diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/saved_model/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/saved_model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b52b15dc1266ed65a564ba9fecd49aff7a9ed65
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/saved_model/__init__.py
@@ -0,0 +1,13 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.__internal__.saved_model namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.__internal__.saved_model import load
+from tensorflow.python.saved_model.builder_impl import _SavedModelBuilder as SavedModelBuilder # line: 48
+from tensorflow.python.saved_model.load import load_partial # line: 916
+from tensorflow.python.saved_model.loader_impl import parse_saved_model # line: 75
+from tensorflow.python.saved_model.nested_structure_coder import decode_proto # line: 109
+from tensorflow.python.saved_model.nested_structure_coder import encode_structure # line: 77
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/saved_model/load/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/saved_model/load/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7beac15fa9d89616ed735e4e7b9edd0a0abe091f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/saved_model/load/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.__internal__.saved_model.load namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.saved_model.revived_types import VersionedTypeRegistration # line: 25
+from tensorflow.python.saved_model.revived_types import get_setter # line: 179
+from tensorflow.python.saved_model.revived_types import register_revived_type # line: 109
+from tensorflow.python.saved_model.revived_types import registered_identifiers # line: 169
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/smart_cond/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/smart_cond/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b27d9157a88dce8c43586fa52f20fa15661cc93c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/smart_cond/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.__internal__.smart_cond namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.smart_cond import smart_cond # line: 24
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/test/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/test/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d04364d2539a039c8c1d6d21d869b56674e0fd9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/test/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.__internal__.test namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.__internal__.test import combinations
+from tensorflow.python.platform.benchmark import ParameterizedBenchmark # line: 150
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/test/combinations/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/test/combinations/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..17f5e961992099e8a213f310283d1b053ed6f42d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/test/combinations/__init__.py
@@ -0,0 +1,14 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.__internal__.test.combinations namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.combinations import generate # line: 83
+from tensorflow.python.framework.test_combinations import NamedObject # line: 437
+from tensorflow.python.framework.test_combinations import OptionalParameter # line: 187
+from tensorflow.python.framework.test_combinations import ParameterModifier # line: 112
+from tensorflow.python.framework.test_combinations import TestCombination # line: 58
+from tensorflow.python.framework.test_combinations import combine # line: 365
+from tensorflow.python.framework.test_combinations import times # line: 403
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/tf2/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/tf2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac7a0cbe0793de05e33951f91ac9d5eeb1cf6ea5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/tf2/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.__internal__.tf2 namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.tf2 import enabled # line: 35
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/tracking/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/tracking/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bcf6b5184f3b97f6ad66ebc8c55138d4b5629bfe
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/tracking/__init__.py
@@ -0,0 +1,21 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.__internal__.tracking namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.checkpoint.checkpoint import register_session_provider # line: 97
+from tensorflow.python.checkpoint.checkpoint import streaming_restore # line: 739
+from tensorflow.python.checkpoint.graph_view import ObjectGraphView # line: 25
+from tensorflow.python.trackable.autotrackable import AutoTrackable # line: 29
+from tensorflow.python.trackable.base import CheckpointInitialValue # line: 123
+from tensorflow.python.trackable.base import CheckpointInitialValueCallable # line: 96
+from tensorflow.python.trackable.base import Trackable # line: 277
+from tensorflow.python.trackable.base import TrackableReference # line: 35
+from tensorflow.python.trackable.base import no_automatic_dependency_tracking # line: 182
+from tensorflow.python.trackable.base_delegate import DelegatingTrackableMixin # line: 32
+from tensorflow.python.trackable.data_structures import TrackableDataStructure # line: 171
+from tensorflow.python.trackable.data_structures import sticky_attribute_assignment # line: 124
+from tensorflow.python.trackable.data_structures import wrap_or_unwrap as wrap # line: 86
+from tensorflow.python.training.saving.saveable_object_util import saveable_objects_from_trackable # line: 499
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/train/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/train/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c726d60cc56e03187d7bde20e9fbe90ad442b31f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/train/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.__internal__.train namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.checkpoint.checkpoint_management import update_checkpoint_state_internal as update_checkpoint_state # line: 175
+from tensorflow.python.training.experimental.mixed_precision_global_state import is_mixed_precision_graph_rewrite_enabled # line: 40
+from tensorflow.python.training.experimental.mixed_precision_global_state import set_using_mixed_precision_policy # line: 63
+from tensorflow.python.training.moving_averages import assign_moving_average # line: 34
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/types/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/types/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..14552f2bca71b37092c952dc14f74a9cc50d5322
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/types/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.__internal__.types namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.__internal__.types import data
+from tensorflow.python.types.core import Tensor # line: 46
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/types/data/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/types/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..04663471ff260665fb05617d7e27a204adf14c22
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__internal__/types/data/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.__internal__.types.data namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.types.data import DatasetV2 as Dataset # line: 22
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__operators__/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__operators__/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..099e8539f105acfb024fc8f44f4989948107d0dd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/__operators__/__init__.py
@@ -0,0 +1,12 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.__operators__ namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.array_ops import _slice_helper as getitem # line: 968
+from tensorflow.python.ops.math_ops import _add_dispatch as add # line: 1841
+from tensorflow.python.ops.math_ops import tensor_equals as eq # line: 2077
+from tensorflow.python.ops.math_ops import tensor_not_equals as ne # line: 2119
+from tensorflow.python.ops.ragged.ragged_getitem import ragged_tensor_getitem as ragged_getitem # line: 34
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/audio/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/audio/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f40f747e668e9da287ff7ccd2bcdf1346ee56eb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/audio/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.audio namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.gen_audio_ops import decode_wav # line: 136
+from tensorflow.python.ops.gen_audio_ops import encode_wav # line: 263
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/autodiff/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/autodiff/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9187a497de14435f522c57541613bb2ba0d60950
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/autodiff/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.autodiff namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.eager.backprop import GradientTape # line: 704
+from tensorflow.python.eager.forwardprop import ForwardAccumulator # line: 229
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/autograph/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/autograph/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2bfa9fabd1c85bc55dda176ef9e1780b73434d09
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/autograph/__init__.py
@@ -0,0 +1,12 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.autograph namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.autograph import experimental
+from tensorflow.python.autograph.impl.api import to_code # line: 904
+from tensorflow.python.autograph.impl.api import to_graph # line: 707
+from tensorflow.python.autograph.utils.ag_logging import set_verbosity # line: 36
+from tensorflow.python.autograph.utils.ag_logging import trace # line: 87
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/autograph/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/autograph/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d98a370ed034a61ff7d752242dee2f39adf179e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/autograph/experimental/__init__.py
@@ -0,0 +1,10 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.autograph.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.autograph.core.converter import Feature # line: 78
+from tensorflow.python.autograph.impl.api import do_not_convert # line: 624
+from tensorflow.python.autograph.lang.directives import set_loop_options # line: 45
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/bitwise/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/bitwise/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ccb08e754cbf7c38470d097581387058f9b6cb2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/bitwise/__init__.py
@@ -0,0 +1,13 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.bitwise namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.gen_bitwise_ops import bitwise_and # line: 29
+from tensorflow.python.ops.gen_bitwise_ops import bitwise_or # line: 135
+from tensorflow.python.ops.gen_bitwise_ops import bitwise_xor # line: 241
+from tensorflow.python.ops.gen_bitwise_ops import invert # line: 347
+from tensorflow.python.ops.gen_bitwise_ops import left_shift # line: 471
+from tensorflow.python.ops.gen_bitwise_ops import right_shift # line: 651
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/compat/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/compat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb0d3fe4d154a776dae088bb83eee5f8a7b4cdf8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/compat/__init__.py
@@ -0,0 +1,22 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.compat namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.compat import v1
+from tensorflow._api.v2.compat.v2.compat import v2
+from tensorflow.python.compat.compat import forward_compatibility_horizon # line: 125
+from tensorflow.python.compat.compat import forward_compatible # line: 65
+from tensorflow.python.framework.tensor_shape import dimension_at_index # line: 139
+from tensorflow.python.framework.tensor_shape import dimension_value # line: 103
+from tensorflow.python.util.compat import as_bytes # line: 121
+from tensorflow.python.util.compat import as_str # line: 122
+from tensorflow.python.util.compat import as_str_any # line: 125
+from tensorflow.python.util.compat import as_text # line: 120
+from tensorflow.python.util.compat import bytes_or_text_types # line: 216
+from tensorflow.python.util.compat import complex_types # line: 212
+from tensorflow.python.util.compat import integral_types # line: 208
+from tensorflow.python.util.compat import path_to_str # line: 145
+from tensorflow.python.util.compat import real_types # line: 210
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/compat/v1/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/compat/v1/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f49156f42e93f82aad620e35dd8abc5566ca4b4b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/compat/v1/__init__.py
@@ -0,0 +1,749 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Bring in all of the public TensorFlow interface into this module."""
+
+# pylint: disable=g-bad-import-order,g-import-not-at-top,protected-access
+
+import os as _os
+import sys as _sys
+import typing as _typing
+
+from tensorflow.python.tools import module_util as _module_util
+from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader
+from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader
+
+from tensorflow._api.v2.compat.v1 import __internal__
+from tensorflow._api.v2.compat.v1 import app
+from tensorflow._api.v2.compat.v1 import audio
+from tensorflow._api.v2.compat.v1 import autograph
+from tensorflow._api.v2.compat.v1 import bitwise
+from tensorflow._api.v2.compat.v1 import compat
+from tensorflow._api.v2.compat.v1 import config
+from tensorflow._api.v2.compat.v1 import data
+from tensorflow._api.v2.compat.v1 import debugging
+from tensorflow._api.v2.compat.v1 import distribute
+from tensorflow._api.v2.compat.v1 import distributions
+from tensorflow._api.v2.compat.v1 import dtypes
+from tensorflow._api.v2.compat.v1 import errors
+from tensorflow._api.v2.compat.v1 import experimental
+from tensorflow._api.v2.compat.v1 import feature_column
+from tensorflow._api.v2.compat.v1 import gfile
+from tensorflow._api.v2.compat.v1 import graph_util
+from tensorflow._api.v2.compat.v1 import image
+from tensorflow._api.v2.compat.v1 import initializers
+from tensorflow._api.v2.compat.v1 import io
+from tensorflow._api.v2.compat.v1 import layers
+from tensorflow._api.v2.compat.v1 import linalg
+from tensorflow._api.v2.compat.v1 import lite
+from tensorflow._api.v2.compat.v1 import logging
+from tensorflow._api.v2.compat.v1 import lookup
+from tensorflow._api.v2.compat.v1 import losses
+from tensorflow._api.v2.compat.v1 import manip
+from tensorflow._api.v2.compat.v1 import math
+from tensorflow._api.v2.compat.v1 import metrics
+from tensorflow._api.v2.compat.v1 import mixed_precision
+from tensorflow._api.v2.compat.v1 import mlir
+from tensorflow._api.v2.compat.v1 import nest
+from tensorflow._api.v2.compat.v1 import nn
+from tensorflow._api.v2.compat.v1 import profiler
+from tensorflow._api.v2.compat.v1 import python_io
+from tensorflow._api.v2.compat.v1 import quantization
+from tensorflow._api.v2.compat.v1 import queue
+from tensorflow._api.v2.compat.v1 import ragged
+from tensorflow._api.v2.compat.v1 import random
+from tensorflow._api.v2.compat.v1 import raw_ops
+from tensorflow._api.v2.compat.v1 import resource_loader
+from tensorflow._api.v2.compat.v1 import saved_model
+from tensorflow._api.v2.compat.v1 import sets
+from tensorflow._api.v2.compat.v1 import signal
+from tensorflow._api.v2.compat.v1 import sparse
+from tensorflow._api.v2.compat.v1 import spectral
+from tensorflow._api.v2.compat.v1 import strings
+from tensorflow._api.v2.compat.v1 import summary
+from tensorflow._api.v2.compat.v1 import sysconfig
+from tensorflow._api.v2.compat.v1 import test
+from tensorflow._api.v2.compat.v1 import tpu
+from tensorflow._api.v2.compat.v1 import train
+from tensorflow._api.v2.compat.v1 import types
+from tensorflow._api.v2.compat.v1 import user_ops
+from tensorflow._api.v2.compat.v1 import version
+from tensorflow._api.v2.compat.v1 import xla
+from tensorflow.python.ops.gen_array_ops import batch_to_space_nd # line: 343
+from tensorflow.python.ops.gen_array_ops import bitcast # line: 558
+from tensorflow.python.ops.gen_array_ops import broadcast_to # line: 829
+from tensorflow.python.ops.gen_array_ops import check_numerics # line: 950
+from tensorflow.python.ops.gen_array_ops import diag # line: 1949
+from tensorflow.python.ops.gen_array_ops import extract_volume_patches # line: 2569
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_args # line: 2698
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_args_gradient # line: 2867
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_vars # line: 3003
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_vars_gradient # line: 3145
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_vars_per_channel # line: 3276
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_vars_per_channel_gradient # line: 3423
+from tensorflow.python.ops.gen_array_ops import identity_n # line: 4226
+from tensorflow.python.ops.gen_array_ops import invert_permutation # line: 4592
+from tensorflow.python.ops.gen_array_ops import matrix_band_part # line: 4879
+from tensorflow.python.ops.gen_array_ops import quantized_concat # line: 8202
+from tensorflow.python.ops.gen_array_ops import reverse_v2 as reverse # line: 9140
+from tensorflow.python.ops.gen_array_ops import reverse_v2 # line: 9140
+from tensorflow.python.ops.gen_array_ops import scatter_nd # line: 9276
+from tensorflow.python.ops.gen_array_ops import space_to_batch_nd # line: 10076
+from tensorflow.python.ops.gen_array_ops import tensor_scatter_add # line: 11226
+from tensorflow.python.ops.gen_array_ops import tensor_scatter_add as tensor_scatter_nd_add # line: 11226
+from tensorflow.python.ops.gen_array_ops import tensor_scatter_max as tensor_scatter_nd_max # line: 11382
+from tensorflow.python.ops.gen_array_ops import tensor_scatter_min as tensor_scatter_nd_min # line: 11488
+from tensorflow.python.ops.gen_array_ops import tensor_scatter_sub as tensor_scatter_nd_sub # line: 11583
+from tensorflow.python.ops.gen_array_ops import tensor_scatter_sub # line: 11583
+from tensorflow.python.ops.gen_array_ops import tile # line: 11970
+from tensorflow.python.ops.gen_array_ops import unravel_index # line: 12731
+from tensorflow.python.ops.gen_control_flow_ops import no_op # line: 475
+from tensorflow.python.ops.gen_data_flow_ops import dynamic_partition # line: 594
+from tensorflow.python.ops.gen_data_flow_ops import dynamic_stitch # line: 736
+from tensorflow.python.ops.gen_io_ops import matching_files # line: 391
+from tensorflow.python.ops.gen_io_ops import write_file # line: 2269
+from tensorflow.python.ops.gen_linalg_ops import cholesky # line: 766
+from tensorflow.python.ops.gen_linalg_ops import matrix_determinant # line: 1370
+from tensorflow.python.ops.gen_linalg_ops import matrix_inverse # line: 1516
+from tensorflow.python.ops.gen_linalg_ops import matrix_solve # line: 1694
+from tensorflow.python.ops.gen_linalg_ops import matrix_square_root # line: 1913
+from tensorflow.python.ops.gen_linalg_ops import qr # line: 2150
+from tensorflow.python.ops.gen_logging_ops import timestamp # line: 886
+from tensorflow.python.ops.gen_math_ops import acosh # line: 231
+from tensorflow.python.ops.gen_math_ops import asin # line: 991
+from tensorflow.python.ops.gen_math_ops import asinh # line: 1091
+from tensorflow.python.ops.gen_math_ops import atan # line: 1184
+from tensorflow.python.ops.gen_math_ops import atan2 # line: 1284
+from tensorflow.python.ops.gen_math_ops import atanh # line: 1383
+from tensorflow.python.ops.gen_math_ops import betainc # line: 1787
+from tensorflow.python.ops.gen_math_ops import cos # line: 2464
+from tensorflow.python.ops.gen_math_ops import cosh # line: 2558
+from tensorflow.python.ops.gen_math_ops import cross # line: 2651
+from tensorflow.python.ops.gen_math_ops import digamma # line: 3161
+from tensorflow.python.ops.gen_math_ops import erf # line: 3454
+from tensorflow.python.ops.gen_math_ops import erfc # line: 3546
+from tensorflow.python.ops.gen_math_ops import expm1 # line: 3847
+from tensorflow.python.ops.gen_math_ops import floor_div # line: 4002
+from tensorflow.python.ops.gen_math_ops import floor_mod as floormod # line: 4092
+from tensorflow.python.ops.gen_math_ops import greater # line: 4186
+from tensorflow.python.ops.gen_math_ops import greater_equal # line: 4287
+from tensorflow.python.ops.gen_math_ops import igamma # line: 4480
+from tensorflow.python.ops.gen_math_ops import igammac # line: 4639
+from tensorflow.python.ops.gen_math_ops import is_finite # line: 4935
+from tensorflow.python.ops.gen_math_ops import is_inf # line: 5031
+from tensorflow.python.ops.gen_math_ops import is_nan # line: 5127
+from tensorflow.python.ops.gen_math_ops import less # line: 5223
+from tensorflow.python.ops.gen_math_ops import less_equal # line: 5324
+from tensorflow.python.ops.gen_math_ops import lgamma # line: 5425
+from tensorflow.python.ops.gen_math_ops import log # line: 5595
+from tensorflow.python.ops.gen_math_ops import log1p # line: 5689
+from tensorflow.python.ops.gen_math_ops import logical_and # line: 5779
+from tensorflow.python.ops.gen_math_ops import logical_not # line: 5918
+from tensorflow.python.ops.gen_math_ops import logical_or # line: 6005
+from tensorflow.python.ops.gen_math_ops import maximum # line: 6310
+from tensorflow.python.ops.gen_math_ops import minimum # line: 6566
+from tensorflow.python.ops.gen_math_ops import floor_mod as mod # line: 4092
+from tensorflow.python.ops.gen_math_ops import neg as negative # line: 6913
+from tensorflow.python.ops.gen_math_ops import polygamma # line: 7167
+from tensorflow.python.ops.gen_math_ops import real_div as realdiv # line: 8068
+from tensorflow.python.ops.gen_math_ops import reciprocal # line: 8159
+from tensorflow.python.ops.gen_math_ops import rint # line: 8656
+from tensorflow.python.ops.gen_math_ops import segment_max # line: 8930
+from tensorflow.python.ops.gen_math_ops import segment_mean # line: 9164
+from tensorflow.python.ops.gen_math_ops import segment_min # line: 9289
+from tensorflow.python.ops.gen_math_ops import segment_prod # line: 9523
+from tensorflow.python.ops.gen_math_ops import segment_sum # line: 9749
+from tensorflow.python.ops.gen_math_ops import sin # line: 10299
+from tensorflow.python.ops.gen_math_ops import sinh # line: 10392
+from tensorflow.python.ops.gen_math_ops import square # line: 11962
+from tensorflow.python.ops.gen_math_ops import squared_difference # line: 12051
+from tensorflow.python.ops.gen_math_ops import tan # line: 12352
+from tensorflow.python.ops.gen_math_ops import tanh # line: 12446
+from tensorflow.python.ops.gen_math_ops import truncate_div as truncatediv # line: 12601
+from tensorflow.python.ops.gen_math_ops import truncate_mod as truncatemod # line: 12695
+from tensorflow.python.ops.gen_math_ops import unsorted_segment_max # line: 12789
+from tensorflow.python.ops.gen_math_ops import unsorted_segment_min # line: 12927
+from tensorflow.python.ops.gen_math_ops import unsorted_segment_prod # line: 13061
+from tensorflow.python.ops.gen_math_ops import unsorted_segment_sum # line: 13195
+from tensorflow.python.ops.gen_math_ops import zeta # line: 13530
+from tensorflow.python.ops.gen_nn_ops import approx_top_k # line: 33
+from tensorflow.python.ops.gen_nn_ops import conv # line: 1061
+from tensorflow.python.ops.gen_nn_ops import conv2d_backprop_filter_v2 # line: 1609
+from tensorflow.python.ops.gen_nn_ops import conv2d_backprop_input_v2 # line: 1977
+from tensorflow.python.ops.gen_parsing_ops import decode_compressed # line: 144
+from tensorflow.python.ops.gen_parsing_ops import parse_tensor # line: 2135
+from tensorflow.python.ops.gen_ragged_array_ops import ragged_fill_empty_rows # line: 196
+from tensorflow.python.ops.gen_ragged_array_ops import ragged_fill_empty_rows_grad # line: 305
+from tensorflow.python.ops.gen_random_index_shuffle_ops import random_index_shuffle # line: 30
+from tensorflow.python.ops.gen_spectral_ops import fft # line: 353
+from tensorflow.python.ops.gen_spectral_ops import fft2d # line: 442
+from tensorflow.python.ops.gen_spectral_ops import fft3d # line: 531
+from tensorflow.python.ops.gen_spectral_ops import fftnd # line: 620
+from tensorflow.python.ops.gen_spectral_ops import ifft # line: 724
+from tensorflow.python.ops.gen_spectral_ops import ifft2d # line: 813
+from tensorflow.python.ops.gen_spectral_ops import ifft3d # line: 902
+from tensorflow.python.ops.gen_spectral_ops import ifftnd # line: 991
+from tensorflow.python.ops.gen_spectral_ops import irfftnd # line: 1347
+from tensorflow.python.ops.gen_spectral_ops import rfftnd # line: 1707
+from tensorflow.python.ops.gen_string_ops import as_string # line: 29
+from tensorflow.python.ops.gen_string_ops import decode_base64 # line: 182
+from tensorflow.python.ops.gen_string_ops import encode_base64 # line: 269
+from tensorflow.python.ops.gen_string_ops import string_strip # line: 1429
+from tensorflow.python.ops.gen_string_ops import string_to_hash_bucket_fast # line: 1583
+from tensorflow.python.ops.gen_string_ops import string_to_hash_bucket_strong # line: 1688
+from tensorflow.python.client.session import InteractiveSession # line: 1720
+from tensorflow.python.client.session import Session # line: 1541
+from tensorflow.python.compat.v2_compat import disable_v2_behavior # line: 83
+from tensorflow.python.compat.v2_compat import enable_v2_behavior # line: 39
+from tensorflow.python.data.ops.optional_ops import OptionalSpec # line: 205
+from tensorflow.python.eager.backprop import GradientTape # line: 704
+from tensorflow.python.eager.context import executing_eagerly_v1 as executing_eagerly # line: 2354
+from tensorflow.python.eager.polymorphic_function.polymorphic_function import function # line: 1276
+from tensorflow.python.eager.wrap_function import wrap_function # line: 576
+from tensorflow.python.framework.constant_op import constant_v1 as constant # line: 106
+from tensorflow.python.framework.device_spec import DeviceSpecV1 as DeviceSpec # line: 420
+from tensorflow.python.framework.dtypes import DType # line: 51
+from tensorflow.python.framework.dtypes import QUANTIZED_DTYPES # line: 771
+from tensorflow.python.framework.dtypes import as_dtype # line: 793
+from tensorflow.python.framework.dtypes import bfloat16 # line: 450
+from tensorflow.python.framework.dtypes import bool # line: 414
+from tensorflow.python.framework.dtypes import complex128 # line: 401
+from tensorflow.python.framework.dtypes import complex64 # line: 394
+from tensorflow.python.framework.dtypes import double # line: 388
+from tensorflow.python.framework.dtypes import float16 # line: 373
+from tensorflow.python.framework.dtypes import float32 # line: 380
+from tensorflow.python.framework.dtypes import float64 # line: 386
+from tensorflow.python.framework.dtypes import half # line: 374
+from tensorflow.python.framework.dtypes import int16 # line: 354
+from tensorflow.python.framework.dtypes import int32 # line: 360
+from tensorflow.python.framework.dtypes import int64 # line: 366
+from tensorflow.python.framework.dtypes import int8 # line: 348
+from tensorflow.python.framework.dtypes import qint16 # line: 426
+from tensorflow.python.framework.dtypes import qint32 # line: 432
+from tensorflow.python.framework.dtypes import qint8 # line: 420
+from tensorflow.python.framework.dtypes import quint16 # line: 444
+from tensorflow.python.framework.dtypes import quint8 # line: 438
+from tensorflow.python.framework.dtypes import resource # line: 312
+from tensorflow.python.framework.dtypes import string # line: 408
+from tensorflow.python.framework.dtypes import uint16 # line: 330
+from tensorflow.python.framework.dtypes import uint32 # line: 336
+from tensorflow.python.framework.dtypes import uint64 # line: 342
+from tensorflow.python.framework.dtypes import uint8 # line: 324
+from tensorflow.python.framework.dtypes import variant # line: 318
+from tensorflow.python.framework.errors_impl import OpError # line: 57
+from tensorflow.python.framework.graph_util_impl import GraphDef # line: 29
+from tensorflow.python.framework.importer import import_graph_def # line: 354
+from tensorflow.python.framework.indexed_slices import IndexedSlices # line: 54
+from tensorflow.python.framework.indexed_slices import IndexedSlicesSpec # line: 203
+from tensorflow.python.framework.indexed_slices import convert_to_tensor_or_indexed_slices # line: 277
+from tensorflow.python.framework.load_library import load_file_system_library # line: 79
+from tensorflow.python.framework.load_library import load_library # line: 120
+from tensorflow.python.framework.load_library import load_op_library # line: 31
+from tensorflow.python.framework.ops import Graph # line: 1912
+from tensorflow.python.framework.ops import GraphKeys # line: 5149
+from tensorflow.python.framework.ops import no_gradient as NoGradient # line: 1687
+from tensorflow.python.framework.ops import no_gradient as NotDifferentiable # line: 1687
+from tensorflow.python.framework.ops import Operation # line: 1032
+from tensorflow.python.framework.ops import RegisterGradient # line: 1638
+from tensorflow.python.framework.ops import add_to_collection # line: 5307
+from tensorflow.python.framework.ops import add_to_collections # line: 5327
+from tensorflow.python.framework.ops import _colocate_with as colocate_with # line: 4420
+from tensorflow.python.framework.ops import container # line: 4377
+from tensorflow.python.framework.ops import control_dependencies # line: 4425
+from tensorflow.python.framework.ops import device # line: 4302
+from tensorflow.python.framework.ops import disable_eager_execution # line: 4863
+from tensorflow.python.framework.ops import enable_eager_execution # line: 4785
+from tensorflow.python.framework.ops import executing_eagerly_outside_functions # line: 4731
+from tensorflow.python.framework.ops import get_collection # line: 5371
+from tensorflow.python.framework.ops import get_collection_ref # line: 5347
+from tensorflow.python.framework.ops import get_default_graph # line: 5010
+from tensorflow.python.framework.ops import init_scope # line: 4626
+from tensorflow.python.framework.ops import is_symbolic_tensor # line: 6076
+from tensorflow.python.framework.ops import name_scope_v1 as name_scope # line: 5525
+from tensorflow.python.framework.ops import no_gradient # line: 1687
+from tensorflow.python.framework.ops import op_scope # line: 5761
+from tensorflow.python.framework.ops import reset_default_graph # line: 4980
+from tensorflow.python.framework.random_seed import get_seed # line: 38
+from tensorflow.python.framework.random_seed import set_random_seed # line: 96
+from tensorflow.python.framework.sparse_tensor import SparseTensor # line: 47
+from tensorflow.python.framework.sparse_tensor import SparseTensorSpec # line: 377
+from tensorflow.python.framework.sparse_tensor import SparseTensorValue # line: 373
+from tensorflow.python.framework.sparse_tensor import convert_to_tensor_or_sparse_tensor # line: 534
+from tensorflow.python.framework.stack import get_default_session # line: 118
+from tensorflow.python.framework.tensor import Tensor # line: 139
+from tensorflow.python.framework.tensor import TensorSpec # line: 918
+from tensorflow.python.framework.tensor import disable_tensor_equality # line: 784
+from tensorflow.python.framework.tensor import enable_tensor_equality # line: 770
+from tensorflow.python.framework.tensor_conversion import convert_to_tensor_v1_with_dispatch as convert_to_tensor # line: 33
+from tensorflow.python.framework.tensor_conversion_registry import register_tensor_conversion_function # line: 80
+from tensorflow.python.framework.tensor_shape import Dimension # line: 188
+from tensorflow.python.framework.tensor_shape import TensorShape # line: 747
+from tensorflow.python.framework.tensor_shape import dimension_at_index # line: 139
+from tensorflow.python.framework.tensor_shape import dimension_value # line: 103
+from tensorflow.python.framework.tensor_shape import disable_v2_tensorshape # line: 91
+from tensorflow.python.framework.tensor_shape import enable_v2_tensorshape # line: 38
+from tensorflow.python.framework.tensor_util import constant_value as get_static_value # line: 896
+from tensorflow.python.framework.tensor_util import is_tf_type as is_tensor # line: 1128
+from tensorflow.python.framework.tensor_util import MakeNdarray as make_ndarray # line: 633
+from tensorflow.python.framework.tensor_util import make_tensor_proto # line: 425
+from tensorflow.python.framework.type_spec import TypeSpec # line: 50
+from tensorflow.python.framework.type_spec import type_spec_from_value # line: 959
+from tensorflow.python.framework.versions import COMPILER_VERSION # line: 41
+from tensorflow.python.framework.versions import CXX11_ABI_FLAG # line: 48
+from tensorflow.python.framework.versions import CXX_VERSION # line: 54
+from tensorflow.python.framework.versions import GIT_VERSION # line: 35
+from tensorflow.python.framework.versions import GRAPH_DEF_VERSION # line: 68
+from tensorflow.python.framework.versions import GRAPH_DEF_VERSION_MIN_CONSUMER # line: 74
+from tensorflow.python.framework.versions import GRAPH_DEF_VERSION_MIN_PRODUCER # line: 82
+from tensorflow.python.framework.versions import MONOLITHIC_BUILD # line: 60
+from tensorflow.python.framework.versions import VERSION # line: 29
+from tensorflow.python.framework.versions import COMPILER_VERSION as __compiler_version__ # line: 41
+from tensorflow.python.framework.versions import CXX11_ABI_FLAG as __cxx11_abi_flag__ # line: 48
+from tensorflow.python.framework.versions import CXX_VERSION as __cxx_version__ # line: 54
+from tensorflow.python.framework.versions import GIT_VERSION as __git_version__ # line: 35
+from tensorflow.python.framework.versions import MONOLITHIC_BUILD as __monolithic_build__ # line: 60
+from tensorflow.python.framework.versions import VERSION as __version__ # line: 29
+from tensorflow.python.module.module import Module # line: 29
+from tensorflow.python.ops.array_ops import batch_gather # line: 5263
+from tensorflow.python.ops.array_ops import batch_to_space # line: 4078
+from tensorflow.python.ops.array_ops import boolean_mask # line: 1691
+from tensorflow.python.ops.array_ops import broadcast_dynamic_shape # line: 528
+from tensorflow.python.ops.array_ops import broadcast_static_shape # line: 562
+from tensorflow.python.ops.array_ops import concat # line: 1596
+from tensorflow.python.ops.array_ops import depth_to_space # line: 4059
+from tensorflow.python.ops.array_ops import dequantize # line: 6139
+from tensorflow.python.ops.array_ops import tensor_diag_part as diag_part # line: 2634
+from tensorflow.python.ops.array_ops import edit_distance # line: 3770
+from tensorflow.python.ops.array_ops import expand_dims # line: 320
+from tensorflow.python.ops.array_ops import extract_image_patches # line: 6538
+from tensorflow.python.ops.array_ops import fill # line: 206
+from tensorflow.python.ops.array_ops import fingerprint # line: 6587
+from tensorflow.python.ops.array_ops import gather # line: 5027
+from tensorflow.python.ops.array_ops import gather_nd # line: 5402
+from tensorflow.python.ops.array_ops import guarantee_const # line: 6926
+from tensorflow.python.ops.array_ops import identity # line: 255
+from tensorflow.python.ops.array_ops import matrix_diag # line: 2322
+from tensorflow.python.ops.array_ops import matrix_diag_part # line: 2491
+from tensorflow.python.ops.array_ops import matrix_set_diag # line: 2679
+from tensorflow.python.ops.array_ops import matrix_transpose # line: 2242
+from tensorflow.python.ops.array_ops import meshgrid # line: 3624
+from tensorflow.python.ops.array_ops import newaxis # line: 58
+from tensorflow.python.ops.array_ops import one_hot # line: 4234
+from tensorflow.python.ops.array_ops import ones # line: 3163
+from tensorflow.python.ops.array_ops import ones_like # line: 3061
+from tensorflow.python.ops.array_ops import pad # line: 3502
+from tensorflow.python.ops.array_ops import parallel_stack # line: 1414
+from tensorflow.python.ops.array_ops import placeholder # line: 3222
+from tensorflow.python.ops.array_ops import placeholder_with_default # line: 3277
+from tensorflow.python.ops.array_ops import quantize # line: 6100
+from tensorflow.python.ops.array_ops import quantize_v2 # line: 6046
+from tensorflow.python.ops.array_ops import rank # line: 879
+from tensorflow.python.ops.array_ops import repeat # line: 6872
+from tensorflow.python.ops.array_ops import required_space_to_batch_paddings # line: 3928
+from tensorflow.python.ops.array_ops import reshape # line: 65
+from tensorflow.python.ops.array_ops import reverse_sequence # line: 4904
+from tensorflow.python.ops.array_ops import searchsorted # line: 6330
+from tensorflow.python.ops.array_ops import sequence_mask # line: 4412
+from tensorflow.python.ops.array_ops import setdiff1d # line: 484
+from tensorflow.python.ops.array_ops import shape # line: 659
+from tensorflow.python.ops.array_ops import shape_n # line: 732
+from tensorflow.python.ops.array_ops import size # line: 802
+from tensorflow.python.ops.array_ops import slice # line: 1171
+from tensorflow.python.ops.array_ops import space_to_batch # line: 4007
+from tensorflow.python.ops.array_ops import space_to_depth # line: 4040
+from tensorflow.python.ops.array_ops import sparse_mask # line: 1845
+from tensorflow.python.ops.array_ops import sparse_placeholder # line: 3329
+from tensorflow.python.ops.array_ops import split # line: 1990
+from tensorflow.python.ops.array_ops import squeeze # line: 4479
+from tensorflow.python.ops.array_ops import stop_gradient # line: 6945
+from tensorflow.python.ops.array_ops import strided_slice # line: 1227
+from tensorflow.python.ops.array_ops import tensor_scatter_nd_update # line: 5757
+from tensorflow.python.ops.array_ops import tensor_scatter_nd_update as tensor_scatter_update # line: 5757
+from tensorflow.python.ops.array_ops import transpose # line: 2153
+from tensorflow.python.ops.array_ops import unique # line: 1889
+from tensorflow.python.ops.array_ops import unique_with_counts # line: 1937
+from tensorflow.python.ops.array_ops import where # line: 4610
+from tensorflow.python.ops.array_ops import where_v2 # line: 4691
+from tensorflow.python.ops.array_ops import zeros # line: 2845
+from tensorflow.python.ops.array_ops import zeros_like # line: 2907
+from tensorflow.python.ops.array_ops_stack import stack # line: 24
+from tensorflow.python.ops.array_ops_stack import unstack # line: 88
+from tensorflow.python.ops.batch_ops import batch_function as nondifferentiable_batch_function # line: 28
+from tensorflow.python.ops.bincount_ops import bincount_v1 as bincount # line: 190
+from tensorflow.python.ops.check_ops import assert_equal # line: 770
+from tensorflow.python.ops.check_ops import assert_greater # line: 987
+from tensorflow.python.ops.check_ops import assert_greater_equal # line: 1005
+from tensorflow.python.ops.check_ops import assert_integer # line: 1448
+from tensorflow.python.ops.check_ops import assert_less # line: 950
+from tensorflow.python.ops.check_ops import assert_less_equal # line: 968
+from tensorflow.python.ops.check_ops import assert_near # line: 858
+from tensorflow.python.ops.check_ops import assert_negative # line: 576
+from tensorflow.python.ops.check_ops import assert_non_negative # line: 685
+from tensorflow.python.ops.check_ops import assert_non_positive # line: 741
+from tensorflow.python.ops.check_ops import assert_none_equal # line: 792
+from tensorflow.python.ops.check_ops import assert_positive # line: 630
+from tensorflow.python.ops.check_ops import assert_proper_iterable # line: 511
+from tensorflow.python.ops.check_ops import assert_rank # line: 1098
+from tensorflow.python.ops.check_ops import assert_rank_at_least # line: 1196
+from tensorflow.python.ops.check_ops import assert_rank_in # line: 1362
+from tensorflow.python.ops.check_ops import assert_same_float_dtype # line: 2118
+from tensorflow.python.ops.check_ops import assert_scalar # line: 2176
+from tensorflow.python.ops.check_ops import assert_type # line: 1522
+from tensorflow.python.ops.check_ops import ensure_shape # line: 2218
+from tensorflow.python.ops.check_ops import is_non_decreasing # line: 1986
+from tensorflow.python.ops.check_ops import is_numeric_tensor # line: 1951
+from tensorflow.python.ops.check_ops import is_strictly_increasing # line: 2028
+from tensorflow.python.ops.clip_ops import clip_by_average_norm # line: 390
+from tensorflow.python.ops.clip_ops import clip_by_global_norm # line: 288
+from tensorflow.python.ops.clip_ops import clip_by_norm # line: 150
+from tensorflow.python.ops.clip_ops import clip_by_value # line: 32
+from tensorflow.python.ops.clip_ops import global_norm # line: 235
+from tensorflow.python.ops.cond import cond # line: 39
+from tensorflow.python.ops.confusion_matrix import confusion_matrix_v1 as confusion_matrix # line: 199
+from tensorflow.python.ops.control_flow_assert import Assert # line: 62
+from tensorflow.python.ops.control_flow_case import case # line: 138
+from tensorflow.python.ops.control_flow_ops import group # line: 1958
+from tensorflow.python.ops.control_flow_ops import tuple # line: 2106
+from tensorflow.python.ops.control_flow_switch_case import switch_case # line: 181
+from tensorflow.python.ops.control_flow_v2_toggles import control_flow_v2_enabled # line: 64
+from tensorflow.python.ops.control_flow_v2_toggles import disable_control_flow_v2 # line: 48
+from tensorflow.python.ops.control_flow_v2_toggles import enable_control_flow_v2 # line: 25
+from tensorflow.python.ops.critical_section_ops import CriticalSection # line: 121
+from tensorflow.python.ops.custom_gradient import custom_gradient # line: 45
+from tensorflow.python.ops.custom_gradient import grad_pass_through # line: 777
+from tensorflow.python.ops.custom_gradient import recompute_grad # line: 604
+from tensorflow.python.ops.data_flow_ops import ConditionalAccumulator # line: 1321
+from tensorflow.python.ops.data_flow_ops import ConditionalAccumulatorBase # line: 1241
+from tensorflow.python.ops.data_flow_ops import FIFOQueue # line: 712
+from tensorflow.python.ops.data_flow_ops import PaddingFIFOQueue # line: 848
+from tensorflow.python.ops.data_flow_ops import PriorityQueue # line: 924
+from tensorflow.python.ops.data_flow_ops import QueueBase # line: 115
+from tensorflow.python.ops.data_flow_ops import RandomShuffleQueue # line: 622
+from tensorflow.python.ops.data_flow_ops import SparseConditionalAccumulator # line: 1412
+from tensorflow.python.ops.functional_ops import foldl # line: 42
+from tensorflow.python.ops.functional_ops import foldr # line: 238
+from tensorflow.python.ops.functional_ops import scan # line: 435
+from tensorflow.python.ops.gradients_impl import gradients # line: 51
+from tensorflow.python.ops.gradients_impl import hessians # line: 384
+from tensorflow.python.ops.gradients_util import AggregationMethod # line: 943
+from tensorflow.python.ops.histogram_ops import histogram_fixed_width # line: 103
+from tensorflow.python.ops.histogram_ops import histogram_fixed_width_bins # line: 31
+from tensorflow.python.ops.init_ops import Constant as constant_initializer # line: 219
+from tensorflow.python.ops.init_ops import GlorotNormal as glorot_normal_initializer # line: 1627
+from tensorflow.python.ops.init_ops import GlorotUniform as glorot_uniform_initializer # line: 1595
+from tensorflow.python.ops.init_ops import Ones as ones_initializer # line: 182
+from tensorflow.python.ops.init_ops import Orthogonal as orthogonal_initializer # line: 895
+from tensorflow.python.ops.init_ops import RandomNormal as random_normal_initializer # line: 487
+from tensorflow.python.ops.init_ops import RandomUniform as random_uniform_initializer # line: 397
+from tensorflow.python.ops.init_ops import TruncatedNormal as truncated_normal_initializer # line: 577
+from tensorflow.python.ops.init_ops import UniformUnitScaling as uniform_unit_scaling_initializer # line: 673
+from tensorflow.python.ops.init_ops import VarianceScaling as variance_scaling_initializer # line: 741
+from tensorflow.python.ops.init_ops import Zeros as zeros_initializer # line: 97
+from tensorflow.python.ops.io_ops import FixedLengthRecordReader # line: 495
+from tensorflow.python.ops.io_ops import IdentityReader # line: 605
+from tensorflow.python.ops.io_ops import LMDBReader # line: 575
+from tensorflow.python.ops.io_ops import ReaderBase # line: 217
+from tensorflow.python.ops.io_ops import TFRecordReader # line: 541
+from tensorflow.python.ops.io_ops import TextLineReader # line: 462
+from tensorflow.python.ops.io_ops import WholeFileReader # line: 431
+from tensorflow.python.ops.io_ops import read_file # line: 97
+from tensorflow.python.ops.io_ops import serialize_tensor # line: 137
+from tensorflow.python.ops.linalg_ops import cholesky_solve # line: 147
+from tensorflow.python.ops.linalg_ops import eye # line: 196
+from tensorflow.python.ops.linalg_ops import matrix_solve_ls # line: 244
+from tensorflow.python.ops.linalg_ops import matrix_triangular_solve # line: 84
+from tensorflow.python.ops.linalg_ops import norm # line: 633
+from tensorflow.python.ops.linalg_ops import self_adjoint_eig # line: 441
+from tensorflow.python.ops.linalg_ops import self_adjoint_eigvals # line: 465
+from tensorflow.python.ops.linalg_ops import svd # line: 489
+from tensorflow.python.ops.logging_ops import Print # line: 75
+from tensorflow.python.ops.logging_ops import print_v2 as print # line: 147
+from tensorflow.python.ops.lookup_ops import initialize_all_tables # line: 50
+from tensorflow.python.ops.lookup_ops import tables_initializer # line: 65
+from tensorflow.python.ops.manip_ops import roll # line: 27
+from tensorflow.python.ops.map_fn import map_fn # line: 41
+from tensorflow.python.ops.math_ops import abs # line: 365
+from tensorflow.python.ops.math_ops import accumulate_n # line: 4161
+from tensorflow.python.ops.math_ops import acos # line: 5747
+from tensorflow.python.ops.math_ops import add # line: 4020
+from tensorflow.python.ops.math_ops import add_n # line: 4101
+from tensorflow.python.ops.math_ops import angle # line: 869
+from tensorflow.python.ops.math_ops import arg_max # line: 232
+from tensorflow.python.ops.math_ops import arg_min # line: 233
+from tensorflow.python.ops.math_ops import argmax # line: 251
+from tensorflow.python.ops.math_ops import argmin # line: 305
+from tensorflow.python.ops.math_ops import cast # line: 944
+from tensorflow.python.ops.math_ops import ceil # line: 5577
+from tensorflow.python.ops.math_ops import complex # line: 699
+from tensorflow.python.ops.math_ops import conj # line: 4534
+from tensorflow.python.ops.math_ops import count_nonzero # line: 2492
+from tensorflow.python.ops.math_ops import cumprod # line: 4424
+from tensorflow.python.ops.math_ops import cumsum # line: 4352
+from tensorflow.python.ops.math_ops import div # line: 1664
+from tensorflow.python.ops.math_ops import div_no_nan # line: 1696
+from tensorflow.python.ops.math_ops import divide # line: 446
+from tensorflow.python.ops.math_ops import equal # line: 2003
+from tensorflow.python.ops.math_ops import exp # line: 5644
+from tensorflow.python.ops.math_ops import floor # line: 5778
+from tensorflow.python.ops.math_ops import floordiv # line: 1804
+from tensorflow.python.ops.math_ops import imag # line: 835
+from tensorflow.python.ops.math_ops import linspace_nd as lin_space # line: 112
+from tensorflow.python.ops.math_ops import linspace_nd as linspace # line: 112
+from tensorflow.python.ops.math_ops import log_sigmoid # line: 4307
+from tensorflow.python.ops.math_ops import logical_xor # line: 1904
+from tensorflow.python.ops.math_ops import matmul # line: 3617
+from tensorflow.python.ops.math_ops import multiply # line: 481
+from tensorflow.python.ops.math_ops import not_equal # line: 2040
+from tensorflow.python.ops.math_ops import pow # line: 669
+from tensorflow.python.ops.math_ops import range # line: 2163
+from tensorflow.python.ops.math_ops import real # line: 794
+from tensorflow.python.ops.math_ops import reduce_all_v1 as reduce_all # line: 3248
+from tensorflow.python.ops.math_ops import reduce_any_v1 as reduce_any # line: 3354
+from tensorflow.python.ops.math_ops import reduce_logsumexp_v1 as reduce_logsumexp # line: 3460
+from tensorflow.python.ops.math_ops import reduce_max_v1 as reduce_max # line: 3123
+from tensorflow.python.ops.math_ops import reduce_mean_v1 as reduce_mean # line: 2646
+from tensorflow.python.ops.math_ops import reduce_min_v1 as reduce_min # line: 2995
+from tensorflow.python.ops.math_ops import reduce_prod_v1 as reduce_prod # line: 2936
+from tensorflow.python.ops.math_ops import reduce_sum_v1 as reduce_sum # line: 2289
+from tensorflow.python.ops.math_ops import round # line: 914
+from tensorflow.python.ops.math_ops import rsqrt # line: 5722
+from tensorflow.python.ops.math_ops import saturate_cast # line: 1028
+from tensorflow.python.ops.math_ops import scalar_mul # line: 592
+from tensorflow.python.ops.math_ops import sigmoid # line: 4254
+from tensorflow.python.ops.math_ops import sign # line: 747
+from tensorflow.python.ops.math_ops import sparse_matmul # line: 3957
+from tensorflow.python.ops.math_ops import sparse_segment_mean # line: 4942
+from tensorflow.python.ops.math_ops import sparse_segment_sqrt_n # line: 5054
+from tensorflow.python.ops.math_ops import sparse_segment_sum # line: 4770
+from tensorflow.python.ops.math_ops import sqrt # line: 5605
+from tensorflow.python.ops.math_ops import subtract # line: 545
+from tensorflow.python.ops.math_ops import tensordot # line: 5152
+from tensorflow.python.ops.math_ops import to_bfloat16 # line: 1271
+from tensorflow.python.ops.math_ops import to_complex128 # line: 1351
+from tensorflow.python.ops.math_ops import to_complex64 # line: 1311
+from tensorflow.python.ops.math_ops import to_double # line: 1151
+from tensorflow.python.ops.math_ops import to_float # line: 1111
+from tensorflow.python.ops.math_ops import to_int32 # line: 1191
+from tensorflow.python.ops.math_ops import to_int64 # line: 1231
+from tensorflow.python.ops.math_ops import trace # line: 3573
+from tensorflow.python.ops.math_ops import truediv # line: 1630
+from tensorflow.python.ops.math_ops import unsorted_segment_mean # line: 4657
+from tensorflow.python.ops.math_ops import unsorted_segment_sqrt_n # line: 4712
+from tensorflow.python.ops.numerics import add_check_numerics_ops # line: 81
+from tensorflow.python.ops.numerics import verify_tensor_all_finite # line: 28
+from tensorflow.python.ops.parallel_for.control_flow_ops import vectorized_map # line: 452
+from tensorflow.python.ops.parsing_config import FixedLenFeature # line: 298
+from tensorflow.python.ops.parsing_config import FixedLenSequenceFeature # line: 318
+from tensorflow.python.ops.parsing_config import SparseFeature # line: 223
+from tensorflow.python.ops.parsing_config import VarLenFeature # line: 44
+from tensorflow.python.ops.parsing_ops import decode_csv # line: 1023
+from tensorflow.python.ops.parsing_ops import decode_json_example # line: 1152
+from tensorflow.python.ops.parsing_ops import decode_raw_v1 as decode_raw # line: 978
+from tensorflow.python.ops.parsing_ops import parse_example # line: 315
+from tensorflow.python.ops.parsing_ops import parse_single_example # line: 375
+from tensorflow.python.ops.parsing_ops import parse_single_sequence_example # line: 696
+from tensorflow.python.ops.partitioned_variables import create_partitioned_variables # line: 275
+from tensorflow.python.ops.partitioned_variables import fixed_size_partitioner # line: 220
+from tensorflow.python.ops.partitioned_variables import min_max_variable_partitioner # line: 155
+from tensorflow.python.ops.partitioned_variables import variable_axis_size_partitioner # line: 67
+from tensorflow.python.ops.ragged.ragged_string_ops import string_split # line: 528
+from tensorflow.python.ops.ragged.ragged_tensor import RaggedTensor # line: 65
+from tensorflow.python.ops.ragged.ragged_tensor import RaggedTensorSpec # line: 2319
+from tensorflow.python.ops.random_crop_ops import random_crop # line: 30
+from tensorflow.python.ops.random_ops import multinomial # line: 362
+from tensorflow.python.ops.random_ops import random_gamma # line: 451
+from tensorflow.python.ops.random_ops import random_normal # line: 39
+from tensorflow.python.ops.random_ops import random_poisson # line: 545
+from tensorflow.python.ops.random_ops import random_shuffle # line: 326
+from tensorflow.python.ops.random_ops import random_uniform # line: 211
+from tensorflow.python.ops.random_ops import truncated_normal # line: 155
+from tensorflow.python.ops.script_ops import numpy_function # line: 804
+from tensorflow.python.ops.script_ops import py_func # line: 795
+from tensorflow.python.ops.script_ops import eager_py_func as py_function # line: 461
+from tensorflow.python.ops.session_ops import delete_session_tensor # line: 219
+from tensorflow.python.ops.session_ops import get_session_handle # line: 135
+from tensorflow.python.ops.session_ops import get_session_tensor # line: 178
+from tensorflow.python.ops.sort_ops import argsort # line: 86
+from tensorflow.python.ops.sort_ops import sort # line: 29
+from tensorflow.python.ops.sparse_ops import deserialize_many_sparse # line: 2356
+from tensorflow.python.ops.sparse_ops import serialize_many_sparse # line: 2224
+from tensorflow.python.ops.sparse_ops import serialize_sparse # line: 2176
+from tensorflow.python.ops.sparse_ops import sparse_add # line: 460
+from tensorflow.python.ops.sparse_ops import sparse_concat # line: 284
+from tensorflow.python.ops.sparse_ops import sparse_fill_empty_rows # line: 2109
+from tensorflow.python.ops.sparse_ops import sparse_maximum # line: 2734
+from tensorflow.python.ops.sparse_ops import sparse_merge # line: 1802
+from tensorflow.python.ops.sparse_ops import sparse_minimum # line: 2780
+from tensorflow.python.ops.sparse_ops import sparse_reduce_max # line: 1341
+from tensorflow.python.ops.sparse_ops import sparse_reduce_max_sparse # line: 1427
+from tensorflow.python.ops.sparse_ops import sparse_reduce_sum # line: 1559
+from tensorflow.python.ops.sparse_ops import sparse_reduce_sum_sparse # line: 1628
+from tensorflow.python.ops.sparse_ops import sparse_reorder # line: 821
+from tensorflow.python.ops.sparse_ops import sparse_reset_shape # line: 2004
+from tensorflow.python.ops.sparse_ops import sparse_reshape # line: 876
+from tensorflow.python.ops.sparse_ops import sparse_retain # line: 1957
+from tensorflow.python.ops.sparse_ops import sparse_slice # line: 1136
+from tensorflow.python.ops.sparse_ops import sparse_softmax # line: 2671
+from tensorflow.python.ops.sparse_ops import sparse_split # line: 991
+from tensorflow.python.ops.sparse_ops import sparse_tensor_dense_matmul # line: 2430
+from tensorflow.python.ops.sparse_ops import sparse_tensor_to_dense # line: 1681
+from tensorflow.python.ops.sparse_ops import sparse_to_dense # line: 1186
+from tensorflow.python.ops.sparse_ops import sparse_to_indicator # line: 1737
+from tensorflow.python.ops.sparse_ops import sparse_transpose # line: 2824
+from tensorflow.python.ops.special_math_ops import einsum # line: 618
+from tensorflow.python.ops.special_math_ops import lbeta # line: 45
+from tensorflow.python.ops.state_ops import assign # line: 277
+from tensorflow.python.ops.state_ops import assign_add # line: 205
+from tensorflow.python.ops.state_ops import assign_sub # line: 133
+from tensorflow.python.ops.state_ops import batch_scatter_update # line: 946
+from tensorflow.python.ops.state_ops import count_up_to # line: 359
+from tensorflow.python.ops.state_ops import scatter_add # line: 499
+from tensorflow.python.ops.state_ops import scatter_div # line: 784
+from tensorflow.python.ops.state_ops import scatter_max # line: 836
+from tensorflow.python.ops.state_ops import scatter_min # line: 891
+from tensorflow.python.ops.state_ops import scatter_mul # line: 732
+from tensorflow.python.ops.state_ops import scatter_nd_add # line: 551
+from tensorflow.python.ops.state_ops import scatter_nd_sub # line: 668
+from tensorflow.python.ops.state_ops import scatter_nd_update # line: 437
+from tensorflow.python.ops.state_ops import scatter_sub # line: 614
+from tensorflow.python.ops.state_ops import scatter_update # line: 383
+from tensorflow.python.ops.string_ops import reduce_join # line: 305
+from tensorflow.python.ops.string_ops import regex_replace # line: 74
+from tensorflow.python.ops.string_ops import string_join # line: 551
+from tensorflow.python.ops.string_ops import string_to_hash_bucket_v1 as string_to_hash_bucket # line: 536
+from tensorflow.python.ops.string_ops import string_to_number_v1 as string_to_number # line: 491
+from tensorflow.python.ops.string_ops import substr_deprecated as substr # line: 422
+from tensorflow.python.ops.template import make_template # line: 33
+from tensorflow.python.ops.tensor_array_ops import TensorArray # line: 971
+from tensorflow.python.ops.tensor_array_ops import TensorArraySpec # line: 1363
+from tensorflow.python.ops.unconnected_gradients import UnconnectedGradients # line: 22
+from tensorflow.python.ops.variable_scope import AUTO_REUSE # line: 200
+from tensorflow.python.ops.variable_scope import VariableScope # line: 1123
+from tensorflow.python.ops.variable_scope import disable_resource_variables # line: 269
+from tensorflow.python.ops.variable_scope import enable_resource_variables # line: 225
+from tensorflow.python.ops.variable_scope import get_local_variable # line: 1790
+from tensorflow.python.ops.variable_scope import get_variable # line: 1614
+from tensorflow.python.ops.variable_scope import get_variable_scope # line: 1480
+from tensorflow.python.ops.variable_scope import no_regularizer # line: 1116
+from tensorflow.python.ops.variable_scope import resource_variables_enabled # line: 247
+from tensorflow.python.ops.variable_scope import variable_creator_scope_v1 as variable_creator_scope # line: 2708
+from tensorflow.python.ops.variable_scope import variable_op_scope # line: 2596
+from tensorflow.python.ops.variable_scope import variable_scope # line: 2156
+from tensorflow.python.ops.variable_v1 import VariableV1 as Variable # line: 55
+from tensorflow.python.ops.variable_v1 import is_variable_initialized # line: 35
+from tensorflow.python.ops.variables import VariableAggregation # line: 139
+from tensorflow.python.ops.variables import VariableSynchronization # line: 61
+from tensorflow.python.ops.variables import all_variables # line: 1735
+from tensorflow.python.ops.variables import assert_variables_initialized # line: 1950
+from tensorflow.python.ops.variables import global_variables # line: 1700
+from tensorflow.python.ops.variables import global_variables_initializer # line: 1896
+from tensorflow.python.ops.variables import initialize_all_variables # line: 1915
+from tensorflow.python.ops.variables import initialize_local_variables # line: 1942
+from tensorflow.python.ops.variables import initialize_variables # line: 1888
+from tensorflow.python.ops.variables import local_variables # line: 1760
+from tensorflow.python.ops.variables import local_variables_initializer # line: 1923
+from tensorflow.python.ops.variables import model_variables # line: 1788
+from tensorflow.python.ops.variables import moving_average_variables # line: 1835
+from tensorflow.python.ops.variables import report_uninitialized_variables # line: 1993
+from tensorflow.python.ops.variables import trainable_variables # line: 1805
+from tensorflow.python.ops.variables import variables_initializer # line: 1857
+from tensorflow.python.ops.while_loop import while_loop # line: 255
+from tensorflow.python.platform.tf_logging import get_logger # line: 93
+from tensorflow.python.proto_exports import AttrValue # line: 26
+from tensorflow.python.proto_exports import ConfigProto # line: 27
+from tensorflow.python.proto_exports import Event # line: 28
+from tensorflow.python.proto_exports import GPUOptions # line: 29
+from tensorflow.python.proto_exports import GraphOptions # line: 30
+from tensorflow.python.proto_exports import HistogramProto # line: 31
+from tensorflow.python.proto_exports import LogMessage # line: 34
+from tensorflow.python.proto_exports import MetaGraphDef # line: 35
+from tensorflow.python.proto_exports import NameAttrList # line: 38
+from tensorflow.python.proto_exports import NodeDef # line: 41
+from tensorflow.python.proto_exports import OptimizerOptions # line: 42
+from tensorflow.python.proto_exports import RunMetadata # line: 45
+from tensorflow.python.proto_exports import RunOptions # line: 46
+from tensorflow.python.proto_exports import SessionLog # line: 47
+from tensorflow.python.proto_exports import Summary # line: 50
+from tensorflow.python.proto_exports import SummaryMetadata # line: 56
+from tensorflow.python.proto_exports import TensorInfo # line: 62
+
+
+
+from tensorflow.python.util import module_wrapper as _module_wrapper
+
+if not isinstance(_sys.modules[__name__], _module_wrapper.TFModuleWrapper):
+  _sys.modules[__name__] = _module_wrapper.TFModuleWrapper(
+      _sys.modules[__name__], "", public_apis=None, deprecation=False,
+      has_lite=False)
+
+
+# Hook external TensorFlow modules.
+_current_module = _sys.modules[__name__]
+
+# Lazy-load estimator.
+_estimator_module = "tensorflow_estimator.python.estimator.api._v1.estimator"
+estimator = _LazyLoader("estimator", globals(), _estimator_module)
+_module_dir = _module_util.get_parent_dir_for_name(_estimator_module)
+if _module_dir:
+  _current_module.__path__ = [_module_dir] + _current_module.__path__
+setattr(_current_module, "estimator", estimator)
+
+# Lazy load Keras v1
+_tf_uses_legacy_keras = (
+    _os.environ.get("TF_USE_LEGACY_KERAS", None) in ("true", "True", "1"))
+setattr(_current_module, "keras", _KerasLazyLoader(globals(), mode="v1"))
+if _tf_uses_legacy_keras:
+  _module_dir = _module_util.get_parent_dir_for_name("tf_keras.api._v1.keras")
+else:
+  _module_dir = _module_util.get_parent_dir_for_name("keras.api._v1.keras")
+_current_module.__path__ = [_module_dir] + _current_module.__path__
+
+
+from tensorflow.python.platform import flags  # pylint: disable=g-import-not-at-top
+_current_module.app.flags = flags  # pylint: disable=undefined-variable
+setattr(_current_module, "flags", flags)
+
+# Add module aliases from Keras to TF.
+# Some tf endpoints actually lives under Keras.
+_current_module.layers = _KerasLazyLoader(
+    globals(),
+    submodule="__internal__.legacy.layers",
+    name="layers",
+    mode="v1")
+if _tf_uses_legacy_keras:
+  _module_dir = _module_util.get_parent_dir_for_name(
+      "tf_keras.api._v1.keras.__internal__.legacy.layers")
+else:
+  _module_dir = _module_util.get_parent_dir_for_name(
+      "keras.api._v1.keras.__internal__.legacy.layers")
+_current_module.__path__ = [_module_dir] + _current_module.__path__
+
+_current_module.nn.rnn_cell = _KerasLazyLoader(
+    globals(),
+    submodule="__internal__.legacy.rnn_cell",
+    name="rnn_cell",
+    mode="v1")
+if _tf_uses_legacy_keras:
+  _module_dir = _module_util.get_parent_dir_for_name(
+      "tf_keras.api._v1.keras.__internal__.legacy.rnn_cell")
+else:
+  _module_dir = _module_util.get_parent_dir_for_name(
+      "keras.api._v1.keras.__internal__.legacy.rnn_cell")
+_current_module.nn.__path__ = [_module_dir] + _current_module.nn.__path__
+
+# Explicitly import lazy-loaded modules to support autocompletion.
+# pylint: disable=g-import-not-at-top
+if _typing.TYPE_CHECKING:
+  from tensorflow_estimator.python.estimator.api._v1 import estimator as estimator
+# pylint: enable=g-import-not-at-top
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/compat/v1/compat/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/compat/v1/compat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed5f0941c4614fb67675f4e7384972b2598ab260
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/compat/v1/compat/__init__.py
@@ -0,0 +1,29 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.compat namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v1.compat import v1
+from tensorflow._api.v2.compat.v1.compat import v2
+from tensorflow.python.compat.compat import forward_compatibility_horizon # line: 125
+from tensorflow.python.compat.compat import forward_compatible # line: 65
+from tensorflow.python.framework.tensor_shape import dimension_at_index # line: 139
+from tensorflow.python.framework.tensor_shape import dimension_value # line: 103
+from tensorflow.python.util.compat import as_bytes # line: 121
+from tensorflow.python.util.compat import as_str # line: 122
+from tensorflow.python.util.compat import as_str_any # line: 125
+from tensorflow.python.util.compat import as_text # line: 120
+from tensorflow.python.util.compat import bytes_or_text_types # line: 216
+from tensorflow.python.util.compat import complex_types # line: 212
+from tensorflow.python.util.compat import integral_types # line: 208
+from tensorflow.python.util.compat import path_to_str # line: 145
+from tensorflow.python.util.compat import real_types # line: 210
+
+from tensorflow.python.util import module_wrapper as _module_wrapper
+
+if not isinstance(_sys.modules[__name__], _module_wrapper.TFModuleWrapper):
+  _sys.modules[__name__] = _module_wrapper.TFModuleWrapper(
+      _sys.modules[__name__], "compat", public_apis=None, deprecation=False,
+      has_lite=False)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/compat/v2/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/compat/v2/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5cf8171806130577957146915fe7f96847d8137
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/compat/v2/__init__.py
@@ -0,0 +1,396 @@
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Bring in all of the public TensorFlow interface into this module."""
+
+# pylint: disable=g-bad-import-order,g-import-not-at-top,protected-access
+
+import logging as _logging
+import os as _os
+import sys as _sys
+import typing as _typing
+
+from tensorflow.python.tools import module_util as _module_util
+from tensorflow.python.util.lazy_loader import LazyLoader as _LazyLoader
+from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader
+
+from tensorflow._api.v2.compat.v2 import __internal__
+from tensorflow._api.v2.compat.v2 import __operators__
+from tensorflow._api.v2.compat.v2 import audio
+from tensorflow._api.v2.compat.v2 import autodiff
+from tensorflow._api.v2.compat.v2 import autograph
+from tensorflow._api.v2.compat.v2 import bitwise
+from tensorflow._api.v2.compat.v2 import compat
+from tensorflow._api.v2.compat.v2 import config
+from tensorflow._api.v2.compat.v2 import data
+from tensorflow._api.v2.compat.v2 import debugging
+from tensorflow._api.v2.compat.v2 import distribute
+from tensorflow._api.v2.compat.v2 import dtypes
+from tensorflow._api.v2.compat.v2 import errors
+from tensorflow._api.v2.compat.v2 import experimental
+from tensorflow._api.v2.compat.v2 import feature_column
+from tensorflow._api.v2.compat.v2 import graph_util
+from tensorflow._api.v2.compat.v2 import image
+from tensorflow._api.v2.compat.v2 import io
+from tensorflow._api.v2.compat.v2 import linalg
+from tensorflow._api.v2.compat.v2 import lite
+from tensorflow._api.v2.compat.v2 import lookup
+from tensorflow._api.v2.compat.v2 import math
+from tensorflow._api.v2.compat.v2 import mlir
+from tensorflow._api.v2.compat.v2 import nest
+from tensorflow._api.v2.compat.v2 import nn
+from tensorflow._api.v2.compat.v2 import profiler
+from tensorflow._api.v2.compat.v2 import quantization
+from tensorflow._api.v2.compat.v2 import queue
+from tensorflow._api.v2.compat.v2 import ragged
+from tensorflow._api.v2.compat.v2 import random
+from tensorflow._api.v2.compat.v2 import raw_ops
+from tensorflow._api.v2.compat.v2 import saved_model
+from tensorflow._api.v2.compat.v2 import sets
+from tensorflow._api.v2.compat.v2 import signal
+from tensorflow._api.v2.compat.v2 import sparse
+from tensorflow._api.v2.compat.v2 import strings
+from tensorflow._api.v2.compat.v2 import summary
+from tensorflow._api.v2.compat.v2 import sysconfig
+from tensorflow._api.v2.compat.v2 import test
+from tensorflow._api.v2.compat.v2 import tpu
+from tensorflow._api.v2.compat.v2 import train
+from tensorflow._api.v2.compat.v2 import types
+from tensorflow._api.v2.compat.v2 import version
+from tensorflow._api.v2.compat.v2 import xla
+from tensorflow.python.ops.gen_array_ops import bitcast # line: 558
+from tensorflow.python.ops.gen_array_ops import broadcast_to # line: 829
+from tensorflow.python.ops.gen_array_ops import extract_volume_patches # line: 2569
+from tensorflow.python.ops.gen_array_ops import identity_n # line: 4226
+from tensorflow.python.ops.gen_array_ops import reverse_v2 as reverse # line: 9140
+from tensorflow.python.ops.gen_array_ops import scatter_nd # line: 9276
+from tensorflow.python.ops.gen_array_ops import space_to_batch_nd # line: 10076
+from tensorflow.python.ops.gen_array_ops import tensor_scatter_add as tensor_scatter_nd_add # line: 11226
+from tensorflow.python.ops.gen_array_ops import tensor_scatter_max as tensor_scatter_nd_max # line: 11382
+from tensorflow.python.ops.gen_array_ops import tensor_scatter_min as tensor_scatter_nd_min # line: 11488
+from tensorflow.python.ops.gen_array_ops import tensor_scatter_sub as tensor_scatter_nd_sub # line: 11583
+from tensorflow.python.ops.gen_array_ops import tile # line: 11970
+from tensorflow.python.ops.gen_array_ops import unravel_index # line: 12731
+from tensorflow.python.ops.gen_control_flow_ops import no_op # line: 475
+from tensorflow.python.ops.gen_data_flow_ops import dynamic_partition # line: 594
+from tensorflow.python.ops.gen_data_flow_ops import dynamic_stitch # line: 736
+from tensorflow.python.ops.gen_linalg_ops import matrix_square_root # line: 1913
+from tensorflow.python.ops.gen_logging_ops import timestamp # line: 886
+from tensorflow.python.ops.gen_math_ops import acosh # line: 231
+from tensorflow.python.ops.gen_math_ops import asin # line: 991
+from tensorflow.python.ops.gen_math_ops import asinh # line: 1091
+from tensorflow.python.ops.gen_math_ops import atan # line: 1184
+from tensorflow.python.ops.gen_math_ops import atan2 # line: 1284
+from tensorflow.python.ops.gen_math_ops import atanh # line: 1383
+from tensorflow.python.ops.gen_math_ops import cos # line: 2464
+from tensorflow.python.ops.gen_math_ops import cosh # line: 2558
+from tensorflow.python.ops.gen_math_ops import greater # line: 4186
+from tensorflow.python.ops.gen_math_ops import greater_equal # line: 4287
+from tensorflow.python.ops.gen_math_ops import less # line: 5223
+from tensorflow.python.ops.gen_math_ops import less_equal # line: 5324
+from tensorflow.python.ops.gen_math_ops import logical_and # line: 5779
+from tensorflow.python.ops.gen_math_ops import logical_not # line: 5918
+from tensorflow.python.ops.gen_math_ops import logical_or # line: 6005
+from tensorflow.python.ops.gen_math_ops import maximum # line: 6310
+from tensorflow.python.ops.gen_math_ops import minimum # line: 6566
+from tensorflow.python.ops.gen_math_ops import neg as negative # line: 6913
+from tensorflow.python.ops.gen_math_ops import real_div as realdiv # line: 8068
+from tensorflow.python.ops.gen_math_ops import sin # line: 10299
+from tensorflow.python.ops.gen_math_ops import sinh # line: 10392
+from tensorflow.python.ops.gen_math_ops import square # line: 11962
+from tensorflow.python.ops.gen_math_ops import tan # line: 12352
+from tensorflow.python.ops.gen_math_ops import tanh # line: 12446
+from tensorflow.python.ops.gen_math_ops import truncate_div as truncatediv # line: 12601
+from tensorflow.python.ops.gen_math_ops import truncate_mod as truncatemod # line: 12695
+from tensorflow.python.ops.gen_nn_ops import approx_top_k # line: 33
+from tensorflow.python.ops.gen_nn_ops import conv # line: 1061
+from tensorflow.python.ops.gen_nn_ops import conv2d_backprop_filter_v2 # line: 1609
+from tensorflow.python.ops.gen_nn_ops import conv2d_backprop_input_v2 # line: 1977
+from tensorflow.python.ops.gen_ragged_array_ops import ragged_fill_empty_rows # line: 196
+from tensorflow.python.ops.gen_ragged_array_ops import ragged_fill_empty_rows_grad # line: 305
+from tensorflow.python.ops.gen_random_index_shuffle_ops import random_index_shuffle # line: 30
+from tensorflow.python.ops.gen_spectral_ops import fftnd # line: 620
+from tensorflow.python.ops.gen_spectral_ops import ifftnd # line: 991
+from tensorflow.python.ops.gen_spectral_ops import irfftnd # line: 1347
+from tensorflow.python.ops.gen_spectral_ops import rfftnd # line: 1707
+from tensorflow.python.ops.gen_string_ops import as_string # line: 29
+from tensorflow.python.data.ops.optional_ops import OptionalSpec # line: 205
+from tensorflow.python.eager.backprop import GradientTape # line: 704
+from tensorflow.python.eager.context import executing_eagerly # line: 2293
+from tensorflow.python.eager.polymorphic_function.polymorphic_function import function # line: 1276
+from tensorflow.python.framework.constant_op import constant # line: 172
+from tensorflow.python.framework.device_spec import DeviceSpecV2 as DeviceSpec # line: 46
+from tensorflow.python.framework.dtypes import DType # line: 51
+from tensorflow.python.framework.dtypes import as_dtype # line: 793
+from tensorflow.python.framework.dtypes import bfloat16 # line: 450
+from tensorflow.python.framework.dtypes import bool # line: 414
+from tensorflow.python.framework.dtypes import complex128 # line: 401
+from tensorflow.python.framework.dtypes import complex64 # line: 394
+from tensorflow.python.framework.dtypes import double # line: 388
+from tensorflow.python.framework.dtypes import float16 # line: 373
+from tensorflow.python.framework.dtypes import float32 # line: 380
+from tensorflow.python.framework.dtypes import float64 # line: 386
+from tensorflow.python.framework.dtypes import half # line: 374
+from tensorflow.python.framework.dtypes import int16 # line: 354
+from tensorflow.python.framework.dtypes import int32 # line: 360
+from tensorflow.python.framework.dtypes import int64 # line: 366
+from tensorflow.python.framework.dtypes import int8 # line: 348
+from tensorflow.python.framework.dtypes import qint16 # line: 426
+from tensorflow.python.framework.dtypes import qint32 # line: 432
+from tensorflow.python.framework.dtypes import qint8 # line: 420
+from tensorflow.python.framework.dtypes import quint16 # line: 444
+from tensorflow.python.framework.dtypes import quint8 # line: 438
+from tensorflow.python.framework.dtypes import resource # line: 312
+from tensorflow.python.framework.dtypes import string # line: 408
+from tensorflow.python.framework.dtypes import uint16 # line: 330
+from tensorflow.python.framework.dtypes import uint32 # line: 336
+from tensorflow.python.framework.dtypes import uint64 # line: 342
+from tensorflow.python.framework.dtypes import uint8 # line: 324
+from tensorflow.python.framework.dtypes import variant # line: 318
+from tensorflow.python.framework.importer import import_graph_def # line: 354
+from tensorflow.python.framework.indexed_slices import IndexedSlices # line: 54
+from tensorflow.python.framework.indexed_slices import IndexedSlicesSpec # line: 203
+from tensorflow.python.framework.load_library import load_library # line: 120
+from tensorflow.python.framework.load_library import load_op_library # line: 31
+from tensorflow.python.framework.ops import Graph # line: 1912
+from tensorflow.python.framework.ops import Operation # line: 1032
+from tensorflow.python.framework.ops import RegisterGradient # line: 1638
+from tensorflow.python.framework.ops import control_dependencies # line: 4425
+from tensorflow.python.framework.ops import device_v2 as device # line: 4338
+from tensorflow.python.framework.ops import get_current_name_scope # line: 5576
+from tensorflow.python.framework.ops import init_scope # line: 4626
+from tensorflow.python.framework.ops import inside_function # line: 4764
+from tensorflow.python.framework.ops import is_symbolic_tensor # line: 6076
+from tensorflow.python.framework.ops import name_scope_v2 as name_scope # line: 5611
+from tensorflow.python.framework.ops import no_gradient # line: 1687
+from tensorflow.python.framework.sparse_tensor import SparseTensor # line: 47
+from tensorflow.python.framework.sparse_tensor import SparseTensorSpec # line: 377
+from tensorflow.python.framework.tensor import Tensor # line: 139
+from tensorflow.python.framework.tensor import TensorSpec # line: 918
+from tensorflow.python.framework.tensor_conversion import convert_to_tensor_v2_with_dispatch as convert_to_tensor # line: 96
+from tensorflow.python.framework.tensor_conversion_registry import register_tensor_conversion_function # line: 80
+from tensorflow.python.framework.tensor_shape import TensorShape # line: 747
+from tensorflow.python.framework.tensor_util import constant_value as get_static_value # line: 896
+from tensorflow.python.framework.tensor_util import is_tf_type as is_tensor # line: 1128
+from tensorflow.python.framework.tensor_util import MakeNdarray as make_ndarray # line: 633
+from tensorflow.python.framework.tensor_util import make_tensor_proto # line: 425
+from tensorflow.python.framework.type_spec import TypeSpec # line: 50
+from tensorflow.python.framework.type_spec import type_spec_from_value # line: 959
+from tensorflow.python.framework.versions import COMPILER_VERSION as __compiler_version__ # line: 41
+from tensorflow.python.framework.versions import CXX11_ABI_FLAG as __cxx11_abi_flag__ # line: 48
+from tensorflow.python.framework.versions import CXX_VERSION as __cxx_version__ # line: 54
+from tensorflow.python.framework.versions import GIT_VERSION as __git_version__ # line: 35
+from tensorflow.python.framework.versions import MONOLITHIC_BUILD as __monolithic_build__ # line: 60
+from tensorflow.python.framework.versions import VERSION as __version__ # line: 29
+from tensorflow.python.module.module import Module # line: 29
+from tensorflow.python.ops.array_ops import batch_to_space_v2 as batch_to_space # line: 4096
+from tensorflow.python.ops.array_ops import boolean_mask_v2 as boolean_mask # line: 1785
+from tensorflow.python.ops.array_ops import broadcast_dynamic_shape # line: 528
+from tensorflow.python.ops.array_ops import broadcast_static_shape # line: 562
+from tensorflow.python.ops.array_ops import concat # line: 1596
+from tensorflow.python.ops.array_ops import edit_distance # line: 3770
+from tensorflow.python.ops.array_ops import expand_dims_v2 as expand_dims # line: 393
+from tensorflow.python.ops.array_ops import fill # line: 206
+from tensorflow.python.ops.array_ops import fingerprint # line: 6587
+from tensorflow.python.ops.array_ops import gather_v2 as gather # line: 5243
+from tensorflow.python.ops.array_ops import gather_nd_v2 as gather_nd # line: 5661
+from tensorflow.python.ops.array_ops import guarantee_const # line: 6926
+from tensorflow.python.ops.array_ops import identity # line: 255
+from tensorflow.python.ops.array_ops import meshgrid # line: 3624
+from tensorflow.python.ops.array_ops import newaxis # line: 58
+from tensorflow.python.ops.array_ops import one_hot # line: 4234
+from tensorflow.python.ops.array_ops import ones # line: 3163
+from tensorflow.python.ops.array_ops import ones_like_v2 as ones_like # line: 3095
+from tensorflow.python.ops.array_ops import pad_v2 as pad # line: 3443
+from tensorflow.python.ops.array_ops import parallel_stack # line: 1414
+from tensorflow.python.ops.array_ops import rank # line: 879
+from tensorflow.python.ops.array_ops import repeat # line: 6872
+from tensorflow.python.ops.array_ops import required_space_to_batch_paddings # line: 3928
+from tensorflow.python.ops.array_ops import reshape # line: 65
+from tensorflow.python.ops.array_ops import reverse_sequence_v2 as reverse_sequence # line: 4970
+from tensorflow.python.ops.array_ops import searchsorted # line: 6330
+from tensorflow.python.ops.array_ops import sequence_mask # line: 4412
+from tensorflow.python.ops.array_ops import shape_v2 as shape # line: 598
+from tensorflow.python.ops.array_ops import shape_n # line: 732
+from tensorflow.python.ops.array_ops import size_v2 as size # line: 762
+from tensorflow.python.ops.array_ops import slice # line: 1171
+from tensorflow.python.ops.array_ops import space_to_batch_v2 as space_to_batch # line: 4031
+from tensorflow.python.ops.array_ops import split # line: 1990
+from tensorflow.python.ops.array_ops import squeeze_v2 as squeeze # line: 4534
+from tensorflow.python.ops.array_ops import stop_gradient # line: 6945
+from tensorflow.python.ops.array_ops import strided_slice # line: 1227
+from tensorflow.python.ops.array_ops import tensor_scatter_nd_update # line: 5757
+from tensorflow.python.ops.array_ops import transpose_v2 as transpose # line: 2072
+from tensorflow.python.ops.array_ops import unique # line: 1889
+from tensorflow.python.ops.array_ops import unique_with_counts # line: 1937
+from tensorflow.python.ops.array_ops import where_v2 as where # line: 4691
+from tensorflow.python.ops.array_ops import zeros # line: 2845
+from tensorflow.python.ops.array_ops import zeros_like_v2 as zeros_like # line: 2947
+from tensorflow.python.ops.array_ops_stack import stack # line: 24
+from tensorflow.python.ops.array_ops_stack import unstack # line: 88
+from tensorflow.python.ops.batch_ops import batch_function as nondifferentiable_batch_function # line: 28
+from tensorflow.python.ops.check_ops import assert_equal_v2 as assert_equal # line: 762
+from tensorflow.python.ops.check_ops import assert_greater_v2 as assert_greater # line: 978
+from tensorflow.python.ops.check_ops import assert_less_v2 as assert_less # line: 942
+from tensorflow.python.ops.check_ops import assert_rank_v2 as assert_rank # line: 1064
+from tensorflow.python.ops.check_ops import ensure_shape # line: 2218
+from tensorflow.python.ops.clip_ops import clip_by_global_norm # line: 288
+from tensorflow.python.ops.clip_ops import clip_by_norm # line: 150
+from tensorflow.python.ops.clip_ops import clip_by_value # line: 32
+from tensorflow.python.ops.cond import cond_for_tf_v2 as cond # line: 243
+from tensorflow.python.ops.control_flow_assert import Assert # line: 62
+from tensorflow.python.ops.control_flow_case import case_v2 as case # line: 33
+from tensorflow.python.ops.control_flow_ops import group # line: 1958
+from tensorflow.python.ops.control_flow_ops import tuple_v2 as tuple # line: 2037
+from tensorflow.python.ops.control_flow_switch_case import switch_case # line: 181
+from tensorflow.python.ops.critical_section_ops import CriticalSection # line: 121
+from tensorflow.python.ops.custom_gradient import custom_gradient # line: 45
+from tensorflow.python.ops.custom_gradient import grad_pass_through # line: 777
+from tensorflow.python.ops.custom_gradient import recompute_grad # line: 604
+from tensorflow.python.ops.functional_ops import foldl_v2 as foldl # line: 161
+from tensorflow.python.ops.functional_ops import foldr_v2 as foldr # line: 358
+from tensorflow.python.ops.functional_ops import scan_v2 as scan # line: 691
+from tensorflow.python.ops.gradients_impl import gradients_v2 as gradients # line: 184
+from tensorflow.python.ops.gradients_impl import HessiansV2 as hessians # line: 451
+from tensorflow.python.ops.gradients_util import AggregationMethod # line: 943
+from tensorflow.python.ops.histogram_ops import histogram_fixed_width # line: 103
+from tensorflow.python.ops.histogram_ops import histogram_fixed_width_bins # line: 31
+from tensorflow.python.ops.init_ops_v2 import Constant as constant_initializer # line: 204
+from tensorflow.python.ops.init_ops_v2 import Ones as ones_initializer # line: 157
+from tensorflow.python.ops.init_ops_v2 import RandomNormal as random_normal_initializer # line: 371
+from tensorflow.python.ops.init_ops_v2 import RandomUniform as random_uniform_initializer # line: 302
+from tensorflow.python.ops.init_ops_v2 import Zeros as zeros_initializer # line: 110
+from tensorflow.python.ops.linalg_ops import eig # line: 382
+from tensorflow.python.ops.linalg_ops import eigvals # line: 414
+from tensorflow.python.ops.linalg_ops import eye # line: 196
+from tensorflow.python.ops.linalg_ops import norm_v2 as norm # line: 561
+from tensorflow.python.ops.logging_ops import print_v2 as print # line: 147
+from tensorflow.python.ops.manip_ops import roll # line: 27
+from tensorflow.python.ops.map_fn import map_fn_v2 as map_fn # line: 614
+from tensorflow.python.ops.math_ops import abs # line: 365
+from tensorflow.python.ops.math_ops import acos # line: 5747
+from tensorflow.python.ops.math_ops import add # line: 4020
+from tensorflow.python.ops.math_ops import add_n # line: 4101
+from tensorflow.python.ops.math_ops import argmax_v2 as argmax # line: 268
+from tensorflow.python.ops.math_ops import argmin_v2 as argmin # line: 322
+from tensorflow.python.ops.math_ops import cast # line: 944
+from tensorflow.python.ops.math_ops import complex # line: 699
+from tensorflow.python.ops.math_ops import cumsum # line: 4352
+from tensorflow.python.ops.math_ops import divide # line: 446
+from tensorflow.python.ops.math_ops import equal # line: 2003
+from tensorflow.python.ops.math_ops import exp # line: 5644
+from tensorflow.python.ops.math_ops import floor # line: 5778
+from tensorflow.python.ops.math_ops import linspace_nd as linspace # line: 112
+from tensorflow.python.ops.math_ops import matmul # line: 3617
+from tensorflow.python.ops.math_ops import multiply # line: 481
+from tensorflow.python.ops.math_ops import not_equal # line: 2040
+from tensorflow.python.ops.math_ops import pow # line: 669
+from tensorflow.python.ops.math_ops import range # line: 2163
+from tensorflow.python.ops.math_ops import reduce_all # line: 3306
+from tensorflow.python.ops.math_ops import reduce_any # line: 3412
+from tensorflow.python.ops.math_ops import reduce_logsumexp # line: 3517
+from tensorflow.python.ops.math_ops import reduce_max # line: 3187
+from tensorflow.python.ops.math_ops import reduce_mean # line: 2713
+from tensorflow.python.ops.math_ops import reduce_min # line: 3059
+from tensorflow.python.ops.math_ops import reduce_prod # line: 2887
+from tensorflow.python.ops.math_ops import reduce_sum # line: 2369
+from tensorflow.python.ops.math_ops import round # line: 914
+from tensorflow.python.ops.math_ops import saturate_cast # line: 1028
+from tensorflow.python.ops.math_ops import scalar_mul_v2 as scalar_mul # line: 660
+from tensorflow.python.ops.math_ops import sigmoid # line: 4254
+from tensorflow.python.ops.math_ops import sign # line: 747
+from tensorflow.python.ops.math_ops import sqrt # line: 5605
+from tensorflow.python.ops.math_ops import subtract # line: 545
+from tensorflow.python.ops.math_ops import tensordot # line: 5152
+from tensorflow.python.ops.math_ops import truediv # line: 1630
+from tensorflow.python.ops.parallel_for.control_flow_ops import vectorized_map # line: 452
+from tensorflow.python.ops.ragged.ragged_tensor import RaggedTensor # line: 65
+from tensorflow.python.ops.ragged.ragged_tensor import RaggedTensorSpec # line: 2319
+from tensorflow.python.ops.script_ops import numpy_function # line: 804
+from tensorflow.python.ops.script_ops import eager_py_func as py_function # line: 461
+from tensorflow.python.ops.sort_ops import argsort # line: 86
+from tensorflow.python.ops.sort_ops import sort # line: 29
+from tensorflow.python.ops.special_math_ops import einsum # line: 618
+from tensorflow.python.ops.tensor_array_ops import TensorArray # line: 971
+from tensorflow.python.ops.tensor_array_ops import TensorArraySpec # line: 1363
+from tensorflow.python.ops.unconnected_gradients import UnconnectedGradients # line: 22
+from tensorflow.python.ops.variable_scope import variable_creator_scope # line: 2784
+from tensorflow.python.ops.variables import Variable # line: 201
+from tensorflow.python.ops.variables import VariableAggregationV2 as VariableAggregation # line: 89
+from tensorflow.python.ops.variables import VariableSynchronization # line: 61
+from tensorflow.python.ops.while_loop import while_loop_v2 as while_loop # line: 35
+from tensorflow.python.platform.tf_logging import get_logger # line: 93
+
+
+
+
+# Hook external TensorFlow modules.
+_current_module = _sys.modules[__name__]
+try:
+  from tensorboard.summary._tf import summary
+  _current_module.__path__ = (
+      [_module_util.get_parent_dir(summary)] + _current_module.__path__)
+  setattr(_current_module, "summary", summary)
+except ImportError:
+  _logging.warning(
+      "Limited tf.compat.v2.summary API due to missing TensorBoard "
+      "installation.")
+
+# Lazy-load estimator.
+_estimator_module = "tensorflow_estimator.python.estimator.api._v2.estimator"
+estimator = _LazyLoader("estimator", globals(), _estimator_module)
+_module_dir = _module_util.get_parent_dir_for_name(_estimator_module)
+if _module_dir:
+  _current_module.__path__ = [_module_dir] + _current_module.__path__
+setattr(_current_module, "estimator", estimator)
+
+# Lazy load Keras v2
+_tf_uses_legacy_keras = (
+    _os.environ.get("TF_USE_LEGACY_KERAS", None) in ("true", "True", "1"))
+setattr(_current_module, "keras", _KerasLazyLoader(globals(), mode="v2"))
+if _tf_uses_legacy_keras:
+  _module_dir = _module_util.get_parent_dir_for_name("tf_keras.api._v2.keras")
+else:
+  _module_dir = _module_util.get_parent_dir_for_name("keras.api._v2.keras")
+_current_module.__path__ = [_module_dir] + _current_module.__path__
+
+
+# We would like the following to work for fully enabling 2.0 in a 1.0 install:
+#
+# import tensorflow.compat.v2 as tf
+# tf.enable_v2_behavior()
+#
+# This make this one symbol available directly.
+from tensorflow.python.compat.v2_compat import enable_v2_behavior  # pylint: disable=g-import-not-at-top
+setattr(_current_module, "enable_v2_behavior", enable_v2_behavior)
+
+# Add module aliases
+_losses = _KerasLazyLoader(
+    globals(), submodule="losses", name="losses", mode="v2")
+_metrics = _KerasLazyLoader(
+    globals(), submodule="metrics", name="metrics", mode="v2")
+_optimizers = _KerasLazyLoader(
+    globals(), submodule="optimizers", name="optimizers", mode="v2")
+_initializers = _KerasLazyLoader(
+    globals(), submodule="initializers", name="initializers", mode="v2")
+setattr(_current_module, "losses", _losses)
+setattr(_current_module, "metrics", _metrics)
+setattr(_current_module, "optimizers", _optimizers)
+setattr(_current_module, "initializers", _initializers)
+
+# Explicitly import lazy-loaded modules to support autocompletion.
+# pylint: disable=g-import-not-at-top
+if _typing.TYPE_CHECKING:
+  from tensorflow_estimator.python.estimator.api._v2 import estimator as estimator
+# pylint: enable=g-import-not-at-top
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/compat/v2/compat/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/compat/v2/compat/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb0d3fe4d154a776dae088bb83eee5f8a7b4cdf8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/compat/v2/compat/__init__.py
@@ -0,0 +1,22 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.compat namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.compat import v1
+from tensorflow._api.v2.compat.v2.compat import v2
+from tensorflow.python.compat.compat import forward_compatibility_horizon # line: 125
+from tensorflow.python.compat.compat import forward_compatible # line: 65
+from tensorflow.python.framework.tensor_shape import dimension_at_index # line: 139
+from tensorflow.python.framework.tensor_shape import dimension_value # line: 103
+from tensorflow.python.util.compat import as_bytes # line: 121
+from tensorflow.python.util.compat import as_str # line: 122
+from tensorflow.python.util.compat import as_str_any # line: 125
+from tensorflow.python.util.compat import as_text # line: 120
+from tensorflow.python.util.compat import bytes_or_text_types # line: 216
+from tensorflow.python.util.compat import complex_types # line: 212
+from tensorflow.python.util.compat import integral_types # line: 208
+from tensorflow.python.util.compat import path_to_str # line: 145
+from tensorflow.python.util.compat import real_types # line: 210
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/config/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..62a58f1ca0d491a4fe837dcd2cab79e632efe604
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/config/__init__.py
@@ -0,0 +1,27 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.config namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.config import experimental
+from tensorflow._api.v2.compat.v2.config import optimizer
+from tensorflow._api.v2.compat.v2.config import threading
+from tensorflow.python.eager.context import LogicalDevice # line: 290
+from tensorflow.python.eager.context import LogicalDeviceConfiguration # line: 308
+from tensorflow.python.eager.context import PhysicalDevice # line: 345
+from tensorflow.python.eager.polymorphic_function.eager_function_run import experimental_functions_run_eagerly # line: 108
+from tensorflow.python.eager.polymorphic_function.eager_function_run import experimental_run_functions_eagerly # line: 82
+from tensorflow.python.eager.polymorphic_function.eager_function_run import functions_run_eagerly # line: 25
+from tensorflow.python.eager.polymorphic_function.eager_function_run import run_functions_eagerly # line: 31
+from tensorflow.python.eager.remote import connect_to_cluster as experimental_connect_to_cluster # line: 76
+from tensorflow.python.eager.remote import connect_to_remote_host as experimental_connect_to_host # line: 37
+from tensorflow.python.framework.config import get_logical_device_configuration # line: 802
+from tensorflow.python.framework.config import get_soft_device_placement # line: 257
+from tensorflow.python.framework.config import get_visible_devices # line: 500
+from tensorflow.python.framework.config import list_logical_devices # line: 461
+from tensorflow.python.framework.config import list_physical_devices # line: 424
+from tensorflow.python.framework.config import set_logical_device_configuration # line: 843
+from tensorflow.python.framework.config import set_soft_device_placement # line: 283
+from tensorflow.python.framework.config import set_visible_devices # line: 533
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/config/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/config/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..47b2375fa234f413b58109b5e20c4303269866b2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/config/experimental/__init__.py
@@ -0,0 +1,30 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.config.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.eager.context import LogicalDeviceConfiguration as VirtualDeviceConfiguration # line: 308
+from tensorflow.python.framework.config import disable_mlir_bridge # line: 926
+from tensorflow.python.framework.config import enable_mlir_bridge # line: 915
+from tensorflow.python.framework.config import enable_op_determinism # line: 932
+from tensorflow.python.framework.config import enable_tensor_float_32_execution # line: 46
+from tensorflow.python.framework.config import get_device_details # line: 757
+from tensorflow.python.framework.config import get_device_policy # line: 312
+from tensorflow.python.framework.config import get_memory_growth # line: 700
+from tensorflow.python.framework.config import get_memory_info # line: 571
+from tensorflow.python.framework.config import get_memory_usage # line: 664
+from tensorflow.python.framework.config import get_synchronous_execution # line: 386
+from tensorflow.python.framework.config import get_logical_device_configuration as get_virtual_device_configuration # line: 802
+from tensorflow.python.framework.config import get_visible_devices # line: 500
+from tensorflow.python.framework.config import list_logical_devices # line: 461
+from tensorflow.python.framework.config import list_physical_devices # line: 424
+from tensorflow.python.framework.config import reset_memory_stats # line: 620
+from tensorflow.python.framework.config import set_device_policy # line: 340
+from tensorflow.python.framework.config import set_memory_growth # line: 729
+from tensorflow.python.framework.config import set_synchronous_execution # line: 399
+from tensorflow.python.framework.config import set_logical_device_configuration as set_virtual_device_configuration # line: 843
+from tensorflow.python.framework.config import set_visible_devices # line: 533
+from tensorflow.python.framework.config import tensor_float_32_execution_enabled # line: 27
+from tensorflow.python.training.server_lib import ClusterDeviceFilters # line: 495
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/config/optimizer/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/config/optimizer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..99ac9b167e6467f647d0bf99079ec8048399c91f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/config/optimizer/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.config.optimizer namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.config import get_optimizer_experimental_options as get_experimental_options # line: 198
+from tensorflow.python.framework.config import get_optimizer_jit as get_jit # line: 158
+from tensorflow.python.framework.config import set_optimizer_experimental_options as set_experimental_options # line: 214
+from tensorflow.python.framework.config import set_optimizer_jit as set_jit # line: 172
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/config/threading/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/config/threading/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e912751472b5ee8cecd9df43957afe403ac6c083
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/config/threading/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.config.threading namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.config import get_inter_op_parallelism_threads # line: 132
+from tensorflow.python.framework.config import get_intra_op_parallelism_threads # line: 104
+from tensorflow.python.framework.config import set_inter_op_parallelism_threads # line: 145
+from tensorflow.python.framework.config import set_intra_op_parallelism_threads # line: 118
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/data/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..522c2df35cf05ebe090f307c229e73de0b6d605a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/data/__init__.py
@@ -0,0 +1,21 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.data namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.data import experimental
+from tensorflow.python.data.ops.dataset_ops import AUTOTUNE # line: 102
+from tensorflow.python.data.ops.dataset_ops import DatasetV2 as Dataset # line: 135
+from tensorflow.python.data.ops.dataset_ops import DatasetSpec # line: 4542
+from tensorflow.python.data.ops.dataset_ops import INFINITE as INFINITE_CARDINALITY # line: 112
+from tensorflow.python.data.ops.dataset_ops import NumpyIterator # line: 4707
+from tensorflow.python.data.ops.dataset_ops import UNKNOWN as UNKNOWN_CARDINALITY # line: 113
+from tensorflow.python.data.ops.iterator_ops import IteratorBase as Iterator # line: 557
+from tensorflow.python.data.ops.iterator_ops import IteratorSpec # line: 913
+from tensorflow.python.data.ops.options import Options # line: 493
+from tensorflow.python.data.ops.options import ThreadingOptions # line: 449
+from tensorflow.python.data.ops.readers import FixedLengthRecordDatasetV2 as FixedLengthRecordDataset # line: 569
+from tensorflow.python.data.ops.readers import TFRecordDatasetV2 as TFRecordDataset # line: 386
+from tensorflow.python.data.ops.readers import TextLineDatasetV2 as TextLineDataset # line: 171
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/data/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/data/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fabbe16bf25538ee3d60c41975e0ea3ab8e611f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/data/experimental/__init__.py
@@ -0,0 +1,67 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.data.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.data.experimental import service
+from tensorflow.python.data.experimental.ops.batching import dense_to_ragged_batch # line: 30
+from tensorflow.python.data.experimental.ops.batching import dense_to_sparse_batch # line: 94
+from tensorflow.python.data.experimental.ops.batching import map_and_batch # line: 210
+from tensorflow.python.data.experimental.ops.batching import unbatch # line: 269
+from tensorflow.python.data.experimental.ops.cardinality import INFINITE as INFINITE_CARDINALITY # line: 26
+from tensorflow.python.data.experimental.ops.cardinality import UNKNOWN as UNKNOWN_CARDINALITY # line: 28
+from tensorflow.python.data.experimental.ops.cardinality import assert_cardinality # line: 67
+from tensorflow.python.data.experimental.ops.cardinality import cardinality # line: 33
+from tensorflow.python.data.experimental.ops.counter import CounterV2 as Counter # line: 23
+from tensorflow.python.data.experimental.ops.distribute import SHARD_HINT # line: 31
+from tensorflow.python.data.experimental.ops.enumerate_ops import enumerate_dataset # line: 21
+from tensorflow.python.data.experimental.ops.error_ops import ignore_errors # line: 20
+from tensorflow.python.data.experimental.ops.from_list import from_list # line: 75
+from tensorflow.python.data.experimental.ops.get_single_element import get_single_element # line: 22
+from tensorflow.python.data.experimental.ops.grouping import Reducer # line: 388
+from tensorflow.python.data.experimental.ops.grouping import bucket_by_sequence_length # line: 112
+from tensorflow.python.data.experimental.ops.grouping import group_by_reducer # line: 28
+from tensorflow.python.data.experimental.ops.grouping import group_by_window # line: 59
+from tensorflow.python.data.experimental.ops.interleave_ops import choose_from_datasets_v2 as choose_from_datasets # line: 175
+from tensorflow.python.data.experimental.ops.interleave_ops import parallel_interleave # line: 28
+from tensorflow.python.data.experimental.ops.interleave_ops import sample_from_datasets_v2 as sample_from_datasets # line: 91
+from tensorflow.python.data.experimental.ops.io import load # line: 108
+from tensorflow.python.data.experimental.ops.io import save # line: 26
+from tensorflow.python.data.experimental.ops.iterator_ops import CheckpointInputPipelineHook # line: 105
+from tensorflow.python.data.experimental.ops.iterator_ops import make_saveable_from_iterator # line: 43
+from tensorflow.python.data.experimental.ops.lookup_ops import DatasetInitializer # line: 54
+from tensorflow.python.data.experimental.ops.lookup_ops import index_table_from_dataset # line: 190
+from tensorflow.python.data.experimental.ops.lookup_ops import table_from_dataset # line: 102
+from tensorflow.python.data.experimental.ops.pad_to_cardinality import pad_to_cardinality # line: 26
+from tensorflow.python.data.experimental.ops.parsing_ops import parse_example_dataset # line: 105
+from tensorflow.python.data.experimental.ops.prefetching_ops import copy_to_device # line: 65
+from tensorflow.python.data.experimental.ops.prefetching_ops import prefetch_to_device # line: 33
+from tensorflow.python.data.experimental.ops.random_access import at # line: 22
+from tensorflow.python.data.experimental.ops.random_ops import RandomDatasetV2 as RandomDataset # line: 27
+from tensorflow.python.data.experimental.ops.readers import CsvDatasetV2 as CsvDataset # line: 668
+from tensorflow.python.data.experimental.ops.readers import SqlDatasetV2 as SqlDataset # line: 1156
+from tensorflow.python.data.experimental.ops.readers import make_batched_features_dataset_v2 as make_batched_features_dataset # line: 916
+from tensorflow.python.data.experimental.ops.readers import make_csv_dataset_v2 as make_csv_dataset # line: 326
+from tensorflow.python.data.experimental.ops.resampling import rejection_resample # line: 21
+from tensorflow.python.data.experimental.ops.scan_ops import scan # line: 21
+from tensorflow.python.data.experimental.ops.shuffle_ops import shuffle_and_repeat # line: 62
+from tensorflow.python.data.experimental.ops.snapshot import snapshot # line: 190
+from tensorflow.python.data.experimental.ops.take_while_ops import take_while # line: 21
+from tensorflow.python.data.experimental.ops.unique import unique # line: 21
+from tensorflow.python.data.experimental.ops.writers import TFRecordWriter # line: 27
+from tensorflow.python.data.ops.dataset_ops import AUTOTUNE # line: 104
+from tensorflow.python.data.ops.dataset_ops import from_variant # line: 4514
+from tensorflow.python.data.ops.dataset_ops import get_structure # line: 4349
+from tensorflow.python.data.ops.dataset_ops import to_variant # line: 4529
+from tensorflow.python.data.ops.debug_mode import enable_debug_mode # line: 24
+from tensorflow.python.data.ops.iterator_ops import get_next_as_optional # line: 999
+from tensorflow.python.data.ops.optional_ops import Optional # line: 31
+from tensorflow.python.data.ops.options import AutoShardPolicy # line: 88
+from tensorflow.python.data.ops.options import AutotuneAlgorithm # line: 30
+from tensorflow.python.data.ops.options import AutotuneOptions # line: 193
+from tensorflow.python.data.ops.options import DistributeOptions # line: 262
+from tensorflow.python.data.ops.options import ExternalStatePolicy # line: 155
+from tensorflow.python.data.ops.options import OptimizationOptions # line: 304
+from tensorflow.python.data.ops.options import ThreadingOptions # line: 449
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/data/experimental/service/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/data/experimental/service/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..40450071641553532311f453e8900bd584061343
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/data/experimental/service/__init__.py
@@ -0,0 +1,16 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.data.experimental.service namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.data.experimental.ops.data_service_ops import CrossTrainerCache # line: 117
+from tensorflow.python.data.experimental.ops.data_service_ops import ShardingPolicy # line: 46
+from tensorflow.python.data.experimental.ops.data_service_ops import distribute # line: 531
+from tensorflow.python.data.experimental.ops.data_service_ops import from_dataset_id # line: 1045
+from tensorflow.python.data.experimental.ops.data_service_ops import register_dataset # line: 839
+from tensorflow.python.data.experimental.service.server_lib import DispatchServer # line: 130
+from tensorflow.python.data.experimental.service.server_lib import DispatcherConfig # line: 44
+from tensorflow.python.data.experimental.service.server_lib import WorkerConfig # line: 292
+from tensorflow.python.data.experimental.service.server_lib import WorkerServer # line: 348
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/debugging/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/debugging/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..daccbba7444e0b6a95b515299a418ec2a847e611
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/debugging/__init__.py
@@ -0,0 +1,39 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.debugging namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.debugging import experimental
+from tensorflow.python.ops.gen_array_ops import check_numerics # line: 950
+from tensorflow.python.debug.lib.check_numerics_callback import disable_check_numerics # line: 445
+from tensorflow.python.debug.lib.check_numerics_callback import enable_check_numerics # line: 336
+from tensorflow.python.eager.context import get_log_device_placement # line: 2520
+from tensorflow.python.eager.context import set_log_device_placement # line: 2530
+from tensorflow.python.ops.check_ops import assert_equal_v2 as assert_equal # line: 762
+from tensorflow.python.ops.check_ops import assert_greater_v2 as assert_greater # line: 978
+from tensorflow.python.ops.check_ops import assert_greater_equal_v2 as assert_greater_equal # line: 996
+from tensorflow.python.ops.check_ops import assert_integer_v2 as assert_integer # line: 1427
+from tensorflow.python.ops.check_ops import assert_less_v2 as assert_less # line: 942
+from tensorflow.python.ops.check_ops import assert_less_equal_v2 as assert_less_equal # line: 959
+from tensorflow.python.ops.check_ops import assert_near_v2 as assert_near # line: 803
+from tensorflow.python.ops.check_ops import assert_negative_v2 as assert_negative # line: 543
+from tensorflow.python.ops.check_ops import assert_non_negative_v2 as assert_non_negative # line: 650
+from tensorflow.python.ops.check_ops import assert_non_positive_v2 as assert_non_positive # line: 706
+from tensorflow.python.ops.check_ops import assert_none_equal_v2 as assert_none_equal # line: 783
+from tensorflow.python.ops.check_ops import assert_positive_v2 as assert_positive # line: 597
+from tensorflow.python.ops.check_ops import assert_proper_iterable # line: 511
+from tensorflow.python.ops.check_ops import assert_rank_v2 as assert_rank # line: 1064
+from tensorflow.python.ops.check_ops import assert_rank_at_least_v2 as assert_rank_at_least # line: 1162
+from tensorflow.python.ops.check_ops import assert_rank_in_v2 as assert_rank_in # line: 1329
+from tensorflow.python.ops.check_ops import assert_same_float_dtype # line: 2118
+from tensorflow.python.ops.check_ops import assert_scalar_v2 as assert_scalar # line: 2153
+from tensorflow.python.ops.check_ops import assert_shapes_v2 as assert_shapes # line: 1611
+from tensorflow.python.ops.check_ops import assert_type_v2 as assert_type # line: 1487
+from tensorflow.python.ops.check_ops import is_numeric_tensor # line: 1951
+from tensorflow.python.ops.control_flow_assert import Assert # line: 62
+from tensorflow.python.ops.numerics import verify_tensor_all_finite_v2 as assert_all_finite # line: 50
+from tensorflow.python.util.traceback_utils import disable_traceback_filtering # line: 76
+from tensorflow.python.util.traceback_utils import enable_traceback_filtering # line: 51
+from tensorflow.python.util.traceback_utils import is_traceback_filtering_enabled # line: 32
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/debugging/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/debugging/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..027fca471e864d65283389ff5742ad6af93e34eb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/debugging/experimental/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.debugging.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.debug.lib.dumping_callback import disable_dump_debug_info # line: 863
+from tensorflow.python.debug.lib.dumping_callback import enable_dump_debug_info # line: 672
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..61702232cabe31b17b1ab5d57b6273a889d06f80
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/__init__.py
@@ -0,0 +1,36 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.distribute namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.distribute import cluster_resolver
+from tensorflow._api.v2.compat.v2.distribute import coordinator
+from tensorflow._api.v2.compat.v2.distribute import experimental
+from tensorflow.python.distribute.collective_all_reduce_strategy import CollectiveAllReduceStrategy as MultiWorkerMirroredStrategy # line: 56
+from tensorflow.python.distribute.cross_device_ops import CrossDeviceOps # line: 251
+from tensorflow.python.distribute.cross_device_ops import HierarchicalCopyAllReduce # line: 996
+from tensorflow.python.distribute.cross_device_ops import NcclAllReduce # line: 959
+from tensorflow.python.distribute.cross_device_ops import ReductionToOneDevice # line: 581
+from tensorflow.python.distribute.distribute_lib import InputContext # line: 841
+from tensorflow.python.distribute.distribute_lib import InputOptions # line: 1015
+from tensorflow.python.distribute.distribute_lib import InputReplicationMode # line: 824
+from tensorflow.python.distribute.distribute_lib import ReplicaContext # line: 3677
+from tensorflow.python.distribute.distribute_lib import RunOptions # line: 980
+from tensorflow.python.distribute.distribute_lib import Strategy # line: 2033
+from tensorflow.python.distribute.distribute_lib import StrategyExtendedV2 as StrategyExtended # line: 2401
+from tensorflow.python.distribute.distribute_lib import experimental_set_strategy # line: 584
+from tensorflow.python.distribute.distribute_lib import get_replica_context # line: 454
+from tensorflow.python.distribute.distribute_lib import get_strategy # line: 543
+from tensorflow.python.distribute.distribute_lib import has_strategy # line: 563
+from tensorflow.python.distribute.distribute_lib import in_cross_replica_context # line: 518
+from tensorflow.python.distribute.mirrored_strategy import MirroredStrategy # line: 199
+from tensorflow.python.distribute.one_device_strategy import OneDeviceStrategy # line: 38
+from tensorflow.python.distribute.parameter_server_strategy_v2 import ParameterServerStrategyV2 as ParameterServerStrategy # line: 72
+from tensorflow.python.distribute.reduce_util import ReduceOp # line: 23
+from tensorflow.python.distribute.tpu_strategy import TPUStrategyV2 as TPUStrategy # line: 242
+from tensorflow.python.training.server_lib import Server # line: 94
+from tensorflow.python.types.distribute import DistributedDatasetInterface as DistributedDataset # line: 307
+from tensorflow.python.types.distribute import DistributedIteratorInterface as DistributedIterator # line: 196
+from tensorflow.python.types.distribute import DistributedValues # line: 62
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/cluster_resolver/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/cluster_resolver/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e2c448e26130bec18027e6193802a70203571c0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/cluster_resolver/__init__.py
@@ -0,0 +1,15 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.distribute.cluster_resolver namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.distribute.cluster_resolver.cluster_resolver import ClusterResolver # line: 55
+from tensorflow.python.distribute.cluster_resolver.cluster_resolver import SimpleClusterResolver # line: 288
+from tensorflow.python.distribute.cluster_resolver.cluster_resolver import UnionClusterResolver as UnionResolver # line: 418
+from tensorflow.python.distribute.cluster_resolver.gce_cluster_resolver import GCEClusterResolver # line: 30
+from tensorflow.python.distribute.cluster_resolver.kubernetes_cluster_resolver import KubernetesClusterResolver # line: 23
+from tensorflow.python.distribute.cluster_resolver.slurm_cluster_resolver import SlurmClusterResolver # line: 163
+from tensorflow.python.distribute.cluster_resolver.tfconfig_cluster_resolver import TFConfigClusterResolver # line: 47
+from tensorflow.python.distribute.cluster_resolver.tpu_cluster_resolver import TPUClusterResolver # line: 24
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/coordinator/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/coordinator/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc20621cc409c281e2e4d4042af36724d380c0b0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/coordinator/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.distribute.coordinator namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.distribute.coordinator.cluster_coordinator import ClusterCoordinator # line: 1397
+from tensorflow.python.distribute.coordinator.coordinator_context import get_current_worker_index as experimental_get_current_worker_index # line: 81
+from tensorflow.python.distribute.coordinator.remote_value import RemoteValue # line: 53
+from tensorflow.python.distribute.coordinator.values import PerWorkerValues as PerWorkerValue # line: 152
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..604ab5939c8533793728b4aaef5b861b4e1a1a43
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/experimental/__init__.py
@@ -0,0 +1,22 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.distribute.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.distribute.experimental import coordinator
+from tensorflow._api.v2.compat.v2.distribute.experimental import partitioners
+from tensorflow._api.v2.compat.v2.distribute.experimental import rpc
+from tensorflow.python.distribute.central_storage_strategy import CentralStorageStrategy # line: 23
+from tensorflow.python.distribute.collective_all_reduce_strategy import _CollectiveAllReduceStrategyExperimental as MultiWorkerMirroredStrategy # line: 229
+from tensorflow.python.distribute.collective_util import CommunicationImplementation as CollectiveCommunication # line: 26
+from tensorflow.python.distribute.collective_util import Hints as CollectiveHints # line: 168
+from tensorflow.python.distribute.collective_util import CommunicationImplementation # line: 26
+from tensorflow.python.distribute.collective_util import _OptionsExported as CommunicationOptions # line: 50
+from tensorflow.python.distribute.distribute_lib import ValueContext # line: 914
+from tensorflow.python.distribute.failure_handling.failure_handling import PreemptionCheckpointHandler # line: 336
+from tensorflow.python.distribute.failure_handling.failure_handling import TerminationConfig # line: 74
+from tensorflow.python.distribute.failure_handling.preemption_watcher import PreemptionWatcher # line: 44
+from tensorflow.python.distribute.parameter_server_strategy_v2 import ParameterServerStrategyV2 as ParameterServerStrategy # line: 72
+from tensorflow.python.distribute.tpu_strategy import TPUStrategy # line: 667
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/experimental/coordinator/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/experimental/coordinator/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9512c26846e5de071be7322332141cc8d767684
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/experimental/coordinator/__init__.py
@@ -0,0 +1,10 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.distribute.experimental.coordinator namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.distribute.coordinator.cluster_coordinator import ClusterCoordinator # line: 1397
+from tensorflow.python.distribute.coordinator.remote_value import RemoteValue # line: 53
+from tensorflow.python.distribute.coordinator.values import PerWorkerValues # line: 152
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/experimental/partitioners/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/experimental/partitioners/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6c0bb99b12e22643a8f05a75f5068c7bd140d30
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/experimental/partitioners/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.distribute.experimental.partitioners namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.distribute.sharded_variable import FixedShardsPartitioner # line: 83
+from tensorflow.python.distribute.sharded_variable import MaxSizePartitioner # line: 175
+from tensorflow.python.distribute.sharded_variable import MinSizePartitioner # line: 114
+from tensorflow.python.distribute.sharded_variable import Partitioner # line: 46
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/experimental/rpc/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/experimental/rpc/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..edd2fea388bb5f3fbb3eb4720d1974c9c37872ba
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/distribute/experimental/rpc/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.distribute.experimental.rpc namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.distribute.experimental.rpc.rpc_ops import Client # line: 117
+from tensorflow.python.distribute.experimental.rpc.rpc_ops import Server # line: 50
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/dtypes/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/dtypes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..745fa03d7768d714028996e1c28cdd435e5501cd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/dtypes/__init__.py
@@ -0,0 +1,39 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.dtypes namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.dtypes import experimental
+from tensorflow.python.framework.dtypes import DType # line: 51
+from tensorflow.python.framework.dtypes import QUANTIZED_DTYPES # line: 771
+from tensorflow.python.framework.dtypes import as_dtype # line: 793
+from tensorflow.python.framework.dtypes import bfloat16 # line: 450
+from tensorflow.python.framework.dtypes import bool # line: 414
+from tensorflow.python.framework.dtypes import complex128 # line: 401
+from tensorflow.python.framework.dtypes import complex64 # line: 394
+from tensorflow.python.framework.dtypes import double # line: 388
+from tensorflow.python.framework.dtypes import float16 # line: 373
+from tensorflow.python.framework.dtypes import float32 # line: 380
+from tensorflow.python.framework.dtypes import float64 # line: 386
+from tensorflow.python.framework.dtypes import half # line: 374
+from tensorflow.python.framework.dtypes import int16 # line: 354
+from tensorflow.python.framework.dtypes import int32 # line: 360
+from tensorflow.python.framework.dtypes import int64 # line: 366
+from tensorflow.python.framework.dtypes import int8 # line: 348
+from tensorflow.python.framework.dtypes import qint16 # line: 426
+from tensorflow.python.framework.dtypes import qint32 # line: 432
+from tensorflow.python.framework.dtypes import qint8 # line: 420
+from tensorflow.python.framework.dtypes import quint16 # line: 444
+from tensorflow.python.framework.dtypes import quint8 # line: 438
+from tensorflow.python.framework.dtypes import resource # line: 312
+from tensorflow.python.framework.dtypes import string # line: 408
+from tensorflow.python.framework.dtypes import uint16 # line: 330
+from tensorflow.python.framework.dtypes import uint32 # line: 336
+from tensorflow.python.framework.dtypes import uint64 # line: 342
+from tensorflow.python.framework.dtypes import uint8 # line: 324
+from tensorflow.python.framework.dtypes import variant # line: 318
+from tensorflow.python.ops.math_ops import cast # line: 944
+from tensorflow.python.ops.math_ops import complex # line: 699
+from tensorflow.python.ops.math_ops import saturate_cast # line: 1028
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/dtypes/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/dtypes/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d17307b985646f250a5485fd51b6551ddf5bf643
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/dtypes/experimental/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.dtypes.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.dtypes import float8_e4m3fn # line: 465
+from tensorflow.python.framework.dtypes import float8_e5m2 # line: 456
+from tensorflow.python.framework.dtypes import int4 # line: 471
+from tensorflow.python.framework.dtypes import uint4 # line: 477
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/errors/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/errors/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..33c6ebfcd059d3966167a016a340fd15603c7278
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/errors/__init__.py
@@ -0,0 +1,42 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.errors namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.errors_impl import ABORTED # line: 214
+from tensorflow.python.framework.errors_impl import ALREADY_EXISTS # line: 201
+from tensorflow.python.framework.errors_impl import AbortedError # line: 401
+from tensorflow.python.framework.errors_impl import AlreadyExistsError # line: 318
+from tensorflow.python.framework.errors_impl import CANCELLED # line: 189
+from tensorflow.python.framework.errors_impl import CancelledError # line: 227
+from tensorflow.python.framework.errors_impl import DATA_LOSS # line: 224
+from tensorflow.python.framework.errors_impl import DEADLINE_EXCEEDED # line: 196
+from tensorflow.python.framework.errors_impl import DataLossError # line: 480
+from tensorflow.python.framework.errors_impl import DeadlineExceededError # line: 290
+from tensorflow.python.framework.errors_impl import FAILED_PRECONDITION # line: 211
+from tensorflow.python.framework.errors_impl import FailedPreconditionError # line: 383
+from tensorflow.python.framework.errors_impl import INTERNAL # line: 220
+from tensorflow.python.framework.errors_impl import INVALID_ARGUMENT # line: 193
+from tensorflow.python.framework.errors_impl import InternalError # line: 454
+from tensorflow.python.framework.errors_impl import InvalidArgumentError # line: 270
+from tensorflow.python.framework.errors_impl import NOT_FOUND # line: 199
+from tensorflow.python.framework.errors_impl import NotFoundError # line: 303
+from tensorflow.python.framework.errors_impl import OK # line: 187
+from tensorflow.python.framework.errors_impl import OUT_OF_RANGE # line: 216
+from tensorflow.python.framework.errors_impl import OpError # line: 57
+from tensorflow.python.framework.errors_impl import OperatorNotAllowedInGraphError # line: 34
+from tensorflow.python.framework.errors_impl import OutOfRangeError # line: 417
+from tensorflow.python.framework.errors_impl import PERMISSION_DENIED # line: 203
+from tensorflow.python.framework.errors_impl import PermissionDeniedError # line: 338
+from tensorflow.python.framework.errors_impl import RESOURCE_EXHAUSTED # line: 208
+from tensorflow.python.framework.errors_impl import ResourceExhaustedError # line: 367
+from tensorflow.python.framework.errors_impl import UNAUTHENTICATED # line: 206
+from tensorflow.python.framework.errors_impl import UNAVAILABLE # line: 222
+from tensorflow.python.framework.errors_impl import UNIMPLEMENTED # line: 218
+from tensorflow.python.framework.errors_impl import UNKNOWN # line: 191
+from tensorflow.python.framework.errors_impl import UnauthenticatedError # line: 354
+from tensorflow.python.framework.errors_impl import UnavailableError # line: 467
+from tensorflow.python.framework.errors_impl import UnimplementedError # line: 437
+from tensorflow.python.framework.errors_impl import UnknownError # line: 254
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9184d1416bcc29e4ff0fd7b11c6229e4c60cc072
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/__init__.py
@@ -0,0 +1,34 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.experimental import dlpack
+from tensorflow._api.v2.compat.v2.experimental import dtensor
+from tensorflow._api.v2.compat.v2.experimental import extension_type
+from tensorflow._api.v2.compat.v2.experimental import numpy
+from tensorflow._api.v2.compat.v2.experimental import tensorrt
+from tensorflow.python.data.ops.optional_ops import Optional # line: 31
+from tensorflow.python.eager.context import async_clear_error # line: 2836
+from tensorflow.python.eager.context import async_scope # line: 2772
+from tensorflow.python.eager.context import function_executor_type # line: 2621
+from tensorflow.python.framework.dtypes import float8_e4m3fn # line: 465
+from tensorflow.python.framework.dtypes import float8_e5m2 # line: 456
+from tensorflow.python.framework.dtypes import int4 # line: 471
+from tensorflow.python.framework.dtypes import uint4 # line: 477
+from tensorflow.python.framework.extension_type import BatchableExtensionType # line: 831
+from tensorflow.python.framework.extension_type import ExtensionType # line: 99
+from tensorflow.python.framework.extension_type import ExtensionTypeBatchEncoder # line: 651
+from tensorflow.python.framework.extension_type import ExtensionTypeSpec # line: 419
+from tensorflow.python.framework.load_library import register_filesystem_plugin # line: 199
+from tensorflow.python.framework.strict_mode import enable_strict_mode # line: 22
+from tensorflow.python.ops.ragged.dynamic_ragged_shape import DynamicRaggedShape # line: 167
+from tensorflow.python.ops.ragged.row_partition import RowPartition # line: 56
+from tensorflow.python.ops.structured.structured_tensor import StructuredTensor # line: 53
+from tensorflow.python.util.dispatch import dispatch_for_api # line: 334
+from tensorflow.python.util.dispatch import dispatch_for_binary_elementwise_apis # line: 878
+from tensorflow.python.util.dispatch import dispatch_for_binary_elementwise_assert_apis # line: 936
+from tensorflow.python.util.dispatch import dispatch_for_unary_elementwise_apis # line: 811
+from tensorflow.python.util.dispatch import unregister_dispatch_for # line: 553
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/dlpack/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/dlpack/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..94b291e4e199347097bef6912c61ee24fb114370
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/dlpack/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.experimental.dlpack namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.dlpack.dlpack import from_dlpack # line: 44
+from tensorflow.python.dlpack.dlpack import to_dlpack # line: 22
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/dtensor/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/dtensor/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d24d5df1af9228736b5d99928d0ded8a12e537e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/dtensor/__init__.py
@@ -0,0 +1,51 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.experimental.dtensor namespace
+"""
+
+import sys as _sys
+
+from tensorflow.dtensor.python.accelerator_util import initialize_accelerator_system # line: 114
+from tensorflow.dtensor.python.accelerator_util import initialize_accelerator_system as initialize_multi_client # line: 114
+from tensorflow.dtensor.python.accelerator_util import initialize_accelerator_system as initialize_tpu_system # line: 114
+from tensorflow.dtensor.python.accelerator_util import shutdown_accelerator_system # line: 268
+from tensorflow.dtensor.python.accelerator_util import shutdown_accelerator_system as shutdown_tpu_system # line: 268
+from tensorflow.dtensor.python.api import reset_dtensor_device as _reset_dtensor_device # line: 505
+from tensorflow.dtensor.python.api import call_with_layout # line: 37
+from tensorflow.dtensor.python.api import check_layout # line: 395
+from tensorflow.dtensor.python.api import copy_to_mesh # line: 167
+from tensorflow.dtensor.python.api import default_mesh # line: 89
+from tensorflow.dtensor.python.api import device_name # line: 131
+from tensorflow.dtensor.python.api import fetch_layout # line: 379
+from tensorflow.dtensor.python.api import get_default_mesh # line: 115
+from tensorflow.dtensor.python.api import is_dtensor # line: 147
+from tensorflow.dtensor.python.api import pack # line: 191
+from tensorflow.dtensor.python.api import relayout # line: 411
+from tensorflow.dtensor.python.api import relayout_like # line: 452
+from tensorflow.dtensor.python.api import run_on # line: 67
+from tensorflow.dtensor.python.api import unpack # line: 342
+from tensorflow.dtensor.python.config import client_id # line: 86
+from tensorflow.dtensor.python.config import full_job_name # line: 117
+from tensorflow.dtensor.python.config import heartbeat_enabled # line: 168
+from tensorflow.dtensor.python.config import job_name # line: 108
+from tensorflow.dtensor.python.config import jobs # line: 148
+from tensorflow.dtensor.python.config import local_devices # line: 46
+from tensorflow.dtensor.python.config import num_clients # line: 100
+from tensorflow.dtensor.python.config import num_global_devices # line: 80
+from tensorflow.dtensor.python.config import num_local_devices # line: 68
+from tensorflow.dtensor.python.config import preferred_device_type # line: 192
+from tensorflow.dtensor.python.d_checkpoint import DTensorCheckpoint # line: 411
+from tensorflow.dtensor.python.d_variable import DVariable # line: 143
+from tensorflow.dtensor.python.input_util import DTensorDataset # line: 384
+from tensorflow.dtensor.python.layout import Layout # line: 351
+from tensorflow.dtensor.python.layout import MATCH # line: 39
+from tensorflow.dtensor.python.layout import Mesh # line: 53
+from tensorflow.dtensor.python.layout import UNSHARDED # line: 36
+from tensorflow.dtensor.python.mesh_util import barrier # line: 243
+from tensorflow.dtensor.python.mesh_util import create_distributed_mesh # line: 138
+from tensorflow.dtensor.python.mesh_util import create_mesh # line: 70
+from tensorflow.dtensor.python.save_restore import enable_save_as_bf16 # line: 83
+from tensorflow.dtensor.python.save_restore import name_based_restore # line: 100
+from tensorflow.dtensor.python.save_restore import name_based_save # line: 176
+from tensorflow.dtensor.python.save_restore import sharded_save # line: 34
+from tensorflow.dtensor.python.tpu_util import create_tpu_mesh # line: 578
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/extension_type/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/extension_type/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..070fcf42ed4902381a6d0d5b105b29d572bbe61e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/extension_type/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.experimental.extension_type namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.extension_type import as_dict # line: 332
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/numpy/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/numpy/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..97da9eb7645364f17fd6ed416a34c735a57d20a0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/numpy/__init__.py
@@ -0,0 +1,236 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.experimental.numpy namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.experimental.numpy import random
+from tensorflow.python.framework.tensor import Tensor as ndarray # line: 139
+from tensorflow.python.ops.numpy_ops.np_array_ops import all # line: 420
+from tensorflow.python.ops.numpy_ops.np_array_ops import amax # line: 672
+from tensorflow.python.ops.numpy_ops.np_array_ops import amin # line: 689
+from tensorflow.python.ops.numpy_ops.np_array_ops import any # line: 427
+from tensorflow.python.ops.numpy_ops.np_array_ops import arange # line: 259
+from tensorflow.python.ops.numpy_ops.np_array_ops import around # line: 820
+from tensorflow.python.ops.numpy_ops.np_array_ops import array # line: 217
+from tensorflow.python.ops.numpy_ops.np_array_ops import asanyarray # line: 246
+from tensorflow.python.ops.numpy_ops.np_array_ops import asarray # line: 234
+from tensorflow.python.ops.numpy_ops.np_array_ops import ascontiguousarray # line: 252
+from tensorflow.python.ops.numpy_ops.np_array_ops import atleast_1d # line: 1293
+from tensorflow.python.ops.numpy_ops.np_array_ops import atleast_2d # line: 1299
+from tensorflow.python.ops.numpy_ops.np_array_ops import atleast_3d # line: 1305
+from tensorflow.python.ops.numpy_ops.np_array_ops import broadcast_arrays # line: 1567
+from tensorflow.python.ops.numpy_ops.np_array_ops import broadcast_to # line: 1192
+from tensorflow.python.ops.numpy_ops.np_array_ops import compress # line: 434
+from tensorflow.python.ops.numpy_ops.np_array_ops import copy # line: 465
+from tensorflow.python.ops.numpy_ops.np_array_ops import cumprod # line: 483
+from tensorflow.python.ops.numpy_ops.np_array_ops import cumsum # line: 500
+from tensorflow.python.ops.numpy_ops.np_array_ops import diag # line: 305
+from tensorflow.python.ops.numpy_ops.np_array_ops import diag_indices # line: 1338
+from tensorflow.python.ops.numpy_ops.np_array_ops import diagflat # line: 389
+from tensorflow.python.ops.numpy_ops.np_array_ops import diagonal # line: 347
+from tensorflow.python.ops.numpy_ops.np_array_ops import dsplit # line: 1187
+from tensorflow.python.ops.numpy_ops.np_array_ops import dstack # line: 1241
+from tensorflow.python.ops.numpy_ops.np_array_ops import empty # line: 53
+from tensorflow.python.ops.numpy_ops.np_array_ops import empty_like # line: 59
+from tensorflow.python.ops.numpy_ops.np_array_ops import expand_dims # line: 875
+from tensorflow.python.ops.numpy_ops.np_array_ops import eye # line: 98
+from tensorflow.python.ops.numpy_ops.np_array_ops import flatten # line: 889
+from tensorflow.python.ops.numpy_ops.np_array_ops import flip # line: 1433
+from tensorflow.python.ops.numpy_ops.np_array_ops import fliplr # line: 1452
+from tensorflow.python.ops.numpy_ops.np_array_ops import flipud # line: 1446
+from tensorflow.python.ops.numpy_ops.np_array_ops import full # line: 136
+from tensorflow.python.ops.numpy_ops.np_array_ops import full_like # line: 148
+from tensorflow.python.ops.numpy_ops.np_array_ops import hsplit # line: 1184
+from tensorflow.python.ops.numpy_ops.np_array_ops import hstack # line: 1214
+from tensorflow.python.ops.numpy_ops.np_array_ops import identity # line: 130
+from tensorflow.python.ops.numpy_ops.np_array_ops import imag # line: 517
+from tensorflow.python.ops.numpy_ops.np_array_ops import isscalar # line: 1114
+from tensorflow.python.ops.numpy_ops.np_array_ops import ix_ # line: 1534
+from tensorflow.python.ops.numpy_ops.np_array_ops import max # line: 1667
+from tensorflow.python.ops.numpy_ops.np_array_ops import mean # line: 657
+from tensorflow.python.ops.numpy_ops.np_array_ops import min # line: 1673
+from tensorflow.python.ops.numpy_ops.np_array_ops import moveaxis # line: 953
+from tensorflow.python.ops.numpy_ops.np_array_ops import ndim # line: 1107
+from tensorflow.python.ops.numpy_ops.np_array_ops import newaxis # line: 48
+from tensorflow.python.ops.numpy_ops.np_array_ops import nonzero # line: 1324
+from tensorflow.python.ops.numpy_ops.np_array_ops import ones # line: 83
+from tensorflow.python.ops.numpy_ops.np_array_ops import ones_like # line: 91
+from tensorflow.python.ops.numpy_ops.np_array_ops import pad # line: 1018
+from tensorflow.python.ops.numpy_ops.np_array_ops import prod # line: 644
+from tensorflow.python.ops.numpy_ops.np_array_ops import ravel # line: 772
+from tensorflow.python.ops.numpy_ops.np_array_ops import real # line: 779
+from tensorflow.python.ops.numpy_ops.np_array_ops import repeat # line: 788
+from tensorflow.python.ops.numpy_ops.np_array_ops import reshape # line: 843
+from tensorflow.python.ops.numpy_ops.np_array_ops import roll # line: 1458
+from tensorflow.python.ops.numpy_ops.np_array_ops import rot90 # line: 1472
+from tensorflow.python.ops.numpy_ops.np_array_ops import round # line: 1679
+from tensorflow.python.ops.numpy_ops.np_array_ops import select # line: 1077
+from tensorflow.python.ops.numpy_ops.np_array_ops import shape # line: 1095
+from tensorflow.python.ops.numpy_ops.np_array_ops import sign # line: 1580
+from tensorflow.python.ops.numpy_ops.np_array_ops import size # line: 615
+from tensorflow.python.ops.numpy_ops.np_array_ops import split # line: 1152
+from tensorflow.python.ops.numpy_ops.np_array_ops import squeeze # line: 882
+from tensorflow.python.ops.numpy_ops.np_array_ops import stack # line: 1198
+from tensorflow.python.ops.numpy_ops.np_array_ops import std # line: 759
+from tensorflow.python.ops.numpy_ops.np_array_ops import sum # line: 631
+from tensorflow.python.ops.numpy_ops.np_array_ops import swapaxes # line: 914
+from tensorflow.python.ops.numpy_ops.np_array_ops import take # line: 1036
+from tensorflow.python.ops.numpy_ops.np_array_ops import take_along_axis # line: 1602
+from tensorflow.python.ops.numpy_ops.np_array_ops import transpose # line: 905
+from tensorflow.python.ops.numpy_ops.np_array_ops import tri # line: 1353
+from tensorflow.python.ops.numpy_ops.np_array_ops import tril # line: 1383
+from tensorflow.python.ops.numpy_ops.np_array_ops import triu # line: 1408
+from tensorflow.python.ops.numpy_ops.np_array_ops import vander # line: 1493
+from tensorflow.python.ops.numpy_ops.np_array_ops import var # line: 706
+from tensorflow.python.ops.numpy_ops.np_array_ops import vsplit # line: 1181
+from tensorflow.python.ops.numpy_ops.np_array_ops import vstack # line: 1230
+from tensorflow.python.ops.numpy_ops.np_array_ops import where # line: 1064
+from tensorflow.python.ops.numpy_ops.np_array_ops import zeros # line: 65
+from tensorflow.python.ops.numpy_ops.np_array_ops import zeros_like # line: 74
+from tensorflow.python.ops.numpy_ops.np_config import enable_numpy_behavior as experimental_enable_numpy_behavior # line: 25
+from tensorflow.python.ops.numpy_ops.np_dtypes import bool_ # line: 27
+from tensorflow.python.ops.numpy_ops.np_dtypes import complex128 # line: 35
+from tensorflow.python.ops.numpy_ops.np_dtypes import complex64 # line: 39
+from tensorflow.python.ops.numpy_ops.np_dtypes import complex_ # line: 31
+from tensorflow.python.ops.numpy_ops.np_dtypes import float16 # line: 47
+from tensorflow.python.ops.numpy_ops.np_dtypes import float32 # line: 51
+from tensorflow.python.ops.numpy_ops.np_dtypes import float64 # line: 55
+from tensorflow.python.ops.numpy_ops.np_dtypes import float_ # line: 43
+from tensorflow.python.ops.numpy_ops.np_dtypes import iinfo # line: 113
+from tensorflow.python.ops.numpy_ops.np_dtypes import inexact # line: 59
+from tensorflow.python.ops.numpy_ops.np_dtypes import int16 # line: 67
+from tensorflow.python.ops.numpy_ops.np_dtypes import int32 # line: 71
+from tensorflow.python.ops.numpy_ops.np_dtypes import int64 # line: 75
+from tensorflow.python.ops.numpy_ops.np_dtypes import int8 # line: 79
+from tensorflow.python.ops.numpy_ops.np_dtypes import int_ # line: 63
+from tensorflow.python.ops.numpy_ops.np_dtypes import issubdtype # line: 118
+from tensorflow.python.ops.numpy_ops.np_dtypes import object_ # line: 83
+from tensorflow.python.ops.numpy_ops.np_dtypes import string_ # line: 87
+from tensorflow.python.ops.numpy_ops.np_dtypes import uint16 # line: 91
+from tensorflow.python.ops.numpy_ops.np_dtypes import uint32 # line: 95
+from tensorflow.python.ops.numpy_ops.np_dtypes import uint64 # line: 99
+from tensorflow.python.ops.numpy_ops.np_dtypes import uint8 # line: 103
+from tensorflow.python.ops.numpy_ops.np_dtypes import unicode_ # line: 107
+from tensorflow.python.ops.numpy_ops.np_math_ops import abs # line: 698
+from tensorflow.python.ops.numpy_ops.np_math_ops import absolute # line: 704
+from tensorflow.python.ops.numpy_ops.np_math_ops import add # line: 90
+from tensorflow.python.ops.numpy_ops.np_math_ops import allclose # line: 542
+from tensorflow.python.ops.numpy_ops.np_math_ops import angle # line: 852
+from tensorflow.python.ops.numpy_ops.np_math_ops import append # line: 1417
+from tensorflow.python.ops.numpy_ops.np_math_ops import arccos # line: 799
+from tensorflow.python.ops.numpy_ops.np_math_ops import arccosh # line: 817
+from tensorflow.python.ops.numpy_ops.np_math_ops import arcsin # line: 793
+from tensorflow.python.ops.numpy_ops.np_math_ops import arcsinh # line: 811
+from tensorflow.python.ops.numpy_ops.np_math_ops import arctan # line: 805
+from tensorflow.python.ops.numpy_ops.np_math_ops import arctan2 # line: 402
+from tensorflow.python.ops.numpy_ops.np_math_ops import arctanh # line: 823
+from tensorflow.python.ops.numpy_ops.np_math_ops import argmax # line: 1405
+from tensorflow.python.ops.numpy_ops.np_math_ops import argmin # line: 1411
+from tensorflow.python.ops.numpy_ops.np_math_ops import argsort # line: 1340
+from tensorflow.python.ops.numpy_ops.np_math_ops import array_equal # line: 1155
+from tensorflow.python.ops.numpy_ops.np_math_ops import average # line: 1426
+from tensorflow.python.ops.numpy_ops.np_math_ops import bitwise_and # line: 630
+from tensorflow.python.ops.numpy_ops.np_math_ops import bitwise_not # line: 648
+from tensorflow.python.ops.numpy_ops.np_math_ops import bitwise_or # line: 636
+from tensorflow.python.ops.numpy_ops.np_math_ops import bitwise_xor # line: 642
+from tensorflow.python.ops.numpy_ops.np_math_ops import cbrt # line: 868
+from tensorflow.python.ops.numpy_ops.np_math_ops import ceil # line: 716
+from tensorflow.python.ops.numpy_ops.np_math_ops import clip # line: 222
+from tensorflow.python.ops.numpy_ops.np_math_ops import concatenate # line: 1298
+from tensorflow.python.ops.numpy_ops.np_math_ops import conj # line: 728
+from tensorflow.python.ops.numpy_ops.np_math_ops import conjugate # line: 879
+from tensorflow.python.ops.numpy_ops.np_math_ops import cos # line: 763
+from tensorflow.python.ops.numpy_ops.np_math_ops import cosh # line: 781
+from tensorflow.python.ops.numpy_ops.np_math_ops import count_nonzero # line: 1334
+from tensorflow.python.ops.numpy_ops.np_math_ops import cross # line: 289
+from tensorflow.python.ops.numpy_ops.np_math_ops import deg2rad # line: 829
+from tensorflow.python.ops.numpy_ops.np_math_ops import diff # line: 1054
+from tensorflow.python.ops.numpy_ops.np_math_ops import divide # line: 144
+from tensorflow.python.ops.numpy_ops.np_math_ops import divmod # line: 182
+from tensorflow.python.ops.numpy_ops.np_math_ops import dot # line: 60
+from tensorflow.python.ops.numpy_ops.np_math_ops import e # line: 51
+from tensorflow.python.ops.numpy_ops.np_math_ops import einsum # line: 1543
+from tensorflow.python.ops.numpy_ops.np_math_ops import equal # line: 1119
+from tensorflow.python.ops.numpy_ops.np_math_ops import exp # line: 686
+from tensorflow.python.ops.numpy_ops.np_math_ops import exp2 # line: 885
+from tensorflow.python.ops.numpy_ops.np_math_ops import expm1 # line: 894
+from tensorflow.python.ops.numpy_ops.np_math_ops import fabs # line: 710
+from tensorflow.python.ops.numpy_ops.np_math_ops import fix # line: 900
+from tensorflow.python.ops.numpy_ops.np_math_ops import float_power # line: 396
+from tensorflow.python.ops.numpy_ops.np_math_ops import floor # line: 722
+from tensorflow.python.ops.numpy_ops.np_math_ops import floor_divide # line: 150
+from tensorflow.python.ops.numpy_ops.np_math_ops import gcd # line: 588
+from tensorflow.python.ops.numpy_ops.np_math_ops import geomspace # line: 1259
+from tensorflow.python.ops.numpy_ops.np_math_ops import greater # line: 1131
+from tensorflow.python.ops.numpy_ops.np_math_ops import greater_equal # line: 1137
+from tensorflow.python.ops.numpy_ops.np_math_ops import heaviside # line: 414
+from tensorflow.python.ops.numpy_ops.np_math_ops import hypot # line: 430
+from tensorflow.python.ops.numpy_ops.np_math_ops import inf # line: 55
+from tensorflow.python.ops.numpy_ops.np_math_ops import inner # line: 273
+from tensorflow.python.ops.numpy_ops.np_math_ops import isclose # line: 524
+from tensorflow.python.ops.numpy_ops.np_math_ops import iscomplex # line: 909
+from tensorflow.python.ops.numpy_ops.np_math_ops import iscomplexobj # line: 921
+from tensorflow.python.ops.numpy_ops.np_math_ops import isfinite # line: 982
+from tensorflow.python.ops.numpy_ops.np_math_ops import isinf # line: 988
+from tensorflow.python.ops.numpy_ops.np_math_ops import isnan # line: 934
+from tensorflow.python.ops.numpy_ops.np_math_ops import isneginf # line: 996
+from tensorflow.python.ops.numpy_ops.np_math_ops import isposinf # line: 1004
+from tensorflow.python.ops.numpy_ops.np_math_ops import isreal # line: 915
+from tensorflow.python.ops.numpy_ops.np_math_ops import isrealobj # line: 928
+from tensorflow.python.ops.numpy_ops.np_math_ops import kron # line: 436
+from tensorflow.python.ops.numpy_ops.np_math_ops import lcm # line: 595
+from tensorflow.python.ops.numpy_ops.np_math_ops import less # line: 1143
+from tensorflow.python.ops.numpy_ops.np_math_ops import less_equal # line: 1149
+from tensorflow.python.ops.numpy_ops.np_math_ops import linspace # line: 1205
+from tensorflow.python.ops.numpy_ops.np_math_ops import log # line: 680
+from tensorflow.python.ops.numpy_ops.np_math_ops import log10 # line: 1018
+from tensorflow.python.ops.numpy_ops.np_math_ops import log1p # line: 1024
+from tensorflow.python.ops.numpy_ops.np_math_ops import log2 # line: 1012
+from tensorflow.python.ops.numpy_ops.np_math_ops import logaddexp # line: 482
+from tensorflow.python.ops.numpy_ops.np_math_ops import logaddexp2 # line: 494
+from tensorflow.python.ops.numpy_ops.np_math_ops import logical_and # line: 1180
+from tensorflow.python.ops.numpy_ops.np_math_ops import logical_not # line: 1198
+from tensorflow.python.ops.numpy_ops.np_math_ops import logical_or # line: 1186
+from tensorflow.python.ops.numpy_ops.np_math_ops import logical_xor # line: 1192
+from tensorflow.python.ops.numpy_ops.np_math_ops import logspace # line: 1246
+from tensorflow.python.ops.numpy_ops.np_math_ops import matmul # line: 236
+from tensorflow.python.ops.numpy_ops.np_math_ops import maximum # line: 188
+from tensorflow.python.ops.numpy_ops.np_math_ops import meshgrid # line: 1514
+from tensorflow.python.ops.numpy_ops.np_math_ops import minimum # line: 210
+from tensorflow.python.ops.numpy_ops.np_math_ops import mod # line: 163
+from tensorflow.python.ops.numpy_ops.np_math_ops import multiply # line: 108
+from tensorflow.python.ops.numpy_ops.np_math_ops import nanmean # line: 965
+from tensorflow.python.ops.numpy_ops.np_math_ops import nanprod # line: 960
+from tensorflow.python.ops.numpy_ops.np_math_ops import nansum # line: 957
+from tensorflow.python.ops.numpy_ops.np_math_ops import negative # line: 734
+from tensorflow.python.ops.numpy_ops.np_math_ops import nextafter # line: 408
+from tensorflow.python.ops.numpy_ops.np_math_ops import not_equal # line: 1125
+from tensorflow.python.ops.numpy_ops.np_math_ops import outer # line: 472
+from tensorflow.python.ops.numpy_ops.np_math_ops import pi # line: 47
+from tensorflow.python.ops.numpy_ops.np_math_ops import polyval # line: 506
+from tensorflow.python.ops.numpy_ops.np_math_ops import positive # line: 1030
+from tensorflow.python.ops.numpy_ops.np_math_ops import power # line: 390
+from tensorflow.python.ops.numpy_ops.np_math_ops import ptp # line: 1290
+from tensorflow.python.ops.numpy_ops.np_math_ops import rad2deg # line: 838
+from tensorflow.python.ops.numpy_ops.np_math_ops import reciprocal # line: 740
+from tensorflow.python.ops.numpy_ops.np_math_ops import remainder # line: 176
+from tensorflow.python.ops.numpy_ops.np_math_ops import signbit # line: 746
+from tensorflow.python.ops.numpy_ops.np_math_ops import sin # line: 757
+from tensorflow.python.ops.numpy_ops.np_math_ops import sinc # line: 1036
+from tensorflow.python.ops.numpy_ops.np_math_ops import sinh # line: 775
+from tensorflow.python.ops.numpy_ops.np_math_ops import sort # line: 1375
+from tensorflow.python.ops.numpy_ops.np_math_ops import sqrt # line: 692
+from tensorflow.python.ops.numpy_ops.np_math_ops import square # line: 1048
+from tensorflow.python.ops.numpy_ops.np_math_ops import subtract # line: 102
+from tensorflow.python.ops.numpy_ops.np_math_ops import tan # line: 769
+from tensorflow.python.ops.numpy_ops.np_math_ops import tanh # line: 787
+from tensorflow.python.ops.numpy_ops.np_math_ops import tensordot # line: 267
+from tensorflow.python.ops.numpy_ops.np_math_ops import tile # line: 1313
+from tensorflow.python.ops.numpy_ops.np_math_ops import trace # line: 1494
+from tensorflow.python.ops.numpy_ops.np_math_ops import true_divide # line: 120
+from tensorflow.python.ops.numpy_ops.np_math_ops import vdot # line: 379
+from tensorflow.python.ops.numpy_ops.np_utils import finfo # line: 486
+from tensorflow.python.ops.numpy_ops.np_utils import promote_types # line: 564
+from tensorflow.python.ops.numpy_ops.np_utils import result_type # line: 518
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/numpy/random/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/numpy/random/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5685f57e5a34b4f331a102216729bf8f07e0ebee
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/numpy/random/__init__.py
@@ -0,0 +1,15 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.experimental.numpy.random namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.numpy_ops.np_random import poisson # line: 94
+from tensorflow.python.ops.numpy_ops.np_random import rand # line: 110
+from tensorflow.python.ops.numpy_ops.np_random import randint # line: 116
+from tensorflow.python.ops.numpy_ops.np_random import randn # line: 53
+from tensorflow.python.ops.numpy_ops.np_random import random # line: 104
+from tensorflow.python.ops.numpy_ops.np_random import seed # line: 33
+from tensorflow.python.ops.numpy_ops.np_random import standard_normal # line: 69
+from tensorflow.python.ops.numpy_ops.np_random import uniform # line: 81
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/tensorrt/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/tensorrt/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e715f32cff77d294e77c175d04a19c41ec580cc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/experimental/tensorrt/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.experimental.tensorrt namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.compiler.tensorrt.trt_convert import TrtConversionParams as ConversionParams # line: 135
+from tensorflow.python.compiler.tensorrt.trt_convert import TrtGraphConverterV2 as Converter # line: 1076
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/feature_column/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/feature_column/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..04ca485d32ef9d80ca1fdb6f864272c444dbde00
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/feature_column/__init__.py
@@ -0,0 +1,24 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.feature_column namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.feature_column.feature_column_v2 import bucketized_column # line: 1124
+from tensorflow.python.feature_column.feature_column_v2 import categorical_column_with_hash_bucket # line: 1211
+from tensorflow.python.feature_column.feature_column_v2 import categorical_column_with_identity # line: 1648
+from tensorflow.python.feature_column.feature_column_v2 import categorical_column_with_vocabulary_file_v2 as categorical_column_with_vocabulary_file # line: 1391
+from tensorflow.python.feature_column.feature_column_v2 import categorical_column_with_vocabulary_list # line: 1529
+from tensorflow.python.feature_column.feature_column_v2 import crossed_column # line: 1849
+from tensorflow.python.feature_column.feature_column_v2 import embedding_column # line: 535
+from tensorflow.python.feature_column.feature_column_v2 import indicator_column # line: 1726
+from tensorflow.python.feature_column.feature_column_v2 import make_parse_example_spec_v2 as make_parse_example_spec # line: 470
+from tensorflow.python.feature_column.feature_column_v2 import numeric_column # line: 1024
+from tensorflow.python.feature_column.feature_column_v2 import shared_embedding_columns_v2 as shared_embeddings # line: 845
+from tensorflow.python.feature_column.feature_column_v2 import weighted_categorical_column # line: 1771
+from tensorflow.python.feature_column.sequence_feature_column import sequence_categorical_column_with_hash_bucket # line: 153
+from tensorflow.python.feature_column.sequence_feature_column import sequence_categorical_column_with_identity # line: 103
+from tensorflow.python.feature_column.sequence_feature_column import sequence_categorical_column_with_vocabulary_file # line: 200
+from tensorflow.python.feature_column.sequence_feature_column import sequence_categorical_column_with_vocabulary_list # line: 270
+from tensorflow.python.feature_column.sequence_feature_column import sequence_numeric_column # line: 337
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/graph_util/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/graph_util/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4563a0266e7ab61d454007c7f8cb7ca7de8bcf4a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/graph_util/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.graph_util namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.importer import import_graph_def # line: 354
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/image/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/image/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe87a77d1782ca84511ca7355570d527b0cf9a7d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/image/__init__.py
@@ -0,0 +1,78 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.image namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.gen_image_ops import hsv_to_rgb # line: 2271
+from tensorflow.python.ops.gen_image_ops import rgb_to_hsv # line: 3276
+from tensorflow.python.ops.array_ops import extract_image_patches_v2 as extract_patches # line: 6416
+from tensorflow.python.ops.image_ops_impl import ResizeMethod # line: 1441
+from tensorflow.python.ops.image_ops_impl import adjust_brightness # line: 2198
+from tensorflow.python.ops.image_ops_impl import adjust_contrast # line: 2251
+from tensorflow.python.ops.image_ops_impl import adjust_gamma # line: 2310
+from tensorflow.python.ops.image_ops_impl import adjust_hue # line: 2734
+from tensorflow.python.ops.image_ops_impl import adjust_jpeg_quality # line: 2920
+from tensorflow.python.ops.image_ops_impl import adjust_saturation # line: 3100
+from tensorflow.python.ops.image_ops_impl import central_crop # line: 857
+from tensorflow.python.ops.image_ops_impl import combined_non_max_suppression # line: 5127
+from tensorflow.python.ops.image_ops_impl import convert_image_dtype # line: 2376
+from tensorflow.python.ops.image_ops_impl import crop_and_resize_v2 as crop_and_resize # line: 4822
+from tensorflow.python.ops.image_ops_impl import crop_to_bounding_box # line: 1168
+from tensorflow.python.ops.image_ops_impl import decode_and_crop_jpeg # line: 3191
+from tensorflow.python.ops.image_ops_impl import decode_bmp # line: 3197
+from tensorflow.python.ops.image_ops_impl import decode_gif # line: 3202
+from tensorflow.python.ops.image_ops_impl import decode_image # line: 3260
+from tensorflow.python.ops.image_ops_impl import decode_jpeg # line: 3207
+from tensorflow.python.ops.image_ops_impl import decode_png # line: 3212
+from tensorflow.python.ops.image_ops_impl import draw_bounding_boxes_v2 as draw_bounding_boxes # line: 5785
+from tensorflow.python.ops.image_ops_impl import encode_jpeg # line: 3218
+from tensorflow.python.ops.image_ops_impl import encode_png # line: 3230
+from tensorflow.python.ops.image_ops_impl import extract_glimpse_v2 as extract_glimpse # line: 5040
+from tensorflow.python.ops.image_ops_impl import extract_jpeg_shape # line: 3223
+from tensorflow.python.ops.image_ops_impl import flip_left_right # line: 546
+from tensorflow.python.ops.image_ops_impl import flip_up_down # line: 581
+from tensorflow.python.ops.image_ops_impl import generate_bounding_box_proposals # line: 5893
+from tensorflow.python.ops.image_ops_impl import grayscale_to_rgb # line: 2597
+from tensorflow.python.ops.image_ops_impl import image_gradients # line: 4604
+from tensorflow.python.ops.image_ops_impl import is_jpeg # line: 3155
+from tensorflow.python.ops.image_ops_impl import non_max_suppression # line: 3775
+from tensorflow.python.ops.image_ops_impl import non_max_suppression_with_overlaps as non_max_suppression_overlaps # line: 3918
+from tensorflow.python.ops.image_ops_impl import non_max_suppression_padded # line: 5395
+from tensorflow.python.ops.image_ops_impl import non_max_suppression_with_scores # line: 3828
+from tensorflow.python.ops.image_ops_impl import pad_to_bounding_box # line: 1004
+from tensorflow.python.ops.image_ops_impl import per_image_standardization # line: 1956
+from tensorflow.python.ops.image_ops_impl import psnr # line: 4159
+from tensorflow.python.ops.image_ops_impl import random_brightness # line: 2015
+from tensorflow.python.ops.image_ops_impl import random_contrast # line: 2105
+from tensorflow.python.ops.image_ops_impl import random_flip_left_right # line: 382
+from tensorflow.python.ops.image_ops_impl import random_flip_up_down # line: 335
+from tensorflow.python.ops.image_ops_impl import random_hue # line: 2634
+from tensorflow.python.ops.image_ops_impl import random_jpeg_quality # line: 2810
+from tensorflow.python.ops.image_ops_impl import random_saturation # line: 2998
+from tensorflow.python.ops.image_ops_impl import resize_images_v2 as resize # line: 1618
+from tensorflow.python.ops.image_ops_impl import resize_image_with_crop_or_pad as resize_with_crop_or_pad # line: 1275
+from tensorflow.python.ops.image_ops_impl import resize_image_with_pad_v2 as resize_with_pad # line: 1915
+from tensorflow.python.ops.image_ops_impl import rgb_to_grayscale # line: 2561
+from tensorflow.python.ops.image_ops_impl import rgb_to_yiq # line: 3972
+from tensorflow.python.ops.image_ops_impl import rgb_to_yuv # line: 4035
+from tensorflow.python.ops.image_ops_impl import rot90 # line: 658
+from tensorflow.python.ops.image_ops_impl import sample_distorted_bounding_box_v2 as sample_distorted_bounding_box # line: 3401
+from tensorflow.python.ops.image_ops_impl import sobel_edges # line: 4678
+from tensorflow.python.ops.image_ops_impl import ssim # line: 4377
+from tensorflow.python.ops.image_ops_impl import ssim_multiscale # line: 4475
+from tensorflow.python.ops.image_ops_impl import stateless_random_brightness # line: 2058
+from tensorflow.python.ops.image_ops_impl import stateless_random_contrast # line: 2151
+from tensorflow.python.ops.image_ops_impl import stateless_random_flip_left_right # line: 430
+from tensorflow.python.ops.image_ops_impl import stateless_random_flip_up_down # line: 461
+from tensorflow.python.ops.image_ops_impl import stateless_random_hue # line: 2683
+from tensorflow.python.ops.image_ops_impl import stateless_random_jpeg_quality # line: 2864
+from tensorflow.python.ops.image_ops_impl import stateless_random_saturation # line: 3050
+from tensorflow.python.ops.image_ops_impl import stateless_sample_distorted_bounding_box # line: 3527
+from tensorflow.python.ops.image_ops_impl import total_variation # line: 3329
+from tensorflow.python.ops.image_ops_impl import transpose # line: 787
+from tensorflow.python.ops.image_ops_impl import yiq_to_rgb # line: 4006
+from tensorflow.python.ops.image_ops_impl import yuv_to_rgb # line: 4065
+from tensorflow.python.ops.random_crop_ops import random_crop # line: 30
+from tensorflow.python.ops.random_crop_ops import stateless_random_crop # line: 85
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/io/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/io/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..37f3426d7ed101468d0b7f1b8610e96e6843ccd8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/io/__init__.py
@@ -0,0 +1,47 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.io namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.io import gfile
+from tensorflow.python.ops.gen_decode_proto_ops import decode_proto_v2 as decode_proto # line: 31
+from tensorflow.python.ops.gen_encode_proto_ops import encode_proto # line: 27
+from tensorflow.python.ops.gen_io_ops import matching_files # line: 391
+from tensorflow.python.ops.gen_io_ops import write_file # line: 2269
+from tensorflow.python.ops.gen_parsing_ops import decode_compressed # line: 144
+from tensorflow.python.ops.gen_parsing_ops import parse_tensor # line: 2135
+from tensorflow.python.ops.gen_string_ops import decode_base64 # line: 182
+from tensorflow.python.ops.gen_string_ops import encode_base64 # line: 269
+from tensorflow.python.framework.graph_io import write_graph # line: 28
+from tensorflow.python.lib.io.tf_record import TFRecordOptions # line: 35
+from tensorflow.python.lib.io.tf_record import TFRecordWriter # line: 211
+from tensorflow.python.ops.image_ops_impl import decode_and_crop_jpeg # line: 3191
+from tensorflow.python.ops.image_ops_impl import decode_bmp # line: 3197
+from tensorflow.python.ops.image_ops_impl import decode_gif # line: 3202
+from tensorflow.python.ops.image_ops_impl import decode_image # line: 3260
+from tensorflow.python.ops.image_ops_impl import decode_jpeg # line: 3207
+from tensorflow.python.ops.image_ops_impl import decode_png # line: 3212
+from tensorflow.python.ops.image_ops_impl import encode_jpeg # line: 3218
+from tensorflow.python.ops.image_ops_impl import encode_png # line: 3230
+from tensorflow.python.ops.image_ops_impl import extract_jpeg_shape # line: 3223
+from tensorflow.python.ops.image_ops_impl import is_jpeg # line: 3155
+from tensorflow.python.ops.io_ops import read_file # line: 97
+from tensorflow.python.ops.io_ops import serialize_tensor # line: 137
+from tensorflow.python.ops.parsing_config import FixedLenFeature # line: 298
+from tensorflow.python.ops.parsing_config import FixedLenSequenceFeature # line: 318
+from tensorflow.python.ops.parsing_config import RaggedFeature # line: 54
+from tensorflow.python.ops.parsing_config import SparseFeature # line: 223
+from tensorflow.python.ops.parsing_config import VarLenFeature # line: 44
+from tensorflow.python.ops.parsing_ops import decode_csv_v2 as decode_csv # line: 1073
+from tensorflow.python.ops.parsing_ops import decode_json_example # line: 1152
+from tensorflow.python.ops.parsing_ops import decode_raw # line: 843
+from tensorflow.python.ops.parsing_ops import parse_example_v2 as parse_example # line: 77
+from tensorflow.python.ops.parsing_ops import parse_sequence_example # line: 452
+from tensorflow.python.ops.parsing_ops import parse_single_example_v2 as parse_single_example # line: 410
+from tensorflow.python.ops.parsing_ops import parse_single_sequence_example # line: 696
+from tensorflow.python.ops.sparse_ops import deserialize_many_sparse # line: 2356
+from tensorflow.python.ops.sparse_ops import serialize_many_sparse_v2 as serialize_many_sparse # line: 2254
+from tensorflow.python.ops.sparse_ops import serialize_sparse_v2 as serialize_sparse # line: 2197
+from tensorflow.python.training.input import match_filenames_once # line: 56
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/io/gfile/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/io/gfile/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b1002208d271902ed287a4257c76fa848edb1f2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/io/gfile/__init__.py
@@ -0,0 +1,22 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.io.gfile namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.lib.io.file_io import copy_v2 as copy # line: 516
+from tensorflow.python.lib.io.file_io import file_exists_v2 as exists # line: 249
+from tensorflow.python.lib.io.file_io import get_registered_schemes # line: 980
+from tensorflow.python.lib.io.file_io import get_matching_files_v2 as glob # line: 385
+from tensorflow.python.lib.io.file_io import is_directory_v2 as isdir # line: 692
+from tensorflow.python.lib.io.file_io import join # line: 781
+from tensorflow.python.lib.io.file_io import list_directory_v2 as listdir # line: 751
+from tensorflow.python.lib.io.file_io import recursive_create_dir_v2 as makedirs # line: 501
+from tensorflow.python.lib.io.file_io import create_dir_v2 as mkdir # line: 470
+from tensorflow.python.lib.io.file_io import delete_file_v2 as remove # line: 318
+from tensorflow.python.lib.io.file_io import rename_v2 as rename # line: 609
+from tensorflow.python.lib.io.file_io import delete_recursively_v2 as rmtree # line: 666
+from tensorflow.python.lib.io.file_io import stat_v2 as stat # line: 911
+from tensorflow.python.lib.io.file_io import walk_v2 as walk # line: 837
+from tensorflow.python.platform.gfile import GFile # line: 36
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/linalg/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/linalg/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa3ca6858e1d8f82e9b2c0dcee68d13304bc9dfd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/linalg/__init__.py
@@ -0,0 +1,76 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.linalg namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.linalg import experimental
+from tensorflow.python.ops.gen_array_ops import matrix_band_part as band_part # line: 4879
+from tensorflow.python.ops.gen_array_ops import diag as tensor_diag # line: 1949
+from tensorflow.python.ops.gen_linalg_ops import cholesky # line: 766
+from tensorflow.python.ops.gen_linalg_ops import matrix_determinant as det # line: 1370
+from tensorflow.python.ops.gen_linalg_ops import matrix_inverse as inv # line: 1516
+from tensorflow.python.ops.gen_linalg_ops import lu # line: 1252
+from tensorflow.python.ops.gen_linalg_ops import qr # line: 2150
+from tensorflow.python.ops.gen_linalg_ops import matrix_solve as solve # line: 1694
+from tensorflow.python.ops.gen_linalg_ops import matrix_square_root as sqrtm # line: 1913
+from tensorflow.python.ops.gen_math_ops import cross # line: 2651
+from tensorflow.python.ops.array_ops import matrix_diag as diag # line: 2322
+from tensorflow.python.ops.array_ops import matrix_diag_part as diag_part # line: 2491
+from tensorflow.python.ops.array_ops import matrix_transpose # line: 2242
+from tensorflow.python.ops.array_ops import matrix_set_diag as set_diag # line: 2679
+from tensorflow.python.ops.array_ops import tensor_diag_part # line: 2634
+from tensorflow.python.ops.clip_ops import global_norm # line: 235
+from tensorflow.python.ops.linalg.linalg_impl import adjoint # line: 102
+from tensorflow.python.ops.linalg.linalg_impl import banded_triangular_solve # line: 350
+from tensorflow.python.ops.linalg.linalg_impl import eigh_tridiagonal # line: 1232
+from tensorflow.python.ops.linalg.linalg_impl import matrix_exponential as expm # line: 232
+from tensorflow.python.ops.linalg.linalg_impl import logdet # line: 68
+from tensorflow.python.ops.linalg.linalg_impl import logm # line: 54
+from tensorflow.python.ops.linalg.linalg_impl import lu_matrix_inverse # line: 1035
+from tensorflow.python.ops.linalg.linalg_impl import lu_reconstruct # line: 1100
+from tensorflow.python.ops.linalg.linalg_impl import lu_solve # line: 937
+from tensorflow.python.ops.linalg.linalg_impl import matrix_rank # line: 768
+from tensorflow.python.ops.linalg.linalg_impl import pinv # line: 807
+from tensorflow.python.ops.linalg.linalg_impl import slogdet # line: 44
+from tensorflow.python.ops.linalg.linalg_impl import tridiagonal_matmul # line: 670
+from tensorflow.python.ops.linalg.linalg_impl import tridiagonal_solve # line: 446
+from tensorflow.python.ops.linalg.linear_operator import LinearOperator # line: 91
+from tensorflow.python.ops.linalg.linear_operator_adjoint import LinearOperatorAdjoint # line: 28
+from tensorflow.python.ops.linalg.linear_operator_block_diag import LinearOperatorBlockDiag # line: 34
+from tensorflow.python.ops.linalg.linear_operator_block_lower_triangular import LinearOperatorBlockLowerTriangular # line: 39
+from tensorflow.python.ops.linalg.linear_operator_circulant import LinearOperatorCirculant # line: 740
+from tensorflow.python.ops.linalg.linear_operator_circulant import LinearOperatorCirculant2D # line: 1035
+from tensorflow.python.ops.linalg.linear_operator_circulant import LinearOperatorCirculant3D # line: 1264
+from tensorflow.python.ops.linalg.linear_operator_composition import LinearOperatorComposition # line: 35
+from tensorflow.python.ops.linalg.linear_operator_diag import LinearOperatorDiag # line: 32
+from tensorflow.python.ops.linalg.linear_operator_full_matrix import LinearOperatorFullMatrix # line: 29
+from tensorflow.python.ops.linalg.linear_operator_householder import LinearOperatorHouseholder # line: 32
+from tensorflow.python.ops.linalg.linear_operator_identity import LinearOperatorIdentity # line: 101
+from tensorflow.python.ops.linalg.linear_operator_identity import LinearOperatorScaledIdentity # line: 538
+from tensorflow.python.ops.linalg.linear_operator_inversion import LinearOperatorInversion # line: 25
+from tensorflow.python.ops.linalg.linear_operator_kronecker import LinearOperatorKronecker # line: 60
+from tensorflow.python.ops.linalg.linear_operator_low_rank_update import LinearOperatorLowRankUpdate # line: 35
+from tensorflow.python.ops.linalg.linear_operator_lower_triangular import LinearOperatorLowerTriangular # line: 31
+from tensorflow.python.ops.linalg.linear_operator_permutation import LinearOperatorPermutation # line: 35
+from tensorflow.python.ops.linalg.linear_operator_toeplitz import LinearOperatorToeplitz # line: 33
+from tensorflow.python.ops.linalg.linear_operator_tridiag import LinearOperatorTridiag # line: 39
+from tensorflow.python.ops.linalg.linear_operator_zeros import LinearOperatorZeros # line: 39
+from tensorflow.python.ops.linalg_ops import cholesky_solve # line: 147
+from tensorflow.python.ops.linalg_ops import eig # line: 382
+from tensorflow.python.ops.linalg_ops import self_adjoint_eig as eigh # line: 441
+from tensorflow.python.ops.linalg_ops import eigvals # line: 414
+from tensorflow.python.ops.linalg_ops import self_adjoint_eigvals as eigvalsh # line: 465
+from tensorflow.python.ops.linalg_ops import eye # line: 196
+from tensorflow.python.ops.linalg_ops import matrix_solve_ls as lstsq # line: 244
+from tensorflow.python.ops.linalg_ops import norm_v2 as norm # line: 561
+from tensorflow.python.ops.linalg_ops import svd # line: 489
+from tensorflow.python.ops.linalg_ops import matrix_triangular_solve as triangular_solve # line: 84
+from tensorflow.python.ops.math_ops import matmul # line: 3617
+from tensorflow.python.ops.math_ops import matvec # line: 3846
+from tensorflow.python.ops.math_ops import tensordot # line: 5152
+from tensorflow.python.ops.math_ops import trace # line: 3573
+from tensorflow.python.ops.nn_impl import l2_normalize # line: 540
+from tensorflow.python.ops.nn_impl import normalize # line: 487
+from tensorflow.python.ops.special_math_ops import einsum # line: 618
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/linalg/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/linalg/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..07230e4ec118e2fe89f8c47ef1ebd82d438299e0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/linalg/experimental/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.linalg.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.linalg.sparse.conjugate_gradient import conjugate_gradient # line: 30
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/lite/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/lite/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..627b6732f6672566107824ca8f23cb0e381afff0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/lite/__init__.py
@@ -0,0 +1,14 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.lite namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.lite import experimental
+from tensorflow.lite.python.convert import OpsSet # line: 169
+from tensorflow.lite.python.interpreter import Interpreter # line: 348
+from tensorflow.lite.python.lite import Optimize # line: 100
+from tensorflow.lite.python.lite import RepresentativeDataset # line: 160
+from tensorflow.lite.python.lite import TFLiteConverterV2 as TFLiteConverter # line: 1915
+from tensorflow.lite.python.lite import TargetSpec # line: 184
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/lite/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/lite/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..eba353df1766ee44be0704e9715a0c5e121fa350
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/lite/experimental/__init__.py
@@ -0,0 +1,13 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.lite.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.lite.experimental import authoring
+from tensorflow.lite.python.analyzer import ModelAnalyzer as Analyzer # line: 35
+from tensorflow.lite.python.interpreter import OpResolverType # line: 303
+from tensorflow.lite.python.interpreter import load_delegate # line: 125
+from tensorflow.lite.tools.optimize.debugging.python.debugger import QuantizationDebugOptions # line: 56
+from tensorflow.lite.tools.optimize.debugging.python.debugger import QuantizationDebugger # line: 120
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/lite/experimental/authoring/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/lite/experimental/authoring/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ab1119e08d20c12ae992ae4e2e34f6568a1541a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/lite/experimental/authoring/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.lite.experimental.authoring namespace
+"""
+
+import sys as _sys
+
+from tensorflow.lite.python.authoring.authoring import compatible # line: 265
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/lookup/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/lookup/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb55cdad83de372fb4e3f4a755aa7c35d1986f4d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/lookup/__init__.py
@@ -0,0 +1,13 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.lookup namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.lookup import experimental
+from tensorflow.python.ops.lookup_ops import KeyValueTensorInitializer # line: 519
+from tensorflow.python.ops.lookup_ops import StaticHashTable # line: 281
+from tensorflow.python.ops.lookup_ops import StaticVocabularyTable # line: 1188
+from tensorflow.python.ops.lookup_ops import TextFileIndex # line: 587
+from tensorflow.python.ops.lookup_ops import TextFileInitializer # line: 607
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/lookup/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/lookup/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f3366bd1230a95f2a66ffb086f8d7aa3be7add3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/lookup/experimental/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.lookup.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.lookup_ops import DenseHashTable # line: 2108
+from tensorflow.python.ops.lookup_ops import MutableHashTable # line: 1796
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/math/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/math/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..afea648baa72b24a06c1713840a5c33bb41008d9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/math/__init__.py
@@ -0,0 +1,139 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.math namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.math import special
+from tensorflow.python.ops.gen_array_ops import invert_permutation # line: 4592
+from tensorflow.python.ops.gen_math_ops import acosh # line: 231
+from tensorflow.python.ops.gen_math_ops import asin # line: 991
+from tensorflow.python.ops.gen_math_ops import asinh # line: 1091
+from tensorflow.python.ops.gen_math_ops import atan # line: 1184
+from tensorflow.python.ops.gen_math_ops import atan2 # line: 1284
+from tensorflow.python.ops.gen_math_ops import atanh # line: 1383
+from tensorflow.python.ops.gen_math_ops import betainc # line: 1787
+from tensorflow.python.ops.gen_math_ops import cos # line: 2464
+from tensorflow.python.ops.gen_math_ops import cosh # line: 2558
+from tensorflow.python.ops.gen_math_ops import digamma # line: 3161
+from tensorflow.python.ops.gen_math_ops import erf # line: 3454
+from tensorflow.python.ops.gen_math_ops import erfc # line: 3546
+from tensorflow.python.ops.gen_math_ops import expm1 # line: 3847
+from tensorflow.python.ops.gen_math_ops import floor_mod as floormod # line: 4092
+from tensorflow.python.ops.gen_math_ops import greater # line: 4186
+from tensorflow.python.ops.gen_math_ops import greater_equal # line: 4287
+from tensorflow.python.ops.gen_math_ops import igamma # line: 4480
+from tensorflow.python.ops.gen_math_ops import igammac # line: 4639
+from tensorflow.python.ops.gen_math_ops import is_finite # line: 4935
+from tensorflow.python.ops.gen_math_ops import is_inf # line: 5031
+from tensorflow.python.ops.gen_math_ops import is_nan # line: 5127
+from tensorflow.python.ops.gen_math_ops import less # line: 5223
+from tensorflow.python.ops.gen_math_ops import less_equal # line: 5324
+from tensorflow.python.ops.gen_math_ops import lgamma # line: 5425
+from tensorflow.python.ops.gen_math_ops import log # line: 5595
+from tensorflow.python.ops.gen_math_ops import log1p # line: 5689
+from tensorflow.python.ops.gen_math_ops import logical_and # line: 5779
+from tensorflow.python.ops.gen_math_ops import logical_not # line: 5918
+from tensorflow.python.ops.gen_math_ops import logical_or # line: 6005
+from tensorflow.python.ops.gen_math_ops import maximum # line: 6310
+from tensorflow.python.ops.gen_math_ops import minimum # line: 6566
+from tensorflow.python.ops.gen_math_ops import floor_mod as mod # line: 4092
+from tensorflow.python.ops.gen_math_ops import neg as negative # line: 6913
+from tensorflow.python.ops.gen_math_ops import next_after as nextafter # line: 6999
+from tensorflow.python.ops.gen_math_ops import polygamma # line: 7167
+from tensorflow.python.ops.gen_math_ops import reciprocal # line: 8159
+from tensorflow.python.ops.gen_math_ops import rint # line: 8656
+from tensorflow.python.ops.gen_math_ops import segment_max # line: 8930
+from tensorflow.python.ops.gen_math_ops import segment_mean # line: 9164
+from tensorflow.python.ops.gen_math_ops import segment_min # line: 9289
+from tensorflow.python.ops.gen_math_ops import segment_prod # line: 9523
+from tensorflow.python.ops.gen_math_ops import segment_sum # line: 9749
+from tensorflow.python.ops.gen_math_ops import sin # line: 10299
+from tensorflow.python.ops.gen_math_ops import sinh # line: 10392
+from tensorflow.python.ops.gen_math_ops import square # line: 11962
+from tensorflow.python.ops.gen_math_ops import squared_difference # line: 12051
+from tensorflow.python.ops.gen_math_ops import tan # line: 12352
+from tensorflow.python.ops.gen_math_ops import tanh # line: 12446
+from tensorflow.python.ops.gen_math_ops import unsorted_segment_max # line: 12789
+from tensorflow.python.ops.gen_math_ops import unsorted_segment_min # line: 12927
+from tensorflow.python.ops.gen_math_ops import unsorted_segment_prod # line: 13061
+from tensorflow.python.ops.gen_math_ops import unsorted_segment_sum # line: 13195
+from tensorflow.python.ops.gen_math_ops import xlogy # line: 13444
+from tensorflow.python.ops.gen_math_ops import zeta # line: 13530
+from tensorflow.python.ops.gen_nn_ops import softsign # line: 12232
+from tensorflow.python.ops.bincount_ops import bincount # line: 29
+from tensorflow.python.ops.check_ops import is_non_decreasing # line: 1986
+from tensorflow.python.ops.check_ops import is_strictly_increasing # line: 2028
+from tensorflow.python.ops.confusion_matrix import confusion_matrix # line: 92
+from tensorflow.python.ops.math_ops import abs # line: 365
+from tensorflow.python.ops.math_ops import accumulate_n # line: 4161
+from tensorflow.python.ops.math_ops import acos # line: 5747
+from tensorflow.python.ops.math_ops import add # line: 4020
+from tensorflow.python.ops.math_ops import add_n # line: 4101
+from tensorflow.python.ops.math_ops import angle # line: 869
+from tensorflow.python.ops.math_ops import argmax_v2 as argmax # line: 268
+from tensorflow.python.ops.math_ops import argmin_v2 as argmin # line: 322
+from tensorflow.python.ops.math_ops import ceil # line: 5577
+from tensorflow.python.ops.math_ops import conj # line: 4534
+from tensorflow.python.ops.math_ops import count_nonzero_v2 as count_nonzero # line: 2570
+from tensorflow.python.ops.math_ops import cumprod # line: 4424
+from tensorflow.python.ops.math_ops import cumsum # line: 4352
+from tensorflow.python.ops.math_ops import cumulative_logsumexp # line: 4478
+from tensorflow.python.ops.math_ops import divide # line: 446
+from tensorflow.python.ops.math_ops import div_no_nan as divide_no_nan # line: 1696
+from tensorflow.python.ops.math_ops import equal # line: 2003
+from tensorflow.python.ops.math_ops import erfcinv # line: 5547
+from tensorflow.python.ops.math_ops import erfinv # line: 5512
+from tensorflow.python.ops.math_ops import exp # line: 5644
+from tensorflow.python.ops.math_ops import floor # line: 5778
+from tensorflow.python.ops.math_ops import floordiv # line: 1804
+from tensorflow.python.ops.math_ops import imag # line: 835
+from tensorflow.python.ops.math_ops import log_sigmoid # line: 4307
+from tensorflow.python.ops.math_ops import logical_xor # line: 1904
+from tensorflow.python.ops.math_ops import multiply # line: 481
+from tensorflow.python.ops.math_ops import multiply_no_nan # line: 1751
+from tensorflow.python.ops.math_ops import ndtri # line: 5531
+from tensorflow.python.ops.math_ops import not_equal # line: 2040
+from tensorflow.python.ops.math_ops import polyval # line: 5334
+from tensorflow.python.ops.math_ops import pow # line: 669
+from tensorflow.python.ops.math_ops import real # line: 794
+from tensorflow.python.ops.math_ops import reciprocal_no_nan # line: 5406
+from tensorflow.python.ops.math_ops import reduce_all # line: 3306
+from tensorflow.python.ops.math_ops import reduce_any # line: 3412
+from tensorflow.python.ops.math_ops import reduce_euclidean_norm # line: 2447
+from tensorflow.python.ops.math_ops import reduce_logsumexp # line: 3517
+from tensorflow.python.ops.math_ops import reduce_max # line: 3187
+from tensorflow.python.ops.math_ops import reduce_mean # line: 2713
+from tensorflow.python.ops.math_ops import reduce_min # line: 3059
+from tensorflow.python.ops.math_ops import reduce_prod # line: 2887
+from tensorflow.python.ops.math_ops import reduce_std # line: 2836
+from tensorflow.python.ops.math_ops import reduce_sum # line: 2369
+from tensorflow.python.ops.math_ops import reduce_variance # line: 2773
+from tensorflow.python.ops.math_ops import round # line: 914
+from tensorflow.python.ops.math_ops import rsqrt # line: 5722
+from tensorflow.python.ops.math_ops import scalar_mul_v2 as scalar_mul # line: 660
+from tensorflow.python.ops.math_ops import sigmoid # line: 4254
+from tensorflow.python.ops.math_ops import sign # line: 747
+from tensorflow.python.ops.math_ops import sobol_sample # line: 5697
+from tensorflow.python.ops.math_ops import softplus # line: 634
+from tensorflow.python.ops.math_ops import sqrt # line: 5605
+from tensorflow.python.ops.math_ops import subtract # line: 545
+from tensorflow.python.ops.math_ops import truediv # line: 1630
+from tensorflow.python.ops.math_ops import unsorted_segment_mean # line: 4657
+from tensorflow.python.ops.math_ops import unsorted_segment_sqrt_n # line: 4712
+from tensorflow.python.ops.math_ops import xdivy # line: 5440
+from tensorflow.python.ops.math_ops import xlog1py # line: 5474
+from tensorflow.python.ops.nn_impl import l2_normalize # line: 540
+from tensorflow.python.ops.nn_impl import zero_fraction # line: 620
+from tensorflow.python.ops.nn_ops import approx_max_k # line: 5882
+from tensorflow.python.ops.nn_ops import approx_min_k # line: 5945
+from tensorflow.python.ops.nn_ops import in_top_k_v2 as in_top_k # line: 6567
+from tensorflow.python.ops.nn_ops import log_softmax_v2 as log_softmax # line: 3955
+from tensorflow.python.ops.nn_ops import softmax_v2 as softmax # line: 3869
+from tensorflow.python.ops.nn_ops import top_k # line: 5815
+from tensorflow.python.ops.special_math_ops import bessel_i0 # line: 253
+from tensorflow.python.ops.special_math_ops import bessel_i0e # line: 282
+from tensorflow.python.ops.special_math_ops import bessel_i1 # line: 309
+from tensorflow.python.ops.special_math_ops import bessel_i1e # line: 338
+from tensorflow.python.ops.special_math_ops import lbeta # line: 45
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/math/special/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/math/special/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f90e74566d849fe62d8b2dd68c8ade4a890d56c4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/math/special/__init__.py
@@ -0,0 +1,24 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.math.special namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.special_math_ops import bessel_i0 # line: 253
+from tensorflow.python.ops.special_math_ops import bessel_i0e # line: 282
+from tensorflow.python.ops.special_math_ops import bessel_i1 # line: 309
+from tensorflow.python.ops.special_math_ops import bessel_i1e # line: 338
+from tensorflow.python.ops.special_math_ops import bessel_j0 # line: 477
+from tensorflow.python.ops.special_math_ops import bessel_j1 # line: 504
+from tensorflow.python.ops.special_math_ops import bessel_k0 # line: 365
+from tensorflow.python.ops.special_math_ops import bessel_k0e # line: 394
+from tensorflow.python.ops.special_math_ops import bessel_k1 # line: 421
+from tensorflow.python.ops.special_math_ops import bessel_k1e # line: 450
+from tensorflow.python.ops.special_math_ops import bessel_y0 # line: 531
+from tensorflow.python.ops.special_math_ops import bessel_y1 # line: 558
+from tensorflow.python.ops.special_math_ops import dawsn # line: 101
+from tensorflow.python.ops.special_math_ops import expint # line: 132
+from tensorflow.python.ops.special_math_ops import fresnel_cos # line: 162
+from tensorflow.python.ops.special_math_ops import fresnel_sin # line: 193
+from tensorflow.python.ops.special_math_ops import spence # line: 223
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/mlir/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/mlir/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f6575e39364955831a77ff2140cd77cf5851df8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/mlir/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.mlir namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.mlir import experimental
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/mlir/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/mlir/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ded7e7a94c56a4b895a5afb4a57299715c2d629
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/mlir/experimental/__init__.py
@@ -0,0 +1,14 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.mlir.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.compiler.mlir.mlir import convert_function # line: 48
+from tensorflow.python.compiler.mlir.mlir import convert_graph_def # line: 21
+from tensorflow.python.compiler.mlir.mlir import convert_saved_model # line: 95
+from tensorflow.python.compiler.mlir.mlir import convert_saved_model_v1 # line: 115
+from tensorflow.python.compiler.mlir.mlir import run_pass_pipeline # line: 152
+from tensorflow.python.compiler.mlir.mlir import tflite_to_tosa_bytecode # line: 181
+from tensorflow.python.compiler.mlir.mlir import experimental_write_bytecode as write_bytecode # line: 170
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/nest/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/nest/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c829f8ac1920d3cf8ce03d31a156fef83658a062
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/nest/__init__.py
@@ -0,0 +1,12 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.nest namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.util.nest import assert_same_structure # line: 301
+from tensorflow.python.util.nest import flatten # line: 201
+from tensorflow.python.util.nest import is_nested # line: 166
+from tensorflow.python.util.nest import map_structure # line: 545
+from tensorflow.python.util.nest import pack_sequence_as # line: 426
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/nn/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/nn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d43b3ee2c934ebb42a36ce979ef8ccbc22b6ed8c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/nn/__init__.py
@@ -0,0 +1,97 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.nn namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.nn import experimental
+from tensorflow.python.ops.gen_math_ops import tanh # line: 12446
+from tensorflow.python.ops.gen_nn_ops import elu # line: 3783
+from tensorflow.python.ops.gen_nn_ops import l2_loss # line: 5717
+from tensorflow.python.ops.gen_nn_ops import lrn as local_response_normalization # line: 5806
+from tensorflow.python.ops.gen_nn_ops import lrn # line: 5806
+from tensorflow.python.ops.gen_nn_ops import relu # line: 11562
+from tensorflow.python.ops.gen_nn_ops import selu # line: 11829
+from tensorflow.python.ops.gen_nn_ops import softsign # line: 12232
+from tensorflow.python.keras.layers.rnn_cell_wrapper_v2 import DeviceWrapper as RNNCellDeviceWrapper # line: 123
+from tensorflow.python.keras.layers.rnn_cell_wrapper_v2 import DropoutWrapper as RNNCellDropoutWrapper # line: 95
+from tensorflow.python.keras.layers.rnn_cell_wrapper_v2 import ResidualWrapper as RNNCellResidualWrapper # line: 111
+from tensorflow.python.ops.array_ops import depth_to_space_v2 as depth_to_space # line: 4069
+from tensorflow.python.ops.array_ops import space_to_batch_v2 as space_to_batch # line: 4031
+from tensorflow.python.ops.array_ops import space_to_depth_v2 as space_to_depth # line: 4050
+from tensorflow.python.ops.candidate_sampling_ops import all_candidate_sampler # line: 430
+from tensorflow.python.ops.candidate_sampling_ops import compute_accidental_hits # line: 466
+from tensorflow.python.ops.candidate_sampling_ops import fixed_unigram_candidate_sampler # line: 306
+from tensorflow.python.ops.candidate_sampling_ops import learned_unigram_candidate_sampler # line: 212
+from tensorflow.python.ops.ctc_ops import collapse_repeated # line: 1175
+from tensorflow.python.ops.ctc_ops import ctc_beam_search_decoder_v2 as ctc_beam_search_decoder # line: 449
+from tensorflow.python.ops.ctc_ops import ctc_greedy_decoder # line: 300
+from tensorflow.python.ops.ctc_ops import ctc_loss_v3 as ctc_loss # line: 886
+from tensorflow.python.ops.ctc_ops import ctc_unique_labels # line: 1273
+from tensorflow.python.ops.embedding_ops import embedding_lookup_v2 as embedding_lookup # line: 371
+from tensorflow.python.ops.embedding_ops import embedding_lookup_sparse_v2 as embedding_lookup_sparse # line: 575
+from tensorflow.python.ops.embedding_ops import safe_embedding_lookup_sparse_v2 as safe_embedding_lookup_sparse # line: 734
+from tensorflow.python.ops.math_ops import sigmoid # line: 4254
+from tensorflow.python.ops.math_ops import softplus # line: 634
+from tensorflow.python.ops.nn_impl import batch_norm_with_global_normalization_v2 as batch_norm_with_global_normalization # line: 1649
+from tensorflow.python.ops.nn_impl import batch_normalization # line: 1420
+from tensorflow.python.ops.nn_impl import depthwise_conv2d_v2 as depthwise_conv2d # line: 792
+from tensorflow.python.ops.nn_impl import l2_normalize # line: 540
+from tensorflow.python.ops.nn_impl import log_poisson_loss # line: 43
+from tensorflow.python.ops.nn_impl import moments_v2 as moments # line: 1281
+from tensorflow.python.ops.nn_impl import nce_loss_v2 as nce_loss # line: 1900
+from tensorflow.python.ops.nn_impl import normalize_moments # line: 1182
+from tensorflow.python.ops.nn_impl import sampled_softmax_loss_v2 as sampled_softmax_loss # line: 2117
+from tensorflow.python.ops.nn_impl import separable_conv2d_v2 as separable_conv2d # line: 1001
+from tensorflow.python.ops.nn_impl import sigmoid_cross_entropy_with_logits_v2 as sigmoid_cross_entropy_with_logits # line: 150
+from tensorflow.python.ops.nn_impl import swish as silu # line: 430
+from tensorflow.python.ops.nn_impl import sufficient_statistics_v2 as sufficient_statistics # line: 1152
+from tensorflow.python.ops.nn_impl import swish # line: 430
+from tensorflow.python.ops.nn_impl import weighted_cross_entropy_with_logits_v2 as weighted_cross_entropy_with_logits # line: 249
+from tensorflow.python.ops.nn_impl import weighted_moments_v2 as weighted_moments # line: 1395
+from tensorflow.python.ops.nn_impl import zero_fraction # line: 620
+from tensorflow.python.ops.nn_impl_distribute import compute_average_loss # line: 70
+from tensorflow.python.ops.nn_impl_distribute import scale_regularization_loss # line: 27
+from tensorflow.python.ops.nn_ops import approx_max_k # line: 5882
+from tensorflow.python.ops.nn_ops import approx_min_k # line: 5945
+from tensorflow.python.ops.nn_ops import atrous_conv2d # line: 1790
+from tensorflow.python.ops.nn_ops import atrous_conv2d_transpose # line: 2814
+from tensorflow.python.ops.nn_ops import avg_pool_v2 as avg_pool # line: 4463
+from tensorflow.python.ops.nn_ops import avg_pool1d # line: 4616
+from tensorflow.python.ops.nn_ops import avg_pool2d # line: 4575
+from tensorflow.python.ops.nn_ops import avg_pool3d # line: 4663
+from tensorflow.python.ops.nn_ops import bias_add # line: 3516
+from tensorflow.python.ops.nn_ops import conv1d_v2 as conv1d # line: 2099
+from tensorflow.python.ops.nn_ops import conv1d_transpose # line: 2169
+from tensorflow.python.ops.nn_ops import conv2d_v2 as conv2d # line: 2260
+from tensorflow.python.ops.nn_ops import conv2d_transpose_v2 as conv2d_transpose # line: 2692
+from tensorflow.python.ops.nn_ops import conv3d_v2 as conv3d # line: 3239
+from tensorflow.python.ops.nn_ops import conv3d_transpose_v2 as conv3d_transpose # line: 3347
+from tensorflow.python.ops.nn_ops import conv_transpose # line: 3428
+from tensorflow.python.ops.nn_ops import convolution_v2 as convolution # line: 1176
+from tensorflow.python.ops.nn_ops import crelu_v2 as crelu # line: 3623
+from tensorflow.python.ops.nn_ops import depthwise_conv2d_native_backprop_filter as depthwise_conv2d_backprop_filter # line: 3126
+from tensorflow.python.ops.nn_ops import depthwise_conv2d_native_backprop_input as depthwise_conv2d_backprop_input # line: 3054
+from tensorflow.python.ops.nn_ops import dilation2d_v2 as dilation2d # line: 482
+from tensorflow.python.ops.nn_ops import dropout_v2 as dropout # line: 5425
+from tensorflow.python.ops.nn_ops import erosion2d_v2 as erosion2d # line: 6459
+from tensorflow.python.ops.nn_ops import fractional_avg_pool_v2 as fractional_avg_pool # line: 6321
+from tensorflow.python.ops.nn_ops import fractional_max_pool_v2 as fractional_max_pool # line: 6141
+from tensorflow.python.ops.nn_ops import gelu # line: 3702
+from tensorflow.python.ops.nn_ops import in_top_k_v2 as in_top_k # line: 6567
+from tensorflow.python.ops.nn_ops import isotonic_regression # line: 6617
+from tensorflow.python.ops.nn_ops import leaky_relu # line: 3667
+from tensorflow.python.ops.nn_ops import log_softmax_v2 as log_softmax # line: 3955
+from tensorflow.python.ops.nn_ops import max_pool_v2 as max_pool # line: 4705
+from tensorflow.python.ops.nn_ops import max_pool1d # line: 4911
+from tensorflow.python.ops.nn_ops import max_pool2d # line: 4971
+from tensorflow.python.ops.nn_ops import max_pool3d # line: 5084
+from tensorflow.python.ops.nn_ops import max_pool_with_argmax_v2 as max_pool_with_argmax # line: 5128
+from tensorflow.python.ops.nn_ops import pool_v2 as pool # line: 1689
+from tensorflow.python.ops.nn_ops import relu6 # line: 3630
+from tensorflow.python.ops.nn_ops import softmax_v2 as softmax # line: 3869
+from tensorflow.python.ops.nn_ops import softmax_cross_entropy_with_logits_v2 as softmax_cross_entropy_with_logits # line: 3989
+from tensorflow.python.ops.nn_ops import sparse_softmax_cross_entropy_with_logits_v2 as sparse_softmax_cross_entropy_with_logits # line: 4402
+from tensorflow.python.ops.nn_ops import top_k # line: 5815
+from tensorflow.python.ops.nn_ops import with_space_to_batch # line: 572
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/nn/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/nn/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..47fa94d259f19d723898910f486905ac0faf5708
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/nn/experimental/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.nn.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.nn_ops import general_dropout # line: 5664
+from tensorflow.python.ops.nn_ops import stateless_dropout # line: 5520
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/profiler/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/profiler/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..976dcfe68a814a35f27073e66fa1c05e71434f04
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/profiler/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.profiler namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.profiler import experimental
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/profiler/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/profiler/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad1438eeca4dc56dd401e7f852aaa7116c51896c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/profiler/experimental/__init__.py
@@ -0,0 +1,14 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.profiler.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.profiler.experimental import client
+from tensorflow._api.v2.compat.v2.profiler.experimental import server
+from tensorflow.python.profiler.profiler_v2 import Profile # line: 183
+from tensorflow.python.profiler.profiler_v2 import ProfilerOptions # line: 45
+from tensorflow.python.profiler.profiler_v2 import start # line: 80
+from tensorflow.python.profiler.profiler_v2 import stop # line: 129
+from tensorflow.python.profiler.trace import Trace # line: 27
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/profiler/experimental/client/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/profiler/experimental/client/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7948c300b88a45b6a34b1f753dbb310d79aabd33
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/profiler/experimental/client/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.profiler.experimental.client namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.profiler.profiler_client import monitor # line: 134
+from tensorflow.python.profiler.profiler_client import trace # line: 25
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/profiler/experimental/server/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/profiler/experimental/server/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..790c00223c2bd2f43ff546735432431d96f67319
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/profiler/experimental/server/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.profiler.experimental.server namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.profiler.profiler_v2 import start_server as start # line: 168
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/quantization/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/quantization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..77af0f6a756df9488fc8656009e99d9dec3b7fc9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/quantization/__init__.py
@@ -0,0 +1,19 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.quantization namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.quantization import experimental
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_args # line: 2698
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_args_gradient # line: 2867
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_vars # line: 3003
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_vars_gradient # line: 3145
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_vars_per_channel # line: 3276
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_vars_per_channel_gradient # line: 3423
+from tensorflow.python.ops.gen_array_ops import quantized_concat # line: 8202
+from tensorflow.python.ops.array_ops import dequantize # line: 6139
+from tensorflow.python.ops.array_ops import quantize # line: 6100
+from tensorflow.python.ops.array_ops import quantize_and_dequantize # line: 6176
+from tensorflow.python.ops.array_ops import quantize_and_dequantize_v2 # line: 6243
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/quantization/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/quantization/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..267d4981e8bcc0b3f894cc33fc1e0c236254a4af
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/quantization/experimental/__init__.py
@@ -0,0 +1,13 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.quantization.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.compiler.mlir.quantization.tensorflow.python.quantize_model import _QuantizationComponentSpec as QuantizationComponentSpec # line: 58
+from tensorflow.compiler.mlir.quantization.tensorflow.python.quantize_model import _QuantizationMethod as QuantizationMethod # line: 54
+from tensorflow.compiler.mlir.quantization.tensorflow.python.quantize_model import _QuantizationOptions as QuantizationOptions # line: 50
+from tensorflow.compiler.mlir.quantization.tensorflow.python.quantize_model import _UnitWiseQuantizationSpec as UnitWiseQuantizationSpec # line: 62
+from tensorflow.compiler.mlir.quantization.tensorflow.python.quantize_model import quantize as quantize_saved_model # line: 1467
+from tensorflow.compiler.mlir.quantization.tensorflow.python.representative_dataset import TfRecordRepresentativeDatasetSaver # line: 78
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/queue/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/queue/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddd979996fbf7408213917cccdd9b6ff4b35d50f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/queue/__init__.py
@@ -0,0 +1,12 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.queue namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.data_flow_ops import FIFOQueue # line: 712
+from tensorflow.python.ops.data_flow_ops import PaddingFIFOQueue # line: 848
+from tensorflow.python.ops.data_flow_ops import PriorityQueue # line: 924
+from tensorflow.python.ops.data_flow_ops import QueueBase # line: 115
+from tensorflow.python.ops.data_flow_ops import RandomShuffleQueue # line: 622
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/ragged/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/ragged/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..748ef1c0f48cce3ca8ea24176f26b1227b77a4e7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/ragged/__init__.py
@@ -0,0 +1,17 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.ragged namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.ragged.ragged_array_ops import boolean_mask # line: 49
+from tensorflow.python.ops.ragged.ragged_array_ops import cross # line: 741
+from tensorflow.python.ops.ragged.ragged_array_ops import cross_hashed # line: 768
+from tensorflow.python.ops.ragged.ragged_array_ops import stack_dynamic_partitions # line: 575
+from tensorflow.python.ops.ragged.ragged_concat_ops import stack # line: 73
+from tensorflow.python.ops.ragged.ragged_factory_ops import constant # line: 35
+from tensorflow.python.ops.ragged.ragged_functional_ops import map_flat_values # line: 25
+from tensorflow.python.ops.ragged.ragged_math_ops import range # line: 44
+from tensorflow.python.ops.ragged.segment_id_ops import row_splits_to_segment_ids # line: 31
+from tensorflow.python.ops.ragged.segment_id_ops import segment_ids_to_row_splits # line: 75
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/random/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/random/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..84f891c6060cb748c28b6742f725231b1ee760a9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/random/__init__.py
@@ -0,0 +1,36 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.random namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.random import experimental
+from tensorflow.python.framework.random_seed import set_seed # line: 210
+from tensorflow.python.ops.candidate_sampling_ops import all_candidate_sampler # line: 430
+from tensorflow.python.ops.candidate_sampling_ops import fixed_unigram_candidate_sampler # line: 306
+from tensorflow.python.ops.candidate_sampling_ops import learned_unigram_candidate_sampler # line: 212
+from tensorflow.python.ops.candidate_sampling_ops import log_uniform_candidate_sampler # line: 116
+from tensorflow.python.ops.candidate_sampling_ops import uniform_candidate_sampler # line: 27
+from tensorflow.python.ops.random_ops import categorical # line: 394
+from tensorflow.python.ops.random_ops import random_gamma as gamma # line: 451
+from tensorflow.python.ops.random_ops import random_normal as normal # line: 39
+from tensorflow.python.ops.random_ops import random_poisson_v2 as poisson # line: 586
+from tensorflow.python.ops.random_ops import random_shuffle as shuffle # line: 326
+from tensorflow.python.ops.random_ops import truncated_normal # line: 155
+from tensorflow.python.ops.random_ops import random_uniform as uniform # line: 211
+from tensorflow.python.ops.random_ops_util import Algorithm # line: 30
+from tensorflow.python.ops.stateful_random_ops import Generator # line: 220
+from tensorflow.python.ops.stateful_random_ops import create_rng_state # line: 174
+from tensorflow.python.ops.stateful_random_ops import get_global_generator # line: 975
+from tensorflow.python.ops.stateful_random_ops import set_global_generator # line: 1000
+from tensorflow.python.ops.stateless_random_ops import fold_in # line: 93
+from tensorflow.python.ops.stateless_random_ops import split # line: 51
+from tensorflow.python.ops.stateless_random_ops import stateless_random_binomial as stateless_binomial # line: 411
+from tensorflow.python.ops.stateless_random_ops import stateless_categorical # line: 793
+from tensorflow.python.ops.stateless_random_ops import stateless_random_gamma as stateless_gamma # line: 481
+from tensorflow.python.ops.stateless_random_ops import stateless_random_normal as stateless_normal # line: 644
+from tensorflow.python.ops.stateless_random_ops import stateless_parameterized_truncated_normal # line: 851
+from tensorflow.python.ops.stateless_random_ops import stateless_random_poisson as stateless_poisson # line: 584
+from tensorflow.python.ops.stateless_random_ops import stateless_truncated_normal # line: 695
+from tensorflow.python.ops.stateless_random_ops import stateless_random_uniform as stateless_uniform # line: 274
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/random/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/random/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8941fae2111531b4998ca26a4321f32e39a0b5ea
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/random/experimental/__init__.py
@@ -0,0 +1,16 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.random.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.random_ops_util import Algorithm # line: 30
+from tensorflow.python.ops.stateful_random_ops import Generator # line: 220
+from tensorflow.python.ops.stateful_random_ops import create_rng_state # line: 174
+from tensorflow.python.ops.stateful_random_ops import get_global_generator # line: 975
+from tensorflow.python.ops.stateful_random_ops import set_global_generator # line: 1000
+from tensorflow.python.ops.stateless_random_ops import index_shuffle # line: 135
+from tensorflow.python.ops.stateless_random_ops import fold_in as stateless_fold_in # line: 93
+from tensorflow.python.ops.stateless_random_ops import stateless_shuffle # line: 226
+from tensorflow.python.ops.stateless_random_ops import split as stateless_split # line: 51
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/raw_ops/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/raw_ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e650c4b2247cad3d39b71e2c6ce5fa7a74699a5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/raw_ops/__init__.py
@@ -0,0 +1,1422 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.raw_ops namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.gen_array_ops import BatchMatrixBandPart # line: 68
+from tensorflow.python.ops.gen_array_ops import BatchMatrixDiag # line: 126
+from tensorflow.python.ops.gen_array_ops import BatchMatrixDiagPart # line: 182
+from tensorflow.python.ops.gen_array_ops import BatchMatrixSetDiag # line: 239
+from tensorflow.python.ops.gen_array_ops import BatchToSpace # line: 319
+from tensorflow.python.ops.gen_array_ops import BatchToSpaceND # line: 533
+from tensorflow.python.ops.gen_array_ops import Bitcast # line: 677
+from tensorflow.python.ops.gen_array_ops import BroadcastArgs # line: 739
+from tensorflow.python.ops.gen_array_ops import BroadcastGradientArgs # line: 807
+from tensorflow.python.ops.gen_array_ops import BroadcastTo # line: 928
+from tensorflow.python.ops.gen_array_ops import CheckNumerics # line: 1041
+from tensorflow.python.ops.gen_array_ops import CheckNumericsV2 # line: 1107
+from tensorflow.python.ops.gen_array_ops import Concat # line: 1174
+from tensorflow.python.ops.gen_array_ops import ConcatOffset # line: 1255
+from tensorflow.python.ops.gen_array_ops import ConcatV2 # line: 1328
+from tensorflow.python.ops.gen_array_ops import ConjugateTranspose # line: 1397
+from tensorflow.python.ops.gen_array_ops import Const # line: 1458
+from tensorflow.python.ops.gen_array_ops import DebugGradientIdentity # line: 1519
+from tensorflow.python.ops.gen_array_ops import DebugGradientRefIdentity # line: 1567
+from tensorflow.python.ops.gen_array_ops import DeepCopy # line: 1613
+from tensorflow.python.ops.gen_array_ops import DepthToSpace # line: 1771
+from tensorflow.python.ops.gen_array_ops import Dequantize # line: 1914
+from tensorflow.python.ops.gen_array_ops import Diag # line: 2032
+from tensorflow.python.ops.gen_array_ops import DiagPart # line: 2109
+from tensorflow.python.ops.gen_array_ops import EditDistance # line: 2204
+from tensorflow.python.ops.gen_array_ops import Empty # line: 2279
+from tensorflow.python.ops.gen_array_ops import EnsureShape # line: 2345
+from tensorflow.python.ops.gen_array_ops import ExpandDims # line: 2439
+from tensorflow.python.ops.gen_array_ops import ExtractImagePatches # line: 2532
+from tensorflow.python.ops.gen_array_ops import ExtractVolumePatches # line: 2667
+from tensorflow.python.ops.gen_array_ops import FakeQuantWithMinMaxArgs # line: 2834
+from tensorflow.python.ops.gen_array_ops import FakeQuantWithMinMaxArgsGradient # line: 2969
+from tensorflow.python.ops.gen_array_ops import FakeQuantWithMinMaxVars # line: 3113
+from tensorflow.python.ops.gen_array_ops import FakeQuantWithMinMaxVarsGradient # line: 3247
+from tensorflow.python.ops.gen_array_ops import FakeQuantWithMinMaxVarsPerChannel # line: 3391
+from tensorflow.python.ops.gen_array_ops import FakeQuantWithMinMaxVarsPerChannelGradient # line: 3530
+from tensorflow.python.ops.gen_array_ops import Fill # line: 3625
+from tensorflow.python.ops.gen_array_ops import Fingerprint # line: 3716
+from tensorflow.python.ops.gen_array_ops import Gather # line: 3810
+from tensorflow.python.ops.gen_array_ops import GatherNd # line: 3980
+from tensorflow.python.ops.gen_array_ops import GatherV2 # line: 4083
+from tensorflow.python.ops.gen_array_ops import GuaranteeConst # line: 4152
+from tensorflow.python.ops.gen_array_ops import Identity # line: 4208
+from tensorflow.python.ops.gen_array_ops import IdentityN # line: 4304
+from tensorflow.python.ops.gen_array_ops import ImmutableConst # line: 4374
+from tensorflow.python.ops.gen_array_ops import InplaceAdd # line: 4439
+from tensorflow.python.ops.gen_array_ops import InplaceSub # line: 4503
+from tensorflow.python.ops.gen_array_ops import InplaceUpdate # line: 4570
+from tensorflow.python.ops.gen_array_ops import InvertPermutation # line: 4672
+from tensorflow.python.ops.gen_array_ops import ListDiff # line: 4766
+from tensorflow.python.ops.gen_array_ops import LowerBound # line: 4854
+from tensorflow.python.ops.gen_array_ops import MatrixBandPart # line: 4991
+from tensorflow.python.ops.gen_array_ops import MatrixDiag # line: 5077
+from tensorflow.python.ops.gen_array_ops import MatrixDiagPart # line: 5162
+from tensorflow.python.ops.gen_array_ops import MatrixDiagPartV2 # line: 5296
+from tensorflow.python.ops.gen_array_ops import MatrixDiagPartV3 # line: 5475
+from tensorflow.python.ops.gen_array_ops import MatrixDiagV2 # line: 5643
+from tensorflow.python.ops.gen_array_ops import MatrixDiagV3 # line: 5851
+from tensorflow.python.ops.gen_array_ops import MatrixSetDiag # line: 5929
+from tensorflow.python.ops.gen_array_ops import MatrixSetDiagV2 # line: 6066
+from tensorflow.python.ops.gen_array_ops import MatrixSetDiagV3 # line: 6249
+from tensorflow.python.ops.gen_array_ops import MirrorPad # line: 6348
+from tensorflow.python.ops.gen_array_ops import MirrorPadGrad # line: 6433
+from tensorflow.python.ops.gen_array_ops import OneHot # line: 6595
+from tensorflow.python.ops.gen_array_ops import OnesLike # line: 6658
+from tensorflow.python.ops.gen_array_ops import Pack # line: 6747
+from tensorflow.python.ops.gen_array_ops import Pad # line: 6837
+from tensorflow.python.ops.gen_array_ops import PadV2 # line: 6924
+from tensorflow.python.ops.gen_array_ops import ParallelConcat # line: 7011
+from tensorflow.python.ops.gen_array_ops import Placeholder # line: 7085
+from tensorflow.python.ops.gen_array_ops import PlaceholderV2 # line: 7154
+from tensorflow.python.ops.gen_array_ops import PlaceholderWithDefault # line: 7215
+from tensorflow.python.ops.gen_array_ops import PreventGradient # line: 7288
+from tensorflow.python.ops.gen_array_ops import QuantizeAndDequantize # line: 7378
+from tensorflow.python.ops.gen_array_ops import QuantizeAndDequantizeV2 # line: 7567
+from tensorflow.python.ops.gen_array_ops import QuantizeAndDequantizeV3 # line: 7680
+from tensorflow.python.ops.gen_array_ops import QuantizeAndDequantizeV4 # line: 7820
+from tensorflow.python.ops.gen_array_ops import QuantizeAndDequantizeV4Grad # line: 7922
+from tensorflow.python.ops.gen_array_ops import QuantizeV2 # line: 8159
+from tensorflow.python.ops.gen_array_ops import QuantizedConcat # line: 8310
+from tensorflow.python.ops.gen_array_ops import QuantizedInstanceNorm # line: 8450
+from tensorflow.python.ops.gen_array_ops import QuantizedReshape # line: 8543
+from tensorflow.python.ops.gen_array_ops import Rank # line: 8616
+from tensorflow.python.ops.gen_array_ops import RefIdentity # line: 8660
+from tensorflow.python.ops.gen_array_ops import Reshape # line: 8770
+from tensorflow.python.ops.gen_array_ops import ResourceStridedSliceAssign # line: 8864
+from tensorflow.python.ops.gen_array_ops import Reverse # line: 8985
+from tensorflow.python.ops.gen_array_ops import ReverseSequence # line: 9113
+from tensorflow.python.ops.gen_array_ops import ReverseV2 # line: 9253
+from tensorflow.python.ops.gen_array_ops import ScatterNd # line: 9434
+from tensorflow.python.ops.gen_array_ops import ScatterNdNonAliasingAdd # line: 9537
+from tensorflow.python.ops.gen_array_ops import Shape # line: 9611
+from tensorflow.python.ops.gen_array_ops import ShapeN # line: 9682
+from tensorflow.python.ops.gen_array_ops import Size # line: 9761
+from tensorflow.python.ops.gen_array_ops import Slice # line: 9837
+from tensorflow.python.ops.gen_array_ops import Snapshot # line: 9895
+from tensorflow.python.ops.gen_array_ops import SpaceToBatch # line: 10051
+from tensorflow.python.ops.gen_array_ops import SpaceToBatchND # line: 10267
+from tensorflow.python.ops.gen_array_ops import SpaceToDepth # line: 10422
+from tensorflow.python.ops.gen_array_ops import Split # line: 10490
+from tensorflow.python.ops.gen_array_ops import SplitV # line: 10559
+from tensorflow.python.ops.gen_array_ops import Squeeze # line: 10648
+from tensorflow.python.ops.gen_array_ops import StopGradient # line: 10769
+from tensorflow.python.ops.gen_array_ops import StridedSlice # line: 10985
+from tensorflow.python.ops.gen_array_ops import StridedSliceAssign # line: 11090
+from tensorflow.python.ops.gen_array_ops import StridedSliceGrad # line: 11186
+from tensorflow.python.ops.gen_array_ops import TensorScatterAdd # line: 11358
+from tensorflow.python.ops.gen_array_ops import TensorScatterMax # line: 11464
+from tensorflow.python.ops.gen_array_ops import TensorScatterMin # line: 11559
+from tensorflow.python.ops.gen_array_ops import TensorScatterSub # line: 11718
+from tensorflow.python.ops.gen_array_ops import TensorScatterUpdate # line: 11820
+from tensorflow.python.ops.gen_array_ops import TensorStridedSliceUpdate # line: 11928
+from tensorflow.python.ops.gen_array_ops import Tile # line: 12064
+from tensorflow.python.ops.gen_array_ops import TileGrad # line: 12127
+from tensorflow.python.ops.gen_array_ops import Transpose # line: 12190
+from tensorflow.python.ops.gen_array_ops import Unique # line: 12284
+from tensorflow.python.ops.gen_array_ops import UniqueV2 # line: 12406
+from tensorflow.python.ops.gen_array_ops import UniqueWithCounts # line: 12499
+from tensorflow.python.ops.gen_array_ops import UniqueWithCountsV2 # line: 12626
+from tensorflow.python.ops.gen_array_ops import Unpack # line: 12708
+from tensorflow.python.ops.gen_array_ops import UnravelIndex # line: 12820
+from tensorflow.python.ops.gen_array_ops import UpperBound # line: 12906
+from tensorflow.python.ops.gen_array_ops import Where # line: 13026
+from tensorflow.python.ops.gen_array_ops import ZerosLike # line: 13082
+from tensorflow.python.ops.gen_audio_ops import AudioSpectrogram # line: 108
+from tensorflow.python.ops.gen_audio_ops import DecodeWav # line: 237
+from tensorflow.python.ops.gen_audio_ops import EncodeWav # line: 338
+from tensorflow.python.ops.gen_audio_ops import Mfcc # line: 443
+from tensorflow.python.ops.gen_batch_ops import Batch # line: 171
+from tensorflow.python.ops.gen_batch_ops import BatchFunction # line: 429
+from tensorflow.python.ops.gen_batch_ops import Unbatch # line: 579
+from tensorflow.python.ops.gen_batch_ops import UnbatchGrad # line: 676
+from tensorflow.python.ops.gen_bitwise_ops import BitwiseAnd # line: 113
+from tensorflow.python.ops.gen_bitwise_ops import BitwiseOr # line: 219
+from tensorflow.python.ops.gen_bitwise_ops import BitwiseXor # line: 325
+from tensorflow.python.ops.gen_bitwise_ops import Invert # line: 450
+from tensorflow.python.ops.gen_bitwise_ops import LeftShift # line: 566
+from tensorflow.python.ops.gen_bitwise_ops import PopulationCount # line: 631
+from tensorflow.python.ops.gen_bitwise_ops import RightShift # line: 749
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesAggregateStats # line: 85
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesBucketize # line: 167
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesCalculateBestFeatureSplit # line: 289
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesCalculateBestFeatureSplitV2 # line: 413
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesCalculateBestGainsPerFeature # line: 536
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesCenterBias # line: 623
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesCreateEnsemble # line: 684
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesCreateQuantileStreamResource # line: 747
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesDeserializeEnsemble # line: 808
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesEnsembleResourceHandleOp # line: 873
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesExampleDebugOutputs # line: 956
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesFlushQuantileSummaries # line: 1031
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesGetEnsembleStates # line: 1099
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesMakeQuantileSummaries # line: 1169
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesMakeStatsSummary # line: 1260
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesPredict # line: 1347
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesQuantileStreamResourceAddSummaries # line: 1416
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesQuantileStreamResourceDeserialize # line: 1480
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesQuantileStreamResourceFlush # line: 1551
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesQuantileStreamResourceGetBucketBoundaries # line: 1619
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesQuantileStreamResourceHandleOp # line: 1685
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesSerializeEnsemble # line: 1755
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesSparseAggregateStats # line: 1857
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesSparseCalculateBestFeatureSplit # line: 1980
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesTrainingPredict # line: 2088
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesUpdateEnsemble # line: 2228
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesUpdateEnsembleV2 # line: 2441
+from tensorflow.python.ops.gen_boosted_trees_ops import IsBoostedTreesEnsembleInitialized # line: 2574
+from tensorflow.python.ops.gen_boosted_trees_ops import IsBoostedTreesQuantileStreamResourceInitialized # line: 2634
+from tensorflow.python.ops.gen_candidate_sampling_ops import AllCandidateSampler # line: 113
+from tensorflow.python.ops.gen_candidate_sampling_ops import ComputeAccidentalHits # line: 214
+from tensorflow.python.ops.gen_candidate_sampling_ops import FixedUnigramCandidateSampler # line: 405
+from tensorflow.python.ops.gen_candidate_sampling_ops import LearnedUnigramCandidateSampler # line: 553
+from tensorflow.python.ops.gen_candidate_sampling_ops import LogUniformCandidateSampler # line: 677
+from tensorflow.python.ops.gen_candidate_sampling_ops import ThreadUnsafeUnigramCandidateSampler # line: 802
+from tensorflow.python.ops.gen_candidate_sampling_ops import UniformCandidateSampler # line: 925
+from tensorflow.python.ops.gen_checkpoint_ops import GenerateVocabRemapping # line: 123
+from tensorflow.python.ops.gen_checkpoint_ops import LoadAndRemapMatrix # line: 264
+from tensorflow.python.ops.gen_clustering_ops import KMC2ChainInitialization # line: 72
+from tensorflow.python.ops.gen_clustering_ops import KmeansPlusPlusInitialization # line: 149
+from tensorflow.python.ops.gen_clustering_ops import NearestNeighbors # line: 225
+from tensorflow.python.ops.gen_collective_ops import CollectiveAllToAllV2 # line: 106
+from tensorflow.python.ops.gen_collective_ops import CollectiveAllToAllV3 # line: 193
+from tensorflow.python.ops.gen_collective_ops import CollectiveAssignGroupV2 # line: 265
+from tensorflow.python.ops.gen_collective_ops import CollectiveBcastRecv # line: 357
+from tensorflow.python.ops.gen_collective_ops import CollectiveBcastRecvV2 # line: 449
+from tensorflow.python.ops.gen_collective_ops import CollectiveBcastSend # line: 548
+from tensorflow.python.ops.gen_collective_ops import CollectiveBcastSendV2 # line: 637
+from tensorflow.python.ops.gen_collective_ops import CollectiveGather # line: 735
+from tensorflow.python.ops.gen_collective_ops import CollectiveGatherV2 # line: 843
+from tensorflow.python.ops.gen_collective_ops import CollectiveInitializeCommunicator # line: 935
+from tensorflow.python.ops.gen_collective_ops import CollectiveReduce # line: 1054
+from tensorflow.python.ops.gen_collective_ops import CollectiveReduceScatterV2 # line: 1194
+from tensorflow.python.ops.gen_collective_ops import CollectiveReduceV2 # line: 1333
+from tensorflow.python.ops.gen_collective_ops import CollectiveReduceV3 # line: 1431
+from tensorflow.python.ops.gen_composite_tensor_ops import CompositeTensorVariantFromComponents # line: 74
+from tensorflow.python.ops.gen_composite_tensor_ops import CompositeTensorVariantToComponents # line: 152
+from tensorflow.python.ops.gen_control_flow_ops import Abort # line: 71
+from tensorflow.python.ops.gen_control_flow_ops import ControlTrigger # line: 120
+from tensorflow.python.ops.gen_control_flow_ops import Enter # line: 196
+from tensorflow.python.ops.gen_control_flow_ops import Exit # line: 262
+from tensorflow.python.ops.gen_control_flow_ops import LoopCond # line: 320
+from tensorflow.python.ops.gen_control_flow_ops import Merge # line: 396
+from tensorflow.python.ops.gen_control_flow_ops import NextIteration # line: 457
+from tensorflow.python.ops.gen_control_flow_ops import NoOp # line: 529
+from tensorflow.python.ops.gen_control_flow_ops import RefEnter # line: 592
+from tensorflow.python.ops.gen_control_flow_ops import RefExit # line: 629
+from tensorflow.python.ops.gen_control_flow_ops import RefMerge # line: 682
+from tensorflow.python.ops.gen_control_flow_ops import RefNextIteration # line: 717
+from tensorflow.python.ops.gen_control_flow_ops import RefSelect # line: 759
+from tensorflow.python.ops.gen_control_flow_ops import RefSwitch # line: 808
+from tensorflow.python.ops.gen_control_flow_ops import Switch # line: 869
+from tensorflow.python.ops.gen_count_ops import DenseCountSparseOutput # line: 103
+from tensorflow.python.ops.gen_count_ops import RaggedCountSparseOutput # line: 211
+from tensorflow.python.ops.gen_count_ops import SparseCountSparseOutput # line: 323
+from tensorflow.python.ops.gen_ctc_ops import CTCBeamSearchDecoder # line: 107
+from tensorflow.python.ops.gen_ctc_ops import CTCGreedyDecoder # line: 212
+from tensorflow.python.ops.gen_ctc_ops import CTCLoss # line: 335
+from tensorflow.python.ops.gen_ctc_ops import CTCLossV2 # line: 463
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNN # line: 151
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNBackprop # line: 337
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNBackpropV2 # line: 524
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNBackpropV3 # line: 733
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNCanonicalToParams # line: 907
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNCanonicalToParamsV2 # line: 1098
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNParamsSize # line: 1267
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNParamsToCanonical # line: 1436
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNParamsToCanonicalV2 # line: 1620
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNV2 # line: 1799
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNV3 # line: 1988
+from tensorflow.python.ops.gen_data_flow_ops import AccumulatorApplyGradient # line: 52
+from tensorflow.python.ops.gen_data_flow_ops import AccumulatorNumAccumulated # line: 84
+from tensorflow.python.ops.gen_data_flow_ops import AccumulatorSetGlobalStep # line: 115
+from tensorflow.python.ops.gen_data_flow_ops import AccumulatorTakeGradient # line: 162
+from tensorflow.python.ops.gen_data_flow_ops import Barrier # line: 244
+from tensorflow.python.ops.gen_data_flow_ops import BarrierClose # line: 284
+from tensorflow.python.ops.gen_data_flow_ops import BarrierIncompleteSize # line: 316
+from tensorflow.python.ops.gen_data_flow_ops import BarrierInsertMany # line: 356
+from tensorflow.python.ops.gen_data_flow_ops import BarrierReadySize # line: 388
+from tensorflow.python.ops.gen_data_flow_ops import BarrierTakeMany # line: 472
+from tensorflow.python.ops.gen_data_flow_ops import ConditionalAccumulator # line: 540
+from tensorflow.python.ops.gen_data_flow_ops import DeleteSessionTensor # line: 577
+from tensorflow.python.ops.gen_data_flow_ops import DynamicPartition # line: 713
+from tensorflow.python.ops.gen_data_flow_ops import DynamicStitch # line: 877
+from tensorflow.python.ops.gen_data_flow_ops import FIFOQueue # line: 977
+from tensorflow.python.ops.gen_data_flow_ops import FIFOQueueV2 # line: 1066
+from tensorflow.python.ops.gen_data_flow_ops import FakeQueue # line: 1129
+from tensorflow.python.ops.gen_data_flow_ops import GetSessionHandle # line: 1175
+from tensorflow.python.ops.gen_data_flow_ops import GetSessionHandleV2 # line: 1231
+from tensorflow.python.ops.gen_data_flow_ops import GetSessionTensor # line: 1290
+from tensorflow.python.ops.gen_data_flow_ops import MapClear # line: 1363
+from tensorflow.python.ops.gen_data_flow_ops import MapIncompleteSize # line: 1460
+from tensorflow.python.ops.gen_data_flow_ops import MapPeek # line: 1567
+from tensorflow.python.ops.gen_data_flow_ops import MapSize # line: 1668
+from tensorflow.python.ops.gen_data_flow_ops import MapStage # line: 1767
+from tensorflow.python.ops.gen_data_flow_ops import MapUnstage # line: 1875
+from tensorflow.python.ops.gen_data_flow_ops import MapUnstageNoKey # line: 1990
+from tensorflow.python.ops.gen_data_flow_ops import OrderedMapClear # line: 2082
+from tensorflow.python.ops.gen_data_flow_ops import OrderedMapIncompleteSize # line: 2180
+from tensorflow.python.ops.gen_data_flow_ops import OrderedMapPeek # line: 2290
+from tensorflow.python.ops.gen_data_flow_ops import OrderedMapSize # line: 2392
+from tensorflow.python.ops.gen_data_flow_ops import OrderedMapStage # line: 2494
+from tensorflow.python.ops.gen_data_flow_ops import OrderedMapUnstage # line: 2603
+from tensorflow.python.ops.gen_data_flow_ops import OrderedMapUnstageNoKey # line: 2721
+from tensorflow.python.ops.gen_data_flow_ops import PaddingFIFOQueue # line: 2833
+from tensorflow.python.ops.gen_data_flow_ops import PaddingFIFOQueueV2 # line: 2930
+from tensorflow.python.ops.gen_data_flow_ops import ParallelDynamicStitch # line: 3084
+from tensorflow.python.ops.gen_data_flow_ops import PriorityQueue # line: 3189
+from tensorflow.python.ops.gen_data_flow_ops import PriorityQueueV2 # line: 3284
+from tensorflow.python.ops.gen_data_flow_ops import QueueClose # line: 3353
+from tensorflow.python.ops.gen_data_flow_ops import QueueCloseV2 # line: 3405
+from tensorflow.python.ops.gen_data_flow_ops import QueueDequeue # line: 3469
+from tensorflow.python.ops.gen_data_flow_ops import QueueDequeueMany # line: 3532
+from tensorflow.python.ops.gen_data_flow_ops import QueueDequeueManyV2 # line: 3611
+from tensorflow.python.ops.gen_data_flow_ops import QueueDequeueUpTo # line: 3697
+from tensorflow.python.ops.gen_data_flow_ops import QueueDequeueUpToV2 # line: 3780
+from tensorflow.python.ops.gen_data_flow_ops import QueueDequeueV2 # line: 3869
+from tensorflow.python.ops.gen_data_flow_ops import QueueEnqueue # line: 3927
+from tensorflow.python.ops.gen_data_flow_ops import QueueEnqueueMany # line: 3972
+from tensorflow.python.ops.gen_data_flow_ops import QueueEnqueueManyV2 # line: 4030
+from tensorflow.python.ops.gen_data_flow_ops import QueueEnqueueV2 # line: 4094
+from tensorflow.python.ops.gen_data_flow_ops import QueueIsClosed # line: 4140
+from tensorflow.python.ops.gen_data_flow_ops import QueueIsClosedV2 # line: 4187
+from tensorflow.python.ops.gen_data_flow_ops import QueueSize # line: 4229
+from tensorflow.python.ops.gen_data_flow_ops import QueueSizeV2 # line: 4273
+from tensorflow.python.ops.gen_data_flow_ops import RandomShuffleQueue # line: 4379
+from tensorflow.python.ops.gen_data_flow_ops import RandomShuffleQueueV2 # line: 4492
+from tensorflow.python.ops.gen_data_flow_ops import RecordInput # line: 4630
+from tensorflow.python.ops.gen_data_flow_ops import ResourceAccumulatorApplyGradient # line: 4708
+from tensorflow.python.ops.gen_data_flow_ops import ResourceAccumulatorNumAccumulated # line: 4762
+from tensorflow.python.ops.gen_data_flow_ops import ResourceAccumulatorSetGlobalStep # line: 4817
+from tensorflow.python.ops.gen_data_flow_ops import ResourceAccumulatorTakeGradient # line: 4887
+from tensorflow.python.ops.gen_data_flow_ops import ResourceConditionalAccumulator # line: 4987
+from tensorflow.python.ops.gen_data_flow_ops import SparseAccumulatorApplyGradient # line: 5059
+from tensorflow.python.ops.gen_data_flow_ops import SparseAccumulatorTakeGradient # line: 5117
+from tensorflow.python.ops.gen_data_flow_ops import SparseConditionalAccumulator # line: 5185
+from tensorflow.python.ops.gen_data_flow_ops import Stack # line: 5225
+from tensorflow.python.ops.gen_data_flow_ops import StackClose # line: 5249
+from tensorflow.python.ops.gen_data_flow_ops import StackCloseV2 # line: 5285
+from tensorflow.python.ops.gen_data_flow_ops import StackPop # line: 5328
+from tensorflow.python.ops.gen_data_flow_ops import StackPopV2 # line: 5376
+from tensorflow.python.ops.gen_data_flow_ops import StackPush # line: 5428
+from tensorflow.python.ops.gen_data_flow_ops import StackPushV2 # line: 5482
+from tensorflow.python.ops.gen_data_flow_ops import StackV2 # line: 5556
+from tensorflow.python.ops.gen_data_flow_ops import Stage # line: 5636
+from tensorflow.python.ops.gen_data_flow_ops import StageClear # line: 5718
+from tensorflow.python.ops.gen_data_flow_ops import StagePeek # line: 5821
+from tensorflow.python.ops.gen_data_flow_ops import StageSize # line: 5921
+from tensorflow.python.ops.gen_data_flow_ops import TensorArray # line: 6007
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayClose # line: 6031
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayCloseV2 # line: 6067
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayCloseV3 # line: 6114
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayConcat # line: 6172
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayConcatV2 # line: 6238
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayConcatV3 # line: 6338
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayGather # line: 6398
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayGatherV2 # line: 6457
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayGatherV3 # line: 6539
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayGrad # line: 6591
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayGradV2 # line: 6639
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayGradV3 # line: 6748
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayGradWithShape # line: 6832
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayPack # line: 6889
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayRead # line: 6928
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayReadV2 # line: 6980
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayReadV3 # line: 7046
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayScatter # line: 7097
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayScatterV2 # line: 7147
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayScatterV3 # line: 7214
+from tensorflow.python.ops.gen_data_flow_ops import TensorArraySize # line: 7260
+from tensorflow.python.ops.gen_data_flow_ops import TensorArraySizeV2 # line: 7305
+from tensorflow.python.ops.gen_data_flow_ops import TensorArraySizeV3 # line: 7363
+from tensorflow.python.ops.gen_data_flow_ops import TensorArraySplit # line: 7412
+from tensorflow.python.ops.gen_data_flow_ops import TensorArraySplitV2 # line: 7462
+from tensorflow.python.ops.gen_data_flow_ops import TensorArraySplitV3 # line: 7554
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayUnpack # line: 7604
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayV2 # line: 7679
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayV3 # line: 7815
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayWrite # line: 7882
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayWriteV2 # line: 7932
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayWriteV3 # line: 7997
+from tensorflow.python.ops.gen_data_flow_ops import Unstage # line: 8087
+from tensorflow.python.ops.gen_dataset_ops import AnonymousIterator # line: 78
+from tensorflow.python.ops.gen_dataset_ops import AnonymousIteratorV2 # line: 164
+from tensorflow.python.ops.gen_dataset_ops import AnonymousIteratorV3 # line: 242
+from tensorflow.python.ops.gen_dataset_ops import AnonymousMemoryCache # line: 312
+from tensorflow.python.ops.gen_dataset_ops import AnonymousMultiDeviceIterator # line: 397
+from tensorflow.python.ops.gen_dataset_ops import AnonymousMultiDeviceIteratorV3 # line: 491
+from tensorflow.python.ops.gen_dataset_ops import AnonymousRandomSeedGenerator # line: 570
+from tensorflow.python.ops.gen_dataset_ops import AnonymousSeedGenerator # line: 637
+from tensorflow.python.ops.gen_dataset_ops import BatchDataset # line: 720
+from tensorflow.python.ops.gen_dataset_ops import BatchDatasetV2 # line: 826
+from tensorflow.python.ops.gen_dataset_ops import CacheDataset # line: 930
+from tensorflow.python.ops.gen_dataset_ops import CacheDatasetV2 # line: 1025
+from tensorflow.python.ops.gen_dataset_ops import ConcatenateDataset # line: 1121
+from tensorflow.python.ops.gen_dataset_ops import DatasetCardinality # line: 1201
+from tensorflow.python.ops.gen_dataset_ops import DatasetToGraph # line: 1289
+from tensorflow.python.ops.gen_dataset_ops import DatasetToGraphV2 # line: 1378
+from tensorflow.python.ops.gen_dataset_ops import DatasetToSingleElement # line: 1463
+from tensorflow.python.ops.gen_dataset_ops import DeleteIterator # line: 1524
+from tensorflow.python.ops.gen_dataset_ops import DeleteMemoryCache # line: 1569
+from tensorflow.python.ops.gen_dataset_ops import DeleteMultiDeviceIterator # line: 1625
+from tensorflow.python.ops.gen_dataset_ops import DeleteRandomSeedGenerator # line: 1678
+from tensorflow.python.ops.gen_dataset_ops import DeleteSeedGenerator # line: 1724
+from tensorflow.python.ops.gen_dataset_ops import DeserializeIterator # line: 1773
+from tensorflow.python.ops.gen_dataset_ops import DummyMemoryCache # line: 1824
+from tensorflow.python.ops.gen_dataset_ops import DummySeedGenerator # line: 1876
+from tensorflow.python.ops.gen_dataset_ops import FilterByLastComponentDataset # line: 1947
+from tensorflow.python.ops.gen_dataset_ops import FilterDataset # line: 2051
+from tensorflow.python.ops.gen_dataset_ops import FinalizeDataset # line: 2147
+from tensorflow.python.ops.gen_dataset_ops import FixedLengthRecordDataset # line: 2238
+from tensorflow.python.ops.gen_dataset_ops import FixedLengthRecordDatasetV2 # line: 2318
+from tensorflow.python.ops.gen_dataset_ops import FlatMapDataset # line: 2417
+from tensorflow.python.ops.gen_dataset_ops import GeneratorDataset # line: 2529
+from tensorflow.python.ops.gen_dataset_ops import GetOptions # line: 2603
+from tensorflow.python.ops.gen_dataset_ops import InterleaveDataset # line: 2700
+from tensorflow.python.ops.gen_dataset_ops import Iterator # line: 2795
+from tensorflow.python.ops.gen_dataset_ops import IteratorFromStringHandle # line: 2887
+from tensorflow.python.ops.gen_dataset_ops import IteratorFromStringHandleV2 # line: 2977
+from tensorflow.python.ops.gen_dataset_ops import IteratorGetNext # line: 3063
+from tensorflow.python.ops.gen_dataset_ops import IteratorGetNextAsOptional # line: 3144
+from tensorflow.python.ops.gen_dataset_ops import IteratorGetNextSync # line: 3231
+from tensorflow.python.ops.gen_dataset_ops import IteratorToStringHandle # line: 3296
+from tensorflow.python.ops.gen_dataset_ops import IteratorV2 # line: 3375
+from tensorflow.python.ops.gen_dataset_ops import MakeIterator # line: 3437
+from tensorflow.python.ops.gen_dataset_ops import MapDataset # line: 3534
+from tensorflow.python.ops.gen_dataset_ops import MapDefun # line: 3658
+from tensorflow.python.ops.gen_dataset_ops import ModelDataset # line: 3767
+from tensorflow.python.ops.gen_dataset_ops import MultiDeviceIterator # line: 3879
+from tensorflow.python.ops.gen_dataset_ops import MultiDeviceIteratorFromStringHandle # line: 3975
+from tensorflow.python.ops.gen_dataset_ops import MultiDeviceIteratorGetNextFromShard # line: 4074
+from tensorflow.python.ops.gen_dataset_ops import MultiDeviceIteratorInit # line: 4148
+from tensorflow.python.ops.gen_dataset_ops import MultiDeviceIteratorToStringHandle # line: 4208
+from tensorflow.python.ops.gen_dataset_ops import OneShotIterator # line: 4314
+from tensorflow.python.ops.gen_dataset_ops import OptimizeDataset # line: 4421
+from tensorflow.python.ops.gen_dataset_ops import OptimizeDatasetV2 # line: 4538
+from tensorflow.python.ops.gen_dataset_ops import OptionsDataset # line: 4644
+from tensorflow.python.ops.gen_dataset_ops import PaddedBatchDataset # line: 4749
+from tensorflow.python.ops.gen_dataset_ops import PaddedBatchDatasetV2 # line: 4867
+from tensorflow.python.ops.gen_dataset_ops import ParallelBatchDataset # line: 4988
+from tensorflow.python.ops.gen_dataset_ops import ParallelFilterDataset # line: 5126
+from tensorflow.python.ops.gen_dataset_ops import ParallelInterleaveDatasetV2 # line: 5271
+from tensorflow.python.ops.gen_dataset_ops import ParallelInterleaveDatasetV3 # line: 5425
+from tensorflow.python.ops.gen_dataset_ops import ParallelInterleaveDatasetV4 # line: 5588
+from tensorflow.python.ops.gen_dataset_ops import ParallelMapDataset # line: 5726
+from tensorflow.python.ops.gen_dataset_ops import ParallelMapDatasetV2 # line: 5867
+from tensorflow.python.ops.gen_dataset_ops import PrefetchDataset # line: 5996
+from tensorflow.python.ops.gen_dataset_ops import RangeDataset # line: 6110
+from tensorflow.python.ops.gen_dataset_ops import ReduceDataset # line: 6230
+from tensorflow.python.ops.gen_dataset_ops import RepeatDataset # line: 6330
+from tensorflow.python.ops.gen_dataset_ops import RewriteDataset # line: 6418
+from tensorflow.python.ops.gen_dataset_ops import SerializeIterator # line: 6493
+from tensorflow.python.ops.gen_dataset_ops import ShardDataset # line: 6585
+from tensorflow.python.ops.gen_dataset_ops import ShuffleAndRepeatDataset # line: 6708
+from tensorflow.python.ops.gen_dataset_ops import ShuffleAndRepeatDatasetV2 # line: 6826
+from tensorflow.python.ops.gen_dataset_ops import ShuffleDataset # line: 6953
+from tensorflow.python.ops.gen_dataset_ops import ShuffleDatasetV2 # line: 7057
+from tensorflow.python.ops.gen_dataset_ops import ShuffleDatasetV3 # line: 7167
+from tensorflow.python.ops.gen_dataset_ops import SkipDataset # line: 7268
+from tensorflow.python.ops.gen_dataset_ops import SparseTensorSliceDataset # line: 7342
+from tensorflow.python.ops.gen_dataset_ops import TFRecordDataset # line: 7416
+from tensorflow.python.ops.gen_dataset_ops import TFRecordDatasetV2 # line: 7496
+from tensorflow.python.ops.gen_dataset_ops import TakeDataset # line: 7582
+from tensorflow.python.ops.gen_dataset_ops import TensorDataset # line: 7666
+from tensorflow.python.ops.gen_dataset_ops import TensorSliceDataset # line: 7759
+from tensorflow.python.ops.gen_dataset_ops import TextLineDataset # line: 7845
+from tensorflow.python.ops.gen_dataset_ops import UnwrapDatasetVariant # line: 7904
+from tensorflow.python.ops.gen_dataset_ops import WindowDataset # line: 8040
+from tensorflow.python.ops.gen_dataset_ops import WindowOp # line: 8129
+from tensorflow.python.ops.gen_dataset_ops import WrapDatasetVariant # line: 8194
+from tensorflow.python.ops.gen_dataset_ops import ZipDataset # line: 8282
+from tensorflow.python.ops.gen_debug_ops import Copy # line: 97
+from tensorflow.python.ops.gen_debug_ops import CopyHost # line: 194
+from tensorflow.python.ops.gen_debug_ops import DebugIdentity # line: 302
+from tensorflow.python.ops.gen_debug_ops import DebugIdentityV2 # line: 444
+from tensorflow.python.ops.gen_debug_ops import DebugIdentityV3 # line: 591
+from tensorflow.python.ops.gen_debug_ops import DebugNanCount # line: 714
+from tensorflow.python.ops.gen_debug_ops import DebugNumericSummary # line: 883
+from tensorflow.python.ops.gen_debug_ops import DebugNumericSummaryV2 # line: 1049
+from tensorflow.python.ops.gen_decode_proto_ops import DecodeProtoV2 # line: 280
+from tensorflow.python.ops.gen_encode_proto_ops import EncodeProto # line: 164
+from tensorflow.python.ops.gen_experimental_dataset_ops import AssertCardinalityDataset # line: 82
+from tensorflow.python.ops.gen_experimental_dataset_ops import AssertNextDataset # line: 180
+from tensorflow.python.ops.gen_experimental_dataset_ops import AssertPrevDataset # line: 277
+from tensorflow.python.ops.gen_experimental_dataset_ops import AutoShardDataset # line: 388
+from tensorflow.python.ops.gen_experimental_dataset_ops import BytesProducedStatsDataset # line: 479
+from tensorflow.python.ops.gen_experimental_dataset_ops import CSVDataset # line: 570
+from tensorflow.python.ops.gen_experimental_dataset_ops import CSVDatasetV2 # line: 666
+from tensorflow.python.ops.gen_experimental_dataset_ops import ChooseFastestBranchDataset # line: 785
+from tensorflow.python.ops.gen_experimental_dataset_ops import ChooseFastestDataset # line: 895
+from tensorflow.python.ops.gen_experimental_dataset_ops import CompressElement # line: 966
+from tensorflow.python.ops.gen_experimental_dataset_ops import ComputeBatchSize # line: 1020
+from tensorflow.python.ops.gen_experimental_dataset_ops import DataServiceDataset # line: 1139
+from tensorflow.python.ops.gen_experimental_dataset_ops import DataServiceDatasetV2 # line: 1295
+from tensorflow.python.ops.gen_experimental_dataset_ops import DataServiceDatasetV3 # line: 1464
+from tensorflow.python.ops.gen_experimental_dataset_ops import DataServiceDatasetV4 # line: 1637
+from tensorflow.python.ops.gen_experimental_dataset_ops import DatasetFromGraph # line: 1731
+from tensorflow.python.ops.gen_experimental_dataset_ops import DatasetToTFRecord # line: 1785
+from tensorflow.python.ops.gen_experimental_dataset_ops import DenseToSparseBatchDataset # line: 1866
+from tensorflow.python.ops.gen_experimental_dataset_ops import DirectedInterleaveDataset # line: 1971
+from tensorflow.python.ops.gen_experimental_dataset_ops import DistributedSave # line: 2046
+from tensorflow.python.ops.gen_experimental_dataset_ops import DummyIterationCounter # line: 2101
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalAssertNextDataset # line: 2175
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalAutoShardDataset # line: 2281
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalBytesProducedStatsDataset # line: 2372
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalCSVDataset # line: 2464
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalChooseFastestDataset # line: 2563
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalDatasetCardinality # line: 2639
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalDatasetToTFRecord # line: 2696
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalDenseToSparseBatchDataset # line: 2779
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalDirectedInterleaveDataset # line: 2877
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalGroupByReducerDataset # line: 3019
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalGroupByWindowDataset # line: 3142
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalIgnoreErrorsDataset # line: 3240
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalIteratorGetDevice # line: 3309
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalLMDBDataset # line: 3381
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalLatencyStatsDataset # line: 3465
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalMapAndBatchDataset # line: 3588
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalMapDataset # line: 3702
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalMatchingFilesDataset # line: 3776
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalMaxIntraOpParallelismDataset # line: 3853
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalNonSerializableDataset # line: 3937
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalParallelInterleaveDataset # line: 4051
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalParseExampleDataset # line: 4208
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalPrivateThreadPoolDataset # line: 4323
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalRandomDataset # line: 4411
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalRebatchDataset # line: 4511
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalScanDataset # line: 4615
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalSetStatsAggregatorDataset # line: 4713
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalSleepDataset # line: 4801
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalSlidingWindowDataset # line: 4899
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalSqlDataset # line: 4989
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalStatsAggregatorHandle # line: 5067
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalStatsAggregatorSummary # line: 5127
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalTakeWhileDataset # line: 5217
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalThreadPoolDataset # line: 5306
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalThreadPoolHandle # line: 5407
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalUnbatchDataset # line: 5491
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalUniqueDataset # line: 5573
+from tensorflow.python.ops.gen_experimental_dataset_ops import GetElementAtIndex # line: 5655
+from tensorflow.python.ops.gen_experimental_dataset_ops import GroupByReducerDataset # line: 5788
+from tensorflow.python.ops.gen_experimental_dataset_ops import GroupByWindowDataset # line: 5914
+from tensorflow.python.ops.gen_experimental_dataset_ops import IgnoreErrorsDataset # line: 6014
+from tensorflow.python.ops.gen_experimental_dataset_ops import InitializeTableFromDataset # line: 6076
+from tensorflow.python.ops.gen_experimental_dataset_ops import IteratorGetDevice # line: 6129
+from tensorflow.python.ops.gen_experimental_dataset_ops import LMDBDataset # line: 6212
+from tensorflow.python.ops.gen_experimental_dataset_ops import LatencyStatsDataset # line: 6294
+from tensorflow.python.ops.gen_experimental_dataset_ops import LegacyParallelInterleaveDatasetV2 # line: 6417
+from tensorflow.python.ops.gen_experimental_dataset_ops import ListDataset # line: 6518
+from tensorflow.python.ops.gen_experimental_dataset_ops import LoadDataset # line: 6616
+from tensorflow.python.ops.gen_experimental_dataset_ops import MapAndBatchDataset # line: 6749
+from tensorflow.python.ops.gen_experimental_dataset_ops import MatchingFilesDataset # line: 6825
+from tensorflow.python.ops.gen_experimental_dataset_ops import MaxIntraOpParallelismDataset # line: 6901
+from tensorflow.python.ops.gen_experimental_dataset_ops import NonSerializableDataset # line: 6984
+from tensorflow.python.ops.gen_experimental_dataset_ops import ParallelInterleaveDataset # line: 7126
+from tensorflow.python.ops.gen_experimental_dataset_ops import ParseExampleDataset # line: 7318
+from tensorflow.python.ops.gen_experimental_dataset_ops import ParseExampleDatasetV2 # line: 7557
+from tensorflow.python.ops.gen_experimental_dataset_ops import PrivateThreadPoolDataset # line: 7692
+from tensorflow.python.ops.gen_experimental_dataset_ops import RandomDataset # line: 7796
+from tensorflow.python.ops.gen_experimental_dataset_ops import RandomDatasetV2 # line: 7920
+from tensorflow.python.ops.gen_experimental_dataset_ops import RebatchDataset # line: 8028
+from tensorflow.python.ops.gen_experimental_dataset_ops import RebatchDatasetV2 # line: 8126
+from tensorflow.python.ops.gen_experimental_dataset_ops import RegisterDataset # line: 8215
+from tensorflow.python.ops.gen_experimental_dataset_ops import RegisterDatasetV2 # line: 8311
+from tensorflow.python.ops.gen_experimental_dataset_ops import SamplingDataset # line: 8409
+from tensorflow.python.ops.gen_experimental_dataset_ops import SaveDataset # line: 8486
+from tensorflow.python.ops.gen_experimental_dataset_ops import SaveDatasetV2 # line: 8585
+from tensorflow.python.ops.gen_experimental_dataset_ops import ScanDataset # line: 8707
+from tensorflow.python.ops.gen_experimental_dataset_ops import SetStatsAggregatorDataset # line: 8809
+from tensorflow.python.ops.gen_experimental_dataset_ops import SleepDataset # line: 8896
+from tensorflow.python.ops.gen_experimental_dataset_ops import SlidingWindowDataset # line: 8998
+from tensorflow.python.ops.gen_experimental_dataset_ops import SnapshotChunkDataset # line: 9093
+from tensorflow.python.ops.gen_experimental_dataset_ops import SnapshotDataset # line: 9286
+from tensorflow.python.ops.gen_experimental_dataset_ops import SnapshotDatasetReader # line: 9430
+from tensorflow.python.ops.gen_experimental_dataset_ops import SnapshotDatasetV2 # line: 9582
+from tensorflow.python.ops.gen_experimental_dataset_ops import SnapshotNestedDatasetReader # line: 9694
+from tensorflow.python.ops.gen_experimental_dataset_ops import SqlDataset # line: 9787
+from tensorflow.python.ops.gen_experimental_dataset_ops import StatsAggregatorHandle # line: 9863
+from tensorflow.python.ops.gen_experimental_dataset_ops import StatsAggregatorHandleV2 # line: 9932
+from tensorflow.python.ops.gen_experimental_dataset_ops import StatsAggregatorSetSummaryWriter # line: 9987
+from tensorflow.python.ops.gen_experimental_dataset_ops import StatsAggregatorSummary # line: 10040
+from tensorflow.python.ops.gen_experimental_dataset_ops import TakeWhileDataset # line: 10134
+from tensorflow.python.ops.gen_experimental_dataset_ops import ThreadPoolDataset # line: 10224
+from tensorflow.python.ops.gen_experimental_dataset_ops import ThreadPoolHandle # line: 10323
+from tensorflow.python.ops.gen_experimental_dataset_ops import UnbatchDataset # line: 10412
+from tensorflow.python.ops.gen_experimental_dataset_ops import UncompressElement # line: 10495
+from tensorflow.python.ops.gen_experimental_dataset_ops import UniqueDataset # line: 10582
+from tensorflow.python.ops.gen_filesystem_ops import FileSystemSetConfiguration # line: 58
+from tensorflow.python.ops.gen_functional_ops import Case # line: 110
+from tensorflow.python.ops.gen_functional_ops import DeviceIndex # line: 191
+from tensorflow.python.ops.gen_functional_ops import FakeParam # line: 260
+from tensorflow.python.ops.gen_functional_ops import For # line: 328
+from tensorflow.python.ops.gen_functional_ops import If # line: 423
+from tensorflow.python.ops.gen_functional_ops import PartitionedCall # line: 524
+from tensorflow.python.ops.gen_functional_ops import RemoteCall # line: 605
+from tensorflow.python.ops.gen_functional_ops import StatefulPartitionedCall # line: 697
+from tensorflow.python.ops.gen_functional_ops import StatelessCase # line: 814
+from tensorflow.python.ops.gen_functional_ops import StatelessIf # line: 926
+from tensorflow.python.ops.gen_functional_ops import StatelessWhile # line: 1029
+from tensorflow.python.ops.gen_functional_ops import SymbolicGradient # line: 1120
+from tensorflow.python.ops.gen_functional_ops import ToBool # line: 1194
+from tensorflow.python.ops.gen_functional_ops import While # line: 1282
+from tensorflow.python.ops.gen_image_ops import AdjustContrast # line: 70
+from tensorflow.python.ops.gen_image_ops import AdjustContrastv2 # line: 143
+from tensorflow.python.ops.gen_image_ops import AdjustHue # line: 209
+from tensorflow.python.ops.gen_image_ops import AdjustSaturation # line: 276
+from tensorflow.python.ops.gen_image_ops import CombinedNonMaxSuppression # line: 402
+from tensorflow.python.ops.gen_image_ops import CropAndResize # line: 524
+from tensorflow.python.ops.gen_image_ops import CropAndResizeGradBoxes # line: 616
+from tensorflow.python.ops.gen_image_ops import CropAndResizeGradImage # line: 710
+from tensorflow.python.ops.gen_image_ops import DecodeAndCropJpeg # line: 847
+from tensorflow.python.ops.gen_image_ops import DecodeBmp # line: 935
+from tensorflow.python.ops.gen_image_ops import DecodeGif # line: 1001
+from tensorflow.python.ops.gen_image_ops import DecodeImage # line: 1098
+from tensorflow.python.ops.gen_image_ops import DecodeJpeg # line: 1234
+from tensorflow.python.ops.gen_image_ops import DecodePng # line: 1338
+from tensorflow.python.ops.gen_image_ops import DrawBoundingBoxes # line: 1416
+from tensorflow.python.ops.gen_image_ops import DrawBoundingBoxesV2 # line: 1492
+from tensorflow.python.ops.gen_image_ops import EncodeJpeg # line: 1633
+from tensorflow.python.ops.gen_image_ops import EncodeJpegVariableQuality # line: 1722
+from tensorflow.python.ops.gen_image_ops import EncodePng # line: 1798
+from tensorflow.python.ops.gen_image_ops import ExtractGlimpse # line: 1919
+from tensorflow.python.ops.gen_image_ops import ExtractGlimpseV2 # line: 2053
+from tensorflow.python.ops.gen_image_ops import ExtractJpegShape # line: 2133
+from tensorflow.python.ops.gen_image_ops import GenerateBoundingBoxProposals # line: 2241
+from tensorflow.python.ops.gen_image_ops import HSVToRGB # line: 2341
+from tensorflow.python.ops.gen_image_ops import ImageProjectiveTransformV2 # line: 2425
+from tensorflow.python.ops.gen_image_ops import ImageProjectiveTransformV3 # line: 2521
+from tensorflow.python.ops.gen_image_ops import NonMaxSuppression # line: 2618
+from tensorflow.python.ops.gen_image_ops import NonMaxSuppressionV2 # line: 2713
+from tensorflow.python.ops.gen_image_ops import NonMaxSuppressionV3 # line: 2810
+from tensorflow.python.ops.gen_image_ops import NonMaxSuppressionV4 # line: 2926
+from tensorflow.python.ops.gen_image_ops import NonMaxSuppressionV5 # line: 3056
+from tensorflow.python.ops.gen_image_ops import NonMaxSuppressionWithOverlaps # line: 3150
+from tensorflow.python.ops.gen_image_ops import QuantizedResizeBilinear # line: 3245
+from tensorflow.python.ops.gen_image_ops import RGBToHSV # line: 3359
+from tensorflow.python.ops.gen_image_ops import RandomCrop # line: 3440
+from tensorflow.python.ops.gen_image_ops import ResizeArea # line: 3527
+from tensorflow.python.ops.gen_image_ops import ResizeBicubic # line: 3608
+from tensorflow.python.ops.gen_image_ops import ResizeBicubicGrad # line: 3693
+from tensorflow.python.ops.gen_image_ops import ResizeBilinear # line: 3778
+from tensorflow.python.ops.gen_image_ops import ResizeBilinearGrad # line: 3864
+from tensorflow.python.ops.gen_image_ops import ResizeNearestNeighbor # line: 3948
+from tensorflow.python.ops.gen_image_ops import ResizeNearestNeighborGrad # line: 4032
+from tensorflow.python.ops.gen_image_ops import SampleDistortedBoundingBox # line: 4227
+from tensorflow.python.ops.gen_image_ops import SampleDistortedBoundingBoxV2 # line: 4442
+from tensorflow.python.ops.gen_image_ops import ScaleAndTranslate # line: 4546
+from tensorflow.python.ops.gen_image_ops import ScaleAndTranslateGrad # line: 4629
+from tensorflow.python.ops.gen_image_ops import StatelessSampleDistortedBoundingBox # line: 4831
+from tensorflow.python.ops.gen_io_ops import FixedLengthRecordReader # line: 89
+from tensorflow.python.ops.gen_io_ops import FixedLengthRecordReaderV2 # line: 184
+from tensorflow.python.ops.gen_io_ops import IdentityReader # line: 263
+from tensorflow.python.ops.gen_io_ops import IdentityReaderV2 # line: 324
+from tensorflow.python.ops.gen_io_ops import LMDBReader # line: 383
+from tensorflow.python.ops.gen_io_ops import MatchingFiles # line: 460
+from tensorflow.python.ops.gen_io_ops import MergeV2Checkpoints # line: 540
+from tensorflow.python.ops.gen_io_ops import ReadFile # line: 599
+from tensorflow.python.ops.gen_io_ops import ReaderNumRecordsProduced # line: 644
+from tensorflow.python.ops.gen_io_ops import ReaderNumRecordsProducedV2 # line: 691
+from tensorflow.python.ops.gen_io_ops import ReaderNumWorkUnitsCompleted # line: 734
+from tensorflow.python.ops.gen_io_ops import ReaderNumWorkUnitsCompletedV2 # line: 779
+from tensorflow.python.ops.gen_io_ops import ReaderRead # line: 836
+from tensorflow.python.ops.gen_io_ops import ReaderReadUpTo # line: 886
+from tensorflow.python.ops.gen_io_ops import ReaderReadUpToV2 # line: 950
+from tensorflow.python.ops.gen_io_ops import ReaderReadV2 # line: 1021
+from tensorflow.python.ops.gen_io_ops import ReaderReset # line: 1056
+from tensorflow.python.ops.gen_io_ops import ReaderResetV2 # line: 1092
+from tensorflow.python.ops.gen_io_ops import ReaderRestoreState # line: 1130
+from tensorflow.python.ops.gen_io_ops import ReaderRestoreStateV2 # line: 1173
+from tensorflow.python.ops.gen_io_ops import ReaderSerializeState # line: 1216
+from tensorflow.python.ops.gen_io_ops import ReaderSerializeStateV2 # line: 1263
+from tensorflow.python.ops.gen_io_ops import Restore # line: 1354
+from tensorflow.python.ops.gen_io_ops import RestoreSlice # line: 1443
+from tensorflow.python.ops.gen_io_ops import RestoreV2 # line: 1536
+from tensorflow.python.ops.gen_io_ops import Save # line: 1599
+from tensorflow.python.ops.gen_io_ops import SaveSlices # line: 1678
+from tensorflow.python.ops.gen_io_ops import SaveV2 # line: 1739
+from tensorflow.python.ops.gen_io_ops import ShardedFilename # line: 1798
+from tensorflow.python.ops.gen_io_ops import ShardedFilespec # line: 1856
+from tensorflow.python.ops.gen_io_ops import TFRecordReader # line: 1917
+from tensorflow.python.ops.gen_io_ops import TFRecordReaderV2 # line: 1981
+from tensorflow.python.ops.gen_io_ops import TextLineReader # line: 2052
+from tensorflow.python.ops.gen_io_ops import TextLineReaderV2 # line: 2118
+from tensorflow.python.ops.gen_io_ops import WholeFileReader # line: 2185
+from tensorflow.python.ops.gen_io_ops import WholeFileReaderV2 # line: 2246
+from tensorflow.python.ops.gen_io_ops import WriteFile # line: 2332
+from tensorflow.python.ops.gen_linalg_ops import BandedTriangularSolve # line: 77
+from tensorflow.python.ops.gen_linalg_ops import BatchCholesky # line: 140
+from tensorflow.python.ops.gen_linalg_ops import BatchCholeskyGrad # line: 197
+from tensorflow.python.ops.gen_linalg_ops import BatchMatrixDeterminant # line: 254
+from tensorflow.python.ops.gen_linalg_ops import BatchMatrixInverse # line: 316
+from tensorflow.python.ops.gen_linalg_ops import BatchMatrixSolve # line: 382
+from tensorflow.python.ops.gen_linalg_ops import BatchMatrixSolveLs # line: 452
+from tensorflow.python.ops.gen_linalg_ops import BatchMatrixTriangularSolve # line: 525
+from tensorflow.python.ops.gen_linalg_ops import BatchSelfAdjointEig # line: 589
+from tensorflow.python.ops.gen_linalg_ops import BatchSelfAdjointEigV2 # line: 658
+from tensorflow.python.ops.gen_linalg_ops import BatchSvd # line: 739
+from tensorflow.python.ops.gen_linalg_ops import Cholesky # line: 845
+from tensorflow.python.ops.gen_linalg_ops import CholeskyGrad # line: 912
+from tensorflow.python.ops.gen_linalg_ops import Eig # line: 1000
+from tensorflow.python.ops.gen_linalg_ops import Einsum # line: 1146
+from tensorflow.python.ops.gen_linalg_ops import LogMatrixDeterminant # line: 1227
+from tensorflow.python.ops.gen_linalg_ops import Lu # line: 1346
+from tensorflow.python.ops.gen_linalg_ops import MatrixDeterminant # line: 1439
+from tensorflow.python.ops.gen_linalg_ops import MatrixExponential # line: 1496
+from tensorflow.python.ops.gen_linalg_ops import MatrixInverse # line: 1597
+from tensorflow.python.ops.gen_linalg_ops import MatrixLogarithm # line: 1674
+from tensorflow.python.ops.gen_linalg_ops import MatrixSolve # line: 1777
+from tensorflow.python.ops.gen_linalg_ops import MatrixSolveLs # line: 1888
+from tensorflow.python.ops.gen_linalg_ops import MatrixSquareRoot # line: 1993
+from tensorflow.python.ops.gen_linalg_ops import MatrixTriangularSolve # line: 2119
+from tensorflow.python.ops.gen_linalg_ops import Qr # line: 2242
+from tensorflow.python.ops.gen_linalg_ops import SelfAdjointEig # line: 2311
+from tensorflow.python.ops.gen_linalg_ops import SelfAdjointEigV2 # line: 2395
+from tensorflow.python.ops.gen_linalg_ops import Svd # line: 2496
+from tensorflow.python.ops.gen_linalg_ops import TridiagonalMatMul # line: 2573
+from tensorflow.python.ops.gen_linalg_ops import TridiagonalSolve # line: 2661
+from tensorflow.python.ops.gen_list_ops import EmptyTensorList # line: 81
+from tensorflow.python.ops.gen_list_ops import TensorListConcat # line: 164
+from tensorflow.python.ops.gen_list_ops import TensorListConcatLists # line: 229
+from tensorflow.python.ops.gen_list_ops import TensorListConcatV2 # line: 318
+from tensorflow.python.ops.gen_list_ops import TensorListElementShape # line: 384
+from tensorflow.python.ops.gen_list_ops import TensorListFromTensor # line: 451
+from tensorflow.python.ops.gen_list_ops import TensorListGather # line: 524
+from tensorflow.python.ops.gen_list_ops import TensorListGetItem # line: 595
+from tensorflow.python.ops.gen_list_ops import TensorListLength # line: 655
+from tensorflow.python.ops.gen_list_ops import TensorListPopBack # line: 733
+from tensorflow.python.ops.gen_list_ops import TensorListPushBack # line: 799
+from tensorflow.python.ops.gen_list_ops import TensorListPushBackBatch # line: 858
+from tensorflow.python.ops.gen_list_ops import TensorListReserve # line: 930
+from tensorflow.python.ops.gen_list_ops import TensorListResize # line: 991
+from tensorflow.python.ops.gen_list_ops import TensorListScatter # line: 1062
+from tensorflow.python.ops.gen_list_ops import TensorListScatterIntoExistingList # line: 1134
+from tensorflow.python.ops.gen_list_ops import TensorListScatterV2 # line: 1213
+from tensorflow.python.ops.gen_list_ops import TensorListSetItem # line: 1292
+from tensorflow.python.ops.gen_list_ops import TensorListSplit # line: 1367
+from tensorflow.python.ops.gen_list_ops import TensorListStack # line: 1445
+from tensorflow.python.ops.gen_logging_ops import Assert # line: 66
+from tensorflow.python.ops.gen_logging_ops import AudioSummary # line: 146
+from tensorflow.python.ops.gen_logging_ops import AudioSummaryV2 # line: 229
+from tensorflow.python.ops.gen_logging_ops import HistogramSummary # line: 299
+from tensorflow.python.ops.gen_logging_ops import ImageSummary # line: 408
+from tensorflow.python.ops.gen_logging_ops import MergeSummary # line: 484
+from tensorflow.python.ops.gen_logging_ops import Print # line: 570
+from tensorflow.python.ops.gen_logging_ops import PrintV2 # line: 640
+from tensorflow.python.ops.gen_logging_ops import ScalarSummary # line: 704
+from tensorflow.python.ops.gen_logging_ops import TensorSummary # line: 788
+from tensorflow.python.ops.gen_logging_ops import TensorSummaryV2 # line: 866
+from tensorflow.python.ops.gen_logging_ops import Timestamp # line: 960
+from tensorflow.python.ops.gen_lookup_ops import AnonymousHashTable # line: 81
+from tensorflow.python.ops.gen_lookup_ops import AnonymousMutableDenseHashTable # line: 190
+from tensorflow.python.ops.gen_lookup_ops import AnonymousMutableHashTable # line: 277
+from tensorflow.python.ops.gen_lookup_ops import AnonymousMutableHashTableOfTensors # line: 360
+from tensorflow.python.ops.gen_lookup_ops import HashTable # line: 442
+from tensorflow.python.ops.gen_lookup_ops import HashTableV2 # line: 524
+from tensorflow.python.ops.gen_lookup_ops import InitializeTable # line: 577
+from tensorflow.python.ops.gen_lookup_ops import InitializeTableFromTextFile # line: 640
+from tensorflow.python.ops.gen_lookup_ops import InitializeTableFromTextFileV2 # line: 720
+from tensorflow.python.ops.gen_lookup_ops import InitializeTableV2 # line: 784
+from tensorflow.python.ops.gen_lookup_ops import LookupTableExport # line: 841
+from tensorflow.python.ops.gen_lookup_ops import LookupTableExportV2 # line: 903
+from tensorflow.python.ops.gen_lookup_ops import LookupTableFind # line: 960
+from tensorflow.python.ops.gen_lookup_ops import LookupTableFindV2 # line: 1017
+from tensorflow.python.ops.gen_lookup_ops import LookupTableImport # line: 1062
+from tensorflow.python.ops.gen_lookup_ops import LookupTableImportV2 # line: 1107
+from tensorflow.python.ops.gen_lookup_ops import LookupTableInsert # line: 1149
+from tensorflow.python.ops.gen_lookup_ops import LookupTableInsertV2 # line: 1194
+from tensorflow.python.ops.gen_lookup_ops import LookupTableRemoveV2 # line: 1246
+from tensorflow.python.ops.gen_lookup_ops import LookupTableSize # line: 1286
+from tensorflow.python.ops.gen_lookup_ops import LookupTableSizeV2 # line: 1330
+from tensorflow.python.ops.gen_lookup_ops import MutableDenseHashTable # line: 1431
+from tensorflow.python.ops.gen_lookup_ops import MutableDenseHashTableV2 # line: 1547
+from tensorflow.python.ops.gen_lookup_ops import MutableHashTable # line: 1649
+from tensorflow.python.ops.gen_lookup_ops import MutableHashTableOfTensors # line: 1721
+from tensorflow.python.ops.gen_lookup_ops import MutableHashTableOfTensorsV2 # line: 1811
+from tensorflow.python.ops.gen_lookup_ops import MutableHashTableV2 # line: 1921
+from tensorflow.python.ops.gen_manip_ops import Roll # line: 100
+from tensorflow.python.ops.gen_map_ops import EmptyTensorMap # line: 64
+from tensorflow.python.ops.gen_map_ops import TensorMapErase # line: 130
+from tensorflow.python.ops.gen_map_ops import TensorMapHasKey # line: 193
+from tensorflow.python.ops.gen_map_ops import TensorMapInsert # line: 260
+from tensorflow.python.ops.gen_map_ops import TensorMapLookup # line: 329
+from tensorflow.python.ops.gen_map_ops import TensorMapSize # line: 388
+from tensorflow.python.ops.gen_map_ops import TensorMapStackKeys # line: 451
+from tensorflow.python.ops.gen_math_ops import Abs # line: 69
+from tensorflow.python.ops.gen_math_ops import AccumulateNV2 # line: 144
+from tensorflow.python.ops.gen_math_ops import Acos # line: 211
+from tensorflow.python.ops.gen_math_ops import Acosh # line: 302
+from tensorflow.python.ops.gen_math_ops import Add # line: 367
+from tensorflow.python.ops.gen_math_ops import AddN # line: 436
+from tensorflow.python.ops.gen_math_ops import AddV2 # line: 501
+from tensorflow.python.ops.gen_math_ops import All # line: 573
+from tensorflow.python.ops.gen_math_ops import Angle # line: 657
+from tensorflow.python.ops.gen_math_ops import Any # line: 731
+from tensorflow.python.ops.gen_math_ops import ApproximateEqual # line: 797
+from tensorflow.python.ops.gen_math_ops import ArgMax # line: 882
+from tensorflow.python.ops.gen_math_ops import ArgMin # line: 967
+from tensorflow.python.ops.gen_math_ops import Asin # line: 1070
+from tensorflow.python.ops.gen_math_ops import Asinh # line: 1163
+from tensorflow.python.ops.gen_math_ops import Atan # line: 1263
+from tensorflow.python.ops.gen_math_ops import Atan2 # line: 1361
+from tensorflow.python.ops.gen_math_ops import Atanh # line: 1457
+from tensorflow.python.ops.gen_math_ops import BatchMatMul # line: 1548
+from tensorflow.python.ops.gen_math_ops import BatchMatMulV2 # line: 1649
+from tensorflow.python.ops.gen_math_ops import BatchMatMulV3 # line: 1758
+from tensorflow.python.ops.gen_math_ops import Betainc # line: 1867
+from tensorflow.python.ops.gen_math_ops import Bincount # line: 1938
+from tensorflow.python.ops.gen_math_ops import Bucketize # line: 2016
+from tensorflow.python.ops.gen_math_ops import Cast # line: 2086
+from tensorflow.python.ops.gen_math_ops import Ceil # line: 2146
+from tensorflow.python.ops.gen_math_ops import ClipByValue # line: 2215
+from tensorflow.python.ops.gen_math_ops import Complex # line: 2294
+from tensorflow.python.ops.gen_math_ops import ComplexAbs # line: 2371
+from tensorflow.python.ops.gen_math_ops import Conj # line: 2444
+from tensorflow.python.ops.gen_math_ops import Cos # line: 2537
+from tensorflow.python.ops.gen_math_ops import Cosh # line: 2630
+from tensorflow.python.ops.gen_math_ops import Cross # line: 2722
+from tensorflow.python.ops.gen_math_ops import Cumprod # line: 2830
+from tensorflow.python.ops.gen_math_ops import Cumsum # line: 2944
+from tensorflow.python.ops.gen_math_ops import CumulativeLogsumexp # line: 3046
+from tensorflow.python.ops.gen_math_ops import DenseBincount # line: 3136
+from tensorflow.python.ops.gen_math_ops import Digamma # line: 3227
+from tensorflow.python.ops.gen_math_ops import Div # line: 3288
+from tensorflow.python.ops.gen_math_ops import DivNoNan # line: 3350
+from tensorflow.python.ops.gen_math_ops import Equal # line: 3429
+from tensorflow.python.ops.gen_math_ops import Erf # line: 3525
+from tensorflow.python.ops.gen_math_ops import Erfc # line: 3610
+from tensorflow.python.ops.gen_math_ops import Erfinv # line: 3667
+from tensorflow.python.ops.gen_math_ops import EuclideanNorm # line: 3740
+from tensorflow.python.ops.gen_math_ops import Exp # line: 3827
+from tensorflow.python.ops.gen_math_ops import Expm1 # line: 3925
+from tensorflow.python.ops.gen_math_ops import Floor # line: 3982
+from tensorflow.python.ops.gen_math_ops import FloorDiv # line: 4070
+from tensorflow.python.ops.gen_math_ops import FloorMod # line: 4164
+from tensorflow.python.ops.gen_math_ops import Greater # line: 4265
+from tensorflow.python.ops.gen_math_ops import GreaterEqual # line: 4366
+from tensorflow.python.ops.gen_math_ops import HistogramFixedWidth # line: 4455
+from tensorflow.python.ops.gen_math_ops import Igamma # line: 4559
+from tensorflow.python.ops.gen_math_ops import IgammaGradA # line: 4618
+from tensorflow.python.ops.gen_math_ops import Igammac # line: 4717
+from tensorflow.python.ops.gen_math_ops import Imag # line: 4793
+from tensorflow.python.ops.gen_math_ops import Inv # line: 4854
+from tensorflow.python.ops.gen_math_ops import InvGrad # line: 4914
+from tensorflow.python.ops.gen_math_ops import IsFinite # line: 5010
+from tensorflow.python.ops.gen_math_ops import IsInf # line: 5106
+from tensorflow.python.ops.gen_math_ops import IsNan # line: 5202
+from tensorflow.python.ops.gen_math_ops import Less # line: 5302
+from tensorflow.python.ops.gen_math_ops import LessEqual # line: 5403
+from tensorflow.python.ops.gen_math_ops import Lgamma # line: 5499
+from tensorflow.python.ops.gen_math_ops import LinSpace # line: 5573
+from tensorflow.python.ops.gen_math_ops import Log # line: 5668
+from tensorflow.python.ops.gen_math_ops import Log1p # line: 5760
+from tensorflow.python.ops.gen_math_ops import LogicalAnd # line: 5898
+from tensorflow.python.ops.gen_math_ops import LogicalNot # line: 5986
+from tensorflow.python.ops.gen_math_ops import LogicalOr # line: 6124
+from tensorflow.python.ops.gen_math_ops import MatMul # line: 6205
+from tensorflow.python.ops.gen_math_ops import Max # line: 6286
+from tensorflow.python.ops.gen_math_ops import Maximum # line: 6390
+from tensorflow.python.ops.gen_math_ops import Mean # line: 6465
+from tensorflow.python.ops.gen_math_ops import Min # line: 6542
+from tensorflow.python.ops.gen_math_ops import Minimum # line: 6649
+from tensorflow.python.ops.gen_math_ops import Mod # line: 6714
+from tensorflow.python.ops.gen_math_ops import Mul # line: 6775
+from tensorflow.python.ops.gen_math_ops import MulNoNan # line: 6836
+from tensorflow.python.ops.gen_math_ops import Ndtri # line: 6893
+from tensorflow.python.ops.gen_math_ops import Neg # line: 6978
+from tensorflow.python.ops.gen_math_ops import NextAfter # line: 7071
+from tensorflow.python.ops.gen_math_ops import NotEqual # line: 7142
+from tensorflow.python.ops.gen_math_ops import Polygamma # line: 7240
+from tensorflow.python.ops.gen_math_ops import Pow # line: 7308
+from tensorflow.python.ops.gen_math_ops import Prod # line: 7382
+from tensorflow.python.ops.gen_math_ops import QuantizeDownAndShrinkRange # line: 7486
+from tensorflow.python.ops.gen_math_ops import QuantizedAdd # line: 7574
+from tensorflow.python.ops.gen_math_ops import QuantizedMatMul # line: 7697
+from tensorflow.python.ops.gen_math_ops import QuantizedMul # line: 7799
+from tensorflow.python.ops.gen_math_ops import RaggedBincount # line: 7891
+from tensorflow.python.ops.gen_math_ops import Range # line: 7970
+from tensorflow.python.ops.gen_math_ops import Real # line: 8045
+from tensorflow.python.ops.gen_math_ops import RealDiv # line: 8137
+from tensorflow.python.ops.gen_math_ops import Reciprocal # line: 8225
+from tensorflow.python.ops.gen_math_ops import ReciprocalGrad # line: 8286
+from tensorflow.python.ops.gen_math_ops import RequantizationRange # line: 8361
+from tensorflow.python.ops.gen_math_ops import RequantizationRangePerChannel # line: 8442
+from tensorflow.python.ops.gen_math_ops import Requantize # line: 8537
+from tensorflow.python.ops.gen_math_ops import RequantizePerChannel # line: 8629
+from tensorflow.python.ops.gen_math_ops import Rint # line: 8730
+from tensorflow.python.ops.gen_math_ops import Round # line: 8790
+from tensorflow.python.ops.gen_math_ops import Rsqrt # line: 8848
+from tensorflow.python.ops.gen_math_ops import RsqrtGrad # line: 8908
+from tensorflow.python.ops.gen_math_ops import SegmentMax # line: 9029
+from tensorflow.python.ops.gen_math_ops import SegmentMaxV2 # line: 9140
+from tensorflow.python.ops.gen_math_ops import SegmentMean # line: 9266
+from tensorflow.python.ops.gen_math_ops import SegmentMin # line: 9388
+from tensorflow.python.ops.gen_math_ops import SegmentMinV2 # line: 9499
+from tensorflow.python.ops.gen_math_ops import SegmentProd # line: 9623
+from tensorflow.python.ops.gen_math_ops import SegmentProdV2 # line: 9725
+from tensorflow.python.ops.gen_math_ops import SegmentSum # line: 9848
+from tensorflow.python.ops.gen_math_ops import SegmentSumV2 # line: 9932
+from tensorflow.python.ops.gen_math_ops import Select # line: 10034
+from tensorflow.python.ops.gen_math_ops import SelectV2 # line: 10094
+from tensorflow.python.ops.gen_math_ops import Sigmoid # line: 10154
+from tensorflow.python.ops.gen_math_ops import SigmoidGrad # line: 10214
+from tensorflow.python.ops.gen_math_ops import Sign # line: 10279
+from tensorflow.python.ops.gen_math_ops import Sin # line: 10371
+from tensorflow.python.ops.gen_math_ops import Sinh # line: 10464
+from tensorflow.python.ops.gen_math_ops import SobolSample # line: 10537
+from tensorflow.python.ops.gen_math_ops import SparseBincount # line: 10627
+from tensorflow.python.ops.gen_math_ops import SparseMatMul # line: 10728
+from tensorflow.python.ops.gen_math_ops import SparseSegmentMean # line: 10821
+from tensorflow.python.ops.gen_math_ops import SparseSegmentMeanGrad # line: 10900
+from tensorflow.python.ops.gen_math_ops import SparseSegmentMeanGradV2 # line: 10987
+from tensorflow.python.ops.gen_math_ops import SparseSegmentMeanWithNumSegments # line: 11078
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSqrtN # line: 11164
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSqrtNGrad # line: 11243
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSqrtNGradV2 # line: 11331
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSqrtNWithNumSegments # line: 11424
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSum # line: 11536
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSumGrad # line: 11615
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSumGradV2 # line: 11702
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSumWithNumSegments # line: 11814
+from tensorflow.python.ops.gen_math_ops import Sqrt # line: 11881
+from tensorflow.python.ops.gen_math_ops import SqrtGrad # line: 11941
+from tensorflow.python.ops.gen_math_ops import Square # line: 12030
+from tensorflow.python.ops.gen_math_ops import SquaredDifference # line: 12119
+from tensorflow.python.ops.gen_math_ops import Sub # line: 12254
+from tensorflow.python.ops.gen_math_ops import Sum # line: 12328
+from tensorflow.python.ops.gen_math_ops import Tan # line: 12425
+from tensorflow.python.ops.gen_math_ops import Tanh # line: 12519
+from tensorflow.python.ops.gen_math_ops import TanhGrad # line: 12580
+from tensorflow.python.ops.gen_math_ops import TruncateDiv # line: 12673
+from tensorflow.python.ops.gen_math_ops import TruncateMod # line: 12765
+from tensorflow.python.ops.gen_math_ops import UnsortedSegmentMax # line: 12901
+from tensorflow.python.ops.gen_math_ops import UnsortedSegmentMin # line: 13035
+from tensorflow.python.ops.gen_math_ops import UnsortedSegmentProd # line: 13169
+from tensorflow.python.ops.gen_math_ops import UnsortedSegmentSum # line: 13304
+from tensorflow.python.ops.gen_math_ops import Xdivy # line: 13365
+from tensorflow.python.ops.gen_math_ops import Xlog1py # line: 13423
+from tensorflow.python.ops.gen_math_ops import Xlogy # line: 13508
+from tensorflow.python.ops.gen_math_ops import Zeta # line: 13600
+from tensorflow.python.ops.gen_nccl_ops import NcclAllReduce # line: 91
+from tensorflow.python.ops.gen_nccl_ops import NcclBroadcast # line: 162
+from tensorflow.python.ops.gen_nccl_ops import NcclReduce # line: 237
+from tensorflow.python.ops.gen_nn_ops import ApproxTopK # line: 167
+from tensorflow.python.ops.gen_nn_ops import AvgPool # line: 279
+from tensorflow.python.ops.gen_nn_ops import AvgPool3D # line: 388
+from tensorflow.python.ops.gen_nn_ops import AvgPool3DGrad # line: 497
+from tensorflow.python.ops.gen_nn_ops import AvgPoolGrad # line: 606
+from tensorflow.python.ops.gen_nn_ops import BatchNormWithGlobalNormalization # line: 712
+from tensorflow.python.ops.gen_nn_ops import BatchNormWithGlobalNormalizationGrad # line: 816
+from tensorflow.python.ops.gen_nn_ops import BiasAdd # line: 895
+from tensorflow.python.ops.gen_nn_ops import BiasAddGrad # line: 973
+from tensorflow.python.ops.gen_nn_ops import BiasAddV1 # line: 1040
+from tensorflow.python.ops.gen_nn_ops import Conv # line: 1218
+from tensorflow.python.ops.gen_nn_ops import Conv2D # line: 1398
+from tensorflow.python.ops.gen_nn_ops import Conv2DBackpropFilter # line: 1559
+from tensorflow.python.ops.gen_nn_ops import Conv2DBackpropFilterV2 # line: 1764
+from tensorflow.python.ops.gen_nn_ops import Conv2DBackpropInput # line: 1927
+from tensorflow.python.ops.gen_nn_ops import Conv2DBackpropInputV2 # line: 2130
+from tensorflow.python.ops.gen_nn_ops import Conv3D # line: 2266
+from tensorflow.python.ops.gen_nn_ops import Conv3DBackpropFilter # line: 2373
+from tensorflow.python.ops.gen_nn_ops import Conv3DBackpropFilterV2 # line: 2538
+from tensorflow.python.ops.gen_nn_ops import Conv3DBackpropInput # line: 2648
+from tensorflow.python.ops.gen_nn_ops import Conv3DBackpropInputV2 # line: 2772
+from tensorflow.python.ops.gen_nn_ops import DataFormatDimMap # line: 2866
+from tensorflow.python.ops.gen_nn_ops import DataFormatVecPermute # line: 2976
+from tensorflow.python.ops.gen_nn_ops import DepthwiseConv2dNative # line: 3111
+from tensorflow.python.ops.gen_nn_ops import DepthwiseConv2dNativeBackpropFilter # line: 3262
+from tensorflow.python.ops.gen_nn_ops import DepthwiseConv2dNativeBackpropInput # line: 3414
+from tensorflow.python.ops.gen_nn_ops import Dilation2D # line: 3549
+from tensorflow.python.ops.gen_nn_ops import Dilation2DBackpropFilter # line: 3649
+from tensorflow.python.ops.gen_nn_ops import Dilation2DBackpropInput # line: 3749
+from tensorflow.python.ops.gen_nn_ops import Elu # line: 3863
+from tensorflow.python.ops.gen_nn_ops import EluGrad # line: 3923
+from tensorflow.python.ops.gen_nn_ops import FractionalAvgPool # line: 4061
+from tensorflow.python.ops.gen_nn_ops import FractionalAvgPoolGrad # line: 4176
+from tensorflow.python.ops.gen_nn_ops import FractionalMaxPool # line: 4343
+from tensorflow.python.ops.gen_nn_ops import FractionalMaxPoolGrad # line: 4455
+from tensorflow.python.ops.gen_nn_ops import FusedBatchNorm # line: 4575
+from tensorflow.python.ops.gen_nn_ops import FusedBatchNormGrad # line: 4704
+from tensorflow.python.ops.gen_nn_ops import FusedBatchNormGradV2 # line: 4830
+from tensorflow.python.ops.gen_nn_ops import FusedBatchNormGradV3 # line: 4964
+from tensorflow.python.ops.gen_nn_ops import FusedBatchNormV2 # line: 5093
+from tensorflow.python.ops.gen_nn_ops import FusedBatchNormV3 # line: 5225
+from tensorflow.python.ops.gen_nn_ops import FusedPadConv2D # line: 5334
+from tensorflow.python.ops.gen_nn_ops import FusedResizeAndPadConv2D # line: 5450
+from tensorflow.python.ops.gen_nn_ops import InTopK # line: 5541
+from tensorflow.python.ops.gen_nn_ops import InTopKV2 # line: 5619
+from tensorflow.python.ops.gen_nn_ops import IsotonicRegression # line: 5694
+from tensorflow.python.ops.gen_nn_ops import L2Loss # line: 5785
+from tensorflow.python.ops.gen_nn_ops import LRN # line: 5908
+from tensorflow.python.ops.gen_nn_ops import LRNGrad # line: 6008
+from tensorflow.python.ops.gen_nn_ops import LeakyRelu # line: 6082
+from tensorflow.python.ops.gen_nn_ops import LeakyReluGrad # line: 6150
+from tensorflow.python.ops.gen_nn_ops import LogSoftmax # line: 6215
+from tensorflow.python.ops.gen_nn_ops import MaxPool # line: 6317
+from tensorflow.python.ops.gen_nn_ops import MaxPool3D # line: 6430
+from tensorflow.python.ops.gen_nn_ops import MaxPool3DGrad # line: 6543
+from tensorflow.python.ops.gen_nn_ops import MaxPool3DGradGrad # line: 6658
+from tensorflow.python.ops.gen_nn_ops import MaxPoolGrad # line: 6781
+from tensorflow.python.ops.gen_nn_ops import MaxPoolGradGrad # line: 6902
+from tensorflow.python.ops.gen_nn_ops import MaxPoolGradGradV2 # line: 7002
+from tensorflow.python.ops.gen_nn_ops import MaxPoolGradGradWithArgmax # line: 7106
+from tensorflow.python.ops.gen_nn_ops import MaxPoolGradV2 # line: 7208
+from tensorflow.python.ops.gen_nn_ops import MaxPoolGradWithArgmax # line: 7312
+from tensorflow.python.ops.gen_nn_ops import MaxPoolV2 # line: 7409
+from tensorflow.python.ops.gen_nn_ops import MaxPoolWithArgmax # line: 7530
+from tensorflow.python.ops.gen_nn_ops import NthElement # line: 7623
+from tensorflow.python.ops.gen_nn_ops import QuantizedAvgPool # line: 7722
+from tensorflow.python.ops.gen_nn_ops import QuantizedBatchNormWithGlobalNormalization # line: 7873
+from tensorflow.python.ops.gen_nn_ops import QuantizedBiasAdd # line: 7975
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2D # line: 8103
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DAndRelu # line: 8241
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DAndReluAndRequantize # line: 8398
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DAndRequantize # line: 8555
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DPerChannel # line: 8701
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DWithBias # line: 8841
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DWithBiasAndRelu # line: 8993
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DWithBiasAndReluAndRequantize # line: 9156
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DWithBiasAndRequantize # line: 9320
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DWithBiasSignedSumAndReluAndRequantize # line: 9493
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DWithBiasSumAndRelu # line: 9653
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DWithBiasSumAndReluAndRequantize # line: 9825
+from tensorflow.python.ops.gen_nn_ops import QuantizedDepthwiseConv2D # line: 9976
+from tensorflow.python.ops.gen_nn_ops import QuantizedDepthwiseConv2DWithBias # line: 10115
+from tensorflow.python.ops.gen_nn_ops import QuantizedDepthwiseConv2DWithBiasAndRelu # line: 10269
+from tensorflow.python.ops.gen_nn_ops import QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize # line: 10443
+from tensorflow.python.ops.gen_nn_ops import QuantizedMatMulWithBias # line: 10599
+from tensorflow.python.ops.gen_nn_ops import QuantizedMatMulWithBiasAndDequantize # line: 10721
+from tensorflow.python.ops.gen_nn_ops import QuantizedMatMulWithBiasAndRelu # line: 10864
+from tensorflow.python.ops.gen_nn_ops import QuantizedMatMulWithBiasAndReluAndRequantize # line: 11020
+from tensorflow.python.ops.gen_nn_ops import QuantizedMatMulWithBiasAndRequantize # line: 11155
+from tensorflow.python.ops.gen_nn_ops import QuantizedMaxPool # line: 11273
+from tensorflow.python.ops.gen_nn_ops import QuantizedRelu # line: 11366
+from tensorflow.python.ops.gen_nn_ops import QuantizedRelu6 # line: 11450
+from tensorflow.python.ops.gen_nn_ops import QuantizedReluX # line: 11536
+from tensorflow.python.ops.gen_nn_ops import Relu # line: 11630
+from tensorflow.python.ops.gen_nn_ops import Relu6 # line: 11687
+from tensorflow.python.ops.gen_nn_ops import Relu6Grad # line: 11747
+from tensorflow.python.ops.gen_nn_ops import ReluGrad # line: 11808
+from tensorflow.python.ops.gen_nn_ops import Selu # line: 11900
+from tensorflow.python.ops.gen_nn_ops import SeluGrad # line: 11960
+from tensorflow.python.ops.gen_nn_ops import Softmax # line: 12022
+from tensorflow.python.ops.gen_nn_ops import SoftmaxCrossEntropyWithLogits # line: 12094
+from tensorflow.python.ops.gen_nn_ops import Softplus # line: 12152
+from tensorflow.python.ops.gen_nn_ops import SoftplusGrad # line: 12211
+from tensorflow.python.ops.gen_nn_ops import Softsign # line: 12295
+from tensorflow.python.ops.gen_nn_ops import SoftsignGrad # line: 12355
+from tensorflow.python.ops.gen_nn_ops import SparseSoftmaxCrossEntropyWithLogits # line: 12434
+from tensorflow.python.ops.gen_nn_ops import TopK # line: 12525
+from tensorflow.python.ops.gen_nn_ops import TopKV2 # line: 12624
+from tensorflow.python.ops.gen_optional_ops import OptionalFromValue # line: 63
+from tensorflow.python.ops.gen_optional_ops import OptionalGetValue # line: 132
+from tensorflow.python.ops.gen_optional_ops import OptionalHasValue # line: 196
+from tensorflow.python.ops.gen_optional_ops import OptionalNone # line: 249
+from tensorflow.python.ops.gen_parsing_ops import DecodeCSV # line: 107
+from tensorflow.python.ops.gen_parsing_ops import DecodeCompressed # line: 228
+from tensorflow.python.ops.gen_parsing_ops import DecodeJSONExample # line: 298
+from tensorflow.python.ops.gen_parsing_ops import DecodePaddedRaw # line: 370
+from tensorflow.python.ops.gen_parsing_ops import DecodeRaw # line: 445
+from tensorflow.python.ops.gen_parsing_ops import ParseExample # line: 593
+from tensorflow.python.ops.gen_parsing_ops import ParseExampleV2 # line: 793
+from tensorflow.python.ops.gen_parsing_ops import ParseSequenceExample # line: 1106
+from tensorflow.python.ops.gen_parsing_ops import ParseSequenceExampleV2 # line: 1510
+from tensorflow.python.ops.gen_parsing_ops import ParseSingleExample # line: 1767
+from tensorflow.python.ops.gen_parsing_ops import ParseSingleSequenceExample # line: 2030
+from tensorflow.python.ops.gen_parsing_ops import ParseTensor # line: 2206
+from tensorflow.python.ops.gen_parsing_ops import SerializeTensor # line: 2264
+from tensorflow.python.ops.gen_parsing_ops import StringToNumber # line: 2335
+from tensorflow.python.ops.gen_ragged_array_ops import RaggedCross # line: 141
+from tensorflow.python.ops.gen_ragged_array_ops import RaggedFillEmptyRows # line: 277
+from tensorflow.python.ops.gen_ragged_array_ops import RaggedFillEmptyRowsGrad # line: 378
+from tensorflow.python.ops.gen_ragged_array_ops import RaggedGather # line: 499
+from tensorflow.python.ops.gen_ragged_conversion_ops import RaggedTensorFromVariant # line: 113
+from tensorflow.python.ops.gen_ragged_conversion_ops import RaggedTensorToSparse # line: 202
+from tensorflow.python.ops.gen_ragged_conversion_ops import RaggedTensorToTensor # line: 339
+from tensorflow.python.ops.gen_ragged_conversion_ops import RaggedTensorToVariant # line: 442
+from tensorflow.python.ops.gen_ragged_conversion_ops import RaggedTensorToVariantGradient # line: 526
+from tensorflow.python.ops.gen_ragged_math_ops import RaggedRange # line: 102
+from tensorflow.python.ops.gen_random_index_shuffle_ops import RandomIndexShuffle # line: 118
+from tensorflow.python.ops.gen_random_ops import Multinomial # line: 90
+from tensorflow.python.ops.gen_random_ops import ParameterizedTruncatedNormal # line: 190
+from tensorflow.python.ops.gen_random_ops import RandomGamma # line: 280
+from tensorflow.python.ops.gen_random_ops import RandomGammaGrad # line: 344
+from tensorflow.python.ops.gen_random_ops import RandomPoisson # line: 415
+from tensorflow.python.ops.gen_random_ops import RandomPoissonV2 # line: 516
+from tensorflow.python.ops.gen_random_ops import RandomShuffle # line: 606
+from tensorflow.python.ops.gen_random_ops import RandomStandardNormal # line: 691
+from tensorflow.python.ops.gen_random_ops import RandomUniform # line: 778
+from tensorflow.python.ops.gen_random_ops import RandomUniformInt # line: 871
+from tensorflow.python.ops.gen_random_ops import TruncatedNormal # line: 960
+from tensorflow.python.ops.gen_resource_variable_ops import AssignAddVariableOp # line: 62
+from tensorflow.python.ops.gen_resource_variable_ops import AssignSubVariableOp # line: 113
+from tensorflow.python.ops.gen_resource_variable_ops import AssignVariableOp # line: 170
+from tensorflow.python.ops.gen_resource_variable_ops import ConsumeMutexLock # line: 226
+from tensorflow.python.ops.gen_resource_variable_ops import DestroyResourceOp # line: 282
+from tensorflow.python.ops.gen_resource_variable_ops import DisableCopyOnRead # line: 331
+from tensorflow.python.ops.gen_resource_variable_ops import MutexLock # line: 421
+from tensorflow.python.ops.gen_resource_variable_ops import MutexV2 # line: 488
+from tensorflow.python.ops.gen_resource_variable_ops import ReadVariableOp # line: 559
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceGather # line: 649
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceGatherNd # line: 720
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceScatterAdd # line: 796
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceScatterDiv # line: 869
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceScatterMax # line: 942
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceScatterMin # line: 1015
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceScatterMul # line: 1088
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceScatterSub # line: 1161
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceScatterUpdate # line: 1224
+from tensorflow.python.ops.gen_resource_variable_ops import VarHandleOp # line: 1319
+from tensorflow.python.ops.gen_resource_variable_ops import VarIsInitializedOp # line: 1392
+from tensorflow.python.ops.gen_resource_variable_ops import VariableShape # line: 1461
+from tensorflow.python.ops.gen_rnn_ops import BlockLSTM # line: 136
+from tensorflow.python.ops.gen_rnn_ops import BlockLSTMGrad # line: 265
+from tensorflow.python.ops.gen_rnn_ops import BlockLSTMGradV2 # line: 385
+from tensorflow.python.ops.gen_rnn_ops import BlockLSTMV2 # line: 512
+from tensorflow.python.ops.gen_rnn_ops import GRUBlockCell # line: 638
+from tensorflow.python.ops.gen_rnn_ops import GRUBlockCellGrad # line: 797
+from tensorflow.python.ops.gen_rnn_ops import LSTMBlockCell # line: 928
+from tensorflow.python.ops.gen_rnn_ops import LSTMBlockCellGrad # line: 1040
+from tensorflow.python.ops.gen_script_ops import EagerPyFunc # line: 84
+from tensorflow.python.ops.gen_script_ops import PyFunc # line: 163
+from tensorflow.python.ops.gen_script_ops import PyFuncStateless # line: 230
+from tensorflow.python.ops.gen_sdca_ops import SdcaFprint # line: 92
+from tensorflow.python.ops.gen_sdca_ops import SdcaOptimizer # line: 366
+from tensorflow.python.ops.gen_sdca_ops import SdcaOptimizerV2 # line: 655
+from tensorflow.python.ops.gen_sdca_ops import SdcaShrinkL1 # line: 795
+from tensorflow.python.ops.gen_sendrecv_ops import Recv # line: 98
+from tensorflow.python.ops.gen_sendrecv_ops import Send # line: 181
+from tensorflow.python.ops.gen_set_ops import DenseToDenseSetOperation # line: 100
+from tensorflow.python.ops.gen_set_ops import DenseToSparseSetOperation # line: 217
+from tensorflow.python.ops.gen_set_ops import SetSize # line: 304
+from tensorflow.python.ops.gen_set_ops import SparseToSparseSetOperation # line: 437
+from tensorflow.python.ops.gen_sparse_ops import AddManySparseToTensorsMap # line: 115
+from tensorflow.python.ops.gen_sparse_ops import AddSparseToTensorsMap # line: 218
+from tensorflow.python.ops.gen_sparse_ops import DeserializeManySparse # line: 339
+from tensorflow.python.ops.gen_sparse_ops import DeserializeSparse # line: 454
+from tensorflow.python.ops.gen_sparse_ops import SerializeManySparse # line: 537
+from tensorflow.python.ops.gen_sparse_ops import SerializeSparse # line: 615
+from tensorflow.python.ops.gen_sparse_ops import SparseAdd # line: 719
+from tensorflow.python.ops.gen_sparse_ops import SparseAddGrad # line: 807
+from tensorflow.python.ops.gen_sparse_ops import SparseConcat # line: 952
+from tensorflow.python.ops.gen_sparse_ops import SparseCross # line: 1133
+from tensorflow.python.ops.gen_sparse_ops import SparseCrossHashed # line: 1293
+from tensorflow.python.ops.gen_sparse_ops import SparseCrossV2 # line: 1441
+from tensorflow.python.ops.gen_sparse_ops import SparseDenseCwiseAdd # line: 1535
+from tensorflow.python.ops.gen_sparse_ops import SparseDenseCwiseDiv # line: 1607
+from tensorflow.python.ops.gen_sparse_ops import SparseDenseCwiseMul # line: 1683
+from tensorflow.python.ops.gen_sparse_ops import SparseFillEmptyRows # line: 1800
+from tensorflow.python.ops.gen_sparse_ops import SparseFillEmptyRowsGrad # line: 1879
+from tensorflow.python.ops.gen_sparse_ops import SparseReduceMax # line: 1969
+from tensorflow.python.ops.gen_sparse_ops import SparseReduceMaxSparse # line: 2073
+from tensorflow.python.ops.gen_sparse_ops import SparseReduceSum # line: 2167
+from tensorflow.python.ops.gen_sparse_ops import SparseReduceSumSparse # line: 2271
+from tensorflow.python.ops.gen_sparse_ops import SparseReorder # line: 2358
+from tensorflow.python.ops.gen_sparse_ops import SparseReshape # line: 2445
+from tensorflow.python.ops.gen_sparse_ops import SparseSlice # line: 2539
+from tensorflow.python.ops.gen_sparse_ops import SparseSliceGrad # line: 2616
+from tensorflow.python.ops.gen_sparse_ops import SparseSoftmax # line: 2698
+from tensorflow.python.ops.gen_sparse_ops import SparseSparseMaximum # line: 2782
+from tensorflow.python.ops.gen_sparse_ops import SparseSparseMinimum # line: 2869
+from tensorflow.python.ops.gen_sparse_ops import SparseSplit # line: 2975
+from tensorflow.python.ops.gen_sparse_ops import SparseTensorDenseAdd # line: 3051
+from tensorflow.python.ops.gen_sparse_ops import SparseTensorDenseMatMul # line: 3147
+from tensorflow.python.ops.gen_sparse_ops import SparseToDense # line: 3259
+from tensorflow.python.ops.gen_sparse_ops import TakeManySparseFromTensorsMap # line: 3404
+from tensorflow.python.ops.gen_special_math_ops import BesselI0 # line: 65
+from tensorflow.python.ops.gen_special_math_ops import BesselI0e # line: 121
+from tensorflow.python.ops.gen_special_math_ops import BesselI1 # line: 177
+from tensorflow.python.ops.gen_special_math_ops import BesselI1e # line: 233
+from tensorflow.python.ops.gen_special_math_ops import BesselJ0 # line: 289
+from tensorflow.python.ops.gen_special_math_ops import BesselJ1 # line: 345
+from tensorflow.python.ops.gen_special_math_ops import BesselK0 # line: 401
+from tensorflow.python.ops.gen_special_math_ops import BesselK0e # line: 457
+from tensorflow.python.ops.gen_special_math_ops import BesselK1 # line: 513
+from tensorflow.python.ops.gen_special_math_ops import BesselK1e # line: 569
+from tensorflow.python.ops.gen_special_math_ops import BesselY0 # line: 625
+from tensorflow.python.ops.gen_special_math_ops import BesselY1 # line: 681
+from tensorflow.python.ops.gen_special_math_ops import Dawsn # line: 737
+from tensorflow.python.ops.gen_special_math_ops import Expint # line: 793
+from tensorflow.python.ops.gen_special_math_ops import FresnelCos # line: 849
+from tensorflow.python.ops.gen_special_math_ops import FresnelSin # line: 905
+from tensorflow.python.ops.gen_special_math_ops import Spence # line: 961
+from tensorflow.python.ops.gen_spectral_ops import BatchFFT # line: 63
+from tensorflow.python.ops.gen_spectral_ops import BatchFFT2D # line: 117
+from tensorflow.python.ops.gen_spectral_ops import BatchFFT3D # line: 171
+from tensorflow.python.ops.gen_spectral_ops import BatchIFFT # line: 225
+from tensorflow.python.ops.gen_spectral_ops import BatchIFFT2D # line: 279
+from tensorflow.python.ops.gen_spectral_ops import BatchIFFT3D # line: 333
+from tensorflow.python.ops.gen_spectral_ops import FFT # line: 421
+from tensorflow.python.ops.gen_spectral_ops import FFT2D # line: 510
+from tensorflow.python.ops.gen_spectral_ops import FFT3D # line: 599
+from tensorflow.python.ops.gen_spectral_ops import FFTND # line: 701
+from tensorflow.python.ops.gen_spectral_ops import IFFT # line: 792
+from tensorflow.python.ops.gen_spectral_ops import IFFT2D # line: 881
+from tensorflow.python.ops.gen_spectral_ops import IFFT3D # line: 970
+from tensorflow.python.ops.gen_spectral_ops import IFFTND # line: 1072
+from tensorflow.python.ops.gen_spectral_ops import IRFFT # line: 1154
+from tensorflow.python.ops.gen_spectral_ops import IRFFT2D # line: 1238
+from tensorflow.python.ops.gen_spectral_ops import IRFFT3D # line: 1322
+from tensorflow.python.ops.gen_spectral_ops import IRFFTND # line: 1435
+from tensorflow.python.ops.gen_spectral_ops import RFFT # line: 1518
+from tensorflow.python.ops.gen_spectral_ops import RFFT2D # line: 1600
+from tensorflow.python.ops.gen_spectral_ops import RFFT3D # line: 1682
+from tensorflow.python.ops.gen_spectral_ops import RFFTND # line: 1795
+from tensorflow.python.ops.gen_state_ops import Assign # line: 75
+from tensorflow.python.ops.gen_state_ops import AssignAdd # line: 122
+from tensorflow.python.ops.gen_state_ops import AssignSub # line: 169
+from tensorflow.python.ops.gen_state_ops import CountUpTo # line: 209
+from tensorflow.python.ops.gen_state_ops import DestroyTemporaryVariable # line: 256
+from tensorflow.python.ops.gen_state_ops import IsVariableInitialized # line: 293
+from tensorflow.python.ops.gen_state_ops import ResourceCountUpTo # line: 347
+from tensorflow.python.ops.gen_state_ops import ResourceScatterNdAdd # line: 447
+from tensorflow.python.ops.gen_state_ops import ResourceScatterNdMax # line: 514
+from tensorflow.python.ops.gen_state_ops import ResourceScatterNdMin # line: 581
+from tensorflow.python.ops.gen_state_ops import ResourceScatterNdSub # line: 682
+from tensorflow.python.ops.gen_state_ops import ResourceScatterNdUpdate # line: 786
+from tensorflow.python.ops.gen_state_ops import ScatterAdd # line: 872
+from tensorflow.python.ops.gen_state_ops import ScatterDiv # line: 942
+from tensorflow.python.ops.gen_state_ops import ScatterMax # line: 1014
+from tensorflow.python.ops.gen_state_ops import ScatterMin # line: 1086
+from tensorflow.python.ops.gen_state_ops import ScatterMul # line: 1156
+from tensorflow.python.ops.gen_state_ops import ScatterNdAdd # line: 1242
+from tensorflow.python.ops.gen_state_ops import ScatterNdMax # line: 1294
+from tensorflow.python.ops.gen_state_ops import ScatterNdMin # line: 1346
+from tensorflow.python.ops.gen_state_ops import ScatterNdSub # line: 1434
+from tensorflow.python.ops.gen_state_ops import ScatterNdUpdate # line: 1521
+from tensorflow.python.ops.gen_state_ops import ScatterSub # line: 1593
+from tensorflow.python.ops.gen_state_ops import ScatterUpdate # line: 1669
+from tensorflow.python.ops.gen_state_ops import TemporaryVariable # line: 1731
+from tensorflow.python.ops.gen_state_ops import Variable # line: 1780
+from tensorflow.python.ops.gen_state_ops import VariableV2 # line: 1838
+from tensorflow.python.ops.gen_stateful_random_ops import NonDeterministicInts # line: 74
+from tensorflow.python.ops.gen_stateful_random_ops import RngReadAndSkip # line: 141
+from tensorflow.python.ops.gen_stateful_random_ops import RngSkip # line: 198
+from tensorflow.python.ops.gen_stateful_random_ops import StatefulRandomBinomial # line: 268
+from tensorflow.python.ops.gen_stateful_random_ops import StatefulStandardNormal # line: 344
+from tensorflow.python.ops.gen_stateful_random_ops import StatefulStandardNormalV2 # line: 419
+from tensorflow.python.ops.gen_stateful_random_ops import StatefulTruncatedNormal # line: 497
+from tensorflow.python.ops.gen_stateful_random_ops import StatefulUniform # line: 574
+from tensorflow.python.ops.gen_stateful_random_ops import StatefulUniformFullInt # line: 649
+from tensorflow.python.ops.gen_stateful_random_ops import StatefulUniformInt # line: 730
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessMultinomial # line: 83
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessParameterizedTruncatedNormal # line: 164
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessRandomBinomial # line: 249
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessRandomGammaV2 # line: 326
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessRandomNormal # line: 401
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessRandomPoisson # line: 481
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessRandomUniform # line: 559
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessRandomUniformFullInt # line: 637
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessRandomUniformInt # line: 713
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessTruncatedNormal # line: 791
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomGammaV3 # line: 81
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomGetAlg # line: 141
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomGetKeyCounter # line: 207
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomGetKeyCounterAlg # line: 276
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomNormalV2 # line: 352
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomUniformFullIntV2 # line: 435
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomUniformIntV2 # line: 517
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomUniformV2 # line: 599
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessShuffle # line: 680
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessTruncatedNormalV2 # line: 760
+from tensorflow.python.ops.gen_string_ops import AsString # line: 147
+from tensorflow.python.ops.gen_string_ops import DecodeBase64 # line: 250
+from tensorflow.python.ops.gen_string_ops import EncodeBase64 # line: 345
+from tensorflow.python.ops.gen_string_ops import ReduceJoin # line: 446
+from tensorflow.python.ops.gen_string_ops import RegexFullMatch # line: 524
+from tensorflow.python.ops.gen_string_ops import RegexReplace # line: 597
+from tensorflow.python.ops.gen_string_ops import StaticRegexFullMatch # line: 666
+from tensorflow.python.ops.gen_string_ops import StaticRegexReplace # line: 738
+from tensorflow.python.ops.gen_string_ops import StringFormat # line: 822
+from tensorflow.python.ops.gen_string_ops import StringJoin # line: 908
+from tensorflow.python.ops.gen_string_ops import StringLength # line: 988
+from tensorflow.python.ops.gen_string_ops import StringLower # line: 1083
+from tensorflow.python.ops.gen_string_ops import StringNGrams # line: 1201
+from tensorflow.python.ops.gen_string_ops import StringSplit # line: 1307
+from tensorflow.python.ops.gen_string_ops import StringSplitV2 # line: 1407
+from tensorflow.python.ops.gen_string_ops import StringStrip # line: 1498
+from tensorflow.python.ops.gen_string_ops import StringToHashBucket # line: 1564
+from tensorflow.python.ops.gen_string_ops import StringToHashBucketFast # line: 1667
+from tensorflow.python.ops.gen_string_ops import StringToHashBucketStrong # line: 1788
+from tensorflow.python.ops.gen_string_ops import StringUpper # line: 1888
+from tensorflow.python.ops.gen_string_ops import Substr # line: 2044
+from tensorflow.python.ops.gen_string_ops import UnicodeDecode # line: 2176
+from tensorflow.python.ops.gen_string_ops import UnicodeDecodeWithOffsets # line: 2325
+from tensorflow.python.ops.gen_string_ops import UnicodeEncode # line: 2450
+from tensorflow.python.ops.gen_string_ops import UnicodeScript # line: 2559
+from tensorflow.python.ops.gen_string_ops import UnicodeTranscode # line: 2745
+from tensorflow.python.ops.gen_string_ops import UnsortedSegmentJoin # line: 2828
+from tensorflow.python.ops.gen_summary_ops import CloseSummaryWriter # line: 55
+from tensorflow.python.ops.gen_summary_ops import CreateSummaryDbWriter # line: 107
+from tensorflow.python.ops.gen_summary_ops import CreateSummaryFileWriter # line: 163
+from tensorflow.python.ops.gen_summary_ops import FlushSummaryWriter # line: 211
+from tensorflow.python.ops.gen_summary_ops import ImportEvent # line: 255
+from tensorflow.python.ops.gen_summary_ops import SummaryWriter # line: 317
+from tensorflow.python.ops.gen_summary_ops import WriteAudioSummary # line: 383
+from tensorflow.python.ops.gen_summary_ops import WriteGraphSummary # line: 438
+from tensorflow.python.ops.gen_summary_ops import WriteHistogramSummary # line: 491
+from tensorflow.python.ops.gen_summary_ops import WriteImageSummary # line: 554
+from tensorflow.python.ops.gen_summary_ops import WriteRawProtoSummary # line: 609
+from tensorflow.python.ops.gen_summary_ops import WriteScalarSummary # line: 662
+from tensorflow.python.ops.gen_summary_ops import WriteSummary # line: 718
+from tensorflow.python.ops.gen_sync_ops import SyncDevice # line: 57
+from tensorflow.python.ops.gen_tpu_ops import AllToAll # line: 104
+from tensorflow.python.ops.gen_tpu_ops import AssignVariableXlaConcatND # line: 228
+from tensorflow.python.ops.gen_tpu_ops import CollectivePermute # line: 313
+from tensorflow.python.ops.gen_tpu_ops import ConfigureDistributedTPU # line: 420
+from tensorflow.python.ops.gen_tpu_ops import ConfigureTPUEmbedding # line: 491
+from tensorflow.python.ops.gen_tpu_ops import CrossReplicaSum # line: 556
+from tensorflow.python.ops.gen_tpu_ops import DynamicEnqueueTPUEmbeddingArbitraryTensorBatch # line: 691
+from tensorflow.python.ops.gen_tpu_ops import DynamicEnqueueTPUEmbeddingRaggedTensorBatch # line: 847
+from tensorflow.python.ops.gen_tpu_ops import EnqueueTPUEmbeddingArbitraryTensorBatch # line: 1038
+from tensorflow.python.ops.gen_tpu_ops import EnqueueTPUEmbeddingIntegerBatch # line: 1142
+from tensorflow.python.ops.gen_tpu_ops import EnqueueTPUEmbeddingRaggedTensorBatch # line: 1311
+from tensorflow.python.ops.gen_tpu_ops import EnqueueTPUEmbeddingSparseBatch # line: 1494
+from tensorflow.python.ops.gen_tpu_ops import EnqueueTPUEmbeddingSparseTensorBatch # line: 1689
+from tensorflow.python.ops.gen_tpu_ops import InfeedDequeue # line: 1805
+from tensorflow.python.ops.gen_tpu_ops import InfeedDequeueTuple # line: 1875
+from tensorflow.python.ops.gen_tpu_ops import InfeedEnqueue # line: 1959
+from tensorflow.python.ops.gen_tpu_ops import InfeedEnqueuePrelinearizedBuffer # line: 2026
+from tensorflow.python.ops.gen_tpu_ops import InfeedEnqueueTuple # line: 2103
+from tensorflow.python.ops.gen_tpu_ops import IsTPUEmbeddingInitialized # line: 2173
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingADAMParameters # line: 2260
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingAdadeltaParameters # line: 2356
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingAdagradMomentumParameters # line: 2452
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingAdagradParameters # line: 2545
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingCenteredRMSPropParameters # line: 2641
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingFTRLParameters # line: 2738
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingFrequencyEstimatorParameters # line: 2833
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingMDLAdagradLightParameters # line: 2932
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingMomentumParameters # line: 3026
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingProximalAdagradParameters # line: 3118
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingProximalYogiParameters # line: 3202
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingRMSPropParameters # line: 3296
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingStochasticGradientDescentParameters # line: 3387
+from tensorflow.python.ops.gen_tpu_ops import OutfeedDequeue # line: 3470
+from tensorflow.python.ops.gen_tpu_ops import OutfeedDequeueTuple # line: 3557
+from tensorflow.python.ops.gen_tpu_ops import OutfeedDequeueTupleV2 # line: 3649
+from tensorflow.python.ops.gen_tpu_ops import OutfeedDequeueV2 # line: 3728
+from tensorflow.python.ops.gen_tpu_ops import OutfeedEnqueue # line: 3778
+from tensorflow.python.ops.gen_tpu_ops import OutfeedEnqueueTuple # line: 3823
+from tensorflow.python.ops.gen_tpu_ops import Prelinearize # line: 3893
+from tensorflow.python.ops.gen_tpu_ops import PrelinearizeTuple # line: 3980
+from tensorflow.python.ops.gen_tpu_ops import ReadVariableXlaSplitND # line: 4115
+from tensorflow.python.ops.gen_tpu_ops import RecvTPUEmbeddingActivations # line: 4199
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingADAMParameters # line: 4293
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingAdadeltaParameters # line: 4398
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingAdagradMomentumParameters # line: 4504
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingAdagradParameters # line: 4608
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingCenteredRMSPropParameters # line: 4715
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingFTRLParameters # line: 4820
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingFrequencyEstimatorParameters # line: 4925
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingMDLAdagradLightParameters # line: 5032
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingMomentumParameters # line: 5136
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingProximalAdagradParameters # line: 5241
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingProximalYogiParameters # line: 5342
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingRMSPropParameters # line: 5447
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingStochasticGradientDescentParameters # line: 5544
+from tensorflow.python.ops.gen_tpu_ops import SendTPUEmbeddingGradients # line: 5630
+from tensorflow.python.ops.gen_tpu_ops import ShutdownDistributedTPU # line: 5687
+from tensorflow.python.ops.gen_tpu_ops import TPUCompilationResult # line: 5741
+from tensorflow.python.ops.gen_tpu_ops import TPUEmbeddingActivations # line: 5817
+from tensorflow.python.ops.gen_tpu_ops import TPUOrdinalSelector # line: 5878
+from tensorflow.python.ops.gen_tpu_ops import TPUPartitionedCall # line: 5949
+from tensorflow.python.ops.gen_tpu_ops import TPUReplicateMetadata # line: 6101
+from tensorflow.python.ops.gen_tpu_ops import TPUReplicatedInput # line: 6247
+from tensorflow.python.ops.gen_tpu_ops import TPUReplicatedOutput # line: 6332
+from tensorflow.python.ops.gen_tpu_ops import WorkerHeartbeat # line: 6391
+from tensorflow.python.ops.gen_tpu_ops import XlaConcatND # line: 6513
+from tensorflow.python.ops.gen_tpu_ops import XlaSplitND # line: 6648
+from tensorflow.python.ops.gen_tpu_partition_ops import TPUPartitionedInput # line: 80
+from tensorflow.python.ops.gen_tpu_partition_ops import TPUPartitionedInputV2 # line: 168
+from tensorflow.python.ops.gen_tpu_partition_ops import TPUPartitionedOutput # line: 252
+from tensorflow.python.ops.gen_tpu_partition_ops import TPUPartitionedOutputV2 # line: 330
+from tensorflow.python.ops.gen_training_ops import ApplyAdaMax # line: 83
+from tensorflow.python.ops.gen_training_ops import ApplyAdadelta # line: 143
+from tensorflow.python.ops.gen_training_ops import ApplyAdagrad # line: 201
+from tensorflow.python.ops.gen_training_ops import ApplyAdagradDA # line: 260
+from tensorflow.python.ops.gen_training_ops import ApplyAdagradV2 # line: 320
+from tensorflow.python.ops.gen_training_ops import ApplyAdam # line: 394
+from tensorflow.python.ops.gen_training_ops import ApplyAddSign # line: 452
+from tensorflow.python.ops.gen_training_ops import ApplyCenteredRMSProp # line: 531
+from tensorflow.python.ops.gen_training_ops import ApplyFtrl # line: 600
+from tensorflow.python.ops.gen_training_ops import ApplyFtrlV2 # line: 673
+from tensorflow.python.ops.gen_training_ops import ApplyGradientDescent # line: 719
+from tensorflow.python.ops.gen_training_ops import ApplyMomentum # line: 784
+from tensorflow.python.ops.gen_training_ops import ApplyPowerSign # line: 842
+from tensorflow.python.ops.gen_training_ops import ApplyProximalAdagrad # line: 898
+from tensorflow.python.ops.gen_training_ops import ApplyProximalGradientDescent # line: 952
+from tensorflow.python.ops.gen_training_ops import ApplyRMSProp # line: 1020
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAdaMax # line: 1087
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAdadelta # line: 1164
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAdagrad # line: 1238
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAdagradDA # line: 1321
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAdagradV2 # line: 1400
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAdam # line: 1496
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAdamWithAmsgrad # line: 1589
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAddSign # line: 1665
+from tensorflow.python.ops.gen_training_ops import ResourceApplyCenteredRMSProp # line: 1759
+from tensorflow.python.ops.gen_training_ops import ResourceApplyFtrl # line: 1850
+from tensorflow.python.ops.gen_training_ops import ResourceApplyFtrlV2 # line: 1946
+from tensorflow.python.ops.gen_training_ops import ResourceApplyGradientDescent # line: 2013
+from tensorflow.python.ops.gen_training_ops import ResourceApplyKerasMomentum # line: 2094
+from tensorflow.python.ops.gen_training_ops import ResourceApplyMomentum # line: 2179
+from tensorflow.python.ops.gen_training_ops import ResourceApplyPowerSign # line: 2256
+from tensorflow.python.ops.gen_training_ops import ResourceApplyProximalAdagrad # line: 2330
+from tensorflow.python.ops.gen_training_ops import ResourceApplyProximalGradientDescent # line: 2403
+from tensorflow.python.ops.gen_training_ops import ResourceApplyRMSProp # line: 2486
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyAdadelta # line: 2563
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyAdagrad # line: 2645
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyAdagradDA # line: 2734
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyAdagradV2 # line: 2821
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyCenteredRMSProp # line: 2924
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyFtrl # line: 3022
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyFtrlV2 # line: 3126
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyKerasMomentum # line: 3222
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyMomentum # line: 3315
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyProximalAdagrad # line: 3401
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyProximalGradientDescent # line: 3481
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyRMSProp # line: 3570
+from tensorflow.python.ops.gen_training_ops import SparseApplyAdadelta # line: 3646
+from tensorflow.python.ops.gen_training_ops import SparseApplyAdagrad # line: 3709
+from tensorflow.python.ops.gen_training_ops import SparseApplyAdagradDA # line: 3773
+from tensorflow.python.ops.gen_training_ops import SparseApplyAdagradV2 # line: 3839
+from tensorflow.python.ops.gen_training_ops import SparseApplyCenteredRMSProp # line: 3921
+from tensorflow.python.ops.gen_training_ops import SparseApplyFtrl # line: 3997
+from tensorflow.python.ops.gen_training_ops import SparseApplyFtrlV2 # line: 4077
+from tensorflow.python.ops.gen_training_ops import SparseApplyMomentum # line: 4149
+from tensorflow.python.ops.gen_training_ops import SparseApplyProximalAdagrad # line: 4212
+from tensorflow.python.ops.gen_training_ops import SparseApplyProximalGradientDescent # line: 4273
+from tensorflow.python.ops.gen_training_ops import SparseApplyRMSProp # line: 4346
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformDequantize # line: 111
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformQuantize # line: 223
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformQuantizedAdd # line: 432
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformQuantizedClipByValue # line: 562
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformQuantizedConvolution # line: 879
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformQuantizedConvolutionHybrid # line: 1174
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformQuantizedDot # line: 1422
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformQuantizedDotHybrid # line: 1565
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformRequantize # line: 1732
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import CSRSparseMatrixComponents # line: 84
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import CSRSparseMatrixToDense # line: 146
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import CSRSparseMatrixToSparseTensor # line: 218
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import DenseToCSRSparseMatrix # line: 279
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixAdd # line: 344
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixMatMul # line: 476
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixMul # line: 563
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixNNZ # line: 618
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixOrderingAMD # line: 722
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixSoftmax # line: 787
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixSoftmaxGrad # line: 849
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixSparseCholesky # line: 992
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixSparseMatMul # line: 1162
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixTranspose # line: 1247
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixZeros # line: 1309
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseTensorToCSRSparseMatrix # line: 1371
+from tensorflow.python.user_ops.ops.gen_user_ops import Fact # line: 62
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/saved_model/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/saved_model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..729df39995f4e7b6170a368e1abaf5f17b1e60aa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/saved_model/__init__.py
@@ -0,0 +1,38 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.saved_model namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.saved_model import experimental
+from tensorflow.python.saved_model.constants import ASSETS_DIRECTORY # line: 26
+from tensorflow.python.saved_model.constants import ASSETS_KEY # line: 37
+from tensorflow.python.saved_model.constants import DEBUG_DIRECTORY # line: 97
+from tensorflow.python.saved_model.constants import DEBUG_INFO_FILENAME_PB # line: 107
+from tensorflow.python.saved_model.constants import SAVED_MODEL_FILENAME_PB # line: 76
+from tensorflow.python.saved_model.constants import SAVED_MODEL_FILENAME_PBTXT # line: 88
+from tensorflow.python.saved_model.constants import SAVED_MODEL_SCHEMA_VERSION # line: 64
+from tensorflow.python.saved_model.constants import VARIABLES_DIRECTORY # line: 116
+from tensorflow.python.saved_model.constants import VARIABLES_FILENAME # line: 125
+from tensorflow.python.saved_model.load import load # line: 819
+from tensorflow.python.saved_model.load_options import LoadOptions # line: 21
+from tensorflow.python.saved_model.loader_impl import contains_saved_model # line: 254
+from tensorflow.python.saved_model.save import save # line: 1145
+from tensorflow.python.saved_model.save_options import SaveOptions # line: 92
+from tensorflow.python.saved_model.signature_constants import CLASSIFY_INPUTS # line: 38
+from tensorflow.python.saved_model.signature_constants import CLASSIFY_METHOD_NAME # line: 47
+from tensorflow.python.saved_model.signature_constants import CLASSIFY_OUTPUT_CLASSES # line: 56
+from tensorflow.python.saved_model.signature_constants import CLASSIFY_OUTPUT_SCORES # line: 65
+from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY # line: 25
+from tensorflow.python.saved_model.signature_constants import PREDICT_INPUTS # line: 77
+from tensorflow.python.saved_model.signature_constants import PREDICT_METHOD_NAME # line: 86
+from tensorflow.python.saved_model.signature_constants import PREDICT_OUTPUTS # line: 95
+from tensorflow.python.saved_model.signature_constants import REGRESS_INPUTS # line: 107
+from tensorflow.python.saved_model.signature_constants import REGRESS_METHOD_NAME # line: 116
+from tensorflow.python.saved_model.signature_constants import REGRESS_OUTPUTS # line: 125
+from tensorflow.python.saved_model.tag_constants import GPU # line: 44
+from tensorflow.python.saved_model.tag_constants import SERVING # line: 23
+from tensorflow.python.saved_model.tag_constants import TPU # line: 51
+from tensorflow.python.saved_model.tag_constants import TRAINING # line: 31
+from tensorflow.python.trackable.asset import Asset # line: 30
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/saved_model/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/saved_model/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..40daaf08990f910291bd6115b3696b52a48702e6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/saved_model/experimental/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.saved_model.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.saved_model.fingerprinting import Fingerprint # line: 26
+from tensorflow.python.saved_model.fingerprinting import read_fingerprint # line: 149
+from tensorflow.python.saved_model.save_options import VariablePolicy # line: 26
+from tensorflow.python.trackable.resource import TrackableResource # line: 233
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/sets/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/sets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d0e25911cd93fe6da9a471cce9f1ae078dd3ac1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/sets/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.sets namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.sets_impl import set_difference as difference # line: 208
+from tensorflow.python.ops.sets_impl import set_intersection as intersection # line: 137
+from tensorflow.python.ops.sets_impl import set_size as size # line: 30
+from tensorflow.python.ops.sets_impl import set_union as union # line: 289
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/signal/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/signal/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..75bf63034ff4df03813c3cefd775e85e6641d868
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/signal/__init__.py
@@ -0,0 +1,41 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.signal namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.gen_spectral_ops import fft # line: 353
+from tensorflow.python.ops.gen_spectral_ops import fft2d # line: 442
+from tensorflow.python.ops.gen_spectral_ops import fft3d # line: 531
+from tensorflow.python.ops.gen_spectral_ops import ifft # line: 724
+from tensorflow.python.ops.gen_spectral_ops import ifft2d # line: 813
+from tensorflow.python.ops.gen_spectral_ops import ifft3d # line: 902
+from tensorflow.python.ops.signal.dct_ops import dct # line: 50
+from tensorflow.python.ops.signal.dct_ops import idct # line: 212
+from tensorflow.python.ops.signal.fft_ops import fftnd # line: 400
+from tensorflow.python.ops.signal.fft_ops import fftshift # line: 603
+from tensorflow.python.ops.signal.fft_ops import ifftnd # line: 404
+from tensorflow.python.ops.signal.fft_ops import ifftshift # line: 648
+from tensorflow.python.ops.signal.fft_ops import irfft # line: 411
+from tensorflow.python.ops.signal.fft_ops import irfft2d # line: 417
+from tensorflow.python.ops.signal.fft_ops import irfft3d # line: 423
+from tensorflow.python.ops.signal.fft_ops import irfftnd # line: 430
+from tensorflow.python.ops.signal.fft_ops import rfft # line: 408
+from tensorflow.python.ops.signal.fft_ops import rfft2d # line: 414
+from tensorflow.python.ops.signal.fft_ops import rfft3d # line: 420
+from tensorflow.python.ops.signal.fft_ops import rfftnd # line: 426
+from tensorflow.python.ops.signal.mel_ops import linear_to_mel_weight_matrix # line: 90
+from tensorflow.python.ops.signal.mfcc_ops import mfccs_from_log_mel_spectrograms # line: 25
+from tensorflow.python.ops.signal.reconstruction_ops import overlap_and_add # line: 26
+from tensorflow.python.ops.signal.shape_ops import frame # line: 55
+from tensorflow.python.ops.signal.spectral_ops import inverse_mdct # line: 367
+from tensorflow.python.ops.signal.spectral_ops import inverse_stft # line: 155
+from tensorflow.python.ops.signal.spectral_ops import inverse_stft_window_fn # line: 95
+from tensorflow.python.ops.signal.spectral_ops import mdct # line: 291
+from tensorflow.python.ops.signal.spectral_ops import stft # line: 34
+from tensorflow.python.ops.signal.window_ops import hamming_window # line: 171
+from tensorflow.python.ops.signal.window_ops import hann_window # line: 145
+from tensorflow.python.ops.signal.window_ops import kaiser_bessel_derived_window # line: 92
+from tensorflow.python.ops.signal.window_ops import kaiser_window # line: 53
+from tensorflow.python.ops.signal.window_ops import vorbis_window # line: 120
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/sparse/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/sparse/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9710725d5c87fddbf25edbe45d961e8e446953c2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/sparse/__init__.py
@@ -0,0 +1,37 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.sparse namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.sparse_tensor import SparseTensor # line: 47
+from tensorflow.python.ops.array_ops import sparse_mask as mask # line: 1845
+from tensorflow.python.ops.math_ops import sparse_segment_mean_v2 as segment_mean # line: 5003
+from tensorflow.python.ops.math_ops import sparse_segment_sqrt_n_v2 as segment_sqrt_n # line: 5105
+from tensorflow.python.ops.math_ops import sparse_segment_sum_v2 as segment_sum # line: 4861
+from tensorflow.python.ops.sparse_ops import sparse_add_v2 as add # line: 524
+from tensorflow.python.ops.sparse_ops import sparse_bincount as bincount # line: 3205
+from tensorflow.python.ops.sparse_ops import sparse_concat_v2 as concat # line: 400
+from tensorflow.python.ops.sparse_ops import sparse_cross as cross # line: 608
+from tensorflow.python.ops.sparse_ops import sparse_cross_hashed as cross_hashed # line: 666
+from tensorflow.python.ops.sparse_ops import sparse_expand_dims as expand_dims # line: 143
+from tensorflow.python.ops.sparse_ops import sparse_eye as eye # line: 249
+from tensorflow.python.ops.sparse_ops import sparse_fill_empty_rows as fill_empty_rows # line: 2109
+from tensorflow.python.ops.sparse_ops import from_dense # line: 111
+from tensorflow.python.ops.sparse_ops import map_values # line: 2932
+from tensorflow.python.ops.sparse_ops import sparse_maximum as maximum # line: 2734
+from tensorflow.python.ops.sparse_ops import sparse_minimum as minimum # line: 2780
+from tensorflow.python.ops.sparse_ops import sparse_reduce_max_v2 as reduce_max # line: 1246
+from tensorflow.python.ops.sparse_ops import sparse_reduce_sum_v2 as reduce_sum # line: 1480
+from tensorflow.python.ops.sparse_ops import sparse_reorder as reorder # line: 821
+from tensorflow.python.ops.sparse_ops import sparse_reset_shape as reset_shape # line: 2004
+from tensorflow.python.ops.sparse_ops import sparse_reshape as reshape # line: 876
+from tensorflow.python.ops.sparse_ops import sparse_retain as retain # line: 1957
+from tensorflow.python.ops.sparse_ops import sparse_slice as slice # line: 1136
+from tensorflow.python.ops.sparse_ops import sparse_softmax as softmax # line: 2671
+from tensorflow.python.ops.sparse_ops import sparse_tensor_dense_matmul as sparse_dense_matmul # line: 2430
+from tensorflow.python.ops.sparse_ops import sparse_split_v2 as split # line: 1067
+from tensorflow.python.ops.sparse_ops import sparse_tensor_to_dense as to_dense # line: 1681
+from tensorflow.python.ops.sparse_ops import sparse_to_indicator as to_indicator # line: 1737
+from tensorflow.python.ops.sparse_ops import sparse_transpose as transpose # line: 2824
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/strings/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/strings/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7fd90a27f3293a73be560b9cde37aab2639f134
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/strings/__init__.py
@@ -0,0 +1,33 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.strings namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.gen_string_ops import as_string # line: 29
+from tensorflow.python.ops.gen_string_ops import string_lower as lower # line: 1009
+from tensorflow.python.ops.gen_string_ops import string_strip as strip # line: 1429
+from tensorflow.python.ops.gen_string_ops import string_to_hash_bucket_fast as to_hash_bucket_fast # line: 1583
+from tensorflow.python.ops.gen_string_ops import string_to_hash_bucket_strong as to_hash_bucket_strong # line: 1688
+from tensorflow.python.ops.gen_string_ops import unicode_script # line: 2477
+from tensorflow.python.ops.gen_string_ops import unicode_transcode # line: 2578
+from tensorflow.python.ops.gen_string_ops import string_upper as upper # line: 1814
+from tensorflow.python.ops.ragged.ragged_string_ops import string_bytes_split as bytes_split # line: 40
+from tensorflow.python.ops.ragged.ragged_string_ops import ngrams # line: 672
+from tensorflow.python.ops.ragged.ragged_string_ops import string_split_v2 as split # line: 466
+from tensorflow.python.ops.ragged.ragged_string_ops import unicode_decode # line: 186
+from tensorflow.python.ops.ragged.ragged_string_ops import unicode_decode_with_offsets # line: 232
+from tensorflow.python.ops.ragged.ragged_string_ops import unicode_encode # line: 88
+from tensorflow.python.ops.ragged.ragged_string_ops import unicode_split # line: 294
+from tensorflow.python.ops.ragged.ragged_string_ops import unicode_split_with_offsets # line: 342
+from tensorflow.python.ops.string_ops import string_format as format # line: 115
+from tensorflow.python.ops.string_ops import string_join as join # line: 551
+from tensorflow.python.ops.string_ops import string_length_v2 as length # line: 412
+from tensorflow.python.ops.string_ops import reduce_join_v2 as reduce_join # line: 332
+from tensorflow.python.ops.string_ops import regex_full_match # line: 47
+from tensorflow.python.ops.string_ops import regex_replace # line: 74
+from tensorflow.python.ops.string_ops import substr_v2 as substr # line: 441
+from tensorflow.python.ops.string_ops import string_to_hash_bucket as to_hash_bucket # line: 505
+from tensorflow.python.ops.string_ops import string_to_number as to_number # line: 462
+from tensorflow.python.ops.string_ops import unsorted_segment_join # line: 586
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/summary/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/summary/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..825d6478cc4d67fe47c8eb30708c3923aa7bb37f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/summary/__init__.py
@@ -0,0 +1,19 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.summary namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.summary import experimental
+from tensorflow.python.ops.summary_ops_v2 import SummaryWriter # line: 239
+from tensorflow.python.ops.summary_ops_v2 import create_file_writer_v2 as create_file_writer # line: 511
+from tensorflow.python.ops.summary_ops_v2 import create_noop_writer # line: 636
+from tensorflow.python.ops.summary_ops_v2 import flush # line: 1136
+from tensorflow.python.ops.summary_ops_v2 import graph # line: 1048
+from tensorflow.python.ops.summary_ops_v2 import record_if # line: 152
+from tensorflow.python.ops.summary_ops_v2 import should_record_summaries # line: 133
+from tensorflow.python.ops.summary_ops_v2 import trace_export # line: 1374
+from tensorflow.python.ops.summary_ops_v2 import trace_off # line: 1425
+from tensorflow.python.ops.summary_ops_v2 import trace_on # line: 1329
+from tensorflow.python.ops.summary_ops_v2 import write # line: 732
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/summary/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/summary/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7152cb936f4610286a0acf0c4dccd669f0c7132
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/summary/experimental/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.summary.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.summary_ops_v2 import get_step # line: 209
+from tensorflow.python.ops.summary_ops_v2 import set_step # line: 220
+from tensorflow.python.ops.summary_ops_v2 import summary_scope # line: 691
+from tensorflow.python.ops.summary_ops_v2 import write_raw_pb # line: 809
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/sysconfig/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/sysconfig/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa18e873703ecf78b4671ef9145116a150d51f08
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/sysconfig/__init__.py
@@ -0,0 +1,15 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.sysconfig namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.versions import CXX11_ABI_FLAG # line: 48
+from tensorflow.python.framework.versions import CXX_VERSION # line: 54
+from tensorflow.python.framework.versions import MONOLITHIC_BUILD # line: 60
+from tensorflow.python.platform.sysconfig import get_build_info # line: 122
+from tensorflow.python.platform.sysconfig import get_compile_flags # line: 59
+from tensorflow.python.platform.sysconfig import get_include # line: 33
+from tensorflow.python.platform.sysconfig import get_lib # line: 48
+from tensorflow.python.platform.sysconfig import get_link_flags # line: 94
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/test/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/test/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..22fc0e394dfdd0f90f73807b3058bc335f3a4011
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/test/__init__.py
@@ -0,0 +1,23 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.test namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.test import experimental
+from tensorflow.python.framework.test_util import TensorFlowTestCase as TestCase # line: 2504
+from tensorflow.python.framework.test_util import assert_equal_graph_def_v2 as assert_equal_graph_def # line: 205
+from tensorflow.python.framework.test_util import create_local_cluster # line: 3921
+from tensorflow.python.framework.test_util import gpu_device_name # line: 156
+from tensorflow.python.framework.test_util import is_gpu_available # line: 1989
+from tensorflow.python.framework.test_util import with_eager_op_as_function # line: 1219
+from tensorflow.python.ops.gradient_checker_v2 import compute_gradient # line: 295
+from tensorflow.python.platform.benchmark import TensorFlowBenchmark as Benchmark # line: 287
+from tensorflow.python.platform.benchmark import benchmark_config # line: 274
+from tensorflow.python.platform.test import disable_with_predicate # line: 131
+from tensorflow.python.platform.test import is_built_with_cuda # line: 89
+from tensorflow.python.platform.test import is_built_with_gpu_support # line: 149
+from tensorflow.python.platform.test import is_built_with_rocm # line: 110
+from tensorflow.python.platform.test import is_built_with_xla # line: 170
+from tensorflow.python.platform.test import main # line: 49
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/test/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/test/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff027d362fbd63221354d71b6ea76dff623f107d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/test/experimental/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.test.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.test_util import sync_devices # line: 4146
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/tpu/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/tpu/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..10fd5d34bff32d9d22acd4a70378e21cb3c45417
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/tpu/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.tpu namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.tpu import experimental
+from tensorflow.python.tpu.tpu import XLAOptions # line: 230
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/tpu/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/tpu/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b503f33182280abfc4431d208297c4b224cf667f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/tpu/experimental/__init__.py
@@ -0,0 +1,15 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.tpu.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.tpu.experimental import embedding
+from tensorflow.python.distribute.cluster_resolver.tpu_cluster_resolver import initialize_tpu_system # line: 25
+from tensorflow.python.distribute.cluster_resolver.tpu_cluster_resolver import shutdown_tpu_system # line: 26
+from tensorflow.python.tpu.device_assignment import DeviceAssignment # line: 68
+from tensorflow.python.tpu.device_assignment import DeviceOrderMode # line: 56
+from tensorflow.python.tpu.topology import Topology # line: 39
+from tensorflow.python.tpu.tpu_hardware_feature import HardwareFeature # line: 21
+from tensorflow.python.tpu.tpu_system_metadata import TPUSystemMetadata # line: 40
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/tpu/experimental/embedding/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/tpu/experimental/embedding/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e0c85f7996d7881327306a678031b4ebffdda6f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/tpu/experimental/embedding/__init__.py
@@ -0,0 +1,19 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.tpu.experimental.embedding namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.tpu.tpu_embedding_for_serving import TPUEmbeddingForServing # line: 42
+from tensorflow.python.tpu.tpu_embedding_for_serving import cpu_embedding_lookup as serving_embedding_lookup # line: 335
+from tensorflow.python.tpu.tpu_embedding_v1 import TPUEmbeddingV0 # line: 38
+from tensorflow.python.tpu.tpu_embedding_v2 import TPUEmbedding # line: 75
+from tensorflow.python.tpu.tpu_embedding_v2_utils import Adagrad # line: 302
+from tensorflow.python.tpu.tpu_embedding_v2_utils import AdagradMomentum # line: 431
+from tensorflow.python.tpu.tpu_embedding_v2_utils import Adam # line: 752
+from tensorflow.python.tpu.tpu_embedding_v2_utils import FTRL # line: 578
+from tensorflow.python.tpu.tpu_embedding_v2_utils import FeatureConfig # line: 1161
+from tensorflow.python.tpu.tpu_embedding_v2_utils import QuantizationConfig # line: 920
+from tensorflow.python.tpu.tpu_embedding_v2_utils import SGD # line: 186
+from tensorflow.python.tpu.tpu_embedding_v2_utils import TableConfig # line: 984
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/train/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/train/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b319845c4f4bdcd3d442e8785b65ed08d4f33d01
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/train/__init__.py
@@ -0,0 +1,34 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.train namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.train import experimental
+from tensorflow.python.checkpoint.checkpoint import Checkpoint # line: 2035
+from tensorflow.python.checkpoint.checkpoint_management import CheckpointManager # line: 518
+from tensorflow.python.checkpoint.checkpoint_management import get_checkpoint_state # line: 250
+from tensorflow.python.checkpoint.checkpoint_management import latest_checkpoint # line: 328
+from tensorflow.python.checkpoint.checkpoint_options import CheckpointOptions # line: 24
+from tensorflow.python.checkpoint.checkpoint_view import CheckpointView # line: 28
+from tensorflow.python.checkpoint.trackable_view import TrackableView # line: 25
+from tensorflow.python.training.checkpoint_utils import checkpoints_iterator # line: 181
+from tensorflow.python.training.checkpoint_utils import list_variables # line: 117
+from tensorflow.python.training.checkpoint_utils import load_checkpoint # line: 46
+from tensorflow.python.training.checkpoint_utils import load_variable # line: 83
+from tensorflow.python.training.coordinator import Coordinator # line: 27
+from tensorflow.python.training.moving_averages import ExponentialMovingAverage # line: 283
+from tensorflow.python.training.server_lib import ClusterSpec # line: 242
+from tensorflow.python.training.training import BytesList # line: 131
+from tensorflow.python.training.training import ClusterDef # line: 132
+from tensorflow.python.training.training import Example # line: 133
+from tensorflow.python.training.training import Feature # line: 134
+from tensorflow.python.training.training import FeatureList # line: 136
+from tensorflow.python.training.training import FeatureLists # line: 137
+from tensorflow.python.training.training import Features # line: 135
+from tensorflow.python.training.training import FloatList # line: 138
+from tensorflow.python.training.training import Int64List # line: 139
+from tensorflow.python.training.training import JobDef # line: 140
+from tensorflow.python.training.training import SequenceExample # line: 142
+from tensorflow.python.training.training import ServerDef # line: 143
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/train/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/train/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a31469418cf82e8e03dd3ad573af2056b35ec72
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/train/experimental/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.train.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.trackable.python_state import PythonState # line: 28
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/types/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/types/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0660b095ff579443a289dac3f27e6d666416effd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/types/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.types namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.types import experimental
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/types/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/types/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a089f943a3826f41920222d4de34d2d207509136
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/types/experimental/__init__.py
@@ -0,0 +1,17 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.types.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.types.experimental import distributed
+from tensorflow.python.types.core import AtomicFunction # line: 143
+from tensorflow.python.types.core import Callable # line: 112
+from tensorflow.python.types.core import ConcreteFunction # line: 170
+from tensorflow.python.types.core import FunctionType # line: 91
+from tensorflow.python.types.core import PolymorphicFunction as GenericFunction # line: 187
+from tensorflow.python.types.core import PolymorphicFunction # line: 187
+from tensorflow.python.types.core import TensorLike # line: 419
+from tensorflow.python.types.trace import SupportsTracingProtocol # line: 307
+from tensorflow.python.types.trace import TraceType # line: 39
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/types/experimental/distributed/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/types/experimental/distributed/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1db2dff92629c06b05e0a4dc3472003807513696
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/types/experimental/distributed/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.types.experimental.distributed namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.types.distribute import Mirrored # line: 182
+from tensorflow.python.types.distribute import PerReplica # line: 169
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/version/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/version/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d494fbe6c828c9f311a543b9882150ad2ab33672
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/version/__init__.py
@@ -0,0 +1,13 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.version namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.versions import COMPILER_VERSION # line: 41
+from tensorflow.python.framework.versions import GIT_VERSION # line: 35
+from tensorflow.python.framework.versions import GRAPH_DEF_VERSION # line: 68
+from tensorflow.python.framework.versions import GRAPH_DEF_VERSION_MIN_CONSUMER # line: 74
+from tensorflow.python.framework.versions import GRAPH_DEF_VERSION_MIN_PRODUCER # line: 82
+from tensorflow.python.framework.versions import VERSION # line: 29
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/xla/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/xla/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..794db274ec42e8ab9412ab9cecd4032193343f50
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/xla/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.xla namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.compat.v2.xla import experimental
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/xla/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/xla/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2c1148ed178739e4a069fc1d4ef5696ec5d8841
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/compat/v2/xla/experimental/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.xla.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.compiler.xla.jit import experimental_jit_scope as jit_scope # line: 37
+from tensorflow.python.compiler.xla.xla import compile # line: 62
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/config/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..75564a3dc4fac135e688c7337f147c2578c6ddec
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/config/__init__.py
@@ -0,0 +1,27 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.config namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.config import experimental
+from tensorflow._api.v2.config import optimizer
+from tensorflow._api.v2.config import threading
+from tensorflow.python.eager.context import LogicalDevice # line: 290
+from tensorflow.python.eager.context import LogicalDeviceConfiguration # line: 308
+from tensorflow.python.eager.context import PhysicalDevice # line: 345
+from tensorflow.python.eager.polymorphic_function.eager_function_run import experimental_functions_run_eagerly # line: 108
+from tensorflow.python.eager.polymorphic_function.eager_function_run import experimental_run_functions_eagerly # line: 82
+from tensorflow.python.eager.polymorphic_function.eager_function_run import functions_run_eagerly # line: 25
+from tensorflow.python.eager.polymorphic_function.eager_function_run import run_functions_eagerly # line: 31
+from tensorflow.python.eager.remote import connect_to_cluster as experimental_connect_to_cluster # line: 76
+from tensorflow.python.eager.remote import connect_to_remote_host as experimental_connect_to_host # line: 37
+from tensorflow.python.framework.config import get_logical_device_configuration # line: 802
+from tensorflow.python.framework.config import get_soft_device_placement # line: 257
+from tensorflow.python.framework.config import get_visible_devices # line: 500
+from tensorflow.python.framework.config import list_logical_devices # line: 461
+from tensorflow.python.framework.config import list_physical_devices # line: 424
+from tensorflow.python.framework.config import set_logical_device_configuration # line: 843
+from tensorflow.python.framework.config import set_soft_device_placement # line: 283
+from tensorflow.python.framework.config import set_visible_devices # line: 533
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/config/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/config/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..47b2375fa234f413b58109b5e20c4303269866b2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/config/experimental/__init__.py
@@ -0,0 +1,30 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.config.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.eager.context import LogicalDeviceConfiguration as VirtualDeviceConfiguration # line: 308
+from tensorflow.python.framework.config import disable_mlir_bridge # line: 926
+from tensorflow.python.framework.config import enable_mlir_bridge # line: 915
+from tensorflow.python.framework.config import enable_op_determinism # line: 932
+from tensorflow.python.framework.config import enable_tensor_float_32_execution # line: 46
+from tensorflow.python.framework.config import get_device_details # line: 757
+from tensorflow.python.framework.config import get_device_policy # line: 312
+from tensorflow.python.framework.config import get_memory_growth # line: 700
+from tensorflow.python.framework.config import get_memory_info # line: 571
+from tensorflow.python.framework.config import get_memory_usage # line: 664
+from tensorflow.python.framework.config import get_synchronous_execution # line: 386
+from tensorflow.python.framework.config import get_logical_device_configuration as get_virtual_device_configuration # line: 802
+from tensorflow.python.framework.config import get_visible_devices # line: 500
+from tensorflow.python.framework.config import list_logical_devices # line: 461
+from tensorflow.python.framework.config import list_physical_devices # line: 424
+from tensorflow.python.framework.config import reset_memory_stats # line: 620
+from tensorflow.python.framework.config import set_device_policy # line: 340
+from tensorflow.python.framework.config import set_memory_growth # line: 729
+from tensorflow.python.framework.config import set_synchronous_execution # line: 399
+from tensorflow.python.framework.config import set_logical_device_configuration as set_virtual_device_configuration # line: 843
+from tensorflow.python.framework.config import set_visible_devices # line: 533
+from tensorflow.python.framework.config import tensor_float_32_execution_enabled # line: 27
+from tensorflow.python.training.server_lib import ClusterDeviceFilters # line: 495
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/config/optimizer/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/config/optimizer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..99ac9b167e6467f647d0bf99079ec8048399c91f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/config/optimizer/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.config.optimizer namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.config import get_optimizer_experimental_options as get_experimental_options # line: 198
+from tensorflow.python.framework.config import get_optimizer_jit as get_jit # line: 158
+from tensorflow.python.framework.config import set_optimizer_experimental_options as set_experimental_options # line: 214
+from tensorflow.python.framework.config import set_optimizer_jit as set_jit # line: 172
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/config/threading/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/config/threading/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e912751472b5ee8cecd9df43957afe403ac6c083
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/config/threading/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.config.threading namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.config import get_inter_op_parallelism_threads # line: 132
+from tensorflow.python.framework.config import get_intra_op_parallelism_threads # line: 104
+from tensorflow.python.framework.config import set_inter_op_parallelism_threads # line: 145
+from tensorflow.python.framework.config import set_intra_op_parallelism_threads # line: 118
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/data/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..728bba8fe353930cc785b02e0e0397dc9a0c0e87
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/data/__init__.py
@@ -0,0 +1,21 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.data namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.data import experimental
+from tensorflow.python.data.ops.dataset_ops import AUTOTUNE # line: 102
+from tensorflow.python.data.ops.dataset_ops import DatasetV2 as Dataset # line: 135
+from tensorflow.python.data.ops.dataset_ops import DatasetSpec # line: 4542
+from tensorflow.python.data.ops.dataset_ops import INFINITE as INFINITE_CARDINALITY # line: 112
+from tensorflow.python.data.ops.dataset_ops import NumpyIterator # line: 4707
+from tensorflow.python.data.ops.dataset_ops import UNKNOWN as UNKNOWN_CARDINALITY # line: 113
+from tensorflow.python.data.ops.iterator_ops import IteratorBase as Iterator # line: 557
+from tensorflow.python.data.ops.iterator_ops import IteratorSpec # line: 913
+from tensorflow.python.data.ops.options import Options # line: 493
+from tensorflow.python.data.ops.options import ThreadingOptions # line: 449
+from tensorflow.python.data.ops.readers import FixedLengthRecordDatasetV2 as FixedLengthRecordDataset # line: 569
+from tensorflow.python.data.ops.readers import TFRecordDatasetV2 as TFRecordDataset # line: 386
+from tensorflow.python.data.ops.readers import TextLineDatasetV2 as TextLineDataset # line: 171
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/data/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/data/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2057f63f9dbb87393bf094a25dd77681bc22cac1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/data/experimental/__init__.py
@@ -0,0 +1,67 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.data.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.data.experimental import service
+from tensorflow.python.data.experimental.ops.batching import dense_to_ragged_batch # line: 30
+from tensorflow.python.data.experimental.ops.batching import dense_to_sparse_batch # line: 94
+from tensorflow.python.data.experimental.ops.batching import map_and_batch # line: 210
+from tensorflow.python.data.experimental.ops.batching import unbatch # line: 269
+from tensorflow.python.data.experimental.ops.cardinality import INFINITE as INFINITE_CARDINALITY # line: 26
+from tensorflow.python.data.experimental.ops.cardinality import UNKNOWN as UNKNOWN_CARDINALITY # line: 28
+from tensorflow.python.data.experimental.ops.cardinality import assert_cardinality # line: 67
+from tensorflow.python.data.experimental.ops.cardinality import cardinality # line: 33
+from tensorflow.python.data.experimental.ops.counter import CounterV2 as Counter # line: 23
+from tensorflow.python.data.experimental.ops.distribute import SHARD_HINT # line: 31
+from tensorflow.python.data.experimental.ops.enumerate_ops import enumerate_dataset # line: 21
+from tensorflow.python.data.experimental.ops.error_ops import ignore_errors # line: 20
+from tensorflow.python.data.experimental.ops.from_list import from_list # line: 75
+from tensorflow.python.data.experimental.ops.get_single_element import get_single_element # line: 22
+from tensorflow.python.data.experimental.ops.grouping import Reducer # line: 388
+from tensorflow.python.data.experimental.ops.grouping import bucket_by_sequence_length # line: 112
+from tensorflow.python.data.experimental.ops.grouping import group_by_reducer # line: 28
+from tensorflow.python.data.experimental.ops.grouping import group_by_window # line: 59
+from tensorflow.python.data.experimental.ops.interleave_ops import choose_from_datasets_v2 as choose_from_datasets # line: 175
+from tensorflow.python.data.experimental.ops.interleave_ops import parallel_interleave # line: 28
+from tensorflow.python.data.experimental.ops.interleave_ops import sample_from_datasets_v2 as sample_from_datasets # line: 91
+from tensorflow.python.data.experimental.ops.io import load # line: 108
+from tensorflow.python.data.experimental.ops.io import save # line: 26
+from tensorflow.python.data.experimental.ops.iterator_ops import CheckpointInputPipelineHook # line: 105
+from tensorflow.python.data.experimental.ops.iterator_ops import make_saveable_from_iterator # line: 43
+from tensorflow.python.data.experimental.ops.lookup_ops import DatasetInitializer # line: 54
+from tensorflow.python.data.experimental.ops.lookup_ops import index_table_from_dataset # line: 190
+from tensorflow.python.data.experimental.ops.lookup_ops import table_from_dataset # line: 102
+from tensorflow.python.data.experimental.ops.pad_to_cardinality import pad_to_cardinality # line: 26
+from tensorflow.python.data.experimental.ops.parsing_ops import parse_example_dataset # line: 105
+from tensorflow.python.data.experimental.ops.prefetching_ops import copy_to_device # line: 65
+from tensorflow.python.data.experimental.ops.prefetching_ops import prefetch_to_device # line: 33
+from tensorflow.python.data.experimental.ops.random_access import at # line: 22
+from tensorflow.python.data.experimental.ops.random_ops import RandomDatasetV2 as RandomDataset # line: 27
+from tensorflow.python.data.experimental.ops.readers import CsvDatasetV2 as CsvDataset # line: 668
+from tensorflow.python.data.experimental.ops.readers import SqlDatasetV2 as SqlDataset # line: 1156
+from tensorflow.python.data.experimental.ops.readers import make_batched_features_dataset_v2 as make_batched_features_dataset # line: 916
+from tensorflow.python.data.experimental.ops.readers import make_csv_dataset_v2 as make_csv_dataset # line: 326
+from tensorflow.python.data.experimental.ops.resampling import rejection_resample # line: 21
+from tensorflow.python.data.experimental.ops.scan_ops import scan # line: 21
+from tensorflow.python.data.experimental.ops.shuffle_ops import shuffle_and_repeat # line: 62
+from tensorflow.python.data.experimental.ops.snapshot import snapshot # line: 190
+from tensorflow.python.data.experimental.ops.take_while_ops import take_while # line: 21
+from tensorflow.python.data.experimental.ops.unique import unique # line: 21
+from tensorflow.python.data.experimental.ops.writers import TFRecordWriter # line: 27
+from tensorflow.python.data.ops.dataset_ops import AUTOTUNE # line: 104
+from tensorflow.python.data.ops.dataset_ops import from_variant # line: 4514
+from tensorflow.python.data.ops.dataset_ops import get_structure # line: 4349
+from tensorflow.python.data.ops.dataset_ops import to_variant # line: 4529
+from tensorflow.python.data.ops.debug_mode import enable_debug_mode # line: 24
+from tensorflow.python.data.ops.iterator_ops import get_next_as_optional # line: 999
+from tensorflow.python.data.ops.optional_ops import Optional # line: 31
+from tensorflow.python.data.ops.options import AutoShardPolicy # line: 88
+from tensorflow.python.data.ops.options import AutotuneAlgorithm # line: 30
+from tensorflow.python.data.ops.options import AutotuneOptions # line: 193
+from tensorflow.python.data.ops.options import DistributeOptions # line: 262
+from tensorflow.python.data.ops.options import ExternalStatePolicy # line: 155
+from tensorflow.python.data.ops.options import OptimizationOptions # line: 304
+from tensorflow.python.data.ops.options import ThreadingOptions # line: 449
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/data/experimental/service/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/data/experimental/service/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..40450071641553532311f453e8900bd584061343
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/data/experimental/service/__init__.py
@@ -0,0 +1,16 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.data.experimental.service namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.data.experimental.ops.data_service_ops import CrossTrainerCache # line: 117
+from tensorflow.python.data.experimental.ops.data_service_ops import ShardingPolicy # line: 46
+from tensorflow.python.data.experimental.ops.data_service_ops import distribute # line: 531
+from tensorflow.python.data.experimental.ops.data_service_ops import from_dataset_id # line: 1045
+from tensorflow.python.data.experimental.ops.data_service_ops import register_dataset # line: 839
+from tensorflow.python.data.experimental.service.server_lib import DispatchServer # line: 130
+from tensorflow.python.data.experimental.service.server_lib import DispatcherConfig # line: 44
+from tensorflow.python.data.experimental.service.server_lib import WorkerConfig # line: 292
+from tensorflow.python.data.experimental.service.server_lib import WorkerServer # line: 348
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/debugging/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/debugging/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e00241aec08ade38a18a7084ae5b2bd159de0c93
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/debugging/__init__.py
@@ -0,0 +1,39 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.debugging namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.debugging import experimental
+from tensorflow.python.ops.gen_array_ops import check_numerics # line: 950
+from tensorflow.python.debug.lib.check_numerics_callback import disable_check_numerics # line: 445
+from tensorflow.python.debug.lib.check_numerics_callback import enable_check_numerics # line: 336
+from tensorflow.python.eager.context import get_log_device_placement # line: 2520
+from tensorflow.python.eager.context import set_log_device_placement # line: 2530
+from tensorflow.python.ops.check_ops import assert_equal_v2 as assert_equal # line: 762
+from tensorflow.python.ops.check_ops import assert_greater_v2 as assert_greater # line: 978
+from tensorflow.python.ops.check_ops import assert_greater_equal_v2 as assert_greater_equal # line: 996
+from tensorflow.python.ops.check_ops import assert_integer_v2 as assert_integer # line: 1427
+from tensorflow.python.ops.check_ops import assert_less_v2 as assert_less # line: 942
+from tensorflow.python.ops.check_ops import assert_less_equal_v2 as assert_less_equal # line: 959
+from tensorflow.python.ops.check_ops import assert_near_v2 as assert_near # line: 803
+from tensorflow.python.ops.check_ops import assert_negative_v2 as assert_negative # line: 543
+from tensorflow.python.ops.check_ops import assert_non_negative_v2 as assert_non_negative # line: 650
+from tensorflow.python.ops.check_ops import assert_non_positive_v2 as assert_non_positive # line: 706
+from tensorflow.python.ops.check_ops import assert_none_equal_v2 as assert_none_equal # line: 783
+from tensorflow.python.ops.check_ops import assert_positive_v2 as assert_positive # line: 597
+from tensorflow.python.ops.check_ops import assert_proper_iterable # line: 511
+from tensorflow.python.ops.check_ops import assert_rank_v2 as assert_rank # line: 1064
+from tensorflow.python.ops.check_ops import assert_rank_at_least_v2 as assert_rank_at_least # line: 1162
+from tensorflow.python.ops.check_ops import assert_rank_in_v2 as assert_rank_in # line: 1329
+from tensorflow.python.ops.check_ops import assert_same_float_dtype # line: 2118
+from tensorflow.python.ops.check_ops import assert_scalar_v2 as assert_scalar # line: 2153
+from tensorflow.python.ops.check_ops import assert_shapes_v2 as assert_shapes # line: 1611
+from tensorflow.python.ops.check_ops import assert_type_v2 as assert_type # line: 1487
+from tensorflow.python.ops.check_ops import is_numeric_tensor # line: 1951
+from tensorflow.python.ops.control_flow_assert import Assert # line: 62
+from tensorflow.python.ops.numerics import verify_tensor_all_finite_v2 as assert_all_finite # line: 50
+from tensorflow.python.util.traceback_utils import disable_traceback_filtering # line: 76
+from tensorflow.python.util.traceback_utils import enable_traceback_filtering # line: 51
+from tensorflow.python.util.traceback_utils import is_traceback_filtering_enabled # line: 32
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/debugging/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/debugging/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..027fca471e864d65283389ff5742ad6af93e34eb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/debugging/experimental/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.debugging.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.debug.lib.dumping_callback import disable_dump_debug_info # line: 863
+from tensorflow.python.debug.lib.dumping_callback import enable_dump_debug_info # line: 672
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..110dbbd41ded24330d6d7e9be3268f7b2c399118
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/__init__.py
@@ -0,0 +1,36 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.distribute namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.distribute import cluster_resolver
+from tensorflow._api.v2.distribute import coordinator
+from tensorflow._api.v2.distribute import experimental
+from tensorflow.python.distribute.collective_all_reduce_strategy import CollectiveAllReduceStrategy as MultiWorkerMirroredStrategy # line: 56
+from tensorflow.python.distribute.cross_device_ops import CrossDeviceOps # line: 251
+from tensorflow.python.distribute.cross_device_ops import HierarchicalCopyAllReduce # line: 996
+from tensorflow.python.distribute.cross_device_ops import NcclAllReduce # line: 959
+from tensorflow.python.distribute.cross_device_ops import ReductionToOneDevice # line: 581
+from tensorflow.python.distribute.distribute_lib import InputContext # line: 841
+from tensorflow.python.distribute.distribute_lib import InputOptions # line: 1015
+from tensorflow.python.distribute.distribute_lib import InputReplicationMode # line: 824
+from tensorflow.python.distribute.distribute_lib import ReplicaContext # line: 3677
+from tensorflow.python.distribute.distribute_lib import RunOptions # line: 980
+from tensorflow.python.distribute.distribute_lib import Strategy # line: 2033
+from tensorflow.python.distribute.distribute_lib import StrategyExtendedV2 as StrategyExtended # line: 2401
+from tensorflow.python.distribute.distribute_lib import experimental_set_strategy # line: 584
+from tensorflow.python.distribute.distribute_lib import get_replica_context # line: 454
+from tensorflow.python.distribute.distribute_lib import get_strategy # line: 543
+from tensorflow.python.distribute.distribute_lib import has_strategy # line: 563
+from tensorflow.python.distribute.distribute_lib import in_cross_replica_context # line: 518
+from tensorflow.python.distribute.mirrored_strategy import MirroredStrategy # line: 199
+from tensorflow.python.distribute.one_device_strategy import OneDeviceStrategy # line: 38
+from tensorflow.python.distribute.parameter_server_strategy_v2 import ParameterServerStrategyV2 as ParameterServerStrategy # line: 72
+from tensorflow.python.distribute.reduce_util import ReduceOp # line: 23
+from tensorflow.python.distribute.tpu_strategy import TPUStrategyV2 as TPUStrategy # line: 242
+from tensorflow.python.training.server_lib import Server # line: 94
+from tensorflow.python.types.distribute import DistributedDatasetInterface as DistributedDataset # line: 307
+from tensorflow.python.types.distribute import DistributedIteratorInterface as DistributedIterator # line: 196
+from tensorflow.python.types.distribute import DistributedValues # line: 62
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/cluster_resolver/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/cluster_resolver/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e2c448e26130bec18027e6193802a70203571c0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/cluster_resolver/__init__.py
@@ -0,0 +1,15 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.distribute.cluster_resolver namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.distribute.cluster_resolver.cluster_resolver import ClusterResolver # line: 55
+from tensorflow.python.distribute.cluster_resolver.cluster_resolver import SimpleClusterResolver # line: 288
+from tensorflow.python.distribute.cluster_resolver.cluster_resolver import UnionClusterResolver as UnionResolver # line: 418
+from tensorflow.python.distribute.cluster_resolver.gce_cluster_resolver import GCEClusterResolver # line: 30
+from tensorflow.python.distribute.cluster_resolver.kubernetes_cluster_resolver import KubernetesClusterResolver # line: 23
+from tensorflow.python.distribute.cluster_resolver.slurm_cluster_resolver import SlurmClusterResolver # line: 163
+from tensorflow.python.distribute.cluster_resolver.tfconfig_cluster_resolver import TFConfigClusterResolver # line: 47
+from tensorflow.python.distribute.cluster_resolver.tpu_cluster_resolver import TPUClusterResolver # line: 24
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/coordinator/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/coordinator/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc20621cc409c281e2e4d4042af36724d380c0b0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/coordinator/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.distribute.coordinator namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.distribute.coordinator.cluster_coordinator import ClusterCoordinator # line: 1397
+from tensorflow.python.distribute.coordinator.coordinator_context import get_current_worker_index as experimental_get_current_worker_index # line: 81
+from tensorflow.python.distribute.coordinator.remote_value import RemoteValue # line: 53
+from tensorflow.python.distribute.coordinator.values import PerWorkerValues as PerWorkerValue # line: 152
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c43a0ab3dab1a28d876b37750f4fe853e347e5e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/experimental/__init__.py
@@ -0,0 +1,22 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.distribute.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.distribute.experimental import coordinator
+from tensorflow._api.v2.distribute.experimental import partitioners
+from tensorflow._api.v2.distribute.experimental import rpc
+from tensorflow.python.distribute.central_storage_strategy import CentralStorageStrategy # line: 23
+from tensorflow.python.distribute.collective_all_reduce_strategy import _CollectiveAllReduceStrategyExperimental as MultiWorkerMirroredStrategy # line: 229
+from tensorflow.python.distribute.collective_util import CommunicationImplementation as CollectiveCommunication # line: 26
+from tensorflow.python.distribute.collective_util import Hints as CollectiveHints # line: 168
+from tensorflow.python.distribute.collective_util import CommunicationImplementation # line: 26
+from tensorflow.python.distribute.collective_util import _OptionsExported as CommunicationOptions # line: 50
+from tensorflow.python.distribute.distribute_lib import ValueContext # line: 914
+from tensorflow.python.distribute.failure_handling.failure_handling import PreemptionCheckpointHandler # line: 336
+from tensorflow.python.distribute.failure_handling.failure_handling import TerminationConfig # line: 74
+from tensorflow.python.distribute.failure_handling.preemption_watcher import PreemptionWatcher # line: 44
+from tensorflow.python.distribute.parameter_server_strategy_v2 import ParameterServerStrategyV2 as ParameterServerStrategy # line: 72
+from tensorflow.python.distribute.tpu_strategy import TPUStrategy # line: 667
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/experimental/coordinator/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/experimental/coordinator/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9512c26846e5de071be7322332141cc8d767684
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/experimental/coordinator/__init__.py
@@ -0,0 +1,10 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.distribute.experimental.coordinator namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.distribute.coordinator.cluster_coordinator import ClusterCoordinator # line: 1397
+from tensorflow.python.distribute.coordinator.remote_value import RemoteValue # line: 53
+from tensorflow.python.distribute.coordinator.values import PerWorkerValues # line: 152
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/experimental/partitioners/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/experimental/partitioners/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6c0bb99b12e22643a8f05a75f5068c7bd140d30
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/experimental/partitioners/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.distribute.experimental.partitioners namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.distribute.sharded_variable import FixedShardsPartitioner # line: 83
+from tensorflow.python.distribute.sharded_variable import MaxSizePartitioner # line: 175
+from tensorflow.python.distribute.sharded_variable import MinSizePartitioner # line: 114
+from tensorflow.python.distribute.sharded_variable import Partitioner # line: 46
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/experimental/rpc/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/experimental/rpc/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..edd2fea388bb5f3fbb3eb4720d1974c9c37872ba
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/distribute/experimental/rpc/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.distribute.experimental.rpc namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.distribute.experimental.rpc.rpc_ops import Client # line: 117
+from tensorflow.python.distribute.experimental.rpc.rpc_ops import Server # line: 50
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/dtypes/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/dtypes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5dd4cab8bc5a6a73c381a00e143e36f7aee3e7f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/dtypes/__init__.py
@@ -0,0 +1,39 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.dtypes namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.dtypes import experimental
+from tensorflow.python.framework.dtypes import DType # line: 51
+from tensorflow.python.framework.dtypes import QUANTIZED_DTYPES # line: 771
+from tensorflow.python.framework.dtypes import as_dtype # line: 793
+from tensorflow.python.framework.dtypes import bfloat16 # line: 450
+from tensorflow.python.framework.dtypes import bool # line: 414
+from tensorflow.python.framework.dtypes import complex128 # line: 401
+from tensorflow.python.framework.dtypes import complex64 # line: 394
+from tensorflow.python.framework.dtypes import double # line: 388
+from tensorflow.python.framework.dtypes import float16 # line: 373
+from tensorflow.python.framework.dtypes import float32 # line: 380
+from tensorflow.python.framework.dtypes import float64 # line: 386
+from tensorflow.python.framework.dtypes import half # line: 374
+from tensorflow.python.framework.dtypes import int16 # line: 354
+from tensorflow.python.framework.dtypes import int32 # line: 360
+from tensorflow.python.framework.dtypes import int64 # line: 366
+from tensorflow.python.framework.dtypes import int8 # line: 348
+from tensorflow.python.framework.dtypes import qint16 # line: 426
+from tensorflow.python.framework.dtypes import qint32 # line: 432
+from tensorflow.python.framework.dtypes import qint8 # line: 420
+from tensorflow.python.framework.dtypes import quint16 # line: 444
+from tensorflow.python.framework.dtypes import quint8 # line: 438
+from tensorflow.python.framework.dtypes import resource # line: 312
+from tensorflow.python.framework.dtypes import string # line: 408
+from tensorflow.python.framework.dtypes import uint16 # line: 330
+from tensorflow.python.framework.dtypes import uint32 # line: 336
+from tensorflow.python.framework.dtypes import uint64 # line: 342
+from tensorflow.python.framework.dtypes import uint8 # line: 324
+from tensorflow.python.framework.dtypes import variant # line: 318
+from tensorflow.python.ops.math_ops import cast # line: 944
+from tensorflow.python.ops.math_ops import complex # line: 699
+from tensorflow.python.ops.math_ops import saturate_cast # line: 1028
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/dtypes/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/dtypes/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d17307b985646f250a5485fd51b6551ddf5bf643
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/dtypes/experimental/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.dtypes.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.dtypes import float8_e4m3fn # line: 465
+from tensorflow.python.framework.dtypes import float8_e5m2 # line: 456
+from tensorflow.python.framework.dtypes import int4 # line: 471
+from tensorflow.python.framework.dtypes import uint4 # line: 477
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/errors/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/errors/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..33c6ebfcd059d3966167a016a340fd15603c7278
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/errors/__init__.py
@@ -0,0 +1,42 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.errors namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.errors_impl import ABORTED # line: 214
+from tensorflow.python.framework.errors_impl import ALREADY_EXISTS # line: 201
+from tensorflow.python.framework.errors_impl import AbortedError # line: 401
+from tensorflow.python.framework.errors_impl import AlreadyExistsError # line: 318
+from tensorflow.python.framework.errors_impl import CANCELLED # line: 189
+from tensorflow.python.framework.errors_impl import CancelledError # line: 227
+from tensorflow.python.framework.errors_impl import DATA_LOSS # line: 224
+from tensorflow.python.framework.errors_impl import DEADLINE_EXCEEDED # line: 196
+from tensorflow.python.framework.errors_impl import DataLossError # line: 480
+from tensorflow.python.framework.errors_impl import DeadlineExceededError # line: 290
+from tensorflow.python.framework.errors_impl import FAILED_PRECONDITION # line: 211
+from tensorflow.python.framework.errors_impl import FailedPreconditionError # line: 383
+from tensorflow.python.framework.errors_impl import INTERNAL # line: 220
+from tensorflow.python.framework.errors_impl import INVALID_ARGUMENT # line: 193
+from tensorflow.python.framework.errors_impl import InternalError # line: 454
+from tensorflow.python.framework.errors_impl import InvalidArgumentError # line: 270
+from tensorflow.python.framework.errors_impl import NOT_FOUND # line: 199
+from tensorflow.python.framework.errors_impl import NotFoundError # line: 303
+from tensorflow.python.framework.errors_impl import OK # line: 187
+from tensorflow.python.framework.errors_impl import OUT_OF_RANGE # line: 216
+from tensorflow.python.framework.errors_impl import OpError # line: 57
+from tensorflow.python.framework.errors_impl import OperatorNotAllowedInGraphError # line: 34
+from tensorflow.python.framework.errors_impl import OutOfRangeError # line: 417
+from tensorflow.python.framework.errors_impl import PERMISSION_DENIED # line: 203
+from tensorflow.python.framework.errors_impl import PermissionDeniedError # line: 338
+from tensorflow.python.framework.errors_impl import RESOURCE_EXHAUSTED # line: 208
+from tensorflow.python.framework.errors_impl import ResourceExhaustedError # line: 367
+from tensorflow.python.framework.errors_impl import UNAUTHENTICATED # line: 206
+from tensorflow.python.framework.errors_impl import UNAVAILABLE # line: 222
+from tensorflow.python.framework.errors_impl import UNIMPLEMENTED # line: 218
+from tensorflow.python.framework.errors_impl import UNKNOWN # line: 191
+from tensorflow.python.framework.errors_impl import UnauthenticatedError # line: 354
+from tensorflow.python.framework.errors_impl import UnavailableError # line: 467
+from tensorflow.python.framework.errors_impl import UnimplementedError # line: 437
+from tensorflow.python.framework.errors_impl import UnknownError # line: 254
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a8386e1dbc09de3d870d553ea9a485858cdd4b8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/__init__.py
@@ -0,0 +1,34 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.experimental import dlpack
+from tensorflow._api.v2.experimental import dtensor
+from tensorflow._api.v2.experimental import extension_type
+from tensorflow._api.v2.experimental import numpy
+from tensorflow._api.v2.experimental import tensorrt
+from tensorflow.python.data.ops.optional_ops import Optional # line: 31
+from tensorflow.python.eager.context import async_clear_error # line: 2836
+from tensorflow.python.eager.context import async_scope # line: 2772
+from tensorflow.python.eager.context import function_executor_type # line: 2621
+from tensorflow.python.framework.dtypes import float8_e4m3fn # line: 465
+from tensorflow.python.framework.dtypes import float8_e5m2 # line: 456
+from tensorflow.python.framework.dtypes import int4 # line: 471
+from tensorflow.python.framework.dtypes import uint4 # line: 477
+from tensorflow.python.framework.extension_type import BatchableExtensionType # line: 831
+from tensorflow.python.framework.extension_type import ExtensionType # line: 99
+from tensorflow.python.framework.extension_type import ExtensionTypeBatchEncoder # line: 651
+from tensorflow.python.framework.extension_type import ExtensionTypeSpec # line: 419
+from tensorflow.python.framework.load_library import register_filesystem_plugin # line: 199
+from tensorflow.python.framework.strict_mode import enable_strict_mode # line: 22
+from tensorflow.python.ops.ragged.dynamic_ragged_shape import DynamicRaggedShape # line: 167
+from tensorflow.python.ops.ragged.row_partition import RowPartition # line: 56
+from tensorflow.python.ops.structured.structured_tensor import StructuredTensor # line: 53
+from tensorflow.python.util.dispatch import dispatch_for_api # line: 334
+from tensorflow.python.util.dispatch import dispatch_for_binary_elementwise_apis # line: 878
+from tensorflow.python.util.dispatch import dispatch_for_binary_elementwise_assert_apis # line: 936
+from tensorflow.python.util.dispatch import dispatch_for_unary_elementwise_apis # line: 811
+from tensorflow.python.util.dispatch import unregister_dispatch_for # line: 553
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/dlpack/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/dlpack/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..94b291e4e199347097bef6912c61ee24fb114370
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/dlpack/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.experimental.dlpack namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.dlpack.dlpack import from_dlpack # line: 44
+from tensorflow.python.dlpack.dlpack import to_dlpack # line: 22
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/dtensor/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/dtensor/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d24d5df1af9228736b5d99928d0ded8a12e537e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/dtensor/__init__.py
@@ -0,0 +1,51 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.experimental.dtensor namespace
+"""
+
+import sys as _sys
+
+from tensorflow.dtensor.python.accelerator_util import initialize_accelerator_system # line: 114
+from tensorflow.dtensor.python.accelerator_util import initialize_accelerator_system as initialize_multi_client # line: 114
+from tensorflow.dtensor.python.accelerator_util import initialize_accelerator_system as initialize_tpu_system # line: 114
+from tensorflow.dtensor.python.accelerator_util import shutdown_accelerator_system # line: 268
+from tensorflow.dtensor.python.accelerator_util import shutdown_accelerator_system as shutdown_tpu_system # line: 268
+from tensorflow.dtensor.python.api import reset_dtensor_device as _reset_dtensor_device # line: 505
+from tensorflow.dtensor.python.api import call_with_layout # line: 37
+from tensorflow.dtensor.python.api import check_layout # line: 395
+from tensorflow.dtensor.python.api import copy_to_mesh # line: 167
+from tensorflow.dtensor.python.api import default_mesh # line: 89
+from tensorflow.dtensor.python.api import device_name # line: 131
+from tensorflow.dtensor.python.api import fetch_layout # line: 379
+from tensorflow.dtensor.python.api import get_default_mesh # line: 115
+from tensorflow.dtensor.python.api import is_dtensor # line: 147
+from tensorflow.dtensor.python.api import pack # line: 191
+from tensorflow.dtensor.python.api import relayout # line: 411
+from tensorflow.dtensor.python.api import relayout_like # line: 452
+from tensorflow.dtensor.python.api import run_on # line: 67
+from tensorflow.dtensor.python.api import unpack # line: 342
+from tensorflow.dtensor.python.config import client_id # line: 86
+from tensorflow.dtensor.python.config import full_job_name # line: 117
+from tensorflow.dtensor.python.config import heartbeat_enabled # line: 168
+from tensorflow.dtensor.python.config import job_name # line: 108
+from tensorflow.dtensor.python.config import jobs # line: 148
+from tensorflow.dtensor.python.config import local_devices # line: 46
+from tensorflow.dtensor.python.config import num_clients # line: 100
+from tensorflow.dtensor.python.config import num_global_devices # line: 80
+from tensorflow.dtensor.python.config import num_local_devices # line: 68
+from tensorflow.dtensor.python.config import preferred_device_type # line: 192
+from tensorflow.dtensor.python.d_checkpoint import DTensorCheckpoint # line: 411
+from tensorflow.dtensor.python.d_variable import DVariable # line: 143
+from tensorflow.dtensor.python.input_util import DTensorDataset # line: 384
+from tensorflow.dtensor.python.layout import Layout # line: 351
+from tensorflow.dtensor.python.layout import MATCH # line: 39
+from tensorflow.dtensor.python.layout import Mesh # line: 53
+from tensorflow.dtensor.python.layout import UNSHARDED # line: 36
+from tensorflow.dtensor.python.mesh_util import barrier # line: 243
+from tensorflow.dtensor.python.mesh_util import create_distributed_mesh # line: 138
+from tensorflow.dtensor.python.mesh_util import create_mesh # line: 70
+from tensorflow.dtensor.python.save_restore import enable_save_as_bf16 # line: 83
+from tensorflow.dtensor.python.save_restore import name_based_restore # line: 100
+from tensorflow.dtensor.python.save_restore import name_based_save # line: 176
+from tensorflow.dtensor.python.save_restore import sharded_save # line: 34
+from tensorflow.dtensor.python.tpu_util import create_tpu_mesh # line: 578
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/extension_type/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/extension_type/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..070fcf42ed4902381a6d0d5b105b29d572bbe61e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/extension_type/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.experimental.extension_type namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.extension_type import as_dict # line: 332
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/numpy/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/numpy/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ae8fa64287b040800eb5c81f5703d88209cb759
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/numpy/__init__.py
@@ -0,0 +1,236 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.experimental.numpy namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.experimental.numpy import random
+from tensorflow.python.framework.tensor import Tensor as ndarray # line: 139
+from tensorflow.python.ops.numpy_ops.np_array_ops import all # line: 420
+from tensorflow.python.ops.numpy_ops.np_array_ops import amax # line: 672
+from tensorflow.python.ops.numpy_ops.np_array_ops import amin # line: 689
+from tensorflow.python.ops.numpy_ops.np_array_ops import any # line: 427
+from tensorflow.python.ops.numpy_ops.np_array_ops import arange # line: 259
+from tensorflow.python.ops.numpy_ops.np_array_ops import around # line: 820
+from tensorflow.python.ops.numpy_ops.np_array_ops import array # line: 217
+from tensorflow.python.ops.numpy_ops.np_array_ops import asanyarray # line: 246
+from tensorflow.python.ops.numpy_ops.np_array_ops import asarray # line: 234
+from tensorflow.python.ops.numpy_ops.np_array_ops import ascontiguousarray # line: 252
+from tensorflow.python.ops.numpy_ops.np_array_ops import atleast_1d # line: 1293
+from tensorflow.python.ops.numpy_ops.np_array_ops import atleast_2d # line: 1299
+from tensorflow.python.ops.numpy_ops.np_array_ops import atleast_3d # line: 1305
+from tensorflow.python.ops.numpy_ops.np_array_ops import broadcast_arrays # line: 1567
+from tensorflow.python.ops.numpy_ops.np_array_ops import broadcast_to # line: 1192
+from tensorflow.python.ops.numpy_ops.np_array_ops import compress # line: 434
+from tensorflow.python.ops.numpy_ops.np_array_ops import copy # line: 465
+from tensorflow.python.ops.numpy_ops.np_array_ops import cumprod # line: 483
+from tensorflow.python.ops.numpy_ops.np_array_ops import cumsum # line: 500
+from tensorflow.python.ops.numpy_ops.np_array_ops import diag # line: 305
+from tensorflow.python.ops.numpy_ops.np_array_ops import diag_indices # line: 1338
+from tensorflow.python.ops.numpy_ops.np_array_ops import diagflat # line: 389
+from tensorflow.python.ops.numpy_ops.np_array_ops import diagonal # line: 347
+from tensorflow.python.ops.numpy_ops.np_array_ops import dsplit # line: 1187
+from tensorflow.python.ops.numpy_ops.np_array_ops import dstack # line: 1241
+from tensorflow.python.ops.numpy_ops.np_array_ops import empty # line: 53
+from tensorflow.python.ops.numpy_ops.np_array_ops import empty_like # line: 59
+from tensorflow.python.ops.numpy_ops.np_array_ops import expand_dims # line: 875
+from tensorflow.python.ops.numpy_ops.np_array_ops import eye # line: 98
+from tensorflow.python.ops.numpy_ops.np_array_ops import flatten # line: 889
+from tensorflow.python.ops.numpy_ops.np_array_ops import flip # line: 1433
+from tensorflow.python.ops.numpy_ops.np_array_ops import fliplr # line: 1452
+from tensorflow.python.ops.numpy_ops.np_array_ops import flipud # line: 1446
+from tensorflow.python.ops.numpy_ops.np_array_ops import full # line: 136
+from tensorflow.python.ops.numpy_ops.np_array_ops import full_like # line: 148
+from tensorflow.python.ops.numpy_ops.np_array_ops import hsplit # line: 1184
+from tensorflow.python.ops.numpy_ops.np_array_ops import hstack # line: 1214
+from tensorflow.python.ops.numpy_ops.np_array_ops import identity # line: 130
+from tensorflow.python.ops.numpy_ops.np_array_ops import imag # line: 517
+from tensorflow.python.ops.numpy_ops.np_array_ops import isscalar # line: 1114
+from tensorflow.python.ops.numpy_ops.np_array_ops import ix_ # line: 1534
+from tensorflow.python.ops.numpy_ops.np_array_ops import max # line: 1667
+from tensorflow.python.ops.numpy_ops.np_array_ops import mean # line: 657
+from tensorflow.python.ops.numpy_ops.np_array_ops import min # line: 1673
+from tensorflow.python.ops.numpy_ops.np_array_ops import moveaxis # line: 953
+from tensorflow.python.ops.numpy_ops.np_array_ops import ndim # line: 1107
+from tensorflow.python.ops.numpy_ops.np_array_ops import newaxis # line: 48
+from tensorflow.python.ops.numpy_ops.np_array_ops import nonzero # line: 1324
+from tensorflow.python.ops.numpy_ops.np_array_ops import ones # line: 83
+from tensorflow.python.ops.numpy_ops.np_array_ops import ones_like # line: 91
+from tensorflow.python.ops.numpy_ops.np_array_ops import pad # line: 1018
+from tensorflow.python.ops.numpy_ops.np_array_ops import prod # line: 644
+from tensorflow.python.ops.numpy_ops.np_array_ops import ravel # line: 772
+from tensorflow.python.ops.numpy_ops.np_array_ops import real # line: 779
+from tensorflow.python.ops.numpy_ops.np_array_ops import repeat # line: 788
+from tensorflow.python.ops.numpy_ops.np_array_ops import reshape # line: 843
+from tensorflow.python.ops.numpy_ops.np_array_ops import roll # line: 1458
+from tensorflow.python.ops.numpy_ops.np_array_ops import rot90 # line: 1472
+from tensorflow.python.ops.numpy_ops.np_array_ops import round # line: 1679
+from tensorflow.python.ops.numpy_ops.np_array_ops import select # line: 1077
+from tensorflow.python.ops.numpy_ops.np_array_ops import shape # line: 1095
+from tensorflow.python.ops.numpy_ops.np_array_ops import sign # line: 1580
+from tensorflow.python.ops.numpy_ops.np_array_ops import size # line: 615
+from tensorflow.python.ops.numpy_ops.np_array_ops import split # line: 1152
+from tensorflow.python.ops.numpy_ops.np_array_ops import squeeze # line: 882
+from tensorflow.python.ops.numpy_ops.np_array_ops import stack # line: 1198
+from tensorflow.python.ops.numpy_ops.np_array_ops import std # line: 759
+from tensorflow.python.ops.numpy_ops.np_array_ops import sum # line: 631
+from tensorflow.python.ops.numpy_ops.np_array_ops import swapaxes # line: 914
+from tensorflow.python.ops.numpy_ops.np_array_ops import take # line: 1036
+from tensorflow.python.ops.numpy_ops.np_array_ops import take_along_axis # line: 1602
+from tensorflow.python.ops.numpy_ops.np_array_ops import transpose # line: 905
+from tensorflow.python.ops.numpy_ops.np_array_ops import tri # line: 1353
+from tensorflow.python.ops.numpy_ops.np_array_ops import tril # line: 1383
+from tensorflow.python.ops.numpy_ops.np_array_ops import triu # line: 1408
+from tensorflow.python.ops.numpy_ops.np_array_ops import vander # line: 1493
+from tensorflow.python.ops.numpy_ops.np_array_ops import var # line: 706
+from tensorflow.python.ops.numpy_ops.np_array_ops import vsplit # line: 1181
+from tensorflow.python.ops.numpy_ops.np_array_ops import vstack # line: 1230
+from tensorflow.python.ops.numpy_ops.np_array_ops import where # line: 1064
+from tensorflow.python.ops.numpy_ops.np_array_ops import zeros # line: 65
+from tensorflow.python.ops.numpy_ops.np_array_ops import zeros_like # line: 74
+from tensorflow.python.ops.numpy_ops.np_config import enable_numpy_behavior as experimental_enable_numpy_behavior # line: 25
+from tensorflow.python.ops.numpy_ops.np_dtypes import bool_ # line: 27
+from tensorflow.python.ops.numpy_ops.np_dtypes import complex128 # line: 35
+from tensorflow.python.ops.numpy_ops.np_dtypes import complex64 # line: 39
+from tensorflow.python.ops.numpy_ops.np_dtypes import complex_ # line: 31
+from tensorflow.python.ops.numpy_ops.np_dtypes import float16 # line: 47
+from tensorflow.python.ops.numpy_ops.np_dtypes import float32 # line: 51
+from tensorflow.python.ops.numpy_ops.np_dtypes import float64 # line: 55
+from tensorflow.python.ops.numpy_ops.np_dtypes import float_ # line: 43
+from tensorflow.python.ops.numpy_ops.np_dtypes import iinfo # line: 113
+from tensorflow.python.ops.numpy_ops.np_dtypes import inexact # line: 59
+from tensorflow.python.ops.numpy_ops.np_dtypes import int16 # line: 67
+from tensorflow.python.ops.numpy_ops.np_dtypes import int32 # line: 71
+from tensorflow.python.ops.numpy_ops.np_dtypes import int64 # line: 75
+from tensorflow.python.ops.numpy_ops.np_dtypes import int8 # line: 79
+from tensorflow.python.ops.numpy_ops.np_dtypes import int_ # line: 63
+from tensorflow.python.ops.numpy_ops.np_dtypes import issubdtype # line: 118
+from tensorflow.python.ops.numpy_ops.np_dtypes import object_ # line: 83
+from tensorflow.python.ops.numpy_ops.np_dtypes import string_ # line: 87
+from tensorflow.python.ops.numpy_ops.np_dtypes import uint16 # line: 91
+from tensorflow.python.ops.numpy_ops.np_dtypes import uint32 # line: 95
+from tensorflow.python.ops.numpy_ops.np_dtypes import uint64 # line: 99
+from tensorflow.python.ops.numpy_ops.np_dtypes import uint8 # line: 103
+from tensorflow.python.ops.numpy_ops.np_dtypes import unicode_ # line: 107
+from tensorflow.python.ops.numpy_ops.np_math_ops import abs # line: 698
+from tensorflow.python.ops.numpy_ops.np_math_ops import absolute # line: 704
+from tensorflow.python.ops.numpy_ops.np_math_ops import add # line: 90
+from tensorflow.python.ops.numpy_ops.np_math_ops import allclose # line: 542
+from tensorflow.python.ops.numpy_ops.np_math_ops import angle # line: 852
+from tensorflow.python.ops.numpy_ops.np_math_ops import append # line: 1417
+from tensorflow.python.ops.numpy_ops.np_math_ops import arccos # line: 799
+from tensorflow.python.ops.numpy_ops.np_math_ops import arccosh # line: 817
+from tensorflow.python.ops.numpy_ops.np_math_ops import arcsin # line: 793
+from tensorflow.python.ops.numpy_ops.np_math_ops import arcsinh # line: 811
+from tensorflow.python.ops.numpy_ops.np_math_ops import arctan # line: 805
+from tensorflow.python.ops.numpy_ops.np_math_ops import arctan2 # line: 402
+from tensorflow.python.ops.numpy_ops.np_math_ops import arctanh # line: 823
+from tensorflow.python.ops.numpy_ops.np_math_ops import argmax # line: 1405
+from tensorflow.python.ops.numpy_ops.np_math_ops import argmin # line: 1411
+from tensorflow.python.ops.numpy_ops.np_math_ops import argsort # line: 1340
+from tensorflow.python.ops.numpy_ops.np_math_ops import array_equal # line: 1155
+from tensorflow.python.ops.numpy_ops.np_math_ops import average # line: 1426
+from tensorflow.python.ops.numpy_ops.np_math_ops import bitwise_and # line: 630
+from tensorflow.python.ops.numpy_ops.np_math_ops import bitwise_not # line: 648
+from tensorflow.python.ops.numpy_ops.np_math_ops import bitwise_or # line: 636
+from tensorflow.python.ops.numpy_ops.np_math_ops import bitwise_xor # line: 642
+from tensorflow.python.ops.numpy_ops.np_math_ops import cbrt # line: 868
+from tensorflow.python.ops.numpy_ops.np_math_ops import ceil # line: 716
+from tensorflow.python.ops.numpy_ops.np_math_ops import clip # line: 222
+from tensorflow.python.ops.numpy_ops.np_math_ops import concatenate # line: 1298
+from tensorflow.python.ops.numpy_ops.np_math_ops import conj # line: 728
+from tensorflow.python.ops.numpy_ops.np_math_ops import conjugate # line: 879
+from tensorflow.python.ops.numpy_ops.np_math_ops import cos # line: 763
+from tensorflow.python.ops.numpy_ops.np_math_ops import cosh # line: 781
+from tensorflow.python.ops.numpy_ops.np_math_ops import count_nonzero # line: 1334
+from tensorflow.python.ops.numpy_ops.np_math_ops import cross # line: 289
+from tensorflow.python.ops.numpy_ops.np_math_ops import deg2rad # line: 829
+from tensorflow.python.ops.numpy_ops.np_math_ops import diff # line: 1054
+from tensorflow.python.ops.numpy_ops.np_math_ops import divide # line: 144
+from tensorflow.python.ops.numpy_ops.np_math_ops import divmod # line: 182
+from tensorflow.python.ops.numpy_ops.np_math_ops import dot # line: 60
+from tensorflow.python.ops.numpy_ops.np_math_ops import e # line: 51
+from tensorflow.python.ops.numpy_ops.np_math_ops import einsum # line: 1543
+from tensorflow.python.ops.numpy_ops.np_math_ops import equal # line: 1119
+from tensorflow.python.ops.numpy_ops.np_math_ops import exp # line: 686
+from tensorflow.python.ops.numpy_ops.np_math_ops import exp2 # line: 885
+from tensorflow.python.ops.numpy_ops.np_math_ops import expm1 # line: 894
+from tensorflow.python.ops.numpy_ops.np_math_ops import fabs # line: 710
+from tensorflow.python.ops.numpy_ops.np_math_ops import fix # line: 900
+from tensorflow.python.ops.numpy_ops.np_math_ops import float_power # line: 396
+from tensorflow.python.ops.numpy_ops.np_math_ops import floor # line: 722
+from tensorflow.python.ops.numpy_ops.np_math_ops import floor_divide # line: 150
+from tensorflow.python.ops.numpy_ops.np_math_ops import gcd # line: 588
+from tensorflow.python.ops.numpy_ops.np_math_ops import geomspace # line: 1259
+from tensorflow.python.ops.numpy_ops.np_math_ops import greater # line: 1131
+from tensorflow.python.ops.numpy_ops.np_math_ops import greater_equal # line: 1137
+from tensorflow.python.ops.numpy_ops.np_math_ops import heaviside # line: 414
+from tensorflow.python.ops.numpy_ops.np_math_ops import hypot # line: 430
+from tensorflow.python.ops.numpy_ops.np_math_ops import inf # line: 55
+from tensorflow.python.ops.numpy_ops.np_math_ops import inner # line: 273
+from tensorflow.python.ops.numpy_ops.np_math_ops import isclose # line: 524
+from tensorflow.python.ops.numpy_ops.np_math_ops import iscomplex # line: 909
+from tensorflow.python.ops.numpy_ops.np_math_ops import iscomplexobj # line: 921
+from tensorflow.python.ops.numpy_ops.np_math_ops import isfinite # line: 982
+from tensorflow.python.ops.numpy_ops.np_math_ops import isinf # line: 988
+from tensorflow.python.ops.numpy_ops.np_math_ops import isnan # line: 934
+from tensorflow.python.ops.numpy_ops.np_math_ops import isneginf # line: 996
+from tensorflow.python.ops.numpy_ops.np_math_ops import isposinf # line: 1004
+from tensorflow.python.ops.numpy_ops.np_math_ops import isreal # line: 915
+from tensorflow.python.ops.numpy_ops.np_math_ops import isrealobj # line: 928
+from tensorflow.python.ops.numpy_ops.np_math_ops import kron # line: 436
+from tensorflow.python.ops.numpy_ops.np_math_ops import lcm # line: 595
+from tensorflow.python.ops.numpy_ops.np_math_ops import less # line: 1143
+from tensorflow.python.ops.numpy_ops.np_math_ops import less_equal # line: 1149
+from tensorflow.python.ops.numpy_ops.np_math_ops import linspace # line: 1205
+from tensorflow.python.ops.numpy_ops.np_math_ops import log # line: 680
+from tensorflow.python.ops.numpy_ops.np_math_ops import log10 # line: 1018
+from tensorflow.python.ops.numpy_ops.np_math_ops import log1p # line: 1024
+from tensorflow.python.ops.numpy_ops.np_math_ops import log2 # line: 1012
+from tensorflow.python.ops.numpy_ops.np_math_ops import logaddexp # line: 482
+from tensorflow.python.ops.numpy_ops.np_math_ops import logaddexp2 # line: 494
+from tensorflow.python.ops.numpy_ops.np_math_ops import logical_and # line: 1180
+from tensorflow.python.ops.numpy_ops.np_math_ops import logical_not # line: 1198
+from tensorflow.python.ops.numpy_ops.np_math_ops import logical_or # line: 1186
+from tensorflow.python.ops.numpy_ops.np_math_ops import logical_xor # line: 1192
+from tensorflow.python.ops.numpy_ops.np_math_ops import logspace # line: 1246
+from tensorflow.python.ops.numpy_ops.np_math_ops import matmul # line: 236
+from tensorflow.python.ops.numpy_ops.np_math_ops import maximum # line: 188
+from tensorflow.python.ops.numpy_ops.np_math_ops import meshgrid # line: 1514
+from tensorflow.python.ops.numpy_ops.np_math_ops import minimum # line: 210
+from tensorflow.python.ops.numpy_ops.np_math_ops import mod # line: 163
+from tensorflow.python.ops.numpy_ops.np_math_ops import multiply # line: 108
+from tensorflow.python.ops.numpy_ops.np_math_ops import nanmean # line: 965
+from tensorflow.python.ops.numpy_ops.np_math_ops import nanprod # line: 960
+from tensorflow.python.ops.numpy_ops.np_math_ops import nansum # line: 957
+from tensorflow.python.ops.numpy_ops.np_math_ops import negative # line: 734
+from tensorflow.python.ops.numpy_ops.np_math_ops import nextafter # line: 408
+from tensorflow.python.ops.numpy_ops.np_math_ops import not_equal # line: 1125
+from tensorflow.python.ops.numpy_ops.np_math_ops import outer # line: 472
+from tensorflow.python.ops.numpy_ops.np_math_ops import pi # line: 47
+from tensorflow.python.ops.numpy_ops.np_math_ops import polyval # line: 506
+from tensorflow.python.ops.numpy_ops.np_math_ops import positive # line: 1030
+from tensorflow.python.ops.numpy_ops.np_math_ops import power # line: 390
+from tensorflow.python.ops.numpy_ops.np_math_ops import ptp # line: 1290
+from tensorflow.python.ops.numpy_ops.np_math_ops import rad2deg # line: 838
+from tensorflow.python.ops.numpy_ops.np_math_ops import reciprocal # line: 740
+from tensorflow.python.ops.numpy_ops.np_math_ops import remainder # line: 176
+from tensorflow.python.ops.numpy_ops.np_math_ops import signbit # line: 746
+from tensorflow.python.ops.numpy_ops.np_math_ops import sin # line: 757
+from tensorflow.python.ops.numpy_ops.np_math_ops import sinc # line: 1036
+from tensorflow.python.ops.numpy_ops.np_math_ops import sinh # line: 775
+from tensorflow.python.ops.numpy_ops.np_math_ops import sort # line: 1375
+from tensorflow.python.ops.numpy_ops.np_math_ops import sqrt # line: 692
+from tensorflow.python.ops.numpy_ops.np_math_ops import square # line: 1048
+from tensorflow.python.ops.numpy_ops.np_math_ops import subtract # line: 102
+from tensorflow.python.ops.numpy_ops.np_math_ops import tan # line: 769
+from tensorflow.python.ops.numpy_ops.np_math_ops import tanh # line: 787
+from tensorflow.python.ops.numpy_ops.np_math_ops import tensordot # line: 267
+from tensorflow.python.ops.numpy_ops.np_math_ops import tile # line: 1313
+from tensorflow.python.ops.numpy_ops.np_math_ops import trace # line: 1494
+from tensorflow.python.ops.numpy_ops.np_math_ops import true_divide # line: 120
+from tensorflow.python.ops.numpy_ops.np_math_ops import vdot # line: 379
+from tensorflow.python.ops.numpy_ops.np_utils import finfo # line: 486
+from tensorflow.python.ops.numpy_ops.np_utils import promote_types # line: 564
+from tensorflow.python.ops.numpy_ops.np_utils import result_type # line: 518
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/numpy/random/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/numpy/random/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5685f57e5a34b4f331a102216729bf8f07e0ebee
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/numpy/random/__init__.py
@@ -0,0 +1,15 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.experimental.numpy.random namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.numpy_ops.np_random import poisson # line: 94
+from tensorflow.python.ops.numpy_ops.np_random import rand # line: 110
+from tensorflow.python.ops.numpy_ops.np_random import randint # line: 116
+from tensorflow.python.ops.numpy_ops.np_random import randn # line: 53
+from tensorflow.python.ops.numpy_ops.np_random import random # line: 104
+from tensorflow.python.ops.numpy_ops.np_random import seed # line: 33
+from tensorflow.python.ops.numpy_ops.np_random import standard_normal # line: 69
+from tensorflow.python.ops.numpy_ops.np_random import uniform # line: 81
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/tensorrt/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/tensorrt/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e715f32cff77d294e77c175d04a19c41ec580cc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/experimental/tensorrt/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.experimental.tensorrt namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.compiler.tensorrt.trt_convert import TrtConversionParams as ConversionParams # line: 135
+from tensorflow.python.compiler.tensorrt.trt_convert import TrtGraphConverterV2 as Converter # line: 1076
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/feature_column/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/feature_column/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..04ca485d32ef9d80ca1fdb6f864272c444dbde00
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/feature_column/__init__.py
@@ -0,0 +1,24 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.feature_column namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.feature_column.feature_column_v2 import bucketized_column # line: 1124
+from tensorflow.python.feature_column.feature_column_v2 import categorical_column_with_hash_bucket # line: 1211
+from tensorflow.python.feature_column.feature_column_v2 import categorical_column_with_identity # line: 1648
+from tensorflow.python.feature_column.feature_column_v2 import categorical_column_with_vocabulary_file_v2 as categorical_column_with_vocabulary_file # line: 1391
+from tensorflow.python.feature_column.feature_column_v2 import categorical_column_with_vocabulary_list # line: 1529
+from tensorflow.python.feature_column.feature_column_v2 import crossed_column # line: 1849
+from tensorflow.python.feature_column.feature_column_v2 import embedding_column # line: 535
+from tensorflow.python.feature_column.feature_column_v2 import indicator_column # line: 1726
+from tensorflow.python.feature_column.feature_column_v2 import make_parse_example_spec_v2 as make_parse_example_spec # line: 470
+from tensorflow.python.feature_column.feature_column_v2 import numeric_column # line: 1024
+from tensorflow.python.feature_column.feature_column_v2 import shared_embedding_columns_v2 as shared_embeddings # line: 845
+from tensorflow.python.feature_column.feature_column_v2 import weighted_categorical_column # line: 1771
+from tensorflow.python.feature_column.sequence_feature_column import sequence_categorical_column_with_hash_bucket # line: 153
+from tensorflow.python.feature_column.sequence_feature_column import sequence_categorical_column_with_identity # line: 103
+from tensorflow.python.feature_column.sequence_feature_column import sequence_categorical_column_with_vocabulary_file # line: 200
+from tensorflow.python.feature_column.sequence_feature_column import sequence_categorical_column_with_vocabulary_list # line: 270
+from tensorflow.python.feature_column.sequence_feature_column import sequence_numeric_column # line: 337
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/graph_util/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/graph_util/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4563a0266e7ab61d454007c7f8cb7ca7de8bcf4a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/graph_util/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.graph_util namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.importer import import_graph_def # line: 354
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/image/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/image/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe87a77d1782ca84511ca7355570d527b0cf9a7d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/image/__init__.py
@@ -0,0 +1,78 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.image namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.gen_image_ops import hsv_to_rgb # line: 2271
+from tensorflow.python.ops.gen_image_ops import rgb_to_hsv # line: 3276
+from tensorflow.python.ops.array_ops import extract_image_patches_v2 as extract_patches # line: 6416
+from tensorflow.python.ops.image_ops_impl import ResizeMethod # line: 1441
+from tensorflow.python.ops.image_ops_impl import adjust_brightness # line: 2198
+from tensorflow.python.ops.image_ops_impl import adjust_contrast # line: 2251
+from tensorflow.python.ops.image_ops_impl import adjust_gamma # line: 2310
+from tensorflow.python.ops.image_ops_impl import adjust_hue # line: 2734
+from tensorflow.python.ops.image_ops_impl import adjust_jpeg_quality # line: 2920
+from tensorflow.python.ops.image_ops_impl import adjust_saturation # line: 3100
+from tensorflow.python.ops.image_ops_impl import central_crop # line: 857
+from tensorflow.python.ops.image_ops_impl import combined_non_max_suppression # line: 5127
+from tensorflow.python.ops.image_ops_impl import convert_image_dtype # line: 2376
+from tensorflow.python.ops.image_ops_impl import crop_and_resize_v2 as crop_and_resize # line: 4822
+from tensorflow.python.ops.image_ops_impl import crop_to_bounding_box # line: 1168
+from tensorflow.python.ops.image_ops_impl import decode_and_crop_jpeg # line: 3191
+from tensorflow.python.ops.image_ops_impl import decode_bmp # line: 3197
+from tensorflow.python.ops.image_ops_impl import decode_gif # line: 3202
+from tensorflow.python.ops.image_ops_impl import decode_image # line: 3260
+from tensorflow.python.ops.image_ops_impl import decode_jpeg # line: 3207
+from tensorflow.python.ops.image_ops_impl import decode_png # line: 3212
+from tensorflow.python.ops.image_ops_impl import draw_bounding_boxes_v2 as draw_bounding_boxes # line: 5785
+from tensorflow.python.ops.image_ops_impl import encode_jpeg # line: 3218
+from tensorflow.python.ops.image_ops_impl import encode_png # line: 3230
+from tensorflow.python.ops.image_ops_impl import extract_glimpse_v2 as extract_glimpse # line: 5040
+from tensorflow.python.ops.image_ops_impl import extract_jpeg_shape # line: 3223
+from tensorflow.python.ops.image_ops_impl import flip_left_right # line: 546
+from tensorflow.python.ops.image_ops_impl import flip_up_down # line: 581
+from tensorflow.python.ops.image_ops_impl import generate_bounding_box_proposals # line: 5893
+from tensorflow.python.ops.image_ops_impl import grayscale_to_rgb # line: 2597
+from tensorflow.python.ops.image_ops_impl import image_gradients # line: 4604
+from tensorflow.python.ops.image_ops_impl import is_jpeg # line: 3155
+from tensorflow.python.ops.image_ops_impl import non_max_suppression # line: 3775
+from tensorflow.python.ops.image_ops_impl import non_max_suppression_with_overlaps as non_max_suppression_overlaps # line: 3918
+from tensorflow.python.ops.image_ops_impl import non_max_suppression_padded # line: 5395
+from tensorflow.python.ops.image_ops_impl import non_max_suppression_with_scores # line: 3828
+from tensorflow.python.ops.image_ops_impl import pad_to_bounding_box # line: 1004
+from tensorflow.python.ops.image_ops_impl import per_image_standardization # line: 1956
+from tensorflow.python.ops.image_ops_impl import psnr # line: 4159
+from tensorflow.python.ops.image_ops_impl import random_brightness # line: 2015
+from tensorflow.python.ops.image_ops_impl import random_contrast # line: 2105
+from tensorflow.python.ops.image_ops_impl import random_flip_left_right # line: 382
+from tensorflow.python.ops.image_ops_impl import random_flip_up_down # line: 335
+from tensorflow.python.ops.image_ops_impl import random_hue # line: 2634
+from tensorflow.python.ops.image_ops_impl import random_jpeg_quality # line: 2810
+from tensorflow.python.ops.image_ops_impl import random_saturation # line: 2998
+from tensorflow.python.ops.image_ops_impl import resize_images_v2 as resize # line: 1618
+from tensorflow.python.ops.image_ops_impl import resize_image_with_crop_or_pad as resize_with_crop_or_pad # line: 1275
+from tensorflow.python.ops.image_ops_impl import resize_image_with_pad_v2 as resize_with_pad # line: 1915
+from tensorflow.python.ops.image_ops_impl import rgb_to_grayscale # line: 2561
+from tensorflow.python.ops.image_ops_impl import rgb_to_yiq # line: 3972
+from tensorflow.python.ops.image_ops_impl import rgb_to_yuv # line: 4035
+from tensorflow.python.ops.image_ops_impl import rot90 # line: 658
+from tensorflow.python.ops.image_ops_impl import sample_distorted_bounding_box_v2 as sample_distorted_bounding_box # line: 3401
+from tensorflow.python.ops.image_ops_impl import sobel_edges # line: 4678
+from tensorflow.python.ops.image_ops_impl import ssim # line: 4377
+from tensorflow.python.ops.image_ops_impl import ssim_multiscale # line: 4475
+from tensorflow.python.ops.image_ops_impl import stateless_random_brightness # line: 2058
+from tensorflow.python.ops.image_ops_impl import stateless_random_contrast # line: 2151
+from tensorflow.python.ops.image_ops_impl import stateless_random_flip_left_right # line: 430
+from tensorflow.python.ops.image_ops_impl import stateless_random_flip_up_down # line: 461
+from tensorflow.python.ops.image_ops_impl import stateless_random_hue # line: 2683
+from tensorflow.python.ops.image_ops_impl import stateless_random_jpeg_quality # line: 2864
+from tensorflow.python.ops.image_ops_impl import stateless_random_saturation # line: 3050
+from tensorflow.python.ops.image_ops_impl import stateless_sample_distorted_bounding_box # line: 3527
+from tensorflow.python.ops.image_ops_impl import total_variation # line: 3329
+from tensorflow.python.ops.image_ops_impl import transpose # line: 787
+from tensorflow.python.ops.image_ops_impl import yiq_to_rgb # line: 4006
+from tensorflow.python.ops.image_ops_impl import yuv_to_rgb # line: 4065
+from tensorflow.python.ops.random_crop_ops import random_crop # line: 30
+from tensorflow.python.ops.random_crop_ops import stateless_random_crop # line: 85
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/io/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/io/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9ed20ea549ed908cbc1568b30676316850e130f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/io/__init__.py
@@ -0,0 +1,47 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.io namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.io import gfile
+from tensorflow.python.ops.gen_decode_proto_ops import decode_proto_v2 as decode_proto # line: 31
+from tensorflow.python.ops.gen_encode_proto_ops import encode_proto # line: 27
+from tensorflow.python.ops.gen_io_ops import matching_files # line: 391
+from tensorflow.python.ops.gen_io_ops import write_file # line: 2269
+from tensorflow.python.ops.gen_parsing_ops import decode_compressed # line: 144
+from tensorflow.python.ops.gen_parsing_ops import parse_tensor # line: 2135
+from tensorflow.python.ops.gen_string_ops import decode_base64 # line: 182
+from tensorflow.python.ops.gen_string_ops import encode_base64 # line: 269
+from tensorflow.python.framework.graph_io import write_graph # line: 28
+from tensorflow.python.lib.io.tf_record import TFRecordOptions # line: 35
+from tensorflow.python.lib.io.tf_record import TFRecordWriter # line: 211
+from tensorflow.python.ops.image_ops_impl import decode_and_crop_jpeg # line: 3191
+from tensorflow.python.ops.image_ops_impl import decode_bmp # line: 3197
+from tensorflow.python.ops.image_ops_impl import decode_gif # line: 3202
+from tensorflow.python.ops.image_ops_impl import decode_image # line: 3260
+from tensorflow.python.ops.image_ops_impl import decode_jpeg # line: 3207
+from tensorflow.python.ops.image_ops_impl import decode_png # line: 3212
+from tensorflow.python.ops.image_ops_impl import encode_jpeg # line: 3218
+from tensorflow.python.ops.image_ops_impl import encode_png # line: 3230
+from tensorflow.python.ops.image_ops_impl import extract_jpeg_shape # line: 3223
+from tensorflow.python.ops.image_ops_impl import is_jpeg # line: 3155
+from tensorflow.python.ops.io_ops import read_file # line: 97
+from tensorflow.python.ops.io_ops import serialize_tensor # line: 137
+from tensorflow.python.ops.parsing_config import FixedLenFeature # line: 298
+from tensorflow.python.ops.parsing_config import FixedLenSequenceFeature # line: 318
+from tensorflow.python.ops.parsing_config import RaggedFeature # line: 54
+from tensorflow.python.ops.parsing_config import SparseFeature # line: 223
+from tensorflow.python.ops.parsing_config import VarLenFeature # line: 44
+from tensorflow.python.ops.parsing_ops import decode_csv_v2 as decode_csv # line: 1073
+from tensorflow.python.ops.parsing_ops import decode_json_example # line: 1152
+from tensorflow.python.ops.parsing_ops import decode_raw # line: 843
+from tensorflow.python.ops.parsing_ops import parse_example_v2 as parse_example # line: 77
+from tensorflow.python.ops.parsing_ops import parse_sequence_example # line: 452
+from tensorflow.python.ops.parsing_ops import parse_single_example_v2 as parse_single_example # line: 410
+from tensorflow.python.ops.parsing_ops import parse_single_sequence_example # line: 696
+from tensorflow.python.ops.sparse_ops import deserialize_many_sparse # line: 2356
+from tensorflow.python.ops.sparse_ops import serialize_many_sparse_v2 as serialize_many_sparse # line: 2254
+from tensorflow.python.ops.sparse_ops import serialize_sparse_v2 as serialize_sparse # line: 2197
+from tensorflow.python.training.input import match_filenames_once # line: 56
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/io/gfile/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/io/gfile/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b1002208d271902ed287a4257c76fa848edb1f2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/io/gfile/__init__.py
@@ -0,0 +1,22 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.io.gfile namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.lib.io.file_io import copy_v2 as copy # line: 516
+from tensorflow.python.lib.io.file_io import file_exists_v2 as exists # line: 249
+from tensorflow.python.lib.io.file_io import get_registered_schemes # line: 980
+from tensorflow.python.lib.io.file_io import get_matching_files_v2 as glob # line: 385
+from tensorflow.python.lib.io.file_io import is_directory_v2 as isdir # line: 692
+from tensorflow.python.lib.io.file_io import join # line: 781
+from tensorflow.python.lib.io.file_io import list_directory_v2 as listdir # line: 751
+from tensorflow.python.lib.io.file_io import recursive_create_dir_v2 as makedirs # line: 501
+from tensorflow.python.lib.io.file_io import create_dir_v2 as mkdir # line: 470
+from tensorflow.python.lib.io.file_io import delete_file_v2 as remove # line: 318
+from tensorflow.python.lib.io.file_io import rename_v2 as rename # line: 609
+from tensorflow.python.lib.io.file_io import delete_recursively_v2 as rmtree # line: 666
+from tensorflow.python.lib.io.file_io import stat_v2 as stat # line: 911
+from tensorflow.python.lib.io.file_io import walk_v2 as walk # line: 837
+from tensorflow.python.platform.gfile import GFile # line: 36
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/linalg/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/linalg/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..254128b05ef55b76e6745a4ee2354aa0c5ce5126
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/linalg/__init__.py
@@ -0,0 +1,76 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.linalg namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.linalg import experimental
+from tensorflow.python.ops.gen_array_ops import matrix_band_part as band_part # line: 4879
+from tensorflow.python.ops.gen_array_ops import diag as tensor_diag # line: 1949
+from tensorflow.python.ops.gen_linalg_ops import cholesky # line: 766
+from tensorflow.python.ops.gen_linalg_ops import matrix_determinant as det # line: 1370
+from tensorflow.python.ops.gen_linalg_ops import matrix_inverse as inv # line: 1516
+from tensorflow.python.ops.gen_linalg_ops import lu # line: 1252
+from tensorflow.python.ops.gen_linalg_ops import qr # line: 2150
+from tensorflow.python.ops.gen_linalg_ops import matrix_solve as solve # line: 1694
+from tensorflow.python.ops.gen_linalg_ops import matrix_square_root as sqrtm # line: 1913
+from tensorflow.python.ops.gen_math_ops import cross # line: 2651
+from tensorflow.python.ops.array_ops import matrix_diag as diag # line: 2322
+from tensorflow.python.ops.array_ops import matrix_diag_part as diag_part # line: 2491
+from tensorflow.python.ops.array_ops import matrix_transpose # line: 2242
+from tensorflow.python.ops.array_ops import matrix_set_diag as set_diag # line: 2679
+from tensorflow.python.ops.array_ops import tensor_diag_part # line: 2634
+from tensorflow.python.ops.clip_ops import global_norm # line: 235
+from tensorflow.python.ops.linalg.linalg_impl import adjoint # line: 102
+from tensorflow.python.ops.linalg.linalg_impl import banded_triangular_solve # line: 350
+from tensorflow.python.ops.linalg.linalg_impl import eigh_tridiagonal # line: 1232
+from tensorflow.python.ops.linalg.linalg_impl import matrix_exponential as expm # line: 232
+from tensorflow.python.ops.linalg.linalg_impl import logdet # line: 68
+from tensorflow.python.ops.linalg.linalg_impl import logm # line: 54
+from tensorflow.python.ops.linalg.linalg_impl import lu_matrix_inverse # line: 1035
+from tensorflow.python.ops.linalg.linalg_impl import lu_reconstruct # line: 1100
+from tensorflow.python.ops.linalg.linalg_impl import lu_solve # line: 937
+from tensorflow.python.ops.linalg.linalg_impl import matrix_rank # line: 768
+from tensorflow.python.ops.linalg.linalg_impl import pinv # line: 807
+from tensorflow.python.ops.linalg.linalg_impl import slogdet # line: 44
+from tensorflow.python.ops.linalg.linalg_impl import tridiagonal_matmul # line: 670
+from tensorflow.python.ops.linalg.linalg_impl import tridiagonal_solve # line: 446
+from tensorflow.python.ops.linalg.linear_operator import LinearOperator # line: 91
+from tensorflow.python.ops.linalg.linear_operator_adjoint import LinearOperatorAdjoint # line: 28
+from tensorflow.python.ops.linalg.linear_operator_block_diag import LinearOperatorBlockDiag # line: 34
+from tensorflow.python.ops.linalg.linear_operator_block_lower_triangular import LinearOperatorBlockLowerTriangular # line: 39
+from tensorflow.python.ops.linalg.linear_operator_circulant import LinearOperatorCirculant # line: 740
+from tensorflow.python.ops.linalg.linear_operator_circulant import LinearOperatorCirculant2D # line: 1035
+from tensorflow.python.ops.linalg.linear_operator_circulant import LinearOperatorCirculant3D # line: 1264
+from tensorflow.python.ops.linalg.linear_operator_composition import LinearOperatorComposition # line: 35
+from tensorflow.python.ops.linalg.linear_operator_diag import LinearOperatorDiag # line: 32
+from tensorflow.python.ops.linalg.linear_operator_full_matrix import LinearOperatorFullMatrix # line: 29
+from tensorflow.python.ops.linalg.linear_operator_householder import LinearOperatorHouseholder # line: 32
+from tensorflow.python.ops.linalg.linear_operator_identity import LinearOperatorIdentity # line: 101
+from tensorflow.python.ops.linalg.linear_operator_identity import LinearOperatorScaledIdentity # line: 538
+from tensorflow.python.ops.linalg.linear_operator_inversion import LinearOperatorInversion # line: 25
+from tensorflow.python.ops.linalg.linear_operator_kronecker import LinearOperatorKronecker # line: 60
+from tensorflow.python.ops.linalg.linear_operator_low_rank_update import LinearOperatorLowRankUpdate # line: 35
+from tensorflow.python.ops.linalg.linear_operator_lower_triangular import LinearOperatorLowerTriangular # line: 31
+from tensorflow.python.ops.linalg.linear_operator_permutation import LinearOperatorPermutation # line: 35
+from tensorflow.python.ops.linalg.linear_operator_toeplitz import LinearOperatorToeplitz # line: 33
+from tensorflow.python.ops.linalg.linear_operator_tridiag import LinearOperatorTridiag # line: 39
+from tensorflow.python.ops.linalg.linear_operator_zeros import LinearOperatorZeros # line: 39
+from tensorflow.python.ops.linalg_ops import cholesky_solve # line: 147
+from tensorflow.python.ops.linalg_ops import eig # line: 382
+from tensorflow.python.ops.linalg_ops import self_adjoint_eig as eigh # line: 441
+from tensorflow.python.ops.linalg_ops import eigvals # line: 414
+from tensorflow.python.ops.linalg_ops import self_adjoint_eigvals as eigvalsh # line: 465
+from tensorflow.python.ops.linalg_ops import eye # line: 196
+from tensorflow.python.ops.linalg_ops import matrix_solve_ls as lstsq # line: 244
+from tensorflow.python.ops.linalg_ops import norm_v2 as norm # line: 561
+from tensorflow.python.ops.linalg_ops import svd # line: 489
+from tensorflow.python.ops.linalg_ops import matrix_triangular_solve as triangular_solve # line: 84
+from tensorflow.python.ops.math_ops import matmul # line: 3617
+from tensorflow.python.ops.math_ops import matvec # line: 3846
+from tensorflow.python.ops.math_ops import tensordot # line: 5152
+from tensorflow.python.ops.math_ops import trace # line: 3573
+from tensorflow.python.ops.nn_impl import l2_normalize # line: 540
+from tensorflow.python.ops.nn_impl import normalize # line: 487
+from tensorflow.python.ops.special_math_ops import einsum # line: 618
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/linalg/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/linalg/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..07230e4ec118e2fe89f8c47ef1ebd82d438299e0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/linalg/experimental/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.linalg.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.linalg.sparse.conjugate_gradient import conjugate_gradient # line: 30
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/lite/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/lite/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c825ca49224ad8f6231e0561c1625af60a3006a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/lite/__init__.py
@@ -0,0 +1,14 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.lite namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.lite import experimental
+from tensorflow.lite.python.convert import OpsSet # line: 169
+from tensorflow.lite.python.interpreter import Interpreter # line: 348
+from tensorflow.lite.python.lite import Optimize # line: 100
+from tensorflow.lite.python.lite import RepresentativeDataset # line: 160
+from tensorflow.lite.python.lite import TFLiteConverterV2 as TFLiteConverter # line: 1915
+from tensorflow.lite.python.lite import TargetSpec # line: 184
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/lite/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/lite/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f5840906d620d942de67110b7478a6e365cbf49
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/lite/experimental/__init__.py
@@ -0,0 +1,13 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.lite.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.lite.experimental import authoring
+from tensorflow.lite.python.analyzer import ModelAnalyzer as Analyzer # line: 35
+from tensorflow.lite.python.interpreter import OpResolverType # line: 303
+from tensorflow.lite.python.interpreter import load_delegate # line: 125
+from tensorflow.lite.tools.optimize.debugging.python.debugger import QuantizationDebugOptions # line: 56
+from tensorflow.lite.tools.optimize.debugging.python.debugger import QuantizationDebugger # line: 120
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/lite/experimental/authoring/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/lite/experimental/authoring/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ab1119e08d20c12ae992ae4e2e34f6568a1541a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/lite/experimental/authoring/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.lite.experimental.authoring namespace
+"""
+
+import sys as _sys
+
+from tensorflow.lite.python.authoring.authoring import compatible # line: 265
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/lookup/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/lookup/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc4d9dd788e172b48aa895cef02865adf679e299
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/lookup/__init__.py
@@ -0,0 +1,13 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.lookup namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.lookup import experimental
+from tensorflow.python.ops.lookup_ops import KeyValueTensorInitializer # line: 519
+from tensorflow.python.ops.lookup_ops import StaticHashTable # line: 281
+from tensorflow.python.ops.lookup_ops import StaticVocabularyTable # line: 1188
+from tensorflow.python.ops.lookup_ops import TextFileIndex # line: 587
+from tensorflow.python.ops.lookup_ops import TextFileInitializer # line: 607
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/lookup/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/lookup/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f3366bd1230a95f2a66ffb086f8d7aa3be7add3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/lookup/experimental/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.lookup.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.lookup_ops import DenseHashTable # line: 2108
+from tensorflow.python.ops.lookup_ops import MutableHashTable # line: 1796
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/math/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/math/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8239d8e6c1fdee72f2f4b26e0281a07524505838
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/math/__init__.py
@@ -0,0 +1,139 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.math namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.math import special
+from tensorflow.python.ops.gen_array_ops import invert_permutation # line: 4592
+from tensorflow.python.ops.gen_math_ops import acosh # line: 231
+from tensorflow.python.ops.gen_math_ops import asin # line: 991
+from tensorflow.python.ops.gen_math_ops import asinh # line: 1091
+from tensorflow.python.ops.gen_math_ops import atan # line: 1184
+from tensorflow.python.ops.gen_math_ops import atan2 # line: 1284
+from tensorflow.python.ops.gen_math_ops import atanh # line: 1383
+from tensorflow.python.ops.gen_math_ops import betainc # line: 1787
+from tensorflow.python.ops.gen_math_ops import cos # line: 2464
+from tensorflow.python.ops.gen_math_ops import cosh # line: 2558
+from tensorflow.python.ops.gen_math_ops import digamma # line: 3161
+from tensorflow.python.ops.gen_math_ops import erf # line: 3454
+from tensorflow.python.ops.gen_math_ops import erfc # line: 3546
+from tensorflow.python.ops.gen_math_ops import expm1 # line: 3847
+from tensorflow.python.ops.gen_math_ops import floor_mod as floormod # line: 4092
+from tensorflow.python.ops.gen_math_ops import greater # line: 4186
+from tensorflow.python.ops.gen_math_ops import greater_equal # line: 4287
+from tensorflow.python.ops.gen_math_ops import igamma # line: 4480
+from tensorflow.python.ops.gen_math_ops import igammac # line: 4639
+from tensorflow.python.ops.gen_math_ops import is_finite # line: 4935
+from tensorflow.python.ops.gen_math_ops import is_inf # line: 5031
+from tensorflow.python.ops.gen_math_ops import is_nan # line: 5127
+from tensorflow.python.ops.gen_math_ops import less # line: 5223
+from tensorflow.python.ops.gen_math_ops import less_equal # line: 5324
+from tensorflow.python.ops.gen_math_ops import lgamma # line: 5425
+from tensorflow.python.ops.gen_math_ops import log # line: 5595
+from tensorflow.python.ops.gen_math_ops import log1p # line: 5689
+from tensorflow.python.ops.gen_math_ops import logical_and # line: 5779
+from tensorflow.python.ops.gen_math_ops import logical_not # line: 5918
+from tensorflow.python.ops.gen_math_ops import logical_or # line: 6005
+from tensorflow.python.ops.gen_math_ops import maximum # line: 6310
+from tensorflow.python.ops.gen_math_ops import minimum # line: 6566
+from tensorflow.python.ops.gen_math_ops import floor_mod as mod # line: 4092
+from tensorflow.python.ops.gen_math_ops import neg as negative # line: 6913
+from tensorflow.python.ops.gen_math_ops import next_after as nextafter # line: 6999
+from tensorflow.python.ops.gen_math_ops import polygamma # line: 7167
+from tensorflow.python.ops.gen_math_ops import reciprocal # line: 8159
+from tensorflow.python.ops.gen_math_ops import rint # line: 8656
+from tensorflow.python.ops.gen_math_ops import segment_max # line: 8930
+from tensorflow.python.ops.gen_math_ops import segment_mean # line: 9164
+from tensorflow.python.ops.gen_math_ops import segment_min # line: 9289
+from tensorflow.python.ops.gen_math_ops import segment_prod # line: 9523
+from tensorflow.python.ops.gen_math_ops import segment_sum # line: 9749
+from tensorflow.python.ops.gen_math_ops import sin # line: 10299
+from tensorflow.python.ops.gen_math_ops import sinh # line: 10392
+from tensorflow.python.ops.gen_math_ops import square # line: 11962
+from tensorflow.python.ops.gen_math_ops import squared_difference # line: 12051
+from tensorflow.python.ops.gen_math_ops import tan # line: 12352
+from tensorflow.python.ops.gen_math_ops import tanh # line: 12446
+from tensorflow.python.ops.gen_math_ops import unsorted_segment_max # line: 12789
+from tensorflow.python.ops.gen_math_ops import unsorted_segment_min # line: 12927
+from tensorflow.python.ops.gen_math_ops import unsorted_segment_prod # line: 13061
+from tensorflow.python.ops.gen_math_ops import unsorted_segment_sum # line: 13195
+from tensorflow.python.ops.gen_math_ops import xlogy # line: 13444
+from tensorflow.python.ops.gen_math_ops import zeta # line: 13530
+from tensorflow.python.ops.gen_nn_ops import softsign # line: 12232
+from tensorflow.python.ops.bincount_ops import bincount # line: 29
+from tensorflow.python.ops.check_ops import is_non_decreasing # line: 1986
+from tensorflow.python.ops.check_ops import is_strictly_increasing # line: 2028
+from tensorflow.python.ops.confusion_matrix import confusion_matrix # line: 92
+from tensorflow.python.ops.math_ops import abs # line: 365
+from tensorflow.python.ops.math_ops import accumulate_n # line: 4161
+from tensorflow.python.ops.math_ops import acos # line: 5747
+from tensorflow.python.ops.math_ops import add # line: 4020
+from tensorflow.python.ops.math_ops import add_n # line: 4101
+from tensorflow.python.ops.math_ops import angle # line: 869
+from tensorflow.python.ops.math_ops import argmax_v2 as argmax # line: 268
+from tensorflow.python.ops.math_ops import argmin_v2 as argmin # line: 322
+from tensorflow.python.ops.math_ops import ceil # line: 5577
+from tensorflow.python.ops.math_ops import conj # line: 4534
+from tensorflow.python.ops.math_ops import count_nonzero_v2 as count_nonzero # line: 2570
+from tensorflow.python.ops.math_ops import cumprod # line: 4424
+from tensorflow.python.ops.math_ops import cumsum # line: 4352
+from tensorflow.python.ops.math_ops import cumulative_logsumexp # line: 4478
+from tensorflow.python.ops.math_ops import divide # line: 446
+from tensorflow.python.ops.math_ops import div_no_nan as divide_no_nan # line: 1696
+from tensorflow.python.ops.math_ops import equal # line: 2003
+from tensorflow.python.ops.math_ops import erfcinv # line: 5547
+from tensorflow.python.ops.math_ops import erfinv # line: 5512
+from tensorflow.python.ops.math_ops import exp # line: 5644
+from tensorflow.python.ops.math_ops import floor # line: 5778
+from tensorflow.python.ops.math_ops import floordiv # line: 1804
+from tensorflow.python.ops.math_ops import imag # line: 835
+from tensorflow.python.ops.math_ops import log_sigmoid # line: 4307
+from tensorflow.python.ops.math_ops import logical_xor # line: 1904
+from tensorflow.python.ops.math_ops import multiply # line: 481
+from tensorflow.python.ops.math_ops import multiply_no_nan # line: 1751
+from tensorflow.python.ops.math_ops import ndtri # line: 5531
+from tensorflow.python.ops.math_ops import not_equal # line: 2040
+from tensorflow.python.ops.math_ops import polyval # line: 5334
+from tensorflow.python.ops.math_ops import pow # line: 669
+from tensorflow.python.ops.math_ops import real # line: 794
+from tensorflow.python.ops.math_ops import reciprocal_no_nan # line: 5406
+from tensorflow.python.ops.math_ops import reduce_all # line: 3306
+from tensorflow.python.ops.math_ops import reduce_any # line: 3412
+from tensorflow.python.ops.math_ops import reduce_euclidean_norm # line: 2447
+from tensorflow.python.ops.math_ops import reduce_logsumexp # line: 3517
+from tensorflow.python.ops.math_ops import reduce_max # line: 3187
+from tensorflow.python.ops.math_ops import reduce_mean # line: 2713
+from tensorflow.python.ops.math_ops import reduce_min # line: 3059
+from tensorflow.python.ops.math_ops import reduce_prod # line: 2887
+from tensorflow.python.ops.math_ops import reduce_std # line: 2836
+from tensorflow.python.ops.math_ops import reduce_sum # line: 2369
+from tensorflow.python.ops.math_ops import reduce_variance # line: 2773
+from tensorflow.python.ops.math_ops import round # line: 914
+from tensorflow.python.ops.math_ops import rsqrt # line: 5722
+from tensorflow.python.ops.math_ops import scalar_mul_v2 as scalar_mul # line: 660
+from tensorflow.python.ops.math_ops import sigmoid # line: 4254
+from tensorflow.python.ops.math_ops import sign # line: 747
+from tensorflow.python.ops.math_ops import sobol_sample # line: 5697
+from tensorflow.python.ops.math_ops import softplus # line: 634
+from tensorflow.python.ops.math_ops import sqrt # line: 5605
+from tensorflow.python.ops.math_ops import subtract # line: 545
+from tensorflow.python.ops.math_ops import truediv # line: 1630
+from tensorflow.python.ops.math_ops import unsorted_segment_mean # line: 4657
+from tensorflow.python.ops.math_ops import unsorted_segment_sqrt_n # line: 4712
+from tensorflow.python.ops.math_ops import xdivy # line: 5440
+from tensorflow.python.ops.math_ops import xlog1py # line: 5474
+from tensorflow.python.ops.nn_impl import l2_normalize # line: 540
+from tensorflow.python.ops.nn_impl import zero_fraction # line: 620
+from tensorflow.python.ops.nn_ops import approx_max_k # line: 5882
+from tensorflow.python.ops.nn_ops import approx_min_k # line: 5945
+from tensorflow.python.ops.nn_ops import in_top_k_v2 as in_top_k # line: 6567
+from tensorflow.python.ops.nn_ops import log_softmax_v2 as log_softmax # line: 3955
+from tensorflow.python.ops.nn_ops import softmax_v2 as softmax # line: 3869
+from tensorflow.python.ops.nn_ops import top_k # line: 5815
+from tensorflow.python.ops.special_math_ops import bessel_i0 # line: 253
+from tensorflow.python.ops.special_math_ops import bessel_i0e # line: 282
+from tensorflow.python.ops.special_math_ops import bessel_i1 # line: 309
+from tensorflow.python.ops.special_math_ops import bessel_i1e # line: 338
+from tensorflow.python.ops.special_math_ops import lbeta # line: 45
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/math/special/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/math/special/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f90e74566d849fe62d8b2dd68c8ade4a890d56c4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/math/special/__init__.py
@@ -0,0 +1,24 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.math.special namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.special_math_ops import bessel_i0 # line: 253
+from tensorflow.python.ops.special_math_ops import bessel_i0e # line: 282
+from tensorflow.python.ops.special_math_ops import bessel_i1 # line: 309
+from tensorflow.python.ops.special_math_ops import bessel_i1e # line: 338
+from tensorflow.python.ops.special_math_ops import bessel_j0 # line: 477
+from tensorflow.python.ops.special_math_ops import bessel_j1 # line: 504
+from tensorflow.python.ops.special_math_ops import bessel_k0 # line: 365
+from tensorflow.python.ops.special_math_ops import bessel_k0e # line: 394
+from tensorflow.python.ops.special_math_ops import bessel_k1 # line: 421
+from tensorflow.python.ops.special_math_ops import bessel_k1e # line: 450
+from tensorflow.python.ops.special_math_ops import bessel_y0 # line: 531
+from tensorflow.python.ops.special_math_ops import bessel_y1 # line: 558
+from tensorflow.python.ops.special_math_ops import dawsn # line: 101
+from tensorflow.python.ops.special_math_ops import expint # line: 132
+from tensorflow.python.ops.special_math_ops import fresnel_cos # line: 162
+from tensorflow.python.ops.special_math_ops import fresnel_sin # line: 193
+from tensorflow.python.ops.special_math_ops import spence # line: 223
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/mlir/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/mlir/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..558ed4a35c398fdc2a1eb56fb55db90c72bc9d65
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/mlir/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.mlir namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.mlir import experimental
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/mlir/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/mlir/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ded7e7a94c56a4b895a5afb4a57299715c2d629
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/mlir/experimental/__init__.py
@@ -0,0 +1,14 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.mlir.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.compiler.mlir.mlir import convert_function # line: 48
+from tensorflow.python.compiler.mlir.mlir import convert_graph_def # line: 21
+from tensorflow.python.compiler.mlir.mlir import convert_saved_model # line: 95
+from tensorflow.python.compiler.mlir.mlir import convert_saved_model_v1 # line: 115
+from tensorflow.python.compiler.mlir.mlir import run_pass_pipeline # line: 152
+from tensorflow.python.compiler.mlir.mlir import tflite_to_tosa_bytecode # line: 181
+from tensorflow.python.compiler.mlir.mlir import experimental_write_bytecode as write_bytecode # line: 170
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/nest/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/nest/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c829f8ac1920d3cf8ce03d31a156fef83658a062
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/nest/__init__.py
@@ -0,0 +1,12 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.nest namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.util.nest import assert_same_structure # line: 301
+from tensorflow.python.util.nest import flatten # line: 201
+from tensorflow.python.util.nest import is_nested # line: 166
+from tensorflow.python.util.nest import map_structure # line: 545
+from tensorflow.python.util.nest import pack_sequence_as # line: 426
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/nn/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/nn/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d65b9b5726872e0dff9efeb1ea1c8cd8f5cefcc0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/nn/__init__.py
@@ -0,0 +1,97 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.nn namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.nn import experimental
+from tensorflow.python.ops.gen_math_ops import tanh # line: 12446
+from tensorflow.python.ops.gen_nn_ops import elu # line: 3783
+from tensorflow.python.ops.gen_nn_ops import l2_loss # line: 5717
+from tensorflow.python.ops.gen_nn_ops import lrn as local_response_normalization # line: 5806
+from tensorflow.python.ops.gen_nn_ops import lrn # line: 5806
+from tensorflow.python.ops.gen_nn_ops import relu # line: 11562
+from tensorflow.python.ops.gen_nn_ops import selu # line: 11829
+from tensorflow.python.ops.gen_nn_ops import softsign # line: 12232
+from tensorflow.python.keras.layers.rnn_cell_wrapper_v2 import DeviceWrapper as RNNCellDeviceWrapper # line: 123
+from tensorflow.python.keras.layers.rnn_cell_wrapper_v2 import DropoutWrapper as RNNCellDropoutWrapper # line: 95
+from tensorflow.python.keras.layers.rnn_cell_wrapper_v2 import ResidualWrapper as RNNCellResidualWrapper # line: 111
+from tensorflow.python.ops.array_ops import depth_to_space_v2 as depth_to_space # line: 4069
+from tensorflow.python.ops.array_ops import space_to_batch_v2 as space_to_batch # line: 4031
+from tensorflow.python.ops.array_ops import space_to_depth_v2 as space_to_depth # line: 4050
+from tensorflow.python.ops.candidate_sampling_ops import all_candidate_sampler # line: 430
+from tensorflow.python.ops.candidate_sampling_ops import compute_accidental_hits # line: 466
+from tensorflow.python.ops.candidate_sampling_ops import fixed_unigram_candidate_sampler # line: 306
+from tensorflow.python.ops.candidate_sampling_ops import learned_unigram_candidate_sampler # line: 212
+from tensorflow.python.ops.ctc_ops import collapse_repeated # line: 1175
+from tensorflow.python.ops.ctc_ops import ctc_beam_search_decoder_v2 as ctc_beam_search_decoder # line: 449
+from tensorflow.python.ops.ctc_ops import ctc_greedy_decoder # line: 300
+from tensorflow.python.ops.ctc_ops import ctc_loss_v3 as ctc_loss # line: 886
+from tensorflow.python.ops.ctc_ops import ctc_unique_labels # line: 1273
+from tensorflow.python.ops.embedding_ops import embedding_lookup_v2 as embedding_lookup # line: 371
+from tensorflow.python.ops.embedding_ops import embedding_lookup_sparse_v2 as embedding_lookup_sparse # line: 575
+from tensorflow.python.ops.embedding_ops import safe_embedding_lookup_sparse_v2 as safe_embedding_lookup_sparse # line: 734
+from tensorflow.python.ops.math_ops import sigmoid # line: 4254
+from tensorflow.python.ops.math_ops import softplus # line: 634
+from tensorflow.python.ops.nn_impl import batch_norm_with_global_normalization_v2 as batch_norm_with_global_normalization # line: 1649
+from tensorflow.python.ops.nn_impl import batch_normalization # line: 1420
+from tensorflow.python.ops.nn_impl import depthwise_conv2d_v2 as depthwise_conv2d # line: 792
+from tensorflow.python.ops.nn_impl import l2_normalize # line: 540
+from tensorflow.python.ops.nn_impl import log_poisson_loss # line: 43
+from tensorflow.python.ops.nn_impl import moments_v2 as moments # line: 1281
+from tensorflow.python.ops.nn_impl import nce_loss_v2 as nce_loss # line: 1900
+from tensorflow.python.ops.nn_impl import normalize_moments # line: 1182
+from tensorflow.python.ops.nn_impl import sampled_softmax_loss_v2 as sampled_softmax_loss # line: 2117
+from tensorflow.python.ops.nn_impl import separable_conv2d_v2 as separable_conv2d # line: 1001
+from tensorflow.python.ops.nn_impl import sigmoid_cross_entropy_with_logits_v2 as sigmoid_cross_entropy_with_logits # line: 150
+from tensorflow.python.ops.nn_impl import swish as silu # line: 430
+from tensorflow.python.ops.nn_impl import sufficient_statistics_v2 as sufficient_statistics # line: 1152
+from tensorflow.python.ops.nn_impl import swish # line: 430
+from tensorflow.python.ops.nn_impl import weighted_cross_entropy_with_logits_v2 as weighted_cross_entropy_with_logits # line: 249
+from tensorflow.python.ops.nn_impl import weighted_moments_v2 as weighted_moments # line: 1395
+from tensorflow.python.ops.nn_impl import zero_fraction # line: 620
+from tensorflow.python.ops.nn_impl_distribute import compute_average_loss # line: 70
+from tensorflow.python.ops.nn_impl_distribute import scale_regularization_loss # line: 27
+from tensorflow.python.ops.nn_ops import approx_max_k # line: 5882
+from tensorflow.python.ops.nn_ops import approx_min_k # line: 5945
+from tensorflow.python.ops.nn_ops import atrous_conv2d # line: 1790
+from tensorflow.python.ops.nn_ops import atrous_conv2d_transpose # line: 2814
+from tensorflow.python.ops.nn_ops import avg_pool_v2 as avg_pool # line: 4463
+from tensorflow.python.ops.nn_ops import avg_pool1d # line: 4616
+from tensorflow.python.ops.nn_ops import avg_pool2d # line: 4575
+from tensorflow.python.ops.nn_ops import avg_pool3d # line: 4663
+from tensorflow.python.ops.nn_ops import bias_add # line: 3516
+from tensorflow.python.ops.nn_ops import conv1d_v2 as conv1d # line: 2099
+from tensorflow.python.ops.nn_ops import conv1d_transpose # line: 2169
+from tensorflow.python.ops.nn_ops import conv2d_v2 as conv2d # line: 2260
+from tensorflow.python.ops.nn_ops import conv2d_transpose_v2 as conv2d_transpose # line: 2692
+from tensorflow.python.ops.nn_ops import conv3d_v2 as conv3d # line: 3239
+from tensorflow.python.ops.nn_ops import conv3d_transpose_v2 as conv3d_transpose # line: 3347
+from tensorflow.python.ops.nn_ops import conv_transpose # line: 3428
+from tensorflow.python.ops.nn_ops import convolution_v2 as convolution # line: 1176
+from tensorflow.python.ops.nn_ops import crelu_v2 as crelu # line: 3623
+from tensorflow.python.ops.nn_ops import depthwise_conv2d_native_backprop_filter as depthwise_conv2d_backprop_filter # line: 3126
+from tensorflow.python.ops.nn_ops import depthwise_conv2d_native_backprop_input as depthwise_conv2d_backprop_input # line: 3054
+from tensorflow.python.ops.nn_ops import dilation2d_v2 as dilation2d # line: 482
+from tensorflow.python.ops.nn_ops import dropout_v2 as dropout # line: 5425
+from tensorflow.python.ops.nn_ops import erosion2d_v2 as erosion2d # line: 6459
+from tensorflow.python.ops.nn_ops import fractional_avg_pool_v2 as fractional_avg_pool # line: 6321
+from tensorflow.python.ops.nn_ops import fractional_max_pool_v2 as fractional_max_pool # line: 6141
+from tensorflow.python.ops.nn_ops import gelu # line: 3702
+from tensorflow.python.ops.nn_ops import in_top_k_v2 as in_top_k # line: 6567
+from tensorflow.python.ops.nn_ops import isotonic_regression # line: 6617
+from tensorflow.python.ops.nn_ops import leaky_relu # line: 3667
+from tensorflow.python.ops.nn_ops import log_softmax_v2 as log_softmax # line: 3955
+from tensorflow.python.ops.nn_ops import max_pool_v2 as max_pool # line: 4705
+from tensorflow.python.ops.nn_ops import max_pool1d # line: 4911
+from tensorflow.python.ops.nn_ops import max_pool2d # line: 4971
+from tensorflow.python.ops.nn_ops import max_pool3d # line: 5084
+from tensorflow.python.ops.nn_ops import max_pool_with_argmax_v2 as max_pool_with_argmax # line: 5128
+from tensorflow.python.ops.nn_ops import pool_v2 as pool # line: 1689
+from tensorflow.python.ops.nn_ops import relu6 # line: 3630
+from tensorflow.python.ops.nn_ops import softmax_v2 as softmax # line: 3869
+from tensorflow.python.ops.nn_ops import softmax_cross_entropy_with_logits_v2 as softmax_cross_entropy_with_logits # line: 3989
+from tensorflow.python.ops.nn_ops import sparse_softmax_cross_entropy_with_logits_v2 as sparse_softmax_cross_entropy_with_logits # line: 4402
+from tensorflow.python.ops.nn_ops import top_k # line: 5815
+from tensorflow.python.ops.nn_ops import with_space_to_batch # line: 572
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/nn/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/nn/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..47fa94d259f19d723898910f486905ac0faf5708
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/nn/experimental/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.nn.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.nn_ops import general_dropout # line: 5664
+from tensorflow.python.ops.nn_ops import stateless_dropout # line: 5520
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/profiler/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/profiler/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..008ea525b0443adcd22f5a7208ff4ec86705ef1a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/profiler/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.profiler namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.profiler import experimental
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/profiler/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/profiler/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c6a30d7a3f226942c45250cacda0e53a1dacfebb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/profiler/experimental/__init__.py
@@ -0,0 +1,14 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.profiler.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.profiler.experimental import client
+from tensorflow._api.v2.profiler.experimental import server
+from tensorflow.python.profiler.profiler_v2 import Profile # line: 183
+from tensorflow.python.profiler.profiler_v2 import ProfilerOptions # line: 45
+from tensorflow.python.profiler.profiler_v2 import start # line: 80
+from tensorflow.python.profiler.profiler_v2 import stop # line: 129
+from tensorflow.python.profiler.trace import Trace # line: 27
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/profiler/experimental/client/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/profiler/experimental/client/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7948c300b88a45b6a34b1f753dbb310d79aabd33
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/profiler/experimental/client/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.profiler.experimental.client namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.profiler.profiler_client import monitor # line: 134
+from tensorflow.python.profiler.profiler_client import trace # line: 25
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/profiler/experimental/server/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/profiler/experimental/server/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..790c00223c2bd2f43ff546735432431d96f67319
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/profiler/experimental/server/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.profiler.experimental.server namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.profiler.profiler_v2 import start_server as start # line: 168
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/quantization/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/quantization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e5fd18fabbd426cd43f7742527e31b7598fe7a1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/quantization/__init__.py
@@ -0,0 +1,19 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.quantization namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.quantization import experimental
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_args # line: 2698
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_args_gradient # line: 2867
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_vars # line: 3003
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_vars_gradient # line: 3145
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_vars_per_channel # line: 3276
+from tensorflow.python.ops.gen_array_ops import fake_quant_with_min_max_vars_per_channel_gradient # line: 3423
+from tensorflow.python.ops.gen_array_ops import quantized_concat # line: 8202
+from tensorflow.python.ops.array_ops import dequantize # line: 6139
+from tensorflow.python.ops.array_ops import quantize # line: 6100
+from tensorflow.python.ops.array_ops import quantize_and_dequantize # line: 6176
+from tensorflow.python.ops.array_ops import quantize_and_dequantize_v2 # line: 6243
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/quantization/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/quantization/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..267d4981e8bcc0b3f894cc33fc1e0c236254a4af
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/quantization/experimental/__init__.py
@@ -0,0 +1,13 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.quantization.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.compiler.mlir.quantization.tensorflow.python.quantize_model import _QuantizationComponentSpec as QuantizationComponentSpec # line: 58
+from tensorflow.compiler.mlir.quantization.tensorflow.python.quantize_model import _QuantizationMethod as QuantizationMethod # line: 54
+from tensorflow.compiler.mlir.quantization.tensorflow.python.quantize_model import _QuantizationOptions as QuantizationOptions # line: 50
+from tensorflow.compiler.mlir.quantization.tensorflow.python.quantize_model import _UnitWiseQuantizationSpec as UnitWiseQuantizationSpec # line: 62
+from tensorflow.compiler.mlir.quantization.tensorflow.python.quantize_model import quantize as quantize_saved_model # line: 1467
+from tensorflow.compiler.mlir.quantization.tensorflow.python.representative_dataset import TfRecordRepresentativeDatasetSaver # line: 78
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/queue/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/queue/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddd979996fbf7408213917cccdd9b6ff4b35d50f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/queue/__init__.py
@@ -0,0 +1,12 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.queue namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.data_flow_ops import FIFOQueue # line: 712
+from tensorflow.python.ops.data_flow_ops import PaddingFIFOQueue # line: 848
+from tensorflow.python.ops.data_flow_ops import PriorityQueue # line: 924
+from tensorflow.python.ops.data_flow_ops import QueueBase # line: 115
+from tensorflow.python.ops.data_flow_ops import RandomShuffleQueue # line: 622
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/ragged/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/ragged/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..748ef1c0f48cce3ca8ea24176f26b1227b77a4e7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/ragged/__init__.py
@@ -0,0 +1,17 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.ragged namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.ragged.ragged_array_ops import boolean_mask # line: 49
+from tensorflow.python.ops.ragged.ragged_array_ops import cross # line: 741
+from tensorflow.python.ops.ragged.ragged_array_ops import cross_hashed # line: 768
+from tensorflow.python.ops.ragged.ragged_array_ops import stack_dynamic_partitions # line: 575
+from tensorflow.python.ops.ragged.ragged_concat_ops import stack # line: 73
+from tensorflow.python.ops.ragged.ragged_factory_ops import constant # line: 35
+from tensorflow.python.ops.ragged.ragged_functional_ops import map_flat_values # line: 25
+from tensorflow.python.ops.ragged.ragged_math_ops import range # line: 44
+from tensorflow.python.ops.ragged.segment_id_ops import row_splits_to_segment_ids # line: 31
+from tensorflow.python.ops.ragged.segment_id_ops import segment_ids_to_row_splits # line: 75
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/random/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/random/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b26fa2ec8a2b3acd3ee83d3cd23d6dd88db102c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/random/__init__.py
@@ -0,0 +1,36 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.random namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.random import experimental
+from tensorflow.python.framework.random_seed import set_seed # line: 210
+from tensorflow.python.ops.candidate_sampling_ops import all_candidate_sampler # line: 430
+from tensorflow.python.ops.candidate_sampling_ops import fixed_unigram_candidate_sampler # line: 306
+from tensorflow.python.ops.candidate_sampling_ops import learned_unigram_candidate_sampler # line: 212
+from tensorflow.python.ops.candidate_sampling_ops import log_uniform_candidate_sampler # line: 116
+from tensorflow.python.ops.candidate_sampling_ops import uniform_candidate_sampler # line: 27
+from tensorflow.python.ops.random_ops import categorical # line: 394
+from tensorflow.python.ops.random_ops import random_gamma as gamma # line: 451
+from tensorflow.python.ops.random_ops import random_normal as normal # line: 39
+from tensorflow.python.ops.random_ops import random_poisson_v2 as poisson # line: 586
+from tensorflow.python.ops.random_ops import random_shuffle as shuffle # line: 326
+from tensorflow.python.ops.random_ops import truncated_normal # line: 155
+from tensorflow.python.ops.random_ops import random_uniform as uniform # line: 211
+from tensorflow.python.ops.random_ops_util import Algorithm # line: 30
+from tensorflow.python.ops.stateful_random_ops import Generator # line: 220
+from tensorflow.python.ops.stateful_random_ops import create_rng_state # line: 174
+from tensorflow.python.ops.stateful_random_ops import get_global_generator # line: 975
+from tensorflow.python.ops.stateful_random_ops import set_global_generator # line: 1000
+from tensorflow.python.ops.stateless_random_ops import fold_in # line: 93
+from tensorflow.python.ops.stateless_random_ops import split # line: 51
+from tensorflow.python.ops.stateless_random_ops import stateless_random_binomial as stateless_binomial # line: 411
+from tensorflow.python.ops.stateless_random_ops import stateless_categorical # line: 793
+from tensorflow.python.ops.stateless_random_ops import stateless_random_gamma as stateless_gamma # line: 481
+from tensorflow.python.ops.stateless_random_ops import stateless_random_normal as stateless_normal # line: 644
+from tensorflow.python.ops.stateless_random_ops import stateless_parameterized_truncated_normal # line: 851
+from tensorflow.python.ops.stateless_random_ops import stateless_random_poisson as stateless_poisson # line: 584
+from tensorflow.python.ops.stateless_random_ops import stateless_truncated_normal # line: 695
+from tensorflow.python.ops.stateless_random_ops import stateless_random_uniform as stateless_uniform # line: 274
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/random/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/random/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8941fae2111531b4998ca26a4321f32e39a0b5ea
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/random/experimental/__init__.py
@@ -0,0 +1,16 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.random.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.random_ops_util import Algorithm # line: 30
+from tensorflow.python.ops.stateful_random_ops import Generator # line: 220
+from tensorflow.python.ops.stateful_random_ops import create_rng_state # line: 174
+from tensorflow.python.ops.stateful_random_ops import get_global_generator # line: 975
+from tensorflow.python.ops.stateful_random_ops import set_global_generator # line: 1000
+from tensorflow.python.ops.stateless_random_ops import index_shuffle # line: 135
+from tensorflow.python.ops.stateless_random_ops import fold_in as stateless_fold_in # line: 93
+from tensorflow.python.ops.stateless_random_ops import stateless_shuffle # line: 226
+from tensorflow.python.ops.stateless_random_ops import split as stateless_split # line: 51
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/raw_ops/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/raw_ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e650c4b2247cad3d39b71e2c6ce5fa7a74699a5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/raw_ops/__init__.py
@@ -0,0 +1,1422 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.raw_ops namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.gen_array_ops import BatchMatrixBandPart # line: 68
+from tensorflow.python.ops.gen_array_ops import BatchMatrixDiag # line: 126
+from tensorflow.python.ops.gen_array_ops import BatchMatrixDiagPart # line: 182
+from tensorflow.python.ops.gen_array_ops import BatchMatrixSetDiag # line: 239
+from tensorflow.python.ops.gen_array_ops import BatchToSpace # line: 319
+from tensorflow.python.ops.gen_array_ops import BatchToSpaceND # line: 533
+from tensorflow.python.ops.gen_array_ops import Bitcast # line: 677
+from tensorflow.python.ops.gen_array_ops import BroadcastArgs # line: 739
+from tensorflow.python.ops.gen_array_ops import BroadcastGradientArgs # line: 807
+from tensorflow.python.ops.gen_array_ops import BroadcastTo # line: 928
+from tensorflow.python.ops.gen_array_ops import CheckNumerics # line: 1041
+from tensorflow.python.ops.gen_array_ops import CheckNumericsV2 # line: 1107
+from tensorflow.python.ops.gen_array_ops import Concat # line: 1174
+from tensorflow.python.ops.gen_array_ops import ConcatOffset # line: 1255
+from tensorflow.python.ops.gen_array_ops import ConcatV2 # line: 1328
+from tensorflow.python.ops.gen_array_ops import ConjugateTranspose # line: 1397
+from tensorflow.python.ops.gen_array_ops import Const # line: 1458
+from tensorflow.python.ops.gen_array_ops import DebugGradientIdentity # line: 1519
+from tensorflow.python.ops.gen_array_ops import DebugGradientRefIdentity # line: 1567
+from tensorflow.python.ops.gen_array_ops import DeepCopy # line: 1613
+from tensorflow.python.ops.gen_array_ops import DepthToSpace # line: 1771
+from tensorflow.python.ops.gen_array_ops import Dequantize # line: 1914
+from tensorflow.python.ops.gen_array_ops import Diag # line: 2032
+from tensorflow.python.ops.gen_array_ops import DiagPart # line: 2109
+from tensorflow.python.ops.gen_array_ops import EditDistance # line: 2204
+from tensorflow.python.ops.gen_array_ops import Empty # line: 2279
+from tensorflow.python.ops.gen_array_ops import EnsureShape # line: 2345
+from tensorflow.python.ops.gen_array_ops import ExpandDims # line: 2439
+from tensorflow.python.ops.gen_array_ops import ExtractImagePatches # line: 2532
+from tensorflow.python.ops.gen_array_ops import ExtractVolumePatches # line: 2667
+from tensorflow.python.ops.gen_array_ops import FakeQuantWithMinMaxArgs # line: 2834
+from tensorflow.python.ops.gen_array_ops import FakeQuantWithMinMaxArgsGradient # line: 2969
+from tensorflow.python.ops.gen_array_ops import FakeQuantWithMinMaxVars # line: 3113
+from tensorflow.python.ops.gen_array_ops import FakeQuantWithMinMaxVarsGradient # line: 3247
+from tensorflow.python.ops.gen_array_ops import FakeQuantWithMinMaxVarsPerChannel # line: 3391
+from tensorflow.python.ops.gen_array_ops import FakeQuantWithMinMaxVarsPerChannelGradient # line: 3530
+from tensorflow.python.ops.gen_array_ops import Fill # line: 3625
+from tensorflow.python.ops.gen_array_ops import Fingerprint # line: 3716
+from tensorflow.python.ops.gen_array_ops import Gather # line: 3810
+from tensorflow.python.ops.gen_array_ops import GatherNd # line: 3980
+from tensorflow.python.ops.gen_array_ops import GatherV2 # line: 4083
+from tensorflow.python.ops.gen_array_ops import GuaranteeConst # line: 4152
+from tensorflow.python.ops.gen_array_ops import Identity # line: 4208
+from tensorflow.python.ops.gen_array_ops import IdentityN # line: 4304
+from tensorflow.python.ops.gen_array_ops import ImmutableConst # line: 4374
+from tensorflow.python.ops.gen_array_ops import InplaceAdd # line: 4439
+from tensorflow.python.ops.gen_array_ops import InplaceSub # line: 4503
+from tensorflow.python.ops.gen_array_ops import InplaceUpdate # line: 4570
+from tensorflow.python.ops.gen_array_ops import InvertPermutation # line: 4672
+from tensorflow.python.ops.gen_array_ops import ListDiff # line: 4766
+from tensorflow.python.ops.gen_array_ops import LowerBound # line: 4854
+from tensorflow.python.ops.gen_array_ops import MatrixBandPart # line: 4991
+from tensorflow.python.ops.gen_array_ops import MatrixDiag # line: 5077
+from tensorflow.python.ops.gen_array_ops import MatrixDiagPart # line: 5162
+from tensorflow.python.ops.gen_array_ops import MatrixDiagPartV2 # line: 5296
+from tensorflow.python.ops.gen_array_ops import MatrixDiagPartV3 # line: 5475
+from tensorflow.python.ops.gen_array_ops import MatrixDiagV2 # line: 5643
+from tensorflow.python.ops.gen_array_ops import MatrixDiagV3 # line: 5851
+from tensorflow.python.ops.gen_array_ops import MatrixSetDiag # line: 5929
+from tensorflow.python.ops.gen_array_ops import MatrixSetDiagV2 # line: 6066
+from tensorflow.python.ops.gen_array_ops import MatrixSetDiagV3 # line: 6249
+from tensorflow.python.ops.gen_array_ops import MirrorPad # line: 6348
+from tensorflow.python.ops.gen_array_ops import MirrorPadGrad # line: 6433
+from tensorflow.python.ops.gen_array_ops import OneHot # line: 6595
+from tensorflow.python.ops.gen_array_ops import OnesLike # line: 6658
+from tensorflow.python.ops.gen_array_ops import Pack # line: 6747
+from tensorflow.python.ops.gen_array_ops import Pad # line: 6837
+from tensorflow.python.ops.gen_array_ops import PadV2 # line: 6924
+from tensorflow.python.ops.gen_array_ops import ParallelConcat # line: 7011
+from tensorflow.python.ops.gen_array_ops import Placeholder # line: 7085
+from tensorflow.python.ops.gen_array_ops import PlaceholderV2 # line: 7154
+from tensorflow.python.ops.gen_array_ops import PlaceholderWithDefault # line: 7215
+from tensorflow.python.ops.gen_array_ops import PreventGradient # line: 7288
+from tensorflow.python.ops.gen_array_ops import QuantizeAndDequantize # line: 7378
+from tensorflow.python.ops.gen_array_ops import QuantizeAndDequantizeV2 # line: 7567
+from tensorflow.python.ops.gen_array_ops import QuantizeAndDequantizeV3 # line: 7680
+from tensorflow.python.ops.gen_array_ops import QuantizeAndDequantizeV4 # line: 7820
+from tensorflow.python.ops.gen_array_ops import QuantizeAndDequantizeV4Grad # line: 7922
+from tensorflow.python.ops.gen_array_ops import QuantizeV2 # line: 8159
+from tensorflow.python.ops.gen_array_ops import QuantizedConcat # line: 8310
+from tensorflow.python.ops.gen_array_ops import QuantizedInstanceNorm # line: 8450
+from tensorflow.python.ops.gen_array_ops import QuantizedReshape # line: 8543
+from tensorflow.python.ops.gen_array_ops import Rank # line: 8616
+from tensorflow.python.ops.gen_array_ops import RefIdentity # line: 8660
+from tensorflow.python.ops.gen_array_ops import Reshape # line: 8770
+from tensorflow.python.ops.gen_array_ops import ResourceStridedSliceAssign # line: 8864
+from tensorflow.python.ops.gen_array_ops import Reverse # line: 8985
+from tensorflow.python.ops.gen_array_ops import ReverseSequence # line: 9113
+from tensorflow.python.ops.gen_array_ops import ReverseV2 # line: 9253
+from tensorflow.python.ops.gen_array_ops import ScatterNd # line: 9434
+from tensorflow.python.ops.gen_array_ops import ScatterNdNonAliasingAdd # line: 9537
+from tensorflow.python.ops.gen_array_ops import Shape # line: 9611
+from tensorflow.python.ops.gen_array_ops import ShapeN # line: 9682
+from tensorflow.python.ops.gen_array_ops import Size # line: 9761
+from tensorflow.python.ops.gen_array_ops import Slice # line: 9837
+from tensorflow.python.ops.gen_array_ops import Snapshot # line: 9895
+from tensorflow.python.ops.gen_array_ops import SpaceToBatch # line: 10051
+from tensorflow.python.ops.gen_array_ops import SpaceToBatchND # line: 10267
+from tensorflow.python.ops.gen_array_ops import SpaceToDepth # line: 10422
+from tensorflow.python.ops.gen_array_ops import Split # line: 10490
+from tensorflow.python.ops.gen_array_ops import SplitV # line: 10559
+from tensorflow.python.ops.gen_array_ops import Squeeze # line: 10648
+from tensorflow.python.ops.gen_array_ops import StopGradient # line: 10769
+from tensorflow.python.ops.gen_array_ops import StridedSlice # line: 10985
+from tensorflow.python.ops.gen_array_ops import StridedSliceAssign # line: 11090
+from tensorflow.python.ops.gen_array_ops import StridedSliceGrad # line: 11186
+from tensorflow.python.ops.gen_array_ops import TensorScatterAdd # line: 11358
+from tensorflow.python.ops.gen_array_ops import TensorScatterMax # line: 11464
+from tensorflow.python.ops.gen_array_ops import TensorScatterMin # line: 11559
+from tensorflow.python.ops.gen_array_ops import TensorScatterSub # line: 11718
+from tensorflow.python.ops.gen_array_ops import TensorScatterUpdate # line: 11820
+from tensorflow.python.ops.gen_array_ops import TensorStridedSliceUpdate # line: 11928
+from tensorflow.python.ops.gen_array_ops import Tile # line: 12064
+from tensorflow.python.ops.gen_array_ops import TileGrad # line: 12127
+from tensorflow.python.ops.gen_array_ops import Transpose # line: 12190
+from tensorflow.python.ops.gen_array_ops import Unique # line: 12284
+from tensorflow.python.ops.gen_array_ops import UniqueV2 # line: 12406
+from tensorflow.python.ops.gen_array_ops import UniqueWithCounts # line: 12499
+from tensorflow.python.ops.gen_array_ops import UniqueWithCountsV2 # line: 12626
+from tensorflow.python.ops.gen_array_ops import Unpack # line: 12708
+from tensorflow.python.ops.gen_array_ops import UnravelIndex # line: 12820
+from tensorflow.python.ops.gen_array_ops import UpperBound # line: 12906
+from tensorflow.python.ops.gen_array_ops import Where # line: 13026
+from tensorflow.python.ops.gen_array_ops import ZerosLike # line: 13082
+from tensorflow.python.ops.gen_audio_ops import AudioSpectrogram # line: 108
+from tensorflow.python.ops.gen_audio_ops import DecodeWav # line: 237
+from tensorflow.python.ops.gen_audio_ops import EncodeWav # line: 338
+from tensorflow.python.ops.gen_audio_ops import Mfcc # line: 443
+from tensorflow.python.ops.gen_batch_ops import Batch # line: 171
+from tensorflow.python.ops.gen_batch_ops import BatchFunction # line: 429
+from tensorflow.python.ops.gen_batch_ops import Unbatch # line: 579
+from tensorflow.python.ops.gen_batch_ops import UnbatchGrad # line: 676
+from tensorflow.python.ops.gen_bitwise_ops import BitwiseAnd # line: 113
+from tensorflow.python.ops.gen_bitwise_ops import BitwiseOr # line: 219
+from tensorflow.python.ops.gen_bitwise_ops import BitwiseXor # line: 325
+from tensorflow.python.ops.gen_bitwise_ops import Invert # line: 450
+from tensorflow.python.ops.gen_bitwise_ops import LeftShift # line: 566
+from tensorflow.python.ops.gen_bitwise_ops import PopulationCount # line: 631
+from tensorflow.python.ops.gen_bitwise_ops import RightShift # line: 749
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesAggregateStats # line: 85
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesBucketize # line: 167
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesCalculateBestFeatureSplit # line: 289
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesCalculateBestFeatureSplitV2 # line: 413
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesCalculateBestGainsPerFeature # line: 536
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesCenterBias # line: 623
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesCreateEnsemble # line: 684
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesCreateQuantileStreamResource # line: 747
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesDeserializeEnsemble # line: 808
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesEnsembleResourceHandleOp # line: 873
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesExampleDebugOutputs # line: 956
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesFlushQuantileSummaries # line: 1031
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesGetEnsembleStates # line: 1099
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesMakeQuantileSummaries # line: 1169
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesMakeStatsSummary # line: 1260
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesPredict # line: 1347
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesQuantileStreamResourceAddSummaries # line: 1416
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesQuantileStreamResourceDeserialize # line: 1480
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesQuantileStreamResourceFlush # line: 1551
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesQuantileStreamResourceGetBucketBoundaries # line: 1619
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesQuantileStreamResourceHandleOp # line: 1685
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesSerializeEnsemble # line: 1755
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesSparseAggregateStats # line: 1857
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesSparseCalculateBestFeatureSplit # line: 1980
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesTrainingPredict # line: 2088
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesUpdateEnsemble # line: 2228
+from tensorflow.python.ops.gen_boosted_trees_ops import BoostedTreesUpdateEnsembleV2 # line: 2441
+from tensorflow.python.ops.gen_boosted_trees_ops import IsBoostedTreesEnsembleInitialized # line: 2574
+from tensorflow.python.ops.gen_boosted_trees_ops import IsBoostedTreesQuantileStreamResourceInitialized # line: 2634
+from tensorflow.python.ops.gen_candidate_sampling_ops import AllCandidateSampler # line: 113
+from tensorflow.python.ops.gen_candidate_sampling_ops import ComputeAccidentalHits # line: 214
+from tensorflow.python.ops.gen_candidate_sampling_ops import FixedUnigramCandidateSampler # line: 405
+from tensorflow.python.ops.gen_candidate_sampling_ops import LearnedUnigramCandidateSampler # line: 553
+from tensorflow.python.ops.gen_candidate_sampling_ops import LogUniformCandidateSampler # line: 677
+from tensorflow.python.ops.gen_candidate_sampling_ops import ThreadUnsafeUnigramCandidateSampler # line: 802
+from tensorflow.python.ops.gen_candidate_sampling_ops import UniformCandidateSampler # line: 925
+from tensorflow.python.ops.gen_checkpoint_ops import GenerateVocabRemapping # line: 123
+from tensorflow.python.ops.gen_checkpoint_ops import LoadAndRemapMatrix # line: 264
+from tensorflow.python.ops.gen_clustering_ops import KMC2ChainInitialization # line: 72
+from tensorflow.python.ops.gen_clustering_ops import KmeansPlusPlusInitialization # line: 149
+from tensorflow.python.ops.gen_clustering_ops import NearestNeighbors # line: 225
+from tensorflow.python.ops.gen_collective_ops import CollectiveAllToAllV2 # line: 106
+from tensorflow.python.ops.gen_collective_ops import CollectiveAllToAllV3 # line: 193
+from tensorflow.python.ops.gen_collective_ops import CollectiveAssignGroupV2 # line: 265
+from tensorflow.python.ops.gen_collective_ops import CollectiveBcastRecv # line: 357
+from tensorflow.python.ops.gen_collective_ops import CollectiveBcastRecvV2 # line: 449
+from tensorflow.python.ops.gen_collective_ops import CollectiveBcastSend # line: 548
+from tensorflow.python.ops.gen_collective_ops import CollectiveBcastSendV2 # line: 637
+from tensorflow.python.ops.gen_collective_ops import CollectiveGather # line: 735
+from tensorflow.python.ops.gen_collective_ops import CollectiveGatherV2 # line: 843
+from tensorflow.python.ops.gen_collective_ops import CollectiveInitializeCommunicator # line: 935
+from tensorflow.python.ops.gen_collective_ops import CollectiveReduce # line: 1054
+from tensorflow.python.ops.gen_collective_ops import CollectiveReduceScatterV2 # line: 1194
+from tensorflow.python.ops.gen_collective_ops import CollectiveReduceV2 # line: 1333
+from tensorflow.python.ops.gen_collective_ops import CollectiveReduceV3 # line: 1431
+from tensorflow.python.ops.gen_composite_tensor_ops import CompositeTensorVariantFromComponents # line: 74
+from tensorflow.python.ops.gen_composite_tensor_ops import CompositeTensorVariantToComponents # line: 152
+from tensorflow.python.ops.gen_control_flow_ops import Abort # line: 71
+from tensorflow.python.ops.gen_control_flow_ops import ControlTrigger # line: 120
+from tensorflow.python.ops.gen_control_flow_ops import Enter # line: 196
+from tensorflow.python.ops.gen_control_flow_ops import Exit # line: 262
+from tensorflow.python.ops.gen_control_flow_ops import LoopCond # line: 320
+from tensorflow.python.ops.gen_control_flow_ops import Merge # line: 396
+from tensorflow.python.ops.gen_control_flow_ops import NextIteration # line: 457
+from tensorflow.python.ops.gen_control_flow_ops import NoOp # line: 529
+from tensorflow.python.ops.gen_control_flow_ops import RefEnter # line: 592
+from tensorflow.python.ops.gen_control_flow_ops import RefExit # line: 629
+from tensorflow.python.ops.gen_control_flow_ops import RefMerge # line: 682
+from tensorflow.python.ops.gen_control_flow_ops import RefNextIteration # line: 717
+from tensorflow.python.ops.gen_control_flow_ops import RefSelect # line: 759
+from tensorflow.python.ops.gen_control_flow_ops import RefSwitch # line: 808
+from tensorflow.python.ops.gen_control_flow_ops import Switch # line: 869
+from tensorflow.python.ops.gen_count_ops import DenseCountSparseOutput # line: 103
+from tensorflow.python.ops.gen_count_ops import RaggedCountSparseOutput # line: 211
+from tensorflow.python.ops.gen_count_ops import SparseCountSparseOutput # line: 323
+from tensorflow.python.ops.gen_ctc_ops import CTCBeamSearchDecoder # line: 107
+from tensorflow.python.ops.gen_ctc_ops import CTCGreedyDecoder # line: 212
+from tensorflow.python.ops.gen_ctc_ops import CTCLoss # line: 335
+from tensorflow.python.ops.gen_ctc_ops import CTCLossV2 # line: 463
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNN # line: 151
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNBackprop # line: 337
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNBackpropV2 # line: 524
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNBackpropV3 # line: 733
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNCanonicalToParams # line: 907
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNCanonicalToParamsV2 # line: 1098
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNParamsSize # line: 1267
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNParamsToCanonical # line: 1436
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNParamsToCanonicalV2 # line: 1620
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNV2 # line: 1799
+from tensorflow.python.ops.gen_cudnn_rnn_ops import CudnnRNNV3 # line: 1988
+from tensorflow.python.ops.gen_data_flow_ops import AccumulatorApplyGradient # line: 52
+from tensorflow.python.ops.gen_data_flow_ops import AccumulatorNumAccumulated # line: 84
+from tensorflow.python.ops.gen_data_flow_ops import AccumulatorSetGlobalStep # line: 115
+from tensorflow.python.ops.gen_data_flow_ops import AccumulatorTakeGradient # line: 162
+from tensorflow.python.ops.gen_data_flow_ops import Barrier # line: 244
+from tensorflow.python.ops.gen_data_flow_ops import BarrierClose # line: 284
+from tensorflow.python.ops.gen_data_flow_ops import BarrierIncompleteSize # line: 316
+from tensorflow.python.ops.gen_data_flow_ops import BarrierInsertMany # line: 356
+from tensorflow.python.ops.gen_data_flow_ops import BarrierReadySize # line: 388
+from tensorflow.python.ops.gen_data_flow_ops import BarrierTakeMany # line: 472
+from tensorflow.python.ops.gen_data_flow_ops import ConditionalAccumulator # line: 540
+from tensorflow.python.ops.gen_data_flow_ops import DeleteSessionTensor # line: 577
+from tensorflow.python.ops.gen_data_flow_ops import DynamicPartition # line: 713
+from tensorflow.python.ops.gen_data_flow_ops import DynamicStitch # line: 877
+from tensorflow.python.ops.gen_data_flow_ops import FIFOQueue # line: 977
+from tensorflow.python.ops.gen_data_flow_ops import FIFOQueueV2 # line: 1066
+from tensorflow.python.ops.gen_data_flow_ops import FakeQueue # line: 1129
+from tensorflow.python.ops.gen_data_flow_ops import GetSessionHandle # line: 1175
+from tensorflow.python.ops.gen_data_flow_ops import GetSessionHandleV2 # line: 1231
+from tensorflow.python.ops.gen_data_flow_ops import GetSessionTensor # line: 1290
+from tensorflow.python.ops.gen_data_flow_ops import MapClear # line: 1363
+from tensorflow.python.ops.gen_data_flow_ops import MapIncompleteSize # line: 1460
+from tensorflow.python.ops.gen_data_flow_ops import MapPeek # line: 1567
+from tensorflow.python.ops.gen_data_flow_ops import MapSize # line: 1668
+from tensorflow.python.ops.gen_data_flow_ops import MapStage # line: 1767
+from tensorflow.python.ops.gen_data_flow_ops import MapUnstage # line: 1875
+from tensorflow.python.ops.gen_data_flow_ops import MapUnstageNoKey # line: 1990
+from tensorflow.python.ops.gen_data_flow_ops import OrderedMapClear # line: 2082
+from tensorflow.python.ops.gen_data_flow_ops import OrderedMapIncompleteSize # line: 2180
+from tensorflow.python.ops.gen_data_flow_ops import OrderedMapPeek # line: 2290
+from tensorflow.python.ops.gen_data_flow_ops import OrderedMapSize # line: 2392
+from tensorflow.python.ops.gen_data_flow_ops import OrderedMapStage # line: 2494
+from tensorflow.python.ops.gen_data_flow_ops import OrderedMapUnstage # line: 2603
+from tensorflow.python.ops.gen_data_flow_ops import OrderedMapUnstageNoKey # line: 2721
+from tensorflow.python.ops.gen_data_flow_ops import PaddingFIFOQueue # line: 2833
+from tensorflow.python.ops.gen_data_flow_ops import PaddingFIFOQueueV2 # line: 2930
+from tensorflow.python.ops.gen_data_flow_ops import ParallelDynamicStitch # line: 3084
+from tensorflow.python.ops.gen_data_flow_ops import PriorityQueue # line: 3189
+from tensorflow.python.ops.gen_data_flow_ops import PriorityQueueV2 # line: 3284
+from tensorflow.python.ops.gen_data_flow_ops import QueueClose # line: 3353
+from tensorflow.python.ops.gen_data_flow_ops import QueueCloseV2 # line: 3405
+from tensorflow.python.ops.gen_data_flow_ops import QueueDequeue # line: 3469
+from tensorflow.python.ops.gen_data_flow_ops import QueueDequeueMany # line: 3532
+from tensorflow.python.ops.gen_data_flow_ops import QueueDequeueManyV2 # line: 3611
+from tensorflow.python.ops.gen_data_flow_ops import QueueDequeueUpTo # line: 3697
+from tensorflow.python.ops.gen_data_flow_ops import QueueDequeueUpToV2 # line: 3780
+from tensorflow.python.ops.gen_data_flow_ops import QueueDequeueV2 # line: 3869
+from tensorflow.python.ops.gen_data_flow_ops import QueueEnqueue # line: 3927
+from tensorflow.python.ops.gen_data_flow_ops import QueueEnqueueMany # line: 3972
+from tensorflow.python.ops.gen_data_flow_ops import QueueEnqueueManyV2 # line: 4030
+from tensorflow.python.ops.gen_data_flow_ops import QueueEnqueueV2 # line: 4094
+from tensorflow.python.ops.gen_data_flow_ops import QueueIsClosed # line: 4140
+from tensorflow.python.ops.gen_data_flow_ops import QueueIsClosedV2 # line: 4187
+from tensorflow.python.ops.gen_data_flow_ops import QueueSize # line: 4229
+from tensorflow.python.ops.gen_data_flow_ops import QueueSizeV2 # line: 4273
+from tensorflow.python.ops.gen_data_flow_ops import RandomShuffleQueue # line: 4379
+from tensorflow.python.ops.gen_data_flow_ops import RandomShuffleQueueV2 # line: 4492
+from tensorflow.python.ops.gen_data_flow_ops import RecordInput # line: 4630
+from tensorflow.python.ops.gen_data_flow_ops import ResourceAccumulatorApplyGradient # line: 4708
+from tensorflow.python.ops.gen_data_flow_ops import ResourceAccumulatorNumAccumulated # line: 4762
+from tensorflow.python.ops.gen_data_flow_ops import ResourceAccumulatorSetGlobalStep # line: 4817
+from tensorflow.python.ops.gen_data_flow_ops import ResourceAccumulatorTakeGradient # line: 4887
+from tensorflow.python.ops.gen_data_flow_ops import ResourceConditionalAccumulator # line: 4987
+from tensorflow.python.ops.gen_data_flow_ops import SparseAccumulatorApplyGradient # line: 5059
+from tensorflow.python.ops.gen_data_flow_ops import SparseAccumulatorTakeGradient # line: 5117
+from tensorflow.python.ops.gen_data_flow_ops import SparseConditionalAccumulator # line: 5185
+from tensorflow.python.ops.gen_data_flow_ops import Stack # line: 5225
+from tensorflow.python.ops.gen_data_flow_ops import StackClose # line: 5249
+from tensorflow.python.ops.gen_data_flow_ops import StackCloseV2 # line: 5285
+from tensorflow.python.ops.gen_data_flow_ops import StackPop # line: 5328
+from tensorflow.python.ops.gen_data_flow_ops import StackPopV2 # line: 5376
+from tensorflow.python.ops.gen_data_flow_ops import StackPush # line: 5428
+from tensorflow.python.ops.gen_data_flow_ops import StackPushV2 # line: 5482
+from tensorflow.python.ops.gen_data_flow_ops import StackV2 # line: 5556
+from tensorflow.python.ops.gen_data_flow_ops import Stage # line: 5636
+from tensorflow.python.ops.gen_data_flow_ops import StageClear # line: 5718
+from tensorflow.python.ops.gen_data_flow_ops import StagePeek # line: 5821
+from tensorflow.python.ops.gen_data_flow_ops import StageSize # line: 5921
+from tensorflow.python.ops.gen_data_flow_ops import TensorArray # line: 6007
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayClose # line: 6031
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayCloseV2 # line: 6067
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayCloseV3 # line: 6114
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayConcat # line: 6172
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayConcatV2 # line: 6238
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayConcatV3 # line: 6338
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayGather # line: 6398
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayGatherV2 # line: 6457
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayGatherV3 # line: 6539
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayGrad # line: 6591
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayGradV2 # line: 6639
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayGradV3 # line: 6748
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayGradWithShape # line: 6832
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayPack # line: 6889
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayRead # line: 6928
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayReadV2 # line: 6980
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayReadV3 # line: 7046
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayScatter # line: 7097
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayScatterV2 # line: 7147
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayScatterV3 # line: 7214
+from tensorflow.python.ops.gen_data_flow_ops import TensorArraySize # line: 7260
+from tensorflow.python.ops.gen_data_flow_ops import TensorArraySizeV2 # line: 7305
+from tensorflow.python.ops.gen_data_flow_ops import TensorArraySizeV3 # line: 7363
+from tensorflow.python.ops.gen_data_flow_ops import TensorArraySplit # line: 7412
+from tensorflow.python.ops.gen_data_flow_ops import TensorArraySplitV2 # line: 7462
+from tensorflow.python.ops.gen_data_flow_ops import TensorArraySplitV3 # line: 7554
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayUnpack # line: 7604
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayV2 # line: 7679
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayV3 # line: 7815
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayWrite # line: 7882
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayWriteV2 # line: 7932
+from tensorflow.python.ops.gen_data_flow_ops import TensorArrayWriteV3 # line: 7997
+from tensorflow.python.ops.gen_data_flow_ops import Unstage # line: 8087
+from tensorflow.python.ops.gen_dataset_ops import AnonymousIterator # line: 78
+from tensorflow.python.ops.gen_dataset_ops import AnonymousIteratorV2 # line: 164
+from tensorflow.python.ops.gen_dataset_ops import AnonymousIteratorV3 # line: 242
+from tensorflow.python.ops.gen_dataset_ops import AnonymousMemoryCache # line: 312
+from tensorflow.python.ops.gen_dataset_ops import AnonymousMultiDeviceIterator # line: 397
+from tensorflow.python.ops.gen_dataset_ops import AnonymousMultiDeviceIteratorV3 # line: 491
+from tensorflow.python.ops.gen_dataset_ops import AnonymousRandomSeedGenerator # line: 570
+from tensorflow.python.ops.gen_dataset_ops import AnonymousSeedGenerator # line: 637
+from tensorflow.python.ops.gen_dataset_ops import BatchDataset # line: 720
+from tensorflow.python.ops.gen_dataset_ops import BatchDatasetV2 # line: 826
+from tensorflow.python.ops.gen_dataset_ops import CacheDataset # line: 930
+from tensorflow.python.ops.gen_dataset_ops import CacheDatasetV2 # line: 1025
+from tensorflow.python.ops.gen_dataset_ops import ConcatenateDataset # line: 1121
+from tensorflow.python.ops.gen_dataset_ops import DatasetCardinality # line: 1201
+from tensorflow.python.ops.gen_dataset_ops import DatasetToGraph # line: 1289
+from tensorflow.python.ops.gen_dataset_ops import DatasetToGraphV2 # line: 1378
+from tensorflow.python.ops.gen_dataset_ops import DatasetToSingleElement # line: 1463
+from tensorflow.python.ops.gen_dataset_ops import DeleteIterator # line: 1524
+from tensorflow.python.ops.gen_dataset_ops import DeleteMemoryCache # line: 1569
+from tensorflow.python.ops.gen_dataset_ops import DeleteMultiDeviceIterator # line: 1625
+from tensorflow.python.ops.gen_dataset_ops import DeleteRandomSeedGenerator # line: 1678
+from tensorflow.python.ops.gen_dataset_ops import DeleteSeedGenerator # line: 1724
+from tensorflow.python.ops.gen_dataset_ops import DeserializeIterator # line: 1773
+from tensorflow.python.ops.gen_dataset_ops import DummyMemoryCache # line: 1824
+from tensorflow.python.ops.gen_dataset_ops import DummySeedGenerator # line: 1876
+from tensorflow.python.ops.gen_dataset_ops import FilterByLastComponentDataset # line: 1947
+from tensorflow.python.ops.gen_dataset_ops import FilterDataset # line: 2051
+from tensorflow.python.ops.gen_dataset_ops import FinalizeDataset # line: 2147
+from tensorflow.python.ops.gen_dataset_ops import FixedLengthRecordDataset # line: 2238
+from tensorflow.python.ops.gen_dataset_ops import FixedLengthRecordDatasetV2 # line: 2318
+from tensorflow.python.ops.gen_dataset_ops import FlatMapDataset # line: 2417
+from tensorflow.python.ops.gen_dataset_ops import GeneratorDataset # line: 2529
+from tensorflow.python.ops.gen_dataset_ops import GetOptions # line: 2603
+from tensorflow.python.ops.gen_dataset_ops import InterleaveDataset # line: 2700
+from tensorflow.python.ops.gen_dataset_ops import Iterator # line: 2795
+from tensorflow.python.ops.gen_dataset_ops import IteratorFromStringHandle # line: 2887
+from tensorflow.python.ops.gen_dataset_ops import IteratorFromStringHandleV2 # line: 2977
+from tensorflow.python.ops.gen_dataset_ops import IteratorGetNext # line: 3063
+from tensorflow.python.ops.gen_dataset_ops import IteratorGetNextAsOptional # line: 3144
+from tensorflow.python.ops.gen_dataset_ops import IteratorGetNextSync # line: 3231
+from tensorflow.python.ops.gen_dataset_ops import IteratorToStringHandle # line: 3296
+from tensorflow.python.ops.gen_dataset_ops import IteratorV2 # line: 3375
+from tensorflow.python.ops.gen_dataset_ops import MakeIterator # line: 3437
+from tensorflow.python.ops.gen_dataset_ops import MapDataset # line: 3534
+from tensorflow.python.ops.gen_dataset_ops import MapDefun # line: 3658
+from tensorflow.python.ops.gen_dataset_ops import ModelDataset # line: 3767
+from tensorflow.python.ops.gen_dataset_ops import MultiDeviceIterator # line: 3879
+from tensorflow.python.ops.gen_dataset_ops import MultiDeviceIteratorFromStringHandle # line: 3975
+from tensorflow.python.ops.gen_dataset_ops import MultiDeviceIteratorGetNextFromShard # line: 4074
+from tensorflow.python.ops.gen_dataset_ops import MultiDeviceIteratorInit # line: 4148
+from tensorflow.python.ops.gen_dataset_ops import MultiDeviceIteratorToStringHandle # line: 4208
+from tensorflow.python.ops.gen_dataset_ops import OneShotIterator # line: 4314
+from tensorflow.python.ops.gen_dataset_ops import OptimizeDataset # line: 4421
+from tensorflow.python.ops.gen_dataset_ops import OptimizeDatasetV2 # line: 4538
+from tensorflow.python.ops.gen_dataset_ops import OptionsDataset # line: 4644
+from tensorflow.python.ops.gen_dataset_ops import PaddedBatchDataset # line: 4749
+from tensorflow.python.ops.gen_dataset_ops import PaddedBatchDatasetV2 # line: 4867
+from tensorflow.python.ops.gen_dataset_ops import ParallelBatchDataset # line: 4988
+from tensorflow.python.ops.gen_dataset_ops import ParallelFilterDataset # line: 5126
+from tensorflow.python.ops.gen_dataset_ops import ParallelInterleaveDatasetV2 # line: 5271
+from tensorflow.python.ops.gen_dataset_ops import ParallelInterleaveDatasetV3 # line: 5425
+from tensorflow.python.ops.gen_dataset_ops import ParallelInterleaveDatasetV4 # line: 5588
+from tensorflow.python.ops.gen_dataset_ops import ParallelMapDataset # line: 5726
+from tensorflow.python.ops.gen_dataset_ops import ParallelMapDatasetV2 # line: 5867
+from tensorflow.python.ops.gen_dataset_ops import PrefetchDataset # line: 5996
+from tensorflow.python.ops.gen_dataset_ops import RangeDataset # line: 6110
+from tensorflow.python.ops.gen_dataset_ops import ReduceDataset # line: 6230
+from tensorflow.python.ops.gen_dataset_ops import RepeatDataset # line: 6330
+from tensorflow.python.ops.gen_dataset_ops import RewriteDataset # line: 6418
+from tensorflow.python.ops.gen_dataset_ops import SerializeIterator # line: 6493
+from tensorflow.python.ops.gen_dataset_ops import ShardDataset # line: 6585
+from tensorflow.python.ops.gen_dataset_ops import ShuffleAndRepeatDataset # line: 6708
+from tensorflow.python.ops.gen_dataset_ops import ShuffleAndRepeatDatasetV2 # line: 6826
+from tensorflow.python.ops.gen_dataset_ops import ShuffleDataset # line: 6953
+from tensorflow.python.ops.gen_dataset_ops import ShuffleDatasetV2 # line: 7057
+from tensorflow.python.ops.gen_dataset_ops import ShuffleDatasetV3 # line: 7167
+from tensorflow.python.ops.gen_dataset_ops import SkipDataset # line: 7268
+from tensorflow.python.ops.gen_dataset_ops import SparseTensorSliceDataset # line: 7342
+from tensorflow.python.ops.gen_dataset_ops import TFRecordDataset # line: 7416
+from tensorflow.python.ops.gen_dataset_ops import TFRecordDatasetV2 # line: 7496
+from tensorflow.python.ops.gen_dataset_ops import TakeDataset # line: 7582
+from tensorflow.python.ops.gen_dataset_ops import TensorDataset # line: 7666
+from tensorflow.python.ops.gen_dataset_ops import TensorSliceDataset # line: 7759
+from tensorflow.python.ops.gen_dataset_ops import TextLineDataset # line: 7845
+from tensorflow.python.ops.gen_dataset_ops import UnwrapDatasetVariant # line: 7904
+from tensorflow.python.ops.gen_dataset_ops import WindowDataset # line: 8040
+from tensorflow.python.ops.gen_dataset_ops import WindowOp # line: 8129
+from tensorflow.python.ops.gen_dataset_ops import WrapDatasetVariant # line: 8194
+from tensorflow.python.ops.gen_dataset_ops import ZipDataset # line: 8282
+from tensorflow.python.ops.gen_debug_ops import Copy # line: 97
+from tensorflow.python.ops.gen_debug_ops import CopyHost # line: 194
+from tensorflow.python.ops.gen_debug_ops import DebugIdentity # line: 302
+from tensorflow.python.ops.gen_debug_ops import DebugIdentityV2 # line: 444
+from tensorflow.python.ops.gen_debug_ops import DebugIdentityV3 # line: 591
+from tensorflow.python.ops.gen_debug_ops import DebugNanCount # line: 714
+from tensorflow.python.ops.gen_debug_ops import DebugNumericSummary # line: 883
+from tensorflow.python.ops.gen_debug_ops import DebugNumericSummaryV2 # line: 1049
+from tensorflow.python.ops.gen_decode_proto_ops import DecodeProtoV2 # line: 280
+from tensorflow.python.ops.gen_encode_proto_ops import EncodeProto # line: 164
+from tensorflow.python.ops.gen_experimental_dataset_ops import AssertCardinalityDataset # line: 82
+from tensorflow.python.ops.gen_experimental_dataset_ops import AssertNextDataset # line: 180
+from tensorflow.python.ops.gen_experimental_dataset_ops import AssertPrevDataset # line: 277
+from tensorflow.python.ops.gen_experimental_dataset_ops import AutoShardDataset # line: 388
+from tensorflow.python.ops.gen_experimental_dataset_ops import BytesProducedStatsDataset # line: 479
+from tensorflow.python.ops.gen_experimental_dataset_ops import CSVDataset # line: 570
+from tensorflow.python.ops.gen_experimental_dataset_ops import CSVDatasetV2 # line: 666
+from tensorflow.python.ops.gen_experimental_dataset_ops import ChooseFastestBranchDataset # line: 785
+from tensorflow.python.ops.gen_experimental_dataset_ops import ChooseFastestDataset # line: 895
+from tensorflow.python.ops.gen_experimental_dataset_ops import CompressElement # line: 966
+from tensorflow.python.ops.gen_experimental_dataset_ops import ComputeBatchSize # line: 1020
+from tensorflow.python.ops.gen_experimental_dataset_ops import DataServiceDataset # line: 1139
+from tensorflow.python.ops.gen_experimental_dataset_ops import DataServiceDatasetV2 # line: 1295
+from tensorflow.python.ops.gen_experimental_dataset_ops import DataServiceDatasetV3 # line: 1464
+from tensorflow.python.ops.gen_experimental_dataset_ops import DataServiceDatasetV4 # line: 1637
+from tensorflow.python.ops.gen_experimental_dataset_ops import DatasetFromGraph # line: 1731
+from tensorflow.python.ops.gen_experimental_dataset_ops import DatasetToTFRecord # line: 1785
+from tensorflow.python.ops.gen_experimental_dataset_ops import DenseToSparseBatchDataset # line: 1866
+from tensorflow.python.ops.gen_experimental_dataset_ops import DirectedInterleaveDataset # line: 1971
+from tensorflow.python.ops.gen_experimental_dataset_ops import DistributedSave # line: 2046
+from tensorflow.python.ops.gen_experimental_dataset_ops import DummyIterationCounter # line: 2101
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalAssertNextDataset # line: 2175
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalAutoShardDataset # line: 2281
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalBytesProducedStatsDataset # line: 2372
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalCSVDataset # line: 2464
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalChooseFastestDataset # line: 2563
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalDatasetCardinality # line: 2639
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalDatasetToTFRecord # line: 2696
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalDenseToSparseBatchDataset # line: 2779
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalDirectedInterleaveDataset # line: 2877
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalGroupByReducerDataset # line: 3019
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalGroupByWindowDataset # line: 3142
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalIgnoreErrorsDataset # line: 3240
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalIteratorGetDevice # line: 3309
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalLMDBDataset # line: 3381
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalLatencyStatsDataset # line: 3465
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalMapAndBatchDataset # line: 3588
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalMapDataset # line: 3702
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalMatchingFilesDataset # line: 3776
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalMaxIntraOpParallelismDataset # line: 3853
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalNonSerializableDataset # line: 3937
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalParallelInterleaveDataset # line: 4051
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalParseExampleDataset # line: 4208
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalPrivateThreadPoolDataset # line: 4323
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalRandomDataset # line: 4411
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalRebatchDataset # line: 4511
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalScanDataset # line: 4615
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalSetStatsAggregatorDataset # line: 4713
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalSleepDataset # line: 4801
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalSlidingWindowDataset # line: 4899
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalSqlDataset # line: 4989
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalStatsAggregatorHandle # line: 5067
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalStatsAggregatorSummary # line: 5127
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalTakeWhileDataset # line: 5217
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalThreadPoolDataset # line: 5306
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalThreadPoolHandle # line: 5407
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalUnbatchDataset # line: 5491
+from tensorflow.python.ops.gen_experimental_dataset_ops import ExperimentalUniqueDataset # line: 5573
+from tensorflow.python.ops.gen_experimental_dataset_ops import GetElementAtIndex # line: 5655
+from tensorflow.python.ops.gen_experimental_dataset_ops import GroupByReducerDataset # line: 5788
+from tensorflow.python.ops.gen_experimental_dataset_ops import GroupByWindowDataset # line: 5914
+from tensorflow.python.ops.gen_experimental_dataset_ops import IgnoreErrorsDataset # line: 6014
+from tensorflow.python.ops.gen_experimental_dataset_ops import InitializeTableFromDataset # line: 6076
+from tensorflow.python.ops.gen_experimental_dataset_ops import IteratorGetDevice # line: 6129
+from tensorflow.python.ops.gen_experimental_dataset_ops import LMDBDataset # line: 6212
+from tensorflow.python.ops.gen_experimental_dataset_ops import LatencyStatsDataset # line: 6294
+from tensorflow.python.ops.gen_experimental_dataset_ops import LegacyParallelInterleaveDatasetV2 # line: 6417
+from tensorflow.python.ops.gen_experimental_dataset_ops import ListDataset # line: 6518
+from tensorflow.python.ops.gen_experimental_dataset_ops import LoadDataset # line: 6616
+from tensorflow.python.ops.gen_experimental_dataset_ops import MapAndBatchDataset # line: 6749
+from tensorflow.python.ops.gen_experimental_dataset_ops import MatchingFilesDataset # line: 6825
+from tensorflow.python.ops.gen_experimental_dataset_ops import MaxIntraOpParallelismDataset # line: 6901
+from tensorflow.python.ops.gen_experimental_dataset_ops import NonSerializableDataset # line: 6984
+from tensorflow.python.ops.gen_experimental_dataset_ops import ParallelInterleaveDataset # line: 7126
+from tensorflow.python.ops.gen_experimental_dataset_ops import ParseExampleDataset # line: 7318
+from tensorflow.python.ops.gen_experimental_dataset_ops import ParseExampleDatasetV2 # line: 7557
+from tensorflow.python.ops.gen_experimental_dataset_ops import PrivateThreadPoolDataset # line: 7692
+from tensorflow.python.ops.gen_experimental_dataset_ops import RandomDataset # line: 7796
+from tensorflow.python.ops.gen_experimental_dataset_ops import RandomDatasetV2 # line: 7920
+from tensorflow.python.ops.gen_experimental_dataset_ops import RebatchDataset # line: 8028
+from tensorflow.python.ops.gen_experimental_dataset_ops import RebatchDatasetV2 # line: 8126
+from tensorflow.python.ops.gen_experimental_dataset_ops import RegisterDataset # line: 8215
+from tensorflow.python.ops.gen_experimental_dataset_ops import RegisterDatasetV2 # line: 8311
+from tensorflow.python.ops.gen_experimental_dataset_ops import SamplingDataset # line: 8409
+from tensorflow.python.ops.gen_experimental_dataset_ops import SaveDataset # line: 8486
+from tensorflow.python.ops.gen_experimental_dataset_ops import SaveDatasetV2 # line: 8585
+from tensorflow.python.ops.gen_experimental_dataset_ops import ScanDataset # line: 8707
+from tensorflow.python.ops.gen_experimental_dataset_ops import SetStatsAggregatorDataset # line: 8809
+from tensorflow.python.ops.gen_experimental_dataset_ops import SleepDataset # line: 8896
+from tensorflow.python.ops.gen_experimental_dataset_ops import SlidingWindowDataset # line: 8998
+from tensorflow.python.ops.gen_experimental_dataset_ops import SnapshotChunkDataset # line: 9093
+from tensorflow.python.ops.gen_experimental_dataset_ops import SnapshotDataset # line: 9286
+from tensorflow.python.ops.gen_experimental_dataset_ops import SnapshotDatasetReader # line: 9430
+from tensorflow.python.ops.gen_experimental_dataset_ops import SnapshotDatasetV2 # line: 9582
+from tensorflow.python.ops.gen_experimental_dataset_ops import SnapshotNestedDatasetReader # line: 9694
+from tensorflow.python.ops.gen_experimental_dataset_ops import SqlDataset # line: 9787
+from tensorflow.python.ops.gen_experimental_dataset_ops import StatsAggregatorHandle # line: 9863
+from tensorflow.python.ops.gen_experimental_dataset_ops import StatsAggregatorHandleV2 # line: 9932
+from tensorflow.python.ops.gen_experimental_dataset_ops import StatsAggregatorSetSummaryWriter # line: 9987
+from tensorflow.python.ops.gen_experimental_dataset_ops import StatsAggregatorSummary # line: 10040
+from tensorflow.python.ops.gen_experimental_dataset_ops import TakeWhileDataset # line: 10134
+from tensorflow.python.ops.gen_experimental_dataset_ops import ThreadPoolDataset # line: 10224
+from tensorflow.python.ops.gen_experimental_dataset_ops import ThreadPoolHandle # line: 10323
+from tensorflow.python.ops.gen_experimental_dataset_ops import UnbatchDataset # line: 10412
+from tensorflow.python.ops.gen_experimental_dataset_ops import UncompressElement # line: 10495
+from tensorflow.python.ops.gen_experimental_dataset_ops import UniqueDataset # line: 10582
+from tensorflow.python.ops.gen_filesystem_ops import FileSystemSetConfiguration # line: 58
+from tensorflow.python.ops.gen_functional_ops import Case # line: 110
+from tensorflow.python.ops.gen_functional_ops import DeviceIndex # line: 191
+from tensorflow.python.ops.gen_functional_ops import FakeParam # line: 260
+from tensorflow.python.ops.gen_functional_ops import For # line: 328
+from tensorflow.python.ops.gen_functional_ops import If # line: 423
+from tensorflow.python.ops.gen_functional_ops import PartitionedCall # line: 524
+from tensorflow.python.ops.gen_functional_ops import RemoteCall # line: 605
+from tensorflow.python.ops.gen_functional_ops import StatefulPartitionedCall # line: 697
+from tensorflow.python.ops.gen_functional_ops import StatelessCase # line: 814
+from tensorflow.python.ops.gen_functional_ops import StatelessIf # line: 926
+from tensorflow.python.ops.gen_functional_ops import StatelessWhile # line: 1029
+from tensorflow.python.ops.gen_functional_ops import SymbolicGradient # line: 1120
+from tensorflow.python.ops.gen_functional_ops import ToBool # line: 1194
+from tensorflow.python.ops.gen_functional_ops import While # line: 1282
+from tensorflow.python.ops.gen_image_ops import AdjustContrast # line: 70
+from tensorflow.python.ops.gen_image_ops import AdjustContrastv2 # line: 143
+from tensorflow.python.ops.gen_image_ops import AdjustHue # line: 209
+from tensorflow.python.ops.gen_image_ops import AdjustSaturation # line: 276
+from tensorflow.python.ops.gen_image_ops import CombinedNonMaxSuppression # line: 402
+from tensorflow.python.ops.gen_image_ops import CropAndResize # line: 524
+from tensorflow.python.ops.gen_image_ops import CropAndResizeGradBoxes # line: 616
+from tensorflow.python.ops.gen_image_ops import CropAndResizeGradImage # line: 710
+from tensorflow.python.ops.gen_image_ops import DecodeAndCropJpeg # line: 847
+from tensorflow.python.ops.gen_image_ops import DecodeBmp # line: 935
+from tensorflow.python.ops.gen_image_ops import DecodeGif # line: 1001
+from tensorflow.python.ops.gen_image_ops import DecodeImage # line: 1098
+from tensorflow.python.ops.gen_image_ops import DecodeJpeg # line: 1234
+from tensorflow.python.ops.gen_image_ops import DecodePng # line: 1338
+from tensorflow.python.ops.gen_image_ops import DrawBoundingBoxes # line: 1416
+from tensorflow.python.ops.gen_image_ops import DrawBoundingBoxesV2 # line: 1492
+from tensorflow.python.ops.gen_image_ops import EncodeJpeg # line: 1633
+from tensorflow.python.ops.gen_image_ops import EncodeJpegVariableQuality # line: 1722
+from tensorflow.python.ops.gen_image_ops import EncodePng # line: 1798
+from tensorflow.python.ops.gen_image_ops import ExtractGlimpse # line: 1919
+from tensorflow.python.ops.gen_image_ops import ExtractGlimpseV2 # line: 2053
+from tensorflow.python.ops.gen_image_ops import ExtractJpegShape # line: 2133
+from tensorflow.python.ops.gen_image_ops import GenerateBoundingBoxProposals # line: 2241
+from tensorflow.python.ops.gen_image_ops import HSVToRGB # line: 2341
+from tensorflow.python.ops.gen_image_ops import ImageProjectiveTransformV2 # line: 2425
+from tensorflow.python.ops.gen_image_ops import ImageProjectiveTransformV3 # line: 2521
+from tensorflow.python.ops.gen_image_ops import NonMaxSuppression # line: 2618
+from tensorflow.python.ops.gen_image_ops import NonMaxSuppressionV2 # line: 2713
+from tensorflow.python.ops.gen_image_ops import NonMaxSuppressionV3 # line: 2810
+from tensorflow.python.ops.gen_image_ops import NonMaxSuppressionV4 # line: 2926
+from tensorflow.python.ops.gen_image_ops import NonMaxSuppressionV5 # line: 3056
+from tensorflow.python.ops.gen_image_ops import NonMaxSuppressionWithOverlaps # line: 3150
+from tensorflow.python.ops.gen_image_ops import QuantizedResizeBilinear # line: 3245
+from tensorflow.python.ops.gen_image_ops import RGBToHSV # line: 3359
+from tensorflow.python.ops.gen_image_ops import RandomCrop # line: 3440
+from tensorflow.python.ops.gen_image_ops import ResizeArea # line: 3527
+from tensorflow.python.ops.gen_image_ops import ResizeBicubic # line: 3608
+from tensorflow.python.ops.gen_image_ops import ResizeBicubicGrad # line: 3693
+from tensorflow.python.ops.gen_image_ops import ResizeBilinear # line: 3778
+from tensorflow.python.ops.gen_image_ops import ResizeBilinearGrad # line: 3864
+from tensorflow.python.ops.gen_image_ops import ResizeNearestNeighbor # line: 3948
+from tensorflow.python.ops.gen_image_ops import ResizeNearestNeighborGrad # line: 4032
+from tensorflow.python.ops.gen_image_ops import SampleDistortedBoundingBox # line: 4227
+from tensorflow.python.ops.gen_image_ops import SampleDistortedBoundingBoxV2 # line: 4442
+from tensorflow.python.ops.gen_image_ops import ScaleAndTranslate # line: 4546
+from tensorflow.python.ops.gen_image_ops import ScaleAndTranslateGrad # line: 4629
+from tensorflow.python.ops.gen_image_ops import StatelessSampleDistortedBoundingBox # line: 4831
+from tensorflow.python.ops.gen_io_ops import FixedLengthRecordReader # line: 89
+from tensorflow.python.ops.gen_io_ops import FixedLengthRecordReaderV2 # line: 184
+from tensorflow.python.ops.gen_io_ops import IdentityReader # line: 263
+from tensorflow.python.ops.gen_io_ops import IdentityReaderV2 # line: 324
+from tensorflow.python.ops.gen_io_ops import LMDBReader # line: 383
+from tensorflow.python.ops.gen_io_ops import MatchingFiles # line: 460
+from tensorflow.python.ops.gen_io_ops import MergeV2Checkpoints # line: 540
+from tensorflow.python.ops.gen_io_ops import ReadFile # line: 599
+from tensorflow.python.ops.gen_io_ops import ReaderNumRecordsProduced # line: 644
+from tensorflow.python.ops.gen_io_ops import ReaderNumRecordsProducedV2 # line: 691
+from tensorflow.python.ops.gen_io_ops import ReaderNumWorkUnitsCompleted # line: 734
+from tensorflow.python.ops.gen_io_ops import ReaderNumWorkUnitsCompletedV2 # line: 779
+from tensorflow.python.ops.gen_io_ops import ReaderRead # line: 836
+from tensorflow.python.ops.gen_io_ops import ReaderReadUpTo # line: 886
+from tensorflow.python.ops.gen_io_ops import ReaderReadUpToV2 # line: 950
+from tensorflow.python.ops.gen_io_ops import ReaderReadV2 # line: 1021
+from tensorflow.python.ops.gen_io_ops import ReaderReset # line: 1056
+from tensorflow.python.ops.gen_io_ops import ReaderResetV2 # line: 1092
+from tensorflow.python.ops.gen_io_ops import ReaderRestoreState # line: 1130
+from tensorflow.python.ops.gen_io_ops import ReaderRestoreStateV2 # line: 1173
+from tensorflow.python.ops.gen_io_ops import ReaderSerializeState # line: 1216
+from tensorflow.python.ops.gen_io_ops import ReaderSerializeStateV2 # line: 1263
+from tensorflow.python.ops.gen_io_ops import Restore # line: 1354
+from tensorflow.python.ops.gen_io_ops import RestoreSlice # line: 1443
+from tensorflow.python.ops.gen_io_ops import RestoreV2 # line: 1536
+from tensorflow.python.ops.gen_io_ops import Save # line: 1599
+from tensorflow.python.ops.gen_io_ops import SaveSlices # line: 1678
+from tensorflow.python.ops.gen_io_ops import SaveV2 # line: 1739
+from tensorflow.python.ops.gen_io_ops import ShardedFilename # line: 1798
+from tensorflow.python.ops.gen_io_ops import ShardedFilespec # line: 1856
+from tensorflow.python.ops.gen_io_ops import TFRecordReader # line: 1917
+from tensorflow.python.ops.gen_io_ops import TFRecordReaderV2 # line: 1981
+from tensorflow.python.ops.gen_io_ops import TextLineReader # line: 2052
+from tensorflow.python.ops.gen_io_ops import TextLineReaderV2 # line: 2118
+from tensorflow.python.ops.gen_io_ops import WholeFileReader # line: 2185
+from tensorflow.python.ops.gen_io_ops import WholeFileReaderV2 # line: 2246
+from tensorflow.python.ops.gen_io_ops import WriteFile # line: 2332
+from tensorflow.python.ops.gen_linalg_ops import BandedTriangularSolve # line: 77
+from tensorflow.python.ops.gen_linalg_ops import BatchCholesky # line: 140
+from tensorflow.python.ops.gen_linalg_ops import BatchCholeskyGrad # line: 197
+from tensorflow.python.ops.gen_linalg_ops import BatchMatrixDeterminant # line: 254
+from tensorflow.python.ops.gen_linalg_ops import BatchMatrixInverse # line: 316
+from tensorflow.python.ops.gen_linalg_ops import BatchMatrixSolve # line: 382
+from tensorflow.python.ops.gen_linalg_ops import BatchMatrixSolveLs # line: 452
+from tensorflow.python.ops.gen_linalg_ops import BatchMatrixTriangularSolve # line: 525
+from tensorflow.python.ops.gen_linalg_ops import BatchSelfAdjointEig # line: 589
+from tensorflow.python.ops.gen_linalg_ops import BatchSelfAdjointEigV2 # line: 658
+from tensorflow.python.ops.gen_linalg_ops import BatchSvd # line: 739
+from tensorflow.python.ops.gen_linalg_ops import Cholesky # line: 845
+from tensorflow.python.ops.gen_linalg_ops import CholeskyGrad # line: 912
+from tensorflow.python.ops.gen_linalg_ops import Eig # line: 1000
+from tensorflow.python.ops.gen_linalg_ops import Einsum # line: 1146
+from tensorflow.python.ops.gen_linalg_ops import LogMatrixDeterminant # line: 1227
+from tensorflow.python.ops.gen_linalg_ops import Lu # line: 1346
+from tensorflow.python.ops.gen_linalg_ops import MatrixDeterminant # line: 1439
+from tensorflow.python.ops.gen_linalg_ops import MatrixExponential # line: 1496
+from tensorflow.python.ops.gen_linalg_ops import MatrixInverse # line: 1597
+from tensorflow.python.ops.gen_linalg_ops import MatrixLogarithm # line: 1674
+from tensorflow.python.ops.gen_linalg_ops import MatrixSolve # line: 1777
+from tensorflow.python.ops.gen_linalg_ops import MatrixSolveLs # line: 1888
+from tensorflow.python.ops.gen_linalg_ops import MatrixSquareRoot # line: 1993
+from tensorflow.python.ops.gen_linalg_ops import MatrixTriangularSolve # line: 2119
+from tensorflow.python.ops.gen_linalg_ops import Qr # line: 2242
+from tensorflow.python.ops.gen_linalg_ops import SelfAdjointEig # line: 2311
+from tensorflow.python.ops.gen_linalg_ops import SelfAdjointEigV2 # line: 2395
+from tensorflow.python.ops.gen_linalg_ops import Svd # line: 2496
+from tensorflow.python.ops.gen_linalg_ops import TridiagonalMatMul # line: 2573
+from tensorflow.python.ops.gen_linalg_ops import TridiagonalSolve # line: 2661
+from tensorflow.python.ops.gen_list_ops import EmptyTensorList # line: 81
+from tensorflow.python.ops.gen_list_ops import TensorListConcat # line: 164
+from tensorflow.python.ops.gen_list_ops import TensorListConcatLists # line: 229
+from tensorflow.python.ops.gen_list_ops import TensorListConcatV2 # line: 318
+from tensorflow.python.ops.gen_list_ops import TensorListElementShape # line: 384
+from tensorflow.python.ops.gen_list_ops import TensorListFromTensor # line: 451
+from tensorflow.python.ops.gen_list_ops import TensorListGather # line: 524
+from tensorflow.python.ops.gen_list_ops import TensorListGetItem # line: 595
+from tensorflow.python.ops.gen_list_ops import TensorListLength # line: 655
+from tensorflow.python.ops.gen_list_ops import TensorListPopBack # line: 733
+from tensorflow.python.ops.gen_list_ops import TensorListPushBack # line: 799
+from tensorflow.python.ops.gen_list_ops import TensorListPushBackBatch # line: 858
+from tensorflow.python.ops.gen_list_ops import TensorListReserve # line: 930
+from tensorflow.python.ops.gen_list_ops import TensorListResize # line: 991
+from tensorflow.python.ops.gen_list_ops import TensorListScatter # line: 1062
+from tensorflow.python.ops.gen_list_ops import TensorListScatterIntoExistingList # line: 1134
+from tensorflow.python.ops.gen_list_ops import TensorListScatterV2 # line: 1213
+from tensorflow.python.ops.gen_list_ops import TensorListSetItem # line: 1292
+from tensorflow.python.ops.gen_list_ops import TensorListSplit # line: 1367
+from tensorflow.python.ops.gen_list_ops import TensorListStack # line: 1445
+from tensorflow.python.ops.gen_logging_ops import Assert # line: 66
+from tensorflow.python.ops.gen_logging_ops import AudioSummary # line: 146
+from tensorflow.python.ops.gen_logging_ops import AudioSummaryV2 # line: 229
+from tensorflow.python.ops.gen_logging_ops import HistogramSummary # line: 299
+from tensorflow.python.ops.gen_logging_ops import ImageSummary # line: 408
+from tensorflow.python.ops.gen_logging_ops import MergeSummary # line: 484
+from tensorflow.python.ops.gen_logging_ops import Print # line: 570
+from tensorflow.python.ops.gen_logging_ops import PrintV2 # line: 640
+from tensorflow.python.ops.gen_logging_ops import ScalarSummary # line: 704
+from tensorflow.python.ops.gen_logging_ops import TensorSummary # line: 788
+from tensorflow.python.ops.gen_logging_ops import TensorSummaryV2 # line: 866
+from tensorflow.python.ops.gen_logging_ops import Timestamp # line: 960
+from tensorflow.python.ops.gen_lookup_ops import AnonymousHashTable # line: 81
+from tensorflow.python.ops.gen_lookup_ops import AnonymousMutableDenseHashTable # line: 190
+from tensorflow.python.ops.gen_lookup_ops import AnonymousMutableHashTable # line: 277
+from tensorflow.python.ops.gen_lookup_ops import AnonymousMutableHashTableOfTensors # line: 360
+from tensorflow.python.ops.gen_lookup_ops import HashTable # line: 442
+from tensorflow.python.ops.gen_lookup_ops import HashTableV2 # line: 524
+from tensorflow.python.ops.gen_lookup_ops import InitializeTable # line: 577
+from tensorflow.python.ops.gen_lookup_ops import InitializeTableFromTextFile # line: 640
+from tensorflow.python.ops.gen_lookup_ops import InitializeTableFromTextFileV2 # line: 720
+from tensorflow.python.ops.gen_lookup_ops import InitializeTableV2 # line: 784
+from tensorflow.python.ops.gen_lookup_ops import LookupTableExport # line: 841
+from tensorflow.python.ops.gen_lookup_ops import LookupTableExportV2 # line: 903
+from tensorflow.python.ops.gen_lookup_ops import LookupTableFind # line: 960
+from tensorflow.python.ops.gen_lookup_ops import LookupTableFindV2 # line: 1017
+from tensorflow.python.ops.gen_lookup_ops import LookupTableImport # line: 1062
+from tensorflow.python.ops.gen_lookup_ops import LookupTableImportV2 # line: 1107
+from tensorflow.python.ops.gen_lookup_ops import LookupTableInsert # line: 1149
+from tensorflow.python.ops.gen_lookup_ops import LookupTableInsertV2 # line: 1194
+from tensorflow.python.ops.gen_lookup_ops import LookupTableRemoveV2 # line: 1246
+from tensorflow.python.ops.gen_lookup_ops import LookupTableSize # line: 1286
+from tensorflow.python.ops.gen_lookup_ops import LookupTableSizeV2 # line: 1330
+from tensorflow.python.ops.gen_lookup_ops import MutableDenseHashTable # line: 1431
+from tensorflow.python.ops.gen_lookup_ops import MutableDenseHashTableV2 # line: 1547
+from tensorflow.python.ops.gen_lookup_ops import MutableHashTable # line: 1649
+from tensorflow.python.ops.gen_lookup_ops import MutableHashTableOfTensors # line: 1721
+from tensorflow.python.ops.gen_lookup_ops import MutableHashTableOfTensorsV2 # line: 1811
+from tensorflow.python.ops.gen_lookup_ops import MutableHashTableV2 # line: 1921
+from tensorflow.python.ops.gen_manip_ops import Roll # line: 100
+from tensorflow.python.ops.gen_map_ops import EmptyTensorMap # line: 64
+from tensorflow.python.ops.gen_map_ops import TensorMapErase # line: 130
+from tensorflow.python.ops.gen_map_ops import TensorMapHasKey # line: 193
+from tensorflow.python.ops.gen_map_ops import TensorMapInsert # line: 260
+from tensorflow.python.ops.gen_map_ops import TensorMapLookup # line: 329
+from tensorflow.python.ops.gen_map_ops import TensorMapSize # line: 388
+from tensorflow.python.ops.gen_map_ops import TensorMapStackKeys # line: 451
+from tensorflow.python.ops.gen_math_ops import Abs # line: 69
+from tensorflow.python.ops.gen_math_ops import AccumulateNV2 # line: 144
+from tensorflow.python.ops.gen_math_ops import Acos # line: 211
+from tensorflow.python.ops.gen_math_ops import Acosh # line: 302
+from tensorflow.python.ops.gen_math_ops import Add # line: 367
+from tensorflow.python.ops.gen_math_ops import AddN # line: 436
+from tensorflow.python.ops.gen_math_ops import AddV2 # line: 501
+from tensorflow.python.ops.gen_math_ops import All # line: 573
+from tensorflow.python.ops.gen_math_ops import Angle # line: 657
+from tensorflow.python.ops.gen_math_ops import Any # line: 731
+from tensorflow.python.ops.gen_math_ops import ApproximateEqual # line: 797
+from tensorflow.python.ops.gen_math_ops import ArgMax # line: 882
+from tensorflow.python.ops.gen_math_ops import ArgMin # line: 967
+from tensorflow.python.ops.gen_math_ops import Asin # line: 1070
+from tensorflow.python.ops.gen_math_ops import Asinh # line: 1163
+from tensorflow.python.ops.gen_math_ops import Atan # line: 1263
+from tensorflow.python.ops.gen_math_ops import Atan2 # line: 1361
+from tensorflow.python.ops.gen_math_ops import Atanh # line: 1457
+from tensorflow.python.ops.gen_math_ops import BatchMatMul # line: 1548
+from tensorflow.python.ops.gen_math_ops import BatchMatMulV2 # line: 1649
+from tensorflow.python.ops.gen_math_ops import BatchMatMulV3 # line: 1758
+from tensorflow.python.ops.gen_math_ops import Betainc # line: 1867
+from tensorflow.python.ops.gen_math_ops import Bincount # line: 1938
+from tensorflow.python.ops.gen_math_ops import Bucketize # line: 2016
+from tensorflow.python.ops.gen_math_ops import Cast # line: 2086
+from tensorflow.python.ops.gen_math_ops import Ceil # line: 2146
+from tensorflow.python.ops.gen_math_ops import ClipByValue # line: 2215
+from tensorflow.python.ops.gen_math_ops import Complex # line: 2294
+from tensorflow.python.ops.gen_math_ops import ComplexAbs # line: 2371
+from tensorflow.python.ops.gen_math_ops import Conj # line: 2444
+from tensorflow.python.ops.gen_math_ops import Cos # line: 2537
+from tensorflow.python.ops.gen_math_ops import Cosh # line: 2630
+from tensorflow.python.ops.gen_math_ops import Cross # line: 2722
+from tensorflow.python.ops.gen_math_ops import Cumprod # line: 2830
+from tensorflow.python.ops.gen_math_ops import Cumsum # line: 2944
+from tensorflow.python.ops.gen_math_ops import CumulativeLogsumexp # line: 3046
+from tensorflow.python.ops.gen_math_ops import DenseBincount # line: 3136
+from tensorflow.python.ops.gen_math_ops import Digamma # line: 3227
+from tensorflow.python.ops.gen_math_ops import Div # line: 3288
+from tensorflow.python.ops.gen_math_ops import DivNoNan # line: 3350
+from tensorflow.python.ops.gen_math_ops import Equal # line: 3429
+from tensorflow.python.ops.gen_math_ops import Erf # line: 3525
+from tensorflow.python.ops.gen_math_ops import Erfc # line: 3610
+from tensorflow.python.ops.gen_math_ops import Erfinv # line: 3667
+from tensorflow.python.ops.gen_math_ops import EuclideanNorm # line: 3740
+from tensorflow.python.ops.gen_math_ops import Exp # line: 3827
+from tensorflow.python.ops.gen_math_ops import Expm1 # line: 3925
+from tensorflow.python.ops.gen_math_ops import Floor # line: 3982
+from tensorflow.python.ops.gen_math_ops import FloorDiv # line: 4070
+from tensorflow.python.ops.gen_math_ops import FloorMod # line: 4164
+from tensorflow.python.ops.gen_math_ops import Greater # line: 4265
+from tensorflow.python.ops.gen_math_ops import GreaterEqual # line: 4366
+from tensorflow.python.ops.gen_math_ops import HistogramFixedWidth # line: 4455
+from tensorflow.python.ops.gen_math_ops import Igamma # line: 4559
+from tensorflow.python.ops.gen_math_ops import IgammaGradA # line: 4618
+from tensorflow.python.ops.gen_math_ops import Igammac # line: 4717
+from tensorflow.python.ops.gen_math_ops import Imag # line: 4793
+from tensorflow.python.ops.gen_math_ops import Inv # line: 4854
+from tensorflow.python.ops.gen_math_ops import InvGrad # line: 4914
+from tensorflow.python.ops.gen_math_ops import IsFinite # line: 5010
+from tensorflow.python.ops.gen_math_ops import IsInf # line: 5106
+from tensorflow.python.ops.gen_math_ops import IsNan # line: 5202
+from tensorflow.python.ops.gen_math_ops import Less # line: 5302
+from tensorflow.python.ops.gen_math_ops import LessEqual # line: 5403
+from tensorflow.python.ops.gen_math_ops import Lgamma # line: 5499
+from tensorflow.python.ops.gen_math_ops import LinSpace # line: 5573
+from tensorflow.python.ops.gen_math_ops import Log # line: 5668
+from tensorflow.python.ops.gen_math_ops import Log1p # line: 5760
+from tensorflow.python.ops.gen_math_ops import LogicalAnd # line: 5898
+from tensorflow.python.ops.gen_math_ops import LogicalNot # line: 5986
+from tensorflow.python.ops.gen_math_ops import LogicalOr # line: 6124
+from tensorflow.python.ops.gen_math_ops import MatMul # line: 6205
+from tensorflow.python.ops.gen_math_ops import Max # line: 6286
+from tensorflow.python.ops.gen_math_ops import Maximum # line: 6390
+from tensorflow.python.ops.gen_math_ops import Mean # line: 6465
+from tensorflow.python.ops.gen_math_ops import Min # line: 6542
+from tensorflow.python.ops.gen_math_ops import Minimum # line: 6649
+from tensorflow.python.ops.gen_math_ops import Mod # line: 6714
+from tensorflow.python.ops.gen_math_ops import Mul # line: 6775
+from tensorflow.python.ops.gen_math_ops import MulNoNan # line: 6836
+from tensorflow.python.ops.gen_math_ops import Ndtri # line: 6893
+from tensorflow.python.ops.gen_math_ops import Neg # line: 6978
+from tensorflow.python.ops.gen_math_ops import NextAfter # line: 7071
+from tensorflow.python.ops.gen_math_ops import NotEqual # line: 7142
+from tensorflow.python.ops.gen_math_ops import Polygamma # line: 7240
+from tensorflow.python.ops.gen_math_ops import Pow # line: 7308
+from tensorflow.python.ops.gen_math_ops import Prod # line: 7382
+from tensorflow.python.ops.gen_math_ops import QuantizeDownAndShrinkRange # line: 7486
+from tensorflow.python.ops.gen_math_ops import QuantizedAdd # line: 7574
+from tensorflow.python.ops.gen_math_ops import QuantizedMatMul # line: 7697
+from tensorflow.python.ops.gen_math_ops import QuantizedMul # line: 7799
+from tensorflow.python.ops.gen_math_ops import RaggedBincount # line: 7891
+from tensorflow.python.ops.gen_math_ops import Range # line: 7970
+from tensorflow.python.ops.gen_math_ops import Real # line: 8045
+from tensorflow.python.ops.gen_math_ops import RealDiv # line: 8137
+from tensorflow.python.ops.gen_math_ops import Reciprocal # line: 8225
+from tensorflow.python.ops.gen_math_ops import ReciprocalGrad # line: 8286
+from tensorflow.python.ops.gen_math_ops import RequantizationRange # line: 8361
+from tensorflow.python.ops.gen_math_ops import RequantizationRangePerChannel # line: 8442
+from tensorflow.python.ops.gen_math_ops import Requantize # line: 8537
+from tensorflow.python.ops.gen_math_ops import RequantizePerChannel # line: 8629
+from tensorflow.python.ops.gen_math_ops import Rint # line: 8730
+from tensorflow.python.ops.gen_math_ops import Round # line: 8790
+from tensorflow.python.ops.gen_math_ops import Rsqrt # line: 8848
+from tensorflow.python.ops.gen_math_ops import RsqrtGrad # line: 8908
+from tensorflow.python.ops.gen_math_ops import SegmentMax # line: 9029
+from tensorflow.python.ops.gen_math_ops import SegmentMaxV2 # line: 9140
+from tensorflow.python.ops.gen_math_ops import SegmentMean # line: 9266
+from tensorflow.python.ops.gen_math_ops import SegmentMin # line: 9388
+from tensorflow.python.ops.gen_math_ops import SegmentMinV2 # line: 9499
+from tensorflow.python.ops.gen_math_ops import SegmentProd # line: 9623
+from tensorflow.python.ops.gen_math_ops import SegmentProdV2 # line: 9725
+from tensorflow.python.ops.gen_math_ops import SegmentSum # line: 9848
+from tensorflow.python.ops.gen_math_ops import SegmentSumV2 # line: 9932
+from tensorflow.python.ops.gen_math_ops import Select # line: 10034
+from tensorflow.python.ops.gen_math_ops import SelectV2 # line: 10094
+from tensorflow.python.ops.gen_math_ops import Sigmoid # line: 10154
+from tensorflow.python.ops.gen_math_ops import SigmoidGrad # line: 10214
+from tensorflow.python.ops.gen_math_ops import Sign # line: 10279
+from tensorflow.python.ops.gen_math_ops import Sin # line: 10371
+from tensorflow.python.ops.gen_math_ops import Sinh # line: 10464
+from tensorflow.python.ops.gen_math_ops import SobolSample # line: 10537
+from tensorflow.python.ops.gen_math_ops import SparseBincount # line: 10627
+from tensorflow.python.ops.gen_math_ops import SparseMatMul # line: 10728
+from tensorflow.python.ops.gen_math_ops import SparseSegmentMean # line: 10821
+from tensorflow.python.ops.gen_math_ops import SparseSegmentMeanGrad # line: 10900
+from tensorflow.python.ops.gen_math_ops import SparseSegmentMeanGradV2 # line: 10987
+from tensorflow.python.ops.gen_math_ops import SparseSegmentMeanWithNumSegments # line: 11078
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSqrtN # line: 11164
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSqrtNGrad # line: 11243
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSqrtNGradV2 # line: 11331
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSqrtNWithNumSegments # line: 11424
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSum # line: 11536
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSumGrad # line: 11615
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSumGradV2 # line: 11702
+from tensorflow.python.ops.gen_math_ops import SparseSegmentSumWithNumSegments # line: 11814
+from tensorflow.python.ops.gen_math_ops import Sqrt # line: 11881
+from tensorflow.python.ops.gen_math_ops import SqrtGrad # line: 11941
+from tensorflow.python.ops.gen_math_ops import Square # line: 12030
+from tensorflow.python.ops.gen_math_ops import SquaredDifference # line: 12119
+from tensorflow.python.ops.gen_math_ops import Sub # line: 12254
+from tensorflow.python.ops.gen_math_ops import Sum # line: 12328
+from tensorflow.python.ops.gen_math_ops import Tan # line: 12425
+from tensorflow.python.ops.gen_math_ops import Tanh # line: 12519
+from tensorflow.python.ops.gen_math_ops import TanhGrad # line: 12580
+from tensorflow.python.ops.gen_math_ops import TruncateDiv # line: 12673
+from tensorflow.python.ops.gen_math_ops import TruncateMod # line: 12765
+from tensorflow.python.ops.gen_math_ops import UnsortedSegmentMax # line: 12901
+from tensorflow.python.ops.gen_math_ops import UnsortedSegmentMin # line: 13035
+from tensorflow.python.ops.gen_math_ops import UnsortedSegmentProd # line: 13169
+from tensorflow.python.ops.gen_math_ops import UnsortedSegmentSum # line: 13304
+from tensorflow.python.ops.gen_math_ops import Xdivy # line: 13365
+from tensorflow.python.ops.gen_math_ops import Xlog1py # line: 13423
+from tensorflow.python.ops.gen_math_ops import Xlogy # line: 13508
+from tensorflow.python.ops.gen_math_ops import Zeta # line: 13600
+from tensorflow.python.ops.gen_nccl_ops import NcclAllReduce # line: 91
+from tensorflow.python.ops.gen_nccl_ops import NcclBroadcast # line: 162
+from tensorflow.python.ops.gen_nccl_ops import NcclReduce # line: 237
+from tensorflow.python.ops.gen_nn_ops import ApproxTopK # line: 167
+from tensorflow.python.ops.gen_nn_ops import AvgPool # line: 279
+from tensorflow.python.ops.gen_nn_ops import AvgPool3D # line: 388
+from tensorflow.python.ops.gen_nn_ops import AvgPool3DGrad # line: 497
+from tensorflow.python.ops.gen_nn_ops import AvgPoolGrad # line: 606
+from tensorflow.python.ops.gen_nn_ops import BatchNormWithGlobalNormalization # line: 712
+from tensorflow.python.ops.gen_nn_ops import BatchNormWithGlobalNormalizationGrad # line: 816
+from tensorflow.python.ops.gen_nn_ops import BiasAdd # line: 895
+from tensorflow.python.ops.gen_nn_ops import BiasAddGrad # line: 973
+from tensorflow.python.ops.gen_nn_ops import BiasAddV1 # line: 1040
+from tensorflow.python.ops.gen_nn_ops import Conv # line: 1218
+from tensorflow.python.ops.gen_nn_ops import Conv2D # line: 1398
+from tensorflow.python.ops.gen_nn_ops import Conv2DBackpropFilter # line: 1559
+from tensorflow.python.ops.gen_nn_ops import Conv2DBackpropFilterV2 # line: 1764
+from tensorflow.python.ops.gen_nn_ops import Conv2DBackpropInput # line: 1927
+from tensorflow.python.ops.gen_nn_ops import Conv2DBackpropInputV2 # line: 2130
+from tensorflow.python.ops.gen_nn_ops import Conv3D # line: 2266
+from tensorflow.python.ops.gen_nn_ops import Conv3DBackpropFilter # line: 2373
+from tensorflow.python.ops.gen_nn_ops import Conv3DBackpropFilterV2 # line: 2538
+from tensorflow.python.ops.gen_nn_ops import Conv3DBackpropInput # line: 2648
+from tensorflow.python.ops.gen_nn_ops import Conv3DBackpropInputV2 # line: 2772
+from tensorflow.python.ops.gen_nn_ops import DataFormatDimMap # line: 2866
+from tensorflow.python.ops.gen_nn_ops import DataFormatVecPermute # line: 2976
+from tensorflow.python.ops.gen_nn_ops import DepthwiseConv2dNative # line: 3111
+from tensorflow.python.ops.gen_nn_ops import DepthwiseConv2dNativeBackpropFilter # line: 3262
+from tensorflow.python.ops.gen_nn_ops import DepthwiseConv2dNativeBackpropInput # line: 3414
+from tensorflow.python.ops.gen_nn_ops import Dilation2D # line: 3549
+from tensorflow.python.ops.gen_nn_ops import Dilation2DBackpropFilter # line: 3649
+from tensorflow.python.ops.gen_nn_ops import Dilation2DBackpropInput # line: 3749
+from tensorflow.python.ops.gen_nn_ops import Elu # line: 3863
+from tensorflow.python.ops.gen_nn_ops import EluGrad # line: 3923
+from tensorflow.python.ops.gen_nn_ops import FractionalAvgPool # line: 4061
+from tensorflow.python.ops.gen_nn_ops import FractionalAvgPoolGrad # line: 4176
+from tensorflow.python.ops.gen_nn_ops import FractionalMaxPool # line: 4343
+from tensorflow.python.ops.gen_nn_ops import FractionalMaxPoolGrad # line: 4455
+from tensorflow.python.ops.gen_nn_ops import FusedBatchNorm # line: 4575
+from tensorflow.python.ops.gen_nn_ops import FusedBatchNormGrad # line: 4704
+from tensorflow.python.ops.gen_nn_ops import FusedBatchNormGradV2 # line: 4830
+from tensorflow.python.ops.gen_nn_ops import FusedBatchNormGradV3 # line: 4964
+from tensorflow.python.ops.gen_nn_ops import FusedBatchNormV2 # line: 5093
+from tensorflow.python.ops.gen_nn_ops import FusedBatchNormV3 # line: 5225
+from tensorflow.python.ops.gen_nn_ops import FusedPadConv2D # line: 5334
+from tensorflow.python.ops.gen_nn_ops import FusedResizeAndPadConv2D # line: 5450
+from tensorflow.python.ops.gen_nn_ops import InTopK # line: 5541
+from tensorflow.python.ops.gen_nn_ops import InTopKV2 # line: 5619
+from tensorflow.python.ops.gen_nn_ops import IsotonicRegression # line: 5694
+from tensorflow.python.ops.gen_nn_ops import L2Loss # line: 5785
+from tensorflow.python.ops.gen_nn_ops import LRN # line: 5908
+from tensorflow.python.ops.gen_nn_ops import LRNGrad # line: 6008
+from tensorflow.python.ops.gen_nn_ops import LeakyRelu # line: 6082
+from tensorflow.python.ops.gen_nn_ops import LeakyReluGrad # line: 6150
+from tensorflow.python.ops.gen_nn_ops import LogSoftmax # line: 6215
+from tensorflow.python.ops.gen_nn_ops import MaxPool # line: 6317
+from tensorflow.python.ops.gen_nn_ops import MaxPool3D # line: 6430
+from tensorflow.python.ops.gen_nn_ops import MaxPool3DGrad # line: 6543
+from tensorflow.python.ops.gen_nn_ops import MaxPool3DGradGrad # line: 6658
+from tensorflow.python.ops.gen_nn_ops import MaxPoolGrad # line: 6781
+from tensorflow.python.ops.gen_nn_ops import MaxPoolGradGrad # line: 6902
+from tensorflow.python.ops.gen_nn_ops import MaxPoolGradGradV2 # line: 7002
+from tensorflow.python.ops.gen_nn_ops import MaxPoolGradGradWithArgmax # line: 7106
+from tensorflow.python.ops.gen_nn_ops import MaxPoolGradV2 # line: 7208
+from tensorflow.python.ops.gen_nn_ops import MaxPoolGradWithArgmax # line: 7312
+from tensorflow.python.ops.gen_nn_ops import MaxPoolV2 # line: 7409
+from tensorflow.python.ops.gen_nn_ops import MaxPoolWithArgmax # line: 7530
+from tensorflow.python.ops.gen_nn_ops import NthElement # line: 7623
+from tensorflow.python.ops.gen_nn_ops import QuantizedAvgPool # line: 7722
+from tensorflow.python.ops.gen_nn_ops import QuantizedBatchNormWithGlobalNormalization # line: 7873
+from tensorflow.python.ops.gen_nn_ops import QuantizedBiasAdd # line: 7975
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2D # line: 8103
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DAndRelu # line: 8241
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DAndReluAndRequantize # line: 8398
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DAndRequantize # line: 8555
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DPerChannel # line: 8701
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DWithBias # line: 8841
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DWithBiasAndRelu # line: 8993
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DWithBiasAndReluAndRequantize # line: 9156
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DWithBiasAndRequantize # line: 9320
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DWithBiasSignedSumAndReluAndRequantize # line: 9493
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DWithBiasSumAndRelu # line: 9653
+from tensorflow.python.ops.gen_nn_ops import QuantizedConv2DWithBiasSumAndReluAndRequantize # line: 9825
+from tensorflow.python.ops.gen_nn_ops import QuantizedDepthwiseConv2D # line: 9976
+from tensorflow.python.ops.gen_nn_ops import QuantizedDepthwiseConv2DWithBias # line: 10115
+from tensorflow.python.ops.gen_nn_ops import QuantizedDepthwiseConv2DWithBiasAndRelu # line: 10269
+from tensorflow.python.ops.gen_nn_ops import QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize # line: 10443
+from tensorflow.python.ops.gen_nn_ops import QuantizedMatMulWithBias # line: 10599
+from tensorflow.python.ops.gen_nn_ops import QuantizedMatMulWithBiasAndDequantize # line: 10721
+from tensorflow.python.ops.gen_nn_ops import QuantizedMatMulWithBiasAndRelu # line: 10864
+from tensorflow.python.ops.gen_nn_ops import QuantizedMatMulWithBiasAndReluAndRequantize # line: 11020
+from tensorflow.python.ops.gen_nn_ops import QuantizedMatMulWithBiasAndRequantize # line: 11155
+from tensorflow.python.ops.gen_nn_ops import QuantizedMaxPool # line: 11273
+from tensorflow.python.ops.gen_nn_ops import QuantizedRelu # line: 11366
+from tensorflow.python.ops.gen_nn_ops import QuantizedRelu6 # line: 11450
+from tensorflow.python.ops.gen_nn_ops import QuantizedReluX # line: 11536
+from tensorflow.python.ops.gen_nn_ops import Relu # line: 11630
+from tensorflow.python.ops.gen_nn_ops import Relu6 # line: 11687
+from tensorflow.python.ops.gen_nn_ops import Relu6Grad # line: 11747
+from tensorflow.python.ops.gen_nn_ops import ReluGrad # line: 11808
+from tensorflow.python.ops.gen_nn_ops import Selu # line: 11900
+from tensorflow.python.ops.gen_nn_ops import SeluGrad # line: 11960
+from tensorflow.python.ops.gen_nn_ops import Softmax # line: 12022
+from tensorflow.python.ops.gen_nn_ops import SoftmaxCrossEntropyWithLogits # line: 12094
+from tensorflow.python.ops.gen_nn_ops import Softplus # line: 12152
+from tensorflow.python.ops.gen_nn_ops import SoftplusGrad # line: 12211
+from tensorflow.python.ops.gen_nn_ops import Softsign # line: 12295
+from tensorflow.python.ops.gen_nn_ops import SoftsignGrad # line: 12355
+from tensorflow.python.ops.gen_nn_ops import SparseSoftmaxCrossEntropyWithLogits # line: 12434
+from tensorflow.python.ops.gen_nn_ops import TopK # line: 12525
+from tensorflow.python.ops.gen_nn_ops import TopKV2 # line: 12624
+from tensorflow.python.ops.gen_optional_ops import OptionalFromValue # line: 63
+from tensorflow.python.ops.gen_optional_ops import OptionalGetValue # line: 132
+from tensorflow.python.ops.gen_optional_ops import OptionalHasValue # line: 196
+from tensorflow.python.ops.gen_optional_ops import OptionalNone # line: 249
+from tensorflow.python.ops.gen_parsing_ops import DecodeCSV # line: 107
+from tensorflow.python.ops.gen_parsing_ops import DecodeCompressed # line: 228
+from tensorflow.python.ops.gen_parsing_ops import DecodeJSONExample # line: 298
+from tensorflow.python.ops.gen_parsing_ops import DecodePaddedRaw # line: 370
+from tensorflow.python.ops.gen_parsing_ops import DecodeRaw # line: 445
+from tensorflow.python.ops.gen_parsing_ops import ParseExample # line: 593
+from tensorflow.python.ops.gen_parsing_ops import ParseExampleV2 # line: 793
+from tensorflow.python.ops.gen_parsing_ops import ParseSequenceExample # line: 1106
+from tensorflow.python.ops.gen_parsing_ops import ParseSequenceExampleV2 # line: 1510
+from tensorflow.python.ops.gen_parsing_ops import ParseSingleExample # line: 1767
+from tensorflow.python.ops.gen_parsing_ops import ParseSingleSequenceExample # line: 2030
+from tensorflow.python.ops.gen_parsing_ops import ParseTensor # line: 2206
+from tensorflow.python.ops.gen_parsing_ops import SerializeTensor # line: 2264
+from tensorflow.python.ops.gen_parsing_ops import StringToNumber # line: 2335
+from tensorflow.python.ops.gen_ragged_array_ops import RaggedCross # line: 141
+from tensorflow.python.ops.gen_ragged_array_ops import RaggedFillEmptyRows # line: 277
+from tensorflow.python.ops.gen_ragged_array_ops import RaggedFillEmptyRowsGrad # line: 378
+from tensorflow.python.ops.gen_ragged_array_ops import RaggedGather # line: 499
+from tensorflow.python.ops.gen_ragged_conversion_ops import RaggedTensorFromVariant # line: 113
+from tensorflow.python.ops.gen_ragged_conversion_ops import RaggedTensorToSparse # line: 202
+from tensorflow.python.ops.gen_ragged_conversion_ops import RaggedTensorToTensor # line: 339
+from tensorflow.python.ops.gen_ragged_conversion_ops import RaggedTensorToVariant # line: 442
+from tensorflow.python.ops.gen_ragged_conversion_ops import RaggedTensorToVariantGradient # line: 526
+from tensorflow.python.ops.gen_ragged_math_ops import RaggedRange # line: 102
+from tensorflow.python.ops.gen_random_index_shuffle_ops import RandomIndexShuffle # line: 118
+from tensorflow.python.ops.gen_random_ops import Multinomial # line: 90
+from tensorflow.python.ops.gen_random_ops import ParameterizedTruncatedNormal # line: 190
+from tensorflow.python.ops.gen_random_ops import RandomGamma # line: 280
+from tensorflow.python.ops.gen_random_ops import RandomGammaGrad # line: 344
+from tensorflow.python.ops.gen_random_ops import RandomPoisson # line: 415
+from tensorflow.python.ops.gen_random_ops import RandomPoissonV2 # line: 516
+from tensorflow.python.ops.gen_random_ops import RandomShuffle # line: 606
+from tensorflow.python.ops.gen_random_ops import RandomStandardNormal # line: 691
+from tensorflow.python.ops.gen_random_ops import RandomUniform # line: 778
+from tensorflow.python.ops.gen_random_ops import RandomUniformInt # line: 871
+from tensorflow.python.ops.gen_random_ops import TruncatedNormal # line: 960
+from tensorflow.python.ops.gen_resource_variable_ops import AssignAddVariableOp # line: 62
+from tensorflow.python.ops.gen_resource_variable_ops import AssignSubVariableOp # line: 113
+from tensorflow.python.ops.gen_resource_variable_ops import AssignVariableOp # line: 170
+from tensorflow.python.ops.gen_resource_variable_ops import ConsumeMutexLock # line: 226
+from tensorflow.python.ops.gen_resource_variable_ops import DestroyResourceOp # line: 282
+from tensorflow.python.ops.gen_resource_variable_ops import DisableCopyOnRead # line: 331
+from tensorflow.python.ops.gen_resource_variable_ops import MutexLock # line: 421
+from tensorflow.python.ops.gen_resource_variable_ops import MutexV2 # line: 488
+from tensorflow.python.ops.gen_resource_variable_ops import ReadVariableOp # line: 559
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceGather # line: 649
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceGatherNd # line: 720
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceScatterAdd # line: 796
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceScatterDiv # line: 869
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceScatterMax # line: 942
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceScatterMin # line: 1015
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceScatterMul # line: 1088
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceScatterSub # line: 1161
+from tensorflow.python.ops.gen_resource_variable_ops import ResourceScatterUpdate # line: 1224
+from tensorflow.python.ops.gen_resource_variable_ops import VarHandleOp # line: 1319
+from tensorflow.python.ops.gen_resource_variable_ops import VarIsInitializedOp # line: 1392
+from tensorflow.python.ops.gen_resource_variable_ops import VariableShape # line: 1461
+from tensorflow.python.ops.gen_rnn_ops import BlockLSTM # line: 136
+from tensorflow.python.ops.gen_rnn_ops import BlockLSTMGrad # line: 265
+from tensorflow.python.ops.gen_rnn_ops import BlockLSTMGradV2 # line: 385
+from tensorflow.python.ops.gen_rnn_ops import BlockLSTMV2 # line: 512
+from tensorflow.python.ops.gen_rnn_ops import GRUBlockCell # line: 638
+from tensorflow.python.ops.gen_rnn_ops import GRUBlockCellGrad # line: 797
+from tensorflow.python.ops.gen_rnn_ops import LSTMBlockCell # line: 928
+from tensorflow.python.ops.gen_rnn_ops import LSTMBlockCellGrad # line: 1040
+from tensorflow.python.ops.gen_script_ops import EagerPyFunc # line: 84
+from tensorflow.python.ops.gen_script_ops import PyFunc # line: 163
+from tensorflow.python.ops.gen_script_ops import PyFuncStateless # line: 230
+from tensorflow.python.ops.gen_sdca_ops import SdcaFprint # line: 92
+from tensorflow.python.ops.gen_sdca_ops import SdcaOptimizer # line: 366
+from tensorflow.python.ops.gen_sdca_ops import SdcaOptimizerV2 # line: 655
+from tensorflow.python.ops.gen_sdca_ops import SdcaShrinkL1 # line: 795
+from tensorflow.python.ops.gen_sendrecv_ops import Recv # line: 98
+from tensorflow.python.ops.gen_sendrecv_ops import Send # line: 181
+from tensorflow.python.ops.gen_set_ops import DenseToDenseSetOperation # line: 100
+from tensorflow.python.ops.gen_set_ops import DenseToSparseSetOperation # line: 217
+from tensorflow.python.ops.gen_set_ops import SetSize # line: 304
+from tensorflow.python.ops.gen_set_ops import SparseToSparseSetOperation # line: 437
+from tensorflow.python.ops.gen_sparse_ops import AddManySparseToTensorsMap # line: 115
+from tensorflow.python.ops.gen_sparse_ops import AddSparseToTensorsMap # line: 218
+from tensorflow.python.ops.gen_sparse_ops import DeserializeManySparse # line: 339
+from tensorflow.python.ops.gen_sparse_ops import DeserializeSparse # line: 454
+from tensorflow.python.ops.gen_sparse_ops import SerializeManySparse # line: 537
+from tensorflow.python.ops.gen_sparse_ops import SerializeSparse # line: 615
+from tensorflow.python.ops.gen_sparse_ops import SparseAdd # line: 719
+from tensorflow.python.ops.gen_sparse_ops import SparseAddGrad # line: 807
+from tensorflow.python.ops.gen_sparse_ops import SparseConcat # line: 952
+from tensorflow.python.ops.gen_sparse_ops import SparseCross # line: 1133
+from tensorflow.python.ops.gen_sparse_ops import SparseCrossHashed # line: 1293
+from tensorflow.python.ops.gen_sparse_ops import SparseCrossV2 # line: 1441
+from tensorflow.python.ops.gen_sparse_ops import SparseDenseCwiseAdd # line: 1535
+from tensorflow.python.ops.gen_sparse_ops import SparseDenseCwiseDiv # line: 1607
+from tensorflow.python.ops.gen_sparse_ops import SparseDenseCwiseMul # line: 1683
+from tensorflow.python.ops.gen_sparse_ops import SparseFillEmptyRows # line: 1800
+from tensorflow.python.ops.gen_sparse_ops import SparseFillEmptyRowsGrad # line: 1879
+from tensorflow.python.ops.gen_sparse_ops import SparseReduceMax # line: 1969
+from tensorflow.python.ops.gen_sparse_ops import SparseReduceMaxSparse # line: 2073
+from tensorflow.python.ops.gen_sparse_ops import SparseReduceSum # line: 2167
+from tensorflow.python.ops.gen_sparse_ops import SparseReduceSumSparse # line: 2271
+from tensorflow.python.ops.gen_sparse_ops import SparseReorder # line: 2358
+from tensorflow.python.ops.gen_sparse_ops import SparseReshape # line: 2445
+from tensorflow.python.ops.gen_sparse_ops import SparseSlice # line: 2539
+from tensorflow.python.ops.gen_sparse_ops import SparseSliceGrad # line: 2616
+from tensorflow.python.ops.gen_sparse_ops import SparseSoftmax # line: 2698
+from tensorflow.python.ops.gen_sparse_ops import SparseSparseMaximum # line: 2782
+from tensorflow.python.ops.gen_sparse_ops import SparseSparseMinimum # line: 2869
+from tensorflow.python.ops.gen_sparse_ops import SparseSplit # line: 2975
+from tensorflow.python.ops.gen_sparse_ops import SparseTensorDenseAdd # line: 3051
+from tensorflow.python.ops.gen_sparse_ops import SparseTensorDenseMatMul # line: 3147
+from tensorflow.python.ops.gen_sparse_ops import SparseToDense # line: 3259
+from tensorflow.python.ops.gen_sparse_ops import TakeManySparseFromTensorsMap # line: 3404
+from tensorflow.python.ops.gen_special_math_ops import BesselI0 # line: 65
+from tensorflow.python.ops.gen_special_math_ops import BesselI0e # line: 121
+from tensorflow.python.ops.gen_special_math_ops import BesselI1 # line: 177
+from tensorflow.python.ops.gen_special_math_ops import BesselI1e # line: 233
+from tensorflow.python.ops.gen_special_math_ops import BesselJ0 # line: 289
+from tensorflow.python.ops.gen_special_math_ops import BesselJ1 # line: 345
+from tensorflow.python.ops.gen_special_math_ops import BesselK0 # line: 401
+from tensorflow.python.ops.gen_special_math_ops import BesselK0e # line: 457
+from tensorflow.python.ops.gen_special_math_ops import BesselK1 # line: 513
+from tensorflow.python.ops.gen_special_math_ops import BesselK1e # line: 569
+from tensorflow.python.ops.gen_special_math_ops import BesselY0 # line: 625
+from tensorflow.python.ops.gen_special_math_ops import BesselY1 # line: 681
+from tensorflow.python.ops.gen_special_math_ops import Dawsn # line: 737
+from tensorflow.python.ops.gen_special_math_ops import Expint # line: 793
+from tensorflow.python.ops.gen_special_math_ops import FresnelCos # line: 849
+from tensorflow.python.ops.gen_special_math_ops import FresnelSin # line: 905
+from tensorflow.python.ops.gen_special_math_ops import Spence # line: 961
+from tensorflow.python.ops.gen_spectral_ops import BatchFFT # line: 63
+from tensorflow.python.ops.gen_spectral_ops import BatchFFT2D # line: 117
+from tensorflow.python.ops.gen_spectral_ops import BatchFFT3D # line: 171
+from tensorflow.python.ops.gen_spectral_ops import BatchIFFT # line: 225
+from tensorflow.python.ops.gen_spectral_ops import BatchIFFT2D # line: 279
+from tensorflow.python.ops.gen_spectral_ops import BatchIFFT3D # line: 333
+from tensorflow.python.ops.gen_spectral_ops import FFT # line: 421
+from tensorflow.python.ops.gen_spectral_ops import FFT2D # line: 510
+from tensorflow.python.ops.gen_spectral_ops import FFT3D # line: 599
+from tensorflow.python.ops.gen_spectral_ops import FFTND # line: 701
+from tensorflow.python.ops.gen_spectral_ops import IFFT # line: 792
+from tensorflow.python.ops.gen_spectral_ops import IFFT2D # line: 881
+from tensorflow.python.ops.gen_spectral_ops import IFFT3D # line: 970
+from tensorflow.python.ops.gen_spectral_ops import IFFTND # line: 1072
+from tensorflow.python.ops.gen_spectral_ops import IRFFT # line: 1154
+from tensorflow.python.ops.gen_spectral_ops import IRFFT2D # line: 1238
+from tensorflow.python.ops.gen_spectral_ops import IRFFT3D # line: 1322
+from tensorflow.python.ops.gen_spectral_ops import IRFFTND # line: 1435
+from tensorflow.python.ops.gen_spectral_ops import RFFT # line: 1518
+from tensorflow.python.ops.gen_spectral_ops import RFFT2D # line: 1600
+from tensorflow.python.ops.gen_spectral_ops import RFFT3D # line: 1682
+from tensorflow.python.ops.gen_spectral_ops import RFFTND # line: 1795
+from tensorflow.python.ops.gen_state_ops import Assign # line: 75
+from tensorflow.python.ops.gen_state_ops import AssignAdd # line: 122
+from tensorflow.python.ops.gen_state_ops import AssignSub # line: 169
+from tensorflow.python.ops.gen_state_ops import CountUpTo # line: 209
+from tensorflow.python.ops.gen_state_ops import DestroyTemporaryVariable # line: 256
+from tensorflow.python.ops.gen_state_ops import IsVariableInitialized # line: 293
+from tensorflow.python.ops.gen_state_ops import ResourceCountUpTo # line: 347
+from tensorflow.python.ops.gen_state_ops import ResourceScatterNdAdd # line: 447
+from tensorflow.python.ops.gen_state_ops import ResourceScatterNdMax # line: 514
+from tensorflow.python.ops.gen_state_ops import ResourceScatterNdMin # line: 581
+from tensorflow.python.ops.gen_state_ops import ResourceScatterNdSub # line: 682
+from tensorflow.python.ops.gen_state_ops import ResourceScatterNdUpdate # line: 786
+from tensorflow.python.ops.gen_state_ops import ScatterAdd # line: 872
+from tensorflow.python.ops.gen_state_ops import ScatterDiv # line: 942
+from tensorflow.python.ops.gen_state_ops import ScatterMax # line: 1014
+from tensorflow.python.ops.gen_state_ops import ScatterMin # line: 1086
+from tensorflow.python.ops.gen_state_ops import ScatterMul # line: 1156
+from tensorflow.python.ops.gen_state_ops import ScatterNdAdd # line: 1242
+from tensorflow.python.ops.gen_state_ops import ScatterNdMax # line: 1294
+from tensorflow.python.ops.gen_state_ops import ScatterNdMin # line: 1346
+from tensorflow.python.ops.gen_state_ops import ScatterNdSub # line: 1434
+from tensorflow.python.ops.gen_state_ops import ScatterNdUpdate # line: 1521
+from tensorflow.python.ops.gen_state_ops import ScatterSub # line: 1593
+from tensorflow.python.ops.gen_state_ops import ScatterUpdate # line: 1669
+from tensorflow.python.ops.gen_state_ops import TemporaryVariable # line: 1731
+from tensorflow.python.ops.gen_state_ops import Variable # line: 1780
+from tensorflow.python.ops.gen_state_ops import VariableV2 # line: 1838
+from tensorflow.python.ops.gen_stateful_random_ops import NonDeterministicInts # line: 74
+from tensorflow.python.ops.gen_stateful_random_ops import RngReadAndSkip # line: 141
+from tensorflow.python.ops.gen_stateful_random_ops import RngSkip # line: 198
+from tensorflow.python.ops.gen_stateful_random_ops import StatefulRandomBinomial # line: 268
+from tensorflow.python.ops.gen_stateful_random_ops import StatefulStandardNormal # line: 344
+from tensorflow.python.ops.gen_stateful_random_ops import StatefulStandardNormalV2 # line: 419
+from tensorflow.python.ops.gen_stateful_random_ops import StatefulTruncatedNormal # line: 497
+from tensorflow.python.ops.gen_stateful_random_ops import StatefulUniform # line: 574
+from tensorflow.python.ops.gen_stateful_random_ops import StatefulUniformFullInt # line: 649
+from tensorflow.python.ops.gen_stateful_random_ops import StatefulUniformInt # line: 730
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessMultinomial # line: 83
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessParameterizedTruncatedNormal # line: 164
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessRandomBinomial # line: 249
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessRandomGammaV2 # line: 326
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessRandomNormal # line: 401
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessRandomPoisson # line: 481
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessRandomUniform # line: 559
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessRandomUniformFullInt # line: 637
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessRandomUniformInt # line: 713
+from tensorflow.python.ops.gen_stateless_random_ops import StatelessTruncatedNormal # line: 791
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomGammaV3 # line: 81
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomGetAlg # line: 141
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomGetKeyCounter # line: 207
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomGetKeyCounterAlg # line: 276
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomNormalV2 # line: 352
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomUniformFullIntV2 # line: 435
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomUniformIntV2 # line: 517
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessRandomUniformV2 # line: 599
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessShuffle # line: 680
+from tensorflow.python.ops.gen_stateless_random_ops_v2 import StatelessTruncatedNormalV2 # line: 760
+from tensorflow.python.ops.gen_string_ops import AsString # line: 147
+from tensorflow.python.ops.gen_string_ops import DecodeBase64 # line: 250
+from tensorflow.python.ops.gen_string_ops import EncodeBase64 # line: 345
+from tensorflow.python.ops.gen_string_ops import ReduceJoin # line: 446
+from tensorflow.python.ops.gen_string_ops import RegexFullMatch # line: 524
+from tensorflow.python.ops.gen_string_ops import RegexReplace # line: 597
+from tensorflow.python.ops.gen_string_ops import StaticRegexFullMatch # line: 666
+from tensorflow.python.ops.gen_string_ops import StaticRegexReplace # line: 738
+from tensorflow.python.ops.gen_string_ops import StringFormat # line: 822
+from tensorflow.python.ops.gen_string_ops import StringJoin # line: 908
+from tensorflow.python.ops.gen_string_ops import StringLength # line: 988
+from tensorflow.python.ops.gen_string_ops import StringLower # line: 1083
+from tensorflow.python.ops.gen_string_ops import StringNGrams # line: 1201
+from tensorflow.python.ops.gen_string_ops import StringSplit # line: 1307
+from tensorflow.python.ops.gen_string_ops import StringSplitV2 # line: 1407
+from tensorflow.python.ops.gen_string_ops import StringStrip # line: 1498
+from tensorflow.python.ops.gen_string_ops import StringToHashBucket # line: 1564
+from tensorflow.python.ops.gen_string_ops import StringToHashBucketFast # line: 1667
+from tensorflow.python.ops.gen_string_ops import StringToHashBucketStrong # line: 1788
+from tensorflow.python.ops.gen_string_ops import StringUpper # line: 1888
+from tensorflow.python.ops.gen_string_ops import Substr # line: 2044
+from tensorflow.python.ops.gen_string_ops import UnicodeDecode # line: 2176
+from tensorflow.python.ops.gen_string_ops import UnicodeDecodeWithOffsets # line: 2325
+from tensorflow.python.ops.gen_string_ops import UnicodeEncode # line: 2450
+from tensorflow.python.ops.gen_string_ops import UnicodeScript # line: 2559
+from tensorflow.python.ops.gen_string_ops import UnicodeTranscode # line: 2745
+from tensorflow.python.ops.gen_string_ops import UnsortedSegmentJoin # line: 2828
+from tensorflow.python.ops.gen_summary_ops import CloseSummaryWriter # line: 55
+from tensorflow.python.ops.gen_summary_ops import CreateSummaryDbWriter # line: 107
+from tensorflow.python.ops.gen_summary_ops import CreateSummaryFileWriter # line: 163
+from tensorflow.python.ops.gen_summary_ops import FlushSummaryWriter # line: 211
+from tensorflow.python.ops.gen_summary_ops import ImportEvent # line: 255
+from tensorflow.python.ops.gen_summary_ops import SummaryWriter # line: 317
+from tensorflow.python.ops.gen_summary_ops import WriteAudioSummary # line: 383
+from tensorflow.python.ops.gen_summary_ops import WriteGraphSummary # line: 438
+from tensorflow.python.ops.gen_summary_ops import WriteHistogramSummary # line: 491
+from tensorflow.python.ops.gen_summary_ops import WriteImageSummary # line: 554
+from tensorflow.python.ops.gen_summary_ops import WriteRawProtoSummary # line: 609
+from tensorflow.python.ops.gen_summary_ops import WriteScalarSummary # line: 662
+from tensorflow.python.ops.gen_summary_ops import WriteSummary # line: 718
+from tensorflow.python.ops.gen_sync_ops import SyncDevice # line: 57
+from tensorflow.python.ops.gen_tpu_ops import AllToAll # line: 104
+from tensorflow.python.ops.gen_tpu_ops import AssignVariableXlaConcatND # line: 228
+from tensorflow.python.ops.gen_tpu_ops import CollectivePermute # line: 313
+from tensorflow.python.ops.gen_tpu_ops import ConfigureDistributedTPU # line: 420
+from tensorflow.python.ops.gen_tpu_ops import ConfigureTPUEmbedding # line: 491
+from tensorflow.python.ops.gen_tpu_ops import CrossReplicaSum # line: 556
+from tensorflow.python.ops.gen_tpu_ops import DynamicEnqueueTPUEmbeddingArbitraryTensorBatch # line: 691
+from tensorflow.python.ops.gen_tpu_ops import DynamicEnqueueTPUEmbeddingRaggedTensorBatch # line: 847
+from tensorflow.python.ops.gen_tpu_ops import EnqueueTPUEmbeddingArbitraryTensorBatch # line: 1038
+from tensorflow.python.ops.gen_tpu_ops import EnqueueTPUEmbeddingIntegerBatch # line: 1142
+from tensorflow.python.ops.gen_tpu_ops import EnqueueTPUEmbeddingRaggedTensorBatch # line: 1311
+from tensorflow.python.ops.gen_tpu_ops import EnqueueTPUEmbeddingSparseBatch # line: 1494
+from tensorflow.python.ops.gen_tpu_ops import EnqueueTPUEmbeddingSparseTensorBatch # line: 1689
+from tensorflow.python.ops.gen_tpu_ops import InfeedDequeue # line: 1805
+from tensorflow.python.ops.gen_tpu_ops import InfeedDequeueTuple # line: 1875
+from tensorflow.python.ops.gen_tpu_ops import InfeedEnqueue # line: 1959
+from tensorflow.python.ops.gen_tpu_ops import InfeedEnqueuePrelinearizedBuffer # line: 2026
+from tensorflow.python.ops.gen_tpu_ops import InfeedEnqueueTuple # line: 2103
+from tensorflow.python.ops.gen_tpu_ops import IsTPUEmbeddingInitialized # line: 2173
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingADAMParameters # line: 2260
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingAdadeltaParameters # line: 2356
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingAdagradMomentumParameters # line: 2452
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingAdagradParameters # line: 2545
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingCenteredRMSPropParameters # line: 2641
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingFTRLParameters # line: 2738
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingFrequencyEstimatorParameters # line: 2833
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingMDLAdagradLightParameters # line: 2932
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingMomentumParameters # line: 3026
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingProximalAdagradParameters # line: 3118
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingProximalYogiParameters # line: 3202
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingRMSPropParameters # line: 3296
+from tensorflow.python.ops.gen_tpu_ops import LoadTPUEmbeddingStochasticGradientDescentParameters # line: 3387
+from tensorflow.python.ops.gen_tpu_ops import OutfeedDequeue # line: 3470
+from tensorflow.python.ops.gen_tpu_ops import OutfeedDequeueTuple # line: 3557
+from tensorflow.python.ops.gen_tpu_ops import OutfeedDequeueTupleV2 # line: 3649
+from tensorflow.python.ops.gen_tpu_ops import OutfeedDequeueV2 # line: 3728
+from tensorflow.python.ops.gen_tpu_ops import OutfeedEnqueue # line: 3778
+from tensorflow.python.ops.gen_tpu_ops import OutfeedEnqueueTuple # line: 3823
+from tensorflow.python.ops.gen_tpu_ops import Prelinearize # line: 3893
+from tensorflow.python.ops.gen_tpu_ops import PrelinearizeTuple # line: 3980
+from tensorflow.python.ops.gen_tpu_ops import ReadVariableXlaSplitND # line: 4115
+from tensorflow.python.ops.gen_tpu_ops import RecvTPUEmbeddingActivations # line: 4199
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingADAMParameters # line: 4293
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingAdadeltaParameters # line: 4398
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingAdagradMomentumParameters # line: 4504
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingAdagradParameters # line: 4608
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingCenteredRMSPropParameters # line: 4715
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingFTRLParameters # line: 4820
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingFrequencyEstimatorParameters # line: 4925
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingMDLAdagradLightParameters # line: 5032
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingMomentumParameters # line: 5136
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingProximalAdagradParameters # line: 5241
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingProximalYogiParameters # line: 5342
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingRMSPropParameters # line: 5447
+from tensorflow.python.ops.gen_tpu_ops import RetrieveTPUEmbeddingStochasticGradientDescentParameters # line: 5544
+from tensorflow.python.ops.gen_tpu_ops import SendTPUEmbeddingGradients # line: 5630
+from tensorflow.python.ops.gen_tpu_ops import ShutdownDistributedTPU # line: 5687
+from tensorflow.python.ops.gen_tpu_ops import TPUCompilationResult # line: 5741
+from tensorflow.python.ops.gen_tpu_ops import TPUEmbeddingActivations # line: 5817
+from tensorflow.python.ops.gen_tpu_ops import TPUOrdinalSelector # line: 5878
+from tensorflow.python.ops.gen_tpu_ops import TPUPartitionedCall # line: 5949
+from tensorflow.python.ops.gen_tpu_ops import TPUReplicateMetadata # line: 6101
+from tensorflow.python.ops.gen_tpu_ops import TPUReplicatedInput # line: 6247
+from tensorflow.python.ops.gen_tpu_ops import TPUReplicatedOutput # line: 6332
+from tensorflow.python.ops.gen_tpu_ops import WorkerHeartbeat # line: 6391
+from tensorflow.python.ops.gen_tpu_ops import XlaConcatND # line: 6513
+from tensorflow.python.ops.gen_tpu_ops import XlaSplitND # line: 6648
+from tensorflow.python.ops.gen_tpu_partition_ops import TPUPartitionedInput # line: 80
+from tensorflow.python.ops.gen_tpu_partition_ops import TPUPartitionedInputV2 # line: 168
+from tensorflow.python.ops.gen_tpu_partition_ops import TPUPartitionedOutput # line: 252
+from tensorflow.python.ops.gen_tpu_partition_ops import TPUPartitionedOutputV2 # line: 330
+from tensorflow.python.ops.gen_training_ops import ApplyAdaMax # line: 83
+from tensorflow.python.ops.gen_training_ops import ApplyAdadelta # line: 143
+from tensorflow.python.ops.gen_training_ops import ApplyAdagrad # line: 201
+from tensorflow.python.ops.gen_training_ops import ApplyAdagradDA # line: 260
+from tensorflow.python.ops.gen_training_ops import ApplyAdagradV2 # line: 320
+from tensorflow.python.ops.gen_training_ops import ApplyAdam # line: 394
+from tensorflow.python.ops.gen_training_ops import ApplyAddSign # line: 452
+from tensorflow.python.ops.gen_training_ops import ApplyCenteredRMSProp # line: 531
+from tensorflow.python.ops.gen_training_ops import ApplyFtrl # line: 600
+from tensorflow.python.ops.gen_training_ops import ApplyFtrlV2 # line: 673
+from tensorflow.python.ops.gen_training_ops import ApplyGradientDescent # line: 719
+from tensorflow.python.ops.gen_training_ops import ApplyMomentum # line: 784
+from tensorflow.python.ops.gen_training_ops import ApplyPowerSign # line: 842
+from tensorflow.python.ops.gen_training_ops import ApplyProximalAdagrad # line: 898
+from tensorflow.python.ops.gen_training_ops import ApplyProximalGradientDescent # line: 952
+from tensorflow.python.ops.gen_training_ops import ApplyRMSProp # line: 1020
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAdaMax # line: 1087
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAdadelta # line: 1164
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAdagrad # line: 1238
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAdagradDA # line: 1321
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAdagradV2 # line: 1400
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAdam # line: 1496
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAdamWithAmsgrad # line: 1589
+from tensorflow.python.ops.gen_training_ops import ResourceApplyAddSign # line: 1665
+from tensorflow.python.ops.gen_training_ops import ResourceApplyCenteredRMSProp # line: 1759
+from tensorflow.python.ops.gen_training_ops import ResourceApplyFtrl # line: 1850
+from tensorflow.python.ops.gen_training_ops import ResourceApplyFtrlV2 # line: 1946
+from tensorflow.python.ops.gen_training_ops import ResourceApplyGradientDescent # line: 2013
+from tensorflow.python.ops.gen_training_ops import ResourceApplyKerasMomentum # line: 2094
+from tensorflow.python.ops.gen_training_ops import ResourceApplyMomentum # line: 2179
+from tensorflow.python.ops.gen_training_ops import ResourceApplyPowerSign # line: 2256
+from tensorflow.python.ops.gen_training_ops import ResourceApplyProximalAdagrad # line: 2330
+from tensorflow.python.ops.gen_training_ops import ResourceApplyProximalGradientDescent # line: 2403
+from tensorflow.python.ops.gen_training_ops import ResourceApplyRMSProp # line: 2486
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyAdadelta # line: 2563
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyAdagrad # line: 2645
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyAdagradDA # line: 2734
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyAdagradV2 # line: 2821
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyCenteredRMSProp # line: 2924
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyFtrl # line: 3022
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyFtrlV2 # line: 3126
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyKerasMomentum # line: 3222
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyMomentum # line: 3315
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyProximalAdagrad # line: 3401
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyProximalGradientDescent # line: 3481
+from tensorflow.python.ops.gen_training_ops import ResourceSparseApplyRMSProp # line: 3570
+from tensorflow.python.ops.gen_training_ops import SparseApplyAdadelta # line: 3646
+from tensorflow.python.ops.gen_training_ops import SparseApplyAdagrad # line: 3709
+from tensorflow.python.ops.gen_training_ops import SparseApplyAdagradDA # line: 3773
+from tensorflow.python.ops.gen_training_ops import SparseApplyAdagradV2 # line: 3839
+from tensorflow.python.ops.gen_training_ops import SparseApplyCenteredRMSProp # line: 3921
+from tensorflow.python.ops.gen_training_ops import SparseApplyFtrl # line: 3997
+from tensorflow.python.ops.gen_training_ops import SparseApplyFtrlV2 # line: 4077
+from tensorflow.python.ops.gen_training_ops import SparseApplyMomentum # line: 4149
+from tensorflow.python.ops.gen_training_ops import SparseApplyProximalAdagrad # line: 4212
+from tensorflow.python.ops.gen_training_ops import SparseApplyProximalGradientDescent # line: 4273
+from tensorflow.python.ops.gen_training_ops import SparseApplyRMSProp # line: 4346
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformDequantize # line: 111
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformQuantize # line: 223
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformQuantizedAdd # line: 432
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformQuantizedClipByValue # line: 562
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformQuantizedConvolution # line: 879
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformQuantizedConvolutionHybrid # line: 1174
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformQuantizedDot # line: 1422
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformQuantizedDotHybrid # line: 1565
+from tensorflow.python.ops.gen_uniform_quant_ops import UniformRequantize # line: 1732
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import CSRSparseMatrixComponents # line: 84
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import CSRSparseMatrixToDense # line: 146
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import CSRSparseMatrixToSparseTensor # line: 218
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import DenseToCSRSparseMatrix # line: 279
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixAdd # line: 344
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixMatMul # line: 476
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixMul # line: 563
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixNNZ # line: 618
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixOrderingAMD # line: 722
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixSoftmax # line: 787
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixSoftmaxGrad # line: 849
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixSparseCholesky # line: 992
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixSparseMatMul # line: 1162
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixTranspose # line: 1247
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseMatrixZeros # line: 1309
+from tensorflow.python.ops.linalg.sparse.gen_sparse_csr_matrix_ops import SparseTensorToCSRSparseMatrix # line: 1371
+from tensorflow.python.user_ops.ops.gen_user_ops import Fact # line: 62
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/saved_model/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/saved_model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c751d9f74e185ee1b8ec798a1ba3648253530136
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/saved_model/__init__.py
@@ -0,0 +1,38 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.saved_model namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.saved_model import experimental
+from tensorflow.python.saved_model.constants import ASSETS_DIRECTORY # line: 26
+from tensorflow.python.saved_model.constants import ASSETS_KEY # line: 37
+from tensorflow.python.saved_model.constants import DEBUG_DIRECTORY # line: 97
+from tensorflow.python.saved_model.constants import DEBUG_INFO_FILENAME_PB # line: 107
+from tensorflow.python.saved_model.constants import SAVED_MODEL_FILENAME_PB # line: 76
+from tensorflow.python.saved_model.constants import SAVED_MODEL_FILENAME_PBTXT # line: 88
+from tensorflow.python.saved_model.constants import SAVED_MODEL_SCHEMA_VERSION # line: 64
+from tensorflow.python.saved_model.constants import VARIABLES_DIRECTORY # line: 116
+from tensorflow.python.saved_model.constants import VARIABLES_FILENAME # line: 125
+from tensorflow.python.saved_model.load import load # line: 819
+from tensorflow.python.saved_model.load_options import LoadOptions # line: 21
+from tensorflow.python.saved_model.loader_impl import contains_saved_model # line: 254
+from tensorflow.python.saved_model.save import save # line: 1145
+from tensorflow.python.saved_model.save_options import SaveOptions # line: 92
+from tensorflow.python.saved_model.signature_constants import CLASSIFY_INPUTS # line: 38
+from tensorflow.python.saved_model.signature_constants import CLASSIFY_METHOD_NAME # line: 47
+from tensorflow.python.saved_model.signature_constants import CLASSIFY_OUTPUT_CLASSES # line: 56
+from tensorflow.python.saved_model.signature_constants import CLASSIFY_OUTPUT_SCORES # line: 65
+from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY # line: 25
+from tensorflow.python.saved_model.signature_constants import PREDICT_INPUTS # line: 77
+from tensorflow.python.saved_model.signature_constants import PREDICT_METHOD_NAME # line: 86
+from tensorflow.python.saved_model.signature_constants import PREDICT_OUTPUTS # line: 95
+from tensorflow.python.saved_model.signature_constants import REGRESS_INPUTS # line: 107
+from tensorflow.python.saved_model.signature_constants import REGRESS_METHOD_NAME # line: 116
+from tensorflow.python.saved_model.signature_constants import REGRESS_OUTPUTS # line: 125
+from tensorflow.python.saved_model.tag_constants import GPU # line: 44
+from tensorflow.python.saved_model.tag_constants import SERVING # line: 23
+from tensorflow.python.saved_model.tag_constants import TPU # line: 51
+from tensorflow.python.saved_model.tag_constants import TRAINING # line: 31
+from tensorflow.python.trackable.asset import Asset # line: 30
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/saved_model/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/saved_model/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..40daaf08990f910291bd6115b3696b52a48702e6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/saved_model/experimental/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.saved_model.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.saved_model.fingerprinting import Fingerprint # line: 26
+from tensorflow.python.saved_model.fingerprinting import read_fingerprint # line: 149
+from tensorflow.python.saved_model.save_options import VariablePolicy # line: 26
+from tensorflow.python.trackable.resource import TrackableResource # line: 233
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/sets/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/sets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d0e25911cd93fe6da9a471cce9f1ae078dd3ac1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/sets/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.sets namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.sets_impl import set_difference as difference # line: 208
+from tensorflow.python.ops.sets_impl import set_intersection as intersection # line: 137
+from tensorflow.python.ops.sets_impl import set_size as size # line: 30
+from tensorflow.python.ops.sets_impl import set_union as union # line: 289
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/signal/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/signal/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..75bf63034ff4df03813c3cefd775e85e6641d868
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/signal/__init__.py
@@ -0,0 +1,41 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.signal namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.gen_spectral_ops import fft # line: 353
+from tensorflow.python.ops.gen_spectral_ops import fft2d # line: 442
+from tensorflow.python.ops.gen_spectral_ops import fft3d # line: 531
+from tensorflow.python.ops.gen_spectral_ops import ifft # line: 724
+from tensorflow.python.ops.gen_spectral_ops import ifft2d # line: 813
+from tensorflow.python.ops.gen_spectral_ops import ifft3d # line: 902
+from tensorflow.python.ops.signal.dct_ops import dct # line: 50
+from tensorflow.python.ops.signal.dct_ops import idct # line: 212
+from tensorflow.python.ops.signal.fft_ops import fftnd # line: 400
+from tensorflow.python.ops.signal.fft_ops import fftshift # line: 603
+from tensorflow.python.ops.signal.fft_ops import ifftnd # line: 404
+from tensorflow.python.ops.signal.fft_ops import ifftshift # line: 648
+from tensorflow.python.ops.signal.fft_ops import irfft # line: 411
+from tensorflow.python.ops.signal.fft_ops import irfft2d # line: 417
+from tensorflow.python.ops.signal.fft_ops import irfft3d # line: 423
+from tensorflow.python.ops.signal.fft_ops import irfftnd # line: 430
+from tensorflow.python.ops.signal.fft_ops import rfft # line: 408
+from tensorflow.python.ops.signal.fft_ops import rfft2d # line: 414
+from tensorflow.python.ops.signal.fft_ops import rfft3d # line: 420
+from tensorflow.python.ops.signal.fft_ops import rfftnd # line: 426
+from tensorflow.python.ops.signal.mel_ops import linear_to_mel_weight_matrix # line: 90
+from tensorflow.python.ops.signal.mfcc_ops import mfccs_from_log_mel_spectrograms # line: 25
+from tensorflow.python.ops.signal.reconstruction_ops import overlap_and_add # line: 26
+from tensorflow.python.ops.signal.shape_ops import frame # line: 55
+from tensorflow.python.ops.signal.spectral_ops import inverse_mdct # line: 367
+from tensorflow.python.ops.signal.spectral_ops import inverse_stft # line: 155
+from tensorflow.python.ops.signal.spectral_ops import inverse_stft_window_fn # line: 95
+from tensorflow.python.ops.signal.spectral_ops import mdct # line: 291
+from tensorflow.python.ops.signal.spectral_ops import stft # line: 34
+from tensorflow.python.ops.signal.window_ops import hamming_window # line: 171
+from tensorflow.python.ops.signal.window_ops import hann_window # line: 145
+from tensorflow.python.ops.signal.window_ops import kaiser_bessel_derived_window # line: 92
+from tensorflow.python.ops.signal.window_ops import kaiser_window # line: 53
+from tensorflow.python.ops.signal.window_ops import vorbis_window # line: 120
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/sparse/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/sparse/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9710725d5c87fddbf25edbe45d961e8e446953c2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/sparse/__init__.py
@@ -0,0 +1,37 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.sparse namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.sparse_tensor import SparseTensor # line: 47
+from tensorflow.python.ops.array_ops import sparse_mask as mask # line: 1845
+from tensorflow.python.ops.math_ops import sparse_segment_mean_v2 as segment_mean # line: 5003
+from tensorflow.python.ops.math_ops import sparse_segment_sqrt_n_v2 as segment_sqrt_n # line: 5105
+from tensorflow.python.ops.math_ops import sparse_segment_sum_v2 as segment_sum # line: 4861
+from tensorflow.python.ops.sparse_ops import sparse_add_v2 as add # line: 524
+from tensorflow.python.ops.sparse_ops import sparse_bincount as bincount # line: 3205
+from tensorflow.python.ops.sparse_ops import sparse_concat_v2 as concat # line: 400
+from tensorflow.python.ops.sparse_ops import sparse_cross as cross # line: 608
+from tensorflow.python.ops.sparse_ops import sparse_cross_hashed as cross_hashed # line: 666
+from tensorflow.python.ops.sparse_ops import sparse_expand_dims as expand_dims # line: 143
+from tensorflow.python.ops.sparse_ops import sparse_eye as eye # line: 249
+from tensorflow.python.ops.sparse_ops import sparse_fill_empty_rows as fill_empty_rows # line: 2109
+from tensorflow.python.ops.sparse_ops import from_dense # line: 111
+from tensorflow.python.ops.sparse_ops import map_values # line: 2932
+from tensorflow.python.ops.sparse_ops import sparse_maximum as maximum # line: 2734
+from tensorflow.python.ops.sparse_ops import sparse_minimum as minimum # line: 2780
+from tensorflow.python.ops.sparse_ops import sparse_reduce_max_v2 as reduce_max # line: 1246
+from tensorflow.python.ops.sparse_ops import sparse_reduce_sum_v2 as reduce_sum # line: 1480
+from tensorflow.python.ops.sparse_ops import sparse_reorder as reorder # line: 821
+from tensorflow.python.ops.sparse_ops import sparse_reset_shape as reset_shape # line: 2004
+from tensorflow.python.ops.sparse_ops import sparse_reshape as reshape # line: 876
+from tensorflow.python.ops.sparse_ops import sparse_retain as retain # line: 1957
+from tensorflow.python.ops.sparse_ops import sparse_slice as slice # line: 1136
+from tensorflow.python.ops.sparse_ops import sparse_softmax as softmax # line: 2671
+from tensorflow.python.ops.sparse_ops import sparse_tensor_dense_matmul as sparse_dense_matmul # line: 2430
+from tensorflow.python.ops.sparse_ops import sparse_split_v2 as split # line: 1067
+from tensorflow.python.ops.sparse_ops import sparse_tensor_to_dense as to_dense # line: 1681
+from tensorflow.python.ops.sparse_ops import sparse_to_indicator as to_indicator # line: 1737
+from tensorflow.python.ops.sparse_ops import sparse_transpose as transpose # line: 2824
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/strings/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/strings/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7fd90a27f3293a73be560b9cde37aab2639f134
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/strings/__init__.py
@@ -0,0 +1,33 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.strings namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.gen_string_ops import as_string # line: 29
+from tensorflow.python.ops.gen_string_ops import string_lower as lower # line: 1009
+from tensorflow.python.ops.gen_string_ops import string_strip as strip # line: 1429
+from tensorflow.python.ops.gen_string_ops import string_to_hash_bucket_fast as to_hash_bucket_fast # line: 1583
+from tensorflow.python.ops.gen_string_ops import string_to_hash_bucket_strong as to_hash_bucket_strong # line: 1688
+from tensorflow.python.ops.gen_string_ops import unicode_script # line: 2477
+from tensorflow.python.ops.gen_string_ops import unicode_transcode # line: 2578
+from tensorflow.python.ops.gen_string_ops import string_upper as upper # line: 1814
+from tensorflow.python.ops.ragged.ragged_string_ops import string_bytes_split as bytes_split # line: 40
+from tensorflow.python.ops.ragged.ragged_string_ops import ngrams # line: 672
+from tensorflow.python.ops.ragged.ragged_string_ops import string_split_v2 as split # line: 466
+from tensorflow.python.ops.ragged.ragged_string_ops import unicode_decode # line: 186
+from tensorflow.python.ops.ragged.ragged_string_ops import unicode_decode_with_offsets # line: 232
+from tensorflow.python.ops.ragged.ragged_string_ops import unicode_encode # line: 88
+from tensorflow.python.ops.ragged.ragged_string_ops import unicode_split # line: 294
+from tensorflow.python.ops.ragged.ragged_string_ops import unicode_split_with_offsets # line: 342
+from tensorflow.python.ops.string_ops import string_format as format # line: 115
+from tensorflow.python.ops.string_ops import string_join as join # line: 551
+from tensorflow.python.ops.string_ops import string_length_v2 as length # line: 412
+from tensorflow.python.ops.string_ops import reduce_join_v2 as reduce_join # line: 332
+from tensorflow.python.ops.string_ops import regex_full_match # line: 47
+from tensorflow.python.ops.string_ops import regex_replace # line: 74
+from tensorflow.python.ops.string_ops import substr_v2 as substr # line: 441
+from tensorflow.python.ops.string_ops import string_to_hash_bucket as to_hash_bucket # line: 505
+from tensorflow.python.ops.string_ops import string_to_number as to_number # line: 462
+from tensorflow.python.ops.string_ops import unsorted_segment_join # line: 586
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/summary/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/summary/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..de01d5f3f24c81c9796f3f487423101d9184197f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/summary/__init__.py
@@ -0,0 +1,19 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.summary namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.summary import experimental
+from tensorflow.python.ops.summary_ops_v2 import SummaryWriter # line: 239
+from tensorflow.python.ops.summary_ops_v2 import create_file_writer_v2 as create_file_writer # line: 511
+from tensorflow.python.ops.summary_ops_v2 import create_noop_writer # line: 636
+from tensorflow.python.ops.summary_ops_v2 import flush # line: 1136
+from tensorflow.python.ops.summary_ops_v2 import graph # line: 1048
+from tensorflow.python.ops.summary_ops_v2 import record_if # line: 152
+from tensorflow.python.ops.summary_ops_v2 import should_record_summaries # line: 133
+from tensorflow.python.ops.summary_ops_v2 import trace_export # line: 1374
+from tensorflow.python.ops.summary_ops_v2 import trace_off # line: 1425
+from tensorflow.python.ops.summary_ops_v2 import trace_on # line: 1329
+from tensorflow.python.ops.summary_ops_v2 import write # line: 732
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/summary/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/summary/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7152cb936f4610286a0acf0c4dccd669f0c7132
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/summary/experimental/__init__.py
@@ -0,0 +1,11 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.summary.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.ops.summary_ops_v2 import get_step # line: 209
+from tensorflow.python.ops.summary_ops_v2 import set_step # line: 220
+from tensorflow.python.ops.summary_ops_v2 import summary_scope # line: 691
+from tensorflow.python.ops.summary_ops_v2 import write_raw_pb # line: 809
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/sysconfig/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/sysconfig/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa18e873703ecf78b4671ef9145116a150d51f08
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/sysconfig/__init__.py
@@ -0,0 +1,15 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.sysconfig namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.versions import CXX11_ABI_FLAG # line: 48
+from tensorflow.python.framework.versions import CXX_VERSION # line: 54
+from tensorflow.python.framework.versions import MONOLITHIC_BUILD # line: 60
+from tensorflow.python.platform.sysconfig import get_build_info # line: 122
+from tensorflow.python.platform.sysconfig import get_compile_flags # line: 59
+from tensorflow.python.platform.sysconfig import get_include # line: 33
+from tensorflow.python.platform.sysconfig import get_lib # line: 48
+from tensorflow.python.platform.sysconfig import get_link_flags # line: 94
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/test/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/test/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..40010eba6dda7a54f69d0e00df806502278ebb77
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/test/__init__.py
@@ -0,0 +1,23 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.test namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.test import experimental
+from tensorflow.python.framework.test_util import TensorFlowTestCase as TestCase # line: 2504
+from tensorflow.python.framework.test_util import assert_equal_graph_def_v2 as assert_equal_graph_def # line: 205
+from tensorflow.python.framework.test_util import create_local_cluster # line: 3921
+from tensorflow.python.framework.test_util import gpu_device_name # line: 156
+from tensorflow.python.framework.test_util import is_gpu_available # line: 1989
+from tensorflow.python.framework.test_util import with_eager_op_as_function # line: 1219
+from tensorflow.python.ops.gradient_checker_v2 import compute_gradient # line: 295
+from tensorflow.python.platform.benchmark import TensorFlowBenchmark as Benchmark # line: 287
+from tensorflow.python.platform.benchmark import benchmark_config # line: 274
+from tensorflow.python.platform.test import disable_with_predicate # line: 131
+from tensorflow.python.platform.test import is_built_with_cuda # line: 89
+from tensorflow.python.platform.test import is_built_with_gpu_support # line: 149
+from tensorflow.python.platform.test import is_built_with_rocm # line: 110
+from tensorflow.python.platform.test import is_built_with_xla # line: 170
+from tensorflow.python.platform.test import main # line: 49
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/test/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/test/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff027d362fbd63221354d71b6ea76dff623f107d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/test/experimental/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.test.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.test_util import sync_devices # line: 4146
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/tpu/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/tpu/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..23b9bf300f5d8f51e2599a18496efe0deb5c0aa8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/tpu/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.tpu namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.tpu import experimental
+from tensorflow.python.tpu.tpu import XLAOptions # line: 230
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/tpu/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/tpu/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..411a4cadba30ceccc512dbe1125a815db75de271
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/tpu/experimental/__init__.py
@@ -0,0 +1,15 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.tpu.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.tpu.experimental import embedding
+from tensorflow.python.distribute.cluster_resolver.tpu_cluster_resolver import initialize_tpu_system # line: 25
+from tensorflow.python.distribute.cluster_resolver.tpu_cluster_resolver import shutdown_tpu_system # line: 26
+from tensorflow.python.tpu.device_assignment import DeviceAssignment # line: 68
+from tensorflow.python.tpu.device_assignment import DeviceOrderMode # line: 56
+from tensorflow.python.tpu.topology import Topology # line: 39
+from tensorflow.python.tpu.tpu_hardware_feature import HardwareFeature # line: 21
+from tensorflow.python.tpu.tpu_system_metadata import TPUSystemMetadata # line: 40
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/tpu/experimental/embedding/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/tpu/experimental/embedding/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e0c85f7996d7881327306a678031b4ebffdda6f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/tpu/experimental/embedding/__init__.py
@@ -0,0 +1,19 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.tpu.experimental.embedding namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.tpu.tpu_embedding_for_serving import TPUEmbeddingForServing # line: 42
+from tensorflow.python.tpu.tpu_embedding_for_serving import cpu_embedding_lookup as serving_embedding_lookup # line: 335
+from tensorflow.python.tpu.tpu_embedding_v1 import TPUEmbeddingV0 # line: 38
+from tensorflow.python.tpu.tpu_embedding_v2 import TPUEmbedding # line: 75
+from tensorflow.python.tpu.tpu_embedding_v2_utils import Adagrad # line: 302
+from tensorflow.python.tpu.tpu_embedding_v2_utils import AdagradMomentum # line: 431
+from tensorflow.python.tpu.tpu_embedding_v2_utils import Adam # line: 752
+from tensorflow.python.tpu.tpu_embedding_v2_utils import FTRL # line: 578
+from tensorflow.python.tpu.tpu_embedding_v2_utils import FeatureConfig # line: 1161
+from tensorflow.python.tpu.tpu_embedding_v2_utils import QuantizationConfig # line: 920
+from tensorflow.python.tpu.tpu_embedding_v2_utils import SGD # line: 186
+from tensorflow.python.tpu.tpu_embedding_v2_utils import TableConfig # line: 984
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/train/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/train/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..04955ac4df55e9b1d83907c69ee15559f55a37e6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/train/__init__.py
@@ -0,0 +1,34 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.train namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.train import experimental
+from tensorflow.python.checkpoint.checkpoint import Checkpoint # line: 2035
+from tensorflow.python.checkpoint.checkpoint_management import CheckpointManager # line: 518
+from tensorflow.python.checkpoint.checkpoint_management import get_checkpoint_state # line: 250
+from tensorflow.python.checkpoint.checkpoint_management import latest_checkpoint # line: 328
+from tensorflow.python.checkpoint.checkpoint_options import CheckpointOptions # line: 24
+from tensorflow.python.checkpoint.checkpoint_view import CheckpointView # line: 28
+from tensorflow.python.checkpoint.trackable_view import TrackableView # line: 25
+from tensorflow.python.training.checkpoint_utils import checkpoints_iterator # line: 181
+from tensorflow.python.training.checkpoint_utils import list_variables # line: 117
+from tensorflow.python.training.checkpoint_utils import load_checkpoint # line: 46
+from tensorflow.python.training.checkpoint_utils import load_variable # line: 83
+from tensorflow.python.training.coordinator import Coordinator # line: 27
+from tensorflow.python.training.moving_averages import ExponentialMovingAverage # line: 283
+from tensorflow.python.training.server_lib import ClusterSpec # line: 242
+from tensorflow.python.training.training import BytesList # line: 131
+from tensorflow.python.training.training import ClusterDef # line: 132
+from tensorflow.python.training.training import Example # line: 133
+from tensorflow.python.training.training import Feature # line: 134
+from tensorflow.python.training.training import FeatureList # line: 136
+from tensorflow.python.training.training import FeatureLists # line: 137
+from tensorflow.python.training.training import Features # line: 135
+from tensorflow.python.training.training import FloatList # line: 138
+from tensorflow.python.training.training import Int64List # line: 139
+from tensorflow.python.training.training import JobDef # line: 140
+from tensorflow.python.training.training import SequenceExample # line: 142
+from tensorflow.python.training.training import ServerDef # line: 143
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/train/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/train/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a31469418cf82e8e03dd3ad573af2056b35ec72
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/train/experimental/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.train.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.trackable.python_state import PythonState # line: 28
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/types/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/types/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e607f10ec1721672fabee801d4540d1316f87ac
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/types/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.types namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.types import experimental
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/types/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/types/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..521eb948ad962969524ddc1db480ae8c21ff5c8f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/types/experimental/__init__.py
@@ -0,0 +1,17 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.types.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.types.experimental import distributed
+from tensorflow.python.types.core import AtomicFunction # line: 143
+from tensorflow.python.types.core import Callable # line: 112
+from tensorflow.python.types.core import ConcreteFunction # line: 170
+from tensorflow.python.types.core import FunctionType # line: 91
+from tensorflow.python.types.core import PolymorphicFunction as GenericFunction # line: 187
+from tensorflow.python.types.core import PolymorphicFunction # line: 187
+from tensorflow.python.types.core import TensorLike # line: 419
+from tensorflow.python.types.trace import SupportsTracingProtocol # line: 307
+from tensorflow.python.types.trace import TraceType # line: 39
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/types/experimental/distributed/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/types/experimental/distributed/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..1db2dff92629c06b05e0a4dc3472003807513696
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/types/experimental/distributed/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.types.experimental.distributed namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.types.distribute import Mirrored # line: 182
+from tensorflow.python.types.distribute import PerReplica # line: 169
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/version/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/version/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d494fbe6c828c9f311a543b9882150ad2ab33672
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/version/__init__.py
@@ -0,0 +1,13 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.version namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.framework.versions import COMPILER_VERSION # line: 41
+from tensorflow.python.framework.versions import GIT_VERSION # line: 35
+from tensorflow.python.framework.versions import GRAPH_DEF_VERSION # line: 68
+from tensorflow.python.framework.versions import GRAPH_DEF_VERSION_MIN_CONSUMER # line: 74
+from tensorflow.python.framework.versions import GRAPH_DEF_VERSION_MIN_PRODUCER # line: 82
+from tensorflow.python.framework.versions import VERSION # line: 29
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/xla/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/xla/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..52f1fa3a290adc5ca0f10a9815de46ff9d40a710
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/xla/__init__.py
@@ -0,0 +1,8 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.xla namespace
+"""
+
+import sys as _sys
+
+from tensorflow._api.v2.xla import experimental
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/xla/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/xla/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2c1148ed178739e4a069fc1d4ef5696ec5d8841
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/_api/v2/xla/experimental/__init__.py
@@ -0,0 +1,9 @@
+# This file is MACHINE GENERATED! Do not edit.
+# Generated by: tensorflow/python/tools/api/generator2/generator/generator.py script.
+"""Public API for tf._api.v2.xla.experimental namespace
+"""
+
+import sys as _sys
+
+from tensorflow.python.compiler.xla.jit import experimental_jit_scope as jit_scope # line: 37
+from tensorflow.python.compiler.xla.xla import compile # line: 62
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/jit/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/jit/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/jit/ops/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/jit/ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/jit/ops/xla_ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/jit/ops/xla_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b488c088f1240a636dc3cb34da3acedf5731844
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/jit/ops/xla_ops.py
@@ -0,0 +1,352 @@
+"""Python wrappers around TensorFlow ops.
+
+This file is MACHINE GENERATED! Do not edit.
+"""
+
+import collections
+
+from tensorflow.python import pywrap_tfe as pywrap_tfe
+from tensorflow.python.eager import context as _context
+from tensorflow.python.eager import core as _core
+from tensorflow.python.eager import execute as _execute
+from tensorflow.python.framework import dtypes as _dtypes
+from tensorflow.security.fuzzing.py import annotation_types as _atypes
+
+from tensorflow.python.framework import op_def_registry as _op_def_registry
+from tensorflow.python.framework import ops as _ops
+from tensorflow.python.framework import op_def_library as _op_def_library
+from tensorflow.python.util.deprecation import deprecated_endpoints
+from tensorflow.python.util import dispatch as _dispatch
+from tensorflow.python.util.tf_export import tf_export
+
+from typing import TypeVar, List, Any
+from typing_extensions import Annotated
+
+TV_XlaClusterOutput_T = TypeVar("TV_XlaClusterOutput_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_cluster_output')
+def xla_cluster_output(input: Annotated[Any, TV_XlaClusterOutput_T], name=None) -> Annotated[Any, TV_XlaClusterOutput_T]:
+  r"""Operator that connects the output of an XLA computation to other consumer graph nodes.
+
+  Args:
+    input: A `Tensor`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaClusterOutput", name, input)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_cluster_output(
+          (input, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_cluster_output_eager_fallback(
+          input, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_cluster_output, (), dict(input=input, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_cluster_output(
+        (input, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaClusterOutput", input=input, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_cluster_output, (), dict(input=input, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaClusterOutput", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaClusterOutput = tf_export("raw_ops.XlaClusterOutput")(_ops.to_raw_op(xla_cluster_output))
+_dispatcher_for_xla_cluster_output = xla_cluster_output._tf_type_based_dispatcher.Dispatch
+
+
+def xla_cluster_output_eager_fallback(input: Annotated[Any, TV_XlaClusterOutput_T], name, ctx) -> Annotated[Any, TV_XlaClusterOutput_T]:
+  _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
+  _inputs_flat = [input]
+  _attrs = ("T", _attr_T)
+  _result = _execute.execute(b"XlaClusterOutput", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaClusterOutput", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_launch')
+def xla_launch(constants, args, resources: Annotated[List[Any], _atypes.Resource], Tresults, function, name=None):
+  r"""XLA Launch Op. For use by the XLA JIT only.
+
+  Args:
+    constants: A list of `Tensor` objects.
+    args: A list of `Tensor` objects.
+    resources: A list of `Tensor` objects with type `resource`.
+    Tresults: A list of `tf.DTypes`.
+    function: A function decorated with @Defun.
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects of type `Tresults`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaLaunch", name, constants, args, resources, "Tresults",
+        Tresults, "function", function)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_launch(
+          (constants, args, resources, Tresults, function, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_launch_eager_fallback(
+          constants, args, resources, Tresults=Tresults, function=function,
+          name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_launch, (), dict(constants=constants, args=args,
+                                 resources=resources, Tresults=Tresults,
+                                 function=function, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_launch(
+        (constants, args, resources, Tresults, function, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if not isinstance(resources, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'resources' argument to "
+        "'xla_launch' Op, not %r." % resources)
+  _attr_Nresources = len(resources)
+  if not isinstance(Tresults, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Tresults' argument to "
+        "'xla_launch' Op, not %r." % Tresults)
+  Tresults = [_execute.make_type(_t, "Tresults") for _t in Tresults]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaLaunch", constants=constants, args=args, resources=resources,
+                     Tresults=Tresults, function=function, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_launch, (), dict(constants=constants, args=args,
+                               resources=resources, Tresults=Tresults,
+                               function=function, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if not _result:
+    return _op
+  if _execute.must_record_gradient():
+    _attrs = ("Tconstants", _op.get_attr("Tconstants"), "Targs",
+              _op.get_attr("Targs"), "Nresources",
+              _op._get_attr_int("Nresources"), "Tresults",
+              _op.get_attr("Tresults"), "function", _op.get_attr("function"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaLaunch", _inputs_flat, _attrs, _result)
+  return _result
+
+XlaLaunch = tf_export("raw_ops.XlaLaunch")(_ops.to_raw_op(xla_launch))
+_dispatcher_for_xla_launch = xla_launch._tf_type_based_dispatcher.Dispatch
+
+
+def xla_launch_eager_fallback(constants, args, resources: Annotated[List[Any], _atypes.Resource], Tresults, function, name, ctx):
+  if not isinstance(resources, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'resources' argument to "
+        "'xla_launch' Op, not %r." % resources)
+  _attr_Nresources = len(resources)
+  if not isinstance(Tresults, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Tresults' argument to "
+        "'xla_launch' Op, not %r." % Tresults)
+  Tresults = [_execute.make_type(_t, "Tresults") for _t in Tresults]
+  _attr_Tconstants, constants = _execute.convert_to_mixed_eager_tensors(constants, ctx)
+  _attr_Targs, args = _execute.convert_to_mixed_eager_tensors(args, ctx)
+  resources = _ops.convert_n_to_tensor(resources, _dtypes.resource)
+  _inputs_flat = list(constants) + list(args) + list(resources)
+  _attrs = ("Tconstants", _attr_Tconstants, "Targs", _attr_Targs,
+  "Nresources", _attr_Nresources, "Tresults", Tresults, "function", function)
+  _result = _execute.execute(b"XlaLaunch", len(Tresults), inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaLaunch", _inputs_flat, _attrs, _result)
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_launch_v2')
+def xla_launch_v2(args, Tresults, constants, resources, function, name=None):
+  r"""XLA Launch Op. For use by the XLA JIT only.
+
+  Args:
+    args: A list of `Tensor` objects.
+    Tresults: A list of `tf.DTypes`.
+    constants: A list of `ints`.
+    resources: A list of `ints`.
+    function: A function decorated with @Defun.
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects of type `Tresults`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaLaunchV2", name, args, "Tresults", Tresults, "constants",
+        constants, "resources", resources, "function", function)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_launch_v2(
+          (args, Tresults, constants, resources, function, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_launch_v2_eager_fallback(
+          args, Tresults=Tresults, constants=constants, resources=resources,
+          function=function, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_launch_v2, (), dict(args=args, Tresults=Tresults,
+                                    constants=constants, resources=resources,
+                                    function=function, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_launch_v2(
+        (args, Tresults, constants, resources, function, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if not isinstance(Tresults, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Tresults' argument to "
+        "'xla_launch_v2' Op, not %r." % Tresults)
+  Tresults = [_execute.make_type(_t, "Tresults") for _t in Tresults]
+  if not isinstance(constants, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'constants' argument to "
+        "'xla_launch_v2' Op, not %r." % constants)
+  constants = [_execute.make_int(_i, "constants") for _i in constants]
+  if not isinstance(resources, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'resources' argument to "
+        "'xla_launch_v2' Op, not %r." % resources)
+  resources = [_execute.make_int(_i, "resources") for _i in resources]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaLaunchV2", args=args, Tresults=Tresults, constants=constants,
+                       resources=resources, function=function, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_launch_v2, (), dict(args=args, Tresults=Tresults,
+                                  constants=constants, resources=resources,
+                                  function=function, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("Targs", _op.get_attr("Targs"), "Tresults",
+              _op.get_attr("Tresults"), "constants",
+              _op.get_attr("constants"), "resources",
+              _op.get_attr("resources"), "function", _op.get_attr("function"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaLaunchV2", _inputs_flat, _attrs, _result)
+  return _result
+
+XlaLaunchV2 = tf_export("raw_ops.XlaLaunchV2")(_ops.to_raw_op(xla_launch_v2))
+_dispatcher_for_xla_launch_v2 = xla_launch_v2._tf_type_based_dispatcher.Dispatch
+
+
+def xla_launch_v2_eager_fallback(args, Tresults, constants, resources, function, name, ctx):
+  if not isinstance(Tresults, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Tresults' argument to "
+        "'xla_launch_v2' Op, not %r." % Tresults)
+  Tresults = [_execute.make_type(_t, "Tresults") for _t in Tresults]
+  if not isinstance(constants, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'constants' argument to "
+        "'xla_launch_v2' Op, not %r." % constants)
+  constants = [_execute.make_int(_i, "constants") for _i in constants]
+  if not isinstance(resources, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'resources' argument to "
+        "'xla_launch_v2' Op, not %r." % resources)
+  resources = [_execute.make_int(_i, "resources") for _i in resources]
+  _attr_Targs, args = _execute.convert_to_mixed_eager_tensors(args, ctx)
+  _inputs_flat = list(args)
+  _attrs = ("Targs", _attr_Targs, "Tresults", Tresults, "constants",
+  constants, "resources", resources, "function", function)
+  _result = _execute.execute(b"XlaLaunchV2", len(Tresults),
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaLaunchV2", _inputs_flat, _attrs, _result)
+  return _result
+
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/jit/ops/xla_ops_grad.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/jit/ops/xla_ops_grad.py
new file mode 100644
index 0000000000000000000000000000000000000000..cca48e48df59e39a176902024ab0e09c8aa9e86c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/jit/ops/xla_ops_grad.py
@@ -0,0 +1,25 @@
+"""Gradients for XLA ops."""
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+from tensorflow.python.framework import ops
+
+
+@ops.RegisterGradient("XlaClusterOutput")
+def _XlaClusterOutputGrad(_, grad):
+  del grad  # unused
+  raise RuntimeError("Gradient computation of graph in xla.compile() is "
+                     "prohibited because it can cause performance degradation."
+                     "Please move gradient computation inside xla.compile().")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/lite/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/lite/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/lite/debug/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/lite/debug/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/lite/debug/debug_options_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/lite/debug/debug_options_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a29dd87fb83f061142402fd48acbe389ec3e674
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/lite/debug/debug_options_pb2.py
@@ -0,0 +1,25 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/mlir/lite/debug/debug_options.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n7tensorflow/compiler/mlir/lite/debug/debug_options.proto\x12\x14tensorflow.converter\"\xa3\x02\n\x0c\x44\x65\x62ugOptions\x12\x17\n\rmlir_dump_dir\x18\x01 \x01(\t:\x00\x12 \n\x14mlir_dump_pass_regex\x18\x02 \x01(\t:\x02.*\x12 \n\x14mlir_dump_func_regex\x18\x03 \x01(\t:\x02.*\x12!\n\x12mlir_enable_timing\x18\x04 \x01(\x08:\x05\x66\x61lse\x12\x1e\n\x14mlir_print_ir_before\x18\x05 \x01(\t:\x00\x12\x1d\n\x13mlir_print_ir_after\x18\x06 \x01(\t:\x00\x12(\n\x1amlir_print_ir_module_scope\x18\x07 \x01(\x08:\x04true\x12*\n\"mlir_elide_elementsattrs_if_larger\x18\x08 \x01(\x03')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.mlir.lite.debug.debug_options_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _DEBUGOPTIONS._serialized_start=82
+  _DEBUGOPTIONS._serialized_end=373
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/stablehlo/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/stablehlo/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/stablehlo/quantization_options_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/stablehlo/quantization_options_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..e108e96ca0887457e073825d5d1b58ab60e8f3b6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/stablehlo/quantization_options_pb2.py
@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/mlir/quantization/stablehlo/quantization_options.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\nJtensorflow/compiler/mlir/quantization/stablehlo/quantization_options.proto\x12\x16stablehlo.quantization\"^\n\x13QuantizationOptions\x12G\n\x13quantization_method\x18\x01 \x01(\x0b\x32*.stablehlo.quantization.QuantizationMethod\"\xdb\x01\n\x12QuantizationMethod\x12V\n\x1apreset_quantization_method\x18\x01 \x01(\x0b\x32\x30.stablehlo.quantization.PresetQuantizationMethodH\x00\x12V\n\x1a\x63ustom_quantization_method\x18\x02 \x01(\x0b\x32\x30.stablehlo.quantization.CustomQuantizationMethodH\x00\x42\x15\n\x13quantization_method\"\x92\x02\n\x18PresetQuantizationMethod\x12T\n\rpreset_method\x18\x01 \x01(\x0e\x32=.stablehlo.quantization.PresetQuantizationMethod.PresetMethod\"\x9f\x01\n\x0cPresetMethod\x12\x16\n\x12METHOD_UNSPECIFIED\x10\x00\x12\x0f\n\x0bWEIGHT_ONLY\x10\x01\x12,\n(POST_TRAINING_QUANTIZATION_DYNAMIC_RANGE\x10\x02\x12\x0b\n\x07\x46LOAT16\x10\x03\x12+\n\'POST_TRAINING_QUANTIZATION_STATIC_RANGE\x10\x04\"r\n\x18\x43ustomQuantizationMethod\x12V\n\x1bquantization_component_spec\x18\x01 \x03(\x0b\x32\x31.stablehlo.quantization.QuantizationComponentSpec\"\xc5\x05\n\x19QuantizationComponentSpec\x12g\n\x16quantization_component\x18\x01 \x01(\x0e\x32G.stablehlo.quantization.QuantizationComponentSpec.QuantizationComponent\x12M\n\tbit_width\x18\x02 \x01(\x0e\x32:.stablehlo.quantization.QuantizationComponentSpec.BitWidth\x12K\n\x08\x62it_type\x18\x03 \x01(\x0e\x32\x39.stablehlo.quantization.QuantizationComponentSpec.BitType\x12\x1b\n\x13\x65nable_narrow_range\x18\x04 \x01(\x08\x12\'\n\x1f\x65nable_per_channel_quantization\x18\x05 \x01(\x08\x12\x18\n\x10\x65nable_symmetric\x18\x06 \x01(\x08\"v\n\x15QuantizationComponent\x12\x19\n\x15\x43OMPONENT_UNSPECIFIED\x10\x00\x12\x18\n\x14\x43OMPONENT_ACTIVATION\x10\x01\x12\x14\n\x10\x43OMPONENT_WEIGHT\x10\x02\x12\x12\n\x0e\x43OMPONENT_BIAS\x10\x03\"k\n\x08\x42itWidth\x12\x19\n\x15\x42IT_WIDTH_UNSPECIFIED\x10\x00\x12\x0f\n\x0b\x42IT_WIDTH_4\x10\x01\x12\x0f\n\x0b\x42IT_WIDTH_8\x10\x02\x12\x10\n\x0c\x42IT_WIDTH_16\x10\x03\x12\x10\n\x0c\x42IT_WIDTH_32\x10\x04\"^\n\x07\x42itType\x12\x18\n\x14\x42IT_TYPE_UNSPECIFIED\x10\x00\x12\x10\n\x0c\x42IT_TYPE_INT\x10\x01\x12\x12\n\x0e\x42IT_TYPE_FLOAT\x10\x02\x12\x13\n\x0f\x42IT_TYPE_BFLOAT\x10\x03\x42\x03\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.mlir.quantization.stablehlo.quantization_options_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _QUANTIZATIONOPTIONS._serialized_start=102
+  _QUANTIZATIONOPTIONS._serialized_end=196
+  _QUANTIZATIONMETHOD._serialized_start=199
+  _QUANTIZATIONMETHOD._serialized_end=418
+  _PRESETQUANTIZATIONMETHOD._serialized_start=421
+  _PRESETQUANTIZATIONMETHOD._serialized_end=695
+  _PRESETQUANTIZATIONMETHOD_PRESETMETHOD._serialized_start=536
+  _PRESETQUANTIZATIONMETHOD_PRESETMETHOD._serialized_end=695
+  _CUSTOMQUANTIZATIONMETHOD._serialized_start=697
+  _CUSTOMQUANTIZATIONMETHOD._serialized_end=811
+  _QUANTIZATIONCOMPONENTSPEC._serialized_start=814
+  _QUANTIZATIONCOMPONENTSPEC._serialized_end=1523
+  _QUANTIZATIONCOMPONENTSPEC_QUANTIZATIONCOMPONENT._serialized_start=1200
+  _QUANTIZATIONCOMPONENTSPEC_QUANTIZATIONCOMPONENT._serialized_end=1318
+  _QUANTIZATIONCOMPONENTSPEC_BITWIDTH._serialized_start=1320
+  _QUANTIZATIONCOMPONENTSPEC_BITWIDTH._serialized_end=1427
+  _QUANTIZATIONCOMPONENTSPEC_BITTYPE._serialized_start=1429
+  _QUANTIZATIONCOMPONENTSPEC_BITTYPE._serialized_end=1523
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/calibrator/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/calibrator/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_algorithm.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_algorithm.py
new file mode 100644
index 0000000000000000000000000000000000000000..af472e76cbe1ec2031d967c0d975e2e6122a35e1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_algorithm.py
@@ -0,0 +1,393 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Defines CalibrationAlgorithm for calculating min and max values calculated by calibration method."""
+import abc
+import itertools
+import logging
+
+import numpy as np
+
+from tensorflow.compiler.mlir.quantization.tensorflow import quantization_options_pb2 as quant_opts_pb2
+from tensorflow.compiler.mlir.quantization.tensorflow.calibrator import calibration_statistics_pb2 as calib_stats_pb2
+
+
+_CalibrationMethod = quant_opts_pb2.CalibrationOptions.CalibrationMethod
+_REGISTRY = {}
+
+
+def _implements(calib_method: _CalibrationMethod):
+  def decorator(cls):
+    assert calib_method not in _REGISTRY
+    _REGISTRY[calib_method] = cls
+    return cls
+
+  return decorator
+
+
+class _CalibrationAlgorithmBase(abc.ABC):
+  """Abstract base class for calibration algorithm."""
+
+  def __init__(
+      self,
+      statistics: calib_stats_pb2.CalibrationStatistics,
+      calib_opts: quant_opts_pb2.CalibrationOptions,
+  ):
+    self._statistics = statistics
+    self._calib_opts = calib_opts
+
+  @abc.abstractmethod
+  def get_min_max_value(self) -> tuple[float, float]:
+    pass
+
+
+class _HistogramCalibrationAlgorithmBase(_CalibrationAlgorithmBase):
+  """Base class for histogram calibrators."""
+
+  def __init__(
+      self,
+      statistics: calib_stats_pb2.CalibrationStatistics,
+      calib_opts: quant_opts_pb2.CalibrationOptions,
+  ):
+    """Builds histogram using statistics.histogram_statistics.
+
+    lower_bound                                    hist_mid
+         v                                            v
+         |=========|=========|=========|=========|=========|
+                    bin width
+
+    Args:
+      statistics: Collected calibration statistics.
+      calib_opts: Calibration options used for calculating min and max.
+    """
+    super().__init__(statistics, calib_opts)
+    hist_stats = statistics.histogram_statistics
+    self._bin_width = hist_stats.bin_width
+    self._lower_bound = hist_stats.lower_bound
+    self._hist_freq = np.array(hist_stats.hist_freq)
+    self._num_bins = len(self._hist_freq)
+    self._num_bits = 8
+    # i-th bin has a range [bins[i], bins[i + 1]).
+    # bins[i] = lower_bound + i * bin_width
+    # bins[i + 1] = lower_bound + (i + 1) * bin_width
+    # So hist_mids[i] = (lower_bound + bin_width / 2) + bin_width * i
+    first_mid = self._lower_bound + self._bin_width / 2
+    last_mid = first_mid + (self._num_bins - 1) * self._bin_width
+    self._hist_mids = np.linspace(first_mid, last_mid, self._num_bins)
+
+  def _get_dequantized_hist_mids_after_quantize(
+      self, quant_min: float, quant_max: float
+  ) -> np.ndarray:
+    """Quantizes and dequantizes hist_mids using quant_min and quant_max.
+
+    Quantization converts the range of numbers from [quant_min, quant_max] to
+    [0, 2^num_bits - 1]. Values less than quant_min are converted to 0, and
+    values greater than quant_max are converted to 2^num_bits - 1.
+
+    The histogram represents the distribution of the data, and our goal is to
+    find the quant_min and quant_max that best describe this distribution. To do
+    this, we quantize hist_mids using quant_min and quant_max and dequantize
+    them again. Then the difference between hist_mids and dequantized hist_mids
+    equates to quantization error when using quant_min and quant_max.
+
+
+    Args:
+      quant_min: The minimum real value that can be represented by a quantized
+        value.
+      quant_max: The maximum real value that can be represented by a quantized
+        value.
+
+    Returns:
+      dequantized hist_mids after quantizing by quant_min and quant_max
+    """
+    maxbound = 2**self._num_bits - 1
+    minbound = 0
+    scale = (quant_max - quant_min) / maxbound
+    zero_point = -quant_min / scale
+
+    # Limit the range of zero_point and scale in case (quant_max - quant_min)
+    # is unusually small.
+    if abs(zero_point) > 9e9:
+      zero_point = 9e9
+    if abs(scale) < 1e-9:
+      scale = 1e-9
+
+    zero_point = round(zero_point)
+    quantized_hist_mids = np.clip(
+        np.round(self._hist_mids / scale) + zero_point, minbound, maxbound
+    )
+    dequantized_hist_mids = scale * (quantized_hist_mids - zero_point)
+    return dequantized_hist_mids
+
+  def _get_weighted_mean_squared_error(
+      self, quant_min, quant_max
+  ) -> tuple[float, float, float]:
+    """Gets mean squared error between hist_mids and dequantized hist_mids.
+
+    Quantization converts the range of numbers from [quant_min, quant_max] to
+    [0, 2^num_bits - 1]. Values less than quant_min are converted to 0, and
+    values greater than quant_max are converted to 2^num_bits - 1.
+
+    Args:
+      quant_min: The minimum real value that can be represented by a quantized
+        value.
+      quant_max: The maximum real value that can be represented by a quantized
+        value.
+
+    Returns:
+      (error, quant_min, quant_max): Tuple of weighted mean squared error.
+      error = (hist_mids - dequantized_hist_mids)**2 * hist_freq
+    """
+    dequantized_hist_mids = self._get_dequantized_hist_mids_after_quantize(
+        quant_min, quant_max
+    )
+    squared_error = (self._hist_mids - dequantized_hist_mids) ** 2
+    weighted_error = np.sum(squared_error * self._hist_freq)
+    return (weighted_error, quant_min, quant_max)
+
+  def _get_min_max_value_by_expanding_range(
+      self, start_idx: int
+  ) -> tuple[float, float]:
+    """Starting from start_idx, expand left and right alternately to find the min value of mse loss.
+
+    Args:
+      start_idx: Index to start quantization.
+
+    Returns:
+      (min_value, max_value): Min and max calculated.
+    """
+    # Tuple of (mse_error, quant_min, quant_max).
+    mse_min = (float('inf'), float('inf'), float('inf'))
+    left, right = start_idx, start_idx
+
+    # If this value is true, it moves left, otherwise it moves right.
+    move_left = True
+    while not (left == 0 and right == self._num_bins - 1):
+      # Decrease left if right can't be moved or move_left is true.
+      if (move_left and left > 0) or (right == self._num_bins - 1):
+        left = max(left - 1, 0)
+      # Else increase right.
+      else:
+        right = min(right + 1, self._num_bins - 1)
+      # Toogle the move_left.
+      move_left = not move_left
+      quant_min, quant_max = self._hist_mids[left], self._hist_mids[right]
+      mse_tuple = self._get_weighted_mean_squared_error(quant_min, quant_max)
+      mse_min = min(mse_tuple, mse_min)
+    # Extract (quant_min, quant_max) from (mse_error, quant_min, quant_max).
+    min_value, max_value = mse_min[1], mse_min[2]
+    return min_value, max_value
+
+
+@_implements(_CalibrationMethod.CALIBRATION_METHOD_MIN_MAX)
+class _MinMax(_CalibrationAlgorithmBase):
+  """MinMaxCalibrationAlgorithm for calculating min and max values of calibration result.
+
+  MinMax calibration calculates the global min and global max values.
+
+  global min = min of given sample inputs
+  global max = max of given sample inputs
+  """
+
+  def get_min_max_value(self) -> tuple[float, float]:
+    """Calculates the global min and max values.
+
+    Returns:
+      (min_value, max_value): Min and max calculated using MinMax
+    """
+    return (
+        self._statistics.min_max_statistics.global_min,
+        self._statistics.min_max_statistics.global_max,
+    )
+
+
+@_implements(_CalibrationMethod.CALIBRATION_METHOD_AVERAGE_MIN_MAX)
+class _AverageMinMax(_CalibrationAlgorithmBase):
+  """AverageMinMaxCalibrationAlgorithm for calculating min and max values of calibration result.
+
+  AverageMinMax calibration calculates the average of min and max values.
+  average of min = sum of min values / number of samples
+  average of max = sum of max values / number of samples
+  """
+
+  def get_min_max_value(self) -> tuple[float, float]:
+    """Calculates the average of min and max values.
+
+    Returns:
+      (min_value, max_value): Min and max calculated using AverageMinMax
+
+    Raises:
+      ValueError: num_samples is 0.
+    """
+    average_min_max_statistics = self._statistics.average_min_max_statistics
+    # num_samples is guaranteed to be larger than 0 because
+    # get_statistics_from_calibrator throws an exception if num_samples == 0.
+    num_samples = average_min_max_statistics.num_samples
+    if num_samples == 0:
+      raise ValueError(
+          'num_samples must not be 0 when calibration method is'
+          f' AverageMinMax: {self._calib_opts}'
+      )
+    min_value, max_value = (
+        average_min_max_statistics.min_sum / num_samples,
+        average_min_max_statistics.max_sum / num_samples,
+    )
+
+    return min_value, max_value
+
+
+@_implements(_CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_PERCENTILE)
+class _HistogramPercentile(_HistogramCalibrationAlgorithmBase):
+  """HistogramPercentile for calculating min and max values of calibration result."""
+
+  def get_min_max_value(self) -> tuple[float, float]:
+    """Calculates min and max from statistics using calibration options.
+
+    A "percentile" is a statistical concept that represents the value below
+    which a given percentage of data falls in a dataset. It involves sorting the
+    data from smallest to largest and then finding the value at a specified
+    percentage position. For example, the 0.01 percentile represents the value
+    in a given data set that corresponds to the lowest 0.01% of the data.
+
+    HistogramPercentile calibration uses min_percentile and max_percentile to
+    find min and max.
+
+    min_percentile and max_percentile must be in range [0, 100].
+    min_percentile is 0.001 by default.
+    max_percentile is 99.999 by default.
+
+    Returns:
+      (min_value, max_value): Min and max calculated using HistogramPercentile
+    """
+    total_freq = sum(self._hist_freq)
+    # hist_freq_cumsum is dividing cumulative sum of hist_freq by total_freq
+    # hist_freq_cumsum's value is in range [0, 1] by its definition
+    hist_freq_cumsum = np.cumsum(self._hist_freq) / total_freq
+
+    # min_percentile and max_percentile are converted from [0, 100] to [0, 1].
+    min_quantile, max_quantile = (
+        self._calib_opts.calibration_parameters.min_percentile / 100.0,
+        self._calib_opts.calibration_parameters.max_percentile / 100.0,
+    )
+
+    # Get index of min/max quantile.
+    min_quantile_idx, max_quantile_idx = (
+        np.searchsorted(hist_freq_cumsum, min_quantile, side='right'),
+        np.searchsorted(hist_freq_cumsum, max_quantile, side='left'),
+    )
+
+    # Get value of min/max quantile index.
+    min_value, max_value = (
+        self._hist_mids[min_quantile_idx],
+        self._hist_mids[max_quantile_idx],
+    )
+
+    return min_value, max_value
+
+
+@_implements(_CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_MSE_BRUTEFORCE)
+class _HistogramMseBruteforce(_HistogramCalibrationAlgorithmBase):
+  """HistogramMseBruteforce for calculating min and max values of calibration result."""
+
+  def get_min_max_value(self) -> tuple[float, float]:
+    """Finds the optimal quant_min and quant_max by testing all possible cases.
+
+    It guarantees optimal quant_min and quant_max for the representative
+    dataset, but not for the test dataset.
+
+    Returns:
+      (min_value, max_value): Min and max calculated using
+      HistogramMseBruteforce.
+    """
+    if self._num_bins > 512:
+      logging.warning(
+          'num_bins=%d is too large. The HISTOGRAM_MSE_BRUTEFORCE method tests'
+          ' all histogram mid value pairs, so it may take a long time.',
+          self._num_bins,
+      )
+    # Tuple of (mse_error, quant_min, quant_max).
+    mse_min = (float('inf'), float('inf'), float('inf'))
+
+    # Calculate the error for all hist_mid pairs.
+    for left, right in itertools.combinations(range(self._num_bins), 2):
+      quant_min, quant_max = self._hist_mids[left], self._hist_mids[right]
+      mse_tuple = self._get_weighted_mean_squared_error(quant_min, quant_max)
+      mse_min = min(mse_tuple, mse_min)
+    min_value, max_value = mse_min[1], mse_min[2]
+
+    return min_value, max_value
+
+
+@_implements(_CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_MSE_MAX_FREQUENCY)
+class _HistogramMseMaxFrequency(_HistogramCalibrationAlgorithmBase):
+  """HistogramMseMaxFrequency for calculating min and max values of calibration result."""
+
+  def get_min_max_value(self) -> tuple[float, float]:
+    """Finds min and max starting from the index of the max frequency.
+
+     The HistogramMseMaxFrequency method starts from the bin with the highest
+     frequency and expands the range to both sides. This performs well when data
+     is well spread on both sides of the max frequency.
+
+    Returns:
+      (min_value, max_value): Min and max calculated using method to expand the
+      range based on max frequency.
+    """
+    # Find the index of max frequency.
+    freq_max_idx = np.argmax(self._hist_freq)
+    return self._get_min_max_value_by_expanding_range(freq_max_idx)
+
+
+@_implements(_CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_MSE_SYMMETRIC)
+class _HistogramMseSymmetric(_HistogramCalibrationAlgorithmBase):
+  """HistogramMseSymmetric for calculating min and max values of calibration result."""
+
+  def get_min_max_value(self) -> tuple[float, float]:
+    """Finds min and max starting from the center index.
+
+    The HistogramMseSymmetric method starts from the center bin and expands the
+    range to both sides. This works better when the data is well-centered.
+
+    Returns:
+      (min_value, max_value): Min and max calculated using the method starting
+      from center and expanding.
+    """
+
+    # This function is currently only called in this method, but will be used in
+    # other methods in the future.
+    return self._get_min_max_value_by_expanding_range(self._num_bins // 2)
+
+
+def get_min_max_value(
+    statistics: calib_stats_pb2.CalibrationStatistics,
+    calib_opts: quant_opts_pb2.CalibrationOptions,
+) -> tuple[float, float]:
+  """Calculates min and max from statistics using calibration options.
+
+  Args:
+    statistics: Collected calibration statistics.
+    calib_opts: Calibration options used for calculating min and max.
+
+  Returns:
+    (min_value, max_value): Min and max calculated using calib_opts.
+
+  Raises:
+    ValueError: Unsupported calibration method is given.
+  """
+  calib_method = calib_opts.calibration_method
+  if calib_method not in _REGISTRY:
+    raise ValueError(f'Unsupported calibration method: {calib_method}')
+
+  calibration_algorithm = _REGISTRY[calib_method](statistics, calib_opts)
+  return calibration_algorithm.get_min_max_value()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_statistics_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_statistics_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..df5929d1bab49045b0ae00aa3f43ea9be0fce5f9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_statistics_pb2.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_statistics.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\nXtensorflow/compiler/mlir/quantization/tensorflow/calibrator/calibration_statistics.proto\x12\x15tensorflow.calibrator\"\x9c\x04\n\x15\x43\x61librationStatistics\x12Y\n\x12min_max_statistics\x18\x01 \x01(\x0b\x32=.tensorflow.calibrator.CalibrationStatistics.MinMaxStatistics\x12h\n\x1a\x61verage_min_max_statistics\x18\x02 \x01(\x0b\x32\x44.tensorflow.calibrator.CalibrationStatistics.AverageMinMaxStatistics\x12^\n\x14histogram_statistics\x18\x03 \x01(\x0b\x32@.tensorflow.calibrator.CalibrationStatistics.HistogramStatistics\x1a:\n\x10MinMaxStatistics\x12\x12\n\nglobal_min\x18\x01 \x01(\x02\x12\x12\n\nglobal_max\x18\x02 \x01(\x02\x1aP\n\x17\x41verageMinMaxStatistics\x12\x0f\n\x07min_sum\x18\x01 \x01(\x02\x12\x0f\n\x07max_sum\x18\x02 \x01(\x02\x12\x13\n\x0bnum_samples\x18\x03 \x01(\x05\x1aP\n\x13HistogramStatistics\x12\x11\n\tbin_width\x18\x01 \x01(\x02\x12\x13\n\x0blower_bound\x18\x02 \x01(\x02\x12\x11\n\thist_freq\x18\x03 \x03(\x03\x42\x03\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.mlir.quantization.tensorflow.calibrator.calibration_statistics_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _CALIBRATIONSTATISTICS._serialized_start=116
+  _CALIBRATIONSTATISTICS._serialized_end=656
+  _CALIBRATIONSTATISTICS_MINMAXSTATISTICS._serialized_start=434
+  _CALIBRATIONSTATISTICS_MINMAXSTATISTICS._serialized_end=492
+  _CALIBRATIONSTATISTICS_AVERAGEMINMAXSTATISTICS._serialized_start=494
+  _CALIBRATIONSTATISTICS_AVERAGEMINMAXSTATISTICS._serialized_end=574
+  _CALIBRATIONSTATISTICS_HISTOGRAMSTATISTICS._serialized_start=576
+  _CALIBRATIONSTATISTICS_HISTOGRAMSTATISTICS._serialized_end=656
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/exported_model_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/exported_model_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..45d3f9c0b26523fa356814d7ca5c35c77bcf71f8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/exported_model_pb2.py
@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/mlir/quantization/tensorflow/exported_model.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import graph_pb2 as tensorflow_dot_core_dot_framework_dot_graph__pb2
+from tensorflow.core.protobuf import meta_graph_pb2 as tensorflow_dot_core_dot_protobuf_dot_meta__graph__pb2
+from tensorflow.core.protobuf import saver_pb2 as tensorflow_dot_core_dot_protobuf_dot_saver__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\nEtensorflow/compiler/mlir/quantization/tensorflow/exported_model.proto\x12\x17tensorflow.quantization\x1a%tensorflow/core/framework/graph.proto\x1a)tensorflow/core/protobuf/meta_graph.proto\x1a$tensorflow/core/protobuf/saver.proto\"\xbe\x03\n\rExportedModel\x12\'\n\tgraph_def\x18\x01 \x01(\x0b\x32\x14.tensorflow.GraphDef\x12\x16\n\x0einit_node_name\x18\x02 \x01(\t\x12\x16\n\x0e\x63heckpoint_dir\x18\x05 \x01(\t\x12U\n\x10\x66unction_aliases\x18\x06 \x03(\x0b\x32;.tensorflow.quantization.ExportedModel.FunctionAliasesEntry\x12\x31\n\x0f\x61sset_file_defs\x18\x08 \x03(\x0b\x32\x18.tensorflow.AssetFileDef\x12\'\n\tsaver_def\x18\n \x01(\x0b\x32\x14.tensorflow.SaverDef\x1a\x36\n\x14\x46unctionAliasesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01J\x04\x08\x03\x10\x04J\x04\x08\x04\x10\x05J\x04\x08\x07\x10\x08J\x04\x08\t\x10\nR\x15variable_shared_namesR\x11restore_node_nameR\x0esave_node_nameR\x17\x66ile_prefix_tensor_nameB\x03\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.mlir.quantization.tensorflow.exported_model_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _EXPORTEDMODEL_FUNCTIONALIASESENTRY._options = None
+  _EXPORTEDMODEL_FUNCTIONALIASESENTRY._serialized_options = b'8\001'
+  _EXPORTEDMODEL._serialized_start=219
+  _EXPORTEDMODEL._serialized_end=665
+  _EXPORTEDMODEL_FUNCTIONALIASESENTRY._serialized_start=504
+  _EXPORTEDMODEL_FUNCTIONALIASESENTRY._serialized_end=558
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/python/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/python/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/python/quantize_model.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/python/quantize_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..43dbd283a67fdf779fa320acf95cd3cd31fcec07
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/python/quantize_model.py
@@ -0,0 +1,1609 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Defines TF Quantization API from SavedModel to SavedModel."""
+
+import collections.abc
+import tempfile
+from typing import Callable, Collection, Dict, Mapping, Optional, Sequence
+import uuid
+
+from absl import logging
+import numpy as np
+
+from tensorflow.compiler.mlir.quantization.tensorflow import exported_model_pb2
+from tensorflow.compiler.mlir.quantization.tensorflow import quantization_options_pb2 as quant_opts_pb2
+from tensorflow.compiler.mlir.quantization.tensorflow.calibrator import calibration_algorithm
+from tensorflow.compiler.mlir.quantization.tensorflow.calibrator import calibration_statistics_pb2 as calib_stats_pb2
+from tensorflow.compiler.mlir.quantization.tensorflow.python import pywrap_quantize_model
+from tensorflow.compiler.mlir.quantization.tensorflow.python import representative_dataset as repr_dataset
+from tensorflow.compiler.mlir.quantization.tensorflow.python import save_model
+from tensorflow.core.framework import graph_pb2
+from tensorflow.core.protobuf import meta_graph_pb2
+from tensorflow.core.protobuf import saver_pb2
+from tensorflow.python.client import session
+from tensorflow.python.eager import context
+from tensorflow.python.eager import wrap_function
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_conversion
+from tensorflow.python.lib.io import file_io
+from tensorflow.python.saved_model import load as saved_model_load
+from tensorflow.python.saved_model import loader_impl as saved_model_loader
+from tensorflow.python.saved_model import signature_constants
+from tensorflow.python.saved_model import tag_constants
+from tensorflow.python.trackable import autotrackable
+from tensorflow.python.types import core
+from tensorflow.python.util import tf_export
+
+# Type aliases for quant_opts_pb2 messages.
+_QuantizationOptions = tf_export.tf_export(
+    'quantization.experimental.QuantizationOptions'
+)(quant_opts_pb2.QuantizationOptions)
+
+_QuantizationMethod = tf_export.tf_export(
+    'quantization.experimental.QuantizationMethod'
+)(quant_opts_pb2.QuantizationMethod)
+
+_QuantizationComponentSpec = tf_export.tf_export(
+    'quantization.experimental.QuantizationComponentSpec'
+)(quant_opts_pb2.QuantizationComponentSpec)
+
+_UnitWiseQuantizationSpec = tf_export.tf_export(
+    'quantization.experimental.UnitWiseQuantizationSpec'
+)(quant_opts_pb2.UnitWiseQuantizationSpec)
+
+_PresetMethod = _QuantizationMethod.PresetMethod
+_CalibrationMethod = quant_opts_pb2.CalibrationOptions.CalibrationMethod
+
+_QuantizationComponent = _QuantizationComponentSpec.QuantizationComponent
+_TensorType = _QuantizationComponentSpec.TensorType
+
+# Mapping of signature def key -> SignatureDef.
+_SignatureDefMap = Mapping[str, meta_graph_pb2.SignatureDef]
+
+# Default minimum number of elements in the weights for them to be quantized
+# during dynamic range quantization (DRQ) and weight-only quantization.
+_DYNAMIC_RANGE_DEFAULT_MIN_NUM_ELEMENTS_FOR_WEIGHTS = 1024
+
+# Name of the saved model assets directory.
+_ASSETS_DIR = 'assets'
+_ASSETS_EXTRA_DIR = 'assets.extra'
+
+
+def _is_qat_saved_model(saved_model_path: str):
+  """Checks if the SavedModel is QAT-enabled by looking for 'FakeQuant' ops."""
+  saved_model_proto = saved_model_loader.parse_saved_model(saved_model_path)
+  for meta_graph in saved_model_proto.meta_graphs:
+    if any(
+        node.op.startswith('FakeQuant') for node in meta_graph.graph_def.node
+    ):
+      return True
+    for function in meta_graph.graph_def.library.function:
+      if any(node.op.startswith('FakeQuant') for node in function.node_def):
+        return True
+  return False
+
+
+def _create_sample_validator(
+    expected_input_keys: Collection[str],
+) -> Callable[
+    [repr_dataset.RepresentativeSample], repr_dataset.RepresentativeSample
+]:
+  """Creates a validator function for a representative sample.
+
+  Args:
+    expected_input_keys: Input keys (keyword argument names) that the function
+      the sample will be used for is expecting to receive.
+
+  Returns:
+    A callable that validates a `RepresentativeSample`.
+  """
+
+  def validator(
+      sample: repr_dataset.RepresentativeSample,
+  ) -> repr_dataset.RepresentativeSample:
+    """Validates a single instance of representative sample.
+
+    This provides a simple check for `sample` that this is a mapping of
+    {input_key: input_value}.
+
+    Args:
+      sample: A `RepresentativeSample` to validate.
+
+    Returns:
+      `sample` iff it is valid.
+
+    Raises:
+      ValueError: iff the sample isn't an instance of `Mapping`.
+      KeyError: iff the sample does not have the set of input keys that match
+        the input keys of the function.
+    """
+    if not isinstance(sample, collections.abc.Mapping):
+      raise ValueError(
+          'Invalid representative sample type. Provide a mapping '
+          '(usually a dict) of {input_key: input_value}. '
+          f'Got type: {type(sample)} instead.'
+      )
+
+    if set(sample.keys()) != expected_input_keys:
+      raise KeyError(
+          'Invalid input keys for representative sample. The function expects '
+          f'input keys of: {set(expected_input_keys)}. '
+          f'Got: {set(sample.keys())}. Please provide correct input keys for '
+          'representative samples.'
+      )
+
+    return sample
+
+  return validator
+
+
+def _validate_representative_dataset(
+    representative_dataset: repr_dataset.RepresentativeDatasetOrMapping,
+    signature_keys: Collection[str],
+) -> None:
+  """Validates the representative dataset, based on the signature keys.
+
+  Representative dataset can be provided in two different forms: a single
+  instance of `RepresentativeDataset` or a map of signature key to the
+  corresponding `RepresentativeDataset`. These have a relationship with
+  `signature_keys`.
+
+  This function validates the following conditions:
+  * If `len(signature_keys) > 1`, then `representative_dataset` should be a
+    mapping where the keys exactly match the elements in `signature_keys`.
+  * If `len(signature_keys) == 1`, then both a mapping and a single instance of
+    `RepresentativeDataset` are allowed.
+  * This function also assumes `len(signature_keys) > 0`.
+
+  Args:
+    representative_dataset: A `RepresentativeDataset` or a map of string to
+      `RepresentativeDataset` to be validated.
+    signature_keys: A collection of strings that contains the signature keys,
+      each identifying a `SignatureDef`.
+
+  Raises:
+    ValueError: Iff `representative_dataset` does not satisfy the conditions
+      above.
+  """
+  if isinstance(representative_dataset, collections.abc.Mapping):
+    if set(signature_keys) != set(representative_dataset.keys()):
+      raise ValueError(
+          'The signature keys and the keys of representative dataset map '
+          f'do not match. Signature keys: {set(signature_keys)}, '
+          f'representative dataset map: {set(representative_dataset.keys())}.'
+      )
+  else:
+    if len(signature_keys) > 1:
+      raise ValueError(
+          'Representative dataset is not a mapping '
+          f'(got: {type(representative_dataset)}), '
+          'but there is more than one signature key provided. '
+          'Please provide a map of {signature_key -> dataset} '
+          'with more than one signature key.'
+      )
+
+
+def _convert_values_to_tf_tensors(
+    sample: repr_dataset.RepresentativeSample,
+) -> Mapping[str, core.Tensor]:
+  """Converts TensorLike values of `sample` to Tensors.
+
+  Creates a copy of `sample`, where each value is converted to Tensors
+  unless it is already a Tensor.
+  The values are not converted in-place (i.e. `sample` is not mutated).
+
+  Args:
+    sample: A representative sample, which is a map of {name -> tensorlike
+      value}.
+
+  Returns:
+    Converted map of {name -> tensor}.
+  """
+  tensor_mapping = {}
+  for name, tensorlike_value in sample.items():
+    if isinstance(tensorlike_value, core.Tensor):
+      tensor_value = tensorlike_value
+    else:
+      tensor_value = tensor_conversion.convert_to_tensor_v2_with_dispatch(
+          tensorlike_value
+      )
+
+    tensor_mapping[name] = tensor_value
+
+  return tensor_mapping
+
+
+def _create_feed_dict_from_input_data(
+    input_data: repr_dataset.RepresentativeSample,
+    signature_def: meta_graph_pb2.SignatureDef,
+) -> Dict[str, np.ndarray]:
+  """Constructs a feed_dict from input data.
+
+  Note: This function should only be used in graph mode.
+
+  This is a helper function that converts an 'input key -> input value' mapping
+  to a feed dict. A feed dict is an 'input tensor name -> input value' mapping
+  and can be directly passed to the `feed_dict` argument of `sess.run()`.
+
+  Args:
+    input_data: Input key -> input value mapping. The input keys should match
+      the input keys of `signature_def`.
+    signature_def: A SignatureDef representing the function that `input_data` is
+      an input to.
+
+  Returns:
+    Feed dict, which is intended to be used as input for `sess.run`. It is
+    essentially a mapping: input tensor name -> input value. Note that the input
+    value in the feed dict is not a `Tensor`.
+  """
+  feed_dict = {}
+  for input_key, input_value in input_data.items():
+    input_tensor_name = signature_def.inputs[input_key].name
+
+    value = input_value
+    if isinstance(input_value, core.Tensor):
+      # Take the data out of the tensor.
+      value = input_value.eval()
+
+    feed_dict[input_tensor_name] = value
+
+  return feed_dict
+
+
+# TODO(b/249918070): Implement a progress bar.
+def _log_sample_num_for_calibration(
+    representative_dataset: repr_dataset.RepresentativeDataset,
+) -> repr_dataset.RepresentativeDataset:
+  """Logs the sample number for calibration.
+
+  If in debug logging level, the "sample number / total num samples" is logged
+  for every 5 iterations.
+
+  This is often useful when tracking the progress of the calibration step which
+  is often slow and may look stale if there's no logs being printed.
+
+  Args:
+    representative_dataset: The representative dataset.
+
+  Yields:
+    The representative samples from `representative_dataset` without any
+    modification.
+  """
+  num_samples: Optional[int] = repr_dataset.get_num_samples(
+      representative_dataset
+  )
+  if num_samples is None:
+    total_num_samples = '?'
+    logging.info('Representative dataset size unknown.')
+  else:
+    total_num_samples = str(num_samples)
+    logging.info('Using representative dataset of size: %s', total_num_samples)
+
+  sample_num = 0
+  for sample in representative_dataset:
+    sample_num += 1
+
+    # Log the sample number for every 5 iterations.
+    logging.log_every_n(
+        logging.DEBUG,
+        'Running representative sample for calibration: %d / %s',
+        5,
+        sample_num,
+        total_num_samples,
+    )
+    yield sample
+
+  logging.info(
+      'Running representative samples complete: %d / %s',
+      sample_num,
+      total_num_samples,
+  )
+
+
+def _run_function_for_calibration_graph_mode(
+    sess: session.Session,
+    signature_def: meta_graph_pb2.SignatureDef,
+    representative_dataset: repr_dataset.RepresentativeDataset,
+) -> None:
+  """Runs the representative dataset through a function for calibration.
+
+  NOTE: This is intended to be run in graph mode (TF1).
+
+  The function is identified by the SignatureDef.
+
+  Args:
+    sess: The Session object to run the function in.
+    signature_def: A SignatureDef that identifies a function by specifying the
+      inputs and outputs.
+    representative_dataset: The representative dataset to run through the
+      function.
+  """
+  output_tensor_names = [
+      output_tensor_info.name
+      for output_tensor_info in signature_def.outputs.values()
+  ]
+
+  sample_validator = _create_sample_validator(
+      expected_input_keys=signature_def.inputs.keys()
+  )
+
+  for sample in map(
+      sample_validator, _log_sample_num_for_calibration(representative_dataset)
+  ):
+    # Create a mapping from input tensor name to the input tensor value.
+    # ex) "Placeholder:0" -> [0, 1, 2]
+    feed_dict = _create_feed_dict_from_input_data(sample, signature_def)
+    sess.run(output_tensor_names, feed_dict=feed_dict)
+
+
+def _replace_tensors_by_numpy_ndarrays(
+    repr_ds_map: repr_dataset.RepresentativeDatasetMapping,
+) -> None:
+  """Replaces tf.Tensors by their evaluated numpy arrays.
+
+  This assumes that tf.Tensors in representative samples are created in the
+  default Graph. It will raise an error if tensors are created in a different
+  graph.
+
+  Args:
+    repr_ds_map: SignatureDef key -> RepresentativeDataset mapping.
+  """
+  with session.Session() as sess:
+    for signature_def_key in repr_ds_map:
+      # Replaces the dataset with a new dataset where tf.Tensors are replaced
+      # by their evaluated values.
+      ds = repr_ds_map[signature_def_key]
+      repr_ds_map[signature_def_key] = (
+          repr_dataset.replace_tensors_by_numpy_ndarrays(ds, sess)
+      )
+
+
+def _run_graph_for_calibration_graph_mode(
+    model_dir: str,
+    tags: Collection[str],
+    representative_dataset_map: repr_dataset.RepresentativeDatasetMapping,
+) -> None:
+  """Runs the graph for calibration in graph mode.
+
+  This function assumes _graph mode_ (used when legacy TF1 is used or when eager
+  mode is explicitly disabled) when running the graph. This step is used in
+  order to collect the statistics in CustomAggregatorOp for quantization using
+  the representative dataset for the actual data provided for inference.
+
+  Args:
+    model_dir: Path to SavedModel directory.
+    tags: Collection of tags identifying the MetaGraphDef within the SavedModel.
+    representative_dataset_map: A map where signature keys are mapped to
+      corresponding representative datasets.
+
+  Raises:
+    ValueError: When running the function with the representative dataset fails.
+  """
+  # Replace tf.Tensors by numpy ndarrays in order to reuse the samples in a
+  # different graph when running the calibration.
+  _replace_tensors_by_numpy_ndarrays(representative_dataset_map)
+
+  # Run the calibration in a new graph to avoid name collision, which could
+  # happen when the same model is loaded multiple times in the default graph.
+  with ops.Graph().as_default(), session.Session() as sess:
+    meta_graph: meta_graph_pb2.MetaGraphDef = saved_model_loader.load(
+        sess, tags, export_dir=model_dir
+    )
+
+    for signature_key, repr_ds in representative_dataset_map.items():
+      sig_def = meta_graph.signature_def[signature_key]
+
+      try:
+        _run_function_for_calibration_graph_mode(
+            sess, signature_def=sig_def, representative_dataset=repr_ds
+        )
+      except Exception as ex:
+        raise ValueError(
+            'Failed to run representative dataset through the '
+            f'function with the signature key: {signature_key}.'
+        ) from ex
+
+
+def _run_function_for_calibration_eager_mode(
+    func: wrap_function.WrappedFunction,
+    representative_dataset: repr_dataset.RepresentativeDataset,
+) -> None:
+  """Runs the representative dataset through a function for calibration.
+
+  NOTE: This is intended to be run in eager mode (TF2).
+
+  Args:
+    func: The function to run the representative samples through.
+    representative_dataset: Representative dataset used for calibration. The
+      input keys and input values of the representative samples should match the
+      keyword arguments of `func`.
+  """
+  _, keyword_args = func.structured_input_signature
+  sample_validator = _create_sample_validator(
+      expected_input_keys=keyword_args.keys()
+  )
+
+  for sample in map(
+      sample_validator, _log_sample_num_for_calibration(representative_dataset)
+  ):
+    # Convert any non-Tensor values from the sample to Tensors.
+    # This conversion is required because the model saved in `model_dir` is
+    # saved using TF1 SavedModelBuilder, which doesn't save the
+    # SavedObjectGraph.
+    # TODO(b/236795224): Remove the need for this conversion by keeping the
+    # FunctionSpec (object graph) in the SavedModel. Related: b/213406917.
+    func_kwargs = _convert_values_to_tf_tensors(sample)
+    func(**func_kwargs)
+
+
+def _run_graph_for_calibration_eager_mode(
+    model_dir: str,
+    tags: Collection[str],
+    representative_dataset_map: repr_dataset.RepresentativeDatasetMapping,
+) -> None:
+  """Runs the graph for calibration in eager mode.
+
+  This function assumes _eager mode_ (enabled in TF2 by default) when running
+  the graph. This step is used in order to collect the statistics in
+  CustomAggregatorOp for quantization using the representative dataset for the
+  actual data provided for inference.
+
+  Args:
+    model_dir: Path to SavedModel directory.
+    tags: Collection of tags identifying the MetaGraphDef within the SavedModel.
+    representative_dataset_map: A map where signature keys are mapped to
+      corresponding representative datasets.
+
+  Raises:
+    ValueError: When running the function with the representative dataset fails.
+  """
+  root: autotrackable.AutoTrackable = saved_model_load.load(model_dir, tags)
+  for signature_key, repr_ds in representative_dataset_map.items():
+    try:
+      _run_function_for_calibration_eager_mode(
+          func=root.signatures[signature_key], representative_dataset=repr_ds
+      )
+    except Exception as ex:
+      raise ValueError(
+          'Failed to run representative dataset through the '
+          f'function with the signature key: {signature_key}.'
+      ) from ex
+
+
+def _run_graph_for_calibration(
+    float_model_dir: str,
+    signature_keys: Sequence[str],
+    tags: Collection[str],
+    representative_dataset: repr_dataset.RepresentativeDatasetOrMapping,
+    force_graph_mode_calibration: bool,
+) -> None:
+  """Runs the graph for calibration using representative datasets.
+
+  Args:
+    float_model_dir: Path to the model to calibrate.
+    signature_keys: Sequence of keys identifying SignatureDef containing inputs
+      and outputs.
+    tags: Collection of tags identifying the MetaGraphDef within the SavedModel
+      to analyze.
+    representative_dataset: An iterator that returns a dictionary of {input_key:
+      input_value} or a mapping from signature keys to such iterators. When
+      `signature_keys` contains more than one signature key,
+      `representative_datsaet` should be a mapping that maps each signature keys
+      to the corresponding representative dataset.
+    force_graph_mode_calibration: If set to true, it forces calibration in graph
+      model instead of eager mode when the context is in eager mode.
+
+  Raises:
+    ValueError iff:
+      * The representative dataset format is invalid.
+      * It fails to run the functions using the representative datasets.
+  """
+  try:
+    _validate_representative_dataset(representative_dataset, signature_keys)
+  except Exception as ex:
+    raise ValueError('Invalid representative dataset.') from ex
+
+  # If `representative_dataset` is not a mapping, convert to a mapping for the
+  # following functions to handle representative datasets more conveniently.
+  representative_dataset_map = representative_dataset
+  if not isinstance(representative_dataset, collections.abc.Mapping):
+    # `signature_keys` is guaranteed to have only one element after the
+    # validation.
+    representative_dataset_map = {signature_keys[0]: representative_dataset}
+
+  try:
+    if context.executing_eagerly() and not force_graph_mode_calibration:
+      logging.info('Calibration step is executed in eager mode.')
+      _run_graph_for_calibration_eager_mode(
+          float_model_dir, tags, representative_dataset_map
+      )
+    else:
+      logging.info('Calibration step is executed in graph mode.')
+      _run_graph_for_calibration_graph_mode(
+          float_model_dir, tags, representative_dataset_map
+      )
+  except Exception as ex:
+    raise ValueError(
+        'Failed to run graph for post-training quantization calibration.'
+    ) from ex
+
+  logging.info('Calibration step complete.')
+
+
+def _copy_assets(src_path: str, dst_path: str) -> None:
+  """Copies the assets directory of the saved model.
+
+  Clones the contents of the assets/ directory from the source saved model
+  directory to the destination saved model directory. Nothing will be copied if
+  there are no assets directory in the source directory.
+
+  Args:
+    src_path: Source saved model directory.
+    dst_path: Destination saved model directory. This directory must exist.
+  """
+  for assets_dir_name in [_ASSETS_DIR, _ASSETS_EXTRA_DIR]:
+    src_assets_path = file_io.join(src_path, assets_dir_name)
+    if not file_io.file_exists_v2(src_assets_path):
+      # Do nothing if the source assets path does not exist.
+      continue
+
+    dst_assets_path = file_io.join(dst_path, assets_dir_name)
+    file_io.create_dir_v2(dst_assets_path)
+
+    for curr_dir, _, files in file_io.walk_v2(src_assets_path):
+      for asset_file_name in files:
+        src_asset_file = file_io.join(curr_dir, asset_file_name)
+
+        # Construct the destination assets file path.
+        curr_dst_dir = curr_dir.replace(src_assets_path, dst_assets_path)
+        dst_asset_file = file_io.join(curr_dst_dir, asset_file_name)
+
+        file_io.copy_v2(src_asset_file, dst_asset_file)
+        logging.info(
+            'Copied asset file: %s -> %s', src_asset_file, dst_asset_file
+        )
+
+
+def _run_static_range_qat(
+    src_saved_model_path: str,
+    dst_saved_model_path: str,
+    quant_opts: _QuantizationOptions,
+    signature_def_map: _SignatureDefMap,
+) -> None:
+  """Runs static-range quantization for a Quantization-Aware Trained model.
+
+  Runs the quantization for a model trained using QAT.
+
+  Args:
+    src_saved_model_path: Path to the source SavedModel directory.
+    dst_saved_model_path: Path to the destination SavedModel directory.
+    quant_opts: Quantization options.
+    signature_def_map: Signature def key -> SignatureDef mapping.
+  """
+  logging.info('Running static-range quantization for QAT model.')
+
+  loader = saved_model_loader.SavedModelLoader(src_saved_model_path)
+  function_aliases = loader.get_meta_graph_def_from_tags(
+      quant_opts.tags
+  ).meta_info_def.function_aliases
+
+  exported_model_serialized = pywrap_quantize_model.quantize_qat_model(
+      src_saved_model_path,
+      list(quant_opts.signature_keys),
+      set(quant_opts.tags),
+      quant_opts.SerializeToString(),
+      dict(function_aliases),
+  )
+
+  exported_model = exported_model_pb2.ExportedModel.FromString(
+      exported_model_serialized
+  )
+
+  save_model.save_model_v1(
+      exported_model.graph_def,
+      dst_saved_model_path,
+      signature_def_map,
+      quant_opts.tags,
+      init_op_name=exported_model.init_node_name,
+      saver_def=_get_saver_def_or_none(exported_model),
+      checkpoint_dir=exported_model.checkpoint_dir,
+      function_aliases=exported_model.function_aliases,
+      asset_file_defs=exported_model.asset_file_defs,
+  )
+
+  _copy_assets(src_saved_model_path, dst_saved_model_path)
+
+
+def _get_min_max_from_calibrator(
+    node_id: bytes,
+    calib_opts: quant_opts_pb2.CalibrationOptions,
+) -> tuple[float, float]:
+  """Calculate min and max from statistics using calibration options.
+
+  Args:
+    node_id: bytes of node id.
+    calib_opts: Calibration options used for calculating min and max.
+
+  Returns:
+    (min_value, max_value): Min and max calculated using calib_opts.
+
+  Raises:
+    ValueError: Unsupported calibration method is given.
+  """
+  statistics: calib_stats_pb2.CalibrationStatistics = (
+      pywrap_quantize_model.get_statistics_from_calibrator(node_id)
+  )
+  min_value, max_value = calibration_algorithm.get_min_max_value(
+      statistics, calib_opts
+  )
+  return min_value, max_value
+
+
+def _add_calibration_statistics(
+    graph_def: graph_pb2.GraphDef,
+    calib_opts: quant_opts_pb2.CalibrationOptions,
+) -> None:
+  """Adds calibration statistics to the graph def.
+
+  This function must be run after running the graph with a representative
+  dataset. Retrieves calibration statistics from the global calibrator and adds
+  them to the corresponding nodes as attributes.
+
+  Args:
+    graph_def: GraphDef to add calibration statistics to.
+    calib_opts: Calibration options to calculate min and max.
+  """
+  for function_def in graph_def.library.function:
+    for node_def in function_def.node_def:
+      if node_def.op != 'CustomAggregator':
+        continue
+
+      node_id = node_def.attr['id'].s
+      try:
+        min_value, max_value = _get_min_max_from_calibrator(node_id, calib_opts)
+        pywrap_quantize_model.clear_data_from_calibrator(node_id)
+
+        node_def.attr['min'].f = min_value
+        node_def.attr['max'].f = max_value
+      except ValueError:
+        logging.warning(
+            (
+                'CustomAggregator id "%s" from FunctionDef "%s" does not have '
+                'min or max values. Parts of this function are not quantized.'
+            ),
+            node_id.decode('utf-8'),
+            function_def.signature.name,
+        )
+
+
+def _enable_dump_tensor(graph_def: graph_pb2.GraphDef) -> None:
+  """Enable DumpTensor in the graph def.
+
+  DumpTensor is disabled by default to avoid logging data during calibration.
+  This function is called after calibration to enable DumpTensor.
+
+  Args:
+    graph_def: GraphDef to enable DumpTensor
+  """
+  for function_def in graph_def.library.function:
+    for node_def in function_def.node_def:
+      if node_def.op != 'DumpTensor':
+        continue
+
+      node_def.attr['enabled'].b = True
+
+
+def _change_dump_tensor_file_name(graph_def: graph_pb2.GraphDef) -> None:
+  """Change file_name used by DumpTensor to quantized_tensor_data.pb.
+
+  In whole model verify, DumpTensor in unquantized model uses file_name
+  unquantized_tensor_data.pb.
+  After unquantized dump model is created, this function allows quantized dump
+  model to use quantized_tensor_data.pb as file_name.
+
+  Args:
+    graph_def: GraphDef to change file_name of DumpTensor
+  """
+  for function_def in graph_def.library.function:
+    for node_def in function_def.node_def:
+      if node_def.op != 'DumpTensor':
+        continue
+
+      node_def.attr['file_name'].s = 'quantized_tensor_data.pb'.encode('utf-8')
+
+
+def _get_saver_def_or_none(
+    exported_model: exported_model_pb2.ExportedModel,
+) -> Optional[saver_pb2.SaverDef]:
+  """Returns the SaverDef from ExportedModel, None otherwise.
+
+  Args:
+    exported_model: ExportedModel to take the SaverDef from.
+
+  Returns:
+    SaverDef instance if the field `saver_def` is set. None otherwise.
+  """
+  if exported_model.HasField('saver_def'):
+    return exported_model.saver_def
+  return None
+
+
+def _run_static_range_ptq(
+    src_saved_model_path: str,
+    dst_saved_model_path: str,
+    quant_opts: _QuantizationOptions,
+    representative_dataset: repr_dataset.RepresentativeDatasetOrMapping,
+    signature_def_map: _SignatureDefMap,
+) -> None:
+  """Runs static-range Post-Training Quantization.
+
+  Runs static-range PTQ for the model. Runs the calibration step with
+  `representative_dataset` to collect statistics required for quantization. This
+  produces the quantized GraphDef along with the SignatureDefs which might have
+  been modified according to the changes in the graph.
+
+  Args:
+    src_saved_model_path: Path to the source SavedModel directory.
+    dst_saved_model_path: Path to the destination SavedModel directory.
+    quant_opts: Quantization options.
+    representative_dataset: Representative dataset used for the calibration
+      step. Representative datasets should exist for each signature def key in
+      `signature_def_keys`.
+    signature_def_map: Signature def key -> SignatureDef mapping.
+
+  Raises:
+    ValueError if the graph doesn't contain a valid signature.
+  """
+  logging.info('Running post-training quantization pre-calibration step.')
+
+  loader = saved_model_loader.SavedModelLoader(src_saved_model_path)
+  function_aliases = loader.get_meta_graph_def_from_tags(
+      quant_opts.tags
+  ).meta_info_def.function_aliases
+
+  exported_model_serialized = (
+      pywrap_quantize_model.quantize_ptq_model_pre_calibration(
+          src_saved_model_path,
+          list(quant_opts.signature_keys),
+          set(quant_opts.tags),
+          quant_opts.SerializeToString(),
+          dict(function_aliases),
+      )
+  )
+
+  exported_model = exported_model_pb2.ExportedModel.FromString(
+      exported_model_serialized
+  )
+
+  graph_def = exported_model.graph_def
+  for function_def in graph_def.library.function:
+    for node_def in function_def.node_def:
+      if node_def.op == 'CustomAggregator':
+        node_def.attr['id'].s = uuid.uuid4().hex.encode('ascii')
+
+  pre_calib_output_model_path = tempfile.mkdtemp()
+  save_model.save_model_v1(
+      graph_def,
+      pre_calib_output_model_path,
+      signature_def_map,
+      quant_opts.tags,
+      exported_model.init_node_name,
+      _get_saver_def_or_none(exported_model),
+      exported_model.checkpoint_dir,
+      exported_model.function_aliases,
+      asset_file_defs=exported_model.asset_file_defs,
+  )
+
+  _copy_assets(src_saved_model_path, pre_calib_output_model_path)
+
+  # Uses the representative dataset to collect statistics for calibration.
+  # Handles the graph mode execution separately in case TF2 is disabled or
+  # eager execution is disabled. The min & max values are stored separately
+  # in a global CalibratorSingleton instance.
+  _run_graph_for_calibration(
+      pre_calib_output_model_path,
+      quant_opts.signature_keys,
+      quant_opts.tags,
+      representative_dataset,
+      quant_opts.force_graph_mode_calibration,
+  )
+
+  _add_calibration_statistics(graph_def, quant_opts.calibration_options)
+
+  if quant_opts.HasField('debugger_options'):
+    # Since DumpTensor was disabled by default, we need to enable them.
+    _enable_dump_tensor(graph_def)
+
+    if (
+        quant_opts.debugger_options.debugger_type
+        == quant_opts_pb2.DebuggerOptions.DebuggerType.DEBUGGER_TYPE_WHOLE_MODEL
+    ):
+      # TODO: b/295139417 - Remove CustomAggregator op in unquantized dump model
+      # TODO: b/296916287 - Create a separate function for saving unquantized
+      # dump model
+      save_model.save_model_v1(
+          graph_def,
+          quant_opts.debugger_options.unquantized_dump_model_path,
+          signature_def_map,
+          quant_opts.tags,
+          exported_model.init_node_name,
+          _get_saver_def_or_none(exported_model),
+          exported_model.checkpoint_dir,
+          exported_model.function_aliases,
+          asset_file_defs=exported_model.asset_file_defs,
+      )
+
+      _copy_assets(
+          src_saved_model_path,
+          quant_opts.debugger_options.unquantized_dump_model_path,
+      )
+
+      _change_dump_tensor_file_name(graph_def)
+
+  calibrated_model_path = tempfile.mkdtemp()
+  save_model.save_model_v1(
+      graph_def,
+      calibrated_model_path,
+      signature_def_map,
+      quant_opts.tags,
+      exported_model.init_node_name,
+      _get_saver_def_or_none(exported_model),
+      exported_model.checkpoint_dir,
+      asset_file_defs=exported_model.asset_file_defs,
+  )
+
+  _copy_assets(pre_calib_output_model_path, calibrated_model_path)
+
+  logging.info('Running post-training quantization post-calibration step.')
+  exported_model_serialized = (
+      pywrap_quantize_model.quantize_ptq_model_post_calibration(
+          calibrated_model_path,
+          list(quant_opts.signature_keys),
+          set(quant_opts.tags),
+          quant_opts.SerializeToString(),
+          dict(exported_model.function_aliases),
+      )
+  )
+
+  exported_model = exported_model_pb2.ExportedModel.FromString(
+      exported_model_serialized
+  )
+
+  save_model.save_model_v1(
+      exported_model.graph_def,
+      dst_saved_model_path,
+      signature_def_map,
+      quant_opts.tags,
+      init_op_name=exported_model.init_node_name,
+      saver_def=_get_saver_def_or_none(exported_model),
+      checkpoint_dir=exported_model.checkpoint_dir,
+      function_aliases=exported_model.function_aliases,
+      asset_file_defs=exported_model.asset_file_defs,
+  )
+
+  _copy_assets(calibrated_model_path, dst_saved_model_path)
+
+
+def _static_range_quantize(
+    saved_model_path: str,
+    output_directory: str,
+    quantization_options: _QuantizationOptions,
+    representative_dataset: Optional[
+        repr_dataset.RepresentativeDatasetOrMapping
+    ] = None,
+) -> autotrackable.AutoTrackable:
+  """Quantizes the given SavedModel via static range quantization.
+
+  If the model is not trained with Quantization-Aware Training (QAT) technique,
+  it requires `representative_dataset` to collect statistics required for
+  quantization. If non-None `representative_dataset` is provided with a QAT
+  model input, `representative_dataset` will be ignored.
+
+  Args:
+    saved_model_path: Path to the saved model. When representative_dataset is
+      not provided, this should be a model trained with QAT.
+    output_directory: The path to save the output SavedModel. The directory will
+      be overwritten if not empty.
+    quantization_options: QuantizationOptions proto describing quantization
+      related config.
+    representative_dataset: a generator that returns a dictionary in {input_key:
+      input_value} format or a tuple with signature key and a dictionary in
+      {input_key: input_value} format that feeds calibration data for quantizing
+      model. This should be provided when the model is not a QAT model.
+
+  Returns:
+    A SavedModel object with TF quantization applied.
+
+  Raises:
+    ValueError: when representative_dataset is not provided for non-QAT model.
+    RuntimeError: When a MetaGraphDef could not be found associated with `tags`
+      in the SavedModel.
+  """
+  logging.info(
+      'Running static range quantization on model: %s', saved_model_path
+  )
+  logging.info('QuantizationOptions: \n%s', quantization_options)
+
+  is_qat_saved_model_or_method_no_quantize = _is_qat_saved_model(
+      saved_model_path
+  ) or (
+      quantization_options.quantization_method.preset_method
+      == _QuantizationMethod.METHOD_NO_QUANTIZE
+  )
+  signature_def_map = save_model.get_signatures_from_saved_model(
+      saved_model_path,
+      quantization_options.signature_keys,
+      set(quantization_options.tags),
+  )
+
+  # Checks if the model is from QAT or method is METHOD_NO_QUANTIZE.
+  if (
+      representative_dataset is None
+      and not is_qat_saved_model_or_method_no_quantize
+  ):
+    raise ValueError(
+        'When `representative_dataset` is not provided, the model should be '
+        'trained with quantization-aware training (QAT).'
+    )
+  if quantization_options.min_num_elements_for_weights > 0:
+    logging.warn(
+        'min_num_elements_for_weights is set but is not supported for the '
+        'Post-training static range quantization. '
+        'The flag is ignored.'
+    )
+
+  if is_qat_saved_model_or_method_no_quantize:
+    _run_static_range_qat(
+        saved_model_path,
+        output_directory,
+        quantization_options,
+        signature_def_map,
+    )
+  else:
+    _run_static_range_ptq(
+        saved_model_path,
+        output_directory,
+        quantization_options,
+        representative_dataset,
+        signature_def_map,
+    )
+
+  return saved_model_load.load(output_directory)
+
+
+def _dynamic_range_quantize(
+    saved_model_path: str,
+    output_directory: str,
+    quantization_options: _QuantizationOptions,
+) -> autotrackable.AutoTrackable:
+  """Quantizes the given SavedModel via post-training dynamic range quantization.
+
+  Args:
+    saved_model_path: Path to the saved model.
+    output_directory: The path to save the output SavedModel. The directory will
+      be overwritten if not empty.
+    quantization_options: QuantizationOptions proto describing quantization
+      related config.
+
+  Returns:
+    A SavedModel object with TF quantization applied.
+
+  Raises:
+    ValueError: when the model is QAT model.
+  """
+  mode_str = 'dynamic-range quantization'
+  if _is_qat_saved_model(saved_model_path):
+    raise ValueError(
+        'The models trained with quantization-aware training (QAT) is not '
+        'supported for %s.' % mode_str
+    )
+
+  logging.info(
+      'Running post-training %s on model: %s', mode_str, saved_model_path
+  )
+  logging.info('QuantizationOptions: \n%s', quantization_options)
+
+  loader = saved_model_loader.SavedModelLoader(saved_model_path)
+
+  function_aliases = loader.get_meta_graph_def_from_tags(
+      quantization_options.tags
+  ).meta_info_def.function_aliases
+
+  # Apply post-training dynamic range quantization to the model.
+  exported_model_serialized = pywrap_quantize_model.quantize_ptq_dynamic_range(
+      saved_model_path,
+      list(quantization_options.signature_keys),
+      set(quantization_options.tags),
+      quantization_options.SerializeToString(),
+      dict(function_aliases),
+  )
+
+  exported_model = exported_model_pb2.ExportedModel.FromString(
+      exported_model_serialized
+  )
+  signature_def_map = save_model.get_signatures_from_saved_model(
+      saved_model_path,
+      quantization_options.signature_keys,
+      quantization_options.tags,
+  )
+
+  save_model.save_model_v1(
+      exported_model.graph_def,
+      output_directory,
+      signature_def_map,
+      quantization_options.tags,
+      init_op_name=exported_model.init_node_name,
+      saver_def=_get_saver_def_or_none(exported_model),
+      checkpoint_dir=exported_model.checkpoint_dir,
+      function_aliases=exported_model.function_aliases,
+      asset_file_defs=exported_model.asset_file_defs,
+  )
+  _copy_assets(saved_model_path, output_directory)
+
+  return saved_model_load.load(output_directory)
+
+
+def _weight_only_quantize(
+    saved_model_path: str,
+    output_directory: str,
+    quantization_options: quant_opts_pb2.QuantizationOptions,
+) -> autotrackable.AutoTrackable:
+  """Quantizes the given SavedModel via weight-only quantization.
+
+  Args:
+    saved_model_path: Path to the saved model.
+    output_directory: The path to save the output SavedModel. The directory will
+      be overwritten if not empty.
+    quantization_options: QuantizationOptions proto describing quantization
+      related config.
+
+  Returns:
+    A SavedModel object with TF quantization applied.
+
+  Raises:
+    ValueError: when the model is QAT model.
+  """
+  mode_str = 'weight-only quantization'
+
+  # QAT weight-only is not supported yet.
+  if _is_qat_saved_model(saved_model_path):
+    raise ValueError(
+        'The models trained with quantization-aware training (QAT) is not '
+        'supported for %s.' % mode_str
+    )
+
+  logging.info(
+      'Running post-training %s on model: %s', mode_str, saved_model_path
+  )
+  logging.info('QuantizationOptions: \n%s', quantization_options)
+
+  loader = saved_model_loader.SavedModelLoader(saved_model_path)
+
+  function_aliases = loader.get_meta_graph_def_from_tags(
+      quantization_options.tags
+  ).meta_info_def.function_aliases
+
+  exported_model_serialized = pywrap_quantize_model.quantize_weight_only(
+      saved_model_path,
+      quantization_options.SerializeToString(),
+      dict(function_aliases),
+  )
+
+  exported_model = exported_model_pb2.ExportedModel.FromString(
+      exported_model_serialized
+  )
+  signature_def_map = save_model.get_signatures_from_saved_model(
+      saved_model_path,
+      list(quantization_options.signature_keys),
+      set(quantization_options.tags),
+  )
+
+  save_model.save_model_v1(
+      exported_model.graph_def,
+      output_directory,
+      signature_def_map,
+      quantization_options.tags,
+      init_op_name=exported_model.init_node_name,
+      saver_def=_get_saver_def_or_none(exported_model),
+      checkpoint_dir=exported_model.checkpoint_dir,
+      function_aliases=exported_model.function_aliases,
+      asset_file_defs=exported_model.asset_file_defs,
+  )
+  _copy_assets(saved_model_path, output_directory)
+
+  return saved_model_load.load(output_directory)
+
+
+def _verify_output_dir(output_dir: Optional[str], overwrite: bool) -> None:
+  """Verifies the output directory.
+
+  Raises an error if `output_dir` is not suitable for writing the output saved
+  model.
+
+  Args:
+    output_dir: Output directory.
+    overwrite: An option allowing to overwrite the existing output directory if
+      set to true. Does not actually create or modify the `output_dir` in this
+      function.
+
+  Raises:
+    FileExistsError: Iff `output_dir` is not empty and `overwrite` is false.
+  """
+  dir_not_empty = (
+      output_dir is not None
+      and file_io.file_exists_v2(output_dir)
+      and file_io.list_directory_v2(output_dir)
+  )
+
+  if dir_not_empty and not overwrite:
+    raise FileExistsError(
+        f'Output directory already exists: {output_dir} . '
+        'Please set overwrite_output_directory to true to '
+        'overwrite the existing directory.'
+    )
+
+
+def _populate_quantization_component_spec(
+    quant_method: _QuantizationMethod,
+) -> None:
+  """Populates default values for QuantizationComponentSpec.
+
+  Args:
+    quant_method: The quantization method to be updated.
+  """
+  # Make sure creating one spec per component.
+  updated_component_spec = dict()
+
+  # Populate default configuration.
+  if (
+      quant_method.preset_method == _PresetMethod.METHOD_STATIC_RANGE_INT8
+      or quant_method.preset_method == _PresetMethod.METHOD_DYNAMIC_RANGE_INT8
+  ):
+    updated_component_spec[_QuantizationComponent.COMPONENT_ACTIVATION] = (
+        _QuantizationComponentSpec(
+            quantization_component=_QuantizationComponent.COMPONENT_ACTIVATION,
+            tensor_type=_TensorType.TENSORTYPE_INT_8,
+        )
+    )
+    updated_component_spec[_QuantizationComponent.COMPONENT_WEIGHT] = (
+        _QuantizationComponentSpec(
+            quantization_component=_QuantizationComponent.COMPONENT_WEIGHT,
+            tensor_type=_TensorType.TENSORTYPE_INT_8,
+        )
+    )
+    updated_component_spec[_QuantizationComponent.COMPONENT_BIAS] = (
+        _QuantizationComponentSpec(
+            quantization_component=_QuantizationComponent.COMPONENT_BIAS,
+            tensor_type=_TensorType.TENSORTYPE_INT_32,
+        )
+    )
+  elif (
+      quant_method.preset_method
+      == _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8
+  ):
+    updated_component_spec[_QuantizationComponent.COMPONENT_WEIGHT] = (
+        _QuantizationComponentSpec(
+            quantization_component=_QuantizationComponent.COMPONENT_WEIGHT,
+            tensor_type=_TensorType.TENSORTYPE_INT_8,
+        )
+    )
+
+  # Override if quantization_component_spec is specified.
+  if quant_method.quantization_component_specs:
+    # Check if the component spec is supported configuration in TF-Quant.
+    for component_spec in quant_method.quantization_component_specs:
+      if component_spec.quantization_component in [
+          _QuantizationComponent.COMPONENT_WEIGHT,
+          _QuantizationComponent.COMPONENT_ACTIVATION,
+      ]:
+        if component_spec.tensor_type != _TensorType.TENSORTYPE_INT_8:
+          raise ValueError(
+              'Only int8 precision is supported for input operands.'
+          )
+      else:
+        if component_spec.tensor_type != _TensorType.TENSORTYPE_INT_32:
+          raise ValueError('Only int32 precision is supported for bias.')
+      # Update with the custom spec.
+      updated_component_spec[component_spec.quantization_component] = (
+          component_spec
+      )
+
+  # Update the componet spec
+  del quant_method.quantization_component_specs[:]
+  quant_method.quantization_component_specs.extend(
+      updated_component_spec.values()
+  )
+
+  if (
+      quant_method.preset_method == _PresetMethod.METHOD_STATIC_RANGE_INT8
+      or quant_method.preset_method == _PresetMethod.METHOD_DYNAMIC_RANGE_INT8
+  ) and (len(quant_method.quantization_component_specs) != 3):
+    raise ValueError('Only 3 components are needed for', quant_method)
+  elif (
+      quant_method.preset_method
+      == _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8
+  ) and len(quant_method.quantization_component_specs) != 1:
+    raise ValueError('At least one component spec needs to be specified.')
+
+
+def _populate_unitwise_quantization_specs(
+    quantization_options: _QuantizationOptions,
+) -> None:
+  """Verifies and pupulates unitwise quantization specs."""
+  if not quantization_options.unit_wise_quantization_specs:
+    return
+
+  sorted_top_level_component_specs = sorted(
+      quantization_options.quantization_method.quantization_component_specs,
+      key=lambda x: x.quantization_component,
+  )
+
+  for unitwise_spec in quantization_options.unit_wise_quantization_specs:
+    if not unitwise_spec.unit:
+      raise ValueError(
+          'UnitWiseQuantizationSpec must contain at least one unit.'
+      )
+
+    for unit in unitwise_spec.unit:
+      if not unit.op_type and not unit.node_name:
+        raise ValueError('Either `op_type` or `node_name` must be specified.')
+
+    _populate_quantization_component_spec(unitwise_spec.quantization_method)
+
+    component_specs = (
+        unitwise_spec.quantization_method.quantization_component_specs
+    )
+    if component_specs and (
+        sorted_top_level_component_specs
+        != sorted(component_specs, key=lambda x: x.quantization_component)
+    ):
+      raise ValueError(
+          'Currently unit-wise quantization spec only supports NO_QUANTIZE and'
+          ' same quantization method as the top-level `quantization_method`'
+      )
+
+
+def _populate_calibration_options(
+    quantization_options: quant_opts_pb2.QuantizationOptions,
+):
+  """Populates default values for CalibrationOptions.
+
+  Args:
+    quantization_options: An instance of QuantizationOptions with a field
+      specifying CalibrationOptions
+  """
+
+  calib_opts = quantization_options.calibration_options
+  if (
+      calib_opts.calibration_method
+      == _CalibrationMethod.CALIBRATION_METHOD_UNSPECIFIED
+  ):
+    calib_opts.calibration_method = (
+        _CalibrationMethod.CALIBRATION_METHOD_MIN_MAX
+    )
+  elif (
+      calib_opts.calibration_method
+      == _CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_PERCENTILE
+  ):
+    if not calib_opts.calibration_parameters.initial_num_bins:
+      calib_opts.calibration_parameters.initial_num_bins = 256
+    if not calib_opts.calibration_parameters.min_percentile:
+      calib_opts.calibration_parameters.min_percentile = 0.001
+    if not calib_opts.calibration_parameters.max_percentile:
+      calib_opts.calibration_parameters.max_percentile = 99.999
+  # Check the activation_tensor_type of HISTOGRAM_MSE methods.
+  elif calib_opts.calibration_method in [
+      _CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_MSE_BRUTEFORCE,
+      _CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_MSE_MAX_FREQUENCY,
+      _CalibrationMethod.CALIBRATION_METHOD_HISTOGRAM_MSE_SYMMETRIC,
+  ]:
+    activation_tensor_type = (
+        quantization_options.quantization_method.quantization_component_specs[
+            _QuantizationComponent.COMPONENT_ACTIVATION
+        ].tensor_type
+    )
+    # Unlike the HISTOGRAM_PERCENTILE method, the HISTOGRAM_MSE method uses
+    # num_bits because it actually quantizes and dequantizes values.
+    if activation_tensor_type != _TensorType.TENSORTYPE_INT_8:
+      raise ValueError(
+          'Only TENSORTYPE_INT_8 is supported for HISTOGRAM_MSE calibration'
+          f' methods. calibration_method={calib_opts.calibration_method}'
+      )
+
+    if not calib_opts.calibration_parameters.initial_num_bins:
+      calib_opts.calibration_parameters.initial_num_bins = 256
+
+
+def _populate_quantization_options_default_values(
+    quantization_options: _QuantizationOptions,
+) -> None:
+  """Populates default values for QuantizationOptions.
+
+  Populates unspecified or unset fields of QuantizationOptions with the default
+  values.
+
+  * If `op_set` is unspecified, it defaults to `OpSet.XLA`.
+  * If `freeze_all_variables` is not set, it defaults to `True`.
+  * Check if configurations are set correctly:
+    - Per-channel quantization is supported for Uniform Quantized opset only.
+
+  Args:
+    quantization_options: An instance of QuantizationOptions.
+  """
+  if quantization_options.op_set == quant_opts_pb2.OpSet.OP_SET_UNSPECIFIED:
+    quantization_options.op_set = quant_opts_pb2.OpSet.XLA
+
+  if not quantization_options.tags:
+    quantization_options.tags.append(tag_constants.SERVING)
+
+  if not quantization_options.signature_keys:
+    quantization_options.signature_keys.append(
+        signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
+    )
+
+  if not quantization_options.HasField('freeze_all_variables'):
+    quantization_options.freeze_all_variables = True
+
+  if quantization_options.enable_legacy_weight_only:
+    raise ValueError(
+        'Legacy weight-only is deprecated. Use weight-only quantization method.'
+    )
+
+  # Check default quantization option values for weight-only quantization.
+  # TODO(b/242805842): Find good minimum_elements_for_weights number for server.
+  # please also update default value in tflite converter:
+  # tensorflow/compiler/mlir/lite/tf_to_tfl_flatbuffer.cc;l=201
+  if (
+      quantization_options.quantization_method.preset_method
+      == _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8
+  ) or (
+      quantization_options.quantization_method.preset_method
+      == _PresetMethod.METHOD_DYNAMIC_RANGE_INT8
+  ):
+    if quantization_options.min_num_elements_for_weights == 0:
+      quantization_options.min_num_elements_for_weights = (
+          _DYNAMIC_RANGE_DEFAULT_MIN_NUM_ELEMENTS_FOR_WEIGHTS
+      )
+      logging.warn(
+          (
+              'QuantizationOptions.min_num_elements_for_weights is not set (0).'
+              ' Setting to the default value: %d.'
+          ),
+          _DYNAMIC_RANGE_DEFAULT_MIN_NUM_ELEMENTS_FOR_WEIGHTS,
+      )
+
+  # TODO(b/281595329): Implement static range quantization per-channel support
+  if quantization_options.enable_per_channel_quantization and not (
+      quantization_options.op_set == quant_opts_pb2.OpSet.UNIFORM_QUANTIZED
+      or quantization_options.quantization_method.preset_method
+      == _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8
+  ):
+    raise ValueError(
+        'Currently, per-channel quantization is supported for Uniform '
+        'Quantized opset and Weight-only.'
+    )
+
+  if (
+      quantization_options.quantization_method.preset_method
+      == _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8
+      and (
+          quantization_options.op_set == quant_opts_pb2.OpSet.UNIFORM_QUANTIZED
+          or quantization_options.op_set == quant_opts_pb2.OpSet.TF
+      )
+  ):
+    raise ValueError('TF/Uniform quantized opset does not support weight-only.')
+
+  if (quantization_options.op_set == quant_opts_pb2.OpSet.STABLEHLO) and (
+      quantization_options.quantization_method.preset_method
+      != _PresetMethod.METHOD_STATIC_RANGE_INT8
+  ):
+    raise ValueError(
+        'StableHLO quantized opset currently only supports static range'
+        ' quantization via TF Quantizer.'
+    )
+
+  # Converter assumes options are specified. So set SRQ explicitly.
+  if (
+      quantization_options.quantization_method.preset_method
+      == _PresetMethod.METHOD_UNSPECIFIED
+  ):
+    logging.debug(
+        '"preset_method" for QuantizationMethod is not specified.'
+        'Static range quantization is used by default.'
+    )
+    quantization_options.quantization_method.preset_method = (
+        _PresetMethod.METHOD_STATIC_RANGE_INT8
+    )
+
+  if quantization_options.HasField('debugger_options'):
+    if not quantization_options.debugger_options.log_dir_path:
+      quantization_options.debugger_options.log_dir_path = '/tmp/dumps'
+
+    if (
+        quantization_options.debugger_options.debugger_type
+        == quant_opts_pb2.DebuggerOptions.DebuggerType.DEBUGGER_TYPE_UNSPECIFIED
+    ):
+      raise ValueError(
+          'Debugger is enabled but debugger type was not specified.'
+      )
+
+    if (
+        quantization_options.debugger_options.debugger_type
+        == quant_opts_pb2.DebuggerOptions.DebuggerType.DEBUGGER_TYPE_WHOLE_MODEL
+        and not quantization_options.debugger_options.unquantized_dump_model_path
+    ):
+      raise ValueError(
+          'Debugger type whole model verify was used but'
+          ' unquantized_dump_model_path was not specified.'
+      )
+
+  # Check and populate quantization component spec.
+  _populate_quantization_component_spec(
+      quantization_options.quantization_method
+  )
+  # Verify and populate unit-wise quantization specs.
+  _populate_unitwise_quantization_specs(quantization_options)
+
+  if (
+      quantization_options.quantization_method.preset_method
+      == _PresetMethod.METHOD_STATIC_RANGE_INT8
+  ):
+    # Check and populate calibration options.
+    _populate_calibration_options(quantization_options)
+
+
+@tf_export.tf_export('quantization.experimental.quantize_saved_model')
+def quantize(
+    saved_model_path: str,
+    output_directory: Optional[str] = None,
+    quantization_options: Optional[_QuantizationOptions] = None,
+    representative_dataset: Optional[
+        repr_dataset.RepresentativeDatasetOrMapping
+    ] = None,
+    *,
+    overwrite_output_directory: bool = False,
+) -> autotrackable.AutoTrackable:
+  """Quantizes the SavedModel with the given quantization options.
+
+  Example usage:
+  ```python
+  # Quantizing a model trained with QAT.
+  quantization_options = tf.quantization.experimental.QuantizationOptions(
+      signature_keys=['your_signature_key'],
+  )
+  tf.quantization.experimental.quantize_saved_model(
+      '/tmp/input_model',
+      '/tmp/output_model',
+      quantization_options=quantization_options,
+  )
+
+  # When quantizing a model trained without QAT (Post-Training Quantization),
+  # a representative dataset is required.
+  representative_dataset = [{"input": tf.random.uniform(shape=(3, 3))}
+                        for _ in range(256)]
+  tf.quantization.experimental.quantize_saved_model(
+      '/tmp/input_model',
+      '/tmp/output_model',
+      quantization_options=quantization_options,
+      representative_dataset={'your_signature_key': representative_dataset},
+    )
+
+  # In addition to preset quantization methods, fine-grained control of
+  # quantization for each component is also supported.
+  _QuantizationComponentSpec = (
+      tf.quantization.experimental.QuantizationComponentSpec
+  )
+  quantization_options = tf.quantization.experimental.QuantizationOptions(
+      signature_keys=['your_signature_key'],
+      quantization_method=tf.quantization.experimental.QuantizationMethod(
+          quantization_component_specs=[
+              _QuantizationComponentSpec(
+                  quantization_component=(
+                      _QuantizationComponentSpec.COMPONENT_ACTIVATION
+                  ),
+                  tensor_type=_QuantizationComponentSpec.TENSORTYPE_INT_8,
+              )
+          ]
+      )
+  )
+  tf.quantization.experimental.quantize_saved_model(
+      '/tmp/input_model',
+      '/tmp/output_model',
+      quantization_options=quantization_options,
+  )
+  ```
+
+  Args:
+    saved_model_path: Path to the saved model. When representative_dataset is
+      not provided, this should be a model trained with QAT.
+    output_directory: The path to save the output SavedModel. Set
+      `overwrite_output_directory` to `True` to overwrite any existing contents
+      in the directory if not empty.
+    quantization_options: A set of options for quantization. If None, it uses
+      post-training static range quantization with XLA opset by default.
+    representative_dataset: an iterator that returns a dictionary of {input_key:
+      input_value} or a map from signature key to a dictionary of {input_key:
+      input_value} that feeds calibration data for quantizing model. The
+      representative should be provided when the model is a PTQ model. It can be
+      provided either via this parameter or via the `representative_datasets`
+      field in `QuantizationOptions`.
+    overwrite_output_directory: If set to true, overwrites the output directory
+      iff it isn't empty. The default value is false.
+
+  Returns:
+    A SavedModel object with TF quantization applied, or None if no quantization
+    is performed.
+
+  Raises:
+    ValueError: When 1) representative_dataset is not provided for non QAT model
+      for enabling static range quantization, 2) invalid value is provided as
+      a quantization method, or 3) provide representative dataset via both
+      argument and QuantizationOptions.
+    ValueError: When the specified quantization method is not yet supported.
+  """
+  _verify_output_dir(output_directory, overwrite_output_directory)
+
+  # Set default values for None arguments.
+  if output_directory is None:
+    output_directory = tempfile.mkdtemp()
+
+  if quantization_options is None:
+    quantization_options = _QuantizationOptions()
+
+  _populate_quantization_options_default_values(quantization_options)
+
+  if (
+      representative_dataset is not None
+      and quantization_options.representative_datasets
+  ):
+    raise ValueError(
+        'Do not specify both the `representative_dataset` argument and'
+        ' the `representative_datasets` field in `QuantizationOptions`.'
+    )
+
+  if quantization_options.representative_datasets:
+    representative_dataset = repr_dataset.RepresentativeDatasetLoader(
+        quantization_options.representative_datasets
+    ).load()
+
+  method: _QuantizationMethod = quantization_options.quantization_method
+  if (
+      method.preset_method == _PresetMethod.METHOD_STATIC_RANGE_INT8
+      or method.preset_method == _PresetMethod.METHOD_NO_QUANTIZE
+  ):
+    return _static_range_quantize(
+        saved_model_path,
+        output_directory,
+        quantization_options,
+        representative_dataset,
+    )
+  elif method.preset_method == _PresetMethod.METHOD_DYNAMIC_RANGE_INT8:
+    return _dynamic_range_quantize(
+        saved_model_path,
+        output_directory,
+        quantization_options,
+    )
+  elif (
+      method.preset_method == _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8
+  ):
+    return _weight_only_quantize(
+        saved_model_path,
+        output_directory,
+        quantization_options,
+    )
+  else:
+    raise ValueError(
+        'Quantization method {method.preset_method} is not supported.'
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/python/representative_dataset.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/python/representative_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3e8cc9d6bcb508c467ca51847cea7e78315009d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/python/representative_dataset.py
@@ -0,0 +1,304 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Defines types required for representative datasets for quantization."""
+
+import collections.abc
+import os
+from typing import Iterable, Mapping, Optional, Union
+
+from tensorflow.compiler.mlir.quantization.tensorflow import quantization_options_pb2
+from tensorflow.python.client import session
+from tensorflow.python.data.ops import readers
+from tensorflow.python.eager import context
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.lib.io import python_io
+from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.types import core
+from tensorflow.python.util import tf_export
+
+# A representative sample is a map of: input_key -> input_value.
+# Ex.: {'dense_input': tf.constant([1, 2, 3])}
+# Ex.: {'x1': np.ndarray([4, 5, 6]}
+RepresentativeSample = Mapping[str, core.TensorLike]
+
+# A representative dataset is an iterable of representative samples.
+RepresentativeDataset = Iterable[RepresentativeSample]
+
+# A type representing a map from: signature key -> representative dataset.
+# Ex.: {'serving_default': [tf.constant([1, 2, 3]), tf.constant([4, 5, 6])],
+#       'other_signature_key': [tf.constant([[2, 2], [9, 9]])]}
+RepresentativeDatasetMapping = Mapping[str, RepresentativeDataset]
+
+# A type alias expressing that it can be either a RepresentativeDataset or
+# a mapping of signature key to RepresentativeDataset.
+RepresentativeDatasetOrMapping = Union[
+    RepresentativeDataset, RepresentativeDatasetMapping
+]
+
+# Type aliases for quantization_options_pb2 messages.
+_RepresentativeDataSample = quantization_options_pb2.RepresentativeDataSample
+_RepresentativeDatasetFile = quantization_options_pb2.RepresentativeDatasetFile
+
+
+class RepresentativeDatasetSaver:
+  """Representative dataset saver.
+
+  Exposes a single method `save` that saves the provided representative dataset
+  into files.
+
+  This is useful when you would like to keep a snapshot of your representative
+  dataset at a file system or when you need to pass the representative dataset
+  as files.
+  """
+
+  def save(
+      self, representative_dataset: RepresentativeDatasetMapping
+  ) -> Mapping[str, _RepresentativeDatasetFile]:
+    """Saves the representative dataset.
+
+    Args:
+      representative_dataset: RepresentativeDatasetMapping which is a
+        signature_def_key -> representative dataset mapping.
+    """
+    raise NotImplementedError('Method "save" is not implemented.')
+
+
+@tf_export.tf_export(
+    'quantization.experimental.TfRecordRepresentativeDatasetSaver'
+)
+class TfRecordRepresentativeDatasetSaver(RepresentativeDatasetSaver):
+  """Representative dataset saver in TFRecord format.
+
+  Saves representative datasets for quantization calibration in TFRecord format.
+  The samples are serialized as `RepresentativeDataSample`.
+
+  The `save` method return a signature key to `RepresentativeDatasetFile` map,
+  which can be used for QuantizationOptions.
+
+  Example usage:
+
+  ```python
+  # Creating the representative dataset.
+  representative_dataset = [{"input": tf.random.uniform(shape=(3, 3))}
+                        for _ in range(256)]
+
+  # Saving to a TFRecord file.
+  dataset_file_map = (
+    tf.quantization.experimental.TfRecordRepresentativeDatasetSaver(
+          path_map={'serving_default': '/tmp/representative_dataset_path'}
+      ).save({'serving_default': representative_dataset})
+  )
+
+  # Using in QuantizationOptions.
+  quantization_options = tf.quantization.experimental.QuantizationOptions(
+      signature_keys=['serving_default'],
+      representative_datasets=dataset_file_map,
+  )
+  tf.quantization.experimental.quantize_saved_model(
+      '/tmp/input_model',
+      '/tmp/output_model',
+      quantization_options=quantization_options,
+  )
+  ```
+  """
+
+  def __init__(self, path_map: Mapping[str, os.PathLike[str]]):
+    """Initializes TFRecord represenatative dataset saver.
+
+    Args:
+      path_map: Signature def key -> path mapping. Each path is a TFRecord file
+        to which a `RepresentativeDataset` is saved. The signature def keys
+        should be a subset of the `SignatureDef` keys of the
+        `representative_dataset` argument of the `save()` call.
+    """
+    self.path_map: Mapping[str, os.PathLike[str]] = path_map
+
+  def _save_tf_record_dataset(
+      self,
+      repr_ds: RepresentativeDataset,
+      signature_def_key: str,
+  ) -> _RepresentativeDatasetFile:
+    """Saves `repr_ds` to a TFRecord file.
+
+    Each sample in `repr_ds` is serialized as `RepresentativeDataSample`.
+
+    Args:
+      repr_ds: `RepresentativeDataset` to save.
+      signature_def_key: The signature def key associated with `repr_ds`.
+
+    Returns:
+      a RepresentativeDatasetFile instance contains the path to the saved file.
+    """
+    tfrecord_file_path = self.path_map[signature_def_key]
+    with python_io.TFRecordWriter(tfrecord_file_path) as writer:
+      for repr_sample in repr_ds:
+        sample = _RepresentativeDataSample()
+        for input_name, input_value in repr_sample.items():
+          sample.tensor_proto_inputs[input_name].CopyFrom(
+              tensor_util.make_tensor_proto(input_value)
+          )
+
+        writer.write(sample.SerializeToString())
+
+    logging.info(
+        'Saved representative dataset for signature def: %s to: %s',
+        signature_def_key,
+        tfrecord_file_path,
+    )
+    return _RepresentativeDatasetFile(
+        tfrecord_file_path=str(tfrecord_file_path)
+    )
+
+  def save(
+      self, representative_dataset: RepresentativeDatasetMapping
+  ) -> Mapping[str, _RepresentativeDatasetFile]:
+    """Saves the representative dataset.
+
+    Args:
+      representative_dataset: Signature def key -> representative dataset
+        mapping. Each dataset is saved in a separate TFRecord file whose path
+        matches the signature def key of `path_map`.
+
+    Raises:
+      ValueError: When the signature def key in `representative_dataset` is not
+      present in the `path_map`.
+
+    Returns:
+      A map from signature key to the RepresentativeDatasetFile instance
+      contains the path to the saved file.
+    """
+    dataset_file_map = {}
+    for signature_def_key, repr_ds in representative_dataset.items():
+      if signature_def_key not in self.path_map:
+        raise ValueError(
+            'SignatureDef key does not exist in the provided path_map:'
+            f' {signature_def_key}'
+        )
+
+      dataset_file_map[signature_def_key] = self._save_tf_record_dataset(
+          repr_ds, signature_def_key
+      )
+    return dataset_file_map
+
+
+class RepresentativeDatasetLoader:
+  """Representative dataset loader.
+
+  Exposes the `load` method that loads the representative dataset from files.
+  """
+
+  def __init__(
+      self,
+      dataset_file_map: Mapping[str, _RepresentativeDatasetFile],
+  ) -> None:
+    """Initializes TFRecord represenatative dataset loader.
+
+    Args:
+      dataset_file_map: Signature key -> `RepresentativeDatasetFile` mapping.
+
+    Raises:
+      DecodeError: If the sample is not RepresentativeDataSample.
+    """
+    self.dataset_file_map = dataset_file_map
+
+  def _load_tf_record(self, tf_record_path: str) -> RepresentativeDataset:
+    """Loads TFRecord containing samples of type`RepresentativeDataSample`."""
+    samples = []
+    with context.eager_mode():
+      for sample_bytes in readers.TFRecordDatasetV2(filenames=[tf_record_path]):
+        sample_proto = _RepresentativeDataSample.FromString(
+            sample_bytes.numpy()
+        )
+        sample = {}
+        for input_key, tensor_proto in sample_proto.tensor_proto_inputs.items():
+          sample[input_key] = tensor_util.MakeNdarray(tensor_proto)
+        samples.append(sample)
+    return samples
+
+  def load(self) -> RepresentativeDatasetMapping:
+    """Loads the representative datasets.
+
+    Returns:
+      representative dataset mapping: A signature def key -> representative
+      mapping. The loader loads `RepresentativeDataset` for each path in
+      `self.dataset_file_map` and associates the loaded dataset to the
+      corresponding signature def key.
+    """
+    repr_dataset_map = {}
+    for signature_def_key, dataset_file in self.dataset_file_map.items():
+      if dataset_file.HasField('tfrecord_file_path'):
+        repr_dataset_map[signature_def_key] = self._load_tf_record(
+            dataset_file.tfrecord_file_path
+        )
+      else:
+        raise ValueError('Unsupported Representative Dataset filetype')
+
+    return repr_dataset_map
+
+
+def replace_tensors_by_numpy_ndarrays(
+    repr_ds: RepresentativeDataset, sess: session.Session
+) -> RepresentativeDataset:
+  """Replaces tf.Tensors in samples by their evaluated numpy arrays.
+
+  Note: This should be run in graph mode (default in TF1) only.
+
+  Args:
+    repr_ds: Representative dataset to replace the tf.Tensors with their
+      evaluated values. `repr_ds` is iterated through, so it may not be reusable
+      (e.g. if it is a generator object).
+    sess: Session instance used to evaluate tf.Tensors.
+
+  Returns:
+    The new representative dataset where each tf.Tensor is replaced by its
+    evaluated numpy ndarrays.
+  """
+  new_repr_ds = []
+  for sample in repr_ds:
+    new_sample = {}
+    for input_key, input_data in sample.items():
+      # Evaluate the Tensor to get the actual value.
+      if isinstance(input_data, core.Tensor):
+        input_data = input_data.eval(session=sess)
+
+      new_sample[input_key] = input_data
+
+    new_repr_ds.append(new_sample)
+  return new_repr_ds
+
+
+def get_num_samples(repr_ds: RepresentativeDataset) -> Optional[int]:
+  """Returns the number of samples if known.
+
+  Args:
+    repr_ds: Representative dataset.
+
+  Returns:
+    Returns the total number of samples in `repr_ds` if it can be determined
+    without iterating the entier dataset. Returns None iff otherwise. When it
+    returns None it does not mean the representative dataset is infinite or it
+    is malformed; it simply means the size cannot be determined without
+    iterating the whole dataset.
+  """
+  if isinstance(repr_ds, collections.abc.Sized):
+    try:
+      return len(repr_ds)
+    except Exception as ex:  # pylint: disable=broad-except
+      # There are some cases where calling __len__() raises an exception.
+      # Handle this as if the size is unknown.
+      logging.info('Cannot determine the size of the dataset (%s).', ex)
+      return None
+  else:
+    return None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/python/save_model.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/python/save_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b4ac4f65fe157de6c58624eafa9af0298f0d2a4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/python/save_model.py
@@ -0,0 +1,343 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Defines utilities involving SavedModel."""
+from typing import Collection, Dict, Mapping, Optional, Sequence
+
+from absl import logging
+
+# pylint: disable=g-importing-member
+from google.protobuf.any_pb2 import Any
+# pylint: enable=g-importing-member
+from tensorflow.core.framework import graph_pb2
+from tensorflow.core.protobuf import meta_graph_pb2
+from tensorflow.core.protobuf import saver_pb2
+from tensorflow.python.client import session
+from tensorflow.python.framework import importer
+from tensorflow.python.framework import ops
+from tensorflow.python.lib.io import file_io
+from tensorflow.python.saved_model import builder
+from tensorflow.python.saved_model import constants as saved_model_constants
+from tensorflow.python.saved_model import loader_impl as saved_model_loader
+from tensorflow.python.saved_model import tag_constants
+from tensorflow.python.training import saver
+
+# Mapping of signature def key -> SignatureDef.
+_SignatureDefMap = Mapping[str, meta_graph_pb2.SignatureDef]
+
+
+def get_signatures_from_saved_model(
+    saved_model_path: str,
+    signature_keys: Optional[Sequence[str]] = None,
+    tags: Optional[Collection[str]] = None,
+) -> Dict[str, meta_graph_pb2.SignatureDef]:
+  """Gets a map from signature keys to their SignatureDef.
+
+  Args:
+    saved_model_path: Path to the saved model.
+    signature_keys: List of keys identifying SignatureDef to retrieve. If None,
+      retrieve all except the init signature.
+    tags: Set of tags identifying the MetaGraphDef within the SavedModel.
+
+  Returns:
+    A map from signature_key to its SignatureDef.
+  """
+  if tags is None:
+    tags = {tag_constants.SERVING}
+
+  loader = saved_model_loader.SavedModelLoader(saved_model_path)
+  meta_graphdef = loader.get_meta_graph_def_from_tags(tags)
+  signatures = {}
+  for key, signature_def in meta_graphdef.signature_def.items():
+    if key == saved_model_constants.INIT_OP_SIGNATURE_KEY:
+      continue
+    if signature_keys is not None and key not in signature_keys:
+      continue
+    signatures[key] = signature_def
+
+  return signatures
+
+
+def _restore_output_tensor_names(
+    graph_def: graph_pb2.GraphDef,
+) -> graph_pb2.GraphDef:
+  """Restores the output tensor names of the converted model.
+
+  During the conversion, the output tensor names of the original model are
+  embedded in the `tf_saved_model.index_path` attribute of the RetVal nodes and
+  might become the name of Retval nodes as well (with an index suffix if there
+  are multiple output tensors from one node). Since Retval nodes are not used in
+  SavedModel, this function removes them and restore the names to the actual
+  output tensors.
+
+  Args:
+    graph_def: the converted GraphDef.
+
+  Returns:
+    The GraphDef with Retval nodes removed and output tensor names restored.
+  """
+  output_renaming_map = {}
+  with session.Session(graph=ops.Graph()):
+    importer.import_graph_def(graph_def, name='')
+    graph = ops.get_default_graph()
+    for op in graph.get_operations():
+      if op.type == '_Retval':
+        expected_node_name = op.name
+        if op.get_attr('tf_saved_model.index_path') is not None:
+          index_path_name = op.get_attr('tf_saved_model.index_path')[0]
+          index_path_name = index_path_name.decode('utf-8').split(':')[0]
+          try:
+            # Only use the index_path name if it points to a Retval node.
+            index_path_node = graph.get_operation_by_name(index_path_name)
+            if index_path_node.type == '_Retval':
+              expected_node_name = index_path_name
+          except KeyError:
+            pass
+        retval_input_node_name = op.inputs[0].op.name
+        output_renaming_map[retval_input_node_name] = expected_node_name
+
+  for node in reversed(graph_def.node):
+    if node.name in output_renaming_map:
+      node.name = output_renaming_map[node.name]
+    elif node.op == '_Retval':
+      graph_def.node.remove(node)
+    else:
+      # Update the inputs referring to the pre-renaming node.
+      for idx, input_name in enumerate(node.input):
+        if input_name in output_renaming_map:
+          node.input[idx] = output_renaming_map[input_name]
+      # Update the control inputs referring to the pre-renaming node.
+      updating_inputs = []
+      for input_name in reversed(node.input):
+        if input_name.startswith('^') and input_name[1:] in output_renaming_map:
+          updating_inputs.append(input_name[1:])
+          node.input.remove(input_name)
+      for updating_input in updating_inputs:
+        node.input.append('^' + output_renaming_map[updating_input])
+  return graph_def
+
+
+def _create_empty_output_dir(output_directory: str) -> None:
+  """Creates the `output_directory`.
+
+  If `output_directory` already exists, it recursively deletes all contents
+  inside the directory.
+
+  Also creates the parent & intermediate directories.
+
+  Args:
+    output_directory: Output directory.
+  """
+  if file_io.file_exists_v2(output_directory):
+    logging.info(
+        'Deleting existing directory for quantized model output: %s .',
+        output_directory,
+    )
+    file_io.delete_recursively_v2(output_directory)
+
+  file_io.recursive_create_dir_v2(output_directory)
+
+
+def _validate_signatures(
+    signature_def_map: _SignatureDefMap, exported_graph: ops.Graph
+) -> _SignatureDefMap:
+  """Validates if the tensor names in signatures are consistent with the graph.
+
+  This function checks if the input and output tensor names in the signatures
+  exist if the graph. The output tensor names might change during conversion,
+  we try to fix that with `_restore_output_tensor_names`. Besides, if there
+  are duplicated tensor names, they we will be prefixed with the signature name.
+  However, if that doesn't work the signatures can't be used with the converted
+  graph.
+
+  Args:
+    signature_def_map: the signatures to validate.
+    exported_graph: The PTQ-exported GraphDef.
+
+  Returns:
+    The signatures with tensor names prefixed with signature name if necessary.
+
+  Raises:
+    ValueError: Iff the signatures are not consistent with the graph.
+  """
+  for signature_key, signature_def in signature_def_map.items():
+    for tensor_info in signature_def.inputs.values():
+      try:
+        exported_graph.get_tensor_by_name(tensor_info.name)
+      except KeyError as exc:
+        try:
+          prefixed_name = signature_key + '_' + tensor_info.name
+          exported_graph.get_tensor_by_name(prefixed_name)
+          tensor_info.name = prefixed_name
+        except KeyError:
+          raise ValueError(
+              'Cannot find the input tensor with name %s in the graph.'
+              % tensor_info.name
+          ) from exc
+
+    for tensor_info in signature_def.outputs.values():
+      try:
+        exported_graph.get_tensor_by_name(tensor_info.name)
+      except KeyError as exc:
+        try:
+          prefixed_name = signature_key + '_' + tensor_info.name
+          exported_graph.get_tensor_by_name(prefixed_name)
+          tensor_info.name = prefixed_name
+        except KeyError:
+          raise ValueError(
+              'Cannot find the output tensor with name %s in the graph.'
+              % tensor_info.name
+          ) from exc
+
+  return signature_def_map
+
+
+def _find_op(
+    graph: ops.Graph, op_name: Optional[str]
+) -> Optional[ops.Operation]:
+  """Finds the operation with `op_name`.
+
+  Args:
+    graph: The graph to find from.
+    op_name: Name of the node.
+
+  Returns:
+    The operation that corresponds to `op_name`. Returns None iff op_name is an
+    empty string or None.
+
+  Raises:
+    ValueError: `op_name` is malformed.
+  """
+  if not op_name:
+    return None
+
+  init_op = graph.get_operation_by_name(op_name)
+  logging.debug('Op found in the graph: %s', op_name)
+
+  return init_op
+
+
+def _save_function_alias(
+    saved_model_dir: str,
+    tags: Collection[str],
+    function_aliases: Mapping[str, str],
+) -> None:
+  """Saves the function alias to the SavedModel.
+
+  SavedModelBuilder (TF1 saved model saver) does not support saving function
+  aliases, so this function loads the SavedModel proto and adds the
+  `function_aliases` field.
+
+  Args:
+    saved_model_dir: Path to the saved model directory.
+    tags: A collection of tags to specify the meta graph.
+    function_aliases: Function name -> function alias mapping.
+  """
+  loader = saved_model_loader.SavedModelLoader(saved_model_dir)
+  meta_graph_def = loader.get_meta_graph_def_from_tags(tags)
+
+  for function_name, function_alias in function_aliases.items():
+    meta_graph_def.meta_info_def.function_aliases[function_name] = (
+        function_alias
+    )
+
+  saved_model_proto_serialized = loader.saved_model.SerializeToString()
+
+  # TODO(b/266015731): Also update and set the SavedModel fingerprint.
+  path = file_io.join(
+      saved_model_dir, saved_model_constants.SAVED_MODEL_FILENAME_PB
+  )
+  file_io.atomic_write_string_to_file(path, saved_model_proto_serialized)
+
+
+def save_model_v1(
+    graph_def: graph_pb2.GraphDef,
+    output_dir: str,
+    signature_def_map: _SignatureDefMap,
+    tags: Collection[str],
+    init_op_name: Optional[str] = None,
+    saver_def: Optional[saver_pb2.SaverDef] = None,
+    checkpoint_dir: Optional[str] = None,
+    function_aliases: Optional[Mapping[str, str]] = None,
+    asset_file_defs: Sequence[meta_graph_pb2.AssetFileDef] = (),
+) -> None:
+  """Saves the model.
+
+  Saves the provided graph def as SavedModel.
+  Uses TF1 SavedModel semantics (i.e. no object graph).
+
+  Args:
+    graph_def: Graph to save.
+    output_dir: Output directory for the SavedModel.
+    signature_def_map: Mapping of signature def key -> SignatureDef.
+    tags: Tags for the meta graph def.
+    init_op_name: Name of the node for initialization.
+    saver_def: `saver_pb2.SaverDef` to create a `saver.Saver` from. The created
+      saver will be used to save and load variables. This may be `None` if no
+      variables exist in the graph.
+    checkpoint_dir: Path to checkpoint file where variable values are saved.
+    function_aliases: Function name -> function alias mapping.
+    asset_file_defs: `AssetFileDef`s that associates the asset files and the
+      name of the tensors to which the asset file names should be fed. The
+      caller should make sure the asset files exist in the output saved model
+      directory.
+
+  Raises:
+    ValueError iff the graph does not contain a valid signature or the file
+    prefix tensor is not found in the graph.
+  """
+  _create_empty_output_dir(output_dir)
+  v1_builder = builder.SavedModelBuilder(output_dir)
+
+  graph_def = _restore_output_tensor_names(graph_def)
+  with session.Session(graph=ops.Graph()) as sess:
+    importer.import_graph_def(graph_def, name='')
+
+    signature_def_map = _validate_signatures(
+        signature_def_map, ops.get_default_graph()
+    )
+
+    # Add `AssetFileDef`s to the collection so that correct values are fed to
+    # the tensors that accept asset file paths.
+    for asset_file_def in asset_file_defs:
+      asset_any_proto = Any()
+      asset_any_proto.Pack(asset_file_def)
+      ops.add_to_collection(
+          saved_model_constants.ASSETS_KEY,
+          asset_any_proto,
+      )
+
+    model_saver = None
+    # If `saver_def` is not None, it means there are variables in the graph.
+    if saver_def:
+      model_saver = saver.Saver(saver_def=saver_def)
+      logging.info('Saver created with SaverDef: %s', saver_def)
+
+      # Variables should be restored once before exporting as saved model
+      # because the variables are not initialized when the GraphDef was
+      # imported.
+      model_saver.restore(sess, checkpoint_dir)
+
+    v1_builder.add_meta_graph_and_variables(
+        sess,
+        tags,
+        signature_def_map=signature_def_map,
+        main_op=_find_op(sess.graph, op_name=init_op_name),
+        saver=model_saver,
+    )
+
+  v1_builder.save()
+
+  if function_aliases:
+    _save_function_alias(output_dir, tags, function_aliases)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/quantization_options_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/quantization_options_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7acc3afca5b47d19999b84c4310d735963279ad
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/quantization/tensorflow/quantization_options_pb2.py
@@ -0,0 +1,65 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\nKtensorflow/compiler/mlir/quantization/tensorflow/quantization_options.proto\x12\x17tensorflow.quantization\x1a&tensorflow/core/framework/tensor.proto\"\xed\x02\n\x12QuantizationMethod\x12O\n\rpreset_method\x18\x04 \x01(\x0e\x32\x38.tensorflow.quantization.QuantizationMethod.PresetMethod\x12X\n\x1cquantization_component_specs\x18\x03 \x03(\x0b\x32\x32.tensorflow.quantization.QuantizationComponentSpec\"\xa5\x01\n\x0cPresetMethod\x12\x16\n\x12METHOD_UNSPECIFIED\x10\x00\x12\x16\n\x12METHOD_NO_QUANTIZE\x10\x01\x12\x1c\n\x18METHOD_STATIC_RANGE_INT8\x10\x02\x12\x1d\n\x19METHOD_DYNAMIC_RANGE_INT8\x10\x03\x12(\n$METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8\x10\x04J\x04\x08\x01\x10\x03\"\xbe\x03\n\x19QuantizationComponentSpec\x12h\n\x16quantization_component\x18\x01 \x01(\x0e\x32H.tensorflow.quantization.QuantizationComponentSpec.QuantizationComponent\x12R\n\x0btensor_type\x18\x02 \x01(\x0e\x32=.tensorflow.quantization.QuantizationComponentSpec.TensorType\"v\n\x15QuantizationComponent\x12\x19\n\x15\x43OMPONENT_UNSPECIFIED\x10\x00\x12\x18\n\x14\x43OMPONENT_ACTIVATION\x10\x01\x12\x14\n\x10\x43OMPONENT_WEIGHT\x10\x02\x12\x12\n\x0e\x43OMPONENT_BIAS\x10\x03\"k\n\nTensorType\x12\x1a\n\x16TENSORTYPE_UNSPECIFIED\x10\x00\x12\x14\n\x10TENSORTYPE_INT_4\x10\x01\x12\x14\n\x10TENSORTYPE_INT_8\x10\x02\x12\x15\n\x11TENSORTYPE_INT_32\x10\x03\"\x87\x02\n\x18UnitWiseQuantizationSpec\x12P\n\x04unit\x18\x05 \x03(\x0b\x32\x42.tensorflow.quantization.UnitWiseQuantizationSpec.QuantizationUnit\x12H\n\x13quantization_method\x18\x06 \x01(\x0b\x32+.tensorflow.quantization.QuantizationMethod\x1aI\n\x10QuantizationUnit\x12\x0f\n\x07op_type\x18\x01 \x01(\t\x12\x11\n\tnode_name\x18\x02 \x01(\t\x12\x11\n\tfunc_name\x18\x03 \x01(\tJ\x04\x08\x01\x10\x05\"\xf9\x04\n\x12\x43\x61librationOptions\x12Y\n\x12\x63\x61libration_method\x18\x01 \x01(\x0e\x32=.tensorflow.quantization.CalibrationOptions.CalibrationMethod\x12\x61\n\x16\x63\x61libration_parameters\x18\x02 \x01(\x0b\x32\x41.tensorflow.quantization.CalibrationOptions.CalibrationParameters\x1a\x61\n\x15\x43\x61librationParameters\x12\x18\n\x10initial_num_bins\x18\x01 \x01(\x05\x12\x16\n\x0emin_percentile\x18\x02 \x01(\x02\x12\x16\n\x0emax_percentile\x18\x03 \x01(\x02\"\xc1\x02\n\x11\x43\x61librationMethod\x12\"\n\x1e\x43\x41LIBRATION_METHOD_UNSPECIFIED\x10\x00\x12\x1e\n\x1a\x43\x41LIBRATION_METHOD_MIN_MAX\x10\x01\x12&\n\"CALIBRATION_METHOD_AVERAGE_MIN_MAX\x10\x02\x12+\n\'CALIBRATION_METHOD_HISTOGRAM_PERCENTILE\x10\x03\x12/\n+CALIBRATION_METHOD_HISTOGRAM_MSE_BRUTEFORCE\x10\x04\x12\x32\n.CALIBRATION_METHOD_HISTOGRAM_MSE_MAX_FREQUENCY\x10\x05\x12.\n*CALIBRATION_METHOD_HISTOGRAM_MSE_SYMMETRIC\x10\x06\"\xd4\x01\n\x18RepresentativeDataSample\x12\x65\n\x13tensor_proto_inputs\x18\x02 \x03(\x0b\x32H.tensorflow.quantization.RepresentativeDataSample.TensorProtoInputsEntry\x1aQ\n\x16TensorProtoInputsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12&\n\x05value\x18\x02 \x01(\x0b\x32\x17.tensorflow.TensorProto:\x02\x38\x01\"I\n\x19RepresentativeDatasetFile\x12\x1c\n\x12tfrecord_file_path\x18\x01 \x01(\tH\x00\x42\x0e\n\x0c\x64\x61taset_file\"\x85\x02\n\x0f\x44\x65\x62uggerOptions\x12L\n\rdebugger_type\x18\x01 \x01(\x0e\x32\x35.tensorflow.quantization.DebuggerOptions.DebuggerType\x12#\n\x1bunquantized_dump_model_path\x18\x02 \x01(\t\x12\x14\n\x0clog_dir_path\x18\x03 \x01(\t\"i\n\x0c\x44\x65\x62uggerType\x12\x1d\n\x19\x44\x45\x42UGGER_TYPE_UNSPECIFIED\x10\x00\x12\x1d\n\x19\x44\x45\x42UGGER_TYPE_WHOLE_MODEL\x10\x01\x12\x1b\n\x17\x44\x45\x42UGGER_TYPE_PER_LAYER\x10\x02\"\xa5\x07\n\x13QuantizationOptions\x12H\n\x13quantization_method\x18\x01 \x01(\x0b\x32+.tensorflow.quantization.QuantizationMethod\x12.\n\x06op_set\x18\x02 \x01(\x0e\x32\x1e.tensorflow.quantization.OpSet\x12W\n\x1cunit_wise_quantization_specs\x18\x11 \x03(\x0b\x32\x31.tensorflow.quantization.UnitWiseQuantizationSpec\x12\x0c\n\x04tags\x18\x05 \x03(\t\x12\x16\n\x0esignature_keys\x18\x06 \x03(\t\x12i\n\x17representative_datasets\x18\x07 \x03(\x0b\x32H.tensorflow.quantization.QuantizationOptions.RepresentativeDatasetsEntry\x12$\n\x1cmin_num_elements_for_weights\x18\x08 \x01(\x03\x12!\n\x14\x66reeze_all_variables\x18\t \x01(\x08H\x00\x88\x01\x01\x12\'\n\x1f\x65nable_per_channel_quantization\x18\n \x01(\x08\x12 \n\x18\x65nable_two_input_tensors\x18\x0b \x01(\x08\x12-\n%experimental_enable_tpu_model_support\x18\x0c \x01(\x08\x12!\n\x19\x65nable_legacy_weight_only\x18\r \x01(\x08\x12$\n\x1c\x66orce_graph_mode_calibration\x18\x0e \x01(\x08\x12H\n\x13\x63\x61libration_options\x18\x0f \x01(\x0b\x32+.tensorflow.quantization.CalibrationOptions\x12\x42\n\x10\x64\x65\x62ugger_options\x18\x10 \x01(\x0b\x32(.tensorflow.quantization.DebuggerOptions\x1aq\n\x1bRepresentativeDatasetsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x41\n\x05value\x18\x02 \x01(\x0b\x32\x32.tensorflow.quantization.RepresentativeDatasetFile:\x02\x38\x01\x42\x17\n\x15_freeze_all_variablesJ\x04\x08\x03\x10\x04*V\n\x05OpSet\x12\x16\n\x12OP_SET_UNSPECIFIED\x10\x00\x12\x06\n\x02TF\x10\x01\x12\x07\n\x03XLA\x10\x02\x12\x15\n\x11UNIFORM_QUANTIZED\x10\x03\x12\r\n\tSTABLEHLO\x10\x04\x42\x03\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.mlir.quantization.tensorflow.quantization_options_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _REPRESENTATIVEDATASAMPLE_TENSORPROTOINPUTSENTRY._options = None
+  _REPRESENTATIVEDATASAMPLE_TENSORPROTOINPUTSENTRY._serialized_options = b'8\001'
+  _QUANTIZATIONOPTIONS_REPRESENTATIVEDATASETSENTRY._options = None
+  _QUANTIZATIONOPTIONS_REPRESENTATIVEDATASETSENTRY._serialized_options = b'8\001'
+  _OPSET._serialized_start=3353
+  _OPSET._serialized_end=3439
+  _QUANTIZATIONMETHOD._serialized_start=145
+  _QUANTIZATIONMETHOD._serialized_end=510
+  _QUANTIZATIONMETHOD_PRESETMETHOD._serialized_start=339
+  _QUANTIZATIONMETHOD_PRESETMETHOD._serialized_end=504
+  _QUANTIZATIONCOMPONENTSPEC._serialized_start=513
+  _QUANTIZATIONCOMPONENTSPEC._serialized_end=959
+  _QUANTIZATIONCOMPONENTSPEC_QUANTIZATIONCOMPONENT._serialized_start=732
+  _QUANTIZATIONCOMPONENTSPEC_QUANTIZATIONCOMPONENT._serialized_end=850
+  _QUANTIZATIONCOMPONENTSPEC_TENSORTYPE._serialized_start=852
+  _QUANTIZATIONCOMPONENTSPEC_TENSORTYPE._serialized_end=959
+  _UNITWISEQUANTIZATIONSPEC._serialized_start=962
+  _UNITWISEQUANTIZATIONSPEC._serialized_end=1225
+  _UNITWISEQUANTIZATIONSPEC_QUANTIZATIONUNIT._serialized_start=1146
+  _UNITWISEQUANTIZATIONSPEC_QUANTIZATIONUNIT._serialized_end=1219
+  _CALIBRATIONOPTIONS._serialized_start=1228
+  _CALIBRATIONOPTIONS._serialized_end=1861
+  _CALIBRATIONOPTIONS_CALIBRATIONPARAMETERS._serialized_start=1440
+  _CALIBRATIONOPTIONS_CALIBRATIONPARAMETERS._serialized_end=1537
+  _CALIBRATIONOPTIONS_CALIBRATIONMETHOD._serialized_start=1540
+  _CALIBRATIONOPTIONS_CALIBRATIONMETHOD._serialized_end=1861
+  _REPRESENTATIVEDATASAMPLE._serialized_start=1864
+  _REPRESENTATIVEDATASAMPLE._serialized_end=2076
+  _REPRESENTATIVEDATASAMPLE_TENSORPROTOINPUTSENTRY._serialized_start=1995
+  _REPRESENTATIVEDATASAMPLE_TENSORPROTOINPUTSENTRY._serialized_end=2076
+  _REPRESENTATIVEDATASETFILE._serialized_start=2078
+  _REPRESENTATIVEDATASETFILE._serialized_end=2151
+  _DEBUGGEROPTIONS._serialized_start=2154
+  _DEBUGGEROPTIONS._serialized_end=2415
+  _DEBUGGEROPTIONS_DEBUGGERTYPE._serialized_start=2310
+  _DEBUGGEROPTIONS_DEBUGGERTYPE._serialized_end=2415
+  _QUANTIZATIONOPTIONS._serialized_start=2418
+  _QUANTIZATIONOPTIONS._serialized_end=3351
+  _QUANTIZATIONOPTIONS_REPRESENTATIVEDATASETSENTRY._serialized_start=3207
+  _QUANTIZATIONOPTIONS_REPRESENTATIVEDATASETSENTRY._serialized_end=3320
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/stablehlo/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/stablehlo/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/stablehlo/stablehlo.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/stablehlo/stablehlo.py
new file mode 100644
index 0000000000000000000000000000000000000000..64c3f1b7be30734d5066a0bcc3f372edaf39dad9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/stablehlo/stablehlo.py
@@ -0,0 +1,26 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""StableHLO Portable Python APIs.
+
+This setup only exports the the StableHLO Portable C++ APIs, which have
+signatures that do not rely on MLIR classes.
+
+Exporting all of MLIR Python bindings to TF OSS has high maintenance
+implications, especially given the frequency that TF updates the revision of
+LLVM used.
+"""
+
+# pylint: disable=wildcard-import
+from .stablehlo_extension import *
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/tensorflow/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/tensorflow/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/tensorflow/gen_mlir_passthrough_op.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/tensorflow/gen_mlir_passthrough_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..63f02ed16b5af0796dfc14c306e532af941fba3a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/mlir/tensorflow/gen_mlir_passthrough_op.py
@@ -0,0 +1,127 @@
+"""Python wrappers around TensorFlow ops.
+
+This file is MACHINE GENERATED! Do not edit.
+"""
+
+import collections
+
+from tensorflow.python import pywrap_tfe as pywrap_tfe
+from tensorflow.python.eager import context as _context
+from tensorflow.python.eager import core as _core
+from tensorflow.python.eager import execute as _execute
+from tensorflow.python.framework import dtypes as _dtypes
+from tensorflow.security.fuzzing.py import annotation_types as _atypes
+
+from tensorflow.python.framework import op_def_registry as _op_def_registry
+from tensorflow.python.framework import ops as _ops
+from tensorflow.python.framework import op_def_library as _op_def_library
+from tensorflow.python.util.deprecation import deprecated_endpoints
+from tensorflow.python.util import dispatch as _dispatch
+from tensorflow.python.util.tf_export import tf_export
+
+from typing import TypeVar, List, Any
+from typing_extensions import Annotated
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('mlir_passthrough_op')
+def mlir_passthrough_op(inputs, mlir_module: str, Toutputs, name=None):
+  r"""TODO: add doc.
+
+  Args:
+    inputs: A list of `Tensor` objects.
+    mlir_module: A `string`.
+    Toutputs: A list of `tf.DTypes`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects of type `Toutputs`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "MlirPassthroughOp", name, inputs, "mlir_module", mlir_module,
+        "Toutputs", Toutputs)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_mlir_passthrough_op(
+          (inputs, mlir_module, Toutputs, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return mlir_passthrough_op_eager_fallback(
+          inputs, mlir_module=mlir_module, Toutputs=Toutputs, name=name,
+          ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            mlir_passthrough_op, (), dict(inputs=inputs,
+                                          mlir_module=mlir_module,
+                                          Toutputs=Toutputs, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_mlir_passthrough_op(
+        (inputs, mlir_module, Toutputs, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  mlir_module = _execute.make_str(mlir_module, "mlir_module")
+  if not isinstance(Toutputs, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Toutputs' argument to "
+        "'mlir_passthrough_op' Op, not %r." % Toutputs)
+  Toutputs = [_execute.make_type(_t, "Toutputs") for _t in Toutputs]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "MlirPassthroughOp", inputs=inputs, mlir_module=mlir_module,
+                             Toutputs=Toutputs, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          mlir_passthrough_op, (), dict(inputs=inputs,
+                                        mlir_module=mlir_module,
+                                        Toutputs=Toutputs, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("mlir_module", _op.get_attr("mlir_module"), "Tinputs",
+              _op.get_attr("Tinputs"), "Toutputs", _op.get_attr("Toutputs"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "MlirPassthroughOp", _inputs_flat, _attrs, _result)
+  return _result
+
+MlirPassthroughOp = tf_export("raw_ops.MlirPassthroughOp")(_ops.to_raw_op(mlir_passthrough_op))
+_dispatcher_for_mlir_passthrough_op = mlir_passthrough_op._tf_type_based_dispatcher.Dispatch
+
+
+def mlir_passthrough_op_eager_fallback(inputs, mlir_module: str, Toutputs, name, ctx):
+  mlir_module = _execute.make_str(mlir_module, "mlir_module")
+  if not isinstance(Toutputs, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Toutputs' argument to "
+        "'mlir_passthrough_op' Op, not %r." % Toutputs)
+  Toutputs = [_execute.make_type(_t, "Toutputs") for _t in Toutputs]
+  _attr_Tinputs, inputs = _execute.convert_to_mixed_eager_tensors(inputs, ctx)
+  _inputs_flat = list(inputs)
+  _attrs = ("mlir_module", mlir_module, "Tinputs", _attr_Tinputs, "Toutputs",
+  Toutputs)
+  _result = _execute.execute(b"MlirPassthroughOp", len(Toutputs),
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "MlirPassthroughOp", _inputs_flat, _attrs, _result)
+  return _result
+
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2tensorrt/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2tensorrt/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2tensorrt/_pywrap_py_utils.pyi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2tensorrt/_pywrap_py_utils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1ef7abbd7d14b6cc491ad6b7f07dc371ae019141
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2tensorrt/_pywrap_py_utils.pyi
@@ -0,0 +1,19 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+def get_linked_tensorrt_version() -> tuple[int,int,int]: ...
+def get_loaded_tensorrt_version() -> tuple[int,int,int]: ...
+def get_registered_op_converters() -> list[str]: ...
+def is_tensorrt_enabled() -> bool: ...
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2tensorrt/ops/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2tensorrt/ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2tensorrt/ops/gen_trt_ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2tensorrt/ops/gen_trt_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..a792cd613ef2d65a9878a6b29344d9fd9dd11bbe
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2tensorrt/ops/gen_trt_ops.py
@@ -0,0 +1,713 @@
+"""Python wrappers around TensorFlow ops.
+
+This file is MACHINE GENERATED! Do not edit.
+"""
+
+import collections
+
+from tensorflow.python import pywrap_tfe as pywrap_tfe
+from tensorflow.python.eager import context as _context
+from tensorflow.python.eager import core as _core
+from tensorflow.python.eager import execute as _execute
+from tensorflow.python.framework import dtypes as _dtypes
+from tensorflow.security.fuzzing.py import annotation_types as _atypes
+
+from tensorflow.python.framework import op_def_registry as _op_def_registry
+from tensorflow.python.framework import ops as _ops
+from tensorflow.python.framework import op_def_library as _op_def_library
+from tensorflow.python.util.deprecation import deprecated_endpoints
+from tensorflow.python.util import dispatch as _dispatch
+from tensorflow.python.util.tf_export import tf_export
+
+from typing import TypeVar, List, Any
+from typing_extensions import Annotated
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('create_trt_resource_handle')
+def create_trt_resource_handle(resource_name: str, name=None) -> Annotated[Any, _atypes.Resource]:
+  r"""TODO: add doc.
+
+  Args:
+    resource_name: A `string`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` of type `resource`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "CreateTRTResourceHandle", name, "resource_name", resource_name)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_create_trt_resource_handle(
+          (resource_name, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return create_trt_resource_handle_eager_fallback(
+          resource_name=resource_name, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            create_trt_resource_handle, (), dict(resource_name=resource_name,
+                                                 name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_create_trt_resource_handle(
+        (resource_name, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  resource_name = _execute.make_str(resource_name, "resource_name")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "CreateTRTResourceHandle", resource_name=resource_name, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          create_trt_resource_handle, (), dict(resource_name=resource_name,
+                                               name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("resource_name", _op.get_attr("resource_name"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "CreateTRTResourceHandle", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+CreateTRTResourceHandle = tf_export("raw_ops.CreateTRTResourceHandle")(_ops.to_raw_op(create_trt_resource_handle))
+_dispatcher_for_create_trt_resource_handle = create_trt_resource_handle._tf_type_based_dispatcher.Dispatch
+
+
+def create_trt_resource_handle_eager_fallback(resource_name: str, name, ctx) -> Annotated[Any, _atypes.Resource]:
+  resource_name = _execute.make_str(resource_name, "resource_name")
+  _inputs_flat = []
+  _attrs = ("resource_name", resource_name)
+  _result = _execute.execute(b"CreateTRTResourceHandle", 1,
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "CreateTRTResourceHandle", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('get_calibration_data_op')
+def get_calibration_data_op(resource_name: Annotated[Any, _atypes.String], name=None) -> Annotated[Any, _atypes.String]:
+  r"""Returns calibration data for the given resource name
+
+  Args:
+    resource_name: A `Tensor` of type `string`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` of type `string`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "GetCalibrationDataOp", name, resource_name)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_get_calibration_data_op(
+          (resource_name, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return get_calibration_data_op_eager_fallback(
+          resource_name, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            get_calibration_data_op, (), dict(resource_name=resource_name,
+                                              name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_get_calibration_data_op(
+        (resource_name, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "GetCalibrationDataOp", resource_name=resource_name, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          get_calibration_data_op, (), dict(resource_name=resource_name,
+                                            name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ()
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "GetCalibrationDataOp", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+GetCalibrationDataOp = tf_export("raw_ops.GetCalibrationDataOp")(_ops.to_raw_op(get_calibration_data_op))
+_dispatcher_for_get_calibration_data_op = get_calibration_data_op._tf_type_based_dispatcher.Dispatch
+
+
+def get_calibration_data_op_eager_fallback(resource_name: Annotated[Any, _atypes.String], name, ctx) -> Annotated[Any, _atypes.String]:
+  resource_name = _ops.convert_to_tensor(resource_name, _dtypes.string)
+  _inputs_flat = [resource_name]
+  _attrs = None
+  _result = _execute.execute(b"GetCalibrationDataOp", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "GetCalibrationDataOp", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('initialize_trt_resource')
+def initialize_trt_resource(resource_handle: Annotated[Any, _atypes.Resource], filename: Annotated[Any, _atypes.String], max_cached_engines_count:int=1, name=None):
+  r"""TODO: add doc.
+
+  Args:
+    resource_handle: A `Tensor` of type `resource`.
+    filename: A `Tensor` of type `string`.
+    max_cached_engines_count: An optional `int`. Defaults to `1`.
+    name: A name for the operation (optional).
+
+  Returns:
+    The created Operation.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "InitializeTRTResource", name, resource_handle, filename,
+        "max_cached_engines_count", max_cached_engines_count)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_initialize_trt_resource(
+          (resource_handle, filename, max_cached_engines_count, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return initialize_trt_resource_eager_fallback(
+          resource_handle, filename,
+          max_cached_engines_count=max_cached_engines_count, name=name,
+          ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            initialize_trt_resource, (), dict(resource_handle=resource_handle,
+                                              filename=filename,
+                                              max_cached_engines_count=max_cached_engines_count,
+                                              name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_initialize_trt_resource(
+        (resource_handle, filename, max_cached_engines_count, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if max_cached_engines_count is None:
+    max_cached_engines_count = 1
+  max_cached_engines_count = _execute.make_int(max_cached_engines_count, "max_cached_engines_count")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "InitializeTRTResource", resource_handle=resource_handle,
+                                 filename=filename,
+                                 max_cached_engines_count=max_cached_engines_count,
+                                 name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          initialize_trt_resource, (), dict(resource_handle=resource_handle,
+                                            filename=filename,
+                                            max_cached_engines_count=max_cached_engines_count,
+                                            name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  return _op
+InitializeTRTResource = tf_export("raw_ops.InitializeTRTResource")(_ops.to_raw_op(initialize_trt_resource))
+_dispatcher_for_initialize_trt_resource = initialize_trt_resource._tf_type_based_dispatcher.Dispatch
+
+
+def initialize_trt_resource_eager_fallback(resource_handle: Annotated[Any, _atypes.Resource], filename: Annotated[Any, _atypes.String], max_cached_engines_count: int, name, ctx):
+  if max_cached_engines_count is None:
+    max_cached_engines_count = 1
+  max_cached_engines_count = _execute.make_int(max_cached_engines_count, "max_cached_engines_count")
+  resource_handle = _ops.convert_to_tensor(resource_handle, _dtypes.resource)
+  filename = _ops.convert_to_tensor(filename, _dtypes.string)
+  _inputs_flat = [resource_handle, filename]
+  _attrs = ("max_cached_engines_count", max_cached_engines_count)
+  _result = _execute.execute(b"InitializeTRTResource", 0, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  _result = None
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('serialize_trt_resource')
+def serialize_trt_resource(resource_name: Annotated[Any, _atypes.String], filename: Annotated[Any, _atypes.String], delete_resource:bool=False, save_gpu_specific_engines:bool=True, name=None):
+  r"""TODO: add doc.
+
+  Args:
+    resource_name: A `Tensor` of type `string`.
+    filename: A `Tensor` of type `string`.
+    delete_resource: An optional `bool`. Defaults to `False`.
+    save_gpu_specific_engines: An optional `bool`. Defaults to `True`.
+    name: A name for the operation (optional).
+
+  Returns:
+    The created Operation.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "SerializeTRTResource", name, resource_name, filename,
+        "delete_resource", delete_resource, "save_gpu_specific_engines",
+        save_gpu_specific_engines)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_serialize_trt_resource(
+          (resource_name, filename, delete_resource,
+          save_gpu_specific_engines, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return serialize_trt_resource_eager_fallback(
+          resource_name, filename, delete_resource=delete_resource,
+          save_gpu_specific_engines=save_gpu_specific_engines, name=name,
+          ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            serialize_trt_resource, (), dict(resource_name=resource_name,
+                                             filename=filename,
+                                             delete_resource=delete_resource,
+                                             save_gpu_specific_engines=save_gpu_specific_engines,
+                                             name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_serialize_trt_resource(
+        (resource_name, filename, delete_resource, save_gpu_specific_engines,
+        name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if delete_resource is None:
+    delete_resource = False
+  delete_resource = _execute.make_bool(delete_resource, "delete_resource")
+  if save_gpu_specific_engines is None:
+    save_gpu_specific_engines = True
+  save_gpu_specific_engines = _execute.make_bool(save_gpu_specific_engines, "save_gpu_specific_engines")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "SerializeTRTResource", resource_name=resource_name,
+                                filename=filename,
+                                delete_resource=delete_resource,
+                                save_gpu_specific_engines=save_gpu_specific_engines,
+                                name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          serialize_trt_resource, (), dict(resource_name=resource_name,
+                                           filename=filename,
+                                           delete_resource=delete_resource,
+                                           save_gpu_specific_engines=save_gpu_specific_engines,
+                                           name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  return _op
+SerializeTRTResource = tf_export("raw_ops.SerializeTRTResource")(_ops.to_raw_op(serialize_trt_resource))
+_dispatcher_for_serialize_trt_resource = serialize_trt_resource._tf_type_based_dispatcher.Dispatch
+
+
+def serialize_trt_resource_eager_fallback(resource_name: Annotated[Any, _atypes.String], filename: Annotated[Any, _atypes.String], delete_resource: bool, save_gpu_specific_engines: bool, name, ctx):
+  if delete_resource is None:
+    delete_resource = False
+  delete_resource = _execute.make_bool(delete_resource, "delete_resource")
+  if save_gpu_specific_engines is None:
+    save_gpu_specific_engines = True
+  save_gpu_specific_engines = _execute.make_bool(save_gpu_specific_engines, "save_gpu_specific_engines")
+  resource_name = _ops.convert_to_tensor(resource_name, _dtypes.string)
+  filename = _ops.convert_to_tensor(filename, _dtypes.string)
+  _inputs_flat = [resource_name, filename]
+  _attrs = ("delete_resource", delete_resource, "save_gpu_specific_engines",
+  save_gpu_specific_engines)
+  _result = _execute.execute(b"SerializeTRTResource", 0, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  _result = None
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('trt_engine_op')
+def trt_engine_op(in_tensor, serialized_segment: str, OutT, workspace_size_bytes: int, precision_mode: str, segment_func="", input_shapes=[], output_shapes=[], max_cached_engines_count:int=1, max_batch_size:int=1, calibration_data:str="", use_calibration:bool=True, segment_funcdef_name:str="", cached_engine_batches=[], fixed_input_size:bool=True, static_engine:bool=True, profile_strategy:str="", use_explicit_precision:bool=False, name=None):
+  r"""TODO: add doc.
+
+  Args:
+    in_tensor: A list of `Tensor` objects with types from: `bool`, `int8`, `half`, `float32`, `int32`, `resource`.
+    serialized_segment: A `string`.
+    OutT: A list of `tf.DTypes` from: `tf.bool, tf.int8, tf.half, tf.float32, tf.int32` that has length `>= 1`.
+    workspace_size_bytes: An `int`.
+    precision_mode: A `string` from: `"FP32", "FP16", "INT8"`.
+    segment_func: An optional function decorated with @Defun. Defaults to `""`.
+    input_shapes: An optional list of shapes (each a `tf.TensorShape` or list of `ints`). Defaults to `[]`.
+    output_shapes: An optional list of shapes (each a `tf.TensorShape` or list of `ints`). Defaults to `[]`.
+    max_cached_engines_count: An optional `int`. Defaults to `1`.
+    max_batch_size: An optional `int`. Defaults to `1`.
+    calibration_data: An optional `string`. Defaults to `""`.
+    use_calibration: An optional `bool`. Defaults to `True`.
+    segment_funcdef_name: An optional `string`. Defaults to `""`.
+    cached_engine_batches: An optional list of `ints`. Defaults to `[]`.
+    fixed_input_size: An optional `bool`. Defaults to `True`.
+    static_engine: An optional `bool`. Defaults to `True`.
+    profile_strategy: An optional `string`. Defaults to `""`.
+    use_explicit_precision: An optional `bool`. Defaults to `False`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects of type `OutT`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "TRTEngineOp", name, in_tensor, "serialized_segment",
+        serialized_segment, "segment_func", segment_func, "OutT", OutT,
+        "input_shapes", input_shapes, "output_shapes", output_shapes,
+        "max_cached_engines_count", max_cached_engines_count,
+        "max_batch_size", max_batch_size, "workspace_size_bytes",
+        workspace_size_bytes, "precision_mode", precision_mode,
+        "calibration_data", calibration_data, "use_calibration",
+        use_calibration, "segment_funcdef_name", segment_funcdef_name,
+        "cached_engine_batches", cached_engine_batches, "fixed_input_size",
+        fixed_input_size, "static_engine", static_engine, "profile_strategy",
+        profile_strategy, "use_explicit_precision", use_explicit_precision)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_trt_engine_op(
+          (in_tensor, serialized_segment, OutT, workspace_size_bytes,
+          precision_mode, segment_func, input_shapes, output_shapes,
+          max_cached_engines_count, max_batch_size, calibration_data,
+          use_calibration, segment_funcdef_name, cached_engine_batches,
+          fixed_input_size, static_engine, profile_strategy,
+          use_explicit_precision, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return trt_engine_op_eager_fallback(
+          in_tensor, serialized_segment=serialized_segment,
+          segment_func=segment_func, OutT=OutT, input_shapes=input_shapes,
+          output_shapes=output_shapes,
+          max_cached_engines_count=max_cached_engines_count,
+          max_batch_size=max_batch_size,
+          workspace_size_bytes=workspace_size_bytes,
+          precision_mode=precision_mode, calibration_data=calibration_data,
+          use_calibration=use_calibration,
+          segment_funcdef_name=segment_funcdef_name,
+          cached_engine_batches=cached_engine_batches,
+          fixed_input_size=fixed_input_size, static_engine=static_engine,
+          profile_strategy=profile_strategy,
+          use_explicit_precision=use_explicit_precision, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            trt_engine_op, (), dict(in_tensor=in_tensor,
+                                    serialized_segment=serialized_segment,
+                                    OutT=OutT,
+                                    workspace_size_bytes=workspace_size_bytes,
+                                    precision_mode=precision_mode,
+                                    segment_func=segment_func,
+                                    input_shapes=input_shapes,
+                                    output_shapes=output_shapes,
+                                    max_cached_engines_count=max_cached_engines_count,
+                                    max_batch_size=max_batch_size,
+                                    calibration_data=calibration_data,
+                                    use_calibration=use_calibration,
+                                    segment_funcdef_name=segment_funcdef_name,
+                                    cached_engine_batches=cached_engine_batches,
+                                    fixed_input_size=fixed_input_size,
+                                    static_engine=static_engine,
+                                    profile_strategy=profile_strategy,
+                                    use_explicit_precision=use_explicit_precision,
+                                    name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_trt_engine_op(
+        (in_tensor, serialized_segment, OutT, workspace_size_bytes,
+        precision_mode, segment_func, input_shapes, output_shapes,
+        max_cached_engines_count, max_batch_size, calibration_data,
+        use_calibration, segment_funcdef_name, cached_engine_batches,
+        fixed_input_size, static_engine, profile_strategy,
+        use_explicit_precision, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  serialized_segment = _execute.make_str(serialized_segment, "serialized_segment")
+  if not isinstance(OutT, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'OutT' argument to "
+        "'trt_engine_op' Op, not %r." % OutT)
+  OutT = [_execute.make_type(_t, "OutT") for _t in OutT]
+  workspace_size_bytes = _execute.make_int(workspace_size_bytes, "workspace_size_bytes")
+  precision_mode = _execute.make_str(precision_mode, "precision_mode")
+  if segment_func is None:
+    segment_func = ""
+  if input_shapes is None:
+    input_shapes = []
+  if not isinstance(input_shapes, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'input_shapes' argument to "
+        "'trt_engine_op' Op, not %r." % input_shapes)
+  input_shapes = [_execute.make_shape(_s, "input_shapes") for _s in input_shapes]
+  if output_shapes is None:
+    output_shapes = []
+  if not isinstance(output_shapes, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'output_shapes' argument to "
+        "'trt_engine_op' Op, not %r." % output_shapes)
+  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
+  if max_cached_engines_count is None:
+    max_cached_engines_count = 1
+  max_cached_engines_count = _execute.make_int(max_cached_engines_count, "max_cached_engines_count")
+  if max_batch_size is None:
+    max_batch_size = 1
+  max_batch_size = _execute.make_int(max_batch_size, "max_batch_size")
+  if calibration_data is None:
+    calibration_data = ""
+  calibration_data = _execute.make_str(calibration_data, "calibration_data")
+  if use_calibration is None:
+    use_calibration = True
+  use_calibration = _execute.make_bool(use_calibration, "use_calibration")
+  if segment_funcdef_name is None:
+    segment_funcdef_name = ""
+  segment_funcdef_name = _execute.make_str(segment_funcdef_name, "segment_funcdef_name")
+  if cached_engine_batches is None:
+    cached_engine_batches = []
+  if not isinstance(cached_engine_batches, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'cached_engine_batches' argument to "
+        "'trt_engine_op' Op, not %r." % cached_engine_batches)
+  cached_engine_batches = [_execute.make_int(_i, "cached_engine_batches") for _i in cached_engine_batches]
+  if fixed_input_size is None:
+    fixed_input_size = True
+  fixed_input_size = _execute.make_bool(fixed_input_size, "fixed_input_size")
+  if static_engine is None:
+    static_engine = True
+  static_engine = _execute.make_bool(static_engine, "static_engine")
+  if profile_strategy is None:
+    profile_strategy = ""
+  profile_strategy = _execute.make_str(profile_strategy, "profile_strategy")
+  if use_explicit_precision is None:
+    use_explicit_precision = False
+  use_explicit_precision = _execute.make_bool(use_explicit_precision, "use_explicit_precision")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "TRTEngineOp", in_tensor=in_tensor,
+                       serialized_segment=serialized_segment, OutT=OutT,
+                       workspace_size_bytes=workspace_size_bytes,
+                       precision_mode=precision_mode,
+                       segment_func=segment_func, input_shapes=input_shapes,
+                       output_shapes=output_shapes,
+                       max_cached_engines_count=max_cached_engines_count,
+                       max_batch_size=max_batch_size,
+                       calibration_data=calibration_data,
+                       use_calibration=use_calibration,
+                       segment_funcdef_name=segment_funcdef_name,
+                       cached_engine_batches=cached_engine_batches,
+                       fixed_input_size=fixed_input_size,
+                       static_engine=static_engine,
+                       profile_strategy=profile_strategy,
+                       use_explicit_precision=use_explicit_precision,
+                       name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          trt_engine_op, (), dict(in_tensor=in_tensor,
+                                  serialized_segment=serialized_segment,
+                                  OutT=OutT,
+                                  workspace_size_bytes=workspace_size_bytes,
+                                  precision_mode=precision_mode,
+                                  segment_func=segment_func,
+                                  input_shapes=input_shapes,
+                                  output_shapes=output_shapes,
+                                  max_cached_engines_count=max_cached_engines_count,
+                                  max_batch_size=max_batch_size,
+                                  calibration_data=calibration_data,
+                                  use_calibration=use_calibration,
+                                  segment_funcdef_name=segment_funcdef_name,
+                                  cached_engine_batches=cached_engine_batches,
+                                  fixed_input_size=fixed_input_size,
+                                  static_engine=static_engine,
+                                  profile_strategy=profile_strategy,
+                                  use_explicit_precision=use_explicit_precision,
+                                  name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("serialized_segment", _op.get_attr("serialized_segment"),
+              "segment_func", _op.get_attr("segment_func"), "InT",
+              _op.get_attr("InT"), "OutT", _op.get_attr("OutT"),
+              "input_shapes", _op.get_attr("input_shapes"), "output_shapes",
+              _op.get_attr("output_shapes"), "max_cached_engines_count",
+              _op._get_attr_int("max_cached_engines_count"), "max_batch_size",
+              _op._get_attr_int("max_batch_size"), "workspace_size_bytes",
+              _op._get_attr_int("workspace_size_bytes"), "precision_mode",
+              _op.get_attr("precision_mode"), "calibration_data",
+              _op.get_attr("calibration_data"), "use_calibration",
+              _op._get_attr_bool("use_calibration"), "segment_funcdef_name",
+              _op.get_attr("segment_funcdef_name"), "cached_engine_batches",
+              _op.get_attr("cached_engine_batches"), "fixed_input_size",
+              _op._get_attr_bool("fixed_input_size"), "static_engine",
+              _op._get_attr_bool("static_engine"), "profile_strategy",
+              _op.get_attr("profile_strategy"), "use_explicit_precision",
+              _op._get_attr_bool("use_explicit_precision"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "TRTEngineOp", _inputs_flat, _attrs, _result)
+  return _result
+
+TRTEngineOp = tf_export("raw_ops.TRTEngineOp")(_ops.to_raw_op(trt_engine_op))
+_dispatcher_for_trt_engine_op = trt_engine_op._tf_type_based_dispatcher.Dispatch
+
+
+def trt_engine_op_eager_fallback(in_tensor, serialized_segment: str, OutT, workspace_size_bytes: int, precision_mode: str, segment_func, input_shapes, output_shapes, max_cached_engines_count: int, max_batch_size: int, calibration_data: str, use_calibration: bool, segment_funcdef_name: str, cached_engine_batches, fixed_input_size: bool, static_engine: bool, profile_strategy: str, use_explicit_precision: bool, name, ctx):
+  serialized_segment = _execute.make_str(serialized_segment, "serialized_segment")
+  if not isinstance(OutT, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'OutT' argument to "
+        "'trt_engine_op' Op, not %r." % OutT)
+  OutT = [_execute.make_type(_t, "OutT") for _t in OutT]
+  workspace_size_bytes = _execute.make_int(workspace_size_bytes, "workspace_size_bytes")
+  precision_mode = _execute.make_str(precision_mode, "precision_mode")
+  if segment_func is None:
+    segment_func = ""
+  if input_shapes is None:
+    input_shapes = []
+  if not isinstance(input_shapes, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'input_shapes' argument to "
+        "'trt_engine_op' Op, not %r." % input_shapes)
+  input_shapes = [_execute.make_shape(_s, "input_shapes") for _s in input_shapes]
+  if output_shapes is None:
+    output_shapes = []
+  if not isinstance(output_shapes, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'output_shapes' argument to "
+        "'trt_engine_op' Op, not %r." % output_shapes)
+  output_shapes = [_execute.make_shape(_s, "output_shapes") for _s in output_shapes]
+  if max_cached_engines_count is None:
+    max_cached_engines_count = 1
+  max_cached_engines_count = _execute.make_int(max_cached_engines_count, "max_cached_engines_count")
+  if max_batch_size is None:
+    max_batch_size = 1
+  max_batch_size = _execute.make_int(max_batch_size, "max_batch_size")
+  if calibration_data is None:
+    calibration_data = ""
+  calibration_data = _execute.make_str(calibration_data, "calibration_data")
+  if use_calibration is None:
+    use_calibration = True
+  use_calibration = _execute.make_bool(use_calibration, "use_calibration")
+  if segment_funcdef_name is None:
+    segment_funcdef_name = ""
+  segment_funcdef_name = _execute.make_str(segment_funcdef_name, "segment_funcdef_name")
+  if cached_engine_batches is None:
+    cached_engine_batches = []
+  if not isinstance(cached_engine_batches, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'cached_engine_batches' argument to "
+        "'trt_engine_op' Op, not %r." % cached_engine_batches)
+  cached_engine_batches = [_execute.make_int(_i, "cached_engine_batches") for _i in cached_engine_batches]
+  if fixed_input_size is None:
+    fixed_input_size = True
+  fixed_input_size = _execute.make_bool(fixed_input_size, "fixed_input_size")
+  if static_engine is None:
+    static_engine = True
+  static_engine = _execute.make_bool(static_engine, "static_engine")
+  if profile_strategy is None:
+    profile_strategy = ""
+  profile_strategy = _execute.make_str(profile_strategy, "profile_strategy")
+  if use_explicit_precision is None:
+    use_explicit_precision = False
+  use_explicit_precision = _execute.make_bool(use_explicit_precision, "use_explicit_precision")
+  _attr_InT, in_tensor = _execute.convert_to_mixed_eager_tensors(in_tensor, ctx)
+  _inputs_flat = list(in_tensor)
+  _attrs = ("serialized_segment", serialized_segment, "segment_func",
+  segment_func, "InT", _attr_InT, "OutT", OutT, "input_shapes", input_shapes,
+  "output_shapes", output_shapes, "max_cached_engines_count",
+  max_cached_engines_count, "max_batch_size", max_batch_size,
+  "workspace_size_bytes", workspace_size_bytes, "precision_mode",
+  precision_mode, "calibration_data", calibration_data, "use_calibration",
+  use_calibration, "segment_funcdef_name", segment_funcdef_name,
+  "cached_engine_batches", cached_engine_batches, "fixed_input_size",
+  fixed_input_size, "static_engine", static_engine, "profile_strategy",
+  profile_strategy, "use_explicit_precision", use_explicit_precision)
+  _result = _execute.execute(b"TRTEngineOp", len(OutT), inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "TRTEngineOp", _inputs_flat, _attrs, _result)
+  return _result
+
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/ops/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/ops/gen_xla_ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/ops/gen_xla_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd3079cf0b1733c91f5996d9732faf03e843a5ac
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/ops/gen_xla_ops.py
@@ -0,0 +1,4862 @@
+"""Python wrappers around TensorFlow ops.
+
+This file is MACHINE GENERATED! Do not edit.
+"""
+
+import collections
+
+from tensorflow.python import pywrap_tfe as pywrap_tfe
+from tensorflow.python.eager import context as _context
+from tensorflow.python.eager import core as _core
+from tensorflow.python.eager import execute as _execute
+from tensorflow.python.framework import dtypes as _dtypes
+from tensorflow.security.fuzzing.py import annotation_types as _atypes
+
+from tensorflow.python.framework import op_def_registry as _op_def_registry
+from tensorflow.python.framework import ops as _ops
+from tensorflow.python.framework import op_def_library as _op_def_library
+from tensorflow.python.util.deprecation import deprecated_endpoints
+from tensorflow.python.util import dispatch as _dispatch
+from tensorflow.python.util.tf_export import tf_export
+
+from typing import TypeVar, List, Any
+from typing_extensions import Annotated
+
+TV_XlaAllReduce_T = TypeVar("TV_XlaAllReduce_T", _atypes.BFloat16, _atypes.Float32, _atypes.Half, _atypes.Int32, _atypes.UInt32)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_all_reduce')
+def xla_all_reduce(input: Annotated[Any, TV_XlaAllReduce_T], group_assignment: Annotated[Any, _atypes.Int32], reduce_op: str, mode: str, name=None) -> Annotated[Any, TV_XlaAllReduce_T]:
+  r"""Wraps the XLA AllReduce operator
+
+    documented at https://www.tensorflow.org/xla/operation_semantics#allreduce.
+
+  Args:
+    input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `int32`, `uint32`.
+      Array or a non-empty tuple of arrays to reduce across replicas.
+    group_assignment: A `Tensor` of type `int32`.
+      Groups between which the reductions are performed.
+    reduce_op: A `string` from: `"Min", "Max", "Mul", "Add", "Mean"`.
+      Reduction computation.
+    mode: A `string` from: `"CrossReplica", "CrossReplicaAndPartition"`.
+      group mode.
+      CrossReplica: group_assignment contains replica_id. Each group contains the
+        replicas for the current partition.
+      CrossReplicaAndPartition: group_assignment contains replica_id. Each group
+        contains the replicas for all partitions.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaAllReduce", name, input, group_assignment, "reduce_op",
+        reduce_op, "mode", mode)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_all_reduce(
+          (input, group_assignment, reduce_op, mode, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_all_reduce_eager_fallback(
+          input, group_assignment, reduce_op=reduce_op, mode=mode, name=name,
+          ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_all_reduce, (), dict(input=input,
+                                     group_assignment=group_assignment,
+                                     reduce_op=reduce_op, mode=mode,
+                                     name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_all_reduce(
+        (input, group_assignment, reduce_op, mode, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  reduce_op = _execute.make_str(reduce_op, "reduce_op")
+  mode = _execute.make_str(mode, "mode")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaAllReduce", input=input, group_assignment=group_assignment,
+                        reduce_op=reduce_op, mode=mode, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_all_reduce, (), dict(input=input,
+                                   group_assignment=group_assignment,
+                                   reduce_op=reduce_op, mode=mode, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "reduce_op",
+              _op.get_attr("reduce_op"), "mode", _op.get_attr("mode"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaAllReduce", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaAllReduce = tf_export("raw_ops.XlaAllReduce")(_ops.to_raw_op(xla_all_reduce))
+_dispatcher_for_xla_all_reduce = xla_all_reduce._tf_type_based_dispatcher.Dispatch
+
+
+def xla_all_reduce_eager_fallback(input: Annotated[Any, TV_XlaAllReduce_T], group_assignment: Annotated[Any, _atypes.Int32], reduce_op: str, mode: str, name, ctx) -> Annotated[Any, TV_XlaAllReduce_T]:
+  reduce_op = _execute.make_str(reduce_op, "reduce_op")
+  mode = _execute.make_str(mode, "mode")
+  _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [_dtypes.half, _dtypes.bfloat16, _dtypes.float32, _dtypes.int32, _dtypes.uint32, ])
+  group_assignment = _ops.convert_to_tensor(group_assignment, _dtypes.int32)
+  _inputs_flat = [input, group_assignment]
+  _attrs = ("T", _attr_T, "reduce_op", reduce_op, "mode", mode)
+  _result = _execute.execute(b"XlaAllReduce", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaAllReduce", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+_XlaBroadcastHelperOutput = collections.namedtuple(
+    "XlaBroadcastHelper",
+    ["lhs_output", "rhs_output"])
+
+
+TV_XlaBroadcastHelper_T = TypeVar("TV_XlaBroadcastHelper_T", _atypes.BFloat16, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+TV_XlaBroadcastHelper_Tindices = TypeVar("TV_XlaBroadcastHelper_Tindices", _atypes.Int32, _atypes.Int64)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_broadcast_helper')
+def xla_broadcast_helper(lhs: Annotated[Any, TV_XlaBroadcastHelper_T], rhs: Annotated[Any, TV_XlaBroadcastHelper_T], broadcast_dims: Annotated[Any, TV_XlaBroadcastHelper_Tindices], name=None):
+  r"""Helper operator for performing XLA-style broadcasts
+
+  Broadcasts `lhs` and `rhs` to the same rank, by adding size 1 dimensions to
+  whichever of `lhs` and `rhs` has the lower rank, using XLA's broadcasting rules
+  for binary operators.
+
+  Args:
+    lhs: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
+      the LHS input tensor
+    rhs: A `Tensor`. Must have the same type as `lhs`. the RHS input tensor
+    broadcast_dims: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+      an XLA-style broadcast dimension specification
+    name: A name for the operation (optional).
+
+  Returns:
+    A tuple of `Tensor` objects (lhs_output, rhs_output).
+
+    lhs_output: A `Tensor`. Has the same type as `lhs`. the broadcasted LHS tensor
+    rhs_output: A `Tensor`. Has the same type as `lhs`. the broadcasted RHS tensor
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaBroadcastHelper", name, lhs, rhs, broadcast_dims)
+      _result = _XlaBroadcastHelperOutput._make(_result)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_broadcast_helper(
+          (lhs, rhs, broadcast_dims, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_broadcast_helper_eager_fallback(
+          lhs, rhs, broadcast_dims, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_broadcast_helper, (), dict(lhs=lhs, rhs=rhs,
+                                           broadcast_dims=broadcast_dims,
+                                           name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_broadcast_helper(
+        (lhs, rhs, broadcast_dims, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaBroadcastHelper", lhs=lhs, rhs=rhs, broadcast_dims=broadcast_dims,
+                              name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_broadcast_helper, (), dict(lhs=lhs, rhs=rhs,
+                                         broadcast_dims=broadcast_dims,
+                                         name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "Tindices",
+              _op._get_attr_type("Tindices"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaBroadcastHelper", _inputs_flat, _attrs, _result)
+  _result = _XlaBroadcastHelperOutput._make(_result)
+  return _result
+
+XlaBroadcastHelper = tf_export("raw_ops.XlaBroadcastHelper")(_ops.to_raw_op(xla_broadcast_helper))
+_dispatcher_for_xla_broadcast_helper = xla_broadcast_helper._tf_type_based_dispatcher.Dispatch
+
+
+def xla_broadcast_helper_eager_fallback(lhs: Annotated[Any, TV_XlaBroadcastHelper_T], rhs: Annotated[Any, TV_XlaBroadcastHelper_T], broadcast_dims: Annotated[Any, TV_XlaBroadcastHelper_Tindices], name, ctx):
+  _attr_T, _inputs_T = _execute.args_to_matching_eager([lhs, rhs], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
+  (lhs, rhs) = _inputs_T
+  _attr_Tindices, (broadcast_dims,) = _execute.args_to_matching_eager([broadcast_dims], ctx, [_dtypes.int32, _dtypes.int64, ])
+  _inputs_flat = [lhs, rhs, broadcast_dims]
+  _attrs = ("T", _attr_T, "Tindices", _attr_Tindices)
+  _result = _execute.execute(b"XlaBroadcastHelper", 2, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaBroadcastHelper", _inputs_flat, _attrs, _result)
+  _result = _XlaBroadcastHelperOutput._make(_result)
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_call_module')
+def xla_call_module(args, version: int, module: str, Sout, Tout, dim_args_spec=[], platforms=[], function_list=[], has_token_input_output:bool=False, disabled_checks=[], name=None):
+  r"""Invokes a StableHLO module.
+
+  This op is used with JAX native serialization in a TensorFlow context with
+  stability guarantees.
+
+  Args:
+    args: A list of `Tensor` objects.
+      A list of `Tensor` with possibly different types to be passed as arguments
+      to the `module`. These are the actual arguments and do not include the
+      platform argument (see `platforms`) nor the dimension arguments (see
+      `dim_args_spec`).
+    version: An `int`.
+      Tracks changes the semantics of the op, to support backwards
+      compatibility. Minimum supported version is 2. From
+      version 2, the op carries a StableHLO text or bytecode `module`. From
+      version 3, the op also supports the `platforms` attribute. From version 4,
+      the op carries a StableHLO module with compatibility guarantees. From version
+      5, XLACallModule can include `stablehlo.custom_call` op to execute tf
+      functions. From version 6 the op supports the `disabled_checks` attribute.
+      See more versioning details at https://github.com/search?q=repo%3Atensorflow%2Ftensorflow+path%3Axla_call_module+%22int+VERSION_MAXIMUM_SUPPORTED%22&type=code.
+    module: A `string`.
+      A serialized computation, a text or bytecode representation of
+      an mlir.Module. The return type must be a tuple if and only if the `Sout` is
+      a list with 0 or more than 1 elements. The length of `Tout` and
+      `Sout` must match. This op always returns a tuple of results, even if the
+      module returns a single result.
+    Sout: A list of shapes (each a `tf.TensorShape` or list of `ints`).
+      List of output tensor shapes.
+    Tout: A list of `tf.DTypes`. List of output tensor data types.
+    dim_args_spec: An optional list of `strings`. Defaults to `[]`.
+      in presence of dynamic shapes, this is the specification for the
+      dimension arguments. In absence of dynamic shapes this list is empty. The
+      `module` takes one 0-dimensional integer tensor dimension argument for each
+      element of `dim_spec_args`. The dimension arguments come after the platform
+      index argument and before the actual arguments. Each specification is a
+      string of the form "<arg_idx>.<axis_idx>" that specifies that the value of
+      the corresponding dimension argument must be "args[arg_idx].shape[axis_idx]",
+      where "args" are the actual array arguments.
+      This attribute is not used anymore in modules serialized with version 5
+      after March 28th, 2023 and JAX OSS versions higher than 0.4.6.
+      TODO(b/283439649): remove support for dim_args_spec.
+    platforms: An optional list of `strings`. Defaults to `[]`.
+      the list of platforms supported by `module`. The list can contain
+      the strings "CPU", "CUDA", "ROCM", or "TPU". It is an error to compile
+      this op for a platform that does not appear in the list. This check can be
+      disabled using `disabled_checks`. If the list contains more than
+      one platform, then the `module` takes one additional 0-dimensional
+      integer-tensor parameter in the first position, encoding the index in
+      `platforms` of the current compilation platform. This parameter has value 0
+      if the plaform is not among `platforms` and the check has been disabled.
+      The list can be empty in old versions (earlier than 6) to denote that no
+      platform checking must be performed at loading time.
+    function_list: An optional list of functions decorated with @Defun. Defaults to `[]`.
+      This list contains the TensorFlow FunctionDefs that are used by
+      the XLACallModule. If the XLACallModule contains `stablehlo.custom_call`
+      operations, they can call TensorFlow graph functions outside of the
+      XLACallModule. This `function_list` attribute registers the dependency of the
+      XLACallModule on those functions. This attribute was added in version 5.
+    has_token_input_output: An optional `bool`. Defaults to `False`.
+      If true, the embedded StableHLO module's main function
+      must take a `!stablehlo.token` as its first argument and returns a token as
+      its first result. This can be used in conjunction with the TF2XLA's side
+      effect mechanism in order to model side effects.
+    disabled_checks: An optional list of `strings`. Defaults to `[]`.
+      A list of strings describing the safety checks that were
+      disabled at serialization time. This attribute was added in version 6.
+      For more details see
+      https://github.com/search?q=repo%3Agoogle%2Fjax+path%3Ajax_export+%22class+DisabledSafetyCheck%22&type=code.
+      This list, supplemented with a comma-separate list of directives specified
+      using the flag --tf_xla_call_module_disabled_checks,
+      is used at module loading time to skip the corresponding checks.
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects of type `Tout`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaCallModule", name, args, "version", version, "module",
+        module, "Sout", Sout, "Tout", Tout, "dim_args_spec", dim_args_spec,
+        "platforms", platforms, "function_list", function_list,
+        "has_token_input_output", has_token_input_output, "disabled_checks",
+        disabled_checks)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_call_module(
+          (args, version, module, Sout, Tout, dim_args_spec, platforms,
+          function_list, has_token_input_output, disabled_checks, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_call_module_eager_fallback(
+          args, version=version, module=module, Sout=Sout, Tout=Tout,
+          dim_args_spec=dim_args_spec, platforms=platforms,
+          function_list=function_list,
+          has_token_input_output=has_token_input_output,
+          disabled_checks=disabled_checks, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_call_module, (), dict(args=args, version=version,
+                                      module=module, Sout=Sout, Tout=Tout,
+                                      dim_args_spec=dim_args_spec,
+                                      platforms=platforms,
+                                      function_list=function_list,
+                                      has_token_input_output=has_token_input_output,
+                                      disabled_checks=disabled_checks,
+                                      name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_call_module(
+        (args, version, module, Sout, Tout, dim_args_spec, platforms,
+        function_list, has_token_input_output, disabled_checks, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  version = _execute.make_int(version, "version")
+  module = _execute.make_str(module, "module")
+  if not isinstance(Sout, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Sout' argument to "
+        "'xla_call_module' Op, not %r." % Sout)
+  Sout = [_execute.make_shape(_s, "Sout") for _s in Sout]
+  if not isinstance(Tout, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Tout' argument to "
+        "'xla_call_module' Op, not %r." % Tout)
+  Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
+  if dim_args_spec is None:
+    dim_args_spec = []
+  if not isinstance(dim_args_spec, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'dim_args_spec' argument to "
+        "'xla_call_module' Op, not %r." % dim_args_spec)
+  dim_args_spec = [_execute.make_str(_s, "dim_args_spec") for _s in dim_args_spec]
+  if platforms is None:
+    platforms = []
+  if not isinstance(platforms, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'platforms' argument to "
+        "'xla_call_module' Op, not %r." % platforms)
+  platforms = [_execute.make_str(_s, "platforms") for _s in platforms]
+  if function_list is None:
+    function_list = []
+  if not isinstance(function_list, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'function_list' argument to "
+        "'xla_call_module' Op, not %r." % function_list)
+  if has_token_input_output is None:
+    has_token_input_output = False
+  has_token_input_output = _execute.make_bool(has_token_input_output, "has_token_input_output")
+  if disabled_checks is None:
+    disabled_checks = []
+  if not isinstance(disabled_checks, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'disabled_checks' argument to "
+        "'xla_call_module' Op, not %r." % disabled_checks)
+  disabled_checks = [_execute.make_str(_s, "disabled_checks") for _s in disabled_checks]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaCallModule", args=args, version=version, module=module, Sout=Sout,
+                         Tout=Tout, dim_args_spec=dim_args_spec,
+                         platforms=platforms, function_list=function_list,
+                         has_token_input_output=has_token_input_output,
+                         disabled_checks=disabled_checks, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_call_module, (), dict(args=args, version=version, module=module,
+                                    Sout=Sout, Tout=Tout,
+                                    dim_args_spec=dim_args_spec,
+                                    platforms=platforms,
+                                    function_list=function_list,
+                                    has_token_input_output=has_token_input_output,
+                                    disabled_checks=disabled_checks,
+                                    name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if not _result:
+    return _op
+  if _execute.must_record_gradient():
+    _attrs = ("version", _op._get_attr_int("version"), "module",
+              _op.get_attr("module"), "Sout", _op.get_attr("Sout"), "Tout",
+              _op.get_attr("Tout"), "Tin", _op.get_attr("Tin"),
+              "dim_args_spec", _op.get_attr("dim_args_spec"), "platforms",
+              _op.get_attr("platforms"), "function_list",
+              _op.get_attr("function_list"), "has_token_input_output",
+              _op._get_attr_bool("has_token_input_output"), "disabled_checks",
+              _op.get_attr("disabled_checks"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaCallModule", _inputs_flat, _attrs, _result)
+  return _result
+
+XlaCallModule = tf_export("raw_ops.XlaCallModule")(_ops.to_raw_op(xla_call_module))
+_dispatcher_for_xla_call_module = xla_call_module._tf_type_based_dispatcher.Dispatch
+
+
+def xla_call_module_eager_fallback(args, version: int, module: str, Sout, Tout, dim_args_spec, platforms, function_list, has_token_input_output: bool, disabled_checks, name, ctx):
+  version = _execute.make_int(version, "version")
+  module = _execute.make_str(module, "module")
+  if not isinstance(Sout, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Sout' argument to "
+        "'xla_call_module' Op, not %r." % Sout)
+  Sout = [_execute.make_shape(_s, "Sout") for _s in Sout]
+  if not isinstance(Tout, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Tout' argument to "
+        "'xla_call_module' Op, not %r." % Tout)
+  Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
+  if dim_args_spec is None:
+    dim_args_spec = []
+  if not isinstance(dim_args_spec, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'dim_args_spec' argument to "
+        "'xla_call_module' Op, not %r." % dim_args_spec)
+  dim_args_spec = [_execute.make_str(_s, "dim_args_spec") for _s in dim_args_spec]
+  if platforms is None:
+    platforms = []
+  if not isinstance(platforms, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'platforms' argument to "
+        "'xla_call_module' Op, not %r." % platforms)
+  platforms = [_execute.make_str(_s, "platforms") for _s in platforms]
+  if function_list is None:
+    function_list = []
+  if not isinstance(function_list, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'function_list' argument to "
+        "'xla_call_module' Op, not %r." % function_list)
+  if has_token_input_output is None:
+    has_token_input_output = False
+  has_token_input_output = _execute.make_bool(has_token_input_output, "has_token_input_output")
+  if disabled_checks is None:
+    disabled_checks = []
+  if not isinstance(disabled_checks, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'disabled_checks' argument to "
+        "'xla_call_module' Op, not %r." % disabled_checks)
+  disabled_checks = [_execute.make_str(_s, "disabled_checks") for _s in disabled_checks]
+  _attr_Tin, args = _execute.convert_to_mixed_eager_tensors(args, ctx)
+  _inputs_flat = list(args)
+  _attrs = ("version", version, "module", module, "Sout", Sout, "Tout", Tout,
+  "Tin", _attr_Tin, "dim_args_spec", dim_args_spec, "platforms", platforms,
+  "function_list", function_list, "has_token_input_output",
+  has_token_input_output, "disabled_checks", disabled_checks)
+  _result = _execute.execute(b"XlaCallModule", len(Tout), inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaCallModule", _inputs_flat, _attrs, _result)
+  return _result
+
+
+TV_XlaConv_T = TypeVar("TV_XlaConv_T", _atypes.BFloat16, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+TV_XlaConv_Tindices = TypeVar("TV_XlaConv_Tindices", _atypes.Int32, _atypes.Int64)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_conv')
+def xla_conv(lhs: Annotated[Any, TV_XlaConv_T], rhs: Annotated[Any, TV_XlaConv_T], window_strides: Annotated[Any, TV_XlaConv_Tindices], padding: Annotated[Any, TV_XlaConv_Tindices], lhs_dilation: Annotated[Any, TV_XlaConv_Tindices], rhs_dilation: Annotated[Any, TV_XlaConv_Tindices], feature_group_count: Annotated[Any, TV_XlaConv_Tindices], dimension_numbers: str, precision_config: str, name=None) -> Annotated[Any, TV_XlaConv_T]:
+  r"""Wraps the XLA ConvGeneralDilated operator, documented at
+
+   https://www.tensorflow.org/performance/xla/operation_semantics#conv_convolution
+  .
+
+  Args:
+    lhs: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
+      the input tensor
+    rhs: A `Tensor`. Must have the same type as `lhs`. the kernel tensor
+    window_strides: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+      the inter-window strides
+    padding: A `Tensor`. Must have the same type as `window_strides`.
+      the padding to apply at the start and end of each input dimensions
+    lhs_dilation: A `Tensor`. Must have the same type as `window_strides`.
+      dilation to apply between input elements
+    rhs_dilation: A `Tensor`. Must have the same type as `window_strides`.
+      dilation to apply between kernel elements
+    feature_group_count: A `Tensor`. Must have the same type as `window_strides`.
+      number of feature groups for grouped convolution.
+    dimension_numbers: A `string`.
+      a serialized xla::ConvolutionDimensionNumbers proto.
+    precision_config: A `string`. a serialized xla::PrecisionConfig proto.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `lhs`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaConv", name, lhs, rhs, window_strides, padding,
+        lhs_dilation, rhs_dilation, feature_group_count, "dimension_numbers",
+        dimension_numbers, "precision_config", precision_config)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_conv(
+          (lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,
+          feature_group_count, dimension_numbers, precision_config, name,),
+          None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_conv_eager_fallback(
+          lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,
+          feature_group_count, dimension_numbers=dimension_numbers,
+          precision_config=precision_config, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_conv, (), dict(lhs=lhs, rhs=rhs,
+                               window_strides=window_strides, padding=padding,
+                               lhs_dilation=lhs_dilation,
+                               rhs_dilation=rhs_dilation,
+                               feature_group_count=feature_group_count,
+                               dimension_numbers=dimension_numbers,
+                               precision_config=precision_config, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_conv(
+        (lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,
+        feature_group_count, dimension_numbers, precision_config, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  dimension_numbers = _execute.make_str(dimension_numbers, "dimension_numbers")
+  precision_config = _execute.make_str(precision_config, "precision_config")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaConv", lhs=lhs, rhs=rhs, window_strides=window_strides,
+                   padding=padding, lhs_dilation=lhs_dilation,
+                   rhs_dilation=rhs_dilation,
+                   feature_group_count=feature_group_count,
+                   dimension_numbers=dimension_numbers,
+                   precision_config=precision_config, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_conv, (), dict(lhs=lhs, rhs=rhs, window_strides=window_strides,
+                             padding=padding, lhs_dilation=lhs_dilation,
+                             rhs_dilation=rhs_dilation,
+                             feature_group_count=feature_group_count,
+                             dimension_numbers=dimension_numbers,
+                             precision_config=precision_config, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "Tindices",
+              _op._get_attr_type("Tindices"), "dimension_numbers",
+              _op.get_attr("dimension_numbers"), "precision_config",
+              _op.get_attr("precision_config"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaConv", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaConv = tf_export("raw_ops.XlaConv")(_ops.to_raw_op(xla_conv))
+_dispatcher_for_xla_conv = xla_conv._tf_type_based_dispatcher.Dispatch
+
+
+def xla_conv_eager_fallback(lhs: Annotated[Any, TV_XlaConv_T], rhs: Annotated[Any, TV_XlaConv_T], window_strides: Annotated[Any, TV_XlaConv_Tindices], padding: Annotated[Any, TV_XlaConv_Tindices], lhs_dilation: Annotated[Any, TV_XlaConv_Tindices], rhs_dilation: Annotated[Any, TV_XlaConv_Tindices], feature_group_count: Annotated[Any, TV_XlaConv_Tindices], dimension_numbers: str, precision_config: str, name, ctx) -> Annotated[Any, TV_XlaConv_T]:
+  dimension_numbers = _execute.make_str(dimension_numbers, "dimension_numbers")
+  precision_config = _execute.make_str(precision_config, "precision_config")
+  _attr_T, _inputs_T = _execute.args_to_matching_eager([lhs, rhs], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
+  (lhs, rhs) = _inputs_T
+  _attr_Tindices, _inputs_Tindices = _execute.args_to_matching_eager([window_strides, padding, lhs_dilation, rhs_dilation, feature_group_count], ctx, [_dtypes.int32, _dtypes.int64, ])
+  (window_strides, padding, lhs_dilation, rhs_dilation, feature_group_count) = _inputs_Tindices
+  _inputs_flat = [lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation, feature_group_count]
+  _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "dimension_numbers",
+  dimension_numbers, "precision_config", precision_config)
+  _result = _execute.execute(b"XlaConv", 1, inputs=_inputs_flat, attrs=_attrs,
+                             ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaConv", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaConvV2_LhsT = TypeVar("TV_XlaConvV2_LhsT", _atypes.BFloat16, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+TV_XlaConvV2_RhsT = TypeVar("TV_XlaConvV2_RhsT", _atypes.BFloat16, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+TV_XlaConvV2_Tindices = TypeVar("TV_XlaConvV2_Tindices", _atypes.Int32, _atypes.Int64)
+TV_XlaConvV2_preferred_element_type = TypeVar("TV_XlaConvV2_preferred_element_type", _atypes.BFloat16, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_conv_v2')
+def xla_conv_v2(lhs: Annotated[Any, TV_XlaConvV2_LhsT], rhs: Annotated[Any, TV_XlaConvV2_RhsT], window_strides: Annotated[Any, TV_XlaConvV2_Tindices], padding: Annotated[Any, TV_XlaConvV2_Tindices], lhs_dilation: Annotated[Any, TV_XlaConvV2_Tindices], rhs_dilation: Annotated[Any, TV_XlaConvV2_Tindices], feature_group_count: Annotated[Any, TV_XlaConvV2_Tindices], dimension_numbers: str, precision_config: str, preferred_element_type: TV_XlaConvV2_preferred_element_type, batch_group_count:int=1, name=None) -> Annotated[Any, TV_XlaConvV2_preferred_element_type]:
+  r"""Wraps the XLA ConvGeneralDilated operator, documented at
+
+   https://www.tensorflow.org/performance/xla/operation_semantics#conv_convolution
+  .
+
+  Args:
+    lhs: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
+      input tensor
+    rhs: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
+      kernel tensor
+    window_strides: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+      inter-window strides
+    padding: A `Tensor`. Must have the same type as `window_strides`.
+      padding to apply at the start and end of each input dimensions
+    lhs_dilation: A `Tensor`. Must have the same type as `window_strides`.
+      dilation to apply between input elements
+    rhs_dilation: A `Tensor`. Must have the same type as `window_strides`.
+      dilation to apply between kernel elements
+    feature_group_count: A `Tensor`. Must have the same type as `window_strides`.
+      number of feature groups for grouped convolution.
+    dimension_numbers: A `string`.
+      serialized xla::ConvolutionDimensionNumbers proto.
+    precision_config: A `string`. serialized xla::PrecisionConfig proto.
+    preferred_element_type: A `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.complex64, tf.int64, tf.qint8, tf.quint8, tf.qint32, tf.bfloat16, tf.qint16, tf.quint16, tf.uint16, tf.complex128, tf.half, tf.uint32, tf.uint64`.
+      type of the tensor.
+    batch_group_count: An optional `int`. Defaults to `1`.
+      number of batch groups or grouped filters.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` of type `preferred_element_type`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaConvV2", name, lhs, rhs, window_strides, padding,
+        lhs_dilation, rhs_dilation, feature_group_count, "dimension_numbers",
+        dimension_numbers, "precision_config", precision_config,
+        "preferred_element_type", preferred_element_type, "batch_group_count",
+        batch_group_count)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_conv_v2(
+          (lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,
+          feature_group_count, dimension_numbers, precision_config,
+          preferred_element_type, batch_group_count, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_conv_v2_eager_fallback(
+          lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,
+          feature_group_count, dimension_numbers=dimension_numbers,
+          precision_config=precision_config,
+          preferred_element_type=preferred_element_type,
+          batch_group_count=batch_group_count, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_conv_v2, (), dict(lhs=lhs, rhs=rhs,
+                                  window_strides=window_strides,
+                                  padding=padding, lhs_dilation=lhs_dilation,
+                                  rhs_dilation=rhs_dilation,
+                                  feature_group_count=feature_group_count,
+                                  dimension_numbers=dimension_numbers,
+                                  precision_config=precision_config,
+                                  preferred_element_type=preferred_element_type,
+                                  batch_group_count=batch_group_count,
+                                  name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_conv_v2(
+        (lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation,
+        feature_group_count, dimension_numbers, precision_config,
+        preferred_element_type, batch_group_count, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  dimension_numbers = _execute.make_str(dimension_numbers, "dimension_numbers")
+  precision_config = _execute.make_str(precision_config, "precision_config")
+  preferred_element_type = _execute.make_type(preferred_element_type, "preferred_element_type")
+  if batch_group_count is None:
+    batch_group_count = 1
+  batch_group_count = _execute.make_int(batch_group_count, "batch_group_count")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaConvV2", lhs=lhs, rhs=rhs, window_strides=window_strides,
+                     padding=padding, lhs_dilation=lhs_dilation,
+                     rhs_dilation=rhs_dilation,
+                     feature_group_count=feature_group_count,
+                     dimension_numbers=dimension_numbers,
+                     precision_config=precision_config,
+                     preferred_element_type=preferred_element_type,
+                     batch_group_count=batch_group_count, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_conv_v2, (), dict(lhs=lhs, rhs=rhs,
+                                window_strides=window_strides,
+                                padding=padding, lhs_dilation=lhs_dilation,
+                                rhs_dilation=rhs_dilation,
+                                feature_group_count=feature_group_count,
+                                dimension_numbers=dimension_numbers,
+                                precision_config=precision_config,
+                                preferred_element_type=preferred_element_type,
+                                batch_group_count=batch_group_count,
+                                name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("LhsT", _op._get_attr_type("LhsT"), "RhsT",
+              _op._get_attr_type("RhsT"), "Tindices",
+              _op._get_attr_type("Tindices"), "dimension_numbers",
+              _op.get_attr("dimension_numbers"), "precision_config",
+              _op.get_attr("precision_config"), "preferred_element_type",
+              _op._get_attr_type("preferred_element_type"),
+              "batch_group_count", _op._get_attr_int("batch_group_count"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaConvV2", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaConvV2 = tf_export("raw_ops.XlaConvV2")(_ops.to_raw_op(xla_conv_v2))
+_dispatcher_for_xla_conv_v2 = xla_conv_v2._tf_type_based_dispatcher.Dispatch
+
+
+def xla_conv_v2_eager_fallback(lhs: Annotated[Any, TV_XlaConvV2_LhsT], rhs: Annotated[Any, TV_XlaConvV2_RhsT], window_strides: Annotated[Any, TV_XlaConvV2_Tindices], padding: Annotated[Any, TV_XlaConvV2_Tindices], lhs_dilation: Annotated[Any, TV_XlaConvV2_Tindices], rhs_dilation: Annotated[Any, TV_XlaConvV2_Tindices], feature_group_count: Annotated[Any, TV_XlaConvV2_Tindices], dimension_numbers: str, precision_config: str, preferred_element_type: TV_XlaConvV2_preferred_element_type, batch_group_count: int, name, ctx) -> Annotated[Any, TV_XlaConvV2_preferred_element_type]:
+  dimension_numbers = _execute.make_str(dimension_numbers, "dimension_numbers")
+  precision_config = _execute.make_str(precision_config, "precision_config")
+  preferred_element_type = _execute.make_type(preferred_element_type, "preferred_element_type")
+  if batch_group_count is None:
+    batch_group_count = 1
+  batch_group_count = _execute.make_int(batch_group_count, "batch_group_count")
+  _attr_LhsT, (lhs,) = _execute.args_to_matching_eager([lhs], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
+  _attr_RhsT, (rhs,) = _execute.args_to_matching_eager([rhs], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
+  _attr_Tindices, _inputs_Tindices = _execute.args_to_matching_eager([window_strides, padding, lhs_dilation, rhs_dilation, feature_group_count], ctx, [_dtypes.int32, _dtypes.int64, ])
+  (window_strides, padding, lhs_dilation, rhs_dilation, feature_group_count) = _inputs_Tindices
+  _inputs_flat = [lhs, rhs, window_strides, padding, lhs_dilation, rhs_dilation, feature_group_count]
+  _attrs = ("LhsT", _attr_LhsT, "RhsT", _attr_RhsT, "Tindices",
+  _attr_Tindices, "dimension_numbers", dimension_numbers, "precision_config",
+  precision_config, "preferred_element_type", preferred_element_type,
+  "batch_group_count", batch_group_count)
+  _result = _execute.execute(b"XlaConvV2", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaConvV2", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaCustomCall_dtype = TypeVar("TV_XlaCustomCall_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_custom_call')
+def xla_custom_call(args, target_name: str, backend_config: str, dtype: TV_XlaCustomCall_dtype, shape, name=None) -> Annotated[Any, TV_XlaCustomCall_dtype]:
+  r"""Wraps the XLA CustomCall operator
+
+    documented at https://www.tensorflow.org/xla/operation_semantics#customcall.
+
+  Args:
+    args: A list of `Tensor` objects.
+      A list of `Tensor` with possibly different types.
+    target_name: A `string`.
+      Name of the function. A call instruction will be emitted which
+      targets this symbol name.
+    backend_config: A `string`.
+      String, used to encode serialized metadata to the backend.
+    dtype: A `tf.DType`. Output tensor data type.
+    shape: A `tf.TensorShape` or list of `ints`. Output tensor shape.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` of type `dtype`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaCustomCall", name, args, "target_name", target_name,
+        "backend_config", backend_config, "dtype", dtype, "shape", shape)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_custom_call(
+          (args, target_name, backend_config, dtype, shape, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_custom_call_eager_fallback(
+          args, target_name=target_name, backend_config=backend_config,
+          dtype=dtype, shape=shape, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_custom_call, (), dict(args=args, target_name=target_name,
+                                      backend_config=backend_config,
+                                      dtype=dtype, shape=shape, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_custom_call(
+        (args, target_name, backend_config, dtype, shape, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  target_name = _execute.make_str(target_name, "target_name")
+  backend_config = _execute.make_str(backend_config, "backend_config")
+  dtype = _execute.make_type(dtype, "dtype")
+  shape = _execute.make_shape(shape, "shape")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaCustomCall", args=args, target_name=target_name,
+                         backend_config=backend_config, dtype=dtype,
+                         shape=shape, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_custom_call, (), dict(args=args, target_name=target_name,
+                                    backend_config=backend_config,
+                                    dtype=dtype, shape=shape, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("target_name", _op.get_attr("target_name"), "backend_config",
+              _op.get_attr("backend_config"), "T", _op.get_attr("T"), "dtype",
+              _op._get_attr_type("dtype"), "shape", _op.get_attr("shape"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaCustomCall", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaCustomCall = tf_export("raw_ops.XlaCustomCall")(_ops.to_raw_op(xla_custom_call))
+_dispatcher_for_xla_custom_call = xla_custom_call._tf_type_based_dispatcher.Dispatch
+
+
+def xla_custom_call_eager_fallback(args, target_name: str, backend_config: str, dtype: TV_XlaCustomCall_dtype, shape, name, ctx) -> Annotated[Any, TV_XlaCustomCall_dtype]:
+  target_name = _execute.make_str(target_name, "target_name")
+  backend_config = _execute.make_str(backend_config, "backend_config")
+  dtype = _execute.make_type(dtype, "dtype")
+  shape = _execute.make_shape(shape, "shape")
+  _attr_T, args = _execute.convert_to_mixed_eager_tensors(args, ctx)
+  _inputs_flat = list(args)
+  _attrs = ("target_name", target_name, "backend_config", backend_config, "T",
+  _attr_T, "dtype", dtype, "shape", shape)
+  _result = _execute.execute(b"XlaCustomCall", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaCustomCall", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_custom_call_v2')
+def xla_custom_call_v2(operands, call_target_name: str, backend_config: str, has_side_effect: bool, result_dtypes, result_shapes, name=None):
+  r"""Emits an HLO `CustomCall` operation with multiple outputs.
+
+  As opposed to `XlaCustomCall`, this operation supports multiple outputs.
+
+  See `CustomCall` specification at
+    https://tensorflow.org/xla/operation_semantics#customcall,
+  and `mhlo.custom_call` specification at
+    https://tensorflow.org/mlir/hlo_ops#mhlocustom_call_mlirmhlocustomcallop.
+
+  Args:
+    operands: A list of `Tensor` objects.
+      A sequence of tensors with possibly different types.
+    call_target_name: A `string`.
+      Name of the user function. The function signature must conform
+      to version 3 of the API, see `API_VERSION_STATUS_RETURNING_UNIFIED`. All
+      operands and results assumed to be in the default layout.
+    backend_config: A `string`.
+      A string that encodes a metadata for the backend.
+    has_side_effect: A `bool`.
+      Indicates whether the custom call has side effects.
+    result_dtypes: A list of `tf.DTypes`. Types of all results.
+    result_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`).
+      Shapes of all results.
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects of type `result_dtypes`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaCustomCallV2", name, operands, "call_target_name",
+        call_target_name, "backend_config", backend_config, "has_side_effect",
+        has_side_effect, "result_dtypes", result_dtypes, "result_shapes",
+        result_shapes)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_custom_call_v2(
+          (operands, call_target_name, backend_config, has_side_effect,
+          result_dtypes, result_shapes, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_custom_call_v2_eager_fallback(
+          operands, call_target_name=call_target_name,
+          backend_config=backend_config, has_side_effect=has_side_effect,
+          result_dtypes=result_dtypes, result_shapes=result_shapes, name=name,
+          ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_custom_call_v2, (), dict(operands=operands,
+                                         call_target_name=call_target_name,
+                                         backend_config=backend_config,
+                                         has_side_effect=has_side_effect,
+                                         result_dtypes=result_dtypes,
+                                         result_shapes=result_shapes,
+                                         name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_custom_call_v2(
+        (operands, call_target_name, backend_config, has_side_effect,
+        result_dtypes, result_shapes, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  call_target_name = _execute.make_str(call_target_name, "call_target_name")
+  backend_config = _execute.make_str(backend_config, "backend_config")
+  has_side_effect = _execute.make_bool(has_side_effect, "has_side_effect")
+  if not isinstance(result_dtypes, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'result_dtypes' argument to "
+        "'xla_custom_call_v2' Op, not %r." % result_dtypes)
+  result_dtypes = [_execute.make_type(_t, "result_dtypes") for _t in result_dtypes]
+  if not isinstance(result_shapes, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'result_shapes' argument to "
+        "'xla_custom_call_v2' Op, not %r." % result_shapes)
+  result_shapes = [_execute.make_shape(_s, "result_shapes") for _s in result_shapes]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaCustomCallV2", operands=operands,
+                           call_target_name=call_target_name,
+                           backend_config=backend_config,
+                           has_side_effect=has_side_effect,
+                           result_dtypes=result_dtypes,
+                           result_shapes=result_shapes, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_custom_call_v2, (), dict(operands=operands,
+                                       call_target_name=call_target_name,
+                                       backend_config=backend_config,
+                                       has_side_effect=has_side_effect,
+                                       result_dtypes=result_dtypes,
+                                       result_shapes=result_shapes, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("call_target_name", _op.get_attr("call_target_name"),
+              "backend_config", _op.get_attr("backend_config"),
+              "has_side_effect", _op._get_attr_bool("has_side_effect"),
+              "operand_dtypes", _op.get_attr("operand_dtypes"),
+              "result_dtypes", _op.get_attr("result_dtypes"), "result_shapes",
+              _op.get_attr("result_shapes"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaCustomCallV2", _inputs_flat, _attrs, _result)
+  return _result
+
+XlaCustomCallV2 = tf_export("raw_ops.XlaCustomCallV2")(_ops.to_raw_op(xla_custom_call_v2))
+_dispatcher_for_xla_custom_call_v2 = xla_custom_call_v2._tf_type_based_dispatcher.Dispatch
+
+
+def xla_custom_call_v2_eager_fallback(operands, call_target_name: str, backend_config: str, has_side_effect: bool, result_dtypes, result_shapes, name, ctx):
+  call_target_name = _execute.make_str(call_target_name, "call_target_name")
+  backend_config = _execute.make_str(backend_config, "backend_config")
+  has_side_effect = _execute.make_bool(has_side_effect, "has_side_effect")
+  if not isinstance(result_dtypes, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'result_dtypes' argument to "
+        "'xla_custom_call_v2' Op, not %r." % result_dtypes)
+  result_dtypes = [_execute.make_type(_t, "result_dtypes") for _t in result_dtypes]
+  if not isinstance(result_shapes, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'result_shapes' argument to "
+        "'xla_custom_call_v2' Op, not %r." % result_shapes)
+  result_shapes = [_execute.make_shape(_s, "result_shapes") for _s in result_shapes]
+  _attr_operand_dtypes, operands = _execute.convert_to_mixed_eager_tensors(operands, ctx)
+  _inputs_flat = list(operands)
+  _attrs = ("call_target_name", call_target_name, "backend_config",
+  backend_config, "has_side_effect", has_side_effect, "operand_dtypes",
+  _attr_operand_dtypes, "result_dtypes", result_dtypes, "result_shapes",
+  result_shapes)
+  _result = _execute.execute(b"XlaCustomCallV2", len(result_dtypes),
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaCustomCallV2", _inputs_flat, _attrs, _result)
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_dequantize')
+def xla_dequantize(input: Annotated[Any, _atypes.UInt32], min_range: float, max_range: float, mode: str, transpose_output: bool, name=None) -> Annotated[Any, _atypes.BFloat16]:
+  r"""Takes the packed uint32 input and unpacks the input to uint8 to do
+
+  Dequantization on device.
+
+  Args:
+    input: A `Tensor` of type `uint32`.
+      Input tensors whose types is uint32, shape is [d0, ..., dn].
+    min_range: A `float`.
+      The minimum scalar value possibly produced for the input.
+    max_range: A `float`.
+      The maximum scalar value possibly produced for the input.
+    mode: A `string`.
+      String to determine the dequantize mode in {"MIN_COMBINED", "MIN_FIRST", "SCALED"}.
+    transpose_output: A `bool`.
+      Boolean to determine if output is transposed. transpose_output
+      is faster when input is large and rank of input is higher than 1.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` of type `bfloat16`.
+    Output tensors whose types is bfloat16. If transpose_output is true,
+    output shape is [dn * 4, dn-1, ..., d1, d0]. If transpose_output
+    is false, output shape is [d0,..., dn * 4].
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaDequantize", name, input, "min_range", min_range,
+        "max_range", max_range, "mode", mode, "transpose_output",
+        transpose_output)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_dequantize(
+          (input, min_range, max_range, mode, transpose_output, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_dequantize_eager_fallback(
+          input, min_range=min_range, max_range=max_range, mode=mode,
+          transpose_output=transpose_output, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_dequantize, (), dict(input=input, min_range=min_range,
+                                     max_range=max_range, mode=mode,
+                                     transpose_output=transpose_output,
+                                     name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_dequantize(
+        (input, min_range, max_range, mode, transpose_output, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  min_range = _execute.make_float(min_range, "min_range")
+  max_range = _execute.make_float(max_range, "max_range")
+  mode = _execute.make_str(mode, "mode")
+  transpose_output = _execute.make_bool(transpose_output, "transpose_output")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaDequantize", input=input, min_range=min_range,
+                         max_range=max_range, mode=mode,
+                         transpose_output=transpose_output, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_dequantize, (), dict(input=input, min_range=min_range,
+                                   max_range=max_range, mode=mode,
+                                   transpose_output=transpose_output,
+                                   name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("min_range", _op.get_attr("min_range"), "max_range",
+              _op.get_attr("max_range"), "mode", _op.get_attr("mode"),
+              "transpose_output", _op._get_attr_bool("transpose_output"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaDequantize", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaDequantize = tf_export("raw_ops.XlaDequantize")(_ops.to_raw_op(xla_dequantize))
+_dispatcher_for_xla_dequantize = xla_dequantize._tf_type_based_dispatcher.Dispatch
+
+
+def xla_dequantize_eager_fallback(input: Annotated[Any, _atypes.UInt32], min_range: float, max_range: float, mode: str, transpose_output: bool, name, ctx) -> Annotated[Any, _atypes.BFloat16]:
+  min_range = _execute.make_float(min_range, "min_range")
+  max_range = _execute.make_float(max_range, "max_range")
+  mode = _execute.make_str(mode, "mode")
+  transpose_output = _execute.make_bool(transpose_output, "transpose_output")
+  input = _ops.convert_to_tensor(input, _dtypes.uint32)
+  _inputs_flat = [input]
+  _attrs = ("min_range", min_range, "max_range", max_range, "mode", mode,
+  "transpose_output", transpose_output)
+  _result = _execute.execute(b"XlaDequantize", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaDequantize", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaDot_T = TypeVar("TV_XlaDot_T", _atypes.BFloat16, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_dot')
+def xla_dot(lhs: Annotated[Any, TV_XlaDot_T], rhs: Annotated[Any, TV_XlaDot_T], dimension_numbers: str, precision_config: str, name=None) -> Annotated[Any, TV_XlaDot_T]:
+  r"""Wraps the XLA DotGeneral operator, documented at
+
+   https://www.tensorflow.org/performance/xla/operation_semantics#dotgeneral
+  .
+
+  Args:
+    lhs: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
+      the LHS tensor
+    rhs: A `Tensor`. Must have the same type as `lhs`. the RHS tensor
+    dimension_numbers: A `string`.
+      a serialized xla::DotDimensionNumbers proto.
+    precision_config: A `string`. a serialized xla::PrecisionConfig proto.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `lhs`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaDot", name, lhs, rhs, "dimension_numbers",
+        dimension_numbers, "precision_config", precision_config)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_dot(
+          (lhs, rhs, dimension_numbers, precision_config, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_dot_eager_fallback(
+          lhs, rhs, dimension_numbers=dimension_numbers,
+          precision_config=precision_config, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_dot, (), dict(lhs=lhs, rhs=rhs,
+                              dimension_numbers=dimension_numbers,
+                              precision_config=precision_config, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_dot(
+        (lhs, rhs, dimension_numbers, precision_config, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  dimension_numbers = _execute.make_str(dimension_numbers, "dimension_numbers")
+  precision_config = _execute.make_str(precision_config, "precision_config")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaDot", lhs=lhs, rhs=rhs, dimension_numbers=dimension_numbers,
+                  precision_config=precision_config, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_dot, (), dict(lhs=lhs, rhs=rhs,
+                            dimension_numbers=dimension_numbers,
+                            precision_config=precision_config, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "dimension_numbers",
+              _op.get_attr("dimension_numbers"), "precision_config",
+              _op.get_attr("precision_config"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaDot", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaDot = tf_export("raw_ops.XlaDot")(_ops.to_raw_op(xla_dot))
+_dispatcher_for_xla_dot = xla_dot._tf_type_based_dispatcher.Dispatch
+
+
+def xla_dot_eager_fallback(lhs: Annotated[Any, TV_XlaDot_T], rhs: Annotated[Any, TV_XlaDot_T], dimension_numbers: str, precision_config: str, name, ctx) -> Annotated[Any, TV_XlaDot_T]:
+  dimension_numbers = _execute.make_str(dimension_numbers, "dimension_numbers")
+  precision_config = _execute.make_str(precision_config, "precision_config")
+  _attr_T, _inputs_T = _execute.args_to_matching_eager([lhs, rhs], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
+  (lhs, rhs) = _inputs_T
+  _inputs_flat = [lhs, rhs]
+  _attrs = ("T", _attr_T, "dimension_numbers", dimension_numbers,
+  "precision_config", precision_config)
+  _result = _execute.execute(b"XlaDot", 1, inputs=_inputs_flat, attrs=_attrs,
+                             ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaDot", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaDotV2_LhsT = TypeVar("TV_XlaDotV2_LhsT", _atypes.BFloat16, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+TV_XlaDotV2_RhsT = TypeVar("TV_XlaDotV2_RhsT", _atypes.BFloat16, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+TV_XlaDotV2_preferred_element_type = TypeVar("TV_XlaDotV2_preferred_element_type", _atypes.BFloat16, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_dot_v2')
+def xla_dot_v2(lhs: Annotated[Any, TV_XlaDotV2_LhsT], rhs: Annotated[Any, TV_XlaDotV2_RhsT], dimension_numbers: str, precision_config: str, preferred_element_type: TV_XlaDotV2_preferred_element_type, name=None) -> Annotated[Any, TV_XlaDotV2_preferred_element_type]:
+  r"""Wraps the XLA DotGeneral operator, documented at
+
+   https://www.tensorflow.org/performance/xla/operation_semantics#dotgeneral
+  .
+
+  Args:
+    lhs: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
+      the LHS tensor
+    rhs: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
+      the RHS tensor
+    dimension_numbers: A `string`.
+      a serialized xla::DotDimensionNumbers proto.
+    precision_config: A `string`. a serialized xla::PrecisionConfig proto.
+    preferred_element_type: A `tf.DType` from: `tf.float32, tf.float64, tf.int32, tf.uint8, tf.int16, tf.int8, tf.complex64, tf.int64, tf.qint8, tf.quint8, tf.qint32, tf.bfloat16, tf.qint16, tf.quint16, tf.uint16, tf.complex128, tf.half, tf.uint32, tf.uint64`.
+      The type of the tensor.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` of type `preferred_element_type`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaDotV2", name, lhs, rhs, "dimension_numbers",
+        dimension_numbers, "precision_config", precision_config,
+        "preferred_element_type", preferred_element_type)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_dot_v2(
+          (lhs, rhs, dimension_numbers, precision_config,
+          preferred_element_type, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_dot_v2_eager_fallback(
+          lhs, rhs, dimension_numbers=dimension_numbers,
+          precision_config=precision_config,
+          preferred_element_type=preferred_element_type, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_dot_v2, (), dict(lhs=lhs, rhs=rhs,
+                                 dimension_numbers=dimension_numbers,
+                                 precision_config=precision_config,
+                                 preferred_element_type=preferred_element_type,
+                                 name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_dot_v2(
+        (lhs, rhs, dimension_numbers, precision_config,
+        preferred_element_type, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  dimension_numbers = _execute.make_str(dimension_numbers, "dimension_numbers")
+  precision_config = _execute.make_str(precision_config, "precision_config")
+  preferred_element_type = _execute.make_type(preferred_element_type, "preferred_element_type")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaDotV2", lhs=lhs, rhs=rhs, dimension_numbers=dimension_numbers,
+                    precision_config=precision_config,
+                    preferred_element_type=preferred_element_type, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_dot_v2, (), dict(lhs=lhs, rhs=rhs,
+                               dimension_numbers=dimension_numbers,
+                               precision_config=precision_config,
+                               preferred_element_type=preferred_element_type,
+                               name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("LhsT", _op._get_attr_type("LhsT"), "RhsT",
+              _op._get_attr_type("RhsT"), "dimension_numbers",
+              _op.get_attr("dimension_numbers"), "precision_config",
+              _op.get_attr("precision_config"), "preferred_element_type",
+              _op._get_attr_type("preferred_element_type"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaDotV2", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaDotV2 = tf_export("raw_ops.XlaDotV2")(_ops.to_raw_op(xla_dot_v2))
+_dispatcher_for_xla_dot_v2 = xla_dot_v2._tf_type_based_dispatcher.Dispatch
+
+
+def xla_dot_v2_eager_fallback(lhs: Annotated[Any, TV_XlaDotV2_LhsT], rhs: Annotated[Any, TV_XlaDotV2_RhsT], dimension_numbers: str, precision_config: str, preferred_element_type: TV_XlaDotV2_preferred_element_type, name, ctx) -> Annotated[Any, TV_XlaDotV2_preferred_element_type]:
+  dimension_numbers = _execute.make_str(dimension_numbers, "dimension_numbers")
+  precision_config = _execute.make_str(precision_config, "precision_config")
+  preferred_element_type = _execute.make_type(preferred_element_type, "preferred_element_type")
+  _attr_LhsT, (lhs,) = _execute.args_to_matching_eager([lhs], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
+  _attr_RhsT, (rhs,) = _execute.args_to_matching_eager([rhs], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
+  _inputs_flat = [lhs, rhs]
+  _attrs = ("LhsT", _attr_LhsT, "RhsT", _attr_RhsT, "dimension_numbers",
+  dimension_numbers, "precision_config", precision_config,
+  "preferred_element_type", preferred_element_type)
+  _result = _execute.execute(b"XlaDotV2", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaDotV2", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaDynamicSlice_T = TypeVar("TV_XlaDynamicSlice_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+TV_XlaDynamicSlice_Tindices = TypeVar("TV_XlaDynamicSlice_Tindices", _atypes.Int32, _atypes.Int64)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_dynamic_slice')
+def xla_dynamic_slice(input: Annotated[Any, TV_XlaDynamicSlice_T], start_indices: Annotated[Any, TV_XlaDynamicSlice_Tindices], size_indices: Annotated[Any, TV_XlaDynamicSlice_Tindices], name=None) -> Annotated[Any, TV_XlaDynamicSlice_T]:
+  r"""Wraps the XLA DynamicSlice operator, documented at
+
+   https://www.tensorflow.org/performance/xla/operation_semantics#dynamicslice
+  .
+
+  DynamicSlice extracts a sub-array from the input array at dynamic
+  start_indices. The size of the slice in each dimension is passed in
+  size_indices, which specify the end point of exclusive slice intervals in each
+  dimension -- [start, start + size). The shape of start_indices must have rank 1,
+  with dimension size equal to the rank of operand.
+
+  Args:
+    input: A `Tensor`. A `Tensor` of type T.
+    start_indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+      List of N integers containing the slice size for each
+      dimension. Each value must be strictly greater than zero, and start + size
+      must be less than or equal to the size of the dimension to avoid
+      implementation defined behavior.
+    size_indices: A `Tensor`. Must have the same type as `start_indices`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaDynamicSlice", name, input, start_indices, size_indices)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_dynamic_slice(
+          (input, start_indices, size_indices, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_dynamic_slice_eager_fallback(
+          input, start_indices, size_indices, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_dynamic_slice, (), dict(input=input,
+                                        start_indices=start_indices,
+                                        size_indices=size_indices, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_dynamic_slice(
+        (input, start_indices, size_indices, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaDynamicSlice", input=input, start_indices=start_indices,
+                           size_indices=size_indices, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_dynamic_slice, (), dict(input=input,
+                                      start_indices=start_indices,
+                                      size_indices=size_indices, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "Tindices",
+              _op._get_attr_type("Tindices"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaDynamicSlice", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaDynamicSlice = tf_export("raw_ops.XlaDynamicSlice")(_ops.to_raw_op(xla_dynamic_slice))
+_dispatcher_for_xla_dynamic_slice = xla_dynamic_slice._tf_type_based_dispatcher.Dispatch
+
+
+def xla_dynamic_slice_eager_fallback(input: Annotated[Any, TV_XlaDynamicSlice_T], start_indices: Annotated[Any, TV_XlaDynamicSlice_Tindices], size_indices: Annotated[Any, TV_XlaDynamicSlice_Tindices], name, ctx) -> Annotated[Any, TV_XlaDynamicSlice_T]:
+  _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
+  _attr_Tindices, _inputs_Tindices = _execute.args_to_matching_eager([start_indices, size_indices], ctx, [_dtypes.int32, _dtypes.int64, ])
+  (start_indices, size_indices) = _inputs_Tindices
+  _inputs_flat = [input, start_indices, size_indices]
+  _attrs = ("T", _attr_T, "Tindices", _attr_Tindices)
+  _result = _execute.execute(b"XlaDynamicSlice", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaDynamicSlice", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaDynamicUpdateSlice_T = TypeVar("TV_XlaDynamicUpdateSlice_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+TV_XlaDynamicUpdateSlice_Tindices = TypeVar("TV_XlaDynamicUpdateSlice_Tindices", _atypes.Int32, _atypes.Int64)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_dynamic_update_slice')
+def xla_dynamic_update_slice(input: Annotated[Any, TV_XlaDynamicUpdateSlice_T], update: Annotated[Any, TV_XlaDynamicUpdateSlice_T], indices: Annotated[Any, TV_XlaDynamicUpdateSlice_Tindices], name=None) -> Annotated[Any, TV_XlaDynamicUpdateSlice_T]:
+  r"""Wraps the XLA DynamicUpdateSlice operator, documented at
+
+   https://www.tensorflow.org/performance/xla/operation_semantics#dynamicupdateslice
+  .
+
+  XlaDynamicUpdateSlice generates a result which is the value of the `input`
+  operand, with a slice update overwritten at `indices`. The shape of `update`
+  determines the shape of the sub-array of the result which is updated. The shape
+  of indices must be rank == 1, with dimension size equal to the rank of `input`.
+
+  Handling of out-of-bounds slice indices is implementation-defined.
+
+  Args:
+    input: A `Tensor`. A `Tensor` of type T.
+    update: A `Tensor`. Must have the same type as `input`.
+      A `Tensor` of type T. Same rank as `input`.
+    indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+      A vector of indices into `input`. Must have length equal to the rank of
+      `input`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`. A `Tensor` of type T.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaDynamicUpdateSlice", name, input, update, indices)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_dynamic_update_slice(
+          (input, update, indices, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_dynamic_update_slice_eager_fallback(
+          input, update, indices, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_dynamic_update_slice, (), dict(input=input, update=update,
+                                               indices=indices, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_dynamic_update_slice(
+        (input, update, indices, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaDynamicUpdateSlice", input=input, update=update, indices=indices,
+                                 name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_dynamic_update_slice, (), dict(input=input, update=update,
+                                             indices=indices, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "Tindices",
+              _op._get_attr_type("Tindices"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaDynamicUpdateSlice", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaDynamicUpdateSlice = tf_export("raw_ops.XlaDynamicUpdateSlice")(_ops.to_raw_op(xla_dynamic_update_slice))
+_dispatcher_for_xla_dynamic_update_slice = xla_dynamic_update_slice._tf_type_based_dispatcher.Dispatch
+
+
+def xla_dynamic_update_slice_eager_fallback(input: Annotated[Any, TV_XlaDynamicUpdateSlice_T], update: Annotated[Any, TV_XlaDynamicUpdateSlice_T], indices: Annotated[Any, TV_XlaDynamicUpdateSlice_Tindices], name, ctx) -> Annotated[Any, TV_XlaDynamicUpdateSlice_T]:
+  _attr_T, _inputs_T = _execute.args_to_matching_eager([input, update], ctx, [])
+  (input, update) = _inputs_T
+  _attr_Tindices, (indices,) = _execute.args_to_matching_eager([indices], ctx, [_dtypes.int32, _dtypes.int64, ])
+  _inputs_flat = [input, update, indices]
+  _attrs = ("T", _attr_T, "Tindices", _attr_Tindices)
+  _result = _execute.execute(b"XlaDynamicUpdateSlice", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaDynamicUpdateSlice", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaEinsum_T = TypeVar("TV_XlaEinsum_T", _atypes.BFloat16, _atypes.Complex64, _atypes.Float32)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_einsum')
+def xla_einsum(a: Annotated[Any, TV_XlaEinsum_T], b: Annotated[Any, TV_XlaEinsum_T], equation: str, name=None) -> Annotated[Any, TV_XlaEinsum_T]:
+  r"""An op which supports basic einsum op with 2 inputs and 1 output.
+
+  This op has better TPU performance since it doesn't have explicitly reshape and
+  transpose operations as tf.einsum does.
+
+  Args:
+    a: A `Tensor`. Must be one of the following types: `complex64`, `bfloat16`, `float32`.
+    b: A `Tensor`. Must have the same type as `a`.
+    equation: A `string`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `a`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaEinsum", name, a, b, "equation", equation)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_einsum(
+          (a, b, equation, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_einsum_eager_fallback(
+          a, b, equation=equation, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_einsum, (), dict(a=a, b=b, equation=equation, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_einsum(
+        (a, b, equation, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  equation = _execute.make_str(equation, "equation")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaEinsum", a=a, b=b, equation=equation, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_einsum, (), dict(a=a, b=b, equation=equation, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("equation", _op.get_attr("equation"), "T",
+              _op._get_attr_type("T"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaEinsum", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaEinsum = tf_export("raw_ops.XlaEinsum")(_ops.to_raw_op(xla_einsum))
+_dispatcher_for_xla_einsum = xla_einsum._tf_type_based_dispatcher.Dispatch
+
+
+def xla_einsum_eager_fallback(a: Annotated[Any, TV_XlaEinsum_T], b: Annotated[Any, TV_XlaEinsum_T], equation: str, name, ctx) -> Annotated[Any, TV_XlaEinsum_T]:
+  equation = _execute.make_str(equation, "equation")
+  _attr_T, _inputs_T = _execute.args_to_matching_eager([a, b], ctx, [_dtypes.complex64, _dtypes.bfloat16, _dtypes.float32, ])
+  (a, b) = _inputs_T
+  _inputs_flat = [a, b]
+  _attrs = ("equation", equation, "T", _attr_T)
+  _result = _execute.execute(b"XlaEinsum", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaEinsum", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaGather_T = TypeVar("TV_XlaGather_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+TV_XlaGather_Tindices = TypeVar("TV_XlaGather_Tindices", _atypes.Int32, _atypes.Int64)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_gather')
+def xla_gather(operand: Annotated[Any, TV_XlaGather_T], start_indices: Annotated[Any, TV_XlaGather_Tindices], slice_sizes: Annotated[Any, TV_XlaGather_Tindices], dimension_numbers: str, indices_are_sorted: bool, name=None) -> Annotated[Any, TV_XlaGather_T]:
+  r"""Wraps the XLA Gather operator documented at
+
+    https://www.tensorflow.org/xla/operation_semantics#gather
+
+  Args:
+    operand: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`, `bool`.
+      The array we're gathering from.
+    start_indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+      Array containing the starting indices of the slices we gather.
+    slice_sizes: A `Tensor`. Must have the same type as `start_indices`.
+      slice_sizes[i] is the bounds for the slice on dimension i.
+    dimension_numbers: A `string`.
+      A serialized xla::GatherDimensionNumbers proto.
+    indices_are_sorted: A `bool`.
+      Boolean indicating if the indices are sorted.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `operand`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaGather", name, operand, start_indices, slice_sizes,
+        "dimension_numbers", dimension_numbers, "indices_are_sorted",
+        indices_are_sorted)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_gather(
+          (operand, start_indices, slice_sizes, dimension_numbers,
+          indices_are_sorted, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_gather_eager_fallback(
+          operand, start_indices, slice_sizes,
+          dimension_numbers=dimension_numbers,
+          indices_are_sorted=indices_are_sorted, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_gather, (), dict(operand=operand, start_indices=start_indices,
+                                 slice_sizes=slice_sizes,
+                                 dimension_numbers=dimension_numbers,
+                                 indices_are_sorted=indices_are_sorted,
+                                 name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_gather(
+        (operand, start_indices, slice_sizes, dimension_numbers,
+        indices_are_sorted, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  dimension_numbers = _execute.make_str(dimension_numbers, "dimension_numbers")
+  indices_are_sorted = _execute.make_bool(indices_are_sorted, "indices_are_sorted")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaGather", operand=operand, start_indices=start_indices,
+                     slice_sizes=slice_sizes,
+                     dimension_numbers=dimension_numbers,
+                     indices_are_sorted=indices_are_sorted, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_gather, (), dict(operand=operand, start_indices=start_indices,
+                               slice_sizes=slice_sizes,
+                               dimension_numbers=dimension_numbers,
+                               indices_are_sorted=indices_are_sorted,
+                               name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("dimension_numbers", _op.get_attr("dimension_numbers"),
+              "indices_are_sorted", _op._get_attr_bool("indices_are_sorted"),
+              "T", _op._get_attr_type("T"), "Tindices",
+              _op._get_attr_type("Tindices"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaGather", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaGather = tf_export("raw_ops.XlaGather")(_ops.to_raw_op(xla_gather))
+_dispatcher_for_xla_gather = xla_gather._tf_type_based_dispatcher.Dispatch
+
+
+def xla_gather_eager_fallback(operand: Annotated[Any, TV_XlaGather_T], start_indices: Annotated[Any, TV_XlaGather_Tindices], slice_sizes: Annotated[Any, TV_XlaGather_Tindices], dimension_numbers: str, indices_are_sorted: bool, name, ctx) -> Annotated[Any, TV_XlaGather_T]:
+  dimension_numbers = _execute.make_str(dimension_numbers, "dimension_numbers")
+  indices_are_sorted = _execute.make_bool(indices_are_sorted, "indices_are_sorted")
+  _attr_T, (operand,) = _execute.args_to_matching_eager([operand], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, _dtypes.bool, ])
+  _attr_Tindices, _inputs_Tindices = _execute.args_to_matching_eager([start_indices, slice_sizes], ctx, [_dtypes.int32, _dtypes.int64, ])
+  (start_indices, slice_sizes) = _inputs_Tindices
+  _inputs_flat = [operand, start_indices, slice_sizes]
+  _attrs = ("dimension_numbers", dimension_numbers, "indices_are_sorted",
+  indices_are_sorted, "T", _attr_T, "Tindices", _attr_Tindices)
+  _result = _execute.execute(b"XlaGather", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaGather", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaIf_Tcond = TypeVar("TV_XlaIf_Tcond", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_if')
+def xla_if(cond: Annotated[Any, TV_XlaIf_Tcond], inputs, then_branch, else_branch, Tout, name=None):
+  r"""output = cond ? then_branch(inputs) : else_branch(inputs).
+
+  Args:
+    cond: A `Tensor`. A boolean scalar.
+    inputs: A list of `Tensor` objects. A list of input tensors.
+    then_branch: A function decorated with @Defun.
+      A function takes 'inputs' and returns a list of tensors,
+      whose types are the same as what else_branch returns.
+    else_branch: A function decorated with @Defun.
+      A function takes 'inputs' and returns a list of tensors.
+      whose types are the same as what then_branch returns.
+    Tout: A list of `tf.DTypes`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects of type `Tout`.
+    A list of tensors returned by either then_branch(inputs) or
+    else_branch(inputs). The input shapes of the then_branch and
+    else_branch must match.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaIf", name, cond, inputs, "then_branch", then_branch,
+        "else_branch", else_branch, "Tout", Tout)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_if(
+          (cond, inputs, then_branch, else_branch, Tout, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_if_eager_fallback(
+          cond, inputs, then_branch=then_branch, else_branch=else_branch,
+          Tout=Tout, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_if, (), dict(cond=cond, inputs=inputs,
+                             then_branch=then_branch, else_branch=else_branch,
+                             Tout=Tout, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_if(
+        (cond, inputs, then_branch, else_branch, Tout, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if not isinstance(Tout, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Tout' argument to "
+        "'xla_if' Op, not %r." % Tout)
+  Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaIf", cond=cond, inputs=inputs, then_branch=then_branch,
+                 else_branch=else_branch, Tout=Tout, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_if, (), dict(cond=cond, inputs=inputs, then_branch=then_branch,
+                           else_branch=else_branch, Tout=Tout, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if not _result:
+    return _op
+  if _execute.must_record_gradient():
+    _attrs = ("Tcond", _op._get_attr_type("Tcond"), "then_branch",
+              _op.get_attr("then_branch"), "else_branch",
+              _op.get_attr("else_branch"), "Tin", _op.get_attr("Tin"), "Tout",
+              _op.get_attr("Tout"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaIf", _inputs_flat, _attrs, _result)
+  return _result
+
+XlaIf = tf_export("raw_ops.XlaIf")(_ops.to_raw_op(xla_if))
+_dispatcher_for_xla_if = xla_if._tf_type_based_dispatcher.Dispatch
+
+
+def xla_if_eager_fallback(cond: Annotated[Any, TV_XlaIf_Tcond], inputs, then_branch, else_branch, Tout, name, ctx):
+  if not isinstance(Tout, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Tout' argument to "
+        "'xla_if' Op, not %r." % Tout)
+  Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
+  _attr_Tcond, (cond,) = _execute.args_to_matching_eager([cond], ctx, [])
+  _attr_Tin, inputs = _execute.convert_to_mixed_eager_tensors(inputs, ctx)
+  _inputs_flat = [cond] + list(inputs)
+  _attrs = ("Tcond", _attr_Tcond, "then_branch", then_branch, "else_branch",
+  else_branch, "Tin", _attr_Tin, "Tout", Tout)
+  _result = _execute.execute(b"XlaIf", len(Tout), inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaIf", _inputs_flat, _attrs, _result)
+  return _result
+
+_XlaKeyValueSortOutput = collections.namedtuple(
+    "XlaKeyValueSort",
+    ["sorted_keys", "sorted_values"])
+
+
+TV_XlaKeyValueSort_K = TypeVar("TV_XlaKeyValueSort_K", _atypes.BFloat16, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+TV_XlaKeyValueSort_V = TypeVar("TV_XlaKeyValueSort_V", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_key_value_sort')
+def xla_key_value_sort(keys: Annotated[Any, TV_XlaKeyValueSort_K], values: Annotated[Any, TV_XlaKeyValueSort_V], name=None):
+  r"""Wraps the XLA Sort operator, documented at
+
+   https://www.tensorflow.org/performance/xla/operation_semantics#sort
+  .
+
+  Sorts a tensor. Currently only sorts in ascending order are supported.
+
+  Args:
+    keys: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
+      A `Tensor` of type K.
+    values: A `Tensor`. A `Tensor` of type V.
+    name: A name for the operation (optional).
+
+  Returns:
+    A tuple of `Tensor` objects (sorted_keys, sorted_values).
+
+    sorted_keys: A `Tensor`. Has the same type as `keys`. A `Tensor` of type K.
+    sorted_values: A `Tensor`. Has the same type as `values`. A `Tensor` of type V.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaKeyValueSort", name, keys, values)
+      _result = _XlaKeyValueSortOutput._make(_result)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_key_value_sort(
+          (keys, values, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_key_value_sort_eager_fallback(
+          keys, values, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_key_value_sort, (), dict(keys=keys, values=values, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_key_value_sort(
+        (keys, values, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaKeyValueSort", keys=keys, values=values, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_key_value_sort, (), dict(keys=keys, values=values, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("K", _op._get_attr_type("K"), "V", _op._get_attr_type("V"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaKeyValueSort", _inputs_flat, _attrs, _result)
+  _result = _XlaKeyValueSortOutput._make(_result)
+  return _result
+
+XlaKeyValueSort = tf_export("raw_ops.XlaKeyValueSort")(_ops.to_raw_op(xla_key_value_sort))
+_dispatcher_for_xla_key_value_sort = xla_key_value_sort._tf_type_based_dispatcher.Dispatch
+
+
+def xla_key_value_sort_eager_fallback(keys: Annotated[Any, TV_XlaKeyValueSort_K], values: Annotated[Any, TV_XlaKeyValueSort_V], name, ctx):
+  _attr_K, (keys,) = _execute.args_to_matching_eager([keys], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.int64, _dtypes.bfloat16, _dtypes.uint16, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
+  _attr_V, (values,) = _execute.args_to_matching_eager([values], ctx, [])
+  _inputs_flat = [keys, values]
+  _attrs = ("K", _attr_K, "V", _attr_V)
+  _result = _execute.execute(b"XlaKeyValueSort", 2, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaKeyValueSort", _inputs_flat, _attrs, _result)
+  _result = _XlaKeyValueSortOutput._make(_result)
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_optimization_barrier')
+def xla_optimization_barrier(input, name=None):
+  r"""Wraps the XLA OptimizationBarrier operator.
+
+  Documented at https://www.tensorflow.org/xla/operation_semantics#optimizationbarrier.
+
+  Args:
+    input: A list of `Tensor` objects. A Tuple of Arrays of any type.
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaOptimizationBarrier", name, input)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_optimization_barrier(
+          (input, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_optimization_barrier_eager_fallback(
+          input, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_optimization_barrier, (), dict(input=input, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_optimization_barrier(
+        (input, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaOptimizationBarrier", input=input, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_optimization_barrier, (), dict(input=input, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op.get_attr("T"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaOptimizationBarrier", _inputs_flat, _attrs, _result)
+  return _result
+
+XlaOptimizationBarrier = tf_export("raw_ops.XlaOptimizationBarrier")(_ops.to_raw_op(xla_optimization_barrier))
+_dispatcher_for_xla_optimization_barrier = xla_optimization_barrier._tf_type_based_dispatcher.Dispatch
+
+
+def xla_optimization_barrier_eager_fallback(input, name, ctx):
+  _attr_T, input = _execute.convert_to_mixed_eager_tensors(input, ctx)
+  _inputs_flat = list(input)
+  _attrs = ("T", _attr_T)
+  _result = _execute.execute(b"XlaOptimizationBarrier", len(input),
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaOptimizationBarrier", _inputs_flat, _attrs, _result)
+  return _result
+
+
+TV_XlaPad_T = TypeVar("TV_XlaPad_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+TV_XlaPad_Tindices = TypeVar("TV_XlaPad_Tindices", _atypes.Int32, _atypes.Int64)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_pad')
+def xla_pad(input: Annotated[Any, TV_XlaPad_T], padding_value: Annotated[Any, TV_XlaPad_T], padding_low: Annotated[Any, TV_XlaPad_Tindices], padding_high: Annotated[Any, TV_XlaPad_Tindices], padding_interior: Annotated[Any, TV_XlaPad_Tindices], name=None) -> Annotated[Any, TV_XlaPad_T]:
+  r"""Wraps the XLA Pad operator, documented at
+
+   https://www.tensorflow.org/performance/xla/operation_semantics#pad
+  .
+
+  Args:
+    input: A `Tensor`. A `Tensor` of type T.
+    padding_value: A `Tensor`. Must have the same type as `input`.
+      A scalar `Tensor` of type T.
+    padding_low: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+      the padding to apply at the start of each input dimensions. Must
+      be a compile-time constant 1D tensor of length equal to rank of input.
+    padding_high: A `Tensor`. Must have the same type as `padding_low`.
+      the padding to apply at the end of each input dimension. Must
+      be a compile-time constant 1D tensor of length equal to rank of input.
+    padding_interior: A `Tensor`. Must have the same type as `padding_low`.
+      the padding to apply between each input element. Must
+      be a compile-time constant 1D tensor of length equal to rank of input,
+      containing only non-negative values.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`. A `Tensor` of type T.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaPad", name, input, padding_value, padding_low, padding_high,
+        padding_interior)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_pad(
+          (input, padding_value, padding_low, padding_high, padding_interior,
+          name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_pad_eager_fallback(
+          input, padding_value, padding_low, padding_high, padding_interior,
+          name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_pad, (), dict(input=input, padding_value=padding_value,
+                              padding_low=padding_low,
+                              padding_high=padding_high,
+                              padding_interior=padding_interior, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_pad(
+        (input, padding_value, padding_low, padding_high, padding_interior,
+        name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaPad", input=input, padding_value=padding_value,
+                  padding_low=padding_low, padding_high=padding_high,
+                  padding_interior=padding_interior, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_pad, (), dict(input=input, padding_value=padding_value,
+                            padding_low=padding_low,
+                            padding_high=padding_high,
+                            padding_interior=padding_interior, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "Tindices",
+              _op._get_attr_type("Tindices"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaPad", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaPad = tf_export("raw_ops.XlaPad")(_ops.to_raw_op(xla_pad))
+_dispatcher_for_xla_pad = xla_pad._tf_type_based_dispatcher.Dispatch
+
+
+def xla_pad_eager_fallback(input: Annotated[Any, TV_XlaPad_T], padding_value: Annotated[Any, TV_XlaPad_T], padding_low: Annotated[Any, TV_XlaPad_Tindices], padding_high: Annotated[Any, TV_XlaPad_Tindices], padding_interior: Annotated[Any, TV_XlaPad_Tindices], name, ctx) -> Annotated[Any, TV_XlaPad_T]:
+  _attr_T, _inputs_T = _execute.args_to_matching_eager([input, padding_value], ctx, [])
+  (input, padding_value) = _inputs_T
+  _attr_Tindices, _inputs_Tindices = _execute.args_to_matching_eager([padding_low, padding_high, padding_interior], ctx, [_dtypes.int32, _dtypes.int64, ])
+  (padding_low, padding_high, padding_interior) = _inputs_Tindices
+  _inputs_flat = [input, padding_value, padding_low, padding_high, padding_interior]
+  _attrs = ("T", _attr_T, "Tindices", _attr_Tindices)
+  _result = _execute.execute(b"XlaPad", 1, inputs=_inputs_flat, attrs=_attrs,
+                             ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaPad", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaRecv_dtype = TypeVar("TV_XlaRecv_dtype", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_recv')
+def xla_recv(dtype: TV_XlaRecv_dtype, tensor_name: str, shape, name=None) -> Annotated[Any, TV_XlaRecv_dtype]:
+  r"""Receives the named tensor from another XLA computation. Wraps the XLA Recv
+
+  operator documented at
+   https://www.tensorflow.org/performance/xla/operation_semantics#recv .
+
+  Args:
+    dtype: A `tf.DType`. The type of the tensor.
+    tensor_name: A `string`. A string key that identifies the channel.
+    shape: A `tf.TensorShape` or list of `ints`. The shape of the tensor.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` of type `dtype`. The tensor to receive.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaRecv", name, "dtype", dtype, "tensor_name", tensor_name,
+        "shape", shape)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_recv(
+          (dtype, tensor_name, shape, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_recv_eager_fallback(
+          dtype=dtype, tensor_name=tensor_name, shape=shape, name=name,
+          ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_recv, (), dict(dtype=dtype, tensor_name=tensor_name,
+                               shape=shape, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_recv(
+        (dtype, tensor_name, shape, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  dtype = _execute.make_type(dtype, "dtype")
+  tensor_name = _execute.make_str(tensor_name, "tensor_name")
+  shape = _execute.make_shape(shape, "shape")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaRecv", dtype=dtype, tensor_name=tensor_name, shape=shape,
+                   name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_recv, (), dict(dtype=dtype, tensor_name=tensor_name,
+                             shape=shape, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("dtype", _op._get_attr_type("dtype"), "tensor_name",
+              _op.get_attr("tensor_name"), "shape", _op.get_attr("shape"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaRecv", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaRecv = tf_export("raw_ops.XlaRecv")(_ops.to_raw_op(xla_recv))
+_dispatcher_for_xla_recv = xla_recv._tf_type_based_dispatcher.Dispatch
+
+
+def xla_recv_eager_fallback(dtype: TV_XlaRecv_dtype, tensor_name: str, shape, name, ctx) -> Annotated[Any, TV_XlaRecv_dtype]:
+  dtype = _execute.make_type(dtype, "dtype")
+  tensor_name = _execute.make_str(tensor_name, "tensor_name")
+  shape = _execute.make_shape(shape, "shape")
+  _inputs_flat = []
+  _attrs = ("dtype", dtype, "tensor_name", tensor_name, "shape", shape)
+  _result = _execute.execute(b"XlaRecv", 1, inputs=_inputs_flat, attrs=_attrs,
+                             ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaRecv", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaReduce_T = TypeVar("TV_XlaReduce_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_reduce')
+def xla_reduce(input: Annotated[Any, TV_XlaReduce_T], init_value: Annotated[Any, TV_XlaReduce_T], dimensions_to_reduce, reducer, name=None) -> Annotated[Any, TV_XlaReduce_T]:
+  r"""Wraps the XLA Reduce operator, documented at
+
+   https://www.tensorflow.org/performance/xla/operation_semantics#reduce .
+
+  Args:
+    input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`, `bool`.
+      the input tensor
+    init_value: A `Tensor`. Must have the same type as `input`.
+      a scalar representing the initial value for the reduction
+    dimensions_to_reduce: A list of `ints`.
+      dimension numbers over which to reduce
+    reducer: A function decorated with @Defun. a reducer function to apply
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaReduce", name, input, init_value, "dimensions_to_reduce",
+        dimensions_to_reduce, "reducer", reducer)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_reduce(
+          (input, init_value, dimensions_to_reduce, reducer, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_reduce_eager_fallback(
+          input, init_value, dimensions_to_reduce=dimensions_to_reduce,
+          reducer=reducer, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_reduce, (), dict(input=input, init_value=init_value,
+                                 dimensions_to_reduce=dimensions_to_reduce,
+                                 reducer=reducer, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_reduce(
+        (input, init_value, dimensions_to_reduce, reducer, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if not isinstance(dimensions_to_reduce, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'dimensions_to_reduce' argument to "
+        "'xla_reduce' Op, not %r." % dimensions_to_reduce)
+  dimensions_to_reduce = [_execute.make_int(_i, "dimensions_to_reduce") for _i in dimensions_to_reduce]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaReduce", input=input, init_value=init_value,
+                     dimensions_to_reduce=dimensions_to_reduce,
+                     reducer=reducer, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_reduce, (), dict(input=input, init_value=init_value,
+                               dimensions_to_reduce=dimensions_to_reduce,
+                               reducer=reducer, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "dimensions_to_reduce",
+              _op.get_attr("dimensions_to_reduce"), "reducer",
+              _op.get_attr("reducer"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaReduce", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaReduce = tf_export("raw_ops.XlaReduce")(_ops.to_raw_op(xla_reduce))
+_dispatcher_for_xla_reduce = xla_reduce._tf_type_based_dispatcher.Dispatch
+
+
+def xla_reduce_eager_fallback(input: Annotated[Any, TV_XlaReduce_T], init_value: Annotated[Any, TV_XlaReduce_T], dimensions_to_reduce, reducer, name, ctx) -> Annotated[Any, TV_XlaReduce_T]:
+  if not isinstance(dimensions_to_reduce, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'dimensions_to_reduce' argument to "
+        "'xla_reduce' Op, not %r." % dimensions_to_reduce)
+  dimensions_to_reduce = [_execute.make_int(_i, "dimensions_to_reduce") for _i in dimensions_to_reduce]
+  _attr_T, _inputs_T = _execute.args_to_matching_eager([input, init_value], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, _dtypes.bool, ])
+  (input, init_value) = _inputs_T
+  _inputs_flat = [input, init_value]
+  _attrs = ("T", _attr_T, "dimensions_to_reduce", dimensions_to_reduce,
+  "reducer", reducer)
+  _result = _execute.execute(b"XlaReduce", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaReduce", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaReducePrecision_T = TypeVar("TV_XlaReducePrecision_T", _atypes.BFloat16, _atypes.Float32, _atypes.Float64, _atypes.Half)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_reduce_precision')
+def xla_reduce_precision(operand: Annotated[Any, TV_XlaReducePrecision_T], exponent_bits: int, mantissa_bits: int, name=None) -> Annotated[Any, TV_XlaReducePrecision_T]:
+  r"""Wraps the XLA ReducePrecision operator
+
+    documented at https://www.tensorflow.org/xla/operation_semantics#reduceprecision.
+
+  Args:
+    operand: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
+      array of floating-point type.
+    exponent_bits: An `int`. number of exponent bits in lower-precision format
+    mantissa_bits: An `int`. number of mantissa bits in lower-precision format
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `operand`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaReducePrecision", name, operand, "exponent_bits",
+        exponent_bits, "mantissa_bits", mantissa_bits)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_reduce_precision(
+          (operand, exponent_bits, mantissa_bits, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_reduce_precision_eager_fallback(
+          operand, exponent_bits=exponent_bits, mantissa_bits=mantissa_bits,
+          name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_reduce_precision, (), dict(operand=operand,
+                                           exponent_bits=exponent_bits,
+                                           mantissa_bits=mantissa_bits,
+                                           name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_reduce_precision(
+        (operand, exponent_bits, mantissa_bits, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  exponent_bits = _execute.make_int(exponent_bits, "exponent_bits")
+  mantissa_bits = _execute.make_int(mantissa_bits, "mantissa_bits")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaReducePrecision", operand=operand, exponent_bits=exponent_bits,
+                              mantissa_bits=mantissa_bits, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_reduce_precision, (), dict(operand=operand,
+                                         exponent_bits=exponent_bits,
+                                         mantissa_bits=mantissa_bits,
+                                         name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "exponent_bits",
+              _op._get_attr_int("exponent_bits"), "mantissa_bits",
+              _op._get_attr_int("mantissa_bits"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaReducePrecision", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaReducePrecision = tf_export("raw_ops.XlaReducePrecision")(_ops.to_raw_op(xla_reduce_precision))
+_dispatcher_for_xla_reduce_precision = xla_reduce_precision._tf_type_based_dispatcher.Dispatch
+
+
+def xla_reduce_precision_eager_fallback(operand: Annotated[Any, TV_XlaReducePrecision_T], exponent_bits: int, mantissa_bits: int, name, ctx) -> Annotated[Any, TV_XlaReducePrecision_T]:
+  exponent_bits = _execute.make_int(exponent_bits, "exponent_bits")
+  mantissa_bits = _execute.make_int(mantissa_bits, "mantissa_bits")
+  _attr_T, (operand,) = _execute.args_to_matching_eager([operand], ctx, [_dtypes.bfloat16, _dtypes.half, _dtypes.float32, _dtypes.float64, ])
+  _inputs_flat = [operand]
+  _attrs = ("T", _attr_T, "exponent_bits", exponent_bits, "mantissa_bits",
+  mantissa_bits)
+  _result = _execute.execute(b"XlaReducePrecision", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaReducePrecision", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaReduceScatter_T = TypeVar("TV_XlaReduceScatter_T", _atypes.BFloat16, _atypes.Float32, _atypes.Half, _atypes.Int32, _atypes.UInt32)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_reduce_scatter')
+def xla_reduce_scatter(input: Annotated[Any, TV_XlaReduceScatter_T], group_assignment: Annotated[Any, _atypes.Int32], scatter_dimension: Annotated[Any, _atypes.Int32], reduce_op: str, name=None) -> Annotated[Any, TV_XlaReduceScatter_T]:
+  r"""Wraps the XLA ReduceScatter operator
+
+    documented at https://www.tensorflow.org/xla/operation_semantics#reducescatter.
+
+  Args:
+    input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `int32`, `uint32`.
+      Array or a non-empty tuple of arrays to reduce across replicas.
+    group_assignment: A `Tensor` of type `int32`.
+      Groups between which the reductions are performed.
+    scatter_dimension: A `Tensor` of type `int32`. Dimension to scatter.
+    reduce_op: A `string` from: `"Min", "Max", "Mul", "Add", "Mean"`.
+      Reduction computation.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaReduceScatter", name, input, group_assignment,
+        scatter_dimension, "reduce_op", reduce_op)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_reduce_scatter(
+          (input, group_assignment, scatter_dimension, reduce_op, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_reduce_scatter_eager_fallback(
+          input, group_assignment, scatter_dimension, reduce_op=reduce_op,
+          name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_reduce_scatter, (), dict(input=input,
+                                         group_assignment=group_assignment,
+                                         scatter_dimension=scatter_dimension,
+                                         reduce_op=reduce_op, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_reduce_scatter(
+        (input, group_assignment, scatter_dimension, reduce_op, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  reduce_op = _execute.make_str(reduce_op, "reduce_op")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaReduceScatter", input=input, group_assignment=group_assignment,
+                            scatter_dimension=scatter_dimension,
+                            reduce_op=reduce_op, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_reduce_scatter, (), dict(input=input,
+                                       group_assignment=group_assignment,
+                                       scatter_dimension=scatter_dimension,
+                                       reduce_op=reduce_op, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "reduce_op",
+              _op.get_attr("reduce_op"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaReduceScatter", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaReduceScatter = tf_export("raw_ops.XlaReduceScatter")(_ops.to_raw_op(xla_reduce_scatter))
+_dispatcher_for_xla_reduce_scatter = xla_reduce_scatter._tf_type_based_dispatcher.Dispatch
+
+
+def xla_reduce_scatter_eager_fallback(input: Annotated[Any, TV_XlaReduceScatter_T], group_assignment: Annotated[Any, _atypes.Int32], scatter_dimension: Annotated[Any, _atypes.Int32], reduce_op: str, name, ctx) -> Annotated[Any, TV_XlaReduceScatter_T]:
+  reduce_op = _execute.make_str(reduce_op, "reduce_op")
+  _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [_dtypes.half, _dtypes.bfloat16, _dtypes.float32, _dtypes.int32, _dtypes.uint32, ])
+  group_assignment = _ops.convert_to_tensor(group_assignment, _dtypes.int32)
+  scatter_dimension = _ops.convert_to_tensor(scatter_dimension, _dtypes.int32)
+  _inputs_flat = [input, group_assignment, scatter_dimension]
+  _attrs = ("T", _attr_T, "reduce_op", reduce_op)
+  _result = _execute.execute(b"XlaReduceScatter", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaReduceScatter", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaReduceWindow_T = TypeVar("TV_XlaReduceWindow_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+TV_XlaReduceWindow_Tindices = TypeVar("TV_XlaReduceWindow_Tindices", _atypes.Int32, _atypes.Int64)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_reduce_window')
+def xla_reduce_window(input: Annotated[Any, TV_XlaReduceWindow_T], init_value: Annotated[Any, TV_XlaReduceWindow_T], window_dimensions: Annotated[Any, TV_XlaReduceWindow_Tindices], window_strides: Annotated[Any, TV_XlaReduceWindow_Tindices], base_dilations: Annotated[Any, TV_XlaReduceWindow_Tindices], window_dilations: Annotated[Any, TV_XlaReduceWindow_Tindices], padding: Annotated[Any, TV_XlaReduceWindow_Tindices], computation, name=None) -> Annotated[Any, TV_XlaReduceWindow_T]:
+  r"""Wraps the XLA ReduceWindow operator, documented at
+
+   https://www.tensorflow.org/performance/xla/operation_semantics#reducewindow .
+
+  Args:
+    input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`, `bool`.
+      the input tensor
+    init_value: A `Tensor`. Must have the same type as `input`.
+      a scalar representing the initial value for the reduction
+    window_dimensions: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+      the shape of the window
+    window_strides: A `Tensor`. Must have the same type as `window_dimensions`.
+      the inter-window strides
+    base_dilations: A `Tensor`. Must have the same type as `window_dimensions`.
+    window_dilations: A `Tensor`. Must have the same type as `window_dimensions`.
+    padding: A `Tensor`. Must have the same type as `window_dimensions`.
+      the padding to apply at the start and end of each input dimensions
+    computation: A function decorated with @Defun. a reducer function to apply
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaReduceWindow", name, input, init_value, window_dimensions,
+        window_strides, base_dilations, window_dilations, padding,
+        "computation", computation)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_reduce_window(
+          (input, init_value, window_dimensions, window_strides,
+          base_dilations, window_dilations, padding, computation, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_reduce_window_eager_fallback(
+          input, init_value, window_dimensions, window_strides,
+          base_dilations, window_dilations, padding, computation=computation,
+          name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_reduce_window, (), dict(input=input, init_value=init_value,
+                                        window_dimensions=window_dimensions,
+                                        window_strides=window_strides,
+                                        base_dilations=base_dilations,
+                                        window_dilations=window_dilations,
+                                        padding=padding,
+                                        computation=computation, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_reduce_window(
+        (input, init_value, window_dimensions, window_strides, base_dilations,
+        window_dilations, padding, computation, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaReduceWindow", input=input, init_value=init_value,
+                           window_dimensions=window_dimensions,
+                           window_strides=window_strides,
+                           base_dilations=base_dilations,
+                           window_dilations=window_dilations, padding=padding,
+                           computation=computation, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_reduce_window, (), dict(input=input, init_value=init_value,
+                                      window_dimensions=window_dimensions,
+                                      window_strides=window_strides,
+                                      base_dilations=base_dilations,
+                                      window_dilations=window_dilations,
+                                      padding=padding,
+                                      computation=computation, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "Tindices",
+              _op._get_attr_type("Tindices"), "computation",
+              _op.get_attr("computation"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaReduceWindow", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaReduceWindow = tf_export("raw_ops.XlaReduceWindow")(_ops.to_raw_op(xla_reduce_window))
+_dispatcher_for_xla_reduce_window = xla_reduce_window._tf_type_based_dispatcher.Dispatch
+
+
+def xla_reduce_window_eager_fallback(input: Annotated[Any, TV_XlaReduceWindow_T], init_value: Annotated[Any, TV_XlaReduceWindow_T], window_dimensions: Annotated[Any, TV_XlaReduceWindow_Tindices], window_strides: Annotated[Any, TV_XlaReduceWindow_Tindices], base_dilations: Annotated[Any, TV_XlaReduceWindow_Tindices], window_dilations: Annotated[Any, TV_XlaReduceWindow_Tindices], padding: Annotated[Any, TV_XlaReduceWindow_Tindices], computation, name, ctx) -> Annotated[Any, TV_XlaReduceWindow_T]:
+  _attr_T, _inputs_T = _execute.args_to_matching_eager([input, init_value], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, _dtypes.bool, ])
+  (input, init_value) = _inputs_T
+  _attr_Tindices, _inputs_Tindices = _execute.args_to_matching_eager([window_dimensions, window_strides, base_dilations, window_dilations, padding], ctx, [_dtypes.int32, _dtypes.int64, ])
+  (window_dimensions, window_strides, base_dilations, window_dilations, padding) = _inputs_Tindices
+  _inputs_flat = [input, init_value, window_dimensions, window_strides, base_dilations, window_dilations, padding]
+  _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "computation",
+  computation)
+  _result = _execute.execute(b"XlaReduceWindow", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaReduceWindow", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaRemoveDynamicDimensionSize_T = TypeVar("TV_XlaRemoveDynamicDimensionSize_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_remove_dynamic_dimension_size')
+def xla_remove_dynamic_dimension_size(input: Annotated[Any, TV_XlaRemoveDynamicDimensionSize_T], dim_index: Annotated[Any, _atypes.Int32], name=None) -> Annotated[Any, TV_XlaRemoveDynamicDimensionSize_T]:
+  r"""Inverse of XlaSetDynamicDimensionSize.
+
+  Make an xla bounded dynamic dimension into a static dimension. The bound of the
+  size of dimension `dim_index` becomes the static dimension size.
+
+  Args:
+    input: A `Tensor`.
+    dim_index: A `Tensor` of type `int32`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaRemoveDynamicDimensionSize", name, input, dim_index)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_remove_dynamic_dimension_size(
+          (input, dim_index, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_remove_dynamic_dimension_size_eager_fallback(
+          input, dim_index, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_remove_dynamic_dimension_size, (), dict(input=input,
+                                                        dim_index=dim_index,
+                                                        name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_remove_dynamic_dimension_size(
+        (input, dim_index, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaRemoveDynamicDimensionSize", input=input, dim_index=dim_index,
+                                         name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_remove_dynamic_dimension_size, (), dict(input=input,
+                                                      dim_index=dim_index,
+                                                      name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaRemoveDynamicDimensionSize", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaRemoveDynamicDimensionSize = tf_export("raw_ops.XlaRemoveDynamicDimensionSize")(_ops.to_raw_op(xla_remove_dynamic_dimension_size))
+_dispatcher_for_xla_remove_dynamic_dimension_size = xla_remove_dynamic_dimension_size._tf_type_based_dispatcher.Dispatch
+
+
+def xla_remove_dynamic_dimension_size_eager_fallback(input: Annotated[Any, TV_XlaRemoveDynamicDimensionSize_T], dim_index: Annotated[Any, _atypes.Int32], name, ctx) -> Annotated[Any, TV_XlaRemoveDynamicDimensionSize_T]:
+  _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
+  dim_index = _ops.convert_to_tensor(dim_index, _dtypes.int32)
+  _inputs_flat = [input, dim_index]
+  _attrs = ("T", _attr_T)
+  _result = _execute.execute(b"XlaRemoveDynamicDimensionSize", 1,
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaRemoveDynamicDimensionSize", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_replica_id')
+def xla_replica_id(name=None) -> Annotated[Any, _atypes.Int32]:
+  r"""Replica ID.
+
+  Args:
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` of type `int32`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaReplicaId", name)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_replica_id(
+          (name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_replica_id_eager_fallback(
+          name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_replica_id, (), dict(name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_replica_id(
+        (name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaReplicaId", name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_replica_id, (), dict(name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ()
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaReplicaId", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaReplicaId = tf_export("raw_ops.XlaReplicaId")(_ops.to_raw_op(xla_replica_id))
+_dispatcher_for_xla_replica_id = xla_replica_id._tf_type_based_dispatcher.Dispatch
+
+
+def xla_replica_id_eager_fallback(name, ctx) -> Annotated[Any, _atypes.Int32]:
+  _inputs_flat = []
+  _attrs = None
+  _result = _execute.execute(b"XlaReplicaId", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaReplicaId", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+_XlaRngBitGeneratorOutput = collections.namedtuple(
+    "XlaRngBitGenerator",
+    ["output_key", "output"])
+
+
+TV_XlaRngBitGenerator_dtype = TypeVar("TV_XlaRngBitGenerator_dtype", _atypes.Int32, _atypes.Int64, _atypes.UInt32, _atypes.UInt64)
+TV_XlaRngBitGenerator_Tshape = TypeVar("TV_XlaRngBitGenerator_Tshape", _atypes.Int32, _atypes.Int64)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_rng_bit_generator')
+def xla_rng_bit_generator(algorithm: Annotated[Any, _atypes.Int32], initial_state: Annotated[Any, _atypes.UInt64], shape: Annotated[Any, TV_XlaRngBitGenerator_Tshape], dtype:TV_XlaRngBitGenerator_dtype=_dtypes.uint64, name=None):
+  r"""Stateless PRNG bit generator.
+
+  Wraps the XLA RngBitGenerator operator, documented at
+   https://www.tensorflow.org/performance/xla/operation_semantics#rngbitgenerator.
+
+  Args:
+    algorithm: A `Tensor` of type `int32`. The PRNG algorithm to use, one of
+      tf.random.Algorithm.{PHILOX, THREEFRY, AUTO_SELECT}.
+    initial_state: A `Tensor` of type `uint64`.
+      Initial state for the PRNG algorithm. For THREEFRY, it should be
+      a u64[2] and for PHILOX a u64[3].
+    shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+      The output shape of the generated data.
+    dtype: An optional `tf.DType` from: `tf.int32, tf.int64, tf.uint32, tf.uint64`. Defaults to `tf.uint64`.
+      The type of the tensor.
+    name: A name for the operation (optional).
+
+  Returns:
+    A tuple of `Tensor` objects (output_key, output).
+
+    output_key: A `Tensor` of type `uint64`.
+    output: A `Tensor` of type `dtype`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaRngBitGenerator", name, algorithm, initial_state, shape,
+        "dtype", dtype)
+      _result = _XlaRngBitGeneratorOutput._make(_result)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_rng_bit_generator(
+          (algorithm, initial_state, shape, dtype, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_rng_bit_generator_eager_fallback(
+          algorithm, initial_state, shape, dtype=dtype, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_rng_bit_generator, (), dict(algorithm=algorithm,
+                                            initial_state=initial_state,
+                                            shape=shape, dtype=dtype,
+                                            name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_rng_bit_generator(
+        (algorithm, initial_state, shape, dtype, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if dtype is None:
+    dtype = _dtypes.uint64
+  dtype = _execute.make_type(dtype, "dtype")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaRngBitGenerator", algorithm=algorithm,
+                              initial_state=initial_state, shape=shape,
+                              dtype=dtype, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_rng_bit_generator, (), dict(algorithm=algorithm,
+                                          initial_state=initial_state,
+                                          shape=shape, dtype=dtype, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("dtype", _op._get_attr_type("dtype"), "Tshape",
+              _op._get_attr_type("Tshape"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaRngBitGenerator", _inputs_flat, _attrs, _result)
+  _result = _XlaRngBitGeneratorOutput._make(_result)
+  return _result
+
+XlaRngBitGenerator = tf_export("raw_ops.XlaRngBitGenerator")(_ops.to_raw_op(xla_rng_bit_generator))
+_dispatcher_for_xla_rng_bit_generator = xla_rng_bit_generator._tf_type_based_dispatcher.Dispatch
+
+
+def xla_rng_bit_generator_eager_fallback(algorithm: Annotated[Any, _atypes.Int32], initial_state: Annotated[Any, _atypes.UInt64], shape: Annotated[Any, TV_XlaRngBitGenerator_Tshape], dtype: TV_XlaRngBitGenerator_dtype, name, ctx):
+  if dtype is None:
+    dtype = _dtypes.uint64
+  dtype = _execute.make_type(dtype, "dtype")
+  _attr_Tshape, (shape,) = _execute.args_to_matching_eager([shape], ctx, [_dtypes.int32, _dtypes.int64, ], _dtypes.int32)
+  algorithm = _ops.convert_to_tensor(algorithm, _dtypes.int32)
+  initial_state = _ops.convert_to_tensor(initial_state, _dtypes.uint64)
+  _inputs_flat = [algorithm, initial_state, shape]
+  _attrs = ("dtype", dtype, "Tshape", _attr_Tshape)
+  _result = _execute.execute(b"XlaRngBitGenerator", 2, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaRngBitGenerator", _inputs_flat, _attrs, _result)
+  _result = _XlaRngBitGeneratorOutput._make(_result)
+  return _result
+
+
+TV_XlaScatter_T = TypeVar("TV_XlaScatter_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+TV_XlaScatter_Tindices = TypeVar("TV_XlaScatter_Tindices", _atypes.Int32, _atypes.Int64)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_scatter')
+def xla_scatter(operand: Annotated[Any, TV_XlaScatter_T], scatter_indices: Annotated[Any, TV_XlaScatter_Tindices], updates: Annotated[Any, TV_XlaScatter_T], update_computation, dimension_numbers: str, indices_are_sorted: bool, name=None) -> Annotated[Any, TV_XlaScatter_T]:
+  r"""Wraps the XLA Scatter operator documented at
+
+    https://www.tensorflow.org/xla/operation_semantics#scatter.
+
+  Args:
+    operand: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`, `bool`.
+      Array to be scattered into.
+    scatter_indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+      Array containing the starting indices of the slices that must
+      be scattered to.
+    updates: A `Tensor`. Must have the same type as `operand`.
+      Array containing the values that must be used for scattering.
+    update_computation: A function decorated with @Defun.
+      Computation to be used for combining the existing values in
+      the input array and the updates during scatter.
+    dimension_numbers: A `string`.
+      A serialized xla::ScatterDimensionNumbers proto.
+    indices_are_sorted: A `bool`.
+      Boolean indicating if the indices are sorted.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `operand`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaScatter", name, operand, scatter_indices, updates,
+        "update_computation", update_computation, "dimension_numbers",
+        dimension_numbers, "indices_are_sorted", indices_are_sorted)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_scatter(
+          (operand, scatter_indices, updates, update_computation,
+          dimension_numbers, indices_are_sorted, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_scatter_eager_fallback(
+          operand, scatter_indices, updates,
+          update_computation=update_computation,
+          dimension_numbers=dimension_numbers,
+          indices_are_sorted=indices_are_sorted, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_scatter, (), dict(operand=operand,
+                                  scatter_indices=scatter_indices,
+                                  updates=updates,
+                                  update_computation=update_computation,
+                                  dimension_numbers=dimension_numbers,
+                                  indices_are_sorted=indices_are_sorted,
+                                  name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_scatter(
+        (operand, scatter_indices, updates, update_computation,
+        dimension_numbers, indices_are_sorted, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  dimension_numbers = _execute.make_str(dimension_numbers, "dimension_numbers")
+  indices_are_sorted = _execute.make_bool(indices_are_sorted, "indices_are_sorted")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaScatter", operand=operand, scatter_indices=scatter_indices,
+                      updates=updates, update_computation=update_computation,
+                      dimension_numbers=dimension_numbers,
+                      indices_are_sorted=indices_are_sorted, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_scatter, (), dict(operand=operand,
+                                scatter_indices=scatter_indices,
+                                updates=updates,
+                                update_computation=update_computation,
+                                dimension_numbers=dimension_numbers,
+                                indices_are_sorted=indices_are_sorted,
+                                name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("update_computation", _op.get_attr("update_computation"),
+              "dimension_numbers", _op.get_attr("dimension_numbers"),
+              "indices_are_sorted", _op._get_attr_bool("indices_are_sorted"),
+              "T", _op._get_attr_type("T"), "Tindices",
+              _op._get_attr_type("Tindices"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaScatter", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaScatter = tf_export("raw_ops.XlaScatter")(_ops.to_raw_op(xla_scatter))
+_dispatcher_for_xla_scatter = xla_scatter._tf_type_based_dispatcher.Dispatch
+
+
+def xla_scatter_eager_fallback(operand: Annotated[Any, TV_XlaScatter_T], scatter_indices: Annotated[Any, TV_XlaScatter_Tindices], updates: Annotated[Any, TV_XlaScatter_T], update_computation, dimension_numbers: str, indices_are_sorted: bool, name, ctx) -> Annotated[Any, TV_XlaScatter_T]:
+  dimension_numbers = _execute.make_str(dimension_numbers, "dimension_numbers")
+  indices_are_sorted = _execute.make_bool(indices_are_sorted, "indices_are_sorted")
+  _attr_T, _inputs_T = _execute.args_to_matching_eager([operand, updates], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, _dtypes.bool, ])
+  (operand, updates) = _inputs_T
+  _attr_Tindices, (scatter_indices,) = _execute.args_to_matching_eager([scatter_indices], ctx, [_dtypes.int32, _dtypes.int64, ])
+  _inputs_flat = [operand, scatter_indices, updates]
+  _attrs = ("update_computation", update_computation, "dimension_numbers",
+  dimension_numbers, "indices_are_sorted", indices_are_sorted, "T", _attr_T,
+  "Tindices", _attr_Tindices)
+  _result = _execute.execute(b"XlaScatter", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaScatter", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaSelectAndScatter_T = TypeVar("TV_XlaSelectAndScatter_T", _atypes.BFloat16, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+TV_XlaSelectAndScatter_Tindices = TypeVar("TV_XlaSelectAndScatter_Tindices", _atypes.Int32, _atypes.Int64)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_select_and_scatter')
+def xla_select_and_scatter(operand: Annotated[Any, TV_XlaSelectAndScatter_T], window_dimensions: Annotated[Any, TV_XlaSelectAndScatter_Tindices], window_strides: Annotated[Any, TV_XlaSelectAndScatter_Tindices], padding: Annotated[Any, TV_XlaSelectAndScatter_Tindices], source: Annotated[Any, TV_XlaSelectAndScatter_T], init_value: Annotated[Any, TV_XlaSelectAndScatter_T], select, scatter, name=None) -> Annotated[Any, TV_XlaSelectAndScatter_T]:
+  r"""Wraps the XLA SelectAndScatter operator, documented at
+
+   https://www.tensorflow.org/performance/xla/operation_semantics#selectandscatter
+  .
+
+  Args:
+    operand: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
+      the input tensor
+    window_dimensions: A `Tensor`. Must be one of the following types: `int32`, `int64`.
+      the shape of the window
+    window_strides: A `Tensor`. Must have the same type as `window_dimensions`.
+      the inter-window strides
+    padding: A `Tensor`. Must have the same type as `window_dimensions`.
+      the padding to apply at the start and end of each input dimensions
+    source: A `Tensor`. Must have the same type as `operand`.
+      a tensor of values to scatter
+    init_value: A `Tensor`. Must have the same type as `operand`.
+      a scalar representing the initial value for the output tensor
+    select: A function decorated with @Defun. a selection function to apply
+    scatter: A function decorated with @Defun. a scatter function to apply
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `operand`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaSelectAndScatter", name, operand, window_dimensions,
+        window_strides, padding, source, init_value, "select", select,
+        "scatter", scatter)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_select_and_scatter(
+          (operand, window_dimensions, window_strides, padding, source,
+          init_value, select, scatter, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_select_and_scatter_eager_fallback(
+          operand, window_dimensions, window_strides, padding, source,
+          init_value, select=select, scatter=scatter, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_select_and_scatter, (), dict(operand=operand,
+                                             window_dimensions=window_dimensions,
+                                             window_strides=window_strides,
+                                             padding=padding, source=source,
+                                             init_value=init_value,
+                                             select=select, scatter=scatter,
+                                             name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_select_and_scatter(
+        (operand, window_dimensions, window_strides, padding, source,
+        init_value, select, scatter, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaSelectAndScatter", operand=operand,
+                               window_dimensions=window_dimensions,
+                               window_strides=window_strides, padding=padding,
+                               source=source, init_value=init_value,
+                               select=select, scatter=scatter, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_select_and_scatter, (), dict(operand=operand,
+                                           window_dimensions=window_dimensions,
+                                           window_strides=window_strides,
+                                           padding=padding, source=source,
+                                           init_value=init_value,
+                                           select=select, scatter=scatter,
+                                           name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "Tindices",
+              _op._get_attr_type("Tindices"), "select",
+              _op.get_attr("select"), "scatter", _op.get_attr("scatter"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaSelectAndScatter", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaSelectAndScatter = tf_export("raw_ops.XlaSelectAndScatter")(_ops.to_raw_op(xla_select_and_scatter))
+_dispatcher_for_xla_select_and_scatter = xla_select_and_scatter._tf_type_based_dispatcher.Dispatch
+
+
+def xla_select_and_scatter_eager_fallback(operand: Annotated[Any, TV_XlaSelectAndScatter_T], window_dimensions: Annotated[Any, TV_XlaSelectAndScatter_Tindices], window_strides: Annotated[Any, TV_XlaSelectAndScatter_Tindices], padding: Annotated[Any, TV_XlaSelectAndScatter_Tindices], source: Annotated[Any, TV_XlaSelectAndScatter_T], init_value: Annotated[Any, TV_XlaSelectAndScatter_T], select, scatter, name, ctx) -> Annotated[Any, TV_XlaSelectAndScatter_T]:
+  _attr_T, _inputs_T = _execute.args_to_matching_eager([operand, source, init_value], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
+  (operand, source, init_value) = _inputs_T
+  _attr_Tindices, _inputs_Tindices = _execute.args_to_matching_eager([window_dimensions, window_strides, padding], ctx, [_dtypes.int32, _dtypes.int64, ])
+  (window_dimensions, window_strides, padding) = _inputs_Tindices
+  _inputs_flat = [operand, window_dimensions, window_strides, padding, source, init_value]
+  _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "select", select,
+  "scatter", scatter)
+  _result = _execute.execute(b"XlaSelectAndScatter", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaSelectAndScatter", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+_XlaSelfAdjointEigOutput = collections.namedtuple(
+    "XlaSelfAdjointEig",
+    ["w", "v"])
+
+
+TV_XlaSelfAdjointEig_T = TypeVar("TV_XlaSelfAdjointEig_T", _atypes.BFloat16, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_self_adjoint_eig')
+def xla_self_adjoint_eig(a: Annotated[Any, TV_XlaSelfAdjointEig_T], lower: bool, max_iter: int, epsilon: float, name=None):
+  r"""Computes the eigen decomposition of a batch of self-adjoint matrices
+
+  (Note: Only real inputs are supported).
+
+  Computes the eigenvalues and eigenvectors of the innermost N-by-N matrices in
+  tensor such that tensor[...,:,:] * v[..., :,i] = e[..., i] * v[...,:,i], for
+  i=0...N-1.
+
+  Args:
+    a: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
+      the input tensor.
+    lower: A `bool`.
+      a boolean specifies whether the calculation is done with the lower
+      triangular part or the upper triangular part.
+    max_iter: An `int`.
+      maximum number of sweep update, i.e., the whole lower triangular
+      part or upper triangular part based on parameter lower. Heuristically, it has
+      been argued that approximately logN sweeps are needed in practice (Ref: Golub &
+      van Loan "Matrix Computation").
+    epsilon: A `float`. the tolerance ratio.
+    name: A name for the operation (optional).
+
+  Returns:
+    A tuple of `Tensor` objects (w, v).
+
+    w: A `Tensor`. Has the same type as `a`. The eigenvalues in ascending order, each repeated according to its
+      multiplicity.
+    v: A `Tensor`. Has the same type as `a`. The column v[..., :, i] is the normalized eigenvector corresponding to the
+      eigenvalue w[..., i].
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaSelfAdjointEig", name, a, "lower", lower, "max_iter",
+        max_iter, "epsilon", epsilon)
+      _result = _XlaSelfAdjointEigOutput._make(_result)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_self_adjoint_eig(
+          (a, lower, max_iter, epsilon, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_self_adjoint_eig_eager_fallback(
+          a, lower=lower, max_iter=max_iter, epsilon=epsilon, name=name,
+          ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_self_adjoint_eig, (), dict(a=a, lower=lower,
+                                           max_iter=max_iter, epsilon=epsilon,
+                                           name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_self_adjoint_eig(
+        (a, lower, max_iter, epsilon, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  lower = _execute.make_bool(lower, "lower")
+  max_iter = _execute.make_int(max_iter, "max_iter")
+  epsilon = _execute.make_float(epsilon, "epsilon")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaSelfAdjointEig", a=a, lower=lower, max_iter=max_iter,
+                             epsilon=epsilon, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_self_adjoint_eig, (), dict(a=a, lower=lower, max_iter=max_iter,
+                                         epsilon=epsilon, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("lower", _op._get_attr_bool("lower"), "max_iter",
+              _op._get_attr_int("max_iter"), "epsilon",
+              _op.get_attr("epsilon"), "T", _op._get_attr_type("T"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaSelfAdjointEig", _inputs_flat, _attrs, _result)
+  _result = _XlaSelfAdjointEigOutput._make(_result)
+  return _result
+
+XlaSelfAdjointEig = tf_export("raw_ops.XlaSelfAdjointEig")(_ops.to_raw_op(xla_self_adjoint_eig))
+_dispatcher_for_xla_self_adjoint_eig = xla_self_adjoint_eig._tf_type_based_dispatcher.Dispatch
+
+
+def xla_self_adjoint_eig_eager_fallback(a: Annotated[Any, TV_XlaSelfAdjointEig_T], lower: bool, max_iter: int, epsilon: float, name, ctx):
+  lower = _execute.make_bool(lower, "lower")
+  max_iter = _execute.make_int(max_iter, "max_iter")
+  epsilon = _execute.make_float(epsilon, "epsilon")
+  _attr_T, (a,) = _execute.args_to_matching_eager([a], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
+  _inputs_flat = [a]
+  _attrs = ("lower", lower, "max_iter", max_iter, "epsilon", epsilon, "T",
+  _attr_T)
+  _result = _execute.execute(b"XlaSelfAdjointEig", 2, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaSelfAdjointEig", _inputs_flat, _attrs, _result)
+  _result = _XlaSelfAdjointEigOutput._make(_result)
+  return _result
+
+
+TV_XlaSend_T = TypeVar("TV_XlaSend_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_send')
+def xla_send(tensor: Annotated[Any, TV_XlaSend_T], tensor_name: str, name=None):
+  r"""Sends the named tensor to another XLA computation. Wraps the XLA Send operator
+
+  documented at
+   https://www.tensorflow.org/performance/xla/operation_semantics#send .
+
+  Args:
+    tensor: A `Tensor`. The tensor to send.
+    tensor_name: A `string`. A string key that identifies the channel.
+    name: A name for the operation (optional).
+
+  Returns:
+    The created Operation.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaSend", name, tensor, "tensor_name", tensor_name)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_send(
+          (tensor, tensor_name, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_send_eager_fallback(
+          tensor, tensor_name=tensor_name, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_send, (), dict(tensor=tensor, tensor_name=tensor_name,
+                               name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_send(
+        (tensor, tensor_name, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  tensor_name = _execute.make_str(tensor_name, "tensor_name")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaSend", tensor=tensor, tensor_name=tensor_name, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_send, (), dict(tensor=tensor, tensor_name=tensor_name,
+                             name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  return _op
+XlaSend = tf_export("raw_ops.XlaSend")(_ops.to_raw_op(xla_send))
+_dispatcher_for_xla_send = xla_send._tf_type_based_dispatcher.Dispatch
+
+
+def xla_send_eager_fallback(tensor: Annotated[Any, TV_XlaSend_T], tensor_name: str, name, ctx):
+  tensor_name = _execute.make_str(tensor_name, "tensor_name")
+  _attr_T, (tensor,) = _execute.args_to_matching_eager([tensor], ctx, [])
+  _inputs_flat = [tensor]
+  _attrs = ("T", _attr_T, "tensor_name", tensor_name)
+  _result = _execute.execute(b"XlaSend", 0, inputs=_inputs_flat, attrs=_attrs,
+                             ctx=ctx, name=name)
+  _result = None
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_set_bound')
+def xla_set_bound(input: Annotated[Any, _atypes.Int32], bound: Annotated[Any, _atypes.Int32], name=None) -> Annotated[Any, _atypes.Int32]:
+  r"""Set a bound for the given input value as a hint to Xla compiler,
+
+          returns the same value.
+
+  Args:
+    input: A `Tensor` of type `int32`.
+    bound: A `Tensor` of type `int32`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` of type `int32`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaSetBound", name, input, bound)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_set_bound(
+          (input, bound, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_set_bound_eager_fallback(
+          input, bound, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_set_bound, (), dict(input=input, bound=bound, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_set_bound(
+        (input, bound, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaSetBound", input=input, bound=bound, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_set_bound, (), dict(input=input, bound=bound, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ()
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaSetBound", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaSetBound = tf_export("raw_ops.XlaSetBound")(_ops.to_raw_op(xla_set_bound))
+_dispatcher_for_xla_set_bound = xla_set_bound._tf_type_based_dispatcher.Dispatch
+
+
+def xla_set_bound_eager_fallback(input: Annotated[Any, _atypes.Int32], bound: Annotated[Any, _atypes.Int32], name, ctx) -> Annotated[Any, _atypes.Int32]:
+  input = _ops.convert_to_tensor(input, _dtypes.int32)
+  bound = _ops.convert_to_tensor(bound, _dtypes.int32)
+  _inputs_flat = [input, bound]
+  _attrs = None
+  _result = _execute.execute(b"XlaSetBound", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaSetBound", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaSetDynamicDimensionSize_T = TypeVar("TV_XlaSetDynamicDimensionSize_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_set_dynamic_dimension_size')
+def xla_set_dynamic_dimension_size(input: Annotated[Any, TV_XlaSetDynamicDimensionSize_T], dim_index: Annotated[Any, _atypes.Int32], size: Annotated[Any, _atypes.Int32], name=None) -> Annotated[Any, TV_XlaSetDynamicDimensionSize_T]:
+  r"""Make a static dimension into a xla bounded dynamic dimension.
+
+          The current static dimension size will become the bound and the second
+          operand becomes the dynamic size of the dimension.
+
+  Args:
+    input: A `Tensor`.
+    dim_index: A `Tensor` of type `int32`.
+    size: A `Tensor` of type `int32`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaSetDynamicDimensionSize", name, input, dim_index, size)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_set_dynamic_dimension_size(
+          (input, dim_index, size, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_set_dynamic_dimension_size_eager_fallback(
+          input, dim_index, size, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_set_dynamic_dimension_size, (), dict(input=input,
+                                                     dim_index=dim_index,
+                                                     size=size, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_set_dynamic_dimension_size(
+        (input, dim_index, size, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaSetDynamicDimensionSize", input=input, dim_index=dim_index,
+                                      size=size, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_set_dynamic_dimension_size, (), dict(input=input,
+                                                   dim_index=dim_index,
+                                                   size=size, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaSetDynamicDimensionSize", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaSetDynamicDimensionSize = tf_export("raw_ops.XlaSetDynamicDimensionSize")(_ops.to_raw_op(xla_set_dynamic_dimension_size))
+_dispatcher_for_xla_set_dynamic_dimension_size = xla_set_dynamic_dimension_size._tf_type_based_dispatcher.Dispatch
+
+
+def xla_set_dynamic_dimension_size_eager_fallback(input: Annotated[Any, TV_XlaSetDynamicDimensionSize_T], dim_index: Annotated[Any, _atypes.Int32], size: Annotated[Any, _atypes.Int32], name, ctx) -> Annotated[Any, TV_XlaSetDynamicDimensionSize_T]:
+  _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
+  dim_index = _ops.convert_to_tensor(dim_index, _dtypes.int32)
+  size = _ops.convert_to_tensor(size, _dtypes.int32)
+  _inputs_flat = [input, dim_index, size]
+  _attrs = ("T", _attr_T)
+  _result = _execute.execute(b"XlaSetDynamicDimensionSize", 1,
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaSetDynamicDimensionSize", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaSharding_T = TypeVar("TV_XlaSharding_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_sharding')
+def xla_sharding(input: Annotated[Any, TV_XlaSharding_T], sharding:str="", unspecified_dims=[], name=None) -> Annotated[Any, TV_XlaSharding_T]:
+  r"""An op which shards the input based on the given sharding attribute. It can
+
+  selectively annotate a subset of tensor dimensions by skipping unspecified_dims,
+  and the sharding annotation should be replicated in those dims.
+
+  Args:
+    input: A `Tensor`.
+    sharding: An optional `string`. Defaults to `""`.
+    unspecified_dims: An optional list of `ints`. Defaults to `[]`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaSharding", name, input, "sharding", sharding,
+        "unspecified_dims", unspecified_dims)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_sharding(
+          (input, sharding, unspecified_dims, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_sharding_eager_fallback(
+          input, sharding=sharding, unspecified_dims=unspecified_dims,
+          name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_sharding, (), dict(input=input, sharding=sharding,
+                                   unspecified_dims=unspecified_dims,
+                                   name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_sharding(
+        (input, sharding, unspecified_dims, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if sharding is None:
+    sharding = ""
+  sharding = _execute.make_str(sharding, "sharding")
+  if unspecified_dims is None:
+    unspecified_dims = []
+  if not isinstance(unspecified_dims, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'unspecified_dims' argument to "
+        "'xla_sharding' Op, not %r." % unspecified_dims)
+  unspecified_dims = [_execute.make_int(_i, "unspecified_dims") for _i in unspecified_dims]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaSharding", input=input, sharding=sharding,
+                       unspecified_dims=unspecified_dims, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_sharding, (), dict(input=input, sharding=sharding,
+                                 unspecified_dims=unspecified_dims, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "sharding",
+              _op.get_attr("sharding"), "unspecified_dims",
+              _op.get_attr("unspecified_dims"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaSharding", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaSharding = tf_export("raw_ops.XlaSharding")(_ops.to_raw_op(xla_sharding))
+_dispatcher_for_xla_sharding = xla_sharding._tf_type_based_dispatcher.Dispatch
+
+
+def xla_sharding_eager_fallback(input: Annotated[Any, TV_XlaSharding_T], sharding: str, unspecified_dims, name, ctx) -> Annotated[Any, TV_XlaSharding_T]:
+  if sharding is None:
+    sharding = ""
+  sharding = _execute.make_str(sharding, "sharding")
+  if unspecified_dims is None:
+    unspecified_dims = []
+  if not isinstance(unspecified_dims, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'unspecified_dims' argument to "
+        "'xla_sharding' Op, not %r." % unspecified_dims)
+  unspecified_dims = [_execute.make_int(_i, "unspecified_dims") for _i in unspecified_dims]
+  _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
+  _inputs_flat = [input]
+  _attrs = ("T", _attr_T, "sharding", sharding, "unspecified_dims",
+  unspecified_dims)
+  _result = _execute.execute(b"XlaSharding", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaSharding", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaSort_T = TypeVar("TV_XlaSort_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_sort')
+def xla_sort(input: Annotated[Any, TV_XlaSort_T], name=None) -> Annotated[Any, TV_XlaSort_T]:
+  r"""Wraps the XLA Sort operator, documented at
+
+   https://www.tensorflow.org/performance/xla/operation_semantics#sort
+  .
+
+  Sorts a tensor. Currently only sorts in ascending order are supported.
+
+  Args:
+    input: A `Tensor`. A `Tensor` of type T.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`. A `Tensor` of type T.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaSort", name, input)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_sort(
+          (input, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_sort_eager_fallback(
+          input, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_sort, (), dict(input=input, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_sort(
+        (input, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaSort", input=input, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_sort, (), dict(input=input, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaSort", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaSort = tf_export("raw_ops.XlaSort")(_ops.to_raw_op(xla_sort))
+_dispatcher_for_xla_sort = xla_sort._tf_type_based_dispatcher.Dispatch
+
+
+def xla_sort_eager_fallback(input: Annotated[Any, TV_XlaSort_T], name, ctx) -> Annotated[Any, TV_XlaSort_T]:
+  _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
+  _inputs_flat = [input]
+  _attrs = ("T", _attr_T)
+  _result = _execute.execute(b"XlaSort", 1, inputs=_inputs_flat, attrs=_attrs,
+                             ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaSort", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaSpmdFullToShardShape_T = TypeVar("TV_XlaSpmdFullToShardShape_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_spmd_full_to_shard_shape')
+def xla_spmd_full_to_shard_shape(input: Annotated[Any, TV_XlaSpmdFullToShardShape_T], manual_sharding: str, dim:int=-1, unspecified_dims=[], name=None) -> Annotated[Any, TV_XlaSpmdFullToShardShape_T]:
+  r"""An op used by XLA SPMD partitioner to switch from automatic partitioning to
+
+  manual partitioning. It annotates the input (full-shape, to be automatically
+  partitioned) with the same sharding used by manual partitioning, and outputs a
+  shard-shaped tensor to be consumed by later manually-partitioned ops. If the
+  shape is not evenly partitionable, the padding region will be masked with 0s.
+  The conversion can happen partially in subgroups, by specifying the dim
+  attribute, where only that dim will be converted.
+
+  Args:
+    input: A `Tensor`.
+    manual_sharding: A `string`.
+    dim: An optional `int`. Defaults to `-1`.
+    unspecified_dims: An optional list of `ints`. Defaults to `[]`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaSpmdFullToShardShape", name, input, "manual_sharding",
+        manual_sharding, "dim", dim, "unspecified_dims", unspecified_dims)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_spmd_full_to_shard_shape(
+          (input, manual_sharding, dim, unspecified_dims, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_spmd_full_to_shard_shape_eager_fallback(
+          input, manual_sharding=manual_sharding, dim=dim,
+          unspecified_dims=unspecified_dims, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_spmd_full_to_shard_shape, (), dict(input=input,
+                                                   manual_sharding=manual_sharding,
+                                                   dim=dim,
+                                                   unspecified_dims=unspecified_dims,
+                                                   name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_spmd_full_to_shard_shape(
+        (input, manual_sharding, dim, unspecified_dims, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  manual_sharding = _execute.make_str(manual_sharding, "manual_sharding")
+  if dim is None:
+    dim = -1
+  dim = _execute.make_int(dim, "dim")
+  if unspecified_dims is None:
+    unspecified_dims = []
+  if not isinstance(unspecified_dims, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'unspecified_dims' argument to "
+        "'xla_spmd_full_to_shard_shape' Op, not %r." % unspecified_dims)
+  unspecified_dims = [_execute.make_int(_i, "unspecified_dims") for _i in unspecified_dims]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaSpmdFullToShardShape", input=input,
+                                   manual_sharding=manual_sharding, dim=dim,
+                                   unspecified_dims=unspecified_dims,
+                                   name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_spmd_full_to_shard_shape, (), dict(input=input,
+                                                 manual_sharding=manual_sharding,
+                                                 dim=dim,
+                                                 unspecified_dims=unspecified_dims,
+                                                 name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "manual_sharding",
+              _op.get_attr("manual_sharding"), "dim",
+              _op._get_attr_int("dim"), "unspecified_dims",
+              _op.get_attr("unspecified_dims"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaSpmdFullToShardShape", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaSpmdFullToShardShape = tf_export("raw_ops.XlaSpmdFullToShardShape")(_ops.to_raw_op(xla_spmd_full_to_shard_shape))
+_dispatcher_for_xla_spmd_full_to_shard_shape = xla_spmd_full_to_shard_shape._tf_type_based_dispatcher.Dispatch
+
+
+def xla_spmd_full_to_shard_shape_eager_fallback(input: Annotated[Any, TV_XlaSpmdFullToShardShape_T], manual_sharding: str, dim: int, unspecified_dims, name, ctx) -> Annotated[Any, TV_XlaSpmdFullToShardShape_T]:
+  manual_sharding = _execute.make_str(manual_sharding, "manual_sharding")
+  if dim is None:
+    dim = -1
+  dim = _execute.make_int(dim, "dim")
+  if unspecified_dims is None:
+    unspecified_dims = []
+  if not isinstance(unspecified_dims, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'unspecified_dims' argument to "
+        "'xla_spmd_full_to_shard_shape' Op, not %r." % unspecified_dims)
+  unspecified_dims = [_execute.make_int(_i, "unspecified_dims") for _i in unspecified_dims]
+  _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
+  _inputs_flat = [input]
+  _attrs = ("T", _attr_T, "manual_sharding", manual_sharding, "dim", dim,
+  "unspecified_dims", unspecified_dims)
+  _result = _execute.execute(b"XlaSpmdFullToShardShape", 1,
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaSpmdFullToShardShape", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_XlaSpmdShardToFullShape_T = TypeVar("TV_XlaSpmdShardToFullShape_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_spmd_shard_to_full_shape')
+def xla_spmd_shard_to_full_shape(input: Annotated[Any, TV_XlaSpmdShardToFullShape_T], manual_sharding: str, full_shape, dim:int=-1, unspecified_dims=[], name=None) -> Annotated[Any, TV_XlaSpmdShardToFullShape_T]:
+  r"""An op used by XLA SPMD partitioner to switch from manual partitioning to
+
+  automatic partitioning. It converts the shard-shaped, manually partitioned input
+  into full-shaped tensor to be partitioned automatically with the same sharding
+  used by manual partitioning. The conversion can happen partially in subgroups,
+  by specifying the dim attribute, where only that dim will be converted.
+
+  Args:
+    input: A `Tensor`.
+    manual_sharding: A `string`.
+    full_shape: A `tf.TensorShape` or list of `ints`.
+    dim: An optional `int`. Defaults to `-1`.
+    unspecified_dims: An optional list of `ints`. Defaults to `[]`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaSpmdShardToFullShape", name, input, "manual_sharding",
+        manual_sharding, "full_shape", full_shape, "dim", dim,
+        "unspecified_dims", unspecified_dims)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_spmd_shard_to_full_shape(
+          (input, manual_sharding, full_shape, dim, unspecified_dims, name,),
+          None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_spmd_shard_to_full_shape_eager_fallback(
+          input, manual_sharding=manual_sharding, full_shape=full_shape,
+          dim=dim, unspecified_dims=unspecified_dims, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_spmd_shard_to_full_shape, (), dict(input=input,
+                                                   manual_sharding=manual_sharding,
+                                                   full_shape=full_shape,
+                                                   dim=dim,
+                                                   unspecified_dims=unspecified_dims,
+                                                   name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_spmd_shard_to_full_shape(
+        (input, manual_sharding, full_shape, dim, unspecified_dims, name,),
+        None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  manual_sharding = _execute.make_str(manual_sharding, "manual_sharding")
+  full_shape = _execute.make_shape(full_shape, "full_shape")
+  if dim is None:
+    dim = -1
+  dim = _execute.make_int(dim, "dim")
+  if unspecified_dims is None:
+    unspecified_dims = []
+  if not isinstance(unspecified_dims, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'unspecified_dims' argument to "
+        "'xla_spmd_shard_to_full_shape' Op, not %r." % unspecified_dims)
+  unspecified_dims = [_execute.make_int(_i, "unspecified_dims") for _i in unspecified_dims]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaSpmdShardToFullShape", input=input,
+                                   manual_sharding=manual_sharding,
+                                   full_shape=full_shape, dim=dim,
+                                   unspecified_dims=unspecified_dims,
+                                   name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_spmd_shard_to_full_shape, (), dict(input=input,
+                                                 manual_sharding=manual_sharding,
+                                                 full_shape=full_shape,
+                                                 dim=dim,
+                                                 unspecified_dims=unspecified_dims,
+                                                 name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "manual_sharding",
+              _op.get_attr("manual_sharding"), "full_shape",
+              _op.get_attr("full_shape"), "dim", _op._get_attr_int("dim"),
+              "unspecified_dims", _op.get_attr("unspecified_dims"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaSpmdShardToFullShape", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+XlaSpmdShardToFullShape = tf_export("raw_ops.XlaSpmdShardToFullShape")(_ops.to_raw_op(xla_spmd_shard_to_full_shape))
+_dispatcher_for_xla_spmd_shard_to_full_shape = xla_spmd_shard_to_full_shape._tf_type_based_dispatcher.Dispatch
+
+
+def xla_spmd_shard_to_full_shape_eager_fallback(input: Annotated[Any, TV_XlaSpmdShardToFullShape_T], manual_sharding: str, full_shape, dim: int, unspecified_dims, name, ctx) -> Annotated[Any, TV_XlaSpmdShardToFullShape_T]:
+  manual_sharding = _execute.make_str(manual_sharding, "manual_sharding")
+  full_shape = _execute.make_shape(full_shape, "full_shape")
+  if dim is None:
+    dim = -1
+  dim = _execute.make_int(dim, "dim")
+  if unspecified_dims is None:
+    unspecified_dims = []
+  if not isinstance(unspecified_dims, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'unspecified_dims' argument to "
+        "'xla_spmd_shard_to_full_shape' Op, not %r." % unspecified_dims)
+  unspecified_dims = [_execute.make_int(_i, "unspecified_dims") for _i in unspecified_dims]
+  _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
+  _inputs_flat = [input]
+  _attrs = ("T", _attr_T, "manual_sharding", manual_sharding, "full_shape",
+  full_shape, "dim", dim, "unspecified_dims", unspecified_dims)
+  _result = _execute.execute(b"XlaSpmdShardToFullShape", 1,
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaSpmdShardToFullShape", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+_XlaSvdOutput = collections.namedtuple(
+    "XlaSvd",
+    ["s", "u", "v"])
+
+
+TV_XlaSvd_T = TypeVar("TV_XlaSvd_T", _atypes.BFloat16, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_svd')
+def xla_svd(a: Annotated[Any, TV_XlaSvd_T], max_iter: int, epsilon: float, precision_config: str, name=None):
+  r"""Computes the eigen decomposition of a batch of self-adjoint matrices
+
+  (Note: Only real inputs are supported).
+
+  Computes the eigenvalues and eigenvectors of the innermost M-by-N matrices in
+  tensor such that tensor[...,:,:] = u[..., :, :] * Diag(s[..., :]) * Transpose(v[...,:,:]).
+
+  Args:
+    a: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
+      the input tensor.
+    max_iter: An `int`.
+      maximum number of sweep update, i.e., the whole lower triangular
+      part or upper triangular part based on parameter lower. Heuristically, it has
+      been argued that approximately log(min (M, N)) sweeps are needed in practice
+      (Ref: Golub & van Loan "Matrix Computation").
+    epsilon: A `float`. the tolerance ratio.
+    precision_config: A `string`. a serialized xla::PrecisionConfig proto.
+    name: A name for the operation (optional).
+
+  Returns:
+    A tuple of `Tensor` objects (s, u, v).
+
+    s: A `Tensor`. Has the same type as `a`. Singular values. The values are sorted in reverse order of magnitude, so
+      s[..., 0] is the largest value, s[..., 1] is the second largest, etc.
+    u: A `Tensor`. Has the same type as `a`. Left singular vectors.
+    v: A `Tensor`. Has the same type as `a`. Right singular vectors.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaSvd", name, a, "max_iter", max_iter, "epsilon", epsilon,
+        "precision_config", precision_config)
+      _result = _XlaSvdOutput._make(_result)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_svd(
+          (a, max_iter, epsilon, precision_config, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_svd_eager_fallback(
+          a, max_iter=max_iter, epsilon=epsilon,
+          precision_config=precision_config, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_svd, (), dict(a=a, max_iter=max_iter, epsilon=epsilon,
+                              precision_config=precision_config, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_svd(
+        (a, max_iter, epsilon, precision_config, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  max_iter = _execute.make_int(max_iter, "max_iter")
+  epsilon = _execute.make_float(epsilon, "epsilon")
+  precision_config = _execute.make_str(precision_config, "precision_config")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaSvd", a=a, max_iter=max_iter, epsilon=epsilon,
+                  precision_config=precision_config, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_svd, (), dict(a=a, max_iter=max_iter, epsilon=epsilon,
+                            precision_config=precision_config, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("max_iter", _op._get_attr_int("max_iter"), "epsilon",
+              _op.get_attr("epsilon"), "precision_config",
+              _op.get_attr("precision_config"), "T", _op._get_attr_type("T"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaSvd", _inputs_flat, _attrs, _result)
+  _result = _XlaSvdOutput._make(_result)
+  return _result
+
+XlaSvd = tf_export("raw_ops.XlaSvd")(_ops.to_raw_op(xla_svd))
+_dispatcher_for_xla_svd = xla_svd._tf_type_based_dispatcher.Dispatch
+
+
+def xla_svd_eager_fallback(a: Annotated[Any, TV_XlaSvd_T], max_iter: int, epsilon: float, precision_config: str, name, ctx):
+  max_iter = _execute.make_int(max_iter, "max_iter")
+  epsilon = _execute.make_float(epsilon, "epsilon")
+  precision_config = _execute.make_str(precision_config, "precision_config")
+  _attr_T, (a,) = _execute.args_to_matching_eager([a], ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, ])
+  _inputs_flat = [a]
+  _attrs = ("max_iter", max_iter, "epsilon", epsilon, "precision_config",
+  precision_config, "T", _attr_T)
+  _result = _execute.execute(b"XlaSvd", 3, inputs=_inputs_flat, attrs=_attrs,
+                             ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaSvd", _inputs_flat, _attrs, _result)
+  _result = _XlaSvdOutput._make(_result)
+  return _result
+
+
+TV_XlaVariadicReduce_T = TypeVar("TV_XlaVariadicReduce_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float32, _atypes.Float64, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.UInt16, _atypes.UInt32, _atypes.UInt64, _atypes.UInt8)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_variadic_reduce')
+def xla_variadic_reduce(input: Annotated[List[Any], TV_XlaVariadicReduce_T], init_value: Annotated[List[Any], TV_XlaVariadicReduce_T], dimensions_to_reduce, reducer, name=None):
+  r"""Wraps the variadic XLA Reduce operator.
+
+  Semantics are documented at
+   https://www.tensorflow.org/performance/xla/operation_semantics#variadic_reduce.
+
+  This version is limited to operands of the same dtype.
+  XlaVariadicReduceV2 is a version that supports heterogeneous operands.
+
+  Args:
+    input: A list of at least 1 `Tensor` objects with the same type in: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`, `bool`.
+      the input tensor(s)
+    init_value: A list with the same length as `input` of `Tensor` objects with the same type as `input`.
+      scalar initial value(s) for the reduction
+    dimensions_to_reduce: A list of `ints`.
+      dimension numbers over which to reduce
+    reducer: A function decorated with @Defun. a reducer function to apply
+    name: A name for the operation (optional).
+
+  Returns:
+    A list with the same length as `input` of `Tensor` objects with the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaVariadicReduce", name, input, init_value,
+        "dimensions_to_reduce", dimensions_to_reduce, "reducer", reducer)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_variadic_reduce(
+          (input, init_value, dimensions_to_reduce, reducer, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_variadic_reduce_eager_fallback(
+          input, init_value, dimensions_to_reduce=dimensions_to_reduce,
+          reducer=reducer, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_variadic_reduce, (), dict(input=input, init_value=init_value,
+                                          dimensions_to_reduce=dimensions_to_reduce,
+                                          reducer=reducer, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_variadic_reduce(
+        (input, init_value, dimensions_to_reduce, reducer, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if not isinstance(input, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'input' argument to "
+        "'xla_variadic_reduce' Op, not %r." % input)
+  _attr_N = len(input)
+  if not isinstance(init_value, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'init_value' argument to "
+        "'xla_variadic_reduce' Op, not %r." % init_value)
+  if len(init_value) != _attr_N:
+    raise ValueError(
+        "List argument 'init_value' to 'xla_variadic_reduce' Op with length %d "
+        "must match length %d of argument 'input'." %
+        (len(init_value), _attr_N))
+  if not isinstance(dimensions_to_reduce, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'dimensions_to_reduce' argument to "
+        "'xla_variadic_reduce' Op, not %r." % dimensions_to_reduce)
+  dimensions_to_reduce = [_execute.make_int(_i, "dimensions_to_reduce") for _i in dimensions_to_reduce]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaVariadicReduce", input=input, init_value=init_value,
+                             dimensions_to_reduce=dimensions_to_reduce,
+                             reducer=reducer, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_variadic_reduce, (), dict(input=input, init_value=init_value,
+                                        dimensions_to_reduce=dimensions_to_reduce,
+                                        reducer=reducer, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("N", _op._get_attr_int("N"), "T", _op._get_attr_type("T"),
+              "dimensions_to_reduce", _op.get_attr("dimensions_to_reduce"),
+              "reducer", _op.get_attr("reducer"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaVariadicReduce", _inputs_flat, _attrs, _result)
+  return _result
+
+XlaVariadicReduce = tf_export("raw_ops.XlaVariadicReduce")(_ops.to_raw_op(xla_variadic_reduce))
+_dispatcher_for_xla_variadic_reduce = xla_variadic_reduce._tf_type_based_dispatcher.Dispatch
+
+
+def xla_variadic_reduce_eager_fallback(input: Annotated[List[Any], TV_XlaVariadicReduce_T], init_value: Annotated[List[Any], TV_XlaVariadicReduce_T], dimensions_to_reduce, reducer, name, ctx):
+  if not isinstance(input, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'input' argument to "
+        "'xla_variadic_reduce' Op, not %r." % input)
+  _attr_N = len(input)
+  if not isinstance(init_value, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'init_value' argument to "
+        "'xla_variadic_reduce' Op, not %r." % init_value)
+  if len(init_value) != _attr_N:
+    raise ValueError(
+        "List argument 'init_value' to 'xla_variadic_reduce' Op with length %d "
+        "must match length %d of argument 'input'." %
+        (len(init_value), _attr_N))
+  if not isinstance(dimensions_to_reduce, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'dimensions_to_reduce' argument to "
+        "'xla_variadic_reduce' Op, not %r." % dimensions_to_reduce)
+  dimensions_to_reduce = [_execute.make_int(_i, "dimensions_to_reduce") for _i in dimensions_to_reduce]
+  _attr_T, _inputs_T = _execute.args_to_matching_eager(list(input) + list(init_value), ctx, [_dtypes.float32, _dtypes.float64, _dtypes.int32, _dtypes.uint8, _dtypes.int16, _dtypes.int8, _dtypes.complex64, _dtypes.int64, _dtypes.qint8, _dtypes.quint8, _dtypes.qint32, _dtypes.bfloat16, _dtypes.qint16, _dtypes.quint16, _dtypes.uint16, _dtypes.complex128, _dtypes.half, _dtypes.uint32, _dtypes.uint64, _dtypes.bool, ])
+  _inputs_T = [_inputs_T[:_attr_N]] + _inputs_T[_attr_N:]
+  _inputs_T = _inputs_T[:1] + [_inputs_T[1:]]
+  (input, init_value) = _inputs_T
+  _inputs_flat = list(input) + list(init_value)
+  _attrs = ("N", _attr_N, "T", _attr_T, "dimensions_to_reduce",
+  dimensions_to_reduce, "reducer", reducer)
+  _result = _execute.execute(b"XlaVariadicReduce", _attr_N,
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaVariadicReduce", _inputs_flat, _attrs, _result)
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_variadic_reduce_v2')
+def xla_variadic_reduce_v2(inputs, init_values, dimensions_to_reduce, reducer, name=None):
+  r"""Wraps the variadic XLA Reduce operator.
+
+  Semantics are documented at
+   https://www.tensorflow.org/performance/xla/operation_semantics#variadic_reduce.
+
+  This is an expanded version of XlaVariadicReduce, with support for
+  operands of different dtypes, and improved shape inference.
+
+  Args:
+    inputs: A list of `Tensor` objects. the input tensor(s)
+    init_values: A list of `Tensor` objects. Must have the same type as `inputs`.
+      scalar initial value(s) for the reduction
+    dimensions_to_reduce: A list of `ints`.
+      dimension numbers over which to reduce
+    reducer: A function decorated with @Defun. a reducer function to apply
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects. Has the same type as `inputs`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaVariadicReduceV2", name, inputs, init_values,
+        "dimensions_to_reduce", dimensions_to_reduce, "reducer", reducer)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_variadic_reduce_v2(
+          (inputs, init_values, dimensions_to_reduce, reducer, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_variadic_reduce_v2_eager_fallback(
+          inputs, init_values, dimensions_to_reduce=dimensions_to_reduce,
+          reducer=reducer, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_variadic_reduce_v2, (), dict(inputs=inputs,
+                                             init_values=init_values,
+                                             dimensions_to_reduce=dimensions_to_reduce,
+                                             reducer=reducer, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_variadic_reduce_v2(
+        (inputs, init_values, dimensions_to_reduce, reducer, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if not isinstance(dimensions_to_reduce, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'dimensions_to_reduce' argument to "
+        "'xla_variadic_reduce_v2' Op, not %r." % dimensions_to_reduce)
+  dimensions_to_reduce = [_execute.make_int(_i, "dimensions_to_reduce") for _i in dimensions_to_reduce]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaVariadicReduceV2", inputs=inputs, init_values=init_values,
+                               dimensions_to_reduce=dimensions_to_reduce,
+                               reducer=reducer, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_variadic_reduce_v2, (), dict(inputs=inputs,
+                                           init_values=init_values,
+                                           dimensions_to_reduce=dimensions_to_reduce,
+                                           reducer=reducer, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op.get_attr("T"), "dimensions_to_reduce",
+              _op.get_attr("dimensions_to_reduce"), "reducer",
+              _op.get_attr("reducer"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaVariadicReduceV2", _inputs_flat, _attrs, _result)
+  return _result
+
+XlaVariadicReduceV2 = tf_export("raw_ops.XlaVariadicReduceV2")(_ops.to_raw_op(xla_variadic_reduce_v2))
+_dispatcher_for_xla_variadic_reduce_v2 = xla_variadic_reduce_v2._tf_type_based_dispatcher.Dispatch
+
+
+def xla_variadic_reduce_v2_eager_fallback(inputs, init_values, dimensions_to_reduce, reducer, name, ctx):
+  if not isinstance(dimensions_to_reduce, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'dimensions_to_reduce' argument to "
+        "'xla_variadic_reduce_v2' Op, not %r." % dimensions_to_reduce)
+  dimensions_to_reduce = [_execute.make_int(_i, "dimensions_to_reduce") for _i in dimensions_to_reduce]
+  _attr_T, (inputs, init_values) = _execute.args_to_mixed_eager_tensors((inputs, init_values), ctx)
+  _inputs_flat = list(inputs) + list(init_values)
+  _attrs = ("T", _attr_T, "dimensions_to_reduce", dimensions_to_reduce,
+  "reducer", reducer)
+  _result = _execute.execute(b"XlaVariadicReduceV2", len(inputs),
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaVariadicReduceV2", _inputs_flat, _attrs, _result)
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_variadic_sort')
+def xla_variadic_sort(inputs, dimension: Annotated[Any, _atypes.Int32], comparator, is_stable: bool, name=None):
+  r"""Wraps the XLA Sort operator, documented at
+
+   https://www.tensorflow.org/performance/xla/operation_semantics#sort
+  .
+
+  Sorts one or more tensors, with support for custom comparator, dimension, and
+  is_stable attributes.
+
+  Args:
+    inputs: A list of `Tensor` objects.
+      A list of `Tensor` of identical shape but possibly different types.
+    dimension: A `Tensor` of type `int32`.
+      The dimension along which to sort. Must be a compile-time constant.
+    comparator: A function decorated with @Defun.
+      A comparator function to apply to 2*N scalars and returning a
+      boolean. N is the number of sort inputs. If you want to sort in ascending
+      order then the comparator should perform a less-than comparison.
+    is_stable: A `bool`. Whether to use stable sort.
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects. Has the same type as `inputs`.
+    A list of `Tensor` of same shape and types as the `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaVariadicSort", name, inputs, dimension, "comparator",
+        comparator, "is_stable", is_stable)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_variadic_sort(
+          (inputs, dimension, comparator, is_stable, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_variadic_sort_eager_fallback(
+          inputs, dimension, comparator=comparator, is_stable=is_stable,
+          name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_variadic_sort, (), dict(inputs=inputs, dimension=dimension,
+                                        comparator=comparator,
+                                        is_stable=is_stable, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_variadic_sort(
+        (inputs, dimension, comparator, is_stable, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  is_stable = _execute.make_bool(is_stable, "is_stable")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaVariadicSort", inputs=inputs, dimension=dimension,
+                           comparator=comparator, is_stable=is_stable,
+                           name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_variadic_sort, (), dict(inputs=inputs, dimension=dimension,
+                                      comparator=comparator,
+                                      is_stable=is_stable, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op.get_attr("T"), "comparator",
+              _op.get_attr("comparator"), "is_stable",
+              _op._get_attr_bool("is_stable"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaVariadicSort", _inputs_flat, _attrs, _result)
+  return _result
+
+XlaVariadicSort = tf_export("raw_ops.XlaVariadicSort")(_ops.to_raw_op(xla_variadic_sort))
+_dispatcher_for_xla_variadic_sort = xla_variadic_sort._tf_type_based_dispatcher.Dispatch
+
+
+def xla_variadic_sort_eager_fallback(inputs, dimension: Annotated[Any, _atypes.Int32], comparator, is_stable: bool, name, ctx):
+  is_stable = _execute.make_bool(is_stable, "is_stable")
+  _attr_T, inputs = _execute.convert_to_mixed_eager_tensors(inputs, ctx)
+  dimension = _ops.convert_to_tensor(dimension, _dtypes.int32)
+  _inputs_flat = list(inputs) + [dimension]
+  _attrs = ("T", _attr_T, "comparator", comparator, "is_stable", is_stable)
+  _result = _execute.execute(b"XlaVariadicSort", len(inputs),
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaVariadicSort", _inputs_flat, _attrs, _result)
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('xla_while')
+def xla_while(input, cond, body, name=None):
+  r"""output = input; While (Cond(output)) { output = Body(output) }
+
+  Args:
+    input: A list of `Tensor` objects.
+      A list of input tensors whose types are T.
+    cond: A function decorated with @Defun.
+      A function takes 'input' and returns a tensor.  If the tensor is
+      a scalar of non-boolean, the scalar is converted to a boolean
+      according to the following rule: if the scalar is a numerical
+      value, non-zero means True and zero means False; if the scalar is
+      a string, non-empty means True and empty means False. If the
+      tensor is not a scalar, non-emptiness means True and False
+      otherwise.
+    body: A function decorated with @Defun.
+      A function that takes a list of tensors and returns another
+      list of tensors. Both lists have the same types as specified by T.
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects. Has the same type as `input`.
+    A list of output tensors whose types are T.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "XlaWhile", name, input, "cond", cond, "body", body)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_xla_while(
+          (input, cond, body, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return xla_while_eager_fallback(
+          input, cond=cond, body=body, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            xla_while, (), dict(input=input, cond=cond, body=body, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_xla_while(
+        (input, cond, body, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "XlaWhile", input=input, cond=cond, body=body, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          xla_while, (), dict(input=input, cond=cond, body=body, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if not _result:
+    return _op
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op.get_attr("T"), "cond", _op.get_attr("cond"), "body",
+              _op.get_attr("body"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "XlaWhile", _inputs_flat, _attrs, _result)
+  return _result
+
+XlaWhile = tf_export("raw_ops.XlaWhile")(_ops.to_raw_op(xla_while))
+_dispatcher_for_xla_while = xla_while._tf_type_based_dispatcher.Dispatch
+
+
+def xla_while_eager_fallback(input, cond, body, name, ctx):
+  _attr_T, input = _execute.convert_to_mixed_eager_tensors(input, ctx)
+  _inputs_flat = list(input)
+  _attrs = ("T", _attr_T, "cond", cond, "body", body)
+  _result = _execute.execute(b"XlaWhile", len(input), inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "XlaWhile", _inputs_flat, _attrs, _result)
+  return _result
+
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/python/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/python/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/python/xla.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/python/xla.py
new file mode 100644
index 0000000000000000000000000000000000000000..792c5143fafd043f2e90b35c00035cf78dfbbded
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/python/xla.py
@@ -0,0 +1,726 @@
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Experimental library that exposes XLA operations directly in TensorFlow.
+
+It is sometimes useful to be able to build HLO programs directly from
+TensorFlow. This file provides Tensorflow operators that mirror the semantics of
+HLO operators as closely as possible.
+
+Note: Most of the operators defined in this module are used by the jax2tf
+converter (see go/jax2tf for details) and are used in SavedModel produced
+by jax2tf. Hence, we need to maintain backwards compatibility for these
+operators. Please reach out to the JAX team if you want to make changes.
+"""
+
+from tensorflow.compiler.tf2xla.ops import gen_xla_ops
+from tensorflow.compiler.xla import xla_data_pb2
+from tensorflow.core.framework import attr_value_pb2
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import bitwise_ops
+from tensorflow.python.ops import gen_math_ops
+from tensorflow.python.ops import gen_random_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import random_ops
+from tensorflow.python.ops import random_ops_util
+from tensorflow.python.ops import special_math_ops
+from tensorflow.python.ops.numpy_ops import np_utils
+
+# TODO(phawkins): provide wrappers for all XLA operators. Currently the missing
+# ops include:
+# infeed/outfeed (available via tf.contrib.tpu)
+# collectives, e.g., cross-replica-sum (available via tf.contrib.tpu)
+# conditional
+# gather/scatter
+# collapse
+
+# This file reuses builtin names (following XLA's names, so we can call things
+# like xla.max), so we capture the builtin versions here.
+# pylint: disable=redefined-builtin
+_max = max
+_min = min
+_slice = slice  # pylint: disable=invalid-name
+
+constant = constant_op.constant
+
+# Unary operators.
+
+# For most arithmetic operators there is a TensorFlow operator
+# that exactly corresponds to each XLA operator. Rather than defining
+# XLA-specific variants, we reuse the corresponding TensorFlow operator.
+# TODO(phawkins): It would be even better to have TensorFlow operators that 1:1
+# wrap every HLO operator, because that would allow us to be confident that the
+# semantics match.
+
+
+def _unary_op(fn):
+  """Wrapper that restricts `fn` to have the correct signature."""
+
+  def unary_op_wrapper(x, name=None):
+    return fn(x, name=name)
+
+  return unary_op_wrapper
+
+
+abs = _unary_op(math_ops.abs)
+# TODO(phawkins): implement clz.
+conj = _unary_op(math_ops.conj)
+cos = _unary_op(math_ops.cos)
+ceil = _unary_op(math_ops.ceil)
+digamma = _unary_op(math_ops.digamma)
+erf = _unary_op(math_ops.erf)
+erfc = _unary_op(math_ops.erfc)
+erfinv = _unary_op(math_ops.erfinv)
+ndtri = _unary_op(math_ops.ndtri)
+exp = _unary_op(math_ops.exp)
+expm1 = _unary_op(math_ops.expm1)
+floor = _unary_op(math_ops.floor)
+imag = _unary_op(math_ops.imag)
+is_finite = _unary_op(math_ops.is_finite)
+lgamma = _unary_op(math_ops.lgamma)
+log = _unary_op(math_ops.log)
+log1p = _unary_op(math_ops.log1p)
+logical_not = _unary_op(math_ops.logical_not)
+neg = _unary_op(math_ops.neg)
+real = _unary_op(math_ops.real)
+# TODO(phawkins): unlike xla::Round, this rounds to even instead of zero for
+# numbers halfway between two integers.
+round = _unary_op(math_ops.round)
+sin = _unary_op(math_ops.sin)
+sign = _unary_op(math_ops.sign)
+tan = _unary_op(math_ops.tan)
+tanh = _unary_op(math_ops.tanh)
+
+# Bessel
+bessel_i0e = _unary_op(special_math_ops.bessel_i0e)
+bessel_i1e = _unary_op(special_math_ops.bessel_i1e)
+
+# Binary operators
+
+# The main difference between TensorFlow and XLA binary ops is the broadcasting
+# semantics. TensorFlow uses Numpy-style broadcasting semantics, whereas XLA
+# requires an explicit specification of which dimensions to broadcast if the
+# arguments have different ranks.
+
+
+def _broadcasting_binary_op(fn):
+  """Wraps a binary Tensorflow operator and performs XLA-style broadcasting."""
+
+  def broadcasting_binary_op_wrapper(x, y, broadcast_dims=None, name=None):
+    """Inner wrapper function."""
+    broadcast_dims = broadcast_dims or []
+    broadcast_dims = ops.convert_to_tensor(broadcast_dims, dtypes.int64)
+    # Rather than relying on having static shape information in the TensorFlow
+    # graph, we use an XlaBroadcastHelper op that can compute the correct shapes
+    # at JIT compilation time.
+    x, y = gen_xla_ops.xla_broadcast_helper(x, y, broadcast_dims)
+    return fn(x, y, name=name)
+
+  return broadcasting_binary_op_wrapper
+
+
+# Map from TF signed types to TF unsigned types.
+_SIGNED_TO_UNSIGNED_TABLE = {
+    dtypes.int8: dtypes.uint8,
+    dtypes.int16: dtypes.uint16,
+    dtypes.int32: dtypes.uint32,
+    dtypes.int64: dtypes.uint64,
+}
+
+# Map from TF unsigned types to TF signed types.
+_UNSIGNED_TO_SIGNED_TABLE = {
+    dtypes.uint8: dtypes.int8,
+    dtypes.uint16: dtypes.int16,
+    dtypes.uint32: dtypes.int32,
+    dtypes.uint64: dtypes.int64,
+}
+
+
+def _shift_right_logical_helper(x, y, name=None):
+  """Performs an integer right logical shift irrespective of input type."""
+  assert y.dtype == x.dtype
+  dtype = x.dtype
+  signed = dtype in _SIGNED_TO_UNSIGNED_TABLE
+  if signed:
+    unsigned_dtype = _SIGNED_TO_UNSIGNED_TABLE[dtype]
+    x = math_ops.cast(x, unsigned_dtype)
+    y = math_ops.cast(y, unsigned_dtype)
+  output = bitwise_ops.right_shift(x, y, name=name)
+  if signed:
+    output = math_ops.cast(output, dtype)
+  return output
+
+
+def _shift_right_arithmetic_helper(x, y, name=None):
+  """Performs an integer right arithmetic shift irrespective of input type."""
+  assert y.dtype == x.dtype
+  dtype = x.dtype
+  unsigned = dtype in _UNSIGNED_TO_SIGNED_TABLE
+  if unsigned:
+    signed_dtype = _UNSIGNED_TO_SIGNED_TABLE[dtype]
+    x = math_ops.cast(x, signed_dtype)
+    y = math_ops.cast(y, signed_dtype)
+  output = bitwise_ops.right_shift(x, y, name=name)
+  if unsigned:
+    output = math_ops.cast(output, dtype)
+  return output
+
+
+add = _broadcasting_binary_op(math_ops.add)
+sub = _broadcasting_binary_op(math_ops.sub)
+mul = _broadcasting_binary_op(math_ops.mul)
+div = _broadcasting_binary_op(math_ops.div)
+rem = _broadcasting_binary_op(gen_math_ops.mod)
+max = _broadcasting_binary_op(math_ops.maximum)
+min = _broadcasting_binary_op(math_ops.minimum)
+atan2 = _broadcasting_binary_op(math_ops.atan2)
+complex = _broadcasting_binary_op(math_ops.complex)
+logical_and = _broadcasting_binary_op(math_ops.logical_and)
+logical_or = _broadcasting_binary_op(math_ops.logical_or)
+logical_xor = _broadcasting_binary_op(math_ops.logical_xor)
+eq = _broadcasting_binary_op(math_ops.equal)
+ne = _broadcasting_binary_op(math_ops.not_equal)
+ge = _broadcasting_binary_op(math_ops.greater_equal)
+gt = _broadcasting_binary_op(math_ops.greater)
+le = _broadcasting_binary_op(math_ops.less_equal)
+lt = _broadcasting_binary_op(math_ops.less)
+pow = _broadcasting_binary_op(math_ops.pow)
+shift_left = _broadcasting_binary_op(bitwise_ops.left_shift)
+shift_right_logical = _broadcasting_binary_op(_shift_right_logical_helper)
+shift_right_arithmetic = _broadcasting_binary_op(_shift_right_arithmetic_helper)
+
+igamma = _broadcasting_binary_op(math_ops.igamma)
+igamma_grad_a = _broadcasting_binary_op(gen_math_ops.igamma_grad_a)
+random_gamma_grad = _broadcasting_binary_op(gen_random_ops.random_gamma_grad)
+igammac = _broadcasting_binary_op(math_ops.igammac)
+polygamma = _broadcasting_binary_op(math_ops.polygamma)
+zeta = _broadcasting_binary_op(math_ops.zeta)
+
+
+def _binary_op(fn):
+  """Wrapper that restricts `fn` to have the correct signature."""
+
+  def binary_op_wrapper(x, y, name=None):
+    return fn(x, y, name=name)
+
+  return binary_op_wrapper
+
+
+transpose = _binary_op(array_ops.transpose)
+rev = _binary_op(array_ops.reverse)
+
+bitcast_convert_type = array_ops.bitcast
+
+
+def broadcast(x, dims, name=None):
+  x = ops.convert_to_tensor(x)
+  shape = array_ops.concat(
+      [constant_op.constant(dims), array_ops.shape(x)], axis=0
+  )
+  return array_ops.broadcast_to(x, shape, name=name)
+
+
+def clamp(a, x, b, name=None):
+  return min(max(a, x, name=name), b, name=name)
+
+
+concatenate = array_ops.concat
+
+
+def conv(
+    lhs,
+    rhs,
+    window_strides,
+    padding,
+    lhs_dilation,
+    rhs_dilation,
+    dimension_numbers,
+    feature_group_count=1,
+    precision_config=None,
+    preferred_element_type=None,
+    name=None,
+    use_v2=False,
+    batch_group_count=1,
+):
+  """Wraps the XLA ConvGeneralDilated operator.
+
+  ConvGeneralDilated is the most general form of XLA convolution and is
+  documented at
+  https://www.tensorflow.org/performance/xla/operation_semantics#conv_convolution
+
+  Args:
+    lhs: the input tensor
+    rhs: the kernel tensor
+    window_strides: the inter-window strides
+    padding: the padding to apply at the start and end of each input dimensions
+    lhs_dilation: dilation to apply between input elements
+    rhs_dilation: dilation to apply between kernel elements
+    dimension_numbers: a `ConvolutionDimensionNumbers` proto.
+    feature_group_count: number of feature groups for grouped convolution.
+    precision_config: a `xla.PrecisionConfig` proto.
+    preferred_element_type: the result `dtype`.
+    name: an optional name for the operator.
+    use_v2: an optional request to use the XlaConvV2 op even if not necessary.
+    batch_group_count: number of batch groups or grouped filters.
+
+  Returns:
+    A tensor representing the output of the convolution.
+  """
+  precision_config_proto = ""
+  if precision_config:
+    precision_config_proto = precision_config.SerializeToString()
+  needs_v2 = (
+      preferred_element_type
+      or (lhs.dtype != rhs.dtype)
+      or batch_group_count > 1
+  )
+  if preferred_element_type is None:
+    preferred_element_type = np_utils.result_type(lhs.dtype, rhs.dtype)
+  if needs_v2 or use_v2:
+    return gen_xla_ops.xla_conv_v2(
+        lhs,
+        rhs,
+        window_strides=window_strides,
+        padding=padding,
+        lhs_dilation=lhs_dilation,
+        rhs_dilation=rhs_dilation,
+        feature_group_count=feature_group_count,
+        batch_group_count=batch_group_count,
+        dimension_numbers=dimension_numbers.SerializeToString(),
+        precision_config=precision_config_proto,
+        preferred_element_type=preferred_element_type,
+        name=name,
+    )
+  return gen_xla_ops.xla_conv(
+      lhs,
+      rhs,
+      window_strides=window_strides,
+      padding=padding,
+      lhs_dilation=lhs_dilation,
+      rhs_dilation=rhs_dilation,
+      feature_group_count=feature_group_count,
+      dimension_numbers=dimension_numbers.SerializeToString(),
+      precision_config=precision_config_proto,
+      name=name,
+  )
+
+
+convert_element_type = math_ops.cast
+
+
+def dot(lhs, rhs, name=None):
+  return math_ops.tensordot(lhs, rhs, axes=1, name=name)
+
+
+DotDimensionNumbers = xla_data_pb2.DotDimensionNumbers
+PrecisionConfig = xla_data_pb2.PrecisionConfig
+
+
+def dot_general(
+    lhs,
+    rhs,
+    dimension_numbers,
+    precision_config=None,
+    preferred_element_type=None,
+    name=None,
+    use_v2=False,
+):
+  precision_config_proto = ""
+  if precision_config:
+    precision_config_proto = precision_config.SerializeToString()
+  needs_v2 = preferred_element_type or (lhs.dtype != rhs.dtype)
+  if preferred_element_type is None:
+    preferred_element_type = np_utils.result_type(lhs.dtype, rhs.dtype)
+  if needs_v2 or use_v2:
+    return gen_xla_ops.xla_dot_v2(
+        lhs,
+        rhs,
+        dimension_numbers=dimension_numbers.SerializeToString(),
+        precision_config=precision_config_proto,
+        preferred_element_type=preferred_element_type,
+        name=name,
+    )
+  return gen_xla_ops.xla_dot(
+      lhs,
+      rhs,
+      dimension_numbers=dimension_numbers.SerializeToString(),
+      precision_config=precision_config_proto,
+      name=name,
+  )
+
+
+def self_adjoint_eig(a, lower, max_iter, epsilon):
+  return gen_xla_ops.xla_self_adjoint_eig(a, lower, max_iter, epsilon)
+
+
+def svd(a, max_iter, epsilon, precision_config=None):
+  precision_config_proto = ""
+  if precision_config:
+    precision_config_proto = precision_config.SerializeToString()
+  return gen_xla_ops.xla_svd(a, max_iter, epsilon, precision_config_proto)
+
+
+dynamic_slice = gen_xla_ops.xla_dynamic_slice
+dynamic_update_slice = gen_xla_ops.xla_dynamic_update_slice
+einsum = gen_xla_ops.xla_einsum
+
+# TODO(phawkins): generalize tf.pad to support interior padding, and then remove
+# the XLA-specific pad operator.
+pad = gen_xla_ops.xla_pad
+
+
+def random_normal(mu, sigma, dims, name=None):
+  mu = ops.convert_to_tensor(mu)
+  return random_ops.random_normal(
+      dims, mean=mu, stddev=sigma, dtype=mu.dtype, name=name
+  )
+
+
+def random_uniform(minval, maxval, dims, name=None):
+  minval = ops.convert_to_tensor(minval)
+  return random_ops.random_uniform(
+      dims, minval, maxval, dtype=minval.dtype, name=name
+  )
+
+
+def rng_bit_generator(algorithm, initial_state, shape, dtype):
+  """Stateless PRNG bit generator.
+
+  Wraps the XLA RngBitGenerator operator, documented at
+    https://www.tensorflow.org/performance/xla/operation_semantics#rngbitgenerator.
+
+  Args:
+    algorithm: The PRNG algorithm to use, one of tf.random.Algorithm.{PHILOX,
+      THREEFRY, AUTO_SELECT}.
+    initial_state: Initial state for the PRNG algorithm. For THREEFRY, it should
+      be a u64[2] and for PHILOX a u64[3].
+    shape: The output shape of the generated data.
+    dtype: The type of the tensor.
+
+  Returns:
+    a tuple with a new state and generated data of the given shape.
+  """
+  alg_int = random_ops_util.convert_alg_to_int(algorithm)
+  return gen_xla_ops.xla_rng_bit_generator(
+      alg_int, initial_state, shape, dtype=dtype
+  )
+
+
+recv = gen_xla_ops.xla_recv
+reduce = gen_xla_ops.xla_reduce
+variadic_reduce = gen_xla_ops.xla_variadic_reduce_v2
+
+ops.no_gradient("XlaVariadicReduce")
+
+
+def reduce_window(
+    operand,
+    init,
+    reducer,
+    window_dimensions,
+    window_strides=None,
+    base_dilations=None,
+    window_dilations=None,
+    padding=None,
+    name=None,
+):
+  """Wraps the XLA ReduceWindow operator.
+
+  ReduceWindow is documented at
+  https://www.tensorflow.org/performance/xla/operation_semantics#reducewindow .
+
+  Args:
+    operand: the input tensor
+    init: a scalar tensor representing the initial value for the reduction
+    reducer: a reduction function that combines a pair of scalars.
+    window_dimensions: shape of the window, as a list of integers
+    window_strides: inter-window strides, as a list of integers. Optional; if
+      omitted, defaults to strides of 1.
+    padding: padding to apply to 'operand'. List of (low, high) pairs of
+      integers that specify the padding to apply before and after each
+      dimension. Optional; if omitted, defaults to no padding.
+    name: the operator name, or None.
+
+  Returns:
+    A tensor that represents the output of the reduce_window operator.
+  """
+  window_strides = window_strides or [1] * len(window_dimensions)
+  base_dilations = base_dilations or [1] * len(window_dimensions)
+  window_dilations = window_dilations or [1] * len(window_dimensions)
+  padding = padding or [(0, 0)] * len(window_dimensions)
+  return gen_xla_ops.xla_reduce_window(
+      input=operand,
+      init_value=init,
+      window_dimensions=window_dimensions,
+      window_strides=window_strides,
+      base_dilations=base_dilations,
+      window_dilations=window_dilations,
+      padding=padding,
+      computation=reducer,
+      name=name,
+  )
+
+
+replica_id = gen_xla_ops.xla_replica_id
+
+# Set a static bound for the given input value as a hint to Xla compiler,
+# returns the same value.
+# Usage:
+# def f(t, p):
+#   p = xla.set_bound(p, 3) # Tells xla the constraint that p <= 3.
+#   return t[:p]            # xla knows the bound of the slice is 3.
+set_bound = gen_xla_ops.xla_set_bound
+
+# Make a static dimension into a xla bounded dynamic dimension. The current
+# static dimension size will become the bound and the second operand becomes the
+# dynamic size of the dimension.
+#
+# This should mostly be used for testing.
+#
+# def f():
+#   array = tf.convert_to_tensor([[1, 2, 3, 4, 5]])
+#   # Tells xla the valid size of the array is 3.
+#   dim = 0
+#   p = xla_set_dynamic_dimension_size(array, dim, 3)
+#   assert(reduce_sum(p) == 6) # xla knows only the first 3 elements are valid.
+set_dynamic_dimension_size = gen_xla_ops.xla_set_dynamic_dimension_size
+
+# Inverse of xla_set_dynamic_dimension_size. Make an xla bounded dynamic
+# dimension into a static dimension. The bound of the size of dimension
+# `dim_index` becomes the static dimension size.
+remove_dynamic_dimension_size = gen_xla_ops.xla_remove_dynamic_dimension_size
+
+
+def reshape(x, new_sizes, dimensions=None, name=None):
+  if dimensions is not None:
+    x = array_ops.transpose(x, dimensions)
+  x = array_ops.reshape(x, new_sizes, name=name)
+  return x
+
+
+def select(condition, x, y, name=None):
+  return array_ops.where(condition, x, y, name)
+
+
+select_and_scatter = gen_xla_ops.xla_select_and_scatter
+send = gen_xla_ops.xla_send
+
+
+def slice(x, start_dims, limit_dims, strides):
+  spec = [
+      _slice(start, limit, stride)
+      for (start, limit, stride) in zip(start_dims, limit_dims, strides)
+  ]
+  return x[tuple(spec)]
+
+
+sharding = gen_xla_ops.xla_sharding
+
+
+@ops.RegisterGradient("XlaSharding")
+def _sharding_grad(op, grad):
+  """Gradient for XlaSharding op."""
+  sharding_attr = op.get_attr("sharding")
+  grad_sharding = gen_xla_ops.xla_sharding(
+      grad,
+      sharding=sharding_attr,
+      unspecified_dims=op.get_attr("unspecified_dims"),
+  )
+  # pylint: disable=protected-access
+  grad_sharding.op._set_attr(
+      "_XlaSharding", attr_value_pb2.AttrValue(s=sharding_attr)
+  )
+  return [grad_sharding]
+
+
+spmd_full_to_shard_shape = gen_xla_ops.xla_spmd_full_to_shard_shape
+spmd_shard_to_full_shape = gen_xla_ops.xla_spmd_shard_to_full_shape
+
+
+@ops.RegisterGradient("XlaSpmdFullToShardShape")
+def _spmd_full_to_shard_shape_grad(op, grad):
+  s2f = gen_xla_ops.xla_spmd_shard_to_full_shape(
+      grad,
+      manual_sharding=op.get_attr("manual_sharding"),
+      full_shape=op.inputs[0].shape.as_list(),
+      dim=op.get_attr("dim"),
+      unspecified_dims=op.get_attr("unspecified_dims"),
+  )
+  return [s2f]
+
+
+@ops.RegisterGradient("XlaSpmdShardToFullShape")
+def _spmd_shard_to_full_shape_grad(op, grad):
+  f2s = gen_xla_ops.xla_spmd_full_to_shard_shape(
+      grad,
+      manual_sharding=op.get_attr("manual_sharding"),
+      dim=op.get_attr("dim"),
+      unspecified_dims=op.get_attr("unspecified_dims"),
+  )
+  return [f2s]
+
+
+sort = gen_xla_ops.xla_sort
+key_value_sort = gen_xla_ops.xla_key_value_sort
+variadic_sort = gen_xla_ops.xla_variadic_sort
+while_loop = gen_xla_ops.xla_while
+dequantize = gen_xla_ops.xla_dequantize
+custom_call = gen_xla_ops.xla_custom_call
+
+
+def custom_call_v2(
+    call_target_name,
+    operands,
+    result_specs,
+    backend_config=None,
+    has_side_effect=None,
+    name=None,
+):
+  """Emits an HLO `CustomCall` operation with multiple outputs.
+
+  See `CustomCall` specification at
+    https://tensorflow.org/xla/operation_semantics#customcall,
+  and `mhlo.custom_call` specification at
+    https://tensorflow.org/mlir/hlo_ops#mhlocustom_call_mlirmhlocustomcallop.
+
+  Args:
+    call_target_name: Name of the user function. The function signature must
+      conform to version 3 of the API, see
+      `API_VERSION_STATUS_RETURNING_UNIFIED`. All operands and results assumed
+      to be in the default layout.
+    operands: A sequence of tensors with possibly different types.
+    result_specs: A sequence of tensor specs for all results.
+    backend_config: A string that encodes a metadata for the backend. Empty
+      string by default.
+    has_side_effect: Indicates whether the custom call has side effects. `False`
+      by default.
+    name: Optional name of the operation.
+
+  Returns:
+    A tuple of output tensors.
+  """
+  return gen_xla_ops.xla_custom_call_v2(
+      operands=operands,
+      call_target_name=call_target_name,
+      backend_config="" if backend_config is None else backend_config,
+      has_side_effect=False if has_side_effect is None else has_side_effect,
+      result_dtypes=tuple(spec.dtype for spec in result_specs),
+      result_shapes=tuple(spec.shape for spec in result_specs),
+      name=name,
+  )
+
+
+# pylint: disable=g-doc-args
+# pylint: disable=g-doc-return-or-yield
+def call_module(
+    args,
+    *,
+    version=4,
+    module,
+    Tout,
+    Sout,
+    platforms=(),
+    function_list=(),
+    has_token_input_output=False,
+    disabled_checks=(),
+):
+  """See documentation for the XlaCallModule op.
+
+  https://github.com/search?q=repo%3Atensorflow%2Ftensorflow+path%3Axla_ops.cc+xlacallmodule&type=code
+  """
+  res = gen_xla_ops.xla_call_module(
+      args,
+      version=version,
+      module=module,
+      dim_args_spec=(),
+      Tout=Tout,
+      Sout=Sout,
+      platforms=platforms,
+      function_list=function_list,
+      has_token_input_output=has_token_input_output,
+      disabled_checks=disabled_checks,
+  )
+  # Since XLACallModule op is stateful, zero return function will return the TF
+  # op under tf.function. It creates trouble for downstream codes.
+  # Here we force it return empty tuple to work around it.
+  # TODO(johnqiangzhang): Figure out a better way to handle control dependency.
+  if isinstance(res, ops.Operation):
+    res = ()
+  return res
+
+
+def call_module_maximum_supported_version():
+  """Maximum version of XlaCallModule op supported.
+
+  See versioning details documentation for the XlaCallModule op at:
+  https://github.com/search?q=repo%3Atensorflow%2Ftensorflow+path%3Axla_call_module+%22int+VERSION_MAXIMUM_SUPPORTED%22&type=code
+  """
+  return 8
+
+# pylint: enable=g-doc-args
+# pylint: enable=g-doc-return-or-yield
+
+
+def call_module_disable_check_platform():
+  # For use with xla_call_module.disabled_checks.
+  return "platform"
+
+
+def gather(
+    operand,
+    start_indices,
+    dimension_numbers,
+    slice_sizes,
+    indices_are_sorted=False,
+    name=None,
+):
+  return gen_xla_ops.xla_gather(
+      operand,
+      start_indices,
+      slice_sizes=slice_sizes,
+      dimension_numbers=dimension_numbers.SerializeToString(),
+      indices_are_sorted=indices_are_sorted,
+      name=name,
+  )
+
+
+def scatter(
+    operand,
+    scatter_indices,
+    updates,
+    update_computation,
+    dimension_numbers,
+    indices_are_sorted=False,
+    name=None,
+):
+  return gen_xla_ops.xla_scatter(
+      operand,
+      scatter_indices,
+      updates,
+      update_computation=update_computation,
+      dimension_numbers=dimension_numbers.SerializeToString(),
+      indices_are_sorted=indices_are_sorted,
+      name=name,
+  )
+
+
+def optimization_barrier(*args):
+  return gen_xla_ops.xla_optimization_barrier(args)
+
+
+def reduce_precision(operand, exponent_bits, mantissa_bits):
+  return gen_xla_ops.xla_reduce_precision(operand, exponent_bits, mantissa_bits)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/tf2xla_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/tf2xla_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..5be7d74bd5adc6a11330569cf0a6e5f4c0b3a5af
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/tf2xla/tf2xla_pb2.py
@@ -0,0 +1,36 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/compiler/tf2xla/tf2xla.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\'tensorflow/compiler/tf2xla/tf2xla.proto\x12\x11tensorflow.tf2xla\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\"3\n\x08TensorId\x12\x11\n\tnode_name\x18\x01 \x01(\t\x12\x14\n\x0coutput_index\x18\x02 \x01(\x03\"\x8e\x01\n\x04\x46\x65\x65\x64\x12\'\n\x02id\x18\x01 \x01(\x0b\x32\x1b.tensorflow.tf2xla.TensorId\x12+\n\x05shape\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12\x0c\n\x04name\x18\x03 \x01(\t\x12\"\n\x04type\x18\x04 \x01(\x0e\x32\x14.tensorflow.DataType\"\x8f\x01\n\x05\x46\x65tch\x12\'\n\x02id\x18\x01 \x01(\x0b\x32\x1b.tensorflow.tf2xla.TensorId\x12\x0c\n\x04name\x18\x02 \x01(\t\x12+\n\x05shape\x18\x03 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12\"\n\x04type\x18\x04 \x01(\x0e\x32\x14.tensorflow.DataType\"\x8e\x01\n\x08Variable\x12\x11\n\tnode_name\x18\x01 \x01(\t\x12\x0c\n\x04name\x18\x02 \x01(\t\x12+\n\x05shape\x18\x03 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12\"\n\x04type\x18\x04 \x01(\x0e\x32\x14.tensorflow.DataType\x12\x10\n\x08readonly\x18\x05 \x01(\x08\"\x87\x01\n\x06\x43onfig\x12%\n\x04\x66\x65\x65\x64\x18\x01 \x03(\x0b\x32\x17.tensorflow.tf2xla.Feed\x12\'\n\x05\x66\x65tch\x18\x02 \x03(\x0b\x32\x18.tensorflow.tf2xla.Fetch\x12-\n\x08variable\x18\x03 \x03(\x0b\x32\x1b.tensorflow.tf2xla.VariableB*\n\x15org.tensorflow.tf2xlaB\x0cTf2XlaProtosP\x01\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.compiler.tf2xla.tf2xla_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\025org.tensorflow.tf2xlaB\014Tf2XlaProtosP\001\370\001\001'
+  _TENSORID._serialized_start=147
+  _TENSORID._serialized_end=198
+  _FEED._serialized_start=201
+  _FEED._serialized_end=343
+  _FETCH._serialized_start=346
+  _FETCH._serialized_end=489
+  _VARIABLE._serialized_start=492
+  _VARIABLE._serialized_end=634
+  _CONFIG._serialized_start=637
+  _CONFIG._serialized_end=772
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/xla/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/xla/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/xla/service/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/xla/service/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/xla/service/hlo_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/xla/service/hlo_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7e8aaa431b979f8b983ee4d20f96354e0213b2a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/xla/service/hlo_pb2.py
@@ -0,0 +1,109 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: xla/service/hlo.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.compiler.xla import xla_data_pb2 as xla_dot_xla__data__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15xla/service/hlo.proto\x12\x03xla\x1a\x12xla/xla_data.proto\"\x8b\x16\n\x13HloInstructionProto\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0e\n\x06opcode\x18\x02 \x01(\t\x12\x1e\n\x05shape\x18\x03 \x01(\x0b\x32\x0f.xla.ShapeProto\x12!\n\x08metadata\x18\x07 \x01(\x0b\x32\x0f.xla.OpMetadata\x12\"\n\x07literal\x18\x08 \x01(\x0b\x32\x11.xla.LiteralProto\x12\x18\n\x10parameter_number\x18\t \x01(\x03\x12\x13\n\x0b\x66usion_kind\x18\x0b \x01(\t\x12\x13\n\x0btuple_index\x18\r \x01(\x03\x12\x12\n\ndimensions\x18\x0e \x03(\x03\x12\x1b\n\x06window\x18\x0f \x01(\x0b\x32\x0b.xla.Window\x12G\n\x1d\x63onvolution_dimension_numbers\x18\x10 \x01(\x0b\x32 .xla.ConvolutionDimensionNumbers\x12\x1b\n\x13\x66\x65\x61ture_group_count\x18\x32 \x01(\x03\x12\x19\n\x11\x62\x61tch_group_count\x18: \x01(\x03\x12\x42\n\x10slice_dimensions\x18\x11 \x03(\x0b\x32(.xla.HloInstructionProto.SliceDimensions\x12\x15\n\rexponent_bits\x18\x12 \x01(\x05\x12\x15\n\rmantissa_bits\x18\x13 \x01(\x05\x12\x1b\n\x13\x64ynamic_slice_sizes\x18\x14 \x03(\x03\x12*\n\x0epadding_config\x18\x15 \x01(\x0b\x32\x12.xla.PaddingConfig\x12\x16\n\x0eoutfeed_config\x18\x16 \x01(\x0c\x12-\n\x0c\x64istribution\x18\x17 \x01(\x0e\x32\x17.xla.RandomDistribution\x12\x0f\n\x07\x65psilon\x18\x18 \x01(\x02\x12\x15\n\rfeature_index\x18\x19 \x01(\x03\x12\x12\n\nchannel_id\x18\x1a \x01(\x03\x12\x15\n\rinfeed_config\x18\x1b \x01(\x0c\x12\x1a\n\x12\x63ustom_call_target\x18\x1c \x01(\t\x12&\n\routfeed_shape\x18\x1d \x01(\x0b\x32\x0f.xla.ShapeProto\x12\x37\n\x15\x64ot_dimension_numbers\x18\x1e \x01(\x0b\x32\x18.xla.DotDimensionNumbers\x12\x1e\n\x08\x66\x66t_type\x18\x1f \x01(\x0e\x32\x0c.xla.FftType\x12\x12\n\nfft_length\x18  \x03(\x03\x12\x1c\n\x14\x63omparison_direction\x18? \x01(\t\x12=\n\x18gather_dimension_numbers\x18! \x01(\x0b\x32\x1b.xla.GatherDimensionNumbers\x12\x1a\n\x12gather_slice_sizes\x18\" \x03(\x03\x12\n\n\x02id\x18# 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+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'xla.service.hlo_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _HLOINSTRUCTIONPROTO.fields_by_name['all_reduce_id']._options = None
+  _HLOINSTRUCTIONPROTO.fields_by_name['all_reduce_id']._serialized_options = b'\030\001'
+  _HLOINSTRUCTIONPROTO.fields_by_name['is_cross_program_prefetch']._options = None
+  _HLOINSTRUCTIONPROTO.fields_by_name['is_cross_program_prefetch']._serialized_options = b'\030\001'
+  _HLOSCHEDULEPROTO_SEQUENCESENTRY._options = None
+  _HLOSCHEDULEPROTO_SEQUENCESENTRY._serialized_options = b'8\001'
+  _LOGICALBUFFERPROTO_LOCATION.fields_by_name['instruction_name']._options = None
+  _LOGICALBUFFERPROTO_LOCATION.fields_by_name['instruction_name']._serialized_options = b'\030\001'
+  _CUSTOMCALLSCHEDULE._serialized_start=7848
+  _CUSTOMCALLSCHEDULE._serialized_end=7931
+  _CUSTOMCALLAPIVERSION._serialized_start=7934
+  _CUSTOMCALLAPIVERSION._serialized_end=8114
+  _KIND._serialized_start=8116
+  _KIND._serialized_end=8174
+  _HLOINSTRUCTIONPROTO._serialized_start=51
+  _HLOINSTRUCTIONPROTO._serialized_end=2878
+  _HLOINSTRUCTIONPROTO_SLICEDIMENSIONS._serialized_start=2533
+  _HLOINSTRUCTIONPROTO_SLICEDIMENSIONS._serialized_end=2596
+  _HLOCOMPUTATIONPROTO._serialized_start=2881
+  _HLOCOMPUTATIONPROTO._serialized_end=3114
+  _HLOSCHEDULEPROTO._serialized_start=3117
+  _HLOSCHEDULEPROTO._serialized_end=3333
+  _HLOSCHEDULEPROTO_INSTRUCTIONSEQUENCE._serialized_start=3194
+  _HLOSCHEDULEPROTO_INSTRUCTIONSEQUENCE._serialized_end=3240
+  _HLOSCHEDULEPROTO_SEQUENCESENTRY._serialized_start=3242
+  _HLOSCHEDULEPROTO_SEQUENCESENTRY._serialized_end=3333
+  _HLOINPUTOUTPUTALIASPROTO._serialized_start=3336
+  _HLOINPUTOUTPUTALIASPROTO._serialized_end=3555
+  _HLOINPUTOUTPUTALIASPROTO_ALIASENTRYPROTO._serialized_start=3428
+  _HLOINPUTOUTPUTALIASPROTO_ALIASENTRYPROTO._serialized_end=3555
+  _HLOBUFFERDONORPROTO._serialized_start=3558
+  _HLOBUFFERDONORPROTO._serialized_end=3726
+  _HLOBUFFERDONORPROTO_BUFFERDONORENTRYPROTO._serialized_start=3646
+  _HLOBUFFERDONORPROTO_BUFFERDONORENTRYPROTO._serialized_end=3726
+  _CROSSPROGRAMPREFETCH._serialized_start=3728
+  _CROSSPROGRAMPREFETCH._serialized_end=3800
+  _STACKFRAMEINDEXPROTO._serialized_start=3803
+  _STACKFRAMEINDEXPROTO._serialized_end=4152
+  _STACKFRAMEINDEXPROTO_FILELOCATION._serialized_start=3995
+  _STACKFRAMEINDEXPROTO_FILELOCATION._serialized_end=4087
+  _STACKFRAMEINDEXPROTO_STACKFRAME._serialized_start=4089
+  _STACKFRAMEINDEXPROTO_STACKFRAME._serialized_end=4152
+  _HLOMODULEPROTO._serialized_start=4155
+  _HLOMODULEPROTO._serialized_end=5236
+  _HLOMODULEPROTO_PROFILEINFO._serialized_start=4944
+  _HLOMODULEPROTO_PROFILEINFO._serialized_end=5132
+  _HLOMODULEPROTO_PROFILETYPE._serialized_start=5134
+  _HLOMODULEPROTO_PROFILETYPE._serialized_end=5203
+  _LOGICALBUFFERPROTO._serialized_start=5239
+  _LOGICALBUFFERPROTO._serialized_end=5447
+  _LOGICALBUFFERPROTO_LOCATION._serialized_start=5356
+  _LOGICALBUFFERPROTO_LOCATION._serialized_end=5447
+  _BUFFERALLOCATIONPROTO._serialized_start=5450
+  _BUFFERALLOCATIONPROTO._serialized_end=5826
+  _BUFFERALLOCATIONPROTO_ASSIGNED._serialized_start=5759
+  _BUFFERALLOCATIONPROTO_ASSIGNED._serialized_end=5826
+  _HEAPSIMULATORTRACE._serialized_start=5829
+  _HEAPSIMULATORTRACE._serialized_end=6171
+  _HEAPSIMULATORTRACE_EVENT._serialized_start=5965
+  _HEAPSIMULATORTRACE_EVENT._serialized_end=6171
+  _HEAPSIMULATORTRACE_EVENT_KIND._serialized_start=6128
+  _HEAPSIMULATORTRACE_EVENT_KIND._serialized_end=6171
+  _HLOMODULEGROUPPROTO._serialized_start=6173
+  _HLOMODULEGROUPPROTO._serialized_end=6250
+  _BUFFERASSIGNMENTPROTO._serialized_start=6253
+  _BUFFERASSIGNMENTPROTO._serialized_end=6595
+  _BUFFERASSIGNMENTPROTO_BUFFERALIAS._serialized_start=6504
+  _BUFFERASSIGNMENTPROTO_BUFFERALIAS._serialized_end=6595
+  _HLOPROTO._serialized_start=6597
+  _HLOPROTO._serialized_end=6723
+  _HLOSNAPSHOT._serialized_start=6726
+  _HLOSNAPSHOT._serialized_end=6868
+  _HLOMODULEMETADATAPROTO._serialized_start=6871
+  _HLOMODULEMETADATAPROTO._serialized_end=7056
+  _HLOPASSMETADATA._serialized_start=7059
+  _HLOPASSMETADATA._serialized_end=7293
+  _ENTRYFUNCTIONATTRIBUTES._serialized_start=7296
+  _ENTRYFUNCTIONATTRIBUTES._serialized_end=7731
+  _ENTRYFUNCTIONATTRIBUTES_SHAPEINDEX._serialized_start=7422
+  _ENTRYFUNCTIONATTRIBUTES_SHAPEINDEX._serialized_end=7451
+  _ENTRYFUNCTIONATTRIBUTES_BUFFERPARAMETERATTRIBUTES._serialized_start=7454
+  _ENTRYFUNCTIONATTRIBUTES_BUFFERPARAMETERATTRIBUTES._serialized_end=7731
+  _XLARUNTIMEEXECUTABLEPROTO._serialized_start=7733
+  _XLARUNTIMEEXECUTABLEPROTO._serialized_end=7846
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/xla/xla_data_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/xla/xla_data_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..896cfef74b7d7a80e56ce30a287b188b63e5213a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/compiler/xla/xla_data_pb2.py
@@ -0,0 +1,130 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: xla/xla_data.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x12xla/xla_data.proto\x12\x03xla\"\xb7\x01\n\rPaddingConfig\x12=\n\ndimensions\x18\x01 \x03(\x0b\x32).xla.PaddingConfig.PaddingConfigDimension\x1ag\n\x16PaddingConfigDimension\x12\x18\n\x10\x65\x64ge_padding_low\x18\x01 \x01(\x03\x12\x19\n\x11\x65\x64ge_padding_high\x18\x02 \x01(\x03\x12\x18\n\x10interior_padding\x18\x03 \x01(\x03\"\x1f\n\tTileProto\x12\x12\n\ndimensions\x18\x01 \x03(\x03\"\xe0\x03\n\x0bLayoutProto\x12*\n\x0f\x64im_level_types\x18\t \x03(\x0e\x32\x11.xla.DimLevelType\x12\x12\n\ndim_unique\x18\r \x03(\x08\x12\x13\n\x0b\x64im_ordered\x18\x0e \x03(\x08\x12\x16\n\x0eminor_to_major\x18\x01 \x03(\x03\x12\x1d\n\x05tiles\x18\x06 \x03(\x0b\x32\x0e.xla.TileProto\x12\x1c\n\x14\x65lement_size_in_bits\x18\x07 \x01(\x03\x12\x14\n\x0cmemory_space\x18\x08 \x01(\x03\x12\x30\n\x14index_primitive_type\x18\x0b \x01(\x0e\x32\x12.xla.PrimitiveType\x12\x32\n\x16pointer_primitive_type\x18\x0c 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+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'xla.xla_data_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _OPMETADATA.fields_by_name['profile_type']._options = None
+  _OPMETADATA.fields_by_name['profile_type']._serialized_options = b'\030\001'
+  _FRONTENDATTRIBUTES_MAPENTRY._options = None
+  _FRONTENDATTRIBUTES_MAPENTRY._serialized_options = b'8\001'
+  _PRIMITIVETYPE._serialized_start=5519
+  _PRIMITIVETYPE._serialized_end=5830
+  _DIMLEVELTYPE._serialized_start=5832
+  _DIMLEVELTYPE._serialized_end=5926
+  _PROFILETYPE._serialized_start=5928
+  _PROFILETYPE._serialized_end=5989
+  _PROFILESOURCE._serialized_start=5991
+  _PROFILESOURCE._serialized_end=6097
+  _COMPILATIONEVENT._serialized_start=6100
+  _COMPILATIONEVENT._serialized_end=6233
+  _PADDINGTYPE._serialized_start=6235
+  _PADDINGTYPE._serialized_end=6306
+  _FFTTYPE._serialized_start=6308
+  _FFTTYPE._serialized_end=6357
+  _RANDOMDISTRIBUTION._serialized_start=6359
+  _RANDOMDISTRIBUTION._serialized_end=6429
+  _RANDOMALGORITHM._serialized_start=6431
+  _RANDOMALGORITHM._serialized_end=6500
+  _PADDINGCONFIG._serialized_start=28
+  _PADDINGCONFIG._serialized_end=211
+  _PADDINGCONFIG_PADDINGCONFIGDIMENSION._serialized_start=108
+  _PADDINGCONFIG_PADDINGCONFIGDIMENSION._serialized_end=211
+  _TILEPROTO._serialized_start=213
+  _TILEPROTO._serialized_end=244
+  _LAYOUTPROTO._serialized_start=247
+  _LAYOUTPROTO._serialized_end=727
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+  _SHAPEPROTO._serialized_end=919
+  _PROGRAMSHAPEPROTO._serialized_start=921
+  _PROGRAMSHAPEPROTO._serialized_end=1035
+  _COMPUTATIONSTATS._serialized_start=1037
+  _COMPUTATIONSTATS._serialized_end=1105
+  _OPMETADATA._serialized_start=1108
+  _OPMETADATA._serialized_end=1695
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+  _OPMETADATA_PROFILEINFO._serialized_end=1689
+  _EXECUTIONPROFILE._serialized_start=1698
+  _EXECUTIONPROFILE._serialized_end=1925
+  _EXECUTIONHANDLE._serialized_start=1927
+  _EXECUTIONHANDLE._serialized_end=1960
+  _GLOBALDATAHANDLE._serialized_start=1962
+  _GLOBALDATAHANDLE._serialized_end=1996
+  _DEVICEHANDLE._serialized_start=1998
+  _DEVICEHANDLE._serialized_end=2050
+  _CHANNELHANDLE._serialized_start=2053
+  _CHANNELHANDLE._serialized_end=2233
+  _CHANNELHANDLE_CHANNELTYPE._serialized_start=2132
+  _CHANNELHANDLE_CHANNELTYPE._serialized_end=2233
+  _DEVICEASSIGNMENTPROTO._serialized_start=2236
+  _DEVICEASSIGNMENTPROTO._serialized_end=2433
+  _DEVICEASSIGNMENTPROTO_COMPUTATIONDEVICE._serialized_start=2386
+  _DEVICEASSIGNMENTPROTO_COMPUTATIONDEVICE._serialized_end=2433
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+  _WINDOWDIMENSION._serialized_start=2891
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+  _WINDOW._serialized_start=3056
+  _WINDOW._serialized_end=3106
+  _GATHERDIMENSIONNUMBERS._serialized_start=3108
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+  _SCATTERDIMENSIONNUMBERS._serialized_end=3384
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+  _CONVOLUTIONDIMENSIONNUMBERS._serialized_end=3731
+  _DOTDIMENSIONNUMBERS._serialized_start=3734
+  _DOTDIMENSIONNUMBERS._serialized_end=3887
+  _TRIANGULARSOLVEOPTIONS._serialized_start=3890
+  _TRIANGULARSOLVEOPTIONS._serialized_end=4113
+  _TRIANGULARSOLVEOPTIONS_TRANSPOSE._serialized_start=4033
+  _TRIANGULARSOLVEOPTIONS_TRANSPOSE._serialized_end=4113
+  _CHOLESKYOPTIONS._serialized_start=4115
+  _CHOLESKYOPTIONS._serialized_end=4147
+  _FRONTENDATTRIBUTES._serialized_start=4149
+  _FRONTENDATTRIBUTES._serialized_end=4260
+  _FRONTENDATTRIBUTES_MAPENTRY._serialized_start=4218
+  _FRONTENDATTRIBUTES_MAPENTRY._serialized_end=4260
+  _STATISTIC._serialized_start=4262
+  _STATISTIC._serialized_end=4310
+  _STATISTICSVIZ._serialized_start=4312
+  _STATISTICSVIZ._serialized_end=4396
+  _OPSHARDING._serialized_start=4399
+  _OPSHARDING._serialized_end=4994
+  _OPSHARDING_TYPE._serialized_start=4876
+  _OPSHARDING_TYPE._serialized_end=4958
+  _OPSHARDING_SHARDGROUPTYPE._serialized_start=4960
+  _OPSHARDING_SHARDGROUPTYPE._serialized_end=4994
+  _REPLICAGROUP._serialized_start=4996
+  _REPLICAGROUP._serialized_end=5031
+  _SOURCETARGET._serialized_start=5033
+  _SOURCETARGET._serialized_end=5079
+  _PRECISIONCONFIG._serialized_start=5082
+  _PRECISIONCONFIG._serialized_end=5226
+  _PRECISIONCONFIG_PRECISION._serialized_start=5160
+  _PRECISIONCONFIG_PRECISION._serialized_end=5226
+  _PARAMETERREPLICATION._serialized_start=5228
+  _PARAMETERREPLICATION._serialized_end=5286
+  _WHILELOOPBACKENDCONFIG._serialized_start=5288
+  _WHILELOOPBACKENDCONFIG._serialized_end=5411
+  _WHILELOOPBACKENDCONFIG_KNOWNTRIPCOUNT._serialized_start=5384
+  _WHILELOOPBACKENDCONFIG_KNOWNTRIPCOUNT._serialized_end=5411
+  _OUTPUTOPERANDALIASING._serialized_start=5413
+  _OUTPUTOPERANDALIASING._serialized_end=5516
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/config/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/config/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/config/flags.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/config/flags.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcc1196e45d666564f1cc63f9ac8740b2e55e916
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/config/flags.py
@@ -0,0 +1,29 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Code to enable/disable feature flags."""
+
+# pylint: disable=invalid-import-order, g-bad-import-order, unused-import
+from tensorflow.python import pywrap_tensorflow
+from tensorflow.python import flags_pybind
+
+
+FLAGS = None
+
+
+def config():
+  global FLAGS
+  if FLAGS is None:
+    FLAGS = flags_pybind.Flags()
+  return FLAGS
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/debug/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/debug/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/debug/debug_service_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/debug/debug_service_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf3922fbfefae1ed2d700cd68374bbcd01593051
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/debug/debug_service_pb2.py
@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/debug/debug_service.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
+from tensorflow.core.profiler import tfprof_log_pb2 as tensorflow_dot_core_dot_profiler_dot_tfprof__log__pb2
+from tensorflow.core.protobuf import debug_pb2 as tensorflow_dot_core_dot_protobuf_dot_debug__pb2
+from tensorflow.core.util import event_pb2 as tensorflow_dot_core_dot_util_dot_event__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n)tensorflow/core/debug/debug_service.proto\x12\ntensorflow\x1a&tensorflow/core/framework/tensor.proto\x1a)tensorflow/core/profiler/tfprof_log.proto\x1a$tensorflow/core/protobuf/debug.proto\x1a tensorflow/core/util/event.proto\"\xde\x02\n\nEventReply\x12I\n\x16\x64\x65\x62ug_op_state_changes\x18\x01 \x03(\x0b\x32).tensorflow.EventReply.DebugOpStateChange\x12\'\n\x06tensor\x18\x02 \x01(\x0b\x32\x17.tensorflow.TensorProto\x1a\xdb\x01\n\x12\x44\x65\x62ugOpStateChange\x12>\n\x05state\x18\x01 \x01(\x0e\x32/.tensorflow.EventReply.DebugOpStateChange.State\x12\x11\n\tnode_name\x18\x02 \x01(\t\x12\x13\n\x0boutput_slot\x18\x03 \x01(\x05\x12\x10\n\x08\x64\x65\x62ug_op\x18\x04 \x01(\t\"K\n\x05State\x12\x15\n\x11STATE_UNSPECIFIED\x10\x00\x12\x0c\n\x08\x44ISABLED\x10\x01\x12\r\n\tREAD_ONLY\x10\x02\x12\x0e\n\nREAD_WRITE\x10\x03\"\xa7\x03\n\rCallTraceback\x12\x35\n\tcall_type\x18\x01 \x01(\x0e\x32\".tensorflow.CallTraceback.CallType\x12\x10\n\x08\x63\x61ll_key\x18\x02 \x01(\t\x12\x30\n\x0corigin_stack\x18\x03 \x01(\x0b\x32\x1a.tensorflow.tfprof.CodeDef\x12L\n\x13origin_id_to_string\x18\x04 \x03(\x0b\x32/.tensorflow.CallTraceback.OriginIdToStringEntry\x12\x36\n\x0fgraph_traceback\x18\x05 \x01(\x0b\x32\x1d.tensorflow.tfprof.OpLogProto\x12\x15\n\rgraph_version\x18\x06 \x01(\x03\x1a\x37\n\x15OriginIdToStringEntry\x12\x0b\n\x03key\x18\x01 \x01(\x03\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"E\n\x08\x43\x61llType\x12\x0f\n\x0bUNSPECIFIED\x10\x00\x12\x13\n\x0fGRAPH_EXECUTION\x10\x01\x12\x13\n\x0f\x45\x41GER_EXECUTION\x10\x02\x32\xdd\x01\n\rEventListener\x12;\n\nSendEvents\x12\x11.tensorflow.Event\x1a\x16.tensorflow.EventReply(\x01\x30\x01\x12\x43\n\x0eSendTracebacks\x12\x19.tensorflow.CallTraceback\x1a\x16.tensorflow.EventReply\x12J\n\x0fSendSourceFiles\x12\x1f.tensorflow.DebuggedSourceFiles\x1a\x16.tensorflow.EventReplyb\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.debug.debug_service_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _CALLTRACEBACK_ORIGINIDTOSTRINGENTRY._options = None
+  _CALLTRACEBACK_ORIGINIDTOSTRINGENTRY._serialized_options = b'8\001'
+  _EVENTREPLY._serialized_start=213
+  _EVENTREPLY._serialized_end=563
+  _EVENTREPLY_DEBUGOPSTATECHANGE._serialized_start=344
+  _EVENTREPLY_DEBUGOPSTATECHANGE._serialized_end=563
+  _EVENTREPLY_DEBUGOPSTATECHANGE_STATE._serialized_start=488
+  _EVENTREPLY_DEBUGOPSTATECHANGE_STATE._serialized_end=563
+  _CALLTRACEBACK._serialized_start=566
+  _CALLTRACEBACK._serialized_end=989
+  _CALLTRACEBACK_ORIGINIDTOSTRINGENTRY._serialized_start=863
+  _CALLTRACEBACK_ORIGINIDTOSTRINGENTRY._serialized_end=918
+  _CALLTRACEBACK_CALLTYPE._serialized_start=920
+  _CALLTRACEBACK_CALLTYPE._serialized_end=989
+  _EVENTLISTENER._serialized_start=992
+  _EVENTLISTENER._serialized_end=1213
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/debug/debug_service_pb2_grpc.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/debug/debug_service_pb2_grpc.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8b581395ba8fc611ceb819a5b639d541520c81a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/debug/debug_service_pb2_grpc.py
@@ -0,0 +1,90 @@
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+import grpc
+
+from tensorflow.core.debug import debug_service_pb2 as tensorflow_dot_core_dot_debug_dot_debug__service__pb2
+from tensorflow.core.protobuf import debug_pb2 as tensorflow_dot_core_dot_protobuf_dot_debug__pb2
+from tensorflow.core.util import event_pb2 as tensorflow_dot_core_dot_util_dot_event__pb2
+
+
+class EventListenerStub(object):
+  """EventListener: Receives Event protos, e.g., from debugged TensorFlow
+  runtime(s).
+  """
+
+  def __init__(self, channel):
+    """Constructor.
+
+    Args:
+      channel: A grpc.Channel.
+    """
+    self.SendEvents = channel.stream_stream(
+        '/tensorflow.EventListener/SendEvents',
+        request_serializer=tensorflow_dot_core_dot_util_dot_event__pb2.Event.SerializeToString,
+        response_deserializer=tensorflow_dot_core_dot_debug_dot_debug__service__pb2.EventReply.FromString,
+        )
+    self.SendTracebacks = channel.unary_unary(
+        '/tensorflow.EventListener/SendTracebacks',
+        request_serializer=tensorflow_dot_core_dot_debug_dot_debug__service__pb2.CallTraceback.SerializeToString,
+        response_deserializer=tensorflow_dot_core_dot_debug_dot_debug__service__pb2.EventReply.FromString,
+        )
+    self.SendSourceFiles = channel.unary_unary(
+        '/tensorflow.EventListener/SendSourceFiles',
+        request_serializer=tensorflow_dot_core_dot_protobuf_dot_debug__pb2.DebuggedSourceFiles.SerializeToString,
+        response_deserializer=tensorflow_dot_core_dot_debug_dot_debug__service__pb2.EventReply.FromString,
+        )
+
+
+class EventListenerServicer(object):
+  """EventListener: Receives Event protos, e.g., from debugged TensorFlow
+  runtime(s).
+  """
+
+  def SendEvents(self, request_iterator, context):
+    """Client(s) can use this RPC method to send the EventListener Event protos.
+    The Event protos can hold information such as:
+    1) intermediate tensors from a debugged graph being executed, which can
+    be sent from DebugIdentity ops configured with grpc URLs.
+    2) GraphDefs of partition graphs, which can be sent from special debug
+    ops that get executed immediately after the beginning of the graph
+    execution.
+    """
+    context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+    context.set_details('Method not implemented!')
+    raise NotImplementedError('Method not implemented!')
+
+  def SendTracebacks(self, request, context):
+    """Send the tracebacks of a TensorFlow execution call.
+    """
+    context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+    context.set_details('Method not implemented!')
+    raise NotImplementedError('Method not implemented!')
+
+  def SendSourceFiles(self, request, context):
+    """Send a collection of source code files being debugged.
+    """
+    context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+    context.set_details('Method not implemented!')
+    raise NotImplementedError('Method not implemented!')
+
+
+def add_EventListenerServicer_to_server(servicer, server):
+  rpc_method_handlers = {
+      'SendEvents': grpc.stream_stream_rpc_method_handler(
+          servicer.SendEvents,
+          request_deserializer=tensorflow_dot_core_dot_util_dot_event__pb2.Event.FromString,
+          response_serializer=tensorflow_dot_core_dot_debug_dot_debug__service__pb2.EventReply.SerializeToString,
+      ),
+      'SendTracebacks': grpc.unary_unary_rpc_method_handler(
+          servicer.SendTracebacks,
+          request_deserializer=tensorflow_dot_core_dot_debug_dot_debug__service__pb2.CallTraceback.FromString,
+          response_serializer=tensorflow_dot_core_dot_debug_dot_debug__service__pb2.EventReply.SerializeToString,
+      ),
+      'SendSourceFiles': grpc.unary_unary_rpc_method_handler(
+          servicer.SendSourceFiles,
+          request_deserializer=tensorflow_dot_core_dot_protobuf_dot_debug__pb2.DebuggedSourceFiles.FromString,
+          response_serializer=tensorflow_dot_core_dot_debug_dot_debug__service__pb2.EventReply.SerializeToString,
+      ),
+  }
+  generic_handler = grpc.method_handlers_generic_handler(
+      'tensorflow.EventListener', rpc_method_handlers)
+  server.add_generic_rpc_handlers((generic_handler,))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/debug/debugger_event_metadata_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/debug/debugger_event_metadata_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cb7ef73c95d41af3ceed1deacc96b4bf3c6d850
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/debug/debugger_event_metadata_pb2.py
@@ -0,0 +1,25 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/debug/debugger_event_metadata.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n3tensorflow/core/debug/debugger_event_metadata.proto\x12!third_party.tensorflow.core.debug\"e\n\x15\x44\x65\x62uggerEventMetadata\x12\x0e\n\x06\x64\x65vice\x18\x01 \x01(\t\x12\x13\n\x0boutput_slot\x18\x02 \x01(\x05\x12\x12\n\nnum_chunks\x18\x03 \x01(\x05\x12\x13\n\x0b\x63hunk_index\x18\x04 \x01(\x05\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.debug.debugger_event_metadata_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _DEBUGGEREVENTMETADATA._serialized_start=90
+  _DEBUGGEREVENTMETADATA._serialized_end=191
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/distributed_runtime/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/distributed_runtime/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/distributed_runtime/preemption/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/distributed_runtime/preemption/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/distributed_runtime/preemption/gen_check_preemption_op.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/distributed_runtime/preemption/gen_check_preemption_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..a54433a5f24a057fd3ad08bc071fea487def0ba6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/distributed_runtime/preemption/gen_check_preemption_op.py
@@ -0,0 +1,101 @@
+"""Python wrappers around TensorFlow ops.
+
+This file is MACHINE GENERATED! Do not edit.
+"""
+
+import collections
+
+from tensorflow.python import pywrap_tfe as pywrap_tfe
+from tensorflow.python.eager import context as _context
+from tensorflow.python.eager import core as _core
+from tensorflow.python.eager import execute as _execute
+from tensorflow.python.framework import dtypes as _dtypes
+from tensorflow.security.fuzzing.py import annotation_types as _atypes
+
+from tensorflow.python.framework import op_def_registry as _op_def_registry
+from tensorflow.python.framework import ops as _ops
+from tensorflow.python.framework import op_def_library as _op_def_library
+from tensorflow.python.util.deprecation import deprecated_endpoints
+from tensorflow.python.util import dispatch as _dispatch
+from tensorflow.python.util.tf_export import tf_export
+
+from typing import TypeVar, List, Any
+from typing_extensions import Annotated
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('check_preemption')
+def check_preemption(preemption_key:str="TF_DEFAULT_PREEMPTION_NOTICE_KEY", name=None):
+  r"""Check if a preemption notice has been received in coordination service.
+
+  Args:
+    preemption_key: An optional `string`. Defaults to `"TF_DEFAULT_PREEMPTION_NOTICE_KEY"`.
+      Key for preemption check in coordination service.
+    name: A name for the operation (optional).
+
+  Returns:
+    The created Operation.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "CheckPreemption", name, "preemption_key", preemption_key)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_check_preemption(
+          (preemption_key, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return check_preemption_eager_fallback(
+          preemption_key=preemption_key, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            check_preemption, (), dict(preemption_key=preemption_key,
+                                       name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_check_preemption(
+        (preemption_key, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if preemption_key is None:
+    preemption_key = "TF_DEFAULT_PREEMPTION_NOTICE_KEY"
+  preemption_key = _execute.make_str(preemption_key, "preemption_key")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "CheckPreemption", preemption_key=preemption_key, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          check_preemption, (), dict(preemption_key=preemption_key, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  return _op
+CheckPreemption = tf_export("raw_ops.CheckPreemption")(_ops.to_raw_op(check_preemption))
+_dispatcher_for_check_preemption = check_preemption._tf_type_based_dispatcher.Dispatch
+
+
+def check_preemption_eager_fallback(preemption_key: str, name, ctx):
+  if preemption_key is None:
+    preemption_key = "TF_DEFAULT_PREEMPTION_NOTICE_KEY"
+  preemption_key = _execute.make_str(preemption_key, "preemption_key")
+  _inputs_flat = []
+  _attrs = ("preemption_key", preemption_key)
+  _result = _execute.execute(b"CheckPreemption", 0, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  _result = None
+  return _result
+
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/example/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/example/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/example/example_parser_configuration_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/example/example_parser_configuration_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed745269a92ef05ebdd8ddbbc109fdd4fc96eec2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/example/example_parser_configuration_pb2.py
@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/example/example_parser_configuration.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n:tensorflow/core/example/example_parser_configuration.proto\x12\ntensorflow\x1a&tensorflow/core/framework/tensor.proto\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\"\xa3\x01\n\x12VarLenFeatureProto\x12#\n\x05\x64type\x18\x01 \x01(\x0e\x32\x14.tensorflow.DataType\x12!\n\x19values_output_tensor_name\x18\x02 \x01(\t\x12\"\n\x1aindices_output_tensor_name\x18\x03 \x01(\t\x12!\n\x19shapes_output_tensor_name\x18\x04 \x01(\t\"\xbb\x01\n\x14\x46ixedLenFeatureProto\x12#\n\x05\x64type\x18\x01 \x01(\x0e\x32\x14.tensorflow.DataType\x12+\n\x05shape\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12.\n\rdefault_value\x18\x03 \x01(\x0b\x32\x17.tensorflow.TensorProto\x12!\n\x19values_output_tensor_name\x18\x04 \x01(\t\"\x9a\x01\n\x14\x46\x65\x61tureConfiguration\x12=\n\x11\x66ixed_len_feature\x18\x01 \x01(\x0b\x32 .tensorflow.FixedLenFeatureProtoH\x00\x12\x39\n\x0fvar_len_feature\x18\x02 \x01(\x0b\x32\x1e.tensorflow.VarLenFeatureProtoH\x00\x42\x08\n\x06\x63onfig\"\xbe\x01\n\x1a\x45xampleParserConfiguration\x12K\n\x0b\x66\x65\x61ture_map\x18\x01 \x03(\x0b\x32\x36.tensorflow.ExampleParserConfiguration.FeatureMapEntry\x1aS\n\x0f\x46\x65\x61tureMapEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12/\n\x05value\x18\x02 \x01(\x0b\x32 .tensorflow.FeatureConfiguration:\x02\x38\x01\x42\xa2\x01\n\x16org.tensorflow.exampleB ExampleParserConfigurationProtosP\x01Zagithub.com/tensorflow/tensorflow/tensorflow/go/core/example/example_parser_configuration_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.example.example_parser_configuration_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\026org.tensorflow.exampleB ExampleParserConfigurationProtosP\001Zagithub.com/tensorflow/tensorflow/tensorflow/go/core/example/example_parser_configuration_go_proto\370\001\001'
+  _EXAMPLEPARSERCONFIGURATION_FEATUREMAPENTRY._options = None
+  _EXAMPLEPARSERCONFIGURATION_FEATUREMAPENTRY._serialized_options = b'8\001'
+  _VARLENFEATUREPROTO._serialized_start=200
+  _VARLENFEATUREPROTO._serialized_end=363
+  _FIXEDLENFEATUREPROTO._serialized_start=366
+  _FIXEDLENFEATUREPROTO._serialized_end=553
+  _FEATURECONFIGURATION._serialized_start=556
+  _FEATURECONFIGURATION._serialized_end=710
+  _EXAMPLEPARSERCONFIGURATION._serialized_start=713
+  _EXAMPLEPARSERCONFIGURATION._serialized_end=903
+  _EXAMPLEPARSERCONFIGURATION_FEATUREMAPENTRY._serialized_start=820
+  _EXAMPLEPARSERCONFIGURATION_FEATUREMAPENTRY._serialized_end=903
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/example/example_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/example/example_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..061a7496259f0c72c8e691ff7244a0105ea218b8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/example/example_pb2.py
@@ -0,0 +1,29 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/example/example.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.example import feature_pb2 as tensorflow_dot_core_dot_example_dot_feature__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n%tensorflow/core/example/example.proto\x12\ntensorflow\x1a%tensorflow/core/example/feature.proto\"1\n\x07\x45xample\x12&\n\x08\x66\x65\x61tures\x18\x01 \x01(\x0b\x32\x14.tensorflow.Features\"i\n\x0fSequenceExample\x12%\n\x07\x63ontext\x18\x01 \x01(\x0b\x32\x14.tensorflow.Features\x12/\n\rfeature_lists\x18\x02 \x01(\x0b\x32\x18.tensorflow.FeatureListsB\x81\x01\n\x16org.tensorflow.exampleB\rExampleProtosP\x01ZSgithub.com/tensorflow/tensorflow/tensorflow/go/core/example/example_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.example.example_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\026org.tensorflow.exampleB\rExampleProtosP\001ZSgithub.com/tensorflow/tensorflow/tensorflow/go/core/example/example_protos_go_proto\370\001\001'
+  _EXAMPLE._serialized_start=92
+  _EXAMPLE._serialized_end=141
+  _SEQUENCEEXAMPLE._serialized_start=143
+  _SEQUENCEEXAMPLE._serialized_end=248
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/example/feature_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/example/feature_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..e29c9f5af57c1d2b91bfe627058bdc3827544b8d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/example/feature_pb2.py
@@ -0,0 +1,50 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/example/feature.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n%tensorflow/core/example/feature.proto\x12\ntensorflow\"\x1a\n\tBytesList\x12\r\n\x05value\x18\x01 \x03(\x0c\"\x1e\n\tFloatList\x12\x11\n\x05value\x18\x01 \x03(\x02\x42\x02\x10\x01\"\x1e\n\tInt64List\x12\x11\n\x05value\x18\x01 \x03(\x03\x42\x02\x10\x01\"\x98\x01\n\x07\x46\x65\x61ture\x12+\n\nbytes_list\x18\x01 \x01(\x0b\x32\x15.tensorflow.BytesListH\x00\x12+\n\nfloat_list\x18\x02 \x01(\x0b\x32\x15.tensorflow.FloatListH\x00\x12+\n\nint64_list\x18\x03 \x01(\x0b\x32\x15.tensorflow.Int64ListH\x00\x42\x06\n\x04kind\"\x83\x01\n\x08\x46\x65\x61tures\x12\x32\n\x07\x66\x65\x61ture\x18\x01 \x03(\x0b\x32!.tensorflow.Features.FeatureEntry\x1a\x43\n\x0c\x46\x65\x61tureEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\"\n\x05value\x18\x02 \x01(\x0b\x32\x13.tensorflow.Feature:\x02\x38\x01\"3\n\x0b\x46\x65\x61tureList\x12$\n\x07\x66\x65\x61ture\x18\x01 \x03(\x0b\x32\x13.tensorflow.Feature\"\x9c\x01\n\x0c\x46\x65\x61tureLists\x12?\n\x0c\x66\x65\x61ture_list\x18\x01 \x03(\x0b\x32).tensorflow.FeatureLists.FeatureListEntry\x1aK\n\x10\x46\x65\x61tureListEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12&\n\x05value\x18\x02 \x01(\x0b\x32\x17.tensorflow.FeatureList:\x02\x38\x01\x42\x81\x01\n\x16org.tensorflow.exampleB\rFeatureProtosP\x01ZSgithub.com/tensorflow/tensorflow/tensorflow/go/core/example/example_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.example.feature_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\026org.tensorflow.exampleB\rFeatureProtosP\001ZSgithub.com/tensorflow/tensorflow/tensorflow/go/core/example/example_protos_go_proto\370\001\001'
+  _FLOATLIST.fields_by_name['value']._options = None
+  _FLOATLIST.fields_by_name['value']._serialized_options = b'\020\001'
+  _INT64LIST.fields_by_name['value']._options = None
+  _INT64LIST.fields_by_name['value']._serialized_options = b'\020\001'
+  _FEATURES_FEATUREENTRY._options = None
+  _FEATURES_FEATUREENTRY._serialized_options = b'8\001'
+  _FEATURELISTS_FEATURELISTENTRY._options = None
+  _FEATURELISTS_FEATURELISTENTRY._serialized_options = b'8\001'
+  _BYTESLIST._serialized_start=53
+  _BYTESLIST._serialized_end=79
+  _FLOATLIST._serialized_start=81
+  _FLOATLIST._serialized_end=111
+  _INT64LIST._serialized_start=113
+  _INT64LIST._serialized_end=143
+  _FEATURE._serialized_start=146
+  _FEATURE._serialized_end=298
+  _FEATURES._serialized_start=301
+  _FEATURES._serialized_end=432
+  _FEATURES_FEATUREENTRY._serialized_start=365
+  _FEATURES_FEATUREENTRY._serialized_end=432
+  _FEATURELIST._serialized_start=434
+  _FEATURELIST._serialized_end=485
+  _FEATURELISTS._serialized_start=488
+  _FEATURELISTS._serialized_end=644
+  _FEATURELISTS_FEATURELISTENTRY._serialized_start=569
+  _FEATURELISTS_FEATURELISTENTRY._serialized_end=644
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/allocation_description_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/allocation_description_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2b3d8b4b5f56f4e613af0c1ffed77c1118368fa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/allocation_description_pb2.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/allocation_description.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n6tensorflow/core/framework/allocation_description.proto\x12\ntensorflow\"\xa3\x01\n\x15\x41llocationDescription\x12\x17\n\x0frequested_bytes\x18\x01 \x01(\x03\x12\x17\n\x0f\x61llocated_bytes\x18\x02 \x01(\x03\x12\x16\n\x0e\x61llocator_name\x18\x03 \x01(\t\x12\x15\n\rallocation_id\x18\x04 \x01(\x03\x12\x1c\n\x14has_single_reference\x18\x05 \x01(\x08\x12\x0b\n\x03ptr\x18\x06 \x01(\x04\x42\x9b\x01\n\x18org.tensorflow.frameworkB\x1b\x41llocationDescriptionProtosP\x01Z]github.com/tensorflow/tensorflow/tensorflow/go/core/framework/allocation_description_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.allocation_description_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\033AllocationDescriptionProtosP\001Z]github.com/tensorflow/tensorflow/tensorflow/go/core/framework/allocation_description_go_proto\370\001\001'
+  _ALLOCATIONDESCRIPTION._serialized_start=71
+  _ALLOCATIONDESCRIPTION._serialized_end=234
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/api_def_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/api_def_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..429e8bf96ea5e916bb7948352b4b8492753964fa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/api_def_pb2.py
@@ -0,0 +1,37 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/api_def.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\'tensorflow/core/framework/api_def.proto\x12\ntensorflow\x1a*tensorflow/core/framework/attr_value.proto\"\xe1\x05\n\x06\x41piDef\x12\x15\n\rgraph_op_name\x18\x01 \x01(\t\x12\x1b\n\x13\x64\x65precation_message\x18\x0c \x01(\t\x12\x1b\n\x13\x64\x65precation_version\x18\r \x01(\x05\x12\x31\n\nvisibility\x18\x02 \x01(\x0e\x32\x1d.tensorflow.ApiDef.Visibility\x12-\n\x08\x65ndpoint\x18\x03 \x03(\x0b\x32\x1b.tensorflow.ApiDef.Endpoint\x12&\n\x06in_arg\x18\x04 \x03(\x0b\x32\x16.tensorflow.ApiDef.Arg\x12\'\n\x07out_arg\x18\x05 \x03(\x0b\x32\x16.tensorflow.ApiDef.Arg\x12\x11\n\targ_order\x18\x0b \x03(\t\x12%\n\x04\x61ttr\x18\x06 \x03(\x0b\x32\x17.tensorflow.ApiDef.Attr\x12\x0f\n\x07summary\x18\x07 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x08 \x01(\t\x12\x1a\n\x12\x64\x65scription_prefix\x18\t \x01(\t\x12\x1a\n\x12\x64\x65scription_suffix\x18\n \x01(\t\x1aI\n\x08\x45ndpoint\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x12\n\ndeprecated\x18\x03 \x01(\x08\x12\x1b\n\x13\x64\x65precation_version\x18\x04 \x01(\x05\x1a;\n\x03\x41rg\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x11\n\trename_to\x18\x02 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x03 \x01(\t\x1aj\n\x04\x41ttr\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x11\n\trename_to\x18\x02 \x01(\t\x12,\n\rdefault_value\x18\x03 \x01(\x0b\x32\x15.tensorflow.AttrValue\x12\x13\n\x0b\x64\x65scription\x18\x04 \x01(\t\"G\n\nVisibility\x12\x16\n\x12\x44\x45\x46\x41ULT_VISIBILITY\x10\x00\x12\x0b\n\x07VISIBLE\x10\x01\x12\x08\n\x04SKIP\x10\x02\x12\n\n\x06HIDDEN\x10\x03\")\n\x07\x41piDefs\x12\x1e\n\x02op\x18\x01 \x03(\x0b\x32\x12.tensorflow.ApiDefB}\n\x18org.tensorflow.frameworkB\x0c\x41piDefProtosP\x01ZNgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/api_def_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.api_def_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\014ApiDefProtosP\001ZNgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/api_def_go_proto\370\001\001'
+  _APIDEF._serialized_start=100
+  _APIDEF._serialized_end=837
+  _APIDEF_ENDPOINT._serialized_start=522
+  _APIDEF_ENDPOINT._serialized_end=595
+  _APIDEF_ARG._serialized_start=597
+  _APIDEF_ARG._serialized_end=656
+  _APIDEF_ATTR._serialized_start=658
+  _APIDEF_ATTR._serialized_end=764
+  _APIDEF_VISIBILITY._serialized_start=766
+  _APIDEF_VISIBILITY._serialized_end=837
+  _APIDEFS._serialized_start=839
+  _APIDEFS._serialized_end=880
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/attr_value_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/attr_value_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..65438a7927e30d6dc4d9a2bcd8055bda1d1ce174
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/attr_value_pb2.py
@@ -0,0 +1,45 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/attr_value.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n*tensorflow/core/framework/attr_value.proto\x12\ntensorflow\x1a&tensorflow/core/framework/tensor.proto\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\"\xa6\x04\n\tAttrValue\x12\x0b\n\x01s\x18\x02 \x01(\x0cH\x00\x12\x0b\n\x01i\x18\x03 \x01(\x03H\x00\x12\x0b\n\x01\x66\x18\x04 \x01(\x02H\x00\x12\x0b\n\x01\x62\x18\x05 \x01(\x08H\x00\x12$\n\x04type\x18\x06 \x01(\x0e\x32\x14.tensorflow.DataTypeH\x00\x12-\n\x05shape\x18\x07 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProtoH\x00\x12)\n\x06tensor\x18\x08 \x01(\x0b\x32\x17.tensorflow.TensorProtoH\x00\x12/\n\x04list\x18\x01 \x01(\x0b\x32\x1f.tensorflow.AttrValue.ListValueH\x00\x12(\n\x04\x66unc\x18\n \x01(\x0b\x32\x18.tensorflow.NameAttrListH\x00\x12\x15\n\x0bplaceholder\x18\t \x01(\tH\x00\x1a\xe9\x01\n\tListValue\x12\t\n\x01s\x18\x02 \x03(\x0c\x12\r\n\x01i\x18\x03 \x03(\x03\x42\x02\x10\x01\x12\r\n\x01\x66\x18\x04 \x03(\x02\x42\x02\x10\x01\x12\r\n\x01\x62\x18\x05 \x03(\x08\x42\x02\x10\x01\x12&\n\x04type\x18\x06 \x03(\x0e\x32\x14.tensorflow.DataTypeB\x02\x10\x01\x12+\n\x05shape\x18\x07 \x03(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12\'\n\x06tensor\x18\x08 \x03(\x0b\x32\x17.tensorflow.TensorProto\x12&\n\x04\x66unc\x18\t \x03(\x0b\x32\x18.tensorflow.NameAttrListB\x07\n\x05value\"\x92\x01\n\x0cNameAttrList\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x30\n\x04\x61ttr\x18\x02 \x03(\x0b\x32\".tensorflow.NameAttrList.AttrEntry\x1a\x42\n\tAttrEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12$\n\x05value\x18\x02 \x01(\x0b\x32\x15.tensorflow.AttrValue:\x02\x38\x01\x42\x83\x01\n\x18org.tensorflow.frameworkB\x0f\x41ttrValueProtosP\x01ZQgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/attr_value_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.attr_value_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\017AttrValueProtosP\001ZQgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/attr_value_go_proto\370\001\001'
+  _ATTRVALUE_LISTVALUE.fields_by_name['i']._options = None
+  _ATTRVALUE_LISTVALUE.fields_by_name['i']._serialized_options = b'\020\001'
+  _ATTRVALUE_LISTVALUE.fields_by_name['f']._options = None
+  _ATTRVALUE_LISTVALUE.fields_by_name['f']._serialized_options = b'\020\001'
+  _ATTRVALUE_LISTVALUE.fields_by_name['b']._options = None
+  _ATTRVALUE_LISTVALUE.fields_by_name['b']._serialized_options = b'\020\001'
+  _ATTRVALUE_LISTVALUE.fields_by_name['type']._options = None
+  _ATTRVALUE_LISTVALUE.fields_by_name['type']._serialized_options = b'\020\001'
+  _NAMEATTRLIST_ATTRENTRY._options = None
+  _NAMEATTRLIST_ATTRENTRY._serialized_options = b'8\001'
+  _ATTRVALUE._serialized_start=184
+  _ATTRVALUE._serialized_end=734
+  _ATTRVALUE_LISTVALUE._serialized_start=492
+  _ATTRVALUE_LISTVALUE._serialized_end=725
+  _NAMEATTRLIST._serialized_start=737
+  _NAMEATTRLIST._serialized_end=883
+  _NAMEATTRLIST_ATTRENTRY._serialized_start=817
+  _NAMEATTRLIST_ATTRENTRY._serialized_end=883
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/cost_graph_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/cost_graph_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..687a4ea3d711ffb58623838ad3206a21b50ddbe7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/cost_graph_pb2.py
@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/cost_graph.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n*tensorflow/core/framework/cost_graph.proto\x12\ntensorflow\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\"\xca\x06\n\x0c\x43ostGraphDef\x12+\n\x04node\x18\x01 \x03(\x0b\x32\x1d.tensorflow.CostGraphDef.Node\x12\x35\n\x04\x63ost\x18\x02 \x03(\x0b\x32\'.tensorflow.CostGraphDef.AggregatedCost\x1a\xa2\x05\n\x04Node\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0e\n\x06\x64\x65vice\x18\x02 \x01(\t\x12\n\n\x02id\x18\x03 \x01(\x05\x12;\n\ninput_info\x18\x04 \x03(\x0b\x32\'.tensorflow.CostGraphDef.Node.InputInfo\x12=\n\x0boutput_info\x18\x05 \x03(\x0b\x32(.tensorflow.CostGraphDef.Node.OutputInfo\x12\x1d\n\x15temporary_memory_size\x18\x06 \x01(\x03\x12\x1e\n\x16persistent_memory_size\x18\x0c \x01(\x03\x12!\n\x15host_temp_memory_size\x18\n \x01(\x03\x42\x02\x18\x01\x12#\n\x17\x64\x65vice_temp_memory_size\x18\x0b \x01(\x03\x42\x02\x18\x01\x12)\n\x1d\x64\x65vice_persistent_memory_size\x18\x10 \x01(\x03\x42\x02\x18\x01\x12\x14\n\x0c\x63ompute_cost\x18\t \x01(\x03\x12\x14\n\x0c\x63ompute_time\x18\x0e \x01(\x03\x12\x13\n\x0bmemory_time\x18\x0f \x01(\x03\x12\x10\n\x08is_final\x18\x07 \x01(\x08\x12\x15\n\rcontrol_input\x18\x08 \x03(\x05\x12\x12\n\ninaccurate\x18\x11 \x01(\x08\x1a;\n\tInputInfo\x12\x16\n\x0epreceding_node\x18\x01 \x01(\x05\x12\x16\n\x0epreceding_port\x18\x02 \x01(\x05\x1a\x86\x01\n\nOutputInfo\x12\x0c\n\x04size\x18\x01 \x01(\x03\x12\x18\n\x10\x61lias_input_port\x18\x02 \x01(\x03\x12+\n\x05shape\x18\x03 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12#\n\x05\x64type\x18\x04 \x01(\x0e\x32\x14.tensorflow.DataType\x1a\x31\n\x0e\x41ggregatedCost\x12\x0c\n\x04\x63ost\x18\x01 \x01(\x02\x12\x11\n\tdimension\x18\x02 \x01(\tB\x83\x01\n\x18org.tensorflow.frameworkB\x0f\x43ostGraphProtosP\x01ZQgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/cost_graph_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.cost_graph_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\017CostGraphProtosP\001ZQgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/cost_graph_go_proto\370\001\001'
+  _COSTGRAPHDEF_NODE.fields_by_name['host_temp_memory_size']._options = None
+  _COSTGRAPHDEF_NODE.fields_by_name['host_temp_memory_size']._serialized_options = b'\030\001'
+  _COSTGRAPHDEF_NODE.fields_by_name['device_temp_memory_size']._options = None
+  _COSTGRAPHDEF_NODE.fields_by_name['device_temp_memory_size']._serialized_options = b'\030\001'
+  _COSTGRAPHDEF_NODE.fields_by_name['device_persistent_memory_size']._options = None
+  _COSTGRAPHDEF_NODE.fields_by_name['device_persistent_memory_size']._serialized_options = b'\030\001'
+  _COSTGRAPHDEF._serialized_start=144
+  _COSTGRAPHDEF._serialized_end=986
+  _COSTGRAPHDEF_NODE._serialized_start=261
+  _COSTGRAPHDEF_NODE._serialized_end=935
+  _COSTGRAPHDEF_NODE_INPUTINFO._serialized_start=739
+  _COSTGRAPHDEF_NODE_INPUTINFO._serialized_end=798
+  _COSTGRAPHDEF_NODE_OUTPUTINFO._serialized_start=801
+  _COSTGRAPHDEF_NODE_OUTPUTINFO._serialized_end=935
+  _COSTGRAPHDEF_AGGREGATEDCOST._serialized_start=937
+  _COSTGRAPHDEF_AGGREGATEDCOST._serialized_end=986
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/cpp_shape_inference_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/cpp_shape_inference_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc39af2d1d576c4c15b5289c38eba5041ffcd5a1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/cpp_shape_inference_pb2.py
@@ -0,0 +1,35 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/cpp_shape_inference.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import full_type_pb2 as tensorflow_dot_core_dot_framework_dot_full__type__pb2
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n3tensorflow/core/framework/cpp_shape_inference.proto\x12\x0ftensorflow.core\x1a)tensorflow/core/framework/full_type.proto\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\"\xa5\x03\n\x17\x43ppShapeInferenceResult\x12+\n\x05shape\x18\x01 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12H\n\x0bhandle_data\x18\x04 \x01(\x0b\x32\x33.tensorflow.core.CppShapeInferenceResult.HandleData\x1a\x93\x01\n\x12HandleShapeAndType\x12+\n\x05shape\x18\x01 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12#\n\x05\x64type\x18\x02 \x01(\x0e\x32\x14.tensorflow.DataType\x12%\n\x04type\x18\x04 \x01(\x0b\x32\x17.tensorflow.FullTypeDefJ\x04\x08\x03\x10\x04\x1aq\n\nHandleData\x12\x0e\n\x06is_set\x18\x01 \x01(\x08\x12S\n\x0eshape_and_type\x18\x02 \x03(\x0b\x32;.tensorflow.core.CppShapeInferenceResult.HandleShapeAndTypeJ\x04\x08\x02\x10\x03J\x04\x08\x03\x10\x04\"e\n\x1d\x43ppShapeInferenceInputsNeeded\x12\x1c\n\x14input_tensors_needed\x18\x01 \x03(\x05\x12&\n\x1einput_tensors_as_shapes_needed\x18\x02 \x03(\x05\x42\x61Z\\github.com/tensorflow/tensorflow/tensorflow/go/python/framework/cpp_shape_inference_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.cpp_shape_inference_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'Z\\github.com/tensorflow/tensorflow/tensorflow/go/python/framework/cpp_shape_inference_go_proto\370\001\001'
+  _CPPSHAPEINFERENCERESULT._serialized_start=201
+  _CPPSHAPEINFERENCERESULT._serialized_end=622
+  _CPPSHAPEINFERENCERESULT_HANDLESHAPEANDTYPE._serialized_start=348
+  _CPPSHAPEINFERENCERESULT_HANDLESHAPEANDTYPE._serialized_end=495
+  _CPPSHAPEINFERENCERESULT_HANDLEDATA._serialized_start=497
+  _CPPSHAPEINFERENCERESULT_HANDLEDATA._serialized_end=610
+  _CPPSHAPEINFERENCEINPUTSNEEDED._serialized_start=624
+  _CPPSHAPEINFERENCEINPUTSNEEDED._serialized_end=725
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/dataset_metadata_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/dataset_metadata_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e2c5938b9a6dd319b804339f9dae9f075ba508e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/dataset_metadata_pb2.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/dataset_metadata.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n0tensorflow/core/framework/dataset_metadata.proto\x12\x0ftensorflow.data\"\x18\n\x08Metadata\x12\x0c\n\x04name\x18\x01 \x01(\x0c\x42YZWgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/dataset_metadata_go_protob\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.dataset_metadata_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZWgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/dataset_metadata_go_proto'
+  _METADATA._serialized_start=69
+  _METADATA._serialized_end=93
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/dataset_options_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/dataset_options_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..a32ec10af6dbf90dec0708b545c9b6b75b0f0c55
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/dataset_options_pb2.py
@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/dataset_options.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import model_pb2 as tensorflow_dot_core_dot_framework_dot_model__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n/tensorflow/core/framework/dataset_options.proto\x12\x0ftensorflow.data\x1a%tensorflow/core/framework/model.proto\"\xf9\x01\n\x0f\x41utotuneOptions\x12\x11\n\x07\x65nabled\x18\x01 \x01(\x08H\x00\x12\x14\n\ncpu_budget\x18\x02 \x01(\x05H\x01\x12\x14\n\nram_budget\x18\x03 \x01(\x03H\x02\x12\x46\n\x12\x61utotune_algorithm\x18\x04 \x01(\x0e\x32(.tensorflow.data.model.AutotuneAlgorithmH\x03\x42\x12\n\x10optional_enabledB\x15\n\x13optional_cpu_budgetB\x15\n\x13optional_ram_budgetB\x1d\n\x1boptional_autotune_algorithm\"\xd1\x01\n\x12\x43\x61rdinalityOptions\x12G\n\rcompute_level\x18\x01 \x01(\x0e\x32\x30.tensorflow.data.CardinalityOptions.ComputeLevel\"r\n\x0c\x43omputeLevel\x12#\n\x1f\x43\x41RDINALITY_COMPUTE_UNSPECIFIED\x10\x00\x12\x1b\n\x17\x43\x41RDINALITY_COMPUTE_LOW\x10\x01\x12 \n\x1c\x43\x41RDINALITY_COMPUTE_MODERATE\x10\x02\"\x7f\n\x11\x44istributeOptions\x12;\n\x11\x61uto_shard_policy\x18\x01 \x01(\x0e\x32 .tensorflow.data.AutoShardPolicy\x12\x15\n\x0bnum_devices\x18\x02 \x01(\x05H\x00\x42\x16\n\x14optional_num_devices\"\xf2\x05\n\x13OptimizationOptions\x12%\n\x1b\x61pply_default_optimizations\x18\x01 \x01(\x08H\x00\x12\x17\n\rfilter_fusion\x18\x06 \x01(\x08H\x01\x12\x1e\n\x14map_and_batch_fusion\x18\t \x01(\x08H\x02\x12\x1f\n\x15map_and_filter_fusion\x18\n \x01(\x08H\x03\x12\x14\n\nmap_fusion\x18\x0b \x01(\x08H\x04\x12\x1d\n\x13map_parallelization\x18\x0c \x01(\x08H\x05\x12\x1a\n\x10noop_elimination\x18\x0e \x01(\x08H\x06\x12\x18\n\x0eparallel_batch\x18\x0f \x01(\x08H\x07\x12#\n\x19shuffle_and_repeat_fusion\x18\x11 \x01(\x08H\x08\x12 \n\x16\x66ilter_parallelization\x18\x12 \x01(\x08H\t\x12\x19\n\x0finject_prefetch\x18\x13 \x01(\x08H\nB&\n$optional_apply_default_optimizationsB\x18\n\x16optional_filter_fusionB\x1f\n\x1doptional_map_and_batch_fusionB \n\x1eoptional_map_and_filter_fusionB\x15\n\x13optional_map_fusionB\x1e\n\x1coptional_map_parallelizationB\x1b\n\x19optional_noop_eliminationB\x19\n\x17optional_parallel_batchB$\n\"optional_shuffle_and_repeat_fusionB!\n\x1foptional_filter_parallelizationB\x1a\n\x18optional_inject_prefetchJ\x04\x08\x02\x10\x03J\x04\x08\x03\x10\x04J\x04\x08\x04\x10\x05J\x04\x08\x05\x10\x06J\x04\x08\x07\x10\x08J\x04\x08\x08\x10\tJ\x04\x08\r\x10\x0eJ\x04\x08\x10\x10\x11J\x04\x08\x14\x10\x15\"\xa2\x01\n\x10ThreadingOptions\x12\"\n\x18max_intra_op_parallelism\x18\x01 \x01(\x05H\x00\x12!\n\x17private_threadpool_size\x18\x02 \x01(\x05H\x01\x42#\n!optional_max_intra_op_parallelismB\"\n optional_private_threadpool_size\"\xb2\x04\n\x07Options\x12\x17\n\rdeterministic\x18\x01 \x01(\x08H\x00\x12:\n\x10\x61utotune_options\x18\x07 \x01(\x0b\x32 .tensorflow.data.AutotuneOptions\x12>\n\x12\x64istribute_options\x18\x02 \x01(\x0b\x32\".tensorflow.data.DistributeOptions\x12\x42\n\x14optimization_options\x18\x03 \x01(\x0b\x32$.tensorflow.data.OptimizationOptions\x12\x0f\n\x05slack\x18\x04 \x01(\x08H\x01\x12<\n\x11threading_options\x18\x05 \x01(\x0b\x32!.tensorflow.data.ThreadingOptions\x12\x45\n\x15\x65xternal_state_policy\x18\x06 \x01(\x0e\x32$.tensorflow.data.ExternalStatePolicyH\x02\x12\x1d\n\x13symbolic_checkpoint\x18\x08 \x01(\x08H\x03\x12\x14\n\nwarm_start\x18\t \x01(\x08H\x04\x42\x18\n\x16optional_deterministicB\x10\n\x0eoptional_slackB \n\x1eoptional_external_state_policyB\x1e\n\x1coptional_symbolic_checkpointB\x15\n\x13optional_warm_start*K\n\x0f\x41utoShardPolicy\x12\x08\n\x04\x41UTO\x10\x00\x12\x08\n\x04\x46ILE\x10\x01\x12\x08\n\x04\x44\x41TA\x10\x02\x12\x08\n\x04HINT\x10\x03\x12\x10\n\x03OFF\x10\xff\xff\xff\xff\xff\xff\xff\xff\xff\x01*J\n\x13\x45xternalStatePolicy\x12\x0f\n\x0bPOLICY_WARN\x10\x00\x12\x11\n\rPOLICY_IGNORE\x10\x01\x12\x0f\n\x0bPOLICY_FAIL\x10\x02\x42XZVgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/dataset_options_go_protob\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.dataset_options_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZVgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/dataset_options_go_proto'
+  _AUTOSHARDPOLICY._serialized_start=2187
+  _AUTOSHARDPOLICY._serialized_end=2262
+  _EXTERNALSTATEPOLICY._serialized_start=2264
+  _EXTERNALSTATEPOLICY._serialized_end=2338
+  _AUTOTUNEOPTIONS._serialized_start=108
+  _AUTOTUNEOPTIONS._serialized_end=357
+  _CARDINALITYOPTIONS._serialized_start=360
+  _CARDINALITYOPTIONS._serialized_end=569
+  _CARDINALITYOPTIONS_COMPUTELEVEL._serialized_start=455
+  _CARDINALITYOPTIONS_COMPUTELEVEL._serialized_end=569
+  _DISTRIBUTEOPTIONS._serialized_start=571
+  _DISTRIBUTEOPTIONS._serialized_end=698
+  _OPTIMIZATIONOPTIONS._serialized_start=701
+  _OPTIMIZATIONOPTIONS._serialized_end=1455
+  _THREADINGOPTIONS._serialized_start=1458
+  _THREADINGOPTIONS._serialized_end=1620
+  _OPTIONS._serialized_start=1623
+  _OPTIONS._serialized_end=2185
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/dataset_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/dataset_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..6651abc2e95bd7825610df26baaee4832b679c55
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/dataset_pb2.py
@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/dataset.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\'tensorflow/core/framework/dataset.proto\x12\x0ftensorflow.data\x1a&tensorflow/core/framework/tensor.proto\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\"\x98\x01\n\x1b\x43ompressedComponentMetadata\x12#\n\x05\x64type\x18\x01 \x01(\x0e\x32\x14.tensorflow.DataType\x12\x32\n\x0ctensor_shape\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12\x1a\n\x12uncompressed_bytes\x18\x04 \x03(\x04J\x04\x08\x03\x10\x04\"|\n\x11\x43ompressedElement\x12\x0c\n\x04\x64\x61ta\x18\x01 \x01(\x0c\x12H\n\x12\x63omponent_metadata\x18\x02 \x03(\x0b\x32,.tensorflow.data.CompressedComponentMetadata\x12\x0f\n\x07version\x18\x03 \x01(\x05\"B\n\x13UncompressedElement\x12+\n\ncomponents\x18\x01 \x03(\x0b\x32\x17.tensorflow.TensorProtoB\x03\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.dataset_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _COMPRESSEDCOMPONENTMETADATA._serialized_start=186
+  _COMPRESSEDCOMPONENTMETADATA._serialized_end=338
+  _COMPRESSEDELEMENT._serialized_start=340
+  _COMPRESSEDELEMENT._serialized_end=464
+  _UNCOMPRESSEDELEMENT._serialized_start=466
+  _UNCOMPRESSEDELEMENT._serialized_end=532
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/device_attributes_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/device_attributes_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2f099f3cd4a5a943e8223abaf37f2110d434cb3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/device_attributes_pb2.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/device_attributes.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n1tensorflow/core/framework/device_attributes.proto\x12\ntensorflow\"E\n\x10InterconnectLink\x12\x11\n\tdevice_id\x18\x01 \x01(\x05\x12\x0c\n\x04type\x18\x02 \x01(\t\x12\x10\n\x08strength\x18\x03 \x01(\x05\"8\n\nLocalLinks\x12*\n\x04link\x18\x01 \x03(\x0b\x32\x1c.tensorflow.InterconnectLink\"Z\n\x0e\x44\x65viceLocality\x12\x0e\n\x06\x62us_id\x18\x01 \x01(\x05\x12\x11\n\tnuma_node\x18\x02 \x01(\x05\x12%\n\x05links\x18\x03 \x01(\x0b\x32\x16.tensorflow.LocalLinks\"\xc3\x01\n\x10\x44\x65viceAttributes\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x13\n\x0b\x64\x65vice_type\x18\x02 \x01(\t\x12\x14\n\x0cmemory_limit\x18\x04 \x01(\x03\x12,\n\x08locality\x18\x05 \x01(\x0b\x32\x1a.tensorflow.DeviceLocality\x12\x13\n\x0bincarnation\x18\x06 \x01(\x06\x12\x1c\n\x14physical_device_desc\x18\x07 \x01(\t\x12\x15\n\rxla_global_id\x18\x08 \x01(\x03\x42\x91\x01\n\x18org.tensorflow.frameworkB\x16\x44\x65viceAttributesProtosP\x01ZXgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/device_attributes_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.device_attributes_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\026DeviceAttributesProtosP\001ZXgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/device_attributes_go_proto\370\001\001'
+  _INTERCONNECTLINK._serialized_start=65
+  _INTERCONNECTLINK._serialized_end=134
+  _LOCALLINKS._serialized_start=136
+  _LOCALLINKS._serialized_end=192
+  _DEVICELOCALITY._serialized_start=194
+  _DEVICELOCALITY._serialized_end=284
+  _DEVICEATTRIBUTES._serialized_start=287
+  _DEVICEATTRIBUTES._serialized_end=482
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/full_type_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/full_type_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffe019e9a0bf365f977b702bbb15638b911655bd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/full_type_pb2.py
@@ -0,0 +1,28 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/full_type.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n)tensorflow/core/framework/full_type.proto\x12\ntensorflow\"\x7f\n\x0b\x46ullTypeDef\x12\'\n\x07type_id\x18\x01 \x01(\x0e\x32\x16.tensorflow.FullTypeId\x12%\n\x04\x61rgs\x18\x02 \x03(\x0b\x32\x17.tensorflow.FullTypeDef\x12\x0b\n\x01s\x18\x03 \x01(\tH\x00\x12\x0b\n\x01i\x18\x04 \x01(\x03H\x00\x42\x06\n\x04\x61ttr*\xda\x04\n\nFullTypeId\x12\r\n\tTFT_UNSET\x10\x00\x12\x0b\n\x07TFT_VAR\x10\x01\x12\x0b\n\x07TFT_ANY\x10\x02\x12\x0f\n\x0bTFT_PRODUCT\x10\x03\x12\r\n\tTFT_NAMED\x10\x04\x12\x10\n\x0cTFT_FOR_EACH\x10\x14\x12\x10\n\x0cTFT_CALLABLE\x10\x64\x12\x0f\n\nTFT_TENSOR\x10\xe8\x07\x12\x0e\n\tTFT_ARRAY\x10\xe9\x07\x12\x11\n\x0cTFT_OPTIONAL\x10\xea\x07\x12\x10\n\x0bTFT_LITERAL\x10\xeb\x07\x12\x10\n\x0bTFT_ENCODED\x10\xec\x07\x12\x15\n\x10TFT_SHAPE_TENSOR\x10\xed\x07\x12\r\n\x08TFT_BOOL\x10\xc8\x01\x12\x0e\n\tTFT_UINT8\x10\xc9\x01\x12\x0f\n\nTFT_UINT16\x10\xca\x01\x12\x0f\n\nTFT_UINT32\x10\xcb\x01\x12\x0f\n\nTFT_UINT64\x10\xcc\x01\x12\r\n\x08TFT_INT8\x10\xcd\x01\x12\x0e\n\tTFT_INT16\x10\xce\x01\x12\x0e\n\tTFT_INT32\x10\xcf\x01\x12\x0e\n\tTFT_INT64\x10\xd0\x01\x12\r\n\x08TFT_HALF\x10\xd1\x01\x12\x0e\n\tTFT_FLOAT\x10\xd2\x01\x12\x0f\n\nTFT_DOUBLE\x10\xd3\x01\x12\x11\n\x0cTFT_BFLOAT16\x10\xd7\x01\x12\x12\n\rTFT_COMPLEX64\x10\xd4\x01\x12\x13\n\x0eTFT_COMPLEX128\x10\xd5\x01\x12\x0f\n\nTFT_STRING\x10\xd6\x01\x12\x10\n\x0bTFT_DATASET\x10\xf6N\x12\x0f\n\nTFT_RAGGED\x10\xf7N\x12\x11\n\x0cTFT_ITERATOR\x10\xf8N\x12\x13\n\x0eTFT_MUTEX_LOCK\x10\xdaO\x12\x17\n\x12TFT_LEGACY_VARIANT\x10\xdbOB\x81\x01\n\x18org.tensorflow.frameworkB\x0e\x46ullTypeProtosP\x01ZPgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/full_type_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.full_type_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\016FullTypeProtosP\001ZPgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/full_type_go_proto\370\001\001'
+  _FULLTYPEID._serialized_start=187
+  _FULLTYPEID._serialized_end=789
+  _FULLTYPEDEF._serialized_start=57
+  _FULLTYPEDEF._serialized_end=184
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/function_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/function_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..d23aa616c3aa86c3a9a6fef0fc9cf0d36530afbc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/function_pb2.py
@@ -0,0 +1,61 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/function.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
+from tensorflow.core.framework import node_def_pb2 as tensorflow_dot_core_dot_framework_dot_node__def__pb2
+from tensorflow.core.framework import op_def_pb2 as tensorflow_dot_core_dot_framework_dot_op__def__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n(tensorflow/core/framework/function.proto\x12\ntensorflow\x1a*tensorflow/core/framework/attr_value.proto\x1a(tensorflow/core/framework/node_def.proto\x1a&tensorflow/core/framework/op_def.proto\"\xa8\x01\n\x12\x46unctionDefLibrary\x12)\n\x08\x66unction\x18\x01 \x03(\x0b\x32\x17.tensorflow.FunctionDef\x12)\n\x08gradient\x18\x02 \x03(\x0b\x32\x17.tensorflow.GradientDef\x12<\n\x14registered_gradients\x18\x03 \x03(\x0b\x32\x1e.tensorflow.RegisteredGradient\"\xc4\x06\n\x0b\x46unctionDef\x12$\n\tsignature\x18\x01 \x01(\x0b\x32\x11.tensorflow.OpDef\x12/\n\x04\x61ttr\x18\x05 \x03(\x0b\x32!.tensorflow.FunctionDef.AttrEntry\x12\x36\n\x08\x61rg_attr\x18\x07 \x03(\x0b\x32$.tensorflow.FunctionDef.ArgAttrEntry\x12P\n\x16resource_arg_unique_id\x18\x08 \x03(\x0b\x32\x30.tensorflow.FunctionDef.ResourceArgUniqueIdEntry\x12%\n\x08node_def\x18\x03 \x03(\x0b\x32\x13.tensorflow.NodeDef\x12-\n\x03ret\x18\x04 \x03(\x0b\x32 .tensorflow.FunctionDef.RetEntry\x12<\n\x0b\x63ontrol_ret\x18\x06 \x03(\x0b\x32\'.tensorflow.FunctionDef.ControlRetEntry\x1a\x42\n\tAttrEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12$\n\x05value\x18\x02 \x01(\x0b\x32\x15.tensorflow.AttrValue:\x02\x38\x01\x1a\x88\x01\n\x08\x41rgAttrs\x12\x38\n\x04\x61ttr\x18\x01 \x03(\x0b\x32*.tensorflow.FunctionDef.ArgAttrs.AttrEntry\x1a\x42\n\tAttrEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12$\n\x05value\x18\x02 \x01(\x0b\x32\x15.tensorflow.AttrValue:\x02\x38\x01\x1aP\n\x0c\x41rgAttrEntry\x12\x0b\n\x03key\x18\x01 \x01(\r\x12/\n\x05value\x18\x02 \x01(\x0b\x32 .tensorflow.FunctionDef.ArgAttrs:\x02\x38\x01\x1a:\n\x18ResourceArgUniqueIdEntry\x12\x0b\n\x03key\x18\x01 \x01(\r\x12\r\n\x05value\x18\x02 \x01(\r:\x02\x38\x01\x1a*\n\x08RetEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\x1a\x31\n\x0f\x43ontrolRetEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01J\x04\x08\x02\x10\x03\";\n\x0bGradientDef\x12\x15\n\rfunction_name\x18\x01 \x01(\t\x12\x15\n\rgradient_func\x18\x02 \x01(\t\"G\n\x12RegisteredGradient\x12\x15\n\rgradient_func\x18\x01 \x01(\t\x12\x1a\n\x12registered_op_type\x18\x02 \x01(\tB\x80\x01\n\x18org.tensorflow.frameworkB\x0e\x46unctionProtosP\x01ZOgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/function_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.function_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\016FunctionProtosP\001ZOgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/function_go_proto\370\001\001'
+  _FUNCTIONDEF_ATTRENTRY._options = None
+  _FUNCTIONDEF_ATTRENTRY._serialized_options = b'8\001'
+  _FUNCTIONDEF_ARGATTRS_ATTRENTRY._options = None
+  _FUNCTIONDEF_ARGATTRS_ATTRENTRY._serialized_options = b'8\001'
+  _FUNCTIONDEF_ARGATTRENTRY._options = None
+  _FUNCTIONDEF_ARGATTRENTRY._serialized_options = b'8\001'
+  _FUNCTIONDEF_RESOURCEARGUNIQUEIDENTRY._options = None
+  _FUNCTIONDEF_RESOURCEARGUNIQUEIDENTRY._serialized_options = b'8\001'
+  _FUNCTIONDEF_RETENTRY._options = None
+  _FUNCTIONDEF_RETENTRY._serialized_options = b'8\001'
+  _FUNCTIONDEF_CONTROLRETENTRY._options = None
+  _FUNCTIONDEF_CONTROLRETENTRY._serialized_options = b'8\001'
+  _FUNCTIONDEFLIBRARY._serialized_start=183
+  _FUNCTIONDEFLIBRARY._serialized_end=351
+  _FUNCTIONDEF._serialized_start=354
+  _FUNCTIONDEF._serialized_end=1190
+  _FUNCTIONDEF_ATTRENTRY._serialized_start=742
+  _FUNCTIONDEF_ATTRENTRY._serialized_end=808
+  _FUNCTIONDEF_ARGATTRS._serialized_start=811
+  _FUNCTIONDEF_ARGATTRS._serialized_end=947
+  _FUNCTIONDEF_ARGATTRS_ATTRENTRY._serialized_start=742
+  _FUNCTIONDEF_ARGATTRS_ATTRENTRY._serialized_end=808
+  _FUNCTIONDEF_ARGATTRENTRY._serialized_start=949
+  _FUNCTIONDEF_ARGATTRENTRY._serialized_end=1029
+  _FUNCTIONDEF_RESOURCEARGUNIQUEIDENTRY._serialized_start=1031
+  _FUNCTIONDEF_RESOURCEARGUNIQUEIDENTRY._serialized_end=1089
+  _FUNCTIONDEF_RETENTRY._serialized_start=1091
+  _FUNCTIONDEF_RETENTRY._serialized_end=1133
+  _FUNCTIONDEF_CONTROLRETENTRY._serialized_start=1135
+  _FUNCTIONDEF_CONTROLRETENTRY._serialized_end=1184
+  _GRADIENTDEF._serialized_start=1192
+  _GRADIENTDEF._serialized_end=1251
+  _REGISTEREDGRADIENT._serialized_start=1253
+  _REGISTEREDGRADIENT._serialized_end=1324
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/graph_debug_info_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/graph_debug_info_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..6bfaaf2fa92262a19a5308ac2a628a194add4c04
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/graph_debug_info_pb2.py
@@ -0,0 +1,48 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/graph_debug_info.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n0tensorflow/core/framework/graph_debug_info.proto\x12\ntensorflow\"\xa3\x06\n\x0eGraphDebugInfo\x12\r\n\x05\x66iles\x18\x01 \x03(\t\x12@\n\x0c\x66rames_by_id\x18\x04 \x03(\x0b\x32*.tensorflow.GraphDebugInfo.FramesByIdEntry\x12@\n\x0ctraces_by_id\x18\x06 \x03(\x0b\x32*.tensorflow.GraphDebugInfo.TracesByIdEntry\x12\x36\n\x06traces\x18\x02 \x03(\x0b\x32&.tensorflow.GraphDebugInfo.TracesEntry\x12G\n\x10name_to_trace_id\x18\x05 \x03(\x0b\x32-.tensorflow.GraphDebugInfo.NameToTraceIdEntry\x1aX\n\x0b\x46ileLineCol\x12\x12\n\nfile_index\x18\x01 \x01(\x05\x12\x0c\n\x04line\x18\x02 \x01(\x05\x12\x0b\n\x03\x63ol\x18\x03 \x01(\x05\x12\x0c\n\x04\x66unc\x18\x04 \x01(\t\x12\x0c\n\x04\x63ode\x18\x05 \x01(\t\x1a\x62\n\nStackTrace\x12>\n\x0e\x66ile_line_cols\x18\x01 \x03(\x0b\x32&.tensorflow.GraphDebugInfo.FileLineCol\x12\x14\n\x08\x66rame_id\x18\x02 \x03(\x06\x42\x02\x10\x01\x1aY\n\x0f\x46ramesByIdEntry\x12\x0b\n\x03key\x18\x01 \x01(\x06\x12\x35\n\x05value\x18\x02 \x01(\x0b\x32&.tensorflow.GraphDebugInfo.FileLineCol:\x02\x38\x01\x1aX\n\x0fTracesByIdEntry\x12\x0b\n\x03key\x18\x01 \x01(\x06\x12\x34\n\x05value\x18\x02 \x01(\x0b\x32%.tensorflow.GraphDebugInfo.StackTrace:\x02\x38\x01\x1aT\n\x0bTracesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x34\n\x05value\x18\x02 \x01(\x0b\x32%.tensorflow.GraphDebugInfo.StackTrace:\x02\x38\x01\x1a\x34\n\x12NameToTraceIdEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\x06:\x02\x38\x01\x42\x8c\x01\n\x18org.tensorflow.frameworkB\x14GraphDebugInfoProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.graph_debug_info_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\024GraphDebugInfoProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _GRAPHDEBUGINFO_STACKTRACE.fields_by_name['frame_id']._options = None
+  _GRAPHDEBUGINFO_STACKTRACE.fields_by_name['frame_id']._serialized_options = b'\020\001'
+  _GRAPHDEBUGINFO_FRAMESBYIDENTRY._options = None
+  _GRAPHDEBUGINFO_FRAMESBYIDENTRY._serialized_options = b'8\001'
+  _GRAPHDEBUGINFO_TRACESBYIDENTRY._options = None
+  _GRAPHDEBUGINFO_TRACESBYIDENTRY._serialized_options = b'8\001'
+  _GRAPHDEBUGINFO_TRACESENTRY._options = None
+  _GRAPHDEBUGINFO_TRACESENTRY._serialized_options = b'8\001'
+  _GRAPHDEBUGINFO_NAMETOTRACEIDENTRY._options = None
+  _GRAPHDEBUGINFO_NAMETOTRACEIDENTRY._serialized_options = b'8\001'
+  _GRAPHDEBUGINFO._serialized_start=65
+  _GRAPHDEBUGINFO._serialized_end=868
+  _GRAPHDEBUGINFO_FILELINECOL._serialized_start=359
+  _GRAPHDEBUGINFO_FILELINECOL._serialized_end=447
+  _GRAPHDEBUGINFO_STACKTRACE._serialized_start=449
+  _GRAPHDEBUGINFO_STACKTRACE._serialized_end=547
+  _GRAPHDEBUGINFO_FRAMESBYIDENTRY._serialized_start=549
+  _GRAPHDEBUGINFO_FRAMESBYIDENTRY._serialized_end=638
+  _GRAPHDEBUGINFO_TRACESBYIDENTRY._serialized_start=640
+  _GRAPHDEBUGINFO_TRACESBYIDENTRY._serialized_end=728
+  _GRAPHDEBUGINFO_TRACESENTRY._serialized_start=730
+  _GRAPHDEBUGINFO_TRACESENTRY._serialized_end=814
+  _GRAPHDEBUGINFO_NAMETOTRACEIDENTRY._serialized_start=816
+  _GRAPHDEBUGINFO_NAMETOTRACEIDENTRY._serialized_end=868
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/graph_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/graph_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..d20b8765eabdf30493580c4c9d29f30487450306
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/graph_pb2.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/graph.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import function_pb2 as tensorflow_dot_core_dot_framework_dot_function__pb2
+from tensorflow.core.framework import graph_debug_info_pb2 as tensorflow_dot_core_dot_framework_dot_graph__debug__info__pb2
+from tensorflow.core.framework import node_def_pb2 as tensorflow_dot_core_dot_framework_dot_node__def__pb2
+from tensorflow.core.framework import versions_pb2 as tensorflow_dot_core_dot_framework_dot_versions__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n%tensorflow/core/framework/graph.proto\x12\ntensorflow\x1a(tensorflow/core/framework/function.proto\x1a\x30tensorflow/core/framework/graph_debug_info.proto\x1a(tensorflow/core/framework/node_def.proto\x1a(tensorflow/core/framework/versions.proto\"\xcd\x01\n\x08GraphDef\x12!\n\x04node\x18\x01 \x03(\x0b\x32\x13.tensorflow.NodeDef\x12(\n\x08versions\x18\x04 \x01(\x0b\x32\x16.tensorflow.VersionDef\x12\x13\n\x07version\x18\x03 \x01(\x05\x42\x02\x18\x01\x12/\n\x07library\x18\x02 \x01(\x0b\x32\x1e.tensorflow.FunctionDefLibrary\x12.\n\ndebug_info\x18\x05 \x01(\x0b\x32\x1a.tensorflow.GraphDebugInfoBz\n\x18org.tensorflow.frameworkB\x0bGraphProtosP\x01ZLgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/graph_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.graph_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\013GraphProtosP\001ZLgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/graph_go_proto\370\001\001'
+  _GRAPHDEF.fields_by_name['version']._options = None
+  _GRAPHDEF.fields_by_name['version']._serialized_options = b'\030\001'
+  _GRAPHDEF._serialized_start=230
+  _GRAPHDEF._serialized_end=435
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/graph_transfer_info_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/graph_transfer_info_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae304545ce58f8a5492031b430a6db3e44d1e7e0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/graph_transfer_info_pb2.py
@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/graph_transfer_info.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n3tensorflow/core/framework/graph_transfer_info.proto\x12\ntensorflow\x1a%tensorflow/core/framework/types.proto\">\n\x16GraphTransferNodeInput\x12\x0f\n\x07node_id\x18\x01 \x01(\x05\x12\x13\n\x0boutput_port\x18\x02 \x01(\x05\"\x9b\x01\n\x15GraphTransferNodeInfo\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07node_id\x18\x02 \x01(\x05\x12\x11\n\ttype_name\x18\x03 \x01(\t\x12\x11\n\tsoc_op_id\x18\x04 \x01(\x05\x12\x12\n\npadding_id\x18\x05 \x01(\x05\x12\x13\n\x0binput_count\x18\x06 \x01(\x05\x12\x14\n\x0coutput_count\x18\x07 \x01(\x05\"}\n\x1aGraphTransferConstNodeInfo\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0f\n\x07node_id\x18\x02 \x01(\x05\x12\r\n\x05shape\x18\x03 \x03(\x03\x12\x0c\n\x04\x64\x61ta\x18\x04 \x01(\x0c\x12#\n\x05\x64type\x18\x05 \x01(\x0e\x32\x14.tensorflow.DataType\"e\n\x1aGraphTransferNodeInputInfo\x12\x0f\n\x07node_id\x18\x01 \x01(\x05\x12\x36\n\nnode_input\x18\x02 \x03(\x0b\x32\".tensorflow.GraphTransferNodeInput\"E\n\x1bGraphTransferNodeOutputInfo\x12\x0f\n\x07node_id\x18\x01 \x01(\x05\x12\x15\n\rmax_byte_size\x18\x02 \x03(\x05\"c\n\x1fGraphTransferGraphInputNodeInfo\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05shape\x18\x02 \x03(\x03\x12#\n\x05\x64type\x18\x03 \x01(\x0e\x32\x14.tensorflow.DataType\"d\n GraphTransferGraphOutputNodeInfo\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05shape\x18\x02 \x03(\x03\x12#\n\x05\x64type\x18\x03 \x01(\x0e\x32\x14.tensorflow.DataType\"\x8d\x04\n\x11GraphTransferInfo\x12\x34\n\tnode_info\x18\x01 \x03(\x0b\x32!.tensorflow.GraphTransferNodeInfo\x12?\n\x0f\x63onst_node_info\x18\x02 \x03(\x0b\x32&.tensorflow.GraphTransferConstNodeInfo\x12?\n\x0fnode_input_info\x18\x03 \x03(\x0b\x32&.tensorflow.GraphTransferNodeInputInfo\x12\x41\n\x10node_output_info\x18\x04 \x03(\x0b\x32\'.tensorflow.GraphTransferNodeOutputInfo\x12J\n\x15graph_input_node_info\x18\x05 \x03(\x0b\x32+.tensorflow.GraphTransferGraphInputNodeInfo\x12L\n\x16graph_output_node_info\x18\x06 \x03(\x0b\x32,.tensorflow.GraphTransferGraphOutputNodeInfo\x12>\n\x0b\x64\x65stination\x18\x07 \x01(\x0e\x32).tensorflow.GraphTransferInfo.Destination\"#\n\x0b\x44\x65stination\x12\x07\n\x03NOP\x10\x00\x12\x0b\n\x07HEXAGON\x10\x01\x42\x93\x01\n\x18org.tensorflow.frameworkB\x16GraphTransferInfoProtoP\x01ZZgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/graph_transfer_info_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.graph_transfer_info_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\026GraphTransferInfoProtoP\001ZZgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/graph_transfer_info_go_proto\370\001\001'
+  _GRAPHTRANSFERNODEINPUT._serialized_start=106
+  _GRAPHTRANSFERNODEINPUT._serialized_end=168
+  _GRAPHTRANSFERNODEINFO._serialized_start=171
+  _GRAPHTRANSFERNODEINFO._serialized_end=326
+  _GRAPHTRANSFERCONSTNODEINFO._serialized_start=328
+  _GRAPHTRANSFERCONSTNODEINFO._serialized_end=453
+  _GRAPHTRANSFERNODEINPUTINFO._serialized_start=455
+  _GRAPHTRANSFERNODEINPUTINFO._serialized_end=556
+  _GRAPHTRANSFERNODEOUTPUTINFO._serialized_start=558
+  _GRAPHTRANSFERNODEOUTPUTINFO._serialized_end=627
+  _GRAPHTRANSFERGRAPHINPUTNODEINFO._serialized_start=629
+  _GRAPHTRANSFERGRAPHINPUTNODEINFO._serialized_end=728
+  _GRAPHTRANSFERGRAPHOUTPUTNODEINFO._serialized_start=730
+  _GRAPHTRANSFERGRAPHOUTPUTNODEINFO._serialized_end=830
+  _GRAPHTRANSFERINFO._serialized_start=833
+  _GRAPHTRANSFERINFO._serialized_end=1358
+  _GRAPHTRANSFERINFO_DESTINATION._serialized_start=1323
+  _GRAPHTRANSFERINFO_DESTINATION._serialized_end=1358
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/kernel_def_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/kernel_def_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..215d45979f67a26a0788b17831f5cbec3a3c2789
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/kernel_def_pb2.py
@@ -0,0 +1,31 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/kernel_def.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n*tensorflow/core/framework/kernel_def.proto\x12\ntensorflow\x1a*tensorflow/core/framework/attr_value.proto\"\xef\x01\n\tKernelDef\x12\n\n\x02op\x18\x01 \x01(\t\x12\x13\n\x0b\x64\x65vice_type\x18\x02 \x01(\t\x12\x38\n\nconstraint\x18\x03 \x03(\x0b\x32$.tensorflow.KernelDef.AttrConstraint\x12\x17\n\x0fhost_memory_arg\x18\x04 \x03(\t\x12\r\n\x05label\x18\x05 \x01(\t\x12\x10\n\x08priority\x18\x06 \x01(\x05\x1aM\n\x0e\x41ttrConstraint\x12\x0c\n\x04name\x18\x01 \x01(\t\x12-\n\x0e\x61llowed_values\x18\x02 \x01(\x0b\x32\x15.tensorflow.AttrValue\"3\n\nKernelList\x12%\n\x06kernel\x18\x01 \x03(\x0b\x32\x15.tensorflow.KernelDefB\x83\x01\n\x18org.tensorflow.frameworkB\x0fKernelDefProtosP\x01ZQgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/kernel_def_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.kernel_def_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\017KernelDefProtosP\001ZQgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/kernel_def_go_proto\370\001\001'
+  _KERNELDEF._serialized_start=103
+  _KERNELDEF._serialized_end=342
+  _KERNELDEF_ATTRCONSTRAINT._serialized_start=265
+  _KERNELDEF_ATTRCONSTRAINT._serialized_end=342
+  _KERNELLIST._serialized_start=344
+  _KERNELLIST._serialized_end=395
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/log_memory_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/log_memory_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1fffcb69a4858282539429d636c99d801186c0f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/log_memory_pb2.py
@@ -0,0 +1,37 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/log_memory.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_description_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__description__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n*tensorflow/core/framework/log_memory.proto\x12\ntensorflow\x1a\x32tensorflow/core/framework/tensor_description.proto\"0\n\rMemoryLogStep\x12\x0f\n\x07step_id\x18\x01 \x01(\x03\x12\x0e\n\x06handle\x18\x02 \x01(\t\"p\n\x19MemoryLogTensorAllocation\x12\x0f\n\x07step_id\x18\x01 \x01(\x03\x12\x13\n\x0bkernel_name\x18\x02 \x01(\t\x12-\n\x06tensor\x18\x03 \x01(\x0b\x32\x1d.tensorflow.TensorDescription\"L\n\x1bMemoryLogTensorDeallocation\x12\x15\n\rallocation_id\x18\x01 \x01(\x03\x12\x16\n\x0e\x61llocator_name\x18\x02 \x01(\t\"{\n\x15MemoryLogTensorOutput\x12\x0f\n\x07step_id\x18\x01 \x01(\x03\x12\x13\n\x0bkernel_name\x18\x02 \x01(\t\x12\r\n\x05index\x18\x03 \x01(\x05\x12-\n\x06tensor\x18\x04 \x01(\x0b\x32\x1d.tensorflow.TensorDescription\"\x8b\x01\n\x16MemoryLogRawAllocation\x12\x0f\n\x07step_id\x18\x01 \x01(\x03\x12\x11\n\toperation\x18\x02 \x01(\t\x12\x11\n\tnum_bytes\x18\x03 \x01(\x03\x12\x0b\n\x03ptr\x18\x04 \x01(\x04\x12\x15\n\rallocation_id\x18\x05 \x01(\x03\x12\x16\n\x0e\x61llocator_name\x18\x06 \x01(\t\"\x7f\n\x18MemoryLogRawDeallocation\x12\x0f\n\x07step_id\x18\x01 \x01(\x03\x12\x11\n\toperation\x18\x02 \x01(\t\x12\x15\n\rallocation_id\x18\x03 \x01(\x03\x12\x16\n\x0e\x61llocator_name\x18\x04 \x01(\t\x12\x10\n\x08\x64\x65\x66\x65rred\x18\x05 \x01(\x08\x42\x83\x01\n\x18org.tensorflow.frameworkB\x0fLogMemoryProtosP\x01ZQgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/log_memory_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.log_memory_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\017LogMemoryProtosP\001ZQgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/log_memory_go_proto\370\001\001'
+  _MEMORYLOGSTEP._serialized_start=110
+  _MEMORYLOGSTEP._serialized_end=158
+  _MEMORYLOGTENSORALLOCATION._serialized_start=160
+  _MEMORYLOGTENSORALLOCATION._serialized_end=272
+  _MEMORYLOGTENSORDEALLOCATION._serialized_start=274
+  _MEMORYLOGTENSORDEALLOCATION._serialized_end=350
+  _MEMORYLOGTENSOROUTPUT._serialized_start=352
+  _MEMORYLOGTENSOROUTPUT._serialized_end=475
+  _MEMORYLOGRAWALLOCATION._serialized_start=478
+  _MEMORYLOGRAWALLOCATION._serialized_end=617
+  _MEMORYLOGRAWDEALLOCATION._serialized_start=619
+  _MEMORYLOGRAWDEALLOCATION._serialized_end=746
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/model_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/model_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..70b948f9b142d8e3272d981fdd33bad094a05bc5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/model_pb2.py
@@ -0,0 +1,40 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/model.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n%tensorflow/core/framework/model.proto\x12\x15tensorflow.data.model\"\x87\x08\n\nModelProto\x12;\n\x05nodes\x18\x01 \x03(\x0b\x32,.tensorflow.data.model.ModelProto.NodesEntry\x12\x0e\n\x06output\x18\x02 \x01(\x03\x12\x12\n\nid_counter\x18\x03 \x01(\x03\x12Q\n\x13optimization_params\x18\x05 \x01(\x0b\x32\x34.tensorflow.data.model.ModelProto.OptimizationParams\x12\x11\n\tgap_times\x18\x06 \x03(\x04\x1a\xbf\x04\n\x04Node\x12\n\n\x02id\x18\x01 \x01(\x03\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\x10\n\x08\x61utotune\x18\x03 \x01(\x08\x12\x16\n\x0e\x62uffered_bytes\x18\x04 \x01(\x03\x12\x19\n\x11\x62uffered_elements\x18\x05 \x01(\x03\x12\x16\n\x0e\x62ytes_consumed\x18\x06 \x01(\x03\x12\x16\n\x0e\x62ytes_produced\x18\x07 \x01(\x03\x12\x14\n\x0cnum_elements\x18\x08 \x01(\x03\x12\x17\n\x0fprocessing_time\x18\t \x01(\x03\x12\x16\n\x0erecord_metrics\x18\n \x01(\x08\x12\x44\n\nparameters\x18\x0b \x03(\x0b\x32\x30.tensorflow.data.model.ModelProto.Node.Parameter\x12!\n\x19input_processing_time_sum\x18\x0c \x01(\x01\x12#\n\x1binput_processing_time_count\x18\r \x01(\x03\x12\x0e\n\x06inputs\x18\x0e \x03(\x03\x12\x34\n\nnode_class\x18\x0f \x01(\x0e\x32 .tensorflow.data.model.NodeClass\x12\r\n\x05ratio\x18\x10 \x01(\x01\x12\x14\n\x0cmemory_ratio\x18\x11 \x01(\x01\x1ah\n\tParameter\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\x01\x12\x13\n\x0bstate_value\x18\x03 \x01(\x01\x12\x0b\n\x03min\x18\x04 \x01(\x01\x12\x0b\n\x03max\x18\x05 \x01(\x01\x12\x0f\n\x07tunable\x18\x06 \x01(\x08\x1aT\n\nNodesEntry\x12\x0b\n\x03key\x18\x01 \x01(\x03\x12\x35\n\x05value\x18\x02 \x01(\x0b\x32&.tensorflow.data.model.ModelProto.Node:\x02\x38\x01\x1a\x93\x01\n\x12OptimizationParams\x12;\n\talgorithm\x18\x01 \x01(\x0e\x32(.tensorflow.data.model.AutotuneAlgorithm\x12\x12\n\ncpu_budget\x18\x02 \x01(\x03\x12\x12\n\nram_budget\x18\x03 \x01(\x03\x12\x18\n\x10model_input_time\x18\x04 \x01(\x01J\x04\x08\x04\x10\x05*\x9c\x01\n\tNodeClass\x12\x0b\n\x07UNKNOWN\x10\x00\x12\x13\n\x0fINTERLEAVE_MANY\x10\x01\x12\x19\n\x15\x41SYNC_INTERLEAVE_MANY\x10\x02\x12\x0f\n\x0bKNOWN_RATIO\x10\x03\x12\x15\n\x11\x41SYNC_KNOWN_RATIO\x10\x04\x12\x11\n\rUNKNOWN_RATIO\x10\x05\x12\x17\n\x13\x41SYNC_UNKNOWN_RATIO\x10\x06*l\n\x11\x41utotuneAlgorithm\x12\x0b\n\x07\x44\x45\x46\x41ULT\x10\x00\x12\x0e\n\nHILL_CLIMB\x10\x01\x12\x14\n\x10GRADIENT_DESCENT\x10\x02\x12\x13\n\x0fMAX_PARALLELISM\x10\x03\x12\x0f\n\x0bSTAGE_BASED\x10\x04\x42QZLgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/model_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.model_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZLgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/model_go_proto\370\001\001'
+  _MODELPROTO_NODESENTRY._options = None
+  _MODELPROTO_NODESENTRY._serialized_options = b'8\001'
+  _NODECLASS._serialized_start=1099
+  _NODECLASS._serialized_end=1255
+  _AUTOTUNEALGORITHM._serialized_start=1257
+  _AUTOTUNEALGORITHM._serialized_end=1365
+  _MODELPROTO._serialized_start=65
+  _MODELPROTO._serialized_end=1096
+  _MODELPROTO_NODE._serialized_start=279
+  _MODELPROTO_NODE._serialized_end=854
+  _MODELPROTO_NODE_PARAMETER._serialized_start=750
+  _MODELPROTO_NODE_PARAMETER._serialized_end=854
+  _MODELPROTO_NODESENTRY._serialized_start=856
+  _MODELPROTO_NODESENTRY._serialized_end=940
+  _MODELPROTO_OPTIMIZATIONPARAMS._serialized_start=943
+  _MODELPROTO_OPTIMIZATIONPARAMS._serialized_end=1090
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/node_def_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/node_def_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..64bb5e7b46fd7e1d48995152da849ff2fbafeaaf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/node_def_pb2.py
@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/node_def.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
+from tensorflow.core.framework import full_type_pb2 as tensorflow_dot_core_dot_framework_dot_full__type__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n(tensorflow/core/framework/node_def.proto\x12\ntensorflow\x1a*tensorflow/core/framework/attr_value.proto\x1a)tensorflow/core/framework/full_type.proto\"\x86\x03\n\x07NodeDef\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\n\n\x02op\x18\x02 \x01(\t\x12\r\n\x05input\x18\x03 \x03(\t\x12\x0e\n\x06\x64\x65vice\x18\x04 \x01(\t\x12+\n\x04\x61ttr\x18\x05 \x03(\x0b\x32\x1d.tensorflow.NodeDef.AttrEntry\x12J\n\x17\x65xperimental_debug_info\x18\x06 \x01(\x0b\x32).tensorflow.NodeDef.ExperimentalDebugInfo\x12\x32\n\x11\x65xperimental_type\x18\x07 \x01(\x0b\x32\x17.tensorflow.FullTypeDef\x1a\x42\n\tAttrEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12$\n\x05value\x18\x02 \x01(\x0b\x32\x15.tensorflow.AttrValue:\x02\x38\x01\x1aQ\n\x15\x45xperimentalDebugInfo\x12\x1b\n\x13original_node_names\x18\x01 \x03(\t\x12\x1b\n\x13original_func_names\x18\x02 \x03(\tB{\n\x18org.tensorflow.frameworkB\tNodeProtoP\x01ZOgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/node_def_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.node_def_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\tNodeProtoP\001ZOgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/node_def_go_proto\370\001\001'
+  _NODEDEF_ATTRENTRY._options = None
+  _NODEDEF_ATTRENTRY._serialized_options = b'8\001'
+  _NODEDEF._serialized_start=144
+  _NODEDEF._serialized_end=534
+  _NODEDEF_ATTRENTRY._serialized_start=385
+  _NODEDEF_ATTRENTRY._serialized_end=451
+  _NODEDEF_EXPERIMENTALDEBUGINFO._serialized_start=453
+  _NODEDEF_EXPERIMENTALDEBUGINFO._serialized_end=534
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/op_def_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/op_def_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc41fa31a8754d33df74da43ef139bb49eba57b9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/op_def_pb2.py
@@ -0,0 +1,38 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/op_def.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
+from tensorflow.core.framework import full_type_pb2 as tensorflow_dot_core_dot_framework_dot_full__type__pb2
+from tensorflow.core.framework import resource_handle_pb2 as tensorflow_dot_core_dot_framework_dot_resource__handle__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n&tensorflow/core/framework/op_def.proto\x12\ntensorflow\x1a*tensorflow/core/framework/attr_value.proto\x1a)tensorflow/core/framework/full_type.proto\x1a/tensorflow/core/framework/resource_handle.proto\x1a%tensorflow/core/framework/types.proto\"\xf3\x06\n\x05OpDef\x12\x0c\n\x04name\x18\x01 \x01(\t\x12+\n\tinput_arg\x18\x02 \x03(\x0b\x32\x18.tensorflow.OpDef.ArgDef\x12,\n\noutput_arg\x18\x03 \x03(\x0b\x32\x18.tensorflow.OpDef.ArgDef\x12\x16\n\x0e\x63ontrol_output\x18\x14 \x03(\t\x12\'\n\x04\x61ttr\x18\x04 \x03(\x0b\x32\x19.tensorflow.OpDef.AttrDef\x12.\n\x0b\x64\x65precation\x18\x08 \x01(\x0b\x32\x19.tensorflow.OpDeprecation\x12\x0f\n\x07summary\x18\x05 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x06 \x01(\t\x12\x16\n\x0eis_commutative\x18\x12 \x01(\x08\x12\x14\n\x0cis_aggregate\x18\x10 \x01(\x08\x12\x13\n\x0bis_stateful\x18\x11 \x01(\x08\x12\"\n\x1a\x61llows_uninitialized_input\x18\x13 \x01(\x08\x12$\n\x1cis_distributed_communication\x18\x15 \x01(\x08\x1a\x9c\x02\n\x06\x41rgDef\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x13\n\x0b\x64\x65scription\x18\x02 \x01(\t\x12\"\n\x04type\x18\x03 \x01(\x0e\x32\x14.tensorflow.DataType\x12\x11\n\ttype_attr\x18\x04 \x01(\t\x12\x13\n\x0bnumber_attr\x18\x05 \x01(\t\x12\x16\n\x0etype_list_attr\x18\x06 \x01(\t\x12\x42\n\x0bhandle_data\x18\x07 \x03(\x0b\x32-.tensorflow.ResourceHandleProto.DtypeAndShape\x12\x0e\n\x06is_ref\x18\x10 \x01(\x08\x12\x37\n\x16\x65xperimental_full_type\x18\x11 \x01(\x0b\x32\x17.tensorflow.FullTypeDef\x1a\xbd\x01\n\x07\x41ttrDef\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0c\n\x04type\x18\x02 \x01(\t\x12,\n\rdefault_value\x18\x03 \x01(\x0b\x32\x15.tensorflow.AttrValue\x12\x13\n\x0b\x64\x65scription\x18\x04 \x01(\t\x12\x13\n\x0bhas_minimum\x18\x05 \x01(\x08\x12\x0f\n\x07minimum\x18\x06 \x01(\x03\x12-\n\x0e\x61llowed_values\x18\x07 \x01(\x0b\x32\x15.tensorflow.AttrValue\"5\n\rOpDeprecation\x12\x0f\n\x07version\x18\x01 \x01(\x05\x12\x13\n\x0b\x65xplanation\x18\x02 \x01(\t\"\'\n\x06OpList\x12\x1d\n\x02op\x18\x01 \x03(\x0b\x32\x11.tensorflow.OpDefB{\n\x18org.tensorflow.frameworkB\x0bOpDefProtosP\x01ZMgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/op_def_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.op_def_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\013OpDefProtosP\001ZMgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/op_def_go_proto\370\001\001'
+  _OPDEF._serialized_start=230
+  _OPDEF._serialized_end=1113
+  _OPDEF_ARGDEF._serialized_start=637
+  _OPDEF_ARGDEF._serialized_end=921
+  _OPDEF_ATTRDEF._serialized_start=924
+  _OPDEF_ATTRDEF._serialized_end=1113
+  _OPDEPRECATION._serialized_start=1115
+  _OPDEPRECATION._serialized_end=1168
+  _OPLIST._serialized_start=1170
+  _OPLIST._serialized_end=1209
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/optimized_function_graph_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/optimized_function_graph_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7a1435dda2b96fe260f8fdb790a1ca2f37d8640
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/optimized_function_graph_pb2.py
@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/optimized_function_graph.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import graph_pb2 as tensorflow_dot_core_dot_framework_dot_graph__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n8tensorflow/core/framework/optimized_function_graph.proto\x12\ntensorflow\x1a%tensorflow/core/framework/graph.proto\x1a%tensorflow/core/framework/types.proto\"\x93\x04\n\x16OptimizedFunctionGraph\x12\x0c\n\x04name\x18\x01 \x01(\t\x12,\n\x0e\x66unction_graph\x18\x02 \x01(\x0b\x32\x14.tensorflow.GraphDef\x12^\n\x18node_name_to_control_ret\x18\x03 \x03(\x0b\x32<.tensorflow.OptimizedFunctionGraph.NodeNameToControlRetEntry\x12\'\n\tret_types\x18\x04 \x03(\x0e\x32\x14.tensorflow.DataType\x12\x18\n\x10num_return_nodes\x18\x05 \x01(\r\x12J\n\x06source\x18\x07 \x01(\x0e\x32\x35.tensorflow.OptimizedFunctionGraph.OptimizationSourceH\x00\x88\x01\x01\x12$\n\x17optimization_time_usecs\x18\x08 \x01(\x04H\x01\x88\x01\x01\x1a;\n\x19NodeNameToControlRetEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\">\n\x12OptimizationSource\x12\x16\n\x12SOURCE_UNSPECIFIED\x10\x00\x12\x07\n\x03\x41OT\x10\x01\x12\x07\n\x03JIT\x10\x02\x42\t\n\x07_sourceB\x1a\n\x18_optimization_time_usecsJ\x04\x08\x06\x10\x07\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.optimized_function_graph_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _OPTIMIZEDFUNCTIONGRAPH_NODENAMETOCONTROLRETENTRY._options = None
+  _OPTIMIZEDFUNCTIONGRAPH_NODENAMETOCONTROLRETENTRY._serialized_options = b'8\001'
+  _OPTIMIZEDFUNCTIONGRAPH._serialized_start=151
+  _OPTIMIZEDFUNCTIONGRAPH._serialized_end=682
+  _OPTIMIZEDFUNCTIONGRAPH_NODENAMETOCONTROLRETENTRY._serialized_start=514
+  _OPTIMIZEDFUNCTIONGRAPH_NODENAMETOCONTROLRETENTRY._serialized_end=573
+  _OPTIMIZEDFUNCTIONGRAPH_OPTIMIZATIONSOURCE._serialized_start=575
+  _OPTIMIZEDFUNCTIONGRAPH_OPTIMIZATIONSOURCE._serialized_end=637
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/reader_base_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/reader_base_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..60de66ab69eaac9f12e4d42bf69ebd9406f57bdc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/reader_base_pb2.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/reader_base.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n+tensorflow/core/framework/reader_base.proto\x12\ntensorflow\"r\n\x0fReaderBaseState\x12\x14\n\x0cwork_started\x18\x01 \x01(\x03\x12\x15\n\rwork_finished\x18\x02 \x01(\x03\x12\x1c\n\x14num_records_produced\x18\x03 \x01(\x03\x12\x14\n\x0c\x63urrent_work\x18\x04 \x01(\x0c\x42\x85\x01\n\x18org.tensorflow.frameworkB\x10ReaderBaseProtosP\x01ZRgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/reader_base_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.reader_base_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\020ReaderBaseProtosP\001ZRgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/reader_base_go_proto\370\001\001'
+  _READERBASESTATE._serialized_start=59
+  _READERBASESTATE._serialized_end=173
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/resource_handle_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/resource_handle_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..401580874db1115664ebfcb8abcffa87539bb648
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/resource_handle_pb2.py
@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/resource_handle.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n/tensorflow/core/framework/resource_handle.proto\x12\ntensorflow\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\"\xa5\x02\n\x13ResourceHandleProto\x12\x0e\n\x06\x64\x65vice\x18\x01 \x01(\t\x12\x11\n\tcontainer\x18\x02 \x01(\t\x12\x0c\n\x04name\x18\x03 \x01(\t\x12\x11\n\thash_code\x18\x04 \x01(\x04\x12\x17\n\x0fmaybe_type_name\x18\x05 \x01(\t\x12H\n\x11\x64types_and_shapes\x18\x06 \x03(\x0b\x32-.tensorflow.ResourceHandleProto.DtypeAndShape\x1a\x61\n\rDtypeAndShape\x12#\n\x05\x64type\x18\x01 \x01(\x0e\x32\x14.tensorflow.DataType\x12+\n\x05shape\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProtoJ\x04\x08\x07\x10\x08\x42\x87\x01\n\x18org.tensorflow.frameworkB\x0eResourceHandleP\x01ZVgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/resource_handle_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.resource_handle_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\016ResourceHandleP\001ZVgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/resource_handle_go_proto\370\001\001'
+  _RESOURCEHANDLEPROTO._serialized_start=149
+  _RESOURCEHANDLEPROTO._serialized_end=442
+  _RESOURCEHANDLEPROTO_DTYPEANDSHAPE._serialized_start=339
+  _RESOURCEHANDLEPROTO_DTYPEANDSHAPE._serialized_end=436
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/step_stats_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/step_stats_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..05a9669ac5c5b60179309eb5c31951e726939988
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/step_stats_pb2.py
@@ -0,0 +1,50 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/step_stats.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import allocation_description_pb2 as tensorflow_dot_core_dot_framework_dot_allocation__description__pb2
+from tensorflow.core.framework import tensor_description_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__description__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n*tensorflow/core/framework/step_stats.proto\x12\ntensorflow\x1a\x36tensorflow/core/framework/allocation_description.proto\x1a\x32tensorflow/core/framework/tensor_description.proto\"=\n\x10\x41llocationRecord\x12\x14\n\x0c\x61lloc_micros\x18\x01 \x01(\x03\x12\x13\n\x0b\x61lloc_bytes\x18\x02 \x01(\x03\"\xc4\x01\n\x13\x41llocatorMemoryUsed\x12\x16\n\x0e\x61llocator_name\x18\x01 \x01(\t\x12\x13\n\x0btotal_bytes\x18\x02 \x01(\x03\x12\x12\n\npeak_bytes\x18\x03 \x01(\x03\x12\x12\n\nlive_bytes\x18\x04 \x01(\x03\x12\x38\n\x12\x61llocation_records\x18\x06 \x03(\x0b\x32\x1c.tensorflow.AllocationRecord\x12\x1e\n\x16\x61llocator_bytes_in_use\x18\x05 \x01(\x03\"U\n\nNodeOutput\x12\x0c\n\x04slot\x18\x01 \x01(\x05\x12\x39\n\x12tensor_description\x18\x03 \x01(\x0b\x32\x1d.tensorflow.TensorDescription\"\xec\x01\n\x0bMemoryStats\x12\x18\n\x10temp_memory_size\x18\x01 \x01(\x03\x12\x1e\n\x16persistent_memory_size\x18\x03 \x01(\x03\x12#\n\x1bpersistent_tensor_alloc_ids\x18\x05 \x03(\x03\x12#\n\x17\x64\x65vice_temp_memory_size\x18\x02 \x01(\x03\x42\x02\x18\x01\x12)\n\x1d\x64\x65vice_persistent_memory_size\x18\x04 \x01(\x03\x42\x02\x18\x01\x12.\n\"device_persistent_tensor_alloc_ids\x18\x06 \x03(\x03\x42\x02\x18\x01\"\x9e\x04\n\rNodeExecStats\x12\x11\n\tnode_name\x18\x01 \x01(\t\x12\x18\n\x10\x61ll_start_micros\x18\x02 \x01(\x03\x12\x1b\n\x13op_start_rel_micros\x18\x03 \x01(\x03\x12\x19\n\x11op_end_rel_micros\x18\x04 \x01(\x03\x12\x1a\n\x12\x61ll_end_rel_micros\x18\x05 \x01(\x03\x12/\n\x06memory\x18\x06 \x03(\x0b\x32\x1f.tensorflow.AllocatorMemoryUsed\x12&\n\x06output\x18\x07 \x03(\x0b\x32\x16.tensorflow.NodeOutput\x12\x16\n\x0etimeline_label\x18\x08 \x01(\t\x12\x18\n\x10scheduled_micros\x18\t \x01(\x03\x12\x11\n\tthread_id\x18\n \x01(\r\x12<\n\x11referenced_tensor\x18\x0b \x03(\x0b\x32!.tensorflow.AllocationDescription\x12-\n\x0cmemory_stats\x18\x0c \x01(\x0b\x32\x17.tensorflow.MemoryStats\x12\x17\n\x0f\x61ll_start_nanos\x18\r \x01(\x03\x12\x1a\n\x12op_start_rel_nanos\x18\x0e \x01(\x03\x12\x18\n\x10op_end_rel_nanos\x18\x0f \x01(\x03\x12\x19\n\x11\x61ll_end_rel_nanos\x18\x10 \x01(\x03\x12\x17\n\x0fscheduled_nanos\x18\x11 \x01(\x03\"\xc8\x01\n\x0f\x44\x65viceStepStats\x12\x0e\n\x06\x64\x65vice\x18\x01 \x01(\t\x12-\n\nnode_stats\x18\x02 \x03(\x0b\x32\x19.tensorflow.NodeExecStats\x12\x42\n\x0cthread_names\x18\x03 \x03(\x0b\x32,.tensorflow.DeviceStepStats.ThreadNamesEntry\x1a\x32\n\x10ThreadNamesEntry\x12\x0b\n\x03key\x18\x01 \x01(\r\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\";\n\tStepStats\x12.\n\tdev_stats\x18\x01 \x03(\x0b\x32\x1b.tensorflow.DeviceStepStatsB\x83\x01\n\x18org.tensorflow.frameworkB\x0fStepStatsProtosP\x01ZQgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/step_stats_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.step_stats_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\017StepStatsProtosP\001ZQgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/step_stats_go_proto\370\001\001'
+  _MEMORYSTATS.fields_by_name['device_temp_memory_size']._options = None
+  _MEMORYSTATS.fields_by_name['device_temp_memory_size']._serialized_options = b'\030\001'
+  _MEMORYSTATS.fields_by_name['device_persistent_memory_size']._options = None
+  _MEMORYSTATS.fields_by_name['device_persistent_memory_size']._serialized_options = b'\030\001'
+  _MEMORYSTATS.fields_by_name['device_persistent_tensor_alloc_ids']._options = None
+  _MEMORYSTATS.fields_by_name['device_persistent_tensor_alloc_ids']._serialized_options = b'\030\001'
+  _DEVICESTEPSTATS_THREADNAMESENTRY._options = None
+  _DEVICESTEPSTATS_THREADNAMESENTRY._serialized_options = b'8\001'
+  _ALLOCATIONRECORD._serialized_start=166
+  _ALLOCATIONRECORD._serialized_end=227
+  _ALLOCATORMEMORYUSED._serialized_start=230
+  _ALLOCATORMEMORYUSED._serialized_end=426
+  _NODEOUTPUT._serialized_start=428
+  _NODEOUTPUT._serialized_end=513
+  _MEMORYSTATS._serialized_start=516
+  _MEMORYSTATS._serialized_end=752
+  _NODEEXECSTATS._serialized_start=755
+  _NODEEXECSTATS._serialized_end=1297
+  _DEVICESTEPSTATS._serialized_start=1300
+  _DEVICESTEPSTATS._serialized_end=1500
+  _DEVICESTEPSTATS_THREADNAMESENTRY._serialized_start=1450
+  _DEVICESTEPSTATS_THREADNAMESENTRY._serialized_end=1500
+  _STEPSTATS._serialized_start=1502
+  _STEPSTATS._serialized_end=1561
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/summary_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/summary_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f1d432cae1c61355a7a478da6768210843e9526
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/summary_pb2.py
@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/summary.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.tsl.protobuf import histogram_pb2 as tsl_dot_protobuf_dot_histogram__pb2
+from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
+
+from tensorflow.tsl.protobuf.histogram_pb2 import *
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\'tensorflow/core/framework/summary.proto\x12\ntensorflow\x1a\x1ctsl/protobuf/histogram.proto\x1a&tensorflow/core/framework/tensor.proto\"\'\n\x12SummaryDescription\x12\x11\n\ttype_hint\x18\x01 \x01(\t\"\xe0\x01\n\x0fSummaryMetadata\x12;\n\x0bplugin_data\x18\x01 \x01(\x0b\x32&.tensorflow.SummaryMetadata.PluginData\x12\x14\n\x0c\x64isplay_name\x18\x02 \x01(\t\x12\x1b\n\x13summary_description\x18\x03 \x01(\t\x12)\n\ndata_class\x18\x04 \x01(\x0e\x32\x15.tensorflow.DataClass\x1a\x32\n\nPluginData\x12\x13\n\x0bplugin_name\x18\x01 \x01(\t\x12\x0f\n\x07\x63ontent\x18\x02 \x01(\x0c\"\xde\x04\n\x07Summary\x12(\n\x05value\x18\x01 \x03(\x0b\x32\x19.tensorflow.Summary.Value\x1aX\n\x05Image\x12\x0e\n\x06height\x18\x01 \x01(\x05\x12\r\n\x05width\x18\x02 \x01(\x05\x12\x12\n\ncolorspace\x18\x03 \x01(\x05\x12\x1c\n\x14\x65ncoded_image_string\x18\x04 \x01(\x0c\x1a}\n\x05\x41udio\x12\x13\n\x0bsample_rate\x18\x01 \x01(\x02\x12\x14\n\x0cnum_channels\x18\x02 \x01(\x03\x12\x15\n\rlength_frames\x18\x03 \x01(\x03\x12\x1c\n\x14\x65ncoded_audio_string\x18\x04 \x01(\x0c\x12\x14\n\x0c\x63ontent_type\x18\x05 \x01(\t\x1a\xcf\x02\n\x05Value\x12\x11\n\tnode_name\x18\x07 \x01(\t\x12\x0b\n\x03tag\x18\x01 \x01(\t\x12-\n\x08metadata\x18\t \x01(\x0b\x32\x1b.tensorflow.SummaryMetadata\x12\x16\n\x0csimple_value\x18\x02 \x01(\x02H\x00\x12&\n\x1cobsolete_old_style_histogram\x18\x03 \x01(\x0cH\x00\x12*\n\x05image\x18\x04 \x01(\x0b\x32\x19.tensorflow.Summary.ImageH\x00\x12+\n\x05histo\x18\x05 \x01(\x0b\x32\x1a.tensorflow.HistogramProtoH\x00\x12*\n\x05\x61udio\x18\x06 \x01(\x0b\x32\x19.tensorflow.Summary.AudioH\x00\x12)\n\x06tensor\x18\x08 \x01(\x0b\x32\x17.tensorflow.TensorProtoH\x00\x42\x07\n\x05value*o\n\tDataClass\x12\x16\n\x12\x44\x41TA_CLASS_UNKNOWN\x10\x00\x12\x15\n\x11\x44\x41TA_CLASS_SCALAR\x10\x01\x12\x15\n\x11\x44\x41TA_CLASS_TENSOR\x10\x02\x12\x1c\n\x18\x44\x41TA_CLASS_BLOB_SEQUENCE\x10\x03\x42~\n\x18org.tensorflow.frameworkB\rSummaryProtosP\x01ZNgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/summary_go_proto\xf8\x01\x01P\x00\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.summary_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\rSummaryProtosP\001ZNgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/summary_go_proto\370\001\001'
+  _DATACLASS._serialized_start=1002
+  _DATACLASS._serialized_end=1113
+  _SUMMARYDESCRIPTION._serialized_start=125
+  _SUMMARYDESCRIPTION._serialized_end=164
+  _SUMMARYMETADATA._serialized_start=167
+  _SUMMARYMETADATA._serialized_end=391
+  _SUMMARYMETADATA_PLUGINDATA._serialized_start=341
+  _SUMMARYMETADATA_PLUGINDATA._serialized_end=391
+  _SUMMARY._serialized_start=394
+  _SUMMARY._serialized_end=1000
+  _SUMMARY_IMAGE._serialized_start=447
+  _SUMMARY_IMAGE._serialized_end=535
+  _SUMMARY_AUDIO._serialized_start=537
+  _SUMMARY_AUDIO._serialized_end=662
+  _SUMMARY_VALUE._serialized_start=665
+  _SUMMARY_VALUE._serialized_end=1000
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/tensor_description_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/tensor_description_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..50a0c30478c0110f23fc6964e98dee24f88ac9ec
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/tensor_description_pb2.py
@@ -0,0 +1,29 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/tensor_description.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import allocation_description_pb2 as tensorflow_dot_core_dot_framework_dot_allocation__description__pb2
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n2tensorflow/core/framework/tensor_description.proto\x12\ntensorflow\x1a\x36tensorflow/core/framework/allocation_description.proto\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\"\xa8\x01\n\x11TensorDescription\x12#\n\x05\x64type\x18\x01 \x01(\x0e\x32\x14.tensorflow.DataType\x12+\n\x05shape\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12\x41\n\x16\x61llocation_description\x18\x04 \x01(\x0b\x32!.tensorflow.AllocationDescriptionB\x93\x01\n\x18org.tensorflow.frameworkB\x17TensorDescriptionProtosP\x01ZYgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_description_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.tensor_description_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\027TensorDescriptionProtosP\001ZYgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_description_go_proto\370\001\001'
+  _TENSORDESCRIPTION._serialized_start=208
+  _TENSORDESCRIPTION._serialized_end=376
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/tensor_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/tensor_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b1fd94655ed63850316f67f17ae0f31cb3f2552
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/tensor_pb2.py
@@ -0,0 +1,51 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/tensor.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import resource_handle_pb2 as tensorflow_dot_core_dot_framework_dot_resource__handle__pb2
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n&tensorflow/core/framework/tensor.proto\x12\ntensorflow\x1a/tensorflow/core/framework/resource_handle.proto\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\"\xa0\x04\n\x0bTensorProto\x12#\n\x05\x64type\x18\x01 \x01(\x0e\x32\x14.tensorflow.DataType\x12\x32\n\x0ctensor_shape\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12\x16\n\x0eversion_number\x18\x03 \x01(\x05\x12\x16\n\x0etensor_content\x18\x04 \x01(\x0c\x12\x14\n\x08half_val\x18\r \x03(\x05\x42\x02\x10\x01\x12\x15\n\tfloat_val\x18\x05 \x03(\x02\x42\x02\x10\x01\x12\x16\n\ndouble_val\x18\x06 \x03(\x01\x42\x02\x10\x01\x12\x13\n\x07int_val\x18\x07 \x03(\x05\x42\x02\x10\x01\x12\x12\n\nstring_val\x18\x08 \x03(\x0c\x12\x18\n\x0cscomplex_val\x18\t \x03(\x02\x42\x02\x10\x01\x12\x15\n\tint64_val\x18\n \x03(\x03\x42\x02\x10\x01\x12\x14\n\x08\x62ool_val\x18\x0b \x03(\x08\x42\x02\x10\x01\x12\x18\n\x0c\x64\x63omplex_val\x18\x0c \x03(\x01\x42\x02\x10\x01\x12<\n\x13resource_handle_val\x18\x0e \x03(\x0b\x32\x1f.tensorflow.ResourceHandleProto\x12\x37\n\x0bvariant_val\x18\x0f \x03(\x0b\x32\".tensorflow.VariantTensorDataProto\x12\x16\n\nuint32_val\x18\x10 \x03(\rB\x02\x10\x01\x12\x16\n\nuint64_val\x18\x11 \x03(\x04\x42\x02\x10\x01\x12\x12\n\nfloat8_val\x18\x12 \x01(\x0c\"g\n\x16VariantTensorDataProto\x12\x11\n\ttype_name\x18\x01 \x01(\t\x12\x10\n\x08metadata\x18\x02 \x01(\x0c\x12(\n\x07tensors\x18\x03 \x03(\x0b\x32\x17.tensorflow.TensorProtoB|\n\x18org.tensorflow.frameworkB\x0cTensorProtosP\x01ZMgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.tensor_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\014TensorProtosP\001ZMgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_go_proto\370\001\001'
+  _TENSORPROTO.fields_by_name['half_val']._options = None
+  _TENSORPROTO.fields_by_name['half_val']._serialized_options = b'\020\001'
+  _TENSORPROTO.fields_by_name['float_val']._options = None
+  _TENSORPROTO.fields_by_name['float_val']._serialized_options = b'\020\001'
+  _TENSORPROTO.fields_by_name['double_val']._options = None
+  _TENSORPROTO.fields_by_name['double_val']._serialized_options = b'\020\001'
+  _TENSORPROTO.fields_by_name['int_val']._options = None
+  _TENSORPROTO.fields_by_name['int_val']._serialized_options = b'\020\001'
+  _TENSORPROTO.fields_by_name['scomplex_val']._options = None
+  _TENSORPROTO.fields_by_name['scomplex_val']._serialized_options = b'\020\001'
+  _TENSORPROTO.fields_by_name['int64_val']._options = None
+  _TENSORPROTO.fields_by_name['int64_val']._serialized_options = b'\020\001'
+  _TENSORPROTO.fields_by_name['bool_val']._options = None
+  _TENSORPROTO.fields_by_name['bool_val']._serialized_options = b'\020\001'
+  _TENSORPROTO.fields_by_name['dcomplex_val']._options = None
+  _TENSORPROTO.fields_by_name['dcomplex_val']._serialized_options = b'\020\001'
+  _TENSORPROTO.fields_by_name['uint32_val']._options = None
+  _TENSORPROTO.fields_by_name['uint32_val']._serialized_options = b'\020\001'
+  _TENSORPROTO.fields_by_name['uint64_val']._options = None
+  _TENSORPROTO.fields_by_name['uint64_val']._serialized_options = b'\020\001'
+  _TENSORPROTO._serialized_start=189
+  _TENSORPROTO._serialized_end=733
+  _VARIANTTENSORDATAPROTO._serialized_start=735
+  _VARIANTTENSORDATAPROTO._serialized_end=838
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/tensor_shape_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/tensor_shape_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..4724ef16413934550b4dc6db490893704bc2da15
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/tensor_shape_pb2.py
@@ -0,0 +1,28 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/tensor_shape.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n,tensorflow/core/framework/tensor_shape.proto\x12\ntensorflow\"z\n\x10TensorShapeProto\x12-\n\x03\x64im\x18\x02 \x03(\x0b\x32 .tensorflow.TensorShapeProto.Dim\x12\x14\n\x0cunknown_rank\x18\x03 \x01(\x08\x1a!\n\x03\x44im\x12\x0c\n\x04size\x18\x01 \x01(\x03\x12\x0c\n\x04name\x18\x02 \x01(\tB\x87\x01\n\x18org.tensorflow.frameworkB\x11TensorShapeProtosP\x01ZSgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.tensor_shape_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\021TensorShapeProtosP\001ZSgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_shape_go_proto\370\001\001'
+  _TENSORSHAPEPROTO._serialized_start=60
+  _TENSORSHAPEPROTO._serialized_end=182
+  _TENSORSHAPEPROTO_DIM._serialized_start=149
+  _TENSORSHAPEPROTO_DIM._serialized_end=182
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/tensor_slice_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/tensor_slice_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..412063f873fb5178692cb7cd1282b69eea37fe3f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/tensor_slice_pb2.py
@@ -0,0 +1,28 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/tensor_slice.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n,tensorflow/core/framework/tensor_slice.proto\x12\ntensorflow\"\x80\x01\n\x10TensorSliceProto\x12\x33\n\x06\x65xtent\x18\x01 \x03(\x0b\x32#.tensorflow.TensorSliceProto.Extent\x1a\x37\n\x06\x45xtent\x12\r\n\x05start\x18\x01 \x01(\x03\x12\x10\n\x06length\x18\x02 \x01(\x03H\x00\x42\x0c\n\nhas_lengthB\x87\x01\n\x18org.tensorflow.frameworkB\x11TensorSliceProtosP\x01ZSgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_slice_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.tensor_slice_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\021TensorSliceProtosP\001ZSgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_slice_go_proto\370\001\001'
+  _TENSORSLICEPROTO._serialized_start=61
+  _TENSORSLICEPROTO._serialized_end=189
+  _TENSORSLICEPROTO_EXTENT._serialized_start=134
+  _TENSORSLICEPROTO_EXTENT._serialized_end=189
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/types_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/types_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f3db1b8bcefe56b45a2a48bca593bfd6eb9ebb6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/types_pb2.py
@@ -0,0 +1,28 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/types.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n%tensorflow/core/framework/types.proto\x12\ntensorflow\"9\n\x0fSerializedDType\x12&\n\x08\x64\x61tatype\x18\x01 \x01(\x0e\x32\x14.tensorflow.DataType*\xc6\x07\n\x08\x44\x61taType\x12\x0e\n\nDT_INVALID\x10\x00\x12\x0c\n\x08\x44T_FLOAT\x10\x01\x12\r\n\tDT_DOUBLE\x10\x02\x12\x0c\n\x08\x44T_INT32\x10\x03\x12\x0c\n\x08\x44T_UINT8\x10\x04\x12\x0c\n\x08\x44T_INT16\x10\x05\x12\x0b\n\x07\x44T_INT8\x10\x06\x12\r\n\tDT_STRING\x10\x07\x12\x10\n\x0c\x44T_COMPLEX64\x10\x08\x12\x0c\n\x08\x44T_INT64\x10\t\x12\x0b\n\x07\x44T_BOOL\x10\n\x12\x0c\n\x08\x44T_QINT8\x10\x0b\x12\r\n\tDT_QUINT8\x10\x0c\x12\r\n\tDT_QINT32\x10\r\x12\x0f\n\x0b\x44T_BFLOAT16\x10\x0e\x12\r\n\tDT_QINT16\x10\x0f\x12\x0e\n\nDT_QUINT16\x10\x10\x12\r\n\tDT_UINT16\x10\x11\x12\x11\n\rDT_COMPLEX128\x10\x12\x12\x0b\n\x07\x44T_HALF\x10\x13\x12\x0f\n\x0b\x44T_RESOURCE\x10\x14\x12\x0e\n\nDT_VARIANT\x10\x15\x12\r\n\tDT_UINT32\x10\x16\x12\r\n\tDT_UINT64\x10\x17\x12\x12\n\x0e\x44T_FLOAT8_E5M2\x10\x18\x12\x14\n\x10\x44T_FLOAT8_E4M3FN\x10\x19\x12\x0b\n\x07\x44T_INT4\x10\x1d\x12\x0c\n\x08\x44T_UINT4\x10\x1e\x12\x10\n\x0c\x44T_FLOAT_REF\x10\x65\x12\x11\n\rDT_DOUBLE_REF\x10\x66\x12\x10\n\x0c\x44T_INT32_REF\x10g\x12\x10\n\x0c\x44T_UINT8_REF\x10h\x12\x10\n\x0c\x44T_INT16_REF\x10i\x12\x0f\n\x0b\x44T_INT8_REF\x10j\x12\x11\n\rDT_STRING_REF\x10k\x12\x14\n\x10\x44T_COMPLEX64_REF\x10l\x12\x10\n\x0c\x44T_INT64_REF\x10m\x12\x0f\n\x0b\x44T_BOOL_REF\x10n\x12\x10\n\x0c\x44T_QINT8_REF\x10o\x12\x11\n\rDT_QUINT8_REF\x10p\x12\x11\n\rDT_QINT32_REF\x10q\x12\x13\n\x0f\x44T_BFLOAT16_REF\x10r\x12\x11\n\rDT_QINT16_REF\x10s\x12\x12\n\x0e\x44T_QUINT16_REF\x10t\x12\x11\n\rDT_UINT16_REF\x10u\x12\x15\n\x11\x44T_COMPLEX128_REF\x10v\x12\x0f\n\x0b\x44T_HALF_REF\x10w\x12\x13\n\x0f\x44T_RESOURCE_REF\x10x\x12\x12\n\x0e\x44T_VARIANT_REF\x10y\x12\x11\n\rDT_UINT32_REF\x10z\x12\x11\n\rDT_UINT64_REF\x10{\x12\x16\n\x12\x44T_FLOAT8_E5M2_REF\x10|\x12\x18\n\x14\x44T_FLOAT8_E4M3FN_REF\x10}\x12\x10\n\x0b\x44T_INT4_REF\x10\x81\x01\x12\x11\n\x0c\x44T_UINT4_REF\x10\x82\x01\x42z\n\x18org.tensorflow.frameworkB\x0bTypesProtosP\x01ZLgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.types_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\013TypesProtosP\001ZLgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/types_go_proto\370\001\001'
+  _DATATYPE._serialized_start=113
+  _DATATYPE._serialized_end=1079
+  _SERIALIZEDDTYPE._serialized_start=53
+  _SERIALIZEDDTYPE._serialized_end=110
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/variable_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/variable_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..953a3335747ddff5b19f49e5ff9a0a147e8b8fcf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/variable_pb2.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/variable.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n(tensorflow/core/framework/variable.proto\x12\ntensorflow\"\xc8\x02\n\x0bVariableDef\x12\x15\n\rvariable_name\x18\x01 \x01(\t\x12\x1a\n\x12initial_value_name\x18\x06 \x01(\t\x12\x18\n\x10initializer_name\x18\x02 \x01(\t\x12\x15\n\rsnapshot_name\x18\x03 \x01(\t\x12\x39\n\x13save_slice_info_def\x18\x04 \x01(\x0b\x32\x1c.tensorflow.SaveSliceInfoDef\x12\x13\n\x0bis_resource\x18\x05 \x01(\x08\x12\x11\n\ttrainable\x18\x07 \x01(\x08\x12<\n\x0fsynchronization\x18\x08 \x01(\x0e\x32#.tensorflow.VariableSynchronization\x12\x34\n\x0b\x61ggregation\x18\t \x01(\x0e\x32\x1f.tensorflow.VariableAggregation\"`\n\x10SaveSliceInfoDef\x12\x11\n\tfull_name\x18\x01 \x01(\t\x12\x12\n\nfull_shape\x18\x02 \x03(\x03\x12\x12\n\nvar_offset\x18\x03 \x03(\x03\x12\x11\n\tvar_shape\x18\x04 \x03(\x03*\xac\x01\n\x17VariableSynchronization\x12!\n\x1dVARIABLE_SYNCHRONIZATION_AUTO\x10\x00\x12!\n\x1dVARIABLE_SYNCHRONIZATION_NONE\x10\x01\x12%\n!VARIABLE_SYNCHRONIZATION_ON_WRITE\x10\x02\x12$\n VARIABLE_SYNCHRONIZATION_ON_READ\x10\x03*\x9e\x01\n\x13VariableAggregation\x12\x1d\n\x19VARIABLE_AGGREGATION_NONE\x10\x00\x12\x1c\n\x18VARIABLE_AGGREGATION_SUM\x10\x01\x12\x1d\n\x19VARIABLE_AGGREGATION_MEAN\x10\x02\x12+\n\'VARIABLE_AGGREGATION_ONLY_FIRST_REPLICA\x10\x03\x42\x80\x01\n\x18org.tensorflow.frameworkB\x0eVariableProtosP\x01ZOgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/variable_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.variable_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\016VariableProtosP\001ZOgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/variable_go_proto\370\001\001'
+  _VARIABLESYNCHRONIZATION._serialized_start=486
+  _VARIABLESYNCHRONIZATION._serialized_end=658
+  _VARIABLEAGGREGATION._serialized_start=661
+  _VARIABLEAGGREGATION._serialized_end=819
+  _VARIABLEDEF._serialized_start=57
+  _VARIABLEDEF._serialized_end=385
+  _SAVESLICEINFODEF._serialized_start=387
+  _SAVESLICEINFODEF._serialized_end=483
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/versions_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/versions_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e3b13ff65da9add5484f0e27b8f85b8efeeb594
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/framework/versions_pb2.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/framework/versions.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n(tensorflow/core/framework/versions.proto\x12\ntensorflow\"K\n\nVersionDef\x12\x10\n\x08producer\x18\x01 \x01(\x05\x12\x14\n\x0cmin_consumer\x18\x02 \x01(\x05\x12\x15\n\rbad_consumers\x18\x03 \x03(\x05\x42\x80\x01\n\x18org.tensorflow.frameworkB\x0eVersionsProtosP\x01ZOgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/versions_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.framework.versions_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\016VersionsProtosP\001ZOgithub.com/tensorflow/tensorflow/tensorflow/go/core/framework/versions_go_proto\370\001\001'
+  _VERSIONDEF._serialized_start=56
+  _VERSIONDEF._serialized_end=131
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/capture/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/capture/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/capture/capture_container.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/capture/capture_container.py
new file mode 100644
index 0000000000000000000000000000000000000000..ede63a6a688959d18a3a161887cc584b41f7db1b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/capture/capture_container.py
@@ -0,0 +1,344 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""FuncGraph and related functionality."""
+
+import collections as py_collections
+import functools
+from typing import Any, Callable, Hashable, Mapping, Optional
+
+from tensorflow.core.function import trace_type
+from tensorflow.python import pywrap_tfe
+from tensorflow.python.framework import dtypes
+from tensorflow.python.types import core
+from tensorflow.python.util import object_identity
+
+
+_EAGER_CONST_THRESHOLD = 128
+
+
+class MutationAwareDict(py_collections.OrderedDict):
+  """A dict with a mutation flag."""
+
+  def __init__(self, *args, **kwargs):
+    super().__init__(*args, **kwargs)
+    self._mutated = True
+
+  def pop(self, key, default=None):
+    self._mutated = True
+    return super().pop(key, default)
+
+  def __setitem__(self, key, value):
+    self._mutated = True
+    return super().__setitem__(key, value)
+
+  def __delitem__(self, key):
+    self._mutated = True
+    return super().__delitem__(key)
+
+  def clear(self):
+    self._mutated = True
+    return super().clear()
+
+  @property
+  def mutated(self):
+    return self._mutated
+
+  @mutated.setter
+  def mutated(self, value):
+    self._mutated = value
+
+
+class FunctionCaptures(object):
+  """A container for all capture usages within FuncGraph."""
+
+  def __init__(self):
+    self._by_ref_internal = py_collections.OrderedDict()
+    self._by_ref_external = py_collections.OrderedDict()
+    self._by_ref_tracetype = py_collections.OrderedDict()
+    self._by_val_internal = MutationAwareDict()
+    self._by_val_external = MutationAwareDict()
+    self._by_val_tracetype = py_collections.OrderedDict()
+
+    # Set of external ops on which the graph has a control dependency
+    self.control = object_identity.ObjectIdentitySet()
+
+    # Cached properties derived from the above.
+    self._cached_by_val_capture_tuples = []
+    self._cached_capture_types = py_collections.OrderedDict()
+
+  def clear(self):
+    self._by_ref_internal.clear()
+    self._by_ref_external.clear()
+    self._by_ref_tracetype.clear()
+    self._by_val_internal.clear()
+    self._by_val_external.clear()
+
+  def capture_by_value(
+      self, graph: Any, tensor: core.Tensor, name: Optional[str] = None
+  ) -> core.Tensor:
+    """Captures `tensor` if it's external to this graph.
+
+    If `tensor` is from a different graph, returns a placeholder for it.
+    `tensor` and the placeholder will appear in self.captures, and the
+    placeholder will appear in self.inputs.  Multiple calls to this method with
+    the same `tensor` argument will return the same placeholder. If `tensor` is
+    from this graph, returns `tensor`.
+
+    Args:
+      graph: The FuncGraph that captures this tensor.
+      tensor: Tensor. May be from this FuncGraph or a different graph.
+      name: Optional name if a placeholder is created.
+
+    Returns:
+      Tensor from this FuncGraph.
+
+    Raises:
+      InaccessibleTensorError: if any tensors are accessed in a manner that
+      bypasses the mechanisms required for the data dependencies to be correctly
+      wired.
+    """
+    if isinstance(tensor, core.Value):
+      if name is None:
+        # A unique (within the program execution) integer.
+        name = str(pywrap_tfe.TFE_Py_UID())
+
+      # Small EagerTensors are captured with Const ops
+      if (
+          tensor.dtype in dtypes.TF_VALUE_DTYPES
+          and functools.reduce(lambda a, b: a * b, tensor.shape, 1)
+          <= _EAGER_CONST_THRESHOLD
+      ):
+        graph_const = self.by_val_internal.get(id(tensor))
+        if graph_const is None:
+          graph_const = tensor._capture_as_const(name)  # pylint: disable=protected-access
+          if graph_const is None:
+            # Some eager tensors, e.g. parallel tensors, are not convertible to
+            # a single constant. We'll use a placeholder for this case.
+            graph_const = self._create_placeholder_helper(graph, tensor, name)
+          self.add_or_replace(
+              key=id(tensor),
+              external=tensor,
+              internal=graph_const,
+              is_by_ref=False,
+          )
+          graph.inputs.append(graph_const)
+        graph_const._record_tape(tensor)  # pylint: disable=protected-access
+        return graph_const
+
+      # Large EagerTensors and resources are captured with Placeholder ops
+      return self._create_placeholder_helper(graph, tensor, name)
+
+    if tensor.graph is not graph:
+      graph._validate_in_scope(tensor)  # pylint: disable=protected-access
+      if name is None:
+        assert tensor.op is not None, (
+            tensor.__class__,
+            dir(tensor),
+            tensor.__class__.__name__,
+        )
+        name = tensor.op.name
+      # cond/while graphs override _capture_helper() so cannot call
+      # self.create_placeholder_helper() here directly.
+      return graph._capture_helper(tensor, name)  # pylint: disable=protected-access
+    return tensor
+
+  def add_or_replace(
+      self,
+      key: Hashable,
+      external: Any,
+      internal: core.Tensor,
+      tracetype: Any = None,
+      is_by_ref: bool = False,
+  ) -> None:
+    """Replace a already exsiting capture, otherwise add it."""
+    if is_by_ref:
+      self._by_ref_external[key] = external
+      self._by_ref_internal[key] = internal
+      self._by_ref_tracetype[key] = tracetype
+    else:
+      self._by_val_internal[key] = internal
+      self._by_val_external[key] = external
+      if tracetype is not None:
+        self._by_val_tracetype[key] = tracetype
+      else:
+        self._by_val_tracetype[key] = trace_type.from_value(external)
+
+  def pop(self, key: Hashable, is_by_ref: bool = False) -> Any:
+    if is_by_ref:
+      return (
+          self._by_ref_external.pop(key, None),
+          self._by_ref_internal.pop(key, None),
+          self._by_ref_tracetype.pop(key, None),
+      )
+    else:
+      return (
+          self._by_val_external.pop(key, None),
+          self._by_val_internal.pop(key, None),
+          self._by_val_tracetype.pop(key, None),
+      )
+
+  def reset_captures(self, tensors, placeholders):
+    """Set the captures with the provided list of captures & placeholder."""
+    self._by_val_external = MutationAwareDict()
+    self._by_val_internal = MutationAwareDict()
+    self._by_val_tracetype = MutationAwareDict()
+    for external, internal in zip(tensors, placeholders):
+      key = id(external)
+      self._by_val_external[key] = external
+      self._by_val_internal[key] = internal
+      self._by_val_tracetype[key] = trace_type.from_value(external)
+
+  # TODO(panzf): make the method public after supporting lam() returns
+  # non-tensor values. Currently, this method is only used by
+  # FuncGraph._experimental_capture_side_input_by_ref(), which contains the
+  # logics for converting non-tensor values to tensor.
+  def _capture_by_ref(
+      self, graph: Any, lam: Callable[[], Any], key: Hashable = None
+  ) -> Any:
+    """Used during tracing process to create/retrive by-ref captures.
+
+    Args:
+      graph: The FuncGraph that captures this tensor.
+      lam: A callable that takes no arguments and returns tensor captures.
+      key: A hashable identifier.
+
+    Returns:
+      Tensor from this FuncGraph.
+    """
+    # Check if the capture exists in self._by_ref
+    if key is not None and key in self._by_ref_internal:
+      return self._by_ref_internal[key]
+    if key is None:
+      key = len(self._by_ref_internal)
+      while key in self._by_ref_internal:
+        key += 1
+
+    value_nested = lam()
+    capture_trace_type = trace_type.from_value(value_nested)
+    ctx = trace_type.InternalPlaceholderContext(graph)
+    internal = capture_trace_type.placeholder_value(ctx)
+
+    def lam_fn():
+      # pytype: disable=attribute-error
+      value = lam()
+      return capture_trace_type.to_tensors(value)
+      # pytype: enable=attribute-error
+
+    self._by_ref_external[key] = lam_fn
+    self._by_ref_internal[key] = internal
+    self._by_ref_tracetype[key] = capture_trace_type
+    return self._by_ref_internal[key]
+
+  def merge_by_ref_with(self, other: "FunctionCaptures") -> None:
+    """Add by-ref captures from `other` to `self` if not exist."""
+    assert isinstance(other, FunctionCaptures)
+    for key in other.by_ref_external:
+      if key not in self._by_ref_external:
+        self._by_ref_external[key] = other.by_ref_external[key]
+        self._by_ref_tracetype[key] = other.by_ref_tracetype[key]
+
+  # TODO(panzf): Return structured values instead of flat tensors.
+  def get_by_ref_snapshot(self) -> Mapping[Hashable, Any]:
+    """Get a snapshot of current values of by-ref captures."""
+    snapshot = {}
+    for key in self._by_ref_external:
+      func = self._by_ref_external[key]
+      try:
+        value = func()
+      except (AttributeError, RuntimeError):
+        # b/269680071 In case of by-ref captures are unavailable at dispatch
+        # time, use the predefined trace_type instead.
+        value = self._by_ref_tracetype[key]
+      snapshot[key] = value
+    return snapshot
+
+  def _create_placeholder_helper(
+      self, graph: Any, tensor: core.Tensor, name: str
+  ):
+    """A helper function to create capture placeholder."""
+    placeholder = self._by_val_internal.get(id(tensor))
+    if placeholder is None:
+      tracing_ctx = trace_type.InternalTracingContext()
+      spec = trace_type.from_value(tensor, tracing_ctx)
+      spec._name = name  # pylint: disable=protected-access
+      if isinstance(tensor, core.Value) and tensor.is_packed:
+        composite_device_name = tensor.device
+      else:
+        composite_device_name = None
+      placeholder_ctx = trace_type.InternalPlaceholderContext(
+          graph,
+          with_none_control_dependencies=True,
+          composite_device_name=composite_device_name,
+      )
+      placeholder = spec.placeholder_value(placeholder_ctx)
+      self.add_or_replace(
+          key=id(tensor), external=tensor, internal=placeholder, is_by_ref=False
+      )
+      graph.inputs.append(placeholder)
+    placeholder._record_tape(tensor)  # pylint: disable=protected-access
+    return placeholder
+
+  def _recompute_cached_properties(self):
+    """Regenerates cached properties if there have been mutations."""
+    self._by_val_internal.mutated = False
+    self._by_val_external.mutated = False
+    assert len(self._by_val_internal) == len(self._by_val_external)
+    self._cached_by_val_capture_tuples = []
+    for key in self._by_val_internal:
+      assert key in self._by_val_external
+      internal = self._by_val_internal[key]
+      external = self._by_val_external[key]
+      self._cached_by_val_capture_tuples.append((external, internal))
+
+    self._cached_capture_types = py_collections.OrderedDict(
+        list(self._by_val_tracetype.items())
+        + list(self._by_ref_tracetype.items())
+    )
+
+  @property
+  def capture_types(self):
+    if self._by_val_internal.mutated or self._by_val_external.mutated:
+      self._recompute_cached_properties()
+    return self._cached_capture_types
+
+  @property
+  def by_val_capture_tuples(self):
+    if self._by_val_internal.mutated or self._by_val_external.mutated:
+      self._recompute_cached_properties()
+    return self._cached_by_val_capture_tuples
+
+  @property
+  def by_ref_internal(self):
+    return self._by_ref_internal
+
+  @property
+  def by_ref_external(self):
+    return self._by_ref_external
+
+  @property
+  def by_ref_tracetype(self):
+    return self._by_ref_tracetype
+
+  @property
+  def by_val_internal(self):
+    return self._by_val_internal
+
+  @property
+  def by_val_external(self):
+    return self._by_val_external
+
+  @property
+  def by_val_tracetype(self):
+    return self._by_val_tracetype
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/capture/restore_captures.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/capture/restore_captures.py
new file mode 100644
index 0000000000000000000000000000000000000000..afdd0c6af6928728935a4ffc1f93059e20c2c337
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/capture/restore_captures.py
@@ -0,0 +1,142 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+# pylint: disable=unidiomatic-typecheck
+"""A shim layer for working with functions exported/restored from saved models.
+
+This functionality should ultimately be moved into a first-class core API.
+"""
+
+import warnings
+
+from tensorflow.core.function.polymorphism import function_type as function_type_lib
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.ops import handle_data_util
+from tensorflow.python.ops import resource_variable_ops
+from tensorflow.python.ops import variables as variables_lib
+from tensorflow.python.trackable import asset
+from tensorflow.python.trackable import resource
+
+
+def get_tensor_from_node(node):
+  """Resolves a saved model graph node into a tensor to be captured.
+
+  Args:
+    node: a tensor, variable, or resource to be resolved into a capturable
+      tensor
+
+  Returns:
+    A list of tensors.
+  Raises:
+    ValueError: if the node cannot be converted into a tensor.
+  """
+  with ops.init_scope():
+    # TODO(b/210144904): Use __tf_tensor__ instead of `is_[...]` checks
+    if getattr(node, "is_distributed_variable", False):
+      return node
+    elif getattr(node, "is_distributed_table", False):
+      return node
+    elif getattr(node, "is_sharded_variable", False):
+      return node
+    elif resource_variable_ops.is_resource_variable(node):
+      return node.handle
+    elif isinstance(node, asset.Asset):
+      return node.asset_path
+    elif tensor_util.is_tf_type(node):
+      return node
+    elif isinstance(node, resource.CapturableResource):
+      # Note: this executes restored functions in the CapturableResource.
+      return node.resource_handle
+    raise ValueError(f"Cannot convert node {node} to tensor.")
+
+
+def restore_captures(concrete_function, inputs):
+  """Restore captures for the concrete function.
+
+  Used at deserialization time.  For functions that are being deserialized,
+  saved model restores objects that tensors were captured from, but functions
+  only know about their tensors -- object information is destroyed by tracing.
+  This additional logic extracts the tensors which the function originally
+  captured.
+
+  Args:
+    concrete_function: the concrete function for which to restore captures
+    inputs: a list tensors or other Python objects (such as variables) which
+      contain tensors that were originally captured by the function
+  """
+  bound_inputs = [get_tensor_from_node(obj) for obj in inputs]
+  # pylint: disable=g-complex-comprehension
+  bound_variables = [
+      obj
+      for obj in inputs
+      if isinstance(
+          obj,
+          (variables_lib.Variable, resource_variable_ops.BaseResourceVariable),
+      )
+  ]
+  # TODO(b/205010575): This is only injecting the captured inputs into the
+  # concrete function, note that we did not modify the FuncGraph
+  # itself.
+  captured_inputs_list = []
+  concrete_function.set_variables(bound_variables)
+  if bound_inputs:
+    for bound_input, internal_capture in zip(
+        bound_inputs, concrete_function.inputs[-len(bound_inputs) :]
+    ):
+      # Distributed inputs have special logic for capturing, so we call their
+      # custom restoration methods
+      if hasattr(bound_input, "__tf_experimental_restore_capture__"):
+        captured_inputs_list.append(
+            bound_input.__tf_experimental_restore_capture__(
+                concrete_function, internal_capture
+            )
+        )
+      else:
+        captured_inputs_list.append(bound_input)
+        concrete_function.graph.replace_capture(bound_input, internal_capture)
+        if internal_capture.dtype == dtypes.resource:
+          if resource_variable_ops.is_resource_variable(bound_input):
+            try:
+              handle = bound_input.handle
+            except ValueError:
+              # For mirrored variables we'll copy handle data for components
+              # as they get captured.
+              pass
+            else:
+              handle_data_util.copy_handle_data(handle, internal_capture)
+          else:
+            # TODO(b/213451747): Remove need to call copy_handle_data
+            handle_data_util.copy_handle_data(bound_input, internal_capture)
+        # Setting "captures" first means "capture" won't create a new
+        # placeholder for this input.
+        concrete_function.graph.capture(bound_input)
+
+  if any([inp is None for inp in captured_inputs_list]):
+    warnings.warn(
+        "Trying to load ShardedVariables using tf.saved_model.load. "
+        "This won't work if using a tf.distribute.Strategy, and may "
+        "use excess memory if not using a Strategy. Ignore this "
+        "warning if using tf.keras.models.load_model."
+    )
+  concrete_function.set_external_captures(captured_inputs_list)
+
+  # Update FunctionType with new captures.
+  if concrete_function.function_type:
+    concrete_function._function_type = function_type_lib.FunctionType(  # pylint: disable=protected-access
+        concrete_function.function_type.parameters.values(),
+        concrete_function.graph.function_captures.capture_types,
+        return_annotation=concrete_function.function_type.output,
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/function_cache.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/function_cache.py
new file mode 100644
index 0000000000000000000000000000000000000000..d22ff95b11a1050f369f9d6f476f24741cfd644d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/function_cache.py
@@ -0,0 +1,103 @@
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Cache to manage functions based on their FunctionType."""
+
+import collections
+from typing import Any, NamedTuple, Optional
+
+from tensorflow.core.function.polymorphism import function_type as function_type_lib
+from tensorflow.core.function.polymorphism import type_dispatch
+
+
+class FunctionContext(NamedTuple):
+  """Contains information regarding tf.function execution context."""
+  context: Any = None
+  scope_type: Any = None
+
+
+class FunctionCache:
+  """A container for managing functions."""
+
+  __slots__ = ["_primary", "_dispatch_dict", "_garbage_collectors"]
+
+  def __init__(self):
+    # Maps (FunctionContext, FunctionType) to a function.
+    self._primary = collections.OrderedDict()
+
+    # Maps FunctionContext to a TypeDispatchTable containing FunctionTypes of
+    # that particular context.
+    self._dispatch_dict = {}
+
+  def lookup(self, function_type: function_type_lib.FunctionType,
+             context: Optional[FunctionContext] = None) -> Optional[Any]:
+    """Looks up a function based on the context and type."""
+    context = context or FunctionContext()
+    if context in self._dispatch_dict:
+      dispatch_type = self._dispatch_dict[context].dispatch(function_type)
+      if dispatch_type:
+        return self._primary[(context, dispatch_type)]
+
+    return None
+
+  def delete(self, function_type: function_type_lib.FunctionType,
+             context: Optional[FunctionContext] = None,
+             ) -> bool:
+    """Deletes a function given the context and type."""
+    context = context or FunctionContext()
+    if (context, function_type) not in self._primary:
+      return False
+
+    del self._primary[(context, function_type)]
+    self._dispatch_dict[context].delete(function_type)
+
+    return True
+
+  def add(self, fn: Any, context: Optional[FunctionContext] = None) -> None:
+    """Adds a new function using its function_type.
+
+    Args:
+      fn: The function to be added to the cache.
+      context: A FunctionContext representing the current context.
+    """
+    context = context or FunctionContext()
+    self._primary[(context, fn.function_type)] = fn
+    if context not in self._dispatch_dict:
+      self._dispatch_dict[context] = type_dispatch.TypeDispatchTable()
+
+    self._dispatch_dict[context].add_target(fn.function_type)
+
+  def generalize(
+      self, context: FunctionContext,
+      function_type: function_type_lib.FunctionType
+  ) -> function_type_lib.FunctionType:
+    """Try to generalize a FunctionType within a FunctionContext."""
+    if context in self._dispatch_dict:
+      return self._dispatch_dict[context].try_generalizing_function_type(
+          function_type)
+    else:
+      return function_type
+
+  # TODO(b/205971333): Remove this function.
+  def clear(self):
+    """Removes all functions from the cache."""
+    self._primary.clear()
+    self._dispatch_dict.clear()
+
+  def values(self):
+    """Returns a list of all functions held by this cache."""
+    return list(self._primary.values())
+
+  def __len__(self):
+    return len(self._primary)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/function_type.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/function_type.py
new file mode 100644
index 0000000000000000000000000000000000000000..e06dcedadab1666361855b640adad2b3ad0bc330
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/function_type.py
@@ -0,0 +1,720 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Represents the types of TF functions."""
+
+import collections
+import inspect
+from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Tuple
+
+from absl import logging
+
+from tensorflow.core.function import trace_type
+from tensorflow.core.function.polymorphism import function_type_pb2
+from tensorflow.core.function.trace_type import serialization
+from tensorflow.python.types import core
+from tensorflow.python.types import trace
+
+
+# Represents a defined parameter default value that is saved alongside the
+# function's captures.
+class CapturedDefaultValue:
+  def __repr__(self):
+    return "<captured_default_value>"
+
+  def __str__(self):
+    return "<captured_default_value>"
+
+CAPTURED_DEFAULT_VALUE = CapturedDefaultValue()
+
+PROTO_TO_PY_ENUM = {
+    function_type_pb2.Parameter.Kind.POSITIONAL_ONLY:
+        inspect.Parameter.POSITIONAL_ONLY,
+    function_type_pb2.Parameter.Kind.POSITIONAL_OR_KEYWORD:
+        inspect.Parameter.POSITIONAL_OR_KEYWORD,
+    function_type_pb2.Parameter.Kind.VAR_POSITIONAL:
+        inspect.Parameter.VAR_POSITIONAL,
+    function_type_pb2.Parameter.Kind.KEYWORD_ONLY:
+        inspect.Parameter.KEYWORD_ONLY,
+    function_type_pb2.Parameter.Kind.VAR_KEYWORD:
+        inspect.Parameter.VAR_KEYWORD,
+}
+
+PY_TO_PROTO_ENUM = {v: k for k, v in PROTO_TO_PY_ENUM.items()}
+
+
+class Parameter(inspect.Parameter):
+  """Represents a parameter to a function."""
+
+  def __init__(self, name: str, kind: Any, optional: bool,
+               type_constraint: Optional[trace.TraceType]):
+    if optional and kind not in [
+        self.POSITIONAL_ONLY, self.KEYWORD_ONLY, self.POSITIONAL_OR_KEYWORD
+    ]:
+      raise ValueError(
+          "Parameter " + name +
+          " is optional and its kind must be one of {POSITIONAL_ONLY, " +
+          "KEYWORD_ONLY, POSITIONAL_OR_KEYWORD}. Got: " + str(kind))
+
+    if type_constraint and kind in [self.VAR_POSITIONAL, self.VAR_KEYWORD]:
+      raise TypeError("Variable args/kwargs can not have type constraints.")
+
+    if not isinstance(type_constraint, (trace.TraceType, type(None))):
+      raise TypeError(
+          "Type constraints can only be an instance of a TraceType but got " +
+          "type_constraint=" + str(type_constraint) + " for Parameter " + name)
+
+    super().__init__(
+        name,
+        kind,
+        default=CAPTURED_DEFAULT_VALUE if optional else self.empty,
+        annotation=type_constraint
+        if type_constraint is not None else self.empty)
+
+  @classmethod
+  def from_proto(cls, proto: Any) -> "Parameter":
+    """Generate a Parameter from the proto representation."""
+    deserialized_type_constraint = serialization.deserialize(
+        proto.type_constraint) if proto.HasField("type_constraint") else None
+    return Parameter(proto.name, PROTO_TO_PY_ENUM[proto.kind],
+                     proto.is_optional, deserialized_type_constraint)
+
+  def to_proto(self) -> function_type_pb2.Parameter:
+    """Generate a proto representation of the Parameter."""
+    serialized_type_constraint = serialization.serialize(
+        self.type_constraint) if self.type_constraint else None
+    return function_type_pb2.Parameter(
+        name=self.name,
+        kind=PY_TO_PROTO_ENUM[self.kind],
+        is_optional=self.optional,
+        type_constraint=serialized_type_constraint)
+
+  @property
+  def optional(self) -> bool:
+    """If this parameter might not be supplied for a call."""
+    return self.default is not self.empty
+
+  @property
+  def type_constraint(self) -> Optional[trace.TraceType]:
+    """A supertype that the parameter's type must subtype for validity."""
+    return self.annotation if self.annotation is not self.empty else None
+
+  def is_subtype_of(self, other: "Parameter") -> bool:
+    """Returns True if self is a supertype of other Parameter."""
+    if not self.type_constraint or not other.type_constraint:
+      raise TypeError(
+          "Can not determine relationship between partially specified types.")
+
+    if ((self.name, self.kind, self.optional) !=
+        (other.name, other.kind, other.optional)):
+      return False
+
+    return self.type_constraint.is_subtype_of(other.type_constraint)
+
+  def most_specific_common_supertype(
+      self, others: Sequence["Parameter"]) -> Optional["Parameter"]:
+    """Returns a common supertype (if exists)."""
+    if not self.type_constraint or any(
+        not other.type_constraint for other in others):
+      raise TypeError(
+          "Can not determine relationship between partially specified types.")
+
+    for other in others:
+      if ((self.name, self.kind, self.optional) !=
+          (other.name, other.kind, other.optional)):
+        return None
+
+    supertyped_constraint = self.type_constraint.most_specific_common_supertype(
+        [other.type_constraint for other in others])
+    if supertyped_constraint:
+      return Parameter(self.name, self.kind, self.optional,
+                       supertyped_constraint)
+    else:
+      return None
+
+  def __eq__(self, other: Any) -> bool:
+    if not isinstance(other, Parameter):
+      return NotImplemented
+
+    return ((self.name, self.kind, self.optional,
+             self.type_constraint) == (other.name, other.kind, other.optional,
+                                       other.type_constraint))
+
+  def __hash__(self):
+    return hash((self.name, self.kind, self.optional, self.type_constraint))
+
+  def __repr__(self):
+    return ("Parameter(name=" + self.name + ", kind=" + str(self.kind) +
+            ", optional=" + repr(self.optional) + ", type_constraint=" +
+            repr(self.type_constraint) + ")")
+
+  def __reduce__(self):
+    return (self.__class__, (self.name, self.kind, self.optional,
+                             self.type_constraint))
+
+
+class FunctionType(core.FunctionType):
+  """Represents the type of a TensorFlow function.
+
+  FunctionType is the canonical way to represent the input/output contract of
+  all kinds of functions within the tf.function domain, including:
+    - Polymorphic Function
+    - Concrete Function
+    - Atomic Function
+
+  It provides consistent, centralized and layered logic for:
+    - Canonicalization of Python input arguments
+    - Type-based dispatch to monomorphic functions
+    - Packing/unpacking structured python values to Tensors
+    - Generation of structured placeholder values for tracing
+
+  Additionaly, it also provides:
+    - Lossless serialization
+    - Native integration with Python function signature representation
+    - Seamless migration from older representation formats
+  """
+
+  def __init__(self,
+               parameters: Sequence[inspect.Parameter],
+               captures: Optional[collections.OrderedDict] = None,
+               **kwargs):
+    super().__init__(parameters, **kwargs)
+    self._captures = captures if captures else collections.OrderedDict()
+
+  @property
+  def parameters(self) -> Mapping[str, Any]:
+    """Returns an ordered mapping of parameter name to specification."""
+    return super().parameters
+
+  @property
+  def captures(self) -> collections.OrderedDict:
+    """Returns an ordered mapping of capture id to type."""
+    return self._captures
+
+  @property
+  def output(self) -> Optional[trace.TraceType]:
+    """Return the output TraceType if specified."""
+    return (
+        self.return_annotation
+        if self.return_annotation is not self.empty
+        else None
+    )
+
+  @classmethod
+  def from_callable(cls,
+                    obj: Callable[..., Any],
+                    *,
+                    follow_wrapped: bool = True) -> "FunctionType":
+    """Generate FunctionType from a python Callable."""
+    signature = super().from_callable(obj, follow_wrapped=follow_wrapped)
+    # TODO(fmuham): Support TraceType-based annotations.
+    parameters = [
+        Parameter(p.name, p.kind, p.default is not p.empty, None)
+        for p in signature.parameters.values()
+    ]
+
+    return FunctionType(parameters)
+
+  @classmethod
+  def get_default_values(cls,
+                         obj: Callable[..., Any],
+                         *,
+                         follow_wrapped: bool = True) -> Dict[str, Any]:
+    """Inspects and returns a dictionary of default values."""
+    signature = super().from_callable(obj, follow_wrapped=follow_wrapped)
+    default_values = {}
+    for p in signature.parameters.values():
+      if p.default is not p.empty:
+        default_values[p.name] = p.default
+    return default_values
+
+  @classmethod
+  def from_proto(cls, proto: Any) -> "FunctionType":
+    """Generate a FunctionType from the proto representation."""
+    return FunctionType([Parameter.from_proto(p) for p in proto.parameters],
+                        collections.OrderedDict([
+                            (c.name,
+                             serialization.deserialize(c.type_constraint))
+                            for c in proto.captures
+                        ]))
+
+  def to_proto(self) -> Any:
+    """Generate a proto representation from the FunctionType."""
+    return function_type_pb2.FunctionType(
+        parameters=[p.to_proto() for p in self.parameters.values()],
+        captures=[
+            function_type_pb2.Capture(
+                name=n, type_constraint=serialization.serialize(t))
+            for n, t in self.captures.items()
+        ])
+
+  def bind_with_defaults(self, args, kwargs, default_values):
+    """Returns BoundArguments with default values filled in."""
+    bound_arguments = self.bind(*args, **kwargs)
+    bound_arguments.apply_defaults()
+
+    with_default_args = collections.OrderedDict()
+    for name, value in bound_arguments.arguments.items():
+      if value is CAPTURED_DEFAULT_VALUE:
+        with_default_args[name] = default_values[name]
+      else:
+        with_default_args[name] = value
+
+    for arg_name in with_default_args:
+      constraint = self.parameters[arg_name].type_constraint
+      if constraint:
+        with_default_args[arg_name] = constraint.cast(
+            with_default_args[arg_name],
+            trace_type.InternalCastContext(allow_specs=True),
+        )
+    bound_arguments = inspect.BoundArguments(self, with_default_args)
+    return bound_arguments
+
+  def is_supertype_of(self, other: "FunctionType") -> bool:
+    """Returns True if self is a supertype of other FunctionType."""
+    if len(self.parameters) != len(other.parameters):
+      return False
+
+    for self_param, other_param in zip(self.parameters.values(),
+                                       other.parameters.values()):
+      # Functions are contravariant on their parameter types.
+      if not self_param.is_subtype_of(other_param):
+        return False
+
+    # Other must have all capture names of self.
+    if not all(name in other.captures for name in self.captures):
+      return False
+
+    # Functions are contravariant upon the capture types.
+    return all(capture_type.is_subtype_of(other.captures[name])
+               for name, capture_type in self.captures.items())
+
+  def most_specific_common_subtype(
+      self, others: Sequence["FunctionType"]) -> Optional["FunctionType"]:
+    """Returns a common subtype (if exists)."""
+    subtyped_parameters = []
+
+    for i, parameter in enumerate(self.parameters.values()):
+      # Functions are contravariant on their parameter types.
+      subtyped_parameter = parameter.most_specific_common_supertype(
+          [list(other.parameters.values())[i] for other in others])
+      if subtyped_parameter is None:
+        return None
+      subtyped_parameters.append(subtyped_parameter)
+
+    if not all(subtyped_parameters):
+      return None
+
+    # Common subtype has superset of all captures.
+    capture_names = set(self.captures.keys())
+    for other in others:
+      capture_names = capture_names.union(other.captures.keys())
+
+    subtyped_captures = collections.OrderedDict()
+    for name in capture_names:
+      containing = [t for t in [self, *others] if name in t.captures]
+      # Pick the first type that has the capture as the base.
+      base = containing[0]
+      relevant_others = containing[1:]
+
+      # Functions are contravariant upon the capture types.
+      common_type = base.captures[name].most_specific_common_supertype(
+          [other.captures[name] for other in relevant_others]
+      )
+      if common_type is None:
+        return None
+      else:
+        subtyped_captures[name] = common_type
+
+    return FunctionType(subtyped_parameters, subtyped_captures)
+
+  def placeholder_arguments(
+      self, placeholder_context: trace.PlaceholderContext
+  ) -> inspect.BoundArguments:
+    """Returns BoundArguments of values that can be used for tracing."""
+    arguments = collections.OrderedDict()
+    for parameter in self.parameters.values():
+      if parameter.kind in {Parameter.VAR_POSITIONAL, Parameter.VAR_KEYWORD}:
+        raise ValueError("Can not generate placeholder values for "
+                         "variable length function type.")
+
+      if not parameter.type_constraint:
+        raise ValueError("Can not generate placeholder value for "
+                         "partially defined function type.")
+      placeholder_context.update_naming_scope(parameter.name)
+      arguments[parameter.name] = parameter.type_constraint.placeholder_value(
+          placeholder_context)
+
+    return inspect.BoundArguments(self, arguments)
+
+  @property
+  def flat_inputs(self) -> List[trace.TraceType]:
+    """Flat tensor inputs accepted by this FunctionType."""
+    if not hasattr(self, "_cached_flat_inputs"):
+      cached_flat_inputs = []
+      for p in self.parameters.values():
+        cached_flat_inputs.extend(p.type_constraint.flatten())
+      self._cached_flat_inputs = cached_flat_inputs
+
+    return self._cached_flat_inputs
+
+  def unpack_inputs(
+      self, bound_parameters: inspect.BoundArguments
+  ) -> List[core.Tensor]:
+    """Unpacks python arguments to flat tensor inputs accepted by this type."""
+    # Sort keyword-only parameters by name.
+    sorted_parameters = []
+    kwonly_parameters = []
+    for p in self.parameters.values():
+      if p.kind is Parameter.KEYWORD_ONLY:
+        kwonly_parameters.append(p)
+      else:
+        sorted_parameters.append(p)
+    sorted_parameters = sorted_parameters + sorted(
+        kwonly_parameters, key=lambda p: p.name
+    )
+
+    flat = []
+    for p in sorted_parameters:
+      flat.extend(
+          p.type_constraint.to_tensors(bound_parameters.arguments[p.name])
+      )
+
+    dealiased_inputs = []
+    ids_used = set()
+    for tensor, input_type in zip(flat, self.flat_inputs):
+      alias_id = input_type._alias_id()  # pylint: disable=protected-access
+      if alias_id is None or alias_id not in ids_used:
+        dealiased_inputs.append(tensor)
+
+      if alias_id is not None:
+        ids_used.add(alias_id)
+
+    return dealiased_inputs
+
+  @property
+  def flat_captures(self) -> List[trace.TraceType]:
+    """Flat tensor captures needed by this FunctionType."""
+    if not hasattr(self, "_cached_flat_captures"):
+      cached_flat_captures = []
+      for t in self.captures.values():
+        cached_flat_captures.extend(t.flatten())
+      self._cached_flat_captures = cached_flat_captures
+
+    return self._cached_flat_captures
+
+  def unpack_captures(self, captures) -> List[core.Tensor]:
+    """Unpacks captures to flat tensors."""
+    flat = []
+    for v, t in zip(captures, self.captures.values()):
+      flat.extend(t.to_tensors(v))
+    if len(flat) != len(self.flat_captures):
+      raise TypeError(
+          f"Flattening captures {captures} with type {self!r} produced"
+          f" {len(flat)} tensors instead of {len(self.flat_captures)}"
+      )
+    return flat
+
+  @property
+  def flat_outputs(self) -> List[trace.TraceType]:
+    """Flat tensor outputs returned by this FunctionType."""
+    if not hasattr(self, "_cached_flat_outputs"):
+      if self.output is not None:
+        self._cached_flat_outputs = self.output.flatten()
+
+    return self._cached_flat_outputs
+
+  def pack_output(self, flat_values: Sequence[core.Tensor]) -> Any:
+    """Packs flat tensors to generate a value of the output type."""
+    if flat_values is None:
+      flat_values = []
+
+    if self.output is None:
+      raise ValueError("Can not pack outputs for undefined output type.")
+    else:
+      return self.output.from_tensors(iter(flat_values))
+
+  def __eq__(self, other: Any) -> bool:
+    if not isinstance(other, FunctionType):
+      return NotImplemented
+
+    return (self.parameters, self.captures) == (other.parameters,
+                                                other.captures)
+
+  def __hash__(self) -> int:
+    return hash((tuple(self.parameters.items()), tuple(self.captures.items())))
+
+  def __repr__(self):
+    if hasattr(self, "_cached_repr"):
+      return self._cached_repr
+
+    lines = ["Input Parameters:"]
+    for parameter in self.parameters.values():
+      lines.append(
+          f"  {parameter.name} ({parameter.kind}): {parameter.type_constraint}"
+      )
+
+    lines.append("Output Type:")
+    lines.append(f"  {self.output}")
+
+    lines.append("Captures:")
+    if self.captures:
+      for capture_id, capture_type in self.captures.items():
+        lines.append(f"  {capture_id}: {capture_type}")
+    else:
+      lines.append("  None")
+
+    self._cached_repr = "\n".join(lines)
+    return self._cached_repr
+
+
+MAX_SANITIZATION_WARNINGS = 5
+sanitization_warnings_given = 0
+
+
+# TODO(fmuham): In future, replace warning with exception.
+# TODO(fmuham): Sanitize to graph node conventions.
+def sanitize_arg_name(name: str) -> str:
+  """Sanitizes function argument names.
+
+  Matches Python symbol naming rules.
+
+  Without sanitization, names that are not legal Python parameter names can be
+  set which makes it challenging to represent callables supporting the named
+  calling capability.
+
+  Args:
+    name: The name to sanitize.
+
+  Returns:
+    A string that meets Python parameter conventions.
+  """
+  # Replace non-alphanumeric chars with '_'
+  swapped = "".join([c if c.isalnum() else "_" for c in name])
+  result = swapped if swapped[0].isalpha() else "arg_" + swapped
+
+  global sanitization_warnings_given
+  if name != result and sanitization_warnings_given < MAX_SANITIZATION_WARNINGS:
+    logging.warning(
+        "`%s` is not a valid tf.function parameter name. Sanitizing to `%s`.",
+        name, result)
+    sanitization_warnings_given += 1
+
+  return result
+
+
+# TODO(fmuham): Consider forcing kind to be always POSITIONAL_OR_KEYWORD.
+def _make_validated_mono_param(
+    name, value, kind, type_context, poly_type
+) -> Parameter:
+  """Generates and validates a parameter for Monomorphic FunctionType."""
+  mono_type = trace_type.from_value(value, type_context)
+
+  if poly_type and not mono_type.is_subtype_of(poly_type):
+    raise TypeError(f"Parameter `{name}` was expected to be of type "
+                    f"{poly_type} but is {mono_type}")
+
+  return Parameter(name, kind, False, mono_type)
+
+
+def canonicalize_to_monomorphic(
+    args: Tuple[Any, ...], kwargs: Dict[Any, Any], default_values: Dict[Any,
+                                                                        Any],
+    capture_types: collections.OrderedDict, polymorphic_type: FunctionType
+) -> Tuple[FunctionType, trace_type.InternalTracingContext]:
+  """Generates a monomorphic type out of polymorphic type for given args."""
+  poly_bound_arguments = polymorphic_type.bind(*args, **kwargs)
+
+  # Inject Default Values.
+  if default_values:
+    poly_bound_arguments.apply_defaults()
+    default_values_injected = poly_bound_arguments.arguments
+    for name, value in default_values_injected.items():
+      if value is CAPTURED_DEFAULT_VALUE:
+        default_values_injected[name] = default_values[name]
+    poly_bound_arguments = inspect.BoundArguments(
+        poly_bound_arguments.signature, default_values_injected
+    )
+
+  parameters = []
+  type_context = trace_type.InternalTracingContext()
+  has_var_positional = any(p.kind is Parameter.VAR_POSITIONAL
+                           for p in polymorphic_type.parameters.values())
+
+  for name, arg in poly_bound_arguments.arguments.items():
+    poly_parameter = polymorphic_type.parameters[name]
+    if (has_var_positional and
+        poly_parameter.kind is Parameter.POSITIONAL_OR_KEYWORD):
+      # If there is a VAR_POSITIONAL, all POSITIONAL_OR_KEYWORD become
+      # POSITIONAL_ONLY.
+      parameters.append(
+          _make_validated_mono_param(name, arg, Parameter.POSITIONAL_ONLY,
+                                     type_context,
+                                     poly_parameter.type_constraint))
+
+    elif poly_parameter.kind is Parameter.VAR_POSITIONAL:
+      # Unbundle VAR_POSITIONAL into individual POSITIONAL_ONLY args.
+      for i, value in enumerate(arg):
+        parameters.append(
+            _make_validated_mono_param(f"{poly_parameter.name}_{i}", value,
+                                       Parameter.POSITIONAL_ONLY, type_context,
+                                       poly_parameter.type_constraint))
+
+    elif poly_parameter.kind is Parameter.VAR_KEYWORD:
+      # Unbundle VAR_KEYWORD into individual KEYWORD_ONLY args.
+      for kwarg_name in sorted(arg.keys()):
+        parameters.append(
+            _make_validated_mono_param(kwarg_name, arg[kwarg_name],
+                                       Parameter.KEYWORD_ONLY, type_context,
+                                       poly_parameter.type_constraint))
+    else:
+      parameters.append(
+          _make_validated_mono_param(name, arg, poly_parameter.kind,
+                                     type_context,
+                                     poly_parameter.type_constraint))
+
+  return FunctionType(parameters, capture_types), type_context
+
+
+# TODO(fmuham): Share code with canonicalize_to_monomorphic.
+# TODO(fmuham): Lift unnecessary restrictions on input_signature validity.
+def add_type_constraints(function_type: FunctionType, input_signature: Any,
+                         default_values: Dict[str, Any]) -> FunctionType:
+  """Adds type constraints to a FunctionType based on the input_signature."""
+  context = trace_type.InternalTracingContext(is_legacy_signature=True)
+  constraints = [trace_type.from_value(c, context) for c in input_signature]
+  parameters = []
+
+  has_var_pos = any(
+      p.kind is p.VAR_POSITIONAL for p in function_type.parameters.values())
+
+  for param in function_type.parameters.values():
+    # VAR_POSITIONAL does not allow POSITIONAL_OR_KEYWORD args.
+    sanitized_kind = (
+        param.POSITIONAL_ONLY if has_var_pos and
+        param.kind is param.POSITIONAL_OR_KEYWORD else param.kind)
+
+    if param.name == "self":
+      # Type constraints do not apply on them.
+      parameters.append(Parameter("self", sanitized_kind, param.optional, None))
+
+    elif param.kind is param.VAR_KEYWORD:
+      # Disabled when input_signature is specified.
+      continue
+
+    elif param.kind is param.VAR_POSITIONAL:
+      # Convert into Positional Only args based on length of constraints.
+      for i in range(len(constraints)):
+        parameters.append(
+            Parameter(param.name + "_" + str(i), Parameter.POSITIONAL_ONLY,
+                      False, constraints.pop(0)))
+
+    elif (param.kind in [
+        param.POSITIONAL_ONLY, param.POSITIONAL_OR_KEYWORD, param.KEYWORD_ONLY
+    ]):
+      if param.kind is param.KEYWORD_ONLY and param.name not in default_values:
+        raise TypeError(
+            "Since input_signature is defined, keyword-only parameter"
+            f" `{param.name}` must have a default value"
+        )
+
+      if constraints:
+        parameters.append(
+            Parameter(param.name, sanitized_kind, param.optional,
+                      constraints.pop(0)))
+      elif param.name in default_values:
+        type_constraint = trace_type.from_value(default_values[param.name])
+        parameters.append(
+            Parameter(param.name, sanitized_kind, param.optional,
+                      type_constraint))
+      else:
+        raise TypeError(
+            f"input_signature missing type constraint for {param.name}")
+
+  if constraints:
+    raise TypeError(
+        f"input_signature contains {len(constraints)} extra type constraints.")
+
+  return FunctionType(parameters)
+
+
+def from_structured_signature(
+    input_signature=None, output_signature=None, capture_types=None
+) -> FunctionType:
+  """Generates a FunctionType from legacy signature representation."""
+  if input_signature is None:
+    input_signature = ((), {})
+
+  args, kwargs = input_signature
+  parameters = []
+
+  for i, arg in enumerate(args):
+    parameters.append(
+        Parameter(
+            "arg_" + str(i),
+            Parameter.POSITIONAL_ONLY,
+            False,
+            trace_type.from_value(
+                arg, trace_type.InternalTracingContext(is_legacy_signature=True)
+            ),
+        )
+    )
+
+  for name, kwarg in kwargs.items():
+    parameters.append(
+        Parameter(
+            sanitize_arg_name(name),
+            Parameter.KEYWORD_ONLY,
+            False,
+            trace_type.from_value(
+                kwarg,
+                trace_type.InternalTracingContext(is_legacy_signature=True),
+            ),
+        )
+    )
+
+  return_type = trace_type.from_value(
+      output_signature,
+      trace_type.InternalTracingContext(is_legacy_signature=True),
+  )
+
+  return FunctionType(
+      parameters, capture_types or {}, return_annotation=return_type
+  )
+
+
+def to_structured_signature(function_type: FunctionType) -> Tuple[Any, Any]:
+  """Returns structured input and output signatures from a FunctionType."""
+  def to_signature(x_type):
+    if x_type is None:
+      raise TypeError(
+          "Can not generate structured signature if FunctionType is not fully"
+          f" specified. Received {function_type}"
+      )
+    return x_type.placeholder_value(
+        trace_type.InternalPlaceholderContext(unnest_only=True)
+    )
+
+  args_signature = []
+  kwargs_signature = {}
+  for p in function_type.parameters.values():
+    if p.kind == Parameter.POSITIONAL_ONLY:
+      args_signature.append(to_signature(p.type_constraint))
+    else:
+      kwargs_signature[p.name] = to_signature(p.type_constraint)
+
+  input_signature = (tuple(args_signature), kwargs_signature)
+  output_signature = to_signature(function_type.output)
+
+  return input_signature, output_signature
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/function_type_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/function_type_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..c143163df8199aaa5cc039f9633738bba98f2fcc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/function_type_pb2.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/function/polymorphism/function_type.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.function.trace_type import serialization_pb2 as tensorflow_dot_core_dot_function_dot_trace__type_dot_serialization__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n9tensorflow/core/function/polymorphism/function_type.proto\x12\x33tensorflow.core.function.polymorphism.function_type\x1a\x37tensorflow/core/function/trace_type/serialization.proto\"\xe0\x02\n\tParameter\x12\x0c\n\x04name\x18\x01 \x01(\t\x12Q\n\x04kind\x18\x02 \x01(\x0e\x32\x43.tensorflow.core.function.polymorphism.function_type.Parameter.Kind\x12\x13\n\x0bis_optional\x18\x03 \x01(\x08\x12_\n\x0ftype_constraint\x18\x04 \x01(\x0b\x32\x46.tensorflow.core.function.trace_type.serialization.SerializedTraceType\"|\n\x04Kind\x12\r\n\tUNDEFINED\x10\x00\x12\x13\n\x0fPOSITIONAL_ONLY\x10\x01\x12\x19\n\x15POSITIONAL_OR_KEYWORD\x10\x02\x12\x12\n\x0eVAR_POSITIONAL\x10\x03\x12\x10\n\x0cKEYWORD_ONLY\x10\x04\x12\x0f\n\x0bVAR_KEYWORD\x10\x05\"x\n\x07\x43\x61pture\x12\x0c\n\x04name\x18\x01 \x01(\t\x12_\n\x0ftype_constraint\x18\x02 \x01(\x0b\x32\x46.tensorflow.core.function.trace_type.serialization.SerializedTraceType\"\xb2\x01\n\x0c\x46unctionType\x12R\n\nparameters\x18\x01 \x03(\x0b\x32>.tensorflow.core.function.polymorphism.function_type.Parameter\x12N\n\x08\x63\x61ptures\x18\x02 \x03(\x0b\x32<.tensorflow.core.function.polymorphism.function_type.Capture')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.function.polymorphism.function_type_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _PARAMETER._serialized_start=172
+  _PARAMETER._serialized_end=524
+  _PARAMETER_KIND._serialized_start=400
+  _PARAMETER_KIND._serialized_end=524
+  _CAPTURE._serialized_start=526
+  _CAPTURE._serialized_end=646
+  _FUNCTIONTYPE._serialized_start=649
+  _FUNCTIONTYPE._serialized_end=827
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/type_dispatch.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/type_dispatch.py
new file mode 100644
index 0000000000000000000000000000000000000000..0898e4676352411471145813e82e1b46ae955f6d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/polymorphism/type_dispatch.py
@@ -0,0 +1,131 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Polymorphic Type Dispatch."""
+
+import collections
+from typing import Optional, Iterable
+
+from tensorflow.core.function.polymorphism import function_type
+
+# The maximum number of dispatch lookups to cache.
+_MAX_DISPATCH_CACHE = 1024
+
+
+class TypeDispatchTable:
+  """Type dispatch table implementation.
+
+  A type dispatch table is a list, L, of target types. Given a request type, R,
+  the table selects a target type, T, according to the following dispatch rules:
+    1. R == T or R is supertype of T (functions are contravariant on args)
+    2. There does not exist O in L such that R is supertype of O and O is a
+       supertype of T (in other words, T is the closest to R, within list L).
+    3. If the above two rules are satisfied by multiple targets, the earliest
+       inserted one is chosen.
+  """
+
+  def __init__(self):
+    """Creates a TypeDispatchTable object."""
+    # Holds all inserted types as keys mapping to None.
+    # (Using OrderedDict as a set for determinism)
+    self._dispatch_table = collections.OrderedDict()
+
+    # LRU cache for dispatch results.
+    # Maps request types to target types (see class description).
+    # Does not contain exact matches, i.e, if cache[a] is b then a is not b.
+    self._dispatch_cache = collections.OrderedDict()
+
+  def add_target(self, target: function_type.FunctionType) -> None:
+    """Adds a new target type."""
+    self._dispatch_table[target] = None
+    for request in self._dispatch_cache:
+      if target.is_supertype_of(self._dispatch_cache[request]):
+        self._dispatch_cache[request] = target
+
+  @property
+  def targets(self) -> Iterable[function_type.FunctionType]:
+    """Returns an iterable to all targets in the table."""
+    return self._dispatch_table.keys()
+
+  def delete(self, target: function_type.FunctionType) -> None:
+    """Deletes a target in the table if it exists."""
+    if target in self._dispatch_table:
+      del self._dispatch_table[target]
+      for request in list(self._dispatch_cache.keys()):
+        if self._dispatch_cache[request] == target:
+          del self._dispatch_cache[request]
+
+  # TODO(b/205971333): remove once FunctionCache 'clear' is removed.
+  def clear(self) -> None:
+    """Deletes all targets in the table."""
+    self._dispatch_table.clear()
+    self._dispatch_cache.clear()
+
+  def dispatch(
+      self, request: function_type.FunctionType
+  ) -> Optional[function_type.FunctionType]:
+    """Returns the most specific supertype target if it exists in the table."""
+    # For known exact matches.
+    if request in self._dispatch_table:
+      return request
+
+    # For known non-exact matches.
+    # (self._dispatch cache does not contain exact matches)
+    if request in self._dispatch_cache:
+      # Move to the front of LRU cache.
+      result = self._dispatch_cache.pop(request)
+      self._dispatch_cache[request] = result
+      return result
+
+    most_specific_supertype = None
+    for other in self._dispatch_table:
+      if request.is_supertype_of(other):
+        if most_specific_supertype is None or other.is_supertype_of(
+            most_specific_supertype):
+          most_specific_supertype = other
+
+    self._cache_dispatch(request, most_specific_supertype)
+    return most_specific_supertype
+
+  def _cache_dispatch(self, request, target):
+    """Caches the dispatch lookup result for a target."""
+    if target is not None:
+      # LRU Cache removes oldest item
+      if len(self._dispatch_cache) > _MAX_DISPATCH_CACHE:
+        self._dispatch_cache.popitem(last=False)
+      self._dispatch_cache[request] = target
+
+  def try_generalizing_function_type(
+      self, target: function_type.FunctionType) -> function_type.FunctionType:
+    """Returns a generalized subtype of the one given.
+
+    This heuristic aims to reduce the number of future traces by computing a
+    type that represents more general function inputs.
+
+    The original "experimental_relax_shapes" heuristic identified a known type
+    which shared a common subtype with the current unknown type and then
+    traced with that common subtype. However, the notion of "common subtype"
+    was only limited to shapes. This heuristic extends that to FunctionType.
+
+    Returns `target` if a generalized subtype can not be found.
+
+    Args:
+      target: The FunctionType to generalize
+    """
+    relaxed = target
+    for other in self._dispatch_table:
+      subtype = relaxed.most_specific_common_subtype([other])
+      if subtype is not None:
+        relaxed = subtype
+    return relaxed
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ae2583b4274b6051e1efe537ce89e2d133cb00a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/__init__.py
@@ -0,0 +1,38 @@
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Trace-time type system for tf.function (TraceType).
+
+Trace-time types describe things like tf.function signatures and type
+constraints in some ops.
+
+This module provides utilities and concrete tf.types.experimental.TraceType
+definitions for common Python types like containers, along with a generic
+implementation for Python objects.
+See also: tf.types.experimental.TraceType
+
+Other implementations of TraceType include tf.TypeSpec and its subclasses.
+"""
+
+from tensorflow.core.function.trace_type.default_types import register_tensor_type
+from tensorflow.core.function.trace_type.default_types import Weakref
+from tensorflow.core.function.trace_type.serialization import deserialize
+from tensorflow.core.function.trace_type.serialization import register_serializable
+from tensorflow.core.function.trace_type.serialization import Serializable
+from tensorflow.core.function.trace_type.serialization import serialize
+from tensorflow.core.function.trace_type.serialization import SerializedTraceType
+from tensorflow.core.function.trace_type.trace_type_builder import from_value
+from tensorflow.core.function.trace_type.trace_type_builder import InternalCastContext
+from tensorflow.core.function.trace_type.trace_type_builder import InternalPlaceholderContext
+from tensorflow.core.function.trace_type.trace_type_builder import InternalTracingContext
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/custom_nest_trace_type.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/custom_nest_trace_type.py
new file mode 100644
index 0000000000000000000000000000000000000000..28216bfe8340a423c08c3895528fcb116a4afaeb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/custom_nest_trace_type.py
@@ -0,0 +1,143 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""TraceType implementations for classes thatimplement the CustomNestProtocol."""
+
+from typing import Any, Iterator, List as PythonList, Optional, Sequence, Tuple as PythonTuple, Type
+
+from tensorflow.core.function.trace_type import util
+from tensorflow.python.types import trace
+from tensorflow.python.util import custom_nest_protocol
+
+
+class CustomNestTraceType(trace.TraceType):
+  """Represents the TraceType of a class implmenting the CustomNestProtocol."""
+
+  def __init__(
+      self,
+      value_type: Type[Any],
+      metadata: Any,
+      components: PythonTuple[trace.TraceType],
+  ):
+    if not issubclass(value_type, custom_nest_protocol.CustomNestProtocol):
+      raise ValueError(f"{value_type!r} does not implement CustomNestProtocol.")
+    self.value_type = value_type
+    self.metadata = metadata
+    self.components = components
+
+  def is_subtype_of(self, other: trace.TraceType) -> bool:
+    if not self._is_same_trace_type(other):
+      return False
+    for c_self, c_other in zip(self.components, other.components):  # pytype: disable=attribute-error
+      if not c_self.is_subtype_of(c_other):
+        return False
+    return True
+
+  def most_specific_common_supertype(
+      self, others: Sequence[trace.TraceType]
+  ) -> Optional["CustomNestTraceType"]:
+    for other in others:
+      if not self._is_same_trace_type(other):
+        return None
+
+    others_components = [other.components for other in others]  # pytype: disable=attribute-error
+    supertyped_components = tuple(
+        self_component.most_specific_common_supertype(others_component)
+        for self_component, *others_component in zip(
+            self.components, *others_components
+        )
+    )
+    return CustomNestTraceType(
+        self.value_type, self.metadata, supertyped_components
+    )
+
+  def __eq__(self, other: trace.TraceType) -> bool:
+    return (
+        isinstance(other, CustomNestTraceType)
+        and self.value_type == other.value_type
+        and self.metadata == other.metadata
+        and self.components == other.components
+    )
+
+  def __hash__(self) -> int:
+    # The hash computation doesn't use self.metadata, so unhashable metadata can
+    # be used. The `self.__eq__` method is used instead to differentiate between
+    # two objects with the same components but different metadata.
+    return hash((self.value_type, self.components))
+
+  def __repr__(self) -> str:
+    return (
+        f"{self.__class__.__name__} [metadata={self.metadata!r}, "
+        f"components={self.components!r}]"
+    )
+
+  def placeholder_value(self, placeholder_context: Any) -> Any:
+    components_placeholder_value = tuple(
+        c.placeholder_value(placeholder_context) for c in self.components
+    )
+    return self.value_type.__tf_unflatten__(
+        self.metadata, components_placeholder_value
+    )
+
+  def to_tensors(self, value: Any) -> PythonList[Any]:
+    if not isinstance(value, self.value_type):
+      raise TypeError(f"{value!r} is not of type {self.value_type}.")
+    _, value_components = value.__tf_flatten__()
+    flattened_values = []
+    for value_comp, type_comp in zip(value_components, self.components):
+      flattened_values.extend(type_comp.to_tensors(value_comp))
+    return flattened_values
+
+  def from_tensors(self, tensors: Iterator[Any]) -> Any:
+    return self.value_type.__tf_unflatten__(
+        self.metadata, tuple(c.from_tensors(tensors) for c in self.components)
+    )
+
+  def flatten(self) -> PythonList[trace.TraceType]:
+    flat_list = []
+    for c in self.components:
+      flat_list.extend(c.flatten())
+    return flat_list
+
+  def cast(self, value: Any, casting_context: Any) -> Any:
+    if not isinstance(value, self.value_type):
+      raise TypeError(f"[{value!r}] is not of type {self.value_type}.")
+    value_metadata, value_components = value.__tf_flatten__()
+    if self.metadata != value_metadata:
+      raise ValueError(
+          f"Metadata mismatch: [{self.metadata!r}] != [{value_metadata!r}]."
+      )
+    if len(self.components) != len(value_components):
+      raise ValueError(
+          f"Lengths of components mismatch: {len(self.components)} != "
+          f"{len(value_components)}."
+      )
+
+    casted_value_components, was_casted = util.cast_and_return_whether_casted(
+        self.components, value_components, casting_context
+    )
+    if was_casted:
+      return self.value_type.__tf_unflatten__(
+          self.metadata, casted_value_components
+      )
+    else:
+      return value
+
+  def _is_same_trace_type(self, other: trace.TraceType) -> bool:
+    return (
+        isinstance(other, CustomNestTraceType)
+        and self.value_type == other.value_type
+        and self.metadata == other.metadata
+        and len(self.components) == len(other.components)
+    )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/default_types.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/default_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..d07bd43c8929d88d6822653128affbba72a5e8c9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/default_types.py
@@ -0,0 +1,826 @@
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""TraceType implementations for common Python types."""
+
+import collections
+import math
+import numbers
+from typing import Any, Dict as PythonDict, Hashable, List as PythonList, Optional, Sequence, Tuple as PythonTuple, Type
+import weakref
+
+from tensorflow.core.function.trace_type import default_types_pb2
+from tensorflow.core.function.trace_type import serialization
+from tensorflow.core.function.trace_type import util
+from tensorflow.python.types import trace
+
+# Register the TraceType of Tensor (aka TensorSpec) to avoid cyclic dependency.
+TENSOR = None
+
+
+def register_tensor_type(tensor_type):
+  global TENSOR
+  if not TENSOR:
+    TENSOR = tensor_type
+  else:
+    raise AssertionError("Tensor type is already registered.")
+
+NanMarker = object()
+
+
+def is_nan(x):
+  """Checks if given value is a Python NaN."""
+  if not isinstance(x, numbers.Number):
+    return False
+
+  if isinstance(x, complex):
+    return math.isnan(x.real) or math.isnan(x.imag)
+  else:
+    return math.isnan(x)
+
+
+class Literal(trace.TraceType, serialization.Serializable):
+  """Represents a Literal type like bool, int or string."""
+
+  def __init__(self, value: Any):
+    # We match nan values against each other even though Python doesn't.
+    if is_nan(value):
+      value = NanMarker
+
+    self.value = value
+    self._value_hash = hash(value)
+
+  def is_subtype_of(self, other: trace.TraceType) -> bool:
+    return self == other
+
+  def most_specific_common_supertype(
+      self, types: Sequence[trace.TraceType]) -> Optional["Literal"]:
+    return self if all(self == other for other in types) else None
+
+  @classmethod
+  def experimental_type_proto(cls) -> Type[default_types_pb2.SerializedLiteral]:
+    return default_types_pb2.SerializedLiteral
+
+  @classmethod
+  def experimental_from_proto(
+      cls, proto: default_types_pb2.SerializedLiteral) -> "Literal":
+    if proto.HasField("bool_value"):
+      return Literal(proto.bool_value)
+
+    if proto.HasField("int_value"):
+      return Literal(proto.int_value)
+
+    if proto.HasField("float_value"):
+      return Literal(proto.float_value)
+
+    if proto.HasField("str_value"):
+      return Literal(proto.str_value)
+
+    if proto.HasField("none_value"):
+      return Literal(None)
+
+    raise ValueError("Malformed Literal proto can not be deserialized")
+
+  def experimental_as_proto(self) -> default_types_pb2.SerializedLiteral:
+    if isinstance(self.value, bool):
+      return default_types_pb2.SerializedLiteral(bool_value=self.value)
+
+    if isinstance(self.value, int):
+      return default_types_pb2.SerializedLiteral(int_value=self.value)
+
+    if isinstance(self.value, float):
+      return default_types_pb2.SerializedLiteral(float_value=self.value)
+
+    if isinstance(self.value, str):
+      return default_types_pb2.SerializedLiteral(str_value=self.value)
+
+    if self.value is None:
+      return default_types_pb2.SerializedLiteral(
+          none_value=default_types_pb2.SerializedLiteral.NoneValue())
+
+    raise ValueError("Can not serialize Literal of type " +
+                     type(self.value).__name__)
+
+  def placeholder_value(self, placeholder_context) -> Any:
+    # TODO(b/263505796): Remove this check when a range's placeholder output
+    # is expected to be a range and not a list.
+    if isinstance(self.value, range):
+      return list(self.value)
+
+    if self.value is NanMarker:
+      return float("nan")
+
+    return self.value
+
+  def cast(self, value: Any, casting_context: Any) -> Any:
+    if self.value is NanMarker and is_nan(value):
+      return value
+
+    if value == self.value:
+      return value
+    else:
+      raise ValueError(f"Can not cast {value!r} to {self!r}")
+
+  def __eq__(self, other) -> bool:
+    if not isinstance(other, trace.TraceType):
+      return NotImplemented
+
+    return isinstance(other, Literal) and self.value == other.value
+
+  def __hash__(self) -> int:
+    return self._value_hash
+
+  def __repr__(self) -> str:
+    return f"{self.__class__.__name__}[{self.value!r}]"
+
+
+class Weakref(trace.TraceType):
+  """Represents weakref of an arbitrary Python object.
+
+  When a function argument is a custom class, instead of making a copy of it
+  just for the sake of function cache, a weakref is instead kept to save memory.
+  """
+
+  def __init__(self, ref: weakref.ReferenceType):
+    self._ref = ref
+    self._ref_hash = hash(ref)
+
+  def is_subtype_of(self, other: trace.TraceType) -> bool:
+    return self == other
+
+  def most_specific_common_supertype(
+      self, types: Sequence[trace.TraceType]) -> Optional["Weakref"]:
+    return self if all(self == other for other in types) else None
+
+  def placeholder_value(self, placeholder_context) -> Any:
+    return self._ref()
+
+  def cast(self, value, _):
+    if value is self._ref() or value == self._ref():
+      return value
+
+    # We unwrap objects when generating the TraceType so we allow matching now.
+    while hasattr(value, "__wrapped__"):
+      value = value.__wrapped__
+      if value is self._ref():
+        return value
+
+    raise ValueError(f"Can not cast {value!r} to {self!r}")
+
+  def __eq__(self, other):
+    if not isinstance(other, trace.TraceType):
+      return NotImplemented
+
+    if not isinstance(other, Weakref):
+      return False
+
+    if self._ref() is None or other._ref() is None:
+      return False
+
+    if self._ref() is other._ref():
+      return True
+
+    return self._ref == other._ref
+
+  def __hash__(self):
+    return self._ref_hash
+
+  def __repr__(self) -> str:
+    return f"{self.__class__.__name__}[{self._ref!r}])"
+
+
+class Tuple(trace.TraceType, serialization.Serializable):
+  """Represents a tuple of TraceType objects."""
+
+  def __init__(self, *components: trace.TraceType):
+    self.components = components
+
+  def is_subtype_of(self, other: trace.TraceType) -> bool:
+    if (not isinstance(other, Tuple) or
+        len(self.components) != len(other.components)):
+      return False
+
+    return all(
+        self_component.is_subtype_of(other_component) for self_component,
+        other_component in zip(self.components, other.components))
+
+  def most_specific_common_supertype(
+      self, others: Sequence[trace.TraceType]) -> Optional["Tuple"]:
+    """See base class."""
+    if not all(
+        isinstance(other, Tuple) and
+        len(self.components) == len(other.components) for other in others):
+      return None
+
+    supertyped_components = []
+    for i, component in enumerate(self.components):
+      supertyped_component = component.most_specific_common_supertype(
+          [other.components[i] for other in others])
+      if supertyped_component is None:
+        return None
+      supertyped_components.append(supertyped_component)
+
+    return Tuple(*supertyped_components)
+
+  @classmethod
+  def experimental_type_proto(cls) -> Type[default_types_pb2.SerializedTuple]:
+    return default_types_pb2.SerializedTuple
+
+  @classmethod
+  def experimental_from_proto(
+      cls, proto: default_types_pb2.SerializedTuple) -> "Tuple":
+    return Tuple(*[serialization.deserialize(c) for c in proto.components])
+
+  def experimental_as_proto(self) -> default_types_pb2.SerializedTuple:
+    return default_types_pb2.SerializedTuple(
+        components=[serialization.serialize(c) for c in self.components])
+
+  def placeholder_value(self, placeholder_context) -> Any:
+    components = [
+        component.placeholder_value(placeholder_context)
+        for component in self.components
+    ]
+    return tuple(components)
+
+  def to_tensors(self, value) -> Any:
+    assert isinstance(value, tuple)
+    flattened_values = []
+    for comp_value, comp_type in zip(value, self.components):
+      flattened_values.extend(comp_type.to_tensors(comp_value))
+    return flattened_values
+
+  def from_tensors(self, tensors) -> Any:
+    return tuple(c.from_tensors(tensors) for c in self.components)
+
+  def flatten(self) -> PythonList[trace.TraceType]:
+    flattened_types = []
+    for component in self.components:
+      flattened_types.extend(component.flatten())
+    return flattened_types
+
+  def cast(self, value: Any, casting_context) -> Any:
+    assert isinstance(value, tuple), f"Can not cast {value!r} to tuple type."
+    assert len(value) == len(
+        self.components
+    ), f"Expected {value} to have length of {len(self.components)}"
+
+    casted_values, was_casted = util.cast_and_return_whether_casted(
+        self.components, value, casting_context
+    )
+    if was_casted:
+      return tuple(casted_values)
+    else:
+      return value
+
+  def __eq__(self, other: Any) -> bool:
+    if not isinstance(other, trace.TraceType):
+      return NotImplemented
+
+    if not isinstance(other, Tuple):
+      return False
+
+    return self.components == other.components
+
+  def __hash__(self) -> int:
+    return hash(self.components)
+
+  def __repr__(self) -> str:
+    return f"Tuple[{', '.join(map(repr, self.components))}]"
+
+
+class List(trace.TraceType, serialization.Serializable):
+  """Represents a list of TraceType objects."""
+
+  def __init__(self, *components: trace.TraceType):
+    self.components_tuple = Tuple(*components)
+
+  def is_subtype_of(self, other: trace.TraceType) -> bool:
+    if not isinstance(other, List):
+      return False
+
+    return self.components_tuple.is_subtype_of(other.components_tuple)
+
+  def most_specific_common_supertype(
+      self, others: Sequence[trace.TraceType]) -> Optional["Tuple"]:
+    """See base class."""
+    if not all(isinstance(other, List) for other in others):
+      return None
+
+    supertyped_components_tuple = (
+        self.components_tuple.most_specific_common_supertype(
+            [other.components_tuple for other in others]
+        )
+    )
+
+    if supertyped_components_tuple is None:
+      return None
+
+    return List(*supertyped_components_tuple.components)
+
+  @classmethod
+  def experimental_type_proto(cls) -> Type[default_types_pb2.SerializedList]:
+    return default_types_pb2.SerializedList
+
+  @classmethod
+  def experimental_from_proto(
+      cls, proto: default_types_pb2.SerializedList) -> "List":
+    return List(
+        *Tuple.experimental_from_proto(proto.components_tuple).components)
+
+  def experimental_as_proto(self) -> default_types_pb2.SerializedList:
+    return default_types_pb2.SerializedList(
+        components_tuple=self.components_tuple.experimental_as_proto())
+
+  def placeholder_value(self, placeholder_context) -> Any:
+    return list(self.components_tuple.placeholder_value(placeholder_context))
+
+  def to_tensors(self, value):
+    assert isinstance(value, list)
+    return self.components_tuple.to_tensors(tuple(value))
+
+  def from_tensors(self, tensors) -> Any:
+    return list(self.components_tuple.from_tensors(tensors))
+
+  def flatten(self) -> PythonList[trace.TraceType]:
+    return self.components_tuple.flatten()
+
+  def cast(self, value: Any, casting_context) -> Any:
+    assert isinstance(value, list), f"Can not cast {value!r} to list type."
+    assert len(value) == len(
+        self.components_tuple.components
+    ), f"Expected {value} to have length of {len(self.components_tuple)}"
+
+    casted_values, was_casted = util.cast_and_return_whether_casted(
+        self.components_tuple.components, value, casting_context
+    )
+    if was_casted:
+      return list(casted_values)
+    else:
+      return value
+
+  def __eq__(self, other: Any) -> bool:
+    if not isinstance(other, trace.TraceType):
+      return NotImplemented
+
+    if not isinstance(other, List):
+      return False
+
+    return self.components_tuple == other.components_tuple
+
+  def __hash__(self) -> int:
+    return hash(self.components_tuple)
+
+  def __repr__(self) -> str:
+    return f"List[{', '.join(map(repr, self.components_tuple.components))}]"
+
+
+class NamedTuple(trace.TraceType, serialization.Serializable):
+  """Represents a NamedTuple of TraceType objects."""
+
+  def __init__(self,
+               type_name: str,
+               attribute_names: PythonTuple[str],
+               attributes: PythonTuple[trace.TraceType],
+               placeholder_type: Optional[Type[Any]] = None):
+    self.type_name = type_name
+    self.attribute_names = attribute_names
+    self.attributes = Tuple(*attributes)
+    self._placeholder_type = placeholder_type
+
+  @classmethod
+  def from_type_and_attributes(
+      cls, named_tuple_type: Any,
+      attributes: PythonTuple[trace.TraceType]) -> "NamedTuple":
+    return NamedTuple(named_tuple_type.__name__, named_tuple_type._fields,
+                      attributes, named_tuple_type)
+
+  def is_subtype_of(self, other: trace.TraceType) -> bool:
+    if not isinstance(other, NamedTuple):
+      return False
+
+    return (self.type_name == other.type_name and
+            self.attribute_names == other.attribute_names and
+            self.attributes.is_subtype_of(other.attributes))
+
+  def most_specific_common_supertype(
+      self, others: Sequence[trace.TraceType]) -> Optional["NamedTuple"]:
+    """See base class."""
+    if not all(
+        isinstance(other, NamedTuple) and self.type_name == other.type_name and
+        self.attribute_names == other.attribute_names for other in others):
+      return None
+
+    supertyped_attributes = self.attributes.most_specific_common_supertype(
+        [other.attributes for other in others])
+
+    if supertyped_attributes is None:
+      return None
+
+    return NamedTuple(self.type_name, self.attribute_names,
+                      supertyped_attributes.components, self._placeholder_type)
+
+  @classmethod
+  def experimental_type_proto(
+      cls) -> Type[default_types_pb2.SerializedNamedTuple]:
+    return default_types_pb2.SerializedNamedTuple
+
+  @classmethod
+  def experimental_from_proto(
+      cls, proto: default_types_pb2.SerializedNamedTuple) -> "NamedTuple":
+    return NamedTuple(
+        proto.type_name, tuple(proto.attribute_names),
+        Tuple.experimental_from_proto(proto.attributes).components)
+
+  def experimental_as_proto(self) -> default_types_pb2.SerializedNamedTuple:
+    return default_types_pb2.SerializedNamedTuple(
+        type_name=self.type_name,
+        attribute_names=list(self.attribute_names),
+        attributes=self.attributes.experimental_as_proto())
+
+  def placeholder_value(self, placeholder_context) -> Any:
+    if self._placeholder_type is None:
+      # We don't need to trace after serialization so it is not needed but we
+      # can generate a placeholder type using the description if ever needed.
+      raise ValueError("Can not generate placeholder value for NamedTuple with"
+                       " unspecified placeholder_type. Note: placeholder_type "
+                       "is lost during serialization.")
+    attribute_placeholders = [
+        attribute.placeholder_value(placeholder_context)
+        for attribute in self.attributes.components
+    ]
+    return self._placeholder_type(*attribute_placeholders)
+
+  def to_tensors(self, value: Any):
+    assert util.is_namedtuple(value)
+    flattened_values = []
+    for attribute_name, attribute_type in zip(
+        self.attribute_names, self.attributes.components):
+      attribute_value = getattr(value, attribute_name)
+      flattened_values.extend(attribute_type.to_tensors(attribute_value))
+    return flattened_values
+
+  def from_tensors(self, tensors) -> Any:
+    if self._placeholder_type is None:
+      raise ValueError("Packing serialized NamedTuples is not supported.")
+
+    return self._placeholder_type(
+        *[c.from_tensors(tensors) for c in self.attributes.components]
+    )
+
+  def flatten(self) -> PythonList[trace.TraceType]:
+    flattened_types = []
+
+    for component in self.attributes.components:
+      flattened_types.extend(component.flatten())
+
+    return flattened_types
+
+  def cast(self, value: Any, casting_context) -> Any:
+    # Value must have same attributes with the TraceType
+    assert util.is_namedtuple(
+        value
+    ), f"Cannot cast {value!r} to type {self._placeholder_type!r}."
+    value_dict = value._asdict()
+    assert set(value_dict.keys()) == set(
+        self.attribute_names
+    ), f"{value!r} has different attributes with the TraceType {self!r}"
+
+    casted_values, was_casted = util.cast_and_return_whether_casted(
+        self.attributes.components,
+        [getattr(value, name) for name in self.attribute_names],
+        casting_context,
+    )
+    if was_casted:
+      return self._placeholder_type(*casted_values)
+    else:
+      return value
+
+  def __hash__(self) -> int:
+    return hash((self.type_name, self.attribute_names, self.attributes))
+
+  def __eq__(self, other: Any) -> bool:
+    if not isinstance(other, trace.TraceType):
+      return NotImplemented
+
+    if not isinstance(other, NamedTuple):
+      return False
+
+    return (self.type_name == other.type_name and
+            self.attribute_names == other.attribute_names and
+            self.attributes == other.attributes)
+
+  def __repr__(self) -> str:
+    paired = [
+        f"[{n!r}, {c!r}]"
+        for n, c in zip(self.attribute_names, self.attributes.components)
+    ]
+    return f"{self.type_name}[{', '.join(paired)}]"
+
+
+class Attrs(trace.TraceType):
+  """Represents a class annotated by attr.s."""
+
+  def __init__(self,
+               type_name: str,
+               attribute_names: PythonTuple[str],
+               attributes: PythonTuple[trace.TraceType],
+               placeholder_type: Optional[Type[Any]] = None):
+    self.named_attributes = NamedTuple(type_name, attribute_names, attributes)
+    self._placeholder_type = placeholder_type
+
+  @classmethod
+  def from_type_and_attributes(
+      cls, attrs_type: Any,
+      attributes: PythonTuple[trace.TraceType]) -> "Attrs":
+    return Attrs(attrs_type.__name__,
+                 tuple(attr.name for attr in attrs_type.__attrs_attrs__),
+                 attributes, attrs_type)
+
+  def is_subtype_of(self, other: trace.TraceType) -> bool:
+    if not isinstance(other, Attrs):
+      return False
+
+    return self.named_attributes.is_subtype_of(other.named_attributes)
+
+  def most_specific_common_supertype(
+      self, others: Sequence[trace.TraceType]) -> Optional["Attrs"]:
+    """See base class."""
+    if not all(isinstance(other, Attrs) for other in others):
+      return None
+
+    supertyped_attributes = (
+        self.named_attributes.most_specific_common_supertype(
+            [other.named_attributes for other in others]
+        )
+    )
+
+    if supertyped_attributes is None:
+      return None
+
+    return Attrs(self.named_attributes.type_name,
+                 self.named_attributes.attribute_names,
+                 supertyped_attributes.attributes.components,
+                 self._placeholder_type)
+
+  @classmethod
+  def experimental_type_proto(cls) -> Type[default_types_pb2.SerializedAttrs]:
+    return default_types_pb2.SerializedAttrs
+
+  @classmethod
+  def experimental_from_proto(
+      cls, proto: default_types_pb2.SerializedAttrs) -> "Attrs":
+    return Attrs(
+        proto.named_attributes.type_name,
+        tuple(proto.named_attributes.attribute_names),
+        Tuple.experimental_from_proto(
+            proto.named_attributes.attributes).components)
+
+  def experimental_as_proto(self) -> default_types_pb2.SerializedAttrs:
+    return default_types_pb2.SerializedAttrs(
+        named_attributes=self.named_attributes.experimental_as_proto())
+
+  def placeholder_value(self, placeholder_context) -> Any:
+    if self._placeholder_type is None:
+      # We don't need to trace after serialization so it is not needed but we
+      # can generate a placeholder type using the description if ever needed.
+      raise ValueError("Can not generate placeholder value for Attrs with"
+                       " unspecified placeholder_type. Note: placeholder_type "
+                       "is lost during serialization.")
+    attribute_placeholders = [
+        attribute.placeholder_value(placeholder_context)
+        for attribute in self.named_attributes.attributes.components
+    ]
+    return self._placeholder_type(*attribute_placeholders)
+
+  def to_tensors(self, value: Any):
+    assert util.is_attrs(value)
+    flattened_values = []
+    for attribute_name, attribute_type in zip(
+        self.named_attributes.attribute_names,
+        self.named_attributes.attributes.components):
+      attribute_value = getattr(value, attribute_name)
+      flattened_values.extend(attribute_type.to_tensors(attribute_value))
+    return flattened_values
+
+  def from_tensors(self, tensors):
+    if self._placeholder_type is None:
+      raise ValueError("Packing serialized NamedTuples is not supported.")
+
+    return self._placeholder_type(
+        *[
+            c.from_tensors(tensors)
+            for c in self.named_attributes.attributes.components
+        ]
+    )
+
+  def flatten(self) -> PythonList[trace.TraceType]:
+    flattened_types = []
+
+    for component in self.named_attributes.attributes.components:
+      flattened_types.extend(component.flatten())
+
+    return flattened_types
+
+  def cast(self, value: Any, casting_context) -> Any:
+    assert util.is_attrs(value)
+
+    attr_names = self.named_attributes.attribute_names
+    casted_values, was_casted = util.cast_and_return_whether_casted(
+        self.named_attributes.attributes.components,
+        [getattr(value, name) for name in attr_names],
+        casting_context,
+    )
+
+    if was_casted:
+      return self._placeholder_type(*casted_values)
+    else:
+      return value
+
+  def __hash__(self) -> int:
+    return hash(self.named_attributes)
+
+  def __eq__(self, other: Any) -> bool:
+    if not isinstance(other, trace.TraceType):
+      return NotImplemented
+
+    if not isinstance(other, Attrs):
+      return False
+
+    return self.named_attributes == other.named_attributes
+
+  def __repr__(self) -> str:
+    name_component_zip = zip(
+        self.named_attributes.attribute_names,
+        self.named_attributes.attributes.components,
+    )
+    paired = [f"[{n!r}, {c!r}]" for n, c in name_component_zip]
+    return f"{self.named_attributes.type_name}[{', '.join(paired)}]"
+
+
+class Dict(trace.TraceType, serialization.Serializable):
+  """Represents a dictionary of TraceType objects.
+
+  Attributes:
+    mapping: A mapping from keys to corresponding TraceTypes of the dict values.
+  """
+
+  def __init__(self,
+               mapping: PythonDict[Hashable, trace.TraceType],
+               placeholder_type: Optional[Type[Any]] = None):
+    self.mapping = mapping
+    self._placeholder_type = placeholder_type
+
+  def _has_same_structure(self, other):
+    if not isinstance(other, Dict):
+      return False
+
+    return self.mapping.keys() == other.mapping.keys()
+
+  def is_subtype_of(self, other: trace.TraceType) -> bool:
+    """See base class."""
+    if not self._has_same_structure(other):
+      return False
+
+    # We need all keys to be present because there can be logic relying on
+    # their existence or lack thereof and hence can not guarantee subtype based
+    # on a subset or superset of keys.
+    # Only the tracing code can explicitly check for key dependencies and inform
+    # that decision.
+    return all(self.mapping[key].is_subtype_of(other.mapping[key])
+               for key in self.mapping)
+
+  def most_specific_common_supertype(
+      self, types: Sequence[trace.TraceType]) -> Optional["Dict"]:
+    """See base class."""
+    if not all(self._has_same_structure(other) for other in types):
+      return None
+
+    new_mapping = {}
+    for key in self.mapping.keys():
+      common = self.mapping[key].most_specific_common_supertype(
+          [other.mapping[key] for other in types])
+      if common is None:
+        return None
+      else:
+        new_mapping[key] = common
+
+    return Dict(new_mapping, self._placeholder_type)
+
+  @classmethod
+  def experimental_type_proto(cls) -> Type[default_types_pb2.SerializedDict]:
+    return default_types_pb2.SerializedDict
+
+  @classmethod
+  def experimental_from_proto(
+      cls, proto: default_types_pb2.SerializedDict) -> "Dict":
+    return Dict({
+        Literal.experimental_from_proto(k).value: serialization.deserialize(v)
+        for k, v in zip(proto.keys, proto.values)
+    })
+
+  def experimental_as_proto(self) -> default_types_pb2.SerializedDict:
+    return default_types_pb2.SerializedDict(
+        keys=[Literal(k).experimental_as_proto() for k in self.mapping.keys()],
+        values=[serialization.serialize(v) for v in self.mapping.values()])
+
+  def placeholder_value(self, placeholder_context) -> Any:
+    if self._placeholder_type is None:
+      raise ValueError("Can not generate placeholder value for Dict with"
+                       " unspecified placeholder_type. Note: placeholder_type "
+                       "is lost during serialization.")
+    attribute_placeholders = [
+        (key, value.placeholder_value(placeholder_context))
+        for key, value in self.mapping.items()
+    ]
+    if self._placeholder_type is collections.defaultdict:
+      return dict(attribute_placeholders)
+    return self._placeholder_type(attribute_placeholders)
+
+  def to_tensors(self, value: Any):
+    assert isinstance(value, collections.abc.Mapping)
+    flattened_values = []
+    for key in sorted(self.mapping.keys()):
+      comp_value, comp_type = value[key], self.mapping[key]
+      flattened_values.extend(comp_type.to_tensors(comp_value))
+    return flattened_values
+
+  def from_tensors(self, tensors):
+    if self._placeholder_type is None:
+      raise ValueError("Packing serialized Dict is not supported.")
+
+    sorted_traversal = {
+        key: self.mapping[key].from_tensors(tensors)
+        for key in sorted(self.mapping)
+    }
+
+    if self._placeholder_type is collections.defaultdict:
+      return {key: sorted_traversal[key] for key in self.mapping}
+
+    return self._placeholder_type(
+        (key, sorted_traversal[key]) for key in self.mapping
+    )
+
+  def flatten(self) -> PythonList[trace.TraceType]:
+    flattened_types = []
+
+    for key in sorted(self.mapping.keys()):
+      flattened_types.extend(self.mapping[key].flatten())
+
+    return flattened_types
+
+  def cast(self, value: Any, casting_context) -> Any:
+    # Value must have same keys with the TraceType
+    assert isinstance(
+        value, collections.abc.Mapping
+    ), f"Can not cast {value!r} to a Dict type."
+    assert set(value.keys()) == set(
+        self.mapping.keys()
+    ), f"{value!r} has different keys with the TraceType {self!r}."
+
+    casted_values, was_casted = util.cast_and_return_whether_casted(
+        self.mapping.values(),
+        [value[k] for k in self.mapping.keys()],
+        casting_context,
+    )
+
+    if was_casted:
+      return self._placeholder_type(
+          **{k: v for k, v in zip(self.mapping.keys(), casted_values)}
+      )
+    else:
+      return value
+
+  def __eq__(self, other) -> bool:
+    if not isinstance(other, trace.TraceType):
+      return NotImplemented
+
+    if not isinstance(other, Dict):
+      return False
+
+    return self.mapping == other.mapping
+
+  def __hash__(self) -> int:
+    return hash(frozenset(self.mapping.keys()))
+
+  def __repr__(self) -> str:
+    paired = [f"[{n!r}, {t!r}]" for n, t in self.mapping.items()]
+    return f"{self.__class__.__name__}[{', '.join(paired)}]"
+
+
+serialization.register_serializable(Literal)
+serialization.register_serializable(Tuple)
+serialization.register_serializable(List)
+serialization.register_serializable(NamedTuple)
+serialization.register_serializable(Attrs)
+serialization.register_serializable(Dict)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/default_types_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/default_types_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..316c7ab33442978631fa41ae73bc09db7b2c9856
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/default_types_pb2.py
@@ -0,0 +1,38 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/function/trace_type/default_types.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.function.trace_type import serialization_pb2 as tensorflow_dot_core_dot_function_dot_trace__type_dot_serialization__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n7tensorflow/core/function/trace_type/default_types.proto\x12\x31tensorflow.core.function.trace_type.default_types\x1a\x37tensorflow/core/function/trace_type/serialization.proto\"\xe6\x01\n\x11SerializedLiteral\x12\x14\n\nbool_value\x18\x01 \x01(\x08H\x00\x12\x13\n\tint_value\x18\x02 \x01(\x03H\x00\x12\x15\n\x0b\x66loat_value\x18\x03 \x01(\x01H\x00\x12\x13\n\tstr_value\x18\x04 \x01(\tH\x00\x12\x64\n\nnone_value\x18\x05 \x01(\x0b\x32N.tensorflow.core.function.trace_type.default_types.SerializedLiteral.NoneValueH\x00\x1a\x0b\n\tNoneValueB\x07\n\x05value\"m\n\x0fSerializedTuple\x12Z\n\ncomponents\x18\x01 \x03(\x0b\x32\x46.tensorflow.core.function.trace_type.serialization.SerializedTraceType\"n\n\x0eSerializedList\x12\\\n\x10\x63omponents_tuple\x18\x01 \x01(\x0b\x32\x42.tensorflow.core.function.trace_type.default_types.SerializedTuple\"\x9a\x01\n\x14SerializedNamedTuple\x12\x11\n\ttype_name\x18\x01 \x01(\t\x12\x17\n\x0f\x61ttribute_names\x18\x02 \x03(\t\x12V\n\nattributes\x18\x03 \x01(\x0b\x32\x42.tensorflow.core.function.trace_type.default_types.SerializedTuple\"t\n\x0fSerializedAttrs\x12\x61\n\x10named_attributes\x18\x01 \x01(\x0b\x32G.tensorflow.core.function.trace_type.default_types.SerializedNamedTuple\"\xbc\x01\n\x0eSerializedDict\x12R\n\x04keys\x18\x01 \x03(\x0b\x32\x44.tensorflow.core.function.trace_type.default_types.SerializedLiteral\x12V\n\x06values\x18\x02 \x03(\x0b\x32\x46.tensorflow.core.function.trace_type.serialization.SerializedTraceType')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.function.trace_type.default_types_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _SERIALIZEDLITERAL._serialized_start=168
+  _SERIALIZEDLITERAL._serialized_end=398
+  _SERIALIZEDLITERAL_NONEVALUE._serialized_start=378
+  _SERIALIZEDLITERAL_NONEVALUE._serialized_end=389
+  _SERIALIZEDTUPLE._serialized_start=400
+  _SERIALIZEDTUPLE._serialized_end=509
+  _SERIALIZEDLIST._serialized_start=511
+  _SERIALIZEDLIST._serialized_end=621
+  _SERIALIZEDNAMEDTUPLE._serialized_start=624
+  _SERIALIZEDNAMEDTUPLE._serialized_end=778
+  _SERIALIZEDATTRS._serialized_start=780
+  _SERIALIZEDATTRS._serialized_end=896
+  _SERIALIZEDDICT._serialized_start=899
+  _SERIALIZEDDICT._serialized_end=1087
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/serialization.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/serialization.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1943e828a06025b6ee23f5488154cc6cb36715f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/serialization.py
@@ -0,0 +1,100 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utils for serializing and deserializing TraceTypes."""
+
+import abc
+from typing import Type
+
+from google.protobuf import message
+from tensorflow.core.function.trace_type import serialization_pb2
+
+SerializedTraceType = serialization_pb2.SerializedTraceType
+
+PROTO_CLASS_TO_PY_CLASS = {}
+
+
+class Serializable(metaclass=abc.ABCMeta):
+  """TraceTypes implementing this additional interface are portable."""
+
+  @classmethod
+  @abc.abstractmethod
+  def experimental_type_proto(cls) -> Type[message.Message]:
+    """Returns the unique type of proto associated with this class."""
+    raise NotImplementedError
+
+  @classmethod
+  @abc.abstractmethod
+  def experimental_from_proto(cls, proto: message.Message) -> "Serializable":
+    """Returns an instance based on a proto."""
+    raise NotImplementedError
+
+  @abc.abstractmethod
+  def experimental_as_proto(self) -> message.Message:
+    """Returns a proto representing this instance."""
+    raise NotImplementedError
+
+
+def register_serializable(cls: Type[Serializable]):
+  """Registers a Python class to support serialization.
+
+  Only register standard TF types. Custom types should NOT be registered.
+
+  Args:
+    cls: Python class to register.
+  """
+  if cls.experimental_type_proto() in PROTO_CLASS_TO_PY_CLASS:
+    raise ValueError(
+        "Existing Python class " +
+        PROTO_CLASS_TO_PY_CLASS[cls.experimental_type_proto()].__name__ +
+        " already has " + cls.experimental_type_proto().__name__ +
+        " as its associated proto representation. Please ensure " +
+        cls.__name__ + " has a unique proto representation.")
+
+  PROTO_CLASS_TO_PY_CLASS[cls.experimental_type_proto()] = cls
+
+
+def serialize(to_serialize: Serializable) -> SerializedTraceType:
+  """Converts Serializable to a proto SerializedTraceType."""
+
+  if not isinstance(to_serialize, Serializable):
+    raise ValueError("Can not serialize " + type(to_serialize).__name__ +
+                     " since it is not Serializable. For object " +
+                     str(to_serialize))
+  actual_proto = to_serialize.experimental_as_proto()
+
+  if not isinstance(actual_proto, to_serialize.experimental_type_proto()):
+    raise ValueError(
+        type(to_serialize).__name__ +
+        " returned different type of proto than specified by " +
+        "experimental_type_proto()")
+
+  serialized = SerializedTraceType()
+  serialized.representation.Pack(actual_proto)
+  return serialized
+
+
+def deserialize(proto: SerializedTraceType) -> Serializable:
+  """Converts a proto SerializedTraceType to instance of Serializable."""
+  for proto_class in PROTO_CLASS_TO_PY_CLASS:
+    if proto.representation.Is(proto_class.DESCRIPTOR):
+      actual_proto = proto_class()
+      proto.representation.Unpack(actual_proto)
+      return PROTO_CLASS_TO_PY_CLASS[proto_class].experimental_from_proto(
+          actual_proto)
+
+  raise ValueError(
+      "Can not deserialize proto of url: ", proto.representation.type_url,
+      " since no matching Python class could be found. For value ",
+      proto.representation.value)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/serialization_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/serialization_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..404a00e4114a676e114ad17e8e21b7e663140685
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/serialization_pb2.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/function/trace_type/serialization.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from google.protobuf import any_pb2 as google_dot_protobuf_dot_any__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n7tensorflow/core/function/trace_type/serialization.proto\x12\x31tensorflow.core.function.trace_type.serialization\x1a\x19google/protobuf/any.proto\"C\n\x13SerializedTraceType\x12,\n\x0erepresentation\x18\x01 \x01(\x0b\x32\x14.google.protobuf.Any')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.function.trace_type.serialization_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _SERIALIZEDTRACETYPE._serialized_start=137
+  _SERIALIZEDTRACETYPE._serialized_end=204
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/serialization_test_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/serialization_test_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..5488f681fafc129a1956ae2f0f9bd1177c4db5f3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/serialization_test_pb2.py
@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/function/trace_type/serialization_test.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.function.trace_type import serialization_pb2 as tensorflow_dot_core_dot_function_dot_trace__type_dot_serialization__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n<tensorflow/core/function/trace_type/serialization_test.proto\x12\x36tensorflow.core.function.trace_type.serialization_test\x1a\x37tensorflow/core/function/trace_type/serialization.proto\"5\n\x16MyCustomRepresentation\x12\r\n\x05index\x18\x01 \x01(\x05\x12\x0c\n\x04name\x18\x02 \x01(\t\"u\n\x19MyCompositeRepresentation\x12X\n\x08\x65lements\x18\x01 \x03(\x0b\x32\x46.tensorflow.core.function.trace_type.serialization.SerializedTraceType\"(\n\x1aMyMultiClassRepresentation\x12\n\n\x02id\x18\x01 \x01(\x05')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.function.trace_type.serialization_test_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _MYCUSTOMREPRESENTATION._serialized_start=177
+  _MYCUSTOMREPRESENTATION._serialized_end=230
+  _MYCOMPOSITEREPRESENTATION._serialized_start=232
+  _MYCOMPOSITEREPRESENTATION._serialized_end=349
+  _MYMULTICLASSREPRESENTATION._serialized_start=351
+  _MYMULTICLASSREPRESENTATION._serialized_end=391
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/trace_type_builder.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/trace_type_builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ee0cd525a94aff365d37661953035e01710c070
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/trace_type_builder.py
@@ -0,0 +1,208 @@
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utitiles for Cache Key generation based on Function Trace Type."""
+
+import collections.abc
+from typing import Any, Dict, Hashable, Optional
+import weakref
+
+from tensorflow.core.function.trace_type import custom_nest_trace_type
+from tensorflow.core.function.trace_type import default_types
+from tensorflow.core.function.trace_type import util
+from tensorflow.python.types import trace
+from tensorflow.python.util import custom_nest_protocol
+
+
+class InternalTracingContext(trace.TracingContext):
+  """Container for variables and flags shared across TraceType generation."""
+
+  def __init__(self, is_legacy_signature: bool = False):
+    self._global_to_local_id = {}
+    self._alias_id_to_placeholder = {}
+    self._is_legacy_signature = is_legacy_signature
+
+  def alias_global_id(self, global_id: Hashable) -> Hashable:
+    if global_id not in self._global_to_local_id:
+      self._global_to_local_id[global_id] = len(self._global_to_local_id)
+
+    return self._global_to_local_id[global_id]
+
+  def add_placeholder(self, alias_id: Hashable, variable) -> None:
+    self._alias_id_to_placeholder[alias_id] = variable
+
+  def get_placeholder_mapping(self) -> Dict[Hashable, Any]:
+    return self._alias_id_to_placeholder
+
+  @property
+  def is_legacy_signature(self) -> bool:
+    """If the value is from a legacy signature representation.
+
+    Legacy signature representations include tf.function.input_signature and
+    ConcreteFunction.structured_input_signature.
+    """
+    return self._is_legacy_signature
+
+
+class InternalPlaceholderContext(trace.PlaceholderContext):
+  """Container with mappings shared across TraceTypes for placeholder values."""
+
+  def __init__(self,
+               context_graph=None,
+               placeholder_mapping=None,
+               unnest_only=False,
+               with_none_control_dependencies=False,
+               composite_device_name=None):
+    self._alias_id_to_placeholder = placeholder_mapping or {}
+    self._naming_scope = None
+    self._context_graph = context_graph
+    self._unnest_only = unnest_only
+    self._with_none_control_dependencies = with_none_control_dependencies
+    self._composite_device_name = composite_device_name
+
+  def has_placeholder(self, alias_id: Hashable) -> bool:
+    return alias_id in self._alias_id_to_placeholder
+
+  def get_placeholder(self, alias_id: Hashable) -> Hashable:
+    if not self.has_placeholder(alias_id):
+      raise KeyError(f"alias_id: {alias_id} not found in this instance of "
+                     "placeholder context.")
+    return self._alias_id_to_placeholder[alias_id]
+
+  def add_placeholder(self, alias_id: Hashable, placeholder: Hashable) -> None:
+    if alias_id in self._alias_id_to_placeholder:
+      raise KeyError(f"alias id: {alias_id} is already stored in this "
+                     "instance of placeholder context.")
+    self._alias_id_to_placeholder[alias_id] = placeholder
+
+  def update_naming_scope(self, naming_scope: Optional[str]) -> None:
+    self._naming_scope = naming_scope
+
+  @property
+  def naming_scope(self) -> Optional[str]:
+    return self._naming_scope
+
+  @property
+  def context_graph(self):
+    return self._context_graph
+
+  @property
+  def unnest_only(self) -> bool:
+    return self._unnest_only
+
+  @property
+  def with_none_control_dependencies(self) -> bool:
+    return self._with_none_control_dependencies
+
+  @property
+  def composite_device_name(self) -> Any:
+    return self._composite_device_name
+
+
+class InternalCastContext(trace.CastContext):
+  """Default casting behaviors."""
+
+  def __init__(self, allow_specs=False):
+    self._allow_specs = allow_specs
+
+  @property
+  def allow_specs(self) -> bool:
+    """Allow TypeSpecs to be casted (instead of the actual CompositeTensors)."""
+    # Public APIs like get_concrete_function allow users to pass in specs
+    # instead which need to pass through input binding etc.
+    return self._allow_specs
+
+
+def from_value(value: Any,
+               context: trace.TracingContext = None) -> trace.TraceType:
+  """Returns a TraceType corresponding to the value based on the context.
+
+  Args:
+    value: The value to generate a TraceType for.
+    context: The TracingContext to be shared during protocol calls.
+
+  Returns:
+    A TraceType object representing the given value.
+  """
+
+  if context is None:
+    context = InternalTracingContext()
+
+  if context.is_legacy_signature and isinstance(value, trace.TraceType):
+    return value
+  elif isinstance(value, trace.SupportsTracingProtocol):
+    generated_type = value.__tf_tracing_type__(context)
+    if not isinstance(generated_type, trace.TraceType):
+      raise TypeError(
+          "Expected an instance of TraceType for Tracing Protocol call to " +
+          str(value) + " but got " + str(generated_type))
+    return generated_type
+
+  # TODO(b/183107079): Allow these once they're handled properly.
+  if isinstance(value, weakref.ref):
+    raise TypeError(
+        f"weakref input {value} not supported for tf.function."
+    )
+
+  if hasattr(value, "__wrapped__"):
+    return from_value(value.__wrapped__, context)
+
+  if isinstance(value, list):
+    return default_types.List(*(from_value(c, context) for c in value))
+
+  if isinstance(value, tuple):
+    if util.is_namedtuple(value):
+      named_tuple_type = type(value)
+      return default_types.NamedTuple.from_type_and_attributes(
+          named_tuple_type, tuple(from_value(c, context) for c in value))
+    else:
+      return default_types.Tuple(*(from_value(c, context) for c in value))
+
+  if isinstance(value, collections.abc.Mapping):
+    mapping_type = type(value)
+    return default_types.Dict(
+        {k: from_value(value[k], context) for k in value}, mapping_type)
+
+  if util.is_attrs(value):
+    return default_types.Attrs.from_type_and_attributes(
+        type(value),
+        tuple(
+            from_value(getattr(value, a.name), context)
+            for a in value.__attrs_attrs__))
+
+  if util.is_np_ndarray(value):
+    ndarray = value.__array__()
+    return default_types.TENSOR(ndarray.shape, ndarray.dtype)
+
+  if isinstance(value, custom_nest_protocol.CustomNestProtocol):
+    metadata, components = value.__tf_flatten__()
+    return custom_nest_trace_type.CustomNestTraceType(
+        type(value), metadata, tuple(from_value(c, context) for c in components)
+    )
+
+  try:
+    ref = weakref.ref(value)
+    if ref is None:
+      raise TypeError(
+          f"Deleted objects are not valid tf.function arguments, Got {value!r}")
+    else:
+      return default_types.Weakref(ref)
+  except TypeError:
+    try:
+      return default_types.Literal(value)
+    except:
+      raise TypeError(  # pylint: disable=raise-missing-from
+          f"Could not generate a generic TraceType for {value!r}."
+          f"Please verify that it is immutable/hashable. Otheriwse, consider "
+          f"implementing the Tracing Protocol for it.")
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..899936b3d66a3b0d16acaa3e456d10ed9a0278e3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/function/trace_type/util.py
@@ -0,0 +1,52 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for the trace_type module."""
+
+from typing import Any, List, Tuple
+
+import numpy as np
+
+
+# TODO(b/225045380): Depend on the abstracted `leaf` lib from 'nest'.
+def is_namedtuple(obj):
+  return hasattr(obj, "_fields") and all(
+      isinstance(field, str) for field in obj._fields)
+
+
+# TODO(b/225045380): Depend on the abstracted `leaf` lib from 'nest'.
+def is_attrs(obj):
+  return hasattr(type(obj), "__attrs_attrs__")
+
+
+# TODO(b/225045380): Depend on the abstracted `leaf` lib from 'nest'.
+def is_np_ndarray(value):
+  return hasattr(value, "__array__") and not (
+      # For legacy reasons we do not automatically promote Numpy strings.
+      isinstance(value, np.str_)
+      # NumPy dtypes have __array__ as unbound methods.
+      or isinstance(value, type))
+
+
+def cast_and_return_whether_casted(
+    trace_types, values, context
+) -> Tuple[List[Any], bool]:
+  did_cast = False
+  casted_values = []
+  for t, v in zip(trace_types, values):
+    casted_v = t.cast(v, context)
+    casted_values.append(casted_v)
+    if casted_v is not v:
+      did_cast = True
+  return casted_values, did_cast
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/grappler/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/grappler/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/grappler/costs/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/grappler/costs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/grappler/costs/op_performance_data_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/grappler/costs/op_performance_data_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f443400a652e94c0c3e70b243dd6cefd8986a73
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/grappler/costs/op_performance_data_pb2.py
@@ -0,0 +1,55 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/grappler/costs/op_performance_data.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
+from tensorflow.core.protobuf import device_properties_pb2 as tensorflow_dot_core_dot_protobuf_dot_device__properties__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n8tensorflow/core/grappler/costs/op_performance_data.proto\x12\ntensorflow\x1a&tensorflow/core/framework/tensor.proto\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\x1a*tensorflow/core/framework/attr_value.proto\x1a\x30tensorflow/core/protobuf/device_properties.proto\"+\n\x0bSessionInfo\x12\x1c\n\x14intra_op_parallelism\x18\x01 \x01(\x03\"\xdb\x03\n\x06OpInfo\x12\n\n\x02op\x18\x01 \x01(\t\x12*\n\x04\x61ttr\x18\x02 \x03(\x0b\x32\x1c.tensorflow.OpInfo.AttrEntry\x12\x33\n\x06inputs\x18\x03 \x03(\x0b\x32#.tensorflow.OpInfo.TensorProperties\x12\x34\n\x07outputs\x18\x05 \x03(\x0b\x32#.tensorflow.OpInfo.TensorProperties\x12,\n\x06\x64\x65vice\x18\x04 \x01(\x0b\x32\x1c.tensorflow.DeviceProperties\x12-\n\x0csession_info\x18\x06 \x01(\x0b\x32\x17.tensorflow.SessionInfo\x1a\x42\n\tAttrEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12$\n\x05value\x18\x02 \x01(\x0b\x32\x15.tensorflow.AttrValue:\x02\x38\x01\x1a\x8c\x01\n\x10TensorProperties\x12#\n\x05\x64type\x18\x01 \x01(\x0e\x32\x14.tensorflow.DataType\x12+\n\x05shape\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12&\n\x05value\x18\x03 \x01(\x0b\x32\x17.tensorflow.TensorProto\"/\n\x12NormalDistribution\x12\n\n\x02mu\x18\x01 \x01(\x01\x12\r\n\x05sigma\x18\x02 \x01(\x01\"2\n\x15LogNormalDistribution\x12\n\n\x02mu\x18\x01 \x01(\x01\x12\r\n\x05sigma\x18\x02 \x01(\x01\"\xf3\x04\n\rOpPerformance\x12\x1e\n\x02op\x18\x01 \x01(\x0b\x32\x12.tensorflow.OpInfo\x12\x31\n\x0csession_info\x18\x0c \x01(\x0b\x32\x17.tensorflow.SessionInfoB\x02\x18\x01\x12\x0c\n\x04node\x18\x05 \x01(\t\x12\x1d\n\x15temporary_memory_size\x18\x02 \x01(\x03\x12\x14\n\x0c\x63ompute_cost\x18\x03 \x01(\x03\x12\x14\n\x0c\x63ompute_time\x18\x06 \x01(\x03\x12\x13\n\x0bmemory_time\x18\x07 \x01(\x03\x12\x1a\n\x12\x63ompute_efficiency\x18\x04 \x01(\x01\x12\x19\n\x11memory_efficiency\x18\x08 \x01(\x01\x12?\n\x15\x65xecution_time_normal\x18\n \x01(\x0b\x32\x1e.tensorflow.NormalDistributionH\x00\x12\x46\n\x19\x65xecution_time_log_normal\x18\x0b \x01(\x0b\x32!.tensorflow.LogNormalDistributionH\x00\x12\x35\n\top_memory\x18\t \x01(\x0b\x32\".tensorflow.OpPerformance.OpMemory\x1a\x97\x01\n\x08OpMemory\x12\x15\n\routput_memory\x18\x01 \x03(\x03\x12\x13\n\x0btemp_memory\x18\x02 \x01(\x03\x12\x19\n\x11persistent_memory\x18\x04 \x01(\x03\x12\x1e\n\x12\x64\x65vice_temp_memory\x18\x03 \x01(\x03\x42\x02\x18\x01\x12$\n\x18\x64\x65vice_persistent_memory\x18\x05 \x01(\x03\x42\x02\x18\x01\x42\x10\n\x0e\x65xecution_time\"F\n\x11OpPerformanceList\x12\x31\n\x0eop_performance\x18\x01 \x03(\x0b\x32\x19.tensorflow.OpPerformanceB\x03\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.grappler.costs.op_performance_data_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _OPINFO_ATTRENTRY._options = None
+  _OPINFO_ATTRENTRY._serialized_options = b'8\001'
+  _OPPERFORMANCE_OPMEMORY.fields_by_name['device_temp_memory']._options = None
+  _OPPERFORMANCE_OPMEMORY.fields_by_name['device_temp_memory']._serialized_options = b'\030\001'
+  _OPPERFORMANCE_OPMEMORY.fields_by_name['device_persistent_memory']._options = None
+  _OPPERFORMANCE_OPMEMORY.fields_by_name['device_persistent_memory']._serialized_options = b'\030\001'
+  _OPPERFORMANCE.fields_by_name['session_info']._options = None
+  _OPPERFORMANCE.fields_by_name['session_info']._serialized_options = b'\030\001'
+  _SESSIONINFO._serialized_start=291
+  _SESSIONINFO._serialized_end=334
+  _OPINFO._serialized_start=337
+  _OPINFO._serialized_end=812
+  _OPINFO_ATTRENTRY._serialized_start=603
+  _OPINFO_ATTRENTRY._serialized_end=669
+  _OPINFO_TENSORPROPERTIES._serialized_start=672
+  _OPINFO_TENSORPROPERTIES._serialized_end=812
+  _NORMALDISTRIBUTION._serialized_start=814
+  _NORMALDISTRIBUTION._serialized_end=861
+  _LOGNORMALDISTRIBUTION._serialized_start=863
+  _LOGNORMALDISTRIBUTION._serialized_end=913
+  _OPPERFORMANCE._serialized_start=916
+  _OPPERFORMANCE._serialized_end=1543
+  _OPPERFORMANCE_OPMEMORY._serialized_start=1374
+  _OPPERFORMANCE_OPMEMORY._serialized_end=1525
+  _OPPERFORMANCELIST._serialized_start=1545
+  _OPPERFORMANCELIST._serialized_end=1615
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/kernels/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/kernels/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/lib/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/lib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/lib/core/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/lib/core/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/lib/core/error_codes_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/lib/core/error_codes_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf0546310a375e6eff757f90b3b77563a1382efc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/lib/core/error_codes_pb2.py
@@ -0,0 +1,25 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/lib/core/error_codes.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.tsl.protobuf import error_codes_pb2 as tsl_dot_protobuf_dot_error__codes__pb2
+
+from tensorflow.tsl.protobuf.error_codes_pb2 import *
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n*tensorflow/core/lib/core/error_codes.proto\x1a\x1etsl/protobuf/error_codes.protoP\x00\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.lib.core.error_codes_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/profile_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/profile_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..73d27da8c8b60762ef3c035114553e9122c60e30
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/profile_pb2.py
@@ -0,0 +1,25 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/profiler/profile.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.tsl.profiler.protobuf import profile_pb2 as tsl_dot_profiler_dot_protobuf_dot_profile__pb2
+
+from tensorflow.tsl.profiler.protobuf.profile_pb2 import *
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n&tensorflow/core/profiler/profile.proto\x12\x1dtensorflow.tfprof.pprof.dummy\x1a#tsl/profiler/protobuf/profile.protoP\x00\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.profiler.profile_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/profiler_options_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/profiler_options_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..f10a0688610aea5554c2c1e92763eeb8ad2e244d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/profiler_options_pb2.py
@@ -0,0 +1,25 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/profiler/profiler_options.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.tsl.profiler.protobuf import profiler_options_pb2 as tsl_dot_profiler_dot_protobuf_dot_profiler__options__pb2
+
+from tensorflow.tsl.profiler.protobuf.profiler_options_pb2 import *
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n/tensorflow/core/profiler/profiler_options.proto\x12\x10tensorflow.dummy\x1a,tsl/profiler/protobuf/profiler_options.protoP\x00\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.profiler.profiler_options_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/protobuf/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/protobuf/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/protobuf/xplane_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/protobuf/xplane_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..f45dad0b1bbb181bcf4062e273bf63ae975ad11a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/protobuf/xplane_pb2.py
@@ -0,0 +1,25 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/profiler/protobuf/xplane.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.tsl.profiler.protobuf import xplane_pb2 as tsl_dot_profiler_dot_protobuf_dot_xplane__pb2
+
+from tensorflow.tsl.profiler.protobuf.xplane_pb2 import *
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n.tensorflow/core/profiler/protobuf/xplane.proto\x12\x19tensorflow.profiler.empty\x1a\"tsl/profiler/protobuf/xplane.protoP\x00\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.profiler.protobuf.xplane_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/tfprof_log_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/tfprof_log_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f494dd038b415a610f90919d2729d10738d654f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/tfprof_log_pb2.py
@@ -0,0 +1,106 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/profiler/tfprof_log.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
+from tensorflow.core.framework import step_stats_pb2 as tensorflow_dot_core_dot_framework_dot_step__stats__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n)tensorflow/core/profiler/tfprof_log.proto\x12\x11tensorflow.tfprof\x1a*tensorflow/core/framework/attr_value.proto\x1a*tensorflow/core/framework/step_stats.proto\"\xdf\x01\n\x07\x43odeDef\x12\x30\n\x06traces\x18\x01 \x03(\x0b\x32 .tensorflow.tfprof.CodeDef.Trace\x1a\xa1\x01\n\x05Trace\x12\x10\n\x04\x66ile\x18\x01 \x01(\tB\x02\x18\x01\x12\x0f\n\x07\x66ile_id\x18\x06 \x01(\x03\x12\x0e\n\x06lineno\x18\x02 \x01(\x05\x12\x14\n\x08\x66unction\x18\x03 \x01(\tB\x02\x18\x01\x12\x13\n\x0b\x66unction_id\x18\x07 \x01(\x03\x12\x10\n\x04line\x18\x04 \x01(\tB\x02\x18\x01\x12\x0f\n\x07line_id\x18\x08 \x01(\x03\x12\x17\n\x0f\x66unc_start_line\x18\x05 \x01(\x05\"j\n\nOpLogEntry\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x11\n\tfloat_ops\x18\x02 \x01(\x03\x12\r\n\x05types\x18\x03 \x03(\t\x12,\n\x08\x63ode_def\x18\x04 \x01(\x0b\x32\x1a.tensorflow.tfprof.CodeDef\"\xb8\x01\n\nOpLogProto\x12\x32\n\x0blog_entries\x18\x01 \x03(\x0b\x32\x1d.tensorflow.tfprof.OpLogEntry\x12\x43\n\x0cid_to_string\x18\x02 \x03(\x0b\x32-.tensorflow.tfprof.OpLogProto.IdToStringEntry\x1a\x31\n\x0fIdToStringEntry\x12\x0b\n\x03key\x18\x01 \x01(\x03\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"\xd4\x02\n\x0cProfileProto\x12\x39\n\x05nodes\x18\x01 \x03(\x0b\x32*.tensorflow.tfprof.ProfileProto.NodesEntry\x12\x11\n\thas_trace\x18\x02 \x01(\x08\x12\x1f\n\x17miss_accelerator_stream\x18\x05 \x01(\x08\x12\r\n\x05steps\x18\x03 \x03(\x03\x12\x45\n\x0cid_to_string\x18\x04 \x03(\x0b\x32/.tensorflow.tfprof.ProfileProto.IdToStringEntry\x1aL\n\nNodesEntry\x12\x0b\n\x03key\x18\x01 \x01(\x03\x12-\n\x05value\x18\x02 \x01(\x0b\x32\x1e.tensorflow.tfprof.ProfileNode:\x02\x38\x01\x1a\x31\n\x0fIdToStringEntry\x12\x0b\n\x03key\x18\x01 \x01(\x03\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"\xd3\x08\n\x0bProfileNode\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\n\n\x02op\x18\t \x01(\t\x12\n\n\x02id\x18\r \x01(\x03\x12:\n\x06inputs\x18\x02 \x03(\x0b\x32*.tensorflow.tfprof.ProfileNode.InputsEntry\x12\x45\n\x0cinput_shapes\x18\x10 \x03(\x0b\x32/.tensorflow.tfprof.ProfileNode.InputShapesEntry\x12<\n\x07outputs\x18\x03 \x03(\x0b\x32+.tensorflow.tfprof.ProfileNode.OutputsEntry\x12G\n\routput_shapes\x18\x0f \x03(\x0b\x32\x30.tensorflow.tfprof.ProfileNode.OutputShapesEntry\x12L\n\x10src_output_index\x18\x0e \x03(\x0b\x32\x32.tensorflow.tfprof.ProfileNode.SrcOutputIndexEntry\x12\r\n\x05shape\x18\x04 \x03(\x03\x12\x10\n\x08op_types\x18\x05 \x03(\t\x12\x18\n\x10\x63\x61nonical_device\x18\x06 \x01(\t\x12\x13\n\x0bhost_device\x18\x07 \x01(\t\x12\x11\n\tfloat_ops\x18\x08 \x01(\x03\x12)\n\x05trace\x18\n \x01(\x0b\x32\x1a.tensorflow.tfprof.CodeDef\x12\x38\n\x05\x61ttrs\x18\x0b \x03(\x0b\x32).tensorflow.tfprof.ProfileNode.AttrsEntry\x12\x38\n\x05\x65xecs\x18\x0c \x03(\x0b\x32).tensorflow.tfprof.ProfileNode.ExecsEntry\x1a-\n\x0bInputsEntry\x12\x0b\n\x03key\x18\x01 \x01(\x05\x12\r\n\x05value\x18\x02 \x01(\x03:\x02\x38\x01\x1aL\n\x10InputShapesEntry\x12\x0b\n\x03key\x18\x01 \x01(\x05\x12\'\n\x05value\x18\x02 \x01(\x0b\x32\x18.tensorflow.tfprof.Tuple:\x02\x38\x01\x1a.\n\x0cOutputsEntry\x12\x0b\n\x03key\x18\x01 \x01(\x05\x12\r\n\x05value\x18\x02 \x01(\x03:\x02\x38\x01\x1aM\n\x11OutputShapesEntry\x12\x0b\n\x03key\x18\x01 \x01(\x05\x12\'\n\x05value\x18\x02 \x01(\x0b\x32\x18.tensorflow.tfprof.Tuple:\x02\x38\x01\x1a\x35\n\x13SrcOutputIndexEntry\x12\x0b\n\x03key\x18\x01 \x01(\x03\x12\r\n\x05value\x18\x02 \x01(\x05:\x02\x38\x01\x1a\x43\n\nAttrsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12$\n\x05value\x18\x02 \x01(\x0b\x32\x15.tensorflow.AttrValue:\x02\x38\x01\x1aL\n\nExecsEntry\x12\x0b\n\x03key\x18\x01 \x01(\x03\x12-\n\x05value\x18\x02 \x01(\x0b\x32\x1e.tensorflow.tfprof.ExecProfile:\x02\x38\x01\"\x84\x04\n\x0b\x45xecProfile\x12\x11\n\trun_count\x18\x01 \x01(\x03\x12\x18\n\x10\x61ll_start_micros\x18\x02 \x01(\x03\x12\x19\n\x11latest_end_micros\x18\x03 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\x01(\x03\x12\x1e\n\x16\x61\x63\x63\x65lerator_temp_bytes\x18\x04 \x01(\x03\x12$\n\x1c\x61\x63\x63\x65lerator_persistent_bytes\x18\x05 \x01(\x03\x12\x17\n\x0frequested_bytes\x18\x06 \x01(\x03\x12\x12\n\npeak_bytes\x18\x07 \x01(\x03\x12\x16\n\x0eresidual_bytes\x18\x08 \x01(\x03\x12\x14\n\x0coutput_bytes\x18\t \x01(\x03\x12\x1e\n\x16\x61llocator_bytes_in_use\x18\n \x01(\x03\x12\x46\n\routput_memory\x18\x0b \x03(\x0b\x32/.tensorflow.tfprof.ExecMemory.OutputMemoryEntry\x1aN\n\x11OutputMemoryEntry\x12\x0b\n\x03key\x18\x01 \x01(\x05\x12(\n\x05value\x18\x02 \x01(\x0b\x32\x19.tensorflow.tfprof.Memory:\x02\x38\x01\"\x1d\n\x05Tuple\x12\x14\n\x0cint64_values\x18\x01 \x03(\x03\"$\n\x06Memory\x12\r\n\x05\x62ytes\x18\x01 \x01(\x03\x12\x0b\n\x03ptr\x18\x02 \x01(\x04\x42RZPgithub.com/tensorflow/tensorflow/tensorflow/go/core/profiler/protos_all_go_protob\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.profiler.tfprof_log_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZPgithub.com/tensorflow/tensorflow/tensorflow/go/core/profiler/protos_all_go_proto'
+  _CODEDEF_TRACE.fields_by_name['file']._options = None
+  _CODEDEF_TRACE.fields_by_name['file']._serialized_options = b'\030\001'
+  _CODEDEF_TRACE.fields_by_name['function']._options = None
+  _CODEDEF_TRACE.fields_by_name['function']._serialized_options = b'\030\001'
+  _CODEDEF_TRACE.fields_by_name['line']._options = None
+  _CODEDEF_TRACE.fields_by_name['line']._serialized_options = b'\030\001'
+  _OPLOGPROTO_IDTOSTRINGENTRY._options = None
+  _OPLOGPROTO_IDTOSTRINGENTRY._serialized_options = b'8\001'
+  _PROFILEPROTO_NODESENTRY._options = None
+  _PROFILEPROTO_NODESENTRY._serialized_options = b'8\001'
+  _PROFILEPROTO_IDTOSTRINGENTRY._options = None
+  _PROFILEPROTO_IDTOSTRINGENTRY._serialized_options = b'8\001'
+  _PROFILENODE_INPUTSENTRY._options = None
+  _PROFILENODE_INPUTSENTRY._serialized_options = b'8\001'
+  _PROFILENODE_INPUTSHAPESENTRY._options = None
+  _PROFILENODE_INPUTSHAPESENTRY._serialized_options = b'8\001'
+  _PROFILENODE_OUTPUTSENTRY._options = None
+  _PROFILENODE_OUTPUTSENTRY._serialized_options = b'8\001'
+  _PROFILENODE_OUTPUTSHAPESENTRY._options = None
+  _PROFILENODE_OUTPUTSHAPESENTRY._serialized_options = b'8\001'
+  _PROFILENODE_SRCOUTPUTINDEXENTRY._options = None
+  _PROFILENODE_SRCOUTPUTINDEXENTRY._serialized_options = b'8\001'
+  _PROFILENODE_ATTRSENTRY._options = None
+  _PROFILENODE_ATTRSENTRY._serialized_options = b'8\001'
+  _PROFILENODE_EXECSENTRY._options = None
+  _PROFILENODE_EXECSENTRY._serialized_options = b'8\001'
+  _EXECPROFILE_ACCELERATOREXECSENTRY._options = None
+  _EXECPROFILE_ACCELERATOREXECSENTRY._serialized_options = b'8\001'
+  _EXECPROFILE_CPUEXECSENTRY._options = None
+  _EXECPROFILE_CPUEXECSENTRY._serialized_options = b'8\001'
+  _EXECMEMORY_OUTPUTMEMORYENTRY._options = None
+  _EXECMEMORY_OUTPUTMEMORYENTRY._serialized_options = b'8\001'
+  _CODEDEF._serialized_start=153
+  _CODEDEF._serialized_end=376
+  _CODEDEF_TRACE._serialized_start=215
+  _CODEDEF_TRACE._serialized_end=376
+  _OPLOGENTRY._serialized_start=378
+  _OPLOGENTRY._serialized_end=484
+  _OPLOGPROTO._serialized_start=487
+  _OPLOGPROTO._serialized_end=671
+  _OPLOGPROTO_IDTOSTRINGENTRY._serialized_start=622
+  _OPLOGPROTO_IDTOSTRINGENTRY._serialized_end=671
+  _PROFILEPROTO._serialized_start=674
+  _PROFILEPROTO._serialized_end=1014
+  _PROFILEPROTO_NODESENTRY._serialized_start=887
+  _PROFILEPROTO_NODESENTRY._serialized_end=963
+  _PROFILEPROTO_IDTOSTRINGENTRY._serialized_start=622
+  _PROFILEPROTO_IDTOSTRINGENTRY._serialized_end=671
+  _PROFILENODE._serialized_start=1017
+  _PROFILENODE._serialized_end=2124
+  _PROFILENODE_INPUTSENTRY._serialized_start=1672
+  _PROFILENODE_INPUTSENTRY._serialized_end=1717
+  _PROFILENODE_INPUTSHAPESENTRY._serialized_start=1719
+  _PROFILENODE_INPUTSHAPESENTRY._serialized_end=1795
+  _PROFILENODE_OUTPUTSENTRY._serialized_start=1797
+  _PROFILENODE_OUTPUTSENTRY._serialized_end=1843
+  _PROFILENODE_OUTPUTSHAPESENTRY._serialized_start=1845
+  _PROFILENODE_OUTPUTSHAPESENTRY._serialized_end=1922
+  _PROFILENODE_SRCOUTPUTINDEXENTRY._serialized_start=1924
+  _PROFILENODE_SRCOUTPUTINDEXENTRY._serialized_end=1977
+  _PROFILENODE_ATTRSENTRY._serialized_start=1979
+  _PROFILENODE_ATTRSENTRY._serialized_end=2046
+  _PROFILENODE_EXECSENTRY._serialized_start=2048
+  _PROFILENODE_EXECSENTRY._serialized_end=2124
+  _EXECPROFILE._serialized_start=2127
+  _EXECPROFILE._serialized_end=2643
+  _EXECPROFILE_ACCELERATOREXECSENTRY._serialized_start=2481
+  _EXECPROFILE_ACCELERATOREXECSENTRY._serialized_end=2565
+  _EXECPROFILE_CPUEXECSENTRY._serialized_start=2567
+  _EXECPROFILE_CPUEXECSENTRY._serialized_end=2643
+  _EXECTIME._serialized_start=2645
+  _EXECTIME._serialized_end=2696
+  _EXECMEMORY._serialized_start=2699
+  _EXECMEMORY._serialized_end=3135
+  _EXECMEMORY_OUTPUTMEMORYENTRY._serialized_start=3057
+  _EXECMEMORY_OUTPUTMEMORYENTRY._serialized_end=3135
+  _TUPLE._serialized_start=3137
+  _TUPLE._serialized_end=3166
+  _MEMORY._serialized_start=3168
+  _MEMORY._serialized_end=3204
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/tfprof_options_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/tfprof_options_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..a19fc363a517d5751f5731fa9e25a476e587806a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/tfprof_options_pb2.py
@@ -0,0 +1,37 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/profiler/tfprof_options.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n-tensorflow/core/profiler/tfprof_options.proto\x12\x11tensorflow.tfprof\"\x95\x04\n\x0cOptionsProto\x12\x11\n\tmax_depth\x18\x01 \x01(\x03\x12\x11\n\tmin_bytes\x18\x02 \x01(\x03\x12\x16\n\x0emin_peak_bytes\x18\x13 \x01(\x03\x12\x1a\n\x12min_residual_bytes\x18\x14 \x01(\x03\x12\x18\n\x10min_output_bytes\x18\x15 \x01(\x03\x12\x12\n\nmin_micros\x18\x03 \x01(\x03\x12\x1e\n\x16min_accelerator_micros\x18\x16 \x01(\x03\x12\x16\n\x0emin_cpu_micros\x18\x17 \x01(\x03\x12\x12\n\nmin_params\x18\x04 \x01(\x03\x12\x15\n\rmin_float_ops\x18\x05 \x01(\x03\x12\x16\n\x0emin_occurrence\x18\x11 \x01(\x03\x12\x0c\n\x04step\x18\x12 \x01(\x03\x12\x10\n\x08order_by\x18\x07 \x01(\t\x12\x1c\n\x14\x61\x63\x63ount_type_regexes\x18\x08 \x03(\t\x12\x1a\n\x12start_name_regexes\x18\t \x03(\t\x12\x19\n\x11trim_name_regexes\x18\n \x03(\t\x12\x19\n\x11show_name_regexes\x18\x0b \x03(\t\x12\x19\n\x11hide_name_regexes\x18\x0c \x03(\t\x12!\n\x19\x61\x63\x63ount_displayed_op_only\x18\r \x01(\x08\x12\x0e\n\x06select\x18\x0e \x03(\t\x12\x0e\n\x06output\x18\x0f \x01(\t\x12\x14\n\x0c\x64ump_to_file\x18\x10 \x01(\t\"\xda\x02\n\x13\x41\x64visorOptionsProto\x12\x46\n\x08\x63heckers\x18\x01 \x03(\x0b\x32\x34.tensorflow.tfprof.AdvisorOptionsProto.CheckersEntry\x1a\x65\n\rCheckersEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x43\n\x05value\x18\x02 \x01(\x0b\x32\x34.tensorflow.tfprof.AdvisorOptionsProto.CheckerOption:\x02\x38\x01\x1a\x93\x01\n\rCheckerOption\x12R\n\x07options\x18\x01 \x03(\x0b\x32\x41.tensorflow.tfprof.AdvisorOptionsProto.CheckerOption.OptionsEntry\x1a.\n\x0cOptionsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.profiler.tfprof_options_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _ADVISOROPTIONSPROTO_CHECKERSENTRY._options = None
+  _ADVISOROPTIONSPROTO_CHECKERSENTRY._serialized_options = b'8\001'
+  _ADVISOROPTIONSPROTO_CHECKEROPTION_OPTIONSENTRY._options = None
+  _ADVISOROPTIONSPROTO_CHECKEROPTION_OPTIONSENTRY._serialized_options = b'8\001'
+  _OPTIONSPROTO._serialized_start=69
+  _OPTIONSPROTO._serialized_end=602
+  _ADVISOROPTIONSPROTO._serialized_start=605
+  _ADVISOROPTIONSPROTO._serialized_end=951
+  _ADVISOROPTIONSPROTO_CHECKERSENTRY._serialized_start=700
+  _ADVISOROPTIONSPROTO_CHECKERSENTRY._serialized_end=801
+  _ADVISOROPTIONSPROTO_CHECKEROPTION._serialized_start=804
+  _ADVISOROPTIONSPROTO_CHECKEROPTION._serialized_end=951
+  _ADVISOROPTIONSPROTO_CHECKEROPTION_OPTIONSENTRY._serialized_start=905
+  _ADVISOROPTIONSPROTO_CHECKEROPTION_OPTIONSENTRY._serialized_end=951
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/tfprof_output_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/tfprof_output_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..09c654f61271c9c7ba85574ad662b5001e140d12
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/profiler/tfprof_output_pb2.py
@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/profiler/tfprof_output.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n,tensorflow/core/profiler/tfprof_output.proto\x12\x11tensorflow.tfprof\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\"v\n\x11TFProfTensorProto\x12#\n\x05\x64type\x18\x01 \x01(\x0e\x32\x14.tensorflow.DataType\x12\x14\n\x0cvalue_double\x18\x02 \x03(\x01\x12\x13\n\x0bvalue_int64\x18\x03 \x03(\x03\x12\x11\n\tvalue_str\x18\x04 \x03(\t\"\x8e\x07\n\x0eGraphNodeProto\x12\x0c\n\x04name\x18\x01 \x01(\t\x12:\n\x0ctensor_value\x18\x0f \x01(\x0b\x32$.tensorflow.tfprof.TFProfTensorProto\x12\x11\n\trun_count\x18\x15 \x01(\x03\x12\x13\n\x0b\x65xec_micros\x18\x02 \x01(\x03\x12\x1f\n\x17\x61\x63\x63\x65lerator_exec_micros\x18\x11 \x01(\x03\x12\x17\n\x0f\x63pu_exec_micros\x18\x12 \x01(\x03\x12\x17\n\x0frequested_bytes\x18\x03 \x01(\x03\x12\x12\n\npeak_bytes\x18\x18 \x01(\x03\x12\x16\n\x0eresidual_bytes\x18\x19 \x01(\x03\x12\x14\n\x0coutput_bytes\x18\x1a \x01(\x03\x12\x12\n\nparameters\x18\x04 \x01(\x03\x12\x11\n\tfloat_ops\x18\r \x01(\x03\x12\x0f\n\x07\x64\x65vices\x18\n \x03(\t\x12\x1e\n\x16total_definition_count\x18\x17 \x01(\x03\x12\x17\n\x0ftotal_run_count\x18\x16 \x01(\x03\x12\x19\n\x11total_exec_micros\x18\x06 \x01(\x03\x12%\n\x1dtotal_accelerator_exec_micros\x18\x13 \x01(\x03\x12\x1d\n\x15total_cpu_exec_micros\x18\x14 \x01(\x03\x12\x1d\n\x15total_requested_bytes\x18\x07 \x01(\x03\x12\x18\n\x10total_peak_bytes\x18\x1b \x01(\x03\x12\x1c\n\x14total_residual_bytes\x18\x1c \x01(\x03\x12\x1a\n\x12total_output_bytes\x18\x1d \x01(\x03\x12\x18\n\x10total_parameters\x18\x08 \x01(\x03\x12\x17\n\x0ftotal_float_ops\x18\x0e \x01(\x03\x12,\n\x06shapes\x18\x0b \x03(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12H\n\x0cinput_shapes\x18\x10 \x03(\x0b\x32\x32.tensorflow.tfprof.GraphNodeProto.InputShapesEntry\x12\x33\n\x08\x63hildren\x18\x0c \x03(\x0b\x32!.tensorflow.tfprof.GraphNodeProto\x1aP\n\x10InputShapesEntry\x12\x0b\n\x03key\x18\x01 \x01(\x05\x12+\n\x05value\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto:\x02\x38\x01\"\xed\x04\n\x13MultiGraphNodeProto\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x13\n\x0b\x65xec_micros\x18\x02 \x01(\x03\x12\x1f\n\x17\x61\x63\x63\x65lerator_exec_micros\x18\x0c \x01(\x03\x12\x17\n\x0f\x63pu_exec_micros\x18\r \x01(\x03\x12\x17\n\x0frequested_bytes\x18\x03 \x01(\x03\x12\x12\n\npeak_bytes\x18\x10 \x01(\x03\x12\x16\n\x0eresidual_bytes\x18\x11 \x01(\x03\x12\x14\n\x0coutput_bytes\x18\x12 \x01(\x03\x12\x12\n\nparameters\x18\x04 \x01(\x03\x12\x11\n\tfloat_ops\x18\x05 \x01(\x03\x12\x19\n\x11total_exec_micros\x18\x06 \x01(\x03\x12%\n\x1dtotal_accelerator_exec_micros\x18\x0e \x01(\x03\x12\x1d\n\x15total_cpu_exec_micros\x18\x0f \x01(\x03\x12\x1d\n\x15total_requested_bytes\x18\x07 \x01(\x03\x12\x18\n\x10total_peak_bytes\x18\x13 \x01(\x03\x12\x1c\n\x14total_residual_bytes\x18\x14 \x01(\x03\x12\x1a\n\x12total_output_bytes\x18\x15 \x01(\x03\x12\x18\n\x10total_parameters\x18\x08 \x01(\x03\x12\x17\n\x0ftotal_float_ops\x18\t \x01(\x03\x12\x36\n\x0bgraph_nodes\x18\n \x03(\x0b\x32!.tensorflow.tfprof.GraphNodeProto\x12\x38\n\x08\x63hildren\x18\x0b \x03(\x0b\x32&.tensorflow.tfprof.MultiGraphNodeProto\"\xc2\x01\n\x0b\x41\x64viceProto\x12>\n\x08\x63heckers\x18\x01 \x03(\x0b\x32,.tensorflow.tfprof.AdviceProto.CheckersEntry\x1aW\n\rCheckersEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x35\n\x05value\x18\x02 \x01(\x0b\x32&.tensorflow.tfprof.AdviceProto.Checker:\x02\x38\x01\x1a\x1a\n\x07\x43hecker\x12\x0f\n\x07reports\x18\x02 \x03(\tb\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.profiler.tfprof_output_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _GRAPHNODEPROTO_INPUTSHAPESENTRY._options = None
+  _GRAPHNODEPROTO_INPUTSHAPESENTRY._serialized_options = b'8\001'
+  _ADVICEPROTO_CHECKERSENTRY._options = None
+  _ADVICEPROTO_CHECKERSENTRY._serialized_options = b'8\001'
+  _TFPROFTENSORPROTO._serialized_start=152
+  _TFPROFTENSORPROTO._serialized_end=270
+  _GRAPHNODEPROTO._serialized_start=273
+  _GRAPHNODEPROTO._serialized_end=1183
+  _GRAPHNODEPROTO_INPUTSHAPESENTRY._serialized_start=1103
+  _GRAPHNODEPROTO_INPUTSHAPESENTRY._serialized_end=1183
+  _MULTIGRAPHNODEPROTO._serialized_start=1186
+  _MULTIGRAPHNODEPROTO._serialized_end=1807
+  _ADVICEPROTO._serialized_start=1810
+  _ADVICEPROTO._serialized_end=2004
+  _ADVICEPROTO_CHECKERSENTRY._serialized_start=1889
+  _ADVICEPROTO_CHECKERSENTRY._serialized_end=1976
+  _ADVICEPROTO_CHECKER._serialized_start=1978
+  _ADVICEPROTO_CHECKER._serialized_end=2004
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/bfc_memory_map_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/bfc_memory_map_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..228e9d87ab1af59ca05c5eaaa212ac62ca540de2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/bfc_memory_map_pb2.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/bfc_memory_map.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.tsl.protobuf import bfc_memory_map_pb2 as tsl_dot_protobuf_dot_bfc__memory__map__pb2
+
+from tensorflow.tsl.protobuf.bfc_memory_map_pb2 import *
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n-tensorflow/core/protobuf/bfc_memory_map.proto\x12\x10tensorflow.dummy\x1a!tsl/protobuf/bfc_memory_map.protoBWZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_protoP\x00\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.bfc_memory_map_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto'
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/cluster_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/cluster_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e63ea73b3f4382cb83dc982d972bf874370b7cd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/cluster_pb2.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/cluster.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n&tensorflow/core/protobuf/cluster.proto\x12\ntensorflow\"r\n\x06JobDef\x12\x0c\n\x04name\x18\x01 \x01(\t\x12,\n\x05tasks\x18\x02 \x03(\x0b\x32\x1d.tensorflow.JobDef.TasksEntry\x1a,\n\nTasksEntry\x12\x0b\n\x03key\x18\x01 \x01(\x05\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"-\n\nClusterDef\x12\x1f\n\x03job\x18\x01 \x03(\x0b\x32\x12.tensorflow.JobDefB\x87\x01\n\x1aorg.tensorflow.distruntimeB\rClusterProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.cluster_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\032org.tensorflow.distruntimeB\rClusterProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _JOBDEF_TASKSENTRY._options = None
+  _JOBDEF_TASKSENTRY._serialized_options = b'8\001'
+  _JOBDEF._serialized_start=54
+  _JOBDEF._serialized_end=168
+  _JOBDEF_TASKSENTRY._serialized_start=124
+  _JOBDEF_TASKSENTRY._serialized_end=168
+  _CLUSTERDEF._serialized_start=170
+  _CLUSTERDEF._serialized_end=215
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/composite_tensor_variant_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/composite_tensor_variant_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..de1a9b6c5060edeb52bc55b5aaf6d04a1452ccfd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/composite_tensor_variant_pb2.py
@@ -0,0 +1,27 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/composite_tensor_variant.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.protobuf import struct_pb2 as tensorflow_dot_core_dot_protobuf_dot_struct__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n7tensorflow/core/protobuf/composite_tensor_variant.proto\x12\ntensorflow\x1a%tensorflow/core/protobuf/struct.proto\"T\n\x1e\x43ompositeTensorVariantMetadata\x12\x32\n\x0ftype_spec_proto\x18\x01 \x01(\x0b\x32\x19.tensorflow.TypeSpecProtoBWZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_protob\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.composite_tensor_variant_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto'
+  _COMPOSITETENSORVARIANTMETADATA._serialized_start=110
+  _COMPOSITETENSORVARIANTMETADATA._serialized_end=194
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/config_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/config_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd242d57780e374f7793dd60b22922d0eeaeb6c1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/config_pb2.py
@@ -0,0 +1,88 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/config.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import cost_graph_pb2 as tensorflow_dot_core_dot_framework_dot_cost__graph__pb2
+from tensorflow.core.framework import graph_pb2 as tensorflow_dot_core_dot_framework_dot_graph__pb2
+from tensorflow.core.framework import step_stats_pb2 as tensorflow_dot_core_dot_framework_dot_step__stats__pb2
+from tensorflow.core.protobuf import cluster_pb2 as tensorflow_dot_core_dot_protobuf_dot_cluster__pb2
+from tensorflow.core.protobuf import debug_pb2 as tensorflow_dot_core_dot_protobuf_dot_debug__pb2
+from tensorflow.core.protobuf import rewriter_config_pb2 as tensorflow_dot_core_dot_protobuf_dot_rewriter__config__pb2
+from tensorflow.core.protobuf import rpc_options_pb2 as tensorflow_dot_core_dot_protobuf_dot_rpc__options__pb2
+try:
+  tsl_dot_protobuf_dot_rpc__options__pb2 = tensorflow_dot_core_dot_protobuf_dot_rpc__options__pb2.tsl_dot_protobuf_dot_rpc__options__pb2
+except AttributeError:
+  tsl_dot_protobuf_dot_rpc__options__pb2 = tensorflow_dot_core_dot_protobuf_dot_rpc__options__pb2.tsl.protobuf.rpc_options_pb2
+from tensorflow.tsl.protobuf import coordination_config_pb2 as tsl_dot_protobuf_dot_coordination__config__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n%tensorflow/core/protobuf/config.proto\x12\ntensorflow\x1a*tensorflow/core/framework/cost_graph.proto\x1a%tensorflow/core/framework/graph.proto\x1a*tensorflow/core/framework/step_stats.proto\x1a&tensorflow/core/protobuf/cluster.proto\x1a$tensorflow/core/protobuf/debug.proto\x1a.tensorflow/core/protobuf/rewriter_config.proto\x1a*tensorflow/core/protobuf/rpc_options.proto\x1a&tsl/protobuf/coordination_config.proto\"\xea\x07\n\nGPUOptions\x12\'\n\x1fper_process_gpu_memory_fraction\x18\x01 \x01(\x01\x12\x14\n\x0c\x61llow_growth\x18\x04 \x01(\x08\x12\x16\n\x0e\x61llocator_type\x18\x02 \x01(\t\x12\x1f\n\x17\x64\x65\x66\x65rred_deletion_bytes\x18\x03 \x01(\x03\x12\x1b\n\x13visible_device_list\x18\x05 \x01(\t\x12\"\n\x1apolling_active_delay_usecs\x18\x06 \x01(\x05\x12$\n\x1cpolling_inactive_delay_msecs\x18\x07 \x01(\x05\x12\x1c\n\x14\x66orce_gpu_compatible\x18\x08 \x01(\x08\x12\x39\n\x0c\x65xperimental\x18\t \x01(\x0b\x32#.tensorflow.GPUOptions.Experimental\x1a\xa3\x05\n\x0c\x45xperimental\x12K\n\x0fvirtual_devices\x18\x01 \x03(\x0b\x32\x32.tensorflow.GPUOptions.Experimental.VirtualDevices\x12#\n\x1bnum_virtual_devices_per_gpu\x18\x0f \x01(\x05\x12\x1a\n\x12use_unified_memory\x18\x02 \x01(\x08\x12#\n\x1bnum_dev_to_dev_copy_streams\x18\x03 \x01(\x05\x12\x1d\n\x15\x63ollective_ring_order\x18\x04 \x01(\t\x12\x1d\n\x15timestamped_allocator\x18\x05 \x01(\x08\x12#\n\x1bkernel_tracker_max_interval\x18\x07 \x01(\x05\x12 \n\x18kernel_tracker_max_bytes\x18\x08 \x01(\x05\x12\"\n\x1akernel_tracker_max_pending\x18\t \x01(\x05\x12\'\n\x1finternal_fragmentation_fraction\x18\n \x01(\x01\x12\x1d\n\x15use_cuda_malloc_async\x18\x0b \x01(\x08\x12,\n$disallow_retry_on_allocation_failure\x18\x0c \x01(\x08\x12 \n\x18gpu_host_mem_limit_in_mb\x18\r \x01(\x02\x12$\n\x1cgpu_host_mem_disallow_growth\x18\x0e \x01(\x08\x12$\n\x1cgpu_system_memory_size_in_mb\x18\x10 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+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.config_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\014ConfigProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _CONFIGPROTO_DEVICECOUNTENTRY._options = None
+  _CONFIGPROTO_DEVICECOUNTENTRY._serialized_options = b'8\001'
+  _CALLABLEOPTIONS_FEEDDEVICESENTRY._options = None
+  _CALLABLEOPTIONS_FEEDDEVICESENTRY._serialized_options = b'8\001'
+  _CALLABLEOPTIONS_FETCHDEVICESENTRY._options = None
+  _CALLABLEOPTIONS_FETCHDEVICESENTRY._serialized_options = b'8\001'
+  _GPUOPTIONS._serialized_start=391
+  _GPUOPTIONS._serialized_end=1393
+  _GPUOPTIONS_EXPERIMENTAL._serialized_start=718
+  _GPUOPTIONS_EXPERIMENTAL._serialized_end=1393
+  _GPUOPTIONS_EXPERIMENTAL_VIRTUALDEVICES._serialized_start=1310
+  _GPUOPTIONS_EXPERIMENTAL_VIRTUALDEVICES._serialized_end=1393
+  _OPTIMIZEROPTIONS._serialized_start=1396
+  _OPTIMIZEROPTIONS._serialized_end=1809
+  _OPTIMIZEROPTIONS_LEVEL._serialized_start=1708
+  _OPTIMIZEROPTIONS_LEVEL._serialized_end=1740
+  _OPTIMIZEROPTIONS_GLOBALJITLEVEL._serialized_start=1742
+  _OPTIMIZEROPTIONS_GLOBALJITLEVEL._serialized_end=1809
+  _GRAPHOPTIONS._serialized_start=1812
+  _GRAPHOPTIONS._serialized_end=2178
+  _THREADPOOLOPTIONPROTO._serialized_start=2180
+  _THREADPOOLOPTIONPROTO._serialized_end=2245
+  _SESSIONMETADATA._serialized_start=2247
+  _SESSIONMETADATA._serialized_end=2295
+  _CONFIGPROTO._serialized_start=2298
+  _CONFIGPROTO._serialized_end=4239
+  _CONFIGPROTO_DEVICECOUNTENTRY._serialized_start=3019
+  _CONFIGPROTO_DEVICECOUNTENTRY._serialized_end=3069
+  _CONFIGPROTO_EXPERIMENTAL._serialized_start=3072
+  _CONFIGPROTO_EXPERIMENTAL._serialized_end=4239
+  _CONFIGPROTO_EXPERIMENTAL_MLIRBRIDGEROLLOUT._serialized_start=3993
+  _CONFIGPROTO_EXPERIMENTAL_MLIRBRIDGEROLLOUT._serialized_end=4215
+  _RUNOPTIONS._serialized_start=4242
+  _RUNOPTIONS._serialized_end=4851
+  _RUNOPTIONS_EXPERIMENTAL._serialized_start=4551
+  _RUNOPTIONS_EXPERIMENTAL._serialized_end=4761
+  _RUNOPTIONS_EXPERIMENTAL_RUNHANDLERPOOLOPTIONS._serialized_start=4720
+  _RUNOPTIONS_EXPERIMENTAL_RUNHANDLERPOOLOPTIONS._serialized_end=4761
+  _RUNOPTIONS_TRACELEVEL._serialized_start=4763
+  _RUNOPTIONS_TRACELEVEL._serialized_end=4845
+  _RUNMETADATA._serialized_start=4854
+  _RUNMETADATA._serialized_end=5300
+  _RUNMETADATA_FUNCTIONGRAPHS._serialized_start=5127
+  _RUNMETADATA_FUNCTIONGRAPHS._serialized_end=5300
+  _TENSORCONNECTION._serialized_start=5302
+  _TENSORCONNECTION._serialized_end=5360
+  _CALLABLEOPTIONS._serialized_start=5363
+  _CALLABLEOPTIONS._serialized_end=5795
+  _CALLABLEOPTIONS_FEEDDEVICESENTRY._serialized_start=5692
+  _CALLABLEOPTIONS_FEEDDEVICESENTRY._serialized_end=5742
+  _CALLABLEOPTIONS_FETCHDEVICESENTRY._serialized_start=5744
+  _CALLABLEOPTIONS_FETCHDEVICESENTRY._serialized_end=5795
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/control_flow_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/control_flow_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..09a96b54833b0e77b395e37ba90758f5c48b6938
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/control_flow_pb2.py
@@ -0,0 +1,36 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/control_flow.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n+tensorflow/core/protobuf/control_flow.proto\x12\ntensorflow\"\x96\x01\n\tValuesDef\x12\x0e\n\x06values\x18\x01 \x03(\t\x12\x42\n\x0f\x65xternal_values\x18\x02 \x03(\x0b\x32).tensorflow.ValuesDef.ExternalValuesEntry\x1a\x35\n\x13\x45xternalValuesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"\x83\x01\n\x15\x43ontrolFlowContextDef\x12/\n\tcond_ctxt\x18\x01 \x01(\x0b\x32\x1a.tensorflow.CondContextDefH\x00\x12\x31\n\nwhile_ctxt\x18\x02 \x01(\x0b\x32\x1b.tensorflow.WhileContextDefH\x00\x42\x06\n\x04\x63txt\"\xc4\x01\n\x0e\x43ondContextDef\x12\x14\n\x0c\x63ontext_name\x18\x01 \x01(\t\x12\x11\n\tpred_name\x18\x02 \x01(\t\x12\x12\n\npivot_name\x18\x03 \x01(\t\x12\x0e\n\x06\x62ranch\x18\x04 \x01(\x05\x12)\n\nvalues_def\x18\x05 \x01(\x0b\x32\x15.tensorflow.ValuesDef\x12:\n\x0fnested_contexts\x18\x06 \x03(\x0b\x32!.tensorflow.ControlFlowContextDef\"\xf5\x02\n\x0fWhileContextDef\x12\x14\n\x0c\x63ontext_name\x18\x01 \x01(\t\x12\x1b\n\x13parallel_iterations\x18\x02 \x01(\x05\x12\x11\n\tback_prop\x18\x03 \x01(\x08\x12\x13\n\x0bswap_memory\x18\x04 \x01(\x08\x12\x12\n\npivot_name\x18\x05 \x01(\t\x12\x1b\n\x13pivot_for_pred_name\x18\x06 \x01(\t\x12\x1b\n\x13pivot_for_body_name\x18\x07 \x01(\t\x12\x17\n\x0floop_exit_names\x18\x08 \x03(\t\x12\x18\n\x10loop_enter_names\x18\n \x03(\t\x12)\n\nvalues_def\x18\t \x01(\x0b\x32\x15.tensorflow.ValuesDef\x12\x1f\n\x17maximum_iterations_name\x18\x0b \x01(\t\x12:\n\x0fnested_contexts\x18\x0c \x03(\x0b\x32!.tensorflow.ControlFlowContextDefB\x89\x01\n\x18org.tensorflow.frameworkB\x11\x43ontrolFlowProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.control_flow_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\021ControlFlowProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _VALUESDEF_EXTERNALVALUESENTRY._options = None
+  _VALUESDEF_EXTERNALVALUESENTRY._serialized_options = b'8\001'
+  _VALUESDEF._serialized_start=60
+  _VALUESDEF._serialized_end=210
+  _VALUESDEF_EXTERNALVALUESENTRY._serialized_start=157
+  _VALUESDEF_EXTERNALVALUESENTRY._serialized_end=210
+  _CONTROLFLOWCONTEXTDEF._serialized_start=213
+  _CONTROLFLOWCONTEXTDEF._serialized_end=344
+  _CONDCONTEXTDEF._serialized_start=347
+  _CONDCONTEXTDEF._serialized_end=543
+  _WHILECONTEXTDEF._serialized_start=546
+  _WHILECONTEXTDEF._serialized_end=919
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/core_platform_payloads_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/core_platform_payloads_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..21f39455ddaaccb7f94e74812a395572f6c55ef4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/core_platform_payloads_pb2.py
@@ -0,0 +1,28 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/core_platform_payloads.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n5tensorflow/core/protobuf/core_platform_payloads.proto\x12\x18tensorflow.core.platform\"\xec\x01\n\x10\x45rrorSourceProto\x12L\n\x0c\x65rror_source\x18\x01 \x01(\x0e\x32\x36.tensorflow.core.platform.ErrorSourceProto.ErrorSource\"\x89\x01\n\x0b\x45rrorSource\x12\x0b\n\x07UNKNOWN\x10\x00\x12\x12\n\x0eTPU_COMPILE_OP\x10\x01\x12\x11\n\rTF_XLA_BRIDGE\x10\x02\x12\x17\n\x13MLIR_BRIDGE_PHASE_1\x10\x03\x12\x17\n\x13MLIR_BRIDGE_PHASE_2\x10\x04\x12\x14\n\x10\x45\x41GER_REMOTE_MGR\x10\x05\x42ZZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.core_platform_payloads_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _ERRORSOURCEPROTO._serialized_start=84
+  _ERRORSOURCEPROTO._serialized_end=320
+  _ERRORSOURCEPROTO_ERRORSOURCE._serialized_start=183
+  _ERRORSOURCEPROTO_ERRORSOURCE._serialized_end=320
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/data_service_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/data_service_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..a332dd20edb792d9012b7b01b767f3f18c09757f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/data_service_pb2.py
@@ -0,0 +1,38 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/data_service.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n+tensorflow/core/protobuf/data_service.proto\x12\x0ftensorflow.data\"\xb7\x01\n\x11ProcessingModeDef\x12J\n\x0fsharding_policy\x18\x01 \x01(\x0e\x32\x31.tensorflow.data.ProcessingModeDef.ShardingPolicy\"V\n\x0eShardingPolicy\x12\x07\n\x03OFF\x10\x00\x12\x0b\n\x07\x44YNAMIC\x10\x01\x12\x08\n\x04\x46ILE\x10\x02\x12\x08\n\x04\x44\x41TA\x10\x03\x12\x10\n\x0c\x46ILE_OR_DATA\x10\x04\x12\x08\n\x04HINT\x10\x05\"\xfb\x01\n\x13\x44\x61taServiceMetadata\x12\x16\n\x0c\x65lement_spec\x18\x01 \x01(\x0cH\x00\x12\x45\n\x0b\x63ompression\x18\x02 \x01(\x0e\x32\x30.tensorflow.data.DataServiceMetadata.Compression\x12\x13\n\x0b\x63\x61rdinality\x18\x03 \x01(\x03\"W\n\x0b\x43ompression\x12\x1b\n\x17\x43OMPRESSION_UNSPECIFIED\x10\x00\x12\x13\n\x0f\x43OMPRESSION_OFF\x10\x01\x12\x16\n\x12\x43OMPRESSION_SNAPPY\x10\x02\x42\x17\n\x15optional_element_spec\".\n\x18\x43rossTrainerCacheOptions\x12\x12\n\ntrainer_id\x18\x01 \x01(\t\"M\n\x11\x44\x61taServiceConfig\x12\x38\n\x0f\x64\x65ployment_mode\x18\x01 \x01(\x0e\x32\x1f.tensorflow.data.DeploymentMode*\x88\x01\n\x0e\x44\x65ploymentMode\x12\x1f\n\x1b\x44\x45PLOYMENT_MODE_UNSPECIFIED\x10\x00\x12\x1d\n\x19\x44\x45PLOYMENT_MODE_COLOCATED\x10\x01\x12\x1a\n\x16\x44\x45PLOYMENT_MODE_REMOTE\x10\x02\x12\x1a\n\x16\x44\x45PLOYMENT_MODE_HYBRID\x10\x03\x42WZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_protob\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.data_service_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto'
+  _DEPLOYMENTMODE._serialized_start=632
+  _DEPLOYMENTMODE._serialized_end=768
+  _PROCESSINGMODEDEF._serialized_start=65
+  _PROCESSINGMODEDEF._serialized_end=248
+  _PROCESSINGMODEDEF_SHARDINGPOLICY._serialized_start=162
+  _PROCESSINGMODEDEF_SHARDINGPOLICY._serialized_end=248
+  _DATASERVICEMETADATA._serialized_start=251
+  _DATASERVICEMETADATA._serialized_end=502
+  _DATASERVICEMETADATA_COMPRESSION._serialized_start=390
+  _DATASERVICEMETADATA_COMPRESSION._serialized_end=477
+  _CROSSTRAINERCACHEOPTIONS._serialized_start=504
+  _CROSSTRAINERCACHEOPTIONS._serialized_end=550
+  _DATASERVICECONFIG._serialized_start=552
+  _DATASERVICECONFIG._serialized_end=629
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/debug_event_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/debug_event_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b456916b657a4adc2ca9ec8501faa835ed52f45
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/debug_event_pb2.py
@@ -0,0 +1,48 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/debug_event.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import graph_debug_info_pb2 as tensorflow_dot_core_dot_framework_dot_graph__debug__info__pb2
+from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n*tensorflow/core/protobuf/debug_event.proto\x12\ntensorflow\x1a\x30tensorflow/core/framework/graph_debug_info.proto\x1a&tensorflow/core/framework/tensor.proto\"\xfe\x03\n\nDebugEvent\x12\x11\n\twall_time\x18\x01 \x01(\x01\x12\x0c\n\x04step\x18\x02 \x01(\x03\x12\x33\n\x0e\x64\x65\x62ug_metadata\x18\x03 \x01(\x0b\x32\x19.tensorflow.DebugMetadataH\x00\x12-\n\x0bsource_file\x18\x04 \x01(\x0b\x32\x16.tensorflow.SourceFileH\x00\x12;\n\x13stack_frame_with_id\x18\x06 \x01(\x0b\x32\x1c.tensorflow.StackFrameWithIdH\x00\x12\x38\n\x11graph_op_creation\x18\x07 \x01(\x0b\x32\x1b.tensorflow.GraphOpCreationH\x00\x12\x33\n\x0e\x64\x65\x62ugged_graph\x18\x08 \x01(\x0b\x32\x19.tensorflow.DebuggedGraphH\x00\x12*\n\texecution\x18\t \x01(\x0b\x32\x15.tensorflow.ExecutionH\x00\x12@\n\x15graph_execution_trace\x18\n \x01(\x0b\x32\x1f.tensorflow.GraphExecutionTraceH\x00\x12\x12\n\x08graph_id\x18\x0b \x01(\tH\x00\x12\x35\n\x0f\x64\x65\x62ugged_device\x18\x0c \x01(\x0b\x32\x1a.tensorflow.DebuggedDeviceH\x00\x42\x06\n\x04what\"W\n\rDebugMetadata\x12\x1a\n\x12tensorflow_version\x18\x01 \x01(\t\x12\x14\n\x0c\x66ile_version\x18\x02 \x01(\t\x12\x14\n\x0ctfdbg_run_id\x18\x03 \x01(\t\"A\n\nSourceFile\x12\x11\n\tfile_path\x18\x01 \x01(\t\x12\x11\n\thost_name\x18\x02 \x01(\t\x12\r\n\x05lines\x18\x03 \x03(\t\"]\n\x10StackFrameWithId\x12\n\n\x02id\x18\x01 \x01(\t\x12=\n\rfile_line_col\x18\x02 \x01(\x0b\x32&.tensorflow.GraphDebugInfo.FileLineCol\":\n\x0c\x43odeLocation\x12\x11\n\thost_name\x18\x01 \x01(\t\x12\x17\n\x0fstack_frame_ids\x18\x02 \x03(\t\"\xe4\x01\n\x0fGraphOpCreation\x12\x0f\n\x07op_type\x18\x01 \x01(\t\x12\x0f\n\x07op_name\x18\x02 \x01(\t\x12\x12\n\ngraph_name\x18\x03 \x01(\t\x12\x10\n\x08graph_id\x18\x04 \x01(\t\x12\x13\n\x0b\x64\x65vice_name\x18\x05 \x01(\t\x12\x13\n\x0binput_names\x18\x06 \x03(\t\x12\x13\n\x0bnum_outputs\x18\x07 \x01(\x05\x12/\n\rcode_location\x18\x08 \x01(\x0b\x32\x18.tensorflow.CodeLocation\x12\x19\n\x11output_tensor_ids\x18\t \x03(\x05\"\xa5\x01\n\rDebuggedGraph\x12\x10\n\x08graph_id\x18\x01 \x01(\t\x12\x12\n\ngraph_name\x18\x02 \x01(\t\x12\x18\n\x10instrumented_ops\x18\x03 \x03(\t\x12\x1a\n\x12original_graph_def\x18\x04 \x01(\x0c\x12\x1e\n\x16instrumented_graph_def\x18\x05 \x01(\x0c\x12\x18\n\x10outer_context_id\x18\x06 \x01(\t\"8\n\x0e\x44\x65\x62uggedDevice\x12\x13\n\x0b\x64\x65vice_name\x18\x01 \x01(\t\x12\x11\n\tdevice_id\x18\x02 \x01(\x05\"\xb3\x02\n\tExecution\x12\x0f\n\x07op_type\x18\x01 \x01(\t\x12\x13\n\x0bnum_outputs\x18\x02 \x01(\x05\x12\x10\n\x08graph_id\x18\x03 \x01(\t\x12\x18\n\x10input_tensor_ids\x18\x04 \x03(\x03\x12\x19\n\x11output_tensor_ids\x18\x05 \x03(\x03\x12\x36\n\x11tensor_debug_mode\x18\x06 \x01(\x0e\x32\x1b.tensorflow.TensorDebugMode\x12.\n\rtensor_protos\x18\x07 \x03(\x0b\x32\x17.tensorflow.TensorProto\x12/\n\rcode_location\x18\x08 \x01(\x0b\x32\x18.tensorflow.CodeLocation\x12 \n\x18output_tensor_device_ids\x18\t \x03(\x05\"\xd1\x01\n\x13GraphExecutionTrace\x12\x18\n\x10tfdbg_context_id\x18\x01 \x01(\t\x12\x0f\n\x07op_name\x18\x02 \x01(\t\x12\x13\n\x0boutput_slot\x18\x03 \x01(\x05\x12\x36\n\x11tensor_debug_mode\x18\x04 \x01(\x0e\x32\x1b.tensorflow.TensorDebugMode\x12-\n\x0ctensor_proto\x18\x05 \x01(\x0b\x32\x17.tensorflow.TensorProto\x12\x13\n\x0b\x64\x65vice_name\x18\x06 \x01(\t*\xb6\x01\n\x0fTensorDebugMode\x12\x0f\n\x0bUNSPECIFIED\x10\x00\x12\r\n\tNO_TENSOR\x10\x01\x12\x0f\n\x0b\x43URT_HEALTH\x10\x02\x12\x12\n\x0e\x43ONCISE_HEALTH\x10\x03\x12\x0f\n\x0b\x46ULL_HEALTH\x10\x04\x12\t\n\x05SHAPE\x10\x05\x12\x11\n\rFULL_NUMERICS\x10\x06\x12\x0f\n\x0b\x46ULL_TENSOR\x10\x07\x12\x1e\n\x1aREDUCE_INF_NAN_THREE_SLOTS\x10\x08\x42\x83\x01\n\x13org.tensorflow.utilB\x10\x44\x65\x62ugEventProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.debug_event_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\023org.tensorflow.utilB\020DebugEventProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _TENSORDEBUGMODE._serialized_start=1952
+  _TENSORDEBUGMODE._serialized_end=2134
+  _DEBUGEVENT._serialized_start=149
+  _DEBUGEVENT._serialized_end=659
+  _DEBUGMETADATA._serialized_start=661
+  _DEBUGMETADATA._serialized_end=748
+  _SOURCEFILE._serialized_start=750
+  _SOURCEFILE._serialized_end=815
+  _STACKFRAMEWITHID._serialized_start=817
+  _STACKFRAMEWITHID._serialized_end=910
+  _CODELOCATION._serialized_start=912
+  _CODELOCATION._serialized_end=970
+  _GRAPHOPCREATION._serialized_start=973
+  _GRAPHOPCREATION._serialized_end=1201
+  _DEBUGGEDGRAPH._serialized_start=1204
+  _DEBUGGEDGRAPH._serialized_end=1369
+  _DEBUGGEDDEVICE._serialized_start=1371
+  _DEBUGGEDDEVICE._serialized_end=1427
+  _EXECUTION._serialized_start=1430
+  _EXECUTION._serialized_end=1737
+  _GRAPHEXECUTIONTRACE._serialized_start=1740
+  _GRAPHEXECUTIONTRACE._serialized_end=1949
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/debug_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/debug_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..dde7cc67cc9b985113c38f05ca85e3c8724d57fd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/debug_pb2.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/debug.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n$tensorflow/core/protobuf/debug.proto\x12\ntensorflow\"\x8e\x01\n\x10\x44\x65\x62ugTensorWatch\x12\x11\n\tnode_name\x18\x01 \x01(\t\x12\x13\n\x0boutput_slot\x18\x02 \x01(\x05\x12\x11\n\tdebug_ops\x18\x03 \x03(\t\x12\x12\n\ndebug_urls\x18\x04 \x03(\t\x12+\n#tolerate_debug_op_creation_failures\x18\x05 \x01(\x08\"\x81\x01\n\x0c\x44\x65\x62ugOptions\x12=\n\x17\x64\x65\x62ug_tensor_watch_opts\x18\x04 \x03(\x0b\x32\x1c.tensorflow.DebugTensorWatch\x12\x13\n\x0bglobal_step\x18\n \x01(\x03\x12\x1d\n\x15reset_disk_byte_usage\x18\x0b \x01(\x08\"j\n\x12\x44\x65\x62uggedSourceFile\x12\x0c\n\x04host\x18\x01 \x01(\t\x12\x11\n\tfile_path\x18\x02 \x01(\t\x12\x15\n\rlast_modified\x18\x03 \x01(\x03\x12\r\n\x05\x62ytes\x18\x04 \x01(\x03\x12\r\n\x05lines\x18\x05 \x03(\t\"K\n\x13\x44\x65\x62uggedSourceFiles\x12\x34\n\x0csource_files\x18\x01 \x03(\x0b\x32\x1e.tensorflow.DebuggedSourceFileB\x83\x01\n\x18org.tensorflow.frameworkB\x0b\x44\x65\x62ugProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.debug_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\013DebugProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _DEBUGTENSORWATCH._serialized_start=53
+  _DEBUGTENSORWATCH._serialized_end=195
+  _DEBUGOPTIONS._serialized_start=198
+  _DEBUGOPTIONS._serialized_end=327
+  _DEBUGGEDSOURCEFILE._serialized_start=329
+  _DEBUGGEDSOURCEFILE._serialized_end=435
+  _DEBUGGEDSOURCEFILES._serialized_start=437
+  _DEBUGGEDSOURCEFILES._serialized_end=512
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/device_filters_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/device_filters_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab119a5d4f1f6eab94c40793fd2ff9b3f7325842
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/device_filters_pb2.py
@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/device_filters.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n-tensorflow/core/protobuf/device_filters.proto\x12\ntensorflow\"+\n\x11TaskDeviceFilters\x12\x16\n\x0e\x64\x65vice_filters\x18\x01 \x03(\t\"\xa5\x01\n\x10JobDeviceFilters\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x36\n\x05tasks\x18\x02 \x03(\x0b\x32\'.tensorflow.JobDeviceFilters.TasksEntry\x1aK\n\nTasksEntry\x12\x0b\n\x03key\x18\x01 \x01(\x05\x12,\n\x05value\x18\x02 \x01(\x0b\x32\x1d.tensorflow.TaskDeviceFilters:\x02\x38\x01\"B\n\x14\x43lusterDeviceFilters\x12*\n\x04jobs\x18\x01 \x03(\x0b\x32\x1c.tensorflow.JobDeviceFiltersB\x8d\x01\n\x1aorg.tensorflow.distruntimeB\x13\x44\x65viceFiltersProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.device_filters_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\032org.tensorflow.distruntimeB\023DeviceFiltersProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _JOBDEVICEFILTERS_TASKSENTRY._options = None
+  _JOBDEVICEFILTERS_TASKSENTRY._serialized_options = b'8\001'
+  _TASKDEVICEFILTERS._serialized_start=61
+  _TASKDEVICEFILTERS._serialized_end=104
+  _JOBDEVICEFILTERS._serialized_start=107
+  _JOBDEVICEFILTERS._serialized_end=272
+  _JOBDEVICEFILTERS_TASKSENTRY._serialized_start=197
+  _JOBDEVICEFILTERS_TASKSENTRY._serialized_end=272
+  _CLUSTERDEVICEFILTERS._serialized_start=274
+  _CLUSTERDEVICEFILTERS._serialized_end=340
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/device_properties_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/device_properties_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa1cf25f203c03049567dcb4c5b5c941542b3ac9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/device_properties_pb2.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/device_properties.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n0tensorflow/core/protobuf/device_properties.proto\x12\ntensorflow\"\x90\x03\n\x10\x44\x65viceProperties\x12\x0c\n\x04type\x18\x01 \x01(\t\x12\x0e\n\x06vendor\x18\x02 \x01(\t\x12\r\n\x05model\x18\x03 \x01(\t\x12\x11\n\tfrequency\x18\x04 \x01(\x03\x12\x11\n\tnum_cores\x18\x05 \x01(\x03\x12\x42\n\x0b\x65nvironment\x18\x06 \x03(\x0b\x32-.tensorflow.DeviceProperties.EnvironmentEntry\x12\x15\n\rnum_registers\x18\x07 \x01(\x03\x12\x15\n\rl1_cache_size\x18\x08 \x01(\x03\x12\x15\n\rl2_cache_size\x18\t \x01(\x03\x12\x15\n\rl3_cache_size\x18\n \x01(\x03\x12-\n%shared_memory_size_per_multiprocessor\x18\x0b \x01(\x03\x12\x13\n\x0bmemory_size\x18\x0c \x01(\x03\x12\x11\n\tbandwidth\x18\r \x01(\x03\x1a\x32\n\x10\x45nvironmentEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\"M\n\x0bNamedDevice\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x30\n\nproperties\x18\x02 \x01(\x0b\x32\x1c.tensorflow.DevicePropertiesBrB\x16\x44\x65vicePropertiesProtosZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.device_properties_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'B\026DevicePropertiesProtosZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _DEVICEPROPERTIES_ENVIRONMENTENTRY._options = None
+  _DEVICEPROPERTIES_ENVIRONMENTENTRY._serialized_options = b'8\001'
+  _DEVICEPROPERTIES._serialized_start=65
+  _DEVICEPROPERTIES._serialized_end=465
+  _DEVICEPROPERTIES_ENVIRONMENTENTRY._serialized_start=415
+  _DEVICEPROPERTIES_ENVIRONMENTENTRY._serialized_end=465
+  _NAMEDDEVICE._serialized_start=467
+  _NAMEDDEVICE._serialized_end=544
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/error_codes_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/error_codes_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ac7922507cdf3cc7f9f71887d5aa3826e3eadd3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/error_codes_pb2.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/error_codes.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.tsl.protobuf import error_codes_pb2 as tsl_dot_protobuf_dot_error__codes__pb2
+
+from tensorflow.tsl.protobuf.error_codes_pb2 import *
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n*tensorflow/core/protobuf/error_codes.proto\x12\x16tensorflow.error.dummy\x1a\x1etsl/protobuf/error_codes.protoBWZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_protoP\x00\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.error_codes_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto'
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/fingerprint_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/fingerprint_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8e9c7c2badb00a217ebb1762f9a15abff3b9616
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/fingerprint_pb2.py
@@ -0,0 +1,27 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/fingerprint.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import versions_pb2 as tensorflow_dot_core_dot_framework_dot_versions__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n*tensorflow/core/protobuf/fingerprint.proto\x12\ntensorflow\x1a(tensorflow/core/framework/versions.proto\"\xcd\x01\n\x0e\x46ingerprintDef\x12\x1c\n\x14saved_model_checksum\x18\x01 \x01(\x04\x12\x1e\n\x16graph_def_program_hash\x18\x02 \x01(\x04\x12\x1a\n\x12signature_def_hash\x18\x03 \x01(\x04\x12\x1f\n\x17saved_object_graph_hash\x18\x04 \x01(\x04\x12\x17\n\x0f\x63heckpoint_hash\x18\x05 \x01(\x04\x12\'\n\x07version\x18\x06 \x01(\x0b\x32\x16.tensorflow.VersionDefB\x89\x01\n\x18org.tensorflow.frameworkB\x11\x46ingerprintProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.fingerprint_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\021FingerprintProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _FINGERPRINTDEF._serialized_start=101
+  _FINGERPRINTDEF._serialized_end=306
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/meta_graph_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/meta_graph_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..d74d5c76d4001dd893b32af660697e77db1006d4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/meta_graph_pb2.py
@@ -0,0 +1,87 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/meta_graph.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from google.protobuf import any_pb2 as google_dot_protobuf_dot_any__pb2
+from tensorflow.core.framework import graph_pb2 as tensorflow_dot_core_dot_framework_dot_graph__pb2
+from tensorflow.core.framework import op_def_pb2 as tensorflow_dot_core_dot_framework_dot_op__def__pb2
+from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+from tensorflow.core.protobuf import saved_object_graph_pb2 as tensorflow_dot_core_dot_protobuf_dot_saved__object__graph__pb2
+from tensorflow.core.protobuf import saver_pb2 as tensorflow_dot_core_dot_protobuf_dot_saver__pb2
+from tensorflow.core.protobuf import struct_pb2 as tensorflow_dot_core_dot_protobuf_dot_struct__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n)tensorflow/core/protobuf/meta_graph.proto\x12\ntensorflow\x1a\x19google/protobuf/any.proto\x1a%tensorflow/core/framework/graph.proto\x1a&tensorflow/core/framework/op_def.proto\x1a&tensorflow/core/framework/tensor.proto\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\x1a\x31tensorflow/core/protobuf/saved_object_graph.proto\x1a$tensorflow/core/protobuf/saver.proto\x1a%tensorflow/core/protobuf/struct.proto\"\xa8\x07\n\x0cMetaGraphDef\x12;\n\rmeta_info_def\x18\x01 \x01(\x0b\x32$.tensorflow.MetaGraphDef.MetaInfoDef\x12\'\n\tgraph_def\x18\x02 \x01(\x0b\x32\x14.tensorflow.GraphDef\x12\'\n\tsaver_def\x18\x03 \x01(\x0b\x32\x14.tensorflow.SaverDef\x12\x43\n\x0e\x63ollection_def\x18\x04 \x03(\x0b\x32+.tensorflow.MetaGraphDef.CollectionDefEntry\x12\x41\n\rsignature_def\x18\x05 \x03(\x0b\x32*.tensorflow.MetaGraphDef.SignatureDefEntry\x12\x30\n\x0e\x61sset_file_def\x18\x06 \x03(\x0b\x32\x18.tensorflow.AssetFileDef\x12\x36\n\x10object_graph_def\x18\x07 \x01(\x0b\x32\x1c.tensorflow.SavedObjectGraph\x1a\xf6\x02\n\x0bMetaInfoDef\x12\x1a\n\x12meta_graph_version\x18\x01 \x01(\t\x12,\n\x10stripped_op_list\x18\x02 \x01(\x0b\x32\x12.tensorflow.OpList\x12&\n\x08\x61ny_info\x18\x03 \x01(\x0b\x32\x14.google.protobuf.Any\x12\x0c\n\x04tags\x18\x04 \x03(\t\x12\x1a\n\x12tensorflow_version\x18\x05 \x01(\t\x12\x1e\n\x16tensorflow_git_version\x18\x06 \x01(\t\x12\x1e\n\x16stripped_default_attrs\x18\x07 \x01(\x08\x12S\n\x10\x66unction_aliases\x18\x08 \x03(\x0b\x32\x39.tensorflow.MetaGraphDef.MetaInfoDef.FunctionAliasesEntry\x1a\x36\n\x14\x46unctionAliasesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\r\n\x05value\x18\x02 \x01(\t:\x02\x38\x01\x1aO\n\x12\x43ollectionDefEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12(\n\x05value\x18\x02 \x01(\x0b\x32\x19.tensorflow.CollectionDef:\x02\x38\x01\x1aM\n\x11SignatureDefEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\'\n\x05value\x18\x02 \x01(\x0b\x32\x18.tensorflow.SignatureDef:\x02\x38\x01\"\xdf\x03\n\rCollectionDef\x12\x37\n\tnode_list\x18\x01 \x01(\x0b\x32\".tensorflow.CollectionDef.NodeListH\x00\x12\x39\n\nbytes_list\x18\x02 \x01(\x0b\x32#.tensorflow.CollectionDef.BytesListH\x00\x12\x39\n\nint64_list\x18\x03 \x01(\x0b\x32#.tensorflow.CollectionDef.Int64ListH\x00\x12\x39\n\nfloat_list\x18\x04 \x01(\x0b\x32#.tensorflow.CollectionDef.FloatListH\x00\x12\x35\n\x08\x61ny_list\x18\x05 \x01(\x0b\x32!.tensorflow.CollectionDef.AnyListH\x00\x1a\x19\n\x08NodeList\x12\r\n\x05value\x18\x01 \x03(\t\x1a\x1a\n\tBytesList\x12\r\n\x05value\x18\x01 \x03(\x0c\x1a\x1e\n\tInt64List\x12\x11\n\x05value\x18\x01 \x03(\x03\x42\x02\x10\x01\x1a\x1e\n\tFloatList\x12\x11\n\x05value\x18\x01 \x03(\x02\x42\x02\x10\x01\x1a.\n\x07\x41nyList\x12#\n\x05value\x18\x01 \x03(\x0b\x32\x14.google.protobuf.AnyB\x06\n\x04kind\"\xd1\x03\n\nTensorInfo\x12\x0e\n\x04name\x18\x01 \x01(\tH\x00\x12\x36\n\ncoo_sparse\x18\x04 \x01(\x0b\x32 .tensorflow.TensorInfo.CooSparseH\x00\x12\x42\n\x10\x63omposite_tensor\x18\x05 \x01(\x0b\x32&.tensorflow.TensorInfo.CompositeTensorH\x00\x12#\n\x05\x64type\x18\x02 \x01(\x0e\x32\x14.tensorflow.DataType\x12\x32\n\x0ctensor_shape\x18\x03 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x1a\x65\n\tCooSparse\x12\x1a\n\x12values_tensor_name\x18\x01 \x01(\t\x12\x1b\n\x13indices_tensor_name\x18\x02 \x01(\t\x12\x1f\n\x17\x64\x65nse_shape_tensor_name\x18\x03 \x01(\t\x1ak\n\x0f\x43ompositeTensor\x12,\n\ttype_spec\x18\x01 \x01(\x0b\x32\x19.tensorflow.TypeSpecProto\x12*\n\ncomponents\x18\x02 \x03(\x0b\x32\x16.tensorflow.TensorInfoB\n\n\x08\x65ncoding\"\xa4\x03\n\x0cSignatureDef\x12\x34\n\x06inputs\x18\x01 \x03(\x0b\x32$.tensorflow.SignatureDef.InputsEntry\x12\x36\n\x07outputs\x18\x02 \x03(\x0b\x32%.tensorflow.SignatureDef.OutputsEntry\x12\x13\n\x0bmethod_name\x18\x03 \x01(\t\x12\x38\n\x08\x64\x65\x66\x61ults\x18\x04 \x03(\x0b\x32&.tensorflow.SignatureDef.DefaultsEntry\x1a\x45\n\x0bInputsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12%\n\x05value\x18\x02 \x01(\x0b\x32\x16.tensorflow.TensorInfo:\x02\x38\x01\x1a\x46\n\x0cOutputsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12%\n\x05value\x18\x02 \x01(\x0b\x32\x16.tensorflow.TensorInfo:\x02\x38\x01\x1aH\n\rDefaultsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12&\n\x05value\x18\x02 \x01(\x0b\x32\x17.tensorflow.TensorProto:\x02\x38\x01\"M\n\x0c\x41ssetFileDef\x12+\n\x0btensor_info\x18\x01 \x01(\x0b\x32\x16.tensorflow.TensorInfo\x12\x10\n\x08\x66ilename\x18\x02 \x01(\tB\x87\x01\n\x18org.tensorflow.frameworkB\x0fMetaGraphProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.meta_graph_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\017MetaGraphProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _METAGRAPHDEF_METAINFODEF_FUNCTIONALIASESENTRY._options = None
+  _METAGRAPHDEF_METAINFODEF_FUNCTIONALIASESENTRY._serialized_options = b'8\001'
+  _METAGRAPHDEF_COLLECTIONDEFENTRY._options = None
+  _METAGRAPHDEF_COLLECTIONDEFENTRY._serialized_options = b'8\001'
+  _METAGRAPHDEF_SIGNATUREDEFENTRY._options = None
+  _METAGRAPHDEF_SIGNATUREDEFENTRY._serialized_options = b'8\001'
+  _COLLECTIONDEF_INT64LIST.fields_by_name['value']._options = None
+  _COLLECTIONDEF_INT64LIST.fields_by_name['value']._serialized_options = b'\020\001'
+  _COLLECTIONDEF_FLOATLIST.fields_by_name['value']._options = None
+  _COLLECTIONDEF_FLOATLIST.fields_by_name['value']._serialized_options = b'\020\001'
+  _SIGNATUREDEF_INPUTSENTRY._options = None
+  _SIGNATUREDEF_INPUTSENTRY._serialized_options = b'8\001'
+  _SIGNATUREDEF_OUTPUTSENTRY._options = None
+  _SIGNATUREDEF_OUTPUTSENTRY._serialized_options = b'8\001'
+  _SIGNATUREDEF_DEFAULTSENTRY._options = None
+  _SIGNATUREDEF_DEFAULTSENTRY._serialized_options = b'8\001'
+  _METAGRAPHDEF._serialized_start=417
+  _METAGRAPHDEF._serialized_end=1353
+  _METAGRAPHDEF_METAINFODEF._serialized_start=819
+  _METAGRAPHDEF_METAINFODEF._serialized_end=1193
+  _METAGRAPHDEF_METAINFODEF_FUNCTIONALIASESENTRY._serialized_start=1139
+  _METAGRAPHDEF_METAINFODEF_FUNCTIONALIASESENTRY._serialized_end=1193
+  _METAGRAPHDEF_COLLECTIONDEFENTRY._serialized_start=1195
+  _METAGRAPHDEF_COLLECTIONDEFENTRY._serialized_end=1274
+  _METAGRAPHDEF_SIGNATUREDEFENTRY._serialized_start=1276
+  _METAGRAPHDEF_SIGNATUREDEFENTRY._serialized_end=1353
+  _COLLECTIONDEF._serialized_start=1356
+  _COLLECTIONDEF._serialized_end=1835
+  _COLLECTIONDEF_NODELIST._serialized_start=1662
+  _COLLECTIONDEF_NODELIST._serialized_end=1687
+  _COLLECTIONDEF_BYTESLIST._serialized_start=1689
+  _COLLECTIONDEF_BYTESLIST._serialized_end=1715
+  _COLLECTIONDEF_INT64LIST._serialized_start=1717
+  _COLLECTIONDEF_INT64LIST._serialized_end=1747
+  _COLLECTIONDEF_FLOATLIST._serialized_start=1749
+  _COLLECTIONDEF_FLOATLIST._serialized_end=1779
+  _COLLECTIONDEF_ANYLIST._serialized_start=1781
+  _COLLECTIONDEF_ANYLIST._serialized_end=1827
+  _TENSORINFO._serialized_start=1838
+  _TENSORINFO._serialized_end=2303
+  _TENSORINFO_COOSPARSE._serialized_start=2081
+  _TENSORINFO_COOSPARSE._serialized_end=2182
+  _TENSORINFO_COMPOSITETENSOR._serialized_start=2184
+  _TENSORINFO_COMPOSITETENSOR._serialized_end=2291
+  _SIGNATUREDEF._serialized_start=2306
+  _SIGNATUREDEF._serialized_end=2726
+  _SIGNATUREDEF_INPUTSENTRY._serialized_start=2511
+  _SIGNATUREDEF_INPUTSENTRY._serialized_end=2580
+  _SIGNATUREDEF_OUTPUTSENTRY._serialized_start=2582
+  _SIGNATUREDEF_OUTPUTSENTRY._serialized_end=2652
+  _SIGNATUREDEF_DEFAULTSENTRY._serialized_start=2654
+  _SIGNATUREDEF_DEFAULTSENTRY._serialized_end=2726
+  _ASSETFILEDEF._serialized_start=2728
+  _ASSETFILEDEF._serialized_end=2805
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/named_tensor_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/named_tensor_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..03c032ae2ad6d5f12e522f467b41b035359c8084
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/named_tensor_pb2.py
@@ -0,0 +1,27 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/named_tensor.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n+tensorflow/core/protobuf/named_tensor.proto\x12\ntensorflow\x1a&tensorflow/core/framework/tensor.proto\"I\n\x10NamedTensorProto\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\'\n\x06tensor\x18\x02 \x01(\x0b\x32\x17.tensorflow.TensorProtoB\x89\x01\n\x18org.tensorflow.frameworkB\x11NamedTensorProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.named_tensor_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\021NamedTensorProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _NAMEDTENSORPROTO._serialized_start=99
+  _NAMEDTENSORPROTO._serialized_end=172
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/queue_runner_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/queue_runner_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb0d24ab3c272bf8d3c48993a80c1fe666bc8407
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/queue_runner_pb2.py
@@ -0,0 +1,31 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/queue_runner.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.protobuf import error_codes_pb2 as tensorflow_dot_core_dot_protobuf_dot_error__codes__pb2
+try:
+  tsl_dot_protobuf_dot_error__codes__pb2 = tensorflow_dot_core_dot_protobuf_dot_error__codes__pb2.tsl_dot_protobuf_dot_error__codes__pb2
+except AttributeError:
+  tsl_dot_protobuf_dot_error__codes__pb2 = tensorflow_dot_core_dot_protobuf_dot_error__codes__pb2.tsl.protobuf.error_codes_pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n+tensorflow/core/protobuf/queue_runner.proto\x12\ntensorflow\x1a*tensorflow/core/protobuf/error_codes.proto\"\xaa\x01\n\x0eQueueRunnerDef\x12\x12\n\nqueue_name\x18\x01 \x01(\t\x12\x17\n\x0f\x65nqueue_op_name\x18\x02 \x03(\t\x12\x15\n\rclose_op_name\x18\x03 \x01(\t\x12\x16\n\x0e\x63\x61ncel_op_name\x18\x04 \x01(\t\x12<\n\x1cqueue_closed_exception_types\x18\x05 \x03(\x0e\x32\x16.tensorflow.error.CodeB\x89\x01\n\x18org.tensorflow.frameworkB\x11QueueRunnerProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.queue_runner_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\021QueueRunnerProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _QUEUERUNNERDEF._serialized_start=104
+  _QUEUERUNNERDEF._serialized_end=274
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/remote_tensor_handle_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/remote_tensor_handle_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..69020292bcbb7add6c74ec911ff0a3010b4c6b8f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/remote_tensor_handle_pb2.py
@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/remote_tensor_handle.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n3tensorflow/core/protobuf/remote_tensor_handle.proto\x12\x10tensorflow.eager\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\"i\n\x15ResourceDtypeAndShape\x12#\n\x05\x64type\x18\x01 \x01(\x0e\x32\x14.tensorflow.DataType\x12+\n\x05shape\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\"\xcc\x01\n\x12RemoteTensorHandle\x12\r\n\x05op_id\x18\x01 \x01(\x03\x12\x12\n\noutput_num\x18\x02 \x01(\x05\x12\x0e\n\x06\x64\x65vice\x18\x03 \x01(\t\x12\x11\n\top_device\x18\x04 \x01(\t\x12#\n\x05\x64type\x18\x05 \x01(\x0e\x32\x14.tensorflow.DataType\x12K\n\x1aresource_dtypes_and_shapes\x18\x06 \x03(\x0b\x32\'.tensorflow.eager.ResourceDtypeAndShapeB\x90\x01\n\x18org.tensorflow.frameworkB\x18RemoteTensorHandleProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.remote_tensor_handle_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\030RemoteTensorHandleProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _RESOURCEDTYPEANDSHAPE._serialized_start=158
+  _RESOURCEDTYPEANDSHAPE._serialized_end=263
+  _REMOTETENSORHANDLE._serialized_start=266
+  _REMOTETENSORHANDLE._serialized_end=470
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/rewriter_config_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/rewriter_config_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a569087c91b583a62666b0d17660c90f9ccee5c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/rewriter_config_pb2.py
@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/rewriter_config.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
+from tensorflow.core.protobuf import verifier_config_pb2 as tensorflow_dot_core_dot_protobuf_dot_verifier__config__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n.tensorflow/core/protobuf/rewriter_config.proto\x12\ntensorflow\x1a*tensorflow/core/framework/attr_value.proto\x1a.tensorflow/core/protobuf/verifier_config.proto\";\n\x13\x41utoParallelOptions\x12\x0e\n\x06\x65nable\x18\x01 \x01(\x08\x12\x14\n\x0cnum_replicas\x18\x02 \x01(\x05\"+\n\x16ScopedAllocatorOptions\x12\x11\n\tenable_op\x18\x01 \x03(\t\"\x95\x16\n\x0eRewriterConfig\x12\x43\n\x15\x63pu_layout_conversion\x18\x32 \x01(\x0e\x32$.tensorflow.RewriterConfig.CpuLayout\x12;\n\x10layout_optimizer\x18\x01 \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12;\n\x10\x63onstant_folding\x18\x03 \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12=\n\x12shape_optimization\x18\r \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12\x34\n\tremapping\x18\x0e \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12\x46\n\x1b\x63ommon_subgraph_elimination\x18\x18 \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12\x42\n\x17\x61rithmetic_optimization\x18\x07 \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12\x42\n\x17\x64\x65pendency_optimization\x18\x08 \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12<\n\x11loop_optimization\x18\t \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12@\n\x15\x66unction_optimization\x18\n \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12\x39\n\x0e\x64\x65\x62ug_stripper\x18\x0b \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12\x1d\n\x15\x64isable_model_pruning\x18\x02 \x01(\x08\x12H\n\x1dscoped_allocator_optimization\x18\x0f \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12\x43\n\x18pin_to_host_optimization\x18\x12 \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12\x42\n\x17implementation_selector\x18\x16 \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12?\n\x14\x61uto_mixed_precision\x18\x17 \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12\x43\n\x18\x61uto_mixed_precision_mkl\x18\x19 \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12O\n$auto_mixed_precision_onednn_bfloat16\x18\x1f \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12\x43\n\x18\x61uto_mixed_precision_cpu\x18\x1d \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12\x1e\n\x16\x64isable_meta_optimizer\x18\x13 \x01(\x08\x12\x1d\n\x15\x64isable_tfg_optimizer\x18  \x01(\x08\x12@\n\x15use_plugin_optimizers\x18\x1c \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12O\n$experimental_conditional_code_motion\x18\x1e \x01(\x0e\x32!.tensorflow.RewriterConfig.Toggle\x12O\n\x19meta_optimizer_iterations\x18\x0c \x01(\x0e\x32,.tensorflow.RewriterConfig.NumIterationsType\x12\x17\n\x0fmin_graph_nodes\x18\x11 \x01(\x05\x12;\n3experimental_disable_compressed_tensor_optimization\x18\x1a \x01(\x08\x12;\n3experimental_disable_folding_quantization_emulation\x18\x1b \x01(\x08\x12\x42\n\x13memory_optimization\x18\x04 \x01(\x0e\x32%.tensorflow.RewriterConfig.MemOptType\x12/\n\'memory_optimizer_target_node_name_scope\x18\x06 \x01(\t\x12!\n\x19meta_optimizer_timeout_ms\x18\x14 \x01(\x03\x12\x36\n\rauto_parallel\x18\x05 \x01(\x0b\x32\x1f.tensorflow.AutoParallelOptions\x12 \n\x18\x66\x61il_on_optimizer_errors\x18\x15 \x01(\x08\x12\x41\n\x15scoped_allocator_opts\x18\x10 \x01(\x0b\x32\".tensorflow.ScopedAllocatorOptions\x12\x12\n\noptimizers\x18\x64 \x03(\t\x12K\n\x11\x63ustom_optimizers\x18\xc8\x01 \x03(\x0b\x32/.tensorflow.RewriterConfig.CustomGraphOptimizer\x12\x44\n\x1finter_optimizer_verifier_config\x18\xac\x02 \x01(\x0b\x32\x1a.tensorflow.VerifierConfig\x12\x46\n!post_optimization_verifier_config\x18\xad\x02 \x01(\x0b\x32\x1a.tensorflow.VerifierConfig\x1a\xca\x01\n\x14\x43ustomGraphOptimizer\x12\x0c\n\x04name\x18\x01 \x01(\t\x12X\n\rparameter_map\x18\x02 \x03(\x0b\x32\x41.tensorflow.RewriterConfig.CustomGraphOptimizer.ParameterMapEntry\x1aJ\n\x11ParameterMapEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12$\n\x05value\x18\x02 \x01(\x0b\x32\x15.tensorflow.AttrValue:\x02\x38\x01\"d\n\x06Toggle\x12\x0b\n\x07\x44\x45\x46\x41ULT\x10\x00\x12\x06\n\x02ON\x10\x01\x12\x07\n\x03OFF\x10\x02\x12\x0e\n\nAGGRESSIVE\x10\x03\x12\x15\n\x11\x45XPERIMENTAL_MLIR\x10\x04\x12\x15\n\x11\x45XPERIMENTAL_BOTH\x10\x05\"I\n\tCpuLayout\x12\x18\n\x14NO_CONVERSION_ON_CPU\x10\x00\x12\x10\n\x0cNCHW_TO_NHWC\x10\x01\x12\x10\n\x0cNHWC_TO_NCHW\x10\x02\"<\n\x11NumIterationsType\x12\x15\n\x11\x44\x45\x46\x41ULT_NUM_ITERS\x10\x00\x12\x07\n\x03ONE\x10\x01\x12\x07\n\x03TWO\x10\x02\"\x9f\x01\n\nMemOptType\x12\x13\n\x0f\x44\x45\x46\x41ULT_MEM_OPT\x10\x00\x12\x0e\n\nNO_MEM_OPT\x10\x01\x12\n\n\x06MANUAL\x10\x02\x12\x17\n\x13SWAPPING_HEURISTICS\x10\x04\x12\x1c\n\x18RECOMPUTATION_HEURISTICS\x10\x05\x12\x19\n\x15SCHEDULING_HEURISTICS\x10\x06\x12\x0e\n\nHEURISTICS\x10\x03\x42\x8c\x01\n\x18org.tensorflow.frameworkB\x14RewriterConfigProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.rewriter_config_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\024RewriterConfigProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _REWRITERCONFIG_CUSTOMGRAPHOPTIMIZER_PARAMETERMAPENTRY._options = None
+  _REWRITERCONFIG_CUSTOMGRAPHOPTIMIZER_PARAMETERMAPENTRY._serialized_options = b'8\001'
+  _AUTOPARALLELOPTIONS._serialized_start=154
+  _AUTOPARALLELOPTIONS._serialized_end=213
+  _SCOPEDALLOCATOROPTIONS._serialized_start=215
+  _SCOPEDALLOCATOROPTIONS._serialized_end=258
+  _REWRITERCONFIG._serialized_start=261
+  _REWRITERCONFIG._serialized_end=3098
+  _REWRITERCONFIG_CUSTOMGRAPHOPTIMIZER._serialized_start=2495
+  _REWRITERCONFIG_CUSTOMGRAPHOPTIMIZER._serialized_end=2697
+  _REWRITERCONFIG_CUSTOMGRAPHOPTIMIZER_PARAMETERMAPENTRY._serialized_start=2623
+  _REWRITERCONFIG_CUSTOMGRAPHOPTIMIZER_PARAMETERMAPENTRY._serialized_end=2697
+  _REWRITERCONFIG_TOGGLE._serialized_start=2699
+  _REWRITERCONFIG_TOGGLE._serialized_end=2799
+  _REWRITERCONFIG_CPULAYOUT._serialized_start=2801
+  _REWRITERCONFIG_CPULAYOUT._serialized_end=2874
+  _REWRITERCONFIG_NUMITERATIONSTYPE._serialized_start=2876
+  _REWRITERCONFIG_NUMITERATIONSTYPE._serialized_end=2936
+  _REWRITERCONFIG_MEMOPTTYPE._serialized_start=2939
+  _REWRITERCONFIG_MEMOPTTYPE._serialized_end=3098
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/rpc_options_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/rpc_options_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..6cce249ecd86c1389835eecdff4852bcd54eb65a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/rpc_options_pb2.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/rpc_options.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.tsl.protobuf import rpc_options_pb2 as tsl_dot_protobuf_dot_rpc__options__pb2
+
+from tensorflow.tsl.protobuf.rpc_options_pb2 import *
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n*tensorflow/core/protobuf/rpc_options.proto\x12\x10tensorflow.dummy\x1a\x1etsl/protobuf/rpc_options.protoBWZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_protoP\x00\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.rpc_options_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto'
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/saved_model_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/saved_model_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..2eca9a7948850c01d91e5db174a86321941416ab
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/saved_model_pb2.py
@@ -0,0 +1,27 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/saved_model.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.protobuf import meta_graph_pb2 as tensorflow_dot_core_dot_protobuf_dot_meta__graph__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n*tensorflow/core/protobuf/saved_model.proto\x12\ntensorflow\x1a)tensorflow/core/protobuf/meta_graph.proto\"_\n\nSavedModel\x12\"\n\x1asaved_model_schema_version\x18\x01 \x01(\x03\x12-\n\x0bmeta_graphs\x18\x02 \x03(\x0b\x32\x18.tensorflow.MetaGraphDefB\x88\x01\n\x18org.tensorflow.frameworkB\x10SavedModelProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.saved_model_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\020SavedModelProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _SAVEDMODEL._serialized_start=101
+  _SAVEDMODEL._serialized_end=196
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/saved_object_graph_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/saved_object_graph_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..d846e399a40b0d4e726d42e1b7b3ecb40b99e1e7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/saved_object_graph_pb2.py
@@ -0,0 +1,69 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/saved_object_graph.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from google.protobuf import any_pb2 as google_dot_protobuf_dot_any__pb2
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+from tensorflow.core.framework import variable_pb2 as tensorflow_dot_core_dot_framework_dot_variable__pb2
+from tensorflow.core.framework import versions_pb2 as tensorflow_dot_core_dot_framework_dot_versions__pb2
+from tensorflow.core.protobuf import struct_pb2 as tensorflow_dot_core_dot_protobuf_dot_struct__pb2
+from tensorflow.core.protobuf import trackable_object_graph_pb2 as tensorflow_dot_core_dot_protobuf_dot_trackable__object__graph__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n1tensorflow/core/protobuf/saved_object_graph.proto\x12\ntensorflow\x1a\x19google/protobuf/any.proto\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\x1a(tensorflow/core/framework/variable.proto\x1a(tensorflow/core/framework/versions.proto\x1a%tensorflow/core/protobuf/struct.proto\x1a\x35tensorflow/core/protobuf/trackable_object_graph.proto\"\xe8\x01\n\x10SavedObjectGraph\x12&\n\x05nodes\x18\x01 \x03(\x0b\x32\x17.tensorflow.SavedObject\x12O\n\x12\x63oncrete_functions\x18\x02 \x03(\x0b\x32\x33.tensorflow.SavedObjectGraph.ConcreteFunctionsEntry\x1a[\n\x16\x43oncreteFunctionsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x30\n\x05value\x18\x02 \x01(\x0b\x32!.tensorflow.SavedConcreteFunction:\x02\x38\x01\"\xd0\x07\n\x0bSavedObject\x12R\n\x08\x63hildren\x18\x01 \x03(\x0b\x32@.tensorflow.TrackableObjectGraph.TrackableObject.ObjectReference\x12V\n\x0c\x64\x65pendencies\x18\x0f \x03(\x0b\x32@.tensorflow.TrackableObjectGraph.TrackableObject.ObjectReference\x12^\n\x0eslot_variables\x18\x03 \x03(\x0b\x32\x46.tensorflow.TrackableObjectGraph.TrackableObject.SlotVariableReference\x12\x32\n\x0buser_object\x18\x04 \x01(\x0b\x32\x1b.tensorflow.SavedUserObjectH\x00\x12\'\n\x05\x61sset\x18\x05 \x01(\x0b\x32\x16.tensorflow.SavedAssetH\x00\x12-\n\x08\x66unction\x18\x06 \x01(\x0b\x32\x19.tensorflow.SavedFunctionH\x00\x12-\n\x08variable\x18\x07 \x01(\x0b\x32\x19.tensorflow.SavedVariableH\x00\x12G\n\x16\x62\x61re_concrete_function\x18\x08 \x01(\x0b\x32%.tensorflow.SavedBareConcreteFunctionH\x00\x12-\n\x08\x63onstant\x18\t \x01(\x0b\x32\x19.tensorflow.SavedConstantH\x00\x12-\n\x08resource\x18\n \x01(\x0b\x32\x19.tensorflow.SavedResourceH\x00\x12\x35\n\x0f\x63\x61ptured_tensor\x18\x0c \x01(\x0b\x32\x1a.tensorflow.CapturedTensorH\x00\x12\x46\n\x10saveable_objects\x18\x0b \x03(\x0b\x32,.tensorflow.SavedObject.SaveableObjectsEntry\x12\x17\n\x0fregistered_name\x18\r \x01(\t\x12\x33\n\x15serialized_user_proto\x18\x0e \x01(\x0b\x32\x14.google.protobuf.Any\x12\x18\n\x10registered_saver\x18\x10 \x01(\t\x1aR\n\x14SaveableObjectsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12)\n\x05value\x18\x02 \x01(\x0b\x32\x1a.tensorflow.SaveableObject:\x02\x38\x01\x42\x06\n\x04kindJ\x04\x08\x02\x10\x03R\nattributes\"d\n\x0fSavedUserObject\x12\x12\n\nidentifier\x18\x01 \x01(\t\x12\'\n\x07version\x18\x02 \x01(\x0b\x32\x16.tensorflow.VersionDef\x12\x14\n\x08metadata\x18\x03 \x01(\tB\x02\x18\x01\"*\n\nSavedAsset\x12\x1c\n\x14\x61sset_file_def_index\x18\x01 \x01(\x05\"\\\n\rSavedFunction\x12\x1a\n\x12\x63oncrete_functions\x18\x01 \x03(\t\x12/\n\rfunction_spec\x18\x02 \x01(\x0b\x32\x18.tensorflow.FunctionSpec\"9\n\x0e\x43\x61pturedTensor\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x19\n\x11\x63oncrete_function\x18\x02 \x01(\t\"\xa8\x01\n\x15SavedConcreteFunction\x12\x14\n\x0c\x62ound_inputs\x18\x02 \x03(\x05\x12\x42\n\x1d\x63\x61nonicalized_input_signature\x18\x03 \x01(\x0b\x32\x1b.tensorflow.StructuredValue\x12\x35\n\x10output_signature\x18\x04 \x01(\x0b\x32\x1b.tensorflow.StructuredValue\"\xad\x01\n\x19SavedBareConcreteFunction\x12\x1e\n\x16\x63oncrete_function_name\x18\x01 \x01(\t\x12\x19\n\x11\x61rgument_keywords\x18\x02 \x03(\t\x12$\n\x1c\x61llowed_positional_arguments\x18\x03 \x01(\x03\x12/\n\rfunction_spec\x18\x04 \x01(\x0b\x32\x18.tensorflow.FunctionSpec\"\"\n\rSavedConstant\x12\x11\n\toperation\x18\x01 \x01(\t\"\xd7\x02\n\rSavedVariable\x12#\n\x05\x64type\x18\x01 \x01(\x0e\x32\x14.tensorflow.DataType\x12+\n\x05shape\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12\x11\n\ttrainable\x18\x03 \x01(\x08\x12<\n\x0fsynchronization\x18\x04 \x01(\x0e\x32#.tensorflow.VariableSynchronization\x12\x34\n\x0b\x61ggregation\x18\x05 \x01(\x0e\x32\x1f.tensorflow.VariableAggregation\x12\x0c\n\x04name\x18\x06 \x01(\t\x12\x0e\n\x06\x64\x65vice\x18\x07 \x01(\t\x12O\n,experimental_distributed_variable_components\x18\x08 \x03(\x0b\x32\x19.tensorflow.SavedVariable\"\xfb\x01\n\x0c\x46unctionSpec\x12\x30\n\x0b\x66ullargspec\x18\x01 \x01(\x0b\x32\x1b.tensorflow.StructuredValue\x12\x11\n\tis_method\x18\x02 \x01(\x08\x12\x34\n\x0finput_signature\x18\x05 \x01(\x0b\x32\x1b.tensorflow.StructuredValue\x12\x38\n\x0bjit_compile\x18\x06 \x01(\x0e\x32#.tensorflow.FunctionSpec.JitCompile\"*\n\nJitCompile\x12\x0b\n\x07\x44\x45\x46\x41ULT\x10\x00\x12\x06\n\x02ON\x10\x01\x12\x07\n\x03OFF\x10\x02J\x04\x08\x03\x10\x04J\x04\x08\x04\x10\x05\"\x1f\n\rSavedResource\x12\x0e\n\x06\x64\x65vice\x18\x01 \x01(\t\"A\n\x0eSaveableObject\x12\x15\n\rsave_function\x18\x02 \x01(\x05\x12\x18\n\x10restore_function\x18\x03 \x01(\x05\x42ZZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.saved_object_graph_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _SAVEDOBJECTGRAPH_CONCRETEFUNCTIONSENTRY._options = None
+  _SAVEDOBJECTGRAPH_CONCRETEFUNCTIONSENTRY._serialized_options = b'8\001'
+  _SAVEDOBJECT_SAVEABLEOBJECTSENTRY._options = None
+  _SAVEDOBJECT_SAVEABLEOBJECTSENTRY._serialized_options = b'8\001'
+  _SAVEDUSEROBJECT.fields_by_name['metadata']._options = None
+  _SAVEDUSEROBJECT.fields_by_name['metadata']._serialized_options = b'\030\001'
+  _SAVEDOBJECTGRAPH._serialized_start=356
+  _SAVEDOBJECTGRAPH._serialized_end=588
+  _SAVEDOBJECTGRAPH_CONCRETEFUNCTIONSENTRY._serialized_start=497
+  _SAVEDOBJECTGRAPH_CONCRETEFUNCTIONSENTRY._serialized_end=588
+  _SAVEDOBJECT._serialized_start=591
+  _SAVEDOBJECT._serialized_end=1567
+  _SAVEDOBJECT_SAVEABLEOBJECTSENTRY._serialized_start=1459
+  _SAVEDOBJECT_SAVEABLEOBJECTSENTRY._serialized_end=1541
+  _SAVEDUSEROBJECT._serialized_start=1569
+  _SAVEDUSEROBJECT._serialized_end=1669
+  _SAVEDASSET._serialized_start=1671
+  _SAVEDASSET._serialized_end=1713
+  _SAVEDFUNCTION._serialized_start=1715
+  _SAVEDFUNCTION._serialized_end=1807
+  _CAPTUREDTENSOR._serialized_start=1809
+  _CAPTUREDTENSOR._serialized_end=1866
+  _SAVEDCONCRETEFUNCTION._serialized_start=1869
+  _SAVEDCONCRETEFUNCTION._serialized_end=2037
+  _SAVEDBARECONCRETEFUNCTION._serialized_start=2040
+  _SAVEDBARECONCRETEFUNCTION._serialized_end=2213
+  _SAVEDCONSTANT._serialized_start=2215
+  _SAVEDCONSTANT._serialized_end=2249
+  _SAVEDVARIABLE._serialized_start=2252
+  _SAVEDVARIABLE._serialized_end=2595
+  _FUNCTIONSPEC._serialized_start=2598
+  _FUNCTIONSPEC._serialized_end=2849
+  _FUNCTIONSPEC_JITCOMPILE._serialized_start=2795
+  _FUNCTIONSPEC_JITCOMPILE._serialized_end=2837
+  _SAVEDRESOURCE._serialized_start=2851
+  _SAVEDRESOURCE._serialized_end=2882
+  _SAVEABLEOBJECT._serialized_start=2884
+  _SAVEABLEOBJECT._serialized_end=2949
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/saver_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/saver_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..c30770110f44b6464bca1a153a7ab0dddc74df9d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/saver_pb2.py
@@ -0,0 +1,28 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/saver.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n$tensorflow/core/protobuf/saver.proto\x12\ntensorflow\"\x9e\x02\n\x08SaverDef\x12\x1c\n\x14\x66ilename_tensor_name\x18\x01 \x01(\t\x12\x18\n\x10save_tensor_name\x18\x02 \x01(\t\x12\x17\n\x0frestore_op_name\x18\x03 \x01(\t\x12\x13\n\x0bmax_to_keep\x18\x04 \x01(\x05\x12\x0f\n\x07sharded\x18\x05 \x01(\x08\x12%\n\x1dkeep_checkpoint_every_n_hours\x18\x06 \x01(\x02\x12=\n\x07version\x18\x07 \x01(\x0e\x32,.tensorflow.SaverDef.CheckpointFormatVersion\"5\n\x17\x43heckpointFormatVersion\x12\n\n\x06LEGACY\x10\x00\x12\x06\n\x02V1\x10\x01\x12\x06\n\x02V2\x10\x02\x42~\n\x13org.tensorflow.utilB\x0bSaverProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.saver_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\023org.tensorflow.utilB\013SaverProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _SAVERDEF._serialized_start=53
+  _SAVERDEF._serialized_end=339
+  _SAVERDEF_CHECKPOINTFORMATVERSION._serialized_start=286
+  _SAVERDEF_CHECKPOINTFORMATVERSION._serialized_end=339
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/service_config_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/service_config_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f0a6f8381811b3cb92cf46f833f3830dea59bb5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/service_config_pb2.py
@@ -0,0 +1,29 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/service_config.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.protobuf import data_service_pb2 as tensorflow_dot_core_dot_protobuf_dot_data__service__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n-tensorflow/core/protobuf/service_config.proto\x12\x1ctensorflow.data.experimental\x1a+tensorflow/core/protobuf/data_service.proto\"\xf3\x02\n\x10\x44ispatcherConfig\x12\x0c\n\x04port\x18\x01 \x01(\x03\x12\x10\n\x08protocol\x18\x02 \x01(\t\x12\x10\n\x08work_dir\x18\x03 \x01(\t\x12\x1b\n\x13\x66\x61ult_tolerant_mode\x18\x04 \x01(\x08\x12\x18\n\x10worker_addresses\x18\x07 \x03(\t\x12\x38\n\x0f\x64\x65ployment_mode\x18\t \x01(\x0e\x32\x1f.tensorflow.data.DeploymentMode\x12 \n\x18job_gc_check_interval_ms\x18\x05 \x01(\x03\x12\x19\n\x11job_gc_timeout_ms\x18\x06 \x01(\x03\x12 \n\x18gc_dynamic_sharding_jobs\x18\x0b \x01(\x08\x12\x19\n\x11\x63lient_timeout_ms\x18\x08 \x01(\x03\x12\x19\n\x11worker_timeout_ms\x18\n \x01(\x03\x12\'\n\x1fworker_max_concurrent_snapshots\x18\x0c \x01(\x03\"\xe5\x02\n\x0cWorkerConfig\x12\x0c\n\x04port\x18\x01 \x01(\x03\x12\x10\n\x08protocol\x18\x02 \x01(\t\x12\x1a\n\x12\x64ispatcher_address\x18\x03 \x01(\t\x12\x16\n\x0eworker_address\x18\x04 \x01(\t\x12\x13\n\x0bworker_tags\x18\n \x03(\t\x12\x1d\n\x15heartbeat_interval_ms\x18\x05 \x01(\x03\x12\x1d\n\x15\x64ispatcher_timeout_ms\x18\x06 \x01(\x03\x12\x1e\n\x16\x64\x61ta_transfer_protocol\x18\x07 \x01(\t\x12\x1d\n\x15\x64\x61ta_transfer_address\x18\x08 \x01(\t\x12&\n\x1e\x63ross_trainer_cache_size_bytes\x18\x0b \x01(\x03\x12%\n\x1dsnapshot_max_chunk_size_bytes\x18\x0c \x01(\x03\x12 \n\x18shutdown_quiet_period_ms\x18\t \x01(\x03\x42WZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_protob\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.service_config_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto'
+  _DISPATCHERCONFIG._serialized_start=125
+  _DISPATCHERCONFIG._serialized_end=496
+  _WORKERCONFIG._serialized_start=499
+  _WORKERCONFIG._serialized_end=856
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/snapshot_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/snapshot_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8cd236cbce28fbfb8ec7b9292ff4a35185188ad
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/snapshot_pb2.py
@@ -0,0 +1,37 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/snapshot.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\'tensorflow/core/protobuf/snapshot.proto\x12\x1ctensorflow.data.experimental\x1a&tensorflow/core/framework/tensor.proto\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\"9\n\x0eSnapshotRecord\x12\'\n\x06tensor\x18\x01 \x03(\x0b\x32\x17.tensorflow.TensorProto\"\xb8\x01\n\x16SnapshotMetadataRecord\x12\x12\n\ngraph_hash\x18\x01 \x01(\t\x12\x0e\n\x06run_id\x18\x02 \x01(\t\x12\x1a\n\x12\x63reation_timestamp\x18\x03 \x01(\x03\x12\x0f\n\x07version\x18\x04 \x01(\x03\x12#\n\x05\x64type\x18\x05 \x03(\x0e\x32\x14.tensorflow.DataType\x12\x14\n\x0cnum_elements\x18\x06 \x01(\x03\x12\x12\n\tfinalized\x18\xe8\x07 \x01(\x08\"_\n\x0eTensorMetadata\x12\x32\n\x0ctensor_shape\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12\x19\n\x11tensor_size_bytes\x18\x03 \x01(\x03\"_\n\x16SnapshotTensorMetadata\x12\x45\n\x0ftensor_metadata\x18\x01 \x03(\x0b\x32,.tensorflow.data.experimental.TensorMetadata\"H\n\x1b\x44istributedSnapshotMetadata\x12\x14\n\x0c\x65lement_spec\x18\x01 \x01(\x0c\x12\x13\n\x0b\x63ompression\x18\x02 \x01(\tBWZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_protob\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.snapshot_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto'
+  _SNAPSHOTRECORD._serialized_start=198
+  _SNAPSHOTRECORD._serialized_end=255
+  _SNAPSHOTMETADATARECORD._serialized_start=258
+  _SNAPSHOTMETADATARECORD._serialized_end=442
+  _TENSORMETADATA._serialized_start=444
+  _TENSORMETADATA._serialized_end=539
+  _SNAPSHOTTENSORMETADATA._serialized_start=541
+  _SNAPSHOTTENSORMETADATA._serialized_end=636
+  _DISTRIBUTEDSNAPSHOTMETADATA._serialized_start=638
+  _DISTRIBUTEDSNAPSHOTMETADATA._serialized_end=710
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/status_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/status_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc50e1864a6c5a588410334bb748d873437e55d7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/status_pb2.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/status.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.tsl.protobuf import status_pb2 as tsl_dot_protobuf_dot_status__pb2
+
+from tensorflow.tsl.protobuf.status_pb2 import *
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n%tensorflow/core/protobuf/status.proto\x12\x10tensorflow.dummy\x1a\x19tsl/protobuf/status.protoBWZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_protoP\x00\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.status_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto'
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/struct_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/struct_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e9389b41c0a752b48bc502e85f9a1fddc32a26c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/struct_pb2.py
@@ -0,0 +1,53 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/struct.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n%tensorflow/core/protobuf/struct.proto\x12\ntensorflow\x1a&tensorflow/core/framework/tensor.proto\x1a,tensorflow/core/framework/tensor_shape.proto\x1a%tensorflow/core/framework/types.proto\"\xf1\x05\n\x0fStructuredValue\x12+\n\nnone_value\x18\x01 \x01(\x0b\x32\x15.tensorflow.NoneValueH\x00\x12\x17\n\rfloat64_value\x18\x0b \x01(\x01H\x00\x12\x15\n\x0bint64_value\x18\x0c \x01(\x12H\x00\x12\x16\n\x0cstring_value\x18\r \x01(\tH\x00\x12\x14\n\nbool_value\x18\x0e \x01(\x08H\x00\x12:\n\x12tensor_shape_value\x18\x1f \x01(\x0b\x32\x1c.tensorflow.TensorShapeProtoH\x00\x12\x32\n\x12tensor_dtype_value\x18  \x01(\x0e\x32\x14.tensorflow.DataTypeH\x00\x12\x38\n\x11tensor_spec_value\x18! \x01(\x0b\x32\x1b.tensorflow.TensorSpecProtoH\x00\x12\x34\n\x0ftype_spec_value\x18\" \x01(\x0b\x32\x19.tensorflow.TypeSpecProtoH\x00\x12G\n\x19\x62ounded_tensor_spec_value\x18# \x01(\x0b\x32\".tensorflow.BoundedTensorSpecProtoH\x00\x12+\n\nlist_value\x18\x33 \x01(\x0b\x32\x15.tensorflow.ListValueH\x00\x12-\n\x0btuple_value\x18\x34 \x01(\x0b\x32\x16.tensorflow.TupleValueH\x00\x12+\n\ndict_value\x18\x35 \x01(\x0b\x32\x15.tensorflow.DictValueH\x00\x12\x38\n\x11named_tuple_value\x18\x36 \x01(\x0b\x32\x1b.tensorflow.NamedTupleValueH\x00\x12/\n\x0ctensor_value\x18\x37 \x01(\x0b\x32\x17.tensorflow.TensorProtoH\x00\x12.\n\x0bnumpy_value\x18\x38 \x01(\x0b\x32\x17.tensorflow.TensorProtoH\x00\x42\x06\n\x04kind\"\x0b\n\tNoneValue\"8\n\tListValue\x12+\n\x06values\x18\x01 \x03(\x0b\x32\x1b.tensorflow.StructuredValue\"9\n\nTupleValue\x12+\n\x06values\x18\x01 \x03(\x0b\x32\x1b.tensorflow.StructuredValue\"\x8a\x01\n\tDictValue\x12\x31\n\x06\x66ields\x18\x01 \x03(\x0b\x32!.tensorflow.DictValue.FieldsEntry\x1aJ\n\x0b\x46ieldsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12*\n\x05value\x18\x02 \x01(\x0b\x32\x1b.tensorflow.StructuredValue:\x02\x38\x01\"D\n\tPairValue\x12\x0b\n\x03key\x18\x01 \x01(\t\x12*\n\x05value\x18\x02 \x01(\x0b\x32\x1b.tensorflow.StructuredValue\"F\n\x0fNamedTupleValue\x12\x0c\n\x04name\x18\x01 \x01(\t\x12%\n\x06values\x18\x02 \x03(\x0b\x32\x15.tensorflow.PairValue\"q\n\x0fTensorSpecProto\x12\x0c\n\x04name\x18\x01 \x01(\t\x12+\n\x05shape\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12#\n\x05\x64type\x18\x03 \x01(\x0e\x32\x14.tensorflow.DataType\"\xcc\x01\n\x16\x42oundedTensorSpecProto\x12\x0c\n\x04name\x18\x01 \x01(\t\x12+\n\x05shape\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12#\n\x05\x64type\x18\x03 \x01(\x0e\x32\x14.tensorflow.DataType\x12(\n\x07minimum\x18\x04 \x01(\x0b\x32\x17.tensorflow.TensorProto\x12(\n\x07maximum\x18\x05 \x01(\x0b\x32\x17.tensorflow.TensorProto\"\xf8\x03\n\rTypeSpecProto\x12@\n\x0ftype_spec_class\x18\x01 \x01(\x0e\x32\'.tensorflow.TypeSpecProto.TypeSpecClass\x12/\n\ntype_state\x18\x02 \x01(\x0b\x32\x1b.tensorflow.StructuredValue\x12\x1c\n\x14type_spec_class_name\x18\x03 \x01(\t\x12\x1b\n\x13num_flat_components\x18\x04 \x01(\x05\"\xb8\x02\n\rTypeSpecClass\x12\x0b\n\x07UNKNOWN\x10\x00\x12\x16\n\x12SPARSE_TENSOR_SPEC\x10\x01\x12\x17\n\x13INDEXED_SLICES_SPEC\x10\x02\x12\x16\n\x12RAGGED_TENSOR_SPEC\x10\x03\x12\x15\n\x11TENSOR_ARRAY_SPEC\x10\x04\x12\x15\n\x11\x44\x41TA_DATASET_SPEC\x10\x05\x12\x16\n\x12\x44\x41TA_ITERATOR_SPEC\x10\x06\x12\x11\n\rOPTIONAL_SPEC\x10\x07\x12\x14\n\x10PER_REPLICA_SPEC\x10\x08\x12\x11\n\rVARIABLE_SPEC\x10\t\x12\x16\n\x12ROW_PARTITION_SPEC\x10\n\x12\x18\n\x14REGISTERED_TYPE_SPEC\x10\x0c\x12\x17\n\x13\x45XTENSION_TYPE_SPEC\x10\r\"\x04\x08\x0b\x10\x0b\x42WZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_protob\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.struct_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto'
+  _DICTVALUE_FIELDSENTRY._options = None
+  _DICTVALUE_FIELDSENTRY._serialized_options = b'8\001'
+  _STRUCTUREDVALUE._serialized_start=179
+  _STRUCTUREDVALUE._serialized_end=932
+  _NONEVALUE._serialized_start=934
+  _NONEVALUE._serialized_end=945
+  _LISTVALUE._serialized_start=947
+  _LISTVALUE._serialized_end=1003
+  _TUPLEVALUE._serialized_start=1005
+  _TUPLEVALUE._serialized_end=1062
+  _DICTVALUE._serialized_start=1065
+  _DICTVALUE._serialized_end=1203
+  _DICTVALUE_FIELDSENTRY._serialized_start=1129
+  _DICTVALUE_FIELDSENTRY._serialized_end=1203
+  _PAIRVALUE._serialized_start=1205
+  _PAIRVALUE._serialized_end=1273
+  _NAMEDTUPLEVALUE._serialized_start=1275
+  _NAMEDTUPLEVALUE._serialized_end=1345
+  _TENSORSPECPROTO._serialized_start=1347
+  _TENSORSPECPROTO._serialized_end=1460
+  _BOUNDEDTENSORSPECPROTO._serialized_start=1463
+  _BOUNDEDTENSORSPECPROTO._serialized_end=1667
+  _TYPESPECPROTO._serialized_start=1670
+  _TYPESPECPROTO._serialized_end=2174
+  _TYPESPECPROTO_TYPESPECCLASS._serialized_start=1862
+  _TYPESPECPROTO_TYPESPECCLASS._serialized_end=2174
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tensor_bundle_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tensor_bundle_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..98c443bfa6dd5fe0aab030f60d3954ebed431981
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tensor_bundle_pb2.py
@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/tensor_bundle.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import tensor_slice_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__slice__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+from tensorflow.core.framework import versions_pb2 as tensorflow_dot_core_dot_framework_dot_versions__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n,tensorflow/core/protobuf/tensor_bundle.proto\x12\ntensorflow\x1a,tensorflow/core/framework/tensor_shape.proto\x1a,tensorflow/core/framework/tensor_slice.proto\x1a%tensorflow/core/framework/types.proto\x1a(tensorflow/core/framework/versions.proto\"\xb1\x01\n\x11\x42undleHeaderProto\x12\x12\n\nnum_shards\x18\x01 \x01(\x05\x12<\n\nendianness\x18\x02 \x01(\x0e\x32(.tensorflow.BundleHeaderProto.Endianness\x12\'\n\x07version\x18\x03 \x01(\x0b\x32\x16.tensorflow.VersionDef\"!\n\nEndianness\x12\n\n\x06LITTLE\x10\x00\x12\x07\n\x03\x42IG\x10\x01\"\xd2\x01\n\x10\x42undleEntryProto\x12#\n\x05\x64type\x18\x01 \x01(\x0e\x32\x14.tensorflow.DataType\x12+\n\x05shape\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12\x10\n\x08shard_id\x18\x03 \x01(\x05\x12\x0e\n\x06offset\x18\x04 \x01(\x03\x12\x0c\n\x04size\x18\x05 \x01(\x03\x12\x0e\n\x06\x63rc32c\x18\x06 \x01(\x07\x12,\n\x06slices\x18\x07 \x03(\x0b\x32\x1c.tensorflow.TensorSliceProtoB\x85\x01\n\x13org.tensorflow.utilB\x12TensorBundleProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.tensor_bundle_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\023org.tensorflow.utilB\022TensorBundleProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _BUNDLEHEADERPROTO._serialized_start=234
+  _BUNDLEHEADERPROTO._serialized_end=411
+  _BUNDLEHEADERPROTO_ENDIANNESS._serialized_start=378
+  _BUNDLEHEADERPROTO_ENDIANNESS._serialized_end=411
+  _BUNDLEENTRYPROTO._serialized_start=414
+  _BUNDLEENTRYPROTO._serialized_end=624
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tensorflow_server_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tensorflow_server_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2d68ddcfc6bb9b0d33af05f4d57e1d1a32ce771
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tensorflow_server_pb2.py
@@ -0,0 +1,29 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/tensorflow_server.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.protobuf import cluster_pb2 as tensorflow_dot_core_dot_protobuf_dot_cluster__pb2
+from tensorflow.core.protobuf import config_pb2 as tensorflow_dot_core_dot_protobuf_dot_config__pb2
+from tensorflow.core.protobuf import device_filters_pb2 as tensorflow_dot_core_dot_protobuf_dot_device__filters__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n0tensorflow/core/protobuf/tensorflow_server.proto\x12\ntensorflow\x1a&tensorflow/core/protobuf/cluster.proto\x1a%tensorflow/core/protobuf/config.proto\x1a-tensorflow/core/protobuf/device_filters.proto\"\x86\x02\n\tServerDef\x12\'\n\x07\x63luster\x18\x01 \x01(\x0b\x32\x16.tensorflow.ClusterDef\x12\x10\n\x08job_name\x18\x02 \x01(\t\x12\x0f\n\x07replica\x18\x08 \x01(\x05\x12\x12\n\ntask_index\x18\x03 \x01(\x05\x12\x37\n\x16\x64\x65\x66\x61ult_session_config\x18\x04 \x01(\x0b\x32\x17.tensorflow.ConfigProto\x12\x10\n\x08protocol\x18\x05 \x01(\t\x12\x0c\n\x04port\x18\x06 \x01(\x05\x12@\n\x16\x63luster_device_filters\x18\x07 \x01(\x0b\x32 .tensorflow.ClusterDeviceFiltersB\x86\x01\n\x1aorg.tensorflow.distruntimeB\x0cServerProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.tensorflow_server_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\032org.tensorflow.distruntimeB\014ServerProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _SERVERDEF._serialized_start=191
+  _SERVERDEF._serialized_end=453
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/compilation_result_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/compilation_result_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..3dead3879bae629eb7b30b2113da53b550efd2aa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/compilation_result_pb2.py
@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/tpu/compilation_result.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.compiler.xla.service import hlo_pb2 as xla_dot_service_dot_hlo__pb2
+from tensorflow.core.protobuf import error_codes_pb2 as tensorflow_dot_core_dot_protobuf_dot_error__codes__pb2
+try:
+  tsl_dot_protobuf_dot_error__codes__pb2 = tensorflow_dot_core_dot_protobuf_dot_error__codes__pb2.tsl_dot_protobuf_dot_error__codes__pb2
+except AttributeError:
+  tsl_dot_protobuf_dot_error__codes__pb2 = tensorflow_dot_core_dot_protobuf_dot_error__codes__pb2.tsl.protobuf.error_codes_pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n5tensorflow/core/protobuf/tpu/compilation_result.proto\x12\x0etensorflow.tpu\x1a\x15xla/service/hlo.proto\x1a*tensorflow/core/protobuf/error_codes.proto\"\xf9\x01\n\x16\x43ompilationResultProto\x12+\n\x0bstatus_code\x18\x01 \x01(\x0e\x32\x16.tensorflow.error.Code\x12\x1c\n\x14status_error_message\x18\x02 \x01(\t\x12!\n\nhlo_protos\x18\x03 \x03(\x0b\x32\r.xla.HloProto\x12\x44\n\nerror_code\x18\x04 \x01(\x0e\x32\x30.tensorflow.tpu.CompilationResultProto.ErrorCode\"+\n\tErrorCode\x12\x0b\n\x07UNKNOWN\x10\x00\x12\x11\n\rOUT_OF_MEMORY\x10\x01\x42\x03\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.tpu.compilation_result_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _COMPILATIONRESULTPROTO._serialized_start=141
+  _COMPILATIONRESULTPROTO._serialized_end=390
+  _COMPILATIONRESULTPROTO_ERRORCODE._serialized_start=347
+  _COMPILATIONRESULTPROTO_ERRORCODE._serialized_end=390
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/dynamic_padding_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/dynamic_padding_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..b69d197f2eafc551c09696bb6240f178cead9641
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/dynamic_padding_pb2.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/tpu/dynamic_padding.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n2tensorflow/core/protobuf/tpu/dynamic_padding.proto\x12\x0etensorflow.tpu\"O\n\nPaddingMap\x12\x11\n\targ_index\x18\x01 \x01(\x05\x12\x13\n\x0bshape_index\x18\x02 \x01(\x05\x12\x19\n\x11padding_arg_index\x18\x03 \x01(\x05\x42\x03\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.tpu.dynamic_padding_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _PADDINGMAP._serialized_start=70
+  _PADDINGMAP._serialized_end=149
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/optimization_parameters_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/optimization_parameters_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..caa4d7485c74a49ffe7c3d2b7b698a855abf188a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/optimization_parameters_pb2.py
@@ -0,0 +1,91 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/tpu/optimization_parameters.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from google.protobuf import wrappers_pb2 as google_dot_protobuf_dot_wrappers__pb2
+from tensorflow.compiler.xla.service import hlo_pb2 as xla_dot_service_dot_hlo__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n:tensorflow/core/protobuf/tpu/optimization_parameters.proto\x12\x0etensorflow.tpu\x1a\x1egoogle/protobuf/wrappers.proto\x1a\x15xla/service/hlo.proto\"h\n\x0e\x43lippingLimits\x12*\n\x05lower\x18\x01 \x01(\x0b\x32\x1b.google.protobuf.FloatValue\x12*\n\x05upper\x18\x02 \x01(\x0b\x32\x1b.google.protobuf.FloatValue\"v\n\x15SimulatedQuantization\x12\x0f\n\x07\x65nabled\x18\x01 \x01(\x08\x12\x37\n\x0f\x63lipping_limits\x18\x02 \x01(\x0b\x32\x1e.tensorflow.tpu.ClippingLimits\x12\x13\n\x0bnum_buckets\x18\x03 \x01(\x05\"\"\n\x13\x44ynamicLearningRate\x12\x0b\n\x03tag\x18\x01 \x01(\x05\"k\n\x0cLearningRate\x12\x12\n\x08\x63onstant\x18\x01 \x01(\x02H\x00\x12\x36\n\x07\x64ynamic\x18\x02 \x01(\x0b\x32#.tensorflow.tpu.DynamicLearningRateH\x00\x42\x0f\n\rlearning_rate\".\n\x11\x41\x64\x61gradParametersJ\x04\x08\x01\x10\x02R\x13initial_accumulator\"u\n\x19\x41\x64\x61gradMomentumParameters\x12\x10\n\x08momentum\x18\x01 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\x01(\x02\x12\x10\n\x08lr_power\x18\x02 \x01(\x02\x12 \n\x18min_servable_mdl_benefit\x18\x03 \x01(\x02\x12\x19\n\x11mdl_mix_in_margin\x18\x04 \x01(\x02\x12 \n\x18mdl_benefit_rampup_coeff\x18\x05 \x01(\x02\x12\x16\n\x0emdl_min_weight\x18\x06 \x01(\x02\x12\x1d\n\x15\x62\x65nefit_revisit_scale\x18\x07 \x01(\x02\x12\x19\n\x11max_event_benefit\x18\x08 \x01(\x02\x12\x19\n\x11max_total_benefit\x18\t \x01(\x02\x12\x16\n\x0emdl_hard_limit\x18\n \x01(\x02\x12\x1e\n\x16hard_limit_min_benefit\x18\x0b \x01(\x08\x12\x16\n\x0emdl_regularize\x18\x0c \x01(\x08J\x04\x08\r\x10\x0eJ\x04\x08\x0e\x10\x0fJ\x04\x08\x0f\x10\x10R\x13initial_accumulatorR\x0einitial_weightR\x0finitial_benefit\"c\n\x12\x41\x64\x61\x64\x65ltaParameters\x12\x0b\n\x03rho\x18\x01 \x01(\x02\x12\x0f\n\x07\x65psilon\x18\x02 \x01(\x02J\x04\x08\x03\x10\x04J\x04\x08\x04\x10\x05R\x13initial_accumulatorR\x0einitial_update\"N\n\x19ProximalAdagradParameters\x12\n\n\x02l1\x18\x01 \x01(\x02\x12\n\n\x02l2\x18\x02 \x01(\x02J\x04\x08\x03\x10\x04R\x13initial_accumulator\"I\n\x14OnlineYogiParameters\x12\n\n\x02l1\x18\x01 \x01(\x02\x12\n\n\x02l2\x18\x02 \x01(\x02\x12\r\n\x05\x62\x65ta2\x18\x03 \x01(\x02J\x04\x08\x06\x10\x07J\x04\x08\x07\x10\x08\"k\n\x16ProximalYogiParameters\x12\n\n\x02l1\x18\x01 \x01(\x02\x12\n\n\x02l2\x18\x02 \x01(\x02\x12\r\n\x05\x62\x65ta1\x18\x03 \x01(\x02\x12\r\n\x05\x62\x65ta2\x18\x04 \x01(\x02\x12\x0f\n\x07\x65psilon\x18\x05 \x01(\x02J\x04\x08\x08\x10\tJ\x04\x08\t\x10\n\"r\n\x1c\x46requencyEstimatorParameters\x12\x0b\n\x03tau\x18\x01 \x01(\x02\x12\x11\n\tmax_delta\x18\x02 \x01(\x02\x12\x19\n\x11outlier_threshold\x18\x03 \x01(\x02\x12\x17\n\x0fweight_exponent\x18\x04 \x01(\x02\"J\n\x1cUserDefinedProgramParameters\x12$\n\x07program\x18\x01 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\x01(\x0b\x32&.tensorflow.tpu.ProximalYogiParametersH\x00\x12K\n\x13\x66requency_estimator\x18\x17 \x01(\x0b\x32,.tensorflow.tpu.FrequencyEstimatorParametersH\x00\x12L\n\x14user_defined_program\x18\x18 \x01(\x0b\x32,.tensorflow.tpu.UserDefinedProgramParametersH\x00\x12\x32\n\x06\x61ssign\x18\x19 \x01(\x0b\x32 .tensorflow.tpu.AssignParametersH\x00\x42\x0c\n\nparametersJ\x04\x08\x01\x10\x02J\x04\x08\x0f\x10\x10\"\x9e\x02\n\x1aStateVariableSpecification\x12\x0c\n\x04name\x18\x01 \x01(\t\x12N\n\x0cuser_defined\x18\x02 \x01(\x0b\x32\x36.tensorflow.tpu.StateVariableSpecification.UserDefinedH\x00\x12Y\n\x12\x66ill_with_constant\x18\x03 \x01(\x0b\x32;.tensorflow.tpu.StateVariableSpecification.FillWithConstantH\x00\x1a\x13\n\x0bUserDefinedJ\x04\x08\x01\x10\x02\x1a)\n\x10\x46illWithConstant\x12\x15\n\rinitial_value\x18\x01 \x01(\x01\x42\x07\n\x05usageb\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.tpu.optimization_parameters_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _FTRLPARAMETERS.fields_by_name['allow_zero_accumulator']._options = None
+  _FTRLPARAMETERS.fields_by_name['allow_zero_accumulator']._serialized_options = b'\030\001'
+  _CLIPPINGLIMITS._serialized_start=133
+  _CLIPPINGLIMITS._serialized_end=237
+  _SIMULATEDQUANTIZATION._serialized_start=239
+  _SIMULATEDQUANTIZATION._serialized_end=357
+  _DYNAMICLEARNINGRATE._serialized_start=359
+  _DYNAMICLEARNINGRATE._serialized_end=393
+  _LEARNINGRATE._serialized_start=395
+  _LEARNINGRATE._serialized_end=502
+  _ADAGRADPARAMETERS._serialized_start=504
+  _ADAGRADPARAMETERS._serialized_end=550
+  _ADAGRADMOMENTUMPARAMETERS._serialized_start=552
+  _ADAGRADMOMENTUMPARAMETERS._serialized_end=669
+  _BOUNDEDADAGRADPARAMETERS._serialized_start=671
+  _BOUNDEDADAGRADPARAMETERS._serialized_end=780
+  _STOCHASTICGRADIENTDESCENTPARAMETERS._serialized_start=782
+  _STOCHASTICGRADIENTDESCENTPARAMETERS._serialized_end=819
+  _FTRLPARAMETERS._serialized_start=822
+  _FTRLPARAMETERS._serialized_end=1004
+  _ADAMPARAMETERS._serialized_start=1007
+  _ADAMPARAMETERS._serialized_end=1160
+  _MOMENTUMPARAMETERS._serialized_start=1162
+  _MOMENTUMPARAMETERS._serialized_end=1243
+  _LIONPARAMETERS._serialized_start=1245
+  _LIONPARAMETERS._serialized_end=1318
+  _RMSPROPPARAMETERS._serialized_start=1320
+  _RMSPROPPARAMETERS._serialized_end=1424
+  _CENTEREDRMSPROPPARAMETERS._serialized_start=1427
+  _CENTEREDRMSPROPPARAMETERS._serialized_end=1557
+  _MDLADAGRADLIGHTPARAMETERS._serialized_start=1560
+  _MDLADAGRADLIGHTPARAMETERS._serialized_end=1973
+  _ADADELTAPARAMETERS._serialized_start=1975
+  _ADADELTAPARAMETERS._serialized_end=2074
+  _PROXIMALADAGRADPARAMETERS._serialized_start=2076
+  _PROXIMALADAGRADPARAMETERS._serialized_end=2154
+  _ONLINEYOGIPARAMETERS._serialized_start=2156
+  _ONLINEYOGIPARAMETERS._serialized_end=2229
+  _PROXIMALYOGIPARAMETERS._serialized_start=2231
+  _PROXIMALYOGIPARAMETERS._serialized_end=2338
+  _FREQUENCYESTIMATORPARAMETERS._serialized_start=2340
+  _FREQUENCYESTIMATORPARAMETERS._serialized_end=2454
+  _USERDEFINEDPROGRAMPARAMETERS._serialized_start=2456
+  _USERDEFINEDPROGRAMPARAMETERS._serialized_end=2530
+  _ASSIGNPARAMETERS._serialized_start=2532
+  _ASSIGNPARAMETERS._serialized_end=2550
+  _GRADIENTACCUMULATIONSTATUS._serialized_start=2552
+  _GRADIENTACCUMULATIONSTATUS._serialized_end=2634
+  _GRADIENTACCUMULATIONSTATUS_STATUS._serialized_start=2582
+  _GRADIENTACCUMULATIONSTATUS_STATUS._serialized_end=2634
+  _LOWDIMENSIONALPACKINGSTATUS._serialized_start=2636
+  _LOWDIMENSIONALPACKINGSTATUS._serialized_end=2719
+  _LOWDIMENSIONALPACKINGSTATUS_STATUS._serialized_start=2582
+  _LOWDIMENSIONALPACKINGSTATUS_STATUS._serialized_end=2634
+  _HOTIDREPLICATIONCONFIGURATION._serialized_start=2722
+  _HOTIDREPLICATIONCONFIGURATION._serialized_end=2897
+  _HOTIDREPLICATIONCONFIGURATION_STATUS._serialized_start=2825
+  _HOTIDREPLICATIONCONFIGURATION_STATUS._serialized_end=2897
+  _OPTIMIZATIONPARAMETERS._serialized_start=2900
+  _OPTIMIZATIONPARAMETERS._serialized_end=4696
+  _STATEVARIABLESPECIFICATION._serialized_start=4699
+  _STATEVARIABLESPECIFICATION._serialized_end=4985
+  _STATEVARIABLESPECIFICATION_USERDEFINED._serialized_start=4914
+  _STATEVARIABLESPECIFICATION_USERDEFINED._serialized_end=4933
+  _STATEVARIABLESPECIFICATION_FILLWITHCONSTANT._serialized_start=4935
+  _STATEVARIABLESPECIFICATION_FILLWITHCONSTANT._serialized_end=4976
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/topology_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/topology_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2af36e168f730a790aef0b9c9427847199605e7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/topology_pb2.py
@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/tpu/topology.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n+tensorflow/core/protobuf/tpu/topology.proto\x12\x0etensorflow.tpu\"\xc1\x01\n\x12TPUHardwareFeature\x12N\n\x11\x65mbedding_feature\x18\x01 \x01(\x0e\x32\x33.tensorflow.tpu.TPUHardwareFeature.EmbeddingFeature\x12&\n\x1enum_embedding_devices_per_chip\x18\x02 \x01(\x05\"3\n\x10\x45mbeddingFeature\x12\x0f\n\x0bUNSUPPORTED\x10\x00\x12\x06\n\x02V1\x10\x01\x12\x06\n\x02V2\x10\x02\"\xb6\x01\n\rTopologyProto\x12\x12\n\nmesh_shape\x18\x01 \x03(\x05\x12\x11\n\tnum_tasks\x18\x02 \x01(\x05\x12 \n\x18num_tpu_devices_per_task\x18\x03 \x01(\x05\x12\x1a\n\x12\x64\x65vice_coordinates\x18\x04 \x03(\x05\x12@\n\x14tpu_hardware_feature\x18\x05 \x01(\x0b\x32\".tensorflow.tpu.TPUHardwareFeatureB\x03\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.tpu.topology_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _TPUHARDWAREFEATURE._serialized_start=64
+  _TPUHARDWAREFEATURE._serialized_end=257
+  _TPUHARDWAREFEATURE_EMBEDDINGFEATURE._serialized_start=206
+  _TPUHARDWAREFEATURE_EMBEDDINGFEATURE._serialized_end=257
+  _TOPOLOGYPROTO._serialized_start=260
+  _TOPOLOGYPROTO._serialized_end=442
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/tpu_embedding_configuration_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/tpu_embedding_configuration_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..13fbe1b7670aa97fa3c48152f88b37a3656e6246
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/tpu/tpu_embedding_configuration_pb2.py
@@ -0,0 +1,38 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/tpu/tpu_embedding_configuration.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.protobuf.tpu import optimization_parameters_pb2 as tensorflow_dot_core_dot_protobuf_dot_tpu_dot_optimization__parameters__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n>tensorflow/core/protobuf/tpu/tpu_embedding_configuration.proto\x12\x0etensorflow.tpu\x1a:tensorflow/core/protobuf/tpu/optimization_parameters.proto\"\x93\x08\n\x19TPUEmbeddingConfiguration\x12S\n\x10table_descriptor\x18\x01 \x03(\x0b\x32\x39.tensorflow.tpu.TPUEmbeddingConfiguration.TableDescriptor\x12<\n\x04mode\x18\x02 \x01(\x0e\x32..tensorflow.tpu.TPUEmbeddingConfiguration.Mode\x12\"\n\x1a\x62\x61tch_size_per_tensor_core\x18\x03 \x01(\x05\x12\x11\n\tnum_hosts\x18\x04 \x01(\x05\x12\x18\n\x10num_tensor_cores\x18\x05 \x01(\x05\x12U\n\x11sharding_strategy\x18\x06 \x01(\x0e\x32:.tensorflow.tpu.TPUEmbeddingConfiguration.ShardingStrategy\x12+\n#pipeline_execution_with_tensor_core\x18\x07 \x01(\x08\x12\x1e\n\x16profile_data_directory\x18\t \x01(\t\x12W\n\x12\x66\x65\x61ture_descriptor\x18\n \x03(\x0b\x32;.tensorflow.tpu.TPUEmbeddingConfiguration.FeatureDescriptor\x12M\n\rspmd_sharding\x18\x0b \x01(\x0b\x32\x36.tensorflow.tpu.TPUEmbeddingConfiguration.SpmdSharding\x1a\xaa\x01\n\x0fTableDescriptor\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x17\n\x0fvocabulary_size\x18\x02 \x01(\x03\x12\x11\n\tdimension\x18\x03 \x01(\x05\x12\x14\n\x0cnum_features\x18\x04 \x01(\x05\x12G\n\x17optimization_parameters\x18\x05 \x01(\x0b\x32&.tensorflow.tpu.OptimizationParameters\x1aH\n\x11\x46\x65\x61tureDescriptor\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x10\n\x08table_id\x18\x02 \x01(\x05\x12\x13\n\x0binput_shape\x18\x03 \x03(\x05\x1a>\n\x0cSpmdSharding\x12\x0f\n\x07\x65nabled\x18\x01 \x01(\x08\x12\x1d\n\x15num_cores_per_replica\x18\x02 \x01(\x05\"L\n\x04Mode\x12\x0f\n\x0bUNSPECIFIED\x10\x00\x12\r\n\tINFERENCE\x10\x01\x12\x0c\n\x08TRAINING\x10\x02\x12\x16\n\x12\x42\x41\x43KWARD_PASS_ONLY\x10\x03\",\n\x10ShardingStrategy\x12\x0f\n\x0b\x44IV_DEFAULT\x10\x00\x12\x07\n\x03MOD\x10\x01J\x04\x08\x08\x10\tR\routput_layout\"\x13\n\x11TPUEmbeddingErrorb\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.tpu.tpu_embedding_configuration_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _TPUEMBEDDINGCONFIGURATION._serialized_start=143
+  _TPUEMBEDDINGCONFIGURATION._serialized_end=1186
+  _TPUEMBEDDINGCONFIGURATION_TABLEDESCRIPTOR._serialized_start=733
+  _TPUEMBEDDINGCONFIGURATION_TABLEDESCRIPTOR._serialized_end=903
+  _TPUEMBEDDINGCONFIGURATION_FEATUREDESCRIPTOR._serialized_start=905
+  _TPUEMBEDDINGCONFIGURATION_FEATUREDESCRIPTOR._serialized_end=977
+  _TPUEMBEDDINGCONFIGURATION_SPMDSHARDING._serialized_start=979
+  _TPUEMBEDDINGCONFIGURATION_SPMDSHARDING._serialized_end=1041
+  _TPUEMBEDDINGCONFIGURATION_MODE._serialized_start=1043
+  _TPUEMBEDDINGCONFIGURATION_MODE._serialized_end=1119
+  _TPUEMBEDDINGCONFIGURATION_SHARDINGSTRATEGY._serialized_start=1121
+  _TPUEMBEDDINGCONFIGURATION_SHARDINGSTRATEGY._serialized_end=1165
+  _TPUEMBEDDINGERROR._serialized_start=1188
+  _TPUEMBEDDINGERROR._serialized_end=1207
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/trackable_object_graph_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/trackable_object_graph_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe236a69b68c38d78a490bc74066c48b87649282
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/trackable_object_graph_pb2.py
@@ -0,0 +1,37 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/trackable_object_graph.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from google.protobuf import wrappers_pb2 as google_dot_protobuf_dot_wrappers__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n5tensorflow/core/protobuf/trackable_object_graph.proto\x12\ntensorflow\x1a\x1egoogle/protobuf/wrappers.proto\"\xf3\x05\n\x14TrackableObjectGraph\x12?\n\x05nodes\x18\x01 \x03(\x0b\x32\x30.tensorflow.TrackableObjectGraph.TrackableObject\x1a\x99\x05\n\x0fTrackableObject\x12R\n\x08\x63hildren\x18\x01 \x03(\x0b\x32@.tensorflow.TrackableObjectGraph.TrackableObject.ObjectReference\x12U\n\nattributes\x18\x02 \x03(\x0b\x32\x41.tensorflow.TrackableObjectGraph.TrackableObject.SerializedTensor\x12^\n\x0eslot_variables\x18\x03 \x03(\x0b\x32\x46.tensorflow.TrackableObjectGraph.TrackableObject.SlotVariableReference\x12\x35\n\x10registered_saver\x18\x04 \x01(\x0b\x32\x1b.tensorflow.RegisteredSaver\x12\x39\n\x15has_checkpoint_values\x18\x05 \x01(\x0b\x32\x1a.google.protobuf.BoolValue\x1a\x36\n\x0fObjectReference\x12\x0f\n\x07node_id\x18\x01 \x01(\x05\x12\x12\n\nlocal_name\x18\x02 \x01(\t\x1a\x63\n\x10SerializedTensor\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x11\n\tfull_name\x18\x02 \x01(\t\x12\x16\n\x0e\x63heckpoint_key\x18\x03 \x01(\tJ\x04\x08\x04\x10\x05R\x10optional_restore\x1al\n\x15SlotVariableReference\x12!\n\x19original_variable_node_id\x18\x01 \x01(\x05\x12\x11\n\tslot_name\x18\x02 \x01(\t\x12\x1d\n\x15slot_variable_node_id\x18\x03 \x01(\x05\"4\n\x0fRegisteredSaver\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x13\n\x0bobject_name\x18\x02 \x01(\tBZZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.trackable_object_graph_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _TRACKABLEOBJECTGRAPH._serialized_start=102
+  _TRACKABLEOBJECTGRAPH._serialized_end=857
+  _TRACKABLEOBJECTGRAPH_TRACKABLEOBJECT._serialized_start=192
+  _TRACKABLEOBJECTGRAPH_TRACKABLEOBJECT._serialized_end=857
+  _TRACKABLEOBJECTGRAPH_TRACKABLEOBJECT_OBJECTREFERENCE._serialized_start=592
+  _TRACKABLEOBJECTGRAPH_TRACKABLEOBJECT_OBJECTREFERENCE._serialized_end=646
+  _TRACKABLEOBJECTGRAPH_TRACKABLEOBJECT_SERIALIZEDTENSOR._serialized_start=648
+  _TRACKABLEOBJECTGRAPH_TRACKABLEOBJECT_SERIALIZEDTENSOR._serialized_end=747
+  _TRACKABLEOBJECTGRAPH_TRACKABLEOBJECT_SLOTVARIABLEREFERENCE._serialized_start=749
+  _TRACKABLEOBJECTGRAPH_TRACKABLEOBJECT_SLOTVARIABLEREFERENCE._serialized_end=857
+  _REGISTEREDSAVER._serialized_start=859
+  _REGISTEREDSAVER._serialized_end=911
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/transport_options_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/transport_options_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..63ef6c9715c53359105df0116eafdf91e32d399c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/transport_options_pb2.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/transport_options.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n0tensorflow/core/protobuf/transport_options.proto\x12\ntensorflow\"*\n\x10RecvBufRespExtra\x12\x16\n\x0etensor_content\x18\x01 \x03(\x0c\x42WZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_protob\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.transport_options_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto'
+  _RECVBUFRESPEXTRA._serialized_start=64
+  _RECVBUFRESPEXTRA._serialized_end=106
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/verifier_config_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/verifier_config_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..5edc7aa3ff2ae16a675b41b7d9db04ebcd8fba6d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/protobuf/verifier_config_pb2.py
@@ -0,0 +1,28 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/protobuf/verifier_config.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n.tensorflow/core/protobuf/verifier_config.proto\x12\ntensorflow\"\x9b\x01\n\x0eVerifierConfig\x12\"\n\x1averification_timeout_in_ms\x18\x01 \x01(\x03\x12=\n\x12structure_verifier\x18\x02 \x01(\x0e\x32!.tensorflow.VerifierConfig.Toggle\"&\n\x06Toggle\x12\x0b\n\x07\x44\x45\x46\x41ULT\x10\x00\x12\x06\n\x02ON\x10\x01\x12\x07\n\x03OFF\x10\x02\x42\x8c\x01\n\x18org.tensorflow.frameworkB\x14VerifierConfigProtosP\x01ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.protobuf.verifier_config_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\030org.tensorflow.frameworkB\024VerifierConfigProtosP\001ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto\370\001\001'
+  _VERIFIERCONFIG._serialized_start=63
+  _VERIFIERCONFIG._serialized_end=218
+  _VERIFIERCONFIG_TOGGLE._serialized_start=180
+  _VERIFIERCONFIG_TOGGLE._serialized_end=218
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/event_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/event_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..7af6b7a46de5c08a195df9c46656e9ac6cd05e7d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/event_pb2.py
@@ -0,0 +1,61 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/util/event.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import summary_pb2 as tensorflow_dot_core_dot_framework_dot_summary__pb2
+try:
+  tsl_dot_protobuf_dot_histogram__pb2 = tensorflow_dot_core_dot_framework_dot_summary__pb2.tsl_dot_protobuf_dot_histogram__pb2
+except AttributeError:
+  tsl_dot_protobuf_dot_histogram__pb2 = tensorflow_dot_core_dot_framework_dot_summary__pb2.tsl.protobuf.histogram_pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n tensorflow/core/util/event.proto\x12\ntensorflow\x1a\'tensorflow/core/framework/summary.proto\"\xf4\x02\n\x05\x45vent\x12\x11\n\twall_time\x18\x01 \x01(\x01\x12\x0c\n\x04step\x18\x02 \x01(\x03\x12\x16\n\x0c\x66ile_version\x18\x03 \x01(\tH\x00\x12\x13\n\tgraph_def\x18\x04 \x01(\x0cH\x00\x12&\n\x07summary\x18\x05 \x01(\x0b\x32\x13.tensorflow.SummaryH\x00\x12\x31\n\x0blog_message\x18\x06 \x01(\x0b\x32\x16.tensorflow.LogMessageB\x02\x18\x01H\x00\x12-\n\x0bsession_log\x18\x07 \x01(\x0b\x32\x16.tensorflow.SessionLogH\x00\x12<\n\x13tagged_run_metadata\x18\x08 \x01(\x0b\x32\x1d.tensorflow.TaggedRunMetadataH\x00\x12\x18\n\x0emeta_graph_def\x18\t \x01(\x0cH\x00\x12\x33\n\x0fsource_metadata\x18\n \x01(\x0b\x32\x1a.tensorflow.SourceMetadataB\x06\n\x04what\" \n\x0eSourceMetadata\x12\x0e\n\x06writer\x18\x01 \x01(\t\"\xa1\x01\n\nLogMessage\x12+\n\x05level\x18\x01 \x01(\x0e\x32\x1c.tensorflow.LogMessage.Level\x12\x0f\n\x07message\x18\x02 \x01(\t\"Q\n\x05Level\x12\x0b\n\x07UNKNOWN\x10\x00\x12\r\n\tDEBUGGING\x10\n\x12\x08\n\x04INFO\x10\x14\x12\x08\n\x04WARN\x10\x1e\x12\t\n\x05\x45RROR\x10(\x12\t\n\x05\x46\x41TAL\x10\x32\x1a\x02\x18\x01:\x02\x18\x01\"\xb6\x01\n\nSessionLog\x12\x34\n\x06status\x18\x01 \x01(\x0e\x32$.tensorflow.SessionLog.SessionStatus\x12\x17\n\x0f\x63heckpoint_path\x18\x02 \x01(\t\x12\x0b\n\x03msg\x18\x03 \x01(\t\"L\n\rSessionStatus\x12\x16\n\x12STATUS_UNSPECIFIED\x10\x00\x12\t\n\x05START\x10\x01\x12\x08\n\x04STOP\x10\x02\x12\x0e\n\nCHECKPOINT\x10\x03\"6\n\x11TaggedRunMetadata\x12\x0b\n\x03tag\x18\x01 \x01(\t\x12\x14\n\x0crun_metadata\x18\x02 \x01(\x0c\"$\n\x0eWatchdogConfig\x12\x12\n\ntimeout_ms\x18\x01 \x01(\x03\"&\n\x11RequestedExitCode\x12\x11\n\texit_code\x18\x01 \x01(\x05\"\xb6\x01\n\x16WorkerHeartbeatRequest\x12\x35\n\rshutdown_mode\x18\x01 \x01(\x0e\x32\x1e.tensorflow.WorkerShutdownMode\x12\x33\n\x0fwatchdog_config\x18\x02 \x01(\x0b\x32\x1a.tensorflow.WatchdogConfig\x12\x30\n\texit_code\x18\x03 \x01(\x0b\x32\x1d.tensorflow.RequestedExitCode\"\x83\x01\n\x17WorkerHeartbeatResponse\x12/\n\rhealth_status\x18\x01 \x01(\x0e\x32\x18.tensorflow.WorkerHealth\x12%\n\nworker_log\x18\x02 \x03(\x0b\x32\x11.tensorflow.Event\x12\x10\n\x08hostname\x18\x03 \x01(\t*[\n\x0cWorkerHealth\x12\x06\n\x02OK\x10\x00\x12\x1c\n\x18RECEIVED_SHUTDOWN_SIGNAL\x10\x01\x12\x12\n\x0eINTERNAL_ERROR\x10\x02\x12\x11\n\rSHUTTING_DOWN\x10\x03*k\n\x12WorkerShutdownMode\x12\x0b\n\x07\x44\x45\x46\x41ULT\x10\x00\x12\x12\n\x0eNOT_CONFIGURED\x10\x01\x12\x18\n\x14WAIT_FOR_COORDINATOR\x10\x02\x12\x1a\n\x16SHUTDOWN_AFTER_TIMEOUT\x10\x03\x42p\n\x13org.tensorflow.utilB\x0b\x45ventProtosP\x01ZGgithub.com/tensorflow/tensorflow/tensorflow/go/core/util/event_go_proto\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.util.event_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\023org.tensorflow.utilB\013EventProtosP\001ZGgithub.com/tensorflow/tensorflow/tensorflow/go/core/util/event_go_proto\370\001\001'
+  _EVENT.fields_by_name['log_message']._options = None
+  _EVENT.fields_by_name['log_message']._serialized_options = b'\030\001'
+  _LOGMESSAGE_LEVEL._options = None
+  _LOGMESSAGE_LEVEL._serialized_options = b'\030\001'
+  _LOGMESSAGE._options = None
+  _LOGMESSAGE._serialized_options = b'\030\001'
+  _WORKERHEALTH._serialized_start=1300
+  _WORKERHEALTH._serialized_end=1391
+  _WORKERSHUTDOWNMODE._serialized_start=1393
+  _WORKERSHUTDOWNMODE._serialized_end=1500
+  _EVENT._serialized_start=90
+  _EVENT._serialized_end=462
+  _SOURCEMETADATA._serialized_start=464
+  _SOURCEMETADATA._serialized_end=496
+  _LOGMESSAGE._serialized_start=499
+  _LOGMESSAGE._serialized_end=660
+  _LOGMESSAGE_LEVEL._serialized_start=575
+  _LOGMESSAGE_LEVEL._serialized_end=656
+  _SESSIONLOG._serialized_start=663
+  _SESSIONLOG._serialized_end=845
+  _SESSIONLOG_SESSIONSTATUS._serialized_start=769
+  _SESSIONLOG_SESSIONSTATUS._serialized_end=845
+  _TAGGEDRUNMETADATA._serialized_start=847
+  _TAGGEDRUNMETADATA._serialized_end=901
+  _WATCHDOGCONFIG._serialized_start=903
+  _WATCHDOGCONFIG._serialized_end=939
+  _REQUESTEDEXITCODE._serialized_start=941
+  _REQUESTEDEXITCODE._serialized_end=979
+  _WORKERHEARTBEATREQUEST._serialized_start=982
+  _WORKERHEARTBEATREQUEST._serialized_end=1164
+  _WORKERHEARTBEATRESPONSE._serialized_start=1167
+  _WORKERHEARTBEATRESPONSE._serialized_end=1298
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/memmapped_file_system_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/memmapped_file_system_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..c470a1d88e88c091a45b782085f1d0eace3495fb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/memmapped_file_system_pb2.py
@@ -0,0 +1,28 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/util/memmapped_file_system.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n0tensorflow/core/util/memmapped_file_system.proto\x12\ntensorflow\"S\n#MemmappedFileSystemDirectoryElement\x12\x0e\n\x06offset\x18\x01 \x01(\x04\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\x0e\n\x06length\x18\x03 \x01(\x04\"`\n\x1cMemmappedFileSystemDirectory\x12@\n\x07\x65lement\x18\x01 \x03(\x0b\x32/.tensorflow.MemmappedFileSystemDirectoryElementB\x03\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.util.memmapped_file_system_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\370\001\001'
+  _MEMMAPPEDFILESYSTEMDIRECTORYELEMENT._serialized_start=64
+  _MEMMAPPEDFILESYSTEMDIRECTORYELEMENT._serialized_end=147
+  _MEMMAPPEDFILESYSTEMDIRECTORY._serialized_start=149
+  _MEMMAPPEDFILESYSTEMDIRECTORY._serialized_end=245
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/quantization/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/quantization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/quantization/uniform_quant_ops_attr_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/quantization/uniform_quant_ops_attr_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c3e4b6ea99cc8f856439db45fc2a7a0a52d35a4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/quantization/uniform_quant_ops_attr_pb2.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/util/quantization/uniform_quant_ops_attr.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n>tensorflow/core/util/quantization/uniform_quant_ops_attr.proto\x12\ntensorflow\"\xec\x02\n/UniformQuantizedConvolutionDimensionNumbersAttr\x12\x1d\n\x15input_batch_dimension\x18\x01 \x01(\x03\x12\x1f\n\x17input_feature_dimension\x18\x02 \x01(\x03\x12 \n\x18input_spatial_dimensions\x18\x03 \x03(\x03\x12&\n\x1ekernel_input_feature_dimension\x18\x04 \x01(\x03\x12\'\n\x1fkernel_output_feature_dimension\x18\x05 \x01(\x03\x12!\n\x19kernel_spatial_dimensions\x18\x06 \x03(\x03\x12\x1e\n\x16output_batch_dimension\x18\x07 \x01(\x03\x12 \n\x18output_feature_dimension\x18\x08 \x01(\x03\x12!\n\x19output_spatial_dimensions\x18\t \x03(\x03\x42WZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_protob\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.util.quantization.uniform_quant_ops_attr_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'ZUgithub.com/tensorflow/tensorflow/tensorflow/go/core/protobuf/for_core_protos_go_proto'
+  _UNIFORMQUANTIZEDCONVOLUTIONDIMENSIONNUMBERSATTR._serialized_start=79
+  _UNIFORMQUANTIZEDCONVOLUTIONDIMENSIONNUMBERSATTR._serialized_end=443
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/saved_tensor_slice_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/saved_tensor_slice_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe2a865b239ac80894a363dc61e249d4eea6f327
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/saved_tensor_slice_pb2.py
@@ -0,0 +1,37 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/util/saved_tensor_slice.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
+from tensorflow.core.framework import tensor_slice_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__slice__pb2
+from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
+from tensorflow.core.framework import types_pb2 as tensorflow_dot_core_dot_framework_dot_types__pb2
+from tensorflow.core.framework import versions_pb2 as tensorflow_dot_core_dot_framework_dot_versions__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n-tensorflow/core/util/saved_tensor_slice.proto\x12\ntensorflow\x1a,tensorflow/core/framework/tensor_shape.proto\x1a,tensorflow/core/framework/tensor_slice.proto\x1a&tensorflow/core/framework/tensor.proto\x1a%tensorflow/core/framework/types.proto\x1a(tensorflow/core/framework/versions.proto\"\x9c\x01\n\x0eSavedSliceMeta\x12\x0c\n\x04name\x18\x01 \x01(\t\x12+\n\x05shape\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorShapeProto\x12\"\n\x04type\x18\x03 \x01(\x0e\x32\x14.tensorflow.DataType\x12+\n\x05slice\x18\x04 \x03(\x0b\x32\x1c.tensorflow.TensorSliceProto\"l\n\x14SavedTensorSliceMeta\x12*\n\x06tensor\x18\x01 \x03(\x0b\x32\x1a.tensorflow.SavedSliceMeta\x12(\n\x08versions\x18\x02 \x01(\x0b\x32\x16.tensorflow.VersionDef\"n\n\nSavedSlice\x12\x0c\n\x04name\x18\x01 \x01(\t\x12+\n\x05slice\x18\x02 \x01(\x0b\x32\x1c.tensorflow.TensorSliceProto\x12%\n\x04\x64\x61ta\x18\x03 \x01(\x0b\x32\x17.tensorflow.TensorProto\"i\n\x11SavedTensorSlices\x12.\n\x04meta\x18\x01 \x01(\x0b\x32 .tensorflow.SavedTensorSliceMeta\x12$\n\x04\x64\x61ta\x18\x02 \x01(\x0b\x32\x16.tensorflow.SavedSliceB2\n\x13org.tensorflow.utilB\x16SavedTensorSliceProtosP\x01\xf8\x01\x01\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.util.saved_tensor_slice_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  DESCRIPTOR._serialized_options = b'\n\023org.tensorflow.utilB\026SavedTensorSliceProtosP\001\370\001\001'
+  _SAVEDSLICEMETA._serialized_start=275
+  _SAVEDSLICEMETA._serialized_end=431
+  _SAVEDTENSORSLICEMETA._serialized_start=433
+  _SAVEDTENSORSLICEMETA._serialized_end=541
+  _SAVEDSLICE._serialized_start=543
+  _SAVEDSLICE._serialized_end=653
+  _SAVEDTENSORSLICES._serialized_start=655
+  _SAVEDTENSORSLICES._serialized_end=760
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/test_log_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/test_log_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..60e8272015cca61b2f8b56907f296bab2f806a2f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/core/util/test_log_pb2.py
@@ -0,0 +1,25 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/core/util/test_log.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.tsl.protobuf import test_log_pb2 as tsl_dot_protobuf_dot_test__log__pb2
+
+from tensorflow.tsl.protobuf.test_log_pb2 import *
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n#tensorflow/core/util/test_log.proto\x12\x10tensorflow.dummy\x1a\x1btsl/protobuf/test_log.protoP\x00\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.core.util.test_log_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/rpc/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/rpc/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/rpc/kernels/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/rpc/kernels/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/rpc/kernels/gen_rpc_ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/rpc/kernels/gen_rpc_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..beb3f4ce5316f83453d1276033c621931c98b4bd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/rpc/kernels/gen_rpc_ops.py
@@ -0,0 +1,763 @@
+"""Python wrappers around TensorFlow ops.
+
+This file is MACHINE GENERATED! Do not edit.
+"""
+
+import collections
+
+from tensorflow.python import pywrap_tfe as pywrap_tfe
+from tensorflow.python.eager import context as _context
+from tensorflow.python.eager import core as _core
+from tensorflow.python.eager import execute as _execute
+from tensorflow.python.framework import dtypes as _dtypes
+from tensorflow.security.fuzzing.py import annotation_types as _atypes
+
+from tensorflow.python.framework import op_def_registry as _op_def_registry
+from tensorflow.python.framework import ops as _ops
+from tensorflow.python.framework import op_def_library as _op_def_library
+from tensorflow.python.util.deprecation import deprecated_endpoints
+from tensorflow.python.util import dispatch as _dispatch
+from tensorflow.python.util.tf_export import tf_export
+
+from typing import TypeVar, List, Any
+from typing_extensions import Annotated
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('delete_rpc_future_resource')
+def delete_rpc_future_resource(handle: Annotated[Any, _atypes.Resource], deleter: Annotated[Any, _atypes.Variant], name=None):
+  r"""TODO: add doc.
+
+  Args:
+    handle: A `Tensor` of type `resource`.
+    deleter: A `Tensor` of type `variant`.
+    name: A name for the operation (optional).
+
+  Returns:
+    The created Operation.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "DeleteRpcFutureResource", name, handle, deleter)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_delete_rpc_future_resource(
+          (handle, deleter, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return delete_rpc_future_resource_eager_fallback(
+          handle, deleter, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            delete_rpc_future_resource, (), dict(handle=handle,
+                                                 deleter=deleter, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_delete_rpc_future_resource(
+        (handle, deleter, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "DeleteRpcFutureResource", handle=handle, deleter=deleter, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          delete_rpc_future_resource, (), dict(handle=handle, deleter=deleter,
+                                               name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  return _op
+DeleteRpcFutureResource = tf_export("raw_ops.DeleteRpcFutureResource")(_ops.to_raw_op(delete_rpc_future_resource))
+_dispatcher_for_delete_rpc_future_resource = delete_rpc_future_resource._tf_type_based_dispatcher.Dispatch
+
+
+def delete_rpc_future_resource_eager_fallback(handle: Annotated[Any, _atypes.Resource], deleter: Annotated[Any, _atypes.Variant], name, ctx):
+  handle = _ops.convert_to_tensor(handle, _dtypes.resource)
+  deleter = _ops.convert_to_tensor(deleter, _dtypes.variant)
+  _inputs_flat = [handle, deleter]
+  _attrs = None
+  _result = _execute.execute(b"DeleteRpcFutureResource", 0,
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  _result = None
+  return _result
+
+_RpcCallOutput = collections.namedtuple(
+    "RpcCall",
+    ["future", "deleter"])
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('rpc_call')
+def rpc_call(client: Annotated[Any, _atypes.Resource], method_name: Annotated[Any, _atypes.String], args, timeout_in_ms: Annotated[Any, _atypes.Int64], name=None):
+  r"""TODO: add doc.
+
+  Args:
+    client: A `Tensor` of type `resource`.
+    method_name: A `Tensor` of type `string`.
+    args: A list of `Tensor` objects.
+    timeout_in_ms: A `Tensor` of type `int64`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A tuple of `Tensor` objects (future, deleter).
+
+    future: A `Tensor` of type `resource`.
+    deleter: A `Tensor` of type `variant`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "RpcCall", name, client, method_name, args, timeout_in_ms)
+      _result = _RpcCallOutput._make(_result)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_rpc_call(
+          (client, method_name, args, timeout_in_ms, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return rpc_call_eager_fallback(
+          client, method_name, args, timeout_in_ms, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            rpc_call, (), dict(client=client, method_name=method_name,
+                               args=args, timeout_in_ms=timeout_in_ms,
+                               name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_rpc_call(
+        (client, method_name, args, timeout_in_ms, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "RpcCall", client=client, method_name=method_name, args=args,
+                   timeout_in_ms=timeout_in_ms, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          rpc_call, (), dict(client=client, method_name=method_name,
+                             args=args, timeout_in_ms=timeout_in_ms,
+                             name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("Tin", _op.get_attr("Tin"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "RpcCall", _inputs_flat, _attrs, _result)
+  _result = _RpcCallOutput._make(_result)
+  return _result
+
+RpcCall = tf_export("raw_ops.RpcCall")(_ops.to_raw_op(rpc_call))
+_dispatcher_for_rpc_call = rpc_call._tf_type_based_dispatcher.Dispatch
+
+
+def rpc_call_eager_fallback(client: Annotated[Any, _atypes.Resource], method_name: Annotated[Any, _atypes.String], args, timeout_in_ms: Annotated[Any, _atypes.Int64], name, ctx):
+  _attr_Tin, args = _execute.convert_to_mixed_eager_tensors(args, ctx)
+  client = _ops.convert_to_tensor(client, _dtypes.resource)
+  method_name = _ops.convert_to_tensor(method_name, _dtypes.string)
+  timeout_in_ms = _ops.convert_to_tensor(timeout_in_ms, _dtypes.int64)
+  _inputs_flat = [client, method_name] + list(args) + [timeout_in_ms]
+  _attrs = ("Tin", _attr_Tin)
+  _result = _execute.execute(b"RpcCall", 2, inputs=_inputs_flat, attrs=_attrs,
+                             ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "RpcCall", _inputs_flat, _attrs, _result)
+  _result = _RpcCallOutput._make(_result)
+  return _result
+
+_RpcCheckStatusOutput = collections.namedtuple(
+    "RpcCheckStatus",
+    ["error_code", "error"])
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('rpc_check_status')
+def rpc_check_status(status_or: Annotated[Any, _atypes.Resource], name=None):
+  r"""TODO: add doc.
+
+  Args:
+    status_or: A `Tensor` of type `resource`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A tuple of `Tensor` objects (error_code, error).
+
+    error_code: A `Tensor` of type `int64`.
+    error: A `Tensor` of type `string`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "RpcCheckStatus", name, status_or)
+      _result = _RpcCheckStatusOutput._make(_result)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_rpc_check_status(
+          (status_or, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return rpc_check_status_eager_fallback(
+          status_or, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            rpc_check_status, (), dict(status_or=status_or, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_rpc_check_status(
+        (status_or, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "RpcCheckStatus", status_or=status_or, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          rpc_check_status, (), dict(status_or=status_or, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ()
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "RpcCheckStatus", _inputs_flat, _attrs, _result)
+  _result = _RpcCheckStatusOutput._make(_result)
+  return _result
+
+RpcCheckStatus = tf_export("raw_ops.RpcCheckStatus")(_ops.to_raw_op(rpc_check_status))
+_dispatcher_for_rpc_check_status = rpc_check_status._tf_type_based_dispatcher.Dispatch
+
+
+def rpc_check_status_eager_fallback(status_or: Annotated[Any, _atypes.Resource], name, ctx):
+  status_or = _ops.convert_to_tensor(status_or, _dtypes.resource)
+  _inputs_flat = [status_or]
+  _attrs = None
+  _result = _execute.execute(b"RpcCheckStatus", 2, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "RpcCheckStatus", _inputs_flat, _attrs, _result)
+  _result = _RpcCheckStatusOutput._make(_result)
+  return _result
+
+_RpcClientOutput = collections.namedtuple(
+    "RpcClient",
+    ["client", "method_specs"])
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('rpc_client')
+def rpc_client(server_address: Annotated[Any, _atypes.String], timeout_in_ms: Annotated[Any, _atypes.Int64], shared_name:str="", list_registered_methods:bool=False, name=None):
+  r"""TODO: add doc.
+
+  Args:
+    server_address: A `Tensor` of type `string`.
+    timeout_in_ms: A `Tensor` of type `int64`.
+    shared_name: An optional `string`. Defaults to `""`.
+    list_registered_methods: An optional `bool`. Defaults to `False`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A tuple of `Tensor` objects (client, method_specs).
+
+    client: A `Tensor` of type `resource`.
+    method_specs: A `Tensor` of type `string`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "RpcClient", name, server_address, timeout_in_ms, "shared_name",
+        shared_name, "list_registered_methods", list_registered_methods)
+      _result = _RpcClientOutput._make(_result)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_rpc_client(
+          (server_address, timeout_in_ms, shared_name,
+          list_registered_methods, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return rpc_client_eager_fallback(
+          server_address, timeout_in_ms, shared_name=shared_name,
+          list_registered_methods=list_registered_methods, name=name,
+          ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            rpc_client, (), dict(server_address=server_address,
+                                 timeout_in_ms=timeout_in_ms,
+                                 shared_name=shared_name,
+                                 list_registered_methods=list_registered_methods,
+                                 name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_rpc_client(
+        (server_address, timeout_in_ms, shared_name, list_registered_methods,
+        name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if shared_name is None:
+    shared_name = ""
+  shared_name = _execute.make_str(shared_name, "shared_name")
+  if list_registered_methods is None:
+    list_registered_methods = False
+  list_registered_methods = _execute.make_bool(list_registered_methods, "list_registered_methods")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "RpcClient", server_address=server_address,
+                     timeout_in_ms=timeout_in_ms, shared_name=shared_name,
+                     list_registered_methods=list_registered_methods,
+                     name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          rpc_client, (), dict(server_address=server_address,
+                               timeout_in_ms=timeout_in_ms,
+                               shared_name=shared_name,
+                               list_registered_methods=list_registered_methods,
+                               name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("shared_name", _op.get_attr("shared_name"),
+              "list_registered_methods",
+              _op._get_attr_bool("list_registered_methods"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "RpcClient", _inputs_flat, _attrs, _result)
+  _result = _RpcClientOutput._make(_result)
+  return _result
+
+RpcClient = tf_export("raw_ops.RpcClient")(_ops.to_raw_op(rpc_client))
+_dispatcher_for_rpc_client = rpc_client._tf_type_based_dispatcher.Dispatch
+
+
+def rpc_client_eager_fallback(server_address: Annotated[Any, _atypes.String], timeout_in_ms: Annotated[Any, _atypes.Int64], shared_name: str, list_registered_methods: bool, name, ctx):
+  if shared_name is None:
+    shared_name = ""
+  shared_name = _execute.make_str(shared_name, "shared_name")
+  if list_registered_methods is None:
+    list_registered_methods = False
+  list_registered_methods = _execute.make_bool(list_registered_methods, "list_registered_methods")
+  server_address = _ops.convert_to_tensor(server_address, _dtypes.string)
+  timeout_in_ms = _ops.convert_to_tensor(timeout_in_ms, _dtypes.int64)
+  _inputs_flat = [server_address, timeout_in_ms]
+  _attrs = ("shared_name", shared_name, "list_registered_methods",
+  list_registered_methods)
+  _result = _execute.execute(b"RpcClient", 2, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "RpcClient", _inputs_flat, _attrs, _result)
+  _result = _RpcClientOutput._make(_result)
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('rpc_get_value')
+def rpc_get_value(status_or: Annotated[Any, _atypes.Resource], Tout, name=None):
+  r"""TODO: add doc.
+
+  Args:
+    status_or: A `Tensor` of type `resource`.
+    Tout: A list of `tf.DTypes`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects of type `Tout`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "RpcGetValue", name, status_or, "Tout", Tout)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_rpc_get_value(
+          (status_or, Tout, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return rpc_get_value_eager_fallback(
+          status_or, Tout=Tout, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            rpc_get_value, (), dict(status_or=status_or, Tout=Tout, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_rpc_get_value(
+        (status_or, Tout, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if not isinstance(Tout, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Tout' argument to "
+        "'rpc_get_value' Op, not %r." % Tout)
+  Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "RpcGetValue", status_or=status_or, Tout=Tout, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          rpc_get_value, (), dict(status_or=status_or, Tout=Tout, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if not _result:
+    return _op
+  if _execute.must_record_gradient():
+    _attrs = ("Tout", _op.get_attr("Tout"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "RpcGetValue", _inputs_flat, _attrs, _result)
+  return _result
+
+RpcGetValue = tf_export("raw_ops.RpcGetValue")(_ops.to_raw_op(rpc_get_value))
+_dispatcher_for_rpc_get_value = rpc_get_value._tf_type_based_dispatcher.Dispatch
+
+
+def rpc_get_value_eager_fallback(status_or: Annotated[Any, _atypes.Resource], Tout, name, ctx):
+  if not isinstance(Tout, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'Tout' argument to "
+        "'rpc_get_value' Op, not %r." % Tout)
+  Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
+  status_or = _ops.convert_to_tensor(status_or, _dtypes.resource)
+  _inputs_flat = [status_or]
+  _attrs = ("Tout", Tout)
+  _result = _execute.execute(b"RpcGetValue", len(Tout), inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "RpcGetValue", _inputs_flat, _attrs, _result)
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('rpc_server')
+def rpc_server(server_address: Annotated[Any, _atypes.String], name=None) -> Annotated[Any, _atypes.Resource]:
+  r"""TODO: add doc.
+
+  Args:
+    server_address: A `Tensor` of type `string`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` of type `resource`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "RpcServer", name, server_address)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_rpc_server(
+          (server_address, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return rpc_server_eager_fallback(
+          server_address, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            rpc_server, (), dict(server_address=server_address, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_rpc_server(
+        (server_address, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "RpcServer", server_address=server_address, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          rpc_server, (), dict(server_address=server_address, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ()
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "RpcServer", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+RpcServer = tf_export("raw_ops.RpcServer")(_ops.to_raw_op(rpc_server))
+_dispatcher_for_rpc_server = rpc_server._tf_type_based_dispatcher.Dispatch
+
+
+def rpc_server_eager_fallback(server_address: Annotated[Any, _atypes.String], name, ctx) -> Annotated[Any, _atypes.Resource]:
+  server_address = _ops.convert_to_tensor(server_address, _dtypes.string)
+  _inputs_flat = [server_address]
+  _attrs = None
+  _result = _execute.execute(b"RpcServer", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "RpcServer", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('rpc_server_register')
+def rpc_server_register(server: Annotated[Any, _atypes.Resource], method_name: Annotated[Any, _atypes.String], captured_inputs, f, output_specs: str, input_specs:str="", name=None):
+  r"""TODO: add doc.
+
+  Args:
+    server: A `Tensor` of type `resource`.
+    method_name: A `Tensor` of type `string`.
+    captured_inputs: A list of `Tensor` objects.
+    f: A function decorated with @Defun.
+    output_specs: A `string`.
+    input_specs: An optional `string`. Defaults to `""`.
+    name: A name for the operation (optional).
+
+  Returns:
+    The created Operation.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "RpcServerRegister", name, server, method_name, captured_inputs,
+        "f", f, "input_specs", input_specs, "output_specs", output_specs)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_rpc_server_register(
+          (server, method_name, captured_inputs, f, output_specs, input_specs,
+          name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return rpc_server_register_eager_fallback(
+          server, method_name, captured_inputs, f=f, input_specs=input_specs,
+          output_specs=output_specs, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            rpc_server_register, (), dict(server=server,
+                                          method_name=method_name,
+                                          captured_inputs=captured_inputs,
+                                          f=f, output_specs=output_specs,
+                                          input_specs=input_specs, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_rpc_server_register(
+        (server, method_name, captured_inputs, f, output_specs, input_specs,
+        name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  output_specs = _execute.make_str(output_specs, "output_specs")
+  if input_specs is None:
+    input_specs = ""
+  input_specs = _execute.make_str(input_specs, "input_specs")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "RpcServerRegister", server=server, method_name=method_name,
+                             captured_inputs=captured_inputs, f=f,
+                             output_specs=output_specs,
+                             input_specs=input_specs, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          rpc_server_register, (), dict(server=server,
+                                        method_name=method_name,
+                                        captured_inputs=captured_inputs, f=f,
+                                        output_specs=output_specs,
+                                        input_specs=input_specs, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  return _op
+RpcServerRegister = tf_export("raw_ops.RpcServerRegister")(_ops.to_raw_op(rpc_server_register))
+_dispatcher_for_rpc_server_register = rpc_server_register._tf_type_based_dispatcher.Dispatch
+
+
+def rpc_server_register_eager_fallback(server: Annotated[Any, _atypes.Resource], method_name: Annotated[Any, _atypes.String], captured_inputs, f, output_specs: str, input_specs: str, name, ctx):
+  output_specs = _execute.make_str(output_specs, "output_specs")
+  if input_specs is None:
+    input_specs = ""
+  input_specs = _execute.make_str(input_specs, "input_specs")
+  _attr_Tin, captured_inputs = _execute.convert_to_mixed_eager_tensors(captured_inputs, ctx)
+  server = _ops.convert_to_tensor(server, _dtypes.resource)
+  method_name = _ops.convert_to_tensor(method_name, _dtypes.string)
+  _inputs_flat = [server, method_name] + list(captured_inputs)
+  _attrs = ("Tin", _attr_Tin, "f", f, "input_specs", input_specs,
+  "output_specs", output_specs)
+  _result = _execute.execute(b"RpcServerRegister", 0, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  _result = None
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('rpc_server_start')
+def rpc_server_start(server: Annotated[Any, _atypes.Resource], name=None):
+  r"""TODO: add doc.
+
+  Args:
+    server: A `Tensor` of type `resource`.
+    name: A name for the operation (optional).
+
+  Returns:
+    The created Operation.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "RpcServerStart", name, server)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_rpc_server_start(
+          (server, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return rpc_server_start_eager_fallback(
+          server, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            rpc_server_start, (), dict(server=server, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_rpc_server_start(
+        (server, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "RpcServerStart", server=server, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          rpc_server_start, (), dict(server=server, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  return _op
+RpcServerStart = tf_export("raw_ops.RpcServerStart")(_ops.to_raw_op(rpc_server_start))
+_dispatcher_for_rpc_server_start = rpc_server_start._tf_type_based_dispatcher.Dispatch
+
+
+def rpc_server_start_eager_fallback(server: Annotated[Any, _atypes.Resource], name, ctx):
+  server = _ops.convert_to_tensor(server, _dtypes.resource)
+  _inputs_flat = [server]
+  _attrs = None
+  _result = _execute.execute(b"RpcServerStart", 0, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  _result = None
+  return _result
+
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/rpc/proto/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/rpc/proto/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/rpc/proto/tf_rpc_service_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/rpc/proto/tf_rpc_service_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..39fa8850a62a590e1f1fd5d521d99462ec88a6ce
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/rpc/proto/tf_rpc_service_pb2.py
@@ -0,0 +1,37 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/distribute/experimental/rpc/proto/tf_rpc_service.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
+from tensorflow.core.protobuf import struct_pb2 as tensorflow_dot_core_dot_protobuf_dot_struct__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\nAtensorflow/distribute/experimental/rpc/proto/tf_rpc_service.proto\x12\x0etensorflow.rpc\x1a&tensorflow/core/framework/tensor.proto\x1a%tensorflow/core/protobuf/struct.proto\"M\n\x0b\x43\x61llRequest\x12\x0e\n\x06method\x18\x01 \x01(\t\x12.\n\rinput_tensors\x18\x02 \x03(\x0b\x32\x17.tensorflow.TensorProto\"?\n\x0c\x43\x61llResponse\x12/\n\x0eoutput_tensors\x18\x01 \x03(\x0b\x32\x17.tensorflow.TensorProto\"\r\n\x0bListRequest\"\x87\x01\n\x10RegisteredMethod\x12\x0e\n\x06method\x18\x01 \x01(\t\x12\x30\n\x0binput_specs\x18\x02 \x01(\x0b\x32\x1b.tensorflow.StructuredValue\x12\x31\n\x0coutput_specs\x18\x03 \x01(\x0b\x32\x1b.tensorflow.StructuredValue\"L\n\x0cListResponse\x12<\n\x12registered_methods\x18\x01 \x03(\x0b\x32 .tensorflow.rpc.RegisteredMethod2\x96\x01\n\nRpcService\x12\x43\n\x04\x43\x61ll\x12\x1b.tensorflow.rpc.CallRequest\x1a\x1c.tensorflow.rpc.CallResponse\"\x00\x12\x43\n\x04List\x12\x1b.tensorflow.rpc.ListRequest\x1a\x1c.tensorflow.rpc.ListResponse\"\x00\x62\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.distribute.experimental.rpc.proto.tf_rpc_service_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _CALLREQUEST._serialized_start=164
+  _CALLREQUEST._serialized_end=241
+  _CALLRESPONSE._serialized_start=243
+  _CALLRESPONSE._serialized_end=306
+  _LISTREQUEST._serialized_start=308
+  _LISTREQUEST._serialized_end=321
+  _REGISTEREDMETHOD._serialized_start=324
+  _REGISTEREDMETHOD._serialized_end=459
+  _LISTRESPONSE._serialized_start=461
+  _LISTRESPONSE._serialized_end=537
+  _RPCSERVICE._serialized_start=540
+  _RPCSERVICE._serialized_end=690
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/rpc/proto/tf_rpc_service_pb2_grpc.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/rpc/proto/tf_rpc_service_pb2_grpc.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe854a3a079e616b21c1effaedfbfa409857573d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/distribute/experimental/rpc/proto/tf_rpc_service_pb2_grpc.py
@@ -0,0 +1,63 @@
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+import grpc
+
+from tensorflow.distribute.experimental.rpc.proto import tf_rpc_service_pb2 as tensorflow_dot_distribute_dot_experimental_dot_rpc_dot_proto_dot_tf__rpc__service__pb2
+
+
+class RpcServiceStub(object):
+  # missing associated documentation comment in .proto file
+  pass
+
+  def __init__(self, channel):
+    """Constructor.
+
+    Args:
+      channel: A grpc.Channel.
+    """
+    self.Call = channel.unary_unary(
+        '/tensorflow.rpc.RpcService/Call',
+        request_serializer=tensorflow_dot_distribute_dot_experimental_dot_rpc_dot_proto_dot_tf__rpc__service__pb2.CallRequest.SerializeToString,
+        response_deserializer=tensorflow_dot_distribute_dot_experimental_dot_rpc_dot_proto_dot_tf__rpc__service__pb2.CallResponse.FromString,
+        )
+    self.List = channel.unary_unary(
+        '/tensorflow.rpc.RpcService/List',
+        request_serializer=tensorflow_dot_distribute_dot_experimental_dot_rpc_dot_proto_dot_tf__rpc__service__pb2.ListRequest.SerializeToString,
+        response_deserializer=tensorflow_dot_distribute_dot_experimental_dot_rpc_dot_proto_dot_tf__rpc__service__pb2.ListResponse.FromString,
+        )
+
+
+class RpcServiceServicer(object):
+  # missing associated documentation comment in .proto file
+  pass
+
+  def Call(self, request, context):
+    """RPC for invoking a registered function on remote server.
+    """
+    context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+    context.set_details('Method not implemented!')
+    raise NotImplementedError('Method not implemented!')
+
+  def List(self, request, context):
+    """RPC for listing available methods in a server.
+    """
+    context.set_code(grpc.StatusCode.UNIMPLEMENTED)
+    context.set_details('Method not implemented!')
+    raise NotImplementedError('Method not implemented!')
+
+
+def add_RpcServiceServicer_to_server(servicer, server):
+  rpc_method_handlers = {
+      'Call': grpc.unary_unary_rpc_method_handler(
+          servicer.Call,
+          request_deserializer=tensorflow_dot_distribute_dot_experimental_dot_rpc_dot_proto_dot_tf__rpc__service__pb2.CallRequest.FromString,
+          response_serializer=tensorflow_dot_distribute_dot_experimental_dot_rpc_dot_proto_dot_tf__rpc__service__pb2.CallResponse.SerializeToString,
+      ),
+      'List': grpc.unary_unary_rpc_method_handler(
+          servicer.List,
+          request_deserializer=tensorflow_dot_distribute_dot_experimental_dot_rpc_dot_proto_dot_tf__rpc__service__pb2.ListRequest.FromString,
+          response_serializer=tensorflow_dot_distribute_dot_experimental_dot_rpc_dot_proto_dot_tf__rpc__service__pb2.ListResponse.SerializeToString,
+      ),
+  }
+  generic_handler = grpc.method_handlers_generic_handler(
+      'tensorflow.rpc.RpcService', rpc_method_handlers)
+  server.add_generic_rpc_handlers((generic_handler,))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/proto/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/proto/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/proto/layout_pb2.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/proto/layout_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..02ae56365aed4ab3ce3abfa91bef883b5c2eab59
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/proto/layout_pb2.py
@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: tensorflow/dtensor/proto/layout.proto
+"""Generated protocol buffer code."""
+from google.protobuf.internal import builder as _builder
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n%tensorflow/dtensor/proto/layout.proto\x12\x12tensorflow.dtensor\"+\n\x0cShardingSpec\x12\x15\n\rsharding_spec\x18\x02 \x01(\tJ\x04\x08\x01\x10\x02\"0\n\x12MeshDimensionProto\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x0c\n\x04size\x18\x02 \x01(\x03\"\xfb\x01\n\x0bLayoutProto\x12\x38\n\x0esharding_specs\x18\x01 \x03(\x0b\x32 .tensorflow.dtensor.ShardingSpec\x12\x32\n\x0bmesh_config\x18\x02 \x01(\x0b\x32\x1d.tensorflow.dtensor.MeshProto\x12\x38\n\x04type\x18\x03 \x01(\x0e\x32*.tensorflow.dtensor.LayoutProto.LayoutType\"D\n\nLayoutType\x12\x0b\n\x07UNKNOWN\x10\x00\x12\n\n\x06STATIC\x10\x01\x12\n\n\x06PARTED\x10\x02\x12\x11\n\rSINGLE_DEVICE\x10\x03\"\xeb\x01\n\tMeshProto\x12?\n\x0fmesh_dimensions\x18\x01 \x03(\x0b\x32&.tensorflow.dtensor.MeshDimensionProto\x12\x19\n\x11global_device_ids\x18\x02 \x03(\x03\x12\x18\n\x10local_device_ids\x18\x04 \x03(\x03\x12\x15\n\rlocal_devices\x18\x05 \x03(\t\x12\x16\n\x0eglobal_devices\x18\x06 \x03(\t\x12\x0c\n\x04name\x18\x03 \x01(\t\x12\x14\n\x0cuse_xla_spmd\x18\x07 \x01(\x08\x12\x15\n\rsingle_device\x18\x08 \x01(\tb\x06proto3')
+
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, globals())
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'tensorflow.dtensor.proto.layout_pb2', globals())
+if _descriptor._USE_C_DESCRIPTORS == False:
+
+  DESCRIPTOR._options = None
+  _SHARDINGSPEC._serialized_start=61
+  _SHARDINGSPEC._serialized_end=104
+  _MESHDIMENSIONPROTO._serialized_start=106
+  _MESHDIMENSIONPROTO._serialized_end=154
+  _LAYOUTPROTO._serialized_start=157
+  _LAYOUTPROTO._serialized_end=408
+  _LAYOUTPROTO_LAYOUTTYPE._serialized_start=340
+  _LAYOUTPROTO_LAYOUTTYPE._serialized_end=408
+  _MESHPROTO._serialized_start=411
+  _MESHPROTO._serialized_end=646
+# @@protoc_insertion_point(module_scope)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b38604828608850d6d3ae6391bb3390e78599cb1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/__init__.py
@@ -0,0 +1,17 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""DTensor Python API."""
+
+# This file is left empty intentionally.
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/accelerator_util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/accelerator_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..e32d54b36c1834546c72d674949e97f726edbc70
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/accelerator_util.py
@@ -0,0 +1,298 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utility for working with accelerator systems."""
+
+from typing import List, Optional
+
+from absl import logging
+
+from tensorflow.core.protobuf import cluster_pb2
+from tensorflow.core.protobuf import tensorflow_server_pb2
+from tensorflow.dtensor.python import config
+from tensorflow.dtensor.python import tpu_util
+from tensorflow.python.eager import context
+from tensorflow.python.framework import config as tf_config
+from tensorflow.python.platform import remote_utils
+from tensorflow.python.util.tf_export import tf_export
+
+_INITIALIZED_ACCELERATOR_SYSTEM_TYPE = None
+
+
+def is_initialized() -> bool:
+  """Returns whether accelerator system has been initialized."""
+  return bool(_INITIALIZED_ACCELERATOR_SYSTEM_TYPE)
+
+
+def set_initialized(value):
+  """Sets if accelerator system has been initialized."""
+  global _INITIALIZED_ACCELERATOR_SYSTEM_TYPE
+  _INITIALIZED_ACCELERATOR_SYSTEM_TYPE = value
+
+
+def initialize_multi_client_cluster(job_name: str,
+                                    dtensor_jobs: List[str],
+                                    client_id: int,
+                                    collective_leader: str,
+                                    port: Optional[int] = None,
+                                    gpu_use_nccl_communication: bool = False,
+                                    enable_coordination_service: bool = True):
+  """Initialize GRPC servers and collectives for multi-client DTensor setup.
+
+  This function can be used to initialize a multi-client cluster and enable
+  collective ops. GRPC servers are necessary in the multi-client mode, even
+  when the number of clientis is 1.
+
+  NOTE: this function must be called in an eager context.
+
+  Args:
+    job_name: The job name used by all clients in the DTensor cluster.
+    dtensor_jobs: A list of the DTensor client jobs participating in the
+      cluster. Must be strings of the form "hostname:port".
+    client_id: The ID of the DTensor client this function is being called in.
+    collective_leader: The job/task that will be used to run collectives.
+    port: The port this client's GRPC server will run on. If omitted, use the
+      port from dtensor_jobs for this client.
+    gpu_use_nccl_communication: if True, configure TensorFlow to use NCCL by
+      default.
+    enable_coordination_service: If true, enable distributed coordination
+      service to make sure that workers know the devices on each other, a
+      prerequisite for data transfer through cross-worker rendezvous.
+
+  Raises:
+    RuntimeError: If running inside a tf.function.
+  """
+  assert context.executing_eagerly()
+
+  if not collective_leader.startswith("/job:"):
+    collective_leader = "/job:" + collective_leader
+
+  context.context().configure_collective_ops(
+      use_nccl_communication=gpu_use_nccl_communication,
+      collective_leader=collective_leader)
+  if enable_coordination_service:
+    context.context().configure_coordination_service(
+        service_type="standalone", service_leader=collective_leader)
+
+  config_proto = context.get_config()
+
+  # Construct server def from the host directly instead of relying on
+  # TF_CONFIG.
+  cluster_def = cluster_pb2.ClusterDef()
+  # Note that for bns addresses, we will currently rely on the sorted string
+  # of job name as the order of assigning task ids. This might be brittle once
+  # we have jobs across multiple cells.
+  cluster_def.job.add(name=job_name, tasks=dict(enumerate(dtensor_jobs)))
+  server_def = tensorflow_server_pb2.ServerDef(
+      cluster=cluster_def,
+      default_session_config=config_proto,
+      job_name=job_name,
+      task_index=client_id,
+      protocol=remote_utils.get_default_communication_protocol(),
+      port=port)
+  server_def.default_session_config.rpc_options.num_channels_per_target = 4
+  server_def.default_session_config.experimental.recv_buf_max_chunk = -1
+
+  logging.info("Enabling collectives with server_def: %s", server_def)
+
+  context.context().enable_collective_ops(server_def)
+
+  context.ensure_initialized()
+
+
+@tf_export(
+    "experimental.dtensor.initialize_accelerator_system",
+    "experimental.dtensor.initialize_tpu_system",
+    "experimental.dtensor.initialize_multi_client",
+    v1=[])
+def initialize_accelerator_system(
+    device_type: Optional[str] = None,
+    enable_coordination_service: Optional[bool] = True,
+    num_logical_cpu_devices: Optional[int] = None,
+    experimental_reset_context: Optional[bool] = False,
+) -> str:
+  """Initializes accelerators and communication fabrics for DTensor.
+
+  DTensor configures TensorFlow to run in the local mode or multi-client mode.
+  - In local mode, a mesh can only use devices attached to the current process.
+  - In multi-client mode, a mesh can span across devices from multiple clients.
+
+  If `DTENSOR_JOBS` is non-empty, DTensor configures TensorFlow to run in the
+  multi-client mode using the distributed runtime. In multi-client mode devices
+  on different clients can communicate with each other.
+
+  The following environment variables controls the behavior of this function.
+
+  - `DTENSOR_JOBS`: string, a comma separated list. Each item in the list is
+      of format `{hostname}:{port}`. If empty, DTensor runs in the local mode.
+      Examples of valid `DTENSOR_JOBS` values:
+      - 4 clients on localhost:
+        `localhost:10000,localhost:10001,localhost:10002,localhost:10003`
+      - 2 clients on host1, 2 clients on host2
+        `host1:10000,host1:10001,host2:10000,host2:10003`
+      If the hostnames are BNS addresses, the items must be sorted in
+      alphabetical order.
+  - `DTENSOR_CLIENT_ID`: integer, between `0` to `num_clients - 1`, to identify
+      the client id of the current process. The default value is `0`.
+  - `DTENSOR_JOB_NAME`: string, a string for the name of the TensorFlow job.
+      The job name controls the job name section of the TensorFlow DeviceSpecs,
+      e.g., `job:worker` in `/job:worker/replica:0/task:0/device:TPU:0` when
+      the job name is `worker`.
+      The default value is `localhost` in local mode, and
+      `worker` when in the multi-client mode. All DTensor clients within the
+      same multi-client cluster share the same job name.
+  - `DTENSOR_USE_PARALLEL_EXECUTOR`: string, with its value being `pw` to
+      specify that the backend is Pathways, and TensorFlow otherwise.
+
+  Args:
+    device_type: Type of accelerator to use, can be CPU, GPU, or TPU. If None,
+      uses `tf.experimental.dtensor.preferred_device_type()`.
+    enable_coordination_service: If true, enable distributed coordination
+      service to make sure that workers know the devices on each other, when
+      there is more than 1 client.
+    num_logical_cpu_devices: the number of logical CPU devices per DTensor
+      client. Default to the current number of logical CPU
+      (`dtensor.num_local_devices("CPU")`),when `device_type` is CPU, otherwise
+      set automatially to match the number of local GPU/TPU devices.
+    experimental_reset_context: Reset the tensorflow context. Behaviors of
+      existing TensorFlow objects (e.g. Tensors) are undefined. Set this to True
+      as an escape hatch, if there is no clear way to refactor your code to call
+      initialize_accelerator_system() before calling TensorFlow APIs that
+      initialize the context.
+
+  Returns:
+    device_type: the type of accelerator that was initialized.
+  """
+  global _INITIALIZED_ACCELERATOR_SYSTEM_TYPE
+  assert context.executing_eagerly()
+
+  if is_initialized():
+    raise ValueError(
+        "Accelerator system has already been initialized. "
+        "Call tf.experimental.dtensor.shutdown_accelerator_system() first.")
+
+  if experimental_reset_context:
+    if context.context()._initialized:    # pylint: disable=protected-access
+      logging.warn(
+          "experimental_reset_context is True. "
+          "Resetting TensorFlow context. Existing TensorFlow objects "
+          "(e.g. Tensors and resources) are invalidated."
+      )
+      context.context().ensure_uninitialized()
+
+  if context.context()._initialized:  # pylint: disable=protected-access
+    raise ValueError(
+        "TensorFlow has already been initialized. "
+        "tf.experimental.dtensor.initialize_accelerator_system() must be "
+        "called before TensorFlow is initialized.")
+
+  context.context()._clear_caches()  # pylint: disable=protected-access
+
+  if device_type is None:
+    device_type = config.preferred_device_type()
+
+  device_type = device_type.upper()
+  if device_type not in {"CPU", "GPU", "TPU"}:
+    raise ValueError(f"Unknown device_type {device_type}. "
+                     "Allowed values are CPU, GPU, or TPU")
+
+  if config.gpu_use_nccl_communication():
+    logical_gpu_count = config.num_local_devices("GPU")
+    physical_gpu_count = len(tf_config.list_physical_devices("GPU"))
+    if logical_gpu_count > physical_gpu_count:
+      raise ValueError(
+          "DTENSOR_GPU_USE_NCCL_COMMUNICATION is set for using NCCL. "
+          "NCCL Collectives require one to one mapping between logical and "
+          "physical GPUs. "
+          f"The number of logical GPU ({logical_gpu_count}) "
+          f"is more than the number of physical GPU ({physical_gpu_count})."
+      )
+
+  # Configure logical host CPU devices for accelerators.
+  if device_type in ("GPU", "TPU"):
+    num_local_devices = config.num_local_devices(device_type)
+    if num_logical_cpu_devices is None:
+      num_logical_cpu_devices = max(
+          config.num_local_devices("CPU"), num_local_devices
+      )
+    else:
+      if num_logical_cpu_devices < num_local_devices:
+        raise ValueError(
+            "If set, `num_logical_cpu_devices`"
+            f" (={num_logical_cpu_devices}) must be greater than or"
+            f" equal to the number of local {device_type} devices"
+            f" (={num_local_devices})"
+        )
+
+  if num_logical_cpu_devices is not None:
+    tf_config.set_logical_device_configuration(
+        tf_config.list_physical_devices("CPU")[0],
+        [context.LogicalDeviceConfiguration()]
+        * num_logical_cpu_devices,
+    )
+
+  if not config.is_local_mode():
+    initialize_multi_client_cluster(
+        job_name=config.job_name(),
+        dtensor_jobs=config.jobs(),
+        client_id=config.client_id(),
+        collective_leader=config.full_job_name(task_id=0),
+        gpu_use_nccl_communication=config.gpu_use_nccl_communication(),
+        enable_coordination_service=enable_coordination_service)
+  else:
+    if device_type == "GPU":
+      # Enables Nccl on local mode.
+      context.context(  # pylint: disable=protected-access
+      )._collective_use_nccl_communication = config.gpu_use_nccl_communication(
+      )
+
+  if device_type == "TPU" and not config.backend_is_pw():
+    tpu_util.initialize_tpu_system()
+
+  _INITIALIZED_ACCELERATOR_SYSTEM_TYPE = device_type
+
+  return device_type
+
+
+@tf_export(
+    "experimental.dtensor.shutdown_accelerator_system",
+    "experimental.dtensor.shutdown_tpu_system",
+    v1=[])
+def shutdown_accelerator_system() -> None:
+  """Shuts down the accelerator system."""
+  global _INITIALIZED_ACCELERATOR_SYSTEM_TYPE
+  try:
+    context.async_wait()
+  finally:
+    if not is_initialized():
+      raise ValueError(
+          "Accelerator system is not initialized. Call "
+          "tf.experimental.dtensor.initialize_accelerator_system first."
+      )
+
+    device_type = _INITIALIZED_ACCELERATOR_SYSTEM_TYPE
+
+    if not config.is_local_mode():
+      raise ValueError(
+          "Shutting down accelerator system under multi-client mode is "
+          "not supported."
+      )
+
+    if device_type == "TPU" and not config.backend_is_pw():
+      tpu_util.shutdown_tpu_system()
+
+    # reset TF context to stop gRPC servers.
+    context._reset_context()  # pylint: disable=protected-access
+    context.context()._clear_caches()  # pylint: disable=protected-access
+    _INITIALIZED_ACCELERATOR_SYSTEM_TYPE = None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/api.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a49cd82fa33943cd64a12eef5f52111979358e7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/api.py
@@ -0,0 +1,568 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Core DTensor Python API."""
+
+import contextlib
+import threading
+from typing import Any, Callable, Optional, Sequence
+
+from tensorflow.dtensor.python import dtensor_device
+from tensorflow.dtensor.python import gen_dtensor_ops
+from tensorflow.dtensor.python import layout as layout_lib
+from tensorflow.python.eager import context
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor as tensor_lib
+from tensorflow.python.util import deprecation
+from tensorflow.python.util.tf_export import tf_export
+
+_dtensor_singleton = None
+_dtensor_singleton_lock = threading.Lock()
+
+# -----------------------------------------------------------------------------
+# Main methods to launch DTensor computations.
+
+
+@tf_export("experimental.dtensor.call_with_layout", v1=[])
+def call_with_layout(fn: Callable[...,
+                                  Any], layout: Optional[layout_lib.Layout],
+                     *args, **kwargs) -> Any:
+  """Calls a function in the DTensor device scope if `layout` is not None.
+
+  If `layout` is not None, `fn` consumes DTensor(s) as input and produces a
+  DTensor as output; a DTensor is a tf.Tensor with layout-related attributes.
+
+  If `layout` is None, `fn` consumes and produces regular tf.Tensors.
+
+  Args:
+    fn: A supported TF API function such as tf.zeros.
+    layout: Optional, the layout of the output DTensor.
+    *args:  Arguments given to `fn`.
+    **kwargs: Keyword arguments given to `fn`.
+
+  Returns:
+    The return value of `fn` transformed to a DTensor if requested.
+  """
+  if layout is not None:
+    if context.executing_eagerly():
+      with default_mesh(layout.mesh):
+        with _dtensor_device()._default_layout(layout):  # pylint: disable=protected-access
+          return fn(*args, **kwargs)
+    else:
+      return relayout(fn(*args, **kwargs), layout)
+  return fn(*args, **kwargs)
+
+
+@tf_export("experimental.dtensor.run_on", v1=[])
+@deprecation.deprecated(None, "Use `dtensor.default_mesh` scope instead.")
+@contextlib.contextmanager
+def run_on(mesh: layout_lib.Mesh):
+  """Runs enclosed functions in the DTensor device scope.
+
+  This function returns a scope. All the ops and tf.functions in this scope will
+  run on the DTensor device using the mesh provided.
+  This is useful for wrapping any tf.function that doesn't take a DTensor as
+  input but would like to produce DTensor as result. The scope will also make
+  sure all small constants be replicated as DTensor.
+
+  Args:
+    mesh: A Mesh instance to extract a default mesh from.
+
+  Yields:
+    A context in which all ops and tf.functions will run on the DTensor device.
+  """
+  with default_mesh(mesh):
+    yield
+
+
+@tf_export("experimental.dtensor.default_mesh", v1=[])
+@contextlib.contextmanager
+def default_mesh(mesh: layout_lib.Mesh):
+  """Sets the default DTensor device mesh to use for enclosed functions.
+
+  This function returns a scope. All the ops and tf.functions in this scope will
+  default to this DTensor mesh if a mesh cannot be inferred from any of the
+  inputs
+  This is useful for wrapping any tf.function that doesn't take a DTensor as
+  input but would like to produce DTensor as result. The scope will also make
+  sure all small constants are replicated as DTensors.
+
+  Args:
+    mesh: A Mesh instance to extract a default mesh from.
+
+  Yields:
+    A context in which all ops and tf.functions will run on the given mesh.
+  """
+  if not isinstance(mesh, layout_lib.Mesh):
+    raise ValueError(f"Expect `mesh` to be `Mesh`, got {type(mesh)}")
+
+  with _dtensor_device()._experimental_default_mesh(mesh):  # pylint: disable=protected-access
+    with ops.device(device_name()):
+      yield
+
+
+@tf_export("experimental.dtensor.get_default_mesh", v1=[])
+def get_default_mesh() -> Optional[layout_lib.Mesh]:
+  """Return the default mesh under the current dtensor device context.
+
+  In the case that dtensor device system is not initialized, this function
+  will return None.
+
+  Returns:
+    The current default mesh for the dtensor device context.
+  """
+  if _dtensor_singleton is None:
+    return None
+  else:
+    return _dtensor_singleton._current_default_mesh   # pylint: disable=protected-access
+
+
+@tf_export("experimental.dtensor.device_name", v1=[])
+def device_name() -> str:
+  """Returns the singleton DTensor device's name.
+
+  This function can be used in the following way:
+
+  ```python
+  import tensorflow as tf
+
+  with tf.device(dtensor.device_name()):
+    # ...
+  ```
+  """
+  return _dtensor_device().name
+
+
+@tf_export("experimental.dtensor.is_dtensor", v1=[])
+def is_dtensor(tensor) -> bool:
+  """Check whether the input tensor is a DTensor.
+
+  In Python, a DTensor has the same type as a `tf.Tensor`. This method will
+  let you check and handle the tensor differently if a tf.Tensor is a DTensor.
+
+  Args:
+    tensor: an object to be checked.
+
+  Returns:
+    bool, True if the given tensor is a DTensor.
+  """
+  return _dtensor_device().is_dtensor(tensor)
+
+
+# -----------------------------------------------------------------------------
+# Data transfer methods.
+
+
+@tf_export("experimental.dtensor.copy_to_mesh", v1=[])
+def copy_to_mesh(
+    tensor: Any,
+    layout: layout_lib.Layout,
+    source_layout: Optional[layout_lib.Layout] = None) -> tensor_lib.Tensor:
+  """Copies a tf.Tensor onto the DTensor device with the given layout.
+
+  Copies a regular tf.Tensor onto the DTensor device. Use the mesh attached to
+  `layout` as target mesh. This method currently only supports replicated
+  layouts, or one-to-one copies for sharded layouts.
+
+  Args:
+    tensor: A regular tf.Tensor to be copied as a DTensor.
+    layout: Target layout (and mesh) for the result DTensor.
+    source_layout: Source layout of the tensor before copy. This argument
+      is deprecated.
+
+  Returns:
+    A DTensor on the DTensor device with the given layout.
+  """
+  del source_layout
+  return relayout(tensor, layout)
+
+
+@tf_export("experimental.dtensor.pack", v1=[])
+def pack(tensors: Sequence[Any], layout: layout_lib.Layout) -> Any:
+  """Packs `tf.Tensor` components into a DTensor.
+
+  Packing and unpacking are inverse operations:
+
+  ```
+  * unpack(pack(tensors)) == tensors
+  * pack(unpack(dtensor)) == dtensor
+  ```
+
+  1. For any DTensor on the mesh, `unpack` returns the raw components placed on
+     each underlying device.
+  2. Packing these raw components in the same order using `pack` returns a
+     DTensor which should be identical to the original DTensor--both the content
+     value and the layout.
+
+  **Shape, Rank, and Scalars**: The rank of the DTensor is the same as the
+  rank of its raw components, i.e., rank is preserved.  This leads to a
+  consistent interpretation for packing scalar values into a DTensor. The only
+  valid layout for a scalar value is fully replicated, and the individual
+  components must be identical scalars.
+
+  Each input `tensors[i]` will be copied to `layout.mesh.local_device[i]`
+  if not already on the local device. Non-local components should not be passed
+  to `pack`; use `copy_to_mesh` and `relayout` to place tensors on all global
+  devices on a mesh.
+
+  It is the caller's responsibility to ensure that the underlying values
+  for `pack` adhere to the specified layout, and that only as many values are
+  specified as there are local devices. Pack does not move data between clients.
+  See examples below for more detail about layouts.
+
+  For example, assume we have a mesh `[X(2), Y(3)]`, which has in total 6
+  underlying devices. Futuremore, assume that the device location mapping is
+  the following:
+
+  ```
+  device_ID  |  location X, Y
+          0     0, 0
+          1     0, 1
+          2     0, 2
+          3     1, 0
+          4     1, 1
+          5     1, 2
+  ```
+
+  1. For 1-D vector DTensor with shape `[128]` with layout `[mesh.X]` and value
+     as `range(128)`, the raw components will have shape `[64]` each, and the
+     raw components will be:
+
+     ```
+     device_ID  |  raw component
+             0     range(0, 64)
+             1     range(0, 64)
+             2     range(0, 64)
+             3     range(64, 128)
+             4     range(64, 128)
+             5     range(64, 128)
+     ```
+
+     This also means for a 1-D DTensor with shape `[2]` and layout `[mesh.X]`,
+     the raw components have shape `[1]` rather than the shape for scalar values
+     `[]`.
+
+  2. For 2-D vector DTensor with shape `[2, 3]` with layout `[mesh.X, mesh.Y]`
+     and value as `range(6)`, this is basically a fully-sharded DTensor.
+
+     From global view, the content looks like
+     ```
+     [
+       [0.0, 1.0, 2.0],
+       [3.0, 4.0, 5.0],
+     ]
+     ```
+
+     The raw components will have shape `[1, 1]` each, and have the following
+     content:
+
+     ```
+     device_ID  |  raw component
+             0     [[0.0]]
+             1     [[1.0]]
+             2     [[2.0]]
+             3     [[3.0]]
+             4     [[4.0]]
+             5     [[5.0]]
+     ```
+
+  3. For a scalar value `123.0` DTensor, it can only have one legitimate layout
+     `[]` (no dimension, but fully replicated).
+
+     The raw components will have shape `[]` each, and have the following
+     content:
+
+     ```
+     device_ID  |  raw component
+             0     123.0
+             1     123.0
+             2     123.0
+             3     123.0
+             4     123.0
+             5     123.0
+     ```
+
+     Again, caller of `pack` is expected to provide 6 identical value raw
+     components with scalar shapes.
+
+  4. For 3-D vector DTensor with shape `[2, 2, 3]` with layout
+     `[X, unsharded, unsharded]` and value as `range(12)`,
+
+     From global view, the content looks like:
+     ```
+     [
+       [
+         [0.0, 1.0, 2.0],
+         [3.0, 4.0, 5.0],
+       ],
+       [
+         [6.0, 7.0, 8.0],
+         [9.0, 10., 11.],
+       ],
+     ]
+     ```
+
+     The raw components will have shape `[1, 2, 3]` each, and have the following
+     content:
+
+     ```
+     device_ID  |  raw component
+             0     range(6).reshape([1, 2, 3])
+             1     range(6).reshape([1, 2, 3])
+             2     range(6).reshape([1, 2, 3])
+             3     range(6, 12).reshape([1, 2, 3])
+             4     range(6, 12).reshape([1, 2, 3])
+             5     range(6, 12).reshape([1, 2, 3])
+     ```
+
+  Args:
+    tensors: The list of local tensor components to pack into a DTensor.
+    layout: The layout of the DTensor to be created.
+
+  Returns:
+    A DTensor created from the individual component tensors.
+
+  Raises:
+    RuntimeError: When `pack` is not called eagerly.
+  """
+  return _dtensor_device().pack(tensors, layout)
+
+
+@tf_export("experimental.dtensor.unpack", v1=[])
+def unpack(tensor: Any) -> Sequence[Any]:
+  """Unpacks a DTensor into `tf.Tensor` components.
+
+  Packing and unpacking are inverse operations:
+
+  ```
+  * unpack(pack(tensors)) == tensors
+  * pack(unpack(dtensor)) == dtensor
+  ```
+
+  1. For any DTensor on the mesh, `unpack` returns the raw components placed on
+     each underlying device.
+  2. Packing these raw components in the same order using `pack` returns a
+     DTensor which should be identical to the original DTensor--both the content
+     value and the layout.
+
+  See the documentation for `pack` for more information about how packing and
+  unpacking works.
+
+  Args:
+    tensor: The DTensor to unpack.
+
+  Returns:
+    The individual component tensors of the DTensor. This will include only the
+    client-local components, i.e. the components placed on the local devices.
+
+  Raises:
+    RuntimeError: When `unpack` is not called eagerly.
+  """
+  return _dtensor_device().unpack(tensor)
+
+
+# -----------------------------------------------------------------------------
+# Layout-related methods.
+
+
+@tf_export("experimental.dtensor.fetch_layout", v1=[])
+def fetch_layout(tensor: tensor_lib.Tensor) -> layout_lib.Layout:
+  """Fetches the layout of a DTensor.
+
+  Args:
+    tensor: The DTensor whose layout is to be fetched.
+
+  Returns:
+    The `Layout` of this DTensor.
+
+  Raises:
+    RuntimeError: When not called eagerly.
+  """
+  return _dtensor_device().fetch_layout(tensor)
+
+
+@tf_export("experimental.dtensor.check_layout", v1=[])
+def check_layout(tensor: tensor_lib.Tensor, layout: layout_lib.Layout) -> None:
+  """Asserts that the layout of the DTensor is `layout`.
+
+  Args:
+    tensor: A DTensor whose layout is to be checked.
+    layout: The `Layout` to compare against.
+
+  Raises:
+    ValueError: If the layout of `tensor` does not match the supplied `layout`.
+  """
+  if fetch_layout(tensor) != layout:
+    raise ValueError("Layout of tensor: " + str(fetch_layout(tensor)) +
+                     ", did not match expected layout: " + str(layout))
+
+
+@tf_export("experimental.dtensor.relayout", v1=[])
+def relayout(
+    tensor: tensor_lib.Tensor,
+    layout: layout_lib.Layout,
+    name: Optional[str] = None,
+) -> tensor_lib.Tensor:
+  """Changes the layout of `tensor`.
+
+  Changes the layout of `tensor` to `layout`. This is used to fine-tune the
+  behavior of ops following/connected to `tensor`, such as choosing one SPMD
+  expansion pattern over another. This works by forward propagating `layout`
+  to connected TensorFlow computation graphs during layout propagation.
+
+  Currently, only converting layouts from replicated to sharded or sharded to
+  replicated per mesh dimension is supported. That is, "x, y" -> "unsharded, y"
+  is supported, while "x, y" -> "z, y" is not supported.
+
+  We also support a special "match" sharding spec, which instructs the relayout
+  to act as an identity operation with respect to any sharding on these
+  mesh dimensions.
+
+  Relayout is internally lowered to a set of Split and/or AllToAll ops. When
+  tensor layouts are converted from replicated to sharded, the cost is
+  comparatively low because we only insert Split ops and no cross-device
+  communication is needed. However, when tensor layouts are converted from
+  sharded to replicated, cross-device communication may occur, causing potential
+  performance impact.
+
+  Args:
+    tensor: A DTensor to specify a new layout for.
+    layout: A Layout object specifying a new sharding spec.
+    name: name of the Op.
+
+  Returns:
+    A DTensor output from the Relayout op.
+  """
+  layout_str = layout.to_string()
+  with default_mesh(layout.mesh):
+    return gen_dtensor_ops.relayout(tensor, layout_str, name=name)
+
+
+@tf_export("experimental.dtensor.relayout_like", v1=[])
+def relayout_like(
+    tensor: tensor_lib.Tensor,
+    layout_tensor: tensor_lib.Tensor,
+    name: Optional[str] = None,
+) -> tensor_lib.Tensor:
+  """Changes the layout of `tensor` to the same as `layout_tensor`.
+
+  `relayout_like` is often used inside a `tf.function`, to ensure a tensor is
+  placed to the same mesh and with the same layout as another tensor.
+
+  The backward gradient of a `relayout` is a `relayout_like` operation, to
+  ensure the backward tensor has the same layout as the forward input tensor:
+
+  ```
+  @ops.RegisterGradient("Relayout")
+  def _relayout_gradient(op, grad):
+    return relayout_like(grad, layout_input=op.inputs[0])
+  ```
+
+  Here is another illustrative example:
+
+  ```
+  @tf.function
+  def func(x):
+    z = tf.ones(x.shape)
+    z = dtensor.relayout_like(z, x)
+    return x + z
+
+  with dtensor.default_mesh(cpu_mesh):
+    x = tf.ones((4, 4))
+
+  with dtensor.default_mesh(gpu_mesh):
+    y = func(x)
+
+  # y would be on the cpu mesh, following the mesh of x.
+  ```
+
+  Args:
+    tensor: A DTensor to specify a new layout for.
+    layout_tensor: A Tensor object whose layout will be used for the layout of
+      result. The shape and type of layout_tensor are irrelevant.
+    name: name of the Op.
+
+  Returns:
+    A DTensor output from the RelayoutLike op.
+  """
+
+  return gen_dtensor_ops.relayout_like(
+      input=tensor, layout_input=layout_tensor, name=name
+  )
+
+
+@tf_export("experimental.dtensor._reset_dtensor_device", v1=[])
+def reset_dtensor_device(is_async: bool) -> None:
+  """Resets the Eager execution device for DTensor.
+
+  This function is only intended for testing and diagnostics.
+
+  Args:
+    is_async: If True, the device uses async execution.
+  """
+  global _dtensor_singleton
+  device = dtensor_device.DTensorDevice(meshes=[], is_async=is_async)
+  _dtensor_singleton = device
+
+
+def _dtensor_device() -> dtensor_device.DTensorDevice:
+  with _dtensor_singleton_lock:
+    if _dtensor_singleton is None:
+      reset_dtensor_device(is_async=True)
+    return _dtensor_singleton
+
+
+def _reset() -> None:
+  global _dtensor_singleton
+  with _dtensor_singleton_lock:
+    if _dtensor_singleton is not None:
+      _dtensor_singleton.clear_tpu_core_ids()
+    _dtensor_singleton = None
+
+
+# ----------------------------------------------------------------------------
+# Gradients
+
+
+@ops.RegisterGradient("Relayout")
+def _relayout_gradient(op, grad):
+  grad = gen_dtensor_ops.relayout_like(grad, layout_input=op.inputs[0])
+  return grad
+
+
+@ops.RegisterGradient("RelayoutLike")
+def _relayout_grad_gradient(op, grad):
+  # Gradient of RelayoutGrad is relayout to the original Relayout's output.
+  grad = gen_dtensor_ops.relayout_like(grad, layout_input=op.inputs[0])
+  # Return None for forward_input's partial gradient since it is not connected
+  # to the target's gradient.
+  return grad, None
+
+
+@ops.RegisterGradient("CopyToMesh")
+def _copy_to_mesh_gradient(op, grad):
+  grad = gen_dtensor_ops.copy_to_mesh_grad(
+      grad,
+      forward_input=op.inputs[0],
+  )
+  return grad
+
+
+@ops.RegisterGradient("CopyToMeshGrad")
+def _copy_to_mesh_grad_gradient(op, grad):
+  grad = gen_dtensor_ops.copy_to_mesh_grad(
+      grad,
+      forward_input=op.inputs[0],
+  )
+  return grad, None
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/config.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..d03491d20bbe708bf474c5c8eeb96c19fc85ce39
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/config.py
@@ -0,0 +1,219 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""DTensor Configuration API."""
+
+import os
+from typing import List, Optional, Union
+
+from tensorflow.python.eager import context
+from tensorflow.python.framework import config as tf_config
+from tensorflow.python.framework import device as tf_device
+from tensorflow.python.util.tf_export import tf_export
+
+_DT_CLIENT_ID = "DTENSOR_CLIENT_ID"
+# DTENSOR_NUM_CLIENTS is removed, but some DTensor users still use this symbol.
+_DT_NUM_CLIENTS = "DTENSOR_NUM_CLIENTS"
+_DT_JOB_NAME = "DTENSOR_JOB_NAME"
+_DT_JOBS = "DTENSOR_JOBS"
+_DT_HEARTBEAT_ENABLED = "DTENSOR_ENABLE_HEARTBEAT"
+
+
+# All functions in this file can be used before calling
+# `tf.experimental.dtensor.initialize_accelerator_system`.
+
+
+# -----------------------------------------------------------------------------
+# Distributed training-related methods.
+#
+# Most users should use DTensor utility methods to create a mesh. The methods
+# here are only for advanced users who want to fully customize their meshes.
+# Note that local_devices and num_local_devices return the actual number of
+# locally attached devices. The others are set through environment variables.
+
+
+@tf_export("experimental.dtensor.local_devices", v1=[])
+def local_devices(
+    device_type: str,
+    for_client_id: Optional[int] = None) -> List[tf_device.DeviceSpec]:
+  """Returns a list of device specs configured on this client."""
+  if device_type.upper() not in ["CPU", "GPU", "TPU"]:
+    raise ValueError(f"Device type {device_type} is not CPU, GPU, or TPU.")
+
+  if for_client_id is None:
+    for_client_id = client_id()
+
+  # Return fully qualified device specs, sorted by increasing device index.
+  return [
+      tf_device.DeviceSpec(  # pylint: disable=g-complex-comprehension
+          job=job_name(),
+          replica=0,  # replica is deprecated and mostly hard-coded now.
+          task=for_client_id,
+          device_type=device_type,
+          device_index=i) for i in range(num_local_devices(device_type))
+  ]
+
+
+@tf_export("experimental.dtensor.num_local_devices", v1=[])
+def num_local_devices(device_type: str) -> int:
+  """Returns the number of devices of device_type configured on this client."""
+
+  # Reads from config because CPU and GPU can use logical devices.
+  if device_type.upper() in ["CPU", "GPU"]:
+    context_config = context.get_config()
+    return context_config.device_count[device_type.upper()]
+
+  return len(tf_config.list_physical_devices(device_type))
+
+
+@tf_export("experimental.dtensor.num_global_devices", v1=[])
+def num_global_devices(device_type: str) -> int:
+  """Returns the number of devices of device_type in this DTensor cluster."""
+  return num_local_devices(device_type) * num_clients()
+
+
+@tf_export("experimental.dtensor.client_id", v1=[])
+def client_id() -> int:
+  """Returns this client's ID."""
+  # If missing, assume running with a single client with client_id of 0.
+  client_id_value = int(os.environ.get(_DT_CLIENT_ID, "0"))
+  if client_id_value < 0:
+    raise ValueError(f"Environment variable {_DT_CLIENT_ID} "
+                     f"must be >= 0, got {client_id_value}. ")
+  if client_id_value >= num_clients():
+    raise ValueError(f"Environment variable {_DT_CLIENT_ID} "
+                     f"must be < {num_clients()}, got {client_id_value}")
+  return client_id_value
+
+
+@tf_export("experimental.dtensor.num_clients", v1=[])
+def num_clients() -> int:
+  """Returns the number of clients in this DTensor cluster."""
+  if is_local_mode():
+    return 1
+  return len(jobs())
+
+
+@tf_export("experimental.dtensor.job_name", v1=[])
+def job_name() -> str:
+  """Returns the job name used by all clients in this DTensor cluster."""
+  # If missing, assumes the program runs locally and use localhost as job name
+  # per TensorFlow convention.
+  return os.environ.get(_DT_JOB_NAME,
+                        "localhost" if num_clients() == 1 else "worker")
+
+
+@tf_export("experimental.dtensor.full_job_name", v1=[])
+def full_job_name(task_id: Optional[int] = None) -> str:
+  """Returns the fully qualified TF job name for this or another task."""
+  # If task_id is None, use this client's ID, which is equal to its task ID.
+  if task_id is None:
+    task_id = client_id()
+  # In local runs and unit tests, there should be exactly one client running
+  # on one TF task.
+  if num_clients() == 1 and task_id != 0:
+    raise ValueError(f"Unexpected task ID {task_id} in local runs")
+  return f"{job_name()}/replica:0/task:{task_id}"
+
+
+def _bns_task_id(job: str) -> Union[int, str]:
+  """Tries to extract an integer task ID from a job name.
+
+  For example, for `job` = '/.../tpu_worker/0:port_name', return 0.
+
+  Args:
+    job: A job name to extract task ID from.
+
+  Returns:
+    The task ID on success, or the original job name on failure.
+  """
+  maybe_task_id = job.rsplit("/")[-1].rsplit(":")[0]
+  try:
+    return int(maybe_task_id)
+  except ValueError:
+    return job
+
+
+@tf_export("experimental.dtensor.jobs", v1=[])
+def jobs() -> List[str]:
+  """Returns a list of job names of all clients in this DTensor cluster."""
+  d_jobs = os.environ.get(_DT_JOBS)
+  if d_jobs is None:
+    return []
+  d_jobs_list = d_jobs.split(",")
+
+  # Validate ordering for BNS style job names.
+  # For definition of BNS, refer to https://research.google/pubs/pub43438/.
+  if any([name.startswith("/bns/") for name in d_jobs_list]):
+    if d_jobs_list != sorted(d_jobs_list, key=_bns_task_id):
+      raise ValueError(
+          f"Unexpected DTENSOR_JOBS content {d_jobs}. Sort entries "
+          "in DTENSOR_JOBS because cluster construction relies on "
+          "the order.")
+
+  return d_jobs_list
+
+
+@tf_export("experimental.dtensor.heartbeat_enabled", v1=[])
+def heartbeat_enabled() -> bool:
+  """Returns true if DTensor heartbeat service is enabled."""
+  return os.environ.get(_DT_HEARTBEAT_ENABLED, "true").lower() in ("true", "1")
+
+
+def is_local_mode() -> bool:
+  """Returns true if DTensor shall run in local mode."""
+  return not jobs()
+
+
+def is_tpu_present() -> bool:
+  """Returns true if TPU devices are present."""
+  # Check if TPU is present from initialized context.
+  # TPU_SYSTEM is a device that indicates TPUs are present.
+  tpu_system_devices = tf_config.list_physical_devices("TPU_SYSTEM")
+  return bool(tpu_system_devices)
+
+
+def is_gpu_present() -> bool:
+  """Returns true if TPU devices are present."""
+  return bool(tf_config.list_physical_devices("GPU"))
+
+
+@tf_export("experimental.dtensor.preferred_device_type", v1=[])
+def preferred_device_type() -> str:
+  """Returns the preferred device type for the accelerators.
+
+  The returned device type is determined by checking the first present device
+  type from all supported device types in the order of 'TPU', 'GPU', 'CPU'.
+  """
+  if is_tpu_present():
+    return "TPU"
+  elif is_gpu_present():
+    return "GPU"
+
+  return "CPU"
+
+
+def use_multi_device_mode() -> bool:
+  """Return True if environment indicates multi-device mode is enabled."""
+  return os.environ.get("DTENSOR_ENABLE_MULTI_DEVICE_EXPANSION", "0") != "0"
+
+
+def gpu_use_nccl_communication() -> bool:
+  """Return True if environment indicates NCCL shall be used for GPU."""
+  return os.environ.get("DTENSOR_GPU_USE_NCCL_COMMUNICATION", "0") != "0"
+
+
+def backend_is_pw() -> bool:
+  """Return True if environment indicates the backend is Pathways."""
+  return os.environ.get("DTENSOR_USE_PARALLEL_EXECUTOR") == "pw"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/d_checkpoint.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/d_checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..366ddc9e24d9b3009e49d1a81d036f2047be551b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/d_checkpoint.py
@@ -0,0 +1,462 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""DTensor Checkpoint.
+
+Note that this module contains deprecated functionality, and the DTensor related
+checkpoint has been integrated with tf.train.Checkpoint. It can be used out of
+the box to save and restore dtensors.
+"""
+
+from typing import Dict, List, Optional
+import weakref
+
+from tensorflow.core.protobuf import trackable_object_graph_pb2
+
+from tensorflow.dtensor.python import api
+from tensorflow.dtensor.python import d_variable
+from tensorflow.dtensor.python import gen_dtensor_ops
+from tensorflow.dtensor.python import layout
+from tensorflow.dtensor.python import save_restore
+from tensorflow.python.checkpoint import checkpoint as util
+from tensorflow.python.checkpoint import checkpoint_options
+from tensorflow.python.checkpoint import graph_view as graph_view_lib
+from tensorflow.python.checkpoint import restore as restore_lib
+from tensorflow.python.eager import context
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import errors_impl
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.trackable import base
+from tensorflow.python.trackable import data_structures
+from tensorflow.python.training import py_checkpoint_reader
+from tensorflow.python.training.saving import saveable_object
+from tensorflow.python.training.saving import saveable_object_util
+from tensorflow.python.util import deprecation
+from tensorflow.python.util import nest
+from tensorflow.python.util.tf_export import tf_export
+
+
+class _DSaver:  # pylint: disable=protected-access
+  """A single device saver that places tensors on DTensor Device."""
+
+  def __init__(self, mesh: layout.Mesh,
+               saveable_objects: List[saveable_object.SaveableObject]):
+    self._saveable_objects = saveable_objects
+    self._mesh = mesh
+
+  def save(
+      self,
+      file_prefix: str,
+      options: Optional[checkpoint_options.CheckpointOptions] = None
+  ) -> Optional[ops.Operation]:
+    """Saves the saveable objects to a checkpoint with `file_prefix`.
+
+    Also query the generated shards from the distributed DTensor SaveV2 ops and
+    do a MergeV2 on those. Each op here is backed by a global_barrier to avoid
+    racing from multiple clients.
+
+    Args:
+      file_prefix: A string or scalar string Tensor containing the prefix to
+        save under.
+      options: Optional `CheckpointOptions` object. This is unused in DTensor.
+
+    Returns:
+      An `Operation`, or None when executing eagerly.
+    """
+    if options is not None and options.experimental_io_device is not None:
+      raise ValueError(
+          "Specified experimental_io_device in DTensor checkpoint is not supported."
+      )
+    del options
+    tensor_names = []
+    tensors = []
+    tensor_slices = []
+    for saveable in self._saveable_objects:
+      for spec in saveable.specs:
+        tensor = spec.tensor
+        # A tensor value of `None` indicates that this SaveableObject gets
+        # recorded in the object graph, but that no value is saved in the
+        # checkpoint.
+        if tensor is not None:
+          if api.device_name() != spec.device:
+            # Some small tensors are placed on CPU0 from save manager and
+            # broadcasted to DTensor mesh, e,g., SaveCounter.
+            tensor = api.pack([tensor] *
+                              self._mesh.host_mesh().num_local_devices(),
+                              layout.Layout.replicated(
+                                  self._mesh.host_mesh(),
+                                  rank=tensor.shape.rank))
+          tensor_names.append(spec.name)
+          tensors.append(tensor)
+          tensor_slices.append(spec.slice_spec)
+    return save_restore.sharded_save(self._mesh, file_prefix, tensor_names,
+                                     tensor_slices, tensors)
+
+  def restore(
+      self,
+      file_prefix: str,
+      options: Optional[checkpoint_options.CheckpointOptions] = None
+  ) -> Dict[str, ops.Operation]:
+    """Restore the saveable objects from a checkpoint with `file_prefix`.
+
+    Args:
+      file_prefix: A string or scalar string Tensor containing the prefix for
+        files to read from.
+      options: Optional `CheckpointOptions` object. This is unused in DTensor.
+
+    Returns:
+      A dictionary mapping from SaveableObject names to restore operations.
+    """
+    if options is not None and options.experimental_io_device is not None:
+      raise ValueError(
+          "Specified experimental_io_device in DTensor checkpoint is not "
+          "supported.")
+    del options
+    restore_specs = []
+    tensor_structure = []
+    for saveable in self._saveable_objects:
+      saveable_tensor_structure = []
+      tensor_structure.append(saveable_tensor_structure)
+      # DTensor change 1 : Gather shapes and layout from original saveable
+      # specs.
+      # Note that this relies on the fact that the variables are already
+      # initialized -- which isn't the behavior we want eventually.
+      # TODO(b/159035705): Handle the variable initialization in restore.
+      for spec in saveable.specs:
+        saveable_tensor_structure.append(spec.name)
+        if isinstance(spec, d_variable.DSaveSpec):
+          restore_specs.append((spec.name, spec.slice_spec, spec.dtype,
+                                spec.layout, spec.global_shape))
+        # Fall back to replicated layouts for non-DTensor saves that constructs
+        # normal SaveSpec.
+        elif isinstance(spec, saveable_object.SaveSpec):
+          restore_specs.append(
+              (spec.name, spec.slice_spec, spec.dtype,
+               layout.Layout.replicated(self._mesh.host_mesh(),
+                                        spec.tensor.shape.rank).to_string(),
+               spec.tensor.shape.as_list()))
+    tensor_names, tensor_slices, tensor_dtypes, layouts, global_shapes = zip(
+        *restore_specs)
+    with ops.device(api.device_name()):
+      # DTensor change 2 : Run on customized DTensor RestoreV2 op rather than
+      # stock TF io_ops.RestoreV2.
+      restored_tensors = gen_dtensor_ops.d_tensor_restore_v2(
+          prefix=file_prefix,
+          tensor_names=tensor_names,
+          shape_and_slices=tensor_slices,
+          input_shapes=global_shapes,
+          input_layouts=layouts,
+          dtypes=tensor_dtypes)
+    structured_restored_tensors = nest.pack_sequence_as(tensor_structure,
+                                                        restored_tensors)
+    restore_ops = {}
+    for saveable, restored_tensors in zip(self._saveable_objects,
+                                          structured_restored_tensors):
+      restore_ops[saveable.name] = saveable.restore(
+          restored_tensors, restored_shapes=None)
+    return restore_ops
+
+
+class _DCheckpointRestoreCoordinator(util._CheckpointRestoreCoordinator):  # pylint: disable=protected-access
+  """Holds the status of an object-based checkpoint load."""
+
+  def __init__(self, mesh: layout.Mesh, **kwargs):
+    super().__init__(**kwargs)
+    self._mesh = mesh
+
+  def restore_saveables(self,
+                        tensor_saveables: Dict[str,
+                                               saveable_object.SaveableObject],
+                        python_positions: List[restore_lib.CheckpointPosition],
+                        registered_savers: Optional[Dict[str, Dict[
+                            str, base.Trackable]]] = None,
+                        reader: py_checkpoint_reader.NewCheckpointReader = None
+                        ) -> Optional[List[ops.Operation]]:
+    """Run or build restore operations for SaveableObjects.
+
+    Args:
+      tensor_saveables: `SaveableObject`s which correspond to Tensors.
+      python_positions: `CheckpointPosition`s which correspond to `PythonState`
+        Trackables bound to the checkpoint.
+      registered_savers: a dict mapping saver names-> object name -> Trackable.
+        This argument is not implemented for DTensorCheckpoint.
+      reader: A CheckpointReader. Creates one lazily if None.
+
+    Returns:
+      When graph building, a list of restore operations, either cached or newly
+      created, to restore `tensor_saveables`.
+    """
+    del registered_savers
+
+    restore_ops = []
+    # Eagerly run restorations for Python state.
+    if python_positions:
+      # Lazily create the NewCheckpointReader, since this requires file access
+      # and we may not have any Python saveables.
+      if reader is None:
+        reader = py_checkpoint_reader.NewCheckpointReader(self.save_path_string)
+      for position in python_positions:
+        key = position.object_proto.attributes[0].checkpoint_key
+        position.trackable.deserialize(reader.get_tensor(key))
+
+    # If we have new SaveableObjects, extract and cache restore ops.
+    if tensor_saveables:
+      validated_saveables = saveable_object_util.validate_and_slice_inputs(
+          tensor_saveables)
+      validated_names = set(saveable.name for saveable in validated_saveables)
+      if set(tensor_saveables.keys()) != validated_names:
+        raise AssertionError(
+            ("Saveable keys changed when validating. Got back %s, was "
+             "expecting %s") % (tensor_saveables.keys(), validated_names))
+      # DTensor change: Use _DSaver that does restore on DTensor with
+      # customized DTensorRestoreV2 op.
+      new_restore_ops = _DSaver(self._mesh, validated_saveables).restore(
+          self.save_path_tensor, self.options)
+      if not context.executing_eagerly():
+        for name, restore_op in sorted(new_restore_ops.items()):
+          restore_ops.append(restore_op)
+          assert name not in self.restore_ops_by_name
+          self.restore_ops_by_name[name] = restore_op
+    return restore_ops
+
+
+class DTrackableSaver(util.TrackableSaver):
+  """A DTensor trackable saver that uses _SingleDeviceSaver."""
+
+  def __init__(self, mesh: layout.Mesh, graph_view):
+    super(DTrackableSaver, self).__init__(graph_view)
+    self._mesh = mesh
+
+  def _gather_saveables(self, object_graph_tensor=None):
+    # Since the base Checkpoint class does not return SaveableObjects, re-use
+    # the saveables cache or generate new Saveables.
+    (serialized_tensors, feed_additions, registered_savers,
+     graph_proto) = self._gather_serialized_tensors(object_graph_tensor)
+
+    saveables_dict = self._saveables_cache
+    if saveables_dict is None:
+      # Get and remove object graph tensor from `serialized_tensors`, because
+      # the function `serialized_tensors_to_saveable_cache` isn't equipped
+      # to handle it.
+      object_graph_tensor = serialized_tensors.pop(
+          None)[base.OBJECT_GRAPH_PROTO_KEY]
+      saveables_dict = (
+          saveable_object_util.serialized_tensors_to_saveable_cache(
+              serialized_tensors))
+    named_saveable_objects = []
+    for saveable_by_name in saveables_dict.values():
+      for saveables in saveable_by_name.values():
+        named_saveable_objects.extend(saveables)
+    named_saveable_objects.append(
+        base.NoRestoreSaveable(
+            tensor=object_graph_tensor,
+            name=base.OBJECT_GRAPH_PROTO_KEY))
+    return (named_saveable_objects, graph_proto, feed_additions,
+            registered_savers)
+
+  def _save_cached_when_graph_building(self,
+                                       file_prefix,
+                                       object_graph_tensor,
+                                       options,
+                                       update_ckpt_state=False):
+    """Create or retrieve save ops, overrides parents's private method.
+
+    Args:
+      file_prefix: The prefix for saved checkpoint files.
+      object_graph_tensor: A `Tensor` to which the current object graph will be
+        fed.
+      options: `CheckpointOptions` object.
+      update_ckpt_state: Optional bool flag. Indiciate whether the internal
+        checkpoint state needs to be updated. This is used for async checkpoint,
+        which DTrackableSaver currently does not support.
+    TODO(chienchunh): Implement async checkpoint for DTrackableSaver.
+
+    Returns:
+      A two-element tuple with a filename tensor and a feed_dict of tensors to
+      feed when running it (if graph building). The feed dict contains the
+      current object graph and any Python state to be saved in the
+      checkpoint. When executing eagerly only the first argument is meaningful.
+    """
+    (named_saveable_objects, graph_proto, feed_additions,
+     unused_registered_savers) = self._gather_saveables(
+         object_graph_tensor=object_graph_tensor)
+    if (self._last_save_object_graph != graph_proto
+        # When executing eagerly, we need to re-create SaveableObjects each time
+        # save() is called so they pick up new Tensors passed to their
+        # constructors. That means the Saver needs to be copied with a new
+        # var_list.
+        or context.executing_eagerly() or ops.inside_function()):
+      # This is needed to avoid MultiDeviceSaver creating unnecessary MergeV2
+      # ops in DTensor. It is an issue when saving TPU Variables on host CPU
+      # mesh given our limited expressiveness in API and hard-coded logic in
+      # broadcasting -- for a small constant Tensor with no extra information,
+      # we place it on the first registered mesh(A.K.A. default mesh).
+      saver = _DSaver(self._mesh, named_saveable_objects)
+      save_op = saver.save(file_prefix, options=options)
+      with ops.device("/cpu:0"):
+        with ops.control_dependencies([save_op]):
+          self._cached_save_operation = array_ops.identity(file_prefix)
+      self._last_save_object_graph = graph_proto
+    return self._cached_save_operation, feed_additions
+
+  # TODO(b/180466245): Use proper mesh placement semantic.
+  def restore(self, save_path, options=None):
+    """Restore a training checkpoint with host mesh placement."""
+    options = options or checkpoint_options.CheckpointOptions()
+    if save_path is None:
+      return util.InitializationOnlyStatus(self._graph_view, ops.uid())
+    reader = py_checkpoint_reader.NewCheckpointReader(save_path)
+    graph_building = not context.executing_eagerly()
+    if graph_building:
+      dtype_map = None
+    else:
+      dtype_map = reader.get_variable_to_dtype_map()
+    try:
+      object_graph_string = reader.get_tensor(base.OBJECT_GRAPH_PROTO_KEY)
+    except errors_impl.NotFoundError:
+      # The object graph proto does not exist in this checkpoint. Try the
+      # name-based compatibility mode.
+      restore_coordinator = util._NameBasedRestoreCoordinator(  # pylint: disable=protected-access
+          save_path=save_path,
+          dtype_map=dtype_map)
+      if not graph_building:
+        for existing_trackable in self._graph_view.list_objects():
+          # pylint: disable=protected-access
+          existing_trackable._maybe_initialize_trackable()
+          existing_trackable._name_based_restores.add(restore_coordinator)
+          existing_trackable._name_based_attribute_restore(restore_coordinator)
+          # pylint: enable=protected-access
+      return util.NameBasedSaverStatus(
+          restore_coordinator, graph_view=self._graph_view)
+
+    if graph_building:
+      if self._file_prefix_placeholder is None:
+        # DTensor change: provide a hint for mesh broadcasting to put the input
+        # onto the host mesh.
+        self._file_prefix_placeholder = api.pack(
+            [constant_op.constant("model")] * self._mesh.num_local_devices(),
+            layout.Layout.replicated(self._mesh.host_mesh(), rank=0))
+      file_prefix_tensor = self._file_prefix_placeholder
+      file_prefix_feed_dict = {self._file_prefix_placeholder: save_path}
+    else:
+      # DTensor change: provide a hint for mesh broadcasting to put the input
+      # onto the host mesh.
+      file_prefix_tensor = api.pack(
+          [constant_op.constant(save_path)] * self._mesh.num_local_devices(),
+          layout.Layout.replicated(self._mesh.host_mesh(), rank=0))
+      file_prefix_feed_dict = None
+    object_graph_proto = (trackable_object_graph_pb2.TrackableObjectGraph())
+    object_graph_proto.ParseFromString(object_graph_string)
+    # DTensor Change: Hook the proper DSaver in restore.
+    checkpoint = _DCheckpointRestoreCoordinator(
+        mesh=self._mesh,
+        object_graph_proto=object_graph_proto,
+        save_path=save_path,
+        save_path_tensor=file_prefix_tensor,
+        reader=reader,
+        restore_op_cache=self._restore_op_cache,
+        graph_view=self._graph_view,
+        options=options,
+        saveables_cache=self._saveables_cache)
+    restore_lib.CheckpointPosition(
+        checkpoint=checkpoint, proto_id=0).restore(self._graph_view.root)
+
+    # Attached dependencies are not attached to the root, so should be restored
+    # separately.
+    if self._graph_view.attached_dependencies:
+      for ref in self._graph_view.attached_dependencies:
+        if ref.name == "root":
+          # Root dependency is automatically added to attached dependencies --
+          # this can be ignored since it maps back to the root object.
+          continue
+        proto_id = None
+        # Find proto ID of attached dependency (if it is in the proto).
+        for proto_ref in object_graph_proto.nodes[0].children:
+          if proto_ref.local_name == ref.name:
+            proto_id = proto_ref.node_id
+            break
+
+        if proto_id in checkpoint.object_by_proto_id:
+          # Object has already been restored. This can happen when there's an
+          # indirect connection from the attached object to the root.
+          continue
+
+        restore_lib.CheckpointPosition(
+            checkpoint=checkpoint, proto_id=proto_id).restore(ref.ref)
+
+    load_status = util.CheckpointLoadStatus(
+        checkpoint,
+        graph_view=self._graph_view,
+        feed_dict=file_prefix_feed_dict)
+    return load_status
+
+
+@deprecation.deprecated(
+    date=None,
+    instructions="Please use tf.train.Checkpoint instead of DTensorCheckpoint. "
+    "DTensor is integrated with tf.train.Checkpoint and it can be "
+    "used out of the box to save and restore dtensors.")
+@tf_export("experimental.dtensor.DTensorCheckpoint", v1=[])
+class DTensorCheckpoint(util.Checkpoint):
+  """Manages saving/restoring trackable values to disk, for DTensor."""
+
+  def __init__(self, mesh: layout.Mesh, root=None, **kwargs):
+    super(DTensorCheckpoint, self).__init__(root=root, **kwargs)
+    self._mesh = mesh
+
+    saver_root = self
+    attached_dependencies = None
+    self._save_counter = None  # Created lazily for restore-on-create.
+    self._save_assign_op = None
+
+    if root:
+      util._assert_trackable(root, "root")
+      saver_root = root
+      attached_dependencies = []
+
+      # All keyword arguments (including root itself) are set as children
+      # of root.
+      kwargs["root"] = root
+      root._maybe_initialize_trackable()
+
+      self._save_counter = data_structures.NoDependency(
+          root._lookup_dependency("save_counter"))
+      self._root = data_structures.NoDependency(root)
+
+    for k, v in sorted(kwargs.items(), key=lambda item: item[0]):
+      setattr(self, k, v)
+
+      # Call getattr instead of directly using v because setattr converts
+      # v to a Trackable data structure when v is a list/dict/tuple.
+      converted_v = getattr(self, k)
+      util._assert_trackable(converted_v, k)
+
+      if root:
+        # Make sure that root doesn't already have dependencies with these names
+        attached_dependencies = attached_dependencies or []
+        child = root._lookup_dependency(k)
+        if child is None:
+          attached_dependencies.append(base.TrackableReference(k, converted_v))
+        elif child != converted_v:
+          raise ValueError(
+              "Cannot create a Checkpoint with keyword argument {name} if "
+              "root.{name} already exists.".format(name=k))
+    # DTensor Change:
+    # Override the parents saver with DTrackableSaver with _SingleDeviceSaver.
+    self._saver = DTrackableSaver(
+        mesh,
+        graph_view_lib.ObjectGraphView(
+            weakref.ref(saver_root),
+            attached_dependencies=attached_dependencies))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/d_variable.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/d_variable.py
new file mode 100644
index 0000000000000000000000000000000000000000..b99875f03fa70ed4f942a0d70c828040a7223e64
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/d_variable.py
@@ -0,0 +1,260 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""DTensor variable and saveable."""
+
+import functools
+
+from tensorflow.dtensor.python import api
+from tensorflow.dtensor.python import layout as layout_lib
+from tensorflow.python.eager import context
+from tensorflow.python.eager import def_function
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors_impl
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import resource_variable_ops
+from tensorflow.python.trackable import base as trackable
+from tensorflow.python.training.saving import saveable_object
+from tensorflow.python.util.tf_export import tf_export
+
+
+class DSaveSpec(saveable_object.SaveSpec):
+  """DTensor SaveSpec that additionaly captures global_shape and layout."""
+
+  def __init__(self,
+               tensor,
+               slice_spec,
+               name,
+               global_shape,
+               layout,
+               dtype=None,
+               device=None):
+    super().__init__(
+        tensor=tensor,
+        slice_spec=slice_spec,
+        name=name,
+        dtype=dtype,
+        device=device)
+    self.global_shape = global_shape
+    self.layout = layout
+
+
+class _DVariableSaveable(saveable_object.SaveableObject):
+  """Class for defining how to save/restore DTensor variable."""
+
+  def __init__(self, dvariable, name):
+    with ops.device(dvariable.device):
+      original_layout = api.fetch_layout(dvariable)
+    # Record original layout to allow restore.
+    self._original_layout = original_layout
+    self._dvariable = dvariable
+
+    def pack(tensors, layout):
+      with ops.device(dvariable.device):
+        return api.pack(tensors, layout)
+
+    host_layout = layout_lib.Layout(original_layout.sharding_specs,
+                                    original_layout.mesh.host_mesh())
+
+    def get_host_dtensor():
+      # Copy to host mesh if needed.
+      if original_layout.mesh.device_type().upper() != 'CPU':
+        # Prefer pack and unpack in eager mode because it supports sharded
+        # layouts.
+        if context.executing_eagerly():
+          host_dtensor = api.pack(
+              api.unpack(dvariable.read_value()), host_layout)
+        else:
+          host_dtensor = api.copy_to_mesh(dvariable.read_value(), host_layout)
+      else:
+        host_dtensor = dvariable.read_value()
+      return (math_ops.cast(host_dtensor, dtypes.bfloat16)
+              if self.should_cast(host_dtensor) else host_dtensor)
+
+    num_local_devices = original_layout.mesh.num_local_devices()
+    super(_DVariableSaveable, self).__init__(
+        None,
+        [
+            DSaveSpec(
+                tensor=get_host_dtensor,
+                slice_spec=pack([''] * num_local_devices,
+                                layout_lib.Layout.replicated(
+                                    original_layout.mesh.host_mesh(), rank=0)),
+                name=pack([name] * num_local_devices,
+                          layout_lib.Layout.replicated(
+                              original_layout.mesh.host_mesh(), rank=0)),
+                global_shape=dvariable.shape,
+                # Layout is attached as attribute, no need to put it as a
+                # Tensor on DTensorDevice.
+                layout=host_layout.to_string(),
+                dtype=dtypes.bfloat16
+                if self.should_cast(dvariable) else dvariable.dtype,
+                device=dvariable.device)
+        ],
+        name)
+
+  def should_cast(self, v):
+    """Returns True if v has float32 dtype and is intructed to save as bf16.
+
+    Args:
+      v : The variable that determines whether to cast.
+
+    Returns:
+      True if current savable DVariable is instructed to save as bfloat16 and
+        the variable has dtype float32.
+    """
+    return self._dvariable.save_as_bf16 and v.dtype == dtypes.float32
+
+  def restore(self, restored_tensors, restored_shapes):
+    """Restores the same value into all variables."""
+    tensor, = restored_tensors
+
+    @def_function.function
+    def _restore(t):
+      with ops.device(self._dvariable.device):
+        return api.copy_to_mesh(t, self._original_layout)
+
+    # This assign establishes connections from restored tensor and tensors
+    # being restored to -- so that restore in SPMD can backtrack the DVariable
+    # and its layout, given that we're using tf.function style restore.
+    # Note that the restored dvaraible is on CPU no matter what as the restoreV2
+    # op must run on CPU.
+    # TODO(b/159035705): Allow restore for Tensor objects as well?
+    # Restore the dvariable back to original layout.
+    if self._original_layout.mesh.device_type().upper() != 'CPU':
+      tensor = _restore(tensor)
+    return self._dvariable.assign(
+        math_ops.cast(tensor, dtype=self._dvariable.dtype) if self._dvariable
+        .save_as_bf16 else tensor)
+
+
+@tf_export('experimental.dtensor.DVariable', v1=[])
+class DVariable(resource_variable_ops.ResourceVariable):
+  """A replacement for tf.Variable which follows initial value placement.
+
+    The class also handles restore/save operations in DTensor. Note that,
+    DVariable may fall back to normal tf.Variable at this moment if
+    `initial_value` is not a DTensor.
+  """
+
+  def __init__(self, initial_value, *args, dtype=None, **kwargs):
+    """Overrides tf.Variable to fix VarHandleOp placements."""
+    # Variables by default use the current device scope for placement. This
+    # wrapper has them follow the initial value's placement instead (which will
+    # be the DTensor device if the initial value has a layout).
+
+    # Pop layout from kwargs since keras make_variable may pass a 'layout'
+    # keyword argument. We need to pop it because we are passing kwargs to
+    # super class constructor.
+    layout = kwargs.pop('layout', None)
+    shape = kwargs.get('shape', None)
+
+    if callable(initial_value):
+      unwrapped = initial_value
+      if issubclass(type(initial_value), functools.partial):
+        unwrapped = initial_value.func
+
+      # If wrapped is a CheckpointInitialValueCallable, this means that
+      # we are creating a Variable during a checkpoint restore.
+      # Thus the restore will happen now through this callable
+      # and we will create the DVariable with the restored dtensor.
+      if issubclass(type(unwrapped), trackable.CheckpointInitialValueCallable):
+        if not shape or not layout:
+          raise ValueError('Expected shape and layout to be not None.')
+
+        # CheckpointInitialValueCallable will call an eager tf.RestoreV2,
+        # which does not have any shape information or layout information
+        # attached. Thus we will do two things to have them correctly specified:
+        #
+        # The default layout scope allows us to correctly specify the output
+        # layout of the tf.RestoreV2 that will be called
+        #
+        # Passing shard_info with the correct shape allows the tf.RestoreV2
+        # ShapeInference to extract the shape.
+        initial_value = api.call_with_layout(
+            initial_value,
+            layout,
+            shard_info=trackable.ShardInfo(
+                shape=shape, offset=[0] * len(shape)))
+      else:
+        initial_value = initial_value()
+
+    # When the initial value came from a Checkpoint restoration, fetch tensor.
+    if isinstance(initial_value, trackable.CheckpointInitialValue):
+      initial_value = initial_value.wrapped_value
+
+    initial_value = ops.convert_to_tensor(initial_value, dtype=dtype)
+    variable_device = initial_value.device
+    self._save_as_bf16 = False
+    # TODO(b/159035705): The following code enables variable creation inside
+    # a tf.function. However, it requires a global dtensor device.
+    # if not variable_device and not tf.executing_eagerly():
+    #   try:
+    #     initial_value.op.get_attr("_layout")
+    #   except ValueError:
+    #     pass
+    #   else:
+    #     # The initial value is a DTensor, but because the DTensor device is
+    #     # only active during eager execution at the moment we need to
+    #     # translate that into a placement for the eager VarHandleOp.
+    #     variable_device = _dtensor_device().name
+    with ops.device(variable_device):
+      # If initial tensor assigned to DVariable is DTensor, record the layout of
+      # the resource so that this can be queried.
+      if context.executing_eagerly():
+        if api.is_dtensor(initial_value):
+          value_layout = api.fetch_layout(initial_value)
+          if layout is not None and layout != value_layout:
+            raise errors_impl.InvalidArgumentError(
+                None,
+                None,
+                'Conflicting layout are provided for initial '
+                f'value layout ({value_layout}) and variable ({layout}).',
+            )
+          layout = value_layout
+        elif layout is not None:
+          initial_value = api.relayout(initial_value, layout)
+        else:
+          raise errors_impl.InvalidArgumentError(
+              None,
+              None,
+              'Neither layout nor DTensor initial value are provided.',
+          )
+        self.layout = layout
+        with api.default_mesh(layout.mesh):
+          super(DVariable, self).__init__(
+              initial_value, *args, dtype=dtype, **kwargs
+          )
+      else:
+        # FIXME(175928457): Record value layout in graph mode.
+        if layout is not None:
+          initial_value = api.relayout(initial_value, layout)
+        super(DVariable, self).__init__(
+            initial_value, *args, dtype=dtype, **kwargs)
+
+  @property
+  def save_as_bf16(self):
+    return self._save_as_bf16
+
+  @save_as_bf16.setter
+  def save_as_bf16(self, save_as_bf16):
+    """Enables saving float32 as bfloat16."""
+    self._save_as_bf16 = save_as_bf16 and self.dtype == dtypes.float32
+
+  def _gather_saveables_for_checkpoint(self):
+    return {
+        trackable.VARIABLE_VALUE_KEY:
+            functools.partial(_DVariableSaveable, self)
+    }
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/dtensor_device.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/dtensor_device.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c6dff23052b66f0c31e945af35bf86716ef5026
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/dtensor_device.py
@@ -0,0 +1,437 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Propagates information about tensor layouts across operations."""
+
+import contextlib
+import logging
+import threading
+from typing import Any, List, Sequence, Set
+
+import numpy as np
+
+from tensorflow.core.framework import attr_value_pb2
+from tensorflow.dtensor.python import config
+from tensorflow.dtensor.python import layout as layout_lib
+from tensorflow.python import _pywrap_dtensor_device
+from tensorflow.python.eager import context
+from tensorflow.python.eager import core
+from tensorflow.python.framework import device as tf_device
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.framework import tensor_util
+from tensorflow.python.util import _pywrap_utils
+
+
+# TODO(allenl): Allow something other than "CUSTOM" so we don't need device
+# numbering hacks to avoid collisions between parallel devices and dtensor
+# devices.
+_next_device_number = 0
+_next_device_number_lock = threading.Lock()
+
+
+class DTensorDevice(object):
+  """Wraps a custom device which attempts to propagate tensor layouts."""
+
+  def __init__(self,
+               meshes: List[layout_lib.Mesh],
+               is_async=True,
+               in_flight_nodes_limit=8):
+    """Create a new DTensorDevice which executes ops on `underlying_device`.
+
+    Args:
+      meshes: A list of `Mesh` objects indicating groups of devices to execute
+        on. These may also be registered lazily.
+      is_async: Indicates whether DTensor operations on this client will return
+        immediately (with "non-ready" handles) or block until executed. This is
+        on by default and is exposed as an option for ease of debugging.
+      in_flight_nodes_limit: Indicates the limit of in-flight nodes before
+        enqueueing of async operations to DTensorDevice is blocked. This limit
+        is per mesh. 0 for no limits from DTensor. Default is 8.
+    """
+    if any(not isinstance(mesh, layout_lib.Mesh) for mesh in meshes):
+      raise TypeError(
+          "Expected a flat list of Mesh objects, got {}".format(meshes))
+    global _next_device_number
+    ctx = context.context()
+    with _next_device_number_lock:
+      self.name = "{}/device:CUSTOM:{}".format(ctx.host_address_space(),
+                                               _next_device_number)
+      _next_device_number += 1
+    device, device_info = _pywrap_dtensor_device.Allocate(
+        self.name, is_async, in_flight_nodes_limit
+    )
+    context.register_custom_device(device, self.name, device_info)
+
+    self._device_info = device_info
+    self._current_output_layout = None
+    self._current_default_mesh = None
+    self._meshes = set()
+    self._mesh_lock = threading.Lock()
+    for mesh in meshes:
+      self._register_mesh(mesh)
+
+  def _create_host_array(self, shape, host_id):
+    """Returns ID and device lists that can be used to create a host mesh."""
+    num_global_devices = np.prod(shape)
+    global_device_ids = np.arange(num_global_devices).reshape(shape)
+    local_device_list = [
+        tf_device.DeviceSpec(
+            job=config.full_job_name(), device_type="CPU", device_index=0)
+    ]
+    num_local_devices = len(local_device_list)
+    local_device_ids = [
+        x + host_id * num_local_devices for x in range(num_local_devices)
+    ]
+    return global_device_ids, local_device_ids, local_device_list
+
+  def _register_mesh(self, mesh: layout_lib.Mesh):
+    """Idempotently register `mesh` with the dtensor device."""
+    with self._mesh_lock:
+      if mesh not in self._meshes:
+        _pywrap_dtensor_device.AddMesh(
+            self._device_info, mesh.to_string(), False
+        )
+        self._meshes.add(mesh)
+        if mesh.device_type().upper() == "TPU":
+          logging.info(
+              "Registering virtual 1:1 mapped host mesh %s for mesh %s",
+              mesh.host_mesh().to_string(), mesh.to_string())
+          _pywrap_dtensor_device.AddMesh(
+              self._device_info, mesh.host_mesh().to_string(), True
+          )
+          self._meshes.add(mesh.host_mesh())
+
+  @property
+  def meshes(self) -> Set[layout_lib.Mesh]:
+    return self._meshes
+
+  def pack(self, tensors: Sequence[Any], layout: layout_lib.Layout) -> Any:
+    """Packs tensors into a DTensor handle on this DTensor device.
+
+    Packing and unpacking are inverse operations:
+
+    ```
+    * unpack(pack(tensors)) == tensors
+    * pack(unpack(dtensor)) == dtensor
+    ```
+
+    Refer to `dtensor.pack` for more information.
+
+    Args:
+      tensors: The list of tensors to pack into a DTensor.
+      layout: The layout of the DTensor to be created.
+
+    Returns:
+      A DTensor created from the individual component tensors.
+
+    Raises:
+      RuntimeError: When not called eagerly.
+    """
+    if not context.executing_eagerly():
+      raise RuntimeError("`pack` must be called eagerly.")
+    self._register_mesh(layout.mesh)
+    with ops.device(self.name):
+      if all(isinstance(t, sparse_tensor.SparseTensor) for t in tensors):
+        if not all(t.shape == tensors[0].shape for t in tensors):
+          raise TypeError("All input SparseTensors to Pack must be same shape.")
+        is_sparse = True
+        tensors = [t.indices for t in tensors] + [t.values for t in tensors] + [
+            ops.convert_to_tensor(t.shape, dtype=dtypes.int64) for t in tensors
+        ]
+      elif any(isinstance(t, sparse_tensor.SparseTensor) for t in tensors):
+        raise TypeError("Cannot Pack SparseTensors with Tensors.")
+      else:
+        is_sparse = False
+      try:
+        return _pywrap_dtensor_device.Pack(
+            context.context()._handle,  # pylint: disable=protected-access
+            tensors,
+            layout.to_string(),
+            self._device_info,
+            is_sparse)
+      except core._NotOkStatusException as e:  # pylint: disable=protected-access
+        raise core._status_to_exception(e) from None  # pylint: disable=protected-access
+
+  def unpack(self, dtensor: Any) -> Sequence[Any]:
+    """Unpacks a DTensor handle on this DTensor device.
+
+    Packing and unpacking are inverse operations:
+
+    ```
+    * unpack(pack(tensors)) == tensors
+    * pack(unpack(dtensor)) == dtensor
+    ```
+
+    Refer to `dtensor.unpack` for more information.
+
+    Args:
+      dtensor: The DTensor to unpack.
+
+    Returns:
+      The raw underlying tensor components of the DTensor.
+
+    Raises:
+      RuntimeError: When not called eagerly.
+    """
+    if not context.executing_eagerly():
+      raise RuntimeError("`unpack` must be called eagerly.")
+    try:
+      tensors = _pywrap_dtensor_device.Unpack(
+          context.context()._handle,  # pylint: disable=protected-access
+          dtensor,
+          self._device_info)
+    except core._NotOkStatusException as e:  # pylint: disable=protected-access
+      raise core._status_to_exception(e) from None  # pylint: disable=protected-access
+
+    is_sparse = _pywrap_dtensor_device.IsSparseDTensor(
+        context.context()._handle,  # pylint: disable=protected-access.
+        dtensor,
+        self._device_info)
+    if is_sparse:
+      result = []
+      for i in range(len(tensors) // 3):
+        result.append(
+            sparse_tensor.SparseTensor(tensors[i],
+                                       tensors[i + len(tensors) // 3],
+                                       tensors[i + 2 * len(tensors) // 3]))
+      return result
+    else:
+      return tensors
+
+  def fetch_layout(self, dtensor: Any) -> layout_lib.Layout:
+    """Fetches the layout of the DTensor.
+
+    Args:
+      dtensor: The DTensor whose layout is to be fetched.
+
+    Returns:
+      The `Layout` of this DTensor.
+
+    Raises:
+      RuntimeError: When not called eagerly.
+    """
+    if not context.executing_eagerly():
+      raise RuntimeError("`fetch_layout` must be called eagerly.")
+    if _pywrap_utils.IsVariable(dtensor):
+      dtensor = dtensor.read_value()
+    try:
+      layout_string = _pywrap_dtensor_device.FetchLayout(
+          context.context()._handle,  # pylint: disable=protected-access
+          dtensor,
+          self._device_info)
+    except core._NotOkStatusException as e:  # pylint: disable=protected-access
+      raise core._status_to_exception(e) from None  # pylint: disable=protected-access
+
+    if layout_string is None:
+      return None
+    return layout_lib.Layout.from_string(layout_string)
+
+  def is_dtensor(self, tensor: Any) -> bool:
+    """Check whether the input tensor is a DTensor.
+
+    In Python, a DTensor has the same type as a `tf.Tensor`. This method will
+    let you check and handle the tensor differently if a tf.Tensor is a DTensor.
+
+    Args:
+      tensor: an object to be checked.
+
+    Returns:
+      bool, True if the given tensor is a DTensor.
+
+    Raises:
+      RuntimeError: When not called eagerly.
+    """
+    if not context.executing_eagerly():
+      raise RuntimeError("`is_dtensor` must be called eagerly.")
+    if not tensor_util.is_tensor(tensor):
+      return False
+    if _pywrap_utils.IsVariable(tensor):
+      tensor = tensor._handle  # pylint: disable=protected-access
+    return _pywrap_dtensor_device.IsDTensor(
+        context.context()._handle,  # pylint: disable=protected-access
+        tensor,
+        self._device_info,
+    )
+
+  def set_tpu_core_ids(self, mesh_name, tpu_core_ids):
+    """Sets the singleton global device ID-to-physical core ID map.
+
+    Args:
+      mesh_name: The name of a mesh. If empty, set the default mapping.
+      tpu_core_ids: TPU core IDs sorted by TF task/device ordinal.
+    """
+    _pywrap_dtensor_device.SetTPUCoreIDs(self._device_info, mesh_name,
+                                         tpu_core_ids)
+
+  def clear_tpu_core_ids(self):
+    _pywrap_dtensor_device.ClearTPUCoreIDs(self._device_info)
+
+  def tpu_core_ids_to_locations(self, tpu_core_ids):
+    """Translates TPU core IDs to TPU core locations.
+
+    Args:
+      tpu_core_ids: A list of TPU core IDs. Each one is an unsigned integer.
+
+    Returns:
+      A list of corresponding TPU core locations.
+    """
+    return _pywrap_dtensor_device.TPUCoreIDsToLocations(
+        context.context()._handle,  # pylint: disable=protected-access
+        self._device_info,
+        tpu_core_ids)
+
+  def tpu_core_locations_to_ids(self, tpu_core_locations):
+    """Translates TPU core locations to TPU core IDs.
+
+    Args:
+      tpu_core_locations: A list of TPU core locations. Each one is a list of
+        four unsigned integers, [x, y, z, core].
+
+    Returns:
+      A list of corresponding TPU core IDs.
+    """
+    return _pywrap_dtensor_device.TPUCoreLocationsToIDs(
+        context.context()._handle,  # pylint: disable=protected-access
+        self._device_info,
+        tpu_core_locations)
+
+  def _get_stats(self):
+    """Returns the number of cache hit and miss for function compilation.
+
+    Returns:
+      A dictionary.
+        'miss': number of cache misses;
+        'hit': number of cache hits; and
+        'size': size of cache;
+      miss count.
+    """
+    return _pywrap_dtensor_device.GetStats(
+        context.context()._handle,  # pylint: disable=protected-access,
+        self._device_info,
+    )
+
+  def set_iterator_element_layouts(self, iterator_resource_dtensor,
+                                   layouts: List[layout_lib.Layout]):
+    """Sets the element layouts on an iterator resource tensor.
+
+    Args:
+      iterator_resource_dtensor: a DTensor created by packing the individiual
+        iterator resource tensors.
+      layouts: the flattened list of layouts to be applied to the elements
+        emitted by the iterator resource DTensor.
+    """
+    _pywrap_dtensor_device.SetIteratorElementLayouts(
+        context.context()._handle,  # pylint: disable=protected-access
+        iterator_resource_dtensor,
+        [layout.to_string() for layout in layouts],
+        self._device_info)
+
+  @contextlib.contextmanager
+  def _experimental_default_mesh(self, mesh: layout_lib.Mesh):
+    """Sets a default mesh for all ops in the scope.
+
+    Note: This is an internal helper method, which is not user facing api.
+
+    Useful for requesting a specific mesh for ops which would have no inferred
+    layout, e.g. tf.zeros.
+
+    Args:
+      mesh: A Mesh to be used for ops without Mesh.
+
+    Yields:
+      Nothing.
+    """
+    previous_default = self._current_default_mesh
+    self._register_mesh(mesh)
+    _pywrap_dtensor_device.ExperimentalSetDefaultMesh(
+        self._device_info,
+        mesh.to_string().encode("utf-8"))
+    self._current_default_mesh = mesh
+    yield
+    _pywrap_dtensor_device.ExperimentalClearDefaultMesh(self._device_info)
+    if previous_default:
+      _pywrap_dtensor_device.ExperimentalSetDefaultMesh(
+          self._device_info,
+          previous_default.to_string().encode("utf-8"))
+    self._current_default_mesh = previous_default
+
+  @contextlib.contextmanager
+  def _default_layout(self, layout: layout_lib.Layout):
+    """Sets a default output layout for all ops in the scope.
+
+    Note: This is an internal helper method, which is not user facing api.
+
+    Useful for requesting a specific layout for ops which would have no inferred
+    layout, e.g. tf.zeros.
+
+    Caveats:
+
+    - Currently only affects the first output of an op. For Op with multiple
+      outputs, this does not support yet.
+
+    - All Ops in the scope will be attached with the same layout. This might not
+      be valid as the rank is different. The current suggestion is: Try to wrap
+      the raw op wheneven possible.
+
+    Args:
+      layout: A Layout for the outputs of all operations in this scope.
+
+    Yields:
+      Nothing.
+    """
+    previous_default = None
+    previous_graph_size = None
+    graph = None
+
+    self._register_mesh(layout.mesh)
+    try:
+      previous_default = self._current_output_layout
+      self._current_output_layout = layout.to_string().encode("utf-8")
+      _pywrap_dtensor_device.ExperimentalSetDefaultLayout(
+          self._device_info, self._current_output_layout)
+      if context.executing_eagerly():
+        with ops.device(self.name):
+          yield
+      else:
+        # Custom devices currently don't affect graph building, so we need a
+        # separate way to indicate layouts.
+        #
+        # TODO(allenl): Remove this case once the DTensor device is active
+        # during tracing.
+        graph = ops.get_default_graph()
+        previous_graph_size = len(graph.get_operations())
+        yield
+    finally:
+      if graph is not None:
+        # Tag operations added under this scope
+        for operation in graph.get_operations()[previous_graph_size:]:
+          # Set layout directly on the Op itself.
+          operation._set_attr(  # pylint: disable=protected-access
+              "_layout",
+              attr_value_pb2.AttrValue(
+                  list=attr_value_pb2.AttrValue.ListValue(
+                      s=[self._current_output_layout])))
+          operation._set_attr(  # pylint: disable=protected-access
+              "_mesh",
+              attr_value_pb2.AttrValue(
+                  s=layout.mesh.to_string().encode("utf-8")))
+
+      self._current_output_layout = previous_default
+      if self._current_output_layout is None:
+        _pywrap_dtensor_device.ExperimentalClearDefaultLayout(self._device_info)
+      else:
+        _pywrap_dtensor_device.ExperimentalSetDefaultLayout(
+            self._device_info, self._current_output_layout.decode("utf-8"))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/gen_dtensor_ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/gen_dtensor_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..adae6de33ef18549f27f8c90b689001d085fd942
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/gen_dtensor_ops.py
@@ -0,0 +1,763 @@
+"""Python wrappers around TensorFlow ops.
+
+This file is MACHINE GENERATED! Do not edit.
+"""
+
+import collections
+
+from tensorflow.python import pywrap_tfe as pywrap_tfe
+from tensorflow.python.eager import context as _context
+from tensorflow.python.eager import core as _core
+from tensorflow.python.eager import execute as _execute
+from tensorflow.python.framework import dtypes as _dtypes
+from tensorflow.security.fuzzing.py import annotation_types as _atypes
+
+from tensorflow.python.framework import op_def_registry as _op_def_registry
+from tensorflow.python.framework import ops as _ops
+from tensorflow.python.framework import op_def_library as _op_def_library
+from tensorflow.python.util.deprecation import deprecated_endpoints
+from tensorflow.python.util import dispatch as _dispatch
+from tensorflow.python.util.tf_export import tf_export
+
+from typing import TypeVar, List, Any
+from typing_extensions import Annotated
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('configure_and_initialize_global_tpu')
+def configure_and_initialize_global_tpu(use_tfrt_host_runtime:bool=True, name=None) -> Annotated[Any, _atypes.Int32]:
+  r"""TODO: add doc.
+
+  Args:
+    use_tfrt_host_runtime: An optional `bool`. Defaults to `True`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` of type `int32`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "ConfigureAndInitializeGlobalTPU", name,
+        "use_tfrt_host_runtime", use_tfrt_host_runtime)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_configure_and_initialize_global_tpu(
+          (use_tfrt_host_runtime, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return configure_and_initialize_global_tpu_eager_fallback(
+          use_tfrt_host_runtime=use_tfrt_host_runtime, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            configure_and_initialize_global_tpu, (), dict(use_tfrt_host_runtime=use_tfrt_host_runtime,
+                                                          name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_configure_and_initialize_global_tpu(
+        (use_tfrt_host_runtime, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if use_tfrt_host_runtime is None:
+    use_tfrt_host_runtime = True
+  use_tfrt_host_runtime = _execute.make_bool(use_tfrt_host_runtime, "use_tfrt_host_runtime")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "ConfigureAndInitializeGlobalTPU", use_tfrt_host_runtime=use_tfrt_host_runtime,
+                                           name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          configure_and_initialize_global_tpu, (), dict(use_tfrt_host_runtime=use_tfrt_host_runtime,
+                                                        name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("use_tfrt_host_runtime",
+              _op._get_attr_bool("use_tfrt_host_runtime"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "ConfigureAndInitializeGlobalTPU", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+ConfigureAndInitializeGlobalTPU = tf_export("raw_ops.ConfigureAndInitializeGlobalTPU")(_ops.to_raw_op(configure_and_initialize_global_tpu))
+_dispatcher_for_configure_and_initialize_global_tpu = configure_and_initialize_global_tpu._tf_type_based_dispatcher.Dispatch
+
+
+def configure_and_initialize_global_tpu_eager_fallback(use_tfrt_host_runtime: bool, name, ctx) -> Annotated[Any, _atypes.Int32]:
+  if use_tfrt_host_runtime is None:
+    use_tfrt_host_runtime = True
+  use_tfrt_host_runtime = _execute.make_bool(use_tfrt_host_runtime, "use_tfrt_host_runtime")
+  _inputs_flat = []
+  _attrs = ("use_tfrt_host_runtime", use_tfrt_host_runtime)
+  _result = _execute.execute(b"ConfigureAndInitializeGlobalTPU", 1,
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "ConfigureAndInitializeGlobalTPU", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_CopyToMesh_T = TypeVar("TV_CopyToMesh_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('copy_to_mesh')
+def copy_to_mesh(input: Annotated[Any, TV_CopyToMesh_T], mesh: str, name=None) -> Annotated[Any, TV_CopyToMesh_T]:
+  r"""TODO: add doc.
+
+  Args:
+    input: A `Tensor`.
+    mesh: A `string`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "CopyToMesh", name, input, "mesh", mesh)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_copy_to_mesh(
+          (input, mesh, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return copy_to_mesh_eager_fallback(
+          input, mesh=mesh, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            copy_to_mesh, (), dict(input=input, mesh=mesh, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_copy_to_mesh(
+        (input, mesh, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  mesh = _execute.make_str(mesh, "mesh")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "CopyToMesh", input=input, mesh=mesh, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          copy_to_mesh, (), dict(input=input, mesh=mesh, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("mesh", _op.get_attr("mesh"), "T", _op._get_attr_type("T"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "CopyToMesh", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+CopyToMesh = tf_export("raw_ops.CopyToMesh")(_ops.to_raw_op(copy_to_mesh))
+_dispatcher_for_copy_to_mesh = copy_to_mesh._tf_type_based_dispatcher.Dispatch
+
+
+def copy_to_mesh_eager_fallback(input: Annotated[Any, TV_CopyToMesh_T], mesh: str, name, ctx) -> Annotated[Any, TV_CopyToMesh_T]:
+  mesh = _execute.make_str(mesh, "mesh")
+  _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
+  _inputs_flat = [input]
+  _attrs = ("mesh", mesh, "T", _attr_T)
+  _result = _execute.execute(b"CopyToMesh", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "CopyToMesh", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_CopyToMeshGrad_T = TypeVar("TV_CopyToMeshGrad_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('copy_to_mesh_grad')
+def copy_to_mesh_grad(input: Annotated[Any, TV_CopyToMeshGrad_T], forward_input: Annotated[Any, TV_CopyToMeshGrad_T], name=None) -> Annotated[Any, TV_CopyToMeshGrad_T]:
+  r"""TODO: add doc.
+
+  Args:
+    input: A `Tensor`.
+    forward_input: A `Tensor`. Must have the same type as `input`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "CopyToMeshGrad", name, input, forward_input)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_copy_to_mesh_grad(
+          (input, forward_input, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return copy_to_mesh_grad_eager_fallback(
+          input, forward_input, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            copy_to_mesh_grad, (), dict(input=input,
+                                        forward_input=forward_input,
+                                        name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_copy_to_mesh_grad(
+        (input, forward_input, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "CopyToMeshGrad", input=input, forward_input=forward_input, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          copy_to_mesh_grad, (), dict(input=input,
+                                      forward_input=forward_input, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "CopyToMeshGrad", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+CopyToMeshGrad = tf_export("raw_ops.CopyToMeshGrad")(_ops.to_raw_op(copy_to_mesh_grad))
+_dispatcher_for_copy_to_mesh_grad = copy_to_mesh_grad._tf_type_based_dispatcher.Dispatch
+
+
+def copy_to_mesh_grad_eager_fallback(input: Annotated[Any, TV_CopyToMeshGrad_T], forward_input: Annotated[Any, TV_CopyToMeshGrad_T], name, ctx) -> Annotated[Any, TV_CopyToMeshGrad_T]:
+  _attr_T, _inputs_T = _execute.args_to_matching_eager([input, forward_input], ctx, [])
+  (input, forward_input) = _inputs_T
+  _inputs_flat = [input, forward_input]
+  _attrs = ("T", _attr_T)
+  _result = _execute.execute(b"CopyToMeshGrad", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "CopyToMeshGrad", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('d_tensor_restore_v2')
+def d_tensor_restore_v2(prefix: Annotated[Any, _atypes.String], tensor_names: Annotated[Any, _atypes.String], shape_and_slices: Annotated[Any, _atypes.String], input_shapes, input_layouts, dtypes, name=None):
+  r"""TODO: add doc.
+
+  Args:
+    prefix: A `Tensor` of type `string`.
+    tensor_names: A `Tensor` of type `string`.
+    shape_and_slices: A `Tensor` of type `string`.
+    input_shapes: A list of shapes (each a `tf.TensorShape` or list of `ints`).
+    input_layouts: A list of `strings`.
+    dtypes: A list of `tf.DTypes` that has length `>= 1`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A list of `Tensor` objects of type `dtypes`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "DTensorRestoreV2", name, prefix, tensor_names,
+        shape_and_slices, "input_shapes", input_shapes, "input_layouts",
+        input_layouts, "dtypes", dtypes)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_d_tensor_restore_v2(
+          (prefix, tensor_names, shape_and_slices, input_shapes,
+          input_layouts, dtypes, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return d_tensor_restore_v2_eager_fallback(
+          prefix, tensor_names, shape_and_slices, input_shapes=input_shapes,
+          input_layouts=input_layouts, dtypes=dtypes, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            d_tensor_restore_v2, (), dict(prefix=prefix,
+                                          tensor_names=tensor_names,
+                                          shape_and_slices=shape_and_slices,
+                                          input_shapes=input_shapes,
+                                          input_layouts=input_layouts,
+                                          dtypes=dtypes, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_d_tensor_restore_v2(
+        (prefix, tensor_names, shape_and_slices, input_shapes, input_layouts,
+        dtypes, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  if not isinstance(input_shapes, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'input_shapes' argument to "
+        "'d_tensor_restore_v2' Op, not %r." % input_shapes)
+  input_shapes = [_execute.make_shape(_s, "input_shapes") for _s in input_shapes]
+  if not isinstance(input_layouts, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'input_layouts' argument to "
+        "'d_tensor_restore_v2' Op, not %r." % input_layouts)
+  input_layouts = [_execute.make_str(_s, "input_layouts") for _s in input_layouts]
+  if not isinstance(dtypes, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'dtypes' argument to "
+        "'d_tensor_restore_v2' Op, not %r." % dtypes)
+  dtypes = [_execute.make_type(_t, "dtypes") for _t in dtypes]
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "DTensorRestoreV2", prefix=prefix, tensor_names=tensor_names,
+                            shape_and_slices=shape_and_slices,
+                            input_shapes=input_shapes,
+                            input_layouts=input_layouts, dtypes=dtypes,
+                            name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          d_tensor_restore_v2, (), dict(prefix=prefix,
+                                        tensor_names=tensor_names,
+                                        shape_and_slices=shape_and_slices,
+                                        input_shapes=input_shapes,
+                                        input_layouts=input_layouts,
+                                        dtypes=dtypes, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if not _result:
+    return _op
+  if _execute.must_record_gradient():
+    _attrs = ("input_shapes", _op.get_attr("input_shapes"), "input_layouts",
+              _op.get_attr("input_layouts"), "dtypes", _op.get_attr("dtypes"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "DTensorRestoreV2", _inputs_flat, _attrs, _result)
+  return _result
+
+DTensorRestoreV2 = tf_export("raw_ops.DTensorRestoreV2")(_ops.to_raw_op(d_tensor_restore_v2))
+_dispatcher_for_d_tensor_restore_v2 = d_tensor_restore_v2._tf_type_based_dispatcher.Dispatch
+
+
+def d_tensor_restore_v2_eager_fallback(prefix: Annotated[Any, _atypes.String], tensor_names: Annotated[Any, _atypes.String], shape_and_slices: Annotated[Any, _atypes.String], input_shapes, input_layouts, dtypes, name, ctx):
+  if not isinstance(input_shapes, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'input_shapes' argument to "
+        "'d_tensor_restore_v2' Op, not %r." % input_shapes)
+  input_shapes = [_execute.make_shape(_s, "input_shapes") for _s in input_shapes]
+  if not isinstance(input_layouts, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'input_layouts' argument to "
+        "'d_tensor_restore_v2' Op, not %r." % input_layouts)
+  input_layouts = [_execute.make_str(_s, "input_layouts") for _s in input_layouts]
+  if not isinstance(dtypes, (list, tuple)):
+    raise TypeError(
+        "Expected list for 'dtypes' argument to "
+        "'d_tensor_restore_v2' Op, not %r." % dtypes)
+  dtypes = [_execute.make_type(_t, "dtypes") for _t in dtypes]
+  prefix = _ops.convert_to_tensor(prefix, _dtypes.string)
+  tensor_names = _ops.convert_to_tensor(tensor_names, _dtypes.string)
+  shape_and_slices = _ops.convert_to_tensor(shape_and_slices, _dtypes.string)
+  _inputs_flat = [prefix, tensor_names, shape_and_slices]
+  _attrs = ("input_shapes", input_shapes, "input_layouts", input_layouts,
+  "dtypes", dtypes)
+  _result = _execute.execute(b"DTensorRestoreV2", len(dtypes),
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "DTensorRestoreV2", _inputs_flat, _attrs, _result)
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('d_tensor_set_global_tpu_array')
+def d_tensor_set_global_tpu_array(topology: Annotated[Any, _atypes.String], name=None):
+  r"""TODO: add doc.
+
+  Args:
+    topology: A `Tensor` of type `string`.
+    name: A name for the operation (optional).
+
+  Returns:
+    The created Operation.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "DTensorSetGlobalTPUArray", name, topology)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_d_tensor_set_global_tpu_array(
+          (topology, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return d_tensor_set_global_tpu_array_eager_fallback(
+          topology, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            d_tensor_set_global_tpu_array, (), dict(topology=topology,
+                                                    name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_d_tensor_set_global_tpu_array(
+        (topology, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "DTensorSetGlobalTPUArray", topology=topology, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          d_tensor_set_global_tpu_array, (), dict(topology=topology,
+                                                  name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  return _op
+DTensorSetGlobalTPUArray = tf_export("raw_ops.DTensorSetGlobalTPUArray")(_ops.to_raw_op(d_tensor_set_global_tpu_array))
+_dispatcher_for_d_tensor_set_global_tpu_array = d_tensor_set_global_tpu_array._tf_type_based_dispatcher.Dispatch
+
+
+def d_tensor_set_global_tpu_array_eager_fallback(topology: Annotated[Any, _atypes.String], name, ctx):
+  topology = _ops.convert_to_tensor(topology, _dtypes.string)
+  _inputs_flat = [topology]
+  _attrs = None
+  _result = _execute.execute(b"DTensorSetGlobalTPUArray", 0,
+                             inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
+                             name=name)
+  _result = None
+  return _result
+
+
+TV_Relayout_T = TypeVar("TV_Relayout_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('relayout')
+def relayout(input: Annotated[Any, TV_Relayout_T], layout: str, name=None) -> Annotated[Any, TV_Relayout_T]:
+  r"""TODO: add doc.
+
+  Args:
+    input: A `Tensor`.
+    layout: A `string`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "Relayout", name, input, "layout", layout)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_relayout(
+          (input, layout, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return relayout_eager_fallback(
+          input, layout=layout, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            relayout, (), dict(input=input, layout=layout, name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_relayout(
+        (input, layout, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  layout = _execute.make_str(layout, "layout")
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "Relayout", input=input, layout=layout, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          relayout, (), dict(input=input, layout=layout, name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("layout", _op.get_attr("layout"), "T", _op._get_attr_type("T"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "Relayout", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+Relayout = tf_export("raw_ops.Relayout")(_ops.to_raw_op(relayout))
+_dispatcher_for_relayout = relayout._tf_type_based_dispatcher.Dispatch
+
+
+def relayout_eager_fallback(input: Annotated[Any, TV_Relayout_T], layout: str, name, ctx) -> Annotated[Any, TV_Relayout_T]:
+  layout = _execute.make_str(layout, "layout")
+  _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
+  _inputs_flat = [input]
+  _attrs = ("layout", layout, "T", _attr_T)
+  _result = _execute.execute(b"Relayout", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "Relayout", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+TV_RelayoutLike_T = TypeVar("TV_RelayoutLike_T", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+TV_RelayoutLike_U = TypeVar("TV_RelayoutLike_U", _atypes.BFloat16, _atypes.Bool, _atypes.Complex128, _atypes.Complex64, _atypes.Float16, _atypes.Float32, _atypes.Float64, _atypes.Float8e4m3fn, _atypes.Float8e5m2, _atypes.Half, _atypes.Int16, _atypes.Int32, _atypes.Int4, _atypes.Int64, _atypes.Int8, _atypes.QInt16, _atypes.QInt32, _atypes.QInt8, _atypes.QUInt16, _atypes.QUInt8, _atypes.Resource, _atypes.String, _atypes.UInt16, _atypes.UInt32, _atypes.UInt4, _atypes.UInt64, _atypes.UInt8, _atypes.Variant)
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('relayout_like')
+def relayout_like(input: Annotated[Any, TV_RelayoutLike_T], layout_input: Annotated[Any, TV_RelayoutLike_U], name=None) -> Annotated[Any, TV_RelayoutLike_T]:
+  r"""TODO: add doc.
+
+  Args:
+    input: A `Tensor`.
+    layout_input: A `Tensor`.
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor`. Has the same type as `input`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "RelayoutLike", name, input, layout_input)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_relayout_like(
+          (input, layout_input, name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return relayout_like_eager_fallback(
+          input, layout_input, name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            relayout_like, (), dict(input=input, layout_input=layout_input,
+                                    name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_relayout_like(
+        (input, layout_input, name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "RelayoutLike", input=input, layout_input=layout_input, name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          relayout_like, (), dict(input=input, layout_input=layout_input,
+                                  name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ("T", _op._get_attr_type("T"), "U", _op._get_attr_type("U"))
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "RelayoutLike", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+RelayoutLike = tf_export("raw_ops.RelayoutLike")(_ops.to_raw_op(relayout_like))
+_dispatcher_for_relayout_like = relayout_like._tf_type_based_dispatcher.Dispatch
+
+
+def relayout_like_eager_fallback(input: Annotated[Any, TV_RelayoutLike_T], layout_input: Annotated[Any, TV_RelayoutLike_U], name, ctx) -> Annotated[Any, TV_RelayoutLike_T]:
+  _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, [])
+  _attr_U, (layout_input,) = _execute.args_to_matching_eager([layout_input], ctx, [])
+  _inputs_flat = [input, layout_input]
+  _attrs = ("T", _attr_T, "U", _attr_U)
+  _result = _execute.execute(b"RelayoutLike", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "RelayoutLike", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+
+@_dispatch.add_fallback_dispatch_list
+@_dispatch.add_type_based_api_dispatcher
+@tf_export('shutdown_tpu_system')
+def shutdown_tpu_system(name=None) -> Annotated[Any, _atypes.Bool]:
+  r"""TODO: add doc.
+
+  Args:
+    name: A name for the operation (optional).
+
+  Returns:
+    A `Tensor` of type `bool`.
+  """
+  _ctx = _context._context or _context.context()
+  tld = _ctx._thread_local_data
+  if tld.is_eager:
+    try:
+      _result = pywrap_tfe.TFE_Py_FastPathExecute(
+        _ctx, "ShutdownTPUSystem", name)
+      return _result
+    except _core._NotOkStatusException as e:
+      _ops.raise_from_not_ok_status(e, name)
+    except _core._FallbackException:
+      pass
+    try:
+      _result = _dispatcher_for_shutdown_tpu_system(
+          (name,), None)
+      if _result is not NotImplemented:
+        return _result
+      return shutdown_tpu_system_eager_fallback(
+          name=name, ctx=_ctx)
+    except _core._SymbolicException:
+      pass  # Add nodes to the TensorFlow graph.
+    except (TypeError, ValueError):
+      _result = _dispatch.dispatch(
+            shutdown_tpu_system, (), dict(name=name)
+          )
+      if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+        return _result
+      raise
+  else:
+    _result = _dispatcher_for_shutdown_tpu_system(
+        (name,), None)
+    if _result is not NotImplemented:
+      return _result
+  # Add nodes to the TensorFlow graph.
+  try:
+    _, _, _op, _outputs = _op_def_library._apply_op_helper(
+        "ShutdownTPUSystem", name=name)
+  except (TypeError, ValueError):
+    _result = _dispatch.dispatch(
+          shutdown_tpu_system, (), dict(name=name)
+        )
+    if _result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
+      return _result
+    raise
+  _result = _outputs[:]
+  if _execute.must_record_gradient():
+    _attrs = ()
+    _inputs_flat = _op.inputs
+    _execute.record_gradient(
+        "ShutdownTPUSystem", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
+ShutdownTPUSystem = tf_export("raw_ops.ShutdownTPUSystem")(_ops.to_raw_op(shutdown_tpu_system))
+_dispatcher_for_shutdown_tpu_system = shutdown_tpu_system._tf_type_based_dispatcher.Dispatch
+
+
+def shutdown_tpu_system_eager_fallback(name, ctx) -> Annotated[Any, _atypes.Bool]:
+  _inputs_flat = []
+  _attrs = None
+  _result = _execute.execute(b"ShutdownTPUSystem", 1, inputs=_inputs_flat,
+                             attrs=_attrs, ctx=ctx, name=name)
+  if _execute.must_record_gradient():
+    _execute.record_gradient(
+        "ShutdownTPUSystem", _inputs_flat, _attrs, _result)
+  _result, = _result
+  return _result
+
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/input_util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/input_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..280fcdabe7d08132ca1ff20c59937dd9d1cdaef1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/input_util.py
@@ -0,0 +1,666 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""APIs to deal with input datasets efficiently in DTensor.
+
+When using tf.data with DTensor, the `DTensorDataset` API can be used to
+efficiently handle loading the input data and correctly packing it to the
+corresponding devices. This API is intended to work with unbatched data and can
+be used for both data and model parallel setups.
+
+Example usage:
+
+>>> # 1-D mesh with 4 devices
+>>> mesh = dtensor.Mesh(dim_names=['batch'], ...)
+>>> layout = dtensor.Layout.batch_sharded(mesh, 'batch', rank=1)
+>>> dataset = tf.data.Dataset.range(256)
+>>> d_dataset = dtensor.DTensorDataset(
+...     dataset=dataset,
+...     global_batch_size=16,
+...     mesh=mesh,
+...     layouts=layout,
+...     batch_dim='batch')
+>>> d_iter = iter(d_dataset)
+>>> # Each batch is a length 16 tensor sharded across 4 devices
+>>> batch_0_dtensor = next(d_iter)
+>>> batch_0_dtensor
+<tf.Tensor: shape=(16,),
+            dtype=int64,
+            value={"CPU:0": [ 0  1  2  4],
+                   "CPU:1": [ 5  6  7  8],
+                   "CPU:2": [ 9 10 11 12],
+                   "CPU:3": [13 14 15 16]}>
+>>> batch_1_dtensor = next(d_iter)
+>>> batch_1_dtensor
+<tf.Tensor: shape=(16,),
+            dtype=int64,
+            value={"CPU:0": [17 18 19 20],
+                   "CPU:1": [21 22 23 24],
+                   "CPU:2": [25 26 27 28],
+                   "CPU:3": [29 30 31 32]}>
+
+For multi-client setups, `DTensorDataset` interacts with tf.data service to
+correctly distribute the dataset among the participating clients. DTensor works
+with tf.data service in co-located mode where each worker is running alongside
+the DTensor client (the Tensorflow Python process). The `TFDataServiceConfig`
+dataclass can be filled with information about the tf.data service cluster, and
+passed to `DTensorDataset` to enable distribution.
+"""
+
+import dataclasses
+import operator
+
+from typing import Any, List, Optional, Sequence, Tuple
+
+from tensorflow.dtensor.python import api
+from tensorflow.dtensor.python import config
+from tensorflow.dtensor.python import layout as layout_lib
+from tensorflow.python.data.experimental.ops import data_service_ops
+from tensorflow.python.data.experimental.ops import distribute
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.data.ops import iterator_ops
+from tensorflow.python.eager import context
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.framework import tensor_spec
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.types import data as data_types
+from tensorflow.python.util import nest
+from tensorflow.python.util.tf_export import tf_export
+
+
+@dataclasses.dataclass
+class TFDataServiceConfig:
+  """Specifies the tf.data service configuration to use.
+
+  Attributes:
+    dispatcher_address: a string specifying the address of the tf.data service
+      dispatcher server.
+    job_name: a non-empty string identifying the shared job that will be created
+      on tf.data service to process this dataset.
+  """
+  dispatcher_address: str
+  job_name: str
+
+
+# TODO(b/223275517): Add support for get_next_as_optional().
+class _DTensorIterator(iterator_ops.OwnedIterator):
+  """An iterator for a tf.data.Dataset distributed using DTensor.
+
+  DTensorIterator encapsulates multiple underlying dataset iterators. It handles
+  retrieving the tensors to be placed on each underlying device and then uses
+  the 'pack' operation to create and return a DTensor. Thus users need only
+  interact with a single DTensorIterator to automatically distribute dataset
+  tensors onto devices.
+  """
+
+  def __init__(
+      self,
+      dtensor_components: Tuple[tensor.Tensor],
+      global_element_spec: tensor_spec.TensorSpec,
+      layouts: Any):
+    """Initializes a distributed iterator for DTensor datasets.
+
+    This iterator encapsulates tf.data iterators for the underlying devices, and
+    treats it as a packed DTensor of iterator resource tensors.
+
+    Args:
+      dtensor_components: a tuple containing the underlying iterator resources
+        packed into a DTensor. This is expected to be a tuple with a single
+        element.
+      global_element_spec: the underlying dataset's element spec from a global
+        view.
+      layouts: a structure of DTensor layouts to be applied to the elements
+        returned by the underlying iterators. This can be a single layout or
+        (possibly nested) tuples or dictionaries of layouts, and the structure
+        must match the structure of the iterator elements.
+    """
+    # dtensor_components is expected to be a single-element tuple.
+    [self._iterator_resource_dtensor] = dtensor_components
+    self._global_element_spec = global_element_spec
+    self._layouts = layouts
+    self._layouts_str = nest.map_structure(
+        lambda layout: layout.to_string(), layouts)
+
+    super().__init__(
+        components=dtensor_components, element_spec=global_element_spec)
+
+  def __next__(self):
+    try:
+      # IteratorGetNext will return a DTensor on the host, so move it to the
+      # device mesh. If the dataset layouts are on the host mesh itself, this
+      # is handled by DTensor as a no-op.
+      host_elem = self._next_internal()
+      context.async_wait()
+      device_elem = nest.map_structure(
+          api.copy_to_mesh, host_elem, self._layouts)
+      context.async_wait()
+      return device_elem
+    except errors.OutOfRangeError as e:
+      # Match TF2 eager executor behavior by raising StopIteration when iterator
+      # is out of range.
+      if context.executing_eagerly():
+        raise StopIteration from e
+      else:
+        raise e
+
+  @property
+  def _type_spec(self):
+    return _DTensorIteratorSpec(self._global_element_spec, self._layouts_str)
+
+
+class _DTensorIteratorSpec(iterator_ops.IteratorSpec):
+  """Type specification for `_DTensorIterator`."""
+
+  __slots__ = ['_global_element_spec', '_layouts_str']
+
+  def __init__(
+      self, global_element_spec: tensor_spec.TensorSpec, layouts_str: Any):
+    super().__init__(global_element_spec)
+    self._global_element_spec = global_element_spec
+    self._layouts_str = layouts_str
+
+  @property
+  def value_type(self):
+    return _DTensorIterator
+
+  def _serialize(self):
+    return (self._global_element_spec, self._layouts_str)
+
+  @property
+  def _component_specs(self):
+    return (tensor_spec.TensorSpec([], dtypes.resource),)
+
+  def _to_components(self, value):
+    return (value._iterator_resource_dtensor,)  # pylint: disable=protected-access
+
+  def _from_components(self, components):
+    layouts = nest.map_structure(
+        layout_lib.Layout.from_string, self._layouts_str)
+    return _DTensorIterator(
+        dtensor_components=components,
+        global_element_spec=self._global_element_spec,
+        layouts=layouts)
+
+  @classmethod
+  def from_value(cls, value):
+    return cls(value._global_element_spec, value._layouts_str)  # pylint: disable=protected-access
+
+
+def _validate_input(flattened_layouts: Sequence[layout_lib.Layout],
+                    flattened_elem_spec: Sequence[tensor_spec.TensorSpec],
+                    dataset_already_batched: bool):
+  """Checks that the dataset's layouts and element specs are compatible.
+
+  Args:
+    flattened_layouts: the flattened list of layouts used to distribute the
+      dataset.
+    flattened_elem_spec: the flattened list of element specs used in the
+      dataset's components.
+    dataset_already_batched: whether the dataset to be validated is already
+      batched.
+
+  Raises:
+    ValueError: if the dataset's inputs are incompatible.
+  """
+  if not flattened_elem_spec:
+    raise ValueError(
+        'Expected input element spec of at least one element, was empty.')
+
+  first_elem_shape = flattened_elem_spec[0].shape
+
+  for layout, elem_spec in zip(flattened_layouts, flattened_elem_spec):
+    if elem_spec.shape.rank is None:
+      raise ValueError(
+          'Dataset element shape must have a valid rank, got spec %s.' %
+          elem_spec)
+
+    # Check that layout's rank matches the element's rank. If dataset is not yet
+    # batched, then the layout's rank must be one greater than the element's
+    # rank.
+    expected_rank = elem_spec.shape.rank
+    if not dataset_already_batched:
+      expected_rank += 1
+    if layout.rank != expected_rank:
+      raise ValueError(
+          ('Expected layout with rank %d for element spec %s, got layout %s. '
+           'Check that the dataset is not batched before passing to '
+           'DTensorDataset.') %
+          (expected_rank, elem_spec, layout.sharding_specs))
+
+    if dataset_already_batched:
+      # Check that the batch dimension size of all dataset elements match.
+      batch_dim_size = first_elem_shape.as_list()[0]
+      if batch_dim_size is None:
+        raise ValueError(
+            ('Size of batch dimension of element spec %s is None. Ensure '
+             'drop_remainder=True when batching the dataset.') % elem_spec)
+
+      if elem_spec.shape.as_list()[0] != batch_dim_size:
+        raise ValueError(
+            ('Size of batch dimension of element spec %s does not match '
+             'expected size %d.') % (elem_spec, batch_dim_size))
+
+
+def _shard_counts(layout: layout_lib.Layout,
+                  batch_dim: Optional[str] = None) -> List[int]:
+  """Computes a list of the number of shards in each dimension of the layout.
+
+  The shard counts are used to slice each dataset element. The batch dimension's
+  count is overridden to 1 since we only consider how many shards to make
+  locally (within each local replica). Sharding across clients is handled by
+  either tf.data.Dataset's shard transformation (in the single-client case) or
+  tf.data service's distribute function (in the multi-client case).
+
+  Args:
+    layout: the layout to compute the shard counts for.
+    batch_dim: the name of the batch dimension of the layout, if present.
+
+  Returns:
+    A list of shard counts, one element per dimension of the layout.
+  """
+  shard_counts = []
+  for spec in layout.sharding_specs:
+    if spec in (batch_dim, layout_lib.UNSHARDED):
+      shard_counts.append(1)
+    else:
+      shard_counts.append(layout.mesh.dim_size(spec))
+  return shard_counts
+
+
+def _index_matrix(layout: layout_lib.Layout,
+                  elem_spec: tensor_spec.TensorSpec) -> tensor.Tensor:
+  """Computes a utility matrix to derive device-based slice offsets.
+
+  This function builds a matrix of shape `[mesh.rank, layout.rank]` for each
+  dataset element. This matrix can be used to slice the DTensor components
+  returned by the iterator according to the local device that component is to be
+  placed on. This can be done by multiplying the device offsets of shape
+  `[1, mesh.rank]` with this index matrix to get a `[1, layout.rank]` shape
+  tensor containing the slice offsets.
+
+  Note: the index on the batch dim is always 0 since sharding on the batch
+  dimension is handled by either tf.data.Dataset's shard transformation (in the
+  single-client case) or tf.data service's distribute function (in the
+  multi-client case). If there is no sharding on the batch dimension (or any
+  other dimension), the slice index remains 0.
+
+  Args:
+    layout: the layout of the dataset element.
+    elem_spec: the spec of the dataset element.
+
+  Returns:
+    The index matrix as a tensor.
+  """
+  matrix = []
+  for dim in layout.mesh.dim_names:
+    row = [0]
+    for layout_idx, spec in enumerate(layout.sharding_specs[1:]):
+      if spec == layout_lib.UNSHARDED or spec != dim:
+        row.append(0)
+      else:
+        row.append(elem_spec.shape[layout_idx] // layout.mesh.dim_size(dim))
+    matrix.append(row)
+
+  return constant_op.constant(matrix, dtype=dtypes.int32)
+
+
+def _pack_iterator_resource_dtensor(
+    datasets: List[Tuple[int, data_types.DatasetV2]],
+    layouts: Any,
+    mesh: layout_lib.Mesh,
+    num_local_devices_per_replica: int):
+  """Creates a DTensor iterator resource for the per-replica datasets.
+
+  Given a list of replica ID to tf.data.Dataset mappings, this function creates
+  iterators for each device and then packs the underlying iterator resource
+  tensors into a single DTensor. This resource tensor is used by the
+  IteratorGetNext op to retrieve the next element in the dataset.
+
+  Args:
+    datasets: a list of tuples of each unique local replica ID to the dataset
+      object whose elements will be placed on the devices corresponding to that
+      replica.
+    layouts: a structure of DTensor layouts to be applied to the elements
+      returned by the underlying iterators. This can be a single layout or
+      (possibly nested) tuples or dictionaries of layouts, and the structure
+      must match the structure of the iterator elements.
+    mesh: the DTensor mesh to place the iterator batches on.
+    num_local_devices_per_replica: the number of devices in each data-parallel
+      replica.
+
+  Returns:
+    A DTensor of the underlying iterator resource tensors.
+  """
+  host_mesh_devices = mesh.host_mesh().local_devices()
+  device_idx = 0
+
+  iterators = []
+  for _, dataset in datasets:
+    for idx in range(num_local_devices_per_replica):
+      with ops.device_v2(host_mesh_devices[device_idx]):
+        device_dataset = dataset.shard(
+            num_shards=num_local_devices_per_replica, index=idx)
+        iterators.append(iter(device_dataset))
+      device_idx += 1
+
+  if device_idx != len(host_mesh_devices):
+    raise ValueError(
+        'The `datasets` argument does not have the correct number of'
+        f' underlying datasets, found {device_idx} but expected'
+        f' {len(host_mesh_devices)}.')
+
+  host_layouts = nest.map_structure(
+      lambda l: layout_lib.Layout(l.sharding_specs, mesh.host_mesh()), layouts)
+
+  # Pack the iterator resource tensors into a replicated 0-dimensional DTensor
+  # and set the element layouts.
+  iterator_resources = [it._iterator_resource for it in iterators]  # pylint: disable=protected-access
+  d_iterator_resource = api.pack(
+      iterator_resources,
+      layout_lib.Layout.replicated(mesh=mesh.host_mesh(), rank=0))
+  api._dtensor_device().set_iterator_element_layouts(  # pylint: disable=protected-access
+      d_iterator_resource, nest.flatten(host_layouts))
+
+  return d_iterator_resource
+
+
+@tf_export('experimental.dtensor.DTensorDataset', v1=[])
+class DTensorDataset(dataset_ops.UnaryUnchangedStructureDataset):
+  """A dataset of DTensors.
+
+  DTensorDataset encapsulates a `tf.data.Dataset` whose elements are
+  automatically packed and returned as DTensors based on a given mesh and
+  layouts.
+  """
+
+  def __init__(self,
+               dataset: data_types.DatasetV2,
+               *,
+               mesh: layout_lib.Mesh,
+               layouts: Any,
+               global_batch_size: int,
+               dataset_already_batched: bool = False,
+               batch_dim: Optional[str] = None,
+               prefetch: Optional[int] = None,
+               tf_data_service_config: Optional[TFDataServiceConfig] = None):
+    """Creates a DTensorDataset.
+
+    DTensorDataset automatically handles distribution of the dataset elements to
+    each client's devices. It can be used to create an iterator that returns
+    DTensors of the input data on each iteration.
+
+    DTensorDataset works best with unbatched datasets. It takes the mesh and the
+    provided layouts to automatically calculate how to batch the input locally
+    for each replica.
+
+    If the provided dataset is already batched according to the per-replica
+    batch size, then `dataset_already_batched` must be set and DTensorDataset
+    will check that the batch size is consistent with the intended
+    `global_batch_size` using the layout information. Each replica receives a
+    separate slice of the global batch, thus the per-replica batch size can be
+    computed as the global batch size divided by the number of model replicas.
+    For a DTensor mesh, the number of replicas is equal to the size of the
+    mesh's batch dimension.
+
+    Note: `tf.experimental.dtensor.DTensorDataset` instances do *not* implement
+    the full interface of `tf.data.Dataset`. It only supports two usages we will
+    mention below: iteration and `element_spec`. We don't support any other APIs
+    to transform or inspect the dataset.
+
+    TODO(b/223275517): add support for input datasets that are already batched
+    to the global batch size.
+
+    Args:
+      dataset: a `tf.data.Dataset` object.
+      mesh: the DTensor mesh to place the dataset batches on.
+      layouts: a structure of DTensor layouts to be applied to the input dataset
+        values. This can be a single layout or (possibly nested) tuples or
+        dictionaries of layouts, and the structure must match the structure of
+        the dataset. Either all or none of the layouts should be sharded on the
+        batch dimension; having only a subset of layouts batch sharded will not
+        work and raises a ValueError.
+      global_batch_size: the desired global batch size.
+      dataset_already_batched: must be set only if the dataset is already
+        batched to the per-replica batch size. The batched dataset must have
+        `drop_remainder=True` set since DTensor requires static shapes for
+        slicing the input tensors.
+      batch_dim: the mesh dimension on which the input's batch dimension is
+        sharded. Set to None if the input layouts do not shard on the batch
+        dimension.
+      prefetch: number of batches to prefetch using Dataset.prefetch.
+      tf_data_service_config: if operating in multi-client mode, this config
+        specifies the tf.data service configuration to use.
+
+    Raises:
+      ValueError: on any of the following situations,
+        1. if the structures and ranks of layouts and the dataset do not match.
+        2. if the shapes in the dataset's spec are not fully defined.
+        3. if batch_dim is specified and all layouts are not batch-sharded.
+        4. if per_replica_batch_size is specified for an already batched Dataset
+           but it does not match the expected per-replica size based on the
+           provided mesh.
+      TypeError: if type of structures of layouts and the dataset do not match.
+    """
+    super().__init__(dataset, dataset_ops.to_variant(dataset))
+
+    # TODO(b/271162918): fix multi-client use case.
+    if tf_data_service_config is not None:
+      raise NotImplementedError(
+          'Multi-client DTensorDataset is currently not supported.'
+          ' Check b/271162918.')
+
+    self._mesh = mesh
+    self._layouts = layouts
+    self._batch_dim = batch_dim
+    self._prefetch = prefetch
+    self._tf_data_service_config = tf_data_service_config
+
+    nest.assert_same_structure(dataset.element_spec, layouts)
+    flattened_layouts = nest.flatten(layouts)
+    flattened_elem_spec = nest.flatten(dataset.element_spec)
+
+    if batch_dim:
+      self.num_global_replicas = mesh.dim_size(batch_dim)
+      self._local_replica_ids = list(
+          dict.fromkeys(
+              [loc[batch_dim] for loc in mesh.local_device_locations()]))
+
+      for layout in flattened_layouts:
+        if batch_dim != layout.sharding_specs[0]:
+          raise ValueError(
+              ('batch_dim %s was specified but at least one layout did not '
+               'contain it: %s') % (batch_dim, layout))
+    else:
+      # Only one replica since there is no sharding on the batch dimension.
+      self.num_global_replicas = 1
+      self._local_replica_ids = [0]
+
+    # Validate layout and element spec compatibility, and raise ValueError if
+    # invalid.
+    _validate_input(
+        flattened_layouts,
+        flattened_elem_spec,
+        dataset_already_batched=dataset_already_batched)
+
+    expected_batch_size = global_batch_size // self.num_global_replicas
+    if not dataset_already_batched:
+      self._batched_dataset = dataset.batch(
+          expected_batch_size, drop_remainder=True)
+    else:
+      per_replica_batch_size = flattened_elem_spec[0].shape.as_list()[0]
+      if per_replica_batch_size != expected_batch_size:
+        raise ValueError(
+            ('per_replica_batch_size does not matched expected size based on '
+             'the mesh, got %d but expected %d.') %
+            (per_replica_batch_size, expected_batch_size))
+      self._batched_dataset = dataset
+
+    # Construct a global element spec of the dataset.
+    flattened_global_elem_spec = []
+    batch_tensor_shape = tensor_shape.as_shape([global_batch_size])
+    for elem_spec in nest.flatten(self._batched_dataset.element_spec):
+      new_elem_spec = tensor_spec.TensorSpec(
+          shape=operator.concat(batch_tensor_shape, elem_spec.shape[1:]),
+          dtype=elem_spec.dtype,
+          name=elem_spec.name)
+      flattened_global_elem_spec.append(new_elem_spec)
+    self._global_element_spec = nest.pack_sequence_as(
+        dataset.element_spec, flattened_global_elem_spec)
+
+    num_global_devices_per_replica = config.num_global_devices(
+        mesh.device_type()) // self.num_global_replicas
+    self._num_local_replicas = len(self._local_replica_ids)
+    self._num_local_devices_per_replica = mesh.num_local_devices(
+    ) // self._num_local_replicas
+    # The number of clients each replica is split over.
+    self._num_clients_per_replica = (
+        num_global_devices_per_replica // self._num_local_devices_per_replica)
+    # In the case where a replica is split across multiple clients, an offset
+    # needs to be added to the index used by the partitioning logic such that
+    # the local devices on that client can be correctly matched to slices of the
+    # input tensor(s). If replicas are wholly contained within a client, then
+    # this offset is always 0.
+    self._partition_offset = (config.client_id() % self._num_clients_per_replica
+                             ) * self._num_local_devices_per_replica
+
+    # Helper data structures used in partitioning the dataset tensors.
+    self._all_shard_counts = [
+        _shard_counts(layout, batch_dim) for layout in flattened_layouts
+    ]
+    self._index_matrices = [
+        _index_matrix(layout, elem_spec)
+        for layout, elem_spec in zip(flattened_layouts, flattened_elem_spec)
+    ]
+
+  def __iter__(self):
+    datasets: List[Tuple[int, data_types.DatasetV2]] = []
+
+    # Start with the batched the dataset.
+    local_dataset = self._batched_dataset
+
+    if self._batch_dim is not None:
+      if self._num_clients_per_replica > 1:
+        # If a replica is split over multiple clients then each batch needs to
+        # be repeated before distribution as many times as there are clients
+        # corresponding to that replica.
+        local_dataset = self._repeat_batch(local_dataset,
+                                           self._num_clients_per_replica)
+        sharding_policy = data_service_ops.ShardingPolicy.DATA
+      else:
+        # Replicas are unique to each client, so FILE based sharding can be used
+        # which is more performant since each worker does not need to read the
+        # entire dataset.
+        sharding_policy = data_service_ops.ShardingPolicy.FILE
+    else:
+      # No batch dimension sharding specified so disable dataset sharding during
+      # the distribute step.
+      sharding_policy = data_service_ops.ShardingPolicy.OFF
+
+    # Apply distribution here (if specified) so all remaining transformations
+    # are executed locally.
+    if self._tf_data_service_config is not None:
+      local_dataset = local_dataset.apply(
+          data_service_ops.distribute(
+              processing_mode=sharding_policy,
+              service=self._tf_data_service_config.dispatcher_address,
+              job_name=f'{self._tf_data_service_config.job_name}_{config.client_id()}',
+              target_workers='LOCAL'))
+
+    for local_replica_idx, replica_id in enumerate(self._local_replica_ids):
+      # Select the shard for the corresponding replica.
+      dataset = distribute._AutoShardDataset(
+          local_dataset,
+          num_workers=self._num_local_replicas,
+          index=local_replica_idx,
+          num_replicas=self.num_global_replicas)
+
+      # Repeat each batch for each local device in the replica.
+      dataset = self._repeat_batch(dataset, self._num_local_devices_per_replica)
+
+      # Slice each shard further for all non-batch dim shards. If there is no
+      # non-batch dim sharding, this slice is essentially a no-op.
+      dataset = self._partition(dataset)
+
+      # Apply prefetch as the last step. Since each batch is repeated, the
+      # number of elements to prefetch has to be scaled by the same size.
+      if self._prefetch is not None:
+        dataset = dataset.prefetch(
+            self._prefetch * self._num_local_devices_per_replica)
+
+      datasets.append((replica_id, dataset))
+
+    # Convert the datasets into iterators placed on the host.
+    d_iterator_resource = _pack_iterator_resource_dtensor(
+        datasets=datasets,
+        layouts=self._layouts,
+        mesh=self._mesh,
+        num_local_devices_per_replica=self._num_local_devices_per_replica)
+
+    return _DTensorIterator(
+        dtensor_components=(d_iterator_resource,),
+        global_element_spec=self._global_element_spec,
+        layouts=self._layouts)
+
+  def _repeat_batch(self, dataset, repeats):
+    if repeats == 1:
+      # Remove this shortcut if tf.data can optimize this away.
+      return dataset
+
+    def repeat(*x):
+      return dataset_ops.DatasetV2.from_tensors(x).repeat(repeats)
+
+    return dataset.flat_map(repeat)
+
+  def _partition(self, dataset):
+    """Slices each dataset element on any sharded non-batch dimension."""
+    if self._num_local_devices_per_replica == 1 and self._partition_offset == 0:
+      # Remove this shortcut if tf.data can optimize this away.
+      return dataset
+
+    # TODO(b/223275517): decouple from self and make testable.
+    def slice_batch(index, batch):
+      flattened_batch = nest.flatten(batch)
+      flattened_output = []
+
+      norm_index = math_ops.cast(
+          index % self._num_local_devices_per_replica, dtype=dtypes.int32)
+      norm_index += self._partition_offset
+      coords = self._mesh.coords(norm_index)
+      coords = array_ops.reshape(coords, (1, -1))
+
+      for element, shard_counts, idx_matrix in zip(flattened_batch,
+                                                   self._all_shard_counts,
+                                                   self._index_matrices):
+        indexes = math_ops.matmul(coords, idx_matrix)
+        start = array_ops.reshape(indexes, (-1,))
+        size = array_ops.shape_v2(
+            element, out_type=dtypes.int32) // shard_counts
+        flattened_output.append(
+            array_ops.slice(element, begin=start, size=size))
+
+      return nest.pack_sequence_as(batch, flattened_output)
+
+    enumerated_dataset = dataset.enumerate()
+    partitioned_dataset = enumerated_dataset.map(slice_batch)
+    return partitioned_dataset
+
+  @property
+  def element_spec(self):
+    return self._global_element_spec
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/layout.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/layout.py
new file mode 100644
index 0000000000000000000000000000000000000000..e55039757f692d8d8f346ce73eb7e08e253af8a2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/layout.py
@@ -0,0 +1,550 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Python definitions for `Mesh` and `Layout`."""
+
+import collections
+import functools
+import itertools
+from typing import List, Dict, Optional, Union
+
+import numpy as np
+
+from tensorflow.dtensor.proto import layout_pb2
+from tensorflow.python import _pywrap_dtensor_device
+from tensorflow.python.framework import device as tf_device
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor
+from tensorflow.python.util.tf_export import tf_export
+
+# UNSHARDED indicates a tensor dimension is not sharded over any mesh dimension.
+UNSHARDED = 'unsharded'
+MATCH = 'match'
+USE_XLA_SPMD = False
+
+tf_export(
+    'experimental.dtensor.UNSHARDED',
+    v1=[]).export_constant(__name__, 'UNSHARDED')
+tf_export(
+    'experimental.dtensor.MATCH', v1=[]).export_constant(__name__, 'MATCH')
+
+MeshDimension = collections.namedtuple('MeshDimension', ['name', 'size'])
+
+
+def _compute_mesh_strides(shape: List[int]) -> List[int]:
+  strides = [1]
+  for idx, dim_size in enumerate(reversed(shape[1:])):
+    strides.append(strides[idx] * dim_size)
+  strides.reverse()
+  return strides
+
+
+@tf_export('experimental.dtensor.Mesh', v1=[])
+class Mesh(_pywrap_dtensor_device.Mesh):
+  """Represents a Mesh configuration over a certain list of Mesh Dimensions.
+
+  A mesh consists of named dimensions with sizes, which describe how a set of
+  devices are arranged. Defining tensor layouts in terms of mesh dimensions
+  allows us to efficiently determine the communication required when computing
+  an operation with tensors of different layouts.
+
+  A mesh provides information not only about the placement of the tensors but
+  also the topology of the underlying devices. For example, we can group 8 TPUs
+  as a 1-D array for data parallelism or a `2x4` grid for (2-way) data
+  parallelism and (4-way) model parallelism.
+
+  Refer to [DTensor Concepts](https://www.tensorflow.org/guide/dtensor_overview)
+  for in depth discussion and examples.
+
+  Note: the utilities `dtensor.create_mesh` and
+  `dtensor.create_distributed_mesh` provide a simpler API to create meshes for
+  single- or multi-client use cases.
+  """
+
+  def __init__(
+      self,
+      dim_names: List[str],
+      global_device_ids: np.ndarray,
+      local_device_ids: List[int],
+      local_devices: List[Union[tf_device.DeviceSpec, str]],
+      mesh_name: str = '',
+      global_devices: Optional[List[Union[tf_device.DeviceSpec, str]]] = None,
+      use_xla_spmd: bool = USE_XLA_SPMD,
+  ):
+    """Builds a Mesh.
+
+    The `dim_names` and `global_device_ids` arguments describe the dimension
+    names and shape for the mesh.
+
+    For example,
+
+    ```python
+      dim_names = ('x', 'y'),
+      global_device_ids = [[0, 1],
+                           [2, 3],
+                           [4, 5]]
+    ```
+
+    defines a 2D mesh of shape 3x2. A reduction over the 'x' dimension will
+    reduce across columns (0, 2, 4) and (1, 3, 5), and a reduction over the 'y'
+    dimension reduces across rows.
+
+    Note: the utilities `dtensor.create_mesh` and
+    `dtensor.create_distributed_mesh` provide a simpler API to create meshes for
+    single- or multi-client use cases.
+
+    Args:
+      dim_names: A list of strings indicating dimension names.
+      global_device_ids: An ndarray of global device IDs is used to compose
+        DeviceSpecs describing the mesh. The shape of this array determines the
+        size of each mesh dimension. Values in this array should increment
+        sequentially from 0. This argument is the same for every DTensor client.
+      local_device_ids: A list of local device IDs equal to a subset of values
+        in global_device_ids. They indicate the position of local devices in the
+        global mesh. Different DTensor clients must contain distinct
+        local_device_ids contents. All local_device_ids from all DTensor clients
+        must cover every element in global_device_ids.
+      local_devices: The list of devices hosted locally. The elements correspond
+        1:1 to those of local_device_ids.
+      mesh_name: The name of the mesh. Currently, this is rarely used, and is
+        mostly used to indicate whether it is a CPU, GPU, or TPU-based mesh.
+      global_devices (optional): The list of global devices. Set when multiple
+        device meshes are in use.
+      use_xla_spmd (optional): Boolean when True, will use XLA SPMD instead of
+        DTensor SPMD.
+    """
+    # Check if input args are valid.
+    if not isinstance(global_device_ids, np.ndarray):
+      raise ValueError('Variable global_device_ids must be an ndarray.')
+    if global_device_ids.size == 0:
+      raise ValueError('Variable global_device_ids must be non-empty.')
+    flat_global_device_ids = global_device_ids.flatten()
+    # global_device_ids are expected to be consecutive numbers.
+    # LINT.IfChange
+    distance = flat_global_device_ids[0]
+    if any(
+        (gid - i != distance) for i, gid in enumerate(flat_global_device_ids)):
+      raise ValueError('global_device_ids must sequentially increase: %s' %
+                       global_device_ids)
+    # LINT.ThenChange(//tensorflow/dtensor/cc/dtensor_device.cc)
+
+    # TODO(b/242201545): This class is only for args type transformation for
+    # exported C++ Mesh class after the unification is complete. Any other
+    # logics should reside in the C++ layer, including validation checks, shall
+    # go to C++.
+
+    if len(dim_names) != global_device_ids.ndim:
+      raise ValueError(
+          'Number of mesh dimensions does not match number of dimension names.')
+
+    if not isinstance(local_device_ids, list):
+      raise ValueError('Variable local_device_ids must be a list of integers.')
+
+    if not isinstance(local_devices, list):
+      raise ValueError('Variable local_devices must be a list of DeviceSpecs.')
+
+    if global_devices and not isinstance(global_devices, list):
+      raise ValueError('Variable global_devices must be a list of DeviceSpecs.')
+
+    if not local_devices and not global_devices:
+      raise ValueError('Empty list of devices not allowed.')
+
+    # Transform args format for C++ Mesh constructor
+    global_device_ids_flatten = global_device_ids.flatten()
+    global_device_ids_shape = global_device_ids.shape
+
+    def to_str(d) -> str:
+      if isinstance(d, tf_device.DeviceSpec):
+        return d.to_string()
+      return d
+
+    def to_spec(d) -> tf_device.DeviceSpec:
+      if not isinstance(d, tf_device.DeviceSpec):
+        return tf_device.DeviceSpec.from_string(d)
+      return d
+
+    local_devices_str = [to_str(d) for d in local_devices]
+    local_devices_spec = [to_spec(d) for d in local_devices]
+    if not global_devices:
+      global_devices = []
+    global_devices_str = [to_str(d) for d in global_devices]
+    global_devices_spec = [to_spec(d) for d in global_devices]
+
+    local_devices_set = set(local_devices_spec)
+    local_device_only_contains_host_cpu = (
+        len(local_devices_set) == 1 and
+        list(local_devices_set)[0].device_type == 'CPU')
+    if not local_device_only_contains_host_cpu and len(local_devices) != len(
+        local_devices_set):
+      raise ValueError('Duplicate devices found in mesh specification %s.' %
+                       [d for d in local_devices if local_devices.count(d) > 1])
+
+    if len(local_device_ids) != len(local_devices):
+      raise ValueError(
+          'Variable local_device_ids does not have same size as local_devices.')
+
+    if len(local_device_ids) > len(np.ravel(global_device_ids)):
+      raise ValueError('Cannot have more local than gobal device IDs.')
+
+    device_types = set([device.device_type for device in local_devices_spec])
+    if not device_types:
+      device_types = set([device.device_type for device in global_devices_spec])
+    if None in device_types:
+      raise ValueError('device_type is required')
+    if len(device_types) > 1:
+      raise ValueError('Devices containing multiple device_types : %s' %
+                       device_types)
+    device_type = device_types.pop()
+    if use_xla_spmd and device_type != 'TPU':
+      raise ValueError('XLA SPMD is not currently not supported for %s mesh.' %
+                       device_type)
+
+    super().__init__(
+        mesh_name,
+        dim_names,
+        global_device_ids_shape,
+        global_device_ids_flatten,
+        global_devices_str,
+        local_device_ids,
+        local_devices_str,
+        use_xla_spmd,
+    )
+
+  @classmethod
+  def _new_object(cls, *args, **kwargs):
+    # Need to explicitly invoke the base class __init__ because
+    # Mesh.__init__ overrode it with a different signature.
+    self = _pywrap_dtensor_device.Mesh.__new__(cls)
+    super().__init__(self, *args, **kwargs)
+    return self
+
+  def global_device_ids(self) -> np.ndarray:
+    """Returns a global device list as an array."""
+    return np.array(super().global_device_ids(), dtype=np.int64).reshape(
+        self.shape()
+    )
+
+  def __getitem__(self, dim_name: str) -> MeshDimension:
+    return MeshDimension(name=dim_name, size=self.dim_size(dim_name))
+
+  def __hash__(self):
+    return hash(self.as_proto().SerializeToString(deterministic=True))
+
+  def __repr__(self) -> str:
+    return f'Mesh.from_string({self.to_string()})'
+
+  # TODO(panzf): change to pybind11 pickle implementation in the last step
+  def __reduce__(self):
+    return Mesh.from_string, (self.to_string(),)
+
+  # TODO(b/242201545): implement this in Mesh C++ class
+  def coords(self, device_idx: int) -> tensor.Tensor:
+    """Converts the device index into a tensor of mesh coordinates."""
+    strides = ops.convert_to_tensor(self.strides)
+    shape = ops.convert_to_tensor(self.shape())
+    return (device_idx // strides) % shape
+
+  @classmethod
+  def from_proto(cls, proto: layout_pb2.MeshProto) -> 'Mesh':
+    """Construct a mesh instance from input `proto`."""
+    return cls._new_object(mesh_proto=proto)
+
+  @classmethod
+  def from_string(cls, mesh_str: str) -> 'Mesh':
+    return cls._new_object(mesh_str=mesh_str)
+
+  @classmethod
+  def from_device(cls, device: str) -> 'Mesh':
+    """Constructs a single device mesh from a device string."""
+    return cls._new_object(single_device=device)
+
+  @classmethod
+  def _from_mesh(cls, mesh: _pywrap_dtensor_device.Mesh):
+    """Creates a copy from an existing pywrap mesh object."""
+    return cls._new_object(mesh=mesh)
+
+  @functools.cached_property
+  def _host_mesh(self) -> 'Mesh':
+    return Mesh._from_mesh(super().host_mesh())
+
+  def host_mesh(self) -> 'Mesh':
+    """Returns a host mesh."""
+    # TODO(b/242201545): Find a way to get the super class to return correct
+    # typed objects.
+    return self._host_mesh
+
+  # TODO(b/242201545): implement this in Mesh C++ class
+  def local_device_locations(self) -> List[Dict[str, int]]:
+    """Returns a list of local device locations.
+
+    A device location is a dictionary from dimension names to indices on those
+    dimensions.
+    """
+    mapping = self.unravel_index()
+    return [mapping[device_id] for device_id in self.local_device_ids()]
+
+  # TODO(b/242201545): implement this in Mesh C++ class
+  @property
+  def strides(self) -> List[int]:
+    """Returns the strides tensor array for this mesh.
+
+    If the mesh shape is `[a, b, c, d]`, then the strides array can be computed
+    as `[b*c*d, c*d, d, 1]`. This array can be useful in computing local device
+    offsets given a device ID. Using the same example, the device coordinates of
+    the mesh can be computed as:
+
+    ```
+    [(device_id / (b*c*d)) % a,
+     (device_id / (c*d))   % b,
+     (device_id / (d))     % c,
+     (device_id)           % d]
+    ```
+
+    This is the same as `(device_id // mesh.strides) % mesh.shape`.
+
+    Returns:
+      The mesh strides as an integer tensor.
+    """
+    return _compute_mesh_strides(self.shape())
+
+  # TODO(b/242201545): implement this in Mesh C++ class
+  def unravel_index(self):
+    """Returns a dictionary from device ID to {dim_name: dim_index}.
+
+    For example, for a 3x2 mesh, return this:
+
+    ```
+      { 0: {'x': 0, 'y', 0},
+        1: {'x': 0, 'y', 1},
+        2: {'x': 1, 'y', 0},
+        3: {'x': 1, 'y', 1},
+        4: {'x': 2, 'y', 0},
+        5: {'x': 2, 'y', 1} }
+    ```
+    """
+    idx_ranges = [range(self.dim_size(dim_name)) for dim_name in self.dim_names]
+    mesh_pos = itertools.product(*idx_ranges)
+    mapping = {}
+    for device_id, device_pos in enumerate(mesh_pos):
+      device_loc = {}
+      for dim_name, dim_index in zip(self.dim_names, device_pos):
+        device_loc[dim_name] = dim_index
+      mapping[device_id] = device_loc
+    return mapping
+
+
+LayoutType = _pywrap_dtensor_device.LayoutType
+
+
+# TODO(hthu): Consider making this class immutable.
+@tf_export('experimental.dtensor.Layout', v1=[])
+class Layout(_pywrap_dtensor_device.Layout):
+  """Represents the layout information of a DTensor.
+
+  A layout describes how a distributed tensor is partitioned across a mesh (and
+  thus across devices). For each axis of the tensor, the corresponding
+  sharding spec indicates which dimension of the mesh it is sharded over. A
+  special sharding spec `UNSHARDED` indicates that axis is replicated on
+  all the devices of that mesh.
+
+  Refer to [DTensor Concepts](https://www.tensorflow.org/guide/dtensor_overview)
+  for in depth discussion and examples.
+
+  For example, let's consider a 1-D mesh:
+
+  ```
+  Mesh(["TPU:0", "TPU:1", "TPU:2", "TPU:3", "TPU:4", "TPU:5"], [("x", 6)])
+  ```
+
+  This mesh arranges 6 TPU devices into a 1-D array. `Layout([UNSHARDED], mesh)`
+  is a layout for rank-1 tensor which is replicated on the 6 devices.
+
+  For another example, let's consider a 2-D mesh:
+
+  ```
+  Mesh(["TPU:0", "TPU:1", "TPU:2", "TPU:3", "TPU:4", "TPU:5"],
+       [("x", 3), ("y", 2)])
+  ```
+
+  This mesh arranges 6 TPU devices into a `3x2` 2-D array.
+  `Layout(["x", UNSHARDED], mesh)` is a layout for rank-2 tensor whose first
+  axis is sharded on mesh dimension "x" and the second axis is replicated. If we
+  place `np.arange(6).reshape((3, 2))` using this layout, the individual
+  components tensors would look like:
+
+  ```
+  Device  |  Component
+   TPU:0     [[0, 1]]
+   TPU:1     [[0, 1]]
+   TPU:2     [[2, 3]]
+   TPU:3     [[2, 3]]
+   TPU:4     [[4, 5]]
+   TPU:5     [[4, 5]]
+  ```
+  """
+
+  def __init__(self, sharding_specs: List[str], mesh: Mesh):
+    """Builds a Layout from a list of dimension names and a Mesh.
+
+    Args:
+      sharding_specs: List of sharding specifications, each corresponding to a
+        tensor axis. Each specification (dim_sharding) can either be a mesh
+        dimension or the special value UNSHARDED.
+      mesh: A mesh configuration for the Tensor.
+
+    Returns:
+      A valid Layout built with given layout & mesh.
+    """
+    # Validate mesh
+    if not isinstance(mesh, Mesh):
+      raise ValueError('mesh is not a valid Mesh object.')
+
+    # Validate sharding spec
+    for _, dim_sharding in enumerate(sharding_specs):
+      # If special value no need to check for uniqueness, just skip.
+      if dim_sharding == UNSHARDED or dim_sharding == MATCH:
+        continue
+      # Check dim_sharding is unique.
+      if sharding_specs.count(dim_sharding) > 1:
+        raise ValueError(
+            ('Mesh dimension {mesh_dim} was repeated in sharding ' +
+             'specification {sharding_specs}. Mesh dimensions must be unique ' +
+             'in a layout.').format(
+                 mesh_dim=dim_sharding, sharding_specs=sharding_specs))
+      # Check dim_sharding is mesh dimension.
+      if dim_sharding not in mesh:
+        raise ValueError(
+            ('{dim_sharding}: A dimension sharding must either be a ' +
+             'valid mesh dimension or UNSHARDED.').format(
+                 dim_sharding=dim_sharding))
+
+    super().__init__(
+        type=LayoutType.STATIC, sharding_specs=sharding_specs, mesh=mesh
+    )
+
+  @classmethod
+  def _new_object(cls, *args, **kwargs):
+    # Need to explicitly invoke the base class __init__ because
+    # Layout.__init__ overrode it with a different signature.
+    self = _pywrap_dtensor_device.Layout.__new__(cls)
+    super().__init__(self, *args, **kwargs)
+    return self
+
+  def __repr__(self) -> str:
+    return f'Layout.from_string({self.to_string()})'
+
+  def __hash__(self):
+    return hash(self.as_proto().SerializeToString(deterministic=True))
+
+  # TODO(panzf): change to pybind11 pickle implementation in the last step
+  def __reduce__(self):
+    return Layout.from_string, (self.to_string(),)
+
+  @property
+  def mesh(self):
+    return Mesh._from_mesh(mesh=super().mesh)  # pylint: disable=protected-access
+
+  @property
+  def shape(self):
+    return self.mesh.shape()
+
+  @classmethod
+  def batch_sharded(
+      cls, mesh: Mesh, batch_dim: str, rank: int, axis: int = 0
+  ) -> 'Layout':
+    """Returns a layout sharded on batch dimension."""
+    return cls._new_object(
+        # Watchout for the different ordering.
+        mesh=mesh,
+        rank=rank,
+        batch_dim=batch_dim,
+        axis=axis,
+    )
+
+  # TODO(b/242201545): Move this to C++ / find the corresponding function there.
+  def delete(self, dims: List[int]) -> 'Layout':
+    """Returns the layout with the give dimensions deleted."""
+    if not isinstance(dims, list):
+      dims = [dims]
+    new_specs = [
+        spec for i, spec in enumerate(self.sharding_specs) if i not in dims
+    ]
+    return Layout(new_specs, self.mesh)
+
+  @classmethod
+  def from_proto(cls, layout_proto: layout_pb2.LayoutProto) -> 'Layout':
+    """Creates an instance from a LayoutProto."""
+    return cls._new_object(layout_proto=layout_proto)
+
+  @classmethod
+  def from_string(cls, layout_str: str) -> 'Layout':
+    """Creates an instance from a human-readable string."""
+    return cls._new_object(layout_str=layout_str)
+
+  def to_parted(self) -> 'Layout':
+    """Returns a "parted" layout from a static layout.
+
+    A parted layout contains axes that are treated as independent by most of
+    SPMD expanders.
+
+    FIXME(b/285905569): The exact semantics is still being investigated.
+    """
+    return Layout._new_object(layout=super().to_parted())
+
+  @classmethod
+  def inner_sharded(cls, mesh: Mesh, inner_dim: str, rank: int) -> 'Layout':
+    """Returns a layout sharded on inner dimension."""
+    return cls.batch_sharded(mesh, inner_dim, rank, axis=rank - 1)
+
+  @classmethod
+  def from_single_device_mesh(cls, mesh: Mesh) -> 'Layout':
+    """Constructs a single device layout from a single device mesh."""
+    return cls._new_object(mesh=mesh)
+
+  @classmethod
+  def from_device(cls, device: str) -> 'Layout':
+    """Constructs a single device layout from a single device mesh."""
+    return cls.from_single_device_mesh(Mesh.from_device(device))
+
+  # TODO(b/242201545): Move this to C++ / find the corresponding function there.
+  def offset_to_shard(self):
+    """Mapping from offset in a flattened list to shard index."""
+    unravel_index = self.mesh.unravel_index()
+    locations = [None] * self.mesh.size
+    for offset, mesh_loc in unravel_index.items():
+      loc = []
+      for dim_sharding in self.sharding_specs:
+        if dim_sharding == UNSHARDED:
+          loc.append(0)
+        else:
+          loc.append(mesh_loc[dim_sharding])
+      locations[offset] = tuple(loc)
+
+    return locations
+
+  # TODO(b/242201545): Move this to C++ / find the corresponding function there.
+  def offset_tuple_to_global_index(self, offset_tuple):
+    """Mapping from offset to index in global tensor."""
+    index = 0
+    for i, o in enumerate(offset_tuple):
+      m = 1
+      for x in range(i + 1, self.rank):
+        m = m * self.num_shards(x)
+      index = index + m * o
+    return index
+
+  @classmethod
+  def replicated(cls, mesh: Mesh, rank: int) -> 'Layout':
+    """Returns a replicated layout of rank `rank`."""
+    return cls._new_object(mesh=mesh, rank=rank)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/mesh_util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/mesh_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..12e86e321e1210fb88096077c808ed44fae80fe0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/mesh_util.py
@@ -0,0 +1,310 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities to help with mesh creation."""
+
+from typing import Dict, List, Optional, Tuple, Union
+
+from absl import logging
+import numpy as np
+
+from tensorflow.dtensor.python import accelerator_util
+from tensorflow.dtensor.python import api
+from tensorflow.dtensor.python import config
+from tensorflow.dtensor.python import layout
+from tensorflow.dtensor.python import tpu_util
+from tensorflow.python.eager import context
+from tensorflow.python.framework import device as tf_device
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.util.tf_export import tf_export
+
+
+def _print_context(num_global_devices: int, num_clients: int, client_id: int,
+                   device_type: str, mesh: layout.Mesh) -> None:
+  logging.info('This is client %d of %d clients', client_id, num_clients)
+  logging.info('Number of global %s devices: %d', device_type.upper(),
+               num_global_devices)
+  # pylint: disable=protected-access
+  logging.info('Global device IDs: %s', mesh.global_device_ids())
+  logging.info('Local device IDs: %s', mesh.local_device_ids())
+  logging.info('Local devices: %s', mesh.local_devices())
+  # pylint: enable=protected-access
+
+
+def _make_device_specs(
+    devices: Optional[List[Union[tf_device.DeviceSpec, str]]] = None,
+    device_type: Optional[str] = None
+) -> Tuple[List[tf_device.DeviceSpec], str]:
+  """Makes device specs for all local devices or from a provided list."""
+
+  if devices is None:
+    if device_type is None:
+      device_type = 'CPU'
+    devices = config.local_devices(device_type)
+  else:
+    if isinstance(devices[0], str):
+      devices = [tf_device.DeviceSpec.from_string(d) for d in devices]
+    if device_type is None:
+      device_type = devices[0].device_type
+
+    if device_type.upper() != devices[0].device_type.upper():
+      raise ValueError(
+          f'Conflicting devices {str(devices)} and device_type {device_type}'
+      )
+
+  return devices, device_type
+
+
+@tf_export('experimental.dtensor.create_mesh', v1=[])
+def create_mesh(
+    mesh_dims: Optional[Union[List[Tuple[str, int]], Dict[str, int]]] = None,
+    mesh_name: str = '',
+    devices: Optional[List[Union[tf_device.DeviceSpec, str]]] = None,
+    device_type: Optional[str] = None,
+    use_xla_spmd: bool = layout.USE_XLA_SPMD,
+) -> layout.Mesh:
+  """Creates a single-client mesh.
+
+  If both `mesh_dims` and `devices` are specified, they must match each otehr.
+  As a special case, when all arguments are missing, this creates a 1D CPU mesh
+  with an empty name, assigning all available devices to that dimension.
+
+  Args:
+    mesh_dims: A dict of dim_name: dim_size, or a list of (dim_name, dim_size)
+      tuples. Defaults to a single batch-parallel dimension called 'x' usin all
+      devices. As a special case, a single-element mesh_dims whose dim_size is
+      -1 also uses all devices.  e.g. `{'x' : 4, 'y' : 1}` or `[('x', 4), ('y',
+      1)]`.
+    mesh_name: Name of the created mesh. Defaults to ''.
+    devices: String representations of devices to use. This is the device part
+      of tf.DeviceSpec, e.g. 'CPU:0'. Defaults to all available logical devices.
+    device_type: If `devices` is missing, the type of devices to use. Defaults
+      to 'CPU'.
+    use_xla_spmd: Boolean when True, will use XLA SPMD instead of DTensor SPMD.
+
+  Returns:
+    A single-client mesh created from specified or default arguments.
+  """
+  device_specs, device_type = _make_device_specs(devices, device_type)
+
+  local_spec = tf_device.DeviceSpec(job=config.job_name(), replica=0, task=0)
+  device_specs = [local_spec.make_merged_spec(d) for d in device_specs]
+
+  if isinstance(mesh_dims, dict):
+    mesh_dims = list(mesh_dims.items())
+  if mesh_dims is None:
+    mesh_dims = [('x', len(device_specs))]
+  elif len(mesh_dims) == 1 and mesh_dims[0][1] == -1:
+    # Replace -1 dim_size in a 1D mesh will the number of all devices.
+    mesh_dims[0] = (mesh_dims[0][0], len(device_specs))
+
+  dim_names = [d[0] for d in mesh_dims]
+  shape = [d[1] for d in mesh_dims]
+
+  if np.prod(shape) != len(device_specs):
+    raise ValueError(f'length of devices ({len(device_specs)}) must be '
+                     f'equal to total size of the mesh of shape {shape}')
+
+  global_device_ids = np.arange(len(device_specs)).reshape(shape)
+  local_device_ids = np.ravel(global_device_ids).tolist()
+  mesh = layout.Mesh(
+      dim_names=dim_names,
+      global_device_ids=global_device_ids,
+      local_device_ids=local_device_ids,
+      local_devices=device_specs,
+      mesh_name=mesh_name,
+      use_xla_spmd=use_xla_spmd)
+  _print_context(
+      num_global_devices=len(device_specs),
+      num_clients=1,
+      client_id=0,
+      device_type=device_type,
+      mesh=mesh)
+  return mesh
+
+
+@tf_export('experimental.dtensor.create_distributed_mesh', v1=[])
+def create_distributed_mesh(
+    mesh_dims: Union[List[Tuple[str, int]], Dict[str, int]],
+    mesh_name: str = '',
+    local_devices: Optional[List[Union[tf_device.DeviceSpec, str]]] = None,
+    device_type: Optional[str] = None,
+    use_xla_spmd: bool = layout.USE_XLA_SPMD,
+) -> layout.Mesh:
+  """Creates a distributed mesh.
+
+  This is similar to `create_mesh`, but with a different set of arguments to
+  create a mesh that spans evenly across a multi-client DTensor cluster.
+
+  For CPU and GPU meshes, users can choose to use fewer local devices than what
+  is available `local_devices`.
+
+  For TPU, only meshes that uses all TPU cores is supported by the DTensor
+  runtime.
+
+  Args:
+    mesh_dims: A dict of dim_name: dim_size, or a list of (dim_name, dim_size)
+      tuples. e.g. `{'x' : 4, 'y' : 1}` or `[('x', 4), ('y', 1)]`.
+    mesh_name: Name of the created mesh. Defaults to ''.
+    local_devices: String representations of devices to use. This is the device
+      part of tf.DeviceSpec, e.g. 'CPU:0'. Defaults to all available local
+      logical devices.
+    device_type: Type of device to build the mesh for. Defaults to 'CPU'.
+      Supported values are 'CPU', 'GPU', 'TPU'.6
+    use_xla_spmd: Boolean when True, will use XLA SPMD instead of DTensor SPMD.
+
+  Returns:
+    A mesh that spans evenly across all DTensor clients in the cluster.
+  """
+  if isinstance(mesh_dims, dict):
+    mesh_dims = list(mesh_dims.items())
+  dim_names, shape = zip(*mesh_dims)
+
+  if not accelerator_util.is_initialized():
+    raise ValueError('Accelerators are uninitialized, please run '
+                     'dtensor.initialize_accelerator_system() first.')
+
+  if device_type and device_type.upper() == 'TPU':
+    # TODO(b/185940495): Allow multi-mesh and partial on TPU.
+    # TPU meshes can only be configured through environment variables that
+    # reflect the actual TPU topology. Do not let users specify custom args.
+    if local_devices is not None:
+      raise ValueError(
+          f'Do not specify devices for {device_type.upper()} meshes. '
+          f'Using a partial list of devices for {device_type.upper()} '
+          f'is not supported.')
+
+  device_specs, device_type = _make_device_specs(local_devices, device_type)
+
+  if device_type.upper() in ['CPU', 'GPU']:
+    # For CPU and GPU meshes, user-specified args take precedence over env vars.
+    # This is particularly useful on single clients when users want to create
+    # meshes that use fewer logical devices than what's available.
+
+    local_spec = tf_device.DeviceSpec(
+        job=config.job_name(), replica=0, task=config.client_id())
+    device_specs = [local_spec.make_merged_spec(d) for d in device_specs]
+
+    # Assumes identical number of local devices per client.
+    num_global_devices = len(device_specs) * config.num_clients()
+
+    if np.prod(shape) != num_global_devices:
+      raise ValueError(
+          f'Global number of devices '
+          f'({len(device_specs)} per client * {config.num_clients()} clients '
+          f'= {num_global_devices}) must be '
+          f'equal to total size of the mesh of shape {shape}')
+
+    global_device_ids = np.arange(num_global_devices).reshape(shape)
+    flattened = np.ravel(global_device_ids).tolist()
+    start_idx = len(device_specs) * config.client_id()
+    local_device_ids = flattened[start_idx:start_idx + len(device_specs)]
+
+    mesh = layout.Mesh(
+        dim_names=dim_names,
+        global_device_ids=global_device_ids,
+        local_device_ids=local_device_ids,
+        local_devices=device_specs,
+        mesh_name=mesh_name,
+        use_xla_spmd=use_xla_spmd)
+    _print_context(num_global_devices, config.num_clients(), config.client_id(),
+                   device_type, mesh)
+    return mesh
+
+  if device_type.upper() == 'TPU':
+    mesh = tpu_util.create_tpu_mesh(
+        mesh_dim_names=dim_names,
+        mesh_shape=shape,
+        mesh_name=mesh_name,
+        use_xla_spmd=use_xla_spmd)
+    _print_context(
+        config.num_global_devices(device_type), config.num_clients(),
+        config.client_id(), device_type, mesh)
+    return mesh
+
+  raise ValueError(f'Device type {device_type} is not CPU, GPU or TPU')
+
+
+_BARRIER_DICT = {}
+
+
+@tf_export('experimental.dtensor.barrier', v1=[])
+def barrier(mesh: layout.Mesh,
+            barrier_name: Optional[str] = None,
+            timeout_in_ms: Optional[int] = None):
+  """Runs a barrier on the mesh.
+
+  Upon returning from the barrier, all operations run before the barrier
+  would have completed across all clients. Currently we allocate a fully
+  sharded tensor with mesh shape and run an all_reduce on it.
+
+  Example:
+
+  A barrier can be used before application exit to ensure completion of pending
+  ops.
+
+  ```python
+
+  x = [1, 2, 3]
+  x = dtensor.relayout(x, dtensor.Layout.batch_sharded(mesh, 'batch', 1))
+  dtensor.barrier(mesh)
+
+  # At this point all devices on all clients in the mesh have completed
+  # operations before the barrier. Therefore it is OK to tear down the clients.
+  sys.exit()
+  ```
+
+  Args:
+    mesh: The mesh to run the barrier on.
+    barrier_name: The name of the barrier. Mainly used for logging purpose.
+    timeout_in_ms: The timeout of the barrier in ms. If omitted, blocks
+      indefinitely till the barrier is reached from all clients.
+  """
+  if barrier_name is None:
+    barrier_name = '(barrier)'
+
+  logging.info('entering barrier before op: %s', barrier_name)
+
+  # Make sure all ops are consumed before running the sync.
+  context.async_wait()
+
+  # Reduction on a fully sharded tensor requires all devices to participate
+  # and serves as a barrier on the mesh.
+  component = array_ops.reshape(1.0, [1] * len(mesh.shape()))
+  ones = api.pack([component] * mesh.num_local_devices(),
+                  layout.Layout(mesh.dim_names, mesh))
+
+  mesh_size = math_ops.reduce_sum(ones)
+  if mesh_size != mesh.size:
+    raise ValueError(
+        'Global barrier produced wrong mesh size : {0} while mesh has actual'
+        'size : {1}'.format(mesh_size, mesh.size))
+
+  # TODO(hthu): This isn't strictly needed but might cause confusing behaviors
+  # from users. Consider dropping this if there is a `big` performance hit.
+  context.async_wait()
+
+  if context.context().coordination_service:
+    if timeout_in_ms is None:
+      timeout_in_ms = 24 * 60 * 60 * 1000  # 24 hours to stand in for infinite.
+
+    num_calls = _BARRIER_DICT.setdefault(barrier_name, 0)
+    _BARRIER_DICT[barrier_name] = num_calls + 1
+
+    barrier_id = f'{barrier_name}:{num_calls}'
+    context.context().wait_at_barrier(barrier_id, timeout_in_ms)
+
+  logging.info('finished running barrier across all clients after '
+               'op: %s', barrier_name)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/numpy_util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/numpy_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..8cab7a592d1bbba14fa1c43fd2f79ea363e02a5f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/numpy_util.py
@@ -0,0 +1,116 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities to convert data buffers to/from DTensor tensors."""
+from typing import List
+
+import numpy as np
+
+from tensorflow.dtensor.python import api
+from tensorflow.dtensor.python import layout as layout_lib
+from tensorflow.python.eager.polymorphic_function import polymorphic_function
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import array_ops_stack
+from tensorflow.python.ops import sparse_ops
+from tensorflow.python.ops import stateless_random_ops
+from tensorflow.python.types.core import Tensor, TensorLike  # pylint: disable=g-multiple-import
+
+# FIXME(b/262894693): Functions in this file are buggy.
+# They do not distinguish between the client-local data and the global view.
+
+
+def _split(value, splits, axis=0, split_fn=np.split, stack_fn=np.stack):
+  """Split `value` into a sharded nparray/tf tensor based on the number of splits.
+  """
+  children = split_fn(value, splits[0], axis=axis)
+  if len(splits) > 1:
+    splits = splits[1:]
+    children = [_split(child, splits, axis + 1) for child in children]
+  return stack_fn(children)
+
+
+def to_numpy(tensor: TensorLike) -> np.ndarray:
+  """Copy `input` DTensor to an equivalent local numpy array."""
+  layout = api.fetch_layout(tensor)
+  if layout.mesh.is_remote():
+    return np.array([None])
+
+  unpacked = [tensor.numpy() for tensor in api.unpack(tensor)]
+  return unpacked_to_numpy(unpacked, layout)
+
+
+def unpacked_to_numpy(unpacked: List[TensorLike],
+                      layout: layout_lib.Layout) -> np.ndarray:
+  """Heals local Tensor components to a numpy array."""
+  if len(unpacked) != len(layout.offset_to_shard()):
+    raise ValueError('Wrong number of component Tensors.')
+
+  unravelled = np.ndarray([layout.num_shards(i) for i in range(layout.rank)],
+                          dtype=object)
+
+  for offset, loc in enumerate(layout.offset_to_shard()):
+    unravelled[loc] = unpacked[offset]
+
+  concat_tensor = np.block(unravelled.tolist())
+
+  # np.block can introduce empty initial dimensions, peel these off until
+  # the output matches the rank of the input tensors.
+  while concat_tensor.ndim > unpacked[0].ndim:
+    concat_tensor = np.squeeze(concat_tensor, axis=0)
+  return concat_tensor
+
+
+# TODO(feyu): rename to slice.
+def unpack(t: TensorLike,
+           layout: layout_lib.Layout,
+           split_fn=np.split,
+           stack_fn=np.stack) -> List[TensorLike]:
+  """Slice `t` into a flattened list of tensors suitable for `pack`."""
+  if not layout.rank:
+    return [t] * layout.mesh.size
+  sharded_tensor = _split(
+      t, [layout.num_shards(i) for i in range(layout.rank)],
+      split_fn=split_fn,
+      stack_fn=stack_fn)
+  flattened = [np.ndarray([])] * layout.mesh.size
+  for offset, shard in enumerate(layout.offset_to_shard()):
+    flattened[offset] = sharded_tensor[tuple(shard)]
+  return flattened
+
+
+def pack_numpy(value: np.ndarray,
+               layout: layout_lib.Layout,
+               make_sparse: bool = False) -> Tensor:
+  assert value is not None
+  unpacked = unpack(value, layout)
+  if make_sparse:
+    return api.pack([sparse_ops.from_dense(t) for t in unpacked], layout)
+  return api.pack(unpacked, layout)
+
+
+def pack_tf_tensor(value: Tensor, layout: layout_lib.Layout) -> Tensor:
+  if value is None:
+    raise ValueError('pack requires values to be passed in')
+  unpacked = unpack(
+      value, layout, split_fn=array_ops.split, stack_fn=array_ops_stack.stack)
+  return api.pack(unpacked, layout)
+
+
+@polymorphic_function.function
+def stateless_random_uniform(shape, seed, layout):
+  """Creates uniform random tensor with the given layout."""
+  return api.relayout(
+      stateless_random_ops.stateless_random_uniform(shape=shape, seed=seed),
+      layout=layout,
+  )
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/save_restore.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/save_restore.py
new file mode 100644
index 0000000000000000000000000000000000000000..25bd78cdf00a991c13181c33218e060c999577b0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/save_restore.py
@@ -0,0 +1,222 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Contains functionaility for Checkpoint/SavedModel in DTensor."""
+
+import collections
+from typing import Dict, List, Union
+
+from tensorflow.dtensor.python import api
+from tensorflow.dtensor.python import d_variable
+from tensorflow.dtensor.python import gen_dtensor_ops
+from tensorflow.dtensor.python import layout as layout_lib
+from tensorflow.dtensor.python import mesh_util
+from tensorflow.python.eager import context
+from tensorflow.python.framework import errors_impl
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor as tensor_lib
+from tensorflow.python.ops import io_ops
+from tensorflow.python.ops import variables as tf_variables
+from tensorflow.python.util.tf_export import tf_export
+
+
+@tf_export('experimental.dtensor.sharded_save', v1=[])
+def sharded_save(
+    mesh: layout_lib.Mesh,
+    file_prefix: Union[str, tensor_lib.Tensor],
+    tensor_names: Union[List[str], tensor_lib.Tensor],
+    shape_and_slices: Union[List[str], tensor_lib.Tensor],
+    tensors: List[Union[tensor_lib.Tensor, tf_variables.Variable]],
+):
+  """Saves given named tensor slices in a sharded, multi-client safe fashion.
+
+  The method makes sure the checkpoint directory state is correct in a sharded
+  mutli-client saving. Namely, we place a barrier after SaveV2 to make sure
+  every client has done writing the files. And another one after
+  MergeV2Checkpoints to make sure all Metadata is properly merged.
+
+  Upon existing, the checkpoint is completed and the all directory operations
+  are done.
+
+  Args:
+    mesh: The Mesh that contains the Tensors to save.
+    file_prefix: The prefix of checkpoint.
+    tensor_names: a list of tensor names used in save op.
+    shape_and_slices: a list of shape and slice specification used in save op.
+      The only supported value is "" as we don't support distributed saving with
+      slices yet.
+    tensors: a list of tensors used in save op. The order should match
+      tensor_names.
+
+  Returns:
+    A MergeV2Checkpoints op that merged all Metadata.
+  """
+  with ops.device(api.device_name()):
+    io_ops.save_v2(file_prefix, tensor_names, shape_and_slices, tensors)
+
+  # Make sure all clients have written the files
+  mesh_util.barrier(mesh.host_mesh(), 'SaveV2')  # pylint: disable=protected-access
+
+  with api.default_mesh(mesh.host_mesh()):
+    merge_op = io_ops.MergeV2Checkpoints(
+        checkpoint_prefixes=[file_prefix],
+        destination_prefix=file_prefix,
+        delete_old_dirs=True)
+
+  # Make sure first device in first host has finished merge.
+  mesh_util.barrier(mesh.host_mesh(), 'MergeV2Checkpoints')
+
+  return merge_op
+
+
+@tf_export('experimental.dtensor.enable_save_as_bf16', v1=[])
+def enable_save_as_bf16(variables: List[tf_variables.Variable]):
+  """Allows float32 DVariables to be checkpointed and restored as bfloat16.
+
+  The method only affects the DVariable part inside the model and leaves
+  non-DTensor Variables/Tensors untouched.
+
+  Args:
+    variables: A list of tf.Variable to be enabled with bfloat16 save/restore.
+      Only has effect on DTensor Variables as they go through d_variables with
+      DTensor Specific logis.
+  """
+  for v in variables:
+    if isinstance(v, d_variable.DVariable):
+      v.save_as_bf16 = True
+
+
+@tf_export('experimental.dtensor.name_based_restore', v1=[])
+def name_based_restore(
+    mesh: layout_lib.Mesh,
+    checkpoint_prefix: str,
+    name_tensor_dict: Dict[
+        str, Union[tensor_lib.Tensor, tf_variables.Variable]],
+):
+  """Restores from checkpoint_prefix to name based DTensors.
+
+  It is required to have already-initialized DTensor variables that have same
+  shape/dtype for the tensors being restored.
+
+  Also, we currently only support a named based restore on a single mesh.
+
+  Args:
+    mesh: The single mesh that all Tensors would be restored to.
+    checkpoint_prefix : The prefix of checkpoint to be restored.
+    name_tensor_dict: A ordered dictionary of tensor_names to a DTensor. The
+      DTensor shape/dtype must match the tensors being saved/restored for now.
+
+  Returns:
+    A dictionary of name to its restored DTensor value.
+  """
+  if not context.executing_eagerly():
+    raise ValueError('name based restore must run eagerly.')
+
+  ordered_name_tensor_dict = name_tensor_dict
+  if not isinstance(name_tensor_dict, collections.OrderedDict):
+    ordered_name_tensor_dict = collections.OrderedDict(name_tensor_dict)
+
+  # Make sure that all tensors are on CPU mesh for now.
+  # This might not be a hard limitation in the future.
+  for name, tensor in ordered_name_tensor_dict.items():
+    try:
+      if api.fetch_layout(tensor).mesh.device_type().upper() != 'CPU':
+        raise ValueError(
+            'Restoring a non CPU Tensor is not supported currently. Offending '
+            'tensor name : {tensor_name}'.format(tensor_name=name))
+    except errors_impl.OpError as op_error:
+      raise ValueError(
+          'Saving/Restoring tensor must be a DTensor') from op_error
+
+  # Now that we have all tensors on CPU mesh, do a DTensorRestoreV2.
+  checkpoint_prefix = api.pack(
+      [checkpoint_prefix] * mesh.num_local_devices(),
+      layout_lib.Layout.replicated(mesh.host_mesh(), rank=0))
+  # Explicitly pack to mesh to avoid implicit small constant extraction, which
+  # does not work larger restores that has lots of names.
+  tensor_names = api.pack(
+      [list(ordered_name_tensor_dict.keys())] * mesh.num_local_devices(),
+      layout_lib.Layout.replicated(mesh.host_mesh(), rank=1))
+  shape_and_slices = api.pack(
+      [[''] * len(ordered_name_tensor_dict)] * mesh.num_local_devices(),
+      layout_lib.Layout.replicated(mesh.host_mesh(), rank=1))
+  # A list of TensorShape representing all shapes for the input tensors.
+  input_shapes = [tensor.shape for tensor in ordered_name_tensor_dict.values()]
+  input_layouts = [
+      api.fetch_layout(tensor).to_string()
+      for tensor in ordered_name_tensor_dict.values()
+  ]
+
+  with ops.device(api.device_name()):
+    restored_cpu_tensors = gen_dtensor_ops.d_tensor_restore_v2(
+        prefix=checkpoint_prefix,
+        tensor_names=tensor_names,
+        shape_and_slices=shape_and_slices,
+        input_shapes=input_shapes,
+        input_layouts=input_layouts,
+        dtypes=[tensor.dtype for tensor in ordered_name_tensor_dict.values()],
+    )
+
+  return collections.OrderedDict(
+      zip(ordered_name_tensor_dict.keys(), restored_cpu_tensors)
+  )
+
+
+@tf_export('experimental.dtensor.name_based_save', v1=[])
+def name_based_save(
+    mesh: layout_lib.Mesh,
+    checkpoint_prefix: Union[str, tensor_lib.Tensor],
+    name_tensor_dict: Dict[
+        str, Union[tensor_lib.Tensor, tf_variables.Variable]],
+):
+  """Saves name based Tensor into a Checkpoint.
+
+  The function prepares the input dictionary to the format of a `sharded_save`,
+  so that it can take advantage of DTensor SPMD based distributed save.
+
+  Same as restore, the function only supports saving on the single mesh.
+
+  Args:
+    mesh: The single mesh that all Tensors would be restored to.
+    checkpoint_prefix : The prefix of checkpoint to be restored.
+    name_tensor_dict: A ordered dictionary of tensor_names to a DTensor. The
+      DTensor shape/dtype must match the tensors being saved/restored for now.
+  """
+  if not context.executing_eagerly():
+    raise ValueError('name based save must run eagerly.')
+
+  ordered_name_tensor_dict = name_tensor_dict
+  if not isinstance(name_tensor_dict, collections.OrderedDict):
+    ordered_name_tensor_dict = collections.OrderedDict(name_tensor_dict)
+
+  # Current _dtensor_device() in api.py is the correct way of specifying
+  # DTensor device singletons. The API itself will be eventually be moved to
+  # a public API and provides global singleton in DTensor context.
+  # For now, we just use the current `internal` API and aim at migrating in
+  # one shot later.
+  # TODO(hthu): Provide _dtensor_device() singleton as a public API.
+  # pylint: disable=protected-access
+  checkpoint_prefix = api.pack([checkpoint_prefix] * mesh.num_local_devices(),
+                               layout_lib.Layout.replicated(
+                                   mesh.host_mesh(), rank=0))
+  tensor_names = api.pack(
+      [list(ordered_name_tensor_dict.keys())] * mesh.num_local_devices(),
+      layout_lib.Layout.replicated(mesh.host_mesh(), rank=1))
+
+  sharded_save(
+      mesh,
+      file_prefix=checkpoint_prefix,
+      tensor_names=tensor_names,
+      shape_and_slices=[''] * len(ordered_name_tensor_dict),
+      tensors=list(ordered_name_tensor_dict.values()))
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/__init__.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/multi_client_test_util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/multi_client_test_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..35d3e7aa10ef982bc4a88f12ca28afd070cfa6ae
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/multi_client_test_util.py
@@ -0,0 +1,141 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utilities for multi-client setup."""
+import os
+import sys
+
+from absl import flags
+import portpicker
+
+from tensorflow.dtensor.python.tests import test_backend_util
+from tensorflow.python.platform import test as tf_test
+
+
+_NUM_LOCAL_DEVICES = flags.DEFINE_integer(
+    'num_local_devices', 4,
+    'Number of local devices. 4 is the only allowed value for TPU.')
+_NUM_CLIENTS = flags.DEFINE_integer(
+    'num_clients', 2,
+    'Number of clients. 0 for local mode. 2 is the only allowed value for TPU.')
+
+
+def multi_client_main(client_config_function):
+  """Creates a Flock of TensorFlow Processes on localhost."""
+  flags.FLAGS(sys.argv, known_only=True)
+  num_clients = _NUM_CLIENTS.value
+  num_process = num_clients or 1
+  num_local_devices = _NUM_LOCAL_DEVICES.value
+
+  # No GPU visible to the flock controller.
+  os.environ['CUDA_VISIBLE_DEVICES'] = ''
+  os.environ['HIP_VISIBLE_DEVICES'] = ''
+
+  # Python multiprocess module in OSS.
+  mp_context = test_backend_util.get_mp_context()
+
+  print('Check per client log in Test artifacts.', flush=True)
+
+  # Inverts the order of ports intentionally to rule out ordering bugs.
+  server_ports = sorted(
+      [portpicker.pick_unused_port() for _ in range(num_process)], reverse=True)
+
+  additional_ports = sorted(
+      [portpicker.pick_unused_port() for _ in range(num_process)]
+  )
+
+  # Starts processes
+  procs = []
+  for client_idx in range(num_process):
+    proc = mp_context.Process(
+        target=run_client,
+        args=(client_idx, num_clients, server_ports, additional_ports,
+              num_local_devices, client_config_function),
+        name=f'Client-{client_idx}',
+    )
+    proc.start()
+    procs.append(proc)
+
+  # Joins processes
+  exitcode = 0
+  for proc in procs:
+    proc.join()
+    if proc.exitcode != 0:
+      exitcode = proc.exitcode
+
+  sys.exit(exitcode)
+
+
+def run_client(idx, num_clients, server_ports, additional_ports,
+               num_local_devices, client_config_function):
+  """Runs test.main() from a DTensor Client process on localhost.
+
+  This function runs in a separate process so that the eager context is
+  properly separated, which resembles real world multi-client setup.
+
+  Virtual devices are configured before test.main() is called.
+
+  Each client is configured to only have access to the physical GPU device
+  corresponding to its client id via CUDA_VISIBLE_DEVICES/HIP_VISIBLE_DEVICES.
+
+  Each client is configured to only have access to some TPU cores
+  corresponding to its client id via flags.
+
+  The clients redirect stdout and stderr to files under Test Artifacts.
+
+  Args:
+    idx: integer task number represents the client's id from global picture.
+    num_clients: total number of clients.
+    server_ports: A list of ports that is allocated and to be used to construct
+      GRPC server. server_ports[idx] will be the GRPC server on the
+      corresponding client.
+    additional_ports: A list of ports that is allocated and to be used to
+      construct the backends.
+    num_local_devices: Number of devices per client.
+    client_config_function: A function, for each of the client to config the
+      local environment variables, etc. Note that the function will be called
+      with a dict of extra params, eg:
+        {'num_clients': 2
+         'client_id': 0,
+         'worker_jobs': ['localhost:port1', 'localhost:port2'],
+         'num_devices': 4,
+        }
+  """
+  test_backend_util.slice_host_devices_for_multiworker(
+      num_clients, idx, additional_ports
+  )
+
+  artifact_dir = os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', '')
+
+  # Redirect extra client's stderr/stdout to undeclared outputs on sponge.
+  if artifact_dir:
+    with open(
+        os.path.join(artifact_dir, f'test-client-process-{idx}.log'),
+        'wb') as fp:
+      os.dup2(fp.fileno(), 1)
+      os.dup2(fp.fileno(), 2)
+
+  # Set up cluster and enable collectives.
+  worker_jobs = [f'localhost:{port:06d}' for port in server_ports]
+  client_config_func_param = {
+      'num_clients': num_clients,
+      'client_id': idx,
+      'worker_jobs': worker_jobs,
+      'num_devices': num_local_devices,
+  }
+  client_config_function(client_config_func_param)
+
+  # The following function call never returns.
+  tf_test.main()
+
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/test_backend_name.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/test_backend_name.py
new file mode 100644
index 0000000000000000000000000000000000000000..0aa665b58bc35934e70dbba6684939351969da77
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/test_backend_name.py
@@ -0,0 +1,40 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""List of test backend names."""
+
+import enum
+import os
+
+
+# LINT.IfChange(backend_name)
+class DTensorTestUtilBackend(enum.Enum):
+  """DTensor backend the test is being run on."""
+  UNSPECIFIED = 'unspecified'
+  CPU = 'cpu'
+  GPU = 'gpu'
+  GPU_2DEVS_BACKEND = '2gpus'
+  TPU = 'tpu'
+  TPU_STREAM_EXECUTOR = 'tpu_se'
+  TPU_V3_DONUT_BACKEND = 'tpu_v3_2x2'
+  TPU_V4_DONUT_BACKEND = 'tpu_v4_2x2'
+  PATHWAYS = 'pw'
+  PATHWAYS_V3_DONUT_BACKEND = 'pw_v3_2x2'
+
+
+DTENSOR_TEST_UTIL_BACKEND = DTensorTestUtilBackend(
+    os.getenv('DTENSOR_TEST_UTIL_BACKEND', default='unspecified')
+)
+
+# LINT.ThenChange()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/test_backend_util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/test_backend_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..02fc82a71b7543482a4d6e07e9858468718365e4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/test_backend_util.py
@@ -0,0 +1,81 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utility to set up DTensor backend in tests."""
+
+# LINT.IfChange
+import multiprocessing
+import os
+
+from tensorflow.dtensor.python import accelerator_util
+from tensorflow.dtensor.python import config
+from tensorflow.dtensor.python import layout as layout_lib
+from tensorflow.dtensor.python.tests.test_backend_name import DTENSOR_TEST_UTIL_BACKEND
+from tensorflow.python.platform import test as tf_test
+
+
+class DTensorTestBackendConfigurator:
+  """Configurate test backends."""
+
+  def __init__(self, test_case: tf_test.TestCase):
+    self._test_case = test_case
+    # TODO(b/260771689): Refactor common backend set up logic to here.
+
+  def tearDown(self):
+    # Only need to explicitly shuts down TPU system in TFRT since in current
+    # runtime, the shutdown is done in initialization process.
+    if accelerator_util.is_initialized():
+      accelerator_util.shutdown_accelerator_system()
+
+
+def config_test_mesh(mesh: layout_lib.Mesh):
+  """No Op.
+
+  Args:
+    mesh: The DTensor mesh.
+  """
+  if config.backend_is_pw():
+    del mesh
+
+
+def slice_host_devices_for_multiworker(num_clients, client_id, ports):
+  """Configure the current process to only use a slice of devices."""
+  if num_clients == 0:
+    # All GPUs are visible to the client.
+    del os.environ['CUDA_VISIBLE_DEVICES']
+    del os.environ['HIP_VISIBLE_DEVICES']
+  else:
+    # Make the client_id-th GPU visible to the client.
+    os.environ['CUDA_VISIBLE_DEVICES'] = f'{client_id}'
+    os.environ['HIP_VISIBLE_DEVICES'] = f'{client_id}'
+    # Make the client_id-th (4x) TPU cores visible to the client.
+    os.environ['CLOUD_TPU_TASK_ID'] = f'{client_id}'
+    if 'tpu' in DTENSOR_TEST_UTIL_BACKEND.value:
+      del ports  # Unused due to lack of implementation.
+      # We need to find out if there is a way to slice a CloudTPU host to
+      # multiple workers.
+      raise NotImplementedError(
+          'OSS multi-client tests of TPU is not supported.'
+      )
+
+
+def get_mp_context():
+  return multiprocessing.get_context('forkserver')
+
+
+def handle_test_main(main, *args, **kwargs):
+  main(*args, **kwargs)
+
+
+# LINT.ThenChange(test_backend_util.py)
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/test_util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/test_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..268e7856794d50df69230a19ced1e689a2b652ce
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/test_util.py
@@ -0,0 +1,408 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utility methods for DTensor testing."""
+import collections
+import copy
+import itertools
+import json
+import os
+import typing
+
+from absl import flags
+from absl.testing import parameterized
+
+import numpy as np
+
+# pylint: disable=g-direct-tensorflow-import
+from tensorflow.dtensor.python import accelerator_util
+from tensorflow.dtensor.python import api
+from tensorflow.dtensor.python import config
+from tensorflow.dtensor.python import layout as layout_lib
+from tensorflow.dtensor.python import numpy_util
+from tensorflow.dtensor.python.config import is_gpu_present  # pylint: disable=unused-import
+from tensorflow.dtensor.python.config import is_tpu_present  # pylint: disable=unused-import
+from tensorflow.dtensor.python.config import preferred_device_type  # pylint: disable=unused-import
+from tensorflow.dtensor.python.config import use_multi_device_mode  # pylint: disable=unused-import
+from tensorflow.dtensor.python.tests import test_backend_util
+from tensorflow.dtensor.python.tests.test_backend_name import DTENSOR_TEST_UTIL_BACKEND
+from tensorflow.dtensor.python.tests.test_backend_name import DTensorTestUtilBackend
+from tensorflow.dtensor.python.tests.test_backend_util import DTensorTestBackendConfigurator
+from tensorflow.python.compat import v2_compat
+from tensorflow.python.eager import context
+from tensorflow.python.framework import config as tf_config
+from tensorflow.python.framework import device as tf_device
+from tensorflow.python.ops import resource_variable_ops
+from tensorflow.python.platform import test as tf_test
+# pylint: enable=g-direct-tensorflow-import
+
+# DTensor only runs with TF V2.
+v2_compat.enable_v2_behavior()
+
+DEFAULT_TOL = 1e-5
+
+_DEFAULT_GPU_MEMORY_LIMIT = 1024  # 1G
+
+
+def get_use_xla_spmd(device_type):
+  """Returns True when device_type is TPU and environment variable is set.
+
+  Args:
+    device_type: A str representing the type of device on the mesh.
+
+  Returns:
+    bool: True when device_type is TPU and environment variable is set.
+  """
+  return device_type == 'TPU' and '0' != os.environ.get(
+      'DTENSOR_TEST_USE_XLA_SPMD', '0'
+  )
+
+
+def create_device_ids_array(shape):
+  device_count = np.prod(shape)
+  return np.arange(device_count).reshape(shape)
+
+
+def create_device_array(shape, device_type):
+  device_count = np.prod(shape)
+  return np.asarray([
+      tf_device.DeviceSpec(  # pylint: disable=g-complex-comprehension
+          job='localhost/replica:0/task:0',
+          device_type=device_type,
+          device_index=i) for i in range(device_count)
+  ]).reshape(shape)
+
+
+def create_device_list(shape, device_type):
+  devices = create_device_array(shape, device_type)
+  return np.ravel(devices).tolist()
+
+
+def reset_context():
+  context._reset_context()  # pylint: disable=protected-access
+
+
+def reset_logical_devices(device_type, count):
+  """Resets logical devices for CPU/GPU.
+
+  Logical devices can only be instantiated once on a particular context. For
+  now, context re-use is triggering some function duplication errors, so we
+  reset the context on each call.
+
+  Args:
+    device_type: The device_type to reset.
+    count: numbers of virtual device to reset to.
+  """
+  reset_context()
+  devices = tf_config.list_physical_devices(device_type)
+  if device_type.upper() not in ('CPU', 'GPU'):
+    raise ValueError('resetting logical device for non-supported device type : '
+                     '%s' % device_type)
+
+  if count < len(devices):
+    devices = devices[:count]
+    tf_config.set_visible_devices(devices, device_type=device_type.upper())
+
+  for i, device in enumerate(devices):
+    n = (i + 1) * count // len(devices) - i * count // len(devices)
+    assert n > 0  # guaranteed if count >= len(devices)
+    configs = []
+    for ordinal in range(n):
+      if device_type.upper() == 'GPU':
+        dev_config = context.LogicalDeviceConfiguration(
+            memory_limit=_DEFAULT_GPU_MEMORY_LIMIT,
+            experimental_device_ordinal=ordinal)
+      else:
+        dev_config = context.LogicalDeviceConfiguration()
+      configs.append(dev_config)
+
+    tf_config.set_logical_device_configuration(device, configs)
+
+
+def list_local_logical_devices(device_type):
+  """Returns a list of local logial devices."""
+
+  # When coordinator service is enabled, list_logical_devices returns
+  # a global list.
+  devices = tf_config.list_logical_devices(device_type)
+
+  def is_local(device):
+    spec = tf_device.DeviceSpec.from_string(device.name)
+    if spec.job is None or spec.job == 'localhost':
+      return True
+    elif spec.job == config.job_name() and spec.task == config.client_id():
+      return True
+    return False
+
+  return [d for d in devices if is_local(d)]
+
+
+def is_tfrt_enabled():
+  return context.is_tfrt_enabled()
+
+
+FLAGS = flags.FLAGS
+
+
+class DTensorBaseTest(tf_test.TestCase, parameterized.TestCase):
+  """Provides comparison helper for dtensor vs local results."""
+
+  @classmethod
+  def setUpClass(cls):
+    super(DTensorBaseTest, cls).setUpClass()
+
+  def setUp(self):
+    super().setUp()
+    self._backend_configurator = DTensorTestBackendConfigurator(self)
+
+  def tearDown(self):
+    # Make sure all async ops finish.
+    try:
+      context.async_wait()
+    finally:
+      # TODO(hthu): Remove the reset once we fixed the CopyToMesh with
+      # DefaultMesh placement issue.
+      reset_dtensor()
+
+      self._backend_configurator.tearDown()
+      super().tearDown()
+
+  @staticmethod
+  def configTestMesh(  # pylint: disable=invalid-name
+      device_type_mesh_map: typing.Dict[typing.Text, layout_lib.Mesh]
+  ) -> layout_lib.Mesh:
+    """Configs corresponding mesh given test context.
+
+    If runs on a CPU mesh, set virtual device on CPU.
+    If runs on a GPU mesh, sets virtual device on GPU with proper memory limits.
+    if runs on a TPU mesh, initializes TPU system.
+
+    Args:
+      device_type_mesh_map: A dictionary containing device_type -> mesh mapping.
+
+    Returns:
+      A properly configured mesh for use in test.
+    """
+    reset_context()
+
+    def get_mesh(device_type):
+      mesh = device_type_mesh_map.get(device_type, None)
+      if mesh is None:
+        raise ValueError('Requires a %s mesh to run test on %s.' %
+                         (device_type, device_type))
+      return mesh
+
+    mesh = None
+    if is_tpu_present():
+      mesh = get_mesh('TPU')
+      reset_context()
+      accelerator_util.initialize_accelerator_system('TPU')
+    elif tf_config.list_physical_devices('GPU'):
+      mesh = get_mesh('GPU')
+      reset_logical_devices('GPU', np.prod(mesh.shape()))
+      accelerator_util.initialize_accelerator_system('GPU')
+    else:
+      mesh = get_mesh('CPU')
+      reset_logical_devices('CPU', np.prod(mesh.shape()))
+      accelerator_util.initialize_accelerator_system('CPU')
+
+    test_backend_util.config_test_mesh(mesh)
+
+    return mesh
+
+  def skipForDeviceType(  # pylint: disable=invalid-name
+      self,
+      device_type: typing.List[str],
+      reason: str,
+      unless_device_count_equals_to=None):
+    """Skip the test for the specific device_type.
+
+    Args:
+      device_type: list of device types, one of "CPU", "GPU", or "TPU".
+      reason: string that describe the reason for skipping the test.
+      unless_device_count_equals_to: Optional int. This parameter only works if
+        device_type is "TPU". If set, the test will be skipped unless the number
+        of TPUs equals to the specified count.
+    """
+    physical_device_types = set(
+        [d.device_type for d in tf_config.list_physical_devices()])
+    for device in device_type:
+      if device == 'TPU' and is_tpu_present():
+        if unless_device_count_equals_to is None:
+          self.skipTest(reason)
+        elif len(list_local_logical_devices(
+            device)) != unless_device_count_equals_to:
+          self.skipTest(reason)
+      if device == 'CPU' and len(
+          physical_device_types) == 1 and 'CPU' in physical_device_types:
+        # Make sure we skip when only `CPU` is present.
+        self.skipTest(reason)
+      if device == 'GPU' and 'GPU' in physical_device_types:
+        self.skipTest(reason)
+
+  def skipForTfrt(self, reason: str):  # pylint: disable=invalid-name
+    if is_tfrt_enabled():
+      self.skipTest(reason)
+
+  def skipTest(self, reason):  # pylint: disable=invalid-name
+    # skipTest() may be called in super().setUp()
+    if hasattr(self, '_backend_configurator'):
+      self._backend_configurator.tearDown()
+    super().skipTest(reason)
+
+  def skipForPathways(self, reason: str):  # pylint: disable=invalid-name
+    if config.backend_is_pw():
+      self.skipTest(reason)
+
+  def assertDTensorEqual(
+      self,  # pylint: disable=invalid-name
+      expected_result,
+      expected_layout,
+      result_dtensor,
+      tol=DEFAULT_TOL):
+    """Asserts DTensor is of the particular value."""
+    if issubclass(
+        type(result_dtensor), resource_variable_ops.BaseResourceVariable):
+      result_dtensor = result_dtensor.value()
+    if expected_layout is not None:
+      # This, the assertEqual, is a pure proto raw bytes comparison. To make it
+      # human-readable, use the `to_string` api for Layout for the dedicated msg
+      # field.
+      #
+      # Futhurmore, as the mesh part is very long and usually identical. Try to
+      # cut them as well, to make it easier to read.
+      expected_str = expected_layout.to_string()
+      got_str = api.fetch_layout(result_dtensor).to_string()
+      index_for_mesh = expected_str.find('mesh:')
+      if index_for_mesh != -1 and got_str.find(
+          expected_str[index_for_mesh:]) != -1:
+        # the mesh part is same. cut them so it is more readable.
+        expected_str = expected_str[:index_for_mesh]
+        got_str = got_str[:got_str.find('mesh:')]
+
+      self.assertEqual(
+          api.fetch_layout(result_dtensor),
+          expected_layout,
+          msg=(
+              '=======\nexpected layout is\n  {}\n\nwhile got layout is\n  {}\n'
+              .format(expected_str, got_str)
+          ),
+      )
+
+    layout = api.fetch_layout(result_dtensor)
+    unpacked = [t.numpy() for t in api.unpack(result_dtensor)]
+
+    # Check global shape.
+    self.assertAllEqual(expected_result.shape, result_dtensor.shape)
+
+    result_dtensor = numpy_util.to_numpy(result_dtensor)
+
+    # Check dtype.
+    # Note: This check needs be after result_dtensor is converted
+    # into numpy, due to failure with Numpy version 1.18.5.
+    self.assertEqual(
+        expected_result.dtype, result_dtensor.dtype, result_dtensor
+    )
+
+    # Check value on concatenated result DTensor.
+    self.assertAllClose(expected_result, result_dtensor, atol=tol, rtol=tol)
+
+    # In addition to check the 'concatenated' DTensor, we also check all
+    # "replicated" parts are same.
+    #
+    # The algorithm is simple:
+    # 1. For a mesh with topology (x,y,z,p), and a DTensor with layout ('',z,x).
+    # 2. Create some data structures:
+    #   - create a mapping from device id (called offset below) to mesh
+    #     location. For the mesh above, loc {x:1,y:2,z:2,p:0} means the device
+    #     is located at that coordinates in the 4-D mesh.
+    #   - create a mapping from mesh location to device id.
+    # 3. Find all replicated mesh dimension names, i.e., 'y' and `p` in the
+    #     example above.
+    # 4. Iterate over all unpacked components, translate the offset (device id)
+    #    to mesh location, called (x',y',z',p').
+    #   - For `y`, which is replicated dim in the mesh, check all unpacked
+    #     components at (x',*,z',p') are same as the component at (x',0,z',p').
+    #   - For `p`, which is also replicated dim in the mesh, check all unpacked
+    #     components at (x',y',z',*) are same as the component at (x',y',z',0).
+
+    def hash_key(loc):
+      """Hash key for Python dict."""
+      # Python dict is unhashable. Creates a sorted dict and dumps as json str.
+      d = collections.OrderedDict(sorted(loc.items(), key=lambda x: x[0]))
+      return json.dumps(d)
+
+    offset_to_mesh_loc_dict = layout.mesh.unravel_index()
+    mesh_loc_to_offset_dict = {}
+    for offset, loc in offset_to_mesh_loc_dict.items():
+      mesh_loc_to_offset_dict[hash_key(loc)] = offset
+
+    # pylint: disable=protected-access
+    replicated_dims = [
+        x for x in layout.mesh.dim_names if x not in layout.sharding_specs
+    ]
+    # pylint: enable=protected-access
+
+    for offset, tensor in enumerate(unpacked):
+      mesh_loc = offset_to_mesh_loc_dict[offset]
+      for dim_sharding in replicated_dims:
+        if mesh_loc[dim_sharding] != 0:
+          mesh_loc = copy.deepcopy(mesh_loc)  # deepcopy as we will mutate
+          mesh_loc[dim_sharding] = 0
+          offset = mesh_loc_to_offset_dict[hash_key(mesh_loc)]
+          # tol is be as low as possible as they should match "exactly". so, we
+          # ignore the `tol` passed by caller and choose the default one.
+          self.assertAllClose(tensor, unpacked[offset])
+
+
+def product(*lists):
+  """Makes a product of names parameters list."""
+  # Each element lists should be a tuple of tuples of the form
+  # (("test1", ...), ("test2", ...), ...).
+  # Function returns the product of the lists with the labels concatenated.
+  return [  # pylint: disable=g-complex-comprehension
+      (''.join(p[0]
+               for p in elt), *sum((p[1:]
+                                    for p in elt), ()))
+      for elt in itertools.product(*lists)
+  ]
+
+
+def reset_dtensor():
+  """Resets the singleton DTensor Device.
+
+  This behavior is not generally exposed and only meant to be used in tests.
+  """
+  api._reset()  # pylint: disable=protected-access
+
+
+__all__ = [
+    'DEFAULT_TOL',
+    'DTensorTestUtilBackend',
+    'DTENSOR_TEST_UTIL_BACKEND',
+    'create_device_ids_array',
+    'create_device_array',
+    'create_device_list',
+    'reset_context',
+    'reset_logical_devices',
+    'list_local_logical_devices',
+    'is_tfrt_enabled',
+    'FLAGS',
+    'DTensorBaseTest',
+    'product',
+    'reset_dtensor',
+    'is_tpu_present',
+    'is_gpu_present',
+    'use_multi_device_mode',
+]
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/test_util_ops.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/test_util_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..89513cff54022136003753c64b3d16a3188e7ecf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tests/test_util_ops.py
@@ -0,0 +1,658 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Utility methods for DTensor testing."""
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import dtypes
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import clip_ops
+from tensorflow.python.ops import gen_array_ops
+from tensorflow.python.ops import gen_bitwise_ops
+from tensorflow.python.ops import gen_math_ops
+from tensorflow.python.ops import gen_nn_ops
+from tensorflow.python.ops import gen_spectral_ops
+from tensorflow.python.ops import gen_stateless_random_ops
+from tensorflow.python.ops import gen_stateless_random_ops_v2
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import special_math_ops
+
+
+def expand_test_config(op_list, test_configs):
+  """Returns a list of test case args that covers ops and test_configs.
+
+  The list is a Cartesian product between op_list and test_configs.
+
+  Args:
+    op_list: A list of dicts, with items keyed by 'testcase_name' and 'op'.
+      Available lists are defined later in this module.
+    test_configs: A list of dicts, additional kwargs to be appended for each
+      test parameters.
+
+  Returns:
+    test_configurations: a list of test parameters that covers all
+      provided ops in op_list and args in test_configs.
+  """
+  test_configurations = []
+  for op_info in op_list:
+    test_index = 0
+    for added_test_config in test_configs:
+      test_config = op_info.copy()
+      test_config.update(added_test_config)
+      test_config['testcase_name'] = op_info['testcase_name'] + '_' + str(
+          test_index)
+      test_index += 1
+      test_configurations.append(test_config)
+  return test_configurations
+
+
+# Disable pyformat for this block to force compact style.
+# pyformat: disable
+#
+# Disable g-long-lambda to make the unit test suits compact (avoid def new func)
+# pylint: disable=g-long-lambda
+UNARY_OPS = [
+    {
+        'testcase_name': 'Identity',
+        'op': array_ops.identity
+    },
+    {
+        'testcase_name': 'ZerosLike',
+        'op': array_ops.zeros_like_v2
+    },
+    {
+        'testcase_name': 'Abs',
+        'op': math_ops.abs
+    },
+    {
+        'testcase_name': 'Negative',
+        'op': gen_math_ops.neg
+    },
+    {
+        'testcase_name': 'Cast',
+        'op': lambda x: math_ops.cast(x, dtypes.int32)
+    },
+    {
+        'testcase_name': 'ErfOp',
+        'op': gen_math_ops.erf
+    },
+    {
+        'testcase_name': 'Softmax',
+        'op': nn_ops.softmax_v2
+    },
+    {
+        'testcase_name': 'LogSoftmax',
+        'op': nn_ops.log_softmax_v2
+    },
+    {
+        'testcase_name': 'StopGradient',
+        'op': array_ops.stop_gradient
+    },
+    {
+        'testcase_name': 'Exp',
+        'op': math_ops.exp
+    },
+    {
+        'testcase_name': 'Sqrt',
+        'op': math_ops.sqrt
+    },
+    {
+        'testcase_name': 'Rsqrt',
+        'op': math_ops.rsqrt
+    },
+    {
+        'testcase_name': 'Reciprocal',
+        'op': gen_math_ops.reciprocal
+    },
+    {
+        'testcase_name': 'Relu',
+        'op': gen_nn_ops.relu
+    },
+    {
+        'testcase_name': 'Square',
+        'op': gen_math_ops.square
+    },
+    {
+        'testcase_name': 'Tanh',
+        'op': gen_math_ops.tanh
+    },
+    {
+        'testcase_name': 'Cos',
+        'op': gen_math_ops.cos
+    },
+    {
+        'testcase_name': 'Sigmoid',
+        'op': math_ops.sigmoid
+    },
+    {
+        'testcase_name': 'Acos',
+        'op': math_ops.acos
+    },
+    {
+        'testcase_name': 'Acosh',
+        'op': gen_math_ops.acosh
+    },
+    {
+        'testcase_name': 'Angle',
+        'op': math_ops.angle
+    },
+    {
+        'testcase_name': 'Asin',
+        'op': gen_math_ops.asin
+    },
+    {
+        'testcase_name': 'Asinh',
+        'op': gen_math_ops.asinh
+    },
+    {
+        'testcase_name': 'Atan',
+        'op': gen_math_ops.atan
+    },
+    {
+        'testcase_name': 'Bessel0e',
+        'op': special_math_ops.bessel_i0e
+    },
+    {
+        'testcase_name': 'Bessel1e',
+        'op': special_math_ops.bessel_i1e
+    },
+    {
+        'testcase_name': 'Bitcast',
+        'op': lambda x: gen_array_ops.bitcast(x, type=dtypes.int32)
+    },
+    {
+        'testcase_name': 'Ceil',
+        'op': math_ops.ceil
+    },
+    {
+        'testcase_name': 'CheckNumbers',
+        'op': (lambda x: gen_array_ops.check_numerics(x, message='bug'))
+    },
+    {
+        'testcase_name': 'ClipByValue',
+        'op': (lambda x: clip_ops.clip_by_value(x, 1.5, 2.5))
+    },
+    {
+        'testcase_name': 'Conj',
+        'op': math_ops.conj
+    },
+    {
+        'testcase_name': 'Cosh',
+        'op': gen_math_ops.cosh
+    },
+    {
+        'testcase_name': 'Digamma',
+        'op': gen_math_ops.digamma
+    },
+    {
+        'testcase_name':
+            'ComplexAbs',
+        'op':
+            lambda x: gen_math_ops.complex_abs(
+                x=math_ops.cast(x, dtypes.complex64), Tout=float, name='raw')
+    },
+    {
+        'testcase_name': 'Sign',
+        'op': math_ops.sign
+    },
+    {
+        'testcase_name': 'Elu',
+        'op': gen_nn_ops.elu
+    },
+    {
+        'testcase_name': 'Erfc',
+        'op': gen_math_ops.erfc
+    },
+    {
+        'testcase_name': 'Expm1',
+        'op': gen_math_ops.expm1
+    },
+    {
+        'testcase_name': 'Floor',
+        'op': math_ops.floor
+    },
+    {
+        'testcase_name': 'Imag',
+        'op': math_ops.imag
+    },
+    {
+        'testcase_name': 'Inv',
+        'op': (lambda x: gen_math_ops.inv(x=x, name='Inv'))
+    },
+    {
+        'testcase_name': 'IsInf',
+        'op': gen_math_ops.is_inf
+    },
+    {
+        'testcase_name': 'IsNan',
+        'op': gen_math_ops.is_nan
+    },
+    {
+        'testcase_name': 'LeakyRelu',
+        'op': (lambda x: nn_ops.leaky_relu((x - 2), alpha=0.3)),
+    },
+    {
+        'testcase_name': 'Lgamma',
+        'op': gen_math_ops.lgamma,
+    },
+    {
+        'testcase_name': 'Log1p',
+        'op': gen_math_ops.log1p,
+    },
+    {
+        'testcase_name': 'Ndtri',
+        'op': (lambda x: math_ops.ndtri(x / 100)),
+    },
+    {
+        'testcase_name': 'Selu',
+        'op': gen_nn_ops.selu,
+    },
+    {
+        'testcase_name': 'Sin',
+        'op': gen_math_ops.sin,
+    },
+    {
+        'testcase_name': 'Sinh',
+        'op': gen_math_ops.sinh,
+    },
+    {
+        'testcase_name': 'Softplus',
+        'op': math_ops.softplus,
+    },
+    {
+        'testcase_name': 'Softsign',
+        'op': gen_nn_ops.softsign,
+    },
+    {
+        'testcase_name': 'Tan',
+        'op': gen_math_ops.tan,
+    },
+    {
+        'testcase_name': 'Round',
+        'op': math_ops.round,
+    },
+    {
+        'testcase_name': 'Rint',
+        'op': gen_math_ops.rint,
+    },
+    {
+        'testcase_name': 'Relu6',
+        'op': nn_ops.relu6,
+    },
+    {
+        'testcase_name': 'Real',
+        'op': math_ops.real,
+    },
+    {
+        'testcase_name': 'PreventGradient',
+        'op': lambda x: gen_array_ops.prevent_gradient(input=x),
+    },
+]
+
+BINARY_ANY_TYPE_OPS_WITH_BROADCASTING_SUPPORT = [
+    {
+        'testcase_name': 'Add',
+        'op': math_ops.add
+    },
+    {
+        'testcase_name': 'Subtract',
+        'op': math_ops.subtract
+    },
+    {
+        'testcase_name': 'Multiply',
+        'op': math_ops.multiply
+    },
+    {
+        'testcase_name': 'Maximum',
+        'op': gen_math_ops.maximum
+    },
+    {
+        'testcase_name': 'Minimum',
+        'op': gen_math_ops.minimum
+    },
+    {
+        'testcase_name': 'Squared_Difference',
+        'op': gen_math_ops.squared_difference
+    },
+    {
+        'testcase_name': 'GreaterEqual',
+        'op': gen_math_ops.greater_equal
+    },
+    {
+        'testcase_name': 'Equal',
+        'op': math_ops.equal
+    },
+    {
+        'testcase_name': 'NotEqual',
+        'op': math_ops.not_equal
+    },
+    {
+        'testcase_name': 'LessEqual',
+        'op': gen_math_ops.less_equal
+    },
+    {
+        'testcase_name': 'Less',
+        'op': gen_math_ops.less
+    },
+    {
+        'testcase_name': 'Pow',
+        'op': math_ops.pow
+    },
+]
+
+BINARY_FLOAT_OPS_WITH_BROADCASTING_SUPPORT = [
+    {
+        'testcase_name': 'Real_Divide',
+        'op': math_ops.divide
+    },
+    {
+        'testcase_name': 'DivNoNan',
+        'op': math_ops.div_no_nan
+    },
+] + BINARY_ANY_TYPE_OPS_WITH_BROADCASTING_SUPPORT
+
+BINARY_INT_OPS_WITH_BROADCASTING_SUPPORT = [
+    {
+        'testcase_name': 'LeftShift',
+        'op': gen_bitwise_ops.left_shift
+    },
+    {
+        'testcase_name': 'RightShift',
+        'op': gen_bitwise_ops.right_shift
+    },
+    {
+        'testcase_name': 'BitwiseOr',
+        'op': gen_bitwise_ops.bitwise_or
+    },
+    {
+        'testcase_name': 'BitwiseAnd',
+        'op': gen_bitwise_ops.bitwise_and
+    },
+    {
+        'testcase_name': 'BitwiseXor',
+        'op': gen_bitwise_ops.bitwise_xor
+    },
+    {
+        'testcase_name': 'TruncateDiv',
+        'op': gen_math_ops.truncate_div
+    },
+    {
+        'testcase_name': 'TruncateMod',
+        'op': gen_math_ops.truncate_mod
+    },
+] + BINARY_ANY_TYPE_OPS_WITH_BROADCASTING_SUPPORT
+
+BINARY_BOOL_OPS = [{
+    'testcase_name': 'LogicalOr',
+    'op': gen_math_ops.logical_or
+}]
+
+BINARY_FLOAT_OPS = [
+    {
+        'testcase_name': 'RsqrtGrad',
+        'op': lambda y, dy: gen_math_ops.rsqrt_grad(y=y, dy=dy)
+    },
+    {
+        'testcase_name': 'SqrtGrad',
+        'op': lambda y, dy: gen_math_ops.sqrt_grad(y=y, dy=dy)
+    },
+    {
+        'testcase_name': 'Atan2',
+        'op': gen_math_ops.atan2
+    },
+    {
+        'testcase_name': 'Betainc',
+        'op': lambda a, b: gen_math_ops.betainc(a, b, 1.0)
+    },
+    {
+        'testcase_name': 'Complex',
+        'op': math_ops.complex
+    },
+    {
+        'testcase_name':
+            'EluGrad',
+        'op': (lambda x, y: gen_nn_ops.elu_grad(
+            gradients=x, outputs=y, name='op_elugrad'))
+    },
+    {
+        'testcase_name': 'Igamma',
+        'op': gen_math_ops.igamma
+    },
+    {
+        'testcase_name':
+            'IgammaGradA',
+        'op': (lambda a, x: gen_math_ops.igamma_grad_a(
+            a=a, x=x, name='IgammaGradA'))
+    },
+    {
+        'testcase_name':
+            'LeakyReluGrad',
+        'op':
+            (lambda x, y: gen_nn_ops.leaky_relu_grad(gradients=x, features=y)),
+    },
+    {
+        'testcase_name': 'MulNoNan',
+        'op': (lambda x, y: gen_math_ops.mul_no_nan(x=x, y=y)),
+    },
+    {
+        'testcase_name': 'NextAfter',
+        'op': gen_math_ops.next_after,
+    },
+    {
+        'testcase_name': 'PolyGamma',
+        'op': gen_math_ops.polygamma,
+    },
+    {
+        'testcase_name': 'SeluGrad',
+        'op': (lambda x, y: gen_nn_ops.selu_grad(gradients=x, outputs=y)),
+    },
+    {
+        'testcase_name': 'Relu6Grad',
+        'op': (lambda x, y: gen_nn_ops.relu6_grad(gradients=x, features=y)),
+    },
+    {
+        'testcase_name': 'ReciprocalGrad',
+        'op': (lambda x, y: gen_math_ops.reciprocal_grad(y=x, dy=y)),
+    },
+    {
+        'testcase_name': 'Xdivy',
+        'op': math_ops.xdivy,
+    },
+    {
+        'testcase_name': 'Xlog1py',
+        'op': math_ops.xlog1py,
+    },
+    {
+        'testcase_name': 'Xlogy',
+        'op': gen_math_ops.xlogy,
+    },
+    {
+        'testcase_name': 'Zeta',
+        'op': gen_math_ops.zeta,
+    },
+] + BINARY_FLOAT_OPS_WITH_BROADCASTING_SUPPORT
+
+BINARY_INT_OPS = [] + BINARY_INT_OPS_WITH_BROADCASTING_SUPPORT
+
+REDUCTION_OPS = [
+    {
+        'testcase_name': 'Sum',
+        'op': math_ops.reduce_sum
+    },
+    {
+        'testcase_name': 'Mean',
+        'op': math_ops.reduce_mean
+    },
+    {
+        'testcase_name': 'Prod',
+        'op': math_ops.reduce_prod
+    },
+    {
+        'testcase_name': 'Max',
+        'op': math_ops.reduce_max
+    },
+    {
+        'testcase_name': 'Min',
+        'op': math_ops.reduce_min
+    },
+]
+
+# TODO(b/171746536): added v2 rng ops here once supported.
+RANDOM_OPS = [{
+    'testcase_name': 'StatelessNorm',
+    'op': gen_stateless_random_ops.stateless_random_normal,
+    'dtype': dtypes.float32,
+    'op_version': 'V1'
+}, {
+    'testcase_name': 'StatelessTruncatedNorm',
+    'op': gen_stateless_random_ops.stateless_truncated_normal,
+    'dtype': dtypes.float32,
+    'op_version': 'V1'
+}, {
+    'testcase_name': 'StatelessUniform',
+    'op': gen_stateless_random_ops.stateless_random_uniform,
+    'dtype': dtypes.float32,
+    'op_version': 'V1'
+}, {
+    'testcase_name': 'StatelessUniformFullInt',
+    'op': gen_stateless_random_ops.stateless_random_uniform_full_int,
+    'dtype': dtypes.int32,
+    'op_version': 'V1'
+}, {
+    'testcase_name': 'StatelessRandomUniformFullIntV2',
+    'op': gen_stateless_random_ops_v2.stateless_random_uniform_full_int_v2,
+    'dtype': dtypes.int32,
+    'op_version': 'V2'
+}, {
+    'testcase_name': 'StatelessRandomNormalV2',
+    'op': gen_stateless_random_ops_v2.stateless_random_normal_v2,
+    'dtype': dtypes.float32,
+    'op_version': 'V2'
+}, {
+    'testcase_name': 'StatelessTruncatedNormalV2',
+    'op': gen_stateless_random_ops_v2.stateless_truncated_normal_v2,
+    'dtype': dtypes.float32,
+    'op_version': 'V2'
+}, {
+    'testcase_name': 'StatelessRandomUniformV2',
+    'op': gen_stateless_random_ops_v2.stateless_random_uniform_v2,
+    'dtype': dtypes.float32,
+    'op_version': 'V2'
+}, {
+    'testcase_name': 'StatelessRandomUniformIntV2',
+    'op': gen_stateless_random_ops_v2.stateless_random_uniform_int_v2,
+    'dtype': dtypes.int32,
+    'op_version': 'V2_RANGE'
+}]
+
+# op(inputs()) is expected to return an NxM tensor (N, M both even) with a
+# flexible output sharding, depending on the context `op` runs in.
+EXPANSION_OPS = [
+    dict(
+        testcase_name='TileFrom1x1Array',
+        inputs=lambda: (constant_op.constant([[1.]]), [4, 4]),
+        op=gen_array_ops.tile),
+    dict(
+        testcase_name='TileFrom2x2Array',
+        inputs=lambda: (constant_op.constant([[1., 2.], [3., 4.]]), [2, 4]),
+        op=gen_array_ops.tile),
+    dict(
+        testcase_name='Fill',
+        inputs=lambda: ([2, 4], constant_op.constant(1.)),
+        op=array_ops.fill),
+]
+
+BATCH_PARALLEL_2D_WINDOW_OPS = [(
+    'AvgPool',
+    nn_ops.avg_pool_v2,
+)]
+
+BATCH_PARALLEL_3D_WINDOW_OPS = [(
+    'MaxPool3D',
+    nn_ops.max_pool3d,
+), (
+    'AvgPool3D',
+    nn_ops.avg_pool3d,
+)]
+
+FFT_OPS = [(
+    'FFT',
+    gen_spectral_ops.fft,
+    1,
+), (
+    'FFT2D',
+    gen_spectral_ops.fft2d,
+    2,
+), (
+    'FFT3D',
+    gen_spectral_ops.fft3d,
+    3,
+), (
+    'IFFT',
+    gen_spectral_ops.ifft,
+    1,
+), (
+    'IFFT2D',
+    gen_spectral_ops.ifft2d,
+    2,
+), (
+    'IFFT3D',
+    gen_spectral_ops.ifft3d,
+    3,
+)]
+
+RFFT_OPS = [(
+    'IRFFT',
+    gen_spectral_ops.irfft,
+    1,
+    dtypes.complex64,
+), (
+    'IRFFT2D',
+    gen_spectral_ops.irfft2d,
+    2,
+    dtypes.complex64,
+), (
+    'IRFFT3D',
+    gen_spectral_ops.irfft3d,
+    3,
+    dtypes.complex64,
+), (
+    'RFFT',
+    gen_spectral_ops.rfft,
+    1,
+    dtypes.float32,
+), (
+    'RFFT2D',
+    gen_spectral_ops.rfft2d,
+    2,
+    dtypes.float32,
+), (
+    'RFFT3D',
+    gen_spectral_ops.rfft3d,
+    3,
+    dtypes.float32,
+)]
+
+PADDINGS = [
+    {
+        'testcase_name': 'SamePadding',
+        'padding': 'SAME'
+    },
+    {
+        'testcase_name': 'ValidPadding',
+        'padding': 'VALID'
+    },
+]
+# pylint: enable=g-long-lambda
+# pyformat: enable
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tpu_util.py b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tpu_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..317d49e37ab59bc9edad8f551f0e0468540b39f0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/dtensor/python/tpu_util.py
@@ -0,0 +1,805 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""TPU-specific utilities for DTensor."""
+
+import functools
+import time
+from typing import List, Optional, Dict
+
+import numpy as np
+
+from tensorflow.dtensor.python import config
+from tensorflow.dtensor.python import dtensor_device
+from tensorflow.dtensor.python import gen_dtensor_ops
+from tensorflow.dtensor.python import layout as layout_lib
+from tensorflow.python.eager import context
+from tensorflow.python.eager import def_function
+from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.tpu import topology
+from tensorflow.python.util.tf_export import tf_export
+
+
+_MESH_DIM_X = "x"
+_TPU_DEVICE_TYPE = "TPU"
+
+# A dedicated, hidden device used to make C++ API calls.
+_dtensor_device = None
+
+# `_topology._mesh_shape` contains the TPU hardware slice size.
+# `_topology.device_coordinates` maps TF task-device ordinals to TPU core IDs.
+_tpu_topology = None
+
+# Cache core ID <-> location mappings so we need not make repeated C++ calls.
+# Both are indexed by TF task-device ordinals.
+_all_core_ids = None
+_all_core_locations = None
+
+
+class _CoreLocation:
+  """Represents a TPU core's location in the mesh."""
+
+  def __init__(self, x: int = 0, y: int = 0, z: int = 0, core: int = 0):
+    self.x = x
+    self.y = y
+    self.z = z
+    self.core = core
+
+  def __eq__(self, other):
+    if not isinstance(other, _CoreLocation):
+      return False
+    return self.x == other.x and self.y == other.y and self.z == other.z and self.core == other.core
+
+  def __ne__(self, other):
+    if not isinstance(other, _CoreLocation):
+      return True
+    return not self == other
+
+  def __hash__(self):
+    return hash((self.x, self.y, self.z, self.core))
+
+  def __repr__(self):
+    return f"{type(self).__name__}(x={self.x}, y={self.y}, z={self.z}, core={self.core})"
+
+  def to_list(self):
+    return [self.x, self.y, self.z, self.core]
+
+
+def _create_device_array(shape, device_type, host_id, local_device_ids=None):
+  """Returns ID and device lists that can be used to create a mesh."""
+  num_global_devices = config.num_global_devices(device_type)
+  global_device_ids = np.arange(num_global_devices).reshape(shape)
+  local_device_list = config.local_devices(device_type)
+
+  # User can specify local_device_ids or use default list for multi host.
+  num_local_devices = len(local_device_list)
+  local_device_ids = [
+      x + host_id * num_local_devices for x in range(num_local_devices)
+  ] if not local_device_ids else local_device_ids
+
+  return global_device_ids, local_device_ids, local_device_list
+
+
+def _create_tpu_topology(core_locations: List[_CoreLocation], num_tasks: int,
+                         num_devices_per_task: int) -> topology.Topology:
+  """Returns a Topology object build from a _CoreLocation list.
+
+  Args:
+    core_locations: A list of _CoreLocation objects sorted first by TF task ID
+      and then by per-task device ordinals.
+    num_tasks: The number of TF tasks in the cluster.
+    num_devices_per_task: The number of TPU devices local to each task.
+  """
+
+  assert min([l.x for l in core_locations]) == 0
+  assert min([l.y for l in core_locations]) == 0
+  assert min([l.z for l in core_locations]) == 0
+  assert min([l.core for l in core_locations]) == 0
+  x_max = max([l.x for l in core_locations])
+  y_max = max([l.y for l in core_locations])
+  z_max = max([l.z for l in core_locations])
+  core_max = max([l.core for l in core_locations])
+  mesh_shape = [x_max + 1, y_max + 1, z_max + 1, core_max + 1]
+
+  device_coordinates = [[l.x, l.y, l.z, l.core] for l in core_locations]
+  device_coordinates = np.asarray(device_coordinates).reshape(
+      num_tasks, num_devices_per_task, 4)
+
+  return topology.Topology(
+      mesh_shape=mesh_shape, device_coordinates=device_coordinates)
+
+
+def shutdown_tpu_system():
+  """Shuts down the TPU system."""
+
+  @def_function.function
+  def _shutdown_tpu_system():
+    return gen_dtensor_ops.shutdown_tpu_system()
+
+  success = _shutdown_tpu_system() if context.is_tfrt_enabled() else True
+  if success:
+    logging.info("TPU system shut down.")
+  else:
+    logging.warning("TPU system fails to shut down.")
+
+
+def tpu_system_init_helper(task_id,
+                           num_tasks,
+                           num_devices,
+                           use_tfrt_host_runtime=True):
+  """A helper function to initialize multi-client tpu system."""
+
+  @def_function.function
+  def _tpu_init_fn():
+    return gen_dtensor_ops.configure_and_initialize_global_tpu(
+        use_tfrt_host_runtime=use_tfrt_host_runtime)
+
+  @def_function.function
+  def _set_global_tpu_array_fn(topology_proto):
+    gen_dtensor_ops.d_tensor_set_global_tpu_array(topology_proto)
+
+  with ops.device("/job:" + config.full_job_name() + "/device:TPU_SYSTEM:0"):  # pylint: disable=protected-access
+    my_core_ids = _tpu_init_fn()
+  logging.info("TPU core IDs: %s", my_core_ids)
+
+  # `my_core_ids` contains the IDs of TPU cores attached to this host.
+  #
+  # To generate correct and efficient XLA AllReduce group assignment, we must
+  # merge these arrays from all hosts and broadcast the result back to all
+  # hosts, so all hosts can use these mappings in their MLIR passes.
+  #
+  # This is essentially doing what WaitForDistributedTpuOp and
+  # SetGlobalTPUArrayOp do, in our multi-client environment.
+  num_devices_per_task = int(num_devices / num_tasks)
+
+  # Create a one-time use mesh and layout just for merging core IDs.
+  mesh = layout_lib.Mesh([_MESH_DIM_X],
+                         *_create_device_array((num_devices,), _TPU_DEVICE_TYPE,
+                                               config.client_id()))
+  layout = layout_lib.Layout([_MESH_DIM_X, layout_lib.UNSHARDED], mesh)
+  device = dtensor_device.DTensorDevice(meshes=[mesh])
+  logging.info("TPU core locations: %s",
+               device.tpu_core_ids_to_locations(my_core_ids))
+
+  # At this point, we don't know which cores are attached to other hosts.
+  # The core ID mappings in the runtime haven't been set yet.
+  #
+  # The core ID merging AllReduce below is carefully written so it works
+  # without needing correct core mappings to be set in the runtime. We will
+  # use this AllReduce's result to set the core ID mappings, and all future
+  # user-initiated AllReduces will use the mappings.
+  #
+  # The runtime is hard-coded to ignore core ID mappings on this AllReduce.
+  all_core_ids = np.zeros([num_devices], dtype=np.int32)
+  for i in range(len(my_core_ids)):
+    all_core_ids[task_id * num_devices_per_task + i] = my_core_ids[i]
+
+  # Only one local device gets a valid input. To give an example, assume we have
+  # 2 tasks and each of them has 8 local devices, then `all_core_ids` in task 0
+  # will have 8 tensors, where 1 of them may have its value as
+  # [0,1,2,3,4,5,6,7,0,0,0,0,0,0,0,0] and the other tensors are all zeros. For
+  # task 1, the case may be one with [0,0,0,0,0,0,0,0,8,9,10,11,12,13,14,15]
+  # and other 7 are all zeros.
+  all_core_ids = constant_op.constant([all_core_ids])
+  zeros = array_ops.zeros_like(all_core_ids)
+  all_core_ids = [all_core_ids] + [zeros] * (num_devices_per_task - 1)
+
+  # All devices on all hosts participate in one AllReduce, whose result will be
+  # core IDs arranged by task-device ordinals. For the above example, the result
+  # will be [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15].
+  with ops.device(device.name):
+    all_core_ids = device.pack(all_core_ids, layout)
+    all_core_ids = math_ops.reduce_sum(all_core_ids, axis=[0])
+    unpacked_all_tpu_ids = device.unpack(all_core_ids)
+
+  all_core_ids = list(unpacked_all_tpu_ids[0].numpy())
+  logging.info("All TPU core IDs: %s", all_core_ids)
+
+  # Set the default core ID mappings in the runtime for legacy code and tests.
+  #
+  # Legacy code and tests create TPU meshes directly without using the
+  # `create_tpu_mesh` function below. Those meshes have global device IDs
+  # equal to TF task-device ordinals. The `all_core_ids` array happens to
+  # arrange core IDs by TF task-device ordinals. Using this array on those
+  # meshes guarantee correct although inefficient results.
+  device.set_tpu_core_ids("", all_core_ids)
+
+  # Remember enough global, immutable information to be able to build any ring
+  # we want prescribed by `create_tpu_mesh` in the future.
+  global _all_core_ids
+  _all_core_ids = all_core_ids
+
+  all_core_locations = device.tpu_core_ids_to_locations(all_core_ids)
+  all_core_locations = [
+      _CoreLocation(l[0], l[1], l[2], l[3]) for l in all_core_locations
+  ]
+  global _all_core_locations
+  _all_core_locations = all_core_locations
+  logging.info("All TPU core locations: %s", all_core_locations)
+
+  tpu_topology = _create_tpu_topology(all_core_locations, num_tasks,
+                                      num_devices_per_task)
+
+  _set_global_tpu_array_fn(tpu_topology.serialized())
+  return tpu_topology, device
+
+
+def initialize_tpu_system():
+  """Initializes the TPU system."""
+
+  # Make sure the server change is fully propagated before attempting to run
+  # the core ID merging logic below.
+  context.ensure_initialized()
+  context.async_wait()
+  context.context()._clear_caches()  # pylint: disable=protected-access
+
+  use_tfrt_host_runtime = context.context().use_tfrt
+  logging.info("Using TFRT host runtime is set to %s", use_tfrt_host_runtime)
+  try:
+    task_id = config.client_id()
+    num_tasks = config.num_clients()
+    num_devices = config.num_global_devices(_TPU_DEVICE_TYPE)
+
+    tpu_topology, device = tpu_system_init_helper(
+        task_id,
+        num_tasks,
+        num_devices,
+        use_tfrt_host_runtime=use_tfrt_host_runtime)
+    global _tpu_topology
+    _tpu_topology = tpu_topology
+    logging.vlog(1, "TPU Topology: %s, %s", tpu_topology.mesh_shape,
+                 tpu_topology.device_coordinates)
+
+    global _dtensor_device
+    _dtensor_device = device
+
+    context.async_wait()
+
+  except errors.InvalidArgumentError as e:
+    raise errors.NotFoundError(
+        None, None,
+        "Initialization failed, no valid TPUs found. " + str(e)) from e
+
+  except errors.InternalError as e:
+    logging.error("Hit internal error during TPU system initialization. "
+                  + "It is likely hardware failure. \nPlease check the error "
+                  + "messages above to see whether that's the case. \nIf so, "
+                  + "consider to restart the job or try another machine.")
+    raise e
+
+  # Clear out the eager context caches since the memory is invalid now.
+  logging.info("Clearing out eager caches")
+  context.context()._clear_caches()  # pylint: disable=protected-access
+
+
+def _enumerate_cores(bounds: List[int], ring_bounds: List[int],
+                     ring_sizes: List[int], host_bounds: List[int],
+                     host_sizes: List[int]) -> List[List[int]]:
+  """Enumerates cores within `bounds` from fatest to slowest varying axes.
+
+  Args:
+    bounds: Upper bounds of axes, from fastest to slowest varying.
+    ring_bounds: Upper bounds of ring size per axis in the same axis order.
+    ring_sizes: Number consecutive cores in the ring built so far, cumulatively.
+    host_bounds: Number of axis values per host in the same axis order.
+    host_sizes: Number consecutive cores on one host, cumulatively.
+
+  Returns:
+    Cores represented as a list of 4 integers in the same axis order.
+  """
+  if not bounds:
+    return [[]]
+
+  # Recursively enumerate cores under all but the slowest varying axis.
+  partials = _enumerate_cores(bounds[:-1], ring_bounds[:-1], ring_sizes[:-1],
+                              host_bounds[:-1], host_sizes[:-1])
+
+  # Append the slowest varying axis to the end of all partial results.
+  # From ring_i|j to host_i|j to core_i|j, use progressively smaller or equal
+  # iteration groupings until every one of the bounds[-1] * len(partials)
+  # combinations is iterated on.
+  # Despite the six levels of nested loops below, the total time complexity for
+  # this invocation is O(N), where N is the number of cores in the topology.
+  results = []
+  for ring_i in range(0, bounds[-1], ring_bounds[-1]):
+    for ring_j in range(0, len(partials), ring_sizes[-1]):
+      for host_i in range(ring_i, ring_i + ring_bounds[-1], host_bounds[-1]):
+        for host_j in range(ring_j, ring_j + ring_sizes[-1], host_sizes[-1]):
+          for i in range(host_i, host_i + host_bounds[-1]):
+            for j in range(host_j, host_j + host_sizes[-1]):
+              results.append(partials[j] + [i])
+  return results
+
+
+def _enumerate_core_locations(bounds: List[int], ring_bounds: List[int],
+                              axes: List[str],
+                              can_split_host_across_rings: bool,
+                              ring_size: int) -> List[_CoreLocation]:
+  """Enumerates all possible core locations under the axis iteration order.
+
+  Args:
+    bounds: A list of 4 positive integers, upper bound values for x, y, z, core.
+    ring_bounds: A list of 4 positive integers, upper bound values for ring size
+      in x, y, z, core axes.
+    axes: A permutation of ["x", "y", "z", "core"], the axis iteration order.
+    can_split_host_across_rings: If true, devices attached to the same host may
+      get assigned to different rings.
+    ring_size: Number of devices in a ring, only for argument validation.
+
+  Returns:
+    A list of all CoreLocation objects defined in a TPU slice of shape `bounds`,
+    sorted by axis iteration order specified by `axes`.
+
+    For example, given bounds=[2, 2, 1, 2] and axes=["core", "z", "y", "x"],
+    return 8 core locations expressed in (x, y, z, core) format but iterated in
+    core -> z -> y -> x order (fatest to slowest varying):
+
+    [_CoreLocation(0, 0, 0, 0),
+     _CoreLocation(0, 0, 0, 1),
+     _CoreLocation(0, 1, 0, 0),
+     _CoreLocation(0, 1, 0, 1),
+     _CoreLocation(1, 0, 0, 0),
+     _CoreLocation(1, 0, 0, 1),
+     _CoreLocation(1, 1, 0, 0),
+     _CoreLocation(1, 1, 0, 1)]
+
+  Raises:
+    ValueError: If ring_size cannot be fulfilled without splitting hosts.
+  """
+
+  num_cores_per_chip = bounds[3]
+  if num_cores_per_chip != 1 and num_cores_per_chip != 2:
+    raise ValueError("Unsupported TPU slice size: %s" % bounds)
+
+  # Translate `axes` from string to integer format.
+  axes = [{"x": 0, "y": 1, "z": 2, "core": 3}[axis] for axis in axes]
+  # Reorder bounds from fastest to slowest varying axes.
+  bounds = [bounds[i] for i in axes]
+
+  # Set and validate host_bounds.
+  if can_split_host_across_rings:
+    # If we can split hosts, shrink every host to effectively contain 1 device.
+    host_bounds = [1, 1, 1, 1]
+  elif np.prod(bounds) <= 2:
+    # We must be running on 1x1 or 1x1x1 Forge.
+    host_bounds = [[1, 1, 1, num_cores_per_chip][i] for i in axes]
+  else:
+    # Other cases including 2x2 Forge and Borg must use a full donut.
+    host_bounds = [[2, 2, 1, num_cores_per_chip][i] for i in axes]
+  # host_sizes is the cumulative products of host_bounts.
+  host_sizes = [1]
+  for host_bound in host_bounds:
+    host_sizes.append(host_sizes[-1] * host_bound)
+  host_size = host_sizes.pop()
+  # When can_split_host_across_rings is false, a ring must contain at least as
+  # many devices as a host has.
+  if ring_size < host_size:
+    assert not can_split_host_across_rings
+    raise ValueError(
+        "Rings too small for can_split_host_across_rings = False: %d" %
+        ring_size)
+
+  # Reorder ring_bounds and validate it's element-wise >= host_bounds.
+  ring_bounds = [ring_bounds[i] for i in axes]
+  if ring_bounds < host_bounds:
+    raise ValueError("ring_bounds %s should be >= host_bounds %s" %
+                     (ring_bounds, host_bounds))
+  ring_sizes = [1]
+  # ring_sizes is the cumulative products of ring_bounds.
+  for ring_bound in ring_bounds:
+    ring_sizes.append(ring_sizes[-1] * ring_bound)
+  ring_sizes.pop()
+
+  # Enumerate cores in the given iteration order. Each core is represented as a
+  # list of int, which are offsets from fatest to slowest varying axes.
+  cores = _enumerate_cores(bounds, ring_bounds, ring_sizes, host_bounds,
+                           host_sizes)
+  # Reorder offsets of each core back to the x, y, z, core order.
+  core_locations = []
+  for core in cores:
+    core = [core[axes.index(i)] for i in range(4)]
+    core_locations.append(_CoreLocation(core[0], core[1], core[2], core[3]))
+  return core_locations
+
+
+def _build_all_reduce_ring(core_locations: List[_CoreLocation],
+                           rotate: bool = False) -> List[int]:
+  """Reorders a list of TPU cores to optimize for AllReduce performance.
+
+  This is ported from the C++ tensorflow::BuildAllReduceRing function,
+  mixed with some logic from TF TPU's device_assignment._ring_3d.
+
+  Args:
+    core_locations: A list of core locations expressed as [x, y, z, core].
+    rotate: If true, scan the cores in a column-major order. False by default.
+
+  Returns:
+    A permutation of the input list such that neighbors in the sequence are
+    nearby in the TPU topology.
+  """
+
+  permutation = list(range(len(core_locations)))
+  if not permutation:
+    return permutation
+  logging.vlog(2, "Core locations in: %s", core_locations)
+
+  first_column = min([l.x for l in core_locations])
+  first_row = min([l.y for l in core_locations])
+  same_z = (len(set([l.z for l in core_locations])) == 1)
+  logging.vlog(2, "first_column: %d", first_column)
+  logging.vlog(2, "first_row: %d", first_row)
+  logging.vlog(2, "same_z: %s", same_z)
+
+  def _cmp_2d(ia: int, ib: int) -> int:
+    if not rotate:
+      a = core_locations[ia]
+      b = core_locations[ib]
+
+      # Order the first column last in the sequence, except for the first row.
+      a_first = (a.x == first_column and a.y != first_row)
+      b_first = (b.x == first_column and b.y != first_row)
+      if a_first != b_first:
+        return -1 if b_first else 1
+
+      # Order rows in increasing order, unless in the first column.
+      if a.y != b.y:
+        return b.y - a.y if a_first else a.y - b.y
+
+      # Order even rows left to right, odd rows right to left.
+      if a.x != b.x:
+        return a.x - b.x if a.y % 2 == 0 else b.x - a.x
+
+      # Order cores in increasing order.
+      return a.core - b.core
+    else:
+      a = core_locations[ia]
+      b = core_locations[ib]
+
+      # Order the first row last in the sequence, except for the first column.
+      a_first = (a.y == first_row and a.x != first_column)
+      b_first = (b.y == first_row and b.x != first_column)
+      if a_first != b_first:
+        return -1 if b_first else 1
+
+      # Order columns in increasing order, unless in the first row.
+      if a.x != b.x:
+        return b.x - a.x if a_first else a.x - b.x
+
+      # Order even columns top down, odd columns bottom up.
+      if a.y != b.y:
+        return a.y - b.y if a.x % 2 == 0 else b.y - a.y
+
+      # Order cores in increasing order.
+      return a.core - b.core
+
+  def _cmp_3d(ia: int, ib: int) -> int:
+    a = core_locations[ia]
+    b = core_locations[ib]
+
+    a_corner = (a.x == first_column and a.y == first_row)
+    b_corner = (b.x == first_column and b.y == first_row)
+
+    # If both are in the corner, order in reverse z then core order.
+    if a_corner and b_corner:
+      return b.z - a.z if a.z != b.z else a.core - b.core
+
+    # Corner cores always go after non-corner cores.
+    if a_corner != b_corner:
+      return -1 if b_corner else 1
+
+    # Both non-corner cores are on the same z-plane. Reverse odd z-planes.
+    if a.z == b.z:
+      return _cmp_2d(ia, ib) if a.z % 2 == 0 else -_cmp_2d(ia, ib)
+
+    # Both non-corner cores are on different z-planes. Smaller z goes first.
+    return a.z - b.z
+
+  # If all cores are on the same z-plane, order as usual. Otherwise, order
+  # neighbor z-planes in opposite orders. Stack all z-planes along the z axis
+  # and connect them in one corner.
+  if same_z:
+    permutation.sort(key=functools.cmp_to_key(_cmp_2d))
+  else:
+    permutation.sort(key=functools.cmp_to_key(_cmp_3d))
+  logging.vlog(2, "Permutation out: %s", permutation)
+  return permutation
+
+
+def _build_orthogonal_rings(
+    core_locations: List[_CoreLocation], ring_size: int,
+    rotate_ring_across_rings: bool) -> List[_CoreLocation]:
+  """Build two all-reduce rings orthogonal to each other.
+
+  One ring includes every `ring_size` consecutive core locations. It is usually
+  applied to the model-parallel dimension of a mesh to achieve best 1D
+  all-reduce performance. The other ring includes core locations separated by
+  a stride of `ring_size`. It is usually applied to the data-parallel dimension
+  of a mesh to get predictable strided all-reduce performance.
+
+  Args:
+    core_locations: A list of core locations expressed as [x, y, z, core].
+    ring_size: The number of core locations in the consecutive ring.
+    rotate_ring_across_rings: Build column-major secondary rings.
+
+  Returns:
+    A permutation of the input list forming the described rings.
+  """
+  # Build a ring for the first `ring_size` cores, and apply that permutation to
+  # every group of `ring_size` cores.
+  num_cores = len(core_locations)
+  permutation = _build_all_reduce_ring(core_locations[:ring_size])
+  for r in range(0, num_cores, ring_size):
+    core_locations[r:r + ring_size] = [
+        core_locations[r + permutation[i]] for i in range(ring_size)
+    ]
+  logging.vlog(1, "Permutated core locations: %s", core_locations)
+
+  # Build a "ring" for the collection of devices consisting of the 0th device
+  # from every group, and apply that permutation to every i-th device group.
+  # This is achieved by transposing the list and back.
+  transposed = []
+  for i in range(ring_size):
+    transposed += [
+        core_locations[g + i] for g in range(0, num_cores, ring_size)
+    ]
+
+  num_rings = int(num_cores / ring_size)
+  permutation = _build_all_reduce_ring(
+      transposed[:num_rings], rotate=rotate_ring_across_rings)
+  for r in range(0, num_cores, num_rings):
+    transposed[r:r + num_rings] = [
+        transposed[r + permutation[i]] for i in range(num_rings)
+    ]
+
+  untransposed = []
+  for i in range(num_rings):
+    untransposed += [transposed[g + i] for g in range(0, num_cores, num_rings)]
+  logging.vlog(1, "Stride-permutated core locations: %s", untransposed)
+
+  return untransposed
+
+
+@tf_export("experimental.dtensor.create_tpu_mesh", v1=[])
+def create_tpu_mesh(
+    mesh_dim_names: List[str],
+    mesh_shape: List[int],
+    mesh_name: str,
+    ring_dims: Optional[int] = None,
+    ring_axes: Optional[List[str]] = None,
+    ring_bounds: Optional[List[int]] = None,
+    can_split_host_across_rings: bool = True,
+    build_ring_across_rings: bool = False,
+    rotate_ring_across_rings: bool = False,
+    use_xla_spmd: bool = layout_lib.USE_XLA_SPMD) -> layout_lib.Mesh:
+  """Returns a distributed TPU mesh optimized for AllReduce ring reductions.
+
+  Only as many as leading axes specified by `ring_axes` as necessary will be
+  used to build rings, as long as the subslice formed by these axes have enough
+  cores to contain a ring of the required size. The leftover axes in `ring_axes`
+  won't affect results.
+
+  This function always uses all TPU devices, and offers more customization than
+  `tf.experimental.dtensor.create_distributed_mesh`.
+
+  Args:
+    mesh_dim_names: List of mesh dimension names.
+    mesh_shape: Shape of the mesh.
+    mesh_name: A unique name for the mesh. If empty, internally generate one.
+    ring_dims: Optional; The number of leading (ring_dims > 0) or trailing
+      (ring_dims < 0) mesh dimensions to build rings for. If unspecified, build
+      rings for all but the first dimension.
+    ring_axes: Optional; A permutation of ["x", "y", "z", "core"], specifying
+      the order of TPU topology axes to build rings in. If unspecified, default
+      to ["core", "x", "y", "z"].
+    ring_bounds: Optional; The maximum number of devices on each axis, in the x,
+      y, z, core order. If unspecified, default to physical topology limits.
+    can_split_host_across_rings: Optional; If true, devices attached to the same
+      host (i.e., DTensor client) may get assigned to different rings. Setting
+      it to false may cause some combinations of arguments to be infeasible; see
+      DeviceAssignmentTest.testCreateMesh[No]SplittingHosts* for examples.
+    build_ring_across_rings: Optional; If true, also build a data-parallel ring
+      across model-parallel rings. This ring could be strided.
+    rotate_ring_across_rings: Optional; If true, build the data-parallel ring in
+      column-major instead of row-major order.
+    use_xla_spmd: Boolean when True, will use XLA SPMD instead of
+      DTensor SPMD.
+  """
+
+  logging.info("Building a TPU mesh %s of shape %s", mesh_name, mesh_shape)
+  logging.info("Requested ring_dims: %s", ring_dims)
+  logging.info("Requested ring_axes: %s", ring_axes)
+  logging.info("Requested ring_bounds: %s", ring_bounds)
+  logging.info("Requested can_split_host_across_rings: %s",
+               can_split_host_across_rings)
+  if not mesh_name:
+    mesh_name = "mesh_%f" % time.time()
+  logging.info("Requested mesh_name: %s", mesh_name)
+
+  # By default, build rings for all but the first (usually batch) dimension.
+  if ring_dims is None:
+    ring_dims = 1 - len(mesh_shape)
+  elif ring_dims < -len(mesh_shape) or ring_dims > len(mesh_shape):
+    raise ValueError("Invalid ring_dims value: %d" % ring_dims)
+  logging.info("Actual ring_dims: %s", ring_dims)
+
+  # By default, vary axes in the core -> x -> y -> z order.
+  if ring_axes is None:
+    ring_axes = ["core", "x", "y", "z"]
+  elif len(ring_axes) != 4:
+    raise ValueError("Expected 4 elements in ring_axes, got %s" % ring_axes)
+  elif sorted(ring_axes) != ["core", "x", "y", "z"]:
+    raise ValueError("Invalid ring_axes value: %s" % ring_axes)
+  logging.info("Actual ring_axes: %s", ring_axes)
+
+  # Validate ring_bounds values.
+  if _tpu_topology is None:
+    raise ValueError(
+        "Invalid TPU topology, run dtensor.initialize_tpu_system() first")
+  topology_shape = list(_tpu_topology.mesh_shape)
+  if ring_bounds is None:
+    ring_bounds = topology_shape
+  elif len(ring_bounds) != 4:
+    raise ValueError("Expected 4 elements in ring_bounds, got %s" % ring_bounds)
+  elif ring_bounds > topology_shape:
+    raise ValueError("ring_bounds %s should be <= topology sizes %s" %
+                     (ring_bounds, topology_shape))
+  logging.info("Actual ring_bounds: %s", ring_bounds)
+
+  # Compute ring_size, the number of cores in a ring.
+  if ring_dims > 0:
+    ring_size = np.prod(mesh_shape[:ring_dims])
+  elif ring_dims < 0:
+    ring_size = np.prod(mesh_shape[ring_dims:])
+  else:
+    ring_size = 1  # single-core rings
+  logging.info("Actual ring_size: %d", ring_size)
+
+  # Rearrange all cores according to the axis iteration order.
+  global_core_locations = _enumerate_core_locations(
+      topology_shape, ring_bounds, ring_axes, can_split_host_across_rings,
+      ring_size)
+  logging.vlog(1, "Enumerated core locations: %s", global_core_locations)
+  num_cores = len(global_core_locations)
+
+  # The mesh to be created must use all TPU cores in the system.
+  mesh_size = np.prod(mesh_shape)
+  if mesh_size != num_cores:
+    raise ValueError(
+        "Invalid mesh size: mesh shape %s cannot 1:1 map to %d TPU cores" %
+        (mesh_shape, num_cores))
+
+  # Build a ring for the `ring_size` dimension and, if required, a strided ring
+  # for the orthogonal dimension.
+  if build_ring_across_rings:
+    global_core_locations = _build_orthogonal_rings(global_core_locations,
+                                                    ring_size,
+                                                    rotate_ring_across_rings)
+  else:
+    permutation = _build_all_reduce_ring(global_core_locations[:ring_size])
+    for r in range(0, num_cores, ring_size):
+      global_core_locations[r:r + ring_size] = [
+          global_core_locations[r + permutation[i]] for i in range(ring_size)
+      ]
+    logging.vlog(1, "Permutated core locations: %s", global_core_locations)
+
+  # For this point on, change from List[CoreLocation] to List[List[int]] for
+  # easier interaction with the C++ API.
+  global_core_locations = [l.to_list() for l in global_core_locations]
+  if _dtensor_device is None:
+    raise ValueError("Invalid system device, "
+                     "run dtensor.initialize_accelerator_system() first")
+  global_core_ids = _dtensor_device.tpu_core_locations_to_ids(
+      global_core_locations)
+
+  # Store a per-mesh mapping in the runtime.
+  _dtensor_device.set_tpu_core_ids(mesh_name, global_core_ids)
+
+  # Create the mesh by manually specifying local_device_ids.
+  local_core_locations = _tpu_topology.device_coordinates[config.client_id()]
+  indexes = [
+      global_core_locations.index(list(local_core_location))
+      for local_core_location in local_core_locations
+  ]
+  global_device_ids, local_device_ids, local_device_list = _create_device_array(
+      mesh_shape, _TPU_DEVICE_TYPE, None, local_device_ids=indexes)
+  return layout_lib.Mesh(
+      mesh_dim_names,
+      global_device_ids,
+      local_device_ids,
+      local_device_list,
+      mesh_name,
+      use_xla_spmd=use_xla_spmd,
+  )
+
+
+def get_device_ids(mesh: layout_lib.Mesh,
+                   client_id: Optional[int] = None) -> List[int]:
+  """Returns the device IDs of all TPU cores local to the given client.
+
+  A device ID is a non-negative integer that uniquely identifies a device in the
+  mesh. For example, for a 2x2 mesh ('x', 'y'), this function returns a
+  permutation of [0, 1, 2, 3].
+
+  Note that device IDs and device locations are equivalent. The former is a
+  linearization of the latter along mesh dimensions.
+
+  Args:
+    mesh: A TPU mesh.
+    client_id: Optional; A DTensor client ID. If empty, query this client.
+  """
+
+  if mesh.device_type() != _TPU_DEVICE_TYPE:
+    raise ValueError("The mesh must be a TPU mesh")
+
+  if client_id is None or client_id == config.client_id():
+    return mesh.local_device_ids()
+
+  # It's not clear we should ever allow a client to query other clients for
+  # their device IDs.
+  raise NotImplementedError(
+      "Looking up other clients' device IDs is not supported")
+
+
+def get_device_locations(
+    mesh: layout_lib.Mesh,
+    client_id: Optional[int] = None) -> List[Dict[str, int]]:
+  """Returns the device locations of all TPU cores local to the given client.
+
+  A device location is a dictionary from dimension names to indices on those
+  dimensions. For example, for a 2x2 mesh ('x', 'y'), this function returns a
+  permutation of this list:
+
+    [{'x': 0, 'y': 0},
+     {'x': 0, 'y': 1},
+     {'x': 1, 'y': 0},
+     {'x': 1, 'y': 1}].
+
+  Note that device IDs and device locations are equivalent. The former is a
+  linearization of the latter along mesh dimensions.
+
+  Args:
+    mesh: A TPU mesh.
+    client_id: Optional; A DTensor client ID. If empty, query this client.
+  """
+
+  if mesh.device_type() != _TPU_DEVICE_TYPE:
+    raise ValueError("The mesh must be a TPU mesh")
+
+  if client_id is None or client_id == config.client_id():
+    return mesh.local_device_locations()
+
+  # It's not clear we should ever allow a client to query other clients for
+  # their device locations.
+  raise NotImplementedError(
+      "Looking up other clients' device locations is not supported")
+
+
+# TODO(b/245589661): Remove dtensor_initialize_tpu_system() and
+# dtensor_shutdown_tpu_system() after users stopped using them.
+def dtensor_initialize_tpu_system(enable_coordination_service=False):
+  """Deprecated way to initialize the TPU system."""
+  from . import accelerator_util  # pylint: disable=g-import-not-at-top
+  accelerator_util.initialize_accelerator_system(
+      "TPU", enable_coordination_service=enable_coordination_service)
+
+
+def dtensor_shutdown_tpu_system():
+  """Deprecated way to shutodwn the TPU system."""
+  from . import accelerator_util  # pylint: disable=g-import-not-at-top
+  accelerator_util.shutdown_accelerator_system()
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/AccelerateSupport b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/AccelerateSupport
new file mode 100644
index 0000000000000000000000000000000000000000..e16f36b9e507ac46dc4c5707d365739fccc18ca6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/AccelerateSupport
@@ -0,0 +1,52 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ACCELERATESUPPORT_MODULE_H
+#define EIGEN_ACCELERATESUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \ingroup Support_modules
+  * \defgroup AccelerateSupport_Module AccelerateSupport module
+  *
+  * This module provides an interface to the Apple Accelerate library.
+  * It provides the seven following main factorization classes:
+  * - class AccelerateLLT: a Cholesky (LL^T) factorization.
+  * - class AccelerateLDLT: the default LDL^T factorization.
+  * - class AccelerateLDLTUnpivoted: a Cholesky-like LDL^T factorization with only 1x1 pivots and no pivoting
+  * - class AccelerateLDLTSBK: an LDL^T factorization with Supernode Bunch-Kaufman and static pivoting
+  * - class AccelerateLDLTTPP: an LDL^T factorization with full threshold partial pivoting
+  * - class AccelerateQR: a QR factorization
+  * - class AccelerateCholeskyAtA: a QR factorization without storing Q (equivalent to A^TA = R^T R)
+  *
+  * \code
+  * #include <Eigen/AccelerateSupport>
+  * \endcode
+  *
+  * In order to use this module, the Accelerate headers must be accessible from
+  * the include paths, and your binary must be linked to the Accelerate framework.
+  * The Accelerate library is only available on Apple hardware.
+  * 
+  * Note that many of the algorithms can be influenced by the UpLo template
+  * argument. All matrices are assumed to be symmetric. For example, the following
+  * creates an LDLT factorization where your matrix is symmetric (implicit) and
+  * uses the lower triangle:
+  * 
+  * \code
+  * AccelerateLDLT<SparseMatrix<float>, Lower> ldlt;
+  * \endcode
+  */
+
+// IWYU pragma: begin_exports
+#include "src/AccelerateSupport/AccelerateSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_ACCELERATESUPPORT_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Cholesky b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Cholesky
new file mode 100644
index 0000000000000000000000000000000000000000..4211151d458d9adcb7f9299c45e50cc19f61f32f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Cholesky
@@ -0,0 +1,43 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CHOLESKY_MODULE_H
+#define EIGEN_CHOLESKY_MODULE_H
+
+#include "Core"
+#include "Jacobi"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup Cholesky_Module Cholesky module
+  *
+  *
+  *
+  * This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices.
+  * Those decompositions are also accessible via the following methods:
+  *  - MatrixBase::llt()
+  *  - MatrixBase::ldlt()
+  *  - SelfAdjointView::llt()
+  *  - SelfAdjointView::ldlt()
+  *
+  * \code
+  * #include <Eigen/Cholesky>
+  * \endcode
+  */
+
+// IWYU pragma: begin_exports
+#include "src/Cholesky/LLT.h"
+#include "src/Cholesky/LDLT.h"
+#ifdef EIGEN_USE_LAPACKE
+#include "src/misc/lapacke_helpers.h"
+#include "src/Cholesky/LLT_LAPACKE.h"
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_CHOLESKY_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/CholmodSupport b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/CholmodSupport
new file mode 100644
index 0000000000000000000000000000000000000000..2ccb21143a4f56aa943797309159277343fed954
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/CholmodSupport
@@ -0,0 +1,50 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
+#define EIGEN_CHOLMODSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+extern "C" {
+  #include <cholmod.h>
+}
+
+/** \ingroup Support_modules
+  * \defgroup CholmodSupport_Module CholmodSupport module
+  *
+  * This module provides an interface to the Cholmod library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
+  * It provides the two following main factorization classes:
+  * - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.
+  * - class CholmodDecomposition: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of the underlying factorization method (supernodal or simplicial).
+  *
+  * For the sake of completeness, this module also propose the two following classes:
+  * - class CholmodSimplicialLLT
+  * - class CholmodSimplicialLDLT
+  * Note that these classes does not bring any particular advantage compared to the built-in
+  * SimplicialLLT and SimplicialLDLT factorization classes.
+  *
+  * \code
+  * #include <Eigen/CholmodSupport>
+  * \endcode
+  *
+  * In order to use this module, the cholmod headers must be accessible from the include paths, and your binary must be linked to the cholmod library and its dependencies.
+  * The dependencies depend on how cholmod has been compiled.
+  * For a cmake based project, you can use our FindCholmod.cmake module to help you in this task.
+  *
+  */
+
+// IWYU pragma: begin_exports
+#include "src/CholmodSupport/CholmodSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_CHOLMODSUPPORT_MODULE_H
+
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Core b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Core
new file mode 100644
index 0000000000000000000000000000000000000000..93d47890a38bf83f64da6a48d6cbfb95de7f84c9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Core
@@ -0,0 +1,413 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2007-2011 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CORE_MODULE_H
+#define EIGEN_CORE_MODULE_H
+
+// first thing Eigen does: stop the compiler from reporting useless warnings.
+#include "src/Core/util/DisableStupidWarnings.h"
+
+// then include this file where all our macros are defined. It's really important to do it first because
+// it's where we do all the compiler/OS/arch detections and define most defaults.
+#include "src/Core/util/Macros.h"
+
+// This detects SSE/AVX/NEON/etc. and configure alignment settings
+#include "src/Core/util/ConfigureVectorization.h"
+
+// We need cuda_runtime.h/hip_runtime.h to ensure that
+// the EIGEN_USING_STD macro works properly on the device side
+#if defined(EIGEN_CUDACC)
+  #include <cuda_runtime.h>
+#elif defined(EIGEN_HIPCC)
+  #include <hip/hip_runtime.h>
+#endif
+
+
+#ifdef EIGEN_EXCEPTIONS
+  #include <new>
+#endif
+
+// Disable the ipa-cp-clone optimization flag with MinGW 6.x or older (enabled by default with -O3)
+// See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=556 for details.
+#if EIGEN_COMP_MINGW && EIGEN_GNUC_STRICT_LESS_THAN(6,0,0)
+  #pragma GCC optimize ("-fno-ipa-cp-clone")
+#endif
+
+// Prevent ICC from specializing std::complex operators that silently fail
+// on device. This allows us to use our own device-compatible specializations
+// instead.
+#if EIGEN_COMP_ICC && defined(EIGEN_GPU_COMPILE_PHASE) \
+    && !defined(_OVERRIDE_COMPLEX_SPECIALIZATION_)
+#define _OVERRIDE_COMPLEX_SPECIALIZATION_ 1
+#endif
+#include <complex>
+
+// this include file manages BLAS and MKL related macros
+// and inclusion of their respective header files
+#include "src/Core/util/MKL_support.h"
+
+
+#if defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)
+  #define EIGEN_HAS_GPU_FP16
+#endif
+
+#if defined(EIGEN_HAS_CUDA_BF16) || defined(EIGEN_HAS_HIP_BF16)
+  #define EIGEN_HAS_GPU_BF16
+#endif
+
+#if (defined _OPENMP) && (!defined EIGEN_DONT_PARALLELIZE)
+  #define EIGEN_HAS_OPENMP
+#endif
+
+#ifdef EIGEN_HAS_OPENMP
+#include <atomic>
+#include <omp.h>
+#endif
+
+// MSVC for windows mobile does not have the errno.h file
+#if !(EIGEN_COMP_MSVC && EIGEN_OS_WINCE) && !EIGEN_COMP_ARM
+#define EIGEN_HAS_ERRNO
+#endif
+
+#ifdef EIGEN_HAS_ERRNO
+#include <cerrno>
+#endif
+#include <cstddef>
+#include <cstdlib>
+#include <cmath>
+#include <functional>
+#ifndef EIGEN_NO_IO
+  #include <sstream>
+  #include <iosfwd>
+#endif
+#include <cstring>
+#include <string>
+#include <limits>
+#include <climits> // for CHAR_BIT
+// for min/max:
+#include <algorithm>
+
+#include <array>
+#include <vector>
+
+// for std::is_nothrow_move_assignable
+#include <type_traits>
+
+// for outputting debug info
+#ifdef EIGEN_DEBUG_ASSIGN
+#include <iostream>
+#endif
+
+// required for __cpuid, needs to be included after cmath
+// also required for _BitScanReverse on Windows on ARM
+#if EIGEN_COMP_MSVC && (EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM64) && !EIGEN_OS_WINCE
+  #include <intrin.h>
+#endif
+
+#if defined(EIGEN_USE_SYCL)
+  #undef min
+  #undef max
+  #undef isnan
+  #undef isinf
+  #undef isfinite
+  #include <CL/sycl.hpp>
+  #include <map>
+  #include <memory>
+  #include <utility>
+  #include <thread>
+  #ifndef EIGEN_SYCL_LOCAL_THREAD_DIM0
+  #define EIGEN_SYCL_LOCAL_THREAD_DIM0 16
+  #endif
+  #ifndef EIGEN_SYCL_LOCAL_THREAD_DIM1
+  #define EIGEN_SYCL_LOCAL_THREAD_DIM1 16
+  #endif
+#endif
+
+
+#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS || defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API || defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS || defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API || defined EIGEN2_SUPPORT
+// This will generate an error message:
+#error Eigen2-support is only available up to version 3.2. Please go to "http://eigen.tuxfamily.org/index.php?title=Eigen2" for further information
+#endif
+
+namespace Eigen {
+
+// we use size_t frequently and we'll never remember to prepend it with std:: every time just to
+// ensure QNX/QCC support
+using std::size_t;
+// gcc 4.6.0 wants std:: for ptrdiff_t
+using std::ptrdiff_t;
+
+}
+
+/** \defgroup Core_Module Core module
+  * This is the main module of Eigen providing dense matrix and vector support
+  * (both fixed and dynamic size) with all the features corresponding to a BLAS library
+  * and much more...
+  *
+  * \code
+  * #include <Eigen/Core>
+  * \endcode
+  */
+
+#ifdef EIGEN_USE_LAPACKE
+  #ifdef EIGEN_USE_MKL
+    #include "mkl_lapacke.h"
+  #else
+    #include "src/misc/lapacke.h"
+  #endif
+#endif
+
+// IWYU pragma: begin_exports
+#include "src/Core/util/Constants.h"
+#include "src/Core/util/Meta.h"
+#include "src/Core/util/Assert.h"
+#include "src/Core/util/ForwardDeclarations.h"
+#include "src/Core/util/StaticAssert.h"
+#include "src/Core/util/XprHelper.h"
+#include "src/Core/util/Memory.h"
+#include "src/Core/util/IntegralConstant.h"
+#include "src/Core/util/Serializer.h"
+#include "src/Core/util/SymbolicIndex.h"
+#include "src/Core/util/EmulateArray.h"
+#include "src/Core/util/MoreMeta.h"
+
+#include "src/Core/NumTraits.h"
+#include "src/Core/MathFunctions.h"
+#include "src/Core/GenericPacketMath.h"
+#include "src/Core/MathFunctionsImpl.h"
+#include "src/Core/arch/Default/ConjHelper.h"
+// Generic half float support
+#include "src/Core/arch/Default/Half.h"
+#include "src/Core/arch/Default/BFloat16.h"
+#include "src/Core/arch/Default/GenericPacketMathFunctionsFwd.h"
+
+#if defined EIGEN_VECTORIZE_AVX512
+  #if defined EIGEN_VECTORIZE_AVX512FP16
+    #include "src/Core/arch/AVX512/PacketMathFP16.h"
+  #endif
+  #include "src/Core/arch/SSE/PacketMath.h"
+  #include "src/Core/arch/SSE/TypeCasting.h"
+  #include "src/Core/arch/SSE/Complex.h"
+  #include "src/Core/arch/AVX/PacketMath.h"
+  #include "src/Core/arch/AVX/TypeCasting.h"
+  #include "src/Core/arch/AVX/Complex.h"
+  #include "src/Core/arch/AVX512/PacketMath.h"
+  #include "src/Core/arch/AVX512/TypeCasting.h"
+  #include "src/Core/arch/AVX512/Complex.h"
+  #include "src/Core/arch/SSE/MathFunctions.h"
+  #include "src/Core/arch/AVX/MathFunctions.h"
+  #include "src/Core/arch/AVX512/MathFunctions.h"
+  #include "src/Core/arch/AVX512/TrsmKernel.h"
+#elif defined EIGEN_VECTORIZE_AVX
+  // Use AVX for floats and doubles, SSE for integers
+  #include "src/Core/arch/SSE/PacketMath.h"
+  #include "src/Core/arch/SSE/TypeCasting.h"
+  #include "src/Core/arch/SSE/Complex.h"
+  #include "src/Core/arch/AVX/PacketMath.h"
+  #include "src/Core/arch/AVX/TypeCasting.h"
+  #include "src/Core/arch/AVX/Complex.h"
+  #include "src/Core/arch/SSE/MathFunctions.h"
+  #include "src/Core/arch/AVX/MathFunctions.h"
+#elif defined EIGEN_VECTORIZE_SSE
+  #include "src/Core/arch/SSE/PacketMath.h"
+  #include "src/Core/arch/SSE/TypeCasting.h"
+  #include "src/Core/arch/SSE/MathFunctions.h"
+  #include "src/Core/arch/SSE/Complex.h"
+#elif defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)
+  #include "src/Core/arch/AltiVec/PacketMath.h"
+  #include "src/Core/arch/AltiVec/TypeCasting.h"
+  #include "src/Core/arch/AltiVec/MathFunctions.h"
+  #include "src/Core/arch/AltiVec/Complex.h"
+#elif defined EIGEN_VECTORIZE_NEON
+  #include "src/Core/arch/NEON/PacketMath.h"
+  #include "src/Core/arch/NEON/TypeCasting.h"
+  #include "src/Core/arch/NEON/MathFunctions.h"
+  #include "src/Core/arch/NEON/Complex.h"
+#elif defined EIGEN_VECTORIZE_SVE
+  #include "src/Core/arch/SVE/PacketMath.h"
+  #include "src/Core/arch/SVE/TypeCasting.h"
+  #include "src/Core/arch/SVE/MathFunctions.h"
+#elif defined EIGEN_VECTORIZE_ZVECTOR
+  #include "src/Core/arch/ZVector/PacketMath.h"
+  #include "src/Core/arch/ZVector/MathFunctions.h"
+  #include "src/Core/arch/ZVector/Complex.h"
+#elif defined EIGEN_VECTORIZE_MSA
+  #include "src/Core/arch/MSA/PacketMath.h"
+  #include "src/Core/arch/MSA/MathFunctions.h"
+  #include "src/Core/arch/MSA/Complex.h"
+#elif defined EIGEN_VECTORIZE_HVX
+  #include "src/Core/arch/HVX/PacketMath.h"
+#endif
+
+#if defined EIGEN_VECTORIZE_GPU
+  #include "src/Core/arch/GPU/PacketMath.h"
+  #include "src/Core/arch/GPU/MathFunctions.h"
+  #include "src/Core/arch/GPU/TypeCasting.h"
+#endif
+
+#if defined(EIGEN_USE_SYCL)
+  #include "src/Core/arch/SYCL/InteropHeaders.h"
+#if !defined(EIGEN_DONT_VECTORIZE_SYCL)
+  #include "src/Core/arch/SYCL/PacketMath.h"
+  #include "src/Core/arch/SYCL/MathFunctions.h"
+  #include "src/Core/arch/SYCL/TypeCasting.h"
+#endif
+#endif
+
+#include "src/Core/arch/Default/Settings.h"
+// This file provides generic implementations valid for scalar as well
+#include "src/Core/arch/Default/GenericPacketMathFunctions.h"
+
+#include "src/Core/functors/TernaryFunctors.h"
+#include "src/Core/functors/BinaryFunctors.h"
+#include "src/Core/functors/UnaryFunctors.h"
+#include "src/Core/functors/NullaryFunctors.h"
+#include "src/Core/functors/StlFunctors.h"
+#include "src/Core/functors/AssignmentFunctors.h"
+
+// Specialized functors for GPU.
+#ifdef EIGEN_GPUCC
+#include "src/Core/arch/GPU/Complex.h"
+#endif
+
+// Specializations of vectorized activation functions for NEON.
+#ifdef EIGEN_VECTORIZE_NEON
+#include "src/Core/arch/NEON/UnaryFunctors.h"
+#endif
+
+#include "src/Core/util/IndexedViewHelper.h"
+#include "src/Core/util/ReshapedHelper.h"
+#include "src/Core/ArithmeticSequence.h"
+#ifndef EIGEN_NO_IO
+  #include "src/Core/IO.h"
+#endif
+#include "src/Core/DenseCoeffsBase.h"
+#include "src/Core/DenseBase.h"
+#include "src/Core/MatrixBase.h"
+#include "src/Core/EigenBase.h"
+
+#include "src/Core/Product.h"
+#include "src/Core/CoreEvaluators.h"
+#include "src/Core/AssignEvaluator.h"
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN // work around Doxygen bug triggered by Assign.h r814874
+                                // at least confirmed with Doxygen 1.5.5 and 1.5.6
+  #include "src/Core/Assign.h"
+#endif
+
+#include "src/Core/ArrayBase.h"
+#include "src/Core/util/BlasUtil.h"
+#include "src/Core/DenseStorage.h"
+#include "src/Core/NestByValue.h"
+
+// #include "src/Core/ForceAlignedAccess.h"
+
+#include "src/Core/ReturnByValue.h"
+#include "src/Core/NoAlias.h"
+#include "src/Core/PlainObjectBase.h"
+#include "src/Core/Matrix.h"
+#include "src/Core/Array.h"
+#include "src/Core/CwiseTernaryOp.h"
+#include "src/Core/CwiseBinaryOp.h"
+#include "src/Core/CwiseUnaryOp.h"
+#include "src/Core/CwiseNullaryOp.h"
+#include "src/Core/CwiseUnaryView.h"
+#include "src/Core/SelfCwiseBinaryOp.h"
+#include "src/Core/Dot.h"
+#include "src/Core/StableNorm.h"
+#include "src/Core/Stride.h"
+#include "src/Core/MapBase.h"
+#include "src/Core/Map.h"
+#include "src/Core/Ref.h"
+#include "src/Core/Block.h"
+#include "src/Core/VectorBlock.h"
+#include "src/Core/IndexedView.h"
+#include "src/Core/Reshaped.h"
+#include "src/Core/Transpose.h"
+#include "src/Core/DiagonalMatrix.h"
+#include "src/Core/Diagonal.h"
+#include "src/Core/DiagonalProduct.h"
+#include "src/Core/SkewSymmetricMatrix3.h"
+#include "src/Core/Redux.h"
+#include "src/Core/Visitor.h"
+#include "src/Core/Fuzzy.h"
+#include "src/Core/Swap.h"
+#include "src/Core/CommaInitializer.h"
+#include "src/Core/GeneralProduct.h"
+#include "src/Core/Solve.h"
+#include "src/Core/Inverse.h"
+#include "src/Core/SolverBase.h"
+#include "src/Core/PermutationMatrix.h"
+#include "src/Core/Transpositions.h"
+#include "src/Core/TriangularMatrix.h"
+#include "src/Core/SelfAdjointView.h"
+#include "src/Core/products/GeneralBlockPanelKernel.h"
+#include "src/Core/products/Parallelizer.h"
+#include "src/Core/ProductEvaluators.h"
+#include "src/Core/products/GeneralMatrixVector.h"
+#include "src/Core/products/GeneralMatrixMatrix.h"
+#include "src/Core/SolveTriangular.h"
+#include "src/Core/products/GeneralMatrixMatrixTriangular.h"
+#include "src/Core/products/SelfadjointMatrixVector.h"
+#include "src/Core/products/SelfadjointMatrixMatrix.h"
+#include "src/Core/products/SelfadjointProduct.h"
+#include "src/Core/products/SelfadjointRank2Update.h"
+#include "src/Core/products/TriangularMatrixVector.h"
+#include "src/Core/products/TriangularMatrixMatrix.h"
+#include "src/Core/products/TriangularSolverMatrix.h"
+#include "src/Core/products/TriangularSolverVector.h"
+#include "src/Core/BandMatrix.h"
+#include "src/Core/CoreIterators.h"
+#include "src/Core/ConditionEstimator.h"
+
+#if defined(EIGEN_VECTORIZE_VSX)
+  #include "src/Core/arch/AltiVec/MatrixProduct.h"
+#elif defined EIGEN_VECTORIZE_NEON
+  #include "src/Core/arch/NEON/GeneralBlockPanelKernel.h"
+#endif
+
+#if defined(EIGEN_VECTORIZE_AVX512)
+  #include "src/Core/arch/AVX512/GemmKernel.h"
+#endif
+
+#if defined(EIGEN_VECTORIZE_HVX)
+  #include "src/Core/arch/HVX/GeneralBlockPanelKernel.h"
+#endif
+
+#include "src/Core/Select.h"
+#include "src/Core/VectorwiseOp.h"
+#include "src/Core/PartialReduxEvaluator.h"
+#include "src/Core/Random.h"
+#include "src/Core/Replicate.h"
+#include "src/Core/Reverse.h"
+#include "src/Core/ArrayWrapper.h"
+#include "src/Core/StlIterators.h"
+
+#ifdef EIGEN_USE_BLAS
+#include "src/Core/products/GeneralMatrixMatrix_BLAS.h"
+#include "src/Core/products/GeneralMatrixVector_BLAS.h"
+#include "src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h"
+#include "src/Core/products/SelfadjointMatrixMatrix_BLAS.h"
+#include "src/Core/products/SelfadjointMatrixVector_BLAS.h"
+#include "src/Core/products/TriangularMatrixMatrix_BLAS.h"
+#include "src/Core/products/TriangularMatrixVector_BLAS.h"
+#include "src/Core/products/TriangularSolverMatrix_BLAS.h"
+#endif // EIGEN_USE_BLAS
+
+#ifdef EIGEN_USE_MKL_VML
+#include "src/Core/Assign_MKL.h"
+#endif
+
+#include "src/Core/GlobalFunctions.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_CORE_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Dense b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Dense
new file mode 100644
index 0000000000000000000000000000000000000000..5768910bd88c43f0761f2f345c6f0e3b46a4d8ec
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Dense
@@ -0,0 +1,7 @@
+#include "Core"
+#include "LU"
+#include "Cholesky"
+#include "QR"
+#include "SVD"
+#include "Geometry"
+#include "Eigenvalues"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Eigen b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Eigen
new file mode 100644
index 0000000000000000000000000000000000000000..654c8dc6380f7bb21d3ba1a9ce916006043552aa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Eigen
@@ -0,0 +1,2 @@
+#include "Dense"
+#include "Sparse"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Eigenvalues b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Eigenvalues
new file mode 100644
index 0000000000000000000000000000000000000000..97c526d91b37f5011e64b1f15dcc5c3cb8ab6ad6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Eigenvalues
@@ -0,0 +1,63 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EIGENVALUES_MODULE_H
+#define EIGEN_EIGENVALUES_MODULE_H
+
+#include "Core"
+
+#include "Cholesky"
+#include "Jacobi"
+#include "Householder"
+#include "LU"
+#include "Geometry"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup Eigenvalues_Module Eigenvalues module
+  *
+  *
+  *
+  * This module mainly provides various eigenvalue solvers.
+  * This module also provides some MatrixBase methods, including:
+  *  - MatrixBase::eigenvalues(),
+  *  - MatrixBase::operatorNorm()
+  *
+  * \code
+  * #include <Eigen/Eigenvalues>
+  * \endcode
+  */
+
+#include "src/misc/RealSvd2x2.h"
+
+// IWYU pragma: begin_exports
+#include "src/Eigenvalues/Tridiagonalization.h"
+#include "src/Eigenvalues/RealSchur.h"
+#include "src/Eigenvalues/EigenSolver.h"
+#include "src/Eigenvalues/SelfAdjointEigenSolver.h"
+#include "src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h"
+#include "src/Eigenvalues/HessenbergDecomposition.h"
+#include "src/Eigenvalues/ComplexSchur.h"
+#include "src/Eigenvalues/ComplexEigenSolver.h"
+#include "src/Eigenvalues/RealQZ.h"
+#include "src/Eigenvalues/GeneralizedEigenSolver.h"
+#include "src/Eigenvalues/MatrixBaseEigenvalues.h"
+#ifdef EIGEN_USE_LAPACKE
+#ifdef EIGEN_USE_MKL
+#include "mkl_lapacke.h"
+#else
+#include "src/misc/lapacke.h"
+#endif
+#include "src/Eigenvalues/RealSchur_LAPACKE.h"
+#include "src/Eigenvalues/ComplexSchur_LAPACKE.h"
+#include "src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h"
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_EIGENVALUES_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Geometry b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Geometry
new file mode 100644
index 0000000000000000000000000000000000000000..0a733c5ecbc3111552098bf9db74274b9f15ca0a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Geometry
@@ -0,0 +1,60 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GEOMETRY_MODULE_H
+#define EIGEN_GEOMETRY_MODULE_H
+
+#include "Core"
+
+#include "SVD"
+#include "LU"
+#include <limits>
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup Geometry_Module Geometry module
+  *
+  * This module provides support for:
+  *  - fixed-size homogeneous transformations
+  *  - translation, scaling, 2D and 3D rotations
+  *  - \link Quaternion quaternions \endlink
+  *  - cross products (\ref MatrixBase::cross, \ref MatrixBase::cross3)
+  *  - orthognal vector generation (\ref MatrixBase::unitOrthogonal)
+  *  - some linear components: \link ParametrizedLine parametrized-lines \endlink and \link Hyperplane hyperplanes \endlink
+  *  - \link AlignedBox axis aligned bounding boxes \endlink
+  *  - \link umeyama least-square transformation fitting \endlink
+  *
+  * \code
+  * #include <Eigen/Geometry>
+  * \endcode
+  */
+
+// IWYU pragma: begin_exports
+#include "src/Geometry/OrthoMethods.h"
+#include "src/Geometry/EulerAngles.h"
+#include "src/Geometry/Homogeneous.h"
+#include "src/Geometry/RotationBase.h"
+#include "src/Geometry/Rotation2D.h"
+#include "src/Geometry/Quaternion.h"
+#include "src/Geometry/AngleAxis.h"
+#include "src/Geometry/Transform.h"
+#include "src/Geometry/Translation.h"
+#include "src/Geometry/Scaling.h"
+#include "src/Geometry/Hyperplane.h"
+#include "src/Geometry/ParametrizedLine.h"
+#include "src/Geometry/AlignedBox.h"
+#include "src/Geometry/Umeyama.h"
+
+// Use the SSE optimized version whenever possible.
+#if (defined EIGEN_VECTORIZE_SSE) || (defined EIGEN_VECTORIZE_NEON)
+#include "src/Geometry/arch/Geometry_SIMD.h"
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_GEOMETRY_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Householder b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Householder
new file mode 100644
index 0000000000000000000000000000000000000000..0f7f9b3d33e7541c6a57423fbdc759e99ecb41f4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Householder
@@ -0,0 +1,31 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_HOUSEHOLDER_MODULE_H
+#define EIGEN_HOUSEHOLDER_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup Householder_Module Householder module
+  * This module provides Householder transformations.
+  *
+  * \code
+  * #include <Eigen/Householder>
+  * \endcode
+  */
+
+// IWYU pragma: begin_exports
+#include "src/Householder/Householder.h"
+#include "src/Householder/HouseholderSequence.h"
+#include "src/Householder/BlockHouseholder.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_HOUSEHOLDER_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/IterativeLinearSolvers b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/IterativeLinearSolvers
new file mode 100644
index 0000000000000000000000000000000000000000..833189b132df7d1840c54b711ff3af11336b198e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/IterativeLinearSolvers
@@ -0,0 +1,50 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
+#define EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
+
+#include "SparseCore"
+#include "OrderingMethods"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** 
+  * \defgroup IterativeLinearSolvers_Module IterativeLinearSolvers module
+  *
+  * This module currently provides iterative methods to solve problems of the form \c A \c x = \c b, where \c A is a squared matrix, usually very large and sparse.
+  * Those solvers are accessible via the following classes:
+  *  - ConjugateGradient for selfadjoint (hermitian) matrices,
+  *  - LeastSquaresConjugateGradient for rectangular least-square problems,
+  *  - BiCGSTAB for general square matrices.
+  *
+  * These iterative solvers are associated with some preconditioners:
+  *  - IdentityPreconditioner - not really useful
+  *  - DiagonalPreconditioner - also called Jacobi preconditioner, work very well on diagonal dominant matrices.
+  *  - IncompleteLUT - incomplete LU factorization with dual thresholding
+  *
+  * Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport, UmfPackSupport, SuperLUSupport, AccelerateSupport.
+  *
+    \code
+    #include <Eigen/IterativeLinearSolvers>
+    \endcode
+  */
+
+// IWYU pragma: begin_exports
+#include "src/IterativeLinearSolvers/SolveWithGuess.h"
+#include "src/IterativeLinearSolvers/IterativeSolverBase.h"
+#include "src/IterativeLinearSolvers/BasicPreconditioners.h"
+#include "src/IterativeLinearSolvers/ConjugateGradient.h"
+#include "src/IterativeLinearSolvers/LeastSquareConjugateGradient.h"
+#include "src/IterativeLinearSolvers/BiCGSTAB.h"
+#include "src/IterativeLinearSolvers/IncompleteLUT.h"
+#include "src/IterativeLinearSolvers/IncompleteCholesky.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Jacobi b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Jacobi
new file mode 100644
index 0000000000000000000000000000000000000000..490a6a8c57e12613b08e3411e677451df669659e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Jacobi
@@ -0,0 +1,34 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_JACOBI_MODULE_H
+#define EIGEN_JACOBI_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup Jacobi_Module Jacobi module
+  * This module provides Jacobi and Givens rotations.
+  *
+  * \code
+  * #include <Eigen/Jacobi>
+  * \endcode
+  *
+  * In addition to listed classes, it defines the two following MatrixBase methods to apply a Jacobi or Givens rotation:
+  *  - MatrixBase::applyOnTheLeft()
+  *  - MatrixBase::applyOnTheRight().
+  */
+
+// IWYU pragma: begin_exports
+#include "src/Jacobi/Jacobi.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_JACOBI_MODULE_H
+
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/KLUSupport b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/KLUSupport
new file mode 100644
index 0000000000000000000000000000000000000000..5c8f8a34c43fc77de8d50a6a22287c49d8d079e7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/KLUSupport
@@ -0,0 +1,43 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_KLUSUPPORT_MODULE_H
+#define EIGEN_KLUSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+extern "C" {
+#include <btf.h>
+#include <klu.h>
+   }
+
+/** \ingroup Support_modules
+  * \defgroup KLUSupport_Module KLUSupport module
+  *
+  * This module provides an interface to the KLU library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
+  * It provides the following factorization class:
+  * - class KLU: a sparse LU factorization, well-suited for circuit simulation.
+  *
+  * \code
+  * #include <Eigen/KLUSupport>
+  * \endcode
+  *
+  * In order to use this module, the klu and btf headers must be accessible from the include paths, and your binary must be linked to the klu library and its dependencies.
+  * The dependencies depend on how umfpack has been compiled.
+  * For a cmake based project, you can use our FindKLU.cmake module to help you in this task.
+  *
+  */
+
+// IWYU pragma: begin_exports
+#include "src/KLUSupport/KLUSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_KLUSUPPORT_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/LU b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/LU
new file mode 100644
index 0000000000000000000000000000000000000000..49d7b91fe051bca6fdb210bddb6b5fb9183e4d66
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/LU
@@ -0,0 +1,46 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LU_MODULE_H
+#define EIGEN_LU_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup LU_Module LU module
+  * This module includes %LU decomposition and related notions such as matrix inversion and determinant.
+  * This module defines the following MatrixBase methods:
+  *  - MatrixBase::inverse()
+  *  - MatrixBase::determinant()
+  *
+  * \code
+  * #include <Eigen/LU>
+  * \endcode
+  */
+
+#include "src/misc/Kernel.h"
+#include "src/misc/Image.h"
+
+// IWYU pragma: begin_exports
+#include "src/LU/FullPivLU.h"
+#include "src/LU/PartialPivLU.h"
+#ifdef EIGEN_USE_LAPACKE
+#include "src/misc/lapacke_helpers.h"
+#include "src/LU/PartialPivLU_LAPACKE.h"
+#endif
+#include "src/LU/Determinant.h"
+#include "src/LU/InverseImpl.h"
+
+#if defined EIGEN_VECTORIZE_SSE || defined EIGEN_VECTORIZE_NEON
+  #include "src/LU/arch/InverseSize4.h"
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_LU_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/MetisSupport b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/MetisSupport
new file mode 100644
index 0000000000000000000000000000000000000000..e74c3bb6ad7398217000ba43823697040711a96c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/MetisSupport
@@ -0,0 +1,36 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_METISSUPPORT_MODULE_H
+#define EIGEN_METISSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+extern "C" {
+#include <metis.h>
+}
+
+
+/** \ingroup Support_modules
+  * \defgroup MetisSupport_Module MetisSupport module
+  *
+  * \code
+  * #include <Eigen/MetisSupport>
+  * \endcode
+  * This module defines an interface to the METIS reordering package (http://glaros.dtc.umn.edu/gkhome/views/metis). 
+  * It can be used just as any other built-in method as explained in \link OrderingMethods_Module here. \endlink
+  */
+
+// IWYU pragma: begin_exports
+#include "src/MetisSupport/MetisSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_METISSUPPORT_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/OrderingMethods b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/OrderingMethods
new file mode 100644
index 0000000000000000000000000000000000000000..5031de7a520be1b50d4e3afec97f787388b9a532
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/OrderingMethods
@@ -0,0 +1,73 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ORDERINGMETHODS_MODULE_H
+#define EIGEN_ORDERINGMETHODS_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** 
+  * \defgroup OrderingMethods_Module OrderingMethods module
+  *
+  * This module is currently for internal use only
+  * 
+  * It defines various built-in and external ordering methods for sparse matrices. 
+  * They are typically used to reduce the number of elements during 
+  * the sparse matrix decomposition (LLT, LU, QR).
+  * Precisely, in a preprocessing step, a permutation matrix P is computed using 
+  * those ordering methods and applied to the columns of the matrix. 
+  * Using for instance the sparse Cholesky decomposition, it is expected that 
+  * the nonzeros elements in LLT(A*P) will be much smaller than that in LLT(A).
+  * 
+  * 
+  * Usage : 
+  * \code
+  * #include <Eigen/OrderingMethods>
+  * \endcode
+  * 
+  * A simple usage is as a template parameter in the sparse decomposition classes : 
+  * 
+  * \code 
+  * SparseLU<MatrixType, COLAMDOrdering<int> > solver;
+  * \endcode 
+  * 
+  * \code 
+  * SparseQR<MatrixType, COLAMDOrdering<int> > solver;
+  * \endcode
+  * 
+  * It is possible as well to call directly a particular ordering method for your own purpose, 
+  * \code 
+  * AMDOrdering<int> ordering;
+  * PermutationMatrix<Dynamic, Dynamic, int> perm;
+  * SparseMatrix<double> A; 
+  * //Fill the matrix ...
+  * 
+  * ordering(A, perm); // Call AMD
+  * \endcode
+  * 
+  * \note Some of these methods (like AMD or METIS), need the sparsity pattern 
+  * of the input matrix to be symmetric. When the matrix is structurally unsymmetric, 
+  * Eigen computes internally the pattern of \f$A^T*A\f$ before calling the method.
+  * If your matrix is already symmetric (at leat in structure), you can avoid that
+  * by calling the method with a SelfAdjointView type.
+  * 
+  * \code
+  *  // Call the ordering on the pattern of the lower triangular matrix A
+  * ordering(A.selfadjointView<Lower>(), perm);
+  * \endcode
+  */
+
+// IWYU pragma: begin_exports
+#include "src/OrderingMethods/Amd.h"
+#include "src/OrderingMethods/Ordering.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_ORDERINGMETHODS_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/PaStiXSupport b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/PaStiXSupport
new file mode 100644
index 0000000000000000000000000000000000000000..5d3cb38fdf3e198099bb572a0a8373b425d4bf33
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/PaStiXSupport
@@ -0,0 +1,51 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PASTIXSUPPORT_MODULE_H
+#define EIGEN_PASTIXSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+extern "C" {
+#include <pastix_nompi.h>
+#include <pastix.h>
+}
+
+#ifdef complex
+#undef complex
+#endif
+
+/** \ingroup Support_modules
+  * \defgroup PaStiXSupport_Module PaStiXSupport module
+  * 
+  * This module provides an interface to the <a href="http://pastix.gforge.inria.fr/">PaSTiX</a> library.
+  * PaSTiX is a general \b supernodal, \b parallel and \b opensource sparse solver.
+  * It provides the two following main factorization classes:
+  * - class PastixLLT : a supernodal, parallel LLt Cholesky factorization.
+  * - class PastixLDLT: a supernodal, parallel LDLt Cholesky factorization.
+  * - class PastixLU : a supernodal, parallel LU factorization (optimized for a symmetric pattern).
+  * 
+  * \code
+  * #include <Eigen/PaStiXSupport>
+  * \endcode
+  *
+  * In order to use this module, the PaSTiX headers must be accessible from the include paths, and your binary must be linked to the PaSTiX library and its dependencies.
+  * This wrapper resuires PaStiX version 5.x compiled without MPI support.
+  * The dependencies depend on how PaSTiX has been compiled.
+  * For a cmake based project, you can use our FindPaSTiX.cmake module to help you in this task.
+  *
+  */
+
+// IWYU pragma: begin_exports
+#include "src/PaStiXSupport/PaStiXSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_PASTIXSUPPORT_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/PardisoSupport b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/PardisoSupport
new file mode 100644
index 0000000000000000000000000000000000000000..c022ce1fb3bd2ad903a504dc1b549587d2fcf01f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/PardisoSupport
@@ -0,0 +1,37 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PARDISOSUPPORT_MODULE_H
+#define EIGEN_PARDISOSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include <mkl_pardiso.h>
+
+/** \ingroup Support_modules
+  * \defgroup PardisoSupport_Module PardisoSupport module
+  *
+  * This module brings support for the Intel(R) MKL PARDISO direct sparse solvers.
+  *
+  * \code
+  * #include <Eigen/PardisoSupport>
+  * \endcode
+  *
+  * In order to use this module, the MKL headers must be accessible from the include paths, and your binary must be linked to the MKL library and its dependencies.
+  * See this \ref TopicUsingIntelMKL "page" for more information on MKL-Eigen integration.
+  * 
+  */
+
+// IWYU pragma: begin_exports
+#include "src/PardisoSupport/PardisoSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_PARDISOSUPPORT_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/QR b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/QR
new file mode 100644
index 0000000000000000000000000000000000000000..2f57a602bc9bca54f359c6650a6b6d8fd2ba4e52
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/QR
@@ -0,0 +1,48 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_QR_MODULE_H
+#define EIGEN_QR_MODULE_H
+
+#include "Core"
+
+#include "Cholesky"
+#include "Jacobi"
+#include "Householder"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup QR_Module QR module
+  *
+  *
+  *
+  * This module provides various QR decompositions
+  * This module also provides some MatrixBase methods, including:
+  *  - MatrixBase::householderQr()
+  *  - MatrixBase::colPivHouseholderQr()
+  *  - MatrixBase::fullPivHouseholderQr()
+  *
+  * \code
+  * #include <Eigen/QR>
+  * \endcode
+  */
+
+// IWYU pragma: begin_exports
+#include "src/QR/HouseholderQR.h"
+#include "src/QR/FullPivHouseholderQR.h"
+#include "src/QR/ColPivHouseholderQR.h"
+#include "src/QR/CompleteOrthogonalDecomposition.h"
+#ifdef EIGEN_USE_LAPACKE
+#include "src/misc/lapacke_helpers.h"
+#include "src/QR/HouseholderQR_LAPACKE.h"
+#include "src/QR/ColPivHouseholderQR_LAPACKE.h"
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_QR_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/QtAlignedMalloc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/QtAlignedMalloc
new file mode 100644
index 0000000000000000000000000000000000000000..6fe82374a5ebdb0dbc17fa6e0119b1a399f126e3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/QtAlignedMalloc
@@ -0,0 +1,39 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_QTMALLOC_MODULE_H
+#define EIGEN_QTMALLOC_MODULE_H
+
+#include "Core"
+
+#if (!EIGEN_MALLOC_ALREADY_ALIGNED)
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+void *qMalloc(std::size_t size)
+{
+  return Eigen::internal::aligned_malloc(size);
+}
+
+void qFree(void *ptr)
+{
+  Eigen::internal::aligned_free(ptr);
+}
+
+void *qRealloc(void *ptr, std::size_t size)
+{
+  void* newPtr = Eigen::internal::aligned_malloc(size);
+  std::memcpy(newPtr, ptr, size);
+  Eigen::internal::aligned_free(ptr);
+  return newPtr;
+}
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif
+
+#endif // EIGEN_QTMALLOC_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SPQRSupport b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SPQRSupport
new file mode 100644
index 0000000000000000000000000000000000000000..a25f5ef9f0137ed1c541e3c7d1e4c433b81376eb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SPQRSupport
@@ -0,0 +1,39 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPQRSUPPORT_MODULE_H
+#define EIGEN_SPQRSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include "SuiteSparseQR.hpp"
+
+/** \ingroup Support_modules
+  * \defgroup SPQRSupport_Module SuiteSparseQR module
+  * 
+  * This module provides an interface to the SPQR library, which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
+  *
+  * \code
+  * #include <Eigen/SPQRSupport>
+  * \endcode
+  *
+  * In order to use this module, the SPQR headers must be accessible from the include paths, and your binary must be linked to the SPQR library and its dependencies (Cholmod, AMD, COLAMD,...).
+  * For a cmake based project, you can use our FindSPQR.cmake and FindCholmod.Cmake modules
+  *
+  */
+
+#include "CholmodSupport"
+
+// IWYU pragma: begin_exports
+#include "src/SPQRSupport/SuiteSparseQRSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SVD b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SVD
new file mode 100644
index 0000000000000000000000000000000000000000..b13ec2fb1caef34127ff2dc9dcf6fed5e35a5121
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SVD
@@ -0,0 +1,55 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SVD_MODULE_H
+#define EIGEN_SVD_MODULE_H
+
+#include "QR"
+#include "Householder"
+#include "Jacobi"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup SVD_Module SVD module
+  *
+  *
+  *
+  * This module provides SVD decomposition for matrices (both real and complex).
+  * Two decomposition algorithms are provided:
+  *  - JacobiSVD implementing two-sided Jacobi iterations is numerically very accurate, fast for small matrices, but very slow for larger ones.
+  *  - BDCSVD implementing a recursive divide & conquer strategy on top of an upper-bidiagonalization which remains fast for large problems.
+  * These decompositions are accessible via the respective classes and following MatrixBase methods:
+  *  - MatrixBase::jacobiSvd()
+  *  - MatrixBase::bdcSvd()
+  *
+  * \code
+  * #include <Eigen/SVD>
+  * \endcode
+  */
+
+// IWYU pragma: begin_exports
+#include "src/misc/RealSvd2x2.h"
+#include "src/SVD/UpperBidiagonalization.h"
+#include "src/SVD/SVDBase.h"
+#include "src/SVD/JacobiSVD.h"
+#include "src/SVD/BDCSVD.h"
+#ifdef EIGEN_USE_LAPACKE
+#ifdef EIGEN_USE_MKL
+#include "mkl_lapacke.h"
+#else
+#include "src/misc/lapacke.h"
+#endif
+#ifndef EIGEN_USE_LAPACKE_STRICT
+#include "src/SVD/JacobiSVD_LAPACKE.h"
+#endif
+#include "src/SVD/BDCSVD_LAPACKE.h"
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_SVD_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Sparse b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Sparse
new file mode 100644
index 0000000000000000000000000000000000000000..a2ef7a66526812617c782f76dea260f04593ea58
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/Sparse
@@ -0,0 +1,34 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_MODULE_H
+#define EIGEN_SPARSE_MODULE_H
+
+/** \defgroup Sparse_Module Sparse meta-module
+  *
+  * Meta-module including all related modules:
+  * - \ref SparseCore_Module
+  * - \ref OrderingMethods_Module
+  * - \ref SparseCholesky_Module
+  * - \ref SparseLU_Module
+  * - \ref SparseQR_Module
+  * - \ref IterativeLinearSolvers_Module
+  *
+    \code
+    #include <Eigen/Sparse>
+    \endcode
+  */
+
+#include "SparseCore"
+#include "OrderingMethods"
+#include "SparseCholesky"
+#include "SparseLU"
+#include "SparseQR"
+#include "IterativeLinearSolvers"
+
+#endif // EIGEN_SPARSE_MODULE_H
+
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SparseCholesky b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SparseCholesky
new file mode 100644
index 0000000000000000000000000000000000000000..967196f049e60b901d83e2aaaf76d135338ec9bf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SparseCholesky
@@ -0,0 +1,40 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2013 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSECHOLESKY_MODULE_H
+#define EIGEN_SPARSECHOLESKY_MODULE_H
+
+#include "SparseCore"
+#include "OrderingMethods"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** 
+  * \defgroup SparseCholesky_Module SparseCholesky module
+  *
+  * This module currently provides two variants of the direct sparse Cholesky decomposition for selfadjoint (hermitian) matrices.
+  * Those decompositions are accessible via the following classes:
+  *  - SimplicialLLt,
+  *  - SimplicialLDLt
+  *
+  * Such problems can also be solved using the ConjugateGradient solver from the IterativeLinearSolvers module.
+  *
+  * \code
+  * #include <Eigen/SparseCholesky>
+  * \endcode
+  */
+
+// IWYU pragma: begin_exports
+#include "src/SparseCholesky/SimplicialCholesky.h"
+#include "src/SparseCholesky/SimplicialCholesky_impl.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_SPARSECHOLESKY_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SparseCore b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SparseCore
new file mode 100644
index 0000000000000000000000000000000000000000..292b08910299db137b7e88e51390cc8dde3aae14
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SparseCore
@@ -0,0 +1,71 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSECORE_MODULE_H
+#define EIGEN_SPARSECORE_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include <vector>
+#include <map>
+#include <cstdlib>
+#include <cstring>
+#include <algorithm>
+#include <numeric>
+
+/** 
+  * \defgroup SparseCore_Module SparseCore module
+  *
+  * This module provides a sparse matrix representation, and basic associated matrix manipulations
+  * and operations.
+  *
+  * See the \ref TutorialSparse "Sparse tutorial"
+  *
+  * \code
+  * #include <Eigen/SparseCore>
+  * \endcode
+  *
+  * This module depends on: Core.
+  */
+
+// IWYU pragma: begin_exports
+#include "src/SparseCore/SparseUtil.h"
+#include "src/SparseCore/SparseMatrixBase.h"
+#include "src/SparseCore/SparseAssign.h"
+#include "src/SparseCore/CompressedStorage.h"
+#include "src/SparseCore/AmbiVector.h"
+#include "src/SparseCore/SparseCompressedBase.h"
+#include "src/SparseCore/SparseMatrix.h"
+#include "src/SparseCore/SparseMap.h"
+#include "src/SparseCore/SparseVector.h"
+#include "src/SparseCore/SparseRef.h"
+#include "src/SparseCore/SparseCwiseUnaryOp.h"
+#include "src/SparseCore/SparseCwiseBinaryOp.h"
+#include "src/SparseCore/SparseTranspose.h"
+#include "src/SparseCore/SparseBlock.h"
+#include "src/SparseCore/SparseDot.h"
+#include "src/SparseCore/SparseRedux.h"
+#include "src/SparseCore/SparseView.h"
+#include "src/SparseCore/SparseDiagonalProduct.h"
+#include "src/SparseCore/ConservativeSparseSparseProduct.h"
+#include "src/SparseCore/SparseSparseProductWithPruning.h"
+#include "src/SparseCore/SparseProduct.h"
+#include "src/SparseCore/SparseDenseProduct.h"
+#include "src/SparseCore/SparseSelfAdjointView.h"
+#include "src/SparseCore/SparseTriangularView.h"
+#include "src/SparseCore/TriangularSolver.h"
+#include "src/SparseCore/SparsePermutation.h"
+#include "src/SparseCore/SparseFuzzy.h"
+#include "src/SparseCore/SparseSolverBase.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_SPARSECORE_MODULE_H
+
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SparseLU b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SparseLU
new file mode 100644
index 0000000000000000000000000000000000000000..9ec6ac73571cbaf9cb5fe0113a45b8fd5b8922b3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SparseLU
@@ -0,0 +1,50 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSELU_MODULE_H
+#define EIGEN_SPARSELU_MODULE_H
+
+#include "SparseCore"
+
+/** 
+  * \defgroup SparseLU_Module SparseLU module
+  * This module defines a supernodal factorization of general sparse matrices.
+  * The code is fully optimized for supernode-panel updates with specialized kernels.
+  * Please, see the documentation of the SparseLU class for more details.
+  */
+
+// Ordering interface
+#include "OrderingMethods"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+// IWYU pragma: begin_exports
+#include "src/SparseLU/SparseLU_Structs.h"
+#include "src/SparseLU/SparseLU_SupernodalMatrix.h"
+#include "src/SparseLU/SparseLUImpl.h"
+#include "src/SparseCore/SparseColEtree.h"
+#include "src/SparseLU/SparseLU_Memory.h"
+#include "src/SparseLU/SparseLU_heap_relax_snode.h"
+#include "src/SparseLU/SparseLU_relax_snode.h"
+#include "src/SparseLU/SparseLU_pivotL.h"
+#include "src/SparseLU/SparseLU_panel_dfs.h"
+#include "src/SparseLU/SparseLU_kernel_bmod.h"
+#include "src/SparseLU/SparseLU_panel_bmod.h"
+#include "src/SparseLU/SparseLU_column_dfs.h"
+#include "src/SparseLU/SparseLU_column_bmod.h"
+#include "src/SparseLU/SparseLU_copy_to_ucol.h"
+#include "src/SparseLU/SparseLU_pruneL.h"
+#include "src/SparseLU/SparseLU_Utils.h"
+#include "src/SparseLU/SparseLU.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_SPARSELU_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SparseQR b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SparseQR
new file mode 100644
index 0000000000000000000000000000000000000000..4bc144ccdae50d0ee15e85a98da9037fda3d872f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SparseQR
@@ -0,0 +1,38 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEQR_MODULE_H
+#define EIGEN_SPARSEQR_MODULE_H
+
+#include "SparseCore"
+#include "OrderingMethods"
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup SparseQR_Module SparseQR module
+  * \brief Provides QR decomposition for sparse matrices
+  * 
+  * This module provides a simplicial version of the left-looking Sparse QR decomposition. 
+  * The columns of the input matrix should be reordered to limit the fill-in during the 
+  * decomposition. Built-in methods (COLAMD, AMD) or external  methods (METIS) can be used to this end.
+  * See the \link OrderingMethods_Module OrderingMethods\endlink module for the list 
+  * of built-in and external ordering methods.
+  * 
+  * \code
+  * #include <Eigen/SparseQR>
+  * \endcode
+  * 
+  * 
+  */
+
+// IWYU pragma: begin_exports
+#include "src/SparseCore/SparseColEtree.h"
+#include "src/SparseQR/SparseQR.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/StdDeque b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/StdDeque
new file mode 100644
index 0000000000000000000000000000000000000000..29550e2a72c8038bb84903b2c85a57bf020f6ed5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/StdDeque
@@ -0,0 +1,29 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STDDEQUE_MODULE_H
+#define EIGEN_STDDEQUE_MODULE_H
+
+#include "Core"
+#include <deque>
+
+#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
+
+#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...)
+
+#else
+
+// IWYU pragma: begin_exports
+#include "src/StlSupport/StdDeque.h"
+// IWYU pragma: end_exports
+
+#endif
+
+#endif // EIGEN_STDDEQUE_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/StdList b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/StdList
new file mode 100644
index 0000000000000000000000000000000000000000..8a22fc8f1069fbceb9b17dcd463bf9178c332faf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/StdList
@@ -0,0 +1,28 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STDLIST_MODULE_H
+#define EIGEN_STDLIST_MODULE_H
+
+#include "Core"
+#include <list>
+
+#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
+
+#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...)
+
+#else
+
+// IWYU pragma: begin_exports
+#include "src/StlSupport/StdList.h"
+// IWYU pragma: end_exports
+
+#endif
+
+#endif // EIGEN_STDLIST_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/StdVector b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/StdVector
new file mode 100644
index 0000000000000000000000000000000000000000..e68f6b5b8ac585ae9ae58074b9f20c55d30c56b1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/StdVector
@@ -0,0 +1,29 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STDVECTOR_MODULE_H
+#define EIGEN_STDVECTOR_MODULE_H
+
+#include "Core"
+#include <vector>
+
+#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && (EIGEN_MAX_STATIC_ALIGN_BYTES<=16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
+
+#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...)
+
+#else
+
+// IWYU pragma: begin_exports
+#include "src/StlSupport/StdVector.h"
+// IWYU pragma: end_exports
+
+#endif
+
+#endif // EIGEN_STDVECTOR_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SuperLUSupport b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SuperLUSupport
new file mode 100644
index 0000000000000000000000000000000000000000..33997fa503674b580bd8844cda9277f90ccec903
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/SuperLUSupport
@@ -0,0 +1,66 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SUPERLUSUPPORT_MODULE_H
+#define EIGEN_SUPERLUSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#ifdef EMPTY
+#define EIGEN_EMPTY_WAS_ALREADY_DEFINED
+#endif
+
+typedef int int_t;
+#include <slu_Cnames.h>
+#include <supermatrix.h>
+#include <slu_util.h>
+
+// slu_util.h defines a preprocessor token named EMPTY which is really polluting,
+// so we remove it in favor of a SUPERLU_EMPTY token.
+// If EMPTY was already defined then we don't undef it.
+
+#if defined(EIGEN_EMPTY_WAS_ALREADY_DEFINED)
+# undef EIGEN_EMPTY_WAS_ALREADY_DEFINED
+#elif defined(EMPTY)
+# undef EMPTY
+#endif
+
+#define SUPERLU_EMPTY (-1)
+
+namespace Eigen { struct SluMatrix; }
+
+/** \ingroup Support_modules
+  * \defgroup SuperLUSupport_Module SuperLUSupport module
+  *
+  * This module provides an interface to the <a href="http://crd-legacy.lbl.gov/~xiaoye/SuperLU/">SuperLU</a> library.
+  * It provides the following factorization class:
+  * - class SuperLU: a supernodal sequential LU factorization.
+  * - class SuperILU: a supernodal sequential incomplete LU factorization (to be used as a preconditioner for iterative methods).
+  *
+  * \warning This wrapper requires at least versions 4.0 of SuperLU. The 3.x versions are not supported.
+  *
+  * \warning When including this module, you have to use SUPERLU_EMPTY instead of EMPTY which is no longer defined because it is too polluting.
+  *
+  * \code
+  * #include <Eigen/SuperLUSupport>
+  * \endcode
+  *
+  * In order to use this module, the superlu headers must be accessible from the include paths, and your binary must be linked to the superlu library and its dependencies.
+  * The dependencies depend on how superlu has been compiled.
+  * For a cmake based project, you can use our FindSuperLU.cmake module to help you in this task.
+  *
+  */
+
+// IWYU pragma: begin_exports
+#include "src/SuperLUSupport/SuperLUSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_SUPERLUSUPPORT_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/ThreadPool b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/ThreadPool
new file mode 100644
index 0000000000000000000000000000000000000000..9be594f8a6453a610872eef151fd5262e6b5a6fe
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/ThreadPool
@@ -0,0 +1,78 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_THREADPOOL_MODULE_H
+#define EIGEN_THREADPOOL_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup ThreadPool_Module ThreadPool Module
+  *
+  * This module provides 2 threadpool implementations
+  *  - a simple reference implementation
+  *  - a faster non blocking implementation
+  *
+  * \code
+  * #include <Eigen/ThreadPool>
+  * \endcode
+  */
+
+#include <cstddef>
+#include <cstring>
+#include <time.h>
+
+#include <vector>
+#include <atomic>
+#include <condition_variable>
+#include <deque>
+#include <mutex>
+#include <thread>
+#include <functional>
+#include <memory>
+#include <utility>
+
+// There are non-parenthesized calls to "max" in the  <unordered_map> header,
+// which trigger a check in test/main.h causing compilation to fail.
+// We work around the check here by removing the check for max in
+// the case where we have to emulate thread_local.
+#ifdef max
+#undef max
+#endif
+#include <unordered_map>
+
+#include "src/Core/util/Meta.h"
+#include "src/Core/util/MaxSizeVector.h"
+
+#ifndef EIGEN_MUTEX
+#define EIGEN_MUTEX std::mutex
+#endif
+#ifndef EIGEN_MUTEX_LOCK
+#define EIGEN_MUTEX_LOCK std::unique_lock<std::mutex>
+#endif
+#ifndef EIGEN_CONDVAR
+#define EIGEN_CONDVAR std::condition_variable
+#endif
+
+// IWYU pragma: begin_exports
+#include "src/ThreadPool/ThreadLocal.h"
+#include "src/ThreadPool/ThreadYield.h"
+#include "src/ThreadPool/ThreadCancel.h"
+#include "src/ThreadPool/EventCount.h"
+#include "src/ThreadPool/RunQueue.h"
+#include "src/ThreadPool/ThreadPoolInterface.h"
+#include "src/ThreadPool/ThreadEnvironment.h"
+#include "src/ThreadPool/Barrier.h"
+#include "src/ThreadPool/NonBlockingThreadPool.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_CXX11_THREADPOOL_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/UmfPackSupport b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/UmfPackSupport
new file mode 100644
index 0000000000000000000000000000000000000000..eea150eedcbf662911e7547577f64e47fceeca8a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/UmfPackSupport
@@ -0,0 +1,42 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_UMFPACKSUPPORT_MODULE_H
+#define EIGEN_UMFPACKSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+extern "C" {
+#include <umfpack.h>
+}
+
+/** \ingroup Support_modules
+  * \defgroup UmfPackSupport_Module UmfPackSupport module
+  *
+  * This module provides an interface to the UmfPack library which is part of the <a href="http://www.suitesparse.com">suitesparse</a> package.
+  * It provides the following factorization class:
+  * - class UmfPackLU: a multifrontal sequential LU factorization.
+  *
+  * \code
+  * #include <Eigen/UmfPackSupport>
+  * \endcode
+  *
+  * In order to use this module, the umfpack headers must be accessible from the include paths, and your binary must be linked to the umfpack library and its dependencies.
+  * The dependencies depend on how umfpack has been compiled.
+  * For a cmake based project, you can use our FindUmfPack.cmake module to help you in this task.
+  *
+  */
+
+// IWYU pragma: begin_exports
+#include "src/UmfPackSupport/UmfPackSupport.h"
+// IWYU pragma: endexports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif // EIGEN_UMFPACKSUPPORT_MODULE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/AccelerateSupport/AccelerateSupport.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/AccelerateSupport/AccelerateSupport.h
new file mode 100644
index 0000000000000000000000000000000000000000..0417688b0c04c8f7c7df1e58cb04495ac11e60f8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/AccelerateSupport/AccelerateSupport.h
@@ -0,0 +1,421 @@
+#ifndef EIGEN_ACCELERATESUPPORT_H
+#define EIGEN_ACCELERATESUPPORT_H
+
+#include <Accelerate/Accelerate.h>
+
+#include <Eigen/Sparse>
+
+namespace Eigen {
+
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+class AccelerateImpl;
+
+/** \ingroup AccelerateSupport_Module
+  * \class AccelerateLLT
+  * \brief A direct Cholesky (LLT) factorization and solver based on Accelerate
+  *
+  * \warning Only single and double precision real scalar types are supported by Accelerate
+  * 
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
+  *
+  * \sa \ref TutorialSparseSolverConcept, class AccelerateLLT
+  */
+template <typename MatrixType, int UpLo = Lower>
+using AccelerateLLT = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationCholesky, true>;
+
+/** \ingroup AccelerateSupport_Module
+  * \class AccelerateLDLT
+  * \brief The default Cholesky (LDLT) factorization and solver based on Accelerate
+  *
+  * \warning Only single and double precision real scalar types are supported by Accelerate
+  * 
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
+  *
+  * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLT
+  */
+template <typename MatrixType, int UpLo = Lower>
+using AccelerateLDLT = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLT, true>;
+
+/** \ingroup AccelerateSupport_Module
+  * \class AccelerateLDLTUnpivoted
+  * \brief A direct Cholesky-like LDL^T factorization and solver based on Accelerate with only 1x1 pivots and no pivoting
+  *
+  * \warning Only single and double precision real scalar types are supported by Accelerate
+  * 
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
+  *
+  * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTUnpivoted
+  */
+template <typename MatrixType, int UpLo = Lower>
+using AccelerateLDLTUnpivoted = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTUnpivoted, true>;
+
+/** \ingroup AccelerateSupport_Module
+  * \class AccelerateLDLTSBK
+  * \brief A direct Cholesky (LDLT) factorization and solver based on Accelerate with Supernode Bunch-Kaufman and static pivoting
+  *
+  * \warning Only single and double precision real scalar types are supported by Accelerate
+  * 
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
+  *
+  * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTSBK
+  */
+template <typename MatrixType, int UpLo = Lower>
+using AccelerateLDLTSBK = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTSBK, true>;
+
+/** \ingroup AccelerateSupport_Module
+  * \class AccelerateLDLTTPP
+  * \brief A direct Cholesky (LDLT) factorization and solver based on Accelerate with full threshold partial pivoting
+  *
+  * \warning Only single and double precision real scalar types are supported by Accelerate
+  * 
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
+  *
+  * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTTPP
+  */
+template <typename MatrixType, int UpLo = Lower>
+using AccelerateLDLTTPP = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTTPP, true>;
+
+/** \ingroup AccelerateSupport_Module
+  * \class AccelerateQR
+  * \brief A QR factorization and solver based on Accelerate
+  *
+  * \warning Only single and double precision real scalar types are supported by Accelerate
+  * 
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  *
+  * \sa \ref TutorialSparseSolverConcept, class AccelerateQR
+  */
+template <typename MatrixType>
+using AccelerateQR = AccelerateImpl<MatrixType, 0, SparseFactorizationQR, false>;
+
+/** \ingroup AccelerateSupport_Module
+  * \class AccelerateCholeskyAtA
+  * \brief A QR factorization and solver based on Accelerate without storing Q (equivalent to A^TA = R^T R)
+  *
+  * \warning Only single and double precision real scalar types are supported by Accelerate
+  * 
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  *
+  * \sa \ref TutorialSparseSolverConcept, class AccelerateCholeskyAtA
+  */
+template <typename MatrixType>
+using AccelerateCholeskyAtA = AccelerateImpl<MatrixType, 0, SparseFactorizationCholeskyAtA, false>;
+
+namespace internal {
+template <typename T>
+struct AccelFactorizationDeleter {
+  void operator()(T* sym) {
+    if (sym) {
+      SparseCleanup(*sym);
+      delete sym;
+      sym = nullptr;
+    }
+  }
+};
+
+template <typename DenseVecT, typename DenseMatT, typename SparseMatT, typename NumFactT>
+struct SparseTypesTraitBase {
+  typedef DenseVecT AccelDenseVector;
+  typedef DenseMatT AccelDenseMatrix;
+  typedef SparseMatT AccelSparseMatrix;
+
+  typedef SparseOpaqueSymbolicFactorization SymbolicFactorization;
+  typedef NumFactT NumericFactorization;
+
+  typedef AccelFactorizationDeleter<SymbolicFactorization> SymbolicFactorizationDeleter;
+  typedef AccelFactorizationDeleter<NumericFactorization> NumericFactorizationDeleter;
+};
+
+template <typename Scalar>
+struct SparseTypesTrait {};
+
+template <>
+struct SparseTypesTrait<double> : SparseTypesTraitBase<DenseVector_Double, DenseMatrix_Double, SparseMatrix_Double,
+                                                       SparseOpaqueFactorization_Double> {};
+
+template <>
+struct SparseTypesTrait<float>
+    : SparseTypesTraitBase<DenseVector_Float, DenseMatrix_Float, SparseMatrix_Float, SparseOpaqueFactorization_Float> {
+};
+
+}  // end namespace internal
+
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+class AccelerateImpl : public SparseSolverBase<AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_> > {
+ protected:
+  using Base = SparseSolverBase<AccelerateImpl>;
+  using Base::derived;
+  using Base::m_isInitialized;
+
+ public:
+  using Base::_solve_impl;
+
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  enum { ColsAtCompileTime = Dynamic, MaxColsAtCompileTime = Dynamic };
+  enum { UpLo = UpLo_ };
+
+  using AccelDenseVector = typename internal::SparseTypesTrait<Scalar>::AccelDenseVector;
+  using AccelDenseMatrix = typename internal::SparseTypesTrait<Scalar>::AccelDenseMatrix;
+  using AccelSparseMatrix = typename internal::SparseTypesTrait<Scalar>::AccelSparseMatrix;
+  using SymbolicFactorization = typename internal::SparseTypesTrait<Scalar>::SymbolicFactorization;
+  using NumericFactorization = typename internal::SparseTypesTrait<Scalar>::NumericFactorization;
+  using SymbolicFactorizationDeleter = typename internal::SparseTypesTrait<Scalar>::SymbolicFactorizationDeleter;
+  using NumericFactorizationDeleter = typename internal::SparseTypesTrait<Scalar>::NumericFactorizationDeleter;
+
+  AccelerateImpl() {
+    m_isInitialized = false;
+
+    auto check_flag_set = [](int value, int flag) { return ((value & flag) == flag); };
+
+    if (check_flag_set(UpLo_, Symmetric)) {
+      m_sparseKind = SparseSymmetric;
+      m_triType = (UpLo_ & Lower) ? SparseLowerTriangle : SparseUpperTriangle;
+    } else if (check_flag_set(UpLo_, UnitLower)) {
+      m_sparseKind = SparseUnitTriangular;
+      m_triType = SparseLowerTriangle;
+    } else if (check_flag_set(UpLo_, UnitUpper)) {
+      m_sparseKind = SparseUnitTriangular;
+      m_triType = SparseUpperTriangle;
+    } else if (check_flag_set(UpLo_, StrictlyLower)) {
+      m_sparseKind = SparseTriangular;
+      m_triType = SparseLowerTriangle;
+    } else if (check_flag_set(UpLo_, StrictlyUpper)) {
+      m_sparseKind = SparseTriangular;
+      m_triType = SparseUpperTriangle;
+    } else if (check_flag_set(UpLo_, Lower)) {
+      m_sparseKind = SparseTriangular;
+      m_triType = SparseLowerTriangle;
+    } else if (check_flag_set(UpLo_, Upper)) {
+      m_sparseKind = SparseTriangular;
+      m_triType = SparseUpperTriangle;
+    } else {
+      m_sparseKind = SparseOrdinary;
+      m_triType = (UpLo_ & Lower) ? SparseLowerTriangle : SparseUpperTriangle;
+    }
+
+    m_order = SparseOrderDefault;
+  }
+
+  explicit AccelerateImpl(const MatrixType& matrix) : AccelerateImpl() { compute(matrix); }
+
+  ~AccelerateImpl() {}
+
+  inline Index cols() const { return m_nCols; }
+  inline Index rows() const { return m_nRows; }
+
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_info;
+  }
+
+  void analyzePattern(const MatrixType& matrix);
+
+  void factorize(const MatrixType& matrix);
+
+  void compute(const MatrixType& matrix);
+
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const;
+
+  /** Sets the ordering algorithm to use. */
+  void setOrder(SparseOrder_t order) { m_order = order; }
+
+ private:
+  template <typename T>
+  void buildAccelSparseMatrix(const SparseMatrix<T>& a, AccelSparseMatrix& A, std::vector<long>& columnStarts) {
+    const Index nColumnsStarts = a.cols() + 1;
+
+    columnStarts.resize(nColumnsStarts);
+
+    for (Index i = 0; i < nColumnsStarts; i++) columnStarts[i] = a.outerIndexPtr()[i];
+
+    SparseAttributes_t attributes{};
+    attributes.transpose = false;
+    attributes.triangle = m_triType;
+    attributes.kind = m_sparseKind;
+
+    SparseMatrixStructure structure{};
+    structure.attributes = attributes;
+    structure.rowCount = static_cast<int>(a.rows());
+    structure.columnCount = static_cast<int>(a.cols());
+    structure.blockSize = 1;
+    structure.columnStarts = columnStarts.data();
+    structure.rowIndices = const_cast<int*>(a.innerIndexPtr());
+
+    A.structure = structure;
+    A.data = const_cast<T*>(a.valuePtr());
+  }
+
+  void doAnalysis(AccelSparseMatrix& A) {
+    m_numericFactorization.reset(nullptr);
+
+    SparseSymbolicFactorOptions opts{};
+    opts.control = SparseDefaultControl;
+    opts.orderMethod = m_order;
+    opts.order = nullptr;
+    opts.ignoreRowsAndColumns = nullptr;
+    opts.malloc = malloc;
+    opts.free = free;
+    opts.reportError = nullptr;
+
+    m_symbolicFactorization.reset(new SymbolicFactorization(SparseFactor(Solver_, A.structure, opts)));
+
+    SparseStatus_t status = m_symbolicFactorization->status;
+
+    updateInfoStatus(status);
+
+    if (status != SparseStatusOK) m_symbolicFactorization.reset(nullptr);
+  }
+
+  void doFactorization(AccelSparseMatrix& A) {
+    SparseStatus_t status = SparseStatusReleased;
+
+    if (m_symbolicFactorization) {
+      m_numericFactorization.reset(new NumericFactorization(SparseFactor(*m_symbolicFactorization, A)));
+
+      status = m_numericFactorization->status;
+
+      if (status != SparseStatusOK) m_numericFactorization.reset(nullptr);
+    }
+
+    updateInfoStatus(status);
+  }
+
+ protected:
+  void updateInfoStatus(SparseStatus_t status) const {
+    switch (status) {
+      case SparseStatusOK:
+        m_info = Success;
+        break;
+      case SparseFactorizationFailed:
+      case SparseMatrixIsSingular:
+        m_info = NumericalIssue;
+        break;
+      case SparseInternalError:
+      case SparseParameterError:
+      case SparseStatusReleased:
+      default:
+        m_info = InvalidInput;
+        break;
+    }
+  }
+
+  mutable ComputationInfo m_info;
+  Index m_nRows, m_nCols;
+  std::unique_ptr<SymbolicFactorization, SymbolicFactorizationDeleter> m_symbolicFactorization;
+  std::unique_ptr<NumericFactorization, NumericFactorizationDeleter> m_numericFactorization;
+  SparseKind_t m_sparseKind;
+  SparseTriangle_t m_triType;
+  SparseOrder_t m_order;
+};
+
+/** Computes the symbolic and numeric decomposition of matrix \a a */
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::compute(const MatrixType& a) {
+  if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
+
+  m_nRows = a.rows();
+  m_nCols = a.cols();
+
+  AccelSparseMatrix A{};
+  std::vector<long> columnStarts;
+
+  buildAccelSparseMatrix(a, A, columnStarts);
+
+  doAnalysis(A);
+
+  if (m_symbolicFactorization) doFactorization(A);
+
+  m_isInitialized = true;
+}
+
+/** Performs a symbolic decomposition on the sparsity pattern of matrix \a a.
+ *
+ * This function is particularly useful when solving for several problems having the same structure.
+ *
+ * \sa factorize()
+ */
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::analyzePattern(const MatrixType& a) {
+  if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
+
+  m_nRows = a.rows();
+  m_nCols = a.cols();
+
+  AccelSparseMatrix A{};
+  std::vector<long> columnStarts;
+
+  buildAccelSparseMatrix(a, A, columnStarts);
+
+  doAnalysis(A);
+
+  m_isInitialized = true;
+}
+
+/** Performs a numeric decomposition of matrix \a a.
+ *
+ * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been performed.
+ *
+ * \sa analyzePattern()
+ */
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::factorize(const MatrixType& a) {
+  eigen_assert(m_symbolicFactorization && "You must first call analyzePattern()");
+  eigen_assert(m_nRows == a.rows() && m_nCols == a.cols());
+
+  if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
+
+  AccelSparseMatrix A{};
+  std::vector<long> columnStarts;
+
+  buildAccelSparseMatrix(a, A, columnStarts);
+
+  doFactorization(A);
+}
+
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+template <typename Rhs, typename Dest>
+void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::_solve_impl(const MatrixBase<Rhs>& b,
+                                                                              MatrixBase<Dest>& x) const {
+  if (!m_numericFactorization) {
+    m_info = InvalidInput;
+    return;
+  }
+
+  eigen_assert(m_nRows == b.rows());
+  eigen_assert(((b.cols() == 1) || b.outerStride() == b.rows()));
+
+  SparseStatus_t status = SparseStatusOK;
+
+  Scalar* b_ptr = const_cast<Scalar*>(b.derived().data());
+  Scalar* x_ptr = const_cast<Scalar*>(x.derived().data());
+
+  AccelDenseMatrix xmat{};
+  xmat.attributes = SparseAttributes_t();
+  xmat.columnCount = static_cast<int>(x.cols());
+  xmat.rowCount = static_cast<int>(x.rows());
+  xmat.columnStride = xmat.rowCount;
+  xmat.data = x_ptr;
+
+  AccelDenseMatrix bmat{};
+  bmat.attributes = SparseAttributes_t();
+  bmat.columnCount = static_cast<int>(b.cols());
+  bmat.rowCount = static_cast<int>(b.rows());
+  bmat.columnStride = bmat.rowCount;
+  bmat.data = b_ptr;
+
+  SparseSolve(*m_numericFactorization, bmat, xmat);
+
+  updateInfoStatus(status);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ACCELERATESUPPORT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/AccelerateSupport/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/AccelerateSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..69bcff50a65b7d856a69b859ba9c23a36771be27
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/AccelerateSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_ACCELERATESUPPORT_MODULE_H
+#error "Please include Eigen/AccelerateSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Cholesky/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Cholesky/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..5de2b219b3f66eaa59773f6d05212c930143856a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Cholesky/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_CHOLESKY_MODULE_H
+#error "Please include Eigen/Cholesky instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Cholesky/LDLT.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Cholesky/LDLT.h
new file mode 100644
index 0000000000000000000000000000000000000000..1d0369b65e40c2422b85119eded4f076161416c8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Cholesky/LDLT.h
@@ -0,0 +1,685 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Keir Mierle <mierle@gmail.com>
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2011 Timothy E. Holy <tim.holy@gmail.com >
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LDLT_H
+#define EIGEN_LDLT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+  template<typename MatrixType_, int UpLo_> struct traits<LDLT<MatrixType_, UpLo_> >
+   : traits<MatrixType_>
+  {
+    typedef MatrixXpr XprKind;
+    typedef SolverStorage StorageKind;
+    typedef int StorageIndex;
+    enum { Flags = 0 };
+  };
+
+  template<typename MatrixType, int UpLo> struct LDLT_Traits;
+
+  // PositiveSemiDef means positive semi-definite and non-zero; same for NegativeSemiDef
+  enum SignMatrix { PositiveSemiDef, NegativeSemiDef, ZeroSign, Indefinite };
+}
+
+/** \ingroup Cholesky_Module
+  *
+  * \class LDLT
+  *
+  * \brief Robust Cholesky decomposition of a matrix with pivoting
+  *
+  * \tparam MatrixType_ the type of the matrix of which to compute the LDL^T Cholesky decomposition
+  * \tparam UpLo_ the triangular part that will be used for the decomposition: Lower (default) or Upper.
+  *             The other triangular part won't be read.
+  *
+  * Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite
+  * matrix \f$ A \f$ such that \f$ A =  P^TLDL^*P \f$, where P is a permutation matrix, L
+  * is lower triangular with a unit diagonal and D is a diagonal matrix.
+  *
+  * The decomposition uses pivoting to ensure stability, so that D will have
+  * zeros in the bottom right rank(A) - n submatrix. Avoiding the square root
+  * on D also stabilizes the computation.
+  *
+  * Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
+  * decomposition to determine whether a system of equations has a solution.
+  *
+  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+  *
+  * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT
+  */
+template<typename MatrixType_, int UpLo_> class LDLT
+        : public SolverBase<LDLT<MatrixType_, UpLo_> >
+{
+  public:
+    typedef MatrixType_ MatrixType;
+    typedef SolverBase<LDLT> Base;
+    friend class SolverBase<LDLT>;
+
+    EIGEN_GENERIC_PUBLIC_INTERFACE(LDLT)
+    enum {
+      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+      UpLo = UpLo_
+    };
+    typedef Matrix<Scalar, RowsAtCompileTime, 1, 0, MaxRowsAtCompileTime, 1> TmpMatrixType;
+
+    typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
+    typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
+
+    typedef internal::LDLT_Traits<MatrixType,UpLo> Traits;
+
+    /** \brief Default Constructor.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via LDLT::compute(const MatrixType&).
+      */
+    LDLT()
+      : m_matrix(),
+        m_transpositions(),
+        m_sign(internal::ZeroSign),
+        m_isInitialized(false)
+    {}
+
+    /** \brief Default Constructor with memory preallocation
+      *
+      * Like the default constructor but with preallocation of the internal data
+      * according to the specified problem \a size.
+      * \sa LDLT()
+      */
+    explicit LDLT(Index size)
+      : m_matrix(size, size),
+        m_transpositions(size),
+        m_temporary(size),
+        m_sign(internal::ZeroSign),
+        m_isInitialized(false)
+    {}
+
+    /** \brief Constructor with decomposition
+      *
+      * This calculates the decomposition for the input \a matrix.
+      *
+      * \sa LDLT(Index size)
+      */
+    template<typename InputType>
+    explicit LDLT(const EigenBase<InputType>& matrix)
+      : m_matrix(matrix.rows(), matrix.cols()),
+        m_transpositions(matrix.rows()),
+        m_temporary(matrix.rows()),
+        m_sign(internal::ZeroSign),
+        m_isInitialized(false)
+    {
+      compute(matrix.derived());
+    }
+
+    /** \brief Constructs a LDLT factorization from a given matrix
+      *
+      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
+      *
+      * \sa LDLT(const EigenBase&)
+      */
+    template<typename InputType>
+    explicit LDLT(EigenBase<InputType>& matrix)
+      : m_matrix(matrix.derived()),
+        m_transpositions(matrix.rows()),
+        m_temporary(matrix.rows()),
+        m_sign(internal::ZeroSign),
+        m_isInitialized(false)
+    {
+      compute(matrix.derived());
+    }
+
+    /** Clear any existing decomposition
+     * \sa rankUpdate(w,sigma)
+     */
+    void setZero()
+    {
+      m_isInitialized = false;
+    }
+
+    /** \returns a view of the upper triangular matrix U */
+    inline typename Traits::MatrixU matrixU() const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return Traits::getU(m_matrix);
+    }
+
+    /** \returns a view of the lower triangular matrix L */
+    inline typename Traits::MatrixL matrixL() const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return Traits::getL(m_matrix);
+    }
+
+    /** \returns the permutation matrix P as a transposition sequence.
+      */
+    inline const TranspositionType& transpositionsP() const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return m_transpositions;
+    }
+
+    /** \returns the coefficients of the diagonal matrix D */
+    inline Diagonal<const MatrixType> vectorD() const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return m_matrix.diagonal();
+    }
+
+    /** \returns true if the matrix is positive (semidefinite) */
+    inline bool isPositive() const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return m_sign == internal::PositiveSemiDef || m_sign == internal::ZeroSign;
+    }
+
+    /** \returns true if the matrix is negative (semidefinite) */
+    inline bool isNegative(void) const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return m_sign == internal::NegativeSemiDef || m_sign == internal::ZeroSign;
+    }
+
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
+    /** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * This function also supports in-place solves using the syntax <tt>x = decompositionObject.solve(x)</tt> .
+      *
+      * \note_about_checking_solutions
+      *
+      * More precisely, this method solves \f$ A x = b \f$ using the decomposition \f$ A = P^T L D L^* P \f$
+      * by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$,
+      * \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then
+      * \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the
+      * least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function
+      * computes the least-square solution of \f$ A x = b \f$ if \f$ A \f$ is singular.
+      *
+      * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt()
+      */
+    template<typename Rhs>
+    inline const Solve<LDLT, Rhs>
+    solve(const MatrixBase<Rhs>& b) const;
+    #endif
+
+    template<typename Derived>
+    bool solveInPlace(MatrixBase<Derived> &bAndX) const;
+
+    template<typename InputType>
+    LDLT& compute(const EigenBase<InputType>& matrix);
+
+    /** \returns an estimate of the reciprocal condition number of the matrix of
+     *  which \c *this is the LDLT decomposition.
+     */
+    RealScalar rcond() const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return internal::rcond_estimate_helper(m_l1_norm, *this);
+    }
+
+    template <typename Derived>
+    LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha=1);
+
+    /** \returns the internal LDLT decomposition matrix
+      *
+      * TODO: document the storage layout
+      */
+    inline const MatrixType& matrixLDLT() const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return m_matrix;
+    }
+
+    MatrixType reconstructedMatrix() const;
+
+    /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.
+      *
+      * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
+      * \code x = decomposition.adjoint().solve(b) \endcode
+      */
+    const LDLT& adjoint() const { return *this; }
+
+    EIGEN_DEVICE_FUNC inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
+    EIGEN_DEVICE_FUNC inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
+
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful,
+      *          \c NumericalIssue if the factorization failed because of a zero pivot.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "LDLT is not initialized.");
+      return m_info;
+    }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename RhsType, typename DstType>
+    void _solve_impl(const RhsType &rhs, DstType &dst) const;
+
+    template<bool Conjugate, typename RhsType, typename DstType>
+    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
+    #endif
+
+  protected:
+
+    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+    /** \internal
+      * Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
+      * The strict upper part is used during the decomposition, the strict lower
+      * part correspond to the coefficients of L (its diagonal is equal to 1 and
+      * is not stored), and the diagonal entries correspond to D.
+      */
+    MatrixType m_matrix;
+    RealScalar m_l1_norm;
+    TranspositionType m_transpositions;
+    TmpMatrixType m_temporary;
+    internal::SignMatrix m_sign;
+    bool m_isInitialized;
+    ComputationInfo m_info;
+};
+
+namespace internal {
+
+template<int UpLo> struct ldlt_inplace;
+
+template<> struct ldlt_inplace<Lower>
+{
+  template<typename MatrixType, typename TranspositionType, typename Workspace>
+  static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign)
+  {
+    using std::abs;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename TranspositionType::StorageIndex IndexType;
+    eigen_assert(mat.rows()==mat.cols());
+    const Index size = mat.rows();
+    bool found_zero_pivot = false;
+    bool ret = true;
+
+    if (size <= 1)
+    {
+      transpositions.setIdentity();
+      if(size==0) sign = ZeroSign;
+      else if (numext::real(mat.coeff(0,0)) > static_cast<RealScalar>(0) ) sign = PositiveSemiDef;
+      else if (numext::real(mat.coeff(0,0)) < static_cast<RealScalar>(0)) sign = NegativeSemiDef;
+      else sign = ZeroSign;
+      return true;
+    }
+
+    for (Index k = 0; k < size; ++k)
+    {
+      // Find largest diagonal element
+      Index index_of_biggest_in_corner;
+      mat.diagonal().tail(size-k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
+      index_of_biggest_in_corner += k;
+
+      transpositions.coeffRef(k) = IndexType(index_of_biggest_in_corner);
+      if(k != index_of_biggest_in_corner)
+      {
+        // apply the transposition while taking care to consider only
+        // the lower triangular part
+        Index s = size-index_of_biggest_in_corner-1; // trailing size after the biggest element
+        mat.row(k).head(k).swap(mat.row(index_of_biggest_in_corner).head(k));
+        mat.col(k).tail(s).swap(mat.col(index_of_biggest_in_corner).tail(s));
+        std::swap(mat.coeffRef(k,k),mat.coeffRef(index_of_biggest_in_corner,index_of_biggest_in_corner));
+        for(Index i=k+1;i<index_of_biggest_in_corner;++i)
+        {
+          Scalar tmp = mat.coeffRef(i,k);
+          mat.coeffRef(i,k) = numext::conj(mat.coeffRef(index_of_biggest_in_corner,i));
+          mat.coeffRef(index_of_biggest_in_corner,i) = numext::conj(tmp);
+        }
+        if(NumTraits<Scalar>::IsComplex)
+          mat.coeffRef(index_of_biggest_in_corner,k) = numext::conj(mat.coeff(index_of_biggest_in_corner,k));
+      }
+
+      // partition the matrix:
+      //       A00 |  -  |  -
+      // lu  = A10 | A11 |  -
+      //       A20 | A21 | A22
+      Index rs = size - k - 1;
+      Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1);
+      Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
+      Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
+
+      if(k>0)
+      {
+        temp.head(k) = mat.diagonal().real().head(k).asDiagonal() * A10.adjoint();
+        mat.coeffRef(k,k) -= (A10 * temp.head(k)).value();
+        if(rs>0)
+          A21.noalias() -= A20 * temp.head(k);
+      }
+
+      // In some previous versions of Eigen (e.g., 3.2.1), the scaling was omitted if the pivot
+      // was smaller than the cutoff value. However, since LDLT is not rank-revealing
+      // we should only make sure that we do not introduce INF or NaN values.
+      // Remark that LAPACK also uses 0 as the cutoff value.
+      RealScalar realAkk = numext::real(mat.coeffRef(k,k));
+      bool pivot_is_valid = (abs(realAkk) > RealScalar(0));
+
+      if(k==0 && !pivot_is_valid)
+      {
+        // The entire diagonal is zero, there is nothing more to do
+        // except filling the transpositions, and checking whether the matrix is zero.
+        sign = ZeroSign;
+        for(Index j = 0; j<size; ++j)
+        {
+          transpositions.coeffRef(j) = IndexType(j);
+          ret = ret && (mat.col(j).tail(size-j-1).array()==Scalar(0)).all();
+        }
+        return ret;
+      }
+
+      if((rs>0) && pivot_is_valid)
+        A21 /= realAkk;
+      else if(rs>0)
+        ret = ret && (A21.array()==Scalar(0)).all();
+
+      if(found_zero_pivot && pivot_is_valid) ret = false; // factorization failed
+      else if(!pivot_is_valid) found_zero_pivot = true;
+
+      if (sign == PositiveSemiDef) {
+        if (realAkk < static_cast<RealScalar>(0)) sign = Indefinite;
+      } else if (sign == NegativeSemiDef) {
+        if (realAkk > static_cast<RealScalar>(0)) sign = Indefinite;
+      } else if (sign == ZeroSign) {
+        if (realAkk > static_cast<RealScalar>(0)) sign = PositiveSemiDef;
+        else if (realAkk < static_cast<RealScalar>(0)) sign = NegativeSemiDef;
+      }
+    }
+
+    return ret;
+  }
+
+  // Reference for the algorithm: Davis and Hager, "Multiple Rank
+  // Modifications of a Sparse Cholesky Factorization" (Algorithm 1)
+  // Trivial rearrangements of their computations (Timothy E. Holy)
+  // allow their algorithm to work for rank-1 updates even if the
+  // original matrix is not of full rank.
+  // Here only rank-1 updates are implemented, to reduce the
+  // requirement for intermediate storage and improve accuracy
+  template<typename MatrixType, typename WDerived>
+  static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w, const typename MatrixType::RealScalar& sigma=1)
+  {
+    using numext::isfinite;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+
+    const Index size = mat.rows();
+    eigen_assert(mat.cols() == size && w.size()==size);
+
+    RealScalar alpha = 1;
+
+    // Apply the update
+    for (Index j = 0; j < size; j++)
+    {
+      // Check for termination due to an original decomposition of low-rank
+      if (!(isfinite)(alpha))
+        break;
+
+      // Update the diagonal terms
+      RealScalar dj = numext::real(mat.coeff(j,j));
+      Scalar wj = w.coeff(j);
+      RealScalar swj2 = sigma*numext::abs2(wj);
+      RealScalar gamma = dj*alpha + swj2;
+
+      mat.coeffRef(j,j) += swj2/alpha;
+      alpha += swj2/dj;
+
+
+      // Update the terms of L
+      Index rs = size-j-1;
+      w.tail(rs) -= wj * mat.col(j).tail(rs);
+      if(!numext::is_exactly_zero(gamma))
+        mat.col(j).tail(rs) += (sigma*numext::conj(wj)/gamma)*w.tail(rs);
+    }
+    return true;
+  }
+
+  template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
+  static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w, const typename MatrixType::RealScalar& sigma=1)
+  {
+    // Apply the permutation to the input w
+    tmp = transpositions * w;
+
+    return ldlt_inplace<Lower>::updateInPlace(mat,tmp,sigma);
+  }
+};
+
+template<> struct ldlt_inplace<Upper>
+{
+  template<typename MatrixType, typename TranspositionType, typename Workspace>
+  static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign)
+  {
+    Transpose<MatrixType> matt(mat);
+    return ldlt_inplace<Lower>::unblocked(matt, transpositions, temp, sign);
+  }
+
+  template<typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
+  static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w, const typename MatrixType::RealScalar& sigma=1)
+  {
+    Transpose<MatrixType> matt(mat);
+    return ldlt_inplace<Lower>::update(matt, transpositions, tmp, w.conjugate(), sigma);
+  }
+};
+
+template<typename MatrixType> struct LDLT_Traits<MatrixType,Lower>
+{
+  typedef const TriangularView<const MatrixType, UnitLower> MatrixL;
+  typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitUpper> MatrixU;
+  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
+  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
+};
+
+template<typename MatrixType> struct LDLT_Traits<MatrixType,Upper>
+{
+  typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitLower> MatrixL;
+  typedef const TriangularView<const MatrixType, UnitUpper> MatrixU;
+  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
+  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
+};
+
+} // end namespace internal
+
+/** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
+  */
+template<typename MatrixType, int UpLo_>
+template<typename InputType>
+LDLT<MatrixType,UpLo_>& LDLT<MatrixType,UpLo_>::compute(const EigenBase<InputType>& a)
+{
+  eigen_assert(a.rows()==a.cols());
+  const Index size = a.rows();
+
+  m_matrix = a.derived();
+
+  // Compute matrix L1 norm = max abs column sum.
+  m_l1_norm = RealScalar(0);
+  // TODO move this code to SelfAdjointView
+  for (Index col = 0; col < size; ++col) {
+    RealScalar abs_col_sum;
+    if (UpLo_ == Lower)
+      abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
+    else
+      abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
+    if (abs_col_sum > m_l1_norm)
+      m_l1_norm = abs_col_sum;
+  }
+
+  m_transpositions.resize(size);
+  m_isInitialized = false;
+  m_temporary.resize(size);
+  m_sign = internal::ZeroSign;
+
+  m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success : NumericalIssue;
+
+  m_isInitialized = true;
+  return *this;
+}
+
+/** Update the LDLT decomposition:  given A = L D L^T, efficiently compute the decomposition of A + sigma w w^T.
+ * \param w a vector to be incorporated into the decomposition.
+ * \param sigma a scalar, +1 for updates and -1 for "downdates," which correspond to removing previously-added column vectors. Optional; default value is +1.
+ * \sa setZero()
+  */
+template<typename MatrixType, int UpLo_>
+template<typename Derived>
+LDLT<MatrixType,UpLo_>& LDLT<MatrixType,UpLo_>::rankUpdate(const MatrixBase<Derived>& w, const typename LDLT<MatrixType,UpLo_>::RealScalar& sigma)
+{
+  typedef typename TranspositionType::StorageIndex IndexType;
+  const Index size = w.rows();
+  if (m_isInitialized)
+  {
+    eigen_assert(m_matrix.rows()==size);
+  }
+  else
+  {
+    m_matrix.resize(size,size);
+    m_matrix.setZero();
+    m_transpositions.resize(size);
+    for (Index i = 0; i < size; i++)
+      m_transpositions.coeffRef(i) = IndexType(i);
+    m_temporary.resize(size);
+    m_sign = sigma>=0 ? internal::PositiveSemiDef : internal::NegativeSemiDef;
+    m_isInitialized = true;
+  }
+
+  internal::ldlt_inplace<UpLo>::update(m_matrix, m_transpositions, m_temporary, w, sigma);
+
+  return *this;
+}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename MatrixType_, int UpLo_>
+template<typename RhsType, typename DstType>
+void LDLT<MatrixType_,UpLo_>::_solve_impl(const RhsType &rhs, DstType &dst) const
+{
+  _solve_impl_transposed<true>(rhs, dst);
+}
+
+template<typename MatrixType_,int UpLo_>
+template<bool Conjugate, typename RhsType, typename DstType>
+void LDLT<MatrixType_,UpLo_>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
+{
+  // dst = P b
+  dst = m_transpositions * rhs;
+
+  // dst = L^-1 (P b)
+  // dst = L^-*T (P b)
+  matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
+
+  // dst = D^-* (L^-1 P b)
+  // dst = D^-1 (L^-*T P b)
+  // more precisely, use pseudo-inverse of D (see bug 241)
+  using std::abs;
+  const typename Diagonal<const MatrixType>::RealReturnType vecD(vectorD());
+  // In some previous versions, tolerance was set to the max of 1/highest (or rather numeric_limits::min())
+  // and the maximal diagonal entry * epsilon as motivated by LAPACK's xGELSS:
+  // RealScalar tolerance = numext::maxi(vecD.array().abs().maxCoeff() * NumTraits<RealScalar>::epsilon(),RealScalar(1) / NumTraits<RealScalar>::highest());
+  // However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the highest
+  // diagonal element is not well justified and leads to numerical issues in some cases.
+  // Moreover, Lapack's xSYTRS routines use 0 for the tolerance.
+  // Using numeric_limits::min() gives us more robustness to denormals.
+  RealScalar tolerance = (std::numeric_limits<RealScalar>::min)();
+  for (Index i = 0; i < vecD.size(); ++i)
+  {
+    if(abs(vecD(i)) > tolerance)
+      dst.row(i) /= vecD(i);
+    else
+      dst.row(i).setZero();
+  }
+
+  // dst = L^-* (D^-* L^-1 P b)
+  // dst = L^-T (D^-1 L^-*T P b)
+  matrixL().transpose().template conjugateIf<Conjugate>().solveInPlace(dst);
+
+  // dst = P^T (L^-* D^-* L^-1 P b) = A^-1 b
+  // dst = P^-T (L^-T D^-1 L^-*T P b) = A^-1 b
+  dst = m_transpositions.transpose() * dst;
+}
+#endif
+
+/** \internal use x = ldlt_object.solve(x);
+  *
+  * This is the \em in-place version of solve().
+  *
+  * \param bAndX represents both the right-hand side matrix b and result x.
+  *
+  * \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
+  *
+  * This version avoids a copy when the right hand side matrix b is not
+  * needed anymore.
+  *
+  * \sa LDLT::solve(), MatrixBase::ldlt()
+  */
+template<typename MatrixType,int UpLo_>
+template<typename Derived>
+bool LDLT<MatrixType,UpLo_>::solveInPlace(MatrixBase<Derived> &bAndX) const
+{
+  eigen_assert(m_isInitialized && "LDLT is not initialized.");
+  eigen_assert(m_matrix.rows() == bAndX.rows());
+
+  bAndX = this->solve(bAndX);
+
+  return true;
+}
+
+/** \returns the matrix represented by the decomposition,
+ * i.e., it returns the product: P^T L D L^* P.
+ * This function is provided for debug purpose. */
+template<typename MatrixType, int UpLo_>
+MatrixType LDLT<MatrixType,UpLo_>::reconstructedMatrix() const
+{
+  eigen_assert(m_isInitialized && "LDLT is not initialized.");
+  const Index size = m_matrix.rows();
+  MatrixType res(size,size);
+
+  // P
+  res.setIdentity();
+  res = transpositionsP() * res;
+  // L^* P
+  res = matrixU() * res;
+  // D(L^*P)
+  res = vectorD().real().asDiagonal() * res;
+  // L(DL^*P)
+  res = matrixL() * res;
+  // P^T (LDL^*P)
+  res = transpositionsP().transpose() * res;
+
+  return res;
+}
+
+/** \cholesky_module
+  * \returns the Cholesky decomposition with full pivoting without square root of \c *this
+  * \sa MatrixBase::ldlt()
+  */
+template<typename MatrixType, unsigned int UpLo>
+inline const LDLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
+SelfAdjointView<MatrixType, UpLo>::ldlt() const
+{
+  return LDLT<PlainObject,UpLo>(m_matrix);
+}
+
+/** \cholesky_module
+  * \returns the Cholesky decomposition with full pivoting without square root of \c *this
+  * \sa SelfAdjointView::ldlt()
+  */
+template<typename Derived>
+inline const LDLT<typename MatrixBase<Derived>::PlainObject>
+MatrixBase<Derived>::ldlt() const
+{
+  return LDLT<PlainObject>(derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_LDLT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Cholesky/LLT.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Cholesky/LLT.h
new file mode 100644
index 0000000000000000000000000000000000000000..1443eac17e718ac81631407bb3b002d2c2d0e197
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Cholesky/LLT.h
@@ -0,0 +1,555 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LLT_H
+#define EIGEN_LLT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal{
+
+template<typename MatrixType_, int UpLo_> struct traits<LLT<MatrixType_, UpLo_> >
+ : traits<MatrixType_>
+{
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef int StorageIndex;
+  enum { Flags = 0 };
+};
+
+template<typename MatrixType, int UpLo> struct LLT_Traits;
+}
+
+/** \ingroup Cholesky_Module
+  *
+  * \class LLT
+  *
+  * \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features
+  *
+  * \tparam MatrixType_ the type of the matrix of which we are computing the LL^T Cholesky decomposition
+  * \tparam UpLo_ the triangular part that will be used for the decomposition: Lower (default) or Upper.
+  *               The other triangular part won't be read.
+  *
+  * This class performs a LL^T Cholesky decomposition of a symmetric, positive definite
+  * matrix A such that A = LL^* = U^*U, where L is lower triangular.
+  *
+  * While the Cholesky decomposition is particularly useful to solve selfadjoint problems like  D^*D x = b,
+  * for that purpose, we recommend the Cholesky decomposition without square root which is more stable
+  * and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other
+  * situations like generalised eigen problems with hermitian matrices.
+  *
+  * Remember that Cholesky decompositions are not rank-revealing. This LLT decomposition is only stable on positive definite matrices,
+  * use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine whether a system of equations
+  * has a solution.
+  *
+  * Example: \include LLT_example.cpp
+  * Output: \verbinclude LLT_example.out
+  *
+  * \b Performance: for best performance, it is recommended to use a column-major storage format
+  * with the Lower triangular part (the default), or, equivalently, a row-major storage format
+  * with the Upper triangular part. Otherwise, you might get a 20% slowdown for the full factorization
+  * step, and rank-updates can be up to 3 times slower.
+  *
+  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+  *
+  * Note that during the decomposition, only the lower (or upper, as defined by UpLo_) triangular part of A is considered.
+  * Therefore, the strict lower part does not have to store correct values.
+  *
+  * \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
+  */
+template<typename MatrixType_, int UpLo_> class LLT
+        : public SolverBase<LLT<MatrixType_, UpLo_> >
+{
+  public:
+    typedef MatrixType_ MatrixType;
+    typedef SolverBase<LLT> Base;
+    friend class SolverBase<LLT>;
+
+    EIGEN_GENERIC_PUBLIC_INTERFACE(LLT)
+    enum {
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+
+    enum {
+      PacketSize = internal::packet_traits<Scalar>::size,
+      AlignmentMask = int(PacketSize)-1,
+      UpLo = UpLo_
+    };
+
+    typedef internal::LLT_Traits<MatrixType,UpLo> Traits;
+
+    /**
+      * \brief Default Constructor.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via LLT::compute(const MatrixType&).
+      */
+    LLT() : m_matrix(), m_isInitialized(false) {}
+
+    /** \brief Default Constructor with memory preallocation
+      *
+      * Like the default constructor but with preallocation of the internal data
+      * according to the specified problem \a size.
+      * \sa LLT()
+      */
+    explicit LLT(Index size) : m_matrix(size, size),
+                    m_isInitialized(false) {}
+
+    template<typename InputType>
+    explicit LLT(const EigenBase<InputType>& matrix)
+      : m_matrix(matrix.rows(), matrix.cols()),
+        m_isInitialized(false)
+    {
+      compute(matrix.derived());
+    }
+
+    /** \brief Constructs a LLT factorization from a given matrix
+      *
+      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
+      * \c MatrixType is a Eigen::Ref.
+      *
+      * \sa LLT(const EigenBase&)
+      */
+    template<typename InputType>
+    explicit LLT(EigenBase<InputType>& matrix)
+      : m_matrix(matrix.derived()),
+        m_isInitialized(false)
+    {
+      compute(matrix.derived());
+    }
+
+    /** \returns a view of the upper triangular matrix U */
+    inline typename Traits::MatrixU matrixU() const
+    {
+      eigen_assert(m_isInitialized && "LLT is not initialized.");
+      return Traits::getU(m_matrix);
+    }
+
+    /** \returns a view of the lower triangular matrix L */
+    inline typename Traits::MatrixL matrixL() const
+    {
+      eigen_assert(m_isInitialized && "LLT is not initialized.");
+      return Traits::getL(m_matrix);
+    }
+
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
+    /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * Since this LLT class assumes anyway that the matrix A is invertible, the solution
+      * theoretically exists and is unique regardless of b.
+      *
+      * Example: \include LLT_solve.cpp
+      * Output: \verbinclude LLT_solve.out
+      *
+      * \sa solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt()
+      */
+    template<typename Rhs>
+    inline const Solve<LLT, Rhs>
+    solve(const MatrixBase<Rhs>& b) const;
+    #endif
+
+    template<typename Derived>
+    void solveInPlace(const MatrixBase<Derived> &bAndX) const;
+
+    template<typename InputType>
+    LLT& compute(const EigenBase<InputType>& matrix);
+
+    /** \returns an estimate of the reciprocal condition number of the matrix of
+      *  which \c *this is the Cholesky decomposition.
+      */
+    RealScalar rcond() const
+    {
+      eigen_assert(m_isInitialized && "LLT is not initialized.");
+      eigen_assert(m_info == Success && "LLT failed because matrix appears to be negative");
+      return internal::rcond_estimate_helper(m_l1_norm, *this);
+    }
+
+    /** \returns the LLT decomposition matrix
+      *
+      * TODO: document the storage layout
+      */
+    inline const MatrixType& matrixLLT() const
+    {
+      eigen_assert(m_isInitialized && "LLT is not initialized.");
+      return m_matrix;
+    }
+
+    MatrixType reconstructedMatrix() const;
+
+
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful,
+      *          \c NumericalIssue if the matrix.appears not to be positive definite.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "LLT is not initialized.");
+      return m_info;
+    }
+
+    /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix is self-adjoint.
+      *
+      * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
+      * \code x = decomposition.adjoint().solve(b) \endcode
+      */
+    const LLT& adjoint() const EIGEN_NOEXCEPT { return *this; }
+
+    inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
+    inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
+
+    template<typename VectorType>
+    LLT & rankUpdate(const VectorType& vec, const RealScalar& sigma = 1);
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename RhsType, typename DstType>
+    void _solve_impl(const RhsType &rhs, DstType &dst) const;
+
+    template<bool Conjugate, typename RhsType, typename DstType>
+    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
+    #endif
+
+  protected:
+
+    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+    /** \internal
+      * Used to compute and store L
+      * The strict upper part is not used and even not initialized.
+      */
+    MatrixType m_matrix;
+    RealScalar m_l1_norm;
+    bool m_isInitialized;
+    ComputationInfo m_info;
+};
+
+namespace internal {
+
+template<typename Scalar, int UpLo> struct llt_inplace;
+
+template<typename MatrixType, typename VectorType>
+static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma)
+{
+  using std::sqrt;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::ColXpr ColXpr;
+  typedef internal::remove_all_t<ColXpr> ColXprCleaned;
+  typedef typename ColXprCleaned::SegmentReturnType ColXprSegment;
+  typedef Matrix<Scalar,Dynamic,1> TempVectorType;
+  typedef typename TempVectorType::SegmentReturnType TempVecSegment;
+
+  Index n = mat.cols();
+  eigen_assert(mat.rows()==n && vec.size()==n);
+
+  TempVectorType temp;
+
+  if(sigma>0)
+  {
+    // This version is based on Givens rotations.
+    // It is faster than the other one below, but only works for updates,
+    // i.e., for sigma > 0
+    temp = sqrt(sigma) * vec;
+
+    for(Index i=0; i<n; ++i)
+    {
+      JacobiRotation<Scalar> g;
+      g.makeGivens(mat(i,i), -temp(i), &mat(i,i));
+
+      Index rs = n-i-1;
+      if(rs>0)
+      {
+        ColXprSegment x(mat.col(i).tail(rs));
+        TempVecSegment y(temp.tail(rs));
+        apply_rotation_in_the_plane(x, y, g);
+      }
+    }
+  }
+  else
+  {
+    temp = vec;
+    RealScalar beta = 1;
+    for(Index j=0; j<n; ++j)
+    {
+      RealScalar Ljj = numext::real(mat.coeff(j,j));
+      RealScalar dj = numext::abs2(Ljj);
+      Scalar wj = temp.coeff(j);
+      RealScalar swj2 = sigma*numext::abs2(wj);
+      RealScalar gamma = dj*beta + swj2;
+
+      RealScalar x = dj + swj2/beta;
+      if (x<=RealScalar(0))
+        return j;
+      RealScalar nLjj = sqrt(x);
+      mat.coeffRef(j,j) = nLjj;
+      beta += swj2/dj;
+
+      // Update the terms of L
+      Index rs = n-j-1;
+      if(rs)
+      {
+        temp.tail(rs) -= (wj/Ljj) * mat.col(j).tail(rs);
+        if(!numext::is_exactly_zero(gamma))
+          mat.col(j).tail(rs) = (nLjj/Ljj) * mat.col(j).tail(rs) + (nLjj * sigma*numext::conj(wj)/gamma)*temp.tail(rs);
+      }
+    }
+  }
+  return -1;
+}
+
+template<typename Scalar> struct llt_inplace<Scalar, Lower>
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  template<typename MatrixType>
+  static Index unblocked(MatrixType& mat)
+  {
+    using std::sqrt;
+
+    eigen_assert(mat.rows()==mat.cols());
+    const Index size = mat.rows();
+    for(Index k = 0; k < size; ++k)
+    {
+      Index rs = size-k-1; // remaining size
+
+      Block<MatrixType,Dynamic,1> A21(mat,k+1,k,rs,1);
+      Block<MatrixType,1,Dynamic> A10(mat,k,0,1,k);
+      Block<MatrixType,Dynamic,Dynamic> A20(mat,k+1,0,rs,k);
+
+      RealScalar x = numext::real(mat.coeff(k,k));
+      if (k>0) x -= A10.squaredNorm();
+      if (x<=RealScalar(0))
+        return k;
+      mat.coeffRef(k,k) = x = sqrt(x);
+      if (k>0 && rs>0) A21.noalias() -= A20 * A10.adjoint();
+      if (rs>0) A21 /= x;
+    }
+    return -1;
+  }
+
+  template<typename MatrixType>
+  static Index blocked(MatrixType& m)
+  {
+    eigen_assert(m.rows()==m.cols());
+    Index size = m.rows();
+    if(size<32)
+      return unblocked(m);
+
+    Index blockSize = size/8;
+    blockSize = (blockSize/16)*16;
+    blockSize = (std::min)((std::max)(blockSize,Index(8)), Index(128));
+
+    for (Index k=0; k<size; k+=blockSize)
+    {
+      // partition the matrix:
+      //       A00 |  -  |  -
+      // lu  = A10 | A11 |  -
+      //       A20 | A21 | A22
+      Index bs = (std::min)(blockSize, size-k);
+      Index rs = size - k - bs;
+      Block<MatrixType,Dynamic,Dynamic> A11(m,k,   k,   bs,bs);
+      Block<MatrixType,Dynamic,Dynamic> A21(m,k+bs,k,   rs,bs);
+      Block<MatrixType,Dynamic,Dynamic> A22(m,k+bs,k+bs,rs,rs);
+
+      Index ret;
+      if((ret=unblocked(A11))>=0) return k+ret;
+      if(rs>0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);
+      if(rs>0) A22.template selfadjointView<Lower>().rankUpdate(A21,typename NumTraits<RealScalar>::Literal(-1)); // bottleneck
+    }
+    return -1;
+  }
+
+  template<typename MatrixType, typename VectorType>
+  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
+  {
+    return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
+  }
+};
+
+template<typename Scalar> struct llt_inplace<Scalar, Upper>
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  template<typename MatrixType>
+  static EIGEN_STRONG_INLINE Index unblocked(MatrixType& mat)
+  {
+    Transpose<MatrixType> matt(mat);
+    return llt_inplace<Scalar, Lower>::unblocked(matt);
+  }
+  template<typename MatrixType>
+  static EIGEN_STRONG_INLINE Index blocked(MatrixType& mat)
+  {
+    Transpose<MatrixType> matt(mat);
+    return llt_inplace<Scalar, Lower>::blocked(matt);
+  }
+  template<typename MatrixType, typename VectorType>
+  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma)
+  {
+    Transpose<MatrixType> matt(mat);
+    return llt_inplace<Scalar, Lower>::rankUpdate(matt, vec.conjugate(), sigma);
+  }
+};
+
+template<typename MatrixType> struct LLT_Traits<MatrixType,Lower>
+{
+  typedef const TriangularView<const MatrixType, Lower> MatrixL;
+  typedef const TriangularView<const typename MatrixType::AdjointReturnType, Upper> MatrixU;
+  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
+  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
+  static bool inplace_decomposition(MatrixType& m)
+  { return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m)==-1; }
+};
+
+template<typename MatrixType> struct LLT_Traits<MatrixType,Upper>
+{
+  typedef const TriangularView<const typename MatrixType::AdjointReturnType, Lower> MatrixL;
+  typedef const TriangularView<const MatrixType, Upper> MatrixU;
+  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
+  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
+  static bool inplace_decomposition(MatrixType& m)
+  { return llt_inplace<typename MatrixType::Scalar, Upper>::blocked(m)==-1; }
+};
+
+} // end namespace internal
+
+/** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix
+  *
+  * \returns a reference to *this
+  *
+  * Example: \include TutorialLinAlgComputeTwice.cpp
+  * Output: \verbinclude TutorialLinAlgComputeTwice.out
+  */
+template<typename MatrixType, int UpLo_>
+template<typename InputType>
+LLT<MatrixType,UpLo_>& LLT<MatrixType,UpLo_>::compute(const EigenBase<InputType>& a)
+{
+  eigen_assert(a.rows()==a.cols());
+  const Index size = a.rows();
+  m_matrix.resize(size, size);
+  if (!internal::is_same_dense(m_matrix, a.derived()))
+    m_matrix = a.derived();
+
+  // Compute matrix L1 norm = max abs column sum.
+  m_l1_norm = RealScalar(0);
+  // TODO move this code to SelfAdjointView
+  for (Index col = 0; col < size; ++col) {
+    RealScalar abs_col_sum;
+    if (UpLo_ == Lower)
+      abs_col_sum = m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
+    else
+      abs_col_sum = m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
+    if (abs_col_sum > m_l1_norm)
+      m_l1_norm = abs_col_sum;
+  }
+
+  m_isInitialized = true;
+  bool ok = Traits::inplace_decomposition(m_matrix);
+  m_info = ok ? Success : NumericalIssue;
+
+  return *this;
+}
+
+/** Performs a rank one update (or dowdate) of the current decomposition.
+  * If A = LL^* before the rank one update,
+  * then after it we have LL^* = A + sigma * v v^* where \a v must be a vector
+  * of same dimension.
+  */
+template<typename MatrixType_, int UpLo_>
+template<typename VectorType>
+LLT<MatrixType_,UpLo_> & LLT<MatrixType_,UpLo_>::rankUpdate(const VectorType& v, const RealScalar& sigma)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType);
+  eigen_assert(v.size()==m_matrix.cols());
+  eigen_assert(m_isInitialized);
+  if(internal::llt_inplace<typename MatrixType::Scalar, UpLo>::rankUpdate(m_matrix,v,sigma)>=0)
+    m_info = NumericalIssue;
+  else
+    m_info = Success;
+
+  return *this;
+}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename MatrixType_,int UpLo_>
+template<typename RhsType, typename DstType>
+void LLT<MatrixType_,UpLo_>::_solve_impl(const RhsType &rhs, DstType &dst) const
+{
+  _solve_impl_transposed<true>(rhs, dst);
+}
+
+template<typename MatrixType_,int UpLo_>
+template<bool Conjugate, typename RhsType, typename DstType>
+void LLT<MatrixType_,UpLo_>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
+{
+    dst = rhs;
+
+    matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
+    matrixU().template conjugateIf<!Conjugate>().solveInPlace(dst);
+}
+#endif
+
+/** \internal use x = llt_object.solve(x);
+  *
+  * This is the \em in-place version of solve().
+  *
+  * \param bAndX represents both the right-hand side matrix b and result x.
+  *
+  * This version avoids a copy when the right hand side matrix b is not needed anymore.
+  *
+  * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
+  * This function will const_cast it, so constness isn't honored here.
+  *
+  * \sa LLT::solve(), MatrixBase::llt()
+  */
+template<typename MatrixType, int UpLo_>
+template<typename Derived>
+void LLT<MatrixType,UpLo_>::solveInPlace(const MatrixBase<Derived> &bAndX) const
+{
+  eigen_assert(m_isInitialized && "LLT is not initialized.");
+  eigen_assert(m_matrix.rows()==bAndX.rows());
+  matrixL().solveInPlace(bAndX);
+  matrixU().solveInPlace(bAndX);
+}
+
+/** \returns the matrix represented by the decomposition,
+ * i.e., it returns the product: L L^*.
+ * This function is provided for debug purpose. */
+template<typename MatrixType, int UpLo_>
+MatrixType LLT<MatrixType,UpLo_>::reconstructedMatrix() const
+{
+  eigen_assert(m_isInitialized && "LLT is not initialized.");
+  return matrixL() * matrixL().adjoint().toDenseMatrix();
+}
+
+/** \cholesky_module
+  * \returns the LLT decomposition of \c *this
+  * \sa SelfAdjointView::llt()
+  */
+template<typename Derived>
+inline const LLT<typename MatrixBase<Derived>::PlainObject>
+MatrixBase<Derived>::llt() const
+{
+  return LLT<PlainObject>(derived());
+}
+
+/** \cholesky_module
+  * \returns the LLT decomposition of \c *this
+  * \sa SelfAdjointView::llt()
+  */
+template<typename MatrixType, unsigned int UpLo>
+inline const LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo>
+SelfAdjointView<MatrixType, UpLo>::llt() const
+{
+  return LLT<PlainObject,UpLo>(m_matrix);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_LLT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Cholesky/LLT_LAPACKE.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Cholesky/LLT_LAPACKE.h
new file mode 100644
index 0000000000000000000000000000000000000000..a32ff2307f09efb292c8a056dfdc3e04357993ef
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Cholesky/LLT_LAPACKE.h
@@ -0,0 +1,123 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *     LLt decomposition based on LAPACKE_?potrf function.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_LLT_LAPACKE_H
+#define EIGEN_LLT_LAPACKE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+namespace lapacke_helpers {
+  // -------------------------------------------------------------------------------------------------------------------
+  //        Dispatch for rank update handling upper and lower parts
+  // -------------------------------------------------------------------------------------------------------------------
+
+  template<UpLoType Mode>
+  struct rank_update {};
+
+  template<>
+  struct rank_update<Lower> {
+      template<typename MatrixType, typename VectorType>
+      static Index run(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
+        return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
+      }
+  };
+
+  template<>
+  struct rank_update<Upper> {
+      template<typename MatrixType, typename VectorType>
+      static Index run(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
+        Transpose<MatrixType> matt(mat);
+        return Eigen::internal::llt_rank_update_lower(matt, vec.conjugate(), sigma);
+      }
+  };
+
+  // -------------------------------------------------------------------------------------------------------------------
+  //        Generic lapacke llt implementation that hands of to the dispatches
+  // -------------------------------------------------------------------------------------------------------------------
+
+  template<typename Scalar, UpLoType Mode>
+  struct lapacke_llt {
+    EIGEN_STATIC_ASSERT(((Mode == Lower) || (Mode == Upper)),MODE_MUST_BE_UPPER_OR_LOWER)
+    template<typename MatrixType>
+    static Index blocked(MatrixType& m)
+    {
+      eigen_assert(m.rows() == m.cols());
+      if(m.rows() == 0) {
+        return -1;
+      }
+      /* Set up parameters for ?potrf */
+      lapack_int size = to_lapack(m.rows());
+      lapack_int matrix_order = lapack_storage_of(m);
+      constexpr char uplo = Mode == Upper ? 'U' : 'L';
+      Scalar* a = &(m.coeffRef(0,0));
+      lapack_int lda = to_lapack(m.outerStride());
+
+      lapack_int info = potrf(matrix_order, uplo, size, to_lapack(a), lda );
+      info = (info==0) ? -1 : info>0 ? info-1 : size;
+      return info;
+    }
+
+    template<typename MatrixType, typename VectorType>
+    static Index rankUpdate(MatrixType& mat, const VectorType& vec, const typename MatrixType::RealScalar& sigma)
+    {
+      return rank_update<Mode>::run(mat, vec, sigma);
+    }
+  };
+}
+// end namespace lapacke_helpers
+
+/*
+ * Here, we just put the generic implementation from lapacke_llt into a full specialization of the llt_inplace
+ * type. By being a full specialization, the versions defined here thus get precedence over the generic implementation
+ * in LLT.h for double, float and complex double, complex float types.
+ */
+
+#define EIGEN_LAPACKE_LLT(EIGTYPE) \
+template<> struct llt_inplace<EIGTYPE, Lower> : public lapacke_helpers::lapacke_llt<EIGTYPE, Lower> {}; \
+template<> struct llt_inplace<EIGTYPE, Upper> : public lapacke_helpers::lapacke_llt<EIGTYPE, Upper> {};
+
+EIGEN_LAPACKE_LLT(double)
+EIGEN_LAPACKE_LLT(float)
+EIGEN_LAPACKE_LLT(std::complex<double>)
+EIGEN_LAPACKE_LLT(std::complex<float>)
+
+#undef EIGEN_LAPACKE_LLT
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_LLT_LAPACKE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/CholmodSupport/CholmodSupport.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/CholmodSupport/CholmodSupport.h
new file mode 100644
index 0000000000000000000000000000000000000000..91c1cfc14e88396605ad25d6ea860e62d37d2c67
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/CholmodSupport/CholmodSupport.h
@@ -0,0 +1,684 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CHOLMODSUPPORT_H
+#define EIGEN_CHOLMODSUPPORT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename Scalar> struct cholmod_configure_matrix;
+
+template<> struct cholmod_configure_matrix<double> {
+  template<typename CholmodType>
+  static void run(CholmodType& mat) {
+    mat.xtype = CHOLMOD_REAL;
+    mat.dtype = CHOLMOD_DOUBLE;
+  }
+};
+
+template<> struct cholmod_configure_matrix<std::complex<double> > {
+  template<typename CholmodType>
+  static void run(CholmodType& mat) {
+    mat.xtype = CHOLMOD_COMPLEX;
+    mat.dtype = CHOLMOD_DOUBLE;
+  }
+};
+
+// Other scalar types are not yet supported by Cholmod
+// template<> struct cholmod_configure_matrix<float> {
+//   template<typename CholmodType>
+//   static void run(CholmodType& mat) {
+//     mat.xtype = CHOLMOD_REAL;
+//     mat.dtype = CHOLMOD_SINGLE;
+//   }
+// };
+//
+// template<> struct cholmod_configure_matrix<std::complex<float> > {
+//   template<typename CholmodType>
+//   static void run(CholmodType& mat) {
+//     mat.xtype = CHOLMOD_COMPLEX;
+//     mat.dtype = CHOLMOD_SINGLE;
+//   }
+// };
+
+} // namespace internal
+
+/** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object.
+  * Note that the data are shared.
+  */
+template<typename Scalar_, int Options_, typename StorageIndex_>
+cholmod_sparse viewAsCholmod(Ref<SparseMatrix<Scalar_,Options_,StorageIndex_> > mat)
+{
+  cholmod_sparse res;
+  res.nzmax   = mat.nonZeros();
+  res.nrow    = mat.rows();
+  res.ncol    = mat.cols();
+  res.p       = mat.outerIndexPtr();
+  res.i       = mat.innerIndexPtr();
+  res.x       = mat.valuePtr();
+  res.z       = 0;
+  res.sorted  = 1;
+  if(mat.isCompressed())
+  {
+    res.packed  = 1;
+    res.nz = 0;
+  }
+  else
+  {
+    res.packed  = 0;
+    res.nz = mat.innerNonZeroPtr();
+  }
+
+  res.dtype   = 0;
+  res.stype   = -1;
+
+  if (internal::is_same<StorageIndex_,int>::value)
+  {
+    res.itype = CHOLMOD_INT;
+  }
+  else if (internal::is_same<StorageIndex_,SuiteSparse_long>::value)
+  {
+    res.itype = CHOLMOD_LONG;
+  }
+  else
+  {
+    eigen_assert(false && "Index type not supported yet");
+  }
+
+  // setup res.xtype
+  internal::cholmod_configure_matrix<Scalar_>::run(res);
+
+  res.stype = 0;
+
+  return res;
+}
+
+template<typename Scalar_, int Options_, typename Index_>
+const cholmod_sparse viewAsCholmod(const SparseMatrix<Scalar_,Options_,Index_>& mat)
+{
+  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_,Options_,Index_> >(mat.const_cast_derived()));
+  return res;
+}
+
+template<typename Scalar_, int Options_, typename Index_>
+const cholmod_sparse viewAsCholmod(const SparseVector<Scalar_,Options_,Index_>& mat)
+{
+  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_,Options_,Index_> >(mat.const_cast_derived()));
+  return res;
+}
+
+/** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix.
+  * The data are not copied but shared. */
+template<typename Scalar_, int Options_, typename Index_, unsigned int UpLo>
+cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<const SparseMatrix<Scalar_,Options_,Index_>, UpLo>& mat)
+{
+  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_,Options_,Index_> >(mat.matrix().const_cast_derived()));
+
+  if(UpLo==Upper) res.stype =  1;
+  if(UpLo==Lower) res.stype = -1;
+  // swap stype for rowmajor matrices (only works for real matrices)
+  EIGEN_STATIC_ASSERT((Options_ & RowMajorBit) == 0 || NumTraits<Scalar_>::IsComplex == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  if(Options_ & RowMajorBit) res.stype *=-1;
+
+  return res;
+}
+
+/** Returns a view of the Eigen \b dense matrix \a mat as Cholmod dense matrix.
+  * The data are not copied but shared. */
+template<typename Derived>
+cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat)
+{
+  EIGEN_STATIC_ASSERT((internal::traits<Derived>::Flags&RowMajorBit)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  typedef typename Derived::Scalar Scalar;
+
+  cholmod_dense res;
+  res.nrow   = mat.rows();
+  res.ncol   = mat.cols();
+  res.nzmax  = res.nrow * res.ncol;
+  res.d      = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride();
+  res.x      = (void*)(mat.derived().data());
+  res.z      = 0;
+
+  internal::cholmod_configure_matrix<Scalar>::run(res);
+
+  return res;
+}
+
+/** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix.
+  * The data are not copied but shared. */
+template<typename Scalar, int Flags, typename StorageIndex>
+Map<SparseMatrix<Scalar,Flags,StorageIndex> > viewAsEigen(cholmod_sparse& cm)
+{
+  return Map<SparseMatrix<Scalar,Flags,StorageIndex> >
+         (cm.nrow, cm.ncol, static_cast<StorageIndex*>(cm.p)[cm.ncol],
+          static_cast<StorageIndex*>(cm.p), static_cast<StorageIndex*>(cm.i),static_cast<Scalar*>(cm.x) );
+}
+
+namespace internal {
+
+// template specializations for int and long that call the correct cholmod method
+
+#define EIGEN_CHOLMOD_SPECIALIZE0(ret, name) \
+    template<typename StorageIndex_> inline ret cm_ ## name       (cholmod_common &Common) { return cholmod_ ## name   (&Common); } \
+    template<>                       inline ret cm_ ## name<SuiteSparse_long> (cholmod_common &Common) { return cholmod_l_ ## name (&Common); }
+
+#define EIGEN_CHOLMOD_SPECIALIZE1(ret, name, t1, a1) \
+    template<typename StorageIndex_> inline ret cm_ ## name       (t1& a1, cholmod_common &Common) { return cholmod_ ## name   (&a1, &Common); } \
+    template<>                       inline ret cm_ ## name<SuiteSparse_long> (t1& a1, cholmod_common &Common) { return cholmod_l_ ## name (&a1, &Common); }
+
+EIGEN_CHOLMOD_SPECIALIZE0(int, start)
+EIGEN_CHOLMOD_SPECIALIZE0(int, finish)
+
+EIGEN_CHOLMOD_SPECIALIZE1(int, free_factor, cholmod_factor*, L)
+EIGEN_CHOLMOD_SPECIALIZE1(int, free_dense,  cholmod_dense*,  X)
+EIGEN_CHOLMOD_SPECIALIZE1(int, free_sparse, cholmod_sparse*, A)
+
+EIGEN_CHOLMOD_SPECIALIZE1(cholmod_factor*, analyze, cholmod_sparse, A)
+
+template<typename StorageIndex_> inline cholmod_dense*  cm_solve         (int sys, cholmod_factor& L, cholmod_dense&  B, cholmod_common &Common) { return cholmod_solve     (sys, &L, &B, &Common); }
+template<>                       inline cholmod_dense*  cm_solve<SuiteSparse_long>   (int sys, cholmod_factor& L, cholmod_dense&  B, cholmod_common &Common) { return cholmod_l_solve   (sys, &L, &B, &Common); }
+
+template<typename StorageIndex_> inline cholmod_sparse* cm_spsolve       (int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common &Common) { return cholmod_spsolve   (sys, &L, &B, &Common); }
+template<>                       inline cholmod_sparse* cm_spsolve<SuiteSparse_long> (int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common &Common) { return cholmod_l_spsolve (sys, &L, &B, &Common); }
+
+template<typename StorageIndex_>
+inline int  cm_factorize_p       (cholmod_sparse*  A, double beta[2], StorageIndex_* fset, std::size_t fsize, cholmod_factor* L, cholmod_common &Common) { return cholmod_factorize_p   (A, beta, fset, fsize, L, &Common); }
+template<>
+inline int  cm_factorize_p<SuiteSparse_long> (cholmod_sparse*  A, double beta[2], SuiteSparse_long* fset,          std::size_t fsize, cholmod_factor* L, cholmod_common &Common) { return cholmod_l_factorize_p (A, beta, fset, fsize, L, &Common); }
+
+#undef EIGEN_CHOLMOD_SPECIALIZE0
+#undef EIGEN_CHOLMOD_SPECIALIZE1
+
+}  // namespace internal
+
+
+enum CholmodMode {
+  CholmodAuto, CholmodSimplicialLLt, CholmodSupernodalLLt, CholmodLDLt
+};
+
+
+/** \ingroup CholmodSupport_Module
+  * \class CholmodBase
+  * \brief The base class for the direct Cholesky factorization of Cholmod
+  * \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT
+  */
+template<typename MatrixType_, int UpLo_, typename Derived>
+class CholmodBase : public SparseSolverBase<Derived>
+{
+  protected:
+    typedef SparseSolverBase<Derived> Base;
+    using Base::derived;
+    using Base::m_isInitialized;
+  public:
+    typedef MatrixType_ MatrixType;
+    enum { UpLo = UpLo_ };
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef MatrixType CholMatrixType;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    enum {
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+
+  public:
+
+    CholmodBase()
+      : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false)
+    {
+      EIGEN_STATIC_ASSERT((internal::is_same<double,RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
+      m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
+      internal::cm_start<StorageIndex>(m_cholmod);
+    }
+
+    explicit CholmodBase(const MatrixType& matrix)
+      : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false)
+    {
+      EIGEN_STATIC_ASSERT((internal::is_same<double,RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
+      m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
+      internal::cm_start<StorageIndex>(m_cholmod);
+      compute(matrix);
+    }
+
+    ~CholmodBase()
+    {
+      if(m_cholmodFactor)
+        internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
+      internal::cm_finish<StorageIndex>(m_cholmod);
+    }
+
+    inline StorageIndex cols() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
+    inline StorageIndex rows() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
+
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful,
+      *          \c NumericalIssue if the matrix.appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+
+    /** Computes the sparse Cholesky decomposition of \a matrix */
+    Derived& compute(const MatrixType& matrix)
+    {
+      analyzePattern(matrix);
+      factorize(matrix);
+      return derived();
+    }
+
+    /** Performs a symbolic decomposition on the sparsity pattern of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      *
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& matrix)
+    {
+      if(m_cholmodFactor)
+      {
+        internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
+        m_cholmodFactor = 0;
+      }
+      cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
+      m_cholmodFactor = internal::cm_analyze<StorageIndex>(A, m_cholmod);
+
+      this->m_isInitialized = true;
+      this->m_info = Success;
+      m_analysisIsOk = true;
+      m_factorizationIsOk = false;
+    }
+
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been performed.
+      *
+      * \sa analyzePattern()
+      */
+    void factorize(const MatrixType& matrix)
+    {
+      eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+      cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
+      internal::cm_factorize_p<StorageIndex>(&A, m_shiftOffset, 0, 0, m_cholmodFactor, m_cholmod);
+
+      // If the factorization failed, minor is the column at which it did. On success minor == n.
+      this->m_info = (m_cholmodFactor->minor == m_cholmodFactor->n ? Success : NumericalIssue);
+      m_factorizationIsOk = true;
+    }
+
+    /** Returns a reference to the Cholmod's configuration structure to get a full control over the performed operations.
+     *  See the Cholmod user guide for details. */
+    cholmod_common& cholmod() { return m_cholmod; }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** \internal */
+    template<typename Rhs,typename Dest>
+    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
+    {
+      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
+      const Index size = m_cholmodFactor->n;
+      EIGEN_UNUSED_VARIABLE(size);
+      eigen_assert(size==b.rows());
+
+      // Cholmod needs column-major storage without inner-stride, which corresponds to the default behavior of Ref.
+      Ref<const Matrix<typename Rhs::Scalar,Dynamic,Dynamic,ColMajor> > b_ref(b.derived());
+
+      cholmod_dense b_cd = viewAsCholmod(b_ref);
+      cholmod_dense* x_cd = internal::cm_solve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cd, m_cholmod);
+      if(!x_cd)
+      {
+        this->m_info = NumericalIssue;
+        return;
+      }
+      // TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
+      // NOTE Actually, the copy can be avoided by calling cholmod_solve2 instead of cholmod_solve
+      dest = Matrix<Scalar,Dest::RowsAtCompileTime,Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),b.rows(),b.cols());
+      internal::cm_free_dense<StorageIndex>(x_cd, m_cholmod);
+    }
+
+    /** \internal */
+    template<typename RhsDerived, typename DestDerived>
+    void _solve_impl(const SparseMatrixBase<RhsDerived> &b, SparseMatrixBase<DestDerived> &dest) const
+    {
+      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
+      const Index size = m_cholmodFactor->n;
+      EIGEN_UNUSED_VARIABLE(size);
+      eigen_assert(size==b.rows());
+
+      // note: cs stands for Cholmod Sparse
+      Ref<SparseMatrix<typename RhsDerived::Scalar,ColMajor,typename RhsDerived::StorageIndex> > b_ref(b.const_cast_derived());
+      cholmod_sparse b_cs = viewAsCholmod(b_ref);
+      cholmod_sparse* x_cs = internal::cm_spsolve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cs, m_cholmod);
+      if(!x_cs)
+      {
+        this->m_info = NumericalIssue;
+        return;
+      }
+      // TODO optimize this copy by swapping when possible (be careful with alignment, etc.)
+      // NOTE cholmod_spsolve in fact just calls the dense solver for blocks of 4 columns at a time (similar to Eigen's sparse solver)
+      dest.derived() = viewAsEigen<typename DestDerived::Scalar,ColMajor,typename DestDerived::StorageIndex>(*x_cs);
+      internal::cm_free_sparse<StorageIndex>(x_cs, m_cholmod);
+    }
+    #endif // EIGEN_PARSED_BY_DOXYGEN
+
+
+    /** Sets the shift parameter that will be used to adjust the diagonal coefficients during the numerical factorization.
+      *
+      * During the numerical factorization, an offset term is added to the diagonal coefficients:\n
+      * \c d_ii = \a offset + \c d_ii
+      *
+      * The default is \a offset=0.
+      *
+      * \returns a reference to \c *this.
+      */
+    Derived& setShift(const RealScalar& offset)
+    {
+      m_shiftOffset[0] = double(offset);
+      return derived();
+    }
+
+    /** \returns the determinant of the underlying matrix from the current factorization */
+    Scalar determinant() const
+    {
+      using std::exp;
+      return exp(logDeterminant());
+    }
+
+    /** \returns the log determinant of the underlying matrix from the current factorization */
+    Scalar logDeterminant() const
+    {
+      using std::log;
+      using numext::real;
+      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
+
+      RealScalar logDet = 0;
+      Scalar *x = static_cast<Scalar*>(m_cholmodFactor->x);
+      if (m_cholmodFactor->is_super)
+      {
+        // Supernodal factorization stored as a packed list of dense column-major blocs,
+        // as described by the following structure:
+
+        // super[k] == index of the first column of the j-th super node
+        StorageIndex *super = static_cast<StorageIndex*>(m_cholmodFactor->super);
+        // pi[k] == offset to the description of row indices
+        StorageIndex *pi = static_cast<StorageIndex*>(m_cholmodFactor->pi);
+        // px[k] == offset to the respective dense block
+        StorageIndex *px = static_cast<StorageIndex*>(m_cholmodFactor->px);
+
+        Index nb_super_nodes = m_cholmodFactor->nsuper;
+        for (Index k=0; k < nb_super_nodes; ++k)
+        {
+          StorageIndex ncols = super[k + 1] - super[k];
+          StorageIndex nrows = pi[k + 1] - pi[k];
+
+          Map<const Array<Scalar,1,Dynamic>, 0, InnerStride<> > sk(x + px[k], ncols, InnerStride<>(nrows+1));
+          logDet += sk.real().log().sum();
+        }
+      }
+      else
+      {
+        // Simplicial factorization stored as standard CSC matrix.
+        StorageIndex *p = static_cast<StorageIndex*>(m_cholmodFactor->p);
+        Index size = m_cholmodFactor->n;
+        for (Index k=0; k<size; ++k)
+          logDet += log(real( x[p[k]] ));
+      }
+      if (m_cholmodFactor->is_ll)
+        logDet *= 2.0;
+      return logDet;
+    }
+
+    template<typename Stream>
+    void dumpMemory(Stream& /*s*/)
+    {}
+
+  protected:
+    mutable cholmod_common m_cholmod;
+    cholmod_factor* m_cholmodFactor;
+    double m_shiftOffset[2];
+    mutable ComputationInfo m_info;
+    int m_factorizationIsOk;
+    int m_analysisIsOk;
+};
+
+/** \ingroup CholmodSupport_Module
+  * \class CholmodSimplicialLLT
+  * \brief A simplicial direct Cholesky (LLT) factorization and solver based on Cholmod
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization
+  * using the Cholmod library.
+  * This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Therefore, it has little practical interest.
+  * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
+  * X and B can be either dense or sparse.
+  *
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+  *               or Upper. Default is Lower.
+  *
+  * \implsparsesolverconcept
+  *
+  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
+  *
+  * \warning Only double precision real and complex scalar types are supported by Cholmod.
+  *
+  * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLLT
+  */
+template<typename MatrixType_, int UpLo_ = Lower>
+class CholmodSimplicialLLT : public CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLLT<MatrixType_, UpLo_> >
+{
+    typedef CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLLT> Base;
+    using Base::m_cholmod;
+
+  public:
+
+    typedef MatrixType_ MatrixType;
+
+    CholmodSimplicialLLT() : Base() { init(); }
+
+    CholmodSimplicialLLT(const MatrixType& matrix) : Base()
+    {
+      init();
+      this->compute(matrix);
+    }
+
+    ~CholmodSimplicialLLT() {}
+  protected:
+    void init()
+    {
+      m_cholmod.final_asis = 0;
+      m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+      m_cholmod.final_ll = 1;
+    }
+};
+
+
+/** \ingroup CholmodSupport_Module
+  * \class CholmodSimplicialLDLT
+  * \brief A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization
+  * using the Cholmod library.
+  * This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Therefore, it has little practical interest.
+  * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
+  * X and B can be either dense or sparse.
+  *
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+  *               or Upper. Default is Lower.
+  *
+  * \implsparsesolverconcept
+  *
+  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
+  *
+  * \warning Only double precision real and complex scalar types are supported by Cholmod.
+  *
+  * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLDLT
+  */
+template<typename MatrixType_, int UpLo_ = Lower>
+class CholmodSimplicialLDLT : public CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLDLT<MatrixType_, UpLo_> >
+{
+    typedef CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLDLT> Base;
+    using Base::m_cholmod;
+
+  public:
+
+    typedef MatrixType_ MatrixType;
+
+    CholmodSimplicialLDLT() : Base() { init(); }
+
+    CholmodSimplicialLDLT(const MatrixType& matrix) : Base()
+    {
+      init();
+      this->compute(matrix);
+    }
+
+    ~CholmodSimplicialLDLT() {}
+  protected:
+    void init()
+    {
+      m_cholmod.final_asis = 1;
+      m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+    }
+};
+
+/** \ingroup CholmodSupport_Module
+  * \class CholmodSupernodalLLT
+  * \brief A supernodal Cholesky (LLT) factorization and solver based on Cholmod
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization
+  * using the Cholmod library.
+  * This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM.
+  * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
+  * X and B can be either dense or sparse.
+  *
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+  *               or Upper. Default is Lower.
+  *
+  * \implsparsesolverconcept
+  *
+  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
+  *
+  * \warning Only double precision real and complex scalar types are supported by Cholmod.
+  *
+  * \sa \ref TutorialSparseSolverConcept
+  */
+template<typename MatrixType_, int UpLo_ = Lower>
+class CholmodSupernodalLLT : public CholmodBase<MatrixType_, UpLo_, CholmodSupernodalLLT<MatrixType_, UpLo_> >
+{
+    typedef CholmodBase<MatrixType_, UpLo_, CholmodSupernodalLLT> Base;
+    using Base::m_cholmod;
+
+  public:
+
+    typedef MatrixType_ MatrixType;
+
+    CholmodSupernodalLLT() : Base() { init(); }
+
+    CholmodSupernodalLLT(const MatrixType& matrix) : Base()
+    {
+      init();
+      this->compute(matrix);
+    }
+
+    ~CholmodSupernodalLLT() {}
+  protected:
+    void init()
+    {
+      m_cholmod.final_asis = 1;
+      m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
+    }
+};
+
+/** \ingroup CholmodSupport_Module
+  * \class CholmodDecomposition
+  * \brief A general Cholesky factorization and solver based on Cholmod
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization
+  * using the Cholmod library. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
+  * X and B can be either dense or sparse.
+  *
+  * This variant permits to change the underlying Cholesky method at runtime.
+  * On the other hand, it does not provide access to the result of the factorization.
+  * The default is to let Cholmod automatically choose between a simplicial and supernodal factorization.
+  *
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+  *               or Upper. Default is Lower.
+  *
+  * \implsparsesolverconcept
+  *
+  * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non compressed.
+  *
+  * \warning Only double precision real and complex scalar types are supported by Cholmod.
+  *
+  * \sa \ref TutorialSparseSolverConcept
+  */
+template<typename MatrixType_, int UpLo_ = Lower>
+class CholmodDecomposition : public CholmodBase<MatrixType_, UpLo_, CholmodDecomposition<MatrixType_, UpLo_> >
+{
+    typedef CholmodBase<MatrixType_, UpLo_, CholmodDecomposition> Base;
+    using Base::m_cholmod;
+
+  public:
+
+    typedef MatrixType_ MatrixType;
+
+    CholmodDecomposition() : Base() { init(); }
+
+    CholmodDecomposition(const MatrixType& matrix) : Base()
+    {
+      init();
+      this->compute(matrix);
+    }
+
+    ~CholmodDecomposition() {}
+
+    void setMode(CholmodMode mode)
+    {
+      switch(mode)
+      {
+        case CholmodAuto:
+          m_cholmod.final_asis = 1;
+          m_cholmod.supernodal = CHOLMOD_AUTO;
+          break;
+        case CholmodSimplicialLLt:
+          m_cholmod.final_asis = 0;
+          m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+          m_cholmod.final_ll = 1;
+          break;
+        case CholmodSupernodalLLt:
+          m_cholmod.final_asis = 1;
+          m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
+          break;
+        case CholmodLDLt:
+          m_cholmod.final_asis = 1;
+          m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+          break;
+        default:
+          break;
+      }
+    }
+  protected:
+    void init()
+    {
+      m_cholmod.final_asis = 1;
+      m_cholmod.supernodal = CHOLMOD_AUTO;
+    }
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CHOLMODSUPPORT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/CholmodSupport/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/CholmodSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..0fb3abc704095b8874a447c99451f7f12f1fd66b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/CholmodSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
+#error "Please include Eigen/CholmodSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ArithmeticSequence.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ArithmeticSequence.h
new file mode 100644
index 0000000000000000000000000000000000000000..81005c5a16084edfe1065a2c84a6bc6278cf1967
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ArithmeticSequence.h
@@ -0,0 +1,258 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARITHMETIC_SEQUENCE_H
+#define EIGEN_ARITHMETIC_SEQUENCE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Helper to cleanup the type of the increment:
+template<typename T> struct cleanup_seq_incr {
+  typedef typename cleanup_index_type<T,DynamicIndex>::type type;
+};
+
+}  // namespace internal
+
+//--------------------------------------------------------------------------------
+// seq(first,last,incr) and seqN(first,size,incr)
+//--------------------------------------------------------------------------------
+
+template<typename FirstType=Index,typename SizeType=Index,typename IncrType=internal::FixedInt<1> >
+class ArithmeticSequence;
+
+template<typename FirstType,typename SizeType,typename IncrType>
+ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
+                   typename internal::cleanup_index_type<SizeType>::type,
+                   typename internal::cleanup_seq_incr<IncrType>::type >
+seqN(FirstType first, SizeType size, IncrType incr);
+
+/** \class ArithmeticSequence
+  * \ingroup Core_Module
+  *
+  * This class represents an arithmetic progression \f$ a_0, a_1, a_2, ..., a_{n-1}\f$ defined by
+  * its \em first value \f$ a_0 \f$, its \em size (aka length) \em n, and the \em increment (aka stride)
+  * that is equal to \f$ a_{i+1}-a_{i}\f$ for any \em i.
+  *
+  * It is internally used as the return type of the Eigen::seq and Eigen::seqN functions, and as the input arguments
+  * of DenseBase::operator()(const RowIndices&, const ColIndices&), and most of the time this is the
+  * only way it is used.
+  *
+  * \tparam FirstType type of the first element, usually an Index,
+  *                   but internally it can be a symbolic expression
+  * \tparam SizeType type representing the size of the sequence, usually an Index
+  *                  or a compile time integral constant. Internally, it can also be a symbolic expression
+  * \tparam IncrType type of the increment, can be a runtime Index, or a compile time integral constant (default is compile-time 1)
+  *
+  * \sa Eigen::seq, Eigen::seqN, DenseBase::operator()(const RowIndices&, const ColIndices&), class IndexedView
+  */
+template<typename FirstType,typename SizeType,typename IncrType>
+class ArithmeticSequence
+{
+public:
+  ArithmeticSequence(FirstType first, SizeType size) : m_first(first), m_size(size) {}
+  ArithmeticSequence(FirstType first, SizeType size, IncrType incr) : m_first(first), m_size(size), m_incr(incr) {}
+
+  enum {
+    SizeAtCompileTime = internal::get_fixed_value<SizeType>::value,
+    IncrAtCompileTime = internal::get_fixed_value<IncrType,DynamicIndex>::value
+  };
+
+  /** \returns the size, i.e., number of elements, of the sequence */
+  Index size()  const { return m_size; }
+
+  /** \returns the first element \f$ a_0 \f$ in the sequence */
+  Index first()  const { return m_first; }
+
+  /** \returns the value \f$ a_i \f$ at index \a i in the sequence. */
+  Index operator[](Index i) const { return m_first + i * m_incr; }
+
+  const FirstType& firstObject() const { return m_first; }
+  const SizeType&  sizeObject()  const { return m_size; }
+  const IncrType&  incrObject()  const { return m_incr; }
+
+protected:
+  FirstType m_first;
+  SizeType  m_size;
+  IncrType  m_incr;
+
+public:
+  auto reverse() const -> decltype(Eigen::seqN(m_first+(m_size+fix<-1>())*m_incr,m_size,-m_incr)) {
+    return seqN(m_first+(m_size+fix<-1>())*m_incr,m_size,-m_incr);
+  }
+};
+
+/** \returns an ArithmeticSequence starting at \a first, of length \a size, and increment \a incr
+  *
+  * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
+template<typename FirstType,typename SizeType,typename IncrType>
+ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,typename internal::cleanup_index_type<SizeType>::type,typename internal::cleanup_seq_incr<IncrType>::type >
+seqN(FirstType first, SizeType size, IncrType incr)  {
+  return ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,typename internal::cleanup_index_type<SizeType>::type,typename internal::cleanup_seq_incr<IncrType>::type>(first,size,incr);
+}
+
+/** \returns an ArithmeticSequence starting at \a first, of length \a size, and unit increment
+  *
+  * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType) */
+template<typename FirstType,typename SizeType>
+ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,typename internal::cleanup_index_type<SizeType>::type >
+seqN(FirstType first, SizeType size)  {
+  return ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,typename internal::cleanup_index_type<SizeType>::type>(first,size);
+}
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+
+/** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and with positive (or negative) increment \a incr
+  *
+  * It is essentially an alias to:
+  * \code
+  * seqN(f, (l-f+incr)/incr, incr);
+  * \endcode
+  *
+  * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType)
+  */
+template<typename FirstType,typename LastType, typename IncrType>
+auto seq(FirstType f, LastType l, IncrType incr);
+
+/** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and unit increment
+  *
+  * It is essentially an alias to:
+  * \code
+  * seqN(f,l-f+1);
+  * \endcode
+  *
+  * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType)
+  */
+template<typename FirstType,typename LastType>
+auto seq(FirstType f, LastType l);
+
+#else // EIGEN_PARSED_BY_DOXYGEN
+
+template<typename FirstType,typename LastType>
+auto seq(FirstType f, LastType l) -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
+                                                   (  typename internal::cleanup_index_type<LastType>::type(l)
+                                                    - typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>())))
+{
+  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
+              (typename internal::cleanup_index_type<LastType>::type(l)
+               -typename internal::cleanup_index_type<FirstType>::type(f)+fix<1>()));
+}
+
+template<typename FirstType,typename LastType, typename IncrType>
+auto seq(FirstType f, LastType l, IncrType incr)
+  -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
+                   (   typename internal::cleanup_index_type<LastType>::type(l)
+                     - typename internal::cleanup_index_type<FirstType>::type(f)+typename internal::cleanup_seq_incr<IncrType>::type(incr)
+                   ) / typename internal::cleanup_seq_incr<IncrType>::type(incr),
+                   typename internal::cleanup_seq_incr<IncrType>::type(incr)))
+{
+  typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
+  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
+              ( typename internal::cleanup_index_type<LastType>::type(l)
+               -typename internal::cleanup_index_type<FirstType>::type(f)+CleanedIncrType(incr)) / CleanedIncrType(incr),
+              CleanedIncrType(incr));
+}
+
+
+#endif // EIGEN_PARSED_BY_DOXYGEN
+
+namespace placeholders {
+
+/** \cpp11
+  * \returns a symbolic ArithmeticSequence representing the last \a size elements with increment \a incr.
+  *
+  * It is a shortcut for: \code seqN(last-(size-fix<1>)*incr, size, incr) \endcode
+  * 
+  * \sa lastN(SizeType), seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
+template<typename SizeType,typename IncrType>
+auto lastN(SizeType size, IncrType incr)
+-> decltype(seqN(Eigen::placeholders::last-(size-fix<1>())*incr, size, incr))
+{
+  return seqN(Eigen::placeholders::last-(size-fix<1>())*incr, size, incr);
+}
+
+/** \cpp11
+  * \returns a symbolic ArithmeticSequence representing the last \a size elements with a unit increment.
+  *
+  *  It is a shortcut for: \code seq(last+fix<1>-size, last) \endcode
+  * 
+  * \sa lastN(SizeType,IncrType, seqN(FirstType,SizeType), seq(FirstType,LastType) */
+template<typename SizeType>
+auto lastN(SizeType size)
+-> decltype(seqN(Eigen::placeholders::last+fix<1>()-size, size))
+{
+  return seqN(Eigen::placeholders::last+fix<1>()-size, size);
+}
+
+}  // namespace placeholders
+
+namespace internal {
+
+// Convert a symbolic span into a usable one (i.e., remove last/end "keywords")
+template<typename T>
+struct make_size_type {
+  typedef std::conditional_t<symbolic::is_symbolic<T>::value, Index, T> type;
+};
+
+template<typename FirstType,typename SizeType,typename IncrType,int XprSize>
+struct IndexedViewCompatibleType<ArithmeticSequence<FirstType,SizeType,IncrType>, XprSize> {
+  typedef ArithmeticSequence<Index,typename make_size_type<SizeType>::type,IncrType> type;
+};
+
+template<typename FirstType,typename SizeType,typename IncrType>
+ArithmeticSequence<Index,typename make_size_type<SizeType>::type,IncrType>
+makeIndexedViewCompatible(const ArithmeticSequence<FirstType,SizeType,IncrType>& ids, Index size,SpecializedType) {
+  return ArithmeticSequence<Index,typename make_size_type<SizeType>::type,IncrType>(
+            eval_expr_given_size(ids.firstObject(),size),eval_expr_given_size(ids.sizeObject(),size),ids.incrObject());
+}
+
+template<typename FirstType,typename SizeType,typename IncrType>
+struct get_compile_time_incr<ArithmeticSequence<FirstType,SizeType,IncrType> > {
+  enum { value = get_fixed_value<IncrType,DynamicIndex>::value };
+};
+
+} // end namespace internal
+
+/** \namespace Eigen::indexing
+  * \ingroup Core_Module
+  * 
+  * The sole purpose of this namespace is to be able to import all functions
+  * and symbols that are expected to be used within operator() for indexing
+  * and slicing. If you already imported the whole Eigen namespace:
+  * \code using namespace Eigen; \endcode
+  * then you are already all set. Otherwise, if you don't want/cannot import
+  * the whole Eigen namespace, the following line:
+  * \code using namespace Eigen::indexing; \endcode
+  * is equivalent to:
+  * \code
+  using Eigen::fix;
+  using Eigen::seq;
+  using Eigen::seqN;
+  using Eigen::placeholders::all;
+  using Eigen::placeholders::last;
+  using Eigen::placeholders::lastN;  // c++11 only
+  using Eigen::placeholders::lastp1;
+  \endcode
+  */
+namespace indexing {
+  using Eigen::fix;
+  using Eigen::seq;
+  using Eigen::seqN;
+  using Eigen::placeholders::all;
+  using Eigen::placeholders::last;
+  using Eigen::placeholders::lastN;
+  using Eigen::placeholders::lastp1;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_ARITHMETIC_SEQUENCE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Array.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Array.h
new file mode 100644
index 0000000000000000000000000000000000000000..d7a5e7aefeb8d997298a90a0ee326fc6be404b32
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Array.h
@@ -0,0 +1,405 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARRAY_H
+#define EIGEN_ARRAY_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template<typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+struct traits<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> > : traits<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> >
+{
+  typedef ArrayXpr XprKind;
+  typedef ArrayBase<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> > XprBase;
+};
+}
+
+/** \class Array
+  * \ingroup Core_Module
+  *
+  * \brief General-purpose arrays with easy API for coefficient-wise operations
+  *
+  * The %Array class is very similar to the Matrix class. It provides
+  * general-purpose one- and two-dimensional arrays. The difference between the
+  * %Array and the %Matrix class is primarily in the API: the API for the
+  * %Array class provides easy access to coefficient-wise operations, while the
+  * API for the %Matrix class provides easy access to linear-algebra
+  * operations.
+  *
+  * See documentation of class Matrix for detailed information on the template parameters
+  * storage layout.
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
+  *
+  * \sa \blank \ref TutorialArrayClass, \ref TopicClassHierarchy
+  */
+template<typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+class Array
+  : public PlainObjectBase<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> >
+{
+  public:
+
+    typedef PlainObjectBase<Array> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Array)
+
+    enum { Options = Options_ };
+    typedef typename Base::PlainObject PlainObject;
+
+  protected:
+    template <typename Derived, typename OtherDerived, bool IsVector>
+    friend struct internal::conservative_resize_like_impl;
+
+    using Base::m_storage;
+
+  public:
+
+    using Base::base;
+    using Base::coeff;
+    using Base::coeffRef;
+
+    /**
+      * The usage of
+      *   using Base::operator=;
+      * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
+      * the usage of 'using'. This should be done only for operator=.
+      */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Array& operator=(const EigenBase<OtherDerived> &other)
+    {
+      return Base::operator=(other);
+    }
+
+    /** Set all the entries to \a value.
+      * \sa DenseBase::setConstant(), DenseBase::fill()
+      */
+    /* This overload is needed because the usage of
+      *   using Base::operator=;
+      * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
+      * the usage of 'using'. This should be done only for operator=.
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Array& operator=(const Scalar &value)
+    {
+      Base::setConstant(value);
+      return *this;
+    }
+
+    /** Copies the value of the expression \a other into \c *this with automatic resizing.
+      *
+      * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
+      * it will be initialized.
+      *
+      * Note that copying a row-vector into a vector (and conversely) is allowed.
+      * The resizing, if any, is then done in the appropriate way so that row-vectors
+      * remain row-vectors and vectors remain vectors.
+      */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Array& operator=(const DenseBase<OtherDerived>& other)
+    {
+      return Base::_set(other);
+    }
+
+    /** This is a special case of the templated operator=. Its purpose is to
+      * prevent a default operator= from hiding the templated operator=.
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Array& operator=(const Array& other)
+    {
+      return Base::_set(other);
+    }
+
+    /** Default constructor.
+      *
+      * For fixed-size matrices, does nothing.
+      *
+      * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix
+      * is called a null matrix. This constructor is the unique way to create null matrices: resizing
+      * a matrix to 0 is not supported.
+      *
+      * \sa resize(Index,Index)
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Array() : Base()
+    {
+      EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+    }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    // FIXME is it still needed ??
+    /** \internal */
+    EIGEN_DEVICE_FUNC
+    Array(internal::constructor_without_unaligned_array_assert)
+      : Base(internal::constructor_without_unaligned_array_assert())
+    {
+      EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+    }
+#endif
+
+    EIGEN_DEVICE_FUNC
+    Array(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
+      : Base(std::move(other))
+    {
+    }
+    EIGEN_DEVICE_FUNC
+    Array& operator=(Array&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
+    {
+      Base::operator=(std::move(other));
+      return *this;
+    }
+
+    /** \copydoc PlainObjectBase(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
+     *
+     * Example: \include Array_variadic_ctor_cxx11.cpp
+     * Output: \verbinclude Array_variadic_ctor_cxx11.out
+     *
+     * \sa Array(const std::initializer_list<std::initializer_list<Scalar>>&)
+     * \sa Array(const Scalar&), Array(const Scalar&,const Scalar&)
+     */
+    template <typename... ArgTypes>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
+      : Base(a0, a1, a2, a3, args...) {}
+
+    /** \brief Constructs an array and initializes it from the coefficients given as initializer-lists grouped by row. \cpp11
+      *
+      * In the general case, the constructor takes a list of rows, each row being represented as a list of coefficients:
+      *
+      * Example: \include Array_initializer_list_23_cxx11.cpp
+      * Output: \verbinclude Array_initializer_list_23_cxx11.out
+      *
+      * Each of the inner initializer lists must contain the exact same number of elements, otherwise an assertion is triggered.
+      *
+      * In the case of a compile-time column 1D array, implicit transposition from a single row is allowed.
+      * Therefore <code> Array<int,Dynamic,1>{{1,2,3,4,5}}</code> is legal and the more verbose syntax
+      * <code>Array<int,Dynamic,1>{{1},{2},{3},{4},{5}}</code> can be avoided:
+      *
+      * Example: \include Array_initializer_list_vector_cxx11.cpp
+      * Output: \verbinclude Array_initializer_list_vector_cxx11.out
+      *
+      * In the case of fixed-sized arrays, the initializer list sizes must exactly match the array sizes,
+      * and implicit transposition is allowed for compile-time 1D arrays only.
+      *
+      * \sa  Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
+      */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr Array(
+        const std::initializer_list<std::initializer_list<Scalar>>& list)
+        : Base(list) {}
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename T>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE explicit Array(const T& x)
+    {
+      Base::template _init1<T>(x);
+    }
+
+    template<typename T0, typename T1>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Array(const T0& val0, const T1& val1)
+    {
+      this->template _init2<T0,T1>(val0, val1);
+    }
+
+    #else
+    /** \brief Constructs a fixed-sized array initialized with coefficients starting at \a data */
+    EIGEN_DEVICE_FUNC explicit Array(const Scalar *data);
+    /** Constructs a vector or row-vector with given dimension. \only_for_vectors
+      *
+      * Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
+      * it is redundant to pass the dimension here, so it makes more sense to use the default
+      * constructor Array() instead.
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE explicit Array(Index dim);
+    /** constructs an initialized 1x1 Array with the given coefficient
+      * \sa const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args */
+    Array(const Scalar& value);
+    /** constructs an uninitialized array with \a rows rows and \a cols columns.
+      *
+      * This is useful for dynamic-size arrays. For fixed-size arrays,
+      * it is redundant to pass these parameters, so one should use the default constructor
+      * Array() instead. */
+    Array(Index rows, Index cols);
+    /** constructs an initialized 2D vector with given coefficients
+      * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) */
+    Array(const Scalar& val0, const Scalar& val1);
+    #endif  // end EIGEN_PARSED_BY_DOXYGEN
+
+    /** constructs an initialized 3D vector with given coefficients
+      * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2)
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3)
+      m_storage.data()[0] = val0;
+      m_storage.data()[1] = val1;
+      m_storage.data()[2] = val2;
+    }
+    /** constructs an initialized 4D vector with given coefficients
+      * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2, const Scalar& val3)
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4)
+      m_storage.data()[0] = val0;
+      m_storage.data()[1] = val1;
+      m_storage.data()[2] = val2;
+      m_storage.data()[3] = val3;
+    }
+
+    /** Copy constructor */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Array(const Array& other)
+            : Base(other)
+    { }
+
+  private:
+    struct PrivateType {};
+  public:
+
+    /** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Array(const EigenBase<OtherDerived> &other,
+                              std::enable_if_t<internal::is_convertible<typename OtherDerived::Scalar,Scalar>::value,
+                                               PrivateType> = PrivateType())
+      : Base(other.derived())
+    { }
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index innerStride() const EIGEN_NOEXCEPT{ return 1; }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index outerStride() const EIGEN_NOEXCEPT { return this->innerSize(); }
+
+    #ifdef EIGEN_ARRAY_PLUGIN
+    #include EIGEN_ARRAY_PLUGIN
+    #endif
+
+  private:
+
+    template<typename MatrixType, typename OtherDerived, bool SwapPointers>
+    friend struct internal::matrix_swap_impl;
+};
+
+/** \defgroup arraytypedefs Global array typedefs
+  * \ingroup Core_Module
+  *
+  * %Eigen defines several typedef shortcuts for most common 1D and 2D array types.
+  *
+  * The general patterns are the following:
+  *
+  * \c ArrayRowsColsType where \c Rows and \c Cols can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size,
+  * and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd
+  * for complex double.
+  *
+  * For example, \c Array33d is a fixed-size 3x3 array type of doubles, and \c ArrayXXf is a dynamic-size matrix of floats.
+  *
+  * There are also \c ArraySizeType which are self-explanatory. For example, \c Array4cf is
+  * a fixed-size 1D array of 4 complex floats.
+  *
+  * With \cpp11, template alias are also defined for common sizes.
+  * They follow the same pattern as above except that the scalar type suffix is replaced by a
+  * template parameter, i.e.:
+  *   - `ArrayRowsCols<Type>` where `Rows` and `Cols` can be \c 2,\c 3,\c 4, or \c X for fixed or dynamic size.
+  *   - `ArraySize<Type>` where `Size` can be \c 2,\c 3,\c 4 or \c X for fixed or dynamic size 1D arrays.
+  *
+  * \sa class Array
+  */
+
+#define EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix)   \
+/** \ingroup arraytypedefs */                                    \
+typedef Array<Type, Size, Size> Array##SizeSuffix##SizeSuffix##TypeSuffix;  \
+/** \ingroup arraytypedefs */                                    \
+typedef Array<Type, Size, 1>    Array##SizeSuffix##TypeSuffix;
+
+#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, Size)         \
+/** \ingroup arraytypedefs */                                    \
+typedef Array<Type, Size, Dynamic> Array##Size##X##TypeSuffix;  \
+/** \ingroup arraytypedefs */                                    \
+typedef Array<Type, Dynamic, Size> Array##X##Size##TypeSuffix;
+
+#define EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
+EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 2, 2) \
+EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 3, 3) \
+EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 4, 4) \
+EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \
+EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \
+EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \
+EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 4)
+
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(int,                  i)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(float,                f)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(double,               d)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<float>,  cf)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<double>, cd)
+
+#undef EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES
+#undef EIGEN_MAKE_ARRAY_TYPEDEFS
+#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
+
+#define EIGEN_MAKE_ARRAY_TYPEDEFS(Size, SizeSuffix)               \
+/** \ingroup arraytypedefs */                                     \
+/** \brief \cpp11 */                                              \
+template <typename Type>                                          \
+using Array##SizeSuffix##SizeSuffix = Array<Type, Size, Size>;    \
+/** \ingroup arraytypedefs */                                     \
+/** \brief \cpp11 */                                              \
+template <typename Type>                                          \
+using Array##SizeSuffix = Array<Type, Size, 1>;
+
+#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Size)                     \
+/** \ingroup arraytypedefs */                                     \
+/** \brief \cpp11 */                                              \
+template <typename Type>                                          \
+using Array##Size##X = Array<Type, Size, Dynamic>;                \
+/** \ingroup arraytypedefs */                                     \
+/** \brief \cpp11 */                                              \
+template <typename Type>                                          \
+using Array##X##Size = Array<Type, Dynamic, Size>;
+
+EIGEN_MAKE_ARRAY_TYPEDEFS(2, 2)
+EIGEN_MAKE_ARRAY_TYPEDEFS(3, 3)
+EIGEN_MAKE_ARRAY_TYPEDEFS(4, 4)
+EIGEN_MAKE_ARRAY_TYPEDEFS(Dynamic, X)
+EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(2)
+EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(3)
+EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(4)
+
+#undef EIGEN_MAKE_ARRAY_TYPEDEFS
+#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
+
+#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \
+using Eigen::Matrix##SizeSuffix##TypeSuffix; \
+using Eigen::Vector##SizeSuffix##TypeSuffix; \
+using Eigen::RowVector##SizeSuffix##TypeSuffix;
+
+#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(TypeSuffix) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X) \
+
+#define EIGEN_USING_ARRAY_TYPEDEFS \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(i) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(f) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(d) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cf) \
+EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cd)
+
+} // end namespace Eigen
+
+#endif // EIGEN_ARRAY_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ArrayBase.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ArrayBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..28397e5cf53e2cfc09ddc1c07441638985fc9c3d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ArrayBase.h
@@ -0,0 +1,228 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARRAYBASE_H
+#define EIGEN_ARRAYBASE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+template<typename ExpressionType> class MatrixWrapper;
+
+/** \class ArrayBase
+  * \ingroup Core_Module
+  *
+  * \brief Base class for all 1D and 2D array, and related expressions
+  *
+  * An array is similar to a dense vector or matrix. While matrices are mathematical
+  * objects with well defined linear algebra operators, an array is just a collection
+  * of scalar values arranged in a one or two dimensional fashion. As the main consequence,
+  * all operations applied to an array are performed coefficient wise. Furthermore,
+  * arrays support scalar math functions of the c++ standard library (e.g., std::sin(x)), and convenient
+  * constructors allowing to easily write generic code working for both scalar values
+  * and arrays.
+  *
+  * This class is the base that is inherited by all array expression types.
+  *
+  * \tparam Derived is the derived type, e.g., an array or an expression type.
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
+  *
+  * \sa class MatrixBase, \ref TopicClassHierarchy
+  */
+template<typename Derived> class ArrayBase
+  : public DenseBase<Derived>
+{
+  public:
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** The base class for a given storage type. */
+    typedef ArrayBase StorageBaseType;
+
+    typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl;
+
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    typedef DenseBase<Derived> Base;
+    using Base::RowsAtCompileTime;
+    using Base::ColsAtCompileTime;
+    using Base::SizeAtCompileTime;
+    using Base::MaxRowsAtCompileTime;
+    using Base::MaxColsAtCompileTime;
+    using Base::MaxSizeAtCompileTime;
+    using Base::IsVectorAtCompileTime;
+    using Base::Flags;
+    
+    using Base::derived;
+    using Base::const_cast_derived;
+    using Base::rows;
+    using Base::cols;
+    using Base::size;
+    using Base::coeff;
+    using Base::coeffRef;
+    using Base::lazyAssign;
+    using Base::operator-;
+    using Base::operator=;
+    using Base::operator+=;
+    using Base::operator-=;
+    using Base::operator*=;
+    using Base::operator/=;
+
+    typedef typename Base::CoeffReturnType CoeffReturnType;
+
+#endif // not EIGEN_PARSED_BY_DOXYGEN
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typedef typename Base::PlainObject PlainObject;
+
+    /** \internal Represents a matrix with all coefficients equal to one another*/
+    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
+#endif // not EIGEN_PARSED_BY_DOXYGEN
+
+#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::ArrayBase
+#define EIGEN_DOC_UNARY_ADDONS(X,Y)
+#   include "../plugins/MatrixCwiseUnaryOps.h"
+#   include "../plugins/ArrayCwiseUnaryOps.h"
+#   include "../plugins/CommonCwiseBinaryOps.h"
+#   include "../plugins/MatrixCwiseBinaryOps.h"
+#   include "../plugins/ArrayCwiseBinaryOps.h"
+#   ifdef EIGEN_ARRAYBASE_PLUGIN
+#     include EIGEN_ARRAYBASE_PLUGIN
+#   endif
+#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
+#undef EIGEN_DOC_UNARY_ADDONS
+
+    /** Special case of the template operator=, in order to prevent the compiler
+      * from generating a default operator= (issue hit with g++ 4.1)
+      */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator=(const ArrayBase& other)
+    {
+      internal::call_assignment(derived(), other.derived());
+      return derived();
+    }
+    
+    /** Set all the entries to \a value.
+      * \sa DenseBase::setConstant(), DenseBase::fill() */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator=(const Scalar &value)
+    { Base::setConstant(value); return derived(); }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator+=(const Scalar& scalar);
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator-=(const Scalar& scalar);
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator+=(const ArrayBase<OtherDerived>& other);
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator-=(const ArrayBase<OtherDerived>& other);
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator*=(const ArrayBase<OtherDerived>& other);
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator/=(const ArrayBase<OtherDerived>& other);
+
+  public:
+    EIGEN_DEVICE_FUNC
+    ArrayBase<Derived>& array() { return *this; }
+    EIGEN_DEVICE_FUNC
+    const ArrayBase<Derived>& array() const { return *this; }
+
+    /** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array
+      * \sa MatrixBase::array() */
+    EIGEN_DEVICE_FUNC
+    MatrixWrapper<Derived> matrix() { return MatrixWrapper<Derived>(derived()); }
+    EIGEN_DEVICE_FUNC
+    const MatrixWrapper<const Derived> matrix() const { return MatrixWrapper<const Derived>(derived()); }
+
+//     template<typename Dest>
+//     inline void evalTo(Dest& dst) const { dst = matrix(); }
+
+  protected:
+    EIGEN_DEFAULT_COPY_CONSTRUCTOR(ArrayBase)
+    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(ArrayBase)
+
+  private:
+    explicit ArrayBase(Index);
+    ArrayBase(Index,Index);
+    template<typename OtherDerived> explicit ArrayBase(const ArrayBase<OtherDerived>&);
+  protected:
+    // mixing arrays and matrices is not legal
+    template<typename OtherDerived> Derived& operator+=(const MatrixBase<OtherDerived>& )
+    {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
+    // mixing arrays and matrices is not legal
+    template<typename OtherDerived> Derived& operator-=(const MatrixBase<OtherDerived>& )
+    {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
+};
+
+/** replaces \c *this by \c *this - \a other.
+  *
+  * \returns a reference to \c *this
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
+ArrayBase<Derived>::operator-=(const ArrayBase<OtherDerived> &other)
+{
+  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+
+/** replaces \c *this by \c *this + \a other.
+  *
+  * \returns a reference to \c *this
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
+ArrayBase<Derived>::operator+=(const ArrayBase<OtherDerived>& other)
+{
+  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+
+/** replaces \c *this by \c *this * \a other coefficient wise.
+  *
+  * \returns a reference to \c *this
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
+ArrayBase<Derived>::operator*=(const ArrayBase<OtherDerived>& other)
+{
+  call_assignment(derived(), other.derived(), internal::mul_assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+
+/** replaces \c *this by \c *this / \a other coefficient wise.
+  *
+  * \returns a reference to \c *this
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
+ArrayBase<Derived>::operator/=(const ArrayBase<OtherDerived>& other)
+{
+  call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_ARRAYBASE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ArrayWrapper.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ArrayWrapper.h
new file mode 100644
index 0000000000000000000000000000000000000000..e65b8fb17df1c26fe4b7afaa9fa1bc2881e73174
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ArrayWrapper.h
@@ -0,0 +1,211 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARRAYWRAPPER_H
+#define EIGEN_ARRAYWRAPPER_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class ArrayWrapper
+  * \ingroup Core_Module
+  *
+  * \brief Expression of a mathematical vector or matrix as an array object
+  *
+  * This class is the return type of MatrixBase::array(), and most of the time
+  * this is the only way it is use.
+  *
+  * \sa MatrixBase::array(), class MatrixWrapper
+  */
+
+namespace internal {
+template<typename ExpressionType>
+struct traits<ArrayWrapper<ExpressionType> >
+  : public traits<remove_all_t<typename ExpressionType::Nested> >
+{
+  typedef ArrayXpr XprKind;
+  // Let's remove NestByRefBit
+  enum {
+    Flags0 = traits<remove_all_t<typename ExpressionType::Nested> >::Flags,
+    LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
+    Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
+  };
+};
+}
+
+template<typename ExpressionType>
+class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> >
+{
+  public:
+    typedef ArrayBase<ArrayWrapper> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper)
+    typedef internal::remove_all_t<ExpressionType> NestedExpression;
+
+    typedef std::conditional_t<
+                       internal::is_lvalue<ExpressionType>::value,
+                       Scalar,
+                       const Scalar
+                     > ScalarWithConstIfNotLvalue;
+
+    typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
+
+    using Base::coeffRef;
+
+    EIGEN_DEVICE_FUNC
+    explicit EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix) : m_expression(matrix) {}
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index outerStride() const EIGEN_NOEXCEPT { return m_expression.outerStride(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index innerStride() const EIGEN_NOEXCEPT { return m_expression.innerStride(); }
+
+    EIGEN_DEVICE_FUNC
+    inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
+    EIGEN_DEVICE_FUNC
+    inline const Scalar* data() const { return m_expression.data(); }
+
+    EIGEN_DEVICE_FUNC
+    inline const Scalar& coeffRef(Index rowId, Index colId) const
+    {
+      return m_expression.coeffRef(rowId, colId);
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline const Scalar& coeffRef(Index index) const
+    {
+      return m_expression.coeffRef(index);
+    }
+
+    template<typename Dest>
+    EIGEN_DEVICE_FUNC
+    inline void evalTo(Dest& dst) const { dst = m_expression; }
+
+    EIGEN_DEVICE_FUNC
+    const internal::remove_all_t<NestedExpressionType>&
+    nestedExpression() const
+    {
+      return m_expression;
+    }
+
+    /** Forwards the resizing request to the nested expression
+      * \sa DenseBase::resize(Index)  */
+    EIGEN_DEVICE_FUNC
+    void resize(Index newSize) { m_expression.resize(newSize); }
+    /** Forwards the resizing request to the nested expression
+      * \sa DenseBase::resize(Index,Index)*/
+    EIGEN_DEVICE_FUNC
+    void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
+
+  protected:
+    NestedExpressionType m_expression;
+};
+
+/** \class MatrixWrapper
+  * \ingroup Core_Module
+  *
+  * \brief Expression of an array as a mathematical vector or matrix
+  *
+  * This class is the return type of ArrayBase::matrix(), and most of the time
+  * this is the only way it is use.
+  *
+  * \sa MatrixBase::matrix(), class ArrayWrapper
+  */
+
+namespace internal {
+template<typename ExpressionType>
+struct traits<MatrixWrapper<ExpressionType> >
+ : public traits<remove_all_t<typename ExpressionType::Nested> >
+{
+  typedef MatrixXpr XprKind;
+  // Let's remove NestByRefBit
+  enum {
+    Flags0 = traits<remove_all_t<typename ExpressionType::Nested> >::Flags,
+    LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
+    Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
+  };
+};
+}
+
+template<typename ExpressionType>
+class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> >
+{
+  public:
+    typedef MatrixBase<MatrixWrapper<ExpressionType> > Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(MatrixWrapper)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(MatrixWrapper)
+    typedef internal::remove_all_t<ExpressionType> NestedExpression;
+
+    typedef std::conditional_t<
+              internal::is_lvalue<ExpressionType>::value,
+              Scalar,
+              const Scalar
+            > ScalarWithConstIfNotLvalue;
+
+    typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
+
+    using Base::coeffRef;
+
+    EIGEN_DEVICE_FUNC
+    explicit inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {}
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index outerStride() const EIGEN_NOEXCEPT { return m_expression.outerStride(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index innerStride() const EIGEN_NOEXCEPT { return m_expression.innerStride(); }
+
+    EIGEN_DEVICE_FUNC
+    inline ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
+    EIGEN_DEVICE_FUNC
+    inline const Scalar* data() const { return m_expression.data(); }
+
+    EIGEN_DEVICE_FUNC
+    inline const Scalar& coeffRef(Index rowId, Index colId) const
+    {
+      return m_expression.derived().coeffRef(rowId, colId);
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline const Scalar& coeffRef(Index index) const
+    {
+      return m_expression.coeffRef(index);
+    }
+
+    EIGEN_DEVICE_FUNC
+    const internal::remove_all_t<NestedExpressionType>&
+    nestedExpression() const
+    {
+      return m_expression;
+    }
+
+    /** Forwards the resizing request to the nested expression
+      * \sa DenseBase::resize(Index)  */
+    EIGEN_DEVICE_FUNC
+    void resize(Index newSize) { m_expression.resize(newSize); }
+    /** Forwards the resizing request to the nested expression
+      * \sa DenseBase::resize(Index,Index)*/
+    EIGEN_DEVICE_FUNC
+    void resize(Index rows, Index cols) { m_expression.resize(rows,cols); }
+
+  protected:
+    NestedExpressionType m_expression;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_ARRAYWRAPPER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Assign.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Assign.h
new file mode 100644
index 0000000000000000000000000000000000000000..dc716d3d8f5e5172c96c2e172c9ab14bf7eb373b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Assign.h
@@ -0,0 +1,92 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007 Michael Olbrich <michael.olbrich@gmx.net>
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ASSIGN_H
+#define EIGEN_ASSIGN_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>
+  ::lazyAssign(const DenseBase<OtherDerived>& other)
+{
+  enum{
+    SameType = internal::is_same<typename Derived::Scalar,typename OtherDerived::Scalar>::value
+  };
+
+  EIGEN_STATIC_ASSERT_LVALUE(Derived)
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived)
+  EIGEN_STATIC_ASSERT(SameType,YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  eigen_assert(rows() == other.rows() && cols() == other.cols());
+  internal::call_assignment_no_alias(derived(),other.derived());
+  
+  return derived();
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
+{
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template<typename Derived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase& other)
+{
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template<typename Derived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const MatrixBase& other)
+{
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template<typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const DenseBase<OtherDerived>& other)
+{
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template<typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const EigenBase<OtherDerived>& other)
+{
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
+{
+  other.derived().evalTo(derived());
+  return derived();
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_ASSIGN_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/AssignEvaluator.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/AssignEvaluator.h
new file mode 100644
index 0000000000000000000000000000000000000000..ec03822409b5e173f89e71f952620d817226b189
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/AssignEvaluator.h
@@ -0,0 +1,1035 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2011-2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ASSIGN_EVALUATOR_H
+#define EIGEN_ASSIGN_EVALUATOR_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// This implementation is based on Assign.h
+
+namespace internal {
+
+/***************************************************************************
+* Part 1 : the logic deciding a strategy for traversal and unrolling       *
+***************************************************************************/
+
+// copy_using_evaluator_traits is based on assign_traits
+
+template <typename DstEvaluator, typename SrcEvaluator, typename AssignFunc, int MaxPacketSize = -1>
+struct copy_using_evaluator_traits
+{
+  typedef typename DstEvaluator::XprType Dst;
+  typedef typename Dst::Scalar DstScalar;
+
+  enum {
+    DstFlags = DstEvaluator::Flags,
+    SrcFlags = SrcEvaluator::Flags
+  };
+
+public:
+  enum {
+    DstAlignment = DstEvaluator::Alignment,
+    SrcAlignment = SrcEvaluator::Alignment,
+    DstHasDirectAccess = (DstFlags & DirectAccessBit) == DirectAccessBit,
+    JointAlignment = plain_enum_min(DstAlignment, SrcAlignment)
+  };
+
+private:
+  enum {
+    InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
+              : int(DstFlags)&RowMajorBit ? int(Dst::ColsAtCompileTime)
+              : int(Dst::RowsAtCompileTime),
+    InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
+              : int(DstFlags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime)
+              : int(Dst::MaxRowsAtCompileTime),
+    RestrictedInnerSize = min_size_prefer_fixed(InnerSize, MaxPacketSize),
+    RestrictedLinearSize = min_size_prefer_fixed(Dst::SizeAtCompileTime, MaxPacketSize),
+    OuterStride = int(outer_stride_at_compile_time<Dst>::ret),
+    MaxSizeAtCompileTime = Dst::SizeAtCompileTime
+  };
+
+  // TODO distinguish between linear traversal and inner-traversals
+  typedef typename find_best_packet<DstScalar,RestrictedLinearSize>::type LinearPacketType;
+  typedef typename find_best_packet<DstScalar,RestrictedInnerSize>::type InnerPacketType;
+
+  enum {
+    LinearPacketSize = unpacket_traits<LinearPacketType>::size,
+    InnerPacketSize = unpacket_traits<InnerPacketType>::size
+  };
+
+public:
+  enum {
+    LinearRequiredAlignment = unpacket_traits<LinearPacketType>::alignment,
+    InnerRequiredAlignment = unpacket_traits<InnerPacketType>::alignment
+  };
+
+private:
+  enum {
+    DstIsRowMajor = DstFlags&RowMajorBit,
+    SrcIsRowMajor = SrcFlags&RowMajorBit,
+    StorageOrdersAgree = (int(DstIsRowMajor) == int(SrcIsRowMajor)),
+    MightVectorize = bool(StorageOrdersAgree)
+                  && (int(DstFlags) & int(SrcFlags) & ActualPacketAccessBit)
+                  && bool(functor_traits<AssignFunc>::PacketAccess),
+    MayInnerVectorize  = MightVectorize
+                       && int(InnerSize)!=Dynamic && int(InnerSize)%int(InnerPacketSize)==0
+                       && int(OuterStride)!=Dynamic && int(OuterStride)%int(InnerPacketSize)==0
+                       && (EIGEN_UNALIGNED_VECTORIZE  || int(JointAlignment)>=int(InnerRequiredAlignment)),
+    MayLinearize = bool(StorageOrdersAgree) && (int(DstFlags) & int(SrcFlags) & LinearAccessBit),
+    MayLinearVectorize = bool(MightVectorize) && bool(MayLinearize) && bool(DstHasDirectAccess)
+                       && (EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)) || MaxSizeAtCompileTime == Dynamic),
+      /* If the destination isn't aligned, we have to do runtime checks and we don't unroll,
+         so it's only good for large enough sizes. */
+    MaySliceVectorize  = bool(MightVectorize) && bool(DstHasDirectAccess)
+                       && (int(InnerMaxSize)==Dynamic || int(InnerMaxSize)>=(EIGEN_UNALIGNED_VECTORIZE?InnerPacketSize:(3*InnerPacketSize)))
+      /* slice vectorization can be slow, so we only want it if the slices are big, which is
+         indicated by InnerMaxSize rather than InnerSize, think of the case of a dynamic block
+         in a fixed-size matrix
+         However, with EIGEN_UNALIGNED_VECTORIZE and unrolling, slice vectorization is still worth it */
+  };
+
+public:
+  enum {
+    Traversal =  int(Dst::SizeAtCompileTime) == 0 ? int(AllAtOnceTraversal) // If compile-size is zero, traversing will fail at compile-time.
+              : (int(MayLinearVectorize) && (LinearPacketSize>InnerPacketSize)) ? int(LinearVectorizedTraversal)
+              : int(MayInnerVectorize)   ? int(InnerVectorizedTraversal)
+              : int(MayLinearVectorize)  ? int(LinearVectorizedTraversal)
+              : int(MaySliceVectorize)   ? int(SliceVectorizedTraversal)
+              : int(MayLinearize)        ? int(LinearTraversal)
+                                         : int(DefaultTraversal),
+    Vectorized = int(Traversal) == InnerVectorizedTraversal
+              || int(Traversal) == LinearVectorizedTraversal
+              || int(Traversal) == SliceVectorizedTraversal
+  };
+
+  typedef std::conditional_t<int(Traversal)==LinearVectorizedTraversal, LinearPacketType, InnerPacketType> PacketType;
+
+private:
+  enum {
+    ActualPacketSize    = int(Traversal)==LinearVectorizedTraversal ? LinearPacketSize
+                        : Vectorized ? InnerPacketSize
+                        : 1,
+    UnrollingLimit      = EIGEN_UNROLLING_LIMIT * ActualPacketSize,
+    MayUnrollCompletely = int(Dst::SizeAtCompileTime) != Dynamic
+                       && int(Dst::SizeAtCompileTime) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit),
+    MayUnrollInner      = int(InnerSize) != Dynamic
+                       && int(InnerSize) * (int(DstEvaluator::CoeffReadCost)+int(SrcEvaluator::CoeffReadCost)) <= int(UnrollingLimit)
+  };
+
+public:
+  enum {
+    Unrolling = (int(Traversal) == int(InnerVectorizedTraversal) || int(Traversal) == int(DefaultTraversal))
+                ? (
+                    int(MayUnrollCompletely) ? int(CompleteUnrolling)
+                  : int(MayUnrollInner)      ? int(InnerUnrolling)
+                                             : int(NoUnrolling)
+                  )
+              : int(Traversal) == int(LinearVectorizedTraversal)
+                ? ( bool(MayUnrollCompletely) && ( EIGEN_UNALIGNED_VECTORIZE || (int(DstAlignment)>=int(LinearRequiredAlignment)))
+                          ? int(CompleteUnrolling)
+                          : int(NoUnrolling) )
+              : int(Traversal) == int(LinearTraversal)
+                ? ( bool(MayUnrollCompletely) ? int(CompleteUnrolling)
+                                              : int(NoUnrolling) )
+#if EIGEN_UNALIGNED_VECTORIZE
+              : int(Traversal) == int(SliceVectorizedTraversal)
+                ? ( bool(MayUnrollInner) ? int(InnerUnrolling)
+                                         : int(NoUnrolling) )
+#endif
+              : int(NoUnrolling)
+  };
+
+#ifdef EIGEN_DEBUG_ASSIGN
+  static void debug()
+  {
+    std::cerr << "DstXpr: " << typeid(typename DstEvaluator::XprType).name() << std::endl;
+    std::cerr << "SrcXpr: " << typeid(typename SrcEvaluator::XprType).name() << std::endl;
+    std::cerr.setf(std::ios::hex, std::ios::basefield);
+    std::cerr << "DstFlags" << " = " << DstFlags << " (" << demangle_flags(DstFlags) << " )" << std::endl;
+    std::cerr << "SrcFlags" << " = " << SrcFlags << " (" << demangle_flags(SrcFlags) << " )" << std::endl;
+    std::cerr.unsetf(std::ios::hex);
+    EIGEN_DEBUG_VAR(DstAlignment)
+    EIGEN_DEBUG_VAR(SrcAlignment)
+    EIGEN_DEBUG_VAR(LinearRequiredAlignment)
+    EIGEN_DEBUG_VAR(InnerRequiredAlignment)
+    EIGEN_DEBUG_VAR(JointAlignment)
+    EIGEN_DEBUG_VAR(InnerSize)
+    EIGEN_DEBUG_VAR(InnerMaxSize)
+    EIGEN_DEBUG_VAR(LinearPacketSize)
+    EIGEN_DEBUG_VAR(InnerPacketSize)
+    EIGEN_DEBUG_VAR(ActualPacketSize)
+    EIGEN_DEBUG_VAR(StorageOrdersAgree)
+    EIGEN_DEBUG_VAR(MightVectorize)
+    EIGEN_DEBUG_VAR(MayLinearize)
+    EIGEN_DEBUG_VAR(MayInnerVectorize)
+    EIGEN_DEBUG_VAR(MayLinearVectorize)
+    EIGEN_DEBUG_VAR(MaySliceVectorize)
+    std::cerr << "Traversal" << " = " << Traversal << " (" << demangle_traversal(Traversal) << ")" << std::endl;
+    EIGEN_DEBUG_VAR(SrcEvaluator::CoeffReadCost)
+    EIGEN_DEBUG_VAR(DstEvaluator::CoeffReadCost)
+    EIGEN_DEBUG_VAR(Dst::SizeAtCompileTime)
+    EIGEN_DEBUG_VAR(UnrollingLimit)
+    EIGEN_DEBUG_VAR(MayUnrollCompletely)
+    EIGEN_DEBUG_VAR(MayUnrollInner)
+    std::cerr << "Unrolling" << " = " << Unrolling << " (" << demangle_unrolling(Unrolling) << ")" << std::endl;
+    std::cerr << std::endl;
+  }
+#endif
+};
+
+/***************************************************************************
+* Part 2 : meta-unrollers
+***************************************************************************/
+
+/************************
+*** Default traversal ***
+************************/
+
+template<typename Kernel, int Index, int Stop>
+struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling
+{
+  // FIXME: this is not very clean, perhaps this information should be provided by the kernel?
+  typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
+  typedef typename DstEvaluatorType::XprType DstXprType;
+
+  enum {
+    outer = Index / DstXprType::InnerSizeAtCompileTime,
+    inner = Index % DstXprType::InnerSizeAtCompileTime
+  };
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
+  {
+    kernel.assignCoeffByOuterInner(outer, inner);
+    copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Index+1, Stop>::run(kernel);
+  }
+};
+
+template<typename Kernel, int Stop>
+struct copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, Stop, Stop>
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel&) { }
+};
+
+template<typename Kernel, int Index_, int Stop>
+struct copy_using_evaluator_DefaultTraversal_InnerUnrolling
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer)
+  {
+    kernel.assignCoeffByOuterInner(outer, Index_);
+    copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Index_+1, Stop>::run(kernel, outer);
+  }
+};
+
+template<typename Kernel, int Stop>
+struct copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, Stop, Stop>
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&, Index) { }
+};
+
+/***********************
+*** Linear traversal ***
+***********************/
+
+template<typename Kernel, int Index, int Stop>
+struct copy_using_evaluator_LinearTraversal_CompleteUnrolling
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel)
+  {
+    kernel.assignCoeff(Index);
+    copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Index+1, Stop>::run(kernel);
+  }
+};
+
+template<typename Kernel, int Stop>
+struct copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, Stop, Stop>
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel&) { }
+};
+
+/**************************
+*** Inner vectorization ***
+**************************/
+
+template<typename Kernel, int Index, int Stop>
+struct copy_using_evaluator_innervec_CompleteUnrolling
+{
+  // FIXME: this is not very clean, perhaps this information should be provided by the kernel?
+  typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
+  typedef typename DstEvaluatorType::XprType DstXprType;
+  typedef typename Kernel::PacketType PacketType;
+
+  enum {
+    outer = Index / DstXprType::InnerSizeAtCompileTime,
+    inner = Index % DstXprType::InnerSizeAtCompileTime,
+    SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
+    DstAlignment = Kernel::AssignmentTraits::DstAlignment
+  };
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
+  {
+    kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
+    enum { NextIndex = Index + unpacket_traits<PacketType>::size };
+    copy_using_evaluator_innervec_CompleteUnrolling<Kernel, NextIndex, Stop>::run(kernel);
+  }
+};
+
+template<typename Kernel, int Stop>
+struct copy_using_evaluator_innervec_CompleteUnrolling<Kernel, Stop, Stop>
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel&) { }
+};
+
+template<typename Kernel, int Index_, int Stop, int SrcAlignment, int DstAlignment>
+struct copy_using_evaluator_innervec_InnerUnrolling
+{
+  typedef typename Kernel::PacketType PacketType;
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel, Index outer)
+  {
+    kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, Index_);
+    enum { NextIndex = Index_ + unpacket_traits<PacketType>::size };
+    copy_using_evaluator_innervec_InnerUnrolling<Kernel, NextIndex, Stop, SrcAlignment, DstAlignment>::run(kernel, outer);
+  }
+};
+
+template<typename Kernel, int Stop, int SrcAlignment, int DstAlignment>
+struct copy_using_evaluator_innervec_InnerUnrolling<Kernel, Stop, Stop, SrcAlignment, DstAlignment>
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &, Index) { }
+};
+
+/***************************************************************************
+* Part 3 : implementation of all cases
+***************************************************************************/
+
+// dense_assignment_loop is based on assign_impl
+
+template<typename Kernel,
+         int Traversal = Kernel::AssignmentTraits::Traversal,
+         int Unrolling = Kernel::AssignmentTraits::Unrolling>
+struct dense_assignment_loop;
+
+/************************
+***** Special Cases *****
+************************/
+
+// Zero-sized assignment is a no-op.
+template<typename Kernel, int Unrolling>
+struct dense_assignment_loop<Kernel, AllAtOnceTraversal, Unrolling>
+{
+  EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE EIGEN_CONSTEXPR run(Kernel& /*kernel*/)
+  {
+    EIGEN_STATIC_ASSERT(int(Kernel::DstEvaluatorType::XprType::SizeAtCompileTime) == 0,
+      EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT)
+  }
+};
+
+/************************
+*** Default traversal ***
+************************/
+
+template<typename Kernel>
+struct dense_assignment_loop<Kernel, DefaultTraversal, NoUnrolling>
+{
+  EIGEN_DEVICE_FUNC static void EIGEN_STRONG_INLINE run(Kernel &kernel)
+  {
+    for(Index outer = 0; outer < kernel.outerSize(); ++outer) {
+      for(Index inner = 0; inner < kernel.innerSize(); ++inner) {
+        kernel.assignCoeffByOuterInner(outer, inner);
+      }
+    }
+  }
+};
+
+template<typename Kernel>
+struct dense_assignment_loop<Kernel, DefaultTraversal, CompleteUnrolling>
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
+  {
+    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
+    copy_using_evaluator_DefaultTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
+  }
+};
+
+template<typename Kernel>
+struct dense_assignment_loop<Kernel, DefaultTraversal, InnerUnrolling>
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
+  {
+    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
+
+    const Index outerSize = kernel.outerSize();
+    for(Index outer = 0; outer < outerSize; ++outer)
+      copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime>::run(kernel, outer);
+  }
+};
+
+/***************************
+*** Linear vectorization ***
+***************************/
+
+
+// The goal of unaligned_dense_assignment_loop is simply to factorize the handling
+// of the non vectorizable beginning and ending parts
+
+template <bool IsAligned = false>
+struct unaligned_dense_assignment_loop
+{
+  // if IsAligned = true, then do nothing
+  template <typename Kernel>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel&, Index, Index) {}
+};
+
+template <>
+struct unaligned_dense_assignment_loop<false>
+{
+  // MSVC must not inline this functions. If it does, it fails to optimize the
+  // packet access path.
+  // FIXME check which version exhibits this issue
+#if EIGEN_COMP_MSVC
+  template <typename Kernel>
+  static EIGEN_DONT_INLINE void run(Kernel &kernel,
+                                    Index start,
+                                    Index end)
+#else
+  template <typename Kernel>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel,
+                                      Index start,
+                                      Index end)
+#endif
+  {
+    for (Index index = start; index < end; ++index)
+      kernel.assignCoeff(index);
+  }
+};
+
+template <typename Kernel, int Index, int Stop>
+struct copy_using_evaluator_linearvec_CompleteUnrolling {
+  // FIXME: this is not very clean, perhaps this information should be provided by the kernel?
+  typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
+  typedef typename DstEvaluatorType::XprType DstXprType;
+  typedef typename Kernel::PacketType PacketType;
+
+  enum {
+    SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
+    DstAlignment = Kernel::AssignmentTraits::DstAlignment
+  };
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel& kernel) {
+    kernel.template assignPacket<DstAlignment, SrcAlignment, PacketType>(Index);
+    enum { NextIndex = Index + unpacket_traits<PacketType>::size };
+    copy_using_evaluator_linearvec_CompleteUnrolling<Kernel, NextIndex, Stop>::run(kernel);
+  }
+};
+
+template <typename Kernel, int Stop>
+struct copy_using_evaluator_linearvec_CompleteUnrolling<Kernel, Stop, Stop> {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel&) {}
+};
+
+template<typename Kernel>
+struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, NoUnrolling>
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel)
+  {
+    const Index size = kernel.size();
+    typedef typename Kernel::Scalar Scalar;
+    typedef typename Kernel::PacketType PacketType;
+    enum {
+      requestedAlignment = Kernel::AssignmentTraits::LinearRequiredAlignment,
+      packetSize = unpacket_traits<PacketType>::size,
+      dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment),
+      dstAlignment = packet_traits<Scalar>::AlignedOnScalar ? int(requestedAlignment)
+                                                            : int(Kernel::AssignmentTraits::DstAlignment),
+      srcAlignment = Kernel::AssignmentTraits::JointAlignment
+    };
+    const Index alignedStart = dstIsAligned ? 0 : internal::first_aligned<requestedAlignment>(kernel.dstDataPtr(), size);
+    const Index alignedEnd = alignedStart + ((size-alignedStart)/packetSize)*packetSize;
+
+    unaligned_dense_assignment_loop<dstIsAligned!=0>::run(kernel, 0, alignedStart);
+
+    for(Index index = alignedStart; index < alignedEnd; index += packetSize)
+      kernel.template assignPacket<dstAlignment, srcAlignment, PacketType>(index);
+
+    unaligned_dense_assignment_loop<>::run(kernel, alignedEnd, size);
+  }
+};
+
+template<typename Kernel>
+struct dense_assignment_loop<Kernel, LinearVectorizedTraversal, CompleteUnrolling>
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel)
+  {
+    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
+    typedef typename Kernel::PacketType PacketType;
+
+    enum { size = DstXprType::SizeAtCompileTime,
+           packetSize =unpacket_traits<PacketType>::size,
+           alignedSize = (int(size)/packetSize)*packetSize };
+
+    copy_using_evaluator_linearvec_CompleteUnrolling<Kernel, 0, alignedSize>::run(kernel);
+    copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, alignedSize, size>::run(kernel);
+  }
+};
+
+/**************************
+*** Inner vectorization ***
+**************************/
+
+template<typename Kernel>
+struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, NoUnrolling>
+{
+  typedef typename Kernel::PacketType PacketType;
+  enum {
+    SrcAlignment = Kernel::AssignmentTraits::SrcAlignment,
+    DstAlignment = Kernel::AssignmentTraits::DstAlignment
+  };
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel)
+  {
+    const Index innerSize = kernel.innerSize();
+    const Index outerSize = kernel.outerSize();
+    const Index packetSize = unpacket_traits<PacketType>::size;
+    for(Index outer = 0; outer < outerSize; ++outer)
+      for(Index inner = 0; inner < innerSize; inner+=packetSize)
+        kernel.template assignPacketByOuterInner<DstAlignment, SrcAlignment, PacketType>(outer, inner);
+  }
+};
+
+template<typename Kernel>
+struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, CompleteUnrolling>
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
+  {
+    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
+    copy_using_evaluator_innervec_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
+  }
+};
+
+template<typename Kernel>
+struct dense_assignment_loop<Kernel, InnerVectorizedTraversal, InnerUnrolling>
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void run(Kernel &kernel)
+  {
+    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
+    typedef typename Kernel::AssignmentTraits Traits;
+    const Index outerSize = kernel.outerSize();
+    for(Index outer = 0; outer < outerSize; ++outer)
+      copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, DstXprType::InnerSizeAtCompileTime,
+                                                   Traits::SrcAlignment, Traits::DstAlignment>::run(kernel, outer);
+  }
+};
+
+/***********************
+*** Linear traversal ***
+***********************/
+
+template<typename Kernel>
+struct dense_assignment_loop<Kernel, LinearTraversal, NoUnrolling>
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel)
+  {
+    const Index size = kernel.size();
+    for(Index i = 0; i < size; ++i)
+      kernel.assignCoeff(i);
+  }
+};
+
+template<typename Kernel>
+struct dense_assignment_loop<Kernel, LinearTraversal, CompleteUnrolling>
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel)
+  {
+    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
+    copy_using_evaluator_LinearTraversal_CompleteUnrolling<Kernel, 0, DstXprType::SizeAtCompileTime>::run(kernel);
+  }
+};
+
+/**************************
+*** Slice vectorization ***
+***************************/
+
+template<typename Kernel>
+struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, NoUnrolling>
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel)
+  {
+    typedef typename Kernel::Scalar Scalar;
+    typedef typename Kernel::PacketType PacketType;
+    enum {
+      packetSize = unpacket_traits<PacketType>::size,
+      requestedAlignment = int(Kernel::AssignmentTraits::InnerRequiredAlignment),
+      alignable = packet_traits<Scalar>::AlignedOnScalar || int(Kernel::AssignmentTraits::DstAlignment)>=sizeof(Scalar),
+      dstIsAligned = int(Kernel::AssignmentTraits::DstAlignment)>=int(requestedAlignment),
+      dstAlignment = alignable ? int(requestedAlignment)
+                               : int(Kernel::AssignmentTraits::DstAlignment)
+    };
+    const Scalar *dst_ptr = kernel.dstDataPtr();
+    if((!bool(dstIsAligned)) && (std::uintptr_t(dst_ptr) % sizeof(Scalar))>0)
+    {
+      // the pointer is not aligned-on scalar, so alignment is not possible
+      return dense_assignment_loop<Kernel,DefaultTraversal,NoUnrolling>::run(kernel);
+    }
+    const Index packetAlignedMask = packetSize - 1;
+    const Index innerSize = kernel.innerSize();
+    const Index outerSize = kernel.outerSize();
+    const Index alignedStep = alignable ? (packetSize - kernel.outerStride() % packetSize) & packetAlignedMask : 0;
+    Index alignedStart = ((!alignable) || bool(dstIsAligned)) ? 0 : internal::first_aligned<requestedAlignment>(dst_ptr, innerSize);
+
+    for(Index outer = 0; outer < outerSize; ++outer)
+    {
+      const Index alignedEnd = alignedStart + ((innerSize-alignedStart) & ~packetAlignedMask);
+      // do the non-vectorizable part of the assignment
+      for(Index inner = 0; inner<alignedStart ; ++inner)
+        kernel.assignCoeffByOuterInner(outer, inner);
+
+      // do the vectorizable part of the assignment
+      for(Index inner = alignedStart; inner<alignedEnd; inner+=packetSize)
+        kernel.template assignPacketByOuterInner<dstAlignment, Unaligned, PacketType>(outer, inner);
+
+      // do the non-vectorizable part of the assignment
+      for(Index inner = alignedEnd; inner<innerSize ; ++inner)
+        kernel.assignCoeffByOuterInner(outer, inner);
+
+      alignedStart = numext::mini((alignedStart+alignedStep)%packetSize, innerSize);
+    }
+  }
+};
+
+#if EIGEN_UNALIGNED_VECTORIZE
+template<typename Kernel>
+struct dense_assignment_loop<Kernel, SliceVectorizedTraversal, InnerUnrolling>
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void run(Kernel &kernel)
+  {
+    typedef typename Kernel::DstEvaluatorType::XprType DstXprType;
+    typedef typename Kernel::PacketType PacketType;
+
+    enum { innerSize = DstXprType::InnerSizeAtCompileTime,
+           packetSize =unpacket_traits<PacketType>::size,
+           vectorizableSize = (int(innerSize) / int(packetSize)) * int(packetSize),
+           size = DstXprType::SizeAtCompileTime };
+
+    for(Index outer = 0; outer < kernel.outerSize(); ++outer)
+    {
+      copy_using_evaluator_innervec_InnerUnrolling<Kernel, 0, vectorizableSize, 0, 0>::run(kernel, outer);
+      copy_using_evaluator_DefaultTraversal_InnerUnrolling<Kernel, vectorizableSize, innerSize>::run(kernel, outer);
+    }
+  }
+};
+#endif
+
+
+/***************************************************************************
+* Part 4 : Generic dense assignment kernel
+***************************************************************************/
+
+// This class generalize the assignment of a coefficient (or packet) from one dense evaluator
+// to another dense writable evaluator.
+// It is parametrized by the two evaluators, and the actual assignment functor.
+// This abstraction level permits to keep the evaluation loops as simple and as generic as possible.
+// One can customize the assignment using this generic dense_assignment_kernel with different
+// functors, or by completely overloading it, by-passing a functor.
+template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version = Specialized>
+class generic_dense_assignment_kernel
+{
+protected:
+  typedef typename DstEvaluatorTypeT::XprType DstXprType;
+  typedef typename SrcEvaluatorTypeT::XprType SrcXprType;
+public:
+
+  typedef DstEvaluatorTypeT DstEvaluatorType;
+  typedef SrcEvaluatorTypeT SrcEvaluatorType;
+  typedef typename DstEvaluatorType::Scalar Scalar;
+  typedef copy_using_evaluator_traits<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor> AssignmentTraits;
+  typedef typename AssignmentTraits::PacketType PacketType;
+
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  generic_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
+    : m_dst(dst), m_src(src), m_functor(func), m_dstExpr(dstExpr)
+  {
+    #ifdef EIGEN_DEBUG_ASSIGN
+    AssignmentTraits::debug();
+    #endif
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index size() const EIGEN_NOEXCEPT { return m_dstExpr.size(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index innerSize() const EIGEN_NOEXCEPT { return m_dstExpr.innerSize(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index outerSize() const EIGEN_NOEXCEPT { return m_dstExpr.outerSize(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_dstExpr.rows(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_dstExpr.cols(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index outerStride() const EIGEN_NOEXCEPT { return m_dstExpr.outerStride(); }
+
+  EIGEN_DEVICE_FUNC DstEvaluatorType& dstEvaluator() EIGEN_NOEXCEPT { return m_dst; }
+  EIGEN_DEVICE_FUNC const SrcEvaluatorType& srcEvaluator() const EIGEN_NOEXCEPT { return m_src; }
+
+  /// Assign src(row,col) to dst(row,col) through the assignment functor.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index row, Index col)
+  {
+    m_functor.assignCoeff(m_dst.coeffRef(row,col), m_src.coeff(row,col));
+  }
+
+  /// \sa assignCoeff(Index,Index)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Index index)
+  {
+    m_functor.assignCoeff(m_dst.coeffRef(index), m_src.coeff(index));
+  }
+
+  /// \sa assignCoeff(Index,Index)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeffByOuterInner(Index outer, Index inner)
+  {
+    Index row = rowIndexByOuterInner(outer, inner);
+    Index col = colIndexByOuterInner(outer, inner);
+    assignCoeff(row, col);
+  }
+
+
+  template<int StoreMode, int LoadMode, typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index row, Index col)
+  {
+    m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(row,col), m_src.template packet<LoadMode,Packet>(row,col));
+  }
+
+  template<int StoreMode, int LoadMode, typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacket(Index index)
+  {
+    m_functor.template assignPacket<StoreMode>(&m_dst.coeffRef(index), m_src.template packet<LoadMode,Packet>(index));
+  }
+
+  template<int StoreMode, int LoadMode, typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignPacketByOuterInner(Index outer, Index inner)
+  {
+    Index row = rowIndexByOuterInner(outer, inner);
+    Index col = colIndexByOuterInner(outer, inner);
+    assignPacket<StoreMode,LoadMode,Packet>(row, col);
+  }
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner)
+  {
+    typedef typename DstEvaluatorType::ExpressionTraits Traits;
+    return int(Traits::RowsAtCompileTime) == 1 ? 0
+      : int(Traits::ColsAtCompileTime) == 1 ? inner
+      : int(DstEvaluatorType::Flags)&RowMajorBit ? outer
+      : inner;
+  }
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner)
+  {
+    typedef typename DstEvaluatorType::ExpressionTraits Traits;
+    return int(Traits::ColsAtCompileTime) == 1 ? 0
+      : int(Traits::RowsAtCompileTime) == 1 ? inner
+      : int(DstEvaluatorType::Flags)&RowMajorBit ? inner
+      : outer;
+  }
+
+  EIGEN_DEVICE_FUNC const Scalar* dstDataPtr() const
+  {
+    return m_dstExpr.data();
+  }
+
+protected:
+  DstEvaluatorType& m_dst;
+  const SrcEvaluatorType& m_src;
+  const Functor &m_functor;
+  // TODO find a way to avoid the needs of the original expression
+  DstXprType& m_dstExpr;
+};
+
+// Special kernel used when computing small products whose operands have dynamic dimensions.  It ensures that the
+// PacketSize used is no larger than 4, thereby increasing the chance that vectorized instructions will be used
+// when computing the product.
+
+template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor>
+class restricted_packet_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, BuiltIn>
+{
+protected:
+  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, BuiltIn> Base;
+ public:
+    typedef typename Base::Scalar Scalar;
+    typedef typename Base::DstXprType DstXprType;
+    typedef copy_using_evaluator_traits<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, 4> AssignmentTraits;
+    typedef typename AssignmentTraits::PacketType PacketType;
+
+    EIGEN_DEVICE_FUNC restricted_packet_dense_assignment_kernel(DstEvaluatorTypeT &dst, const SrcEvaluatorTypeT &src, const Functor &func, DstXprType& dstExpr)
+    : Base(dst, src, func, dstExpr)
+  {
+  }
+ };
+
+/***************************************************************************
+* Part 5 : Entry point for dense rectangular assignment
+***************************************************************************/
+
+template<typename DstXprType,typename SrcXprType, typename Functor>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+void resize_if_allowed(DstXprType &dst, const SrcXprType& src, const Functor &/*func*/)
+{
+  EIGEN_ONLY_USED_FOR_DEBUG(dst);
+  EIGEN_ONLY_USED_FOR_DEBUG(src);
+  eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
+}
+
+template<typename DstXprType,typename SrcXprType, typename T1, typename T2>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+void resize_if_allowed(DstXprType &dst, const SrcXprType& src, const internal::assign_op<T1,T2> &/*func*/)
+{
+  Index dstRows = src.rows();
+  Index dstCols = src.cols();
+  if(((dst.rows()!=dstRows) || (dst.cols()!=dstCols)))
+    dst.resize(dstRows, dstCols);
+  eigen_assert(dst.rows() == dstRows && dst.cols() == dstCols);
+}
+
+template<typename DstXprType, typename SrcXprType, typename Functor>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src, const Functor &func)
+{
+  typedef evaluator<DstXprType> DstEvaluatorType;
+  typedef evaluator<SrcXprType> SrcEvaluatorType;
+
+  SrcEvaluatorType srcEvaluator(src);
+
+  // NOTE To properly handle A = (A*A.transpose())/s with A rectangular,
+  // we need to resize the destination after the source evaluator has been created.
+  resize_if_allowed(dst, src, func);
+
+  DstEvaluatorType dstEvaluator(dst);
+
+  typedef generic_dense_assignment_kernel<DstEvaluatorType,SrcEvaluatorType,Functor> Kernel;
+  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
+
+  dense_assignment_loop<Kernel>::run(kernel);
+}
+
+// Specialization for filling the destination with a constant value.
+#ifndef EIGEN_GPU_COMPILE_PHASE
+template<typename DstXprType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const Eigen::CwiseNullaryOp<Eigen::internal::scalar_constant_op<typename DstXprType::Scalar>, DstXprType>& src, const internal::assign_op<typename DstXprType::Scalar,typename DstXprType::Scalar>& func)
+{
+  resize_if_allowed(dst, src, func);
+  std::fill_n(dst.data(), dst.size(), src.functor()());
+}
+#endif
+
+template<typename DstXprType, typename SrcXprType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void call_dense_assignment_loop(DstXprType& dst, const SrcXprType& src)
+{
+  call_dense_assignment_loop(dst, src, internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
+}
+
+/***************************************************************************
+* Part 6 : Generic assignment
+***************************************************************************/
+
+// Based on the respective shapes of the destination and source,
+// the class AssignmentKind determine the kind of assignment mechanism.
+// AssignmentKind must define a Kind typedef.
+template<typename DstShape, typename SrcShape> struct AssignmentKind;
+
+// Assignment kind defined in this file:
+struct Dense2Dense {};
+struct EigenBase2EigenBase {};
+
+template<typename,typename> struct AssignmentKind { typedef EigenBase2EigenBase Kind; };
+template<> struct AssignmentKind<DenseShape,DenseShape> { typedef Dense2Dense Kind; };
+
+// This is the main assignment class
+template< typename DstXprType, typename SrcXprType, typename Functor,
+          typename Kind = typename AssignmentKind< typename evaluator_traits<DstXprType>::Shape , typename evaluator_traits<SrcXprType>::Shape >::Kind,
+          typename EnableIf = void>
+struct Assignment;
+
+
+// The only purpose of this call_assignment() function is to deal with noalias() / "assume-aliasing" and automatic transposition.
+// Indeed, I (Gael) think that this concept of "assume-aliasing" was a mistake, and it makes thing quite complicated.
+// So this intermediate function removes everything related to "assume-aliasing" such that Assignment
+// does not has to bother about these annoying details.
+
+template<typename Dst, typename Src>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+void call_assignment(Dst& dst, const Src& src)
+{
+  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
+}
+template<typename Dst, typename Src>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+void call_assignment(const Dst& dst, const Src& src)
+{
+  call_assignment(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
+}
+
+// Deal with "assume-aliasing"
+template<typename Dst, typename Src, typename Func>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+void call_assignment(Dst& dst, const Src& src, const Func& func, std::enable_if_t< evaluator_assume_aliasing<Src>::value, void*> = 0)
+{
+  typename plain_matrix_type<Src>::type tmp(src);
+  call_assignment_no_alias(dst, tmp, func);
+}
+
+template<typename Dst, typename Src, typename Func>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+void call_assignment(Dst& dst, const Src& src, const Func& func, std::enable_if_t<!evaluator_assume_aliasing<Src>::value, void*> = 0)
+{
+  call_assignment_no_alias(dst, src, func);
+}
+
+// by-pass "assume-aliasing"
+// When there is no aliasing, we require that 'dst' has been properly resized
+template<typename Dst, template <typename> class StorageBase, typename Src, typename Func>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+void call_assignment(NoAlias<Dst,StorageBase>& dst, const Src& src, const Func& func)
+{
+  call_assignment_no_alias(dst.expression(), src, func);
+}
+
+
+template<typename Dst, typename Src, typename Func>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+void call_assignment_no_alias(Dst& dst, const Src& src, const Func& func)
+{
+  enum {
+    NeedToTranspose = (    (int(Dst::RowsAtCompileTime) == 1 && int(Src::ColsAtCompileTime) == 1)
+                        || (int(Dst::ColsAtCompileTime) == 1 && int(Src::RowsAtCompileTime) == 1)
+                      ) && int(Dst::SizeAtCompileTime) != 1
+  };
+
+  typedef std::conditional_t<NeedToTranspose, Transpose<Dst>, Dst> ActualDstTypeCleaned;
+  typedef std::conditional_t<NeedToTranspose, Transpose<Dst>, Dst&> ActualDstType;
+  ActualDstType actualDst(dst);
+
+  // TODO check whether this is the right place to perform these checks:
+  EIGEN_STATIC_ASSERT_LVALUE(Dst)
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(ActualDstTypeCleaned,Src)
+  EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename ActualDstTypeCleaned::Scalar,typename Src::Scalar);
+
+  Assignment<ActualDstTypeCleaned,Src,Func>::run(actualDst, src, func);
+}
+
+template<typename Dst, typename Src, typename Func>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+void call_restricted_packet_assignment_no_alias(Dst& dst, const Src& src, const Func& func)
+{
+    typedef evaluator<Dst> DstEvaluatorType;
+    typedef evaluator<Src> SrcEvaluatorType;
+    typedef restricted_packet_dense_assignment_kernel<DstEvaluatorType,SrcEvaluatorType,Func> Kernel;
+
+    EIGEN_STATIC_ASSERT_LVALUE(Dst)
+    EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename Dst::Scalar,typename Src::Scalar);
+
+    SrcEvaluatorType srcEvaluator(src);
+    resize_if_allowed(dst, src, func);
+
+    DstEvaluatorType dstEvaluator(dst);
+    Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
+
+    dense_assignment_loop<Kernel>::run(kernel);
+}
+
+template<typename Dst, typename Src>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+void call_assignment_no_alias(Dst& dst, const Src& src)
+{
+  call_assignment_no_alias(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
+}
+
+template<typename Dst, typename Src, typename Func>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src, const Func& func)
+{
+  // TODO check whether this is the right place to perform these checks:
+  EIGEN_STATIC_ASSERT_LVALUE(Dst)
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Dst,Src)
+  EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename Dst::Scalar,typename Src::Scalar);
+
+  Assignment<Dst,Src,Func>::run(dst, src, func);
+}
+template<typename Dst, typename Src>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+void call_assignment_no_alias_no_transpose(Dst& dst, const Src& src)
+{
+  call_assignment_no_alias_no_transpose(dst, src, internal::assign_op<typename Dst::Scalar,typename Src::Scalar>());
+}
+
+// forward declaration
+template<typename Dst, typename Src> EIGEN_DEVICE_FUNC void check_for_aliasing(const Dst &dst, const Src &src);
+
+// Generic Dense to Dense assignment
+// Note that the last template argument "Weak" is needed to make it possible to perform
+// both partial specialization+SFINAE without ambiguous specialization
+template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
+struct Assignment<DstXprType, SrcXprType, Functor, Dense2Dense, Weak>
+{
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
+  {
+#ifndef EIGEN_NO_DEBUG
+    internal::check_for_aliasing(dst, src);
+#endif
+
+    call_dense_assignment_loop(dst, src, func);
+  }
+};
+
+// Generic assignment through evalTo.
+// TODO: not sure we have to keep that one, but it helps porting current code to new evaluator mechanism.
+// Note that the last template argument "Weak" is needed to make it possible to perform
+// both partial specialization+SFINAE without ambiguous specialization
+template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
+struct Assignment<DstXprType, SrcXprType, Functor, EigenBase2EigenBase, Weak>
+{
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
+  {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+      dst.resize(dstRows, dstCols);
+
+    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
+    src.evalTo(dst);
+  }
+
+  // NOTE The following two functions are templated to avoid their instantiation if not needed
+  //      This is needed because some expressions supports evalTo only and/or have 'void' as scalar type.
+  template<typename SrcScalarType>
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,SrcScalarType> &/*func*/)
+  {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+      dst.resize(dstRows, dstCols);
+
+    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
+    src.addTo(dst);
+  }
+
+  template<typename SrcScalarType>
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,SrcScalarType> &/*func*/)
+  {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+      dst.resize(dstRows, dstCols);
+
+    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
+    src.subTo(dst);
+  }
+};
+
+} // namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_ASSIGN_EVALUATOR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Assign_MKL.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Assign_MKL.h
new file mode 100644
index 0000000000000000000000000000000000000000..f9b86c8eb7fc09dd10a7caa676d177570b4c84d2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Assign_MKL.h
@@ -0,0 +1,180 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+ Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+ 
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *   MKL VML support for coefficient-wise unary Eigen expressions like a=b.sin()
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_ASSIGN_VML_H
+#define EIGEN_ASSIGN_VML_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename Dst, typename Src>
+class vml_assign_traits
+{
+  private:
+    enum {
+      DstHasDirectAccess = Dst::Flags & DirectAccessBit,
+      SrcHasDirectAccess = Src::Flags & DirectAccessBit,
+      StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)),
+      InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
+                : int(Dst::Flags)&RowMajorBit ? int(Dst::ColsAtCompileTime)
+                : int(Dst::RowsAtCompileTime),
+      InnerMaxSize  = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
+                    : int(Dst::Flags)&RowMajorBit ? int(Dst::MaxColsAtCompileTime)
+                    : int(Dst::MaxRowsAtCompileTime),
+      MaxSizeAtCompileTime = Dst::SizeAtCompileTime,
+
+      MightEnableVml = StorageOrdersAgree && DstHasDirectAccess && SrcHasDirectAccess && Src::InnerStrideAtCompileTime==1 && Dst::InnerStrideAtCompileTime==1,
+      MightLinearize = MightEnableVml && (int(Dst::Flags) & int(Src::Flags) & LinearAccessBit),
+      VmlSize = MightLinearize ? MaxSizeAtCompileTime : InnerMaxSize,
+      LargeEnough = VmlSize==Dynamic || VmlSize>=EIGEN_MKL_VML_THRESHOLD
+    };
+  public:
+    enum {
+      EnableVml = MightEnableVml && LargeEnough,
+      Traversal = MightLinearize ? LinearTraversal : DefaultTraversal
+    };
+};
+
+#define EIGEN_PP_EXPAND(ARG) ARG
+#if !defined (EIGEN_FAST_MATH) || (EIGEN_FAST_MATH != 1)
+#define EIGEN_VMLMODE_EXPAND_xLA , VML_HA
+#else
+#define EIGEN_VMLMODE_EXPAND_xLA , VML_LA
+#endif
+
+#define EIGEN_VMLMODE_EXPAND_x_
+
+#define EIGEN_VMLMODE_PREFIX_xLA vm
+#define EIGEN_VMLMODE_PREFIX_x_  v
+#define EIGEN_VMLMODE_PREFIX(VMLMODE) EIGEN_CAT(EIGEN_VMLMODE_PREFIX_x,VMLMODE)
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE)                                           \
+  template< typename DstXprType, typename SrcXprNested>                                                                         \
+  struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>, assign_op<EIGENTYPE,EIGENTYPE>,   \
+                   Dense2Dense, std::enable_if_t<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>> {              \
+    typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType;                                            \
+    static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) {                       \
+      resize_if_allowed(dst, src, func);                                                                                        \
+      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());                                                       \
+      if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal) {                                              \
+        VMLOP(dst.size(), (const VMLTYPE*)src.nestedExpression().data(),                                                        \
+              (VMLTYPE*)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE) );                                           \
+      } else {                                                                                                                  \
+        const Index outerSize = dst.outerSize();                                                                                \
+        for(Index outer = 0; outer < outerSize; ++outer) {                                                                      \
+          const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer,0)) :                             \
+                                                      &(src.nestedExpression().coeffRef(0, outer));                             \
+          EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer));                           \
+          VMLOP( dst.innerSize(), (const VMLTYPE*)src_ptr,                                                                      \
+                (VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                                             \
+        }                                                                                                                       \
+      }                                                                                                                         \
+    }                                                                                                                           \
+  };                                                                                                                            \
+
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE)                                                         \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),s##VMLOP), float, float, VMLMODE)           \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),d##VMLOP), double, double, VMLMODE)
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)                                                         \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),c##VMLOP), scomplex, MKL_Complex8, VMLMODE) \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE),z##VMLOP), dcomplex, MKL_Complex16, VMLMODE)
+  
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP, VMLMODE)                                                              \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE)                                                               \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)
+
+  
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sin,   Sin,   LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(asin,  Asin,  LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sinh,  Sinh,  LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cos,   Cos,   LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(acos,  Acos,  LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cosh,  Cosh,  LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tan,   Tan,   LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(atan,  Atan,  LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tanh,  Tanh,  LA)
+// EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs,   Abs,    _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(exp,   Exp,   LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log,   Ln,    LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log10, Log10, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sqrt,  Sqrt,  _)
+
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr,   _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(arg, Arg,      _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(round, Round,  _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(floor, Floor,  _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(ceil,  Ceil,   _)
+
+#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE)                                           \
+  template< typename DstXprType, typename SrcXprNested, typename Plain>                                                       \
+  struct Assignment<DstXprType, CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested,                       \
+                    const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> >, assign_op<EIGENTYPE,EIGENTYPE>,    \
+                   Dense2Dense, std::enable_if_t<vml_assign_traits<DstXprType,SrcXprNested>::EnableVml>> {            \
+    typedef CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE,EIGENTYPE>, SrcXprNested,                                           \
+                    const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>,Plain> > SrcXprType;                         \
+    static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE,EIGENTYPE> &func) {                     \
+      resize_if_allowed(dst, src, func);                                                                                      \
+      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());                                                     \
+      VMLTYPE exponent = reinterpret_cast<const VMLTYPE&>(src.rhs().functor().m_other);                                       \
+      if(vml_assign_traits<DstXprType,SrcXprNested>::Traversal==LinearTraversal)                                              \
+      {                                                                                                                       \
+        VMLOP( dst.size(), (const VMLTYPE*)src.lhs().data(), exponent,                                                        \
+              (VMLTYPE*)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE) );                                         \
+      } else {                                                                                                                \
+        const Index outerSize = dst.outerSize();                                                                              \
+        for(Index outer = 0; outer < outerSize; ++outer) {                                                                    \
+          const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.lhs().coeffRef(outer,0)) :                                        \
+                                                      &(src.lhs().coeffRef(0, outer));                                        \
+          EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer,0)) : &(dst.coeffRef(0, outer));                         \
+          VMLOP( dst.innerSize(), (const VMLTYPE*)src_ptr, exponent,                                                          \
+                 (VMLTYPE*)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                                          \
+        }                                                                                                                     \
+      }                                                                                                                       \
+    }                                                                                                                         \
+  };
+  
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmsPowx, float,    float,         LA)
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdPowx, double,   double,        LA)
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcPowx, scomplex, MKL_Complex8,  LA)
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzPowx, dcomplex, MKL_Complex16, LA)
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_ASSIGN_VML_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/BandMatrix.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/BandMatrix.h
new file mode 100644
index 0000000000000000000000000000000000000000..dcb0d13861c4442d9a07d17eb86768789a250e0b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/BandMatrix.h
@@ -0,0 +1,355 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BANDMATRIX_H
+#define EIGEN_BANDMATRIX_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename Derived>
+class BandMatrixBase : public EigenBase<Derived>
+{
+  public:
+
+    enum {
+      Flags = internal::traits<Derived>::Flags,
+      CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
+      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+      MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
+      Supers = internal::traits<Derived>::Supers,
+      Subs   = internal::traits<Derived>::Subs,
+      Options = internal::traits<Derived>::Options
+    };
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> DenseMatrixType;
+    typedef typename DenseMatrixType::StorageIndex StorageIndex;
+    typedef typename internal::traits<Derived>::CoefficientsType CoefficientsType;
+    typedef EigenBase<Derived> Base;
+
+  protected:
+    enum {
+      DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic))
+                            ? 1 + Supers + Subs
+                            : Dynamic,
+      SizeAtCompileTime = min_size_prefer_dynamic(RowsAtCompileTime,ColsAtCompileTime)
+    };
+
+  public:
+
+    using Base::derived;
+    using Base::rows;
+    using Base::cols;
+
+    /** \returns the number of super diagonals */
+    inline Index supers() const { return derived().supers(); }
+
+    /** \returns the number of sub diagonals */
+    inline Index subs() const { return derived().subs(); }
+
+    /** \returns an expression of the underlying coefficient matrix */
+    inline const CoefficientsType& coeffs() const { return derived().coeffs(); }
+
+    /** \returns an expression of the underlying coefficient matrix */
+    inline CoefficientsType& coeffs() { return derived().coeffs(); }
+
+    /** \returns a vector expression of the \a i -th column,
+      * only the meaningful part is returned.
+      * \warning the internal storage must be column major. */
+    inline Block<CoefficientsType,Dynamic,1> col(Index i)
+    {
+      EIGEN_STATIC_ASSERT((int(Options) & int(RowMajor)) == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+      Index start = 0;
+      Index len = coeffs().rows();
+      if (i<=supers())
+      {
+        start = supers()-i;
+        len = (std::min)(rows(),std::max<Index>(0,coeffs().rows() - (supers()-i)));
+      }
+      else if (i>=rows()-subs())
+        len = std::max<Index>(0,coeffs().rows() - (i + 1 - rows() + subs()));
+      return Block<CoefficientsType,Dynamic,1>(coeffs(), start, i, len, 1);
+    }
+
+    /** \returns a vector expression of the main diagonal */
+    inline Block<CoefficientsType,1,SizeAtCompileTime> diagonal()
+    { return Block<CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
+
+    /** \returns a vector expression of the main diagonal (const version) */
+    inline const Block<const CoefficientsType,1,SizeAtCompileTime> diagonal() const
+    { return Block<const CoefficientsType,1,SizeAtCompileTime>(coeffs(),supers(),0,1,(std::min)(rows(),cols())); }
+
+    template<int Index> struct DiagonalIntReturnType {
+      enum {
+        ReturnOpposite = (int(Options) & int(SelfAdjoint)) && (((Index) > 0 && Supers == 0) || ((Index) < 0 && Subs == 0)),
+        Conjugate = ReturnOpposite && NumTraits<Scalar>::IsComplex,
+        ActualIndex = ReturnOpposite ? -Index : Index,
+        DiagonalSize = (RowsAtCompileTime==Dynamic || ColsAtCompileTime==Dynamic)
+                     ? Dynamic
+                     : (ActualIndex<0
+                     ? min_size_prefer_dynamic(ColsAtCompileTime, RowsAtCompileTime + ActualIndex)
+                     : min_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime - ActualIndex))
+      };
+      typedef Block<CoefficientsType,1, DiagonalSize> BuildType;
+      typedef std::conditional_t<Conjugate,
+                 CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>,BuildType >,
+                 BuildType> Type;
+    };
+
+    /** \returns a vector expression of the \a N -th sub or super diagonal */
+    template<int N> inline typename DiagonalIntReturnType<N>::Type diagonal()
+    {
+      return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
+    }
+
+    /** \returns a vector expression of the \a N -th sub or super diagonal */
+    template<int N> inline const typename DiagonalIntReturnType<N>::Type diagonal() const
+    {
+      return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers()-N, (std::max)(0,N), 1, diagonalLength(N));
+    }
+
+    /** \returns a vector expression of the \a i -th sub or super diagonal */
+    inline Block<CoefficientsType,1,Dynamic> diagonal(Index i)
+    {
+      eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers()));
+      return Block<CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i));
+    }
+
+    /** \returns a vector expression of the \a i -th sub or super diagonal */
+    inline const Block<const CoefficientsType,1,Dynamic> diagonal(Index i) const
+    {
+      eigen_assert((i<0 && -i<=subs()) || (i>=0 && i<=supers()));
+      return Block<const CoefficientsType,1,Dynamic>(coeffs(), supers()-i, std::max<Index>(0,i), 1, diagonalLength(i));
+    }
+
+    template<typename Dest> inline void evalTo(Dest& dst) const
+    {
+      dst.resize(rows(),cols());
+      dst.setZero();
+      dst.diagonal() = diagonal();
+      for (Index i=1; i<=supers();++i)
+        dst.diagonal(i) = diagonal(i);
+      for (Index i=1; i<=subs();++i)
+        dst.diagonal(-i) = diagonal(-i);
+    }
+
+    DenseMatrixType toDenseMatrix() const
+    {
+      DenseMatrixType res(rows(),cols());
+      evalTo(res);
+      return res;
+    }
+
+  protected:
+
+    inline Index diagonalLength(Index i) const
+    { return i<0 ? (std::min)(cols(),rows()+i) : (std::min)(rows(),cols()-i); }
+};
+
+/**
+  * \class BandMatrix
+  * \ingroup Core_Module
+  *
+  * \brief Represents a rectangular matrix with a banded storage
+  *
+  * \tparam Scalar_ Numeric type, i.e. float, double, int
+  * \tparam Rows_ Number of rows, or \b Dynamic
+  * \tparam Cols_ Number of columns, or \b Dynamic
+  * \tparam Supers_ Number of super diagonal
+  * \tparam Subs_ Number of sub diagonal
+  * \tparam Options_ A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
+  *                  The former controls \ref TopicStorageOrders "storage order", and defaults to
+  *                  column-major. The latter controls whether the matrix represents a selfadjoint
+  *                  matrix in which case either Supers of Subs have to be null.
+  *
+  * \sa class TridiagonalMatrix
+  */
+
+template<typename Scalar_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+struct traits<BandMatrix<Scalar_,Rows_,Cols_,Supers_,Subs_,Options_> >
+{
+  typedef Scalar_ Scalar;
+  typedef Dense StorageKind;
+  typedef Eigen::Index StorageIndex;
+  enum {
+    CoeffReadCost = NumTraits<Scalar>::ReadCost,
+    RowsAtCompileTime = Rows_,
+    ColsAtCompileTime = Cols_,
+    MaxRowsAtCompileTime = Rows_,
+    MaxColsAtCompileTime = Cols_,
+    Flags = LvalueBit,
+    Supers = Supers_,
+    Subs = Subs_,
+    Options = Options_,
+    DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
+  };
+  typedef Matrix<Scalar, DataRowsAtCompileTime, ColsAtCompileTime, int(Options) & int(RowMajor) ? RowMajor : ColMajor> CoefficientsType;
+};
+
+template<typename Scalar_, int Rows, int Cols, int Supers, int Subs, int Options>
+class BandMatrix : public BandMatrixBase<BandMatrix<Scalar_,Rows,Cols,Supers,Subs,Options> >
+{
+  public:
+
+    typedef typename internal::traits<BandMatrix>::Scalar Scalar;
+    typedef typename internal::traits<BandMatrix>::StorageIndex StorageIndex;
+    typedef typename internal::traits<BandMatrix>::CoefficientsType CoefficientsType;
+
+    explicit inline BandMatrix(Index rows=Rows, Index cols=Cols, Index supers=Supers, Index subs=Subs)
+      : m_coeffs(1+supers+subs,cols),
+        m_rows(rows), m_supers(supers), m_subs(subs)
+    {
+    }
+
+    /** \returns the number of columns */
+    inline EIGEN_CONSTEXPR Index rows() const { return m_rows.value(); }
+
+    /** \returns the number of rows */
+    inline EIGEN_CONSTEXPR Index cols() const { return m_coeffs.cols(); }
+
+    /** \returns the number of super diagonals */
+    inline EIGEN_CONSTEXPR Index supers() const { return m_supers.value(); }
+
+    /** \returns the number of sub diagonals */
+    inline EIGEN_CONSTEXPR Index subs() const { return m_subs.value(); }
+
+    inline const CoefficientsType& coeffs() const { return m_coeffs; }
+    inline CoefficientsType& coeffs() { return m_coeffs; }
+
+  protected:
+
+    CoefficientsType m_coeffs;
+    internal::variable_if_dynamic<Index, Rows>   m_rows;
+    internal::variable_if_dynamic<Index, Supers> m_supers;
+    internal::variable_if_dynamic<Index, Subs>   m_subs;
+};
+
+template<typename CoefficientsType_,int Rows_, int Cols_, int Supers_, int Subs_,int Options_>
+class BandMatrixWrapper;
+
+template<typename CoefficientsType_,int Rows_, int Cols_, int Supers_, int Subs_,int Options_>
+struct traits<BandMatrixWrapper<CoefficientsType_,Rows_,Cols_,Supers_,Subs_,Options_> >
+{
+  typedef typename CoefficientsType_::Scalar Scalar;
+  typedef typename CoefficientsType_::StorageKind StorageKind;
+  typedef typename CoefficientsType_::StorageIndex StorageIndex;
+  enum {
+    CoeffReadCost = internal::traits<CoefficientsType_>::CoeffReadCost,
+    RowsAtCompileTime = Rows_,
+    ColsAtCompileTime = Cols_,
+    MaxRowsAtCompileTime = Rows_,
+    MaxColsAtCompileTime = Cols_,
+    Flags = LvalueBit,
+    Supers = Supers_,
+    Subs = Subs_,
+    Options = Options_,
+    DataRowsAtCompileTime = ((Supers!=Dynamic) && (Subs!=Dynamic)) ? 1 + Supers + Subs : Dynamic
+  };
+  typedef CoefficientsType_ CoefficientsType;
+};
+
+template<typename CoefficientsType_,int Rows_, int Cols_, int Supers_, int Subs_,int Options_>
+class BandMatrixWrapper : public BandMatrixBase<BandMatrixWrapper<CoefficientsType_,Rows_,Cols_,Supers_,Subs_,Options_> >
+{
+  public:
+
+    typedef typename internal::traits<BandMatrixWrapper>::Scalar Scalar;
+    typedef typename internal::traits<BandMatrixWrapper>::CoefficientsType CoefficientsType;
+    typedef typename internal::traits<BandMatrixWrapper>::StorageIndex StorageIndex;
+
+    explicit inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows=Rows_, Index cols=Cols_, Index supers=Supers_, Index subs=Subs_)
+      : m_coeffs(coeffs),
+        m_rows(rows), m_supers(supers), m_subs(subs)
+    {
+      EIGEN_UNUSED_VARIABLE(cols);
+      // eigen_assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows());
+    }
+
+    /** \returns the number of columns */
+    inline EIGEN_CONSTEXPR Index rows() const { return m_rows.value(); }
+
+    /** \returns the number of rows */
+    inline EIGEN_CONSTEXPR Index cols() const { return m_coeffs.cols(); }
+
+    /** \returns the number of super diagonals */
+    inline EIGEN_CONSTEXPR Index supers() const { return m_supers.value(); }
+
+    /** \returns the number of sub diagonals */
+    inline EIGEN_CONSTEXPR Index subs() const { return m_subs.value(); }
+
+    inline const CoefficientsType& coeffs() const { return m_coeffs; }
+
+  protected:
+
+    const CoefficientsType& m_coeffs;
+    internal::variable_if_dynamic<Index, Rows_>   m_rows;
+    internal::variable_if_dynamic<Index, Supers_> m_supers;
+    internal::variable_if_dynamic<Index, Subs_>   m_subs;
+};
+
+/**
+  * \class TridiagonalMatrix
+  * \ingroup Core_Module
+  *
+  * \brief Represents a tridiagonal matrix with a compact banded storage
+  *
+  * \tparam Scalar Numeric type, i.e. float, double, int
+  * \tparam Size Number of rows and cols, or \b Dynamic
+  * \tparam Options Can be 0 or \b SelfAdjoint
+  *
+  * \sa class BandMatrix
+  */
+template<typename Scalar, int Size, int Options>
+class TridiagonalMatrix : public BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor>
+{
+    typedef BandMatrix<Scalar,Size,Size,Options&SelfAdjoint?0:1,1,Options|RowMajor> Base;
+    typedef typename Base::StorageIndex StorageIndex;
+  public:
+    explicit TridiagonalMatrix(Index size = Size) : Base(size,size,Options&SelfAdjoint?0:1,1) {}
+
+    inline typename Base::template DiagonalIntReturnType<1>::Type super()
+    { return Base::template diagonal<1>(); }
+    inline const typename Base::template DiagonalIntReturnType<1>::Type super() const
+    { return Base::template diagonal<1>(); }
+    inline typename Base::template DiagonalIntReturnType<-1>::Type sub()
+    { return Base::template diagonal<-1>(); }
+    inline const typename Base::template DiagonalIntReturnType<-1>::Type sub() const
+    { return Base::template diagonal<-1>(); }
+  protected:
+};
+
+
+struct BandShape {};
+
+template<typename Scalar_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+struct evaluator_traits<BandMatrix<Scalar_,Rows_,Cols_,Supers_,Subs_,Options_> >
+  : public evaluator_traits_base<BandMatrix<Scalar_,Rows_,Cols_,Supers_,Subs_,Options_> >
+{
+  typedef BandShape Shape;
+};
+
+template<typename CoefficientsType_,int Rows_, int Cols_, int Supers_, int Subs_,int Options_>
+struct evaluator_traits<BandMatrixWrapper<CoefficientsType_,Rows_,Cols_,Supers_,Subs_,Options_> >
+  : public evaluator_traits_base<BandMatrixWrapper<CoefficientsType_,Rows_,Cols_,Supers_,Subs_,Options_> >
+{
+  typedef BandShape Shape;
+};
+
+template<> struct AssignmentKind<DenseShape,BandShape> { typedef EigenBase2EigenBase Kind; };
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_BANDMATRIX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Block.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Block.h
new file mode 100644
index 0000000000000000000000000000000000000000..dad2af802afe600dbfba377adf1851bfe433aeff
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Block.h
@@ -0,0 +1,465 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BLOCK_H
+#define EIGEN_BLOCK_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+struct traits<Block<XprType, BlockRows, BlockCols, InnerPanel> > : traits<XprType>
+{
+  typedef typename traits<XprType>::Scalar Scalar;
+  typedef typename traits<XprType>::StorageKind StorageKind;
+  typedef typename traits<XprType>::XprKind XprKind;
+  typedef typename ref_selector<XprType>::type XprTypeNested;
+  typedef std::remove_reference_t<XprTypeNested> XprTypeNested_;
+  enum{
+    MatrixRows = traits<XprType>::RowsAtCompileTime,
+    MatrixCols = traits<XprType>::ColsAtCompileTime,
+    RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows,
+    ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols,
+    MaxRowsAtCompileTime = BlockRows==0 ? 0
+                         : RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime)
+                         : int(traits<XprType>::MaxRowsAtCompileTime),
+    MaxColsAtCompileTime = BlockCols==0 ? 0
+                         : ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime)
+                         : int(traits<XprType>::MaxColsAtCompileTime),
+
+    XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0,
+    IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
+               : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
+               : XprTypeIsRowMajor,
+    HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
+    InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
+    InnerStrideAtCompileTime = HasSameStorageOrderAsXprType
+                             ? int(inner_stride_at_compile_time<XprType>::ret)
+                             : int(outer_stride_at_compile_time<XprType>::ret),
+    OuterStrideAtCompileTime = HasSameStorageOrderAsXprType
+                             ? int(outer_stride_at_compile_time<XprType>::ret)
+                             : int(inner_stride_at_compile_time<XprType>::ret),
+
+    // FIXME, this traits is rather specialized for dense object and it needs to be cleaned further
+    FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
+    FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
+    Flags = (traits<XprType>::Flags & (DirectAccessBit | (InnerPanel?CompressedAccessBit:0))) | FlagsLvalueBit | FlagsRowMajorBit,
+    // FIXME DirectAccessBit should not be handled by expressions
+    //
+    // Alignment is needed by MapBase's assertions
+    // We can sefely set it to false here. Internal alignment errors will be detected by an eigen_internal_assert in the respective evaluator
+    Alignment = 0
+  };
+};
+
+template<typename XprType, int BlockRows=Dynamic, int BlockCols=Dynamic, bool InnerPanel = false,
+         bool HasDirectAccess = internal::has_direct_access<XprType>::ret> class BlockImpl_dense;
+
+} // end namespace internal
+
+template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, typename StorageKind> class BlockImpl;
+
+/** \class Block
+  * \ingroup Core_Module
+  *
+  * \brief Expression of a fixed-size or dynamic-size block
+  *
+  * \tparam XprType the type of the expression in which we are taking a block
+  * \tparam BlockRows the number of rows of the block we are taking at compile time (optional)
+  * \tparam BlockCols the number of columns of the block we are taking at compile time (optional)
+  * \tparam InnerPanel is true, if the block maps to a set of rows of a row major matrix or
+  *         to set of columns of a column major matrix (optional). The parameter allows to determine
+  *         at compile time whether aligned access is possible on the block expression.
+  *
+  * This class represents an expression of either a fixed-size or dynamic-size block. It is the return
+  * type of DenseBase::block(Index,Index,Index,Index) and DenseBase::block<int,int>(Index,Index) and
+  * most of the time this is the only way it is used.
+  *
+  * However, if you want to directly maniputate block expressions,
+  * for instance if you want to write a function returning such an expression, you
+  * will need to use this class.
+  *
+  * Here is an example illustrating the dynamic case:
+  * \include class_Block.cpp
+  * Output: \verbinclude class_Block.out
+  *
+  * \note Even though this expression has dynamic size, in the case where \a XprType
+  * has fixed size, this expression inherits a fixed maximal size which means that evaluating
+  * it does not cause a dynamic memory allocation.
+  *
+  * Here is an example illustrating the fixed-size case:
+  * \include class_FixedBlock.cpp
+  * Output: \verbinclude class_FixedBlock.out
+  *
+  * \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock
+  */
+template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel> class Block
+  : public BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind>
+{
+    typedef BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> Impl;
+  public:
+    //typedef typename Impl::Base Base;
+    typedef Impl Base;
+    EIGEN_GENERIC_PUBLIC_INTERFACE(Block)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
+
+    typedef internal::remove_all_t<XprType> NestedExpression;
+
+    /** Column or Row constructor
+      */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Block(XprType& xpr, Index i) : Impl(xpr,i)
+    {
+      eigen_assert( (i>=0) && (
+          ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && i<xpr.rows())
+        ||((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && i<xpr.cols())));
+    }
+
+    /** Fixed-size constructor
+      */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Block(XprType& xpr, Index startRow, Index startCol)
+      : Impl(xpr, startRow, startCol)
+    {
+      EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
+      eigen_assert(startRow >= 0 && BlockRows >= 0 && startRow + BlockRows <= xpr.rows()
+             && startCol >= 0 && BlockCols >= 0 && startCol + BlockCols <= xpr.cols());
+    }
+
+    /** Dynamic-size constructor
+      */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Block(XprType& xpr,
+          Index startRow, Index startCol,
+          Index blockRows, Index blockCols)
+      : Impl(xpr, startRow, startCol, blockRows, blockCols)
+    {
+      eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==blockRows)
+          && (ColsAtCompileTime==Dynamic || ColsAtCompileTime==blockCols));
+      eigen_assert(startRow >= 0 && blockRows >= 0 && startRow  <= xpr.rows() - blockRows
+          && startCol >= 0 && blockCols >= 0 && startCol <= xpr.cols() - blockCols);
+    }
+};
+
+// The generic default implementation for dense block simplu forward to the internal::BlockImpl_dense
+// that must be specialized for direct and non-direct access...
+template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+class BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, Dense>
+  : public internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel>
+{
+    typedef internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> Impl;
+    typedef typename XprType::StorageIndex StorageIndex;
+  public:
+    typedef Impl Base;
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index i) : Impl(xpr,i) {}
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol) : Impl(xpr, startRow, startCol) {}
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
+      : Impl(xpr, startRow, startCol, blockRows, blockCols) {}
+};
+
+namespace internal {
+
+/** \internal Internal implementation of dense Blocks in the general case. */
+template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess> class BlockImpl_dense
+  : public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel> >::type
+{
+    typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
+    typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
+  public:
+
+    typedef typename internal::dense_xpr_base<BlockType>::type Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
+
+    // class InnerIterator; // FIXME apparently never used
+
+    /** Column or Row constructor
+      */
+    EIGEN_DEVICE_FUNC
+    inline BlockImpl_dense(XprType& xpr, Index i)
+      : m_xpr(xpr),
+        // It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime,
+        // and it is a column if and only if BlockRows==XprType::RowsAtCompileTime and BlockCols==1,
+        // all other cases are invalid.
+        // The case a 1x1 matrix seems ambiguous, but the result is the same anyway.
+        m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0),
+        m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0),
+        m_blockRows(BlockRows==1 ? 1 : xpr.rows()),
+        m_blockCols(BlockCols==1 ? 1 : xpr.cols())
+    {}
+
+    /** Fixed-size constructor
+      */
+    EIGEN_DEVICE_FUNC
+    inline BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
+      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
+                    m_blockRows(BlockRows), m_blockCols(BlockCols)
+    {}
+
+    /** Dynamic-size constructor
+      */
+    EIGEN_DEVICE_FUNC
+    inline BlockImpl_dense(XprType& xpr,
+          Index startRow, Index startCol,
+          Index blockRows, Index blockCols)
+      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol),
+                    m_blockRows(blockRows), m_blockCols(blockCols)
+    {}
+
+    EIGEN_DEVICE_FUNC inline Index rows() const { return m_blockRows.value(); }
+    EIGEN_DEVICE_FUNC inline Index cols() const { return m_blockCols.value(); }
+
+    EIGEN_DEVICE_FUNC
+    inline Scalar& coeffRef(Index rowId, Index colId)
+    {
+      EIGEN_STATIC_ASSERT_LVALUE(XprType)
+      return m_xpr.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline const Scalar& coeffRef(Index rowId, Index colId) const
+    {
+      return m_xpr.derived().coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const
+    {
+      return m_xpr.coeff(rowId + m_startRow.value(), colId + m_startCol.value());
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline Scalar& coeffRef(Index index)
+    {
+      EIGEN_STATIC_ASSERT_LVALUE(XprType)
+      return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                            m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline const Scalar& coeffRef(Index index) const
+    {
+      return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                            m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline const CoeffReturnType coeff(Index index) const
+    {
+      return m_xpr.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                         m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+    }
+
+    template<int LoadMode>
+    EIGEN_DEVICE_FUNC inline PacketScalar packet(Index rowId, Index colId) const
+    {
+      return m_xpr.template packet<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value());
+    }
+
+    template<int LoadMode>
+    EIGEN_DEVICE_FUNC inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
+    {
+      m_xpr.template writePacket<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value(), val);
+    }
+
+    template<int LoadMode>
+    EIGEN_DEVICE_FUNC inline PacketScalar packet(Index index) const
+    {
+      return m_xpr.template packet<Unaligned>
+              (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+               m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+    }
+
+    template<int LoadMode>
+    EIGEN_DEVICE_FUNC inline void writePacket(Index index, const PacketScalar& val)
+    {
+      m_xpr.template writePacket<Unaligned>
+         (m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), val);
+    }
+
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
+    /** \sa MapBase::data() */
+    EIGEN_DEVICE_FUNC inline const Scalar* data() const;
+    EIGEN_DEVICE_FUNC inline Index innerStride() const;
+    EIGEN_DEVICE_FUNC inline Index outerStride() const;
+    #endif
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const internal::remove_all_t<XprTypeNested>& nestedExpression() const
+    {
+      return m_xpr;
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    XprType& nestedExpression() { return m_xpr; }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    StorageIndex startRow() const EIGEN_NOEXCEPT
+    {
+      return m_startRow.value();
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    StorageIndex startCol() const EIGEN_NOEXCEPT
+    {
+      return m_startCol.value();
+    }
+
+  protected:
+
+    XprTypeNested m_xpr;
+    const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
+    const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
+    const internal::variable_if_dynamic<StorageIndex, RowsAtCompileTime> m_blockRows;
+    const internal::variable_if_dynamic<StorageIndex, ColsAtCompileTime> m_blockCols;
+};
+
+/** \internal Internal implementation of dense Blocks in the direct access case.*/
+template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+class BlockImpl_dense<XprType,BlockRows,BlockCols, InnerPanel,true>
+  : public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel> >
+{
+    typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
+    typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
+    enum {
+      XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0
+    };
+
+    /** \internal Returns base+offset (unless base is null, in which case returns null).
+      * Adding an offset to nullptr is undefined behavior, so we must avoid it.
+      */
+    template <typename Scalar>
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE
+    static Scalar* add_to_nullable_pointer(Scalar* base, Index offset)
+    {
+      return base != nullptr ? base+offset : nullptr;
+    }
+
+  public:
+
+    typedef MapBase<BlockType> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
+
+    /** Column or Row constructor
+      */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    BlockImpl_dense(XprType& xpr, Index i)
+      : Base((BlockRows == 0 || BlockCols == 0) ? nullptr : add_to_nullable_pointer(xpr.data(),
+                 i * (    ((BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) && (!XprTypeIsRowMajor))
+                       || ((BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) && ( XprTypeIsRowMajor)) ? xpr.innerStride() : xpr.outerStride())),
+             BlockRows==1 ? 1 : xpr.rows(),
+             BlockCols==1 ? 1 : xpr.cols()),
+        m_xpr(xpr),
+        m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? i : 0),
+        m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? i : 0)
+    {
+      init();
+    }
+
+    /** Fixed-size constructor
+      */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
+      : Base((BlockRows == 0 || BlockCols == 0) ? nullptr : add_to_nullable_pointer(xpr.data(),
+                 xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol))),
+        m_xpr(xpr), m_startRow(startRow), m_startCol(startCol)
+    {
+      init();
+    }
+
+    /** Dynamic-size constructor
+      */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    BlockImpl_dense(XprType& xpr,
+          Index startRow, Index startCol,
+          Index blockRows, Index blockCols)
+      : Base((blockRows == 0 || blockCols == 0) ? nullptr : add_to_nullable_pointer(xpr.data(),
+                 xpr.innerStride()*(XprTypeIsRowMajor?startCol:startRow) + xpr.outerStride()*(XprTypeIsRowMajor?startRow:startCol)),
+             blockRows, blockCols),
+        m_xpr(xpr), m_startRow(startRow), m_startCol(startCol)
+    {
+      init();
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const internal::remove_all_t<XprTypeNested>& nestedExpression() const EIGEN_NOEXCEPT
+    {
+      return m_xpr;
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    XprType& nestedExpression() { return m_xpr; }
+
+    /** \sa MapBase::innerStride() */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index innerStride() const EIGEN_NOEXCEPT
+    {
+      return internal::traits<BlockType>::HasSameStorageOrderAsXprType
+             ? m_xpr.innerStride()
+             : m_xpr.outerStride();
+    }
+
+    /** \sa MapBase::outerStride() */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index outerStride() const EIGEN_NOEXCEPT
+    {
+      return internal::traits<BlockType>::HasSameStorageOrderAsXprType
+                    ? m_xpr.outerStride()
+                    : m_xpr.innerStride();
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    StorageIndex startRow() const EIGEN_NOEXCEPT { return m_startRow.value(); }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    StorageIndex startCol() const EIGEN_NOEXCEPT { return m_startCol.value(); }
+
+  #ifndef __SUNPRO_CC
+  // FIXME sunstudio is not friendly with the above friend...
+  // META-FIXME there is no 'friend' keyword around here. Is this obsolete?
+  protected:
+  #endif
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** \internal used by allowAligned() */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    BlockImpl_dense(XprType& xpr, const Scalar* data, Index blockRows, Index blockCols)
+      : Base(data, blockRows, blockCols), m_xpr(xpr)
+    {
+      init();
+    }
+    #endif
+
+  protected:
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    void init()
+    {
+      m_outerStride = internal::traits<BlockType>::HasSameStorageOrderAsXprType
+                    ? m_xpr.outerStride()
+                    : m_xpr.innerStride();
+    }
+
+    XprTypeNested m_xpr;
+    const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
+    const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
+    Index m_outerStride;
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_BLOCK_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CommaInitializer.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CommaInitializer.h
new file mode 100644
index 0000000000000000000000000000000000000000..7c2eea8a606062d4509f9c108697221c4b3482a5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CommaInitializer.h
@@ -0,0 +1,166 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMMAINITIALIZER_H
+#define EIGEN_COMMAINITIALIZER_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \class CommaInitializer
+  * \ingroup Core_Module
+  *
+  * \brief Helper class used by the comma initializer operator
+  *
+  * This class is internally used to implement the comma initializer feature. It is
+  * the return type of MatrixBase::operator<<, and most of the time this is the only
+  * way it is used.
+  *
+  * \sa \blank \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
+  */
+template<typename XprType>
+struct CommaInitializer
+{
+  typedef typename XprType::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC
+  inline CommaInitializer(XprType& xpr, const Scalar& s)
+    : m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1)
+  {
+    eigen_assert(m_xpr.rows() > 0 && m_xpr.cols() > 0
+      && "Cannot comma-initialize a 0x0 matrix (operator<<)");
+    m_xpr.coeffRef(0,0) = s;
+  }
+
+  template<typename OtherDerived>
+  EIGEN_DEVICE_FUNC
+  inline CommaInitializer(XprType& xpr, const DenseBase<OtherDerived>& other)
+    : m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows())
+  {
+    eigen_assert(m_xpr.rows() >= other.rows() && m_xpr.cols() >= other.cols()
+      && "Cannot comma-initialize a 0x0 matrix (operator<<)");
+    m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>(0, 0, other.rows(), other.cols()) = other;
+  }
+
+  /* Copy/Move constructor which transfers ownership. This is crucial in 
+   * absence of return value optimization to avoid assertions during destruction. */
+  // FIXME in C++11 mode this could be replaced by a proper RValue constructor
+  EIGEN_DEVICE_FUNC
+  inline CommaInitializer(const CommaInitializer& o)
+  : m_xpr(o.m_xpr), m_row(o.m_row), m_col(o.m_col), m_currentBlockRows(o.m_currentBlockRows) {
+    // Mark original object as finished. In absence of R-value references we need to const_cast:
+    const_cast<CommaInitializer&>(o).m_row = m_xpr.rows();
+    const_cast<CommaInitializer&>(o).m_col = m_xpr.cols();
+    const_cast<CommaInitializer&>(o).m_currentBlockRows = 0;
+  }
+
+  /* inserts a scalar value in the target matrix */
+  EIGEN_DEVICE_FUNC
+  CommaInitializer& operator,(const Scalar& s)
+  {
+    if (m_col==m_xpr.cols())
+    {
+      m_row+=m_currentBlockRows;
+      m_col = 0;
+      m_currentBlockRows = 1;
+      eigen_assert(m_row<m_xpr.rows()
+        && "Too many rows passed to comma initializer (operator<<)");
+    }
+    eigen_assert(m_col<m_xpr.cols()
+      && "Too many coefficients passed to comma initializer (operator<<)");
+    eigen_assert(m_currentBlockRows==1);
+    m_xpr.coeffRef(m_row, m_col++) = s;
+    return *this;
+  }
+
+  /* inserts a matrix expression in the target matrix */
+  template<typename OtherDerived>
+  EIGEN_DEVICE_FUNC
+  CommaInitializer& operator,(const DenseBase<OtherDerived>& other)
+  {
+    if (m_col==m_xpr.cols() && (other.cols()!=0 || other.rows()!=m_currentBlockRows))
+    {
+      m_row+=m_currentBlockRows;
+      m_col = 0;
+      m_currentBlockRows = other.rows();
+      eigen_assert(m_row+m_currentBlockRows<=m_xpr.rows()
+        && "Too many rows passed to comma initializer (operator<<)");
+    }
+    eigen_assert((m_col + other.cols() <= m_xpr.cols())
+      && "Too many coefficients passed to comma initializer (operator<<)");
+    eigen_assert(m_currentBlockRows==other.rows());
+    m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>
+                    (m_row, m_col, other.rows(), other.cols()) = other;
+    m_col += other.cols();
+    return *this;
+  }
+
+  EIGEN_DEVICE_FUNC
+  inline ~CommaInitializer()
+#if defined VERIFY_RAISES_ASSERT && (!defined EIGEN_NO_ASSERTION_CHECKING) && defined EIGEN_EXCEPTIONS
+  EIGEN_EXCEPTION_SPEC(Eigen::eigen_assert_exception)
+#endif
+  {
+    finished();
+  }
+
+  /** \returns the built matrix once all its coefficients have been set.
+    * Calling finished is 100% optional. Its purpose is to write expressions
+    * like this:
+    * \code
+    * quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished());
+    * \endcode
+    */
+  EIGEN_DEVICE_FUNC
+  inline XprType& finished() {
+      eigen_assert(((m_row+m_currentBlockRows) == m_xpr.rows() || m_xpr.cols() == 0)
+           && m_col == m_xpr.cols()
+           && "Too few coefficients passed to comma initializer (operator<<)");
+      return m_xpr;
+  }
+
+  XprType& m_xpr;           // target expression
+  Index m_row;              // current row id
+  Index m_col;              // current col id
+  Index m_currentBlockRows; // current block height
+};
+
+/** \anchor MatrixBaseCommaInitRef
+  * Convenient operator to set the coefficients of a matrix.
+  *
+  * The coefficients must be provided in a row major order and exactly match
+  * the size of the matrix. Otherwise an assertion is raised.
+  *
+  * Example: \include MatrixBase_set.cpp
+  * Output: \verbinclude MatrixBase_set.out
+  * 
+  * \note According the c++ standard, the argument expressions of this comma initializer are evaluated in arbitrary order.
+  *
+  * \sa CommaInitializer::finished(), class CommaInitializer
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline CommaInitializer<Derived> DenseBase<Derived>::operator<< (const Scalar& s)
+{
+  return CommaInitializer<Derived>(*static_cast<Derived*>(this), s);
+}
+
+/** \sa operator<<(const Scalar&) */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC inline CommaInitializer<Derived>
+DenseBase<Derived>::operator<<(const DenseBase<OtherDerived>& other)
+{
+  return CommaInitializer<Derived>(*static_cast<Derived *>(this), other);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_COMMAINITIALIZER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ConditionEstimator.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ConditionEstimator.h
new file mode 100644
index 0000000000000000000000000000000000000000..694be8ba7a0cc1c17341b744295365e278ee0cbc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ConditionEstimator.h
@@ -0,0 +1,177 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Rasmus Munk Larsen (rmlarsen@google.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CONDITIONESTIMATOR_H
+#define EIGEN_CONDITIONESTIMATOR_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Vector, typename RealVector, bool IsComplex>
+struct rcond_compute_sign {
+  static inline Vector run(const Vector& v) {
+    const RealVector v_abs = v.cwiseAbs();
+    return (v_abs.array() == static_cast<typename Vector::RealScalar>(0))
+            .select(Vector::Ones(v.size()), v.cwiseQuotient(v_abs));
+  }
+};
+
+// Partial specialization to avoid elementwise division for real vectors.
+template <typename Vector>
+struct rcond_compute_sign<Vector, Vector, false> {
+  static inline Vector run(const Vector& v) {
+    return (v.array() < static_cast<typename Vector::RealScalar>(0))
+           .select(-Vector::Ones(v.size()), Vector::Ones(v.size()));
+  }
+};
+
+/**
+  * \returns an estimate of ||inv(matrix)||_1 given a decomposition of
+  * \a matrix that implements .solve() and .adjoint().solve() methods.
+  *
+  * This function implements Algorithms 4.1 and 5.1 from
+  *   http://www.maths.manchester.ac.uk/~higham/narep/narep135.pdf
+  * which also forms the basis for the condition number estimators in
+  * LAPACK. Since at most 10 calls to the solve method of dec are
+  * performed, the total cost is O(dims^2), as opposed to O(dims^3)
+  * needed to compute the inverse matrix explicitly.
+  *
+  * The most common usage is in estimating the condition number
+  * ||matrix||_1 * ||inv(matrix)||_1. The first term ||matrix||_1 can be
+  * computed directly in O(n^2) operations.
+  *
+  * Supports the following decompositions: FullPivLU, PartialPivLU, LDLT, and
+  * LLT.
+  *
+  * \sa FullPivLU, PartialPivLU, LDLT, LLT.
+  */
+template <typename Decomposition>
+typename Decomposition::RealScalar rcond_invmatrix_L1_norm_estimate(const Decomposition& dec)
+{
+  typedef typename Decomposition::MatrixType MatrixType;
+  typedef typename Decomposition::Scalar Scalar;
+  typedef typename Decomposition::RealScalar RealScalar;
+  typedef typename internal::plain_col_type<MatrixType>::type Vector;
+  typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVector;
+  const bool is_complex = (NumTraits<Scalar>::IsComplex != 0);
+
+  eigen_assert(dec.rows() == dec.cols());
+  const Index n = dec.rows();
+  if (n == 0)
+    return 0;
+
+  // Disable Index to float conversion warning
+#ifdef __INTEL_COMPILER
+  #pragma warning push
+  #pragma warning ( disable : 2259 )
+#endif
+  Vector v = dec.solve(Vector::Ones(n) / Scalar(n));
+#ifdef __INTEL_COMPILER
+  #pragma warning pop
+#endif
+
+  // lower_bound is a lower bound on
+  //   ||inv(matrix)||_1  = sup_v ||inv(matrix) v||_1 / ||v||_1
+  // and is the objective maximized by the ("super-") gradient ascent
+  // algorithm below.
+  RealScalar lower_bound = v.template lpNorm<1>();
+  if (n == 1)
+    return lower_bound;
+
+  // Gradient ascent algorithm follows: We know that the optimum is achieved at
+  // one of the simplices v = e_i, so in each iteration we follow a
+  // super-gradient to move towards the optimal one.
+  RealScalar old_lower_bound = lower_bound;
+  Vector sign_vector(n);
+  Vector old_sign_vector;
+  Index v_max_abs_index = -1;
+  Index old_v_max_abs_index = v_max_abs_index;
+  for (int k = 0; k < 4; ++k)
+  {
+    sign_vector = internal::rcond_compute_sign<Vector, RealVector, is_complex>::run(v);
+    if (k > 0 && !is_complex && sign_vector == old_sign_vector) {
+      // Break if the solution stagnated.
+      break;
+    }
+    // v_max_abs_index = argmax |real( inv(matrix)^T * sign_vector )|
+    v = dec.adjoint().solve(sign_vector);
+    v.real().cwiseAbs().maxCoeff(&v_max_abs_index);
+    if (v_max_abs_index == old_v_max_abs_index) {
+      // Break if the solution stagnated.
+      break;
+    }
+    // Move to the new simplex e_j, where j = v_max_abs_index.
+    v = dec.solve(Vector::Unit(n, v_max_abs_index));  // v = inv(matrix) * e_j.
+    lower_bound = v.template lpNorm<1>();
+    if (lower_bound <= old_lower_bound) {
+      // Break if the gradient step did not increase the lower_bound.
+      break;
+    }
+    if (!is_complex) {
+      old_sign_vector = sign_vector;
+    }
+    old_v_max_abs_index = v_max_abs_index;
+    old_lower_bound = lower_bound;
+  }
+  // The following calculates an independent estimate of ||matrix||_1 by
+  // multiplying matrix by a vector with entries of slowly increasing
+  // magnitude and alternating sign:
+  //   v_i = (-1)^{i} (1 + (i / (dim-1))), i = 0,...,dim-1.
+  // This improvement to Hager's algorithm above is due to Higham. It was
+  // added to make the algorithm more robust in certain corner cases where
+  // large elements in the matrix might otherwise escape detection due to
+  // exact cancellation (especially when op and op_adjoint correspond to a
+  // sequence of backsubstitutions and permutations), which could cause
+  // Hager's algorithm to vastly underestimate ||matrix||_1.
+  Scalar alternating_sign(RealScalar(1));
+  for (Index i = 0; i < n; ++i) {
+    // The static_cast is needed when Scalar is a complex and RealScalar implements expression templates
+    v[i] = alternating_sign * static_cast<RealScalar>(RealScalar(1) + (RealScalar(i) / (RealScalar(n - 1))));
+    alternating_sign = -alternating_sign;
+  }
+  v = dec.solve(v);
+  const RealScalar alternate_lower_bound = (2 * v.template lpNorm<1>()) / (3 * RealScalar(n));
+  return numext::maxi(lower_bound, alternate_lower_bound);
+}
+
+/** \brief Reciprocal condition number estimator.
+  *
+  * Computing a decomposition of a dense matrix takes O(n^3) operations, while
+  * this method estimates the condition number quickly and reliably in O(n^2)
+  * operations.
+  *
+  * \returns an estimate of the reciprocal condition number
+  * (1 / (||matrix||_1 * ||inv(matrix)||_1)) of matrix, given ||matrix||_1 and
+  * its decomposition. Supports the following decompositions: FullPivLU,
+  * PartialPivLU, LDLT, and LLT.
+  *
+  * \sa FullPivLU, PartialPivLU, LDLT, LLT.
+  */
+template <typename Decomposition>
+typename Decomposition::RealScalar
+rcond_estimate_helper(typename Decomposition::RealScalar matrix_norm, const Decomposition& dec)
+{
+  typedef typename Decomposition::RealScalar RealScalar;
+  eigen_assert(dec.rows() == dec.cols());
+  if (dec.rows() == 0)                        return NumTraits<RealScalar>::infinity();
+  if (numext::is_exactly_zero(matrix_norm)) return RealScalar(0);
+  if (dec.rows() == 1)                        return RealScalar(1);
+  const RealScalar inverse_matrix_norm = rcond_invmatrix_L1_norm_estimate(dec);
+  return (numext::is_exactly_zero(inverse_matrix_norm) ? RealScalar(0)
+                                                       : (RealScalar(1) / inverse_matrix_norm) / matrix_norm);
+}
+
+}  // namespace internal
+
+}  // namespace Eigen
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CoreEvaluators.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CoreEvaluators.h
new file mode 100644
index 0000000000000000000000000000000000000000..32371c55bf6971f97814c46e7180e2407a09cd1f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CoreEvaluators.h
@@ -0,0 +1,1945 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2011-2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+
+#ifndef EIGEN_COREEVALUATORS_H
+#define EIGEN_COREEVALUATORS_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// This class returns the evaluator kind from the expression storage kind.
+// Default assumes index based accessors
+template<typename StorageKind>
+struct storage_kind_to_evaluator_kind {
+  typedef IndexBased Kind;
+};
+
+// This class returns the evaluator shape from the expression storage kind.
+// It can be Dense, Sparse, Triangular, Diagonal, SelfAdjoint, Band, etc.
+template<typename StorageKind> struct storage_kind_to_shape;
+
+template<> struct storage_kind_to_shape<Dense>                  { typedef DenseShape Shape;           };
+template<> struct storage_kind_to_shape<SolverStorage>          { typedef SolverShape Shape;           };
+template<> struct storage_kind_to_shape<PermutationStorage>     { typedef PermutationShape Shape;     };
+template<> struct storage_kind_to_shape<TranspositionsStorage>  { typedef TranspositionsShape Shape;  };
+
+// Evaluators have to be specialized with respect to various criteria such as:
+//  - storage/structure/shape
+//  - scalar type
+//  - etc.
+// Therefore, we need specialization of evaluator providing additional template arguments for each kind of evaluators.
+// We currently distinguish the following kind of evaluators:
+// - unary_evaluator    for expressions taking only one arguments (CwiseUnaryOp, CwiseUnaryView, Transpose, MatrixWrapper, ArrayWrapper, Reverse, Replicate)
+// - binary_evaluator   for expression taking two arguments (CwiseBinaryOp)
+// - ternary_evaluator   for expression taking three arguments (CwiseTernaryOp)
+// - product_evaluator  for linear algebra products (Product); special case of binary_evaluator because it requires additional tags for dispatching.
+// - mapbase_evaluator  for Map, Block, Ref
+// - block_evaluator    for Block (special dispatching to a mapbase_evaluator or unary_evaluator)
+
+template< typename T,
+          typename Arg1Kind   = typename evaluator_traits<typename T::Arg1>::Kind,
+          typename Arg2Kind   = typename evaluator_traits<typename T::Arg2>::Kind,
+          typename Arg3Kind   = typename evaluator_traits<typename T::Arg3>::Kind,
+          typename Arg1Scalar = typename traits<typename T::Arg1>::Scalar,
+          typename Arg2Scalar = typename traits<typename T::Arg2>::Scalar,
+          typename Arg3Scalar = typename traits<typename T::Arg3>::Scalar> struct ternary_evaluator;
+
+template< typename T,
+          typename LhsKind   = typename evaluator_traits<typename T::Lhs>::Kind,
+          typename RhsKind   = typename evaluator_traits<typename T::Rhs>::Kind,
+          typename LhsScalar = typename traits<typename T::Lhs>::Scalar,
+          typename RhsScalar = typename traits<typename T::Rhs>::Scalar> struct binary_evaluator;
+
+template< typename T,
+          typename Kind   = typename evaluator_traits<typename T::NestedExpression>::Kind,
+          typename Scalar = typename T::Scalar> struct unary_evaluator;
+
+// evaluator_traits<T> contains traits for evaluator<T>
+
+template<typename T>
+struct evaluator_traits_base
+{
+  // by default, get evaluator kind and shape from storage
+  typedef typename storage_kind_to_evaluator_kind<typename traits<T>::StorageKind>::Kind Kind;
+  typedef typename storage_kind_to_shape<typename traits<T>::StorageKind>::Shape Shape;
+};
+
+// Default evaluator traits
+template<typename T>
+struct evaluator_traits : public evaluator_traits_base<T>
+{
+};
+
+template<typename T, typename Shape = typename evaluator_traits<T>::Shape >
+struct evaluator_assume_aliasing {
+  static const bool value = false;
+};
+
+// By default, we assume a unary expression:
+template<typename T>
+struct evaluator : public unary_evaluator<T>
+{
+  typedef unary_evaluator<T> Base;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit evaluator(const T& xpr) : Base(xpr) {}
+};
+
+
+// TODO: Think about const-correctness
+template<typename T>
+struct evaluator<const T>
+  : evaluator<T>
+{
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit evaluator(const T& xpr) : evaluator<T>(xpr) {}
+};
+
+// ---------- base class for all evaluators ----------
+
+template<typename ExpressionType>
+struct evaluator_base
+{
+  // TODO that's not very nice to have to propagate all these traits. They are currently only needed to handle outer,inner indices.
+  typedef traits<ExpressionType> ExpressionTraits;
+
+  enum {
+    Alignment = 0
+  };
+  // noncopyable:
+  // Don't make this class inherit noncopyable as this kills EBO (Empty Base Optimization)
+  // and make complex evaluator much larger than then should do.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE evaluator_base() {}
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ~evaluator_base() {}
+private:
+  EIGEN_DEVICE_FUNC evaluator_base(const evaluator_base&);
+  EIGEN_DEVICE_FUNC const evaluator_base& operator=(const evaluator_base&);
+};
+
+// -------------------- Matrix and Array --------------------
+//
+// evaluator<PlainObjectBase> is a common base class for the
+// Matrix and Array evaluators.
+// Here we directly specialize evaluator. This is not really a unary expression, and it is, by definition, dense,
+// so no need for more sophisticated dispatching.
+
+// this helper permits to completely eliminate m_outerStride if it is known at compiletime.
+template<typename Scalar,int OuterStride> class plainobjectbase_evaluator_data {
+public:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  plainobjectbase_evaluator_data(const Scalar* ptr, Index outerStride) : data(ptr)
+  {
+#ifndef EIGEN_INTERNAL_DEBUGGING
+    EIGEN_UNUSED_VARIABLE(outerStride);
+#endif
+    eigen_internal_assert(outerStride==OuterStride);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+  Index outerStride() const EIGEN_NOEXCEPT { return OuterStride; }
+  const Scalar *data;
+};
+
+template<typename Scalar> class plainobjectbase_evaluator_data<Scalar,Dynamic> {
+public:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  plainobjectbase_evaluator_data(const Scalar* ptr, Index outerStride) : data(ptr), m_outerStride(outerStride) {}
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Index outerStride() const { return m_outerStride; }
+  const Scalar *data;
+protected:
+  Index m_outerStride;
+};
+
+template<typename Derived>
+struct evaluator<PlainObjectBase<Derived> >
+  : evaluator_base<Derived>
+{
+  typedef PlainObjectBase<Derived> PlainObjectType;
+  typedef typename PlainObjectType::Scalar Scalar;
+  typedef typename PlainObjectType::CoeffReturnType CoeffReturnType;
+
+  enum {
+    IsRowMajor = PlainObjectType::IsRowMajor,
+    IsVectorAtCompileTime = PlainObjectType::IsVectorAtCompileTime,
+    RowsAtCompileTime = PlainObjectType::RowsAtCompileTime,
+    ColsAtCompileTime = PlainObjectType::ColsAtCompileTime,
+
+    CoeffReadCost = NumTraits<Scalar>::ReadCost,
+    Flags = traits<Derived>::EvaluatorFlags,
+    Alignment = traits<Derived>::Alignment
+  };
+  enum {
+    // We do not need to know the outer stride for vectors
+    OuterStrideAtCompileTime = IsVectorAtCompileTime  ? 0
+                                                      : int(IsRowMajor) ? ColsAtCompileTime
+                                                                        : RowsAtCompileTime
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  evaluator()
+    : m_d(0,OuterStrideAtCompileTime)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit evaluator(const PlainObjectType& m)
+    : m_d(m.data(),IsVectorAtCompileTime ? 0 : m.outerStride())
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index row, Index col) const
+  {
+    if (IsRowMajor)
+      return m_d.data[row * m_d.outerStride() + col];
+    else
+      return m_d.data[row + col * m_d.outerStride()];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index index) const
+  {
+    return m_d.data[index];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index row, Index col)
+  {
+    if (IsRowMajor)
+      return const_cast<Scalar*>(m_d.data)[row * m_d.outerStride() + col];
+    else
+      return const_cast<Scalar*>(m_d.data)[row + col * m_d.outerStride()];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index index)
+  {
+    return const_cast<Scalar*>(m_d.data)[index];
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index row, Index col) const
+  {
+    if (IsRowMajor)
+      return ploadt<PacketType, LoadMode>(m_d.data + row * m_d.outerStride() + col);
+    else
+      return ploadt<PacketType, LoadMode>(m_d.data + row + col * m_d.outerStride());
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index index) const
+  {
+    return ploadt<PacketType, LoadMode>(m_d.data + index);
+  }
+
+  template<int StoreMode,typename PacketType>
+  EIGEN_STRONG_INLINE
+  void writePacket(Index row, Index col, const PacketType& x)
+  {
+    if (IsRowMajor)
+      return pstoret<Scalar, PacketType, StoreMode>
+	            (const_cast<Scalar*>(m_d.data) + row * m_d.outerStride() + col, x);
+    else
+      return pstoret<Scalar, PacketType, StoreMode>
+                    (const_cast<Scalar*>(m_d.data) + row + col * m_d.outerStride(), x);
+  }
+
+  template<int StoreMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketType& x)
+  {
+    return pstoret<Scalar, PacketType, StoreMode>(const_cast<Scalar*>(m_d.data) + index, x);
+  }
+
+protected:
+
+  plainobjectbase_evaluator_data<Scalar,OuterStrideAtCompileTime> m_d;
+};
+
+template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+struct evaluator<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
+  : evaluator<PlainObjectBase<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > >
+{
+  typedef Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> XprType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  evaluator() {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit evaluator(const XprType& m)
+    : evaluator<PlainObjectBase<XprType> >(m)
+  { }
+};
+
+template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+struct evaluator<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
+  : evaluator<PlainObjectBase<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > >
+{
+  typedef Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> XprType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  evaluator() {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit evaluator(const XprType& m)
+    : evaluator<PlainObjectBase<XprType> >(m)
+  { }
+};
+
+// -------------------- Transpose --------------------
+
+template<typename ArgType>
+struct unary_evaluator<Transpose<ArgType>, IndexBased>
+  : evaluator_base<Transpose<ArgType> >
+{
+  typedef Transpose<ArgType> XprType;
+
+  enum {
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+    Flags = evaluator<ArgType>::Flags ^ RowMajorBit,
+    Alignment = evaluator<ArgType>::Alignment
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit unary_evaluator(const XprType& t) : m_argImpl(t.nestedExpression()) {}
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index row, Index col) const
+  {
+    return m_argImpl.coeff(col, row);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index index) const
+  {
+    return m_argImpl.coeff(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index row, Index col)
+  {
+    return m_argImpl.coeffRef(col, row);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  typename XprType::Scalar& coeffRef(Index index)
+  {
+    return m_argImpl.coeffRef(index);
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index row, Index col) const
+  {
+    return m_argImpl.template packet<LoadMode,PacketType>(col, row);
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index index) const
+  {
+    return m_argImpl.template packet<LoadMode,PacketType>(index);
+  }
+
+  template<int StoreMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  void writePacket(Index row, Index col, const PacketType& x)
+  {
+    m_argImpl.template writePacket<StoreMode,PacketType>(col, row, x);
+  }
+
+  template<int StoreMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketType& x)
+  {
+    m_argImpl.template writePacket<StoreMode,PacketType>(index, x);
+  }
+
+protected:
+  evaluator<ArgType> m_argImpl;
+};
+
+// -------------------- CwiseNullaryOp --------------------
+// Like Matrix and Array, this is not really a unary expression, so we directly specialize evaluator.
+// Likewise, there is not need to more sophisticated dispatching here.
+
+template<typename Scalar,typename NullaryOp,
+         bool has_nullary = has_nullary_operator<NullaryOp>::value,
+         bool has_unary   = has_unary_operator<NullaryOp>::value,
+         bool has_binary  = has_binary_operator<NullaryOp>::value>
+struct nullary_wrapper
+{
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j) const { return op(i,j); }
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const { return op(i); }
+
+  template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const { return op.template packetOp<T>(i,j); }
+  template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const { return op.template packetOp<T>(i); }
+};
+
+template<typename Scalar,typename NullaryOp>
+struct nullary_wrapper<Scalar,NullaryOp,true,false,false>
+{
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType=0, IndexType=0) const { return op(); }
+  template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType=0, IndexType=0) const { return op.template packetOp<T>(); }
+};
+
+template<typename Scalar,typename NullaryOp>
+struct nullary_wrapper<Scalar,NullaryOp,false,false,true>
+{
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j=0) const { return op(i,j); }
+  template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j=0) const { return op.template packetOp<T>(i,j); }
+};
+
+// We need the following specialization for vector-only functors assigned to a runtime vector,
+// for instance, using linspace and assigning a RowVectorXd to a MatrixXd or even a row of a MatrixXd.
+// In this case, i==0 and j is used for the actual iteration.
+template<typename Scalar,typename NullaryOp>
+struct nullary_wrapper<Scalar,NullaryOp,false,true,false>
+{
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j) const {
+    eigen_assert(i==0 || j==0);
+    return op(i+j);
+  }
+  template <typename T, typename IndexType> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const {
+    eigen_assert(i==0 || j==0);
+    return op.template packetOp<T>(i+j);
+  }
+
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const { return op(i); }
+  template <typename T, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const { return op.template packetOp<T>(i); }
+};
+
+template<typename Scalar,typename NullaryOp>
+struct nullary_wrapper<Scalar,NullaryOp,false,false,false> {};
+
+#if 0 && EIGEN_COMP_MSVC>0
+// Disable this ugly workaround. This is now handled in traits<Ref>::match,
+// but this piece of code might still become handly if some other weird compilation
+// erros pop up again.
+
+// MSVC exhibits a weird compilation error when
+// compiling:
+//    Eigen::MatrixXf A = MatrixXf::Random(3,3);
+//    Ref<const MatrixXf> R = 2.f*A;
+// and that has_*ary_operator<scalar_constant_op<float>> have not been instantiated yet.
+// The "problem" is that evaluator<2.f*A> is instantiated by traits<Ref>::match<2.f*A>
+// and at that time has_*ary_operator<T> returns true regardless of T.
+// Then nullary_wrapper is badly instantiated as nullary_wrapper<.,.,true,true,true>.
+// The trick is thus to defer the proper instantiation of nullary_wrapper when coeff(),
+// and packet() are really instantiated as implemented below:
+
+// This is a simple wrapper around Index to enforce the re-instantiation of
+// has_*ary_operator when needed.
+template<typename T> struct nullary_wrapper_workaround_msvc {
+  nullary_wrapper_workaround_msvc(const T&);
+  operator T()const;
+};
+
+template<typename Scalar,typename NullaryOp>
+struct nullary_wrapper<Scalar,NullaryOp,true,true,true>
+{
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i, IndexType j) const {
+    return nullary_wrapper<Scalar,NullaryOp,
+    has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
+    has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
+    has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().operator()(op,i,j);
+  }
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const NullaryOp& op, IndexType i) const {
+    return nullary_wrapper<Scalar,NullaryOp,
+    has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
+    has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
+    has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().operator()(op,i);
+  }
+
+  template <typename T, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i, IndexType j) const {
+    return nullary_wrapper<Scalar,NullaryOp,
+    has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
+    has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
+    has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().template packetOp<T>(op,i,j);
+  }
+  template <typename T, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T packetOp(const NullaryOp& op, IndexType i) const {
+    return nullary_wrapper<Scalar,NullaryOp,
+    has_nullary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
+    has_unary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value,
+    has_binary_operator<NullaryOp,nullary_wrapper_workaround_msvc<IndexType> >::value>().template packetOp<T>(op,i);
+  }
+};
+#endif // MSVC workaround
+
+template<typename NullaryOp, typename PlainObjectType>
+struct evaluator<CwiseNullaryOp<NullaryOp,PlainObjectType> >
+  : evaluator_base<CwiseNullaryOp<NullaryOp,PlainObjectType> >
+{
+  typedef CwiseNullaryOp<NullaryOp,PlainObjectType> XprType;
+  typedef internal::remove_all_t<PlainObjectType> PlainObjectTypeCleaned;
+
+  enum {
+    CoeffReadCost = internal::functor_traits<NullaryOp>::Cost,
+
+    Flags = (evaluator<PlainObjectTypeCleaned>::Flags
+          &  (  HereditaryBits
+              | (functor_has_linear_access<NullaryOp>::ret  ? LinearAccessBit : 0)
+              | (functor_traits<NullaryOp>::PacketAccess    ? PacketAccessBit : 0)))
+          | (functor_traits<NullaryOp>::IsRepeatable ? 0 : EvalBeforeNestingBit),
+    Alignment = AlignedMax
+  };
+
+  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& n)
+    : m_functor(n.functor()), m_wrapper()
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(IndexType row, IndexType col) const
+  {
+    return m_wrapper(m_functor, row, col);
+  }
+
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(IndexType index) const
+  {
+    return m_wrapper(m_functor,index);
+  }
+
+  template<int LoadMode, typename PacketType, typename IndexType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(IndexType row, IndexType col) const
+  {
+    return m_wrapper.template packetOp<PacketType>(m_functor, row, col);
+  }
+
+  template<int LoadMode, typename PacketType, typename IndexType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(IndexType index) const
+  {
+    return m_wrapper.template packetOp<PacketType>(m_functor, index);
+  }
+
+protected:
+  const NullaryOp m_functor;
+  const internal::nullary_wrapper<CoeffReturnType,NullaryOp> m_wrapper;
+};
+
+// -------------------- CwiseUnaryOp --------------------
+
+template<typename UnaryOp, typename ArgType>
+struct unary_evaluator<CwiseUnaryOp<UnaryOp, ArgType>, IndexBased >
+  : evaluator_base<CwiseUnaryOp<UnaryOp, ArgType> >
+{
+  typedef CwiseUnaryOp<UnaryOp, ArgType> XprType;
+
+  enum {
+    CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<UnaryOp>::Cost),
+
+    Flags = evaluator<ArgType>::Flags
+          & (HereditaryBits | LinearAccessBit | (functor_traits<UnaryOp>::PacketAccess ? PacketAccessBit : 0)),
+    Alignment = evaluator<ArgType>::Alignment
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit unary_evaluator(const XprType& op) : m_d(op)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<UnaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index row, Index col) const
+  {
+    return m_d.func()(m_d.argImpl.coeff(row, col));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index index) const
+  {
+    return m_d.func()(m_d.argImpl.coeff(index));
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index row, Index col) const
+  {
+    return m_d.func().packetOp(m_d.argImpl.template packet<LoadMode, PacketType>(row, col));
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index index) const
+  {
+    return m_d.func().packetOp(m_d.argImpl.template packet<LoadMode, PacketType>(index));
+  }
+
+protected:
+
+  // this helper permits to completely eliminate the functor if it is empty
+  struct Data
+  {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Data(const XprType& xpr) : op(xpr.functor()), argImpl(xpr.nestedExpression()) {}
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const UnaryOp& func() const { return op; }
+    UnaryOp op;
+    evaluator<ArgType> argImpl;
+  };
+
+  Data m_d;
+};
+
+// ----------------------- Casting ---------------------
+
+template <typename SrcType, typename DstType, typename ArgType>
+struct unary_evaluator<CwiseUnaryOp<core_cast_op<SrcType, DstType>, ArgType>, IndexBased> {
+  using CastOp = core_cast_op<SrcType, DstType>;
+  using XprType = CwiseUnaryOp<CastOp, ArgType>;
+
+  // Use the largest packet type by default
+  using SrcPacketType = typename packet_traits<SrcType>::type;
+  static constexpr int SrcPacketSize = unpacket_traits<SrcPacketType>::size;
+  static constexpr int SrcPacketBytes = SrcPacketSize * sizeof(SrcType);
+
+  enum {
+    CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<CastOp>::Cost),
+    PacketAccess = functor_traits<CastOp>::PacketAccess,
+    ActualPacketAccessBit = PacketAccess ? PacketAccessBit : 0,
+    Flags = evaluator<ArgType>::Flags & (HereditaryBits | LinearAccessBit | ActualPacketAccessBit),
+    IsRowMajor = (evaluator<ArgType>::Flags & RowMajorBit),
+    Alignment = evaluator<ArgType>::Alignment
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit unary_evaluator(const XprType& xpr)
+      : m_argImpl(xpr.nestedExpression()), m_rows(xpr.rows()), m_cols(xpr.cols()) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<CastOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  template <typename DstPacketType>
+  using AltSrcScalarOp = std::enable_if_t<(unpacket_traits<DstPacketType>::size < SrcPacketSize && !find_packet_by_size<SrcType, unpacket_traits<DstPacketType>::size>::value), bool>;
+  template <typename DstPacketType>
+  using SrcPacketArgs1 = std::enable_if_t<(find_packet_by_size<SrcType, unpacket_traits<DstPacketType>::size>::value), bool>;
+  template <typename DstPacketType>
+  using SrcPacketArgs2 = std::enable_if_t<(unpacket_traits<DstPacketType>::size) == (2 * SrcPacketSize), bool>;
+  template <typename DstPacketType>
+  using SrcPacketArgs4 = std::enable_if_t<(unpacket_traits<DstPacketType>::size) == (4 * SrcPacketSize), bool>;
+  template <typename DstPacketType>
+  using SrcPacketArgs8 = std::enable_if_t<(unpacket_traits<DstPacketType>::size) == (8 * SrcPacketSize), bool>;
+
+  template <bool UseRowMajor = IsRowMajor, std::enable_if_t<UseRowMajor, bool> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool check_array_bounds(Index, Index col, Index packetSize) const {
+    return col + packetSize <= cols();
+  }
+  template <bool UseRowMajor = IsRowMajor, std::enable_if_t<!UseRowMajor, bool> = true>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool check_array_bounds(Index row, Index, Index packetSize) const {
+    return row + packetSize <= rows();
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool check_array_bounds(Index index, Index packetSize) const {
+    return index + packetSize <= size();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE SrcType srcCoeff(Index row, Index col, Index offset) const {
+    Index actualRow = IsRowMajor ? row : row + offset;
+    Index actualCol = IsRowMajor ? col + offset : col;
+    return m_argImpl.coeff(actualRow, actualCol);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE SrcType srcCoeff(Index index, Index offset) const {
+    Index actualIndex = index + offset;
+    return m_argImpl.coeff(actualIndex);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstType coeff(Index row, Index col) const {
+    return cast<SrcType, DstType>(srcCoeff(row, col, 0));
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DstType coeff(Index index) const { return cast<SrcType, DstType>(srcCoeff(index, 0)); }
+
+  template <int LoadMode, typename PacketType = SrcPacketType>
+  EIGEN_STRONG_INLINE PacketType srcPacket(Index row, Index col, Index offset) const {
+    constexpr int PacketSize = unpacket_traits<PacketType>::size;
+    Index actualRow = IsRowMajor ? row : row + (offset * PacketSize);
+    Index actualCol = IsRowMajor ? col + (offset * PacketSize) : col;
+    eigen_assert(check_array_bounds(actualRow, actualCol, PacketSize) && "Array index out of bounds");
+    return m_argImpl.template packet<LoadMode, PacketType>(actualRow, actualCol);
+  }
+  template <int LoadMode, typename PacketType = SrcPacketType>
+  EIGEN_STRONG_INLINE PacketType srcPacket(Index index, Index offset) const {
+    constexpr int PacketSize = unpacket_traits<PacketType>::size;
+    Index actualIndex = index + (offset * PacketSize);
+    eigen_assert(check_array_bounds(actualIndex, PacketSize) && "Array index out of bounds");
+    return m_argImpl.template packet<LoadMode, PacketType>(actualIndex);
+  }
+
+  // There is no source packet type with equal or fewer elements than DstPacketType.
+  // This is problematic as the evaluation loop may attempt to access data outside the bounds of the array.
+  // For example, consider the cast utilizing pcast<Packet4f,Packet2d> with an array of size 4: {0.0f,1.0f,2.0f,3.0f}. 
+  // The first iteration of the evaulation loop will load 16 bytes: {0.0f,1.0f,2.0f,3.0f} and cast to {0.0,1.0}, which is acceptable.
+  // The second iteration will load 16 bytes: {2.0f,3.0f,?,?}, which is outside the bounds of the array.
+
+  // Instead, perform runtime check to determine if the load would access data outside the bounds of the array.
+  // If not, perform full load. Otherwise, revert to a scalar loop to perform a partial load.
+  // In either case, perform a vectorized cast of the source packet.
+  template <int LoadMode, typename DstPacketType, AltSrcScalarOp<DstPacketType> = true>
+  EIGEN_STRONG_INLINE DstPacketType packet(Index row, Index col) const {
+    constexpr int DstPacketSize = unpacket_traits<DstPacketType>::size;
+    constexpr int SrcBytesIncrement = DstPacketSize * sizeof(SrcType);
+    constexpr int SrcLoadMode = plain_enum_min(SrcBytesIncrement, LoadMode);
+    SrcPacketType src;
+    if (EIGEN_PREDICT_TRUE(check_array_bounds(row, col, SrcPacketSize))) {
+      src = srcPacket<SrcLoadMode>(row, col, 0);
+    } else {
+      Array<SrcType, SrcPacketSize, 1> srcArray;
+      for (size_t k = 0; k < DstPacketSize; k++) srcArray[k] = srcCoeff(row, col, k);
+      for (size_t k = DstPacketSize; k < SrcPacketSize; k++) srcArray[k] = SrcType(0);
+      src = pload<SrcPacketType>(srcArray.data());
+    }
+    return pcast<SrcPacketType, DstPacketType>(src);
+  }
+  // Use the source packet type with the same size as DstPacketType, if it exists
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs1<DstPacketType> = true>
+  EIGEN_STRONG_INLINE DstPacketType packet(Index row, Index col) const {
+    constexpr int DstPacketSize = unpacket_traits<DstPacketType>::size;
+    using SizedSrcPacketType = typename find_packet_by_size<SrcType, DstPacketSize>::type;
+    constexpr int SrcBytesIncrement = DstPacketSize * sizeof(SrcType);
+    constexpr int SrcLoadMode = plain_enum_min(SrcBytesIncrement, LoadMode);
+    return pcast<SizedSrcPacketType, DstPacketType>(
+        srcPacket<SrcLoadMode, SizedSrcPacketType>(row, col, 0));
+  }
+  // unpacket_traits<DstPacketType>::size == 2 * SrcPacketSize
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs2<DstPacketType> = true>
+  EIGEN_STRONG_INLINE DstPacketType packet(Index row, Index col) const {
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(
+        srcPacket<SrcLoadMode>(row, col, 0), srcPacket<SrcLoadMode>(row, col, 1));
+  }
+  // unpacket_traits<DstPacketType>::size == 4 * SrcPacketSize
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs4<DstPacketType> = true>
+  EIGEN_STRONG_INLINE DstPacketType packet(Index row, Index col) const {
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(
+        srcPacket<SrcLoadMode>(row, col, 0), srcPacket<SrcLoadMode>(row, col, 1),
+        srcPacket<SrcLoadMode>(row, col, 2), srcPacket<SrcLoadMode>(row, col, 3));
+  }
+  // unpacket_traits<DstPacketType>::size == 8 * SrcPacketSize
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs8<DstPacketType> = true>
+  EIGEN_STRONG_INLINE DstPacketType packet(Index row, Index col) const {
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(
+        srcPacket<SrcLoadMode>(row, col, 0), srcPacket<SrcLoadMode>(row, col, 1), 
+        srcPacket<SrcLoadMode>(row, col, 2), srcPacket<SrcLoadMode>(row, col, 3), 
+        srcPacket<SrcLoadMode>(row, col, 4), srcPacket<SrcLoadMode>(row, col, 5),
+        srcPacket<SrcLoadMode>(row, col, 6), srcPacket<SrcLoadMode>(row, col, 7));
+  }
+
+  // Analagous routines for linear access.
+  template <int LoadMode, typename DstPacketType, AltSrcScalarOp<DstPacketType> = true>
+  EIGEN_STRONG_INLINE DstPacketType packet(Index index) const {
+    constexpr int DstPacketSize = unpacket_traits<DstPacketType>::size;
+    constexpr int SrcBytesIncrement = DstPacketSize * sizeof(SrcType);
+    constexpr int SrcLoadMode = plain_enum_min(SrcBytesIncrement, LoadMode);
+    SrcPacketType src;
+    if (EIGEN_PREDICT_TRUE(check_array_bounds(index, SrcPacketSize))) {
+      src = srcPacket<SrcLoadMode>(index, 0);
+    } else {
+      Array<SrcType, SrcPacketSize, 1> srcArray;
+      for (size_t k = 0; k < DstPacketSize; k++) srcArray[k] = srcCoeff(index, k);
+      for (size_t k = DstPacketSize; k < SrcPacketSize; k++) srcArray[k] = SrcType(0);
+      src = pload<SrcPacketType>(srcArray.data());
+    }
+    return pcast<SrcPacketType, DstPacketType>(src);
+  }
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs1<DstPacketType> = true>
+  EIGEN_STRONG_INLINE DstPacketType packet(Index index) const {
+    constexpr int DstPacketSize = unpacket_traits<DstPacketType>::size;
+    using SizedSrcPacketType = typename find_packet_by_size<SrcType, DstPacketSize>::type;
+    constexpr int SrcBytesIncrement = DstPacketSize * sizeof(SrcType);
+    constexpr int SrcLoadMode = plain_enum_min(SrcBytesIncrement, LoadMode);
+    return pcast<SizedSrcPacketType, DstPacketType>(
+        srcPacket<SrcLoadMode, SizedSrcPacketType>(index, 0));
+  }
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs2<DstPacketType> = true>
+  EIGEN_STRONG_INLINE DstPacketType packet(Index index) const {
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(
+        srcPacket<SrcLoadMode>(index, 0), srcPacket<SrcLoadMode>(index, 1));
+  }
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs4<DstPacketType> = true>
+  EIGEN_STRONG_INLINE DstPacketType packet(Index index) const {
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(
+        srcPacket<SrcLoadMode>(index, 0), srcPacket<SrcLoadMode>(index, 1),
+        srcPacket<SrcLoadMode>(index, 2), srcPacket<SrcLoadMode>(index, 3));
+  }
+  template <int LoadMode, typename DstPacketType, SrcPacketArgs8<DstPacketType> = true>
+  EIGEN_STRONG_INLINE DstPacketType packet(Index index) const {
+    constexpr int SrcLoadMode = plain_enum_min(SrcPacketBytes, LoadMode);
+    return pcast<SrcPacketType, DstPacketType>(
+        srcPacket<SrcLoadMode>(index, 0), srcPacket<SrcLoadMode>(index, 1),
+        srcPacket<SrcLoadMode>(index, 2), srcPacket<SrcLoadMode>(index, 3),
+        srcPacket<SrcLoadMode>(index, 4), srcPacket<SrcLoadMode>(index, 5),
+        srcPacket<SrcLoadMode>(index, 6), srcPacket<SrcLoadMode>(index, 7));
+  }
+
+  constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index rows() const { return m_rows; }
+  constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index cols() const { return m_cols; }
+  constexpr EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index size() const { return m_rows * m_cols; }
+
+ protected:
+  const evaluator<ArgType> m_argImpl;
+  const variable_if_dynamic<Index, XprType::RowsAtCompileTime> m_rows;
+  const variable_if_dynamic<Index, XprType::ColsAtCompileTime> m_cols;
+};
+
+// -------------------- CwiseTernaryOp --------------------
+
+// this is a ternary expression
+template<typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
+struct evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >
+  : public ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >
+{
+  typedef CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> XprType;
+  typedef ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> > Base;
+
+  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr) : Base(xpr) {}
+};
+
+template<typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
+struct ternary_evaluator<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3>, IndexBased, IndexBased>
+  : evaluator_base<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >
+{
+  typedef CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> XprType;
+
+  enum {
+    CoeffReadCost = int(evaluator<Arg1>::CoeffReadCost) + int(evaluator<Arg2>::CoeffReadCost) + int(evaluator<Arg3>::CoeffReadCost) + int(functor_traits<TernaryOp>::Cost),
+
+    Arg1Flags = evaluator<Arg1>::Flags,
+    Arg2Flags = evaluator<Arg2>::Flags,
+    Arg3Flags = evaluator<Arg3>::Flags,
+    SameType = is_same<typename Arg1::Scalar,typename Arg2::Scalar>::value && is_same<typename Arg1::Scalar,typename Arg3::Scalar>::value,
+    StorageOrdersAgree = (int(Arg1Flags)&RowMajorBit)==(int(Arg2Flags)&RowMajorBit) && (int(Arg1Flags)&RowMajorBit)==(int(Arg3Flags)&RowMajorBit),
+    Flags0 = (int(Arg1Flags) | int(Arg2Flags) | int(Arg3Flags)) & (
+        HereditaryBits
+        | (int(Arg1Flags) & int(Arg2Flags) & int(Arg3Flags) &
+           ( (StorageOrdersAgree ? LinearAccessBit : 0)
+           | (functor_traits<TernaryOp>::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0)
+           )
+        )
+     ),
+    Flags = (Flags0 & ~RowMajorBit) | (Arg1Flags & RowMajorBit),
+    Alignment = plain_enum_min(
+            plain_enum_min(evaluator<Arg1>::Alignment, evaluator<Arg2>::Alignment),
+            evaluator<Arg3>::Alignment)
+  };
+
+  EIGEN_DEVICE_FUNC explicit ternary_evaluator(const XprType& xpr) : m_d(xpr)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<TernaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index row, Index col) const
+  {
+    return m_d.func()(m_d.arg1Impl.coeff(row, col), m_d.arg2Impl.coeff(row, col), m_d.arg3Impl.coeff(row, col));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index index) const
+  {
+    return m_d.func()(m_d.arg1Impl.coeff(index), m_d.arg2Impl.coeff(index), m_d.arg3Impl.coeff(index));
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index row, Index col) const
+  {
+    return m_d.func().packetOp(m_d.arg1Impl.template packet<LoadMode,PacketType>(row, col),
+                               m_d.arg2Impl.template packet<LoadMode,PacketType>(row, col),
+                               m_d.arg3Impl.template packet<LoadMode,PacketType>(row, col));
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index index) const
+  {
+    return m_d.func().packetOp(m_d.arg1Impl.template packet<LoadMode,PacketType>(index),
+                               m_d.arg2Impl.template packet<LoadMode,PacketType>(index),
+                               m_d.arg3Impl.template packet<LoadMode,PacketType>(index));
+  }
+
+protected:
+  // this helper permits to completely eliminate the functor if it is empty
+  struct Data
+  {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Data(const XprType& xpr) : op(xpr.functor()), arg1Impl(xpr.arg1()), arg2Impl(xpr.arg2()), arg3Impl(xpr.arg3()) {}
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const TernaryOp& func() const { return op; }
+    TernaryOp op;
+    evaluator<Arg1> arg1Impl;
+    evaluator<Arg2> arg2Impl;
+    evaluator<Arg3> arg3Impl;
+  };
+
+  Data m_d;
+};
+
+// -------------------- CwiseBinaryOp --------------------
+
+// this is a binary expression
+template<typename BinaryOp, typename Lhs, typename Rhs>
+struct evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
+  : public binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
+{
+  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
+  typedef binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs> > Base;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit evaluator(const XprType& xpr) : Base(xpr) {}
+};
+
+template<typename BinaryOp, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IndexBased, IndexBased>
+  : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
+{
+  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
+
+  enum {
+    CoeffReadCost = int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
+
+    LhsFlags = evaluator<Lhs>::Flags,
+    RhsFlags = evaluator<Rhs>::Flags,
+    SameType = is_same<typename Lhs::Scalar,typename Rhs::Scalar>::value,
+    StorageOrdersAgree = (int(LhsFlags)&RowMajorBit)==(int(RhsFlags)&RowMajorBit),
+    Flags0 = (int(LhsFlags) | int(RhsFlags)) & (
+        HereditaryBits
+      | (int(LhsFlags) & int(RhsFlags) &
+           ( (StorageOrdersAgree ? LinearAccessBit : 0)
+           | (functor_traits<BinaryOp>::PacketAccess && StorageOrdersAgree && SameType ? PacketAccessBit : 0)
+           )
+        )
+     ),
+    Flags = (Flags0 & ~RowMajorBit) | (LhsFlags & RowMajorBit),
+    Alignment = plain_enum_min(evaluator<Lhs>::Alignment, evaluator<Rhs>::Alignment)
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit binary_evaluator(const XprType& xpr) : m_d(xpr)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index row, Index col) const
+  {
+    return m_d.func()(m_d.lhsImpl.coeff(row, col), m_d.rhsImpl.coeff(row, col));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index index) const
+  {
+    return m_d.func()(m_d.lhsImpl.coeff(index), m_d.rhsImpl.coeff(index));
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index row, Index col) const
+  {
+    return m_d.func().packetOp(m_d.lhsImpl.template packet<LoadMode,PacketType>(row, col),
+                               m_d.rhsImpl.template packet<LoadMode,PacketType>(row, col));
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index index) const
+  {
+    return m_d.func().packetOp(m_d.lhsImpl.template packet<LoadMode,PacketType>(index),
+                               m_d.rhsImpl.template packet<LoadMode,PacketType>(index));
+  }
+
+protected:
+
+  // this helper permits to completely eliminate the functor if it is empty
+  struct Data
+  {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Data(const XprType& xpr) : op(xpr.functor()), lhsImpl(xpr.lhs()), rhsImpl(xpr.rhs()) {}
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const BinaryOp& func() const { return op; }
+    BinaryOp op;
+    evaluator<Lhs> lhsImpl;
+    evaluator<Rhs> rhsImpl;
+  };
+
+  Data m_d;
+};
+
+// -------------------- CwiseUnaryView --------------------
+
+template<typename UnaryOp, typename ArgType, typename StrideType>
+struct unary_evaluator<CwiseUnaryView<UnaryOp, ArgType, StrideType>, IndexBased>
+  : evaluator_base<CwiseUnaryView<UnaryOp, ArgType, StrideType> >
+{
+  typedef CwiseUnaryView<UnaryOp, ArgType, StrideType> XprType;
+
+  enum {
+    CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<UnaryOp>::Cost),
+
+    Flags = (evaluator<ArgType>::Flags & (HereditaryBits | LinearAccessBit | DirectAccessBit)),
+
+    Alignment = 0 // FIXME it is not very clear why alignment is necessarily lost...
+  };
+
+  EIGEN_DEVICE_FUNC explicit unary_evaluator(const XprType& op) : m_d(op)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<UnaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index row, Index col) const
+  {
+    return m_d.func()(m_d.argImpl.coeff(row, col));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index index) const
+  {
+    return m_d.func()(m_d.argImpl.coeff(index));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index row, Index col)
+  {
+    return m_d.func()(m_d.argImpl.coeffRef(row, col));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index index)
+  {
+    return m_d.func()(m_d.argImpl.coeffRef(index));
+  }
+
+protected:
+
+  // this helper permits to completely eliminate the functor if it is empty
+  struct Data
+  {
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Data(const XprType& xpr) : op(xpr.functor()), argImpl(xpr.nestedExpression()) {}
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const UnaryOp& func() const { return op; }
+    UnaryOp op;
+    evaluator<ArgType> argImpl;
+  };
+
+  Data m_d;
+};
+
+// -------------------- Map --------------------
+
+// FIXME perhaps the PlainObjectType could be provided by Derived::PlainObject ?
+// but that might complicate template specialization
+template<typename Derived, typename PlainObjectType>
+struct mapbase_evaluator;
+
+template<typename Derived, typename PlainObjectType>
+struct mapbase_evaluator : evaluator_base<Derived>
+{
+  typedef Derived  XprType;
+  typedef typename XprType::PointerType PointerType;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  enum {
+    IsRowMajor = XprType::RowsAtCompileTime,
+    ColsAtCompileTime = XprType::ColsAtCompileTime,
+    CoeffReadCost = NumTraits<Scalar>::ReadCost
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit mapbase_evaluator(const XprType& map)
+    : m_data(const_cast<PointerType>(map.data())),
+      m_innerStride(map.innerStride()),
+      m_outerStride(map.outerStride())
+  {
+    EIGEN_STATIC_ASSERT(check_implication((evaluator<Derived>::Flags & PacketAccessBit) != 0,
+                                          internal::inner_stride_at_compile_time<Derived>::ret == 1),
+                        PACKET_ACCESS_REQUIRES_TO_HAVE_INNER_STRIDE_FIXED_TO_1);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index row, Index col) const
+  {
+    return m_data[col * colStride() + row * rowStride()];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index index) const
+  {
+    return m_data[index * m_innerStride.value()];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index row, Index col)
+  {
+    return m_data[col * colStride() + row * rowStride()];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index index)
+  {
+    return m_data[index * m_innerStride.value()];
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index row, Index col) const
+  {
+    PointerType ptr = m_data + row * rowStride() + col * colStride();
+    return internal::ploadt<PacketType, LoadMode>(ptr);
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index index) const
+  {
+    return internal::ploadt<PacketType, LoadMode>(m_data + index * m_innerStride.value());
+  }
+
+  template<int StoreMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  void writePacket(Index row, Index col, const PacketType& x)
+  {
+    PointerType ptr = m_data + row * rowStride() + col * colStride();
+    return internal::pstoret<Scalar, PacketType, StoreMode>(ptr, x);
+  }
+
+  template<int StoreMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketType& x)
+  {
+    internal::pstoret<Scalar, PacketType, StoreMode>(m_data + index * m_innerStride.value(), x);
+  }
+protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+  Index rowStride() const EIGEN_NOEXCEPT {
+    return XprType::IsRowMajor ? m_outerStride.value() : m_innerStride.value();
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+  Index colStride() const EIGEN_NOEXCEPT {
+     return XprType::IsRowMajor ? m_innerStride.value() : m_outerStride.value();
+  }
+
+  PointerType m_data;
+  const internal::variable_if_dynamic<Index, XprType::InnerStrideAtCompileTime> m_innerStride;
+  const internal::variable_if_dynamic<Index, XprType::OuterStrideAtCompileTime> m_outerStride;
+};
+
+template<typename PlainObjectType, int MapOptions, typename StrideType>
+struct evaluator<Map<PlainObjectType, MapOptions, StrideType> >
+  : public mapbase_evaluator<Map<PlainObjectType, MapOptions, StrideType>, PlainObjectType>
+{
+  typedef Map<PlainObjectType, MapOptions, StrideType> XprType;
+  typedef typename XprType::Scalar Scalar;
+  // TODO: should check for smaller packet types once we can handle multi-sized packet types
+  typedef typename packet_traits<Scalar>::type PacketScalar;
+
+  enum {
+    InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
+                             ? int(PlainObjectType::InnerStrideAtCompileTime)
+                             : int(StrideType::InnerStrideAtCompileTime),
+    OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
+                             ? int(PlainObjectType::OuterStrideAtCompileTime)
+                             : int(StrideType::OuterStrideAtCompileTime),
+    HasNoInnerStride = InnerStrideAtCompileTime == 1,
+    HasNoOuterStride = StrideType::OuterStrideAtCompileTime == 0,
+    HasNoStride = HasNoInnerStride && HasNoOuterStride,
+    IsDynamicSize = PlainObjectType::SizeAtCompileTime==Dynamic,
+
+    PacketAccessMask = bool(HasNoInnerStride) ? ~int(0) : ~int(PacketAccessBit),
+    LinearAccessMask = bool(HasNoStride) || bool(PlainObjectType::IsVectorAtCompileTime) ? ~int(0) : ~int(LinearAccessBit),
+    Flags = int( evaluator<PlainObjectType>::Flags) & (LinearAccessMask&PacketAccessMask),
+
+    Alignment = int(MapOptions)&int(AlignedMask)
+  };
+
+  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& map)
+    : mapbase_evaluator<XprType, PlainObjectType>(map)
+  { }
+};
+
+// -------------------- Ref --------------------
+
+template<typename PlainObjectType, int RefOptions, typename StrideType>
+struct evaluator<Ref<PlainObjectType, RefOptions, StrideType> >
+  : public mapbase_evaluator<Ref<PlainObjectType, RefOptions, StrideType>, PlainObjectType>
+{
+  typedef Ref<PlainObjectType, RefOptions, StrideType> XprType;
+
+  enum {
+    Flags = evaluator<Map<PlainObjectType, RefOptions, StrideType> >::Flags,
+    Alignment = evaluator<Map<PlainObjectType, RefOptions, StrideType> >::Alignment
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit evaluator(const XprType& ref)
+    : mapbase_evaluator<XprType, PlainObjectType>(ref)
+  { }
+};
+
+// -------------------- Block --------------------
+
+template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel,
+         bool HasDirectAccess = internal::has_direct_access<ArgType>::ret> struct block_evaluator;
+
+template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
+struct evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel> >
+  : block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel>
+{
+  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
+  typedef typename XprType::Scalar Scalar;
+  // TODO: should check for smaller packet types once we can handle multi-sized packet types
+  typedef typename packet_traits<Scalar>::type PacketScalar;
+
+  enum {
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+
+    RowsAtCompileTime = traits<XprType>::RowsAtCompileTime,
+    ColsAtCompileTime = traits<XprType>::ColsAtCompileTime,
+    MaxRowsAtCompileTime = traits<XprType>::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = traits<XprType>::MaxColsAtCompileTime,
+
+    ArgTypeIsRowMajor = (int(evaluator<ArgType>::Flags)&RowMajorBit) != 0,
+    IsRowMajor = (MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1) ? 1
+               : (MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1) ? 0
+               : ArgTypeIsRowMajor,
+    HasSameStorageOrderAsArgType = (IsRowMajor == ArgTypeIsRowMajor),
+    InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
+    InnerStrideAtCompileTime = HasSameStorageOrderAsArgType
+                             ? int(inner_stride_at_compile_time<ArgType>::ret)
+                             : int(outer_stride_at_compile_time<ArgType>::ret),
+    OuterStrideAtCompileTime = HasSameStorageOrderAsArgType
+                             ? int(outer_stride_at_compile_time<ArgType>::ret)
+                             : int(inner_stride_at_compile_time<ArgType>::ret),
+    MaskPacketAccessBit = (InnerStrideAtCompileTime == 1 || HasSameStorageOrderAsArgType) ? PacketAccessBit : 0,
+
+    FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1 || (InnerPanel && (evaluator<ArgType>::Flags&LinearAccessBit))) ? LinearAccessBit : 0,
+    FlagsRowMajorBit = XprType::Flags&RowMajorBit,
+    Flags0 = evaluator<ArgType>::Flags & ( (HereditaryBits & ~RowMajorBit) |
+                                           DirectAccessBit |
+                                           MaskPacketAccessBit),
+    Flags = Flags0 | FlagsLinearAccessBit | FlagsRowMajorBit,
+
+    PacketAlignment = unpacket_traits<PacketScalar>::alignment,
+    Alignment0 = (InnerPanel && (OuterStrideAtCompileTime!=Dynamic)
+                             && (OuterStrideAtCompileTime!=0)
+                             && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % int(PacketAlignment)) == 0)) ? int(PacketAlignment) : 0,
+    Alignment = plain_enum_min(evaluator<ArgType>::Alignment, Alignment0)
+  };
+  typedef block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel> block_evaluator_type;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit evaluator(const XprType& block) : block_evaluator_type(block)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+};
+
+// no direct-access => dispatch to a unary evaluator
+template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
+struct block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel, /*HasDirectAccess*/ false>
+  : unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel> >
+{
+  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit block_evaluator(const XprType& block)
+    : unary_evaluator<XprType>(block)
+  {}
+};
+
+template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
+struct unary_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>, IndexBased>
+  : evaluator_base<Block<ArgType, BlockRows, BlockCols, InnerPanel> >
+{
+  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit unary_evaluator(const XprType& block)
+    : m_argImpl(block.nestedExpression()),
+      m_startRow(block.startRow()),
+      m_startCol(block.startCol()),
+      m_linear_offset(ForwardLinearAccess?(ArgType::IsRowMajor ? block.startRow()*block.nestedExpression().cols() + block.startCol() : block.startCol()*block.nestedExpression().rows() + block.startRow()):0)
+  { }
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  enum {
+    RowsAtCompileTime = XprType::RowsAtCompileTime,
+    ForwardLinearAccess = (InnerPanel || int(XprType::IsRowMajor)==int(ArgType::IsRowMajor)) && bool(evaluator<ArgType>::Flags&LinearAccessBit)
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index row, Index col) const
+  {
+    return m_argImpl.coeff(m_startRow.value() + row, m_startCol.value() + col);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index index) const
+  {
+    return linear_coeff_impl(index, bool_constant<ForwardLinearAccess>());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index row, Index col)
+  {
+    return m_argImpl.coeffRef(m_startRow.value() + row, m_startCol.value() + col);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index index)
+  {
+    return linear_coeffRef_impl(index, bool_constant<ForwardLinearAccess>());
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index row, Index col) const
+  {
+    return m_argImpl.template packet<LoadMode,PacketType>(m_startRow.value() + row, m_startCol.value() + col);
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index index) const
+  {
+    if (ForwardLinearAccess)
+      return m_argImpl.template packet<LoadMode,PacketType>(m_linear_offset.value() + index);
+    else
+      return packet<LoadMode,PacketType>(RowsAtCompileTime == 1 ? 0 : index,
+                                         RowsAtCompileTime == 1 ? index : 0);
+  }
+
+  template<int StoreMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  void writePacket(Index row, Index col, const PacketType& x)
+  {
+    return m_argImpl.template writePacket<StoreMode,PacketType>(m_startRow.value() + row, m_startCol.value() + col, x);
+  }
+
+  template<int StoreMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketType& x)
+  {
+    if (ForwardLinearAccess)
+      return m_argImpl.template writePacket<StoreMode,PacketType>(m_linear_offset.value() + index, x);
+    else
+      return writePacket<StoreMode,PacketType>(RowsAtCompileTime == 1 ? 0 : index,
+                                              RowsAtCompileTime == 1 ? index : 0,
+                                              x);
+  }
+
+protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType linear_coeff_impl(Index index, internal::true_type /* ForwardLinearAccess */) const
+  {
+    return m_argImpl.coeff(m_linear_offset.value() + index);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType linear_coeff_impl(Index index, internal::false_type /* not ForwardLinearAccess */) const
+  {
+    return coeff(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& linear_coeffRef_impl(Index index, internal::true_type /* ForwardLinearAccess */)
+  {
+    return m_argImpl.coeffRef(m_linear_offset.value() + index);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& linear_coeffRef_impl(Index index, internal::false_type /* not ForwardLinearAccess */)
+  {
+    return coeffRef(RowsAtCompileTime == 1 ? 0 : index, RowsAtCompileTime == 1 ? index : 0);
+  }
+
+  evaluator<ArgType> m_argImpl;
+  const variable_if_dynamic<Index, (ArgType::RowsAtCompileTime == 1 && BlockRows==1) ? 0 : Dynamic> m_startRow;
+  const variable_if_dynamic<Index, (ArgType::ColsAtCompileTime == 1 && BlockCols==1) ? 0 : Dynamic> m_startCol;
+  const variable_if_dynamic<Index, ForwardLinearAccess ? Dynamic : 0> m_linear_offset;
+};
+
+// TODO: This evaluator does not actually use the child evaluator;
+// all action is via the data() as returned by the Block expression.
+
+template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
+struct block_evaluator<ArgType, BlockRows, BlockCols, InnerPanel, /* HasDirectAccess */ true>
+  : mapbase_evaluator<Block<ArgType, BlockRows, BlockCols, InnerPanel>,
+                      typename Block<ArgType, BlockRows, BlockCols, InnerPanel>::PlainObject>
+{
+  typedef Block<ArgType, BlockRows, BlockCols, InnerPanel> XprType;
+  typedef typename XprType::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit block_evaluator(const XprType& block)
+    : mapbase_evaluator<XprType, typename XprType::PlainObject>(block)
+  {
+    eigen_internal_assert((internal::is_constant_evaluated() || (std::uintptr_t(block.data()) % plain_enum_max(1,evaluator<XprType>::Alignment)) == 0) \
+      && "data is not aligned");
+  }
+};
+
+
+// -------------------- Select --------------------
+// NOTE shall we introduce a ternary_evaluator?
+
+// TODO enable vectorization for Select
+template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType>
+struct evaluator<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
+  : evaluator_base<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
+{
+  typedef Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> XprType;
+  enum {
+    CoeffReadCost = evaluator<ConditionMatrixType>::CoeffReadCost
+                  + plain_enum_max(evaluator<ThenMatrixType>::CoeffReadCost,
+                                             evaluator<ElseMatrixType>::CoeffReadCost),
+
+    Flags = (unsigned int)evaluator<ThenMatrixType>::Flags & evaluator<ElseMatrixType>::Flags & HereditaryBits,
+
+    Alignment = plain_enum_min(evaluator<ThenMatrixType>::Alignment, evaluator<ElseMatrixType>::Alignment)
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit evaluator(const XprType& select)
+    : m_conditionImpl(select.conditionMatrix()),
+      m_thenImpl(select.thenMatrix()),
+      m_elseImpl(select.elseMatrix())
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index row, Index col) const
+  {
+    if (m_conditionImpl.coeff(row, col))
+      return m_thenImpl.coeff(row, col);
+    else
+      return m_elseImpl.coeff(row, col);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index index) const
+  {
+    if (m_conditionImpl.coeff(index))
+      return m_thenImpl.coeff(index);
+    else
+      return m_elseImpl.coeff(index);
+  }
+
+protected:
+  evaluator<ConditionMatrixType> m_conditionImpl;
+  evaluator<ThenMatrixType> m_thenImpl;
+  evaluator<ElseMatrixType> m_elseImpl;
+};
+
+
+// -------------------- Replicate --------------------
+
+template<typename ArgType, int RowFactor, int ColFactor>
+struct unary_evaluator<Replicate<ArgType, RowFactor, ColFactor> >
+  : evaluator_base<Replicate<ArgType, RowFactor, ColFactor> >
+{
+  typedef Replicate<ArgType, RowFactor, ColFactor> XprType;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  enum {
+    Factor = (RowFactor==Dynamic || ColFactor==Dynamic) ? Dynamic : RowFactor*ColFactor
+  };
+  typedef typename internal::nested_eval<ArgType,Factor>::type ArgTypeNested;
+  typedef internal::remove_all_t<ArgTypeNested> ArgTypeNestedCleaned;
+
+  enum {
+    CoeffReadCost = evaluator<ArgTypeNestedCleaned>::CoeffReadCost,
+    LinearAccessMask = XprType::IsVectorAtCompileTime ? LinearAccessBit : 0,
+    Flags = (evaluator<ArgTypeNestedCleaned>::Flags & (HereditaryBits|LinearAccessMask) & ~RowMajorBit) | (traits<XprType>::Flags & RowMajorBit),
+
+    Alignment = evaluator<ArgTypeNestedCleaned>::Alignment
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit unary_evaluator(const XprType& replicate)
+    : m_arg(replicate.nestedExpression()),
+      m_argImpl(m_arg),
+      m_rows(replicate.nestedExpression().rows()),
+      m_cols(replicate.nestedExpression().cols())
+  {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index row, Index col) const
+  {
+    // try to avoid using modulo; this is a pure optimization strategy
+    const Index actual_row = internal::traits<XprType>::RowsAtCompileTime==1 ? 0
+                           : RowFactor==1 ? row
+                           : row % m_rows.value();
+    const Index actual_col = internal::traits<XprType>::ColsAtCompileTime==1 ? 0
+                           : ColFactor==1 ? col
+                           : col % m_cols.value();
+
+    return m_argImpl.coeff(actual_row, actual_col);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index index) const
+  {
+    // try to avoid using modulo; this is a pure optimization strategy
+    const Index actual_index = internal::traits<XprType>::RowsAtCompileTime==1
+                                  ? (ColFactor==1 ?  index : index%m_cols.value())
+                                  : (RowFactor==1 ?  index : index%m_rows.value());
+
+    return m_argImpl.coeff(actual_index);
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index row, Index col) const
+  {
+    const Index actual_row = internal::traits<XprType>::RowsAtCompileTime==1 ? 0
+                           : RowFactor==1 ? row
+                           : row % m_rows.value();
+    const Index actual_col = internal::traits<XprType>::ColsAtCompileTime==1 ? 0
+                           : ColFactor==1 ? col
+                           : col % m_cols.value();
+
+    return m_argImpl.template packet<LoadMode,PacketType>(actual_row, actual_col);
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index index) const
+  {
+    const Index actual_index = internal::traits<XprType>::RowsAtCompileTime==1
+                                  ? (ColFactor==1 ?  index : index%m_cols.value())
+                                  : (RowFactor==1 ?  index : index%m_rows.value());
+
+    return m_argImpl.template packet<LoadMode,PacketType>(actual_index);
+  }
+
+protected:
+  const ArgTypeNested m_arg;
+  evaluator<ArgTypeNestedCleaned> m_argImpl;
+  const variable_if_dynamic<Index, ArgType::RowsAtCompileTime> m_rows;
+  const variable_if_dynamic<Index, ArgType::ColsAtCompileTime> m_cols;
+};
+
+// -------------------- MatrixWrapper and ArrayWrapper --------------------
+//
+// evaluator_wrapper_base<T> is a common base class for the
+// MatrixWrapper and ArrayWrapper evaluators.
+
+template<typename XprType>
+struct evaluator_wrapper_base
+  : evaluator_base<XprType>
+{
+  typedef remove_all_t<typename XprType::NestedExpressionType> ArgType;
+  enum {
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+    Flags = evaluator<ArgType>::Flags,
+    Alignment = evaluator<ArgType>::Alignment
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit evaluator_wrapper_base(const ArgType& arg) : m_argImpl(arg) {}
+
+  typedef typename ArgType::Scalar Scalar;
+  typedef typename ArgType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index row, Index col) const
+  {
+    return m_argImpl.coeff(row, col);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index index) const
+  {
+    return m_argImpl.coeff(index);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index row, Index col)
+  {
+    return m_argImpl.coeffRef(row, col);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index index)
+  {
+    return m_argImpl.coeffRef(index);
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index row, Index col) const
+  {
+    return m_argImpl.template packet<LoadMode,PacketType>(row, col);
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index index) const
+  {
+    return m_argImpl.template packet<LoadMode,PacketType>(index);
+  }
+
+  template<int StoreMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  void writePacket(Index row, Index col, const PacketType& x)
+  {
+    m_argImpl.template writePacket<StoreMode>(row, col, x);
+  }
+
+  template<int StoreMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketType& x)
+  {
+    m_argImpl.template writePacket<StoreMode>(index, x);
+  }
+
+protected:
+  evaluator<ArgType> m_argImpl;
+};
+
+template<typename TArgType>
+struct unary_evaluator<MatrixWrapper<TArgType> >
+  : evaluator_wrapper_base<MatrixWrapper<TArgType> >
+{
+  typedef MatrixWrapper<TArgType> XprType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit unary_evaluator(const XprType& wrapper)
+    : evaluator_wrapper_base<MatrixWrapper<TArgType> >(wrapper.nestedExpression())
+  { }
+};
+
+template<typename TArgType>
+struct unary_evaluator<ArrayWrapper<TArgType> >
+  : evaluator_wrapper_base<ArrayWrapper<TArgType> >
+{
+  typedef ArrayWrapper<TArgType> XprType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit unary_evaluator(const XprType& wrapper)
+    : evaluator_wrapper_base<ArrayWrapper<TArgType> >(wrapper.nestedExpression())
+  { }
+};
+
+
+// -------------------- Reverse --------------------
+
+// defined in Reverse.h:
+template<typename PacketType, bool ReversePacket> struct reverse_packet_cond;
+
+template<typename ArgType, int Direction>
+struct unary_evaluator<Reverse<ArgType, Direction> >
+  : evaluator_base<Reverse<ArgType, Direction> >
+{
+  typedef Reverse<ArgType, Direction> XprType;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  enum {
+    IsRowMajor = XprType::IsRowMajor,
+    IsColMajor = !IsRowMajor,
+    ReverseRow = (Direction == Vertical)   || (Direction == BothDirections),
+    ReverseCol = (Direction == Horizontal) || (Direction == BothDirections),
+    ReversePacket = (Direction == BothDirections)
+                    || ((Direction == Vertical)   && IsColMajor)
+                    || ((Direction == Horizontal) && IsRowMajor),
+
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+
+    // let's enable LinearAccess only with vectorization because of the product overhead
+    // FIXME enable DirectAccess with negative strides?
+    Flags0 = evaluator<ArgType>::Flags,
+    LinearAccess = ( (Direction==BothDirections) && (int(Flags0)&PacketAccessBit) )
+                  || ((ReverseRow && XprType::ColsAtCompileTime==1) || (ReverseCol && XprType::RowsAtCompileTime==1))
+                 ? LinearAccessBit : 0,
+
+    Flags = int(Flags0) & (HereditaryBits | PacketAccessBit | LinearAccess),
+
+    Alignment = 0 // FIXME in some rare cases, Alignment could be preserved, like a Vector4f.
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit unary_evaluator(const XprType& reverse)
+    : m_argImpl(reverse.nestedExpression()),
+      m_rows(ReverseRow ? reverse.nestedExpression().rows() : 1),
+      m_cols(ReverseCol ? reverse.nestedExpression().cols() : 1)
+  { }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index row, Index col) const
+  {
+    return m_argImpl.coeff(ReverseRow ? m_rows.value() - row - 1 : row,
+                           ReverseCol ? m_cols.value() - col - 1 : col);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index index) const
+  {
+    return m_argImpl.coeff(m_rows.value() * m_cols.value() - index - 1);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index row, Index col)
+  {
+    return m_argImpl.coeffRef(ReverseRow ? m_rows.value() - row - 1 : row,
+                              ReverseCol ? m_cols.value() - col - 1 : col);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index index)
+  {
+    return m_argImpl.coeffRef(m_rows.value() * m_cols.value() - index - 1);
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index row, Index col) const
+  {
+    enum {
+      PacketSize = unpacket_traits<PacketType>::size,
+      OffsetRow  = ReverseRow && IsColMajor ? PacketSize : 1,
+      OffsetCol  = ReverseCol && IsRowMajor ? PacketSize : 1
+    };
+    typedef internal::reverse_packet_cond<PacketType,ReversePacket> reverse_packet;
+    return reverse_packet::run(m_argImpl.template packet<LoadMode,PacketType>(
+                                  ReverseRow ? m_rows.value() - row - OffsetRow : row,
+                                  ReverseCol ? m_cols.value() - col - OffsetCol : col));
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  PacketType packet(Index index) const
+  {
+    enum { PacketSize = unpacket_traits<PacketType>::size };
+    return preverse(m_argImpl.template packet<LoadMode,PacketType>(m_rows.value() * m_cols.value() - index - PacketSize));
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  void writePacket(Index row, Index col, const PacketType& x)
+  {
+    // FIXME we could factorize some code with packet(i,j)
+    enum {
+      PacketSize = unpacket_traits<PacketType>::size,
+      OffsetRow  = ReverseRow && IsColMajor ? PacketSize : 1,
+      OffsetCol  = ReverseCol && IsRowMajor ? PacketSize : 1
+    };
+    typedef internal::reverse_packet_cond<PacketType,ReversePacket> reverse_packet;
+    m_argImpl.template writePacket<LoadMode>(
+                                  ReverseRow ? m_rows.value() - row - OffsetRow : row,
+                                  ReverseCol ? m_cols.value() - col - OffsetCol : col,
+                                  reverse_packet::run(x));
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE
+  void writePacket(Index index, const PacketType& x)
+  {
+    enum { PacketSize = unpacket_traits<PacketType>::size };
+    m_argImpl.template writePacket<LoadMode>
+      (m_rows.value() * m_cols.value() - index - PacketSize, preverse(x));
+  }
+
+protected:
+  evaluator<ArgType> m_argImpl;
+
+  // If we do not reverse rows, then we do not need to know the number of rows; same for columns
+  // Nonetheless, in this case it is important to set to 1 such that the coeff(index) method works fine for vectors.
+  const variable_if_dynamic<Index, ReverseRow ? ArgType::RowsAtCompileTime : 1> m_rows;
+  const variable_if_dynamic<Index, ReverseCol ? ArgType::ColsAtCompileTime : 1> m_cols;
+};
+
+
+// -------------------- Diagonal --------------------
+
+template<typename ArgType, int DiagIndex>
+struct evaluator<Diagonal<ArgType, DiagIndex> >
+  : evaluator_base<Diagonal<ArgType, DiagIndex> >
+{
+  typedef Diagonal<ArgType, DiagIndex> XprType;
+
+  enum {
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+
+    Flags = (unsigned int)(evaluator<ArgType>::Flags & (HereditaryBits | DirectAccessBit) & ~RowMajorBit) | LinearAccessBit,
+
+    Alignment = 0
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit evaluator(const XprType& diagonal)
+    : m_argImpl(diagonal.nestedExpression()),
+      m_index(diagonal.index())
+  { }
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index row, Index) const
+  {
+    return m_argImpl.coeff(row + rowOffset(), row + colOffset());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index index) const
+  {
+    return m_argImpl.coeff(index + rowOffset(), index + colOffset());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index row, Index)
+  {
+    return m_argImpl.coeffRef(row + rowOffset(), row + colOffset());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index index)
+  {
+    return m_argImpl.coeffRef(index + rowOffset(), index + colOffset());
+  }
+
+protected:
+  evaluator<ArgType> m_argImpl;
+  const internal::variable_if_dynamicindex<Index, XprType::DiagIndex> m_index;
+
+private:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+  Index rowOffset() const { return m_index.value() > 0 ? 0 : -m_index.value(); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+  Index colOffset() const { return m_index.value() > 0 ? m_index.value() : 0; }
+};
+
+
+//----------------------------------------------------------------------
+// deprecated code
+//----------------------------------------------------------------------
+
+// -------------------- EvalToTemp --------------------
+
+// expression class for evaluating nested expression to a temporary
+
+template<typename ArgType> class EvalToTemp;
+
+template<typename ArgType>
+struct traits<EvalToTemp<ArgType> >
+  : public traits<ArgType>
+{ };
+
+template<typename ArgType>
+class EvalToTemp
+  : public dense_xpr_base<EvalToTemp<ArgType> >::type
+{
+ public:
+
+  typedef typename dense_xpr_base<EvalToTemp>::type Base;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(EvalToTemp)
+
+  explicit EvalToTemp(const ArgType& arg)
+    : m_arg(arg)
+  { }
+
+  const ArgType& arg() const
+  {
+    return m_arg;
+  }
+
+  EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT
+  {
+    return m_arg.rows();
+  }
+
+  EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT
+  {
+    return m_arg.cols();
+  }
+
+ private:
+  const ArgType& m_arg;
+};
+
+template<typename ArgType>
+struct evaluator<EvalToTemp<ArgType> >
+  : public evaluator<typename ArgType::PlainObject>
+{
+  typedef EvalToTemp<ArgType>                   XprType;
+  typedef typename ArgType::PlainObject         PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr)
+    : m_result(xpr.arg())
+  {
+    internal::construct_at<Base>(this, m_result);
+  }
+
+  // This constructor is used when nesting an EvalTo evaluator in another evaluator
+  EIGEN_DEVICE_FUNC evaluator(const ArgType& arg)
+    : m_result(arg)
+  {
+    internal::construct_at<Base>(this, m_result);
+  }
+
+protected:
+  PlainObject m_result;
+};
+
+} // namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_COREEVALUATORS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CoreIterators.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CoreIterators.h
new file mode 100644
index 0000000000000000000000000000000000000000..f74568a174f85c582f2ae9d654fb30ee053ed4b3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CoreIterators.h
@@ -0,0 +1,134 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COREITERATORS_H
+#define EIGEN_COREITERATORS_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/* This file contains the respective InnerIterator definition of the expressions defined in Eigen/Core
+ */
+
+namespace internal {
+
+template<typename XprType, typename EvaluatorKind>
+class inner_iterator_selector;
+
+}
+
+/** \class InnerIterator
+  * \brief An InnerIterator allows to loop over the element of any matrix expression.
+  * 
+  * \warning To be used with care because an evaluator is constructed every time an InnerIterator iterator is constructed.
+  * 
+  * TODO: add a usage example
+  */
+template<typename XprType>
+class InnerIterator
+{
+protected:
+  typedef internal::inner_iterator_selector<XprType, typename internal::evaluator_traits<XprType>::Kind> IteratorType;
+  typedef internal::evaluator<XprType> EvaluatorType;
+  typedef typename internal::traits<XprType>::Scalar Scalar;
+public:
+  /** Construct an iterator over the \a outerId -th row or column of \a xpr */
+  InnerIterator(const XprType &xpr, const Index &outerId)
+    : m_eval(xpr), m_iter(m_eval, outerId, xpr.innerSize())
+  {}
+  
+  /// \returns the value of the current coefficient.
+  EIGEN_STRONG_INLINE Scalar value() const          { return m_iter.value(); }
+  /** Increment the iterator \c *this to the next non-zero coefficient.
+    * Explicit zeros are not skipped over. To skip explicit zeros, see class SparseView
+    */
+  EIGEN_STRONG_INLINE InnerIterator& operator++()   { m_iter.operator++(); return *this; }
+  EIGEN_STRONG_INLINE InnerIterator& operator+=(Index i) { m_iter.operator+=(i); return *this; }
+  EIGEN_STRONG_INLINE InnerIterator operator+(Index i) 
+  { InnerIterator result(*this); result+=i; return result; }
+    
+
+  /// \returns the column or row index of the current coefficient.
+  EIGEN_STRONG_INLINE Index index() const           { return m_iter.index(); }
+  /// \returns the row index of the current coefficient.
+  EIGEN_STRONG_INLINE Index row() const             { return m_iter.row(); }
+  /// \returns the column index of the current coefficient.
+  EIGEN_STRONG_INLINE Index col() const             { return m_iter.col(); }
+  /// \returns \c true if the iterator \c *this still references a valid coefficient.
+  EIGEN_STRONG_INLINE operator bool() const         { return m_iter; }
+  
+protected:
+  EvaluatorType m_eval;
+  IteratorType m_iter;
+private:
+  // If you get here, then you're not using the right InnerIterator type, e.g.:
+  //   SparseMatrix<double,RowMajor> A;
+  //   SparseMatrix<double>::InnerIterator it(A,0);
+  template<typename T> InnerIterator(const EigenBase<T>&,Index outer);
+};
+
+namespace internal {
+
+// Generic inner iterator implementation for dense objects
+template<typename XprType>
+class inner_iterator_selector<XprType, IndexBased>
+{
+protected:
+  typedef evaluator<XprType> EvaluatorType;
+  typedef typename traits<XprType>::Scalar Scalar;
+  enum { IsRowMajor = (XprType::Flags&RowMajorBit)==RowMajorBit };
+  
+public:
+  EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &innerSize)
+    : m_eval(eval), m_inner(0), m_outer(outerId), m_end(innerSize)
+  {}
+
+  EIGEN_STRONG_INLINE Scalar value() const
+  {
+    return (IsRowMajor) ? m_eval.coeff(m_outer, m_inner)
+                        : m_eval.coeff(m_inner, m_outer);
+  }
+
+  EIGEN_STRONG_INLINE inner_iterator_selector& operator++() { m_inner++; return *this; }
+
+  EIGEN_STRONG_INLINE Index index() const { return m_inner; }
+  inline Index row() const { return IsRowMajor ? m_outer : index(); }
+  inline Index col() const { return IsRowMajor ? index() : m_outer; }
+
+  EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; }
+
+protected:
+  const EvaluatorType& m_eval;
+  Index m_inner;
+  const Index m_outer;
+  const Index m_end;
+};
+
+// For iterator-based evaluator, inner-iterator is already implemented as
+// evaluator<>::InnerIterator
+template<typename XprType>
+class inner_iterator_selector<XprType, IteratorBased>
+ : public evaluator<XprType>::InnerIterator
+{
+protected:
+  typedef typename evaluator<XprType>::InnerIterator Base;
+  typedef evaluator<XprType> EvaluatorType;
+  
+public:
+  EIGEN_STRONG_INLINE inner_iterator_selector(const EvaluatorType &eval, const Index &outerId, const Index &/*innerSize*/)
+    : Base(eval, outerId)
+  {}  
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_COREITERATORS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CwiseBinaryOp.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CwiseBinaryOp.h
new file mode 100644
index 0000000000000000000000000000000000000000..21a061a00da8f45e8219fcb7c9f142dc22b0b1bb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CwiseBinaryOp.h
@@ -0,0 +1,185 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CWISE_BINARY_OP_H
+#define EIGEN_CWISE_BINARY_OP_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template<typename BinaryOp, typename Lhs, typename Rhs>
+struct traits<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
+{
+  // we must not inherit from traits<Lhs> since it has
+  // the potential to cause problems with MSVC
+  typedef remove_all_t<Lhs> Ancestor;
+  typedef typename traits<Ancestor>::XprKind XprKind;
+  enum {
+    RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
+    ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
+    MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
+  };
+
+  // even though we require Lhs and Rhs to have the same scalar type (see CwiseBinaryOp constructor),
+  // we still want to handle the case when the result type is different.
+  typedef typename result_of<
+                     BinaryOp(
+                       const typename Lhs::Scalar&,
+                       const typename Rhs::Scalar&
+                     )
+                   >::type Scalar;
+  typedef typename cwise_promote_storage_type<typename traits<Lhs>::StorageKind,
+                                              typename traits<Rhs>::StorageKind,
+                                              BinaryOp>::ret StorageKind;
+  typedef typename promote_index_type<typename traits<Lhs>::StorageIndex,
+                                      typename traits<Rhs>::StorageIndex>::type StorageIndex;
+  typedef typename Lhs::Nested LhsNested;
+  typedef typename Rhs::Nested RhsNested;
+  typedef std::remove_reference_t<LhsNested> LhsNested_;
+  typedef std::remove_reference_t<RhsNested> RhsNested_;
+  enum {
+    Flags = cwise_promote_storage_order<typename traits<Lhs>::StorageKind,typename traits<Rhs>::StorageKind,LhsNested_::Flags & RowMajorBit,RhsNested_::Flags & RowMajorBit>::value
+  };
+};
+} // end namespace internal
+
+template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
+class CwiseBinaryOpImpl;
+
+/** \class CwiseBinaryOp
+  * \ingroup Core_Module
+  *
+  * \brief Generic expression where a coefficient-wise binary operator is applied to two expressions
+  *
+  * \tparam BinaryOp template functor implementing the operator
+  * \tparam LhsType the type of the left-hand side
+  * \tparam RhsType the type of the right-hand side
+  *
+  * This class represents an expression  where a coefficient-wise binary operator is applied to two expressions.
+  * It is the return type of binary operators, by which we mean only those binary operators where
+  * both the left-hand side and the right-hand side are Eigen expressions.
+  * For example, the return type of matrix1+matrix2 is a CwiseBinaryOp.
+  *
+  * Most of the time, this is the only way that it is used, so you typically don't have to name
+  * CwiseBinaryOp types explicitly.
+  *
+  * \sa MatrixBase::binaryExpr(const MatrixBase<OtherDerived> &,const CustomBinaryOp &) const, class CwiseUnaryOp, class CwiseNullaryOp
+  */
+template<typename BinaryOp, typename LhsType, typename RhsType>
+class CwiseBinaryOp :
+  public CwiseBinaryOpImpl<
+          BinaryOp, LhsType, RhsType,
+          typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
+                                                        typename internal::traits<RhsType>::StorageKind,
+                                                        BinaryOp>::ret>,
+  internal::no_assignment_operator
+{
+  public:
+
+    typedef internal::remove_all_t<BinaryOp> Functor;
+    typedef internal::remove_all_t<LhsType> Lhs;
+    typedef internal::remove_all_t<RhsType> Rhs;
+
+    typedef typename CwiseBinaryOpImpl<
+        BinaryOp, LhsType, RhsType,
+        typename internal::cwise_promote_storage_type<typename internal::traits<LhsType>::StorageKind,
+                                                      typename internal::traits<Rhs>::StorageKind,
+                                                      BinaryOp>::ret>::Base Base;
+    EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseBinaryOp)
+
+    EIGEN_CHECK_BINARY_COMPATIBILIY(BinaryOp,typename Lhs::Scalar,typename Rhs::Scalar)
+    EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Lhs, Rhs)
+
+    typedef typename internal::ref_selector<LhsType>::type LhsNested;
+    typedef typename internal::ref_selector<RhsType>::type RhsNested;
+    typedef std::remove_reference_t<LhsNested> LhsNested_;
+    typedef std::remove_reference_t<RhsNested> RhsNested_;
+
+#if EIGEN_COMP_MSVC
+    //Required for Visual Studio or the Copy constructor will probably not get inlined!
+    EIGEN_STRONG_INLINE
+    CwiseBinaryOp(const CwiseBinaryOp<BinaryOp,LhsType,RhsType>&) = default;
+#endif
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    CwiseBinaryOp(const Lhs& aLhs, const Rhs& aRhs, const BinaryOp& func = BinaryOp())
+      : m_lhs(aLhs), m_rhs(aRhs), m_functor(func)
+    {
+      eigen_assert(aLhs.rows() == aRhs.rows() && aLhs.cols() == aRhs.cols());
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index rows() const EIGEN_NOEXCEPT {
+      // return the fixed size type if available to enable compile time optimizations
+      return internal::traits<internal::remove_all_t<LhsNested>>::RowsAtCompileTime==Dynamic ? m_rhs.rows() : m_lhs.rows();
+    }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index cols() const EIGEN_NOEXCEPT {
+      // return the fixed size type if available to enable compile time optimizations
+      return internal::traits<internal::remove_all_t<LhsNested>>::ColsAtCompileTime==Dynamic ? m_rhs.cols() : m_lhs.cols();
+    }
+
+    /** \returns the left hand side nested expression */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const LhsNested_& lhs() const { return m_lhs; }
+    /** \returns the right hand side nested expression */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const RhsNested_& rhs() const { return m_rhs; }
+    /** \returns the functor representing the binary operation */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const BinaryOp& functor() const { return m_functor; }
+
+  protected:
+    LhsNested m_lhs;
+    RhsNested m_rhs;
+    const BinaryOp m_functor;
+};
+
+// Generic API dispatcher
+template<typename BinaryOp, typename Lhs, typename Rhs, typename StorageKind>
+class CwiseBinaryOpImpl
+  : public internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type
+{
+public:
+  typedef typename internal::generic_xpr_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >::type Base;
+};
+
+/** replaces \c *this by \c *this - \a other.
+  *
+  * \returns a reference to \c *this
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
+MatrixBase<Derived>::operator-=(const MatrixBase<OtherDerived> &other)
+{
+  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+
+/** replaces \c *this by \c *this + \a other.
+  *
+  * \returns a reference to \c *this
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived &
+MatrixBase<Derived>::operator+=(const MatrixBase<OtherDerived>& other)
+{
+  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_CWISE_BINARY_OP_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CwiseNullaryOp.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CwiseNullaryOp.h
new file mode 100644
index 0000000000000000000000000000000000000000..b33c052c3f12e19e0c6734d2e62829f43be1ac03
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CwiseNullaryOp.h
@@ -0,0 +1,1030 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CWISE_NULLARY_OP_H
+#define EIGEN_CWISE_NULLARY_OP_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template<typename NullaryOp, typename PlainObjectType>
+struct traits<CwiseNullaryOp<NullaryOp, PlainObjectType> > : traits<PlainObjectType>
+{
+  enum {
+    Flags = traits<PlainObjectType>::Flags & RowMajorBit
+  };
+};
+
+} // namespace internal
+
+/** \class CwiseNullaryOp
+  * \ingroup Core_Module
+  *
+  * \brief Generic expression of a matrix where all coefficients are defined by a functor
+  *
+  * \tparam NullaryOp template functor implementing the operator
+  * \tparam PlainObjectType the underlying plain matrix/array type
+  *
+  * This class represents an expression of a generic nullary operator.
+  * It is the return type of the Ones(), Zero(), Constant(), Identity() and Random() methods,
+  * and most of the time this is the only way it is used.
+  *
+  * However, if you want to write a function returning such an expression, you
+  * will need to use this class.
+  *
+  * The functor NullaryOp must expose one of the following method:
+    <table class="manual">
+    <tr            ><td>\c operator()() </td><td>if the procedural generation does not depend on the coefficient entries (e.g., random numbers)</td></tr>
+    <tr class="alt"><td>\c operator()(Index i)</td><td>if the procedural generation makes sense for vectors only and that it depends on the coefficient index \c i (e.g., linspace) </td></tr>
+    <tr            ><td>\c operator()(Index i,Index j)</td><td>if the procedural generation depends on the matrix coordinates \c i, \c j (e.g., to generate a checkerboard with 0 and 1)</td></tr>
+    </table>
+  * It is also possible to expose the last two operators if the generation makes sense for matrices but can be optimized for vectors.
+  *
+  * See DenseBase::NullaryExpr(Index,const CustomNullaryOp&) for an example binding
+  * C++11 random number generators.
+  *
+  * A nullary expression can also be used to implement custom sophisticated matrix manipulations
+  * that cannot be covered by the existing set of natively supported matrix manipulations.
+  * See this \ref TopicCustomizing_NullaryExpr "page" for some examples and additional explanations
+  * on the behavior of CwiseNullaryOp.
+  *
+  * \sa class CwiseUnaryOp, class CwiseBinaryOp, DenseBase::NullaryExpr
+  */
+template<typename NullaryOp, typename PlainObjectType>
+class CwiseNullaryOp : public internal::dense_xpr_base< CwiseNullaryOp<NullaryOp, PlainObjectType> >::type, internal::no_assignment_operator
+{
+  public:
+
+    typedef typename internal::dense_xpr_base<CwiseNullaryOp>::type Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(CwiseNullaryOp)
+
+    EIGEN_DEVICE_FUNC
+    CwiseNullaryOp(Index rows, Index cols, const NullaryOp& func = NullaryOp())
+      : m_rows(rows), m_cols(cols), m_functor(func)
+    {
+      eigen_assert(rows >= 0
+            && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
+            &&  cols >= 0
+            && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols));
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index rows() const { return m_rows.value(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index cols() const { return m_cols.value(); }
+
+    /** \returns the functor representing the nullary operation */
+    EIGEN_DEVICE_FUNC
+    const NullaryOp& functor() const { return m_functor; }
+
+  protected:
+    const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows;
+    const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
+    const NullaryOp m_functor;
+};
+
+
+/** \returns an expression of a matrix defined by a custom functor \a func
+  *
+  * The parameters \a rows and \a cols are the number of rows and of columns of
+  * the returned matrix. Must be compatible with this MatrixBase type.
+  *
+  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
+  * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
+  * instead.
+  *
+  * The template parameter \a CustomNullaryOp is the type of the functor.
+  *
+  * \sa class CwiseNullaryOp
+  */
+template<typename Derived>
+template<typename CustomNullaryOp>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const CwiseNullaryOp<CustomNullaryOp,typename DenseBase<Derived>::PlainObject>
+#else
+const CwiseNullaryOp<CustomNullaryOp,PlainObject>
+#endif
+DenseBase<Derived>::NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func)
+{
+  return CwiseNullaryOp<CustomNullaryOp, PlainObject>(rows, cols, func);
+}
+
+/** \returns an expression of a matrix defined by a custom functor \a func
+  *
+  * The parameter \a size is the size of the returned vector.
+  * Must be compatible with this MatrixBase type.
+  *
+  * \only_for_vectors
+  *
+  * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
+  * it is redundant to pass \a size as argument, so Zero() should be used
+  * instead.
+  *
+  * The template parameter \a CustomNullaryOp is the type of the functor.
+  *
+  * Here is an example with C++11 random generators: \include random_cpp11.cpp
+  * Output: \verbinclude random_cpp11.out
+  *
+  * \sa class CwiseNullaryOp
+  */
+template<typename Derived>
+template<typename CustomNullaryOp>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
+#else
+const CwiseNullaryOp<CustomNullaryOp, PlainObject>
+#endif
+DenseBase<Derived>::NullaryExpr(Index size, const CustomNullaryOp& func)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  if(RowsAtCompileTime == 1) return CwiseNullaryOp<CustomNullaryOp, PlainObject>(1, size, func);
+  else return CwiseNullaryOp<CustomNullaryOp, PlainObject>(size, 1, func);
+}
+
+/** \returns an expression of a matrix defined by a custom functor \a func
+  *
+  * This variant is only for fixed-size DenseBase types. For dynamic-size types, you
+  * need to use the variants taking size arguments.
+  *
+  * The template parameter \a CustomNullaryOp is the type of the functor.
+  *
+  * \sa class CwiseNullaryOp
+  */
+template<typename Derived>
+template<typename CustomNullaryOp>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const CwiseNullaryOp<CustomNullaryOp, typename DenseBase<Derived>::PlainObject>
+#else
+const CwiseNullaryOp<CustomNullaryOp, PlainObject>
+#endif
+DenseBase<Derived>::NullaryExpr(const CustomNullaryOp& func)
+{
+  return CwiseNullaryOp<CustomNullaryOp, PlainObject>(RowsAtCompileTime, ColsAtCompileTime, func);
+}
+
+/** \returns an expression of a constant matrix of value \a value
+  *
+  * The parameters \a rows and \a cols are the number of rows and of columns of
+  * the returned matrix. Must be compatible with this DenseBase type.
+  *
+  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
+  * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
+  * instead.
+  *
+  * The template parameter \a CustomNullaryOp is the type of the functor.
+  *
+  * \sa class CwiseNullaryOp
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
+DenseBase<Derived>::Constant(Index rows, Index cols, const Scalar& value)
+{
+  return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_constant_op<Scalar>(value));
+}
+
+/** \returns an expression of a constant matrix of value \a value
+  *
+  * The parameter \a size is the size of the returned vector.
+  * Must be compatible with this DenseBase type.
+  *
+  * \only_for_vectors
+  *
+  * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
+  * it is redundant to pass \a size as argument, so Zero() should be used
+  * instead.
+  *
+  * The template parameter \a CustomNullaryOp is the type of the functor.
+  *
+  * \sa class CwiseNullaryOp
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
+DenseBase<Derived>::Constant(Index size, const Scalar& value)
+{
+  return DenseBase<Derived>::NullaryExpr(size, internal::scalar_constant_op<Scalar>(value));
+}
+
+/** \returns an expression of a constant matrix of value \a value
+  *
+  * This variant is only for fixed-size DenseBase types. For dynamic-size types, you
+  * need to use the variants taking size arguments.
+  *
+  * The template parameter \a CustomNullaryOp is the type of the functor.
+  *
+  * \sa class CwiseNullaryOp
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
+DenseBase<Derived>::Constant(const Scalar& value)
+{
+  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
+  return DenseBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_constant_op<Scalar>(value));
+}
+
+/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(Index,const Scalar&,const Scalar&)
+  *
+  * \only_for_vectors
+  *
+  * Example: \include DenseBase_LinSpaced_seq_deprecated.cpp
+  * Output: \verbinclude DenseBase_LinSpaced_seq_deprecated.out
+  *
+  * \sa LinSpaced(Index,const Scalar&, const Scalar&), setLinSpaced(Index,const Scalar&,const Scalar&)
+  */
+template<typename Derived>
+EIGEN_DEPRECATED EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
+DenseBase<Derived>::LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar>(low,high,size));
+}
+
+/** \deprecated because of accuracy loss. In Eigen 3.3, it is an alias for LinSpaced(const Scalar&,const Scalar&)
+  *
+  * \sa LinSpaced(const Scalar&, const Scalar&)
+  */
+template<typename Derived>
+EIGEN_DEPRECATED EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
+DenseBase<Derived>::LinSpaced(Sequential_t, const Scalar& low, const Scalar& high)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
+  return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar>(low,high,Derived::SizeAtCompileTime));
+}
+
+/**
+  * \brief Sets a linearly spaced vector.
+  *
+  * The function generates 'size' equally spaced values in the closed interval [low,high].
+  * When size is set to 1, a vector of length 1 containing 'high' is returned.
+  *
+  * \only_for_vectors
+  *
+  * Example: \include DenseBase_LinSpaced.cpp
+  * Output: \verbinclude DenseBase_LinSpaced.out
+  *
+  * For integer scalar types, an even spacing is possible if and only if the length of the range,
+  * i.e., \c high-low is a scalar multiple of \c size-1, or if \c size is a scalar multiple of the
+  * number of values \c high-low+1 (meaning each value can be repeated the same number of time).
+  * If one of these two considions is not satisfied, then \c high is lowered to the largest value
+  * satisfying one of this constraint.
+  * Here are some examples:
+  *
+  * Example: \include DenseBase_LinSpacedInt.cpp
+  * Output: \verbinclude DenseBase_LinSpacedInt.out
+  *
+  * \sa setLinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
+DenseBase<Derived>::LinSpaced(Index size, const Scalar& low, const Scalar& high)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return DenseBase<Derived>::NullaryExpr(size, internal::linspaced_op<Scalar>(low,high,size));
+}
+
+/**
+  * \copydoc DenseBase::LinSpaced(Index, const Scalar&, const Scalar&)
+  * Special version for fixed size types which does not require the size parameter.
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessLinSpacedReturnType
+DenseBase<Derived>::LinSpaced(const Scalar& low, const Scalar& high)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
+  return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::linspaced_op<Scalar>(low,high,Derived::SizeAtCompileTime));
+}
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessEqualSpacedReturnType
+DenseBase<Derived>::EqualSpaced(Index size, const Scalar& low, const Scalar& step) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return DenseBase<Derived>::NullaryExpr(size, internal::equalspaced_op<Scalar>(low, step));
+}
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::RandomAccessEqualSpacedReturnType
+DenseBase<Derived>::EqualSpaced(const Scalar& low, const Scalar& step) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return DenseBase<Derived>::NullaryExpr(Derived::SizeAtCompileTime, internal::equalspaced_op<Scalar>(low, step));
+}
+
+/** \returns true if all coefficients in this matrix are approximately equal to \a val, to within precision \a prec */
+template<typename Derived>
+EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isApproxToConstant
+(const Scalar& val, const RealScalar& prec) const
+{
+  typename internal::nested_eval<Derived,1>::type self(derived());
+  for(Index j = 0; j < cols(); ++j)
+    for(Index i = 0; i < rows(); ++i)
+      if(!internal::isApprox(self.coeff(i, j), val, prec))
+        return false;
+  return true;
+}
+
+/** This is just an alias for isApproxToConstant().
+  *
+  * \returns true if all coefficients in this matrix are approximately equal to \a value, to within precision \a prec */
+template<typename Derived>
+EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isConstant
+(const Scalar& val, const RealScalar& prec) const
+{
+  return isApproxToConstant(val, prec);
+}
+
+/** Alias for setConstant(): sets all coefficients in this expression to \a val.
+  *
+  * \sa setConstant(), Constant(), class CwiseNullaryOp
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void DenseBase<Derived>::fill(const Scalar& val)
+{
+  setConstant(val);
+}
+
+/** Sets all coefficients in this expression to value \a val.
+  *
+  * \sa fill(), setConstant(Index,const Scalar&), setConstant(Index,Index,const Scalar&), setZero(), setOnes(), Constant(), class CwiseNullaryOp, setZero(), setOnes()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setConstant(const Scalar& val)
+{
+  return derived() = Constant(rows(), cols(), val);
+}
+
+/** Resizes to the given \a size, and sets all coefficients in this expression to the given value \a val.
+  *
+  * \only_for_vectors
+  *
+  * Example: \include Matrix_setConstant_int.cpp
+  * Output: \verbinclude Matrix_setConstant_int.out
+  *
+  * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setConstant(Index size, const Scalar& val)
+{
+  resize(size);
+  return setConstant(val);
+}
+
+/** Resizes to the given size, and sets all coefficients in this expression to the given value \a val.
+  *
+  * \param rows the new number of rows
+  * \param cols the new number of columns
+  * \param val the value to which all coefficients are set
+  *
+  * Example: \include Matrix_setConstant_int_int.cpp
+  * Output: \verbinclude Matrix_setConstant_int_int.out
+  *
+  * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setConstant(Index rows, Index cols, const Scalar& val)
+{
+  resize(rows, cols);
+  return setConstant(val);
+}
+
+/** Resizes to the given size, changing only the number of columns, and sets all
+  * coefficients in this expression to the given value \a val. For the parameter
+  * of type NoChange_t, just pass the special value \c NoChange.
+  *
+  * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setConstant(NoChange_t, Index cols, const Scalar& val)
+{
+  return setConstant(rows(), cols, val);
+}
+
+/** Resizes to the given size, changing only the number of rows, and sets all
+  * coefficients in this expression to the given value \a val. For the parameter
+  * of type NoChange_t, just pass the special value \c NoChange.
+  *
+  * \sa MatrixBase::setConstant(const Scalar&), setConstant(Index,const Scalar&), class CwiseNullaryOp, MatrixBase::Constant(const Scalar&)
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setConstant(Index rows, NoChange_t, const Scalar& val)
+{
+  return setConstant(rows, cols(), val);
+}
+
+
+/**
+  * \brief Sets a linearly spaced vector.
+  *
+  * The function generates 'size' equally spaced values in the closed interval [low,high].
+  * When size is set to 1, a vector of length 1 containing 'high' is returned.
+  *
+  * \only_for_vectors
+  *
+  * Example: \include DenseBase_setLinSpaced.cpp
+  * Output: \verbinclude DenseBase_setLinSpaced.out
+  *
+  * For integer scalar types, do not miss the explanations on the definition
+  * of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink.
+  *
+  * \sa LinSpaced(Index,const Scalar&,const Scalar&), CwiseNullaryOp
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(Index newSize, const Scalar& low, const Scalar& high)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return derived() = Derived::NullaryExpr(newSize, internal::linspaced_op<Scalar>(low,high,newSize));
+}
+
+/**
+  * \brief Sets a linearly spaced vector.
+  *
+  * The function fills \c *this with equally spaced values in the closed interval [low,high].
+  * When size is set to 1, a vector of length 1 containing 'high' is returned.
+  *
+  * \only_for_vectors
+  *
+  * For integer scalar types, do not miss the explanations on the definition
+  * of \link LinSpaced(Index,const Scalar&,const Scalar&) even spacing \endlink.
+  *
+  * \sa LinSpaced(Index,const Scalar&,const Scalar&), setLinSpaced(Index, const Scalar&, const Scalar&), CwiseNullaryOp
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setLinSpaced(const Scalar& low, const Scalar& high)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return setLinSpaced(size(), low, high);
+}
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setEqualSpaced(Index newSize, const Scalar& low,
+                                                                                  const Scalar& step) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return derived() = Derived::NullaryExpr(newSize, internal::equalspaced_op<Scalar>(low, step));
+}
+template <typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setEqualSpaced(const Scalar& low,
+                                                                                  const Scalar& step) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return setEqualSpaced(size(), low, step);
+}
+
+// zero:
+
+/** \returns an expression of a zero matrix.
+  *
+  * The parameters \a rows and \a cols are the number of rows and of columns of
+  * the returned matrix. Must be compatible with this MatrixBase type.
+  *
+  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
+  * it is redundant to pass \a rows and \a cols as arguments, so Zero() should be used
+  * instead.
+  *
+  * Example: \include MatrixBase_zero_int_int.cpp
+  * Output: \verbinclude MatrixBase_zero_int_int.out
+  *
+  * \sa Zero(), Zero(Index)
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
+DenseBase<Derived>::Zero(Index rows, Index cols)
+{
+  return Constant(rows, cols, Scalar(0));
+}
+
+/** \returns an expression of a zero vector.
+  *
+  * The parameter \a size is the size of the returned vector.
+  * Must be compatible with this MatrixBase type.
+  *
+  * \only_for_vectors
+  *
+  * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
+  * it is redundant to pass \a size as argument, so Zero() should be used
+  * instead.
+  *
+  * Example: \include MatrixBase_zero_int.cpp
+  * Output: \verbinclude MatrixBase_zero_int.out
+  *
+  * \sa Zero(), Zero(Index,Index)
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
+DenseBase<Derived>::Zero(Index size)
+{
+  return Constant(size, Scalar(0));
+}
+
+/** \returns an expression of a fixed-size zero matrix or vector.
+  *
+  * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
+  * need to use the variants taking size arguments.
+  *
+  * Example: \include MatrixBase_zero.cpp
+  * Output: \verbinclude MatrixBase_zero.out
+  *
+  * \sa Zero(Index), Zero(Index,Index)
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
+DenseBase<Derived>::Zero()
+{
+  return Constant(Scalar(0));
+}
+
+/** \returns true if *this is approximately equal to the zero matrix,
+  *          within the precision given by \a prec.
+  *
+  * Example: \include MatrixBase_isZero.cpp
+  * Output: \verbinclude MatrixBase_isZero.out
+  *
+  * \sa class CwiseNullaryOp, Zero()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isZero(const RealScalar& prec) const
+{
+  typename internal::nested_eval<Derived,1>::type self(derived());
+  for(Index j = 0; j < cols(); ++j)
+    for(Index i = 0; i < rows(); ++i)
+      if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<Scalar>(1), prec))
+        return false;
+  return true;
+}
+
+/** Sets all coefficients in this expression to zero.
+  *
+  * Example: \include MatrixBase_setZero.cpp
+  * Output: \verbinclude MatrixBase_setZero.out
+  *
+  * \sa class CwiseNullaryOp, Zero()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setZero()
+{
+  return setConstant(Scalar(0));
+}
+
+/** Resizes to the given \a size, and sets all coefficients in this expression to zero.
+  *
+  * \only_for_vectors
+  *
+  * Example: \include Matrix_setZero_int.cpp
+  * Output: \verbinclude Matrix_setZero_int.out
+  *
+  * \sa DenseBase::setZero(), setZero(Index,Index), class CwiseNullaryOp, DenseBase::Zero()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setZero(Index newSize)
+{
+  resize(newSize);
+  return setConstant(Scalar(0));
+}
+
+/** Resizes to the given size, and sets all coefficients in this expression to zero.
+  *
+  * \param rows the new number of rows
+  * \param cols the new number of columns
+  *
+  * Example: \include Matrix_setZero_int_int.cpp
+  * Output: \verbinclude Matrix_setZero_int_int.out
+  *
+  * \sa DenseBase::setZero(), setZero(Index), class CwiseNullaryOp, DenseBase::Zero()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setZero(Index rows, Index cols)
+{
+  resize(rows, cols);
+  return setConstant(Scalar(0));
+}
+
+/** Resizes to the given size, changing only the number of columns, and sets all
+  * coefficients in this expression to zero. For the parameter of type NoChange_t,
+  * just pass the special value \c NoChange.
+  *
+  * \sa DenseBase::setZero(), setZero(Index), setZero(Index, Index), setZero(Index, NoChange_t), class CwiseNullaryOp, DenseBase::Zero()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setZero(NoChange_t, Index cols)
+{
+  return setZero(rows(), cols);
+}
+
+/** Resizes to the given size, changing only the number of rows, and sets all
+  * coefficients in this expression to zero. For the parameter of type NoChange_t,
+  * just pass the special value \c NoChange.
+  *
+  * \sa DenseBase::setZero(), setZero(Index), setZero(Index, Index), setZero(NoChange_t, Index), class CwiseNullaryOp, DenseBase::Zero()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setZero(Index rows, NoChange_t)
+{
+  return setZero(rows, cols());
+}
+
+// ones:
+
+/** \returns an expression of a matrix where all coefficients equal one.
+  *
+  * The parameters \a rows and \a cols are the number of rows and of columns of
+  * the returned matrix. Must be compatible with this MatrixBase type.
+  *
+  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
+  * it is redundant to pass \a rows and \a cols as arguments, so Ones() should be used
+  * instead.
+  *
+  * Example: \include MatrixBase_ones_int_int.cpp
+  * Output: \verbinclude MatrixBase_ones_int_int.out
+  *
+  * \sa Ones(), Ones(Index), isOnes(), class Ones
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
+DenseBase<Derived>::Ones(Index rows, Index cols)
+{
+  return Constant(rows, cols, Scalar(1));
+}
+
+/** \returns an expression of a vector where all coefficients equal one.
+  *
+  * The parameter \a newSize is the size of the returned vector.
+  * Must be compatible with this MatrixBase type.
+  *
+  * \only_for_vectors
+  *
+  * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
+  * it is redundant to pass \a size as argument, so Ones() should be used
+  * instead.
+  *
+  * Example: \include MatrixBase_ones_int.cpp
+  * Output: \verbinclude MatrixBase_ones_int.out
+  *
+  * \sa Ones(), Ones(Index,Index), isOnes(), class Ones
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
+DenseBase<Derived>::Ones(Index newSize)
+{
+  return Constant(newSize, Scalar(1));
+}
+
+/** \returns an expression of a fixed-size matrix or vector where all coefficients equal one.
+  *
+  * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
+  * need to use the variants taking size arguments.
+  *
+  * Example: \include MatrixBase_ones.cpp
+  * Output: \verbinclude MatrixBase_ones.out
+  *
+  * \sa Ones(Index), Ones(Index,Index), isOnes(), class Ones
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename DenseBase<Derived>::ConstantReturnType
+DenseBase<Derived>::Ones()
+{
+  return Constant(Scalar(1));
+}
+
+/** \returns true if *this is approximately equal to the matrix where all coefficients
+  *          are equal to 1, within the precision given by \a prec.
+  *
+  * Example: \include MatrixBase_isOnes.cpp
+  * Output: \verbinclude MatrixBase_isOnes.out
+  *
+  * \sa class CwiseNullaryOp, Ones()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isOnes
+(const RealScalar& prec) const
+{
+  return isApproxToConstant(Scalar(1), prec);
+}
+
+/** Sets all coefficients in this expression to one.
+  *
+  * Example: \include MatrixBase_setOnes.cpp
+  * Output: \verbinclude MatrixBase_setOnes.out
+  *
+  * \sa class CwiseNullaryOp, Ones()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::setOnes()
+{
+  return setConstant(Scalar(1));
+}
+
+/** Resizes to the given \a newSize, and sets all coefficients in this expression to one.
+  *
+  * \only_for_vectors
+  *
+  * Example: \include Matrix_setOnes_int.cpp
+  * Output: \verbinclude Matrix_setOnes_int.out
+  *
+  * \sa MatrixBase::setOnes(), setOnes(Index,Index), class CwiseNullaryOp, MatrixBase::Ones()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setOnes(Index newSize)
+{
+  resize(newSize);
+  return setConstant(Scalar(1));
+}
+
+/** Resizes to the given size, and sets all coefficients in this expression to one.
+  *
+  * \param rows the new number of rows
+  * \param cols the new number of columns
+  *
+  * Example: \include Matrix_setOnes_int_int.cpp
+  * Output: \verbinclude Matrix_setOnes_int_int.out
+  *
+  * \sa MatrixBase::setOnes(), setOnes(Index), class CwiseNullaryOp, MatrixBase::Ones()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setOnes(Index rows, Index cols)
+{
+  resize(rows, cols);
+  return setConstant(Scalar(1));
+}
+
+/** Resizes to the given size, changing only the number of rows, and sets all
+  * coefficients in this expression to one. For the parameter of type NoChange_t,
+  * just pass the special value \c NoChange.
+  *
+ * \sa MatrixBase::setOnes(), setOnes(Index), setOnes(Index, Index), setOnes(NoChange_t, Index), class CwiseNullaryOp, MatrixBase::Ones()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setOnes(Index rows, NoChange_t)
+{
+  return setOnes(rows, cols());
+}
+
+/** Resizes to the given size, changing only the number of columns, and sets all
+  * coefficients in this expression to one. For the parameter of type NoChange_t,
+  * just pass the special value \c NoChange.
+  *
+ * \sa MatrixBase::setOnes(), setOnes(Index), setOnes(Index, Index), setOnes(Index, NoChange_t) class CwiseNullaryOp, MatrixBase::Ones()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setOnes(NoChange_t, Index cols)
+{
+  return setOnes(rows(), cols);
+}
+
+// Identity:
+
+/** \returns an expression of the identity matrix (not necessarily square).
+  *
+  * The parameters \a rows and \a cols are the number of rows and of columns of
+  * the returned matrix. Must be compatible with this MatrixBase type.
+  *
+  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
+  * it is redundant to pass \a rows and \a cols as arguments, so Identity() should be used
+  * instead.
+  *
+  * Example: \include MatrixBase_identity_int_int.cpp
+  * Output: \verbinclude MatrixBase_identity_int_int.out
+  *
+  * \sa Identity(), setIdentity(), isIdentity()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
+MatrixBase<Derived>::Identity(Index rows, Index cols)
+{
+  return DenseBase<Derived>::NullaryExpr(rows, cols, internal::scalar_identity_op<Scalar>());
+}
+
+/** \returns an expression of the identity matrix (not necessarily square).
+  *
+  * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
+  * need to use the variant taking size arguments.
+  *
+  * Example: \include MatrixBase_identity.cpp
+  * Output: \verbinclude MatrixBase_identity.out
+  *
+  * \sa Identity(Index,Index), setIdentity(), isIdentity()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::IdentityReturnType
+MatrixBase<Derived>::Identity()
+{
+  EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
+  return MatrixBase<Derived>::NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_identity_op<Scalar>());
+}
+
+/** \returns true if *this is approximately equal to the identity matrix
+  *          (not necessarily square),
+  *          within the precision given by \a prec.
+  *
+  * Example: \include MatrixBase_isIdentity.cpp
+  * Output: \verbinclude MatrixBase_isIdentity.out
+  *
+  * \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), setIdentity()
+  */
+template<typename Derived>
+bool MatrixBase<Derived>::isIdentity
+(const RealScalar& prec) const
+{
+  typename internal::nested_eval<Derived,1>::type self(derived());
+  for(Index j = 0; j < cols(); ++j)
+  {
+    for(Index i = 0; i < rows(); ++i)
+    {
+      if(i == j)
+      {
+        if(!internal::isApprox(self.coeff(i, j), static_cast<Scalar>(1), prec))
+          return false;
+      }
+      else
+      {
+        if(!internal::isMuchSmallerThan(self.coeff(i, j), static_cast<RealScalar>(1), prec))
+          return false;
+      }
+    }
+  }
+  return true;
+}
+
+namespace internal {
+
+template<typename Derived, bool Big = (Derived::SizeAtCompileTime>=16)>
+struct setIdentity_impl
+{
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Derived& run(Derived& m)
+  {
+    return m = Derived::Identity(m.rows(), m.cols());
+  }
+};
+
+template<typename Derived>
+struct setIdentity_impl<Derived, true>
+{
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Derived& run(Derived& m)
+  {
+    m.setZero();
+    const Index size = numext::mini(m.rows(), m.cols());
+    for(Index i = 0; i < size; ++i) m.coeffRef(i,i) = typename Derived::Scalar(1);
+    return m;
+  }
+};
+
+} // end namespace internal
+
+/** Writes the identity expression (not necessarily square) into *this.
+  *
+  * Example: \include MatrixBase_setIdentity.cpp
+  * Output: \verbinclude MatrixBase_setIdentity.out
+  *
+  * \sa class CwiseNullaryOp, Identity(), Identity(Index,Index), isIdentity()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity()
+{
+  return internal::setIdentity_impl<Derived>::run(derived());
+}
+
+/** \brief Resizes to the given size, and writes the identity expression (not necessarily square) into *this.
+  *
+  * \param rows the new number of rows
+  * \param cols the new number of columns
+  *
+  * Example: \include Matrix_setIdentity_int_int.cpp
+  * Output: \verbinclude Matrix_setIdentity_int_int.out
+  *
+  * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Identity()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setIdentity(Index rows, Index cols)
+{
+  derived().resize(rows, cols);
+  return setIdentity();
+}
+
+/** \returns an expression of the i-th unit (basis) vector.
+  *
+  * \only_for_vectors
+  *
+  * \sa MatrixBase::Unit(Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index newSize, Index i)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return BasisReturnType(SquareMatrixType::Identity(newSize,newSize), i);
+}
+
+/** \returns an expression of the i-th unit (basis) vector.
+  *
+  * \only_for_vectors
+  *
+  * This variant is for fixed-size vector only.
+  *
+  * \sa MatrixBase::Unit(Index,Index), MatrixBase::UnitX(), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::Unit(Index i)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return BasisReturnType(SquareMatrixType::Identity(),i);
+}
+
+/** \returns an expression of the X axis unit vector (1{,0}^*)
+  *
+  * \only_for_vectors
+  *
+  * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitX()
+{ return Derived::Unit(0); }
+
+/** \returns an expression of the Y axis unit vector (0,1{,0}^*)
+  *
+  * \only_for_vectors
+  *
+  * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitY()
+{ return Derived::Unit(1); }
+
+/** \returns an expression of the Z axis unit vector (0,0,1{,0}^*)
+  *
+  * \only_for_vectors
+  *
+  * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitZ()
+{ return Derived::Unit(2); }
+
+/** \returns an expression of the W axis unit vector (0,0,0,1)
+  *
+  * \only_for_vectors
+  *
+  * \sa MatrixBase::Unit(Index,Index), MatrixBase::Unit(Index), MatrixBase::UnitY(), MatrixBase::UnitZ(), MatrixBase::UnitW()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::BasisReturnType MatrixBase<Derived>::UnitW()
+{ return Derived::Unit(3); }
+
+/** \brief Set the coefficients of \c *this to the i-th unit (basis) vector
+  *
+  * \param i index of the unique coefficient to be set to 1
+  *
+  * \only_for_vectors
+  *
+  * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Unit(Index,Index)
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setUnit(Index i)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  eigen_assert(i<size());
+  derived().setZero();
+  derived().coeffRef(i) = Scalar(1);
+  return derived();
+}
+
+/** \brief Resizes to the given \a newSize, and writes the i-th unit (basis) vector into *this.
+  *
+  * \param newSize the new size of the vector
+  * \param i index of the unique coefficient to be set to 1
+  *
+  * \only_for_vectors
+  *
+  * \sa MatrixBase::setIdentity(), class CwiseNullaryOp, MatrixBase::Unit(Index,Index)
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::setUnit(Index newSize, Index i)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  eigen_assert(i<newSize);
+  derived().resize(newSize);
+  return setUnit(i);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_CWISE_NULLARY_OP_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CwiseTernaryOp.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CwiseTernaryOp.h
new file mode 100644
index 0000000000000000000000000000000000000000..8d24a483aa54b601356aabe50835cb8a3450847a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CwiseTernaryOp.h
@@ -0,0 +1,199 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CWISE_TERNARY_OP_H
+#define EIGEN_CWISE_TERNARY_OP_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>
+struct traits<CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> > {
+  // we must not inherit from traits<Arg1> since it has
+  // the potential to cause problems with MSVC
+  typedef remove_all_t<Arg1> Ancestor;
+  typedef typename traits<Ancestor>::XprKind XprKind;
+  enum {
+    RowsAtCompileTime = traits<Ancestor>::RowsAtCompileTime,
+    ColsAtCompileTime = traits<Ancestor>::ColsAtCompileTime,
+    MaxRowsAtCompileTime = traits<Ancestor>::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = traits<Ancestor>::MaxColsAtCompileTime
+  };
+
+  // even though we require Arg1, Arg2, and Arg3 to have the same scalar type
+  // (see CwiseTernaryOp constructor),
+  // we still want to handle the case when the result type is different.
+  typedef typename result_of<TernaryOp(
+      const typename Arg1::Scalar&, const typename Arg2::Scalar&,
+      const typename Arg3::Scalar&)>::type Scalar;
+
+  typedef typename internal::traits<Arg1>::StorageKind StorageKind;
+  typedef typename internal::traits<Arg1>::StorageIndex StorageIndex;
+
+  typedef typename Arg1::Nested Arg1Nested;
+  typedef typename Arg2::Nested Arg2Nested;
+  typedef typename Arg3::Nested Arg3Nested;
+  typedef std::remove_reference_t<Arg1Nested> Arg1Nested_;
+  typedef std::remove_reference_t<Arg2Nested> Arg2Nested_;
+  typedef std::remove_reference_t<Arg3Nested> Arg3Nested_;
+  enum { Flags = Arg1Nested_::Flags & RowMajorBit };
+};
+}  // end namespace internal
+
+template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3,
+          typename StorageKind>
+class CwiseTernaryOpImpl;
+
+/** \class CwiseTernaryOp
+  * \ingroup Core_Module
+  *
+  * \brief Generic expression where a coefficient-wise ternary operator is
+ * applied to two expressions
+  *
+  * \tparam TernaryOp template functor implementing the operator
+  * \tparam Arg1Type the type of the first argument
+  * \tparam Arg2Type the type of the second argument
+  * \tparam Arg3Type the type of the third argument
+  *
+  * This class represents an expression where a coefficient-wise ternary
+ * operator is applied to three expressions.
+  * It is the return type of ternary operators, by which we mean only those
+ * ternary operators where
+  * all three arguments are Eigen expressions.
+  * For example, the return type of betainc(matrix1, matrix2, matrix3) is a
+ * CwiseTernaryOp.
+  *
+  * Most of the time, this is the only way that it is used, so you typically
+ * don't have to name
+  * CwiseTernaryOp types explicitly.
+  *
+  * \sa MatrixBase::ternaryExpr(const MatrixBase<Argument2> &, const
+ * MatrixBase<Argument3> &, const CustomTernaryOp &) const, class CwiseBinaryOp,
+ * class CwiseUnaryOp, class CwiseNullaryOp
+  */
+template <typename TernaryOp, typename Arg1Type, typename Arg2Type,
+          typename Arg3Type>
+class CwiseTernaryOp : public CwiseTernaryOpImpl<
+                           TernaryOp, Arg1Type, Arg2Type, Arg3Type,
+                           typename internal::traits<Arg1Type>::StorageKind>,
+                       internal::no_assignment_operator
+{
+ public:
+  typedef internal::remove_all_t<Arg1Type> Arg1;
+  typedef internal::remove_all_t<Arg2Type> Arg2;
+  typedef internal::remove_all_t<Arg3Type> Arg3;
+
+  // require the sizes to match
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg2)
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Arg1, Arg3)
+
+  // The index types should match
+  EIGEN_STATIC_ASSERT((internal::is_same<
+                       typename internal::traits<Arg1Type>::StorageKind,
+                       typename internal::traits<Arg2Type>::StorageKind>::value),
+                      STORAGE_KIND_MUST_MATCH)
+  EIGEN_STATIC_ASSERT((internal::is_same<
+                       typename internal::traits<Arg1Type>::StorageKind,
+                       typename internal::traits<Arg3Type>::StorageKind>::value),
+                      STORAGE_KIND_MUST_MATCH)
+
+  typedef typename CwiseTernaryOpImpl<
+      TernaryOp, Arg1Type, Arg2Type, Arg3Type,
+      typename internal::traits<Arg1Type>::StorageKind>::Base Base;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseTernaryOp)
+
+  typedef typename internal::ref_selector<Arg1Type>::type Arg1Nested;
+  typedef typename internal::ref_selector<Arg2Type>::type Arg2Nested;
+  typedef typename internal::ref_selector<Arg3Type>::type Arg3Nested;
+  typedef std::remove_reference_t<Arg1Nested> Arg1Nested_;
+  typedef std::remove_reference_t<Arg2Nested> Arg2Nested_;
+  typedef std::remove_reference_t<Arg3Nested> Arg3Nested_;
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE CwiseTernaryOp(const Arg1& a1, const Arg2& a2,
+                                     const Arg3& a3,
+                                     const TernaryOp& func = TernaryOp())
+      : m_arg1(a1), m_arg2(a2), m_arg3(a3), m_functor(func) {
+    eigen_assert(a1.rows() == a2.rows() && a1.cols() == a2.cols() &&
+                 a1.rows() == a3.rows() && a1.cols() == a3.cols());
+  }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE Index rows() const {
+    // return the fixed size type if available to enable compile time
+    // optimizations
+    if (internal::traits<internal::remove_all_t<Arg1Nested>>::
+                RowsAtCompileTime == Dynamic &&
+        internal::traits<internal::remove_all_t<Arg2Nested>>::
+                RowsAtCompileTime == Dynamic)
+      return m_arg3.rows();
+    else if (internal::traits<internal::remove_all_t<Arg1Nested>>::
+                     RowsAtCompileTime == Dynamic &&
+             internal::traits<internal::remove_all_t<Arg3Nested>>::
+                     RowsAtCompileTime == Dynamic)
+      return m_arg2.rows();
+    else
+      return m_arg1.rows();
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE Index cols() const {
+    // return the fixed size type if available to enable compile time
+    // optimizations
+    if (internal::traits<internal::remove_all_t<Arg1Nested>>::
+                ColsAtCompileTime == Dynamic &&
+        internal::traits<internal::remove_all_t<Arg2Nested>>::
+                ColsAtCompileTime == Dynamic)
+      return m_arg3.cols();
+    else if (internal::traits<internal::remove_all_t<Arg1Nested>>::
+                     ColsAtCompileTime == Dynamic &&
+             internal::traits<internal::remove_all_t<Arg3Nested>>::
+                     ColsAtCompileTime == Dynamic)
+      return m_arg2.cols();
+    else
+      return m_arg1.cols();
+  }
+
+  /** \returns the first argument nested expression */
+  EIGEN_DEVICE_FUNC
+  const Arg1Nested_& arg1() const { return m_arg1; }
+  /** \returns the first argument nested expression */
+  EIGEN_DEVICE_FUNC
+  const Arg2Nested_& arg2() const { return m_arg2; }
+  /** \returns the third argument nested expression */
+  EIGEN_DEVICE_FUNC
+  const Arg3Nested_& arg3() const { return m_arg3; }
+  /** \returns the functor representing the ternary operation */
+  EIGEN_DEVICE_FUNC
+  const TernaryOp& functor() const { return m_functor; }
+
+ protected:
+  Arg1Nested m_arg1;
+  Arg2Nested m_arg2;
+  Arg3Nested m_arg3;
+  const TernaryOp m_functor;
+};
+
+// Generic API dispatcher
+template <typename TernaryOp, typename Arg1, typename Arg2, typename Arg3,
+          typename StorageKind>
+class CwiseTernaryOpImpl
+    : public internal::generic_xpr_base<
+          CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >::type {
+ public:
+  typedef typename internal::generic_xpr_base<
+      CwiseTernaryOp<TernaryOp, Arg1, Arg2, Arg3> >::type Base;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CWISE_TERNARY_OP_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CwiseUnaryOp.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CwiseUnaryOp.h
new file mode 100644
index 0000000000000000000000000000000000000000..ff7d0b9482f191260a0427e25d352f9074123f75
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CwiseUnaryOp.h
@@ -0,0 +1,105 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CWISE_UNARY_OP_H
+#define EIGEN_CWISE_UNARY_OP_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template<typename UnaryOp, typename XprType>
+struct traits<CwiseUnaryOp<UnaryOp, XprType> >
+ : traits<XprType>
+{
+  typedef typename result_of<
+                     UnaryOp(const typename XprType::Scalar&)
+                   >::type Scalar;
+  typedef typename XprType::Nested XprTypeNested;
+  typedef std::remove_reference_t<XprTypeNested> XprTypeNested_;
+  enum {
+    Flags = XprTypeNested_::Flags & RowMajorBit
+  };
+};
+}
+
+template<typename UnaryOp, typename XprType, typename StorageKind>
+class CwiseUnaryOpImpl;
+
+/** \class CwiseUnaryOp
+  * \ingroup Core_Module
+  *
+  * \brief Generic expression where a coefficient-wise unary operator is applied to an expression
+  *
+  * \tparam UnaryOp template functor implementing the operator
+  * \tparam XprType the type of the expression to which we are applying the unary operator
+  *
+  * This class represents an expression where a unary operator is applied to an expression.
+  * It is the return type of all operations taking exactly 1 input expression, regardless of the
+  * presence of other inputs such as scalars. For example, the operator* in the expression 3*matrix
+  * is considered unary, because only the right-hand side is an expression, and its
+  * return type is a specialization of CwiseUnaryOp.
+  *
+  * Most of the time, this is the only way that it is used, so you typically don't have to name
+  * CwiseUnaryOp types explicitly.
+  *
+  * \sa MatrixBase::unaryExpr(const CustomUnaryOp &) const, class CwiseBinaryOp, class CwiseNullaryOp
+  */
+template<typename UnaryOp, typename XprType>
+class CwiseUnaryOp : public CwiseUnaryOpImpl<UnaryOp, XprType, typename internal::traits<XprType>::StorageKind>, internal::no_assignment_operator
+{
+  public:
+
+    typedef typename CwiseUnaryOpImpl<UnaryOp, XprType,typename internal::traits<XprType>::StorageKind>::Base Base;
+    EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryOp)
+    typedef typename internal::ref_selector<XprType>::type XprTypeNested;
+    typedef internal::remove_all_t<XprType> NestedExpression;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    explicit CwiseUnaryOp(const XprType& xpr, const UnaryOp& func = UnaryOp())
+      : m_xpr(xpr), m_functor(func) {}
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index rows() const EIGEN_NOEXCEPT { return m_xpr.rows(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index cols() const EIGEN_NOEXCEPT { return m_xpr.cols(); }
+
+    /** \returns the functor representing the unary operation */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const UnaryOp& functor() const { return m_functor; }
+
+    /** \returns the nested expression */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const internal::remove_all_t<XprTypeNested>&
+    nestedExpression() const { return m_xpr; }
+
+    /** \returns the nested expression */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    internal::remove_all_t<XprTypeNested>&
+    nestedExpression() { return m_xpr; }
+
+  protected:
+    XprTypeNested m_xpr;
+    const UnaryOp m_functor;
+};
+
+// Generic API dispatcher
+template<typename UnaryOp, typename XprType, typename StorageKind>
+class CwiseUnaryOpImpl
+  : public internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type
+{
+public:
+  typedef typename internal::generic_xpr_base<CwiseUnaryOp<UnaryOp, XprType> >::type Base;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CWISE_UNARY_OP_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CwiseUnaryView.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CwiseUnaryView.h
new file mode 100644
index 0000000000000000000000000000000000000000..b4539a6b2c9577e642abdd559134a0fb8c82a0a9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/CwiseUnaryView.h
@@ -0,0 +1,143 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CWISE_UNARY_VIEW_H
+#define EIGEN_CWISE_UNARY_VIEW_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template<typename ViewOp, typename MatrixType, typename StrideType>
+struct traits<CwiseUnaryView<ViewOp, MatrixType, StrideType> >
+ : traits<MatrixType>
+{
+  typedef typename result_of<
+                     ViewOp(const typename traits<MatrixType>::Scalar&)
+                   >::type Scalar;
+  typedef typename MatrixType::Nested MatrixTypeNested;
+  typedef remove_all_t<MatrixTypeNested> MatrixTypeNested_;
+  enum {
+    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
+    Flags = traits<MatrixTypeNested_>::Flags & (RowMajorBit | FlagsLvalueBit | DirectAccessBit), // FIXME DirectAccessBit should not be handled by expressions
+    MatrixTypeInnerStride =  inner_stride_at_compile_time<MatrixType>::ret,
+    // need to cast the sizeof's from size_t to int explicitly, otherwise:
+    // "error: no integral type can represent all of the enumerator values
+    InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
+                             ? (MatrixTypeInnerStride == Dynamic
+                               ? int(Dynamic)
+                               : int(MatrixTypeInnerStride) * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)))
+                             : int(StrideType::InnerStrideAtCompileTime),
+
+    OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
+                             ? (outer_stride_at_compile_time<MatrixType>::ret == Dynamic
+                               ? int(Dynamic)
+                               : outer_stride_at_compile_time<MatrixType>::ret * int(sizeof(typename traits<MatrixType>::Scalar) / sizeof(Scalar)))
+                             : int(StrideType::OuterStrideAtCompileTime)
+  };
+};
+}
+
+template<typename ViewOp, typename MatrixType, typename StrideType, typename StorageKind>
+class CwiseUnaryViewImpl;
+
+/** \class CwiseUnaryView
+  * \ingroup Core_Module
+  *
+  * \brief Generic lvalue expression of a coefficient-wise unary operator of a matrix or a vector
+  *
+  * \tparam ViewOp template functor implementing the view
+  * \tparam MatrixType the type of the matrix we are applying the unary operator
+  *
+  * This class represents a lvalue expression of a generic unary view operator of a matrix or a vector.
+  * It is the return type of real() and imag(), and most of the time this is the only way it is used.
+  *
+  * \sa MatrixBase::unaryViewExpr(const CustomUnaryOp &) const, class CwiseUnaryOp
+  */
+template<typename ViewOp, typename MatrixType, typename StrideType>
+class CwiseUnaryView : public CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, typename internal::traits<MatrixType>::StorageKind>
+{
+  public:
+
+    typedef typename CwiseUnaryViewImpl<ViewOp, MatrixType, StrideType, typename internal::traits<MatrixType>::StorageKind>::Base Base;
+    EIGEN_GENERIC_PUBLIC_INTERFACE(CwiseUnaryView)
+    typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
+    typedef internal::remove_all_t<MatrixType> NestedExpression;
+
+    explicit EIGEN_DEVICE_FUNC inline CwiseUnaryView(MatrixType& mat, const ViewOp& func = ViewOp())
+      : m_matrix(mat), m_functor(func) {}
+
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryView)
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
+
+    /** \returns the functor representing unary operation */
+    EIGEN_DEVICE_FUNC const ViewOp& functor() const { return m_functor; }
+
+    /** \returns the nested expression */
+    EIGEN_DEVICE_FUNC const internal::remove_all_t<MatrixTypeNested>&
+    nestedExpression() const { return m_matrix; }
+
+    /** \returns the nested expression */
+    EIGEN_DEVICE_FUNC std::remove_reference_t<MatrixTypeNested>&
+    nestedExpression() { return m_matrix; }
+
+  protected:
+    MatrixTypeNested m_matrix;
+    ViewOp m_functor;
+};
+
+// Generic API dispatcher
+template<typename ViewOp, typename XprType, typename StrideType, typename StorageKind>
+class CwiseUnaryViewImpl
+  : public internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType, StrideType> >::type
+{
+public:
+  typedef typename internal::generic_xpr_base<CwiseUnaryView<ViewOp, XprType, StrideType> >::type Base;
+};
+
+template<typename ViewOp, typename MatrixType, typename StrideType>
+class CwiseUnaryViewImpl<ViewOp,MatrixType,StrideType,Dense>
+  : public internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType, StrideType> >::type
+{
+  public:
+
+    typedef CwiseUnaryView<ViewOp, MatrixType,StrideType> Derived;
+    typedef typename internal::dense_xpr_base< CwiseUnaryView<ViewOp, MatrixType,StrideType> >::type Base;
+
+    EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CwiseUnaryViewImpl)
+
+    EIGEN_DEVICE_FUNC inline Scalar* data() { return &(this->coeffRef(0)); }
+    EIGEN_DEVICE_FUNC inline const Scalar* data() const { return &(this->coeff(0)); }
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index innerStride() const
+    {
+      return StrideType::InnerStrideAtCompileTime != 0
+             ? int(StrideType::InnerStrideAtCompileTime)
+             : derived().nestedExpression().innerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index outerStride() const
+    {
+      return StrideType::OuterStrideAtCompileTime != 0
+             ? int(StrideType::OuterStrideAtCompileTime)
+             : derived().nestedExpression().outerStride() * sizeof(typename internal::traits<MatrixType>::Scalar) / sizeof(Scalar);
+    }
+  protected:
+    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(CwiseUnaryViewImpl)
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CWISE_UNARY_VIEW_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/DenseBase.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/DenseBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..aa045686be17452b849087953b1e2d267b10a4c1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/DenseBase.h
@@ -0,0 +1,702 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DENSEBASE_H
+#define EIGEN_DENSEBASE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// The index type defined by EIGEN_DEFAULT_DENSE_INDEX_TYPE must be a signed type.
+EIGEN_STATIC_ASSERT(NumTraits<DenseIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE)
+
+/** \class DenseBase
+  * \ingroup Core_Module
+  *
+  * \brief Base class for all dense matrices, vectors, and arrays
+  *
+  * This class is the base that is inherited by all dense objects (matrix, vector, arrays,
+  * and related expression types). The common Eigen API for dense objects is contained in this class.
+  *
+  * \tparam Derived is the derived type, e.g., a matrix type or an expression.
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_DENSEBASE_PLUGIN.
+  *
+  * \sa \blank \ref TopicClassHierarchy
+  */
+template<typename Derived> class DenseBase
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  : public DenseCoeffsBase<Derived, internal::accessors_level<Derived>::value>
+#else
+  : public DenseCoeffsBase<Derived,DirectWriteAccessors>
+#endif // not EIGEN_PARSED_BY_DOXYGEN
+{
+  public:
+
+    /** Inner iterator type to iterate over the coefficients of a row or column.
+      * \sa class InnerIterator
+      */
+    typedef Eigen::InnerIterator<Derived> InnerIterator;
+
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+
+    /**
+      * \brief The type used to store indices
+      * \details This typedef is relevant for types that store multiple indices such as
+      *          PermutationMatrix or Transpositions, otherwise it defaults to Eigen::Index
+      * \sa \blank \ref TopicPreprocessorDirectives, Eigen::Index, SparseMatrixBase.
+     */
+    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+
+    /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc. */
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+
+    /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc.
+      *
+      * It is an alias for the Scalar type */
+    typedef Scalar value_type;
+
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef DenseCoeffsBase<Derived, internal::accessors_level<Derived>::value> Base;
+
+    using Base::derived;
+    using Base::const_cast_derived;
+    using Base::rows;
+    using Base::cols;
+    using Base::size;
+    using Base::rowIndexByOuterInner;
+    using Base::colIndexByOuterInner;
+    using Base::coeff;
+    using Base::coeffByOuterInner;
+    using Base::operator();
+    using Base::operator[];
+    using Base::x;
+    using Base::y;
+    using Base::z;
+    using Base::w;
+    using Base::stride;
+    using Base::innerStride;
+    using Base::outerStride;
+    using Base::rowStride;
+    using Base::colStride;
+    typedef typename Base::CoeffReturnType CoeffReturnType;
+
+    enum {
+
+      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+        /**< The number of rows at compile-time. This is just a copy of the value provided
+          * by the \a Derived type. If a value is not known at compile-time,
+          * it is set to the \a Dynamic constant.
+          * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
+
+      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+        /**< The number of columns at compile-time. This is just a copy of the value provided
+          * by the \a Derived type. If a value is not known at compile-time,
+          * it is set to the \a Dynamic constant.
+          * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
+
+
+      SizeAtCompileTime = (internal::size_of_xpr_at_compile_time<Derived>::ret),
+        /**< This is equal to the number of coefficients, i.e. the number of
+          * rows times the number of columns, or to \a Dynamic if this is not
+          * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
+
+      MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
+        /**< This value is equal to the maximum possible number of rows that this expression
+          * might have. If this expression might have an arbitrarily high number of rows,
+          * this value is set to \a Dynamic.
+          *
+          * This value is useful to know when evaluating an expression, in order to determine
+          * whether it is possible to avoid doing a dynamic memory allocation.
+          *
+          * \sa RowsAtCompileTime, MaxColsAtCompileTime, MaxSizeAtCompileTime
+          */
+
+      MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
+        /**< This value is equal to the maximum possible number of columns that this expression
+          * might have. If this expression might have an arbitrarily high number of columns,
+          * this value is set to \a Dynamic.
+          *
+          * This value is useful to know when evaluating an expression, in order to determine
+          * whether it is possible to avoid doing a dynamic memory allocation.
+          *
+          * \sa ColsAtCompileTime, MaxRowsAtCompileTime, MaxSizeAtCompileTime
+          */
+
+      MaxSizeAtCompileTime = internal::size_at_compile_time(internal::traits<Derived>::MaxRowsAtCompileTime,
+                                                            internal::traits<Derived>::MaxColsAtCompileTime),
+        /**< This value is equal to the maximum possible number of coefficients that this expression
+          * might have. If this expression might have an arbitrarily high number of coefficients,
+          * this value is set to \a Dynamic.
+          *
+          * This value is useful to know when evaluating an expression, in order to determine
+          * whether it is possible to avoid doing a dynamic memory allocation.
+          *
+          * \sa SizeAtCompileTime, MaxRowsAtCompileTime, MaxColsAtCompileTime
+          */
+
+      IsVectorAtCompileTime = internal::traits<Derived>::RowsAtCompileTime == 1
+                           || internal::traits<Derived>::ColsAtCompileTime == 1,
+        /**< This is set to true if either the number of rows or the number of
+          * columns is known at compile-time to be equal to 1. Indeed, in that case,
+          * we are dealing with a column-vector (if there is only one column) or with
+          * a row-vector (if there is only one row). */
+
+      NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0 : bool(IsVectorAtCompileTime) ? 1 : 2,
+        /**< This value is equal to Tensor::NumDimensions, i.e. 0 for scalars, 1 for vectors,
+         * and 2 for matrices.
+         */
+
+      Flags = internal::traits<Derived>::Flags,
+        /**< This stores expression \ref flags flags which may or may not be inherited by new expressions
+          * constructed from this one. See the \ref flags "list of flags".
+          */
+
+      IsRowMajor = int(Flags) & RowMajorBit, /**< True if this expression has row-major storage order. */
+
+      InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime)
+                             : int(IsRowMajor) ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
+
+      InnerStrideAtCompileTime = internal::inner_stride_at_compile_time<Derived>::ret,
+      OuterStrideAtCompileTime = internal::outer_stride_at_compile_time<Derived>::ret
+    };
+
+    typedef typename internal::find_best_packet<Scalar,SizeAtCompileTime>::type PacketScalar;
+
+    enum { IsPlainObjectBase = 0 };
+
+    /** The plain matrix type corresponding to this expression.
+      * \sa PlainObject */
+    typedef Matrix<typename internal::traits<Derived>::Scalar,
+                internal::traits<Derived>::RowsAtCompileTime,
+                internal::traits<Derived>::ColsAtCompileTime,
+                AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
+                internal::traits<Derived>::MaxRowsAtCompileTime,
+                internal::traits<Derived>::MaxColsAtCompileTime
+          > PlainMatrix;
+
+    /** The plain array type corresponding to this expression.
+      * \sa PlainObject */
+    typedef Array<typename internal::traits<Derived>::Scalar,
+                internal::traits<Derived>::RowsAtCompileTime,
+                internal::traits<Derived>::ColsAtCompileTime,
+                AutoAlign | (internal::traits<Derived>::Flags&RowMajorBit ? RowMajor : ColMajor),
+                internal::traits<Derived>::MaxRowsAtCompileTime,
+                internal::traits<Derived>::MaxColsAtCompileTime
+          > PlainArray;
+
+    /** \brief The plain matrix or array type corresponding to this expression.
+      *
+      * This is not necessarily exactly the return type of eval(). In the case of plain matrices,
+      * the return type of eval() is a const reference to a matrix, not a matrix! It is however guaranteed
+      * that the return type of eval() is either PlainObject or const PlainObject&.
+      */
+    typedef std::conditional_t<internal::is_same<typename internal::traits<Derived>::XprKind,MatrixXpr >::value,
+                                 PlainMatrix, PlainArray> PlainObject;
+
+    /** \returns the outer size.
+      *
+      * \note For a vector, this returns just 1. For a matrix (non-vector), this is the major dimension
+      * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of columns for a
+      * column-major matrix, and the number of rows for a row-major matrix. */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    Index outerSize() const
+    {
+      return IsVectorAtCompileTime ? 1
+           : int(IsRowMajor) ? this->rows() : this->cols();
+    }
+
+    /** \returns the inner size.
+      *
+      * \note For a vector, this is just the size. For a matrix (non-vector), this is the minor dimension
+      * with respect to the \ref TopicStorageOrders "storage order", i.e., the number of rows for a
+      * column-major matrix, and the number of columns for a row-major matrix. */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    Index innerSize() const
+    {
+      return IsVectorAtCompileTime ? this->size()
+           : int(IsRowMajor) ? this->cols() : this->rows();
+    }
+
+    /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
+      * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
+      * nothing else.
+      */
+    EIGEN_DEVICE_FUNC
+    void resize(Index newSize)
+    {
+      EIGEN_ONLY_USED_FOR_DEBUG(newSize);
+      eigen_assert(newSize == this->size()
+                && "DenseBase::resize() does not actually allow to resize.");
+    }
+    /** Only plain matrices/arrays, not expressions, may be resized; therefore the only useful resize methods are
+      * Matrix::resize() and Array::resize(). The present method only asserts that the new size equals the old size, and does
+      * nothing else.
+      */
+    EIGEN_DEVICE_FUNC
+    void resize(Index rows, Index cols)
+    {
+      EIGEN_ONLY_USED_FOR_DEBUG(rows);
+      EIGEN_ONLY_USED_FOR_DEBUG(cols);
+      eigen_assert(rows == this->rows() && cols == this->cols()
+                && "DenseBase::resize() does not actually allow to resize.");
+    }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** \internal Represents a matrix with all coefficients equal to one another*/
+    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
+    /** \internal \deprecated Represents a vector with linearly spaced coefficients that allows sequential access only. */
+    EIGEN_DEPRECATED typedef CwiseNullaryOp<internal::linspaced_op<Scalar>,PlainObject> SequentialLinSpacedReturnType;
+    /** \internal Represents a vector with linearly spaced coefficients that allows random access. */
+    typedef CwiseNullaryOp<internal::linspaced_op<Scalar>,PlainObject> RandomAccessLinSpacedReturnType;
+    /** \internal Represents a vector with equally spaced coefficients that allows random access. */
+    typedef CwiseNullaryOp<internal::equalspaced_op<Scalar>, PlainObject> RandomAccessEqualSpacedReturnType;
+    /** \internal the return type of MatrixBase::eigenvalues() */
+    typedef Matrix<typename NumTraits<typename internal::traits<Derived>::Scalar>::Real, internal::traits<Derived>::ColsAtCompileTime, 1> EigenvaluesReturnType;
+
+#endif // not EIGEN_PARSED_BY_DOXYGEN
+
+    /** Copies \a other into *this. \returns a reference to *this. */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator=(const DenseBase<OtherDerived>& other);
+
+    /** Special case of the template operator=, in order to prevent the compiler
+      * from generating a default operator= (issue hit with g++ 4.1)
+      */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator=(const DenseBase& other);
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    Derived& operator=(const EigenBase<OtherDerived> &other);
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    Derived& operator+=(const EigenBase<OtherDerived> &other);
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    Derived& operator-=(const EigenBase<OtherDerived> &other);
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    Derived& operator=(const ReturnByValue<OtherDerived>& func);
+
+    /** \internal
+      * Copies \a other into *this without evaluating other. \returns a reference to *this. */
+    template<typename OtherDerived>
+    /** \deprecated */
+    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC
+    Derived& lazyAssign(const DenseBase<OtherDerived>& other);
+
+    EIGEN_DEVICE_FUNC
+    CommaInitializer<Derived> operator<< (const Scalar& s);
+
+    template<unsigned int Added,unsigned int Removed>
+    /** \deprecated it now returns \c *this */
+    EIGEN_DEPRECATED
+    const Derived& flagged() const
+    { return derived(); }
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    CommaInitializer<Derived> operator<< (const DenseBase<OtherDerived>& other);
+
+    typedef Transpose<Derived> TransposeReturnType;
+    EIGEN_DEVICE_FUNC
+    TransposeReturnType transpose();
+    typedef Transpose<const Derived> ConstTransposeReturnType;
+    EIGEN_DEVICE_FUNC
+    const ConstTransposeReturnType transpose() const;
+    EIGEN_DEVICE_FUNC
+    void transposeInPlace();
+
+    EIGEN_DEVICE_FUNC static const ConstantReturnType
+    Constant(Index rows, Index cols, const Scalar& value);
+    EIGEN_DEVICE_FUNC static const ConstantReturnType
+    Constant(Index size, const Scalar& value);
+    EIGEN_DEVICE_FUNC static const ConstantReturnType
+    Constant(const Scalar& value);
+
+    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
+    LinSpaced(Sequential_t, Index size, const Scalar& low, const Scalar& high);
+    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
+    LinSpaced(Sequential_t, const Scalar& low, const Scalar& high);
+
+    EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
+    LinSpaced(Index size, const Scalar& low, const Scalar& high);
+    EIGEN_DEVICE_FUNC static const RandomAccessLinSpacedReturnType
+    LinSpaced(const Scalar& low, const Scalar& high);
+
+    EIGEN_DEVICE_FUNC static const RandomAccessEqualSpacedReturnType
+    EqualSpaced(Index size, const Scalar& low, const Scalar& step);
+    EIGEN_DEVICE_FUNC static const RandomAccessEqualSpacedReturnType
+    EqualSpaced(const Scalar& low, const Scalar& step);
+
+    template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
+    static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
+    NullaryExpr(Index rows, Index cols, const CustomNullaryOp& func);
+    template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
+    static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
+    NullaryExpr(Index size, const CustomNullaryOp& func);
+    template<typename CustomNullaryOp> EIGEN_DEVICE_FUNC
+    static const CwiseNullaryOp<CustomNullaryOp, PlainObject>
+    NullaryExpr(const CustomNullaryOp& func);
+
+    EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index rows, Index cols);
+    EIGEN_DEVICE_FUNC static const ConstantReturnType Zero(Index size);
+    EIGEN_DEVICE_FUNC static const ConstantReturnType Zero();
+    EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index rows, Index cols);
+    EIGEN_DEVICE_FUNC static const ConstantReturnType Ones(Index size);
+    EIGEN_DEVICE_FUNC static const ConstantReturnType Ones();
+
+    EIGEN_DEVICE_FUNC void fill(const Scalar& value);
+    EIGEN_DEVICE_FUNC Derived& setConstant(const Scalar& value);
+    EIGEN_DEVICE_FUNC Derived& setLinSpaced(Index size, const Scalar& low, const Scalar& high);
+    EIGEN_DEVICE_FUNC Derived& setLinSpaced(const Scalar& low, const Scalar& high);
+    EIGEN_DEVICE_FUNC Derived& setEqualSpaced(Index size, const Scalar& low, const Scalar& step);
+    EIGEN_DEVICE_FUNC Derived& setEqualSpaced(const Scalar& low, const Scalar& step);
+    EIGEN_DEVICE_FUNC Derived& setZero();
+    EIGEN_DEVICE_FUNC Derived& setOnes();
+    EIGEN_DEVICE_FUNC Derived& setRandom();
+
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC
+    bool isApprox(const DenseBase<OtherDerived>& other,
+                  const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+    EIGEN_DEVICE_FUNC
+    bool isMuchSmallerThan(const RealScalar& other,
+                           const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC
+    bool isMuchSmallerThan(const DenseBase<OtherDerived>& other,
+                           const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+
+    EIGEN_DEVICE_FUNC bool isApproxToConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+    EIGEN_DEVICE_FUNC bool isConstant(const Scalar& value, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+    EIGEN_DEVICE_FUNC bool isZero(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+    EIGEN_DEVICE_FUNC bool isOnes(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+
+    EIGEN_DEVICE_FUNC inline bool hasNaN() const;
+    EIGEN_DEVICE_FUNC inline bool allFinite() const;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator*=(const Scalar& other);
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator/=(const Scalar& other);
+
+    typedef internal::add_const_on_value_type_t<typename internal::eval<Derived>::type> EvalReturnType;
+    /** \returns the matrix or vector obtained by evaluating this expression.
+      *
+      * Notice that in the case of a plain matrix or vector (not an expression) this function just returns
+      * a const reference, in order to avoid a useless copy.
+      *
+      * \warning Be careful with eval() and the auto C++ keyword, as detailed in this \link TopicPitfalls_auto_keyword page \endlink.
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE EvalReturnType eval() const
+    {
+      // Even though MSVC does not honor strong inlining when the return type
+      // is a dynamic matrix, we desperately need strong inlining for fixed
+      // size types on MSVC.
+      return typename internal::eval<Derived>::type(derived());
+    }
+
+    /** swaps *this with the expression \a other.
+      *
+      */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    void swap(const DenseBase<OtherDerived>& other)
+    {
+      EIGEN_STATIC_ASSERT(!OtherDerived::IsPlainObjectBase,THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
+      eigen_assert(rows()==other.rows() && cols()==other.cols());
+      call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
+    }
+
+    /** swaps *this with the matrix or array \a other.
+      *
+      */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    void swap(PlainObjectBase<OtherDerived>& other)
+    {
+      eigen_assert(rows()==other.rows() && cols()==other.cols());
+      call_assignment(derived(), other.derived(), internal::swap_assign_op<Scalar>());
+    }
+
+    EIGEN_DEVICE_FUNC inline const NestByValue<Derived> nestByValue() const;
+    EIGEN_DEVICE_FUNC inline const ForceAlignedAccess<Derived> forceAlignedAccess() const;
+    EIGEN_DEVICE_FUNC inline ForceAlignedAccess<Derived> forceAlignedAccess();
+    template<bool Enable> EIGEN_DEVICE_FUNC
+    inline const std::conditional_t<Enable,ForceAlignedAccess<Derived>,Derived&> forceAlignedAccessIf() const;
+    template<bool Enable> EIGEN_DEVICE_FUNC
+    inline std::conditional_t<Enable,ForceAlignedAccess<Derived>,Derived&> forceAlignedAccessIf();
+
+    EIGEN_DEVICE_FUNC Scalar sum() const;
+    EIGEN_DEVICE_FUNC Scalar mean() const;
+    EIGEN_DEVICE_FUNC Scalar trace() const;
+
+    EIGEN_DEVICE_FUNC Scalar prod() const;
+
+    template<int NaNPropagation>
+    EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar minCoeff() const;
+    template<int NaNPropagation>
+    EIGEN_DEVICE_FUNC typename internal::traits<Derived>::Scalar maxCoeff() const;
+
+
+    // By default, the fastest version with undefined NaN propagation semantics is
+    // used.
+    // TODO(rmlarsen): Replace with default template argument when we move to
+    // c++11 or beyond.
+    EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar minCoeff() const {
+      return minCoeff<PropagateFast>();
+    }
+    EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar maxCoeff() const {
+      return maxCoeff<PropagateFast>();
+    }
+
+    template<int NaNPropagation, typename IndexType>
+    EIGEN_DEVICE_FUNC
+    typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const;
+    template<int NaNPropagation, typename IndexType>
+    EIGEN_DEVICE_FUNC
+    typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const;
+    template<int NaNPropagation, typename IndexType>
+    EIGEN_DEVICE_FUNC
+    typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const;
+    template<int NaNPropagation, typename IndexType>
+    EIGEN_DEVICE_FUNC
+    typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const;
+
+    // TODO(rmlarsen): Replace these methods with a default template argument.
+    template<typename IndexType>
+    EIGEN_DEVICE_FUNC inline
+    typename internal::traits<Derived>::Scalar minCoeff(IndexType* row, IndexType* col) const {
+      return minCoeff<PropagateFast>(row, col);
+    }
+    template<typename IndexType>
+    EIGEN_DEVICE_FUNC inline
+    typename internal::traits<Derived>::Scalar maxCoeff(IndexType* row, IndexType* col) const {
+      return maxCoeff<PropagateFast>(row, col);
+    }
+    template<typename IndexType>
+     EIGEN_DEVICE_FUNC inline
+    typename internal::traits<Derived>::Scalar minCoeff(IndexType* index) const {
+      return minCoeff<PropagateFast>(index);
+    }
+    template<typename IndexType>
+    EIGEN_DEVICE_FUNC inline
+    typename internal::traits<Derived>::Scalar maxCoeff(IndexType* index) const {
+      return maxCoeff<PropagateFast>(index);
+    }
+  
+    template<typename BinaryOp>
+    EIGEN_DEVICE_FUNC
+    Scalar redux(const BinaryOp& func) const;
+
+    template<typename Visitor>
+    EIGEN_DEVICE_FUNC
+    void visit(Visitor& func) const;
+
+    /** \returns a WithFormat proxy object allowing to print a matrix the with given
+      * format \a fmt.
+      *
+      * See class IOFormat for some examples.
+      *
+      * \sa class IOFormat, class WithFormat
+      */
+    inline const WithFormat<Derived> format(const IOFormat& fmt) const
+    {
+      return WithFormat<Derived>(derived(), fmt);
+    }
+
+    /** \returns the unique coefficient of a 1x1 expression */
+    EIGEN_DEVICE_FUNC
+    CoeffReturnType value() const
+    {
+      EIGEN_STATIC_ASSERT_SIZE_1x1(Derived)
+      eigen_assert(this->rows() == 1 && this->cols() == 1);
+      return derived().coeff(0,0);
+    }
+
+    EIGEN_DEVICE_FUNC bool all() const;
+    EIGEN_DEVICE_FUNC bool any() const;
+    EIGEN_DEVICE_FUNC Index count() const;
+
+    typedef VectorwiseOp<Derived, Horizontal> RowwiseReturnType;
+    typedef const VectorwiseOp<const Derived, Horizontal> ConstRowwiseReturnType;
+    typedef VectorwiseOp<Derived, Vertical> ColwiseReturnType;
+    typedef const VectorwiseOp<const Derived, Vertical> ConstColwiseReturnType;
+
+    /** \returns a VectorwiseOp wrapper of *this for broadcasting and partial reductions
+    *
+    * Example: \include MatrixBase_rowwise.cpp
+    * Output: \verbinclude MatrixBase_rowwise.out
+    *
+    * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
+    */
+    //Code moved here due to a CUDA compiler bug
+    EIGEN_DEVICE_FUNC inline ConstRowwiseReturnType rowwise() const {
+      return ConstRowwiseReturnType(derived());
+    }
+    EIGEN_DEVICE_FUNC RowwiseReturnType rowwise();
+
+    /** \returns a VectorwiseOp wrapper of *this broadcasting and partial reductions
+    *
+    * Example: \include MatrixBase_colwise.cpp
+    * Output: \verbinclude MatrixBase_colwise.out
+    *
+    * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
+    */
+    EIGEN_DEVICE_FUNC inline ConstColwiseReturnType colwise() const {
+      return ConstColwiseReturnType(derived());
+    }
+    EIGEN_DEVICE_FUNC ColwiseReturnType colwise();
+
+    typedef CwiseNullaryOp<internal::scalar_random_op<Scalar>,PlainObject> RandomReturnType;
+    static const RandomReturnType Random(Index rows, Index cols);
+    static const RandomReturnType Random(Index size);
+    static const RandomReturnType Random();
+
+    template <typename ThenDerived, typename ElseDerived>
+    inline EIGEN_DEVICE_FUNC
+        CwiseTernaryOp<internal::scalar_boolean_select_op<typename DenseBase<ThenDerived>::Scalar,
+                           typename DenseBase<ElseDerived>::Scalar, Scalar>,
+        ThenDerived, ElseDerived, Derived>
+        select(const DenseBase<ThenDerived>& thenMatrix, const DenseBase<ElseDerived>& elseMatrix) const;
+
+    template <typename ThenDerived>
+    inline EIGEN_DEVICE_FUNC
+        CwiseTernaryOp<internal::scalar_boolean_select_op<typename DenseBase<ThenDerived>::Scalar,
+                           typename DenseBase<ThenDerived>::Scalar, Scalar>,
+        ThenDerived, typename DenseBase<ThenDerived>::ConstantReturnType, Derived>
+        select(const DenseBase<ThenDerived>& thenMatrix,
+               const typename DenseBase<ThenDerived>::Scalar& elseScalar) const;
+
+    template <typename ElseDerived>
+    inline EIGEN_DEVICE_FUNC
+        CwiseTernaryOp<internal::scalar_boolean_select_op<typename DenseBase<ElseDerived>::Scalar,
+                           typename DenseBase<ElseDerived>::Scalar, Scalar>,
+        typename DenseBase<ElseDerived>::ConstantReturnType, ElseDerived, Derived>
+        select(const typename DenseBase<ElseDerived>::Scalar& thenScalar,
+               const DenseBase<ElseDerived>& elseMatrix) const;
+
+    template<int p> RealScalar lpNorm() const;
+
+    template<int RowFactor, int ColFactor>
+    EIGEN_DEVICE_FUNC
+    const Replicate<Derived,RowFactor,ColFactor> replicate() const;
+    /**
+    * \return an expression of the replication of \c *this
+    *
+    * Example: \include MatrixBase_replicate_int_int.cpp
+    * Output: \verbinclude MatrixBase_replicate_int_int.out
+    *
+    * \sa VectorwiseOp::replicate(), DenseBase::replicate<int,int>(), class Replicate
+    */
+    //Code moved here due to a CUDA compiler bug
+    EIGEN_DEVICE_FUNC
+    const Replicate<Derived, Dynamic, Dynamic> replicate(Index rowFactor, Index colFactor) const
+    {
+      return Replicate<Derived, Dynamic, Dynamic>(derived(), rowFactor, colFactor);
+    }
+
+    typedef Reverse<Derived, BothDirections> ReverseReturnType;
+    typedef const Reverse<const Derived, BothDirections> ConstReverseReturnType;
+    EIGEN_DEVICE_FUNC ReverseReturnType reverse();
+    /** This is the const version of reverse(). */
+    //Code moved here due to a CUDA compiler bug
+    EIGEN_DEVICE_FUNC ConstReverseReturnType reverse() const
+    {
+      return ConstReverseReturnType(derived());
+    }
+    EIGEN_DEVICE_FUNC void reverseInPlace();
+
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
+    /** STL-like <a href="https://en.cppreference.com/w/cpp/named_req/RandomAccessIterator">RandomAccessIterator</a>
+      * iterator type as returned by the begin() and end() methods.
+      */
+    typedef random_access_iterator_type iterator;
+    /** This is the const version of iterator (aka read-only) */
+    typedef random_access_iterator_type const_iterator;
+    #else
+    typedef std::conditional_t< (Flags&DirectAccessBit)==DirectAccessBit,
+                                     internal::pointer_based_stl_iterator<Derived>,
+                                     internal::generic_randaccess_stl_iterator<Derived>
+                                   > iterator_type;
+
+    typedef std::conditional_t< (Flags&DirectAccessBit)==DirectAccessBit,
+                                     internal::pointer_based_stl_iterator<const Derived>,
+                                     internal::generic_randaccess_stl_iterator<const Derived>
+                                   > const_iterator_type;
+
+    // Stl-style iterators are supported only for vectors.
+
+    typedef std::conditional_t<IsVectorAtCompileTime, iterator_type, void> iterator;
+
+    typedef std::conditional_t<IsVectorAtCompileTime, const_iterator_type, void> const_iterator;
+    #endif
+
+    inline iterator begin();
+    inline const_iterator begin() const;
+    inline const_iterator cbegin() const;
+    inline iterator end();
+    inline const_iterator end() const;
+    inline const_iterator cend() const;
+
+#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::DenseBase
+#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND)
+#define EIGEN_DOC_UNARY_ADDONS(X,Y)
+#   include "../plugins/CommonCwiseUnaryOps.h"
+#   include "../plugins/BlockMethods.h"
+#   include "../plugins/IndexedViewMethods.h"
+#   include "../plugins/ReshapedMethods.h"
+#   ifdef EIGEN_DENSEBASE_PLUGIN
+#     include EIGEN_DENSEBASE_PLUGIN
+#   endif
+#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
+#undef EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+#undef EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF
+#undef EIGEN_DOC_UNARY_ADDONS
+
+    // disable the use of evalTo for dense objects with a nice compilation error
+    template<typename Dest>
+    EIGEN_DEVICE_FUNC
+    inline void evalTo(Dest& ) const
+    {
+      EIGEN_STATIC_ASSERT((internal::is_same<Dest,void>::value),THE_EVAL_EVALTO_FUNCTION_SHOULD_NEVER_BE_CALLED_FOR_DENSE_OBJECTS);
+    }
+
+  protected:
+    EIGEN_DEFAULT_COPY_CONSTRUCTOR(DenseBase)
+    /** Default constructor. Do nothing. */
+    EIGEN_DEVICE_FUNC constexpr DenseBase() {
+      /* Just checks for self-consistency of the flags.
+       * Only do it when debugging Eigen, as this borders on paranoia and could slow compilation down
+       */
+#ifdef EIGEN_INTERNAL_DEBUGGING
+      EIGEN_STATIC_ASSERT((internal::check_implication(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, int(IsRowMajor))
+                        && internal::check_implication(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, int(!IsRowMajor))),
+                          INVALID_STORAGE_ORDER_FOR_THIS_VECTOR_EXPRESSION)
+#endif
+    }
+
+  private:
+    EIGEN_DEVICE_FUNC explicit DenseBase(int);
+    EIGEN_DEVICE_FUNC DenseBase(int,int);
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC explicit DenseBase(const DenseBase<OtherDerived>&);
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_DENSEBASE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/DenseCoeffsBase.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/DenseCoeffsBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..7f0bcf471808998027d628244b1bd302fb5189a9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/DenseCoeffsBase.h
@@ -0,0 +1,687 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DENSECOEFFSBASE_H
+#define EIGEN_DENSECOEFFSBASE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template<typename T> struct add_const_on_value_type_if_arithmetic
+{
+  typedef std::conditional_t<is_arithmetic<T>::value, T, add_const_on_value_type_t<T>> type;
+};
+}
+
+/** \brief Base class providing read-only coefficient access to matrices and arrays.
+  * \ingroup Core_Module
+  * \tparam Derived Type of the derived class
+  *
+  * \note #ReadOnlyAccessors Constant indicating read-only access
+  *
+  * This class defines the \c operator() \c const function and friends, which can be used to read specific
+  * entries of a matrix or array.
+  *
+  * \sa DenseCoeffsBase<Derived, WriteAccessors>, DenseCoeffsBase<Derived, DirectAccessors>,
+  *     \ref TopicClassHierarchy
+  */
+template<typename Derived>
+class DenseCoeffsBase<Derived,ReadOnlyAccessors> : public EigenBase<Derived>
+{
+  public:
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+
+    // Explanation for this CoeffReturnType typedef.
+    // - This is the return type of the coeff() method.
+    // - The LvalueBit means exactly that we can offer a coeffRef() method, which means exactly that we can get references
+    // to coeffs, which means exactly that we can have coeff() return a const reference (as opposed to returning a value).
+    // - The is_arithmetic check is required since "const int", "const double", etc. will cause warnings on some systems
+    // while the declaration of "const T", where T is a non arithmetic type does not. Always returning "const Scalar&" is
+    // not possible, since the underlying expressions might not offer a valid address the reference could be referring to.
+    typedef std::conditional_t<bool(internal::traits<Derived>::Flags&LvalueBit),
+                const Scalar&,
+                std::conditional_t<internal::is_arithmetic<Scalar>::value, Scalar, const Scalar>
+            > CoeffReturnType;
+
+    typedef typename internal::add_const_on_value_type_if_arithmetic<
+                         typename internal::packet_traits<Scalar>::type
+                     >::type PacketReturnType;
+
+    typedef EigenBase<Derived> Base;
+    using Base::rows;
+    using Base::cols;
+    using Base::size;
+    using Base::derived;
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Index rowIndexByOuterInner(Index outer, Index inner) const
+    {
+      return int(Derived::RowsAtCompileTime) == 1 ? 0
+          : int(Derived::ColsAtCompileTime) == 1 ? inner
+          : int(Derived::Flags)&RowMajorBit ? outer
+          : inner;
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Index colIndexByOuterInner(Index outer, Index inner) const
+    {
+      return int(Derived::ColsAtCompileTime) == 1 ? 0
+          : int(Derived::RowsAtCompileTime) == 1 ? inner
+          : int(Derived::Flags)&RowMajorBit ? inner
+          : outer;
+    }
+
+    /** Short version: don't use this function, use
+      * \link operator()(Index,Index) const \endlink instead.
+      *
+      * Long version: this function is similar to
+      * \link operator()(Index,Index) const \endlink, but without the assertion.
+      * Use this for limiting the performance cost of debugging code when doing
+      * repeated coefficient access. Only use this when it is guaranteed that the
+      * parameters \a row and \a col are in range.
+      *
+      * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
+      * function equivalent to \link operator()(Index,Index) const \endlink.
+      *
+      * \sa operator()(Index,Index) const, coeffRef(Index,Index), coeff(Index) const
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const
+    {
+      eigen_internal_assert(row >= 0 && row < rows()
+                         && col >= 0 && col < cols());
+      return internal::evaluator<Derived>(derived()).coeff(row,col);
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
+    {
+      return coeff(rowIndexByOuterInner(outer, inner),
+                   colIndexByOuterInner(outer, inner));
+    }
+
+    /** \returns the coefficient at given the given row and column.
+      *
+      * \sa operator()(Index,Index), operator[](Index)
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE CoeffReturnType operator()(Index row, Index col) const
+    {
+      eigen_assert(row >= 0 && row < rows()
+          && col >= 0 && col < cols());
+      return coeff(row, col);
+    }
+
+    /** Short version: don't use this function, use
+      * \link operator[](Index) const \endlink instead.
+      *
+      * Long version: this function is similar to
+      * \link operator[](Index) const \endlink, but without the assertion.
+      * Use this for limiting the performance cost of debugging code when doing
+      * repeated coefficient access. Only use this when it is guaranteed that the
+      * parameter \a index is in range.
+      *
+      * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
+      * function equivalent to \link operator[](Index) const \endlink.
+      *
+      * \sa operator[](Index) const, coeffRef(Index), coeff(Index,Index) const
+      */
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE CoeffReturnType
+    coeff(Index index) const
+    {
+      EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
+                          THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
+      eigen_internal_assert(index >= 0 && index < size());
+      return internal::evaluator<Derived>(derived()).coeff(index);
+    }
+
+
+    /** \returns the coefficient at given index.
+      *
+      * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
+      *
+      * \sa operator[](Index), operator()(Index,Index) const, x() const, y() const,
+      * z() const, w() const
+      */
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE CoeffReturnType
+    operator[](Index index) const
+    {
+      EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
+                          THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
+      eigen_assert(index >= 0 && index < size());
+      return coeff(index);
+    }
+
+    /** \returns the coefficient at given index.
+      *
+      * This is synonymous to operator[](Index) const.
+      *
+      * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
+      *
+      * \sa operator[](Index), operator()(Index,Index) const, x() const, y() const,
+      * z() const, w() const
+      */
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE CoeffReturnType
+    operator()(Index index) const
+    {
+      eigen_assert(index >= 0 && index < size());
+      return coeff(index);
+    }
+
+    /** equivalent to operator[](0).  */
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE CoeffReturnType
+    x() const { return (*this)[0]; }
+
+    /** equivalent to operator[](1).  */
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE CoeffReturnType
+    y() const
+    {
+      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=2, OUT_OF_RANGE_ACCESS);
+      return (*this)[1];
+    }
+
+    /** equivalent to operator[](2).  */
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE CoeffReturnType
+    z() const
+    {
+      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=3, OUT_OF_RANGE_ACCESS);
+      return (*this)[2];
+    }
+
+    /** equivalent to operator[](3).  */
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE CoeffReturnType
+    w() const
+    {
+      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=4, OUT_OF_RANGE_ACCESS);
+      return (*this)[3];
+    }
+
+    /** \internal
+      * \returns the packet of coefficients starting at the given row and column. It is your responsibility
+      * to ensure that a packet really starts there. This method is only available on expressions having the
+      * PacketAccessBit.
+      *
+      * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
+      * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
+      * starting at an address which is a multiple of the packet size.
+      */
+
+    template<int LoadMode>
+    EIGEN_STRONG_INLINE PacketReturnType packet(Index row, Index col) const
+    {
+      typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
+      eigen_internal_assert(row >= 0 && row < rows() && col >= 0 && col < cols());
+      return internal::evaluator<Derived>(derived()).template packet<LoadMode,DefaultPacketType>(row,col);
+    }
+
+
+    /** \internal */
+    template<int LoadMode>
+    EIGEN_STRONG_INLINE PacketReturnType packetByOuterInner(Index outer, Index inner) const
+    {
+      return packet<LoadMode>(rowIndexByOuterInner(outer, inner),
+                              colIndexByOuterInner(outer, inner));
+    }
+
+    /** \internal
+      * \returns the packet of coefficients starting at the given index. It is your responsibility
+      * to ensure that a packet really starts there. This method is only available on expressions having the
+      * PacketAccessBit and the LinearAccessBit.
+      *
+      * The \a LoadMode parameter may have the value \a #Aligned or \a #Unaligned. Its effect is to select
+      * the appropriate vectorization instruction. Aligned access is faster, but is only possible for packets
+      * starting at an address which is a multiple of the packet size.
+      */
+
+    template<int LoadMode>
+    EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
+    {
+      EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
+                          THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
+      typedef typename internal::packet_traits<Scalar>::type DefaultPacketType;
+      eigen_internal_assert(index >= 0 && index < size());
+      return internal::evaluator<Derived>(derived()).template packet<LoadMode,DefaultPacketType>(index);
+    }
+
+  protected:
+    // explanation: DenseBase is doing "using ..." on the methods from DenseCoeffsBase.
+    // But some methods are only available in the DirectAccess case.
+    // So we add dummy methods here with these names, so that "using... " doesn't fail.
+    // It's not private so that the child class DenseBase can access them, and it's not public
+    // either since it's an implementation detail, so has to be protected.
+    void coeffRef();
+    void coeffRefByOuterInner();
+    void writePacket();
+    void writePacketByOuterInner();
+    void copyCoeff();
+    void copyCoeffByOuterInner();
+    void copyPacket();
+    void copyPacketByOuterInner();
+    void stride();
+    void innerStride();
+    void outerStride();
+    void rowStride();
+    void colStride();
+};
+
+/** \brief Base class providing read/write coefficient access to matrices and arrays.
+  * \ingroup Core_Module
+  * \tparam Derived Type of the derived class
+  *
+  * \note #WriteAccessors Constant indicating read/write access
+  *
+  * This class defines the non-const \c operator() function and friends, which can be used to write specific
+  * entries of a matrix or array. This class inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which
+  * defines the const variant for reading specific entries.
+  *
+  * \sa DenseCoeffsBase<Derived, DirectAccessors>, \ref TopicClassHierarchy
+  */
+template<typename Derived>
+class DenseCoeffsBase<Derived, WriteAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors>
+{
+  public:
+
+    typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
+
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    using Base::coeff;
+    using Base::rows;
+    using Base::cols;
+    using Base::size;
+    using Base::derived;
+    using Base::rowIndexByOuterInner;
+    using Base::colIndexByOuterInner;
+    using Base::operator[];
+    using Base::operator();
+    using Base::x;
+    using Base::y;
+    using Base::z;
+    using Base::w;
+
+    /** Short version: don't use this function, use
+      * \link operator()(Index,Index) \endlink instead.
+      *
+      * Long version: this function is similar to
+      * \link operator()(Index,Index) \endlink, but without the assertion.
+      * Use this for limiting the performance cost of debugging code when doing
+      * repeated coefficient access. Only use this when it is guaranteed that the
+      * parameters \a row and \a col are in range.
+      *
+      * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
+      * function equivalent to \link operator()(Index,Index) \endlink.
+      *
+      * \sa operator()(Index,Index), coeff(Index, Index) const, coeffRef(Index)
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar& coeffRef(Index row, Index col)
+    {
+      eigen_internal_assert(row >= 0 && row < rows()
+                         && col >= 0 && col < cols());
+      return internal::evaluator<Derived>(derived()).coeffRef(row,col);
+    }
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar&
+    coeffRefByOuterInner(Index outer, Index inner)
+    {
+      return coeffRef(rowIndexByOuterInner(outer, inner),
+                      colIndexByOuterInner(outer, inner));
+    }
+
+    /** \returns a reference to the coefficient at given the given row and column.
+      *
+      * \sa operator[](Index)
+      */
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar&
+    operator()(Index row, Index col)
+    {
+      eigen_assert(row >= 0 && row < rows()
+          && col >= 0 && col < cols());
+      return coeffRef(row, col);
+    }
+
+
+    /** Short version: don't use this function, use
+      * \link operator[](Index) \endlink instead.
+      *
+      * Long version: this function is similar to
+      * \link operator[](Index) \endlink, but without the assertion.
+      * Use this for limiting the performance cost of debugging code when doing
+      * repeated coefficient access. Only use this when it is guaranteed that the
+      * parameters \a row and \a col are in range.
+      *
+      * If EIGEN_INTERNAL_DEBUGGING is defined, an assertion will be made, making this
+      * function equivalent to \link operator[](Index) \endlink.
+      *
+      * \sa operator[](Index), coeff(Index) const, coeffRef(Index,Index)
+      */
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar&
+    coeffRef(Index index)
+    {
+      EIGEN_STATIC_ASSERT(internal::evaluator<Derived>::Flags & LinearAccessBit,
+                          THIS_COEFFICIENT_ACCESSOR_TAKING_ONE_ACCESS_IS_ONLY_FOR_EXPRESSIONS_ALLOWING_LINEAR_ACCESS)
+      eigen_internal_assert(index >= 0 && index < size());
+      return internal::evaluator<Derived>(derived()).coeffRef(index);
+    }
+
+    /** \returns a reference to the coefficient at given index.
+      *
+      * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
+      *
+      * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
+      */
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar&
+    operator[](Index index)
+    {
+      EIGEN_STATIC_ASSERT(Derived::IsVectorAtCompileTime,
+                          THE_BRACKET_OPERATOR_IS_ONLY_FOR_VECTORS__USE_THE_PARENTHESIS_OPERATOR_INSTEAD)
+      eigen_assert(index >= 0 && index < size());
+      return coeffRef(index);
+    }
+
+    /** \returns a reference to the coefficient at given index.
+      *
+      * This is synonymous to operator[](Index).
+      *
+      * This method is allowed only for vector expressions, and for matrix expressions having the LinearAccessBit.
+      *
+      * \sa operator[](Index) const, operator()(Index,Index), x(), y(), z(), w()
+      */
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar&
+    operator()(Index index)
+    {
+      eigen_assert(index >= 0 && index < size());
+      return coeffRef(index);
+    }
+
+    /** equivalent to operator[](0).  */
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar&
+    x() { return (*this)[0]; }
+
+    /** equivalent to operator[](1).  */
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar&
+    y()
+    {
+      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=2, OUT_OF_RANGE_ACCESS);
+      return (*this)[1];
+    }
+
+    /** equivalent to operator[](2).  */
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar&
+    z()
+    {
+      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=3, OUT_OF_RANGE_ACCESS);
+      return (*this)[2];
+    }
+
+    /** equivalent to operator[](3).  */
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Scalar&
+    w()
+    {
+      EIGEN_STATIC_ASSERT(Derived::SizeAtCompileTime==-1 || Derived::SizeAtCompileTime>=4, OUT_OF_RANGE_ACCESS);
+      return (*this)[3];
+    }
+};
+
+/** \brief Base class providing direct read-only coefficient access to matrices and arrays.
+  * \ingroup Core_Module
+  * \tparam Derived Type of the derived class
+  *
+  * \note #DirectAccessors Constant indicating direct access
+  *
+  * This class defines functions to work with strides which can be used to access entries directly. This class
+  * inherits DenseCoeffsBase<Derived, ReadOnlyAccessors> which defines functions to access entries read-only using
+  * \c operator() .
+  *
+  * \sa \blank \ref TopicClassHierarchy
+  */
+template<typename Derived>
+class DenseCoeffsBase<Derived, DirectAccessors> : public DenseCoeffsBase<Derived, ReadOnlyAccessors>
+{
+  public:
+
+    typedef DenseCoeffsBase<Derived, ReadOnlyAccessors> Base;
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    using Base::rows;
+    using Base::cols;
+    using Base::size;
+    using Base::derived;
+
+    /** \returns the pointer increment between two consecutive elements within a slice in the inner direction.
+      *
+      * \sa outerStride(), rowStride(), colStride()
+      */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index innerStride() const
+    {
+      return derived().innerStride();
+    }
+
+    /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns
+      *          in a column-major matrix).
+      *
+      * \sa innerStride(), rowStride(), colStride()
+      */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index outerStride() const
+    {
+      return derived().outerStride();
+    }
+
+    // FIXME shall we remove it ?
+    EIGEN_CONSTEXPR inline Index stride() const
+    {
+      return Derived::IsVectorAtCompileTime ? innerStride() : outerStride();
+    }
+
+    /** \returns the pointer increment between two consecutive rows.
+      *
+      * \sa innerStride(), outerStride(), colStride()
+      */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index rowStride() const
+    {
+      return Derived::IsRowMajor ? outerStride() : innerStride();
+    }
+
+    /** \returns the pointer increment between two consecutive columns.
+      *
+      * \sa innerStride(), outerStride(), rowStride()
+      */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index colStride() const
+    {
+      return Derived::IsRowMajor ? innerStride() : outerStride();
+    }
+};
+
+/** \brief Base class providing direct read/write coefficient access to matrices and arrays.
+  * \ingroup Core_Module
+  * \tparam Derived Type of the derived class
+  *
+  * \note #DirectWriteAccessors Constant indicating direct access
+  *
+  * This class defines functions to work with strides which can be used to access entries directly. This class
+  * inherits DenseCoeffsBase<Derived, WriteAccessors> which defines functions to access entries read/write using
+  * \c operator().
+  *
+  * \sa \blank \ref TopicClassHierarchy
+  */
+template<typename Derived>
+class DenseCoeffsBase<Derived, DirectWriteAccessors>
+  : public DenseCoeffsBase<Derived, WriteAccessors>
+{
+  public:
+
+    typedef DenseCoeffsBase<Derived, WriteAccessors> Base;
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    using Base::rows;
+    using Base::cols;
+    using Base::size;
+    using Base::derived;
+
+    /** \returns the pointer increment between two consecutive elements within a slice in the inner direction.
+      *
+      * \sa outerStride(), rowStride(), colStride()
+      */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index innerStride() const EIGEN_NOEXCEPT
+    {
+      return derived().innerStride();
+    }
+
+    /** \returns the pointer increment between two consecutive inner slices (for example, between two consecutive columns
+      *          in a column-major matrix).
+      *
+      * \sa innerStride(), rowStride(), colStride()
+      */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index outerStride() const EIGEN_NOEXCEPT
+    {
+      return derived().outerStride();
+    }
+
+    // FIXME shall we remove it ?
+    EIGEN_CONSTEXPR inline Index stride() const EIGEN_NOEXCEPT
+    {
+      return Derived::IsVectorAtCompileTime ? innerStride() : outerStride();
+    }
+
+    /** \returns the pointer increment between two consecutive rows.
+      *
+      * \sa innerStride(), outerStride(), colStride()
+      */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index rowStride() const EIGEN_NOEXCEPT
+    {
+      return Derived::IsRowMajor ? outerStride() : innerStride();
+    }
+
+    /** \returns the pointer increment between two consecutive columns.
+      *
+      * \sa innerStride(), outerStride(), rowStride()
+      */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index colStride() const EIGEN_NOEXCEPT
+    {
+      return Derived::IsRowMajor ? innerStride() : outerStride();
+    }
+};
+
+namespace internal {
+
+template<int Alignment, typename Derived, bool JustReturnZero>
+struct first_aligned_impl
+{
+  static EIGEN_CONSTEXPR inline Index run(const Derived&) EIGEN_NOEXCEPT
+  { return 0; }
+};
+
+template<int Alignment, typename Derived>
+struct first_aligned_impl<Alignment, Derived, false>
+{
+  static inline Index run(const Derived& m)
+  {
+    return internal::first_aligned<Alignment>(m.data(), m.size());
+  }
+};
+
+/** \internal \returns the index of the first element of the array stored by \a m that is properly aligned with respect to \a Alignment for vectorization.
+  *
+  * \tparam Alignment requested alignment in Bytes.
+  *
+  * There is also the variant first_aligned(const Scalar*, Integer) defined in Memory.h. See it for more
+  * documentation.
+  */
+template<int Alignment, typename Derived>
+static inline Index first_aligned(const DenseBase<Derived>& m)
+{
+  enum { ReturnZero = (int(evaluator<Derived>::Alignment) >= Alignment) || !(Derived::Flags & DirectAccessBit) };
+  return first_aligned_impl<Alignment, Derived, ReturnZero>::run(m.derived());
+}
+
+template<typename Derived>
+static inline Index first_default_aligned(const DenseBase<Derived>& m)
+{
+  typedef typename Derived::Scalar Scalar;
+  typedef typename packet_traits<Scalar>::type DefaultPacketType;
+  return internal::first_aligned<int(unpacket_traits<DefaultPacketType>::alignment),Derived>(m);
+}
+
+template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>
+struct inner_stride_at_compile_time
+{
+  enum { ret = traits<Derived>::InnerStrideAtCompileTime };
+};
+
+template<typename Derived>
+struct inner_stride_at_compile_time<Derived, false>
+{
+  enum { ret = 0 };
+};
+
+template<typename Derived, bool HasDirectAccess = has_direct_access<Derived>::ret>
+struct outer_stride_at_compile_time
+{
+  enum { ret = traits<Derived>::OuterStrideAtCompileTime };
+};
+
+template<typename Derived>
+struct outer_stride_at_compile_time<Derived, false>
+{
+  enum { ret = 0 };
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_DENSECOEFFSBASE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/DenseStorage.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/DenseStorage.h
new file mode 100644
index 0000000000000000000000000000000000000000..11205fa4037d599f7985b9286adcdf1e69e0df0d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/DenseStorage.h
@@ -0,0 +1,688 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2010-2013 Hauke Heibel <hauke.heibel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIXSTORAGE_H
+#define EIGEN_MATRIXSTORAGE_H
+
+#ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+  #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X) X; EIGEN_DENSE_STORAGE_CTOR_PLUGIN;
+#else
+  #define EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(X)
+#endif
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+struct constructor_without_unaligned_array_assert {};
+
+template <typename T, int Size>
+EIGEN_DEVICE_FUNC constexpr void check_static_allocation_size() {
+// if EIGEN_STACK_ALLOCATION_LIMIT is defined to 0, then no limit
+#if EIGEN_STACK_ALLOCATION_LIMIT
+  EIGEN_STATIC_ASSERT(Size * sizeof(T) <= EIGEN_STACK_ALLOCATION_LIMIT, OBJECT_ALLOCATED_ON_STACK_IS_TOO_BIG);
+#endif
+}
+
+/** \internal
+  * Static array. If the MatrixOrArrayOptions require auto-alignment, the array will be automatically aligned:
+  * to 16 bytes boundary if the total size is a multiple of 16 bytes.
+  */
+template <typename T, int Size, int MatrixOrArrayOptions,
+          int Alignment = (MatrixOrArrayOptions&DontAlign) ? 0
+                        : compute_default_alignment<T,Size>::value >
+struct plain_array
+{
+  T array[Size];
+
+  EIGEN_DEVICE_FUNC constexpr plain_array() { check_static_allocation_size<T, Size>(); }
+
+  EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {
+    check_static_allocation_size<T, Size>();
+  }
+};
+
+#if defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
+  #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask)
+#else
+  #define EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(sizemask) \
+    eigen_assert((internal::is_constant_evaluated() || (std::uintptr_t(array) & (sizemask)) == 0) \
+              && "this assertion is explained here: " \
+              "http://eigen.tuxfamily.org/dox-devel/group__TopicUnalignedArrayAssert.html" \
+              " **** READ THIS WEB PAGE !!! ****");
+#endif
+
+template <typename T, int Size, int MatrixOrArrayOptions>
+struct plain_array<T, Size, MatrixOrArrayOptions, 8>
+{
+  EIGEN_ALIGN_TO_BOUNDARY(8) T array[Size];
+
+  EIGEN_DEVICE_FUNC constexpr plain_array() {
+    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(7);
+    check_static_allocation_size<T,Size>();
+  }
+
+  EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {
+    check_static_allocation_size<T, Size>();
+  }
+};
+
+template <typename T, int Size, int MatrixOrArrayOptions>
+struct plain_array<T, Size, MatrixOrArrayOptions, 16>
+{
+  EIGEN_ALIGN_TO_BOUNDARY(16) T array[Size];
+
+  EIGEN_DEVICE_FUNC constexpr plain_array() {
+    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(15);
+    check_static_allocation_size<T,Size>();
+  }
+
+  EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {
+    check_static_allocation_size<T, Size>();
+  }
+};
+
+template <typename T, int Size, int MatrixOrArrayOptions>
+struct plain_array<T, Size, MatrixOrArrayOptions, 32>
+{
+  EIGEN_ALIGN_TO_BOUNDARY(32) T array[Size];
+
+  EIGEN_DEVICE_FUNC constexpr plain_array() {
+    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(31);
+    check_static_allocation_size<T,Size>();
+  }
+
+  EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {
+    check_static_allocation_size<T, Size>();
+  }
+};
+
+template <typename T, int Size, int MatrixOrArrayOptions>
+struct plain_array<T, Size, MatrixOrArrayOptions, 64>
+{
+  EIGEN_ALIGN_TO_BOUNDARY(64) T array[Size];
+
+  EIGEN_DEVICE_FUNC constexpr plain_array() {
+    EIGEN_MAKE_UNALIGNED_ARRAY_ASSERT(63);
+    check_static_allocation_size<T,Size>();
+  }
+
+  EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {
+    check_static_allocation_size<T, Size>();
+  }
+};
+
+template <typename T, int MatrixOrArrayOptions, int Alignment>
+struct plain_array<T, 0, MatrixOrArrayOptions, Alignment>
+{
+  T array[1];
+  EIGEN_DEVICE_FUNC constexpr plain_array() {}
+  EIGEN_DEVICE_FUNC constexpr plain_array(constructor_without_unaligned_array_assert) {}
+};
+
+struct plain_array_helper {
+  template<typename T, int Size, int MatrixOrArrayOptions, int Alignment>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  static void copy(const plain_array<T, Size, MatrixOrArrayOptions, Alignment>& src, const Eigen::Index size,
+                         plain_array<T, Size, MatrixOrArrayOptions, Alignment>& dst) {
+    smart_copy(src.array, src.array + size, dst.array);
+  }
+  
+  template<typename T, int Size, int MatrixOrArrayOptions, int Alignment>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  static void swap(plain_array<T, Size, MatrixOrArrayOptions, Alignment>& a, const Eigen::Index a_size,
+                   plain_array<T, Size, MatrixOrArrayOptions, Alignment>& b, const Eigen::Index b_size) {
+    if (a_size < b_size) {
+      std::swap_ranges(b.array, b.array + a_size, a.array);
+      smart_move(b.array + a_size, b.array + b_size, a.array + a_size);
+    } else if (a_size > b_size) {
+      std::swap_ranges(a.array, a.array + b_size, b.array);
+      smart_move(a.array + b_size, a.array + a_size, b.array + b_size);
+    } else {
+      std::swap_ranges(a.array, a.array + a_size, b.array);
+    }
+  }
+};
+
+} // end namespace internal
+
+/** \internal
+  *
+  * \class DenseStorage
+  * \ingroup Core_Module
+  *
+  * \brief Stores the data of a matrix
+  *
+  * This class stores the data of fixed-size, dynamic-size or mixed matrices
+  * in a way as compact as possible.
+  *
+  * \sa Matrix
+  */
+template<typename T, int Size, int Rows_, int Cols_, int Options_> class DenseStorage;
+
+// purely fixed-size matrix
+template<typename T, int Size, int Rows_, int Cols_, int Options_> class DenseStorage
+{
+    internal::plain_array<T,Size,Options_> m_data;
+  public:
+    constexpr EIGEN_DEVICE_FUNC DenseStorage() {
+      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
+    }
+    EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert)
+      : m_data(internal::constructor_without_unaligned_array_assert()) {}
+#if defined(EIGEN_DENSE_STORAGE_CTOR_PLUGIN)
+    EIGEN_DEVICE_FUNC constexpr
+    DenseStorage(const DenseStorage& other) : m_data(other.m_data) {
+      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = Size)
+    }
+#else
+    EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage&) = default;
+#endif
+    EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(const DenseStorage&) = default;
+    EIGEN_DEVICE_FUNC constexpr DenseStorage(DenseStorage&&) = default;
+    EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(DenseStorage&&) = default;
+    EIGEN_DEVICE_FUNC constexpr DenseStorage(Index size, Index rows, Index cols) {
+      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+      eigen_internal_assert(size == rows * cols && rows == Rows_ && cols == Cols_);
+      EIGEN_UNUSED_VARIABLE(size);
+      EIGEN_UNUSED_VARIABLE(rows);
+      EIGEN_UNUSED_VARIABLE(cols);
+    }
+    EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
+      numext::swap(m_data, other.m_data);
+    }
+    EIGEN_DEVICE_FUNC static constexpr Index rows(void) EIGEN_NOEXCEPT { return Rows_; }
+    EIGEN_DEVICE_FUNC static constexpr Index cols(void) EIGEN_NOEXCEPT { return Cols_; }
+    EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index, Index, Index) {}
+    EIGEN_DEVICE_FUNC constexpr void resize(Index, Index, Index) {}
+    EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
+    EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
+};
+
+// null matrix
+template<typename T, int Rows_, int Cols_, int Options_>
+class DenseStorage<T, 0, Rows_, Cols_, Options_>
+{
+  public:
+    static_assert(Rows_ * Cols_ == 0, "The fixed number of rows times columns must equal the storage size.");
+    EIGEN_DEVICE_FUNC constexpr DenseStorage() {}
+    EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert) {}
+    EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage&) {}
+    EIGEN_DEVICE_FUNC constexpr DenseStorage& operator=(const DenseStorage&) { return *this; }
+    EIGEN_DEVICE_FUNC constexpr DenseStorage(Index,Index,Index) {}
+    EIGEN_DEVICE_FUNC constexpr void swap(DenseStorage& ) {}
+    EIGEN_DEVICE_FUNC static constexpr Index rows(void) EIGEN_NOEXCEPT {return Rows_;}
+    EIGEN_DEVICE_FUNC static constexpr Index cols(void) EIGEN_NOEXCEPT {return Cols_;}
+    EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index,Index,Index) {}
+    EIGEN_DEVICE_FUNC constexpr void resize(Index,Index,Index) {}
+    EIGEN_DEVICE_FUNC constexpr const T *data() const { return 0; }
+    EIGEN_DEVICE_FUNC constexpr T *data() { return 0; }
+};
+
+// more specializations for null matrices; these are necessary to resolve ambiguities
+template<typename T, int Options_>
+class DenseStorage<T, 0, Dynamic, Dynamic, Options_> {
+    Index m_rows;
+    Index m_cols;
+  public:
+    EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0), m_cols(0) {}
+    EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : DenseStorage() {}
+    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_rows(other.m_rows), m_cols(other.m_cols) {}
+    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) {
+      m_rows = other.m_rows;
+      m_cols = other.m_cols;
+      return *this;
+    }
+    EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {
+      eigen_assert(m_rows * m_cols == 0 && "The number of rows times columns must equal the storage size.");
+    }
+    EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
+      numext::swap(m_rows,other.m_rows);
+      numext::swap(m_cols,other.m_cols);
+    }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT {return m_rows;}
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT {return m_cols;}
+    EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index cols) {
+      m_rows = rows;
+      m_cols = cols;
+      eigen_assert(m_rows * m_cols == 0 && "The number of rows times columns must equal the storage size.");
+    }
+    EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index cols) {
+      m_rows = rows;
+      m_cols = cols;
+      eigen_assert(m_rows * m_cols == 0 && "The number of rows times columns must equal the storage size.");
+    }
+    EIGEN_DEVICE_FUNC const T *data() const { return nullptr; }
+    EIGEN_DEVICE_FUNC T *data() { return nullptr; }
+};
+
+template<typename T, int Rows_, int Options_>
+class DenseStorage<T, 0, Rows_, Dynamic, Options_> {
+    Index m_cols;
+  public:
+    EIGEN_DEVICE_FUNC DenseStorage() : m_cols(0) {}
+    EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : DenseStorage() {}
+    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_cols(other.m_cols) {}
+    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) {
+      m_cols = other.m_cols;
+      return *this;
+    }
+    EIGEN_DEVICE_FUNC DenseStorage(Index, Index, Index cols) : m_cols(cols) {
+      eigen_assert(Rows_ * m_cols == 0 && "The number of rows times columns must equal the storage size.");
+    }
+    EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
+      numext::swap(m_cols, other.m_cols);
+    }
+    EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index rows(void) EIGEN_NOEXCEPT {return Rows_;}
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols(void) const EIGEN_NOEXCEPT {return m_cols;}
+    EIGEN_DEVICE_FUNC void conservativeResize(Index, Index, Index cols) {
+      m_cols = cols;
+      eigen_assert(Rows_ * m_cols == 0 && "The number of rows times columns must equal the storage size.");
+    }
+    EIGEN_DEVICE_FUNC void resize(Index, Index, Index cols) {
+      m_cols = cols;
+      eigen_assert(Rows_ * m_cols == 0 && "The number of rows times columns must equal the storage size.");
+    }
+    EIGEN_DEVICE_FUNC const T *data() const { return nullptr; }
+    EIGEN_DEVICE_FUNC T *data() { return nullptr; }
+};
+
+template<typename T, int Cols_, int Options_>
+class DenseStorage<T, 0, Dynamic, Cols_, Options_> {
+    Index m_rows;
+  public:
+    EIGEN_DEVICE_FUNC DenseStorage() : m_rows(0) {}
+    EIGEN_DEVICE_FUNC explicit DenseStorage(internal::constructor_without_unaligned_array_assert) : DenseStorage() {}
+    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other) : m_rows(other.m_rows) {}
+    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other) {
+      m_rows = other.m_rows;
+      return *this;
+    }
+    EIGEN_DEVICE_FUNC DenseStorage(Index, Index rows, Index) : m_rows(rows) {
+      eigen_assert(m_rows * Cols_ == 0 && "The number of rows times columns must equal the storage size.");
+    }
+    EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
+      numext::swap(m_rows, other.m_rows);
+    }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows(void) const EIGEN_NOEXCEPT {return m_rows;}
+    EIGEN_DEVICE_FUNC static EIGEN_CONSTEXPR Index cols(void) EIGEN_NOEXCEPT {return Cols_;}
+    EIGEN_DEVICE_FUNC void conservativeResize(Index, Index rows, Index) {
+      m_rows = rows;
+      eigen_assert(m_rows * Cols_ == 0 && "The number of rows times columns must equal the storage size.");
+    }
+    EIGEN_DEVICE_FUNC void resize(Index, Index rows, Index) {
+      m_rows = rows;
+      eigen_assert(m_rows * Cols_ == 0 && "The number of rows times columns must equal the storage size.");
+    }
+    EIGEN_DEVICE_FUNC const T *data() const { return nullptr; }
+    EIGEN_DEVICE_FUNC T *data() { return nullptr; }
+};
+
+// dynamic-size matrix with fixed-size storage
+template<typename T, int Size, int Options_>
+class DenseStorage<T, Size, Dynamic, Dynamic, Options_>
+{
+    internal::plain_array<T,Size,Options_> m_data;
+    Index m_rows;
+    Index m_cols;
+  public:
+   EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_data(), m_rows(0), m_cols(0) {}
+   EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert)
+       : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0), m_cols(0) {}
+   EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage& other)
+       : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(other.m_rows), m_cols(other.m_cols) {
+    internal::plain_array_helper::copy(other.m_data, m_rows * m_cols, m_data);
+   }
+    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
+    {
+      if (this != &other)
+      {
+        m_rows = other.m_rows;
+        m_cols = other.m_cols;
+        internal::plain_array_helper::copy(other.m_data, m_rows * m_cols, m_data);
+      }
+      return *this;
+    }
+    EIGEN_DEVICE_FUNC constexpr DenseStorage(Index, Index rows, Index cols) : m_rows(rows), m_cols(cols) {}
+    EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
+    {
+      internal::plain_array_helper::swap(m_data, m_rows * m_cols, other.m_data, other.m_rows * other.m_cols);
+      numext::swap(m_rows,other.m_rows);
+      numext::swap(m_cols,other.m_cols);
+    }
+    EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_rows; }
+    EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_cols; }
+    EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index, Index rows, Index cols) {
+      m_rows = rows;
+      m_cols = cols;
+    }
+    EIGEN_DEVICE_FUNC constexpr void resize(Index, Index rows, Index cols) {
+      m_rows = rows;
+      m_cols = cols;
+    }
+    EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
+    EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
+};
+
+// dynamic-size matrix with fixed-size storage and fixed width
+template<typename T, int Size, int Cols_, int Options_>
+class DenseStorage<T, Size, Dynamic, Cols_, Options_>
+{
+    internal::plain_array<T,Size,Options_> m_data;
+    Index m_rows;
+  public:
+   EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_rows(0) {}
+   EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert)
+       : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(0) {}
+   EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage& other)
+       : m_data(internal::constructor_without_unaligned_array_assert()), m_rows(other.m_rows) {
+      internal::plain_array_helper::copy(other.m_data, m_rows * Cols_, m_data);
+   }
+
+    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
+    {
+      if (this != &other)
+      {
+        m_rows = other.m_rows;
+        internal::plain_array_helper::copy(other.m_data, m_rows * Cols_, m_data);
+      }
+      return *this;
+    }
+    EIGEN_DEVICE_FUNC constexpr DenseStorage(Index, Index rows, Index) : m_rows(rows) {}
+    EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
+    { 
+      internal::plain_array_helper::swap(m_data, m_rows * Cols_, other.m_data, other.m_rows * Cols_);
+      numext::swap(m_rows, other.m_rows);
+    }
+    EIGEN_DEVICE_FUNC constexpr Index rows(void) const EIGEN_NOEXCEPT { return m_rows; }
+    EIGEN_DEVICE_FUNC constexpr Index cols(void) const EIGEN_NOEXCEPT { return Cols_; }
+    EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index, Index rows, Index) { m_rows = rows; }
+    EIGEN_DEVICE_FUNC constexpr void resize(Index, Index rows, Index) { m_rows = rows; }
+    EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
+    EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
+};
+
+// dynamic-size matrix with fixed-size storage and fixed height
+template<typename T, int Size, int Rows_, int Options_>
+class DenseStorage<T, Size, Rows_, Dynamic, Options_>
+{
+    internal::plain_array<T,Size,Options_> m_data;
+    Index m_cols;
+  public:
+   EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_cols(0) {}
+   EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert)
+       : m_data(internal::constructor_without_unaligned_array_assert()), m_cols(0) {}
+   EIGEN_DEVICE_FUNC constexpr DenseStorage(const DenseStorage& other)
+       : m_data(internal::constructor_without_unaligned_array_assert()), m_cols(other.m_cols) {
+      internal::plain_array_helper::copy(other.m_data, Rows_ * m_cols, m_data);
+   }
+    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
+    {
+      if (this != &other)
+      {
+        m_cols = other.m_cols;
+        internal::plain_array_helper::copy(other.m_data, Rows_ * m_cols, m_data);
+      }
+      return *this;
+    }
+    EIGEN_DEVICE_FUNC DenseStorage(Index, Index, Index cols) : m_cols(cols) {}
+    EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
+      internal::plain_array_helper::swap(m_data, Rows_ * m_cols, other.m_data, Rows_ * other.m_cols);
+      numext::swap(m_cols, other.m_cols);
+    }
+    EIGEN_DEVICE_FUNC constexpr Index rows(void) const EIGEN_NOEXCEPT { return Rows_; }
+    EIGEN_DEVICE_FUNC constexpr Index cols(void) const EIGEN_NOEXCEPT { return m_cols; }
+    EIGEN_DEVICE_FUNC constexpr void conservativeResize(Index, Index, Index cols) { m_cols = cols; }
+    EIGEN_DEVICE_FUNC constexpr void resize(Index, Index, Index cols) { m_cols = cols; }
+    EIGEN_DEVICE_FUNC constexpr const T* data() const { return m_data.array; }
+    EIGEN_DEVICE_FUNC constexpr T* data() { return m_data.array; }
+};
+
+// purely dynamic matrix.
+template<typename T, int Options_>
+class DenseStorage<T, Dynamic, Dynamic, Dynamic, Options_>
+{
+    T *m_data;
+    Index m_rows;
+    Index m_cols;
+  public:
+   EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_data(0), m_rows(0), m_cols(0) {}
+   EIGEN_DEVICE_FUNC explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert)
+       : m_data(0), m_rows(0), m_cols(0) {}
+   EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols)
+       : m_data(internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(size)),
+         m_rows(rows),
+         m_cols(cols) {
+      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+      eigen_internal_assert(size==rows*cols && rows>=0 && cols >=0);
+   }
+    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
+      : m_data(internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(other.m_rows*other.m_cols))
+      , m_rows(other.m_rows)
+      , m_cols(other.m_cols)
+    {
+      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*m_cols)
+      internal::smart_copy(other.m_data, other.m_data+other.m_rows*other.m_cols, m_data);
+    }
+    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
+    {
+      if (this != &other)
+      {
+        DenseStorage tmp(other);
+        this->swap(tmp);
+      }
+      return *this;
+    }
+    EIGEN_DEVICE_FUNC
+    DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
+      : m_data(std::move(other.m_data))
+      , m_rows(std::move(other.m_rows))
+      , m_cols(std::move(other.m_cols))
+    {
+      other.m_data = nullptr;
+      other.m_rows = 0;
+      other.m_cols = 0;
+    }
+    EIGEN_DEVICE_FUNC
+    DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
+    {
+      numext::swap(m_data, other.m_data);
+      numext::swap(m_rows, other.m_rows);
+      numext::swap(m_cols, other.m_cols);
+      return *this;
+    }
+    EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, m_rows*m_cols); }
+    EIGEN_DEVICE_FUNC void swap(DenseStorage& other)
+    {
+      numext::swap(m_data,other.m_data);
+      numext::swap(m_rows,other.m_rows);
+      numext::swap(m_cols,other.m_cols);
+    }
+    EIGEN_DEVICE_FUNC Index rows(void) const EIGEN_NOEXCEPT {return m_rows;}
+    EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT {return m_cols;}
+    void conservativeResize(Index size, Index rows, Index cols)
+    {
+      m_data = internal::conditional_aligned_realloc_new_auto<T,(Options_&DontAlign)==0>(m_data, size, m_rows*m_cols);
+      m_rows = rows;
+      m_cols = cols;
+    }
+    EIGEN_DEVICE_FUNC void resize(Index size, Index rows, Index cols)
+    {
+      if(size != m_rows*m_cols)
+      {
+        internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, m_rows*m_cols);
+        if (size>0) // >0 and not simply !=0 to let the compiler knows that size cannot be negative
+          m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(size);
+        else
+          m_data = 0;
+        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+      }
+      m_rows = rows;
+      m_cols = cols;
+    }
+    EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
+    EIGEN_DEVICE_FUNC T *data() { return m_data; }
+};
+
+// matrix with dynamic width and fixed height (so that matrix has dynamic size).
+template<typename T, int Rows_, int Options_>
+class DenseStorage<T, Dynamic, Rows_, Dynamic, Options_> {
+    T *m_data;
+    Index m_cols;
+  public:
+   EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_data(0), m_cols(0) {}
+   explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_cols(0) {}
+   EIGEN_DEVICE_FUNC DenseStorage(Index size, Index rows, Index cols)
+       : m_data(internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(size)), m_cols(cols) {
+      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+      eigen_internal_assert(size==rows*cols && rows==Rows_ && cols >=0);
+      EIGEN_UNUSED_VARIABLE(rows);
+   }
+    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
+      : m_data(internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(Rows_*other.m_cols))
+      , m_cols(other.m_cols)
+    {
+      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_cols*Rows_)
+      internal::smart_copy(other.m_data, other.m_data+Rows_*m_cols, m_data);
+    }
+    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
+    {
+      if (this != &other)
+      {
+        DenseStorage tmp(other);
+        this->swap(tmp);
+      }
+      return *this;
+    }
+    EIGEN_DEVICE_FUNC
+    DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
+      : m_data(std::move(other.m_data))
+      , m_cols(std::move(other.m_cols))
+    {
+      other.m_data = nullptr;
+      other.m_cols = 0;
+    }
+    EIGEN_DEVICE_FUNC
+    DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
+    {
+      numext::swap(m_data, other.m_data);
+      numext::swap(m_cols, other.m_cols);
+      return *this;
+    }
+    EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, Rows_*m_cols); }
+    EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
+      numext::swap(m_data,other.m_data);
+      numext::swap(m_cols,other.m_cols);
+    }
+    EIGEN_DEVICE_FUNC static constexpr Index rows(void) EIGEN_NOEXCEPT { return Rows_; }
+    EIGEN_DEVICE_FUNC Index cols(void) const EIGEN_NOEXCEPT {return m_cols;}
+    EIGEN_DEVICE_FUNC void conservativeResize(Index size, Index, Index cols)
+    {
+      m_data = internal::conditional_aligned_realloc_new_auto<T,(Options_&DontAlign)==0>(m_data, size, Rows_*m_cols);
+      m_cols = cols;
+    }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index, Index cols)
+    {
+      if(size != Rows_*m_cols)
+      {
+        internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, Rows_*m_cols);
+        if (size>0) // >0 and not simply !=0 to let the compiler knows that size cannot be negative
+          m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(size);
+        else
+          m_data = 0;
+        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+      }
+      m_cols = cols;
+    }
+    EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
+    EIGEN_DEVICE_FUNC T *data() { return m_data; }
+};
+
+// matrix with dynamic height and fixed width (so that matrix has dynamic size).
+template<typename T, int Cols_, int Options_>
+class DenseStorage<T, Dynamic, Dynamic, Cols_, Options_>
+{
+    T *m_data;
+    Index m_rows;
+  public:
+   EIGEN_DEVICE_FUNC constexpr DenseStorage() : m_data(0), m_rows(0) {}
+   explicit constexpr DenseStorage(internal::constructor_without_unaligned_array_assert) : m_data(0), m_rows(0) {}
+   EIGEN_DEVICE_FUNC constexpr DenseStorage(Index size, Index rows, Index cols)
+       : m_data(internal::conditional_aligned_new_auto<T, (Options_ & DontAlign) == 0>(size)), m_rows(rows) {
+      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+      eigen_internal_assert(size==rows*cols && rows>=0 && cols == Cols_);
+      EIGEN_UNUSED_VARIABLE(cols);
+   }
+    EIGEN_DEVICE_FUNC DenseStorage(const DenseStorage& other)
+      : m_data(internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(other.m_rows*Cols_))
+      , m_rows(other.m_rows)
+    {
+      EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN(Index size = m_rows*Cols_)
+      internal::smart_copy(other.m_data, other.m_data+other.m_rows*Cols_, m_data);
+    }
+    EIGEN_DEVICE_FUNC DenseStorage& operator=(const DenseStorage& other)
+    {
+      if (this != &other)
+      {
+        DenseStorage tmp(other);
+        this->swap(tmp);
+      }
+      return *this;
+    }
+    EIGEN_DEVICE_FUNC
+    DenseStorage(DenseStorage&& other) EIGEN_NOEXCEPT
+      : m_data(std::move(other.m_data))
+      , m_rows(std::move(other.m_rows))
+    {
+      other.m_data = nullptr;
+      other.m_rows = 0;
+    }
+    EIGEN_DEVICE_FUNC
+    DenseStorage& operator=(DenseStorage&& other) EIGEN_NOEXCEPT
+    {
+      numext::swap(m_data, other.m_data);
+      numext::swap(m_rows, other.m_rows);
+      return *this;
+    }
+    EIGEN_DEVICE_FUNC ~DenseStorage() { internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, Cols_*m_rows); }
+    EIGEN_DEVICE_FUNC void swap(DenseStorage& other) {
+      numext::swap(m_data,other.m_data);
+      numext::swap(m_rows,other.m_rows);
+    }
+    EIGEN_DEVICE_FUNC Index rows(void) const EIGEN_NOEXCEPT {return m_rows;}
+    EIGEN_DEVICE_FUNC static constexpr Index cols(void) { return Cols_; }
+    void conservativeResize(Index size, Index rows, Index)
+    {
+      m_data = internal::conditional_aligned_realloc_new_auto<T,(Options_&DontAlign)==0>(m_data, size, m_rows*Cols_);
+      m_rows = rows;
+    }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void resize(Index size, Index rows, Index)
+    {
+      if(size != m_rows*Cols_)
+      {
+        internal::conditional_aligned_delete_auto<T,(Options_&DontAlign)==0>(m_data, Cols_*m_rows);
+        if (size>0) // >0 and not simply !=0 to let the compiler knows that size cannot be negative
+          m_data = internal::conditional_aligned_new_auto<T,(Options_&DontAlign)==0>(size);
+        else
+          m_data = 0;
+        EIGEN_INTERNAL_DENSE_STORAGE_CTOR_PLUGIN({})
+      }
+      m_rows = rows;
+    }
+    EIGEN_DEVICE_FUNC const T *data() const { return m_data; }
+    EIGEN_DEVICE_FUNC T *data() { return m_data; }
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATRIX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Diagonal.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Diagonal.h
new file mode 100644
index 0000000000000000000000000000000000000000..4af17dd39230bd7b90f95e7baaaad0c5999a7140
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Diagonal.h
@@ -0,0 +1,261 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DIAGONAL_H
+#define EIGEN_DIAGONAL_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class Diagonal
+  * \ingroup Core_Module
+  *
+  * \brief Expression of a diagonal/subdiagonal/superdiagonal in a matrix
+  *
+  * \tparam MatrixType the type of the object in which we are taking a sub/main/super diagonal
+  * \tparam DiagIndex the index of the sub/super diagonal. The default is 0 and it means the main diagonal.
+  *              A positive value means a superdiagonal, a negative value means a subdiagonal.
+  *              You can also use DynamicIndex so the index can be set at runtime.
+  *
+  * The matrix is not required to be square.
+  *
+  * This class represents an expression of the main diagonal, or any sub/super diagonal
+  * of a square matrix. It is the return type of MatrixBase::diagonal() and MatrixBase::diagonal(Index) and most of the
+  * time this is the only way it is used.
+  *
+  * \sa MatrixBase::diagonal(), MatrixBase::diagonal(Index)
+  */
+
+namespace internal {
+template<typename MatrixType, int DiagIndex>
+struct traits<Diagonal<MatrixType,DiagIndex> >
+ : traits<MatrixType>
+{
+  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
+  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_;
+  typedef typename MatrixType::StorageKind StorageKind;
+  enum {
+    RowsAtCompileTime = (int(DiagIndex) == DynamicIndex || int(MatrixType::SizeAtCompileTime) == Dynamic) ? Dynamic
+                      : (plain_enum_min(MatrixType::RowsAtCompileTime - plain_enum_max(-DiagIndex, 0),
+                                        MatrixType::ColsAtCompileTime - plain_enum_max( DiagIndex, 0))),
+    ColsAtCompileTime = 1,
+    MaxRowsAtCompileTime = int(MatrixType::MaxSizeAtCompileTime) == Dynamic ? Dynamic
+                         : DiagIndex == DynamicIndex ? min_size_prefer_fixed(MatrixType::MaxRowsAtCompileTime,
+                                                                             MatrixType::MaxColsAtCompileTime)
+                         : (plain_enum_min(MatrixType::MaxRowsAtCompileTime - plain_enum_max(-DiagIndex, 0),
+                                           MatrixType::MaxColsAtCompileTime - plain_enum_max( DiagIndex, 0))),
+    MaxColsAtCompileTime = 1,
+    MaskLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
+    Flags = (unsigned int)MatrixTypeNested_::Flags & (RowMajorBit | MaskLvalueBit | DirectAccessBit) & ~RowMajorBit, // FIXME DirectAccessBit should not be handled by expressions
+    MatrixTypeOuterStride = outer_stride_at_compile_time<MatrixType>::ret,
+    InnerStrideAtCompileTime = MatrixTypeOuterStride == Dynamic ? Dynamic : MatrixTypeOuterStride+1,
+    OuterStrideAtCompileTime = 0
+  };
+};
+}
+
+template<typename MatrixType, int DiagIndex_> class Diagonal
+   : public internal::dense_xpr_base< Diagonal<MatrixType,DiagIndex_> >::type
+{
+  public:
+
+    enum { DiagIndex = DiagIndex_ };
+    typedef typename internal::dense_xpr_base<Diagonal>::type Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Diagonal)
+
+    EIGEN_DEVICE_FUNC
+    explicit inline Diagonal(MatrixType& matrix, Index a_index = DiagIndex) : m_matrix(matrix), m_index(a_index)
+    {
+      eigen_assert( a_index <= m_matrix.cols() && -a_index <= m_matrix.rows() );
+    }
+
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Diagonal)
+
+    EIGEN_DEVICE_FUNC
+    inline Index rows() const
+    {
+      return m_index.value()<0 ? numext::mini<Index>(m_matrix.cols(),m_matrix.rows()+m_index.value())
+                               : numext::mini<Index>(m_matrix.rows(),m_matrix.cols()-m_index.value());
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index cols() const EIGEN_NOEXCEPT { return 1; }
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index innerStride() const EIGEN_NOEXCEPT {
+      return m_matrix.outerStride() + 1;
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index outerStride() const EIGEN_NOEXCEPT { return 0; }
+
+    typedef std::conditional_t<
+              internal::is_lvalue<MatrixType>::value,
+              Scalar,
+              const Scalar
+            > ScalarWithConstIfNotLvalue;
+
+    EIGEN_DEVICE_FUNC
+    inline ScalarWithConstIfNotLvalue* data() { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
+    EIGEN_DEVICE_FUNC
+    inline const Scalar* data() const { return &(m_matrix.coeffRef(rowOffset(), colOffset())); }
+
+    EIGEN_DEVICE_FUNC
+    inline Scalar& coeffRef(Index row, Index)
+    {
+      EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
+      return m_matrix.coeffRef(row+rowOffset(), row+colOffset());
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline const Scalar& coeffRef(Index row, Index) const
+    {
+      return m_matrix.coeffRef(row+rowOffset(), row+colOffset());
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline CoeffReturnType coeff(Index row, Index) const
+    {
+      return m_matrix.coeff(row+rowOffset(), row+colOffset());
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline Scalar& coeffRef(Index idx)
+    {
+      EIGEN_STATIC_ASSERT_LVALUE(MatrixType)
+      return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline const Scalar& coeffRef(Index idx) const
+    {
+      return m_matrix.coeffRef(idx+rowOffset(), idx+colOffset());
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline CoeffReturnType coeff(Index idx) const
+    {
+      return m_matrix.coeff(idx+rowOffset(), idx+colOffset());
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline const internal::remove_all_t<typename MatrixType::Nested>&
+    nestedExpression() const
+    {
+      return m_matrix;
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline Index index() const
+    {
+      return m_index.value();
+    }
+
+  protected:
+    typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
+    const internal::variable_if_dynamicindex<Index, DiagIndex> m_index;
+
+  private:
+    // some compilers may fail to optimize std::max etc in case of compile-time constants...
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index absDiagIndex() const EIGEN_NOEXCEPT { return m_index.value()>0 ? m_index.value() : -m_index.value(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index rowOffset() const EIGEN_NOEXCEPT { return m_index.value()>0 ? 0 : -m_index.value(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index colOffset() const EIGEN_NOEXCEPT { return m_index.value()>0 ? m_index.value() : 0; }
+    // trigger a compile-time error if someone try to call packet
+    template<int LoadMode> typename MatrixType::PacketReturnType packet(Index) const;
+    template<int LoadMode> typename MatrixType::PacketReturnType packet(Index,Index) const;
+};
+
+/** \returns an expression of the main diagonal of the matrix \c *this
+  *
+  * \c *this is not required to be square.
+  *
+  * Example: \include MatrixBase_diagonal.cpp
+  * Output: \verbinclude MatrixBase_diagonal.out
+  *
+  * \sa class Diagonal */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::DiagonalReturnType
+MatrixBase<Derived>::diagonal()
+{
+  return DiagonalReturnType(derived());
+}
+
+/** This is the const version of diagonal(). */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline
+const typename MatrixBase<Derived>::ConstDiagonalReturnType
+MatrixBase<Derived>::diagonal() const
+{
+  return ConstDiagonalReturnType(derived());
+}
+
+/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
+  *
+  * \c *this is not required to be square.
+  *
+  * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
+  * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
+  *
+  * Example: \include MatrixBase_diagonal_int.cpp
+  * Output: \verbinclude MatrixBase_diagonal_int.out
+  *
+  * \sa MatrixBase::diagonal(), class Diagonal */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline Diagonal<Derived, DynamicIndex>
+MatrixBase<Derived>::diagonal(Index index)
+{
+  return Diagonal<Derived, DynamicIndex>(derived(), index);
+}
+
+/** This is the const version of diagonal(Index). */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline const Diagonal<const Derived, DynamicIndex>
+MatrixBase<Derived>::diagonal(Index index) const
+{
+  return Diagonal<const Derived, DynamicIndex>(derived(), index);
+}
+
+/** \returns an expression of the \a DiagIndex-th sub or super diagonal of the matrix \c *this
+  *
+  * \c *this is not required to be square.
+  *
+  * The template parameter \a DiagIndex represent a super diagonal if \a DiagIndex > 0
+  * and a sub diagonal otherwise. \a DiagIndex == 0 is equivalent to the main diagonal.
+  *
+  * Example: \include MatrixBase_diagonal_template_int.cpp
+  * Output: \verbinclude MatrixBase_diagonal_template_int.out
+  *
+  * \sa MatrixBase::diagonal(), class Diagonal */
+template<typename Derived>
+template<int Index_>
+EIGEN_DEVICE_FUNC
+inline Diagonal<Derived, Index_>
+MatrixBase<Derived>::diagonal()
+{
+  return Diagonal<Derived, Index_>(derived());
+}
+
+/** This is the const version of diagonal<int>(). */
+template<typename Derived>
+template<int Index_>
+EIGEN_DEVICE_FUNC
+inline const Diagonal<const Derived, Index_>
+MatrixBase<Derived>::diagonal() const
+{
+  return  Diagonal<const Derived, Index_>(derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_DIAGONAL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/DiagonalMatrix.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/DiagonalMatrix.h
new file mode 100644
index 0000000000000000000000000000000000000000..3c389b972d9b0cba2c43ffad6614d8efb0794a77
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/DiagonalMatrix.h
@@ -0,0 +1,450 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DIAGONALMATRIX_H
+#define EIGEN_DIAGONALMATRIX_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class DiagonalBase
+ * \ingroup Core_Module
+ *
+ * \brief Base class for diagonal matrices and expressions
+ *
+ * This is the base class that is inherited by diagonal matrix and related expression
+ * types, which internally use a vector for storing the diagonal entries. Diagonal
+ * types always represent square matrices.
+ *
+ * \tparam Derived is the derived type, a DiagonalMatrix or DiagonalWrapper.
+ *
+ * \sa class DiagonalMatrix, class DiagonalWrapper
+ */
+template<typename Derived>
+class DiagonalBase : public EigenBase<Derived>
+{
+  public:
+    typedef typename internal::traits<Derived>::DiagonalVectorType DiagonalVectorType;
+    typedef typename DiagonalVectorType::Scalar Scalar;
+    typedef typename DiagonalVectorType::RealScalar RealScalar;
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+
+    enum {
+      RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
+      ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
+      MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
+      MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
+      IsVectorAtCompileTime = 0,
+      Flags = NoPreferredStorageOrderBit
+    };
+
+    typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime> DenseMatrixType;
+    typedef DenseMatrixType DenseType;
+    typedef DiagonalMatrix<Scalar,DiagonalVectorType::SizeAtCompileTime,DiagonalVectorType::MaxSizeAtCompileTime> PlainObject;
+
+    /** \returns a reference to the derived object. */
+    EIGEN_DEVICE_FUNC
+    inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
+    /** \returns a const reference to the derived object. */
+    EIGEN_DEVICE_FUNC
+    inline Derived& derived() { return *static_cast<Derived*>(this); }
+
+    /**
+     * Constructs a dense matrix from \c *this. Note, this directly returns a dense matrix type,
+     * not an expression.
+     * \returns A dense matrix, with its diagonal entries set from the the derived object. */
+    EIGEN_DEVICE_FUNC
+    DenseMatrixType toDenseMatrix() const { return derived(); }
+
+    /** \returns a reference to the derived object's vector of diagonal coefficients. */
+    EIGEN_DEVICE_FUNC
+    inline const DiagonalVectorType& diagonal() const { return derived().diagonal(); }
+    /** \returns a const reference to the derived object's vector of diagonal coefficients. */
+    EIGEN_DEVICE_FUNC
+    inline DiagonalVectorType& diagonal() { return derived().diagonal(); }
+
+    /** \returns the value of the coefficient as if \c *this was a dense matrix. */
+    EIGEN_DEVICE_FUNC
+    inline Scalar coeff(Index row, Index col) const {
+      eigen_assert(row >= 0 && col >= 0 && row < rows() && col <= cols());
+      return row == col ? diagonal().coeff(row) : Scalar(0);
+    }
+
+    /** \returns the number of rows. */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR 
+    inline Index rows() const { return diagonal().size(); }
+    /** \returns the number of columns. */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR 
+    inline Index cols() const { return diagonal().size(); }
+
+    /** \returns the diagonal matrix product of \c *this by the dense matrix, \a matrix */
+    template<typename MatrixDerived>
+    EIGEN_DEVICE_FUNC
+    const Product<Derived,MatrixDerived,LazyProduct>
+    operator*(const MatrixBase<MatrixDerived> &matrix) const
+    {
+      return Product<Derived, MatrixDerived, LazyProduct>(derived(),matrix.derived());
+    }
+
+    template <typename OtherDerived>
+    using DiagonalProductReturnType = DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
+        DiagonalVectorType, typename OtherDerived::DiagonalVectorType, product)>;
+
+    /** \returns the diagonal matrix product of \c *this by the diagonal matrix \a other */
+    template <typename OtherDerived>
+    EIGEN_DEVICE_FUNC const DiagonalProductReturnType<OtherDerived> operator*(
+        const DiagonalBase<OtherDerived>& other) const {
+      return diagonal().cwiseProduct(other.diagonal()).asDiagonal();
+    }
+
+    using DiagonalInverseReturnType =
+        DiagonalWrapper<const CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const DiagonalVectorType>>;
+
+    /** \returns the inverse \c *this. Computed as the coefficient-wise inverse of the diagonal. */
+    EIGEN_DEVICE_FUNC
+    inline const DiagonalInverseReturnType inverse() const { return diagonal().cwiseInverse().asDiagonal(); }
+
+    using DiagonalScaleReturnType =
+        DiagonalWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(DiagonalVectorType, Scalar, product)>;
+
+    /** \returns the product of \c *this by the scalar \a scalar */
+    EIGEN_DEVICE_FUNC
+    inline const DiagonalScaleReturnType operator*(const Scalar& scalar) const {
+      return (diagonal() * scalar).asDiagonal();
+    }
+
+    using ScaleDiagonalReturnType =
+        DiagonalWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar, DiagonalVectorType, product)>;
+
+    /** \returns the product of a scalar and the diagonal matrix \a other */
+    EIGEN_DEVICE_FUNC
+    friend inline const ScaleDiagonalReturnType operator*(const Scalar& scalar, const DiagonalBase& other) {
+      return (scalar * other.diagonal()).asDiagonal();
+    }
+
+    template <typename OtherDerived>
+    using DiagonalSumReturnType = DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
+        DiagonalVectorType, typename OtherDerived::DiagonalVectorType, sum)>;
+
+    /** \returns the sum of \c *this and the diagonal matrix \a other */
+    template <typename OtherDerived>
+    EIGEN_DEVICE_FUNC inline const DiagonalSumReturnType<OtherDerived> operator+(
+        const DiagonalBase<OtherDerived>& other) const {
+      return (diagonal() + other.diagonal()).asDiagonal();
+    }
+
+    template <typename OtherDerived>
+    using DiagonalDifferenceReturnType = DiagonalWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
+        DiagonalVectorType, typename OtherDerived::DiagonalVectorType, difference)>;
+
+    /** \returns the difference of \c *this and the diagonal matrix \a other */
+    template <typename OtherDerived>
+    EIGEN_DEVICE_FUNC inline const DiagonalDifferenceReturnType<OtherDerived> operator-(
+        const DiagonalBase<OtherDerived>& other) const {
+      return (diagonal() - other.diagonal()).asDiagonal();
+    }
+};
+
+/** \class DiagonalMatrix
+ * \ingroup Core_Module
+ *
+ * \brief Represents a diagonal matrix with its storage
+ *
+ * \tparam Scalar_ the type of coefficients
+ * \tparam SizeAtCompileTime the dimension of the matrix, or Dynamic
+ * \tparam MaxSizeAtCompileTime the dimension of the matrix, or Dynamic. This parameter is optional and defaults
+ *        to SizeAtCompileTime. Most of the time, you do not need to specify it.
+ *
+ * \sa class DiagonalBase, class DiagonalWrapper
+ */
+
+namespace internal {
+template<typename Scalar_, int SizeAtCompileTime, int MaxSizeAtCompileTime>
+struct traits<DiagonalMatrix<Scalar_,SizeAtCompileTime,MaxSizeAtCompileTime> >
+ : traits<Matrix<Scalar_,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
+{
+  typedef Matrix<Scalar_,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1> DiagonalVectorType;
+  typedef DiagonalShape StorageKind;
+  enum {
+    Flags = LvalueBit | NoPreferredStorageOrderBit | NestByRefBit
+  };
+};
+}
+template<typename Scalar_, int SizeAtCompileTime, int MaxSizeAtCompileTime>
+class DiagonalMatrix
+  : public DiagonalBase<DiagonalMatrix<Scalar_,SizeAtCompileTime,MaxSizeAtCompileTime> >
+{
+  public:
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    typedef typename internal::traits<DiagonalMatrix>::DiagonalVectorType DiagonalVectorType;
+    typedef const DiagonalMatrix& Nested;
+    typedef Scalar_ Scalar;
+    typedef typename internal::traits<DiagonalMatrix>::StorageKind StorageKind;
+    typedef typename internal::traits<DiagonalMatrix>::StorageIndex StorageIndex;
+    #endif
+
+  protected:
+
+    DiagonalVectorType m_diagonal;
+
+  public:
+
+    /** const version of diagonal(). */
+    EIGEN_DEVICE_FUNC
+    inline const DiagonalVectorType& diagonal() const { return m_diagonal; }
+    /** \returns a reference to the stored vector of diagonal coefficients. */
+    EIGEN_DEVICE_FUNC
+    inline DiagonalVectorType& diagonal() { return m_diagonal; }
+
+    /** Default constructor without initialization */
+    EIGEN_DEVICE_FUNC
+    inline DiagonalMatrix() {}
+
+    /** Constructs a diagonal matrix with given dimension  */
+    EIGEN_DEVICE_FUNC
+    explicit inline DiagonalMatrix(Index dim) : m_diagonal(dim) {}
+
+    /** 2D constructor. */
+    EIGEN_DEVICE_FUNC
+    inline DiagonalMatrix(const Scalar& x, const Scalar& y) : m_diagonal(x,y) {}
+
+    /** 3D constructor. */
+    EIGEN_DEVICE_FUNC
+    inline DiagonalMatrix(const Scalar& x, const Scalar& y, const Scalar& z) : m_diagonal(x,y,z) {}
+
+    /** \brief Construct a diagonal matrix with fixed size from an arbitrary number of coefficients.
+      * 
+      * \warning To construct a diagonal matrix of fixed size, the number of values passed to this 
+      * constructor must match the fixed dimension of \c *this.
+      * 
+      * \sa DiagonalMatrix(const Scalar&, const Scalar&)
+      * \sa DiagonalMatrix(const Scalar&, const Scalar&, const Scalar&)
+      */
+    template <typename... ArgTypes>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    DiagonalMatrix(const Scalar& a0, const Scalar& a1, const Scalar& a2, const ArgTypes&... args)
+      : m_diagonal(a0, a1, a2, args...) {}
+
+    /** \brief Constructs a DiagonalMatrix and initializes it by elements given by an initializer list of initializer
+      * lists \cpp11
+      */
+    EIGEN_DEVICE_FUNC
+    explicit EIGEN_STRONG_INLINE DiagonalMatrix(const std::initializer_list<std::initializer_list<Scalar>>& list)
+      : m_diagonal(list) {}
+
+    /** \brief Constructs a DiagonalMatrix from an r-value diagonal vector type */
+    EIGEN_DEVICE_FUNC
+    explicit inline DiagonalMatrix(DiagonalVectorType&& diag) : m_diagonal(std::move(diag)) {}
+
+    /** Copy constructor. */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    inline DiagonalMatrix(const DiagonalBase<OtherDerived>& other) : m_diagonal(other.diagonal()) {}
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** copy constructor. prevent a default copy constructor from hiding the other templated constructor */
+    inline DiagonalMatrix(const DiagonalMatrix& other) : m_diagonal(other.diagonal()) {}
+    #endif
+
+    /** generic constructor from expression of the diagonal coefficients */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    explicit inline DiagonalMatrix(const MatrixBase<OtherDerived>& other) : m_diagonal(other)
+    {}
+
+    /** Copy operator. */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    DiagonalMatrix& operator=(const DiagonalBase<OtherDerived>& other)
+    {
+      m_diagonal = other.diagonal();
+      return *this;
+    }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** This is a special case of the templated operator=. Its purpose is to
+      * prevent a default operator= from hiding the templated operator=.
+      */
+    EIGEN_DEVICE_FUNC
+    DiagonalMatrix& operator=(const DiagonalMatrix& other)
+    {
+      m_diagonal = other.diagonal();
+      return *this;
+    }
+    #endif
+
+    typedef DiagonalWrapper<const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, DiagonalVectorType>>
+        InitializeReturnType;
+
+    /** Initializes a diagonal matrix of size SizeAtCompileTime with coefficients set to zero */
+    EIGEN_DEVICE_FUNC
+    static const InitializeReturnType Zero() { return DiagonalVectorType::Zero().asDiagonal(); }
+    /** Initializes a diagonal matrix of size dim with coefficients set to zero */
+    EIGEN_DEVICE_FUNC
+    static const InitializeReturnType Zero(Index size) { return DiagonalVectorType::Zero(size).asDiagonal(); }
+    /** Initializes a identity matrix of size SizeAtCompileTime */
+    EIGEN_DEVICE_FUNC
+    static const InitializeReturnType Identity() { return DiagonalVectorType::Ones().asDiagonal(); }
+    /** Initializes a identity matrix of size dim */
+    EIGEN_DEVICE_FUNC
+    static const InitializeReturnType Identity(Index size) { return DiagonalVectorType::Ones(size).asDiagonal(); }
+
+    /** Resizes to given size. */
+    EIGEN_DEVICE_FUNC
+    inline void resize(Index size) { m_diagonal.resize(size); }
+    /** Sets all coefficients to zero. */
+    EIGEN_DEVICE_FUNC
+    inline void setZero() { m_diagonal.setZero(); }
+    /** Resizes and sets all coefficients to zero. */
+    EIGEN_DEVICE_FUNC
+    inline void setZero(Index size) { m_diagonal.setZero(size); }
+    /** Sets this matrix to be the identity matrix of the current size. */
+    EIGEN_DEVICE_FUNC
+    inline void setIdentity() { m_diagonal.setOnes(); }
+    /** Sets this matrix to be the identity matrix of the given size. */
+    EIGEN_DEVICE_FUNC
+    inline void setIdentity(Index size) { m_diagonal.setOnes(size); }
+};
+
+/** \class DiagonalWrapper
+  * \ingroup Core_Module
+  *
+  * \brief Expression of a diagonal matrix
+  *
+  * \tparam DiagonalVectorType_ the type of the vector of diagonal coefficients
+  *
+  * This class is an expression of a diagonal matrix, but not storing its own vector of diagonal coefficients,
+  * instead wrapping an existing vector expression. It is the return type of MatrixBase::asDiagonal()
+  * and most of the time this is the only way that it is used.
+  *
+  * \sa class DiagonalMatrix, class DiagonalBase, MatrixBase::asDiagonal()
+  */
+
+namespace internal {
+template<typename DiagonalVectorType_>
+struct traits<DiagonalWrapper<DiagonalVectorType_> >
+{
+  typedef DiagonalVectorType_ DiagonalVectorType;
+  typedef typename DiagonalVectorType::Scalar Scalar;
+  typedef typename DiagonalVectorType::StorageIndex StorageIndex;
+  typedef DiagonalShape StorageKind;
+  typedef typename traits<DiagonalVectorType>::XprKind XprKind;
+  enum {
+    RowsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
+    ColsAtCompileTime = DiagonalVectorType::SizeAtCompileTime,
+    MaxRowsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
+    MaxColsAtCompileTime = DiagonalVectorType::MaxSizeAtCompileTime,
+    Flags =  (traits<DiagonalVectorType>::Flags & LvalueBit) | NoPreferredStorageOrderBit
+  };
+};
+}
+
+template<typename DiagonalVectorType_>
+class DiagonalWrapper
+  : public DiagonalBase<DiagonalWrapper<DiagonalVectorType_> >, internal::no_assignment_operator
+{
+  public:
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    typedef DiagonalVectorType_ DiagonalVectorType;
+    typedef DiagonalWrapper Nested;
+    #endif
+
+    /** Constructor from expression of diagonal coefficients to wrap. */
+    EIGEN_DEVICE_FUNC
+    explicit inline DiagonalWrapper(DiagonalVectorType& a_diagonal) : m_diagonal(a_diagonal) {}
+
+    /** \returns a const reference to the wrapped expression of diagonal coefficients. */
+    EIGEN_DEVICE_FUNC
+    const DiagonalVectorType& diagonal() const { return m_diagonal; }
+
+  protected:
+    typename DiagonalVectorType::Nested m_diagonal;
+};
+
+/** \returns a pseudo-expression of a diagonal matrix with *this as vector of diagonal coefficients
+  *
+  * \only_for_vectors
+  *
+  * Example: \include MatrixBase_asDiagonal.cpp
+  * Output: \verbinclude MatrixBase_asDiagonal.out
+  *
+  * \sa class DiagonalWrapper, class DiagonalMatrix, diagonal(), isDiagonal()
+  **/
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline const DiagonalWrapper<const Derived>
+MatrixBase<Derived>::asDiagonal() const
+{
+  return DiagonalWrapper<const Derived>(derived());
+}
+
+/** \returns true if *this is approximately equal to a diagonal matrix,
+  *          within the precision given by \a prec.
+  *
+  * Example: \include MatrixBase_isDiagonal.cpp
+  * Output: \verbinclude MatrixBase_isDiagonal.out
+  *
+  * \sa asDiagonal()
+  */
+template<typename Derived>
+bool MatrixBase<Derived>::isDiagonal(const RealScalar& prec) const
+{
+  if(cols() != rows()) return false;
+  RealScalar maxAbsOnDiagonal = static_cast<RealScalar>(-1);
+  for(Index j = 0; j < cols(); ++j)
+  {
+    RealScalar absOnDiagonal = numext::abs(coeff(j,j));
+    if(absOnDiagonal > maxAbsOnDiagonal) maxAbsOnDiagonal = absOnDiagonal;
+  }
+  for(Index j = 0; j < cols(); ++j)
+    for(Index i = 0; i < j; ++i)
+    {
+      if(!internal::isMuchSmallerThan(coeff(i, j), maxAbsOnDiagonal, prec)) return false;
+      if(!internal::isMuchSmallerThan(coeff(j, i), maxAbsOnDiagonal, prec)) return false;
+    }
+  return true;
+}
+
+namespace internal {
+
+template<> struct storage_kind_to_shape<DiagonalShape> { typedef DiagonalShape Shape; };
+
+struct Diagonal2Dense {};
+
+template<> struct AssignmentKind<DenseShape,DiagonalShape> { typedef Diagonal2Dense Kind; };
+
+// Diagonal matrix to Dense assignment
+template< typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Dense>
+{
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
+  {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+      dst.resize(dstRows, dstCols);
+    
+    dst.setZero();
+    dst.diagonal() = src.diagonal();
+  }
+  
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
+  { dst.diagonal() += src.diagonal(); }
+  
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
+  { dst.diagonal() -= src.diagonal(); }
+};
+
+} // namespace internal
+
+}  // end namespace Eigen
+
+#endif // EIGEN_DIAGONALMATRIX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/DiagonalProduct.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/DiagonalProduct.h
new file mode 100644
index 0000000000000000000000000000000000000000..3cd34ba3f8ddb176652e628fff505b0dd6614e2a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/DiagonalProduct.h
@@ -0,0 +1,30 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DIAGONALPRODUCT_H
+#define EIGEN_DIAGONALPRODUCT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \returns the diagonal matrix product of \c *this by the diagonal matrix \a diagonal.
+  */
+template<typename Derived>
+template<typename DiagonalDerived>
+EIGEN_DEVICE_FUNC inline const Product<Derived, DiagonalDerived, LazyProduct>
+MatrixBase<Derived>::operator*(const DiagonalBase<DiagonalDerived> &a_diagonal) const
+{
+  return Product<Derived, DiagonalDerived, LazyProduct>(derived(),a_diagonal.derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_DIAGONALPRODUCT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Dot.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Dot.h
new file mode 100644
index 0000000000000000000000000000000000000000..139e2ca98b1f57726a39940e670aa8c2d7be4163
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Dot.h
@@ -0,0 +1,316 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008, 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DOT_H
+#define EIGEN_DOT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+// helper function for dot(). The problem is that if we put that in the body of dot(), then upon calling dot
+// with mismatched types, the compiler emits errors about failing to instantiate cwiseProduct BEFORE
+// looking at the static assertions. Thus this is a trick to get better compile errors.
+template<typename T, typename U,
+         bool NeedToTranspose = T::IsVectorAtCompileTime && U::IsVectorAtCompileTime &&
+                ((int(T::RowsAtCompileTime) == 1 && int(U::ColsAtCompileTime) == 1) ||
+                 (int(T::ColsAtCompileTime) == 1 && int(U::RowsAtCompileTime) == 1))>
+struct dot_nocheck
+{
+  typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
+  typedef typename conj_prod::result_type ResScalar;
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE
+  static ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
+  {
+    return a.template binaryExpr<conj_prod>(b).sum();
+  }
+};
+
+template<typename T, typename U>
+struct dot_nocheck<T, U, true>
+{
+  typedef scalar_conj_product_op<typename traits<T>::Scalar,typename traits<U>::Scalar> conj_prod;
+  typedef typename conj_prod::result_type ResScalar;
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE
+  static ResScalar run(const MatrixBase<T>& a, const MatrixBase<U>& b)
+  {
+    return a.transpose().template binaryExpr<conj_prod>(b).sum();
+  }
+};
+
+} // end namespace internal
+
+/** \fn MatrixBase::dot
+  * \returns the dot product of *this with other.
+  *
+  * \only_for_vectors
+  *
+  * \note If the scalar type is complex numbers, then this function returns the hermitian
+  * (sesquilinear) dot product, conjugate-linear in the first variable and linear in the
+  * second variable.
+  *
+  * \sa squaredNorm(), norm()
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE
+typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
+MatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
+#if !(defined(EIGEN_NO_STATIC_ASSERT) && defined(EIGEN_NO_DEBUG))
+  EIGEN_CHECK_BINARY_COMPATIBILIY(
+      Eigen::internal::scalar_conj_product_op<Scalar EIGEN_COMMA typename OtherDerived::Scalar>, 
+      Scalar, typename OtherDerived::Scalar);
+#endif
+  
+  eigen_assert(size() == other.size());
+
+  return internal::dot_nocheck<Derived,OtherDerived>::run(*this, other);
+}
+
+//---------- implementation of L2 norm and related functions ----------
+
+/** \returns, for vectors, the squared \em l2 norm of \c *this, and for matrices the squared Frobenius norm.
+  * In both cases, it consists in the sum of the square of all the matrix entries.
+  * For vectors, this is also equals to the dot product of \c *this with itself.
+  *
+  * \sa dot(), norm(), lpNorm()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::squaredNorm() const
+{
+  return numext::real((*this).cwiseAbs2().sum());
+}
+
+/** \returns, for vectors, the \em l2 norm of \c *this, and for matrices the Frobenius norm.
+  * In both cases, it consists in the square root of the sum of the square of all the matrix entries.
+  * For vectors, this is also equals to the square root of the dot product of \c *this with itself.
+  *
+  * \sa lpNorm(), dot(), squaredNorm()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename NumTraits<typename internal::traits<Derived>::Scalar>::Real MatrixBase<Derived>::norm() const
+{
+  return numext::sqrt(squaredNorm());
+}
+
+/** \returns an expression of the quotient of \c *this by its own norm.
+  *
+  * \warning If the input vector is too small (i.e., this->norm()==0),
+  *          then this function returns a copy of the input.
+  *
+  * \only_for_vectors
+  *
+  * \sa norm(), normalize()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
+MatrixBase<Derived>::normalized() const
+{
+  typedef typename internal::nested_eval<Derived,2>::type Nested_;
+  Nested_ n(derived());
+  RealScalar z = n.squaredNorm();
+  // NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
+  if(z>RealScalar(0))
+    return n / numext::sqrt(z);
+  else
+    return n;
+}
+
+/** Normalizes the vector, i.e. divides it by its own norm.
+  *
+  * \only_for_vectors
+  *
+  * \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
+  *
+  * \sa norm(), normalized()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase<Derived>::normalize()
+{
+  RealScalar z = squaredNorm();
+  // NOTE: after extensive benchmarking, this conditional does not impact performance, at least on recent x86 CPU
+  if(z>RealScalar(0))
+    derived() /= numext::sqrt(z);
+}
+
+/** \returns an expression of the quotient of \c *this by its own norm while avoiding underflow and overflow.
+  *
+  * \only_for_vectors
+  *
+  * This method is analogue to the normalized() method, but it reduces the risk of
+  * underflow and overflow when computing the norm.
+  *
+  * \warning If the input vector is too small (i.e., this->norm()==0),
+  *          then this function returns a copy of the input.
+  *
+  * \sa stableNorm(), stableNormalize(), normalized()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename MatrixBase<Derived>::PlainObject
+MatrixBase<Derived>::stableNormalized() const
+{
+  typedef typename internal::nested_eval<Derived,3>::type Nested_;
+  Nested_ n(derived());
+  RealScalar w = n.cwiseAbs().maxCoeff();
+  RealScalar z = (n/w).squaredNorm();
+  if(z>RealScalar(0))
+    return n / (numext::sqrt(z)*w);
+  else
+    return n;
+}
+
+/** Normalizes the vector while avoid underflow and overflow
+  *
+  * \only_for_vectors
+  *
+  * This method is analogue to the normalize() method, but it reduces the risk of
+  * underflow and overflow when computing the norm.
+  *
+  * \warning If the input vector is too small (i.e., this->norm()==0), then \c *this is left unchanged.
+  *
+  * \sa stableNorm(), stableNormalized(), normalize()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void MatrixBase<Derived>::stableNormalize()
+{
+  RealScalar w = cwiseAbs().maxCoeff();
+  RealScalar z = (derived()/w).squaredNorm();
+  if(z>RealScalar(0))
+    derived() /= numext::sqrt(z)*w;
+}
+
+//---------- implementation of other norms ----------
+
+namespace internal {
+
+template<typename Derived, int p>
+struct lpNorm_selector
+{
+  typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar run(const MatrixBase<Derived>& m)
+  {
+    EIGEN_USING_STD(pow)
+    return pow(m.cwiseAbs().array().pow(p).sum(), RealScalar(1)/p);
+  }
+};
+
+template<typename Derived>
+struct lpNorm_selector<Derived, 1>
+{
+  EIGEN_DEVICE_FUNC
+  static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
+  {
+    return m.cwiseAbs().sum();
+  }
+};
+
+template<typename Derived>
+struct lpNorm_selector<Derived, 2>
+{
+  EIGEN_DEVICE_FUNC
+  static inline typename NumTraits<typename traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
+  {
+    return m.norm();
+  }
+};
+
+template<typename Derived>
+struct lpNorm_selector<Derived, Infinity>
+{
+  typedef typename NumTraits<typename traits<Derived>::Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar run(const MatrixBase<Derived>& m)
+  {
+    if(Derived::SizeAtCompileTime==0 || (Derived::SizeAtCompileTime==Dynamic && m.size()==0))
+      return RealScalar(0);
+    return m.cwiseAbs().maxCoeff();
+  }
+};
+
+} // end namespace internal
+
+/** \returns the \b coefficient-wise \f$ \ell^p \f$ norm of \c *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values
+  *          of the coefficients of \c *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^\infty \f$
+  *          norm, that is the maximum of the absolute values of the coefficients of \c *this.
+  *
+  * In all cases, if \c *this is empty, then the value 0 is returned.
+  *
+  * \note For matrices, this function does not compute the <a href="https://en.wikipedia.org/wiki/Operator_norm">operator-norm</a>. That is, if \c *this is a matrix, then its coefficients are interpreted as a 1D vector. Nonetheless, you can easily compute the 1-norm and \f$\infty\f$-norm matrix operator norms using \link TutorialReductionsVisitorsBroadcastingReductionsNorm partial reductions \endlink.
+  *
+  * \sa norm()
+  */
+template<typename Derived>
+template<int p>
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+EIGEN_DEVICE_FUNC inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+#else
+EIGEN_DEVICE_FUNC MatrixBase<Derived>::RealScalar
+#endif
+MatrixBase<Derived>::lpNorm() const
+{
+  return internal::lpNorm_selector<Derived, p>::run(*this);
+}
+
+//---------- implementation of isOrthogonal / isUnitary ----------
+
+/** \returns true if *this is approximately orthogonal to \a other,
+  *          within the precision given by \a prec.
+  *
+  * Example: \include MatrixBase_isOrthogonal.cpp
+  * Output: \verbinclude MatrixBase_isOrthogonal.out
+  */
+template<typename Derived>
+template<typename OtherDerived>
+bool MatrixBase<Derived>::isOrthogonal
+(const MatrixBase<OtherDerived>& other, const RealScalar& prec) const
+{
+  typename internal::nested_eval<Derived,2>::type nested(derived());
+  typename internal::nested_eval<OtherDerived,2>::type otherNested(other.derived());
+  return numext::abs2(nested.dot(otherNested)) <= prec * prec * nested.squaredNorm() * otherNested.squaredNorm();
+}
+
+/** \returns true if *this is approximately an unitary matrix,
+  *          within the precision given by \a prec. In the case where the \a Scalar
+  *          type is real numbers, a unitary matrix is an orthogonal matrix, whence the name.
+  *
+  * \note This can be used to check whether a family of vectors forms an orthonormal basis.
+  *       Indeed, \c m.isUnitary() returns true if and only if the columns (equivalently, the rows) of m form an
+  *       orthonormal basis.
+  *
+  * Example: \include MatrixBase_isUnitary.cpp
+  * Output: \verbinclude MatrixBase_isUnitary.out
+  */
+template<typename Derived>
+bool MatrixBase<Derived>::isUnitary(const RealScalar& prec) const
+{
+  typename internal::nested_eval<Derived,1>::type self(derived());
+  for(Index i = 0; i < cols(); ++i)
+  {
+    if(!internal::isApprox(self.col(i).squaredNorm(), static_cast<RealScalar>(1), prec))
+      return false;
+    for(Index j = 0; j < i; ++j)
+      if(!internal::isMuchSmallerThan(self.col(i).dot(self.col(j)), static_cast<Scalar>(1), prec))
+        return false;
+  }
+  return true;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_DOT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/EigenBase.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/EigenBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..105488dca0f9fc51dbfcc885a03f21b8b26ba725
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/EigenBase.h
@@ -0,0 +1,162 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EIGENBASE_H
+#define EIGEN_EIGENBASE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class EigenBase
+  * \ingroup Core_Module
+  *
+  * Common base class for all classes T such that MatrixBase has an operator=(T) and a constructor MatrixBase(T).
+  *
+  * In other words, an EigenBase object is an object that can be copied into a MatrixBase.
+  *
+  * Besides MatrixBase-derived classes, this also includes special matrix classes such as diagonal matrices, etc.
+  *
+  * Notice that this class is trivial, it is only used to disambiguate overloaded functions.
+  *
+  * \sa \blank \ref TopicClassHierarchy
+  */
+template<typename Derived> struct EigenBase
+{
+//   typedef typename internal::plain_matrix_type<Derived>::type PlainObject;
+
+  /** \brief The interface type of indices
+    * \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
+    * \sa StorageIndex, \ref TopicPreprocessorDirectives.
+    * DEPRECATED: Since Eigen 3.3, its usage is deprecated. Use Eigen::Index instead.
+    * Deprecation is not marked with a doxygen comment because there are too many existing usages to add the deprecation attribute.
+    */
+  typedef Eigen::Index Index;
+
+  // FIXME is it needed?
+  typedef typename internal::traits<Derived>::StorageKind StorageKind;
+
+  /** \returns a reference to the derived object */
+  EIGEN_DEVICE_FUNC
+  Derived& derived() { return *static_cast<Derived*>(this); }
+  /** \returns a const reference to the derived object */
+  EIGEN_DEVICE_FUNC
+  const Derived& derived() const { return *static_cast<const Derived*>(this); }
+
+  EIGEN_DEVICE_FUNC
+  inline Derived& const_cast_derived() const
+  { return *static_cast<Derived*>(const_cast<EigenBase*>(this)); }
+  EIGEN_DEVICE_FUNC
+  inline const Derived& const_derived() const
+  { return *static_cast<const Derived*>(this); }
+
+  /** \returns the number of rows. \sa cols(), RowsAtCompileTime */
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  inline Index rows() const EIGEN_NOEXCEPT { return derived().rows(); }
+  /** \returns the number of columns. \sa rows(), ColsAtCompileTime*/
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  inline Index cols() const EIGEN_NOEXCEPT { return derived().cols(); }
+  /** \returns the number of coefficients, which is rows()*cols().
+    * \sa rows(), cols(), SizeAtCompileTime. */
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  inline Index size() const EIGEN_NOEXCEPT { return rows() * cols(); }
+
+  /** \internal Don't use it, but do the equivalent: \code dst = *this; \endcode */
+  template<typename Dest>
+  EIGEN_DEVICE_FUNC
+  inline void evalTo(Dest& dst) const
+  { derived().evalTo(dst); }
+
+  /** \internal Don't use it, but do the equivalent: \code dst += *this; \endcode */
+  template<typename Dest>
+  EIGEN_DEVICE_FUNC
+  inline void addTo(Dest& dst) const
+  {
+    // This is the default implementation,
+    // derived class can reimplement it in a more optimized way.
+    typename Dest::PlainObject res(rows(),cols());
+    evalTo(res);
+    dst += res;
+  }
+
+  /** \internal Don't use it, but do the equivalent: \code dst -= *this; \endcode */
+  template<typename Dest>
+  EIGEN_DEVICE_FUNC
+  inline void subTo(Dest& dst) const
+  {
+    // This is the default implementation,
+    // derived class can reimplement it in a more optimized way.
+    typename Dest::PlainObject res(rows(),cols());
+    evalTo(res);
+    dst -= res;
+  }
+
+  /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheRight(*this); \endcode */
+  template<typename Dest>
+  EIGEN_DEVICE_FUNC inline void applyThisOnTheRight(Dest& dst) const
+  {
+    // This is the default implementation,
+    // derived class can reimplement it in a more optimized way.
+    dst = dst * this->derived();
+  }
+
+  /** \internal Don't use it, but do the equivalent: \code dst.applyOnTheLeft(*this); \endcode */
+  template<typename Dest>
+  EIGEN_DEVICE_FUNC inline void applyThisOnTheLeft(Dest& dst) const
+  {
+    // This is the default implementation,
+    // derived class can reimplement it in a more optimized way.
+    dst = this->derived() * dst;
+  }
+
+};
+
+/***************************************************************************
+* Implementation of matrix base methods
+***************************************************************************/
+
+/** \brief Copies the generic expression \a other into *this.
+  *
+  * \details The expression must provide a (templated) evalTo(Derived& dst) const
+  * function which does the actual job. In practice, this allows any user to write
+  * its own special matrix without having to modify MatrixBase
+  *
+  * \returns a reference to *this.
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+Derived& DenseBase<Derived>::operator=(const EigenBase<OtherDerived> &other)
+{
+  call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+Derived& DenseBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
+{
+  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+Derived& DenseBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
+{
+  call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_EIGENBASE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ForceAlignedAccess.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ForceAlignedAccess.h
new file mode 100644
index 0000000000000000000000000000000000000000..b00785e3d27290b04dd67e771ce567fa0cb928a2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ForceAlignedAccess.h
@@ -0,0 +1,152 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_FORCEALIGNEDACCESS_H
+#define EIGEN_FORCEALIGNEDACCESS_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class ForceAlignedAccess
+  * \ingroup Core_Module
+  *
+  * \brief Enforce aligned packet loads and stores regardless of what is requested
+  *
+  * \param ExpressionType the type of the object of which we are forcing aligned packet access
+  *
+  * This class is the return type of MatrixBase::forceAlignedAccess()
+  * and most of the time this is the only way it is used.
+  *
+  * \sa MatrixBase::forceAlignedAccess()
+  */
+
+namespace internal {
+template<typename ExpressionType>
+struct traits<ForceAlignedAccess<ExpressionType> > : public traits<ExpressionType>
+{};
+}
+
+template<typename ExpressionType> class ForceAlignedAccess
+  : public internal::dense_xpr_base< ForceAlignedAccess<ExpressionType> >::type
+{
+  public:
+
+    typedef typename internal::dense_xpr_base<ForceAlignedAccess>::type Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(ForceAlignedAccess)
+
+    EIGEN_DEVICE_FUNC explicit inline ForceAlignedAccess(const ExpressionType& matrix) : m_expression(matrix) {}
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index outerStride() const EIGEN_NOEXCEPT { return m_expression.outerStride(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index innerStride() const EIGEN_NOEXCEPT { return m_expression.innerStride(); }
+
+    EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index row, Index col) const
+    {
+      return m_expression.coeff(row, col);
+    }
+
+    EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index row, Index col)
+    {
+      return m_expression.const_cast_derived().coeffRef(row, col);
+    }
+
+    EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index index) const
+    {
+      return m_expression.coeff(index);
+    }
+
+    EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index)
+    {
+      return m_expression.const_cast_derived().coeffRef(index);
+    }
+
+    template<int LoadMode>
+    inline const PacketScalar packet(Index row, Index col) const
+    {
+      return m_expression.template packet<Aligned>(row, col);
+    }
+
+    template<int LoadMode>
+    inline void writePacket(Index row, Index col, const PacketScalar& x)
+    {
+      m_expression.const_cast_derived().template writePacket<Aligned>(row, col, x);
+    }
+
+    template<int LoadMode>
+    inline const PacketScalar packet(Index index) const
+    {
+      return m_expression.template packet<Aligned>(index);
+    }
+
+    template<int LoadMode>
+    inline void writePacket(Index index, const PacketScalar& x)
+    {
+      m_expression.const_cast_derived().template writePacket<Aligned>(index, x);
+    }
+
+    EIGEN_DEVICE_FUNC operator const ExpressionType&() const { return m_expression; }
+
+  protected:
+    const ExpressionType& m_expression;
+
+  private:
+    ForceAlignedAccess& operator=(const ForceAlignedAccess&);
+};
+
+/** \returns an expression of *this with forced aligned access
+  * \sa forceAlignedAccessIf(),class ForceAlignedAccess
+  */
+template<typename Derived>
+inline const ForceAlignedAccess<Derived>
+MatrixBase<Derived>::forceAlignedAccess() const
+{
+  return ForceAlignedAccess<Derived>(derived());
+}
+
+/** \returns an expression of *this with forced aligned access
+  * \sa forceAlignedAccessIf(), class ForceAlignedAccess
+  */
+template<typename Derived>
+inline ForceAlignedAccess<Derived>
+MatrixBase<Derived>::forceAlignedAccess()
+{
+  return ForceAlignedAccess<Derived>(derived());
+}
+
+/** \returns an expression of *this with forced aligned access if \a Enable is true.
+  * \sa forceAlignedAccess(), class ForceAlignedAccess
+  */
+template<typename Derived>
+template<bool Enable>
+inline add_const_on_value_type_t<std::conditional_t<Enable,ForceAlignedAccess<Derived>,Derived&>>
+MatrixBase<Derived>::forceAlignedAccessIf() const
+{
+  return derived();  // FIXME This should not work but apparently is never used
+}
+
+/** \returns an expression of *this with forced aligned access if \a Enable is true.
+  * \sa forceAlignedAccess(), class ForceAlignedAccess
+  */
+template<typename Derived>
+template<bool Enable>
+inline std::conditional_t<Enable,ForceAlignedAccess<Derived>,Derived&>
+MatrixBase<Derived>::forceAlignedAccessIf()
+{
+  return derived();  // FIXME This should not work but apparently is never used
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_FORCEALIGNEDACCESS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Fuzzy.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Fuzzy.h
new file mode 100644
index 0000000000000000000000000000000000000000..1842b47639ad5806558fdeb11e597fefd623dedb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Fuzzy.h
@@ -0,0 +1,157 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_FUZZY_H
+#define EIGEN_FUZZY_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal
+{
+
+template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
+struct isApprox_selector
+{
+  EIGEN_DEVICE_FUNC
+  static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
+  {
+    typename internal::nested_eval<Derived,2>::type nested(x);
+    typename internal::nested_eval<OtherDerived,2>::type otherNested(y);
+    return (nested.matrix() - otherNested.matrix()).cwiseAbs2().sum() <= prec * prec * numext::mini(nested.cwiseAbs2().sum(), otherNested.cwiseAbs2().sum());
+  }
+};
+
+template<typename Derived, typename OtherDerived>
+struct isApprox_selector<Derived, OtherDerived, true>
+{
+  EIGEN_DEVICE_FUNC
+  static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar&)
+  {
+    return x.matrix() == y.matrix();
+  }
+};
+
+template<typename Derived, typename OtherDerived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
+struct isMuchSmallerThan_object_selector
+{
+  EIGEN_DEVICE_FUNC
+  static bool run(const Derived& x, const OtherDerived& y, const typename Derived::RealScalar& prec)
+  {
+    return x.cwiseAbs2().sum() <= numext::abs2(prec) * y.cwiseAbs2().sum();
+  }
+};
+
+template<typename Derived, typename OtherDerived>
+struct isMuchSmallerThan_object_selector<Derived, OtherDerived, true>
+{
+  EIGEN_DEVICE_FUNC
+  static bool run(const Derived& x, const OtherDerived&, const typename Derived::RealScalar&)
+  {
+    return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
+  }
+};
+
+template<typename Derived, bool is_integer = NumTraits<typename Derived::Scalar>::IsInteger>
+struct isMuchSmallerThan_scalar_selector
+{
+  EIGEN_DEVICE_FUNC
+  static bool run(const Derived& x, const typename Derived::RealScalar& y, const typename Derived::RealScalar& prec)
+  {
+    return x.cwiseAbs2().sum() <= numext::abs2(prec * y);
+  }
+};
+
+template<typename Derived>
+struct isMuchSmallerThan_scalar_selector<Derived, true>
+{
+  EIGEN_DEVICE_FUNC
+  static bool run(const Derived& x, const typename Derived::RealScalar&, const typename Derived::RealScalar&)
+  {
+    return x.matrix() == Derived::Zero(x.rows(), x.cols()).matrix();
+  }
+};
+
+} // end namespace internal
+
+
+/** \returns \c true if \c *this is approximately equal to \a other, within the precision
+  * determined by \a prec.
+  *
+  * \note The fuzzy compares are done multiplicatively. Two vectors \f$ v \f$ and \f$ w \f$
+  * are considered to be approximately equal within precision \f$ p \f$ if
+  * \f[ \Vert v - w \Vert \leqslant p\,\min(\Vert v\Vert, \Vert w\Vert). \f]
+  * For matrices, the comparison is done using the Hilbert-Schmidt norm (aka Frobenius norm
+  * L2 norm).
+  *
+  * \note Because of the multiplicativeness of this comparison, one can't use this function
+  * to check whether \c *this is approximately equal to the zero matrix or vector.
+  * Indeed, \c isApprox(zero) returns false unless \c *this itself is exactly the zero matrix
+  * or vector. If you want to test whether \c *this is zero, use internal::isMuchSmallerThan(const
+  * RealScalar&, RealScalar) instead.
+  *
+  * \sa internal::isMuchSmallerThan(const RealScalar&, RealScalar) const
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isApprox(
+  const DenseBase<OtherDerived>& other,
+  const RealScalar& prec
+) const
+{
+  return internal::isApprox_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);
+}
+
+/** \returns \c true if the norm of \c *this is much smaller than \a other,
+  * within the precision determined by \a prec.
+  *
+  * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
+  * considered to be much smaller than \f$ x \f$ within precision \f$ p \f$ if
+  * \f[ \Vert v \Vert \leqslant p\,\vert x\vert. \f]
+  *
+  * For matrices, the comparison is done using the Hilbert-Schmidt norm. For this reason,
+  * the value of the reference scalar \a other should come from the Hilbert-Schmidt norm
+  * of a reference matrix of same dimensions.
+  *
+  * \sa isApprox(), isMuchSmallerThan(const DenseBase<OtherDerived>&, RealScalar) const
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isMuchSmallerThan(
+  const typename NumTraits<Scalar>::Real& other,
+  const RealScalar& prec
+) const
+{
+  return internal::isMuchSmallerThan_scalar_selector<Derived>::run(derived(), other, prec);
+}
+
+/** \returns \c true if the norm of \c *this is much smaller than the norm of \a other,
+  * within the precision determined by \a prec.
+  *
+  * \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
+  * considered to be much smaller than a vector \f$ w \f$ within precision \f$ p \f$ if
+  * \f[ \Vert v \Vert \leqslant p\,\Vert w\Vert. \f]
+  * For matrices, the comparison is done using the Hilbert-Schmidt norm.
+  *
+  * \sa isApprox(), isMuchSmallerThan(const RealScalar&, RealScalar) const
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC bool DenseBase<Derived>::isMuchSmallerThan(
+  const DenseBase<OtherDerived>& other,
+  const RealScalar& prec
+) const
+{
+  return internal::isMuchSmallerThan_object_selector<Derived, OtherDerived>::run(derived(), other.derived(), prec);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_FUZZY_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/GeneralProduct.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/GeneralProduct.h
new file mode 100644
index 0000000000000000000000000000000000000000..9f693baa2c90912f235d6127a8e3b3359bacb225
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/GeneralProduct.h
@@ -0,0 +1,467 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERAL_PRODUCT_H
+#define EIGEN_GENERAL_PRODUCT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+enum {
+  Large = 2,
+  Small = 3
+};
+
+// Define the threshold value to fallback from the generic matrix-matrix product
+// implementation (heavy) to the lightweight coeff-based product one.
+// See generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct>
+// in products/GeneralMatrixMatrix.h for more details.
+// TODO This threshold should also be used in the compile-time selector below.
+#ifndef EIGEN_GEMM_TO_COEFFBASED_THRESHOLD
+// This default value has been obtained on a Haswell architecture.
+#define EIGEN_GEMM_TO_COEFFBASED_THRESHOLD 20
+#endif
+
+namespace internal {
+
+template<int Rows, int Cols, int Depth> struct product_type_selector;
+
+template<int Size, int MaxSize> struct product_size_category
+{
+  enum {
+    #ifndef EIGEN_GPU_COMPILE_PHASE
+    is_large = MaxSize == Dynamic ||
+               Size >= EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD ||
+               (Size==Dynamic && MaxSize>=EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD),
+    #else
+    is_large = 0,
+    #endif
+    value = is_large  ? Large
+          : Size == 1 ? 1
+                      : Small
+  };
+};
+
+template<typename Lhs, typename Rhs> struct product_type
+{
+  typedef remove_all_t<Lhs> Lhs_;
+  typedef remove_all_t<Rhs> Rhs_;
+  enum {
+    MaxRows = traits<Lhs_>::MaxRowsAtCompileTime,
+    Rows    = traits<Lhs_>::RowsAtCompileTime,
+    MaxCols = traits<Rhs_>::MaxColsAtCompileTime,
+    Cols    = traits<Rhs_>::ColsAtCompileTime,
+    MaxDepth = min_size_prefer_fixed(traits<Lhs_>::MaxColsAtCompileTime,
+                                     traits<Rhs_>::MaxRowsAtCompileTime),
+    Depth = min_size_prefer_fixed(traits<Lhs_>::ColsAtCompileTime,
+                                  traits<Rhs_>::RowsAtCompileTime)
+  };
+
+  // the splitting into different lines of code here, introducing the _select enums and the typedef below,
+  // is to work around an internal compiler error with gcc 4.1 and 4.2.
+private:
+  enum {
+    rows_select = product_size_category<Rows,MaxRows>::value,
+    cols_select = product_size_category<Cols,MaxCols>::value,
+    depth_select = product_size_category<Depth,MaxDepth>::value
+  };
+  typedef product_type_selector<rows_select, cols_select, depth_select> selector;
+
+public:
+  enum {
+    value = selector::ret,
+    ret = selector::ret
+  };
+#ifdef EIGEN_DEBUG_PRODUCT
+  static void debug()
+  {
+      EIGEN_DEBUG_VAR(Rows);
+      EIGEN_DEBUG_VAR(Cols);
+      EIGEN_DEBUG_VAR(Depth);
+      EIGEN_DEBUG_VAR(rows_select);
+      EIGEN_DEBUG_VAR(cols_select);
+      EIGEN_DEBUG_VAR(depth_select);
+      EIGEN_DEBUG_VAR(value);
+  }
+#endif
+};
+
+/* The following allows to select the kind of product at compile time
+ * based on the three dimensions of the product.
+ * This is a compile time mapping from {1,Small,Large}^3 -> {product types} */
+// FIXME I'm not sure the current mapping is the ideal one.
+template<int M, int N>  struct product_type_selector<M,N,1>              { enum { ret = OuterProduct }; };
+template<int M>         struct product_type_selector<M, 1, 1>            { enum { ret = LazyCoeffBasedProductMode }; };
+template<int N>         struct product_type_selector<1, N, 1>            { enum { ret = LazyCoeffBasedProductMode }; };
+template<int Depth>     struct product_type_selector<1,    1,    Depth>  { enum { ret = InnerProduct }; };
+template<>              struct product_type_selector<1,    1,    1>      { enum { ret = InnerProduct }; };
+template<>              struct product_type_selector<Small,1,    Small>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<1,    Small,Small>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<Small,Small,Small>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<Small, Small, 1>    { enum { ret = LazyCoeffBasedProductMode }; };
+template<>              struct product_type_selector<Small, Large, 1>    { enum { ret = LazyCoeffBasedProductMode }; };
+template<>              struct product_type_selector<Large, Small, 1>    { enum { ret = LazyCoeffBasedProductMode }; };
+template<>              struct product_type_selector<1,    Large,Small>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<1,    Large,Large>  { enum { ret = GemvProduct }; };
+template<>              struct product_type_selector<1,    Small,Large>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<Large,1,    Small>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<Large,1,    Large>  { enum { ret = GemvProduct }; };
+template<>              struct product_type_selector<Small,1,    Large>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<Small,Small,Large>  { enum { ret = GemmProduct }; };
+template<>              struct product_type_selector<Large,Small,Large>  { enum { ret = GemmProduct }; };
+template<>              struct product_type_selector<Small,Large,Large>  { enum { ret = GemmProduct }; };
+template<>              struct product_type_selector<Large,Large,Large>  { enum { ret = GemmProduct }; };
+template<>              struct product_type_selector<Large,Small,Small>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<Small,Large,Small>  { enum { ret = CoeffBasedProductMode }; };
+template<>              struct product_type_selector<Large,Large,Small>  { enum { ret = GemmProduct }; };
+
+} // end namespace internal
+
+/***********************************************************************
+*  Implementation of Inner Vector Vector Product
+***********************************************************************/
+
+// FIXME : maybe the "inner product" could return a Scalar
+// instead of a 1x1 matrix ??
+// Pro: more natural for the user
+// Cons: this could be a problem if in a meta unrolled algorithm a matrix-matrix
+// product ends up to a row-vector times col-vector product... To tackle this use
+// case, we could have a specialization for Block<MatrixType,1,1> with: operator=(Scalar x);
+
+/***********************************************************************
+*  Implementation of Outer Vector Vector Product
+***********************************************************************/
+
+/***********************************************************************
+*  Implementation of General Matrix Vector Product
+***********************************************************************/
+
+/*  According to the shape/flags of the matrix we have to distinghish 3 different cases:
+ *   1 - the matrix is col-major, BLAS compatible and M is large => call fast BLAS-like colmajor routine
+ *   2 - the matrix is row-major, BLAS compatible and N is large => call fast BLAS-like rowmajor routine
+ *   3 - all other cases are handled using a simple loop along the outer-storage direction.
+ *  Therefore we need a lower level meta selector.
+ *  Furthermore, if the matrix is the rhs, then the product has to be transposed.
+ */
+namespace internal {
+
+template<int Side, int StorageOrder, bool BlasCompatible>
+struct gemv_dense_selector;
+
+} // end namespace internal
+
+namespace internal {
+
+template<typename Scalar,int Size,int MaxSize,bool Cond> struct gemv_static_vector_if;
+
+template<typename Scalar,int Size,int MaxSize>
+struct gemv_static_vector_if<Scalar,Size,MaxSize,false>
+{
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Scalar* data() { eigen_internal_assert(false && "should never be called"); return 0; }
+};
+
+template<typename Scalar,int Size>
+struct gemv_static_vector_if<Scalar,Size,Dynamic,true>
+{
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Scalar* data() { return 0; }
+};
+
+template<typename Scalar,int Size,int MaxSize>
+struct gemv_static_vector_if<Scalar,Size,MaxSize,true>
+{
+  enum {
+    ForceAlignment  = internal::packet_traits<Scalar>::Vectorizable,
+    PacketSize      = internal::packet_traits<Scalar>::size
+  };
+  #if EIGEN_MAX_STATIC_ALIGN_BYTES!=0
+  internal::plain_array<Scalar, internal::min_size_prefer_fixed(Size, MaxSize), 0,
+                        internal::plain_enum_min(AlignedMax, PacketSize)> m_data;
+  EIGEN_STRONG_INLINE Scalar* data() { return m_data.array; }
+  #else
+  // Some architectures cannot align on the stack,
+  // => let's manually enforce alignment by allocating more data and return the address of the first aligned element.
+  internal::plain_array<Scalar, internal::min_size_prefer_fixed(Size, MaxSize)+(ForceAlignment?EIGEN_MAX_ALIGN_BYTES:0),0> m_data;
+  EIGEN_STRONG_INLINE Scalar* data() {
+    return ForceAlignment
+            ? reinterpret_cast<Scalar*>((std::uintptr_t(m_data.array) & ~(std::size_t(EIGEN_MAX_ALIGN_BYTES-1))) + EIGEN_MAX_ALIGN_BYTES)
+            : m_data.array;
+  }
+  #endif
+};
+
+// The vector is on the left => transposition
+template<int StorageOrder, bool BlasCompatible>
+struct gemv_dense_selector<OnTheLeft,StorageOrder,BlasCompatible>
+{
+  template<typename Lhs, typename Rhs, typename Dest>
+  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
+  {
+    Transpose<Dest> destT(dest);
+    enum { OtherStorageOrder = StorageOrder == RowMajor ? ColMajor : RowMajor };
+    gemv_dense_selector<OnTheRight,OtherStorageOrder,BlasCompatible>
+      ::run(rhs.transpose(), lhs.transpose(), destT, alpha);
+  }
+};
+
+template<> struct gemv_dense_selector<OnTheRight,ColMajor,true>
+{
+  template<typename Lhs, typename Rhs, typename Dest>
+  static inline void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
+  {
+    typedef typename Lhs::Scalar   LhsScalar;
+    typedef typename Rhs::Scalar   RhsScalar;
+    typedef typename Dest::Scalar  ResScalar;
+    
+    typedef internal::blas_traits<Lhs> LhsBlasTraits;
+    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+    typedef internal::blas_traits<Rhs> RhsBlasTraits;
+    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+  
+    typedef Map<Matrix<ResScalar,Dynamic,1>, plain_enum_min(AlignedMax, internal::packet_traits<ResScalar>::size)> MappedDest;
+
+    ActualLhsType actualLhs = LhsBlasTraits::extract(lhs);
+    ActualRhsType actualRhs = RhsBlasTraits::extract(rhs);
+
+    ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);
+
+    // make sure Dest is a compile-time vector type (bug 1166)
+    typedef std::conditional_t<Dest::IsVectorAtCompileTime, Dest, typename Dest::ColXpr> ActualDest;
+
+    enum {
+      // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
+      // on, the other hand it is good for the cache to pack the vector anyways...
+      EvalToDestAtCompileTime = (ActualDest::InnerStrideAtCompileTime==1),
+      ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex),
+      MightCannotUseDest = ((!EvalToDestAtCompileTime) || ComplexByReal) && (ActualDest::MaxSizeAtCompileTime!=0)
+    };
+
+    typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper;
+    typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper;
+    RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);
+
+    if(!MightCannotUseDest)
+    {
+      // shortcut if we are sure to be able to use dest directly,
+      // this ease the compiler to generate cleaner and more optimzized code for most common cases
+      general_matrix_vector_product
+          <Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
+          actualLhs.rows(), actualLhs.cols(),
+          LhsMapper(actualLhs.data(), actualLhs.outerStride()),
+          RhsMapper(actualRhs.data(), actualRhs.innerStride()),
+          dest.data(), 1,
+          compatibleAlpha);
+    }
+    else
+    {
+      gemv_static_vector_if<ResScalar,ActualDest::SizeAtCompileTime,ActualDest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
+
+      const bool alphaIsCompatible = (!ComplexByReal) || (numext::is_exactly_zero(numext::imag(actualAlpha)));
+      const bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
+
+      ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
+                                                    evalToDest ? dest.data() : static_dest.data());
+
+      if(!evalToDest)
+      {
+        #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+        Index size = dest.size();
+        EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+        #endif
+        if(!alphaIsCompatible)
+        {
+          MappedDest(actualDestPtr, dest.size()).setZero();
+          compatibleAlpha = RhsScalar(1);
+        }
+        else
+          MappedDest(actualDestPtr, dest.size()) = dest;
+      }
+
+      general_matrix_vector_product
+          <Index,LhsScalar,LhsMapper,ColMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
+          actualLhs.rows(), actualLhs.cols(),
+          LhsMapper(actualLhs.data(), actualLhs.outerStride()),
+          RhsMapper(actualRhs.data(), actualRhs.innerStride()),
+          actualDestPtr, 1,
+          compatibleAlpha);
+
+      if (!evalToDest)
+      {
+        if(!alphaIsCompatible)
+          dest.matrix() += actualAlpha * MappedDest(actualDestPtr, dest.size());
+        else
+          dest = MappedDest(actualDestPtr, dest.size());
+      }
+    }
+  }
+};
+
+template<> struct gemv_dense_selector<OnTheRight,RowMajor,true>
+{
+  template<typename Lhs, typename Rhs, typename Dest>
+  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
+  {
+    typedef typename Lhs::Scalar   LhsScalar;
+    typedef typename Rhs::Scalar   RhsScalar;
+    typedef typename Dest::Scalar  ResScalar;
+    
+    typedef internal::blas_traits<Lhs> LhsBlasTraits;
+    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+    typedef internal::blas_traits<Rhs> RhsBlasTraits;
+    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+    typedef internal::remove_all_t<ActualRhsType> ActualRhsTypeCleaned;
+
+    std::add_const_t<ActualLhsType> actualLhs = LhsBlasTraits::extract(lhs);
+    std::add_const_t<ActualRhsType> actualRhs = RhsBlasTraits::extract(rhs);
+
+    ResScalar actualAlpha = combine_scalar_factors(alpha, lhs, rhs);
+
+    enum {
+      // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
+      // on, the other hand it is good for the cache to pack the vector anyways...
+      DirectlyUseRhs = ActualRhsTypeCleaned::InnerStrideAtCompileTime==1 || ActualRhsTypeCleaned::MaxSizeAtCompileTime==0
+    };
+
+    gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs;
+
+    ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(),
+        DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data());
+
+    if(!DirectlyUseRhs)
+    {
+      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      Index size = actualRhs.size();
+      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      #endif
+      Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
+    }
+
+    typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper;
+    typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper;
+    general_matrix_vector_product
+        <Index,LhsScalar,LhsMapper,RowMajor,LhsBlasTraits::NeedToConjugate,RhsScalar,RhsMapper,RhsBlasTraits::NeedToConjugate>::run(
+        actualLhs.rows(), actualLhs.cols(),
+        LhsMapper(actualLhs.data(), actualLhs.outerStride()),
+        RhsMapper(actualRhsPtr, 1),
+        dest.data(), dest.col(0).innerStride(), //NOTE  if dest is not a vector at compile-time, then dest.innerStride() might be wrong. (bug 1166)
+        actualAlpha);
+  }
+};
+
+template<> struct gemv_dense_selector<OnTheRight,ColMajor,false>
+{
+  template<typename Lhs, typename Rhs, typename Dest>
+  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
+  {
+    EIGEN_STATIC_ASSERT((!nested_eval<Lhs,1>::Evaluate),EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
+    // TODO if rhs is large enough it might be beneficial to make sure that dest is sequentially stored in memory, otherwise use a temp
+    typename nested_eval<Rhs,1>::type actual_rhs(rhs);
+    const Index size = rhs.rows();
+    for(Index k=0; k<size; ++k)
+      dest += (alpha*actual_rhs.coeff(k)) * lhs.col(k);
+  }
+};
+
+template<> struct gemv_dense_selector<OnTheRight,RowMajor,false>
+{
+  template<typename Lhs, typename Rhs, typename Dest>
+  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
+  {
+    EIGEN_STATIC_ASSERT((!nested_eval<Lhs,1>::Evaluate),EIGEN_INTERNAL_COMPILATION_ERROR_OR_YOU_MADE_A_PROGRAMMING_MISTAKE);
+    typename nested_eval<Rhs,Lhs::RowsAtCompileTime>::type actual_rhs(rhs);
+    const Index rows = dest.rows();
+    for(Index i=0; i<rows; ++i)
+      dest.coeffRef(i) += alpha * (lhs.row(i).cwiseProduct(actual_rhs.transpose())).sum();
+  }
+};
+
+} // end namespace internal
+
+/***************************************************************************
+* Implementation of matrix base methods
+***************************************************************************/
+
+/** \returns the matrix product of \c *this and \a other.
+  *
+  * \note If instead of the matrix product you want the coefficient-wise product, see Cwise::operator*().
+  *
+  * \sa lazyProduct(), operator*=(const MatrixBase&), Cwise::operator*()
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const Product<Derived, OtherDerived>
+MatrixBase<Derived>::operator*(const MatrixBase<OtherDerived> &other) const
+{
+  // A note regarding the function declaration: In MSVC, this function will sometimes
+  // not be inlined since DenseStorage is an unwindable object for dynamic
+  // matrices and product types are holding a member to store the result.
+  // Thus it does not help tagging this function with EIGEN_STRONG_INLINE.
+  enum {
+    ProductIsValid =  Derived::ColsAtCompileTime==Dynamic
+                   || OtherDerived::RowsAtCompileTime==Dynamic
+                   || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime),
+    AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
+    SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived)
+  };
+  // note to the lost user:
+  //    * for a dot product use: v1.dot(v2)
+  //    * for a coeff-wise product use: v1.cwiseProduct(v2)
+  EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
+    INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
+  EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
+    INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
+  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
+#ifdef EIGEN_DEBUG_PRODUCT
+  internal::product_type<Derived,OtherDerived>::debug();
+#endif
+
+  return Product<Derived, OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the matrix product of \c *this and \a other without implicit evaluation.
+  *
+  * The returned product will behave like any other expressions: the coefficients of the product will be
+  * computed once at a time as requested. This might be useful in some extremely rare cases when only
+  * a small and no coherent fraction of the result's coefficients have to be computed.
+  *
+  * \warning This version of the matrix product can be much much slower. So use it only if you know
+  * what you are doing and that you measured a true speed improvement.
+  *
+  * \sa operator*(const MatrixBase&)
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const Product<Derived,OtherDerived,LazyProduct>
+MatrixBase<Derived>::lazyProduct(const MatrixBase<OtherDerived> &other) const
+{
+  enum {
+    ProductIsValid =  Derived::ColsAtCompileTime==Dynamic
+                   || OtherDerived::RowsAtCompileTime==Dynamic
+                   || int(Derived::ColsAtCompileTime)==int(OtherDerived::RowsAtCompileTime),
+    AreVectors = Derived::IsVectorAtCompileTime && OtherDerived::IsVectorAtCompileTime,
+    SameSizes = EIGEN_PREDICATE_SAME_MATRIX_SIZE(Derived,OtherDerived)
+  };
+  // note to the lost user:
+  //    * for a dot product use: v1.dot(v2)
+  //    * for a coeff-wise product use: v1.cwiseProduct(v2)
+  EIGEN_STATIC_ASSERT(ProductIsValid || !(AreVectors && SameSizes),
+    INVALID_VECTOR_VECTOR_PRODUCT__IF_YOU_WANTED_A_DOT_OR_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTIONS)
+  EIGEN_STATIC_ASSERT(ProductIsValid || !(SameSizes && !AreVectors),
+    INVALID_MATRIX_PRODUCT__IF_YOU_WANTED_A_COEFF_WISE_PRODUCT_YOU_MUST_USE_THE_EXPLICIT_FUNCTION)
+  EIGEN_STATIC_ASSERT(ProductIsValid || SameSizes, INVALID_MATRIX_PRODUCT)
+
+  return Product<Derived,OtherDerived,LazyProduct>(derived(), other.derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_PRODUCT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/GenericPacketMath.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/GenericPacketMath.h
new file mode 100644
index 0000000000000000000000000000000000000000..f3d607aeb0aa6de1429018cb5972a54ecc2fff93
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/GenericPacketMath.h
@@ -0,0 +1,1392 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERIC_PACKET_MATH_H
+#define EIGEN_GENERIC_PACKET_MATH_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+  * \file GenericPacketMath.h
+  *
+  * Default implementation for types not supported by the vectorization.
+  * In practice these functions are provided to make easier the writing
+  * of generic vectorized code.
+  */
+
+#ifndef EIGEN_DEBUG_ALIGNED_LOAD
+#define EIGEN_DEBUG_ALIGNED_LOAD
+#endif
+
+#ifndef EIGEN_DEBUG_UNALIGNED_LOAD
+#define EIGEN_DEBUG_UNALIGNED_LOAD
+#endif
+
+#ifndef EIGEN_DEBUG_ALIGNED_STORE
+#define EIGEN_DEBUG_ALIGNED_STORE
+#endif
+
+#ifndef EIGEN_DEBUG_UNALIGNED_STORE
+#define EIGEN_DEBUG_UNALIGNED_STORE
+#endif
+
+struct default_packet_traits
+{
+  enum {
+    HasAdd       = 1,
+    HasSub       = 1,
+    HasShift     = 1,
+    HasMul       = 1,
+    HasNegate    = 1,
+    HasAbs       = 1,
+    HasArg       = 0,
+    HasAbs2      = 1,
+    HasAbsDiff   = 0,
+    HasMin       = 1,
+    HasMax       = 1,
+    HasConj      = 1,
+    HasSetLinear = 1,
+    HasSign      = 1,
+    HasBlend     = 0,
+    // This flag is used to indicate whether packet comparison is supported.
+    // pcmp_eq, pcmp_lt and pcmp_le should be defined for it to be true.
+    HasCmp       = 0,
+
+    HasDiv    = 0,
+    HasReciprocal = 0,
+    HasSqrt   = 0,
+    HasRsqrt  = 0,
+    HasExp    = 0,
+    HasExpm1  = 0,
+    HasLog    = 0,
+    HasLog1p  = 0,
+    HasLog10  = 0,
+    HasPow    = 0,
+
+    HasSin    = 0,
+    HasCos    = 0,
+    HasTan    = 0,
+    HasASin   = 0,
+    HasACos   = 0,
+    HasATan   = 0,
+    HasATanh  = 0,
+    HasSinh   = 0,
+    HasCosh   = 0,
+    HasTanh   = 0,
+    HasLGamma = 0,
+    HasDiGamma = 0,
+    HasZeta = 0,
+    HasPolygamma = 0,
+    HasErf = 0,
+    HasErfc = 0,
+    HasNdtri = 0,
+    HasBessel = 0,
+    HasIGamma = 0,
+    HasIGammaDerA = 0,
+    HasGammaSampleDerAlpha = 0,
+    HasIGammac = 0,
+    HasBetaInc = 0,
+
+    HasRound  = 0,
+    HasRint   = 0,
+    HasFloor  = 0,
+    HasCeil   = 0
+  };
+};
+
+template<typename T> struct packet_traits : default_packet_traits
+{
+  typedef T type;
+  typedef T half;
+  enum {
+    Vectorizable = 0,
+    size = 1,
+    AlignedOnScalar = 0,
+  };
+  enum {
+    HasAdd    = 0,
+    HasSub    = 0,
+    HasMul    = 0,
+    HasNegate = 0,
+    HasAbs    = 0,
+    HasAbs2   = 0,
+    HasMin    = 0,
+    HasMax    = 0,
+    HasConj   = 0,
+    HasSetLinear = 0
+  };
+};
+
+template<typename T> struct packet_traits<const T> : packet_traits<T> { };
+
+template<typename T> struct unpacket_traits
+{
+  typedef T type;
+  typedef T half;
+  enum
+  {
+    size = 1,
+    alignment = 1,
+    vectorizable = false,
+    masked_load_available=false,
+    masked_store_available=false
+  };
+};
+
+template<typename T> struct unpacket_traits<const T> : unpacket_traits<T> { };
+
+/** \internal A convenience utility for determining if the type is a scalar.
+ * This is used to enable some generic packet implementations.
+ */
+template <typename Packet>
+struct is_scalar {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  enum { value = internal::is_same<Packet, Scalar>::value };
+};
+
+// automatically and succinctly define combinations of pcast<SrcPacket,TgtPacket> when 
+// 1) the packets are the same type, or
+// 2) the packets differ only in sign. 
+// In both of these cases, preinterpret (bit_cast) is equivalent to pcast (static_cast)
+template <typename SrcPacket, typename TgtPacket,
+          bool Scalar = is_scalar<SrcPacket>::value && is_scalar<TgtPacket>::value>
+struct is_degenerate_helper : is_same<SrcPacket, TgtPacket> {};
+template <>
+struct is_degenerate_helper<int8_t, uint8_t, true> : std::true_type {};
+template <>
+struct is_degenerate_helper<int16_t, uint16_t, true> : std::true_type {};
+template <>
+struct is_degenerate_helper<int32_t, uint32_t, true> : std::true_type {};
+template <>
+struct is_degenerate_helper<int64_t, uint64_t, true> : std::true_type {};
+
+template <typename SrcPacket, typename TgtPacket>
+struct is_degenerate_helper<SrcPacket, TgtPacket, false> {
+  using SrcScalar = typename unpacket_traits<SrcPacket>::type;
+  static constexpr int SrcSize = unpacket_traits<SrcPacket>::size;
+  using TgtScalar = typename unpacket_traits<TgtPacket>::type;
+  static constexpr int TgtSize = unpacket_traits<TgtPacket>::size;
+  static constexpr bool value = is_degenerate_helper<SrcScalar, TgtScalar, true>::value && (SrcSize == TgtSize);
+};
+
+// is_degenerate<T1,T2>::value == is_degenerate<T2,T1>::value
+template <typename SrcPacket, typename TgtPacket>
+struct is_degenerate {
+  static constexpr bool value =
+      is_degenerate_helper<SrcPacket, TgtPacket>::value || is_degenerate_helper<TgtPacket, SrcPacket>::value;
+};
+
+template <typename Packet>
+struct is_half {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  static constexpr int Size = unpacket_traits<Packet>::size;
+  using DefaultPacket = typename packet_traits<Scalar>::type;
+  static constexpr int DefaultSize = unpacket_traits<DefaultPacket>::size;
+  static constexpr bool value = Size < DefaultSize;
+};
+
+template <typename Src, typename Tgt>
+struct type_casting_traits {
+  enum {
+    VectorizedCast =
+        is_degenerate<Src, Tgt>::value && packet_traits<Src>::Vectorizable && packet_traits<Tgt>::Vectorizable,
+    SrcCoeffRatio = 1,
+    TgtCoeffRatio = 1
+  };
+};
+
+// provides a succint template to define vectorized casting traits with respect to the largest accessible packet types
+template <typename Src, typename Tgt>
+struct vectorized_type_casting_traits {
+  enum : int {
+    DefaultSrcPacketSize = packet_traits<Src>::size,
+    DefaultTgtPacketSize = packet_traits<Tgt>::size,
+    VectorizedCast = 1,
+    SrcCoeffRatio = plain_enum_max(DefaultTgtPacketSize / DefaultSrcPacketSize, 1),
+    TgtCoeffRatio = plain_enum_max(DefaultSrcPacketSize / DefaultTgtPacketSize, 1)
+  };
+};
+
+/** \internal Wrapper to ensure that multiple packet types can map to the same
+    same underlying vector type. */
+template<typename T, int unique_id = 0>
+struct eigen_packet_wrapper
+{
+  EIGEN_ALWAYS_INLINE operator T&() { return m_val; }
+  EIGEN_ALWAYS_INLINE operator const T&() const { return m_val; }
+  EIGEN_ALWAYS_INLINE eigen_packet_wrapper() = default;
+  EIGEN_ALWAYS_INLINE eigen_packet_wrapper(const T &v) : m_val(v) {}
+  EIGEN_ALWAYS_INLINE eigen_packet_wrapper& operator=(const T &v) {
+    m_val = v;
+    return *this;
+  }
+
+  T m_val;
+};
+
+template <typename Target, typename Packet, bool IsSame = is_same<Target, Packet>::value>
+struct preinterpret_generic;
+
+template <typename Target, typename Packet>
+struct preinterpret_generic<Target, Packet, false> {
+  // the packets are not the same, attempt scalar bit_cast
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Target run(const Packet& a) {
+    return numext::bit_cast<Target, Packet>(a);
+  }
+};
+
+template <typename Packet>
+struct preinterpret_generic<Packet, Packet, true> {
+  // the packets are the same type: do nothing
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& a) { return a; }
+};
+
+/** \internal \returns reinterpret_cast<Target>(a) */
+template <typename Target, typename Packet>
+EIGEN_DEVICE_FUNC inline Target preinterpret(const Packet& a) {
+  return preinterpret_generic<Target, Packet>::run(a);
+}
+
+template <typename SrcPacket, typename TgtPacket, bool Degenerate = is_degenerate<SrcPacket, TgtPacket>::value, bool TgtIsHalf = is_half<TgtPacket>::value>
+struct pcast_generic;
+
+template <typename SrcPacket, typename TgtPacket>
+struct pcast_generic<SrcPacket, TgtPacket, false, false> {
+  // the packets are not degenerate: attempt scalar static_cast
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket run(const SrcPacket& a) {
+    return cast_impl<SrcPacket, TgtPacket>::run(a);
+  }
+};
+
+template <typename Packet>
+struct pcast_generic<Packet, Packet, true, false> {
+  // the packets are the same: do nothing
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& a) { return a; }
+};
+
+template <typename SrcPacket, typename TgtPacket, bool TgtIsHalf>
+struct pcast_generic<SrcPacket, TgtPacket, true, TgtIsHalf> {
+  // the packets are degenerate: preinterpret is equivalent to pcast
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket run(const SrcPacket& a) { return preinterpret<TgtPacket>(a); }
+};
+
+
+
+/** \internal \returns static_cast<TgtType>(a) (coeff-wise) */
+template <typename SrcPacket, typename TgtPacket>
+EIGEN_DEVICE_FUNC inline TgtPacket pcast(const SrcPacket& a) {
+  return pcast_generic<SrcPacket, TgtPacket>::run(a);
+}
+template <typename SrcPacket, typename TgtPacket>
+EIGEN_DEVICE_FUNC inline TgtPacket pcast(const SrcPacket& a, const SrcPacket& b) {
+  return pcast_generic<SrcPacket, TgtPacket>::run(a, b);
+}
+template <typename SrcPacket, typename TgtPacket>
+EIGEN_DEVICE_FUNC inline TgtPacket pcast(const SrcPacket& a, const SrcPacket& b, const SrcPacket& c,
+                                         const SrcPacket& d) {
+  return pcast_generic<SrcPacket, TgtPacket>::run(a, b, c, d);
+}
+template <typename SrcPacket, typename TgtPacket>
+EIGEN_DEVICE_FUNC inline TgtPacket pcast(const SrcPacket& a, const SrcPacket& b, const SrcPacket& c, const SrcPacket& d,
+                                         const SrcPacket& e, const SrcPacket& f, const SrcPacket& g,
+                                         const SrcPacket& h) {
+  return pcast_generic<SrcPacket, TgtPacket>::run(a, b, c, d, e, f, g, h);
+}
+
+template <typename SrcPacket, typename TgtPacket>
+struct pcast_generic<SrcPacket, TgtPacket, false, true> {
+  // TgtPacket is a half packet of some other type
+  // perform cast and truncate result
+  using DefaultTgtPacket = typename is_half<TgtPacket>::DefaultPacket;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TgtPacket run(const SrcPacket& a) {
+    return preinterpret<TgtPacket>(pcast<SrcPacket, DefaultTgtPacket>(a));
+  }
+};
+
+/** \internal \returns a + b (coeff-wise) */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+padd(const Packet& a, const Packet& b) { return a+b; }
+// Avoid compiler warning for boolean algebra.
+template<> EIGEN_DEVICE_FUNC inline bool
+padd(const bool& a, const bool& b) { return a || b; }
+
+/** \internal \returns a packet version of \a *from, (un-aligned masked add)
+ * There is no generic implementation. We only have implementations for specialized
+ * cases. Generic case should not be called.
+ */
+template<typename Packet> EIGEN_DEVICE_FUNC inline
+std::enable_if_t<unpacket_traits<Packet>::masked_fpops_available, Packet>
+padd(const Packet& a, const Packet& b, typename unpacket_traits<Packet>::mask_t umask);
+
+
+/** \internal \returns a - b (coeff-wise) */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+psub(const Packet& a, const Packet& b) { return a-b; }
+
+/** \internal \returns -a (coeff-wise) */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pnegate(const Packet& a) { return -a; }
+
+template<> EIGEN_DEVICE_FUNC inline bool
+pnegate(const bool& a) { return !a; }
+
+/** \internal \returns conj(a) (coeff-wise) */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pconj(const Packet& a) { return numext::conj(a); }
+
+/** \internal \returns a * b (coeff-wise) */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pmul(const Packet& a, const Packet& b) { return a*b; }
+// Avoid compiler warning for boolean algebra.
+template<> EIGEN_DEVICE_FUNC inline bool
+pmul(const bool& a, const bool& b) { return a && b; }
+
+/** \internal \returns a / b (coeff-wise) */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pdiv(const Packet& a, const Packet& b) { return a/b; }
+
+// In the generic case, memset to all one bits.
+template<typename Packet, typename EnableIf = void>
+struct ptrue_impl {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& /*a*/){
+    Packet b;
+    memset(static_cast<void*>(&b), 0xff, sizeof(Packet));
+    return b;
+  }
+};
+
+// For non-trivial scalars, set to Scalar(1) (i.e. a non-zero value).
+// Although this is technically not a valid bitmask, the scalar path for pselect
+// uses a comparison to zero, so this should still work in most cases. We don't
+// have another option, since the scalar type requires initialization.
+template<typename T>
+struct ptrue_impl<T, 
+    std::enable_if_t<is_scalar<T>::value && NumTraits<T>::RequireInitialization> > {
+  static EIGEN_DEVICE_FUNC inline T run(const T& /*a*/){
+    return T(1);
+  }
+};
+
+/** \internal \returns one bits. */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+ptrue(const Packet& a) {
+  return ptrue_impl<Packet>::run(a);
+}
+
+// In the general case, memset to zero.
+template<typename Packet, typename EnableIf = void>
+struct pzero_impl {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& /*a*/) {
+    Packet b;
+    memset(static_cast<void*>(&b), 0x00, sizeof(Packet));
+    return b;
+  }
+};
+
+// For scalars, explicitly set to Scalar(0), since the underlying representation
+// for zero may not consist of all-zero bits.
+template<typename T>
+struct pzero_impl<T,
+    std::enable_if_t<is_scalar<T>::value>> {
+  static EIGEN_DEVICE_FUNC inline T run(const T& /*a*/) {
+    return T(0);
+  }
+};
+
+/** \internal \returns packet of zeros */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pzero(const Packet& a) {
+  return pzero_impl<Packet>::run(a);
+}
+
+/** \internal \returns a <= b as a bit mask */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pcmp_le(const Packet& a, const Packet& b)  { return a<=b ? ptrue(a) : pzero(a); }
+
+/** \internal \returns a < b as a bit mask */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pcmp_lt(const Packet& a, const Packet& b)  { return a<b ? ptrue(a) : pzero(a); }
+
+/** \internal \returns a == b as a bit mask */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pcmp_eq(const Packet& a, const Packet& b) { return a==b ? ptrue(a) : pzero(a); }
+
+/** \internal \returns a < b or a==NaN or b==NaN as a bit mask */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pcmp_lt_or_nan(const Packet& a, const Packet& b) { return a>=b ? pzero(a) : ptrue(a); }
+
+template<typename T>
+struct bit_and {
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a, const T& b) const {
+    return a & b;
+  }
+};
+
+template<typename T>
+struct bit_or {
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a, const T& b) const {
+    return a | b;
+  }
+};
+
+template<typename T>
+struct bit_xor {
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a, const T& b) const {
+    return a ^ b;
+  }
+};
+
+template<typename T>
+struct bit_not {
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR EIGEN_ALWAYS_INLINE T operator()(const T& a) const {
+    return ~a;
+  }
+};
+
+// Use operators &, |, ^, ~.
+template<typename T>
+struct operator_bitwise_helper {
+  EIGEN_DEVICE_FUNC static inline T bitwise_and(const T& a, const T& b) { return bit_and<T>()(a, b); }
+  EIGEN_DEVICE_FUNC static inline T bitwise_or(const T& a, const T& b) { return bit_or<T>()(a, b); }
+  EIGEN_DEVICE_FUNC static inline T bitwise_xor(const T& a, const T& b) { return bit_xor<T>()(a, b); }
+  EIGEN_DEVICE_FUNC static inline T bitwise_not(const T& a) { return bit_not<T>()(a); }
+};
+
+// Apply binary operations byte-by-byte
+template<typename T>
+struct bytewise_bitwise_helper {
+  EIGEN_DEVICE_FUNC static inline T bitwise_and(const T& a, const T& b) {
+    return binary(a, b, bit_and<unsigned char>());
+  }
+  EIGEN_DEVICE_FUNC static inline T bitwise_or(const T& a, const T& b) {
+    return binary(a, b, bit_or<unsigned char>());
+   }
+  EIGEN_DEVICE_FUNC static inline T bitwise_xor(const T& a, const T& b) {
+    return binary(a, b, bit_xor<unsigned char>());
+  }
+  EIGEN_DEVICE_FUNC static inline T bitwise_not(const T& a) {
+    return unary(a,bit_not<unsigned char>());
+   }
+
+ private:
+  template<typename Op>
+  EIGEN_DEVICE_FUNC static inline T unary(const T& a, Op op) {
+    const unsigned char* a_ptr = reinterpret_cast<const unsigned char*>(&a);
+    T c;
+    unsigned char* c_ptr = reinterpret_cast<unsigned char*>(&c);
+    for (size_t i = 0; i < sizeof(T); ++i) {
+      *c_ptr++ = op(*a_ptr++);
+    }
+    return c;
+  }
+
+  template<typename Op>
+  EIGEN_DEVICE_FUNC static inline T binary(const T& a, const T& b, Op op) {
+    const unsigned char* a_ptr = reinterpret_cast<const unsigned char*>(&a);
+    const unsigned char* b_ptr = reinterpret_cast<const unsigned char*>(&b);
+    T c;
+    unsigned char* c_ptr = reinterpret_cast<unsigned char*>(&c);
+    for (size_t i = 0; i < sizeof(T); ++i) {
+      *c_ptr++ = op(*a_ptr++, *b_ptr++);
+    }
+    return c;
+  }
+};
+
+// In the general case, use byte-by-byte manipulation.
+template<typename T, typename EnableIf = void>
+struct bitwise_helper : public bytewise_bitwise_helper<T> {};
+
+// For integers or non-trivial scalars, use binary operators.
+template<typename T>
+struct bitwise_helper<T,
+  typename std::enable_if_t<
+    is_scalar<T>::value && (NumTraits<T>::IsInteger || NumTraits<T>::RequireInitialization)>
+  > : public operator_bitwise_helper<T> {};
+
+/** \internal \returns the bitwise and of \a a and \a b */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pand(const Packet& a, const Packet& b) {
+  return bitwise_helper<Packet>::bitwise_and(a, b);
+}
+
+/** \internal \returns the bitwise or of \a a and \a b */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+por(const Packet& a, const Packet& b) {
+  return bitwise_helper<Packet>::bitwise_or(a, b);
+}
+
+/** \internal \returns the bitwise xor of \a a and \a b */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pxor(const Packet& a, const Packet& b) {
+  return bitwise_helper<Packet>::bitwise_xor(a, b);
+}
+
+/** \internal \returns the bitwise not of \a a */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pnot(const Packet& a) {
+  return bitwise_helper<Packet>::bitwise_not(a);
+}
+
+/** \internal \returns the bitwise and of \a a and not \a b */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pandnot(const Packet& a, const Packet& b) { return pand(a, pnot(b)); }
+
+/** \internal \returns isnan(a) */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pisnan(const Packet& a) {
+  return pandnot(ptrue(a), pcmp_eq(a, a));
+}
+
+// In the general case, use bitwise select.
+template<typename Packet, typename EnableIf = void>
+struct pselect_impl {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& mask, const Packet& a, const Packet& b) {
+    return por(pand(a,mask),pandnot(b,mask));
+  }
+};
+
+// For scalars, use ternary select.
+template<typename Packet>
+struct pselect_impl<Packet, 
+    std::enable_if_t<is_scalar<Packet>::value> > {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& mask, const Packet& a, const Packet& b) {
+    return numext::equal_strict(mask, Packet(0)) ? b : a;
+  }
+};
+
+/** \internal \returns \a or \b for each field in packet according to \mask */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pselect(const Packet& mask, const Packet& a, const Packet& b) {
+  return pselect_impl<Packet>::run(mask, a, b);
+}
+
+template<> EIGEN_DEVICE_FUNC inline bool pselect<bool>(
+    const bool& cond, const bool& a, const bool& b) {
+  return cond ? a : b;
+}
+
+/** \internal \returns the min or of \a a and \a b (coeff-wise)
+    If either \a a or \a b are NaN, the result is implementation defined. */
+template<int NaNPropagation>
+struct pminmax_impl {
+  template <typename Packet, typename Op>
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a, const Packet& b, Op op) {
+    return op(a,b);
+  }
+};
+
+/** \internal \returns the min or max of \a a and \a b (coeff-wise)
+    If either \a a or \a b are NaN, NaN is returned. */
+template<>
+struct pminmax_impl<PropagateNaN> {
+  template <typename Packet, typename Op>
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a, const Packet& b, Op op) {
+  Packet not_nan_mask_a = pcmp_eq(a, a);
+  Packet not_nan_mask_b = pcmp_eq(b, b);
+  return pselect(not_nan_mask_a,
+                 pselect(not_nan_mask_b, op(a, b), b),
+                 a);
+  }
+};
+
+/** \internal \returns the min or max of \a a and \a b (coeff-wise)
+    If both \a a and \a b are NaN, NaN is returned.
+    Equivalent to std::fmin(a, b).  */
+template<>
+struct pminmax_impl<PropagateNumbers> {
+  template <typename Packet, typename Op>
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a, const Packet& b, Op op) {
+  Packet not_nan_mask_a = pcmp_eq(a, a);
+  Packet not_nan_mask_b = pcmp_eq(b, b);
+  return pselect(not_nan_mask_a,
+                 pselect(not_nan_mask_b, op(a, b), a),
+                 b);
+  }
+};
+
+
+#ifndef SYCL_DEVICE_ONLY
+#define EIGEN_BINARY_OP_NAN_PROPAGATION(Type, Func) Func
+#else
+#define EIGEN_BINARY_OP_NAN_PROPAGATION(Type, Func) \
+[](const Type& a, const Type& b) { \
+        return Func(a, b);}
+#endif
+
+/** \internal \returns the min of \a a and \a b  (coeff-wise).
+    If \a a or \b b is NaN, the return value is implementation defined. */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pmin(const Packet& a, const Packet& b) { return numext::mini(a,b); }
+
+/** \internal \returns the min of \a a and \a b  (coeff-wise).
+    NaNPropagation determines the NaN propagation semantics. */
+template <int NaNPropagation, typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pmin(const Packet& a, const Packet& b) {
+  return pminmax_impl<NaNPropagation>::run(a, b, EIGEN_BINARY_OP_NAN_PROPAGATION(Packet, (pmin<Packet>)));
+}
+
+/** \internal \returns the max of \a a and \a b  (coeff-wise)
+    If \a a or \b b is NaN, the return value is implementation defined. */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pmax(const Packet& a, const Packet& b) { return numext::maxi(a, b); }
+
+/** \internal \returns the max of \a a and \a b  (coeff-wise).
+    NaNPropagation determines the NaN propagation semantics. */
+template <int NaNPropagation, typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pmax(const Packet& a, const Packet& b) {
+  return pminmax_impl<NaNPropagation>::run(a, b, EIGEN_BINARY_OP_NAN_PROPAGATION(Packet,(pmax<Packet>)));
+}
+
+/** \internal \returns the absolute value of \a a */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pabs(const Packet& a) { return numext::abs(a); }
+template<> EIGEN_DEVICE_FUNC inline unsigned int
+pabs(const unsigned int& a) { return a; }
+template<> EIGEN_DEVICE_FUNC inline unsigned long
+pabs(const unsigned long& a) { return a; }
+template<> EIGEN_DEVICE_FUNC inline unsigned long long
+pabs(const unsigned long long& a) { return a; }
+
+/** \internal \returns the addsub value of \a a,b */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+paddsub(const Packet& a, const Packet& b) {
+  return pselect(peven_mask(a), padd(a, b), psub(a, b));
+ }
+
+/** \internal \returns the phase angle of \a a */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+parg(const Packet& a) { using numext::arg; return arg(a); }
+
+
+/** \internal \returns \a a arithmetically shifted by N bits to the right */
+template<int N> EIGEN_DEVICE_FUNC inline int
+parithmetic_shift_right(const int& a) { return a >> N; }
+template<int N> EIGEN_DEVICE_FUNC inline long int
+parithmetic_shift_right(const long int& a) { return a >> N; }
+
+/** \internal \returns \a a logically shifted by N bits to the right */
+template<int N> EIGEN_DEVICE_FUNC inline int
+plogical_shift_right(const int& a) { return static_cast<int>(static_cast<unsigned int>(a) >> N); }
+template<int N> EIGEN_DEVICE_FUNC inline long int
+plogical_shift_right(const long int& a) { return static_cast<long>(static_cast<unsigned long>(a) >> N); }
+
+/** \internal \returns \a a shifted by N bits to the left */
+template<int N> EIGEN_DEVICE_FUNC inline int
+plogical_shift_left(const int& a) { return a << N; }
+template<int N> EIGEN_DEVICE_FUNC inline long int
+plogical_shift_left(const long int& a) { return a << N; }
+
+/** \internal \returns the significant and exponent of the underlying floating point numbers
+  * See https://en.cppreference.com/w/cpp/numeric/math/frexp
+  */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pfrexp(const Packet& a, Packet& exponent) {
+  int exp;
+  EIGEN_USING_STD(frexp);
+  Packet result = static_cast<Packet>(frexp(a, &exp));
+  exponent = static_cast<Packet>(exp);
+  return result;
+}
+
+/** \internal \returns a * 2^((int)exponent)
+  * See https://en.cppreference.com/w/cpp/numeric/math/ldexp
+  */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pldexp(const Packet &a, const Packet &exponent) {
+  EIGEN_USING_STD(ldexp)
+  return static_cast<Packet>(ldexp(a, static_cast<int>(exponent)));
+}
+
+/** \internal \returns the min of \a a and \a b  (coeff-wise) */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pabsdiff(const Packet& a, const Packet& b) { return pselect(pcmp_lt(a, b), psub(b, a), psub(a, b)); }
+
+/** \internal \returns a packet version of \a *from, from must be properly aligned */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pload(const typename unpacket_traits<Packet>::type* from) { return *from; }
+
+/** \internal \returns n elements of a packet version of \a *from, from must be properly aligned
+  * offset indicates the starting element in which to load and
+  * offset + n <= unpacket_traits::size
+  * All elements before offset and after the last element loaded will initialized with zero */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pload_partial(const typename unpacket_traits<Packet>::type* from, const Index n, const Index offset = 0)
+{
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_assert(n + offset <= packet_size && "number of elements plus offset will read past end of packet");
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  EIGEN_ALIGN_MAX Scalar elements[packet_size] = { Scalar(0) };
+  for (Index i = offset; i < numext::mini(n+offset,packet_size); i++) {
+    elements[i] = from[i-offset];
+  }
+  return pload<Packet>(elements);
+}
+
+/** \internal \returns a packet version of \a *from, (un-aligned load) */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+ploadu(const typename unpacket_traits<Packet>::type* from) { return *from; }
+
+/** \internal \returns n elements of a packet version of \a *from, (un-aligned load)
+  * All elements after the last element loaded will initialized with zero */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+ploadu_partial(const typename unpacket_traits<Packet>::type* from, const Index n, const Index offset = 0)
+{
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_assert(n + offset <= packet_size && "number of elements plus offset will read past end of packet");
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  EIGEN_ALIGN_MAX Scalar elements[packet_size] = { Scalar(0) };
+  for (Index i = offset; i < numext::mini(n+offset,packet_size); i++) {
+    elements[i] = from[i-offset];
+  }
+  return pload<Packet>(elements);
+}
+
+/** \internal \returns a packet version of \a *from, (un-aligned masked load)
+ * There is no generic implementation. We only have implementations for specialized
+ * cases. Generic case should not be called.
+ */
+template<typename Packet> EIGEN_DEVICE_FUNC inline
+std::enable_if_t<unpacket_traits<Packet>::masked_load_available, Packet>
+ploadu(const typename unpacket_traits<Packet>::type* from, typename unpacket_traits<Packet>::mask_t umask);
+
+/** \internal \returns a packet with constant coefficients \a a, e.g.: (a,a,a,a) */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pset1(const typename unpacket_traits<Packet>::type& a) { return a; }
+
+/** \internal \returns a packet with constant coefficients set from bits */
+template<typename Packet,typename BitsType> EIGEN_DEVICE_FUNC inline Packet
+pset1frombits(BitsType a);
+
+/** \internal \returns a packet with constant coefficients \a a[0], e.g.: (a[0],a[0],a[0],a[0]) */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pload1(const typename unpacket_traits<Packet>::type  *a) { return pset1<Packet>(*a); }
+
+/** \internal \returns a packet with elements of \a *from duplicated.
+  * For instance, for a packet of 8 elements, 4 scalars will be read from \a *from and
+  * duplicated to form: {from[0],from[0],from[1],from[1],from[2],from[2],from[3],from[3]}
+  * Currently, this function is only used for scalar * complex products.
+  */
+template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet
+ploaddup(const typename unpacket_traits<Packet>::type* from) { return *from; }
+
+/** \internal \returns a packet with elements of \a *from quadrupled.
+  * For instance, for a packet of 8 elements, 2 scalars will be read from \a *from and
+  * replicated to form: {from[0],from[0],from[0],from[0],from[1],from[1],from[1],from[1]}
+  * Currently, this function is only used in matrix products.
+  * For packet-size smaller or equal to 4, this function is equivalent to pload1
+  */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+ploadquad(const typename unpacket_traits<Packet>::type* from)
+{ return pload1<Packet>(from); }
+
+/** \internal equivalent to
+  * \code
+  * a0 = pload1(a+0);
+  * a1 = pload1(a+1);
+  * a2 = pload1(a+2);
+  * a3 = pload1(a+3);
+  * \endcode
+  * \sa pset1, pload1, ploaddup, pbroadcast2
+  */
+template<typename Packet> EIGEN_DEVICE_FUNC
+inline void pbroadcast4(const typename unpacket_traits<Packet>::type *a,
+                        Packet& a0, Packet& a1, Packet& a2, Packet& a3)
+{
+  a0 = pload1<Packet>(a+0);
+  a1 = pload1<Packet>(a+1);
+  a2 = pload1<Packet>(a+2);
+  a3 = pload1<Packet>(a+3);
+}
+
+/** \internal equivalent to
+  * \code
+  * a0 = pload1(a+0);
+  * a1 = pload1(a+1);
+  * \endcode
+  * \sa pset1, pload1, ploaddup, pbroadcast4
+  */
+template<typename Packet> EIGEN_DEVICE_FUNC
+inline void pbroadcast2(const typename unpacket_traits<Packet>::type *a,
+                        Packet& a0, Packet& a1)
+{
+  a0 = pload1<Packet>(a+0);
+  a1 = pload1<Packet>(a+1);
+}
+
+/** \internal \brief Returns a packet with coefficients (a,a+1,...,a+packet_size-1). */
+template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet
+plset(const typename unpacket_traits<Packet>::type& a) { return a; }
+
+/** \internal \returns a packet with constant coefficients \a a, e.g.: (x, 0, x, 0),
+     where x is the value of all 1-bits. */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+peven_mask(const Packet& /*a*/) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  const size_t n = unpacket_traits<Packet>::size;
+  EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) Scalar elements[n];
+  for(size_t i = 0; i < n; ++i) {
+    memset(elements+i, ((i & 1) == 0 ? 0xff : 0), sizeof(Scalar));
+  }
+  return ploadu<Packet>(elements);
+}
+
+
+/** \internal copy the packet \a from to \a *to, \a to must be properly aligned */
+template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstore(Scalar* to, const Packet& from)
+{ (*to) = from; }
+
+/** \internal copy n elements of the packet \a from to \a *to, \a to must be properly aligned
+ * offset indicates the starting element in which to store and
+ * offset + n <= unpacket_traits::size */
+template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstore_partial(Scalar* to, const Packet& from, const Index n, const Index offset = 0)
+{
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_assert(n + offset <= packet_size && "number of elements plus offset will write past end of packet");
+  EIGEN_ALIGN_MAX Scalar elements[packet_size];
+  pstore<Scalar>(elements, from);
+  for (Index i = 0; i < numext::mini(n,packet_size-offset); i++) {
+    to[i] = elements[i + offset];
+  }
+}
+
+/** \internal copy the packet \a from to \a *to, (un-aligned store) */
+template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstoreu(Scalar* to, const Packet& from)
+{  (*to) = from; }
+
+/** \internal copy n elements of the packet \a from to \a *to, (un-aligned store) */
+template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pstoreu_partial(Scalar* to, const Packet& from, const Index n, const Index offset = 0)
+{
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_assert(n + offset <= packet_size && "number of elements plus offset will write past end of packet");
+  EIGEN_ALIGN_MAX Scalar elements[packet_size];
+  pstore<Scalar>(elements, from);
+  for (Index i = 0; i < numext::mini(n,packet_size-offset); i++) {
+    to[i] = elements[i + offset];
+  }
+}
+
+/** \internal copy the packet \a from to \a *to, (un-aligned store with a mask)
+ * There is no generic implementation. We only have implementations for specialized
+ * cases. Generic case should not be called.
+ */
+template<typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline
+std::enable_if_t<unpacket_traits<Packet>::masked_store_available, void>
+pstoreu(Scalar* to, const Packet& from, typename unpacket_traits<Packet>::mask_t umask);
+
+template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather(const Scalar* from, Index /*stride*/)
+{ return ploadu<Packet>(from); }
+
+template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline Packet pgather_partial(const Scalar* from, Index stride, const Index n)
+{
+  const Index packet_size = unpacket_traits<Packet>::size;
+  EIGEN_ALIGN_MAX Scalar elements[packet_size] = { Scalar(0) };
+  for (Index i = 0; i < numext::mini(n,packet_size); i++) {
+    elements[i] = from[i*stride];
+  }
+  return pload<Packet>(elements);
+}
+
+template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pscatter(Scalar* to, const Packet& from, Index /*stride*/)
+{ pstore(to, from); }
+
+template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC inline void pscatter_partial(Scalar* to, const Packet& from, Index stride, const Index n)
+{
+  const Index packet_size = unpacket_traits<Packet>::size;
+  EIGEN_ALIGN_MAX Scalar elements[packet_size];
+  pstore<Scalar>(elements, from);
+  for (Index i = 0; i < numext::mini(n,packet_size); i++) {
+    to[i*stride] = elements[i];
+  }
+}
+
+/** \internal tries to do cache prefetching of \a addr */
+template<typename Scalar> EIGEN_DEVICE_FUNC inline void prefetch(const Scalar* addr)
+{
+#if defined(EIGEN_HIP_DEVICE_COMPILE)
+  // do nothing
+#elif defined(EIGEN_CUDA_ARCH)
+#if defined(__LP64__) || EIGEN_OS_WIN64
+  // 64-bit pointer operand constraint for inlined asm
+  asm(" prefetch.L1 [ %1 ];" : "=l"(addr) : "l"(addr));
+#else
+  // 32-bit pointer operand constraint for inlined asm
+  asm(" prefetch.L1 [ %1 ];" : "=r"(addr) : "r"(addr));
+#endif
+#elif (!EIGEN_COMP_MSVC) && (EIGEN_COMP_GNUC || EIGEN_COMP_CLANG || EIGEN_COMP_ICC)
+  __builtin_prefetch(addr);
+#endif
+}
+
+/** \internal \returns the reversed elements of \a a*/
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet preverse(const Packet& a)
+{ return a; }
+
+/** \internal \returns \a a with real and imaginary part flipped (for complex type only) */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet pcplxflip(const Packet& a)
+{
+  return Packet(numext::imag(a),numext::real(a));
+}
+
+/**************************
+* Special math functions
+***************************/
+
+/** \internal \returns the sine of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet psin(const Packet& a) { EIGEN_USING_STD(sin); return sin(a); }
+
+/** \internal \returns the cosine of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pcos(const Packet& a) { EIGEN_USING_STD(cos); return cos(a); }
+
+/** \internal \returns the tan of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet ptan(const Packet& a) { EIGEN_USING_STD(tan); return tan(a); }
+
+/** \internal \returns the arc sine of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pasin(const Packet& a) { EIGEN_USING_STD(asin); return asin(a); }
+
+/** \internal \returns the arc cosine of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pacos(const Packet& a) { EIGEN_USING_STD(acos); return acos(a); }
+
+
+/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet psinh(const Packet& a) { EIGEN_USING_STD(sinh); return sinh(a); }
+
+/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pcosh(const Packet& a) { EIGEN_USING_STD(cosh); return cosh(a); }
+
+/** \internal \returns the arc tangent of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet patan(const Packet& a) { EIGEN_USING_STD(atan); return atan(a); }
+
+/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet ptanh(const Packet& a) { EIGEN_USING_STD(tanh); return tanh(a); }
+
+/** \internal \returns the arc tangent of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet patanh(const Packet& a) { EIGEN_USING_STD(atanh); return atanh(a); }
+
+/** \internal \returns the exp of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pexp(const Packet& a) { EIGEN_USING_STD(exp); return exp(a); }
+
+/** \internal \returns the expm1 of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pexpm1(const Packet& a) { return numext::expm1(a); }
+
+/** \internal \returns the log of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet plog(const Packet& a) { EIGEN_USING_STD(log); return log(a); }
+
+/** \internal \returns the log1p of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet plog1p(const Packet& a) { return numext::log1p(a); }
+
+/** \internal \returns the log10 of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet plog10(const Packet& a) { EIGEN_USING_STD(log10); return log10(a); }
+
+/** \internal \returns the log10 of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet plog2(const Packet& a) {
+  typedef typename internal::unpacket_traits<Packet>::type Scalar;
+  return pmul(pset1<Packet>(Scalar(EIGEN_LOG2E)), plog(a));
+}
+
+/** \internal \returns the square-root of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet psqrt(const Packet& a) { return numext::sqrt(a); }
+
+/** \internal \returns the rounded value of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pround(const Packet& a) { using numext::round; return round(a); }
+
+/** \internal \returns the floor of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pfloor(const Packet& a) { using numext::floor; return floor(a); }
+
+/** \internal \returns the rounded value of \a a (coeff-wise) with current
+ * rounding mode */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet print(const Packet& a) { using numext::rint; return rint(a); }
+
+/** \internal \returns the ceil of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pceil(const Packet& a) { using numext::ceil; return ceil(a); }
+
+template<typename Packet, typename EnableIf = void>
+struct psign_impl {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a) {
+    return numext::sign(a);
+  }
+};
+
+/** \internal \returns the sign of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+psign(const Packet& a) {
+  return psign_impl<Packet>::run(a);
+}
+
+template<> EIGEN_DEVICE_FUNC inline bool
+psign(const bool& a) {
+  return a;
+}
+
+/** \internal \returns the first element of a packet */
+template<typename Packet>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type
+pfirst(const Packet& a)
+{ return a; }
+
+/** \internal \returns the sum of the elements of upper and lower half of \a a if \a a is larger than 4.
+  * For a packet {a0, a1, a2, a3, a4, a5, a6, a7}, it returns a half packet {a0+a4, a1+a5, a2+a6, a3+a7}
+  * For packet-size smaller or equal to 4, this boils down to a noop.
+  */
+template<typename Packet>
+EIGEN_DEVICE_FUNC inline std::conditional_t<(unpacket_traits<Packet>::size%8)==0,typename unpacket_traits<Packet>::half,Packet>
+predux_half_dowto4(const Packet& a)
+{ return a; }
+
+// Slow generic implementation of Packet reduction.
+template <typename Packet, typename Op>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type
+predux_helper(const Packet& a, Op op) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  const size_t n = unpacket_traits<Packet>::size;
+  EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) Scalar elements[n];
+  pstoreu<Scalar>(elements, a);
+  for(size_t k = n / 2; k > 0; k /= 2)  {
+    for(size_t i = 0; i < k; ++i) {
+      elements[i] = op(elements[i], elements[i + k]);
+    }
+  }
+  return elements[0];
+}
+
+/** \internal \returns the sum of the elements of \a a*/
+template<typename Packet>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type
+predux(const Packet& a)
+{
+  return a;
+}
+
+/** \internal \returns the product of the elements of \a a */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_mul(
+    const Packet& a) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmul<Scalar>)));
+}
+
+/** \internal \returns the min of the elements of \a a */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(
+    const Packet &a) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmin<PropagateFast, Scalar>)));
+}
+
+template <int NaNPropagation, typename Packet>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_min(
+    const Packet& a) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmin<NaNPropagation, Scalar>)));
+}
+
+/** \internal \returns the min of the elements of \a a */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_max(
+    const Packet &a) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmax<PropagateFast, Scalar>)));
+}
+
+template <int NaNPropagation, typename Packet>
+EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type predux_max(
+    const Packet& a) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  return predux_helper(a, EIGEN_BINARY_OP_NAN_PROPAGATION(Scalar, (pmax<NaNPropagation, Scalar>)));
+}
+
+#undef EIGEN_BINARY_OP_NAN_PROPAGATION
+
+/** \internal \returns true if all coeffs of \a a means "true"
+  * It is supposed to be called on values returned by pcmp_*.
+  */
+// not needed yet
+// template<typename Packet> EIGEN_DEVICE_FUNC inline bool predux_all(const Packet& a)
+// { return bool(a); }
+
+/** \internal \returns true if any coeffs of \a a means "true"
+  * It is supposed to be called on values returned by pcmp_*.
+  */
+template<typename Packet> EIGEN_DEVICE_FUNC inline bool predux_any(const Packet& a)
+{
+  // Dirty but generic implementation where "true" is assumed to be non 0 and all the sames.
+  // It is expected that "true" is either:
+  //  - Scalar(1)
+  //  - bits full of ones (NaN for floats),
+  //  - or first bit equals to 1 (1 for ints, smallest denormal for floats).
+  // For all these cases, taking the sum is just fine, and this boils down to a no-op for scalars.
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  return numext::not_equal_strict(predux(a), Scalar(0));
+}
+
+/***************************************************************************
+* The following functions might not have to be overwritten for vectorized types
+***************************************************************************/
+
+// FMA instructions.
+/** \internal \returns a * b + c (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pmadd(const Packet& a, const Packet& b,
+                                      const Packet& c) {
+  return padd(pmul(a, b), c);
+}
+
+/** \internal \returns a * b - c (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pmsub(const Packet& a, const Packet& b,
+                                      const Packet& c) {
+  return psub(pmul(a, b), c);
+}
+
+/** \internal \returns -(a * b) + c (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pnmadd(const Packet& a, const Packet& b,
+                                       const Packet& c) {
+  return padd(pnegate(pmul(a, b)), c);
+}
+
+/** \internal \returns -(a * b) - c (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pnmsub(const Packet& a, const Packet& b,
+                                       const Packet& c) {
+  return psub(pnegate(pmul(a, b)), c);
+}
+
+/** \internal copy a packet with constant coefficient \a a (e.g., [a,a,a,a]) to \a *to. \a to must be 16 bytes aligned */
+// NOTE: this function must really be templated on the packet type (think about different packet types for the same scalar type)
+template<typename Packet>
+inline void pstore1(typename unpacket_traits<Packet>::type* to, const typename unpacket_traits<Packet>::type& a)
+{
+  pstore(to, pset1<Packet>(a));
+}
+
+/** \internal \returns a packet version of \a *from.
+  * The pointer \a from must be aligned on a \a Alignment bytes boundary. */
+template<typename Packet, int Alignment>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt(const typename unpacket_traits<Packet>::type* from)
+{
+  if(Alignment >= unpacket_traits<Packet>::alignment)
+    return pload<Packet>(from);
+  else
+    return ploadu<Packet>(from);
+}
+
+/** \internal \returns n elements of a packet version of \a *from.
+  * The pointer \a from must be aligned on a \a Alignment bytes boundary. */
+template<typename Packet, int Alignment>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt_partial(const typename unpacket_traits<Packet>::type* from, const Index n, const Index offset = 0)
+{
+  if(Alignment >= unpacket_traits<Packet>::alignment)
+    return pload_partial<Packet>(from, n, offset);
+  else
+    return ploadu_partial<Packet>(from, n, offset);
+}
+
+/** \internal copy the packet \a from to \a *to.
+  * The pointer \a from must be aligned on a \a Alignment bytes boundary. */
+template<typename Scalar, typename Packet, int Alignment>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret(Scalar* to, const Packet& from)
+{
+  if(Alignment >= unpacket_traits<Packet>::alignment)
+    pstore(to, from);
+  else
+    pstoreu(to, from);
+}
+
+/** \internal copy n elements of the packet \a from to \a *to.
+  * The pointer \a from must be aligned on a \a Alignment bytes boundary. */
+template<typename Scalar, typename Packet, int Alignment>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstoret_partial(Scalar* to, const Packet& from, const Index n, const Index offset = 0)
+{
+  if(Alignment >= unpacket_traits<Packet>::alignment)
+    pstore_partial(to, from, n, offset);
+  else
+    pstoreu_partial(to, from, n, offset);
+}
+
+/** \internal \returns a packet version of \a *from.
+  * Unlike ploadt, ploadt_ro takes advantage of the read-only memory path on the
+  * hardware if available to speedup the loading of data that won't be modified
+  * by the current computation.
+  */
+template<typename Packet, int LoadMode>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet ploadt_ro(const typename unpacket_traits<Packet>::type* from)
+{
+  return ploadt<Packet, LoadMode>(from);
+}
+
+/***************************************************************************
+* Fast complex products (GCC generates a function call which is very slow)
+***************************************************************************/
+
+// Eigen+CUDA does not support complexes.
+#if !defined(EIGEN_GPUCC)
+
+template<> inline std::complex<float> pmul(const std::complex<float>& a, const std::complex<float>& b)
+{ return std::complex<float>(a.real()*b.real() - a.imag()*b.imag(), a.imag()*b.real() + a.real()*b.imag()); }
+
+template<> inline std::complex<double> pmul(const std::complex<double>& a, const std::complex<double>& b)
+{ return std::complex<double>(a.real()*b.real() - a.imag()*b.imag(), a.imag()*b.real() + a.real()*b.imag()); }
+
+#endif
+
+
+/***************************************************************************
+ * PacketBlock, that is a collection of N packets where the number of words
+ * in the packet is a multiple of N.
+***************************************************************************/
+template <typename Packet,int N=unpacket_traits<Packet>::size> struct PacketBlock {
+  Packet packet[N];
+};
+
+template<typename Packet> EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet,1>& /*kernel*/) {
+  // Nothing to do in the scalar case, i.e. a 1x1 matrix.
+}
+
+/***************************************************************************
+ * Selector, i.e. vector of N boolean values used to select (i.e. blend)
+ * words from 2 packets.
+***************************************************************************/
+template <size_t N> struct Selector {
+  bool select[N];
+};
+
+template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
+pblend(const Selector<unpacket_traits<Packet>::size>& ifPacket, const Packet& thenPacket, const Packet& elsePacket) {
+  return ifPacket.select[0] ? thenPacket : elsePacket;
+}
+
+/** \internal \returns 1 / a (coeff-wise) */
+template <typename Packet>
+EIGEN_DEVICE_FUNC inline Packet preciprocal(const Packet& a) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  return pdiv(pset1<Packet>(Scalar(1)), a);
+}
+
+/** \internal \returns the reciprocal square-root of \a a (coeff-wise) */
+template<typename Packet> EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet prsqrt(const Packet& a) {
+  return preciprocal<Packet>(psqrt(a));
+}
+
+template <typename Packet, bool IsScalar = is_scalar<Packet>::value,
+          bool IsInteger = NumTraits<typename unpacket_traits<Packet>::type>::IsInteger>
+struct psignbit_impl;
+template <typename Packet, bool IsInteger>
+struct psignbit_impl<Packet, true, IsInteger> {
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static constexpr Packet run(const Packet& a) { return numext::signbit(a); }
+};
+template <typename Packet>
+struct psignbit_impl<Packet, false, false> {
+  // generic implementation if not specialized in PacketMath.h
+  // slower than arithmetic shift
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static Packet run(const Packet& a) {
+    const Packet cst_pos_one = pset1<Packet>(Scalar(1));
+    const Packet cst_neg_one = pset1<Packet>(Scalar(-1));
+    return pcmp_eq(por(pand(a, cst_neg_one), cst_pos_one), cst_neg_one);
+  }
+};
+template <typename Packet>
+struct psignbit_impl<Packet, false, true> {
+  // generic implementation for integer packets
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static constexpr Packet run(const Packet& a) { return pcmp_lt(a, pzero(a)); }
+};
+/** \internal \returns the sign bit of \a a as a bitmask*/
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE constexpr Packet
+psignbit(const Packet& a) { return psignbit_impl<Packet>::run(a); }
+
+/** \internal \returns the 2-argument arc tangent of \a y and \a x (coeff-wise) */
+template <typename Packet, std::enable_if_t<is_scalar<Packet>::value, int> = 0>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet patan2(const Packet& y, const Packet& x) {
+  return numext::atan2(y, x);
+}
+
+/** \internal \returns the 2-argument arc tangent of \a y and \a x (coeff-wise) */
+template <typename Packet, std::enable_if_t<!is_scalar<Packet>::value, int> = 0>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet patan2(const Packet& y, const Packet& x) {
+  typedef typename internal::unpacket_traits<Packet>::type Scalar;
+
+  // See https://en.cppreference.com/w/cpp/numeric/math/atan2
+  // for how corner cases are supposed to be handled according to the
+  // IEEE floating-point standard (IEC 60559).
+  const Packet kSignMask = pset1<Packet>(-Scalar(0));
+  const Packet kZero = pzero(x);
+  const Packet kOne = pset1<Packet>(Scalar(1));
+  const Packet kPi = pset1<Packet>(Scalar(EIGEN_PI));
+
+  const Packet x_has_signbit = psignbit(x);
+  const Packet y_signmask = pand(y, kSignMask);
+  const Packet x_signmask = pand(x, kSignMask);
+  const Packet result_signmask = pxor(y_signmask, x_signmask);
+  const Packet shift = por(pand(x_has_signbit, kPi), y_signmask);
+
+  const Packet x_and_y_are_same = pcmp_eq(pabs(x), pabs(y));
+  const Packet x_and_y_are_zero = pcmp_eq(por(x, y), kZero);
+
+  Packet arg = pdiv(y, x);
+  arg = pselect(x_and_y_are_same, por(kOne, result_signmask), arg);
+  arg = pselect(x_and_y_are_zero, result_signmask, arg);
+
+  Packet result = patan(arg);
+  result = padd(result, shift);
+  return result;
+}
+
+/** \internal \returns the argument of \a a as a complex number */
+template <typename Packet, std::enable_if_t<is_scalar<Packet>::value, int> = 0>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet pcarg(const Packet& a) {
+  return Packet(numext::arg(a));
+}
+
+/** \internal \returns the argument of \a a as a complex number */
+template <typename Packet, std::enable_if_t<!is_scalar<Packet>::value, int> = 0>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet pcarg(const Packet& a) {
+  EIGEN_STATIC_ASSERT(NumTraits<typename unpacket_traits<Packet>::type>::IsComplex, THIS METHOD IS FOR COMPLEX TYPES ONLY)
+  using RealPacket = typename unpacket_traits<Packet>::as_real;
+  // a                                              // r     i    r     i    ...
+  RealPacket aflip = pcplxflip(a).v;                // i     r    i     r    ...
+  RealPacket result = patan2(aflip, a.v);           // atan2 crap atan2 crap ...
+  return (Packet)pand(result, peven_mask(result));  // atan2 0    atan2 0    ...
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_GENERIC_PACKET_MATH_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/GlobalFunctions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/GlobalFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..30f2a38af3182015092d5e128827202065a9ed13
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/GlobalFunctions.h
@@ -0,0 +1,213 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GLOBAL_FUNCTIONS_H
+#define EIGEN_GLOBAL_FUNCTIONS_H
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+
+#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR,DOC_OP,DOC_DETAILS) \
+  /** \returns an expression of the coefficient-wise DOC_OP of \a x
+
+    DOC_DETAILS
+
+    \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_##NAME">Math functions</a>, class CwiseUnaryOp
+    */ \
+  template<typename Derived> \
+  inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \
+  NAME(const Eigen::ArrayBase<Derived>& x);
+
+#else
+
+#define EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(NAME,FUNCTOR,DOC_OP,DOC_DETAILS) \
+  template<typename Derived> \
+  inline const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> \
+  (NAME)(const Eigen::ArrayBase<Derived>& x) { \
+    return Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived>(x.derived()); \
+  }
+
+#endif // EIGEN_PARSED_BY_DOXYGEN
+
+#define EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(NAME,FUNCTOR) \
+  \
+  template<typename Derived> \
+  struct NAME##_retval<ArrayBase<Derived> > \
+  { \
+    typedef const Eigen::CwiseUnaryOp<Eigen::internal::FUNCTOR<typename Derived::Scalar>, const Derived> type; \
+  }; \
+  template<typename Derived> \
+  struct NAME##_impl<ArrayBase<Derived> > \
+  { \
+    static inline typename NAME##_retval<ArrayBase<Derived> >::type run(const Eigen::ArrayBase<Derived>& x) \
+    { \
+      return typename NAME##_retval<ArrayBase<Derived> >::type(x.derived()); \
+    } \
+  };
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen
+{
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(real,scalar_real_op,real part,\sa ArrayBase::real)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(imag,scalar_imag_op,imaginary part,\sa ArrayBase::imag)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(conj,scalar_conjugate_op,complex conjugate,\sa ArrayBase::conjugate)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(inverse,scalar_inverse_op,inverse,\sa ArrayBase::inverse)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sin,scalar_sin_op,sine,\sa ArrayBase::sin)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cos,scalar_cos_op,cosine,\sa ArrayBase::cos)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tan,scalar_tan_op,tangent,\sa ArrayBase::tan)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atan,scalar_atan_op,arc-tangent,\sa ArrayBase::atan)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asin,scalar_asin_op,arc-sine,\sa ArrayBase::asin)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acos,scalar_acos_op,arc-consine,\sa ArrayBase::acos)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sinh,scalar_sinh_op,hyperbolic sine,\sa ArrayBase::sinh)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cosh,scalar_cosh_op,hyperbolic cosine,\sa ArrayBase::cosh)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(tanh,scalar_tanh_op,hyperbolic tangent,\sa ArrayBase::tanh)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(asinh,scalar_asinh_op,inverse hyperbolic sine,\sa ArrayBase::asinh)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(acosh,scalar_acosh_op,inverse hyperbolic cosine,\sa ArrayBase::acosh)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(atanh,scalar_atanh_op,inverse hyperbolic tangent,\sa ArrayBase::atanh)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(logistic,scalar_logistic_op,logistic function,\sa ArrayBase::logistic)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(lgamma,scalar_lgamma_op,natural logarithm of the gamma function,\sa ArrayBase::lgamma)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(digamma,scalar_digamma_op,derivative of lgamma,\sa ArrayBase::digamma)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erf,scalar_erf_op,error function,\sa ArrayBase::erf)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(erfc,scalar_erfc_op,complement error function,\sa ArrayBase::erfc)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ndtri,scalar_ndtri_op,inverse normal distribution function,\sa ArrayBase::ndtri)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(exp,scalar_exp_op,exponential,\sa ArrayBase::exp)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(expm1,scalar_expm1_op,exponential of a value minus 1,\sa ArrayBase::expm1)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log,scalar_log_op,natural logarithm,\sa Eigen::log10 DOXCOMMA ArrayBase::log)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log1p,scalar_log1p_op,natural logarithm of 1 plus the value,\sa ArrayBase::log1p)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log10,scalar_log10_op,base 10 logarithm,\sa Eigen::log DOXCOMMA ArrayBase::log10)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(log2,scalar_log2_op,base 2 logarithm,\sa Eigen::log DOXCOMMA ArrayBase::log2)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs,scalar_abs_op,absolute value,\sa ArrayBase::abs DOXCOMMA MatrixBase::cwiseAbs)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(abs2,scalar_abs2_op,squared absolute value,\sa ArrayBase::abs2 DOXCOMMA MatrixBase::cwiseAbs2)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(arg,scalar_arg_op,complex argument,\sa ArrayBase::arg DOXCOMMA MatrixBase::cwiseArg)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(carg, scalar_carg_op, complex argument, \sa ArrayBase::carg DOXCOMMA MatrixBase::cwiseCArg)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sqrt,scalar_sqrt_op,square root,\sa ArrayBase::sqrt DOXCOMMA MatrixBase::cwiseSqrt)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(rsqrt,scalar_rsqrt_op,reciprocal square root,\sa ArrayBase::rsqrt)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(square,scalar_square_op,square (power 2),\sa Eigen::abs2 DOXCOMMA Eigen::pow DOXCOMMA ArrayBase::square)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(cube,scalar_cube_op,cube (power 3),\sa Eigen::pow DOXCOMMA ArrayBase::cube)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(rint,scalar_rint_op,nearest integer,\sa Eigen::floor DOXCOMMA Eigen::ceil DOXCOMMA ArrayBase::round)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(round,scalar_round_op,nearest integer,\sa Eigen::floor DOXCOMMA Eigen::ceil DOXCOMMA ArrayBase::round)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(floor,scalar_floor_op,nearest integer not greater than the giben value,\sa Eigen::ceil DOXCOMMA ArrayBase::floor)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(ceil,scalar_ceil_op,nearest integer not less than the giben value,\sa Eigen::floor DOXCOMMA ArrayBase::ceil)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isnan,scalar_isnan_op,not-a-number test,\sa Eigen::isinf DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isnan)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isinf,scalar_isinf_op,infinite value test,\sa Eigen::isnan DOXCOMMA Eigen::isfinite DOXCOMMA ArrayBase::isinf)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(isfinite,scalar_isfinite_op,finite value test,\sa Eigen::isinf DOXCOMMA Eigen::isnan DOXCOMMA ArrayBase::isfinite)
+  EIGEN_ARRAY_DECLARE_GLOBAL_UNARY(sign,scalar_sign_op,sign (or 0),\sa ArrayBase::sign)
+
+  template <typename Derived, typename ScalarExponent>
+  using GlobalUnaryPowReturnType = std::enable_if_t<
+      !internal::is_arithmetic<typename NumTraits<Derived>::Real>::value &&
+          internal::is_arithmetic<typename NumTraits<ScalarExponent>::Real>::value,
+      CwiseUnaryOp<internal::scalar_unary_pow_op<typename Derived::Scalar, ScalarExponent>, const Derived> >;
+
+  /** \returns an expression of the coefficient-wise power of \a x to the given constant \a exponent.
+   *
+   * \tparam ScalarExponent is the scalar type of \a exponent. It must be compatible with the scalar type of the given
+   * expression (\c Derived::Scalar).
+   *
+   * \sa ArrayBase::pow()
+   *
+   * \relates ArrayBase
+   */
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  template <typename Derived, typename ScalarExponent>
+  EIGEN_DEVICE_FUNC inline const GlobalUnaryPowReturnType<Derived, ScalarExponent> pow(
+      const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent);
+#else
+  template <typename Derived, typename ScalarExponent>
+  EIGEN_DEVICE_FUNC inline const GlobalUnaryPowReturnType<Derived, ScalarExponent> pow(
+      const Eigen::ArrayBase<Derived>& x, const ScalarExponent& exponent) {
+    return GlobalUnaryPowReturnType<Derived, ScalarExponent>(
+        x.derived(), internal::scalar_unary_pow_op<typename Derived::Scalar, ScalarExponent>(exponent));
+  }
+#endif
+
+  /** \returns an expression of the coefficient-wise power of \a x to the given array of \a exponents.
+    *
+    * This function computes the coefficient-wise power.
+    *
+    * Example: \include Cwise_array_power_array.cpp
+    * Output: \verbinclude Cwise_array_power_array.out
+    *
+    * \sa ArrayBase::pow()
+    *
+    * \relates ArrayBase
+    */
+  template<typename Derived,typename ExponentDerived>
+  inline const Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>
+  pow(const Eigen::ArrayBase<Derived>& x, const Eigen::ArrayBase<ExponentDerived>& exponents)
+  {
+    return Eigen::CwiseBinaryOp<Eigen::internal::scalar_pow_op<typename Derived::Scalar, typename ExponentDerived::Scalar>, const Derived, const ExponentDerived>(
+      x.derived(),
+      exponents.derived()
+    );
+  }
+
+  /** \returns an expression of the coefficient-wise power of the scalar \a x to the given array of \a exponents.
+    *
+    * This function computes the coefficient-wise power between a scalar and an array of exponents.
+    *
+    * \tparam Scalar is the scalar type of \a x. It must be compatible with the scalar type of the given array expression (\c Derived::Scalar).
+    *
+    * Example: \include Cwise_scalar_power_array.cpp
+    * Output: \verbinclude Cwise_scalar_power_array.out
+    *
+    * \sa ArrayBase::pow()
+    *
+    * \relates ArrayBase
+    */
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  template<typename Scalar,typename Derived>
+  inline const CwiseBinaryOp<internal::scalar_pow_op<Scalar,Derived::Scalar>,Constant<Scalar>,Derived>
+  pow(const Scalar& x,const Eigen::ArrayBase<Derived>& x);
+#else
+  template <typename Scalar, typename Derived>
+  EIGEN_DEVICE_FUNC inline
+    const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename internal::promote_scalar_arg<typename Derived::Scalar
+                                                 EIGEN_COMMA Scalar EIGEN_COMMA
+                                                 EIGEN_SCALAR_BINARY_SUPPORTED(pow,Scalar,typename Derived::Scalar)>::type,Derived,pow)
+  pow(const Scalar& x, const Eigen::ArrayBase<Derived>& exponents) {
+    typedef typename internal::promote_scalar_arg<typename Derived::Scalar,Scalar,
+                                                  EIGEN_SCALAR_BINARY_SUPPORTED(pow,Scalar,typename Derived::Scalar)>::type PromotedScalar;
+    return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(PromotedScalar,Derived,pow)(
+           typename internal::plain_constant_type<Derived,PromotedScalar>::type(exponents.derived().rows(), exponents.derived().cols(), internal::scalar_constant_op<PromotedScalar>(x)), exponents.derived());
+  }
+#endif
+
+  /** \returns an expression of the coefficient-wise atan2(\a x, \a y). \a x and \a y must be of the same type.
+    *
+    * This function computes the coefficient-wise atan2().
+    *
+    * \sa ArrayBase::atan2()
+    *
+    * \relates ArrayBase
+    */
+  template <typename LhsDerived, typename RhsDerived>
+  inline const std::enable_if_t<
+      std::is_same<typename LhsDerived::Scalar, typename RhsDerived::Scalar>::value,
+      Eigen::CwiseBinaryOp<Eigen::internal::scalar_atan2_op<typename LhsDerived::Scalar, typename RhsDerived::Scalar>, const LhsDerived, const RhsDerived>
+      >
+  atan2(const Eigen::ArrayBase<LhsDerived>& x, const Eigen::ArrayBase<RhsDerived>& exponents) {
+    return Eigen::CwiseBinaryOp<Eigen::internal::scalar_atan2_op<typename LhsDerived::Scalar, typename RhsDerived::Scalar>, const LhsDerived, const RhsDerived>(
+      x.derived(),
+      exponents.derived()
+    );
+  }
+
+  namespace internal
+  {
+    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(real,scalar_real_op)
+    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(imag,scalar_imag_op)
+    EIGEN_ARRAY_DECLARE_GLOBAL_EIGEN_UNARY(abs2,scalar_abs2_op)
+  }
+}
+
+// TODO: cleanly disable those functions that are not supported on Array (numext::real_ref, internal::random, internal::isApprox...)
+
+#endif // EIGEN_GLOBAL_FUNCTIONS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/IO.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/IO.h
new file mode 100644
index 0000000000000000000000000000000000000000..69937292b4aed5205e8d6fb6093b73c488401685
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/IO.h
@@ -0,0 +1,263 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_IO_H
+#define EIGEN_IO_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+enum { DontAlignCols = 1 };
+enum { StreamPrecision = -1,
+       FullPrecision = -2 };
+
+namespace internal {
+template<typename Derived>
+std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt);
+}
+
+/** \class IOFormat
+  * \ingroup Core_Module
+  *
+  * \brief Stores a set of parameters controlling the way matrices are printed
+  *
+  * List of available parameters:
+  *  - \b precision number of digits for floating point values, or one of the special constants \c StreamPrecision and \c FullPrecision.
+  *                 The default is the special value \c StreamPrecision which means to use the
+  *                 stream's own precision setting, as set for instance using \c cout.precision(3). The other special value
+  *                 \c FullPrecision means that the number of digits will be computed to match the full precision of each floating-point
+  *                 type.
+  *  - \b flags an OR-ed combination of flags, the default value is 0, the only currently available flag is \c DontAlignCols which
+  *             allows to disable the alignment of columns, resulting in faster code.
+  *  - \b coeffSeparator string printed between two coefficients of the same row
+  *  - \b rowSeparator string printed between two rows
+  *  - \b rowPrefix string printed at the beginning of each row
+  *  - \b rowSuffix string printed at the end of each row
+  *  - \b matPrefix string printed at the beginning of the matrix
+  *  - \b matSuffix string printed at the end of the matrix
+  *  - \b fill character printed to fill the empty space in aligned columns
+  *
+  * Example: \include IOFormat.cpp
+  * Output: \verbinclude IOFormat.out
+  *
+  * \sa DenseBase::format(), class WithFormat
+  */
+struct IOFormat
+{
+  /** Default constructor, see class IOFormat for the meaning of the parameters */
+  IOFormat(int _precision = StreamPrecision, int _flags = 0,
+    const std::string& _coeffSeparator = " ",
+    const std::string& _rowSeparator = "\n", const std::string& _rowPrefix="", const std::string& _rowSuffix="",
+    const std::string& _matPrefix="", const std::string& _matSuffix="", const char _fill=' ')
+  : matPrefix(_matPrefix), matSuffix(_matSuffix), rowPrefix(_rowPrefix), rowSuffix(_rowSuffix), rowSeparator(_rowSeparator),
+    rowSpacer(""), coeffSeparator(_coeffSeparator), fill(_fill), precision(_precision), flags(_flags)
+  {
+    // TODO check if rowPrefix, rowSuffix or rowSeparator contains a newline
+    // don't add rowSpacer if columns are not to be aligned
+    if((flags & DontAlignCols))
+      return;
+    int i = int(matSuffix.length())-1;
+    while (i>=0 && matSuffix[i]!='\n')
+    {
+      rowSpacer += ' ';
+      i--;
+    }
+  }
+  std::string matPrefix, matSuffix;
+  std::string rowPrefix, rowSuffix, rowSeparator, rowSpacer;
+  std::string coeffSeparator;
+  char fill;
+  int precision;
+  int flags;
+};
+
+/** \class WithFormat
+  * \ingroup Core_Module
+  *
+  * \brief Pseudo expression providing matrix output with given format
+  *
+  * \tparam ExpressionType the type of the object on which IO stream operations are performed
+  *
+  * This class represents an expression with stream operators controlled by a given IOFormat.
+  * It is the return type of DenseBase::format()
+  * and most of the time this is the only way it is used.
+  *
+  * See class IOFormat for some examples.
+  *
+  * \sa DenseBase::format(), class IOFormat
+  */
+template<typename ExpressionType>
+class WithFormat
+{
+  public:
+
+    WithFormat(const ExpressionType& matrix, const IOFormat& format)
+      : m_matrix(matrix), m_format(format)
+    {}
+
+    friend std::ostream & operator << (std::ostream & s, const WithFormat& wf)
+    {
+      return internal::print_matrix(s, wf.m_matrix.eval(), wf.m_format);
+    }
+
+  protected:
+    typename ExpressionType::Nested m_matrix;
+    IOFormat m_format;
+};
+
+namespace internal {
+
+// NOTE: This helper is kept for backward compatibility with previous code specializing
+//       this internal::significant_decimals_impl structure. In the future we should directly
+//       call max_digits10().
+template<typename Scalar>
+struct significant_decimals_impl
+{
+  static inline int run()
+  {
+    return NumTraits<Scalar>::max_digits10();
+  }
+};
+
+/** \internal
+  * print the matrix \a _m to the output stream \a s using the output format \a fmt */
+template<typename Derived>
+std::ostream & print_matrix(std::ostream & s, const Derived& _m, const IOFormat& fmt)
+{
+  using internal::is_same;
+
+  if(_m.size() == 0)
+  {
+    s << fmt.matPrefix << fmt.matSuffix;
+    return s;
+  }
+  
+  typename Derived::Nested m = _m;
+  typedef typename Derived::Scalar Scalar;
+  typedef std::conditional_t<
+          is_same<Scalar, char>::value ||
+            is_same<Scalar, unsigned char>::value ||
+            is_same<Scalar, numext::int8_t>::value ||
+            is_same<Scalar, numext::uint8_t>::value,
+          int,
+          std::conditional_t<
+              is_same<Scalar, std::complex<char> >::value ||
+                is_same<Scalar, std::complex<unsigned char> >::value ||
+                is_same<Scalar, std::complex<numext::int8_t> >::value ||
+                is_same<Scalar, std::complex<numext::uint8_t> >::value,
+              std::complex<int>,
+              const Scalar&
+            >
+        > PrintType;
+
+  Index width = 0;
+
+  std::streamsize explicit_precision;
+  if(fmt.precision == StreamPrecision)
+  {
+    explicit_precision = 0;
+  }
+  else if(fmt.precision == FullPrecision)
+  {
+    if (NumTraits<Scalar>::IsInteger)
+    {
+      explicit_precision = 0;
+    }
+    else
+    {
+      explicit_precision = significant_decimals_impl<Scalar>::run();
+    }
+  }
+  else
+  {
+    explicit_precision = fmt.precision;
+  }
+
+  std::streamsize old_precision = 0;
+  if(explicit_precision) old_precision = s.precision(explicit_precision);
+
+  bool align_cols = !(fmt.flags & DontAlignCols);
+  if(align_cols)
+  {
+    // compute the largest width
+    for(Index j = 0; j < m.cols(); ++j)
+      for(Index i = 0; i < m.rows(); ++i)
+      {
+        std::stringstream sstr;
+        sstr.copyfmt(s);
+        sstr << static_cast<PrintType>(m.coeff(i,j));
+        width = std::max<Index>(width, Index(sstr.str().length()));
+      }
+  }
+  std::streamsize old_width = s.width();
+  char old_fill_character = s.fill();
+  s << fmt.matPrefix;
+  for(Index i = 0; i < m.rows(); ++i)
+  {
+    if (i)
+      s << fmt.rowSpacer;
+    s << fmt.rowPrefix;
+    if(width) {
+      s.fill(fmt.fill);
+      s.width(width);
+    }
+    s << static_cast<PrintType>(m.coeff(i, 0));
+    for(Index j = 1; j < m.cols(); ++j)
+    {
+      s << fmt.coeffSeparator;
+      if(width) {
+        s.fill(fmt.fill);
+        s.width(width);
+      }
+      s << static_cast<PrintType>(m.coeff(i, j));
+    }
+    s << fmt.rowSuffix;
+    if( i < m.rows() - 1)
+      s << fmt.rowSeparator;
+  }
+  s << fmt.matSuffix;
+  if(explicit_precision) s.precision(old_precision);
+  if(width) {
+    s.fill(old_fill_character);
+    s.width(old_width);
+  }
+  return s;
+}
+
+} // end namespace internal
+
+/** \relates DenseBase
+  *
+  * Outputs the matrix, to the given stream.
+  *
+  * If you wish to print the matrix with a format different than the default, use DenseBase::format().
+  *
+  * It is also possible to change the default format by defining EIGEN_DEFAULT_IO_FORMAT before including Eigen headers.
+  * If not defined, this will automatically be defined to Eigen::IOFormat(), that is the Eigen::IOFormat with default parameters.
+  *
+  * \sa DenseBase::format()
+  */
+template<typename Derived>
+std::ostream & operator <<
+(std::ostream & s,
+ const DenseBase<Derived> & m)
+{
+  return internal::print_matrix(s, m.eval(), EIGEN_DEFAULT_IO_FORMAT);
+}
+
+template <typename Derived>
+std::ostream& operator<<(std::ostream& s, const DiagonalBase<Derived>& m) {
+  return internal::print_matrix(s, m.derived(), EIGEN_DEFAULT_IO_FORMAT);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_IO_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/IndexedView.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/IndexedView.h
new file mode 100644
index 0000000000000000000000000000000000000000..feab3a9a313997b6c2b8008b9dcc742aab05c7ab
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/IndexedView.h
@@ -0,0 +1,248 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_INDEXED_VIEW_H
+#define EIGEN_INDEXED_VIEW_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename XprType, typename RowIndices, typename ColIndices>
+struct traits<IndexedView<XprType, RowIndices, ColIndices> >
+ : traits<XprType>
+{
+  enum {
+    RowsAtCompileTime = int(array_size<RowIndices>::value),
+    ColsAtCompileTime = int(array_size<ColIndices>::value),
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+
+    XprTypeIsRowMajor = (int(traits<XprType>::Flags)&RowMajorBit) != 0,
+    IsRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
+               : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
+               : XprTypeIsRowMajor,
+
+    RowIncr = int(get_compile_time_incr<RowIndices>::value),
+    ColIncr = int(get_compile_time_incr<ColIndices>::value),
+    InnerIncr = IsRowMajor ? ColIncr : RowIncr,
+    OuterIncr = IsRowMajor ? RowIncr : ColIncr,
+
+    HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
+    XprInnerStride = HasSameStorageOrderAsXprType ? int(inner_stride_at_compile_time<XprType>::ret) : int(outer_stride_at_compile_time<XprType>::ret),
+    XprOuterstride = HasSameStorageOrderAsXprType ? int(outer_stride_at_compile_time<XprType>::ret) : int(inner_stride_at_compile_time<XprType>::ret),
+
+    InnerSize = XprTypeIsRowMajor ? ColsAtCompileTime : RowsAtCompileTime,
+    IsBlockAlike = InnerIncr==1 && OuterIncr==1,
+    IsInnerPannel = HasSameStorageOrderAsXprType && is_same<AllRange<InnerSize>,std::conditional_t<XprTypeIsRowMajor,ColIndices,RowIndices>>::value,
+
+    InnerStrideAtCompileTime = InnerIncr<0 || InnerIncr==DynamicIndex || XprInnerStride==Dynamic || InnerIncr==UndefinedIncr ? Dynamic : XprInnerStride * InnerIncr,
+    OuterStrideAtCompileTime = OuterIncr<0 || OuterIncr==DynamicIndex || XprOuterstride==Dynamic || OuterIncr==UndefinedIncr ? Dynamic : XprOuterstride * OuterIncr,
+
+    ReturnAsScalar = is_same<RowIndices,SingleRange>::value && is_same<ColIndices,SingleRange>::value,
+    ReturnAsBlock = (!ReturnAsScalar) && IsBlockAlike,
+    ReturnAsIndexedView = (!ReturnAsScalar) && (!ReturnAsBlock),
+
+    // FIXME we deal with compile-time strides if and only if we have DirectAccessBit flag,
+    // but this is too strict regarding negative strides...
+    DirectAccessMask = (int(InnerIncr)!=UndefinedIncr && int(OuterIncr)!=UndefinedIncr && InnerIncr>=0 && OuterIncr>=0) ? DirectAccessBit : 0,
+    FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
+    FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
+    FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0,
+    Flags = (traits<XprType>::Flags & (HereditaryBits | DirectAccessMask )) | FlagsLvalueBit | FlagsRowMajorBit | FlagsLinearAccessBit
+  };
+
+  typedef Block<XprType,RowsAtCompileTime,ColsAtCompileTime,IsInnerPannel> BlockType;
+};
+
+}
+
+template<typename XprType, typename RowIndices, typename ColIndices, typename StorageKind>
+class IndexedViewImpl;
+
+
+/** \class IndexedView
+  * \ingroup Core_Module
+  *
+  * \brief Expression of a non-sequential sub-matrix defined by arbitrary sequences of row and column indices
+  *
+  * \tparam XprType the type of the expression in which we are taking the intersections of sub-rows and sub-columns
+  * \tparam RowIndices the type of the object defining the sequence of row indices
+  * \tparam ColIndices the type of the object defining the sequence of column indices
+  *
+  * This class represents an expression of a sub-matrix (or sub-vector) defined as the intersection
+  * of sub-sets of rows and columns, that are themself defined by generic sequences of row indices \f$ \{r_0,r_1,..r_{m-1}\} \f$
+  * and column indices \f$ \{c_0,c_1,..c_{n-1} \}\f$. Let \f$ A \f$  be the nested matrix, then the resulting matrix \f$ B \f$ has \c m
+  * rows and \c n columns, and its entries are given by: \f$ B(i,j) = A(r_i,c_j) \f$.
+  *
+  * The \c RowIndices and \c ColIndices types must be compatible with the following API:
+  * \code
+  * <integral type> operator[](Index) const;
+  * Index size() const;
+  * \endcode
+  *
+  * Typical supported types thus include:
+  *  - std::vector<int>
+  *  - std::valarray<int>
+  *  - std::array<int>
+  *  - Eigen::ArrayXi
+  *  - decltype(ArrayXi::LinSpaced(...))
+  *  - Any view/expressions of the previous types
+  *  - Eigen::ArithmeticSequence
+  *  - Eigen::internal::AllRange     (helper for Eigen::placeholders::all)
+  *  - Eigen::internal::SingleRange  (helper for single index)
+  *  - etc.
+  *
+  * In typical usages of %Eigen, this class should never be used directly. It is the return type of
+  * DenseBase::operator()(const RowIndices&, const ColIndices&).
+  *
+  * \sa class Block
+  */
+template<typename XprType, typename RowIndices, typename ColIndices>
+class IndexedView : public IndexedViewImpl<XprType, RowIndices, ColIndices, typename internal::traits<XprType>::StorageKind>
+{
+public:
+  typedef typename IndexedViewImpl<XprType, RowIndices, ColIndices, typename internal::traits<XprType>::StorageKind>::Base Base;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(IndexedView)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(IndexedView)
+
+  typedef typename internal::ref_selector<XprType>::non_const_type MatrixTypeNested;
+  typedef internal::remove_all_t<XprType> NestedExpression;
+
+  template<typename T0, typename T1>
+  IndexedView(XprType& xpr, const T0& rowIndices, const T1& colIndices)
+    : m_xpr(xpr), m_rowIndices(rowIndices), m_colIndices(colIndices)
+  {}
+
+  /** \returns number of rows */
+  Index rows() const { return internal::index_list_size(m_rowIndices); }
+
+  /** \returns number of columns */
+  Index cols() const { return internal::index_list_size(m_colIndices); }
+
+  /** \returns the nested expression */
+  const internal::remove_all_t<XprType>&
+  nestedExpression() const { return m_xpr; }
+
+  /** \returns the nested expression */
+  std::remove_reference_t<XprType>&
+  nestedExpression() { return m_xpr; }
+
+  /** \returns a const reference to the object storing/generating the row indices */
+  const RowIndices& rowIndices() const { return m_rowIndices; }
+
+  /** \returns a const reference to the object storing/generating the column indices */
+  const ColIndices& colIndices() const { return m_colIndices; }
+
+protected:
+  MatrixTypeNested m_xpr;
+  RowIndices m_rowIndices;
+  ColIndices m_colIndices;
+};
+
+
+// Generic API dispatcher
+template<typename XprType, typename RowIndices, typename ColIndices, typename StorageKind>
+class IndexedViewImpl
+  : public internal::generic_xpr_base<IndexedView<XprType, RowIndices, ColIndices> >::type
+{
+public:
+  typedef typename internal::generic_xpr_base<IndexedView<XprType, RowIndices, ColIndices> >::type Base;
+};
+
+namespace internal {
+
+
+template<typename ArgType, typename RowIndices, typename ColIndices>
+struct unary_evaluator<IndexedView<ArgType, RowIndices, ColIndices>, IndexBased>
+  : evaluator_base<IndexedView<ArgType, RowIndices, ColIndices> >
+{
+  typedef IndexedView<ArgType, RowIndices, ColIndices> XprType;
+
+  enum {
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost /* TODO + cost of row/col index */,
+
+    FlagsLinearAccessBit = (traits<XprType>::RowsAtCompileTime == 1 || traits<XprType>::ColsAtCompileTime == 1) ? LinearAccessBit : 0,
+
+    FlagsRowMajorBit = traits<XprType>::FlagsRowMajorBit, 
+
+    Flags = (evaluator<ArgType>::Flags & (HereditaryBits & ~RowMajorBit /*| LinearAccessBit | DirectAccessBit*/)) | FlagsLinearAccessBit | FlagsRowMajorBit,
+
+    Alignment = 0
+  };
+
+  EIGEN_DEVICE_FUNC explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_xpr(xpr)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeff(Index row, Index col) const
+  {
+    eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows()
+                 && m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
+    return m_argImpl.coeff(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index row, Index col)
+  {
+    eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows()
+                 && m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
+    return m_argImpl.coeffRef(m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Scalar& coeffRef(Index index)
+  {
+    EIGEN_STATIC_ASSERT_LVALUE(XprType)
+    Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
+    Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
+    eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows()
+                 && m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
+    return m_argImpl.coeffRef( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const Scalar& coeffRef(Index index) const
+  {
+    Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
+    Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
+    eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows()
+                 && m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
+    return m_argImpl.coeffRef( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const CoeffReturnType coeff(Index index) const
+  {
+    Index row = XprType::RowsAtCompileTime == 1 ? 0 : index;
+    Index col = XprType::RowsAtCompileTime == 1 ? index : 0;
+    eigen_assert(m_xpr.rowIndices()[row] >= 0 && m_xpr.rowIndices()[row] < m_xpr.nestedExpression().rows()
+                 && m_xpr.colIndices()[col] >= 0 && m_xpr.colIndices()[col] < m_xpr.nestedExpression().cols());
+    return m_argImpl.coeff( m_xpr.rowIndices()[row], m_xpr.colIndices()[col]);
+  }
+
+protected:
+
+  evaluator<ArgType> m_argImpl;
+  const XprType& m_xpr;
+
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_INDEXED_VIEW_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..1cea572dcf9875c3abf175513397c2a0be053c90
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_CORE_MODULE_H
+#error "Please include Eigen/Core instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Inverse.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Inverse.h
new file mode 100644
index 0000000000000000000000000000000000000000..9c70733b18204e6bd1ae50778a25d48ac736ada3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Inverse.h
@@ -0,0 +1,119 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_INVERSE_H
+#define EIGEN_INVERSE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template<typename XprType,typename StorageKind> class InverseImpl;
+
+namespace internal {
+
+template<typename XprType>
+struct traits<Inverse<XprType> >
+  : traits<typename XprType::PlainObject>
+{
+  typedef typename XprType::PlainObject PlainObject;
+  typedef traits<PlainObject> BaseTraits;
+  enum {
+    Flags = BaseTraits::Flags & RowMajorBit
+  };
+};
+
+} // end namespace internal
+
+/** \class Inverse
+  *
+  * \brief Expression of the inverse of another expression
+  *
+  * \tparam XprType the type of the expression we are taking the inverse
+  *
+  * This class represents an abstract expression of A.inverse()
+  * and most of the time this is the only way it is used.
+  *
+  */
+template<typename XprType>
+class Inverse : public InverseImpl<XprType,typename internal::traits<XprType>::StorageKind>
+{
+public:
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename XprType::Scalar                            Scalar;
+  typedef typename internal::ref_selector<XprType>::type      XprTypeNested;
+  typedef internal::remove_all_t<XprTypeNested>  XprTypeNestedCleaned;
+  typedef typename internal::ref_selector<Inverse>::type Nested;
+  typedef internal::remove_all_t<XprType> NestedExpression;
+
+  explicit EIGEN_DEVICE_FUNC Inverse(const XprType &xpr)
+    : m_xpr(xpr)
+  {}
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR  Index rows() const EIGEN_NOEXCEPT { return m_xpr.cols(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR  Index cols() const EIGEN_NOEXCEPT { return m_xpr.rows(); }
+
+  EIGEN_DEVICE_FUNC const XprTypeNestedCleaned& nestedExpression() const { return m_xpr; }
+
+protected:
+  XprTypeNested m_xpr;
+};
+
+// Generic API dispatcher
+template<typename XprType, typename StorageKind>
+class InverseImpl
+  : public internal::generic_xpr_base<Inverse<XprType> >::type
+{
+public:
+  typedef typename internal::generic_xpr_base<Inverse<XprType> >::type Base;
+  typedef typename XprType::Scalar Scalar;
+private:
+
+  Scalar coeff(Index row, Index col) const;
+  Scalar coeff(Index i) const;
+};
+
+namespace internal {
+
+/** \internal
+  * \brief Default evaluator for Inverse expression.
+  *
+  * This default evaluator for Inverse expression simply evaluate the inverse into a temporary
+  * by a call to internal::call_assignment_no_alias.
+  * Therefore, inverse implementers only have to specialize Assignment<Dst,Inverse<...>, ...> for
+  * there own nested expression.
+  *
+  * \sa class Inverse
+  */
+template<typename ArgType>
+struct unary_evaluator<Inverse<ArgType> >
+  : public evaluator<typename Inverse<ArgType>::PlainObject>
+{
+  typedef Inverse<ArgType> InverseType;
+  typedef typename InverseType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  enum { Flags = Base::Flags | EvalBeforeNestingBit };
+
+  unary_evaluator(const InverseType& inv_xpr)
+    : m_result(inv_xpr.rows(), inv_xpr.cols())
+  {
+    internal::construct_at<Base>(this, m_result);
+    internal::call_assignment_no_alias(m_result, inv_xpr);
+  }
+
+protected:
+  PlainObject m_result;
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_INVERSE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Map.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Map.h
new file mode 100644
index 0000000000000000000000000000000000000000..56d1ff86fea5197fa4ce43a72440b9a5061ef3f2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Map.h
@@ -0,0 +1,170 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MAP_H
+#define EIGEN_MAP_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template<typename PlainObjectType, int MapOptions, typename StrideType>
+struct traits<Map<PlainObjectType, MapOptions, StrideType> >
+  : public traits<PlainObjectType>
+{
+  typedef traits<PlainObjectType> TraitsBase;
+  enum {
+    PlainObjectTypeInnerSize = ((traits<PlainObjectType>::Flags&RowMajorBit)==RowMajorBit)
+                             ? PlainObjectType::ColsAtCompileTime
+                             : PlainObjectType::RowsAtCompileTime,
+
+    InnerStrideAtCompileTime = StrideType::InnerStrideAtCompileTime == 0
+                             ? int(PlainObjectType::InnerStrideAtCompileTime)
+                             : int(StrideType::InnerStrideAtCompileTime),
+    OuterStrideAtCompileTime = StrideType::OuterStrideAtCompileTime == 0
+                             ? (InnerStrideAtCompileTime==Dynamic || PlainObjectTypeInnerSize==Dynamic
+                                ? Dynamic
+                                : int(InnerStrideAtCompileTime) * int(PlainObjectTypeInnerSize))
+                             : int(StrideType::OuterStrideAtCompileTime),
+    Alignment = int(MapOptions)&int(AlignedMask),
+    Flags0 = TraitsBase::Flags & (~NestByRefBit),
+    Flags = is_lvalue<PlainObjectType>::value ? int(Flags0) : (int(Flags0) & ~LvalueBit)
+  };
+private:
+  enum { Options }; // Expressions don't have Options
+};
+}
+
+/** \class Map
+  * \ingroup Core_Module
+  *
+  * \brief A matrix or vector expression mapping an existing array of data.
+  *
+  * \tparam PlainObjectType the equivalent matrix type of the mapped data
+  * \tparam MapOptions specifies the pointer alignment in bytes. It can be: \c #Aligned128, \c #Aligned64, \c #Aligned32, \c #Aligned16, \c #Aligned8 or \c #Unaligned.
+  *                The default is \c #Unaligned.
+  * \tparam StrideType optionally specifies strides. By default, Map assumes the memory layout
+  *                   of an ordinary, contiguous array. This can be overridden by specifying strides.
+  *                   The type passed here must be a specialization of the Stride template, see examples below.
+  *
+  * This class represents a matrix or vector expression mapping an existing array of data.
+  * It can be used to let Eigen interface without any overhead with non-Eigen data structures,
+  * such as plain C arrays or structures from other libraries. By default, it assumes that the
+  * data is laid out contiguously in memory. You can however override this by explicitly specifying
+  * inner and outer strides.
+  *
+  * Here's an example of simply mapping a contiguous array as a \ref TopicStorageOrders "column-major" matrix:
+  * \include Map_simple.cpp
+  * Output: \verbinclude Map_simple.out
+  *
+  * If you need to map non-contiguous arrays, you can do so by specifying strides:
+  *
+  * Here's an example of mapping an array as a vector, specifying an inner stride, that is, the pointer
+  * increment between two consecutive coefficients. Here, we're specifying the inner stride as a compile-time
+  * fixed value.
+  * \include Map_inner_stride.cpp
+  * Output: \verbinclude Map_inner_stride.out
+  *
+  * Here's an example of mapping an array while specifying an outer stride. Here, since we're mapping
+  * as a column-major matrix, 'outer stride' means the pointer increment between two consecutive columns.
+  * Here, we're specifying the outer stride as a runtime parameter. Note that here \c OuterStride<> is
+  * a short version of \c OuterStride<Dynamic> because the default template parameter of OuterStride
+  * is  \c Dynamic
+  * \include Map_outer_stride.cpp
+  * Output: \verbinclude Map_outer_stride.out
+  *
+  * For more details and for an example of specifying both an inner and an outer stride, see class Stride.
+  *
+  * \b Tip: to change the array of data mapped by a Map object, you can use the C++
+  * placement new syntax:
+  *
+  * Example: \include Map_placement_new.cpp
+  * Output: \verbinclude Map_placement_new.out
+  *
+  * This class is the return type of PlainObjectBase::Map() but can also be used directly.
+  *
+  * \sa PlainObjectBase::Map(), \ref TopicStorageOrders
+  */
+template<typename PlainObjectType, int MapOptions, typename StrideType> class Map
+  : public MapBase<Map<PlainObjectType, MapOptions, StrideType> >
+{
+  public:
+
+    typedef MapBase<Map> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Map)
+
+    typedef typename Base::PointerType PointerType;
+    typedef PointerType PointerArgType;
+    EIGEN_DEVICE_FUNC
+    inline PointerType cast_to_pointer_type(PointerArgType ptr) { return ptr; }
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index innerStride() const
+    {
+      return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index outerStride() const
+    {
+      return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
+           : internal::traits<Map>::OuterStrideAtCompileTime != Dynamic ? Index(internal::traits<Map>::OuterStrideAtCompileTime)
+           : IsVectorAtCompileTime ? (this->size() * innerStride())
+           : int(Flags)&RowMajorBit ? (this->cols() * innerStride())
+           : (this->rows() * innerStride());
+    }
+
+    /** Constructor in the fixed-size case.
+      *
+      * \param dataPtr pointer to the array to map
+      * \param stride optional Stride object, passing the strides.
+      */
+    EIGEN_DEVICE_FUNC
+    explicit inline Map(PointerArgType dataPtr, const StrideType& stride = StrideType())
+      : Base(cast_to_pointer_type(dataPtr)), m_stride(stride)
+    {
+    }
+
+    /** Constructor in the dynamic-size vector case.
+      *
+      * \param dataPtr pointer to the array to map
+      * \param size the size of the vector expression
+      * \param stride optional Stride object, passing the strides.
+      */
+    EIGEN_DEVICE_FUNC
+    inline Map(PointerArgType dataPtr, Index size, const StrideType& stride = StrideType())
+      : Base(cast_to_pointer_type(dataPtr), size), m_stride(stride)
+    {
+    }
+
+    /** Constructor in the dynamic-size matrix case.
+      *
+      * \param dataPtr pointer to the array to map
+      * \param rows the number of rows of the matrix expression
+      * \param cols the number of columns of the matrix expression
+      * \param stride optional Stride object, passing the strides.
+      */
+    EIGEN_DEVICE_FUNC
+    inline Map(PointerArgType dataPtr, Index rows, Index cols, const StrideType& stride = StrideType())
+      : Base(cast_to_pointer_type(dataPtr), rows, cols), m_stride(stride)
+    {
+    }
+
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)
+
+  protected:
+    StrideType m_stride;
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_MAP_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/MapBase.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/MapBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..4dda24d211f167d89be4223ea12cb3cccc0eacc4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/MapBase.h
@@ -0,0 +1,312 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MAPBASE_H
+#define EIGEN_MAPBASE_H
+
+#define EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived) \
+      EIGEN_STATIC_ASSERT((int(internal::evaluator<Derived>::Flags) & LinearAccessBit) || Derived::IsVectorAtCompileTime, \
+                          YOU_ARE_TRYING_TO_USE_AN_INDEX_BASED_ACCESSOR_ON_AN_EXPRESSION_THAT_DOES_NOT_SUPPORT_THAT)
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup Core_Module
+  *
+  * \brief Base class for dense Map and Block expression with direct access
+  *
+  * This base class provides the const low-level accessors (e.g. coeff, coeffRef) of dense
+  * Map and Block objects with direct access.
+  * Typical users do not have to directly deal with this class.
+  *
+  * This class can be extended by through the macro plugin \c EIGEN_MAPBASE_PLUGIN.
+  * See \link TopicCustomizing_Plugins customizing Eigen \endlink for details.
+  *
+  * The \c Derived class has to provide the following two methods describing the memory layout:
+  *  \code Index innerStride() const; \endcode
+  *  \code Index outerStride() const; \endcode
+  *
+  * \sa class Map, class Block
+  */
+template<typename Derived> class MapBase<Derived, ReadOnlyAccessors>
+  : public internal::dense_xpr_base<Derived>::type
+{
+  public:
+
+    typedef typename internal::dense_xpr_base<Derived>::type Base;
+    enum {
+      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+      InnerStrideAtCompileTime = internal::traits<Derived>::InnerStrideAtCompileTime,
+      SizeAtCompileTime = Base::SizeAtCompileTime
+    };
+
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef std::conditional_t<
+                bool(internal::is_lvalue<Derived>::value),
+                Scalar *,
+                const Scalar *>
+            PointerType;
+
+    using Base::derived;
+//    using Base::RowsAtCompileTime;
+//    using Base::ColsAtCompileTime;
+//    using Base::SizeAtCompileTime;
+    using Base::MaxRowsAtCompileTime;
+    using Base::MaxColsAtCompileTime;
+    using Base::MaxSizeAtCompileTime;
+    using Base::IsVectorAtCompileTime;
+    using Base::Flags;
+    using Base::IsRowMajor;
+
+    using Base::rows;
+    using Base::cols;
+    using Base::size;
+    using Base::coeff;
+    using Base::coeffRef;
+    using Base::lazyAssign;
+    using Base::eval;
+
+    using Base::innerStride;
+    using Base::outerStride;
+    using Base::rowStride;
+    using Base::colStride;
+
+    // bug 217 - compile error on ICC 11.1
+    using Base::operator=;
+
+    typedef typename Base::CoeffReturnType CoeffReturnType;
+
+    /** \copydoc DenseBase::rows() */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index rows() const EIGEN_NOEXCEPT { return m_rows.value(); }
+    /** \copydoc DenseBase::cols() */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index cols() const EIGEN_NOEXCEPT { return m_cols.value(); }
+
+    /** Returns a pointer to the first coefficient of the matrix or vector.
+      *
+      * \note When addressing this data, make sure to honor the strides returned by innerStride() and outerStride().
+      *
+      * \sa innerStride(), outerStride()
+      */
+    EIGEN_DEVICE_FUNC inline const Scalar* data() const { return m_data; }
+
+    /** \copydoc PlainObjectBase::coeff(Index,Index) const */
+    EIGEN_DEVICE_FUNC
+    inline const Scalar& coeff(Index rowId, Index colId) const
+    {
+      return m_data[colId * colStride() + rowId * rowStride()];
+    }
+
+    /** \copydoc PlainObjectBase::coeff(Index) const */
+    EIGEN_DEVICE_FUNC
+    inline const Scalar& coeff(Index index) const
+    {
+      EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
+      return m_data[index * innerStride()];
+    }
+
+    /** \copydoc PlainObjectBase::coeffRef(Index,Index) const */
+    EIGEN_DEVICE_FUNC
+    inline const Scalar& coeffRef(Index rowId, Index colId) const
+    {
+      return this->m_data[colId * colStride() + rowId * rowStride()];
+    }
+
+    /** \copydoc PlainObjectBase::coeffRef(Index) const */
+    EIGEN_DEVICE_FUNC
+    inline const Scalar& coeffRef(Index index) const
+    {
+      EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
+      return this->m_data[index * innerStride()];
+    }
+
+    /** \internal */
+    template<int LoadMode>
+    inline PacketScalar packet(Index rowId, Index colId) const
+    {
+      return internal::ploadt<PacketScalar, LoadMode>
+               (m_data + (colId * colStride() + rowId * rowStride()));
+    }
+
+    /** \internal */
+    template<int LoadMode>
+    inline PacketScalar packet(Index index) const
+    {
+      EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
+      return internal::ploadt<PacketScalar, LoadMode>(m_data + index * innerStride());
+    }
+
+    /** \internal Constructor for fixed size matrices or vectors */
+    EIGEN_DEVICE_FUNC
+    explicit inline MapBase(PointerType dataPtr) : m_data(dataPtr), m_rows(RowsAtCompileTime), m_cols(ColsAtCompileTime)
+    {
+      EIGEN_STATIC_ASSERT_FIXED_SIZE(Derived)
+      checkSanity<Derived>();
+    }
+
+    /** \internal Constructor for dynamically sized vectors */
+    EIGEN_DEVICE_FUNC
+    inline MapBase(PointerType dataPtr, Index vecSize)
+            : m_data(dataPtr),
+              m_rows(RowsAtCompileTime == Dynamic ? vecSize : Index(RowsAtCompileTime)),
+              m_cols(ColsAtCompileTime == Dynamic ? vecSize : Index(ColsAtCompileTime))
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+      eigen_assert(vecSize >= 0);
+      eigen_assert(dataPtr == 0 || SizeAtCompileTime == Dynamic || SizeAtCompileTime == vecSize);
+      checkSanity<Derived>();
+    }
+
+    /** \internal Constructor for dynamically sized matrices */
+    EIGEN_DEVICE_FUNC
+    inline MapBase(PointerType dataPtr, Index rows, Index cols)
+            : m_data(dataPtr), m_rows(rows), m_cols(cols)
+    {
+      eigen_assert( (dataPtr == 0)
+              || (   rows >= 0 && (RowsAtCompileTime == Dynamic || RowsAtCompileTime == rows)
+                  && cols >= 0 && (ColsAtCompileTime == Dynamic || ColsAtCompileTime == cols)));
+      checkSanity<Derived>();
+    }
+
+    #ifdef EIGEN_MAPBASE_PLUGIN
+    #include EIGEN_MAPBASE_PLUGIN
+    #endif
+
+  protected:
+    EIGEN_DEFAULT_COPY_CONSTRUCTOR(MapBase)
+    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(MapBase)
+
+    template<typename T>
+    EIGEN_DEVICE_FUNC
+    void checkSanity(std::enable_if_t<(internal::traits<T>::Alignment>0),void*> = 0) const
+    {
+#if EIGEN_MAX_ALIGN_BYTES>0
+      // innerStride() is not set yet when this function is called, so we optimistically assume the lowest plausible value:
+      const Index minInnerStride = InnerStrideAtCompileTime == Dynamic ? 1 : Index(InnerStrideAtCompileTime);
+      EIGEN_ONLY_USED_FOR_DEBUG(minInnerStride);
+      eigen_assert((   ((std::uintptr_t(m_data) % internal::traits<Derived>::Alignment) == 0)
+                    || (cols() * rows() * minInnerStride * sizeof(Scalar)) < internal::traits<Derived>::Alignment ) && "data is not aligned");
+#endif
+    }
+
+    template<typename T>
+    EIGEN_DEVICE_FUNC
+    void checkSanity(std::enable_if_t<internal::traits<T>::Alignment==0,void*> = 0) const
+    {}
+
+    PointerType m_data;
+    const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows;
+    const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
+};
+
+/** \ingroup Core_Module
+  *
+  * \brief Base class for non-const dense Map and Block expression with direct access
+  *
+  * This base class provides the non-const low-level accessors (e.g. coeff and coeffRef) of
+  * dense Map and Block objects with direct access.
+  * It inherits MapBase<Derived, ReadOnlyAccessors> which defines the const variant for reading specific entries.
+  *
+  * \sa class Map, class Block
+  */
+template<typename Derived> class MapBase<Derived, WriteAccessors>
+  : public MapBase<Derived, ReadOnlyAccessors>
+{
+    typedef MapBase<Derived, ReadOnlyAccessors> ReadOnlyMapBase;
+  public:
+
+    typedef MapBase<Derived, ReadOnlyAccessors> Base;
+
+    typedef typename Base::Scalar Scalar;
+    typedef typename Base::PacketScalar PacketScalar;
+    typedef typename Base::StorageIndex StorageIndex;
+    typedef typename Base::PointerType PointerType;
+
+    using Base::derived;
+    using Base::rows;
+    using Base::cols;
+    using Base::size;
+    using Base::coeff;
+    using Base::coeffRef;
+
+    using Base::innerStride;
+    using Base::outerStride;
+    using Base::rowStride;
+    using Base::colStride;
+
+    typedef std::conditional_t<
+                    internal::is_lvalue<Derived>::value,
+                    Scalar,
+                    const Scalar
+                  > ScalarWithConstIfNotLvalue;
+
+    EIGEN_DEVICE_FUNC
+    inline const Scalar* data() const { return this->m_data; }
+    EIGEN_DEVICE_FUNC
+    inline ScalarWithConstIfNotLvalue* data() { return this->m_data; } // no const-cast here so non-const-correct code will give a compile error
+
+    EIGEN_DEVICE_FUNC
+    inline ScalarWithConstIfNotLvalue& coeffRef(Index row, Index col)
+    {
+      return this->m_data[col * colStride() + row * rowStride()];
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline ScalarWithConstIfNotLvalue& coeffRef(Index index)
+    {
+      EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
+      return this->m_data[index * innerStride()];
+    }
+
+    template<int StoreMode>
+    inline void writePacket(Index row, Index col, const PacketScalar& val)
+    {
+      internal::pstoret<Scalar, PacketScalar, StoreMode>
+               (this->m_data + (col * colStride() + row * rowStride()), val);
+    }
+
+    template<int StoreMode>
+    inline void writePacket(Index index, const PacketScalar& val)
+    {
+      EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS(Derived)
+      internal::pstoret<Scalar, PacketScalar, StoreMode>
+                (this->m_data + index * innerStride(), val);
+    }
+
+    EIGEN_DEVICE_FUNC explicit inline MapBase(PointerType dataPtr) : Base(dataPtr) {}
+    EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index vecSize) : Base(dataPtr, vecSize) {}
+    EIGEN_DEVICE_FUNC inline MapBase(PointerType dataPtr, Index rows, Index cols) : Base(dataPtr, rows, cols) {}
+
+    EIGEN_DEVICE_FUNC
+    Derived& operator=(const MapBase& other)
+    {
+      ReadOnlyMapBase::Base::operator=(other);
+      return derived();
+    }
+
+    // In theory we could simply refer to Base:Base::operator=, but MSVC does not like Base::Base,
+    // see bugs 821 and 920.
+    using ReadOnlyMapBase::Base::operator=;
+  protected:
+    EIGEN_DEFAULT_COPY_CONSTRUCTOR(MapBase)
+    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(MapBase)
+};
+
+#undef EIGEN_STATIC_ASSERT_INDEX_BASED_ACCESS
+
+} // end namespace Eigen
+
+#endif // EIGEN_MAPBASE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/MathFunctions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/MathFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..55077071b0933e461524ae93eee3f5be5d1a1e06
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/MathFunctions.h
@@ -0,0 +1,1993 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATHFUNCTIONS_H
+#define EIGEN_MATHFUNCTIONS_H
+
+// TODO this should better be moved to NumTraits
+// Source: WolframAlpha
+#define EIGEN_PI    3.141592653589793238462643383279502884197169399375105820974944592307816406L
+#define EIGEN_LOG2E 1.442695040888963407359924681001892137426645954152985934135449406931109219L
+#define EIGEN_LN2   0.693147180559945309417232121458176568075500134360255254120680009493393621L
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal \class global_math_functions_filtering_base
+  *
+  * What it does:
+  * Defines a typedef 'type' as follows:
+  * - if type T has a member typedef Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl, then
+  *   global_math_functions_filtering_base<T>::type is a typedef for it.
+  * - otherwise, global_math_functions_filtering_base<T>::type is a typedef for T.
+  *
+  * How it's used:
+  * To allow to defined the global math functions (like sin...) in certain cases, like the Array expressions.
+  * When you do sin(array1+array2), the object array1+array2 has a complicated expression type, all what you want to know
+  * is that it inherits ArrayBase. So we implement a partial specialization of sin_impl for ArrayBase<Derived>.
+  * So we must make sure to use sin_impl<ArrayBase<Derived> > and not sin_impl<Derived>, otherwise our partial specialization
+  * won't be used. How does sin know that? That's exactly what global_math_functions_filtering_base tells it.
+  *
+  * How it's implemented:
+  * SFINAE in the style of enable_if. Highly susceptible of breaking compilers. With GCC, it sure does work, but if you replace
+  * the typename dummy by an integer template parameter, it doesn't work anymore!
+  */
+
+template<typename T, typename dummy = void>
+struct global_math_functions_filtering_base
+{
+  typedef T type;
+};
+
+template<typename T> struct always_void { typedef void type; };
+
+template<typename T>
+struct global_math_functions_filtering_base
+  <T,
+   typename always_void<typename T::Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl>::type
+  >
+{
+  typedef typename T::Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl type;
+};
+
+#define EIGEN_MATHFUNC_IMPL(func, scalar) Eigen::internal::func##_impl<typename Eigen::internal::global_math_functions_filtering_base<scalar>::type>
+#define EIGEN_MATHFUNC_RETVAL(func, scalar) typename Eigen::internal::func##_retval<typename Eigen::internal::global_math_functions_filtering_base<scalar>::type>::type
+
+/****************************************************************************
+* Implementation of real                                                 *
+****************************************************************************/
+
+template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
+struct real_default_impl
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar run(const Scalar& x)
+  {
+    return x;
+  }
+};
+
+template<typename Scalar>
+struct real_default_impl<Scalar,true>
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar run(const Scalar& x)
+  {
+    using std::real;
+    return real(x);
+  }
+};
+
+template<typename Scalar> struct real_impl : real_default_impl<Scalar> {};
+
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+template<typename T>
+struct real_impl<std::complex<T> >
+{
+  typedef T RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline T run(const std::complex<T>& x)
+  {
+    return x.real();
+  }
+};
+#endif
+
+template<typename Scalar>
+struct real_retval
+{
+  typedef typename NumTraits<Scalar>::Real type;
+};
+
+/****************************************************************************
+* Implementation of imag                                                 *
+****************************************************************************/
+
+template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
+struct imag_default_impl
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar run(const Scalar&)
+  {
+    return RealScalar(0);
+  }
+};
+
+template<typename Scalar>
+struct imag_default_impl<Scalar,true>
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar run(const Scalar& x)
+  {
+    using std::imag;
+    return imag(x);
+  }
+};
+
+template<typename Scalar> struct imag_impl : imag_default_impl<Scalar> {};
+
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+template<typename T>
+struct imag_impl<std::complex<T> >
+{
+  typedef T RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline T run(const std::complex<T>& x)
+  {
+    return x.imag();
+  }
+};
+#endif
+
+template<typename Scalar>
+struct imag_retval
+{
+  typedef typename NumTraits<Scalar>::Real type;
+};
+
+/****************************************************************************
+* Implementation of real_ref                                             *
+****************************************************************************/
+
+template<typename Scalar>
+struct real_ref_impl
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar& run(Scalar& x)
+  {
+    return reinterpret_cast<RealScalar*>(&x)[0];
+  }
+  EIGEN_DEVICE_FUNC
+  static inline const RealScalar& run(const Scalar& x)
+  {
+    return reinterpret_cast<const RealScalar*>(&x)[0];
+  }
+};
+
+template<typename Scalar>
+struct real_ref_retval
+{
+  typedef typename NumTraits<Scalar>::Real & type;
+};
+
+/****************************************************************************
+* Implementation of imag_ref                                             *
+****************************************************************************/
+
+template<typename Scalar, bool IsComplex>
+struct imag_ref_default_impl
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar& run(Scalar& x)
+  {
+    return reinterpret_cast<RealScalar*>(&x)[1];
+  }
+  EIGEN_DEVICE_FUNC
+  static inline const RealScalar& run(const Scalar& x)
+  {
+    return reinterpret_cast<RealScalar*>(&x)[1];
+  }
+};
+
+template<typename Scalar>
+struct imag_ref_default_impl<Scalar, false>
+{
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline Scalar run(Scalar&)
+  {
+    return Scalar(0);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline const Scalar run(const Scalar&)
+  {
+    return Scalar(0);
+  }
+};
+
+template<typename Scalar>
+struct imag_ref_impl : imag_ref_default_impl<Scalar, NumTraits<Scalar>::IsComplex> {};
+
+template<typename Scalar>
+struct imag_ref_retval
+{
+  typedef typename NumTraits<Scalar>::Real & type;
+};
+
+/****************************************************************************
+* Implementation of conj                                                 *
+****************************************************************************/
+
+template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
+struct conj_default_impl
+{
+  EIGEN_DEVICE_FUNC
+  static inline Scalar run(const Scalar& x)
+  {
+    return x;
+  }
+};
+
+template<typename Scalar>
+struct conj_default_impl<Scalar,true>
+{
+  EIGEN_DEVICE_FUNC
+  static inline Scalar run(const Scalar& x)
+  {
+    using std::conj;
+    return conj(x);
+  }
+};
+
+template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
+struct conj_impl : conj_default_impl<Scalar, IsComplex> {};
+
+template<typename Scalar>
+struct conj_retval
+{
+  typedef Scalar type;
+};
+
+/****************************************************************************
+* Implementation of abs2                                                 *
+****************************************************************************/
+
+template<typename Scalar,bool IsComplex>
+struct abs2_impl_default
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar run(const Scalar& x)
+  {
+    return x*x;
+  }
+};
+
+template<typename Scalar>
+struct abs2_impl_default<Scalar, true> // IsComplex
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar run(const Scalar& x)
+  {
+    return x.real()*x.real() + x.imag()*x.imag();
+  }
+};
+
+template<typename Scalar>
+struct abs2_impl
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar run(const Scalar& x)
+  {
+    return abs2_impl_default<Scalar,NumTraits<Scalar>::IsComplex>::run(x);
+  }
+};
+
+template<typename Scalar>
+struct abs2_retval
+{
+  typedef typename NumTraits<Scalar>::Real type;
+};
+
+/****************************************************************************
+* Implementation of sqrt/rsqrt                                             *
+****************************************************************************/
+
+template<typename Scalar>
+struct sqrt_impl
+{
+  EIGEN_DEVICE_FUNC
+  static EIGEN_ALWAYS_INLINE Scalar run(const Scalar& x)
+  {
+    EIGEN_USING_STD(sqrt);
+    return sqrt(x);
+  }
+};
+
+// Complex sqrt defined in MathFunctionsImpl.h.
+template<typename T> EIGEN_DEVICE_FUNC std::complex<T> complex_sqrt(const std::complex<T>& a_x);
+
+// Custom implementation is faster than `std::sqrt`, works on
+// GPU, and correctly handles special cases (unlike MSVC).
+template<typename T>
+struct sqrt_impl<std::complex<T> >
+{
+  EIGEN_DEVICE_FUNC
+  static EIGEN_ALWAYS_INLINE std::complex<T> run(const std::complex<T>& x)
+  {
+    return complex_sqrt<T>(x);
+  }
+};
+
+template<typename Scalar>
+struct sqrt_retval
+{
+  typedef Scalar type;
+};
+
+// Default implementation relies on numext::sqrt, at bottom of file.
+template<typename T>
+struct rsqrt_impl;
+
+// Complex rsqrt defined in MathFunctionsImpl.h.
+template<typename T> EIGEN_DEVICE_FUNC std::complex<T> complex_rsqrt(const std::complex<T>& a_x);
+
+template<typename T>
+struct rsqrt_impl<std::complex<T> >
+{
+  EIGEN_DEVICE_FUNC
+  static EIGEN_ALWAYS_INLINE std::complex<T> run(const std::complex<T>& x)
+  {
+    return complex_rsqrt<T>(x);
+  }
+};
+
+template<typename Scalar>
+struct rsqrt_retval
+{
+  typedef Scalar type;
+};
+
+/****************************************************************************
+* Implementation of norm1                                                *
+****************************************************************************/
+
+template<typename Scalar, bool IsComplex>
+struct norm1_default_impl;
+
+template<typename Scalar>
+struct norm1_default_impl<Scalar,true>
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar run(const Scalar& x)
+  {
+    EIGEN_USING_STD(abs);
+    return abs(x.real()) + abs(x.imag());
+  }
+};
+
+template<typename Scalar>
+struct norm1_default_impl<Scalar, false>
+{
+  EIGEN_DEVICE_FUNC
+  static inline Scalar run(const Scalar& x)
+  {
+    EIGEN_USING_STD(abs);
+    return abs(x);
+  }
+};
+
+template<typename Scalar>
+struct norm1_impl : norm1_default_impl<Scalar, NumTraits<Scalar>::IsComplex> {};
+
+template<typename Scalar>
+struct norm1_retval
+{
+  typedef typename NumTraits<Scalar>::Real type;
+};
+
+/****************************************************************************
+* Implementation of hypot                                                *
+****************************************************************************/
+
+template<typename Scalar> struct hypot_impl;
+
+template<typename Scalar>
+struct hypot_retval
+{
+  typedef typename NumTraits<Scalar>::Real type;
+};
+
+/****************************************************************************
+* Implementation of cast                                                 *
+****************************************************************************/
+
+template<typename OldType, typename NewType, typename EnableIf = void>
+struct cast_impl
+{
+  EIGEN_DEVICE_FUNC
+  static inline NewType run(const OldType& x)
+  {
+    return static_cast<NewType>(x);
+  }
+};
+
+template <typename OldType>
+struct cast_impl<OldType, bool> {
+  EIGEN_DEVICE_FUNC
+  static inline bool run(const OldType& x) { return x != OldType(0); }
+};
+
+
+// Casting from S -> Complex<T> leads to an implicit conversion from S to T,
+// generating warnings on clang.  Here we explicitly cast the real component.
+template<typename OldType, typename NewType>
+struct cast_impl<OldType, NewType,
+  typename std::enable_if_t<
+    !NumTraits<OldType>::IsComplex && NumTraits<NewType>::IsComplex
+  >>
+{
+  EIGEN_DEVICE_FUNC
+  static inline NewType run(const OldType& x)
+  {
+    typedef typename NumTraits<NewType>::Real NewReal;
+    return static_cast<NewType>(static_cast<NewReal>(x));
+  }
+};
+
+// here, for once, we're plainly returning NewType: we don't want cast to do weird things.
+
+template<typename OldType, typename NewType>
+EIGEN_DEVICE_FUNC
+inline NewType cast(const OldType& x)
+{
+  return cast_impl<OldType, NewType>::run(x);
+}
+
+/****************************************************************************
+* Implementation of arg                                                     *
+****************************************************************************/
+
+// Visual Studio 2017 has a bug where arg(float) returns 0 for negative inputs.
+// This seems to be fixed in VS 2019.
+#if (!EIGEN_COMP_MSVC || EIGEN_COMP_MSVC >= 1920)
+// std::arg is only defined for types of std::complex, or integer types or float/double/long double
+template<typename Scalar,
+          bool HasStdImpl = NumTraits<Scalar>::IsComplex || is_integral<Scalar>::value
+                            || is_same<Scalar, float>::value || is_same<Scalar, double>::value
+                            || is_same<Scalar, long double>::value >
+struct arg_default_impl;
+
+template<typename Scalar>
+struct arg_default_impl<Scalar, true> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar run(const Scalar& x)
+  {
+    // There is no official ::arg on device in CUDA/HIP, so we always need to use std::arg.
+    using std::arg;
+    return static_cast<RealScalar>(arg(x));
+  }
+};
+
+// Must be non-complex floating-point type (e.g. half/bfloat16).
+template<typename Scalar>
+struct arg_default_impl<Scalar, false> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar run(const Scalar& x)
+  {
+    return (x < Scalar(0)) ? RealScalar(EIGEN_PI) : RealScalar(0);
+  }
+};
+#else
+template<typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
+struct arg_default_impl
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar run(const Scalar& x)
+  {
+    return (x < RealScalar(0)) ? RealScalar(EIGEN_PI) : RealScalar(0);
+  }
+};
+
+template<typename Scalar>
+struct arg_default_impl<Scalar,true>
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline RealScalar run(const Scalar& x)
+  {
+    EIGEN_USING_STD(arg);
+    return arg(x);
+  }
+};
+#endif
+template<typename Scalar> struct arg_impl : arg_default_impl<Scalar> {};
+
+template<typename Scalar>
+struct arg_retval
+{
+  typedef typename NumTraits<Scalar>::Real type;
+};
+
+/****************************************************************************
+* Implementation of expm1                                                   *
+****************************************************************************/
+
+// This implementation is based on GSL Math's expm1.
+namespace std_fallback {
+  // fallback expm1 implementation in case there is no expm1(Scalar) function in namespace of Scalar,
+  // or that there is no suitable std::expm1 function available. Implementation
+  // attributed to Kahan. See: http://www.plunk.org/~hatch/rightway.php.
+  template<typename Scalar>
+  EIGEN_DEVICE_FUNC inline Scalar expm1(const Scalar& x) {
+    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    EIGEN_USING_STD(exp);
+    Scalar u = exp(x);
+    if (numext::equal_strict(u, Scalar(1))) {
+      return x;
+    }
+    Scalar um1 = u - RealScalar(1);
+    if (numext::equal_strict(um1, Scalar(-1))) {
+      return RealScalar(-1);
+    }
+
+    EIGEN_USING_STD(log);
+    Scalar logu = log(u);
+    return numext::equal_strict(u, logu) ? u : (u - RealScalar(1)) * x / logu;
+  }
+}
+
+template<typename Scalar>
+struct expm1_impl {
+  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x)
+  {
+    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+    EIGEN_USING_STD(expm1);
+    return expm1(x);
+  }
+};
+
+template<typename Scalar>
+struct expm1_retval
+{
+  typedef Scalar type;
+};
+
+/****************************************************************************
+* Implementation of log                                                     *
+****************************************************************************/
+
+// Complex log defined in MathFunctionsImpl.h.
+template<typename T> EIGEN_DEVICE_FUNC std::complex<T> complex_log(const std::complex<T>& z);
+
+template<typename Scalar>
+struct log_impl {
+  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x)
+  {
+    EIGEN_USING_STD(log);
+    return static_cast<Scalar>(log(x));
+  }
+};
+
+template<typename Scalar>
+struct log_impl<std::complex<Scalar> > {
+  EIGEN_DEVICE_FUNC static inline std::complex<Scalar> run(const std::complex<Scalar>& z)
+  {
+    return complex_log(z);
+  }
+};
+
+/****************************************************************************
+* Implementation of log1p                                                   *
+****************************************************************************/
+
+namespace std_fallback {
+  // fallback log1p implementation in case there is no log1p(Scalar) function in namespace of Scalar,
+  // or that there is no suitable std::log1p function available
+  template<typename Scalar>
+  EIGEN_DEVICE_FUNC inline Scalar log1p(const Scalar& x) {
+    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    EIGEN_USING_STD(log);
+    Scalar x1p = RealScalar(1) + x;
+    Scalar log_1p = log_impl<Scalar>::run(x1p);
+    const bool is_small = numext::equal_strict(x1p, Scalar(1));
+    const bool is_inf = numext::equal_strict(x1p, log_1p);
+    return (is_small || is_inf) ? x : x * (log_1p / (x1p - RealScalar(1)));
+  }
+}
+
+template<typename Scalar>
+struct log1p_impl {
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  EIGEN_DEVICE_FUNC static inline Scalar run(const Scalar& x)
+  {
+    EIGEN_USING_STD(log1p);
+    return log1p(x);
+  }
+};
+
+// Specialization for complex types that are not supported by std::log1p.
+template <typename RealScalar>
+struct log1p_impl<std::complex<RealScalar> > {
+  EIGEN_STATIC_ASSERT_NON_INTEGER(RealScalar)
+
+  EIGEN_DEVICE_FUNC static inline std::complex<RealScalar> run(
+      const std::complex<RealScalar>& x) {
+    return std_fallback::log1p(x);
+  }
+};
+
+template<typename Scalar>
+struct log1p_retval
+{
+  typedef Scalar type;
+};
+
+/****************************************************************************
+* Implementation of pow                                                  *
+****************************************************************************/
+
+template<typename ScalarX,typename ScalarY, bool IsInteger = NumTraits<ScalarX>::IsInteger&&NumTraits<ScalarY>::IsInteger>
+struct pow_impl
+{
+  //typedef Scalar retval;
+  typedef typename ScalarBinaryOpTraits<ScalarX,ScalarY,internal::scalar_pow_op<ScalarX,ScalarY> >::ReturnType result_type;
+  static EIGEN_DEVICE_FUNC inline result_type run(const ScalarX& x, const ScalarY& y)
+  {
+    EIGEN_USING_STD(pow);
+    return pow(x, y);
+  }
+};
+
+template<typename ScalarX,typename ScalarY>
+struct pow_impl<ScalarX,ScalarY, true>
+{
+  typedef ScalarX result_type;
+  static EIGEN_DEVICE_FUNC inline ScalarX run(ScalarX x, ScalarY y)
+  {
+    ScalarX res(1);
+    eigen_assert(!NumTraits<ScalarY>::IsSigned || y >= 0);
+    if(y & 1) res *= x;
+    y >>= 1;
+    while(y)
+    {
+      x *= x;
+      if(y&1) res *= x;
+      y >>= 1;
+    }
+    return res;
+  }
+};
+
+/****************************************************************************
+* Implementation of random                                               *
+****************************************************************************/
+
+template<typename Scalar,
+         bool IsComplex,
+         bool IsInteger>
+struct random_default_impl {};
+
+template<typename Scalar>
+struct random_impl : random_default_impl<Scalar, NumTraits<Scalar>::IsComplex, NumTraits<Scalar>::IsInteger> {};
+
+template<typename Scalar>
+struct random_retval
+{
+  typedef Scalar type;
+};
+
+template<typename Scalar> inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random(const Scalar& x, const Scalar& y);
+template<typename Scalar> inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random();
+
+template<typename Scalar>
+struct random_default_impl<Scalar, false, false>
+{
+  static inline Scalar run(const Scalar& x, const Scalar& y)
+  {
+    return x + (y-x) * Scalar(std::rand()) / Scalar(RAND_MAX);
+  }
+  static inline Scalar run()
+  {
+    return run(Scalar(NumTraits<Scalar>::IsSigned ? -1 : 0), Scalar(1));
+  }
+};
+
+enum {
+  meta_floor_log2_terminate,
+  meta_floor_log2_move_up,
+  meta_floor_log2_move_down,
+  meta_floor_log2_bogus
+};
+
+template<unsigned int n, int lower, int upper> struct meta_floor_log2_selector
+{
+  enum { middle = (lower + upper) / 2,
+         value = (upper <= lower + 1) ? int(meta_floor_log2_terminate)
+               : (n < (1 << middle)) ? int(meta_floor_log2_move_down)
+               : (n==0) ? int(meta_floor_log2_bogus)
+               : int(meta_floor_log2_move_up)
+  };
+};
+
+template<unsigned int n,
+         int lower = 0,
+         int upper = sizeof(unsigned int) * CHAR_BIT - 1,
+         int selector = meta_floor_log2_selector<n, lower, upper>::value>
+struct meta_floor_log2 {};
+
+template<unsigned int n, int lower, int upper>
+struct meta_floor_log2<n, lower, upper, meta_floor_log2_move_down>
+{
+  enum { value = meta_floor_log2<n, lower, meta_floor_log2_selector<n, lower, upper>::middle>::value };
+};
+
+template<unsigned int n, int lower, int upper>
+struct meta_floor_log2<n, lower, upper, meta_floor_log2_move_up>
+{
+  enum { value = meta_floor_log2<n, meta_floor_log2_selector<n, lower, upper>::middle, upper>::value };
+};
+
+template<unsigned int n, int lower, int upper>
+struct meta_floor_log2<n, lower, upper, meta_floor_log2_terminate>
+{
+  enum { value = (n >= ((unsigned int)(1) << (lower+1))) ? lower+1 : lower };
+};
+
+template<unsigned int n, int lower, int upper>
+struct meta_floor_log2<n, lower, upper, meta_floor_log2_bogus>
+{
+  // no value, error at compile time
+};
+
+template<typename Scalar>
+struct random_default_impl<Scalar, false, true>
+{
+  static inline Scalar run(const Scalar& x, const Scalar& y)
+  {
+    if (y <= x)
+      return x;
+    // ScalarU is the unsigned counterpart of Scalar, possibly Scalar itself.
+    typedef typename make_unsigned<Scalar>::type ScalarU;
+    // ScalarX is the widest of ScalarU and unsigned int.
+    // We'll deal only with ScalarX and unsigned int below thus avoiding signed
+    // types and arithmetic and signed overflows (which are undefined behavior).
+    typedef std::conditional_t<(ScalarU(-1) > unsigned(-1)), ScalarU, unsigned> ScalarX;
+    // The following difference doesn't overflow, provided our integer types are two's
+    // complement and have the same number of padding bits in signed and unsigned variants.
+    // This is the case in most modern implementations of C++.
+    ScalarX range = ScalarX(y) - ScalarX(x);
+    ScalarX offset = 0;
+    ScalarX divisor = 1;
+    ScalarX multiplier = 1;
+    const unsigned rand_max = RAND_MAX;
+    if (range <= rand_max) divisor = (rand_max + 1) / (range + 1);
+    else                   multiplier = 1 + range / (rand_max + 1);
+    // Rejection sampling.
+    do {
+      offset = (unsigned(std::rand()) * multiplier) / divisor;
+    } while (offset > range);
+    return Scalar(ScalarX(x) + offset);
+  }
+
+  static inline Scalar run()
+  {
+#ifdef EIGEN_MAKING_DOCS
+    return run(Scalar(NumTraits<Scalar>::IsSigned ? -10 : 0), Scalar(10));
+#else
+    enum { rand_bits = meta_floor_log2<(unsigned int)(RAND_MAX)+1>::value,
+           scalar_bits = sizeof(Scalar) * CHAR_BIT,
+           shift = plain_enum_max(0, int(rand_bits) - int(scalar_bits)),
+           offset = NumTraits<Scalar>::IsSigned ? (1 << (plain_enum_min(rand_bits, scalar_bits)-1)) : 0
+    };
+    return Scalar((std::rand() >> shift) - offset);
+#endif
+  }
+};
+
+template<typename Scalar>
+struct random_default_impl<Scalar, true, false>
+{
+  static inline Scalar run(const Scalar& x, const Scalar& y)
+  {
+    return Scalar(random(x.real(), y.real()),
+                  random(x.imag(), y.imag()));
+  }
+  static inline Scalar run()
+  {
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    return Scalar(random<RealScalar>(), random<RealScalar>());
+  }
+};
+
+template<typename Scalar>
+inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random(const Scalar& x, const Scalar& y)
+{
+  return EIGEN_MATHFUNC_IMPL(random, Scalar)::run(x, y);
+}
+
+template<typename Scalar>
+inline EIGEN_MATHFUNC_RETVAL(random, Scalar) random()
+{
+  return EIGEN_MATHFUNC_IMPL(random, Scalar)::run();
+}
+
+// Implementation of is* functions
+
+template <typename T>
+EIGEN_DEVICE_FUNC std::enable_if_t<!(std::numeric_limits<T>::has_infinity ||
+                                     std::numeric_limits<T>::has_quiet_NaN ||
+                                     std::numeric_limits<T>::has_signaling_NaN),
+                                   bool>
+isfinite_impl(const T&) {
+  return true;
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC std::enable_if_t<(std::numeric_limits<T>::has_infinity || std::numeric_limits<T>::has_quiet_NaN ||
+                                    std::numeric_limits<T>::has_signaling_NaN) &&
+                                       (!NumTraits<T>::IsComplex),
+                                   bool>
+isfinite_impl(const T& x) {
+  EIGEN_USING_STD(isfinite);
+  return isfinite EIGEN_NOT_A_MACRO (x);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC std::enable_if_t<!std::numeric_limits<T>::has_infinity, bool>
+isinf_impl(const T&) {
+  return false;
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC std::enable_if_t<
+    (std::numeric_limits<T>::has_infinity && !NumTraits<T>::IsComplex), bool>
+isinf_impl(const T& x) {
+  EIGEN_USING_STD(isinf);
+  return isinf EIGEN_NOT_A_MACRO (x);
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC std::enable_if_t<!(std::numeric_limits<T>::has_quiet_NaN ||
+                                     std::numeric_limits<T>::has_signaling_NaN),
+                                   bool>
+isnan_impl(const T&) {
+  return false;
+}
+
+template <typename T>
+EIGEN_DEVICE_FUNC
+    std::enable_if_t<(std::numeric_limits<T>::has_quiet_NaN ||
+                      std::numeric_limits<T>::has_signaling_NaN) &&
+                         (!NumTraits<T>::IsComplex),
+                     bool>
+    isnan_impl(const T& x) {
+  EIGEN_USING_STD(isnan);
+  return isnan EIGEN_NOT_A_MACRO (x);
+}
+
+// The following overload are defined at the end of this file
+template <typename T>
+EIGEN_DEVICE_FUNC bool isfinite_impl(const std::complex<T>& x);
+template <typename T>
+EIGEN_DEVICE_FUNC bool isnan_impl(const std::complex<T>& x);
+template <typename T>
+EIGEN_DEVICE_FUNC bool isinf_impl(const std::complex<T>& x);
+template <typename T>
+T generic_fast_tanh_float(const T& a_x);
+
+/****************************************************************************
+ * Implementation of sign                                                 *
+ ****************************************************************************/
+template <typename Scalar, bool IsComplex = (NumTraits<Scalar>::IsComplex != 0),
+          bool IsInteger = (NumTraits<Scalar>::IsInteger != 0)>
+struct sign_impl {
+  EIGEN_DEVICE_FUNC
+  static inline Scalar run(const Scalar& a) {
+    return Scalar((a > Scalar(0)) - (a < Scalar(0)));
+  }
+};
+
+template <typename Scalar>
+struct sign_impl<Scalar, false, false> {
+  EIGEN_DEVICE_FUNC
+  static inline Scalar run(const Scalar& a) {
+    return (isnan_impl<Scalar>)(a) ? a
+                                   : Scalar((a > Scalar(0)) - (a < Scalar(0)));
+  }
+};
+
+template <typename Scalar, bool IsInteger>
+struct sign_impl<Scalar, true, IsInteger> {
+  EIGEN_DEVICE_FUNC
+  static inline Scalar run(const Scalar& a) {
+    using real_type = typename NumTraits<Scalar>::Real;
+    EIGEN_USING_STD(abs);
+    real_type aa = abs(a);
+    if (aa == real_type(0)) return Scalar(0);
+    aa = real_type(1) / aa;
+    return Scalar(a.real() * aa, a.imag() * aa);
+  }
+};
+
+// The sign function for bool is the identity.
+template <>
+struct sign_impl<bool, false, true> {
+  EIGEN_DEVICE_FUNC
+  static inline bool run(const bool& a) { return a; }
+};
+
+template <typename Scalar>
+struct sign_retval {
+  typedef Scalar type;
+};
+
+
+template <typename Scalar, bool IsInteger = NumTraits<typename unpacket_traits<Scalar>::type>::IsInteger>
+struct nearest_integer_impl {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_floor(const Scalar& x) { EIGEN_USING_STD(floor) return floor(x); }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_ceil(const Scalar& x) { EIGEN_USING_STD(ceil) return ceil(x); }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_rint(const Scalar& x) { EIGEN_USING_STD(rint) return rint(x); }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_round(const Scalar& x) { EIGEN_USING_STD(round) return round(x); }
+};
+template <typename Scalar>
+struct nearest_integer_impl<Scalar, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_floor(const Scalar& x) { return x; }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_ceil(const Scalar& x) { return x; }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_rint(const Scalar& x) { return x; }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_round(const Scalar& x) { return x; }
+};
+
+} // end namespace internal
+
+/****************************************************************************
+* Generic math functions                                                    *
+****************************************************************************/
+
+namespace numext {
+
+#if (!defined(EIGEN_GPUCC) || defined(EIGEN_CONSTEXPR_ARE_DEVICE_FUNC))
+template<typename T>
+EIGEN_DEVICE_FUNC
+EIGEN_ALWAYS_INLINE T mini(const T& x, const T& y)
+{
+  EIGEN_USING_STD(min)
+  return min EIGEN_NOT_A_MACRO (x,y);
+}
+
+template<typename T>
+EIGEN_DEVICE_FUNC
+EIGEN_ALWAYS_INLINE T maxi(const T& x, const T& y)
+{
+  EIGEN_USING_STD(max)
+  return max EIGEN_NOT_A_MACRO (x,y);
+}
+#else
+template<typename T>
+EIGEN_DEVICE_FUNC
+EIGEN_ALWAYS_INLINE T mini(const T& x, const T& y)
+{
+  return y < x ? y : x;
+}
+template<>
+EIGEN_DEVICE_FUNC
+EIGEN_ALWAYS_INLINE float mini(const float& x, const float& y)
+{
+  return fminf(x, y);
+}
+template<>
+EIGEN_DEVICE_FUNC
+EIGEN_ALWAYS_INLINE double mini(const double& x, const double& y)
+{
+  return fmin(x, y);
+}
+
+#ifndef EIGEN_GPU_COMPILE_PHASE
+template<>
+EIGEN_DEVICE_FUNC
+EIGEN_ALWAYS_INLINE long double mini(const long double& x, const long double& y)
+{
+#if defined(EIGEN_HIPCC)
+  // no "fminl" on HIP yet
+  return (x < y) ? x : y;
+#else
+  return fminl(x, y);
+#endif
+}
+#endif
+
+template<typename T>
+EIGEN_DEVICE_FUNC
+EIGEN_ALWAYS_INLINE T maxi(const T& x, const T& y)
+{
+  return x < y ? y : x;
+}
+template<>
+EIGEN_DEVICE_FUNC
+EIGEN_ALWAYS_INLINE float maxi(const float& x, const float& y)
+{
+  return fmaxf(x, y);
+}
+template<>
+EIGEN_DEVICE_FUNC
+EIGEN_ALWAYS_INLINE double maxi(const double& x, const double& y)
+{
+  return fmax(x, y);
+}
+#ifndef EIGEN_GPU_COMPILE_PHASE
+template<>
+EIGEN_DEVICE_FUNC
+EIGEN_ALWAYS_INLINE long double maxi(const long double& x, const long double& y)
+{
+#if defined(EIGEN_HIPCC)
+  // no "fmaxl" on HIP yet
+  return (x > y) ? x : y;
+#else
+  return fmaxl(x, y);
+#endif
+}
+#endif
+#endif
+
+#if defined(SYCL_DEVICE_ONLY)
+
+
+#define SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_char)   \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_short)  \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_int)    \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_long)
+#define SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_char)   \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_short)  \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_int)    \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_long)
+#define SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_uchar)  \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_ushort) \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_uint)   \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_ulong)
+#define SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_uchar)  \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_ushort) \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_uint)   \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_ulong)
+#define SYCL_SPECIALIZE_INTEGER_TYPES_BINARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_BINARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_BINARY(NAME, FUNC)
+#define SYCL_SPECIALIZE_INTEGER_TYPES_UNARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_UNARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY(NAME, FUNC)
+#define SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, cl::sycl::cl_float) \
+  SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC,cl::sycl::cl_double)
+#define SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(NAME, FUNC) \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, cl::sycl::cl_float) \
+  SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC,cl::sycl::cl_double)
+#define SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(NAME, FUNC, RET_TYPE) \
+  SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, RET_TYPE, cl::sycl::cl_float) \
+  SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, RET_TYPE, cl::sycl::cl_double)
+
+#define SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE) \
+template<>                                               \
+  EIGEN_DEVICE_FUNC                                      \
+  EIGEN_ALWAYS_INLINE RET_TYPE NAME(const ARG_TYPE& x) { \
+    return cl::sycl::FUNC(x);                            \
+  }
+
+#define SYCL_SPECIALIZE_UNARY_FUNC(NAME, FUNC, TYPE) \
+  SYCL_SPECIALIZE_GEN_UNARY_FUNC(NAME, FUNC, TYPE, TYPE)
+
+#define SYCL_SPECIALIZE_GEN1_BINARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE1, ARG_TYPE2) \
+  template<>                                                                  \
+  EIGEN_DEVICE_FUNC                                                           \
+  EIGEN_ALWAYS_INLINE RET_TYPE NAME(const ARG_TYPE1& x, const ARG_TYPE2& y) { \
+    return cl::sycl::FUNC(x, y);                                              \
+  }
+
+#define SYCL_SPECIALIZE_GEN2_BINARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE) \
+  SYCL_SPECIALIZE_GEN1_BINARY_FUNC(NAME, FUNC, RET_TYPE, ARG_TYPE, ARG_TYPE)
+
+#define SYCL_SPECIALIZE_BINARY_FUNC(NAME, FUNC, TYPE) \
+  SYCL_SPECIALIZE_GEN2_BINARY_FUNC(NAME, FUNC, TYPE, TYPE)
+
+SYCL_SPECIALIZE_INTEGER_TYPES_BINARY(mini, min)
+SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(mini, fmin)
+SYCL_SPECIALIZE_INTEGER_TYPES_BINARY(maxi, max)
+SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(maxi, fmax)
+
+#endif
+
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+inline EIGEN_MATHFUNC_RETVAL(real, Scalar) real(const Scalar& x)
+{
+  return EIGEN_MATHFUNC_IMPL(real, Scalar)::run(x);
+}
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+inline internal::add_const_on_value_type_t< EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) > real_ref(const Scalar& x)
+{
+  return internal::real_ref_impl<Scalar>::run(x);
+}
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+inline EIGEN_MATHFUNC_RETVAL(real_ref, Scalar) real_ref(Scalar& x)
+{
+  return EIGEN_MATHFUNC_IMPL(real_ref, Scalar)::run(x);
+}
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+inline EIGEN_MATHFUNC_RETVAL(imag, Scalar) imag(const Scalar& x)
+{
+  return EIGEN_MATHFUNC_IMPL(imag, Scalar)::run(x);
+}
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+inline EIGEN_MATHFUNC_RETVAL(arg, Scalar) arg(const Scalar& x)
+{
+  return EIGEN_MATHFUNC_IMPL(arg, Scalar)::run(x);
+}
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+inline internal::add_const_on_value_type_t< EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) > imag_ref(const Scalar& x)
+{
+  return internal::imag_ref_impl<Scalar>::run(x);
+}
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+inline EIGEN_MATHFUNC_RETVAL(imag_ref, Scalar) imag_ref(Scalar& x)
+{
+  return EIGEN_MATHFUNC_IMPL(imag_ref, Scalar)::run(x);
+}
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+inline EIGEN_MATHFUNC_RETVAL(conj, Scalar) conj(const Scalar& x)
+{
+  return EIGEN_MATHFUNC_IMPL(conj, Scalar)::run(x);
+}
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+inline EIGEN_MATHFUNC_RETVAL(sign, Scalar) sign(const Scalar& x)
+{
+  return EIGEN_MATHFUNC_IMPL(sign, Scalar)::run(x);
+}
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+inline EIGEN_MATHFUNC_RETVAL(abs2, Scalar) abs2(const Scalar& x)
+{
+  return EIGEN_MATHFUNC_IMPL(abs2, Scalar)::run(x);
+}
+
+EIGEN_DEVICE_FUNC
+inline bool abs2(bool x) { return x; }
+
+template<typename T>
+EIGEN_DEVICE_FUNC
+EIGEN_ALWAYS_INLINE T absdiff(const T& x, const T& y)
+{
+  return x > y ? x - y : y - x;
+}
+template<>
+EIGEN_DEVICE_FUNC
+EIGEN_ALWAYS_INLINE float absdiff(const float& x, const float& y)
+{
+  return fabsf(x - y);
+}
+template<>
+EIGEN_DEVICE_FUNC
+EIGEN_ALWAYS_INLINE double absdiff(const double& x, const double& y)
+{
+  return fabs(x - y);
+}
+
+// HIP and CUDA do not support long double.
+#ifndef EIGEN_GPU_COMPILE_PHASE
+template<>
+EIGEN_DEVICE_FUNC
+EIGEN_ALWAYS_INLINE long double absdiff(const long double& x, const long double& y) {
+  return fabsl(x - y);
+}
+#endif
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+inline EIGEN_MATHFUNC_RETVAL(norm1, Scalar) norm1(const Scalar& x)
+{
+  return EIGEN_MATHFUNC_IMPL(norm1, Scalar)::run(x);
+}
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+inline EIGEN_MATHFUNC_RETVAL(hypot, Scalar) hypot(const Scalar& x, const Scalar& y)
+{
+  return EIGEN_MATHFUNC_IMPL(hypot, Scalar)::run(x, y);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+  SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(hypot, hypot)
+#endif
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+inline EIGEN_MATHFUNC_RETVAL(log1p, Scalar) log1p(const Scalar& x)
+{
+  return EIGEN_MATHFUNC_IMPL(log1p, Scalar)::run(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(log1p, log1p)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float log1p(const float &x) { return ::log1pf(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double log1p(const double &x) { return ::log1p(x); }
+#endif
+
+template<typename ScalarX,typename ScalarY>
+EIGEN_DEVICE_FUNC
+inline typename internal::pow_impl<ScalarX,ScalarY>::result_type pow(const ScalarX& x, const ScalarY& y)
+{
+  return internal::pow_impl<ScalarX,ScalarY>::run(x, y);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(pow, pow)
+#endif
+
+template<typename T> EIGEN_DEVICE_FUNC bool (isnan)   (const T &x) { return internal::isnan_impl(x); }
+template<typename T> EIGEN_DEVICE_FUNC bool (isinf)   (const T &x) { return internal::isinf_impl(x); }
+template<typename T> EIGEN_DEVICE_FUNC bool (isfinite)(const T &x) { return internal::isfinite_impl(x); }
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(isnan, isnan, bool)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(isinf, isinf, bool)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE(isfinite, isfinite, bool)
+#endif
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+Scalar rint(const Scalar& x)
+{
+  return internal::nearest_integer_impl<Scalar>::run_rint(x);
+}
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+Scalar round(const Scalar& x)
+{
+  return internal::nearest_integer_impl<Scalar>::run_round(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(round, round)
+#endif
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+Scalar (floor)(const Scalar& x)
+{
+  return internal::nearest_integer_impl<Scalar>::run_floor(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(floor, floor)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float floor(const float &x) { return ::floorf(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double floor(const double &x) { return ::floor(x); }
+#endif
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+Scalar (ceil)(const Scalar& x)
+{
+  return internal::nearest_integer_impl<Scalar>::run_ceil(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(ceil, ceil)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float ceil(const float &x) { return ::ceilf(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double ceil(const double &x) { return ::ceil(x); }
+#endif
+
+
+/** Log base 2 for 32 bits positive integers.
+  * Conveniently returns 0 for x==0. */
+inline int log2(int x)
+{
+  eigen_assert(x>=0);
+  unsigned int v(x);
+  static const int table[32] = { 0, 9, 1, 10, 13, 21, 2, 29, 11, 14, 16, 18, 22, 25, 3, 30, 8, 12, 20, 28, 15, 17, 24, 7, 19, 27, 23, 6, 26, 5, 4, 31 };
+  v |= v >> 1;
+  v |= v >> 2;
+  v |= v >> 4;
+  v |= v >> 8;
+  v |= v >> 16;
+  return table[(v * 0x07C4ACDDU) >> 27];
+}
+
+/** \returns the square root of \a x.
+  *
+  * It is essentially equivalent to
+  * \code using std::sqrt; return sqrt(x); \endcode
+  * but slightly faster for float/double and some compilers (e.g., gcc), thanks to
+  * specializations when SSE is enabled.
+  *
+  * It's usage is justified in performance critical functions, like norm/normalize.
+  */
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+EIGEN_ALWAYS_INLINE EIGEN_MATHFUNC_RETVAL(sqrt, Scalar) sqrt(const Scalar& x)
+{
+  return EIGEN_MATHFUNC_IMPL(sqrt, Scalar)::run(x);
+}
+
+// Boolean specialization, avoids implicit float to bool conversion (-Wimplicit-conversion-floating-point-to-bool).
+template<>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_DEVICE_FUNC
+bool sqrt<bool>(const bool &x) { return x; }
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sqrt, sqrt)
+#endif
+
+/** \returns the reciprocal square root of \a x. **/
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T rsqrt(const T& x)
+{
+  return internal::rsqrt_impl<T>::run(x);
+}
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T log(const T &x) {
+  return internal::log_impl<T>::run(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(log, log)
+#endif
+
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float log(const float &x) { return ::logf(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double log(const double &x) { return ::log(x); }
+#endif
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+std::enable_if_t<NumTraits<T>::IsSigned || NumTraits<T>::IsComplex,typename NumTraits<T>::Real>
+abs(const T &x) {
+  EIGEN_USING_STD(abs);
+  return abs(x);
+}
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+std::enable_if_t<!(NumTraits<T>::IsSigned || NumTraits<T>::IsComplex),typename NumTraits<T>::Real>
+abs(const T &x) {
+  return x;
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_INTEGER_TYPES_UNARY(abs, abs)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(abs, fabs)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float abs(const float &x) { return ::fabsf(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double abs(const double &x) { return ::fabs(x); }
+
+template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float abs(const std::complex<float>& x) {
+  return ::hypotf(x.real(), x.imag());
+}
+
+template <> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double abs(const std::complex<double>& x) {
+  return ::hypot(x.real(), x.imag());
+}
+#endif
+
+template <typename Scalar, bool IsInteger = NumTraits<Scalar>::IsInteger, bool IsSigned = NumTraits<Scalar>::IsSigned>
+struct signbit_impl;
+template <typename Scalar>
+struct signbit_impl<Scalar, false, true> {
+  static constexpr size_t Size = sizeof(Scalar);
+  static constexpr size_t Shift = (CHAR_BIT * Size) - 1;
+  using intSize_t = typename get_integer_by_size<Size>::signed_type;
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static Scalar run(const Scalar& x) {
+    intSize_t a = bit_cast<intSize_t, Scalar>(x);
+    a = a >> Shift;
+    Scalar result = bit_cast<Scalar, intSize_t>(a);
+    return result;
+  }
+};
+template <typename Scalar>
+struct signbit_impl<Scalar, true, true> {
+  static constexpr size_t Size = sizeof(Scalar);
+  static constexpr size_t Shift = (CHAR_BIT * Size) - 1;
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static constexpr Scalar run(const Scalar& x) { return x >> Shift; }
+};
+template <typename Scalar>
+struct signbit_impl<Scalar, true, false> {
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static constexpr Scalar run(const Scalar&  ) {
+    return Scalar(0);
+  }
+};
+template <typename Scalar>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static constexpr Scalar signbit(const Scalar& x) {
+  return signbit_impl<Scalar>::run(x);
+}
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T exp(const T &x) {
+  EIGEN_USING_STD(exp);
+  return exp(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(exp, exp)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float exp(const float &x) { return ::expf(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double exp(const double &x) { return ::exp(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+std::complex<float> exp(const std::complex<float>& x) {
+  float com = ::expf(x.real());
+  float res_real = com * ::cosf(x.imag());
+  float res_imag = com * ::sinf(x.imag());
+  return std::complex<float>(res_real, res_imag);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+std::complex<double> exp(const std::complex<double>& x) {
+  double com = ::exp(x.real());
+  double res_real = com * ::cos(x.imag());
+  double res_imag = com * ::sin(x.imag());
+  return std::complex<double>(res_real, res_imag);
+}
+#endif
+
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+inline EIGEN_MATHFUNC_RETVAL(expm1, Scalar) expm1(const Scalar& x)
+{
+  return EIGEN_MATHFUNC_IMPL(expm1, Scalar)::run(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(expm1, expm1)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float expm1(const float &x) { return ::expm1f(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double expm1(const double &x) { return ::expm1(x); }
+#endif
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T cos(const T &x) {
+  EIGEN_USING_STD(cos);
+  return cos(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(cos,cos)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float cos(const float &x) { return ::cosf(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double cos(const double &x) { return ::cos(x); }
+#endif
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T sin(const T &x) {
+  EIGEN_USING_STD(sin);
+  return sin(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sin, sin)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float sin(const float &x) { return ::sinf(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double sin(const double &x) { return ::sin(x); }
+#endif
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T tan(const T &x) {
+  EIGEN_USING_STD(tan);
+  return tan(x);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(tan, tan)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float tan(const float &x) { return ::tanf(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double tan(const double &x) { return ::tan(x); }
+#endif
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T acos(const T &x) {
+  EIGEN_USING_STD(acos);
+  return acos(x);
+}
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T acosh(const T &x) {
+  EIGEN_USING_STD(acosh);
+  return static_cast<T>(acosh(x));
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(acos, acos)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(acosh, acosh)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float acos(const float &x) { return ::acosf(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double acos(const double &x) { return ::acos(x); }
+#endif
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T asin(const T &x) {
+  EIGEN_USING_STD(asin);
+  return asin(x);
+}
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T asinh(const T &x) {
+  EIGEN_USING_STD(asinh);
+  return static_cast<T>(asinh(x));
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(asin, asin)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(asinh, asinh)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float asin(const float &x) { return ::asinf(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double asin(const double &x) { return ::asin(x); }
+#endif
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T atan(const T &x) {
+  EIGEN_USING_STD(atan);
+  return static_cast<T>(atan(x));
+}
+
+template <typename T, std::enable_if_t<!NumTraits<T>::IsComplex, int> = 0>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE T atan2(const T& y, const T& x) {
+  EIGEN_USING_STD(atan2);
+  return static_cast<T>(atan2(y, x));
+}
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T atanh(const T &x) {
+  EIGEN_USING_STD(atanh);
+  return static_cast<T>(atanh(x));
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(atan, atan)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(atanh, atanh)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float atan(const float &x) { return ::atanf(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double atan(const double &x) { return ::atan(x); }
+#endif
+
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T cosh(const T &x) {
+  EIGEN_USING_STD(cosh);
+  return static_cast<T>(cosh(x));
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(cosh, cosh)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float cosh(const float &x) { return ::coshf(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double cosh(const double &x) { return ::cosh(x); }
+#endif
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T sinh(const T &x) {
+  EIGEN_USING_STD(sinh);
+  return static_cast<T>(sinh(x));
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(sinh, sinh)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float sinh(const float &x) { return ::sinhf(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double sinh(const double &x) { return ::sinh(x); }
+#endif
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T tanh(const T &x) {
+  EIGEN_USING_STD(tanh);
+  return tanh(x);
+}
+
+#if (!defined(EIGEN_GPUCC)) && EIGEN_FAST_MATH && !defined(SYCL_DEVICE_ONLY)
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float tanh(float x) { return internal::generic_fast_tanh_float(x); }
+#endif
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_UNARY(tanh, tanh)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float tanh(const float &x) { return ::tanhf(x); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double tanh(const double &x) { return ::tanh(x); }
+#endif
+
+template <typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+T fmod(const T& a, const T& b) {
+  EIGEN_USING_STD(fmod);
+  return fmod(a, b);
+}
+
+#if defined(SYCL_DEVICE_ONLY)
+SYCL_SPECIALIZE_FLOATING_TYPES_BINARY(fmod, fmod)
+#endif
+
+#if defined(EIGEN_GPUCC)
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float fmod(const float& a, const float& b) {
+  return ::fmodf(a, b);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double fmod(const double& a, const double& b) {
+  return ::fmod(a, b);
+}
+#endif
+
+#if defined(SYCL_DEVICE_ONLY)
+#undef SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_BINARY
+#undef SYCL_SPECIALIZE_SIGNED_INTEGER_TYPES_UNARY
+#undef SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_BINARY
+#undef SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY
+#undef SYCL_SPECIALIZE_INTEGER_TYPES_BINARY
+#undef SYCL_SPECIALIZE_UNSIGNED_INTEGER_TYPES_UNARY
+#undef SYCL_SPECIALIZE_FLOATING_TYPES_BINARY
+#undef SYCL_SPECIALIZE_FLOATING_TYPES_UNARY
+#undef SYCL_SPECIALIZE_FLOATING_TYPES_UNARY_FUNC_RET_TYPE
+#undef SYCL_SPECIALIZE_GEN_UNARY_FUNC
+#undef SYCL_SPECIALIZE_UNARY_FUNC
+#undef SYCL_SPECIALIZE_GEN1_BINARY_FUNC
+#undef SYCL_SPECIALIZE_GEN2_BINARY_FUNC
+#undef SYCL_SPECIALIZE_BINARY_FUNC
+#endif
+
+} // end namespace numext
+
+namespace internal {
+
+template<typename T>
+EIGEN_DEVICE_FUNC bool isfinite_impl(const std::complex<T>& x)
+{
+  return (numext::isfinite)(numext::real(x)) && (numext::isfinite)(numext::imag(x));
+}
+
+template<typename T>
+EIGEN_DEVICE_FUNC bool isnan_impl(const std::complex<T>& x)
+{
+  return (numext::isnan)(numext::real(x)) || (numext::isnan)(numext::imag(x));
+}
+
+template<typename T>
+EIGEN_DEVICE_FUNC bool isinf_impl(const std::complex<T>& x)
+{
+  return ((numext::isinf)(numext::real(x)) || (numext::isinf)(numext::imag(x))) && (!(numext::isnan)(x));
+}
+
+/****************************************************************************
+* Implementation of fuzzy comparisons                                       *
+****************************************************************************/
+
+template<typename Scalar,
+         bool IsComplex,
+         bool IsInteger>
+struct scalar_fuzzy_default_impl {};
+
+template<typename Scalar>
+struct scalar_fuzzy_default_impl<Scalar, false, false>
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  template<typename OtherScalar> EIGEN_DEVICE_FUNC
+  static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, const RealScalar& prec)
+  {
+    return numext::abs(x) <= numext::abs(y) * prec;
+  }
+  EIGEN_DEVICE_FUNC
+  static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec)
+  {
+    return numext::abs(x - y) <= numext::mini(numext::abs(x), numext::abs(y)) * prec;
+  }
+  EIGEN_DEVICE_FUNC
+  static inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, const RealScalar& prec)
+  {
+    return x <= y || isApprox(x, y, prec);
+  }
+};
+
+template<typename Scalar>
+struct scalar_fuzzy_default_impl<Scalar, false, true>
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  template<typename OtherScalar> EIGEN_DEVICE_FUNC
+  static inline bool isMuchSmallerThan(const Scalar& x, const Scalar&, const RealScalar&)
+  {
+    return x == Scalar(0);
+  }
+  EIGEN_DEVICE_FUNC
+  static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar&)
+  {
+    return x == y;
+  }
+  EIGEN_DEVICE_FUNC
+  static inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y, const RealScalar&)
+  {
+    return x <= y;
+  }
+};
+
+template<typename Scalar>
+struct scalar_fuzzy_default_impl<Scalar, true, false>
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  template<typename OtherScalar> EIGEN_DEVICE_FUNC
+  static inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y, const RealScalar& prec)
+  {
+    return numext::abs2(x) <= numext::abs2(y) * prec * prec;
+  }
+  EIGEN_DEVICE_FUNC
+  static inline bool isApprox(const Scalar& x, const Scalar& y, const RealScalar& prec)
+  {
+    return numext::abs2(x - y) <= numext::mini(numext::abs2(x), numext::abs2(y)) * prec * prec;
+  }
+};
+
+template<typename Scalar>
+struct scalar_fuzzy_impl : scalar_fuzzy_default_impl<Scalar, NumTraits<Scalar>::IsComplex, NumTraits<Scalar>::IsInteger> {};
+
+template<typename Scalar, typename OtherScalar> EIGEN_DEVICE_FUNC
+inline bool isMuchSmallerThan(const Scalar& x, const OtherScalar& y,
+                              const typename NumTraits<Scalar>::Real &precision = NumTraits<Scalar>::dummy_precision())
+{
+  return scalar_fuzzy_impl<Scalar>::template isMuchSmallerThan<OtherScalar>(x, y, precision);
+}
+
+template<typename Scalar> EIGEN_DEVICE_FUNC
+inline bool isApprox(const Scalar& x, const Scalar& y,
+                     const typename NumTraits<Scalar>::Real &precision = NumTraits<Scalar>::dummy_precision())
+{
+  return scalar_fuzzy_impl<Scalar>::isApprox(x, y, precision);
+}
+
+template<typename Scalar> EIGEN_DEVICE_FUNC
+inline bool isApproxOrLessThan(const Scalar& x, const Scalar& y,
+                               const typename NumTraits<Scalar>::Real &precision = NumTraits<Scalar>::dummy_precision())
+{
+  return scalar_fuzzy_impl<Scalar>::isApproxOrLessThan(x, y, precision);
+}
+
+/******************************************
+***  The special case of the  bool type ***
+******************************************/
+
+template<> struct random_impl<bool>
+{
+  static inline bool run()
+  {
+    return random<int>(0,1)==0 ? false : true;
+  }
+
+  static inline bool run(const bool& a, const bool& b)
+  {
+    return random<int>(a, b)==0 ? false : true;
+  }
+};
+
+template<> struct scalar_fuzzy_impl<bool>
+{
+  typedef bool RealScalar;
+
+  template<typename OtherScalar> EIGEN_DEVICE_FUNC
+  static inline bool isMuchSmallerThan(const bool& x, const bool&, const bool&)
+  {
+    return !x;
+  }
+
+  EIGEN_DEVICE_FUNC
+  static inline bool isApprox(bool x, bool y, bool)
+  {
+    return x == y;
+  }
+
+  EIGEN_DEVICE_FUNC
+  static inline bool isApproxOrLessThan(const bool& x, const bool& y, const bool&)
+  {
+    return (!x) || y;
+  }
+
+};
+
+} // end namespace internal
+
+// Default implementations that rely on other numext implementations
+namespace internal {
+
+// Specialization for complex types that are not supported by std::expm1.
+template <typename RealScalar>
+struct expm1_impl<std::complex<RealScalar> > {
+  EIGEN_STATIC_ASSERT_NON_INTEGER(RealScalar)
+
+  EIGEN_DEVICE_FUNC static inline std::complex<RealScalar> run(
+      const std::complex<RealScalar>& x) {
+    RealScalar xr = x.real();
+    RealScalar xi = x.imag();
+    // expm1(z) = exp(z) - 1
+    //          = exp(x +  i * y) - 1
+    //          = exp(x) * (cos(y) + i * sin(y)) - 1
+    //          = exp(x) * cos(y) - 1 + i * exp(x) * sin(y)
+    // Imag(expm1(z)) = exp(x) * sin(y)
+    // Real(expm1(z)) = exp(x) * cos(y) - 1
+    //          = exp(x) * cos(y) - 1.
+    //          = expm1(x) + exp(x) * (cos(y) - 1)
+    //          = expm1(x) + exp(x) * (2 * sin(y / 2) ** 2)
+    RealScalar erm1 = numext::expm1<RealScalar>(xr);
+    RealScalar er = erm1 + RealScalar(1.);
+    RealScalar sin2 = numext::sin(xi / RealScalar(2.));
+    sin2 = sin2 * sin2;
+    RealScalar s = numext::sin(xi);
+    RealScalar real_part = erm1 - RealScalar(2.) * er * sin2;
+    return std::complex<RealScalar>(real_part, er * s);
+  }
+};
+
+template<typename T>
+struct rsqrt_impl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_ALWAYS_INLINE T run(const T& x) {
+    return T(1)/numext::sqrt(x);
+  }
+};
+
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+template<typename T>
+struct conj_impl<std::complex<T>, true>
+{
+  EIGEN_DEVICE_FUNC
+  static inline std::complex<T> run(const std::complex<T>& x)
+  {
+    return std::complex<T>(numext::real(x), -numext::imag(x));
+  }
+};
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATHFUNCTIONS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/MathFunctionsImpl.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/MathFunctionsImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..86c06b2b13970244fadd4db93e642de16ed029b0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/MathFunctionsImpl.h
@@ -0,0 +1,337 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATHFUNCTIONSIMPL_H
+#define EIGEN_MATHFUNCTIONSIMPL_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal Fast reciprocal using Newton-Raphson's method.
+
+ Preconditions:
+   1. The starting guess provided in approx_a_recip must have at least half
+      the leading mantissa bits in the correct result, such that a single
+      Newton-Raphson step is sufficient to get within 1-2 ulps of the currect
+      result.
+   2. If a is zero, approx_a_recip must be infinite with the same sign as a.
+   3. If a is infinite, approx_a_recip must be zero with the same sign as a.
+
+   If the preconditions are satisfied, which they are for for the _*_rcp_ps
+   instructions on x86, the result has a maximum relative error of 2 ulps,
+   and correctly handles reciprocals of zero, infinity, and NaN.
+*/
+template <typename Packet, int Steps>
+struct generic_reciprocal_newton_step {
+  static_assert(Steps > 0, "Steps must be at least 1.");
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE  Packet
+  run(const Packet& a, const Packet& approx_a_recip) {
+    using Scalar = typename unpacket_traits<Packet>::type;
+    const Packet two = pset1<Packet>(Scalar(2));
+    // Refine the approximation using one Newton-Raphson step:
+    //   x_{i} = x_{i-1} * (2 - a * x_{i-1})
+     const Packet x =
+         generic_reciprocal_newton_step<Packet,Steps - 1>::run(a, approx_a_recip);
+     const Packet tmp = pnmadd(a, x, two);
+     // If tmp is NaN, it means that a is either +/-0 or +/-Inf.
+     // In this case return the approximation directly.
+     const Packet is_not_nan = pcmp_eq(tmp, tmp);
+     return pselect(is_not_nan, pmul(x, tmp), x);
+  }
+};
+
+template<typename Packet>
+struct generic_reciprocal_newton_step<Packet, 0> {
+   EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Packet
+   run(const Packet& /*unused*/, const Packet& approx_rsqrt) {
+    return approx_rsqrt;
+  }
+};
+
+
+/** \internal Fast reciprocal sqrt using Newton-Raphson's method.
+
+ Preconditions:
+   1. The starting guess provided in approx_a_recip must have at least half
+      the leading mantissa bits in the correct result, such that a single
+      Newton-Raphson step is sufficient to get within 1-2 ulps of the currect
+      result.
+   2. If a is zero, approx_a_recip must be infinite with the same sign as a.
+   3. If a is infinite, approx_a_recip must be zero with the same sign as a.
+
+   If the preconditions are satisfied, which they are for for the _*_rcp_ps
+   instructions on x86, the result has a maximum relative error of 2 ulps,
+   and correctly handles zero, infinity, and NaN. Positive denormals are
+   treated as zero.
+*/
+template <typename Packet, int Steps>
+struct generic_rsqrt_newton_step {
+  static_assert(Steps > 0, "Steps must be at least 1.");
+  using Scalar = typename unpacket_traits<Packet>::type;
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Packet
+  run(const Packet& a, const Packet& approx_rsqrt) {
+    constexpr Scalar kMinusHalf = Scalar(-1)/Scalar(2);
+    const Packet cst_minus_half = pset1<Packet>(kMinusHalf);
+    const Packet cst_minus_one = pset1<Packet>(Scalar(-1));
+
+    Packet inv_sqrt = approx_rsqrt;
+    for (int step = 0; step < Steps; ++step) {
+      // Refine the approximation using one Newton-Raphson step:
+      // h_n = (x * inv_sqrt) * inv_sqrt - 1 (so that h_n is nearly 0).
+      // inv_sqrt = inv_sqrt - 0.5 * inv_sqrt * h_n
+      Packet r2 = pmul(a, inv_sqrt);
+      Packet half_r = pmul(inv_sqrt, cst_minus_half);
+      Packet h_n = pmadd(r2, inv_sqrt, cst_minus_one);
+      inv_sqrt = pmadd(half_r, h_n, inv_sqrt);
+    }
+
+    // If x is NaN, then either:
+    // 1) the input is NaN
+    // 2) zero and infinity were multiplied
+    // In either of these cases, return approx_rsqrt
+    return pselect(pisnan(inv_sqrt), approx_rsqrt, inv_sqrt);
+  }
+};
+
+template<typename Packet>
+struct generic_rsqrt_newton_step<Packet, 0> {
+   EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Packet
+   run(const Packet& /*unused*/, const Packet& approx_rsqrt) {
+    return approx_rsqrt;
+  }
+};
+
+/** \internal Fast sqrt using Newton-Raphson's method.
+
+ Preconditions:
+   1. The starting guess for the reciprocal sqrt provided in approx_rsqrt must
+      have at least half the leading mantissa bits in the correct result, such
+      that a single Newton-Raphson step is sufficient to get within 1-2 ulps of
+      the currect result.
+   2. If a is zero, approx_rsqrt must be infinite.
+   3. If a is infinite, approx_rsqrt must be zero.
+
+   If the preconditions are satisfied, which they are for for the _*_rsqrt_ps
+   instructions on x86, the result has a maximum relative error of 2 ulps,
+   and correctly handles zero and infinity, and NaN. Positive denormal inputs
+   are treated as zero.
+*/
+template <typename Packet, int Steps=1>
+struct generic_sqrt_newton_step {
+  static_assert(Steps > 0, "Steps must be at least 1.");
+
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE  Packet
+  run(const Packet& a, const Packet& approx_rsqrt) {
+    using Scalar = typename unpacket_traits<Packet>::type;
+    const Packet one_point_five = pset1<Packet>(Scalar(1.5));
+    const Packet minus_half = pset1<Packet>(Scalar(-0.5));
+    // If a is inf or zero, return a directly.
+    const Packet inf_mask = pcmp_eq(a, pset1<Packet>(NumTraits<Scalar>::infinity()));
+    const Packet return_a = por(pcmp_eq(a, pzero(a)), inf_mask);
+    // Do a single step of Newton's iteration for reciprocal square root:
+    //   x_{n+1} = x_n * (1.5 + (-0.5 * x_n) * (a * x_n))).
+    // The Newton's step is computed this way to avoid over/under-flows.
+    Packet rsqrt = pmul(approx_rsqrt, pmadd(pmul(minus_half, approx_rsqrt), pmul(a, approx_rsqrt), one_point_five));
+    for (int step = 1; step < Steps; ++step) {
+      rsqrt = pmul(rsqrt, pmadd(pmul(minus_half, rsqrt), pmul(a, rsqrt), one_point_five));
+    }
+
+    // Return sqrt(x) = x * rsqrt(x) for non-zero finite positive arguments.
+    // Return a itself for 0 or +inf, NaN for negative arguments.
+    return pselect(return_a, a, pmul(a, rsqrt));
+  }
+};
+
+/** \internal \returns the hyperbolic tan of \a a (coeff-wise)
+    Doesn't do anything fancy, just a 13/6-degree rational interpolant which
+    is accurate up to a couple of ulps in the (approximate) range [-8, 8],
+    outside of which tanh(x) = +/-1 in single precision. The input is clamped
+    to the range [-c, c]. The value c is chosen as the smallest value where
+    the approximation evaluates to exactly 1. In the reange [-0.0004, 0.0004]
+    the approximation tanh(x) ~= x is used for better accuracy as x tends to zero.
+
+    This implementation works on both scalars and packets.
+*/
+template<typename T>
+T generic_fast_tanh_float(const T& a_x)
+{
+  // Clamp the inputs to the range [-c, c]
+#ifdef EIGEN_VECTORIZE_FMA
+  const T plus_clamp = pset1<T>(7.99881172180175781f);
+  const T minus_clamp = pset1<T>(-7.99881172180175781f);
+#else
+  const T plus_clamp = pset1<T>(7.90531110763549805f);
+  const T minus_clamp = pset1<T>(-7.90531110763549805f);
+#endif
+  const T tiny = pset1<T>(0.0004f);
+  const T x = pmax(pmin(a_x, plus_clamp), minus_clamp);
+  const T tiny_mask = pcmp_lt(pabs(a_x), tiny);
+  // The monomial coefficients of the numerator polynomial (odd).
+  const T alpha_1 = pset1<T>(4.89352455891786e-03f);
+  const T alpha_3 = pset1<T>(6.37261928875436e-04f);
+  const T alpha_5 = pset1<T>(1.48572235717979e-05f);
+  const T alpha_7 = pset1<T>(5.12229709037114e-08f);
+  const T alpha_9 = pset1<T>(-8.60467152213735e-11f);
+  const T alpha_11 = pset1<T>(2.00018790482477e-13f);
+  const T alpha_13 = pset1<T>(-2.76076847742355e-16f);
+
+  // The monomial coefficients of the denominator polynomial (even).
+  const T beta_0 = pset1<T>(4.89352518554385e-03f);
+  const T beta_2 = pset1<T>(2.26843463243900e-03f);
+  const T beta_4 = pset1<T>(1.18534705686654e-04f);
+  const T beta_6 = pset1<T>(1.19825839466702e-06f);
+
+  // Since the polynomials are odd/even, we need x^2.
+  const T x2 = pmul(x, x);
+
+  // Evaluate the numerator polynomial p.
+  T p = pmadd(x2, alpha_13, alpha_11);
+  p = pmadd(x2, p, alpha_9);
+  p = pmadd(x2, p, alpha_7);
+  p = pmadd(x2, p, alpha_5);
+  p = pmadd(x2, p, alpha_3);
+  p = pmadd(x2, p, alpha_1);
+  p = pmul(x, p);
+
+  // Evaluate the denominator polynomial q.
+  T q = pmadd(x2, beta_6, beta_4);
+  q = pmadd(x2, q, beta_2);
+  q = pmadd(x2, q, beta_0);
+
+  // Divide the numerator by the denominator.
+  return pselect(tiny_mask, x, pdiv(p, q));
+}
+
+template<typename RealScalar>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+RealScalar positive_real_hypot(const RealScalar& x, const RealScalar& y)
+{
+  // IEEE IEC 6059 special cases.
+  if ((numext::isinf)(x) || (numext::isinf)(y))
+    return NumTraits<RealScalar>::infinity();
+  if ((numext::isnan)(x) || (numext::isnan)(y))
+    return NumTraits<RealScalar>::quiet_NaN();
+    
+  EIGEN_USING_STD(sqrt);
+  RealScalar p, qp;
+  p = numext::maxi(x,y);
+  if(numext::is_exactly_zero(p)) return RealScalar(0);
+  qp = numext::mini(y,x) / p;
+  return p * sqrt(RealScalar(1) + qp*qp);
+}
+
+template<typename Scalar>
+struct hypot_impl
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  static EIGEN_DEVICE_FUNC
+  inline RealScalar run(const Scalar& x, const Scalar& y)
+  {
+    EIGEN_USING_STD(abs);
+    return positive_real_hypot<RealScalar>(abs(x), abs(y));
+  }
+};
+
+// Generic complex sqrt implementation that correctly handles corner cases
+// according to https://en.cppreference.com/w/cpp/numeric/complex/sqrt
+template<typename T>
+EIGEN_DEVICE_FUNC std::complex<T> complex_sqrt(const std::complex<T>& z) {
+  // Computes the principal sqrt of the input.
+  //
+  // For a complex square root of the number x + i*y. We want to find real
+  // numbers u and v such that
+  //    (u + i*v)^2 = x + i*y  <=>
+  //    u^2 - v^2 + i*2*u*v = x + i*v.
+  // By equating the real and imaginary parts we get:
+  //    u^2 - v^2 = x
+  //    2*u*v = y.
+  //
+  // For x >= 0, this has the numerically stable solution
+  //    u = sqrt(0.5 * (x + sqrt(x^2 + y^2)))
+  //    v = y / (2 * u)
+  // and for x < 0,
+  //    v = sign(y) * sqrt(0.5 * (-x + sqrt(x^2 + y^2)))
+  //    u = y / (2 * v)
+  //
+  // Letting w = sqrt(0.5 * (|x| + |z|)),
+  //   if x == 0: u = w, v = sign(y) * w
+  //   if x > 0:  u = w, v = y / (2 * w)
+  //   if x < 0:  u = |y| / (2 * w), v = sign(y) * w
+
+  const T x = numext::real(z);
+  const T y = numext::imag(z);
+  const T zero = T(0);
+  const T w = numext::sqrt(T(0.5) * (numext::abs(x) + numext::hypot(x, y)));
+
+  return
+    (numext::isinf)(y) ? std::complex<T>(NumTraits<T>::infinity(), y)
+      : numext::is_exactly_zero(x) ? std::complex<T>(w, y < zero ? -w : w)
+                                   : x > zero ? std::complex<T>(w, y / (2 * w))
+      : std::complex<T>(numext::abs(y) / (2 * w), y < zero ? -w : w );
+}
+
+// Generic complex rsqrt implementation.
+template<typename T>
+EIGEN_DEVICE_FUNC std::complex<T> complex_rsqrt(const std::complex<T>& z) {
+  // Computes the principal reciprocal sqrt of the input.
+  //
+  // For a complex reciprocal square root of the number z = x + i*y. We want to
+  // find real numbers u and v such that
+  //    (u + i*v)^2 = 1 / (x + i*y)  <=>
+  //    u^2 - v^2 + i*2*u*v = x/|z|^2 - i*v/|z|^2.
+  // By equating the real and imaginary parts we get:
+  //    u^2 - v^2 = x/|z|^2
+  //    2*u*v = y/|z|^2.
+  //
+  // For x >= 0, this has the numerically stable solution
+  //    u = sqrt(0.5 * (x + |z|)) / |z|
+  //    v = -y / (2 * u * |z|)
+  // and for x < 0,
+  //    v = -sign(y) * sqrt(0.5 * (-x + |z|)) / |z|
+  //    u = -y / (2 * v * |z|)
+  //
+  // Letting w = sqrt(0.5 * (|x| + |z|)),
+  //   if x == 0: u = w / |z|, v = -sign(y) * w / |z|
+  //   if x > 0:  u = w / |z|, v = -y / (2 * w * |z|)
+  //   if x < 0:  u = |y| / (2 * w * |z|), v = -sign(y) * w / |z|
+
+  const T x = numext::real(z);
+  const T y = numext::imag(z);
+  const T zero = T(0);
+
+  const T abs_z = numext::hypot(x, y);
+  const T w = numext::sqrt(T(0.5) * (numext::abs(x) + abs_z));
+  const T woz = w / abs_z;
+  // Corner cases consistent with 1/sqrt(z) on gcc/clang.
+  return
+          numext::is_exactly_zero(abs_z) ? std::complex<T>(NumTraits<T>::infinity(), NumTraits<T>::quiet_NaN())
+                                         : ((numext::isinf)(x) || (numext::isinf)(y)) ? std::complex<T>(zero, zero)
+      : numext::is_exactly_zero(x) ? std::complex<T>(woz, y < zero ? woz : -woz)
+                                   : x > zero ? std::complex<T>(woz, -y / (2 * w * abs_z))
+      : std::complex<T>(numext::abs(y) / (2 * w * abs_z), y < zero ? woz : -woz );
+}
+
+template<typename T>
+EIGEN_DEVICE_FUNC std::complex<T> complex_log(const std::complex<T>& z) {
+  // Computes complex log.
+  T a = numext::abs(z);
+  EIGEN_USING_STD(atan2);
+  T b = atan2(z.imag(), z.real());
+  return std::complex<T>(numext::log(a), b);
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATHFUNCTIONSIMPL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Matrix.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Matrix.h
new file mode 100644
index 0000000000000000000000000000000000000000..c7747f16d23f24320fdc3532ba76d14c1d157278
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Matrix.h
@@ -0,0 +1,557 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_H
+#define EIGEN_MATRIX_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template<typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+struct traits<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> >
+{
+private:
+  constexpr static int size = internal::size_at_compile_time(Rows_,Cols_);
+  typedef typename find_best_packet<Scalar_,size>::type PacketScalar;
+  enum {
+      row_major_bit = Options_&RowMajor ? RowMajorBit : 0,
+      is_dynamic_size_storage = MaxRows_==Dynamic || MaxCols_==Dynamic,
+      max_size = is_dynamic_size_storage ? Dynamic : MaxRows_*MaxCols_,
+      default_alignment = compute_default_alignment<Scalar_,max_size>::value,
+      actual_alignment = ((Options_&DontAlign)==0) ? default_alignment : 0,
+      required_alignment = unpacket_traits<PacketScalar>::alignment,
+      packet_access_bit = (packet_traits<Scalar_>::Vectorizable && (EIGEN_UNALIGNED_VECTORIZE || (actual_alignment>=required_alignment))) ? PacketAccessBit : 0
+    };
+
+public:
+  typedef Scalar_ Scalar;
+  typedef Dense StorageKind;
+  typedef Eigen::Index StorageIndex;
+  typedef MatrixXpr XprKind;
+  enum {
+    RowsAtCompileTime = Rows_,
+    ColsAtCompileTime = Cols_,
+    MaxRowsAtCompileTime = MaxRows_,
+    MaxColsAtCompileTime = MaxCols_,
+    Flags = compute_matrix_flags(Options_),
+    Options = Options_,
+    InnerStrideAtCompileTime = 1,
+    OuterStrideAtCompileTime = (Options&RowMajor) ? ColsAtCompileTime : RowsAtCompileTime,
+
+    // FIXME, the following flag in only used to define NeedsToAlign in PlainObjectBase
+    EvaluatorFlags = LinearAccessBit | DirectAccessBit | packet_access_bit | row_major_bit,
+    Alignment = actual_alignment
+  };
+};
+}
+
+/** \class Matrix
+  * \ingroup Core_Module
+  *
+  * \brief The matrix class, also used for vectors and row-vectors
+  *
+  * The %Matrix class is the work-horse for all \em dense (\ref dense "note") matrices and vectors within Eigen.
+  * Vectors are matrices with one column, and row-vectors are matrices with one row.
+  *
+  * The %Matrix class encompasses \em both fixed-size and dynamic-size objects (\ref fixedsize "note").
+  *
+  * The first three template parameters are required:
+  * \tparam Scalar_ Numeric type, e.g. float, double, int or std::complex<float>.
+  *                 User defined scalar types are supported as well (see \ref user_defined_scalars "here").
+  * \tparam Rows_ Number of rows, or \b Dynamic
+  * \tparam Cols_ Number of columns, or \b Dynamic
+  *
+  * The remaining template parameters are optional -- in most cases you don't have to worry about them.
+  * \tparam Options_ A combination of either \b #RowMajor or \b #ColMajor, and of either
+  *                 \b #AutoAlign or \b #DontAlign.
+  *                 The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required
+  *                 for vectorization. It defaults to aligning matrices except for fixed sizes that aren't a multiple of the packet size.
+  * \tparam MaxRows_ Maximum number of rows. Defaults to \a Rows_ (\ref maxrows "note").
+  * \tparam MaxCols_ Maximum number of columns. Defaults to \a Cols_ (\ref maxrows "note").
+  *
+  * Eigen provides a number of typedefs covering the usual cases. Here are some examples:
+  *
+  * \li \c Matrix2d is a 2x2 square matrix of doubles (\c Matrix<double, 2, 2>)
+  * \li \c Vector4f is a vector of 4 floats (\c Matrix<float, 4, 1>)
+  * \li \c RowVector3i is a row-vector of 3 ints (\c Matrix<int, 1, 3>)
+  *
+  * \li \c MatrixXf is a dynamic-size matrix of floats (\c Matrix<float, Dynamic, Dynamic>)
+  * \li \c VectorXf is a dynamic-size vector of floats (\c Matrix<float, Dynamic, 1>)
+  *
+  * \li \c Matrix2Xf is a partially fixed-size (dynamic-size) matrix of floats (\c Matrix<float, 2, Dynamic>)
+  * \li \c MatrixX3d is a partially dynamic-size (fixed-size) matrix of double (\c Matrix<double, Dynamic, 3>)
+  *
+  * See \link matrixtypedefs this page \endlink for a complete list of predefined \em %Matrix and \em Vector typedefs.
+  *
+  * You can access elements of vectors and matrices using normal subscripting:
+  *
+  * \code
+  * Eigen::VectorXd v(10);
+  * v[0] = 0.1;
+  * v[1] = 0.2;
+  * v(0) = 0.3;
+  * v(1) = 0.4;
+  *
+  * Eigen::MatrixXi m(10, 10);
+  * m(0, 1) = 1;
+  * m(0, 2) = 2;
+  * m(0, 3) = 3;
+  * \endcode
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIX_PLUGIN.
+  *
+  * <i><b>Some notes:</b></i>
+  *
+  * <dl>
+  * <dt><b>\anchor dense Dense versus sparse:</b></dt>
+  * <dd>This %Matrix class handles dense, not sparse matrices and vectors. For sparse matrices and vectors, see the Sparse module.
+  *
+  * Dense matrices and vectors are plain usual arrays of coefficients. All the coefficients are stored, in an ordinary contiguous array.
+  * This is unlike Sparse matrices and vectors where the coefficients are stored as a list of nonzero coefficients.</dd>
+  *
+  * <dt><b>\anchor fixedsize Fixed-size versus dynamic-size:</b></dt>
+  * <dd>Fixed-size means that the numbers of rows and columns are known are compile-time. In this case, Eigen allocates the array
+  * of coefficients as a fixed-size array, as a class member. This makes sense for very small matrices, typically up to 4x4, sometimes up
+  * to 16x16. Larger matrices should be declared as dynamic-size even if one happens to know their size at compile-time.
+  *
+  * Dynamic-size means that the numbers of rows or columns are not necessarily known at compile-time. In this case they are runtime
+  * variables, and the array of coefficients is allocated dynamically on the heap.
+  *
+  * Note that \em dense matrices, be they Fixed-size or Dynamic-size, <em>do not</em> expand dynamically in the sense of a std::map.
+  * If you want this behavior, see the Sparse module.</dd>
+  *
+  * <dt><b>\anchor maxrows MaxRows_ and MaxCols_:</b></dt>
+  * <dd>In most cases, one just leaves these parameters to the default values.
+  * These parameters mean the maximum size of rows and columns that the matrix may have. They are useful in cases
+  * when the exact numbers of rows and columns are not known are compile-time, but it is known at compile-time that they cannot
+  * exceed a certain value. This happens when taking dynamic-size blocks inside fixed-size matrices: in this case MaxRows_ and MaxCols_
+  * are the dimensions of the original matrix, while Rows_ and Cols_ are Dynamic.</dd>
+  * </dl>
+  *
+  * <i><b>ABI and storage layout</b></i>
+  *
+  * The table below summarizes the ABI of some possible Matrix instances which is fixed thorough the lifetime of Eigen 3.
+  * <table  class="manual">
+  * <tr><th>Matrix type</th><th>Equivalent C structure</th></tr>
+  * <tr><td>\code Matrix<T,Dynamic,Dynamic> \endcode</td><td>\code
+  * struct {
+  *   T *data;                  // with (size_t(data)%EIGEN_MAX_ALIGN_BYTES)==0
+  *   Eigen::Index rows, cols;
+  *  };
+  * \endcode</td></tr>
+  * <tr class="alt"><td>\code
+  * Matrix<T,Dynamic,1>
+  * Matrix<T,1,Dynamic> \endcode</td><td>\code
+  * struct {
+  *   T *data;                  // with (size_t(data)%EIGEN_MAX_ALIGN_BYTES)==0
+  *   Eigen::Index size;
+  *  };
+  * \endcode</td></tr>
+  * <tr><td>\code Matrix<T,Rows,Cols> \endcode</td><td>\code
+  * struct {
+  *   T data[Rows*Cols];        // with (size_t(data)%A(Rows*Cols*sizeof(T)))==0
+  *  };
+  * \endcode</td></tr>
+  * <tr class="alt"><td>\code Matrix<T,Dynamic,Dynamic,0,MaxRows,MaxCols> \endcode</td><td>\code
+  * struct {
+  *   T data[MaxRows*MaxCols];  // with (size_t(data)%A(MaxRows*MaxCols*sizeof(T)))==0
+  *   Eigen::Index rows, cols;
+  *  };
+  * \endcode</td></tr>
+  * </table>
+  * Note that in this table Rows, Cols, MaxRows and MaxCols are all positive integers. A(S) is defined to the largest possible power-of-two
+  * smaller to EIGEN_MAX_STATIC_ALIGN_BYTES.
+  *
+  * \see MatrixBase for the majority of the API methods for matrices, \ref TopicClassHierarchy,
+  * \ref TopicStorageOrders
+  */
+
+template<typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+class Matrix
+  : public PlainObjectBase<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> >
+{
+  public:
+
+    /** \brief Base class typedef.
+      * \sa PlainObjectBase
+      */
+    typedef PlainObjectBase<Matrix> Base;
+
+    enum { Options = Options_ };
+
+    EIGEN_DENSE_PUBLIC_INTERFACE(Matrix)
+
+    typedef typename Base::PlainObject PlainObject;
+
+    using Base::base;
+    using Base::coeffRef;
+
+    /**
+      * \brief Assigns matrices to each other.
+      *
+      * \note This is a special case of the templated operator=. Its purpose is
+      * to prevent a default operator= from hiding the templated operator=.
+      *
+      * \callgraph
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Matrix& operator=(const Matrix& other)
+    {
+      return Base::_set(other);
+    }
+
+    /** \internal
+      * \brief Copies the value of the expression \a other into \c *this with automatic resizing.
+      *
+      * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
+      * it will be initialized.
+      *
+      * Note that copying a row-vector into a vector (and conversely) is allowed.
+      * The resizing, if any, is then done in the appropriate way so that row-vectors
+      * remain row-vectors and vectors remain vectors.
+      */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Matrix& operator=(const DenseBase<OtherDerived>& other)
+    {
+      return Base::_set(other);
+    }
+
+    /* Here, doxygen failed to copy the brief information when using \copydoc */
+
+    /**
+      * \brief Copies the generic expression \a other into *this.
+      * \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
+      */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Matrix& operator=(const EigenBase<OtherDerived> &other)
+    {
+      return Base::operator=(other);
+    }
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Matrix& operator=(const ReturnByValue<OtherDerived>& func)
+    {
+      return Base::operator=(func);
+    }
+
+    /** \brief Default constructor.
+      *
+      * For fixed-size matrices, does nothing.
+      *
+      * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix
+      * is called a null matrix. This constructor is the unique way to create null matrices: resizing
+      * a matrix to 0 is not supported.
+      *
+      * \sa resize(Index,Index)
+      */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Matrix() : Base()
+    {
+      EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+    }
+
+    // FIXME is it still needed
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    explicit Matrix(internal::constructor_without_unaligned_array_assert)
+      : Base(internal::constructor_without_unaligned_array_assert())
+    { EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Matrix(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
+      : Base(std::move(other)) {}
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Matrix& operator=(Matrix&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
+    {
+      Base::operator=(std::move(other));
+      return *this;
+    }
+
+    /** \copydoc PlainObjectBase(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&... args)
+     *
+     * Example: \include Matrix_variadic_ctor_cxx11.cpp
+     * Output: \verbinclude Matrix_variadic_ctor_cxx11.out
+     *
+     * \sa Matrix(const std::initializer_list<std::initializer_list<Scalar>>&)
+     */
+    template <typename... ArgTypes>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Matrix(const Scalar& a0, const Scalar& a1, const Scalar& a2,  const Scalar& a3, const ArgTypes&... args)
+      : Base(a0, a1, a2, a3, args...) {}
+
+    /** \brief Constructs a Matrix and initializes it from the coefficients given as initializer-lists grouped by row. \cpp11
+      *
+      * In the general case, the constructor takes a list of rows, each row being represented as a list of coefficients:
+      *
+      * Example: \include Matrix_initializer_list_23_cxx11.cpp
+      * Output: \verbinclude Matrix_initializer_list_23_cxx11.out
+      *
+      * Each of the inner initializer lists must contain the exact same number of elements, otherwise an assertion is triggered.
+      *
+      * In the case of a compile-time column vector, implicit transposition from a single row is allowed.
+      * Therefore <code>VectorXd{{1,2,3,4,5}}</code> is legal and the more verbose syntax
+      * <code>RowVectorXd{{1},{2},{3},{4},{5}}</code> can be avoided:
+      *
+      * Example: \include Matrix_initializer_list_vector_cxx11.cpp
+      * Output: \verbinclude Matrix_initializer_list_vector_cxx11.out
+      *
+      * In the case of fixed-sized matrices, the initializer list sizes must exactly match the matrix sizes,
+      * and implicit transposition is allowed for compile-time vectors only.
+      *
+      * \sa Matrix(const Scalar& a0, const Scalar& a1, const Scalar& a2,  const Scalar& a3, const ArgTypes&... args)
+      */
+    EIGEN_DEVICE_FUNC explicit constexpr EIGEN_STRONG_INLINE Matrix(
+        const std::initializer_list<std::initializer_list<Scalar>>& list)
+        : Base(list) {}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+    // This constructor is for both 1x1 matrices and dynamic vectors
+    template<typename T>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    explicit Matrix(const T& x)
+    {
+      Base::template _init1<T>(x);
+    }
+
+    template<typename T0, typename T1>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Matrix(const T0& x, const T1& y)
+    {
+      Base::template _init2<T0,T1>(x, y);
+    }
+
+
+#else
+    /** \brief Constructs a fixed-sized matrix initialized with coefficients starting at \a data */
+    EIGEN_DEVICE_FUNC
+    explicit Matrix(const Scalar *data);
+
+    /** \brief Constructs a vector or row-vector with given dimension. \only_for_vectors
+      *
+      * This is useful for dynamic-size vectors. For fixed-size vectors,
+      * it is redundant to pass these parameters, so one should use the default constructor
+      * Matrix() instead.
+      *
+      * \warning This constructor is disabled for fixed-size \c 1x1 matrices. For instance,
+      * calling Matrix<double,1,1>(1) will call the initialization constructor: Matrix(const Scalar&).
+      * For fixed-size \c 1x1 matrices it is therefore recommended to use the default
+      * constructor Matrix() instead, especially when using one of the non standard
+      * \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives).
+      */
+    EIGEN_STRONG_INLINE explicit Matrix(Index dim);
+    /** \brief Constructs an initialized 1x1 matrix with the given coefficient
+      * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...) */
+    Matrix(const Scalar& x);
+    /** \brief Constructs an uninitialized matrix with \a rows rows and \a cols columns.
+      *
+      * This is useful for dynamic-size matrices. For fixed-size matrices,
+      * it is redundant to pass these parameters, so one should use the default constructor
+      * Matrix() instead.
+      *
+      * \warning This constructor is disabled for fixed-size \c 1x2 and \c 2x1 vectors. For instance,
+      * calling Matrix2f(2,1) will call the initialization constructor: Matrix(const Scalar& x, const Scalar& y).
+      * For fixed-size \c 1x2 or \c 2x1 vectors it is therefore recommended to use the default
+      * constructor Matrix() instead, especially when using one of the non standard
+      * \c EIGEN_INITIALIZE_MATRICES_BY_{ZERO,\c NAN} macros (see \ref TopicPreprocessorDirectives).
+      */
+    EIGEN_DEVICE_FUNC
+    Matrix(Index rows, Index cols);
+
+    /** \brief Constructs an initialized 2D vector with given coefficients
+      * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...) */
+    Matrix(const Scalar& x, const Scalar& y);
+    #endif  // end EIGEN_PARSED_BY_DOXYGEN
+
+    /** \brief Constructs an initialized 3D vector with given coefficients
+      * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...)
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z)
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 3)
+      m_storage.data()[0] = x;
+      m_storage.data()[1] = y;
+      m_storage.data()[2] = z;
+    }
+    /** \brief Constructs an initialized 4D vector with given coefficients
+      * \sa Matrix(const Scalar&, const Scalar&, const Scalar&,  const Scalar&, const ArgTypes&...)
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Matrix(const Scalar& x, const Scalar& y, const Scalar& z, const Scalar& w)
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Matrix, 4)
+      m_storage.data()[0] = x;
+      m_storage.data()[1] = y;
+      m_storage.data()[2] = z;
+      m_storage.data()[3] = w;
+    }
+
+
+    /** \brief Copy constructor */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Matrix(const Matrix& other) : Base(other)
+    { }
+
+    /** \brief Copy constructor for generic expressions.
+      * \sa MatrixBase::operator=(const EigenBase<OtherDerived>&)
+      */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Matrix(const EigenBase<OtherDerived> &other)
+      : Base(other.derived())
+    { }
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index innerStride() const EIGEN_NOEXCEPT { return 1; }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index outerStride() const EIGEN_NOEXCEPT { return this->innerSize(); }
+
+    /////////// Geometry module ///////////
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    explicit Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    Matrix& operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r);
+
+    // allow to extend Matrix outside Eigen
+    #ifdef EIGEN_MATRIX_PLUGIN
+    #include EIGEN_MATRIX_PLUGIN
+    #endif
+
+  protected:
+    template <typename Derived, typename OtherDerived, bool IsVector>
+    friend struct internal::conservative_resize_like_impl;
+
+    using Base::m_storage;
+};
+
+/** \defgroup matrixtypedefs Global matrix typedefs
+  *
+  * \ingroup Core_Module
+  *
+  * %Eigen defines several typedef shortcuts for most common matrix and vector types.
+  *
+  * The general patterns are the following:
+  *
+  * \c MatrixSizeType where \c Size can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size,
+  * and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c cd
+  * for complex double.
+  *
+  * For example, \c Matrix3d is a fixed-size 3x3 matrix type of doubles, and \c MatrixXf is a dynamic-size matrix of floats.
+  *
+  * There are also \c VectorSizeType and \c RowVectorSizeType which are self-explanatory. For example, \c Vector4cf is
+  * a fixed-size vector of 4 complex floats.
+  *
+  * With \cpp11, template alias are also defined for common sizes.
+  * They follow the same pattern as above except that the scalar type suffix is replaced by a
+  * template parameter, i.e.:
+  *   - `MatrixSize<Type>` where `Size` can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for dynamic size.
+  *   - `MatrixXSize<Type>` and `MatrixSizeX<Type>` where `Size` can be \c 2,\c 3,\c 4 for hybrid dynamic/fixed matrices.
+  *   - `VectorSize<Type>` and `RowVectorSize<Type>` for column and row vectors.
+  *
+  * With \cpp11, you can also use fully generic column and row vector types: `Vector<Type,Size>` and `RowVector<Type,Size>`.
+  *
+  * \sa class Matrix
+  */
+
+#define EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix)   \
+/** \ingroup matrixtypedefs */                                    \
+/** \brief `Size`&times;`Size` matrix of type `Type`. */          \
+typedef Matrix<Type, Size, Size> Matrix##SizeSuffix##TypeSuffix;  \
+/** \ingroup matrixtypedefs */                                    \
+/** \brief `Size`&times;`1` vector of type `Type`. */             \
+typedef Matrix<Type, Size, 1>    Vector##SizeSuffix##TypeSuffix;  \
+/** \ingroup matrixtypedefs */                                    \
+/** \brief `1`&times;`Size` vector of type `Type`. */             \
+typedef Matrix<Type, 1, Size>    RowVector##SizeSuffix##TypeSuffix;
+
+#define EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, Size)         \
+/** \ingroup matrixtypedefs */                                    \
+/** \brief `Size`&times;`Dynamic` matrix of type `Type`. */       \
+typedef Matrix<Type, Size, Dynamic> Matrix##Size##X##TypeSuffix;  \
+/** \ingroup matrixtypedefs */                                    \
+/** \brief `Dynamic`&times;`Size` matrix of type `Type`. */       \
+typedef Matrix<Type, Dynamic, Size> Matrix##X##Size##TypeSuffix;
+
+#define EIGEN_MAKE_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
+EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 2, 2) \
+EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 3, 3) \
+EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 4, 4) \
+EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Dynamic, X) \
+EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 2) \
+EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 3) \
+EIGEN_MAKE_FIXED_TYPEDEFS(Type, TypeSuffix, 4)
+
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(int,                  i)
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(float,                f)
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(double,               d)
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(std::complex<float>,  cf)
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(std::complex<double>, cd)
+
+#undef EIGEN_MAKE_TYPEDEFS_ALL_SIZES
+#undef EIGEN_MAKE_TYPEDEFS
+#undef EIGEN_MAKE_FIXED_TYPEDEFS
+
+#define EIGEN_MAKE_TYPEDEFS(Size, SizeSuffix)                        \
+/** \ingroup matrixtypedefs */                                       \
+/** \brief \cpp11 `Size`&times;`Size` matrix of type `Type`.*/       \
+template <typename Type>                                             \
+using Matrix##SizeSuffix = Matrix<Type, Size, Size>;                 \
+/** \ingroup matrixtypedefs */                                       \
+/** \brief \cpp11 `Size`&times;`1` vector of type `Type`.*/          \
+template <typename Type>                                             \
+using Vector##SizeSuffix = Matrix<Type, Size, 1>;                    \
+/** \ingroup matrixtypedefs */                                       \
+/** \brief \cpp11 `1`&times;`Size` vector of type `Type`.*/          \
+template <typename Type>                                             \
+using RowVector##SizeSuffix = Matrix<Type, 1, Size>;
+
+#define EIGEN_MAKE_FIXED_TYPEDEFS(Size)                            \
+/** \ingroup matrixtypedefs */                                     \
+/** \brief \cpp11 `Size`&times;`Dynamic` matrix of type `Type` */  \
+template <typename Type>                                           \
+using Matrix##Size##X = Matrix<Type, Size, Dynamic>;               \
+/** \ingroup matrixtypedefs */                                     \
+/** \brief \cpp11 `Dynamic`&times;`Size` matrix of type `Type`. */ \
+template <typename Type>                                           \
+using Matrix##X##Size = Matrix<Type, Dynamic, Size>;
+
+EIGEN_MAKE_TYPEDEFS(2, 2)
+EIGEN_MAKE_TYPEDEFS(3, 3)
+EIGEN_MAKE_TYPEDEFS(4, 4)
+EIGEN_MAKE_TYPEDEFS(Dynamic, X)
+EIGEN_MAKE_FIXED_TYPEDEFS(2)
+EIGEN_MAKE_FIXED_TYPEDEFS(3)
+EIGEN_MAKE_FIXED_TYPEDEFS(4)
+
+/** \ingroup matrixtypedefs
+  * \brief \cpp11 `Size`&times;`1` vector of type `Type`. */
+template <typename Type, int Size>
+using Vector = Matrix<Type, Size, 1>;
+
+/** \ingroup matrixtypedefs
+  * \brief \cpp11 `1`&times;`Size` vector of type `Type`. */
+template <typename Type, int Size>
+using RowVector = Matrix<Type, 1, Size>;
+
+#undef EIGEN_MAKE_TYPEDEFS
+#undef EIGEN_MAKE_FIXED_TYPEDEFS
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATRIX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/MatrixBase.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/MatrixBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..6eb064b6132d621eee0a0b2836c238a32d2715eb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/MatrixBase.h
@@ -0,0 +1,551 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIXBASE_H
+#define EIGEN_MATRIXBASE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class MatrixBase
+  * \ingroup Core_Module
+  *
+  * \brief Base class for all dense matrices, vectors, and expressions
+  *
+  * This class is the base that is inherited by all matrix, vector, and related expression
+  * types. Most of the Eigen API is contained in this class, and its base classes. Other important
+  * classes for the Eigen API are Matrix, and VectorwiseOp.
+  *
+  * Note that some methods are defined in other modules such as the \ref LU_Module LU module
+  * for all functions related to matrix inversions.
+  *
+  * \tparam Derived is the derived type, e.g. a matrix type, or an expression, etc.
+  *
+  * When writing a function taking Eigen objects as argument, if you want your function
+  * to take as argument any matrix, vector, or expression, just let it take a
+  * MatrixBase argument. As an example, here is a function printFirstRow which, given
+  * a matrix, vector, or expression \a x, prints the first row of \a x.
+  *
+  * \code
+    template<typename Derived>
+    void printFirstRow(const Eigen::MatrixBase<Derived>& x)
+    {
+      cout << x.row(0) << endl;
+    }
+  * \endcode
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_MATRIXBASE_PLUGIN.
+  *
+  * \sa \blank \ref TopicClassHierarchy
+  */
+template<typename Derived> class MatrixBase
+  : public DenseBase<Derived>
+{
+  public:
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    typedef MatrixBase StorageBaseType;
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    typedef DenseBase<Derived> Base;
+    using Base::RowsAtCompileTime;
+    using Base::ColsAtCompileTime;
+    using Base::SizeAtCompileTime;
+    using Base::MaxRowsAtCompileTime;
+    using Base::MaxColsAtCompileTime;
+    using Base::MaxSizeAtCompileTime;
+    using Base::IsVectorAtCompileTime;
+    using Base::Flags;
+
+    using Base::derived;
+    using Base::const_cast_derived;
+    using Base::rows;
+    using Base::cols;
+    using Base::size;
+    using Base::coeff;
+    using Base::coeffRef;
+    using Base::lazyAssign;
+    using Base::eval;
+    using Base::operator-;
+    using Base::operator+=;
+    using Base::operator-=;
+    using Base::operator*=;
+    using Base::operator/=;
+
+    typedef typename Base::CoeffReturnType CoeffReturnType;
+    typedef typename Base::ConstTransposeReturnType ConstTransposeReturnType;
+    typedef typename Base::RowXpr RowXpr;
+    typedef typename Base::ColXpr ColXpr;
+#endif // not EIGEN_PARSED_BY_DOXYGEN
+
+
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** type of the equivalent square matrix */
+    typedef Matrix<Scalar, internal::max_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime),
+                           internal::max_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime)> SquareMatrixType;
+#endif // not EIGEN_PARSED_BY_DOXYGEN
+
+    /** \returns the size of the main diagonal, which is min(rows(),cols()).
+      * \sa rows(), cols(), SizeAtCompileTime. */
+    EIGEN_DEVICE_FUNC
+    inline Index diagonalSize() const { return (numext::mini)(rows(),cols()); }
+
+    typedef typename Base::PlainObject PlainObject;
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** \internal Represents a matrix with all coefficients equal to one another*/
+    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,PlainObject> ConstantReturnType;
+    /** \internal the return type of MatrixBase::adjoint() */
+    typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
+               CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, ConstTransposeReturnType>,
+               ConstTransposeReturnType
+            > AdjointReturnType;
+    /** \internal Return type of eigenvalues() */
+    typedef Matrix<std::complex<RealScalar>, internal::traits<Derived>::ColsAtCompileTime, 1, ColMajor> EigenvaluesReturnType;
+    /** \internal the return type of identity */
+    typedef CwiseNullaryOp<internal::scalar_identity_op<Scalar>,PlainObject> IdentityReturnType;
+    /** \internal the return type of unit vectors */
+    typedef Block<const CwiseNullaryOp<internal::scalar_identity_op<Scalar>, SquareMatrixType>,
+                  internal::traits<Derived>::RowsAtCompileTime,
+                  internal::traits<Derived>::ColsAtCompileTime> BasisReturnType;
+#endif // not EIGEN_PARSED_BY_DOXYGEN
+
+#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::MatrixBase
+#define EIGEN_DOC_UNARY_ADDONS(X,Y)
+#   include "../plugins/CommonCwiseBinaryOps.h"
+#   include "../plugins/MatrixCwiseUnaryOps.h"
+#   include "../plugins/MatrixCwiseBinaryOps.h"
+#   ifdef EIGEN_MATRIXBASE_PLUGIN
+#     include EIGEN_MATRIXBASE_PLUGIN
+#   endif
+#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
+#undef EIGEN_DOC_UNARY_ADDONS
+
+    /** Special case of the template operator=, in order to prevent the compiler
+      * from generating a default operator= (issue hit with g++ 4.1)
+      */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator=(const MatrixBase& other);
+
+    // We cannot inherit here via Base::operator= since it is causing
+    // trouble with MSVC.
+
+    template <typename OtherDerived>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator=(const DenseBase<OtherDerived>& other);
+
+    template <typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    Derived& operator=(const EigenBase<OtherDerived>& other);
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    Derived& operator=(const ReturnByValue<OtherDerived>& other);
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator+=(const MatrixBase<OtherDerived>& other);
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Derived& operator-=(const MatrixBase<OtherDerived>& other);
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    const Product<Derived,OtherDerived>
+    operator*(const MatrixBase<OtherDerived> &other) const;
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    const Product<Derived,OtherDerived,LazyProduct>
+    lazyProduct(const MatrixBase<OtherDerived> &other) const;
+
+    template<typename OtherDerived>
+    Derived& operator*=(const EigenBase<OtherDerived>& other);
+
+    template<typename OtherDerived>
+    void applyOnTheLeft(const EigenBase<OtherDerived>& other);
+
+    template<typename OtherDerived>
+    void applyOnTheRight(const EigenBase<OtherDerived>& other);
+
+    template<typename DiagonalDerived>
+    EIGEN_DEVICE_FUNC
+    const Product<Derived, DiagonalDerived, LazyProduct>
+    operator*(const DiagonalBase<DiagonalDerived> &diagonal) const;
+
+    template<typename SkewDerived>
+    EIGEN_DEVICE_FUNC
+    const Product<Derived, SkewDerived, LazyProduct>
+    operator*(const SkewSymmetricBase<SkewDerived> &skew) const;
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType
+    dot(const MatrixBase<OtherDerived>& other) const;
+
+    EIGEN_DEVICE_FUNC RealScalar squaredNorm() const;
+    EIGEN_DEVICE_FUNC RealScalar norm() const;
+    RealScalar stableNorm() const;
+    RealScalar blueNorm() const;
+    RealScalar hypotNorm() const;
+    EIGEN_DEVICE_FUNC const PlainObject normalized() const;
+    EIGEN_DEVICE_FUNC const PlainObject stableNormalized() const;
+    EIGEN_DEVICE_FUNC void normalize();
+    EIGEN_DEVICE_FUNC void stableNormalize();
+
+    EIGEN_DEVICE_FUNC const AdjointReturnType adjoint() const;
+    EIGEN_DEVICE_FUNC void adjointInPlace();
+
+    typedef Diagonal<Derived> DiagonalReturnType;
+    EIGEN_DEVICE_FUNC
+    DiagonalReturnType diagonal();
+
+    typedef Diagonal<const Derived> ConstDiagonalReturnType;
+    EIGEN_DEVICE_FUNC
+    const ConstDiagonalReturnType diagonal() const;
+
+    template<int Index>
+    EIGEN_DEVICE_FUNC
+    Diagonal<Derived, Index> diagonal();
+
+    template<int Index>
+    EIGEN_DEVICE_FUNC
+    const Diagonal<const Derived, Index> diagonal() const;
+
+    EIGEN_DEVICE_FUNC
+    Diagonal<Derived, DynamicIndex> diagonal(Index index);
+    EIGEN_DEVICE_FUNC
+    const Diagonal<const Derived, DynamicIndex> diagonal(Index index) const;
+
+    template<unsigned int Mode> struct TriangularViewReturnType { typedef TriangularView<Derived, Mode> Type; };
+    template<unsigned int Mode> struct ConstTriangularViewReturnType { typedef const TriangularView<const Derived, Mode> Type; };
+
+    template<unsigned int Mode>
+    EIGEN_DEVICE_FUNC
+    typename TriangularViewReturnType<Mode>::Type triangularView();
+    template<unsigned int Mode>
+    EIGEN_DEVICE_FUNC
+    typename ConstTriangularViewReturnType<Mode>::Type triangularView() const;
+
+    template<unsigned int UpLo> struct SelfAdjointViewReturnType { typedef SelfAdjointView<Derived, UpLo> Type; };
+    template<unsigned int UpLo> struct ConstSelfAdjointViewReturnType { typedef const SelfAdjointView<const Derived, UpLo> Type; };
+
+    template<unsigned int UpLo>
+    EIGEN_DEVICE_FUNC
+    typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
+    template<unsigned int UpLo>
+    EIGEN_DEVICE_FUNC
+    typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
+
+    const SparseView<Derived> sparseView(const Scalar& m_reference = Scalar(0),
+                                         const typename NumTraits<Scalar>::Real& m_epsilon = NumTraits<Scalar>::dummy_precision()) const;
+    EIGEN_DEVICE_FUNC static const IdentityReturnType Identity();
+    EIGEN_DEVICE_FUNC static const IdentityReturnType Identity(Index rows, Index cols);
+    EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index size, Index i);
+    EIGEN_DEVICE_FUNC static const BasisReturnType Unit(Index i);
+    EIGEN_DEVICE_FUNC static const BasisReturnType UnitX();
+    EIGEN_DEVICE_FUNC static const BasisReturnType UnitY();
+    EIGEN_DEVICE_FUNC static const BasisReturnType UnitZ();
+    EIGEN_DEVICE_FUNC static const BasisReturnType UnitW();
+
+    EIGEN_DEVICE_FUNC
+    const DiagonalWrapper<const Derived> asDiagonal() const;
+    const PermutationWrapper<const Derived> asPermutation() const;
+    EIGEN_DEVICE_FUNC
+    const SkewSymmetricWrapper<const Derived> asSkewSymmetric() const;
+
+    EIGEN_DEVICE_FUNC
+    Derived& setIdentity();
+    EIGEN_DEVICE_FUNC
+    Derived& setIdentity(Index rows, Index cols);
+    EIGEN_DEVICE_FUNC Derived& setUnit(Index i);
+    EIGEN_DEVICE_FUNC Derived& setUnit(Index newSize, Index i);
+
+    bool isIdentity(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+    bool isDiagonal(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+
+    bool isUpperTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+    bool isLowerTriangular(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+
+    bool isSkewSymmetric(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+
+    template<typename OtherDerived>
+    bool isOrthogonal(const MatrixBase<OtherDerived>& other,
+                      const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+    bool isUnitary(const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+
+    /** \returns true if each coefficients of \c *this and \a other are all exactly equal.
+      * \warning When using floating point scalar values you probably should rather use a
+      *          fuzzy comparison such as isApprox()
+      * \sa isApprox(), operator!= */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC inline bool operator==(const MatrixBase<OtherDerived>& other) const
+    { return cwiseEqual(other).all(); }
+
+    /** \returns true if at least one pair of coefficients of \c *this and \a other are not exactly equal to each other.
+      * \warning When using floating point scalar values you probably should rather use a
+      *          fuzzy comparison such as isApprox()
+      * \sa isApprox(), operator== */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC inline bool operator!=(const MatrixBase<OtherDerived>& other) const
+    { return cwiseNotEqual(other).any(); }
+
+    NoAlias<Derived,Eigen::MatrixBase > EIGEN_DEVICE_FUNC noalias();
+
+    // TODO forceAlignedAccess is temporarily disabled
+    // Need to find a nicer workaround.
+    inline const Derived& forceAlignedAccess() const { return derived(); }
+    inline Derived& forceAlignedAccess() { return derived(); }
+    template<bool Enable> inline const Derived& forceAlignedAccessIf() const { return derived(); }
+    template<bool Enable> inline Derived& forceAlignedAccessIf() { return derived(); }
+
+    EIGEN_DEVICE_FUNC Scalar trace() const;
+
+    template<int p> EIGEN_DEVICE_FUNC RealScalar lpNorm() const;
+
+    EIGEN_DEVICE_FUNC MatrixBase<Derived>& matrix() { return *this; }
+    EIGEN_DEVICE_FUNC const MatrixBase<Derived>& matrix() const { return *this; }
+
+    /** \returns an \link Eigen::ArrayBase Array \endlink expression of this matrix
+      * \sa ArrayBase::matrix() */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ArrayWrapper<Derived> array() { return ArrayWrapper<Derived>(derived()); }
+    /** \returns a const \link Eigen::ArrayBase Array \endlink expression of this matrix
+      * \sa ArrayBase::matrix() */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const ArrayWrapper<const Derived> array() const { return ArrayWrapper<const Derived>(derived()); }
+
+/////////// LU module ///////////
+
+    template<typename PermutationIndex = DefaultPermutationIndex> inline const FullPivLU<PlainObject, PermutationIndex> fullPivLu() const;
+    template<typename PermutationIndex = DefaultPermutationIndex> inline const PartialPivLU<PlainObject, PermutationIndex> partialPivLu() const;
+
+    template<typename PermutationIndex = DefaultPermutationIndex> inline const PartialPivLU<PlainObject, PermutationIndex> lu() const;
+
+    EIGEN_DEVICE_FUNC
+    inline const Inverse<Derived> inverse() const;
+
+    template<typename ResultType>
+    inline void computeInverseAndDetWithCheck(
+      ResultType& inverse,
+      typename ResultType::Scalar& determinant,
+      bool& invertible,
+      const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision()
+    ) const;
+
+    template<typename ResultType>
+    inline void computeInverseWithCheck(
+      ResultType& inverse,
+      bool& invertible,
+      const RealScalar& absDeterminantThreshold = NumTraits<Scalar>::dummy_precision()
+    ) const;
+
+    EIGEN_DEVICE_FUNC
+    Scalar determinant() const;
+
+/////////// Cholesky module ///////////
+
+    inline const LLT<PlainObject>  llt() const;
+    inline const LDLT<PlainObject> ldlt() const;
+
+/////////// QR module ///////////
+
+    inline const HouseholderQR<PlainObject> householderQr() const;
+    template<typename PermutationIndex = DefaultPermutationIndex> inline const ColPivHouseholderQR<PlainObject, PermutationIndex> colPivHouseholderQr() const;
+    template<typename PermutationIndex = DefaultPermutationIndex> inline const FullPivHouseholderQR<PlainObject, PermutationIndex> fullPivHouseholderQr() const;
+    template<typename PermutationIndex = DefaultPermutationIndex> inline const CompleteOrthogonalDecomposition<PlainObject, PermutationIndex> completeOrthogonalDecomposition() const;
+
+/////////// Eigenvalues module ///////////
+
+    inline EigenvaluesReturnType eigenvalues() const;
+    inline RealScalar operatorNorm() const;
+
+/////////// SVD module ///////////
+
+    template<int Options = 0>
+    inline JacobiSVD<PlainObject, Options> jacobiSvd() const;
+    template<int Options = 0>
+    EIGEN_DEPRECATED
+    inline JacobiSVD<PlainObject, Options> jacobiSvd(unsigned int computationOptions) const;
+
+    template<int Options = 0>
+    inline BDCSVD<PlainObject, Options> bdcSvd() const;
+    template<int Options = 0>
+    EIGEN_DEPRECATED
+    inline BDCSVD<PlainObject, Options> bdcSvd(unsigned int computationOptions) const;
+
+/////////// Geometry module ///////////
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    inline typename internal::cross_impl<Derived, OtherDerived>::return_type
+    cross(const MatrixBase<OtherDerived>& other) const;
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    inline PlainObject cross3(const MatrixBase<OtherDerived>& other) const;
+
+    EIGEN_DEVICE_FUNC
+    inline PlainObject unitOrthogonal(void) const;
+
+    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC
+    inline Matrix<Scalar,3,1> eulerAngles(Index a0, Index a1, Index a2) const;
+
+    EIGEN_DEVICE_FUNC
+    inline Matrix<Scalar,3,1> canonicalEulerAngles(Index a0, Index a1, Index a2) const;
+
+    // put this as separate enum value to work around possible GCC 4.3 bug (?)
+    enum { HomogeneousReturnTypeDirection = ColsAtCompileTime==1&&RowsAtCompileTime==1 ? ((internal::traits<Derived>::Flags&RowMajorBit)==RowMajorBit ? Horizontal : Vertical)
+                                          : ColsAtCompileTime==1 ? Vertical : Horizontal };
+    typedef Homogeneous<Derived, HomogeneousReturnTypeDirection> HomogeneousReturnType;
+    EIGEN_DEVICE_FUNC
+    inline HomogeneousReturnType homogeneous() const;
+
+    enum {
+      SizeMinusOne = SizeAtCompileTime==Dynamic ? Dynamic : SizeAtCompileTime-1
+    };
+    typedef Block<const Derived,
+                  internal::traits<Derived>::ColsAtCompileTime==1 ? SizeMinusOne : 1,
+                  internal::traits<Derived>::ColsAtCompileTime==1 ? 1 : SizeMinusOne> ConstStartMinusOne;
+    typedef EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(ConstStartMinusOne,Scalar,quotient) HNormalizedReturnType;
+    EIGEN_DEVICE_FUNC
+    inline const HNormalizedReturnType hnormalized() const;
+
+////////// Householder module ///////////
+
+    EIGEN_DEVICE_FUNC
+    void makeHouseholderInPlace(Scalar& tau, RealScalar& beta);
+    template<typename EssentialPart>
+    EIGEN_DEVICE_FUNC
+    void makeHouseholder(EssentialPart& essential,
+                         Scalar& tau, RealScalar& beta) const;
+    template<typename EssentialPart>
+    EIGEN_DEVICE_FUNC
+    void applyHouseholderOnTheLeft(const EssentialPart& essential,
+                                   const Scalar& tau,
+                                   Scalar* workspace);
+    template<typename EssentialPart>
+    EIGEN_DEVICE_FUNC
+    void applyHouseholderOnTheRight(const EssentialPart& essential,
+                                    const Scalar& tau,
+                                    Scalar* workspace);
+
+///////// Jacobi module /////////
+
+    template<typename OtherScalar>
+    EIGEN_DEVICE_FUNC
+    void applyOnTheLeft(Index p, Index q, const JacobiRotation<OtherScalar>& j);
+    template<typename OtherScalar>
+    EIGEN_DEVICE_FUNC
+    void applyOnTheRight(Index p, Index q, const JacobiRotation<OtherScalar>& j);
+
+///////// SparseCore module /////////
+
+    template<typename OtherDerived>
+    EIGEN_STRONG_INLINE const typename SparseMatrixBase<OtherDerived>::template CwiseProductDenseReturnType<Derived>::Type
+    cwiseProduct(const SparseMatrixBase<OtherDerived> &other) const
+    {
+      return other.cwiseProduct(derived());
+    }
+
+///////// MatrixFunctions module /////////
+
+    typedef typename internal::stem_function<Scalar>::type StemFunction;
+#define EIGEN_MATRIX_FUNCTION(ReturnType, Name, Description) \
+    /** \returns an expression of the matrix Description of \c *this. \brief This function requires the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>. To compute the coefficient-wise Description use ArrayBase::##Name . */ \
+    const ReturnType<Derived> Name() const;
+#define EIGEN_MATRIX_FUNCTION_1(ReturnType, Name, Description, Argument) \
+    /** \returns an expression of the matrix Description of \c *this. \brief This function requires the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>. To compute the coefficient-wise Description use ArrayBase::##Name . */ \
+    const ReturnType<Derived> Name(Argument) const;
+
+    EIGEN_MATRIX_FUNCTION(MatrixExponentialReturnValue, exp, exponential)
+    /** \brief Helper function for the <a href="unsupported/group__MatrixFunctions__Module.html"> unsupported MatrixFunctions module</a>.*/
+    const MatrixFunctionReturnValue<Derived> matrixFunction(StemFunction f) const;
+    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cosh, hyperbolic cosine)
+    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sinh, hyperbolic sine)
+    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, atanh, inverse hyperbolic cosine)
+    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, acosh, inverse hyperbolic cosine)
+    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, asinh, inverse hyperbolic sine)
+    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, cos, cosine)
+    EIGEN_MATRIX_FUNCTION(MatrixFunctionReturnValue, sin, sine)
+    EIGEN_MATRIX_FUNCTION(MatrixSquareRootReturnValue, sqrt, square root)
+    EIGEN_MATRIX_FUNCTION(MatrixLogarithmReturnValue, log, logarithm)
+    EIGEN_MATRIX_FUNCTION_1(MatrixPowerReturnValue,        pow, power to \c p, const RealScalar& p)
+    EIGEN_MATRIX_FUNCTION_1(MatrixComplexPowerReturnValue, pow, power to \c p, const std::complex<RealScalar>& p)
+
+  protected:
+    EIGEN_DEFAULT_COPY_CONSTRUCTOR(MatrixBase)
+    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(MatrixBase)
+
+  private:
+    EIGEN_DEVICE_FUNC explicit MatrixBase(int);
+    EIGEN_DEVICE_FUNC MatrixBase(int,int);
+    template<typename OtherDerived> EIGEN_DEVICE_FUNC explicit MatrixBase(const MatrixBase<OtherDerived>&);
+  protected:
+    // mixing arrays and matrices is not legal
+    template<typename OtherDerived> Derived& operator+=(const ArrayBase<OtherDerived>& )
+    {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
+    // mixing arrays and matrices is not legal
+    template<typename OtherDerived> Derived& operator-=(const ArrayBase<OtherDerived>& )
+    {EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar))==-1,YOU_CANNOT_MIX_ARRAYS_AND_MATRICES); return *this;}
+};
+
+
+/***************************************************************************
+* Implementation of matrix base methods
+***************************************************************************/
+
+/** replaces \c *this by \c *this * \a other.
+  *
+  * \returns a reference to \c *this
+  *
+  * Example: \include MatrixBase_applyOnTheRight.cpp
+  * Output: \verbinclude MatrixBase_applyOnTheRight.out
+  */
+template<typename Derived>
+template<typename OtherDerived>
+inline Derived&
+MatrixBase<Derived>::operator*=(const EigenBase<OtherDerived> &other)
+{
+  other.derived().applyThisOnTheRight(derived());
+  return derived();
+}
+
+/** replaces \c *this by \c *this * \a other. It is equivalent to MatrixBase::operator*=().
+  *
+  * Example: \include MatrixBase_applyOnTheRight.cpp
+  * Output: \verbinclude MatrixBase_applyOnTheRight.out
+  */
+template<typename Derived>
+template<typename OtherDerived>
+inline void MatrixBase<Derived>::applyOnTheRight(const EigenBase<OtherDerived> &other)
+{
+  other.derived().applyThisOnTheRight(derived());
+}
+
+/** replaces \c *this by \a other * \c *this.
+  *
+  * Example: \include MatrixBase_applyOnTheLeft.cpp
+  * Output: \verbinclude MatrixBase_applyOnTheLeft.out
+  */
+template<typename Derived>
+template<typename OtherDerived>
+inline void MatrixBase<Derived>::applyOnTheLeft(const EigenBase<OtherDerived> &other)
+{
+  other.derived().applyThisOnTheLeft(derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATRIXBASE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/NestByValue.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/NestByValue.h
new file mode 100644
index 0000000000000000000000000000000000000000..311cb5a3df8e6136b6798fd1250dee0c8c47cb4c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/NestByValue.h
@@ -0,0 +1,101 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NESTBYVALUE_H
+#define EIGEN_NESTBYVALUE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template<typename ExpressionType>
+struct traits<NestByValue<ExpressionType> > : public traits<ExpressionType>
+{
+  enum {
+    Flags = traits<ExpressionType>::Flags & ~NestByRefBit
+  };
+};
+}
+
+/** \class NestByValue
+  * \ingroup Core_Module
+  *
+  * \brief Expression which must be nested by value
+  *
+  * \tparam ExpressionType the type of the object of which we are requiring nesting-by-value
+  *
+  * This class is the return type of MatrixBase::nestByValue()
+  * and most of the time this is the only way it is used.
+  *
+  * \sa MatrixBase::nestByValue()
+  */
+template<typename ExpressionType> class NestByValue
+  : public internal::dense_xpr_base< NestByValue<ExpressionType> >::type
+{
+  public:
+
+    typedef typename internal::dense_xpr_base<NestByValue>::type Base;
+    static constexpr bool HasDirectAccess = internal::has_direct_access<ExpressionType>::ret;
+    
+    EIGEN_DENSE_PUBLIC_INTERFACE(NestByValue)
+
+    EIGEN_DEVICE_FUNC explicit inline NestByValue(const ExpressionType& matrix) : m_expression(matrix) {}
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return m_expression.rows(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return m_expression.cols(); }
+
+    EIGEN_DEVICE_FUNC operator const ExpressionType&() const { return m_expression; }
+
+    EIGEN_DEVICE_FUNC const ExpressionType& nestedExpression() const { return m_expression; }
+
+    EIGEN_DEVICE_FUNC typename std::enable_if<HasDirectAccess, const Scalar*>::type data() const {
+      return m_expression.data();
+    }
+    
+    EIGEN_DEVICE_FUNC typename std::enable_if<HasDirectAccess, Index>::type innerStride() const {
+      return m_expression.innerStride();
+    }
+    
+    EIGEN_DEVICE_FUNC typename std::enable_if<HasDirectAccess, Index>::type outerStride() const {
+      return m_expression.outerStride();
+    }
+
+  protected:
+    const ExpressionType m_expression;
+};
+
+/** \returns an expression of the temporary version of *this.
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline const NestByValue<Derived>
+DenseBase<Derived>::nestByValue() const
+{
+  return NestByValue<Derived>(derived());
+}
+
+namespace internal {
+
+// Evaluator of Solve -> eval into a temporary
+template<typename ArgType>
+struct evaluator<NestByValue<ArgType> >
+  : public evaluator<ArgType>
+{
+  typedef evaluator<ArgType> Base;
+
+  EIGEN_DEVICE_FUNC explicit evaluator(const NestByValue<ArgType>& xpr)
+    : Base(xpr.nestedExpression())
+  {}
+};
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_NESTBYVALUE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/NoAlias.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/NoAlias.h
new file mode 100644
index 0000000000000000000000000000000000000000..09c0aac556f80ce0b9ff53e04a2b0a101bf4c3d5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/NoAlias.h
@@ -0,0 +1,111 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NOALIAS_H
+#define EIGEN_NOALIAS_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class NoAlias
+  * \ingroup Core_Module
+  *
+  * \brief Pseudo expression providing an operator = assuming no aliasing
+  *
+  * \tparam ExpressionType the type of the object on which to do the lazy assignment
+  *
+  * This class represents an expression with special assignment operators
+  * assuming no aliasing between the target expression and the source expression.
+  * More precisely it alloas to bypass the EvalBeforeAssignBit flag of the source expression.
+  * It is the return type of MatrixBase::noalias()
+  * and most of the time this is the only way it is used.
+  *
+  * \sa MatrixBase::noalias()
+  */
+template<typename ExpressionType, template <typename> class StorageBase>
+class NoAlias
+{
+  public:
+    typedef typename ExpressionType::Scalar Scalar;
+    
+    EIGEN_DEVICE_FUNC
+    explicit NoAlias(ExpressionType& expression) : m_expression(expression) {}
+    
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE ExpressionType& operator=(const StorageBase<OtherDerived>& other)
+    {
+      call_assignment_no_alias(m_expression, other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
+      return m_expression;
+    }
+    
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE ExpressionType& operator+=(const StorageBase<OtherDerived>& other)
+    {
+      call_assignment_no_alias(m_expression, other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
+      return m_expression;
+    }
+    
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE ExpressionType& operator-=(const StorageBase<OtherDerived>& other)
+    {
+      call_assignment_no_alias(m_expression, other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
+      return m_expression;
+    }
+
+    EIGEN_DEVICE_FUNC
+    ExpressionType& expression() const
+    {
+      return m_expression;
+    }
+
+  protected:
+    ExpressionType& m_expression;
+};
+
+/** \returns a pseudo expression of \c *this with an operator= assuming
+  * no aliasing between \c *this and the source expression.
+  *
+  * More precisely, noalias() allows to bypass the EvalBeforeAssignBit flag.
+  * Currently, even though several expressions may alias, only product
+  * expressions have this flag. Therefore, noalias() is only useful when
+  * the source expression contains a matrix product.
+  *
+  * Here are some examples where noalias is useful:
+  * \code
+  * D.noalias()  = A * B;
+  * D.noalias() += A.transpose() * B;
+  * D.noalias() -= 2 * A * B.adjoint();
+  * \endcode
+  *
+  * On the other hand the following example will lead to a \b wrong result:
+  * \code
+  * A.noalias() = A * B;
+  * \endcode
+  * because the result matrix A is also an operand of the matrix product. Therefore,
+  * there is no alternative than evaluating A * B in a temporary, that is the default
+  * behavior when you write:
+  * \code
+  * A = A * B;
+  * \endcode
+  *
+  * \sa class NoAlias
+  */
+template<typename Derived>
+NoAlias<Derived,MatrixBase> EIGEN_DEVICE_FUNC MatrixBase<Derived>::noalias()
+{
+  return NoAlias<Derived, Eigen::MatrixBase >(derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_NOALIAS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/NumTraits.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/NumTraits.h
new file mode 100644
index 0000000000000000000000000000000000000000..f5275d8042ae89a1960d1b077dbc8138f67efeba
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/NumTraits.h
@@ -0,0 +1,390 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NUMTRAITS_H
+#define EIGEN_NUMTRAITS_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// default implementation of digits(), based on numeric_limits if specialized,
+// 0 for integer types, and log2(epsilon()) otherwise.
+template< typename T,
+          bool use_numeric_limits = std::numeric_limits<T>::is_specialized,
+          bool is_integer = NumTraits<T>::IsInteger>
+struct default_digits_impl
+{
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static int run() { return std::numeric_limits<T>::digits; }
+};
+
+template<typename T>
+struct default_digits_impl<T,false,false> // Floating point
+{
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static int run() {
+    using std::log2;
+    using std::ceil;
+    typedef typename NumTraits<T>::Real Real;
+    return int(ceil(-log2(NumTraits<Real>::epsilon())));
+  }
+};
+
+template<typename T>
+struct default_digits_impl<T,false,true> // Integer
+{
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static int run() { return 0; }
+};
+
+// default implementation of digits10(), based on numeric_limits if specialized,
+// 0 for integer types, and floor((digits()-1)*log10(2)) otherwise.
+template< typename T,
+          bool use_numeric_limits = std::numeric_limits<T>::is_specialized,
+          bool is_integer = NumTraits<T>::IsInteger>
+struct default_digits10_impl
+{
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static int run() { return std::numeric_limits<T>::digits10; }
+};
+
+template<typename T>
+struct default_digits10_impl<T,false,false> // Floating point
+{
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static int run() {
+    using std::log10;
+    using std::floor;
+    typedef typename NumTraits<T>::Real Real;
+    return int(floor((internal::default_digits_impl<Real>::run()-1)*log10(2)));
+  }
+};
+
+template<typename T>
+struct default_digits10_impl<T,false,true> // Integer
+{
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static int run() { return 0; }
+};
+
+// default implementation of max_digits10(), based on numeric_limits if specialized,
+// 0 for integer types, and log10(2) * digits() + 1 otherwise.
+template< typename T,
+          bool use_numeric_limits = std::numeric_limits<T>::is_specialized,
+          bool is_integer = NumTraits<T>::IsInteger>
+struct default_max_digits10_impl
+{
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static int run() { return std::numeric_limits<T>::max_digits10; }
+};
+
+template<typename T>
+struct default_max_digits10_impl<T,false,false> // Floating point
+{
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static int run() {
+    using std::log10;
+    using std::ceil;
+    typedef typename NumTraits<T>::Real Real;
+    return int(ceil(internal::default_digits_impl<Real>::run()*log10(2)+1));
+  }
+};
+
+template<typename T>
+struct default_max_digits10_impl<T,false,true> // Integer
+{
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static int run() { return 0; }
+};
+
+} // end namespace internal
+
+namespace numext {
+/** \internal bit-wise cast without changing the underlying bit representation. */
+
+// TODO: Replace by std::bit_cast (available in C++20)
+template <typename Tgt, typename Src>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Tgt bit_cast(const Src& src) {
+  // The behaviour of memcpy is not specified for non-trivially copyable types
+  EIGEN_STATIC_ASSERT(std::is_trivially_copyable<Src>::value, THIS_TYPE_IS_NOT_SUPPORTED);
+  EIGEN_STATIC_ASSERT(std::is_trivially_copyable<Tgt>::value && std::is_default_constructible<Tgt>::value,
+                      THIS_TYPE_IS_NOT_SUPPORTED);
+  EIGEN_STATIC_ASSERT(sizeof(Src) == sizeof(Tgt), THIS_TYPE_IS_NOT_SUPPORTED);
+
+  Tgt tgt;
+  // Load src into registers first. This allows the memcpy to be elided by CUDA.
+  const Src staged = src;
+  EIGEN_USING_STD(memcpy)
+  memcpy(static_cast<void*>(&tgt),static_cast<const void*>(&staged), sizeof(Tgt));
+  return tgt;
+}
+}  // namespace numext
+
+/** \class NumTraits
+  * \ingroup Core_Module
+  *
+  * \brief Holds information about the various numeric (i.e. scalar) types allowed by Eigen.
+  *
+  * \tparam T the numeric type at hand
+  *
+  * This class stores enums, typedefs and static methods giving information about a numeric type.
+  *
+  * The provided data consists of:
+  * \li A typedef \c Real, giving the "real part" type of \a T. If \a T is already real,
+  *     then \c Real is just a typedef to \a T. If \a T is \c std::complex<U> then \c Real
+  *     is a typedef to \a U.
+  * \li A typedef \c NonInteger, giving the type that should be used for operations producing non-integral values,
+  *     such as quotients, square roots, etc. If \a T is a floating-point type, then this typedef just gives
+  *     \a T again. Note however that many Eigen functions such as internal::sqrt simply refuse to
+  *     take integers. Outside of a few cases, Eigen doesn't do automatic type promotion. Thus, this typedef is
+  *     only intended as a helper for code that needs to explicitly promote types.
+  * \li A typedef \c Literal giving the type to use for numeric literals such as "2" or "0.5". For instance, for \c std::complex<U>, Literal is defined as \c U.
+  *     Of course, this type must be fully compatible with \a T. In doubt, just use \a T here.
+  * \li A typedef \a Nested giving the type to use to nest a value inside of the expression tree. If you don't know what
+  *     this means, just use \a T here.
+  * \li An enum value \a IsComplex. It is equal to 1 if \a T is a \c std::complex
+  *     type, and to 0 otherwise.
+  * \li An enum value \a IsInteger. It is equal to \c 1 if \a T is an integer type such as \c int,
+  *     and to \c 0 otherwise.
+  * \li Enum values ReadCost, AddCost and MulCost representing a rough estimate of the number of CPU cycles needed
+  *     to by move / add / mul instructions respectively, assuming the data is already stored in CPU registers.
+  *     Stay vague here. No need to do architecture-specific stuff. If you don't know what this means, just use \c Eigen::HugeCost.
+  * \li An enum value \a IsSigned. It is equal to \c 1 if \a T is a signed type and to 0 if \a T is unsigned.
+  * \li An enum value \a RequireInitialization. It is equal to \c 1 if the constructor of the numeric type \a T must
+  *     be called, and to 0 if it is safe not to call it. Default is 0 if \a T is an arithmetic type, and 1 otherwise.
+  * \li An epsilon() function which, unlike <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/epsilon">std::numeric_limits::epsilon()</a>,
+  *     it returns a \a Real instead of a \a T.
+  * \li A dummy_precision() function returning a weak epsilon value. It is mainly used as a default
+  *     value by the fuzzy comparison operators.
+  * \li highest() and lowest() functions returning the highest and lowest possible values respectively.
+  * \li digits() function returning the number of radix digits (non-sign digits for integers, mantissa for floating-point). This is
+  *     the analogue of <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/digits">std::numeric_limits<T>::digits</a>
+  *     which is used as the default implementation if specialized.
+  * \li digits10() function returning the number of decimal digits that can be represented without change. This is
+  *     the analogue of <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/digits10">std::numeric_limits<T>::digits10</a>
+  *     which is used as the default implementation if specialized.
+  * \li max_digits10() function returning the number of decimal digits required to uniquely represent all distinct values of the type. This is
+  *     the analogue of <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/max_digits10">std::numeric_limits<T>::max_digits10</a>
+  *     which is used as the default implementation if specialized.
+  * \li min_exponent() and max_exponent() functions returning the highest and lowest possible values, respectively,
+  *     such that the radix raised to the power exponent-1 is a normalized floating-point number.  These are equivalent to
+  *     <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/min_exponent">std::numeric_limits<T>::min_exponent</a>/
+  *     <a href="http://en.cppreference.com/w/cpp/types/numeric_limits/max_exponent">std::numeric_limits<T>::max_exponent</a>.
+  * \li infinity() function returning a representation of positive infinity, if available.
+  * \li quiet_NaN function returning a non-signaling "not-a-number", if available.
+  */
+
+template<typename T> struct GenericNumTraits
+{
+  enum {
+    IsInteger = std::numeric_limits<T>::is_integer,
+    IsSigned = std::numeric_limits<T>::is_signed,
+    IsComplex = 0,
+    RequireInitialization = internal::is_arithmetic<T>::value ? 0 : 1,
+    ReadCost = 1,
+    AddCost = 1,
+    MulCost = 1
+  };
+
+  typedef T Real;
+  typedef std::conditional_t<IsInteger, std::conditional_t<sizeof(T)<=2, float, double>, T> NonInteger;
+  typedef T Nested;
+  typedef T Literal;
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline Real epsilon()
+  {
+    return numext::numeric_limits<T>::epsilon();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline int digits10()
+  {
+    return internal::default_digits10_impl<T>::run();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline int max_digits10()
+  {
+    return internal::default_max_digits10_impl<T>::run();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline int digits()
+  {
+    return internal::default_digits_impl<T>::run();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline int min_exponent()
+  {
+    return numext::numeric_limits<T>::min_exponent;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline int max_exponent()
+  {
+    return numext::numeric_limits<T>::max_exponent;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline Real dummy_precision()
+  {
+    // make sure to override this for floating-point types
+    return Real(0);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline T highest() {
+    return (numext::numeric_limits<T>::max)();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline T lowest()  {
+    return IsInteger ? (numext::numeric_limits<T>::min)()
+                     : static_cast<T>(-(numext::numeric_limits<T>::max)());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline T infinity() {
+    return numext::numeric_limits<T>::infinity();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline T quiet_NaN() {
+    return numext::numeric_limits<T>::quiet_NaN();
+  }
+};
+
+template<typename T> struct NumTraits : GenericNumTraits<T>
+{};
+
+template<> struct NumTraits<float>
+  : GenericNumTraits<float>
+{
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline float dummy_precision() { return 1e-5f; }
+};
+
+template<> struct NumTraits<double> : GenericNumTraits<double>
+{
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline double dummy_precision() { return 1e-12; }
+};
+
+// GPU devices treat `long double` as `double`.
+#ifndef EIGEN_GPU_COMPILE_PHASE
+template<> struct NumTraits<long double>
+  : GenericNumTraits<long double>
+{
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline long double dummy_precision() { return static_cast<long double>(1e-15l); }
+
+#if defined(EIGEN_ARCH_PPC) && (__LDBL_MANT_DIG__ == 106)
+  // PowerPC double double causes issues with some values
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline long double epsilon()
+  {
+    // 2^(-(__LDBL_MANT_DIG__)+1)
+    return static_cast<long double>(2.4651903288156618919116517665087e-32l);
+  }
+#endif
+};
+#endif
+
+template<typename Real_> struct NumTraits<std::complex<Real_> >
+  : GenericNumTraits<std::complex<Real_> >
+{
+  typedef Real_ Real;
+  typedef typename NumTraits<Real_>::Literal Literal;
+  enum {
+    IsComplex = 1,
+    RequireInitialization = NumTraits<Real_>::RequireInitialization,
+    ReadCost = 2 * NumTraits<Real_>::ReadCost,
+    AddCost = 2 * NumTraits<Real>::AddCost,
+    MulCost = 4 * NumTraits<Real>::MulCost + 2 * NumTraits<Real>::AddCost
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline Real epsilon() { return NumTraits<Real>::epsilon(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline Real dummy_precision() { return NumTraits<Real>::dummy_precision(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline int digits10() { return NumTraits<Real>::digits10(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline int max_digits10() { return NumTraits<Real>::max_digits10(); }
+};
+
+template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+struct NumTraits<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> >
+{
+  typedef Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> ArrayType;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Array<RealScalar, Rows, Cols, Options, MaxRows, MaxCols> Real;
+  typedef typename NumTraits<Scalar>::NonInteger NonIntegerScalar;
+  typedef Array<NonIntegerScalar, Rows, Cols, Options, MaxRows, MaxCols> NonInteger;
+  typedef ArrayType & Nested;
+  typedef typename NumTraits<Scalar>::Literal Literal;
+
+  enum {
+    IsComplex = NumTraits<Scalar>::IsComplex,
+    IsInteger = NumTraits<Scalar>::IsInteger,
+    IsSigned  = NumTraits<Scalar>::IsSigned,
+    RequireInitialization = 1,
+    ReadCost = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * int(NumTraits<Scalar>::ReadCost),
+    AddCost  = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * int(NumTraits<Scalar>::AddCost),
+    MulCost  = ArrayType::SizeAtCompileTime==Dynamic ? HugeCost : ArrayType::SizeAtCompileTime * int(NumTraits<Scalar>::MulCost)
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline RealScalar epsilon() { return NumTraits<RealScalar>::epsilon(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  static inline RealScalar dummy_precision() { return NumTraits<RealScalar>::dummy_precision(); }
+
+  EIGEN_CONSTEXPR
+  static inline int digits10() { return NumTraits<Scalar>::digits10(); }
+  EIGEN_CONSTEXPR
+  static inline int max_digits10() { return NumTraits<Scalar>::max_digits10(); }
+};
+
+template<> struct NumTraits<std::string>
+  : GenericNumTraits<std::string>
+{
+  enum {
+    RequireInitialization = 1,
+    ReadCost = HugeCost,
+    AddCost  = HugeCost,
+    MulCost  = HugeCost
+  };
+
+  EIGEN_CONSTEXPR
+  static inline int digits10() { return 0; }
+  EIGEN_CONSTEXPR
+  static inline int max_digits10() { return 0; }
+
+ private:
+  static inline std::string epsilon();
+  static inline std::string dummy_precision();
+  static inline std::string lowest();
+  static inline std::string highest();
+  static inline std::string infinity();
+  static inline std::string quiet_NaN();
+};
+
+// Empty specialization for void to allow template specialization based on NumTraits<T>::Real with T==void and SFINAE.
+template<> struct NumTraits<void> {};
+
+template<> struct NumTraits<bool> : GenericNumTraits<bool> {};
+
+} // end namespace Eigen
+
+#endif // EIGEN_NUMTRAITS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/PartialReduxEvaluator.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/PartialReduxEvaluator.h
new file mode 100644
index 0000000000000000000000000000000000000000..e5d77ecad869e8d15e8bddba80a4f5a6aca115cc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/PartialReduxEvaluator.h
@@ -0,0 +1,239 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011-2018 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PARTIALREDUX_H
+#define EIGEN_PARTIALREDUX_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+
+/***************************************************************************
+*
+* This file provides evaluators for partial reductions.
+* There are two modes:
+*
+*  - scalar path: simply calls the respective function on the column or row.
+*    -> nothing special here, all the tricky part is handled by the return
+*       types of VectorwiseOp's members. They embed the functor calling the
+*       respective DenseBase's member function.
+*
+*  - vectorized path: implements a packet-wise reductions followed by
+*    some (optional) processing of the outcome, e.g., division by n for mean.
+*
+* For the vectorized path let's observe that the packet-size and outer-unrolling
+* are both decided by the assignment logic. So all we have to do is to decide
+* on the inner unrolling.
+*
+* For the unrolling, we can reuse "internal::redux_vec_unroller" from Redux.h,
+* but be need to be careful to specify correct increment.
+*
+***************************************************************************/
+
+
+/* logic deciding a strategy for unrolling of vectorized paths */
+template<typename Func, typename Evaluator>
+struct packetwise_redux_traits
+{
+  enum {
+    OuterSize = int(Evaluator::IsRowMajor) ? Evaluator::RowsAtCompileTime : Evaluator::ColsAtCompileTime,
+    Cost = OuterSize == Dynamic ? HugeCost
+         : OuterSize * Evaluator::CoeffReadCost + (OuterSize-1) * functor_traits<Func>::Cost,
+    Unrolling = Cost <= EIGEN_UNROLLING_LIMIT ? CompleteUnrolling : NoUnrolling
+  };
+
+};
+
+/* Value to be returned when size==0 , by default let's return 0 */
+template<typename PacketType,typename Func>
+EIGEN_DEVICE_FUNC
+PacketType packetwise_redux_empty_value(const Func& ) {
+  const typename unpacket_traits<PacketType>::type zero(0);
+  return pset1<PacketType>(zero);
+}
+
+/* For products the default is 1 */
+template<typename PacketType,typename Scalar>
+EIGEN_DEVICE_FUNC
+PacketType packetwise_redux_empty_value(const scalar_product_op<Scalar,Scalar>& ) {
+  return pset1<PacketType>(Scalar(1));
+}
+
+/* Perform the actual reduction */
+template<typename Func, typename Evaluator,
+         int Unrolling = packetwise_redux_traits<Func, Evaluator>::Unrolling
+>
+struct packetwise_redux_impl;
+
+/* Perform the actual reduction with unrolling */
+template<typename Func, typename Evaluator>
+struct packetwise_redux_impl<Func, Evaluator, CompleteUnrolling>
+{
+  typedef redux_novec_unroller<Func,Evaluator, 0, Evaluator::SizeAtCompileTime> Base;
+  typedef typename Evaluator::Scalar Scalar;
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
+  PacketType run(const Evaluator &eval, const Func& func, Index /*size*/)
+  {
+    return redux_vec_unroller<Func, Evaluator, 0, packetwise_redux_traits<Func, Evaluator>::OuterSize>::template run<PacketType>(eval,func);
+  }
+};
+
+/* Add a specialization of redux_vec_unroller for size==0 at compiletime.
+ * This specialization is not required for general reductions, which is
+ * why it is defined here.
+ */
+template<typename Func, typename Evaluator, Index Start>
+struct redux_vec_unroller<Func, Evaluator, Start, 0>
+{
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE PacketType run(const Evaluator &, const Func& f)
+  {
+    return packetwise_redux_empty_value<PacketType>(f);
+  }
+};
+
+/* Perform the actual reduction for dynamic sizes */
+template<typename Func, typename Evaluator>
+struct packetwise_redux_impl<Func, Evaluator, NoUnrolling>
+{
+  typedef typename Evaluator::Scalar Scalar;
+  typedef typename redux_traits<Func, Evaluator>::PacketType PacketScalar;
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC
+  static PacketType run(const Evaluator &eval, const Func& func, Index size)
+  {
+    if(size==0)
+      return packetwise_redux_empty_value<PacketType>(func);
+    
+    const Index size4 = (size-1)&(~3);
+    PacketType p = eval.template packetByOuterInner<Unaligned,PacketType>(0,0);
+    Index i = 1;
+    // This loop is optimized for instruction pipelining:
+    // - each iteration generates two independent instructions
+    // - thanks to branch prediction and out-of-order execution we have independent instructions across loops
+    for(; i<size4; i+=4)
+      p = func.packetOp(p,
+            func.packetOp(
+              func.packetOp(eval.template packetByOuterInner<Unaligned,PacketType>(i+0,0),eval.template packetByOuterInner<Unaligned,PacketType>(i+1,0)),
+              func.packetOp(eval.template packetByOuterInner<Unaligned,PacketType>(i+2,0),eval.template packetByOuterInner<Unaligned,PacketType>(i+3,0))));
+    for(; i<size; ++i)
+      p = func.packetOp(p, eval.template packetByOuterInner<Unaligned,PacketType>(i,0));
+    return p;
+  }
+};
+
+template< typename ArgType, typename MemberOp, int Direction>
+struct evaluator<PartialReduxExpr<ArgType, MemberOp, Direction> >
+  : evaluator_base<PartialReduxExpr<ArgType, MemberOp, Direction> >
+{
+  typedef PartialReduxExpr<ArgType, MemberOp, Direction> XprType;
+  typedef typename internal::nested_eval<ArgType,1>::type ArgTypeNested;
+  typedef add_const_on_value_type_t<ArgTypeNested> ConstArgTypeNested;
+  typedef internal::remove_all_t<ArgTypeNested> ArgTypeNestedCleaned;
+  typedef typename ArgType::Scalar InputScalar;
+  typedef typename XprType::Scalar Scalar;
+  enum {
+    TraversalSize = Direction==int(Vertical) ? int(ArgType::RowsAtCompileTime) :  int(ArgType::ColsAtCompileTime)
+  };
+  typedef typename MemberOp::template Cost<int(TraversalSize)> CostOpType;
+  enum {
+    CoeffReadCost = TraversalSize==Dynamic ? HugeCost
+                  : TraversalSize==0 ? 1
+                  : int(TraversalSize) * int(evaluator<ArgType>::CoeffReadCost) + int(CostOpType::value),
+    
+    ArgFlags_ = evaluator<ArgType>::Flags,
+
+    Vectorizable_ =  bool(int(ArgFlags_)&PacketAccessBit)
+                  && bool(MemberOp::Vectorizable)
+                  && (Direction==int(Vertical) ? bool(ArgFlags_&RowMajorBit) : (ArgFlags_&RowMajorBit)==0)
+                  && (TraversalSize!=0),
+                  
+    Flags = (traits<XprType>::Flags&RowMajorBit)
+          | (evaluator<ArgType>::Flags&(HereditaryBits&(~RowMajorBit)))
+          | (Vectorizable_ ? PacketAccessBit : 0)
+          | LinearAccessBit,
+    
+    Alignment = 0 // FIXME this will need to be improved once PartialReduxExpr is vectorized
+  };
+
+  EIGEN_DEVICE_FUNC explicit evaluator(const XprType xpr)
+    : m_arg(xpr.nestedExpression()), m_functor(xpr.functor())
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(TraversalSize==Dynamic ? HugeCost : (TraversalSize==0 ? 1 : int(CostOpType::value)));
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const Scalar coeff(Index i, Index j) const
+  {
+    return coeff(Direction==Vertical ? j : i);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const Scalar coeff(Index index) const
+  {
+    return m_functor(m_arg.template subVector<DirectionType(Direction)>(index));
+  }
+
+  template<int LoadMode,typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  PacketType packet(Index i, Index j) const
+  {
+    return packet<LoadMode,PacketType>(Direction==Vertical ? j : i);
+  }
+  
+  template<int LoadMode,typename PacketType>
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+  PacketType packet(Index idx) const
+  {
+    enum { PacketSize = internal::unpacket_traits<PacketType>::size };
+    typedef Block<const ArgTypeNestedCleaned,
+                  Direction==Vertical ? int(ArgType::RowsAtCompileTime) : int(PacketSize),
+                  Direction==Vertical ? int(PacketSize) : int(ArgType::ColsAtCompileTime),
+                  true /* InnerPanel */> PanelType;
+    
+    PanelType panel(m_arg,
+                    Direction==Vertical ? 0 : idx,
+                    Direction==Vertical ? idx : 0,
+                    Direction==Vertical ? m_arg.rows() : Index(PacketSize),
+                    Direction==Vertical ? Index(PacketSize) : m_arg.cols());
+
+    // FIXME
+    // See bug 1612, currently if PacketSize==1 (i.e. complex<double> with 128bits registers) then the storage-order of panel get reversed
+    // and methods like packetByOuterInner do not make sense anymore in this context.
+    // So let's just by pass "vectorization" in this case:
+    if(PacketSize==1)
+      return internal::pset1<PacketType>(coeff(idx));
+    
+    typedef typename internal::redux_evaluator<PanelType> PanelEvaluator;
+    PanelEvaluator panel_eval(panel);
+    typedef typename MemberOp::BinaryOp BinaryOp;
+    PacketType p = internal::packetwise_redux_impl<BinaryOp,PanelEvaluator>::template run<PacketType>(panel_eval,m_functor.binaryFunc(),m_arg.outerSize());
+    return p;
+  }
+
+protected:
+  ConstArgTypeNested m_arg;
+  const MemberOp m_functor;
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_PARTIALREDUX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/PermutationMatrix.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/PermutationMatrix.h
new file mode 100644
index 0000000000000000000000000000000000000000..73a73006b45c414bb2b4de441d146ed0ccb24a7d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/PermutationMatrix.h
@@ -0,0 +1,607 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PERMUTATIONMATRIX_H
+#define EIGEN_PERMUTATIONMATRIX_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+enum PermPermProduct_t {PermPermProduct};
+
+} // end namespace internal
+
+/** \class PermutationBase
+  * \ingroup Core_Module
+  *
+  * \brief Base class for permutations
+  *
+  * \tparam Derived the derived class
+  *
+  * This class is the base class for all expressions representing a permutation matrix,
+  * internally stored as a vector of integers.
+  * The convention followed here is that if \f$ \sigma \f$ is a permutation, the corresponding permutation matrix
+  * \f$ P_\sigma \f$ is such that if \f$ (e_1,\ldots,e_p) \f$ is the canonical basis, we have:
+  *  \f[ P_\sigma(e_i) = e_{\sigma(i)}. \f]
+  * This convention ensures that for any two permutations \f$ \sigma, \tau \f$, we have:
+  *  \f[ P_{\sigma\circ\tau} = P_\sigma P_\tau. \f]
+  *
+  * Permutation matrices are square and invertible.
+  *
+  * Notice that in addition to the member functions and operators listed here, there also are non-member
+  * operator* to multiply any kind of permutation object with any kind of matrix expression (MatrixBase)
+  * on either side.
+  *
+  * \sa class PermutationMatrix, class PermutationWrapper
+  */
+template<typename Derived>
+class PermutationBase : public EigenBase<Derived>
+{
+    typedef internal::traits<Derived> Traits;
+    typedef EigenBase<Derived> Base;
+  public:
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    typedef typename Traits::IndicesType IndicesType;
+    enum {
+      Flags = Traits::Flags,
+      RowsAtCompileTime = Traits::RowsAtCompileTime,
+      ColsAtCompileTime = Traits::ColsAtCompileTime,
+      MaxRowsAtCompileTime = Traits::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = Traits::MaxColsAtCompileTime
+    };
+    typedef typename Traits::StorageIndex StorageIndex;
+    typedef Matrix<StorageIndex,RowsAtCompileTime,ColsAtCompileTime,0,MaxRowsAtCompileTime,MaxColsAtCompileTime>
+            DenseMatrixType;
+    typedef PermutationMatrix<IndicesType::SizeAtCompileTime,IndicesType::MaxSizeAtCompileTime,StorageIndex>
+            PlainPermutationType;
+    typedef PlainPermutationType PlainObject;
+    using Base::derived;
+    typedef Inverse<Derived> InverseReturnType;
+    typedef void Scalar;
+    #endif
+
+    /** Copies the other permutation into *this */
+    template<typename OtherDerived>
+    Derived& operator=(const PermutationBase<OtherDerived>& other)
+    {
+      indices() = other.indices();
+      return derived();
+    }
+
+    /** Assignment from the Transpositions \a tr */
+    template<typename OtherDerived>
+    Derived& operator=(const TranspositionsBase<OtherDerived>& tr)
+    {
+      setIdentity(tr.size());
+      for(Index k=size()-1; k>=0; --k)
+        applyTranspositionOnTheRight(k,tr.coeff(k));
+      return derived();
+    }
+
+    /** \returns the number of rows */
+    inline EIGEN_DEVICE_FUNC Index rows() const { return Index(indices().size()); }
+
+    /** \returns the number of columns */
+    inline EIGEN_DEVICE_FUNC Index cols() const { return Index(indices().size()); }
+
+    /** \returns the size of a side of the respective square matrix, i.e., the number of indices */
+    inline EIGEN_DEVICE_FUNC Index size() const { return Index(indices().size()); }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename DenseDerived>
+    void evalTo(MatrixBase<DenseDerived>& other) const
+    {
+      other.setZero();
+      for (Index i=0; i<rows(); ++i)
+        other.coeffRef(indices().coeff(i),i) = typename DenseDerived::Scalar(1);
+    }
+    #endif
+
+    /** \returns a Matrix object initialized from this permutation matrix. Notice that it
+      * is inefficient to return this Matrix object by value. For efficiency, favor using
+      * the Matrix constructor taking EigenBase objects.
+      */
+    DenseMatrixType toDenseMatrix() const
+    {
+      return derived();
+    }
+
+    /** const version of indices(). */
+    const IndicesType& indices() const { return derived().indices(); }
+    /** \returns a reference to the stored array representing the permutation. */
+    IndicesType& indices() { return derived().indices(); }
+
+    /** Resizes to given size.
+      */
+    inline void resize(Index newSize)
+    {
+      indices().resize(newSize);
+    }
+
+    /** Sets *this to be the identity permutation matrix */
+    void setIdentity()
+    {
+      StorageIndex n = StorageIndex(size());
+      for(StorageIndex i = 0; i < n; ++i)
+        indices().coeffRef(i) = i;
+    }
+
+    /** Sets *this to be the identity permutation matrix of given size.
+      */
+    void setIdentity(Index newSize)
+    {
+      resize(newSize);
+      setIdentity();
+    }
+
+    /** Multiplies *this by the transposition \f$(ij)\f$ on the left.
+      *
+      * \returns a reference to *this.
+      *
+      * \warning This is much slower than applyTranspositionOnTheRight(Index,Index):
+      * this has linear complexity and requires a lot of branching.
+      *
+      * \sa applyTranspositionOnTheRight(Index,Index)
+      */
+    Derived& applyTranspositionOnTheLeft(Index i, Index j)
+    {
+      eigen_assert(i>=0 && j>=0 && i<size() && j<size());
+      for(Index k = 0; k < size(); ++k)
+      {
+        if(indices().coeff(k) == i) indices().coeffRef(k) = StorageIndex(j);
+        else if(indices().coeff(k) == j) indices().coeffRef(k) = StorageIndex(i);
+      }
+      return derived();
+    }
+
+    /** Multiplies *this by the transposition \f$(ij)\f$ on the right.
+      *
+      * \returns a reference to *this.
+      *
+      * This is a fast operation, it only consists in swapping two indices.
+      *
+      * \sa applyTranspositionOnTheLeft(Index,Index)
+      */
+    Derived& applyTranspositionOnTheRight(Index i, Index j)
+    {
+      eigen_assert(i>=0 && j>=0 && i<size() && j<size());
+      std::swap(indices().coeffRef(i), indices().coeffRef(j));
+      return derived();
+    }
+
+    /** \returns the inverse permutation matrix.
+      *
+      * \note \blank \note_try_to_help_rvo
+      */
+    inline InverseReturnType inverse() const
+    { return InverseReturnType(derived()); }
+    /** \returns the tranpose permutation matrix.
+      *
+      * \note \blank \note_try_to_help_rvo
+      */
+    inline InverseReturnType transpose() const
+    { return InverseReturnType(derived()); }
+
+    /**** multiplication helpers to hopefully get RVO ****/
+
+  
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  protected:
+    template<typename OtherDerived>
+    void assignTranspose(const PermutationBase<OtherDerived>& other)
+    {
+      for (Index i=0; i<rows();++i) indices().coeffRef(other.indices().coeff(i)) = i;
+    }
+    template<typename Lhs,typename Rhs>
+    void assignProduct(const Lhs& lhs, const Rhs& rhs)
+    {
+      eigen_assert(lhs.cols() == rhs.rows());
+      for (Index i=0; i<rows();++i) indices().coeffRef(i) = lhs.indices().coeff(rhs.indices().coeff(i));
+    }
+#endif
+
+  public:
+
+    /** \returns the product permutation matrix.
+      *
+      * \note \blank \note_try_to_help_rvo
+      */
+    template<typename Other>
+    inline PlainPermutationType operator*(const PermutationBase<Other>& other) const
+    { return PlainPermutationType(internal::PermPermProduct, derived(), other.derived()); }
+
+    /** \returns the product of a permutation with another inverse permutation.
+      *
+      * \note \blank \note_try_to_help_rvo
+      */
+    template<typename Other>
+    inline PlainPermutationType operator*(const InverseImpl<Other,PermutationStorage>& other) const
+    { return PlainPermutationType(internal::PermPermProduct, *this, other.eval()); }
+
+    /** \returns the product of an inverse permutation with another permutation.
+      *
+      * \note \blank \note_try_to_help_rvo
+      */
+    template<typename Other> friend
+    inline PlainPermutationType operator*(const InverseImpl<Other, PermutationStorage>& other, const PermutationBase& perm)
+    { return PlainPermutationType(internal::PermPermProduct, other.eval(), perm); }
+    
+    /** \returns the determinant of the permutation matrix, which is either 1 or -1 depending on the parity of the permutation.
+      *
+      * This function is O(\c n) procedure allocating a buffer of \c n booleans.
+      */
+    Index determinant() const
+    {
+      Index res = 1;
+      Index n = size();
+      Matrix<bool,RowsAtCompileTime,1,0,MaxRowsAtCompileTime> mask(n);
+      mask.fill(false);
+      Index r = 0;
+      while(r < n)
+      {
+        // search for the next seed
+        while(r<n && mask[r]) r++;
+        if(r>=n)
+          break;
+        // we got one, let's follow it until we are back to the seed
+        Index k0 = r++;
+        mask.coeffRef(k0) = true;
+        for(Index k=indices().coeff(k0); k!=k0; k=indices().coeff(k))
+        {
+          mask.coeffRef(k) = true;
+          res = -res;
+        }
+      }
+      return res;
+    }
+
+  protected:
+
+};
+
+namespace internal {
+template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_>
+struct traits<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_> >
+ : traits<Matrix<StorageIndex_,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
+{
+  typedef PermutationStorage StorageKind;
+  typedef Matrix<StorageIndex_, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
+  typedef StorageIndex_ StorageIndex;
+  typedef void Scalar;
+};
+}
+
+/** \class PermutationMatrix
+  * \ingroup Core_Module
+  *
+  * \brief Permutation matrix
+  *
+  * \tparam SizeAtCompileTime the number of rows/cols, or Dynamic
+  * \tparam MaxSizeAtCompileTime the maximum number of rows/cols, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it.
+  * \tparam StorageIndex_ the integer type of the indices
+  *
+  * This class represents a permutation matrix, internally stored as a vector of integers.
+  *
+  * \sa class PermutationBase, class PermutationWrapper, class DiagonalMatrix
+  */
+template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_>
+class PermutationMatrix : public PermutationBase<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_> >
+{
+    typedef PermutationBase<PermutationMatrix> Base;
+    typedef internal::traits<PermutationMatrix> Traits;
+  public:
+
+    typedef const PermutationMatrix& Nested;
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    typedef typename Traits::IndicesType IndicesType;
+    typedef typename Traits::StorageIndex StorageIndex;
+    #endif
+
+    inline PermutationMatrix()
+    {}
+
+    /** Constructs an uninitialized permutation matrix of given size.
+      */
+    explicit inline PermutationMatrix(Index size) : m_indices(size)
+    {
+      eigen_internal_assert(size <= NumTraits<StorageIndex>::highest());
+    }
+
+    /** Copy constructor. */
+    template<typename OtherDerived>
+    inline PermutationMatrix(const PermutationBase<OtherDerived>& other)
+      : m_indices(other.indices()) {}
+
+    /** Generic constructor from expression of the indices. The indices
+      * array has the meaning that the permutations sends each integer i to indices[i].
+      *
+      * \warning It is your responsibility to check that the indices array that you passes actually
+      * describes a permutation, i.e., each value between 0 and n-1 occurs exactly once, where n is the
+      * array's size.
+      */
+    template<typename Other>
+    explicit inline PermutationMatrix(const MatrixBase<Other>& indices) : m_indices(indices)
+    {}
+
+    /** Convert the Transpositions \a tr to a permutation matrix */
+    template<typename Other>
+    explicit PermutationMatrix(const TranspositionsBase<Other>& tr)
+      : m_indices(tr.size())
+    {
+      *this = tr;
+    }
+
+    /** Copies the other permutation into *this */
+    template<typename Other>
+    PermutationMatrix& operator=(const PermutationBase<Other>& other)
+    {
+      m_indices = other.indices();
+      return *this;
+    }
+
+    /** Assignment from the Transpositions \a tr */
+    template<typename Other>
+    PermutationMatrix& operator=(const TranspositionsBase<Other>& tr)
+    {
+      return Base::operator=(tr.derived());
+    }
+
+    /** const version of indices(). */
+    const IndicesType& indices() const { return m_indices; }
+    /** \returns a reference to the stored array representing the permutation. */
+    IndicesType& indices() { return m_indices; }
+
+
+    /**** multiplication helpers to hopefully get RVO ****/
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename Other>
+    PermutationMatrix(const InverseImpl<Other,PermutationStorage>& other)
+      : m_indices(other.derived().nestedExpression().size())
+    {
+      eigen_internal_assert(m_indices.size() <= NumTraits<StorageIndex>::highest());
+      StorageIndex end = StorageIndex(m_indices.size());
+      for (StorageIndex i=0; i<end;++i)
+        m_indices.coeffRef(other.derived().nestedExpression().indices().coeff(i)) = i;
+    }
+    template<typename Lhs,typename Rhs>
+    PermutationMatrix(internal::PermPermProduct_t, const Lhs& lhs, const Rhs& rhs)
+      : m_indices(lhs.indices().size())
+    {
+      Base::assignProduct(lhs,rhs);
+    }
+#endif
+
+  protected:
+
+    IndicesType m_indices;
+};
+
+
+namespace internal {
+template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_, int PacketAccess_>
+struct traits<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_>,PacketAccess_> >
+ : traits<Matrix<StorageIndex_,SizeAtCompileTime,SizeAtCompileTime,0,MaxSizeAtCompileTime,MaxSizeAtCompileTime> >
+{
+  typedef PermutationStorage StorageKind;
+  typedef Map<const Matrix<StorageIndex_, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1>, PacketAccess_> IndicesType;
+  typedef StorageIndex_ StorageIndex;
+  typedef void Scalar;
+};
+}
+
+template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_, int PacketAccess_>
+class Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_>,PacketAccess_>
+  : public PermutationBase<Map<PermutationMatrix<SizeAtCompileTime, MaxSizeAtCompileTime, StorageIndex_>,PacketAccess_> >
+{
+    typedef PermutationBase<Map> Base;
+    typedef internal::traits<Map> Traits;
+  public:
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    typedef typename Traits::IndicesType IndicesType;
+    typedef typename IndicesType::Scalar StorageIndex;
+    #endif
+
+    inline Map(const StorageIndex* indicesPtr)
+      : m_indices(indicesPtr)
+    {}
+
+    inline Map(const StorageIndex* indicesPtr, Index size)
+      : m_indices(indicesPtr,size)
+    {}
+
+    /** Copies the other permutation into *this */
+    template<typename Other>
+    Map& operator=(const PermutationBase<Other>& other)
+    { return Base::operator=(other.derived()); }
+
+    /** Assignment from the Transpositions \a tr */
+    template<typename Other>
+    Map& operator=(const TranspositionsBase<Other>& tr)
+    { return Base::operator=(tr.derived()); }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** This is a special case of the templated operator=. Its purpose is to
+      * prevent a default operator= from hiding the templated operator=.
+      */
+    Map& operator=(const Map& other)
+    {
+      m_indices = other.m_indices;
+      return *this;
+    }
+    #endif
+
+    /** const version of indices(). */
+    const IndicesType& indices() const { return m_indices; }
+    /** \returns a reference to the stored array representing the permutation. */
+    IndicesType& indices() { return m_indices; }
+
+  protected:
+
+    IndicesType m_indices;
+};
+
+template<typename IndicesType_> class TranspositionsWrapper;
+namespace internal {
+template<typename IndicesType_>
+struct traits<PermutationWrapper<IndicesType_> >
+{
+  typedef PermutationStorage StorageKind;
+  typedef void Scalar;
+  typedef typename IndicesType_::Scalar StorageIndex;
+  typedef IndicesType_ IndicesType;
+  enum {
+    RowsAtCompileTime = IndicesType_::SizeAtCompileTime,
+    ColsAtCompileTime = IndicesType_::SizeAtCompileTime,
+    MaxRowsAtCompileTime = IndicesType::MaxSizeAtCompileTime,
+    MaxColsAtCompileTime = IndicesType::MaxSizeAtCompileTime,
+    Flags = 0
+  };
+};
+}
+
+/** \class PermutationWrapper
+  * \ingroup Core_Module
+  *
+  * \brief Class to view a vector of integers as a permutation matrix
+  *
+  * \tparam IndicesType_ the type of the vector of integer (can be any compatible expression)
+  *
+  * This class allows to view any vector expression of integers as a permutation matrix.
+  *
+  * \sa class PermutationBase, class PermutationMatrix
+  */
+template<typename IndicesType_>
+class PermutationWrapper : public PermutationBase<PermutationWrapper<IndicesType_> >
+{
+    typedef PermutationBase<PermutationWrapper> Base;
+    typedef internal::traits<PermutationWrapper> Traits;
+  public:
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    typedef typename Traits::IndicesType IndicesType;
+    #endif
+
+    inline PermutationWrapper(const IndicesType& indices)
+      : m_indices(indices)
+    {}
+
+    /** const version of indices(). */
+    const internal::remove_all_t<typename IndicesType::Nested>&
+    indices() const { return m_indices; }
+
+  protected:
+
+    typename IndicesType::Nested m_indices;
+};
+
+
+/** \returns the matrix with the permutation applied to the columns.
+  */
+template<typename MatrixDerived, typename PermutationDerived>
+EIGEN_DEVICE_FUNC
+const Product<MatrixDerived, PermutationDerived, AliasFreeProduct>
+operator*(const MatrixBase<MatrixDerived> &matrix,
+          const PermutationBase<PermutationDerived>& permutation)
+{
+  return Product<MatrixDerived, PermutationDerived, AliasFreeProduct>
+            (matrix.derived(), permutation.derived());
+}
+
+/** \returns the matrix with the permutation applied to the rows.
+  */
+template<typename PermutationDerived, typename MatrixDerived>
+EIGEN_DEVICE_FUNC
+const Product<PermutationDerived, MatrixDerived, AliasFreeProduct>
+operator*(const PermutationBase<PermutationDerived> &permutation,
+          const MatrixBase<MatrixDerived>& matrix)
+{
+  return Product<PermutationDerived, MatrixDerived, AliasFreeProduct>
+            (permutation.derived(), matrix.derived());
+}
+
+
+template<typename PermutationType>
+class InverseImpl<PermutationType, PermutationStorage>
+  : public EigenBase<Inverse<PermutationType> >
+{
+    typedef typename PermutationType::PlainPermutationType PlainPermutationType;
+    typedef internal::traits<PermutationType> PermTraits;
+  protected:
+    InverseImpl() {}
+  public:
+    typedef Inverse<PermutationType> InverseType;
+    using EigenBase<Inverse<PermutationType> >::derived;
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    typedef typename PermutationType::DenseMatrixType DenseMatrixType;
+    enum {
+      RowsAtCompileTime = PermTraits::RowsAtCompileTime,
+      ColsAtCompileTime = PermTraits::ColsAtCompileTime,
+      MaxRowsAtCompileTime = PermTraits::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = PermTraits::MaxColsAtCompileTime
+    };
+    #endif
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename DenseDerived>
+    void evalTo(MatrixBase<DenseDerived>& other) const
+    {
+      other.setZero();
+      for (Index i=0; i<derived().rows();++i)
+        other.coeffRef(i, derived().nestedExpression().indices().coeff(i)) = typename DenseDerived::Scalar(1);
+    }
+    #endif
+
+    /** \return the equivalent permutation matrix */
+    PlainPermutationType eval() const { return derived(); }
+
+    DenseMatrixType toDenseMatrix() const { return derived(); }
+
+    /** \returns the matrix with the inverse permutation applied to the columns.
+      */
+    template<typename OtherDerived> friend
+    const Product<OtherDerived, InverseType, AliasFreeProduct>
+    operator*(const MatrixBase<OtherDerived>& matrix, const InverseType& trPerm)
+    {
+      return Product<OtherDerived, InverseType, AliasFreeProduct>(matrix.derived(), trPerm.derived());
+    }
+
+    /** \returns the matrix with the inverse permutation applied to the rows.
+      */
+    template<typename OtherDerived>
+    const Product<InverseType, OtherDerived, AliasFreeProduct>
+    operator*(const MatrixBase<OtherDerived>& matrix) const
+    {
+      return Product<InverseType, OtherDerived, AliasFreeProduct>(derived(), matrix.derived());
+    }
+};
+
+template<typename Derived>
+const PermutationWrapper<const Derived> MatrixBase<Derived>::asPermutation() const
+{
+  return derived();
+}
+
+namespace internal {
+
+template<> struct AssignmentKind<DenseShape,PermutationShape> { typedef EigenBase2EigenBase Kind; };
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_PERMUTATIONMATRIX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/PlainObjectBase.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/PlainObjectBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..ba6e51d3162dc9f0ea05f0097d4d17214c7765df
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/PlainObjectBase.h
@@ -0,0 +1,1099 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DENSESTORAGEBASE_H
+#define EIGEN_DENSESTORAGEBASE_H
+
+#if defined(EIGEN_INITIALIZE_MATRICES_BY_ZERO)
+# define EIGEN_INITIALIZE_COEFFS
+# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED for(Index i=0;i<base().size();++i) coeffRef(i)=Scalar(0);
+#elif defined(EIGEN_INITIALIZE_MATRICES_BY_NAN)
+# define EIGEN_INITIALIZE_COEFFS
+# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED for(Index i=0;i<base().size();++i) coeffRef(i)=std::numeric_limits<Scalar>::quiet_NaN();
+#else
+# undef EIGEN_INITIALIZE_COEFFS
+# define EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+#endif
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <int MaxSizeAtCompileTime, int MaxRowsAtCompileTime, int MaxColsAtCompileTime>
+struct check_rows_cols_for_overflow {
+  EIGEN_STATIC_ASSERT(MaxRowsAtCompileTime * MaxColsAtCompileTime == MaxSizeAtCompileTime,YOU MADE A PROGRAMMING MISTAKE)
+  template <typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE constexpr void run(Index, Index) {}
+};
+
+template <int MaxRowsAtCompileTime>
+struct check_rows_cols_for_overflow<Dynamic, MaxRowsAtCompileTime, Dynamic> {
+  template <typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE constexpr void run(Index, Index cols) {
+    constexpr Index MaxIndex = NumTraits<Index>::highest();
+    bool error = cols > MaxIndex / MaxRowsAtCompileTime;
+    if (error) throw_std_bad_alloc();
+  }
+};
+
+template <int MaxColsAtCompileTime>
+struct check_rows_cols_for_overflow<Dynamic, Dynamic, MaxColsAtCompileTime> {
+  template <typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE constexpr void run(Index rows, Index) {
+    constexpr Index MaxIndex = NumTraits<Index>::highest();
+    bool error = rows > MaxIndex / MaxColsAtCompileTime;
+    if (error) throw_std_bad_alloc();
+  }
+};
+
+template <>
+struct check_rows_cols_for_overflow<Dynamic, Dynamic, Dynamic> {
+  template <typename Index>
+  EIGEN_DEVICE_FUNC static EIGEN_ALWAYS_INLINE constexpr void run(Index rows, Index cols) {
+    constexpr Index MaxIndex = NumTraits<Index>::highest();
+    bool error = cols == 0 ? false : (rows > MaxIndex / cols);
+    if (error) throw_std_bad_alloc();
+  }
+};
+
+template <typename Derived,
+          typename OtherDerived = Derived,
+          bool IsVector = bool(Derived::IsVectorAtCompileTime) && bool(OtherDerived::IsVectorAtCompileTime)>
+struct conservative_resize_like_impl;
+
+template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers> struct matrix_swap_impl;
+
+} // end namespace internal
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+namespace doxygen {
+
+// This is a workaround to doxygen not being able to understand the inheritance logic
+// when it is hidden by the dense_xpr_base helper struct.
+// Moreover, doxygen fails to include members that are not documented in the declaration body of
+// MatrixBase if we inherits MatrixBase<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> >,
+// this is why we simply inherits MatrixBase, though this does not make sense.
+
+/** This class is just a workaround for Doxygen and it does not not actually exist. */
+template<typename Derived> struct dense_xpr_base_dispatcher;
+/** This class is just a workaround for Doxygen and it does not not actually exist. */
+template<typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+struct dense_xpr_base_dispatcher<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> >
+    : public MatrixBase {};
+/** This class is just a workaround for Doxygen and it does not not actually exist. */
+template<typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+struct dense_xpr_base_dispatcher<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_> >
+    : public ArrayBase {};
+
+} // namespace doxygen
+
+/** \class PlainObjectBase
+  * \ingroup Core_Module
+  * \brief %Dense storage base class for matrices and arrays.
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_PLAINOBJECTBASE_PLUGIN.
+  *
+  * \tparam Derived is the derived type, e.g., a Matrix or Array
+  *
+  * \sa \ref TopicClassHierarchy
+  */
+template<typename Derived>
+class PlainObjectBase : public doxygen::dense_xpr_base_dispatcher<Derived>
+#else
+template<typename Derived>
+class PlainObjectBase : public internal::dense_xpr_base<Derived>::type
+#endif
+{
+  public:
+    enum { Options = internal::traits<Derived>::Options };
+    typedef typename internal::dense_xpr_base<Derived>::type Base;
+
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+
+    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef Derived DenseType;
+
+    using Base::RowsAtCompileTime;
+    using Base::ColsAtCompileTime;
+    using Base::SizeAtCompileTime;
+    using Base::MaxRowsAtCompileTime;
+    using Base::MaxColsAtCompileTime;
+    using Base::MaxSizeAtCompileTime;
+    using Base::IsVectorAtCompileTime;
+    using Base::Flags;
+
+    typedef Eigen::Map<Derived, Unaligned>  MapType;
+    typedef const Eigen::Map<const Derived, Unaligned> ConstMapType;
+    typedef Eigen::Map<Derived, AlignedMax> AlignedMapType;
+    typedef const Eigen::Map<const Derived, AlignedMax> ConstAlignedMapType;
+    template<typename StrideType> struct StridedMapType { typedef Eigen::Map<Derived, Unaligned, StrideType> type; };
+    template<typename StrideType> struct StridedConstMapType { typedef Eigen::Map<const Derived, Unaligned, StrideType> type; };
+    template<typename StrideType> struct StridedAlignedMapType { typedef Eigen::Map<Derived, AlignedMax, StrideType> type; };
+    template<typename StrideType> struct StridedConstAlignedMapType { typedef Eigen::Map<const Derived, AlignedMax, StrideType> type; };
+
+  protected:
+    DenseStorage<Scalar, Base::MaxSizeAtCompileTime, Base::RowsAtCompileTime, Base::ColsAtCompileTime, Options> m_storage;
+
+  public:
+    enum { NeedsToAlign = (SizeAtCompileTime != Dynamic) && (internal::traits<Derived>::Alignment>0) };
+    EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
+
+    EIGEN_STATIC_ASSERT(internal::check_implication(MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1, (int(Options)&RowMajor)==RowMajor), INVALID_MATRIX_TEMPLATE_PARAMETERS)
+    EIGEN_STATIC_ASSERT(internal::check_implication(MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1, (int(Options)&RowMajor)==0), INVALID_MATRIX_TEMPLATE_PARAMETERS)
+    EIGEN_STATIC_ASSERT((RowsAtCompileTime == Dynamic) || (RowsAtCompileTime >= 0), INVALID_MATRIX_TEMPLATE_PARAMETERS)
+    EIGEN_STATIC_ASSERT((ColsAtCompileTime == Dynamic) || (ColsAtCompileTime >= 0), INVALID_MATRIX_TEMPLATE_PARAMETERS)
+    EIGEN_STATIC_ASSERT((MaxRowsAtCompileTime == Dynamic) || (MaxRowsAtCompileTime >= 0), INVALID_MATRIX_TEMPLATE_PARAMETERS)
+    EIGEN_STATIC_ASSERT((MaxColsAtCompileTime == Dynamic) || (MaxColsAtCompileTime >= 0), INVALID_MATRIX_TEMPLATE_PARAMETERS)
+    EIGEN_STATIC_ASSERT((MaxRowsAtCompileTime == RowsAtCompileTime || RowsAtCompileTime==Dynamic), INVALID_MATRIX_TEMPLATE_PARAMETERS)
+    EIGEN_STATIC_ASSERT((MaxColsAtCompileTime == ColsAtCompileTime || ColsAtCompileTime==Dynamic), INVALID_MATRIX_TEMPLATE_PARAMETERS)
+    EIGEN_STATIC_ASSERT(((Options & (DontAlign|RowMajor)) == Options), INVALID_MATRIX_TEMPLATE_PARAMETERS)
+
+    EIGEN_DEVICE_FUNC
+    Base& base() { return *static_cast<Base*>(this); }
+    EIGEN_DEVICE_FUNC
+    const Base& base() const { return *static_cast<const Base*>(this); }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index rows() const EIGEN_NOEXCEPT { return m_storage.rows(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index cols() const EIGEN_NOEXCEPT { return m_storage.cols(); }
+
+    /** This is an overloaded version of DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index,Index) const
+      * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
+      *
+      * See DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const for details. */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr const Scalar& coeff(Index rowId, Index colId) const {
+      if (Flags & RowMajorBit)
+        return m_storage.data()[colId + rowId * m_storage.cols()];
+      else  // column-major
+        return m_storage.data()[rowId + colId * m_storage.rows()];
+    }
+
+    /** This is an overloaded version of DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const
+      * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
+      *
+      * See DenseCoeffsBase<Derived,ReadOnlyAccessors>::coeff(Index) const for details. */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const Scalar& coeff(Index index) const
+    {
+      return m_storage.data()[index];
+    }
+
+    /** This is an overloaded version of DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index,Index) const
+      * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
+      *
+      * See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index,Index) const for details. */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr Scalar& coeffRef(Index rowId, Index colId) {
+      if (Flags & RowMajorBit)
+        return m_storage.data()[colId + rowId * m_storage.cols()];
+      else  // column-major
+        return m_storage.data()[rowId + colId * m_storage.rows()];
+    }
+
+    /** This is an overloaded version of DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index) const
+      * provided to by-pass the creation of an evaluator of the expression, thus saving compilation efforts.
+      *
+      * See DenseCoeffsBase<Derived,WriteAccessors>::coeffRef(Index) const for details. */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr Scalar& coeffRef(Index index) { return m_storage.data()[index]; }
+
+    /** This is the const version of coeffRef(Index,Index) which is thus synonym of coeff(Index,Index).
+      * It is provided for convenience. */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr const Scalar& coeffRef(Index rowId, Index colId) const {
+      if (Flags & RowMajorBit)
+        return m_storage.data()[colId + rowId * m_storage.cols()];
+      else  // column-major
+        return m_storage.data()[rowId + colId * m_storage.rows()];
+    }
+
+    /** This is the const version of coeffRef(Index) which is thus synonym of coeff(Index).
+      * It is provided for convenience. */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr const Scalar& coeffRef(Index index) const {
+      return m_storage.data()[index];
+    }
+
+    /** \internal */
+    template<int LoadMode>
+    EIGEN_STRONG_INLINE PacketScalar packet(Index rowId, Index colId) const
+    {
+      return internal::ploadt<PacketScalar, LoadMode>
+               (m_storage.data() + (Flags & RowMajorBit
+                                   ? colId + rowId * m_storage.cols()
+                                   : rowId + colId * m_storage.rows()));
+    }
+
+    /** \internal */
+    template<int LoadMode>
+    EIGEN_STRONG_INLINE PacketScalar packet(Index index) const
+    {
+      return internal::ploadt<PacketScalar, LoadMode>(m_storage.data() + index);
+    }
+
+    /** \internal */
+    template<int StoreMode>
+    EIGEN_STRONG_INLINE void writePacket(Index rowId, Index colId, const PacketScalar& val)
+    {
+      internal::pstoret<Scalar, PacketScalar, StoreMode>
+              (m_storage.data() + (Flags & RowMajorBit
+                                   ? colId + rowId * m_storage.cols()
+                                   : rowId + colId * m_storage.rows()), val);
+    }
+
+    /** \internal */
+    template<int StoreMode>
+    EIGEN_STRONG_INLINE void writePacket(Index index, const PacketScalar& val)
+    {
+      internal::pstoret<Scalar, PacketScalar, StoreMode>(m_storage.data() + index, val);
+    }
+
+    /** \returns a const pointer to the data array of this matrix */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar *data() const
+    { return m_storage.data(); }
+
+    /** \returns a pointer to the data array of this matrix */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar *data()
+    { return m_storage.data(); }
+
+    /** Resizes \c *this to a \a rows x \a cols matrix.
+      *
+      * This method is intended for dynamic-size matrices, although it is legal to call it on any
+      * matrix as long as fixed dimensions are left unchanged. If you only want to change the number
+      * of rows and/or of columns, you can use resize(NoChange_t, Index), resize(Index, NoChange_t).
+      *
+      * If the current number of coefficients of \c *this exactly matches the
+      * product \a rows * \a cols, then no memory allocation is performed and
+      * the current values are left unchanged. In all other cases, including
+      * shrinking, the data is reallocated and all previous values are lost.
+      *
+      * Example: \include Matrix_resize_int_int.cpp
+      * Output: \verbinclude Matrix_resize_int_int.out
+      *
+      * \sa resize(Index) for vectors, resize(NoChange_t, Index), resize(Index, NoChange_t)
+      */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void resize(Index rows, Index cols) {
+      eigen_assert(internal::check_implication(RowsAtCompileTime!=Dynamic, rows==RowsAtCompileTime)
+                   && internal::check_implication(ColsAtCompileTime!=Dynamic, cols==ColsAtCompileTime)
+                   && internal::check_implication(RowsAtCompileTime==Dynamic && MaxRowsAtCompileTime!=Dynamic, rows<=MaxRowsAtCompileTime)
+                   && internal::check_implication(ColsAtCompileTime==Dynamic && MaxColsAtCompileTime!=Dynamic, cols<=MaxColsAtCompileTime)
+                   && rows>=0 && cols>=0 && "Invalid sizes when resizing a matrix or array.");
+      internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime, MaxRowsAtCompileTime, MaxColsAtCompileTime>::run(rows, cols);
+      #ifdef EIGEN_INITIALIZE_COEFFS
+        Index size = rows*cols;
+        bool size_changed = size != this->size();
+        m_storage.resize(size, rows, cols);
+        if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+      #else
+        m_storage.resize(rows*cols, rows, cols);
+      #endif
+    }
+
+    /** Resizes \c *this to a vector of length \a size
+      *
+      * \only_for_vectors. This method does not work for
+      * partially dynamic matrices when the static dimension is anything other
+      * than 1. For example it will not work with Matrix<double, 2, Dynamic>.
+      *
+      * Example: \include Matrix_resize_int.cpp
+      * Output: \verbinclude Matrix_resize_int.out
+      *
+      * \sa resize(Index,Index), resize(NoChange_t, Index), resize(Index, NoChange_t)
+      */
+    EIGEN_DEVICE_FUNC inline constexpr void resize(Index size) {
+        EIGEN_STATIC_ASSERT_VECTOR_ONLY(PlainObjectBase)
+        eigen_assert(
+            ((SizeAtCompileTime == Dynamic && (MaxSizeAtCompileTime == Dynamic || size <= MaxSizeAtCompileTime)) ||
+             SizeAtCompileTime == size) &&
+            size >= 0);
+#ifdef EIGEN_INITIALIZE_COEFFS
+        bool size_changed = size != this->size();
+      #endif
+      if(RowsAtCompileTime == 1)
+        m_storage.resize(size, 1, size);
+      else
+        m_storage.resize(size, size, 1);
+      #ifdef EIGEN_INITIALIZE_COEFFS
+        if(size_changed) EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+      #endif
+    }
+
+    /** Resizes the matrix, changing only the number of columns. For the parameter of type NoChange_t, just pass the special value \c NoChange
+      * as in the example below.
+      *
+      * Example: \include Matrix_resize_NoChange_int.cpp
+      * Output: \verbinclude Matrix_resize_NoChange_int.out
+      *
+      * \sa resize(Index,Index)
+      */
+    EIGEN_DEVICE_FUNC inline constexpr void resize(NoChange_t, Index cols) { resize(rows(), cols); }
+
+    /** Resizes the matrix, changing only the number of rows. For the parameter of type NoChange_t, just pass the special value \c NoChange
+      * as in the example below.
+      *
+      * Example: \include Matrix_resize_int_NoChange.cpp
+      * Output: \verbinclude Matrix_resize_int_NoChange.out
+      *
+      * \sa resize(Index,Index)
+      */
+    EIGEN_DEVICE_FUNC inline constexpr void resize(Index rows, NoChange_t) { resize(rows, cols()); }
+
+    /** Resizes \c *this to have the same dimensions as \a other.
+      * Takes care of doing all the checking that's needed.
+      *
+      * Note that copying a row-vector into a vector (and conversely) is allowed.
+      * The resizing, if any, is then done in the appropriate way so that row-vectors
+      * remain row-vectors and vectors remain vectors.
+      */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void resizeLike(const EigenBase<OtherDerived>& _other)
+    {
+      const OtherDerived& other = _other.derived();
+      internal::check_rows_cols_for_overflow<MaxSizeAtCompileTime, MaxRowsAtCompileTime, MaxColsAtCompileTime>::run(other.rows(), other.cols());
+      const Index othersize = other.rows()*other.cols();
+      if(RowsAtCompileTime == 1)
+      {
+        eigen_assert(other.rows() == 1 || other.cols() == 1);
+        resize(1, othersize);
+      }
+      else if(ColsAtCompileTime == 1)
+      {
+        eigen_assert(other.rows() == 1 || other.cols() == 1);
+        resize(othersize, 1);
+      }
+      else resize(other.rows(), other.cols());
+    }
+
+    /** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
+      *
+      * The method is intended for matrices of dynamic size. If you only want to change the number
+      * of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or
+      * conservativeResize(Index, NoChange_t).
+      *
+      * Matrices are resized relative to the top-left element. In case values need to be
+      * appended to the matrix they will be uninitialized.
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void conservativeResize(Index rows, Index cols)
+    {
+      internal::conservative_resize_like_impl<Derived>::run(*this, rows, cols);
+    }
+
+    /** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
+      *
+      * As opposed to conservativeResize(Index rows, Index cols), this version leaves
+      * the number of columns unchanged.
+      *
+      * In case the matrix is growing, new rows will be uninitialized.
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void conservativeResize(Index rows, NoChange_t)
+    {
+      // Note: see the comment in conservativeResize(Index,Index)
+      conservativeResize(rows, cols());
+    }
+
+    /** Resizes the matrix to \a rows x \a cols while leaving old values untouched.
+      *
+      * As opposed to conservativeResize(Index rows, Index cols), this version leaves
+      * the number of rows unchanged.
+      *
+      * In case the matrix is growing, new columns will be uninitialized.
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void conservativeResize(NoChange_t, Index cols)
+    {
+      // Note: see the comment in conservativeResize(Index,Index)
+      conservativeResize(rows(), cols);
+    }
+
+    /** Resizes the vector to \a size while retaining old values.
+      *
+      * \only_for_vectors. This method does not work for
+      * partially dynamic matrices when the static dimension is anything other
+      * than 1. For example it will not work with Matrix<double, 2, Dynamic>.
+      *
+      * When values are appended, they will be uninitialized.
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void conservativeResize(Index size)
+    {
+      internal::conservative_resize_like_impl<Derived>::run(*this, size);
+    }
+
+    /** Resizes the matrix to \a rows x \a cols of \c other, while leaving old values untouched.
+      *
+      * The method is intended for matrices of dynamic size. If you only want to change the number
+      * of rows and/or of columns, you can use conservativeResize(NoChange_t, Index) or
+      * conservativeResize(Index, NoChange_t).
+      *
+      * Matrices are resized relative to the top-left element. In case values need to be
+      * appended to the matrix they will copied from \c other.
+      */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void conservativeResizeLike(const DenseBase<OtherDerived>& other)
+    {
+      internal::conservative_resize_like_impl<Derived,OtherDerived>::run(*this, other);
+    }
+
+    /** This is a special case of the templated operator=. Its purpose is to
+      * prevent a default operator= from hiding the templated operator=.
+      */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& operator=(const PlainObjectBase& other)
+    {
+      return _set(other);
+    }
+
+    /** \sa MatrixBase::lazyAssign() */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& lazyAssign(const DenseBase<OtherDerived>& other)
+    {
+      _resize_to_match(other);
+      return Base::lazyAssign(other.derived());
+    }
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& operator=(const ReturnByValue<OtherDerived>& func)
+    {
+      resize(func.rows(), func.cols());
+      return Base::operator=(func);
+    }
+
+    // Prevent user from trying to instantiate PlainObjectBase objects
+    // by making all its constructor protected. See bug 1074.
+  protected:
+
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE PlainObjectBase() : m_storage()
+    {
+//       EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+    }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    // FIXME is it still needed ?
+    /** \internal */
+    EIGEN_DEVICE_FUNC
+    explicit PlainObjectBase(internal::constructor_without_unaligned_array_assert)
+      : m_storage(internal::constructor_without_unaligned_array_assert())
+    {
+      // EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+    }
+#endif
+
+    EIGEN_DEVICE_FUNC
+    PlainObjectBase(PlainObjectBase&& other) EIGEN_NOEXCEPT
+      : m_storage( std::move(other.m_storage) )
+    {
+    }
+
+    EIGEN_DEVICE_FUNC
+    PlainObjectBase& operator=(PlainObjectBase&& other) EIGEN_NOEXCEPT
+    {
+      m_storage = std::move(other.m_storage);
+      return *this;
+    }
+
+    /** Copy constructor */
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE PlainObjectBase(const PlainObjectBase& other)
+      : Base(), m_storage(other.m_storage) { }
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE PlainObjectBase(Index size, Index rows, Index cols)
+      : m_storage(size, rows, cols)
+    {
+//       EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED
+    }
+
+    /** \brief Construct a row of column vector with fixed size from an arbitrary number of coefficients.
+      *
+      * \only_for_vectors
+      *
+      * This constructor is for 1D array or vectors with more than 4 coefficients.
+      *
+      * \warning To construct a column (resp. row) vector of fixed length, the number of values passed to this
+      * constructor must match the the fixed number of rows (resp. columns) of \c *this.
+      */
+    template <typename... ArgTypes>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    PlainObjectBase(const Scalar& a0, const Scalar& a1, const Scalar& a2,  const Scalar& a3, const ArgTypes&... args)
+      : m_storage()
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, sizeof...(args) + 4);
+      m_storage.data()[0] = a0;
+      m_storage.data()[1] = a1;
+      m_storage.data()[2] = a2;
+      m_storage.data()[3] = a3;
+      Index i = 4;
+      auto x = {(m_storage.data()[i++] = args, 0)...};
+      static_cast<void>(x);
+    }
+
+    /** \brief Constructs a Matrix or Array and initializes it by elements given by an initializer list of initializer
+      * lists
+      */
+    EIGEN_DEVICE_FUNC explicit constexpr EIGEN_STRONG_INLINE PlainObjectBase(
+        const std::initializer_list<std::initializer_list<Scalar>>& list)
+        : m_storage() {
+      size_t list_size = 0;
+      if (list.begin() != list.end()) {
+        list_size = list.begin()->size();
+      }
+
+      // This is to allow syntax like VectorXi {{1, 2, 3, 4}}
+      if (ColsAtCompileTime == 1 && list.size() == 1) {
+        eigen_assert(list_size == static_cast<size_t>(RowsAtCompileTime) || RowsAtCompileTime == Dynamic);
+        resize(list_size, ColsAtCompileTime);
+        if (list.begin()->begin() != nullptr) {
+          std::copy(list.begin()->begin(), list.begin()->end(), m_storage.data());
+        }
+      } else {
+        eigen_assert(list.size() == static_cast<size_t>(RowsAtCompileTime) || RowsAtCompileTime == Dynamic);
+        eigen_assert(list_size == static_cast<size_t>(ColsAtCompileTime) || ColsAtCompileTime == Dynamic);
+        resize(list.size(), list_size);
+
+        Index row_index = 0;
+        for (const std::initializer_list<Scalar>& row : list) {
+          eigen_assert(list_size == row.size());
+          Index col_index = 0;
+          for (const Scalar& e : row) {
+            coeffRef(row_index, col_index) = e;
+            ++col_index;
+          }
+          ++row_index;
+        }
+      }
+    }
+
+    /** \sa PlainObjectBase::operator=(const EigenBase<OtherDerived>&) */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE PlainObjectBase(const DenseBase<OtherDerived> &other)
+      : m_storage()
+    {
+      resizeLike(other);
+      _set_noalias(other);
+    }
+
+    /** \sa PlainObjectBase::operator=(const EigenBase<OtherDerived>&) */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE PlainObjectBase(const EigenBase<OtherDerived> &other)
+      : m_storage()
+    {
+      resizeLike(other);
+      *this = other.derived();
+    }
+    /** \brief Copy constructor with in-place evaluation */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE PlainObjectBase(const ReturnByValue<OtherDerived>& other)
+    {
+      // FIXME this does not automatically transpose vectors if necessary
+      resize(other.rows(), other.cols());
+      other.evalTo(this->derived());
+    }
+
+  public:
+
+    /** \brief Copies the generic expression \a other into *this.
+      * \copydetails DenseBase::operator=(const EigenBase<OtherDerived> &other)
+      */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& operator=(const EigenBase<OtherDerived> &other)
+    {
+      _resize_to_match(other);
+      Base::operator=(other.derived());
+      return this->derived();
+    }
+
+    /** \name Map
+      * These are convenience functions returning Map objects. The Map() static functions return unaligned Map objects,
+      * while the AlignedMap() functions return aligned Map objects and thus should be called only with 16-byte-aligned
+      * \a data pointers.
+      *
+      * Here is an example using strides:
+      * \include Matrix_Map_stride.cpp
+      * Output: \verbinclude Matrix_Map_stride.out
+      *
+      * \see class Map
+      */
+    ///@{
+    static inline ConstMapType Map(const Scalar* data)
+    { return ConstMapType(data); }
+    static inline MapType Map(Scalar* data)
+    { return MapType(data); }
+    static inline ConstMapType Map(const Scalar* data, Index size)
+    { return ConstMapType(data, size); }
+    static inline MapType Map(Scalar* data, Index size)
+    { return MapType(data, size); }
+    static inline ConstMapType Map(const Scalar* data, Index rows, Index cols)
+    { return ConstMapType(data, rows, cols); }
+    static inline MapType Map(Scalar* data, Index rows, Index cols)
+    { return MapType(data, rows, cols); }
+
+    static inline ConstAlignedMapType MapAligned(const Scalar* data)
+    { return ConstAlignedMapType(data); }
+    static inline AlignedMapType MapAligned(Scalar* data)
+    { return AlignedMapType(data); }
+    static inline ConstAlignedMapType MapAligned(const Scalar* data, Index size)
+    { return ConstAlignedMapType(data, size); }
+    static inline AlignedMapType MapAligned(Scalar* data, Index size)
+    { return AlignedMapType(data, size); }
+    static inline ConstAlignedMapType MapAligned(const Scalar* data, Index rows, Index cols)
+    { return ConstAlignedMapType(data, rows, cols); }
+    static inline AlignedMapType MapAligned(Scalar* data, Index rows, Index cols)
+    { return AlignedMapType(data, rows, cols); }
+
+    template<int Outer, int Inner>
+    static inline typename StridedConstMapType<Stride<Outer, Inner> >::type Map(const Scalar* data, const Stride<Outer, Inner>& stride)
+    { return typename StridedConstMapType<Stride<Outer, Inner> >::type(data, stride); }
+    template<int Outer, int Inner>
+    static inline typename StridedMapType<Stride<Outer, Inner> >::type Map(Scalar* data, const Stride<Outer, Inner>& stride)
+    { return typename StridedMapType<Stride<Outer, Inner> >::type(data, stride); }
+    template<int Outer, int Inner>
+    static inline typename StridedConstMapType<Stride<Outer, Inner> >::type Map(const Scalar* data, Index size, const Stride<Outer, Inner>& stride)
+    { return typename StridedConstMapType<Stride<Outer, Inner> >::type(data, size, stride); }
+    template<int Outer, int Inner>
+    static inline typename StridedMapType<Stride<Outer, Inner> >::type Map(Scalar* data, Index size, const Stride<Outer, Inner>& stride)
+    { return typename StridedMapType<Stride<Outer, Inner> >::type(data, size, stride); }
+    template<int Outer, int Inner>
+    static inline typename StridedConstMapType<Stride<Outer, Inner> >::type Map(const Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
+    { return typename StridedConstMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
+    template<int Outer, int Inner>
+    static inline typename StridedMapType<Stride<Outer, Inner> >::type Map(Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
+    { return typename StridedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
+
+    template<int Outer, int Inner>
+    static inline typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type MapAligned(const Scalar* data, const Stride<Outer, Inner>& stride)
+    { return typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type(data, stride); }
+    template<int Outer, int Inner>
+    static inline typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, const Stride<Outer, Inner>& stride)
+    { return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, stride); }
+    template<int Outer, int Inner>
+    static inline typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type MapAligned(const Scalar* data, Index size, const Stride<Outer, Inner>& stride)
+    { return typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type(data, size, stride); }
+    template<int Outer, int Inner>
+    static inline typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, Index size, const Stride<Outer, Inner>& stride)
+    { return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, size, stride); }
+    template<int Outer, int Inner>
+    static inline typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type MapAligned(const Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
+    { return typename StridedConstAlignedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
+    template<int Outer, int Inner>
+    static inline typename StridedAlignedMapType<Stride<Outer, Inner> >::type MapAligned(Scalar* data, Index rows, Index cols, const Stride<Outer, Inner>& stride)
+    { return typename StridedAlignedMapType<Stride<Outer, Inner> >::type(data, rows, cols, stride); }
+    ///@}
+
+    using Base::setConstant;
+    EIGEN_DEVICE_FUNC Derived& setConstant(Index size, const Scalar& val);
+    EIGEN_DEVICE_FUNC Derived& setConstant(Index rows, Index cols, const Scalar& val);
+    EIGEN_DEVICE_FUNC Derived& setConstant(NoChange_t, Index cols, const Scalar& val);
+    EIGEN_DEVICE_FUNC Derived& setConstant(Index rows, NoChange_t, const Scalar& val);
+
+    using Base::setZero;
+    EIGEN_DEVICE_FUNC Derived& setZero(Index size);
+    EIGEN_DEVICE_FUNC Derived& setZero(Index rows, Index cols);
+    EIGEN_DEVICE_FUNC Derived& setZero(NoChange_t, Index cols);
+    EIGEN_DEVICE_FUNC Derived& setZero(Index rows, NoChange_t);
+
+    using Base::setOnes;
+    EIGEN_DEVICE_FUNC Derived& setOnes(Index size);
+    EIGEN_DEVICE_FUNC Derived& setOnes(Index rows, Index cols);
+    EIGEN_DEVICE_FUNC Derived& setOnes(NoChange_t, Index cols);
+    EIGEN_DEVICE_FUNC Derived& setOnes(Index rows, NoChange_t);
+
+    using Base::setRandom;
+    Derived& setRandom(Index size);
+    Derived& setRandom(Index rows, Index cols);
+    Derived& setRandom(NoChange_t, Index cols);
+    Derived& setRandom(Index rows, NoChange_t);
+
+    #ifdef EIGEN_PLAINOBJECTBASE_PLUGIN
+    #include EIGEN_PLAINOBJECTBASE_PLUGIN
+    #endif
+
+  protected:
+    /** \internal Resizes *this in preparation for assigning \a other to it.
+      * Takes care of doing all the checking that's needed.
+      *
+      * Note that copying a row-vector into a vector (and conversely) is allowed.
+      * The resizing, if any, is then done in the appropriate way so that row-vectors
+      * remain row-vectors and vectors remain vectors.
+      */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _resize_to_match(const EigenBase<OtherDerived>& other)
+    {
+      #ifdef EIGEN_NO_AUTOMATIC_RESIZING
+      eigen_assert((this->size()==0 || (IsVectorAtCompileTime ? (this->size() == other.size())
+                 : (rows() == other.rows() && cols() == other.cols())))
+        && "Size mismatch. Automatic resizing is disabled because EIGEN_NO_AUTOMATIC_RESIZING is defined");
+      EIGEN_ONLY_USED_FOR_DEBUG(other);
+      #else
+      resizeLike(other);
+      #endif
+    }
+
+    /**
+      * \brief Copies the value of the expression \a other into \c *this with automatic resizing.
+      *
+      * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
+      * it will be initialized.
+      *
+      * Note that copying a row-vector into a vector (and conversely) is allowed.
+      * The resizing, if any, is then done in the appropriate way so that row-vectors
+      * remain row-vectors and vectors remain vectors.
+      *
+      * \sa operator=(const MatrixBase<OtherDerived>&), _set_noalias()
+      *
+      * \internal
+      */
+    // aliasing is dealt once in internal::call_assignment
+    // so at this stage we have to assume aliasing... and resising has to be done later.
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& _set(const DenseBase<OtherDerived>& other)
+    {
+      internal::call_assignment(this->derived(), other.derived());
+      return this->derived();
+    }
+
+    /** \internal Like _set() but additionally makes the assumption that no aliasing effect can happen (which
+      * is the case when creating a new matrix) so one can enforce lazy evaluation.
+      *
+      * \sa operator=(const MatrixBase<OtherDerived>&), _set()
+      */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE Derived& _set_noalias(const DenseBase<OtherDerived>& other)
+    {
+      // I don't think we need this resize call since the lazyAssign will anyways resize
+      // and lazyAssign will be called by the assign selector.
+      //_resize_to_match(other);
+      // the 'false' below means to enforce lazy evaluation. We don't use lazyAssign() because
+      // it wouldn't allow to copy a row-vector into a column-vector.
+      internal::call_assignment_no_alias(this->derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
+      return this->derived();
+    }
+
+    template<typename T0, typename T1>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _init2(Index rows, Index cols, std::enable_if_t<Base::SizeAtCompileTime!=2,T0>* = 0)
+    {
+      EIGEN_STATIC_ASSERT(internal::is_valid_index_type<T0>::value &&
+                          internal::is_valid_index_type<T1>::value,
+                          T0 AND T1 MUST BE INTEGER TYPES)
+      resize(rows,cols);
+    }
+
+    template<typename T0, typename T1>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _init2(const T0& val0, const T1& val1, std::enable_if_t<Base::SizeAtCompileTime==2,T0>* = 0)
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
+      m_storage.data()[0] = Scalar(val0);
+      m_storage.data()[1] = Scalar(val1);
+    }
+
+    template<typename T0, typename T1>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _init2(const Index& val0, const Index& val1,
+                                    std::enable_if_t<    (!internal::is_same<Index,Scalar>::value)
+                                                      && (internal::is_same<T0,Index>::value)
+                                                      && (internal::is_same<T1,Index>::value)
+                                                      && Base::SizeAtCompileTime==2,T1>* = 0)
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 2)
+      m_storage.data()[0] = Scalar(val0);
+      m_storage.data()[1] = Scalar(val1);
+    }
+
+    // The argument is convertible to the Index type and we either have a non 1x1 Matrix, or a dynamic-sized Array,
+    // then the argument is meant to be the size of the object.
+    template<typename T>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _init1(Index size, std::enable_if_t<    (Base::SizeAtCompileTime!=1 || !internal::is_convertible<T, Scalar>::value)
+                                                                  && ((!internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value || Base::SizeAtCompileTime==Dynamic)),T>* = 0)
+    {
+      // NOTE MSVC 2008 complains if we directly put bool(NumTraits<T>::IsInteger) as the EIGEN_STATIC_ASSERT argument.
+      const bool is_integer_alike = internal::is_valid_index_type<T>::value;
+      EIGEN_UNUSED_VARIABLE(is_integer_alike);
+      EIGEN_STATIC_ASSERT(is_integer_alike,
+                          FLOATING_POINT_ARGUMENT_PASSED__INTEGER_WAS_EXPECTED)
+      resize(size);
+    }
+
+    // We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type can be implicitly converted)
+    template<typename T>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _init1(const Scalar& val0, std::enable_if_t<Base::SizeAtCompileTime==1 && internal::is_convertible<T, Scalar>::value,T>* = 0)
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1)
+      m_storage.data()[0] = val0;
+    }
+
+    // We have a 1x1 matrix/array => the argument is interpreted as the value of the unique coefficient (case where scalar type match the index type)
+    template<typename T>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _init1(const Index& val0,
+                                    std::enable_if_t<    (!internal::is_same<Index,Scalar>::value)
+                                                      && (internal::is_same<Index,T>::value)
+                                                      && Base::SizeAtCompileTime==1
+                                                      && internal::is_convertible<T, Scalar>::value,T*>* = 0)
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(PlainObjectBase, 1)
+      m_storage.data()[0] = Scalar(val0);
+    }
+
+    // Initialize a fixed size matrix from a pointer to raw data
+    template<typename T>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _init1(const Scalar* data){
+      this->_set_noalias(ConstMapType(data));
+    }
+
+    // Initialize an arbitrary matrix from a dense expression
+    template<typename T, typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _init1(const DenseBase<OtherDerived>& other){
+      this->_set_noalias(other);
+    }
+
+    // Initialize an arbitrary matrix from an object convertible to the Derived type.
+    template<typename T>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _init1(const Derived& other){
+      this->_set_noalias(other);
+    }
+
+    // Initialize an arbitrary matrix from a generic Eigen expression
+    template<typename T, typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _init1(const EigenBase<OtherDerived>& other){
+      this->derived() = other;
+    }
+
+    template<typename T, typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _init1(const ReturnByValue<OtherDerived>& other)
+    {
+      resize(other.rows(), other.cols());
+      other.evalTo(this->derived());
+    }
+
+    template<typename T, typename OtherDerived, int ColsAtCompileTime>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _init1(const RotationBase<OtherDerived,ColsAtCompileTime>& r)
+    {
+      this->derived() = r;
+    }
+
+    // For fixed-size Array<Scalar,...>
+    template<typename T>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _init1(const Scalar& val0,
+                                    std::enable_if_t<    Base::SizeAtCompileTime!=Dynamic
+                                                      && Base::SizeAtCompileTime!=1
+                                                      && internal::is_convertible<T, Scalar>::value
+                                                      && internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value,T>* = 0)
+    {
+      Base::setConstant(val0);
+    }
+
+    // For fixed-size Array<Index,...>
+    template<typename T>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _init1(const Index& val0,
+                                    std::enable_if_t<    (!internal::is_same<Index,Scalar>::value)
+                                                      && (internal::is_same<Index,T>::value)
+                                                      && Base::SizeAtCompileTime!=Dynamic
+                                                      && Base::SizeAtCompileTime!=1
+                                                      && internal::is_convertible<T, Scalar>::value
+                                                      && internal::is_same<typename internal::traits<Derived>::XprKind,ArrayXpr>::value,T*>* = 0)
+    {
+      Base::setConstant(val0);
+    }
+
+    template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
+    friend struct internal::matrix_swap_impl;
+
+  public:
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** \internal
+      * \brief Override DenseBase::swap() since for dynamic-sized matrices
+      * of same type it is enough to swap the data pointers.
+      */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    void swap(DenseBase<OtherDerived> & other)
+    {
+      enum { SwapPointers = internal::is_same<Derived, OtherDerived>::value && Base::SizeAtCompileTime==Dynamic };
+      internal::matrix_swap_impl<Derived, OtherDerived, bool(SwapPointers)>::run(this->derived(), other.derived());
+    }
+
+    /** \internal
+      * \brief const version forwarded to DenseBase::swap
+      */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    void swap(DenseBase<OtherDerived> const & other)
+    { Base::swap(other.derived()); }
+
+    enum { IsPlainObjectBase = 1 };
+#endif
+  public:
+    // These apparently need to be down here for nvcc+icc to prevent duplicate
+    // Map symbol.
+    template<typename PlainObjectType, int MapOptions, typename StrideType> friend class Eigen::Map;
+    friend class Eigen::Map<Derived, Unaligned>;
+    friend class Eigen::Map<const Derived, Unaligned>;
+#if EIGEN_MAX_ALIGN_BYTES>0
+    // for EIGEN_MAX_ALIGN_BYTES==0, AlignedMax==Unaligned, and many compilers generate warnings for friend-ing a class twice.
+    friend class Eigen::Map<Derived, AlignedMax>;
+    friend class Eigen::Map<const Derived, AlignedMax>;
+#endif
+};
+
+namespace internal {
+
+template <typename Derived, typename OtherDerived, bool IsVector>
+struct conservative_resize_like_impl
+{
+  static constexpr bool IsRelocatable = std::is_trivially_copyable<typename Derived::Scalar>::value;
+  static void run(DenseBase<Derived>& _this, Index rows, Index cols)
+  {
+    if (_this.rows() == rows && _this.cols() == cols) return;
+    EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived)
+
+    if ( IsRelocatable
+          && (( Derived::IsRowMajor && _this.cols() == cols) ||  // row-major and we change only the number of rows
+              (!Derived::IsRowMajor && _this.rows() == rows) ))  // column-major and we change only the number of columns
+    {
+      internal::check_rows_cols_for_overflow<Derived::MaxSizeAtCompileTime, Derived::MaxRowsAtCompileTime, Derived::MaxColsAtCompileTime>::run(rows, cols);
+      _this.derived().m_storage.conservativeResize(rows*cols,rows,cols);
+    }
+    else
+    {
+      // The storage order does not allow us to use reallocation.
+      Derived tmp(rows,cols);
+      const Index common_rows = numext::mini(rows, _this.rows());
+      const Index common_cols = numext::mini(cols, _this.cols());
+      tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
+      _this.derived().swap(tmp);
+    }
+  }
+
+  static void run(DenseBase<Derived>& _this, const DenseBase<OtherDerived>& other)
+  {
+    if (_this.rows() == other.rows() && _this.cols() == other.cols()) return;
+
+    // Note: Here is space for improvement. Basically, for conservativeResize(Index,Index),
+    // neither RowsAtCompileTime or ColsAtCompileTime must be Dynamic. If only one of the
+    // dimensions is dynamic, one could use either conservativeResize(Index rows, NoChange_t) or
+    // conservativeResize(NoChange_t, Index cols). For these methods new static asserts like
+    // EIGEN_STATIC_ASSERT_DYNAMIC_ROWS and EIGEN_STATIC_ASSERT_DYNAMIC_COLS would be good.
+    EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(Derived)
+    EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(OtherDerived)
+
+    if ( IsRelocatable &&
+          (( Derived::IsRowMajor && _this.cols() == other.cols()) ||  // row-major and we change only the number of rows
+           (!Derived::IsRowMajor && _this.rows() == other.rows()) ))  // column-major and we change only the number of columns
+    {
+      const Index new_rows = other.rows() - _this.rows();
+      const Index new_cols = other.cols() - _this.cols();
+      _this.derived().m_storage.conservativeResize(other.size(),other.rows(),other.cols());
+      if (new_rows>0)
+        _this.bottomRightCorner(new_rows, other.cols()) = other.bottomRows(new_rows);
+      else if (new_cols>0)
+        _this.bottomRightCorner(other.rows(), new_cols) = other.rightCols(new_cols);
+    }
+    else
+    {
+      // The storage order does not allow us to use reallocation.
+      Derived tmp(other);
+      const Index common_rows = numext::mini(tmp.rows(), _this.rows());
+      const Index common_cols = numext::mini(tmp.cols(), _this.cols());
+      tmp.block(0,0,common_rows,common_cols) = _this.block(0,0,common_rows,common_cols);
+      _this.derived().swap(tmp);
+    }
+  }
+};
+
+// Here, the specialization for vectors inherits from the general matrix case
+// to allow calling .conservativeResize(rows,cols) on vectors.
+template <typename Derived, typename OtherDerived>
+struct conservative_resize_like_impl<Derived,OtherDerived,true>
+  : conservative_resize_like_impl<Derived,OtherDerived,false>
+{
+  typedef conservative_resize_like_impl<Derived,OtherDerived,false> Base;
+  using Base::run;
+  using Base::IsRelocatable;
+
+  static void run(DenseBase<Derived>& _this, Index size)
+  {
+    const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : size;
+    const Index new_cols = Derived::RowsAtCompileTime==1 ? size : 1;
+    if(IsRelocatable)
+      _this.derived().m_storage.conservativeResize(size,new_rows,new_cols);
+    else
+      Base::run(_this.derived(), new_rows, new_cols);
+  }
+
+  static void run(DenseBase<Derived>& _this, const DenseBase<OtherDerived>& other)
+  {
+    if (_this.rows() == other.rows() && _this.cols() == other.cols()) return;
+
+    const Index num_new_elements = other.size() - _this.size();
+
+    const Index new_rows = Derived::RowsAtCompileTime==1 ? 1 : other.rows();
+    const Index new_cols = Derived::RowsAtCompileTime==1 ? other.cols() : 1;
+    if(IsRelocatable)
+      _this.derived().m_storage.conservativeResize(other.size(),new_rows,new_cols);
+    else
+      Base::run(_this.derived(), new_rows, new_cols);
+
+    if (num_new_elements > 0)
+      _this.tail(num_new_elements) = other.tail(num_new_elements);
+  }
+};
+
+template<typename MatrixTypeA, typename MatrixTypeB, bool SwapPointers>
+struct matrix_swap_impl
+{
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE void run(MatrixTypeA& a, MatrixTypeB& b)
+  {
+    a.base().swap(b);
+  }
+};
+
+template<typename MatrixTypeA, typename MatrixTypeB>
+struct matrix_swap_impl<MatrixTypeA, MatrixTypeB, true>
+{
+  EIGEN_DEVICE_FUNC
+  static inline void run(MatrixTypeA& a, MatrixTypeB& b)
+  {
+    static_cast<typename MatrixTypeA::Base&>(a).m_storage.swap(static_cast<typename MatrixTypeB::Base&>(b).m_storage);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_DENSESTORAGEBASE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Product.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Product.h
new file mode 100644
index 0000000000000000000000000000000000000000..85842d1a5cfd6550f8fdd122cad1b8ce34c6705e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Product.h
@@ -0,0 +1,193 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PRODUCT_H
+#define EIGEN_PRODUCT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template<typename Lhs, typename Rhs, int Option, typename StorageKind> class ProductImpl;
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, int Option>
+struct traits<Product<Lhs, Rhs, Option> >
+{
+  typedef remove_all_t<Lhs> LhsCleaned;
+  typedef remove_all_t<Rhs> RhsCleaned;
+  typedef traits<LhsCleaned> LhsTraits;
+  typedef traits<RhsCleaned> RhsTraits;
+
+  typedef MatrixXpr XprKind;
+
+  typedef typename ScalarBinaryOpTraits<typename traits<LhsCleaned>::Scalar, typename traits<RhsCleaned>::Scalar>::ReturnType Scalar;
+  typedef typename product_promote_storage_type<typename LhsTraits::StorageKind,
+                                                typename RhsTraits::StorageKind,
+                                                internal::product_type<Lhs,Rhs>::ret>::ret StorageKind;
+  typedef typename promote_index_type<typename LhsTraits::StorageIndex,
+                                      typename RhsTraits::StorageIndex>::type StorageIndex;
+
+  enum {
+    RowsAtCompileTime    = LhsTraits::RowsAtCompileTime,
+    ColsAtCompileTime    = RhsTraits::ColsAtCompileTime,
+    MaxRowsAtCompileTime = LhsTraits::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = RhsTraits::MaxColsAtCompileTime,
+
+    // FIXME: only needed by GeneralMatrixMatrixTriangular
+    InnerSize = min_size_prefer_fixed(LhsTraits::ColsAtCompileTime, RhsTraits::RowsAtCompileTime),
+
+    // The storage order is somewhat arbitrary here. The correct one will be determined through the evaluator.
+    Flags = (MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1) ? RowMajorBit
+          : (MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1) ? 0
+          : (   ((LhsTraits::Flags&NoPreferredStorageOrderBit) && (RhsTraits::Flags&RowMajorBit))
+             || ((RhsTraits::Flags&NoPreferredStorageOrderBit) && (LhsTraits::Flags&RowMajorBit)) ) ? RowMajorBit
+          : NoPreferredStorageOrderBit
+  };
+};
+
+} // end namespace internal
+
+/** \class Product
+  * \ingroup Core_Module
+  *
+  * \brief Expression of the product of two arbitrary matrices or vectors
+  *
+  * \tparam Lhs_ the type of the left-hand side expression
+  * \tparam Rhs_ the type of the right-hand side expression
+  *
+  * This class represents an expression of the product of two arbitrary matrices.
+  *
+  * The other template parameters are:
+  * \tparam Option     can be DefaultProduct, AliasFreeProduct, or LazyProduct
+  *
+  */
+template<typename Lhs_, typename Rhs_, int Option>
+class Product : public ProductImpl<Lhs_,Rhs_,Option,
+                                   typename internal::product_promote_storage_type<typename internal::traits<Lhs_>::StorageKind,
+                                                                                   typename internal::traits<Rhs_>::StorageKind,
+                                                                                   internal::product_type<Lhs_,Rhs_>::ret>::ret>
+{
+  public:
+
+    typedef Lhs_ Lhs;
+    typedef Rhs_ Rhs;
+
+    typedef typename ProductImpl<
+        Lhs, Rhs, Option,
+        typename internal::product_promote_storage_type<typename internal::traits<Lhs>::StorageKind,
+                                                        typename internal::traits<Rhs>::StorageKind,
+                                                        internal::product_type<Lhs,Rhs>::ret>::ret>::Base Base;
+    EIGEN_GENERIC_PUBLIC_INTERFACE(Product)
+
+    typedef typename internal::ref_selector<Lhs>::type LhsNested;
+    typedef typename internal::ref_selector<Rhs>::type RhsNested;
+    typedef internal::remove_all_t<LhsNested> LhsNestedCleaned;
+    typedef internal::remove_all_t<RhsNested> RhsNestedCleaned;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Product(const Lhs& lhs, const Rhs& rhs) : m_lhs(lhs), m_rhs(rhs)
+    {
+      eigen_assert(lhs.cols() == rhs.rows()
+        && "invalid matrix product"
+        && "if you wanted a coeff-wise or a dot product use the respective explicit functions");
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index rows() const EIGEN_NOEXCEPT { return m_lhs.rows(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const LhsNestedCleaned& lhs() const { return m_lhs; }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const RhsNestedCleaned& rhs() const { return m_rhs; }
+
+  protected:
+
+    LhsNested m_lhs;
+    RhsNested m_rhs;
+};
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, int Option, int ProductTag = internal::product_type<Lhs,Rhs>::ret>
+class dense_product_base
+ : public internal::dense_xpr_base<Product<Lhs,Rhs,Option> >::type
+{};
+
+/** Conversion to scalar for inner-products */
+template<typename Lhs, typename Rhs, int Option>
+class dense_product_base<Lhs, Rhs, Option, InnerProduct>
+ : public internal::dense_xpr_base<Product<Lhs,Rhs,Option> >::type
+{
+  typedef Product<Lhs,Rhs,Option> ProductXpr;
+  typedef typename internal::dense_xpr_base<ProductXpr>::type Base;
+public:
+  using Base::derived;
+  typedef typename Base::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE operator const Scalar() const
+  {
+    return internal::evaluator<ProductXpr>(derived()).coeff(0,0);
+  }
+};
+
+} // namespace internal
+
+// Generic API dispatcher
+template<typename Lhs, typename Rhs, int Option, typename StorageKind>
+class ProductImpl : public internal::generic_xpr_base<Product<Lhs,Rhs,Option>, MatrixXpr, StorageKind>::type
+{
+  public:
+    typedef typename internal::generic_xpr_base<Product<Lhs,Rhs,Option>, MatrixXpr, StorageKind>::type Base;
+};
+
+template<typename Lhs, typename Rhs, int Option>
+class ProductImpl<Lhs,Rhs,Option,Dense>
+  : public internal::dense_product_base<Lhs,Rhs,Option>
+{
+    typedef Product<Lhs, Rhs, Option> Derived;
+
+  public:
+
+    typedef typename internal::dense_product_base<Lhs, Rhs, Option> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
+  protected:
+    enum {
+      IsOneByOne = (RowsAtCompileTime == 1 || RowsAtCompileTime == Dynamic) &&
+                   (ColsAtCompileTime == 1 || ColsAtCompileTime == Dynamic),
+      EnableCoeff = IsOneByOne || Option==LazyProduct
+    };
+
+  public:
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index row, Index col) const
+    {
+      EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
+      eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) );
+
+      return internal::evaluator<Derived>(derived()).coeff(row,col);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar coeff(Index i) const
+    {
+      EIGEN_STATIC_ASSERT(EnableCoeff, THIS_METHOD_IS_ONLY_FOR_INNER_OR_LAZY_PRODUCTS);
+      eigen_assert( (Option==LazyProduct) || (this->rows() == 1 && this->cols() == 1) );
+
+      return internal::evaluator<Derived>(derived()).coeff(i);
+    }
+
+
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_PRODUCT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ProductEvaluators.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ProductEvaluators.h
new file mode 100644
index 0000000000000000000000000000000000000000..9da240671409f88d11a2584dc9831e2441f2c913
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ProductEvaluators.h
@@ -0,0 +1,1215 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2011 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+
+#ifndef EIGEN_PRODUCTEVALUATORS_H
+#define EIGEN_PRODUCTEVALUATORS_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+  * Evaluator of a product expression.
+  * Since products require special treatments to handle all possible cases,
+  * we simply defer the evaluation logic to a product_evaluator class
+  * which offers more partial specialization possibilities.
+  *
+  * \sa class product_evaluator
+  */
+template<typename Lhs, typename Rhs, int Options>
+struct evaluator<Product<Lhs, Rhs, Options> >
+ : public product_evaluator<Product<Lhs, Rhs, Options> >
+{
+  typedef Product<Lhs, Rhs, Options> XprType;
+  typedef product_evaluator<XprType> Base;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr) : Base(xpr) {}
+};
+
+// Catch "scalar * ( A * B )" and transform it to "(A*scalar) * B"
+// TODO we should apply that rule only if that's really helpful
+template<typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
+struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
+                                               const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
+                                               const Product<Lhs, Rhs, DefaultProduct> > >
+{
+  static const bool value = true;
+};
+template<typename Lhs, typename Rhs, typename Scalar1, typename Scalar2, typename Plain1>
+struct evaluator<CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
+                               const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
+                               const Product<Lhs, Rhs, DefaultProduct> > >
+ : public evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1,Lhs,product), Rhs, DefaultProduct> >
+{
+  typedef CwiseBinaryOp<internal::scalar_product_op<Scalar1,Scalar2>,
+                               const CwiseNullaryOp<internal::scalar_constant_op<Scalar1>, Plain1>,
+                               const Product<Lhs, Rhs, DefaultProduct> > XprType;
+  typedef evaluator<Product<EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar1,Lhs,product), Rhs, DefaultProduct> > Base;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr)
+    : Base(xpr.lhs().functor().m_other * xpr.rhs().lhs() * xpr.rhs().rhs())
+  {}
+};
+
+
+template<typename Lhs, typename Rhs, int DiagIndex>
+struct evaluator<Diagonal<const Product<Lhs, Rhs, DefaultProduct>, DiagIndex> >
+ : public evaluator<Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex> >
+{
+  typedef Diagonal<const Product<Lhs, Rhs, DefaultProduct>, DiagIndex> XprType;
+  typedef evaluator<Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex> > Base;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit evaluator(const XprType& xpr)
+    : Base(Diagonal<const Product<Lhs, Rhs, LazyProduct>, DiagIndex>(
+        Product<Lhs, Rhs, LazyProduct>(xpr.nestedExpression().lhs(), xpr.nestedExpression().rhs()),
+        xpr.index() ))
+  {}
+};
+
+
+// Helper class to perform a matrix product with the destination at hand.
+// Depending on the sizes of the factors, there are different evaluation strategies
+// as controlled by internal::product_type.
+template< typename Lhs, typename Rhs,
+          typename LhsShape = typename evaluator_traits<Lhs>::Shape,
+          typename RhsShape = typename evaluator_traits<Rhs>::Shape,
+          int ProductType = internal::product_type<Lhs,Rhs>::value>
+struct generic_product_impl;
+
+template<typename Lhs, typename Rhs>
+struct evaluator_assume_aliasing<Product<Lhs, Rhs, DefaultProduct> > {
+  static const bool value = true;
+};
+
+// This is the default evaluator implementation for products:
+// It creates a temporary and call generic_product_impl
+template<typename Lhs, typename Rhs, int Options, int ProductTag, typename LhsShape, typename RhsShape>
+struct product_evaluator<Product<Lhs, Rhs, Options>, ProductTag, LhsShape, RhsShape>
+  : public evaluator<typename Product<Lhs, Rhs, Options>::PlainObject>
+{
+  typedef Product<Lhs, Rhs, Options> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+  enum {
+    Flags = Base::Flags | EvalBeforeNestingBit
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit product_evaluator(const XprType& xpr)
+    : m_result(xpr.rows(), xpr.cols())
+  {
+    internal::construct_at<Base>(this, m_result);
+
+// FIXME shall we handle nested_eval here?,
+// if so, then we must take care at removing the call to nested_eval in the specializations (e.g., in permutation_matrix_product, transposition_matrix_product, etc.)
+//     typedef typename internal::nested_eval<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
+//     typedef typename internal::nested_eval<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
+//     typedef internal::remove_all_t<LhsNested> LhsNestedCleaned;
+//     typedef internal::remove_all_t<RhsNested> RhsNestedCleaned;
+//
+//     const LhsNested lhs(xpr.lhs());
+//     const RhsNested rhs(xpr.rhs());
+//
+//     generic_product_impl<LhsNestedCleaned, RhsNestedCleaned>::evalTo(m_result, lhs, rhs);
+
+    generic_product_impl<Lhs, Rhs, LhsShape, RhsShape, ProductTag>::evalTo(m_result, xpr.lhs(), xpr.rhs());
+  }
+
+protected:
+  PlainObject m_result;
+};
+
+// The following three shortcuts are enabled only if the scalar types match exactly.
+// TODO: we could enable them for different scalar types when the product is not vectorized.
+
+// Dense = Product
+template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
+struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::assign_op<Scalar,Scalar>, Dense2Dense,
+  std::enable_if_t<(Options==DefaultProduct || Options==AliasFreeProduct)>>
+{
+  typedef Product<Lhs,Rhs,Options> SrcXprType;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
+  {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+      dst.resize(dstRows, dstCols);
+    // FIXME shall we handle nested_eval here?
+    generic_product_impl<Lhs, Rhs>::evalTo(dst, src.lhs(), src.rhs());
+  }
+};
+
+// Dense += Product
+template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
+struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::add_assign_op<Scalar,Scalar>, Dense2Dense,
+  std::enable_if_t<(Options==DefaultProduct || Options==AliasFreeProduct)>>
+{
+  typedef Product<Lhs,Rhs,Options> SrcXprType;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar,Scalar> &)
+  {
+    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
+    // FIXME shall we handle nested_eval here?
+    generic_product_impl<Lhs, Rhs>::addTo(dst, src.lhs(), src.rhs());
+  }
+};
+
+// Dense -= Product
+template< typename DstXprType, typename Lhs, typename Rhs, int Options, typename Scalar>
+struct Assignment<DstXprType, Product<Lhs,Rhs,Options>, internal::sub_assign_op<Scalar,Scalar>, Dense2Dense,
+  std::enable_if_t<(Options==DefaultProduct || Options==AliasFreeProduct)>>
+{
+  typedef Product<Lhs,Rhs,Options> SrcXprType;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar,Scalar> &)
+  {
+    eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());
+    // FIXME shall we handle nested_eval here?
+    generic_product_impl<Lhs, Rhs>::subTo(dst, src.lhs(), src.rhs());
+  }
+};
+
+
+// Dense ?= scalar * Product
+// TODO we should apply that rule if that's really helpful
+// for instance, this is not good for inner products
+template< typename DstXprType, typename Lhs, typename Rhs, typename AssignFunc, typename Scalar, typename ScalarBis, typename Plain>
+struct Assignment<DstXprType, CwiseBinaryOp<internal::scalar_product_op<ScalarBis,Scalar>, const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>,Plain>,
+                                           const Product<Lhs,Rhs,DefaultProduct> >, AssignFunc, Dense2Dense>
+{
+  typedef CwiseBinaryOp<internal::scalar_product_op<ScalarBis,Scalar>,
+                        const CwiseNullaryOp<internal::scalar_constant_op<ScalarBis>,Plain>,
+                        const Product<Lhs,Rhs,DefaultProduct> > SrcXprType;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void run(DstXprType &dst, const SrcXprType &src, const AssignFunc& func)
+  {
+    call_assignment_no_alias(dst, (src.lhs().functor().m_other * src.rhs().lhs())*src.rhs().rhs(), func);
+  }
+};
+
+//----------------------------------------
+// Catch "Dense ?= xpr + Product<>" expression to save one temporary
+// FIXME we could probably enable these rules for any product, i.e., not only Dense and DefaultProduct
+
+template<typename OtherXpr, typename Lhs, typename Rhs>
+struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_sum_op<typename OtherXpr::Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, const OtherXpr,
+                                               const Product<Lhs,Rhs,DefaultProduct> >, DenseShape > {
+  static const bool value = true;
+};
+
+template<typename OtherXpr, typename Lhs, typename Rhs>
+struct evaluator_assume_aliasing<CwiseBinaryOp<internal::scalar_difference_op<typename OtherXpr::Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, const OtherXpr,
+                                               const Product<Lhs,Rhs,DefaultProduct> >, DenseShape > {
+  static const bool value = true;
+};
+
+template<typename DstXprType, typename OtherXpr, typename ProductType, typename Func1, typename Func2>
+struct assignment_from_xpr_op_product
+{
+  template<typename SrcXprType, typename InitialFunc>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void run(DstXprType &dst, const SrcXprType &src, const InitialFunc& /*func*/)
+  {
+    call_assignment_no_alias(dst, src.lhs(), Func1());
+    call_assignment_no_alias(dst, src.rhs(), Func2());
+  }
+};
+
+#define EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(ASSIGN_OP,BINOP,ASSIGN_OP2) \
+  template< typename DstXprType, typename OtherXpr, typename Lhs, typename Rhs, typename DstScalar, typename SrcScalar, typename OtherScalar,typename ProdScalar> \
+  struct Assignment<DstXprType, CwiseBinaryOp<internal::BINOP<OtherScalar,ProdScalar>, const OtherXpr, \
+                                            const Product<Lhs,Rhs,DefaultProduct> >, internal::ASSIGN_OP<DstScalar,SrcScalar>, Dense2Dense> \
+    : assignment_from_xpr_op_product<DstXprType, OtherXpr, Product<Lhs,Rhs,DefaultProduct>, internal::ASSIGN_OP<DstScalar,OtherScalar>, internal::ASSIGN_OP2<DstScalar,ProdScalar> > \
+  {}
+
+EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op,    scalar_sum_op,add_assign_op);
+EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op,scalar_sum_op,add_assign_op);
+EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op,scalar_sum_op,sub_assign_op);
+
+EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(assign_op,    scalar_difference_op,sub_assign_op);
+EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(add_assign_op,scalar_difference_op,sub_assign_op);
+EIGEN_CATCH_ASSIGN_XPR_OP_PRODUCT(sub_assign_op,scalar_difference_op,add_assign_op);
+
+//----------------------------------------
+
+template<typename Lhs, typename Rhs>
+struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,InnerProduct>
+{
+  template<typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    dst.coeffRef(0,0) = (lhs.transpose().cwiseProduct(rhs)).sum();
+  }
+
+  template<typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    dst.coeffRef(0,0) += (lhs.transpose().cwiseProduct(rhs)).sum();
+  }
+
+  template<typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+  { dst.coeffRef(0,0) -= (lhs.transpose().cwiseProduct(rhs)).sum(); }
+};
+
+
+/***********************************************************************
+*  Implementation of outer dense * dense vector product
+***********************************************************************/
+
+// Column major result
+template<typename Dst, typename Lhs, typename Rhs, typename Func>
+void EIGEN_DEVICE_FUNC outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const false_type&)
+{
+  evaluator<Rhs> rhsEval(rhs);
+  ei_declare_local_nested_eval(Lhs,lhs,Rhs::SizeAtCompileTime,actual_lhs);
+  // FIXME if cols is large enough, then it might be useful to make sure that lhs is sequentially stored
+  // FIXME not very good if rhs is real and lhs complex while alpha is real too
+  const Index cols = dst.cols();
+  for (Index j=0; j<cols; ++j)
+    func(dst.col(j), rhsEval.coeff(Index(0),j) * actual_lhs);
+}
+
+// Row major result
+template<typename Dst, typename Lhs, typename Rhs, typename Func>
+void EIGEN_DEVICE_FUNC outer_product_selector_run(Dst& dst, const Lhs &lhs, const Rhs &rhs, const Func& func, const true_type&)
+{
+  evaluator<Lhs> lhsEval(lhs);
+  ei_declare_local_nested_eval(Rhs,rhs,Lhs::SizeAtCompileTime,actual_rhs);
+  // FIXME if rows is large enough, then it might be useful to make sure that rhs is sequentially stored
+  // FIXME not very good if lhs is real and rhs complex while alpha is real too
+  const Index rows = dst.rows();
+  for (Index i=0; i<rows; ++i)
+    func(dst.row(i), lhsEval.coeff(i,Index(0)) * actual_rhs);
+}
+
+template<typename Lhs, typename Rhs>
+struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,OuterProduct>
+{
+  template<typename T> struct is_row_major : std::conditional_t<(int(T::Flags)&RowMajorBit), internal::true_type, internal::false_type> {};
+  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
+
+  // TODO it would be nice to be able to exploit our *_assign_op functors for that purpose
+  struct set  { template<typename Dst, typename Src> EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived()  = src; } };
+  struct add  { template<typename Dst, typename Src> EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() += src; } };
+  struct sub  { template<typename Dst, typename Src> EIGEN_DEVICE_FUNC void operator()(const Dst& dst, const Src& src) const { dst.const_cast_derived() -= src; } };
+  struct adds {
+    Scalar m_scale;
+    explicit adds(const Scalar& s) : m_scale(s) {}
+    template<typename Dst, typename Src> void EIGEN_DEVICE_FUNC operator()(const Dst& dst, const Src& src) const {
+      dst.const_cast_derived() += m_scale * src;
+    }
+  };
+
+  template<typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    internal::outer_product_selector_run(dst, lhs, rhs, set(), is_row_major<Dst>());
+  }
+
+  template<typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    internal::outer_product_selector_run(dst, lhs, rhs, add(), is_row_major<Dst>());
+  }
+
+  template<typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    internal::outer_product_selector_run(dst, lhs, rhs, sub(), is_row_major<Dst>());
+  }
+
+  template<typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
+  {
+    internal::outer_product_selector_run(dst, lhs, rhs, adds(alpha), is_row_major<Dst>());
+  }
+
+};
+
+
+// This base class provides default implementations for evalTo, addTo, subTo, in terms of scaleAndAddTo
+template<typename Lhs, typename Rhs, typename Derived>
+struct generic_product_impl_base
+{
+  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
+
+  template<typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+  { dst.setZero(); scaleAndAddTo(dst, lhs, rhs, Scalar(1)); }
+
+  template<typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+  { scaleAndAddTo(dst,lhs, rhs, Scalar(1)); }
+
+  template<typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+  { scaleAndAddTo(dst, lhs, rhs, Scalar(-1)); }
+
+  template<typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
+  { Derived::scaleAndAddTo(dst,lhs,rhs,alpha); }
+
+};
+
+template<typename Lhs, typename Rhs>
+struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemvProduct>
+  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemvProduct> >
+{
+  typedef typename nested_eval<Lhs,1>::type LhsNested;
+  typedef typename nested_eval<Rhs,1>::type RhsNested;
+  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
+  enum { Side = Lhs::IsVectorAtCompileTime ? OnTheLeft : OnTheRight };
+  typedef internal::remove_all_t<std::conditional_t<int(Side)==OnTheRight,LhsNested,RhsNested>> MatrixType;
+
+  template<typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
+  {
+    // Fallback to inner product if both the lhs and rhs is a runtime vector.
+    if (lhs.rows() == 1 && rhs.cols() == 1) {
+      dst.coeffRef(0,0) += alpha * lhs.row(0).conjugate().dot(rhs.col(0));
+      return;
+    }
+    LhsNested actual_lhs(lhs);
+    RhsNested actual_rhs(rhs);
+    internal::gemv_dense_selector<Side,
+                            (int(MatrixType::Flags)&RowMajorBit) ? RowMajor : ColMajor,
+                            bool(internal::blas_traits<MatrixType>::HasUsableDirectAccess)
+                           >::run(actual_lhs, actual_rhs, dst, alpha);
+  }
+};
+
+template<typename Lhs, typename Rhs>
+struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode>
+{
+  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
+
+  template<typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    // Same as: dst.noalias() = lhs.lazyProduct(rhs);
+    // but easier on the compiler side
+    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::assign_op<typename Dst::Scalar,Scalar>());
+  }
+
+  template<typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    // dst.noalias() += lhs.lazyProduct(rhs);
+    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::add_assign_op<typename Dst::Scalar,Scalar>());
+  }
+
+  template<typename Dst>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    // dst.noalias() -= lhs.lazyProduct(rhs);
+    call_assignment_no_alias(dst, lhs.lazyProduct(rhs), internal::sub_assign_op<typename Dst::Scalar,Scalar>());
+  }
+
+  // This is a special evaluation path called from generic_product_impl<...,GemmProduct> in file GeneralMatrixMatrix.h
+  // This variant tries to extract scalar multiples from both the LHS and RHS and factor them out. For instance:
+  //   dst {,+,-}= (s1*A)*(B*s2)
+  // will be rewritten as:
+  //   dst {,+,-}= (s1*s2) * (A.lazyProduct(B))
+  // There are at least four benefits of doing so:
+  //  1 - huge performance gain for heap-allocated matrix types as it save costly allocations.
+  //  2 - it is faster than simply by-passing the heap allocation through stack allocation.
+  //  3 - it makes this fallback consistent with the heavy GEMM routine.
+  //  4 - it fully by-passes huge stack allocation attempts when multiplying huge fixed-size matrices.
+  //      (see https://stackoverflow.com/questions/54738495)
+  // For small fixed sizes matrices, however, the gains are less obvious, it is sometimes x2 faster, but sometimes x3 slower,
+  // and the behavior depends also a lot on the compiler... This is why this re-writing strategy is currently
+  // enabled only when falling back from the main GEMM.
+  template<typename Dst, typename Func>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void eval_dynamic(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Func &func)
+  {
+    enum {
+      HasScalarFactor = blas_traits<Lhs>::HasScalarFactor || blas_traits<Rhs>::HasScalarFactor,
+      ConjLhs = blas_traits<Lhs>::NeedToConjugate,
+      ConjRhs = blas_traits<Rhs>::NeedToConjugate
+    };
+    // FIXME: in c++11 this should be auto, and extractScalarFactor should also return auto
+    //        this is important for real*complex_mat
+    Scalar actualAlpha = combine_scalar_factors<Scalar>(lhs, rhs);
+
+    eval_dynamic_impl(dst,
+                      blas_traits<Lhs>::extract(lhs).template conjugateIf<ConjLhs>(),
+                      blas_traits<Rhs>::extract(rhs).template conjugateIf<ConjRhs>(),
+                      func,
+                      actualAlpha,
+                      std::conditional_t<HasScalarFactor,true_type,false_type>());
+  }
+
+protected:
+
+  template<typename Dst, typename LhsT, typename RhsT, typename Func, typename Scalar>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void eval_dynamic_impl(Dst& dst, const LhsT& lhs, const RhsT& rhs, const Func &func, const Scalar&  s /* == 1 */, false_type)
+  {
+    EIGEN_UNUSED_VARIABLE(s);
+    eigen_internal_assert(numext::is_exactly_one(s));
+    call_restricted_packet_assignment_no_alias(dst, lhs.lazyProduct(rhs), func);
+  }
+
+  template<typename Dst, typename LhsT, typename RhsT, typename Func, typename Scalar>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void eval_dynamic_impl(Dst& dst, const LhsT& lhs, const RhsT& rhs, const Func &func, const Scalar& s, true_type)
+  {
+    call_restricted_packet_assignment_no_alias(dst, s * lhs.lazyProduct(rhs), func);
+  }
+};
+
+// This specialization enforces the use of a coefficient-based evaluation strategy
+template<typename Lhs, typename Rhs>
+struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,LazyCoeffBasedProductMode>
+  : generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode> {};
+
+// Case 2: Evaluate coeff by coeff
+//
+// This is mostly taken from CoeffBasedProduct.h
+// The main difference is that we add an extra argument to the etor_product_*_impl::run() function
+// for the inner dimension of the product, because evaluator object do not know their size.
+
+template<int Traversal, int UnrollingIndex, typename Lhs, typename Rhs, typename RetScalar>
+struct etor_product_coeff_impl;
+
+template<int StorageOrder, int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl;
+
+template<typename Lhs, typename Rhs, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape, DenseShape>
+    : evaluator_base<Product<Lhs, Rhs, LazyProduct> >
+{
+  typedef Product<Lhs, Rhs, LazyProduct> XprType;
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit product_evaluator(const XprType& xpr)
+    : m_lhs(xpr.lhs()),
+      m_rhs(xpr.rhs()),
+      m_lhsImpl(m_lhs),     // FIXME the creation of the evaluator objects should result in a no-op, but check that!
+      m_rhsImpl(m_rhs),     //       Moreover, they are only useful for the packet path, so we could completely disable them when not needed,
+                            //       or perhaps declare them on the fly on the packet method... We have experiment to check what's best.
+      m_innerDim(xpr.lhs().cols())
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::MulCost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::AddCost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+#if 0
+    std::cerr << "LhsOuterStrideBytes=  " << LhsOuterStrideBytes << "\n";
+    std::cerr << "RhsOuterStrideBytes=  " << RhsOuterStrideBytes << "\n";
+    std::cerr << "LhsAlignment=         " << LhsAlignment << "\n";
+    std::cerr << "RhsAlignment=         " << RhsAlignment << "\n";
+    std::cerr << "CanVectorizeLhs=      " << CanVectorizeLhs << "\n";
+    std::cerr << "CanVectorizeRhs=      " << CanVectorizeRhs << "\n";
+    std::cerr << "CanVectorizeInner=    " << CanVectorizeInner << "\n";
+    std::cerr << "EvalToRowMajor=       " << EvalToRowMajor << "\n";
+    std::cerr << "Alignment=            " << Alignment << "\n";
+    std::cerr << "Flags=                " << Flags << "\n";
+#endif
+  }
+
+  // Everything below here is taken from CoeffBasedProduct.h
+
+  typedef typename internal::nested_eval<Lhs,Rhs::ColsAtCompileTime>::type LhsNested;
+  typedef typename internal::nested_eval<Rhs,Lhs::RowsAtCompileTime>::type RhsNested;
+
+  typedef internal::remove_all_t<LhsNested> LhsNestedCleaned;
+  typedef internal::remove_all_t<RhsNested> RhsNestedCleaned;
+
+  typedef evaluator<LhsNestedCleaned> LhsEtorType;
+  typedef evaluator<RhsNestedCleaned> RhsEtorType;
+
+  enum {
+    RowsAtCompileTime = LhsNestedCleaned::RowsAtCompileTime,
+    ColsAtCompileTime = RhsNestedCleaned::ColsAtCompileTime,
+    InnerSize = min_size_prefer_fixed(LhsNestedCleaned::ColsAtCompileTime, RhsNestedCleaned::RowsAtCompileTime),
+    MaxRowsAtCompileTime = LhsNestedCleaned::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = RhsNestedCleaned::MaxColsAtCompileTime
+  };
+
+  typedef typename find_best_packet<Scalar,RowsAtCompileTime>::type LhsVecPacketType;
+  typedef typename find_best_packet<Scalar,ColsAtCompileTime>::type RhsVecPacketType;
+
+  enum {
+
+    LhsCoeffReadCost = LhsEtorType::CoeffReadCost,
+    RhsCoeffReadCost = RhsEtorType::CoeffReadCost,
+    CoeffReadCost = InnerSize==0 ? NumTraits<Scalar>::ReadCost
+                  : InnerSize == Dynamic ? HugeCost
+                    : InnerSize * (NumTraits<Scalar>::MulCost + int(LhsCoeffReadCost) + int(RhsCoeffReadCost))
+                    + (InnerSize - 1) * NumTraits<Scalar>::AddCost,
+
+    Unroll = CoeffReadCost <= EIGEN_UNROLLING_LIMIT,
+
+    LhsFlags = LhsEtorType::Flags,
+    RhsFlags = RhsEtorType::Flags,
+
+    LhsRowMajor = LhsFlags & RowMajorBit,
+    RhsRowMajor = RhsFlags & RowMajorBit,
+
+    LhsVecPacketSize = unpacket_traits<LhsVecPacketType>::size,
+    RhsVecPacketSize = unpacket_traits<RhsVecPacketType>::size,
+
+    // Here, we don't care about alignment larger than the usable packet size.
+    LhsAlignment = plain_enum_min(LhsEtorType::Alignment, LhsVecPacketSize*int(sizeof(typename LhsNestedCleaned::Scalar))),
+    RhsAlignment = plain_enum_min(RhsEtorType::Alignment, RhsVecPacketSize*int(sizeof(typename RhsNestedCleaned::Scalar))),
+
+    SameType = is_same<typename LhsNestedCleaned::Scalar,typename RhsNestedCleaned::Scalar>::value,
+
+    CanVectorizeRhs = bool(RhsRowMajor) && (RhsFlags & PacketAccessBit) && (ColsAtCompileTime!=1),
+    CanVectorizeLhs = (!LhsRowMajor) && (LhsFlags & PacketAccessBit) && (RowsAtCompileTime!=1),
+
+    EvalToRowMajor = (MaxRowsAtCompileTime==1&&MaxColsAtCompileTime!=1) ? 1
+                    : (MaxColsAtCompileTime==1&&MaxRowsAtCompileTime!=1) ? 0
+                    : (bool(RhsRowMajor) && !CanVectorizeLhs),
+
+    Flags = ((int(LhsFlags) | int(RhsFlags)) & HereditaryBits & ~RowMajorBit)
+          | (EvalToRowMajor ? RowMajorBit : 0)
+          // TODO enable vectorization for mixed types
+          | (SameType && (CanVectorizeLhs || CanVectorizeRhs) ? PacketAccessBit : 0)
+          | (XprType::IsVectorAtCompileTime ? LinearAccessBit : 0),
+
+    LhsOuterStrideBytes = int(LhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename LhsNestedCleaned::Scalar)),
+    RhsOuterStrideBytes = int(RhsNestedCleaned::OuterStrideAtCompileTime) * int(sizeof(typename RhsNestedCleaned::Scalar)),
+
+    Alignment = bool(CanVectorizeLhs) ? (LhsOuterStrideBytes<=0 || (int(LhsOuterStrideBytes) % plain_enum_max(1, LhsAlignment))!=0 ? 0 : LhsAlignment)
+              : bool(CanVectorizeRhs) ? (RhsOuterStrideBytes<=0 || (int(RhsOuterStrideBytes) % plain_enum_max(1, RhsAlignment))!=0 ? 0 : RhsAlignment)
+              : 0,
+
+    /* CanVectorizeInner deserves special explanation. It does not affect the product flags. It is not used outside
+     * of Product. If the Product itself is not a packet-access expression, there is still a chance that the inner
+     * loop of the product might be vectorized. This is the meaning of CanVectorizeInner. Since it doesn't affect
+     * the Flags, it is safe to make this value depend on ActualPacketAccessBit, that doesn't affect the ABI.
+     */
+    CanVectorizeInner =    SameType
+                        && LhsRowMajor
+                        && (!RhsRowMajor)
+                        && (int(LhsFlags) & int(RhsFlags) & ActualPacketAccessBit)
+                        && (int(InnerSize) % packet_traits<Scalar>::size == 0)
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index row, Index col) const
+  {
+    return (m_lhs.row(row).transpose().cwiseProduct( m_rhs.col(col) )).sum();
+  }
+
+  /* Allow index-based non-packet access. It is impossible though to allow index-based packed access,
+   * which is why we don't set the LinearAccessBit.
+   * TODO: this seems possible when the result is a vector
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const CoeffReturnType coeff(Index index) const
+  {
+    const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? 0 : index;
+    const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? index : 0;
+    return (m_lhs.row(row).transpose().cwiseProduct( m_rhs.col(col) )).sum();
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const PacketType packet(Index row, Index col) const
+  {
+    PacketType res;
+    typedef etor_product_packet_impl<bool(int(Flags)&RowMajorBit) ? RowMajor : ColMajor,
+                                     Unroll ? int(InnerSize) : Dynamic,
+                                     LhsEtorType, RhsEtorType, PacketType, LoadMode> PacketImpl;
+    PacketImpl::run(row, col, m_lhsImpl, m_rhsImpl, m_innerDim, res);
+    return res;
+  }
+
+  template<int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const PacketType packet(Index index) const
+  {
+    const Index row = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? 0 : index;
+    const Index col = (RowsAtCompileTime == 1 || MaxRowsAtCompileTime==1) ? index : 0;
+    return packet<LoadMode,PacketType>(row,col);
+  }
+
+protected:
+  add_const_on_value_type_t<LhsNested> m_lhs;
+  add_const_on_value_type_t<RhsNested> m_rhs;
+
+  LhsEtorType m_lhsImpl;
+  RhsEtorType m_rhsImpl;
+
+  // TODO: Get rid of m_innerDim if known at compile time
+  Index m_innerDim;
+};
+
+template<typename Lhs, typename Rhs>
+struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, LazyCoeffBasedProductMode, DenseShape, DenseShape>
+  : product_evaluator<Product<Lhs, Rhs, LazyProduct>, CoeffBasedProductMode, DenseShape, DenseShape>
+{
+  typedef Product<Lhs, Rhs, DefaultProduct> XprType;
+  typedef Product<Lhs, Rhs, LazyProduct> BaseProduct;
+  typedef product_evaluator<BaseProduct, CoeffBasedProductMode, DenseShape, DenseShape> Base;
+  enum {
+    Flags = Base::Flags | EvalBeforeNestingBit
+  };
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit product_evaluator(const XprType& xpr)
+    : Base(BaseProduct(xpr.lhs(),xpr.rhs()))
+  {}
+};
+
+/****************************************
+*** Coeff based product, Packet path  ***
+****************************************/
+
+template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<RowMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode>
+{
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res)
+  {
+    etor_product_packet_impl<RowMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, innerDim, res);
+    res =  pmadd(pset1<Packet>(lhs.coeff(row, Index(UnrollingIndex-1))), rhs.template packet<LoadMode,Packet>(Index(UnrollingIndex-1), col), res);
+  }
+};
+
+template<int UnrollingIndex, typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<ColMajor, UnrollingIndex, Lhs, Rhs, Packet, LoadMode>
+{
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet &res)
+  {
+    etor_product_packet_impl<ColMajor, UnrollingIndex-1, Lhs, Rhs, Packet, LoadMode>::run(row, col, lhs, rhs, innerDim, res);
+    res =  pmadd(lhs.template packet<LoadMode,Packet>(row, Index(UnrollingIndex-1)), pset1<Packet>(rhs.coeff(Index(UnrollingIndex-1), col)), res);
+  }
+};
+
+template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<RowMajor, 1, Lhs, Rhs, Packet, LoadMode>
+{
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res)
+  {
+    res = pmul(pset1<Packet>(lhs.coeff(row, Index(0))),rhs.template packet<LoadMode,Packet>(Index(0), col));
+  }
+};
+
+template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<ColMajor, 1, Lhs, Rhs, Packet, LoadMode>
+{
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index /*innerDim*/, Packet &res)
+  {
+    res = pmul(lhs.template packet<LoadMode,Packet>(row, Index(0)), pset1<Packet>(rhs.coeff(Index(0), col)));
+  }
+};
+
+template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<RowMajor, 0, Lhs, Rhs, Packet, LoadMode>
+{
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/, const Rhs& /*rhs*/, Index /*innerDim*/, Packet &res)
+  {
+    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
+  }
+};
+
+template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<ColMajor, 0, Lhs, Rhs, Packet, LoadMode>
+{
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index /*row*/, Index /*col*/, const Lhs& /*lhs*/, const Rhs& /*rhs*/, Index /*innerDim*/, Packet &res)
+  {
+    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
+  }
+};
+
+template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<RowMajor, Dynamic, Lhs, Rhs, Packet, LoadMode>
+{
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res)
+  {
+    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
+    for(Index i = 0; i < innerDim; ++i)
+      res =  pmadd(pset1<Packet>(lhs.coeff(row, i)), rhs.template packet<LoadMode,Packet>(i, col), res);
+  }
+};
+
+template<typename Lhs, typename Rhs, typename Packet, int LoadMode>
+struct etor_product_packet_impl<ColMajor, Dynamic, Lhs, Rhs, Packet, LoadMode>
+{
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Index row, Index col, const Lhs& lhs, const Rhs& rhs, Index innerDim, Packet& res)
+  {
+    res = pset1<Packet>(typename unpacket_traits<Packet>::type(0));
+    for(Index i = 0; i < innerDim; ++i)
+      res =  pmadd(lhs.template packet<LoadMode,Packet>(row, i), pset1<Packet>(rhs.coeff(i, col)), res);
+  }
+};
+
+
+/***************************************************************************
+* Triangular products
+***************************************************************************/
+template<int Mode, bool LhsIsTriangular,
+         typename Lhs, bool LhsIsVector,
+         typename Rhs, bool RhsIsVector>
+struct triangular_product_impl;
+
+template<typename Lhs, typename Rhs, int ProductTag>
+struct generic_product_impl<Lhs,Rhs,TriangularShape,DenseShape,ProductTag>
+  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,TriangularShape,DenseShape,ProductTag> >
+{
+  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
+
+  template<typename Dest>
+  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
+  {
+    triangular_product_impl<Lhs::Mode,true,typename Lhs::MatrixType,false,Rhs, Rhs::ColsAtCompileTime==1>
+        ::run(dst, lhs.nestedExpression(), rhs, alpha);
+  }
+};
+
+template<typename Lhs, typename Rhs, int ProductTag>
+struct generic_product_impl<Lhs,Rhs,DenseShape,TriangularShape,ProductTag>
+: generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,TriangularShape,ProductTag> >
+{
+  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
+
+  template<typename Dest>
+  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
+  {
+    triangular_product_impl<Rhs::Mode,false,Lhs,Lhs::RowsAtCompileTime==1, typename Rhs::MatrixType, false>::run(dst, lhs, rhs.nestedExpression(), alpha);
+  }
+};
+
+
+/***************************************************************************
+* SelfAdjoint products
+***************************************************************************/
+template <typename Lhs, int LhsMode, bool LhsIsVector,
+          typename Rhs, int RhsMode, bool RhsIsVector>
+struct selfadjoint_product_impl;
+
+template<typename Lhs, typename Rhs, int ProductTag>
+struct generic_product_impl<Lhs,Rhs,SelfAdjointShape,DenseShape,ProductTag>
+  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,SelfAdjointShape,DenseShape,ProductTag> >
+{
+  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
+
+  template<typename Dest>
+  static EIGEN_DEVICE_FUNC
+  void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
+  {
+    selfadjoint_product_impl<typename Lhs::MatrixType,Lhs::Mode,false,Rhs,0,Rhs::IsVectorAtCompileTime>::run(dst, lhs.nestedExpression(), rhs, alpha);
+  }
+};
+
+template<typename Lhs, typename Rhs, int ProductTag>
+struct generic_product_impl<Lhs,Rhs,DenseShape,SelfAdjointShape,ProductTag>
+: generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,SelfAdjointShape,ProductTag> >
+{
+  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
+
+  template<typename Dest>
+  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
+  {
+    selfadjoint_product_impl<Lhs,0,Lhs::IsVectorAtCompileTime,typename Rhs::MatrixType,Rhs::Mode,false>::run(dst, lhs, rhs.nestedExpression(), alpha);
+  }
+};
+
+
+/***************************************************************************
+* Diagonal products
+***************************************************************************/
+
+template<typename MatrixType, typename DiagonalType, typename Derived, int ProductOrder>
+struct diagonal_product_evaluator_base
+  : evaluator_base<Derived>
+{
+   typedef typename ScalarBinaryOpTraits<typename MatrixType::Scalar, typename DiagonalType::Scalar>::ReturnType Scalar;
+public:
+  enum {
+    CoeffReadCost = int(NumTraits<Scalar>::MulCost) + int(evaluator<MatrixType>::CoeffReadCost) + int(evaluator<DiagonalType>::CoeffReadCost),
+
+    MatrixFlags = evaluator<MatrixType>::Flags,
+    DiagFlags = evaluator<DiagonalType>::Flags,
+
+    StorageOrder_ = (Derived::MaxRowsAtCompileTime==1 && Derived::MaxColsAtCompileTime!=1) ? RowMajor
+                  : (Derived::MaxColsAtCompileTime==1 && Derived::MaxRowsAtCompileTime!=1) ? ColMajor
+                  : MatrixFlags & RowMajorBit ? RowMajor : ColMajor,
+    SameStorageOrder_ = StorageOrder_ == (MatrixFlags & RowMajorBit ? RowMajor : ColMajor),
+
+    ScalarAccessOnDiag_ =  !((int(StorageOrder_) == ColMajor && int(ProductOrder) == OnTheLeft)
+                           ||(int(StorageOrder_) == RowMajor && int(ProductOrder) == OnTheRight)),
+    SameTypes_ = is_same<typename MatrixType::Scalar, typename DiagonalType::Scalar>::value,
+    // FIXME currently we need same types, but in the future the next rule should be the one
+    //Vectorizable_ = bool(int(MatrixFlags)&PacketAccessBit) && ((!_PacketOnDiag) || (SameTypes_ && bool(int(DiagFlags)&PacketAccessBit))),
+    Vectorizable_ =   bool(int(MatrixFlags)&PacketAccessBit)
+                  &&  SameTypes_
+                  && (SameStorageOrder_ || (MatrixFlags&LinearAccessBit)==LinearAccessBit)
+                  && (ScalarAccessOnDiag_ || (bool(int(DiagFlags)&PacketAccessBit))),
+    LinearAccessMask_ = (MatrixType::RowsAtCompileTime==1 || MatrixType::ColsAtCompileTime==1) ? LinearAccessBit : 0,
+    Flags = ((HereditaryBits|LinearAccessMask_) & (unsigned int)(MatrixFlags)) | (Vectorizable_ ? PacketAccessBit : 0),
+    Alignment = evaluator<MatrixType>::Alignment,
+
+    AsScalarProduct =     (DiagonalType::SizeAtCompileTime==1)
+                      ||  (DiagonalType::SizeAtCompileTime==Dynamic && MatrixType::RowsAtCompileTime==1 && ProductOrder==OnTheLeft)
+                      ||  (DiagonalType::SizeAtCompileTime==Dynamic && MatrixType::ColsAtCompileTime==1 && ProductOrder==OnTheRight)
+  };
+
+  EIGEN_DEVICE_FUNC diagonal_product_evaluator_base(const MatrixType &mat, const DiagonalType &diag)
+    : m_diagImpl(diag), m_matImpl(mat)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(NumTraits<Scalar>::MulCost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index idx) const
+  {
+    if(AsScalarProduct)
+      return m_diagImpl.coeff(0) * m_matImpl.coeff(idx);
+    else
+      return m_diagImpl.coeff(idx) * m_matImpl.coeff(idx);
+  }
+
+protected:
+  template<int LoadMode,typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet_impl(Index row, Index col, Index id, internal::true_type) const
+  {
+    return internal::pmul(m_matImpl.template packet<LoadMode,PacketType>(row, col),
+                          internal::pset1<PacketType>(m_diagImpl.coeff(id)));
+  }
+
+  template<int LoadMode,typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet_impl(Index row, Index col, Index id, internal::false_type) const
+  {
+    enum {
+      InnerSize = (MatrixType::Flags & RowMajorBit) ? MatrixType::ColsAtCompileTime : MatrixType::RowsAtCompileTime,
+      DiagonalPacketLoadMode = plain_enum_min(LoadMode,((InnerSize%16) == 0) ? int(Aligned16) : int(evaluator<DiagonalType>::Alignment)) // FIXME hardcoded 16!!
+    };
+    return internal::pmul(m_matImpl.template packet<LoadMode,PacketType>(row, col),
+                          m_diagImpl.template packet<DiagonalPacketLoadMode,PacketType>(id));
+  }
+
+  evaluator<DiagonalType> m_diagImpl;
+  evaluator<MatrixType>   m_matImpl;
+};
+
+// diagonal * dense
+template<typename Lhs, typename Rhs, int ProductKind, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DiagonalShape, DenseShape>
+  : diagonal_product_evaluator_base<Rhs, typename Lhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheLeft>
+{
+  typedef diagonal_product_evaluator_base<Rhs, typename Lhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheLeft> Base;
+  using Base::m_diagImpl;
+  using Base::m_matImpl;
+  using Base::coeff;
+  typedef typename Base::Scalar Scalar;
+
+  typedef Product<Lhs, Rhs, ProductKind> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+  typedef typename Lhs::DiagonalVectorType DiagonalType;
+
+
+  enum { StorageOrder = Base::StorageOrder_ };
+
+  EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
+    : Base(xpr.rhs(), xpr.lhs().diagonal())
+  {
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
+  {
+    return m_diagImpl.coeff(row) * m_matImpl.coeff(row, col);
+  }
+
+#ifndef EIGEN_GPUCC
+  template<int LoadMode,typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const
+  {
+    // FIXME: NVCC used to complain about the template keyword, but we have to check whether this is still the case.
+    // See also similar calls below.
+    return this->template packet_impl<LoadMode,PacketType>(row,col, row,
+                                 std::conditional_t<int(StorageOrder)==RowMajor, internal::true_type, internal::false_type>());
+  }
+
+  template<int LoadMode,typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet(Index idx) const
+  {
+    return packet<LoadMode,PacketType>(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
+  }
+#endif
+};
+
+// dense * diagonal
+template<typename Lhs, typename Rhs, int ProductKind, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, ProductKind>, ProductTag, DenseShape, DiagonalShape>
+  : diagonal_product_evaluator_base<Lhs, typename Rhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheRight>
+{
+  typedef diagonal_product_evaluator_base<Lhs, typename Rhs::DiagonalVectorType, Product<Lhs, Rhs, LazyProduct>, OnTheRight> Base;
+  using Base::m_diagImpl;
+  using Base::m_matImpl;
+  using Base::coeff;
+  typedef typename Base::Scalar Scalar;
+
+  typedef Product<Lhs, Rhs, ProductKind> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+
+  enum { StorageOrder = Base::StorageOrder_ };
+
+  EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
+    : Base(xpr.lhs(), xpr.rhs().diagonal())
+  {
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar coeff(Index row, Index col) const
+  {
+    return m_matImpl.coeff(row, col) * m_diagImpl.coeff(col);
+  }
+
+#ifndef EIGEN_GPUCC
+  template<int LoadMode,typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet(Index row, Index col) const
+  {
+    return this->template packet_impl<LoadMode,PacketType>(row,col, col,
+                                 std::conditional_t<int(StorageOrder)==ColMajor, internal::true_type, internal::false_type>());
+  }
+
+  template<int LoadMode,typename PacketType>
+  EIGEN_STRONG_INLINE PacketType packet(Index idx) const
+  {
+    return packet<LoadMode,PacketType>(int(StorageOrder)==ColMajor?idx:0,int(StorageOrder)==ColMajor?0:idx);
+  }
+#endif
+};
+
+/***************************************************************************
+* Products with permutation matrices
+***************************************************************************/
+
+/** \internal
+  * \class permutation_matrix_product
+  * Internal helper class implementing the product between a permutation matrix and a matrix.
+  * This class is specialized for DenseShape below and for SparseShape in SparseCore/SparsePermutation.h
+  */
+template<typename ExpressionType, int Side, bool Transposed, typename ExpressionShape>
+struct permutation_matrix_product;
+
+template<typename ExpressionType, int Side, bool Transposed>
+struct permutation_matrix_product<ExpressionType, Side, Transposed, DenseShape>
+{
+    typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
+    typedef remove_all_t<MatrixType> MatrixTypeCleaned;
+
+    template<typename Dest, typename PermutationType>
+    static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Dest& dst, const PermutationType& perm, const ExpressionType& xpr)
+    {
+      MatrixType mat(xpr);
+      const Index n = Side==OnTheLeft ? mat.rows() : mat.cols();
+      // FIXME we need an is_same for expression that is not sensitive to constness. For instance
+      // is_same_xpr<Block<const Matrix>, Block<Matrix> >::value should be true.
+      //if(is_same<MatrixTypeCleaned,Dest>::value && extract_data(dst) == extract_data(mat))
+      if(is_same_dense(dst, mat))
+      {
+        // apply the permutation inplace
+        Matrix<bool,PermutationType::RowsAtCompileTime,1,0,PermutationType::MaxRowsAtCompileTime> mask(perm.size());
+        mask.fill(false);
+        Index r = 0;
+        while(r < perm.size())
+        {
+          // search for the next seed
+          while(r<perm.size() && mask[r]) r++;
+          if(r>=perm.size())
+            break;
+          // we got one, let's follow it until we are back to the seed
+          Index k0 = r++;
+          Index kPrev = k0;
+          mask.coeffRef(k0) = true;
+          for(Index k=perm.indices().coeff(k0); k!=k0; k=perm.indices().coeff(k))
+          {
+                  Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>(dst, k)
+            .swap(Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
+                       (dst,((Side==OnTheLeft) ^ Transposed) ? k0 : kPrev));
+
+            mask.coeffRef(k) = true;
+            kPrev = k;
+          }
+        }
+      }
+      else
+      {
+        for(Index i = 0; i < n; ++i)
+        {
+          Block<Dest, Side==OnTheLeft ? 1 : Dest::RowsAtCompileTime, Side==OnTheRight ? 1 : Dest::ColsAtCompileTime>
+               (dst, ((Side==OnTheLeft) ^ Transposed) ? perm.indices().coeff(i) : i)
+
+          =
+
+          Block<const MatrixTypeCleaned,Side==OnTheLeft ? 1 : MatrixTypeCleaned::RowsAtCompileTime,Side==OnTheRight ? 1 : MatrixTypeCleaned::ColsAtCompileTime>
+               (mat, ((Side==OnTheRight) ^ Transposed) ? perm.indices().coeff(i) : i);
+        }
+      }
+    }
+};
+
+template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Rhs, PermutationShape, MatrixShape, ProductTag>
+{
+  template<typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    permutation_matrix_product<Rhs, OnTheLeft, false, MatrixShape>::run(dst, lhs, rhs);
+  }
+};
+
+template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Rhs, MatrixShape, PermutationShape, ProductTag>
+{
+  template<typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    permutation_matrix_product<Lhs, OnTheRight, false, MatrixShape>::run(dst, rhs, lhs);
+  }
+};
+
+template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Inverse<Lhs>, Rhs, PermutationShape, MatrixShape, ProductTag>
+{
+  template<typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Inverse<Lhs>& lhs, const Rhs& rhs)
+  {
+    permutation_matrix_product<Rhs, OnTheLeft, true, MatrixShape>::run(dst, lhs.nestedExpression(), rhs);
+  }
+};
+
+template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Inverse<Rhs>, MatrixShape, PermutationShape, ProductTag>
+{
+  template<typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Inverse<Rhs>& rhs)
+  {
+    permutation_matrix_product<Lhs, OnTheRight, true, MatrixShape>::run(dst, rhs.nestedExpression(), lhs);
+  }
+};
+
+
+/***************************************************************************
+* Products with transpositions matrices
+***************************************************************************/
+
+// FIXME could we unify Transpositions and Permutation into a single "shape"??
+
+/** \internal
+  * \class transposition_matrix_product
+  * Internal helper class implementing the product between a permutation matrix and a matrix.
+  */
+template<typename ExpressionType, int Side, bool Transposed, typename ExpressionShape>
+struct transposition_matrix_product
+{
+  typedef typename nested_eval<ExpressionType, 1>::type MatrixType;
+  typedef remove_all_t<MatrixType> MatrixTypeCleaned;
+
+  template<typename Dest, typename TranspositionType>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Dest& dst, const TranspositionType& tr, const ExpressionType& xpr)
+  {
+    MatrixType mat(xpr);
+    typedef typename TranspositionType::StorageIndex StorageIndex;
+    const Index size = tr.size();
+    StorageIndex j = 0;
+
+    if(!is_same_dense(dst,mat))
+      dst = mat;
+
+    for(Index k=(Transposed?size-1:0) ; Transposed?k>=0:k<size ; Transposed?--k:++k)
+      if(Index(j=tr.coeff(k))!=k)
+      {
+        if(Side==OnTheLeft)        dst.row(k).swap(dst.row(j));
+        else if(Side==OnTheRight)  dst.col(k).swap(dst.col(j));
+      }
+  }
+};
+
+template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Rhs, TranspositionsShape, MatrixShape, ProductTag>
+{
+  template<typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    transposition_matrix_product<Rhs, OnTheLeft, false, MatrixShape>::run(dst, lhs, rhs);
+  }
+};
+
+template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Rhs, MatrixShape, TranspositionsShape, ProductTag>
+{
+  template<typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    transposition_matrix_product<Lhs, OnTheRight, false, MatrixShape>::run(dst, rhs, lhs);
+  }
+};
+
+
+template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Transpose<Lhs>, Rhs, TranspositionsShape, MatrixShape, ProductTag>
+{
+  template<typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Transpose<Lhs>& lhs, const Rhs& rhs)
+  {
+    transposition_matrix_product<Rhs, OnTheLeft, true, MatrixShape>::run(dst, lhs.nestedExpression(), rhs);
+  }
+};
+
+template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Transpose<Rhs>, MatrixShape, TranspositionsShape, ProductTag>
+{
+  template<typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Transpose<Rhs>& rhs)
+  {
+    transposition_matrix_product<Lhs, OnTheRight, true, MatrixShape>::run(dst, rhs.nestedExpression(), lhs);
+  }
+};
+
+/***************************************************************************
+* skew symmetric products
+* for now we just call the generic implementation
+***************************************************************************/
+template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Rhs, SkewSymmetricShape, MatrixShape, ProductTag>
+{
+  template<typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    generic_product_impl<typename Lhs::DenseMatrixType , Rhs, DenseShape, MatrixShape, ProductTag>::evalTo(dst, lhs, rhs);
+  }
+};
+
+template<typename Lhs, typename Rhs, int ProductTag, typename MatrixShape>
+struct generic_product_impl<Lhs, Rhs, MatrixShape, SkewSymmetricShape, ProductTag>
+{
+  template<typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    generic_product_impl<Lhs, typename Rhs::DenseMatrixType, MatrixShape, DenseShape, ProductTag>::evalTo(dst, lhs, rhs);
+  }
+};
+
+template<typename Lhs, typename Rhs, int ProductTag>
+struct generic_product_impl<Lhs, Rhs, SkewSymmetricShape, SkewSymmetricShape, ProductTag>
+{
+  template<typename Dest>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    generic_product_impl<typename Lhs::DenseMatrixType, typename Rhs::DenseMatrixType, DenseShape, DenseShape, ProductTag>::evalTo(dst, lhs, rhs);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_PRODUCT_EVALUATORS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Random.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Random.h
new file mode 100644
index 0000000000000000000000000000000000000000..fab6889dca6de86af53db357a3d76eef52a4cc40
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Random.h
@@ -0,0 +1,219 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_RANDOM_H
+#define EIGEN_RANDOM_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename Scalar> struct scalar_random_op {
+  inline const Scalar operator() () const { return random<Scalar>(); }
+};
+
+template<typename Scalar>
+struct functor_traits<scalar_random_op<Scalar> >
+{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false, IsRepeatable = false }; };
+
+} // end namespace internal
+
+/** \returns a random matrix expression
+  *
+  * Numbers are uniformly spread through their whole definition range for integer types,
+  * and in the [-1:1] range for floating point scalar types.
+  * 
+  * The parameters \a rows and \a cols are the number of rows and of columns of
+  * the returned matrix. Must be compatible with this MatrixBase type.
+  *
+  * \not_reentrant
+  * 
+  * This variant is meant to be used for dynamic-size matrix types. For fixed-size types,
+  * it is redundant to pass \a rows and \a cols as arguments, so Random() should be used
+  * instead.
+  * 
+  *
+  * Example: \include MatrixBase_random_int_int.cpp
+  * Output: \verbinclude MatrixBase_random_int_int.out
+  *
+  * This expression has the "evaluate before nesting" flag so that it will be evaluated into
+  * a temporary matrix whenever it is nested in a larger expression. This prevents unexpected
+  * behavior with expressions involving random matrices.
+  * 
+  * See DenseBase::NullaryExpr(Index, const CustomNullaryOp&) for an example using C++11 random generators.
+  *
+  * \sa DenseBase::setRandom(), DenseBase::Random(Index), DenseBase::Random()
+  */
+template<typename Derived>
+inline const typename DenseBase<Derived>::RandomReturnType
+DenseBase<Derived>::Random(Index rows, Index cols)
+{
+  return NullaryExpr(rows, cols, internal::scalar_random_op<Scalar>());
+}
+
+/** \returns a random vector expression
+  *
+  * Numbers are uniformly spread through their whole definition range for integer types,
+  * and in the [-1:1] range for floating point scalar types.
+  *
+  * The parameter \a size is the size of the returned vector.
+  * Must be compatible with this MatrixBase type.
+  *
+  * \only_for_vectors
+  * \not_reentrant
+  *
+  * This variant is meant to be used for dynamic-size vector types. For fixed-size types,
+  * it is redundant to pass \a size as argument, so Random() should be used
+  * instead.
+  *
+  * Example: \include MatrixBase_random_int.cpp
+  * Output: \verbinclude MatrixBase_random_int.out
+  *
+  * This expression has the "evaluate before nesting" flag so that it will be evaluated into
+  * a temporary vector whenever it is nested in a larger expression. This prevents unexpected
+  * behavior with expressions involving random matrices.
+  *
+  * \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random()
+  */
+template<typename Derived>
+inline const typename DenseBase<Derived>::RandomReturnType
+DenseBase<Derived>::Random(Index size)
+{
+  return NullaryExpr(size, internal::scalar_random_op<Scalar>());
+}
+
+/** \returns a fixed-size random matrix or vector expression
+  *
+  * Numbers are uniformly spread through their whole definition range for integer types,
+  * and in the [-1:1] range for floating point scalar types.
+  * 
+  * This variant is only for fixed-size MatrixBase types. For dynamic-size types, you
+  * need to use the variants taking size arguments.
+  *
+  * Example: \include MatrixBase_random.cpp
+  * Output: \verbinclude MatrixBase_random.out
+  *
+  * This expression has the "evaluate before nesting" flag so that it will be evaluated into
+  * a temporary matrix whenever it is nested in a larger expression. This prevents unexpected
+  * behavior with expressions involving random matrices.
+  * 
+  * \not_reentrant
+  *
+  * \sa DenseBase::setRandom(), DenseBase::Random(Index,Index), DenseBase::Random(Index)
+  */
+template<typename Derived>
+inline const typename DenseBase<Derived>::RandomReturnType
+DenseBase<Derived>::Random()
+{
+  return NullaryExpr(RowsAtCompileTime, ColsAtCompileTime, internal::scalar_random_op<Scalar>());
+}
+
+/** Sets all coefficients in this expression to random values.
+  *
+  * Numbers are uniformly spread through their whole definition range for integer types,
+  * and in the [-1:1] range for floating point scalar types.
+  * 
+  * \not_reentrant
+  * 
+  * Example: \include MatrixBase_setRandom.cpp
+  * Output: \verbinclude MatrixBase_setRandom.out
+  *
+  * \sa class CwiseNullaryOp, setRandom(Index), setRandom(Index,Index)
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline Derived& DenseBase<Derived>::setRandom()
+{
+  return *this = Random(rows(), cols());
+}
+
+/** Resizes to the given \a newSize, and sets all coefficients in this expression to random values.
+  *
+  * Numbers are uniformly spread through their whole definition range for integer types,
+  * and in the [-1:1] range for floating point scalar types.
+  * 
+  * \only_for_vectors
+  * \not_reentrant
+  *
+  * Example: \include Matrix_setRandom_int.cpp
+  * Output: \verbinclude Matrix_setRandom_int.out
+  *
+  * \sa DenseBase::setRandom(), setRandom(Index,Index), class CwiseNullaryOp, DenseBase::Random()
+  */
+template<typename Derived>
+EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setRandom(Index newSize)
+{
+  resize(newSize);
+  return setRandom();
+}
+
+/** Resizes to the given size, and sets all coefficients in this expression to random values.
+  *
+  * Numbers are uniformly spread through their whole definition range for integer types,
+  * and in the [-1:1] range for floating point scalar types.
+  *
+  * \not_reentrant
+  * 
+  * \param rows the new number of rows
+  * \param cols the new number of columns
+  *
+  * Example: \include Matrix_setRandom_int_int.cpp
+  * Output: \verbinclude Matrix_setRandom_int_int.out
+  *
+  * \sa DenseBase::setRandom(), setRandom(Index), class CwiseNullaryOp, DenseBase::Random()
+  */
+template<typename Derived>
+EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setRandom(Index rows, Index cols)
+{
+  resize(rows, cols);
+  return setRandom();
+}
+
+/** Resizes to the given size, changing only the number of columns, and sets all
+  * coefficients in this expression to random values. For the parameter of type
+  * NoChange_t, just pass the special value \c NoChange.
+  *
+  * Numbers are uniformly spread through their whole definition range for integer types,
+  * and in the [-1:1] range for floating point scalar types.
+  *
+  * \not_reentrant
+  *
+  * \sa DenseBase::setRandom(), setRandom(Index), setRandom(Index, NoChange_t), class CwiseNullaryOp, DenseBase::Random()
+  */
+template<typename Derived>
+EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setRandom(NoChange_t, Index cols)
+{
+  return setRandom(rows(), cols);
+}
+
+/** Resizes to the given size, changing only the number of rows, and sets all
+  * coefficients in this expression to random values. For the parameter of type
+  * NoChange_t, just pass the special value \c NoChange.
+  *
+  * Numbers are uniformly spread through their whole definition range for integer types,
+  * and in the [-1:1] range for floating point scalar types.
+  *
+  * \not_reentrant
+  *
+  * \sa DenseBase::setRandom(), setRandom(Index), setRandom(NoChange_t, Index), class CwiseNullaryOp, DenseBase::Random()
+  */
+template<typename Derived>
+EIGEN_STRONG_INLINE Derived&
+PlainObjectBase<Derived>::setRandom(Index rows, NoChange_t)
+{
+  return setRandom(rows, cols());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_RANDOM_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Redux.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Redux.h
new file mode 100644
index 0000000000000000000000000000000000000000..8f5f761ac1c5572f111f3684ec26e39c88f824d6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Redux.h
@@ -0,0 +1,602 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REDUX_H
+#define EIGEN_REDUX_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+// TODO
+//  * implement other kind of vectorization
+//  * factorize code
+
+/***************************************************************************
+* Part 1 : the logic deciding a strategy for vectorization and unrolling
+***************************************************************************/
+
+template<typename Func, typename Evaluator>
+struct redux_traits
+{
+public:
+    typedef typename find_best_packet<typename Evaluator::Scalar,Evaluator::SizeAtCompileTime>::type PacketType;
+  enum {
+    PacketSize = unpacket_traits<PacketType>::size,
+    InnerMaxSize = int(Evaluator::IsRowMajor)
+                 ? Evaluator::MaxColsAtCompileTime
+                 : Evaluator::MaxRowsAtCompileTime,
+    OuterMaxSize = int(Evaluator::IsRowMajor)
+                 ? Evaluator::MaxRowsAtCompileTime
+                 : Evaluator::MaxColsAtCompileTime,
+    SliceVectorizedWork = int(InnerMaxSize)==Dynamic ? Dynamic
+                        : int(OuterMaxSize)==Dynamic ? (int(InnerMaxSize)>=int(PacketSize) ? Dynamic : 0)
+                        : (int(InnerMaxSize)/int(PacketSize)) * int(OuterMaxSize)
+  };
+
+  enum {
+    MayLinearize = (int(Evaluator::Flags) & LinearAccessBit),
+    MightVectorize = (int(Evaluator::Flags)&ActualPacketAccessBit)
+                  && (functor_traits<Func>::PacketAccess),
+    MayLinearVectorize = bool(MightVectorize) && bool(MayLinearize),
+    MaySliceVectorize  = bool(MightVectorize) && (int(SliceVectorizedWork)==Dynamic || int(SliceVectorizedWork)>=3)
+  };
+
+public:
+  enum {
+    Traversal = int(MayLinearVectorize) ? int(LinearVectorizedTraversal)
+              : int(MaySliceVectorize)  ? int(SliceVectorizedTraversal)
+              : int(MayLinearize)       ? int(LinearTraversal)
+                                        : int(DefaultTraversal)
+  };
+
+public:
+  enum {
+    Cost = Evaluator::SizeAtCompileTime == Dynamic ? HugeCost
+         : int(Evaluator::SizeAtCompileTime) * int(Evaluator::CoeffReadCost) + (Evaluator::SizeAtCompileTime-1) * functor_traits<Func>::Cost,
+    UnrollingLimit = EIGEN_UNROLLING_LIMIT * (int(Traversal) == int(DefaultTraversal) ? 1 : int(PacketSize))
+  };
+
+public:
+  enum {
+    Unrolling = Cost <= UnrollingLimit ? CompleteUnrolling : NoUnrolling
+  };
+  
+#ifdef EIGEN_DEBUG_ASSIGN
+  static void debug()
+  {
+    std::cerr << "Xpr: " << typeid(typename Evaluator::XprType).name() << std::endl;
+    std::cerr.setf(std::ios::hex, std::ios::basefield);
+    EIGEN_DEBUG_VAR(Evaluator::Flags)
+    std::cerr.unsetf(std::ios::hex);
+    EIGEN_DEBUG_VAR(InnerMaxSize)
+    EIGEN_DEBUG_VAR(OuterMaxSize)
+    EIGEN_DEBUG_VAR(SliceVectorizedWork)
+    EIGEN_DEBUG_VAR(PacketSize)
+    EIGEN_DEBUG_VAR(MightVectorize)
+    EIGEN_DEBUG_VAR(MayLinearVectorize)
+    EIGEN_DEBUG_VAR(MaySliceVectorize)
+    std::cerr << "Traversal" << " = " << Traversal << " (" << demangle_traversal(Traversal) << ")" << std::endl;
+    EIGEN_DEBUG_VAR(UnrollingLimit)
+    std::cerr << "Unrolling" << " = " << Unrolling << " (" << demangle_unrolling(Unrolling) << ")" << std::endl;
+    std::cerr << std::endl;
+  }
+#endif
+};
+
+/***************************************************************************
+* Part 2 : unrollers
+***************************************************************************/
+
+/*** no vectorization ***/
+
+template<typename Func, typename Evaluator, Index Start, Index Length>
+struct redux_novec_unroller
+{
+  static constexpr Index HalfLength = Length/2;
+
+  typedef typename Evaluator::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar run(const Evaluator &eval, const Func& func)
+  {
+    return func(redux_novec_unroller<Func, Evaluator, Start, HalfLength>::run(eval,func),
+                redux_novec_unroller<Func, Evaluator, Start+HalfLength, Length-HalfLength>::run(eval,func));
+  }
+};
+
+template<typename Func, typename Evaluator, Index Start>
+struct redux_novec_unroller<Func, Evaluator, Start, 1>
+{
+  static constexpr Index outer = Start / Evaluator::InnerSizeAtCompileTime;
+  static constexpr Index inner = Start % Evaluator::InnerSizeAtCompileTime;
+
+  typedef typename Evaluator::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar run(const Evaluator &eval, const Func&)
+  {
+    return eval.coeffByOuterInner(outer, inner);
+  }
+};
+
+// This is actually dead code and will never be called. It is required
+// to prevent false warnings regarding failed inlining though
+// for 0 length run() will never be called at all.
+template<typename Func, typename Evaluator, Index Start>
+struct redux_novec_unroller<Func, Evaluator, Start, 0>
+{
+  typedef typename Evaluator::Scalar Scalar;
+  EIGEN_DEVICE_FUNC 
+  static EIGEN_STRONG_INLINE Scalar run(const Evaluator&, const Func&) { return Scalar(); }
+};
+
+template<typename Func, typename Evaluator, Index Start, Index Length>
+struct redux_novec_linear_unroller
+{
+  static constexpr Index HalfLength = Length/2;
+
+  typedef typename Evaluator::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar run(const Evaluator &eval, const Func& func)
+  {
+    return func(redux_novec_linear_unroller<Func, Evaluator, Start, HalfLength>::run(eval,func),
+                redux_novec_linear_unroller<Func, Evaluator, Start+HalfLength, Length-HalfLength>::run(eval,func));
+  }
+};
+
+template<typename Func, typename Evaluator, Index Start>
+struct redux_novec_linear_unroller<Func, Evaluator, Start, 1>
+{
+  typedef typename Evaluator::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Scalar run(const Evaluator &eval, const Func&)
+  {
+    return eval.coeff(Start);
+  }
+};
+
+// This is actually dead code and will never be called. It is required
+// to prevent false warnings regarding failed inlining though
+// for 0 length run() will never be called at all.
+template<typename Func, typename Evaluator, Index Start>
+struct redux_novec_linear_unroller<Func, Evaluator, Start, 0>
+{
+  typedef typename Evaluator::Scalar Scalar;
+  EIGEN_DEVICE_FUNC 
+  static EIGEN_STRONG_INLINE Scalar run(const Evaluator&, const Func&) { return Scalar(); }
+};
+
+/*** vectorization ***/
+
+template<typename Func, typename Evaluator, Index Start, Index Length>
+struct redux_vec_unroller
+{
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE PacketType run(const Evaluator &eval, const Func& func)
+  {
+    constexpr Index HalfLength = Length/2;
+
+    return func.packetOp(
+            redux_vec_unroller<Func, Evaluator, Start, HalfLength>::template run<PacketType>(eval,func),
+            redux_vec_unroller<Func, Evaluator, Start+HalfLength, Length-HalfLength>::template run<PacketType>(eval,func) );
+  }
+};
+
+template<typename Func, typename Evaluator, Index Start>
+struct redux_vec_unroller<Func, Evaluator, Start, 1>
+{
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE PacketType run(const Evaluator &eval, const Func&)
+  {
+    constexpr Index PacketSize = unpacket_traits<PacketType>::size;
+    constexpr Index index = Start * PacketSize;
+    constexpr Index outer = index / int(Evaluator::InnerSizeAtCompileTime);
+    constexpr Index inner = index % int(Evaluator::InnerSizeAtCompileTime);
+    constexpr int alignment = Evaluator::Alignment;
+
+    return eval.template packetByOuterInner<alignment,PacketType>(outer, inner);
+  }
+};
+
+template<typename Func, typename Evaluator, Index Start, Index Length>
+struct redux_vec_linear_unroller
+{
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE PacketType run(const Evaluator &eval, const Func& func)
+  {
+    constexpr Index HalfLength = Length/2;
+
+    return func.packetOp(
+            redux_vec_linear_unroller<Func, Evaluator, Start, HalfLength>::template run<PacketType>(eval,func),
+            redux_vec_linear_unroller<Func, Evaluator, Start+HalfLength, Length-HalfLength>::template run<PacketType>(eval,func) );
+  }
+};
+
+template<typename Func, typename Evaluator, Index Start>
+struct redux_vec_linear_unroller<Func, Evaluator, Start, 1>
+{
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE PacketType run(const Evaluator &eval, const Func&)
+  {
+    constexpr Index PacketSize = unpacket_traits<PacketType>::size;
+    constexpr Index index = (Start * PacketSize);
+    constexpr int alignment = Evaluator::Alignment;
+    return eval.template packet<alignment,PacketType>(index);
+  }
+};
+
+/***************************************************************************
+* Part 3 : implementation of all cases
+***************************************************************************/
+
+template<typename Func, typename Evaluator,
+         int Traversal = redux_traits<Func, Evaluator>::Traversal,
+         int Unrolling = redux_traits<Func, Evaluator>::Unrolling
+>
+struct redux_impl;
+
+template<typename Func, typename Evaluator>
+struct redux_impl<Func, Evaluator, DefaultTraversal, NoUnrolling>
+{
+  typedef typename Evaluator::Scalar Scalar;
+
+  template<typename XprType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
+  Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr)
+  {
+    eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix");
+    Scalar res = eval.coeffByOuterInner(0, 0);
+    for(Index i = 1; i < xpr.innerSize(); ++i)
+      res = func(res, eval.coeffByOuterInner(0, i));
+    for(Index i = 1; i < xpr.outerSize(); ++i)
+      for(Index j = 0; j < xpr.innerSize(); ++j)
+        res = func(res, eval.coeffByOuterInner(i, j));
+    return res;
+  }
+};
+
+template<typename Func, typename Evaluator>
+struct redux_impl<Func, Evaluator, LinearTraversal, NoUnrolling>
+{
+  typedef typename Evaluator::Scalar Scalar;
+
+  template<typename XprType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
+  Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr)
+  {
+    eigen_assert(xpr.size()>0 && "you are using an empty matrix");
+    Scalar res = eval.coeff(0);
+    for(Index k = 1; k < xpr.size(); ++k)
+      res = func(res, eval.coeff(k));
+    return res;
+  }
+};
+
+template<typename Func, typename Evaluator>
+struct redux_impl<Func,Evaluator, DefaultTraversal, CompleteUnrolling>
+  : redux_novec_unroller<Func,Evaluator, 0, Evaluator::SizeAtCompileTime>
+{
+  typedef redux_novec_unroller<Func,Evaluator, 0, Evaluator::SizeAtCompileTime> Base;
+  typedef typename Evaluator::Scalar Scalar;
+  template<typename XprType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
+  Scalar run(const Evaluator &eval, const Func& func, const XprType& /*xpr*/)
+  {
+    return Base::run(eval,func);
+  }
+};
+
+template<typename Func, typename Evaluator>
+struct redux_impl<Func,Evaluator, LinearTraversal, CompleteUnrolling>
+  : redux_novec_linear_unroller<Func,Evaluator, 0, Evaluator::SizeAtCompileTime>
+{
+  typedef redux_novec_linear_unroller<Func,Evaluator, 0, Evaluator::SizeAtCompileTime> Base;
+  typedef typename Evaluator::Scalar Scalar;
+  template<typename XprType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
+  Scalar run(const Evaluator &eval, const Func& func, const XprType& /*xpr*/)
+  {
+    return Base::run(eval,func);
+  }
+};
+
+template<typename Func, typename Evaluator>
+struct redux_impl<Func, Evaluator, LinearVectorizedTraversal, NoUnrolling>
+{
+  typedef typename Evaluator::Scalar Scalar;
+  typedef typename redux_traits<Func, Evaluator>::PacketType PacketScalar;
+
+  template<typename XprType>
+  static Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr)
+  {
+    const Index size = xpr.size();
+    
+    constexpr Index packetSize = redux_traits<Func, Evaluator>::PacketSize;
+    constexpr int packetAlignment = unpacket_traits<PacketScalar>::alignment;
+    constexpr int alignment0 = (bool(Evaluator::Flags & DirectAccessBit) && bool(packet_traits<Scalar>::AlignedOnScalar)) ? int(packetAlignment) : int(Unaligned);
+    constexpr int alignment = plain_enum_max(alignment0, Evaluator::Alignment);
+    const Index alignedStart = internal::first_default_aligned(xpr);
+    const Index alignedSize2 = ((size-alignedStart)/(2*packetSize))*(2*packetSize);
+    const Index alignedSize = ((size-alignedStart)/(packetSize))*(packetSize);
+    const Index alignedEnd2 = alignedStart + alignedSize2;
+    const Index alignedEnd  = alignedStart + alignedSize;
+    Scalar res;
+    if(alignedSize)
+    {
+      PacketScalar packet_res0 = eval.template packet<alignment,PacketScalar>(alignedStart);
+      if(alignedSize>packetSize) // we have at least two packets to partly unroll the loop
+      {
+        PacketScalar packet_res1 = eval.template packet<alignment,PacketScalar>(alignedStart+packetSize);
+        for(Index index = alignedStart + 2*packetSize; index < alignedEnd2; index += 2*packetSize)
+        {
+          packet_res0 = func.packetOp(packet_res0, eval.template packet<alignment,PacketScalar>(index));
+          packet_res1 = func.packetOp(packet_res1, eval.template packet<alignment,PacketScalar>(index+packetSize));
+        }
+
+        packet_res0 = func.packetOp(packet_res0,packet_res1);
+        if(alignedEnd>alignedEnd2)
+          packet_res0 = func.packetOp(packet_res0, eval.template packet<alignment,PacketScalar>(alignedEnd2));
+      }
+      res = func.predux(packet_res0);
+
+      for(Index index = 0; index < alignedStart; ++index)
+        res = func(res,eval.coeff(index));
+
+      for(Index index = alignedEnd; index < size; ++index)
+        res = func(res,eval.coeff(index));
+    }
+    else // too small to vectorize anything.
+         // since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize.
+    {
+      res = eval.coeff(0);
+      for(Index index = 1; index < size; ++index)
+        res = func(res,eval.coeff(index));
+    }
+
+    return res;
+  }
+};
+
+// NOTE: for SliceVectorizedTraversal we simply bypass unrolling
+template<typename Func, typename Evaluator, int Unrolling>
+struct redux_impl<Func, Evaluator, SliceVectorizedTraversal, Unrolling>
+{
+  typedef typename Evaluator::Scalar Scalar;
+  typedef typename redux_traits<Func, Evaluator>::PacketType PacketType;
+
+  template<typename XprType>
+  EIGEN_DEVICE_FUNC static Scalar run(const Evaluator &eval, const Func& func, const XprType& xpr)
+  {
+    eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix");
+    constexpr Index packetSize = redux_traits<Func, Evaluator>::PacketSize;
+    const Index innerSize = xpr.innerSize();
+    const Index outerSize = xpr.outerSize();
+    const Index packetedInnerSize = ((innerSize)/packetSize)*packetSize;
+    Scalar res;
+    if(packetedInnerSize)
+    {
+      PacketType packet_res = eval.template packet<Unaligned,PacketType>(0,0);
+      for(Index j=0; j<outerSize; ++j)
+        for(Index i=(j==0?packetSize:0); i<packetedInnerSize; i+=Index(packetSize))
+          packet_res = func.packetOp(packet_res, eval.template packetByOuterInner<Unaligned,PacketType>(j,i));
+
+      res = func.predux(packet_res);
+      for(Index j=0; j<outerSize; ++j)
+        for(Index i=packetedInnerSize; i<innerSize; ++i)
+          res = func(res, eval.coeffByOuterInner(j,i));
+    }
+    else // too small to vectorize anything.
+         // since this is dynamic-size hence inefficient anyway for such small sizes, don't try to optimize.
+    {
+      res = redux_impl<Func, Evaluator, DefaultTraversal, NoUnrolling>::run(eval, func, xpr);
+    }
+
+    return res;
+  }
+};
+
+template<typename Func, typename Evaluator>
+struct redux_impl<Func, Evaluator, LinearVectorizedTraversal, CompleteUnrolling>
+{
+  typedef typename Evaluator::Scalar Scalar;
+
+  typedef typename redux_traits<Func, Evaluator>::PacketType PacketType;
+  static constexpr Index PacketSize = redux_traits<Func, Evaluator>::PacketSize;
+  static constexpr Index  Size = Evaluator::SizeAtCompileTime;
+  static constexpr Index  VectorizedSize = (int(Size) / int(PacketSize)) * int(PacketSize);
+
+  template<typename XprType>
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE
+  Scalar run(const Evaluator &eval, const Func& func, const XprType &xpr)
+  {
+    EIGEN_ONLY_USED_FOR_DEBUG(xpr)
+    eigen_assert(xpr.rows()>0 && xpr.cols()>0 && "you are using an empty matrix");
+    if (VectorizedSize > 0) {
+      Scalar res = func.predux(redux_vec_linear_unroller<Func, Evaluator, 0, Size / PacketSize>::template run<PacketType>(eval,func));
+      if (VectorizedSize != Size)
+        res = func(res,redux_novec_linear_unroller<Func, Evaluator, VectorizedSize, Size-VectorizedSize>::run(eval,func));
+      return res;
+    }
+    else {
+      return redux_novec_linear_unroller<Func, Evaluator, 0, Size>::run(eval,func);
+    }
+  }
+};
+
+// evaluator adaptor
+template<typename XprType_>
+class redux_evaluator : public internal::evaluator<XprType_>
+{
+  typedef internal::evaluator<XprType_> Base;
+public:
+  typedef XprType_ XprType;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit redux_evaluator(const XprType &xpr) : Base(xpr) {}
+  
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+  typedef typename XprType::PacketScalar PacketScalar;
+  
+  enum {
+    MaxRowsAtCompileTime = XprType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = XprType::MaxColsAtCompileTime,
+    // TODO we should not remove DirectAccessBit and rather find an elegant way to query the alignment offset at runtime from the evaluator
+    Flags = Base::Flags & ~DirectAccessBit,
+    IsRowMajor = XprType::IsRowMajor,
+    SizeAtCompileTime = XprType::SizeAtCompileTime,
+    InnerSizeAtCompileTime = XprType::InnerSizeAtCompileTime
+  };
+  
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  CoeffReturnType coeffByOuterInner(Index outer, Index inner) const
+  { return Base::coeff(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
+  
+  template<int LoadMode, typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  PacketType packetByOuterInner(Index outer, Index inner) const
+  { return Base::template packet<LoadMode,PacketType>(IsRowMajor ? outer : inner, IsRowMajor ? inner : outer); }
+  
+};
+
+} // end namespace internal
+
+/***************************************************************************
+* Part 4 : public API
+***************************************************************************/
+
+
+/** \returns the result of a full redux operation on the whole matrix or vector using \a func
+  *
+  * The template parameter \a BinaryOp is the type of the functor \a func which must be
+  * an associative operator. Both current C++98 and C++11 functor styles are handled.
+  *
+  * \warning the matrix must be not empty, otherwise an assertion is triggered.
+  *
+  * \sa DenseBase::sum(), DenseBase::minCoeff(), DenseBase::maxCoeff(), MatrixBase::colwise(), MatrixBase::rowwise()
+  */
+template<typename Derived>
+template<typename Func>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
+DenseBase<Derived>::redux(const Func& func) const
+{
+  eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
+
+  typedef typename internal::redux_evaluator<Derived> ThisEvaluator;
+  ThisEvaluator thisEval(derived());
+
+  // The initial expression is passed to the reducer as an additional argument instead of
+  // passing it as a member of redux_evaluator to help  
+  return internal::redux_impl<Func, ThisEvaluator>::run(thisEval, func, derived());
+}
+
+/** \returns the minimum of all coefficients of \c *this.
+  * In case \c *this contains NaN, NaNPropagation determines the behavior:
+  *   NaNPropagation == PropagateFast : undefined
+  *   NaNPropagation == PropagateNaN : result is NaN
+  *   NaNPropagation == PropagateNumbers : result is minimum of elements that are not NaN
+  * \warning the matrix must be not empty, otherwise an assertion is triggered.
+  */
+template<typename Derived>
+template<int NaNPropagation>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
+DenseBase<Derived>::minCoeff() const
+{
+  return derived().redux(Eigen::internal::scalar_min_op<Scalar,Scalar, NaNPropagation>());
+}
+
+/** \returns the maximum of all coefficients of \c *this. 
+  * In case \c *this contains NaN, NaNPropagation determines the behavior:
+  *   NaNPropagation == PropagateFast : undefined
+  *   NaNPropagation == PropagateNaN : result is NaN
+  *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
+  * \warning the matrix must be not empty, otherwise an assertion is triggered.
+  */
+template<typename Derived>
+template<int NaNPropagation>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
+DenseBase<Derived>::maxCoeff() const
+{
+  return derived().redux(Eigen::internal::scalar_max_op<Scalar,Scalar, NaNPropagation>());
+}
+
+/** \returns the sum of all coefficients of \c *this
+  *
+  * If \c *this is empty, then the value 0 is returned.
+  *
+  * \sa trace(), prod(), mean()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
+DenseBase<Derived>::sum() const
+{
+  if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0))
+    return Scalar(0);
+  return derived().redux(Eigen::internal::scalar_sum_op<Scalar,Scalar>());
+}
+
+/** \returns the mean of all coefficients of *this
+*
+* \sa trace(), prod(), sum()
+*/
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
+DenseBase<Derived>::mean() const
+{
+#ifdef __INTEL_COMPILER
+  #pragma warning push
+  #pragma warning ( disable : 2259 )
+#endif
+  return Scalar(derived().redux(Eigen::internal::scalar_sum_op<Scalar,Scalar>())) / Scalar(this->size());
+#ifdef __INTEL_COMPILER
+  #pragma warning pop
+#endif
+}
+
+/** \returns the product of all coefficients of *this
+  *
+  * Example: \include MatrixBase_prod.cpp
+  * Output: \verbinclude MatrixBase_prod.out
+  *
+  * \sa sum(), mean(), trace()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
+DenseBase<Derived>::prod() const
+{
+  if(SizeAtCompileTime==0 || (SizeAtCompileTime==Dynamic && size()==0))
+    return Scalar(1);
+  return derived().redux(Eigen::internal::scalar_product_op<Scalar>());
+}
+
+/** \returns the trace of \c *this, i.e. the sum of the coefficients on the main diagonal.
+  *
+  * \c *this can be any matrix, not necessarily square.
+  *
+  * \sa diagonal(), sum()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::traits<Derived>::Scalar
+MatrixBase<Derived>::trace() const
+{
+  return derived().diagonal().sum();
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_REDUX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Ref.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Ref.h
new file mode 100644
index 0000000000000000000000000000000000000000..32d51d2369b1869684a05d404ac4829cd5777060
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Ref.h
@@ -0,0 +1,408 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REF_H
+#define EIGEN_REF_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename PlainObjectType_, int Options_, typename StrideType_>
+struct traits<Ref<PlainObjectType_, Options_, StrideType_> >
+  : public traits<Map<PlainObjectType_, Options_, StrideType_> >
+{
+  typedef PlainObjectType_ PlainObjectType;
+  typedef StrideType_ StrideType;
+  enum {
+    Options = Options_,
+    Flags = traits<Map<PlainObjectType_, Options_, StrideType_> >::Flags | NestByRefBit,
+    Alignment = traits<Map<PlainObjectType_, Options_, StrideType_> >::Alignment
+  };
+
+  template<typename Derived> struct match {
+    enum {
+      IsVectorAtCompileTime = PlainObjectType::IsVectorAtCompileTime || Derived::IsVectorAtCompileTime,
+      HasDirectAccess = internal::has_direct_access<Derived>::ret,
+      StorageOrderMatch = IsVectorAtCompileTime || ((PlainObjectType::Flags&RowMajorBit)==(Derived::Flags&RowMajorBit)),
+      InnerStrideMatch = int(StrideType::InnerStrideAtCompileTime)==int(Dynamic)
+                      || int(StrideType::InnerStrideAtCompileTime)==int(Derived::InnerStrideAtCompileTime)
+                      || (int(StrideType::InnerStrideAtCompileTime)==0 && int(Derived::InnerStrideAtCompileTime)==1),
+      OuterStrideMatch = IsVectorAtCompileTime
+                      || int(StrideType::OuterStrideAtCompileTime)==int(Dynamic) || int(StrideType::OuterStrideAtCompileTime)==int(Derived::OuterStrideAtCompileTime),
+      // NOTE, this indirection of evaluator<Derived>::Alignment is needed
+      // to workaround a very strange bug in MSVC related to the instantiation
+      // of has_*ary_operator in evaluator<CwiseNullaryOp>.
+      // This line is surprisingly very sensitive. For instance, simply adding parenthesis
+      // as "DerivedAlignment = (int(evaluator<Derived>::Alignment))," will make MSVC fail...
+      DerivedAlignment = int(evaluator<Derived>::Alignment),
+      AlignmentMatch = (int(traits<PlainObjectType>::Alignment)==int(Unaligned)) || (DerivedAlignment >= int(Alignment)), // FIXME the first condition is not very clear, it should be replaced by the required alignment
+      ScalarTypeMatch = internal::is_same<typename PlainObjectType::Scalar, typename Derived::Scalar>::value,
+      MatchAtCompileTime = HasDirectAccess && StorageOrderMatch && InnerStrideMatch && OuterStrideMatch && AlignmentMatch && ScalarTypeMatch
+    };
+    typedef std::conditional_t<MatchAtCompileTime,internal::true_type,internal::false_type> type;
+  };
+
+};
+
+template<typename Derived>
+struct traits<RefBase<Derived> > : public traits<Derived> {};
+
+}
+
+template<typename Derived> class RefBase
+ : public MapBase<Derived>
+{
+  typedef typename internal::traits<Derived>::PlainObjectType PlainObjectType;
+  typedef typename internal::traits<Derived>::StrideType StrideType;
+
+public:
+
+  typedef MapBase<Derived> Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(RefBase)
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index innerStride() const
+  {
+    return StrideType::InnerStrideAtCompileTime != 0 ? m_stride.inner() : 1;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index outerStride() const
+  {
+    return StrideType::OuterStrideAtCompileTime != 0 ? m_stride.outer()
+         : IsVectorAtCompileTime ? this->size()
+         : int(Flags)&RowMajorBit ? this->cols()
+         : this->rows();
+  }
+
+  EIGEN_DEVICE_FUNC RefBase()
+    : Base(0,RowsAtCompileTime==Dynamic?0:RowsAtCompileTime,ColsAtCompileTime==Dynamic?0:ColsAtCompileTime),
+      // Stride<> does not allow default ctor for Dynamic strides, so let' initialize it with dummy values:
+      m_stride(StrideType::OuterStrideAtCompileTime==Dynamic?0:StrideType::OuterStrideAtCompileTime,
+               StrideType::InnerStrideAtCompileTime==Dynamic?0:StrideType::InnerStrideAtCompileTime)
+  {}
+
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(RefBase)
+
+protected:
+
+  typedef Stride<StrideType::OuterStrideAtCompileTime,StrideType::InnerStrideAtCompileTime> StrideBase;
+
+  // Resolves inner stride if default 0.
+  static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index resolveInnerStride(Index inner) {
+    return inner == 0 ? 1 : inner;
+  }
+
+  // Resolves outer stride if default 0.
+  static EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index resolveOuterStride(Index inner, Index outer, Index rows, Index cols, bool isVectorAtCompileTime, bool isRowMajor) {
+    return outer == 0 ? isVectorAtCompileTime ? inner * rows * cols : isRowMajor ? inner * cols : inner * rows : outer;
+  }
+
+  // Returns true if construction is valid, false if there is a stride mismatch,
+  // and fails if there is a size mismatch.
+  template<typename Expression>
+  EIGEN_DEVICE_FUNC bool construct(Expression& expr)
+  {
+    // Check matrix sizes.  If this is a compile-time vector, we do allow
+    // implicitly transposing.
+    EIGEN_STATIC_ASSERT(
+      EIGEN_PREDICATE_SAME_MATRIX_SIZE(PlainObjectType, Expression)
+      // If it is a vector, the transpose sizes might match.
+      || ( PlainObjectType::IsVectorAtCompileTime
+            && ((int(PlainObjectType::RowsAtCompileTime)==Eigen::Dynamic
+              || int(Expression::ColsAtCompileTime)==Eigen::Dynamic
+              || int(PlainObjectType::RowsAtCompileTime)==int(Expression::ColsAtCompileTime))
+            &&  (int(PlainObjectType::ColsAtCompileTime)==Eigen::Dynamic
+              || int(Expression::RowsAtCompileTime)==Eigen::Dynamic
+              || int(PlainObjectType::ColsAtCompileTime)==int(Expression::RowsAtCompileTime)))),
+      YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES
+    )
+
+    // Determine runtime rows and columns.
+    Index rows = expr.rows();
+    Index cols = expr.cols();
+    if(PlainObjectType::RowsAtCompileTime==1)
+    {
+      eigen_assert(expr.rows()==1 || expr.cols()==1);
+      rows = 1;
+      cols = expr.size();
+    }
+    else if(PlainObjectType::ColsAtCompileTime==1)
+    {
+      eigen_assert(expr.rows()==1 || expr.cols()==1);
+      rows = expr.size();
+      cols = 1;
+    }
+    // Verify that the sizes are valid.
+    eigen_assert(
+      (PlainObjectType::RowsAtCompileTime == Dynamic) || (PlainObjectType::RowsAtCompileTime == rows));
+    eigen_assert(
+      (PlainObjectType::ColsAtCompileTime == Dynamic) || (PlainObjectType::ColsAtCompileTime == cols));
+
+
+    // If this is a vector, we might be transposing, which means that stride should swap.
+    const bool transpose = PlainObjectType::IsVectorAtCompileTime && (rows != expr.rows());
+    // If the storage format differs, we also need to swap the stride.
+    const bool row_major = ((PlainObjectType::Flags)&RowMajorBit) != 0;
+    const bool expr_row_major = (Expression::Flags&RowMajorBit) != 0;
+    const bool storage_differs =  (row_major != expr_row_major);
+
+    const bool swap_stride = (transpose != storage_differs);
+
+    // Determine expr's actual strides, resolving any defaults if zero.
+    const Index expr_inner_actual = resolveInnerStride(expr.innerStride());
+    const Index expr_outer_actual = resolveOuterStride(expr_inner_actual,
+                                                       expr.outerStride(),
+                                                       expr.rows(),
+                                                       expr.cols(),
+                                                       Expression::IsVectorAtCompileTime != 0,
+                                                       expr_row_major);
+
+    // If this is a column-major row vector or row-major column vector, the inner-stride
+    // is arbitrary, so set it to either the compile-time inner stride or 1.
+    const bool row_vector = (rows == 1);
+    const bool col_vector = (cols == 1);
+    const Index inner_stride =
+        ( (!row_major && row_vector) || (row_major && col_vector) ) ?
+            ( StrideType::InnerStrideAtCompileTime > 0 ? Index(StrideType::InnerStrideAtCompileTime) : 1)
+            : swap_stride ? expr_outer_actual : expr_inner_actual;
+
+    // If this is a column-major column vector or row-major row vector, the outer-stride
+    // is arbitrary, so set it to either the compile-time outer stride or vector size.
+    const Index outer_stride =
+      ( (!row_major && col_vector) || (row_major && row_vector) ) ?
+          ( StrideType::OuterStrideAtCompileTime > 0 ? Index(StrideType::OuterStrideAtCompileTime) : rows * cols * inner_stride)
+          : swap_stride ? expr_inner_actual : expr_outer_actual;
+
+    // Check if given inner/outer strides are compatible with compile-time strides.
+    const bool inner_valid = (StrideType::InnerStrideAtCompileTime == Dynamic)
+        || (resolveInnerStride(Index(StrideType::InnerStrideAtCompileTime)) == inner_stride);
+    if (!inner_valid) {
+      return false;
+    }
+
+    const bool outer_valid = (StrideType::OuterStrideAtCompileTime == Dynamic)
+        || (resolveOuterStride(
+              inner_stride,
+              Index(StrideType::OuterStrideAtCompileTime),
+              rows, cols, PlainObjectType::IsVectorAtCompileTime != 0,
+              row_major)
+            == outer_stride);
+    if (!outer_valid) {
+      return false;
+    }
+
+    internal::construct_at<Base>(this, expr.data(), rows, cols);
+    internal::construct_at(&m_stride,
+      (StrideType::OuterStrideAtCompileTime == 0) ? 0 : outer_stride,
+      (StrideType::InnerStrideAtCompileTime == 0) ? 0 : inner_stride );
+    return true;
+  }
+
+  StrideBase m_stride;
+};
+
+/** \class Ref
+  * \ingroup Core_Module
+  *
+  * \brief A matrix or vector expression mapping an existing expression
+  *
+  * \tparam PlainObjectType the equivalent matrix type of the mapped data
+  * \tparam Options specifies the pointer alignment in bytes. It can be: \c #Aligned128, , \c #Aligned64, \c #Aligned32, \c #Aligned16, \c #Aligned8 or \c #Unaligned.
+  *                 The default is \c #Unaligned.
+  * \tparam StrideType optionally specifies strides. By default, Ref implies a contiguous storage along the inner dimension (inner stride==1),
+  *                   but accepts a variable outer stride (leading dimension).
+  *                   This can be overridden by specifying strides.
+  *                   The type passed here must be a specialization of the Stride template, see examples below.
+  *
+  * This class provides a way to write non-template functions taking Eigen objects as parameters while limiting the number of copies.
+  * A Ref<> object can represent either a const expression or a l-value:
+  * \code
+  * // in-out argument:
+  * void foo1(Ref<VectorXf> x);
+  *
+  * // read-only const argument:
+  * void foo2(const Ref<const VectorXf>& x);
+  * \endcode
+  *
+  * In the in-out case, the input argument must satisfy the constraints of the actual Ref<> type, otherwise a compilation issue will be triggered.
+  * By default, a Ref<VectorXf> can reference any dense vector expression of float having a contiguous memory layout.
+  * Likewise, a Ref<MatrixXf> can reference any column-major dense matrix expression of float whose column's elements are contiguously stored with
+  * the possibility to have a constant space in-between each column, i.e. the inner stride must be equal to 1, but the outer stride (or leading dimension)
+  * can be greater than the number of rows.
+  *
+  * In the const case, if the input expression does not match the above requirement, then it is evaluated into a temporary before being passed to the function.
+  * Here are some examples:
+  * \code
+  * MatrixXf A;
+  * VectorXf a;
+  * foo1(a.head());             // OK
+  * foo1(A.col());              // OK
+  * foo1(A.row());              // Compilation error because here innerstride!=1
+  * foo2(A.row());              // Compilation error because A.row() is a 1xN object while foo2 is expecting a Nx1 object
+  * foo2(A.row().transpose());  // The row is copied into a contiguous temporary
+  * foo2(2*a);                  // The expression is evaluated into a temporary
+  * foo2(A.col().segment(2,4)); // No temporary
+  * \endcode
+  *
+  * The range of inputs that can be referenced without temporary can be enlarged using the last two template parameters.
+  * Here is an example accepting an innerstride!=1:
+  * \code
+  * // in-out argument:
+  * void foo3(Ref<VectorXf,0,InnerStride<> > x);
+  * foo3(A.row());              // OK
+  * \endcode
+  * The downside here is that the function foo3 might be significantly slower than foo1 because it won't be able to exploit vectorization, and will involve more
+  * expensive address computations even if the input is contiguously stored in memory. To overcome this issue, one might propose to overload internally calling a
+  * template function, e.g.:
+  * \code
+  * // in the .h:
+  * void foo(const Ref<MatrixXf>& A);
+  * void foo(const Ref<MatrixXf,0,Stride<> >& A);
+  *
+  * // in the .cpp:
+  * template<typename TypeOfA> void foo_impl(const TypeOfA& A) {
+  *     ... // crazy code goes here
+  * }
+  * void foo(const Ref<MatrixXf>& A) { foo_impl(A); }
+  * void foo(const Ref<MatrixXf,0,Stride<> >& A) { foo_impl(A); }
+  * \endcode
+  *
+  * See also the following stackoverflow questions for further references:
+  *  - <a href="http://stackoverflow.com/questions/21132538/correct-usage-of-the-eigenref-class">Correct usage of the Eigen::Ref<> class</a>
+  *
+  * \sa PlainObjectBase::Map(), \ref TopicStorageOrders
+  */
+template<typename PlainObjectType, int Options, typename StrideType> class Ref
+  : public RefBase<Ref<PlainObjectType, Options, StrideType> >
+{
+  private:
+    typedef internal::traits<Ref> Traits;
+    template<typename Derived>
+    EIGEN_DEVICE_FUNC inline Ref(const PlainObjectBase<Derived>& expr,
+                                 std::enable_if_t<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>* = 0);
+  public:
+
+    typedef RefBase<Ref> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Ref)
+
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename Derived>
+    EIGEN_DEVICE_FUNC inline Ref(PlainObjectBase<Derived>& expr,
+                                 std::enable_if_t<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>* = 0)
+    {
+      EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
+      // Construction must pass since we will not create temporary storage in the non-const case.
+      const bool success = Base::construct(expr.derived());
+      EIGEN_UNUSED_VARIABLE(success)
+      eigen_assert(success);
+    }
+    template<typename Derived>
+    EIGEN_DEVICE_FUNC inline Ref(const DenseBase<Derived>& expr,
+                                 std::enable_if_t<bool(Traits::template match<Derived>::MatchAtCompileTime),Derived>* = 0)
+    #else
+    /** Implicit constructor from any dense expression */
+    template<typename Derived>
+    inline Ref(DenseBase<Derived>& expr)
+    #endif
+    {
+      EIGEN_STATIC_ASSERT(bool(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
+      EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
+      EIGEN_STATIC_ASSERT(!Derived::IsPlainObjectBase,THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
+      // Construction must pass since we will not create temporary storage in the non-const case.
+      const bool success = Base::construct(expr.const_cast_derived());
+      EIGEN_UNUSED_VARIABLE(success)
+      eigen_assert(success);
+    }
+
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Ref)
+
+};
+
+// this is the const ref version
+template<typename TPlainObjectType, int Options, typename StrideType> class Ref<const TPlainObjectType, Options, StrideType>
+  : public RefBase<Ref<const TPlainObjectType, Options, StrideType> >
+{
+    typedef internal::traits<Ref> Traits;
+
+    static constexpr bool may_map_m_object_successfully = 
+      (static_cast<int>(StrideType::InnerStrideAtCompileTime) == 0 ||
+       static_cast<int>(StrideType::InnerStrideAtCompileTime) == 1 ||
+       static_cast<int>(StrideType::InnerStrideAtCompileTime) == Dynamic) &&
+      (TPlainObjectType::IsVectorAtCompileTime ||
+       static_cast<int>(StrideType::OuterStrideAtCompileTime) == 0 ||
+       static_cast<int>(StrideType::OuterStrideAtCompileTime) == Dynamic ||
+       static_cast<int>(StrideType::OuterStrideAtCompileTime) == static_cast<int>(TPlainObjectType::InnerSizeAtCompileTime) ||
+       static_cast<int>(TPlainObjectType::InnerSizeAtCompileTime) == Dynamic);
+  public:
+
+    typedef RefBase<Ref> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Ref)
+
+    template<typename Derived>
+    EIGEN_DEVICE_FUNC inline Ref(const DenseBase<Derived>& expr,
+                                 std::enable_if_t<bool(Traits::template match<Derived>::ScalarTypeMatch),Derived>* = 0)
+    {
+//      std::cout << match_helper<Derived>::HasDirectAccess << "," << match_helper<Derived>::OuterStrideMatch << "," << match_helper<Derived>::InnerStrideMatch << "\n";
+//      std::cout << int(StrideType::OuterStrideAtCompileTime) << " - " << int(Derived::OuterStrideAtCompileTime) << "\n";
+//      std::cout << int(StrideType::InnerStrideAtCompileTime) << " - " << int(Derived::InnerStrideAtCompileTime) << "\n";
+      EIGEN_STATIC_ASSERT(Traits::template match<Derived>::type::value || may_map_m_object_successfully,
+                          STORAGE_LAYOUT_DOES_NOT_MATCH);
+      construct(expr.derived(), typename Traits::template match<Derived>::type());
+    }
+
+    EIGEN_DEVICE_FUNC inline Ref(const Ref& other) : Base(other) {
+      // copy constructor shall not copy the m_object, to avoid unnecessary malloc and copy
+    }
+
+    EIGEN_DEVICE_FUNC inline Ref(Ref&& other) {
+      if (other.data() == other.m_object.data()) {
+        m_object = std::move(other.m_object);
+        Base::construct(m_object);
+      }
+      else
+        Base::construct(other);
+    }
+
+    template<typename OtherRef>
+    EIGEN_DEVICE_FUNC inline Ref(const RefBase<OtherRef>& other) {
+      EIGEN_STATIC_ASSERT(Traits::template match<OtherRef>::type::value || may_map_m_object_successfully,
+                          STORAGE_LAYOUT_DOES_NOT_MATCH);
+      construct(other.derived(), typename Traits::template match<OtherRef>::type());
+    }
+
+  protected:
+
+    template<typename Expression>
+    EIGEN_DEVICE_FUNC void construct(const Expression& expr,internal::true_type)
+    {
+      // Check if we can use the underlying expr's storage directly, otherwise call the copy version.
+      if (!Base::construct(expr)) {
+        construct(expr, internal::false_type());
+      }
+    }
+
+    template<typename Expression>
+    EIGEN_DEVICE_FUNC void construct(const Expression& expr, internal::false_type)
+    {
+      internal::call_assignment_no_alias(m_object,expr,internal::assign_op<Scalar,Scalar>());
+      const bool success = Base::construct(m_object);
+      EIGEN_ONLY_USED_FOR_DEBUG(success)
+      eigen_assert(success);
+    }
+
+  protected:
+    TPlainObjectType m_object;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_REF_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Replicate.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Replicate.h
new file mode 100644
index 0000000000000000000000000000000000000000..4f91bbe4cdb33497704e530261fbad5fa74aedfb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Replicate.h
@@ -0,0 +1,144 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REPLICATE_H
+#define EIGEN_REPLICATE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template<typename MatrixType,int RowFactor,int ColFactor>
+struct traits<Replicate<MatrixType,RowFactor,ColFactor> >
+ : traits<MatrixType>
+{
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename traits<MatrixType>::StorageKind StorageKind;
+  typedef typename traits<MatrixType>::XprKind XprKind;
+  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
+  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_;
+  enum {
+    RowsAtCompileTime = RowFactor==Dynamic || int(MatrixType::RowsAtCompileTime)==Dynamic
+                      ? Dynamic
+                      : RowFactor * MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = ColFactor==Dynamic || int(MatrixType::ColsAtCompileTime)==Dynamic
+                      ? Dynamic
+                      : ColFactor * MatrixType::ColsAtCompileTime,
+   //FIXME we don't propagate the max sizes !!!
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+    IsRowMajor = MaxRowsAtCompileTime==1 && MaxColsAtCompileTime!=1 ? 1
+               : MaxColsAtCompileTime==1 && MaxRowsAtCompileTime!=1 ? 0
+               : (MatrixType::Flags & RowMajorBit) ? 1 : 0,
+
+    // FIXME enable DirectAccess with negative strides?
+    Flags = IsRowMajor ? RowMajorBit : 0
+  };
+};
+}
+
+/**
+  * \class Replicate
+  * \ingroup Core_Module
+  *
+  * \brief Expression of the multiple replication of a matrix or vector
+  *
+  * \tparam MatrixType the type of the object we are replicating
+  * \tparam RowFactor number of repetitions at compile time along the vertical direction, can be Dynamic.
+  * \tparam ColFactor number of repetitions at compile time along the horizontal direction, can be Dynamic.
+  *
+  * This class represents an expression of the multiple replication of a matrix or vector.
+  * It is the return type of DenseBase::replicate() and most of the time
+  * this is the only way it is used.
+  *
+  * \sa DenseBase::replicate()
+  */
+template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
+  : public internal::dense_xpr_base< Replicate<MatrixType,RowFactor,ColFactor> >::type
+{
+    typedef typename internal::traits<Replicate>::MatrixTypeNested MatrixTypeNested;
+    typedef typename internal::traits<Replicate>::MatrixTypeNested_ MatrixTypeNested_;
+  public:
+
+    typedef typename internal::dense_xpr_base<Replicate>::type Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Replicate)
+    typedef internal::remove_all_t<MatrixType> NestedExpression;
+
+    template<typename OriginalMatrixType>
+    EIGEN_DEVICE_FUNC
+    inline explicit Replicate(const OriginalMatrixType& matrix)
+      : m_matrix(matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor)
+    {
+      EIGEN_STATIC_ASSERT((internal::is_same<std::remove_const_t<MatrixType>,OriginalMatrixType>::value),
+                          THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
+      eigen_assert(RowFactor!=Dynamic && ColFactor!=Dynamic);
+    }
+
+    template<typename OriginalMatrixType>
+    EIGEN_DEVICE_FUNC
+    inline Replicate(const OriginalMatrixType& matrix, Index rowFactor, Index colFactor)
+      : m_matrix(matrix), m_rowFactor(rowFactor), m_colFactor(colFactor)
+    {
+      EIGEN_STATIC_ASSERT((internal::is_same<std::remove_const_t<MatrixType>,OriginalMatrixType>::value),
+                          THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index rows() const { return m_matrix.rows() * m_rowFactor.value(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index cols() const { return m_matrix.cols() * m_colFactor.value(); }
+
+    EIGEN_DEVICE_FUNC
+    const MatrixTypeNested_& nestedExpression() const
+    {
+      return m_matrix;
+    }
+
+  protected:
+    MatrixTypeNested m_matrix;
+    const internal::variable_if_dynamic<Index, RowFactor> m_rowFactor;
+    const internal::variable_if_dynamic<Index, ColFactor> m_colFactor;
+};
+
+/**
+  * \return an expression of the replication of \c *this
+  *
+  * Example: \include MatrixBase_replicate.cpp
+  * Output: \verbinclude MatrixBase_replicate.out
+  *
+  * \sa VectorwiseOp::replicate(), DenseBase::replicate(Index,Index), class Replicate
+  */
+template<typename Derived>
+template<int RowFactor, int ColFactor>
+EIGEN_DEVICE_FUNC const Replicate<Derived,RowFactor,ColFactor>
+DenseBase<Derived>::replicate() const
+{
+  return Replicate<Derived,RowFactor,ColFactor>(derived());
+}
+
+/**
+  * \return an expression of the replication of each column (or row) of \c *this
+  *
+  * Example: \include DirectionWise_replicate_int.cpp
+  * Output: \verbinclude DirectionWise_replicate_int.out
+  *
+  * \sa VectorwiseOp::replicate(), DenseBase::replicate(), class Replicate
+  */
+template<typename ExpressionType, int Direction>
+EIGEN_DEVICE_FUNC const typename VectorwiseOp<ExpressionType,Direction>::ReplicateReturnType
+VectorwiseOp<ExpressionType,Direction>::replicate(Index factor) const
+{
+  return typename VectorwiseOp<ExpressionType,Direction>::ReplicateReturnType
+          (_expression(),Direction==Vertical?factor:1,Direction==Horizontal?factor:1);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_REPLICATE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Reshaped.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Reshaped.h
new file mode 100644
index 0000000000000000000000000000000000000000..508bc25297d367da54a5dee1677985b8f7068a46
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Reshaped.h
@@ -0,0 +1,455 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2014 yoco <peter.xiau@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_RESHAPED_H
+#define EIGEN_RESHAPED_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class Reshaped
+  * \ingroup Core_Module
+  *
+  * \brief Expression of a fixed-size or dynamic-size reshape
+  *
+  * \tparam XprType the type of the expression in which we are taking a reshape
+  * \tparam Rows the number of rows of the reshape we are taking at compile time (optional)
+  * \tparam Cols the number of columns of the reshape we are taking at compile time (optional)
+  * \tparam Order can be ColMajor or RowMajor, default is ColMajor.
+  *
+  * This class represents an expression of either a fixed-size or dynamic-size reshape.
+  * It is the return type of DenseBase::reshaped(NRowsType,NColsType) and
+  * most of the time this is the only way it is used.
+  *
+  * If you want to directly manipulate reshaped expressions,
+  * for instance if you want to write a function returning such an expression,
+  * it is advised to use the \em auto keyword for such use cases.
+  *
+  * Here is an example illustrating the dynamic case:
+  * \include class_Reshaped.cpp
+  * Output: \verbinclude class_Reshaped.out
+  *
+  * Here is an example illustrating the fixed-size case:
+  * \include class_FixedReshaped.cpp
+  * Output: \verbinclude class_FixedReshaped.out
+  *
+  * \sa DenseBase::reshaped(NRowsType,NColsType)
+  */
+
+namespace internal {
+
+template<typename XprType, int Rows, int Cols, int Order>
+struct traits<Reshaped<XprType, Rows, Cols, Order> > : traits<XprType>
+{
+  typedef typename traits<XprType>::Scalar Scalar;
+  typedef typename traits<XprType>::StorageKind StorageKind;
+  typedef typename traits<XprType>::XprKind XprKind;
+  enum{
+    MatrixRows = traits<XprType>::RowsAtCompileTime,
+    MatrixCols = traits<XprType>::ColsAtCompileTime,
+    RowsAtCompileTime = Rows,
+    ColsAtCompileTime = Cols,
+    MaxRowsAtCompileTime = Rows,
+    MaxColsAtCompileTime = Cols,
+    XpxStorageOrder = ((int(traits<XprType>::Flags) & RowMajorBit) == RowMajorBit) ? RowMajor : ColMajor,
+    ReshapedStorageOrder = (RowsAtCompileTime == 1 && ColsAtCompileTime != 1) ? RowMajor
+                         : (ColsAtCompileTime == 1 && RowsAtCompileTime != 1) ? ColMajor
+                         : XpxStorageOrder,
+    HasSameStorageOrderAsXprType = (ReshapedStorageOrder == XpxStorageOrder),
+    InnerSize = (ReshapedStorageOrder==int(RowMajor)) ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
+    InnerStrideAtCompileTime = HasSameStorageOrderAsXprType
+                             ? int(inner_stride_at_compile_time<XprType>::ret)
+                             : Dynamic,
+    OuterStrideAtCompileTime = Dynamic,
+
+    HasDirectAccess = internal::has_direct_access<XprType>::ret
+                    && (Order==int(XpxStorageOrder))
+                    && ((evaluator<XprType>::Flags&LinearAccessBit)==LinearAccessBit),
+
+    MaskPacketAccessBit = (InnerSize == Dynamic || (InnerSize % packet_traits<Scalar>::size) == 0)
+                       && (InnerStrideAtCompileTime == 1)
+                        ? PacketAccessBit : 0,
+    //MaskAlignedBit = ((OuterStrideAtCompileTime!=Dynamic) && (((OuterStrideAtCompileTime * int(sizeof(Scalar))) % 16) == 0)) ? AlignedBit : 0,
+    FlagsLinearAccessBit = (RowsAtCompileTime == 1 || ColsAtCompileTime == 1) ? LinearAccessBit : 0,
+    FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
+    FlagsRowMajorBit = (ReshapedStorageOrder==int(RowMajor)) ? RowMajorBit : 0,
+    FlagsDirectAccessBit = HasDirectAccess ? DirectAccessBit : 0,
+    Flags0 = traits<XprType>::Flags & ( (HereditaryBits & ~RowMajorBit) | MaskPacketAccessBit),
+
+    Flags = (Flags0 | FlagsLinearAccessBit | FlagsLvalueBit | FlagsRowMajorBit | FlagsDirectAccessBit)
+  };
+};
+
+template<typename XprType, int Rows, int Cols, int Order, bool HasDirectAccess> class ReshapedImpl_dense;
+
+} // end namespace internal
+
+template<typename XprType, int Rows, int Cols, int Order, typename StorageKind> class ReshapedImpl;
+
+template<typename XprType, int Rows, int Cols, int Order> class Reshaped
+  : public ReshapedImpl<XprType, Rows, Cols, Order, typename internal::traits<XprType>::StorageKind>
+{
+    typedef ReshapedImpl<XprType, Rows, Cols, Order, typename internal::traits<XprType>::StorageKind> Impl;
+  public:
+    //typedef typename Impl::Base Base;
+    typedef Impl Base;
+    EIGEN_GENERIC_PUBLIC_INTERFACE(Reshaped)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Reshaped)
+
+    /** Fixed-size constructor
+      */
+    EIGEN_DEVICE_FUNC
+    inline Reshaped(XprType& xpr)
+      : Impl(xpr)
+    {
+      EIGEN_STATIC_ASSERT(RowsAtCompileTime!=Dynamic && ColsAtCompileTime!=Dynamic,THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
+      eigen_assert(Rows * Cols == xpr.rows() * xpr.cols());
+    }
+
+    /** Dynamic-size constructor
+      */
+    EIGEN_DEVICE_FUNC
+    inline Reshaped(XprType& xpr,
+          Index reshapeRows, Index reshapeCols)
+      : Impl(xpr, reshapeRows, reshapeCols)
+    {
+      eigen_assert((RowsAtCompileTime==Dynamic || RowsAtCompileTime==reshapeRows)
+          && (ColsAtCompileTime==Dynamic || ColsAtCompileTime==reshapeCols));
+      eigen_assert(reshapeRows * reshapeCols == xpr.rows() * xpr.cols());
+    }
+};
+
+// The generic default implementation for dense reshape simply forward to the internal::ReshapedImpl_dense
+// that must be specialized for direct and non-direct access...
+template<typename XprType, int Rows, int Cols, int Order>
+class ReshapedImpl<XprType, Rows, Cols, Order, Dense>
+  : public internal::ReshapedImpl_dense<XprType, Rows, Cols, Order,internal::traits<Reshaped<XprType,Rows,Cols,Order> >::HasDirectAccess>
+{
+    typedef internal::ReshapedImpl_dense<XprType, Rows, Cols, Order,internal::traits<Reshaped<XprType,Rows,Cols,Order> >::HasDirectAccess> Impl;
+  public:
+    typedef Impl Base;
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl)
+    EIGEN_DEVICE_FUNC inline ReshapedImpl(XprType& xpr) : Impl(xpr) {}
+    EIGEN_DEVICE_FUNC inline ReshapedImpl(XprType& xpr, Index reshapeRows, Index reshapeCols)
+      : Impl(xpr, reshapeRows, reshapeCols) {}
+};
+
+namespace internal {
+
+/** \internal Internal implementation of dense Reshaped in the general case. */
+template<typename XprType, int Rows, int Cols, int Order>
+class ReshapedImpl_dense<XprType,Rows,Cols,Order,false>
+  : public internal::dense_xpr_base<Reshaped<XprType, Rows, Cols, Order> >::type
+{
+    typedef Reshaped<XprType, Rows, Cols, Order> ReshapedType;
+  public:
+
+    typedef typename internal::dense_xpr_base<ReshapedType>::type Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(ReshapedType)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl_dense)
+
+    typedef typename internal::ref_selector<XprType>::non_const_type MatrixTypeNested;
+    typedef internal::remove_all_t<XprType> NestedExpression;
+
+    class InnerIterator;
+
+    /** Fixed-size constructor
+      */
+    EIGEN_DEVICE_FUNC
+    inline ReshapedImpl_dense(XprType& xpr)
+      : m_xpr(xpr), m_rows(Rows), m_cols(Cols)
+    {}
+
+    /** Dynamic-size constructor
+      */
+    EIGEN_DEVICE_FUNC
+    inline ReshapedImpl_dense(XprType& xpr, Index nRows, Index nCols)
+      : m_xpr(xpr), m_rows(nRows), m_cols(nCols)
+    {}
+
+    EIGEN_DEVICE_FUNC Index rows() const { return m_rows; }
+    EIGEN_DEVICE_FUNC Index cols() const { return m_cols; }
+
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
+    /** \sa MapBase::data() */
+    EIGEN_DEVICE_FUNC inline const Scalar* data() const;
+    EIGEN_DEVICE_FUNC inline Index innerStride() const;
+    EIGEN_DEVICE_FUNC inline Index outerStride() const;
+    #endif
+
+    /** \returns the nested expression */
+    EIGEN_DEVICE_FUNC
+    const internal::remove_all_t<XprType>&
+    nestedExpression() const { return m_xpr; }
+
+    /** \returns the nested expression */
+    EIGEN_DEVICE_FUNC
+    std::remove_reference_t<XprType>&
+    nestedExpression() { return m_xpr; }
+
+  protected:
+
+    MatrixTypeNested m_xpr;
+    const internal::variable_if_dynamic<Index, Rows> m_rows;
+    const internal::variable_if_dynamic<Index, Cols> m_cols;
+};
+
+
+/** \internal Internal implementation of dense Reshaped in the direct access case. */
+template<typename XprType, int Rows, int Cols, int Order>
+class ReshapedImpl_dense<XprType, Rows, Cols, Order, true>
+  : public MapBase<Reshaped<XprType, Rows, Cols, Order> >
+{
+    typedef Reshaped<XprType, Rows, Cols, Order> ReshapedType;
+    typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
+  public:
+
+    typedef MapBase<ReshapedType> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(ReshapedType)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ReshapedImpl_dense)
+
+    /** Fixed-size constructor
+      */
+    EIGEN_DEVICE_FUNC
+    inline ReshapedImpl_dense(XprType& xpr)
+      : Base(xpr.data()), m_xpr(xpr)
+    {}
+
+    /** Dynamic-size constructor
+      */
+    EIGEN_DEVICE_FUNC
+    inline ReshapedImpl_dense(XprType& xpr, Index nRows, Index nCols)
+      : Base(xpr.data(), nRows, nCols),
+        m_xpr(xpr)
+    {}
+
+    EIGEN_DEVICE_FUNC
+    const internal::remove_all_t<XprTypeNested>& nestedExpression() const
+    {
+      return m_xpr;
+    }
+
+    EIGEN_DEVICE_FUNC
+    XprType& nestedExpression() { return m_xpr; }
+
+    /** \sa MapBase::innerStride() */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index innerStride() const
+    {
+      return m_xpr.innerStride();
+    }
+
+    /** \sa MapBase::outerStride() */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index outerStride() const
+    {
+      return (((Flags&RowMajorBit)==RowMajorBit) ? this->cols() : this->rows()) * m_xpr.innerStride();
+    }
+
+  protected:
+
+    XprTypeNested m_xpr;
+};
+
+// Evaluators
+template<typename ArgType, int Rows, int Cols, int Order, bool HasDirectAccess> struct reshaped_evaluator;
+
+template<typename ArgType, int Rows, int Cols, int Order>
+struct evaluator<Reshaped<ArgType, Rows, Cols, Order> >
+  : reshaped_evaluator<ArgType, Rows, Cols, Order, traits<Reshaped<ArgType,Rows,Cols,Order> >::HasDirectAccess>
+{
+  typedef Reshaped<ArgType, Rows, Cols, Order> XprType;
+  typedef typename XprType::Scalar Scalar;
+  // TODO: should check for smaller packet types
+  typedef typename packet_traits<Scalar>::type PacketScalar;
+
+  enum {
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+    HasDirectAccess = traits<XprType>::HasDirectAccess,
+
+//     RowsAtCompileTime = traits<XprType>::RowsAtCompileTime,
+//     ColsAtCompileTime = traits<XprType>::ColsAtCompileTime,
+//     MaxRowsAtCompileTime = traits<XprType>::MaxRowsAtCompileTime,
+//     MaxColsAtCompileTime = traits<XprType>::MaxColsAtCompileTime,
+//
+//     InnerStrideAtCompileTime = traits<XprType>::HasSameStorageOrderAsXprType
+//                              ? int(inner_stride_at_compile_time<ArgType>::ret)
+//                              : Dynamic,
+//     OuterStrideAtCompileTime = Dynamic,
+
+    FlagsLinearAccessBit = (traits<XprType>::RowsAtCompileTime == 1 || traits<XprType>::ColsAtCompileTime == 1 || HasDirectAccess) ? LinearAccessBit : 0,
+    FlagsRowMajorBit = (traits<XprType>::ReshapedStorageOrder==int(RowMajor)) ? RowMajorBit : 0,
+    FlagsDirectAccessBit =  HasDirectAccess ? DirectAccessBit : 0,
+    Flags0 = evaluator<ArgType>::Flags & (HereditaryBits & ~RowMajorBit),
+    Flags = Flags0 | FlagsLinearAccessBit | FlagsRowMajorBit | FlagsDirectAccessBit,
+
+    PacketAlignment = unpacket_traits<PacketScalar>::alignment,
+    Alignment = evaluator<ArgType>::Alignment
+  };
+  typedef reshaped_evaluator<ArgType, Rows, Cols, Order, HasDirectAccess> reshaped_evaluator_type;
+  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr) : reshaped_evaluator_type(xpr)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+};
+
+template<typename ArgType, int Rows, int Cols, int Order>
+struct reshaped_evaluator<ArgType, Rows, Cols, Order, /* HasDirectAccess */ false>
+  : evaluator_base<Reshaped<ArgType, Rows, Cols, Order> >
+{
+  typedef Reshaped<ArgType, Rows, Cols, Order> XprType;
+
+  enum {
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost /* TODO + cost of index computations */,
+
+    Flags = (evaluator<ArgType>::Flags & (HereditaryBits /*| LinearAccessBit | DirectAccessBit*/)),
+
+    Alignment = 0
+  };
+
+  EIGEN_DEVICE_FUNC explicit reshaped_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_xpr(xpr)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::CoeffReturnType CoeffReturnType;
+
+  typedef std::pair<Index, Index> RowCol;
+
+  EIGEN_DEVICE_FUNC inline RowCol index_remap(Index rowId, Index colId) const
+  {
+    if(Order==ColMajor)
+    {
+      const Index nth_elem_idx = colId * m_xpr.rows() + rowId;
+      return RowCol(nth_elem_idx % m_xpr.nestedExpression().rows(),
+                    nth_elem_idx / m_xpr.nestedExpression().rows());
+    }
+    else
+    {
+      const Index nth_elem_idx = colId + rowId * m_xpr.cols();
+      return RowCol(nth_elem_idx / m_xpr.nestedExpression().cols(),
+                    nth_elem_idx % m_xpr.nestedExpression().cols());
+    }
+  }
+
+  EIGEN_DEVICE_FUNC
+  inline Scalar& coeffRef(Index rowId, Index colId)
+  {
+    EIGEN_STATIC_ASSERT_LVALUE(XprType)
+    const RowCol row_col = index_remap(rowId, colId);
+    return m_argImpl.coeffRef(row_col.first, row_col.second);
+  }
+
+  EIGEN_DEVICE_FUNC
+  inline const Scalar& coeffRef(Index rowId, Index colId) const
+  {
+    const RowCol row_col = index_remap(rowId, colId);
+    return m_argImpl.coeffRef(row_col.first, row_col.second);
+  }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const
+  {
+    const RowCol row_col = index_remap(rowId, colId);
+    return m_argImpl.coeff(row_col.first, row_col.second);
+  }
+
+  EIGEN_DEVICE_FUNC
+  inline Scalar& coeffRef(Index index)
+  {
+    EIGEN_STATIC_ASSERT_LVALUE(XprType)
+    const RowCol row_col = index_remap(Rows == 1 ? 0 : index,
+                                       Rows == 1 ? index : 0);
+    return m_argImpl.coeffRef(row_col.first, row_col.second);
+
+  }
+
+  EIGEN_DEVICE_FUNC
+  inline const Scalar& coeffRef(Index index) const
+  {
+    const RowCol row_col = index_remap(Rows == 1 ? 0 : index,
+                                       Rows == 1 ? index : 0);
+    return m_argImpl.coeffRef(row_col.first, row_col.second);
+  }
+
+  EIGEN_DEVICE_FUNC
+  inline const CoeffReturnType coeff(Index index) const
+  {
+    const RowCol row_col = index_remap(Rows == 1 ? 0 : index,
+                                       Rows == 1 ? index : 0);
+    return m_argImpl.coeff(row_col.first, row_col.second);
+  }
+#if 0
+  EIGEN_DEVICE_FUNC
+  template<int LoadMode>
+  inline PacketScalar packet(Index rowId, Index colId) const
+  {
+    const RowCol row_col = index_remap(rowId, colId);
+    return m_argImpl.template packet<Unaligned>(row_col.first, row_col.second);
+
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC
+  inline void writePacket(Index rowId, Index colId, const PacketScalar& val)
+  {
+    const RowCol row_col = index_remap(rowId, colId);
+    m_argImpl.const_cast_derived().template writePacket<Unaligned>
+            (row_col.first, row_col.second, val);
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC
+  inline PacketScalar packet(Index index) const
+  {
+    const RowCol row_col = index_remap(RowsAtCompileTime == 1 ? 0 : index,
+                                        RowsAtCompileTime == 1 ? index : 0);
+    return m_argImpl.template packet<Unaligned>(row_col.first, row_col.second);
+  }
+
+  template<int LoadMode>
+  EIGEN_DEVICE_FUNC
+  inline void writePacket(Index index, const PacketScalar& val)
+  {
+    const RowCol row_col = index_remap(RowsAtCompileTime == 1 ? 0 : index,
+                                        RowsAtCompileTime == 1 ? index : 0);
+    return m_argImpl.template packet<Unaligned>(row_col.first, row_col.second, val);
+  }
+#endif
+protected:
+
+  evaluator<ArgType> m_argImpl;
+  const XprType& m_xpr;
+
+};
+
+template<typename ArgType, int Rows, int Cols, int Order>
+struct reshaped_evaluator<ArgType, Rows, Cols, Order, /* HasDirectAccess */ true>
+: mapbase_evaluator<Reshaped<ArgType, Rows, Cols, Order>,
+                      typename Reshaped<ArgType, Rows, Cols, Order>::PlainObject>
+{
+  typedef Reshaped<ArgType, Rows, Cols, Order> XprType;
+  typedef typename XprType::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC explicit reshaped_evaluator(const XprType& xpr)
+    : mapbase_evaluator<XprType, typename XprType::PlainObject>(xpr)
+  {
+    // TODO: for the 3.4 release, this should be turned to an internal assertion, but let's keep it as is for the beta lifetime
+    eigen_assert(((std::uintptr_t(xpr.data()) % plain_enum_max(1, evaluator<XprType>::Alignment)) == 0) && "data is not aligned");
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_RESHAPED_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ReturnByValue.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ReturnByValue.h
new file mode 100644
index 0000000000000000000000000000000000000000..90252829bce62e310d1dcc31dd5606644a80c0f7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/ReturnByValue.h
@@ -0,0 +1,121 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_RETURNBYVALUE_H
+#define EIGEN_RETURNBYVALUE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename Derived>
+struct traits<ReturnByValue<Derived> >
+  : public traits<typename traits<Derived>::ReturnType>
+{
+  enum {
+    // We're disabling the DirectAccess because e.g. the constructor of
+    // the Block-with-DirectAccess expression requires to have a coeffRef method.
+    // Also, we don't want to have to implement the stride stuff.
+    Flags = (traits<typename traits<Derived>::ReturnType>::Flags
+             | EvalBeforeNestingBit) & ~DirectAccessBit
+  };
+};
+
+/* The ReturnByValue object doesn't even have a coeff() method.
+ * So the only way that nesting it in an expression can work, is by evaluating it into a plain matrix.
+ * So internal::nested always gives the plain return matrix type.
+ *
+ * FIXME: I don't understand why we need this specialization: isn't this taken care of by the EvalBeforeNestingBit ??
+ * Answer: EvalBeforeNestingBit should be deprecated since we have the evaluators
+ */
+template<typename Derived,int n,typename PlainObject>
+struct nested_eval<ReturnByValue<Derived>, n, PlainObject>
+{
+  typedef typename traits<Derived>::ReturnType type;
+};
+
+} // end namespace internal
+
+/** \class ReturnByValue
+  * \ingroup Core_Module
+  *
+  */
+template<typename Derived> class ReturnByValue
+  : public internal::dense_xpr_base< ReturnByValue<Derived> >::type, internal::no_assignment_operator
+{
+  public:
+    typedef typename internal::traits<Derived>::ReturnType ReturnType;
+
+    typedef typename internal::dense_xpr_base<ReturnByValue>::type Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(ReturnByValue)
+
+    template<typename Dest>
+    EIGEN_DEVICE_FUNC
+    inline void evalTo(Dest& dst) const
+    { static_cast<const Derived*>(this)->evalTo(dst); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index rows() const EIGEN_NOEXCEPT { return static_cast<const Derived*>(this)->rows(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index cols() const EIGEN_NOEXCEPT { return static_cast<const Derived*>(this)->cols(); }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+#define Unusable YOU_ARE_TRYING_TO_ACCESS_A_SINGLE_COEFFICIENT_IN_A_SPECIAL_EXPRESSION_WHERE_THAT_IS_NOT_ALLOWED_BECAUSE_THAT_WOULD_BE_INEFFICIENT
+    class Unusable{
+      Unusable(const Unusable&) {}
+      Unusable& operator=(const Unusable&) {return *this;}
+    };
+    const Unusable& coeff(Index) const { return *reinterpret_cast<const Unusable*>(this); }
+    const Unusable& coeff(Index,Index) const { return *reinterpret_cast<const Unusable*>(this); }
+    Unusable& coeffRef(Index) { return *reinterpret_cast<Unusable*>(this); }
+    Unusable& coeffRef(Index,Index) { return *reinterpret_cast<Unusable*>(this); }
+#undef Unusable
+#endif
+};
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC Derived& DenseBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
+{
+  other.evalTo(derived());
+  return derived();
+}
+
+namespace internal {
+
+// Expression is evaluated in a temporary; default implementation of Assignment is bypassed so that
+// when a ReturnByValue expression is assigned, the evaluator is not constructed.
+// TODO: Finalize port to new regime; ReturnByValue should not exist in the expression world
+
+template<typename Derived>
+struct evaluator<ReturnByValue<Derived> >
+  : public evaluator<typename internal::traits<Derived>::ReturnType>
+{
+  typedef ReturnByValue<Derived> XprType;
+  typedef typename internal::traits<Derived>::ReturnType PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  EIGEN_DEVICE_FUNC explicit evaluator(const XprType& xpr)
+    : m_result(xpr.rows(), xpr.cols())
+  {
+    internal::construct_at<Base>(this, m_result);
+    xpr.evalTo(m_result);
+  }
+
+protected:
+  PlainObject m_result;
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_RETURNBYVALUE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Reverse.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Reverse.h
new file mode 100644
index 0000000000000000000000000000000000000000..97e1d68e9e30ba3fbe88925a1073807d32a804ff
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Reverse.h
@@ -0,0 +1,219 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009 Ricard Marxer <email@ricardmarxer.com>
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REVERSE_H
+#define EIGEN_REVERSE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename MatrixType, int Direction>
+struct traits<Reverse<MatrixType, Direction> >
+ : traits<MatrixType>
+{
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename traits<MatrixType>::StorageKind StorageKind;
+  typedef typename traits<MatrixType>::XprKind XprKind;
+  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
+  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_;
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+    Flags = MatrixTypeNested_::Flags & (RowMajorBit | LvalueBit)
+  };
+};
+
+template<typename PacketType, bool ReversePacket> struct reverse_packet_cond
+{
+  static inline PacketType run(const PacketType& x) { return preverse(x); }
+};
+
+template<typename PacketType> struct reverse_packet_cond<PacketType,false>
+{
+  static inline PacketType run(const PacketType& x) { return x; }
+};
+
+} // end namespace internal
+
+/** \class Reverse
+  * \ingroup Core_Module
+  *
+  * \brief Expression of the reverse of a vector or matrix
+  *
+  * \tparam MatrixType the type of the object of which we are taking the reverse
+  * \tparam Direction defines the direction of the reverse operation, can be Vertical, Horizontal, or BothDirections
+  *
+  * This class represents an expression of the reverse of a vector.
+  * It is the return type of MatrixBase::reverse() and VectorwiseOp::reverse()
+  * and most of the time this is the only way it is used.
+  *
+  * \sa MatrixBase::reverse(), VectorwiseOp::reverse()
+  */
+template<typename MatrixType, int Direction> class Reverse
+  : public internal::dense_xpr_base< Reverse<MatrixType, Direction> >::type
+{
+  public:
+
+    typedef typename internal::dense_xpr_base<Reverse>::type Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Reverse)
+    typedef internal::remove_all_t<MatrixType> NestedExpression;
+    using Base::IsRowMajor;
+
+  protected:
+    enum {
+      PacketSize = internal::packet_traits<Scalar>::size,
+      IsColMajor = !IsRowMajor,
+      ReverseRow = (Direction == Vertical)   || (Direction == BothDirections),
+      ReverseCol = (Direction == Horizontal) || (Direction == BothDirections),
+      OffsetRow  = ReverseRow && IsColMajor ? PacketSize : 1,
+      OffsetCol  = ReverseCol && IsRowMajor ? PacketSize : 1,
+      ReversePacket = (Direction == BothDirections)
+                    || ((Direction == Vertical)   && IsColMajor)
+                    || ((Direction == Horizontal) && IsRowMajor)
+    };
+    typedef internal::reverse_packet_cond<PacketScalar,ReversePacket> reverse_packet;
+  public:
+
+    EIGEN_DEVICE_FUNC explicit inline Reverse(const MatrixType& matrix) : m_matrix(matrix) { }
+
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Reverse)
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
+
+    EIGEN_DEVICE_FUNC inline Index innerStride() const
+    {
+      return -m_matrix.innerStride();
+    }
+
+    EIGEN_DEVICE_FUNC const internal::remove_all_t<typename MatrixType::Nested>&
+    nestedExpression() const
+    {
+      return m_matrix;
+    }
+
+  protected:
+    typename MatrixType::Nested m_matrix;
+};
+
+/** \returns an expression of the reverse of *this.
+  *
+  * Example: \include MatrixBase_reverse.cpp
+  * Output: \verbinclude MatrixBase_reverse.out
+  *
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::ReverseReturnType
+DenseBase<Derived>::reverse()
+{
+  return ReverseReturnType(derived());
+}
+
+
+//reverse const overload moved DenseBase.h due to a CUDA compiler bug
+
+/** This is the "in place" version of reverse: it reverses \c *this.
+  *
+  * In most cases it is probably better to simply use the reversed expression
+  * of a matrix. However, when reversing the matrix data itself is really needed,
+  * then this "in-place" version is probably the right choice because it provides
+  * the following additional benefits:
+  *  - less error prone: doing the same operation with .reverse() requires special care:
+  *    \code m = m.reverse().eval(); \endcode
+  *  - this API enables reverse operations without the need for a temporary
+  *  - it allows future optimizations (cache friendliness, etc.)
+  *
+  * \sa VectorwiseOp::reverseInPlace(), reverse() */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline void DenseBase<Derived>::reverseInPlace()
+{
+  if(cols()>rows())
+  {
+    Index half = cols()/2;
+    leftCols(half).swap(rightCols(half).reverse());
+    if((cols()%2)==1)
+    {
+      Index half2 = rows()/2;
+      col(half).head(half2).swap(col(half).tail(half2).reverse());
+    }
+  }
+  else
+  {
+    Index half = rows()/2;
+    topRows(half).swap(bottomRows(half).reverse());
+    if((rows()%2)==1)
+    {
+      Index half2 = cols()/2;
+      row(half).head(half2).swap(row(half).tail(half2).reverse());
+    }
+  }
+}
+
+namespace internal {
+
+template<int Direction>
+struct vectorwise_reverse_inplace_impl;
+
+template<>
+struct vectorwise_reverse_inplace_impl<Vertical>
+{
+  template<typename ExpressionType>
+  static void run(ExpressionType &xpr)
+  {
+    constexpr Index HalfAtCompileTime = ExpressionType::RowsAtCompileTime==Dynamic?Dynamic:ExpressionType::RowsAtCompileTime/2;
+    Index half = xpr.rows()/2;
+    xpr.template topRows<HalfAtCompileTime>(half)
+       .swap(xpr.template bottomRows<HalfAtCompileTime>(half).colwise().reverse());
+  }
+};
+
+template<>
+struct vectorwise_reverse_inplace_impl<Horizontal>
+{
+  template<typename ExpressionType>
+  static void run(ExpressionType &xpr)
+  {
+    constexpr Index HalfAtCompileTime = ExpressionType::ColsAtCompileTime==Dynamic?Dynamic:ExpressionType::ColsAtCompileTime/2;
+    Index half = xpr.cols()/2;
+    xpr.template leftCols<HalfAtCompileTime>(half)
+       .swap(xpr.template rightCols<HalfAtCompileTime>(half).rowwise().reverse());
+  }
+};
+
+} // end namespace internal
+
+/** This is the "in place" version of VectorwiseOp::reverse: it reverses each column or row of \c *this.
+  *
+  * In most cases it is probably better to simply use the reversed expression
+  * of a matrix. However, when reversing the matrix data itself is really needed,
+  * then this "in-place" version is probably the right choice because it provides
+  * the following additional benefits:
+  *  - less error prone: doing the same operation with .reverse() requires special care:
+  *    \code m = m.reverse().eval(); \endcode
+  *  - this API enables reverse operations without the need for a temporary
+  *
+  * \sa DenseBase::reverseInPlace(), reverse() */
+template<typename ExpressionType, int Direction>
+EIGEN_DEVICE_FUNC void VectorwiseOp<ExpressionType,Direction>::reverseInPlace()
+{
+  internal::vectorwise_reverse_inplace_impl<Direction>::run(m_matrix);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_REVERSE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Select.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Select.h
new file mode 100644
index 0000000000000000000000000000000000000000..fdc93c5cdb3f9f670236171596f6f1ccc64bfd92
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Select.h
@@ -0,0 +1,191 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELECT_H
+#define EIGEN_SELECT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class Select
+  * \ingroup Core_Module
+  *
+  * \brief Expression of a coefficient wise version of the C++ ternary operator ?:
+  *
+  * \tparam ConditionMatrixType the type of the \em condition expression which must be a boolean matrix
+  * \tparam ThenMatrixType the type of the \em then expression
+  * \tparam ElseMatrixType the type of the \em else expression
+  *
+  * This class represents an expression of a coefficient wise version of the C++ ternary operator ?:.
+  * It is the return type of DenseBase::select() and most of the time this is the only way it is used.
+  *
+  * \sa DenseBase::select(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const
+  */
+
+namespace internal {
+template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType>
+struct traits<Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >
+ : traits<ThenMatrixType>
+{
+  typedef typename traits<ThenMatrixType>::Scalar Scalar;
+  typedef Dense StorageKind;
+  typedef typename traits<ThenMatrixType>::XprKind XprKind;
+  typedef typename ConditionMatrixType::Nested ConditionMatrixNested;
+  typedef typename ThenMatrixType::Nested ThenMatrixNested;
+  typedef typename ElseMatrixType::Nested ElseMatrixNested;
+  enum {
+    RowsAtCompileTime = ConditionMatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = ConditionMatrixType::ColsAtCompileTime,
+    MaxRowsAtCompileTime = ConditionMatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = ConditionMatrixType::MaxColsAtCompileTime,
+    Flags = (unsigned int)ThenMatrixType::Flags & ElseMatrixType::Flags & RowMajorBit
+  };
+};
+}
+
+template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType>
+class Select : public internal::dense_xpr_base< Select<ConditionMatrixType, ThenMatrixType, ElseMatrixType> >::type,
+               internal::no_assignment_operator
+{
+  public:
+
+    typedef typename internal::dense_xpr_base<Select>::type Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Select)
+
+    inline EIGEN_DEVICE_FUNC
+    Select(const ConditionMatrixType& a_conditionMatrix,
+           const ThenMatrixType& a_thenMatrix,
+           const ElseMatrixType& a_elseMatrix)
+      : m_condition(a_conditionMatrix), m_then(a_thenMatrix), m_else(a_elseMatrix)
+    {
+      eigen_assert(m_condition.rows() == m_then.rows() && m_condition.rows() == m_else.rows());
+      eigen_assert(m_condition.cols() == m_then.cols() && m_condition.cols() == m_else.cols());
+    }
+
+    inline EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    Index rows() const EIGEN_NOEXCEPT { return m_condition.rows(); }
+    inline EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    Index cols() const EIGEN_NOEXCEPT { return m_condition.cols(); }
+
+    inline EIGEN_DEVICE_FUNC
+    const Scalar coeff(Index i, Index j) const
+    {
+      if (m_condition.coeff(i,j))
+        return m_then.coeff(i,j);
+      else
+        return m_else.coeff(i,j);
+    }
+
+    inline EIGEN_DEVICE_FUNC
+    const Scalar coeff(Index i) const
+    {
+      if (m_condition.coeff(i))
+        return m_then.coeff(i);
+      else
+        return m_else.coeff(i);
+    }
+
+    inline EIGEN_DEVICE_FUNC const ConditionMatrixType& conditionMatrix() const
+    {
+      return m_condition;
+    }
+
+    inline EIGEN_DEVICE_FUNC const ThenMatrixType& thenMatrix() const
+    {
+      return m_then;
+    }
+
+    inline EIGEN_DEVICE_FUNC const ElseMatrixType& elseMatrix() const
+    {
+      return m_else;
+    }
+
+  protected:
+    typename ConditionMatrixType::Nested m_condition;
+    typename ThenMatrixType::Nested m_then;
+    typename ElseMatrixType::Nested m_else;
+};
+
+/** \returns a matrix where each coefficient (i,j) is equal to \a thenMatrix(i,j)
+ * if \c *this(i,j) != Scalar(0), and \a elseMatrix(i,j) otherwise.
+ *
+ * Example: \include MatrixBase_select.cpp
+ * Output: \verbinclude MatrixBase_select.out
+ *
+ * \sa DenseBase::bitwiseSelect(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&)
+ */
+template <typename Derived>
+template <typename ThenDerived, typename ElseDerived>
+inline EIGEN_DEVICE_FUNC CwiseTernaryOp<
+    internal::scalar_boolean_select_op<typename DenseBase<ThenDerived>::Scalar,
+                                       typename DenseBase<ElseDerived>::Scalar,
+                                       typename DenseBase<Derived>::Scalar>,
+    ThenDerived, ElseDerived, Derived>
+DenseBase<Derived>::select(const DenseBase<ThenDerived>& thenMatrix,
+                           const DenseBase<ElseDerived>& elseMatrix) const {
+    using Op = internal::scalar_boolean_select_op<
+        typename DenseBase<ThenDerived>::Scalar,
+        typename DenseBase<ElseDerived>::Scalar, Scalar>;
+    return CwiseTernaryOp<Op, ThenDerived, ElseDerived, Derived>(
+        thenMatrix.derived(), elseMatrix.derived(), derived(), Op());
+}
+/** Version of DenseBase::select(const DenseBase&, const DenseBase&) with
+ * the \em else expression being a scalar value.
+ *
+ * \sa DenseBase::booleanSelect(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const, class Select
+ */
+template <typename Derived>
+template <typename ThenDerived>
+inline EIGEN_DEVICE_FUNC CwiseTernaryOp<
+    internal::scalar_boolean_select_op<typename DenseBase<ThenDerived>::Scalar,
+                                       typename DenseBase<ThenDerived>::Scalar,
+                                       typename DenseBase<Derived>::Scalar>,
+    ThenDerived, typename DenseBase<ThenDerived>::ConstantReturnType, Derived>
+DenseBase<Derived>::select(
+    const DenseBase<ThenDerived>& thenMatrix,
+    const typename DenseBase<ThenDerived>::Scalar& elseScalar) const {
+    using ElseConstantType =
+        typename DenseBase<ThenDerived>::ConstantReturnType;
+    using Op = internal::scalar_boolean_select_op<
+        typename DenseBase<ThenDerived>::Scalar,
+        typename DenseBase<ThenDerived>::Scalar, Scalar>;
+    return CwiseTernaryOp<Op, ThenDerived, ElseConstantType, Derived>(
+        thenMatrix.derived(), ElseConstantType(rows(), cols(), elseScalar),
+        derived(), Op());
+}
+/** Version of DenseBase::select(const DenseBase&, const DenseBase&) with
+ * the \em then expression being a scalar value.
+ *
+ * \sa DenseBase::booleanSelect(const DenseBase<ThenDerived>&, const DenseBase<ElseDerived>&) const, class Select
+ */
+template <typename Derived>
+template <typename ElseDerived>
+inline EIGEN_DEVICE_FUNC CwiseTernaryOp<
+    internal::scalar_boolean_select_op<typename DenseBase<ElseDerived>::Scalar,
+                                       typename DenseBase<ElseDerived>::Scalar,
+                                       typename DenseBase<Derived>::Scalar>,
+    typename DenseBase<ElseDerived>::ConstantReturnType, ElseDerived,
+    Derived>
+DenseBase<Derived>::select(
+    const typename DenseBase<ElseDerived>::Scalar& thenScalar,
+    const DenseBase<ElseDerived>& elseMatrix) const {
+    using ThenConstantType =
+        typename DenseBase<ElseDerived>::ConstantReturnType;
+    using Op = internal::scalar_boolean_select_op<
+        typename DenseBase<ElseDerived>::Scalar,
+        typename DenseBase<ElseDerived>::Scalar, Scalar>;
+    return CwiseTernaryOp<Op, ThenConstantType, ElseDerived, Derived>(
+        ThenConstantType(rows(), cols(), thenScalar), elseMatrix.derived(),
+        derived(), Op());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SELECT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/SelfAdjointView.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/SelfAdjointView.h
new file mode 100644
index 0000000000000000000000000000000000000000..7a930dbd2f2502c6b383e90972906a5a8a2334fb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/SelfAdjointView.h
@@ -0,0 +1,365 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELFADJOINTMATRIX_H
+#define EIGEN_SELFADJOINTMATRIX_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class SelfAdjointView
+  * \ingroup Core_Module
+  *
+  *
+  * \brief Expression of a selfadjoint matrix from a triangular part of a dense matrix
+  *
+  * \tparam MatrixType the type of the dense matrix storing the coefficients
+  * \tparam TriangularPart can be either \c #Lower or \c #Upper
+  *
+  * This class is an expression of a sefladjoint matrix from a triangular part of a matrix
+  * with given dense storage of the coefficients. It is the return type of MatrixBase::selfadjointView()
+  * and most of the time this is the only way that it is used.
+  *
+  * \sa class TriangularBase, MatrixBase::selfadjointView()
+  */
+
+namespace internal {
+template<typename MatrixType, unsigned int UpLo>
+struct traits<SelfAdjointView<MatrixType, UpLo> > : traits<MatrixType>
+{
+  typedef typename ref_selector<MatrixType>::non_const_type MatrixTypeNested;
+  typedef remove_all_t<MatrixTypeNested> MatrixTypeNestedCleaned;
+  typedef MatrixType ExpressionType;
+  typedef typename MatrixType::PlainObject FullMatrixType;
+  enum {
+    Mode = UpLo | SelfAdjoint,
+    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
+    Flags =  MatrixTypeNestedCleaned::Flags & (HereditaryBits|FlagsLvalueBit)
+           & (~(PacketAccessBit | DirectAccessBit | LinearAccessBit)) // FIXME these flags should be preserved
+  };
+};
+}
+
+
+template<typename MatrixType_, unsigned int UpLo> class SelfAdjointView
+  : public TriangularBase<SelfAdjointView<MatrixType_, UpLo> >
+{
+  public:
+    EIGEN_STATIC_ASSERT(UpLo==Lower || UpLo==Upper,SELFADJOINTVIEW_ACCEPTS_UPPER_AND_LOWER_MODE_ONLY)
+
+    typedef MatrixType_ MatrixType;
+    typedef TriangularBase<SelfAdjointView> Base;
+    typedef typename internal::traits<SelfAdjointView>::MatrixTypeNested MatrixTypeNested;
+    typedef typename internal::traits<SelfAdjointView>::MatrixTypeNestedCleaned MatrixTypeNestedCleaned;
+    typedef MatrixTypeNestedCleaned NestedExpression;
+
+    /** \brief The type of coefficients in this matrix */
+    typedef typename internal::traits<SelfAdjointView>::Scalar Scalar;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    typedef internal::remove_all_t<typename MatrixType::ConjugateReturnType> MatrixConjugateReturnType;
+    typedef SelfAdjointView<std::add_const_t<MatrixType>, UpLo> ConstSelfAdjointView;
+
+    enum {
+      Mode = internal::traits<SelfAdjointView>::Mode,
+      Flags = internal::traits<SelfAdjointView>::Flags,
+      TransposeMode = ((int(Mode) & int(Upper)) ? Lower : 0) | ((int(Mode) & int(Lower)) ? Upper : 0)
+    };
+    typedef typename MatrixType::PlainObject PlainObject;
+
+    EIGEN_DEVICE_FUNC
+    explicit inline SelfAdjointView(MatrixType& matrix) : m_matrix(matrix) { }
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index outerStride() const EIGEN_NOEXCEPT { return m_matrix.outerStride(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index innerStride() const EIGEN_NOEXCEPT { return m_matrix.innerStride(); }
+
+    /** \sa MatrixBase::coeff()
+      * \warning the coordinates must fit into the referenced triangular part
+      */
+    EIGEN_DEVICE_FUNC
+    inline Scalar coeff(Index row, Index col) const
+    {
+      Base::check_coordinates_internal(row, col);
+      return m_matrix.coeff(row, col);
+    }
+
+    /** \sa MatrixBase::coeffRef()
+      * \warning the coordinates must fit into the referenced triangular part
+      */
+    EIGEN_DEVICE_FUNC
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      EIGEN_STATIC_ASSERT_LVALUE(SelfAdjointView);
+      Base::check_coordinates_internal(row, col);
+      return m_matrix.coeffRef(row, col);
+    }
+
+    /** \internal */
+    EIGEN_DEVICE_FUNC
+    const MatrixTypeNestedCleaned& _expression() const { return m_matrix; }
+
+    EIGEN_DEVICE_FUNC
+    const MatrixTypeNestedCleaned& nestedExpression() const { return m_matrix; }
+    EIGEN_DEVICE_FUNC
+    MatrixTypeNestedCleaned& nestedExpression() { return m_matrix; }
+
+    /** Efficient triangular matrix times vector/matrix product */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    const Product<SelfAdjointView,OtherDerived>
+    operator*(const MatrixBase<OtherDerived>& rhs) const
+    {
+      return Product<SelfAdjointView,OtherDerived>(*this, rhs.derived());
+    }
+
+    /** Efficient vector/matrix times triangular matrix product */
+    template<typename OtherDerived> friend
+    EIGEN_DEVICE_FUNC
+    const Product<OtherDerived,SelfAdjointView>
+    operator*(const MatrixBase<OtherDerived>& lhs, const SelfAdjointView& rhs)
+    {
+      return Product<OtherDerived,SelfAdjointView>(lhs.derived(),rhs);
+    }
+
+    friend EIGEN_DEVICE_FUNC
+    const SelfAdjointView<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar,MatrixType,product),UpLo>
+    operator*(const Scalar& s, const SelfAdjointView& mat)
+    {
+      return (s*mat.nestedExpression()).template selfadjointView<UpLo>();
+    }
+
+    /** Perform a symmetric rank 2 update of the selfadjoint matrix \c *this:
+      * \f$ this = this + \alpha u v^* + conj(\alpha) v u^* \f$
+      * \returns a reference to \c *this
+      *
+      * The vectors \a u and \c v \b must be column vectors, however they can be
+      * a adjoint expression without any overhead. Only the meaningful triangular
+      * part of the matrix is updated, the rest is left unchanged.
+      *
+      * \sa rankUpdate(const MatrixBase<DerivedU>&, Scalar)
+      */
+    template<typename DerivedU, typename DerivedV>
+    EIGEN_DEVICE_FUNC
+    SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, const Scalar& alpha = Scalar(1));
+
+    /** Perform a symmetric rank K update of the selfadjoint matrix \c *this:
+      * \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix.
+      *
+      * \returns a reference to \c *this
+      *
+      * Note that to perform \f$ this = this + \alpha ( u^* u ) \f$ you can simply
+      * call this function with u.adjoint().
+      *
+      * \sa rankUpdate(const MatrixBase<DerivedU>&, const MatrixBase<DerivedV>&, Scalar)
+      */
+    template<typename DerivedU>
+    EIGEN_DEVICE_FUNC
+    SelfAdjointView& rankUpdate(const MatrixBase<DerivedU>& u, const Scalar& alpha = Scalar(1));
+
+    /** \returns an expression of a triangular view extracted from the current selfadjoint view of a given triangular part
+      *
+      * The parameter \a TriMode can have the following values: \c #Upper, \c #StrictlyUpper, \c #UnitUpper,
+      * \c #Lower, \c #StrictlyLower, \c #UnitLower.
+      *
+      * If \c TriMode references the same triangular part than \c *this, then this method simply return a \c TriangularView of the nested expression,
+      * otherwise, the nested expression is first transposed, thus returning a \c TriangularView<Transpose<MatrixType>> object.
+      *
+      * \sa MatrixBase::triangularView(), class TriangularView
+      */
+    template<unsigned int TriMode>
+    EIGEN_DEVICE_FUNC
+    std::conditional_t<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)),
+                            TriangularView<MatrixType,TriMode>,
+                            TriangularView<typename MatrixType::AdjointReturnType,TriMode> >
+    triangularView() const
+    {
+      std::conditional_t<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)), MatrixType&, typename MatrixType::ConstTransposeReturnType> tmp1(m_matrix);
+      std::conditional_t<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)), MatrixType&, typename MatrixType::AdjointReturnType> tmp2(tmp1);
+      return std::conditional_t<(TriMode&(Upper|Lower))==(UpLo&(Upper|Lower)),
+                          TriangularView<MatrixType,TriMode>,
+                          TriangularView<typename MatrixType::AdjointReturnType,TriMode> >(tmp2);
+    }
+
+    typedef SelfAdjointView<const MatrixConjugateReturnType,UpLo> ConjugateReturnType;
+    /** \sa MatrixBase::conjugate() const */
+    EIGEN_DEVICE_FUNC
+    inline const ConjugateReturnType conjugate() const
+    { return ConjugateReturnType(m_matrix.conjugate()); }
+
+    /** \returns an expression of the complex conjugate of \c *this if Cond==true,
+     *           returns \c *this otherwise.
+     */
+    template<bool Cond>
+    EIGEN_DEVICE_FUNC
+    inline std::conditional_t<Cond,ConjugateReturnType,ConstSelfAdjointView>
+    conjugateIf() const
+    {
+      typedef std::conditional_t<Cond,ConjugateReturnType,ConstSelfAdjointView> ReturnType;
+      return ReturnType(m_matrix.template conjugateIf<Cond>());
+    }
+
+    typedef SelfAdjointView<const typename MatrixType::AdjointReturnType,TransposeMode> AdjointReturnType;
+    /** \sa MatrixBase::adjoint() const */
+    EIGEN_DEVICE_FUNC
+    inline const AdjointReturnType adjoint() const
+    { return AdjointReturnType(m_matrix.adjoint()); }
+
+    typedef SelfAdjointView<typename MatrixType::TransposeReturnType,TransposeMode> TransposeReturnType;
+     /** \sa MatrixBase::transpose() */
+    template<class Dummy=int>
+    EIGEN_DEVICE_FUNC
+    inline TransposeReturnType transpose(std::enable_if_t<Eigen::internal::is_lvalue<MatrixType>::value, Dummy*> = nullptr)
+    {
+      typename MatrixType::TransposeReturnType tmp(m_matrix);
+      return TransposeReturnType(tmp);
+    }
+
+    typedef SelfAdjointView<const typename MatrixType::ConstTransposeReturnType,TransposeMode> ConstTransposeReturnType;
+    /** \sa MatrixBase::transpose() const */
+    EIGEN_DEVICE_FUNC
+    inline const ConstTransposeReturnType transpose() const
+    {
+      return ConstTransposeReturnType(m_matrix.transpose());
+    }
+
+    /** \returns a const expression of the main diagonal of the matrix \c *this
+      *
+      * This method simply returns the diagonal of the nested expression, thus by-passing the SelfAdjointView decorator.
+      *
+      * \sa MatrixBase::diagonal(), class Diagonal */
+    EIGEN_DEVICE_FUNC
+    typename MatrixType::ConstDiagonalReturnType diagonal() const
+    {
+      return typename MatrixType::ConstDiagonalReturnType(m_matrix);
+    }
+
+/////////// Cholesky module ///////////
+
+    const LLT<PlainObject, UpLo> llt() const;
+    const LDLT<PlainObject, UpLo> ldlt() const;
+
+/////////// Eigenvalue module ///////////
+
+    /** Real part of #Scalar */
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    /** Return type of eigenvalues() */
+    typedef Matrix<RealScalar, internal::traits<MatrixType>::ColsAtCompileTime, 1> EigenvaluesReturnType;
+
+    EIGEN_DEVICE_FUNC
+    EigenvaluesReturnType eigenvalues() const;
+    EIGEN_DEVICE_FUNC
+    RealScalar operatorNorm() const;
+
+  protected:
+    MatrixTypeNested m_matrix;
+};
+
+
+// template<typename OtherDerived, typename MatrixType, unsigned int UpLo>
+// internal::selfadjoint_matrix_product_returntype<OtherDerived,SelfAdjointView<MatrixType,UpLo> >
+// operator*(const MatrixBase<OtherDerived>& lhs, const SelfAdjointView<MatrixType,UpLo>& rhs)
+// {
+//   return internal::matrix_selfadjoint_product_returntype<OtherDerived,SelfAdjointView<MatrixType,UpLo> >(lhs.derived(),rhs);
+// }
+
+// selfadjoint to dense matrix
+
+namespace internal {
+
+// TODO currently a selfadjoint expression has the form SelfAdjointView<.,.>
+//      in the future selfadjoint-ness should be defined by the expression traits
+//      such that Transpose<SelfAdjointView<.,.> > is valid. (currently TriangularBase::transpose() is overloaded to make it work)
+template<typename MatrixType, unsigned int Mode>
+struct evaluator_traits<SelfAdjointView<MatrixType,Mode> >
+{
+  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
+  typedef SelfAdjointShape Shape;
+};
+
+template<int UpLo, int SetOpposite, typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version>
+class triangular_dense_assignment_kernel<UpLo,SelfAdjoint,SetOpposite,DstEvaluatorTypeT,SrcEvaluatorTypeT,Functor,Version>
+  : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>
+{
+protected:
+  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version> Base;
+  typedef typename Base::DstXprType DstXprType;
+  typedef typename Base::SrcXprType SrcXprType;
+  using Base::m_dst;
+  using Base::m_src;
+  using Base::m_functor;
+public:
+
+  typedef typename Base::DstEvaluatorType DstEvaluatorType;
+  typedef typename Base::SrcEvaluatorType SrcEvaluatorType;
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::AssignmentTraits AssignmentTraits;
+
+
+  EIGEN_DEVICE_FUNC triangular_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
+    : Base(dst, src, func, dstExpr)
+  {}
+
+  EIGEN_DEVICE_FUNC void assignCoeff(Index row, Index col)
+  {
+    eigen_internal_assert(row!=col);
+    Scalar tmp = m_src.coeff(row,col);
+    m_functor.assignCoeff(m_dst.coeffRef(row,col), tmp);
+    m_functor.assignCoeff(m_dst.coeffRef(col,row), numext::conj(tmp));
+  }
+
+  EIGEN_DEVICE_FUNC void assignDiagonalCoeff(Index id)
+  {
+    Base::assignCoeff(id,id);
+  }
+
+  EIGEN_DEVICE_FUNC void assignOppositeCoeff(Index, Index)
+  { eigen_internal_assert(false && "should never be called"); }
+};
+
+} // end namespace internal
+
+/***************************************************************************
+* Implementation of MatrixBase methods
+***************************************************************************/
+
+/** This is the const version of MatrixBase::selfadjointView() */
+template<typename Derived>
+template<unsigned int UpLo>
+EIGEN_DEVICE_FUNC typename MatrixBase<Derived>::template ConstSelfAdjointViewReturnType<UpLo>::Type
+MatrixBase<Derived>::selfadjointView() const
+{
+  return typename ConstSelfAdjointViewReturnType<UpLo>::Type(derived());
+}
+
+/** \returns an expression of a symmetric/self-adjoint view extracted from the upper or lower triangular part of the current matrix
+  *
+  * The parameter \a UpLo can be either \c #Upper or \c #Lower
+  *
+  * Example: \include MatrixBase_selfadjointView.cpp
+  * Output: \verbinclude MatrixBase_selfadjointView.out
+  *
+  * \sa class SelfAdjointView
+  */
+template<typename Derived>
+template<unsigned int UpLo>
+EIGEN_DEVICE_FUNC typename MatrixBase<Derived>::template SelfAdjointViewReturnType<UpLo>::Type
+MatrixBase<Derived>::selfadjointView()
+{
+  return typename SelfAdjointViewReturnType<UpLo>::Type(derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SELFADJOINTMATRIX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/SelfCwiseBinaryOp.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/SelfCwiseBinaryOp.h
new file mode 100644
index 0000000000000000000000000000000000000000..14dbec0001e233bdcf86879b538b1da4d0446e80
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/SelfCwiseBinaryOp.h
@@ -0,0 +1,49 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELFCWISEBINARYOP_H
+#define EIGEN_SELFCWISEBINARYOP_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+// TODO generalize the scalar type of 'other'
+
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator*=(const Scalar& other)
+{
+  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::mul_assign_op<Scalar,Scalar>());
+  return derived();
+}
+
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator+=(const Scalar& other)
+{
+  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::add_assign_op<Scalar,Scalar>());
+  return derived();
+}
+
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& ArrayBase<Derived>::operator-=(const Scalar& other)
+{
+  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::sub_assign_op<Scalar,Scalar>());
+  return derived();
+}
+
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator/=(const Scalar& other)
+{
+  internal::call_assignment(this->derived(), PlainObject::Constant(rows(),cols(),other), internal::div_assign_op<Scalar,Scalar>());
+  return derived();
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SELFCWISEBINARYOP_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/SkewSymmetricMatrix3.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/SkewSymmetricMatrix3.h
new file mode 100644
index 0000000000000000000000000000000000000000..5efbc4432b9f564b9c1f5732a82063c3e6faee13
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/SkewSymmetricMatrix3.h
@@ -0,0 +1,414 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SKEWSYMMETRICMATRIX3_H
+#define EIGEN_SKEWSYMMETRICMATRIX3_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class SkewSymmetricBase
+ * \ingroup Core_Module
+ *
+ * \brief Base class for skew symmetric matrices and expressions
+ *
+ * This is the base class that is inherited by SkewSymmetricMatrix3 and related expression
+ * types, which internally use a three vector for storing the entries. SkewSymmetric
+ * types always represent square three times three matrices.
+ *
+ * This implementations follows class DiagonalMatrix
+ *
+ * \tparam Derived is the derived type, a SkewSymmetricMatrix3 or SkewSymmetricWrapper.
+ *
+ * \sa class SkewSymmetricMatrix3, class SkewSymmetricWrapper
+ */
+template<typename Derived>
+class SkewSymmetricBase : public EigenBase<Derived>
+{
+  public:
+    typedef typename internal::traits<Derived>::SkewSymmetricVectorType SkewSymmetricVectorType;
+    typedef typename SkewSymmetricVectorType::Scalar Scalar;
+    typedef typename SkewSymmetricVectorType::RealScalar RealScalar;
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+
+    enum {
+      RowsAtCompileTime = SkewSymmetricVectorType::SizeAtCompileTime,
+      ColsAtCompileTime = SkewSymmetricVectorType::SizeAtCompileTime,
+      MaxRowsAtCompileTime = SkewSymmetricVectorType::MaxSizeAtCompileTime,
+      MaxColsAtCompileTime = SkewSymmetricVectorType::MaxSizeAtCompileTime,
+      IsVectorAtCompileTime = 0,
+      Flags = NoPreferredStorageOrderBit
+    };
+
+    typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime, 0, MaxRowsAtCompileTime, MaxColsAtCompileTime> DenseMatrixType;
+    typedef DenseMatrixType DenseType;
+    typedef SkewSymmetricMatrix3<Scalar> PlainObject;
+
+    /** \returns a reference to the derived object. */
+    EIGEN_DEVICE_FUNC
+    inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
+    /** \returns a const reference to the derived object. */
+    EIGEN_DEVICE_FUNC
+    inline Derived& derived() { return *static_cast<Derived*>(this); }
+
+    /**
+     * Constructs a dense matrix from \c *this. Note, this directly returns a dense matrix type,
+     * not an expression.
+     * \returns A dense matrix, with its entries set from the the derived object. */
+    EIGEN_DEVICE_FUNC
+    DenseMatrixType toDenseMatrix() const { return derived(); }
+
+    /** Determinant vanishes */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Scalar determinant() const { return 0; }
+
+    /** A.transpose() = -A */
+    EIGEN_DEVICE_FUNC
+    PlainObject transpose() const { return (-vector()).asSkewSymmetric(); }
+
+    /** \returns the exponential of this matrix using Rodrigues’ formula */
+    EIGEN_DEVICE_FUNC
+    DenseMatrixType exponential() const {
+      DenseMatrixType retVal = DenseMatrixType::Identity();
+      const SkewSymmetricVectorType& v = vector();
+      if (v.isZero()) {
+        return retVal;
+      }
+      const Scalar norm2 = v.squaredNorm();
+      const Scalar norm = numext::sqrt(norm2);
+      retVal += ((((1 - numext::cos(norm))/norm2)*derived())*derived()) + (numext::sin(norm)/norm)*derived().toDenseMatrix();
+      return retVal;
+    }
+
+    /** \returns a reference to the derived object's vector of coefficients. */
+    EIGEN_DEVICE_FUNC
+    inline const SkewSymmetricVectorType& vector() const { return derived().vector(); }
+    /** \returns a const reference to the derived object's vector of coefficients. */
+    EIGEN_DEVICE_FUNC
+    inline SkewSymmetricVectorType& vector() { return derived().vector(); }
+
+    /** \returns the number of rows. */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR 
+    inline Index rows() const { return 3; }
+    /** \returns the number of columns. */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR 
+    inline Index cols() const { return 3; }
+
+    /** \returns the matrix product of \c *this by the dense matrix, \a matrix */
+    template<typename MatrixDerived>
+    EIGEN_DEVICE_FUNC
+    Product<Derived,MatrixDerived,LazyProduct>
+    operator*(const MatrixBase<MatrixDerived> &matrix) const
+    {
+      return Product<Derived, MatrixDerived, LazyProduct>(derived(), matrix.derived());
+    }
+
+    /** \returns the matrix product of \c *this by the skew symmetric matrix, \a matrix */
+    template<typename MatrixDerived>
+    EIGEN_DEVICE_FUNC
+    Product<Derived,MatrixDerived,LazyProduct>
+    operator*(const SkewSymmetricBase<MatrixDerived> &matrix) const
+    {
+      return Product<Derived, MatrixDerived, LazyProduct>(derived(), matrix.derived());
+    }
+
+    template <typename OtherDerived>
+    using SkewSymmetricProductReturnType = SkewSymmetricWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
+        SkewSymmetricVectorType, typename OtherDerived::SkewSymmetricVectorType, product)>;
+
+    /** \returns the wedge product of \c *this by the skew symmetric matrix \a other
+     *  A wedge B = AB - BA */
+    template <typename OtherDerived>
+    EIGEN_DEVICE_FUNC SkewSymmetricProductReturnType<OtherDerived> wedge(
+        const SkewSymmetricBase<OtherDerived>& other) const {
+      return vector().cross(other.vector()).asSkewSymmetric();
+    }
+
+    using SkewSymmetricScaleReturnType =
+        SkewSymmetricWrapper<const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(SkewSymmetricVectorType, Scalar, product)>;
+
+    /** \returns the product of \c *this by the scalar \a scalar */
+    EIGEN_DEVICE_FUNC
+    inline SkewSymmetricScaleReturnType operator*(const Scalar& scalar) const {
+      return (vector() * scalar).asSkewSymmetric();
+    }
+
+    using ScaleSkewSymmetricReturnType =
+        SkewSymmetricWrapper<const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(Scalar, SkewSymmetricVectorType, product)>;
+
+    /** \returns the product of a scalar and the skew symmetric matrix \a other */
+    EIGEN_DEVICE_FUNC
+    friend inline ScaleSkewSymmetricReturnType operator*(const Scalar& scalar, const SkewSymmetricBase& other) {
+      return (scalar * other.vector()).asSkewSymmetric();
+    }
+
+    template <typename OtherDerived>
+    using SkewSymmetricSumReturnType = SkewSymmetricWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
+        SkewSymmetricVectorType, typename OtherDerived::SkewSymmetricVectorType, sum)>;
+
+    /** \returns the sum of \c *this and the skew symmetric matrix \a other */
+    template <typename OtherDerived>
+    EIGEN_DEVICE_FUNC inline SkewSymmetricSumReturnType<OtherDerived> operator+(
+        const SkewSymmetricBase<OtherDerived>& other) const {
+      return (vector() + other.vector()).asSkewSymmetric();
+    }
+
+    template <typename OtherDerived>
+    using SkewSymmetricDifferenceReturnType = SkewSymmetricWrapper<const EIGEN_CWISE_BINARY_RETURN_TYPE(
+        SkewSymmetricVectorType, typename OtherDerived::SkewSymmetricVectorType, difference)>;
+
+    /** \returns the difference of \c *this and the skew symmetric matrix \a other */
+    template <typename OtherDerived>
+    EIGEN_DEVICE_FUNC inline SkewSymmetricDifferenceReturnType<OtherDerived> operator-(
+        const SkewSymmetricBase<OtherDerived>& other) const {
+      return (vector() - other.vector()).asSkewSymmetric();
+    }
+};
+
+/** \class SkewSymmetricMatrix3
+ * \ingroup Core_Module
+ *
+ * \brief Represents a 3x3 skew symmetric matrix with its storage
+ *
+ * \tparam Scalar_ the type of coefficients
+ *
+ * \sa class SkewSymmetricBase, class SkewSymmetricWrapper
+ */
+
+namespace internal {
+template<typename Scalar_>
+struct traits<SkewSymmetricMatrix3<Scalar_> >
+ : traits<Matrix<Scalar_,3,3,0,3,3> >
+{
+  typedef Matrix<Scalar_,3,1,0,3,1> SkewSymmetricVectorType;
+  typedef SkewSymmetricShape StorageKind;
+  enum {
+    Flags = LvalueBit | NoPreferredStorageOrderBit | NestByRefBit
+  };
+};
+}
+template<typename Scalar_>
+class SkewSymmetricMatrix3
+  : public SkewSymmetricBase<SkewSymmetricMatrix3<Scalar_> >
+{
+  public:
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    typedef typename internal::traits<SkewSymmetricMatrix3>::SkewSymmetricVectorType SkewSymmetricVectorType;
+    typedef const SkewSymmetricMatrix3& Nested;
+    typedef Scalar_ Scalar;
+    typedef typename internal::traits<SkewSymmetricMatrix3>::StorageKind StorageKind;
+    typedef typename internal::traits<SkewSymmetricMatrix3>::StorageIndex StorageIndex;
+    #endif
+
+  protected:
+
+    SkewSymmetricVectorType m_vector;
+
+  public:
+
+    /** const version of vector(). */
+    EIGEN_DEVICE_FUNC
+    inline const SkewSymmetricVectorType& vector() const { return m_vector; }
+    /** \returns a reference to the stored vector of coefficients. */
+    EIGEN_DEVICE_FUNC
+    inline SkewSymmetricVectorType& vector() { return m_vector; }
+
+    /** Default constructor without initialization */
+    EIGEN_DEVICE_FUNC
+    inline SkewSymmetricMatrix3() {}
+
+    /** Constructor from three scalars */
+    EIGEN_DEVICE_FUNC
+    inline SkewSymmetricMatrix3(const Scalar& x, const Scalar& y, const Scalar& z) : m_vector(x,y,z) {}
+
+    /** \brief Constructs a SkewSymmetricMatrix3 from an r-value vector type */
+    EIGEN_DEVICE_FUNC
+    explicit inline SkewSymmetricMatrix3(SkewSymmetricVectorType&& vec) : m_vector(std::move(vec)) {}
+
+    /** generic constructor from expression of the coefficients */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+        explicit inline SkewSymmetricMatrix3(const MatrixBase<OtherDerived>& other) : m_vector(other)
+    {}
+
+    /** Copy constructor. */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    inline SkewSymmetricMatrix3(const SkewSymmetricBase<OtherDerived>& other) : m_vector(other.vector()) {}
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** copy constructor. prevent a default copy constructor from hiding the other templated constructor */
+    inline SkewSymmetricMatrix3(const SkewSymmetricMatrix3& other) : m_vector(other.vector()) {}
+    #endif
+
+    /** Copy operator. */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    SkewSymmetricMatrix3& operator=(const SkewSymmetricBase<OtherDerived>& other)
+    {
+      m_vector = other.vector();
+      return *this;
+    }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** This is a special case of the templated operator=. Its purpose is to
+      * prevent a default operator= from hiding the templated operator=.
+      */
+    EIGEN_DEVICE_FUNC
+    SkewSymmetricMatrix3& operator=(const SkewSymmetricMatrix3& other)
+    {
+      m_vector = other.vector();
+      return *this;
+    }
+    #endif
+
+    typedef SkewSymmetricWrapper<const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, SkewSymmetricVectorType>>
+        InitializeReturnType;
+
+    /** Initializes a skew symmetric matrix with coefficients set to zero */
+    EIGEN_DEVICE_FUNC
+    static InitializeReturnType Zero() { return SkewSymmetricVectorType::Zero().asSkewSymmetric(); }
+
+    /** Sets all coefficients to zero. */
+    EIGEN_DEVICE_FUNC
+    inline void setZero() { m_vector.setZero(); }
+};
+
+/** \class SkewSymmetricWrapper
+  * \ingroup Core_Module
+  *
+  * \brief Expression of a skew symmetric matrix
+  *
+  * \tparam SkewSymmetricVectorType_ the type of the vector of coefficients
+  *
+  * This class is an expression of a skew symmetric matrix, but not storing its own vector of coefficients,
+  * instead wrapping an existing vector expression. It is the return type of MatrixBase::asSkewSymmetric()
+  * and most of the time this is the only way that it is used.
+  *
+  * \sa class SkewSymmetricMatrix3, class SkewSymmetricBase, MatrixBase::asSkewSymmetric()
+  */
+
+namespace internal {
+template<typename SkewSymmetricVectorType_>
+struct traits<SkewSymmetricWrapper<SkewSymmetricVectorType_> >
+{
+  typedef SkewSymmetricVectorType_ SkewSymmetricVectorType;
+  typedef typename SkewSymmetricVectorType::Scalar Scalar;
+  typedef typename SkewSymmetricVectorType::StorageIndex StorageIndex;
+  typedef SkewSymmetricShape StorageKind;
+  typedef typename traits<SkewSymmetricVectorType>::XprKind XprKind;
+  enum {
+    RowsAtCompileTime = SkewSymmetricVectorType::SizeAtCompileTime,
+    ColsAtCompileTime = SkewSymmetricVectorType::SizeAtCompileTime,
+    MaxRowsAtCompileTime = SkewSymmetricVectorType::MaxSizeAtCompileTime,
+    MaxColsAtCompileTime = SkewSymmetricVectorType::MaxSizeAtCompileTime,
+    Flags =  (traits<SkewSymmetricVectorType>::Flags & LvalueBit) | NoPreferredStorageOrderBit
+  };
+};
+}
+
+template<typename SkewSymmetricVectorType_>
+class SkewSymmetricWrapper
+  : public SkewSymmetricBase<SkewSymmetricWrapper<SkewSymmetricVectorType_> >, internal::no_assignment_operator
+{
+  public:
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    typedef SkewSymmetricVectorType_ SkewSymmetricVectorType;
+    typedef SkewSymmetricWrapper Nested;
+    #endif
+
+    /** Constructor from expression of coefficients to wrap. */
+    EIGEN_DEVICE_FUNC
+    explicit inline SkewSymmetricWrapper(SkewSymmetricVectorType& a_vector) : m_vector(a_vector) {}
+
+    /** \returns a const reference to the wrapped expression of coefficients. */
+    EIGEN_DEVICE_FUNC
+    const SkewSymmetricVectorType& vector() const { return m_vector; }
+
+  protected:
+    typename SkewSymmetricVectorType::Nested m_vector;
+};
+
+/** \returns a pseudo-expression of a skew symmetric matrix with *this as vector of coefficients
+  *
+  * \only_for_vectors
+  *
+  * \sa class SkewSymmetricWrapper, class SkewSymmetricMatrix3, vector(), isSkewSymmetric()
+  **/
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline const SkewSymmetricWrapper<const Derived>
+MatrixBase<Derived>::asSkewSymmetric() const
+{
+  return SkewSymmetricWrapper<const Derived>(derived());
+}
+
+/** \returns true if *this is approximately equal to a skew symmetric matrix,
+  *          within the precision given by \a prec.
+  */
+template<typename Derived>
+bool MatrixBase<Derived>::isSkewSymmetric(const RealScalar& prec) const
+{
+  if(cols() != rows()) return false;
+  return (this->transpose() + *this).isZero(prec);
+}
+
+/** \returns the matrix product of \c *this by the skew symmetric matrix \skew.
+ */
+template<typename Derived>
+template<typename SkewDerived>
+EIGEN_DEVICE_FUNC inline const Product<Derived, SkewDerived, LazyProduct>
+MatrixBase<Derived>::operator*(const SkewSymmetricBase<SkewDerived> &skew) const
+{
+  return Product<Derived, SkewDerived, LazyProduct>(derived(), skew.derived());
+}
+
+namespace internal {
+
+template<> struct storage_kind_to_shape<SkewSymmetricShape> { typedef SkewSymmetricShape Shape; };
+
+struct SkewSymmetric2Dense {};
+
+template<> struct AssignmentKind<DenseShape,SkewSymmetricShape> { typedef SkewSymmetric2Dense Kind; };
+
+// SkewSymmetric matrix to Dense assignment
+template< typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, SkewSymmetric2Dense>
+{
+  EIGEN_DEVICE_FUNC
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
+  {
+    if((dst.rows()!=3) || (dst.cols()!=3)) {
+      dst.resize(3, 3);
+    }
+    dst.diagonal().setZero();
+    const typename SrcXprType::SkewSymmetricVectorType v = src.vector();
+    dst(0, 1) = -v(2);
+    dst(1, 0) = v(2);
+    dst(0, 2) = v(1);
+    dst(2, 0) = -v(1);
+    dst(1, 2) = -v(0);
+    dst(2, 1) = v(0);
+  }
+  EIGEN_DEVICE_FUNC
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
+  { dst.vector() += src.vector(); }
+
+  EIGEN_DEVICE_FUNC
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
+  { dst.vector() -= src.vector(); }
+};
+
+} // namespace internal
+
+}  // end namespace Eigen
+
+#endif // EIGEN_SKEWSYMMETRICMATRIX3_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Solve.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Solve.h
new file mode 100644
index 0000000000000000000000000000000000000000..f77eac91797e791eec4f5de70fa2ea0e56d291d2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Solve.h
@@ -0,0 +1,190 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SOLVE_H
+#define EIGEN_SOLVE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template<typename Decomposition, typename RhsType, typename StorageKind> class SolveImpl;
+
+/** \class Solve
+  * \ingroup Core_Module
+  *
+  * \brief Pseudo expression representing a solving operation
+  *
+  * \tparam Decomposition the type of the matrix or decomposition object
+  * \tparam Rhstype the type of the right-hand side
+  *
+  * This class represents an expression of A.solve(B)
+  * and most of the time this is the only way it is used.
+  *
+  */
+namespace internal {
+
+// this solve_traits class permits to determine the evaluation type with respect to storage kind (Dense vs Sparse)
+template<typename Decomposition, typename RhsType,typename StorageKind> struct solve_traits;
+
+template<typename Decomposition, typename RhsType>
+struct solve_traits<Decomposition,RhsType,Dense>
+{
+  typedef typename make_proper_matrix_type<typename RhsType::Scalar,
+                 Decomposition::ColsAtCompileTime,
+                 RhsType::ColsAtCompileTime,
+                 RhsType::PlainObject::Options,
+                 Decomposition::MaxColsAtCompileTime,
+                 RhsType::MaxColsAtCompileTime>::type PlainObject;
+};
+
+template<typename Decomposition, typename RhsType>
+struct traits<Solve<Decomposition, RhsType> >
+  : traits<typename solve_traits<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>::PlainObject>
+{
+  typedef typename solve_traits<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>::PlainObject PlainObject;
+  typedef typename promote_index_type<typename Decomposition::StorageIndex, typename RhsType::StorageIndex>::type StorageIndex;
+  typedef traits<PlainObject> BaseTraits;
+  enum {
+    Flags = BaseTraits::Flags & RowMajorBit,
+    CoeffReadCost = HugeCost
+  };
+};
+
+}
+
+
+template<typename Decomposition, typename RhsType>
+class Solve : public SolveImpl<Decomposition,RhsType,typename internal::traits<RhsType>::StorageKind>
+{
+public:
+  typedef typename internal::traits<Solve>::PlainObject PlainObject;
+  typedef typename internal::traits<Solve>::StorageIndex StorageIndex;
+
+  Solve(const Decomposition &dec, const RhsType &rhs)
+    : m_dec(dec), m_rhs(rhs)
+  {}
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_dec.cols(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
+
+  EIGEN_DEVICE_FUNC const Decomposition& dec() const { return m_dec; }
+  EIGEN_DEVICE_FUNC const RhsType&       rhs() const { return m_rhs; }
+
+protected:
+  const Decomposition &m_dec;
+  const typename internal::ref_selector<RhsType>::type m_rhs;
+};
+
+
+// Specialization of the Solve expression for dense results
+template<typename Decomposition, typename RhsType>
+class SolveImpl<Decomposition,RhsType,Dense>
+  : public MatrixBase<Solve<Decomposition,RhsType> >
+{
+  typedef Solve<Decomposition,RhsType> Derived;
+
+public:
+
+  typedef MatrixBase<Solve<Decomposition,RhsType> > Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(Derived)
+
+private:
+
+  Scalar coeff(Index row, Index col) const;
+  Scalar coeff(Index i) const;
+};
+
+// Generic API dispatcher
+template<typename Decomposition, typename RhsType, typename StorageKind>
+class SolveImpl : public internal::generic_xpr_base<Solve<Decomposition,RhsType>, MatrixXpr, StorageKind>::type
+{
+  public:
+    typedef typename internal::generic_xpr_base<Solve<Decomposition,RhsType>, MatrixXpr, StorageKind>::type Base;
+};
+
+namespace internal {
+
+// Evaluator of Solve -> eval into a temporary
+template<typename Decomposition, typename RhsType>
+struct evaluator<Solve<Decomposition,RhsType> >
+  : public evaluator<typename Solve<Decomposition,RhsType>::PlainObject>
+{
+  typedef Solve<Decomposition,RhsType> SolveType;
+  typedef typename SolveType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  enum { Flags = Base::Flags | EvalBeforeNestingBit };
+
+  EIGEN_DEVICE_FUNC explicit evaluator(const SolveType& solve)
+    : m_result(solve.rows(), solve.cols())
+  {
+    internal::construct_at<Base>(this, m_result);
+    solve.dec()._solve_impl(solve.rhs(), m_result);
+  }
+
+protected:
+  PlainObject m_result;
+};
+
+// Specialization for "dst = dec.solve(rhs)"
+// NOTE we need to specialize it for Dense2Dense to avoid ambiguous specialization error and a Sparse2Sparse specialization must exist somewhere
+template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
+struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar,Scalar>, Dense2Dense>
+{
+  typedef Solve<DecType,RhsType> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
+  {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+      dst.resize(dstRows, dstCols);
+
+    src.dec()._solve_impl(src.rhs(), dst);
+  }
+};
+
+// Specialization for "dst = dec.transpose().solve(rhs)"
+template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
+struct Assignment<DstXprType, Solve<Transpose<const DecType>,RhsType>, internal::assign_op<Scalar,Scalar>, Dense2Dense>
+{
+  typedef Solve<Transpose<const DecType>,RhsType> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
+  {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+      dst.resize(dstRows, dstCols);
+
+    src.dec().nestedExpression().template _solve_impl_transposed<false>(src.rhs(), dst);
+  }
+};
+
+// Specialization for "dst = dec.adjoint().solve(rhs)"
+template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
+struct Assignment<DstXprType, Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,RhsType>,
+                  internal::assign_op<Scalar,Scalar>, Dense2Dense>
+{
+  typedef Solve<CwiseUnaryOp<internal::scalar_conjugate_op<typename DecType::Scalar>, const Transpose<const DecType> >,RhsType> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
+  {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+      dst.resize(dstRows, dstCols);
+
+    src.dec().nestedExpression().nestedExpression().template _solve_impl_transposed<true>(src.rhs(), dst);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SOLVE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/SolveTriangular.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/SolveTriangular.h
new file mode 100644
index 0000000000000000000000000000000000000000..23df5082a081eb977d622ca76fb6cbb6aba30cfa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/SolveTriangular.h
@@ -0,0 +1,242 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SOLVETRIANGULAR_H
+#define EIGEN_SOLVETRIANGULAR_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Forward declarations:
+// The following two routines are implemented in the products/TriangularSolver*.h files
+template<typename LhsScalar, typename RhsScalar, typename Index, int Side, int Mode, bool Conjugate, int StorageOrder>
+struct triangular_solve_vector;
+
+template <typename Scalar, typename Index, int Side, int Mode, bool Conjugate, int TriStorageOrder, int OtherStorageOrder, int OtherInnerStride>
+struct triangular_solve_matrix;
+
+// small helper struct extracting some traits on the underlying solver operation
+template<typename Lhs, typename Rhs, int Side>
+class trsolve_traits
+{
+  private:
+    enum {
+      RhsIsVectorAtCompileTime = (Side==OnTheLeft ? Rhs::ColsAtCompileTime : Rhs::RowsAtCompileTime)==1
+    };
+  public:
+    enum {
+      Unrolling   = (RhsIsVectorAtCompileTime && Rhs::SizeAtCompileTime != Dynamic && Rhs::SizeAtCompileTime <= 8)
+                  ? CompleteUnrolling : NoUnrolling,
+      RhsVectors  = RhsIsVectorAtCompileTime ? 1 : Dynamic
+    };
+};
+
+template<typename Lhs, typename Rhs,
+  int Side, // can be OnTheLeft/OnTheRight
+  int Mode, // can be Upper/Lower | UnitDiag
+  int Unrolling = trsolve_traits<Lhs,Rhs,Side>::Unrolling,
+  int RhsVectors = trsolve_traits<Lhs,Rhs,Side>::RhsVectors
+  >
+struct triangular_solver_selector;
+
+template<typename Lhs, typename Rhs, int Side, int Mode>
+struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,1>
+{
+  typedef typename Lhs::Scalar LhsScalar;
+  typedef typename Rhs::Scalar RhsScalar;
+  typedef blas_traits<Lhs> LhsProductTraits;
+  typedef typename LhsProductTraits::ExtractType ActualLhsType;
+  typedef Map<Matrix<RhsScalar,Dynamic,1>, Aligned> MappedRhs;
+  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
+  {
+    ActualLhsType actualLhs = LhsProductTraits::extract(lhs);
+
+    // FIXME find a way to allow an inner stride if packet_traits<Scalar>::size==1
+
+    bool useRhsDirectly = Rhs::InnerStrideAtCompileTime==1 || rhs.innerStride()==1;
+
+    ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhs,rhs.size(),
+                                                  (useRhsDirectly ? rhs.data() : 0));
+
+    if(!useRhsDirectly)
+      MappedRhs(actualRhs,rhs.size()) = rhs;
+
+    triangular_solve_vector<LhsScalar, RhsScalar, Index, Side, Mode, LhsProductTraits::NeedToConjugate,
+                            (int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor>
+      ::run(actualLhs.cols(), actualLhs.data(), actualLhs.outerStride(), actualRhs);
+
+    if(!useRhsDirectly)
+      rhs = MappedRhs(actualRhs, rhs.size());
+  }
+};
+
+// the rhs is a matrix
+template<typename Lhs, typename Rhs, int Side, int Mode>
+struct triangular_solver_selector<Lhs,Rhs,Side,Mode,NoUnrolling,Dynamic>
+{
+  typedef typename Rhs::Scalar Scalar;
+  typedef blas_traits<Lhs> LhsProductTraits;
+  typedef typename LhsProductTraits::DirectLinearAccessType ActualLhsType;
+
+  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
+  {
+    add_const_on_value_type_t<ActualLhsType> actualLhs = LhsProductTraits::extract(lhs);
+
+    const Index size = lhs.rows();
+    const Index othersize = Side==OnTheLeft? rhs.cols() : rhs.rows();
+
+    typedef internal::gemm_blocking_space<(Rhs::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar,
+              Rhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxRowsAtCompileTime,4> BlockingType;
+
+    // Nothing to solve.
+    if (actualLhs.size() == 0 || rhs.size() == 0) {
+      return;
+    }
+
+    BlockingType blocking(rhs.rows(), rhs.cols(), size, 1, false);
+
+    triangular_solve_matrix<Scalar,Index,Side,Mode,LhsProductTraits::NeedToConjugate,(int(Lhs::Flags) & RowMajorBit) ? RowMajor : ColMajor,
+                               (Rhs::Flags&RowMajorBit) ? RowMajor : ColMajor, Rhs::InnerStrideAtCompileTime>
+      ::run(size, othersize, &actualLhs.coeffRef(0,0), actualLhs.outerStride(), &rhs.coeffRef(0,0), rhs.innerStride(), rhs.outerStride(), blocking);
+  }
+};
+
+/***************************************************************************
+* meta-unrolling implementation
+***************************************************************************/
+
+template<typename Lhs, typename Rhs, int Mode, int LoopIndex, int Size,
+         bool Stop = LoopIndex==Size>
+struct triangular_solver_unroller;
+
+template<typename Lhs, typename Rhs, int Mode, int LoopIndex, int Size>
+struct triangular_solver_unroller<Lhs,Rhs,Mode,LoopIndex,Size,false> {
+  enum {
+    IsLower = ((Mode&Lower)==Lower),
+    DiagIndex  = IsLower ? LoopIndex : Size - LoopIndex - 1,
+    StartIndex = IsLower ? 0         : DiagIndex+1
+  };
+  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
+  {
+    if (LoopIndex>0)
+      rhs.coeffRef(DiagIndex) -= lhs.row(DiagIndex).template segment<LoopIndex>(StartIndex).transpose()
+                                .cwiseProduct(rhs.template segment<LoopIndex>(StartIndex)).sum();
+
+    if(!(Mode & UnitDiag))
+      rhs.coeffRef(DiagIndex) /= lhs.coeff(DiagIndex,DiagIndex);
+
+    triangular_solver_unroller<Lhs,Rhs,Mode,LoopIndex+1,Size>::run(lhs,rhs);
+  }
+};
+
+template<typename Lhs, typename Rhs, int Mode, int LoopIndex, int Size>
+struct triangular_solver_unroller<Lhs,Rhs,Mode,LoopIndex,Size,true> {
+  static EIGEN_DEVICE_FUNC void run(const Lhs&, Rhs&) {}
+};
+
+template<typename Lhs, typename Rhs, int Mode>
+struct triangular_solver_selector<Lhs,Rhs,OnTheLeft,Mode,CompleteUnrolling,1> {
+  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
+  { triangular_solver_unroller<Lhs,Rhs,Mode,0,Rhs::SizeAtCompileTime>::run(lhs,rhs); }
+};
+
+template<typename Lhs, typename Rhs, int Mode>
+struct triangular_solver_selector<Lhs,Rhs,OnTheRight,Mode,CompleteUnrolling,1> {
+  static EIGEN_DEVICE_FUNC void run(const Lhs& lhs, Rhs& rhs)
+  {
+    Transpose<const Lhs> trLhs(lhs);
+    Transpose<Rhs> trRhs(rhs);
+
+    triangular_solver_unroller<Transpose<const Lhs>,Transpose<Rhs>,
+                              ((Mode&Upper)==Upper ? Lower : Upper) | (Mode&UnitDiag),
+                              0,Rhs::SizeAtCompileTime>::run(trLhs,trRhs);
+  }
+};
+
+} // end namespace internal
+
+/***************************************************************************
+* TriangularView methods
+***************************************************************************/
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename MatrixType, unsigned int Mode>
+template<int Side, typename OtherDerived>
+EIGEN_DEVICE_FUNC void TriangularViewImpl<MatrixType,Mode,Dense>::solveInPlace(const MatrixBase<OtherDerived>& _other) const
+{
+  OtherDerived& other = _other.const_cast_derived();
+  eigen_assert( derived().cols() == derived().rows() && ((Side==OnTheLeft && derived().cols() == other.rows()) || (Side==OnTheRight && derived().cols() == other.cols())) );
+  eigen_assert((!(int(Mode) & int(ZeroDiag))) && bool(int(Mode) & (int(Upper) | int(Lower))));
+  // If solving for a 0x0 matrix, nothing to do, simply return.
+  if (derived().cols() == 0)
+    return;
+
+  enum { copy = (internal::traits<OtherDerived>::Flags & RowMajorBit)  && OtherDerived::IsVectorAtCompileTime && OtherDerived::SizeAtCompileTime!=1};
+  typedef std::conditional_t<copy,
+    typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&> OtherCopy;
+  OtherCopy otherCopy(other);
+
+  internal::triangular_solver_selector<MatrixType, std::remove_reference_t<OtherCopy>,
+    Side, Mode>::run(derived().nestedExpression(), otherCopy);
+
+  if (copy)
+    other = otherCopy;
+}
+
+template<typename Derived, unsigned int Mode>
+template<int Side, typename Other>
+const internal::triangular_solve_retval<Side,TriangularView<Derived,Mode>,Other>
+TriangularViewImpl<Derived,Mode,Dense>::solve(const MatrixBase<Other>& other) const
+{
+  return internal::triangular_solve_retval<Side,TriangularViewType,Other>(derived(), other.derived());
+}
+#endif
+
+namespace internal {
+
+
+template<int Side, typename TriangularType, typename Rhs>
+struct traits<triangular_solve_retval<Side, TriangularType, Rhs> >
+{
+  typedef typename internal::plain_matrix_type_column_major<Rhs>::type ReturnType;
+};
+
+template<int Side, typename TriangularType, typename Rhs> struct triangular_solve_retval
+ : public ReturnByValue<triangular_solve_retval<Side, TriangularType, Rhs> >
+{
+  typedef remove_all_t<typename Rhs::Nested> RhsNestedCleaned;
+  typedef ReturnByValue<triangular_solve_retval> Base;
+
+  triangular_solve_retval(const TriangularType& tri, const Rhs& rhs)
+    : m_triangularMatrix(tri), m_rhs(rhs)
+  {}
+
+  inline EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_rhs.rows(); }
+  inline EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
+
+  template<typename Dest> inline void evalTo(Dest& dst) const
+  {
+    if(!is_same_dense(dst,m_rhs))
+      dst = m_rhs;
+    m_triangularMatrix.template solveInPlace<Side>(dst);
+  }
+
+  protected:
+    const TriangularType& m_triangularMatrix;
+    typename Rhs::Nested m_rhs;
+};
+
+} // namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SOLVETRIANGULAR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/SolverBase.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/SolverBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..7396e0417bba6eb02d5c34be569bac26c106dc0d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/SolverBase.h
@@ -0,0 +1,169 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SOLVERBASE_H
+#define EIGEN_SOLVERBASE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename Derived>
+struct solve_assertion {
+    template<bool Transpose_, typename Rhs>
+    static void run(const Derived& solver, const Rhs& b) { solver.template _check_solve_assertion<Transpose_>(b); }
+};
+
+template<typename Derived>
+struct solve_assertion<Transpose<Derived> >
+{
+    typedef Transpose<Derived> type;
+
+    template<bool Transpose_, typename Rhs>
+    static void run(const type& transpose, const Rhs& b)
+    {
+        internal::solve_assertion<internal::remove_all_t<Derived>>::template run<true>(transpose.nestedExpression(), b);
+    }
+};
+
+template<typename Scalar, typename Derived>
+struct solve_assertion<CwiseUnaryOp<Eigen::internal::scalar_conjugate_op<Scalar>, const Transpose<Derived> > >
+{
+    typedef CwiseUnaryOp<Eigen::internal::scalar_conjugate_op<Scalar>, const Transpose<Derived> > type;
+
+    template<bool Transpose_, typename Rhs>
+    static void run(const type& adjoint, const Rhs& b)
+    {
+        internal::solve_assertion<internal::remove_all_t<Transpose<Derived> >>::template run<true>(adjoint.nestedExpression(), b);
+    }
+};
+} // end namespace internal
+
+/** \class SolverBase
+  * \brief A base class for matrix decomposition and solvers
+  *
+  * \tparam Derived the actual type of the decomposition/solver.
+  *
+  * Any matrix decomposition inheriting this base class provide the following API:
+  *
+  * \code
+  * MatrixType A, b, x;
+  * DecompositionType dec(A);
+  * x = dec.solve(b);             // solve A   * x = b
+  * x = dec.transpose().solve(b); // solve A^T * x = b
+  * x = dec.adjoint().solve(b);   // solve A'  * x = b
+  * \endcode
+  *
+  * \warning Currently, any other usage of transpose() and adjoint() are not supported and will produce compilation errors.
+  *
+  * \sa class PartialPivLU, class FullPivLU, class HouseholderQR, class ColPivHouseholderQR, class FullPivHouseholderQR, class CompleteOrthogonalDecomposition, class LLT, class LDLT, class SVDBase
+  */
+template<typename Derived>
+class SolverBase : public EigenBase<Derived>
+{
+  public:
+
+    typedef EigenBase<Derived> Base;
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    typedef Scalar CoeffReturnType;
+
+    template<typename Derived_>
+    friend struct internal::solve_assertion;
+
+    enum {
+      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+      SizeAtCompileTime = (internal::size_of_xpr_at_compile_time<Derived>::ret),
+      MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
+      MaxSizeAtCompileTime = internal::size_at_compile_time(internal::traits<Derived>::MaxRowsAtCompileTime,
+                                                            internal::traits<Derived>::MaxColsAtCompileTime),
+      IsVectorAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime == 1
+                           || internal::traits<Derived>::MaxColsAtCompileTime == 1,
+      NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0 : bool(IsVectorAtCompileTime) ? 1 : 2
+    };
+
+    /** Default constructor */
+    SolverBase()
+    {}
+
+    ~SolverBase()
+    {}
+
+    using Base::derived;
+
+    /** \returns an expression of the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      */
+    template<typename Rhs>
+    inline const Solve<Derived, Rhs>
+    solve(const MatrixBase<Rhs>& b) const
+    {
+      internal::solve_assertion<internal::remove_all_t<Derived>>::template run<false>(derived(), b);
+      return Solve<Derived, Rhs>(derived(), b.derived());
+    }
+
+    /** \internal the return type of transpose() */
+    typedef Transpose<const Derived> ConstTransposeReturnType;
+    /** \returns an expression of the transposed of the factored matrix.
+      *
+      * A typical usage is to solve for the transposed problem A^T x = b:
+      * \code x = dec.transpose().solve(b); \endcode
+      *
+      * \sa adjoint(), solve()
+      */
+    inline const ConstTransposeReturnType transpose() const
+    {
+      return ConstTransposeReturnType(derived());
+    }
+
+    /** \internal the return type of adjoint() */
+    typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
+               CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const ConstTransposeReturnType>,
+               const ConstTransposeReturnType
+            > AdjointReturnType;
+    /** \returns an expression of the adjoint of the factored matrix
+      *
+      * A typical usage is to solve for the adjoint problem A' x = b:
+      * \code x = dec.adjoint().solve(b); \endcode
+      *
+      * For real scalar types, this function is equivalent to transpose().
+      *
+      * \sa transpose(), solve()
+      */
+    inline const AdjointReturnType adjoint() const
+    {
+      return AdjointReturnType(derived().transpose());
+    }
+
+  protected:
+
+    template<bool Transpose_, typename Rhs>
+    void _check_solve_assertion(const Rhs& b) const {
+        EIGEN_ONLY_USED_FOR_DEBUG(b);
+        eigen_assert(derived().m_isInitialized && "Solver is not initialized.");
+        eigen_assert((Transpose_?derived().cols():derived().rows())==b.rows() && "SolverBase::solve(): invalid number of rows of the right hand side matrix b");
+    }
+};
+
+namespace internal {
+
+template<typename Derived>
+struct generic_xpr_base<Derived, MatrixXpr, SolverStorage>
+{
+  typedef SolverBase<Derived> type;
+
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SOLVERBASE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/StableNorm.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/StableNorm.h
new file mode 100644
index 0000000000000000000000000000000000000000..a3bc918aae9d9dd31715531a83b3b7075778416e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/StableNorm.h
@@ -0,0 +1,253 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STABLENORM_H
+#define EIGEN_STABLENORM_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename ExpressionType, typename Scalar>
+inline void stable_norm_kernel(const ExpressionType& bl, Scalar& ssq, Scalar& scale, Scalar& invScale)
+{
+  Scalar maxCoeff = bl.cwiseAbs().maxCoeff();
+  
+  if(maxCoeff>scale)
+  {
+    ssq = ssq * numext::abs2(scale/maxCoeff);
+    Scalar tmp = Scalar(1)/maxCoeff;
+    if(tmp > NumTraits<Scalar>::highest())
+    {
+      invScale = NumTraits<Scalar>::highest();
+      scale = Scalar(1)/invScale;
+    }
+    else if(maxCoeff>NumTraits<Scalar>::highest()) // we got a INF
+    {
+      invScale = Scalar(1);
+      scale = maxCoeff;
+    }
+    else
+    {
+      scale = maxCoeff;
+      invScale = tmp;
+    }
+  }
+  else if(maxCoeff!=maxCoeff) // we got a NaN
+  {
+    scale = maxCoeff;
+  }
+  
+  // TODO if the maxCoeff is much much smaller than the current scale,
+  // then we can neglect this sub vector
+  if(scale>Scalar(0)) // if scale==0, then bl is 0 
+    ssq += (bl*invScale).squaredNorm();
+}
+
+template<typename VectorType, typename RealScalar>
+void stable_norm_impl_inner_step(const VectorType &vec, RealScalar& ssq, RealScalar& scale, RealScalar& invScale)
+{
+  typedef typename VectorType::Scalar Scalar;
+  const Index blockSize = 4096;
+  
+  typedef typename internal::nested_eval<VectorType,2>::type VectorTypeCopy;
+  typedef internal::remove_all_t<VectorTypeCopy> VectorTypeCopyClean;
+  const VectorTypeCopy copy(vec);
+  
+  enum {
+    CanAlign = (   (int(VectorTypeCopyClean::Flags)&DirectAccessBit)
+                || (int(internal::evaluator<VectorTypeCopyClean>::Alignment)>0) // FIXME Alignment)>0 might not be enough
+               ) && (blockSize*sizeof(Scalar)*2<EIGEN_STACK_ALLOCATION_LIMIT)
+                 && (EIGEN_MAX_STATIC_ALIGN_BYTES>0) // if we cannot allocate on the stack, then let's not bother about this optimization
+  };
+  typedef std::conditional_t<CanAlign, Ref<const Matrix<Scalar,Dynamic,1,0,blockSize,1>, internal::evaluator<VectorTypeCopyClean>::Alignment>,
+                                                   typename VectorTypeCopyClean::ConstSegmentReturnType> SegmentWrapper;
+  Index n = vec.size();
+  
+  Index bi = internal::first_default_aligned(copy);
+  if (bi>0)
+    internal::stable_norm_kernel(copy.head(bi), ssq, scale, invScale);
+  for (; bi<n; bi+=blockSize)
+    internal::stable_norm_kernel(SegmentWrapper(copy.segment(bi,numext::mini(blockSize, n - bi))), ssq, scale, invScale);
+}
+
+template<typename VectorType>
+typename VectorType::RealScalar
+stable_norm_impl(const VectorType &vec, std::enable_if_t<VectorType::IsVectorAtCompileTime>* = 0 )
+{
+  using std::sqrt;
+  using std::abs;
+
+  Index n = vec.size();
+
+  if(n==1)
+    return abs(vec.coeff(0));
+
+  typedef typename VectorType::RealScalar RealScalar;
+  RealScalar scale(0);
+  RealScalar invScale(1);
+  RealScalar ssq(0); // sum of squares
+
+  stable_norm_impl_inner_step(vec, ssq, scale, invScale);
+  
+  return scale * sqrt(ssq);
+}
+
+template<typename MatrixType>
+typename MatrixType::RealScalar
+stable_norm_impl(const MatrixType &mat, std::enable_if_t<!MatrixType::IsVectorAtCompileTime>* = 0 )
+{
+  using std::sqrt;
+
+  typedef typename MatrixType::RealScalar RealScalar;
+  RealScalar scale(0);
+  RealScalar invScale(1);
+  RealScalar ssq(0); // sum of squares
+
+  for(Index j=0; j<mat.outerSize(); ++j)
+    stable_norm_impl_inner_step(mat.innerVector(j), ssq, scale, invScale);
+  return scale * sqrt(ssq);
+}
+
+template<typename Derived>
+inline typename NumTraits<typename traits<Derived>::Scalar>::Real
+blueNorm_impl(const EigenBase<Derived>& _vec)
+{
+  typedef typename Derived::RealScalar RealScalar;  
+  using std::pow;
+  using std::sqrt;
+  using std::abs;
+
+  // This program calculates the machine-dependent constants
+  // bl, b2, slm, s2m, relerr overfl
+  // from the "basic" machine-dependent numbers
+  // nbig, ibeta, it, iemin, iemax, rbig.
+  // The following define the basic machine-dependent constants.
+  // For portability, the PORT subprograms "ilmaeh" and "rlmach"
+  // are used. For any specific computer, each of the assignment
+  // statements can be replaced
+  static const int ibeta = std::numeric_limits<RealScalar>::radix;  // base for floating-point numbers
+  static const int it    = NumTraits<RealScalar>::digits();  // number of base-beta digits in mantissa
+  static const int iemin = NumTraits<RealScalar>::min_exponent();  // minimum exponent
+  static const int iemax = NumTraits<RealScalar>::max_exponent();  // maximum exponent
+  static const RealScalar rbig   = NumTraits<RealScalar>::highest();  // largest floating-point number
+  static const RealScalar b1     = RealScalar(pow(RealScalar(ibeta),RealScalar(-((1-iemin)/2))));  // lower boundary of midrange
+  static const RealScalar b2     = RealScalar(pow(RealScalar(ibeta),RealScalar((iemax + 1 - it)/2)));  // upper boundary of midrange
+  static const RealScalar s1m    = RealScalar(pow(RealScalar(ibeta),RealScalar((2-iemin)/2)));  // scaling factor for lower range
+  static const RealScalar s2m    = RealScalar(pow(RealScalar(ibeta),RealScalar(- ((iemax+it)/2))));  // scaling factor for upper range
+  static const RealScalar eps    = RealScalar(pow(double(ibeta), 1-it));
+  static const RealScalar relerr = sqrt(eps);  // tolerance for neglecting asml
+
+  const Derived& vec(_vec.derived());
+  Index n = vec.size();
+  RealScalar ab2 = b2 / RealScalar(n);
+  RealScalar asml = RealScalar(0);
+  RealScalar amed = RealScalar(0);
+  RealScalar abig = RealScalar(0);
+
+  for(Index j=0; j<vec.outerSize(); ++j)
+  {
+    for(typename Derived::InnerIterator iter(vec, j); iter; ++iter)
+    {
+      RealScalar ax = abs(iter.value());
+      if(ax > ab2)     abig += numext::abs2(ax*s2m);
+      else if(ax < b1) asml += numext::abs2(ax*s1m);
+      else             amed += numext::abs2(ax);
+    }
+  }
+  if(amed!=amed)
+    return amed;  // we got a NaN
+  if(abig > RealScalar(0))
+  {
+    abig = sqrt(abig);
+    if(abig > rbig) // overflow, or *this contains INF values
+      return abig;  // return INF
+    if(amed > RealScalar(0))
+    {
+      abig = abig/s2m;
+      amed = sqrt(amed);
+    }
+    else
+      return abig/s2m;
+  }
+  else if(asml > RealScalar(0))
+  {
+    if (amed > RealScalar(0))
+    {
+      abig = sqrt(amed);
+      amed = sqrt(asml) / s1m;
+    }
+    else
+      return sqrt(asml)/s1m;
+  }
+  else
+    return sqrt(amed);
+  asml = numext::mini(abig, amed);
+  abig = numext::maxi(abig, amed);
+  if(asml <= abig*relerr)
+    return abig;
+  else
+    return abig * sqrt(RealScalar(1) + numext::abs2(asml/abig));
+}
+
+} // end namespace internal
+
+/** \returns the \em l2 norm of \c *this avoiding underflow and overflow.
+  * This version use a blockwise two passes algorithm:
+  *  1 - find the absolute largest coefficient \c s
+  *  2 - compute \f$ s \Vert \frac{*this}{s} \Vert \f$ in a standard way
+  *
+  * For architecture/scalar types supporting vectorization, this version
+  * is faster than blueNorm(). Otherwise the blueNorm() is much faster.
+  *
+  * \sa norm(), blueNorm(), hypotNorm()
+  */
+template<typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+MatrixBase<Derived>::stableNorm() const
+{
+  return internal::stable_norm_impl(derived());
+}
+
+/** \returns the \em l2 norm of \c *this using the Blue's algorithm.
+  * A Portable Fortran Program to Find the Euclidean Norm of a Vector,
+  * ACM TOMS, Vol 4, Issue 1, 1978.
+  *
+  * For architecture/scalar types without vectorization, this version
+  * is much faster than stableNorm(). Otherwise the stableNorm() is faster.
+  *
+  * \sa norm(), stableNorm(), hypotNorm()
+  */
+template<typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+MatrixBase<Derived>::blueNorm() const
+{
+  return internal::blueNorm_impl(*this);
+}
+
+/** \returns the \em l2 norm of \c *this avoiding undeflow and overflow.
+  * This version use a concatenation of hypot() calls, and it is very slow.
+  *
+  * \sa norm(), stableNorm()
+  */
+template<typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+MatrixBase<Derived>::hypotNorm() const
+{
+  if(size()==1)
+    return numext::abs(coeff(0,0));
+  else
+    return this->cwiseAbs().redux(internal::scalar_hypot_op<RealScalar>());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_STABLENORM_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/StlIterators.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/StlIterators.h
new file mode 100644
index 0000000000000000000000000000000000000000..d5d3971f68916c8d86fe5df7b19341b2d58e6a1d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/StlIterators.h
@@ -0,0 +1,465 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STLITERATORS_H
+#define EIGEN_STLITERATORS_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename IteratorType>
+struct indexed_based_stl_iterator_traits;
+
+template<typename  Derived>
+class indexed_based_stl_iterator_base
+{
+protected:
+  typedef indexed_based_stl_iterator_traits<Derived> traits;
+  typedef typename traits::XprType XprType;
+  typedef indexed_based_stl_iterator_base<typename traits::non_const_iterator> non_const_iterator;
+  typedef indexed_based_stl_iterator_base<typename traits::const_iterator> const_iterator;
+  typedef std::conditional_t<internal::is_const<XprType>::value,non_const_iterator,const_iterator> other_iterator;
+  // NOTE: in C++03 we cannot declare friend classes through typedefs because we need to write friend class:
+  friend class indexed_based_stl_iterator_base<typename traits::const_iterator>;
+  friend class indexed_based_stl_iterator_base<typename traits::non_const_iterator>;
+public:
+  typedef Index difference_type;
+  typedef std::random_access_iterator_tag iterator_category;
+
+  indexed_based_stl_iterator_base() EIGEN_NO_THROW : mp_xpr(0), m_index(0) {}
+  indexed_based_stl_iterator_base(XprType& xpr, Index index) EIGEN_NO_THROW : mp_xpr(&xpr), m_index(index) {}
+
+  indexed_based_stl_iterator_base(const non_const_iterator& other) EIGEN_NO_THROW
+    : mp_xpr(other.mp_xpr), m_index(other.m_index)
+  {}
+
+  indexed_based_stl_iterator_base& operator=(const non_const_iterator& other)
+  {
+    mp_xpr = other.mp_xpr;
+    m_index = other.m_index;
+    return *this;
+  }
+
+  Derived& operator++() { ++m_index; return derived(); }
+  Derived& operator--() { --m_index; return derived(); }
+
+  Derived operator++(int) { Derived prev(derived()); operator++(); return prev;}
+  Derived operator--(int) { Derived prev(derived()); operator--(); return prev;}
+
+  friend Derived operator+(const indexed_based_stl_iterator_base& a, Index b) { Derived ret(a.derived()); ret += b; return ret; }
+  friend Derived operator-(const indexed_based_stl_iterator_base& a, Index b) { Derived ret(a.derived()); ret -= b; return ret; }
+  friend Derived operator+(Index a, const indexed_based_stl_iterator_base& b) { Derived ret(b.derived()); ret += a; return ret; }
+  friend Derived operator-(Index a, const indexed_based_stl_iterator_base& b) { Derived ret(b.derived()); ret -= a; return ret; }
+  
+  Derived& operator+=(Index b) { m_index += b; return derived(); }
+  Derived& operator-=(Index b) { m_index -= b; return derived(); }
+
+  difference_type operator-(const indexed_based_stl_iterator_base& other) const
+  {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index - other.m_index;
+  }
+
+  difference_type operator-(const other_iterator& other) const
+  {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return m_index - other.m_index;
+  }
+
+  bool operator==(const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index == other.m_index; }
+  bool operator!=(const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index != other.m_index; }
+  bool operator< (const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <  other.m_index; }
+  bool operator<=(const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <= other.m_index; }
+  bool operator> (const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >  other.m_index; }
+  bool operator>=(const indexed_based_stl_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >= other.m_index; }
+
+  bool operator==(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index == other.m_index; }
+  bool operator!=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index != other.m_index; }
+  bool operator< (const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <  other.m_index; }
+  bool operator<=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <= other.m_index; }
+  bool operator> (const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >  other.m_index; }
+  bool operator>=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >= other.m_index; }
+
+protected:
+
+  Derived& derived() { return static_cast<Derived&>(*this); }
+  const Derived& derived() const { return static_cast<const Derived&>(*this); }
+
+  XprType *mp_xpr;
+  Index m_index;
+};
+
+template<typename  Derived>
+class indexed_based_stl_reverse_iterator_base
+{
+protected:
+  typedef indexed_based_stl_iterator_traits<Derived> traits;
+  typedef typename traits::XprType XprType;
+  typedef indexed_based_stl_reverse_iterator_base<typename traits::non_const_iterator> non_const_iterator;
+  typedef indexed_based_stl_reverse_iterator_base<typename traits::const_iterator> const_iterator;
+  typedef std::conditional_t<internal::is_const<XprType>::value,non_const_iterator,const_iterator> other_iterator;
+  // NOTE: in C++03 we cannot declare friend classes through typedefs because we need to write friend class:
+  friend class indexed_based_stl_reverse_iterator_base<typename traits::const_iterator>;
+  friend class indexed_based_stl_reverse_iterator_base<typename traits::non_const_iterator>;
+public:
+  typedef Index difference_type;
+  typedef std::random_access_iterator_tag iterator_category;
+
+  indexed_based_stl_reverse_iterator_base() : mp_xpr(0), m_index(0) {}
+  indexed_based_stl_reverse_iterator_base(XprType& xpr, Index index) : mp_xpr(&xpr), m_index(index) {}
+
+  indexed_based_stl_reverse_iterator_base(const non_const_iterator& other)
+    : mp_xpr(other.mp_xpr), m_index(other.m_index)
+  {}
+
+  indexed_based_stl_reverse_iterator_base& operator=(const non_const_iterator& other)
+  {
+    mp_xpr = other.mp_xpr;
+    m_index = other.m_index;
+    return *this;
+  }
+
+  Derived& operator++() { --m_index; return derived(); }
+  Derived& operator--() { ++m_index; return derived(); }
+
+  Derived operator++(int) { Derived prev(derived()); operator++(); return prev;}
+  Derived operator--(int) { Derived prev(derived()); operator--(); return prev;}
+
+  friend Derived operator+(const indexed_based_stl_reverse_iterator_base& a, Index b) { Derived ret(a.derived()); ret += b; return ret; }
+  friend Derived operator-(const indexed_based_stl_reverse_iterator_base& a, Index b) { Derived ret(a.derived()); ret -= b; return ret; }
+  friend Derived operator+(Index a, const indexed_based_stl_reverse_iterator_base& b) { Derived ret(b.derived()); ret += a; return ret; }
+  friend Derived operator-(Index a, const indexed_based_stl_reverse_iterator_base& b) { Derived ret(b.derived()); ret -= a; return ret; }
+  
+  Derived& operator+=(Index b) { m_index -= b; return derived(); }
+  Derived& operator-=(Index b) { m_index += b; return derived(); }
+
+  difference_type operator-(const indexed_based_stl_reverse_iterator_base& other) const
+  {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return other.m_index - m_index;
+  }
+
+  difference_type operator-(const other_iterator& other) const
+  {
+    eigen_assert(mp_xpr == other.mp_xpr);
+    return other.m_index - m_index;
+  }
+
+  bool operator==(const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index == other.m_index; }
+  bool operator!=(const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index != other.m_index; }
+  bool operator< (const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >  other.m_index; }
+  bool operator<=(const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >= other.m_index; }
+  bool operator> (const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <  other.m_index; }
+  bool operator>=(const indexed_based_stl_reverse_iterator_base& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <= other.m_index; }
+
+  bool operator==(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index == other.m_index; }
+  bool operator!=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index != other.m_index; }
+  bool operator< (const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >  other.m_index; }
+  bool operator<=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index >= other.m_index; }
+  bool operator> (const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <  other.m_index; }
+  bool operator>=(const other_iterator& other) const { eigen_assert(mp_xpr == other.mp_xpr); return m_index <= other.m_index; }
+
+protected:
+
+  Derived& derived() { return static_cast<Derived&>(*this); }
+  const Derived& derived() const { return static_cast<const Derived&>(*this); }
+
+  XprType *mp_xpr;
+  Index m_index;
+};
+
+template<typename XprType>
+class pointer_based_stl_iterator
+{
+  enum { is_lvalue  = internal::is_lvalue<XprType>::value };
+  typedef pointer_based_stl_iterator<std::remove_const_t<XprType>> non_const_iterator;
+  typedef pointer_based_stl_iterator<std::add_const_t<XprType>> const_iterator;
+  typedef std::conditional_t<internal::is_const<XprType>::value,non_const_iterator,const_iterator> other_iterator;
+  // NOTE: in C++03 we cannot declare friend classes through typedefs because we need to write friend class:
+  friend class pointer_based_stl_iterator<std::add_const_t<XprType>>;
+  friend class pointer_based_stl_iterator<std::remove_const_t<XprType>>;
+public:
+  typedef Index difference_type;
+  typedef typename XprType::Scalar value_type;
+  typedef std::random_access_iterator_tag iterator_category;
+  typedef std::conditional_t<bool(is_lvalue), value_type*, const value_type*> pointer;
+  typedef std::conditional_t<bool(is_lvalue), value_type&, const value_type&> reference;
+
+
+  pointer_based_stl_iterator() EIGEN_NO_THROW : m_ptr(0) {}
+  pointer_based_stl_iterator(XprType& xpr, Index index) EIGEN_NO_THROW : m_incr(xpr.innerStride())
+  {
+    m_ptr = xpr.data() + index * m_incr.value();
+  }
+
+  pointer_based_stl_iterator(const non_const_iterator& other) EIGEN_NO_THROW
+    : m_ptr(other.m_ptr), m_incr(other.m_incr)
+  {}
+
+  pointer_based_stl_iterator& operator=(const non_const_iterator& other) EIGEN_NO_THROW
+  {
+    m_ptr = other.m_ptr;
+    m_incr.setValue(other.m_incr);
+    return *this;
+  }
+
+  reference operator*()         const { return *m_ptr;   }
+  reference operator[](Index i) const { return *(m_ptr+i*m_incr.value()); }
+  pointer   operator->()        const { return m_ptr;    }
+
+  pointer_based_stl_iterator& operator++() { m_ptr += m_incr.value(); return *this; }
+  pointer_based_stl_iterator& operator--() { m_ptr -= m_incr.value(); return *this; }
+
+  pointer_based_stl_iterator operator++(int) { pointer_based_stl_iterator prev(*this); operator++(); return prev;}
+  pointer_based_stl_iterator operator--(int) { pointer_based_stl_iterator prev(*this); operator--(); return prev;}
+
+  friend pointer_based_stl_iterator operator+(const pointer_based_stl_iterator& a, Index b) { pointer_based_stl_iterator ret(a); ret += b; return ret; }
+  friend pointer_based_stl_iterator operator-(const pointer_based_stl_iterator& a, Index b) { pointer_based_stl_iterator ret(a); ret -= b; return ret; }
+  friend pointer_based_stl_iterator operator+(Index a, const pointer_based_stl_iterator& b) { pointer_based_stl_iterator ret(b); ret += a; return ret; }
+  friend pointer_based_stl_iterator operator-(Index a, const pointer_based_stl_iterator& b) { pointer_based_stl_iterator ret(b); ret -= a; return ret; }
+  
+  pointer_based_stl_iterator& operator+=(Index b) { m_ptr += b*m_incr.value(); return *this; }
+  pointer_based_stl_iterator& operator-=(Index b) { m_ptr -= b*m_incr.value(); return *this; }
+
+  difference_type operator-(const pointer_based_stl_iterator& other) const {
+    return (m_ptr - other.m_ptr)/m_incr.value();
+  }
+
+  difference_type operator-(const other_iterator& other) const {
+    return (m_ptr - other.m_ptr)/m_incr.value();
+  }
+
+  bool operator==(const pointer_based_stl_iterator& other) const { return m_ptr == other.m_ptr; }
+  bool operator!=(const pointer_based_stl_iterator& other) const { return m_ptr != other.m_ptr; }
+  bool operator< (const pointer_based_stl_iterator& other) const { return m_ptr <  other.m_ptr; }
+  bool operator<=(const pointer_based_stl_iterator& other) const { return m_ptr <= other.m_ptr; }
+  bool operator> (const pointer_based_stl_iterator& other) const { return m_ptr >  other.m_ptr; }
+  bool operator>=(const pointer_based_stl_iterator& other) const { return m_ptr >= other.m_ptr; }
+
+  bool operator==(const other_iterator& other) const { return m_ptr == other.m_ptr; }
+  bool operator!=(const other_iterator& other) const { return m_ptr != other.m_ptr; }
+  bool operator< (const other_iterator& other) const { return m_ptr <  other.m_ptr; }
+  bool operator<=(const other_iterator& other) const { return m_ptr <= other.m_ptr; }
+  bool operator> (const other_iterator& other) const { return m_ptr >  other.m_ptr; }
+  bool operator>=(const other_iterator& other) const { return m_ptr >= other.m_ptr; }
+
+protected:
+
+  pointer m_ptr;
+  internal::variable_if_dynamic<Index, XprType::InnerStrideAtCompileTime> m_incr;
+};
+
+template<typename XprType_>
+struct indexed_based_stl_iterator_traits<generic_randaccess_stl_iterator<XprType_> >
+{
+  typedef XprType_ XprType;
+  typedef generic_randaccess_stl_iterator<std::remove_const_t<XprType>> non_const_iterator;
+  typedef generic_randaccess_stl_iterator<std::add_const_t<XprType>> const_iterator;
+};
+
+template<typename XprType>
+class generic_randaccess_stl_iterator : public indexed_based_stl_iterator_base<generic_randaccess_stl_iterator<XprType> >
+{
+public:
+  typedef typename XprType::Scalar value_type;
+
+protected:
+
+  enum {
+    has_direct_access = (internal::traits<XprType>::Flags & DirectAccessBit) ? 1 : 0,
+    is_lvalue  = internal::is_lvalue<XprType>::value
+  };
+
+  typedef indexed_based_stl_iterator_base<generic_randaccess_stl_iterator> Base;
+  using Base::m_index;
+  using Base::mp_xpr;
+
+  // TODO currently const Transpose/Reshape expressions never returns const references,
+  // so lets return by value too.
+  //typedef std::conditional_t<bool(has_direct_access), const value_type&, const value_type> read_only_ref_t;
+  typedef const value_type read_only_ref_t;
+
+public:
+  
+  typedef std::conditional_t<bool(is_lvalue), value_type *, const value_type *> pointer;
+  typedef std::conditional_t<bool(is_lvalue), value_type&, read_only_ref_t> reference;
+  
+  generic_randaccess_stl_iterator() : Base() {}
+  generic_randaccess_stl_iterator(XprType& xpr, Index index) : Base(xpr,index) {}
+  generic_randaccess_stl_iterator(const typename Base::non_const_iterator& other) : Base(other) {}
+  using Base::operator=;
+
+  reference operator*()         const { return   (*mp_xpr)(m_index);   }
+  reference operator[](Index i) const { return   (*mp_xpr)(m_index+i); }
+  pointer   operator->()        const { return &((*mp_xpr)(m_index)); }
+};
+
+template<typename XprType_, DirectionType Direction>
+struct indexed_based_stl_iterator_traits<subvector_stl_iterator<XprType_,Direction> >
+{
+  typedef XprType_ XprType;
+  typedef subvector_stl_iterator<std::remove_const_t<XprType>, Direction> non_const_iterator;
+  typedef subvector_stl_iterator<std::add_const_t<XprType>, Direction> const_iterator;
+};
+
+template<typename XprType, DirectionType Direction>
+class subvector_stl_iterator : public indexed_based_stl_iterator_base<subvector_stl_iterator<XprType,Direction> >
+{
+protected:
+
+  enum { is_lvalue  = internal::is_lvalue<XprType>::value };
+
+  typedef indexed_based_stl_iterator_base<subvector_stl_iterator> Base;
+  using Base::m_index;
+  using Base::mp_xpr;
+
+  typedef std::conditional_t<Direction==Vertical,typename XprType::ColXpr,typename XprType::RowXpr> SubVectorType;
+  typedef std::conditional_t<Direction==Vertical,typename XprType::ConstColXpr,typename XprType::ConstRowXpr> ConstSubVectorType;
+
+
+public:
+  typedef std::conditional_t<bool(is_lvalue), SubVectorType, ConstSubVectorType> reference;
+  typedef typename reference::PlainObject value_type;
+
+private:
+  class subvector_stl_iterator_ptr
+  {
+  public:
+      subvector_stl_iterator_ptr(const reference &subvector) : m_subvector(subvector) {}
+      reference* operator->() { return &m_subvector; }
+  private:
+      reference m_subvector;
+  };
+public:
+
+  typedef subvector_stl_iterator_ptr pointer;
+  
+  subvector_stl_iterator() : Base() {}
+  subvector_stl_iterator(XprType& xpr, Index index) : Base(xpr,index) {}
+
+  reference operator*()         const { return (*mp_xpr).template subVector<Direction>(m_index); }
+  reference operator[](Index i) const { return (*mp_xpr).template subVector<Direction>(m_index+i); }
+  pointer   operator->()        const { return (*mp_xpr).template subVector<Direction>(m_index); }
+};
+
+template<typename XprType_, DirectionType Direction>
+struct indexed_based_stl_iterator_traits<subvector_stl_reverse_iterator<XprType_,Direction> >
+{
+  typedef XprType_ XprType;
+  typedef subvector_stl_reverse_iterator<std::remove_const_t<XprType>, Direction> non_const_iterator;
+  typedef subvector_stl_reverse_iterator<std::add_const_t<XprType>, Direction> const_iterator;
+};
+
+template<typename XprType, DirectionType Direction>
+class subvector_stl_reverse_iterator : public indexed_based_stl_reverse_iterator_base<subvector_stl_reverse_iterator<XprType,Direction> >
+{
+protected:
+
+  enum { is_lvalue  = internal::is_lvalue<XprType>::value };
+
+  typedef indexed_based_stl_reverse_iterator_base<subvector_stl_reverse_iterator> Base;
+  using Base::m_index;
+  using Base::mp_xpr;
+
+  typedef std::conditional_t<Direction==Vertical,typename XprType::ColXpr,typename XprType::RowXpr> SubVectorType;
+  typedef std::conditional_t<Direction==Vertical,typename XprType::ConstColXpr,typename XprType::ConstRowXpr> ConstSubVectorType;
+
+
+public:
+  typedef std::conditional_t<bool(is_lvalue), SubVectorType, ConstSubVectorType> reference;
+  typedef typename reference::PlainObject value_type;
+
+private:
+  class subvector_stl_reverse_iterator_ptr
+  {
+  public:
+      subvector_stl_reverse_iterator_ptr(const reference &subvector) : m_subvector(subvector) {}
+      reference* operator->() { return &m_subvector; }
+  private:
+      reference m_subvector;
+  };
+public:
+
+  typedef subvector_stl_reverse_iterator_ptr pointer;
+  
+  subvector_stl_reverse_iterator() : Base() {}
+  subvector_stl_reverse_iterator(XprType& xpr, Index index) : Base(xpr,index) {}
+
+  reference operator*()         const { return (*mp_xpr).template subVector<Direction>(m_index); }
+  reference operator[](Index i) const { return (*mp_xpr).template subVector<Direction>(m_index+i); }
+  pointer   operator->()        const { return (*mp_xpr).template subVector<Direction>(m_index); }
+};
+
+} // namespace internal
+
+
+/** returns an iterator to the first element of the 1D vector or array
+  * \only_for_vectors
+  * \sa end(), cbegin()
+  */
+template<typename Derived>
+inline typename DenseBase<Derived>::iterator DenseBase<Derived>::begin()
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  return iterator(derived(), 0);
+}
+
+/** const version of begin() */
+template<typename Derived>
+inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::begin() const
+{
+  return cbegin();
+}
+
+/** returns a read-only const_iterator to the first element of the 1D vector or array
+  * \only_for_vectors
+  * \sa cend(), begin()
+  */
+template<typename Derived>
+inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::cbegin() const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  return const_iterator(derived(), 0);
+}
+
+/** returns an iterator to the element following the last element of the 1D vector or array
+  * \only_for_vectors
+  * \sa begin(), cend()
+  */
+template<typename Derived>
+inline typename DenseBase<Derived>::iterator DenseBase<Derived>::end()
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  return iterator(derived(), size());
+}
+
+/** const version of end() */
+template<typename Derived>
+inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::end() const
+{
+  return cend();
+}
+
+/** returns a read-only const_iterator to the element following the last element of the 1D vector or array
+  * \only_for_vectors
+  * \sa begin(), cend()
+  */
+template<typename Derived>
+inline typename DenseBase<Derived>::const_iterator DenseBase<Derived>::cend() const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  return const_iterator(derived(), size());
+}
+
+} // namespace Eigen
+
+#endif // EIGEN_STLITERATORS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Stride.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Stride.h
new file mode 100644
index 0000000000000000000000000000000000000000..2832e8014f21bfe0b03084191540c4540af69520
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Stride.h
@@ -0,0 +1,122 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STRIDE_H
+#define EIGEN_STRIDE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class Stride
+  * \ingroup Core_Module
+  *
+  * \brief Holds strides information for Map
+  *
+  * This class holds the strides information for mapping arrays with strides with class Map.
+  *
+  * It holds two values: the inner stride and the outer stride.
+  *
+  * The inner stride is the pointer increment between two consecutive entries within a given row of a
+  * row-major matrix or within a given column of a column-major matrix.
+  *
+  * The outer stride is the pointer increment between two consecutive rows of a row-major matrix or
+  * between two consecutive columns of a column-major matrix.
+  *
+  * These two values can be passed either at compile-time as template parameters, or at runtime as
+  * arguments to the constructor.
+  *
+  * Indeed, this class takes two template parameters:
+  *  \tparam OuterStrideAtCompileTime_ the outer stride, or Dynamic if you want to specify it at runtime.
+  *  \tparam InnerStrideAtCompileTime_ the inner stride, or Dynamic if you want to specify it at runtime.
+  *
+  * Here is an example:
+  * \include Map_general_stride.cpp
+  * Output: \verbinclude Map_general_stride.out
+  *
+  * Both strides can be negative. However, a negative stride of -1 cannot be specified at compile time
+  * because of the ambiguity with Dynamic which is defined to -1 (historically, negative strides were
+  * not allowed).
+  *
+  * Note that for compile-time vectors (ColsAtCompileTime==1 or RowsAtCompile==1),
+  * the inner stride is the pointer increment between two consecutive elements,
+  * regardless of storage layout.
+  *
+  * \sa class InnerStride, class OuterStride, \ref TopicStorageOrders
+  */
+template<int OuterStrideAtCompileTime_, int InnerStrideAtCompileTime_>
+class Stride
+{
+  public:
+    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+    enum {
+      InnerStrideAtCompileTime = InnerStrideAtCompileTime_,
+      OuterStrideAtCompileTime = OuterStrideAtCompileTime_
+    };
+
+    /** Default constructor, for use when strides are fixed at compile time */
+    EIGEN_DEVICE_FUNC
+    Stride()
+      : m_outer(OuterStrideAtCompileTime), m_inner(InnerStrideAtCompileTime)
+    {
+      // FIXME: for Eigen 4 we should use DynamicIndex instead of Dynamic.
+      // FIXME: for Eigen 4 we should also unify this API with fix<>
+      eigen_assert(InnerStrideAtCompileTime != Dynamic && OuterStrideAtCompileTime != Dynamic);
+    }
+
+    /** Constructor allowing to pass the strides at runtime */
+    EIGEN_DEVICE_FUNC
+    Stride(Index outerStride, Index innerStride)
+      : m_outer(outerStride), m_inner(innerStride)
+    {
+    }
+
+    /** Copy constructor */
+    EIGEN_DEVICE_FUNC
+    Stride(const Stride& other)
+      : m_outer(other.outer()), m_inner(other.inner())
+    {}
+
+    /** \returns the outer stride */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index outer() const { return m_outer.value(); }
+    /** \returns the inner stride */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index inner() const { return m_inner.value(); }
+
+  protected:
+    internal::variable_if_dynamic<Index, OuterStrideAtCompileTime> m_outer;
+    internal::variable_if_dynamic<Index, InnerStrideAtCompileTime> m_inner;
+};
+
+/** \brief Convenience specialization of Stride to specify only an inner stride
+  * See class Map for some examples */
+template<int Value>
+class InnerStride : public Stride<0, Value>
+{
+    typedef Stride<0, Value> Base;
+  public:
+    EIGEN_DEVICE_FUNC InnerStride() : Base() {}
+    EIGEN_DEVICE_FUNC InnerStride(Index v) : Base(0, v) {} // FIXME making this explicit could break valid code
+};
+
+/** \brief Convenience specialization of Stride to specify only an outer stride
+  * See class Map for some examples */
+template<int Value>
+class OuterStride : public Stride<Value, 0>
+{
+    typedef Stride<Value, 0> Base;
+  public:
+    EIGEN_DEVICE_FUNC OuterStride() : Base() {}
+    EIGEN_DEVICE_FUNC OuterStride(Index v) : Base(v,0) {} // FIXME making this explicit could break valid code
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_STRIDE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Swap.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Swap.h
new file mode 100644
index 0000000000000000000000000000000000000000..b2e7511770535759e2431254df897fb271d9f23a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Swap.h
@@ -0,0 +1,70 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SWAP_H
+#define EIGEN_SWAP_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+// Overload default assignPacket behavior for swapping them
+template<typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT>
+class generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, Specialized>
+ : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn>
+{
+protected:
+  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, swap_assign_op<typename DstEvaluatorTypeT::Scalar>, BuiltIn> Base;
+  using Base::m_dst;
+  using Base::m_src;
+  using Base::m_functor;
+  
+public:
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::DstXprType DstXprType;
+  typedef swap_assign_op<Scalar> Functor;
+  
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  generic_dense_assignment_kernel(DstEvaluatorTypeT &dst, const SrcEvaluatorTypeT &src, const Functor &func, DstXprType& dstExpr)
+    : Base(dst, src, func, dstExpr)
+  {}
+  
+  template<int StoreMode, int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE void assignPacket(Index row, Index col)
+  {
+    PacketType tmp = m_src.template packet<LoadMode,PacketType>(row,col);
+    const_cast<SrcEvaluatorTypeT&>(m_src).template writePacket<LoadMode>(row,col, m_dst.template packet<StoreMode,PacketType>(row,col));
+    m_dst.template writePacket<StoreMode>(row,col,tmp);
+  }
+  
+  template<int StoreMode, int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE void assignPacket(Index index)
+  {
+    PacketType tmp = m_src.template packet<LoadMode,PacketType>(index);
+    const_cast<SrcEvaluatorTypeT&>(m_src).template writePacket<LoadMode>(index, m_dst.template packet<StoreMode,PacketType>(index));
+    m_dst.template writePacket<StoreMode>(index,tmp);
+  }
+  
+  // TODO find a simple way not to have to copy/paste this function from generic_dense_assignment_kernel, by simple I mean no CRTP (Gael)
+  template<int StoreMode, int LoadMode, typename PacketType>
+  EIGEN_STRONG_INLINE void assignPacketByOuterInner(Index outer, Index inner)
+  {
+    Index row = Base::rowIndexByOuterInner(outer, inner); 
+    Index col = Base::colIndexByOuterInner(outer, inner);
+    assignPacket<StoreMode,LoadMode,PacketType>(row, col);
+  }
+};
+
+} // namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SWAP_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Transpose.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Transpose.h
new file mode 100644
index 0000000000000000000000000000000000000000..c56318c7ae156f454119fccc1d3b8a488e53a2ac
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Transpose.h
@@ -0,0 +1,466 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRANSPOSE_H
+#define EIGEN_TRANSPOSE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template<typename MatrixType>
+struct traits<Transpose<MatrixType> > : public traits<MatrixType>
+{
+  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
+  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNestedPlain;
+  enum {
+    RowsAtCompileTime = MatrixType::ColsAtCompileTime,
+    ColsAtCompileTime = MatrixType::RowsAtCompileTime,
+    MaxRowsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
+    Flags0 = traits<MatrixTypeNestedPlain>::Flags & ~(LvalueBit | NestByRefBit),
+    Flags1 = Flags0 | FlagsLvalueBit,
+    Flags = Flags1 ^ RowMajorBit,
+    InnerStrideAtCompileTime = inner_stride_at_compile_time<MatrixType>::ret,
+    OuterStrideAtCompileTime = outer_stride_at_compile_time<MatrixType>::ret
+  };
+};
+}
+
+template<typename MatrixType, typename StorageKind> class TransposeImpl;
+
+/** \class Transpose
+  * \ingroup Core_Module
+  *
+  * \brief Expression of the transpose of a matrix
+  *
+  * \tparam MatrixType the type of the object of which we are taking the transpose
+  *
+  * This class represents an expression of the transpose of a matrix.
+  * It is the return type of MatrixBase::transpose() and MatrixBase::adjoint()
+  * and most of the time this is the only way it is used.
+  *
+  * \sa MatrixBase::transpose(), MatrixBase::adjoint()
+  */
+template<typename MatrixType> class Transpose
+  : public TransposeImpl<MatrixType,typename internal::traits<MatrixType>::StorageKind>
+{
+  public:
+
+    typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
+
+    typedef typename TransposeImpl<MatrixType,typename internal::traits<MatrixType>::StorageKind>::Base Base;
+    EIGEN_GENERIC_PUBLIC_INTERFACE(Transpose)
+    typedef internal::remove_all_t<MatrixType> NestedExpression;
+
+    EIGEN_DEVICE_FUNC
+    explicit EIGEN_STRONG_INLINE Transpose(MatrixType& matrix) : m_matrix(matrix) {}
+
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Transpose)
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index rows() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    Index cols() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
+
+    /** \returns the nested expression */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const internal::remove_all_t<MatrixTypeNested>&
+    nestedExpression() const { return m_matrix; }
+
+    /** \returns the nested expression */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    std::remove_reference_t<MatrixTypeNested>&
+    nestedExpression() { return m_matrix; }
+
+    /** \internal */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    void resize(Index nrows, Index ncols) {
+      m_matrix.resize(ncols,nrows);
+    }
+
+  protected:
+    typename internal::ref_selector<MatrixType>::non_const_type m_matrix;
+};
+
+namespace internal {
+
+template<typename MatrixType, bool HasDirectAccess = has_direct_access<MatrixType>::ret>
+struct TransposeImpl_base
+{
+  typedef typename dense_xpr_base<Transpose<MatrixType> >::type type;
+};
+
+template<typename MatrixType>
+struct TransposeImpl_base<MatrixType, false>
+{
+  typedef typename dense_xpr_base<Transpose<MatrixType> >::type type;
+};
+
+} // end namespace internal
+
+// Generic API dispatcher
+template<typename XprType, typename StorageKind>
+class TransposeImpl
+  : public internal::generic_xpr_base<Transpose<XprType> >::type
+{
+public:
+  typedef typename internal::generic_xpr_base<Transpose<XprType> >::type Base;
+};
+
+template<typename MatrixType> class TransposeImpl<MatrixType,Dense>
+  : public internal::TransposeImpl_base<MatrixType>::type
+{
+  public:
+
+    typedef typename internal::TransposeImpl_base<MatrixType>::type Base;
+    using Base::coeffRef;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Transpose<MatrixType>)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(TransposeImpl)
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Index innerStride() const { return derived().nestedExpression().innerStride(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    Index outerStride() const { return derived().nestedExpression().outerStride(); }
+
+    typedef std::conditional_t<
+              internal::is_lvalue<MatrixType>::value,
+              Scalar,
+              const Scalar
+            > ScalarWithConstIfNotLvalue;
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    ScalarWithConstIfNotLvalue* data() { return derived().nestedExpression().data(); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const Scalar* data() const { return derived().nestedExpression().data(); }
+
+    // FIXME: shall we keep the const version of coeffRef?
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const Scalar& coeffRef(Index rowId, Index colId) const
+    {
+      return derived().nestedExpression().coeffRef(colId, rowId);
+    }
+
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    const Scalar& coeffRef(Index index) const
+    {
+      return derived().nestedExpression().coeffRef(index);
+    }
+  protected:
+    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(TransposeImpl)
+};
+
+/** \returns an expression of the transpose of *this.
+  *
+  * Example: \include MatrixBase_transpose.cpp
+  * Output: \verbinclude MatrixBase_transpose.out
+  *
+  * \warning If you want to replace a matrix by its own transpose, do \b NOT do this:
+  * \code
+  * m = m.transpose(); // bug!!! caused by aliasing effect
+  * \endcode
+  * Instead, use the transposeInPlace() method:
+  * \code
+  * m.transposeInPlace();
+  * \endcode
+  * which gives Eigen good opportunities for optimization, or alternatively you can also do:
+  * \code
+  * m = m.transpose().eval();
+  * \endcode
+  *
+  * \sa transposeInPlace(), adjoint() */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename DenseBase<Derived>::TransposeReturnType
+DenseBase<Derived>::transpose()
+{
+  return TransposeReturnType(derived());
+}
+
+/** This is the const version of transpose().
+  *
+  * Make sure you read the warning for transpose() !
+  *
+  * \sa transposeInPlace(), adjoint() */
+template<typename Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const typename DenseBase<Derived>::ConstTransposeReturnType
+DenseBase<Derived>::transpose() const
+{
+  return ConstTransposeReturnType(derived());
+}
+
+/** \returns an expression of the adjoint (i.e. conjugate transpose) of *this.
+  *
+  * Example: \include MatrixBase_adjoint.cpp
+  * Output: \verbinclude MatrixBase_adjoint.out
+  *
+  * \warning If you want to replace a matrix by its own adjoint, do \b NOT do this:
+  * \code
+  * m = m.adjoint(); // bug!!! caused by aliasing effect
+  * \endcode
+  * Instead, use the adjointInPlace() method:
+  * \code
+  * m.adjointInPlace();
+  * \endcode
+  * which gives Eigen good opportunities for optimization, or alternatively you can also do:
+  * \code
+  * m = m.adjoint().eval();
+  * \endcode
+  *
+  * \sa adjointInPlace(), transpose(), conjugate(), class Transpose, class internal::scalar_conjugate_op */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline const typename MatrixBase<Derived>::AdjointReturnType
+MatrixBase<Derived>::adjoint() const
+{
+  return AdjointReturnType(this->transpose());
+}
+
+/***************************************************************************
+* "in place" transpose implementation
+***************************************************************************/
+
+namespace internal {
+
+template<typename MatrixType,
+  bool IsSquare = (MatrixType::RowsAtCompileTime == MatrixType::ColsAtCompileTime) && MatrixType::RowsAtCompileTime!=Dynamic,
+  bool MatchPacketSize =
+        (int(MatrixType::RowsAtCompileTime) == int(internal::packet_traits<typename MatrixType::Scalar>::size))
+    &&  (internal::evaluator<MatrixType>::Flags&PacketAccessBit) >
+struct inplace_transpose_selector;
+
+template<typename MatrixType>
+struct inplace_transpose_selector<MatrixType,true,false> { // square matrix
+  static void run(MatrixType& m) {
+    m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose().template triangularView<StrictlyUpper>());
+  }
+};
+
+template<typename MatrixType>
+struct inplace_transpose_selector<MatrixType,true,true> { // PacketSize x PacketSize
+  static void run(MatrixType& m) {
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename internal::packet_traits<typename MatrixType::Scalar>::type Packet;
+    const Index PacketSize = internal::packet_traits<Scalar>::size;
+    const Index Alignment = internal::evaluator<MatrixType>::Alignment;
+    PacketBlock<Packet> A;
+    for (Index i=0; i<PacketSize; ++i)
+      A.packet[i] = m.template packetByOuterInner<Alignment>(i,0);
+    internal::ptranspose(A);
+    for (Index i=0; i<PacketSize; ++i)
+      m.template writePacket<Alignment>(m.rowIndexByOuterInner(i,0), m.colIndexByOuterInner(i,0), A.packet[i]);
+  }
+};
+
+
+template <typename MatrixType, Index Alignment>
+void BlockedInPlaceTranspose(MatrixType& m) {
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename internal::packet_traits<typename MatrixType::Scalar>::type Packet;
+  const Index PacketSize = internal::packet_traits<Scalar>::size;
+  eigen_assert(m.rows() == m.cols());
+  int row_start = 0;
+  for (; row_start + PacketSize <= m.rows(); row_start += PacketSize) {
+    for (int col_start = row_start; col_start + PacketSize <= m.cols(); col_start += PacketSize) {
+      PacketBlock<Packet> A;
+      if (row_start == col_start) {
+        for (Index i=0; i<PacketSize; ++i)
+          A.packet[i] = m.template packetByOuterInner<Alignment>(row_start + i,col_start);
+        internal::ptranspose(A);
+        for (Index i=0; i<PacketSize; ++i)
+          m.template writePacket<Alignment>(m.rowIndexByOuterInner(row_start + i, col_start), m.colIndexByOuterInner(row_start + i,col_start), A.packet[i]);
+      } else {
+        PacketBlock<Packet> B;
+        for (Index i=0; i<PacketSize; ++i) {
+          A.packet[i] = m.template packetByOuterInner<Alignment>(row_start + i,col_start);
+          B.packet[i] = m.template packetByOuterInner<Alignment>(col_start + i, row_start);
+        }
+        internal::ptranspose(A);
+        internal::ptranspose(B);
+        for (Index i=0; i<PacketSize; ++i) {
+          m.template writePacket<Alignment>(m.rowIndexByOuterInner(row_start + i, col_start), m.colIndexByOuterInner(row_start + i,col_start), B.packet[i]);
+          m.template writePacket<Alignment>(m.rowIndexByOuterInner(col_start + i, row_start), m.colIndexByOuterInner(col_start + i,row_start), A.packet[i]);
+        }
+      }
+    }
+  }
+  for (Index row = row_start; row < m.rows(); ++row) {
+    m.matrix().row(row).head(row).swap(
+        m.matrix().col(row).head(row).transpose());
+  }
+}
+
+template<typename MatrixType,bool MatchPacketSize>
+struct inplace_transpose_selector<MatrixType,false,MatchPacketSize> { // non square or dynamic matrix
+  static void run(MatrixType& m) {
+    typedef typename MatrixType::Scalar Scalar;
+    if (m.rows() == m.cols()) {
+      const Index PacketSize = internal::packet_traits<Scalar>::size;
+      if (!NumTraits<Scalar>::IsComplex && m.rows() >= PacketSize) {
+        if ((m.rows() % PacketSize) == 0)
+          BlockedInPlaceTranspose<MatrixType,internal::evaluator<MatrixType>::Alignment>(m);
+        else
+          BlockedInPlaceTranspose<MatrixType,Unaligned>(m);
+      }
+      else {
+        m.matrix().template triangularView<StrictlyUpper>().swap(m.matrix().transpose().template triangularView<StrictlyUpper>());
+      }
+    } else {
+      m = m.transpose().eval();
+    }
+  }
+};
+
+
+} // end namespace internal
+
+/** This is the "in place" version of transpose(): it replaces \c *this by its own transpose.
+  * Thus, doing
+  * \code
+  * m.transposeInPlace();
+  * \endcode
+  * has the same effect on m as doing
+  * \code
+  * m = m.transpose().eval();
+  * \endcode
+  * and is faster and also safer because in the latter line of code, forgetting the eval() results
+  * in a bug caused by \ref TopicAliasing "aliasing".
+  *
+  * Notice however that this method is only useful if you want to replace a matrix by its own transpose.
+  * If you just need the transpose of a matrix, use transpose().
+  *
+  * \note if the matrix is not square, then \c *this must be a resizable matrix.
+  * This excludes (non-square) fixed-size matrices, block-expressions and maps.
+  *
+  * \sa transpose(), adjoint(), adjointInPlace() */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline void DenseBase<Derived>::transposeInPlace()
+{
+  eigen_assert((rows() == cols() || (RowsAtCompileTime == Dynamic && ColsAtCompileTime == Dynamic))
+               && "transposeInPlace() called on a non-square non-resizable matrix");
+  internal::inplace_transpose_selector<Derived>::run(derived());
+}
+
+/***************************************************************************
+* "in place" adjoint implementation
+***************************************************************************/
+
+/** This is the "in place" version of adjoint(): it replaces \c *this by its own transpose.
+  * Thus, doing
+  * \code
+  * m.adjointInPlace();
+  * \endcode
+  * has the same effect on m as doing
+  * \code
+  * m = m.adjoint().eval();
+  * \endcode
+  * and is faster and also safer because in the latter line of code, forgetting the eval() results
+  * in a bug caused by aliasing.
+  *
+  * Notice however that this method is only useful if you want to replace a matrix by its own adjoint.
+  * If you just need the adjoint of a matrix, use adjoint().
+  *
+  * \note if the matrix is not square, then \c *this must be a resizable matrix.
+  * This excludes (non-square) fixed-size matrices, block-expressions and maps.
+  *
+  * \sa transpose(), adjoint(), transposeInPlace() */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline void MatrixBase<Derived>::adjointInPlace()
+{
+  derived() = adjoint().eval();
+}
+
+#ifndef EIGEN_NO_DEBUG
+
+// The following is to detect aliasing problems in most common cases.
+
+namespace internal {
+
+template<bool DestIsTransposed, typename OtherDerived>
+struct check_transpose_aliasing_compile_time_selector
+{
+  enum { ret = bool(blas_traits<OtherDerived>::IsTransposed) != DestIsTransposed };
+};
+
+template<bool DestIsTransposed, typename BinOp, typename DerivedA, typename DerivedB>
+struct check_transpose_aliasing_compile_time_selector<DestIsTransposed,CwiseBinaryOp<BinOp,DerivedA,DerivedB> >
+{
+  enum { ret =    bool(blas_traits<DerivedA>::IsTransposed) != DestIsTransposed
+               || bool(blas_traits<DerivedB>::IsTransposed) != DestIsTransposed
+  };
+};
+
+template<typename Scalar, bool DestIsTransposed, typename OtherDerived>
+struct check_transpose_aliasing_run_time_selector
+{
+  EIGEN_DEVICE_FUNC static bool run(const Scalar* dest, const OtherDerived& src)
+  {
+    return (bool(blas_traits<OtherDerived>::IsTransposed) != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src));
+  }
+};
+
+template<typename Scalar, bool DestIsTransposed, typename BinOp, typename DerivedA, typename DerivedB>
+struct check_transpose_aliasing_run_time_selector<Scalar,DestIsTransposed,CwiseBinaryOp<BinOp,DerivedA,DerivedB> >
+{
+  EIGEN_DEVICE_FUNC static bool run(const Scalar* dest, const CwiseBinaryOp<BinOp,DerivedA,DerivedB>& src)
+  {
+    return ((blas_traits<DerivedA>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src.lhs())))
+        || ((blas_traits<DerivedB>::IsTransposed != DestIsTransposed) && (dest!=0 && dest==(const Scalar*)extract_data(src.rhs())));
+  }
+};
+
+// the following selector, checkTransposeAliasing_impl, based on MightHaveTransposeAliasing,
+// is because when the condition controlling the assert is known at compile time, ICC emits a warning.
+// This is actually a good warning: in expressions that don't have any transposing, the condition is
+// known at compile time to be false, and using that, we can avoid generating the code of the assert again
+// and again for all these expressions that don't need it.
+
+template<typename Derived, typename OtherDerived,
+         bool MightHaveTransposeAliasing
+                 = check_transpose_aliasing_compile_time_selector
+                     <blas_traits<Derived>::IsTransposed,OtherDerived>::ret
+        >
+struct checkTransposeAliasing_impl
+{
+    EIGEN_DEVICE_FUNC static void run(const Derived& dst, const OtherDerived& other)
+    {
+        eigen_assert((!check_transpose_aliasing_run_time_selector
+                      <typename Derived::Scalar,blas_traits<Derived>::IsTransposed,OtherDerived>
+                      ::run(extract_data(dst), other))
+          && "aliasing detected during transposition, use transposeInPlace() "
+             "or evaluate the rhs into a temporary using .eval()");
+
+    }
+};
+
+template<typename Derived, typename OtherDerived>
+struct checkTransposeAliasing_impl<Derived, OtherDerived, false>
+{
+    EIGEN_DEVICE_FUNC static void run(const Derived&, const OtherDerived&)
+    {
+    }
+};
+
+template<typename Dst, typename Src>
+EIGEN_DEVICE_FUNC inline void check_for_aliasing(const Dst &dst, const Src &src)
+{
+  if((!Dst::IsVectorAtCompileTime) && dst.rows()>1 && dst.cols()>1)
+    internal::checkTransposeAliasing_impl<Dst, Src>::run(dst, src);
+}
+
+} // end namespace internal
+
+#endif // EIGEN_NO_DEBUG
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRANSPOSE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Transpositions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Transpositions.h
new file mode 100644
index 0000000000000000000000000000000000000000..84a9773267198175e94220ae5418c0abd469e7a7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Transpositions.h
@@ -0,0 +1,388 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRANSPOSITIONS_H
+#define EIGEN_TRANSPOSITIONS_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template<typename Derived>
+class TranspositionsBase
+{
+    typedef internal::traits<Derived> Traits;
+
+  public:
+
+    typedef typename Traits::IndicesType IndicesType;
+    typedef typename IndicesType::Scalar StorageIndex;
+    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+
+    EIGEN_DEVICE_FUNC
+    Derived& derived() { return *static_cast<Derived*>(this); }
+    EIGEN_DEVICE_FUNC
+    const Derived& derived() const { return *static_cast<const Derived*>(this); }
+
+    /** Copies the \a other transpositions into \c *this */
+    template<typename OtherDerived>
+    Derived& operator=(const TranspositionsBase<OtherDerived>& other)
+    {
+      indices() = other.indices();
+      return derived();
+    }
+
+    /** \returns the number of transpositions */
+    EIGEN_DEVICE_FUNC
+    Index size() const { return indices().size(); }
+    /** \returns the number of rows of the equivalent permutation matrix */
+    EIGEN_DEVICE_FUNC
+    Index rows() const { return indices().size(); }
+    /** \returns the number of columns of the equivalent permutation matrix */
+    EIGEN_DEVICE_FUNC
+    Index cols() const { return indices().size(); }
+
+    /** Direct access to the underlying index vector */
+    EIGEN_DEVICE_FUNC
+    inline const StorageIndex& coeff(Index i) const { return indices().coeff(i); }
+    /** Direct access to the underlying index vector */
+    inline StorageIndex& coeffRef(Index i) { return indices().coeffRef(i); }
+    /** Direct access to the underlying index vector */
+    inline const StorageIndex& operator()(Index i) const { return indices()(i); }
+    /** Direct access to the underlying index vector */
+    inline StorageIndex& operator()(Index i) { return indices()(i); }
+    /** Direct access to the underlying index vector */
+    inline const StorageIndex& operator[](Index i) const { return indices()(i); }
+    /** Direct access to the underlying index vector */
+    inline StorageIndex& operator[](Index i) { return indices()(i); }
+
+    /** const version of indices(). */
+    EIGEN_DEVICE_FUNC
+    const IndicesType& indices() const { return derived().indices(); }
+    /** \returns a reference to the stored array representing the transpositions. */
+    EIGEN_DEVICE_FUNC
+    IndicesType& indices() { return derived().indices(); }
+
+    /** Resizes to given size. */
+    inline void resize(Index newSize)
+    {
+      indices().resize(newSize);
+    }
+
+    /** Sets \c *this to represents an identity transformation */
+    void setIdentity()
+    {
+      for(StorageIndex i = 0; i < indices().size(); ++i)
+        coeffRef(i) = i;
+    }
+
+    // FIXME: do we want such methods ?
+    // might be useful when the target matrix expression is complex, e.g.:
+    // object.matrix().block(..,..,..,..) = trans * object.matrix().block(..,..,..,..);
+    /*
+    template<typename MatrixType>
+    void applyForwardToRows(MatrixType& mat) const
+    {
+      for(Index k=0 ; k<size() ; ++k)
+        if(m_indices(k)!=k)
+          mat.row(k).swap(mat.row(m_indices(k)));
+    }
+
+    template<typename MatrixType>
+    void applyBackwardToRows(MatrixType& mat) const
+    {
+      for(Index k=size()-1 ; k>=0 ; --k)
+        if(m_indices(k)!=k)
+          mat.row(k).swap(mat.row(m_indices(k)));
+    }
+    */
+
+    /** \returns the inverse transformation */
+    inline Transpose<TranspositionsBase> inverse() const
+    { return Transpose<TranspositionsBase>(derived()); }
+
+    /** \returns the tranpose transformation */
+    inline Transpose<TranspositionsBase> transpose() const
+    { return Transpose<TranspositionsBase>(derived()); }
+
+  protected:
+};
+
+namespace internal {
+template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_>
+struct traits<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,StorageIndex_> >
+ : traits<PermutationMatrix<SizeAtCompileTime,MaxSizeAtCompileTime,StorageIndex_> >
+{
+  typedef Matrix<StorageIndex_, SizeAtCompileTime, 1, 0, MaxSizeAtCompileTime, 1> IndicesType;
+  typedef TranspositionsStorage StorageKind;
+};
+}
+
+/** \class Transpositions
+  * \ingroup Core_Module
+  *
+  * \brief Represents a sequence of transpositions (row/column interchange)
+  *
+  * \tparam SizeAtCompileTime the number of transpositions, or Dynamic
+  * \tparam MaxSizeAtCompileTime the maximum number of transpositions, or Dynamic. This optional parameter defaults to SizeAtCompileTime. Most of the time, you should not have to specify it.
+  *
+  * This class represents a permutation transformation as a sequence of \em n transpositions
+  * \f$[T_{n-1} \ldots T_{i} \ldots T_{0}]\f$. It is internally stored as a vector of integers \c indices.
+  * Each transposition \f$ T_{i} \f$ applied on the left of a matrix (\f$ T_{i} M\f$) interchanges
+  * the rows \c i and \c indices[i] of the matrix \c M.
+  * A transposition applied on the right (e.g., \f$ M T_{i}\f$) yields a column interchange.
+  *
+  * Compared to the class PermutationMatrix, such a sequence of transpositions is what is
+  * computed during a decomposition with pivoting, and it is faster when applying the permutation in-place.
+  *
+  * To apply a sequence of transpositions to a matrix, simply use the operator * as in the following example:
+  * \code
+  * Transpositions tr;
+  * MatrixXf mat;
+  * mat = tr * mat;
+  * \endcode
+  * In this example, we detect that the matrix appears on both side, and so the transpositions
+  * are applied in-place without any temporary or extra copy.
+  *
+  * \sa class PermutationMatrix
+  */
+
+template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_>
+class Transpositions : public TranspositionsBase<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,StorageIndex_> >
+{
+    typedef internal::traits<Transpositions> Traits;
+  public:
+
+    typedef TranspositionsBase<Transpositions> Base;
+    typedef typename Traits::IndicesType IndicesType;
+    typedef typename IndicesType::Scalar StorageIndex;
+
+    inline Transpositions() {}
+
+    /** Copy constructor. */
+    template<typename OtherDerived>
+    inline Transpositions(const TranspositionsBase<OtherDerived>& other)
+      : m_indices(other.indices()) {}
+
+    /** Generic constructor from expression of the transposition indices. */
+    template<typename Other>
+    explicit inline Transpositions(const MatrixBase<Other>& indices) : m_indices(indices)
+    {}
+
+    /** Copies the \a other transpositions into \c *this */
+    template<typename OtherDerived>
+    Transpositions& operator=(const TranspositionsBase<OtherDerived>& other)
+    {
+      return Base::operator=(other);
+    }
+
+    /** Constructs an uninitialized permutation matrix of given size.
+      */
+    inline Transpositions(Index size) : m_indices(size)
+    {}
+
+    /** const version of indices(). */
+    EIGEN_DEVICE_FUNC
+    const IndicesType& indices() const { return m_indices; }
+    /** \returns a reference to the stored array representing the transpositions. */
+    EIGEN_DEVICE_FUNC
+    IndicesType& indices() { return m_indices; }
+
+  protected:
+
+    IndicesType m_indices;
+};
+
+
+namespace internal {
+template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_, int PacketAccess_>
+struct traits<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,StorageIndex_>,PacketAccess_> >
+ : traits<PermutationMatrix<SizeAtCompileTime,MaxSizeAtCompileTime,StorageIndex_> >
+{
+  typedef Map<const Matrix<StorageIndex_,SizeAtCompileTime,1,0,MaxSizeAtCompileTime,1>, PacketAccess_> IndicesType;
+  typedef StorageIndex_ StorageIndex;
+  typedef TranspositionsStorage StorageKind;
+};
+}
+
+template<int SizeAtCompileTime, int MaxSizeAtCompileTime, typename StorageIndex_, int PacketAccess>
+class Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,StorageIndex_>,PacketAccess>
+ : public TranspositionsBase<Map<Transpositions<SizeAtCompileTime,MaxSizeAtCompileTime,StorageIndex_>,PacketAccess> >
+{
+    typedef internal::traits<Map> Traits;
+  public:
+
+    typedef TranspositionsBase<Map> Base;
+    typedef typename Traits::IndicesType IndicesType;
+    typedef typename IndicesType::Scalar StorageIndex;
+
+    explicit inline Map(const StorageIndex* indicesPtr)
+      : m_indices(indicesPtr)
+    {}
+
+    inline Map(const StorageIndex* indicesPtr, Index size)
+      : m_indices(indicesPtr,size)
+    {}
+
+    /** Copies the \a other transpositions into \c *this */
+    template<typename OtherDerived>
+    Map& operator=(const TranspositionsBase<OtherDerived>& other)
+    {
+      return Base::operator=(other);
+    }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** This is a special case of the templated operator=. Its purpose is to
+      * prevent a default operator= from hiding the templated operator=.
+      */
+    Map& operator=(const Map& other)
+    {
+      m_indices = other.m_indices;
+      return *this;
+    }
+    #endif
+
+    /** const version of indices(). */
+    EIGEN_DEVICE_FUNC
+    const IndicesType& indices() const { return m_indices; }
+
+    /** \returns a reference to the stored array representing the transpositions. */
+    EIGEN_DEVICE_FUNC
+    IndicesType& indices() { return m_indices; }
+
+  protected:
+
+    IndicesType m_indices;
+};
+
+namespace internal {
+template<typename IndicesType_>
+struct traits<TranspositionsWrapper<IndicesType_> >
+ : traits<PermutationWrapper<IndicesType_> >
+{
+  typedef TranspositionsStorage StorageKind;
+};
+}
+
+template<typename IndicesType_>
+class TranspositionsWrapper
+ : public TranspositionsBase<TranspositionsWrapper<IndicesType_> >
+{
+    typedef internal::traits<TranspositionsWrapper> Traits;
+  public:
+
+    typedef TranspositionsBase<TranspositionsWrapper> Base;
+    typedef typename Traits::IndicesType IndicesType;
+    typedef typename IndicesType::Scalar StorageIndex;
+
+    explicit inline TranspositionsWrapper(IndicesType& indices)
+      : m_indices(indices)
+    {}
+
+    /** Copies the \a other transpositions into \c *this */
+    template<typename OtherDerived>
+    TranspositionsWrapper& operator=(const TranspositionsBase<OtherDerived>& other)
+    {
+      return Base::operator=(other);
+    }
+
+    /** const version of indices(). */
+    EIGEN_DEVICE_FUNC
+    const IndicesType& indices() const { return m_indices; }
+
+    /** \returns a reference to the stored array representing the transpositions. */
+    EIGEN_DEVICE_FUNC
+    IndicesType& indices() { return m_indices; }
+
+  protected:
+
+    typename IndicesType::Nested m_indices;
+};
+
+
+
+/** \returns the \a matrix with the \a transpositions applied to the columns.
+  */
+template<typename MatrixDerived, typename TranspositionsDerived>
+EIGEN_DEVICE_FUNC
+const Product<MatrixDerived, TranspositionsDerived, AliasFreeProduct>
+operator*(const MatrixBase<MatrixDerived> &matrix,
+          const TranspositionsBase<TranspositionsDerived>& transpositions)
+{
+  return Product<MatrixDerived, TranspositionsDerived, AliasFreeProduct>
+            (matrix.derived(), transpositions.derived());
+}
+
+/** \returns the \a matrix with the \a transpositions applied to the rows.
+  */
+template<typename TranspositionsDerived, typename MatrixDerived>
+EIGEN_DEVICE_FUNC
+const Product<TranspositionsDerived, MatrixDerived, AliasFreeProduct>
+operator*(const TranspositionsBase<TranspositionsDerived> &transpositions,
+          const MatrixBase<MatrixDerived>& matrix)
+{
+  return Product<TranspositionsDerived, MatrixDerived, AliasFreeProduct>
+            (transpositions.derived(), matrix.derived());
+}
+
+// Template partial specialization for transposed/inverse transpositions
+
+namespace internal {
+
+template<typename Derived>
+struct traits<Transpose<TranspositionsBase<Derived> > >
+ : traits<Derived>
+{};
+
+} // end namespace internal
+
+template<typename TranspositionsDerived>
+class Transpose<TranspositionsBase<TranspositionsDerived> >
+{
+    typedef TranspositionsDerived TranspositionType;
+    typedef typename TranspositionType::IndicesType IndicesType;
+  public:
+
+    explicit Transpose(const TranspositionType& t) : m_transpositions(t) {}
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    Index size() const EIGEN_NOEXCEPT { return m_transpositions.size(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    Index rows() const EIGEN_NOEXCEPT { return m_transpositions.size(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    Index cols() const EIGEN_NOEXCEPT { return m_transpositions.size(); }
+
+    /** \returns the \a matrix with the inverse transpositions applied to the columns.
+      */
+    template<typename OtherDerived> friend
+    const Product<OtherDerived, Transpose, AliasFreeProduct>
+    operator*(const MatrixBase<OtherDerived>& matrix, const Transpose& trt)
+    {
+      return Product<OtherDerived, Transpose, AliasFreeProduct>(matrix.derived(), trt);
+    }
+
+    /** \returns the \a matrix with the inverse transpositions applied to the rows.
+      */
+    template<typename OtherDerived>
+    const Product<Transpose, OtherDerived, AliasFreeProduct>
+    operator*(const MatrixBase<OtherDerived>& matrix) const
+    {
+      return Product<Transpose, OtherDerived, AliasFreeProduct>(*this, matrix.derived());
+    }
+
+    EIGEN_DEVICE_FUNC
+    const TranspositionType& nestedExpression() const { return m_transpositions; }
+
+  protected:
+    const TranspositionType& m_transpositions;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRANSPOSITIONS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/TriangularMatrix.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/TriangularMatrix.h
new file mode 100644
index 0000000000000000000000000000000000000000..c1bd13afc1821c9d287682d5a6f0667fc3738a98
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/TriangularMatrix.h
@@ -0,0 +1,1003 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRIANGULARMATRIX_H
+#define EIGEN_TRIANGULARMATRIX_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<int Side, typename TriangularType, typename Rhs> struct triangular_solve_retval;
+
+}
+
+/** \class TriangularBase
+  * \ingroup Core_Module
+  *
+  * \brief Base class for triangular part in a matrix
+  */
+template<typename Derived> class TriangularBase : public EigenBase<Derived>
+{
+  public:
+
+    enum {
+      Mode = internal::traits<Derived>::Mode,
+      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+      MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
+
+      SizeAtCompileTime = (internal::size_of_xpr_at_compile_time<Derived>::ret),
+      /**< This is equal to the number of coefficients, i.e. the number of
+          * rows times the number of columns, or to \a Dynamic if this is not
+          * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
+
+      MaxSizeAtCompileTime = internal::size_at_compile_time(internal::traits<Derived>::MaxRowsAtCompileTime,
+                                                            internal::traits<Derived>::MaxColsAtCompileTime)
+
+    };
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+    typedef typename internal::traits<Derived>::FullMatrixType DenseMatrixType;
+    typedef DenseMatrixType DenseType;
+    typedef Derived const& Nested;
+
+    EIGEN_DEVICE_FUNC
+    inline TriangularBase() { eigen_assert(!((int(Mode) & int(UnitDiag)) && (int(Mode) & int(ZeroDiag)))); }
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index rows() const EIGEN_NOEXCEPT { return derived().rows(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index cols() const EIGEN_NOEXCEPT { return derived().cols(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index outerStride() const EIGEN_NOEXCEPT { return derived().outerStride(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index innerStride() const EIGEN_NOEXCEPT { return derived().innerStride(); }
+
+    // dummy resize function
+    EIGEN_DEVICE_FUNC
+    void resize(Index rows, Index cols)
+    {
+      EIGEN_UNUSED_VARIABLE(rows);
+      EIGEN_UNUSED_VARIABLE(cols);
+      eigen_assert(rows==this->rows() && cols==this->cols());
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline Scalar coeff(Index row, Index col) const  { return derived().coeff(row,col); }
+    EIGEN_DEVICE_FUNC
+    inline Scalar& coeffRef(Index row, Index col) { return derived().coeffRef(row,col); }
+
+    /** \see MatrixBase::copyCoeff(row,col)
+      */
+    template<typename Other>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void copyCoeff(Index row, Index col, Other& other)
+    {
+      derived().coeffRef(row, col) = other.coeff(row, col);
+    }
+
+    EIGEN_DEVICE_FUNC
+    inline Scalar operator()(Index row, Index col) const
+    {
+      check_coordinates(row, col);
+      return coeff(row,col);
+    }
+    EIGEN_DEVICE_FUNC
+    inline Scalar& operator()(Index row, Index col)
+    {
+      check_coordinates(row, col);
+      return coeffRef(row,col);
+    }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    EIGEN_DEVICE_FUNC
+    inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
+    EIGEN_DEVICE_FUNC
+    inline Derived& derived() { return *static_cast<Derived*>(this); }
+    #endif // not EIGEN_PARSED_BY_DOXYGEN
+
+    template<typename DenseDerived>
+    EIGEN_DEVICE_FUNC
+    void evalTo(MatrixBase<DenseDerived> &other) const;
+    template<typename DenseDerived>
+    EIGEN_DEVICE_FUNC
+    void evalToLazy(MatrixBase<DenseDerived> &other) const;
+
+    EIGEN_DEVICE_FUNC
+    DenseMatrixType toDenseMatrix() const
+    {
+      DenseMatrixType res(rows(), cols());
+      evalToLazy(res);
+      return res;
+    }
+
+  protected:
+
+    void check_coordinates(Index row, Index col) const
+    {
+      EIGEN_ONLY_USED_FOR_DEBUG(row);
+      EIGEN_ONLY_USED_FOR_DEBUG(col);
+      eigen_assert(col>=0 && col<cols() && row>=0 && row<rows());
+      const int mode = int(Mode) & ~SelfAdjoint;
+      EIGEN_ONLY_USED_FOR_DEBUG(mode);
+      eigen_assert((mode==Upper && col>=row)
+                || (mode==Lower && col<=row)
+                || ((mode==StrictlyUpper || mode==UnitUpper) && col>row)
+                || ((mode==StrictlyLower || mode==UnitLower) && col<row));
+    }
+
+    #ifdef EIGEN_INTERNAL_DEBUGGING
+    void check_coordinates_internal(Index row, Index col) const
+    {
+      check_coordinates(row, col);
+    }
+    #else
+    void check_coordinates_internal(Index , Index ) const {}
+    #endif
+
+};
+
+/** \class TriangularView
+  * \ingroup Core_Module
+  *
+  * \brief Expression of a triangular part in a matrix
+  *
+  * \tparam MatrixType the type of the object in which we are taking the triangular part
+  * \tparam Mode the kind of triangular matrix expression to construct. Can be #Upper,
+  *             #Lower, #UnitUpper, #UnitLower, #StrictlyUpper, or #StrictlyLower.
+  *             This is in fact a bit field; it must have either #Upper or #Lower,
+  *             and additionally it may have #UnitDiag or #ZeroDiag or neither.
+  *
+  * This class represents a triangular part of a matrix, not necessarily square. Strictly speaking, for rectangular
+  * matrices one should speak of "trapezoid" parts. This class is the return type
+  * of MatrixBase::triangularView() and SparseMatrixBase::triangularView(), and most of the time this is the only way it is used.
+  *
+  * \sa MatrixBase::triangularView()
+  */
+namespace internal {
+template<typename MatrixType, unsigned int Mode_>
+struct traits<TriangularView<MatrixType, Mode_> > : traits<MatrixType>
+{
+  typedef typename ref_selector<MatrixType>::non_const_type MatrixTypeNested;
+  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNestedNonRef;
+  typedef remove_all_t<MatrixTypeNested> MatrixTypeNestedCleaned;
+  typedef typename MatrixType::PlainObject FullMatrixType;
+  typedef MatrixType ExpressionType;
+  enum {
+    Mode = Mode_,
+    FlagsLvalueBit = is_lvalue<MatrixType>::value ? LvalueBit : 0,
+    Flags = (MatrixTypeNestedCleaned::Flags & (HereditaryBits | FlagsLvalueBit) & (~(PacketAccessBit | DirectAccessBit | LinearAccessBit)))
+  };
+};
+}
+
+template<typename MatrixType_, unsigned int Mode_, typename StorageKind> class TriangularViewImpl;
+
+template<typename MatrixType_, unsigned int Mode_> class TriangularView
+  : public TriangularViewImpl<MatrixType_, Mode_, typename internal::traits<MatrixType_>::StorageKind >
+{
+  public:
+
+    typedef TriangularViewImpl<MatrixType_, Mode_, typename internal::traits<MatrixType_>::StorageKind > Base;
+    typedef typename internal::traits<TriangularView>::Scalar Scalar;
+    typedef MatrixType_ MatrixType;
+
+  protected:
+    typedef typename internal::traits<TriangularView>::MatrixTypeNested MatrixTypeNested;
+    typedef typename internal::traits<TriangularView>::MatrixTypeNestedNonRef MatrixTypeNestedNonRef;
+
+    typedef internal::remove_all_t<typename MatrixType::ConjugateReturnType> MatrixConjugateReturnType;
+    typedef TriangularView<std::add_const_t<MatrixType>, Mode_> ConstTriangularView;
+
+  public:
+
+    typedef typename internal::traits<TriangularView>::StorageKind StorageKind;
+    typedef typename internal::traits<TriangularView>::MatrixTypeNestedCleaned NestedExpression;
+
+    enum {
+      Mode = Mode_,
+      Flags = internal::traits<TriangularView>::Flags,
+      TransposeMode = (Mode & Upper ? Lower : 0)
+                    | (Mode & Lower ? Upper : 0)
+                    | (Mode & (UnitDiag))
+                    | (Mode & (ZeroDiag)),
+      IsVectorAtCompileTime = false
+    };
+
+    EIGEN_DEVICE_FUNC
+    explicit inline TriangularView(MatrixType& matrix) : m_matrix(matrix)
+    {}
+
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(TriangularView)
+
+    /** \copydoc EigenBase::rows() */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
+    /** \copydoc EigenBase::cols() */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
+
+    /** \returns a const reference to the nested expression */
+    EIGEN_DEVICE_FUNC
+    const NestedExpression& nestedExpression() const { return m_matrix; }
+
+    /** \returns a reference to the nested expression */
+    EIGEN_DEVICE_FUNC
+    NestedExpression& nestedExpression() { return m_matrix; }
+
+    typedef TriangularView<const MatrixConjugateReturnType,Mode> ConjugateReturnType;
+    /** \sa MatrixBase::conjugate() const */
+    EIGEN_DEVICE_FUNC
+    inline const ConjugateReturnType conjugate() const
+    { return ConjugateReturnType(m_matrix.conjugate()); }
+
+    /** \returns an expression of the complex conjugate of \c *this if Cond==true,
+     *           returns \c *this otherwise.
+     */
+    template<bool Cond>
+    EIGEN_DEVICE_FUNC
+    inline std::conditional_t<Cond,ConjugateReturnType,ConstTriangularView>
+    conjugateIf() const
+    {
+      typedef std::conditional_t<Cond,ConjugateReturnType,ConstTriangularView> ReturnType;
+      return ReturnType(m_matrix.template conjugateIf<Cond>());
+    }
+
+    typedef TriangularView<const typename MatrixType::AdjointReturnType,TransposeMode> AdjointReturnType;
+    /** \sa MatrixBase::adjoint() const */
+    EIGEN_DEVICE_FUNC
+    inline const AdjointReturnType adjoint() const
+    { return AdjointReturnType(m_matrix.adjoint()); }
+
+    typedef TriangularView<typename MatrixType::TransposeReturnType,TransposeMode> TransposeReturnType;
+     /** \sa MatrixBase::transpose() */
+    template<class Dummy=int>
+    EIGEN_DEVICE_FUNC
+    inline TransposeReturnType transpose(std::enable_if_t<Eigen::internal::is_lvalue<MatrixType>::value, Dummy*> = nullptr)
+    {
+      typename MatrixType::TransposeReturnType tmp(m_matrix);
+      return TransposeReturnType(tmp);
+    }
+
+    typedef TriangularView<const typename MatrixType::ConstTransposeReturnType,TransposeMode> ConstTransposeReturnType;
+    /** \sa MatrixBase::transpose() const */
+    EIGEN_DEVICE_FUNC
+    inline const ConstTransposeReturnType transpose() const
+    {
+      return ConstTransposeReturnType(m_matrix.transpose());
+    }
+
+    template<typename Other>
+    EIGEN_DEVICE_FUNC
+    inline const Solve<TriangularView, Other>
+    solve(const MatrixBase<Other>& other) const
+    { return Solve<TriangularView, Other>(*this, other.derived()); }
+
+  // workaround MSVC ICE
+  #if EIGEN_COMP_MSVC
+    template<int Side, typename Other>
+    EIGEN_DEVICE_FUNC
+    inline const internal::triangular_solve_retval<Side,TriangularView, Other>
+    solve(const MatrixBase<Other>& other) const
+    { return Base::template solve<Side>(other); }
+  #else
+    using Base::solve;
+  #endif
+
+    /** \returns a selfadjoint view of the referenced triangular part which must be either \c #Upper or \c #Lower.
+      *
+      * This is a shortcut for \code this->nestedExpression().selfadjointView<(*this)::Mode>() \endcode
+      * \sa MatrixBase::selfadjointView() */
+    EIGEN_DEVICE_FUNC
+    SelfAdjointView<MatrixTypeNestedNonRef,Mode> selfadjointView()
+    {
+      EIGEN_STATIC_ASSERT((Mode&(UnitDiag|ZeroDiag))==0,PROGRAMMING_ERROR);
+      return SelfAdjointView<MatrixTypeNestedNonRef,Mode>(m_matrix);
+    }
+
+    /** This is the const version of selfadjointView() */
+    EIGEN_DEVICE_FUNC
+    const SelfAdjointView<MatrixTypeNestedNonRef,Mode> selfadjointView() const
+    {
+      EIGEN_STATIC_ASSERT((Mode&(UnitDiag|ZeroDiag))==0,PROGRAMMING_ERROR);
+      return SelfAdjointView<MatrixTypeNestedNonRef,Mode>(m_matrix);
+    }
+
+
+    /** \returns the determinant of the triangular matrix
+      * \sa MatrixBase::determinant() */
+    EIGEN_DEVICE_FUNC
+    Scalar determinant() const
+    {
+      if (Mode & UnitDiag)
+        return 1;
+      else if (Mode & ZeroDiag)
+        return 0;
+      else
+        return m_matrix.diagonal().prod();
+    }
+
+  protected:
+
+    MatrixTypeNested m_matrix;
+};
+
+/** \ingroup Core_Module
+  *
+  * \brief Base class for a triangular part in a \b dense matrix
+  *
+  * This class is an abstract base class of class TriangularView, and objects of type TriangularViewImpl cannot be instantiated.
+  * It extends class TriangularView with additional methods which available for dense expressions only.
+  *
+  * \sa class TriangularView, MatrixBase::triangularView()
+  */
+template<typename MatrixType_, unsigned int Mode_> class TriangularViewImpl<MatrixType_,Mode_,Dense>
+  : public TriangularBase<TriangularView<MatrixType_, Mode_> >
+{
+  public:
+
+    typedef TriangularView<MatrixType_, Mode_> TriangularViewType;
+
+    typedef TriangularBase<TriangularViewType> Base;
+    typedef typename internal::traits<TriangularViewType>::Scalar Scalar;
+
+    typedef MatrixType_ MatrixType;
+    typedef typename MatrixType::PlainObject DenseMatrixType;
+    typedef DenseMatrixType PlainObject;
+
+  public:
+    using Base::evalToLazy;
+    using Base::derived;
+
+    typedef typename internal::traits<TriangularViewType>::StorageKind StorageKind;
+
+    enum {
+      Mode = Mode_,
+      Flags = internal::traits<TriangularViewType>::Flags
+    };
+
+    /** \returns the outer-stride of the underlying dense matrix
+      * \sa DenseCoeffsBase::outerStride() */
+    EIGEN_DEVICE_FUNC
+    inline Index outerStride() const { return derived().nestedExpression().outerStride(); }
+    /** \returns the inner-stride of the underlying dense matrix
+      * \sa DenseCoeffsBase::innerStride() */
+    EIGEN_DEVICE_FUNC
+    inline Index innerStride() const { return derived().nestedExpression().innerStride(); }
+
+    /** \sa MatrixBase::operator+=() */
+    template<typename Other>
+    EIGEN_DEVICE_FUNC
+    TriangularViewType&  operator+=(const DenseBase<Other>& other) {
+      internal::call_assignment_no_alias(derived(), other.derived(), internal::add_assign_op<Scalar,typename Other::Scalar>());
+      return derived();
+    }
+    /** \sa MatrixBase::operator-=() */
+    template<typename Other>
+    EIGEN_DEVICE_FUNC
+    TriangularViewType&  operator-=(const DenseBase<Other>& other) {
+      internal::call_assignment_no_alias(derived(), other.derived(), internal::sub_assign_op<Scalar,typename Other::Scalar>());
+      return derived();
+    }
+
+    /** \sa MatrixBase::operator*=() */
+    EIGEN_DEVICE_FUNC
+    TriangularViewType&  operator*=(const typename internal::traits<MatrixType>::Scalar& other) { return *this = derived().nestedExpression() * other; }
+    /** \sa DenseBase::operator/=() */
+    EIGEN_DEVICE_FUNC
+    TriangularViewType&  operator/=(const typename internal::traits<MatrixType>::Scalar& other) { return *this = derived().nestedExpression() / other; }
+
+    /** \sa MatrixBase::fill() */
+    EIGEN_DEVICE_FUNC
+    void fill(const Scalar& value) { setConstant(value); }
+    /** \sa MatrixBase::setConstant() */
+    EIGEN_DEVICE_FUNC
+    TriangularViewType& setConstant(const Scalar& value)
+    { return *this = MatrixType::Constant(derived().rows(), derived().cols(), value); }
+    /** \sa MatrixBase::setZero() */
+    EIGEN_DEVICE_FUNC
+    TriangularViewType& setZero() { return setConstant(Scalar(0)); }
+    /** \sa MatrixBase::setOnes() */
+    EIGEN_DEVICE_FUNC
+    TriangularViewType& setOnes() { return setConstant(Scalar(1)); }
+
+    /** \sa MatrixBase::coeff()
+      * \warning the coordinates must fit into the referenced triangular part
+      */
+    EIGEN_DEVICE_FUNC
+    inline Scalar coeff(Index row, Index col) const
+    {
+      Base::check_coordinates_internal(row, col);
+      return derived().nestedExpression().coeff(row, col);
+    }
+
+    /** \sa MatrixBase::coeffRef()
+      * \warning the coordinates must fit into the referenced triangular part
+      */
+    EIGEN_DEVICE_FUNC
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      EIGEN_STATIC_ASSERT_LVALUE(TriangularViewType);
+      Base::check_coordinates_internal(row, col);
+      return derived().nestedExpression().coeffRef(row, col);
+    }
+
+    /** Assigns a triangular matrix to a triangular part of a dense matrix */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    TriangularViewType& operator=(const TriangularBase<OtherDerived>& other);
+
+    /** Shortcut for\code *this = other.other.triangularView<(*this)::Mode>() \endcode */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    TriangularViewType& operator=(const MatrixBase<OtherDerived>& other);
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    EIGEN_DEVICE_FUNC
+    TriangularViewType& operator=(const TriangularViewImpl& other)
+    { return *this = other.derived().nestedExpression(); }
+
+    template<typename OtherDerived>
+    /** \deprecated */
+    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC
+    void lazyAssign(const TriangularBase<OtherDerived>& other);
+
+    template<typename OtherDerived>
+    /** \deprecated */
+    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC
+    void lazyAssign(const MatrixBase<OtherDerived>& other);
+#endif
+
+    /** Efficient triangular matrix times vector/matrix product */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    const Product<TriangularViewType,OtherDerived>
+    operator*(const MatrixBase<OtherDerived>& rhs) const
+    {
+      return Product<TriangularViewType,OtherDerived>(derived(), rhs.derived());
+    }
+
+    /** Efficient vector/matrix times triangular matrix product */
+    template<typename OtherDerived> friend
+    EIGEN_DEVICE_FUNC
+    const Product<OtherDerived,TriangularViewType>
+    operator*(const MatrixBase<OtherDerived>& lhs, const TriangularViewImpl& rhs)
+    {
+      return Product<OtherDerived,TriangularViewType>(lhs.derived(),rhs.derived());
+    }
+
+    /** \returns the product of the inverse of \c *this with \a other, \a *this being triangular.
+      *
+      * This function computes the inverse-matrix matrix product inverse(\c *this) * \a other if
+      * \a Side==OnTheLeft (the default), or the right-inverse-multiply  \a other * inverse(\c *this) if
+      * \a Side==OnTheRight.
+      *
+      * Note that the template parameter \c Side can be omitted, in which case \c Side==OnTheLeft
+      *
+      * The matrix \c *this must be triangular and invertible (i.e., all the coefficients of the
+      * diagonal must be non zero). It works as a forward (resp. backward) substitution if \c *this
+      * is an upper (resp. lower) triangular matrix.
+      *
+      * Example: \include Triangular_solve.cpp
+      * Output: \verbinclude Triangular_solve.out
+      *
+      * This function returns an expression of the inverse-multiply and can works in-place if it is assigned
+      * to the same matrix or vector \a other.
+      *
+      * For users coming from BLAS, this function (and more specifically solveInPlace()) offer
+      * all the operations supported by the \c *TRSV and \c *TRSM BLAS routines.
+      *
+      * \sa TriangularView::solveInPlace()
+      */
+    template<int Side, typename Other>
+    inline const internal::triangular_solve_retval<Side,TriangularViewType, Other>
+    solve(const MatrixBase<Other>& other) const;
+
+    /** "in-place" version of TriangularView::solve() where the result is written in \a other
+      *
+      * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
+      * This function will const_cast it, so constness isn't honored here.
+      *
+      * Note that the template parameter \c Side can be omitted, in which case \c Side==OnTheLeft
+      *
+      * See TriangularView:solve() for the details.
+      */
+    template<int Side, typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    void solveInPlace(const MatrixBase<OtherDerived>& other) const;
+
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    void solveInPlace(const MatrixBase<OtherDerived>& other) const
+    { return solveInPlace<OnTheLeft>(other); }
+
+    /** Swaps the coefficients of the common triangular parts of two matrices */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+    void swap(TriangularBase<OtherDerived> &other)
+#else
+    void swap(TriangularBase<OtherDerived> const & other)
+#endif
+    {
+      EIGEN_STATIC_ASSERT_LVALUE(OtherDerived);
+      call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
+    }
+
+    /** Shortcut for \code (*this).swap(other.triangularView<(*this)::Mode>()) \endcode */
+    template<typename OtherDerived>
+    /** \deprecated */
+    EIGEN_DEPRECATED EIGEN_DEVICE_FUNC
+    void swap(MatrixBase<OtherDerived> const & other)
+    {
+      EIGEN_STATIC_ASSERT_LVALUE(OtherDerived);
+      call_assignment(derived(), other.const_cast_derived(), internal::swap_assign_op<Scalar>());
+    }
+
+    template<typename RhsType, typename DstType>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _solve_impl(const RhsType &rhs, DstType &dst) const {
+      if(!internal::is_same_dense(dst,rhs))
+        dst = rhs;
+      this->solveInPlace(dst);
+    }
+
+    template<typename ProductType>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE TriangularViewType& _assignProduct(const ProductType& prod, const Scalar& alpha, bool beta);
+  protected:
+    EIGEN_DEFAULT_COPY_CONSTRUCTOR(TriangularViewImpl)
+    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(TriangularViewImpl)
+
+};
+
+/***************************************************************************
+* Implementation of triangular evaluation/assignment
+***************************************************************************/
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+// FIXME should we keep that possibility
+template<typename MatrixType, unsigned int Mode>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC inline TriangularView<MatrixType, Mode>&
+TriangularViewImpl<MatrixType, Mode, Dense>::operator=(const MatrixBase<OtherDerived>& other)
+{
+  internal::call_assignment_no_alias(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+
+// FIXME should we keep that possibility
+template<typename MatrixType, unsigned int Mode>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC void TriangularViewImpl<MatrixType, Mode, Dense>::lazyAssign(const MatrixBase<OtherDerived>& other)
+{
+  internal::call_assignment_no_alias(derived(), other.template triangularView<Mode>());
+}
+
+
+
+template<typename MatrixType, unsigned int Mode>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC inline TriangularView<MatrixType, Mode>&
+TriangularViewImpl<MatrixType, Mode, Dense>::operator=(const TriangularBase<OtherDerived>& other)
+{
+  eigen_assert(Mode == int(OtherDerived::Mode));
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template<typename MatrixType, unsigned int Mode>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC void TriangularViewImpl<MatrixType, Mode, Dense>::lazyAssign(const TriangularBase<OtherDerived>& other)
+{
+  eigen_assert(Mode == int(OtherDerived::Mode));
+  internal::call_assignment_no_alias(derived(), other.derived());
+}
+#endif
+
+/***************************************************************************
+* Implementation of TriangularBase methods
+***************************************************************************/
+
+/** Assigns a triangular or selfadjoint matrix to a dense matrix.
+  * If the matrix is triangular, the opposite part is set to zero. */
+template<typename Derived>
+template<typename DenseDerived>
+EIGEN_DEVICE_FUNC void TriangularBase<Derived>::evalTo(MatrixBase<DenseDerived> &other) const
+{
+  evalToLazy(other.derived());
+}
+
+/***************************************************************************
+* Implementation of TriangularView methods
+***************************************************************************/
+
+/***************************************************************************
+* Implementation of MatrixBase methods
+***************************************************************************/
+
+/**
+  * \returns an expression of a triangular view extracted from the current matrix
+  *
+  * The parameter \a Mode can have the following values: \c #Upper, \c #StrictlyUpper, \c #UnitUpper,
+  * \c #Lower, \c #StrictlyLower, \c #UnitLower.
+  *
+  * Example: \include MatrixBase_triangularView.cpp
+  * Output: \verbinclude MatrixBase_triangularView.out
+  *
+  * \sa class TriangularView
+  */
+template<typename Derived>
+template<unsigned int Mode>
+EIGEN_DEVICE_FUNC
+typename MatrixBase<Derived>::template TriangularViewReturnType<Mode>::Type
+MatrixBase<Derived>::triangularView()
+{
+  return typename TriangularViewReturnType<Mode>::Type(derived());
+}
+
+/** This is the const version of MatrixBase::triangularView() */
+template<typename Derived>
+template<unsigned int Mode>
+EIGEN_DEVICE_FUNC
+typename MatrixBase<Derived>::template ConstTriangularViewReturnType<Mode>::Type
+MatrixBase<Derived>::triangularView() const
+{
+  return typename ConstTriangularViewReturnType<Mode>::Type(derived());
+}
+
+/** \returns true if *this is approximately equal to an upper triangular matrix,
+  *          within the precision given by \a prec.
+  *
+  * \sa isLowerTriangular()
+  */
+template<typename Derived>
+bool MatrixBase<Derived>::isUpperTriangular(const RealScalar& prec) const
+{
+  RealScalar maxAbsOnUpperPart = static_cast<RealScalar>(-1);
+  for(Index j = 0; j < cols(); ++j)
+  {
+    Index maxi = numext::mini(j, rows()-1);
+    for(Index i = 0; i <= maxi; ++i)
+    {
+      RealScalar absValue = numext::abs(coeff(i,j));
+      if(absValue > maxAbsOnUpperPart) maxAbsOnUpperPart = absValue;
+    }
+  }
+  RealScalar threshold = maxAbsOnUpperPart * prec;
+  for(Index j = 0; j < cols(); ++j)
+    for(Index i = j+1; i < rows(); ++i)
+      if(numext::abs(coeff(i, j)) > threshold) return false;
+  return true;
+}
+
+/** \returns true if *this is approximately equal to a lower triangular matrix,
+  *          within the precision given by \a prec.
+  *
+  * \sa isUpperTriangular()
+  */
+template<typename Derived>
+bool MatrixBase<Derived>::isLowerTriangular(const RealScalar& prec) const
+{
+  RealScalar maxAbsOnLowerPart = static_cast<RealScalar>(-1);
+  for(Index j = 0; j < cols(); ++j)
+    for(Index i = j; i < rows(); ++i)
+    {
+      RealScalar absValue = numext::abs(coeff(i,j));
+      if(absValue > maxAbsOnLowerPart) maxAbsOnLowerPart = absValue;
+    }
+  RealScalar threshold = maxAbsOnLowerPart * prec;
+  for(Index j = 1; j < cols(); ++j)
+  {
+    Index maxi = numext::mini(j, rows()-1);
+    for(Index i = 0; i < maxi; ++i)
+      if(numext::abs(coeff(i, j)) > threshold) return false;
+  }
+  return true;
+}
+
+
+/***************************************************************************
+****************************************************************************
+* Evaluators and Assignment of triangular expressions
+***************************************************************************
+***************************************************************************/
+
+namespace internal {
+
+
+// TODO currently a triangular expression has the form TriangularView<.,.>
+//      in the future triangular-ness should be defined by the expression traits
+//      such that Transpose<TriangularView<.,.> > is valid. (currently TriangularBase::transpose() is overloaded to make it work)
+template<typename MatrixType, unsigned int Mode>
+struct evaluator_traits<TriangularView<MatrixType,Mode> >
+{
+  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
+  typedef typename glue_shapes<typename evaluator_traits<MatrixType>::Shape, TriangularShape>::type Shape;
+};
+
+template<typename MatrixType, unsigned int Mode>
+struct unary_evaluator<TriangularView<MatrixType,Mode>, IndexBased>
+ : evaluator<internal::remove_all_t<MatrixType>>
+{
+  typedef TriangularView<MatrixType,Mode> XprType;
+  typedef evaluator<internal::remove_all_t<MatrixType>> Base;
+  EIGEN_DEVICE_FUNC
+  unary_evaluator(const XprType &xpr) : Base(xpr.nestedExpression()) {}
+};
+
+// Additional assignment kinds:
+struct Triangular2Triangular    {};
+struct Triangular2Dense         {};
+struct Dense2Triangular         {};
+
+
+template<typename Kernel, unsigned int Mode, int UnrollCount, bool ClearOpposite> struct triangular_assignment_loop;
+
+
+/** \internal Specialization of the dense assignment kernel for triangular matrices.
+  * The main difference is that the triangular, diagonal, and opposite parts are processed through three different functions.
+  * \tparam UpLo must be either Lower or Upper
+  * \tparam Mode must be either 0, UnitDiag, ZeroDiag, or SelfAdjoint
+  */
+template<int UpLo, int Mode, int SetOpposite, typename DstEvaluatorTypeT, typename SrcEvaluatorTypeT, typename Functor, int Version = Specialized>
+class triangular_dense_assignment_kernel : public generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version>
+{
+protected:
+  typedef generic_dense_assignment_kernel<DstEvaluatorTypeT, SrcEvaluatorTypeT, Functor, Version> Base;
+  typedef typename Base::DstXprType DstXprType;
+  typedef typename Base::SrcXprType SrcXprType;
+  using Base::m_dst;
+  using Base::m_src;
+  using Base::m_functor;
+public:
+
+  typedef typename Base::DstEvaluatorType DstEvaluatorType;
+  typedef typename Base::SrcEvaluatorType SrcEvaluatorType;
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::AssignmentTraits AssignmentTraits;
+
+
+  EIGEN_DEVICE_FUNC triangular_dense_assignment_kernel(DstEvaluatorType &dst, const SrcEvaluatorType &src, const Functor &func, DstXprType& dstExpr)
+    : Base(dst, src, func, dstExpr)
+  {}
+
+#ifdef EIGEN_INTERNAL_DEBUGGING
+  EIGEN_DEVICE_FUNC void assignCoeff(Index row, Index col)
+  {
+    eigen_internal_assert(row!=col);
+    Base::assignCoeff(row,col);
+  }
+#else
+  using Base::assignCoeff;
+#endif
+
+  EIGEN_DEVICE_FUNC void assignDiagonalCoeff(Index id)
+  {
+         if(Mode==UnitDiag && SetOpposite) m_functor.assignCoeff(m_dst.coeffRef(id,id), Scalar(1));
+    else if(Mode==ZeroDiag && SetOpposite) m_functor.assignCoeff(m_dst.coeffRef(id,id), Scalar(0));
+    else if(Mode==0)                       Base::assignCoeff(id,id);
+  }
+
+  EIGEN_DEVICE_FUNC void assignOppositeCoeff(Index row, Index col)
+  {
+    eigen_internal_assert(row!=col);
+    if(SetOpposite)
+      m_functor.assignCoeff(m_dst.coeffRef(row,col), Scalar(0));
+  }
+};
+
+template<int Mode, bool SetOpposite, typename DstXprType, typename SrcXprType, typename Functor>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+void call_triangular_assignment_loop(DstXprType& dst, const SrcXprType& src, const Functor &func)
+{
+  typedef evaluator<DstXprType> DstEvaluatorType;
+  typedef evaluator<SrcXprType> SrcEvaluatorType;
+
+  SrcEvaluatorType srcEvaluator(src);
+
+  Index dstRows = src.rows();
+  Index dstCols = src.cols();
+  if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+    dst.resize(dstRows, dstCols);
+  DstEvaluatorType dstEvaluator(dst);
+
+  typedef triangular_dense_assignment_kernel< Mode&(Lower|Upper),Mode&(UnitDiag|ZeroDiag|SelfAdjoint),SetOpposite,
+                                              DstEvaluatorType,SrcEvaluatorType,Functor> Kernel;
+  Kernel kernel(dstEvaluator, srcEvaluator, func, dst.const_cast_derived());
+
+  enum {
+      unroll = DstXprType::SizeAtCompileTime != Dynamic
+            && SrcEvaluatorType::CoeffReadCost < HugeCost
+            && DstXprType::SizeAtCompileTime * (int(DstEvaluatorType::CoeffReadCost) + int(SrcEvaluatorType::CoeffReadCost)) / 2 <= EIGEN_UNROLLING_LIMIT
+    };
+
+  triangular_assignment_loop<Kernel, Mode, unroll ? int(DstXprType::SizeAtCompileTime) : Dynamic, SetOpposite>::run(kernel);
+}
+
+template<int Mode, bool SetOpposite, typename DstXprType, typename SrcXprType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+void call_triangular_assignment_loop(DstXprType& dst, const SrcXprType& src)
+{
+  call_triangular_assignment_loop<Mode,SetOpposite>(dst, src, internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>());
+}
+
+template<> struct AssignmentKind<TriangularShape,TriangularShape> { typedef Triangular2Triangular Kind; };
+template<> struct AssignmentKind<DenseShape,TriangularShape>      { typedef Triangular2Dense      Kind; };
+template<> struct AssignmentKind<TriangularShape,DenseShape>      { typedef Dense2Triangular      Kind; };
+
+
+template< typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, Triangular2Triangular>
+{
+  EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
+  {
+    eigen_assert(int(DstXprType::Mode) == int(SrcXprType::Mode));
+
+    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);
+  }
+};
+
+template< typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, Triangular2Dense>
+{
+  EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
+  {
+    call_triangular_assignment_loop<SrcXprType::Mode, (int(SrcXprType::Mode) & int(SelfAdjoint)) == 0>(dst, src, func);
+  }
+};
+
+template< typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, Dense2Triangular>
+{
+  EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
+  {
+    call_triangular_assignment_loop<DstXprType::Mode, false>(dst, src, func);
+  }
+};
+
+
+template<typename Kernel, unsigned int Mode, int UnrollCount, bool SetOpposite>
+struct triangular_assignment_loop
+{
+  // FIXME: this is not very clean, perhaps this information should be provided by the kernel?
+  typedef typename Kernel::DstEvaluatorType DstEvaluatorType;
+  typedef typename DstEvaluatorType::XprType DstXprType;
+
+  enum {
+    col = (UnrollCount-1) / DstXprType::RowsAtCompileTime,
+    row = (UnrollCount-1) % DstXprType::RowsAtCompileTime
+  };
+
+  typedef typename Kernel::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC
+  static inline void run(Kernel &kernel)
+  {
+    triangular_assignment_loop<Kernel, Mode, UnrollCount-1, SetOpposite>::run(kernel);
+
+    if(row==col)
+      kernel.assignDiagonalCoeff(row);
+    else if( ((Mode&Lower) && row>col) || ((Mode&Upper) && row<col) )
+      kernel.assignCoeff(row,col);
+    else if(SetOpposite)
+      kernel.assignOppositeCoeff(row,col);
+  }
+};
+
+// prevent buggy user code from causing an infinite recursion
+template<typename Kernel, unsigned int Mode, bool SetOpposite>
+struct triangular_assignment_loop<Kernel, Mode, 0, SetOpposite>
+{
+  EIGEN_DEVICE_FUNC
+  static inline void run(Kernel &) {}
+};
+
+
+
+// TODO: experiment with a recursive assignment procedure splitting the current
+//       triangular part into one rectangular and two triangular parts.
+
+
+template<typename Kernel, unsigned int Mode, bool SetOpposite>
+struct triangular_assignment_loop<Kernel, Mode, Dynamic, SetOpposite>
+{
+  typedef typename Kernel::Scalar Scalar;
+  EIGEN_DEVICE_FUNC
+  static inline void run(Kernel &kernel)
+  {
+    for(Index j = 0; j < kernel.cols(); ++j)
+    {
+      Index maxi = numext::mini(j, kernel.rows());
+      Index i = 0;
+      if (((Mode&Lower) && SetOpposite) || (Mode&Upper))
+      {
+        for(; i < maxi; ++i)
+          if(Mode&Upper) kernel.assignCoeff(i, j);
+          else           kernel.assignOppositeCoeff(i, j);
+      }
+      else
+        i = maxi;
+
+      if(i<kernel.rows()) // then i==j
+        kernel.assignDiagonalCoeff(i++);
+
+      if (((Mode&Upper) && SetOpposite) || (Mode&Lower))
+      {
+        for(; i < kernel.rows(); ++i)
+          if(Mode&Lower) kernel.assignCoeff(i, j);
+          else           kernel.assignOppositeCoeff(i, j);
+      }
+    }
+  }
+};
+
+} // end namespace internal
+
+/** Assigns a triangular or selfadjoint matrix to a dense matrix.
+  * If the matrix is triangular, the opposite part is set to zero. */
+template<typename Derived>
+template<typename DenseDerived>
+EIGEN_DEVICE_FUNC void TriangularBase<Derived>::evalToLazy(MatrixBase<DenseDerived> &other) const
+{
+  other.derived().resize(this->rows(), this->cols());
+  internal::call_triangular_assignment_loop<Derived::Mode, (int(Derived::Mode) & int(SelfAdjoint)) == 0 /* SetOpposite */>(other.derived(), derived().nestedExpression());
+}
+
+namespace internal {
+
+// Triangular = Product
+template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
+struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::assign_op<Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, Dense2Triangular>
+{
+  typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename SrcXprType::Scalar> &)
+  {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+      dst.resize(dstRows, dstCols);
+
+    dst._assignProduct(src, Scalar(1), false);
+  }
+};
+
+// Triangular += Product
+template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
+struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::add_assign_op<Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, Dense2Triangular>
+{
+  typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<Scalar,typename SrcXprType::Scalar> &)
+  {
+    dst._assignProduct(src, Scalar(1), true);
+  }
+};
+
+// Triangular -= Product
+template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar>
+struct Assignment<DstXprType, Product<Lhs,Rhs,DefaultProduct>, internal::sub_assign_op<Scalar,typename Product<Lhs,Rhs,DefaultProduct>::Scalar>, Dense2Triangular>
+{
+  typedef Product<Lhs,Rhs,DefaultProduct> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<Scalar,typename SrcXprType::Scalar> &)
+  {
+    dst._assignProduct(src, Scalar(-1), true);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRIANGULARMATRIX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/VectorBlock.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/VectorBlock.h
new file mode 100644
index 0000000000000000000000000000000000000000..2715a1e3036a2055ccd426dd6640ed60070254c9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/VectorBlock.h
@@ -0,0 +1,94 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_VECTORBLOCK_H
+#define EIGEN_VECTORBLOCK_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+template<typename VectorType, int Size>
+struct traits<VectorBlock<VectorType, Size> >
+  : public traits<Block<VectorType,
+                     traits<VectorType>::Flags & RowMajorBit ? 1 : Size,
+                     traits<VectorType>::Flags & RowMajorBit ? Size : 1> >
+{
+};
+}
+
+/** \class VectorBlock
+  * \ingroup Core_Module
+  *
+  * \brief Expression of a fixed-size or dynamic-size sub-vector
+  *
+  * \tparam VectorType the type of the object in which we are taking a sub-vector
+  * \tparam Size size of the sub-vector we are taking at compile time (optional)
+  *
+  * This class represents an expression of either a fixed-size or dynamic-size sub-vector.
+  * It is the return type of DenseBase::segment(Index,Index) and DenseBase::segment<int>(Index) and
+  * most of the time this is the only way it is used.
+  *
+  * However, if you want to directly manipulate sub-vector expressions,
+  * for instance if you want to write a function returning such an expression, you
+  * will need to use this class.
+  *
+  * Here is an example illustrating the dynamic case:
+  * \include class_VectorBlock.cpp
+  * Output: \verbinclude class_VectorBlock.out
+  *
+  * \note Even though this expression has dynamic size, in the case where \a VectorType
+  * has fixed size, this expression inherits a fixed maximal size which means that evaluating
+  * it does not cause a dynamic memory allocation.
+  *
+  * Here is an example illustrating the fixed-size case:
+  * \include class_FixedVectorBlock.cpp
+  * Output: \verbinclude class_FixedVectorBlock.out
+  *
+  * \sa class Block, DenseBase::segment(Index,Index,Index,Index), DenseBase::segment(Index,Index)
+  */
+template<typename VectorType, int Size> class VectorBlock
+  : public Block<VectorType,
+                     internal::traits<VectorType>::Flags & RowMajorBit ? 1 : Size,
+                     internal::traits<VectorType>::Flags & RowMajorBit ? Size : 1>
+{
+    typedef Block<VectorType,
+                     internal::traits<VectorType>::Flags & RowMajorBit ? 1 : Size,
+                     internal::traits<VectorType>::Flags & RowMajorBit ? Size : 1> Base;
+    enum {
+      IsColVector = !(internal::traits<VectorType>::Flags & RowMajorBit)
+    };
+  public:
+    EIGEN_DENSE_PUBLIC_INTERFACE(VectorBlock)
+    EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorBlock)
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(VectorBlock)
+
+    /** Dynamic-size constructor
+      */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    VectorBlock(VectorType& vector, Index start, Index size)
+      : Base(vector,
+             IsColVector ? start : 0, IsColVector ? 0 : start,
+             IsColVector ? size  : 1, IsColVector ? 1 : size)
+    { }
+
+    /** Fixed-size constructor
+      */
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    VectorBlock(VectorType& vector, Index start)
+      : Base(vector, IsColVector ? start : 0, IsColVector ? 0 : start)
+    { }
+};
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_VECTORBLOCK_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/VectorwiseOp.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/VectorwiseOp.h
new file mode 100644
index 0000000000000000000000000000000000000000..737df139ae37d518644de081f99d51951168f00c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/VectorwiseOp.h
@@ -0,0 +1,785 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PARTIAL_REDUX_H
+#define EIGEN_PARTIAL_REDUX_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class PartialReduxExpr
+  * \ingroup Core_Module
+  *
+  * \brief Generic expression of a partially reduxed matrix
+  *
+  * \tparam MatrixType the type of the matrix we are applying the redux operation
+  * \tparam MemberOp type of the member functor
+  * \tparam Direction indicates the direction of the redux (#Vertical or #Horizontal)
+  *
+  * This class represents an expression of a partial redux operator of a matrix.
+  * It is the return type of some VectorwiseOp functions,
+  * and most of the time this is the only way it is used.
+  *
+  * \sa class VectorwiseOp
+  */
+
+template< typename MatrixType, typename MemberOp, int Direction>
+class PartialReduxExpr;
+
+namespace internal {
+template<typename MatrixType, typename MemberOp, int Direction>
+struct traits<PartialReduxExpr<MatrixType, MemberOp, Direction> >
+ : traits<MatrixType>
+{
+  typedef typename MemberOp::result_type Scalar;
+  typedef typename traits<MatrixType>::StorageKind StorageKind;
+  typedef typename traits<MatrixType>::XprKind XprKind;
+  typedef typename MatrixType::Scalar InputScalar;
+  enum {
+    RowsAtCompileTime = Direction==Vertical   ? 1 : MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::ColsAtCompileTime,
+    MaxRowsAtCompileTime = Direction==Vertical   ? 1 : MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = Direction==Horizontal ? 1 : MatrixType::MaxColsAtCompileTime,
+    Flags = RowsAtCompileTime == 1 ? RowMajorBit : 0,
+    TraversalSize = Direction==Vertical ? MatrixType::RowsAtCompileTime :  MatrixType::ColsAtCompileTime
+  };
+};
+}
+
+template< typename MatrixType, typename MemberOp, int Direction>
+class PartialReduxExpr : public internal::dense_xpr_base< PartialReduxExpr<MatrixType, MemberOp, Direction> >::type,
+                         internal::no_assignment_operator
+{
+  public:
+
+    typedef typename internal::dense_xpr_base<PartialReduxExpr>::type Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(PartialReduxExpr)
+
+    EIGEN_DEVICE_FUNC
+    explicit PartialReduxExpr(const MatrixType& mat, const MemberOp& func = MemberOp())
+      : m_matrix(mat), m_functor(func) {}
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    Index rows() const EIGEN_NOEXCEPT { return (Direction==Vertical   ? 1 : m_matrix.rows()); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    Index cols() const EIGEN_NOEXCEPT { return (Direction==Horizontal ? 1 : m_matrix.cols()); }
+
+    EIGEN_DEVICE_FUNC
+    typename MatrixType::Nested nestedExpression() const { return m_matrix; }
+
+    EIGEN_DEVICE_FUNC
+    const MemberOp& functor() const { return m_functor; }
+
+  protected:
+    typename MatrixType::Nested m_matrix;
+    const MemberOp m_functor;
+};
+
+template<typename A,typename B> struct partial_redux_dummy_func;
+
+#define EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(MEMBER,COST,VECTORIZABLE,BINARYOP)                \
+  template <typename ResultType,typename Scalar>                                                            \
+  struct member_##MEMBER {                                                                  \
+    typedef ResultType result_type;                                                         \
+    typedef BINARYOP<Scalar,Scalar> BinaryOp;   \
+    template<int Size> struct Cost { enum { value = COST }; };             \
+    enum { Vectorizable = VECTORIZABLE };                                                   \
+    template<typename XprType>                                                              \
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                   \
+    ResultType operator()(const XprType& mat) const                                         \
+    { return mat.MEMBER(); }                                                                \
+    BinaryOp binaryFunc() const { return BinaryOp(); }                                      \
+  }
+
+#define EIGEN_MEMBER_FUNCTOR(MEMBER,COST) \
+  EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(MEMBER,COST,0,partial_redux_dummy_func)
+
+namespace internal {
+
+EIGEN_MEMBER_FUNCTOR(norm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
+EIGEN_MEMBER_FUNCTOR(stableNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
+EIGEN_MEMBER_FUNCTOR(blueNorm, (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost);
+EIGEN_MEMBER_FUNCTOR(hypotNorm, (Size-1) * functor_traits<scalar_hypot_op<Scalar> >::Cost );
+EIGEN_MEMBER_FUNCTOR(all, (Size-1)*NumTraits<Scalar>::AddCost);
+EIGEN_MEMBER_FUNCTOR(any, (Size-1)*NumTraits<Scalar>::AddCost);
+EIGEN_MEMBER_FUNCTOR(count, (Size-1)*NumTraits<Scalar>::AddCost);
+
+EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(sum, (Size-1)*NumTraits<Scalar>::AddCost, 1, internal::scalar_sum_op);
+EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(minCoeff, (Size-1)*NumTraits<Scalar>::AddCost, 1, internal::scalar_min_op);
+EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(maxCoeff, (Size-1)*NumTraits<Scalar>::AddCost, 1, internal::scalar_max_op);
+EIGEN_MAKE_PARTIAL_REDUX_FUNCTOR(prod, (Size-1)*NumTraits<Scalar>::MulCost, 1, internal::scalar_product_op);
+
+template <int p, typename ResultType,typename Scalar>
+struct member_lpnorm {
+  typedef ResultType result_type;
+  enum { Vectorizable = 0 };
+  template<int Size> struct Cost
+  { enum { value = (Size+5) * NumTraits<Scalar>::MulCost + (Size-1)*NumTraits<Scalar>::AddCost }; };
+  EIGEN_DEVICE_FUNC member_lpnorm() {}
+  template<typename XprType>
+  EIGEN_DEVICE_FUNC inline ResultType operator()(const XprType& mat) const
+  { return mat.template lpNorm<p>(); }
+};
+
+template <typename BinaryOpT, typename Scalar>
+struct member_redux {
+  typedef BinaryOpT BinaryOp;
+  typedef typename result_of<
+                     BinaryOp(const Scalar&,const Scalar&)
+                   >::type  result_type;
+
+  enum { Vectorizable = functor_traits<BinaryOp>::PacketAccess };
+  template<int Size> struct Cost { enum { value = (Size-1) * functor_traits<BinaryOp>::Cost }; };
+  EIGEN_DEVICE_FUNC explicit member_redux(const BinaryOp func) : m_functor(func) {}
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC inline result_type operator()(const DenseBase<Derived>& mat) const
+  { return mat.redux(m_functor); }
+  const BinaryOp& binaryFunc() const { return m_functor; }
+  const BinaryOp m_functor;
+};
+}
+
+/** \class VectorwiseOp
+  * \ingroup Core_Module
+  *
+  * \brief Pseudo expression providing broadcasting and partial reduction operations
+  *
+  * \tparam ExpressionType the type of the object on which to do partial reductions
+  * \tparam Direction indicates whether to operate on columns (#Vertical) or rows (#Horizontal)
+  *
+  * This class represents a pseudo expression with broadcasting and partial reduction features.
+  * It is the return type of DenseBase::colwise() and DenseBase::rowwise()
+  * and most of the time this is the only way it is explicitly used.
+  *
+  * To understand the logic of rowwise/colwise expression, let's consider a generic case `A.colwise().foo()`
+  * where `foo` is any method of `VectorwiseOp`. This expression is equivalent to applying `foo()` to each
+  * column of `A` and then re-assemble the outputs in a matrix expression:
+  * \code [A.col(0).foo(), A.col(1).foo(), ..., A.col(A.cols()-1).foo()] \endcode
+  *
+  * Example: \include MatrixBase_colwise.cpp
+  * Output: \verbinclude MatrixBase_colwise.out
+  *
+  * The begin() and end() methods are obviously exceptions to the previous rule as they
+  * return STL-compatible begin/end iterators to the rows or columns of the nested expression.
+  * Typical use cases include for-range-loop and calls to STL algorithms:
+  *
+  * Example: \include MatrixBase_colwise_iterator_cxx11.cpp
+  * Output: \verbinclude MatrixBase_colwise_iterator_cxx11.out
+  *
+  * For a partial reduction on an empty input, some rules apply.
+  * For the sake of clarity, let's consider a vertical reduction:
+  *   - If the number of columns is zero, then a 1x0 row-major vector expression is returned.
+  *   - Otherwise, if the number of rows is zero, then
+  *       - a row vector of zeros is returned for sum-like reductions (sum, squaredNorm, norm, etc.)
+  *       - a row vector of ones is returned for a product reduction (e.g., <code>MatrixXd(n,0).colwise().prod()</code>)
+  *       - an assert is triggered for all other reductions (minCoeff,maxCoeff,redux(bin_op))
+  *
+  * \sa DenseBase::colwise(), DenseBase::rowwise(), class PartialReduxExpr
+  */
+template<typename ExpressionType, int Direction> class VectorwiseOp
+{
+  public:
+
+    typedef typename ExpressionType::Scalar Scalar;
+    typedef typename ExpressionType::RealScalar RealScalar;
+    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+    typedef typename internal::ref_selector<ExpressionType>::non_const_type ExpressionTypeNested;
+    typedef internal::remove_all_t<ExpressionTypeNested> ExpressionTypeNestedCleaned;
+
+    template<template<typename OutScalar,typename InputScalar> class Functor,
+                      typename ReturnScalar=Scalar> struct ReturnType
+    {
+      typedef PartialReduxExpr<ExpressionType,
+                               Functor<ReturnScalar,Scalar>,
+                               Direction
+                              > Type;
+    };
+
+    template<typename BinaryOp> struct ReduxReturnType
+    {
+      typedef PartialReduxExpr<ExpressionType,
+                               internal::member_redux<BinaryOp,Scalar>,
+                               Direction
+                              > Type;
+    };
+
+    enum {
+      isVertical   = (Direction==Vertical) ? 1 : 0,
+      isHorizontal = (Direction==Horizontal) ? 1 : 0
+    };
+
+  protected:
+
+    template<typename OtherDerived> struct ExtendedType {
+      typedef Replicate<OtherDerived,
+                        isVertical   ? 1 : ExpressionType::RowsAtCompileTime,
+                        isHorizontal ? 1 : ExpressionType::ColsAtCompileTime> Type;
+    };
+
+    /** \internal
+      * Replicates a vector to match the size of \c *this */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    typename ExtendedType<OtherDerived>::Type
+    extendedTo(const DenseBase<OtherDerived>& other) const
+    {
+      EIGEN_STATIC_ASSERT(internal::check_implication(isVertical, OtherDerived::MaxColsAtCompileTime==1),
+                          YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
+      EIGEN_STATIC_ASSERT(internal::check_implication(isHorizontal, OtherDerived::MaxRowsAtCompileTime==1),
+                          YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
+      return typename ExtendedType<OtherDerived>::Type
+                      (other.derived(),
+                       isVertical   ? 1 : m_matrix.rows(),
+                       isHorizontal ? 1 : m_matrix.cols());
+    }
+
+    template<typename OtherDerived> struct OppositeExtendedType {
+      typedef Replicate<OtherDerived,
+                        isHorizontal ? 1 : ExpressionType::RowsAtCompileTime,
+                        isVertical   ? 1 : ExpressionType::ColsAtCompileTime> Type;
+    };
+
+    /** \internal
+      * Replicates a vector in the opposite direction to match the size of \c *this */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    typename OppositeExtendedType<OtherDerived>::Type
+    extendedToOpposite(const DenseBase<OtherDerived>& other) const
+    {
+      EIGEN_STATIC_ASSERT(internal::check_implication(isHorizontal, OtherDerived::MaxColsAtCompileTime==1),
+                          YOU_PASSED_A_ROW_VECTOR_BUT_A_COLUMN_VECTOR_WAS_EXPECTED)
+      EIGEN_STATIC_ASSERT(internal::check_implication(isVertical, OtherDerived::MaxRowsAtCompileTime==1),
+                          YOU_PASSED_A_COLUMN_VECTOR_BUT_A_ROW_VECTOR_WAS_EXPECTED)
+      return typename OppositeExtendedType<OtherDerived>::Type
+                      (other.derived(),
+                       isHorizontal  ? 1 : m_matrix.rows(),
+                       isVertical    ? 1 : m_matrix.cols());
+    }
+
+  public:
+    EIGEN_DEVICE_FUNC
+    explicit inline VectorwiseOp(ExpressionType& matrix) : m_matrix(matrix) {}
+
+    /** \internal */
+    EIGEN_DEVICE_FUNC
+    inline const ExpressionType& _expression() const { return m_matrix; }
+
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
+    /** STL-like <a href="https://en.cppreference.com/w/cpp/named_req/RandomAccessIterator">RandomAccessIterator</a>
+      * iterator type over the columns or rows as returned by the begin() and end() methods.
+      */
+    random_access_iterator_type iterator;
+    /** This is the const version of iterator (aka read-only) */
+    random_access_iterator_type const_iterator;
+    #else
+    typedef internal::subvector_stl_iterator<ExpressionType,               DirectionType(Direction)> iterator;
+    typedef internal::subvector_stl_iterator<const ExpressionType,         DirectionType(Direction)> const_iterator;
+    typedef internal::subvector_stl_reverse_iterator<ExpressionType,       DirectionType(Direction)> reverse_iterator;
+    typedef internal::subvector_stl_reverse_iterator<const ExpressionType, DirectionType(Direction)> const_reverse_iterator;
+    #endif
+
+    /** returns an iterator to the first row (rowwise) or column (colwise) of the nested expression.
+      * \sa end(), cbegin()
+      */
+    iterator                 begin()       { return iterator      (m_matrix, 0); }
+    /** const version of begin() */
+    const_iterator           begin() const { return const_iterator(m_matrix, 0); }
+    /** const version of begin() */
+    const_iterator          cbegin() const { return const_iterator(m_matrix, 0); }
+
+    /** returns a reverse iterator to the last row (rowwise) or column (colwise) of the nested expression.
+      * \sa rend(), crbegin()
+      */
+    reverse_iterator        rbegin()       { return reverse_iterator       (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()-1); }
+	/** const version of rbegin() */
+    const_reverse_iterator  rbegin() const { return const_reverse_iterator (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()-1); }
+	/** const version of rbegin() */
+	const_reverse_iterator crbegin() const { return const_reverse_iterator (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()-1); }
+
+    /** returns an iterator to the row (resp. column) following the last row (resp. column) of the nested expression
+      * \sa begin(), cend()
+      */
+    iterator                 end()         { return iterator      (m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()); }
+    /** const version of end() */
+    const_iterator           end()  const  { return const_iterator(m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()); }
+    /** const version of end() */
+    const_iterator          cend()  const  { return const_iterator(m_matrix, m_matrix.template subVectors<DirectionType(Direction)>()); }
+
+    /** returns a reverse iterator to the row (resp. column) before the first row (resp. column) of the nested expression
+      * \sa begin(), cend()
+      */
+    reverse_iterator        rend()         { return reverse_iterator       (m_matrix, -1); }
+    /** const version of rend() */
+    const_reverse_iterator  rend()  const  { return const_reverse_iterator (m_matrix, -1); }
+    /** const version of rend() */
+    const_reverse_iterator crend()  const  { return const_reverse_iterator (m_matrix, -1); }
+
+    /** \returns a row or column vector expression of \c *this reduxed by \a func
+      *
+      * The template parameter \a BinaryOp is the type of the functor
+      * of the custom redux operator. Note that func must be an associative operator.
+      *
+      * \warning the size along the reduction direction must be strictly positive,
+      *          otherwise an assertion is triggered.
+      *
+      * \sa class VectorwiseOp, DenseBase::colwise(), DenseBase::rowwise()
+      */
+    template<typename BinaryOp>
+    EIGEN_DEVICE_FUNC
+    const typename ReduxReturnType<BinaryOp>::Type
+    redux(const BinaryOp& func = BinaryOp()) const
+    {
+      eigen_assert(redux_length()>0 && "you are using an empty matrix");
+      return typename ReduxReturnType<BinaryOp>::Type(_expression(), internal::member_redux<BinaryOp,Scalar>(func));
+    }
+
+    typedef typename ReturnType<internal::member_minCoeff>::Type MinCoeffReturnType;
+    typedef typename ReturnType<internal::member_maxCoeff>::Type MaxCoeffReturnType;
+    typedef PartialReduxExpr<const CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const ExpressionTypeNestedCleaned>,internal::member_sum<RealScalar,RealScalar>,Direction> SquaredNormReturnType;
+    typedef CwiseUnaryOp<internal::scalar_sqrt_op<RealScalar>, const SquaredNormReturnType> NormReturnType;
+    typedef typename ReturnType<internal::member_blueNorm,RealScalar>::Type BlueNormReturnType;
+    typedef typename ReturnType<internal::member_stableNorm,RealScalar>::Type StableNormReturnType;
+    typedef typename ReturnType<internal::member_hypotNorm,RealScalar>::Type HypotNormReturnType;
+    typedef typename ReturnType<internal::member_sum>::Type SumReturnType;
+    typedef EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(SumReturnType,Scalar,quotient) MeanReturnType;
+    typedef typename ReturnType<internal::member_all, bool>::Type AllReturnType;
+    typedef typename ReturnType<internal::member_any, bool>::Type AnyReturnType;
+    typedef PartialReduxExpr<ExpressionType, internal::member_count<Index,Scalar>, Direction> CountReturnType;
+    typedef typename ReturnType<internal::member_prod>::Type ProdReturnType;
+    typedef Reverse<const ExpressionType, Direction> ConstReverseReturnType;
+    typedef Reverse<ExpressionType, Direction> ReverseReturnType;
+
+    template<int p> struct LpNormReturnType {
+      typedef PartialReduxExpr<ExpressionType, internal::member_lpnorm<p,RealScalar,Scalar>,Direction> Type;
+    };
+
+    /** \returns a row (or column) vector expression of the smallest coefficient
+      * of each column (or row) of the referenced expression.
+      *
+      * \warning the size along the reduction direction must be strictly positive,
+      *          otherwise an assertion is triggered.
+      *
+      * \warning the result is undefined if \c *this contains NaN.
+      *
+      * Example: \include PartialRedux_minCoeff.cpp
+      * Output: \verbinclude PartialRedux_minCoeff.out
+      *
+      * \sa DenseBase::minCoeff() */
+    EIGEN_DEVICE_FUNC
+    const MinCoeffReturnType minCoeff() const
+    {
+      eigen_assert(redux_length()>0 && "you are using an empty matrix");
+      return MinCoeffReturnType(_expression());
+    }
+
+    /** \returns a row (or column) vector expression of the largest coefficient
+      * of each column (or row) of the referenced expression.
+      *
+      * \warning the size along the reduction direction must be strictly positive,
+      *          otherwise an assertion is triggered.
+      *
+      * \warning the result is undefined if \c *this contains NaN.
+      *
+      * Example: \include PartialRedux_maxCoeff.cpp
+      * Output: \verbinclude PartialRedux_maxCoeff.out
+      *
+      * \sa DenseBase::maxCoeff() */
+    EIGEN_DEVICE_FUNC
+    const MaxCoeffReturnType maxCoeff() const
+    {
+      eigen_assert(redux_length()>0 && "you are using an empty matrix");
+      return MaxCoeffReturnType(_expression());
+    }
+
+    /** \returns a row (or column) vector expression of the squared norm
+      * of each column (or row) of the referenced expression.
+      * This is a vector with real entries, even if the original matrix has complex entries.
+      *
+      * Example: \include PartialRedux_squaredNorm.cpp
+      * Output: \verbinclude PartialRedux_squaredNorm.out
+      *
+      * \sa DenseBase::squaredNorm() */
+    EIGEN_DEVICE_FUNC
+    const SquaredNormReturnType squaredNorm() const
+    { return SquaredNormReturnType(m_matrix.cwiseAbs2()); }
+
+    /** \returns a row (or column) vector expression of the norm
+      * of each column (or row) of the referenced expression.
+      * This is a vector with real entries, even if the original matrix has complex entries.
+      *
+      * Example: \include PartialRedux_norm.cpp
+      * Output: \verbinclude PartialRedux_norm.out
+      *
+      * \sa DenseBase::norm() */
+    EIGEN_DEVICE_FUNC
+    const NormReturnType norm() const
+    { return NormReturnType(squaredNorm()); }
+
+    /** \returns a row (or column) vector expression of the norm
+      * of each column (or row) of the referenced expression.
+      * This is a vector with real entries, even if the original matrix has complex entries.
+      *
+      * Example: \include PartialRedux_norm.cpp
+      * Output: \verbinclude PartialRedux_norm.out
+      *
+      * \sa DenseBase::norm() */
+    template<int p>
+    EIGEN_DEVICE_FUNC
+    const typename LpNormReturnType<p>::Type lpNorm() const
+    { return typename LpNormReturnType<p>::Type(_expression()); }
+
+
+    /** \returns a row (or column) vector expression of the norm
+      * of each column (or row) of the referenced expression, using
+      * Blue's algorithm.
+      * This is a vector with real entries, even if the original matrix has complex entries.
+      *
+      * \sa DenseBase::blueNorm() */
+    EIGEN_DEVICE_FUNC
+    const BlueNormReturnType blueNorm() const
+    { return BlueNormReturnType(_expression()); }
+
+
+    /** \returns a row (or column) vector expression of the norm
+      * of each column (or row) of the referenced expression, avoiding
+      * underflow and overflow.
+      * This is a vector with real entries, even if the original matrix has complex entries.
+      *
+      * \sa DenseBase::stableNorm() */
+    EIGEN_DEVICE_FUNC
+    const StableNormReturnType stableNorm() const
+    { return StableNormReturnType(_expression()); }
+
+
+    /** \returns a row (or column) vector expression of the norm
+      * of each column (or row) of the referenced expression, avoiding
+      * underflow and overflow using a concatenation of hypot() calls.
+      * This is a vector with real entries, even if the original matrix has complex entries.
+      *
+      * \sa DenseBase::hypotNorm() */
+    EIGEN_DEVICE_FUNC
+    const HypotNormReturnType hypotNorm() const
+    { return HypotNormReturnType(_expression()); }
+
+    /** \returns a row (or column) vector expression of the sum
+      * of each column (or row) of the referenced expression.
+      *
+      * Example: \include PartialRedux_sum.cpp
+      * Output: \verbinclude PartialRedux_sum.out
+      *
+      * \sa DenseBase::sum() */
+    EIGEN_DEVICE_FUNC
+    const SumReturnType sum() const
+    { return SumReturnType(_expression()); }
+
+    /** \returns a row (or column) vector expression of the mean
+    * of each column (or row) of the referenced expression.
+    *
+    * \sa DenseBase::mean() */
+    EIGEN_DEVICE_FUNC
+    const MeanReturnType mean() const
+    { return sum() / Scalar(Direction==Vertical?m_matrix.rows():m_matrix.cols()); }
+
+    /** \returns a row (or column) vector expression representing
+      * whether \b all coefficients of each respective column (or row) are \c true.
+      * This expression can be assigned to a vector with entries of type \c bool.
+      *
+      * \sa DenseBase::all() */
+    EIGEN_DEVICE_FUNC
+    const AllReturnType all() const
+    { return AllReturnType(_expression()); }
+
+    /** \returns a row (or column) vector expression representing
+      * whether \b at \b least one coefficient of each respective column (or row) is \c true.
+      * This expression can be assigned to a vector with entries of type \c bool.
+      *
+      * \sa DenseBase::any() */
+    EIGEN_DEVICE_FUNC
+    const AnyReturnType any() const
+    { return AnyReturnType(_expression()); }
+
+    /** \returns a row (or column) vector expression representing
+      * the number of \c true coefficients of each respective column (or row).
+      * This expression can be assigned to a vector whose entries have the same type as is used to
+      * index entries of the original matrix; for dense matrices, this is \c std::ptrdiff_t .
+      *
+      * Example: \include PartialRedux_count.cpp
+      * Output: \verbinclude PartialRedux_count.out
+      *
+      * \sa DenseBase::count() */
+    EIGEN_DEVICE_FUNC
+    const CountReturnType count() const
+    { return CountReturnType(_expression()); }
+
+    /** \returns a row (or column) vector expression of the product
+      * of each column (or row) of the referenced expression.
+      *
+      * Example: \include PartialRedux_prod.cpp
+      * Output: \verbinclude PartialRedux_prod.out
+      *
+      * \sa DenseBase::prod() */
+    EIGEN_DEVICE_FUNC
+    const ProdReturnType prod() const
+    { return ProdReturnType(_expression()); }
+
+
+    /** \returns a matrix expression
+      * where each column (or row) are reversed.
+      *
+      * Example: \include Vectorwise_reverse.cpp
+      * Output: \verbinclude Vectorwise_reverse.out
+      *
+      * \sa DenseBase::reverse() */
+    EIGEN_DEVICE_FUNC
+    const ConstReverseReturnType reverse() const
+    { return ConstReverseReturnType( _expression() ); }
+
+    /** \returns a writable matrix expression
+      * where each column (or row) are reversed.
+      *
+      * \sa reverse() const */
+    EIGEN_DEVICE_FUNC
+    ReverseReturnType reverse()
+    { return ReverseReturnType( _expression() ); }
+
+    typedef Replicate<ExpressionType,(isVertical?Dynamic:1),(isHorizontal?Dynamic:1)> ReplicateReturnType;
+    EIGEN_DEVICE_FUNC
+    const ReplicateReturnType replicate(Index factor) const;
+
+    /**
+      * \return an expression of the replication of each column (or row) of \c *this
+      *
+      * Example: \include DirectionWise_replicate.cpp
+      * Output: \verbinclude DirectionWise_replicate.out
+      *
+      * \sa VectorwiseOp::replicate(Index), DenseBase::replicate(), class Replicate
+      */
+    // NOTE implemented here because of sunstudio's compilation errors
+    // isVertical*Factor+isHorizontal instead of (isVertical?Factor:1) to handle CUDA bug with ternary operator
+    template<int Factor> const Replicate<ExpressionType,isVertical*Factor+isHorizontal,isHorizontal*Factor+isVertical>
+    EIGEN_DEVICE_FUNC
+    replicate(Index factor = Factor) const
+    {
+      return Replicate<ExpressionType,(isVertical?Factor:1),(isHorizontal?Factor:1)>
+          (_expression(),isVertical?factor:1,isHorizontal?factor:1);
+    }
+
+/////////// Artithmetic operators ///////////
+
+    /** Copies the vector \a other to each subvector of \c *this */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    ExpressionType& operator=(const DenseBase<OtherDerived>& other)
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+      //eigen_assert((m_matrix.isNull()) == (other.isNull())); FIXME
+      return m_matrix = extendedTo(other.derived());
+    }
+
+    /** Adds the vector \a other to each subvector of \c *this */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    ExpressionType& operator+=(const DenseBase<OtherDerived>& other)
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+      return m_matrix += extendedTo(other.derived());
+    }
+
+    /** Subtracts the vector \a other to each subvector of \c *this */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    ExpressionType& operator-=(const DenseBase<OtherDerived>& other)
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+      return m_matrix -= extendedTo(other.derived());
+    }
+
+    /** Multiplies each subvector of \c *this by the vector \a other */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    ExpressionType& operator*=(const DenseBase<OtherDerived>& other)
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+      EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
+      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+      m_matrix *= extendedTo(other.derived());
+      return m_matrix;
+    }
+
+    /** Divides each subvector of \c *this by the vector \a other */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    ExpressionType& operator/=(const DenseBase<OtherDerived>& other)
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+      EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
+      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+      m_matrix /= extendedTo(other.derived());
+      return m_matrix;
+    }
+
+    /** Returns the expression of the sum of the vector \a other to each subvector of \c *this */
+    template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+    CwiseBinaryOp<internal::scalar_sum_op<Scalar,typename OtherDerived::Scalar>,
+                  const ExpressionTypeNestedCleaned,
+                  const typename ExtendedType<OtherDerived>::Type>
+    operator+(const DenseBase<OtherDerived>& other) const
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+      return m_matrix + extendedTo(other.derived());
+    }
+
+    /** Returns the expression of the difference between each subvector of \c *this and the vector \a other */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    CwiseBinaryOp<internal::scalar_difference_op<Scalar,typename OtherDerived::Scalar>,
+                  const ExpressionTypeNestedCleaned,
+                  const typename ExtendedType<OtherDerived>::Type>
+    operator-(const DenseBase<OtherDerived>& other) const
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+      return m_matrix - extendedTo(other.derived());
+    }
+
+    /** Returns the expression where each subvector is the product of the vector \a other
+      * by the corresponding subvector of \c *this */
+    template<typename OtherDerived> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+    CwiseBinaryOp<internal::scalar_product_op<Scalar>,
+                  const ExpressionTypeNestedCleaned,
+                  const typename ExtendedType<OtherDerived>::Type>
+    EIGEN_DEVICE_FUNC
+    operator*(const DenseBase<OtherDerived>& other) const
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+      EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
+      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+      return m_matrix * extendedTo(other.derived());
+    }
+
+    /** Returns the expression where each subvector is the quotient of the corresponding
+      * subvector of \c *this by the vector \a other */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    CwiseBinaryOp<internal::scalar_quotient_op<Scalar>,
+                  const ExpressionTypeNestedCleaned,
+                  const typename ExtendedType<OtherDerived>::Type>
+    operator/(const DenseBase<OtherDerived>& other) const
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+      EIGEN_STATIC_ASSERT_ARRAYXPR(ExpressionType)
+      EIGEN_STATIC_ASSERT_SAME_XPR_KIND(ExpressionType, OtherDerived)
+      return m_matrix / extendedTo(other.derived());
+    }
+
+    /** \returns an expression where each column (or row) of the referenced matrix are normalized.
+      * The referenced matrix is \b not modified.
+      * \sa MatrixBase::normalized(), normalize()
+      */
+    EIGEN_DEVICE_FUNC
+    CwiseBinaryOp<internal::scalar_quotient_op<Scalar>,
+                  const ExpressionTypeNestedCleaned,
+                  const typename OppositeExtendedType<NormReturnType>::Type>
+    normalized() const { return m_matrix.cwiseQuotient(extendedToOpposite(this->norm())); }
+
+
+    /** Normalize in-place each row or columns of the referenced matrix.
+      * \sa MatrixBase::normalize(), normalized()
+      */
+    EIGEN_DEVICE_FUNC void normalize() {
+      m_matrix = this->normalized();
+    }
+
+    EIGEN_DEVICE_FUNC inline void reverseInPlace();
+
+/////////// Geometry module ///////////
+
+    typedef Homogeneous<ExpressionType,Direction> HomogeneousReturnType;
+    EIGEN_DEVICE_FUNC
+    HomogeneousReturnType homogeneous() const;
+
+    typedef typename ExpressionType::PlainObject CrossReturnType;
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC
+    const CrossReturnType cross(const MatrixBase<OtherDerived>& other) const;
+
+    enum {
+      HNormalized_Size = Direction==Vertical ? internal::traits<ExpressionType>::RowsAtCompileTime
+                                             : internal::traits<ExpressionType>::ColsAtCompileTime,
+      HNormalized_SizeMinusOne = HNormalized_Size==Dynamic ? Dynamic : HNormalized_Size-1
+    };
+    typedef Block<const ExpressionType,
+                  Direction==Vertical   ? int(HNormalized_SizeMinusOne)
+                                        : int(internal::traits<ExpressionType>::RowsAtCompileTime),
+                  Direction==Horizontal ? int(HNormalized_SizeMinusOne)
+                                        : int(internal::traits<ExpressionType>::ColsAtCompileTime)>
+            HNormalized_Block;
+    typedef Block<const ExpressionType,
+                  Direction==Vertical   ? 1 : int(internal::traits<ExpressionType>::RowsAtCompileTime),
+                  Direction==Horizontal ? 1 : int(internal::traits<ExpressionType>::ColsAtCompileTime)>
+            HNormalized_Factors;
+    typedef CwiseBinaryOp<internal::scalar_quotient_op<typename internal::traits<ExpressionType>::Scalar>,
+                const HNormalized_Block,
+                const Replicate<HNormalized_Factors,
+                  Direction==Vertical   ? HNormalized_SizeMinusOne : 1,
+                  Direction==Horizontal ? HNormalized_SizeMinusOne : 1> >
+            HNormalizedReturnType;
+
+    EIGEN_DEVICE_FUNC
+    const HNormalizedReturnType hnormalized() const;
+
+#   ifdef EIGEN_VECTORWISEOP_PLUGIN
+#     include EIGEN_VECTORWISEOP_PLUGIN
+#   endif
+
+  protected:
+    EIGEN_DEVICE_FUNC Index redux_length() const
+    {
+      return Direction==Vertical ? m_matrix.rows() : m_matrix.cols();
+    }
+    ExpressionTypeNested m_matrix;
+};
+
+//const colwise moved to DenseBase.h due to CUDA compiler bug
+
+
+/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
+  *
+  * \sa rowwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::ColwiseReturnType
+DenseBase<Derived>::colwise()
+{
+  return ColwiseReturnType(derived());
+}
+
+//const rowwise moved to DenseBase.h due to CUDA compiler bug
+
+
+/** \returns a writable VectorwiseOp wrapper of *this providing additional partial reduction operations
+  *
+  * \sa colwise(), class VectorwiseOp, \ref TutorialReductionsVisitorsBroadcasting
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline typename DenseBase<Derived>::RowwiseReturnType
+DenseBase<Derived>::rowwise()
+{
+  return RowwiseReturnType(derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_PARTIAL_REDUX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Visitor.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Visitor.h
new file mode 100644
index 0000000000000000000000000000000000000000..079a37db56cfb329e9e42f5fa9c808153ff00c42
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/Visitor.h
@@ -0,0 +1,841 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_VISITOR_H
+#define EIGEN_VISITOR_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Visitor, typename Derived, int UnrollCount,
+          bool Vectorize = (Derived::PacketAccess && functor_traits<Visitor>::PacketAccess), bool LinearAccess = false,
+          bool ShortCircuitEvaluation = false>
+struct visitor_impl;
+
+template <typename Visitor, bool ShortCircuitEvaluation = false>
+struct short_circuit_eval_impl {
+  // if short circuit evaluation is not used, do nothing
+  static EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(const Visitor&) { return false; }
+};
+template <typename Visitor>
+struct short_circuit_eval_impl<Visitor, true> {
+  // if short circuit evaluation is used, check the visitor
+  static EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool run(const Visitor& visitor) {
+    return visitor.done();
+  }
+};
+
+// unrolled inner-outer traversal
+template <typename Visitor, typename Derived, int UnrollCount, bool Vectorize, bool ShortCircuitEvaluation>
+struct visitor_impl<Visitor, Derived, UnrollCount, Vectorize, false, ShortCircuitEvaluation> {
+  // don't use short circuit evaulation for unrolled version
+  using Scalar = typename Derived::Scalar;
+  using Packet = typename packet_traits<Scalar>::type;
+  static constexpr bool RowMajor = Derived::IsRowMajor;
+  static constexpr int RowsAtCompileTime = Derived::RowsAtCompileTime;
+  static constexpr int ColsAtCompileTime = Derived::ColsAtCompileTime;
+  static constexpr int PacketSize = packet_traits<Scalar>::size;
+
+  static constexpr bool CanVectorize(int K) {
+    constexpr int InnerSizeAtCompileTime = RowMajor ? ColsAtCompileTime : RowsAtCompileTime;
+    if(InnerSizeAtCompileTime < PacketSize) return false;
+    return Vectorize && (InnerSizeAtCompileTime - (K % InnerSizeAtCompileTime) >= PacketSize);
+  }
+
+  template <int K = 0,
+      bool Empty = (K == UnrollCount),
+      std::enable_if_t<Empty, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived&, Visitor&) {}
+
+  template <int K = 0,
+      bool Empty = (K == UnrollCount),
+      bool Initialize = (K == 0),
+      bool DoVectorOp = CanVectorize(K),
+      std::enable_if_t<!Empty && Initialize && !DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor)
+  {
+    visitor.init(mat.coeff(0, 0), 0, 0);
+    run<1>(mat, visitor);
+  }
+
+  template <int K = 0,
+      bool Empty = (K == UnrollCount),
+      bool Initialize = (K == 0),
+      bool DoVectorOp = CanVectorize(K),
+      std::enable_if_t<!Empty && !Initialize && !DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor)
+  {
+    static constexpr int R = RowMajor ? (K / ColsAtCompileTime) : (K % RowsAtCompileTime);
+    static constexpr int C = RowMajor ? (K % ColsAtCompileTime) : (K / RowsAtCompileTime);
+    visitor(mat.coeff(R, C), R, C);
+    run<K + 1>(mat, visitor);
+  }
+
+  template <int K = 0,
+      bool Empty = (K == UnrollCount),
+      bool Initialize = (K == 0),
+      bool DoVectorOp = CanVectorize(K),
+      std::enable_if_t<!Empty && Initialize && DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor)
+  {
+    Packet P = mat.template packet<Packet>(0, 0);
+    visitor.initpacket(P, 0, 0);
+    run<PacketSize>(mat, visitor);
+  }
+
+  template <int K = 0,
+      bool Empty = (K == UnrollCount),
+      bool Initialize = (K == 0),
+      bool DoVectorOp = CanVectorize(K),
+      std::enable_if_t<!Empty && !Initialize && DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor)
+  {
+    static constexpr int R = RowMajor ? (K / ColsAtCompileTime) : (K % RowsAtCompileTime);
+    static constexpr int C = RowMajor ? (K % ColsAtCompileTime) : (K / RowsAtCompileTime);
+    Packet P = mat.template packet<Packet>(R, C);
+    visitor.packet(P, R, C);
+    run<K + PacketSize>(mat, visitor);
+  }
+};
+
+// unrolled linear traversal
+template <typename Visitor, typename Derived, int UnrollCount, bool Vectorize, bool ShortCircuitEvaluation>
+struct visitor_impl<Visitor, Derived, UnrollCount, Vectorize, true, ShortCircuitEvaluation> {
+  // don't use short circuit evaulation for unrolled version
+  using Scalar = typename Derived::Scalar;
+  using Packet = typename packet_traits<Scalar>::type;
+  static constexpr int PacketSize = packet_traits<Scalar>::size;
+
+  static constexpr bool CanVectorize(int K) {
+      return Vectorize && ((UnrollCount - K) >= PacketSize);
+  }
+
+  // empty
+  template <int K = 0,
+      bool Empty = (K == UnrollCount),
+      std::enable_if_t<Empty, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived&, Visitor&) {}
+
+  // scalar initialization
+  template <int K = 0,
+      bool Empty = (K == UnrollCount),
+      bool Initialize = (K == 0),
+      bool DoVectorOp = CanVectorize(K),
+      std::enable_if_t<!Empty && Initialize && !DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    visitor.init(mat.coeff(0), 0);
+    run<1>(mat, visitor);
+  }
+
+  // scalar iteration
+  template <int K = 0,
+      bool Empty = (K == UnrollCount),
+      bool Initialize = (K == 0),
+      bool DoVectorOp = CanVectorize(K),
+      std::enable_if_t<!Empty && !Initialize && !DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    visitor(mat.coeff(K), K);
+    run<K + 1>(mat, visitor);
+  }
+
+  // vector initialization
+  template <int K = 0,
+      bool Empty = (K == UnrollCount),
+      bool Initialize = (K == 0),
+      bool DoVectorOp = CanVectorize(K),
+      std::enable_if_t<!Empty && Initialize && DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    Packet P = mat.template packet<Packet>(0);
+    visitor.initpacket(P, 0);
+    run<PacketSize>(mat, visitor);
+  }
+
+  // vector iteration
+  template <int K = 0,
+      bool Empty = (K == UnrollCount),
+      bool Initialize = (K == 0),
+      bool DoVectorOp = CanVectorize(K),
+      std::enable_if_t<!Empty && !Initialize && DoVectorOp, bool> = true>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    Packet P = mat.template packet<Packet>(K);
+    visitor.packet(P, K);
+    run<K + PacketSize>(mat, visitor);
+  }
+};
+
+// dynamic scalar outer-inner traversal
+template <typename Visitor, typename Derived, bool ShortCircuitEvaluation>
+struct visitor_impl<Visitor, Derived, Dynamic, /*Vectorize=*/false, /*LinearAccess=*/false, ShortCircuitEvaluation> {
+  using short_circuit = short_circuit_eval_impl<Visitor, ShortCircuitEvaluation>;
+  static constexpr bool RowMajor = Derived::IsRowMajor;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    const Index innerSize = RowMajor ? mat.cols() : mat.rows();
+    const Index outerSize = RowMajor ? mat.rows() : mat.cols();
+    if (innerSize == 0 || outerSize == 0) return;
+    {
+      visitor.init(mat.coeff(0, 0), 0, 0);
+      if (short_circuit::run(visitor)) return;
+      for (Index i = 1; i < innerSize; ++i) {
+        Index r = RowMajor ? 0 : i;
+        Index c = RowMajor ? i : 0;
+        visitor(mat.coeff(r, c), r, c);
+        if EIGEN_PREDICT_FALSE(short_circuit::run(visitor)) return;
+      }
+    }
+    for (Index j = 1; j < outerSize; j++) {
+      for (Index i = 0; i < innerSize; ++i) {
+        Index r = RowMajor ? j : i;
+        Index c = RowMajor ? i : j;
+        visitor(mat.coeff(r, c), r, c);
+        if EIGEN_PREDICT_FALSE(short_circuit::run(visitor)) return;
+      }
+    }
+  }
+};
+
+// dynamic vectorized outer-inner traversal
+template <typename Visitor, typename Derived, bool ShortCircuitEvaluation>
+struct visitor_impl<Visitor, Derived, Dynamic, /*Vectorize=*/true, /*LinearAccess=*/false, ShortCircuitEvaluation> {
+  using Scalar = typename Derived::Scalar;
+  using Packet = typename packet_traits<Scalar>::type;
+  static constexpr int PacketSize = packet_traits<Scalar>::size;
+  using short_circuit = short_circuit_eval_impl<Visitor, ShortCircuitEvaluation>;
+  static constexpr bool RowMajor = Derived::IsRowMajor;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    const Index innerSize = RowMajor ? mat.cols() : mat.rows();
+    const Index outerSize = RowMajor ? mat.rows() : mat.cols();
+    if (innerSize == 0 || outerSize == 0) return;
+    {
+      Index i = 0;
+      if (innerSize < PacketSize) {
+        visitor.init(mat.coeff(0, 0), 0, 0);
+        i = 1;
+      } else {
+        Packet p = mat.template packet<Packet>(0, 0);
+        visitor.initpacket(p, 0, 0);
+        i = PacketSize;
+      }
+      if EIGEN_PREDICT_FALSE(short_circuit::run(visitor)) return;
+      for (; i + PacketSize - 1 < innerSize; i += PacketSize) {
+        Index r = RowMajor ? 0 : i;
+        Index c = RowMajor ? i : 0;
+        Packet p = mat.template packet<Packet>(r, c);
+        visitor.packet(p, r, c);
+        if EIGEN_PREDICT_FALSE(short_circuit::run(visitor)) return;
+      }
+      for (; i < innerSize; ++i) {
+        Index r = RowMajor ? 0 : i;
+        Index c = RowMajor ? i : 0;
+        visitor(mat.coeff(r, c), r, c);
+        if EIGEN_PREDICT_FALSE(short_circuit::run(visitor)) return;
+      }
+    }
+    for (Index j = 1; j < outerSize; j++) {
+      Index i = 0;
+      for (; i + PacketSize - 1 < innerSize; i += PacketSize) {
+        Index r = RowMajor ? j : i;
+        Index c = RowMajor ? i : j;
+        Packet p = mat.template packet<Packet>(r, c);
+        visitor.packet(p, r, c);
+        if EIGEN_PREDICT_FALSE(short_circuit::run(visitor)) return;
+      }
+      for (; i < innerSize; ++i) {
+        Index r = RowMajor ? j : i;
+        Index c = RowMajor ? i : j;
+        visitor(mat.coeff(r, c), r, c);
+        if EIGEN_PREDICT_FALSE(short_circuit::run(visitor)) return;
+      }
+    }
+  }
+};
+
+// dynamic scalar linear traversal
+template <typename Visitor, typename Derived, bool ShortCircuitEvaluation>
+struct visitor_impl<Visitor, Derived, Dynamic, /*Vectorize=*/false, /*LinearAccess=*/true, ShortCircuitEvaluation> {
+  using short_circuit = short_circuit_eval_impl<Visitor, ShortCircuitEvaluation>;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    const Index size = mat.size();
+    if (size == 0) return;
+    visitor.init(mat.coeff(0), 0);
+    if EIGEN_PREDICT_FALSE(short_circuit::run(visitor)) return;
+    for (Index k = 1; k < size; k++) {
+      visitor(mat.coeff(k), k);
+      if EIGEN_PREDICT_FALSE(short_circuit::run(visitor)) return;
+    }
+  }
+};
+
+// dynamic vectorized linear traversal
+template <typename Visitor, typename Derived, bool ShortCircuitEvaluation>
+struct visitor_impl<Visitor, Derived, Dynamic, /*Vectorize=*/true, /*LinearAccess=*/true, ShortCircuitEvaluation> {
+  using Scalar = typename Derived::Scalar;
+  using Packet = typename packet_traits<Scalar>::type;
+  static constexpr int PacketSize = packet_traits<Scalar>::size;
+  using short_circuit = short_circuit_eval_impl<Visitor, ShortCircuitEvaluation>;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Derived& mat, Visitor& visitor) {
+    const Index size = mat.size();
+    if (size == 0) return;
+    Index k = 0;
+    if (size < PacketSize) {
+      visitor.init(mat.coeff(0), 0);
+      k = 1;
+    } else {
+      Packet p = mat.template packet<Packet>(k);
+      visitor.initpacket(p, k);
+      k = PacketSize;
+    }
+    if EIGEN_PREDICT_FALSE(short_circuit::run(visitor)) return;
+    for (; k + PacketSize - 1 < size; k += PacketSize) {
+      Packet p = mat.template packet<Packet>(k);
+      visitor.packet(p, k);
+      if EIGEN_PREDICT_FALSE(short_circuit::run(visitor)) return;
+    }
+    for (; k < size; k++) {
+      visitor(mat.coeff(k), k);
+      if EIGEN_PREDICT_FALSE(short_circuit::run(visitor)) return;
+    }
+  }
+};
+
+// evaluator adaptor
+template<typename XprType>
+class visitor_evaluator
+{
+public:
+  typedef evaluator<XprType> Evaluator;
+  typedef typename XprType::Scalar Scalar;
+  using Packet = typename packet_traits<Scalar>::type;
+  typedef std::remove_const_t<typename XprType::CoeffReturnType> CoeffReturnType;
+
+  static constexpr bool PacketAccess = static_cast<bool>(Evaluator::Flags & PacketAccessBit);
+  static constexpr bool LinearAccess = static_cast<bool>(Evaluator::Flags & LinearAccessBit);
+  static constexpr bool IsRowMajor = static_cast<bool>(XprType::IsRowMajor);
+  static constexpr int RowsAtCompileTime = XprType::RowsAtCompileTime;
+  static constexpr int ColsAtCompileTime = XprType::ColsAtCompileTime;
+  static constexpr int XprAlignment = Evaluator::Alignment;
+  static constexpr int CoeffReadCost = Evaluator::CoeffReadCost;
+
+  EIGEN_DEVICE_FUNC
+  explicit visitor_evaluator(const XprType &xpr) : m_evaluator(xpr), m_xpr(xpr) { }
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_xpr.rows(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_xpr.cols(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index size() const EIGEN_NOEXCEPT { return m_xpr.size(); }
+  // outer-inner access
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index row, Index col) const { return m_evaluator.coeff(row, col); }
+  template <typename Packet, int Alignment = Unaligned>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packet(Index row, Index col) const {
+    return m_evaluator.template packet<Alignment, Packet>(row, col);
+  }
+  // linear access
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_evaluator.coeff(index); }
+  template <typename Packet, int Alignment = XprAlignment>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packet(Index index) const {
+    return m_evaluator.template packet<Alignment, Packet>(index);
+  }
+
+protected:
+  Evaluator m_evaluator;
+  const XprType &m_xpr;
+};
+
+template <typename Derived, typename Visitor, bool ShortCircuitEvaulation>
+struct visit_impl {
+  using Evaluator = visitor_evaluator<Derived>;
+  using Scalar = typename DenseBase<Derived>::Scalar;
+
+  static constexpr bool IsRowMajor = DenseBase<Derived>::IsRowMajor;
+  static constexpr int SizeAtCompileTime = DenseBase<Derived>::SizeAtCompileTime;
+  static constexpr int RowsAtCompileTime = DenseBase<Derived>::RowsAtCompileTime;
+  static constexpr int ColsAtCompileTime = DenseBase<Derived>::ColsAtCompileTime;
+  static constexpr int InnerSizeAtCompileTime = IsRowMajor ? ColsAtCompileTime : RowsAtCompileTime;
+  static constexpr int OuterSizeAtCompileTime = IsRowMajor ? RowsAtCompileTime : ColsAtCompileTime;
+
+  static constexpr bool LinearAccess = Evaluator::LinearAccess && static_cast<bool>(functor_traits<Visitor>::LinearAccess);
+  static constexpr bool Vectorize = Evaluator::PacketAccess && static_cast<bool>(functor_traits<Visitor>::PacketAccess);
+
+  static constexpr int PacketSize = packet_traits<Scalar>::size;
+  static constexpr int VectorOps = Vectorize ? (LinearAccess ? (SizeAtCompileTime / PacketSize) : (OuterSizeAtCompileTime * (InnerSizeAtCompileTime / PacketSize))) : 0;
+  static constexpr int ScalarOps = SizeAtCompileTime - (VectorOps * PacketSize);
+  // treat vector op and scalar op as same cost for unroll logic
+  static constexpr int TotalOps = VectorOps + ScalarOps;
+
+  static constexpr int UnrollCost = int(Evaluator::CoeffReadCost) + int(functor_traits<Visitor>::Cost);
+  static constexpr bool Unroll = (SizeAtCompileTime != Dynamic) && ((TotalOps * UnrollCost) <= EIGEN_UNROLLING_LIMIT);
+  static constexpr int UnrollCount = Unroll ? int(SizeAtCompileTime) : Dynamic;
+
+
+  using impl = visitor_impl<Visitor, Evaluator, UnrollCount, Vectorize, LinearAccess, ShortCircuitEvaulation>;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const DenseBase<Derived>& mat, Visitor& visitor) {
+    Evaluator evaluator(mat.derived());
+    impl::run(evaluator, visitor);
+  }
+};
+
+} // end namespace internal
+
+/** Applies the visitor \a visitor to the whole coefficients of the matrix or vector.
+  *
+  * The template parameter \a Visitor is the type of the visitor and provides the following interface:
+  * \code
+  * struct MyVisitor {
+  *   // called for the first coefficient
+  *   void init(const Scalar& value, Index i, Index j);
+  *   // called for all other coefficients
+  *   void operator() (const Scalar& value, Index i, Index j);
+  * };
+  * \endcode
+  *
+  * \note compared to one or two \em for \em loops, visitors offer automatic
+  * unrolling for small fixed size matrix.
+  *
+  * \note if the matrix is empty, then the visitor is left unchanged.
+  *
+  * \sa minCoeff(Index*,Index*), maxCoeff(Index*,Index*), DenseBase::redux()
+  */
+template<typename Derived>
+template<typename Visitor>
+EIGEN_DEVICE_FUNC
+void DenseBase<Derived>::visit(Visitor& visitor) const
+{
+  using impl = internal::visit_impl<Derived, Visitor, /*ShortCircuitEvaulation*/false>;
+  impl::run(derived(), visitor);
+}
+
+namespace internal {
+
+/** \internal
+  * \brief Base class to implement min and max visitors
+  */
+template <typename Derived>
+struct coeff_visitor
+{
+  // default initialization to avoid countless invalid maybe-uninitialized warnings by gcc
+  EIGEN_DEVICE_FUNC
+  coeff_visitor() : row(-1), col(-1), res(0) {}
+  typedef typename Derived::Scalar Scalar;
+  Index row, col;
+  Scalar res;
+  EIGEN_DEVICE_FUNC
+  inline void init(const Scalar& value, Index i, Index j)
+  {
+    res = value;
+    row = i;
+    col = j;
+  }
+};
+
+
+template <typename Scalar, int NaNPropagation, bool is_min = true>
+struct minmax_compare {
+  typedef typename packet_traits<Scalar>::type Packet;
+  static EIGEN_DEVICE_FUNC inline bool compare(Scalar a, Scalar b) { return a < b; }
+  static EIGEN_DEVICE_FUNC inline Scalar predux(const Packet& p) { return predux_min<NaNPropagation>(p); }
+};
+
+template <typename Scalar, int NaNPropagation>
+struct minmax_compare<Scalar, NaNPropagation, false> {
+  typedef typename packet_traits<Scalar>::type Packet;
+  static EIGEN_DEVICE_FUNC inline bool compare(Scalar a, Scalar b) { return a > b; }
+  static EIGEN_DEVICE_FUNC inline Scalar predux(const Packet& p) { return predux_max<NaNPropagation>(p); }
+};
+
+// Default implementation used by non-floating types, where we do not
+// need special logic for NaN handling.
+template <typename Derived, bool is_min, int NaNPropagation,
+          bool isInt = NumTraits<typename Derived::Scalar>::IsInteger>
+struct minmax_coeff_visitor : coeff_visitor<Derived> {
+  using Scalar = typename Derived::Scalar;
+  using Packet = typename packet_traits<Scalar>::type;
+  using Comparator = minmax_compare<Scalar, NaNPropagation, is_min>;
+  static constexpr Index PacketSize = packet_traits<Scalar>::size;
+
+  EIGEN_DEVICE_FUNC inline void operator()(const Scalar& value, Index i, Index j) {
+    if (Comparator::compare(value, this->res)) {
+      this->res = value;
+      this->row = i;
+      this->col = j;
+    }
+  }
+  EIGEN_DEVICE_FUNC inline void packet(const Packet& p, Index i, Index j) {
+    Scalar value = Comparator::predux(p);
+    if (Comparator::compare(value, this->res)) {
+      const Packet range = preverse(plset<Packet>(Scalar(1)));
+      Packet mask = pcmp_eq(pset1<Packet>(value), p);
+      Index max_idx = PacketSize - static_cast<Index>(predux_max(pand(range, mask)));
+      this->res = value;
+      this->row = Derived::IsRowMajor ? i : i + max_idx;
+      this->col = Derived::IsRowMajor ? j + max_idx : j;
+    }
+  }
+  EIGEN_DEVICE_FUNC inline void initpacket(const Packet& p, Index i, Index j) {
+    Scalar value = Comparator::predux(p);
+    const Packet range = preverse(plset<Packet>(Scalar(1)));
+    Packet mask = pcmp_eq(pset1<Packet>(value), p);
+    Index max_idx = PacketSize - static_cast<Index>(predux_max(pand(range, mask)));
+    this->res = value;
+    this->row = Derived::IsRowMajor ? i : i + max_idx;
+    this->col = Derived::IsRowMajor ? j + max_idx : j;
+  }
+};
+
+// Suppress NaN. The only case in which we return NaN is if the matrix is all NaN,
+// in which case, row=0, col=0 is returned for the location.
+template <typename Derived, bool is_min>
+struct minmax_coeff_visitor<Derived, is_min, PropagateNumbers, false> : coeff_visitor<Derived> {
+  typedef typename Derived::Scalar Scalar;
+  using Packet = typename packet_traits<Scalar>::type;
+  using Comparator = minmax_compare<Scalar, PropagateNumbers, is_min>;
+
+  EIGEN_DEVICE_FUNC inline void operator()(const Scalar& value, Index i, Index j) {
+    if ((!(numext::isnan)(value) && (numext::isnan)(this->res)) || Comparator::compare(value, this->res)) {
+      this->res = value;
+      this->row = i;
+      this->col = j;
+    }
+  }
+  EIGEN_DEVICE_FUNC inline void packet(const Packet& p, Index i, Index j) {
+    const Index PacketSize = packet_traits<Scalar>::size;
+    Scalar value = Comparator::predux(p);
+    if ((!(numext::isnan)(value) && (numext::isnan)(this->res)) || Comparator::compare(value, this->res)) {
+      const Packet range = preverse(plset<Packet>(Scalar(1)));
+      /* mask will be zero for NaNs, so they will be ignored. */
+      Packet mask = pcmp_eq(pset1<Packet>(value), p);
+      Index max_idx = PacketSize - static_cast<Index>(predux_max(pand(range, mask)));
+      this->res = value;
+      this->row = Derived::IsRowMajor ? i : i + max_idx;
+      this->col = Derived::IsRowMajor ? j + max_idx : j;
+    }
+  }
+  EIGEN_DEVICE_FUNC inline void initpacket(const Packet& p, Index i, Index j) {
+    const Index PacketSize = packet_traits<Scalar>::size;
+    Scalar value = Comparator::predux(p);
+    if ((numext::isnan)(value)) {
+      this->res = value;
+      this->row = 0;
+      this->col = 0;
+      return;
+    }
+    const Packet range = preverse(plset<Packet>(Scalar(1)));
+    /* mask will be zero for NaNs, so they will be ignored. */
+    Packet mask = pcmp_eq(pset1<Packet>(value), p);
+    Index max_idx = PacketSize - static_cast<Index>(predux_max(pand(range, mask)));
+    this->res = value;
+    this->row = Derived::IsRowMajor ? i : i + max_idx;
+    this->col = Derived::IsRowMajor ? j + max_idx : j;
+  }
+};
+
+// Propagate NaNs. If the matrix contains NaN, the location of the first NaN
+// will be returned in row and col.
+template <typename Derived, bool is_min, int NaNPropagation>
+    struct minmax_coeff_visitor<Derived, is_min, NaNPropagation, false> : coeff_visitor<Derived> {
+  typedef typename Derived::Scalar Scalar;
+  using Packet = typename packet_traits<Scalar>::type;
+  using Comparator = minmax_compare<Scalar, PropagateNaN, is_min>;
+
+  EIGEN_DEVICE_FUNC inline void operator()(const Scalar& value, Index i, Index j) {
+    const bool value_is_nan = (numext::isnan)(value);
+    if ((value_is_nan && !(numext::isnan)(this->res)) || Comparator::compare(value, this->res)) {
+      this->res = value;
+      this->row = i;
+      this->col = j;
+    }
+  }
+  EIGEN_DEVICE_FUNC inline void packet(const Packet& p, Index i, Index j) {
+    const Index PacketSize = packet_traits<Scalar>::size;
+    Scalar value = Comparator::predux(p);
+    const bool value_is_nan = (numext::isnan)(value);
+    if ((value_is_nan && !(numext::isnan)(this->res)) || Comparator::compare(value, this->res)) {
+      const Packet range = preverse(plset<Packet>(Scalar(1)));
+      // If the value is NaN, pick the first position of a NaN, otherwise pick the first extremal value.
+      Packet mask = value_is_nan ? pnot(pcmp_eq(p, p)) : pcmp_eq(pset1<Packet>(value), p);
+      Index max_idx = PacketSize - static_cast<Index>(predux_max(pand(range, mask)));
+      this->res = value;
+      this->row = Derived::IsRowMajor ? i : i + max_idx;
+      this->col = Derived::IsRowMajor ? j + max_idx : j;
+    }
+  }
+  EIGEN_DEVICE_FUNC inline void initpacket(const Packet& p, Index i, Index j) {
+    const Index PacketSize = packet_traits<Scalar>::size;
+    Scalar value = Comparator::predux(p);
+    const bool value_is_nan = (numext::isnan)(value);
+    const Packet range = preverse(plset<Packet>(Scalar(1)));
+    // If the value is NaN, pick the first position of a NaN, otherwise pick the first extremal value.
+    Packet mask = value_is_nan ? pnot(pcmp_eq(p, p)) : pcmp_eq(pset1<Packet>(value), p);
+    Index max_idx = PacketSize - static_cast<Index>(predux_max(pand(range, mask)));
+    this->res = value;
+    this->row = Derived::IsRowMajor ? i : i + max_idx;
+    this->col = Derived::IsRowMajor ? j + max_idx : j;
+  }
+};
+
+template<typename Derived, bool is_min, int NaNPropagation>
+struct functor_traits<minmax_coeff_visitor<Derived, is_min, NaNPropagation> > {
+  using Scalar = typename Derived::Scalar;
+  enum {
+    Cost = NumTraits<Scalar>::AddCost,
+    LinearAccess = false,
+    PacketAccess = packet_traits<Scalar>::HasCmp
+  };
+};
+
+template <typename Scalar>
+struct all_visitor {
+  using result_type = bool;
+  using Packet = typename packet_traits<Scalar>::type;
+  EIGEN_DEVICE_FUNC inline void init(const Scalar& value, Index, Index) { res = (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline void init(const Scalar& value, Index) { res = (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline bool all_predux(const Packet& p) const { return !predux_any(pcmp_eq(p, pzero(p))); }
+  EIGEN_DEVICE_FUNC inline void initpacket(const Packet& p, Index, Index) { res = all_predux(p); }
+  EIGEN_DEVICE_FUNC inline void initpacket(const Packet& p, Index) { res = all_predux(p); }
+  EIGEN_DEVICE_FUNC inline void operator()(const Scalar& value, Index, Index) { res = res && (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline void operator()(const Scalar& value, Index) { res = res && (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline void packet(const Packet& p, Index, Index) { res = res && all_predux(p); }
+  EIGEN_DEVICE_FUNC inline void packet(const Packet& p, Index) { res = res && all_predux(p); }
+  EIGEN_DEVICE_FUNC inline bool done() const { return !res; }
+  bool res = true;
+};
+template <typename Scalar>
+struct functor_traits<all_visitor<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::ReadCost, LinearAccess = true, PacketAccess = packet_traits<Scalar>::HasCmp };
+};
+
+template <typename Scalar>
+struct any_visitor {
+  using result_type = bool;
+  using Packet = typename packet_traits<Scalar>::type;
+  EIGEN_DEVICE_FUNC inline void init(const Scalar& value, Index, Index) { res = (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline void init(const Scalar& value, Index) { res = (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline bool any_predux(const Packet& p) const {
+    return predux_any(pandnot(ptrue(p), pcmp_eq(p, pzero(p))));
+  }
+  EIGEN_DEVICE_FUNC inline void initpacket(const Packet& p, Index, Index) { res = any_predux(p); }
+  EIGEN_DEVICE_FUNC inline void initpacket(const Packet& p, Index) { res = any_predux(p); }
+  EIGEN_DEVICE_FUNC inline void operator()(const Scalar& value, Index, Index) { res = res || (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline void operator()(const Scalar& value, Index) { res = res || (value != Scalar(0)); }
+  EIGEN_DEVICE_FUNC inline void packet(const Packet& p, Index, Index) { res = res || any_predux(p); }
+  EIGEN_DEVICE_FUNC inline void packet(const Packet& p, Index) { res = res || any_predux(p); }
+  EIGEN_DEVICE_FUNC inline bool done() const { return res; }
+  bool res = false;
+};
+template <typename Scalar>
+struct functor_traits<any_visitor<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::ReadCost, LinearAccess = true, PacketAccess = packet_traits<Scalar>::HasCmp };
+};
+
+template <typename Scalar>
+struct count_visitor {
+  using result_type = Index;
+  using Packet = typename packet_traits<Scalar>::type;
+  EIGEN_DEVICE_FUNC inline void init(const Scalar& value, Index, Index) { res = value != Scalar(0) ? 1 : 0; }
+  EIGEN_DEVICE_FUNC inline void init(const Scalar& value, Index) { res = value != Scalar(0) ? 1 : 0; }
+  EIGEN_DEVICE_FUNC inline Index count_redux(const Packet& p) const {
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    Packet true_vals = pandnot(cst_one, pcmp_eq(p, pzero(p)));
+    Scalar num_true = predux(true_vals);
+    return static_cast<Index>(num_true);
+  }
+  EIGEN_DEVICE_FUNC inline void initpacket(const Packet& p, Index, Index) { res = count_redux(p); }
+  EIGEN_DEVICE_FUNC inline void initpacket(const Packet& p, Index) { res = count_redux(p); }
+  EIGEN_DEVICE_FUNC inline void operator()(const Scalar& value, Index, Index) {
+    if (value != Scalar(0)) res++;
+  }
+  EIGEN_DEVICE_FUNC inline void operator()(const Scalar& value, Index) {
+    if (value != Scalar(0)) res++;
+  }
+  EIGEN_DEVICE_FUNC inline void packet(const Packet& p, Index, Index) { res += count_redux(p); }
+  EIGEN_DEVICE_FUNC inline void packet(const Packet& p, Index) { res += count_redux(p); }
+  Index res = 0;
+};
+
+template <typename Scalar>
+struct functor_traits<count_visitor<Scalar>> {
+  enum {
+    Cost = NumTraits<Scalar>::AddCost,
+    LinearAccess = true,
+    // predux is problematic for bool
+    PacketAccess = packet_traits<Scalar>::HasCmp && packet_traits<Scalar>::HasAdd && !is_same<Scalar, bool>::value
+  };
+};
+
+} // end namespace internal
+
+/** \fn DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
+  * \returns the minimum of all coefficients of *this and puts in *row and *col its location.
+  *
+  * In case \c *this contains NaN, NaNPropagation determines the behavior:
+  *   NaNPropagation == PropagateFast : undefined
+  *   NaNPropagation == PropagateNaN : result is NaN
+  *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
+  * \warning the matrix must be not empty, otherwise an assertion is triggered.
+  *
+  * \sa DenseBase::minCoeff(Index*), DenseBase::maxCoeff(Index*,Index*), DenseBase::visit(), DenseBase::minCoeff()
+  */
+template<typename Derived>
+template<int NaNPropagation, typename IndexType>
+EIGEN_DEVICE_FUNC
+typename internal::traits<Derived>::Scalar
+DenseBase<Derived>::minCoeff(IndexType* rowId, IndexType* colId) const
+{
+  eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
+
+  internal::minmax_coeff_visitor<Derived, true, NaNPropagation> minVisitor;
+  this->visit(minVisitor);
+  *rowId = minVisitor.row;
+  if (colId) *colId = minVisitor.col;
+  return minVisitor.res;
+}
+
+/** \returns the minimum of all coefficients of *this and puts in *index its location.
+  *
+  * In case \c *this contains NaN, NaNPropagation determines the behavior:
+  *   NaNPropagation == PropagateFast : undefined
+  *   NaNPropagation == PropagateNaN : result is NaN
+  *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
+  * \warning the matrix must be not empty, otherwise an assertion is triggered.
+  *
+  * \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::visit(), DenseBase::minCoeff()
+  */
+template<typename Derived>
+template<int NaNPropagation, typename IndexType>
+EIGEN_DEVICE_FUNC
+typename internal::traits<Derived>::Scalar
+DenseBase<Derived>::minCoeff(IndexType* index) const
+{
+  eigen_assert(this->rows() > 0 && this->cols() > 0 && "you are using an empty matrix");
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+
+  internal::minmax_coeff_visitor<Derived, true, NaNPropagation> minVisitor;
+  this->visit(minVisitor);
+  *index = IndexType((RowsAtCompileTime==1) ? minVisitor.col : minVisitor.row);
+  return minVisitor.res;
+}
+
+/** \fn DenseBase<Derived>::maxCoeff(IndexType* rowId, IndexType* colId) const
+  * \returns the maximum of all coefficients of *this and puts in *row and *col its location.
+  *
+  * In case \c *this contains NaN, NaNPropagation determines the behavior:
+  *   NaNPropagation == PropagateFast : undefined
+  *   NaNPropagation == PropagateNaN : result is NaN
+  *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
+  * \warning the matrix must be not empty, otherwise an assertion is triggered.
+  *
+  * \sa DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visit(), DenseBase::maxCoeff()
+  */
+template<typename Derived>
+template<int NaNPropagation, typename IndexType>
+EIGEN_DEVICE_FUNC
+typename internal::traits<Derived>::Scalar
+DenseBase<Derived>::maxCoeff(IndexType* rowPtr, IndexType* colPtr) const
+{
+  eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
+
+  internal::minmax_coeff_visitor<Derived, false, NaNPropagation> maxVisitor;
+  this->visit(maxVisitor);
+  *rowPtr = maxVisitor.row;
+  if (colPtr) *colPtr = maxVisitor.col;
+  return maxVisitor.res;
+}
+
+/** \returns the maximum of all coefficients of *this and puts in *index its location.
+  *
+  * In case \c *this contains NaN, NaNPropagation determines the behavior:
+  *   NaNPropagation == PropagateFast : undefined
+  *   NaNPropagation == PropagateNaN : result is NaN
+  *   NaNPropagation == PropagateNumbers : result is maximum of elements that are not NaN
+  * \warning the matrix must be not empty, otherwise an assertion is triggered.
+  *
+  * \sa DenseBase::maxCoeff(IndexType*,IndexType*), DenseBase::minCoeff(IndexType*,IndexType*), DenseBase::visitor(), DenseBase::maxCoeff()
+  */
+template<typename Derived>
+template<int NaNPropagation, typename IndexType>
+EIGEN_DEVICE_FUNC
+typename internal::traits<Derived>::Scalar
+DenseBase<Derived>::maxCoeff(IndexType* index) const
+{
+  eigen_assert(this->rows()>0 && this->cols()>0 && "you are using an empty matrix");
+
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  internal::minmax_coeff_visitor<Derived, false, NaNPropagation> maxVisitor;
+  this->visit(maxVisitor);
+  *index = (RowsAtCompileTime==1) ? maxVisitor.col : maxVisitor.row;
+  return maxVisitor.res;
+}
+
+/** \returns true if all coefficients are true
+  *
+  * Example: \include MatrixBase_all.cpp
+  * Output: \verbinclude MatrixBase_all.out
+  *
+  * \sa any(), Cwise::operator<()
+  */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::all() const {
+  using Visitor = internal::all_visitor<Scalar>;
+  using impl = internal::visit_impl<Derived, Visitor, /*ShortCircuitEvaulation*/true>;
+  Visitor visitor;
+  impl::run(derived(), visitor);
+  return visitor.res;
+}
+
+/** \returns true if at least one coefficient is true
+  *
+  * \sa all()
+  */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::any() const {
+  using Visitor = internal::any_visitor<Scalar>;
+  using impl = internal::visit_impl<Derived, Visitor, /*ShortCircuitEvaulation*/true>;
+  Visitor visitor;
+  impl::run(derived(), visitor);
+  return visitor.res;
+}
+
+/** \returns the number of coefficients which evaluate to true
+  *
+  * \sa all(), any()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC
+Index DenseBase<Derived>::count() const
+{
+  using Visitor = internal::count_visitor<Scalar>;
+  using impl = internal::visit_impl<Derived, Visitor, /*ShortCircuitEvaulation*/false>;
+  Visitor visitor;
+  impl::run(derived(), visitor);
+  return visitor.res;
+
+}
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::hasNaN() const {
+  return derived().cwiseTypedNotEqual(derived()).any();
+}
+
+/** \returns true if \c *this contains only finite numbers, i.e., no NaN and no +/-INF values.
+  *
+  * \sa hasNaN()
+  */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline bool DenseBase<Derived>::allFinite() const {
+  return derived().array().isFinite().all(); 
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_VISITOR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX/Complex.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX/Complex.h
new file mode 100644
index 0000000000000000000000000000000000000000..cd90496d5a41da81747f9ef12fe944e130082f2a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX/Complex.h
@@ -0,0 +1,368 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner (benoit.steiner.goog@gmail.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_AVX_H
+#define EIGEN_COMPLEX_AVX_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+//---------- float ----------
+struct Packet4cf
+{
+  EIGEN_STRONG_INLINE Packet4cf() {}
+  EIGEN_STRONG_INLINE explicit Packet4cf(const __m256& a) : v(a) {}
+  __m256  v;
+};
+
+#ifndef EIGEN_VECTORIZE_AVX512
+template<> struct packet_traits<std::complex<float> >  : default_packet_traits
+{
+  typedef Packet4cf type;
+  typedef Packet2cf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasDiv    = 1,
+    HasNegate = 1,
+    HasSqrt   = 1,
+    HasAbs    = 0,
+    HasAbs2   = 0,
+    HasMin    = 0,
+    HasMax    = 0,
+    HasSetLinear = 0
+  };
+};
+#endif
+
+template<> struct unpacket_traits<Packet4cf> {
+  typedef std::complex<float> type;
+  typedef Packet2cf half;
+  typedef Packet8f as_real;
+  enum {
+    size=4,
+    alignment=Aligned32,
+    vectorizable=true,
+    masked_load_available=false,
+    masked_store_available=false
+  };
+};
+
+template<> EIGEN_STRONG_INLINE Packet4cf padd<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_add_ps(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet4cf psub<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_sub_ps(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet4cf pnegate(const Packet4cf& a)
+{
+  return Packet4cf(pnegate(a.v));
+}
+template<> EIGEN_STRONG_INLINE Packet4cf pconj(const Packet4cf& a)
+{
+  const __m256 mask = _mm256_castsi256_ps(_mm256_setr_epi32(0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000));
+  return Packet4cf(_mm256_xor_ps(a.v,mask));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4cf pmul<Packet4cf>(const Packet4cf& a, const Packet4cf& b)
+{
+  __m256 tmp1 = _mm256_mul_ps(_mm256_moveldup_ps(a.v), b.v);
+  __m256 tmp2 = _mm256_mul_ps(_mm256_movehdup_ps(a.v), _mm256_permute_ps(b.v, _MM_SHUFFLE(2,3,0,1)));
+  __m256 result = _mm256_addsub_ps(tmp1, tmp2);
+  return Packet4cf(result);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf pcmp_eq(const Packet4cf& a, const Packet4cf& b) {
+  __m256 eq = _mm256_cmp_ps(a.v, b.v, _CMP_EQ_OQ);
+  return Packet4cf(_mm256_and_ps(eq, _mm256_permute_ps(eq, 0xb1)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4cf ptrue<Packet4cf>(const Packet4cf& a) { return Packet4cf(ptrue(Packet8f(a.v))); }
+template<> EIGEN_STRONG_INLINE Packet4cf pand   <Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_and_ps(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet4cf por    <Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_or_ps(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet4cf pxor   <Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_xor_ps(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet4cf pandnot<Packet4cf>(const Packet4cf& a, const Packet4cf& b) { return Packet4cf(_mm256_andnot_ps(b.v,a.v)); }
+
+template<> EIGEN_STRONG_INLINE Packet4cf pload <Packet4cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet4cf(pload<Packet8f>(&numext::real_ref(*from))); }
+template<> EIGEN_STRONG_INLINE Packet4cf ploadu<Packet4cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet4cf(ploadu<Packet8f>(&numext::real_ref(*from))); }
+
+
+template<> EIGEN_STRONG_INLINE Packet4cf pset1<Packet4cf>(const std::complex<float>& from)
+{
+  const float re = std::real(from);
+  const float im = std::imag(from);
+  return Packet4cf(_mm256_set_ps(im, re, im, re, im, re, im, re));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4cf ploaddup<Packet4cf>(const std::complex<float>* from)
+{
+  // FIXME The following might be optimized using _mm256_movedup_pd
+  Packet2cf a = ploaddup<Packet2cf>(from);
+  Packet2cf b = ploaddup<Packet2cf>(from+1);
+  return  Packet4cf(_mm256_insertf128_ps(_mm256_castps128_ps256(a.v), b.v, 1));
+}
+
+template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float>* to, const Packet4cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), from.v); }
+template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet4cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), from.v); }
+
+template<> EIGEN_DEVICE_FUNC inline Packet4cf pgather<std::complex<float>, Packet4cf>(const std::complex<float>* from, Index stride)
+{
+  return Packet4cf(_mm256_set_ps(std::imag(from[3*stride]), std::real(from[3*stride]),
+                                 std::imag(from[2*stride]), std::real(from[2*stride]),
+                                 std::imag(from[1*stride]), std::real(from[1*stride]),
+                                 std::imag(from[0*stride]), std::real(from[0*stride])));
+}
+
+template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet4cf>(std::complex<float>* to, const Packet4cf& from, Index stride)
+{
+  __m128 low = _mm256_extractf128_ps(from.v, 0);
+  to[stride*0] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(low, low, 0)),
+                                     _mm_cvtss_f32(_mm_shuffle_ps(low, low, 1)));
+  to[stride*1] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(low, low, 2)),
+                                     _mm_cvtss_f32(_mm_shuffle_ps(low, low, 3)));
+
+  __m128 high = _mm256_extractf128_ps(from.v, 1);
+  to[stride*2] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(high, high, 0)),
+                                     _mm_cvtss_f32(_mm_shuffle_ps(high, high, 1)));
+  to[stride*3] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(high, high, 2)),
+                                     _mm_cvtss_f32(_mm_shuffle_ps(high, high, 3)));
+
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet4cf>(const Packet4cf& a)
+{
+  return pfirst(Packet2cf(_mm256_castps256_ps128(a.v)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4cf preverse(const Packet4cf& a) {
+  __m128 low  = _mm256_extractf128_ps(a.v, 0);
+  __m128 high = _mm256_extractf128_ps(a.v, 1);
+  __m128d lowd  = _mm_castps_pd(low);
+  __m128d highd = _mm_castps_pd(high);
+  low  = _mm_castpd_ps(_mm_shuffle_pd(lowd,lowd,0x1));
+  high = _mm_castpd_ps(_mm_shuffle_pd(highd,highd,0x1));
+  __m256 result = _mm256_setzero_ps();
+  result = _mm256_insertf128_ps(result, low, 1);
+  result = _mm256_insertf128_ps(result, high, 0);
+  return Packet4cf(result);
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet4cf>(const Packet4cf& a)
+{
+  return predux(padd(Packet2cf(_mm256_extractf128_ps(a.v,0)),
+                     Packet2cf(_mm256_extractf128_ps(a.v,1))));
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet4cf>(const Packet4cf& a)
+{
+  return predux_mul(pmul(Packet2cf(_mm256_extractf128_ps(a.v, 0)),
+                         Packet2cf(_mm256_extractf128_ps(a.v, 1))));
+}
+
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet4cf,Packet8f)
+
+template<> EIGEN_STRONG_INLINE Packet4cf pdiv<Packet4cf>(const Packet4cf& a, const Packet4cf& b)
+{
+  return pdiv_complex(a, b);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4cf pcplxflip<Packet4cf>(const Packet4cf& x)
+{
+  return Packet4cf(_mm256_shuffle_ps(x.v, x.v, _MM_SHUFFLE(2, 3, 0 ,1)));
+}
+
+//---------- double ----------
+struct Packet2cd
+{
+  EIGEN_STRONG_INLINE Packet2cd() {}
+  EIGEN_STRONG_INLINE explicit Packet2cd(const __m256d& a) : v(a) {}
+  __m256d  v;
+};
+
+#ifndef EIGEN_VECTORIZE_AVX512
+template<> struct packet_traits<std::complex<double> >  : default_packet_traits
+{
+  typedef Packet2cd type;
+  typedef Packet1cd half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 0,
+    size = 2,
+
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasDiv    = 1,
+    HasNegate = 1,
+    HasSqrt   = 1,
+    HasAbs    = 0,
+    HasAbs2   = 0,
+    HasMin    = 0,
+    HasMax    = 0,
+    HasSetLinear = 0
+  };
+};
+#endif
+
+template<> struct unpacket_traits<Packet2cd> {
+  typedef std::complex<double> type;
+  typedef Packet1cd half;
+  typedef Packet4d as_real;
+  enum {
+    size=2,
+    alignment=Aligned32,
+    vectorizable=true,
+    masked_load_available=false,
+    masked_store_available=false
+  };
+};
+
+template<> EIGEN_STRONG_INLINE Packet2cd padd<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_add_pd(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cd psub<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_sub_pd(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cd pnegate(const Packet2cd& a) { return Packet2cd(pnegate(a.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cd pconj(const Packet2cd& a)
+{
+  const __m256d mask = _mm256_castsi256_pd(_mm256_set_epi32(0x80000000,0x0,0x0,0x0,0x80000000,0x0,0x0,0x0));
+  return Packet2cd(_mm256_xor_pd(a.v,mask));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cd pmul<Packet2cd>(const Packet2cd& a, const Packet2cd& b)
+{
+  __m256d tmp1 = _mm256_shuffle_pd(a.v,a.v,0x0);
+  __m256d even = _mm256_mul_pd(tmp1, b.v);
+  __m256d tmp2 = _mm256_shuffle_pd(a.v,a.v,0xF);
+  __m256d tmp3 = _mm256_shuffle_pd(b.v,b.v,0x5);
+  __m256d odd  = _mm256_mul_pd(tmp2, tmp3);
+  return Packet2cd(_mm256_addsub_pd(even, odd));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cd pcmp_eq(const Packet2cd& a, const Packet2cd& b) {
+  __m256d eq = _mm256_cmp_pd(a.v, b.v, _CMP_EQ_OQ);
+  return Packet2cd(pand(eq, _mm256_permute_pd(eq, 0x5)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cd ptrue<Packet2cd>(const Packet2cd& a) { return Packet2cd(ptrue(Packet4d(a.v))); }
+template<> EIGEN_STRONG_INLINE Packet2cd pand   <Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_and_pd(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cd por    <Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_or_pd(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cd pxor   <Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_xor_pd(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cd pandnot<Packet2cd>(const Packet2cd& a, const Packet2cd& b) { return Packet2cd(_mm256_andnot_pd(b.v,a.v)); }
+
+template<> EIGEN_STRONG_INLINE Packet2cd pload <Packet2cd>(const std::complex<double>* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return Packet2cd(pload<Packet4d>((const double*)from)); }
+template<> EIGEN_STRONG_INLINE Packet2cd ploadu<Packet2cd>(const std::complex<double>* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cd(ploadu<Packet4d>((const double*)from)); }
+
+template<> EIGEN_STRONG_INLINE Packet2cd pset1<Packet2cd>(const std::complex<double>& from)
+{
+  // in case casting to a __m128d* is really not safe, then we can still fallback to this version: (much slower though)
+//   return Packet2cd(_mm256_loadu2_m128d((const double*)&from,(const double*)&from));
+    return Packet2cd(_mm256_broadcast_pd((const __m128d*)(const void*)&from));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cd ploaddup<Packet2cd>(const std::complex<double>* from) { return pset1<Packet2cd>(*from); }
+
+template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *   to, const Packet2cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
+template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *   to, const Packet2cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
+
+template<> EIGEN_DEVICE_FUNC inline Packet2cd pgather<std::complex<double>, Packet2cd>(const std::complex<double>* from, Index stride)
+{
+  return Packet2cd(_mm256_set_pd(std::imag(from[1*stride]), std::real(from[1*stride]),
+				 std::imag(from[0*stride]), std::real(from[0*stride])));
+}
+
+template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet2cd>(std::complex<double>* to, const Packet2cd& from, Index stride)
+{
+  __m128d low = _mm256_extractf128_pd(from.v, 0);
+  to[stride*0] = std::complex<double>(_mm_cvtsd_f64(low), _mm_cvtsd_f64(_mm_shuffle_pd(low, low, 1)));
+  __m128d high = _mm256_extractf128_pd(from.v, 1);
+  to[stride*1] = std::complex<double>(_mm_cvtsd_f64(high), _mm_cvtsd_f64(_mm_shuffle_pd(high, high, 1)));
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet2cd>(const Packet2cd& a)
+{
+  __m128d low = _mm256_extractf128_pd(a.v, 0);
+  EIGEN_ALIGN16 double res[2];
+  _mm_store_pd(res, low);
+  return std::complex<double>(res[0],res[1]);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cd preverse(const Packet2cd& a) {
+  __m256d result = _mm256_permute2f128_pd(a.v, a.v, 1);
+  return Packet2cd(result);
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet2cd>(const Packet2cd& a)
+{
+  return predux(padd(Packet1cd(_mm256_extractf128_pd(a.v,0)),
+                     Packet1cd(_mm256_extractf128_pd(a.v,1))));
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet2cd>(const Packet2cd& a)
+{
+  return predux(pmul(Packet1cd(_mm256_extractf128_pd(a.v,0)),
+                     Packet1cd(_mm256_extractf128_pd(a.v,1))));
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cd,Packet4d)
+
+template<> EIGEN_STRONG_INLINE Packet2cd pdiv<Packet2cd>(const Packet2cd& a, const Packet2cd& b)
+{
+  return pdiv_complex(a, b);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cd pcplxflip<Packet2cd>(const Packet2cd& x)
+{
+  return Packet2cd(_mm256_shuffle_pd(x.v, x.v, 0x5));
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet4cf,4>& kernel) {
+  __m256d P0 = _mm256_castps_pd(kernel.packet[0].v);
+  __m256d P1 = _mm256_castps_pd(kernel.packet[1].v);
+  __m256d P2 = _mm256_castps_pd(kernel.packet[2].v);
+  __m256d P3 = _mm256_castps_pd(kernel.packet[3].v);
+
+  __m256d T0 = _mm256_shuffle_pd(P0, P1, 15);
+  __m256d T1 = _mm256_shuffle_pd(P0, P1, 0);
+  __m256d T2 = _mm256_shuffle_pd(P2, P3, 15);
+  __m256d T3 = _mm256_shuffle_pd(P2, P3, 0);
+
+  kernel.packet[1].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T0, T2, 32));
+  kernel.packet[3].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T0, T2, 49));
+  kernel.packet[0].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T1, T3, 32));
+  kernel.packet[2].v = _mm256_castpd_ps(_mm256_permute2f128_pd(T1, T3, 49));
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet2cd,2>& kernel) {
+  __m256d tmp = _mm256_permute2f128_pd(kernel.packet[0].v, kernel.packet[1].v, 0+(2<<4));
+  kernel.packet[1].v = _mm256_permute2f128_pd(kernel.packet[0].v, kernel.packet[1].v, 1+(3<<4));
+ kernel.packet[0].v = tmp;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cd psqrt<Packet2cd>(const Packet2cd& a) {
+  return psqrt_complex<Packet2cd>(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4cf psqrt<Packet4cf>(const Packet4cf& a) {
+  return psqrt_complex<Packet4cf>(a);
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_COMPLEX_AVX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX/MathFunctions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX/MathFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..a3320b8523d6c2d866344d78095759371f95b662
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX/MathFunctions.h
@@ -0,0 +1,113 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATH_FUNCTIONS_AVX_H
+#define EIGEN_MATH_FUNCTIONS_AVX_H
+
+/* The sin and cos functions of this file are loosely derived from
+ * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
+ */
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet8f)
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_DOUBLE(Packet4d)
+
+// Notice that for newer processors, it is counterproductive to use Newton
+// iteration for square root. In particular, Skylake and Zen2 processors
+// have approximately doubled throughput of the _mm_sqrt_ps instruction
+// compared to their predecessors.
+template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet8f psqrt<Packet8f>(const Packet8f& _x) {
+  return _mm256_sqrt_ps(_x);
+}
+template <> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet4d psqrt<Packet4d>(const Packet4d& _x) {
+  return _mm256_sqrt_pd(_x);
+}
+
+
+// Even on Skylake, using Newton iteration is a win for reciprocal square root.
+#if EIGEN_FAST_MATH
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet8f prsqrt<Packet8f>(const Packet8f& a) {
+  // _mm256_rsqrt_ps returns -inf for negative denormals.
+  // _mm512_rsqrt**_ps returns -NaN for negative denormals.  We may want
+  // consistency here.
+  // const Packet8f rsqrt = pselect(pcmp_lt(a, pzero(a)),
+  //                                pset1<Packet8f>(-NumTraits<float>::quiet_NaN()),
+  //                                _mm256_rsqrt_ps(a));
+  return generic_rsqrt_newton_step<Packet8f, /*Steps=*/1>::run(a, _mm256_rsqrt_ps(a));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f preciprocal<Packet8f>(const Packet8f& a) {
+  return generic_reciprocal_newton_step<Packet8f, /*Steps=*/1>::run(a, _mm256_rcp_ps(a));
+}
+
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pfrexp(const Packet8h& a, Packet8h& exponent) {
+  Packet8f fexponent;
+  const Packet8h out = float2half(pfrexp<Packet8f>(half2float(a), fexponent));
+  exponent = float2half(fexponent);
+  return out;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pldexp(const Packet8h& a, const Packet8h& exponent) {
+  return float2half(pldexp<Packet8f>(half2float(a), half2float(exponent)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pfrexp(const Packet8bf& a, Packet8bf& exponent) {
+  Packet8f fexponent;
+  const Packet8bf out = F32ToBf16(pfrexp<Packet8f>(Bf16ToF32(a), fexponent));
+  exponent = F32ToBf16(fexponent);
+  return out;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pldexp(const Packet8bf& a, const Packet8bf& exponent) {
+  return F32ToBf16(pldexp<Packet8f>(Bf16ToF32(a), Bf16ToF32(exponent)));
+}
+
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pcos)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pexp)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, pexpm1)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, plog)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, plog1p)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, plog2)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, preciprocal)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, prsqrt)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, psin)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, psqrt)
+BF16_PACKET_FUNCTION(Packet8f, Packet8bf, ptanh)
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pcos)
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pexp)
+F16_PACKET_FUNCTION(Packet8f, Packet8h, pexpm1)
+F16_PACKET_FUNCTION(Packet8f, Packet8h, plog)
+F16_PACKET_FUNCTION(Packet8f, Packet8h, plog1p)
+F16_PACKET_FUNCTION(Packet8f, Packet8h, plog2)
+F16_PACKET_FUNCTION(Packet8f, Packet8h, preciprocal)
+F16_PACKET_FUNCTION(Packet8f, Packet8h, prsqrt)
+F16_PACKET_FUNCTION(Packet8f, Packet8h, psin)
+F16_PACKET_FUNCTION(Packet8f, Packet8h, psqrt)
+F16_PACKET_FUNCTION(Packet8f, Packet8h, ptanh)
+
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_AVX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX/PacketMath.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX/PacketMath.h
new file mode 100644
index 0000000000000000000000000000000000000000..9bbbc13f614c66860f67c30651d253ffa9023ccf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX/PacketMath.h
@@ -0,0 +1,2411 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner (benoit.steiner.goog@gmail.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_AVX_H
+#define EIGEN_PACKET_MATH_AVX_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+#if !defined(EIGEN_VECTORIZE_AVX512) && !defined(EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS)
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 16
+#endif
+
+#ifdef EIGEN_VECTORIZE_FMA
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+#endif
+
+typedef __m256  Packet8f;
+typedef eigen_packet_wrapper<__m256i, 0> Packet8i;
+typedef __m256d Packet4d;
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+typedef eigen_packet_wrapper<__m128i, 2> Packet8h;
+#endif
+typedef eigen_packet_wrapper<__m128i, 3> Packet8bf;
+typedef eigen_packet_wrapper<__m256i, 4> Packet8ui;
+
+#ifdef EIGEN_VECTORIZE_AVX2
+// Start from 3 to be compatible with AVX512
+typedef eigen_packet_wrapper<__m256i, 3> Packet4l;
+typedef eigen_packet_wrapper<__m256i, 5> Packet4ul;
+#endif
+
+template<> struct is_arithmetic<__m256>  { enum { value = true }; };
+template<> struct is_arithmetic<__m256i> { enum { value = true }; };
+template<> struct is_arithmetic<__m256d> { enum { value = true }; };
+template<> struct is_arithmetic<Packet8i> { enum { value = true }; };
+// Note that `Packet8ui` uses the underlying type `__m256i`, which is
+// interpreted as a vector of _signed_ `int32`s, which breaks some arithmetic
+// operations used in `GenericPacketMath.h`.
+template<> struct is_arithmetic<Packet8ui> { enum { value = false }; };
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template<> struct is_arithmetic<Packet8h> { enum { value = true }; };
+#endif
+template<> struct is_arithmetic<Packet8bf> { enum { value = true }; };
+#ifdef EIGEN_VECTORIZE_AVX2
+template<> struct is_arithmetic<Packet4l> { enum { value = true }; };
+// Note that `Packet4ul` uses the underlying type `__m256i`, which is
+// interpreted as a vector of _signed_ `int32`s, which breaks some arithmetic
+// operations used in `GenericPacketMath.h`.
+template<> struct is_arithmetic<Packet4ul> { enum { value = false }; };
+#endif
+
+// Use the packet_traits defined in AVX512/PacketMath.h instead if we're going
+// to leverage AVX512 instructions.
+#ifndef EIGEN_VECTORIZE_AVX512
+template<> struct packet_traits<float>  : default_packet_traits
+{
+  typedef Packet8f type;
+  typedef Packet4f half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasCmp  = 1,
+    HasDiv = 1,
+    HasReciprocal = EIGEN_FAST_MATH,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasACos = 1,
+    HasASin = 1,
+    HasATan = 1,
+    HasATanh = 1,
+    HasLog = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasExp = 1,
+    HasNdtri = 1,
+    HasBessel = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasBlend = 1,
+    HasRound = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasRint = 1
+  };
+};
+template<> struct packet_traits<double> : default_packet_traits
+{
+  typedef Packet4d type;
+  typedef Packet2d half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size=4,
+
+    HasCmp  = 1,
+    HasDiv  = 1,
+    HasLog  = 1,
+    HasExp  = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasATan = 1,
+    HasBlend = 1,
+    HasRound = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasRint = 1
+  };
+};
+
+template <>
+struct packet_traits<Eigen::half> : default_packet_traits {
+  typedef Packet8h type;
+  // There is no half-size packet for Packet8h.
+  typedef Packet8h half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasCmp    = 1,
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasDiv    = 1,
+    HasSin    = EIGEN_FAST_MATH,
+    HasCos    = EIGEN_FAST_MATH,
+    HasNegate = 1,
+    HasAbs    = 1,
+    HasAbs2   = 0,
+    HasMin    = 1,
+    HasMax    = 1,
+    HasConj   = 1,
+    HasSetLinear = 0,
+    HasLog    = 1,
+    HasLog1p  = 1,
+    HasExpm1  = 1,
+    HasExp    = 1,
+    HasSqrt   = 1,
+    HasRsqrt  = 1,
+    HasTanh   = EIGEN_FAST_MATH,
+    HasErf    = EIGEN_FAST_MATH,
+    HasBlend  = 0,
+    HasRound  = 1,
+    HasFloor  = 1,
+    HasCeil   = 1,
+    HasRint   = 1,
+    HasBessel = 1,
+    HasNdtri  = 1
+  };
+};
+
+template <>
+struct packet_traits<bfloat16> : default_packet_traits {
+  typedef Packet8bf type;
+  // There is no half-size packet for current Packet8bf.
+  // TODO: support as SSE path.
+  typedef Packet8bf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasNegate = 1,
+    HasAbs    = 1,
+    HasAbs2   = 0,
+    HasMin    = 1,
+    HasMax    = 1,
+    HasConj   = 1,
+    HasSetLinear = 0,
+    HasLog = 1,
+    HasLog1p  = 1,
+    HasExpm1  = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasBlend = 0,
+    HasRound = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasRint = 1,
+    HasBessel = 1,
+    HasNdtri  = 1
+  };
+};
+
+template<> struct packet_traits<int> : default_packet_traits
+{
+  typedef Packet8i type;
+  typedef Packet4i half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    HasCmp = 1,
+    HasDiv = 1,
+    size=8
+  };
+};
+template<> struct packet_traits<uint32_t> : default_packet_traits
+{
+  typedef Packet8ui type;
+  typedef Packet4ui half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasDiv = 0,
+    HasNegate = 0,
+    HasSqrt = 0,
+
+    HasCmp = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasShift = 1
+  };
+};
+
+#ifdef EIGEN_VECTORIZE_AVX2
+template<> struct packet_traits<int64_t> : default_packet_traits
+{
+  typedef Packet4l type;
+  // There is no half-size packet for current Packet4l.
+  // TODO: support as SSE path.
+  typedef Packet4l half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    HasCmp = 1,
+    size=4
+  };
+};
+template<> struct packet_traits<uint64_t> : default_packet_traits
+{
+  typedef Packet4ul type;
+  // There is no half-size packet for current Packet4ul.
+  // TODO: support as SSE path.
+  typedef Packet4ul half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    // HasMin = 0,
+    // HasMax = 0,
+    HasDiv = 0,
+    HasBlend = 0,
+    HasTranspose = 0,
+    HasNegate = 0,
+    HasSqrt = 0,
+    HasCmp = 1,
+    HasShift = 1
+  };
+};
+#endif
+
+#endif
+
+template<> struct scalar_div_cost<float,true> { enum { value = 14 }; };
+template<> struct scalar_div_cost<double,true> { enum { value = 16 }; };
+
+template<> struct unpacket_traits<Packet8f> {
+  typedef float     type;
+  typedef Packet4f  half;
+  typedef Packet8i  integer_packet;
+  typedef uint8_t   mask_t;
+  enum {size=8, alignment=Aligned32, vectorizable=true, masked_load_available=true, masked_store_available=true
+#ifdef EIGEN_VECTORIZE_AVX512
+    , masked_fpops_available=true
+#endif
+  };
+};
+template<> struct unpacket_traits<Packet4d> {
+  typedef double type;
+  typedef Packet2d half;
+  enum {size=4, alignment=Aligned32, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+template<> struct unpacket_traits<Packet8i> {
+  typedef int    type;
+  typedef Packet4i half;
+  enum {size=8, alignment=Aligned32, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+template<> struct unpacket_traits<Packet8ui> {
+  typedef uint32_t type;
+  typedef Packet4ui half;
+  enum {size = 8, alignment = Aligned32, vectorizable = true, masked_load_available = false, masked_store_available = false};
+};
+#ifdef EIGEN_VECTORIZE_AVX2
+template<> struct unpacket_traits<Packet4l> {
+  typedef int64_t    type;
+  typedef Packet4l half;
+  enum {size=4, alignment=Aligned32, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+template<> struct unpacket_traits<Packet4ul> {
+  typedef uint64_t type;
+  typedef Packet4ul half;
+  enum {size = 4, alignment = Aligned32, vectorizable = true, masked_load_available = false, masked_store_available = false};
+};
+#endif
+template<> struct unpacket_traits<Packet8bf> {
+  typedef bfloat16 type;
+  typedef Packet8bf half;
+  enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+
+// Helper function for bit packing snippet of low precision comparison.
+// It packs the flags from 16x16 to 8x16.
+EIGEN_STRONG_INLINE __m128i Pack16To8(Packet8f rf) {
+  return _mm_packs_epi32(_mm256_extractf128_si256(_mm256_castps_si256(rf), 0),
+                         _mm256_extractf128_si256(_mm256_castps_si256(rf), 1));
+}
+
+#ifdef EIGEN_VECTORIZE_AVX2
+template <>
+EIGEN_STRONG_INLINE Packet4l pset1<Packet4l>(const int64_t& from) {
+  return _mm256_set1_epi64x(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pset1<Packet4ul>(const uint64_t& from) {
+  return _mm256_set1_epi64x(numext::bit_cast<uint64_t>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pzero(const Packet4l& /*a*/) {
+  return _mm256_setzero_si256();
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pzero(const Packet4ul& /*a*/) {
+  return _mm256_setzero_si256();
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l peven_mask(const Packet4l& /*a*/) {
+  return _mm256_set_epi64x(0ll, -1ll, 0ll, -1ll);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul peven_mask(const Packet4ul& /*a*/) {
+  return _mm256_set_epi64x(0ll, -1ll, 0ll, -1ll);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pload1<Packet4l>(const int64_t* from) {
+  return _mm256_set1_epi64x(*from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pload1<Packet4ul>(const uint64_t* from) {
+  return _mm256_set1_epi64x(*from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l padd<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  return _mm256_add_epi64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul padd<Packet4ul>(const Packet4ul& a, const Packet4ul& b) {
+  return _mm256_add_epi64(a, b);
+}
+template<>
+EIGEN_STRONG_INLINE Packet4l plset<Packet4l>(const int64_t& a) {
+  return padd(pset1<Packet4l>(a), Packet4l(_mm256_set_epi64x(3ll, 2ll, 1ll, 0ll)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul plset<Packet4ul>(const uint64_t& a) {
+  return padd(pset1<Packet4ul>(a), Packet4ul(_mm256_set_epi64x(3ll, 2ll, 1ll, 0ll)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l psub<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  return _mm256_sub_epi64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul psub<Packet4ul>(const Packet4ul& a, const Packet4ul& b) {
+  return _mm256_sub_epi64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pnegate(const Packet4l& a) {
+  return psub(pzero(a), a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pconj(const Packet4l& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pcmp_le(const Packet4l& a, const Packet4l& b) {
+  return _mm256_xor_si256(_mm256_cmpgt_epi64(a, b), _mm256_set1_epi32(-1));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pcmp_le(const Packet4ul& a, const Packet4ul& b) {
+  return (Packet4ul)pcmp_le((Packet4l)psub(a, pset1<Packet4ul>(0x8000000000000000UL)),
+                            (Packet4l)psub(b, pset1<Packet4ul>(0x8000000000000000UL)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pcmp_lt(const Packet4l& a, const Packet4l& b) {
+  return _mm256_cmpgt_epi64(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pcmp_lt(const Packet4ul& a, const Packet4ul& b) {
+  return (Packet4ul)pcmp_lt((Packet4l)psub(a, pset1<Packet4ul>(0x8000000000000000UL)),
+                            (Packet4l)psub(b, pset1<Packet4ul>(0x8000000000000000UL)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pcmp_eq(const Packet4l& a, const Packet4l& b) {
+  return _mm256_cmpeq_epi64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pcmp_eq(const Packet4ul& a, const Packet4ul& b) {
+  return _mm256_cmpeq_epi64(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l ptrue<Packet4l>(const Packet4l& a) {
+  return _mm256_cmpeq_epi64(a, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul ptrue<Packet4ul>(const Packet4ul& a) {
+  return _mm256_cmpeq_epi64(a, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pand<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  return _mm256_and_si256(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l por<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  return _mm256_or_si256(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pxor<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  return _mm256_xor_si256(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pxor<Packet4ul>(const Packet4ul& a, const Packet4ul& b) {
+  return _mm256_xor_si256(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pandnot<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  return _mm256_andnot_si256(b, a);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4l plogical_shift_right(Packet4l a) {
+  return _mm256_srli_epi64(a, N);
+}
+template <int N>
+EIGEN_STRONG_INLINE Packet4l plogical_shift_left(Packet4l a) {
+  return _mm256_slli_epi64(a, N);
+}
+#ifdef EIGEN_VECTORIZE_AVX512FP16
+template <int N>
+EIGEN_STRONG_INLINE Packet4l parithmetic_shift_right(Packet4l a) { return _mm256_srai_epi64(a, N); }
+#else
+template <int N>
+EIGEN_STRONG_INLINE std::enable_if_t< (N == 0), Packet4l> parithmetic_shift_right(Packet4l a) {
+  return a;
+}
+template <int N>
+EIGEN_STRONG_INLINE std::enable_if_t< (N > 0) && (N < 32), Packet4l> parithmetic_shift_right(Packet4l a) {
+  __m256i hi_word = _mm256_srai_epi32(a, N);
+  __m256i lo_word = _mm256_srli_epi64(a, N);
+  return _mm256_blend_epi32(hi_word, lo_word, 0b01010101);
+}
+template <int N>
+EIGEN_STRONG_INLINE std::enable_if_t< (N >= 32) && (N < 63), Packet4l> parithmetic_shift_right(Packet4l a) {
+  __m256i hi_word = _mm256_srai_epi32(a, 31);
+  __m256i lo_word = _mm256_shuffle_epi32(_mm256_srai_epi32(a, N - 32), (shuffle_mask<1, 1, 3, 3>::mask));
+  return _mm256_blend_epi32(hi_word, lo_word, 0b01010101);
+}
+template <int N>
+EIGEN_STRONG_INLINE std::enable_if_t< (N == 63), Packet4l> parithmetic_shift_right(Packet4l a) {
+  return _mm256_shuffle_epi32(_mm256_srai_epi32(a, 31), (shuffle_mask<1, 1, 3, 3>::mask));
+}
+template <int N>
+EIGEN_STRONG_INLINE std::enable_if_t< (N < 0) || (N > 63), Packet4l> parithmetic_shift_right(Packet4l a) {
+  return parithmetic_shift_right<int(N&63)>(a);
+}
+#endif
+template <>
+EIGEN_STRONG_INLINE Packet4l pload<Packet4l>(const int64_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pload<Packet4ul>(const uint64_t* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l ploadu<Packet4l>(const int64_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul ploadu<Packet4ul>(const uint64_t* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
+}
+// Loads 2 int64_ts from memory a returns the packet {a0, a0, a1, a1}
+template <>
+EIGEN_STRONG_INLINE Packet4l ploaddup<Packet4l>(const int64_t* from) {
+  const Packet4l a = _mm256_castsi128_si256(_mm_loadu_si128(reinterpret_cast<const __m128i*>(from)));
+  return _mm256_permutevar8x32_epi32(a, _mm256_setr_epi32(0, 1, 0, 1, 2, 3, 2, 3));
+}
+// Loads 2 uint64_ts from memory a returns the packet {a0, a0, a1, a1}
+template <>
+EIGEN_STRONG_INLINE Packet4ul ploaddup<Packet4ul>(const uint64_t* from) {
+  const Packet4ul a = _mm256_castsi128_si256(_mm_loadu_si128(reinterpret_cast<const __m128i*>(from)));
+  return _mm256_permutevar8x32_epi32(a, _mm256_setr_epi32(0, 1, 0, 1, 2, 3, 2, 3));
+}
+template<>
+EIGEN_STRONG_INLINE void pstore<int64_t>(int64_t* to, const Packet4l& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm256_store_si256(reinterpret_cast<__m256i*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<uint64_t>(uint64_t* to, const Packet4ul& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm256_store_si256(reinterpret_cast<__m256i*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int64_t>(int64_t* to, const Packet4l& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<uint64_t>(uint64_t* to, const Packet4ul& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from);
+}
+template <>
+EIGEN_DEVICE_FUNC inline Packet4l pgather<int64_t, Packet4l>(const int64_t* from, Index stride) {
+  return _mm256_set_epi64x(from[3 * stride], from[2 * stride], from[1 * stride], from[0 * stride]);
+}
+template <>
+EIGEN_DEVICE_FUNC inline Packet4ul pgather<uint64_t, Packet4ul>(const uint64_t* from, Index stride) {
+  return _mm256_set_epi64x(from[3 * stride], from[2 * stride], from[1 * stride], from[0 * stride]);
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<int64_t, Packet4l>(int64_t* to, const Packet4l& from, Index stride) {
+  __m128i low = _mm256_extractf128_si256(from, 0);
+  to[stride * 0] = _mm_extract_epi64(low, 0);
+  to[stride * 1] = _mm_extract_epi64(low, 1);
+
+  __m128i high = _mm256_extractf128_si256(from, 1);
+  to[stride * 2] = _mm_extract_epi64(high, 0);
+  to[stride * 3] = _mm_extract_epi64(high, 1);
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<uint64_t, Packet4ul>(uint64_t* to, const Packet4ul& from, Index stride) {
+  __m128i low = _mm256_extractf128_si256(from, 0);
+  to[stride * 0] = _mm_extract_epi64(low, 0);
+  to[stride * 1] = _mm_extract_epi64(low, 1);
+
+  __m128i high = _mm256_extractf128_si256(from, 1);
+  to[stride * 2] = _mm_extract_epi64(high, 0);
+  to[stride * 3] = _mm_extract_epi64(high, 1);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet4l>(int64_t* to, const int64_t& a) {
+  Packet4l pa = pset1<Packet4l>(a);
+  pstore(to, pa);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet4ul>(uint64_t* to, const uint64_t& a) {
+  Packet4ul pa = pset1<Packet4ul>(a);
+  pstore(to, pa);
+}
+template<>
+EIGEN_STRONG_INLINE int64_t pfirst<Packet4l>(const Packet4l& a) {
+  return _mm_cvtsi128_si64(_mm256_castsi256_si128(a));
+}
+template <>
+EIGEN_STRONG_INLINE uint64_t pfirst<Packet4ul>(const Packet4ul& a) {
+  return _mm_cvtsi128_si64(_mm256_castsi256_si128(a));
+}
+template <>
+EIGEN_STRONG_INLINE int64_t predux<Packet4l>(const Packet4l& a) {
+  __m128i r = _mm_add_epi64(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1));
+  return _mm_extract_epi64(r, 0) + _mm_extract_epi64(r, 1);
+}
+template <>
+EIGEN_STRONG_INLINE uint64_t predux<Packet4ul>(const Packet4ul& a) {
+  __m128i r = _mm_add_epi64(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1));
+  return numext::bit_cast<uint64_t>(_mm_extract_epi64(r, 0) + _mm_extract_epi64(r, 1));
+}
+#define MM256_SHUFFLE_EPI64(A, B, M) _mm256_shuffle_pd(_mm256_castsi256_pd(A), _mm256_castsi256_pd(B), M)
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4l, 4>& kernel) {
+  __m256d T0 = MM256_SHUFFLE_EPI64(kernel.packet[0], kernel.packet[1], 15);
+  __m256d T1 = MM256_SHUFFLE_EPI64(kernel.packet[0], kernel.packet[1], 0);
+  __m256d T2 = MM256_SHUFFLE_EPI64(kernel.packet[2], kernel.packet[3], 15);
+  __m256d T3 = MM256_SHUFFLE_EPI64(kernel.packet[2], kernel.packet[3], 0);
+
+  kernel.packet[1] = _mm256_castpd_si256(_mm256_permute2f128_pd(T0, T2, 32));
+  kernel.packet[3] = _mm256_castpd_si256(_mm256_permute2f128_pd(T0, T2, 49));
+  kernel.packet[0] = _mm256_castpd_si256(_mm256_permute2f128_pd(T1, T3, 32));
+  kernel.packet[2] = _mm256_castpd_si256(_mm256_permute2f128_pd(T1, T3, 49));
+}
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4ul, 4>& kernel) {
+  ptranspose((PacketBlock<Packet4l, 4>&)kernel);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pmin<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  __m256i cmp = _mm256_cmpgt_epi64(a, b);
+  __m256i a_min = _mm256_andnot_si256(cmp, a);
+  __m256i b_min = _mm256_and_si256(cmp, b);
+  return Packet4l(_mm256_or_si256(a_min, b_min));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pmin<Packet4ul>(const Packet4ul& a, const Packet4ul& b) {
+  return padd((Packet4ul)pmin((Packet4l)psub(a, pset1<Packet4ul>(0x8000000000000000UL)),
+                              (Packet4l)psub(b, pset1<Packet4ul>(0x8000000000000000UL))),
+              pset1<Packet4ul>(0x8000000000000000UL));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pmax<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  __m256i cmp = _mm256_cmpgt_epi64(a, b);
+  __m256i a_min = _mm256_and_si256(cmp, a);
+  __m256i b_min = _mm256_andnot_si256(cmp, b);
+  return Packet4l(_mm256_or_si256(a_min, b_min));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pmax<Packet4ul>(const Packet4ul& a, const Packet4ul& b) {
+  return padd((Packet4ul)pmax((Packet4l)psub(a, pset1<Packet4ul>(0x8000000000000000UL)),
+                              (Packet4l)psub(b, pset1<Packet4ul>(0x8000000000000000UL))),
+              pset1<Packet4ul>(0x8000000000000000UL));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pabs<Packet4l>(const Packet4l& a) {
+  Packet4l pz = pzero<Packet4l>(a);
+  Packet4l cmp = _mm256_cmpgt_epi64(a, pz);
+  return psub(cmp, pxor(a, cmp));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pabs<Packet4ul>(const Packet4ul& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet4l pmul<Packet4l>(const Packet4l& a, const Packet4l& b) {
+  // 64-bit mul requires avx512, so do this with 32-bit multiplication
+  __m256i upper32_a = _mm256_srli_epi64(a, 32);
+  __m256i upper32_b = _mm256_srli_epi64(b, 32);
+
+  // upper * lower
+  __m256i mul1 = _mm256_mul_epu32(upper32_a, b);
+  __m256i mul2 = _mm256_mul_epu32(upper32_b, a);
+  // Gives us both upper*upper and lower*lower
+  __m256i mul3 = _mm256_mul_epu32(a, b);
+
+  __m256i high = _mm256_slli_epi64(_mm256_add_epi64(mul1, mul2), 32);
+  return _mm256_add_epi64(high, mul3);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ul pmul<Packet4ul>(const Packet4ul& a, const Packet4ul& b) {
+  return (Packet4ul)pmul<Packet4l>((Packet4l)a, (Packet4l)b);
+}
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet8f pset1<Packet8f>(const float&  from) { return _mm256_set1_ps(from); }
+template<> EIGEN_STRONG_INLINE Packet4d pset1<Packet4d>(const double& from) { return _mm256_set1_pd(from); }
+template<> EIGEN_STRONG_INLINE Packet8i pset1<Packet8i>(const int&    from) { return _mm256_set1_epi32(from); }
+template<> EIGEN_STRONG_INLINE Packet8ui pset1<Packet8ui>(const uint32_t& from) { return _mm256_set1_epi32(from); }
+
+template<> EIGEN_STRONG_INLINE Packet8f pset1frombits<Packet8f>(unsigned int from) { return _mm256_castsi256_ps(pset1<Packet8i>(from)); }
+template<> EIGEN_STRONG_INLINE Packet4d pset1frombits<Packet4d>(uint64_t from) { return _mm256_castsi256_pd(_mm256_set1_epi64x(from)); }
+
+template<> EIGEN_STRONG_INLINE Packet8f pzero(const Packet8f& /*a*/) { return _mm256_setzero_ps(); }
+template<> EIGEN_STRONG_INLINE Packet4d pzero(const Packet4d& /*a*/) { return _mm256_setzero_pd(); }
+template<> EIGEN_STRONG_INLINE Packet8i pzero(const Packet8i& /*a*/) { return _mm256_setzero_si256(); }
+template<> EIGEN_STRONG_INLINE Packet8ui pzero(const Packet8ui& /*a*/) { return _mm256_setzero_si256(); }
+
+
+template<> EIGEN_STRONG_INLINE Packet8f peven_mask(const Packet8f& /*a*/) { return _mm256_castsi256_ps(_mm256_set_epi32(0, -1, 0, -1, 0, -1, 0, -1)); }
+template<> EIGEN_STRONG_INLINE Packet8i peven_mask(const Packet8i& /*a*/) { return _mm256_set_epi32(0, -1, 0, -1, 0, -1, 0, -1); }
+template<> EIGEN_STRONG_INLINE Packet8ui peven_mask(const Packet8ui& /*a*/) { return _mm256_set_epi32(0, -1, 0, -1, 0, -1, 0, -1); }
+template<> EIGEN_STRONG_INLINE Packet4d peven_mask(const Packet4d& /*a*/) { return _mm256_castsi256_pd(_mm256_set_epi32(0, 0, -1, -1, 0, 0, -1, -1)); }
+
+template<> EIGEN_STRONG_INLINE Packet8f pload1<Packet8f>(const float*  from) { return _mm256_broadcast_ss(from); }
+template<> EIGEN_STRONG_INLINE Packet4d pload1<Packet4d>(const double* from) { return _mm256_broadcast_sd(from); }
+
+template<> EIGEN_STRONG_INLINE Packet8f padd<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_add_ps(a,b); }
+#ifdef EIGEN_VECTORIZE_AVX512
+template <>
+EIGEN_STRONG_INLINE Packet8f padd<Packet8f>(const Packet8f& a, const Packet8f& b, uint8_t umask) {
+  __mmask16 mask = static_cast<__mmask16>(umask & 0x00FF);
+  return _mm512_castps512_ps256(_mm512_maskz_add_ps(
+                                    mask,
+                                    _mm512_castps256_ps512(a),
+                                    _mm512_castps256_ps512(b)));
+}
+#endif
+template<> EIGEN_STRONG_INLINE Packet4d padd<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_add_pd(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8i padd<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_add_epi32(a,b);
+#else
+  __m128i lo = _mm_add_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_add_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8ui padd<Packet8ui>(const Packet8ui& a, const Packet8ui& b)
+{
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_add_epi32(a, b);
+#else
+  __m128i lo = _mm_add_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_add_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f plset<Packet8f>(const float& a) { return padd(pset1<Packet8f>(a), _mm256_set_ps(7.0,6.0,5.0,4.0,3.0,2.0,1.0,0.0)); }
+template<> EIGEN_STRONG_INLINE Packet4d plset<Packet4d>(const double& a) { return padd(pset1<Packet4d>(a), _mm256_set_pd(3.0,2.0,1.0,0.0)); }
+template<> EIGEN_STRONG_INLINE Packet8i plset<Packet8i>(const int& a) { return padd(pset1<Packet8i>(a), (Packet8i)_mm256_set_epi32(7,6,5,4,3,2,1,0)); }
+template<> EIGEN_STRONG_INLINE Packet8ui plset<Packet8ui>(const uint32_t& a) { return padd(pset1<Packet8ui>(a), (Packet8ui)_mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0)); }
+
+template<> EIGEN_STRONG_INLINE Packet8f psub<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_sub_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4d psub<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_sub_pd(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8i psub<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_sub_epi32(a,b);
+#else
+  __m128i lo = _mm_sub_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_sub_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8ui psub<Packet8ui>(const Packet8ui& a, const Packet8ui& b)
+{
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_sub_epi32(a, b);
+#else
+  __m128i lo = _mm_sub_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_sub_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pnegate(const Packet8f& a)
+{
+  const Packet8f mask = _mm256_castsi256_ps(_mm256_set1_epi32(0x80000000));
+  return _mm256_xor_ps(a, mask);
+}
+template<> EIGEN_STRONG_INLINE Packet4d pnegate(const Packet4d& a)
+{
+  const Packet4d mask = _mm256_castsi256_pd(_mm256_set1_epi64x(0x8000000000000000ULL));
+  return _mm256_xor_pd(a, mask);
+}
+template<> EIGEN_STRONG_INLINE Packet8i pnegate(const Packet8i& a)
+{
+  return psub(pzero(a), a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pconj(const Packet8f& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet4d pconj(const Packet4d& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet8i pconj(const Packet8i& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE Packet8f pmul<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_mul_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4d pmul<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_mul_pd(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8i pmul<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_mullo_epi32(a,b);
+#else
+  const __m128i lo = _mm_mullo_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  const __m128i hi = _mm_mullo_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8ui pmul<Packet8ui>(const Packet8ui& a, const Packet8ui& b)
+{
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_mullo_epi32(a, b);
+#else
+  const __m128i lo = _mm_mullo_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  const __m128i hi = _mm_mullo_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pdiv<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_div_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4d pdiv<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_div_pd(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet8i pdiv<Packet8i>(const Packet8i& a, const Packet8i& b)
+{
+#ifdef EIGEN_VECTORIZE_AVX512
+  return _mm512_cvttpd_epi32(_mm512_div_pd(_mm512_cvtepi32_pd(a), _mm512_cvtepi32_pd(b)));
+#else
+  Packet4i lo = pdiv<Packet4i>(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  Packet4i hi = pdiv<Packet4i>(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), hi, 1);
+#endif
+}
+
+#ifdef EIGEN_VECTORIZE_FMA
+template <>
+EIGEN_STRONG_INLINE Packet8f pmadd(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
+  return _mm256_fmadd_ps(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d pmadd(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
+  return _mm256_fmadd_pd(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pmsub(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
+  return _mm256_fmsub_ps(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pmsub(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
+  return _mm256_fmsub_pd(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pnmadd(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
+  return _mm256_fnmadd_ps(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pnmadd(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
+  return _mm256_fnmadd_pd(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pnmsub(const Packet8f& a, const Packet8f& b, const Packet8f& c) {
+  return _mm256_fnmsub_ps(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4d pnmsub(const Packet4d& a, const Packet4d& b, const Packet4d& c) {
+  return _mm256_fnmsub_pd(a, b, c);
+}
+
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet8f pcmp_le(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a,b,_CMP_LE_OQ); }
+template<> EIGEN_STRONG_INLINE Packet8f pcmp_lt(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a,b,_CMP_LT_OQ); }
+template<> EIGEN_STRONG_INLINE Packet8f pcmp_lt_or_nan(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a, b, _CMP_NGE_UQ); }
+template<> EIGEN_STRONG_INLINE Packet8f pcmp_eq(const Packet8f& a, const Packet8f& b) { return _mm256_cmp_ps(a,b,_CMP_EQ_OQ); }
+template<> EIGEN_STRONG_INLINE Packet8f pisnan(const Packet8f& a) { return _mm256_cmp_ps(a,a,_CMP_UNORD_Q); }
+
+template<> EIGEN_STRONG_INLINE Packet4d pcmp_le(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a,b,_CMP_LE_OQ); }
+template<> EIGEN_STRONG_INLINE Packet4d pcmp_lt(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a,b,_CMP_LT_OQ); }
+template<> EIGEN_STRONG_INLINE Packet4d pcmp_lt_or_nan(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a, b, _CMP_NGE_UQ); }
+template<> EIGEN_STRONG_INLINE Packet4d pcmp_eq(const Packet4d& a, const Packet4d& b) { return _mm256_cmp_pd(a,b,_CMP_EQ_OQ); }
+
+template<> EIGEN_STRONG_INLINE Packet8i pcmp_le(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_xor_si256(_mm256_cmpgt_epi32(a,b), _mm256_set1_epi32(-1));
+#else
+  __m128i lo = _mm_cmpgt_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  lo = _mm_xor_si128(lo, _mm_set1_epi32(-1));
+  __m128i hi = _mm_cmpgt_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  hi = _mm_xor_si128(hi, _mm_set1_epi32(-1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8i pcmp_lt(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_cmpgt_epi32(b,a);
+#else
+  __m128i lo = _mm_cmpgt_epi32(_mm256_extractf128_si256(b, 0), _mm256_extractf128_si256(a, 0));
+  __m128i hi = _mm_cmpgt_epi32(_mm256_extractf128_si256(b, 1), _mm256_extractf128_si256(a, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8i pcmp_eq(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_cmpeq_epi32(a,b);
+#else
+  __m128i lo = _mm_cmpeq_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_cmpeq_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8ui pcmp_eq(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_cmpeq_epi32(a, b);
+#else
+  __m128i lo = _mm_cmpeq_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_cmpeq_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pmin<Packet8f>(const Packet8f& a, const Packet8f& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6,3,0)
+  // There appears to be a bug in GCC, by which the optimizer may flip
+  // the argument order in calls to _mm_min_ps/_mm_max_ps, so we have to
+  // resort to inline ASM here. This is supposed to be fixed in gcc6.3,
+  // see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
+  Packet8f res;
+  asm("vminps %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
+  return res;
+#else
+  // Arguments are swapped to match NaN propagation behavior of std::min.
+  return _mm256_min_ps(b,a);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet4d pmin<Packet4d>(const Packet4d& a, const Packet4d& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6,3,0)
+  // See pmin above
+  Packet4d res;
+  asm("vminpd %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
+  return res;
+#else
+  // Arguments are swapped to match NaN propagation behavior of std::min.
+  return _mm256_min_pd(b,a);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8i pmin<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_min_epi32(a, b);
+#else
+  __m128i lo = _mm_min_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_min_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8ui pmin<Packet8ui>(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_min_epu32(a, b);
+#else
+  __m128i lo = _mm_min_epu32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_min_epu32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pmax<Packet8f>(const Packet8f& a, const Packet8f& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6,3,0)
+  // See pmin above
+  Packet8f res;
+  asm("vmaxps %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
+  return res;
+#else
+  // Arguments are swapped to match NaN propagation behavior of std::max.
+  return _mm256_max_ps(b,a);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet4d pmax<Packet4d>(const Packet4d& a, const Packet4d& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6,3,0)
+  // See pmin above
+  Packet4d res;
+  asm("vmaxpd %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
+  return res;
+#else
+  // Arguments are swapped to match NaN propagation behavior of std::max.
+  return _mm256_max_pd(b,a);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8i pmax<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_max_epi32(a, b);
+#else
+  __m128i lo = _mm_max_epi32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_max_epi32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8ui pmax<Packet8ui>(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_max_epu32(a, b);
+#else
+  __m128i lo = _mm_max_epu32(_mm256_extractf128_si256(a, 0), _mm256_extractf128_si256(b, 0));
+  __m128i hi = _mm_max_epu32(_mm256_extractf128_si256(a, 1), _mm256_extractf128_si256(b, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+#ifdef EIGEN_VECTORIZE_AVX2
+template<> EIGEN_STRONG_INLINE Packet8i psign(const Packet8i& a) {
+  return _mm256_sign_epi32(_mm256_set1_epi32(1), a);
+}
+#endif
+
+// Add specializations for min/max with prescribed NaN progation.
+template<>
+EIGEN_STRONG_INLINE Packet8f pmin<PropagateNumbers, Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return pminmax_propagate_numbers(a, b, pmin<Packet8f>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet4d pmin<PropagateNumbers, Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return pminmax_propagate_numbers(a, b, pmin<Packet4d>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet8f pmax<PropagateNumbers, Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return pminmax_propagate_numbers(a, b, pmax<Packet8f>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet4d pmax<PropagateNumbers, Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return pminmax_propagate_numbers(a, b, pmax<Packet4d>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet8f pmin<PropagateNaN, Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return pminmax_propagate_nan(a, b, pmin<Packet8f>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet4d pmin<PropagateNaN, Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return pminmax_propagate_nan(a, b, pmin<Packet4d>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet8f pmax<PropagateNaN, Packet8f>(const Packet8f& a, const Packet8f& b) {
+  return pminmax_propagate_nan(a, b, pmax<Packet8f>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet4d pmax<PropagateNaN, Packet4d>(const Packet4d& a, const Packet4d& b) {
+  return pminmax_propagate_nan(a, b, pmax<Packet4d>);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f print<Packet8f>(const Packet8f& a) { return _mm256_round_ps(a, _MM_FROUND_CUR_DIRECTION); }
+template<> EIGEN_STRONG_INLINE Packet4d print<Packet4d>(const Packet4d& a) { return _mm256_round_pd(a, _MM_FROUND_CUR_DIRECTION); }
+
+template<> EIGEN_STRONG_INLINE Packet8f pceil<Packet8f>(const Packet8f& a) { return _mm256_ceil_ps(a); }
+template<> EIGEN_STRONG_INLINE Packet4d pceil<Packet4d>(const Packet4d& a) { return _mm256_ceil_pd(a); }
+
+template<> EIGEN_STRONG_INLINE Packet8f pfloor<Packet8f>(const Packet8f& a) { return _mm256_floor_ps(a); }
+template<> EIGEN_STRONG_INLINE Packet4d pfloor<Packet4d>(const Packet4d& a) { return _mm256_floor_pd(a); }
+
+
+template<> EIGEN_STRONG_INLINE Packet8i ptrue<Packet8i>(const Packet8i& a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  // vpcmpeqd has lower latency than the more general vcmpps
+  return _mm256_cmpeq_epi32(a,a);
+#else
+  const __m256 b = _mm256_castsi256_ps(a);
+  return _mm256_castps_si256(_mm256_cmp_ps(b,b,_CMP_TRUE_UQ));
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f ptrue<Packet8f>(const Packet8f& a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  // vpcmpeqd has lower latency than the more general vcmpps
+  const __m256i b = _mm256_castps_si256(a);
+  return _mm256_castsi256_ps(_mm256_cmpeq_epi32(b,b));
+#else
+  return _mm256_cmp_ps(a,a,_CMP_TRUE_UQ);
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet4d ptrue<Packet4d>(const Packet4d& a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  // vpcmpeqq has lower latency than the more general vcmppd
+  const __m256i b = _mm256_castpd_si256(a);
+  return _mm256_castsi256_pd(_mm256_cmpeq_epi64(b,b));
+#else
+  return _mm256_cmp_pd(a,a,_CMP_TRUE_UQ);
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pand<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_and_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4d pand<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_and_pd(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8i pand<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_and_si256(a,b);
+#else
+  return _mm256_castps_si256(_mm256_and_ps(_mm256_castsi256_ps(a),_mm256_castsi256_ps(b)));
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8ui pand<Packet8ui>(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_and_si256(a,b);
+#else
+  return _mm256_castps_si256(_mm256_and_ps(_mm256_castsi256_ps(a),_mm256_castsi256_ps(b)));
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f por<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_or_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4d por<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_or_pd(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8i por<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_or_si256(a,b);
+#else
+  return _mm256_castps_si256(_mm256_or_ps(_mm256_castsi256_ps(a),_mm256_castsi256_ps(b)));
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8ui por<Packet8ui>(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_or_si256(a,b);
+#else
+  return _mm256_castps_si256(_mm256_or_ps(_mm256_castsi256_ps(a),_mm256_castsi256_ps(b)));
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pxor<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_xor_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4d pxor<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_xor_pd(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8i pxor<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_xor_si256(a,b);
+#else
+  return _mm256_castps_si256(_mm256_xor_ps(_mm256_castsi256_ps(a),_mm256_castsi256_ps(b)));
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8ui pxor<Packet8ui>(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_xor_si256(a, b);
+#else
+  return _mm256_castps_si256(_mm256_xor_ps(_mm256_castsi256_ps(a), _mm256_castsi256_ps(b)));
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pandnot<Packet8f>(const Packet8f& a, const Packet8f& b) { return _mm256_andnot_ps(b,a); }
+template<> EIGEN_STRONG_INLINE Packet4d pandnot<Packet4d>(const Packet4d& a, const Packet4d& b) { return _mm256_andnot_pd(b,a); }
+template<> EIGEN_STRONG_INLINE Packet8i pandnot<Packet8i>(const Packet8i& a, const Packet8i& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_andnot_si256(b,a);
+#else
+  return _mm256_castps_si256(_mm256_andnot_ps(_mm256_castsi256_ps(b),_mm256_castsi256_ps(a)));
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8ui pandnot<Packet8ui>(const Packet8ui& a, const Packet8ui& b) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_andnot_si256(b,a);
+#else
+  return _mm256_castps_si256(_mm256_andnot_ps(_mm256_castsi256_ps(b),_mm256_castsi256_ps(a)));
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet8ui pcmp_lt(const Packet8ui& a, const Packet8ui& b) {
+  return pxor(pcmp_eq(a, pmax(a, b)), ptrue(a));
+}
+template<> EIGEN_STRONG_INLINE Packet8ui pcmp_le(const Packet8ui& a, const Packet8ui& b) {
+  return pcmp_eq(a, pmin(a, b));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pround<Packet8f>(const Packet8f& a)
+{
+  const Packet8f mask = pset1frombits<Packet8f>(static_cast<numext::uint32_t>(0x80000000u));
+  const Packet8f prev0dot5 = pset1frombits<Packet8f>(static_cast<numext::uint32_t>(0x3EFFFFFFu));
+  return _mm256_round_ps(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+template<> EIGEN_STRONG_INLINE Packet4d pround<Packet4d>(const Packet4d& a)
+{
+  const Packet4d mask = pset1frombits<Packet4d>(static_cast<numext::uint64_t>(0x8000000000000000ull));
+  const Packet4d prev0dot5 = pset1frombits<Packet4d>(static_cast<numext::uint64_t>(0x3FDFFFFFFFFFFFFFull));
+  return _mm256_round_pd(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pselect<Packet8f>(const Packet8f& mask, const Packet8f& a, const Packet8f& b)
+{ return _mm256_blendv_ps(b,a,mask); }
+template<> EIGEN_STRONG_INLINE Packet8i pselect<Packet8i>(const Packet8i& mask, const Packet8i& a, const Packet8i& b)
+{ return _mm256_castps_si256(_mm256_blendv_ps(_mm256_castsi256_ps(b), _mm256_castsi256_ps(a), _mm256_castsi256_ps(mask))); }
+template<> EIGEN_STRONG_INLINE Packet8ui pselect<Packet8ui>(const Packet8ui& mask, const Packet8ui& a, const Packet8ui& b)
+{ return _mm256_castps_si256(_mm256_blendv_ps(_mm256_castsi256_ps(b), _mm256_castsi256_ps(a), _mm256_castsi256_ps(mask))); }
+
+template<> EIGEN_STRONG_INLINE Packet4d pselect<Packet4d>(const Packet4d& mask, const Packet4d& a, const Packet4d& b)
+{ return _mm256_blendv_pd(b,a,mask); }
+
+template<int N> EIGEN_STRONG_INLINE Packet8i parithmetic_shift_right(Packet8i a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_srai_epi32(a, N);
+#else
+  __m128i lo = _mm_srai_epi32(_mm256_extractf128_si256(a, 0), N);
+  __m128i hi = _mm_srai_epi32(_mm256_extractf128_si256(a, 1), N);
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template<int N> EIGEN_STRONG_INLINE Packet8i plogical_shift_right(Packet8i a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_srli_epi32(a, N);
+#else
+  __m128i lo = _mm_srli_epi32(_mm256_extractf128_si256(a, 0), N);
+  __m128i hi = _mm_srli_epi32(_mm256_extractf128_si256(a, 1), N);
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template<int N> EIGEN_STRONG_INLINE Packet8i plogical_shift_left(Packet8i a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_slli_epi32(a, N);
+#else
+  __m128i lo = _mm_slli_epi32(_mm256_extractf128_si256(a, 0), N);
+  __m128i hi = _mm_slli_epi32(_mm256_extractf128_si256(a, 1), N);
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+
+template<int N> EIGEN_STRONG_INLINE Packet8ui parithmetic_shift_right(Packet8ui a) {
+  return (Packet8ui)plogical_shift_right<N>((Packet8i)a);
+}
+template<int N> EIGEN_STRONG_INLINE Packet8ui plogical_shift_right(Packet8ui a) {
+  return (Packet8ui)plogical_shift_right<N>((Packet8i)a);
+}
+template<int N> EIGEN_STRONG_INLINE Packet8ui plogical_shift_left(Packet8ui a) {
+  return (Packet8ui)plogical_shift_left<N>((Packet8i)a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pload<Packet8f>(const float*   from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_ps(from); }
+template<> EIGEN_STRONG_INLINE Packet4d pload<Packet4d>(const double*  from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_pd(from); }
+template<> EIGEN_STRONG_INLINE Packet8i pload<Packet8i>(const int*     from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_si256(reinterpret_cast<const __m256i*>(from)); }
+template<> EIGEN_STRONG_INLINE Packet8ui pload<Packet8ui>(const uint32_t* from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm256_load_si256(reinterpret_cast<const __m256i*>(from)); }
+
+template<> EIGEN_STRONG_INLINE Packet8f ploadu<Packet8f>(const float* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_ps(from); }
+template<> EIGEN_STRONG_INLINE Packet4d ploadu<Packet4d>(const double* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_pd(from); }
+template<> EIGEN_STRONG_INLINE Packet8i ploadu<Packet8i>(const int* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from)); }
+template<> EIGEN_STRONG_INLINE Packet8ui ploadu<Packet8ui>(const uint32_t* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from)); }
+
+template<> EIGEN_STRONG_INLINE Packet8f ploadu<Packet8f>(const float* from, uint8_t umask) {
+#ifdef EIGEN_VECTORIZE_AVX512
+  __mmask16 mask = static_cast<__mmask16>(umask & 0x00FF);
+  EIGEN_DEBUG_UNALIGNED_LOAD return  _mm512_castps512_ps256(_mm512_maskz_loadu_ps(mask, from));
+#else
+  Packet8i mask = _mm256_set1_epi8(static_cast<char>(umask));
+  const Packet8i bit_mask = _mm256_set_epi32(0xffffff7f, 0xffffffbf, 0xffffffdf, 0xffffffef, 0xfffffff7, 0xfffffffb, 0xfffffffd, 0xfffffffe);
+  mask = por<Packet8i>(mask, bit_mask);
+  mask = pcmp_eq<Packet8i>(mask, _mm256_set1_epi32(0xffffffff));
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm256_maskload_ps(from, mask);
+#endif
+}
+
+// Loads 4 floats from memory a returns the packet {a0, a0  a1, a1, a2, a2, a3, a3}
+template<> EIGEN_STRONG_INLINE Packet8f ploaddup<Packet8f>(const float* from)
+{
+  // TODO try to find a way to avoid the need of a temporary register
+  //   Packet8f tmp  = _mm256_castps128_ps256(_mm_loadu_ps(from));
+//   tmp = _mm256_insertf128_ps(tmp, _mm_movehl_ps(_mm256_castps256_ps128(tmp),_mm256_castps256_ps128(tmp)), 1);
+//   return _mm256_unpacklo_ps(tmp,tmp);
+
+  // _mm256_insertf128_ps is very slow on Haswell, thus:
+  Packet8f tmp = _mm256_broadcast_ps((const __m128*)(const void*)from);
+  // mimic an "inplace" permutation of the lower 128bits using a blend
+  tmp = _mm256_blend_ps(tmp,_mm256_castps128_ps256(_mm_permute_ps( _mm256_castps256_ps128(tmp), _MM_SHUFFLE(1,0,1,0))), 15);
+  // then we can perform a consistent permutation on the global register to get everything in shape:
+  return  _mm256_permute_ps(tmp, _MM_SHUFFLE(3,3,2,2));
+}
+// Loads 2 doubles from memory a returns the packet {a0, a0, a1, a1}
+template<> EIGEN_STRONG_INLINE Packet4d ploaddup<Packet4d>(const double* from)
+{
+  Packet4d tmp = _mm256_broadcast_pd((const __m128d*)(const void*)from);
+  return  _mm256_permute_pd(tmp, 3<<2);
+}
+// Loads 4 integers from memory a returns the packet {a0, a0, a1, a1, a2, a2, a3, a3}
+template<> EIGEN_STRONG_INLINE Packet8i ploaddup<Packet8i>(const int* from)
+{
+#ifdef EIGEN_VECTORIZE_AVX2
+  const Packet8i a = _mm256_castsi128_si256(ploadu<Packet4i>(from));
+  return _mm256_permutevar8x32_epi32(a, _mm256_setr_epi32(0, 0, 1, 1, 2, 2, 3, 3));
+#else
+  __m256 tmp = _mm256_broadcast_ps((const __m128*)(const void*)from);
+  // mimic an "inplace" permutation of the lower 128bits using a blend
+  tmp = _mm256_blend_ps(tmp,_mm256_castps128_ps256(_mm_permute_ps( _mm256_castps256_ps128(tmp), _MM_SHUFFLE(1,0,1,0))), 15);
+  // then we can perform a consistent permutation on the global register to get everything in shape:
+  return  _mm256_castps_si256(_mm256_permute_ps(tmp, _MM_SHUFFLE(3,3,2,2)));
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8ui ploaddup<Packet8ui>(const uint32_t* from) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  const Packet8ui a = _mm256_castsi128_si256(ploadu<Packet4ui>(from));
+  return _mm256_permutevar8x32_epi32(a, _mm256_setr_epi32(0, 0, 1, 1, 2, 2, 3, 3));
+#else
+  __m256 tmp = _mm256_broadcast_ps((const __m128*)(const void*)from);
+  // mimic an "inplace" permutation of the lower 128bits using a blend
+  tmp = _mm256_blend_ps(
+      tmp, _mm256_castps128_ps256(_mm_permute_ps(_mm256_castps256_ps128(tmp), _MM_SHUFFLE(1, 0, 1, 0))), 15);
+  // then we can perform a consistent permutation on the global register to get
+  // everything in shape:
+  return _mm256_castps_si256(_mm256_permute_ps(tmp, _MM_SHUFFLE(3, 3, 2, 2)));
+#endif
+}
+
+// Loads 2 floats from memory a returns the packet {a0, a0  a0, a0, a1, a1, a1, a1}
+template<> EIGEN_STRONG_INLINE Packet8f ploadquad<Packet8f>(const float* from)
+{
+  Packet8f tmp = _mm256_castps128_ps256(_mm_broadcast_ss(from));
+  return _mm256_insertf128_ps(tmp, _mm_broadcast_ss(from+1), 1);
+}
+template<> EIGEN_STRONG_INLINE Packet8i ploadquad<Packet8i>(const int* from)
+{
+  return _mm256_insertf128_si256(_mm256_set1_epi32(*from), _mm_set1_epi32(*(from+1)), 1);
+}
+template<> EIGEN_STRONG_INLINE Packet8ui ploadquad<Packet8ui>(const uint32_t* from) {
+  return _mm256_insertf128_si256(_mm256_set1_epi32(*from), _mm_set1_epi32(*(from + 1)), 1);
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<float>(float*   to, const Packet8f& from) { EIGEN_DEBUG_ALIGNED_STORE _mm256_store_ps(to, from); }
+template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet4d& from) { EIGEN_DEBUG_ALIGNED_STORE _mm256_store_pd(to, from); }
+template<> EIGEN_STRONG_INLINE void pstore<int>(int*       to, const Packet8i& from) { EIGEN_DEBUG_ALIGNED_STORE _mm256_store_si256(reinterpret_cast<__m256i*>(to), from); }
+template<> EIGEN_STRONG_INLINE void pstore<uint32_t>(uint32_t* to, const Packet8ui& from) { EIGEN_DEBUG_ALIGNED_STORE _mm256_store_si256(reinterpret_cast<__m256i*>(to), from); }
+
+template<> EIGEN_STRONG_INLINE void pstoreu<float>(float*   to, const Packet8f& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_ps(to, from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet4d& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_pd(to, from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<int>(int*       to, const Packet8i& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<uint32_t>(uint32_t* to, const Packet8ui& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from); }
+
+template<> EIGEN_STRONG_INLINE void pstoreu<float>(float*   to, const Packet8f& from, uint8_t umask) {
+#ifdef EIGEN_VECTORIZE_AVX512
+  __mmask16 mask = static_cast<__mmask16>(umask & 0x00FF);
+  EIGEN_DEBUG_UNALIGNED_STORE _mm512_mask_storeu_ps(to, mask, _mm512_castps256_ps512(from));
+#else
+  Packet8i mask = _mm256_set1_epi8(static_cast<char>(umask));
+  const Packet8i bit_mask = _mm256_set_epi32(0x7f7f7f7f, 0xbfbfbfbf, 0xdfdfdfdf, 0xefefefef, 0xf7f7f7f7, 0xfbfbfbfb, 0xfdfdfdfd, 0xfefefefe);
+  mask = por<Packet8i>(mask, bit_mask);
+  mask = pcmp_eq<Packet8i>(mask, _mm256_set1_epi32(0xffffffff));
+#if EIGEN_COMP_MSVC
+  // MSVC sometimes seems to use a bogus mask with maskstore.
+  const __m256i ifrom = _mm256_castps_si256(from);
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_maskmoveu_si128(_mm256_extractf128_si256(ifrom, 0), _mm256_extractf128_si256(mask, 0), reinterpret_cast<char*>(to));
+  EIGEN_DEBUG_UNALIGNED_STORE _mm_maskmoveu_si128(_mm256_extractf128_si256(ifrom, 1), _mm256_extractf128_si256(mask, 1), reinterpret_cast<char*>(to + 4));
+#else
+  EIGEN_DEBUG_UNALIGNED_STORE _mm256_maskstore_ps(to, mask, from);
+#endif
+#endif
+}
+
+// NOTE: leverage _mm256_i32gather_ps and _mm256_i32gather_pd if AVX2 instructions are available
+// NOTE: for the record the following seems to be slower: return _mm256_i32gather_ps(from, _mm256_set1_epi32(stride), 4);
+template<> EIGEN_DEVICE_FUNC inline Packet8f pgather<float, Packet8f>(const float* from, Index stride)
+{
+  return _mm256_set_ps(from[7*stride], from[6*stride], from[5*stride], from[4*stride],
+                       from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
+}
+template<> EIGEN_DEVICE_FUNC inline Packet4d pgather<double, Packet4d>(const double* from, Index stride)
+{
+  return _mm256_set_pd(from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
+}
+template<> EIGEN_DEVICE_FUNC inline Packet8i pgather<int, Packet8i>(const int* from, Index stride)
+{
+  return _mm256_set_epi32(from[7*stride], from[6*stride], from[5*stride], from[4*stride],
+                          from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
+}
+template<> EIGEN_DEVICE_FUNC inline Packet8ui pgather<uint32_t, Packet8ui>(const uint32_t* from, Index stride) {
+  return (Packet8ui)pgather<int, Packet8i>((int*)from, stride);
+}
+
+template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet8f>(float* to, const Packet8f& from, Index stride)
+{
+  __m128 low = _mm256_extractf128_ps(from, 0);
+  to[stride*0] = _mm_cvtss_f32(low);
+  to[stride*1] = _mm_cvtss_f32(_mm_shuffle_ps(low, low, 1));
+  to[stride*2] = _mm_cvtss_f32(_mm_shuffle_ps(low, low, 2));
+  to[stride*3] = _mm_cvtss_f32(_mm_shuffle_ps(low, low, 3));
+
+  __m128 high = _mm256_extractf128_ps(from, 1);
+  to[stride*4] = _mm_cvtss_f32(high);
+  to[stride*5] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 1));
+  to[stride*6] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 2));
+  to[stride*7] = _mm_cvtss_f32(_mm_shuffle_ps(high, high, 3));
+}
+template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet4d>(double* to, const Packet4d& from, Index stride)
+{
+  __m128d low = _mm256_extractf128_pd(from, 0);
+  to[stride*0] = _mm_cvtsd_f64(low);
+  to[stride*1] = _mm_cvtsd_f64(_mm_shuffle_pd(low, low, 1));
+  __m128d high = _mm256_extractf128_pd(from, 1);
+  to[stride*2] = _mm_cvtsd_f64(high);
+  to[stride*3] = _mm_cvtsd_f64(_mm_shuffle_pd(high, high, 1));
+}
+template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet8i>(int* to, const Packet8i& from, Index stride)
+{
+  __m128i low = _mm256_extractf128_si256(from, 0);
+  to[stride*0] = _mm_extract_epi32(low, 0);
+  to[stride*1] = _mm_extract_epi32(low, 1);
+  to[stride*2] = _mm_extract_epi32(low, 2);
+  to[stride*3] = _mm_extract_epi32(low, 3);
+
+  __m128i high = _mm256_extractf128_si256(from, 1);
+  to[stride*4] = _mm_extract_epi32(high, 0);
+  to[stride*5] = _mm_extract_epi32(high, 1);
+  to[stride*6] = _mm_extract_epi32(high, 2);
+  to[stride*7] = _mm_extract_epi32(high, 3);
+}
+template<> EIGEN_DEVICE_FUNC inline void pscatter<uint32_t, Packet8ui>(uint32_t* to, const Packet8ui& from, Index stride) {
+  pscatter<int, Packet8i>((int*)to, (Packet8i)from, stride);
+}
+
+template<> EIGEN_STRONG_INLINE void pstore1<Packet8f>(float* to, const float& a)
+{
+  Packet8f pa = pset1<Packet8f>(a);
+  pstore(to, pa);
+}
+template<> EIGEN_STRONG_INLINE void pstore1<Packet4d>(double* to, const double& a)
+{
+  Packet4d pa = pset1<Packet4d>(a);
+  pstore(to, pa);
+}
+template<> EIGEN_STRONG_INLINE void pstore1<Packet8i>(int* to, const int& a)
+{
+  Packet8i pa = pset1<Packet8i>(a);
+  pstore(to, pa);
+}
+
+#ifndef EIGEN_VECTORIZE_AVX512
+template<> EIGEN_STRONG_INLINE void prefetch<float>(const float*   addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
+template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
+template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*       addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
+template<> EIGEN_STRONG_INLINE void prefetch<uint32_t>(const uint32_t*       addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
+#endif
+
+template<> EIGEN_STRONG_INLINE float  pfirst<Packet8f>(const Packet8f& a) {
+  return _mm_cvtss_f32(_mm256_castps256_ps128(a));
+}
+template<> EIGEN_STRONG_INLINE double pfirst<Packet4d>(const Packet4d& a) {
+  return _mm_cvtsd_f64(_mm256_castpd256_pd128(a));
+}
+template<> EIGEN_STRONG_INLINE int    pfirst<Packet8i>(const Packet8i& a) {
+  return _mm_cvtsi128_si32(_mm256_castsi256_si128(a));
+}
+template<> EIGEN_STRONG_INLINE uint32_t pfirst<Packet8ui>(const Packet8ui& a) {
+  return numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(_mm256_castsi256_si128(a)));
+}
+
+
+template<> EIGEN_STRONG_INLINE Packet8f preverse(const Packet8f& a)
+{
+  __m256 tmp = _mm256_shuffle_ps(a,a,0x1b);
+  return _mm256_permute2f128_ps(tmp, tmp, 1);
+}
+template<> EIGEN_STRONG_INLINE Packet4d preverse(const Packet4d& a)
+{
+   __m256d tmp = _mm256_shuffle_pd(a,a,5);
+  return _mm256_permute2f128_pd(tmp, tmp, 1);
+#if 0
+  // This version is unlikely to be faster as _mm256_shuffle_ps and _mm256_permute_pd
+  // exhibit the same latency/throughput, but it is here for future reference/benchmarking...
+  __m256d swap_halves = _mm256_permute2f128_pd(a,a,1);
+    return _mm256_permute_pd(swap_halves,5);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8i preverse(const Packet8i& a)
+{
+  return _mm256_castps_si256(preverse(_mm256_castsi256_ps(a)));
+}
+template<> EIGEN_STRONG_INLINE Packet8ui preverse(const Packet8ui& a) {
+  return _mm256_castps_si256(preverse(_mm256_castsi256_ps(a)));
+}
+
+#ifdef EIGEN_VECTORIZE_AVX2
+template<> EIGEN_STRONG_INLINE Packet4l preverse(const Packet4l& a)
+    {
+  return _mm256_castpd_si256(preverse(_mm256_castsi256_pd(a)));
+}
+template<> EIGEN_STRONG_INLINE Packet4ul preverse(const Packet4ul& a) {
+  return _mm256_castpd_si256(preverse(_mm256_castsi256_pd(a)));
+}
+#endif
+
+// pabs should be ok
+template<> EIGEN_STRONG_INLINE Packet8f pabs(const Packet8f& a)
+{
+  const Packet8f mask = _mm256_castsi256_ps(_mm256_setr_epi32(0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF));
+  return _mm256_and_ps(a,mask);
+}
+template<> EIGEN_STRONG_INLINE Packet4d pabs(const Packet4d& a)
+{
+  const Packet4d mask = _mm256_castsi256_pd(_mm256_setr_epi32(0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF));
+  return _mm256_and_pd(a,mask);
+}
+template<> EIGEN_STRONG_INLINE Packet8i pabs(const Packet8i& a)
+{
+#ifdef EIGEN_VECTORIZE_AVX2
+  return _mm256_abs_epi32(a);
+#else
+  __m128i lo = _mm_abs_epi32(_mm256_extractf128_si256(a, 0));
+  __m128i hi = _mm_abs_epi32(_mm256_extractf128_si256(a, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(lo), (hi), 1);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8ui pabs(const Packet8ui& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE Packet8h  psignbit(const Packet8h&  a) { return _mm_srai_epi16(a, 15); }
+template<> EIGEN_STRONG_INLINE Packet8bf psignbit(const Packet8bf& a) { return _mm_srai_epi16(a, 15); }
+template<> EIGEN_STRONG_INLINE Packet8f  psignbit(const Packet8f&  a) { return _mm256_castsi256_ps(parithmetic_shift_right<31>((Packet8i)_mm256_castps_si256(a))); }
+template<> EIGEN_STRONG_INLINE Packet8ui  psignbit(const Packet8ui& a)  { return pzero(a); }
+#ifdef EIGEN_VECTORIZE_AVX2
+template<> EIGEN_STRONG_INLINE Packet4d  psignbit(const Packet4d& a)  { return _mm256_castsi256_pd(parithmetic_shift_right<63>((Packet4l)_mm256_castpd_si256(a))); }
+template<> EIGEN_STRONG_INLINE Packet4ul  psignbit(const Packet4ul& a)  { return pzero(a); }
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet8f pfrexp<Packet8f>(const Packet8f& a, Packet8f& exponent) {
+  return pfrexp_generic(a,exponent);
+}
+
+// Extract exponent without existence of Packet4l.
+template<>
+EIGEN_STRONG_INLINE  
+Packet4d pfrexp_generic_get_biased_exponent(const Packet4d& a) {
+  const Packet4d cst_exp_mask  = pset1frombits<Packet4d>(static_cast<uint64_t>(0x7ff0000000000000ull));
+  __m256i a_expo = _mm256_castpd_si256(pand(a, cst_exp_mask));
+#ifdef EIGEN_VECTORIZE_AVX2
+  a_expo = _mm256_srli_epi64(a_expo, 52);
+  __m128i lo = _mm256_extractf128_si256(a_expo, 0);
+  __m128i hi = _mm256_extractf128_si256(a_expo, 1);
+#else
+  __m128i lo = _mm256_extractf128_si256(a_expo, 0);
+  __m128i hi = _mm256_extractf128_si256(a_expo, 1);
+  lo = _mm_srli_epi64(lo, 52);
+  hi = _mm_srli_epi64(hi, 52);
+#endif
+  Packet2d exponent_lo = _mm_cvtepi32_pd(vec4i_swizzle1(lo, 0, 2, 1, 3));
+  Packet2d exponent_hi = _mm_cvtepi32_pd(vec4i_swizzle1(hi, 0, 2, 1, 3));
+  Packet4d exponent = _mm256_insertf128_pd(_mm256_setzero_pd(), exponent_lo, 0);
+  exponent = _mm256_insertf128_pd(exponent, exponent_hi, 1);
+  return exponent;
+}
+
+
+template<> EIGEN_STRONG_INLINE Packet4d pfrexp<Packet4d>(const Packet4d& a, Packet4d& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pldexp<Packet8f>(const Packet8f& a, const Packet8f& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4d pldexp<Packet4d>(const Packet4d& a, const Packet4d& exponent) {
+  // Clamp exponent to [-2099, 2099]
+  const Packet4d max_exponent = pset1<Packet4d>(2099.0);
+  const Packet4i e = _mm256_cvtpd_epi32(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
+
+  // Split 2^e into four factors and multiply.
+  const Packet4i bias = pset1<Packet4i>(1023);
+  Packet4i b = parithmetic_shift_right<2>(e);  // floor(e/4)
+
+  // 2^b
+  Packet4i hi = vec4i_swizzle1(padd(b, bias), 0, 2, 1, 3);
+  Packet4i lo = _mm_slli_epi64(hi, 52);
+  hi = _mm_slli_epi64(_mm_srli_epi64(hi, 32), 52);
+  Packet4d c = _mm256_castsi256_pd(_mm256_insertf128_si256(_mm256_castsi128_si256(lo), hi, 1));
+  Packet4d out = pmul(pmul(pmul(a, c), c), c);  // a * 2^(3b)
+
+  // 2^(e - 3b)
+  b = psub(psub(psub(e, b), b), b);  // e - 3b
+  hi = vec4i_swizzle1(padd(b, bias), 0, 2, 1, 3);
+  lo = _mm_slli_epi64(hi, 52);
+  hi = _mm_slli_epi64(_mm_srli_epi64(hi, 32), 52);
+  c = _mm256_castsi256_pd(_mm256_insertf128_si256(_mm256_castsi128_si256(lo), hi, 1));
+  out = pmul(out, c); // a * 2^e
+  return out;
+}
+
+template<> EIGEN_STRONG_INLINE float predux<Packet8f>(const Packet8f& a)
+{
+  return predux(Packet4f(_mm_add_ps(_mm256_castps256_ps128(a),_mm256_extractf128_ps(a,1))));
+}
+template<> EIGEN_STRONG_INLINE double predux<Packet4d>(const Packet4d& a)
+{
+  return predux(Packet2d(_mm_add_pd(_mm256_castpd256_pd128(a),_mm256_extractf128_pd(a,1))));
+}
+template<> EIGEN_STRONG_INLINE int predux<Packet8i>(const Packet8i& a)
+{
+  return predux(Packet4i(_mm_add_epi32(_mm256_castsi256_si128(a),_mm256_extractf128_si256(a,1))));
+}
+template<> EIGEN_STRONG_INLINE uint32_t predux<Packet8ui>(const Packet8ui& a) {
+  return predux(Packet4ui(_mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1))));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f predux_half_dowto4<Packet8f>(const Packet8f& a)
+{
+  return _mm_add_ps(_mm256_castps256_ps128(a),_mm256_extractf128_ps(a,1));
+}
+template<> EIGEN_STRONG_INLINE Packet4i predux_half_dowto4<Packet8i>(const Packet8i& a)
+{
+  return _mm_add_epi32(_mm256_castsi256_si128(a),_mm256_extractf128_si256(a,1));
+}
+template<> EIGEN_STRONG_INLINE Packet4ui predux_half_dowto4<Packet8ui>(const Packet8ui& a) {
+  return _mm_add_epi32(_mm256_castsi256_si128(a), _mm256_extractf128_si256(a, 1));
+}
+
+template<> EIGEN_STRONG_INLINE float predux_mul<Packet8f>(const Packet8f& a)
+{
+  Packet8f tmp;
+  tmp = _mm256_mul_ps(a, _mm256_permute2f128_ps(a,a,1));
+  tmp = _mm256_mul_ps(tmp, _mm256_shuffle_ps(tmp,tmp,_MM_SHUFFLE(1,0,3,2)));
+  return pfirst(_mm256_mul_ps(tmp, _mm256_shuffle_ps(tmp,tmp,1)));
+}
+template<> EIGEN_STRONG_INLINE double predux_mul<Packet4d>(const Packet4d& a)
+{
+  Packet4d tmp;
+  tmp = _mm256_mul_pd(a, _mm256_permute2f128_pd(a,a,1));
+  return pfirst(_mm256_mul_pd(tmp, _mm256_shuffle_pd(tmp,tmp,1)));
+}
+
+template<> EIGEN_STRONG_INLINE float predux_min<Packet8f>(const Packet8f& a)
+{
+  Packet8f tmp = _mm256_min_ps(a, _mm256_permute2f128_ps(a,a,1));
+  tmp = _mm256_min_ps(tmp, _mm256_shuffle_ps(tmp,tmp,_MM_SHUFFLE(1,0,3,2)));
+  return pfirst(_mm256_min_ps(tmp, _mm256_shuffle_ps(tmp,tmp,1)));
+}
+template<> EIGEN_STRONG_INLINE double predux_min<Packet4d>(const Packet4d& a)
+{
+  Packet4d tmp = _mm256_min_pd(a, _mm256_permute2f128_pd(a,a,1));
+  return pfirst(_mm256_min_pd(tmp, _mm256_shuffle_pd(tmp, tmp, 1)));
+}
+
+template<> EIGEN_STRONG_INLINE float predux_max<Packet8f>(const Packet8f& a)
+{
+  Packet8f tmp = _mm256_max_ps(a, _mm256_permute2f128_ps(a,a,1));
+  tmp = _mm256_max_ps(tmp, _mm256_shuffle_ps(tmp,tmp,_MM_SHUFFLE(1,0,3,2)));
+  return pfirst(_mm256_max_ps(tmp, _mm256_shuffle_ps(tmp,tmp,1)));
+}
+
+template<> EIGEN_STRONG_INLINE double predux_max<Packet4d>(const Packet4d& a)
+{
+  Packet4d tmp = _mm256_max_pd(a, _mm256_permute2f128_pd(a,a,1));
+  return pfirst(_mm256_max_pd(tmp, _mm256_shuffle_pd(tmp, tmp, 1)));
+}
+
+// not needed yet
+// template<> EIGEN_STRONG_INLINE bool predux_all(const Packet8f& x)
+// {
+//   return _mm256_movemask_ps(x)==0xFF;
+// }
+
+template<> EIGEN_STRONG_INLINE bool predux_any(const Packet8f& x)
+{
+  return _mm256_movemask_ps(x) != 0;
+}
+
+template<> EIGEN_STRONG_INLINE bool predux_any(const Packet8i& x)
+{
+  return _mm256_movemask_ps(_mm256_castsi256_ps(x)) != 0;
+}
+template<> EIGEN_STRONG_INLINE bool predux_any(const Packet8ui& x)
+{
+  return _mm256_movemask_ps(_mm256_castsi256_ps(x)) != 0;
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet8f,8>& kernel) {
+  __m256 T0 = _mm256_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
+  __m256 T1 = _mm256_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
+  __m256 T2 = _mm256_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
+  __m256 T3 = _mm256_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
+  __m256 T4 = _mm256_unpacklo_ps(kernel.packet[4], kernel.packet[5]);
+  __m256 T5 = _mm256_unpackhi_ps(kernel.packet[4], kernel.packet[5]);
+  __m256 T6 = _mm256_unpacklo_ps(kernel.packet[6], kernel.packet[7]);
+  __m256 T7 = _mm256_unpackhi_ps(kernel.packet[6], kernel.packet[7]);
+  __m256 S0 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(1,0,1,0));
+  __m256 S1 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(3,2,3,2));
+  __m256 S2 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(1,0,1,0));
+  __m256 S3 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(3,2,3,2));
+  __m256 S4 = _mm256_shuffle_ps(T4,T6,_MM_SHUFFLE(1,0,1,0));
+  __m256 S5 = _mm256_shuffle_ps(T4,T6,_MM_SHUFFLE(3,2,3,2));
+  __m256 S6 = _mm256_shuffle_ps(T5,T7,_MM_SHUFFLE(1,0,1,0));
+  __m256 S7 = _mm256_shuffle_ps(T5,T7,_MM_SHUFFLE(3,2,3,2));
+  kernel.packet[0] = _mm256_permute2f128_ps(S0, S4, 0x20);
+  kernel.packet[1] = _mm256_permute2f128_ps(S1, S5, 0x20);
+  kernel.packet[2] = _mm256_permute2f128_ps(S2, S6, 0x20);
+  kernel.packet[3] = _mm256_permute2f128_ps(S3, S7, 0x20);
+  kernel.packet[4] = _mm256_permute2f128_ps(S0, S4, 0x31);
+  kernel.packet[5] = _mm256_permute2f128_ps(S1, S5, 0x31);
+  kernel.packet[6] = _mm256_permute2f128_ps(S2, S6, 0x31);
+  kernel.packet[7] = _mm256_permute2f128_ps(S3, S7, 0x31);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet8f,4>& kernel) {
+  __m256 T0 = _mm256_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
+  __m256 T1 = _mm256_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
+  __m256 T2 = _mm256_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
+  __m256 T3 = _mm256_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
+
+  __m256 S0 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(1,0,1,0));
+  __m256 S1 = _mm256_shuffle_ps(T0,T2,_MM_SHUFFLE(3,2,3,2));
+  __m256 S2 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(1,0,1,0));
+  __m256 S3 = _mm256_shuffle_ps(T1,T3,_MM_SHUFFLE(3,2,3,2));
+
+  kernel.packet[0] = _mm256_permute2f128_ps(S0, S1, 0x20);
+  kernel.packet[1] = _mm256_permute2f128_ps(S2, S3, 0x20);
+  kernel.packet[2] = _mm256_permute2f128_ps(S0, S1, 0x31);
+  kernel.packet[3] = _mm256_permute2f128_ps(S2, S3, 0x31);
+}
+
+#define MM256_SHUFFLE_EPI32(A, B, M) \
+  _mm256_castps_si256(_mm256_shuffle_ps(_mm256_castsi256_ps(A), _mm256_castsi256_ps(B), M))
+
+#ifndef EIGEN_VECTORIZE_AVX2
+#define MM256_UNPACKLO_EPI32(A, B) \
+  _mm256_castps_si256(_mm256_unpacklo_ps(_mm256_castsi256_ps(A), _mm256_castsi256_ps(B)))
+#define MM256_UNPACKHI_EPI32(A, B) \
+  _mm256_castps_si256(_mm256_unpackhi_ps(_mm256_castsi256_ps(A), _mm256_castsi256_ps(B)))
+#else
+#define MM256_UNPACKLO_EPI32(A, B) _mm256_unpacklo_epi32(A, B)
+#define MM256_UNPACKHI_EPI32(A, B) _mm256_unpackhi_epi32(A, B)
+#endif
+
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet8i,8>& kernel) {
+  __m256i T0 = MM256_UNPACKLO_EPI32(kernel.packet[0], kernel.packet[1]);
+  __m256i T1 = MM256_UNPACKHI_EPI32(kernel.packet[0], kernel.packet[1]);
+  __m256i T2 = MM256_UNPACKLO_EPI32(kernel.packet[2], kernel.packet[3]);
+  __m256i T3 = MM256_UNPACKHI_EPI32(kernel.packet[2], kernel.packet[3]);
+  __m256i T4 = MM256_UNPACKLO_EPI32(kernel.packet[4], kernel.packet[5]);
+  __m256i T5 = MM256_UNPACKHI_EPI32(kernel.packet[4], kernel.packet[5]);
+  __m256i T6 = MM256_UNPACKLO_EPI32(kernel.packet[6], kernel.packet[7]);
+  __m256i T7 = MM256_UNPACKHI_EPI32(kernel.packet[6], kernel.packet[7]);
+  __m256i S0 = MM256_SHUFFLE_EPI32(T0,T2,_MM_SHUFFLE(1,0,1,0));
+  __m256i S1 = MM256_SHUFFLE_EPI32(T0,T2,_MM_SHUFFLE(3,2,3,2));
+  __m256i S2 = MM256_SHUFFLE_EPI32(T1,T3,_MM_SHUFFLE(1,0,1,0));
+  __m256i S3 = MM256_SHUFFLE_EPI32(T1,T3,_MM_SHUFFLE(3,2,3,2));
+  __m256i S4 = MM256_SHUFFLE_EPI32(T4,T6,_MM_SHUFFLE(1,0,1,0));
+  __m256i S5 = MM256_SHUFFLE_EPI32(T4,T6,_MM_SHUFFLE(3,2,3,2));
+  __m256i S6 = MM256_SHUFFLE_EPI32(T5,T7,_MM_SHUFFLE(1,0,1,0));
+  __m256i S7 = MM256_SHUFFLE_EPI32(T5,T7,_MM_SHUFFLE(3,2,3,2));
+  kernel.packet[0] = _mm256_permute2f128_si256(S0, S4, 0x20);
+  kernel.packet[1] = _mm256_permute2f128_si256(S1, S5, 0x20);
+  kernel.packet[2] = _mm256_permute2f128_si256(S2, S6, 0x20);
+  kernel.packet[3] = _mm256_permute2f128_si256(S3, S7, 0x20);
+  kernel.packet[4] = _mm256_permute2f128_si256(S0, S4, 0x31);
+  kernel.packet[5] = _mm256_permute2f128_si256(S1, S5, 0x31);
+  kernel.packet[6] = _mm256_permute2f128_si256(S2, S6, 0x31);
+  kernel.packet[7] = _mm256_permute2f128_si256(S3, S7, 0x31);
+}
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8ui, 8>& kernel) {
+  ptranspose((PacketBlock<Packet8i, 8>&)kernel);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet8i,4>& kernel) {
+  __m256i T0 = MM256_UNPACKLO_EPI32(kernel.packet[0], kernel.packet[1]);
+  __m256i T1 = MM256_UNPACKHI_EPI32(kernel.packet[0], kernel.packet[1]);
+  __m256i T2 = MM256_UNPACKLO_EPI32(kernel.packet[2], kernel.packet[3]);
+  __m256i T3 = MM256_UNPACKHI_EPI32(kernel.packet[2], kernel.packet[3]);
+
+  __m256i S0 = MM256_SHUFFLE_EPI32(T0,T2,_MM_SHUFFLE(1,0,1,0));
+  __m256i S1 = MM256_SHUFFLE_EPI32(T0,T2,_MM_SHUFFLE(3,2,3,2));
+  __m256i S2 = MM256_SHUFFLE_EPI32(T1,T3,_MM_SHUFFLE(1,0,1,0));
+  __m256i S3 = MM256_SHUFFLE_EPI32(T1,T3,_MM_SHUFFLE(3,2,3,2));
+
+  kernel.packet[0] = _mm256_permute2f128_si256(S0, S1, 0x20);
+  kernel.packet[1] = _mm256_permute2f128_si256(S2, S3, 0x20);
+  kernel.packet[2] = _mm256_permute2f128_si256(S0, S1, 0x31);
+  kernel.packet[3] = _mm256_permute2f128_si256(S2, S3, 0x31);
+}
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8ui, 4>& kernel) {
+  ptranspose((PacketBlock<Packet8i, 4>&)kernel);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet4d,4>& kernel) {
+  __m256d T0 = _mm256_shuffle_pd(kernel.packet[0], kernel.packet[1], 15);
+  __m256d T1 = _mm256_shuffle_pd(kernel.packet[0], kernel.packet[1], 0);
+  __m256d T2 = _mm256_shuffle_pd(kernel.packet[2], kernel.packet[3], 15);
+  __m256d T3 = _mm256_shuffle_pd(kernel.packet[2], kernel.packet[3], 0);
+
+  kernel.packet[1] = _mm256_permute2f128_pd(T0, T2, 32);
+  kernel.packet[3] = _mm256_permute2f128_pd(T0, T2, 49);
+  kernel.packet[0] = _mm256_permute2f128_pd(T1, T3, 32);
+  kernel.packet[2] = _mm256_permute2f128_pd(T1, T3, 49);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pblend(const Selector<8>& ifPacket, const Packet8f& thenPacket, const Packet8f& elsePacket) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  const __m256i zero = _mm256_setzero_si256();
+  const __m256i select = _mm256_set_epi32(ifPacket.select[7], ifPacket.select[6], ifPacket.select[5], ifPacket.select[4], ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
+  __m256i false_mask = _mm256_cmpeq_epi32(zero, select);
+  return _mm256_blendv_ps(thenPacket, elsePacket, _mm256_castsi256_ps(false_mask));
+#else
+  const __m256 zero = _mm256_setzero_ps();
+  const __m256 select = _mm256_set_ps(ifPacket.select[7], ifPacket.select[6], ifPacket.select[5], ifPacket.select[4], ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
+  __m256 false_mask = _mm256_cmp_ps(select, zero, _CMP_EQ_UQ);
+  return _mm256_blendv_ps(thenPacket, elsePacket, false_mask);
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet4d pblend(const Selector<4>& ifPacket, const Packet4d& thenPacket, const Packet4d& elsePacket) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  const __m256i zero = _mm256_setzero_si256();
+  const __m256i select = _mm256_set_epi64x(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
+  __m256i false_mask = _mm256_cmpeq_epi64(select, zero);
+  return _mm256_blendv_pd(thenPacket, elsePacket, _mm256_castsi256_pd(false_mask));
+#else
+  const __m256d zero = _mm256_setzero_pd();
+  const __m256d select = _mm256_set_pd(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
+  __m256d false_mask = _mm256_cmp_pd(select, zero, _CMP_EQ_UQ);
+  return _mm256_blendv_pd(thenPacket, elsePacket, false_mask);
+#endif
+}
+
+// Packet math for Eigen::half
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template<> struct unpacket_traits<Packet8h> { typedef Eigen::half type; enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet8h half; };
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet8h pset1<Packet8h>(const Eigen::half& from) {
+  return _mm_set1_epi16(numext::bit_cast<numext::uint16_t>(from));
+}
+
+template<> EIGEN_STRONG_INLINE Eigen::half pfirst<Packet8h>(const Packet8h& from) {
+  return numext::bit_cast<Eigen::half>(static_cast<numext::uint16_t>(_mm_extract_epi16(from, 0)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h pload<Packet8h>(const Eigen::half* from) {
+  return _mm_load_si128(reinterpret_cast<const __m128i*>(from));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h ploadu<Packet8h>(const Eigen::half* from) {
+  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet8h& from) {
+  _mm_store_si128(reinterpret_cast<__m128i*>(to), from);
+}
+
+template<> EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet8h& from) {
+  _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h
+ploaddup<Packet8h>(const Eigen::half*  from) {
+  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
+  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
+  const numext::uint16_t c = numext::bit_cast<numext::uint16_t>(from[2]);
+  const numext::uint16_t d = numext::bit_cast<numext::uint16_t>(from[3]);
+  return _mm_set_epi16(d, d, c, c, b, b, a, a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h
+ploadquad<Packet8h>(const Eigen::half* from) {
+  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
+  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
+  return _mm_set_epi16(b, b, b, b, a, a, a, a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h ptrue(const Packet8h& a) {
+  return _mm_cmpeq_epi32(a, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pabs(const Packet8h& a) {
+  const __m128i sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
+  return _mm_andnot_si128(sign_mask, a);
+}
+
+EIGEN_STRONG_INLINE Packet8f half2float(const Packet8h& a) {
+#ifdef EIGEN_HAS_FP16_C
+  return _mm256_cvtph_ps(a);
+#else
+  Eigen::internal::Packet8f pp = _mm256_castsi256_ps(_mm256_insertf128_si256(
+      _mm256_castsi128_si256(half2floatsse(a)), half2floatsse(_mm_srli_si128(a, 8)), 1));
+  return pp;
+#endif
+}
+
+EIGEN_STRONG_INLINE Packet8h float2half(const Packet8f& a) {
+#ifdef EIGEN_HAS_FP16_C
+  return _mm256_cvtps_ph(a, _MM_FROUND_TO_NEAREST_INT);
+#else
+  __m128i lo = float2half(_mm256_extractf128_ps(a, 0));
+  __m128i hi = float2half(_mm256_extractf128_ps(a, 1));
+  return   _mm_packus_epi32(lo, hi);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pmin<Packet8h>(const Packet8h& a,
+                                            const Packet8h& b) {
+  return float2half(pmin<Packet8f>(half2float(a), half2float(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pmax<Packet8h>(const Packet8h& a,
+                                            const Packet8h& b) {
+  return float2half(pmax<Packet8f>(half2float(a), half2float(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h plset<Packet8h>(const half& a) {
+  return float2half(plset<Packet8f>(static_cast<float>(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h por(const Packet8h& a,const Packet8h& b) {
+  // in some cases Packet4i is a wrapper around __m128i, so we either need to
+  // cast to Packet4i to directly call the intrinsics as below:
+  return _mm_or_si128(a,b);
+}
+template<> EIGEN_STRONG_INLINE Packet8h pxor(const Packet8h& a,const Packet8h& b) {
+  return _mm_xor_si128(a,b);
+}
+template<> EIGEN_STRONG_INLINE Packet8h pand(const Packet8h& a,const Packet8h& b) {
+  return _mm_and_si128(a,b);
+}
+template<> EIGEN_STRONG_INLINE Packet8h pandnot(const Packet8h& a,const Packet8h& b) {
+  return _mm_andnot_si128(b,a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h pselect(const Packet8h& mask, const Packet8h& a, const Packet8h& b) {
+  return _mm_blendv_epi8(b, a, mask);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h pround<Packet8h>(const Packet8h& a) {
+  return float2half(pround<Packet8f>(half2float(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h print<Packet8h>(const Packet8h& a) {
+  return float2half(print<Packet8f>(half2float(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h pceil<Packet8h>(const Packet8h& a) {
+  return float2half(pceil<Packet8f>(half2float(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h pfloor<Packet8h>(const Packet8h& a) {
+  return float2half(pfloor<Packet8f>(half2float(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h pcmp_eq(const Packet8h& a,const Packet8h& b) {
+  return Pack16To8(pcmp_eq(half2float(a), half2float(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h pcmp_le(const Packet8h& a,const Packet8h& b) {
+  return Pack16To8(pcmp_le(half2float(a), half2float(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h pcmp_lt(const Packet8h& a,const Packet8h& b) {
+  return Pack16To8(pcmp_lt(half2float(a), half2float(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h pcmp_lt_or_nan(const Packet8h& a,const Packet8h& b) {
+  return Pack16To8(pcmp_lt_or_nan(half2float(a), half2float(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h pconj(const Packet8h& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE Packet8h pnegate(const Packet8h& a) {
+  Packet8h sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
+  return _mm_xor_si128(a, sign_mask);
+}
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template<> EIGEN_STRONG_INLINE Packet8h padd<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  Packet8f af = half2float(a);
+  Packet8f bf = half2float(b);
+  Packet8f rf = padd(af, bf);
+  return float2half(rf);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h psub<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  Packet8f af = half2float(a);
+  Packet8f bf = half2float(b);
+  Packet8f rf = psub(af, bf);
+  return float2half(rf);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h pmul<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  Packet8f af = half2float(a);
+  Packet8f bf = half2float(b);
+  Packet8f rf = pmul(af, bf);
+  return float2half(rf);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h pdiv<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  Packet8f af = half2float(a);
+  Packet8f bf = half2float(b);
+  Packet8f rf = pdiv(af, bf);
+  return float2half(rf);
+}
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet8h pgather<Eigen::half, Packet8h>(const Eigen::half* from, Index stride)
+{
+  const numext::uint16_t s0 = numext::bit_cast<numext::uint16_t>(from[0*stride]);
+  const numext::uint16_t s1 = numext::bit_cast<numext::uint16_t>(from[1*stride]);
+  const numext::uint16_t s2 = numext::bit_cast<numext::uint16_t>(from[2*stride]);
+  const numext::uint16_t s3 = numext::bit_cast<numext::uint16_t>(from[3*stride]);
+  const numext::uint16_t s4 = numext::bit_cast<numext::uint16_t>(from[4*stride]);
+  const numext::uint16_t s5 = numext::bit_cast<numext::uint16_t>(from[5*stride]);
+  const numext::uint16_t s6 = numext::bit_cast<numext::uint16_t>(from[6*stride]);
+  const numext::uint16_t s7 = numext::bit_cast<numext::uint16_t>(from[7*stride]);
+  return _mm_set_epi16(s7, s6, s5, s4, s3, s2, s1, s0);
+}
+
+template<> EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet8h>(Eigen::half* to, const Packet8h& from, Index stride)
+{
+  EIGEN_ALIGN32 Eigen::half aux[8];
+  pstore(aux, from);
+  to[stride*0] = aux[0];
+  to[stride*1] = aux[1];
+  to[stride*2] = aux[2];
+  to[stride*3] = aux[3];
+  to[stride*4] = aux[4];
+  to[stride*5] = aux[5];
+  to[stride*6] = aux[6];
+  to[stride*7] = aux[7];
+}
+
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template<> EIGEN_STRONG_INLINE Eigen::half predux<Packet8h>(const Packet8h& a) {
+  Packet8f af = half2float(a);
+  float reduced = predux<Packet8f>(af);
+  return Eigen::half(reduced);
+}
+#endif
+
+template<> EIGEN_STRONG_INLINE Eigen::half predux_max<Packet8h>(const Packet8h& a) {
+  Packet8f af = half2float(a);
+  float reduced = predux_max<Packet8f>(af);
+  return Eigen::half(reduced);
+}
+
+template<> EIGEN_STRONG_INLINE Eigen::half predux_min<Packet8h>(const Packet8h& a) {
+  Packet8f af = half2float(a);
+  float reduced = predux_min<Packet8f>(af);
+  return Eigen::half(reduced);
+}
+
+template<> EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet8h>(const Packet8h& a) {
+  Packet8f af = half2float(a);
+  float reduced = predux_mul<Packet8f>(af);
+  return Eigen::half(reduced);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h preverse(const Packet8h& a)
+{
+  __m128i m = _mm_setr_epi8(14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1);
+  return _mm_shuffle_epi8(a,m);
+}
+
+EIGEN_STRONG_INLINE void
+ptranspose(PacketBlock<Packet8h,8>& kernel) {
+  __m128i a = kernel.packet[0];
+  __m128i b = kernel.packet[1];
+  __m128i c = kernel.packet[2];
+  __m128i d = kernel.packet[3];
+  __m128i e = kernel.packet[4];
+  __m128i f = kernel.packet[5];
+  __m128i g = kernel.packet[6];
+  __m128i h = kernel.packet[7];
+
+  __m128i a03b03 = _mm_unpacklo_epi16(a, b);
+  __m128i c03d03 = _mm_unpacklo_epi16(c, d);
+  __m128i e03f03 = _mm_unpacklo_epi16(e, f);
+  __m128i g03h03 = _mm_unpacklo_epi16(g, h);
+  __m128i a47b47 = _mm_unpackhi_epi16(a, b);
+  __m128i c47d47 = _mm_unpackhi_epi16(c, d);
+  __m128i e47f47 = _mm_unpackhi_epi16(e, f);
+  __m128i g47h47 = _mm_unpackhi_epi16(g, h);
+
+  __m128i a01b01c01d01 = _mm_unpacklo_epi32(a03b03, c03d03);
+  __m128i a23b23c23d23 = _mm_unpackhi_epi32(a03b03, c03d03);
+  __m128i e01f01g01h01 = _mm_unpacklo_epi32(e03f03, g03h03);
+  __m128i e23f23g23h23 = _mm_unpackhi_epi32(e03f03, g03h03);
+  __m128i a45b45c45d45 = _mm_unpacklo_epi32(a47b47, c47d47);
+  __m128i a67b67c67d67 = _mm_unpackhi_epi32(a47b47, c47d47);
+  __m128i e45f45g45h45 = _mm_unpacklo_epi32(e47f47, g47h47);
+  __m128i e67f67g67h67 = _mm_unpackhi_epi32(e47f47, g47h47);
+
+  __m128i a0b0c0d0e0f0g0h0 = _mm_unpacklo_epi64(a01b01c01d01, e01f01g01h01);
+  __m128i a1b1c1d1e1f1g1h1 = _mm_unpackhi_epi64(a01b01c01d01, e01f01g01h01);
+  __m128i a2b2c2d2e2f2g2h2 = _mm_unpacklo_epi64(a23b23c23d23, e23f23g23h23);
+  __m128i a3b3c3d3e3f3g3h3 = _mm_unpackhi_epi64(a23b23c23d23, e23f23g23h23);
+  __m128i a4b4c4d4e4f4g4h4 = _mm_unpacklo_epi64(a45b45c45d45, e45f45g45h45);
+  __m128i a5b5c5d5e5f5g5h5 = _mm_unpackhi_epi64(a45b45c45d45, e45f45g45h45);
+  __m128i a6b6c6d6e6f6g6h6 = _mm_unpacklo_epi64(a67b67c67d67, e67f67g67h67);
+  __m128i a7b7c7d7e7f7g7h7 = _mm_unpackhi_epi64(a67b67c67d67, e67f67g67h67);
+
+  kernel.packet[0] = a0b0c0d0e0f0g0h0;
+  kernel.packet[1] = a1b1c1d1e1f1g1h1;
+  kernel.packet[2] = a2b2c2d2e2f2g2h2;
+  kernel.packet[3] = a3b3c3d3e3f3g3h3;
+  kernel.packet[4] = a4b4c4d4e4f4g4h4;
+  kernel.packet[5] = a5b5c5d5e5f5g5h5;
+  kernel.packet[6] = a6b6c6d6e6f6g6h6;
+  kernel.packet[7] = a7b7c7d7e7f7g7h7;
+}
+
+EIGEN_STRONG_INLINE void
+ptranspose(PacketBlock<Packet8h,4>& kernel) {
+  EIGEN_ALIGN32 Eigen::half in[4][8];
+  pstore<Eigen::half>(in[0], kernel.packet[0]);
+  pstore<Eigen::half>(in[1], kernel.packet[1]);
+  pstore<Eigen::half>(in[2], kernel.packet[2]);
+  pstore<Eigen::half>(in[3], kernel.packet[3]);
+
+  EIGEN_ALIGN32 Eigen::half out[4][8];
+
+  for (int i = 0; i < 4; ++i) {
+    for (int j = 0; j < 4; ++j) {
+      out[i][j] = in[j][2*i];
+    }
+    for (int j = 0; j < 4; ++j) {
+      out[i][j+4] = in[j][2*i+1];
+    }
+  }
+
+  kernel.packet[0] = pload<Packet8h>(out[0]);
+  kernel.packet[1] = pload<Packet8h>(out[1]);
+  kernel.packet[2] = pload<Packet8h>(out[2]);
+  kernel.packet[3] = pload<Packet8h>(out[3]);
+}
+
+// BFloat16 implementation.
+
+EIGEN_STRONG_INLINE Packet8f Bf16ToF32(const Packet8bf& a) {
+#ifdef EIGEN_VECTORIZE_AVX2
+  __m256i extend = _mm256_cvtepu16_epi32(a);
+  return _mm256_castsi256_ps(_mm256_slli_epi32(extend, 16));
+#else
+  __m128i lo = _mm_cvtepu16_epi32(a);
+  __m128i hi = _mm_cvtepu16_epi32(_mm_srli_si128(a, 8));
+  __m128i lo_shift = _mm_slli_epi32(lo, 16);
+  __m128i hi_shift = _mm_slli_epi32(hi, 16);
+  return _mm256_castsi256_ps(_mm256_insertf128_si256(_mm256_castsi128_si256(lo_shift), hi_shift, 1));
+#endif
+}
+
+// Convert float to bfloat16 according to round-to-nearest-even/denormals algorithm.
+EIGEN_STRONG_INLINE Packet8bf F32ToBf16(const Packet8f& a) {
+
+  __m256i input = _mm256_castps_si256(a);
+
+#ifdef EIGEN_VECTORIZE_AVX2
+  // uint32_t lsb = (input >> 16);
+  __m256i t = _mm256_srli_epi32(input, 16);
+  // uint32_t lsb = lsb & 1;
+  t = _mm256_and_si256(t, _mm256_set1_epi32(1));
+  // uint32_t rounding_bias = 0x7fff + lsb;
+  t = _mm256_add_epi32(t, _mm256_set1_epi32(0x7fff));
+  // input += rounding_bias;
+  t = _mm256_add_epi32(t, input);
+  // input = input >> 16;
+  t = _mm256_srli_epi32(t, 16);
+  // Check NaN before converting back to bf16
+  __m256 mask = _mm256_cmp_ps(a, a, _CMP_ORD_Q);
+  __m256i nan = _mm256_set1_epi32(0x7fc0);
+  t = _mm256_blendv_epi8(nan, t, _mm256_castps_si256(mask));
+  // output = numext::bit_cast<uint16_t>(input);
+  return _mm_packus_epi32(_mm256_extractf128_si256(t, 0),
+                         _mm256_extractf128_si256(t, 1));
+#else
+  // uint32_t lsb = (input >> 16);
+  __m128i lo = _mm_srli_epi32(_mm256_extractf128_si256(input, 0), 16);
+  __m128i hi = _mm_srli_epi32(_mm256_extractf128_si256(input, 1), 16);
+  // uint32_t lsb = lsb & 1;
+  lo = _mm_and_si128(lo, _mm_set1_epi32(1));
+  hi = _mm_and_si128(hi, _mm_set1_epi32(1));
+  // uint32_t rounding_bias = 0x7fff + lsb;
+  lo = _mm_add_epi32(lo, _mm_set1_epi32(0x7fff));
+  hi = _mm_add_epi32(hi, _mm_set1_epi32(0x7fff));
+  // input += rounding_bias;
+  lo = _mm_add_epi32(lo, _mm256_extractf128_si256(input, 0));
+  hi = _mm_add_epi32(hi, _mm256_extractf128_si256(input, 1));
+  // input = input >> 16;
+  lo = _mm_srli_epi32(lo, 16);
+  hi = _mm_srli_epi32(hi, 16);
+  // Check NaN before converting back to bf16
+  __m256 mask = _mm256_cmp_ps(a, a, _CMP_ORD_Q);
+  __m128i nan = _mm_set1_epi32(0x7fc0);
+  lo = _mm_blendv_epi8(nan, lo, _mm_castps_si128(_mm256_castps256_ps128(mask)));
+  hi = _mm_blendv_epi8(nan, hi, _mm_castps_si128(_mm256_extractf128_ps(mask, 1)));
+  // output = numext::bit_cast<uint16_t>(input);
+  return _mm_packus_epi32(lo, hi);
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pset1<Packet8bf>(const bfloat16& from) {
+  return _mm_set1_epi16(numext::bit_cast<numext::uint16_t>(from));
+}
+
+template<> EIGEN_STRONG_INLINE bfloat16 pfirst<Packet8bf>(const Packet8bf& from) {
+  return numext::bit_cast<bfloat16>(static_cast<numext::uint16_t>(_mm_extract_epi16(from, 0)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pload<Packet8bf>(const bfloat16* from) {
+  return _mm_load_si128(reinterpret_cast<const __m128i*>(from));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf ploadu<Packet8bf>(const bfloat16* from) {
+  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to, const Packet8bf& from) {
+  _mm_store_si128(reinterpret_cast<__m128i*>(to), from);
+}
+
+template<> EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to, const Packet8bf& from) {
+  _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf
+ploaddup<Packet8bf>(const bfloat16* from) {
+  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
+  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
+  const numext::uint16_t c = numext::bit_cast<numext::uint16_t>(from[2]);
+  const numext::uint16_t d = numext::bit_cast<numext::uint16_t>(from[3]);
+  return _mm_set_epi16(d, d, c, c, b, b, a, a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf
+ploadquad<Packet8bf>(const bfloat16* from) {
+  const numext::uint16_t a = numext::bit_cast<numext::uint16_t>(from[0]);
+  const numext::uint16_t b = numext::bit_cast<numext::uint16_t>(from[1]);
+  return _mm_set_epi16(b, b, b, b, a, a, a, a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf ptrue(const Packet8bf& a) {
+  return _mm_cmpeq_epi32(a, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pabs(const Packet8bf& a) {
+  const __m128i sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
+  return _mm_andnot_si128(sign_mask, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pmin<Packet8bf>(const Packet8bf& a,
+                                                const Packet8bf& b) {
+  return F32ToBf16(pmin<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf pmax<Packet8bf>(const Packet8bf& a,
+                                                const Packet8bf& b) {
+  return F32ToBf16(pmax<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf plset<Packet8bf>(const bfloat16& a) {
+  return F32ToBf16(plset<Packet8f>(static_cast<float>(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf por(const Packet8bf& a,const Packet8bf& b) {
+  return _mm_or_si128(a,b);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf pxor(const Packet8bf& a,const Packet8bf& b) {
+  return _mm_xor_si128(a,b);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf pand(const Packet8bf& a,const Packet8bf& b) {
+  return _mm_and_si128(a,b);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf pandnot(const Packet8bf& a,const Packet8bf& b) {
+  return _mm_andnot_si128(b,a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pselect(const Packet8bf& mask, const Packet8bf& a, const Packet8bf& b) {
+  return _mm_blendv_epi8(b, a, mask);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pround<Packet8bf>(const Packet8bf& a)
+{
+  return F32ToBf16(pround<Packet8f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf print<Packet8bf>(const Packet8bf& a) {
+  return F32ToBf16(print<Packet8f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pceil<Packet8bf>(const Packet8bf& a) {
+  return F32ToBf16(pceil<Packet8f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pfloor<Packet8bf>(const Packet8bf& a) {
+  return F32ToBf16(pfloor<Packet8f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pcmp_eq(const Packet8bf& a,const Packet8bf& b) {
+  return Pack16To8(pcmp_eq(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pcmp_le(const Packet8bf& a,const Packet8bf& b) {
+  return Pack16To8(pcmp_le(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pcmp_lt(const Packet8bf& a,const Packet8bf& b) {
+  return Pack16To8(pcmp_lt(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pcmp_lt_or_nan(const Packet8bf& a,const Packet8bf& b) {
+  return Pack16To8(pcmp_lt_or_nan(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pconj(const Packet8bf& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE Packet8bf pnegate(const Packet8bf& a) {
+  Packet8bf sign_mask = _mm_set1_epi16(static_cast<numext::uint16_t>(0x8000));
+  return _mm_xor_si128(a, sign_mask);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf padd<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  return F32ToBf16(padd<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf psub<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  return F32ToBf16(psub<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pmul<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  return F32ToBf16(pmul<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pdiv<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  return F32ToBf16(pdiv<Packet8f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+
+template<> EIGEN_STRONG_INLINE Packet8bf pgather<bfloat16, Packet8bf>(const bfloat16* from, Index stride)
+{
+  const numext::uint16_t s0 = numext::bit_cast<numext::uint16_t>(from[0*stride]);
+  const numext::uint16_t s1 = numext::bit_cast<numext::uint16_t>(from[1*stride]);
+  const numext::uint16_t s2 = numext::bit_cast<numext::uint16_t>(from[2*stride]);
+  const numext::uint16_t s3 = numext::bit_cast<numext::uint16_t>(from[3*stride]);
+  const numext::uint16_t s4 = numext::bit_cast<numext::uint16_t>(from[4*stride]);
+  const numext::uint16_t s5 = numext::bit_cast<numext::uint16_t>(from[5*stride]);
+  const numext::uint16_t s6 = numext::bit_cast<numext::uint16_t>(from[6*stride]);
+  const numext::uint16_t s7 = numext::bit_cast<numext::uint16_t>(from[7*stride]);
+  return _mm_set_epi16(s7, s6, s5, s4, s3, s2, s1, s0);
+}
+
+template<> EIGEN_STRONG_INLINE void pscatter<bfloat16, Packet8bf>(bfloat16* to, const Packet8bf& from, Index stride)
+{
+  EIGEN_ALIGN32 bfloat16 aux[8];
+  pstore(aux, from);
+  to[stride*0] = aux[0];
+  to[stride*1] = aux[1];
+  to[stride*2] = aux[2];
+  to[stride*3] = aux[3];
+  to[stride*4] = aux[4];
+  to[stride*5] = aux[5];
+  to[stride*6] = aux[6];
+  to[stride*7] = aux[7];
+}
+
+template<> EIGEN_STRONG_INLINE bfloat16 predux<Packet8bf>(const Packet8bf& a) {
+  return static_cast<bfloat16>(predux<Packet8f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE bfloat16 predux_max<Packet8bf>(const Packet8bf& a) {
+  return static_cast<bfloat16>(predux_max<Packet8f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE bfloat16 predux_min<Packet8bf>(const Packet8bf& a) {
+  return static_cast<bfloat16>(predux_min<Packet8f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet8bf>(const Packet8bf& a) {
+  return static_cast<bfloat16>(predux_mul<Packet8f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf preverse(const Packet8bf& a)
+{
+  __m128i m = _mm_setr_epi8(14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1);
+  return _mm_shuffle_epi8(a,m);
+}
+
+EIGEN_STRONG_INLINE void
+ptranspose(PacketBlock<Packet8bf,8>& kernel) {
+  __m128i a = kernel.packet[0];
+  __m128i b = kernel.packet[1];
+  __m128i c = kernel.packet[2];
+  __m128i d = kernel.packet[3];
+  __m128i e = kernel.packet[4];
+  __m128i f = kernel.packet[5];
+  __m128i g = kernel.packet[6];
+  __m128i h = kernel.packet[7];
+
+  __m128i a03b03 = _mm_unpacklo_epi16(a, b);
+  __m128i c03d03 = _mm_unpacklo_epi16(c, d);
+  __m128i e03f03 = _mm_unpacklo_epi16(e, f);
+  __m128i g03h03 = _mm_unpacklo_epi16(g, h);
+  __m128i a47b47 = _mm_unpackhi_epi16(a, b);
+  __m128i c47d47 = _mm_unpackhi_epi16(c, d);
+  __m128i e47f47 = _mm_unpackhi_epi16(e, f);
+  __m128i g47h47 = _mm_unpackhi_epi16(g, h);
+
+  __m128i a01b01c01d01 = _mm_unpacklo_epi32(a03b03, c03d03);
+  __m128i a23b23c23d23 = _mm_unpackhi_epi32(a03b03, c03d03);
+  __m128i e01f01g01h01 = _mm_unpacklo_epi32(e03f03, g03h03);
+  __m128i e23f23g23h23 = _mm_unpackhi_epi32(e03f03, g03h03);
+  __m128i a45b45c45d45 = _mm_unpacklo_epi32(a47b47, c47d47);
+  __m128i a67b67c67d67 = _mm_unpackhi_epi32(a47b47, c47d47);
+  __m128i e45f45g45h45 = _mm_unpacklo_epi32(e47f47, g47h47);
+  __m128i e67f67g67h67 = _mm_unpackhi_epi32(e47f47, g47h47);
+
+  kernel.packet[0] = _mm_unpacklo_epi64(a01b01c01d01, e01f01g01h01);
+  kernel.packet[1] = _mm_unpackhi_epi64(a01b01c01d01, e01f01g01h01);
+  kernel.packet[2] = _mm_unpacklo_epi64(a23b23c23d23, e23f23g23h23);
+  kernel.packet[3] = _mm_unpackhi_epi64(a23b23c23d23, e23f23g23h23);
+  kernel.packet[4] = _mm_unpacklo_epi64(a45b45c45d45, e45f45g45h45);
+  kernel.packet[5] = _mm_unpackhi_epi64(a45b45c45d45, e45f45g45h45);
+  kernel.packet[6] = _mm_unpacklo_epi64(a67b67c67d67, e67f67g67h67);
+  kernel.packet[7] = _mm_unpackhi_epi64(a67b67c67d67, e67f67g67h67);
+}
+
+EIGEN_STRONG_INLINE void
+ptranspose(PacketBlock<Packet8bf,4>& kernel) {
+  __m128i a = kernel.packet[0];
+  __m128i b = kernel.packet[1];
+  __m128i c = kernel.packet[2];
+  __m128i d = kernel.packet[3];
+
+  __m128i ab_03 = _mm_unpacklo_epi16(a, b);
+  __m128i cd_03 = _mm_unpacklo_epi16(c, d);
+  __m128i ab_47 = _mm_unpackhi_epi16(a, b);
+  __m128i cd_47 = _mm_unpackhi_epi16(c, d);
+
+  kernel.packet[0] = _mm_unpacklo_epi32(ab_03, cd_03);
+  kernel.packet[1] = _mm_unpackhi_epi32(ab_03, cd_03);
+  kernel.packet[2] = _mm_unpacklo_epi32(ab_47, cd_47);
+  kernel.packet[3] = _mm_unpackhi_epi32(ab_47, cd_47);
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_PACKET_MATH_AVX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX/TypeCasting.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX/TypeCasting.h
new file mode 100644
index 0000000000000000000000000000000000000000..98533477bd36aeb330528fdf8d54ff9e89250bf0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX/TypeCasting.h
@@ -0,0 +1,192 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_AVX_H
+#define EIGEN_TYPE_CASTING_AVX_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_VECTORIZE_AVX512
+template<> struct type_casting_traits<float, bool> : vectorized_type_casting_traits<float, bool> {};
+template<> struct type_casting_traits<bool, float> : vectorized_type_casting_traits<bool, float> {};
+
+template<> struct type_casting_traits<float, int> : vectorized_type_casting_traits<float, int> {};
+template<> struct type_casting_traits<int, float> : vectorized_type_casting_traits<int, float> {};
+
+template<> struct type_casting_traits<float, double> : vectorized_type_casting_traits<float, double> {};
+template<> struct type_casting_traits<double, float> : vectorized_type_casting_traits<double, float> {};
+
+template<> struct type_casting_traits<double, int> : vectorized_type_casting_traits<double, int> {};
+template<> struct type_casting_traits<int, double> : vectorized_type_casting_traits<int, double> {};
+
+template<> struct type_casting_traits<half, float> : vectorized_type_casting_traits<half, float> {};
+template<> struct type_casting_traits<float, half> : vectorized_type_casting_traits<float, half> {};
+
+template<> struct type_casting_traits<bfloat16, float> : vectorized_type_casting_traits<bfloat16, float> {};
+template<> struct type_casting_traits<float, bfloat16> : vectorized_type_casting_traits<float, bfloat16> {};
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet16b pcast<Packet8f, Packet16b>(const Packet8f& a,
+                                                         const Packet8f& b) {
+  __m256 nonzero_a = _mm256_cmp_ps(a, pzero(a), _CMP_NEQ_UQ);
+  __m256 nonzero_b = _mm256_cmp_ps(b, pzero(b), _CMP_NEQ_UQ);
+  constexpr char kFF = '\255';
+#ifndef EIGEN_VECTORIZE_AVX2
+  __m128i shuffle_mask128_a_lo = _mm_set_epi8(kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, 12, 8, 4, 0);
+  __m128i shuffle_mask128_a_hi = _mm_set_epi8(kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, 12, 8, 4, 0, kFF, kFF, kFF, kFF);
+  __m128i shuffle_mask128_b_lo = _mm_set_epi8(kFF, kFF, kFF, kFF, 12, 8, 4, 0, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF);
+  __m128i shuffle_mask128_b_hi = _mm_set_epi8(12, 8, 4, 0, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF);
+  __m128i a_hi = _mm_shuffle_epi8(_mm256_extractf128_si256(_mm256_castps_si256(nonzero_a), 1), shuffle_mask128_a_hi);
+  __m128i a_lo = _mm_shuffle_epi8(_mm256_extractf128_si256(_mm256_castps_si256(nonzero_a), 0), shuffle_mask128_a_lo);
+  __m128i b_hi = _mm_shuffle_epi8(_mm256_extractf128_si256(_mm256_castps_si256(nonzero_b), 1), shuffle_mask128_b_hi);
+  __m128i b_lo = _mm_shuffle_epi8(_mm256_extractf128_si256(_mm256_castps_si256(nonzero_b), 0), shuffle_mask128_b_lo);
+  __m128i merged = _mm_or_si128(_mm_or_si128(b_lo, b_hi), _mm_or_si128(a_lo, a_hi));
+  return _mm_and_si128(merged, _mm_set1_epi8(1));
+ #else
+  __m256i a_shuffle_mask = _mm256_set_epi8(kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF,  12,   8,   4,   0, kFF, kFF, kFF, kFF,
+                                           kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF,  12,   8,   4,   0);
+  __m256i b_shuffle_mask = _mm256_set_epi8( 12,   8,   4,   0, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF,
+                                           kFF, kFF, kFF, kFF,  12,   8,   4,   0, kFF, kFF, kFF, kFF, kFF, kFF, kFF, kFF);
+  __m256i a_shuff = _mm256_shuffle_epi8(_mm256_castps_si256(nonzero_a), a_shuffle_mask);
+  __m256i b_shuff = _mm256_shuffle_epi8(_mm256_castps_si256(nonzero_b), b_shuffle_mask);
+  __m256i a_or_b = _mm256_or_si256(a_shuff, b_shuff);
+  __m256i merged = _mm256_or_si256(a_or_b, _mm256_castsi128_si256(_mm256_extractf128_si256(a_or_b, 1)));
+  return _mm256_castsi256_si128(_mm256_and_si256(merged, _mm256_set1_epi8(1)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pcast<Packet16b, Packet8f>(const Packet16b& a) {
+  const __m256 cst_one = _mm256_set1_ps(1.0f);
+  #ifdef EIGEN_VECTORIZE_AVX2
+  __m256i a_extended = _mm256_cvtepi8_epi32(a);
+  __m256i abcd_efgh = _mm256_cmpeq_epi32(a_extended, _mm256_setzero_si256());
+  #else
+  __m128i abcd_efhg_ijkl_mnop = _mm_cmpeq_epi8(a, _mm_setzero_si128());
+  __m128i aabb_ccdd_eeff_gghh = _mm_unpacklo_epi8(abcd_efhg_ijkl_mnop, abcd_efhg_ijkl_mnop);
+  __m128i aaaa_bbbb_cccc_dddd = _mm_unpacklo_epi8(aabb_ccdd_eeff_gghh, aabb_ccdd_eeff_gghh);
+  __m128i eeee_ffff_gggg_hhhh = _mm_unpackhi_epi8(aabb_ccdd_eeff_gghh, aabb_ccdd_eeff_gghh);
+  __m256i abcd_efgh = _mm256_setr_m128i(aaaa_bbbb_cccc_dddd, eeee_ffff_gggg_hhhh);
+  #endif
+  __m256 result = _mm256_andnot_ps(_mm256_castsi256_ps(abcd_efgh), cst_one);
+  return result;
+}
+
+template<> EIGEN_STRONG_INLINE Packet8i pcast<Packet8f, Packet8i>(const Packet8f& a) {
+  return _mm256_cvttps_epi32(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8i pcast<Packet4d, Packet8i>(const Packet4d& a, const Packet4d& b) {
+  return _mm256_set_m128i(_mm256_cvttpd_epi32(b), _mm256_cvttpd_epi32(a));
+}
+
+template <> EIGEN_STRONG_INLINE Packet4i pcast<Packet4d, Packet4i>(const Packet4d& a) {
+  return _mm256_cvttpd_epi32(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pcast<Packet8i, Packet8f>(const Packet8i& a) {
+  return _mm256_cvtepi32_ps(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pcast<Packet4d, Packet8f>(const Packet4d& a, const Packet4d& b) {
+  return _mm256_set_m128(_mm256_cvtpd_ps(b), _mm256_cvtpd_ps(a));
+}
+
+template <> EIGEN_STRONG_INLINE Packet4f pcast<Packet4d, Packet4f>(const Packet4d& a) {
+  return _mm256_cvtpd_ps(a);
+}
+
+template <> EIGEN_STRONG_INLINE Packet4d pcast<Packet8i, Packet4d>(const Packet8i& a) {
+  return _mm256_cvtepi32_pd(_mm256_castsi256_si128(a));
+}
+
+template <> EIGEN_STRONG_INLINE Packet4d pcast<Packet4i, Packet4d>(const Packet4i& a) {
+  return _mm256_cvtepi32_pd(a);
+}
+
+template <> EIGEN_STRONG_INLINE Packet4d pcast<Packet8f, Packet4d>(const Packet8f& a) {
+  return _mm256_cvtps_pd(_mm256_castps256_ps128(a));
+}
+
+template <> EIGEN_STRONG_INLINE Packet4d pcast<Packet4f, Packet4d>(const Packet4f& a) {
+  return _mm256_cvtps_pd(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8i preinterpret<Packet8i,Packet8f>(const Packet8f& a) {
+  return _mm256_castps_si256(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f preinterpret<Packet8f,Packet8i>(const Packet8i& a) {
+  return _mm256_castsi256_ps(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8ui preinterpret<Packet8ui, Packet8i>(const Packet8i& a) {
+  return Packet8ui(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8i preinterpret<Packet8i, Packet8ui>(const Packet8ui& a) {
+  return Packet8i(a);
+}
+
+// truncation operations
+
+template<> EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet8f>(const Packet8f& a) {
+  return _mm256_castps256_ps128(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet4d>(const Packet4d& a) {
+  return _mm256_castpd256_pd128(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet8i>(const Packet8i& a) {
+  return _mm256_castsi256_si128(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet8ui>(const Packet8ui& a) {
+  return _mm256_castsi256_si128(a);
+}
+
+
+#ifdef EIGEN_VECTORIZE_AVX2
+template<> EIGEN_STRONG_INLINE Packet4ul preinterpret<Packet4ul, Packet4l>(const Packet4l& a) {
+  return Packet4ul(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4l preinterpret<Packet4l, Packet4ul>(const Packet4ul& a) {
+  return Packet4l(a);
+}
+
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet8f pcast<Packet8h, Packet8f>(const Packet8h& a) {
+  return half2float(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f pcast<Packet8bf, Packet8f>(const Packet8bf& a) {
+  return Bf16ToF32(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h pcast<Packet8f, Packet8h>(const Packet8f& a) {
+  return float2half(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pcast<Packet8f, Packet8bf>(const Packet8f& a) {
+  return F32ToBf16(a);
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TYPE_CASTING_AVX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/Complex.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/Complex.h
new file mode 100644
index 0000000000000000000000000000000000000000..0372e95dccf656e4739491a5421a164d12625c9f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/Complex.h
@@ -0,0 +1,380 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_AVX512_H
+#define EIGEN_COMPLEX_AVX512_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+//---------- float ----------
+struct Packet8cf
+{
+  EIGEN_STRONG_INLINE Packet8cf() {}
+  EIGEN_STRONG_INLINE explicit Packet8cf(const __m512& a) : v(a) {}
+  __m512  v;
+};
+
+template<> struct packet_traits<std::complex<float> >  : default_packet_traits
+{
+  typedef Packet8cf type;
+  typedef Packet4cf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasDiv    = 1,
+    HasNegate = 1,
+    HasSqrt   = 1,
+    HasAbs    = 0,
+    HasAbs2   = 0,
+    HasMin    = 0,
+    HasMax    = 0,
+    HasSetLinear = 0
+  };
+};
+
+template<> struct unpacket_traits<Packet8cf> {
+  typedef std::complex<float> type;
+  typedef Packet4cf half;
+  typedef Packet16f as_real;
+  enum {
+    size = 8,
+    alignment=unpacket_traits<Packet16f>::alignment,
+    vectorizable=true,
+    masked_load_available=false,
+    masked_store_available=false
+  };
+};
+
+template<> EIGEN_STRONG_INLINE Packet8cf ptrue<Packet8cf>(const Packet8cf& a) { return Packet8cf(ptrue(Packet16f(a.v))); }
+template<> EIGEN_STRONG_INLINE Packet8cf padd<Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(_mm512_add_ps(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet8cf psub<Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(_mm512_sub_ps(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet8cf pnegate(const Packet8cf& a)
+{
+  return Packet8cf(pnegate(a.v));
+}
+template<> EIGEN_STRONG_INLINE Packet8cf pconj(const Packet8cf& a)
+{
+  const __m512 mask = _mm512_castsi512_ps(_mm512_setr_epi32(
+    0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000,
+    0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000,0x00000000,0x80000000));
+  return Packet8cf(pxor(a.v,mask));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8cf pmul<Packet8cf>(const Packet8cf& a, const Packet8cf& b)
+{
+  __m512 tmp2 = _mm512_mul_ps(_mm512_movehdup_ps(a.v), _mm512_permute_ps(b.v, _MM_SHUFFLE(2,3,0,1)));
+  return Packet8cf(_mm512_fmaddsub_ps(_mm512_moveldup_ps(a.v), b.v, tmp2));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8cf pand   <Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(pand(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet8cf por    <Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(por(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet8cf pxor   <Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(pxor(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet8cf pandnot<Packet8cf>(const Packet8cf& a, const Packet8cf& b) { return Packet8cf(pandnot(a.v,b.v)); }
+
+template <>
+EIGEN_STRONG_INLINE Packet8cf pcmp_eq(const Packet8cf& a, const Packet8cf& b) {
+  __m512 eq = pcmp_eq<Packet16f>(a.v, b.v);
+  return Packet8cf(pand(eq, _mm512_permute_ps(eq, 0xB1)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8cf pload <Packet8cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet8cf(pload<Packet16f>(&numext::real_ref(*from))); }
+template<> EIGEN_STRONG_INLINE Packet8cf ploadu<Packet8cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet8cf(ploadu<Packet16f>(&numext::real_ref(*from))); }
+
+
+template<> EIGEN_STRONG_INLINE Packet8cf pset1<Packet8cf>(const std::complex<float>& from)
+{
+  const float re = std::real(from);
+  const float im = std::imag(from);
+  return Packet8cf(_mm512_set_ps(im, re, im, re, im, re, im, re, im, re, im, re, im, re, im, re));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8cf ploaddup<Packet8cf>(const std::complex<float>* from)
+{
+  return Packet8cf( _mm512_castpd_ps( ploaddup<Packet8d>((const double*)(const void*)from )) );
+}
+template<> EIGEN_STRONG_INLINE Packet8cf ploadquad<Packet8cf>(const std::complex<float>* from)
+{
+  return Packet8cf( _mm512_castpd_ps( ploadquad<Packet8d>((const double*)(const void*)from )) );
+}
+
+template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float>* to, const Packet8cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), from.v); }
+template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to, const Packet8cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), from.v); }
+
+template<> EIGEN_DEVICE_FUNC inline Packet8cf pgather<std::complex<float>, Packet8cf>(const std::complex<float>* from, Index stride)
+{
+  return Packet8cf(_mm512_castpd_ps(pgather<double,Packet8d>((const double*)(const void*)from, stride)));
+}
+
+template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet8cf>(std::complex<float>* to, const Packet8cf& from, Index stride)
+{
+  pscatter((double*)(void*)to, _mm512_castps_pd(from.v), stride);
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet8cf>(const Packet8cf& a)
+{
+  return pfirst(Packet2cf(_mm512_castps512_ps128(a.v)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8cf preverse(const Packet8cf& a) {
+  return Packet8cf(_mm512_castsi512_ps(
+            _mm512_permutexvar_epi64( _mm512_set_epi32(0, 0, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7),
+                                      _mm512_castps_si512(a.v))));
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet8cf>(const Packet8cf& a)
+{
+  return predux(padd(Packet4cf(extract256<0>(a.v)),
+                     Packet4cf(extract256<1>(a.v))));
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet8cf>(const Packet8cf& a)
+{
+  return predux_mul(pmul(Packet4cf(extract256<0>(a.v)),
+                         Packet4cf(extract256<1>(a.v))));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4cf predux_half_dowto4<Packet8cf>(const Packet8cf& a) {
+  __m256 lane0 = extract256<0>(a.v);
+  __m256 lane1 = extract256<1>(a.v);
+  __m256 res = _mm256_add_ps(lane0, lane1);
+  return Packet4cf(res);
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet8cf,Packet16f)
+
+template<> EIGEN_STRONG_INLINE Packet8cf pdiv<Packet8cf>(const Packet8cf& a, const Packet8cf& b)
+{
+  return pdiv_complex(a, b);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8cf pcplxflip<Packet8cf>(const Packet8cf& x)
+{
+  return Packet8cf(_mm512_shuffle_ps(x.v, x.v, _MM_SHUFFLE(2, 3, 0 ,1)));
+}
+
+//---------- double ----------
+struct Packet4cd
+{
+  EIGEN_STRONG_INLINE Packet4cd() {}
+  EIGEN_STRONG_INLINE explicit Packet4cd(const __m512d& a) : v(a) {}
+  __m512d  v;
+};
+
+template<> struct packet_traits<std::complex<double> >  : default_packet_traits
+{
+  typedef Packet4cd type;
+  typedef Packet2cd half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 0,
+    size = 4,
+
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasDiv    = 1,
+    HasNegate = 1,
+    HasSqrt   = 1,
+    HasAbs    = 0,
+    HasAbs2   = 0,
+    HasMin    = 0,
+    HasMax    = 0,
+    HasSetLinear = 0
+  };
+};
+
+template<> struct unpacket_traits<Packet4cd> {
+  typedef std::complex<double> type;
+  typedef Packet2cd half;
+  typedef Packet8d as_real;
+  enum {
+    size = 4,
+    alignment = unpacket_traits<Packet8d>::alignment,
+    vectorizable=true,
+    masked_load_available=false,
+    masked_store_available=false
+  };
+};
+
+template<> EIGEN_STRONG_INLINE Packet4cd padd<Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(_mm512_add_pd(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet4cd psub<Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(_mm512_sub_pd(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet4cd pnegate(const Packet4cd& a) { return Packet4cd(pnegate(a.v)); }
+template<> EIGEN_STRONG_INLINE Packet4cd pconj(const Packet4cd& a)
+{
+  const __m512d mask = _mm512_castsi512_pd(
+          _mm512_set_epi32(0x80000000,0x0,0x0,0x0,0x80000000,0x0,0x0,0x0,
+                           0x80000000,0x0,0x0,0x0,0x80000000,0x0,0x0,0x0));
+  return Packet4cd(pxor(a.v,mask));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4cd pmul<Packet4cd>(const Packet4cd& a, const Packet4cd& b)
+{
+  __m512d tmp1 = _mm512_shuffle_pd(a.v,a.v,0x0);
+  __m512d tmp2 = _mm512_shuffle_pd(a.v,a.v,0xFF);
+  __m512d tmp3 = _mm512_shuffle_pd(b.v,b.v,0x55);
+  __m512d odd  = _mm512_mul_pd(tmp2, tmp3);
+  return Packet4cd(_mm512_fmaddsub_pd(tmp1, b.v, odd));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4cd ptrue<Packet4cd>(const Packet4cd& a) { return Packet4cd(ptrue(Packet8d(a.v))); }
+template<> EIGEN_STRONG_INLINE Packet4cd pand   <Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(pand(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet4cd por    <Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(por(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet4cd pxor   <Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(pxor(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet4cd pandnot<Packet4cd>(const Packet4cd& a, const Packet4cd& b) { return Packet4cd(pandnot(a.v,b.v)); }
+
+template <>
+EIGEN_STRONG_INLINE Packet4cd pcmp_eq(const Packet4cd& a, const Packet4cd& b) {
+  __m512d eq = pcmp_eq<Packet8d>(a.v, b.v);
+  return Packet4cd(pand(eq, _mm512_permute_pd(eq, 0x55)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4cd pload <Packet4cd>(const std::complex<double>* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return Packet4cd(pload<Packet8d>((const double*)from)); }
+template<> EIGEN_STRONG_INLINE Packet4cd ploadu<Packet4cd>(const std::complex<double>* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet4cd(ploadu<Packet8d>((const double*)from)); }
+
+template<> EIGEN_STRONG_INLINE Packet4cd pset1<Packet4cd>(const std::complex<double>& from)
+{
+  return Packet4cd(_mm512_castps_pd(_mm512_broadcast_f32x4( _mm_castpd_ps(pset1<Packet1cd>(from).v))));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4cd ploaddup<Packet4cd>(const std::complex<double>* from) {
+  return Packet4cd(_mm512_insertf64x4(
+          _mm512_castpd256_pd512(ploaddup<Packet2cd>(from).v), ploaddup<Packet2cd>(from+1).v, 1));
+}
+
+template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *   to, const Packet4cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
+template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *   to, const Packet4cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
+
+template<> EIGEN_DEVICE_FUNC inline Packet4cd pgather<std::complex<double>, Packet4cd>(const std::complex<double>* from, Index stride)
+{
+  return Packet4cd(_mm512_insertf64x4(_mm512_castpd256_pd512(
+            _mm256_insertf128_pd(_mm256_castpd128_pd256(ploadu<Packet1cd>(from+0*stride).v), ploadu<Packet1cd>(from+1*stride).v,1)),
+            _mm256_insertf128_pd(_mm256_castpd128_pd256(ploadu<Packet1cd>(from+2*stride).v), ploadu<Packet1cd>(from+3*stride).v,1), 1));
+}
+
+template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet4cd>(std::complex<double>* to, const Packet4cd& from, Index stride)
+{
+  __m512i fromi = _mm512_castpd_si512(from.v);
+  double* tod = (double*)(void*)to;
+  _mm_storeu_pd(tod+0*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,0)) );
+  _mm_storeu_pd(tod+2*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,1)) );
+  _mm_storeu_pd(tod+4*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,2)) );
+  _mm_storeu_pd(tod+6*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,3)) );
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet4cd>(const Packet4cd& a)
+{
+  __m128d low = extract128<0>(a.v);
+  EIGEN_ALIGN16 double res[2];
+  _mm_store_pd(res, low);
+  return std::complex<double>(res[0],res[1]);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4cd preverse(const Packet4cd& a) {
+  return Packet4cd(_mm512_shuffle_f64x2(a.v, a.v, (shuffle_mask<3,2,1,0>::mask)));
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet4cd>(const Packet4cd& a)
+{
+  return predux(padd(Packet2cd(_mm512_extractf64x4_pd(a.v,0)),
+                     Packet2cd(_mm512_extractf64x4_pd(a.v,1))));
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet4cd>(const Packet4cd& a)
+{
+  return predux_mul(pmul(Packet2cd(_mm512_extractf64x4_pd(a.v,0)),
+                         Packet2cd(_mm512_extractf64x4_pd(a.v,1))));
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet4cd,Packet8d)
+
+template<> EIGEN_STRONG_INLINE Packet4cd pdiv<Packet4cd>(const Packet4cd& a, const Packet4cd& b)
+{
+  return pdiv_complex(a, b);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4cd pcplxflip<Packet4cd>(const Packet4cd& x)
+{
+  return Packet4cd(_mm512_permute_pd(x.v,0x55));
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet8cf,4>& kernel) {
+  PacketBlock<Packet8d,4> pb;
+  
+  pb.packet[0] = _mm512_castps_pd(kernel.packet[0].v);
+  pb.packet[1] = _mm512_castps_pd(kernel.packet[1].v);
+  pb.packet[2] = _mm512_castps_pd(kernel.packet[2].v);
+  pb.packet[3] = _mm512_castps_pd(kernel.packet[3].v);
+  ptranspose(pb);
+  kernel.packet[0].v = _mm512_castpd_ps(pb.packet[0]);
+  kernel.packet[1].v = _mm512_castpd_ps(pb.packet[1]);
+  kernel.packet[2].v = _mm512_castpd_ps(pb.packet[2]);
+  kernel.packet[3].v = _mm512_castpd_ps(pb.packet[3]);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet8cf,8>& kernel) {
+  PacketBlock<Packet8d,8> pb;
+  
+  pb.packet[0] = _mm512_castps_pd(kernel.packet[0].v);
+  pb.packet[1] = _mm512_castps_pd(kernel.packet[1].v);
+  pb.packet[2] = _mm512_castps_pd(kernel.packet[2].v);
+  pb.packet[3] = _mm512_castps_pd(kernel.packet[3].v);
+  pb.packet[4] = _mm512_castps_pd(kernel.packet[4].v);
+  pb.packet[5] = _mm512_castps_pd(kernel.packet[5].v);
+  pb.packet[6] = _mm512_castps_pd(kernel.packet[6].v);
+  pb.packet[7] = _mm512_castps_pd(kernel.packet[7].v);
+  ptranspose(pb);
+  kernel.packet[0].v = _mm512_castpd_ps(pb.packet[0]);
+  kernel.packet[1].v = _mm512_castpd_ps(pb.packet[1]);
+  kernel.packet[2].v = _mm512_castpd_ps(pb.packet[2]);
+  kernel.packet[3].v = _mm512_castpd_ps(pb.packet[3]);
+  kernel.packet[4].v = _mm512_castpd_ps(pb.packet[4]);
+  kernel.packet[5].v = _mm512_castpd_ps(pb.packet[5]);
+  kernel.packet[6].v = _mm512_castpd_ps(pb.packet[6]);
+  kernel.packet[7].v = _mm512_castpd_ps(pb.packet[7]);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet4cd,4>& kernel) {
+  __m512d T0 = _mm512_shuffle_f64x2(kernel.packet[0].v, kernel.packet[1].v, (shuffle_mask<0,1,0,1>::mask)); // [a0 a1 b0 b1]
+  __m512d T1 = _mm512_shuffle_f64x2(kernel.packet[0].v, kernel.packet[1].v, (shuffle_mask<2,3,2,3>::mask)); // [a2 a3 b2 b3]
+  __m512d T2 = _mm512_shuffle_f64x2(kernel.packet[2].v, kernel.packet[3].v, (shuffle_mask<0,1,0,1>::mask)); // [c0 c1 d0 d1]
+  __m512d T3 = _mm512_shuffle_f64x2(kernel.packet[2].v, kernel.packet[3].v, (shuffle_mask<2,3,2,3>::mask)); // [c2 c3 d2 d3]
+
+  kernel.packet[3] = Packet4cd(_mm512_shuffle_f64x2(T1, T3, (shuffle_mask<1,3,1,3>::mask))); // [a3 b3 c3 d3]
+  kernel.packet[2] = Packet4cd(_mm512_shuffle_f64x2(T1, T3, (shuffle_mask<0,2,0,2>::mask))); // [a2 b2 c2 d2]
+  kernel.packet[1] = Packet4cd(_mm512_shuffle_f64x2(T0, T2, (shuffle_mask<1,3,1,3>::mask))); // [a1 b1 c1 d1]
+  kernel.packet[0] = Packet4cd(_mm512_shuffle_f64x2(T0, T2, (shuffle_mask<0,2,0,2>::mask))); // [a0 b0 c0 d0]
+}
+
+template<> EIGEN_STRONG_INLINE Packet4cd psqrt<Packet4cd>(const Packet4cd& a) {
+  return psqrt_complex<Packet4cd>(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8cf psqrt<Packet8cf>(const Packet8cf& a) {
+  return psqrt_complex<Packet8cf>(a);
+}
+
+} // end namespace internal
+} // end namespace Eigen
+
+#endif // EIGEN_COMPLEX_AVX512_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/GemmKernel.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/GemmKernel.h
new file mode 100644
index 0000000000000000000000000000000000000000..2d33ca3f9bcb701bb894202550ca753552823826
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/GemmKernel.h
@@ -0,0 +1,1244 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2022 Intel Corporation
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CORE_ARCH_AVX512_GEMM_KERNEL_H
+#define EIGEN_CORE_ARCH_AVX512_GEMM_KERNEL_H
+
+#if EIGEN_COMP_MSVC
+#include <intrin.h>
+#else
+#include <x86intrin.h>
+#endif
+#include <immintrin.h>
+#include <type_traits>
+
+#include "../../InternalHeaderCheck.h"
+
+#if !defined(EIGEN_USE_AVX512_GEMM_KERNELS)
+#define EIGEN_USE_AVX512_GEMM_KERNELS 1
+#endif
+
+#define SECOND_FETCH (32)
+#if (EIGEN_COMP_GNUC_STRICT != 0) && !defined(EIGEN_ARCH_AVX512_GEMM_KERNEL_USE_LESS_A_REGS)
+// Use less registers to load A elements to workaround compiler spills. Loose a
+// bit of performance (less than ~2%).
+#define EIGEN_ARCH_AVX512_GEMM_KERNEL_USE_LESS_A_REGS
+#endif
+
+namespace Eigen {
+namespace internal {
+
+template <typename Scalar, bool is_unit_inc>
+class gemm_class {
+  using vec = typename packet_traits<Scalar>::type;
+  using vec_ymm = typename unpacket_traits<vec>::half;
+  using vec_xmm = typename unpacket_traits<vec_ymm>::half;
+  using umask_t = typename unpacket_traits<vec>::mask_t;
+
+  static constexpr bool is_f32 = sizeof(Scalar) == sizeof(float);
+  static constexpr bool is_f64 = sizeof(Scalar) == sizeof(double);
+
+#ifndef EIGEN_ARCH_AVX512_GEMM_KERNEL_USE_LESS_A_REGS
+  static constexpr bool use_less_a_regs = !is_unit_inc;
+#else
+  static constexpr bool use_less_a_regs = true;
+#endif
+#ifndef EIGEN_ARCH_AVX512_GEMM_KERNEL_USE_LESS_B_REGS
+  static constexpr bool use_less_b_regs = !is_unit_inc;
+#else
+  static constexpr bool use_less_b_regs = true;
+#endif
+
+  static constexpr int a_regs[] = {0, 1, 2, use_less_a_regs ? 0 : 3, use_less_a_regs ? 1 : 4, use_less_a_regs ? 2 : 5};
+  static constexpr int b_regs[] = {6, use_less_b_regs ? 6 : 7};
+  static constexpr int c_regs[] = {
+      8, 16, 24, 9, 17, 25, 10, 18, 26, 11, 19, 27, 12, 20, 28, 13, 21, 29, 14, 22, 30, 15, 23, 31,
+  };
+
+  static constexpr int alpha_load_reg = 0;
+  static constexpr int c_load_regs[] = {1, 2, 6};
+
+  static constexpr int a_shift = 128;
+  static constexpr int b_shift = 128;
+
+  static constexpr int nelems_in_cache_line = is_f32 ? 16 : 8;
+  static constexpr int a_prefetch_size = nelems_in_cache_line * 2;
+  static constexpr int b_prefetch_size = nelems_in_cache_line * 8;
+
+  vec zmm[32];
+  umask_t mask;
+
+  // gemm arguments.
+  Index m;
+  const Index n, k, ldc;
+  const Index inc;
+  const Scalar *alpha;
+
+  const Scalar *a, *b;
+  Scalar *c;
+
+  const bool is_alpha1;
+  const bool is_beta0;
+
+  const Index a_stride, b_stride;
+  const Index a_off, b_off;
+
+  static EIGEN_ALWAYS_INLINE constexpr int div_up(int a, int b) { return a == 0 ? 0 : (a - 1) / b + 1; }
+
+  EIGEN_ALWAYS_INLINE void prefetch_a(const Scalar *a_addr) {
+    _mm_prefetch((char *)(a_prefetch_size + a_addr - a_shift), _MM_HINT_T0);
+  }
+
+  EIGEN_ALWAYS_INLINE void prefetch_b(const Scalar *b_addr) {
+    _mm_prefetch((char *)(b_prefetch_size + b_addr - b_shift), _MM_HINT_T0);
+  }
+
+  EIGEN_ALWAYS_INLINE void prefetch_x(const Scalar *x_addr) { _mm_prefetch((char *)(x_addr - a_shift), _MM_HINT_T2); }
+
+  EIGEN_ALWAYS_INLINE void prefetch_c(const Scalar *c_addr) {
+#if defined(__PRFCHW__) && __PRFCHW__ == 1
+    _m_prefetchw((void *)c_addr);
+#else
+    _mm_prefetch((char *)c_addr, _MM_HINT_T0);
+#endif
+  }
+
+  template <int nelems>
+  EIGEN_ALWAYS_INLINE void a_load(vec &a_reg, const Scalar *a_addr) {
+    switch (nelems * sizeof(*a_addr) * 8) {
+      default:
+      case 512 * 3:
+        a_reg = ploadu<vec>(a_addr);
+        break;
+      case 512 * 2:
+        a_reg = ploadu<vec>(a_addr);
+        break;
+      case 512 * 1:
+        a_reg = ploadu<vec>(a_addr);
+        break;
+      case 256 * 1:
+        a_reg = preinterpret<vec>(_mm512_broadcast_f64x4(ploadu<Packet4d>(reinterpret_cast<const double *>(a_addr))));
+        break;
+      case 128 * 1:
+        a_reg = preinterpret<vec>(_mm512_broadcast_f32x4(ploadu<Packet4f>(reinterpret_cast<const float *>(a_addr))));
+        break;
+      case 64 * 1:
+        a_reg = preinterpret<vec>(pload1<Packet8d>(reinterpret_cast<const double *>(a_addr)));
+        break;
+      case 32 * 1:
+        a_reg = pload1<vec>(a_addr);
+        break;
+    }
+  }
+
+  EIGEN_ALWAYS_INLINE void b_load(vec &b_reg, const Scalar *b_addr) { b_reg = pload1<vec>(b_addr); }
+
+  template <int nelems>
+  EIGEN_ALWAYS_INLINE void c_store(Scalar *mem, vec &src) {
+    if (is_unit_inc) {
+      switch (nelems * sizeof(*mem) * 8) {
+        default:
+        case 512 * 3:
+          pstoreu(mem, src);
+          break;
+        case 512 * 2:
+          pstoreu(mem, src);
+          break;
+        case 512 * 1:
+          pstoreu(mem, src);
+          break;
+        case 256 * 1:
+          pstoreu(mem, preinterpret<vec_ymm>(src));
+          break;
+        case 128 * 1:
+          pstoreu(mem, preinterpret<vec_xmm>(src));
+          break;
+        case 64 * 1:
+          pstorel(mem, preinterpret<vec_xmm>(src));
+          break;
+        case 32 * 1:
+          pstores(mem, preinterpret<vec_xmm>(src));
+          break;
+      }
+    } else {
+      switch (nelems * sizeof(*mem) * 8) {
+        default:
+        case 512 * 3:
+          pscatter(mem, src, inc);
+          break;
+        case 512 * 2:
+          pscatter(mem, src, inc);
+          break;
+        case 512 * 1:
+          pscatter(mem, src, inc);
+          break;
+        case 256 * 1:
+          pscatter(mem, src, inc, mask);
+          break;
+        case 128 * 1:
+          pscatter(mem, src, inc, mask);
+          break;
+        case 64 * 1:
+          pscatter(mem, src, inc, mask);
+          break;
+        case 32 * 1:
+          pscatter(mem, src, inc, mask);
+          break;
+      }
+    }
+  }
+
+  template <int nelems>
+  EIGEN_ALWAYS_INLINE void vaddm(vec &dst, const Scalar *mem, vec &src, vec &reg) {
+    if (is_unit_inc) {
+      switch (nelems * sizeof(*mem) * 8) {
+        default:
+        case 512 * 3:
+          dst = padd(src, ploadu<vec>(mem));
+          break;
+        case 512 * 2:
+          dst = padd(src, ploadu<vec>(mem));
+          break;
+        case 512 * 1:
+          dst = padd(src, ploadu<vec>(mem));
+          break;
+        case 256 * 1:
+          dst = preinterpret<vec>(padd(preinterpret<vec_ymm>(src), ploadu<vec_ymm>(mem)));
+          break;
+        case 128 * 1:
+          dst = preinterpret<vec>(padd(preinterpret<vec_xmm>(src), ploadu<vec_xmm>(mem)));
+          break;
+        case 64 * 1:
+          dst = preinterpret<vec>(padd(preinterpret<vec_xmm>(src), ploadl<vec_xmm>(mem)));
+          break;
+        case 32 * 1:
+          dst = preinterpret<vec>(padds(preinterpret<vec_xmm>(src), ploads<vec_xmm>(mem)));
+          break;
+      }
+    } else {
+      // Zero out scratch register
+      reg = pzero(reg);
+
+      switch (nelems * sizeof(*mem) * 8) {
+        default:
+        case 512 * 3:
+          reg = pgather<Scalar, vec>(mem, inc);
+          dst = padd(src, reg);
+          break;
+        case 512 * 2:
+          reg = pgather<Scalar, vec>(mem, inc);
+          dst = padd(src, reg);
+          break;
+        case 512 * 1:
+          reg = pgather<Scalar, vec>(mem, inc);
+          dst = padd(src, reg);
+          break;
+        case 256 * 1:
+          reg = preinterpret<vec>(pgather<Scalar, vec_ymm>(mem, inc));
+          dst = preinterpret<vec>(padd(preinterpret<vec_ymm>(src), preinterpret<vec_ymm>(reg)));
+          break;
+        case 128 * 1:
+          reg = preinterpret<vec>(pgather<Scalar, vec_xmm>(mem, inc));
+          dst = preinterpret<vec>(padd(preinterpret<vec_xmm>(src), preinterpret<vec_xmm>(reg)));
+          break;
+        case 64 * 1:
+          if (is_f32) {
+            reg = pgather(reg, mem, inc, mask);
+            dst = preinterpret<vec>(padd(preinterpret<vec_xmm>(src), preinterpret<vec_xmm>(reg)));
+          } else {
+            dst = preinterpret<vec>(padd(preinterpret<vec_xmm>(src), ploadl<vec_xmm>(mem)));
+          }
+          break;
+        case 32 * 1:
+          dst = preinterpret<vec>(padds(preinterpret<vec_xmm>(src), ploads<vec_xmm>(mem)));
+          break;
+      }
+    }
+  }
+
+  EIGEN_STRONG_INLINE void vfmadd(vec &dst, const vec &src1, const vec &src2) {
+    dst = pmadd(src1, src2, dst);
+
+#if (EIGEN_COMP_GNUC != 0) || (EIGEN_COMP_CLANG != 0)
+    // Workaround register spills for gcc and clang
+    __asm__("#" : [dst] "+v"(dst) : [src1] "%v"(src1), [src2] "v"(src2));
+#endif
+  }
+
+  template <int nelems>
+  EIGEN_ALWAYS_INLINE void vfmaddm(vec &dst, const Scalar *mem, vec &src, vec &scale, vec &reg) {
+    if (is_unit_inc) {
+      switch (nelems * sizeof(*mem) * 8) {
+        default:
+        case 512 * 3:
+          dst = pmadd(scale, src, ploadu<vec>(mem));
+          break;
+        case 512 * 2:
+          dst = pmadd(scale, src, ploadu<vec>(mem));
+          break;
+        case 512 * 1:
+          dst = pmadd(scale, src, ploadu<vec>(mem));
+          break;
+        case 256 * 1:
+          dst =
+              preinterpret<vec>(pmadd(preinterpret<vec_ymm>(scale), preinterpret<vec_ymm>(src), ploadu<vec_ymm>(mem)));
+          break;
+        case 128 * 1:
+          dst =
+              preinterpret<vec>(pmadd(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), ploadu<vec_xmm>(mem)));
+          break;
+        case 64 * 1:
+          dst =
+              preinterpret<vec>(pmadd(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), ploadl<vec_xmm>(mem)));
+          break;
+        case 32 * 1:
+          dst =
+              preinterpret<vec>(pmadds(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), ploads<vec_xmm>(mem)));
+          break;
+      }
+    } else {
+      // Zero out scratch register
+      reg = pzero(reg);
+
+      switch (nelems * sizeof(*mem) * 8) {
+        default:
+        case 512 * 3:
+          reg = pgather<Scalar, vec>(mem, inc);
+          dst = pmadd(scale, src, reg);
+          break;
+        case 512 * 2:
+          reg = pgather<Scalar, vec>(mem, inc);
+          dst = pmadd(scale, src, reg);
+          break;
+        case 512 * 1:
+          reg = pgather<Scalar, vec>(mem, inc);
+          dst = pmadd(scale, src, reg);
+          break;
+        case 256 * 1:
+          reg = preinterpret<vec>(pgather<Scalar, vec_ymm>(mem, inc));
+          dst = preinterpret<vec>(
+              pmadd(preinterpret<vec_ymm>(scale), preinterpret<vec_ymm>(src), preinterpret<vec_ymm>(reg)));
+          break;
+        case 128 * 1:
+          reg = preinterpret<vec>(pgather<Scalar, vec_xmm>(mem, inc));
+          dst = preinterpret<vec>(
+              pmadd(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), preinterpret<vec_xmm>(reg)));
+          break;
+        case 64 * 1:
+          if (is_f32) {
+            reg = pgather(reg, mem, inc, mask);
+            dst = preinterpret<vec>(
+                pmadd(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), preinterpret<vec_xmm>(reg)));
+          } else {
+            dst = preinterpret<vec>(
+                pmadd(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), ploadl<vec_xmm>(mem)));
+          }
+          break;
+        case 32 * 1:
+          dst =
+              preinterpret<vec>(pmadds(preinterpret<vec_xmm>(scale), preinterpret<vec_xmm>(src), ploads<vec_xmm>(mem)));
+          break;
+      }
+    }
+  }
+
+  template <int j, int endX, int i, int endY, int nelems>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(j > endX) || (i > endY)> a_loads(const Scalar *ao) {
+    EIGEN_UNUSED_VARIABLE(ao);
+  }
+
+  template <int j, int endX, int i, int endY, int nelems>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(j <= endX) && (i <= endY)> a_loads(const Scalar *ao) {
+    if (j < endX) {
+      if (i < endY) {
+        auto &a_reg = zmm[a_regs[i + (j % 2) * 3]];
+        const Scalar *a_addr = ao + nelems * j + nelems_in_cache_line * i - a_shift;
+        a_load<nelems>(a_reg, a_addr);
+
+        a_loads<j, endX, i + 1, endY, nelems>(ao);
+      } else {
+        a_loads<j + 1, endX, 0, endY, nelems>(ao);
+      }
+    }
+  }
+
+  template <int un, int max_b_unroll, int i, int um_vecs, int a_unroll, int b_unroll>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(un > max_b_unroll) || (i > um_vecs)> prefetch_cs(const Scalar *co1,
+                                                                                         const Scalar *co2) {
+    EIGEN_UNUSED_VARIABLE(co1);
+    EIGEN_UNUSED_VARIABLE(co2);
+  }
+
+  /* C prefetch loop structure.
+   * for (int un = 0; un < 8; un++) {
+   *     if (b_unroll >= un + 1) {
+   *         if (un == 4) co2 = co1 + 4 * ldc;
+   *
+   *         for (int i = 0; i < um_vecs; i++) {
+   *             Scalar *co = (un + 1 <= 4) ? co1 : co2;
+   *             auto co_off = (un % 4) * ldc + a_unroll - 1 + i * nelems_in_cache_line * sizeof *co;
+   *             prefetch_c(co + co_off);
+   *         }
+   *     }
+   * }
+   */
+
+  template <int un, int max_b_unroll, int i, int um_vecs, int a_unroll, int b_unroll>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(un <= max_b_unroll) && (i <= um_vecs)> prefetch_cs(Scalar *&co1, Scalar *&co2) {
+    if (un < max_b_unroll) {
+      if (b_unroll >= un + 1) {
+        if (un == 4 && i == 0) co2 = co1 + 4 * ldc;
+
+        if (i < um_vecs) {
+          Scalar *co = (un + 1 <= 4) ? co1 : co2;
+          auto co_off = (un % 4) * ldc + a_unroll - 1 + i * nelems_in_cache_line * sizeof *co;
+          prefetch_c(co + co_off);
+
+          prefetch_cs<un, max_b_unroll, i + 1, um_vecs, a_unroll, b_unroll>(co1, co2);
+        } else {
+          prefetch_cs<un + 1, max_b_unroll, 0, um_vecs, a_unroll, b_unroll>(co1, co2);
+        }
+
+      } else {
+        prefetch_cs<un + 1, max_b_unroll, 0, um_vecs, a_unroll, b_unroll>(co1, co2);
+      }
+    }
+  }
+
+  // load_c
+  template <int i, int um_vecs, int idx, int nelems>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(i > um_vecs)> scale_load_c(const Scalar *cox, vec &alpha_reg) {
+    EIGEN_UNUSED_VARIABLE(cox);
+    EIGEN_UNUSED_VARIABLE(alpha_reg);
+  }
+
+  template <int i, int um_vecs, int idx, int nelems>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(i <= um_vecs)> scale_load_c(const Scalar *cox, vec &alpha_reg) {
+    if (i < um_vecs) {
+      auto &c_reg = zmm[c_regs[i + idx * 3]];
+      auto &c_load_reg = zmm[c_load_regs[i % 3]];
+      auto c_mem = cox;
+      if (is_unit_inc)
+        c_mem += i * nelems_in_cache_line;
+      else
+        c_mem += i * nelems_in_cache_line * inc;
+
+      if (!is_beta0 && is_alpha1)
+        vaddm<nelems>(c_reg, c_mem, c_reg, c_load_reg);
+      else if (!is_beta0 && !is_alpha1)
+        vfmaddm<nelems>(c_reg, c_mem, c_reg, alpha_reg, c_load_reg);
+      else if (is_beta0 && !is_alpha1)
+        c_reg = pmul(alpha_reg, c_reg);
+
+      scale_load_c<i + 1, um_vecs, idx, nelems>(cox, alpha_reg);
+    }
+  }
+
+  // store_c
+  template <int i, int um_vecs, int idx, int nelems>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(i > um_vecs)> write_c(Scalar *cox) {
+    EIGEN_UNUSED_VARIABLE(cox);
+  }
+
+  template <int i, int um_vecs, int idx, int nelems>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(i <= um_vecs)> write_c(Scalar *cox) {
+    if (i < um_vecs) {
+      auto &c_reg = zmm[c_regs[i + idx * 3]];
+      auto c_mem = cox;
+      if (is_unit_inc)
+        c_mem += i * nelems_in_cache_line;
+      else
+        c_mem += i * nelems_in_cache_line * inc;
+
+      c_store<nelems>(c_mem, c_reg);
+      c_reg = pzero(c_reg);
+
+      write_c<i + 1, um_vecs, idx, nelems>(cox);
+    }
+  }
+
+  /*  C update loop structure.
+   *  co2 = co1 + ldc;
+   *
+   *  auto &alpha_reg = zmm[alpha_load_reg];
+   *  if (!is_alpha1) alpha_reg = pload1<vec>(alpha);
+   *
+   *  int idx = 0;
+   *  for (pow = 1; pow <= 8; pow <<= 1) {
+   *
+   *      if (b_unroll >= pow) {
+   *          for (count = 1; count < (pow + 1) / 2 + 1;  count++) {
+   *              if (pow >= 4) co2 += ldc;
+   *
+   *              const Scalar *cox = (idx == 0) ? co1 : co2;
+   *
+   *              const int um_vecs = div_up(a_unroll, nelems_in_cache_line);
+   *              scale_load_c<0, um_vecs, idx, a_unroll>(cox, alpha_reg);
+   *              write_c<0, um_vecs, idx, a_unroll>(cox);
+   *
+   *              idx++;
+   *          }
+   *      }
+   *  }
+   *
+   *  if (b_unroll == 1)
+   *      co1 += ldc;
+   *  else
+   *      co1 = co2 + ldc;
+   */
+
+  template <int pow, int a_unroll, int idx>
+  EIGEN_ALWAYS_INLINE void c_update_1count(Scalar *&cox) {
+    if (pow >= 4) cox += ldc;
+
+    const int um_vecs = div_up(a_unroll, nelems_in_cache_line);
+    auto &alpha_reg = zmm[alpha_load_reg];
+
+    scale_load_c<0, um_vecs, idx, a_unroll>(cox, alpha_reg);
+    write_c<0, um_vecs, idx, a_unroll>(cox);
+  }
+
+  template <int pow, int a_unroll>
+  EIGEN_ALWAYS_INLINE void c_update_1pow(Scalar *&co1, Scalar *&co2) {
+    constexpr int idx = pow / 2;
+    Scalar *&cox = idx == 0 ? co1 : co2;
+
+    constexpr int max_count = (pow + 1) / 2;
+    static_assert(max_count <= 4, "Unsupported max_count.");
+
+    if (1 <= max_count) c_update_1count<pow, a_unroll, idx + 0>(cox);
+    if (2 <= max_count) c_update_1count<pow, a_unroll, idx + 1>(cox);
+    if (3 <= max_count) c_update_1count<pow, a_unroll, idx + 2>(cox);
+    if (4 <= max_count) c_update_1count<pow, a_unroll, idx + 3>(cox);
+  }
+
+  template <int max_b_unroll, int a_unroll, int b_unroll>
+  EIGEN_ALWAYS_INLINE void c_update(Scalar *&co1, Scalar *&co2) {
+    auto &alpha_reg = zmm[alpha_load_reg];
+
+    co2 = co1 + ldc;
+    if (!is_alpha1) alpha_reg = pload1<vec>(alpha);
+    if (!is_unit_inc && a_unroll < nelems_in_cache_line) mask = static_cast<umask_t>((1ull << a_unroll) - 1);
+
+    static_assert(max_b_unroll <= 8, "Unsupported max_b_unroll");
+
+    if (1 <= max_b_unroll && 1 <= b_unroll) c_update_1pow<1, a_unroll>(co1, co2);
+    if (2 <= max_b_unroll && 2 <= b_unroll) c_update_1pow<2, a_unroll>(co1, co2);
+    if (4 <= max_b_unroll && 4 <= b_unroll) c_update_1pow<4, a_unroll>(co1, co2);
+    if (8 <= max_b_unroll && 8 <= b_unroll) c_update_1pow<8, a_unroll>(co1, co2);
+
+    if (b_unroll == 1)
+      co1 += ldc;
+    else
+      co1 = co2 + ldc;
+  }
+
+  // compute
+  template <int um, int um_vecs, int idx, int uk, bool fetch_x, bool ktail>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(um > um_vecs)> compute(const Scalar *ao, const Scalar *bo, int &fetchA_idx,
+                                                               int &fetchB_idx, vec &b_reg) {
+    EIGEN_UNUSED_VARIABLE(ao);
+    EIGEN_UNUSED_VARIABLE(bo);
+    EIGEN_UNUSED_VARIABLE(fetchA_idx);
+    EIGEN_UNUSED_VARIABLE(fetchB_idx);
+    EIGEN_UNUSED_VARIABLE(b_reg);
+  }
+
+  template <int um, int um_vecs, int idx, int uk, bool fetch_x, bool ktail>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(um <= um_vecs)> compute(const Scalar *ao, const Scalar *bo, int &fetchA_idx,
+                                                                int &fetchB_idx, vec &b_reg) {
+    if (um < um_vecs) {
+      auto &c_reg = zmm[c_regs[um + idx * 3]];
+      auto &a_reg = zmm[a_regs[um + (uk % 2) * 3]];
+
+      vfmadd(c_reg, a_reg, b_reg);
+
+      if (!fetch_x && um == 0 &&
+          (((idx == 0 || idx == 6) && (uk % 2 == 0 || is_f64 || ktail)) ||
+           (idx == 3 && (uk % 2 == 1 || is_f64 || ktail)))) {
+        prefetch_a(ao + nelems_in_cache_line * fetchA_idx);
+        fetchA_idx++;
+      }
+
+      if (um == 0 && idx == 1 && (uk % 2 == 0 || is_f64 || ktail)) {
+        prefetch_b(bo + nelems_in_cache_line * fetchB_idx);
+        fetchB_idx++;
+      }
+
+      compute<um + 1, um_vecs, idx, uk, fetch_x, ktail>(ao, bo, fetchA_idx, fetchB_idx, b_reg);
+    }
+  }
+
+  // load_a
+  template <int um, int um_vecs, int uk, int nelems, bool ktail>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(um > um_vecs)> load_a(const Scalar *ao) {
+    EIGEN_UNUSED_VARIABLE(ao);
+  }
+
+  template <int um, int um_vecs, int uk, int nelems, bool ktail>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(um <= um_vecs)> load_a(const Scalar *ao) {
+    if (um < um_vecs) {
+      auto &a_reg = zmm[a_regs[um + (uk % 2) * 3]];
+      const Scalar *a_addr = ao + nelems * (1 + !ktail * !use_less_a_regs + uk) + nelems_in_cache_line * um - a_shift;
+      a_load<nelems>(a_reg, a_addr);
+
+      load_a<um + 1, um_vecs, uk, nelems, ktail>(ao);
+    }
+  }
+  template <int uk, int pow, int count, int um_vecs, int b_unroll, bool ktail, bool fetch_x, bool c_fetch>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(count > (pow + 1) / 2)> innerkernel_1pow(const Scalar *&aa,
+                                                                                 const Scalar *const &ao,
+                                                                                 const Scalar *const &bo, Scalar *&co2,
+                                                                                 int &fetchA_idx, int &fetchB_idx) {
+    EIGEN_UNUSED_VARIABLE(aa);
+    EIGEN_UNUSED_VARIABLE(ao);
+    EIGEN_UNUSED_VARIABLE(bo);
+    EIGEN_UNUSED_VARIABLE(co2);
+    EIGEN_UNUSED_VARIABLE(fetchA_idx);
+    EIGEN_UNUSED_VARIABLE(fetchB_idx);
+  }
+
+  template <int uk, int pow, int count, int um_vecs, int b_unroll, bool ktail, bool fetch_x, bool c_fetch>
+  EIGEN_ALWAYS_INLINE std::enable_if_t<(count <= (pow + 1) / 2)> innerkernel_1pow(const Scalar *&aa,
+                                                                                  const Scalar *const &ao,
+                                                                                  const Scalar *const &bo, Scalar *&co2,
+                                                                                  int &fetchA_idx, int &fetchB_idx) {
+    const int idx = (pow / 2) + count;
+
+    if (count < (pow + 1) / 2) {
+      auto &b_reg = zmm[b_regs[idx % 2]];
+
+      if (fetch_x && uk == 3 && idx == 0) prefetch_x(aa);
+      if (fetch_x && uk == 3 && idx == 4) aa += 8;
+
+      if (b_unroll >= pow) {
+        compute<0, um_vecs, idx, uk, fetch_x, ktail>(ao, bo, fetchA_idx, fetchB_idx, b_reg);
+
+        const Scalar *b_addr = bo + b_unroll * uk + idx + 1 + (b_unroll > 1) * !use_less_b_regs - b_shift;
+        b_load(b_reg, b_addr);
+      }
+
+      // Go to the next count.
+      innerkernel_1pow<uk, pow, count + 1, um_vecs, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx,
+                                                                                       fetchB_idx);
+
+    } else {
+      // Maybe prefetch C data after count-loop.
+      if (pow == 2 && c_fetch) {
+        if (uk % 3 == 0 && uk > 0) {
+          co2 += ldc;
+        } else {
+          prefetch_c(co2 + (uk % 3) * nelems_in_cache_line);
+        }
+      }
+    }
+  }
+
+  template <int uk, int max_b_unroll, int a_unroll, int b_unroll, bool ktail, bool fetch_x, bool c_fetch, bool no_a_preload = false>
+  EIGEN_ALWAYS_INLINE void innerkernel_1uk(const Scalar *&aa, const Scalar *const &ao, const Scalar *const &bo,
+                                           Scalar *&co2, int &fetchA_idx, int &fetchB_idx) {
+    const int um_vecs = div_up(a_unroll, nelems_in_cache_line);
+
+    if (max_b_unroll >= 1)
+      innerkernel_1pow<uk, 1, 0, um_vecs, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx);
+    if (max_b_unroll >= 2)
+      innerkernel_1pow<uk, 2, 0, um_vecs, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx);
+    if (max_b_unroll >= 4)
+      innerkernel_1pow<uk, 4, 0, um_vecs, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx);
+    if (max_b_unroll >= 8)
+      innerkernel_1pow<uk, 8, 0, um_vecs, b_unroll, ktail, fetch_x, c_fetch>(aa, ao, bo, co2, fetchA_idx, fetchB_idx);
+
+    // Load A after pow-loop. Skip this at the end to prevent running over the buffer
+    if (!no_a_preload) load_a<0, um_vecs, uk, a_unroll, ktail>(ao);
+  }
+
+  /*  Inner kernel loop structure.
+   *  for (int uk = 0; uk < kfactor; uk++) {
+   *      int idx = 0;
+   *
+   *      for (pow = 1; pow < max_b_unroll << 1; pow <<= 1) {
+   *          for (int count = 0; count < (pow + 1) / 2; count++) {
+   *              auto &b_reg = zmm[b_regs[idx % 2]];
+   *
+   *              if (fetch_x && uk == 3 && idx == 0) prefetch_x(aa);
+   *              if (fetch_x && uk == 3 && idx == 4) aa += 8;
+   *
+   *              if (b_unroll >= pow) {
+   *                  compute<0, um_vecs, idx, uk, fetchx, ktail>(ao, bo, fetchA_idx, fetchB_idx, b_reg);
+   *
+   *                  const Scalar *b_addr = bo + b_unroll * uk + idx + 1 + (b_unroll > 1) - b_shift ;
+   *                  b_load(b_reg, b_addr);
+   *              }
+   *              idx++;
+   *          }
+   *
+   *          Maybe prefetch C data.
+   *          if (pow == 2 && c_fetch) {
+   *              if (uk % 3 == 0 && uk > 0) {
+   *                  co2 += ldc;
+   *              } else {
+   *                  prefetch_c(co2 + (uk % 3) * nelems_in_cache_line);
+   *              }
+   *          }
+   *      }
+   *
+   *      Load A.
+   *      load_a<0, um_vecs, uk, ktail, a_unroll>(ao);
+   *  }
+   *
+   *  Advance A/B pointers after uk-loop.
+   *  ao += a_unroll * kfactor;
+   *  bo += b_unroll * kfactor;
+   */
+
+  template <int a_unroll, int b_unroll, int k_factor, int max_b_unroll, int max_k_factor, bool c_fetch, bool no_a_preload = false>
+  EIGEN_ALWAYS_INLINE void innerkernel(const Scalar *&aa, const Scalar *&ao, const Scalar *&bo, Scalar *&co2) {
+    int fetchA_idx = 0;
+    int fetchB_idx = 0;
+
+    const bool fetch_x = k_factor == max_k_factor;
+    const bool ktail = k_factor == 1;
+
+    static_assert(k_factor <= 4 && k_factor > 0, "innerkernel maximum k_factor supported is 4");
+    static_assert(no_a_preload == false || (no_a_preload == true && k_factor == 1), "skipping a preload only allowed when k unroll is 1");
+
+    if (k_factor > 0)
+      innerkernel_1uk<0, max_b_unroll, a_unroll, b_unroll, ktail, fetch_x, c_fetch, no_a_preload>(aa, ao, bo, co2, fetchA_idx,
+                                                                                    fetchB_idx);
+    if (k_factor > 1)
+      innerkernel_1uk<1, max_b_unroll, a_unroll, b_unroll, ktail, fetch_x, c_fetch, no_a_preload>(aa, ao, bo, co2, fetchA_idx,
+                                                                                    fetchB_idx);
+    if (k_factor > 2)
+      innerkernel_1uk<2, max_b_unroll, a_unroll, b_unroll, ktail, fetch_x, c_fetch, no_a_preload>(aa, ao, bo, co2, fetchA_idx,
+                                                                                    fetchB_idx);
+    if (k_factor > 3)
+      innerkernel_1uk<3, max_b_unroll, a_unroll, b_unroll, ktail, fetch_x, c_fetch, no_a_preload>(aa, ao, bo, co2, fetchA_idx,
+                                                                                    fetchB_idx);
+
+    // Advance A/B pointers after uk-loop.
+    ao += a_unroll * k_factor;
+    bo += b_unroll * k_factor;
+  }
+
+  template <int a_unroll, int b_unroll, int max_b_unroll>
+  EIGEN_ALWAYS_INLINE void kloop(const Scalar *&aa, const Scalar *&ao, const Scalar *&bo, Scalar *&co1, Scalar *&co2) {
+    const int um_vecs = div_up(a_unroll, nelems_in_cache_line);
+    if (!use_less_a_regs && k > 1)
+      a_loads<0, 2, 0, um_vecs, a_unroll>(ao);
+    else
+      a_loads<0, 1, 0, um_vecs, a_unroll>(ao);
+
+    b_load(zmm[b_regs[0]], bo - b_shift + 0);
+    if (!use_less_b_regs) b_load(zmm[b_regs[1]], bo - b_shift + 1);
+
+#ifndef SECOND_FETCH
+    prefetch_cs<0, max_b_unroll, 0, um_vecs, a_unroll, b_unroll>(co1, co2);
+#endif  // SECOND_FETCH
+
+    // Unrolling k-loop by a factor of 4.
+    const int max_k_factor = 4;
+    Index kRem = k % max_k_factor;
+    Index k_ = k - kRem;
+    if (k_ >= max_k_factor) {
+      k_ -= max_k_factor;
+      kRem += max_k_factor;
+    }
+    Index loop_count = k_ / max_k_factor;
+
+    if (loop_count > 0) {
+#ifdef SECOND_FETCH
+      loop_count -= SECOND_FETCH;
+#endif
+      while (loop_count > 0) {
+        innerkernel<a_unroll, b_unroll, max_k_factor, max_b_unroll, max_k_factor, 0>(aa, ao, bo, co2);
+        loop_count--;
+      }
+#ifdef SECOND_FETCH
+      co2 = co1 + nelems_in_cache_line - 1;
+
+      loop_count += b_unroll;
+      while (loop_count > 0) {
+        innerkernel<a_unroll, b_unroll, max_k_factor, max_b_unroll, max_k_factor, 1>(aa, ao, bo, co2);
+        loop_count--;
+      }
+
+      loop_count += SECOND_FETCH - b_unroll;
+      while (loop_count > 0) {
+        innerkernel<a_unroll, b_unroll, max_k_factor, max_b_unroll, max_k_factor, 0>(aa, ao, bo, co2);
+        loop_count--;
+      }
+#endif
+    }
+
+    // k-loop remainder handling.
+    loop_count = kRem;
+    while (loop_count > 1) {
+      innerkernel<a_unroll, b_unroll, 1, max_b_unroll, max_k_factor, 0>(aa, ao, bo, co2);
+      loop_count--;
+    }
+    if (loop_count > 0) {
+      innerkernel<a_unroll, b_unroll, 1, max_b_unroll, max_k_factor, 0, true>(aa, ao, bo, co2);
+    }
+
+    // Update C matrix.
+    c_update<max_b_unroll, a_unroll, b_unroll>(co1, co2);
+  }
+
+  template <int a_unroll, int b_unroll, int max_b_unroll>
+  EIGEN_ALWAYS_INLINE void nloop(const Scalar *&aa, const Scalar *&ao, const Scalar *&bo, Scalar *&co1, Scalar *&co2) {
+    // Set A matrix pointer.
+    ao = a + a_off * a_unroll;
+
+    // Set B matrix pointer if needed.
+    bo += b_unroll * b_off;
+
+    kloop<a_unroll, b_unroll, max_b_unroll>(aa, ao, bo, co1, co2);
+
+    // Advance B matrix pointer if needed.
+    bo += b_unroll * (b_stride - k - b_off);
+
+    // Advance prefetch A pointer.
+    aa += 16;
+  }
+
+  template <int a_unroll, int max_a_unroll, int max_b_unroll>
+  EIGEN_ALWAYS_INLINE void mloop(const Scalar *&ao, const Scalar *&bo, Scalar *&co1, Scalar *&co2) {
+    // Set prefetch A pointers.
+    const Scalar *aa = a + a_unroll * a_stride;
+
+    // Set C matrix pointers.
+    co1 = c;
+    if (a_unroll >= max_a_unroll) co2 = c + 2 * ldc;
+    if (is_unit_inc)
+      c += a_unroll;
+    else
+      c += a_unroll * inc;
+
+    // Set B matrix pointer.
+    bo = b;
+
+    // Main n-loop.
+    for (Index i = n / max_b_unroll; i > 0; i--) nloop<a_unroll, max_b_unroll, max_b_unroll>(aa, ao, bo, co1, co2);
+
+    // n-remainders.
+    if (n & 4 && max_b_unroll > 4) nloop<a_unroll, 4, max_b_unroll>(aa, ao, bo, co1, co2);
+#if 0
+        if (n & 2 && max_b_unroll > 2) nloop<a_unroll, 2, max_b_unroll>(aa, ao, bo, co1, co2);
+        if (n & 1 && max_b_unroll > 1) nloop<a_unroll, 1, max_b_unroll>(aa, ao, bo, co1, co2);
+#else
+    // Copy kernels don't support tails of n = 2 for single/double precision.
+    // Loop over ones.
+    int n_rem = 2 * ((n & 2) != 0) + 1 * ((n & 1) != 0);
+    while (n_rem > 0) {
+      nloop<a_unroll, 1, max_b_unroll>(aa, ao, bo, co1, co2);
+      n_rem--;
+    }
+#endif
+
+    // Advance A matrix pointer.
+    a = ao + a_unroll * (a_stride - k - a_off);
+  }
+
+ public:
+  // Compute kernel unrolling C matrix by max_a_unroll x max_b_unroll.
+  template <int max_a_unroll, int max_b_unroll>
+  EIGEN_ALWAYS_INLINE void compute_kern() {
+    a -= -a_shift;
+    b -= -b_shift;
+
+    const Scalar *ao = nullptr;
+    const Scalar *bo = nullptr;
+    Scalar *co1 = nullptr;
+    Scalar *co2 = nullptr;
+
+    // Main m-loop.
+    for (; m >= max_a_unroll; m -= max_a_unroll) mloop<max_a_unroll, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+
+    // m-remainders.
+    if (m & 32 && max_a_unroll > 32) mloop<32, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+    if (m & 16 && max_a_unroll > 16) mloop<16, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+    if (m & 8 && max_a_unroll > 8) mloop<8, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+    if (m & 4 && max_a_unroll > 4) mloop<4, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+    if (m & 2 && max_a_unroll > 2 && is_f64) mloop<2, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+    if (m & 1 && max_a_unroll > 1 && is_f64) mloop<1, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+
+    // Copy kernels don't support tails of m = 2 for single precision.
+    // Loop over ones.
+    if (is_f32) {
+      int m_rem = 2 * ((m & 2) != 0) + 1 * ((m & 1) != 0);
+      while (m_rem > 0) {
+        mloop<1, max_a_unroll, max_b_unroll>(ao, bo, co1, co2);
+        m_rem--;
+      }
+    }
+  }
+
+  gemm_class(Index m_, Index n_, Index k_, Index ldc_, Index inc_, const Scalar *alpha_, const Scalar *a_,
+             const Scalar *b_, Scalar *c_, bool is_alpha1_, bool is_beta0_, Index a_stride_, Index b_stride_,
+             Index a_off_, Index b_off_)
+      : m(m_),
+        n(n_),
+        k(k_),
+        ldc(ldc_),
+        inc(inc_),
+        alpha(alpha_),
+        a(a_),
+        b(b_),
+        c(c_),
+        is_alpha1(is_alpha1_),
+        is_beta0(is_beta0_),
+        a_stride(a_stride_),
+        b_stride(b_stride_),
+        a_off(a_off_),
+        b_off(b_off_) {
+    // Zero out all accumulation registers.
+    zmm[8] = pzero(zmm[8]);
+    zmm[9] = pzero(zmm[9]);
+    zmm[10] = pzero(zmm[10]);
+    zmm[11] = pzero(zmm[11]);
+    zmm[12] = pzero(zmm[12]);
+    zmm[13] = pzero(zmm[13]);
+    zmm[14] = pzero(zmm[14]);
+    zmm[15] = pzero(zmm[15]);
+    zmm[16] = pzero(zmm[16]);
+    zmm[17] = pzero(zmm[17]);
+    zmm[18] = pzero(zmm[18]);
+    zmm[19] = pzero(zmm[19]);
+    zmm[20] = pzero(zmm[20]);
+    zmm[21] = pzero(zmm[21]);
+    zmm[22] = pzero(zmm[22]);
+    zmm[23] = pzero(zmm[23]);
+    zmm[24] = pzero(zmm[24]);
+    zmm[25] = pzero(zmm[25]);
+    zmm[26] = pzero(zmm[26]);
+    zmm[27] = pzero(zmm[27]);
+    zmm[28] = pzero(zmm[28]);
+    zmm[29] = pzero(zmm[29]);
+    zmm[30] = pzero(zmm[30]);
+    zmm[31] = pzero(zmm[31]);
+  }
+};
+
+// Compute kernel with max unroll support of:
+//   Single precision:
+//     max_a_unroll: 48, 32, 16, 8, 4, 2, 1
+//     max_b_unroll: 8, 4, 2, 1
+//   Double precision:
+//     max_a_unroll: 24, 16, 8, 4, 2, 1
+//     max_b_unroll: 8, 4, 2, 1
+template <typename Scalar, int max_a_unroll, int max_b_unroll, bool is_alpha1, bool is_beta0, bool is_unit_inc>
+EIGEN_DONT_INLINE void gemm_kern_avx512(Index m, Index n, Index k, Scalar *alpha, const Scalar *a, const Scalar *b,
+                                        Scalar *c, Index ldc, Index inc = 1, Index a_stride = -1, Index b_stride = -1,
+                                        Index a_off = 0, Index b_off = 0) {
+  if (a_stride == -1) a_stride = k;
+  if (b_stride == -1) b_stride = k;
+
+  gemm_class<Scalar, is_unit_inc> g(m, n, k, ldc, inc, alpha, a, b, c, is_alpha1, is_beta0, a_stride, b_stride, a_off,
+                                    b_off);
+  g.template compute_kern<max_a_unroll, max_b_unroll>();
+}
+
+// Template specializations of GEBP kernels with nr = 8.
+#if EIGEN_USE_AVX512_GEMM_KERNELS
+template <bool ConjLhs_, bool ConjRhs_, int PacketSize_>
+class gebp_traits<float, float, ConjLhs_, ConjRhs_, Architecture::Target, PacketSize_>
+    : public gebp_traits<float, float, ConjLhs_, ConjRhs_, Architecture::Generic, PacketSize_> {
+  using Base = gebp_traits<float, float, ConjLhs_, ConjRhs_, Architecture::Generic, PacketSize_>;
+
+ public:
+  enum { nr = Base::Vectorizable ? 8 : 4 };
+};
+
+template <bool ConjLhs_, bool ConjRhs_, int PacketSize_>
+class gebp_traits<double, double, ConjLhs_, ConjRhs_, Architecture::Target, PacketSize_>
+    : public gebp_traits<double, double, ConjLhs_, ConjRhs_, Architecture::Generic, PacketSize_> {
+  using Base = gebp_traits<double, double, ConjLhs_, ConjRhs_, Architecture::Generic, PacketSize_>;
+
+ public:
+  enum { nr = Base::Vectorizable ? 8 : 4 };
+};
+
+template <typename Scalar, typename Index, typename DataMapper, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<Scalar, Index, DataMapper, 8, ColMajor, Conjugate, PanelMode> {
+  typedef typename packet_traits<Scalar>::type Packet;
+  typedef typename DataMapper::LinearMapper LinearMapper;
+  enum { PacketSize = packet_traits<Scalar>::size };
+  EIGEN_DONT_INLINE void operator()(Scalar *blockB, const DataMapper &rhs, Index depth, Index cols, Index stride = 0,
+                                    Index offset = 0);
+};
+
+template <typename Scalar, typename Index, typename DataMapper, bool Conjugate, bool PanelMode>
+EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, DataMapper, 8, ColMajor, Conjugate, PanelMode>::operator()(
+    Scalar *blockB, const DataMapper &rhs, Index depth, Index cols, Index stride, Index offset) {
+  constexpr int nr = 8;
+  EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR");
+  EIGEN_UNUSED_VARIABLE(stride);
+  EIGEN_UNUSED_VARIABLE(offset);
+  eigen_assert(((!PanelMode) && stride == 0 && offset == 0) || (PanelMode && stride >= depth && offset <= stride));
+  conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
+  Index packet_cols8 = nr >= 8 ? (cols / 8) * 8 : 0;
+  Index packet_cols4 = nr >= 4 ? (cols / 4) * 4 : 0;
+  Index count = 0;
+  const Index peeled_k = (depth / PacketSize) * PacketSize;
+  if (nr >= 8) {
+    for (Index j2 = 0; j2 < packet_cols8; j2 += 8) {
+      // skip what we have before
+      if (PanelMode) count += 8 * offset;
+      const LinearMapper dm0 = rhs.getLinearMapper(0, j2 + 0);
+      const LinearMapper dm1 = rhs.getLinearMapper(0, j2 + 1);
+      const LinearMapper dm2 = rhs.getLinearMapper(0, j2 + 2);
+      const LinearMapper dm3 = rhs.getLinearMapper(0, j2 + 3);
+      const LinearMapper dm4 = rhs.getLinearMapper(0, j2 + 4);
+      const LinearMapper dm5 = rhs.getLinearMapper(0, j2 + 5);
+      const LinearMapper dm6 = rhs.getLinearMapper(0, j2 + 6);
+      const LinearMapper dm7 = rhs.getLinearMapper(0, j2 + 7);
+      Index k = 0;
+      if ((PacketSize % 8) == 0)  // TODO enable vectorized transposition for PacketSize==4
+      {
+        for (; k < peeled_k; k += PacketSize) {
+          PacketBlock<Packet, (PacketSize % 8) == 0 ? 8 : PacketSize> kernel;
+
+          kernel.packet[0] = dm0.template loadPacket<Packet>(k);
+          kernel.packet[1] = dm1.template loadPacket<Packet>(k);
+          kernel.packet[2] = dm2.template loadPacket<Packet>(k);
+          kernel.packet[3] = dm3.template loadPacket<Packet>(k);
+          kernel.packet[4] = dm4.template loadPacket<Packet>(k);
+          kernel.packet[5] = dm5.template loadPacket<Packet>(k);
+          kernel.packet[6] = dm6.template loadPacket<Packet>(k);
+          kernel.packet[7] = dm7.template loadPacket<Packet>(k);
+
+          ptranspose(kernel);
+
+          pstoreu(blockB + count + 0 * PacketSize, cj.pconj(kernel.packet[0]));
+          pstoreu(blockB + count + 1 * PacketSize, cj.pconj(kernel.packet[1 % PacketSize]));
+          pstoreu(blockB + count + 2 * PacketSize, cj.pconj(kernel.packet[2 % PacketSize]));
+          pstoreu(blockB + count + 3 * PacketSize, cj.pconj(kernel.packet[3 % PacketSize]));
+          pstoreu(blockB + count + 4 * PacketSize, cj.pconj(kernel.packet[4 % PacketSize]));
+          pstoreu(blockB + count + 5 * PacketSize, cj.pconj(kernel.packet[5 % PacketSize]));
+          pstoreu(blockB + count + 6 * PacketSize, cj.pconj(kernel.packet[6 % PacketSize]));
+          pstoreu(blockB + count + 7 * PacketSize, cj.pconj(kernel.packet[7 % PacketSize]));
+          count += 8 * PacketSize;
+        }
+      }
+      for (; k < depth; k++) {
+        blockB[count + 0] = cj(dm0(k));
+        blockB[count + 1] = cj(dm1(k));
+        blockB[count + 2] = cj(dm2(k));
+        blockB[count + 3] = cj(dm3(k));
+        blockB[count + 4] = cj(dm4(k));
+        blockB[count + 5] = cj(dm5(k));
+        blockB[count + 6] = cj(dm6(k));
+        blockB[count + 7] = cj(dm7(k));
+        count += 8;
+      }
+      // skip what we have after
+      if (PanelMode) count += 8 * (stride - offset - depth);
+    }
+  }
+
+  if (nr >= 4) {
+    for (Index j2 = packet_cols8; j2 < packet_cols4; j2 += 4) {
+      // skip what we have before
+      if (PanelMode) count += 4 * offset;
+      const LinearMapper dm0 = rhs.getLinearMapper(0, j2 + 0);
+      const LinearMapper dm1 = rhs.getLinearMapper(0, j2 + 1);
+      const LinearMapper dm2 = rhs.getLinearMapper(0, j2 + 2);
+      const LinearMapper dm3 = rhs.getLinearMapper(0, j2 + 3);
+
+      Index k = 0;
+      if ((PacketSize % 4) == 0)  // TODO enable vectorized transposition for PacketSize==2 ??
+      {
+        for (; k < peeled_k; k += PacketSize) {
+          PacketBlock<Packet, (PacketSize % 4) == 0 ? 4 : PacketSize> kernel;
+          kernel.packet[0] = dm0.template loadPacket<Packet>(k);
+          kernel.packet[1 % PacketSize] = dm1.template loadPacket<Packet>(k);
+          kernel.packet[2 % PacketSize] = dm2.template loadPacket<Packet>(k);
+          kernel.packet[3 % PacketSize] = dm3.template loadPacket<Packet>(k);
+          ptranspose(kernel);
+          pstoreu(blockB + count + 0 * PacketSize, cj.pconj(kernel.packet[0]));
+          pstoreu(blockB + count + 1 * PacketSize, cj.pconj(kernel.packet[1 % PacketSize]));
+          pstoreu(blockB + count + 2 * PacketSize, cj.pconj(kernel.packet[2 % PacketSize]));
+          pstoreu(blockB + count + 3 * PacketSize, cj.pconj(kernel.packet[3 % PacketSize]));
+          count += 4 * PacketSize;
+        }
+      }
+      for (; k < depth; k++) {
+        blockB[count + 0] = cj(dm0(k));
+        blockB[count + 1] = cj(dm1(k));
+        blockB[count + 2] = cj(dm2(k));
+        blockB[count + 3] = cj(dm3(k));
+        count += 4;
+      }
+      // skip what we have after
+      if (PanelMode) count += 4 * (stride - offset - depth);
+    }
+  }
+
+  // copy the remaining columns one at a time (nr==1)
+  for (Index j2 = packet_cols4; j2 < cols; ++j2) {
+    if (PanelMode) count += offset;
+    const LinearMapper dm0 = rhs.getLinearMapper(0, j2);
+    for (Index k = 0; k < depth; k++) {
+      blockB[count] = cj(dm0(k));
+      count += 1;
+    }
+    if (PanelMode) count += (stride - offset - depth);
+  }
+}
+
+template <typename Scalar, typename Index, typename DataMapper, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<Scalar, Index, DataMapper, 8, RowMajor, Conjugate, PanelMode> {
+  typedef typename packet_traits<Scalar>::type Packet;
+  typedef typename unpacket_traits<Packet>::half HalfPacket;
+  typedef typename unpacket_traits<typename unpacket_traits<Packet>::half>::half QuarterPacket;
+  typedef typename DataMapper::LinearMapper LinearMapper;
+  enum {
+    PacketSize = packet_traits<Scalar>::size,
+    HalfPacketSize = unpacket_traits<HalfPacket>::size,
+    QuarterPacketSize = unpacket_traits<QuarterPacket>::size
+  };
+  EIGEN_DONT_INLINE void operator()(Scalar *blockB, const DataMapper &rhs, Index depth, Index cols, Index stride = 0,
+                                    Index offset = 0) {
+    constexpr int nr = 8;
+    EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR");
+    EIGEN_UNUSED_VARIABLE(stride);
+    EIGEN_UNUSED_VARIABLE(offset);
+    eigen_assert(((!PanelMode) && stride == 0 && offset == 0) || (PanelMode && stride >= depth && offset <= stride));
+    const bool HasHalf = (int)HalfPacketSize < (int)PacketSize;
+    const bool HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize;
+    conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
+    Index packet_cols8 = nr >= 8 ? (cols / 8) * 8 : 0;
+    Index packet_cols4 = nr >= 4 ? (cols / 4) * 4 : 0;
+    Index count = 0;
+
+    if (nr >= 8) {
+      for (Index j2 = 0; j2 < packet_cols8; j2 += 8) {
+        // skip what we have before
+        if (PanelMode) count += 8 * offset;
+        for (Index k = 0; k < depth; k++) {
+          if (PacketSize == 8) {
+            // Packet A = ploadu<Packet>(&rhs.data()[k*rhs.stride() + j2]);
+            Packet A = rhs.template loadPacket<Packet>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+          } else if (HasHalf && HalfPacketSize == 8) {
+            HalfPacket A = rhs.template loadPacket<HalfPacket>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+          } else if (HasQuarter && QuarterPacketSize == 8) {
+            QuarterPacket A = rhs.template loadPacket<QuarterPacket>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+          } else if (PacketSize == 4) {
+            // Packet A = ploadu<Packet>(&rhs.data()[k*rhs.stride() + j2]);
+            // Packet B = ploadu<Packet>(&rhs.data()[k*rhs.stride() + j2 + PacketSize]);
+            Packet A = rhs.template loadPacket<Packet>(k, j2);
+            Packet B = rhs.template loadPacket<Packet>(k, j2 + PacketSize);
+            pstoreu(blockB + count, cj.pconj(A));
+            pstoreu(blockB + count + PacketSize, cj.pconj(B));
+          } else {
+            // const Scalar* b0 = &rhs.data()[k*rhs.stride() + j2];
+            const LinearMapper dm0 = rhs.getLinearMapper(k, j2);
+            blockB[count + 0] = cj(dm0(0));
+            blockB[count + 1] = cj(dm0(1));
+            blockB[count + 2] = cj(dm0(2));
+            blockB[count + 3] = cj(dm0(3));
+            blockB[count + 4] = cj(dm0(4));
+            blockB[count + 5] = cj(dm0(5));
+            blockB[count + 6] = cj(dm0(6));
+            blockB[count + 7] = cj(dm0(7));
+          }
+          count += 8;
+        }
+        // skip what we have after
+        if (PanelMode) count += 8 * (stride - offset - depth);
+      }
+    }
+
+    if (nr >= 4) {
+      for (Index j2 = packet_cols8; j2 < packet_cols4; j2 += 4) {
+        // skip what we have before
+        if (PanelMode) count += 4 * offset;
+        for (Index k = 0; k < depth; k++) {
+          if (PacketSize == 4) {
+            Packet A = rhs.template loadPacket<Packet>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+            count += PacketSize;
+          } else if (HasHalf && HalfPacketSize == 4) {
+            HalfPacket A = rhs.template loadPacket<HalfPacket>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+            count += HalfPacketSize;
+          } else if (HasQuarter && QuarterPacketSize == 4) {
+            QuarterPacket A = rhs.template loadPacket<QuarterPacket>(k, j2);
+            pstoreu(blockB + count, cj.pconj(A));
+            count += QuarterPacketSize;
+          } else {
+            const LinearMapper dm0 = rhs.getLinearMapper(k, j2);
+            blockB[count + 0] = cj(dm0(0));
+            blockB[count + 1] = cj(dm0(1));
+            blockB[count + 2] = cj(dm0(2));
+            blockB[count + 3] = cj(dm0(3));
+            count += 4;
+          }
+        }
+        // skip what we have after
+        if (PanelMode) count += 4 * (stride - offset - depth);
+      }
+    }
+    // copy the remaining columns one at a time (nr==1)
+    for (Index j2 = packet_cols4; j2 < cols; ++j2) {
+      if (PanelMode) count += offset;
+      for (Index k = 0; k < depth; k++) {
+        blockB[count] = cj(rhs(k, j2));
+        count += 1;
+      }
+      if (PanelMode) count += stride - offset - depth;
+    }
+  }
+};
+
+template <typename Scalar, typename Index, typename DataMapper, int mr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<Scalar, Scalar, Index, DataMapper, mr, 8, ConjugateLhs, ConjugateRhs> {
+  EIGEN_ALWAYS_INLINE
+  void operator()(const DataMapper &res, const Scalar *blockA, const Scalar *blockB, Index rows, Index depth,
+                  Index cols, Scalar alpha, Index strideA = -1, Index strideB = -1, Index offsetA = 0,
+                  Index offsetB = 0);
+};
+
+template <typename Scalar, typename Index, typename DataMapper, int mr, bool ConjugateLhs, bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE void gebp_kernel<Scalar, Scalar, Index, DataMapper, mr, 8, ConjugateLhs, ConjugateRhs>::operator()(
+    const DataMapper &res, const Scalar *blockA, const Scalar *blockB, Index rows, Index depth, Index cols,
+    Scalar alpha, Index strideA, Index strideB, Index offsetA, Index offsetB) {
+  if (res.incr() == 1) {
+    if (alpha == 1) {
+      gemm_kern_avx512<Scalar, mr, 8, true, false, true>(rows, cols, depth, &alpha, blockA, blockB,
+                                                         (Scalar *)res.data(), res.stride(), res.incr(), strideA,
+                                                         strideB, offsetA, offsetB);
+    } else {
+      gemm_kern_avx512<Scalar, mr, 8, false, false, true>(rows, cols, depth, &alpha, blockA, blockB,
+                                                          (Scalar *)res.data(), res.stride(), res.incr(), strideA,
+                                                          strideB, offsetA, offsetB);
+    }
+  } else {
+    if (alpha == 1) {
+      gemm_kern_avx512<Scalar, mr, 8, true, false, false>(rows, cols, depth, &alpha, blockA, blockB,
+                                                          (Scalar *)res.data(), res.stride(), res.incr(), strideA,
+                                                          strideB, offsetA, offsetB);
+    } else {
+      gemm_kern_avx512<Scalar, mr, 8, false, false, false>(rows, cols, depth, &alpha, blockA, blockB,
+                                                           (Scalar *)res.data(), res.stride(), res.incr(), strideA,
+                                                           strideB, offsetA, offsetB);
+    }
+  }
+}
+#endif // EIGEN_USE_AVX512_GEMM_KERNELS
+
+}  // namespace internal
+}  // namespace Eigen
+
+#undef SECOND_FETCH
+
+#endif  // EIGEN_CORE_ARCH_AVX512_GEMM_KERNEL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/MathFunctions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/MathFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..b327988bcef084daf89ce90dfac86ec94a08d99a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/MathFunctions.h
@@ -0,0 +1,139 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Pedro Gonnet (pedro.gonnet@gmail.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef THIRD_PARTY_EIGEN3_EIGEN_SRC_CORE_ARCH_AVX512_MATHFUNCTIONS_H_
+#define THIRD_PARTY_EIGEN3_EIGEN_SRC_CORE_ARCH_AVX512_MATHFUNCTIONS_H_
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet16f)
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_DOUBLE(Packet8d)
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pfrexp(const Packet16h& a, Packet16h& exponent) {
+  Packet16f fexponent;
+  const Packet16h out = float2half(pfrexp<Packet16f>(half2float(a), fexponent));
+  exponent = float2half(fexponent);
+  return out;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pldexp(const Packet16h& a, const Packet16h& exponent) {
+  return float2half(pldexp<Packet16f>(half2float(a), half2float(exponent)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pfrexp(const Packet16bf& a, Packet16bf& exponent) {
+  Packet16f fexponent;
+  const Packet16bf out = F32ToBf16(pfrexp<Packet16f>(Bf16ToF32(a), fexponent));
+  exponent = F32ToBf16(fexponent);
+  return out;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pldexp(const Packet16bf& a, const Packet16bf& exponent) {
+  return F32ToBf16(pldexp<Packet16f>(Bf16ToF32(a), Bf16ToF32(exponent)));
+}
+
+#if EIGEN_FAST_MATH
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16f
+psqrt<Packet16f>(const Packet16f& _x) {
+  return generic_sqrt_newton_step<Packet16f>::run(_x, _mm512_rsqrt14_ps(_x));
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8d
+psqrt<Packet8d>(const Packet8d& _x) {
+#ifdef EIGEN_VECTORIZE_AVX512ER
+  return generic_sqrt_newton_step<Packet8d, /*Steps=*/1>::run(_x, _mm512_rsqrt28_pd(_x));
+#else
+  return generic_sqrt_newton_step<Packet8d, /*Steps=*/2>::run(_x, _mm512_rsqrt14_pd(_x));
+#endif
+}
+#else
+template <>
+EIGEN_STRONG_INLINE Packet16f psqrt<Packet16f>(const Packet16f& x) {
+  return _mm512_sqrt_ps(x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d psqrt<Packet8d>(const Packet8d& x) {
+  return _mm512_sqrt_pd(x);
+}
+#endif
+
+// prsqrt for float.
+#if defined(EIGEN_VECTORIZE_AVX512ER)
+template <>
+EIGEN_STRONG_INLINE Packet16f prsqrt<Packet16f>(const Packet16f& x) {
+  return _mm512_rsqrt28_ps(x);
+}
+#elif EIGEN_FAST_MATH
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet16f
+prsqrt<Packet16f>(const Packet16f& _x) {
+  return generic_rsqrt_newton_step<Packet16f, /*Steps=*/1>::run(_x, _mm512_rsqrt14_ps(_x));
+}
+#endif
+
+
+// prsqrt for double.
+#if EIGEN_FAST_MATH
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet8d
+prsqrt<Packet8d>(const Packet8d& _x) {
+  #ifdef EIGEN_VECTORIZE_AVX512ER
+    return generic_rsqrt_newton_step<Packet8d, /*Steps=*/1>::run(_x, _mm512_rsqrt28_pd(_x));
+  #else
+    return generic_rsqrt_newton_step<Packet8d, /*Steps=*/2>::run(_x, _mm512_rsqrt14_pd(_x));
+  #endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f preciprocal<Packet16f>(const Packet16f& a) {
+#ifdef EIGEN_VECTORIZE_AVX512ER
+  return _mm512_rcp28_ps(a);
+#else
+  return generic_reciprocal_newton_step<Packet16f, /*Steps=*/1>::run(a, _mm512_rcp14_ps(a));
+#endif
+}
+#endif
+
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pcos)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pexp)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, pexpm1)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, plog)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, plog1p)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, plog2)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, preciprocal)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, prsqrt)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, psin)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, psqrt)
+BF16_PACKET_FUNCTION(Packet16f, Packet16bf, ptanh)
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pcos)
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pexp)
+F16_PACKET_FUNCTION(Packet16f, Packet16h, pexpm1)
+F16_PACKET_FUNCTION(Packet16f, Packet16h, plog)
+F16_PACKET_FUNCTION(Packet16f, Packet16h, plog1p)
+F16_PACKET_FUNCTION(Packet16f, Packet16h, plog2)
+F16_PACKET_FUNCTION(Packet16f, Packet16h, preciprocal)
+F16_PACKET_FUNCTION(Packet16f, Packet16h, prsqrt)
+F16_PACKET_FUNCTION(Packet16f, Packet16h, psin)
+F16_PACKET_FUNCTION(Packet16f, Packet16h, psqrt)
+F16_PACKET_FUNCTION(Packet16f, Packet16h, ptanh)
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // THIRD_PARTY_EIGEN3_EIGEN_SRC_CORE_ARCH_AVX512_MATHFUNCTIONS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/PacketMath.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/PacketMath.h
new file mode 100644
index 0000000000000000000000000000000000000000..129a68cf5c22eea38680490e6b4c18aacb0cce4b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/PacketMath.h
@@ -0,0 +1,2757 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner (benoit.steiner.goog@gmail.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_AVX512_H
+#define EIGEN_PACKET_MATH_AVX512_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
+#endif
+
+#ifdef EIGEN_VECTORIZE_FMA
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+#endif
+
+typedef __m512 Packet16f;
+typedef __m512i Packet16i;
+typedef __m512d Packet8d;
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+typedef eigen_packet_wrapper<__m256i, 1> Packet16h;
+#endif
+typedef eigen_packet_wrapper<__m256i, 2> Packet16bf;
+
+template <>
+struct is_arithmetic<__m512> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<__m512i> {
+  enum { value = true };
+};
+template <>
+struct is_arithmetic<__m512d> {
+  enum { value = true };
+};
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template<> struct is_arithmetic<Packet16h> { enum { value = true }; };
+
+template <>
+struct packet_traits<half> : default_packet_traits {
+  typedef Packet16h type;
+  // There is no half-size packet for Packet16h.
+  typedef Packet16h half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+
+    HasCmp    = 1,
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasDiv    = 1,
+    HasNegate = 1,
+    HasAbs    = 1,
+    HasAbs2   = 0,
+    HasMin    = 1,
+    HasMax    = 1,
+    HasConj   = 1,
+    HasSetLinear = 0,
+    HasSqrt   = 1,
+    HasRsqrt  = 1,
+    HasLog    = 1,
+    HasLog1p  = 1,
+    HasExp    = 1,
+    HasExpm1  = 1,
+    HasBessel = 1,
+    HasNdtri  = 1,
+    HasSin    = EIGEN_FAST_MATH,
+    HasCos    = EIGEN_FAST_MATH,
+    HasTanh   = EIGEN_FAST_MATH,
+    HasErf    = EIGEN_FAST_MATH,
+    HasBlend  = 0,
+    HasRound  = 1,
+    HasFloor  = 1,
+    HasCeil   = 1,
+    HasRint   = 1
+  };
+};
+#endif
+
+template<> struct packet_traits<float>  : default_packet_traits
+{
+  typedef Packet16f type;
+  typedef Packet8f half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+
+    HasAbs = 1,
+    HasMin   = 1,
+    HasMax   = 1,
+    HasConj  = 1,
+    HasBlend = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasACos = 1,
+    HasASin = 1,
+    HasATan = 1,
+    HasATanh = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasLog = 1,
+    HasLog1p  = 1,
+    HasExpm1  = 1,
+    HasNdtri = 1,
+    HasBessel  = 1,
+    HasExp = 1,
+    HasReciprocal = EIGEN_FAST_MATH,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasCmp  = 1,
+    HasDiv = 1,
+    HasRound = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasRint = 1
+  };
+ };
+template<> struct packet_traits<double> : default_packet_traits
+{
+  typedef Packet8d type;
+  typedef Packet4d half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+    HasBlend = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasLog  = 1,
+    HasExp = 1,
+    HasATan = 1,
+    HasCmp  = 1,
+    HasDiv = 1,
+    HasRound = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasRint = 1
+  };
+};
+
+template<> struct packet_traits<int> : default_packet_traits
+{
+  typedef Packet16i type;
+  typedef Packet8i half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    HasBlend = 0,
+    HasCmp = 1,
+    HasDiv = 1,
+    size=16
+  };
+};
+
+template <>
+struct unpacket_traits<Packet16f> {
+  typedef float type;
+  typedef Packet8f half;
+  typedef Packet16i integer_packet;
+  typedef uint16_t mask_t;
+  enum { size = 16, alignment=Aligned64, vectorizable=true, masked_load_available=true, masked_store_available=true, masked_fpops_available=true };
+};
+template <>
+struct unpacket_traits<Packet8d> {
+  typedef double type;
+  typedef Packet4d half;
+  typedef uint8_t mask_t;
+  enum { size = 8, alignment=Aligned64, vectorizable=true, masked_load_available=true, masked_store_available=true, masked_fpops_available=true };
+};
+template <>
+struct unpacket_traits<Packet16i> {
+  typedef int type;
+  typedef Packet8i half;
+  enum { size = 16, alignment=Aligned64, vectorizable=true, masked_load_available=false, masked_store_available=false };
+};
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template<>
+struct unpacket_traits<Packet16h> {
+  typedef Eigen::half type;
+  typedef Packet8h half;
+  enum {size=16, alignment=Aligned32, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pset1<Packet16f>(const float& from) {
+  return _mm512_set1_ps(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pset1<Packet8d>(const double& from) {
+  return _mm512_set1_pd(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pset1<Packet16i>(const int& from) {
+  return _mm512_set1_epi32(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pset1frombits<Packet16f>(unsigned int from) {
+  return _mm512_castsi512_ps(_mm512_set1_epi32(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pset1frombits<Packet8d>(const numext::uint64_t from) {
+  return _mm512_castsi512_pd(_mm512_set1_epi64(from));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f pzero(const Packet16f& /*a*/) { return _mm512_setzero_ps(); }
+template<> EIGEN_STRONG_INLINE Packet8d pzero(const Packet8d& /*a*/) { return _mm512_setzero_pd(); }
+template<> EIGEN_STRONG_INLINE Packet16i pzero(const Packet16i& /*a*/) { return _mm512_setzero_si512(); }
+
+template<> EIGEN_STRONG_INLINE Packet16f peven_mask(const Packet16f& /*a*/) {
+  return _mm512_castsi512_ps(_mm512_set_epi32(0, -1, 0, -1, 0, -1, 0, -1,
+                                              0, -1, 0, -1, 0, -1, 0, -1));
+}
+template<> EIGEN_STRONG_INLINE Packet16i peven_mask(const Packet16i& /*a*/) {
+  return _mm512_set_epi32(0, -1, 0, -1, 0, -1, 0, -1,
+                          0, -1, 0, -1, 0, -1, 0, -1);
+}
+template<> EIGEN_STRONG_INLINE Packet8d peven_mask(const Packet8d& /*a*/) {
+  return _mm512_castsi512_pd(_mm512_set_epi32(0, 0, -1, -1, 0, 0, -1, -1,
+                                              0, 0, -1, -1, 0, 0, -1, -1));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pload1<Packet16f>(const float* from) {
+#if (EIGEN_COMP_GNUC != 0) || (EIGEN_COMP_CLANG != 0)
+  // Inline asm here helps reduce some register spilling in TRSM kernels.
+  // See note in unrolls::gemm::microKernel in TrsmKernel.h
+  Packet16f ret;
+  __asm__  ("vbroadcastss %[mem], %[dst]" : [dst] "=v" (ret) : [mem] "m" (*from));
+  return ret;
+#else
+  return _mm512_broadcastss_ps(_mm_load_ps1(from));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pload1<Packet8d>(const double* from) {
+#if (EIGEN_COMP_GNUC != 0) || (EIGEN_COMP_CLANG != 0)
+  Packet8d ret;
+  __asm__  ("vbroadcastsd %[mem], %[dst]" : [dst] "=v" (ret) : [mem] "m" (*from));
+  return ret;
+#else
+  return _mm512_set1_pd(*from);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f plset<Packet16f>(const float& a) {
+  return _mm512_add_ps(
+      _mm512_set1_ps(a),
+      _mm512_set_ps(15.0f, 14.0f, 13.0f, 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f,
+                    4.0f, 3.0f, 2.0f, 1.0f, 0.0f));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d plset<Packet8d>(const double& a) {
+  return _mm512_add_pd(_mm512_set1_pd(a),
+                       _mm512_set_pd(7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i plset<Packet16i>(const int& a) {
+  return _mm512_add_epi32(
+      _mm512_set1_epi32(a),
+      _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f padd<Packet16f>(const Packet16f& a,
+                                              const Packet16f& b) {
+  return _mm512_add_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d padd<Packet8d>(const Packet8d& a,
+                                            const Packet8d& b) {
+  return _mm512_add_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i padd<Packet16i>(const Packet16i& a,
+                                              const Packet16i& b) {
+  return _mm512_add_epi32(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f padd<Packet16f>(const Packet16f& a,
+                                              const Packet16f& b,
+                                              uint16_t umask) {
+  __mmask16 mask = static_cast<__mmask16>(umask);
+  return _mm512_maskz_add_ps(mask, a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d padd<Packet8d>(const Packet8d& a,
+                                            const Packet8d& b,
+                                            uint8_t umask) {
+  __mmask8 mask = static_cast<__mmask8>(umask);
+  return _mm512_maskz_add_pd(mask, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f psub<Packet16f>(const Packet16f& a,
+                                              const Packet16f& b) {
+  return _mm512_sub_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d psub<Packet8d>(const Packet8d& a,
+                                            const Packet8d& b) {
+  return _mm512_sub_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i psub<Packet16i>(const Packet16i& a,
+                                              const Packet16i& b) {
+  return _mm512_sub_epi32(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pnegate(const Packet16f& a) {
+  // NOTE: MSVC seems to struggle with _mm512_set1_epi32, leading to random results.
+  //       The intel docs give it a relatively high latency as well, so we're probably
+  //       better off with using _mm512_set_epi32 directly anyways.
+  const __m512i mask = _mm512_set_epi32(0x80000000,0x80000000,0x80000000,0x80000000,
+                                        0x80000000,0x80000000,0x80000000,0x80000000,
+                                        0x80000000,0x80000000,0x80000000,0x80000000,
+                                        0x80000000,0x80000000,0x80000000,0x80000000);
+  return _mm512_castsi512_ps(_mm512_xor_epi32(_mm512_castps_si512(a), mask));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pnegate(const Packet8d& a) {
+  const __m512i mask = _mm512_set_epi64(0x8000000000000000ULL, 0x8000000000000000ULL, 0x8000000000000000ULL, 0x8000000000000000ULL,
+                                        0x8000000000000000ULL, 0x8000000000000000ULL, 0x8000000000000000ULL, 0x8000000000000000ULL);
+  return _mm512_castsi512_pd(_mm512_xor_epi64(_mm512_castpd_si512(a), mask));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pnegate(const Packet16i& a) {
+  return _mm512_sub_epi32(_mm512_setzero_si512(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pconj(const Packet16f& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pconj(const Packet8d& a) {
+  return a;
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pconj(const Packet16i& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pmul<Packet16f>(const Packet16f& a,
+                                              const Packet16f& b) {
+  return _mm512_mul_ps(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pmul<Packet8d>(const Packet8d& a,
+                                            const Packet8d& b) {
+  return _mm512_mul_pd(a, b);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pmul<Packet16i>(const Packet16i& a,
+                                              const Packet16i& b) {
+  return _mm512_mullo_epi32(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pdiv<Packet16f>(const Packet16f& a,
+                                              const Packet16f& b) {
+  return _mm512_div_ps(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pdiv<Packet8d>(const Packet8d& a,
+                                            const Packet8d& b) {
+  return _mm512_div_pd(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i pdiv<Packet16i>(const Packet16i& a,
+                                              const Packet16i& b) {
+  Packet8i q_lo = pdiv<Packet8i>(_mm512_extracti64x4_epi64(a, 0), _mm512_extracti64x4_epi64(b,0));
+  Packet8i q_hi = pdiv<Packet8i>(_mm512_extracti64x4_epi64(a, 1), _mm512_extracti64x4_epi64(b, 1));
+  return _mm512_inserti64x4(_mm512_castsi256_si512(q_lo), q_hi, 1);
+}
+
+#ifdef EIGEN_VECTORIZE_FMA
+template <>
+EIGEN_STRONG_INLINE Packet16f pmadd(const Packet16f& a, const Packet16f& b,
+                                    const Packet16f& c) {
+  return _mm512_fmadd_ps(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pmadd(const Packet8d& a, const Packet8d& b,
+                                   const Packet8d& c) {
+  return _mm512_fmadd_pd(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pmsub(const Packet16f& a, const Packet16f& b,
+                                    const Packet16f& c) {
+  return _mm512_fmsub_ps(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pmsub(const Packet8d& a, const Packet8d& b,
+                                   const Packet8d& c) {
+  return _mm512_fmsub_pd(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pnmadd(const Packet16f& a, const Packet16f& b,
+                                    const Packet16f& c) {
+  return _mm512_fnmadd_ps(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pnmadd(const Packet8d& a, const Packet8d& b,
+                                   const Packet8d& c) {
+  return _mm512_fnmadd_pd(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pnmsub(const Packet16f& a, const Packet16f& b,
+                                    const Packet16f& c) {
+  return _mm512_fnmsub_ps(a, b, c);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pnmsub(const Packet8d& a, const Packet8d& b,
+                                   const Packet8d& c) {
+  return _mm512_fnmsub_pd(a, b, c);
+}
+#endif
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet16f pselect(const Packet16f& mask,
+                                           const Packet16f& a,
+                                           const Packet16f& b) {
+  __mmask16 mask16 = _mm512_cmpeq_epi32_mask(_mm512_castps_si512(mask), _mm512_setzero_epi32());
+  return _mm512_mask_blend_ps(mask16, a, b);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet16i pselect(const Packet16i& mask,
+                                           const Packet16i& a,
+                                           const Packet16i& b) {
+  __mmask16 mask16 = _mm512_cmpeq_epi32_mask(mask, _mm512_setzero_epi32());
+  return _mm512_mask_blend_epi32(mask16, a, b);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet8d pselect(const Packet8d& mask,
+                                          const Packet8d& a,
+                                          const Packet8d& b) {
+  __mmask8 mask8 = _mm512_cmp_epi64_mask(_mm512_castpd_si512(mask),
+                                         _mm512_setzero_epi32(), _MM_CMPINT_EQ);
+  return _mm512_mask_blend_pd(mask8, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pmin<Packet16f>(const Packet16f& a,
+                                              const Packet16f& b) {
+  // Arguments are reversed to match NaN propagation behavior of std::min.
+  return _mm512_min_ps(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pmin<Packet8d>(const Packet8d& a,
+                                            const Packet8d& b) {
+  // Arguments are reversed to match NaN propagation behavior of std::min.
+  return _mm512_min_pd(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pmin<Packet16i>(const Packet16i& a,
+                                              const Packet16i& b) {
+  return _mm512_min_epi32(b, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pmax<Packet16f>(const Packet16f& a,
+                                              const Packet16f& b) {
+  // Arguments are reversed to match NaN propagation behavior of std::max.
+  return _mm512_max_ps(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pmax<Packet8d>(const Packet8d& a,
+                                            const Packet8d& b) {
+  // Arguments are reversed to match NaN propagation behavior of std::max.
+  return _mm512_max_pd(b, a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pmax<Packet16i>(const Packet16i& a,
+                                              const Packet16i& b) {
+  return _mm512_max_epi32(b, a);
+}
+
+// Add specializations for min/max with prescribed NaN progation.
+template<>
+EIGEN_STRONG_INLINE Packet16f pmin<PropagateNumbers, Packet16f>(const Packet16f& a, const Packet16f& b) {
+  return pminmax_propagate_numbers(a, b, pmin<Packet16f>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet8d pmin<PropagateNumbers, Packet8d>(const Packet8d& a, const Packet8d& b) {
+  return pminmax_propagate_numbers(a, b, pmin<Packet8d>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet16f pmax<PropagateNumbers, Packet16f>(const Packet16f& a, const Packet16f& b) {
+  return pminmax_propagate_numbers(a, b, pmax<Packet16f>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet8d pmax<PropagateNumbers, Packet8d>(const Packet8d& a, const Packet8d& b) {
+  return pminmax_propagate_numbers(a, b, pmax<Packet8d>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet16f pmin<PropagateNaN, Packet16f>(const Packet16f& a, const Packet16f& b) {
+  return pminmax_propagate_nan(a, b, pmin<Packet16f>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet8d pmin<PropagateNaN, Packet8d>(const Packet8d& a, const Packet8d& b) {
+  return pminmax_propagate_nan(a, b, pmin<Packet8d>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet16f pmax<PropagateNaN, Packet16f>(const Packet16f& a, const Packet16f& b) {
+  return pminmax_propagate_nan(a, b, pmax<Packet16f>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet8d pmax<PropagateNaN, Packet8d>(const Packet8d& a, const Packet8d& b) {
+  return pminmax_propagate_nan(a, b, pmax<Packet8d>);
+}
+
+
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+template<int I_> EIGEN_STRONG_INLINE Packet8f extract256(Packet16f x) { return _mm512_extractf32x8_ps(x,I_); }
+template<int I_> EIGEN_STRONG_INLINE Packet2d extract128(Packet8d x) { return _mm512_extractf64x2_pd(x,I_); }
+EIGEN_STRONG_INLINE Packet16f cat256(Packet8f a, Packet8f b) { return _mm512_insertf32x8(_mm512_castps256_ps512(a),b,1); }
+EIGEN_STRONG_INLINE Packet16i cat256i(Packet8i a, Packet8i b) { return _mm512_inserti32x8(_mm512_castsi256_si512(a), b, 1); }
+#else
+// AVX512F does not define _mm512_extractf32x8_ps to extract _m256 from _m512
+template<int I_> EIGEN_STRONG_INLINE Packet8f extract256(Packet16f x) {
+  return  _mm256_castsi256_ps(_mm512_extracti64x4_epi64( _mm512_castps_si512(x),I_));
+}
+
+// AVX512F does not define _mm512_extractf64x2_pd to extract _m128 from _m512
+template<int I_> EIGEN_STRONG_INLINE Packet2d extract128(Packet8d x) {
+  return _mm_castsi128_pd(_mm512_extracti32x4_epi32( _mm512_castpd_si512(x),I_));
+}
+
+EIGEN_STRONG_INLINE Packet16f cat256(Packet8f a, Packet8f b) {
+  return _mm512_castsi512_ps(_mm512_inserti64x4(_mm512_castsi256_si512(_mm256_castps_si256(a)),
+                                                _mm256_castps_si256(b),1));
+}
+EIGEN_STRONG_INLINE Packet16i cat256i(Packet8i a, Packet8i b) {
+  return _mm512_inserti64x4(_mm512_castsi256_si512(a), b, 1);
+}
+#endif
+
+// Helper function for bit packing snippet of low precision comparison.
+// It packs the flags from 32x16 to 16x16.
+EIGEN_STRONG_INLINE __m256i Pack32To16(Packet16f rf) {
+  // Split data into small pieces and handle with AVX instructions
+  // to guarantee internal order of vector.
+  // Operation:
+  //   dst[15:0]    := Saturate16(rf[31:0])
+  //   dst[31:16]   := Saturate16(rf[63:32])
+  //   ...
+  //   dst[255:240] := Saturate16(rf[255:224])
+  __m256i lo = _mm256_castps_si256(extract256<0>(rf));
+  __m256i hi = _mm256_castps_si256(extract256<1>(rf));
+  __m128i result_lo = _mm_packs_epi32(_mm256_extractf128_si256(lo, 0),
+                                      _mm256_extractf128_si256(lo, 1));
+  __m128i result_hi = _mm_packs_epi32(_mm256_extractf128_si256(hi, 0),
+                                      _mm256_extractf128_si256(hi, 1));
+  return _mm256_insertf128_si256(_mm256_castsi128_si256(result_lo), result_hi, 1);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pisnan(const Packet16f& a) {
+  __mmask16 mask = _mm512_cmp_ps_mask(a, a, _CMP_UNORD_Q);
+  return _mm512_castsi512_ps(_mm512_maskz_set1_epi32(mask, 0xffffffffu));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pcmp_eq(const Packet16f& a, const Packet16f& b) {
+  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_EQ_OQ);
+  return _mm512_castsi512_ps(
+      _mm512_mask_set1_epi32(_mm512_setzero_epi32(), mask, 0xffffffffu));
+}
+template<> EIGEN_STRONG_INLINE Packet16f pcmp_le(const Packet16f& a, const Packet16f& b) {
+  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_LE_OQ);
+  return _mm512_castsi512_ps(
+      _mm512_mask_set1_epi32(_mm512_setzero_epi32(), mask, 0xffffffffu));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f pcmp_lt(const Packet16f& a, const Packet16f& b) {
+  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_LT_OQ);
+  return _mm512_castsi512_ps(
+      _mm512_mask_set1_epi32(_mm512_setzero_epi32(), mask, 0xffffffffu));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f pcmp_lt_or_nan(const Packet16f& a, const Packet16f& b) {
+  __mmask16 mask = _mm512_cmp_ps_mask(a, b, _CMP_NGE_UQ);
+  return _mm512_castsi512_ps(
+      _mm512_mask_set1_epi32(_mm512_setzero_epi32(), mask, 0xffffffffu));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16i pcmp_eq(const Packet16i& a, const Packet16i& b) {
+  __mmask16 mask = _mm512_cmp_epi32_mask(a, b, _MM_CMPINT_EQ);
+  return _mm512_mask_set1_epi32(_mm512_setzero_epi32(), mask, 0xffffffffu);
+}
+template<> EIGEN_STRONG_INLINE Packet16i pcmp_le(const Packet16i& a, const Packet16i& b) {
+  __mmask16 mask = _mm512_cmp_epi32_mask(a, b, _MM_CMPINT_LE);
+  return _mm512_mask_set1_epi32(_mm512_setzero_epi32(), mask, 0xffffffffu);
+}
+template<> EIGEN_STRONG_INLINE Packet16i pcmp_lt(const Packet16i& a, const Packet16i& b) {
+  __mmask16 mask = _mm512_cmp_epi32_mask(a, b, _MM_CMPINT_LT);
+  return _mm512_mask_set1_epi32(_mm512_setzero_epi32(), mask, 0xffffffffu);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pcmp_eq(const Packet8d& a, const Packet8d& b) {
+  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_EQ_OQ);
+  return _mm512_castsi512_pd(
+      _mm512_mask_set1_epi64(_mm512_setzero_epi32(), mask, 0xffffffffffffffffu));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pcmp_le(const Packet8d& a, const Packet8d& b) {
+  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_LE_OQ);
+  return _mm512_castsi512_pd(
+      _mm512_mask_set1_epi64(_mm512_setzero_epi32(), mask, 0xffffffffffffffffu));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pcmp_lt(const Packet8d& a, const Packet8d& b) {
+  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_LT_OQ);
+  return _mm512_castsi512_pd(
+      _mm512_mask_set1_epi64(_mm512_setzero_epi32(), mask, 0xffffffffffffffffu));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pcmp_lt_or_nan(const Packet8d& a, const Packet8d& b) {
+  __mmask8 mask = _mm512_cmp_pd_mask(a, b, _CMP_NGE_UQ);
+  return _mm512_castsi512_pd(
+      _mm512_mask_set1_epi64(_mm512_setzero_epi32(), mask, 0xffffffffffffffffu));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f print<Packet16f>(const Packet16f& a) { return _mm512_roundscale_ps(a, _MM_FROUND_CUR_DIRECTION); }
+template<> EIGEN_STRONG_INLINE Packet8d print<Packet8d>(const Packet8d& a) { return _mm512_roundscale_pd(a, _MM_FROUND_CUR_DIRECTION); }
+
+template<> EIGEN_STRONG_INLINE Packet16f pceil<Packet16f>(const Packet16f& a) { return _mm512_roundscale_ps(a, _MM_FROUND_TO_POS_INF); }
+template<> EIGEN_STRONG_INLINE Packet8d pceil<Packet8d>(const Packet8d& a) { return _mm512_roundscale_pd(a, _MM_FROUND_TO_POS_INF); }
+
+template<> EIGEN_STRONG_INLINE Packet16f pfloor<Packet16f>(const Packet16f& a) { return _mm512_roundscale_ps(a, _MM_FROUND_TO_NEG_INF); }
+template<> EIGEN_STRONG_INLINE Packet8d pfloor<Packet8d>(const Packet8d& a) { return _mm512_roundscale_pd(a, _MM_FROUND_TO_NEG_INF); }
+
+template <>
+EIGEN_STRONG_INLINE Packet16i ptrue<Packet16i>(const Packet16i& /*a*/) {
+  return _mm512_set1_epi32(0xffffffffu);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f ptrue<Packet16f>(const Packet16f& a) {
+  return _mm512_castsi512_ps(ptrue<Packet16i>(_mm512_castps_si512(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d ptrue<Packet8d>(const Packet8d& a) {
+  return _mm512_castsi512_pd(ptrue<Packet16i>(_mm512_castpd_si512(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i pand<Packet16i>(const Packet16i& a,
+                                              const Packet16i& b) {
+  return _mm512_and_si512(a,b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pand<Packet16f>(const Packet16f& a,
+                                              const Packet16f& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_and_ps(a, b);
+#else
+  return _mm512_castsi512_ps(pand(_mm512_castps_si512(a),_mm512_castps_si512(b)));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pand<Packet8d>(const Packet8d& a,
+                                            const Packet8d& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_and_pd(a, b);
+#else
+  Packet8d res = _mm512_undefined_pd();
+  Packet4d lane0_a = _mm512_extractf64x4_pd(a, 0);
+  Packet4d lane0_b = _mm512_extractf64x4_pd(b, 0);
+  res = _mm512_insertf64x4(res, _mm256_and_pd(lane0_a, lane0_b), 0);
+
+  Packet4d lane1_a = _mm512_extractf64x4_pd(a, 1);
+  Packet4d lane1_b = _mm512_extractf64x4_pd(b, 1);
+  return _mm512_insertf64x4(res, _mm256_and_pd(lane1_a, lane1_b), 1);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i por<Packet16i>(const Packet16i& a, const Packet16i& b) {
+  return _mm512_or_si512(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f por<Packet16f>(const Packet16f& a, const Packet16f& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_or_ps(a, b);
+#else
+  return _mm512_castsi512_ps(por(_mm512_castps_si512(a),_mm512_castps_si512(b)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d por<Packet8d>(const Packet8d& a,
+                                           const Packet8d& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_or_pd(a, b);
+#else
+  return _mm512_castsi512_pd(por(_mm512_castpd_si512(a),_mm512_castpd_si512(b)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i pxor<Packet16i>(const Packet16i& a, const Packet16i& b) {
+  return _mm512_xor_si512(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pxor<Packet16f>(const Packet16f& a, const Packet16f& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_xor_ps(a, b);
+#else
+  return _mm512_castsi512_ps(pxor(_mm512_castps_si512(a),_mm512_castps_si512(b)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8d pxor<Packet8d>(const Packet8d& a, const Packet8d& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_xor_pd(a, b);
+#else
+  return _mm512_castsi512_pd(pxor(_mm512_castpd_si512(a),_mm512_castpd_si512(b)));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16i pandnot<Packet16i>(const Packet16i& a, const Packet16i& b) {
+  return _mm512_andnot_si512(b, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pandnot<Packet16f>(const Packet16f& a, const Packet16f& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_andnot_ps(b, a);
+#else
+  return _mm512_castsi512_ps(pandnot(_mm512_castps_si512(a),_mm512_castps_si512(b)));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pandnot<Packet8d>(const Packet8d& a,const Packet8d& b) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_andnot_pd(b, a);
+#else
+  return _mm512_castsi512_pd(pandnot(_mm512_castpd_si512(a),_mm512_castpd_si512(b)));
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f pround<Packet16f>(const Packet16f& a)
+{
+  // Work-around for default std::round rounding mode.
+  const Packet16f mask = pset1frombits<Packet16f>(static_cast<numext::uint32_t>(0x80000000u));
+  const Packet16f prev0dot5 = pset1frombits<Packet16f>(static_cast<numext::uint32_t>(0x3EFFFFFFu));
+  return _mm512_roundscale_ps(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+template<> EIGEN_STRONG_INLINE Packet8d pround<Packet8d>(const Packet8d& a)
+{
+  // Work-around for default std::round rounding mode.
+  const Packet8d mask = pset1frombits<Packet8d>(static_cast<numext::uint64_t>(0x8000000000000000ull));
+  const Packet8d prev0dot5 = pset1frombits<Packet8d>(static_cast<numext::uint64_t>(0x3FDFFFFFFFFFFFFFull));
+  return _mm512_roundscale_pd(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+
+template<int N> EIGEN_STRONG_INLINE Packet16i parithmetic_shift_right(Packet16i a) {
+  return _mm512_srai_epi32(a, N);
+}
+
+template<int N> EIGEN_STRONG_INLINE Packet16i plogical_shift_right(Packet16i a) {
+  return _mm512_srli_epi32(a, N);
+}
+
+template<int N> EIGEN_STRONG_INLINE Packet16i plogical_shift_left(Packet16i a) {
+  return _mm512_slli_epi32(a, N);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pload<Packet16f>(const float* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_ps(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pload<Packet8d>(const double* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_pd(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i pload<Packet16i>(const int* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_si512(
+    reinterpret_cast<const __m512i*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f ploadu<Packet16f>(const float* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_loadu_ps(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d ploadu<Packet8d>(const double* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_loadu_pd(from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet16i ploadu<Packet16i>(const int* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_loadu_si512(
+      reinterpret_cast<const __m512i*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f ploadu<Packet16f>(const float* from, uint16_t umask) {
+  __mmask16 mask = static_cast<__mmask16>(umask);
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_maskz_loadu_ps(mask, from);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d ploadu<Packet8d>(const double* from, uint8_t umask) {
+  __mmask8 mask = static_cast<__mmask8>(umask);
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_maskz_loadu_pd(mask, from);
+}
+
+// Loads 8 floats from memory a returns the packet
+// {a0, a0  a1, a1, a2, a2, a3, a3, a4, a4, a5, a5, a6, a6, a7, a7}
+template <>
+EIGEN_STRONG_INLINE Packet16f ploaddup<Packet16f>(const float* from) {
+  // an unaligned load is required here as there is no requirement
+  // on the alignment of input pointer 'from'
+  __m256i low_half = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
+  __m512 even_elements = _mm512_castsi512_ps(_mm512_cvtepu32_epi64(low_half));
+  __m512 pairs = _mm512_permute_ps(even_elements, _MM_SHUFFLE(2, 2, 0, 0));
+  return pairs;
+}
+
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+// FIXME: this does not look optimal, better load a Packet4d and shuffle...
+// Loads 4 doubles from memory a returns the packet {a0, a0  a1, a1, a2, a2, a3,
+// a3}
+template <>
+EIGEN_STRONG_INLINE Packet8d ploaddup<Packet8d>(const double* from) {
+ __m512d x = _mm512_setzero_pd();
+  x = _mm512_insertf64x2(x, _mm_loaddup_pd(&from[0]), 0);
+  x = _mm512_insertf64x2(x, _mm_loaddup_pd(&from[1]), 1);
+  x = _mm512_insertf64x2(x, _mm_loaddup_pd(&from[2]), 2);
+  x = _mm512_insertf64x2(x, _mm_loaddup_pd(&from[3]), 3);
+  return x;
+}
+#else
+template <>
+EIGEN_STRONG_INLINE Packet8d ploaddup<Packet8d>(const double* from) {
+  __m512d x = _mm512_setzero_pd();
+  x = _mm512_mask_broadcastsd_pd(x, 0x3<<0, _mm_load_sd(from+0));
+  x = _mm512_mask_broadcastsd_pd(x, 0x3<<2, _mm_load_sd(from+1));
+  x = _mm512_mask_broadcastsd_pd(x, 0x3<<4, _mm_load_sd(from+2));
+  x = _mm512_mask_broadcastsd_pd(x, 0x3<<6, _mm_load_sd(from+3));
+  return x;
+}
+#endif
+
+// Loads 8 integers from memory and returns the packet
+// {a0, a0  a1, a1, a2, a2, a3, a3, a4, a4, a5, a5, a6, a6, a7, a7}
+template <>
+EIGEN_STRONG_INLINE Packet16i ploaddup<Packet16i>(const int* from) {
+  __m256i low_half = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
+  __m512 even_elements = _mm512_castsi512_ps(_mm512_cvtepu32_epi64(low_half));
+  __m512 pairs = _mm512_permute_ps(even_elements, _MM_SHUFFLE(2, 2, 0, 0));
+  return _mm512_castps_si512(pairs);
+}
+
+// Loads 4 floats from memory a returns the packet
+// {a0, a0  a0, a0, a1, a1, a1, a1, a2, a2, a2, a2, a3, a3, a3, a3}
+template <>
+EIGEN_STRONG_INLINE Packet16f ploadquad<Packet16f>(const float* from) {
+  Packet16f tmp = _mm512_castps128_ps512(ploadu<Packet4f>(from));
+  const Packet16i scatter_mask = _mm512_set_epi32(3,3,3,3, 2,2,2,2, 1,1,1,1, 0,0,0,0);
+  return _mm512_permutexvar_ps(scatter_mask, tmp);
+}
+
+// Loads 2 doubles from memory a returns the packet
+// {a0, a0  a0, a0, a1, a1, a1, a1}
+template <>
+EIGEN_STRONG_INLINE Packet8d ploadquad<Packet8d>(const double* from) {
+  __m256d lane0 = _mm256_set1_pd(*from);
+  __m256d lane1 = _mm256_set1_pd(*(from+1));
+  __m512d tmp = _mm512_undefined_pd();
+  tmp = _mm512_insertf64x4(tmp, lane0, 0);
+  return _mm512_insertf64x4(tmp, lane1, 1);
+}
+
+// Loads 4 integers from memory and returns the packet
+// {a0, a0  a0, a0, a1, a1, a1, a1, a2, a2, a2, a2, a3, a3, a3, a3}
+template <>
+EIGEN_STRONG_INLINE Packet16i ploadquad<Packet16i>(const int* from) {
+  Packet16i tmp = _mm512_castsi128_si512(ploadu<Packet4i>(from));
+  const Packet16i scatter_mask = _mm512_set_epi32(3,3,3,3, 2,2,2,2, 1,1,1,1, 0,0,0,0);
+  return _mm512_permutexvar_epi32(scatter_mask, tmp);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet16f& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm512_store_ps(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet8d& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm512_store_pd(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore<int>(int* to, const Packet16i& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm512_storeu_si512(reinterpret_cast<__m512i*>(to),
+                                                from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet16f& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm512_storeu_ps(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet8d& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm512_storeu_pd(to, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int>(int* to, const Packet16i& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm512_storeu_si512(
+      reinterpret_cast<__m512i*>(to), from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet16f& from, uint16_t umask) {
+  __mmask16 mask = static_cast<__mmask16>(umask);
+  EIGEN_DEBUG_UNALIGNED_STORE return _mm512_mask_storeu_ps(to, mask, from);
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet8d& from, uint8_t umask) {
+  __mmask8 mask = static_cast<__mmask8>(umask);
+  EIGEN_DEBUG_UNALIGNED_STORE return _mm512_mask_storeu_pd(to, mask, from);
+}
+
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline Packet pgather(const Packet& src, const Scalar* from,
+    Index stride, typename unpacket_traits<Packet>::mask_t umask);
+template <>
+EIGEN_DEVICE_FUNC inline Packet16f pgather<float, Packet16f>(const Packet16f& src,
+                                                             const float* from,
+                                                             Index stride,
+                                                             uint16_t umask) {
+  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
+  Packet16i stride_multiplier =
+      _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
+  __mmask16 mask = static_cast<__mmask16>(umask);
+
+  return _mm512_mask_i32gather_ps(src, mask, indices, from, 4);
+}
+template <>
+EIGEN_DEVICE_FUNC inline Packet8d pgather<double, Packet8d>(const Packet8d& src,
+                                                            const double* from,
+                                                            Index stride,
+                                                            uint8_t umask) {
+  Packet8i stride_vector = _mm256_set1_epi32(convert_index<int>(stride));
+  Packet8i stride_multiplier = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
+  Packet8i indices = _mm256_mullo_epi32(stride_vector, stride_multiplier);
+  __mmask8 mask = static_cast<__mmask8>(umask);
+
+  return _mm512_mask_i32gather_pd(src, mask, indices, from, 8);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet16f pgather<float, Packet16f>(const float* from,
+                                                             Index stride) {
+  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
+  Packet16i stride_multiplier =
+      _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
+
+  return _mm512_i32gather_ps(indices, from, 4);
+}
+template <>
+EIGEN_DEVICE_FUNC inline Packet8d pgather<double, Packet8d>(const double* from,
+                                                            Index stride) {
+  Packet8i stride_vector = _mm256_set1_epi32(convert_index<int>(stride));
+  Packet8i stride_multiplier = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
+  Packet8i indices = _mm256_mullo_epi32(stride_vector, stride_multiplier);
+
+  return _mm512_i32gather_pd(indices, from, 8);
+}
+template <>
+EIGEN_DEVICE_FUNC inline Packet16i pgather<int, Packet16i>(const int* from,
+                                                           Index stride) {
+  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
+  Packet16i stride_multiplier =
+      _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
+  return _mm512_i32gather_epi32(indices, from, 4);
+}
+
+template <typename Scalar, typename Packet>
+EIGEN_DEVICE_FUNC inline void pscatter(Scalar* to, const Packet& from,
+    Index stride, typename unpacket_traits<Packet>::mask_t umask);
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<float, Packet16f>(float* to,
+                                                         const Packet16f& from,
+                                                         Index stride,
+                                                         uint16_t umask) {
+  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
+  Packet16i stride_multiplier =
+      _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
+  __mmask16 mask = static_cast<__mmask16>(umask);
+  _mm512_mask_i32scatter_ps(to, mask, indices, from, 4);
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<double, Packet8d>(double* to,
+                                                         const Packet8d& from,
+                                                         Index stride,
+                                                         uint8_t umask) {
+  Packet8i stride_vector = _mm256_set1_epi32(convert_index<int>(stride));
+  Packet8i stride_multiplier = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
+  Packet8i indices = _mm256_mullo_epi32(stride_vector, stride_multiplier);
+  __mmask8 mask = static_cast<__mmask8>(umask);
+  _mm512_mask_i32scatter_pd(to, mask, indices, from, 8);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<float, Packet16f>(float* to,
+                                                         const Packet16f& from,
+                                                         Index stride) {
+  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
+  Packet16i stride_multiplier =
+      _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
+  _mm512_i32scatter_ps(to, indices, from, 4);
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<double, Packet8d>(double* to,
+                                                         const Packet8d& from,
+                                                         Index stride) {
+  Packet8i stride_vector = _mm256_set1_epi32(convert_index<int>(stride));
+  Packet8i stride_multiplier = _mm256_set_epi32(7, 6, 5, 4, 3, 2, 1, 0);
+  Packet8i indices = _mm256_mullo_epi32(stride_vector, stride_multiplier);
+  _mm512_i32scatter_pd(to, indices, from, 8);
+}
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<int, Packet16i>(int* to,
+                                                       const Packet16i& from,
+                                                       Index stride) {
+  Packet16i stride_vector = _mm512_set1_epi32(convert_index<int>(stride));
+  Packet16i stride_multiplier =
+      _mm512_set_epi32(15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0);
+  Packet16i indices = _mm512_mullo_epi32(stride_vector, stride_multiplier);
+  _mm512_i32scatter_epi32(to, indices, from, 4);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet16f>(float* to, const float& a) {
+  Packet16f pa = pset1<Packet16f>(a);
+  pstore(to, pa);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet8d>(double* to, const double& a) {
+  Packet8d pa = pset1<Packet8d>(a);
+  pstore(to, pa);
+}
+template <>
+EIGEN_STRONG_INLINE void pstore1<Packet16i>(int* to, const int& a) {
+  Packet16i pa = pset1<Packet16i>(a);
+  pstore(to, pa);
+}
+
+template<> EIGEN_STRONG_INLINE void prefetch<float>(const float*   addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
+template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
+template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*       addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
+
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet16f>(const Packet16f& a) {
+  return _mm_cvtss_f32(_mm512_extractf32x4_ps(a, 0));
+}
+template <>
+EIGEN_STRONG_INLINE double pfirst<Packet8d>(const Packet8d& a) {
+  return _mm_cvtsd_f64(_mm256_extractf128_pd(_mm512_extractf64x4_pd(a, 0), 0));
+}
+template <>
+EIGEN_STRONG_INLINE int pfirst<Packet16i>(const Packet16i& a) {
+  return _mm_extract_epi32(_mm512_extracti32x4_epi32(a, 0), 0);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f preverse(const Packet16f& a)
+{
+  return _mm512_permutexvar_ps(_mm512_set_epi32(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15), a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8d preverse(const Packet8d& a)
+{
+  return _mm512_permutexvar_pd(_mm512_set_epi32(0, 0, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0, 7), a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16i preverse(const Packet16i& a)
+{
+  return _mm512_permutexvar_epi32(_mm512_set_epi32(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15), a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f pabs(const Packet16f& a)
+{
+  // _mm512_abs_ps intrinsic not found, so hack around it
+  return _mm512_castsi512_ps(_mm512_and_si512(_mm512_castps_si512(a), _mm512_set1_epi32(0x7fffffff)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pabs(const Packet8d& a) {
+  // _mm512_abs_ps intrinsic not found, so hack around it
+  return _mm512_castsi512_pd(_mm512_and_si512(_mm512_castpd_si512(a),
+                                   _mm512_set1_epi64(0x7fffffffffffffff)));
+}
+template<> EIGEN_STRONG_INLINE Packet16i pabs(const Packet16i& a)
+{
+  return _mm512_abs_epi32(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h  psignbit(const Packet16h&  a) { return _mm256_srai_epi16(a, 15); }
+template<> EIGEN_STRONG_INLINE Packet16bf psignbit(const Packet16bf& a) { return _mm256_srai_epi16(a, 15); }
+template<> EIGEN_STRONG_INLINE Packet16f  psignbit(const Packet16f&  a) { return _mm512_castsi512_ps(_mm512_srai_epi32(_mm512_castps_si512(a), 31)); }
+template<> EIGEN_STRONG_INLINE Packet8d   psignbit(const Packet8d&   a) { return _mm512_castsi512_pd(_mm512_srai_epi64(_mm512_castpd_si512(a), 63)); }
+
+template<>
+EIGEN_STRONG_INLINE Packet16f pfrexp<Packet16f>(const Packet16f& a, Packet16f& exponent){
+  return pfrexp_generic(a, exponent);
+}
+
+// Extract exponent without existence of Packet8l.
+template<>
+EIGEN_STRONG_INLINE
+Packet8d pfrexp_generic_get_biased_exponent(const Packet8d& a) {
+  const Packet8d cst_exp_mask  = pset1frombits<Packet8d>(static_cast<uint64_t>(0x7ff0000000000000ull));
+  #ifdef EIGEN_VECTORIZE_AVX512DQ
+  return _mm512_cvtepi64_pd(_mm512_srli_epi64(_mm512_castpd_si512(pand(a, cst_exp_mask)), 52));
+  #else
+  return _mm512_cvtepi32_pd(_mm512_cvtepi64_epi32(_mm512_srli_epi64(_mm512_castpd_si512(pand(a, cst_exp_mask)), 52)));
+  #endif
+}
+
+template<>
+EIGEN_STRONG_INLINE Packet8d pfrexp<Packet8d>(const Packet8d& a, Packet8d& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f pldexp<Packet16f>(const Packet16f& a, const Packet16f& exponent) {
+  return pldexp_generic(a, exponent);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8d pldexp<Packet8d>(const Packet8d& a, const Packet8d& exponent) {
+  // Clamp exponent to [-2099, 2099]
+  const Packet8d max_exponent = pset1<Packet8d>(2099.0);
+  const Packet8i e = _mm512_cvtpd_epi32(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
+
+  // Split 2^e into four factors and multiply.
+  const Packet8i bias = pset1<Packet8i>(1023);
+  Packet8i b = parithmetic_shift_right<2>(e);  // floor(e/4)
+
+  // 2^b
+  const Packet8i permute_idx = _mm256_setr_epi32(0, 4, 1, 5, 2, 6, 3, 7);
+  Packet8i hi = _mm256_permutevar8x32_epi32(padd(b, bias), permute_idx);
+  Packet8i lo = _mm256_slli_epi64(hi, 52);
+  hi = _mm256_slli_epi64(_mm256_srli_epi64(hi, 32), 52);
+  Packet8d c = _mm512_castsi512_pd(_mm512_inserti64x4(_mm512_castsi256_si512(lo), hi, 1));
+  Packet8d out = pmul(pmul(pmul(a, c), c), c);  // a * 2^(3b)
+
+  // 2^(e - 3b)
+  b = psub(psub(psub(e, b), b), b);  // e - 3b
+  hi = _mm256_permutevar8x32_epi32(padd(b, bias), permute_idx);
+  lo = _mm256_slli_epi64(hi, 52);
+  hi = _mm256_slli_epi64(_mm256_srli_epi64(hi, 32), 52);
+  c = _mm512_castsi512_pd(_mm512_inserti64x4(_mm512_castsi256_si512(lo), hi, 1));
+  out = pmul(out, c);  // a * 2^e
+  return out;
+}
+
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+// AVX512F does not define _mm512_extractf32x8_ps to extract _m256 from _m512
+#define EIGEN_EXTRACT_8f_FROM_16f(INPUT, OUTPUT)                           \
+  __m256 OUTPUT##_0 = _mm512_extractf32x8_ps(INPUT, 0);                    \
+  __m256 OUTPUT##_1 = _mm512_extractf32x8_ps(INPUT, 1)
+
+// AVX512F does not define _mm512_extracti32x8_epi32 to extract _m256i from _m512i
+#define EIGEN_EXTRACT_8i_FROM_16i(INPUT, OUTPUT)                           \
+  __m256i OUTPUT##_0 = _mm512_extracti32x8_epi32(INPUT, 0);                \
+  __m256i OUTPUT##_1 = _mm512_extracti32x8_epi32(INPUT, 1)
+#else
+#define EIGEN_EXTRACT_8f_FROM_16f(INPUT, OUTPUT)                \
+  __m256 OUTPUT##_0 = _mm256_insertf128_ps(                     \
+      _mm256_castps128_ps256(_mm512_extractf32x4_ps(INPUT, 0)), \
+      _mm512_extractf32x4_ps(INPUT, 1), 1);                     \
+  __m256 OUTPUT##_1 = _mm256_insertf128_ps(                     \
+      _mm256_castps128_ps256(_mm512_extractf32x4_ps(INPUT, 2)), \
+      _mm512_extractf32x4_ps(INPUT, 3), 1)
+
+#define EIGEN_EXTRACT_8i_FROM_16i(INPUT, OUTPUT)                    \
+  __m256i OUTPUT##_0 = _mm256_insertf128_si256(                     \
+      _mm256_castsi128_si256(_mm512_extracti32x4_epi32(INPUT, 0)),  \
+      _mm512_extracti32x4_epi32(INPUT, 1), 1);                      \
+  __m256i OUTPUT##_1 = _mm256_insertf128_si256(                     \
+      _mm256_castsi128_si256(_mm512_extracti32x4_epi32(INPUT, 2)),  \
+      _mm512_extracti32x4_epi32(INPUT, 3), 1)
+#endif
+
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+#define EIGEN_INSERT_8f_INTO_16f(OUTPUT, INPUTA, INPUTB) \
+  OUTPUT = _mm512_insertf32x8(_mm512_castps256_ps512(INPUTA), INPUTB, 1);
+
+#define EIGEN_INSERT_8i_INTO_16i(OUTPUT, INPUTA, INPUTB) \
+  OUTPUT = _mm512_inserti32x8(_mm512_castsi256_si512(INPUTA), INPUTB, 1);
+#else
+#define EIGEN_INSERT_8f_INTO_16f(OUTPUT, INPUTA, INPUTB)                    \
+  OUTPUT = _mm512_undefined_ps();                                           \
+  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTA, 0), 0); \
+  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTA, 1), 1); \
+  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTB, 0), 2); \
+  OUTPUT = _mm512_insertf32x4(OUTPUT, _mm256_extractf128_ps(INPUTB, 1), 3);
+
+#define EIGEN_INSERT_8i_INTO_16i(OUTPUT, INPUTA, INPUTB)                    \
+  OUTPUT = _mm512_undefined_epi32();                                           \
+  OUTPUT = _mm512_inserti32x4(OUTPUT, _mm256_extractf128_si256(INPUTA, 0), 0); \
+  OUTPUT = _mm512_inserti32x4(OUTPUT, _mm256_extractf128_si256(INPUTA, 1), 1); \
+  OUTPUT = _mm512_inserti32x4(OUTPUT, _mm256_extractf128_si256(INPUTB, 0), 2); \
+  OUTPUT = _mm512_inserti32x4(OUTPUT, _mm256_extractf128_si256(INPUTB, 1), 3);
+#endif
+
+template <>
+EIGEN_STRONG_INLINE float predux<Packet16f>(const Packet16f& a) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  __m256 lane0 = _mm512_extractf32x8_ps(a, 0);
+  __m256 lane1 = _mm512_extractf32x8_ps(a, 1);
+  Packet8f x = _mm256_add_ps(lane0, lane1);
+  return predux<Packet8f>(x);
+#else
+  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
+  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
+  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
+  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
+  __m128 sum = _mm_add_ps(_mm_add_ps(lane0, lane1), _mm_add_ps(lane2, lane3));
+  sum = _mm_hadd_ps(sum, sum);
+  sum = _mm_hadd_ps(sum, _mm_permute_ps(sum, 1));
+  return _mm_cvtss_f32(sum);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE double predux<Packet8d>(const Packet8d& a) {
+  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
+  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
+  __m256d sum = _mm256_add_pd(lane0, lane1);
+  __m256d tmp0 = _mm256_hadd_pd(sum, _mm256_permute2f128_pd(sum, sum, 1));
+  return _mm_cvtsd_f64(_mm256_castpd256_pd128(_mm256_hadd_pd(tmp0, tmp0)));
+}
+template <>
+EIGEN_STRONG_INLINE int predux<Packet16i>(const Packet16i& a) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  __m256i lane0 = _mm512_extracti32x8_epi32(a, 0);
+  __m256i lane1 = _mm512_extracti32x8_epi32(a, 1);
+  Packet8i x = _mm256_add_epi32(lane0, lane1);
+  return predux<Packet8i>(x);
+#else
+  __m128i lane0 = _mm512_extracti32x4_epi32(a, 0);
+  __m128i lane1 = _mm512_extracti32x4_epi32(a, 1);
+  __m128i lane2 = _mm512_extracti32x4_epi32(a, 2);
+  __m128i lane3 = _mm512_extracti32x4_epi32(a, 3);
+  __m128i sum = _mm_add_epi32(_mm_add_epi32(lane0, lane1), _mm_add_epi32(lane2, lane3));
+  sum = _mm_hadd_epi32(sum, sum);
+  sum = _mm_hadd_epi32(sum, _mm_castps_si128(_mm_permute_ps(_mm_castsi128_ps(sum), 1)));
+  return _mm_cvtsi128_si32(sum);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f predux_half_dowto4<Packet16f>(const Packet16f& a) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  __m256 lane0 = _mm512_extractf32x8_ps(a, 0);
+  __m256 lane1 = _mm512_extractf32x8_ps(a, 1);
+  return _mm256_add_ps(lane0, lane1);
+#else
+  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
+  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
+  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
+  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
+  __m128 sum0 = _mm_add_ps(lane0, lane2);
+  __m128 sum1 = _mm_add_ps(lane1, lane3);
+  return _mm256_insertf128_ps(_mm256_castps128_ps256(sum0), sum1, 1);
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE Packet4d predux_half_dowto4<Packet8d>(const Packet8d& a) {
+  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
+  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
+  return _mm256_add_pd(lane0, lane1);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8i predux_half_dowto4<Packet16i>(const Packet16i& a) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  __m256i lane0 = _mm512_extracti32x8_epi32(a, 0);
+  __m256i lane1 = _mm512_extracti32x8_epi32(a, 1);
+  return _mm256_add_epi32(lane0, lane1);
+#else
+  __m128i lane0 = _mm512_extracti32x4_epi32(a, 0);
+  __m128i lane1 = _mm512_extracti32x4_epi32(a, 1);
+  __m128i lane2 = _mm512_extracti32x4_epi32(a, 2);
+  __m128i lane3 = _mm512_extracti32x4_epi32(a, 3);
+  __m128i sum0 = _mm_add_epi32(lane0, lane2);
+  __m128i sum1 = _mm_add_epi32(lane1, lane3);
+  return _mm256_inserti128_si256(_mm256_castsi128_si256(sum0), sum1, 1);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_mul<Packet16f>(const Packet16f& a) {
+//#ifdef EIGEN_VECTORIZE_AVX512DQ
+#if 0
+  Packet8f lane0 = _mm512_extractf32x8_ps(a, 0);
+  Packet8f lane1 = _mm512_extractf32x8_ps(a, 1);
+  Packet8f res = pmul(lane0, lane1);
+  res = pmul(res, _mm256_permute2f128_ps(res, res, 1));
+  res = pmul(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 3, 2)));
+  return pfirst(pmul(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 0, 1))));
+#else
+  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
+  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
+  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
+  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
+  __m128 res = pmul(pmul(lane0, lane1), pmul(lane2, lane3));
+  res = pmul(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 3, 2)));
+  return pfirst(pmul(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 0, 1))));
+#endif
+}
+template <>
+EIGEN_STRONG_INLINE double predux_mul<Packet8d>(const Packet8d& a) {
+  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
+  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
+  __m256d res = pmul(lane0, lane1);
+  res = pmul(res, _mm256_permute2f128_pd(res, res, 1));
+  return pfirst(pmul(res, _mm256_shuffle_pd(res, res, 1)));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min<Packet16f>(const Packet16f& a) {
+  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
+  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
+  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
+  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
+  __m128 res = _mm_min_ps(_mm_min_ps(lane0, lane1), _mm_min_ps(lane2, lane3));
+  res = _mm_min_ps(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 3, 2)));
+  return pfirst(_mm_min_ps(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 0, 1))));
+}
+template <>
+EIGEN_STRONG_INLINE double predux_min<Packet8d>(const Packet8d& a) {
+  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
+  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
+  __m256d res = _mm256_min_pd(lane0, lane1);
+  res = _mm256_min_pd(res, _mm256_permute2f128_pd(res, res, 1));
+  return pfirst(_mm256_min_pd(res, _mm256_shuffle_pd(res, res, 1)));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max<Packet16f>(const Packet16f& a) {
+  __m128 lane0 = _mm512_extractf32x4_ps(a, 0);
+  __m128 lane1 = _mm512_extractf32x4_ps(a, 1);
+  __m128 lane2 = _mm512_extractf32x4_ps(a, 2);
+  __m128 lane3 = _mm512_extractf32x4_ps(a, 3);
+  __m128 res = _mm_max_ps(_mm_max_ps(lane0, lane1), _mm_max_ps(lane2, lane3));
+  res = _mm_max_ps(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 3, 2)));
+  return pfirst(_mm_max_ps(res, _mm_permute_ps(res, _MM_SHUFFLE(0, 0, 0, 1))));
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux_max<Packet8d>(const Packet8d& a) {
+  __m256d lane0 = _mm512_extractf64x4_pd(a, 0);
+  __m256d lane1 = _mm512_extractf64x4_pd(a, 1);
+  __m256d res = _mm256_max_pd(lane0, lane1);
+  res = _mm256_max_pd(res, _mm256_permute2f128_pd(res, res, 1));
+  return pfirst(_mm256_max_pd(res, _mm256_shuffle_pd(res, res, 1)));
+}
+
+template<> EIGEN_STRONG_INLINE bool predux_any(const Packet16f& x)
+{
+  Packet16i xi = _mm512_castps_si512(x);
+  __mmask16 tmp = _mm512_test_epi32_mask(xi,xi);
+  return !_mm512_kortestz(tmp,tmp);
+}
+
+template<> EIGEN_STRONG_INLINE bool predux_any(const Packet16i& x)
+{
+  __mmask16 tmp = _mm512_test_epi32_mask(x,x);
+  return !_mm512_kortestz(tmp,tmp);
+}
+
+#define PACK_OUTPUT(OUTPUT, INPUT, INDEX, STRIDE) \
+  EIGEN_INSERT_8f_INTO_16f(OUTPUT[INDEX], INPUT[INDEX], INPUT[INDEX + STRIDE]);
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16f, 16>& kernel) {
+  __m512 T0 = _mm512_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
+  __m512 T1 = _mm512_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
+  __m512 T2 = _mm512_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
+  __m512 T3 = _mm512_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
+  __m512 T4 = _mm512_unpacklo_ps(kernel.packet[4], kernel.packet[5]);
+  __m512 T5 = _mm512_unpackhi_ps(kernel.packet[4], kernel.packet[5]);
+  __m512 T6 = _mm512_unpacklo_ps(kernel.packet[6], kernel.packet[7]);
+  __m512 T7 = _mm512_unpackhi_ps(kernel.packet[6], kernel.packet[7]);
+  __m512 T8 = _mm512_unpacklo_ps(kernel.packet[8], kernel.packet[9]);
+  __m512 T9 = _mm512_unpackhi_ps(kernel.packet[8], kernel.packet[9]);
+  __m512 T10 = _mm512_unpacklo_ps(kernel.packet[10], kernel.packet[11]);
+  __m512 T11 = _mm512_unpackhi_ps(kernel.packet[10], kernel.packet[11]);
+  __m512 T12 = _mm512_unpacklo_ps(kernel.packet[12], kernel.packet[13]);
+  __m512 T13 = _mm512_unpackhi_ps(kernel.packet[12], kernel.packet[13]);
+  __m512 T14 = _mm512_unpacklo_ps(kernel.packet[14], kernel.packet[15]);
+  __m512 T15 = _mm512_unpackhi_ps(kernel.packet[14], kernel.packet[15]);
+  __m512 S0 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S1 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S2 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S3 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S4 = _mm512_shuffle_ps(T4, T6, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S5 = _mm512_shuffle_ps(T4, T6, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S6 = _mm512_shuffle_ps(T5, T7, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S7 = _mm512_shuffle_ps(T5, T7, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S8 = _mm512_shuffle_ps(T8, T10, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S9 = _mm512_shuffle_ps(T8, T10, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S10 = _mm512_shuffle_ps(T9, T11, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S11 = _mm512_shuffle_ps(T9, T11, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S12 = _mm512_shuffle_ps(T12, T14, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S13 = _mm512_shuffle_ps(T12, T14, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S14 = _mm512_shuffle_ps(T13, T15, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S15 = _mm512_shuffle_ps(T13, T15, _MM_SHUFFLE(3, 2, 3, 2));
+
+  EIGEN_EXTRACT_8f_FROM_16f(S0, S0);
+  EIGEN_EXTRACT_8f_FROM_16f(S1, S1);
+  EIGEN_EXTRACT_8f_FROM_16f(S2, S2);
+  EIGEN_EXTRACT_8f_FROM_16f(S3, S3);
+  EIGEN_EXTRACT_8f_FROM_16f(S4, S4);
+  EIGEN_EXTRACT_8f_FROM_16f(S5, S5);
+  EIGEN_EXTRACT_8f_FROM_16f(S6, S6);
+  EIGEN_EXTRACT_8f_FROM_16f(S7, S7);
+  EIGEN_EXTRACT_8f_FROM_16f(S8, S8);
+  EIGEN_EXTRACT_8f_FROM_16f(S9, S9);
+  EIGEN_EXTRACT_8f_FROM_16f(S10, S10);
+  EIGEN_EXTRACT_8f_FROM_16f(S11, S11);
+  EIGEN_EXTRACT_8f_FROM_16f(S12, S12);
+  EIGEN_EXTRACT_8f_FROM_16f(S13, S13);
+  EIGEN_EXTRACT_8f_FROM_16f(S14, S14);
+  EIGEN_EXTRACT_8f_FROM_16f(S15, S15);
+
+  PacketBlock<Packet8f, 32> tmp;
+
+  tmp.packet[0] = _mm256_permute2f128_ps(S0_0, S4_0, 0x20);
+  tmp.packet[1] = _mm256_permute2f128_ps(S1_0, S5_0, 0x20);
+  tmp.packet[2] = _mm256_permute2f128_ps(S2_0, S6_0, 0x20);
+  tmp.packet[3] = _mm256_permute2f128_ps(S3_0, S7_0, 0x20);
+  tmp.packet[4] = _mm256_permute2f128_ps(S0_0, S4_0, 0x31);
+  tmp.packet[5] = _mm256_permute2f128_ps(S1_0, S5_0, 0x31);
+  tmp.packet[6] = _mm256_permute2f128_ps(S2_0, S6_0, 0x31);
+  tmp.packet[7] = _mm256_permute2f128_ps(S3_0, S7_0, 0x31);
+
+  tmp.packet[8] = _mm256_permute2f128_ps(S0_1, S4_1, 0x20);
+  tmp.packet[9] = _mm256_permute2f128_ps(S1_1, S5_1, 0x20);
+  tmp.packet[10] = _mm256_permute2f128_ps(S2_1, S6_1, 0x20);
+  tmp.packet[11] = _mm256_permute2f128_ps(S3_1, S7_1, 0x20);
+  tmp.packet[12] = _mm256_permute2f128_ps(S0_1, S4_1, 0x31);
+  tmp.packet[13] = _mm256_permute2f128_ps(S1_1, S5_1, 0x31);
+  tmp.packet[14] = _mm256_permute2f128_ps(S2_1, S6_1, 0x31);
+  tmp.packet[15] = _mm256_permute2f128_ps(S3_1, S7_1, 0x31);
+
+  // Second set of _m256 outputs
+  tmp.packet[16] = _mm256_permute2f128_ps(S8_0, S12_0, 0x20);
+  tmp.packet[17] = _mm256_permute2f128_ps(S9_0, S13_0, 0x20);
+  tmp.packet[18] = _mm256_permute2f128_ps(S10_0, S14_0, 0x20);
+  tmp.packet[19] = _mm256_permute2f128_ps(S11_0, S15_0, 0x20);
+  tmp.packet[20] = _mm256_permute2f128_ps(S8_0, S12_0, 0x31);
+  tmp.packet[21] = _mm256_permute2f128_ps(S9_0, S13_0, 0x31);
+  tmp.packet[22] = _mm256_permute2f128_ps(S10_0, S14_0, 0x31);
+  tmp.packet[23] = _mm256_permute2f128_ps(S11_0, S15_0, 0x31);
+
+  tmp.packet[24] = _mm256_permute2f128_ps(S8_1, S12_1, 0x20);
+  tmp.packet[25] = _mm256_permute2f128_ps(S9_1, S13_1, 0x20);
+  tmp.packet[26] = _mm256_permute2f128_ps(S10_1, S14_1, 0x20);
+  tmp.packet[27] = _mm256_permute2f128_ps(S11_1, S15_1, 0x20);
+  tmp.packet[28] = _mm256_permute2f128_ps(S8_1, S12_1, 0x31);
+  tmp.packet[29] = _mm256_permute2f128_ps(S9_1, S13_1, 0x31);
+  tmp.packet[30] = _mm256_permute2f128_ps(S10_1, S14_1, 0x31);
+  tmp.packet[31] = _mm256_permute2f128_ps(S11_1, S15_1, 0x31);
+
+  // Pack them into the output
+  PACK_OUTPUT(kernel.packet, tmp.packet, 0, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 1, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 2, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 3, 16);
+
+  PACK_OUTPUT(kernel.packet, tmp.packet, 4, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 5, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 6, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 7, 16);
+
+  PACK_OUTPUT(kernel.packet, tmp.packet, 8, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 9, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 10, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 11, 16);
+
+  PACK_OUTPUT(kernel.packet, tmp.packet, 12, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 13, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 14, 16);
+  PACK_OUTPUT(kernel.packet, tmp.packet, 15, 16);
+}
+#define PACK_OUTPUT_2(OUTPUT, INPUT, INDEX, STRIDE)         \
+  EIGEN_INSERT_8f_INTO_16f(OUTPUT[INDEX], INPUT[2 * INDEX], \
+                           INPUT[2 * INDEX + STRIDE]);
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16f, 8>& kernel) {
+  __m512 T0 = _mm512_unpacklo_ps(kernel.packet[0],kernel.packet[1]);
+  __m512 T1 = _mm512_unpackhi_ps(kernel.packet[0],kernel.packet[1]);
+  __m512 T2 = _mm512_unpacklo_ps(kernel.packet[2],kernel.packet[3]);
+  __m512 T3 = _mm512_unpackhi_ps(kernel.packet[2],kernel.packet[3]);
+  __m512 T4 = _mm512_unpacklo_ps(kernel.packet[4],kernel.packet[5]);
+  __m512 T5 = _mm512_unpackhi_ps(kernel.packet[4],kernel.packet[5]);
+  __m512 T6 = _mm512_unpacklo_ps(kernel.packet[6],kernel.packet[7]);
+  __m512 T7 = _mm512_unpackhi_ps(kernel.packet[6],kernel.packet[7]);
+
+  kernel.packet[0] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T0),_mm512_castps_pd(T2)));
+  kernel.packet[1] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T0),_mm512_castps_pd(T2)));
+  kernel.packet[2] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T1),_mm512_castps_pd(T3)));
+  kernel.packet[3] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T1),_mm512_castps_pd(T3)));
+  kernel.packet[4] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T4),_mm512_castps_pd(T6)));
+  kernel.packet[5] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T4),_mm512_castps_pd(T6)));
+  kernel.packet[6] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T5),_mm512_castps_pd(T7)));
+  kernel.packet[7] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T5),_mm512_castps_pd(T7)));
+
+  T0 = _mm512_shuffle_f32x4(kernel.packet[0], kernel.packet[4], 0x44);
+  T1 = _mm512_shuffle_f32x4(kernel.packet[0], kernel.packet[4], 0xee);
+  T2 = _mm512_shuffle_f32x4(kernel.packet[1], kernel.packet[5], 0x44);
+  T3 = _mm512_shuffle_f32x4(kernel.packet[1], kernel.packet[5], 0xee);
+  T4 = _mm512_shuffle_f32x4(kernel.packet[2], kernel.packet[6], 0x44);
+  T5 = _mm512_shuffle_f32x4(kernel.packet[2], kernel.packet[6], 0xee);
+  T6 = _mm512_shuffle_f32x4(kernel.packet[3], kernel.packet[7], 0x44);
+  T7 = _mm512_shuffle_f32x4(kernel.packet[3], kernel.packet[7], 0xee);
+
+  kernel.packet[0] = _mm512_shuffle_f32x4(T0, T2, 0x88);
+  kernel.packet[2] = _mm512_shuffle_f32x4(T0, T2, 0xdd);
+  kernel.packet[1] = _mm512_shuffle_f32x4(T4, T6, 0x88);
+  kernel.packet[3] = _mm512_shuffle_f32x4(T4, T6, 0xdd);
+  kernel.packet[4] = _mm512_shuffle_f32x4(T1, T3, 0x88);
+  kernel.packet[6] = _mm512_shuffle_f32x4(T1, T3, 0xdd);
+  kernel.packet[5] = _mm512_shuffle_f32x4(T5, T7, 0x88);
+  kernel.packet[7] = _mm512_shuffle_f32x4(T5, T7, 0xdd);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16f, 4>& kernel) {
+  __m512 T0 = _mm512_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
+  __m512 T1 = _mm512_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
+  __m512 T2 = _mm512_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
+  __m512 T3 = _mm512_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
+
+  __m512 S0 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S1 = _mm512_shuffle_ps(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512 S2 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512 S3 = _mm512_shuffle_ps(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
+
+  EIGEN_EXTRACT_8f_FROM_16f(S0, S0);
+  EIGEN_EXTRACT_8f_FROM_16f(S1, S1);
+  EIGEN_EXTRACT_8f_FROM_16f(S2, S2);
+  EIGEN_EXTRACT_8f_FROM_16f(S3, S3);
+
+  PacketBlock<Packet8f, 8> tmp;
+
+  tmp.packet[0] = _mm256_permute2f128_ps(S0_0, S1_0, 0x20);
+  tmp.packet[1] = _mm256_permute2f128_ps(S2_0, S3_0, 0x20);
+  tmp.packet[2] = _mm256_permute2f128_ps(S0_0, S1_0, 0x31);
+  tmp.packet[3] = _mm256_permute2f128_ps(S2_0, S3_0, 0x31);
+
+  tmp.packet[4] = _mm256_permute2f128_ps(S0_1, S1_1, 0x20);
+  tmp.packet[5] = _mm256_permute2f128_ps(S2_1, S3_1, 0x20);
+  tmp.packet[6] = _mm256_permute2f128_ps(S0_1, S1_1, 0x31);
+  tmp.packet[7] = _mm256_permute2f128_ps(S2_1, S3_1, 0x31);
+
+  PACK_OUTPUT_2(kernel.packet, tmp.packet, 0, 1);
+  PACK_OUTPUT_2(kernel.packet, tmp.packet, 1, 1);
+  PACK_OUTPUT_2(kernel.packet, tmp.packet, 2, 1);
+  PACK_OUTPUT_2(kernel.packet, tmp.packet, 3, 1);
+}
+
+#define PACK_OUTPUT_SQ_D(OUTPUT, INPUT, INDEX, STRIDE)                \
+  OUTPUT[INDEX] = _mm512_insertf64x4(OUTPUT[INDEX], INPUT[INDEX], 0); \
+  OUTPUT[INDEX] = _mm512_insertf64x4(OUTPUT[INDEX], INPUT[INDEX + STRIDE], 1);
+
+#define PACK_OUTPUT_D(OUTPUT, INPUT, INDEX, STRIDE)                         \
+  OUTPUT[INDEX] = _mm512_insertf64x4(OUTPUT[INDEX], INPUT[(2 * INDEX)], 0); \
+  OUTPUT[INDEX] =                                                           \
+      _mm512_insertf64x4(OUTPUT[INDEX], INPUT[(2 * INDEX) + STRIDE], 1);
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8d, 4>& kernel) {
+  __m512d T0 = _mm512_shuffle_pd(kernel.packet[0], kernel.packet[1], 0);
+  __m512d T1 = _mm512_shuffle_pd(kernel.packet[0], kernel.packet[1], 0xff);
+  __m512d T2 = _mm512_shuffle_pd(kernel.packet[2], kernel.packet[3], 0);
+  __m512d T3 = _mm512_shuffle_pd(kernel.packet[2], kernel.packet[3], 0xff);
+
+  PacketBlock<Packet4d, 8> tmp;
+
+  tmp.packet[0] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 0),
+                                         _mm512_extractf64x4_pd(T2, 0), 0x20);
+  tmp.packet[1] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 0),
+                                         _mm512_extractf64x4_pd(T3, 0), 0x20);
+  tmp.packet[2] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 0),
+                                         _mm512_extractf64x4_pd(T2, 0), 0x31);
+  tmp.packet[3] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 0),
+                                         _mm512_extractf64x4_pd(T3, 0), 0x31);
+
+  tmp.packet[4] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 1),
+                                         _mm512_extractf64x4_pd(T2, 1), 0x20);
+  tmp.packet[5] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 1),
+                                         _mm512_extractf64x4_pd(T3, 1), 0x20);
+  tmp.packet[6] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T0, 1),
+                                         _mm512_extractf64x4_pd(T2, 1), 0x31);
+  tmp.packet[7] = _mm256_permute2f128_pd(_mm512_extractf64x4_pd(T1, 1),
+                                         _mm512_extractf64x4_pd(T3, 1), 0x31);
+
+  PACK_OUTPUT_D(kernel.packet, tmp.packet, 0, 1);
+  PACK_OUTPUT_D(kernel.packet, tmp.packet, 1, 1);
+  PACK_OUTPUT_D(kernel.packet, tmp.packet, 2, 1);
+  PACK_OUTPUT_D(kernel.packet, tmp.packet, 3, 1);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet8d, 8>& kernel) {
+    __m512d T0 = _mm512_unpacklo_pd(kernel.packet[0],kernel.packet[1]);
+    __m512d T1 = _mm512_unpackhi_pd(kernel.packet[0],kernel.packet[1]);
+    __m512d T2 = _mm512_unpacklo_pd(kernel.packet[2],kernel.packet[3]);
+    __m512d T3 = _mm512_unpackhi_pd(kernel.packet[2],kernel.packet[3]);
+    __m512d T4 = _mm512_unpacklo_pd(kernel.packet[4],kernel.packet[5]);
+    __m512d T5 = _mm512_unpackhi_pd(kernel.packet[4],kernel.packet[5]);
+    __m512d T6 = _mm512_unpacklo_pd(kernel.packet[6],kernel.packet[7]);
+    __m512d T7 = _mm512_unpackhi_pd(kernel.packet[6],kernel.packet[7]);
+
+    kernel.packet[0] = _mm512_permutex_pd(T2, 0x4E);
+    kernel.packet[0] = _mm512_mask_blend_pd(0xCC, T0, kernel.packet[0]);
+    kernel.packet[2] = _mm512_permutex_pd(T0, 0x4E);
+    kernel.packet[2] = _mm512_mask_blend_pd(0xCC, kernel.packet[2], T2);
+    kernel.packet[1] = _mm512_permutex_pd(T3, 0x4E);
+    kernel.packet[1] = _mm512_mask_blend_pd(0xCC, T1, kernel.packet[1]);
+    kernel.packet[3] = _mm512_permutex_pd(T1, 0x4E);
+    kernel.packet[3] = _mm512_mask_blend_pd(0xCC, kernel.packet[3], T3);
+    kernel.packet[4] = _mm512_permutex_pd(T6, 0x4E);
+    kernel.packet[4] = _mm512_mask_blend_pd(0xCC, T4, kernel.packet[4]);
+    kernel.packet[6] = _mm512_permutex_pd(T4, 0x4E);
+    kernel.packet[6] = _mm512_mask_blend_pd(0xCC, kernel.packet[6], T6);
+    kernel.packet[5] = _mm512_permutex_pd(T7, 0x4E);
+    kernel.packet[5] = _mm512_mask_blend_pd(0xCC, T5, kernel.packet[5]);
+    kernel.packet[7] = _mm512_permutex_pd(T5, 0x4E);
+    kernel.packet[7] = _mm512_mask_blend_pd(0xCC, kernel.packet[7], T7);
+
+    T0 = _mm512_shuffle_f64x2(kernel.packet[4], kernel.packet[4], 0x4E);
+    T0 = _mm512_mask_blend_pd(0xF0, kernel.packet[0], T0);
+    T4 = _mm512_shuffle_f64x2(kernel.packet[0], kernel.packet[0], 0x4E);
+    T4 = _mm512_mask_blend_pd(0xF0, T4, kernel.packet[4]);
+    T1 = _mm512_shuffle_f64x2(kernel.packet[5], kernel.packet[5], 0x4E);
+    T1 = _mm512_mask_blend_pd(0xF0, kernel.packet[1], T1);
+    T5 = _mm512_shuffle_f64x2(kernel.packet[1], kernel.packet[1], 0x4E);
+    T5 = _mm512_mask_blend_pd(0xF0, T5, kernel.packet[5]);
+    T2 = _mm512_shuffle_f64x2(kernel.packet[6], kernel.packet[6], 0x4E);
+    T2 = _mm512_mask_blend_pd(0xF0, kernel.packet[2], T2);
+    T6 = _mm512_shuffle_f64x2(kernel.packet[2], kernel.packet[2], 0x4E);
+    T6 = _mm512_mask_blend_pd(0xF0, T6, kernel.packet[6]);
+    T3 = _mm512_shuffle_f64x2(kernel.packet[7], kernel.packet[7], 0x4E);
+    T3 = _mm512_mask_blend_pd(0xF0, kernel.packet[3], T3);
+    T7 = _mm512_shuffle_f64x2(kernel.packet[3], kernel.packet[3], 0x4E);
+    T7 = _mm512_mask_blend_pd(0xF0, T7, kernel.packet[7]);
+
+    kernel.packet[0] = T0; kernel.packet[1] = T1;
+    kernel.packet[2] = T2; kernel.packet[3] = T3;
+    kernel.packet[4] = T4; kernel.packet[5] = T5;
+    kernel.packet[6] = T6; kernel.packet[7] = T7;
+}
+
+#define PACK_OUTPUT_I32(OUTPUT, INPUT, INDEX, STRIDE) \
+  EIGEN_INSERT_8i_INTO_16i(OUTPUT[INDEX], INPUT[INDEX], INPUT[INDEX + STRIDE]);
+
+#define PACK_OUTPUT_I32_2(OUTPUT, INPUT, INDEX, STRIDE)     \
+  EIGEN_INSERT_8i_INTO_16i(OUTPUT[INDEX], INPUT[2 * INDEX], \
+                           INPUT[2 * INDEX + STRIDE]);
+
+#define SHUFFLE_EPI32(A, B, M) \
+  _mm512_castps_si512(_mm512_shuffle_ps(_mm512_castsi512_ps(A), _mm512_castsi512_ps(B), M))
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16i, 16>& kernel) {
+  __m512i T0 = _mm512_unpacklo_epi32(kernel.packet[0], kernel.packet[1]);
+  __m512i T1 = _mm512_unpackhi_epi32(kernel.packet[0], kernel.packet[1]);
+  __m512i T2 = _mm512_unpacklo_epi32(kernel.packet[2], kernel.packet[3]);
+  __m512i T3 = _mm512_unpackhi_epi32(kernel.packet[2], kernel.packet[3]);
+  __m512i T4 = _mm512_unpacklo_epi32(kernel.packet[4], kernel.packet[5]);
+  __m512i T5 = _mm512_unpackhi_epi32(kernel.packet[4], kernel.packet[5]);
+  __m512i T6 = _mm512_unpacklo_epi32(kernel.packet[6], kernel.packet[7]);
+  __m512i T7 = _mm512_unpackhi_epi32(kernel.packet[6], kernel.packet[7]);
+  __m512i T8 = _mm512_unpacklo_epi32(kernel.packet[8], kernel.packet[9]);
+  __m512i T9 = _mm512_unpackhi_epi32(kernel.packet[8], kernel.packet[9]);
+  __m512i T10 = _mm512_unpacklo_epi32(kernel.packet[10], kernel.packet[11]);
+  __m512i T11 = _mm512_unpackhi_epi32(kernel.packet[10], kernel.packet[11]);
+  __m512i T12 = _mm512_unpacklo_epi32(kernel.packet[12], kernel.packet[13]);
+  __m512i T13 = _mm512_unpackhi_epi32(kernel.packet[12], kernel.packet[13]);
+  __m512i T14 = _mm512_unpacklo_epi32(kernel.packet[14], kernel.packet[15]);
+  __m512i T15 = _mm512_unpackhi_epi32(kernel.packet[14], kernel.packet[15]);
+  __m512i S0 = SHUFFLE_EPI32(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S1 = SHUFFLE_EPI32(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S2 = SHUFFLE_EPI32(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S3 = SHUFFLE_EPI32(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S4 = SHUFFLE_EPI32(T4, T6, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S5 = SHUFFLE_EPI32(T4, T6, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S6 = SHUFFLE_EPI32(T5, T7, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S7 = SHUFFLE_EPI32(T5, T7, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S8 = SHUFFLE_EPI32(T8, T10, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S9 = SHUFFLE_EPI32(T8, T10, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S10 = SHUFFLE_EPI32(T9, T11, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S11 = SHUFFLE_EPI32(T9, T11, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S12 = SHUFFLE_EPI32(T12, T14, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S13 = SHUFFLE_EPI32(T12, T14, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S14 = SHUFFLE_EPI32(T13, T15, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S15 = SHUFFLE_EPI32(T13, T15, _MM_SHUFFLE(3, 2, 3, 2));
+
+  EIGEN_EXTRACT_8i_FROM_16i(S0, S0);
+  EIGEN_EXTRACT_8i_FROM_16i(S1, S1);
+  EIGEN_EXTRACT_8i_FROM_16i(S2, S2);
+  EIGEN_EXTRACT_8i_FROM_16i(S3, S3);
+  EIGEN_EXTRACT_8i_FROM_16i(S4, S4);
+  EIGEN_EXTRACT_8i_FROM_16i(S5, S5);
+  EIGEN_EXTRACT_8i_FROM_16i(S6, S6);
+  EIGEN_EXTRACT_8i_FROM_16i(S7, S7);
+  EIGEN_EXTRACT_8i_FROM_16i(S8, S8);
+  EIGEN_EXTRACT_8i_FROM_16i(S9, S9);
+  EIGEN_EXTRACT_8i_FROM_16i(S10, S10);
+  EIGEN_EXTRACT_8i_FROM_16i(S11, S11);
+  EIGEN_EXTRACT_8i_FROM_16i(S12, S12);
+  EIGEN_EXTRACT_8i_FROM_16i(S13, S13);
+  EIGEN_EXTRACT_8i_FROM_16i(S14, S14);
+  EIGEN_EXTRACT_8i_FROM_16i(S15, S15);
+
+  PacketBlock<Packet8i, 32> tmp;
+
+  tmp.packet[0] = _mm256_permute2f128_si256(S0_0, S4_0, 0x20);
+  tmp.packet[1] = _mm256_permute2f128_si256(S1_0, S5_0, 0x20);
+  tmp.packet[2] = _mm256_permute2f128_si256(S2_0, S6_0, 0x20);
+  tmp.packet[3] = _mm256_permute2f128_si256(S3_0, S7_0, 0x20);
+  tmp.packet[4] = _mm256_permute2f128_si256(S0_0, S4_0, 0x31);
+  tmp.packet[5] = _mm256_permute2f128_si256(S1_0, S5_0, 0x31);
+  tmp.packet[6] = _mm256_permute2f128_si256(S2_0, S6_0, 0x31);
+  tmp.packet[7] = _mm256_permute2f128_si256(S3_0, S7_0, 0x31);
+
+  tmp.packet[8] = _mm256_permute2f128_si256(S0_1, S4_1, 0x20);
+  tmp.packet[9] = _mm256_permute2f128_si256(S1_1, S5_1, 0x20);
+  tmp.packet[10] = _mm256_permute2f128_si256(S2_1, S6_1, 0x20);
+  tmp.packet[11] = _mm256_permute2f128_si256(S3_1, S7_1, 0x20);
+  tmp.packet[12] = _mm256_permute2f128_si256(S0_1, S4_1, 0x31);
+  tmp.packet[13] = _mm256_permute2f128_si256(S1_1, S5_1, 0x31);
+  tmp.packet[14] = _mm256_permute2f128_si256(S2_1, S6_1, 0x31);
+  tmp.packet[15] = _mm256_permute2f128_si256(S3_1, S7_1, 0x31);
+
+  // Second set of _m256 outputs
+  tmp.packet[16] = _mm256_permute2f128_si256(S8_0, S12_0, 0x20);
+  tmp.packet[17] = _mm256_permute2f128_si256(S9_0, S13_0, 0x20);
+  tmp.packet[18] = _mm256_permute2f128_si256(S10_0, S14_0, 0x20);
+  tmp.packet[19] = _mm256_permute2f128_si256(S11_0, S15_0, 0x20);
+  tmp.packet[20] = _mm256_permute2f128_si256(S8_0, S12_0, 0x31);
+  tmp.packet[21] = _mm256_permute2f128_si256(S9_0, S13_0, 0x31);
+  tmp.packet[22] = _mm256_permute2f128_si256(S10_0, S14_0, 0x31);
+  tmp.packet[23] = _mm256_permute2f128_si256(S11_0, S15_0, 0x31);
+
+  tmp.packet[24] = _mm256_permute2f128_si256(S8_1, S12_1, 0x20);
+  tmp.packet[25] = _mm256_permute2f128_si256(S9_1, S13_1, 0x20);
+  tmp.packet[26] = _mm256_permute2f128_si256(S10_1, S14_1, 0x20);
+  tmp.packet[27] = _mm256_permute2f128_si256(S11_1, S15_1, 0x20);
+  tmp.packet[28] = _mm256_permute2f128_si256(S8_1, S12_1, 0x31);
+  tmp.packet[29] = _mm256_permute2f128_si256(S9_1, S13_1, 0x31);
+  tmp.packet[30] = _mm256_permute2f128_si256(S10_1, S14_1, 0x31);
+  tmp.packet[31] = _mm256_permute2f128_si256(S11_1, S15_1, 0x31);
+
+  // Pack them into the output
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 0, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 1, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 2, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 3, 16);
+
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 4, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 5, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 6, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 7, 16);
+
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 8, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 9, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 10, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 11, 16);
+
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 12, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 13, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 14, 16);
+  PACK_OUTPUT_I32(kernel.packet, tmp.packet, 15, 16);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet16i, 4>& kernel) {
+  __m512i T0 = _mm512_unpacklo_epi32(kernel.packet[0], kernel.packet[1]);
+  __m512i T1 = _mm512_unpackhi_epi32(kernel.packet[0], kernel.packet[1]);
+  __m512i T2 = _mm512_unpacklo_epi32(kernel.packet[2], kernel.packet[3]);
+  __m512i T3 = _mm512_unpackhi_epi32(kernel.packet[2], kernel.packet[3]);
+
+  __m512i S0 = SHUFFLE_EPI32(T0, T2, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S1 = SHUFFLE_EPI32(T0, T2, _MM_SHUFFLE(3, 2, 3, 2));
+  __m512i S2 = SHUFFLE_EPI32(T1, T3, _MM_SHUFFLE(1, 0, 1, 0));
+  __m512i S3 = SHUFFLE_EPI32(T1, T3, _MM_SHUFFLE(3, 2, 3, 2));
+
+  EIGEN_EXTRACT_8i_FROM_16i(S0, S0);
+  EIGEN_EXTRACT_8i_FROM_16i(S1, S1);
+  EIGEN_EXTRACT_8i_FROM_16i(S2, S2);
+  EIGEN_EXTRACT_8i_FROM_16i(S3, S3);
+
+  PacketBlock<Packet8i, 8> tmp;
+
+  tmp.packet[0] = _mm256_permute2f128_si256(S0_0, S1_0, 0x20);
+  tmp.packet[1] = _mm256_permute2f128_si256(S2_0, S3_0, 0x20);
+  tmp.packet[2] = _mm256_permute2f128_si256(S0_0, S1_0, 0x31);
+  tmp.packet[3] = _mm256_permute2f128_si256(S2_0, S3_0, 0x31);
+
+  tmp.packet[4] = _mm256_permute2f128_si256(S0_1, S1_1, 0x20);
+  tmp.packet[5] = _mm256_permute2f128_si256(S2_1, S3_1, 0x20);
+  tmp.packet[6] = _mm256_permute2f128_si256(S0_1, S1_1, 0x31);
+  tmp.packet[7] = _mm256_permute2f128_si256(S2_1, S3_1, 0x31);
+
+  PACK_OUTPUT_I32_2(kernel.packet, tmp.packet, 0, 1);
+  PACK_OUTPUT_I32_2(kernel.packet, tmp.packet, 1, 1);
+  PACK_OUTPUT_I32_2(kernel.packet, tmp.packet, 2, 1);
+  PACK_OUTPUT_I32_2(kernel.packet, tmp.packet, 3, 1);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pblend(const Selector<16>& ifPacket,
+                                     const Packet16f& thenPacket,
+                                     const Packet16f& elsePacket) {
+  __mmask16 m = (ifPacket.select[0]) | (ifPacket.select[1] << 1) | (ifPacket.select[2] << 2) |
+                (ifPacket.select[3] << 3) | (ifPacket.select[4] << 4) | (ifPacket.select[5] << 5) |
+                (ifPacket.select[6] << 6) | (ifPacket.select[7] << 7) | (ifPacket.select[8] << 8) |
+                (ifPacket.select[9] << 9) | (ifPacket.select[10] << 10) | (ifPacket.select[11] << 11) |
+                (ifPacket.select[12] << 12) | (ifPacket.select[13] << 13) | (ifPacket.select[14] << 14) |
+                (ifPacket.select[15] << 15);
+  return _mm512_mask_blend_ps(m, elsePacket, thenPacket);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8d pblend(const Selector<8>& ifPacket,
+                                    const Packet8d& thenPacket,
+                                    const Packet8d& elsePacket) {
+  __mmask8 m = (ifPacket.select[0]   )
+             | (ifPacket.select[1]<<1)
+             | (ifPacket.select[2]<<2)
+             | (ifPacket.select[3]<<3)
+             | (ifPacket.select[4]<<4)
+             | (ifPacket.select[5]<<5)
+             | (ifPacket.select[6]<<6)
+             | (ifPacket.select[7]<<7);
+  return _mm512_mask_blend_pd(m, elsePacket, thenPacket);
+}
+
+// Packet math for Eigen::half
+template<> EIGEN_STRONG_INLINE Packet16h pset1<Packet16h>(const Eigen::half& from) {
+  return _mm256_set1_epi16(from.x);
+}
+
+template<> EIGEN_STRONG_INLINE Eigen::half pfirst<Packet16h>(const Packet16h& from) {
+  return half_impl::raw_uint16_to_half(static_cast<unsigned short>(_mm256_extract_epi16(from, 0)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h pload<Packet16h>(const Eigen::half* from) {
+  return _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h ploadu<Packet16h>(const Eigen::half* from) {
+  return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<half>(Eigen::half* to, const Packet16h& from) {
+  // (void*) -> workaround clang warning:
+  // cast from 'Eigen::half *' to '__m256i *' increases required alignment from 2 to 32
+  _mm256_store_si256((__m256i*)(void*)to, from);
+}
+
+template<> EIGEN_STRONG_INLINE void pstoreu<half>(Eigen::half* to, const Packet16h& from) {
+  // (void*) -> workaround clang warning:
+  // cast from 'Eigen::half *' to '__m256i *' increases required alignment from 2 to 32
+  _mm256_storeu_si256((__m256i*)(void*)to, from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h
+ploaddup<Packet16h>(const Eigen::half*  from) {
+  unsigned short a = from[0].x;
+  unsigned short b = from[1].x;
+  unsigned short c = from[2].x;
+  unsigned short d = from[3].x;
+  unsigned short e = from[4].x;
+  unsigned short f = from[5].x;
+  unsigned short g = from[6].x;
+  unsigned short h = from[7].x;
+  return _mm256_set_epi16(h, h, g, g, f, f, e, e, d, d, c, c, b, b, a, a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h
+ploadquad(const Eigen::half* from) {
+  unsigned short a = from[0].x;
+  unsigned short b = from[1].x;
+  unsigned short c = from[2].x;
+  unsigned short d = from[3].x;
+  return _mm256_set_epi16(d, d, d, d, c, c, c, c, b, b, b, b, a, a, a, a);
+}
+
+EIGEN_STRONG_INLINE Packet16f half2float(const Packet16h& a) {
+  return _mm512_cvtph_ps(a);
+}
+
+EIGEN_STRONG_INLINE Packet16h float2half(const Packet16f& a) {
+  return _mm512_cvtps_ph(a, _MM_FROUND_TO_NEAREST_INT|_MM_FROUND_NO_EXC);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h ptrue(const Packet16h& a) {
+  return Packet16h(ptrue(Packet8i(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pabs(const Packet16h& a) {
+  const __m256i sign_mask = _mm256_set1_epi16(static_cast<numext::uint16_t>(0x8000));
+  return _mm256_andnot_si256(sign_mask, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pmin<Packet16h>(const Packet16h& a,
+                                              const Packet16h& b) {
+  return float2half(pmin<Packet16f>(half2float(a), half2float(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pmax<Packet16h>(const Packet16h& a,
+                                              const Packet16h& b) {
+  return float2half(pmax<Packet16f>(half2float(a), half2float(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h plset<Packet16h>(const half& a) {
+  return float2half(plset<Packet16f>(static_cast<float>(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h por(const Packet16h& a,const Packet16h& b) {
+  // in some cases Packet8i is a wrapper around __m256i, so we need to
+  // cast to Packet8i to call the correct overload.
+  return Packet16h(por(Packet8i(a),Packet8i(b)));
+}
+template<> EIGEN_STRONG_INLINE Packet16h pxor(const Packet16h& a,const Packet16h& b) {
+  return Packet16h(pxor(Packet8i(a),Packet8i(b)));
+}
+template<> EIGEN_STRONG_INLINE Packet16h pand(const Packet16h& a,const Packet16h& b) {
+  return Packet16h(pand(Packet8i(a),Packet8i(b)));
+}
+template<> EIGEN_STRONG_INLINE Packet16h pandnot(const Packet16h& a,const Packet16h& b) {
+  return Packet16h(pandnot(Packet8i(a),Packet8i(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h pselect(const Packet16h& mask, const Packet16h& a, const Packet16h& b) {
+  return _mm256_blendv_epi8(b, a, mask);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h pround<Packet16h>(const Packet16h& a) {
+  return float2half(pround<Packet16f>(half2float(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h print<Packet16h>(const Packet16h& a) {
+  return float2half(print<Packet16f>(half2float(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h pceil<Packet16h>(const Packet16h& a) {
+  return float2half(pceil<Packet16f>(half2float(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h pfloor<Packet16h>(const Packet16h& a) {
+  return float2half(pfloor<Packet16f>(half2float(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h pcmp_eq(const Packet16h& a,const Packet16h& b) {
+  Packet16f af = half2float(a);
+  Packet16f bf = half2float(b);
+  return Pack32To16(pcmp_eq(af, bf));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h pcmp_le(const Packet16h& a,const Packet16h& b) {
+  return Pack32To16(pcmp_le(half2float(a), half2float(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h pcmp_lt(const Packet16h& a,const Packet16h& b) {
+  return Pack32To16(pcmp_lt(half2float(a), half2float(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h pcmp_lt_or_nan(const Packet16h& a,const Packet16h& b) {
+  return Pack32To16(pcmp_lt_or_nan(half2float(a), half2float(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h pconj(const Packet16h& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE Packet16h pnegate(const Packet16h& a) {
+  Packet16h sign_mask = _mm256_set1_epi16(static_cast<unsigned short>(0x8000));
+  return _mm256_xor_si256(a, sign_mask);
+}
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+template<> EIGEN_STRONG_INLINE Packet16h padd<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  Packet16f af = half2float(a);
+  Packet16f bf = half2float(b);
+  Packet16f rf = padd(af, bf);
+  return float2half(rf);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h psub<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  Packet16f af = half2float(a);
+  Packet16f bf = half2float(b);
+  Packet16f rf = psub(af, bf);
+  return float2half(rf);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h pmul<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  Packet16f af = half2float(a);
+  Packet16f bf = half2float(b);
+  Packet16f rf = pmul(af, bf);
+  return float2half(rf);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h pdiv<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  Packet16f af = half2float(a);
+  Packet16f bf = half2float(b);
+  Packet16f rf = pdiv(af, bf);
+  return float2half(rf);
+}
+
+template<> EIGEN_STRONG_INLINE half predux<Packet16h>(const Packet16h& from) {
+  Packet16f from_float = half2float(from);
+  return half(predux(from_float));
+}
+
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet8h predux_half_dowto4<Packet16h>(const Packet16h& a) {
+  Packet8h lane0 = _mm256_extractf128_si256(a, 0);
+  Packet8h lane1 = _mm256_extractf128_si256(a, 1);
+  return padd<Packet8h>(lane0, lane1);
+}
+
+template<> EIGEN_STRONG_INLINE Eigen::half predux_max<Packet16h>(const Packet16h& a) {
+  Packet16f af = half2float(a);
+  float reduced = predux_max<Packet16f>(af);
+  return Eigen::half(reduced);
+}
+
+template<> EIGEN_STRONG_INLINE Eigen::half predux_min<Packet16h>(const Packet16h& a) {
+  Packet16f af = half2float(a);
+  float reduced = predux_min<Packet16f>(af);
+  return Eigen::half(reduced);
+}
+
+template<> EIGEN_STRONG_INLINE half predux_mul<Packet16h>(const Packet16h& from) {
+  Packet16f from_float = half2float(from);
+  return half(predux_mul(from_float));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h preverse(const Packet16h& a)
+{
+  __m128i m = _mm_setr_epi8(14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1);
+  return _mm256_insertf128_si256(
+                    _mm256_castsi128_si256(_mm_shuffle_epi8(_mm256_extractf128_si256(a,1),m)),
+                                           _mm_shuffle_epi8(_mm256_extractf128_si256(a,0),m), 1);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h pgather<Eigen::half, Packet16h>(const Eigen::half* from, Index stride)
+{
+  return _mm256_set_epi16(
+      from[15*stride].x, from[14*stride].x, from[13*stride].x, from[12*stride].x,
+      from[11*stride].x, from[10*stride].x, from[9*stride].x, from[8*stride].x,
+      from[7*stride].x, from[6*stride].x, from[5*stride].x, from[4*stride].x,
+      from[3*stride].x, from[2*stride].x, from[1*stride].x, from[0*stride].x);
+}
+
+template<> EIGEN_STRONG_INLINE void pscatter<half, Packet16h>(half* to, const Packet16h& from, Index stride)
+{
+  EIGEN_ALIGN64 half aux[16];
+  pstore(aux, from);
+  to[stride*0] = aux[0];
+  to[stride*1] = aux[1];
+  to[stride*2] = aux[2];
+  to[stride*3] = aux[3];
+  to[stride*4] = aux[4];
+  to[stride*5] = aux[5];
+  to[stride*6] = aux[6];
+  to[stride*7] = aux[7];
+  to[stride*8] = aux[8];
+  to[stride*9] = aux[9];
+  to[stride*10] = aux[10];
+  to[stride*11] = aux[11];
+  to[stride*12] = aux[12];
+  to[stride*13] = aux[13];
+  to[stride*14] = aux[14];
+  to[stride*15] = aux[15];
+}
+
+EIGEN_STRONG_INLINE void
+ptranspose(PacketBlock<Packet16h,16>& kernel) {
+  __m256i a = kernel.packet[0];
+  __m256i b = kernel.packet[1];
+  __m256i c = kernel.packet[2];
+  __m256i d = kernel.packet[3];
+  __m256i e = kernel.packet[4];
+  __m256i f = kernel.packet[5];
+  __m256i g = kernel.packet[6];
+  __m256i h = kernel.packet[7];
+  __m256i i = kernel.packet[8];
+  __m256i j = kernel.packet[9];
+  __m256i k = kernel.packet[10];
+  __m256i l = kernel.packet[11];
+  __m256i m = kernel.packet[12];
+  __m256i n = kernel.packet[13];
+  __m256i o = kernel.packet[14];
+  __m256i p = kernel.packet[15];
+
+  __m256i ab_07 = _mm256_unpacklo_epi16(a, b);
+  __m256i cd_07 = _mm256_unpacklo_epi16(c, d);
+  __m256i ef_07 = _mm256_unpacklo_epi16(e, f);
+  __m256i gh_07 = _mm256_unpacklo_epi16(g, h);
+  __m256i ij_07 = _mm256_unpacklo_epi16(i, j);
+  __m256i kl_07 = _mm256_unpacklo_epi16(k, l);
+  __m256i mn_07 = _mm256_unpacklo_epi16(m, n);
+  __m256i op_07 = _mm256_unpacklo_epi16(o, p);
+
+  __m256i ab_8f = _mm256_unpackhi_epi16(a, b);
+  __m256i cd_8f = _mm256_unpackhi_epi16(c, d);
+  __m256i ef_8f = _mm256_unpackhi_epi16(e, f);
+  __m256i gh_8f = _mm256_unpackhi_epi16(g, h);
+  __m256i ij_8f = _mm256_unpackhi_epi16(i, j);
+  __m256i kl_8f = _mm256_unpackhi_epi16(k, l);
+  __m256i mn_8f = _mm256_unpackhi_epi16(m, n);
+  __m256i op_8f = _mm256_unpackhi_epi16(o, p);
+
+  __m256i abcd_03 = _mm256_unpacklo_epi32(ab_07, cd_07);
+  __m256i abcd_47 = _mm256_unpackhi_epi32(ab_07, cd_07);
+  __m256i efgh_03 = _mm256_unpacklo_epi32(ef_07, gh_07);
+  __m256i efgh_47 = _mm256_unpackhi_epi32(ef_07, gh_07);
+  __m256i ijkl_03 = _mm256_unpacklo_epi32(ij_07, kl_07);
+  __m256i ijkl_47 = _mm256_unpackhi_epi32(ij_07, kl_07);
+  __m256i mnop_03 = _mm256_unpacklo_epi32(mn_07, op_07);
+  __m256i mnop_47 = _mm256_unpackhi_epi32(mn_07, op_07);
+
+  __m256i abcd_8b = _mm256_unpacklo_epi32(ab_8f, cd_8f);
+  __m256i abcd_cf = _mm256_unpackhi_epi32(ab_8f, cd_8f);
+  __m256i efgh_8b = _mm256_unpacklo_epi32(ef_8f, gh_8f);
+  __m256i efgh_cf = _mm256_unpackhi_epi32(ef_8f, gh_8f);
+  __m256i ijkl_8b = _mm256_unpacklo_epi32(ij_8f, kl_8f);
+  __m256i ijkl_cf = _mm256_unpackhi_epi32(ij_8f, kl_8f);
+  __m256i mnop_8b = _mm256_unpacklo_epi32(mn_8f, op_8f);
+  __m256i mnop_cf = _mm256_unpackhi_epi32(mn_8f, op_8f);
+
+  __m256i abcdefgh_01 = _mm256_unpacklo_epi64(abcd_03, efgh_03);
+  __m256i abcdefgh_23 = _mm256_unpackhi_epi64(abcd_03, efgh_03);
+  __m256i ijklmnop_01 = _mm256_unpacklo_epi64(ijkl_03, mnop_03);
+  __m256i ijklmnop_23 = _mm256_unpackhi_epi64(ijkl_03, mnop_03);
+  __m256i abcdefgh_45 = _mm256_unpacklo_epi64(abcd_47, efgh_47);
+  __m256i abcdefgh_67 = _mm256_unpackhi_epi64(abcd_47, efgh_47);
+  __m256i ijklmnop_45 = _mm256_unpacklo_epi64(ijkl_47, mnop_47);
+  __m256i ijklmnop_67 = _mm256_unpackhi_epi64(ijkl_47, mnop_47);
+  __m256i abcdefgh_89 = _mm256_unpacklo_epi64(abcd_8b, efgh_8b);
+  __m256i abcdefgh_ab = _mm256_unpackhi_epi64(abcd_8b, efgh_8b);
+  __m256i ijklmnop_89 = _mm256_unpacklo_epi64(ijkl_8b, mnop_8b);
+  __m256i ijklmnop_ab = _mm256_unpackhi_epi64(ijkl_8b, mnop_8b);
+  __m256i abcdefgh_cd = _mm256_unpacklo_epi64(abcd_cf, efgh_cf);
+  __m256i abcdefgh_ef = _mm256_unpackhi_epi64(abcd_cf, efgh_cf);
+  __m256i ijklmnop_cd = _mm256_unpacklo_epi64(ijkl_cf, mnop_cf);
+  __m256i ijklmnop_ef = _mm256_unpackhi_epi64(ijkl_cf, mnop_cf);
+
+  // NOTE: no unpacklo/hi instr in this case, so using permute instr.
+  __m256i a_p_0 = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x20);
+  __m256i a_p_1 = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x20);
+  __m256i a_p_2 = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x20);
+  __m256i a_p_3 = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x20);
+  __m256i a_p_4 = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x20);
+  __m256i a_p_5 = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x20);
+  __m256i a_p_6 = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x20);
+  __m256i a_p_7 = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x20);
+  __m256i a_p_8 = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x31);
+  __m256i a_p_9 = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x31);
+  __m256i a_p_a = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x31);
+  __m256i a_p_b = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x31);
+  __m256i a_p_c = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x31);
+  __m256i a_p_d = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x31);
+  __m256i a_p_e = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x31);
+  __m256i a_p_f = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x31);
+
+  kernel.packet[0] = a_p_0;
+  kernel.packet[1] = a_p_1;
+  kernel.packet[2] = a_p_2;
+  kernel.packet[3] = a_p_3;
+  kernel.packet[4] = a_p_4;
+  kernel.packet[5] = a_p_5;
+  kernel.packet[6] = a_p_6;
+  kernel.packet[7] = a_p_7;
+  kernel.packet[8] = a_p_8;
+  kernel.packet[9] = a_p_9;
+  kernel.packet[10] = a_p_a;
+  kernel.packet[11] = a_p_b;
+  kernel.packet[12] = a_p_c;
+  kernel.packet[13] = a_p_d;
+  kernel.packet[14] = a_p_e;
+  kernel.packet[15] = a_p_f;
+}
+
+EIGEN_STRONG_INLINE void
+ptranspose(PacketBlock<Packet16h,8>& kernel) {
+  EIGEN_ALIGN64 half in[8][16];
+  pstore<half>(in[0], kernel.packet[0]);
+  pstore<half>(in[1], kernel.packet[1]);
+  pstore<half>(in[2], kernel.packet[2]);
+  pstore<half>(in[3], kernel.packet[3]);
+  pstore<half>(in[4], kernel.packet[4]);
+  pstore<half>(in[5], kernel.packet[5]);
+  pstore<half>(in[6], kernel.packet[6]);
+  pstore<half>(in[7], kernel.packet[7]);
+
+  EIGEN_ALIGN64 half out[8][16];
+
+  for (int i = 0; i < 8; ++i) {
+    for (int j = 0; j < 8; ++j) {
+      out[i][j] = in[j][2*i];
+    }
+    for (int j = 0; j < 8; ++j) {
+      out[i][j+8] = in[j][2*i+1];
+    }
+  }
+
+  kernel.packet[0] = pload<Packet16h>(out[0]);
+  kernel.packet[1] = pload<Packet16h>(out[1]);
+  kernel.packet[2] = pload<Packet16h>(out[2]);
+  kernel.packet[3] = pload<Packet16h>(out[3]);
+  kernel.packet[4] = pload<Packet16h>(out[4]);
+  kernel.packet[5] = pload<Packet16h>(out[5]);
+  kernel.packet[6] = pload<Packet16h>(out[6]);
+  kernel.packet[7] = pload<Packet16h>(out[7]);
+}
+
+EIGEN_STRONG_INLINE void
+ptranspose(PacketBlock<Packet16h,4>& kernel) {
+  EIGEN_ALIGN64 half in[4][16];
+  pstore<half>(in[0], kernel.packet[0]);
+  pstore<half>(in[1], kernel.packet[1]);
+  pstore<half>(in[2], kernel.packet[2]);
+  pstore<half>(in[3], kernel.packet[3]);
+
+  EIGEN_ALIGN64 half out[4][16];
+
+  for (int i = 0; i < 4; ++i) {
+    for (int j = 0; j < 4; ++j) {
+      out[i][j] = in[j][4*i];
+    }
+    for (int j = 0; j < 4; ++j) {
+      out[i][j+4] = in[j][4*i+1];
+    }
+    for (int j = 0; j < 4; ++j) {
+      out[i][j+8] = in[j][4*i+2];
+    }
+    for (int j = 0; j < 4; ++j) {
+      out[i][j+12] = in[j][4*i+3];
+    }
+  }
+
+  kernel.packet[0] = pload<Packet16h>(out[0]);
+  kernel.packet[1] = pload<Packet16h>(out[1]);
+  kernel.packet[2] = pload<Packet16h>(out[2]);
+  kernel.packet[3] = pload<Packet16h>(out[3]);
+}
+
+template <> struct is_arithmetic<Packet16bf> { enum { value = true }; };
+
+template <>
+struct packet_traits<bfloat16> : default_packet_traits {
+  typedef Packet16bf type;
+  typedef Packet8bf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+    HasBlend = 0,
+    HasInsert = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+    HasLog = 1,  // Currently fails test with bad accuracy.
+    HasLog1p  = 1,
+    HasExpm1  = 1,
+    HasNdtri = 1,
+    HasBessel = 1,
+#endif
+    HasExp = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasCmp  = 1,
+    HasDiv = 1
+  };
+};
+
+template <>
+struct unpacket_traits<Packet16bf>
+{
+  typedef bfloat16 type;
+  enum {size=16, alignment=Aligned32, vectorizable=true, masked_load_available=false, masked_store_available=false};
+  typedef Packet8bf half;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pset1<Packet16bf>(const bfloat16& from) {
+  return _mm256_set1_epi16(from.value);
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 pfirst<Packet16bf>(const Packet16bf& from) {
+  bfloat16 t;
+  t.value = static_cast<unsigned short>(_mm256_extract_epi16(from, 0));
+  return t;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pload<Packet16bf>(const bfloat16* from) {
+  return _mm256_load_si256(reinterpret_cast<const __m256i*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf ploadu<Packet16bf>(const bfloat16* from) {
+  return _mm256_loadu_si256(reinterpret_cast<const __m256i*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to,
+                                          const Packet16bf& from) {
+  _mm256_store_si256(reinterpret_cast<__m256i*>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to,
+                                           const Packet16bf& from) {
+  _mm256_storeu_si256(reinterpret_cast<__m256i*>(to), from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16bf
+ploaddup<Packet16bf>(const bfloat16* from) {
+  unsigned short a = from[0].value;
+  unsigned short b = from[1].value;
+  unsigned short c = from[2].value;
+  unsigned short d = from[3].value;
+  unsigned short e = from[4].value;
+  unsigned short f = from[5].value;
+  unsigned short g = from[6].value;
+  unsigned short h = from[7].value;
+  return _mm256_set_epi16(h, h, g, g, f, f, e, e, d, d, c, c, b, b, a, a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16bf
+ploadquad(const bfloat16* from) {
+  unsigned short a = from[0].value;
+  unsigned short b = from[1].value;
+  unsigned short c = from[2].value;
+  unsigned short d = from[3].value;
+  return _mm256_set_epi16(d, d, d, d, c, c, c, c, b, b, b, b, a, a, a, a);
+}
+
+EIGEN_STRONG_INLINE Packet16f Bf16ToF32(const Packet16bf& a) {
+  return _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(a), 16));
+}
+
+// Convert float to bfloat16 according to round-to-nearest-even/denormals algorithm.
+EIGEN_STRONG_INLINE Packet16bf F32ToBf16(const Packet16f& a) {
+  Packet16bf r;
+
+#if defined(EIGEN_VECTORIZE_AVX512BF16) && EIGEN_GNUC_STRICT_AT_LEAST(10,1,0)
+  // Since GCC 10.1 supports avx512bf16 and C style explicit cast
+  // (C++ static_cast is not supported yet), do conversion via intrinsic
+  // and register path for performance.
+  r = (__m256i)(_mm512_cvtneps_pbh(a));
+
+#else
+  __m512i t;
+  __m512i input = _mm512_castps_si512(a);
+  __m512i nan = _mm512_set1_epi32(0x7fc0);
+
+  // uint32_t lsb = (input >> 16) & 1;
+  t = _mm512_and_si512(_mm512_srli_epi32(input, 16), _mm512_set1_epi32(1));
+  // uint32_t rounding_bias = 0x7fff + lsb;
+  t = _mm512_add_epi32(t, _mm512_set1_epi32(0x7fff));
+  // input += rounding_bias;
+  t = _mm512_add_epi32(t, input);
+  // input = input >> 16;
+  t = _mm512_srli_epi32(t, 16);
+
+  // Check NaN before converting back to bf16
+  __mmask16 mask = _mm512_cmp_ps_mask(a, a, _CMP_ORD_Q);
+
+  t = _mm512_mask_blend_epi32(mask, nan, t);
+  // output.value = static_cast<uint16_t>(input);
+  r = _mm512_cvtepi32_epi16(t);
+#endif // EIGEN_VECTORIZE_AVX512BF16
+
+  return r;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf ptrue(const Packet16bf& a) {
+  return Packet16bf(ptrue<Packet8i>(Packet8i(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf por(const Packet16bf& a, const Packet16bf& b) {
+  return Packet16bf(por<Packet8i>(Packet8i(a), Packet8i(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pxor(const Packet16bf& a, const Packet16bf& b) {
+  return Packet16bf(pxor<Packet8i>(Packet8i(a), Packet8i(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pand(const Packet16bf& a, const Packet16bf& b) {
+  return Packet16bf(pand<Packet8i>(Packet8i(a), Packet8i(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pandnot(const Packet16bf& a,
+                                       const Packet16bf& b) {
+  return Packet16bf(pandnot<Packet8i>(Packet8i(a), Packet8i(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pselect(const Packet16bf& mask,
+                                       const Packet16bf& a,
+                                       const Packet16bf& b) {
+  // Input mask is expected to be all 0/1, handle it with 8-bit
+  // intrinsic for performance.
+  return _mm256_blendv_epi8(b, a, mask);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16bf pround<Packet16bf>(const Packet16bf& a)
+{
+  return F32ToBf16(pround<Packet16f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16bf print<Packet16bf>(const Packet16bf& a) {
+  return F32ToBf16(print<Packet16f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16bf pceil<Packet16bf>(const Packet16bf& a) {
+  return F32ToBf16(pceil<Packet16f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16bf pfloor<Packet16bf>(const Packet16bf& a) {
+  return F32ToBf16(pfloor<Packet16f>(Bf16ToF32(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pcmp_eq(const Packet16bf& a,
+                                       const Packet16bf& b) {
+  return Pack32To16(pcmp_eq(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pcmp_le(const Packet16bf& a,
+                                       const Packet16bf& b) {
+  return Pack32To16(pcmp_le(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pcmp_lt(const Packet16bf& a,
+                                       const Packet16bf& b) {
+  return Pack32To16(pcmp_lt(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pcmp_lt_or_nan(const Packet16bf& a,
+                                              const Packet16bf& b) {
+  return Pack32To16(pcmp_lt_or_nan(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pnegate(const Packet16bf& a) {
+  Packet16bf sign_mask = _mm256_set1_epi16(static_cast<unsigned short>(0x8000));
+  return _mm256_xor_si256(a, sign_mask);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pconj(const Packet16bf& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pabs(const Packet16bf& a) {
+  const __m256i sign_mask = _mm256_set1_epi16(static_cast<numext::uint16_t>(0x8000));
+  return _mm256_andnot_si256(sign_mask, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf padd<Packet16bf>(const Packet16bf& a,
+                                                const Packet16bf& b) {
+  return F32ToBf16(padd<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf psub<Packet16bf>(const Packet16bf& a,
+                                                const Packet16bf& b) {
+  return F32ToBf16(psub<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pmul<Packet16bf>(const Packet16bf& a,
+                                                const Packet16bf& b) {
+  return F32ToBf16(pmul<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pdiv<Packet16bf>(const Packet16bf& a,
+                                                const Packet16bf& b) {
+  return F32ToBf16(pdiv<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pmin<Packet16bf>(const Packet16bf& a,
+                                                const Packet16bf& b) {
+  return F32ToBf16(pmin<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pmax<Packet16bf>(const Packet16bf& a,
+                                                const Packet16bf& b) {
+  return F32ToBf16(pmax<Packet16f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf plset<Packet16bf>(const bfloat16& a) {
+  return F32ToBf16(plset<Packet16f>(static_cast<float>(a)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8bf predux_half_dowto4<Packet16bf>(const Packet16bf& a) {
+  Packet8bf lane0 = _mm256_extractf128_si256(a, 0);
+  Packet8bf lane1 = _mm256_extractf128_si256(a, 1);
+  return padd<Packet8bf>(lane0, lane1);
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux<Packet16bf>(const Packet16bf& p) {
+  return static_cast<bfloat16>(predux<Packet16f>(Bf16ToF32(p)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet16bf>(const Packet16bf& from) {
+  return static_cast<bfloat16>(predux_mul<Packet16f>(Bf16ToF32(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_min<Packet16bf>(const Packet16bf& from) {
+  return static_cast<bfloat16>(predux_min<Packet16f>(Bf16ToF32(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE bfloat16 predux_max<Packet16bf>(const Packet16bf& from) {
+  return static_cast<bfloat16>(predux_max<Packet16f>(Bf16ToF32(from)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf preverse(const Packet16bf& a) {
+  __m256i m = _mm256_setr_epi8(14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1,
+                               14,15,12,13,10,11,8,9,6,7,4,5,2,3,0,1);
+
+  Packet16bf res;
+  // Swap hi and lo first because shuffle is in 128-bit lanes.
+  res = _mm256_permute2x128_si256(a, a, 1);
+  // Shuffle 8-bit values in src within 2*128-bit lanes.
+  return _mm256_shuffle_epi8(res, m);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16bf pgather<bfloat16, Packet16bf>(const bfloat16* from,
+                                                             Index stride) {
+  return _mm256_set_epi16(
+      from[15*stride].value, from[14*stride].value, from[13*stride].value, from[12*stride].value,
+      from[11*stride].value, from[10*stride].value, from[9*stride].value, from[8*stride].value,
+      from[7*stride].value, from[6*stride].value, from[5*stride].value, from[4*stride].value,
+      from[3*stride].value, from[2*stride].value, from[1*stride].value, from[0*stride].value);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pscatter<bfloat16, Packet16bf>(bfloat16* to,
+                                                        const Packet16bf& from,
+                                                        Index stride) {
+  EIGEN_ALIGN64 bfloat16 aux[16];
+  pstore(aux, from);
+  to[stride*0] = aux[0];
+  to[stride*1] = aux[1];
+  to[stride*2] = aux[2];
+  to[stride*3] = aux[3];
+  to[stride*4] = aux[4];
+  to[stride*5] = aux[5];
+  to[stride*6] = aux[6];
+  to[stride*7] = aux[7];
+  to[stride*8] = aux[8];
+  to[stride*9] = aux[9];
+  to[stride*10] = aux[10];
+  to[stride*11] = aux[11];
+  to[stride*12] = aux[12];
+  to[stride*13] = aux[13];
+  to[stride*14] = aux[14];
+  to[stride*15] = aux[15];
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16bf,16>& kernel) {
+  __m256i a = kernel.packet[0];
+  __m256i b = kernel.packet[1];
+  __m256i c = kernel.packet[2];
+  __m256i d = kernel.packet[3];
+  __m256i e = kernel.packet[4];
+  __m256i f = kernel.packet[5];
+  __m256i g = kernel.packet[6];
+  __m256i h = kernel.packet[7];
+  __m256i i = kernel.packet[8];
+  __m256i j = kernel.packet[9];
+  __m256i k = kernel.packet[10];
+  __m256i l = kernel.packet[11];
+  __m256i m = kernel.packet[12];
+  __m256i n = kernel.packet[13];
+  __m256i o = kernel.packet[14];
+  __m256i p = kernel.packet[15];
+
+  __m256i ab_07 = _mm256_unpacklo_epi16(a, b);
+  __m256i cd_07 = _mm256_unpacklo_epi16(c, d);
+  __m256i ef_07 = _mm256_unpacklo_epi16(e, f);
+  __m256i gh_07 = _mm256_unpacklo_epi16(g, h);
+  __m256i ij_07 = _mm256_unpacklo_epi16(i, j);
+  __m256i kl_07 = _mm256_unpacklo_epi16(k, l);
+  __m256i mn_07 = _mm256_unpacklo_epi16(m, n);
+  __m256i op_07 = _mm256_unpacklo_epi16(o, p);
+
+  __m256i ab_8f = _mm256_unpackhi_epi16(a, b);
+  __m256i cd_8f = _mm256_unpackhi_epi16(c, d);
+  __m256i ef_8f = _mm256_unpackhi_epi16(e, f);
+  __m256i gh_8f = _mm256_unpackhi_epi16(g, h);
+  __m256i ij_8f = _mm256_unpackhi_epi16(i, j);
+  __m256i kl_8f = _mm256_unpackhi_epi16(k, l);
+  __m256i mn_8f = _mm256_unpackhi_epi16(m, n);
+  __m256i op_8f = _mm256_unpackhi_epi16(o, p);
+
+  __m256i abcd_03 = _mm256_unpacklo_epi32(ab_07, cd_07);
+  __m256i abcd_47 = _mm256_unpackhi_epi32(ab_07, cd_07);
+  __m256i efgh_03 = _mm256_unpacklo_epi32(ef_07, gh_07);
+  __m256i efgh_47 = _mm256_unpackhi_epi32(ef_07, gh_07);
+  __m256i ijkl_03 = _mm256_unpacklo_epi32(ij_07, kl_07);
+  __m256i ijkl_47 = _mm256_unpackhi_epi32(ij_07, kl_07);
+  __m256i mnop_03 = _mm256_unpacklo_epi32(mn_07, op_07);
+  __m256i mnop_47 = _mm256_unpackhi_epi32(mn_07, op_07);
+
+  __m256i abcd_8b = _mm256_unpacklo_epi32(ab_8f, cd_8f);
+  __m256i abcd_cf = _mm256_unpackhi_epi32(ab_8f, cd_8f);
+  __m256i efgh_8b = _mm256_unpacklo_epi32(ef_8f, gh_8f);
+  __m256i efgh_cf = _mm256_unpackhi_epi32(ef_8f, gh_8f);
+  __m256i ijkl_8b = _mm256_unpacklo_epi32(ij_8f, kl_8f);
+  __m256i ijkl_cf = _mm256_unpackhi_epi32(ij_8f, kl_8f);
+  __m256i mnop_8b = _mm256_unpacklo_epi32(mn_8f, op_8f);
+  __m256i mnop_cf = _mm256_unpackhi_epi32(mn_8f, op_8f);
+
+  __m256i abcdefgh_01 = _mm256_unpacklo_epi64(abcd_03, efgh_03);
+  __m256i abcdefgh_23 = _mm256_unpackhi_epi64(abcd_03, efgh_03);
+  __m256i ijklmnop_01 = _mm256_unpacklo_epi64(ijkl_03, mnop_03);
+  __m256i ijklmnop_23 = _mm256_unpackhi_epi64(ijkl_03, mnop_03);
+  __m256i abcdefgh_45 = _mm256_unpacklo_epi64(abcd_47, efgh_47);
+  __m256i abcdefgh_67 = _mm256_unpackhi_epi64(abcd_47, efgh_47);
+  __m256i ijklmnop_45 = _mm256_unpacklo_epi64(ijkl_47, mnop_47);
+  __m256i ijklmnop_67 = _mm256_unpackhi_epi64(ijkl_47, mnop_47);
+  __m256i abcdefgh_89 = _mm256_unpacklo_epi64(abcd_8b, efgh_8b);
+  __m256i abcdefgh_ab = _mm256_unpackhi_epi64(abcd_8b, efgh_8b);
+  __m256i ijklmnop_89 = _mm256_unpacklo_epi64(ijkl_8b, mnop_8b);
+  __m256i ijklmnop_ab = _mm256_unpackhi_epi64(ijkl_8b, mnop_8b);
+  __m256i abcdefgh_cd = _mm256_unpacklo_epi64(abcd_cf, efgh_cf);
+  __m256i abcdefgh_ef = _mm256_unpackhi_epi64(abcd_cf, efgh_cf);
+  __m256i ijklmnop_cd = _mm256_unpacklo_epi64(ijkl_cf, mnop_cf);
+  __m256i ijklmnop_ef = _mm256_unpackhi_epi64(ijkl_cf, mnop_cf);
+
+  // NOTE: no unpacklo/hi instr in this case, so using permute instr.
+  kernel.packet[0] = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x20);
+  kernel.packet[1] = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x20);
+  kernel.packet[2] = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x20);
+  kernel.packet[3] = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x20);
+  kernel.packet[4] = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x20);
+  kernel.packet[5] = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x20);
+  kernel.packet[6] = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x20);
+  kernel.packet[7] = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x20);
+  kernel.packet[8] = _mm256_permute2x128_si256(abcdefgh_01, ijklmnop_01, 0x31);
+  kernel.packet[9] = _mm256_permute2x128_si256(abcdefgh_23, ijklmnop_23, 0x31);
+  kernel.packet[10] = _mm256_permute2x128_si256(abcdefgh_45, ijklmnop_45, 0x31);
+  kernel.packet[11] = _mm256_permute2x128_si256(abcdefgh_67, ijklmnop_67, 0x31);
+  kernel.packet[12] = _mm256_permute2x128_si256(abcdefgh_89, ijklmnop_89, 0x31);
+  kernel.packet[13] = _mm256_permute2x128_si256(abcdefgh_ab, ijklmnop_ab, 0x31);
+  kernel.packet[14] = _mm256_permute2x128_si256(abcdefgh_cd, ijklmnop_cd, 0x31);
+  kernel.packet[15] = _mm256_permute2x128_si256(abcdefgh_ef, ijklmnop_ef, 0x31);
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16bf,4>& kernel) {
+  __m256i a = kernel.packet[0];
+  __m256i b = kernel.packet[1];
+  __m256i c = kernel.packet[2];
+  __m256i d = kernel.packet[3];
+
+  __m256i ab_07 = _mm256_unpacklo_epi16(a, b);
+  __m256i cd_07 = _mm256_unpacklo_epi16(c, d);
+  __m256i ab_8f = _mm256_unpackhi_epi16(a, b);
+  __m256i cd_8f = _mm256_unpackhi_epi16(c, d);
+
+  __m256i abcd_03 = _mm256_unpacklo_epi32(ab_07, cd_07);
+  __m256i abcd_47 = _mm256_unpackhi_epi32(ab_07, cd_07);
+  __m256i abcd_8b = _mm256_unpacklo_epi32(ab_8f, cd_8f);
+  __m256i abcd_cf = _mm256_unpackhi_epi32(ab_8f, cd_8f);
+
+  // NOTE: no unpacklo/hi instr in this case, so using permute instr.
+  kernel.packet[0] = _mm256_permute2x128_si256(abcd_03, abcd_47, 0x20);
+  kernel.packet[1] = _mm256_permute2x128_si256(abcd_8b, abcd_cf, 0x20);
+  kernel.packet[2] = _mm256_permute2x128_si256(abcd_03, abcd_47, 0x31);
+  kernel.packet[3] = _mm256_permute2x128_si256(abcd_8b, abcd_cf, 0x31);
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_PACKET_MATH_AVX512_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/PacketMathFP16.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/PacketMathFP16.h
new file mode 100644
index 0000000000000000000000000000000000000000..52fee1e95e678b233310c51c85f71528f8bdcd90
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/PacketMathFP16.h
@@ -0,0 +1,869 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+//
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_FP16_AVX512_H
+#define EIGEN_PACKET_MATH_FP16_AVX512_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+typedef __m512h Packet32h;
+typedef eigen_packet_wrapper<__m256i, 1> Packet16h;
+typedef eigen_packet_wrapper<__m128i, 2> Packet8h;
+
+template <>
+struct is_arithmetic<Packet8h> {
+  enum { value = true };
+};
+
+template <>
+struct packet_traits<half> : default_packet_traits {
+  typedef Packet32h type;
+  typedef Packet16h half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 32,
+
+    HasCmp = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasAbs2 = 0,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasLog = 1,
+    HasLog1p = 1,
+    HasExp = 1,
+    HasExpm1 = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    // These ones should be implemented in future
+    HasBessel = 0,
+    HasNdtri = 0,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = 0,  // EIGEN_FAST_MATH,
+    HasBlend = 0,
+    HasRound = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasRint = 1
+  };
+};
+
+template <>
+struct unpacket_traits<Packet32h> {
+  typedef Eigen::half type;
+  typedef Packet16h half;
+  enum {
+    size = 32,
+    alignment = Aligned64,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet16h> {
+  typedef Eigen::half type;
+  typedef Packet8h half;
+  enum {
+    size = 16,
+    alignment = Aligned32,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet8h> {
+  typedef Eigen::half type;
+  typedef Packet8h half;
+  enum {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+// Memory functions
+
+// pset1
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pset1<Packet32h>(const Eigen::half& from) {
+  return _mm512_set1_ph(static_cast<_Float16>(from));
+}
+
+// pset1frombits
+template <>
+EIGEN_STRONG_INLINE Packet32h pset1frombits<Packet32h>(unsigned short from) {
+  return _mm512_castsi512_ph(_mm512_set1_epi16(from));
+}
+
+// pfirst
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half pfirst<Packet32h>(const Packet32h& from) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  return half_impl::raw_uint16_to_half(
+      static_cast<unsigned short>(_mm256_extract_epi16(_mm512_extracti32x8_epi32(_mm512_castph_si512(from), 0), 0)));
+#else
+  Eigen::half dest[32];
+  _mm512_storeu_ph(dest, from);
+  return dest[0];
+#endif
+}
+
+// pload
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pload<Packet32h>(const Eigen::half* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return _mm512_load_ph(from);
+}
+
+// ploadu
+
+template <>
+EIGEN_STRONG_INLINE Packet32h ploadu<Packet32h>(const Eigen::half* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return _mm512_loadu_ph(from);
+}
+
+// pstore
+
+template <>
+EIGEN_STRONG_INLINE void pstore<half>(Eigen::half* to, const Packet32h& from) {
+  EIGEN_DEBUG_ALIGNED_STORE _mm512_store_ph(to, from);
+}
+
+// pstoreu
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<half>(Eigen::half* to, const Packet32h& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE _mm512_storeu_ph(to, from);
+}
+
+// ploaddup
+template <>
+EIGEN_STRONG_INLINE Packet32h ploaddup<Packet32h>(const Eigen::half* from) {
+  __m512h a = _mm512_castph256_ph512(_mm256_loadu_ph(from));
+  return _mm512_permutexvar_ph(_mm512_set_epi16(15, 15, 14, 14, 13, 13, 12, 12, 11, 11, 10, 10, 9, 9, 8, 8, 7, 7, 6, 6,
+                                                5, 5, 4, 4, 3, 3, 2, 2, 1, 1, 0, 0),
+                               a);
+}
+
+// ploadquad
+template <>
+EIGEN_STRONG_INLINE Packet32h ploadquad<Packet32h>(const Eigen::half* from) {
+  __m512h a = _mm512_castph128_ph512(_mm_loadu_ph(from));
+  return _mm512_permutexvar_ph(
+      _mm512_set_epi16(7, 7, 7, 7, 6, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0),
+      a);
+}
+
+// pabs
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pabs<Packet32h>(const Packet32h& a) {
+  return _mm512_abs_ph(a);
+}
+
+// psignbit
+
+template <>
+EIGEN_STRONG_INLINE Packet32h psignbit<Packet32h>(const Packet32h& a) {
+  return _mm512_castsi512_ph(_mm512_srai_epi16(_mm512_castph_si512(a), 15));
+}
+
+// pmin
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pmin<Packet32h>(const Packet32h& a, const Packet32h& b) {
+  return _mm512_min_ph(a, b);
+}
+
+// pmax
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pmax<Packet32h>(const Packet32h& a, const Packet32h& b) {
+  return _mm512_max_ph(a, b);
+}
+
+// plset
+template <>
+EIGEN_STRONG_INLINE Packet32h plset<Packet32h>(const half& a) {
+  return _mm512_add_ph(_mm512_set1_ph(a),
+                       _mm512_set_ph(31.0f, 30.0f, 29.0f, 28.0f, 27.0f, 26.0f, 25.0f, 24.0f, 23.0f, 22.0f, 21.0f, 20.0f,
+                                     19.0f, 18.0f, 17.0f, 16.0f, 15.0f, 14.0f, 13.0f, 12.0f, 11.0f, 10.0f, 9.0f, 8.0f,
+                                     7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f, 0.0f));
+}
+
+// por
+
+template <>
+EIGEN_STRONG_INLINE Packet32h por(const Packet32h& a, const Packet32h& b) {
+  return _mm512_castsi512_ph(_mm512_or_si512(_mm512_castph_si512(a), _mm512_castph_si512(b)));
+}
+
+// pxor
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pxor(const Packet32h& a, const Packet32h& b) {
+  return _mm512_castsi512_ph(_mm512_xor_si512(_mm512_castph_si512(a), _mm512_castph_si512(b)));
+}
+
+// pand
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pand(const Packet32h& a, const Packet32h& b) {
+  return _mm512_castsi512_ph(_mm512_and_si512(_mm512_castph_si512(a), _mm512_castph_si512(b)));
+}
+
+// pandnot
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pandnot(const Packet32h& a, const Packet32h& b) {
+  return _mm512_castsi512_ph(_mm512_andnot_si512(_mm512_castph_si512(b), _mm512_castph_si512(a)));
+}
+
+// pselect
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet32h pselect(const Packet32h& mask, const Packet32h& a, const Packet32h& b) {
+  __mmask32 mask32 = _mm512_cmp_epi16_mask(_mm512_castph_si512(mask), _mm512_setzero_epi32(), _MM_CMPINT_EQ);
+  return _mm512_mask_blend_ph(mask32, a, b);
+}
+
+// pcmp_eq
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pcmp_eq(const Packet32h& a, const Packet32h& b) {
+  __mmask32 mask = _mm512_cmp_ph_mask(a, b, _CMP_EQ_OQ);
+  return _mm512_castsi512_ph(_mm512_mask_set1_epi16(_mm512_set1_epi32(0), mask, 0xffffu));
+}
+
+// pcmp_le
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pcmp_le(const Packet32h& a, const Packet32h& b) {
+  __mmask32 mask = _mm512_cmp_ph_mask(a, b, _CMP_LE_OQ);
+  return _mm512_castsi512_ph(_mm512_mask_set1_epi16(_mm512_set1_epi32(0), mask, 0xffffu));
+}
+
+// pcmp_lt
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pcmp_lt(const Packet32h& a, const Packet32h& b) {
+  __mmask32 mask = _mm512_cmp_ph_mask(a, b, _CMP_LT_OQ);
+  return _mm512_castsi512_ph(_mm512_mask_set1_epi16(_mm512_set1_epi32(0), mask, 0xffffu));
+}
+
+// pcmp_lt_or_nan
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pcmp_lt_or_nan(const Packet32h& a, const Packet32h& b) {
+  __mmask32 mask = _mm512_cmp_ph_mask(a, b, _CMP_NGE_UQ);
+  return _mm512_castsi512_ph(_mm512_mask_set1_epi16(_mm512_set1_epi16(0), mask, 0xffffu));
+}
+
+// padd
+
+template <>
+EIGEN_STRONG_INLINE Packet32h padd<Packet32h>(const Packet32h& a, const Packet32h& b) {
+  return _mm512_add_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h padd<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  return _mm256_castph_si256(_mm256_add_ph(_mm256_castsi256_ph(a), _mm256_castsi256_ph(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h padd<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  return _mm_castph_si128(_mm_add_ph(_mm_castsi128_ph(a), _mm_castsi128_ph(b)));
+}
+
+// psub
+
+template <>
+EIGEN_STRONG_INLINE Packet32h psub<Packet32h>(const Packet32h& a, const Packet32h& b) {
+  return _mm512_sub_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h psub<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  return _mm256_castph_si256(_mm256_sub_ph(_mm256_castsi256_ph(a), _mm256_castsi256_ph(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h psub<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  return _mm_castph_si128(_mm_sub_ph(_mm_castsi128_ph(a), _mm_castsi128_ph(b)));
+}
+
+// pmul
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pmul<Packet32h>(const Packet32h& a, const Packet32h& b) {
+  return _mm512_mul_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pmul<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  return _mm256_castph_si256(_mm256_mul_ph(_mm256_castsi256_ph(a), _mm256_castsi256_ph(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pmul<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  return _mm_castph_si128(_mm_mul_ph(_mm_castsi128_ph(a), _mm_castsi128_ph(b)));
+}
+
+// pdiv
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pdiv<Packet32h>(const Packet32h& a, const Packet32h& b) {
+  return _mm512_div_ph(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pdiv<Packet16h>(const Packet16h& a, const Packet16h& b) {
+  return _mm256_castph_si256(_mm256_div_ph(_mm256_castsi256_ph(a), _mm256_castsi256_ph(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pdiv<Packet8h>(const Packet8h& a, const Packet8h& b) {
+  return _mm_castph_si128(_mm_div_ph(_mm_castsi128_ph(a), _mm_castsi128_ph(b)));
+}
+
+// pround
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pround<Packet32h>(const Packet32h& a) {
+  // Work-around for default std::round rounding mode.
+
+  // Mask for the sign bit
+  const Packet32h signMask = pset1frombits<Packet32h>(static_cast<numext::uint16_t>(0x8000u));
+  // The largest half-preicision float less than 0.5
+  const Packet32h prev0dot5 = pset1frombits<Packet32h>(static_cast<numext::uint16_t>(0x37FFu));
+
+  return _mm512_roundscale_ph(padd(por(pand(a, signMask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+
+// print
+
+template <>
+EIGEN_STRONG_INLINE Packet32h print<Packet32h>(const Packet32h& a) {
+  return _mm512_roundscale_ph(a, _MM_FROUND_CUR_DIRECTION);
+}
+
+// pceil
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pceil<Packet32h>(const Packet32h& a) {
+  return _mm512_roundscale_ph(a, _MM_FROUND_TO_POS_INF);
+}
+
+// pfloor
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pfloor<Packet32h>(const Packet32h& a) {
+  return _mm512_roundscale_ph(a, _MM_FROUND_TO_NEG_INF);
+}
+
+// predux
+template <>
+EIGEN_STRONG_INLINE half predux<Packet32h>(const Packet32h& a) {
+  return (half)_mm512_reduce_add_ph(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux<Packet16h>(const Packet16h& a) {
+  return (half)_mm256_reduce_add_ph(_mm256_castsi256_ph(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE half predux<Packet8h>(const Packet8h& a) {
+  return (half)_mm_reduce_add_ph(_mm_castsi128_ph(a));
+}
+
+// predux_half_dowto4
+template <>
+EIGEN_STRONG_INLINE Packet16h predux_half_dowto4<Packet32h>(const Packet32h& a) {
+#ifdef EIGEN_VECTORIZE_AVX512DQ
+  __m256i lowHalf = _mm256_castps_si256(_mm512_extractf32x8_ps(_mm512_castph_ps(a), 0));
+  __m256i highHalf = _mm256_castps_si256(_mm512_extractf32x8_ps(_mm512_castph_ps(a), 1));
+
+  return Packet16h(padd<Packet16h>(lowHalf, highHalf));
+#else
+  Eigen::half data[32];
+  _mm512_storeu_ph(data, a);
+
+  __m256i lowHalf = _mm256_castph_si256(_mm256_loadu_ph(data));
+  __m256i highHalf = _mm256_castph_si256(_mm256_loadu_ph(data + 16));
+
+  return Packet16h(padd<Packet16h>(lowHalf, highHalf));
+#endif
+}
+
+// predux_max
+
+// predux_min
+
+// predux_mul
+
+#ifdef EIGEN_VECTORIZE_FMA
+
+// pmadd
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pmadd(const Packet32h& a, const Packet32h& b, const Packet32h& c) {
+  return _mm512_fmadd_ph(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pmadd(const Packet16h& a, const Packet16h& b, const Packet16h& c) {
+  return _mm256_castph_si256(_mm256_fmadd_ph(_mm256_castsi256_ph(a), _mm256_castsi256_ph(b), _mm256_castsi256_ph(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pmadd(const Packet8h& a, const Packet8h& b, const Packet8h& c) {
+  return _mm_castph_si128(_mm_fmadd_ph(_mm_castsi128_ph(a), _mm_castsi128_ph(b), _mm_castsi128_ph(c)));
+}
+
+// pmsub
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pmsub(const Packet32h& a, const Packet32h& b, const Packet32h& c) {
+  return _mm512_fmsub_ph(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pmsub(const Packet16h& a, const Packet16h& b, const Packet16h& c) {
+  return _mm256_castph_si256(_mm256_fmsub_ph(_mm256_castsi256_ph(a), _mm256_castsi256_ph(b), _mm256_castsi256_ph(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pmsub(const Packet8h& a, const Packet8h& b, const Packet8h& c) {
+  return _mm_castph_si128(_mm_fmsub_ph(_mm_castsi128_ph(a), _mm_castsi128_ph(b), _mm_castsi128_ph(c)));
+}
+
+// pnmadd
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pnmadd(const Packet32h& a, const Packet32h& b, const Packet32h& c) {
+  return _mm512_fnmadd_ph(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pnmadd(const Packet16h& a, const Packet16h& b, const Packet16h& c) {
+  return _mm256_castph_si256(_mm256_fnmadd_ph(_mm256_castsi256_ph(a), _mm256_castsi256_ph(b), _mm256_castsi256_ph(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pnmadd(const Packet8h& a, const Packet8h& b, const Packet8h& c) {
+  return _mm_castph_si128(_mm_fnmadd_ph(_mm_castsi128_ph(a), _mm_castsi128_ph(b), _mm_castsi128_ph(c)));
+}
+
+// pnmsub
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pnmsub(const Packet32h& a, const Packet32h& b, const Packet32h& c) {
+  return _mm512_fnmsub_ph(a, b, c);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pnmsub(const Packet16h& a, const Packet16h& b, const Packet16h& c) {
+  return _mm256_castph_si256(_mm256_fnmsub_ph(_mm256_castsi256_ph(a), _mm256_castsi256_ph(b), _mm256_castsi256_ph(c)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pnmsub(const Packet8h& a, const Packet8h& b, const Packet8h& c) {
+  return _mm_castph_si128(_mm_fnmsub_ph(_mm_castsi128_ph(a), _mm_castsi128_ph(b), _mm_castsi128_ph(c)));
+}
+
+#endif
+
+// pnegate
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pnegate<Packet32h>(const Packet32h& a) {
+  return _mm512_sub_ph(_mm512_set1_ph(0.0), a);
+}
+
+// pconj
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pconj<Packet32h>(const Packet32h& a) {
+  return a;
+}
+
+// psqrt
+
+template <>
+EIGEN_STRONG_INLINE Packet32h psqrt<Packet32h>(const Packet32h& a) {
+  return _mm512_sqrt_ph(a);
+}
+
+// prsqrt
+
+template <>
+EIGEN_STRONG_INLINE Packet32h prsqrt<Packet32h>(const Packet32h& a) {
+  return _mm512_rsqrt_ph(a);
+}
+
+// preciprocal
+
+template <>
+EIGEN_STRONG_INLINE Packet32h preciprocal<Packet32h>(const Packet32h& a) {
+  return _mm512_rcp_ph(a);
+}
+
+// ptranspose
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet32h, 32>& a) {
+  __m512i t[32];
+
+  EIGEN_UNROLL_LOOP
+  for (int i = 0; i < 16; i++) {
+    t[2 * i] = _mm512_unpacklo_epi16(_mm512_castph_si512(a.packet[2 * i]), _mm512_castph_si512(a.packet[2 * i + 1]));
+    t[2 * i + 1] =
+        _mm512_unpackhi_epi16(_mm512_castph_si512(a.packet[2 * i]), _mm512_castph_si512(a.packet[2 * i + 1]));
+  }
+
+  __m512i p[32];
+
+  EIGEN_UNROLL_LOOP
+  for (int i = 0; i < 8; i++) {
+    p[4 * i] = _mm512_unpacklo_epi32(t[4 * i], t[4 * i + 2]);
+    p[4 * i + 1] = _mm512_unpackhi_epi32(t[4 * i], t[4 * i + 2]);
+    p[4 * i + 2] = _mm512_unpacklo_epi32(t[4 * i + 1], t[4 * i + 3]);
+    p[4 * i + 3] = _mm512_unpackhi_epi32(t[4 * i + 1], t[4 * i + 3]);
+  }
+
+  __m512i q[32];
+
+  EIGEN_UNROLL_LOOP
+  for (int i = 0; i < 4; i++) {
+    q[8 * i] = _mm512_unpacklo_epi64(p[8 * i], p[8 * i + 4]);
+    q[8 * i + 1] = _mm512_unpackhi_epi64(p[8 * i], p[8 * i + 4]);
+    q[8 * i + 2] = _mm512_unpacklo_epi64(p[8 * i + 1], p[8 * i + 5]);
+    q[8 * i + 3] = _mm512_unpackhi_epi64(p[8 * i + 1], p[8 * i + 5]);
+    q[8 * i + 4] = _mm512_unpacklo_epi64(p[8 * i + 2], p[8 * i + 6]);
+    q[8 * i + 5] = _mm512_unpackhi_epi64(p[8 * i + 2], p[8 * i + 6]);
+    q[8 * i + 6] = _mm512_unpacklo_epi64(p[8 * i + 3], p[8 * i + 7]);
+    q[8 * i + 7] = _mm512_unpackhi_epi64(p[8 * i + 3], p[8 * i + 7]);
+  }
+
+  __m512i f[32];
+
+#define PACKET32H_TRANSPOSE_HELPER(X, Y)                                                            \
+  do {                                                                                              \
+    f[Y * 8] = _mm512_inserti32x4(f[Y * 8], _mm512_extracti32x4_epi32(q[X * 8], Y), X);             \
+    f[Y * 8 + 1] = _mm512_inserti32x4(f[Y * 8 + 1], _mm512_extracti32x4_epi32(q[X * 8 + 1], Y), X); \
+    f[Y * 8 + 2] = _mm512_inserti32x4(f[Y * 8 + 2], _mm512_extracti32x4_epi32(q[X * 8 + 2], Y), X); \
+    f[Y * 8 + 3] = _mm512_inserti32x4(f[Y * 8 + 3], _mm512_extracti32x4_epi32(q[X * 8 + 3], Y), X); \
+    f[Y * 8 + 4] = _mm512_inserti32x4(f[Y * 8 + 4], _mm512_extracti32x4_epi32(q[X * 8 + 4], Y), X); \
+    f[Y * 8 + 5] = _mm512_inserti32x4(f[Y * 8 + 5], _mm512_extracti32x4_epi32(q[X * 8 + 5], Y), X); \
+    f[Y * 8 + 6] = _mm512_inserti32x4(f[Y * 8 + 6], _mm512_extracti32x4_epi32(q[X * 8 + 6], Y), X); \
+    f[Y * 8 + 7] = _mm512_inserti32x4(f[Y * 8 + 7], _mm512_extracti32x4_epi32(q[X * 8 + 7], Y), X); \
+  } while (false);
+
+  PACKET32H_TRANSPOSE_HELPER(0, 0);
+  PACKET32H_TRANSPOSE_HELPER(1, 1);
+  PACKET32H_TRANSPOSE_HELPER(2, 2);
+  PACKET32H_TRANSPOSE_HELPER(3, 3);
+
+  PACKET32H_TRANSPOSE_HELPER(1, 0);
+  PACKET32H_TRANSPOSE_HELPER(2, 0);
+  PACKET32H_TRANSPOSE_HELPER(3, 0);
+  PACKET32H_TRANSPOSE_HELPER(2, 1);
+  PACKET32H_TRANSPOSE_HELPER(3, 1);
+  PACKET32H_TRANSPOSE_HELPER(3, 2);
+
+  PACKET32H_TRANSPOSE_HELPER(0, 1);
+  PACKET32H_TRANSPOSE_HELPER(0, 2);
+  PACKET32H_TRANSPOSE_HELPER(0, 3);
+  PACKET32H_TRANSPOSE_HELPER(1, 2);
+  PACKET32H_TRANSPOSE_HELPER(1, 3);
+  PACKET32H_TRANSPOSE_HELPER(2, 3);
+
+#undef PACKET32H_TRANSPOSE_HELPER
+
+  EIGEN_UNROLL_LOOP
+  for (int i = 0; i < 32; i++) {
+    a.packet[i] = _mm512_castsi512_ph(f[i]);
+  }
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet32h, 4>& a) {
+  __m512i p0, p1, p2, p3, t0, t1, t2, t3, a0, a1, a2, a3;
+  t0 = _mm512_unpacklo_epi16(_mm512_castph_si512(a.packet[0]), _mm512_castph_si512(a.packet[1]));
+  t1 = _mm512_unpackhi_epi16(_mm512_castph_si512(a.packet[0]), _mm512_castph_si512(a.packet[1]));
+  t2 = _mm512_unpacklo_epi16(_mm512_castph_si512(a.packet[2]), _mm512_castph_si512(a.packet[3]));
+  t3 = _mm512_unpackhi_epi16(_mm512_castph_si512(a.packet[2]), _mm512_castph_si512(a.packet[3]));
+
+  p0 = _mm512_unpacklo_epi32(t0, t2);
+  p1 = _mm512_unpackhi_epi32(t0, t2);
+  p2 = _mm512_unpacklo_epi32(t1, t3);
+  p3 = _mm512_unpackhi_epi32(t1, t3);
+
+  a0 = p0;
+  a1 = p1;
+  a2 = p2;
+  a3 = p3;
+
+  a0 = _mm512_inserti32x4(a0, _mm512_extracti32x4_epi32(p1, 0), 1);
+  a1 = _mm512_inserti32x4(a1, _mm512_extracti32x4_epi32(p0, 1), 0);
+
+  a0 = _mm512_inserti32x4(a0, _mm512_extracti32x4_epi32(p2, 0), 2);
+  a2 = _mm512_inserti32x4(a2, _mm512_extracti32x4_epi32(p0, 2), 0);
+
+  a0 = _mm512_inserti32x4(a0, _mm512_extracti32x4_epi32(p3, 0), 3);
+  a3 = _mm512_inserti32x4(a3, _mm512_extracti32x4_epi32(p0, 3), 0);
+
+  a1 = _mm512_inserti32x4(a1, _mm512_extracti32x4_epi32(p2, 1), 2);
+  a2 = _mm512_inserti32x4(a2, _mm512_extracti32x4_epi32(p1, 2), 1);
+
+  a2 = _mm512_inserti32x4(a2, _mm512_extracti32x4_epi32(p3, 2), 3);
+  a3 = _mm512_inserti32x4(a3, _mm512_extracti32x4_epi32(p2, 3), 2);
+
+  a1 = _mm512_inserti32x4(a1, _mm512_extracti32x4_epi32(p3, 1), 3);
+  a3 = _mm512_inserti32x4(a3, _mm512_extracti32x4_epi32(p1, 3), 1);
+
+  a.packet[0] = _mm512_castsi512_ph(a0);
+  a.packet[1] = _mm512_castsi512_ph(a1);
+  a.packet[2] = _mm512_castsi512_ph(a2);
+  a.packet[3] = _mm512_castsi512_ph(a3);
+}
+
+// preverse
+
+template <>
+EIGEN_STRONG_INLINE Packet32h preverse(const Packet32h& a) {
+  return _mm512_permutexvar_ph(_mm512_set_epi16(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
+                                                20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31),
+                               a);
+}
+
+// pscatter
+
+template <>
+EIGEN_STRONG_INLINE void pscatter<half, Packet32h>(half* to, const Packet32h& from, Index stride) {
+  EIGEN_ALIGN64 half aux[32];
+  pstore(aux, from);
+
+  EIGEN_UNROLL_LOOP
+  for (int i = 0; i < 32; i++) {
+    to[stride * i] = aux[i];
+  }
+}
+
+// pgather
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pgather<Eigen::half, Packet32h>(const Eigen::half* from, Index stride) {
+  return _mm512_castsi512_ph(_mm512_set_epi16(
+      from[31 * stride].x, from[30 * stride].x, from[29 * stride].x, from[28 * stride].x, from[27 * stride].x,
+      from[26 * stride].x, from[25 * stride].x, from[24 * stride].x, from[23 * stride].x, from[22 * stride].x,
+      from[21 * stride].x, from[20 * stride].x, from[19 * stride].x, from[18 * stride].x, from[17 * stride].x,
+      from[16 * stride].x, from[15 * stride].x, from[14 * stride].x, from[13 * stride].x, from[12 * stride].x,
+      from[11 * stride].x, from[10 * stride].x, from[9 * stride].x, from[8 * stride].x, from[7 * stride].x,
+      from[6 * stride].x, from[5 * stride].x, from[4 * stride].x, from[3 * stride].x, from[2 * stride].x,
+      from[1 * stride].x, from[0 * stride].x));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pcos<Packet16h>(const Packet16h&);
+template <>
+EIGEN_STRONG_INLINE Packet16h psin<Packet16h>(const Packet16h&);
+template <>
+EIGEN_STRONG_INLINE Packet16h plog<Packet16h>(const Packet16h&);
+template <>
+EIGEN_STRONG_INLINE Packet16h plog2<Packet16h>(const Packet16h&);
+template <>
+EIGEN_STRONG_INLINE Packet16h plog1p<Packet16h>(const Packet16h&);
+template <>
+EIGEN_STRONG_INLINE Packet16h pexp<Packet16h>(const Packet16h&);
+template <>
+EIGEN_STRONG_INLINE Packet16h pexpm1<Packet16h>(const Packet16h&);
+template <>
+EIGEN_STRONG_INLINE Packet16h ptanh<Packet16h>(const Packet16h&);
+template <>
+EIGEN_STRONG_INLINE Packet16h pfrexp<Packet16h>(const Packet16h&, Packet16h&);
+template <>
+EIGEN_STRONG_INLINE Packet16h pldexp<Packet16h>(const Packet16h&, const Packet16h&);
+
+EIGEN_STRONG_INLINE Packet32h combine2Packet16h(const Packet16h& a, const Packet16h& b) {
+  __m512d result = _mm512_undefined_pd();
+  result = _mm512_insertf64x4(result, _mm256_castsi256_pd(a), 0);
+  result = _mm512_insertf64x4(result, _mm256_castsi256_pd(b), 1);
+  return _mm512_castpd_ph(result);
+}
+
+EIGEN_STRONG_INLINE void extract2Packet16h(const Packet32h& x, Packet16h& a, Packet16h& b) {
+  a = _mm256_castpd_si256(_mm512_extractf64x4_pd(_mm512_castph_pd(x), 0));
+  b = _mm256_castpd_si256(_mm512_extractf64x4_pd(_mm512_castph_pd(x), 1));
+}
+
+// psin
+template <>
+EIGEN_STRONG_INLINE Packet32h psin<Packet32h>(const Packet32h& a) {
+  Packet16h low;
+  Packet16h high;
+  extract2Packet16h(a, low, high);
+
+  Packet16h lowOut = psin(low);
+  Packet16h highOut = psin(high);
+
+  return combine2Packet16h(lowOut, highOut);
+}
+
+// pcos
+template <>
+EIGEN_STRONG_INLINE Packet32h pcos<Packet32h>(const Packet32h& a) {
+  Packet16h low;
+  Packet16h high;
+  extract2Packet16h(a, low, high);
+
+  Packet16h lowOut = pcos(low);
+  Packet16h highOut = pcos(high);
+
+  return combine2Packet16h(lowOut, highOut);
+}
+
+// plog
+template <>
+EIGEN_STRONG_INLINE Packet32h plog<Packet32h>(const Packet32h& a) {
+  Packet16h low;
+  Packet16h high;
+  extract2Packet16h(a, low, high);
+
+  Packet16h lowOut = plog(low);
+  Packet16h highOut = plog(high);
+
+  return combine2Packet16h(lowOut, highOut);
+}
+
+// plog2
+template <>
+EIGEN_STRONG_INLINE Packet32h plog2<Packet32h>(const Packet32h& a) {
+  Packet16h low;
+  Packet16h high;
+  extract2Packet16h(a, low, high);
+
+  Packet16h lowOut = plog2(low);
+  Packet16h highOut = plog2(high);
+
+  return combine2Packet16h(lowOut, highOut);
+}
+
+// plog1p
+template <>
+EIGEN_STRONG_INLINE Packet32h plog1p<Packet32h>(const Packet32h& a) {
+  Packet16h low;
+  Packet16h high;
+  extract2Packet16h(a, low, high);
+
+  Packet16h lowOut = plog1p(low);
+  Packet16h highOut = plog1p(high);
+
+  return combine2Packet16h(lowOut, highOut);
+}
+
+// pexp
+template <>
+EIGEN_STRONG_INLINE Packet32h pexp<Packet32h>(const Packet32h& a) {
+  Packet16h low;
+  Packet16h high;
+  extract2Packet16h(a, low, high);
+
+  Packet16h lowOut = pexp(low);
+  Packet16h highOut = pexp(high);
+
+  return combine2Packet16h(lowOut, highOut);
+}
+
+// pexpm1
+template <>
+EIGEN_STRONG_INLINE Packet32h pexpm1<Packet32h>(const Packet32h& a) {
+  Packet16h low;
+  Packet16h high;
+  extract2Packet16h(a, low, high);
+
+  Packet16h lowOut = pexpm1(low);
+  Packet16h highOut = pexpm1(high);
+
+  return combine2Packet16h(lowOut, highOut);
+}
+
+// ptanh
+template <>
+EIGEN_STRONG_INLINE Packet32h ptanh<Packet32h>(const Packet32h& a) {
+  Packet16h low;
+  Packet16h high;
+  extract2Packet16h(a, low, high);
+
+  Packet16h lowOut = ptanh(low);
+  Packet16h highOut = ptanh(high);
+
+  return combine2Packet16h(lowOut, highOut);
+}
+
+// pfrexp
+template <>
+EIGEN_STRONG_INLINE Packet32h pfrexp<Packet32h>(const Packet32h& a, Packet32h& exponent) {
+  Packet16h low;
+  Packet16h high;
+  extract2Packet16h(a, low, high);
+
+  Packet16h exp1 = _mm256_undefined_si256();
+  Packet16h exp2 = _mm256_undefined_si256();
+
+  Packet16h lowOut = pfrexp(low, exp1);
+  Packet16h highOut = pfrexp(high, exp2);
+
+  exponent = combine2Packet16h(exp1, exp2);
+
+  return combine2Packet16h(lowOut, highOut);
+}
+
+// pldexp
+template <>
+EIGEN_STRONG_INLINE Packet32h pldexp<Packet32h>(const Packet32h& a, const Packet32h& exponent) {
+  Packet16h low;
+  Packet16h high;
+  extract2Packet16h(a, low, high);
+
+  Packet16h exp1;
+  Packet16h exp2;
+  extract2Packet16h(exponent, exp1, exp2);
+
+  Packet16h lowOut = pldexp(low, exp1);
+  Packet16h highOut = pldexp(high, exp2);
+
+  return combine2Packet16h(lowOut, highOut);
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_FP16_AVX512_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/TrsmKernel.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/TrsmKernel.h
new file mode 100644
index 0000000000000000000000000000000000000000..714afacec733a170bcdefddc5a6366bdaed3f671
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/TrsmKernel.h
@@ -0,0 +1,1164 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2022 Intel Corporation
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CORE_ARCH_AVX512_TRSM_KERNEL_H
+#define EIGEN_CORE_ARCH_AVX512_TRSM_KERNEL_H
+
+#include "../../InternalHeaderCheck.h"
+
+#if !defined(EIGEN_USE_AVX512_TRSM_KERNELS)
+#define EIGEN_USE_AVX512_TRSM_KERNELS 1
+#endif
+
+// TRSM kernels currently unconditionally rely on malloc with AVX512.
+// Disable them if malloc is explicitly disabled at compile-time.
+#ifdef EIGEN_NO_MALLOC
+#undef EIGEN_USE_AVX512_TRSM_KERNELS
+#define EIGEN_USE_AVX512_TRSM_KERNELS 0
+#endif
+
+#if EIGEN_USE_AVX512_TRSM_KERNELS
+#if !defined(EIGEN_USE_AVX512_TRSM_R_KERNELS)
+#define EIGEN_USE_AVX512_TRSM_R_KERNELS 1
+#endif
+#if !defined(EIGEN_USE_AVX512_TRSM_L_KERNELS)
+#define EIGEN_USE_AVX512_TRSM_L_KERNELS 1
+#endif
+#else  // EIGEN_USE_AVX512_TRSM_KERNELS == 0
+#define EIGEN_USE_AVX512_TRSM_R_KERNELS 0
+#define EIGEN_USE_AVX512_TRSM_L_KERNELS 0
+#endif
+
+// Need this for some std::min calls.
+#ifdef min
+#undef min
+#endif
+
+namespace Eigen {
+namespace internal {
+
+#define EIGEN_AVX_MAX_NUM_ACC (int64_t(24))
+#define EIGEN_AVX_MAX_NUM_ROW (int64_t(8))  // Denoted L in code.
+#define EIGEN_AVX_MAX_K_UNROL (int64_t(4))
+#define EIGEN_AVX_B_LOAD_SETS (int64_t(2))
+#define EIGEN_AVX_MAX_A_BCAST (int64_t(2))
+typedef Packet16f vecFullFloat;
+typedef Packet8d vecFullDouble;
+typedef Packet8f vecHalfFloat;
+typedef Packet4d vecHalfDouble;
+
+// Compile-time unrolls are implemented here.
+// Note: this depends on macros and typedefs above.
+#include "TrsmUnrolls.inc"
+
+#if (EIGEN_USE_AVX512_TRSM_KERNELS) && (EIGEN_COMP_CLANG != 0)
+/**
+ * For smaller problem sizes, and certain compilers, using the optimized kernels trsmKernelL/R directly
+ * is faster than the packed versions in TriangularSolverMatrix.h.
+ *
+ * The current heuristic is based on having having all arrays used in the largest gemm-update
+ * in triSolve fit in roughly L2Cap (percentage) of the L2 cache. These cutoffs are a bit conservative and could be
+ * larger for some trsm cases.
+ * The formula:
+ *
+ *   (L*M + M*N + L*N)*sizeof(Scalar) < L2Cache*L2Cap
+ *
+ *  L = number of rows to solve at a time
+ *  N = number of rhs
+ *  M = Dimension of triangular matrix
+ *
+ */
+#if !defined(EIGEN_ENABLE_AVX512_NOCOPY_TRSM_CUTOFFS)
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_CUTOFFS 1
+#endif
+
+#if EIGEN_ENABLE_AVX512_NOCOPY_TRSM_CUTOFFS
+
+#if EIGEN_USE_AVX512_TRSM_R_KERNELS
+#if !defined(EIGEN_ENABLE_AVX512_NOCOPY_TRSM_R_CUTOFFS)
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_R_CUTOFFS 1
+#endif  // !defined(EIGEN_ENABLE_AVX512_NOCOPY_TRSM_R_CUTOFFS)
+#endif
+
+#if EIGEN_USE_AVX512_TRSM_L_KERNELS
+#if !defined(EIGEN_ENABLE_AVX512_NOCOPY_TRSM_L_CUTOFFS)
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_L_CUTOFFS 1
+#endif
+#endif  // EIGEN_USE_AVX512_TRSM_L_KERNELS
+
+#else  // EIGEN_ENABLE_AVX512_NOCOPY_TRSM_CUTOFFS == 0
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_R_CUTOFFS 0
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_L_CUTOFFS 0
+#endif  // EIGEN_ENABLE_AVX512_NOCOPY_TRSM_CUTOFFS
+
+template <typename Scalar>
+int64_t avx512_trsm_cutoff(int64_t L2Size, int64_t N, double L2Cap) {
+  const int64_t U3 = 3 * packet_traits<Scalar>::size;
+  const int64_t MaxNb = 5 * U3;
+  int64_t Nb = std::min(MaxNb, N);
+  double cutoff_d =
+      (((L2Size * L2Cap) / (sizeof(Scalar))) - (EIGEN_AVX_MAX_NUM_ROW)*Nb) / ((EIGEN_AVX_MAX_NUM_ROW) + Nb);
+  int64_t cutoff_l = static_cast<int64_t>(cutoff_d);
+  return (cutoff_l / EIGEN_AVX_MAX_NUM_ROW) * EIGEN_AVX_MAX_NUM_ROW;
+}
+#else // !(EIGEN_USE_AVX512_TRSM_KERNELS) || !(EIGEN_COMP_CLANG != 0)
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_CUTOFFS 0
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_R_CUTOFFS 0
+#define EIGEN_ENABLE_AVX512_NOCOPY_TRSM_L_CUTOFFS 0
+#endif
+
+/**
+ * Used by gemmKernel for the case A/B row-major and C col-major.
+ */
+template <typename Scalar, typename vec, int64_t unrollM, int64_t unrollN, bool remM, bool remN>
+EIGEN_ALWAYS_INLINE void transStoreC(PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+                                            Scalar *C_arr, int64_t LDC, int64_t remM_ = 0, int64_t remN_ = 0) {
+  EIGEN_UNUSED_VARIABLE(remN_);
+  EIGEN_UNUSED_VARIABLE(remM_);
+  using urolls = unrolls::trans<Scalar>;
+
+  constexpr int64_t U3 = urolls::PacketSize * 3;
+  constexpr int64_t U2 = urolls::PacketSize * 2;
+  constexpr int64_t U1 = urolls::PacketSize * 1;
+
+  static_assert(unrollN == U1 || unrollN == U2 || unrollN == U3, "unrollN should be a multiple of PacketSize");
+  static_assert(unrollM == EIGEN_AVX_MAX_NUM_ROW, "unrollM should be equal to EIGEN_AVX_MAX_NUM_ROW");
+
+  urolls::template transpose<unrollN, 0>(zmm);
+  EIGEN_IF_CONSTEXPR(unrollN > U2) urolls::template transpose<unrollN, 2>(zmm);
+  EIGEN_IF_CONSTEXPR(unrollN > U1) urolls::template transpose<unrollN, 1>(zmm);
+
+  static_assert((remN && unrollN == U1) || !remN, "When handling N remainder set unrollN=U1");
+  EIGEN_IF_CONSTEXPR(!remN) {
+    urolls::template storeC<std::min(unrollN, U1), unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+    EIGEN_IF_CONSTEXPR(unrollN > U1) {
+      constexpr int64_t unrollN_ = std::min(unrollN - U1, U1);
+      urolls::template storeC<unrollN_, unrollN, 1, remM>(C_arr + U1 * LDC, LDC, zmm, remM_);
+    }
+    EIGEN_IF_CONSTEXPR(unrollN > U2) {
+      constexpr int64_t unrollN_ = std::min(unrollN - U2, U1);
+      urolls::template storeC<unrollN_, unrollN, 2, remM>(C_arr + U2 * LDC, LDC, zmm, remM_);
+    }
+  }
+  else {
+    EIGEN_IF_CONSTEXPR((std::is_same<Scalar, float>::value)) {
+      // Note: without "if constexpr" this section of code will also be
+      // parsed by the compiler so each of the storeC will still be instantiated.
+      // We use enable_if in aux_storeC to set it to an empty function for
+      // these cases.
+      if (remN_ == 15)
+        urolls::template storeC<15, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 14)
+        urolls::template storeC<14, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 13)
+        urolls::template storeC<13, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 12)
+        urolls::template storeC<12, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 11)
+        urolls::template storeC<11, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 10)
+        urolls::template storeC<10, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 9)
+        urolls::template storeC<9, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 8)
+        urolls::template storeC<8, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 7)
+        urolls::template storeC<7, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 6)
+        urolls::template storeC<6, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 5)
+        urolls::template storeC<5, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 4)
+        urolls::template storeC<4, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 3)
+        urolls::template storeC<3, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 2)
+        urolls::template storeC<2, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 1)
+        urolls::template storeC<1, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+    }
+    else {
+      if (remN_ == 7)
+        urolls::template storeC<7, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 6)
+        urolls::template storeC<6, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 5)
+        urolls::template storeC<5, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 4)
+        urolls::template storeC<4, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 3)
+        urolls::template storeC<3, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 2)
+        urolls::template storeC<2, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+      else if (remN_ == 1)
+        urolls::template storeC<1, unrollN, 0, remM>(C_arr, LDC, zmm, remM_);
+    }
+  }
+}
+
+/**
+ * GEMM like operation for trsm panel updates.
+ * Computes: C -= A*B
+ * K must be multipe of 4.
+ *
+ * Unrolls used are {1,2,4,8}x{U1,U2,U3};
+ * For good performance we want K to be large with M/N relatively small, but also large enough
+ * to use the {8,U3} unroll block.
+ *
+ * isARowMajor: is A_arr row-major?
+ * isCRowMajor: is C_arr row-major? (B_arr is assumed to be row-major).
+ * isAdd: C += A*B or C -= A*B (used by trsm)
+ * handleKRem: Handle arbitrary K? This is not needed for trsm.
+ */
+template <typename Scalar, bool isARowMajor, bool isCRowMajor, bool isAdd, bool handleKRem>
+void gemmKernel(Scalar *A_arr, Scalar *B_arr, Scalar *C_arr, int64_t M, int64_t N, int64_t K, int64_t LDA, int64_t LDB,
+                int64_t LDC) {
+  using urolls = unrolls::gemm<Scalar, isAdd>;
+  constexpr int64_t U3 = urolls::PacketSize * 3;
+  constexpr int64_t U2 = urolls::PacketSize * 2;
+  constexpr int64_t U1 = urolls::PacketSize * 1;
+  using vec = typename std::conditional<std::is_same<Scalar, float>::value, vecFullFloat, vecFullDouble>::type;
+  int64_t N_ = (N / U3) * U3;
+  int64_t M_ = (M / EIGEN_AVX_MAX_NUM_ROW) * EIGEN_AVX_MAX_NUM_ROW;
+  int64_t K_ = (K / EIGEN_AVX_MAX_K_UNROL) * EIGEN_AVX_MAX_K_UNROL;
+  int64_t j = 0;
+  for (; j < N_; j += U3) {
+    constexpr int64_t EIGEN_AVX_MAX_B_LOAD = EIGEN_AVX_B_LOAD_SETS * 3;
+    int64_t i = 0;
+    for (; i < M_; i += EIGEN_AVX_MAX_NUM_ROW) {
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)], *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<3, EIGEN_AVX_MAX_NUM_ROW>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 3, EIGEN_AVX_MAX_NUM_ROW, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 3, EIGEN_AVX_MAX_NUM_ROW, 1, EIGEN_AVX_B_LOAD_SETS * 3,
+                                       EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<3, EIGEN_AVX_MAX_NUM_ROW>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<3, EIGEN_AVX_MAX_NUM_ROW>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U3, false, false>(zmm, &C_arr[i + j * LDC], LDC);
+      }
+    }
+    if (M - i >= 4) {  // Note: this block assumes EIGEN_AVX_MAX_NUM_ROW = 8. Should be removed otherwise
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<3, 4>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 3, 4, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_B_LOAD_SETS * 3,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 3, 4, 1, EIGEN_AVX_B_LOAD_SETS * 3, EIGEN_AVX_MAX_A_BCAST>(
+              B_t, A_t, LDB, LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<3, 4>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<3, 4>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U3, true, false>(zmm, &C_arr[i + j * LDC], LDC, 4);
+      }
+      i += 4;
+    }
+    if (M - i >= 2) {
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<3, 2>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 3, 2, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_B_LOAD_SETS * 3,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 3, 2, 1, EIGEN_AVX_B_LOAD_SETS * 3, EIGEN_AVX_MAX_A_BCAST>(
+              B_t, A_t, LDB, LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<3, 2>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<3, 2>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U3, true, false>(zmm, &C_arr[i + j * LDC], LDC, 2);
+      }
+      i += 2;
+    }
+    if (M - i > 0) {
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<3, 1>(zmm);
+      {
+        for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+          urolls::template microKernel<isARowMajor, 3, 1, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_B_LOAD_SETS * 3, 1>(
+              B_t, A_t, LDB, LDA, zmm);
+          B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+          else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+        }
+        EIGEN_IF_CONSTEXPR(handleKRem) {
+          for (int64_t k = K_; k < K; k++) {
+            urolls::template microKernel<isARowMajor, 3, 1, 1, EIGEN_AVX_B_LOAD_SETS * 3, 1>(B_t, A_t, LDB, LDA, zmm);
+            B_t += LDB;
+            EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+            else A_t += LDA;
+          }
+        }
+        EIGEN_IF_CONSTEXPR(isCRowMajor) {
+          urolls::template updateC<3, 1>(&C_arr[i * LDC + j], LDC, zmm);
+          urolls::template storeC<3, 1>(&C_arr[i * LDC + j], LDC, zmm);
+        }
+        else {
+          transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U3, true, false>(zmm, &C_arr[i + j * LDC], LDC, 1);
+        }
+      }
+    }
+  }
+  if (N - j >= U2) {
+    constexpr int64_t EIGEN_AVX_MAX_B_LOAD = EIGEN_AVX_B_LOAD_SETS * 2;
+    int64_t i = 0;
+    for (; i < M_; i += EIGEN_AVX_MAX_NUM_ROW) {
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)], *B_t = &B_arr[0 * LDB + j];
+      EIGEN_IF_CONSTEXPR(isCRowMajor) B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<2, EIGEN_AVX_MAX_NUM_ROW>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 2, EIGEN_AVX_MAX_NUM_ROW, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 2, EIGEN_AVX_MAX_NUM_ROW, 1, EIGEN_AVX_MAX_B_LOAD,
+                                       EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<2, EIGEN_AVX_MAX_NUM_ROW>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<2, EIGEN_AVX_MAX_NUM_ROW>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U2, false, false>(zmm, &C_arr[i + j * LDC], LDC);
+      }
+    }
+    if (M - i >= 4) {  // Note: this block assumes EIGEN_AVX_MAX_NUM_ROW = 8. Should be removed otherwise
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<2, 4>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 2, 4, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 2, 4, 1, EIGEN_AVX_MAX_B_LOAD, EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB,
+                                                                                                          LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<2, 4>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<2, 4>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U2, true, false>(zmm, &C_arr[i + j * LDC], LDC, 4);
+      }
+      i += 4;
+    }
+    if (M - i >= 2) {
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<2, 2>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 2, 2, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 2, 2, 1, EIGEN_AVX_MAX_B_LOAD, EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB,
+                                                                                                          LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<2, 2>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<2, 2>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U2, true, false>(zmm, &C_arr[i + j * LDC], LDC, 2);
+      }
+      i += 2;
+    }
+    if (M - i > 0) {
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<2, 1>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 2, 1, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD, 1>(B_t, A_t, LDB,
+                                                                                                        LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 2, 1, 1, EIGEN_AVX_MAX_B_LOAD, 1>(B_t, A_t, LDB, LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<2, 1>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<2, 1>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U2, true, false>(zmm, &C_arr[i + j * LDC], LDC, 1);
+      }
+    }
+    j += U2;
+  }
+  if (N - j >= U1) {
+    constexpr int64_t EIGEN_AVX_MAX_B_LOAD = EIGEN_AVX_B_LOAD_SETS * 1;
+    int64_t i = 0;
+    for (; i < M_; i += EIGEN_AVX_MAX_NUM_ROW) {
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)], *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, EIGEN_AVX_MAX_NUM_ROW>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 1, EIGEN_AVX_MAX_NUM_ROW, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 1, EIGEN_AVX_MAX_NUM_ROW, 1, EIGEN_AVX_B_LOAD_SETS * 1,
+                                       EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<1, EIGEN_AVX_MAX_NUM_ROW>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<1, EIGEN_AVX_MAX_NUM_ROW>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, false, false>(zmm, &C_arr[i + j * LDC], LDC);
+      }
+    }
+    if (M - i >= 4) {  // Note: this block assumes EIGEN_AVX_MAX_NUM_ROW = 8. Should be removed otherwise
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, 4>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 1, 4, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 1, 4, 1, EIGEN_AVX_MAX_B_LOAD, EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB,
+                                                                                                          LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<1, 4>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<1, 4>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, true, false>(zmm, &C_arr[i + j * LDC], LDC, 4);
+      }
+      i += 4;
+    }
+    if (M - i >= 2) {
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, 2>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 1, 2, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB, LDA, zmm);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 1, 2, 1, EIGEN_AVX_MAX_B_LOAD, EIGEN_AVX_MAX_A_BCAST>(B_t, A_t, LDB,
+                                                                                                          LDA, zmm);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<1, 2>(&C_arr[i * LDC + j], LDC, zmm);
+        urolls::template storeC<1, 2>(&C_arr[i * LDC + j], LDC, zmm);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, true, false>(zmm, &C_arr[i + j * LDC], LDC, 2);
+      }
+      i += 2;
+    }
+    if (M - i > 0) {
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, 1>(zmm);
+      {
+        for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+          urolls::template microKernel<isARowMajor, 1, 1, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD, 1>(B_t, A_t, LDB,
+                                                                                                          LDA, zmm);
+          B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+          else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+        }
+        EIGEN_IF_CONSTEXPR(handleKRem) {
+          for (int64_t k = K_; k < K; k++) {
+            urolls::template microKernel<isARowMajor, 1, 1, 1, EIGEN_AVX_B_LOAD_SETS * 1, 1>(B_t, A_t, LDB, LDA, zmm);
+            B_t += LDB;
+            EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+            else A_t += LDA;
+          }
+        }
+        EIGEN_IF_CONSTEXPR(isCRowMajor) {
+          urolls::template updateC<1, 1>(&C_arr[i * LDC + j], LDC, zmm);
+          urolls::template storeC<1, 1>(&C_arr[i * LDC + j], LDC, zmm);
+        }
+        else {
+          transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, true, false>(zmm, &C_arr[i + j * LDC], LDC, 1);
+        }
+      }
+    }
+    j += U1;
+  }
+  if (N - j > 0) {
+    constexpr int64_t EIGEN_AVX_MAX_B_LOAD = EIGEN_AVX_B_LOAD_SETS * 1;
+    int64_t i = 0;
+    for (; i < M_; i += EIGEN_AVX_MAX_NUM_ROW) {
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, EIGEN_AVX_MAX_NUM_ROW>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 1, EIGEN_AVX_MAX_NUM_ROW, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST, true>(B_t, A_t, LDB, LDA, zmm, N - j);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 1, EIGEN_AVX_MAX_NUM_ROW, 1, EIGEN_AVX_MAX_B_LOAD,
+                                       EIGEN_AVX_MAX_A_BCAST, true>(B_t, A_t, LDB, LDA, zmm, N - j);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<1, EIGEN_AVX_MAX_NUM_ROW, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+        urolls::template storeC<1, EIGEN_AVX_MAX_NUM_ROW, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, false, true>(zmm, &C_arr[i + j * LDC], LDC, 0, N - j);
+      }
+    }
+    if (M - i >= 4) {  // Note: this block assumes EIGEN_AVX_MAX_NUM_ROW = 8. Should be removed otherwise
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, 4>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 1, 4, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST, true>(B_t, A_t, LDB, LDA, zmm, N - j);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 1, 4, 1, EIGEN_AVX_MAX_B_LOAD, EIGEN_AVX_MAX_A_BCAST, true>(
+              B_t, A_t, LDB, LDA, zmm, N - j);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<1, 4, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+        urolls::template storeC<1, 4, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, true, true>(zmm, &C_arr[i + j * LDC], LDC, 4, N - j);
+      }
+      i += 4;
+    }
+    if (M - i >= 2) {
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, 2>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 1, 2, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD,
+                                     EIGEN_AVX_MAX_A_BCAST, true>(B_t, A_t, LDB, LDA, zmm, N - j);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 1, 2, 1, EIGEN_AVX_MAX_B_LOAD, EIGEN_AVX_MAX_A_BCAST, true>(
+              B_t, A_t, LDB, LDA, zmm, N - j);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<1, 2, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+        urolls::template storeC<1, 2, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, true, true>(zmm, &C_arr[i + j * LDC], LDC, 2, N - j);
+      }
+      i += 2;
+    }
+    if (M - i > 0) {
+      Scalar *A_t = &A_arr[idA<isARowMajor>(i, 0, LDA)];
+      Scalar *B_t = &B_arr[0 * LDB + j];
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> zmm;
+      urolls::template setzero<1, 1>(zmm);
+      for (int64_t k = 0; k < K_; k += EIGEN_AVX_MAX_K_UNROL) {
+        urolls::template microKernel<isARowMajor, 1, 1, EIGEN_AVX_MAX_K_UNROL, EIGEN_AVX_MAX_B_LOAD, 1, true>(
+            B_t, A_t, LDB, LDA, zmm, N - j);
+        B_t += EIGEN_AVX_MAX_K_UNROL * LDB;
+        EIGEN_IF_CONSTEXPR(isARowMajor) A_t += EIGEN_AVX_MAX_K_UNROL;
+        else A_t += EIGEN_AVX_MAX_K_UNROL * LDA;
+      }
+      EIGEN_IF_CONSTEXPR(handleKRem) {
+        for (int64_t k = K_; k < K; k++) {
+          urolls::template microKernel<isARowMajor, 1, 1, 1, EIGEN_AVX_MAX_B_LOAD, 1, true>(B_t, A_t, LDB, LDA, zmm,
+                                                                                            N - j);
+          B_t += LDB;
+          EIGEN_IF_CONSTEXPR(isARowMajor) A_t++;
+          else A_t += LDA;
+        }
+      }
+      EIGEN_IF_CONSTEXPR(isCRowMajor) {
+        urolls::template updateC<1, 1, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+        urolls::template storeC<1, 1, true>(&C_arr[i * LDC + j], LDC, zmm, N - j);
+      }
+      else {
+        transStoreC<Scalar, vec, EIGEN_AVX_MAX_NUM_ROW, U1, true, true>(zmm, &C_arr[i + j * LDC], LDC, 1, N - j);
+      }
+    }
+  }
+}
+
+/**
+ * Triangular solve kernel with A on left with K number of rhs. dim(A) = unrollM
+ *
+ * unrollM: dimension of A matrix (triangular matrix). unrollM should be <= EIGEN_AVX_MAX_NUM_ROW
+ * isFWDSolve: is forward solve?
+ * isUnitDiag: is the diagonal of A all ones?
+ * The B matrix (RHS) is assumed to be row-major
+ */
+template <typename Scalar, typename vec, int64_t unrollM, bool isARowMajor, bool isFWDSolve, bool isUnitDiag>
+EIGEN_ALWAYS_INLINE void triSolveKernel(Scalar *A_arr, Scalar *B_arr, int64_t K, int64_t LDA, int64_t LDB) {
+  static_assert(unrollM <= EIGEN_AVX_MAX_NUM_ROW, "unrollM should be equal to EIGEN_AVX_MAX_NUM_ROW");
+  using urolls = unrolls::trsm<Scalar>;
+  constexpr int64_t U3 = urolls::PacketSize * 3;
+  constexpr int64_t U2 = urolls::PacketSize * 2;
+  constexpr int64_t U1 = urolls::PacketSize * 1;
+
+  PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> RHSInPacket;
+  PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> AInPacket;
+
+  int64_t k = 0;
+  while (K - k >= U3) {
+    urolls::template loadRHS<isFWDSolve, unrollM, 3>(B_arr + k, LDB, RHSInPacket);
+    urolls::template triSolveMicroKernel<isARowMajor, isFWDSolve, isUnitDiag, unrollM, 3>(A_arr, LDA, RHSInPacket,
+                                                                                          AInPacket);
+    urolls::template storeRHS<isFWDSolve, unrollM, 3>(B_arr + k, LDB, RHSInPacket);
+    k += U3;
+  }
+  if (K - k >= U2) {
+    urolls::template loadRHS<isFWDSolve, unrollM, 2>(B_arr + k, LDB, RHSInPacket);
+    urolls::template triSolveMicroKernel<isARowMajor, isFWDSolve, isUnitDiag, unrollM, 2>(A_arr, LDA, RHSInPacket,
+                                                                                          AInPacket);
+    urolls::template storeRHS<isFWDSolve, unrollM, 2>(B_arr + k, LDB, RHSInPacket);
+    k += U2;
+  }
+  if (K - k >= U1) {
+    urolls::template loadRHS<isFWDSolve, unrollM, 1>(B_arr + k, LDB, RHSInPacket);
+    urolls::template triSolveMicroKernel<isARowMajor, isFWDSolve, isUnitDiag, unrollM, 1>(A_arr, LDA, RHSInPacket,
+                                                                                          AInPacket);
+    urolls::template storeRHS<isFWDSolve, unrollM, 1>(B_arr + k, LDB, RHSInPacket);
+    k += U1;
+  }
+  if (K - k > 0) {
+    // Handle remaining number of RHS
+    urolls::template loadRHS<isFWDSolve, unrollM, 1, true>(B_arr + k, LDB, RHSInPacket, K - k);
+    urolls::template triSolveMicroKernel<isARowMajor, isFWDSolve, isUnitDiag, unrollM, 1>(A_arr, LDA, RHSInPacket,
+                                                                                          AInPacket);
+    urolls::template storeRHS<isFWDSolve, unrollM, 1, true>(B_arr + k, LDB, RHSInPacket, K - k);
+  }
+}
+
+/**
+ * Triangular solve routine with A on left and dimension of at most L with K number of rhs. This is essentially
+ * a wrapper for triSolveMicrokernel for M = {1,2,3,4,5,6,7,8}.
+ *
+ * isFWDSolve: is forward solve?
+ * isUnitDiag: is the diagonal of A all ones?
+ * The B matrix (RHS) is assumed to be row-major
+ */
+template <typename Scalar, bool isARowMajor, bool isFWDSolve, bool isUnitDiag>
+void triSolveKernelLxK(Scalar *A_arr, Scalar *B_arr, int64_t M, int64_t K, int64_t LDA, int64_t LDB) {
+  // Note: this assumes EIGEN_AVX_MAX_NUM_ROW = 8. Unrolls should be adjusted
+  // accordingly if EIGEN_AVX_MAX_NUM_ROW is smaller.
+  using vec = typename std::conditional<std::is_same<Scalar, float>::value, vecFullFloat, vecFullDouble>::type;
+  if (M == 8)
+    triSolveKernel<Scalar, vec, 8, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  else if (M == 7)
+    triSolveKernel<Scalar, vec, 7, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  else if (M == 6)
+    triSolveKernel<Scalar, vec, 6, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  else if (M == 5)
+    triSolveKernel<Scalar, vec, 5, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  else if (M == 4)
+    triSolveKernel<Scalar, vec, 4, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  else if (M == 3)
+    triSolveKernel<Scalar, vec, 3, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  else if (M == 2)
+    triSolveKernel<Scalar, vec, 2, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  else if (M == 1)
+    triSolveKernel<Scalar, vec, 1, isARowMajor, isFWDSolve, isUnitDiag>(A_arr, B_arr, K, LDA, LDB);
+  return;
+}
+
+/**
+ * This routine is used to copy B to/from a temporary array (row-major) for cases where B is column-major.
+ *
+ * toTemp: true => copy to temporary array, false => copy from temporary array
+ * remM: true = need to handle remainder values for M (M < EIGEN_AVX_MAX_NUM_ROW)
+ *
+ */
+template <typename Scalar, bool toTemp = true, bool remM = false>
+EIGEN_ALWAYS_INLINE void copyBToRowMajor(Scalar *B_arr, int64_t LDB, int64_t K, Scalar *B_temp, int64_t LDB_,
+                                                int64_t remM_ = 0) {
+  EIGEN_UNUSED_VARIABLE(remM_);
+  using urolls = unrolls::transB<Scalar>;
+  using vecHalf = typename std::conditional<std::is_same<Scalar, float>::value, vecHalfFloat, vecFullDouble>::type;
+  PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> ymm;
+  constexpr int64_t U3 = urolls::PacketSize * 3;
+  constexpr int64_t U2 = urolls::PacketSize * 2;
+  constexpr int64_t U1 = urolls::PacketSize * 1;
+  int64_t K_ = K / U3 * U3;
+  int64_t k = 0;
+
+  for (; k < K_; k += U3) {
+    urolls::template transB_kernel<U3, toTemp, remM>(B_arr + k * LDB, LDB, B_temp, LDB_, ymm, remM_);
+    B_temp += U3;
+  }
+  if (K - k >= U2) {
+    urolls::template transB_kernel<U2, toTemp, remM>(B_arr + k * LDB, LDB, B_temp, LDB_, ymm, remM_);
+    B_temp += U2;
+    k += U2;
+  }
+  if (K - k >= U1) {
+    urolls::template transB_kernel<U1, toTemp, remM>(B_arr + k * LDB, LDB, B_temp, LDB_, ymm, remM_);
+    B_temp += U1;
+    k += U1;
+  }
+  EIGEN_IF_CONSTEXPR(U1 > 8) {
+    // Note: without "if constexpr" this section of code will also be
+    // parsed by the compiler so there is an additional check in {load/store}BBlock
+    // to make sure the counter is not non-negative.
+    if (K - k >= 8) {
+      urolls::template transB_kernel<8, toTemp, remM>(B_arr + k * LDB, LDB, B_temp, LDB_, ymm, remM_);
+      B_temp += 8;
+      k += 8;
+    }
+  }
+  EIGEN_IF_CONSTEXPR(U1 > 4) {
+    // Note: without "if constexpr" this section of code will also be
+    // parsed by the compiler so there is an additional check in {load/store}BBlock
+    // to make sure the counter is not non-negative.
+    if (K - k >= 4) {
+      urolls::template transB_kernel<4, toTemp, remM>(B_arr + k * LDB, LDB, B_temp, LDB_, ymm, remM_);
+      B_temp += 4;
+      k += 4;
+    }
+  }
+  if (K - k >= 2) {
+    urolls::template transB_kernel<2, toTemp, remM>(B_arr + k * LDB, LDB, B_temp, LDB_, ymm, remM_);
+    B_temp += 2;
+    k += 2;
+  }
+  if (K - k >= 1) {
+    urolls::template transB_kernel<1, toTemp, remM>(B_arr + k * LDB, LDB, B_temp, LDB_, ymm, remM_);
+    B_temp += 1;
+    k += 1;
+  }
+}
+
+/**
+ * Main triangular solve driver
+ *
+ * Triangular solve with A on the left.
+ * Scalar:    Scalar precision, only float/double is supported.
+ * isARowMajor:  is A row-major?
+ * isBRowMajor:  is B row-major?
+ * isFWDSolve:   is this forward solve or backward (true => forward)?
+ * isUnitDiag: is diagonal of A unit or nonunit (true => A has unit diagonal)?
+ *
+ * M: dimension of A
+ * numRHS: number of right hand sides (coincides with K dimension for gemm updates)
+ *
+ * Here are the mapping between the different TRSM cases (col-major) and triSolve:
+ *
+ * LLN (left , lower, A non-transposed) ::  isARowMajor=false, isBRowMajor=false, isFWDSolve=true
+ * LUT (left , upper, A transposed)     ::  isARowMajor=true,  isBRowMajor=false, isFWDSolve=true
+ * LUN (left , upper, A non-transposed) ::  isARowMajor=false, isBRowMajor=false, isFWDSolve=false
+ * LLT (left , lower, A transposed)     ::  isARowMajor=true,  isBRowMajor=false, isFWDSolve=false
+ * RUN (right, upper, A non-transposed) ::  isARowMajor=true,  isBRowMajor=true,  isFWDSolve=true
+ * RLT (right, lower, A transposed)     ::  isARowMajor=false, isBRowMajor=true,  isFWDSolve=true
+ * RUT (right, upper, A transposed)     ::  isARowMajor=false, isBRowMajor=true,  isFWDSolve=false
+ * RLN (right, lower, A non-transposed) ::  isARowMajor=true,  isBRowMajor=true,  isFWDSolve=false
+ *
+ * Note: For RXX cases M,numRHS should be swapped.
+ *
+ */
+template <typename Scalar, bool isARowMajor = true, bool isBRowMajor = true, bool isFWDSolve = true,
+          bool isUnitDiag = false>
+void triSolve(Scalar *A_arr, Scalar *B_arr, int64_t M, int64_t numRHS, int64_t LDA, int64_t LDB) {
+  constexpr int64_t psize = packet_traits<Scalar>::size;
+  /**
+   * The values for kB, numM were determined experimentally.
+   * kB: Number of RHS we process at a time.
+   * numM: number of rows of B we will store in a temporary array (see below.) This should be a multiple of L.
+   *
+   * kB was determined by initially setting kB = numRHS and benchmarking triSolve (TRSM-RUN case)
+   * performance with M=numRHS.
+   * It was observed that performance started to drop around M=numRHS=240. This is likely machine dependent.
+   *
+   * numM was chosen "arbitrarily". It should be relatively small so B_temp is not too large, but it should be
+   * large enough to allow GEMM updates to have larger "K"s (see below.) No benchmarking has been done so far to
+   * determine optimal values for numM.
+   */
+  constexpr int64_t kB = (3 * psize) * 5;  // 5*U3
+  constexpr int64_t numM = 8 * EIGEN_AVX_MAX_NUM_ROW;
+
+  int64_t sizeBTemp = 0;
+  Scalar *B_temp = NULL;
+  EIGEN_IF_CONSTEXPR(!isBRowMajor) {
+    /**
+     * If B is col-major, we copy it to a fixed-size temporary array of size at most ~numM*kB and
+     * transpose it to row-major. Call the solve routine, and copy+transpose it back to the original array.
+     * The updated row-major copy of B is reused in the GEMM updates.
+     */
+    sizeBTemp = (((std::min(kB, numRHS) + psize - 1) / psize + 4) * psize) * numM;
+  }
+
+  EIGEN_IF_CONSTEXPR(!isBRowMajor) B_temp = (Scalar *)handmade_aligned_malloc(sizeof(Scalar) * sizeBTemp, 64);
+
+  for (int64_t k = 0; k < numRHS; k += kB) {
+    int64_t bK = numRHS - k > kB ? kB : numRHS - k;
+    int64_t M_ = (M / EIGEN_AVX_MAX_NUM_ROW) * EIGEN_AVX_MAX_NUM_ROW, gemmOff = 0;
+
+    // bK rounded up to next multiple of L=EIGEN_AVX_MAX_NUM_ROW. When B_temp is used, we solve for bkL RHS
+    // instead of bK RHS in triSolveKernelLxK.
+    int64_t bkL = ((bK + (EIGEN_AVX_MAX_NUM_ROW - 1)) / EIGEN_AVX_MAX_NUM_ROW) * EIGEN_AVX_MAX_NUM_ROW;
+    const int64_t numScalarPerCache = 64 / sizeof(Scalar);
+    // Leading dimension of B_temp, will be a multiple of the cache line size.
+    int64_t LDT = ((bkL + (numScalarPerCache - 1)) / numScalarPerCache) * numScalarPerCache;
+    int64_t offsetBTemp = 0;
+    for (int64_t i = 0; i < M_; i += EIGEN_AVX_MAX_NUM_ROW) {
+      EIGEN_IF_CONSTEXPR(!isBRowMajor) {
+        int64_t indA_i = isFWDSolve ? i : M - 1 - i;
+        int64_t indB_i = isFWDSolve ? i : M - (i + EIGEN_AVX_MAX_NUM_ROW);
+        int64_t offB_1 = isFWDSolve ? offsetBTemp : sizeBTemp - EIGEN_AVX_MAX_NUM_ROW * LDT - offsetBTemp;
+        int64_t offB_2 = isFWDSolve ? offsetBTemp : sizeBTemp - LDT - offsetBTemp;
+        // Copy values from B to B_temp.
+        copyBToRowMajor<Scalar, true, false>(B_arr + indB_i + k * LDB, LDB, bK, B_temp + offB_1, LDT);
+        // Triangular solve with a small block of A and long horizontal blocks of B (or B_temp if B col-major)
+        triSolveKernelLxK<Scalar, isARowMajor, isFWDSolve, isUnitDiag>(
+            &A_arr[idA<isARowMajor>(indA_i, indA_i, LDA)], B_temp + offB_2, EIGEN_AVX_MAX_NUM_ROW, bkL, LDA, LDT);
+        // Copy values from B_temp back to B. B_temp will be reused in gemm call below.
+        copyBToRowMajor<Scalar, false, false>(B_arr + indB_i + k * LDB, LDB, bK, B_temp + offB_1, LDT);
+
+        offsetBTemp += EIGEN_AVX_MAX_NUM_ROW * LDT;
+      }
+      else {
+        int64_t ind = isFWDSolve ? i : M - 1 - i;
+        triSolveKernelLxK<Scalar, isARowMajor, isFWDSolve, isUnitDiag>(
+            &A_arr[idA<isARowMajor>(ind, ind, LDA)], B_arr + k + ind * LDB, EIGEN_AVX_MAX_NUM_ROW, bK, LDA, LDB);
+      }
+      if (i + EIGEN_AVX_MAX_NUM_ROW < M_) {
+        /**
+         * For the GEMM updates, we want "K" (K=i+8 in this case) to be large as soon as possible
+         * to reuse the accumulators in GEMM as much as possible. So we only update 8xbK blocks of
+         * B as follows:
+         *
+         *        A             B
+         *     __
+         *    |__|__           |__|
+         *    |__|__|__        |__|
+         *    |__|__|__|__     |__|
+         *    |********|__|    |**|
+         */
+        EIGEN_IF_CONSTEXPR(isBRowMajor) {
+          int64_t indA_i = isFWDSolve ? i + EIGEN_AVX_MAX_NUM_ROW : M - (i + 2 * EIGEN_AVX_MAX_NUM_ROW);
+          int64_t indA_j = isFWDSolve ? 0 : M - (i + EIGEN_AVX_MAX_NUM_ROW);
+          int64_t indB_i = isFWDSolve ? 0 : M - (i + EIGEN_AVX_MAX_NUM_ROW);
+          int64_t indB_i2 = isFWDSolve ? i + EIGEN_AVX_MAX_NUM_ROW : M - (i + 2 * EIGEN_AVX_MAX_NUM_ROW);
+          gemmKernel<Scalar, isARowMajor, isBRowMajor, false, false>(
+              &A_arr[idA<isARowMajor>(indA_i, indA_j, LDA)], B_arr + k + indB_i * LDB, B_arr + k + indB_i2 * LDB,
+              EIGEN_AVX_MAX_NUM_ROW, bK, i + EIGEN_AVX_MAX_NUM_ROW, LDA, LDB, LDB);
+        }
+        else {
+          if (offsetBTemp + EIGEN_AVX_MAX_NUM_ROW * LDT > sizeBTemp) {
+            /**
+             * Similar idea as mentioned above, but here we are limited by the number of updated values of B
+             * that can be stored (row-major) in B_temp.
+             *
+             * If there is not enough space to store the next batch of 8xbK of B in B_temp, we call GEMM
+             * update and partially update the remaining old values of B which depends on the new values
+             * of B stored in B_temp. These values are then no longer needed and can be overwritten.
+             */
+            int64_t indA_i = isFWDSolve ? i + EIGEN_AVX_MAX_NUM_ROW : 0;
+            int64_t indA_j = isFWDSolve ? gemmOff : M - (i + EIGEN_AVX_MAX_NUM_ROW);
+            int64_t indB_i = isFWDSolve ? i + EIGEN_AVX_MAX_NUM_ROW : 0;
+            int64_t offB_1 = isFWDSolve ? 0 : sizeBTemp - offsetBTemp;
+            gemmKernel<Scalar, isARowMajor, isBRowMajor, false, false>(
+                &A_arr[idA<isARowMajor>(indA_i, indA_j, LDA)], B_temp + offB_1, B_arr + indB_i + (k)*LDB,
+                M - (i + EIGEN_AVX_MAX_NUM_ROW), bK, i + EIGEN_AVX_MAX_NUM_ROW - gemmOff, LDA, LDT, LDB);
+            offsetBTemp = 0;
+            gemmOff = i + EIGEN_AVX_MAX_NUM_ROW;
+          } else {
+            /**
+             * If there is enough space in B_temp, we only update the next 8xbK values of B.
+             */
+            int64_t indA_i = isFWDSolve ? i + EIGEN_AVX_MAX_NUM_ROW : M - (i + 2 * EIGEN_AVX_MAX_NUM_ROW);
+            int64_t indA_j = isFWDSolve ? gemmOff : M - (i + EIGEN_AVX_MAX_NUM_ROW);
+            int64_t indB_i = isFWDSolve ? i + EIGEN_AVX_MAX_NUM_ROW : M - (i + 2 * EIGEN_AVX_MAX_NUM_ROW);
+            int64_t offB_1 = isFWDSolve ? 0 : sizeBTemp - offsetBTemp;
+            gemmKernel<Scalar, isARowMajor, isBRowMajor, false, false>(
+                &A_arr[idA<isARowMajor>(indA_i, indA_j, LDA)], B_temp + offB_1, B_arr + indB_i + (k)*LDB,
+                EIGEN_AVX_MAX_NUM_ROW, bK, i + EIGEN_AVX_MAX_NUM_ROW - gemmOff, LDA, LDT, LDB);
+          }
+        }
+      }
+    }
+    // Handle M remainder..
+    int64_t bM = M - M_;
+    if (bM > 0) {
+      if (M_ > 0) {
+        EIGEN_IF_CONSTEXPR(isBRowMajor) {
+          int64_t indA_i = isFWDSolve ? M_ : 0;
+          int64_t indA_j = isFWDSolve ? 0 : bM;
+          int64_t indB_i = isFWDSolve ? 0 : bM;
+          int64_t indB_i2 = isFWDSolve ? M_ : 0;
+          gemmKernel<Scalar, isARowMajor, isBRowMajor, false, false>(
+              &A_arr[idA<isARowMajor>(indA_i, indA_j, LDA)], B_arr + k + indB_i * LDB, B_arr + k + indB_i2 * LDB, bM,
+              bK, M_, LDA, LDB, LDB);
+        }
+        else {
+          int64_t indA_i = isFWDSolve ? M_ : 0;
+          int64_t indA_j = isFWDSolve ? gemmOff : bM;
+          int64_t indB_i = isFWDSolve ? M_ : 0;
+          int64_t offB_1 = isFWDSolve ? 0 : sizeBTemp - offsetBTemp;
+          gemmKernel<Scalar, isARowMajor, isBRowMajor, false, false>(&A_arr[idA<isARowMajor>(indA_i, indA_j, LDA)],
+                                                                     B_temp + offB_1, B_arr + indB_i + (k)*LDB, bM, bK,
+                                                                     M_ - gemmOff, LDA, LDT, LDB);
+        }
+      }
+      EIGEN_IF_CONSTEXPR(!isBRowMajor) {
+        int64_t indA_i = isFWDSolve ? M_ : M - 1 - M_;
+        int64_t indB_i = isFWDSolve ? M_ : 0;
+        int64_t offB_1 = isFWDSolve ? 0 : (bM - 1) * bkL;
+        copyBToRowMajor<Scalar, true, true>(B_arr + indB_i + k * LDB, LDB, bK, B_temp, bkL, bM);
+        triSolveKernelLxK<Scalar, isARowMajor, isFWDSolve, isUnitDiag>(&A_arr[idA<isARowMajor>(indA_i, indA_i, LDA)],
+                                                                       B_temp + offB_1, bM, bkL, LDA, bkL);
+        copyBToRowMajor<Scalar, false, true>(B_arr + indB_i + k * LDB, LDB, bK, B_temp, bkL, bM);
+      }
+      else {
+        int64_t ind = isFWDSolve ? M_ : M - 1 - M_;
+        triSolveKernelLxK<Scalar, isARowMajor, isFWDSolve, isUnitDiag>(&A_arr[idA<isARowMajor>(ind, ind, LDA)],
+                                                                       B_arr + k + ind * LDB, bM, bK, LDA, LDB);
+      }
+    }
+  }
+
+  EIGEN_IF_CONSTEXPR(!isBRowMajor) handmade_aligned_free(B_temp);
+}
+
+// Template specializations of trsmKernelL/R for float/double and inner strides of 1.
+#if (EIGEN_USE_AVX512_TRSM_KERNELS)
+#if (EIGEN_USE_AVX512_TRSM_R_KERNELS)
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride, bool Specialized>
+struct trsmKernelR;
+
+template <typename Index, int Mode, int TriStorageOrder>
+struct trsmKernelR<float, Index, Mode, false, TriStorageOrder, 1, true> {
+  static void kernel(Index size, Index otherSize, const float *_tri, Index triStride, float *_other, Index otherIncr,
+                     Index otherStride);
+};
+
+template <typename Index, int Mode, int TriStorageOrder>
+struct trsmKernelR<double, Index, Mode, false, TriStorageOrder, 1, true> {
+  static void kernel(Index size, Index otherSize, const double *_tri, Index triStride, double *_other, Index otherIncr,
+                     Index otherStride);
+};
+
+template <typename Index, int Mode, int TriStorageOrder>
+EIGEN_DONT_INLINE void trsmKernelR<float, Index, Mode, false, TriStorageOrder, 1, true>::kernel(
+    Index size, Index otherSize, const float *_tri, Index triStride, float *_other, Index otherIncr,
+    Index otherStride) {
+  EIGEN_UNUSED_VARIABLE(otherIncr);
+#ifdef EIGEN_NO_RUNTIME_MALLOC
+  if (!is_malloc_allowed()) {
+    trsmKernelR<float, Index, Mode, false, TriStorageOrder, 1, /*Specialized=*/false>::kernel(
+          size, otherSize, _tri, triStride, _other, otherIncr, otherStride);
+    return;
+  }
+#endif
+  triSolve<float, TriStorageOrder != RowMajor, true, (Mode & Lower) != Lower, (Mode & UnitDiag) != 0>(
+      const_cast<float *>(_tri), _other, size, otherSize, triStride, otherStride);
+}
+
+template <typename Index, int Mode, int TriStorageOrder>
+EIGEN_DONT_INLINE void trsmKernelR<double, Index, Mode, false, TriStorageOrder, 1, true>::kernel(
+    Index size, Index otherSize, const double *_tri, Index triStride, double *_other, Index otherIncr,
+    Index otherStride) {
+  EIGEN_UNUSED_VARIABLE(otherIncr);
+#ifdef EIGEN_NO_RUNTIME_MALLOC
+  if (!is_malloc_allowed()) {
+    trsmKernelR<double, Index, Mode, false, TriStorageOrder, 1, /*Specialized=*/false>::kernel(
+          size, otherSize, _tri, triStride, _other, otherIncr, otherStride);
+    return;
+  }
+#endif
+  triSolve<double, TriStorageOrder != RowMajor, true, (Mode & Lower) != Lower, (Mode & UnitDiag) != 0>(
+      const_cast<double *>(_tri), _other, size, otherSize, triStride, otherStride);
+}
+#endif  // (EIGEN_USE_AVX512_TRSM_R_KERNELS)
+
+// These trsm kernels require temporary memory allocation
+#if (EIGEN_USE_AVX512_TRSM_L_KERNELS)
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride, bool Specialized = true>
+struct trsmKernelL;
+
+template <typename Index, int Mode, int TriStorageOrder>
+struct trsmKernelL<float, Index, Mode, false, TriStorageOrder, 1, true> {
+  static void kernel(Index size, Index otherSize, const float *_tri, Index triStride, float *_other, Index otherIncr,
+                     Index otherStride);
+};
+
+template <typename Index, int Mode, int TriStorageOrder>
+struct trsmKernelL<double, Index, Mode, false, TriStorageOrder, 1, true> {
+  static void kernel(Index size, Index otherSize, const double *_tri, Index triStride, double *_other, Index otherIncr,
+                     Index otherStride);
+};
+
+template <typename Index, int Mode, int TriStorageOrder>
+EIGEN_DONT_INLINE void trsmKernelL<float, Index, Mode, false, TriStorageOrder, 1, true>::kernel(
+    Index size, Index otherSize, const float *_tri, Index triStride, float *_other, Index otherIncr,
+    Index otherStride) {
+  EIGEN_UNUSED_VARIABLE(otherIncr);
+#ifdef EIGEN_NO_RUNTIME_MALLOC
+  if (!is_malloc_allowed()) {
+    trsmKernelL<float, Index, Mode, false, TriStorageOrder, 1, /*Specialized=*/false>::kernel(
+          size, otherSize, _tri, triStride, _other, otherIncr, otherStride);
+    return;
+  }
+#endif
+  triSolve<float, TriStorageOrder == RowMajor, false, (Mode & Lower) == Lower, (Mode & UnitDiag) != 0>(
+      const_cast<float *>(_tri), _other, size, otherSize, triStride, otherStride);
+}
+
+template <typename Index, int Mode, int TriStorageOrder>
+EIGEN_DONT_INLINE void trsmKernelL<double, Index, Mode, false, TriStorageOrder, 1, true>::kernel(
+    Index size, Index otherSize, const double *_tri, Index triStride, double *_other, Index otherIncr,
+    Index otherStride) {
+  EIGEN_UNUSED_VARIABLE(otherIncr);
+#ifdef EIGEN_NO_RUNTIME_MALLOC
+  if (!is_malloc_allowed()) {
+    trsmKernelL<double, Index, Mode, false, TriStorageOrder, 1, /*Specialized=*/false>::kernel(
+          size, otherSize, _tri, triStride, _other, otherIncr, otherStride);
+    return;
+  }
+#endif
+  triSolve<double, TriStorageOrder == RowMajor, false, (Mode & Lower) == Lower, (Mode & UnitDiag) != 0>(
+      const_cast<double *>(_tri), _other, size, otherSize, triStride, otherStride);
+}
+#endif  // EIGEN_USE_AVX512_TRSM_L_KERNELS
+#endif  // EIGEN_USE_AVX512_TRSM_KERNELS
+}  // namespace internal
+}  // namespace Eigen
+#endif  // EIGEN_CORE_ARCH_AVX512_TRSM_KERNEL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/TrsmUnrolls.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/TrsmUnrolls.inc
new file mode 100644
index 0000000000000000000000000000000000000000..4c6116c692243d23966b863558711f657d8ddba0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/TrsmUnrolls.inc
@@ -0,0 +1,1218 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2022 Intel Corporation
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CORE_ARCH_AVX512_TRSM_UNROLLS_H
+#define EIGEN_CORE_ARCH_AVX512_TRSM_UNROLLS_H
+
+template <bool isARowMajor = true>
+EIGEN_ALWAYS_INLINE int64_t idA(int64_t i, int64_t j, int64_t LDA) {
+  EIGEN_IF_CONSTEXPR(isARowMajor) return i * LDA + j;
+  else return i + j * LDA;
+}
+
+/**
+ * This namespace contains various classes used to generate compile-time unrolls which are
+ * used throughout the trsm/gemm kernels. The unrolls are characterized as for-loops (1-D), nested
+ * for-loops (2-D), or triple nested for-loops (3-D). Unrolls are generated using template recursion
+ *
+ * Example, the 2-D for-loop is unrolled recursively by first flattening to a 1-D loop.
+ *
+ * for(startI = 0; startI < endI; startI++)             for(startC = 0; startC < endI*endJ; startC++)
+ *   for(startJ = 0; startJ < endJ; startJ++)  ---->      startI = (startC)/(endJ)
+ *     func(startI,startJ)                                startJ = (startC)%(endJ)
+ *                                                        func(...)
+ *
+ * The 1-D loop can be unrolled recursively by using enable_if and defining an auxillary function
+ * with a template parameter used as a counter.
+ *
+ * template <endI, endJ, counter>
+ * std::enable_if_t<(counter <= 0)>  <---- tail case.
+ * aux_func {}
+ *
+ * template <endI, endJ, counter>
+ * std::enable_if_t<(counter > 0)>   <---- actual for-loop
+ * aux_func {
+ *   startC = endI*endJ - counter
+ *   startI = (startC)/(endJ)
+ *   startJ = (startC)%(endJ)
+ *   func(startI, startJ)
+ *   aux_func<endI, endJ, counter-1>()
+ * }
+ *
+ * Note: Additional wrapper functions are provided for aux_func which hides the counter template
+ * parameter since counter usually depends on endI, endJ, etc...
+ *
+ * Conventions:
+ * 1) endX: specifies the terminal value for the for-loop, (ex: for(startX = 0; startX < endX; startX++))
+ *
+ * 2) rem, remM, remK template parameters are used for deciding whether to use masked operations for
+ *    handling remaining tails (when sizes are not multiples of PacketSize or EIGEN_AVX_MAX_NUM_ROW)
+ */
+namespace unrolls {
+
+template <int64_t N>
+EIGEN_ALWAYS_INLINE auto remMask(int64_t m) {
+  EIGEN_IF_CONSTEXPR(N == 16) { return 0xFFFF >> (16 - m); }
+  else EIGEN_IF_CONSTEXPR(N == 8) {
+    return 0xFF >> (8 - m);
+  }
+  else EIGEN_IF_CONSTEXPR(N == 4) {
+    return 0x0F >> (4 - m);
+  }
+  return 0;
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE void trans8x8blocks(PacketBlock<Packet, 8> &kernel);
+
+template <>
+EIGEN_ALWAYS_INLINE void trans8x8blocks(PacketBlock<Packet16f, 8> &kernel) {
+  __m512 T0 = _mm512_unpacklo_ps(kernel.packet[0], kernel.packet[1]);
+  __m512 T1 = _mm512_unpackhi_ps(kernel.packet[0], kernel.packet[1]);
+  __m512 T2 = _mm512_unpacklo_ps(kernel.packet[2], kernel.packet[3]);
+  __m512 T3 = _mm512_unpackhi_ps(kernel.packet[2], kernel.packet[3]);
+  __m512 T4 = _mm512_unpacklo_ps(kernel.packet[4], kernel.packet[5]);
+  __m512 T5 = _mm512_unpackhi_ps(kernel.packet[4], kernel.packet[5]);
+  __m512 T6 = _mm512_unpacklo_ps(kernel.packet[6], kernel.packet[7]);
+  __m512 T7 = _mm512_unpackhi_ps(kernel.packet[6], kernel.packet[7]);
+
+  kernel.packet[0] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T0), _mm512_castps_pd(T2)));
+  kernel.packet[1] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T0), _mm512_castps_pd(T2)));
+  kernel.packet[2] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T1), _mm512_castps_pd(T3)));
+  kernel.packet[3] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T1), _mm512_castps_pd(T3)));
+  kernel.packet[4] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T4), _mm512_castps_pd(T6)));
+  kernel.packet[5] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T4), _mm512_castps_pd(T6)));
+  kernel.packet[6] = _mm512_castpd_ps(_mm512_unpacklo_pd(_mm512_castps_pd(T5), _mm512_castps_pd(T7)));
+  kernel.packet[7] = _mm512_castpd_ps(_mm512_unpackhi_pd(_mm512_castps_pd(T5), _mm512_castps_pd(T7)));
+
+  T0 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[4]), 0x4E));
+  T0 = _mm512_mask_blend_ps(0xF0F0, kernel.packet[0], T0);
+  T4 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[0]), 0x4E));
+  T4 = _mm512_mask_blend_ps(0xF0F0, T4, kernel.packet[4]);
+  T1 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[5]), 0x4E));
+  T1 = _mm512_mask_blend_ps(0xF0F0, kernel.packet[1], T1);
+  T5 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[1]), 0x4E));
+  T5 = _mm512_mask_blend_ps(0xF0F0, T5, kernel.packet[5]);
+  T2 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[6]), 0x4E));
+  T2 = _mm512_mask_blend_ps(0xF0F0, kernel.packet[2], T2);
+  T6 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[2]), 0x4E));
+  T6 = _mm512_mask_blend_ps(0xF0F0, T6, kernel.packet[6]);
+  T3 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[7]), 0x4E));
+  T3 = _mm512_mask_blend_ps(0xF0F0, kernel.packet[3], T3);
+  T7 = _mm512_castpd_ps(_mm512_permutex_pd(_mm512_castps_pd(kernel.packet[3]), 0x4E));
+  T7 = _mm512_mask_blend_ps(0xF0F0, T7, kernel.packet[7]);
+
+  kernel.packet[0] = T0;
+  kernel.packet[1] = T1;
+  kernel.packet[2] = T2;
+  kernel.packet[3] = T3;
+  kernel.packet[4] = T4;
+  kernel.packet[5] = T5;
+  kernel.packet[6] = T6;
+  kernel.packet[7] = T7;
+}
+
+template <>
+EIGEN_ALWAYS_INLINE void trans8x8blocks(PacketBlock<Packet8d, 8> &kernel) {
+  ptranspose(kernel);
+}
+
+/***
+ * Unrolls for tranposed C stores
+ */
+template <typename Scalar>
+class trans {
+ public:
+  using vec = typename std::conditional<std::is_same<Scalar, float>::value, vecFullFloat, vecFullDouble>::type;
+  using vecHalf = typename std::conditional<std::is_same<Scalar, float>::value, vecHalfFloat, vecFullDouble>::type;
+  static constexpr int64_t PacketSize = packet_traits<Scalar>::size;
+
+  /***********************************
+   * Auxillary Functions for:
+   *  - storeC
+   ***********************************
+   */
+
+  /**
+   * aux_storeC
+   *
+   * 1-D unroll
+   *      for(startN = 0; startN < endN; startN++)
+   *
+   * (endN <= PacketSize) is required to handle the fp32 case, see comments in transStoreC
+   *
+   **/
+  template <int64_t endN, int64_t counter, int64_t unrollN, int64_t packetIndexOffset, bool remM>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0 && endN <= PacketSize)> aux_storeC(
+      Scalar *C_arr, int64_t LDC, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t remM_ = 0) {
+    constexpr int64_t counterReverse = endN - counter;
+    constexpr int64_t startN = counterReverse;
+
+    EIGEN_IF_CONSTEXPR(startN < EIGEN_AVX_MAX_NUM_ROW) {
+      EIGEN_IF_CONSTEXPR(remM) {
+        pstoreu<Scalar>(
+            C_arr + LDC * startN,
+            padd(ploadu<vecHalf>((const Scalar *)C_arr + LDC * startN, remMask<EIGEN_AVX_MAX_NUM_ROW>(remM_)),
+                 preinterpret<vecHalf>(zmm.packet[packetIndexOffset + (unrollN / PacketSize) * startN]),
+                 remMask<EIGEN_AVX_MAX_NUM_ROW>(remM_)),
+            remMask<EIGEN_AVX_MAX_NUM_ROW>(remM_));
+      }
+      else {
+        pstoreu<Scalar>(C_arr + LDC * startN,
+                        padd(ploadu<vecHalf>((const Scalar *)C_arr + LDC * startN),
+                             preinterpret<vecHalf>(zmm.packet[packetIndexOffset + (unrollN / PacketSize) * startN])));
+      }
+    }
+    else {  // This block is only needed for fp32 case
+      // Reinterpret as __m512 for _mm512_shuffle_f32x4
+      vecFullFloat zmm2vecFullFloat = preinterpret<vecFullFloat>(
+          zmm.packet[packetIndexOffset + (unrollN / PacketSize) * (startN - EIGEN_AVX_MAX_NUM_ROW)]);
+      // Swap lower and upper half of avx register.
+      zmm.packet[packetIndexOffset + (unrollN / PacketSize) * (startN - EIGEN_AVX_MAX_NUM_ROW)] =
+          preinterpret<vec>(_mm512_shuffle_f32x4(zmm2vecFullFloat, zmm2vecFullFloat, 0b01001110));
+
+      EIGEN_IF_CONSTEXPR(remM) {
+        pstoreu<Scalar>(
+            C_arr + LDC * startN,
+            padd(ploadu<vecHalf>((const Scalar *)C_arr + LDC * startN, remMask<EIGEN_AVX_MAX_NUM_ROW>(remM_)),
+                 preinterpret<vecHalf>(
+                     zmm.packet[packetIndexOffset + (unrollN / PacketSize) * (startN - EIGEN_AVX_MAX_NUM_ROW)])),
+            remMask<EIGEN_AVX_MAX_NUM_ROW>(remM_));
+      }
+      else {
+        pstoreu<Scalar>(
+            C_arr + LDC * startN,
+            padd(ploadu<vecHalf>((const Scalar *)C_arr + LDC * startN),
+                 preinterpret<vecHalf>(
+                     zmm.packet[packetIndexOffset + (unrollN / PacketSize) * (startN - EIGEN_AVX_MAX_NUM_ROW)])));
+      }
+    }
+    aux_storeC<endN, counter - 1, unrollN, packetIndexOffset, remM>(C_arr, LDC, zmm, remM_);
+  }
+
+  template <int64_t endN, int64_t counter, int64_t unrollN, int64_t packetIndexOffset, bool remM>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<!(counter > 0 && endN <= PacketSize)> aux_storeC(
+      Scalar *C_arr, int64_t LDC, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t remM_ = 0) {
+    EIGEN_UNUSED_VARIABLE(C_arr);
+    EIGEN_UNUSED_VARIABLE(LDC);
+    EIGEN_UNUSED_VARIABLE(zmm);
+    EIGEN_UNUSED_VARIABLE(remM_);
+  }
+
+  template <int64_t endN, int64_t unrollN, int64_t packetIndexOffset, bool remM>
+  static EIGEN_ALWAYS_INLINE void storeC(Scalar *C_arr, int64_t LDC,
+                                         PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+                                         int64_t remM_ = 0) {
+    aux_storeC<endN, endN, unrollN, packetIndexOffset, remM>(C_arr, LDC, zmm, remM_);
+  }
+
+  /**
+   * Transposes LxunrollN row major block of matrices stored EIGEN_AVX_MAX_NUM_ACC zmm registers to
+   * "unrollN"xL ymm registers to be stored col-major into C.
+   *
+   *  For 8x48, the 8x48 block (row-major) is stored in zmm as follows:
+   *
+   *  row0: zmm0 zmm1 zmm2
+   *  row1: zmm3 zmm4 zmm5
+   *    .
+   *    .
+   *  row7: zmm21 zmm22 zmm23
+   *
+   *  For 8x32, the 8x32 block (row-major) is stored in zmm as follows:
+   *
+   *  row0: zmm0 zmm1
+   *  row1: zmm2 zmm3
+   *    .
+   *    .
+   *  row7: zmm14 zmm15
+   *
+   *
+   * In general we will have {1,2,3} groups of avx registers each of size
+   * EIGEN_AVX_MAX_NUM_ROW. packetIndexOffset is used to select which "block" of
+   * avx registers are being transposed.
+   */
+  template <int64_t unrollN, int64_t packetIndexOffset>
+  static EIGEN_ALWAYS_INLINE void transpose(PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm) {
+    // Note: this assumes EIGEN_AVX_MAX_NUM_ROW = 8. Unrolls should be adjusted
+    // accordingly if EIGEN_AVX_MAX_NUM_ROW is smaller.
+    constexpr int64_t zmmStride = unrollN / PacketSize;
+    PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> r;
+    r.packet[0] = zmm.packet[packetIndexOffset + zmmStride * 0];
+    r.packet[1] = zmm.packet[packetIndexOffset + zmmStride * 1];
+    r.packet[2] = zmm.packet[packetIndexOffset + zmmStride * 2];
+    r.packet[3] = zmm.packet[packetIndexOffset + zmmStride * 3];
+    r.packet[4] = zmm.packet[packetIndexOffset + zmmStride * 4];
+    r.packet[5] = zmm.packet[packetIndexOffset + zmmStride * 5];
+    r.packet[6] = zmm.packet[packetIndexOffset + zmmStride * 6];
+    r.packet[7] = zmm.packet[packetIndexOffset + zmmStride * 7];
+    trans8x8blocks(r);
+    zmm.packet[packetIndexOffset + zmmStride * 0] = r.packet[0];
+    zmm.packet[packetIndexOffset + zmmStride * 1] = r.packet[1];
+    zmm.packet[packetIndexOffset + zmmStride * 2] = r.packet[2];
+    zmm.packet[packetIndexOffset + zmmStride * 3] = r.packet[3];
+    zmm.packet[packetIndexOffset + zmmStride * 4] = r.packet[4];
+    zmm.packet[packetIndexOffset + zmmStride * 5] = r.packet[5];
+    zmm.packet[packetIndexOffset + zmmStride * 6] = r.packet[6];
+    zmm.packet[packetIndexOffset + zmmStride * 7] = r.packet[7];
+  }
+};
+
+/**
+ * Unrolls for copyBToRowMajor
+ *
+ * Idea:
+ *  1) Load a block of right-hand sides to registers (using loadB).
+ *  2) Convert the block from column-major to row-major (transposeLxL)
+ *  3) Store the blocks from register either to a temp array (toTemp == true), or back to B (toTemp == false).
+ *
+ *  We use at most EIGEN_AVX_MAX_NUM_ACC avx registers to store the blocks of B. The remaining registers are
+ *  used as temps for transposing.
+ *
+ *  Blocks will be of size Lx{U1,U2,U3}. packetIndexOffset is used to index between these subblocks
+ *  For fp32, PacketSize = 2*EIGEN_AVX_MAX_NUM_ROW, so we reinterpret packets as packets half the size (zmm -> ymm).
+ */
+template <typename Scalar>
+class transB {
+ public:
+  using vec = typename std::conditional<std::is_same<Scalar, float>::value, vecFullFloat, vecFullDouble>::type;
+  using vecHalf = typename std::conditional<std::is_same<Scalar, float>::value, vecHalfFloat, vecFullDouble>::type;
+  static constexpr int64_t PacketSize = packet_traits<Scalar>::size;
+
+  /***********************************
+   * Auxillary Functions for:
+   *  - loadB
+   *  - storeB
+   *  - loadBBlock
+   *  - storeBBlock
+   ***********************************
+   */
+
+  /**
+   * aux_loadB
+   *
+   * 1-D unroll
+   *      for(startN = 0; startN < endN; startN++)
+   **/
+  template <int64_t endN, int64_t counter, int64_t packetIndexOffset, bool remM, int64_t remN_>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_loadB(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm,
+      int64_t remM_ = 0) {
+    constexpr int64_t counterReverse = endN - counter;
+    constexpr int64_t startN = counterReverse;
+
+    EIGEN_IF_CONSTEXPR(remM) {
+      ymm.packet[packetIndexOffset + startN] =
+          ploadu<vecHalf>((const Scalar *)&B_arr[startN * LDB], remMask<EIGEN_AVX_MAX_NUM_ROW>(remM_));
+    }
+    else {
+      EIGEN_IF_CONSTEXPR(remN_ == 0) {
+        ymm.packet[packetIndexOffset + startN] = ploadu<vecHalf>((const Scalar *)&B_arr[startN * LDB]);
+      }
+      else ymm.packet[packetIndexOffset + startN] =
+          ploadu<vecHalf>((const Scalar *)&B_arr[startN * LDB], remMask<EIGEN_AVX_MAX_NUM_ROW>(remN_));
+    }
+
+    aux_loadB<endN, counter - 1, packetIndexOffset, remM, remN_>(B_arr, LDB, ymm, remM_);
+  }
+
+  template <int64_t endN, int64_t counter, int64_t packetIndexOffset, bool remM, int64_t remN_>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_loadB(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm,
+      int64_t remM_ = 0) {
+    EIGEN_UNUSED_VARIABLE(B_arr);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(ymm);
+    EIGEN_UNUSED_VARIABLE(remM_);
+  }
+
+  /**
+   * aux_storeB
+   *
+   * 1-D unroll
+   *      for(startN = 0; startN < endN; startN++)
+   **/
+  template <int64_t endN, int64_t counter, int64_t packetIndexOffset, bool remK, bool remM>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_storeB(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm, int64_t rem_ = 0) {
+    constexpr int64_t counterReverse = endN - counter;
+    constexpr int64_t startN = counterReverse;
+
+    EIGEN_IF_CONSTEXPR(remK || remM) {
+      pstoreu<Scalar>(&B_arr[startN * LDB], ymm.packet[packetIndexOffset + startN],
+                      remMask<EIGEN_AVX_MAX_NUM_ROW>(rem_));
+    }
+    else {
+      pstoreu<Scalar>(&B_arr[startN * LDB], ymm.packet[packetIndexOffset + startN]);
+    }
+
+    aux_storeB<endN, counter - 1, packetIndexOffset, remK, remM>(B_arr, LDB, ymm, rem_);
+  }
+
+  template <int64_t endN, int64_t counter, int64_t packetIndexOffset, bool remK, bool remM>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_storeB(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(B_arr);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(ymm);
+    EIGEN_UNUSED_VARIABLE(rem_);
+  }
+
+  /**
+   * aux_loadBBlock
+   *
+   * 1-D unroll
+   *      for(startN = 0; startN < endN; startN += EIGEN_AVX_MAX_NUM_ROW)
+   **/
+  template <int64_t endN, int64_t counter, bool toTemp, bool remM, int64_t remN_>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_loadBBlock(
+      Scalar *B_arr, int64_t LDB, Scalar *B_temp, int64_t LDB_,
+      PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm, int64_t remM_ = 0) {
+    constexpr int64_t counterReverse = endN - counter;
+    constexpr int64_t startN = counterReverse;
+    transB::template loadB<EIGEN_AVX_MAX_NUM_ROW, startN, false, (toTemp ? 0 : remN_)>(&B_temp[startN], LDB_, ymm);
+    aux_loadBBlock<endN, counter - EIGEN_AVX_MAX_NUM_ROW, toTemp, remM, remN_>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+  }
+
+  template <int64_t endN, int64_t counter, bool toTemp, bool remM, int64_t remN_>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_loadBBlock(
+      Scalar *B_arr, int64_t LDB, Scalar *B_temp, int64_t LDB_,
+      PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm, int64_t remM_ = 0) {
+    EIGEN_UNUSED_VARIABLE(B_arr);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(B_temp);
+    EIGEN_UNUSED_VARIABLE(LDB_);
+    EIGEN_UNUSED_VARIABLE(ymm);
+    EIGEN_UNUSED_VARIABLE(remM_);
+  }
+
+  /**
+   * aux_storeBBlock
+   *
+   * 1-D unroll
+   *      for(startN = 0; startN < endN; startN += EIGEN_AVX_MAX_NUM_ROW)
+   **/
+  template <int64_t endN, int64_t counter, bool toTemp, bool remM, int64_t remK_>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_storeBBlock(
+      Scalar *B_arr, int64_t LDB, Scalar *B_temp, int64_t LDB_,
+      PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm, int64_t remM_ = 0) {
+    constexpr int64_t counterReverse = endN - counter;
+    constexpr int64_t startN = counterReverse;
+
+    EIGEN_IF_CONSTEXPR(toTemp) {
+      transB::template storeB<EIGEN_AVX_MAX_NUM_ROW, startN, remK_ != 0, false>(&B_temp[startN], LDB_, ymm, remK_);
+    }
+    else {
+      transB::template storeB<std::min(EIGEN_AVX_MAX_NUM_ROW, endN), startN, false, remM>(&B_arr[0 + startN * LDB], LDB,
+                                                                                          ymm, remM_);
+    }
+    aux_storeBBlock<endN, counter - EIGEN_AVX_MAX_NUM_ROW, toTemp, remM, remK_>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+  }
+
+  template <int64_t endN, int64_t counter, bool toTemp, bool remM, int64_t remK_>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_storeBBlock(
+      Scalar *B_arr, int64_t LDB, Scalar *B_temp, int64_t LDB_,
+      PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm, int64_t remM_ = 0) {
+    EIGEN_UNUSED_VARIABLE(B_arr);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(B_temp);
+    EIGEN_UNUSED_VARIABLE(LDB_);
+    EIGEN_UNUSED_VARIABLE(ymm);
+    EIGEN_UNUSED_VARIABLE(remM_);
+  }
+
+  /********************************************************
+   * Wrappers for aux_XXXX to hide counter parameter
+   ********************************************************/
+
+  template <int64_t endN, int64_t packetIndexOffset, bool remM, int64_t remN_>
+  static EIGEN_ALWAYS_INLINE void loadB(Scalar *B_arr, int64_t LDB,
+                                        PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm,
+                                        int64_t remM_ = 0) {
+    aux_loadB<endN, endN, packetIndexOffset, remM, remN_>(B_arr, LDB, ymm, remM_);
+  }
+
+  template <int64_t endN, int64_t packetIndexOffset, bool remK, bool remM>
+  static EIGEN_ALWAYS_INLINE void storeB(Scalar *B_arr, int64_t LDB,
+                                         PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm,
+                                         int64_t rem_ = 0) {
+    aux_storeB<endN, endN, packetIndexOffset, remK, remM>(B_arr, LDB, ymm, rem_);
+  }
+
+  template <int64_t unrollN, bool toTemp, bool remM, int64_t remN_ = 0>
+  static EIGEN_ALWAYS_INLINE void loadBBlock(Scalar *B_arr, int64_t LDB, Scalar *B_temp, int64_t LDB_,
+                                             PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm,
+                                             int64_t remM_ = 0) {
+    EIGEN_IF_CONSTEXPR(toTemp) { transB::template loadB<unrollN, 0, remM, 0>(&B_arr[0], LDB, ymm, remM_); }
+    else {
+      aux_loadBBlock<unrollN, unrollN, toTemp, remM, remN_>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+    }
+  }
+
+  template <int64_t unrollN, bool toTemp, bool remM, int64_t remK_>
+  static EIGEN_ALWAYS_INLINE void storeBBlock(Scalar *B_arr, int64_t LDB, Scalar *B_temp, int64_t LDB_,
+                                              PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm,
+                                              int64_t remM_ = 0) {
+    aux_storeBBlock<unrollN, unrollN, toTemp, remM, remK_>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+  }
+
+  template <int64_t packetIndexOffset>
+  static EIGEN_ALWAYS_INLINE void transposeLxL(PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm) {
+    // Note: this assumes EIGEN_AVX_MAX_NUM_ROW = 8. Unrolls should be adjusted
+    // accordingly if EIGEN_AVX_MAX_NUM_ROW is smaller.
+    PacketBlock<vecHalf, EIGEN_AVX_MAX_NUM_ROW> r;
+    r.packet[0] = ymm.packet[packetIndexOffset + 0];
+    r.packet[1] = ymm.packet[packetIndexOffset + 1];
+    r.packet[2] = ymm.packet[packetIndexOffset + 2];
+    r.packet[3] = ymm.packet[packetIndexOffset + 3];
+    r.packet[4] = ymm.packet[packetIndexOffset + 4];
+    r.packet[5] = ymm.packet[packetIndexOffset + 5];
+    r.packet[6] = ymm.packet[packetIndexOffset + 6];
+    r.packet[7] = ymm.packet[packetIndexOffset + 7];
+    ptranspose(r);
+    ymm.packet[packetIndexOffset + 0] = r.packet[0];
+    ymm.packet[packetIndexOffset + 1] = r.packet[1];
+    ymm.packet[packetIndexOffset + 2] = r.packet[2];
+    ymm.packet[packetIndexOffset + 3] = r.packet[3];
+    ymm.packet[packetIndexOffset + 4] = r.packet[4];
+    ymm.packet[packetIndexOffset + 5] = r.packet[5];
+    ymm.packet[packetIndexOffset + 6] = r.packet[6];
+    ymm.packet[packetIndexOffset + 7] = r.packet[7];
+  }
+
+  template <int64_t unrollN, bool toTemp, bool remM>
+  static EIGEN_ALWAYS_INLINE void transB_kernel(Scalar *B_arr, int64_t LDB, Scalar *B_temp, int64_t LDB_,
+                                                PacketBlock<vecHalf, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &ymm,
+                                                int64_t remM_ = 0) {
+    constexpr int64_t U3 = PacketSize * 3;
+    constexpr int64_t U2 = PacketSize * 2;
+    constexpr int64_t U1 = PacketSize * 1;
+    /**
+     *  Unrolls needed for each case:
+     *   - AVX512 fp32 48 32 16 8 4 2 1
+     *   - AVX512 fp64 24 16 8  4 2 1
+     *
+     *  For fp32 L and U1 are 1:2 so for U3/U2 cases the loads/stores need to be split up.
+     */
+    EIGEN_IF_CONSTEXPR(unrollN == U3) {
+      // load LxU3 B col major, transpose LxU3 row major
+      constexpr int64_t maxUBlock = std::min(3 * EIGEN_AVX_MAX_NUM_ROW, U3);
+      transB::template loadBBlock<maxUBlock, toTemp, remM>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+      transB::template transposeLxL<0 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+      transB::template transposeLxL<1 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+      transB::template transposeLxL<2 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+      transB::template storeBBlock<maxUBlock, toTemp, remM, 0>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+
+      EIGEN_IF_CONSTEXPR(maxUBlock < U3) {
+        transB::template loadBBlock<maxUBlock, toTemp, remM>(&B_arr[maxUBlock * LDB], LDB, &B_temp[maxUBlock], LDB_,
+                                                             ymm, remM_);
+        transB::template transposeLxL<0 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+        transB::template transposeLxL<1 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+        transB::template transposeLxL<2 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+        transB::template storeBBlock<maxUBlock, toTemp, remM, 0>(&B_arr[maxUBlock * LDB], LDB, &B_temp[maxUBlock], LDB_,
+                                                                 ymm, remM_);
+      }
+    }
+    else EIGEN_IF_CONSTEXPR(unrollN == U2) {
+      // load LxU2 B col major, transpose LxU2 row major
+      constexpr int64_t maxUBlock = std::min(3 * EIGEN_AVX_MAX_NUM_ROW, U2);
+      transB::template loadBBlock<maxUBlock, toTemp, remM>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+      transB::template transposeLxL<0 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+      transB::template transposeLxL<1 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+      EIGEN_IF_CONSTEXPR(maxUBlock < U2) transB::template transposeLxL<2 * EIGEN_AVX_MAX_NUM_ROW>(ymm);
+      transB::template storeBBlock<maxUBlock, toTemp, remM, 0>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+
+      EIGEN_IF_CONSTEXPR(maxUBlock < U2) {
+        transB::template loadBBlock<EIGEN_AVX_MAX_NUM_ROW, toTemp, remM>(&B_arr[maxUBlock * LDB], LDB,
+                                                                         &B_temp[maxUBlock], LDB_, ymm, remM_);
+        transB::template transposeLxL<0>(ymm);
+        transB::template storeBBlock<EIGEN_AVX_MAX_NUM_ROW, toTemp, remM, 0>(&B_arr[maxUBlock * LDB], LDB,
+                                                                             &B_temp[maxUBlock], LDB_, ymm, remM_);
+      }
+    }
+    else EIGEN_IF_CONSTEXPR(unrollN == U1) {
+      // load LxU1 B col major, transpose LxU1 row major
+      transB::template loadBBlock<U1, toTemp, remM>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+      transB::template transposeLxL<0>(ymm);
+      EIGEN_IF_CONSTEXPR(EIGEN_AVX_MAX_NUM_ROW < U1) { transB::template transposeLxL<1 * EIGEN_AVX_MAX_NUM_ROW>(ymm); }
+      transB::template storeBBlock<U1, toTemp, remM, 0>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+    }
+    else EIGEN_IF_CONSTEXPR(unrollN == 8 && U1 > 8) {
+      // load Lx4 B col major, transpose Lx4 row major
+      transB::template loadBBlock<8, toTemp, remM>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+      transB::template transposeLxL<0>(ymm);
+      transB::template storeBBlock<8, toTemp, remM, 8>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+    }
+    else EIGEN_IF_CONSTEXPR(unrollN == 4 && U1 > 4) {
+      // load Lx4 B col major, transpose Lx4 row major
+      transB::template loadBBlock<4, toTemp, remM>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+      transB::template transposeLxL<0>(ymm);
+      transB::template storeBBlock<4, toTemp, remM, 4>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+    }
+    else EIGEN_IF_CONSTEXPR(unrollN == 2) {
+      // load Lx2 B col major, transpose Lx2 row major
+      transB::template loadBBlock<2, toTemp, remM, 2>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+      transB::template transposeLxL<0>(ymm);
+      transB::template storeBBlock<2, toTemp, remM, 2>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+    }
+    else EIGEN_IF_CONSTEXPR(unrollN == 1) {
+      // load Lx1 B col major, transpose Lx1 row major
+      transB::template loadBBlock<1, toTemp, remM, 1>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+      transB::template transposeLxL<0>(ymm);
+      transB::template storeBBlock<1, toTemp, remM, 1>(B_arr, LDB, B_temp, LDB_, ymm, remM_);
+    }
+  }
+};
+
+/**
+ * Unrolls for triSolveKernel
+ *
+ * Idea:
+ *  1) Load a block of right-hand sides to registers in RHSInPacket (using loadRHS).
+ *  2) Do triangular solve with RHSInPacket and a small block of A (triangular matrix)
+ *     stored in AInPacket (using triSolveMicroKernel).
+ *  3) Store final results (in avx registers) back into memory (using storeRHS).
+ *
+ *  RHSInPacket uses at most EIGEN_AVX_MAX_NUM_ACC avx registers and AInPacket uses at most
+ *  EIGEN_AVX_MAX_NUM_ROW registers.
+ */
+template <typename Scalar>
+class trsm {
+ public:
+  using vec = typename std::conditional<std::is_same<Scalar, float>::value, vecFullFloat, vecFullDouble>::type;
+  static constexpr int64_t PacketSize = packet_traits<Scalar>::size;
+
+  /***********************************
+   * Auxillary Functions for:
+   *  - loadRHS
+   *  - storeRHS
+   *  - divRHSByDiag
+   *  - updateRHS
+   *  - triSolveMicroKernel
+   ************************************/
+  /**
+   * aux_loadRHS
+   *
+   * 2-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   *        for(startK = 0; startK < endK; startK++)
+   **/
+  template <bool isFWDSolve, int64_t endM, int64_t endK, int64_t counter, bool krem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_loadRHS(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, int64_t rem = 0) {
+    constexpr int64_t counterReverse = endM * endK - counter;
+    constexpr int64_t startM = counterReverse / (endK);
+    constexpr int64_t startK = counterReverse % endK;
+
+    constexpr int64_t packetIndex = startM * endK + startK;
+    constexpr int64_t startM_ = isFWDSolve ? startM : -startM;
+    const int64_t rhsIndex = (startK * PacketSize) + startM_ * LDB;
+    EIGEN_IF_CONSTEXPR(krem) {
+      RHSInPacket.packet[packetIndex] = ploadu<vec>(&B_arr[rhsIndex], remMask<PacketSize>(rem));
+    }
+    else {
+      RHSInPacket.packet[packetIndex] = ploadu<vec>(&B_arr[rhsIndex]);
+    }
+    aux_loadRHS<isFWDSolve, endM, endK, counter - 1, krem>(B_arr, LDB, RHSInPacket, rem);
+  }
+
+  template <bool isFWDSolve, int64_t endM, int64_t endK, int64_t counter, bool krem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_loadRHS(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, int64_t rem = 0) {
+    EIGEN_UNUSED_VARIABLE(B_arr);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(RHSInPacket);
+    EIGEN_UNUSED_VARIABLE(rem);
+  }
+
+  /**
+   * aux_storeRHS
+   *
+   * 2-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   *        for(startK = 0; startK < endK; startK++)
+   **/
+  template <bool isFWDSolve, int64_t endM, int64_t endK, int64_t counter, bool krem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_storeRHS(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, int64_t rem = 0) {
+    constexpr int64_t counterReverse = endM * endK - counter;
+    constexpr int64_t startM = counterReverse / (endK);
+    constexpr int64_t startK = counterReverse % endK;
+
+    constexpr int64_t packetIndex = startM * endK + startK;
+    constexpr int64_t startM_ = isFWDSolve ? startM : -startM;
+    const int64_t rhsIndex = (startK * PacketSize) + startM_ * LDB;
+    EIGEN_IF_CONSTEXPR(krem) {
+      pstoreu<Scalar>(&B_arr[rhsIndex], RHSInPacket.packet[packetIndex], remMask<PacketSize>(rem));
+    }
+    else {
+      pstoreu<Scalar>(&B_arr[rhsIndex], RHSInPacket.packet[packetIndex]);
+    }
+    aux_storeRHS<isFWDSolve, endM, endK, counter - 1, krem>(B_arr, LDB, RHSInPacket, rem);
+  }
+
+  template <bool isFWDSolve, int64_t endM, int64_t endK, int64_t counter, bool krem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_storeRHS(
+      Scalar *B_arr, int64_t LDB, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, int64_t rem = 0) {
+    EIGEN_UNUSED_VARIABLE(B_arr);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(RHSInPacket);
+    EIGEN_UNUSED_VARIABLE(rem);
+  }
+
+  /**
+   * aux_divRHSByDiag
+   *
+   * currM may be -1, (currM >=0) in enable_if checks for this
+   *
+   * 1-D unroll
+   *      for(startK = 0; startK < endK; startK++)
+   **/
+  template <int64_t currM, int64_t endK, int64_t counter>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0 && currM >= 0)> aux_divRHSByDiag(
+      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    constexpr int64_t counterReverse = endK - counter;
+    constexpr int64_t startK = counterReverse;
+
+    constexpr int64_t packetIndex = currM * endK + startK;
+    RHSInPacket.packet[packetIndex] = pmul(AInPacket.packet[currM], RHSInPacket.packet[packetIndex]);
+    aux_divRHSByDiag<currM, endK, counter - 1>(RHSInPacket, AInPacket);
+  }
+
+  template <int64_t currM, int64_t endK, int64_t counter>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<!(counter > 0 && currM >= 0)> aux_divRHSByDiag(
+      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    EIGEN_UNUSED_VARIABLE(RHSInPacket);
+    EIGEN_UNUSED_VARIABLE(AInPacket);
+  }
+
+  /**
+   * aux_updateRHS
+   *
+   * 2-D unroll
+   *      for(startM = initM; startM < endM; startM++)
+   *        for(startK = 0; startK < endK; startK++)
+   **/
+  template <bool isARowMajor, bool isFWDSolve, bool isUnitDiag, int64_t initM, int64_t endM, int64_t endK,
+            int64_t counter, int64_t currentM>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_updateRHS(
+      Scalar *A_arr, int64_t LDA, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket,
+      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    constexpr int64_t counterReverse = (endM - initM) * endK - counter;
+    constexpr int64_t startM = initM + counterReverse / (endK);
+    constexpr int64_t startK = counterReverse % endK;
+
+    // For each row of A, first update all corresponding RHS
+    constexpr int64_t packetIndex = startM * endK + startK;
+    EIGEN_IF_CONSTEXPR(currentM > 0) {
+      RHSInPacket.packet[packetIndex] =
+          pnmadd(AInPacket.packet[startM], RHSInPacket.packet[(currentM - 1) * endK + startK],
+                 RHSInPacket.packet[packetIndex]);
+    }
+
+    EIGEN_IF_CONSTEXPR(startK == endK - 1) {
+      // Once all RHS for previous row of A is updated, we broadcast the next element in the column A_{i, currentM}.
+      EIGEN_IF_CONSTEXPR(startM == currentM && !isUnitDiag) {
+        // If diagonal is not unit, we broadcast reciprocals of diagonals AinPacket.packet[currentM].
+        // This will be used in divRHSByDiag
+        EIGEN_IF_CONSTEXPR(isFWDSolve)
+        AInPacket.packet[currentM] = pset1<vec>(Scalar(1) / A_arr[idA<isARowMajor>(currentM, currentM, LDA)]);
+        else AInPacket.packet[currentM] = pset1<vec>(Scalar(1) / A_arr[idA<isARowMajor>(-currentM, -currentM, LDA)]);
+      }
+      else {
+        // Broadcast next off diagonal element of A
+        EIGEN_IF_CONSTEXPR(isFWDSolve)
+        AInPacket.packet[startM] = pset1<vec>(A_arr[idA<isARowMajor>(startM, currentM, LDA)]);
+        else AInPacket.packet[startM] = pset1<vec>(A_arr[idA<isARowMajor>(-startM, -currentM, LDA)]);
+      }
+    }
+
+    aux_updateRHS<isARowMajor, isFWDSolve, isUnitDiag, initM, endM, endK, counter - 1, currentM>(
+        A_arr, LDA, RHSInPacket, AInPacket);
+  }
+
+  template <bool isARowMajor, bool isFWDSolve, bool isUnitDiag, int64_t initM, int64_t endM, int64_t endK,
+            int64_t counter, int64_t currentM>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_updateRHS(
+      Scalar *A_arr, int64_t LDA, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket,
+      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    EIGEN_UNUSED_VARIABLE(A_arr);
+    EIGEN_UNUSED_VARIABLE(LDA);
+    EIGEN_UNUSED_VARIABLE(RHSInPacket);
+    EIGEN_UNUSED_VARIABLE(AInPacket);
+  }
+
+  /**
+   * aux_triSolverMicroKernel
+   *
+   * 1-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   **/
+  template <bool isARowMajor, bool isFWDSolve, bool isUnitDiag, int64_t endM, int64_t counter, int64_t numK>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_triSolveMicroKernel(
+      Scalar *A_arr, int64_t LDA, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket,
+      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    constexpr int64_t counterReverse = endM - counter;
+    constexpr int64_t startM = counterReverse;
+
+    constexpr int64_t currentM = startM;
+    // Divides the right-hand side in row startM, by digonal value of A
+    // broadcasted to AInPacket.packet[startM-1] in the previous iteration.
+    //
+    // Without "if constexpr" the compiler instantiates the case <-1, numK>
+    // this is handled with enable_if to prevent out-of-bound warnings
+    // from the compiler
+    EIGEN_IF_CONSTEXPR(!isUnitDiag && startM > 0)
+    trsm::template divRHSByDiag<startM - 1, numK>(RHSInPacket, AInPacket);
+
+    // After division, the rhs corresponding to subsequent rows of A can be partially updated
+    // We also broadcast the reciprocal of the next diagonal to AInPacket.packet[currentM] (if needed)
+    // to be used in the next iteration.
+    trsm::template updateRHS<isARowMajor, isFWDSolve, isUnitDiag, startM, endM, numK, currentM>(A_arr, LDA, RHSInPacket,
+                                                                                                AInPacket);
+
+    // Handle division for the RHS corresponding to the final row of A.
+    EIGEN_IF_CONSTEXPR(!isUnitDiag && startM == endM - 1)
+    trsm::template divRHSByDiag<startM, numK>(RHSInPacket, AInPacket);
+
+    aux_triSolveMicroKernel<isARowMajor, isFWDSolve, isUnitDiag, endM, counter - 1, numK>(A_arr, LDA, RHSInPacket,
+                                                                                          AInPacket);
+  }
+
+  template <bool isARowMajor, bool isFWDSolve, bool isUnitDiag, int64_t endM, int64_t counter, int64_t numK>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_triSolveMicroKernel(
+      Scalar *A_arr, int64_t LDA, PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket,
+      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    EIGEN_UNUSED_VARIABLE(A_arr);
+    EIGEN_UNUSED_VARIABLE(LDA);
+    EIGEN_UNUSED_VARIABLE(RHSInPacket);
+    EIGEN_UNUSED_VARIABLE(AInPacket);
+  }
+
+  /********************************************************
+   * Wrappers for aux_XXXX to hide counter parameter
+   ********************************************************/
+
+  /**
+   * Load endMxendK block of B to RHSInPacket
+   * Masked loads are used for cases where endK is not a multiple of PacketSize
+   */
+  template <bool isFWDSolve, int64_t endM, int64_t endK, bool krem = false>
+  static EIGEN_ALWAYS_INLINE void loadRHS(Scalar *B_arr, int64_t LDB,
+                                          PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, int64_t rem = 0) {
+    aux_loadRHS<isFWDSolve, endM, endK, endM * endK, krem>(B_arr, LDB, RHSInPacket, rem);
+  }
+
+  /**
+   * Load endMxendK block of B to RHSInPacket
+   * Masked loads are used for cases where endK is not a multiple of PacketSize
+   */
+  template <bool isFWDSolve, int64_t endM, int64_t endK, bool krem = false>
+  static EIGEN_ALWAYS_INLINE void storeRHS(Scalar *B_arr, int64_t LDB,
+                                           PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket, int64_t rem = 0) {
+    aux_storeRHS<isFWDSolve, endM, endK, endM * endK, krem>(B_arr, LDB, RHSInPacket, rem);
+  }
+
+  /**
+   * Only used if Triangular matrix has non-unit diagonal values
+   */
+  template <int64_t currM, int64_t endK>
+  static EIGEN_ALWAYS_INLINE void divRHSByDiag(PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket,
+                                               PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    aux_divRHSByDiag<currM, endK, endK>(RHSInPacket, AInPacket);
+  }
+
+  /**
+   * Update right-hand sides (stored in avx registers)
+   * Traversing along the column A_{i,currentM}, where currentM <= i <= endM, and broadcasting each value to AInPacket.
+   **/
+  template <bool isARowMajor, bool isFWDSolve, bool isUnitDiag, int64_t startM, int64_t endM, int64_t endK,
+            int64_t currentM>
+  static EIGEN_ALWAYS_INLINE void updateRHS(Scalar *A_arr, int64_t LDA,
+                                            PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket,
+                                            PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    aux_updateRHS<isARowMajor, isFWDSolve, isUnitDiag, startM, endM, endK, (endM - startM) * endK, currentM>(
+        A_arr, LDA, RHSInPacket, AInPacket);
+  }
+
+  /**
+   * endM: dimension of A. 1 <= endM <= EIGEN_AVX_MAX_NUM_ROW
+   * numK: number of avx registers to use for each row of B (ex fp32: 48 rhs => 3 avx reg used). 1 <= endK <= 3.
+   * isFWDSolve: true => forward substitution, false => backwards substitution
+   * isUnitDiag: true => triangular matrix has unit diagonal.
+   */
+  template <bool isARowMajor, bool isFWDSolve, bool isUnitDiag, int64_t endM, int64_t numK>
+  static EIGEN_ALWAYS_INLINE void triSolveMicroKernel(Scalar *A_arr, int64_t LDA,
+                                                      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ACC> &RHSInPacket,
+                                                      PacketBlock<vec, EIGEN_AVX_MAX_NUM_ROW> &AInPacket) {
+    static_assert(numK >= 1 && numK <= 3, "numK out of range");
+    aux_triSolveMicroKernel<isARowMajor, isFWDSolve, isUnitDiag, endM, endM, numK>(A_arr, LDA, RHSInPacket, AInPacket);
+  }
+};
+
+/**
+ * Unrolls for gemm kernel
+ *
+ * isAdd: true => C += A*B, false => C -= A*B
+ */
+template <typename Scalar, bool isAdd>
+class gemm {
+ public:
+  using vec = typename std::conditional<std::is_same<Scalar, float>::value, vecFullFloat, vecFullDouble>::type;
+  static constexpr int64_t PacketSize = packet_traits<Scalar>::size;
+
+  /***********************************
+   * Auxillary Functions for:
+   *  - setzero
+   *  - updateC
+   *  - storeC
+   *  - startLoadB
+   *  - triSolveMicroKernel
+   ************************************/
+
+  /**
+   * aux_setzero
+   *
+   * 2-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   *        for(startN = 0; startN < endN; startN++)
+   **/
+  template <int64_t endM, int64_t endN, int64_t counter>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_setzero(
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm) {
+    constexpr int64_t counterReverse = endM * endN - counter;
+    constexpr int64_t startM = counterReverse / (endN);
+    constexpr int64_t startN = counterReverse % endN;
+
+    zmm.packet[startN * endM + startM] = pzero(zmm.packet[startN * endM + startM]);
+    aux_setzero<endM, endN, counter - 1>(zmm);
+  }
+
+  template <int64_t endM, int64_t endN, int64_t counter>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_setzero(
+      PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm) {
+    EIGEN_UNUSED_VARIABLE(zmm);
+  }
+
+  /**
+   * aux_updateC
+   *
+   * 2-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   *        for(startN = 0; startN < endN; startN++)
+   **/
+  template <int64_t endM, int64_t endN, int64_t counter, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_updateC(
+      Scalar *C_arr, int64_t LDC, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    constexpr int64_t counterReverse = endM * endN - counter;
+    constexpr int64_t startM = counterReverse / (endN);
+    constexpr int64_t startN = counterReverse % endN;
+
+    EIGEN_IF_CONSTEXPR(rem)
+    zmm.packet[startN * endM + startM] =
+        padd(ploadu<vec>(&C_arr[(startN)*LDC + startM * PacketSize], remMask<PacketSize>(rem_)),
+             zmm.packet[startN * endM + startM], remMask<PacketSize>(rem_));
+    else zmm.packet[startN * endM + startM] =
+        padd(ploadu<vec>(&C_arr[(startN)*LDC + startM * PacketSize]), zmm.packet[startN * endM + startM]);
+    aux_updateC<endM, endN, counter - 1, rem>(C_arr, LDC, zmm, rem_);
+  }
+
+  template <int64_t endM, int64_t endN, int64_t counter, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_updateC(
+      Scalar *C_arr, int64_t LDC, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(C_arr);
+    EIGEN_UNUSED_VARIABLE(LDC);
+    EIGEN_UNUSED_VARIABLE(zmm);
+    EIGEN_UNUSED_VARIABLE(rem_);
+  }
+
+  /**
+   * aux_storeC
+   *
+   * 2-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   *        for(startN = 0; startN < endN; startN++)
+   **/
+  template <int64_t endM, int64_t endN, int64_t counter, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_storeC(
+      Scalar *C_arr, int64_t LDC, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    constexpr int64_t counterReverse = endM * endN - counter;
+    constexpr int64_t startM = counterReverse / (endN);
+    constexpr int64_t startN = counterReverse % endN;
+
+    EIGEN_IF_CONSTEXPR(rem)
+    pstoreu<Scalar>(&C_arr[(startN)*LDC + startM * PacketSize], zmm.packet[startN * endM + startM],
+                    remMask<PacketSize>(rem_));
+    else pstoreu<Scalar>(&C_arr[(startN)*LDC + startM * PacketSize], zmm.packet[startN * endM + startM]);
+    aux_storeC<endM, endN, counter - 1, rem>(C_arr, LDC, zmm, rem_);
+  }
+
+  template <int64_t endM, int64_t endN, int64_t counter, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_storeC(
+      Scalar *C_arr, int64_t LDC, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(C_arr);
+    EIGEN_UNUSED_VARIABLE(LDC);
+    EIGEN_UNUSED_VARIABLE(zmm);
+    EIGEN_UNUSED_VARIABLE(rem_);
+  }
+
+  /**
+   * aux_startLoadB
+   *
+   * 1-D unroll
+   *      for(startL = 0; startL < endL; startL++)
+   **/
+  template <int64_t unrollM, int64_t unrollN, int64_t endL, int64_t counter, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_startLoadB(
+      Scalar *B_t, int64_t LDB, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    constexpr int64_t counterReverse = endL - counter;
+    constexpr int64_t startL = counterReverse;
+
+    EIGEN_IF_CONSTEXPR(rem)
+    zmm.packet[unrollM * unrollN + startL] =
+        ploadu<vec>(&B_t[(startL / unrollM) * LDB + (startL % unrollM) * PacketSize], remMask<PacketSize>(rem_));
+    else zmm.packet[unrollM * unrollN + startL] =
+        ploadu<vec>(&B_t[(startL / unrollM) * LDB + (startL % unrollM) * PacketSize]);
+
+    aux_startLoadB<unrollM, unrollN, endL, counter - 1, rem>(B_t, LDB, zmm, rem_);
+  }
+
+  template <int64_t unrollM, int64_t unrollN, int64_t endL, int64_t counter, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_startLoadB(
+      Scalar *B_t, int64_t LDB, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(B_t);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(zmm);
+    EIGEN_UNUSED_VARIABLE(rem_);
+  }
+
+  /**
+   * aux_startBCastA
+   *
+   * 1-D unroll
+   *      for(startB = 0; startB < endB; startB++)
+   **/
+  template <bool isARowMajor, int64_t unrollM, int64_t unrollN, int64_t endB, int64_t counter, int64_t numLoad>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_startBCastA(
+      Scalar *A_t, int64_t LDA, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm) {
+    constexpr int64_t counterReverse = endB - counter;
+    constexpr int64_t startB = counterReverse;
+
+    zmm.packet[unrollM * unrollN + numLoad + startB] = pload1<vec>(&A_t[idA<isARowMajor>(startB, 0, LDA)]);
+
+    aux_startBCastA<isARowMajor, unrollM, unrollN, endB, counter - 1, numLoad>(A_t, LDA, zmm);
+  }
+
+  template <bool isARowMajor, int64_t unrollM, int64_t unrollN, int64_t endB, int64_t counter, int64_t numLoad>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_startBCastA(
+      Scalar *A_t, int64_t LDA, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm) {
+    EIGEN_UNUSED_VARIABLE(A_t);
+    EIGEN_UNUSED_VARIABLE(LDA);
+    EIGEN_UNUSED_VARIABLE(zmm);
+  }
+
+  /**
+   * aux_loadB
+   * currK: current K
+   *
+   * 1-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   **/
+  template <int64_t endM, int64_t counter, int64_t unrollN, int64_t currK, int64_t unrollK, int64_t numLoad,
+            int64_t numBCast, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_loadB(
+      Scalar *B_t, int64_t LDB, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    if ((numLoad / endM + currK < unrollK)) {
+      constexpr int64_t counterReverse = endM - counter;
+      constexpr int64_t startM = counterReverse;
+
+      EIGEN_IF_CONSTEXPR(rem) {
+        zmm.packet[endM * unrollN + (startM + currK * endM) % numLoad] =
+            ploadu<vec>(&B_t[(numLoad / endM + currK) * LDB + startM * PacketSize], remMask<PacketSize>(rem_));
+      }
+      else {
+        zmm.packet[endM * unrollN + (startM + currK * endM) % numLoad] =
+            ploadu<vec>(&B_t[(numLoad / endM + currK) * LDB + startM * PacketSize]);
+      }
+
+      aux_loadB<endM, counter - 1, unrollN, currK, unrollK, numLoad, numBCast, rem>(B_t, LDB, zmm, rem_);
+    }
+  }
+
+  template <int64_t endM, int64_t counter, int64_t unrollN, int64_t currK, int64_t unrollK, int64_t numLoad,
+            int64_t numBCast, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_loadB(
+      Scalar *B_t, int64_t LDB, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm, int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(B_t);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(zmm);
+    EIGEN_UNUSED_VARIABLE(rem_);
+  }
+
+  /**
+   * aux_microKernel
+   *
+   * 3-D unroll
+   *      for(startM = 0; startM < endM; startM++)
+   *        for(startN = 0; startN < endN; startN++)
+   *          for(startK = 0; startK < endK; startK++)
+   **/
+  template <bool isARowMajor, int64_t endM, int64_t endN, int64_t endK, int64_t counter, int64_t numLoad,
+            int64_t numBCast, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter > 0)> aux_microKernel(
+      Scalar *B_t, Scalar *A_t, int64_t LDB, int64_t LDA, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+      int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    constexpr int64_t counterReverse = endM * endN * endK - counter;
+    constexpr int startK = counterReverse / (endM * endN);
+    constexpr int startN = (counterReverse / (endM)) % endN;
+    constexpr int startM = counterReverse % endM;
+
+    EIGEN_IF_CONSTEXPR(startK == 0 && startM == 0 && startN == 0) {
+      gemm::template startLoadB<endM, endN, numLoad, rem>(B_t, LDB, zmm, rem_);
+      gemm::template startBCastA<isARowMajor, endM, endN, numBCast, numLoad>(A_t, LDA, zmm);
+    }
+
+    {
+      // Interleave FMA and Bcast
+      EIGEN_IF_CONSTEXPR(isAdd) {
+        zmm.packet[startN * endM + startM] =
+            pmadd(zmm.packet[endM * endN + numLoad + (startN + startK * endN) % numBCast],
+                  zmm.packet[endM * endN + (startM + startK * endM) % numLoad], zmm.packet[startN * endM + startM]);
+      }
+      else {
+        zmm.packet[startN * endM + startM] =
+            pnmadd(zmm.packet[endM * endN + numLoad + (startN + startK * endN) % numBCast],
+                   zmm.packet[endM * endN + (startM + startK * endM) % numLoad], zmm.packet[startN * endM + startM]);
+      }
+      // Bcast
+      EIGEN_IF_CONSTEXPR(startM == endM - 1 && (numBCast + startN + startK * endN < endK * endN)) {
+        zmm.packet[endM * endN + numLoad + (startN + startK * endN) % numBCast] = pload1<vec>(&A_t[idA<isARowMajor>(
+            (numBCast + startN + startK * endN) % endN, (numBCast + startN + startK * endN) / endN, LDA)]);
+      }
+    }
+
+    // We have updated all accumlators, time to load next set of B's
+    EIGEN_IF_CONSTEXPR((startN == endN - 1) && (startM == endM - 1)) {
+      gemm::template loadB<endM, endN, startK, endK, numLoad, numBCast, rem>(B_t, LDB, zmm, rem_);
+    }
+    aux_microKernel<isARowMajor, endM, endN, endK, counter - 1, numLoad, numBCast, rem>(B_t, A_t, LDB, LDA, zmm, rem_);
+  }
+
+  template <bool isARowMajor, int64_t endM, int64_t endN, int64_t endK, int64_t counter, int64_t numLoad,
+            int64_t numBCast, bool rem>
+  static EIGEN_ALWAYS_INLINE std::enable_if_t<(counter <= 0)> aux_microKernel(
+      Scalar *B_t, Scalar *A_t, int64_t LDB, int64_t LDA, PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+      int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(B_t);
+    EIGEN_UNUSED_VARIABLE(A_t);
+    EIGEN_UNUSED_VARIABLE(LDB);
+    EIGEN_UNUSED_VARIABLE(LDA);
+    EIGEN_UNUSED_VARIABLE(zmm);
+    EIGEN_UNUSED_VARIABLE(rem_);
+  }
+
+  /********************************************************
+   * Wrappers for aux_XXXX to hide counter parameter
+   ********************************************************/
+
+  template <int64_t endM, int64_t endN>
+  static EIGEN_ALWAYS_INLINE void setzero(PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm) {
+    aux_setzero<endM, endN, endM * endN>(zmm);
+  }
+
+  /**
+   * Ideally the compiler folds these into vaddp{s,d} with an embedded memory load.
+   */
+  template <int64_t endM, int64_t endN, bool rem = false>
+  static EIGEN_ALWAYS_INLINE void updateC(Scalar *C_arr, int64_t LDC,
+                                          PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+                                          int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    aux_updateC<endM, endN, endM * endN, rem>(C_arr, LDC, zmm, rem_);
+  }
+
+  template <int64_t endM, int64_t endN, bool rem = false>
+  static EIGEN_ALWAYS_INLINE void storeC(Scalar *C_arr, int64_t LDC,
+                                         PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+                                         int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    aux_storeC<endM, endN, endM * endN, rem>(C_arr, LDC, zmm, rem_);
+  }
+
+  /**
+   * Use numLoad registers for loading B at start of microKernel
+   */
+  template <int64_t unrollM, int64_t unrollN, int64_t endL, bool rem>
+  static EIGEN_ALWAYS_INLINE void startLoadB(Scalar *B_t, int64_t LDB,
+                                             PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+                                             int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    aux_startLoadB<unrollM, unrollN, endL, endL, rem>(B_t, LDB, zmm, rem_);
+  }
+
+  /**
+   * Use numBCast registers for broadcasting A at start of microKernel
+   */
+  template <bool isARowMajor, int64_t unrollM, int64_t unrollN, int64_t endB, int64_t numLoad>
+  static EIGEN_ALWAYS_INLINE void startBCastA(Scalar *A_t, int64_t LDA,
+                                              PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm) {
+    aux_startBCastA<isARowMajor, unrollM, unrollN, endB, endB, numLoad>(A_t, LDA, zmm);
+  }
+
+  /**
+   * Loads next set of B into vector registers between each K unroll.
+   */
+  template <int64_t endM, int64_t unrollN, int64_t currK, int64_t unrollK, int64_t numLoad, int64_t numBCast, bool rem>
+  static EIGEN_ALWAYS_INLINE void loadB(Scalar *B_t, int64_t LDB,
+                                        PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+                                        int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    aux_loadB<endM, endM, unrollN, currK, unrollK, numLoad, numBCast, rem>(B_t, LDB, zmm, rem_);
+  }
+
+  /**
+   * Generates a microkernel for gemm (row-major) with unrolls {1,2,4,8}x{U1,U2,U3} to compute C -= A*B.
+   * A matrix can be row/col-major. B matrix is assumed row-major.
+   *
+   * isARowMajor: is A row major
+   * endM: Number registers per row
+   * endN: Number of rows
+   * endK: Loop unroll for K.
+   * numLoad: Number of registers for loading B.
+   * numBCast: Number of registers for broadcasting A.
+   *
+   * Ex: microkernel<isARowMajor,0,3,0,4,0,4,6,2>: 8x48 unroll (24 accumulators), k unrolled 4 times,
+   * 6 register for loading B, 2 for broadcasting A.
+   *
+   * Note: Ideally the microkernel should not have any register spilling.
+   * The avx instruction counts should be:
+   *   - endK*endN vbroadcasts{s,d}
+   *   - endK*endM vmovup{s,d}
+   *   - endK*endN*endM FMAs
+   *
+   * From testing, there are no register spills with clang. There are register spills with GNU, which
+   * causes a performance hit.
+   */
+  template <bool isARowMajor, int64_t endM, int64_t endN, int64_t endK, int64_t numLoad, int64_t numBCast,
+            bool rem = false>
+  static EIGEN_ALWAYS_INLINE void microKernel(Scalar *B_t, Scalar *A_t, int64_t LDB, int64_t LDA,
+                                              PacketBlock<vec, EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS> &zmm,
+                                              int64_t rem_ = 0) {
+    EIGEN_UNUSED_VARIABLE(rem_);
+    aux_microKernel<isARowMajor, endM, endN, endK, endM * endN * endK, numLoad, numBCast, rem>(B_t, A_t, LDB, LDA, zmm,
+                                                                                               rem_);
+  }
+};
+}  // namespace unrolls
+
+#endif  // EIGEN_CORE_ARCH_AVX512_TRSM_UNROLLS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/TypeCasting.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/TypeCasting.h
new file mode 100644
index 0000000000000000000000000000000000000000..02c56282f3593b0373f5965fd9577334bdc156f4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AVX512/TypeCasting.h
@@ -0,0 +1,232 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2019 Rasmus Munk Larsen <rmlarsen@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_AVX512_H
+#define EIGEN_TYPE_CASTING_AVX512_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<> struct type_casting_traits<float, bool> : vectorized_type_casting_traits<float, bool> {};
+template<> struct type_casting_traits<bool, float> : vectorized_type_casting_traits<bool, float> {};
+
+template<> struct type_casting_traits<float, int> : vectorized_type_casting_traits<float, int> {};
+template<> struct type_casting_traits<int, float> : vectorized_type_casting_traits<int, float> {};
+
+template<> struct type_casting_traits<float, double> : vectorized_type_casting_traits<float, double> {};
+template<> struct type_casting_traits<double, float> : vectorized_type_casting_traits<double, float> {};
+
+template<> struct type_casting_traits<double, int> : vectorized_type_casting_traits<double, int> {};
+template<> struct type_casting_traits<int, double> : vectorized_type_casting_traits<int, double> {};
+
+template<> struct type_casting_traits<half, float> : vectorized_type_casting_traits<half, float> {};
+template<> struct type_casting_traits<float, half> : vectorized_type_casting_traits<float, half> {};
+
+template<> struct type_casting_traits<bfloat16, float> : vectorized_type_casting_traits<bfloat16, float> {};
+template<> struct type_casting_traits<float, bfloat16> : vectorized_type_casting_traits<float, bfloat16> {};
+
+template<> EIGEN_STRONG_INLINE Packet16b pcast<Packet16f, Packet16b>(const Packet16f& a) {
+  __mmask16 mask = _mm512_cmpneq_ps_mask(a, pzero(a));
+  return _mm512_maskz_cvtepi32_epi8(mask, _mm512_set1_epi32(1));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f pcast<Packet16b, Packet16f>(const Packet16b& a) {
+  return _mm512_cvtepi32_ps(_mm512_and_si512(_mm512_cvtepi8_epi32(a), _mm512_set1_epi32(1)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16i pcast<Packet16f, Packet16i>(const Packet16f& a) {
+  return _mm512_cvttps_epi32(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8d pcast<Packet16f, Packet8d>(const Packet16f& a) {
+  return _mm512_cvtps_pd(_mm512_castps512_ps256(a));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8d pcast<Packet8f, Packet8d>(const Packet8f& a) {
+  return _mm512_cvtps_pd(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f pcast<Packet16i, Packet16f>(const Packet16i& a) {
+  return _mm512_cvtepi32_ps(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8d pcast<Packet16i, Packet8d>(const Packet16i& a) {
+  return _mm512_cvtepi32_pd(_mm512_castsi512_si256(a));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8d pcast<Packet8i, Packet8d>(const Packet8i& a) {
+  return _mm512_cvtepi32_pd(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f pcast<Packet8d, Packet16f>(const Packet8d& a, const Packet8d& b) {
+  return  cat256(_mm512_cvtpd_ps(a), _mm512_cvtpd_ps(b));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16i pcast<Packet8d, Packet16i>(const Packet8d& a, const Packet8d& b) {
+  return  cat256i(_mm512_cvttpd_epi32(a), _mm512_cvttpd_epi32(b));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8i pcast<Packet8d, Packet8i>(const Packet8d& a) {
+  return _mm512_cvtpd_epi32(a);
+}
+template<> EIGEN_STRONG_INLINE Packet8f pcast<Packet8d, Packet8f>(const Packet8d& a) {
+  return _mm512_cvtpd_ps(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16i preinterpret<Packet16i, Packet16f>(const Packet16f& a) {
+  return _mm512_castps_si512(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f preinterpret<Packet16f, Packet16i>(const Packet16i& a) {
+  return _mm512_castsi512_ps(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8d preinterpret<Packet8d, Packet16f>(const Packet16f& a) {
+  return _mm512_castps_pd(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f preinterpret<Packet16f, Packet8d>(const Packet8d& a) {
+  return _mm512_castpd_ps(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8f preinterpret<Packet8f, Packet16f>(const Packet16f& a) {
+  return _mm512_castps512_ps256(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet16f>(const Packet16f& a) {
+  return _mm512_castps512_ps128(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4d preinterpret<Packet4d, Packet8d>(const Packet8d& a) {
+  return _mm512_castpd512_pd256(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet8d>(const Packet8d& a) {
+  return _mm512_castpd512_pd128(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f preinterpret<Packet16f, Packet8f>(const Packet8f& a) {
+  return _mm512_castps256_ps512(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16f preinterpret<Packet16f, Packet4f>(const Packet4f& a) {
+  return _mm512_castps128_ps512(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8d preinterpret<Packet8d, Packet4d>(const Packet4d& a) {
+  return _mm512_castpd256_pd512(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8d preinterpret<Packet8d, Packet2d>(const Packet2d& a) {
+  return _mm512_castpd128_pd512(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8i preinterpret<Packet8i, Packet16i>(const Packet16i& a) {
+  return _mm512_castsi512_si256(a);
+}
+template<> EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet16i>(const Packet16i& a) {
+  return _mm512_castsi512_si128(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8h preinterpret<Packet8h, Packet16h>(const Packet16h& a) {
+  return _mm256_castsi256_si128(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf preinterpret<Packet8bf, Packet16bf>(const Packet16bf& a) {
+  return _mm256_castsi256_si128(a);
+}
+
+#ifndef EIGEN_VECTORIZE_AVX512FP16
+
+template<> EIGEN_STRONG_INLINE Packet16f pcast<Packet16h, Packet16f>(const Packet16h& a) {
+  return half2float(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16h pcast<Packet16f, Packet16h>(const Packet16f& a) {
+  return float2half(a);
+}
+
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet16f pcast<Packet16bf, Packet16f>(const Packet16bf& a) {
+  return Bf16ToF32(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16bf pcast<Packet16f, Packet16bf>(const Packet16f& a) {
+  return F32ToBf16(a);
+}
+
+#ifdef EIGEN_VECTORIZE_AVX512FP16
+
+template<> EIGEN_STRONG_INLINE Packet16h preinterpret<Packet16h, Packet32h>(const Packet32h& a) {
+  return _mm256_castpd_si256(_mm512_extractf64x4_pd(_mm512_castph_pd(a), 0));
+}
+template<> EIGEN_STRONG_INLINE Packet8h preinterpret<Packet8h, Packet32h>(const Packet32h& a) {
+  return _mm256_castsi256_si128(preinterpret<Packet16h>(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet16f pcast<Packet32h, Packet16f>(const Packet32h& a) {
+  // Discard second-half of input.
+  Packet16h low = _mm256_castpd_si256(_mm512_extractf64x4_pd(_mm512_castph_pd(a), 0));
+  return _mm512_cvtxph_ps(_mm256_castsi256_ph(low));
+}
+
+
+template <>
+EIGEN_STRONG_INLINE Packet32h pcast<Packet16f, Packet32h>(const Packet16f& a, const Packet16f& b) {
+  __m512d result = _mm512_undefined_pd();
+  result = _mm512_insertf64x4(result, _mm256_castsi256_pd(_mm512_cvtps_ph(a, _MM_FROUND_TO_NEAREST_INT|_MM_FROUND_NO_EXC)), 0);
+  result = _mm512_insertf64x4(result, _mm256_castsi256_pd(_mm512_cvtps_ph(b, _MM_FROUND_TO_NEAREST_INT|_MM_FROUND_NO_EXC)), 1);
+  return _mm512_castpd_ph(result);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8f pcast<Packet16h, Packet8f>(const Packet16h& a) {
+  // Discard second-half of input.
+  Packet8h low = _mm_castps_si128(_mm256_extractf32x4_ps(_mm256_castsi256_ps(a), 0));
+  return _mm256_cvtxph_ps(_mm_castsi128_ph(low));
+}
+
+
+template <>
+EIGEN_STRONG_INLINE Packet16h pcast<Packet8f, Packet16h>(const Packet8f& a, const Packet8f& b) {
+  __m256d result = _mm256_undefined_pd();
+  result = _mm256_insertf64x2(result, _mm_castsi128_pd(_mm256_cvtps_ph(a, _MM_FROUND_TO_NEAREST_INT|_MM_FROUND_NO_EXC)), 0);
+  result = _mm256_insertf64x2(result, _mm_castsi128_pd(_mm256_cvtps_ph(b, _MM_FROUND_TO_NEAREST_INT|_MM_FROUND_NO_EXC)), 1);
+  return _mm256_castpd_si256(result);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet8h, Packet4f>(const Packet8h& a) {
+  Packet8f full = _mm256_cvtxph_ps(_mm_castsi128_ph(a));
+  // Discard second-half of input.
+  return _mm256_extractf32x4_ps(full, 0);
+}
+
+
+template <>
+EIGEN_STRONG_INLINE Packet8h pcast<Packet4f, Packet8h>(const Packet4f& a, const Packet4f& b) {
+  __m256 result = _mm256_undefined_ps();
+  result = _mm256_insertf128_ps(result, a, 0);
+  result = _mm256_insertf128_ps(result, b, 1);
+  return _mm256_cvtps_ph(result, _MM_FROUND_TO_NEAREST_INT);
+}
+
+
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TYPE_CASTING_AVX512_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/Complex.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/Complex.h
new file mode 100644
index 0000000000000000000000000000000000000000..e24581fd41210653e16f0e81522086424cede957
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/Complex.h
@@ -0,0 +1,482 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010-2016 Konstantinos Margaritis <markos@freevec.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX32_ALTIVEC_H
+#define EIGEN_COMPLEX32_ALTIVEC_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+static Packet4ui  p4ui_CONJ_XOR = vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_MZERO);//{ 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
+#ifdef EIGEN_VECTORIZE_VSX
+#if defined(_BIG_ENDIAN)
+static Packet2ul  p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2d_MZERO, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
+static Packet2ul  p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO,  (Packet4ui) p2d_MZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
+#else
+static Packet2ul  p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO,  (Packet4ui) p2d_MZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
+static Packet2ul  p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2d_MZERO, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
+#endif
+#endif
+
+//---------- float ----------
+struct Packet2cf
+{
+  EIGEN_STRONG_INLINE explicit Packet2cf() {}
+  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {}
+
+  EIGEN_STRONG_INLINE Packet2cf pmul(const Packet2cf& a, const Packet2cf& b)
+  {
+    Packet4f v1, v2;
+
+    // Permute and multiply the real parts of a and b
+    v1 = vec_perm(a.v, a.v, p16uc_PSET32_WODD);
+    // Get the imaginary parts of a
+    v2 = vec_perm(a.v, a.v, p16uc_PSET32_WEVEN);
+    // multiply a_re * b
+    v1 = vec_madd(v1, b.v, p4f_ZERO);
+    // multiply a_im * b and get the conjugate result
+    v2 = vec_madd(v2, b.v, p4f_ZERO);
+    v2 = reinterpret_cast<Packet4f>(pxor(v2, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR)));
+    // permute back to a proper order
+    v2 = vec_perm(v2, v2, p16uc_COMPLEX32_REV);
+
+    return Packet2cf(padd<Packet4f>(v1, v2));
+  }
+
+  EIGEN_STRONG_INLINE Packet2cf& operator*=(const Packet2cf& b) {
+    v = pmul(Packet2cf(*this), b).v;
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator*(const Packet2cf& b) const {
+    return Packet2cf(*this) *= b;
+  }
+
+  EIGEN_STRONG_INLINE Packet2cf& operator+=(const Packet2cf& b) {
+    v = padd(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator+(const Packet2cf& b) const {
+    return Packet2cf(*this) += b;
+  }
+  EIGEN_STRONG_INLINE Packet2cf& operator-=(const Packet2cf& b) {
+    v = psub(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator-(const Packet2cf& b) const {
+    return Packet2cf(*this) -= b;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator-(void) const {
+    return Packet2cf(-v);
+  }
+
+  Packet4f  v;
+};
+
+template<> struct packet_traits<std::complex<float> >  : default_packet_traits
+{
+  typedef Packet2cf type;
+  typedef Packet2cf half;
+  typedef Packet4f as_real;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasDiv    = 1,
+    HasNegate = 1,
+    HasAbs    = 0,
+    HasAbs2   = 0,
+    HasMin    = 0,
+    HasMax    = 0,
+    HasSqrt   = 1,
+#ifdef EIGEN_VECTORIZE_VSX
+    HasBlend  = 1,
+#endif
+    HasSetLinear = 0
+  };
+};
+
+template<> struct unpacket_traits<Packet2cf> { typedef std::complex<float> type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet2cf half; typedef Packet4f as_real; };
+
+template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>&  from)
+{
+  Packet2cf res;
+#ifdef EIGEN_VECTORIZE_VSX
+  // Load a single std::complex<float> from memory and duplicate
+  //
+  // Using pload would read past the end of the reference in this case
+  // Using vec_xl_len + vec_splat, generates poor assembly
+  __asm__ ("lxvdsx %x0,%y1" : "=wa" (res.v) : "Z" (from));
+#else
+  if((std::ptrdiff_t(&from) % 16) == 0)
+    res.v = pload<Packet4f>((const float *)&from);
+  else
+    res.v = ploadu<Packet4f>((const float *)&from);
+  res.v = vec_perm(res.v, res.v, p16uc_PSET64_HI);
+#endif
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>*        from) { return Packet2cf(pload<Packet4f>((const float *) from)); }
+template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>*       from) { return Packet2cf(ploadu<Packet4f>((const float*) from)); }
+template<> EIGEN_ALWAYS_INLINE Packet2cf pload_partial<Packet2cf>(const std::complex<float>* from, const Index n, const Index offset)
+{
+  return Packet2cf(pload_partial<Packet4f>((const float *) from, n * 2, offset * 2));
+}
+template<> EIGEN_ALWAYS_INLINE Packet2cf ploadu_partial<Packet2cf>(const std::complex<float>* from, const Index n, const Index offset)
+{
+  return Packet2cf(ploadu_partial<Packet4f>((const float*) from, n * 2, offset * 2));
+}
+template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>*     from) { return pset1<Packet2cf>(*from); }
+
+template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *   to, const Packet2cf& from) { pstore((float*)to, from.v); }
+template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *   to, const Packet2cf& from) { pstoreu((float*)to, from.v); }
+template<> EIGEN_ALWAYS_INLINE void pstore_partial <std::complex<float> >(std::complex<float> *  to, const Packet2cf& from, const Index n, const Index offset) { pstore_partial((float*)to, from.v, n * 2, offset * 2); }
+template<> EIGEN_ALWAYS_INLINE void pstoreu_partial<std::complex<float> >(std::complex<float> *  to, const Packet2cf& from, const Index n, const Index offset) { pstoreu_partial((float*)to, from.v, n * 2, offset * 2); }
+
+EIGEN_STRONG_INLINE Packet2cf pload2(const std::complex<float>& from0, const std::complex<float>& from1)
+{
+  Packet4f res0, res1;
+#ifdef EIGEN_VECTORIZE_VSX
+  // Load two std::complex<float> from memory and combine
+  __asm__ ("lxsdx %x0,%y1" : "=wa" (res0) : "Z" (from0));
+  __asm__ ("lxsdx %x0,%y1" : "=wa" (res1) : "Z" (from1));
+#ifdef _BIG_ENDIAN
+  __asm__ ("xxpermdi %x0, %x1, %x2, 0" : "=wa" (res0) : "wa" (res0), "wa" (res1));
+#else
+  __asm__ ("xxpermdi %x0, %x2, %x1, 0" : "=wa" (res0) : "wa" (res0), "wa" (res1));
+#endif
+#else
+  *reinterpret_cast<std::complex<float> *>(&res0) = from0;
+  *reinterpret_cast<std::complex<float> *>(&res1) = from1;
+  res0 = vec_perm(res0, res1, p16uc_TRANSPOSE64_HI);
+#endif
+  return Packet2cf(res0);
+}
+
+template<> EIGEN_ALWAYS_INLINE Packet2cf pload_ignore<Packet2cf>(const std::complex<float>*     from)
+{
+  Packet2cf res;
+  res.v = pload_ignore<Packet4f>(reinterpret_cast<const float*>(from));
+  return res;
+}
+
+template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet pgather_complex_size2(const Scalar* from, Index stride, const Index n = 2)
+{
+  eigen_internal_assert(n <= unpacket_traits<Packet>::size && "number of elements will gather past end of packet");
+  EIGEN_ALIGN16 Scalar af[2];
+  for (Index i = 0; i < n; i++) {
+    af[i] = from[i*stride];
+  }
+  return pload_ignore<Packet>(af);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, Index stride)
+{
+  return pgather_complex_size2<std::complex<float>, Packet2cf>(from, stride);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet2cf pgather_partial<std::complex<float>, Packet2cf>(const std::complex<float>* from, Index stride, const Index n)
+{
+  return pgather_complex_size2<std::complex<float>, Packet2cf>(from, stride, n);
+}
+template<typename Scalar, typename Packet> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_complex_size2(Scalar* to, const Packet& from, Index stride, const Index n = 2)
+{
+  eigen_internal_assert(n <= unpacket_traits<Packet>::size && "number of elements will scatter past end of packet");
+  EIGEN_ALIGN16 Scalar af[2];
+  pstore<Scalar>((Scalar *) af, from);
+  for (Index i = 0; i < n; i++) {
+    to[i*stride] = af[i];
+  }
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, Index stride)
+{
+  pscatter_complex_size2<std::complex<float>, Packet2cf>(to, from, stride);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, Index stride, const Index n)
+{
+  pscatter_complex_size2<std::complex<float>, Packet2cf>(to, from, stride, n);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(a.v + b.v); }
+template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(a.v - b.v); }
+template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) { return Packet2cf(pnegate(a.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) { return Packet2cf(pxor<Packet4f>(a.v, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR))); }
+
+template<> EIGEN_STRONG_INLINE Packet2cf pand   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pand<Packet4f>(a.v, b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf por    <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(por<Packet4f>(a.v, b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf pxor   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pxor<Packet4f>(a.v, b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pandnot<Packet4f>(a.v, b.v)); }
+
+template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> * addr)    { EIGEN_PPC_PREFETCH(addr); }
+
+template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet2cf>(const Packet2cf& a)
+{
+  EIGEN_ALIGN16 std::complex<float> res[2];
+  pstore((float *)&res, a.v);
+
+  return res[0];
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a)
+{
+  Packet4f rev_a;
+  rev_a = vec_sld(a.v, a.v, 8);
+  return Packet2cf(rev_a);
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
+{
+  Packet4f b;
+  b = vec_sld(a.v, a.v, 8);
+  b = padd<Packet4f>(a.v, b);
+  return pfirst<Packet2cf>(Packet2cf(b));
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a)
+{
+  Packet4f b;
+  Packet2cf prod;
+  b = vec_sld(a.v, a.v, 8);
+  prod = pmul<Packet2cf>(a, Packet2cf(b));
+
+  return pfirst<Packet2cf>(prod);
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
+
+template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
+{
+  return pdiv_complex(a, b);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& x)
+{
+  return Packet2cf(vec_perm(x.v, x.v, p16uc_COMPLEX32_REV));
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel)
+{
+#ifdef EIGEN_VECTORIZE_VSX
+  Packet4f tmp = reinterpret_cast<Packet4f>(vec_mergeh(reinterpret_cast<Packet2d>(kernel.packet[0].v), reinterpret_cast<Packet2d>(kernel.packet[1].v)));
+  kernel.packet[1].v = reinterpret_cast<Packet4f>(vec_mergel(reinterpret_cast<Packet2d>(kernel.packet[0].v), reinterpret_cast<Packet2d>(kernel.packet[1].v)));
+#else
+  Packet4f tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
+  kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
+#endif
+  kernel.packet[0].v = tmp;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b) {
+  Packet4f eq = reinterpret_cast<Packet4f>(vec_cmpeq(a.v,b.v));
+  return Packet2cf(vec_and(eq, vec_perm(eq, eq, p16uc_COMPLEX32_REV)));
+}
+
+#ifdef EIGEN_VECTORIZE_VSX
+template<> EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket, const Packet2cf& elsePacket) {
+  Packet2cf result;
+  result.v = reinterpret_cast<Packet4f>(pblend<Packet2d>(ifPacket, reinterpret_cast<Packet2d>(thenPacket.v), reinterpret_cast<Packet2d>(elsePacket.v)));
+  return result;
+}
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a)
+{
+  return psqrt_complex<Packet2cf>(a);
+}
+
+//---------- double ----------
+#ifdef EIGEN_VECTORIZE_VSX
+struct Packet1cd
+{
+  EIGEN_STRONG_INLINE Packet1cd() {}
+  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
+
+  EIGEN_STRONG_INLINE Packet1cd pmul(const Packet1cd& a, const Packet1cd& b)
+  {
+    Packet2d a_re, a_im, v1, v2;
+
+    // Permute and multiply the real parts of a and b
+    a_re = vec_perm(a.v, a.v, p16uc_PSET64_HI);
+    // Get the imaginary parts of a
+    a_im = vec_perm(a.v, a.v, p16uc_PSET64_LO);
+    // multiply a_re * b
+    v1 = vec_madd(a_re, b.v, p2d_ZERO);
+    // multiply a_im * b and get the conjugate result
+    v2 = vec_madd(a_im, b.v, p2d_ZERO);
+    v2 = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(v2), reinterpret_cast<Packet4ui>(v2), 8));
+    v2 = pxor(v2, reinterpret_cast<Packet2d>(p2ul_CONJ_XOR1));
+
+    return Packet1cd(padd<Packet2d>(v1, v2));
+  }
+
+  EIGEN_STRONG_INLINE Packet1cd& operator*=(const Packet1cd& b) {
+    v = pmul(Packet1cd(*this), b).v;
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator*(const Packet1cd& b) const {
+    return Packet1cd(*this) *= b;
+  }
+
+  EIGEN_STRONG_INLINE Packet1cd& operator+=(const Packet1cd& b) {
+    v = padd(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator+(const Packet1cd& b) const {
+    return Packet1cd(*this) += b;
+  }
+  EIGEN_STRONG_INLINE Packet1cd& operator-=(const Packet1cd& b) {
+    v = psub(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator-(const Packet1cd& b) const {
+    return Packet1cd(*this) -= b;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator-(void) const {
+    return Packet1cd(-v);
+  }
+
+  Packet2d v;
+};
+
+template<> struct packet_traits<std::complex<double> >  : default_packet_traits
+{
+  typedef Packet1cd type;
+  typedef Packet1cd half;
+  typedef Packet2d as_real;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 0,
+    size = 1,
+
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasDiv    = 1,
+    HasNegate = 1,
+    HasAbs    = 0,
+    HasAbs2   = 0,
+    HasMin    = 0,
+    HasMax    = 0,
+    HasSqrt   = 1,
+    HasSetLinear = 0
+  };
+};
+
+template<> struct unpacket_traits<Packet1cd> { typedef std::complex<double> type; enum {size=1, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet1cd half; typedef Packet2d as_real; };
+
+template<> EIGEN_STRONG_INLINE Packet1cd pload <Packet1cd>(const std::complex<double>* from) { return Packet1cd(pload<Packet2d>((const double*)from)); }
+template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) { return Packet1cd(ploadu<Packet2d>((const double*)from)); }
+template<> EIGEN_ALWAYS_INLINE Packet1cd pload_partial<Packet1cd>(const std::complex<double>* from, const Index n, const Index offset)
+{
+  return Packet1cd(pload_partial<Packet2d>((const double*)from, n * 2, offset * 2));
+}
+template<> EIGEN_ALWAYS_INLINE Packet1cd ploadu_partial<Packet1cd>(const std::complex<double>* from, const Index n, const Index offset)
+{
+  return Packet1cd(ploadu_partial<Packet2d>((const double*)from, n * 2, offset * 2));
+}
+template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { pstore((double*)to, from.v); }
+template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { pstoreu((double*)to, from.v); }
+template<> EIGEN_ALWAYS_INLINE void pstore_partial <std::complex<double> >(std::complex<double> *  to, const Packet1cd& from, const Index n, const Index offset) { pstore_partial((double*)to, from.v, n * 2, offset * 2); }
+template<> EIGEN_ALWAYS_INLINE void pstoreu_partial<std::complex<double> >(std::complex<double> *  to, const Packet1cd& from, const Index n, const Index offset) { pstoreu_partial((double*)to, from.v, n * 2, offset * 2); }
+
+template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>&  from)
+{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from, Index)
+{
+  return pload<Packet1cd>(from);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet1cd pgather_partial<std::complex<double>, Packet1cd>(const std::complex<double>* from, Index, const Index)
+{
+  return pload<Packet1cd>(from);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from, Index)
+{
+  pstore<std::complex<double> >(to, from);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from, Index, const Index)
+{
+  pstore<std::complex<double> >(to, from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v + b.v); }
+template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v - b.v); }
+template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate(Packet2d(a.v))); }
+template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) { return Packet1cd(pxor(a.v, reinterpret_cast<Packet2d>(p2ul_CONJ_XOR2))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cd pand   <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(pand(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet1cd por    <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(por(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet1cd pxor   <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(pxor(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(pandnot(a.v, b.v)); }
+
+template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>*     from)  { return pset1<Packet1cd>(*from); }
+
+template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> * addr)    { EIGEN_PPC_PREFETCH(addr); }
+
+template<> EIGEN_STRONG_INLINE std::complex<double>  pfirst<Packet1cd>(const Packet1cd& a)
+{
+  EIGEN_ALIGN16 std::complex<double> res[1];
+  pstore<std::complex<double> >(res, a);
+
+  return res[0];
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
+
+template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
+
+template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
+{
+  return pdiv_complex(a, b);
+}
+
+EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)
+{
+  return Packet1cd(preverse(Packet2d(x.v)));
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd,2>& kernel)
+{
+  Packet2d tmp = vec_mergeh(kernel.packet[0].v, kernel.packet[1].v);
+  kernel.packet[1].v = vec_mergel(kernel.packet[0].v, kernel.packet[1].v);
+  kernel.packet[0].v = tmp;
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b) {
+  // Compare real and imaginary parts of a and b to get the mask vector:
+  // [re(a)==re(b), im(a)==im(b)]
+  Packet2d eq = reinterpret_cast<Packet2d>(vec_cmpeq(a.v,b.v));
+  // Swap real/imag elements in the mask in to get:
+  // [im(a)==im(b), re(a)==re(b)]
+  Packet2d eq_swapped = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(eq), reinterpret_cast<Packet4ui>(eq), 8));
+  // Return re(a)==re(b) & im(a)==im(b) by computing bitwise AND of eq and eq_swapped
+  return Packet1cd(vec_and(eq, eq_swapped));
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cd psqrt<Packet1cd>(const Packet1cd& a)
+{
+  return psqrt_complex<Packet1cd>(a);
+}
+
+#endif // __VSX__
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_COMPLEX32_ALTIVEC_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MathFunctions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MathFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..609e443267a73039c91453d14967e507d2fc5158
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MathFunctions.h
@@ -0,0 +1,83 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007 Julien Pommier
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATH_FUNCTIONS_ALTIVEC_H
+#define EIGEN_MATH_FUNCTIONS_ALTIVEC_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet4f)
+#ifdef EIGEN_VECTORIZE_VSX
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_DOUBLE(Packet2d)
+#endif
+
+#ifdef EIGEN_VECTORIZE_VSX
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet4f psqrt<Packet4f>(const Packet4f& x)
+{
+  return  vec_sqrt(x);
+}
+
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet2d psqrt<Packet2d>(const Packet2d& x)
+{
+  return  vec_sqrt(x);
+}
+
+#if !EIGEN_COMP_CLANG
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet4f prsqrt<Packet4f>(const Packet4f& x)
+{
+  return pset1<Packet4f>(1.0f) / psqrt<Packet4f>(x);
+//  vec_rsqrt returns different results from the generic version
+//  return  vec_rsqrt(x);
+}
+
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet2d prsqrt<Packet2d>(const Packet2d& x)
+{
+  return pset1<Packet2d>(1.0) / psqrt<Packet2d>(x);
+//  vec_rsqrt returns different results from the generic version
+//  return  vec_rsqrt(x);
+}
+
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet8bf psqrt<Packet8bf> (const Packet8bf& a){
+  BF16_TO_F32_UNARY_OP_WRAPPER(psqrt<Packet4f>, a);
+}
+
+#if !EIGEN_COMP_CLANG
+template<> EIGEN_STRONG_INLINE Packet8bf prsqrt<Packet8bf> (const Packet8bf& a){
+  BF16_TO_F32_UNARY_OP_WRAPPER(prsqrt<Packet4f>, a);
+}
+#endif
+#else
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet4f psqrt<Packet4f>(const Packet4f& x)
+{
+  Packet4f a;
+  for (Index i = 0; i < packet_traits<float>::size; i++) {
+    a[i] = numext::sqrt(x[i]);
+  }
+  return a;
+}
+#endif
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_ALTIVEC_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MatrixProduct.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
new file mode 100644
index 0000000000000000000000000000000000000000..1c5c04859bc7b993e267e580cca30f9793ba5928
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MatrixProduct.h
@@ -0,0 +1,3812 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2020 Everton Constantino (everton.constantino@ibm.com)
+// Copyright (C) 2021 Chip Kerchner (chip.kerchner@ibm.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_PRODUCT_ALTIVEC_H
+#define EIGEN_MATRIX_PRODUCT_ALTIVEC_H
+
+#ifndef EIGEN_ALTIVEC_USE_CUSTOM_PACK
+#define EIGEN_ALTIVEC_USE_CUSTOM_PACK    1
+#endif
+
+#if !defined(EIGEN_ALTIVEC_DISABLE_MMA)
+#define EIGEN_ALTIVEC_DISABLE_MMA 0
+#endif
+
+// Check for MMA builtin support. 
+#if !EIGEN_ALTIVEC_DISABLE_MMA && defined(__has_builtin)
+#if __has_builtin(__builtin_mma_assemble_acc)
+  #define EIGEN_ALTIVEC_MMA_SUPPORT
+#endif
+#endif
+
+// Check if and how we should actually use MMA if supported.
+#if defined(EIGEN_ALTIVEC_MMA_SUPPORT)
+
+#if !defined(EIGEN_ALTIVEC_ENABLE_MMA_DYNAMIC_DISPATCH)
+#define EIGEN_ALTIVEC_ENABLE_MMA_DYNAMIC_DISPATCH 0
+#endif
+
+// Check if we want to enable dynamic dispatch. Not supported by LLVM.
+#if EIGEN_ALTIVEC_ENABLE_MMA_DYNAMIC_DISPATCH && !EIGEN_COMP_LLVM
+#define EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH 1
+// Otherwise, use MMA by default if available.
+#elif defined(__MMA__)
+#define EIGEN_ALTIVEC_MMA_ONLY 1
+#endif
+
+#endif // EIGEN_ALTIVEC_MMA_SUPPORT
+
+#include "MatrixProductCommon.h"
+
+#if defined(EIGEN_ALTIVEC_MMA_ONLY) || defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
+  #include "MatrixProductMMA.h"
+#endif
+
+/**************************************************************************************************
+ * TODO                                                                                           *
+ * - Check StorageOrder on dhs_pack (the innermost second loop seems unvectorized when it could). *
+ * - Check the possibility of transposing as GETREAL and GETIMAG when needed.                     *
+ **************************************************************************************************/
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/**************************
+ * Constants and typedefs *
+ **************************/
+template<typename Scalar>
+struct quad_traits
+{
+  typedef typename packet_traits<Scalar>::type    vectortype;
+  typedef PacketBlock<vectortype,4>                     type;
+  typedef vectortype                                 rhstype;
+  enum
+  {
+    vectorsize = packet_traits<Scalar>::size,
+    size = 4,
+    rows = 4
+  };
+};
+
+template<>
+struct quad_traits<double>
+{
+  typedef Packet2d                        vectortype;
+  typedef PacketBlock<vectortype,4>             type;
+  typedef PacketBlock<Packet2d,2>            rhstype;
+  enum
+  {
+    vectorsize = packet_traits<double>::size,
+    size = 2,
+    rows = 4
+  };
+};
+
+template<>
+struct quad_traits<bfloat16>
+{
+  typedef Packet8bf                       vectortype;
+  typedef PacketBlock<vectortype,4>             type;
+  typedef vectortype                         rhstype;
+  enum
+  {
+    vectorsize = packet_traits<bfloat16>::size,
+    size = 8,
+    rows = 4
+  };
+};
+
+// MatrixProduct decomposes real/imaginary vectors into a real vector and an imaginary vector, this turned out
+// to be faster than Eigen's usual approach of having real/imaginary pairs on a single vector. This constants then
+// are responsible to extract from convert between Eigen's and MatrixProduct approach.
+
+const static Packet16uc p16uc_GETREAL32 = {  0,  1,  2,  3,
+                                             8,  9, 10, 11,
+                                            16, 17, 18, 19,
+                                            24, 25, 26, 27};
+
+const static Packet16uc p16uc_GETIMAG32 = {  4,  5,  6,  7,
+                                            12, 13, 14, 15,
+                                            20, 21, 22, 23,
+                                            28, 29, 30, 31};
+
+/*********************************************
+ * Single precision real and complex packing *
+ * *******************************************/
+
+/**
+ * Symm packing is related to packing of symmetric adjoint blocks, as expected the packing leaves
+ * the diagonal real, whatever is below it is copied from the respective upper diagonal element and 
+ * conjugated. There's no PanelMode available for symm packing.
+ *
+ * Packing in general is supposed to leave the lhs block and the rhs block easy to be read by gemm using 
+ * its respective rank-update instructions. The float32/64 versions are different because at this moment
+ * the size of the accumulator is fixed at 512-bits so you can't have a 4x4 accumulator of 64-bit elements.
+ * 
+ * As mentioned earlier MatrixProduct breaks complex numbers into a real vector and a complex vector so packing has
+ * to take that into account, at the moment, we run pack the real part and then the imaginary part, this is the main
+ * reason why packing for complex is broken down into several different parts, also the reason why we endup having a
+ * float32/64 and complex float32/64 version.
+ **/
+template<typename Scalar, int StorageOrder>
+EIGEN_ALWAYS_INLINE std::complex<Scalar> getAdjointVal(Index i, Index j, const_blas_data_mapper<std::complex<Scalar>, Index, StorageOrder>& dt)
+{
+  std::complex<Scalar> v;
+  if(i < j)
+  {
+    v.real( dt(j,i).real());
+    v.imag(-dt(j,i).imag());
+  } else if(i > j)
+  {
+    v.real( dt(i,j).real());
+    v.imag( dt(i,j).imag());
+  } else {
+    v.real( dt(i,j).real());
+    v.imag((Scalar)0.0);
+  }
+  return v;
+}
+
+template<typename Scalar, int StorageOrder, int N>
+EIGEN_STRONG_INLINE void symm_pack_complex_rhs_helper(std::complex<Scalar>* blockB, const std::complex<Scalar>* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
+{
+  const Index depth = k2 + rows;
+  const_blas_data_mapper<std::complex<Scalar>, Index, StorageOrder> rhs(_rhs, rhsStride);
+  const Index vectorSize = N*quad_traits<Scalar>::vectorsize;
+  const Index vectorDelta = vectorSize * rows;
+  Scalar* blockBf = reinterpret_cast<Scalar *>(blockB);
+
+  Index rir = 0, rii, j = 0;
+  for(; j + vectorSize <= cols; j+=vectorSize)
+  {
+    rii = rir + vectorDelta;
+
+    for(Index i = k2; i < depth; i++)
+    {
+      for(Index k = 0; k < vectorSize; k++)
+      {
+        std::complex<Scalar> v = getAdjointVal<Scalar, StorageOrder>(i, j + k, rhs);
+
+        blockBf[rir + k] = v.real();
+        blockBf[rii + k] = v.imag();
+      }
+      rir += vectorSize;
+      rii += vectorSize;
+    }
+
+    rir += vectorDelta;
+  }
+
+  for(; j < cols; j++)
+  {
+    rii = rir + rows;
+
+    for(Index i = k2; i < depth; i++)
+    {
+      std::complex<Scalar> v = getAdjointVal<Scalar, StorageOrder>(i, j, rhs);
+
+      blockBf[rir] = v.real();
+      blockBf[rii] = v.imag();
+
+      rir += 1;
+      rii += 1;
+    }
+
+    rir += rows;
+  }
+}
+
+template<typename Scalar, int StorageOrder>
+EIGEN_STRONG_INLINE void symm_pack_complex_lhs_helper(std::complex<Scalar>* blockA, const std::complex<Scalar>* _lhs, Index lhsStride, Index cols, Index rows)
+{
+  const Index depth = cols;
+  const_blas_data_mapper<std::complex<Scalar>, Index, StorageOrder> lhs(_lhs, lhsStride);
+  const Index vectorSize = quad_traits<Scalar>::vectorsize;
+  const Index vectorDelta = vectorSize * depth;
+  Scalar* blockAf = reinterpret_cast<Scalar *>(blockA);
+
+  Index rir = 0, rii, j = 0;
+  for(; j + vectorSize <= rows; j+=vectorSize)
+  {
+    rii = rir + vectorDelta;
+
+    for(Index i = 0; i < depth; i++)
+    {
+      for(Index k = 0; k < vectorSize; k++)
+      {
+        std::complex<Scalar> v = getAdjointVal<Scalar, StorageOrder>(j+k, i, lhs);
+
+        blockAf[rir + k] = v.real();
+        blockAf[rii + k] = v.imag();
+      }
+      rir += vectorSize;
+      rii += vectorSize;
+    }
+
+    rir += vectorDelta;
+  }
+
+  if (j < rows)
+  {
+    rii = rir + ((rows - j) * depth);
+
+    for(Index i = 0; i < depth; i++)
+    {
+      Index k = j;
+      for(; k < rows; k++)
+      {
+        std::complex<Scalar> v = getAdjointVal<Scalar, StorageOrder>(k, i, lhs);
+
+        blockAf[rir] = v.real();
+        blockAf[rii] = v.imag();
+
+        rir += 1;
+        rii += 1;
+      }
+    }
+  }
+}
+
+template<typename Scalar, int StorageOrder, int N>
+EIGEN_STRONG_INLINE void symm_pack_rhs_helper(Scalar* blockB, const Scalar* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
+{
+  const Index depth = k2 + rows;
+  const_blas_data_mapper<Scalar, Index, StorageOrder> rhs(_rhs, rhsStride);
+  const Index vectorSize = quad_traits<Scalar>::vectorsize;
+
+  Index ri = 0, j = 0;
+  for(; j + N*vectorSize <= cols; j+=N*vectorSize)
+  {
+    Index i = k2;
+    for(; i < depth; i++)
+    {
+      for(Index k = 0; k < N*vectorSize; k++)
+      {
+        if(i <= j+k)
+          blockB[ri + k] = rhs(j+k, i);
+        else
+          blockB[ri + k] = rhs(i, j+k);
+      }
+      ri += N*vectorSize;
+    }
+  }
+
+  for(; j < cols; j++)
+  {
+    for(Index i = k2; i < depth; i++)
+    {
+      if(j <= i)
+        blockB[ri] = rhs(i, j);
+      else
+        blockB[ri] = rhs(j, i);
+      ri += 1;
+    }
+  }
+}
+
+template<typename Scalar, int StorageOrder>
+EIGEN_STRONG_INLINE void symm_pack_lhs_helper(Scalar* blockA, const Scalar* _lhs, Index lhsStride, Index cols, Index rows)
+{
+  const Index depth = cols;
+  const_blas_data_mapper<Scalar, Index, StorageOrder> lhs(_lhs, lhsStride);
+  const Index vectorSize = quad_traits<Scalar>::vectorsize;
+
+  Index ri = 0, j = 0;
+  for(; j + vectorSize <= rows; j+=vectorSize)
+  {
+    Index i = 0;
+
+    for(; i < depth; i++)
+    {
+      for(Index k = 0; k < vectorSize; k++)
+      {
+        if(i <= j+k)
+          blockA[ri + k] = lhs(j+k, i);
+        else
+          blockA[ri + k] = lhs(i, j+k);
+      }
+      ri += vectorSize;
+    }
+  }
+
+  if (j < rows)
+  {
+    for(Index i = 0; i < depth; i++)
+    {
+      Index k = j;
+      for(; k < rows; k++)
+      {
+        if(i <= k)
+          blockA[ri] = lhs(k, i);
+        else
+          blockA[ri] = lhs(i, k);
+        ri += 1;
+      }
+    }
+  }
+}
+
+template<typename Index, int nr, int StorageOrder>
+struct symm_pack_rhs<std::complex<float>, Index, nr, StorageOrder>
+{
+  void operator()(std::complex<float>* blockB, const std::complex<float>* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
+  {
+    symm_pack_complex_rhs_helper<float, StorageOrder, 1>(blockB, _rhs, rhsStride, rows, cols, k2);
+  }
+};
+
+template<typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
+struct symm_pack_lhs<std::complex<float>, Index, Pack1, Pack2_dummy, StorageOrder>
+{
+  void operator()(std::complex<float>* blockA, const std::complex<float>* _lhs, Index lhsStride, Index cols, Index rows)
+  {
+    symm_pack_complex_lhs_helper<float, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
+  }
+};
+
+// *********** symm_pack std::complex<float64> ***********
+
+template<typename Index, int nr, int StorageOrder>
+struct symm_pack_rhs<std::complex<double>, Index, nr, StorageOrder>
+{
+  void operator()(std::complex<double>* blockB, const std::complex<double>* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
+  {
+    symm_pack_complex_rhs_helper<double, StorageOrder, 2>(blockB, _rhs, rhsStride, rows, cols, k2);
+  }
+};
+
+template<typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
+struct symm_pack_lhs<std::complex<double>, Index, Pack1, Pack2_dummy, StorageOrder>
+{
+  void operator()(std::complex<double>* blockA, const std::complex<double>* _lhs, Index lhsStride, Index cols, Index rows)
+  {
+    symm_pack_complex_lhs_helper<double, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
+  }
+};
+
+// *********** symm_pack float32 ***********
+template<typename Index, int nr, int StorageOrder>
+struct symm_pack_rhs<float, Index, nr, StorageOrder>
+{
+  void operator()(float* blockB, const float* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
+  {
+    symm_pack_rhs_helper<float, StorageOrder, 1>(blockB, _rhs, rhsStride, rows, cols, k2);
+  }
+};
+
+template<typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
+struct symm_pack_lhs<float, Index, Pack1, Pack2_dummy, StorageOrder>
+{
+  void operator()(float* blockA, const float* _lhs, Index lhsStride, Index cols, Index rows)
+  {
+    symm_pack_lhs_helper<float, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
+  }
+};
+
+// *********** symm_pack float64 ***********
+template<typename Index, int nr, int StorageOrder>
+struct symm_pack_rhs<double, Index, nr, StorageOrder>
+{
+  void operator()(double* blockB, const double* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
+  {
+    symm_pack_rhs_helper<double, StorageOrder, 2>(blockB, _rhs, rhsStride, rows, cols, k2);
+  }
+};
+
+template<typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
+struct symm_pack_lhs<double, Index, Pack1, Pack2_dummy, StorageOrder>
+{
+  void operator()(double* blockA, const double* _lhs, Index lhsStride, Index cols, Index rows)
+  {
+    symm_pack_lhs_helper<double, StorageOrder>(blockA, _lhs, lhsStride, cols, rows);
+  }
+};
+
+/**
+ * PanelMode
+ * Packing might be called several times before being multiplied by gebp_kernel, this happens because 
+ * on special occasions it fills part of block with other parts of the matrix. Two variables control
+ * how PanelMode should behave: offset and stride. The idea is that those variables represent whatever
+ * is going to be the real offset and stride in the future and this is what you should obey. The process
+ * is to behave as you would with normal packing but leave the start of each part with the correct offset
+ * and the end as well respecting the real stride the block will have. Gebp is aware of both blocks stride
+ * and offset and behaves accordingly.
+ **/
+
+template<typename Scalar, typename Packet, int N>
+EIGEN_ALWAYS_INLINE void storeBlock(Scalar* to, PacketBlock<Packet,N>& block)
+{
+  const Index size = 16 / sizeof(Scalar);
+  pstore<Scalar>(to + (0 * size), block.packet[0]);
+  pstore<Scalar>(to + (1 * size), block.packet[1]);
+  if (N > 2) {
+    pstore<Scalar>(to + (2 * size), block.packet[2]);
+  }
+  if (N > 3) {
+    pstore<Scalar>(to + (3 * size), block.packet[3]);
+  }
+}
+
+// General template for lhs & rhs complex packing.
+template<typename Scalar, typename DataMapper, typename Packet, typename PacketC, int StorageOrder, bool Conjugate, bool PanelMode, bool UseLhs>
+struct dhs_cpack {
+  EIGEN_STRONG_INLINE void operator()(std::complex<Scalar>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+  {
+    const Index vectorSize = quad_traits<Scalar>::vectorsize;
+    const Index vectorDelta = vectorSize * ((PanelMode) ? stride : depth);
+    Index rir = ((PanelMode) ? (vectorSize*offset) : 0), rii;
+    Scalar* blockAt = reinterpret_cast<Scalar *>(blockA);
+    Index j = 0;
+
+    for(; j + vectorSize <= rows; j+=vectorSize)
+    {
+      const DataMapper lhs2 = UseLhs ? lhs.getSubMapper(j, 0) : lhs.getSubMapper(0, j);
+      Index i = 0;
+
+      rii = rir + vectorDelta;
+
+      for(; i + vectorSize <= depth; i+=vectorSize)
+      {
+        PacketBlock<Packet,4> blockr, blocki;
+        PacketBlock<PacketC,8> cblock;
+
+        if (UseLhs) {
+          bload<DataMapper, PacketC, 2, StorageOrder, true, 4>(cblock, lhs2, 0, i);
+        } else {
+          bload<DataMapper, PacketC, 2, StorageOrder, true, 4>(cblock, lhs2, i, 0);
+        }
+
+        blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[4].v, p16uc_GETREAL32);
+        blockr.packet[1] = vec_perm(cblock.packet[1].v, cblock.packet[5].v, p16uc_GETREAL32);
+        blockr.packet[2] = vec_perm(cblock.packet[2].v, cblock.packet[6].v, p16uc_GETREAL32);
+        blockr.packet[3] = vec_perm(cblock.packet[3].v, cblock.packet[7].v, p16uc_GETREAL32);
+
+        blocki.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[4].v, p16uc_GETIMAG32);
+        blocki.packet[1] = vec_perm(cblock.packet[1].v, cblock.packet[5].v, p16uc_GETIMAG32);
+        blocki.packet[2] = vec_perm(cblock.packet[2].v, cblock.packet[6].v, p16uc_GETIMAG32);
+        blocki.packet[3] = vec_perm(cblock.packet[3].v, cblock.packet[7].v, p16uc_GETIMAG32);
+
+        if(Conjugate)
+        {
+          blocki.packet[0] = -blocki.packet[0];
+          blocki.packet[1] = -blocki.packet[1];
+          blocki.packet[2] = -blocki.packet[2];
+          blocki.packet[3] = -blocki.packet[3];
+        }
+
+        if(((StorageOrder == RowMajor) && UseLhs) || (((StorageOrder == ColMajor) && !UseLhs)))
+        {
+          ptranspose(blockr);
+          ptranspose(blocki);
+        }
+
+        storeBlock<Scalar, Packet, 4>(blockAt + rir, blockr);
+        storeBlock<Scalar, Packet, 4>(blockAt + rii, blocki);
+
+        rir += 4*vectorSize;
+        rii += 4*vectorSize;
+      }
+      for(; i < depth; i++)
+      {
+        PacketBlock<Packet,1> blockr, blocki;
+        PacketBlock<PacketC,2> cblock;
+
+        if(((StorageOrder == ColMajor) && UseLhs) || (((StorageOrder == RowMajor) && !UseLhs)))
+        {
+          if (UseLhs) {
+            cblock.packet[0] = lhs2.template loadPacket<PacketC>(0, i);
+            cblock.packet[1] = lhs2.template loadPacket<PacketC>(2, i);
+          } else {
+            cblock.packet[0] = lhs2.template loadPacket<PacketC>(i, 0);
+            cblock.packet[1] = lhs2.template loadPacket<PacketC>(i, 2);
+          }
+        } else {
+          if (UseLhs) {
+            cblock.packet[0] = pload2(lhs2(0, i), lhs2(1, i));
+            cblock.packet[1] = pload2(lhs2(2, i), lhs2(3, i));
+          } else {
+            cblock.packet[0] = pload2(lhs2(i, 0), lhs2(i, 1));
+            cblock.packet[1] = pload2(lhs2(i, 2), lhs2(i, 3));
+          }
+        }
+
+        blockr.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETREAL32);
+        blocki.packet[0] = vec_perm(cblock.packet[0].v, cblock.packet[1].v, p16uc_GETIMAG32);
+
+        if(Conjugate)
+        {
+          blocki.packet[0] = -blocki.packet[0];
+        }
+
+        pstore<Scalar>(blockAt + rir, blockr.packet[0]);
+        pstore<Scalar>(blockAt + rii, blocki.packet[0]);
+
+        rir += vectorSize;
+        rii += vectorSize;
+      }
+
+      rir += ((PanelMode) ? (vectorSize*(2*stride - depth)) : vectorDelta);
+    }
+
+    if (!UseLhs)
+    {
+      if(PanelMode) rir -= (offset*(vectorSize - 1));
+
+      for(; j < rows; j++)
+      {
+        const DataMapper lhs2 = lhs.getSubMapper(0, j);
+        rii = rir + ((PanelMode) ? stride : depth);
+
+        for(Index i = 0; i < depth; i++)
+        {
+          blockAt[rir] = lhs2(i, 0).real();
+
+          if(Conjugate)
+            blockAt[rii] = -lhs2(i, 0).imag();
+          else
+            blockAt[rii] =  lhs2(i, 0).imag();
+
+          rir += 1;
+          rii += 1;
+        }
+
+        rir += ((PanelMode) ? (2*stride - depth) : depth);
+      }
+    } else {
+      if (j < rows)
+      {
+        if(PanelMode) rir += (offset*(rows - j - vectorSize));
+        rii = rir + (((PanelMode) ? stride : depth) * (rows - j));
+
+        for(Index i = 0; i < depth; i++)
+        {
+          Index k = j;
+          for(; k < rows; k++)
+          {
+            blockAt[rir] = lhs(k, i).real();
+
+            if(Conjugate)
+              blockAt[rii] = -lhs(k, i).imag();
+            else
+              blockAt[rii] =  lhs(k, i).imag();
+
+            rir += 1;
+            rii += 1;
+          }
+        }
+      }
+    }
+  }
+};
+
+// General template for lhs & rhs packing.
+template<typename Scalar, typename DataMapper, typename Packet, int StorageOrder, bool PanelMode, bool UseLhs>
+struct dhs_pack{
+  EIGEN_STRONG_INLINE void operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+  {
+    const Index vectorSize = quad_traits<Scalar>::vectorsize;
+    Index ri = 0, j = 0;
+
+    for(; j + vectorSize <= rows; j+=vectorSize)
+    {
+      const DataMapper lhs2 = UseLhs ? lhs.getSubMapper(j, 0) : lhs.getSubMapper(0, j);
+      Index i = 0;
+
+      if(PanelMode) ri += vectorSize*offset;
+
+      for(; i + vectorSize <= depth; i+=vectorSize)
+      {
+        PacketBlock<Packet,4> block;
+
+        if (UseLhs) {
+          bload<DataMapper, Packet, 4, StorageOrder, false, 4>(block, lhs2, 0, i);
+        } else {
+          bload<DataMapper, Packet, 4, StorageOrder, false, 4>(block, lhs2, i, 0);
+        }
+        if(((StorageOrder == RowMajor) && UseLhs) || ((StorageOrder == ColMajor) && !UseLhs))
+        {
+          ptranspose(block);
+        }
+
+        storeBlock<Scalar, Packet, 4>(blockA + ri, block);
+
+        ri += 4*vectorSize;
+      }
+      for(; i < depth; i++)
+      {
+        if(((StorageOrder == RowMajor) && UseLhs) || ((StorageOrder == ColMajor) && !UseLhs))
+        {
+          if (UseLhs) {
+            blockA[ri+0] = lhs2(0, i);
+            blockA[ri+1] = lhs2(1, i);
+            blockA[ri+2] = lhs2(2, i);
+            blockA[ri+3] = lhs2(3, i);
+          } else {
+            blockA[ri+0] = lhs2(i, 0);
+            blockA[ri+1] = lhs2(i, 1);
+            blockA[ri+2] = lhs2(i, 2);
+            blockA[ri+3] = lhs2(i, 3);
+          }
+        } else {
+          Packet lhsV;
+          if (UseLhs) {
+            lhsV = lhs2.template loadPacket<Packet>(0, i);
+          } else {
+            lhsV = lhs2.template loadPacket<Packet>(i, 0);
+          }
+          pstore<Scalar>(blockA + ri, lhsV);
+        }
+
+        ri += vectorSize;
+      }
+
+      if(PanelMode) ri += vectorSize*(stride - offset - depth);
+    }
+
+    if (!UseLhs)
+    {
+      if(PanelMode) ri += offset;
+
+      for(; j < rows; j++)
+      {
+        const DataMapper lhs2 = lhs.getSubMapper(0, j);
+        for(Index i = 0; i < depth; i++)
+        {
+          blockA[ri] = lhs2(i, 0);
+          ri += 1;
+        }
+
+        if(PanelMode) ri += stride - depth;
+      }
+    } else {
+      if (j < rows)
+      {
+        if(PanelMode) ri += offset*(rows - j);
+
+        for(Index i = 0; i < depth; i++)
+        {
+          Index k = j;
+          for(; k < rows; k++)
+          {
+            blockA[ri] = lhs(k, i);
+            ri += 1;
+          }
+        }
+      }
+    }
+  }
+};
+
+// General template for lhs packing, float64 specialization.
+template<typename DataMapper, int StorageOrder, bool PanelMode>
+struct dhs_pack<double, DataMapper, Packet2d, StorageOrder, PanelMode, true>
+{
+  EIGEN_STRONG_INLINE void operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+  {
+    const Index vectorSize = quad_traits<double>::vectorsize;
+    Index ri = 0, j = 0;
+
+    for(; j + vectorSize <= rows; j+=vectorSize)
+    {
+      const DataMapper lhs2 = lhs.getSubMapper(j, 0);
+      Index i = 0;
+
+      if(PanelMode) ri += vectorSize*offset;
+
+      for(; i + vectorSize <= depth; i+=vectorSize)
+      {
+        PacketBlock<Packet2d,2> block;
+        if(StorageOrder == RowMajor)
+        {
+          block.packet[0] = lhs2.template loadPacket<Packet2d>(0, i);
+          block.packet[1] = lhs2.template loadPacket<Packet2d>(1, i);
+
+          ptranspose(block);
+        } else {
+          block.packet[0] = lhs2.template loadPacket<Packet2d>(0, i + 0);
+          block.packet[1] = lhs2.template loadPacket<Packet2d>(0, i + 1);
+        }
+
+        storeBlock<double, Packet2d, 2>(blockA + ri, block);
+
+        ri += 2*vectorSize;
+      }
+      for(; i < depth; i++)
+      {
+        if(StorageOrder == RowMajor)
+        {
+          blockA[ri+0] = lhs2(0, i);
+          blockA[ri+1] = lhs2(1, i);
+        } else {
+          Packet2d lhsV = lhs2.template loadPacket<Packet2d>(0, i);
+          pstore<double>(blockA + ri, lhsV);
+        }
+
+        ri += vectorSize;
+      }
+
+      if(PanelMode) ri += vectorSize*(stride - offset - depth);
+    }
+
+    if (j < rows)
+    {
+      if(PanelMode) ri += offset*(rows - j);
+
+      for(Index i = 0; i < depth; i++)
+      {
+        Index k = j;
+        for(; k < rows; k++)
+        {
+          blockA[ri] = lhs(k, i);
+          ri += 1;
+        }
+      }
+    }
+  }
+};
+
+// General template for rhs packing, float64 specialization.
+template<typename DataMapper, int StorageOrder, bool PanelMode>
+struct dhs_pack<double, DataMapper, Packet2d, StorageOrder, PanelMode, false>
+{
+  EIGEN_STRONG_INLINE void operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
+  {
+    const Index vectorSize = quad_traits<double>::vectorsize;
+    Index ri = 0, j = 0;
+
+    for(; j + 2*vectorSize <= cols; j+=2*vectorSize)
+    {
+      const DataMapper rhs2 = rhs.getSubMapper(0, j);
+      Index i = 0;
+
+      if(PanelMode) ri += offset*(2*vectorSize);
+
+      for(; i + vectorSize <= depth; i+=vectorSize)
+      {
+        PacketBlock<Packet2d,4> block;
+        if(StorageOrder == ColMajor)
+        {
+          PacketBlock<Packet2d,2> block1, block2;
+          block1.packet[0] = rhs2.template loadPacket<Packet2d>(i, 0);
+          block1.packet[1] = rhs2.template loadPacket<Packet2d>(i, 1);
+          block2.packet[0] = rhs2.template loadPacket<Packet2d>(i, 2);
+          block2.packet[1] = rhs2.template loadPacket<Packet2d>(i, 3);
+
+          ptranspose(block1);
+          ptranspose(block2);
+
+          pstore<double>(blockB + ri    , block1.packet[0]);
+          pstore<double>(blockB + ri + 2, block2.packet[0]);
+          pstore<double>(blockB + ri + 4, block1.packet[1]);
+          pstore<double>(blockB + ri + 6, block2.packet[1]);
+        } else {
+          block.packet[0] = rhs2.template loadPacket<Packet2d>(i + 0, 0); //[a1 a2]
+          block.packet[1] = rhs2.template loadPacket<Packet2d>(i + 0, 2); //[a3 a4]
+          block.packet[2] = rhs2.template loadPacket<Packet2d>(i + 1, 0); //[b1 b2]
+          block.packet[3] = rhs2.template loadPacket<Packet2d>(i + 1, 2); //[b3 b4]
+
+          storeBlock<double, Packet2d, 4>(blockB + ri, block);
+        }
+
+        ri += 4*vectorSize;
+      }
+      for(; i < depth; i++)
+      {
+        if(StorageOrder == ColMajor)
+        {
+          blockB[ri+0] = rhs2(i, 0);
+          blockB[ri+1] = rhs2(i, 1);
+
+          ri += vectorSize;
+
+          blockB[ri+0] = rhs2(i, 2);
+          blockB[ri+1] = rhs2(i, 3);
+        } else {
+          Packet2d rhsV = rhs2.template loadPacket<Packet2d>(i, 0);
+          pstore<double>(blockB + ri, rhsV);
+
+          ri += vectorSize;
+
+          rhsV = rhs2.template loadPacket<Packet2d>(i, 2);
+          pstore<double>(blockB + ri, rhsV);
+        }
+        ri += vectorSize;
+      }
+
+      if(PanelMode) ri += (2*vectorSize)*(stride - offset - depth);
+    }
+
+    if(PanelMode) ri += offset;
+
+    for(; j < cols; j++)
+    {
+      const DataMapper rhs2 = rhs.getSubMapper(0, j);
+      for(Index i = 0; i < depth; i++)
+      {
+        blockB[ri] = rhs2(i, 0);
+        ri += 1;
+      }
+
+      if(PanelMode) ri += stride - depth;
+    }
+  }
+};
+
+// General template for lhs packing, bfloat16 specialization.
+template<typename DataMapper, int StorageOrder, bool PanelMode>
+struct dhs_pack<bfloat16, DataMapper, Packet8bf, StorageOrder, PanelMode, true>
+{
+  EIGEN_STRONG_INLINE void operator()(bfloat16* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+  {
+    const Index vectorSize = quad_traits<bfloat16>::vectorsize;
+    Index ri = 0, j = 0;
+
+    for(; j + 2*vectorSize <= rows; j+=2*vectorSize)
+    {
+      const DataMapper lhs2 = lhs.getSubMapper(j, 0);
+      Index i = 0;
+
+      if(PanelMode) ri += 2*vectorSize*offset;
+
+      if(StorageOrder == ColMajor)
+      {
+        for(; i + 2 <= depth; i+=2)
+        {
+          PacketBlock<Packet8bf,4> block;
+
+          block.packet[0] = lhs2.template loadPacket<Packet8bf>(0 * vectorSize, i + 0);
+          block.packet[1] = lhs2.template loadPacket<Packet8bf>(1 * vectorSize, i + 0);
+          block.packet[2] = lhs2.template loadPacket<Packet8bf>(0 * vectorSize, i + 1);
+          block.packet[3] = lhs2.template loadPacket<Packet8bf>(1 * vectorSize, i + 1);
+
+          Packet8bf t0, t1;
+          t0              = vec_mergeh(block.packet[0].m_val, block.packet[2].m_val);
+          t1              = vec_mergel(block.packet[0].m_val, block.packet[2].m_val);
+          block.packet[2] = vec_mergeh(block.packet[1].m_val, block.packet[3].m_val);
+          block.packet[3] = vec_mergel(block.packet[1].m_val, block.packet[3].m_val);
+          block.packet[0] = t0;
+          block.packet[1] = t1;
+
+          storeBlock<bfloat16, Packet8bf, 4>(blockA + ri, block);
+
+          ri += 2*2*vectorSize;
+        }
+        if (depth & 1)
+        {
+          PacketBlock<Packet8bf,2> block;
+
+          block.packet[0] = lhs2.template loadPacket<Packet8bf>(0 * vectorSize, i + 0);
+          block.packet[1] = lhs2.template loadPacket<Packet8bf>(1 * vectorSize, i + 0);
+
+          storeBlock<bfloat16, Packet8bf, 2>(blockA + ri, block);
+
+          ri += 2*vectorSize;
+        }
+      } else {
+        for(; i + vectorSize <= depth; i+=vectorSize)
+        {
+          PacketBlock<Packet8bf,8> block1, block2;
+
+          bload<DataMapper, Packet8bf, 8, StorageOrder, false, 8>(block1, lhs2, 0 * vectorSize, i);
+          bload<DataMapper, Packet8bf, 8, StorageOrder, false, 8>(block2, lhs2, 1 * vectorSize, i);
+
+          Packet4ui v1[8], v2[8];
+
+          v1[0] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[0].m_val), reinterpret_cast<Packet4ui>(block1.packet[1].m_val));
+          v1[1] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[0].m_val), reinterpret_cast<Packet4ui>(block1.packet[1].m_val));
+          v1[2] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[2].m_val), reinterpret_cast<Packet4ui>(block1.packet[3].m_val));
+          v1[3] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[2].m_val), reinterpret_cast<Packet4ui>(block1.packet[3].m_val));
+          v1[4] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[4].m_val), reinterpret_cast<Packet4ui>(block1.packet[5].m_val));
+          v1[5] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[4].m_val), reinterpret_cast<Packet4ui>(block1.packet[5].m_val));
+          v1[6] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[6].m_val), reinterpret_cast<Packet4ui>(block1.packet[7].m_val));
+          v1[7] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[6].m_val), reinterpret_cast<Packet4ui>(block1.packet[7].m_val));
+          v2[0] = vec_mergeh(reinterpret_cast<Packet4ui>(block2.packet[0].m_val), reinterpret_cast<Packet4ui>(block2.packet[1].m_val));
+          v2[1] = vec_mergel(reinterpret_cast<Packet4ui>(block2.packet[0].m_val), reinterpret_cast<Packet4ui>(block2.packet[1].m_val));
+          v2[2] = vec_mergeh(reinterpret_cast<Packet4ui>(block2.packet[2].m_val), reinterpret_cast<Packet4ui>(block2.packet[3].m_val));
+          v2[3] = vec_mergel(reinterpret_cast<Packet4ui>(block2.packet[2].m_val), reinterpret_cast<Packet4ui>(block2.packet[3].m_val));
+          v2[4] = vec_mergeh(reinterpret_cast<Packet4ui>(block2.packet[4].m_val), reinterpret_cast<Packet4ui>(block2.packet[5].m_val));
+          v2[5] = vec_mergel(reinterpret_cast<Packet4ui>(block2.packet[4].m_val), reinterpret_cast<Packet4ui>(block2.packet[5].m_val));
+          v2[6] = vec_mergeh(reinterpret_cast<Packet4ui>(block2.packet[6].m_val), reinterpret_cast<Packet4ui>(block2.packet[7].m_val));
+          v2[7] = vec_mergel(reinterpret_cast<Packet4ui>(block2.packet[6].m_val), reinterpret_cast<Packet4ui>(block2.packet[7].m_val));
+
+#ifdef EIGEN_VECTORIZE_VSX
+          block1.packet[0] = reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(v1[0]),reinterpret_cast<Packet2ul>(v1[2])));
+          block1.packet[2] = reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(v1[0]),reinterpret_cast<Packet2ul>(v1[2])));
+          block1.packet[4] = reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(v1[1]),reinterpret_cast<Packet2ul>(v1[3])));
+          block1.packet[6] = reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(v1[1]),reinterpret_cast<Packet2ul>(v1[3])));
+          block1.packet[1] = reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(v1[4]),reinterpret_cast<Packet2ul>(v1[6])));
+          block1.packet[3] = reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(v1[4]),reinterpret_cast<Packet2ul>(v1[6])));
+          block1.packet[5] = reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(v1[5]),reinterpret_cast<Packet2ul>(v1[7])));
+          block1.packet[7] = reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(v1[5]),reinterpret_cast<Packet2ul>(v1[7])));
+          block2.packet[0] = reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(v2[0]),reinterpret_cast<Packet2ul>(v2[2])));
+          block2.packet[2] = reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(v2[0]),reinterpret_cast<Packet2ul>(v2[2])));
+          block2.packet[4] = reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(v2[1]),reinterpret_cast<Packet2ul>(v2[3])));
+          block2.packet[6] = reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(v2[1]),reinterpret_cast<Packet2ul>(v2[3])));
+          block2.packet[1] = reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(v2[4]),reinterpret_cast<Packet2ul>(v2[6])));
+          block2.packet[3] = reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(v2[4]),reinterpret_cast<Packet2ul>(v2[6])));
+          block2.packet[5] = reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(v2[5]),reinterpret_cast<Packet2ul>(v2[7])));
+          block2.packet[7] = reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(v2[5]),reinterpret_cast<Packet2ul>(v2[7])));
+#else
+          block1.packet[0] = reinterpret_cast<Packet8us>(vec_perm(v1[0],v1[2],p16uc_TRANSPOSE64_HI));
+          block1.packet[2] = reinterpret_cast<Packet8us>(vec_perm(v1[0],v1[2],p16uc_TRANSPOSE64_LO));
+          block1.packet[4] = reinterpret_cast<Packet8us>(vec_perm(v1[1],v1[3],p16uc_TRANSPOSE64_HI));
+          block1.packet[6] = reinterpret_cast<Packet8us>(vec_perm(v1[1],v1[3],p16uc_TRANSPOSE64_LO));
+          block1.packet[1] = reinterpret_cast<Packet8us>(vec_perm(v1[4],v1[6],p16uc_TRANSPOSE64_HI));
+          block1.packet[3] = reinterpret_cast<Packet8us>(vec_perm(v1[4],v1[6],p16uc_TRANSPOSE64_LO));
+          block1.packet[5] = reinterpret_cast<Packet8us>(vec_perm(v1[5],v1[7],p16uc_TRANSPOSE64_HI));
+          block1.packet[7] = reinterpret_cast<Packet8us>(vec_perm(v1[5],v1[7],p16uc_TRANSPOSE64_LO));
+          block2.packet[0] = reinterpret_cast<Packet8us>(vec_perm(v2[0],v2[2],p16uc_TRANSPOSE64_HI));
+          block2.packet[2] = reinterpret_cast<Packet8us>(vec_perm(v2[0],v2[2],p16uc_TRANSPOSE64_LO));
+          block2.packet[4] = reinterpret_cast<Packet8us>(vec_perm(v2[1],v2[3],p16uc_TRANSPOSE64_HI));
+          block2.packet[6] = reinterpret_cast<Packet8us>(vec_perm(v2[1],v2[3],p16uc_TRANSPOSE64_LO));
+          block2.packet[1] = reinterpret_cast<Packet8us>(vec_perm(v2[4],v2[6],p16uc_TRANSPOSE64_HI));
+          block2.packet[3] = reinterpret_cast<Packet8us>(vec_perm(v2[4],v2[6],p16uc_TRANSPOSE64_LO));
+          block2.packet[5] = reinterpret_cast<Packet8us>(vec_perm(v2[5],v2[7],p16uc_TRANSPOSE64_HI));
+          block2.packet[7] = reinterpret_cast<Packet8us>(vec_perm(v2[5],v2[7],p16uc_TRANSPOSE64_LO));
+#endif
+
+          for(Index M = 0; M < 8; M+=2) {
+            pstore<bfloat16>(blockA + ri + (0 * vectorSize) + (2*vectorSize * M), block1.packet[M+0]);
+            pstore<bfloat16>(blockA + ri + (1 * vectorSize) + (2*vectorSize * M), block1.packet[M+1]);
+            pstore<bfloat16>(blockA + ri + (2 * vectorSize) + (2*vectorSize * M), block2.packet[M+0]);
+            pstore<bfloat16>(blockA + ri + (3 * vectorSize) + (2*vectorSize * M), block2.packet[M+1]);
+          }
+
+          ri += 2*vectorSize*vectorSize;
+        }
+        for(; i + 2 <= depth; i+=2)
+        {
+          for(Index M = 0; M < 2*vectorSize; M++) {
+            blockA[ri + (M * 2) + 0] = lhs2(M, i + 0);
+            blockA[ri + (M * 2) + 1] = lhs2(M, i + 1);
+          }
+
+          ri += 2*2*vectorSize;
+        }
+        if (depth & 1)
+        {
+          for(Index M = 0; M < 2*vectorSize; M++) {
+            blockA[ri + M] = lhs2(M, i);
+          }
+          ri += 2*vectorSize;
+        }
+      }
+
+      if(PanelMode) ri += 2*vectorSize*(stride - offset - depth);
+    }
+    for(; j + vectorSize <= rows; j+=vectorSize)
+    {
+      const DataMapper lhs2 = lhs.getSubMapper(j, 0);
+      Index i = 0;
+
+      if(PanelMode) ri += vectorSize*offset;
+
+      if(StorageOrder == ColMajor)
+      {
+        for(; i + 2 <= depth; i+=2)
+        {
+          PacketBlock<Packet8bf,2> block;
+
+          block.packet[0] = lhs2.template loadPacket<Packet8bf>(0 * vectorSize, i + 0);
+          block.packet[1] = lhs2.template loadPacket<Packet8bf>(0 * vectorSize, i + 1);
+
+          Packet8bf t0;
+          t0              = vec_mergeh(block.packet[0].m_val, block.packet[1].m_val);
+          block.packet[1] = vec_mergel(block.packet[0].m_val, block.packet[1].m_val);
+          block.packet[0] = t0;
+
+          storeBlock<bfloat16, Packet8bf, 2>(blockA + ri, block);
+
+          ri += 2*vectorSize;
+        }
+        if (depth & 1)
+        {
+          Packet8bf lhsV = lhs2.template loadPacket<Packet8bf>(0 * vectorSize, i + 0);
+          pstore<bfloat16>(blockA + ri, lhsV);
+
+          ri += vectorSize;
+        }
+      } else {
+        for(; i + vectorSize <= depth; i+=vectorSize)
+        {
+          PacketBlock<Packet8bf,8> block1;
+
+          bload<DataMapper, Packet8bf, 8, StorageOrder, false, 8>(block1, lhs2, 0 * vectorSize, i);
+
+          Packet4ui v1[8];
+
+          // This is transposing and interleaving data
+          v1[0] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[0].m_val), reinterpret_cast<Packet4ui>(block1.packet[1].m_val));
+          v1[1] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[0].m_val), reinterpret_cast<Packet4ui>(block1.packet[1].m_val));
+          v1[2] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[2].m_val), reinterpret_cast<Packet4ui>(block1.packet[3].m_val));
+          v1[3] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[2].m_val), reinterpret_cast<Packet4ui>(block1.packet[3].m_val));
+          v1[4] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[4].m_val), reinterpret_cast<Packet4ui>(block1.packet[5].m_val));
+          v1[5] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[4].m_val), reinterpret_cast<Packet4ui>(block1.packet[5].m_val));
+          v1[6] = vec_mergeh(reinterpret_cast<Packet4ui>(block1.packet[6].m_val), reinterpret_cast<Packet4ui>(block1.packet[7].m_val));
+          v1[7] = vec_mergel(reinterpret_cast<Packet4ui>(block1.packet[6].m_val), reinterpret_cast<Packet4ui>(block1.packet[7].m_val));
+
+#ifdef EIGEN_VECTORIZE_VSX
+          block1.packet[0] = reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(v1[0]),reinterpret_cast<Packet2ul>(v1[2])));
+          block1.packet[2] = reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(v1[0]),reinterpret_cast<Packet2ul>(v1[2])));
+          block1.packet[4] = reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(v1[1]),reinterpret_cast<Packet2ul>(v1[3])));
+          block1.packet[6] = reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(v1[1]),reinterpret_cast<Packet2ul>(v1[3])));
+          block1.packet[1] = reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(v1[4]),reinterpret_cast<Packet2ul>(v1[6])));
+          block1.packet[3] = reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(v1[4]),reinterpret_cast<Packet2ul>(v1[6])));
+          block1.packet[5] = reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(v1[5]),reinterpret_cast<Packet2ul>(v1[7])));
+          block1.packet[7] = reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(v1[5]),reinterpret_cast<Packet2ul>(v1[7])));
+#else
+          block1.packet[0] = reinterpret_cast<Packet8us>(vec_perm(v1[0],v1[2],p16uc_TRANSPOSE64_HI));
+          block1.packet[2] = reinterpret_cast<Packet8us>(vec_perm(v1[0],v1[2],p16uc_TRANSPOSE64_LO));
+          block1.packet[4] = reinterpret_cast<Packet8us>(vec_perm(v1[1],v1[3],p16uc_TRANSPOSE64_HI));
+          block1.packet[6] = reinterpret_cast<Packet8us>(vec_perm(v1[1],v1[3],p16uc_TRANSPOSE64_LO));
+          block1.packet[1] = reinterpret_cast<Packet8us>(vec_perm(v1[4],v1[6],p16uc_TRANSPOSE64_HI));
+          block1.packet[3] = reinterpret_cast<Packet8us>(vec_perm(v1[4],v1[6],p16uc_TRANSPOSE64_LO));
+          block1.packet[5] = reinterpret_cast<Packet8us>(vec_perm(v1[5],v1[7],p16uc_TRANSPOSE64_HI));
+          block1.packet[7] = reinterpret_cast<Packet8us>(vec_perm(v1[5],v1[7],p16uc_TRANSPOSE64_LO));
+#endif
+
+          for(Index M = 0; M < 8; M++) {
+            pstore<bfloat16>(blockA + ri + (vectorSize * M), block1.packet[M]);
+          }
+
+          ri += vectorSize*vectorSize;
+        }
+        for(; i + 2 <= depth; i+=2)
+        {
+          for(Index M = 0; M < vectorSize; M++) {
+            blockA[ri + (M * 2) + 0] = lhs2(M, i + 0);
+            blockA[ri + (M * 2) + 1] = lhs2(M, i + 1);
+          }
+
+          ri += 2*vectorSize;
+        }
+        if (depth & 1)
+        {
+          for(Index M = 0; M < vectorSize; M++) {
+            blockA[ri + M] = lhs2(M, i);
+          }
+
+          ri += vectorSize;
+        }
+      }
+
+      if(PanelMode) ri += vectorSize*(stride - offset - depth);
+    }
+    if(j + 4 <= rows)
+    {
+      const DataMapper lhs2 = lhs.getSubMapper(j, 0);
+      Index i = 0;
+
+      if(PanelMode) ri += 4*offset;
+
+      for(; i + 2 <= depth; i+=2)
+      {
+        if(StorageOrder == ColMajor)
+        {
+          PacketBlock<Packet8bf,2> block;
+
+          block.packet[0] = lhs2.template loadPacketPartial<Packet8bf>(0, i + 0, 4);
+          block.packet[1] = lhs2.template loadPacketPartial<Packet8bf>(0, i + 1, 4);
+
+          block.packet[0] = vec_mergeh(block.packet[0].m_val, block.packet[1].m_val);
+
+          pstore<bfloat16>(blockA + ri, block.packet[0]);
+        } else {
+          blockA[ri+0] = lhs2(0, i + 0);
+          blockA[ri+1] = lhs2(0, i + 1);
+          blockA[ri+2] = lhs2(1, i + 0);
+          blockA[ri+3] = lhs2(1, i + 1);
+          blockA[ri+4] = lhs2(2, i + 0);
+          blockA[ri+5] = lhs2(2, i + 1);
+          blockA[ri+6] = lhs2(3, i + 0);
+          blockA[ri+7] = lhs2(3, i + 1);
+        }
+
+        ri += 2*4;
+      }
+      if (depth & 1)
+      {
+        if(StorageOrder == ColMajor)
+        {
+          Packet8bf lhsV = lhs2.template loadPacketPartial<Packet8bf>(0, i + 0, 4);
+
+          pstore_partial<bfloat16>(blockA + ri, lhsV, 4);
+        } else {
+          blockA[ri+0] = lhs2(0, i);
+          blockA[ri+1] = lhs2(1, i);
+          blockA[ri+2] = lhs2(2, i);
+          blockA[ri+3] = lhs2(3, i);
+        }
+
+        ri += 4;
+      }
+
+      if(PanelMode) ri += 4*(stride - offset - depth);
+      j += 4;
+    }
+
+    if (j < rows)
+    {
+      if(PanelMode) ri += offset*(rows - j);
+
+      Index i = 0;
+      for(; i + 2 <= depth; i+=2)
+      {
+        Index k = j;
+        for(; k < rows; k++)
+        {
+          blockA[ri+0] = lhs(k, i + 0);
+          blockA[ri+1] = lhs(k, i + 1);
+          ri += 2;
+        }
+      }
+      if (depth & 1)
+      {
+        for(; j < rows; j++)
+        {
+          blockA[ri] = lhs(j, i);
+          ri += 1;
+        }
+      }
+    }
+  }
+};
+
+// General template for rhs packing, bfloat16 specialization.
+template<typename DataMapper, int StorageOrder, bool PanelMode>
+struct dhs_pack<bfloat16, DataMapper, Packet8bf, StorageOrder, PanelMode, false>
+{
+  EIGEN_STRONG_INLINE void operator()(bfloat16* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
+  {
+    const Index vectorSize = quad_traits<bfloat16>::vectorsize;
+    Index ri = 0, j = 0;
+
+    for(; j + 4 <= cols; j+=4)
+    {
+      const DataMapper rhs2 = rhs.getSubMapper(0, j);
+      Index i = 0;
+
+      if(PanelMode) ri += 4*offset;
+
+      for(; i + vectorSize <= depth; i+=vectorSize)
+      {
+        if(StorageOrder == ColMajor)
+        {
+          PacketBlock<Packet8bf,4> block;
+
+          bload<DataMapper, Packet8bf, 4, StorageOrder, false, 4>(block, rhs2, i, 0);
+
+          Packet4ui t0, t1, t2, t3;
+
+          t0 = vec_mergeh(reinterpret_cast<Packet4ui>(block.packet[0].m_val), reinterpret_cast<Packet4ui>(block.packet[1].m_val));
+          t1 = vec_mergel(reinterpret_cast<Packet4ui>(block.packet[0].m_val), reinterpret_cast<Packet4ui>(block.packet[1].m_val));
+          t2 = vec_mergeh(reinterpret_cast<Packet4ui>(block.packet[2].m_val), reinterpret_cast<Packet4ui>(block.packet[3].m_val));
+          t3 = vec_mergel(reinterpret_cast<Packet4ui>(block.packet[2].m_val), reinterpret_cast<Packet4ui>(block.packet[3].m_val));
+
+#ifdef EIGEN_VECTORIZE_VSX
+          block.packet[0] = reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(t0),reinterpret_cast<Packet2ul>(t2)));
+          block.packet[1] = reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(t0),reinterpret_cast<Packet2ul>(t2)));
+          block.packet[2] = reinterpret_cast<Packet8us>(vec_mergeh(reinterpret_cast<Packet2ul>(t1),reinterpret_cast<Packet2ul>(t3)));
+          block.packet[3] = reinterpret_cast<Packet8us>(vec_mergel(reinterpret_cast<Packet2ul>(t1),reinterpret_cast<Packet2ul>(t3)));
+#else
+          block.packet[0] = reinterpret_cast<Packet8us>(vec_perm(t0,t2,p16uc_TRANSPOSE64_HI));
+          block.packet[1] = reinterpret_cast<Packet8us>(vec_perm(t0,t2,p16uc_TRANSPOSE64_LO));
+          block.packet[2] = reinterpret_cast<Packet8us>(vec_perm(t1,t3,p16uc_TRANSPOSE64_HI));
+          block.packet[3] = reinterpret_cast<Packet8us>(vec_perm(t1,t3,p16uc_TRANSPOSE64_LO));
+#endif
+
+          storeBlock<bfloat16, Packet8bf, 4>(blockB + ri, block);
+        } else {
+          PacketBlock<Packet8bf,8> block;
+
+          for (int M = 0; M < 8; M++) {
+            block.packet[M] = rhs2.template loadPacketPartial<Packet8bf>(i + M, 0, 4);
+          }
+
+          block.packet[0] = vec_mergeh(block.packet[0].m_val, block.packet[1].m_val);
+          block.packet[1] = vec_mergeh(block.packet[2].m_val, block.packet[3].m_val);
+          block.packet[2] = vec_mergeh(block.packet[4].m_val, block.packet[5].m_val);
+          block.packet[3] = vec_mergeh(block.packet[6].m_val, block.packet[7].m_val);
+
+          const Index size = 16 / sizeof(bfloat16);
+
+          for (int M = 0; M < 4; M++) {
+            pstore<bfloat16>(blockB + ri + (M * size), block.packet[M]);
+          }
+        }
+
+        ri += 4*vectorSize;
+      }
+      for (; i + 2 <= depth; i += 2) {
+        if(StorageOrder == ColMajor)
+        {
+          blockB[ri+0] = rhs2(i + 0, 0);
+          blockB[ri+1] = rhs2(i + 1, 0);
+          blockB[ri+2] = rhs2(i + 0, 1);
+          blockB[ri+3] = rhs2(i + 1, 1);
+          blockB[ri+4] = rhs2(i + 0, 2);
+          blockB[ri+5] = rhs2(i + 1, 2);
+          blockB[ri+6] = rhs2(i + 0, 3);
+          blockB[ri+7] = rhs2(i + 1, 3);
+        } else {
+          PacketBlock<Packet8bf,2> block;
+
+          for (int M = 0; M < 2; M++) {
+            block.packet[M] = rhs2.template loadPacketPartial<Packet8bf>(i + M, 0, 4);
+          }
+
+          block.packet[0] = vec_mergeh(block.packet[0].m_val, block.packet[1].m_val);
+
+          pstore<bfloat16>(blockB + ri, block.packet[0]);
+        }
+
+        ri += 4*2;
+      }
+      if (depth & 1)
+      {
+        blockB[ri+0] = rhs2(i, 0);
+        blockB[ri+1] = rhs2(i, 1);
+        blockB[ri+2] = rhs2(i, 2);
+        blockB[ri+3] = rhs2(i, 3);
+
+        ri += 4;
+      }
+
+      if(PanelMode) ri += 4*(stride - offset - depth);
+    }
+
+    if (j < cols)
+    {
+      if(PanelMode) ri += offset*(cols - j);
+
+      Index i = 0;
+      for(; i + 2 <= depth; i+=2)
+      {
+        Index k = j;
+        for(; k < cols; k++)
+        {
+          blockB[ri+0] = rhs(i + 0, k);
+          blockB[ri+1] = rhs(i + 1, k);
+          ri += 2;
+        }
+      }
+      if (depth & 1)
+      {
+        for(; j < cols; j++)
+        {
+          blockB[ri] = rhs(i, j);
+          ri += 1;
+        }
+      }
+    }
+  }
+};
+
+// General template for lhs complex packing, float64 specialization.
+template<typename DataMapper, typename Packet, typename PacketC, int StorageOrder, bool Conjugate, bool PanelMode>
+struct dhs_cpack<double, DataMapper, Packet, PacketC, StorageOrder, Conjugate, PanelMode, true>
+{
+  EIGEN_STRONG_INLINE void operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+  {
+    const Index vectorSize = quad_traits<double>::vectorsize;
+    const Index vectorDelta = vectorSize * ((PanelMode) ? stride : depth);
+    Index rir = ((PanelMode) ? (vectorSize*offset) : 0), rii;
+    double* blockAt = reinterpret_cast<double *>(blockA);
+    Index j = 0;
+
+    for(; j + vectorSize <= rows; j+=vectorSize)
+    {
+      const DataMapper lhs2 = lhs.getSubMapper(j, 0);
+      Index i = 0;
+
+      rii = rir + vectorDelta;
+
+      for(; i + vectorSize <= depth; i+=vectorSize)
+      {
+        PacketBlock<Packet,2> blockr, blocki;
+        PacketBlock<PacketC,4> cblock;
+
+        if(StorageOrder == ColMajor)
+        {
+          cblock.packet[0] = lhs2.template loadPacket<PacketC>(0, i + 0); //[a1 a1i]
+          cblock.packet[1] = lhs2.template loadPacket<PacketC>(0, i + 1); //[b1 b1i]
+
+          cblock.packet[2] = lhs2.template loadPacket<PacketC>(1, i + 0); //[a2 a2i]
+          cblock.packet[3] = lhs2.template loadPacket<PacketC>(1, i + 1); //[b2 b2i]
+
+          blockr.packet[0] = vec_mergeh(cblock.packet[0].v, cblock.packet[2].v); //[a1 a2]
+          blockr.packet[1] = vec_mergeh(cblock.packet[1].v, cblock.packet[3].v); //[b1 b2]
+
+          blocki.packet[0] = vec_mergel(cblock.packet[0].v, cblock.packet[2].v);
+          blocki.packet[1] = vec_mergel(cblock.packet[1].v, cblock.packet[3].v);
+        } else {
+          cblock.packet[0] = lhs2.template loadPacket<PacketC>(0, i); //[a1 a1i]
+          cblock.packet[1] = lhs2.template loadPacket<PacketC>(1, i); //[a2 a2i]
+
+          cblock.packet[2] = lhs2.template loadPacket<PacketC>(0, i + 1); //[b1 b1i]
+          cblock.packet[3] = lhs2.template loadPacket<PacketC>(1, i + 1); //[b2 b2i
+
+          blockr.packet[0] = vec_mergeh(cblock.packet[0].v, cblock.packet[1].v); //[a1 a2]
+          blockr.packet[1] = vec_mergeh(cblock.packet[2].v, cblock.packet[3].v); //[b1 b2]
+
+          blocki.packet[0] = vec_mergel(cblock.packet[0].v, cblock.packet[1].v);
+          blocki.packet[1] = vec_mergel(cblock.packet[2].v, cblock.packet[3].v);
+        }
+
+        if(Conjugate)
+        {
+          blocki.packet[0] = -blocki.packet[0];
+          blocki.packet[1] = -blocki.packet[1];
+        }
+
+        storeBlock<double, Packet, 2>(blockAt + rir, blockr);
+        storeBlock<double, Packet, 2>(blockAt + rii, blocki);
+
+        rir += 2*vectorSize;
+        rii += 2*vectorSize;
+      }
+      for(; i < depth; i++)
+      {
+        PacketBlock<Packet,1> blockr, blocki;
+        PacketBlock<PacketC,2> cblock;
+
+        cblock.packet[0] = lhs2.template loadPacket<PacketC>(0, i);
+        cblock.packet[1] = lhs2.template loadPacket<PacketC>(1, i);
+
+        blockr.packet[0] = vec_mergeh(cblock.packet[0].v, cblock.packet[1].v);
+        blocki.packet[0] = vec_mergel(cblock.packet[0].v, cblock.packet[1].v);
+
+        if(Conjugate)
+        {
+          blocki.packet[0] = -blocki.packet[0];
+        }
+
+        pstore<double>(blockAt + rir, blockr.packet[0]);
+        pstore<double>(blockAt + rii, blocki.packet[0]);
+
+        rir += vectorSize;
+        rii += vectorSize;
+      }
+
+      rir += ((PanelMode) ? (vectorSize*(2*stride - depth)) : vectorDelta);
+    }
+
+    if (j < rows)
+    {
+      if(PanelMode) rir += (offset*(rows - j - vectorSize));
+      rii = rir + (((PanelMode) ? stride : depth) * (rows - j));
+
+      for(Index i = 0; i < depth; i++)
+      {
+        Index k = j;
+        for(; k < rows; k++)
+        {
+          blockAt[rir] = lhs(k, i).real();
+
+          if(Conjugate)
+            blockAt[rii] = -lhs(k, i).imag();
+          else
+            blockAt[rii] =  lhs(k, i).imag();
+
+          rir += 1;
+          rii += 1;
+        }
+      }
+    }
+  }
+};
+
+// General template for rhs complex packing, float64 specialization.
+template<typename DataMapper, typename Packet, typename PacketC, int StorageOrder, bool Conjugate, bool PanelMode>
+struct dhs_cpack<double, DataMapper, Packet, PacketC, StorageOrder, Conjugate, PanelMode, false>
+{
+  EIGEN_STRONG_INLINE void operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
+  {
+    const Index vectorSize = quad_traits<double>::vectorsize;
+    const Index vectorDelta = 2*vectorSize * ((PanelMode) ? stride : depth);
+    Index rir = ((PanelMode) ? (2*vectorSize*offset) : 0), rii;
+    double* blockBt = reinterpret_cast<double *>(blockB);
+    Index j = 0;
+
+    for(; j + 2*vectorSize <= cols; j+=2*vectorSize)
+    {
+      const DataMapper rhs2 = rhs.getSubMapper(0, j);
+      Index i = 0;
+
+      rii = rir + vectorDelta;
+
+      for(; i < depth; i++)
+      {
+        PacketBlock<PacketC,4> cblock;
+        PacketBlock<Packet,2> blockr, blocki;
+
+        bload<DataMapper, PacketC, 2, ColMajor, false, 4>(cblock, rhs2, i, 0);
+
+        blockr.packet[0] = vec_mergeh(cblock.packet[0].v, cblock.packet[1].v);
+        blockr.packet[1] = vec_mergeh(cblock.packet[2].v, cblock.packet[3].v);
+
+        blocki.packet[0] = vec_mergel(cblock.packet[0].v, cblock.packet[1].v);
+        blocki.packet[1] = vec_mergel(cblock.packet[2].v, cblock.packet[3].v);
+
+        if(Conjugate)
+        {
+          blocki.packet[0] = -blocki.packet[0];
+          blocki.packet[1] = -blocki.packet[1];
+        }
+
+        storeBlock<double, Packet, 2>(blockBt + rir, blockr);
+        storeBlock<double, Packet, 2>(blockBt + rii, blocki);
+
+        rir += 2*vectorSize;
+        rii += 2*vectorSize;
+      }
+
+      rir += ((PanelMode) ? (2*vectorSize*(2*stride - depth)) : vectorDelta);
+    }
+
+    if(PanelMode) rir -= (offset*(2*vectorSize - 1));
+
+    for(; j < cols; j++)
+    {
+      const DataMapper rhs2 = rhs.getSubMapper(0, j);
+      rii = rir + ((PanelMode) ? stride : depth);
+
+      for(Index i = 0; i < depth; i++)
+      {
+        blockBt[rir] = rhs2(i, 0).real();
+
+        if(Conjugate)
+          blockBt[rii] = -rhs2(i, 0).imag();
+        else
+          blockBt[rii] =  rhs2(i, 0).imag();
+
+        rir += 1;
+        rii += 1;
+      }
+
+      rir += ((PanelMode) ? (2*stride - depth) : depth);
+    }
+  }
+};
+
+/**************
+ * GEMM utils *
+ **************/
+
+// 512-bits rank1-update of acc. It can either positive or negative accumulate (useful for complex gemm).
+template<typename Packet, bool NegativeAccumulate, int N>
+EIGEN_ALWAYS_INLINE void pger_common(PacketBlock<Packet,N>* acc, const Packet& lhsV, const Packet* rhsV)
+{
+  if(NegativeAccumulate)
+  {
+    for (int M = 0; M < N; M++) {
+      acc->packet[M] = vec_nmsub(lhsV, rhsV[M], acc->packet[M]);
+    }
+  } else {
+    for (int M = 0; M < N; M++) {
+      acc->packet[M] = vec_madd(lhsV, rhsV[M], acc->packet[M]);
+    }
+  }
+}
+
+template<int N, typename Scalar, typename Packet, bool NegativeAccumulate>
+EIGEN_ALWAYS_INLINE void pger(PacketBlock<Packet,N>* acc, const Scalar* lhs, const Packet* rhsV)
+{
+  Packet lhsV = pload<Packet>(lhs);
+
+  pger_common<Packet, NegativeAccumulate, N>(acc, lhsV, rhsV);
+}
+
+// 512-bits rank1-update of complex acc. It takes decoupled accumulators as entries. It also takes cares of mixed types real * complex and complex * real.
+template<int N, typename Packet, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void pgerc_common(PacketBlock<Packet,N>* accReal, PacketBlock<Packet,N>* accImag, const Packet &lhsV, Packet &lhsVi, const Packet* rhsV, const Packet* rhsVi)
+{
+  pger_common<Packet, false, N>(accReal, lhsV, rhsV);
+  if(LhsIsReal)
+  {
+    pger_common<Packet, ConjugateRhs, N>(accImag, lhsV, rhsVi);
+    EIGEN_UNUSED_VARIABLE(lhsVi);
+  } else {
+    if (!RhsIsReal) {
+      pger_common<Packet, ConjugateLhs == ConjugateRhs, N>(accReal, lhsVi, rhsVi);
+      pger_common<Packet, ConjugateRhs, N>(accImag, lhsV, rhsVi);
+    } else {
+      EIGEN_UNUSED_VARIABLE(rhsVi);
+    }
+    pger_common<Packet, ConjugateLhs, N>(accImag, lhsVi, rhsV);
+  }
+}
+
+template<int N, typename Scalar, typename Packet, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void pgerc(PacketBlock<Packet,N>* accReal, PacketBlock<Packet,N>* accImag, const Scalar* lhs_ptr, const Scalar* lhs_ptr_imag, const Packet* rhsV, const Packet* rhsVi)
+{
+  Packet lhsV = ploadLhs<Packet>(lhs_ptr);
+  Packet lhsVi;
+  if(!LhsIsReal) lhsVi = ploadLhs<Packet>(lhs_ptr_imag);
+  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
+
+  pgerc_common<N, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(accReal, accImag, lhsV, lhsVi, rhsV, rhsVi);
+}
+
+template<typename Packet>
+EIGEN_ALWAYS_INLINE Packet ploadLhs(const __UNPACK_TYPE__(Packet)* lhs)
+{
+  return ploadu<Packet>(lhs);
+}
+
+// Zero the accumulator on PacketBlock.
+template<typename Packet, int N>
+EIGEN_ALWAYS_INLINE void bsetzero(PacketBlock<Packet,N>& acc)
+{
+  for (int M = 0; M < N; M++) {
+    acc.packet[M] = pset1<Packet>((__UNPACK_TYPE__(Packet))0);
+  }
+}
+
+template<typename Packet, int N>
+EIGEN_ALWAYS_INLINE void bscalec_common(PacketBlock<Packet,N>& acc, PacketBlock<Packet,N>& accZ, const Packet& pAlpha)
+{
+  for (int M = 0; M < N; M++) {
+    acc.packet[M] = vec_mul(accZ.packet[M], pAlpha);
+  }
+}
+
+template<typename Packet, int N>
+EIGEN_ALWAYS_INLINE void band(PacketBlock<Packet,N>& acc, const Packet& pMask)
+{
+  for (int M = 0; M < N; M++) {
+    acc.packet[M] = pand<Packet>(acc.packet[M], pMask);
+  }
+}
+
+// Complex version of PacketBlock scaling.
+template<typename Packet, int N, bool mask>
+EIGEN_ALWAYS_INLINE void bscalec(PacketBlock<Packet,N>& aReal, PacketBlock<Packet,N>& aImag, const Packet& bReal, const Packet& bImag, PacketBlock<Packet,N>& cReal, PacketBlock<Packet,N>& cImag, const Packet& pMask)
+{
+  if (mask && (sizeof(__UNPACK_TYPE__(Packet)) == sizeof(float))) {
+    band<Packet, N>(aReal, pMask);
+    band<Packet, N>(aImag, pMask);
+  } else {
+    EIGEN_UNUSED_VARIABLE(pMask);
+  }
+
+  bscalec_common<Packet, N>(cReal, aReal, bReal);
+
+  bscalec_common<Packet, N>(cImag, aImag, bReal);
+
+  pger_common<Packet, true, N>(&cReal, bImag, aImag.packet);
+
+  pger_common<Packet, false, N>(&cImag, bImag, aReal.packet);
+}
+
+// Load a PacketBlock, the N parameters make tunning gemm easier so we can add more accumulators as needed.
+//
+// full = operate (load) on the entire PacketBlock or only half
+template<typename DataMapper, typename Packet, const Index accCols, int StorageOrder, bool Complex, int N, bool full>
+EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,N*(Complex?2:1)>& acc, const DataMapper& res, Index row, Index col)
+{
+  if (StorageOrder == RowMajor) {
+    for (int M = 0; M < N; M++) {
+      acc.packet[M] = res.template loadPacket<Packet>(row + M, col);
+    }
+    if (Complex) {
+      for (int M = 0; M < N; M++) {
+        acc.packet[M+N] = res.template loadPacket<Packet>(row + M, col + accCols);
+      }
+    }
+  } else {
+    for (int M = 0; M < N; M++) {
+      acc.packet[M] = res.template loadPacket<Packet>(row, col + M);
+    }
+    if (Complex && full) {
+      for (int M = 0; M < N; M++) {
+        acc.packet[M+N] = res.template loadPacket<Packet>(row + accCols, col + M);
+      }
+    }
+  }
+}
+
+template<typename DataMapper, typename Packet, int N>
+EIGEN_ALWAYS_INLINE void bstore(PacketBlock<Packet,N>& acc, const DataMapper& res, Index row)
+{
+  for (int M = 0; M < N; M++) {
+    res.template storePacket<Packet>(row, M, acc.packet[M]);
+  }
+}
+
+#ifdef USE_PARTIAL_PACKETS
+template<typename DataMapper, typename Packet, const Index accCols, bool Complex, Index N, bool full>
+EIGEN_ALWAYS_INLINE void bload_partial(PacketBlock<Packet,N*(Complex?2:1)>& acc, const DataMapper& res, Index row, Index elements)
+{
+  for (Index M = 0; M < N; M++) {
+    acc.packet[M] = res.template loadPacketPartial<Packet>(row, M, elements);
+  }
+  if (Complex && full) {
+    for (Index M = 0; M < N; M++) {
+      acc.packet[M+N] = res.template loadPacketPartial<Packet>(row + accCols, M, elements);
+    }
+  }
+}
+
+template<typename DataMapper, typename Packet, Index N>
+EIGEN_ALWAYS_INLINE void bstore_partial(PacketBlock<Packet,N>& acc, const DataMapper& res, Index row, Index elements)
+{
+  for (Index M = 0; M < N; M++) {
+    res.template storePacketPartial<Packet>(row, M, acc.packet[M], elements);
+  }
+}
+#endif
+
+#ifdef _ARCH_PWR10
+#define USE_P10_AND_PVIPR2_0 (EIGEN_COMP_LLVM || (__GNUC__ >= 11))
+#else
+#define USE_P10_AND_PVIPR2_0 0
+#endif
+
+#if !USE_P10_AND_PVIPR2_0
+const static Packet4i mask4[4] = { {  0,  0,  0,  0 }, { -1,  0,  0,  0 }, { -1, -1,  0,  0 }, { -1, -1, -1,  0 } };
+#endif
+
+template<typename Packet>
+EIGEN_ALWAYS_INLINE Packet bmask(const Index remaining_rows)
+{
+#if USE_P10_AND_PVIPR2_0
+#ifdef _BIG_ENDIAN
+  return Packet(vec_reve(vec_genwm((1 << remaining_rows) - 1)));
+#else
+  return Packet(vec_genwm((1 << remaining_rows) - 1));
+#endif
+#else
+  return Packet(mask4[remaining_rows]);
+#endif
+}
+
+template<>
+EIGEN_ALWAYS_INLINE Packet2d bmask<Packet2d>(const Index remaining_rows)
+{
+#if USE_P10_AND_PVIPR2_0
+  Packet2d mask2 = Packet2d(vec_gendm(remaining_rows));
+#ifdef _BIG_ENDIAN
+  return preverse(mask2);
+#else
+  return mask2;
+#endif
+#else
+  Packet2l ret = { -remaining_rows, 0 };
+  return Packet2d(ret);
+#endif
+}
+
+template<typename Packet, int N>
+EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,N>& acc, PacketBlock<Packet,N>& accZ, const Packet& pAlpha)
+{
+  for (int M = 0; M < N; M++) {
+    acc.packet[M] = pmadd<Packet>(pAlpha, accZ.packet[M], acc.packet[M]);
+  }
+}
+
+// Scale the PacketBlock vectors by alpha.
+template<typename Packet, int N, bool mask>
+EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,N>& acc, PacketBlock<Packet,N>& accZ, const Packet& pAlpha, const Packet& pMask)
+{
+  if (mask) {
+    band<Packet, N>(accZ, pMask);
+  } else {
+    EIGEN_UNUSED_VARIABLE(pMask);
+  }
+
+  bscale<Packet, N>(acc, accZ, pAlpha);
+}
+
+template<typename Packet, int N, bool real>
+EIGEN_ALWAYS_INLINE void pbroadcastN(const __UNPACK_TYPE__(Packet) *ap0,
+        const __UNPACK_TYPE__(Packet) *ap1, const __UNPACK_TYPE__(Packet) *ap2,
+        Packet& a0, Packet& a1, Packet& a2, Packet& a3)
+{
+  a0 = pset1<Packet>(ap0[0]);
+  if (N == 4) {
+    a1 = pset1<Packet>(ap0[1]);
+    a2 = pset1<Packet>(ap0[2]);
+    a3 = pset1<Packet>(ap0[3]);
+    EIGEN_UNUSED_VARIABLE(ap1);
+    EIGEN_UNUSED_VARIABLE(ap2);
+  } else {
+    if (N > 1) {
+      a1 = pset1<Packet>(ap1[0]);
+    } else {
+      EIGEN_UNUSED_VARIABLE(a1);
+      EIGEN_UNUSED_VARIABLE(ap1);
+    }
+    if (N > 2) {
+      a2 = pset1<Packet>(ap2[0]);
+    } else {
+      EIGEN_UNUSED_VARIABLE(a2);
+      EIGEN_UNUSED_VARIABLE(ap2);
+    }
+  }
+}
+
+template<> EIGEN_ALWAYS_INLINE void
+pbroadcastN<Packet4f,4,true>(const float *ap0, const float *, const float *,
+                             Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
+{
+  pbroadcast4<Packet4f>(ap0, a0, a1, a2, a3);
+}
+
+template<> EIGEN_ALWAYS_INLINE void
+pbroadcastN<Packet4f,4,false>(const float *ap0, const float *ap1, const float *ap2,
+                              Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
+{
+  pbroadcastN<Packet4f,4,true>(ap0, ap1, ap2, a0, a1, a2, a3);
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void pbroadcastN<Packet2d,4,false>(const double* ap0, const double *,
+    const double *, Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
+{
+  a1 = pload<Packet2d>(ap0);
+  a3 = pload<Packet2d>(ap0 + 2);
+  a0 = vec_splat(a1, 0);
+  a1 = vec_splat(a1, 1);
+  a2 = vec_splat(a3, 0);
+  a3 = vec_splat(a3, 1);
+}
+
+// Grab two decouples real/imaginary PacketBlocks and return two coupled (real/imaginary pairs) PacketBlocks.
+template<typename Packet, typename Packetc, int N, bool full>
+EIGEN_ALWAYS_INLINE void bcouple_common(PacketBlock<Packet,N>& taccReal, PacketBlock<Packet,N>& taccImag, PacketBlock<Packetc, N>& acc1, PacketBlock<Packetc, N>& acc2)
+{
+  for (int M = 0; M < N; M++) {
+    acc1.packet[M].v = vec_mergeh(taccReal.packet[M], taccImag.packet[M]);
+  }
+
+  if (full) {
+    for (int M = 0; M < N; M++) {
+      acc2.packet[M].v = vec_mergel(taccReal.packet[M], taccImag.packet[M]);
+    }
+  }
+}
+
+template<typename Packet, typename Packetc, int N, bool full>
+EIGEN_ALWAYS_INLINE void bcouple(PacketBlock<Packet,N>& taccReal, PacketBlock<Packet,N>& taccImag, PacketBlock<Packetc,N*2>& tRes, PacketBlock<Packetc, N>& acc1, PacketBlock<Packetc, N>& acc2)
+{
+  bcouple_common<Packet, Packetc, N, full>(taccReal, taccImag, acc1, acc2);
+
+  for (int M = 0; M < N; M++) {
+    acc1.packet[M] = padd<Packetc>(tRes.packet[M], acc1.packet[M]);
+  }
+
+  if (full) {
+    for (int M = 0; M < N; M++) {
+      acc2.packet[M] = padd<Packetc>(tRes.packet[M+N], acc2.packet[M]);
+    }
+  }
+}
+
+// PEEL loop factor.
+#define PEEL 7
+#define PEEL_ROW 7
+
+#define MICRO_UNROLL(func) \
+  func(0) func(1) func(2) func(3) func(4) func(5) func(6) func(7)
+
+#define MICRO_NORMAL_ROWS \
+  accRows == quad_traits<Scalar>::rows || accRows == 1
+
+#define MICRO_NEW_ROWS ((MICRO_NORMAL_ROWS) ? accRows : 1)
+
+#define MICRO_RHS(ptr, N) rhs_##ptr##N
+
+#define MICRO_ZERO_PEEL(peel) \
+  if ((PEEL_ROW > peel) && (peel != 0)) { \
+    bsetzero<Packet, accRows>(accZero##peel); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(accZero##peel); \
+  }
+
+#define MICRO_ADD(ptr, N) \
+  if (MICRO_NORMAL_ROWS) { \
+    MICRO_RHS(ptr,0) += (accRows * N); \
+  } else { \
+    MICRO_RHS(ptr,0) += N; \
+    MICRO_RHS(ptr,1) += N; \
+    if (accRows == 3) { \
+       MICRO_RHS(ptr,2) += N; \
+    } \
+  }
+
+#define MICRO_ADD_ROWS(N) MICRO_ADD(ptr, N)
+
+#define MICRO_BROADCAST1(peel, ptr, rhsV, real) \
+  if (MICRO_NORMAL_ROWS) { \
+    pbroadcastN<Packet,accRows,real>(MICRO_RHS(ptr,0) + (accRows * peel), MICRO_RHS(ptr,0), MICRO_RHS(ptr,0), rhsV##peel[0], rhsV##peel[1], rhsV##peel[2], rhsV##peel[3]); \
+  } else { \
+    pbroadcastN<Packet,accRows,real>(MICRO_RHS(ptr,0) + peel, MICRO_RHS(ptr,1) + peel, MICRO_RHS(ptr,2) + peel, rhsV##peel[0], rhsV##peel[1], rhsV##peel[2], rhsV##peel[3]); \
+  }
+
+#define MICRO_BROADCAST(peel) MICRO_BROADCAST1(peel, ptr, rhsV, true)
+
+#define MICRO_BROADCAST_EXTRA1(ptr, rhsV, real) \
+  pbroadcastN<Packet,accRows,real>(MICRO_RHS(ptr,0), MICRO_RHS(ptr,1), MICRO_RHS(ptr,2), rhsV[0], rhsV[1], rhsV[2], rhsV[3]);
+
+#define MICRO_BROADCAST_EXTRA \
+  Packet rhsV[4]; \
+  MICRO_BROADCAST_EXTRA1(ptr, rhsV, true) \
+  MICRO_ADD_ROWS(1)
+
+#define MICRO_SRC2(ptr, N, M) \
+  if (MICRO_NORMAL_ROWS) { \
+    EIGEN_UNUSED_VARIABLE(strideB); \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr,1)); \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr,2)); \
+  } else { \
+    MICRO_RHS(ptr,1) = rhs_base + N + M; \
+    if (accRows == 3) { \
+      MICRO_RHS(ptr,2) = rhs_base + N*2 + M; \
+    } else { \
+      EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr,2)); \
+    } \
+  }
+
+#define MICRO_SRC2_PTR MICRO_SRC2(ptr, strideB, 0)
+
+#define MICRO_ZERO_PEEL_ROW MICRO_UNROLL(MICRO_ZERO_PEEL)
+
+#define MICRO_WORK_PEEL(peel) \
+  if (PEEL_ROW > peel) { \
+    MICRO_BROADCAST(peel) \
+    pger<accRows, Scalar, Packet, false>(&accZero##peel, lhs_ptr + (remaining_rows * peel), rhsV##peel); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
+  }
+
+#define MICRO_WORK_PEEL_ROW \
+  Packet rhsV0[4], rhsV1[4], rhsV2[4], rhsV3[4], rhsV4[4], rhsV5[4], rhsV6[4], rhsV7[4]; \
+  MICRO_UNROLL(MICRO_WORK_PEEL) \
+  lhs_ptr += (remaining_rows * PEEL_ROW); \
+  MICRO_ADD_ROWS(PEEL_ROW)
+
+#define MICRO_ADD_PEEL(peel, sum) \
+  if (PEEL_ROW > peel) { \
+    for (Index i = 0; i < accRows; i++) { \
+      accZero##sum.packet[i] += accZero##peel.packet[i]; \
+    } \
+  }
+
+#define MICRO_ADD_PEEL_ROW \
+  MICRO_ADD_PEEL(4, 0) MICRO_ADD_PEEL(5, 1) MICRO_ADD_PEEL(6, 2) MICRO_ADD_PEEL(7, 3) \
+  MICRO_ADD_PEEL(2, 0) MICRO_ADD_PEEL(3, 1) MICRO_ADD_PEEL(1, 0)
+
+#define MICRO_PREFETCHN1(ptr, N) \
+  EIGEN_POWER_PREFETCH(MICRO_RHS(ptr,0)); \
+  if (N == 2 || N == 3) { \
+    EIGEN_POWER_PREFETCH(MICRO_RHS(ptr,1)); \
+    if (N == 3) { \
+      EIGEN_POWER_PREFETCH(MICRO_RHS(ptr,2)); \
+    } \
+  }
+
+#define MICRO_PREFETCHN(N) MICRO_PREFETCHN1(ptr, N)
+
+#define MICRO_COMPLEX_PREFETCHN(N) \
+  MICRO_PREFETCHN1(ptr_real, N); \
+  if(!RhsIsReal) { \
+    MICRO_PREFETCHN1(ptr_imag, N); \
+  }
+
+template<typename Scalar, typename Packet, const Index accRows, const Index remaining_rows>
+EIGEN_ALWAYS_INLINE void MICRO_EXTRA_ROW(
+  const Scalar* &lhs_ptr,
+  const Scalar* &rhs_ptr0,
+  const Scalar* &rhs_ptr1,
+  const Scalar* &rhs_ptr2,
+  PacketBlock<Packet,accRows> &accZero)
+{
+  MICRO_BROADCAST_EXTRA
+  pger<accRows, Scalar, Packet, false>(&accZero, lhs_ptr, rhsV);
+  lhs_ptr += remaining_rows;
+}
+
+template<typename Scalar, typename Packet, typename DataMapper, const Index accRows, const Index accCols, const Index remaining_rows>
+EIGEN_ALWAYS_INLINE void gemm_unrolled_row_iteration(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index row,
+  Index rows,
+  const Packet& pAlpha,
+  const Packet& pMask)
+{
+  const Scalar* rhs_ptr0 = rhs_base, * rhs_ptr1 = NULL, * rhs_ptr2 = NULL;
+  const Scalar* lhs_ptr = lhs_base + row*strideA + remaining_rows*offsetA;
+  PacketBlock<Packet,accRows> accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7, acc;
+
+  MICRO_SRC2_PTR
+  bsetzero<Packet, accRows>(accZero0);
+
+  Index remaining_depth = depth & -quad_traits<Scalar>::rows;
+  Index k = 0;
+  if (remaining_depth >= PEEL_ROW) {
+    MICRO_ZERO_PEEL_ROW
+    do
+    {
+      MICRO_PREFETCHN(accRows)
+      EIGEN_POWER_PREFETCH(lhs_ptr);
+      MICRO_WORK_PEEL_ROW
+    } while ((k += PEEL_ROW) + PEEL_ROW <= remaining_depth);
+    MICRO_ADD_PEEL_ROW
+  }
+  for(; k < depth; k++)
+  {
+    MICRO_EXTRA_ROW<Scalar, Packet, accRows, remaining_rows>(lhs_ptr, rhs_ptr0, rhs_ptr1, rhs_ptr2, accZero0);
+  }
+
+#ifdef USE_PARTIAL_PACKETS
+  EIGEN_UNUSED_VARIABLE(rows);
+  EIGEN_UNUSED_VARIABLE(pMask);
+  bload_partial<DataMapper, Packet, 0, false, accRows>(acc, res, row, remaining_rows);
+  bscale<Packet,accRows>(acc, accZero0, pAlpha);
+  bstore_partial<DataMapper, Packet, accRows>(acc, res, row, remaining_rows);
+#else
+  bload<DataMapper, Packet, 0, ColMajor, false, accRows>(acc, res, row, 0);
+  if ((accRows == 1) || (rows >= accCols))
+  {
+    bscale<Packet,accRows,true>(acc, accZero0, pAlpha, pMask);
+    bstore<DataMapper, Packet, accRows>(acc, res, row);
+  } else {
+    bscale<Packet,accRows,false>(acc, accZero0, pAlpha, pMask);
+    for(Index j = 0; j < accRows; j++) {
+      for(Index i = 0; i < remaining_rows; i++) {
+        res(row + i, j) = acc.packet[j][i];
+      }
+    }
+  }
+#endif
+}
+
+#define MICRO_EXTRA(MICRO_EXTRA_UNROLL, value, is_col) \
+  switch(value) { \
+    default: \
+      MICRO_EXTRA_UNROLL(1) \
+      break; \
+    case 2: \
+      if (is_col || (sizeof(Scalar) == sizeof(float))) { \
+        MICRO_EXTRA_UNROLL(2) \
+      } \
+      break; \
+    case 3: \
+      if (is_col || (sizeof(Scalar) == sizeof(float))) { \
+        MICRO_EXTRA_UNROLL(3) \
+      } \
+      break; \
+  }
+
+#define MICRO_EXTRA_ROWS(N) \
+  gemm_unrolled_row_iteration<Scalar, Packet, DataMapper, accRows, accCols, N>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, rows, pAlpha, pMask);
+
+template<typename Scalar, typename Packet, typename DataMapper, const Index accRows, const Index accCols>
+EIGEN_ALWAYS_INLINE void gemm_extra_row(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index row,
+  Index rows,
+  Index remaining_rows,
+  const Packet& pAlpha,
+  const Packet& pMask)
+{
+  MICRO_EXTRA(MICRO_EXTRA_ROWS, remaining_rows, false)
+}
+
+#define MICRO_UNROLL_WORK(func, func2, peel) \
+  MICRO_UNROLL(func2); \
+  func(0,peel) func(1,peel) func(2,peel) func(3,peel) \
+  func(4,peel) func(5,peel) func(6,peel) func(7,peel)
+
+#define MICRO_WORK_ONE(iter, peel) \
+  if (unroll_factor > iter) { \
+    pger_common<Packet, false, accRows>(&accZero##iter, lhsV##iter, rhsV##peel); \
+  }
+
+#define MICRO_TYPE_PEEL4(func, func2, peel) \
+  if (PEEL > peel) { \
+    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4, lhsV5, lhsV6, lhsV7; \
+    MICRO_BROADCAST(peel) \
+    MICRO_UNROLL_WORK(func, func2, peel) \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
+  }
+
+#define MICRO_UNROLL_TYPE_PEEL(M, func, func1, func2) \
+  Packet rhsV0[M], rhsV1[M], rhsV2[M], rhsV3[M], rhsV4[M], rhsV5[M], rhsV6[M], rhsV7[M]; \
+  func(func1,func2,0) func(func1,func2,1) \
+  func(func1,func2,2) func(func1,func2,3) \
+  func(func1,func2,4) func(func1,func2,5) \
+  func(func1,func2,6) func(func1,func2,7)
+
+#define MICRO_UNROLL_TYPE_ONE(M, func, func1, func2) \
+  Packet rhsV0[M]; \
+  func(func1,func2,0)
+
+#define MICRO_UNROLL_TYPE(MICRO_TYPE, size) \
+  MICRO_TYPE(4, MICRO_TYPE_PEEL4, MICRO_WORK_ONE, MICRO_LOAD_ONE) \
+  MICRO_ADD_ROWS(size)
+
+#define MICRO_ONE_PEEL4 MICRO_UNROLL_TYPE(MICRO_UNROLL_TYPE_PEEL, PEEL)
+
+#define MICRO_ONE4 MICRO_UNROLL_TYPE(MICRO_UNROLL_TYPE_ONE, 1)
+
+#define MICRO_DST_PTR_ONE(iter) \
+  if (unroll_factor > iter) { \
+    bsetzero<Packet, accRows>(accZero##iter); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(accZero##iter); \
+  }
+
+#define MICRO_DST_PTR MICRO_UNROLL(MICRO_DST_PTR_ONE)
+
+#define MICRO_SRC_PTR MICRO_UNROLL(MICRO_SRC_PTR_ONE)
+
+#define MICRO_PREFETCH MICRO_UNROLL(MICRO_PREFETCH_ONE)
+
+#ifdef USE_PARTIAL_PACKETS
+#define MICRO_STORE_ONE(iter) \
+  if (unroll_factor > iter) { \
+    if (MICRO_NORMAL_PARTIAL(iter)) { \
+      bload<DataMapper, Packet, 0, ColMajor, false, accRows>(acc, res, row + iter*accCols, 0); \
+      bscale<Packet,accRows>(acc, accZero##iter, pAlpha); \
+      bstore<DataMapper, Packet, accRows>(acc, res, row + iter*accCols); \
+    } else { \
+      bload_partial<DataMapper, Packet, 0, false, accRows>(acc, res, row + iter*accCols, accCols2); \
+      bscale<Packet,accRows>(acc, accZero##iter, pAlpha); \
+      bstore_partial<DataMapper, Packet, accRows>(acc, res, row + iter*accCols, accCols2); \
+    } \
+  }
+#else
+#define MICRO_STORE_ONE(iter) \
+  if (unroll_factor > iter) { \
+    bload<DataMapper, Packet, 0, ColMajor, false, accRows>(acc, res, row + iter*accCols, 0); \
+    bscale<Packet,accRows,!(MICRO_NORMAL(iter))>(acc, accZero##iter, pAlpha, pMask); \
+    bstore<DataMapper, Packet, accRows>(acc, res, row + iter*accCols); \
+  }
+#endif
+
+#define MICRO_STORE MICRO_UNROLL(MICRO_STORE_ONE)
+
+#ifdef USE_PARTIAL_PACKETS
+template<int unroll_factor, typename Scalar, typename Packet, typename DataMapper, const Index accRows, const Index accCols, bool full>
+#else
+template<int unroll_factor, typename Scalar, typename Packet, typename DataMapper, const Index accRows, const Index accCols, const Index accCols2>
+#endif
+EIGEN_ALWAYS_INLINE void gemm_unrolled_iteration(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index& row,
+  const Packet& pAlpha,
+#ifdef USE_PARTIAL_PACKETS
+  Index accCols2
+#else
+  const Packet& pMask
+#endif
+  )
+{
+  const Scalar* rhs_ptr0 = rhs_base, * rhs_ptr1 = NULL, * rhs_ptr2 = NULL;
+  const Scalar* lhs_ptr0 = NULL, * lhs_ptr1 = NULL, * lhs_ptr2 = NULL, * lhs_ptr3 = NULL, * lhs_ptr4 = NULL, * lhs_ptr5 = NULL, * lhs_ptr6 = NULL, * lhs_ptr7 = NULL;
+  PacketBlock<Packet,accRows> accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7;
+  PacketBlock<Packet,accRows> acc;
+
+  MICRO_SRC2_PTR
+  MICRO_SRC_PTR
+  MICRO_DST_PTR
+
+  Index k = 0;
+  for(; k + PEEL <= depth; k+= PEEL)
+  {
+    MICRO_PREFETCHN(accRows)
+    MICRO_PREFETCH
+    MICRO_ONE_PEEL4
+  }
+  for(; k < depth; k++)
+  {
+    MICRO_ONE4
+  }
+  MICRO_STORE
+
+  MICRO_UPDATE
+}
+
+#ifdef USE_PARTIAL_PACKETS
+#define MICRO_UNROLL_ITER2(N, M) \
+  gemm_unrolled_iteration<N + ((M) ? 1 : 0), Scalar, Packet, DataMapper, accRows, accCols, !M>(res3, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, pAlpha, M ? remaining_rows : accCols); \
+  if (M) return;
+#else
+#define MICRO_UNROLL_ITER2(N, M) \
+  gemm_unrolled_iteration<N + ((M) ? 1 : 0), Scalar, Packet, DataMapper, accRows, accCols, M ? M : accCols>(res3, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, pAlpha, pMask); \
+  if (M) return;
+#endif
+
+template<typename Scalar, typename Packet, typename DataMapper, const Index accRows, const Index accCols>
+EIGEN_ALWAYS_INLINE void gemm_cols(
+  const DataMapper& res,
+  const Scalar* blockA,
+  const Scalar* blockB,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index offsetB,
+  Index col,
+  Index rows,
+  Index remaining_rows,
+  const Packet& pAlpha,
+  const Packet& pMask)
+{
+  const DataMapper res3 = res.getSubMapper(0, col);
+
+  const Scalar* rhs_base = blockB + col*strideB + MICRO_NEW_ROWS*offsetB;
+  const Scalar* lhs_base = blockA + accCols*offsetA;
+  Index row = 0;
+
+#define MAX_UNROLL 7
+  while(row + MAX_UNROLL*accCols <= rows) {
+    MICRO_UNROLL_ITER2(MAX_UNROLL, 0);
+  }
+  switch( (rows-row)/accCols ) {
+#if MAX_UNROLL > 7
+    case 7:
+      MICRO_UNROLL_ITER(MICRO_UNROLL_ITER2, 7)
+      break;
+#endif
+#if MAX_UNROLL > 6
+    case 6:
+      MICRO_UNROLL_ITER(MICRO_UNROLL_ITER2, 6)
+      break;
+#endif
+#if MAX_UNROLL > 5
+    case 5:
+      MICRO_UNROLL_ITER(MICRO_UNROLL_ITER2, 5)
+      break;
+#endif
+#if MAX_UNROLL > 4
+    case 4:
+      MICRO_UNROLL_ITER(MICRO_UNROLL_ITER2, 4)
+      break;
+#endif
+#if MAX_UNROLL > 3
+    case 3:
+      MICRO_UNROLL_ITER(MICRO_UNROLL_ITER2, 3)
+      break;
+#endif
+#if MAX_UNROLL > 2
+    case 2:
+      MICRO_UNROLL_ITER(MICRO_UNROLL_ITER2, 2)
+      break;
+#endif
+#if MAX_UNROLL > 1
+    case 1:
+      MICRO_UNROLL_ITER(MICRO_UNROLL_ITER2, 1)
+      break;
+#endif
+    default:
+      break;
+  }
+#undef MAX_UNROLL
+
+  if(remaining_rows > 0)
+  {
+    gemm_extra_row<Scalar, Packet, DataMapper, accRows, accCols>(res3, blockA, rhs_base, depth, strideA, offsetA, strideB, row, rows, remaining_rows, pAlpha, pMask);
+  }
+}
+
+#define MICRO_EXTRA_COLS(N) \
+  gemm_cols<Scalar, Packet, DataMapper, N, accCols>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, remaining_rows, pAlpha, pMask);
+
+template<typename Scalar, typename Packet, typename DataMapper, const Index accCols>
+EIGEN_ALWAYS_INLINE void gemm_extra_cols(
+  const DataMapper& res,
+  const Scalar* blockA,
+  const Scalar* blockB,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index offsetB,
+  Index col,
+  Index rows,
+  Index cols,
+  Index remaining_rows,
+  const Packet& pAlpha,
+  const Packet& pMask)
+{
+  MICRO_EXTRA(MICRO_EXTRA_COLS, cols-col, true)
+}
+
+/****************
+ * GEMM kernels *
+ * **************/
+template<typename Scalar, typename Packet, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols>
+EIGEN_STRONG_INLINE void gemm(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index rows, Index depth, Index cols, Scalar alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
+{
+      const Index remaining_rows = rows % accCols;
+
+      if( strideA == -1 ) strideA = depth;
+      if( strideB == -1 ) strideB = depth;
+
+      const Packet pAlpha = pset1<Packet>(alpha);
+      const Packet pMask  = bmask<Packet>(remaining_rows);
+
+      Index col = 0;
+      for(; col + accRows <= cols; col += accRows)
+      {
+        gemm_cols<Scalar, Packet, DataMapper, accRows, accCols>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, remaining_rows, pAlpha, pMask);
+      }
+
+      if (col != cols)
+      {
+        gemm_extra_cols<Scalar, Packet, DataMapper, accCols>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols, remaining_rows, pAlpha, pMask);
+      }
+}
+
+#define accColsC (accCols / 2)
+#define advanceRows ((LhsIsReal) ? 1 : 2)
+#define advanceCols ((RhsIsReal) ? 1 : 2)
+
+// PEEL_COMPLEX loop factor.
+#define PEEL_COMPLEX 3
+#define PEEL_COMPLEX_ROW 3
+
+#define MICRO_COMPLEX_UNROLL(func) \
+  func(0) func(1) func(2) func(3)
+
+#define MICRO_COMPLEX_ZERO_PEEL(peel) \
+  if ((PEEL_COMPLEX_ROW > peel) && (peel != 0)) { \
+    bsetzero<Packet, accRows>(accReal##peel); \
+    bsetzero<Packet, accRows>(accImag##peel); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(accReal##peel); \
+    EIGEN_UNUSED_VARIABLE(accImag##peel); \
+  }
+
+#define MICRO_COMPLEX_ADD_ROWS(N, used) \
+  MICRO_ADD(ptr_real, N) \
+  if (!RhsIsReal) { \
+    MICRO_ADD(ptr_imag, N) \
+  } else if (used) { \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr_imag,0)); \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr_imag,1)); \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr_imag,2)); \
+  }
+
+#define MICRO_COMPLEX_BROADCAST(peel) \
+  MICRO_BROADCAST1(peel, ptr_real, rhsV, false) \
+  if (!RhsIsReal) { \
+    MICRO_BROADCAST1(peel, ptr_imag, rhsVi, false) \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
+  }
+
+#define MICRO_COMPLEX_BROADCAST_EXTRA \
+  Packet rhsV[4], rhsVi[4]; \
+  MICRO_BROADCAST_EXTRA1(ptr_real, rhsV, false) \
+  if(!RhsIsReal) { \
+    MICRO_BROADCAST_EXTRA1(ptr_imag, rhsVi, false) \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(rhsVi); \
+  } \
+  MICRO_COMPLEX_ADD_ROWS(1, true)
+
+#define MICRO_COMPLEX_SRC2_PTR \
+  MICRO_SRC2(ptr_real, strideB*advanceCols, 0) \
+  if (!RhsIsReal) { \
+    MICRO_RHS(ptr_imag,0) = rhs_base + MICRO_NEW_ROWS*strideB; \
+    MICRO_SRC2(ptr_imag, strideB*advanceCols, strideB) \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr_imag,0)); \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr_imag,1)); \
+    EIGEN_UNUSED_VARIABLE(MICRO_RHS(ptr_imag,2)); \
+  }
+
+#define MICRO_COMPLEX_ZERO_PEEL_ROW MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_ZERO_PEEL)
+
+#define MICRO_COMPLEX_WORK_PEEL(peel) \
+  if (PEEL_COMPLEX_ROW > peel) { \
+    MICRO_COMPLEX_BROADCAST(peel) \
+    pgerc<accRows, Scalar, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal##peel, &accImag##peel, lhs_ptr_real + (remaining_rows * peel), lhs_ptr_imag + (remaining_rows * peel), rhsV##peel, rhsVi##peel); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
+    EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
+  }
+
+#define MICRO_COMPLEX_ADD_COLS(size) \
+  lhs_ptr_real += (remaining_rows * size); \
+  if(!LhsIsReal) lhs_ptr_imag += (remaining_rows * size); \
+  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
+
+#define MICRO_COMPLEX_WORK_PEEL_ROW \
+  Packet rhsV0[4], rhsV1[4], rhsV2[4], rhsV3[4]; \
+  Packet rhsVi0[4], rhsVi1[4], rhsVi2[4], rhsVi3[4]; \
+  MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_WORK_PEEL) \
+  MICRO_COMPLEX_ADD_COLS(PEEL_COMPLEX_ROW) \
+  MICRO_COMPLEX_ADD_ROWS(PEEL_COMPLEX_ROW, false)
+
+#define MICRO_COMPLEX_ADD_PEEL(peel, sum) \
+  if (PEEL_COMPLEX_ROW > peel) { \
+    for (Index i = 0; i < accRows; i++) { \
+      accReal##sum.packet[i] += accReal##peel.packet[i]; \
+      accImag##sum.packet[i] += accImag##peel.packet[i]; \
+    } \
+  }
+
+#define MICRO_COMPLEX_ADD_PEEL_ROW \
+  MICRO_COMPLEX_ADD_PEEL(2, 0) MICRO_COMPLEX_ADD_PEEL(3, 1) \
+  MICRO_COMPLEX_ADD_PEEL(1, 0)
+
+template<typename Scalar, typename Packet, const Index accRows, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal, const Index remaining_rows>
+EIGEN_ALWAYS_INLINE void MICRO_COMPLEX_EXTRA_ROW(
+  const Scalar* &lhs_ptr_real, const Scalar* &lhs_ptr_imag,
+  const Scalar* &rhs_ptr_real0, const Scalar* &rhs_ptr_real1, const Scalar* &rhs_ptr_real2,
+  const Scalar* &rhs_ptr_imag0, const Scalar* &rhs_ptr_imag1, const Scalar* &rhs_ptr_imag2,
+  PacketBlock<Packet,accRows> &accReal, PacketBlock<Packet,accRows> &accImag)
+{
+  MICRO_COMPLEX_BROADCAST_EXTRA
+  pgerc<accRows, Scalar, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal, &accImag, lhs_ptr_real, lhs_ptr_imag, rhsV, rhsVi);
+  MICRO_COMPLEX_ADD_COLS(1)
+}
+
+template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal, const Index remaining_rows>
+EIGEN_ALWAYS_INLINE void gemm_unrolled_complex_row_iteration(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index row,
+  Index rows,
+  const Packet& pAlphaReal,
+  const Packet& pAlphaImag,
+  const Packet& pMask)
+{
+  const Scalar* rhs_ptr_real0 = rhs_base, * rhs_ptr_real1 = NULL, * rhs_ptr_real2 = NULL;
+  const Scalar* rhs_ptr_imag0 = NULL, * rhs_ptr_imag1 = NULL, * rhs_ptr_imag2 = NULL;
+  const Scalar* lhs_ptr_real = lhs_base + advanceRows*row*strideA + remaining_rows*offsetA;
+  const Scalar* lhs_ptr_imag = NULL;
+  if(!LhsIsReal) lhs_ptr_imag = lhs_ptr_real + remaining_rows*strideA;
+  else EIGEN_UNUSED_VARIABLE(lhs_ptr_imag);
+  PacketBlock<Packet,accRows> accReal0, accImag0, accReal1, accImag1, accReal2, accImag2, accReal3, accImag3;
+  PacketBlock<Packet,accRows> taccReal, taccImag;
+  PacketBlock<Packetc,accRows> acc0, acc1;
+  PacketBlock<Packetc,accRows*2> tRes;
+
+  MICRO_COMPLEX_SRC2_PTR
+
+  bsetzero<Packet, accRows>(accReal0);
+  bsetzero<Packet, accRows>(accImag0);
+
+  Index remaining_depth = depth & -quad_traits<Scalar>::rows;
+  Index k = 0;
+  if (remaining_depth >= PEEL_COMPLEX_ROW) {
+    MICRO_COMPLEX_ZERO_PEEL_ROW
+    do
+    {
+      MICRO_COMPLEX_PREFETCHN(accRows)
+      EIGEN_POWER_PREFETCH(lhs_ptr_real);
+      if(!LhsIsReal) {
+        EIGEN_POWER_PREFETCH(lhs_ptr_imag);
+      }
+      MICRO_COMPLEX_WORK_PEEL_ROW
+    } while ((k += PEEL_COMPLEX_ROW) + PEEL_COMPLEX_ROW <= remaining_depth);
+    MICRO_COMPLEX_ADD_PEEL_ROW
+  }
+  for(; k < depth; k++)
+  {
+    MICRO_COMPLEX_EXTRA_ROW<Scalar, Packet, accRows, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal, remaining_rows>(lhs_ptr_real, lhs_ptr_imag, rhs_ptr_real0, rhs_ptr_real1, rhs_ptr_real2, rhs_ptr_imag0, rhs_ptr_imag1, rhs_ptr_imag2, accReal0, accImag0);
+  }
+
+  constexpr bool full = (remaining_rows > accColsC);
+  bload<DataMapper, Packetc, accColsC, ColMajor, true, accRows, full>(tRes, res, row, 0);
+  if ((accRows == 1) || (rows >= accCols))
+  {
+    bscalec<Packet,accRows,true>(accReal0, accImag0, pAlphaReal, pAlphaImag, taccReal, taccImag, pMask);
+    bcouple<Packet, Packetc, accRows, full>(taccReal, taccImag, tRes, acc0, acc1);
+    bstore<DataMapper, Packetc, accRows>(acc0, res, row + 0);
+    if (full) {
+      bstore<DataMapper, Packetc, accRows>(acc1, res, row + accColsC);
+    }
+  } else {
+    bscalec<Packet,accRows,false>(accReal0, accImag0, pAlphaReal, pAlphaImag, taccReal, taccImag, pMask);
+    bcouple<Packet, Packetc, accRows, full>(taccReal, taccImag, tRes, acc0, acc1);
+
+    if ((sizeof(Scalar) == sizeof(float)) && (remaining_rows == 1))
+    {
+      for(Index j = 0; j < accRows; j++) {
+        res(row + 0, j) = pfirst<Packetc>(acc0.packet[j]);
+      }
+    } else {
+      bstore<DataMapper, Packetc, accRows>(acc0, res, row + 0);
+      if (full) {
+        for(Index j = 0; j < accRows; j++) {
+          res(row + accColsC, j) = pfirst<Packetc>(acc1.packet[j]);
+        }
+      }
+    }
+  }
+}
+
+#define MICRO_COMPLEX_EXTRA_ROWS(N) \
+  gemm_unrolled_complex_row_iteration<Scalar, Packet, Packetc, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal, N>(res, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, rows, pAlphaReal, pAlphaImag, pMask);
+
+template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void gemm_complex_extra_row(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index row,
+  Index rows,
+  Index remaining_rows,
+  const Packet& pAlphaReal,
+  const Packet& pAlphaImag,
+  const Packet& pMask)
+{
+  MICRO_EXTRA(MICRO_COMPLEX_EXTRA_ROWS, remaining_rows, false)
+}
+
+#define MICRO_COMPLEX_UNROLL_WORK(func, func2, peel) \
+  MICRO_COMPLEX_UNROLL(func2); \
+  func(0,peel) func(1,peel) func(2,peel) func(3,peel)
+
+#define MICRO_COMPLEX_WORK_ONE4(iter, peel) \
+  if (unroll_factor > iter) { \
+    pgerc_common<accRows, Packet, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal##iter, &accImag##iter, lhsV##iter, lhsVi##iter, rhsV##peel, rhsVi##peel); \
+  }
+
+#define MICRO_COMPLEX_TYPE_PEEL4(func, func2, peel) \
+  if (PEEL_COMPLEX > peel) { \
+    Packet lhsV0, lhsV1, lhsV2, lhsV3; \
+    Packet lhsVi0, lhsVi1, lhsVi2, lhsVi3; \
+    MICRO_COMPLEX_BROADCAST(peel) \
+    MICRO_COMPLEX_UNROLL_WORK(func, func2, peel) \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(rhsV##peel); \
+    EIGEN_UNUSED_VARIABLE(rhsVi##peel); \
+  }
+
+#define MICRO_COMPLEX_UNROLL_TYPE_PEEL(M, func, func1, func2) \
+  Packet rhsV0[M], rhsV1[M], rhsV2[M], rhsV3[M]; \
+  Packet rhsVi0[M], rhsVi1[M], rhsVi2[M], rhsVi3[M]; \
+  func(func1,func2,0) func(func1,func2,1) \
+  func(func1,func2,2) func(func1,func2,3)
+
+#define MICRO_COMPLEX_UNROLL_TYPE_ONE(M, func, func1, func2) \
+  Packet rhsV0[M], rhsVi0[M];\
+  func(func1,func2,0)
+
+#define MICRO_COMPLEX_UNROLL_TYPE(MICRO_COMPLEX_TYPE, size) \
+  MICRO_COMPLEX_TYPE(4, MICRO_COMPLEX_TYPE_PEEL4, MICRO_COMPLEX_WORK_ONE4, MICRO_COMPLEX_LOAD_ONE) \
+  MICRO_COMPLEX_ADD_ROWS(size, false)
+
+#define MICRO_COMPLEX_ONE_PEEL4 MICRO_COMPLEX_UNROLL_TYPE(MICRO_COMPLEX_UNROLL_TYPE_PEEL, PEEL_COMPLEX)
+
+#define MICRO_COMPLEX_ONE4 MICRO_COMPLEX_UNROLL_TYPE(MICRO_COMPLEX_UNROLL_TYPE_ONE, 1)
+
+#define MICRO_COMPLEX_DST_PTR_ONE(iter) \
+  if (unroll_factor > iter) { \
+    bsetzero<Packet, accRows>(accReal##iter); \
+    bsetzero<Packet, accRows>(accImag##iter); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(accReal##iter); \
+    EIGEN_UNUSED_VARIABLE(accImag##iter); \
+  }
+
+#define MICRO_COMPLEX_DST_PTR MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_DST_PTR_ONE)
+
+#define MICRO_COMPLEX_SRC_PTR MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_SRC_PTR_ONE)
+
+#define MICRO_COMPLEX_PREFETCH MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_PREFETCH_ONE)
+
+#define MICRO_COMPLEX_STORE_ONE(iter) \
+  if (unroll_factor > iter) { \
+    constexpr bool full = ((MICRO_NORMAL(iter)) || (accCols2 > accColsC)); \
+    bload<DataMapper, Packetc, accColsC, ColMajor, true, accRows, full>(tRes, res, row + iter*accCols, 0); \
+    bscalec<Packet,accRows,!(MICRO_NORMAL(iter))>(accReal##iter, accImag##iter, pAlphaReal, pAlphaImag, taccReal, taccImag, pMask); \
+    bcouple<Packet, Packetc, accRows, full>(taccReal, taccImag, tRes, acc0, acc1); \
+    bstore<DataMapper, Packetc, accRows>(acc0, res, row + iter*accCols + 0); \
+    if (full) { \
+      bstore<DataMapper, Packetc, accRows>(acc1, res, row + iter*accCols + accColsC); \
+    } \
+  }
+
+#define MICRO_COMPLEX_STORE MICRO_COMPLEX_UNROLL(MICRO_COMPLEX_STORE_ONE)
+
+template<int unroll_factor, typename Scalar, typename Packet, typename Packetc, typename DataMapper, const Index accRows, const Index accCols, const Index accCols2, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void gemm_complex_unrolled_iteration(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index& row,
+  const Packet& pAlphaReal,
+  const Packet& pAlphaImag,
+  const Packet& pMask)
+{
+  const Scalar* rhs_ptr_real0 = rhs_base, * rhs_ptr_real1 = NULL, * rhs_ptr_real2 = NULL;
+  const Scalar* rhs_ptr_imag0 = NULL, * rhs_ptr_imag1 = NULL, * rhs_ptr_imag2 = NULL;
+  const Index imag_delta = accCols*strideA;
+  const Index imag_delta2 = accCols2*strideA;
+  const Scalar* lhs_ptr_real0 = NULL, * lhs_ptr_real1 = NULL;
+  const Scalar* lhs_ptr_real2 = NULL, * lhs_ptr_real3 = NULL;
+  PacketBlock<Packet,accRows> accReal0, accImag0, accReal1, accImag1;
+  PacketBlock<Packet,accRows> accReal2, accImag2, accReal3, accImag3;
+  PacketBlock<Packet,accRows> taccReal, taccImag;
+  PacketBlock<Packetc,accRows> acc0, acc1;
+  PacketBlock<Packetc,accRows*2> tRes;
+
+  MICRO_COMPLEX_SRC2_PTR
+  MICRO_COMPLEX_SRC_PTR
+  MICRO_COMPLEX_DST_PTR
+
+  Index k = 0;
+  for(; k + PEEL_COMPLEX <= depth; k+= PEEL_COMPLEX)
+  {
+    MICRO_COMPLEX_PREFETCHN(accRows)
+    MICRO_COMPLEX_PREFETCH
+    MICRO_COMPLEX_ONE_PEEL4
+  }
+  for(; k < depth; k++)
+  {
+    MICRO_COMPLEX_ONE4
+  }
+  MICRO_COMPLEX_STORE
+
+  MICRO_COMPLEX_UPDATE
+}
+
+#define MICRO_COMPLEX_UNROLL_ITER2(N, M) \
+  gemm_complex_unrolled_iteration<N + (M ? 1 : 0), Scalar, Packet, Packetc, DataMapper, accRows, accCols, M ? M : accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, pAlphaReal, pAlphaImag, pMask); \
+  if (M) return;
+
+template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void gemm_complex_cols(
+  const DataMapper& res,
+  const Scalar* blockA,
+  const Scalar* blockB,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index offsetB,
+  Index col,
+  Index rows,
+  Index remaining_rows,
+  const Packet& pAlphaReal,
+  const Packet& pAlphaImag,
+  const Packet& pMask)
+{
+  const DataMapper res3 = res.getSubMapper(0, col);
+
+  const Scalar* rhs_base = blockB + advanceCols*col*strideB + MICRO_NEW_ROWS*offsetB;
+  const Scalar* lhs_base = blockA + accCols*offsetA;
+  Index row = 0;
+
+#define MAX_COMPLEX_UNROLL 4
+  while(row + MAX_COMPLEX_UNROLL*accCols <= rows) {
+    MICRO_COMPLEX_UNROLL_ITER2(MAX_COMPLEX_UNROLL, 0);
+  }
+  switch( (rows-row)/accCols ) {
+#if MAX_COMPLEX_UNROLL > 4
+    case 4:
+      MICRO_COMPLEX_UNROLL_ITER(MICRO_COMPLEX_UNROLL_ITER2, 4)
+      break;
+#endif
+#if MAX_COMPLEX_UNROLL > 3
+    case 3:
+      MICRO_COMPLEX_UNROLL_ITER(MICRO_COMPLEX_UNROLL_ITER2, 3)
+      break;
+#endif
+#if MAX_COMPLEX_UNROLL > 2
+    case 2:
+      MICRO_COMPLEX_UNROLL_ITER(MICRO_COMPLEX_UNROLL_ITER2, 2)
+      break;
+#endif
+#if MAX_COMPLEX_UNROLL > 1
+    case 1:
+      MICRO_COMPLEX_UNROLL_ITER(MICRO_COMPLEX_UNROLL_ITER2, 1)
+      break;
+#endif
+    default:
+      break;
+  }
+#undef MAX_COMPLEX_UNROLL
+
+  if(remaining_rows > 0)
+  {
+    gemm_complex_extra_row<Scalar, Packet, Packetc, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, blockA, rhs_base, depth, strideA, offsetA, strideB, row, rows, remaining_rows, pAlphaReal, pAlphaImag, pMask);
+  }
+}
+
+#define MICRO_COMPLEX_EXTRA_COLS(N) \
+  gemm_complex_cols<Scalar, Packet, Packetc, DataMapper, N, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, remaining_rows, pAlphaReal, pAlphaImag, pMask);
+
+template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void gemm_complex_extra_cols(
+  const DataMapper& res,
+  const Scalar* blockA,
+  const Scalar* blockB,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index offsetB,
+  Index col,
+  Index rows,
+  Index cols,
+  Index remaining_rows,
+  const Packet& pAlphaReal,
+  const Packet& pAlphaImag,
+  const Packet& pMask)
+{
+  MICRO_EXTRA(MICRO_COMPLEX_EXTRA_COLS, cols-col, true)
+}
+
+template<typename LhsScalar, typename RhsScalar, typename Scalarc, typename Scalar, typename Packet, typename Packetc, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_STRONG_INLINE void gemm_complex(const DataMapper& res, const LhsScalar* blockAc, const RhsScalar* blockBc, Index rows, Index depth, Index cols, Scalarc alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
+{
+      const Index remaining_rows = rows % accCols;
+
+      if( strideA == -1 ) strideA = depth;
+      if( strideB == -1 ) strideB = depth;
+
+      const Packet pAlphaReal = pset1<Packet>(alpha.real());
+      const Packet pAlphaImag = pset1<Packet>(alpha.imag());
+      const Packet pMask = bmask<Packet>(remaining_rows);
+
+      const Scalar* blockA = (Scalar *) blockAc;
+      const Scalar* blockB = (Scalar *) blockBc;
+
+      Index col = 0;
+      for(; col + accRows <= cols; col += accRows)
+      {
+        gemm_complex_cols<Scalar, Packet, Packetc, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, remaining_rows, pAlphaReal, pAlphaImag, pMask);
+      }
+
+      if (col != cols)
+      {
+        gemm_complex_extra_cols<Scalar, Packet, Packetc, DataMapper, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols, remaining_rows, pAlphaReal, pAlphaImag, pMask);
+      }
+}
+
+#undef accColsC
+#undef advanceCols
+#undef advanceRows
+
+EIGEN_ALWAYS_INLINE bool supportsMMA()
+{
+#if defined(EIGEN_ALTIVEC_MMA_ONLY)
+  return true;
+#elif defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH) && defined(__BUILTIN_CPU_SUPPORTS__)
+  return __builtin_cpu_supports ("arch_3_1") && __builtin_cpu_supports ("mma");
+#else
+  return false;  // No dynamic dispatch for LLVM or older GCC
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet4f loadAndMultiplyF32(Packet4f acc, const Packet4f pAlpha, float* result)
+{
+  Packet4f result_block = ploadu<Packet4f>(result);
+  return pmadd(acc, pAlpha, result_block);
+}
+
+template<bool lhsExtraRows>
+EIGEN_ALWAYS_INLINE void storeF32(float*& result, Packet4f result_block, Index rows, Index extra_rows)
+{
+  if (lhsExtraRows) {
+    pstoreu_partial(result, result_block, extra_rows);
+  } else {
+    pstoreu(result, result_block);
+  }
+  result += rows;
+}
+
+template<bool rhsExtraCols, bool lhsExtraRows>
+EIGEN_ALWAYS_INLINE void storeResults(Packet4f (&acc)[4], Index rows, const Packet4f pAlpha, float* result, Index extra_cols, Index extra_rows)
+{
+  Index x = 0;
+  if (rhsExtraCols) {
+    do{
+      Packet4f result_block = loadAndMultiplyF32(acc[x], pAlpha, result);
+      storeF32<lhsExtraRows>(result, result_block, rows, extra_rows);
+    } while (++x < extra_cols);
+  } else {
+    Packet4f result_block[4];
+    float *result2 = result;
+    do{
+      result_block[x] = loadAndMultiplyF32(acc[x], pAlpha, result);
+      result += rows;
+    } while (++x < 4);
+    x = 0;
+    do{
+      storeF32<lhsExtraRows>(result2, result_block[x], rows, extra_rows);
+    } while (++x < 4);
+  }
+}
+
+EIGEN_ALWAYS_INLINE Packet4f oneConvertBF16Hi(Packet8us data)
+{
+  Packet8us z = pset1<Packet8us>(0);
+#ifdef _BIG_ENDIAN
+  return reinterpret_cast<Packet4f>(vec_mergeh(data, z));
+#else
+  return reinterpret_cast<Packet4f>(vec_mergeh(z, data));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet4f oneConvertBF16Lo(Packet8us data)
+{
+  Packet8us z = pset1<Packet8us>(0);
+#ifdef _BIG_ENDIAN
+  return reinterpret_cast<Packet4f>(vec_mergel(data, z));
+#else
+  return reinterpret_cast<Packet4f>(vec_mergel(z, data));
+#endif
+}
+
+template<Index N, Index M>
+EIGEN_ALWAYS_INLINE void storeConvertTwoBF16(float* to, PacketBlock<Packet8bf,(N+7)/8>& block, Index extra = 0)
+{
+  if (N < 4) {
+    pstoreu_partial(to +  0, oneConvertBF16Hi(block.packet[0].m_val), extra);
+  } else if (N >= (M*8+4)) {
+    pstoreu(to + 0, oneConvertBF16Hi(block.packet[M].m_val));
+    if (N >= 8) {
+      pstoreu(to + 4, oneConvertBF16Lo(block.packet[M].m_val));
+    }
+  }
+}
+
+template<Index N>
+EIGEN_ALWAYS_INLINE void storeConvertBlockBF16(float* to, PacketBlock<Packet8bf,(N+7)/8>& block, Index extra)
+{
+  storeConvertTwoBF16<N, 0>(to + 0, block, extra);
+  if (N >= 16) {
+    storeConvertTwoBF16<N, 1>(to + 8, block);
+  }
+  if (N >= 32) {
+    storeConvertTwoBF16<N, 2>(to + 16, block);
+    storeConvertTwoBF16<N, 3>(to + 24, block);
+  }
+}
+
+template<bool non_unit_stride, Index delta>
+EIGEN_ALWAYS_INLINE Packet8bf loadBF16fromResult(bfloat16* src, Index resInc)
+{
+  if (non_unit_stride) {
+    return pgather<bfloat16, Packet8bf>(src + delta*resInc, resInc);
+  } else {
+    return ploadu<Packet8bf>(src + delta);
+  }
+}
+
+static Packet16uc p16uc_MERGE16_32_1 = {  0, 1, 16,17,  2, 3, 18,19,  0, 1, 16,17,  2, 3, 18,19 };
+static Packet16uc p16uc_MERGE16_32_2 = {  4, 5, 20,21,  6, 7, 22,23,  4, 5, 20,21,  6, 7, 22,23 };
+static Packet16uc p16uc_MERGE16_32_3 = {  8, 9, 24,25, 10,11, 26,27,  8, 9, 24,25, 10,11, 26,27 };
+static Packet16uc p16uc_MERGE16_32_4 = { 12,13, 28,29, 14,15, 30,31, 12,13, 28,29, 14,15, 30,31 };
+
+static Packet16uc p16uc_MERGE16_32_5 = { 0,1, 16,17, 16,17, 16,17, 0,1, 16,17, 16,17, 16,17 };
+static Packet16uc p16uc_MERGE16_32_6 = { 2,3, 18,19, 18,19, 18,19, 2,3, 18,19, 18,19, 18,19 };
+static Packet16uc p16uc_MERGE16_32_7 = { 4,5, 20,21, 20,21, 20,21, 4,5, 20,21, 20,21, 20,21 };
+static Packet16uc p16uc_MERGE16_32_8 = { 6,7, 22,23, 22,23, 22,23, 6,7, 22,23, 22,23, 22,23 };
+
+EIGEN_ALWAYS_INLINE Packet4f oneConvertBF16Perm(Packet8us data, Packet16uc mask)
+{
+  Packet8us z = pset1<Packet8us>(0);
+#ifdef _BIG_ENDIAN
+  return reinterpret_cast<Packet4f>(vec_perm(data, z, mask));
+#else
+  return reinterpret_cast<Packet4f>(vec_perm(z, data, mask));
+#endif
+}
+
+template<bool lhsExtraRows, bool odd, Index size>
+EIGEN_ALWAYS_INLINE void convertArrayPointerBF16toF32DupOne(float *result, Index rows, const bfloat16* src, Index extra_rows)
+{
+  Packet4f dup[4*4];
+  Packet8bf data[4];
+
+  for (Index i = 0; i < size; i++) {
+    data[i] = ploadu<Packet8bf>(src + rows*i);
+  }
+
+  for (Index i = 0, j = 0; i < size; i++, j += 4) {
+    dup[j+0] = oneConvertBF16Perm(data[i].m_val, odd ? p16uc_MERGE16_32_5 : p16uc_MERGE16_32_1);
+    dup[j+1] = oneConvertBF16Perm(data[i].m_val, odd ? p16uc_MERGE16_32_6 : p16uc_MERGE16_32_2);
+    dup[j+2] = oneConvertBF16Perm(data[i].m_val, odd ? p16uc_MERGE16_32_7 : p16uc_MERGE16_32_3);
+    dup[j+3] = oneConvertBF16Perm(data[i].m_val, odd ? p16uc_MERGE16_32_8 : p16uc_MERGE16_32_4);
+  }
+
+  for (Index j = 0; j < 4*size; j += 4) {
+    if (lhsExtraRows) {
+      Packet4f z = pset1<Packet4f>(float(0));
+      Index i = 0;
+      do {
+        pstoreu(result + (j+i)*4, dup[j+i]);
+      } while (++i < extra_rows);
+      do {
+        pstoreu(result + (j+i)*4, z);
+      } while (++i < 4);
+    } else {
+      for (Index i = 0; i < 4; i++) {
+        pstoreu(result + (j+i)*4, dup[j+i]);
+      }
+    }
+  }
+}
+
+template<bool lhsExtraRows>
+EIGEN_ALWAYS_INLINE void convertArrayPointerBF16toF32Dup(float *result, Index cols, Index rows, const bfloat16* src, Index delta, Index extra_rows)
+{
+  Index col = 0;
+  src += delta*2;
+  for(; col + 4*2 <= cols; col += 4*2, result += 4*4*4, src += 4*rows) {
+    convertArrayPointerBF16toF32DupOne<lhsExtraRows,false,4>(result, rows, src, extra_rows);
+  }
+  for(; col + 2 <= cols; col += 2, result += 4*4, src += rows) {
+    convertArrayPointerBF16toF32DupOne<lhsExtraRows,false,1>(result, rows, src, extra_rows);
+  }
+  if (cols & 1) {
+    convertArrayPointerBF16toF32DupOne<lhsExtraRows,true,1>(result, rows, src - delta, extra_rows);
+  }
+}
+
+template<const Index size, bool non_unit_stride>
+EIGEN_ALWAYS_INLINE void convertPointerBF16toF32(Index& i, float *result, Index rows, bfloat16*& src, Index resInc)
+{
+  constexpr Index extra = ((size < 4) ? 4 : size);
+  while (i + size <= rows) {
+    PacketBlock<Packet8bf,(size+7)/8> r32;
+    r32.packet[0] = loadBF16fromResult<non_unit_stride, 0>(src, resInc);
+    if (size >= 16) {
+      r32.packet[1] = loadBF16fromResult<non_unit_stride, 8>(src, resInc);
+    }
+    if (size >= 32) {
+      r32.packet[2] = loadBF16fromResult<non_unit_stride, 16>(src, resInc);
+      r32.packet[3] = loadBF16fromResult<non_unit_stride, 24>(src, resInc);
+    }
+    storeConvertBlockBF16<size>(result + i, r32, rows & 3);
+    i += extra; src += extra*resInc;
+    if (size != 32) break;
+  }
+}
+
+template<bool non_unit_stride>
+EIGEN_ALWAYS_INLINE void convertArrayPointerBF16toF32(float *result, Index cols, Index rows, bfloat16* src, Index resInc)
+{
+  for(Index col = 0; col < cols; col++, src += (rows*resInc), result += rows) {
+    Index i = 0;
+    bfloat16* src2 = src;
+    convertPointerBF16toF32<32, non_unit_stride>(i, result, rows, src2, resInc);
+    convertPointerBF16toF32<16, non_unit_stride>(i, result, rows, src2, resInc);
+    convertPointerBF16toF32<8,  non_unit_stride>(i, result, rows, src2, resInc);
+    convertPointerBF16toF32<4,  non_unit_stride>(i, result, rows, src2, resInc);
+    convertPointerBF16toF32<1,  non_unit_stride>(i, result, rows, src2, resInc);
+  }
+}
+
+template<Index num_acc, Index size = 4>
+EIGEN_ALWAYS_INLINE void zeroAccumulators(Packet4f (&acc)[num_acc][size])
+{
+  Packet4f z = pset1<Packet4f>(float(0));
+
+  for(Index k = 0; k < num_acc; k++) {
+    for(Index j = 0; j < size; j++) {
+      acc[k][j] = z;
+    }
+  }
+}
+
+template<Index num_acc>
+EIGEN_ALWAYS_INLINE void tranposeResults(Packet4f (&acc)[num_acc][4])
+{
+  for(Index i = 0; i < num_acc; i++) {
+    Packet4ui t0, t1, t2, t3;
+    t0 = vec_mergeh(reinterpret_cast<Packet4ui>(acc[i][0]), reinterpret_cast<Packet4ui>(acc[i][2]));
+    t1 = vec_mergel(reinterpret_cast<Packet4ui>(acc[i][0]), reinterpret_cast<Packet4ui>(acc[i][2]));
+    t2 = vec_mergeh(reinterpret_cast<Packet4ui>(acc[i][1]), reinterpret_cast<Packet4ui>(acc[i][3]));
+    t3 = vec_mergel(reinterpret_cast<Packet4ui>(acc[i][1]), reinterpret_cast<Packet4ui>(acc[i][3]));
+    acc[i][0] = reinterpret_cast<Packet4f>(vec_mergeh(t0, t2));
+    acc[i][1] = reinterpret_cast<Packet4f>(vec_mergel(t0, t2));
+    acc[i][2] = reinterpret_cast<Packet4f>(vec_mergeh(t1, t3));
+    acc[i][3] = reinterpret_cast<Packet4f>(vec_mergel(t1, t3));
+  }
+}
+
+template<Index num_acc>
+EIGEN_ALWAYS_INLINE void addResults(Packet4f (&acc)[num_acc][4])
+{
+  for(Index i = 0, j = 0; j < num_acc; i++, j += 2) {
+    for(Index x = 0, y = 0; x < 2; x++, y += 2) {
+      for(Index w = 0, z = 0; w < 2; w++, z += 2) {
+        acc[i][y+w] = acc[j+x][z+0] + acc[j+x][z+1];
+      }
+    }
+  }
+}
+
+template<Index num_acc, bool rhsExtraCols, bool lhsExtraRows, Index num_rhs>
+EIGEN_ALWAYS_INLINE void outputResultsVSX(Packet4f (&acc)[num_acc][4], Index rows, const Packet4f pAlpha, float* result, const Index extra_cols, Index extra_rows)
+{
+  tranposeResults<num_acc>(acc);
+  addResults<num_acc>(acc);
+
+  constexpr Index real_rhs = ((num_rhs / 2) - (rhsExtraCols ? 1 : 0));
+  Index k = 0;
+  for(Index i = 0; i < real_rhs; i++, result += 4*rows, k++){
+    storeResults<false, lhsExtraRows>(acc[k], rows, pAlpha, result, extra_cols, extra_rows);
+  }
+  if(rhsExtraCols) {
+    storeResults<rhsExtraCols, lhsExtraRows>(acc[k], rows, pAlpha, result, extra_cols, extra_rows);
+  }
+}
+
+template<bool zero>
+EIGEN_ALWAYS_INLINE void loadTwoRhsFloat32(const float* block, Index strideB, Index i, Packet4f& dhs0, Packet4f &dhs1)
+{
+  dhs0 = ploadu<Packet4f>(block + strideB*i + 0);
+  if (zero) {
+    Packet4f dhs2 = pset1<Packet4f>(float(0));
+    dhs1 = vec_mergel(dhs0, dhs2);
+    dhs0 = vec_mergeh(dhs0, dhs2);
+  } else {
+    dhs1 = ploadu<Packet4f>(block + strideB*i + 4);
+  }
+}
+
+template<Index num_acc, bool zero, bool rhsExtraCols, Index num_rhs>
+EIGEN_ALWAYS_INLINE void KLoop
+(
+  const float* indexA,
+  const float* indexB,
+  Packet4f (&acc)[num_acc][4],
+  Index strideB,
+  Index k,
+  Index offsetB,
+  Index extra_cols
+)
+{
+  constexpr Index num_lhs = 4;
+  Packet4f lhs[num_lhs], rhs[num_rhs];
+
+  constexpr Index real_rhs = (num_rhs - (rhsExtraCols ? 2 : 0));
+  for(Index i = 0; i < real_rhs; i += 2){
+    loadTwoRhsFloat32<zero>(indexB + k*4, strideB, i, rhs[i + 0], rhs[i + 1]);
+  }
+  if(rhsExtraCols) {
+    loadTwoRhsFloat32<zero>(indexB + k*extra_cols - offsetB, strideB, real_rhs, rhs[real_rhs + 0], rhs[real_rhs + 1]);
+  }
+
+  indexA += 2*k*4;
+  for(Index j = 0; j < num_lhs; j++) {
+    lhs[j] = ploadu<Packet4f>(indexA + j*4);
+  }
+
+  for(Index j = 0; j < num_rhs; j++) {
+    for(Index i = 0; i < num_lhs; i++) {
+      acc[j][i] = pmadd(rhs[j], lhs[i], acc[j][i]);
+    }
+  }
+}
+
+template<const Index num_acc, bool rhsExtraCols, bool lhsExtraRows>
+EIGEN_ALWAYS_INLINE void colVSXLoopBodyIter(Index depth, Index rows, const Packet4f pAlpha, const float* indexA, const float* indexB, Index strideB, Index offsetB, float* result, const Index extra_cols, const Index extra_rows)
+{
+  constexpr Index num_rhs = num_acc;
+
+  Packet4f acc[num_acc][4];
+
+  zeroAccumulators<num_acc>(acc);
+
+  Index k;
+  for(k = 0; k + 2 <= depth; k += 2){
+    KLoop<num_acc, false, rhsExtraCols, num_rhs>(indexA, indexB, acc, strideB, k, offsetB, extra_cols);
+  }
+  if(depth&1){
+    KLoop<num_acc, true, rhsExtraCols, num_rhs>(indexA, indexB, acc, strideB, k, offsetB, extra_cols);
+  }
+
+  outputResultsVSX<num_acc, rhsExtraCols, lhsExtraRows, num_rhs>(acc, rows, pAlpha, result, extra_cols, extra_rows);
+}
+
+// No more than 4 (uses 2X the accumulators or 8X the number of VSX registers)
+#define MAX_BFLOAT16_ACC_VSX   4
+
+template<const Index num_acc, bool rhsExtraCols, bool lhsExtraRows>
+void colVSXLoopBody(Index& col, Index depth, Index cols, Index rows, const Packet4f pAlpha, const float* indexA, const float* indexB, Index strideB, Index offsetB, float* result)
+{
+  constexpr Index step = (num_acc * 4); // each accumulator has 4 elements
+  const Index extra_cols = (rhsExtraCols) ? (cols & 3) : 0;
+  const Index extra_rows = (lhsExtraRows) ? (rows & 3) : 0;
+  constexpr bool multiIters = !rhsExtraCols && (num_acc == MAX_BFLOAT16_ACC_VSX);
+
+  do{
+    colVSXLoopBodyIter<num_acc*2, rhsExtraCols, lhsExtraRows>(depth, rows, pAlpha, indexA, indexB, strideB, offsetB, result, extra_cols, extra_rows);
+
+    indexB += strideB*(num_acc * 2);
+    result += rows*step;
+  } while(multiIters && (step <= cols - (col += step)));
+}
+
+template<const Index num_acc, bool rhsExtraCols, bool lhsExtraRows>
+EIGEN_ALWAYS_INLINE void colVSXLoopBodyExtraN(Index col, Index depth, Index cols, Index rows, const Packet4f pAlpha, const float* indexA, const float* blockB, Index strideB, Index offsetB, float* result)
+{
+  if (MAX_BFLOAT16_ACC_VSX > num_acc) {
+    colVSXLoopBody<num_acc + (rhsExtraCols ? 1 : 0), rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+  }
+}
+
+template<bool rhsExtraCols, bool lhsExtraRows>
+void colVSXLoopBodyExtra(Index col, Index depth, Index cols, Index rows, const Packet4f pAlpha, const float* indexA, const float* blockB, Index strideB, Index offsetB, float* result)
+{
+  switch ((cols - col) >> 2) {
+  case 3:
+    colVSXLoopBodyExtraN<3, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+    break;
+  case 2:
+    colVSXLoopBodyExtraN<2, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+    break;
+  case 1:
+    colVSXLoopBodyExtraN<1, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+    break;
+  default:
+    if (rhsExtraCols) {
+      colVSXLoopBody<1, true, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+    }
+    break;
+  }
+}
+
+template<Index size, bool lhsExtraRows = false>
+EIGEN_ALWAYS_INLINE void colVSXLoops(Index depth, Index cols, Index rows, const Packet4f pAlpha, const bfloat16* indexA, const float* indexA2, const float* blockB2, Index strideA, Index strideB, Index offsetB, float* result2)
+{
+  Index delta_rows = 2*(lhsExtraRows ? (rows & 3) : size);
+  for (Index row = 0; row < size; row += 4) {
+    convertArrayPointerBF16toF32Dup<lhsExtraRows>(const_cast<float *>(indexA2), strideA, delta_rows, indexA, row, rows & 3);
+
+    const float *blockB = blockB2;
+    float *result = result2 + row;
+
+    Index col = 0;
+    if (cols >= (MAX_BFLOAT16_ACC_VSX * 4)) {
+      colVSXLoopBody<MAX_BFLOAT16_ACC_VSX, false, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA2, blockB, strideB, 0, result);
+      blockB += (strideB >> 1)*col;
+      result += rows*col;
+    }
+    if (cols & 3) {
+      colVSXLoopBodyExtra<true, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA2, blockB, strideB, offsetB, result);
+    } else {
+      colVSXLoopBodyExtra<false, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA2, blockB, strideB, 0, result);
+    }
+  }
+}
+
+template<Index size>
+EIGEN_ALWAYS_INLINE void calcVSXColLoops(const bfloat16*& indexA, const float* indexA2, Index& row, Index depth, Index cols, Index rows, const Packet4f pAlpha, const float* indexB, Index strideA, Index strideB, Index offsetA, Index offsetB, Index bigSuffix, float *result)
+{
+  if ((size == 16) || (rows & size)) {
+    indexA += size*offsetA;
+    colVSXLoops<size>(depth, cols, rows, pAlpha, indexA, indexA2, indexB, strideA, strideB, offsetB, result + row);
+    row += size;
+    indexA += bigSuffix*size/16;
+  }
+}
+
+template<const Index size, typename DataMapper>
+EIGEN_ALWAYS_INLINE void convertBF16toF32(Index& i, float *result, Index rows, const DataMapper& src)
+{
+  constexpr Index extra = ((size < 4) ? 4 : size);
+  while (i + size <= rows) {
+    PacketBlock<Packet8bf,(size+7)/8> r32;
+    r32.packet[0] = src.template loadPacket<Packet8bf>(i +  0);
+    if (size >= 16) {
+      r32.packet[1] = src.template loadPacket<Packet8bf>(i +  8);
+    }
+    if (size >= 32) {
+      r32.packet[2] = src.template loadPacket<Packet8bf>(i + 16);
+      r32.packet[3] = src.template loadPacket<Packet8bf>(i + 24);
+    }
+    storeConvertBlockBF16<size>(result + i, r32, rows & 3);
+    i += extra;
+    if (size != 32) break;
+  }
+}
+
+template<typename DataMapper>
+EIGEN_ALWAYS_INLINE void convertArrayBF16toF32(float *result, Index cols, Index rows, const DataMapper& src)
+{
+  typedef typename DataMapper::LinearMapper LinearMapper;
+  for(Index j = 0; j < cols; j++, result += rows){
+    const LinearMapper src2 = src.getLinearMapper(0, j);
+    Index i = 0;
+    convertBF16toF32<32, LinearMapper>(i, result, rows, src2);
+    convertBF16toF32<16, LinearMapper>(i, result, rows, src2);
+    convertBF16toF32<8,  LinearMapper>(i, result, rows, src2);
+    convertBF16toF32<4,  LinearMapper>(i, result, rows, src2);
+    convertBF16toF32<1,  LinearMapper>(i, result, rows, src2);
+  }
+}
+
+EIGEN_ALWAYS_INLINE Packet8bf convertF32toBF16VSX(const float *res)
+{
+  return F32ToBf16Both(ploadu<Packet4f>(res + 0), ploadu<Packet4f>(res + 4));
+}
+
+template<typename DataMapper, const Index size>
+EIGEN_ALWAYS_INLINE void convertArrayF32toBF16ColVSX(float *result, Index col, Index rows, const DataMapper& res)
+{
+  const DataMapper res2 = res.getSubMapper(0, col);
+  Index row;
+  float *result2 = result + col*rows;
+  for(row = 0; row + 8 <= rows; row += 8, result2 += 8){
+    // get and save block
+    PacketBlock<Packet8bf,size> block;
+    for(Index j = 0; j < size; j++){
+      block.packet[j] = convertF32toBF16VSX(result2 + j*rows);
+    }
+    res2.template storePacketBlock<Packet8bf,size>(row, 0, block);
+  }
+  // extra rows
+  if(row < rows){
+    for(Index j = 0; j < size; j++){
+      Packet8bf fp16 = convertF32toBF16VSX(result2 + j*rows);
+      res2.template storePacketPartial<Packet8bf>(row, j, fp16, rows & 7);
+    }
+  }
+}
+
+template<typename DataMapper>
+EIGEN_ALWAYS_INLINE void convertArrayF32toBF16VSX(float *result, Index cols, Index rows, const DataMapper& res)
+{
+  Index col;
+  for(col = 0; col + 4 <= cols; col += 4){
+    convertArrayF32toBF16ColVSX<DataMapper,4>(result, col, rows, res);
+  }
+  // extra cols
+  switch (cols - col) {
+  case 1:
+    convertArrayF32toBF16ColVSX<DataMapper,1>(result, col, rows, res);
+    break;
+  case 2:
+    convertArrayF32toBF16ColVSX<DataMapper,2>(result, col, rows, res);
+    break;
+  case 3:
+    convertArrayF32toBF16ColVSX<DataMapper,3>(result, col, rows, res);
+    break;
+  }
+}
+
+template<typename DataMapper>
+void gemmbfloat16(const DataMapper& res, const bfloat16* indexA, const bfloat16* indexB, Index rows, Index depth, Index cols, bfloat16 alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
+{
+  float falpha = Eigen::bfloat16_impl::bfloat16_to_float(alpha);
+  const Packet4f pAlpha = pset1<Packet4f>(falpha);
+
+  if( strideA == -1 ) strideA = depth;
+  if( strideB == -1 ) strideB = depth;
+
+  ei_declare_aligned_stack_constructed_variable(float, result, cols*rows, 0);
+  ei_declare_aligned_stack_constructed_variable(float, indexB2, strideB*cols, 0);
+  ei_declare_aligned_stack_constructed_variable(float, indexA2, ((strideA + 1) & -2)*4*2, 0);
+
+  convertArrayBF16toF32<DataMapper>(result, cols, rows, res);
+  convertArrayPointerBF16toF32(indexB2, cols, strideB, const_cast<bfloat16 *>(indexB));
+
+  Index bigSuffix = 2*8*(strideA-offsetA);
+  float* indexBF32 = indexB2 + 4*offsetB;
+  offsetB *= 3;
+  strideB *= 2;
+
+  Index row = 0;
+  // LHS (8x16) block
+  while(row + 16 <= rows){
+    calcVSXColLoops<16>(indexA, indexA2, row, depth, cols, rows, pAlpha, indexBF32, strideA, strideB, offsetA, offsetB, bigSuffix, result);
+  }
+  // LHS (8x8) block
+  calcVSXColLoops<8>(indexA, indexA2, row, depth, cols, rows, pAlpha, indexBF32, strideA, strideB, offsetA, offsetB, bigSuffix, result);
+  // LHS (8x4) block
+  calcVSXColLoops<4>(indexA, indexA2, row, depth, cols, rows, pAlpha, indexBF32, strideA, strideB, offsetA, offsetB, bigSuffix, result);
+  // extra rows
+  if(rows & 3){
+    // This index is the beginning of remaining block.
+    colVSXLoops<4, true>(depth, cols, rows, pAlpha, indexA, indexA2, indexBF32, strideA, strideB, offsetB, result + row);
+  }
+
+  // Convert back to bfloat16
+  convertArrayF32toBF16VSX<DataMapper>(result, cols, rows, res);
+}
+
+#undef MAX_BFLOAT16_ACC_VSX
+
+#include "MatrixVectorProduct.h"
+
+/************************************
+ * ppc64le template specializations *
+ * **********************************/
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
+{
+  void operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
+  ::operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+{
+    dhs_pack<double, DataMapper, Packet2d, ColMajor, PanelMode, true> pack;
+    pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
+{
+  void operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<double, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
+  ::operator()(double* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+{
+    dhs_pack<double, DataMapper, Packet2d, RowMajor, PanelMode, true> pack;
+    pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+#if EIGEN_ALTIVEC_USE_CUSTOM_PACK
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<double, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
+{
+  void operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<double, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
+  ::operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
+{
+  dhs_pack<double, DataMapper, Packet2d, ColMajor, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<double, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
+{
+  void operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<double, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
+  ::operator()(double* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
+{
+  dhs_pack<double, DataMapper, Packet2d, RowMajor, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<bfloat16, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
+{
+  void operator()(bfloat16* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<bfloat16, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
+  ::operator()(bfloat16* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
+{
+  dhs_pack<bfloat16, DataMapper, Packet8bf, ColMajor, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<bfloat16, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
+{
+  void operator()(bfloat16* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<bfloat16, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
+  ::operator()(bfloat16* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
+{
+  dhs_pack<bfloat16, DataMapper, Packet8bf, RowMajor, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+#endif
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<bfloat16, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
+{
+  void operator()(bfloat16* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<bfloat16, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
+  ::operator()(bfloat16* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+{
+  dhs_pack<bfloat16, DataMapper, Packet8bf, ColMajor, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<bfloat16, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
+{
+  void operator()(bfloat16* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<bfloat16, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
+  ::operator()(bfloat16* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+{
+  dhs_pack<bfloat16, DataMapper, Packet8bf, RowMajor, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
+{
+  void operator()(float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
+  ::operator()(float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+{
+  dhs_pack<float, DataMapper, Packet4f, RowMajor, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
+{
+  void operator()(float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<float, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
+  ::operator()(float* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+{
+  dhs_pack<float, DataMapper, Packet4f, ColMajor, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
+{
+  void operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
+  ::operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+{
+  dhs_cpack<float, DataMapper, Packet4f, Packet2cf, RowMajor, Conjugate, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
+{
+  void operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<std::complex<float>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
+  ::operator()(std::complex<float>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+{
+  dhs_cpack<float, DataMapper, Packet4f, Packet2cf, ColMajor, Conjugate, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+#if EIGEN_ALTIVEC_USE_CUSTOM_PACK
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<float, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
+{
+  void operator()(float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<float, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
+  ::operator()(float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
+{
+  dhs_pack<float, DataMapper, Packet4f, ColMajor, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<float, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
+{
+  void operator()(float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<float, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
+  ::operator()(float* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
+{
+  dhs_pack<float, DataMapper, Packet4f, RowMajor, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+#endif
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
+{
+  void operator()(std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
+  ::operator()(std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
+{
+  dhs_cpack<float, DataMapper, Packet4f, Packet2cf, ColMajor, Conjugate, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
+{
+  void operator()(std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<std::complex<float>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
+  ::operator()(std::complex<float>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
+{
+  dhs_cpack<float, DataMapper, Packet4f, Packet2cf, RowMajor, Conjugate, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
+{
+  void operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
+  ::operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+{
+  dhs_cpack<double, DataMapper, Packet2d, Packet1cd, RowMajor, Conjugate, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
+{
+  void operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+void gemm_pack_lhs<std::complex<double>, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
+  ::operator()(std::complex<double>* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+{
+  dhs_cpack<double, DataMapper, Packet2d, Packet1cd, ColMajor, Conjugate, PanelMode, true> pack;
+  pack(blockA, lhs, depth, rows, stride, offset);
+}
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
+{
+  void operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
+  ::operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
+{
+  dhs_cpack<double, DataMapper, Packet2d, Packet1cd, ColMajor, Conjugate, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
+{
+  void operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
+};
+
+template<typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+void gemm_pack_rhs<std::complex<double>, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
+  ::operator()(std::complex<double>* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
+{
+  dhs_cpack<double, DataMapper, Packet2d, Packet1cd, RowMajor, Conjugate, PanelMode, false> pack;
+  pack(blockB, rhs, depth, cols, stride, offset);
+}
+
+// ********* gebp specializations *********
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<float, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+{
+  typedef typename quad_traits<float>::vectortype   Packet;
+  typedef typename quad_traits<float>::rhstype      RhsPacket;
+
+  void operator()(const DataMapper& res, const float* blockA, const float* blockB,
+                  Index rows, Index depth, Index cols, float alpha,
+                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
+};
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<float, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+  ::operator()(const DataMapper& res, const float* blockA, const float* blockB,
+               Index rows, Index depth, Index cols, float alpha,
+               Index strideA, Index strideB, Index offsetA, Index offsetB)
+  {
+    const Index accRows = quad_traits<float>::rows;
+    const Index accCols = quad_traits<float>::size;
+    static void (*gemm_function)(const DataMapper&, const float*, const float*, Index, Index, Index, float, Index, Index, Index, Index) =
+    #ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ?
+        &Eigen::internal::gemmMMA<float, Packet, RhsPacket, DataMapper, accRows, accCols> :
+    #endif
+        &Eigen::internal::gemm<float, Packet, RhsPacket, DataMapper, accRows, accCols>;
+    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+  }
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<std::complex<float>, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+{
+  typedef Packet4f   Packet;
+  typedef Packet2cf  Packetc;
+  typedef Packet4f   RhsPacket;
+
+  void operator()(const DataMapper& res, const std::complex<float>* blockA, const std::complex<float>* blockB,
+                  Index rows, Index depth, Index cols, std::complex<float> alpha,
+                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
+};
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<std::complex<float>, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+  ::operator()(const DataMapper& res, const std::complex<float>* blockA, const std::complex<float>* blockB,
+               Index rows, Index depth, Index cols, std::complex<float> alpha,
+               Index strideA, Index strideB, Index offsetA, Index offsetB)
+  {
+    const Index accRows = quad_traits<float>::rows;
+    const Index accCols = quad_traits<float>::size;
+    static void (*gemm_function)(const DataMapper&, const std::complex<float>*, const std::complex<float>*,
+          Index, Index, Index, std::complex<float>, Index, Index, Index, Index) =
+    #ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ?
+        &Eigen::internal::gemm_complexMMA<std::complex<float>, std::complex<float>, std::complex<float>, float, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false> :
+    #endif
+        &Eigen::internal::gemm_complex<std::complex<float>, std::complex<float>, std::complex<float>, float, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
+    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+  }
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<float, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+{
+  typedef Packet4f   Packet;
+  typedef Packet2cf  Packetc;
+  typedef Packet4f   RhsPacket;
+
+  void operator()(const DataMapper& res, const float* blockA, const std::complex<float>* blockB,
+                  Index rows, Index depth, Index cols, std::complex<float> alpha,
+                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
+};
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<float, std::complex<float>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+  ::operator()(const DataMapper& res, const float* blockA, const std::complex<float>* blockB,
+               Index rows, Index depth, Index cols, std::complex<float> alpha,
+               Index strideA, Index strideB, Index offsetA, Index offsetB)
+  {
+    const Index accRows = quad_traits<float>::rows;
+    const Index accCols = quad_traits<float>::size;
+    static void (*gemm_function)(const DataMapper&, const float*, const std::complex<float>*,
+          Index, Index, Index, std::complex<float>, Index, Index, Index, Index) =
+    #ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ?
+        &Eigen::internal::gemm_complexMMA<float, std::complex<float>, std::complex<float>, float, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false> :
+    #endif
+        &Eigen::internal::gemm_complex<float, std::complex<float>, std::complex<float>, float, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
+    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+  }
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<std::complex<float>, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+{
+  typedef Packet4f   Packet;
+  typedef Packet2cf  Packetc;
+  typedef Packet4f   RhsPacket;
+
+  void operator()(const DataMapper& res, const std::complex<float>* blockA, const float* blockB,
+                  Index rows, Index depth, Index cols, std::complex<float> alpha,
+                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
+};
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<std::complex<float>, float, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+  ::operator()(const DataMapper& res, const std::complex<float>* blockA, const float* blockB,
+               Index rows, Index depth, Index cols, std::complex<float> alpha,
+               Index strideA, Index strideB, Index offsetA, Index offsetB)
+  {
+    const Index accRows = quad_traits<float>::rows;
+    const Index accCols = quad_traits<float>::size;
+    static void (*gemm_function)(const DataMapper&, const std::complex<float>*, const float*,
+          Index, Index, Index, std::complex<float>, Index, Index, Index, Index) =
+    #ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ?
+        &Eigen::internal::gemm_complexMMA<std::complex<float>, float, std::complex<float>, float, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true> :
+    #endif
+        &Eigen::internal::gemm_complex<std::complex<float>, float, std::complex<float>, float, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
+    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+  }
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<double, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+{
+  typedef typename quad_traits<double>::vectortype  Packet;
+  typedef typename quad_traits<double>::rhstype     RhsPacket;
+
+  void operator()(const DataMapper& res, const double* blockA, const double* blockB,
+                  Index rows, Index depth, Index cols, double alpha,
+                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
+};
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<double, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+  ::operator()(const DataMapper& res, const double* blockA, const double* blockB,
+               Index rows, Index depth, Index cols, double alpha,
+               Index strideA, Index strideB, Index offsetA, Index offsetB)
+  {
+    const Index accRows = quad_traits<double>::rows;
+    const Index accCols = quad_traits<double>::size;
+    static void (*gemm_function)(const DataMapper&, const double*, const double*, Index, Index, Index, double, Index, Index, Index, Index) =
+    #ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ?
+        &Eigen::internal::gemmMMA<double, Packet, RhsPacket, DataMapper, accRows, accCols> :
+    #endif
+        &Eigen::internal::gemm<double, Packet, RhsPacket, DataMapper, accRows, accCols>;
+    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+  }
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<std::complex<double>, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+{
+  typedef quad_traits<double>::vectortype   Packet;
+  typedef Packet1cd  Packetc;
+  typedef quad_traits<double>::rhstype   RhsPacket;
+
+  void operator()(const DataMapper& res, const std::complex<double>* blockA, const std::complex<double>* blockB,
+                  Index rows, Index depth, Index cols, std::complex<double> alpha,
+                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
+};
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<std::complex<double>, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+  ::operator()(const DataMapper& res, const std::complex<double>* blockA, const std::complex<double>* blockB,
+               Index rows, Index depth, Index cols, std::complex<double> alpha,
+               Index strideA, Index strideB, Index offsetA, Index offsetB)
+  {
+    const Index accRows = quad_traits<double>::rows;
+    const Index accCols = quad_traits<double>::size;
+    static void (*gemm_function)(const DataMapper&, const std::complex<double>*, const std::complex<double>*,
+          Index, Index, Index, std::complex<double>, Index, Index, Index, Index) =
+    #ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ?
+        &Eigen::internal::gemm_complexMMA<std::complex<double>, std::complex<double>, std::complex<double>, double, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false> :
+    #endif
+        &Eigen::internal::gemm_complex<std::complex<double>, std::complex<double>, std::complex<double>, double, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, false>;
+    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+  }
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<std::complex<double>, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+{
+  typedef quad_traits<double>::vectortype   Packet;
+  typedef Packet1cd  Packetc;
+  typedef quad_traits<double>::rhstype   RhsPacket;
+
+  void operator()(const DataMapper& res, const std::complex<double>* blockA, const double* blockB,
+                  Index rows, Index depth, Index cols, std::complex<double> alpha,
+                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
+};
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<std::complex<double>, double, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+  ::operator()(const DataMapper& res, const std::complex<double>* blockA, const double* blockB,
+               Index rows, Index depth, Index cols, std::complex<double> alpha,
+               Index strideA, Index strideB, Index offsetA, Index offsetB)
+  {
+    const Index accRows = quad_traits<double>::rows;
+    const Index accCols = quad_traits<double>::size;
+    static void (*gemm_function)(const DataMapper&, const std::complex<double>*, const double*,
+          Index, Index, Index, std::complex<double>, Index, Index, Index, Index) =
+    #ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ?
+        &Eigen::internal::gemm_complexMMA<std::complex<double>, double, std::complex<double>, double, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true> :
+    #endif
+        &Eigen::internal::gemm_complex<std::complex<double>, double, std::complex<double>, double, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, false, true>;
+    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+  }
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<double, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+{
+  typedef quad_traits<double>::vectortype   Packet;
+  typedef Packet1cd  Packetc;
+  typedef quad_traits<double>::rhstype   RhsPacket;
+
+  void operator()(const DataMapper& res, const double* blockA, const std::complex<double>* blockB,
+                  Index rows, Index depth, Index cols, std::complex<double> alpha,
+                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
+};
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<double, std::complex<double>, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+  ::operator()(const DataMapper& res, const double* blockA, const std::complex<double>* blockB,
+               Index rows, Index depth, Index cols, std::complex<double> alpha,
+               Index strideA, Index strideB, Index offsetA, Index offsetB)
+  {
+    const Index accRows = quad_traits<double>::rows;
+    const Index accCols = quad_traits<double>::size;
+    static void (*gemm_function)(const DataMapper&, const double*, const std::complex<double>*,
+          Index, Index, Index, std::complex<double>, Index, Index, Index, Index) =
+    #ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ?
+        &Eigen::internal::gemm_complexMMA<double, std::complex<double>, std::complex<double>, double, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false> :
+    #endif
+        &Eigen::internal::gemm_complex<double, std::complex<double>, std::complex<double>, double, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, true, false>;
+    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+  }
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel<bfloat16, bfloat16, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+{
+  typedef typename quad_traits<bfloat16>::vectortype   Packet;
+  typedef typename quad_traits<bfloat16>::rhstype      RhsPacket;
+
+  void operator()(const DataMapper& res, const bfloat16* blockA, const bfloat16* blockB,
+                  Index rows, Index depth, Index cols, bfloat16 alpha,
+                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
+};
+
+template<typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+void gebp_kernel<bfloat16, bfloat16, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs>
+  ::operator()(const DataMapper& res, const bfloat16* blockA, const bfloat16* blockB,
+               Index rows, Index depth, Index cols, bfloat16 alpha,
+               Index strideA, Index strideB, Index offsetA, Index offsetB)
+  {
+    static void (*gemm_function)(const DataMapper&, const bfloat16*, const bfloat16*, Index, Index, Index, bfloat16, Index, Index, Index, Index) =
+    #ifdef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+      (supportsMMA()) ?
+        &Eigen::internal::gemmMMAbfloat16<DataMapper> :
+    #endif
+        &Eigen::internal::gemmbfloat16<DataMapper>;
+    gemm_function(res, blockA, blockB, rows, depth, cols, alpha, strideA, strideB, offsetA, offsetB);
+  }
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATRIX_PRODUCT_ALTIVEC_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
new file mode 100644
index 0000000000000000000000000000000000000000..daed8c1659647ec949d03a33ba993d0e259a2ce6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MatrixProductCommon.h
@@ -0,0 +1,237 @@
+//#define EIGEN_POWER_USE_PREFETCH  // Use prefetching in gemm routines
+#ifdef EIGEN_POWER_USE_PREFETCH
+#define EIGEN_POWER_PREFETCH(p)  prefetch(p)
+#else
+#define EIGEN_POWER_PREFETCH(p)
+#endif
+
+#if defined(_ARCH_PWR9) || defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
+#define USE_PARTIAL_PACKETS
+#endif
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename Scalar, typename Packet, typename DataMapper, const Index accRows, const Index accCols>
+EIGEN_ALWAYS_INLINE void gemm_extra_row(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index row,
+  Index rows,
+  Index remaining_rows,
+  const Packet& pAlpha,
+  const Packet& pMask);
+
+template<typename Scalar, typename Packet, typename DataMapper, const Index accCols>
+EIGEN_ALWAYS_INLINE void gemm_extra_cols(
+  const DataMapper& res,
+  const Scalar* blockA,
+  const Scalar* blockB,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index offsetB,
+  Index col,
+  Index rows,
+  Index cols,
+  Index remaining_rows,
+  const Packet& pAlpha,
+  const Packet& pMask);
+
+template<typename Packet>
+EIGEN_ALWAYS_INLINE Packet bmask(const Index remaining_rows);
+
+template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void gemm_complex_extra_row(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index row,
+  Index rows,
+  Index remaining_rows,
+  const Packet& pAlphaReal,
+  const Packet& pAlphaImag,
+  const Packet& pMask);
+
+template<typename Scalar, typename Packet, typename Packetc, typename DataMapper, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void gemm_complex_extra_cols(
+  const DataMapper& res,
+  const Scalar* blockA,
+  const Scalar* blockB,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index offsetB,
+  Index col,
+  Index rows,
+  Index cols,
+  Index remaining_rows,
+  const Packet& pAlphaReal,
+  const Packet& pAlphaImag,
+  const Packet& pMask);
+
+template<typename DataMapper>
+EIGEN_ALWAYS_INLINE void convertArrayBF16toF32(float *result, Index cols, Index rows, const DataMapper& src);
+
+template<const Index size, bool non_unit_stride, Index delta>
+EIGEN_ALWAYS_INLINE void storeBF16fromResult(bfloat16* dst, Packet8bf data, Index resInc, Index extra = 0);
+
+template<bool non_unit_stride = false>
+EIGEN_ALWAYS_INLINE void convertArrayPointerBF16toF32(float *result, Index cols, Index rows, bfloat16* src, Index resInc = 1);
+
+template<bool rhsExtraCols, bool lhsExtraRows>
+EIGEN_ALWAYS_INLINE void storeResults(Packet4f (&acc)[4], Index rows, const Packet4f pAlpha, float* result, Index extra_cols, Index extra_rows);
+
+template<Index num_acc, bool extraRows, Index size = 4>
+EIGEN_ALWAYS_INLINE void outputVecColResults(Packet4f (&acc)[num_acc][size], float *result, Packet4f pAlpha, Index extra_rows);
+
+template<Index num_acc, Index size = 4>
+EIGEN_ALWAYS_INLINE void outputVecResults(Packet4f (&acc)[num_acc][size], float *result, Packet4f pAlpha);
+
+template<typename RhsMapper, bool linear>
+EIGEN_ALWAYS_INLINE Packet8bf loadColData(RhsMapper& rhs, Index j);
+
+template<typename Packet>
+EIGEN_ALWAYS_INLINE Packet ploadLhs(const __UNPACK_TYPE__(Packet)* lhs);
+
+template<typename DataMapper, typename Packet, const Index accCols, int StorageOrder, bool Complex, int N, bool full = true>
+EIGEN_ALWAYS_INLINE void bload(PacketBlock<Packet,N*(Complex?2:1)>& acc, const DataMapper& res, Index row, Index col);
+
+template<typename DataMapper, typename Packet, int N>
+EIGEN_ALWAYS_INLINE void bstore(PacketBlock<Packet,N>& acc, const DataMapper& res, Index row);
+
+#ifdef USE_PARTIAL_PACKETS
+template<typename DataMapper, typename Packet, const Index accCols, bool Complex, Index N, bool full = true>
+EIGEN_ALWAYS_INLINE void bload_partial(PacketBlock<Packet,N*(Complex?2:1)>& acc, const DataMapper& res, Index row, Index elements);
+
+template<typename DataMapper, typename Packet, Index N>
+EIGEN_ALWAYS_INLINE void bstore_partial(PacketBlock<Packet,N>& acc, const DataMapper& res, Index row, Index elements);
+#endif
+
+template<typename Packet, int N>
+EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,N>& acc, PacketBlock<Packet,N>& accZ, const Packet& pAlpha);
+
+template<typename Packet, int N, bool mask>
+EIGEN_ALWAYS_INLINE void bscale(PacketBlock<Packet,N>& acc, PacketBlock<Packet,N>& accZ, const Packet& pAlpha, const Packet& pMask);
+
+template<typename Packet, int N, bool mask>
+EIGEN_ALWAYS_INLINE void bscalec(PacketBlock<Packet,N>& aReal, PacketBlock<Packet,N>& aImag, const Packet& bReal, const Packet& bImag, PacketBlock<Packet,N>& cReal, PacketBlock<Packet,N>& cImag, const Packet& pMask);
+
+template<typename Packet, typename Packetc, int N, bool full>
+EIGEN_ALWAYS_INLINE void bcouple(PacketBlock<Packet,N>& taccReal, PacketBlock<Packet,N>& taccImag, PacketBlock<Packetc,N*2>& tRes, PacketBlock<Packetc, N>& acc1, PacketBlock<Packetc, N>& acc2);
+
+#define MICRO_NORMAL(iter) \
+  (accCols == accCols2) || (unroll_factor != (iter + 1))
+
+#define MICRO_UNROLL_ITER1(func, N) \
+  switch (remaining_rows) { \
+    default: \
+      func(N, 0) \
+      break; \
+    case 1: \
+      func(N, 1) \
+      break; \
+    case 2: \
+      if (sizeof(Scalar) == sizeof(float)) { \
+        func(N, 2) \
+      } \
+      break; \
+    case 3: \
+      if (sizeof(Scalar) == sizeof(float)) { \
+        func(N, 3) \
+      } \
+      break; \
+  }
+
+#ifdef USE_PARTIAL_PACKETS
+#define MICRO_UNROLL_ITER(func, N) \
+  if (remaining_rows) { \
+    func(N, true); \
+  } else { \
+    func(N, false); \
+  }
+
+#define MICRO_NORMAL_PARTIAL(iter) \
+  full || (unroll_factor != (iter + 1))
+#else
+#define MICRO_UNROLL_ITER(func, N) MICRO_UNROLL_ITER1(func, N)
+#endif
+
+#define MICRO_COMPLEX_UNROLL_ITER(func, N) MICRO_UNROLL_ITER1(func, N)
+
+#define MICRO_NORMAL_COLS(iter, a, b) ((MICRO_NORMAL(iter)) ? a : b)
+
+#define MICRO_LOAD1(lhs_ptr, iter) \
+  if (unroll_factor > iter) { \
+    lhsV##iter = ploadLhs<Packet>(lhs_ptr##iter); \
+    lhs_ptr##iter += MICRO_NORMAL_COLS(iter, accCols, accCols2); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(lhsV##iter); \
+  }
+
+#define MICRO_LOAD_ONE(iter) MICRO_LOAD1(lhs_ptr, iter)
+
+#define MICRO_COMPLEX_LOAD_ONE(iter) \
+  if (!LhsIsReal && (unroll_factor > iter)) { \
+    lhsVi##iter = ploadLhs<Packet>(lhs_ptr_real##iter + MICRO_NORMAL_COLS(iter, imag_delta, imag_delta2)); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(lhsVi##iter); \
+  } \
+  MICRO_LOAD1(lhs_ptr_real, iter) \
+
+#define MICRO_SRC_PTR1(lhs_ptr, advRows, iter) \
+  if (unroll_factor > iter) { \
+    lhs_ptr##iter = lhs_base + (row+(iter*accCols))*strideA*advRows - MICRO_NORMAL_COLS(iter, 0, (accCols-accCols2)*offsetA); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(lhs_ptr##iter); \
+  }
+
+#define MICRO_SRC_PTR_ONE(iter) MICRO_SRC_PTR1(lhs_ptr, 1, iter)
+
+#define MICRO_COMPLEX_SRC_PTR_ONE(iter) MICRO_SRC_PTR1(lhs_ptr_real, advanceRows, iter)
+
+#define MICRO_PREFETCH1(lhs_ptr, iter) \
+  if (unroll_factor > iter) { \
+    EIGEN_POWER_PREFETCH(lhs_ptr##iter); \
+  }
+
+#define MICRO_PREFETCH_ONE(iter) MICRO_PREFETCH1(lhs_ptr, iter)
+
+#define MICRO_COMPLEX_PREFETCH_ONE(iter) MICRO_PREFETCH1(lhs_ptr_real, iter)
+
+#ifdef USE_PARTIAL_PACKETS
+#define MICRO_UPDATE_MASK
+#else
+#define MICRO_UPDATE_MASK EIGEN_UNUSED_VARIABLE(pMask);
+#endif
+
+#define MICRO_UPDATE \
+  if (accCols == accCols2) { \
+    MICRO_UPDATE_MASK \
+    EIGEN_UNUSED_VARIABLE(offsetA); \
+    row += unroll_factor*accCols; \
+  }
+
+#define MICRO_COMPLEX_UPDATE \
+  MICRO_UPDATE \
+  if(LhsIsReal || (accCols == accCols2)) { \
+    EIGEN_UNUSED_VARIABLE(imag_delta2); \
+  }
+
+
+} // end namespace internal
+} // end namespace Eigen
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
new file mode 100644
index 0000000000000000000000000000000000000000..e4013a7479fe3775a0db35597456bcd0da563ab8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MatrixProductMMA.h
@@ -0,0 +1,757 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2020 Everton Constantino (everton.constantino@ibm.com)
+// Copyright (C) 2021 Chip Kerchner (chip.kerchner@ibm.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+#define EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+
+// If using dynamic dispatch, set the CPU target.
+#if defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
+#pragma GCC push_options
+#pragma GCC target("cpu=power10,htm")
+#endif
+
+#ifdef __has_builtin
+#if !__has_builtin(__builtin_vsx_assemble_pair)
+#define __builtin_vsx_assemble_pair __builtin_mma_assemble_pair
+#endif
+#if !__has_builtin(__builtin_vsx_disassemble_pair)
+#define __builtin_vsx_disassemble_pair __builtin_mma_disassemble_pair
+#endif
+#endif
+
+#include "../../InternalHeaderCheck.h"
+
+#include "MatrixProductMMAbfloat16.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#define accColsC (accCols / 2)
+
+EIGEN_ALWAYS_INLINE void bsetzeroMMA(__vector_quad* acc)
+{
+  __builtin_mma_xxsetaccz(acc);
+}
+
+#ifdef USE_PARTIAL_PACKETS
+template<typename DataMapper, typename Packet, bool full>
+EIGEN_ALWAYS_INLINE void storeAccumulator(Index i, const DataMapper& data, const Packet& alpha, const Index elements, __vector_quad* acc)
+#else
+template<typename DataMapper, typename Packet, const Index accCols, const Index accCols2>
+EIGEN_ALWAYS_INLINE void storeAccumulator(Index i, const DataMapper& data, const Packet& alpha, const Packet& pMask, __vector_quad* acc)
+#endif
+{
+  PacketBlock<Packet, 4> result;
+  __builtin_mma_disassemble_acc(&result.packet, acc);
+
+  PacketBlock<Packet, 4> tRes;
+#ifdef USE_PARTIAL_PACKETS
+  if (full) {
+    EIGEN_UNUSED_VARIABLE(elements);
+    bload<DataMapper, Packet, 0, ColMajor, false, 4>(tRes, data, i, 0);
+    bscale<Packet, 4>(tRes, result, alpha);
+    bstore<DataMapper, Packet, 4>(tRes, data, i);
+  } else {
+    bload_partial<DataMapper, Packet, 0, false, 4>(tRes, data, i, elements);
+    bscale<Packet, 4>(tRes, result, alpha);
+    bstore_partial<DataMapper, Packet, 4>(tRes, data, i, elements);
+  }
+#else
+  bload<DataMapper, Packet, 0, ColMajor, false, 4>(tRes, data, i, 0);
+  bscale<Packet, 4, (accCols != accCols2)>(tRes, result, alpha, pMask);
+  bstore<DataMapper, Packet, 4>(tRes, data, i);
+#endif
+}
+
+template<typename DataMapper, typename Packet, typename Packetc, const Index accCols, const Index accCols2>
+EIGEN_ALWAYS_INLINE void storeComplexAccumulator(Index i, const DataMapper& data, const Packet& alphaReal, const Packet& alphaImag, const Packet& pMask, __vector_quad* accReal, __vector_quad* accImag)
+{
+  constexpr bool full = (accCols2 > accColsC);
+  PacketBlock<Packet, 4> resultReal, resultImag;
+  __builtin_mma_disassemble_acc(&resultReal.packet, accReal);
+  __builtin_mma_disassemble_acc(&resultImag.packet, accImag);
+
+  PacketBlock<Packetc, 8> tRes;
+  bload<DataMapper, Packetc, accColsC, ColMajor, true, 4, full>(tRes, data, i, 0);
+
+  PacketBlock<Packet, 4> taccReal, taccImag;
+  bscalec<Packet, 4, (accCols != accCols2)>(resultReal, resultImag, alphaReal, alphaImag, taccReal, taccImag, pMask);
+
+  PacketBlock<Packetc, 4> acc1, acc2;
+  bcouple<Packet, Packetc, 4, full>(taccReal, taccImag, tRes, acc1, acc2);
+
+  bstore<DataMapper, Packetc, 4>(acc1, data, i);
+  if (full) {
+    bstore<DataMapper, Packetc, 4>(acc2, data, i + accColsC);
+  }
+}
+
+// Defaults to float32, since Eigen still supports C++03 we can't use default template arguments
+template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
+EIGEN_ALWAYS_INLINE void pgerMMA(__vector_quad* acc, const RhsPacket& a, const LhsPacket& b)
+{
+  if(NegativeAccumulate)
+  {
+    __builtin_mma_xvf32gernp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
+  } else {
+    __builtin_mma_xvf32gerpp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
+  }
+}
+
+template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
+EIGEN_ALWAYS_INLINE void pgerMMA(__vector_quad* acc, const __vector_pair& a, const Packet2d& b)
+{
+  if(NegativeAccumulate)
+  {
+    __builtin_mma_xvf64gernp(acc, (__vector_pair)a, (__vector unsigned char)b);
+  } else {
+    __builtin_mma_xvf64gerpp(acc, (__vector_pair)a, (__vector unsigned char)b);
+  }
+}
+
+template<typename Packet, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void pgercMMA(__vector_quad* accReal, __vector_quad* accImag, const Packet& lhsV, Packet& lhsVi, const RhsPacket& rhsV, RhsPacket& rhsVi)
+{
+  pgerMMA<Packet, RhsPacket, false>(accReal,  rhsV,  lhsV);
+  if(LhsIsReal) {
+    pgerMMA<Packet, RhsPacket, ConjugateRhs>(accImag, rhsVi,  lhsV);
+    EIGEN_UNUSED_VARIABLE(lhsVi);
+  } else {
+    if(!RhsIsReal) {
+      pgerMMA<Packet, RhsPacket, ConjugateLhs == ConjugateRhs>(accReal, rhsVi, lhsVi);
+      pgerMMA<Packet, RhsPacket, ConjugateRhs>(accImag, rhsVi,  lhsV);
+    } else {
+      EIGEN_UNUSED_VARIABLE(rhsVi);
+    }
+    pgerMMA<Packet, RhsPacket, ConjugateLhs>(accImag,  rhsV, lhsVi);
+  }
+}
+
+// This is necessary because ploadRhs for double returns a pair of vectors when MMA is enabled.
+template<typename Packet>
+EIGEN_ALWAYS_INLINE Packet ploadRhs(const __UNPACK_TYPE__(Packet)* rhs)
+{
+  return ploadu<Packet>(rhs);
+}
+
+template<typename Scalar, typename Packet>
+EIGEN_ALWAYS_INLINE void ploadRhsMMA(const Scalar* rhs, Packet& rhsV)
+{
+  rhsV = ploadRhs<Packet>(rhs);
+} 
+
+template<>
+EIGEN_ALWAYS_INLINE void ploadRhsMMA(const double* rhs, __vector_pair& rhsV)
+{
+#if EIGEN_COMP_LLVM
+  __builtin_vsx_assemble_pair(&rhsV,
+    reinterpret_cast<__vector unsigned char>(ploadRhs<Packet2d>(rhs + (sizeof(Packet2d) / sizeof(double)))),
+    reinterpret_cast<__vector unsigned char>(ploadRhs<Packet2d>(rhs)));
+#else
+  rhsV = *reinterpret_cast<__vector_pair *>(const_cast<double *>(rhs));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE void ploadLhsMMA(const double* lhs, __vector_pair& lhsV)
+{
+  ploadRhsMMA(lhs, lhsV);
+}
+
+#if (EIGEN_COMP_LLVM || (__GNUC__ >= 11))
+#define VECTOR_PAIR_LOADS_LHS
+#endif
+
+// PEEL_MMA loop factor.
+#define PEEL_MMA 7
+
+#define MICRO_MMA_UNROLL(func) \
+  func(0) func(1) func(2) func(3) func(4) func(5) func(6) func(7)
+
+#define MICRO_MMA_WORK(func, type, peel) \
+  func(0,type,peel) func(1,type,peel) func(2,type,peel) func(3,type,peel) \
+  func(4,type,peel) func(5,type,peel) func(6,type,peel) func(7,type,peel)
+
+#define MICRO_MMA_WORK_ONE(iter, type, peel) \
+  if (unroll_factor > iter) { \
+    pgerMMA<Packet, type, false>(&accZero##iter, rhsV[peel], lhsV##iter); \
+  }
+
+#ifdef VECTOR_PAIR_LOADS_LHS
+#define MICRO_MMA_WORK_TWO(iter, type, peel) \
+  if (unroll_factor > iter) { \
+    pgerMMA<Packet, type, false>(&accZero##iter, rhsV[peel], lhsV2##iter.packet[peel & 1]); \
+  }
+
+#define MICRO_MMA_LOAD1_TWO(lhs_ptr, iter) \
+  if (unroll_factor > iter) { \
+    if (MICRO_NORMAL(iter)) { \
+      ploadLhsMMA(reinterpret_cast<const double*>(lhs_ptr##iter), plhsV##iter); \
+      __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(&lhsV2##iter.packet), &plhsV##iter); \
+      lhs_ptr##iter += accCols*2; \
+    } else { \
+      lhsV2##iter.packet[0] = ploadLhs<Packet>(lhs_ptr##iter); \
+      lhsV2##iter.packet[1] = ploadLhs<Packet>(lhs_ptr##iter + accCols2); \
+      lhs_ptr##iter += accCols2*2; \
+      EIGEN_UNUSED_VARIABLE(plhsV##iter) \
+    } \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(lhsV2##iter); \
+    EIGEN_UNUSED_VARIABLE(plhsV##iter) \
+  }
+
+#define MICRO_MMA_LOAD_TWO(iter) MICRO_MMA_LOAD1_TWO(lhs_ptr, iter)
+#endif
+
+#define MICRO_MMA_TYPE_PEEL(funcw, funcl, type, peel) \
+  if (PEEL_MMA > peel) { \
+    Packet lhsV0, lhsV1, lhsV2, lhsV3, lhsV4, lhsV5, lhsV6, lhsV7; \
+    ploadRhsMMA(rhs_ptr + (accRows * peel), rhsV[peel]); \
+    MICRO_MMA_UNROLL(funcl) \
+    MICRO_MMA_WORK(funcw, type, peel) \
+  }
+
+#ifndef VECTOR_PAIR_LOADS_LHS
+#define MICRO_MMA_UNROLL_TYPE_PEEL(funcw, funcl, type) \
+  type rhsV[8]; \
+  MICRO_MMA_TYPE_PEEL(funcw,funcl,type,0) MICRO_MMA_TYPE_PEEL(funcw,funcl,type,1) \
+  MICRO_MMA_TYPE_PEEL(funcw,funcl,type,2) MICRO_MMA_TYPE_PEEL(funcw,funcl,type,3) \
+  MICRO_MMA_TYPE_PEEL(funcw,funcl,type,4) MICRO_MMA_TYPE_PEEL(funcw,funcl,type,5) \
+  MICRO_MMA_TYPE_PEEL(funcw,funcl,type,6) MICRO_MMA_TYPE_PEEL(funcw,funcl,type,7)
+#else
+#define MICRO_MMA_TYPE_PEEL2(funcw1, funcl1, funcw2, funcl2, type, peel1, peel2) \
+  if (PEEL_MMA > peel2) { \
+    PacketBlock<Packet,2> lhsV20, lhsV21, lhsV22, lhsV23, lhsV24, lhsV25, lhsV26, lhsV27; \
+    __vector_pair plhsV0, plhsV1, plhsV2, plhsV3, plhsV4, plhsV5, plhsV6, plhsV7; \
+    if (sizeof(type) == 16) { \
+      ploadRhsMMA(reinterpret_cast<const double*>(rhs_ptr + (accRows * peel1)), prhsV##peel1); \
+      __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(&rhsV[peel1]), &prhsV##peel1); \
+    } else { \
+      EIGEN_UNUSED_VARIABLE(prhsV##peel1); \
+      ploadRhsMMA(rhs_ptr + (accRows * peel1), rhsV[peel1]); \
+      ploadRhsMMA(rhs_ptr + (accRows * peel2), rhsV[peel2]); \
+    } \
+    MICRO_MMA_UNROLL(funcl2) \
+    MICRO_MMA_WORK(funcw2, type, peel1) \
+    MICRO_MMA_WORK(funcw2, type, peel2) \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(prhsV##peel1); \
+    MICRO_MMA_TYPE_PEEL(funcw1, funcl1, type, peel1) \
+  }
+
+#define MICRO_MMA_UNROLL_TYPE_PEEL2(funcw1, funcl1, funcw2, funcl2, type) \
+  type rhsV[8]; \
+  __vector_pair prhsV0, prhsV2, prhsV4, prhsV6; \
+  MICRO_MMA_TYPE_PEEL2(funcw1,funcl1,funcw2,funcl2,type,0,1) \
+  MICRO_MMA_TYPE_PEEL2(funcw1,funcl1,funcw2,funcl2,type,2,3) \
+  MICRO_MMA_TYPE_PEEL2(funcw1,funcl1,funcw2,funcl2,type,4,5) \
+  MICRO_MMA_TYPE_PEEL2(funcw1,funcl1,funcw2,funcl2,type,6,7)
+#endif
+
+#define MICRO_MMA_UNROLL_TYPE_ONE(funcw, funcl, type) \
+  type rhsV[1]; \
+  MICRO_MMA_TYPE_PEEL(funcw,funcl,type,0)
+
+#define MICRO_MMA_UNROLL_TYPE(MICRO_MMA_TYPE, size) \
+  MICRO_MMA_TYPE(MICRO_MMA_WORK_ONE, MICRO_LOAD_ONE, RhsPacket) \
+  rhs_ptr += (accRows * size);
+
+#ifndef VECTOR_PAIR_LOADS_LHS
+#define MICRO_MMA_ONE_PEEL MICRO_MMA_UNROLL_TYPE(MICRO_MMA_UNROLL_TYPE_PEEL, PEEL_MMA)
+#else
+#define MICRO_MMA_UNROLL_TYPE2(MICRO_MMA_TYPE, size) \
+  MICRO_MMA_TYPE(MICRO_MMA_WORK_ONE, MICRO_LOAD_ONE, MICRO_MMA_WORK_TWO, MICRO_MMA_LOAD_TWO, RhsPacket) \
+  rhs_ptr += (accRows * size);
+
+#define MICRO_MMA_ONE_PEEL MICRO_MMA_UNROLL_TYPE2(MICRO_MMA_UNROLL_TYPE_PEEL2, PEEL_MMA)
+#endif
+
+#define MICRO_MMA_ONE MICRO_MMA_UNROLL_TYPE(MICRO_MMA_UNROLL_TYPE_ONE, 1)
+
+#define MICRO_MMA_DST_PTR_ONE(iter) \
+  if (unroll_factor > iter) { \
+    bsetzeroMMA(&accZero##iter); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(accZero##iter); \
+  }
+
+#define MICRO_MMA_DST_PTR MICRO_MMA_UNROLL(MICRO_MMA_DST_PTR_ONE)
+
+#define MICRO_MMA_SRC_PTR MICRO_MMA_UNROLL(MICRO_SRC_PTR_ONE)
+
+#define MICRO_MMA_PREFETCH MICRO_MMA_UNROLL(MICRO_PREFETCH_ONE)
+
+#ifdef USE_PARTIAL_PACKETS
+#define MICRO_MMA_STORE_ONE(iter) \
+  if (unroll_factor > iter) { \
+    storeAccumulator<DataMapper, Packet, MICRO_NORMAL_PARTIAL(iter)>(row + iter*accCols, res, pAlpha, accCols2, &accZero##iter); \
+  }
+#else
+#define MICRO_MMA_STORE_ONE(iter) \
+  if (unroll_factor > iter) { \
+    storeAccumulator<DataMapper, Packet, accCols, (unroll_factor != (iter + 1)) ? accCols : accCols2>(row + iter*accCols, res, pAlpha, pMask, &accZero##iter); \
+  }
+#endif
+
+#define MICRO_MMA_STORE MICRO_MMA_UNROLL(MICRO_MMA_STORE_ONE)
+
+#ifdef USE_PARTIAL_PACKETS
+template<int unroll_factor, typename Scalar, typename Packet, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols, bool full>
+#else
+template<int unroll_factor, typename Scalar, typename Packet, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols, const Index accCols2>
+#endif
+EIGEN_ALWAYS_INLINE void gemm_unrolled_MMA_iteration(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index& row,
+  const Packet& pAlpha,
+#ifdef USE_PARTIAL_PACKETS
+  Index accCols2
+#else
+  const Packet& pMask
+#endif
+  )
+{
+  const Scalar* rhs_ptr = rhs_base;
+  const Scalar* lhs_ptr0 = NULL, * lhs_ptr1 = NULL, * lhs_ptr2 = NULL, * lhs_ptr3 = NULL, * lhs_ptr4 = NULL, * lhs_ptr5 = NULL, * lhs_ptr6 = NULL, * lhs_ptr7 = NULL;
+  __vector_quad accZero0, accZero1, accZero2, accZero3, accZero4, accZero5, accZero6, accZero7;
+
+  MICRO_MMA_SRC_PTR
+  MICRO_MMA_DST_PTR
+
+  Index k = 0, depth2 = depth - PEEL_MMA;
+  for(; k <= depth2; k += PEEL_MMA)
+  {
+    EIGEN_POWER_PREFETCH(rhs_ptr);
+    MICRO_MMA_PREFETCH
+    MICRO_MMA_ONE_PEEL
+  }
+  for(; k < depth; k++)
+  {
+    MICRO_MMA_ONE
+  }
+  MICRO_MMA_STORE
+
+  MICRO_UPDATE
+}
+
+#ifdef USE_PARTIAL_PACKETS
+#define MICRO_MMA_UNROLL_ITER2(N, M) \
+  gemm_unrolled_MMA_iteration<N + (M ? 1 : 0), Scalar, Packet, RhsPacket, DataMapper, accRows, accCols, !M>(res3, lhs_base, rhs_base, depth, strideA, offsetA, row, pAlpha, M ? remaining_rows : accCols); \
+  if (M) return;
+#else
+#define MICRO_MMA_UNROLL_ITER2(N, M) \
+  gemm_unrolled_MMA_iteration<N + (M ? 1 : 0), Scalar, Packet, RhsPacket, DataMapper, accRows, accCols, M ? M : accCols>(res3, lhs_base, rhs_base, depth, strideA, offsetA, row, pAlpha, pMask); \
+  if (M) return;
+#endif
+
+template<typename Scalar, typename Packet, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols>
+EIGEN_ALWAYS_INLINE void gemmMMA_cols(
+  const DataMapper& res,
+  const Scalar* blockA,
+  const Scalar* blockB,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index offsetB,
+  Index col,
+  Index rows,
+  Index remaining_rows,
+  const Packet& pAlpha,
+  const Packet& pMask)
+{
+  const DataMapper res3 = res.getSubMapper(0, col);
+
+  const Scalar* rhs_base = blockB + col*strideB + accRows*offsetB;
+  const Scalar* lhs_base = blockA + accCols*offsetA;
+  Index row = 0;
+
+#define MAX_MMA_UNROLL 7
+  while(row + MAX_MMA_UNROLL*accCols <= rows) {
+    MICRO_MMA_UNROLL_ITER2(MAX_MMA_UNROLL, 0);
+  }
+  switch( (rows-row)/accCols ) {
+#if MAX_MMA_UNROLL > 7
+    case 7:
+      MICRO_UNROLL_ITER(MICRO_MMA_UNROLL_ITER2, 7)
+      break;
+#endif
+#if MAX_MMA_UNROLL > 6
+    case 6:
+      MICRO_UNROLL_ITER(MICRO_MMA_UNROLL_ITER2, 6)
+      break;
+#endif
+#if MAX_MMA_UNROLL > 5
+    case 5:
+      MICRO_UNROLL_ITER(MICRO_MMA_UNROLL_ITER2, 5)
+      break;
+#endif
+#if MAX_MMA_UNROLL > 4
+    case 4:
+      MICRO_UNROLL_ITER(MICRO_MMA_UNROLL_ITER2, 4)
+      break;
+#endif
+#if MAX_MMA_UNROLL > 3
+    case 3:
+      MICRO_UNROLL_ITER(MICRO_MMA_UNROLL_ITER2, 3)
+      break;
+#endif
+#if MAX_MMA_UNROLL > 2
+    case 2:
+      MICRO_UNROLL_ITER(MICRO_MMA_UNROLL_ITER2, 2)
+      break;
+#endif
+#if MAX_MMA_UNROLL > 1
+    case 1:
+      MICRO_UNROLL_ITER(MICRO_MMA_UNROLL_ITER2, 1)
+      break;
+#endif
+    default:
+      break;
+  }
+#undef MAX_MMA_UNROLL
+
+  if(remaining_rows > 0)
+  {
+    gemm_extra_row<Scalar, Packet, DataMapper, accRows, accCols>(res3, blockA, rhs_base, depth, strideA, offsetA, strideB, row, rows, remaining_rows, pAlpha, pMask);
+  }
+}
+
+template<typename Scalar, typename Packet, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols>
+void gemmMMA(const DataMapper& res, const Scalar* blockA, const Scalar* blockB, Index rows, Index depth, Index cols, Scalar alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
+{
+      const Index remaining_rows = rows % accCols;
+
+      if( strideA == -1 ) strideA = depth;
+      if( strideB == -1 ) strideB = depth;
+
+      const Packet pAlpha = pset1<Packet>(alpha);
+      const Packet pMask  = bmask<Packet>(remaining_rows);
+
+      typedef typename std::conditional_t<(sizeof(Scalar) == sizeof(float)), RhsPacket, __vector_pair> RhsPacket2;
+
+      Index col = 0;
+      for(; col + accRows <= cols; col += accRows)
+      {
+        gemmMMA_cols<Scalar, Packet, RhsPacket2, DataMapper, accRows, accCols>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, remaining_rows, pAlpha, pMask);
+      }
+
+      if (col != cols)
+      {
+        gemm_extra_cols<Scalar, Packet, DataMapper, accCols>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols, remaining_rows, pAlpha, pMask);
+      }
+}
+
+#define advanceRows ((LhsIsReal) ? 1 : 2)
+#define advanceCols ((RhsIsReal) ? 1 : 2)
+
+// PEEL_COMPLEX_MMA loop factor.
+#define PEEL_COMPLEX_MMA 3
+
+#define MICRO_COMPLEX_MMA_UNROLL(func) \
+  func(0) func(1) func(2) func(3)
+
+#define MICRO_COMPLEX_MMA_WORK(func, type, peel) \
+  func(0,type,peel) func(1,type,peel) func(2,type,peel) func(3,type,peel)
+
+#define MICRO_COMPLEX_MMA_WORK_ONE(iter, type, peel) \
+  if (unroll_factor > iter) { \
+    pgercMMA<Packet, type, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal##iter, &accImag##iter, lhsV##iter, lhsVi##iter, rhsV[peel], rhsVi[peel]); \
+  }
+
+#ifdef VECTOR_PAIR_LOADS_LHS
+#define MICRO_COMPLEX_MMA_WORK_TWO(iter, type, peel) \
+  if (unroll_factor > iter) { \
+    pgercMMA<Packet, type, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(&accReal##iter, &accImag##iter, lhsV2##iter.packet[peel & 1], lhsVi2##iter.packet[peel & 1], rhsV[peel], rhsVi[peel]); \
+  }
+
+#define MICRO_COMPLEX_MMA_LOAD1_TWO(lhs_ptr, iter) \
+  if (!LhsIsReal && (unroll_factor > iter)) { \
+    if (MICRO_NORMAL(iter)) { \
+      ploadLhsMMA(reinterpret_cast<const double*>(lhs_ptr_real##iter + imag_delta), plhsVi##iter); \
+      __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(&lhsVi2##iter.packet), &plhsVi##iter); \
+    } else { \
+      lhsVi2##iter.packet[0] = ploadLhs<Packet>(lhs_ptr_real##iter + imag_delta2); \
+      lhsVi2##iter.packet[1] = ploadLhs<Packet>(lhs_ptr_real##iter + imag_delta2 + accCols2); \
+      EIGEN_UNUSED_VARIABLE(plhsVi##iter) \
+    } \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(lhsVi2##iter); \
+    EIGEN_UNUSED_VARIABLE(plhsVi##iter) \
+  } \
+  MICRO_MMA_LOAD1_TWO(lhs_ptr_real, iter)
+
+#define MICRO_COMPLEX_MMA_LOAD_TWO(iter) MICRO_COMPLEX_MMA_LOAD1_TWO(lhs_ptr, iter)
+#endif
+
+#define MICRO_COMPLEX_MMA_TYPE_PEEL(funcw, funcl, type, peel) \
+  if (PEEL_COMPLEX_MMA > peel) { \
+    Packet lhsV0, lhsV1, lhsV2, lhsV3; \
+    Packet lhsVi0, lhsVi1, lhsVi2, lhsVi3; \
+    ploadRhsMMA(rhs_ptr_real + (accRows * peel), rhsV[peel]); \
+    if(!RhsIsReal) { \
+      ploadRhsMMA(rhs_ptr_imag + (accRows * peel), rhsVi[peel]); \
+    } \
+    MICRO_COMPLEX_MMA_UNROLL(funcl) \
+    MICRO_COMPLEX_MMA_WORK(funcw, type, peel) \
+  }
+
+#ifndef VECTOR_PAIR_LOADS_LHS
+#define MICRO_COMPLEX_MMA_UNROLL_TYPE_PEEL(funcw, funcl, type) \
+  type rhsV[4], rhsVi[4]; \
+  MICRO_COMPLEX_MMA_TYPE_PEEL(funcw,funcl,type,0) MICRO_COMPLEX_MMA_TYPE_PEEL(funcw,funcl,type,1) \
+  MICRO_COMPLEX_MMA_TYPE_PEEL(funcw,funcl,type,2) MICRO_COMPLEX_MMA_TYPE_PEEL(funcw,funcl,type,3)
+#else
+#define MICRO_COMPLEX_MMA_TYPE_PEEL2(funcw1, funcl1, funcw2, funcl2, type, peel1, peel2) \
+  if (PEEL_COMPLEX_MMA > peel2) { \
+    PacketBlock<Packet,2> lhsV20, lhsV21, lhsV22, lhsV23; \
+    PacketBlock<Packet,2> lhsVi20, lhsVi21, lhsVi22, lhsVi23; \
+    __vector_pair plhsV0, plhsV1, plhsV2, plhsV3; \
+    __vector_pair plhsVi0, plhsVi1, plhsVi2, plhsVi3; \
+    if (sizeof(type) == 16) { \
+      ploadRhsMMA(reinterpret_cast<const double*>(rhs_ptr_real + (accRows * peel1)), prhsV##peel1); \
+      __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(&rhsV[peel1]), &prhsV##peel1); \
+      if(!RhsIsReal) { \
+        ploadRhsMMA(reinterpret_cast<const double*>(rhs_ptr_imag + (accRows * peel1)), prhsVi##peel1); \
+        __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(&rhsVi[peel1]), &prhsVi##peel1); \
+      } else { \
+        EIGEN_UNUSED_VARIABLE(prhsVi##peel1); \
+      } \
+    } else { \
+      EIGEN_UNUSED_VARIABLE(prhsV##peel1); \
+      EIGEN_UNUSED_VARIABLE(prhsVi##peel1); \
+      ploadRhsMMA(rhs_ptr_real + (accRows * peel1), rhsV[peel1]); \
+      ploadRhsMMA(rhs_ptr_real + (accRows * peel2), rhsV[peel2]); \
+      if(!RhsIsReal) { \
+        ploadRhsMMA(rhs_ptr_imag + (accRows * peel1), rhsVi[peel1]); \
+        ploadRhsMMA(rhs_ptr_imag + (accRows * peel2), rhsVi[peel2]); \
+      } \
+    } \
+    MICRO_COMPLEX_MMA_UNROLL(funcl2) \
+    MICRO_COMPLEX_MMA_WORK(funcw2, type, peel1) \
+    MICRO_COMPLEX_MMA_WORK(funcw2, type, peel2) \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(prhsV##peel1); \
+    EIGEN_UNUSED_VARIABLE(prhsVi##peel1); \
+    MICRO_COMPLEX_MMA_TYPE_PEEL(funcw1, funcl1, type, peel1) \
+  }
+
+#define MICRO_COMPLEX_MMA_UNROLL_TYPE_PEEL2(funcw1, funcl1, funcw2, funcl2, type) \
+  type rhsV[4], rhsVi[4]; \
+  __vector_pair prhsV0, prhsV2; \
+  __vector_pair prhsVi0, prhsVi2; \
+  MICRO_COMPLEX_MMA_TYPE_PEEL2(funcw1,funcl1,funcw2,funcl2,type,0,1) \
+  MICRO_COMPLEX_MMA_TYPE_PEEL2(funcw1,funcl1,funcw2,funcl2,type,2,3)
+#endif
+
+#define MICRO_COMPLEX_MMA_UNROLL_TYPE_ONE(funcw, funcl, type) \
+  type rhsV[1], rhsVi[1]; \
+  MICRO_COMPLEX_MMA_TYPE_PEEL(funcw,funcl,type,0)
+
+#define MICRO_COMPLEX_MMA_UNROLL_TYPE(MICRO_COMPLEX_MMA_TYPE, size) \
+  MICRO_COMPLEX_MMA_TYPE(MICRO_COMPLEX_MMA_WORK_ONE, MICRO_COMPLEX_LOAD_ONE, RhsPacket) \
+  rhs_ptr_real += (accRows * size); \
+  if(!RhsIsReal) rhs_ptr_imag += (accRows * size);
+
+#ifndef VECTOR_PAIR_LOADS_LHS
+#define MICRO_COMPLEX_MMA_ONE_PEEL MICRO_COMPLEX_MMA_UNROLL_TYPE(MICRO_COMPLEX_MMA_UNROLL_TYPE_PEEL, PEEL_COMPLEX_MMA)
+#else
+#define MICRO_COMPLEX_MMA_UNROLL_TYPE2(MICRO_COMPLEX_MMA_TYPE, size) \
+  MICRO_COMPLEX_MMA_TYPE(MICRO_COMPLEX_MMA_WORK_ONE, MICRO_COMPLEX_LOAD_ONE, MICRO_COMPLEX_MMA_WORK_TWO, MICRO_COMPLEX_MMA_LOAD_TWO, RhsPacket) \
+  rhs_ptr_real += (accRows * size); \
+  if(!RhsIsReal) rhs_ptr_imag += (accRows * size);
+
+#define MICRO_COMPLEX_MMA_ONE_PEEL MICRO_COMPLEX_MMA_UNROLL_TYPE2(MICRO_COMPLEX_MMA_UNROLL_TYPE_PEEL2, PEEL_COMPLEX_MMA)
+#endif
+
+#define MICRO_COMPLEX_MMA_ONE MICRO_COMPLEX_MMA_UNROLL_TYPE(MICRO_COMPLEX_MMA_UNROLL_TYPE_ONE, 1)
+
+#define MICRO_COMPLEX_MMA_DST_PTR_ONE(iter) \
+  if (unroll_factor > iter) { \
+    bsetzeroMMA(&accReal##iter); \
+    bsetzeroMMA(&accImag##iter); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(accReal##iter); \
+    EIGEN_UNUSED_VARIABLE(accImag##iter); \
+  }
+
+#define MICRO_COMPLEX_MMA_DST_PTR MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_MMA_DST_PTR_ONE)
+
+#define MICRO_COMPLEX_MMA_SRC_PTR MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_SRC_PTR_ONE)
+
+#define MICRO_COMPLEX_MMA_PREFETCH MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_PREFETCH_ONE)
+
+#define MICRO_COMPLEX_MMA_STORE_ONE(iter) \
+  if (unroll_factor > iter) { \
+    storeComplexAccumulator<DataMapper, Packet, Packetc, accCols, (unroll_factor != (iter + 1)) ? accCols : accCols2>(row + iter*accCols, res, pAlphaReal, pAlphaImag, pMask, &accReal##iter, &accImag##iter); \
+  }
+
+#define MICRO_COMPLEX_MMA_STORE MICRO_COMPLEX_MMA_UNROLL(MICRO_COMPLEX_MMA_STORE_ONE)
+
+template<int unroll_factor, typename Scalar, typename Packet, typename Packetc, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols, const Index accCols2, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void gemm_complex_unrolled_MMA_iteration(
+  const DataMapper& res,
+  const Scalar* lhs_base,
+  const Scalar* rhs_base,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index& row,
+  const Packet& pAlphaReal,
+  const Packet& pAlphaImag,
+  const Packet& pMask)
+{
+  const Scalar* rhs_ptr_real = rhs_base;
+  const Scalar* rhs_ptr_imag = NULL;
+  const Index imag_delta = accCols*strideA;
+  const Index imag_delta2 = accCols2*strideA;
+  if(!RhsIsReal) {
+    rhs_ptr_imag = rhs_base + accRows*strideB;
+  } else {
+    EIGEN_UNUSED_VARIABLE(rhs_ptr_imag);
+  }
+  const Scalar* lhs_ptr_real0 = NULL, * lhs_ptr_real1 = NULL;
+  const Scalar* lhs_ptr_real2 = NULL, * lhs_ptr_real3 = NULL;
+  __vector_quad accReal0, accImag0, accReal1, accImag1, accReal2, accImag2, accReal3, accImag3;
+
+  MICRO_COMPLEX_MMA_SRC_PTR
+  MICRO_COMPLEX_MMA_DST_PTR
+
+  Index k = 0, depth2 = depth - PEEL_COMPLEX_MMA;
+  for(; k <= depth2; k += PEEL_COMPLEX_MMA)
+  {
+    EIGEN_POWER_PREFETCH(rhs_ptr_real);
+    if(!RhsIsReal) {
+      EIGEN_POWER_PREFETCH(rhs_ptr_imag);
+    }
+    MICRO_COMPLEX_MMA_PREFETCH
+    MICRO_COMPLEX_MMA_ONE_PEEL
+  }
+  for(; k < depth; k++)
+  {
+    MICRO_COMPLEX_MMA_ONE
+  }
+  MICRO_COMPLEX_MMA_STORE
+
+  MICRO_COMPLEX_UPDATE
+}
+
+#define MICRO_COMPLEX_MMA_UNROLL_ITER2(N, M) \
+  gemm_complex_unrolled_MMA_iteration<N + (M ? 1 : 0), Scalar, Packet, Packetc, RhsPacket, DataMapper, accRows, accCols, M ? M : accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, lhs_base, rhs_base, depth, strideA, offsetA, strideB, row, pAlphaReal, pAlphaImag, pMask); \
+  if (M) return;
+
+template<typename Scalar, typename Packet, typename Packetc, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+EIGEN_ALWAYS_INLINE void gemmMMA_complex_cols(
+  const DataMapper& res,
+  const Scalar* blockA,
+  const Scalar* blockB,
+  Index depth,
+  Index strideA,
+  Index offsetA,
+  Index strideB,
+  Index offsetB,
+  Index col,
+  Index rows,
+  Index remaining_rows,
+  const Packet& pAlphaReal,
+  const Packet& pAlphaImag,
+  const Packet& pMask)
+{
+  const DataMapper res3 = res.getSubMapper(0, col);
+
+  const Scalar* rhs_base = blockB + advanceCols*col*strideB + accRows*offsetB;
+  const Scalar* lhs_base = blockA + accCols*offsetA;
+  Index row = 0;
+
+#define MAX_COMPLEX_MMA_UNROLL 4
+  while(row + MAX_COMPLEX_MMA_UNROLL*accCols <= rows) {
+    MICRO_COMPLEX_MMA_UNROLL_ITER2(MAX_COMPLEX_MMA_UNROLL, 0);
+  }
+  switch( (rows-row)/accCols ) {
+#if MAX_COMPLEX_MMA_UNROLL > 4
+    case 4:
+      MICRO_COMPLEX_UNROLL_ITER(MICRO_COMPLEX_MMA_UNROLL_ITER2, 4)
+      break;
+#endif
+#if MAX_COMPLEX_MMA_UNROLL > 3
+    case 3:
+      MICRO_COMPLEX_UNROLL_ITER(MICRO_COMPLEX_MMA_UNROLL_ITER2, 3)
+      break;
+#endif
+#if MAX_COMPLEX_MMA_UNROLL > 2
+    case 2:
+      MICRO_COMPLEX_UNROLL_ITER(MICRO_COMPLEX_MMA_UNROLL_ITER2, 2)
+      break;
+#endif
+#if MAX_COMPLEX_MMA_UNROLL > 1
+    case 1:
+      MICRO_COMPLEX_UNROLL_ITER(MICRO_COMPLEX_MMA_UNROLL_ITER2, 1)
+      break;
+#endif
+    default:
+      break;
+  }
+#undef MAX_COMPLEX_MMA_UNROLL
+
+  if(remaining_rows > 0)
+  {
+    gemm_complex_extra_row<Scalar, Packet, Packetc, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res3, blockA, rhs_base, depth, strideA, offsetA, strideB, row, rows, remaining_rows, pAlphaReal, pAlphaImag, pMask);
+  }
+}
+
+template<typename LhsScalar, typename RhsScalar, typename Scalarc, typename Scalar, typename Packet, typename Packetc, typename RhsPacket, typename DataMapper, const Index accRows, const Index accCols, bool ConjugateLhs, bool ConjugateRhs, bool LhsIsReal, bool RhsIsReal>
+void gemm_complexMMA(const DataMapper& res, const LhsScalar* blockAc, const RhsScalar* blockBc, Index rows, Index depth, Index cols, Scalarc alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
+{
+      const Index remaining_rows = rows % accCols;
+
+      if( strideA == -1 ) strideA = depth;
+      if( strideB == -1 ) strideB = depth;
+
+      const Packet pAlphaReal = pset1<Packet>(alpha.real());
+      const Packet pAlphaImag = pset1<Packet>(alpha.imag());
+      const Packet pMask = bmask<Packet>(remaining_rows);
+
+      const Scalar* blockA = (Scalar *) blockAc;
+      const Scalar* blockB = (Scalar *) blockBc;
+
+      typedef typename std::conditional_t<(sizeof(Scalar) == sizeof(float)), RhsPacket, __vector_pair> RhsPacket2;
+
+      Index col = 0;
+      for(; col + accRows <= cols; col += accRows)
+      {
+        gemmMMA_complex_cols<Scalar, Packet, Packetc, RhsPacket2, DataMapper, accRows, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, remaining_rows, pAlphaReal, pAlphaImag, pMask);
+      }
+
+      if (col != cols)
+      {
+        gemm_complex_extra_cols<Scalar, Packet, Packetc, DataMapper, accCols, ConjugateLhs, ConjugateRhs, LhsIsReal, RhsIsReal>(res, blockA, blockB, depth, strideA, offsetA, strideB, offsetB, col, rows, cols, remaining_rows, pAlphaReal, pAlphaImag, pMask);
+      }
+}
+
+#undef accColsC
+#undef advanceRows
+#undef advanceCols
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#if defined(EIGEN_ALTIVEC_MMA_DYNAMIC_DISPATCH)
+#pragma GCC pop_options
+#endif
+
+#endif // EIGEN_MATRIX_PRODUCT_MMA_ALTIVEC_H
+
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MatrixProductMMAbfloat16.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MatrixProductMMAbfloat16.h
new file mode 100644
index 0000000000000000000000000000000000000000..509411839319f905e75b60f595451fc4ce97cdcf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MatrixProductMMAbfloat16.h
@@ -0,0 +1,749 @@
+#ifndef EIGEN_MATRIX_PRODUCT_MMA_BFLOAT16_ALTIVEC_H
+#define EIGEN_MATRIX_PRODUCT_MMA_BFLOAT16_ALTIVEC_H
+
+#if EIGEN_COMP_LLVM
+#define BFLOAT16_UNROLL _Pragma("unroll 8")
+#else
+#define BFLOAT16_UNROLL _Pragma("GCC unroll(8)")
+#endif
+
+namespace Eigen {
+
+namespace internal {
+
+template<bool zero>
+EIGEN_ALWAYS_INLINE Packet8bf loadBfloat16(const bfloat16* indexA)
+{
+  Packet8bf lhs1 = ploadu<Packet8bf>(indexA);
+  if(zero){
+    Packet8bf lhs2 = pset1<Packet8bf>(Eigen::bfloat16(0));
+    return vec_mergeh(lhs1.m_val, lhs2.m_val);
+  } else {
+    return lhs1;
+  }
+}
+
+template<bool zero>
+EIGEN_ALWAYS_INLINE Packet8bf loadRhsBfloat16(const bfloat16* blockB, Index strideB, Index i)
+{
+  return loadBfloat16<zero>(blockB + strideB*i);
+}
+
+template<Index num_acc, Index num_packets, bool zero, bool rhsExtraCols, bool lhsExtraRows, Index num_rhs, Index num_lhs>
+EIGEN_ALWAYS_INLINE void KLoop
+(
+  const bfloat16* indexA,
+  const bfloat16* indexB,
+  __vector_quad (&quad_acc)[num_acc],
+  Index strideB,
+  Index k,
+  Index offsetB,
+  Index extra_cols,
+  Index extra_rows
+)
+{
+  Packet8bf lhs[num_lhs], rhs[num_rhs];
+
+  BFLOAT16_UNROLL
+  for(Index i = 0; i < (num_rhs - (rhsExtraCols ? 1 : 0)); i++){
+    rhs[i] = loadRhsBfloat16<zero>(indexB + k*4, strideB, i);
+  }
+  if(rhsExtraCols) {
+    rhs[num_rhs - 1] = loadRhsBfloat16<zero>(indexB + k*extra_cols - offsetB, strideB, num_rhs - 1);
+  }
+
+  indexA += k*(lhsExtraRows ? extra_rows : num_packets);
+  if (num_lhs == 1) {
+    lhs[0] = loadBfloat16<zero>(indexA);
+  } else {
+    BFLOAT16_UNROLL
+    for(Index j = 0; j < num_lhs; j += 2) {
+      Packet8bf lhs1 = ploadu<Packet8bf>(indexA + (j + 0)*(zero ? 4 : 8));
+      if (zero) {
+        Packet8bf lhs2 = pset1<Packet8bf>(Eigen::bfloat16(0));
+        lhs[j + 0] = vec_mergeh(lhs1.m_val, lhs2.m_val);
+        lhs[j + 1] = vec_mergel(lhs1.m_val, lhs2.m_val);
+      } else {
+        lhs[j + 0] = lhs1;
+        lhs[j + 1] = ploadu<Packet8bf>(indexA + (j + 1)*8);
+      }
+    }
+  }
+
+  BFLOAT16_UNROLL
+  for(Index i = 0, x = 0; i < num_rhs; i++) {
+    BFLOAT16_UNROLL
+    for(Index j = 0; j < num_lhs; j++, x++) {
+      __builtin_mma_xvbf16ger2pp(&(quad_acc[x]), reinterpret_cast<Packet16uc>(rhs[i].m_val), reinterpret_cast<Packet16uc>(lhs[j].m_val));
+    }
+  }
+}
+
+template<Index num_acc>
+EIGEN_ALWAYS_INLINE void zeroAccumulators(__vector_quad (&quad_acc)[num_acc])
+{
+  BFLOAT16_UNROLL
+  for(Index k = 0; k < num_acc; k++)
+    __builtin_mma_xxsetaccz(&(quad_acc[k]));
+}
+
+template<Index num_acc>
+EIGEN_ALWAYS_INLINE void disassembleAccumulators(__vector_quad (&quad_acc)[num_acc], Packet4f (&acc)[num_acc][4])
+{
+  BFLOAT16_UNROLL
+  for(Index k = 0; k < num_acc; k++)
+    __builtin_mma_disassemble_acc((void*)acc[k], &(quad_acc[k]));
+}
+
+template<Index num_acc, bool rhsExtraCols, bool lhsExtraRows, Index num_rhs, Index num_lhs>
+EIGEN_ALWAYS_INLINE void outputResults(Packet4f (&acc)[num_acc][4], Index rows, const Packet4f pAlpha, float* result, const Index extra_cols, Index extra_rows)
+{
+  BFLOAT16_UNROLL
+  for(Index i = 0, k = 0; i < num_rhs - (rhsExtraCols ? 1 : 0); i++, result += 4*rows){
+    BFLOAT16_UNROLL
+    for(Index j = 0; j < num_lhs; j++, k++) {
+      storeResults<false, lhsExtraRows>(acc[k], rows, pAlpha, result + j*4, extra_cols, extra_rows);
+    }
+  }
+  if(rhsExtraCols) {
+    storeResults<rhsExtraCols, lhsExtraRows>(acc[num_acc - 1], rows, pAlpha, result, extra_cols, extra_rows);
+  }
+}
+
+template<const Index num_acc, const Index num_packets, bool rhsExtraCols, bool lhsExtraRows, bool multiIter = false>
+EIGEN_ALWAYS_INLINE void colLoopBodyIter(Index depth, Index rows, const Packet4f pAlpha, const bfloat16* indexA, const bfloat16* indexB, Index strideB, Index offsetB, float* result, const Index extra_cols, const Index extra_rows)
+{
+  constexpr Index num_lhs = multiIter ? (num_packets / 4) : 1;
+  constexpr Index num_rhs = (num_acc + num_lhs - 1) / num_lhs;
+
+  for(Index offset_row = 0; offset_row < num_packets; offset_row += 4, indexA += (multiIter ? 0 : 8), indexB += (multiIter ? (num_rhs*strideB) : 0), result += (multiIter ? (4*rows*num_rhs) : 4)) {
+    Packet4f acc[num_acc][4];
+    __vector_quad quad_acc[num_acc];
+
+    zeroAccumulators<num_acc>(quad_acc);
+
+    Index k;
+    for(k = 0; k + 2 <= depth; k += 2){
+      KLoop<num_acc, num_packets, false, rhsExtraCols, lhsExtraRows, num_rhs, num_lhs>(indexA, indexB, quad_acc, strideB, k, offsetB, extra_cols, extra_rows);
+    }
+    if(depth&1){
+      KLoop<num_acc, num_packets, true, rhsExtraCols, lhsExtraRows, num_rhs, num_lhs>(indexA - (multiIter ? 0 : offset_row), indexB, quad_acc, strideB, k, offsetB, extra_cols, extra_rows);
+    }
+
+    disassembleAccumulators<num_acc>(quad_acc, acc);
+
+    outputResults<num_acc, rhsExtraCols, lhsExtraRows, num_rhs, num_lhs>(acc, rows, pAlpha, result, extra_cols, extra_rows);
+  }
+}
+
+#define MAX_BFLOAT16_ACC   8
+
+template<const Index num_acc, const Index num_packets, bool rhsExtraCols, bool lhsExtraRows>
+void colLoopBody(Index& col, Index depth, Index cols, Index rows, const Packet4f pAlpha, const bfloat16* indexA, const bfloat16* indexB, Index strideB, Index offsetB, float* result)
+{
+  constexpr Index step = (num_acc * 4); // each accumulator has 4 elements
+  const Index extra_cols = (rhsExtraCols) ? (cols & 3) : 0;
+  const Index extra_rows = (lhsExtraRows) ? (rows & 3) : 0;
+  constexpr bool multiIters = !rhsExtraCols && (num_acc == MAX_BFLOAT16_ACC);
+  constexpr bool normIters = multiIters && ((num_acc % (num_packets / 4)) == 0);
+
+  do{
+    colLoopBodyIter<num_acc, num_packets, rhsExtraCols, lhsExtraRows, normIters>(depth, rows, pAlpha, indexA, indexB, strideB, offsetB, result, extra_cols, extra_rows);
+
+    indexB += strideB*num_acc;
+    result += rows*step;
+  } while(multiIters && (step <= cols - (col += step)));
+}
+
+template<const Index num_acc, const Index num_packets, bool rhsExtraCols, bool lhsExtraRows>
+EIGEN_ALWAYS_INLINE void colLoopBodyExtraN(Index col, Index depth, Index cols, Index rows, const Packet4f pAlpha, const bfloat16* indexA, const bfloat16* blockB, Index strideB, Index offsetB, float* result)
+{
+  if (MAX_BFLOAT16_ACC > num_acc) {
+    colLoopBody<num_acc + (rhsExtraCols ? 1 : 0), num_packets, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+  }
+}
+
+template<const Index num_packets, bool rhsExtraCols, bool lhsExtraRows>
+void colLoopBodyExtra(Index col, Index depth, Index cols, Index rows, const Packet4f pAlpha, const bfloat16* indexA, const bfloat16* blockB, Index strideB, Index offsetB, float* result)
+{
+  switch ((cols - col) >> 2) {
+  case 7:
+    colLoopBodyExtraN<7, num_packets, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+    break;
+  case 6:
+    colLoopBodyExtraN<6, num_packets, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+    break;
+  case 5:
+    colLoopBodyExtraN<5, num_packets, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+    break;
+  case 4:
+    colLoopBodyExtraN<4, num_packets, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+    break;
+  case 3:
+    colLoopBodyExtraN<3, num_packets, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+    break;
+  case 2:
+    colLoopBodyExtraN<2, num_packets, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+    break;
+  case 1:
+    colLoopBodyExtraN<1, num_packets, rhsExtraCols, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+    break;
+  default:
+    if (rhsExtraCols) {
+      colLoopBody<1, num_packets, true, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+    }
+    break;
+  }
+}
+
+template<const Index num_packets, bool lhsExtraRows = false>
+EIGEN_ALWAYS_INLINE void colLoops(Index depth, Index cols, Index rows, const Packet4f pAlpha, const bfloat16* indexA, const bfloat16* blockB, Index strideB, Index offsetB, float* result)
+{
+  Index col = 0;
+  if (cols >= (MAX_BFLOAT16_ACC * 4)) {
+    colLoopBody<MAX_BFLOAT16_ACC, num_packets, false, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, 0, result);
+    blockB += (strideB >> 2)*col;
+    result += rows*col;
+  }
+  if (cols & 3) {
+    colLoopBodyExtra<num_packets, true, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, offsetB, result);
+  } else {
+    colLoopBodyExtra<num_packets, false, lhsExtraRows>(col, depth, cols, rows, pAlpha, indexA, blockB, strideB, 0, result);
+  }
+}
+
+EIGEN_ALWAYS_INLINE Packet8bf convertF32toBF16(const float *res)
+{
+  Packet16uc fp16[2];
+  __vector_pair fp16_vp = *reinterpret_cast<__vector_pair *>(const_cast<float *>(res));
+  __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(fp16), &fp16_vp);
+  fp16[0] = __builtin_vsx_xvcvspbf16(fp16[0]);
+  fp16[1] = __builtin_vsx_xvcvspbf16(fp16[1]);
+  return vec_pack(reinterpret_cast<Packet4ui>(fp16[0]), reinterpret_cast<Packet4ui>(fp16[1]));
+}
+
+template<typename DataMapper, const Index size>
+EIGEN_ALWAYS_INLINE void convertArrayF32toBF16Col(float *result, Index col, Index rows, const DataMapper& res)
+{
+  const DataMapper res2 = res.getSubMapper(0, col);
+  Index row;
+  float *result2 = result + col*rows;
+  for(row = 0; row + 8 <= rows; row += 8, result2 += 8){
+    // get and save block
+    PacketBlock<Packet8bf,size> block;
+    BFLOAT16_UNROLL
+    for(Index j = 0; j < size; j++){
+      block.packet[j] = convertF32toBF16(result2 + j*rows);
+    }
+    res2.template storePacketBlock<Packet8bf,size>(row, 0, block);
+  }
+  // extra rows
+  if(row < rows){
+    BFLOAT16_UNROLL
+    for(Index j = 0; j < size; j++){
+      Packet8bf fp16 = convertF32toBF16(result2 + j*rows);
+      res2.template storePacketPartial<Packet8bf>(row, j, fp16, rows & 7);
+    }
+  }
+}
+
+template<const Index size, bool non_unit_stride = false>
+EIGEN_ALWAYS_INLINE void convertPointerF32toBF16(Index& i, float* result, Index rows, bfloat16*& dst, Index resInc = 1)
+{
+  constexpr Index extra = ((size < 8) ? 8 : size);
+  while (i + size <= rows){
+    PacketBlock<Packet8bf,(size+7)/8> r32;
+    r32.packet[0] = convertF32toBF16(result + i +  0);
+    if (size >= 16) {
+      r32.packet[1] = convertF32toBF16(result + i +  8);
+    }
+    if (size >= 32) {
+      r32.packet[2] = convertF32toBF16(result + i + 16);
+      r32.packet[3] = convertF32toBF16(result + i + 24);
+    }
+    storeBF16fromResult<size, non_unit_stride, 0>(dst, r32.packet[0], resInc, rows & 7);
+    if (size >= 16) {
+      storeBF16fromResult<size, non_unit_stride, 8>(dst, r32.packet[1], resInc);
+    }
+    if (size >= 32) {
+      storeBF16fromResult<size, non_unit_stride, 16>(dst, r32.packet[2], resInc);
+      storeBF16fromResult<size, non_unit_stride, 24>(dst, r32.packet[3], resInc);
+    }
+    i += extra; dst += extra*resInc;
+    if (size != 32) break;
+  }
+}
+
+template<bool non_unit_stride = false>
+EIGEN_ALWAYS_INLINE void convertArrayPointerF32toBF16(float *result, Index rows, bfloat16* dst, Index resInc = 1)
+{
+  Index i = 0;
+  convertPointerF32toBF16<32,non_unit_stride>(i, result, rows, dst, resInc);
+  convertPointerF32toBF16<16,non_unit_stride>(i, result, rows, dst, resInc);
+  convertPointerF32toBF16<8,non_unit_stride>(i, result, rows, dst, resInc);
+  convertPointerF32toBF16<1,non_unit_stride>(i, result, rows, dst, resInc);
+}
+
+template<typename DataMapper>
+EIGEN_ALWAYS_INLINE void convertArrayF32toBF16(float *result, Index cols, Index rows, const DataMapper& res)
+{
+  Index col;
+  for(col = 0; col + 4 <= cols; col += 4){
+    convertArrayF32toBF16Col<DataMapper,4>(result, col, rows, res);
+  }
+  // extra cols
+  switch (cols - col) {
+  case 1:
+    convertArrayF32toBF16Col<DataMapper,1>(result, col, rows, res);
+    break;
+  case 2:
+    convertArrayF32toBF16Col<DataMapper,2>(result, col, rows, res);
+    break;
+  case 3:
+    convertArrayF32toBF16Col<DataMapper,3>(result, col, rows, res);
+    break;
+  }
+}
+
+template<Index size>
+EIGEN_ALWAYS_INLINE void calcColLoops(const bfloat16*& indexA, Index& row, Index depth, Index cols, Index rows, const Packet4f pAlpha, const bfloat16* indexB, Index strideB, Index offsetA, Index offsetB, Index bigSuffix, float *result)
+{
+  if ((size == 16) || (rows & size)) {
+    indexA += size*offsetA;
+    colLoops<size>(depth, cols, rows, pAlpha, indexA, indexB, strideB, offsetB, result + row);
+    row += size;
+    indexA += bigSuffix*size/16;
+  }
+}
+
+template<typename DataMapper>
+void gemmMMAbfloat16(const DataMapper& res, const bfloat16* indexA, const bfloat16* indexB, Index rows, Index depth, Index cols, bfloat16 alpha, Index strideA, Index strideB, Index offsetA, Index offsetB)
+{
+  float falpha = Eigen::bfloat16_impl::bfloat16_to_float(alpha);
+  const Packet4f pAlpha = pset1<Packet4f>(falpha);
+  ei_declare_aligned_stack_constructed_variable(float, result, cols*rows, 0);
+
+  convertArrayBF16toF32<DataMapper>(result, cols, rows, res);
+
+  if( strideA == -1 ) strideA = depth;
+  if( strideB == -1 ) strideB = depth;
+  // Packing is done in blocks.
+  // There's 4 possible sizes of blocks
+  // Blocks of 8 columns with 16 elements (8x16)
+  // Blocks of 8 columns with 8 elements (8x8). This happens when there's 16 > rows >= 8
+  // Blocks of 8 columns with 4 elements (8x4). This happens when there's 8 > rows >= 4
+  // Blocks of 8 columns with < 4 elements. This happens when there's less than 4 remaining rows
+
+  // Loop for LHS standard block (8x16)
+  Index bigSuffix = (2*8) * (strideA-offsetA);
+  indexB += 4*offsetB;
+  strideB *= 4;
+  offsetB *= 3;
+
+  Index row = 0;
+  while(row + 16 <= rows){
+    calcColLoops<16>(indexA, row, depth, cols, rows, pAlpha, indexB, strideB, offsetA, offsetB, bigSuffix, result);
+  }
+  // LHS (8x8) block
+  calcColLoops<8>(indexA, row, depth, cols, rows, pAlpha, indexB, strideB, offsetA, offsetB, bigSuffix, result);
+  // LHS (8x4) block
+  calcColLoops<4>(indexA, row, depth, cols, rows, pAlpha, indexB, strideB, offsetA, offsetB, bigSuffix, result);
+  // extra rows
+  if(rows & 3){
+    // This index is the beginning of remaining block.
+    colLoops<4, true>(depth, cols, rows, pAlpha, indexA, indexB, strideB, offsetB, result + row);
+  }
+
+  // Convert back to bfloat16
+  convertArrayF32toBF16<DataMapper>(result, cols, rows, res);
+}
+
+#undef MAX_BFLOAT16_ACC
+
+#if !EIGEN_ALTIVEC_DISABLE_MMA
+template<Index num_acc, typename LhsMapper, bool zero>
+EIGEN_ALWAYS_INLINE void loadVecLoop(Index k, LhsMapper& lhs, Packet8bf (&a0)[num_acc], Packet8bf b1)
+{
+  a0[k + 0] = lhs.template loadPacket<Packet8bf>(k*4, 0);
+  if (!zero) {
+    b1 = lhs.template loadPacket<Packet8bf>(k*4, 1);
+  }
+  if (num_acc > (k + 1)) {
+    a0[k + 1] = vec_mergel(a0[k + 0].m_val, b1.m_val);
+  }
+  a0[k + 0] = vec_mergeh(a0[k + 0].m_val, b1.m_val);
+}
+
+template<Index num_acc>
+EIGEN_ALWAYS_INLINE void multVec(__vector_quad (&quad_acc)[num_acc], Packet8bf (&a0)[num_acc], Packet8bf b0)
+{
+  BFLOAT16_UNROLL
+  for(Index k = 0; k < num_acc; k++) {
+    __builtin_mma_xvbf16ger2pp(&(quad_acc[k]), reinterpret_cast<Packet16uc>(b0.m_val), reinterpret_cast<Packet16uc>(a0[k].m_val));
+  }
+}
+
+template<Index num_acc, typename LhsMapper, typename RhsMapper, bool zero, bool linear>
+EIGEN_ALWAYS_INLINE void vecColLoop(Index j, LhsMapper& lhs, RhsMapper& rhs, __vector_quad (&quad_acc)[num_acc])
+{
+  Packet8bf a0[num_acc];
+  Packet8bf b1 = pset1<Packet8bf>(Eigen::bfloat16(0));
+  Packet8bf b0 = loadColData<RhsMapper, linear>(rhs, j);
+
+  if (zero) {
+    b0 = vec_mergeh(b0.m_val, b1.m_val);
+  }
+
+  using LhsSubMapper = typename LhsMapper::SubMapper;
+
+  LhsSubMapper lhs2 = lhs.getSubMapper(0, j);
+  BFLOAT16_UNROLL
+  for(Index k = 0; k < num_acc; k += 2) {
+    loadVecLoop<num_acc, LhsSubMapper, zero>(k, lhs2, a0, b1);
+  }
+
+  multVec<num_acc>(quad_acc, a0, b0);
+}
+
+#define MAX_BFLOAT16_VEC_ACC   8
+
+template<const Index num_acc, typename LhsMapper, typename RhsMapper, bool extraRows, bool linear>
+void colVecColLoopBody(Index& row, Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  constexpr Index step = (num_acc * 4);
+  const Index extra_rows = (extraRows) ? (rows & 3) : 0;
+  constexpr bool multiIters = !extraRows && (num_acc == MAX_BFLOAT16_VEC_ACC);
+
+  do{
+    Packet4f acc[num_acc][4];
+    __vector_quad quad_acc[num_acc];
+
+    zeroAccumulators<num_acc>(quad_acc);
+
+    using LhsSubMapper = typename LhsMapper::SubMapper;
+
+    LhsSubMapper lhs2 = lhs.getSubMapper(row, 0);
+    for(Index j = 0; j + 2 <= cend; j += 2) {
+      vecColLoop<num_acc, LhsSubMapper, RhsMapper, false, linear>(j, lhs2, rhs, quad_acc);
+    }
+    if (cend & 1) {
+      vecColLoop<num_acc, LhsSubMapper, RhsMapper, true, linear>(cend - 1, lhs2, rhs, quad_acc);
+    }
+
+    disassembleAccumulators<num_acc>(quad_acc, acc);
+
+    outputVecColResults<num_acc, extraRows>(acc, result, pAlpha, extra_rows);
+
+    result += step;
+  } while(multiIters && (step <= rows - (row += step)));
+}
+
+template<const Index num_acc, typename LhsMapper, typename RhsMapper, bool extraRows, bool linear>
+EIGEN_ALWAYS_INLINE void colVecColLoopBodyExtraN(Index& row, Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  if (MAX_BFLOAT16_VEC_ACC > num_acc) {
+    colVecColLoopBody<num_acc + (extraRows ? 1 : 0), LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+  }
+}
+
+template<typename LhsMapper, typename RhsMapper, bool extraRows, bool linear>
+EIGEN_ALWAYS_INLINE void colVecColLoopBodyExtra(Index& row, Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  switch ((rows - row) >> 2) {
+  case 7:
+    colVecColLoopBodyExtraN<7, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 6:
+    colVecColLoopBodyExtraN<6, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 5:
+    colVecColLoopBodyExtraN<5, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 4:
+    colVecColLoopBodyExtraN<4, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 3:
+    colVecColLoopBodyExtraN<3, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 2:
+    colVecColLoopBodyExtraN<2, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 1:
+    colVecColLoopBodyExtraN<1, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    break;
+  default:
+    if (extraRows) {
+      colVecColLoopBody<1, LhsMapper, RhsMapper, true, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    }
+    break;
+  }
+}
+
+template<typename LhsMapper, typename RhsMapper, bool linear>
+EIGEN_ALWAYS_INLINE void calcVecColLoops(Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  Index row = 0;
+  if (rows >= (MAX_BFLOAT16_VEC_ACC * 4)) {
+    colVecColLoopBody<MAX_BFLOAT16_VEC_ACC, LhsMapper, RhsMapper, false, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    result += row;
+  }
+  if (rows & 3) {
+    colVecColLoopBodyExtra<LhsMapper, RhsMapper, true, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+  } else {
+    colVecColLoopBodyExtra<LhsMapper, RhsMapper, false, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+  }
+}
+
+template<typename RhsMapper, typename LhsMapper, typename = void>
+struct UseMMAStride : std::false_type {
+  static EIGEN_ALWAYS_INLINE void run(Index j2, Index jend, Index rows, LhsMapper& lhs, RhsMapper& rhs, Packet4f pAlpha, float *result)
+  {
+    using RhsSubMapper = typename RhsMapper::SubMapper;
+
+    RhsSubMapper rhs2 = rhs.getSubMapper(j2, 0);
+    calcVecColLoops<LhsMapper, RhsSubMapper, false>(jend - j2, rows, lhs, rhs2, pAlpha, result);
+  }
+};
+
+template<typename RhsMapper, typename LhsMapper>
+struct UseMMAStride<RhsMapper, LhsMapper, std::enable_if_t<std::is_member_function_pointer<
+                           decltype(&RhsMapper::stride)>::value>> : std::true_type {
+  static EIGEN_ALWAYS_INLINE void run(Index j2, Index jend, Index rows, LhsMapper& lhs, RhsMapper& rhs, Packet4f pAlpha, float *result)
+  {
+    using RhsSubMapper = typename RhsMapper::SubMapper;
+
+    RhsSubMapper rhs2 = rhs.getSubMapper(j2, 0);
+    if (rhs.stride() == 1) {
+      calcVecColLoops<LhsMapper, RhsSubMapper, true>(jend - j2, rows, lhs, rhs2, pAlpha, result);
+    } else {
+      calcVecColLoops<LhsMapper, RhsSubMapper, false>(jend - j2, rows, lhs, rhs2, pAlpha, result);
+    }
+  }
+};
+
+template<typename LhsMapper, typename RhsMapper>
+void gemvMMA_bfloat16_col(
+  Index rows, Index cols,
+  const LhsMapper& alhs,
+  const RhsMapper& rhs,
+  bfloat16* res, Index resIncr,
+  bfloat16 alpha)
+{
+  EIGEN_UNUSED_VARIABLE(resIncr);
+  eigen_internal_assert(resIncr == 1);
+
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate proper code.
+  LhsMapper lhs(alhs);
+  RhsMapper rhs2(rhs);
+
+  const Index lhsStride = lhs.stride();
+
+  // TODO: improve the following heuristic:
+  const Index block_cols = cols < 128 ? cols : (lhsStride * sizeof(bfloat16) < 16000 ? 16 : 8);
+  float falpha = Eigen::bfloat16_impl::bfloat16_to_float(alpha);
+  Packet4f pAlpha = pset1<Packet4f>(falpha);
+
+  ei_declare_aligned_stack_constructed_variable(float, result, rows, 0);
+
+  convertArrayPointerBF16toF32(result, 1, rows, res);
+
+  for (Index j2 = 0; j2 < cols; j2 += block_cols)
+  {
+    Index jend = numext::mini(j2 + block_cols, cols);
+
+    using LhsSubMapper = typename LhsMapper::SubMapper;
+
+    LhsSubMapper lhs2 = lhs.getSubMapper(0, j2);
+    UseMMAStride<RhsMapper, LhsSubMapper>::run(j2, jend, rows, lhs2, rhs2, pAlpha, result);
+  }
+
+  convertArrayPointerF32toBF16(result, rows, res);
+}
+
+static Packet16uc p16uc_ELEMENT_VEC3 = { 0x0c,0x0d,0x0e,0x0f, 0x1c,0x1d,0x1e,0x1f, 0x0c,0x0d,0x0e,0x0f, 0x1c,0x1d,0x1e,0x1f };
+
+template<Index num_acc>
+EIGEN_ALWAYS_INLINE void preduxVecResults2(Packet4f (&acc)[num_acc][4], Index k)
+{
+  if (num_acc > (k + 1)) {
+    acc[k][0] = vec_mergeh(acc[k][0], acc[k + 1][0]);
+    acc[k][1] = vec_mergeo(acc[k][1], acc[k + 1][1]);
+    acc[k][2] = vec_mergel(acc[k][2], acc[k + 1][2]);
+    acc[k][3] = vec_perm(acc[k][3], acc[k + 1][3], p16uc_ELEMENT_VEC3);
+
+    acc[k][0] = (acc[k][0] + acc[k][2]) + (acc[k][1] + acc[k][3]);
+  } else {
+    acc[k][0] = vec_mergeh(acc[k][0], acc[k][1]);
+    acc[k][0] += vec_mergel(acc[k][2], acc[k][3]);
+#ifdef _BIG_ENDIAN
+    acc[k][0] += vec_sld(acc[k][0], acc[k][0], 12);
+#else
+    acc[k][0] += vec_sld(acc[k][0], acc[k][0], 4);
+#endif
+  }
+}
+
+template<Index num_acc>
+EIGEN_ALWAYS_INLINE void preduxVecResults(Packet4f (&acc)[num_acc][4])
+{
+  BFLOAT16_UNROLL
+  for(Index k = 0; k < num_acc; k += 4) {
+    preduxVecResults2<num_acc>(acc, k + 0);
+    if (num_acc > (k + 2)) {
+      preduxVecResults2<num_acc>(acc, k + 2);
+      acc[k + 0][0] = reinterpret_cast<Packet4f>(vec_mergeh(reinterpret_cast<Packet2ul>(acc[k + 0][0]), reinterpret_cast<Packet2ul>(acc[k + 2][0])));
+    }
+  }
+}
+
+template<Index num_acc, typename LhsMapper, typename RhsMapper, bool extra>
+EIGEN_ALWAYS_INLINE void multVecLoop(__vector_quad (&quad_acc)[num_acc], const LhsMapper& lhs, RhsMapper& rhs, Index j, Index extra_cols)
+{
+  Packet8bf a0[num_acc], b0;
+
+  if (extra) {
+    b0 = rhs.template loadPacketPartial<Packet8bf>(j, extra_cols);
+  } else {
+    b0 = rhs.template loadPacket<Packet8bf>(j);
+  }
+
+  const LhsMapper lhs2 = lhs.getSubMapper(0, j);
+  BFLOAT16_UNROLL
+  for(Index k = 0; k < num_acc; k++) {
+    if (extra) {
+      a0[k] = lhs2.template loadPacketPartial<Packet8bf>(k, 0, extra_cols);
+    } else {
+      a0[k] = lhs2.template loadPacket<Packet8bf>(k, 0);
+    }
+  }
+
+  multVec<num_acc>(quad_acc, a0, b0);
+}
+
+template<Index num_acc, typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void vecLoop(Index cols, const LhsMapper& lhs, RhsMapper& rhs, __vector_quad (&quad_acc)[num_acc], Index extra_cols)
+{
+  Index j = 0;
+  for(; j + 8 <= cols; j += 8){
+    multVecLoop<num_acc, LhsMapper, RhsMapper, false>(quad_acc, lhs, rhs, j, extra_cols);
+  }
+
+  if (extra_cols) {
+    multVecLoop<num_acc, LhsMapper, RhsMapper, true>(quad_acc, lhs, rhs, j, extra_cols);
+  }
+}
+
+template<const Index num_acc, typename LhsMapper, typename RhsMapper>
+void colVecLoopBody(Index& row, Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  constexpr bool multiIters = (num_acc == MAX_BFLOAT16_VEC_ACC);
+  const Index extra_cols = (cols & 7);
+
+  do{
+    Packet4f acc[num_acc][4];
+    __vector_quad quad_acc[num_acc];
+
+    zeroAccumulators<num_acc>(quad_acc);
+
+    const LhsMapper lhs2 = lhs.getSubMapper(row, 0);
+    vecLoop<num_acc, LhsMapper, RhsMapper>(cols, lhs2, rhs, quad_acc, extra_cols);
+
+    disassembleAccumulators<num_acc>(quad_acc, acc);
+
+    preduxVecResults<num_acc>(acc);
+
+    outputVecResults<num_acc>(acc, result, pAlpha);
+
+    result += num_acc;
+  } while(multiIters && (num_acc <= rows - (row += num_acc)));
+}
+
+template<const Index num_acc, typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void colVecLoopBodyExtraN(Index& row, Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  if (MAX_BFLOAT16_VEC_ACC > num_acc) {
+    colVecLoopBody<num_acc, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+  }
+}
+
+template<typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void colVecLoopBodyExtra(Index& row, Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  switch (rows - row) {
+  case 7:
+    colVecLoopBodyExtraN<7, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 6:
+    colVecLoopBodyExtraN<6, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 5:
+    colVecLoopBodyExtraN<5, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 4:
+    colVecLoopBodyExtraN<4, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 3:
+    colVecLoopBodyExtraN<3, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 2:
+    colVecLoopBodyExtraN<2, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 1:
+    colVecLoopBodyExtraN<1, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    break;
+  }
+}
+
+template<typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void calcVecLoops(Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  Index row = 0;
+  if (rows >= MAX_BFLOAT16_VEC_ACC) {
+    colVecLoopBody<MAX_BFLOAT16_VEC_ACC, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    result += row;
+  }
+  colVecLoopBodyExtra<LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+}
+
+template<typename LhsMapper, typename RhsMapper>
+EIGEN_STRONG_INLINE void gemvMMA_bfloat16_row(
+  Index rows, Index cols,
+  const LhsMapper& alhs,
+  const RhsMapper& rhs,
+  bfloat16* res, Index resIncr,
+  bfloat16 alpha)
+{
+  typedef typename RhsMapper::LinearMapper LinearMapper;
+
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate proper code.
+  LhsMapper lhs(alhs);
+  LinearMapper rhs2 = rhs.getLinearMapper(0, 0);
+
+  eigen_internal_assert(rhs.stride() == 1);
+
+  float falpha = Eigen::bfloat16_impl::bfloat16_to_float(alpha);
+  const Packet4f pAlpha = pset1<Packet4f>(falpha);
+
+  ei_declare_aligned_stack_constructed_variable(float, result, rows, 0);
+  if (resIncr == 1) {
+    convertArrayPointerBF16toF32(result, 1, rows, res);
+  } else {
+    convertArrayPointerBF16toF32<true>(result, 1, rows, res, resIncr);
+  }
+  calcVecLoops<LhsMapper, LinearMapper>(cols, rows, lhs, rhs2, pAlpha, result);
+  if (resIncr == 1) {
+    convertArrayPointerF32toBF16(result, rows, res);
+  } else {
+    convertArrayPointerF32toBF16<true>(result, rows, res, resIncr);
+  }
+}
+#endif
+
+#undef MAX_BFLOAT16_VEC_ACC
+#undef BFLOAT16_UNROLL
+
+}
+}
+#endif //EIGEN_MATRIX_PRODUCT_MMA_BFLOAT16_ALTIVEC_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MatrixVectorProduct.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MatrixVectorProduct.h
new file mode 100644
index 0000000000000000000000000000000000000000..91702301376ee97d586e5ef00f7544e053a6284e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/MatrixVectorProduct.h
@@ -0,0 +1,2989 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2021 Chip Kerchner (chip.kerchner@ibm.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIX_VECTOR_PRODUCT_ALTIVEC_H
+#define EIGEN_MATRIX_VECTOR_PRODUCT_ALTIVEC_H
+
+#include "../../InternalHeaderCheck.h"
+
+#if defined(__MMA__) && !EIGEN_ALTIVEC_DISABLE_MMA
+#if EIGEN_COMP_LLVM || (__GNUC__ > 10 || __GNUC_MINOR__ >= 3)
+#define USE_GEMV_MMA
+#endif
+
+#if !EIGEN_COMP_LLVM && (__GNUC__ < 11)
+// Only allow one vector_pair in buggy gcc - gcc 10.x has a bug
+#define GCC_ONE_VECTORPAIR_BUG
+#endif
+#endif
+
+//#define USE_SLOWER_GEMV_MMA   // MMA is currently not as fast as VSX in complex double GEMV (revisit when gcc is improved)
+
+//#define EIGEN_POWER_USE_GEMV_PREFETCH
+#ifdef EIGEN_POWER_USE_GEMV_PREFETCH
+#define EIGEN_POWER_GEMV_PREFETCH(p)  prefetch(p)
+#else
+#define EIGEN_POWER_GEMV_PREFETCH(p)
+#endif
+
+#ifdef __has_builtin
+#if !__has_builtin(__builtin_vsx_assemble_pair)
+#define __builtin_vsx_assemble_pair __builtin_mma_assemble_pair
+#endif
+#if !__has_builtin(__builtin_vsx_disassemble_pair)
+#define __builtin_vsx_disassemble_pair __builtin_mma_disassemble_pair
+#endif
+#endif
+
+#if EIGEN_COMP_LLVM
+#define GEMV_BUILDPAIR_MMA(dst, src1, src2) \
+  __builtin_vsx_assemble_pair(&dst, (__vector unsigned char)src2, (__vector unsigned char)src1)
+#else
+#if (__GNUC__ <= 10)
+#if (__GNUC_MINOR__ > 3)
+#define GEMV_BUILDPAIR_MMA(dst, src1, src2) \
+  __builtin_vsx_assemble_pair(&dst, (__vector unsigned char)src2, (__vector unsigned char)src1)
+#else
+#define GEMV_BUILDPAIR_MMA(dst, src1, src2) \
+  __builtin_vsx_assemble_pair(&dst, (__vector unsigned char)src1, (__vector unsigned char)src2)
+#endif
+#else
+#define GEMV_BUILDPAIR_MMA(dst, src1, src2) \
+  __builtin_vsx_build_pair(&dst, (__vector unsigned char)src1, (__vector unsigned char)src2)
+#endif
+#endif
+
+#define GEMV_IS_COMPLEX_COMPLEX ((sizeof(LhsPacket) == 16) && (sizeof(RhsPacket) == 16))
+#define GEMV_IS_FLOAT           (ResPacketSize == (16 / sizeof(float)))
+#define GEMV_IS_SCALAR          (sizeof(ResPacket) != 16)
+#define GEMV_IS_COMPLEX_FLOAT   (ResPacketSize == (16 / sizeof(std::complex<float>)))
+
+/** \internal multiply and add and store results */
+template<typename ResPacket, typename ResScalar>
+EIGEN_ALWAYS_INLINE void storeMaddData(ResScalar* res, ResPacket& palpha, ResPacket& data)
+{
+    pstoreu(res, pmadd(data, palpha, ploadu<ResPacket>(res)));
+}
+
+template<typename ResScalar>
+EIGEN_ALWAYS_INLINE void storeMaddData(ResScalar* res, ResScalar& alpha, ResScalar& data)
+{
+    *res += (alpha * data);
+}
+
+#define GEMV_UNROLL(func, N) \
+  func(0, N) func(1, N) func(2, N) func(3, N) \
+  func(4, N) func(5, N) func(6, N) func(7, N)
+
+#define GEMV_UNROLL_HALF(func, N) \
+  func(0, 0, 1, N) func(1, 2, 3, N) func(2, 4, 5, N) func(3, 6, 7, N)
+
+#define GEMV_GETN(N) (((N) * ResPacketSize) >> 2)
+
+#define GEMV_LOADPACKET_COL(iter) \
+  lhs.template load<LhsPacket, LhsAlignment>(i + ((iter) * LhsPacketSize), j)
+
+#ifdef USE_GEMV_MMA
+#define GEMV_UNROLL3(func, N, which) \
+  func(0, N, which) func(1, N, which) func(2, N, which) func(3, N, which) \
+  func(4, N, which) func(5, N, which) func(6, N, which) func(7, N, which)
+
+#define GEMV_UNUSED_VAR(iter, N, which) \
+  if (GEMV_GETN(N) <= iter) { \
+    EIGEN_UNUSED_VARIABLE(which##iter); \
+  }
+
+#define GEMV_UNUSED_EXTRA_VAR(iter, N, which) \
+  if (N <= iter) { \
+    EIGEN_UNUSED_VARIABLE(which##iter); \
+  }
+
+#define GEMV_UNUSED_EXTRA(N, which) \
+  GEMV_UNROLL3(GEMV_UNUSED_EXTRA_VAR, N, which)
+
+#define GEMV_UNUSED(N, which) \
+  GEMV_UNROLL3(GEMV_UNUSED_VAR, N, which)
+
+#define GEMV_INIT_MMA(iter, N) \
+  if (GEMV_GETN(N) > iter) { \
+    __builtin_mma_xxsetaccz(&e##iter); \
+  }
+
+#if EIGEN_COMP_LLVM
+#define GEMV_LOADPAIR_COL_MMA(iter1, iter2) \
+  GEMV_BUILDPAIR_MMA(b##iter1, GEMV_LOADPACKET_COL(iter2), GEMV_LOADPACKET_COL((iter2) + 1));
+#else
+#define GEMV_LOADPAIR_COL_MMA(iter1, iter2) \
+  const LhsScalar& src##iter1 = lhs(i + ((iter1 * 32) / sizeof(LhsScalar)), j); \
+  b##iter1 = *reinterpret_cast<__vector_pair *>(const_cast<LhsScalar *>(&src##iter1));
+#endif
+
+#define GEMV_LOAD1A_COL_MMA(iter, N) \
+  if (GEMV_GETN(N) > iter) { \
+    if (GEMV_IS_FLOAT) { \
+      g##iter = GEMV_LOADPACKET_COL(iter); \
+      EIGEN_UNUSED_VARIABLE(b##iter); \
+    } else { \
+      GEMV_LOADPAIR_COL_MMA(iter, iter << 1) \
+      EIGEN_UNUSED_VARIABLE(g##iter); \
+    } \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(b##iter); \
+    EIGEN_UNUSED_VARIABLE(g##iter); \
+  }
+
+#define GEMV_WORK1A_COL_MMA(iter, N) \
+  if (GEMV_GETN(N) > iter) { \
+    if (GEMV_IS_FLOAT) { \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter, a0, g##iter); \
+    } else { \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter, b##iter, a0); \
+    } \
+  }
+
+#define GEMV_LOAD1B_COL_MMA(iter1, iter2, iter3, N) \
+  if (GEMV_GETN(N) > iter1) { \
+    if (GEMV_IS_FLOAT) { \
+      GEMV_LOADPAIR_COL_MMA(iter2, iter2) \
+      EIGEN_UNUSED_VARIABLE(b##iter3); \
+    } else { \
+      GEMV_LOADPAIR_COL_MMA(iter2, iter2 << 1) \
+      GEMV_LOADPAIR_COL_MMA(iter3, iter3 << 1) \
+    } \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(b##iter2); \
+    EIGEN_UNUSED_VARIABLE(b##iter3); \
+  } \
+  EIGEN_UNUSED_VARIABLE(g##iter2); \
+  EIGEN_UNUSED_VARIABLE(g##iter3);
+
+#define GEMV_WORK1B_COL_MMA(iter1, iter2, iter3, N) \
+  if (GEMV_GETN(N) > iter1) { \
+    if (GEMV_IS_FLOAT) { \
+      LhsPacket h[2]; \
+      __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(h), &b##iter2); \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter2, a0, h[0]); \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter3, a0, h[1]); \
+    } else { \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter2, b##iter2, a0); \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&e##iter3, b##iter3, a0); \
+    } \
+  }
+
+#if EIGEN_COMP_LLVM
+#define GEMV_LOAD_COL_MMA(N) \
+  if (GEMV_GETN(N) > 1) { \
+    GEMV_UNROLL_HALF(GEMV_LOAD1B_COL_MMA, (N >> 1)) \
+  } else { \
+    GEMV_UNROLL(GEMV_LOAD1A_COL_MMA, N) \
+  }
+
+#define GEMV_WORK_COL_MMA(N) \
+  if (GEMV_GETN(N) > 1) { \
+    GEMV_UNROLL_HALF(GEMV_WORK1B_COL_MMA, (N >> 1)) \
+  } else { \
+    GEMV_UNROLL(GEMV_WORK1A_COL_MMA, N) \
+  }
+#else
+#define GEMV_LOAD_COL_MMA(N) \
+  GEMV_UNROLL(GEMV_LOAD1A_COL_MMA, N)
+
+#define GEMV_WORK_COL_MMA(N) \
+  GEMV_UNROLL(GEMV_WORK1A_COL_MMA, N)
+#endif
+
+#define GEMV_DISASSEMBLE_MMA(iter, N) \
+  if (GEMV_GETN(N) > iter) { \
+    __builtin_mma_disassemble_acc(&result##iter.packet, &e##iter); \
+    if (!GEMV_IS_FLOAT) { \
+      result##iter.packet[0][1] = result##iter.packet[1][0]; \
+      result##iter.packet[2][1] = result##iter.packet[3][0]; \
+    } \
+  }
+
+#define GEMV_LOADPAIR2_COL_MMA(iter1, iter2) \
+  b##iter1 = *reinterpret_cast<__vector_pair *>(res + i + ((iter2) * ResPacketSize));
+
+#define GEMV_LOAD2_COL_MMA(iter1, iter2, iter3, N) \
+  if (GEMV_GETN(N) > iter1) { \
+    if (GEMV_IS_FLOAT) { \
+      GEMV_LOADPAIR2_COL_MMA(iter2, iter2); \
+      EIGEN_UNUSED_VARIABLE(b##iter3); \
+    } else { \
+      GEMV_LOADPAIR2_COL_MMA(iter2, iter2 << 1); \
+      GEMV_LOADPAIR2_COL_MMA(iter3, iter3 << 1); \
+    } \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(b##iter2); \
+    EIGEN_UNUSED_VARIABLE(b##iter3); \
+  }
+
+#if EIGEN_COMP_LLVM
+#define GEMV_WORKPAIR2_COL_MMA(iter2, iter3, iter4) \
+  ResPacket f##iter2[2]; \
+  __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(f##iter2), &b##iter2); \
+  f##iter2[0] = pmadd(result##iter2.packet[0], palpha, f##iter2[0]); \
+  f##iter2[1] = pmadd(result##iter3.packet[(iter2 == iter3) ? 2 : 0], palpha, f##iter2[1]); \
+  GEMV_BUILDPAIR_MMA(b##iter2, f##iter2[0], f##iter2[1]);
+#else
+#define GEMV_WORKPAIR2_COL_MMA(iter2, iter3, iter4) \
+  if (GEMV_IS_FLOAT) { \
+    __asm__ ("xvmaddasp %0,%x1,%x3\n\txvmaddasp %L0,%x2,%x3" : "+&d" (b##iter2) : "wa" (result##iter3.packet[0]), "wa" (result##iter2.packet[0]), "wa" (palpha)); \
+  } else { \
+    __asm__ ("xvmaddadp %0,%x1,%x3\n\txvmaddadp %L0,%x2,%x3" : "+&d" (b##iter2) : "wa" (result##iter2.packet[2]), "wa" (result##iter2.packet[0]), "wa" (palpha)); \
+  }
+#endif
+
+#define GEMV_WORK2_COL_MMA(iter1, iter2, iter3, N) \
+  if (GEMV_GETN(N) > iter1) { \
+    if (GEMV_IS_FLOAT) { \
+      GEMV_WORKPAIR2_COL_MMA(iter2, iter3, iter2); \
+    } else { \
+      GEMV_WORKPAIR2_COL_MMA(iter2, iter2, iter2 << 1); \
+      GEMV_WORKPAIR2_COL_MMA(iter3, iter3, iter3 << 1); \
+    } \
+  }
+
+#define GEMV_STOREPAIR2_COL_MMA(iter1, iter2) \
+  *reinterpret_cast<__vector_pair *>(res + i + ((iter2) * ResPacketSize)) = b##iter1;
+
+#define GEMV_STORE_COL_MMA(iter, N) \
+  if (GEMV_GETN(N) > iter) { \
+    if (GEMV_IS_FLOAT) { \
+      storeMaddData<ResPacket, ResScalar>(res + i + (iter * ResPacketSize), palpha, result##iter.packet[0]); \
+    } else { \
+      GEMV_LOADPAIR2_COL_MMA(iter, iter << 1) \
+      GEMV_WORKPAIR2_COL_MMA(iter, iter, iter << 1) \
+      GEMV_STOREPAIR2_COL_MMA(iter, iter << 1) \
+    } \
+  }
+
+#define GEMV_STORE2_COL_MMA(iter1, iter2, iter3, N) \
+  if (GEMV_GETN(N) > iter1) { \
+    if (GEMV_IS_FLOAT) { \
+      GEMV_STOREPAIR2_COL_MMA(iter2, iter2); \
+    } else { \
+      GEMV_STOREPAIR2_COL_MMA(iter2, iter2 << 1) \
+      GEMV_STOREPAIR2_COL_MMA(iter3, iter3 << 1) \
+    } \
+  }
+
+#define GEMV_PROCESS_COL_ONE_MMA(N) \
+  GEMV_UNROLL(GEMV_INIT_MMA, N) \
+  Index j = j2; \
+  __vector_pair b0, b1, b2, b3, b4, b5, b6, b7; \
+  do { \
+    LhsPacket g0, g1, g2, g3, g4, g5, g6, g7; \
+    RhsPacket a0 = pset1<RhsPacket>(rhs2(j, 0)); \
+    GEMV_UNROLL(GEMV_PREFETCH, N) \
+    GEMV_LOAD_COL_MMA(N) \
+    GEMV_WORK_COL_MMA(N) \
+  } while (++j < jend); \
+  GEMV_UNROLL(GEMV_DISASSEMBLE_MMA, N) \
+  if (GEMV_GETN(N) <= 1) { \
+    GEMV_UNROLL(GEMV_STORE_COL_MMA, N) \
+  } else { \
+    GEMV_UNROLL_HALF(GEMV_LOAD2_COL_MMA, (N >> 1)) \
+    GEMV_UNROLL_HALF(GEMV_WORK2_COL_MMA, (N >> 1)) \
+    GEMV_UNROLL_HALF(GEMV_STORE2_COL_MMA, (N >> 1)) \
+  } \
+  i += (ResPacketSize * N);
+#endif
+
+#define GEMV_INIT(iter, N) \
+  if (N > iter) { \
+    c##iter = pset1<ResPacket>(ResScalar(0)); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(c##iter); \
+  }
+
+#ifdef EIGEN_POWER_USE_GEMV_PREFETCH
+#define GEMV_PREFETCH(iter, N) \
+  if (GEMV_GETN(N) > ((iter >> 1) + ((N >> 1) * (iter & 1)))) { \
+    lhs.prefetch(i + (iter * LhsPacketSize) + prefetch_dist, j); \
+  }
+#else
+#define GEMV_PREFETCH(iter, N)
+#endif
+
+#define GEMV_WORK_COL(iter, N) \
+  if (N > iter) { \
+    c##iter = pcj.pmadd(GEMV_LOADPACKET_COL(iter), a0, c##iter); \
+  }
+
+#define GEMV_STORE_COL(iter, N) \
+  if (N > iter) { \
+    pstoreu(res + i + (iter * ResPacketSize), pmadd(c##iter, palpha, ploadu<ResPacket>(res + i + (iter * ResPacketSize)))); \
+  }
+
+/** \internal main macro for gemv_col - initialize accumulators, multiply and add inputs, and store results */
+#define GEMV_PROCESS_COL_ONE(N) \
+  GEMV_UNROLL(GEMV_INIT, N) \
+  Index j = j2; \
+  do { \
+    RhsPacket a0 = pset1<RhsPacket>(rhs2(j, 0)); \
+    GEMV_UNROLL(GEMV_PREFETCH, N) \
+    GEMV_UNROLL(GEMV_WORK_COL, N) \
+  } while (++j < jend); \
+  GEMV_UNROLL(GEMV_STORE_COL, N) \
+  i += (ResPacketSize * N);
+
+#ifdef USE_GEMV_MMA
+#define GEMV_PROCESS_COL(N) \
+  GEMV_PROCESS_COL_ONE_MMA(N)
+#else
+#define GEMV_PROCESS_COL(N) \
+  GEMV_PROCESS_COL_ONE(N)
+#endif
+
+/** \internal perform a matrix multiply and accumulate of packet a and packet b */
+#ifdef USE_GEMV_MMA
+template<typename LhsPacket, typename RhsPacket, bool accumulate>
+EIGEN_ALWAYS_INLINE void pger_vecMMA_acc(__vector_quad* acc, const RhsPacket& a, const LhsPacket& b)
+{
+    if (accumulate)
+    {
+        __builtin_mma_xvf32gerpp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
+    }
+    else
+    {
+        __builtin_mma_xvf32ger(acc, (__vector unsigned char)a, (__vector unsigned char)b);
+    }
+}
+
+/** \internal perform a matrix multiply and accumulate of vector_pair a and packet b */
+template<typename LhsPacket, typename RhsPacket, bool accumulate>
+EIGEN_ALWAYS_INLINE void pger_vecMMA_acc(__vector_quad* acc, __vector_pair& a, const LhsPacket& b)
+{
+    if (accumulate)
+    {
+        __builtin_mma_xvf64gerpp(acc, a, (__vector unsigned char)b);
+    }
+    else
+    {
+        __builtin_mma_xvf64ger(acc, a, (__vector unsigned char)b);
+    }
+}
+#endif
+
+template<typename LhsScalar, typename LhsMapper, typename RhsScalar, typename RhsMapper, typename ResScalar>
+EIGEN_STRONG_INLINE void gemv_col(
+    Index rows, Index cols,
+    const LhsMapper& alhs,
+    const RhsMapper& rhs,
+    ResScalar* res, Index resIncr,
+    ResScalar alpha)
+{
+    typedef gemv_traits<LhsScalar, RhsScalar> Traits;
+
+    typedef typename Traits::LhsPacket LhsPacket;
+    typedef typename Traits::RhsPacket RhsPacket;
+    typedef typename Traits::ResPacket ResPacket;
+
+    EIGEN_UNUSED_VARIABLE(resIncr);
+    eigen_internal_assert(resIncr == 1);
+
+    // The following copy tells the compiler that lhs's attributes are not modified outside this function
+    // This helps GCC to generate proper code.
+    LhsMapper lhs(alhs);
+    RhsMapper rhs2(rhs);
+
+    conj_helper<LhsScalar, RhsScalar, false, false> cj;
+    conj_helper<LhsPacket, RhsPacket, false, false> pcj;
+
+    const Index lhsStride = lhs.stride();
+    // TODO: for padded aligned inputs, we could enable aligned reads
+    enum {
+        LhsAlignment = Unaligned,
+        ResPacketSize = Traits::ResPacketSize,
+        LhsPacketSize = Traits::LhsPacketSize,
+        RhsPacketSize = Traits::RhsPacketSize,
+    };
+
+#ifndef GCC_ONE_VECTORPAIR_BUG
+    const Index n8 = rows - 8 * ResPacketSize + 1;
+    const Index n4 = rows - 4 * ResPacketSize + 1;
+    const Index n2 = rows - 2 * ResPacketSize + 1;
+#endif
+    const Index n1 = rows - 1 * ResPacketSize + 1;
+#ifdef EIGEN_POWER_USE_GEMV_PREFETCH
+    const Index prefetch_dist = 64 * LhsPacketSize;
+#endif
+
+    // TODO: improve the following heuristic:
+    const Index block_cols = cols < 128 ? cols : (lhsStride * sizeof(LhsScalar) < 16000 ? 16 : 8);
+    ResPacket palpha = pset1<ResPacket>(alpha);
+
+    for (Index j2 = 0; j2 < cols; j2 += block_cols)
+    {
+        Index jend = numext::mini(j2 + block_cols, cols);
+        Index i = 0;
+        ResPacket c0, c1, c2, c3, c4, c5, c6, c7;
+#ifdef USE_GEMV_MMA
+        __vector_quad e0, e1, e2, e3, e4, e5, e6, e7;
+        PacketBlock<ResPacket, 4> result0, result1, result2, result3, result4, result5, result6, result7;
+        GEMV_UNUSED(8, e)
+        GEMV_UNUSED(8, result)
+        GEMV_UNUSED_EXTRA(1, c)
+#endif
+#ifndef GCC_ONE_VECTORPAIR_BUG
+        while (i < n8)
+        {
+            GEMV_PROCESS_COL(8)
+        }
+        if (i < n4)
+        {
+            GEMV_PROCESS_COL(4)
+        }
+        if (i < n2)
+        {
+            GEMV_PROCESS_COL(2)
+        }
+        if (i < n1)
+#else
+        while (i < n1)
+#endif
+        {
+            GEMV_PROCESS_COL_ONE(1)
+        }
+        for (;i < rows;++i)
+        {
+            ResScalar d0(0);
+            Index j = j2;
+            do {
+                d0 += cj.pmul(lhs(i, j), rhs2(j, 0));
+            } while (++j < jend);
+            res[i] += alpha * d0;
+        }
+    }
+}
+
+template<bool extraRows>
+EIGEN_ALWAYS_INLINE void outputVecCol(Packet4f acc, float *result, Packet4f pAlpha, Index extra_rows)
+{
+  Packet4f d0 = ploadu<Packet4f>(result);
+  d0 = pmadd(acc, pAlpha, d0);
+  if (extraRows) {
+    pstoreu_partial(result, d0, extra_rows);
+  } else {
+    pstoreu(result, d0);
+  }
+}
+
+template<Index num_acc, bool extraRows, Index size>
+EIGEN_ALWAYS_INLINE void outputVecColResults(Packet4f (&acc)[num_acc][size], float *result, Packet4f pAlpha, Index extra_rows)
+{
+  constexpr Index real_acc = (num_acc - (extraRows ? 1 : 0));
+  for(Index k = 0; k < real_acc; k++) {
+    outputVecCol<false>(acc[k][0], result + k*4, pAlpha, extra_rows);
+  }
+  if (extraRows) {
+    outputVecCol<true>(acc[real_acc][0], result + real_acc*4, pAlpha, extra_rows);
+  }
+}
+
+static Packet16uc p16uc_MERGE16_32_V1 = {  0, 1, 16,17,  0, 1, 16,17,  0, 1, 16,17,  0, 1, 16,17 };
+static Packet16uc p16uc_MERGE16_32_V2 = {  2, 3, 18,19,  2, 3, 18,19,  2, 3, 18,19,  2, 3, 18,19 };
+
+template<Index num_acc, typename LhsMapper, bool zero>
+EIGEN_ALWAYS_INLINE void loadVecLoopVSX(Index k, LhsMapper& lhs, Packet4f (&a0)[num_acc][2])
+{
+  Packet8bf c0 = lhs.template loadPacket<Packet8bf>(k*4, 0);
+  Packet8bf b1;
+  if (!zero) {
+    b1 = lhs.template loadPacket<Packet8bf>(k*4, 1);
+
+    a0[k + 0][1] = oneConvertBF16Hi(b1.m_val);
+  }
+  a0[k + 0][0] = oneConvertBF16Hi(c0.m_val);
+
+  if (num_acc > (k + 1)) {
+    a0[k + 1][0] = oneConvertBF16Lo(c0.m_val);
+    if (!zero) {
+      a0[k + 1][1] = oneConvertBF16Lo(b1.m_val);
+    }
+  }
+}
+
+template<Index num_acc, bool zero>
+EIGEN_ALWAYS_INLINE void multVecVSX(Packet4f (&acc)[num_acc][2], Packet4f (&a0)[num_acc][2], Packet4f (&b0)[2])
+{
+  for(Index k = 0; k < num_acc; k++) {
+    for(Index i = 0; i < (zero ? 1 : 2); i++) {
+      acc[k][i] = pmadd(b0[i], a0[k][i], acc[k][i]);
+    }
+  }
+}
+
+template<typename RhsMapper, bool linear>
+struct loadColData_impl
+{
+  // linear == false
+  static EIGEN_ALWAYS_INLINE Packet8bf run(RhsMapper& rhs, Index j)
+  {
+    const Index n = unpacket_traits<Packet8bf>::size;
+    EIGEN_ALIGN16 bfloat16 to[n];
+    LOAD_STORE_UNROLL_16
+    for (Index i = 0; i < n; i++) {
+      to[i] = rhs(j + i, 0);
+    }
+    return pload<Packet8bf>(to);
+  }
+};
+
+template<typename RhsMapper>
+struct loadColData_impl<RhsMapper, true>
+{
+  // linear == true
+  static EIGEN_ALWAYS_INLINE Packet8bf run(RhsMapper& rhs, Index j)
+  {
+    return rhs.template loadPacket<Packet8bf>(j + 0, 0);
+  }
+};
+
+template<typename RhsMapper, bool linear>
+EIGEN_ALWAYS_INLINE Packet8bf loadColData(RhsMapper& rhs, Index j)
+{
+  return loadColData_impl<RhsMapper, linear>::run(rhs, j);
+}
+
+template<Index num_acc, typename LhsMapper, typename RhsMapper, bool zero, bool linear>
+EIGEN_ALWAYS_INLINE void vecColLoopVSX(Index j, LhsMapper& lhs, RhsMapper& rhs, Packet4f (&acc)[num_acc][2])
+{
+  Packet4f a0[num_acc][2], b0[2];
+  Packet8bf b2 = loadColData<RhsMapper, linear>(rhs, j);
+
+  b0[0] = oneConvertBF16Perm(b2.m_val, p16uc_MERGE16_32_V1);
+  if (!zero) {
+    b0[1] = oneConvertBF16Perm(b2.m_val, p16uc_MERGE16_32_V2);
+  }
+
+  using LhsSubMapper = typename LhsMapper::SubMapper;
+
+  LhsSubMapper lhs2 = lhs.getSubMapper(0, j);
+  for(Index k = 0; k < num_acc; k += 2) {
+    loadVecLoopVSX<num_acc, LhsSubMapper, zero>(k, lhs2, a0);
+  }
+
+  multVecVSX<num_acc, zero>(acc, a0, b0);
+}
+
+template<Index num_acc>
+EIGEN_ALWAYS_INLINE void addResultsVSX(Packet4f (&acc)[num_acc][2])
+{
+  for(Index i = 0; i < num_acc; i++) {
+    acc[i][0] = acc[i][0] + acc[i][1];
+  }
+}
+
+// Uses 2X the accumulators or 4X the number of VSX registers
+#define MAX_BFLOAT16_VEC_ACC_VSX   8
+
+template<const Index num_acc, typename LhsMapper, typename RhsMapper, bool extraRows, bool linear>
+void colVSXVecColLoopBody(Index& row, Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  constexpr Index step = (num_acc * 4);
+  const Index extra_rows = (extraRows) ? (rows & 3) : 0;
+  constexpr bool multiIters = !extraRows && (num_acc == MAX_BFLOAT16_VEC_ACC_VSX);
+
+  do{
+    Packet4f acc[num_acc][2];
+
+    zeroAccumulators<num_acc, 2>(acc);
+
+    using LhsSubMapper = typename LhsMapper::SubMapper;
+
+    LhsSubMapper lhs2 = lhs.getSubMapper(row, 0);
+    for(Index j = 0; j + 2 <= cend; j += 2) {
+      vecColLoopVSX<num_acc, LhsSubMapper, RhsMapper, false, linear>(j, lhs2, rhs, acc);
+    }
+    if (cend & 1) {
+      vecColLoopVSX<num_acc, LhsSubMapper, RhsMapper, true, linear>(cend - 1, lhs2, rhs, acc);
+    }
+
+    addResultsVSX<num_acc>(acc);
+
+    outputVecColResults<num_acc, extraRows, 2>(acc, result, pAlpha, extra_rows);
+
+    result += step;
+  } while(multiIters && (step <= rows - (row += step)));
+}
+
+template<const Index num_acc, typename LhsMapper, typename RhsMapper, bool extraRows, bool linear>
+EIGEN_ALWAYS_INLINE void colVSXVecColLoopBodyExtraN(Index& row, Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  if (MAX_BFLOAT16_VEC_ACC_VSX > num_acc) {
+    colVSXVecColLoopBody<num_acc + (extraRows ? 1 : 0), LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+  }
+}
+
+template<typename LhsMapper, typename RhsMapper, bool extraRows, bool linear>
+EIGEN_ALWAYS_INLINE void colVSXVecColLoopBodyExtra(Index& row, Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  switch ((rows - row) >> 2) {
+  case 7:
+    colVSXVecColLoopBodyExtraN<7, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 6:
+    colVSXVecColLoopBodyExtraN<6, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 5:
+    colVSXVecColLoopBodyExtraN<5, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 4:
+    colVSXVecColLoopBodyExtraN<4, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 3:
+    colVSXVecColLoopBodyExtraN<3, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 2:
+    colVSXVecColLoopBodyExtraN<2, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 1:
+    colVSXVecColLoopBodyExtraN<1, LhsMapper, RhsMapper, extraRows, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    break;
+  default:
+    if (extraRows) {
+      colVSXVecColLoopBody<1, LhsMapper, RhsMapper, true, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    }
+    break;
+  }
+}
+
+template<typename LhsMapper, typename RhsMapper, bool linear>
+EIGEN_ALWAYS_INLINE void calcVSXVecColLoops(Index cend, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  Index row = 0;
+  if (rows >= (MAX_BFLOAT16_VEC_ACC_VSX * 4)) {
+    colVSXVecColLoopBody<MAX_BFLOAT16_VEC_ACC_VSX, LhsMapper, RhsMapper, false, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+    result += row;
+  }
+  if (rows & 3) {
+    colVSXVecColLoopBodyExtra<LhsMapper, RhsMapper, true, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+  } else {
+    colVSXVecColLoopBodyExtra<LhsMapper, RhsMapper, false, linear>(row, cend, rows, lhs, rhs, pAlpha, result);
+  }
+}
+
+template<const Index size, bool inc, Index delta>
+EIGEN_ALWAYS_INLINE void storeBF16fromResult(bfloat16* dst, Packet8bf data, Index resInc, Index extra)
+{
+  if (inc) {
+    if (size < 8) {
+      pscatter_partial(dst + delta*resInc, data, resInc, extra);
+    } else {
+      pscatter(dst + delta*resInc, data, resInc);
+    }
+  } else {
+    if (size < 8) {
+      pstoreu_partial(dst + delta, data, extra);
+    } else {
+      pstoreu(dst + delta, data);
+    }
+  }
+}
+
+template<const Index size, bool inc = false>
+EIGEN_ALWAYS_INLINE void convertPointerF32toBF16VSX(Index& i, float* result, Index rows, bfloat16*& dst, Index resInc = 1)
+{
+  constexpr Index extra = ((size < 8) ? 8 : size);
+  while (i + size <= rows) {
+    PacketBlock<Packet8bf,(size+7)/8> r32;
+    r32.packet[0] = convertF32toBF16VSX(result + i +  0);
+    if (size >= 16) {
+      r32.packet[1] = convertF32toBF16VSX(result + i +  8);
+    }
+    if (size >= 32) {
+      r32.packet[2] = convertF32toBF16VSX(result + i + 16);
+      r32.packet[3] = convertF32toBF16VSX(result + i + 24);
+    }
+    storeBF16fromResult<size, inc, 0>(dst, r32.packet[0], resInc, rows & 7);
+    if (size >= 16) {
+      storeBF16fromResult<size, inc, 8>(dst, r32.packet[1], resInc);
+    }
+    if (size >= 32) {
+      storeBF16fromResult<size, inc, 16>(dst, r32.packet[2], resInc);
+      storeBF16fromResult<size, inc, 24>(dst, r32.packet[3], resInc);
+    }
+    i += extra; dst += extra*resInc;
+    if (size != 32) break;
+  }
+}
+
+template<bool inc = false>
+EIGEN_ALWAYS_INLINE void convertArrayPointerF32toBF16VSX(float *result, Index rows, bfloat16* dst, Index resInc = 1)
+{
+  Index i = 0;
+  convertPointerF32toBF16VSX<32,inc>(i, result, rows, dst, resInc);
+  convertPointerF32toBF16VSX<16,inc>(i, result, rows, dst, resInc);
+  convertPointerF32toBF16VSX<8,inc>(i, result, rows, dst, resInc);
+  convertPointerF32toBF16VSX<1,inc>(i, result, rows, dst, resInc);
+}
+
+template<typename RhsMapper, typename LhsMapper, typename = void>
+struct UseStride : std::false_type {
+  static EIGEN_ALWAYS_INLINE void run(Index j2, Index jend, Index rows, LhsMapper& lhs, RhsMapper& rhs, Packet4f pAlpha, float *result)
+  {
+    using RhsSubMapper = typename RhsMapper::SubMapper;
+
+    RhsSubMapper rhs2 = rhs.getSubMapper(j2, 0);
+    calcVSXVecColLoops<LhsMapper, RhsSubMapper, false>(jend - j2, rows, lhs, rhs2, pAlpha, result);
+  }
+};
+
+template<typename RhsMapper, typename LhsMapper>
+struct UseStride<RhsMapper, LhsMapper, std::enable_if_t<std::is_member_function_pointer<
+                           decltype(&RhsMapper::stride)>::value>> : std::true_type {
+  static EIGEN_ALWAYS_INLINE void run(Index j2, Index jend, Index rows, LhsMapper& lhs, RhsMapper& rhs, Packet4f pAlpha, float *result)
+  {
+    using RhsSubMapper = typename RhsMapper::SubMapper;
+
+    RhsSubMapper rhs2 = rhs.getSubMapper(j2, 0);
+    if (rhs.stride() == 1) {
+      calcVSXVecColLoops<LhsMapper, RhsSubMapper, true>(jend - j2, rows, lhs, rhs2, pAlpha, result);
+    } else {
+      calcVSXVecColLoops<LhsMapper, RhsSubMapper, false>(jend - j2, rows, lhs, rhs2, pAlpha, result);
+    }
+  }
+};
+
+template<typename LhsMapper, typename RhsMapper>
+void gemv_bfloat16_col(
+  Index rows, Index cols,
+  const LhsMapper& alhs,
+  const RhsMapper& rhs,
+  bfloat16* res, Index resIncr,
+  bfloat16 alpha)
+{
+  EIGEN_UNUSED_VARIABLE(resIncr);
+  eigen_internal_assert(resIncr == 1);
+
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate proper code.
+  LhsMapper lhs(alhs);
+  RhsMapper rhs2(rhs);
+
+  const Index lhsStride = lhs.stride();
+
+  // TODO: improve the following heuristic:
+  const Index block_cols = cols < 128 ? cols : (lhsStride * sizeof(bfloat16) < 16000 ? 16 : 8);
+  float falpha = Eigen::bfloat16_impl::bfloat16_to_float(alpha);
+  Packet4f pAlpha = pset1<Packet4f>(falpha);
+
+  ei_declare_aligned_stack_constructed_variable(float, result, rows, 0);
+
+  convertArrayPointerBF16toF32(result, 1, rows, res);
+
+  for (Index j2 = 0; j2 < cols; j2 += block_cols)
+  {
+    Index jend = numext::mini(j2 + block_cols, cols);
+
+    using LhsSubMapper = typename LhsMapper::SubMapper;
+
+    LhsSubMapper lhs2 = lhs.getSubMapper(0, j2);
+    UseStride<RhsMapper, LhsSubMapper>::run(j2, jend, rows, lhs2, rhs2, pAlpha, result);
+  }
+
+  convertArrayPointerF32toBF16VSX(result, rows, res);
+}
+
+template<Index num_acc, Index size>
+EIGEN_ALWAYS_INLINE void outputVecResults(Packet4f (&acc)[num_acc][size], float *result, Packet4f pAlpha)
+{
+  constexpr Index extra = num_acc & 3;
+
+  for(Index k = 0; k < num_acc; k += 4) {
+    Packet4f d0 = ploadu<Packet4f>(result + k);
+    d0 = pmadd(acc[k + 0][0], pAlpha, d0);
+
+    if (num_acc > (k + 3)) {
+      pstoreu(result + k, d0);
+    } else {
+      if (extra == 3) {
+        pstoreu_partial(result + k, d0, extra);
+      } else {
+        memcpy((void *)(result + k), (void *)(&d0), sizeof(float) * extra);
+      }
+    }
+  }
+}
+
+template<Index num_acc>
+EIGEN_ALWAYS_INLINE void preduxVecResults2VSX(Packet4f (&acc)[num_acc][2], Index k)
+{
+  if (num_acc > (k + 1)) {
+    acc[k][1] = vec_mergel(acc[k + 0][0], acc[k + 1][0]);
+    acc[k][0] = vec_mergeh(acc[k + 0][0], acc[k + 1][0]);
+    acc[k][0] = acc[k][0] + acc[k][1];
+    acc[k][0] += vec_sld(acc[k][0], acc[k][0], 8);
+  } else {
+    acc[k][0] += vec_sld(acc[k][0], acc[k][0], 8);
+#ifdef _BIG_ENDIAN
+    acc[k][0] += vec_sld(acc[k][0], acc[k][0], 12);
+#else
+    acc[k][0] += vec_sld(acc[k][0], acc[k][0], 4);
+#endif
+  }
+}
+
+template<Index num_acc>
+EIGEN_ALWAYS_INLINE void preduxVecResultsVSX(Packet4f (&acc)[num_acc][2])
+{
+  for(Index k = 0; k < num_acc; k += 4) {
+    preduxVecResults2VSX<num_acc>(acc, k + 0);
+    if (num_acc > (k + 2)) {
+      preduxVecResults2VSX<num_acc>(acc, k + 2);
+#ifdef EIGEN_VECTORIZE_VSX
+      acc[k + 0][0] = reinterpret_cast<Packet4f>(vec_mergeh(reinterpret_cast<Packet2ul>(acc[k + 0][0]), reinterpret_cast<Packet2ul>(acc[k + 2][0])));
+#else
+      acc[k + 0][0] = reinterpret_cast<Packet4f>(vec_perm(acc[k + 0][0],acc[k + 2][0],p16uc_TRANSPOSE64_HI));
+#endif
+    }
+  }
+}
+
+#ifndef _ARCH_PWR9
+EIGEN_ALWAYS_INLINE Packet8us loadPacketPartialZero(Packet8us data, Index extra_cols)
+{
+  Packet16uc shift = pset1<Packet16uc>(8 * 2 * (8 - extra_cols));
+#ifdef _BIG_ENDIAN
+  return reinterpret_cast<Packet8us>(vec_slo(vec_sro(reinterpret_cast<Packet16uc>(data), shift), shift));
+#else
+  return reinterpret_cast<Packet8us>(vec_sro(vec_slo(reinterpret_cast<Packet16uc>(data), shift), shift));
+#endif
+}
+#endif
+
+template<Index num_acc, typename LhsMapper, typename RhsMapper, bool extra>
+EIGEN_ALWAYS_INLINE void multVSXVecLoop(Packet4f (&acc)[num_acc][2], const LhsMapper& lhs, RhsMapper& rhs, Index j, Index extra_cols)
+{
+  Packet4f a0[num_acc][2], b0[2];
+  Packet8bf a1, b1;
+
+  if (extra) {
+    b1 = rhs.template loadPacketPartial<Packet8bf>(j, extra_cols);
+#ifndef _ARCH_PWR9
+    b1 = loadPacketPartialZero(b1.m_val, extra_cols);
+#endif
+  } else {
+    b1 = rhs.template loadPacket<Packet8bf>(j);
+  }
+  b0[0] = oneConvertBF16Hi(b1.m_val);
+  b0[1] = oneConvertBF16Lo(b1.m_val);
+
+  const LhsMapper lhs2 = lhs.getSubMapper(0, j);
+  for(Index k = 0; k < num_acc; k++) {
+    if (extra) {
+      a1 = lhs2.template loadPacketPartial<Packet8bf>(k, 0, extra_cols);
+#ifndef _ARCH_PWR9
+      a1 = loadPacketPartialZero(a1.m_val, extra_cols);
+#endif
+    } else {
+      a1 = lhs2.template loadPacket<Packet8bf>(k, 0);
+    }
+    a0[k][0] = oneConvertBF16Hi(a1.m_val);
+    a0[k][1] = oneConvertBF16Lo(a1.m_val);
+  }
+
+  multVecVSX<num_acc, false>(acc, a0, b0);
+}
+
+template<Index num_acc, typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void vecVSXLoop(Index cols, const LhsMapper& lhs, RhsMapper& rhs, Packet4f (&acc)[num_acc][2], Index extra_cols)
+{
+  Index j = 0;
+  for(; j + 8 <= cols; j += 8){
+    multVSXVecLoop<num_acc, LhsMapper, RhsMapper, false>(acc, lhs, rhs, j, extra_cols);
+  }
+
+  if (extra_cols) {
+    multVSXVecLoop<num_acc, LhsMapper, RhsMapper, true>(acc, lhs, rhs, j, extra_cols);
+  }
+}
+
+template<const Index num_acc, typename LhsMapper, typename RhsMapper>
+void colVSXVecLoopBody(Index& row, Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  constexpr bool multiIters = (num_acc == MAX_BFLOAT16_VEC_ACC_VSX);
+  const Index extra_cols = (cols & 7);
+
+  do{
+    Packet4f acc[num_acc][2];
+
+    zeroAccumulators<num_acc, 2>(acc);
+
+    const LhsMapper lhs2 = lhs.getSubMapper(row, 0);
+    vecVSXLoop<num_acc, LhsMapper, RhsMapper>(cols, lhs2, rhs, acc, extra_cols);
+
+    addResultsVSX<num_acc>(acc);
+
+    preduxVecResultsVSX<num_acc>(acc);
+
+    outputVecResults<num_acc, 2>(acc, result, pAlpha);
+
+    result += num_acc;
+  } while(multiIters && (num_acc <= rows - (row += num_acc)));
+}
+
+template<const Index num_acc, typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void colVSXVecLoopBodyExtraN(Index& row, Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  if (MAX_BFLOAT16_VEC_ACC_VSX > num_acc) {
+    colVSXVecLoopBody<num_acc, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+  }
+}
+
+template<typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void colVSXVecLoopBodyExtra(Index& row, Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  switch (rows - row) {
+  case 7:
+    colVSXVecLoopBodyExtraN<7, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 6:
+    colVSXVecLoopBodyExtraN<6, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 5:
+    colVSXVecLoopBodyExtraN<5, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 4:
+    colVSXVecLoopBodyExtraN<4, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 3:
+    colVSXVecLoopBodyExtraN<3, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 2:
+    colVSXVecLoopBodyExtraN<2, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    break;
+  case 1:
+    colVSXVecLoopBodyExtraN<1, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    break;
+  }
+}
+
+template<typename LhsMapper, typename RhsMapper>
+EIGEN_ALWAYS_INLINE void calcVSXVecLoops(Index cols, Index rows, LhsMapper& lhs, RhsMapper& rhs, const Packet4f pAlpha, float *result)
+{
+  Index row = 0;
+  if (rows >= MAX_BFLOAT16_VEC_ACC_VSX) {
+    colVSXVecLoopBody<MAX_BFLOAT16_VEC_ACC_VSX, LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+    result += row;
+  }
+  colVSXVecLoopBodyExtra<LhsMapper, RhsMapper>(row, cols, rows, lhs, rhs, pAlpha, result);
+}
+
+template<typename LhsMapper, typename RhsMapper>
+EIGEN_STRONG_INLINE void gemv_bfloat16_row(
+  Index rows, Index cols,
+  const LhsMapper& alhs,
+  const RhsMapper& rhs,
+  bfloat16* res, Index resIncr,
+  bfloat16 alpha)
+{
+  typedef typename RhsMapper::LinearMapper LinearMapper;
+
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate proper code.
+  LhsMapper lhs(alhs);
+  LinearMapper rhs2 = rhs.getLinearMapper(0, 0);
+
+  eigen_internal_assert(rhs.stride() == 1);
+
+  float falpha = Eigen::bfloat16_impl::bfloat16_to_float(alpha);
+  const Packet4f pAlpha = pset1<Packet4f>(falpha);
+
+  ei_declare_aligned_stack_constructed_variable(float, result, rows, 0);
+  if (resIncr == 1) {
+    convertArrayPointerBF16toF32(result, 1, rows, res);
+  } else {
+    convertArrayPointerBF16toF32<true>(result, 1, rows, res, resIncr);
+  }
+  calcVSXVecLoops<LhsMapper, LinearMapper>(cols, rows, lhs, rhs2, pAlpha, result);
+  if (resIncr == 1) {
+    convertArrayPointerF32toBF16VSX(result, rows, res);
+  } else {
+    convertArrayPointerF32toBF16VSX<true>(result, rows, res, resIncr);
+  }
+}
+
+#undef MAX_BFLOAT16_VEC_ACC_VSX
+
+const Packet16uc p16uc_COMPLEX32_XORFLIP = { 0x44,0x55,0x66,0x77, 0x00,0x11,0x22,0x33, 0xcc,0xdd,0xee,0xff, 0x88,0x99,0xaa,0xbb };
+const Packet16uc p16uc_COMPLEX64_XORFLIP = { 0x88,0x99,0xaa,0xbb, 0xcc,0xdd,0xee,0xff, 0x00,0x11,0x22,0x33, 0x44,0x55,0x66,0x77 };
+
+#ifdef _BIG_ENDIAN
+const Packet16uc p16uc_COMPLEX32_CONJ_XOR  = { 0x00,0x00,0x00,0x00, 0x80,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x80,0x00,0x00,0x00 };
+const Packet16uc p16uc_COMPLEX64_CONJ_XOR  = { 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x80,0x00,0x00,0x00, 0x00,0x00,0x00,0x00 };
+const Packet16uc p16uc_COMPLEX32_CONJ_XOR2 = { 0x80,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x80,0x00,0x00,0x00, 0x00,0x00,0x00,0x00 };
+const Packet16uc p16uc_COMPLEX64_CONJ_XOR2 = { 0x80,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x00 };
+const Packet16uc p16uc_COMPLEX32_NEGATE    = { 0x80,0x00,0x00,0x00, 0x80,0x00,0x00,0x00, 0x80,0x00,0x00,0x00, 0x80,0x00,0x00,0x00 };
+const Packet16uc p16uc_COMPLEX64_NEGATE    = { 0x80,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x80,0x00,0x00,0x00, 0x00,0x00,0x00,0x00 };
+#else
+const Packet16uc p16uc_COMPLEX32_CONJ_XOR  = { 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x80 };
+const Packet16uc p16uc_COMPLEX64_CONJ_XOR  = { 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x80 };
+const Packet16uc p16uc_COMPLEX32_CONJ_XOR2 = { 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x00 };
+const Packet16uc p16uc_COMPLEX64_CONJ_XOR2 = { 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x00 };
+const Packet16uc p16uc_COMPLEX32_NEGATE    = { 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x80 };
+const Packet16uc p16uc_COMPLEX64_NEGATE    = { 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x80, 0x00,0x00,0x00,0x00, 0x00,0x00,0x00,0x80 };
+#endif
+
+#ifdef _BIG_ENDIAN
+#define COMPLEX_DELTA  0
+#else
+#define COMPLEX_DELTA  2
+#endif
+
+/** \internal packet conjugate (same as pconj but uses the constants in pcplxflipconj for better code generation) */
+EIGEN_ALWAYS_INLINE Packet2cf pconj2(const Packet2cf& a) {
+    return Packet2cf(pxor(a.v, reinterpret_cast<Packet4f>(p16uc_COMPLEX32_CONJ_XOR)));
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd pconj2(const Packet1cd& a) {
+    return Packet1cd(pxor(a.v, reinterpret_cast<Packet2d>(p16uc_COMPLEX64_CONJ_XOR)));
+}
+
+/** \internal packet conjugate with real & imaginary operation inverted */
+EIGEN_ALWAYS_INLINE Packet2cf pconjinv(const Packet2cf& a) {
+#ifdef __POWER8_VECTOR__
+    return Packet2cf(Packet4f(vec_neg(Packet2d(a.v))));
+#else
+    return Packet2cf(pxor(a.v, reinterpret_cast<Packet4f>(p16uc_COMPLEX32_CONJ_XOR2)));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd pconjinv(const Packet1cd& a) {
+    return Packet1cd(pxor(a.v, reinterpret_cast<Packet2d>(p16uc_COMPLEX64_CONJ_XOR2)));
+}
+
+#if defined(_ARCH_PWR8) && (!EIGEN_COMP_LLVM || __clang_major__ >= 12)
+#define PERMXOR_GOOD  // Clang had a bug with vec_permxor and endianness prior to version 12
+#endif
+
+/** \internal flip the real & imaginary results and packet conjugate */
+EIGEN_ALWAYS_INLINE Packet2cf pcplxflipconj(Packet2cf a)
+{
+#ifdef PERMXOR_GOOD
+    return Packet2cf(Packet4f(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX32_CONJ_XOR, p16uc_COMPLEX32_XORFLIP)));
+#else
+    return pcplxflip(pconj2(a));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd pcplxflipconj(Packet1cd a)
+{
+#ifdef PERMXOR_GOOD
+    return Packet1cd(Packet2d(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX64_CONJ_XOR, p16uc_COMPLEX64_XORFLIP)));
+#else
+    return pcplxflip(pconj2(a));
+#endif
+}
+
+/** \internal packet conjugate and flip the real & imaginary results */
+EIGEN_ALWAYS_INLINE Packet2cf pcplxconjflip(Packet2cf a)
+{
+#ifdef PERMXOR_GOOD
+    return Packet2cf(Packet4f(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX32_CONJ_XOR2, p16uc_COMPLEX32_XORFLIP)));
+#else
+    return pconj2(pcplxflip(a));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd pcplxconjflip(Packet1cd a)
+{
+#ifdef PERMXOR_GOOD
+    return Packet1cd(Packet2d(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX64_CONJ_XOR2, p16uc_COMPLEX64_XORFLIP)));
+#else
+    return pconj2(pcplxflip(a));
+#endif
+}
+
+/** \internal packet negate */
+EIGEN_ALWAYS_INLINE Packet2cf pnegate2(Packet2cf a)
+{
+#ifdef __POWER8_VECTOR__
+    return Packet2cf(vec_neg(a.v));
+#else
+    return Packet2cf(pxor(a.v, reinterpret_cast<Packet4f>(p16uc_COMPLEX32_NEGATE)));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd pnegate2(Packet1cd a)
+{
+#ifdef __POWER8_VECTOR__
+    return Packet1cd(vec_neg(a.v));
+#else
+    return Packet1cd(pxor(a.v, reinterpret_cast<Packet2d>(p16uc_COMPLEX64_NEGATE)));
+#endif
+}
+
+/** \internal flip the real & imaginary results and negate */
+EIGEN_ALWAYS_INLINE Packet2cf pcplxflipnegate(Packet2cf a)
+{
+#ifdef PERMXOR_GOOD
+    return Packet2cf(Packet4f(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX32_NEGATE, p16uc_COMPLEX32_XORFLIP)));
+#else
+    return pcplxflip(pnegate2(a));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd pcplxflipnegate(Packet1cd a)
+{
+#ifdef PERMXOR_GOOD
+    return Packet1cd(Packet2d(vec_permxor(Packet16uc(a.v), p16uc_COMPLEX64_NEGATE, p16uc_COMPLEX64_XORFLIP)));
+#else
+    return pcplxflip(pnegate2(a));
+#endif
+}
+
+/** \internal flip the real & imaginary results */
+EIGEN_ALWAYS_INLINE Packet2cf pcplxflip2(Packet2cf a)
+{
+    return Packet2cf(Packet4f(vec_perm(Packet16uc(a.v), Packet16uc(a.v), p16uc_COMPLEX32_XORFLIP)));
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd pcplxflip2(Packet1cd a)
+{
+#ifdef EIGEN_VECTORIZE_VSX
+    return Packet1cd(__builtin_vsx_xxpermdi(a.v, a.v, 2));
+#else
+    return Packet1cd(Packet2d(vec_perm(Packet16uc(a.v), Packet16uc(a.v), p16uc_COMPLEX64_XORFLIP)));
+#endif
+}
+
+/** \internal load half a vector with one complex value */
+EIGEN_ALWAYS_INLINE Packet4f pload_complex_half(std::complex<float>* src)
+{
+    Packet4f t;
+#ifdef EIGEN_VECTORIZE_VSX
+    // Load float64/two float32 (doubleword alignment)
+    __asm__("lxsdx %x0,%y1" : "=wa" (t) : "Z" (*src));
+#else
+    *reinterpret_cast<std::complex<float>*>(reinterpret_cast<float*>(&t) + COMPLEX_DELTA) = *src;
+#endif
+    return t;
+}
+
+/** \internal load two vectors from the real and imaginary portions of a complex value */
+template<typename RhsScalar>
+EIGEN_ALWAYS_INLINE void pload_realimag(RhsScalar* src, Packet4f& r, Packet4f& i)
+{
+#ifdef _ARCH_PWR9
+    __asm__("lxvwsx %x0,%y1" : "=wa" (r) : "Z" (*(reinterpret_cast<float*>(src) + 0)));
+    __asm__("lxvwsx %x0,%y1" : "=wa" (i) : "Z" (*(reinterpret_cast<float*>(src) + 1)));
+#else
+    Packet4f t = pload_complex_half(src);
+    r = vec_splat(t, COMPLEX_DELTA + 0);
+    i = vec_splat(t, COMPLEX_DELTA + 1);
+#endif
+}
+
+template<typename RhsScalar>
+EIGEN_ALWAYS_INLINE void pload_realimag(RhsScalar* src, Packet2d& r, Packet2d& i)
+{
+#ifdef EIGEN_VECTORIZE_VSX
+    __asm__("lxvdsx %x0,%y1" : "=wa" (r) : "Z" (*(reinterpret_cast<double*>(src) + 0)));
+    __asm__("lxvdsx %x0,%y1" : "=wa" (i) : "Z" (*(reinterpret_cast<double*>(src) + 1)));
+#else
+    Packet2d t = ploadu<Packet2d>(reinterpret_cast<double*>(src));
+    r = vec_splat(t, 0);
+    i = vec_splat(t, 1);
+#endif
+}
+
+#ifndef __POWER8_VECTOR__
+const Packet16uc p16uc_MERGEE = { 0x00, 0x01, 0x02, 0x03, 0x10, 0x11, 0x12, 0x13, 0x08, 0x09, 0x0A, 0x0B, 0x18, 0x19, 0x1A, 0x1B };
+
+const Packet16uc p16uc_MERGEO = { 0x04, 0x05, 0x06, 0x07, 0x14, 0x15, 0x16, 0x17, 0x0C, 0x0D, 0x0E, 0x0F, 0x1C, 0x1D, 0x1E, 0x1F };
+#endif
+
+/** \internal load two vectors from the interleaved real & imaginary values of src */
+template<typename RhsScalar>
+EIGEN_ALWAYS_INLINE void pload_realimag_row(RhsScalar* src, Packet4f& r, Packet4f& i)
+{
+    Packet4f t = ploadu<Packet4f>(reinterpret_cast<float*>(src));
+#ifdef __POWER8_VECTOR__
+    r = vec_mergee(t, t);
+    i = vec_mergeo(t, t);
+#else
+    r = vec_perm(t, t, p16uc_MERGEE);
+    i = vec_perm(t, t, p16uc_MERGEO);
+#endif
+}
+
+template<typename RhsScalar>
+EIGEN_ALWAYS_INLINE void pload_realimag_row(RhsScalar* src, Packet2d& r, Packet2d& i)
+{
+    return pload_realimag(src, r, i);
+}
+
+/** \internal load and splat a complex value into a vector - column-wise */
+EIGEN_ALWAYS_INLINE Packet4f pload_realimag_combine(std::complex<float>* src)
+{
+#ifdef EIGEN_VECTORIZE_VSX
+    Packet4f ret;
+    __asm__("lxvdsx %x0,%y1" : "=wa" (ret) : "Z" (*(reinterpret_cast<double*>(src) + 0)));
+    return ret;
+#else
+    return Packet4f(ploaddup<Packet2d>(reinterpret_cast<double *>(src)));
+#endif
+}
+
+EIGEN_ALWAYS_INLINE Packet2d pload_realimag_combine(std::complex<double>* src)
+{
+    return ploadu<Packet1cd>(src).v;
+}
+
+/** \internal load a complex value into a vector - row-wise */
+EIGEN_ALWAYS_INLINE Packet4f pload_realimag_combine_row(std::complex<float>* src)
+{
+    return ploadu<Packet2cf>(src).v;
+}
+
+EIGEN_ALWAYS_INLINE Packet2d pload_realimag_combine_row(std::complex<double>* src)
+{
+    return ploadu<Packet1cd>(src).v;
+}
+
+/** \internal load a scalar or a vector from complex location */
+template<typename ResPacket>
+EIGEN_ALWAYS_INLINE Packet4f pload_complex(std::complex<float>* src)
+{
+    if (GEMV_IS_SCALAR) {
+        return pload_complex_half(src);
+    }
+    else
+    {
+        return ploadu<Packet4f>(reinterpret_cast<float*>(src));
+    }
+}
+
+template<typename ResPacket>
+EIGEN_ALWAYS_INLINE Packet2d pload_complex(std::complex<double>* src)
+{
+    return ploadu<Packet2d>(reinterpret_cast<double*>(src));
+}
+
+/** \internal load from a complex vector and convert to a real vector */
+template<typename ResPacket>
+EIGEN_ALWAYS_INLINE Packet4f pload_complex(Packet2cf* src)
+{
+    return src->v;
+}
+
+template<typename ResPacket>
+EIGEN_ALWAYS_INLINE Packet2d pload_complex(Packet1cd* src)
+{
+    return src->v;
+}
+
+/** \internal load a full vector from complex location - column-wise */
+EIGEN_ALWAYS_INLINE Packet4f pload_complex_full(std::complex<float>* src)
+{
+    return Packet4f(ploaddup<Packet2d>(reinterpret_cast<double *>(src)));
+}
+
+EIGEN_ALWAYS_INLINE Packet2d pload_complex_full(std::complex<double>* src)
+{
+    return ploadu<Packet1cd>(src).v;
+}
+
+/** \internal load a full vector from complex location - row-wise */
+EIGEN_ALWAYS_INLINE Packet4f pload_complex_full_row(std::complex<float>* src)
+{
+    return ploadu<Packet2cf>(src).v;
+}
+
+EIGEN_ALWAYS_INLINE Packet2d pload_complex_full_row(std::complex<double>* src)
+{
+    return pload_complex_full(src);
+}
+
+/** \internal load a vector from a real-only scalar location - column-wise */
+EIGEN_ALWAYS_INLINE Packet4f pload_real(float* src)
+{
+    return pset1<Packet4f>(*src);
+}
+
+EIGEN_ALWAYS_INLINE Packet2d pload_real(double* src)
+{
+    return pset1<Packet2d>(*src);
+}
+
+EIGEN_ALWAYS_INLINE Packet4f pload_real(Packet4f& src)
+{
+    return src;
+}
+
+EIGEN_ALWAYS_INLINE Packet2d pload_real(Packet2d& src)
+{
+    return src;
+}
+
+/** \internal load a vector from a real-only vector location */
+EIGEN_ALWAYS_INLINE Packet4f pload_real_full(float* src)
+{
+    Packet4f ret = ploadu<Packet4f>(src);
+    return vec_mergeh(ret, ret);
+}
+
+EIGEN_ALWAYS_INLINE Packet2d pload_real_full(double* src)
+{
+    return pload_real(src);
+}
+
+EIGEN_ALWAYS_INLINE Packet4f pload_real_full(std::complex<float>* src)
+{
+    return pload_complex_full(src);   // Just for compilation
+}
+
+EIGEN_ALWAYS_INLINE Packet2d pload_real_full(std::complex<double>* src)
+{
+    return pload_complex_full(src);   // Just for compilation
+}
+
+/** \internal load a vector from a real-only scalar location - row-wise */
+template<typename ResPacket>
+EIGEN_ALWAYS_INLINE Packet4f pload_real_row(float* src)
+{
+    if (GEMV_IS_SCALAR) {
+        return pload_real_full(src);
+    }
+    else {
+        return ploadu<Packet4f>(src);
+    }
+}
+
+template<typename ResPacket>
+EIGEN_ALWAYS_INLINE Packet2d pload_real_row(double* src)
+{
+    return pload_real(src);
+}
+
+EIGEN_ALWAYS_INLINE Packet2cf padd(Packet2cf& a, std::complex<float>& b)
+{
+    EIGEN_UNUSED_VARIABLE(b);
+    return a;  // Just for compilation
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd padd(Packet1cd& a, std::complex<double>& b)
+{
+    EIGEN_UNUSED_VARIABLE(b);
+    return a;  // Just for compilation
+}
+
+/** \internal set a scalar from complex location */
+template<typename Scalar, typename ResScalar>
+EIGEN_ALWAYS_INLINE Scalar pset1_realimag(ResScalar& alpha, int which, int conj)
+{
+    return (which) ? ((conj) ? -alpha.real() : alpha.real()) : ((conj) ? -alpha.imag() : alpha.imag());
+}
+
+/** \internal set a vector from complex location */
+template<typename Scalar, typename ResScalar, typename ResPacket, int which>
+EIGEN_ALWAYS_INLINE Packet2cf pset1_complex(std::complex<float>& alpha)
+{
+    Packet2cf ret;
+    ret.v[COMPLEX_DELTA + 0] = pset1_realimag<Scalar, ResScalar>(alpha, (which & 0x01), (which & 0x04));
+    ret.v[COMPLEX_DELTA + 1] = pset1_realimag<Scalar, ResScalar>(alpha, (which & 0x02), (which & 0x08));
+    ret.v[2 - COMPLEX_DELTA] = ret.v[COMPLEX_DELTA + 0];
+    ret.v[3 - COMPLEX_DELTA] = ret.v[COMPLEX_DELTA + 1];
+    return ret;
+}
+
+template<typename Scalar, typename ResScalar, typename ResPacket, int which>
+EIGEN_ALWAYS_INLINE Packet1cd pset1_complex(std::complex<double>& alpha)
+{
+    Packet1cd ret;
+    ret.v[0] = pset1_realimag<Scalar, ResScalar>(alpha, (which & 0x01), (which & 0x04));
+    ret.v[1] = pset1_realimag<Scalar, ResScalar>(alpha, (which & 0x02), (which & 0x08));
+    return ret;
+}
+
+/** \internal zero out a vector for real or complex forms */
+template<typename Packet>
+EIGEN_ALWAYS_INLINE Packet pset_zero()
+{
+    return pset1<Packet>(__UNPACK_TYPE__(Packet)(0));
+}
+
+template<>
+EIGEN_ALWAYS_INLINE Packet2cf pset_zero<Packet2cf>()
+{
+    return Packet2cf(pset1<Packet4f>(float(0)));
+}
+
+template<>
+EIGEN_ALWAYS_INLINE Packet1cd pset_zero<Packet1cd>()
+{
+    return Packet1cd(pset1<Packet2d>(double(0)));
+}
+
+/** \internal initialize a vector from another vector */
+template<typename Packet, typename LhsPacket, typename RhsPacket>
+EIGEN_ALWAYS_INLINE Packet pset_init(Packet& c1)
+{
+    if (GEMV_IS_COMPLEX_COMPLEX) {
+        EIGEN_UNUSED_VARIABLE(c1);
+        return pset_zero<Packet>();
+    }
+    else
+    {
+        return c1;  // Intentionally left uninitialized
+    }
+}
+
+template<typename PResPacket, typename ResPacket, typename ResScalar, typename Scalar>
+struct alpha_store
+{
+    alpha_store(ResScalar& alpha) {
+        separate.r = pset1_complex<Scalar, ResScalar, ResPacket, 0x3>(alpha);
+        separate.i = pset1_complex<Scalar, ResScalar, ResPacket, 0x0>(alpha);
+    }
+    struct ri {
+        PResPacket r;
+        PResPacket i;
+    } separate;
+};
+
+/** \internal multiply and add for complex math */
+template<typename ScalarPacket, typename AlphaData>
+EIGEN_ALWAYS_INLINE ScalarPacket pmadd_complex(ScalarPacket& c0, ScalarPacket& c2, ScalarPacket& c4, AlphaData& b0)
+{
+    return pmadd(c2, b0.separate.i.v, pmadd(c0, b0.separate.r.v, c4));
+}
+
+/** \internal store and madd for complex math */
+template<typename Scalar, typename ScalarPacket, typename PResPacket, typename ResPacket, typename ResScalar, typename AlphaData>
+EIGEN_ALWAYS_INLINE void pstoreu_pmadd_complex(PResPacket& c0, AlphaData& b0, ResScalar* res)
+{
+    PResPacket c2 = pcplxflipconj(c0);
+    if (GEMV_IS_SCALAR) {
+        ScalarPacket c4 = ploadu<ScalarPacket>(reinterpret_cast<Scalar*>(res));
+        ScalarPacket c3 = pmadd_complex<ScalarPacket, AlphaData>(c0.v, c2.v, c4, b0);
+        pstoreu(reinterpret_cast<Scalar*>(res), c3);
+    } else {
+        ScalarPacket c4 = pload_complex<ResPacket>(res);
+        PResPacket c3 = PResPacket(pmadd_complex<ScalarPacket, AlphaData>(c0.v, c2.v, c4, b0));
+        pstoreu(res, c3);
+    }
+}
+
+template<typename ScalarPacket, typename PResPacket, typename ResPacket, typename ResScalar, typename AlphaData, Index ResPacketSize, Index iter2>
+EIGEN_ALWAYS_INLINE void pstoreu_pmadd_complex(PResPacket& c0, PResPacket& c1, AlphaData& b0, ResScalar* res)
+{
+    PResPacket c2 = pcplxflipconj(c0);
+    PResPacket c3 = pcplxflipconj(c1);
+#if !defined(_ARCH_PWR10)
+    ScalarPacket c4 = pload_complex<ResPacket>(res + (iter2 * ResPacketSize));
+    ScalarPacket c5 = pload_complex<ResPacket>(res + ((iter2 + 1) * ResPacketSize));
+    PResPacket c6 = PResPacket(pmadd_complex<ScalarPacket, AlphaData>(c0.v, c2.v, c4, b0));
+    PResPacket c7 = PResPacket(pmadd_complex<ScalarPacket, AlphaData>(c1.v, c3.v, c5, b0));
+    pstoreu(res + (iter2 * ResPacketSize), c6);
+    pstoreu(res + ((iter2 + 1) * ResPacketSize), c7);
+#else
+    __vector_pair a = *reinterpret_cast<__vector_pair *>(res + (iter2 * ResPacketSize));
+#if EIGEN_COMP_LLVM
+    PResPacket c6[2];
+    __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(c6), &a);
+    c6[0] = PResPacket(pmadd_complex<ScalarPacket, AlphaData>(c0.v, c2.v, c6[0].v, b0));
+    c6[1] = PResPacket(pmadd_complex<ScalarPacket, AlphaData>(c1.v, c3.v, c6[1].v, b0));
+    GEMV_BUILDPAIR_MMA(a, c6[0].v, c6[1].v);
+#else
+    if (GEMV_IS_COMPLEX_FLOAT) {
+        __asm__ ("xvmaddasp %L0,%x1,%x2\n\txvmaddasp %0,%x1,%x3" : "+&d" (a) : "wa" (b0.separate.r.v), "wa" (c0.v), "wa" (c1.v));
+        __asm__ ("xvmaddasp %L0,%x1,%x2\n\txvmaddasp %0,%x1,%x3" : "+&d" (a) : "wa" (b0.separate.i.v), "wa" (c2.v), "wa" (c3.v));
+    } else {
+        __asm__ ("xvmaddadp %L0,%x1,%x2\n\txvmaddadp %0,%x1,%x3" : "+&d" (a) : "wa" (b0.separate.r.v), "wa" (c0.v), "wa" (c1.v));
+        __asm__ ("xvmaddadp %L0,%x1,%x2\n\txvmaddadp %0,%x1,%x3" : "+&d" (a) : "wa" (b0.separate.i.v), "wa" (c2.v), "wa" (c3.v));
+    }
+#endif
+    *reinterpret_cast<__vector_pair *>(res + (iter2 * ResPacketSize)) = a;
+#endif
+}
+
+/** \internal load lhs packet */
+template<typename Scalar, typename LhsScalar, typename LhsMapper, typename LhsPacket>
+EIGEN_ALWAYS_INLINE LhsPacket loadLhsPacket(LhsMapper& lhs, Index i, Index j)
+{
+    if (sizeof(Scalar) == sizeof(LhsScalar)) {
+        const LhsScalar& src = lhs(i + 0, j);
+        return LhsPacket(pload_real_full(const_cast<LhsScalar*>(&src)));
+    }
+    return lhs.template load<LhsPacket, Unaligned>(i + 0, j);
+}
+
+/** \internal madd for complex times complex */
+template<typename ComplexPacket, typename RealPacket, bool ConjugateLhs, bool ConjugateRhs, bool Negate>
+EIGEN_ALWAYS_INLINE RealPacket pmadd_complex_complex(RealPacket& a, RealPacket& b, RealPacket& c)
+{
+    if (ConjugateLhs && ConjugateRhs) {
+        return vec_madd(a, pconj2(ComplexPacket(b)).v, c);
+    }
+    else if (Negate && !ConjugateLhs && ConjugateRhs) {
+        return vec_nmsub(a, b, c);
+    }
+    else {
+        return vec_madd(a, b, c);
+    }
+}
+
+/** \internal madd for complex times real */
+template<typename ComplexPacket, typename RealPacket, bool Conjugate>
+EIGEN_ALWAYS_INLINE RealPacket pmadd_complex_real(RealPacket& a, RealPacket& b, RealPacket& c)
+{
+    if (Conjugate) {
+        return vec_madd(a, pconj2(ComplexPacket(b)).v, c);
+    }
+    else {
+        return vec_madd(a, b, c);
+    }
+}
+
+template<typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_generic(LhsPacket& a0, RhsScalar* b, PResPacket& c0)
+{
+    conj_helper<LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs> pcj;
+    RhsPacket b0;
+    if (StorageOrder == ColMajor) {
+        b0 = pset1<RhsPacket>(*b);
+    }
+    else {
+        b0 = ploadu<RhsPacket>(b);
+    }
+    c0 = pcj.pmadd(a0, b0, c0);
+}
+
+/** \internal core multiply operation for vectors - complex times complex */
+template<typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_complex_complex(LhsPacket& a0, RhsScalar* b, PResPacket& c0, ResPacket& c1)
+{
+    ScalarPacket br, bi;
+    if (StorageOrder == ColMajor) {
+        pload_realimag<RhsScalar>(b, br, bi);
+    }
+    else {
+        pload_realimag_row<RhsScalar>(b, br, bi);
+    }
+    if (ConjugateLhs && !ConjugateRhs) a0 = pconj2(a0);
+    LhsPacket a1 = pcplxflipconj(a0);
+    ScalarPacket cr = pmadd_complex_complex<LhsPacket, ScalarPacket, ConjugateLhs, ConjugateRhs, false>(a0.v, br, c0.v);
+    ScalarPacket ci = pmadd_complex_complex<LhsPacket, ScalarPacket, ConjugateLhs, ConjugateRhs, true>(a1.v, bi, c1.v);
+    c1 = ResPacket(ci);
+    c0 = PResPacket(cr);
+}
+
+/** \internal core multiply operation for vectors - real times complex */
+template<typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_real_complex(LhsPacket& a0, RhsScalar* b, PResPacket& c0)
+{
+    ScalarPacket b0;
+    if (StorageOrder == ColMajor) {
+        b0 = pload_complex_full(b);
+    }
+    else {
+        b0 = pload_complex_full_row(b);
+    }
+    ScalarPacket cri = pmadd_complex_real<PResPacket, ScalarPacket, ConjugateRhs>(a0, b0, c0.v);
+    c0 = PResPacket(cri);
+}
+
+/** \internal core multiply operation for vectors - complex times real */
+template<typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_complex_real(LhsPacket& a0, RhsScalar* b, PResPacket& c0)
+{
+    ScalarPacket a1 = pload_complex<ResPacket>(&a0);
+    ScalarPacket b0;
+    if (StorageOrder == ColMajor) {
+        b0 = pload_real(b);
+    }
+    else {
+        b0 = pload_real_row<ResPacket>(b);
+    }
+    ScalarPacket cri = pmadd_complex_real<PResPacket, ScalarPacket, ConjugateLhs>(a1, b0, c0.v);
+    c0 = PResPacket(cri);
+}
+
+#define GEMV_MULT_COMPLEX_COMPLEX(LhsType, RhsType, ResType) \
+template<typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder> \
+EIGEN_ALWAYS_INLINE void gemv_mult_complex(LhsType& a0, RhsType* b, ResType& c0, ResType& c1) \
+{ \
+    gemv_mult_complex_complex<ScalarPacket, LhsPacket, RhsScalar, RhsPacket, PResPacket, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0, c1); \
+}
+
+GEMV_MULT_COMPLEX_COMPLEX(Packet2cf, std::complex<float>,  Packet2cf)
+GEMV_MULT_COMPLEX_COMPLEX(Packet1cd, std::complex<double>, Packet1cd)
+
+#define GEMV_MULT_REAL_COMPLEX(LhsType, RhsType, ResType) \
+template<typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder> \
+EIGEN_ALWAYS_INLINE void gemv_mult_complex(LhsType& a0, RhsType* b, ResType& c0, RhsType&) \
+{ \
+    gemv_mult_real_complex<ScalarPacket, LhsPacket, RhsScalar, RhsPacket, PResPacket, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0); \
+}
+
+GEMV_MULT_REAL_COMPLEX(float,    std::complex<float>,  Packet2cf)
+GEMV_MULT_REAL_COMPLEX(double,   std::complex<double>, Packet1cd)
+GEMV_MULT_REAL_COMPLEX(Packet4f, std::complex<float>,  Packet2cf)
+GEMV_MULT_REAL_COMPLEX(Packet2d, std::complex<double>, Packet1cd)
+
+#define GEMV_MULT_COMPLEX_REAL(LhsType, RhsType, ResType1, ResType2) \
+template<typename ScalarPacket, typename LhsPacket, typename RhsScalar, typename RhsPacket, typename PResPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder> \
+EIGEN_ALWAYS_INLINE void gemv_mult_complex(LhsType& a0, RhsType* b, ResType1& c0, ResType2&) \
+{ \
+    gemv_mult_complex_real<ScalarPacket, LhsPacket, RhsScalar, RhsPacket, PResPacket, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0); \
+}
+
+GEMV_MULT_COMPLEX_REAL(Packet2cf,             float, Packet2cf, std::complex<float>)
+GEMV_MULT_COMPLEX_REAL(Packet1cd,            double, Packet1cd, std::complex<double>)
+GEMV_MULT_COMPLEX_REAL(std::complex<float>,   float, Packet2cf, std::complex<float>)
+GEMV_MULT_COMPLEX_REAL(std::complex<double>, double, Packet1cd, std::complex<double>)
+
+#ifdef USE_GEMV_MMA
+/** \internal convert packet to real form */
+template<typename T>
+EIGEN_ALWAYS_INLINE T convertReal(T a)
+{
+    return a;
+}
+
+EIGEN_ALWAYS_INLINE Packet4f convertReal(Packet2cf a)
+{
+    return a.v;
+}
+
+EIGEN_ALWAYS_INLINE Packet2d convertReal(Packet1cd a)
+{
+    return a.v;
+}
+
+/** \internal convert packet to complex form */
+template<typename T>
+EIGEN_ALWAYS_INLINE T convertComplex(T a)
+{
+    return a;
+}
+
+EIGEN_ALWAYS_INLINE Packet2cf convertComplex(Packet4f a)
+{
+    return Packet2cf(a);
+}
+
+EIGEN_ALWAYS_INLINE Packet1cd convertComplex(Packet2d a)
+{
+    return Packet1cd(a);
+}
+
+/** \internal load a vector from a complex location (for MMA version) */
+template<typename ScalarPacket, typename LhsPacket, typename SLhsPacket, typename ResPacket>
+EIGEN_ALWAYS_INLINE void pload_complex_MMA(SLhsPacket& a)
+{
+    a = SLhsPacket(pload_complex<ResPacket>(&a));
+}
+
+template<typename ScalarPacket, typename LhsPacket, typename SLhsPacket, typename ResPacket>
+EIGEN_ALWAYS_INLINE void pload_complex_MMA(__vector_pair&)
+{
+    // Pass thru
+}
+
+/** \internal perform a matrix multiply and accumulate (positive and negative) of packet a and packet b */
+template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
+EIGEN_ALWAYS_INLINE void pger_vecMMA(__vector_quad* acc, RhsPacket& a, LhsPacket& b)
+{
+    if (NegativeAccumulate)
+    {
+        __builtin_mma_xvf32gernp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
+    }
+    else {
+        __builtin_mma_xvf32gerpp(acc, (__vector unsigned char)a, (__vector unsigned char)b);
+    }
+}
+
+/** \internal perform a matrix multiply and accumulate (positive and negative) of vector_pair a and packet b */
+template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
+EIGEN_ALWAYS_INLINE void pger_vecMMA(__vector_quad* acc, __vector_pair& a, Packet2d& b)
+{
+    if (NegativeAccumulate)
+    {
+        __builtin_mma_xvf64gernp(acc, (__vector_pair)a, (__vector unsigned char)b);
+    }
+    else {
+        __builtin_mma_xvf64gerpp(acc, (__vector_pair)a, (__vector unsigned char)b);
+    }
+}
+
+template<typename LhsPacket, typename RhsPacket, bool NegativeAccumulate>
+EIGEN_ALWAYS_INLINE void pger_vecMMA(__vector_quad*, __vector_pair&, Packet4f&)
+{
+    // Just for compilation
+}
+
+/** \internal madd for complex times complex (MMA version) */
+template<typename RealPacket, typename LhsPacket, bool ConjugateLhs, bool ConjugateRhs, bool Negate>
+EIGEN_ALWAYS_INLINE void pmadd_complex_complex_MMA(LhsPacket& a, RealPacket& b, __vector_quad* c)
+{
+    if (ConjugateLhs && ConjugateRhs) {
+        RealPacket b2 = pconj2(convertComplex(b)).v;
+        return pger_vecMMA<RealPacket, RealPacket, false>(c, b2, a.v);
+    }
+    else if (Negate && !ConjugateLhs && ConjugateRhs) {
+        return pger_vecMMA<RealPacket, RealPacket, true>(c, b, a.v);
+    }
+    else {
+        return pger_vecMMA<RealPacket, RealPacket, false>(c, b, a.v);
+    }
+}
+
+template<typename RealPacket, typename LhsPacket, bool ConjugateLhs, bool ConjugateRhs, bool Negate>
+EIGEN_ALWAYS_INLINE void pmadd_complex_complex_MMA(__vector_pair& a, RealPacket& b, __vector_quad* c)
+{
+    if (ConjugateLhs && ConjugateRhs) {
+        RealPacket b2 = pconj2(convertComplex(b)).v;
+        return pger_vecMMA<RealPacket, __vector_pair, false>(c, a, b2);
+    }
+    else if (Negate && !ConjugateLhs && ConjugateRhs) {
+        return pger_vecMMA<RealPacket, __vector_pair, true>(c, a, b);
+    }
+    else {
+        return pger_vecMMA<RealPacket, __vector_pair, false>(c, a, b);
+    }
+}
+
+/** \internal madd for complex times real (MMA version) */
+template<typename RealPacket, typename LhsPacket, bool Conjugate, int StorageOrder>
+EIGEN_ALWAYS_INLINE void pmadd_complex_real_MMA(LhsPacket& a, RealPacket& b, __vector_quad* c)
+{
+    RealPacket a2 = convertReal(a);
+    if (Conjugate) {
+        RealPacket b2 = pconj2(convertComplex(b)).v;
+        if (StorageOrder == ColMajor) {
+            return pger_vecMMA<RealPacket, RealPacket, false>(c, b2, a2);
+        } else {
+            return pger_vecMMA<RealPacket, RealPacket, false>(c, a2, b2);
+        }
+    }
+    else {
+        if (StorageOrder == ColMajor) {
+            return pger_vecMMA<RealPacket, RealPacket, false>(c, b, a2);
+        } else {
+            return pger_vecMMA<RealPacket, RealPacket, false>(c, a2, b);
+        }
+    }
+}
+
+/** \internal madd for real times complex (MMA version) */
+template<typename RealPacket, typename LhsPacket, bool Conjugate, int StorageOrder>
+EIGEN_ALWAYS_INLINE void pmadd_complex_real_MMA(__vector_pair& a, RealPacket& b, __vector_quad* c)
+{
+    if (Conjugate) {
+        RealPacket b2 = pconj2(convertComplex(b)).v;
+        return pger_vecMMA<RealPacket, __vector_pair, false>(c, a, b2);
+    }
+    else {
+        return pger_vecMMA<RealPacket, __vector_pair, false>(c, a, b);
+    }
+}
+
+/** \internal core multiply operation for vectors (MMA version) - complex times complex */
+template<typename ScalarPacket, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_complex_complex_MMA(SLhsPacket& a0, RhsScalar* b, __vector_quad* c0)
+{
+    ScalarPacket b0;
+    if (StorageOrder == ColMajor) {
+        b0 = pload_realimag_combine(b);
+    } else {
+        b0 = pload_realimag_combine_row(b);
+    }
+    pmadd_complex_complex_MMA<ScalarPacket, LhsPacket, ConjugateLhs, ConjugateRhs, false>(a0, b0, c0);
+}
+
+/** \internal core multiply operation for vectors (MMA version) - complex times real */
+template<typename ScalarPacket, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_complex_real_MMA(SLhsPacket& a0, RhsScalar* b, __vector_quad* c0)
+{
+    pload_complex_MMA<ScalarPacket, LhsPacket, SLhsPacket, ResPacket>(a0);
+    ScalarPacket b0;
+    if (StorageOrder == ColMajor) {
+        b0 = pload_real(b);
+    }
+    else {
+        b0 = pload_real_row<ResPacket>(b);
+    }
+    pmadd_complex_real_MMA<ScalarPacket, LhsPacket, ConjugateLhs, ColMajor>(a0, b0, c0);
+}
+
+/** \internal core multiply operation for vectors (MMA version) - real times complex */
+template<typename ScalarPacket, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_real_complex_MMA(SLhsPacket& a0, RhsScalar* b, __vector_quad* c0)
+{
+    ScalarPacket b0;
+    if (StorageOrder == ColMajor) {
+        b0 = pload_complex_full(b);
+    }
+    else {
+        b0 = pload_complex_full_row(b);
+    }
+    pmadd_complex_real_MMA<ScalarPacket, LhsPacket, ConjugateRhs, (sizeof(RhsScalar) == sizeof(std::complex<float>)) ? StorageOrder : ColMajor>(a0, b0, c0);
+}
+
+#define GEMV_MULT_COMPLEX_COMPLEX_MMA(LhsType, RhsType) \
+template<typename ScalarPacket, typename LhsScalar, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename RhsPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder> \
+EIGEN_ALWAYS_INLINE void gemv_mult_complex_MMA(LhsType& a0, RhsType* b, __vector_quad* c0) \
+{ \
+    gemv_mult_complex_complex_MMA<ScalarPacket, LhsPacket, SLhsPacket, RhsScalar, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0); \
+}
+
+GEMV_MULT_COMPLEX_COMPLEX_MMA(Packet2cf,     std::complex<float>)
+GEMV_MULT_COMPLEX_COMPLEX_MMA(__vector_pair, std::complex<float>)
+GEMV_MULT_COMPLEX_COMPLEX_MMA(Packet1cd,     std::complex<double>)
+
+/** \internal core multiply operation for vectors (MMA version) - complex times complex */
+template<typename ScalarPacket, typename LhsScalar, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename RhsPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder>
+EIGEN_ALWAYS_INLINE void gemv_mult_complex_MMA(__vector_pair& a0, std::complex<double>* b, __vector_quad* c0)
+{
+    if (sizeof(LhsScalar) == 16) {
+        gemv_mult_complex_complex_MMA<ScalarPacket, LhsPacket, SLhsPacket, RhsScalar, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0);
+    }
+    else {
+        gemv_mult_real_complex_MMA<ScalarPacket, LhsPacket, SLhsPacket, RhsScalar, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0);
+    }
+}
+
+#define GEMV_MULT_REAL_COMPLEX_MMA(LhsType, RhsType) \
+template<typename ScalarPacket, typename LhsScalar, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename RhsPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder> \
+EIGEN_ALWAYS_INLINE void gemv_mult_complex_MMA(LhsType& a0, RhsType* b, __vector_quad* c0) \
+{ \
+    gemv_mult_real_complex_MMA<ScalarPacket, LhsPacket, SLhsPacket, RhsScalar, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0); \
+}
+
+GEMV_MULT_REAL_COMPLEX_MMA(Packet4f, std::complex<float>)
+GEMV_MULT_REAL_COMPLEX_MMA(Packet2d, std::complex<double>)
+
+#define GEMV_MULT_COMPLEX_REAL_MMA(LhsType, RhsType) \
+template<typename ScalarPacket, typename LhsScalar, typename LhsPacket, typename SLhsPacket, typename RhsScalar, typename RhsPacket, typename ResPacket, bool ConjugateLhs, bool ConjugateRhs, int StorageOrder> \
+EIGEN_ALWAYS_INLINE void gemv_mult_complex_MMA(LhsType& a0, RhsType* b, __vector_quad* c0) \
+{ \
+    gemv_mult_complex_real_MMA<ScalarPacket, LhsPacket, SLhsPacket, RhsScalar, ResPacket, ConjugateLhs, ConjugateRhs, StorageOrder>(a0, b, c0); \
+}
+
+GEMV_MULT_COMPLEX_REAL_MMA(Packet2cf,     float)
+GEMV_MULT_COMPLEX_REAL_MMA(Packet1cd,     double)
+GEMV_MULT_COMPLEX_REAL_MMA(__vector_pair, float)
+GEMV_MULT_COMPLEX_REAL_MMA(__vector_pair, double)
+
+/** \internal disassemble MMA accumulator results into packets */
+template <typename Scalar, typename ScalarPacket, typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE void disassembleResults2(__vector_quad* c0, PacketBlock<ScalarPacket, 4>& result0)
+{
+    __builtin_mma_disassemble_acc(&result0.packet, c0);
+    if (sizeof(LhsPacket) == 16) {
+        if (sizeof(RhsPacket) == 16) {
+            ScalarPacket tmp0, tmp2;
+            tmp2 = vec_mergeh(result0.packet[2], result0.packet[3]);
+            tmp0 = vec_mergeh(result0.packet[0], result0.packet[1]);
+            result0.packet[3] = vec_mergel(result0.packet[3], result0.packet[2]);
+            result0.packet[1] = vec_mergel(result0.packet[1], result0.packet[0]);
+            result0.packet[2] = tmp2;
+            result0.packet[0] = tmp0;
+
+            if (ConjugateLhs) {
+                result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
+                result0.packet[2] = pconj2(convertComplex(result0.packet[2])).v;
+            } else if (ConjugateRhs) {
+                result0.packet[1] = pconj2(convertComplex(result0.packet[1])).v;
+                result0.packet[3] = pconj2(convertComplex(result0.packet[3])).v;
+            } else {
+                result0.packet[1] = pconjinv(convertComplex(result0.packet[1])).v;
+                result0.packet[3] = pconjinv(convertComplex(result0.packet[3])).v;
+            }
+            result0.packet[0] = vec_add(result0.packet[0], result0.packet[1]);
+            result0.packet[2] = vec_add(result0.packet[2], result0.packet[3]);
+        } else {
+            result0.packet[0][1] = result0.packet[1][1];
+            result0.packet[2][1] = result0.packet[3][1];
+        }
+    }
+}
+
+template <typename Scalar, typename ScalarPacket, typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE void disassembleResults4(__vector_quad* c0, PacketBlock<ScalarPacket, 4>& result0)
+{
+    __builtin_mma_disassemble_acc(&result0.packet, c0);
+    if (GEMV_IS_COMPLEX_COMPLEX) {
+        if (ConjugateLhs) {
+            result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
+            result0.packet[1] = pcplxflip2(convertComplex(result0.packet[1])).v;
+        } else {
+            if (ConjugateRhs) {
+                result0.packet[1] = pcplxconjflip(convertComplex(result0.packet[1])).v;
+            } else {
+                result0.packet[1] = pcplxflipconj(convertComplex(result0.packet[1])).v;
+            }
+        }
+        result0.packet[0] = vec_add(result0.packet[0], result0.packet[1]);
+    } else if (sizeof(LhsPacket) == sizeof(std::complex<float>)) {
+        if (ConjugateLhs) {
+            result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
+        }
+    } else {
+        result0.packet[0] = vec_mergee(result0.packet[0], result0.packet[1]);
+    }
+}
+
+template <typename Scalar, typename ScalarPacket, int ResPacketSize, typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE void disassembleResults(__vector_quad* c0, PacketBlock<ScalarPacket, 4>& result0)
+{
+    if (!GEMV_IS_COMPLEX_FLOAT) {
+        disassembleResults2<Scalar, ScalarPacket, LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(c0, result0);
+    } else {
+        disassembleResults4<Scalar, ScalarPacket, LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(c0, result0);
+    }
+}
+#endif
+
+#define GEMV_GETN_COMPLEX(N) (((N) * ResPacketSize) >> 1)
+
+#define GEMV_LOADPACKET_COL_COMPLEX(iter) \
+  loadLhsPacket<Scalar, LhsScalar, LhsMapper, PLhsPacket>(lhs, i + ((iter) * ResPacketSize), j)
+
+#define GEMV_LOADPACKET_COL_COMPLEX_DATA(iter) \
+  convertReal(GEMV_LOADPACKET_COL_COMPLEX(iter))
+
+#ifdef USE_GEMV_MMA
+#define GEMV_INIT_COL_COMPLEX_MMA(iter, N) \
+  if (GEMV_GETN_COMPLEX(N) > iter) { \
+    __builtin_mma_xxsetaccz(&e0##iter); \
+  }
+
+#if EIGEN_COMP_LLVM
+#define GEMV_LOADPAIR_COL_COMPLEX_MMA(iter1, iter2) \
+  GEMV_BUILDPAIR_MMA(a##iter1, GEMV_LOADPACKET_COL_COMPLEX_DATA(iter2), GEMV_LOADPACKET_COL_COMPLEX_DATA((iter2) + 1)); \
+  EIGEN_UNUSED_VARIABLE(f##iter1);
+#else
+#define GEMV_LOADPAIR_COL_COMPLEX_MMA(iter1, iter2) \
+  if (sizeof(LhsPacket) == 16) { \
+    const LhsScalar& src = lhs(i + ((32 * iter1) / sizeof(LhsScalar)), j); \
+    a##iter1 = *reinterpret_cast<__vector_pair *>(const_cast<LhsScalar *>(&src)); \
+    EIGEN_UNUSED_VARIABLE(f##iter1); \
+  } else { \
+    f##iter1 = lhs.template load<PLhsPacket, Unaligned>(i + ((iter2) * ResPacketSize), j); \
+    GEMV_BUILDPAIR_MMA(a##iter1, vec_splat(convertReal(f##iter1), 0), vec_splat(convertReal(f##iter1), 1)); \
+  }
+#endif
+
+#define GEMV_LOAD1_COL_COMPLEX_MMA(iter, N) \
+  if (GEMV_GETN_COMPLEX(N) > iter) { \
+    if (GEMV_IS_COMPLEX_FLOAT) { \
+      f##iter = GEMV_LOADPACKET_COL_COMPLEX(iter); \
+      EIGEN_UNUSED_VARIABLE(a##iter); \
+    } else { \
+      GEMV_LOADPAIR_COL_COMPLEX_MMA(iter, iter << 1) \
+    } \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(a##iter); \
+    EIGEN_UNUSED_VARIABLE(f##iter); \
+  }
+
+#define GEMV_WORK1_COL_COMPLEX_MMA(iter, N) \
+  if (GEMV_GETN_COMPLEX(N) > iter) { \
+    if (GEMV_IS_COMPLEX_FLOAT) { \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, PLhsPacket, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, ColMajor>(f##iter, b, &e0##iter); \
+    } else { \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, __vector_pair, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, ColMajor>(a##iter, b, &e0##iter); \
+    } \
+  }
+
+#define GEMV_LOADPAIR2_COL_COMPLEX_MMA(iter1, iter2) \
+  GEMV_BUILDPAIR_MMA(a##iter1, GEMV_LOADPACKET_COL_COMPLEX_DATA(iter2), GEMV_LOADPACKET_COL_COMPLEX_DATA((iter2) + 1));
+
+#define GEMV_LOAD2_COL_COMPLEX_MMA(iter1, iter2, iter3, N) \
+  if (GEMV_GETN_COMPLEX(N) > iter1) { \
+    if (GEMV_IS_COMPLEX_FLOAT) { \
+      GEMV_LOADPAIR2_COL_COMPLEX_MMA(iter2, iter2); \
+      EIGEN_UNUSED_VARIABLE(a##iter3) \
+    } else { \
+      GEMV_LOADPAIR2_COL_COMPLEX_MMA(iter2, iter2 << 1); \
+      GEMV_LOADPAIR2_COL_COMPLEX_MMA(iter3, iter3 << 1); \
+    } \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(a##iter2); \
+    EIGEN_UNUSED_VARIABLE(a##iter3); \
+  } \
+  EIGEN_UNUSED_VARIABLE(f##iter2); \
+  EIGEN_UNUSED_VARIABLE(f##iter3);
+
+#define GEMV_WORK2_COL_COMPLEX_MMA(iter1, iter2, iter3, N) \
+  if (GEMV_GETN_COMPLEX(N) > iter1) { \
+    if (GEMV_IS_COMPLEX_FLOAT) { \
+      PLhsPacket g[2]; \
+      __builtin_vsx_disassemble_pair(reinterpret_cast<void*>(g), &a##iter2); \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, PLhsPacket, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, ColMajor>(g[0], b, &e0##iter2); \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, PLhsPacket, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, ColMajor>(g[1], b, &e0##iter3); \
+    } else { \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, __vector_pair, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, ColMajor>(a##iter2, b, &e0##iter2); \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, __vector_pair, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, ColMajor>(a##iter3, b, &e0##iter3); \
+    } \
+  }
+
+#if EIGEN_COMP_LLVM
+#define GEMV_LOAD_COL_COMPLEX_MMA(N) \
+  if (GEMV_GETN_COMPLEX(N) > 1) { \
+    GEMV_UNROLL_HALF(GEMV_LOAD2_COL_COMPLEX_MMA, (N >> 1)) \
+  } else { \
+    GEMV_UNROLL(GEMV_LOAD1_COL_COMPLEX_MMA, N) \
+  }
+
+#define GEMV_WORK_COL_COMPLEX_MMA(N) \
+  if (GEMV_GETN_COMPLEX(N) > 1) { \
+    GEMV_UNROLL_HALF(GEMV_WORK2_COL_COMPLEX_MMA, (N >> 1)) \
+  } else { \
+    GEMV_UNROLL(GEMV_WORK1_COL_COMPLEX_MMA, N) \
+  }
+#else
+#define GEMV_LOAD_COL_COMPLEX_MMA(N) \
+  GEMV_UNROLL(GEMV_LOAD1_COL_COMPLEX_MMA, N)
+
+#define GEMV_WORK_COL_COMPLEX_MMA(N) \
+  GEMV_UNROLL(GEMV_WORK1_COL_COMPLEX_MMA, N)
+#endif
+
+#define GEMV_DISASSEMBLE_COMPLEX_MMA(iter) \
+  disassembleResults<Scalar, ScalarPacket, ResPacketSize, LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(&e0##iter, result0##iter);
+
+#define GEMV_STORE_COL_COMPLEX_MMA(iter, N) \
+  if (GEMV_GETN_COMPLEX(N) > iter) { \
+    GEMV_DISASSEMBLE_COMPLEX_MMA(iter); \
+    c0##iter = PResPacket(result0##iter.packet[0]); \
+    if (GEMV_IS_COMPLEX_FLOAT) { \
+      pstoreu_pmadd_complex<Scalar, ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData>(c0##iter, alpha_data, res + i + (iter * ResPacketSize)); \
+    } else { \
+      pstoreu_pmadd_complex<Scalar, ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData>(c0##iter, alpha_data, res + i + ((iter << 1) * ResPacketSize)); \
+      c0##iter = PResPacket(result0##iter.packet[2]); \
+      pstoreu_pmadd_complex<Scalar, ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData>(c0##iter, alpha_data, res + i + (((iter << 1) + 1) * ResPacketSize)); \
+    } \
+  }
+
+#define GEMV_STORE2_COL_COMPLEX_MMA(iter1, iter2, iter3, N) \
+  if (GEMV_GETN_COMPLEX(N) > iter1) { \
+    GEMV_DISASSEMBLE_COMPLEX_MMA(iter2); \
+    GEMV_DISASSEMBLE_COMPLEX_MMA(iter3); \
+    c0##iter2 = PResPacket(result0##iter2.packet[0]); \
+    if (GEMV_IS_COMPLEX_FLOAT) { \
+      c0##iter3 = PResPacket(result0##iter3.packet[0]); \
+      pstoreu_pmadd_complex<ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData, ResPacketSize, iter2>(c0##iter2, c0##iter3, alpha_data, res + i); \
+    } else { \
+      c0##iter3 = PResPacket(result0##iter2.packet[2]); \
+      pstoreu_pmadd_complex<ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData, ResPacketSize, iter2 << 1>(c0##iter2, c0##iter3, alpha_data, res + i); \
+      c0##iter2 = PResPacket(result0##iter3.packet[0]); \
+      c0##iter3 = PResPacket(result0##iter3.packet[2]); \
+      pstoreu_pmadd_complex<ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData, ResPacketSize, iter3 << 1>(c0##iter2, c0##iter3, alpha_data, res + i); \
+    } \
+  }
+
+#define GEMV_PROCESS_COL_COMPLEX_ONE_MMA(N) \
+  GEMV_UNROLL(GEMV_INIT_COL_COMPLEX_MMA, N) \
+  Index j = j2; \
+  do { \
+    const RhsScalar& b1 = rhs2(j, 0); \
+    RhsScalar* b = const_cast<RhsScalar *>(&b1); \
+    GEMV_UNROLL(GEMV_PREFETCH, N) \
+    GEMV_LOAD_COL_COMPLEX_MMA(N) \
+    GEMV_WORK_COL_COMPLEX_MMA(N) \
+  } while (++j < jend); \
+  if (GEMV_GETN(N) <= 2) { \
+    GEMV_UNROLL(GEMV_STORE_COL_COMPLEX_MMA, N) \
+  } else { \
+    GEMV_UNROLL_HALF(GEMV_STORE2_COL_COMPLEX_MMA, (N >> 1)) \
+  } \
+  i += (ResPacketSize * N);
+#endif
+
+#define GEMV_INIT_COMPLEX(iter, N) \
+  if (N > iter) { \
+    c0##iter = pset_zero<PResPacket>(); \
+    c1##iter = pset_init<ResPacket, LhsPacket, RhsPacket>(c1##iter); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(c0##iter); \
+    EIGEN_UNUSED_VARIABLE(c1##iter); \
+  }
+
+#define GEMV_WORK_COL_COMPLEX(iter, N) \
+  if (N > iter) { \
+    f##iter = GEMV_LOADPACKET_COL_COMPLEX(iter); \
+    gemv_mult_complex<ScalarPacket, PLhsPacket, RhsScalar, RhsPacket, PResPacket, ResPacket, ConjugateLhs, ConjugateRhs, ColMajor>(f##iter, b, c0##iter, c1##iter); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(f##iter); \
+  }
+
+#define GEMV_STORE_COL_COMPLEX(iter, N) \
+  if (N > iter) { \
+    if (GEMV_IS_COMPLEX_COMPLEX) { \
+      c0##iter = padd(c0##iter, c1##iter); \
+    } \
+    pstoreu_pmadd_complex<Scalar, ScalarPacket, PResPacket, ResPacket, ResScalar, AlphaData>(c0##iter, alpha_data, res + i + (iter * ResPacketSize)); \
+  }
+
+/** \internal main macro for gemv_complex_col - initialize accumulators, multiply and add inputs, and store results */
+#define GEMV_PROCESS_COL_COMPLEX_ONE(N) \
+  GEMV_UNROLL(GEMV_INIT_COMPLEX, N) \
+  Index j = j2; \
+  do { \
+    const RhsScalar& b1 = rhs2(j, 0); \
+    RhsScalar* b = const_cast<RhsScalar *>(&b1); \
+    GEMV_UNROLL(GEMV_PREFETCH, N) \
+    GEMV_UNROLL(GEMV_WORK_COL_COMPLEX, N) \
+  } while (++j < jend); \
+  GEMV_UNROLL(GEMV_STORE_COL_COMPLEX, N) \
+  i += (ResPacketSize * N);
+
+#if defined(USE_GEMV_MMA) && (EIGEN_COMP_LLVM || defined(USE_SLOWER_GEMV_MMA))
+#define USE_GEMV_COL_COMPLEX_MMA
+#endif
+
+#ifdef USE_GEMV_COL_COMPLEX_MMA
+#define GEMV_PROCESS_COL_COMPLEX(N) \
+  GEMV_PROCESS_COL_COMPLEX_ONE_MMA(N)
+#else
+#if defined(USE_GEMV_MMA) && (__GNUC__ > 10)
+#define GEMV_PROCESS_COL_COMPLEX(N) \
+  if (sizeof(Scalar) != sizeof(LhsPacket)) { \
+    GEMV_PROCESS_COL_COMPLEX_ONE_MMA(N) \
+  } else { \
+    GEMV_PROCESS_COL_COMPLEX_ONE(N) \
+  }
+#else
+#define GEMV_PROCESS_COL_COMPLEX(N) \
+  GEMV_PROCESS_COL_COMPLEX_ONE(N)
+#endif
+#endif
+
+template<typename Scalar, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, bool LhsIsReal, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, bool RhsIsReal, typename ResScalar>
+EIGEN_STRONG_INLINE void gemv_complex_col(
+    Index rows, Index cols,
+    const LhsMapper& alhs,
+    const RhsMapper& rhs,
+    ResScalar* res, Index resIncr,
+    ResScalar alpha)
+{
+    typedef gemv_traits<LhsScalar, RhsScalar> Traits;
+
+    typedef typename Traits::LhsPacket LhsPacket;
+    typedef typename Traits::RhsPacket RhsPacket;
+    typedef typename Traits::ResPacket ResPacket;
+
+    typedef typename packet_traits<Scalar>::type ScalarPacket;
+    typedef typename packet_traits<LhsScalar>::type PLhsPacket;
+    typedef typename packet_traits<ResScalar>::type PResPacket;
+    typedef gemv_traits<ResPacket, ResPacket> PTraits;
+
+    EIGEN_UNUSED_VARIABLE(resIncr);
+    eigen_internal_assert(resIncr == 1);
+
+    // The following copy tells the compiler that lhs's attributes are not modified outside this function
+    // This helps GCC to generate proper code.
+    LhsMapper lhs(alhs);
+    RhsMapper rhs2(rhs);
+
+    conj_helper<LhsScalar, RhsScalar, ConjugateLhs, ConjugateRhs> cj;
+
+    const Index lhsStride = lhs.stride();
+    // TODO: for padded aligned inputs, we could enable aligned reads
+    enum {
+        LhsAlignment = Unaligned,
+        ResPacketSize = PTraits::ResPacketSize,
+        LhsPacketSize = PTraits::LhsPacketSize,
+        RhsPacketSize = PTraits::RhsPacketSize,
+    };
+#ifdef EIGEN_POWER_USE_GEMV_PREFETCH
+    const Index prefetch_dist = 64 * LhsPacketSize;
+#endif
+
+#ifndef GCC_ONE_VECTORPAIR_BUG
+    const Index n8 = rows - 8 * ResPacketSize + 1;
+    const Index n4 = rows - 4 * ResPacketSize + 1;
+    const Index n2 = rows - 2 * ResPacketSize + 1;
+#endif
+    const Index n1 = rows - 1 * ResPacketSize + 1;
+
+    // TODO: improve the following heuristic:
+    const Index block_cols = cols < 128 ? cols : (lhsStride * sizeof(LhsScalar) < 16000 ? 16 : 8);
+
+    typedef alpha_store<PResPacket, ResPacket, ResScalar, Scalar> AlphaData;
+    AlphaData alpha_data(alpha);
+
+    for (Index j2 = 0; j2 < cols; j2 += block_cols)
+    {
+        Index jend = numext::mini(j2 + block_cols, cols);
+        Index i = 0;
+        PResPacket c00, c01, c02, c03, c04, c05, c06, c07;
+        ResPacket c10, c11, c12, c13, c14, c15, c16, c17;
+        PLhsPacket f0, f1, f2, f3, f4, f5, f6, f7;
+#ifdef USE_GEMV_MMA
+        __vector_quad e00, e01, e02, e03, e04, e05, e06, e07;
+        __vector_pair a0, a1, a2, a3, a4, a5, a6, a7;
+        PacketBlock<ScalarPacket, 4> result00, result01, result02, result03, result04, result05, result06, result07;
+        GEMV_UNUSED(8, e0)
+        GEMV_UNUSED(8, result0)
+        GEMV_UNUSED(8, a)
+        GEMV_UNUSED(8, f)
+#if !defined(GCC_ONE_VECTORPAIR_BUG) && defined(USE_GEMV_COL_COMPLEX_MMA)
+        if (GEMV_IS_COMPLEX_COMPLEX || !GEMV_IS_COMPLEX_FLOAT)
+#endif
+#endif
+#ifndef GCC_ONE_VECTORPAIR_BUG
+        {
+            while (i < n8)
+            {
+                GEMV_PROCESS_COL_COMPLEX(8)
+            }
+        }
+        while (i < n4)
+        {
+            GEMV_PROCESS_COL_COMPLEX(4)
+        }
+        if (i < n2)
+        {
+            GEMV_PROCESS_COL_COMPLEX(2)
+        }
+        if (i < n1)
+#else
+        while (i < n1)
+#endif
+        {
+            GEMV_PROCESS_COL_COMPLEX_ONE(1)
+        }
+        for (;i < rows;++i)
+        {
+            ResScalar d0(0);
+            Index j = j2;
+            do {
+                d0 += cj.pmul(lhs(i, j), rhs2(j, 0));
+            } while (++j < jend);
+            res[i] += alpha * d0;
+        }
+    }
+}
+
+template <typename Scalar, int N> struct ScalarBlock {
+    Scalar scalar[N];
+};
+
+#ifdef USE_GEMV_MMA
+static Packet16uc p16uc_ELEMENT_3 = { 0x0c,0x0d,0x0e,0x0f, 0x1c,0x1d,0x1e,0x1f, 0x0c,0x0d,0x0e,0x0f, 0x1c,0x1d,0x1e,0x1f };
+
+/** \internal predux (add elements of a vector) from a MMA accumulator - real results */
+template<typename ResScalar, typename ResPacket>
+EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_real(__vector_quad* acc0, __vector_quad* acc1)
+{
+    PacketBlock<ResPacket, 4> result0, result1;
+    __builtin_mma_disassemble_acc(&result0.packet, acc0);
+    __builtin_mma_disassemble_acc(&result1.packet, acc1);
+    result0.packet[0] = vec_mergeh(result0.packet[0], result1.packet[0]);
+    result0.packet[1] = vec_mergeo(result0.packet[1], result1.packet[1]);
+    result0.packet[2] = vec_mergel(result0.packet[2], result1.packet[2]);
+    result0.packet[3] = vec_perm(result0.packet[3], result1.packet[3], p16uc_ELEMENT_3);
+    result0.packet[0] = vec_add(vec_add(result0.packet[0], result0.packet[2]), vec_add(result0.packet[1], result0.packet[3]));
+    return *reinterpret_cast<ScalarBlock<ResScalar, 2> *>(&result0.packet[0]);
+}
+
+template<>
+EIGEN_ALWAYS_INLINE ScalarBlock<double, 2> predux_real<double, Packet2d>(__vector_quad* acc0, __vector_quad* acc1)
+{
+    PacketBlock<Packet2d, 4> result0, result1;
+    __builtin_mma_disassemble_acc(&result0.packet, acc0);
+    __builtin_mma_disassemble_acc(&result1.packet, acc1);
+    result0.packet[0] = vec_add(vec_mergeh(result0.packet[0], result1.packet[0]), vec_mergel(result0.packet[1], result1.packet[1]));
+    return *reinterpret_cast<ScalarBlock<double, 2> *>(&result0.packet[0]);
+}
+
+/** \internal add complex results together */
+template<typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE ScalarBlock<std::complex<float>, 2> addComplexResults(PacketBlock<Packet4f, 4>& result0, PacketBlock<Packet4f, 4>& result1)
+{
+    ScalarBlock<std::complex<float>, 2> cc0;
+    result0.packet[0] = reinterpret_cast<Packet4f>(vec_mergeh(reinterpret_cast<Packet2d>(result0.packet[0]), reinterpret_cast<Packet2d>(result1.packet[0])));
+    result0.packet[2] = reinterpret_cast<Packet4f>(vec_mergel(reinterpret_cast<Packet2d>(result0.packet[2]), reinterpret_cast<Packet2d>(result1.packet[2])));
+    result0.packet[0] = vec_add(result0.packet[0], result0.packet[2]);
+    if (GEMV_IS_COMPLEX_COMPLEX) {
+        result0.packet[1] = reinterpret_cast<Packet4f>(vec_mergeh(reinterpret_cast<Packet2d>(result0.packet[1]), reinterpret_cast<Packet2d>(result1.packet[1])));
+        result0.packet[3] = reinterpret_cast<Packet4f>(vec_mergel(reinterpret_cast<Packet2d>(result0.packet[3]), reinterpret_cast<Packet2d>(result1.packet[3])));
+        result0.packet[1] = vec_add(result0.packet[1], result0.packet[3]);
+        if (ConjugateLhs) {
+            result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
+            result0.packet[1] = pcplxflip2(convertComplex(result0.packet[1])).v;
+        } else if (ConjugateRhs) {
+            result0.packet[1] = pcplxconjflip(convertComplex(result0.packet[1])).v;
+        } else {
+            result0.packet[1] = pcplxflipconj(convertComplex(result0.packet[1])).v;
+        }
+        result0.packet[0] = vec_add(result0.packet[0], result0.packet[1]);
+    } else {
+        if (ConjugateLhs && (sizeof(LhsPacket) == sizeof(std::complex<float>))) {
+            result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
+        }
+    }
+    cc0.scalar[0].real(result0.packet[0][0]);
+    cc0.scalar[0].imag(result0.packet[0][1]);
+    cc0.scalar[1].real(result0.packet[0][2]);
+    cc0.scalar[1].imag(result0.packet[0][3]);
+    return cc0;
+}
+
+template<typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE ScalarBlock<std::complex<double>, 2> addComplexResults(PacketBlock<Packet2d, 4>&, PacketBlock<Packet2d, 4>&)
+{
+    ScalarBlock<std::complex<double>, 2> cc0;
+    EIGEN_UNUSED_VARIABLE(cc0);
+    return cc0;  // Just for compilation
+}
+
+/** \internal predux (add elements of a vector) from a MMA accumulator - complex results */
+template<typename ResScalar, typename ResPacket, typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_complex(__vector_quad* acc0, __vector_quad* acc1)
+{
+    PacketBlock<ResPacket, 4> result0, result1;
+    __builtin_mma_disassemble_acc(&result0.packet, acc0);
+    __builtin_mma_disassemble_acc(&result1.packet, acc1);
+    return addComplexResults<LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(result0, result1);
+}
+
+template<typename ResScalar, typename ResPacket>
+EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_real(__vector_quad* acc0)
+{
+    PacketBlock<ResPacket, 4> result0;
+    __builtin_mma_disassemble_acc(&result0.packet, acc0);
+    result0.packet[0] = vec_add(vec_mergeh(result0.packet[0], result0.packet[2]), vec_mergel(result0.packet[1], result0.packet[3]));
+    return *reinterpret_cast<ScalarBlock<ResScalar, 2> *>(&result0.packet[0]);
+}
+
+template<typename ResScalar, typename ResPacket, typename LhsPacket, typename RhsPacket, bool ConjugateLhs, bool ConjugateRhs>
+EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_complex(__vector_quad* acc0)
+{
+    ScalarBlock<ResScalar, 2> cc0;
+    PacketBlock<ResPacket, 4> result0;
+    __builtin_mma_disassemble_acc(&result0.packet, acc0);
+    if (GEMV_IS_COMPLEX_COMPLEX) {
+        if (ConjugateLhs) {
+            result0.packet[1] = pconjinv(convertComplex(result0.packet[1])).v;
+            result0.packet[3] = pconjinv(convertComplex(result0.packet[3])).v;
+        } else if (ConjugateRhs) {
+            result0.packet[0] = pconj2(convertComplex(result0.packet[0])).v;
+            result0.packet[2] = pconj2(convertComplex(result0.packet[2])).v;
+        } else {
+            result0.packet[1] = pconj2(convertComplex(result0.packet[1])).v;
+            result0.packet[3] = pconj2(convertComplex(result0.packet[3])).v;
+        }
+        result0.packet[0] = vec_add(result0.packet[0], __builtin_vsx_xxpermdi(result0.packet[1], result0.packet[1], 2));
+        result0.packet[2] = vec_add(result0.packet[2], __builtin_vsx_xxpermdi(result0.packet[3], result0.packet[3], 2));
+    } else {
+        result0.packet[0] = __builtin_vsx_xxpermdi(result0.packet[0], result0.packet[1], 1);
+        result0.packet[2] = __builtin_vsx_xxpermdi(result0.packet[2], result0.packet[3], 1);
+    }
+    cc0.scalar[0].real(result0.packet[0][0]);
+    cc0.scalar[0].imag(result0.packet[0][1]);
+    cc0.scalar[1].real(result0.packet[2][0]);
+    cc0.scalar[1].imag(result0.packet[2][1]);
+    return cc0;
+}
+#endif
+
+template<typename ResScalar, typename ResPacket>
+EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_real(ResPacket& a, ResPacket& b)
+{
+    ScalarBlock<ResScalar, 2> cc0;
+    cc0.scalar[0] = predux(a);
+    cc0.scalar[1] = predux(b);
+    return cc0;
+}
+
+template<typename ResScalar, typename ResPacket>
+EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_complex(ResPacket& a, ResPacket& b)
+{
+    return predux_real<ResScalar, ResPacket>(a, b);
+}
+
+#define GEMV_UNROLL_ROW(func, N) \
+  func(0, N) func(1, N) func(2, N) func(3, N) func(4, N) func(5, N) func(6, N) func(7, N)
+
+#define GEMV_UNROLL_ROW_HALF(func, N) \
+  func(0, 0, 1, N) func(1, 2, 3, N) func(2, 4, 5, N) func(3, 6, 7, N)
+
+#define GEMV_LOADPACKET_ROW(iter) \
+  lhs.template load<LhsPacket, Unaligned>(i + (iter), j)
+
+#ifdef USE_GEMV_MMA
+#define GEMV_UNROLL3_ROW(func, N, which) \
+  func(0, N, which) func(1, N, which) func(2, N, which) func(3, N, which) \
+  func(4, N, which) func(5, N, which) func(6, N, which) func(7, N, which)
+
+#define GEMV_UNUSED_ROW(N, which) \
+  GEMV_UNROLL3_ROW(GEMV_UNUSED_VAR, N, which)
+
+#define GEMV_INIT_ROW(iter, N) \
+  if (GEMV_GETN(N) > iter) { \
+    __builtin_mma_xxsetaccz(&c##iter); \
+  }
+
+#define GEMV_LOADPAIR_ROW(iter1, iter2) \
+  GEMV_BUILDPAIR_MMA(b##iter1, GEMV_LOADPACKET_ROW(iter2), GEMV_LOADPACKET_ROW((iter2) + 1));
+
+#define GEMV_WORK_ROW(iter, N) \
+  if (GEMV_GETN(N) > iter) { \
+    if (GEMV_IS_FLOAT) { \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&c##iter, a0, GEMV_LOADPACKET_ROW(iter)); \
+    } else { \
+      __vector_pair b##iter; \
+      GEMV_LOADPAIR_ROW(iter, iter << 1) \
+      pger_vecMMA_acc<LhsPacket, RhsPacket, true>(&c##iter, b##iter, a0); \
+    } \
+  }
+
+#define GEMV_PREDUX2(iter1, iter2, iter3, N) \
+  if (N > iter1) { \
+    if (GEMV_IS_FLOAT) { \
+      cc##iter1 = predux_real<ResScalar, ResPacket>(&c##iter2, &c##iter3); \
+    } else { \
+      cc##iter1 = predux_real<ResScalar, ResPacket>(&c##iter1); \
+    } \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(cc##iter1); \
+  }
+#else
+#define GEMV_INIT_ROW(iter, N) \
+  if (N > iter) { \
+    c##iter = pset1<ResPacket>(ResScalar(0)); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(c##iter); \
+  }
+
+#define GEMV_WORK_ROW(iter, N) \
+  if (N > iter) { \
+    c##iter = pcj.pmadd(GEMV_LOADPACKET_ROW(iter), a0, c##iter); \
+  }
+
+#define GEMV_PREDUX2(iter1, iter2, iter3, N) \
+  if (N > iter1) { \
+    cc##iter1 = predux_real<ResScalar, ResPacket>(c##iter2, c##iter3); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(cc##iter1); \
+  }
+#endif
+
+#define GEMV_MULT(iter1, iter2, iter3, N) \
+  if (N > iter1) { \
+    cc##iter1.scalar[0] += cj.pmul(lhs(i + iter2, j), a0); \
+    cc##iter1.scalar[1] += cj.pmul(lhs(i + iter3, j), a0); \
+  }
+
+#define GEMV_STORE_ROW(iter1, iter2, iter3, N) \
+  if (N > iter1) { \
+    storeMaddData<ResScalar>(res + ((i + iter2) * resIncr), alpha, cc##iter1.scalar[0]); \
+    storeMaddData<ResScalar>(res + ((i + iter3) * resIncr), alpha, cc##iter1.scalar[1]); \
+  }
+
+/** \internal main macro for gemv_row - initialize accumulators, multiply and add inputs, predux and store results */
+#define GEMV_PROCESS_ROW(N) \
+  for (; i < n##N; i += N) { \
+    GEMV_UNROLL_ROW(GEMV_INIT_ROW, N) \
+    Index j = 0; \
+    for (; j + LhsPacketSize <= cols; j += LhsPacketSize) { \
+      RhsPacket a0 = rhs2.template load<RhsPacket, Unaligned>(j); \
+      GEMV_UNROLL_ROW(GEMV_WORK_ROW, N) \
+    } \
+    GEMV_UNROLL_ROW_HALF(GEMV_PREDUX2, (N >> 1)) \
+    for (; j < cols; ++j) { \
+      RhsScalar a0 = rhs2(j); \
+      GEMV_UNROLL_ROW_HALF(GEMV_MULT, (N >> 1)) \
+    } \
+    GEMV_UNROLL_ROW_HALF(GEMV_STORE_ROW, (N >> 1)) \
+  }
+
+template<typename LhsScalar, typename LhsMapper, typename RhsScalar, typename RhsMapper, typename ResScalar>
+EIGEN_STRONG_INLINE void gemv_row(
+    Index rows, Index cols,
+    const LhsMapper& alhs,
+    const RhsMapper& rhs,
+    ResScalar* res, Index resIncr,
+    ResScalar alpha)
+{
+    typedef gemv_traits<LhsScalar, RhsScalar> Traits;
+
+    typedef typename Traits::LhsPacket LhsPacket;
+    typedef typename Traits::RhsPacket RhsPacket;
+    typedef typename Traits::ResPacket ResPacket;
+
+    // The following copy tells the compiler that lhs's attributes are not modified outside this function
+    // This helps GCC to generate proper code.
+    LhsMapper lhs(alhs);
+    typename RhsMapper::LinearMapper rhs2 = rhs.getLinearMapper(0, 0);
+
+    eigen_internal_assert(rhs.stride() == 1);
+    conj_helper<LhsScalar, RhsScalar, false, false> cj;
+    conj_helper<LhsPacket, RhsPacket, false, false> pcj;
+
+    // TODO: fine tune the following heuristic. The rationale is that if the matrix is very large,
+    //       processing 8 rows at once might be counter productive wrt cache.
+#ifndef GCC_ONE_VECTORPAIR_BUG
+    const Index n8 = lhs.stride() * sizeof(LhsScalar) > 32000 ? (rows - 7) : (rows - 7);
+    const Index n4 = rows - 3;
+    const Index n2 = rows - 1;
+#endif
+
+    // TODO: for padded aligned inputs, we could enable aligned reads
+    enum {
+        LhsAlignment = Unaligned,
+        ResPacketSize = Traits::ResPacketSize,
+        LhsPacketSize = Traits::LhsPacketSize,
+        RhsPacketSize = Traits::RhsPacketSize,
+    };
+
+    Index i = 0;
+#ifdef USE_GEMV_MMA
+    __vector_quad c0, c1, c2, c3, c4, c5, c6, c7;
+    GEMV_UNUSED_ROW(8, c)
+#else
+    ResPacket c0, c1, c2, c3, c4, c5, c6, c7;
+#endif
+#ifndef GCC_ONE_VECTORPAIR_BUG
+    ScalarBlock<ResScalar, 2> cc0, cc1, cc2, cc3;
+    GEMV_PROCESS_ROW(8)
+    GEMV_PROCESS_ROW(4)
+    GEMV_PROCESS_ROW(2)
+#endif
+    for (; i < rows; ++i)
+    {
+        ResPacket d0 = pset1<ResPacket>(ResScalar(0));
+        Index j = 0;
+        for (; j + LhsPacketSize <= cols; j += LhsPacketSize)
+        {
+            RhsPacket b0 = rhs2.template load<RhsPacket, Unaligned>(j);
+
+            d0 = pcj.pmadd(lhs.template load<LhsPacket, LhsAlignment>(i + 0, j), b0, d0);
+        }
+        ResScalar dd0 = predux(d0);
+        for (; j < cols; ++j)
+        {
+            dd0 += cj.pmul(lhs(i, j), rhs2(j));
+        }
+        res[i * resIncr] += alpha * dd0;
+    }
+}
+
+#define EIGEN_POWER_GEMV_REAL_SPECIALIZE_COL(Scalar) \
+template<typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
+struct general_matrix_vector_product<Index, Scalar, LhsMapper, ColMajor, ConjugateLhs, Scalar, RhsMapper, ConjugateRhs, Version> \
+{ \
+    typedef typename ScalarBinaryOpTraits<Scalar, Scalar>::ReturnType ResScalar; \
+\
+    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run( \
+        Index rows, Index cols, \
+        const LhsMapper& lhs, \
+        const RhsMapper& rhs, \
+        ResScalar* res, Index resIncr, \
+        ResScalar alpha) { \
+        gemv_col<Scalar, LhsMapper, Scalar, RhsMapper, ResScalar>(rows, cols, lhs, rhs, res, resIncr, alpha); \
+    } \
+};
+
+#define EIGEN_POWER_GEMV_REAL_SPECIALIZE_ROW(Scalar) \
+template<typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
+struct general_matrix_vector_product<Index, Scalar, LhsMapper, RowMajor, ConjugateLhs, Scalar, RhsMapper, ConjugateRhs, Version> \
+{ \
+    typedef typename ScalarBinaryOpTraits<Scalar, Scalar>::ReturnType ResScalar; \
+\
+    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run( \
+        Index rows, Index cols, \
+        const LhsMapper& lhs, \
+        const RhsMapper& rhs, \
+        ResScalar* res, Index resIncr, \
+        ResScalar alpha) { \
+        gemv_row<Scalar, LhsMapper, Scalar, RhsMapper, ResScalar>(rows, cols, lhs, rhs, res, resIncr, alpha); \
+    } \
+};
+
+EIGEN_POWER_GEMV_REAL_SPECIALIZE_COL(float)
+EIGEN_POWER_GEMV_REAL_SPECIALIZE_COL(double)
+EIGEN_POWER_GEMV_REAL_SPECIALIZE_ROW(float)
+EIGEN_POWER_GEMV_REAL_SPECIALIZE_ROW(double)
+
+#ifdef USE_GEMV_MMA
+#define gemv_bf16_col  gemvMMA_bfloat16_col
+#define gemv_bf16_row  gemvMMA_bfloat16_row
+#else
+#define gemv_bf16_col  gemv_bfloat16_col
+#define gemv_bf16_row  gemv_bfloat16_row
+#endif
+
+#define EIGEN_POWER_GEMV_REAL_SPECIALIZE_COL_BFLOAT16() \
+template<typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
+struct general_matrix_vector_product<Index, bfloat16, LhsMapper, ColMajor, ConjugateLhs, bfloat16, RhsMapper, ConjugateRhs, Version> \
+{ \
+    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run( \
+        Index rows, Index cols, \
+        const LhsMapper& lhs, \
+        const RhsMapper& rhs, \
+        bfloat16* res, Index resIncr, \
+        bfloat16 alpha) { \
+        gemv_bf16_col<LhsMapper, RhsMapper>(rows, cols, lhs, rhs, res, resIncr, alpha); \
+    } \
+};
+
+#define EIGEN_POWER_GEMV_REAL_SPECIALIZE_ROW_BFLOAT16() \
+template<typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
+struct general_matrix_vector_product<Index, bfloat16, LhsMapper, RowMajor, ConjugateLhs, bfloat16, RhsMapper, ConjugateRhs, Version> \
+{ \
+    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run( \
+        Index rows, Index cols, \
+        const LhsMapper& lhs, \
+        const RhsMapper& rhs, \
+        bfloat16* res, Index resIncr, \
+        bfloat16 alpha) { \
+        gemv_bf16_row<LhsMapper, RhsMapper>(rows, cols, lhs, rhs, res, resIncr, alpha); \
+    } \
+};
+
+EIGEN_POWER_GEMV_REAL_SPECIALIZE_COL_BFLOAT16()
+EIGEN_POWER_GEMV_REAL_SPECIALIZE_ROW_BFLOAT16()
+
+template<typename ResScalar, typename PResPacket, typename ResPacket, typename LhsPacket, typename RhsPacket>
+EIGEN_ALWAYS_INLINE ScalarBlock<ResScalar, 2> predux_complex(PResPacket& a0, PResPacket& b0, ResPacket& a1, ResPacket& b1)
+{
+    if (GEMV_IS_COMPLEX_COMPLEX) {
+        a0 = padd(a0, a1);
+        b0 = padd(b0, b1);
+    }
+    return predux_complex<ResScalar, PResPacket>(a0, b0);
+}
+
+#define GEMV_LOADPACKET_ROW_COMPLEX(iter) \
+  loadLhsPacket<Scalar, LhsScalar, LhsMapper, PLhsPacket>(lhs, i + (iter), j)
+
+#define GEMV_LOADPACKET_ROW_COMPLEX_DATA(iter) \
+  convertReal(GEMV_LOADPACKET_ROW_COMPLEX(iter))
+
+#define GEMV_PROCESS_ROW_COMPLEX_SINGLE_WORK(which, N) \
+  j = 0; \
+  for (; j + LhsPacketSize <= cols; j += LhsPacketSize) { \
+    const RhsScalar& b1 = rhs2(j); \
+    RhsScalar* b = const_cast<RhsScalar *>(&b1); \
+    GEMV_UNROLL_ROW(which, N) \
+  }
+
+#define GEMV_PROCESS_END_ROW_COMPLEX(N) \
+  for (; j < cols; ++j) { \
+    RhsScalar b0 = rhs2(j); \
+    GEMV_UNROLL_ROW_HALF(GEMV_MULT_COMPLEX, (N >> 1)) \
+  } \
+  GEMV_UNROLL_ROW_HALF(GEMV_STORE_ROW_COMPLEX, (N >> 1))
+
+#ifdef USE_GEMV_MMA
+#define GEMV_INIT_ROW_COMPLEX_MMA(iter, N) \
+  if (GEMV_GETN_COMPLEX(N) > iter) { \
+    __builtin_mma_xxsetaccz(&e0##iter); \
+  }
+
+#define GEMV_LOADPAIR_ROW_COMPLEX_MMA(iter1, iter2) \
+  GEMV_BUILDPAIR_MMA(a##iter1, GEMV_LOADPACKET_ROW_COMPLEX_DATA(iter2), GEMV_LOADPACKET_ROW_COMPLEX_DATA((iter2) + 1));
+
+#define GEMV_WORK_ROW_COMPLEX_MMA(iter, N) \
+  if (GEMV_GETN_COMPLEX(N) > iter) { \
+    if (GEMV_IS_COMPLEX_FLOAT) { \
+      PLhsPacket a##iter = GEMV_LOADPACKET_ROW_COMPLEX(iter); \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, PLhsPacket, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, RowMajor>(a##iter, b, &e0##iter); \
+    } else { \
+      __vector_pair a##iter; \
+      GEMV_LOADPAIR_ROW_COMPLEX_MMA(iter, iter << 1) \
+      gemv_mult_complex_MMA<ScalarPacket, LhsScalar, PLhsPacket, __vector_pair, RhsScalar, RhsPacket, ResPacket, ConjugateLhs, ConjugateRhs, RowMajor>(a##iter, b, &e0##iter); \
+    } \
+  }
+
+#define GEMV_PREDUX4_COMPLEX_MMA(iter1, iter2, iter3, N) \
+  if (N > iter1) { \
+    if (GEMV_IS_COMPLEX_FLOAT) { \
+      cc##iter1 = predux_complex<ResScalar, ScalarPacket, LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(&e0##iter2, &e0##iter3); \
+    } else { \
+      cc##iter1 = predux_complex<ResScalar, ScalarPacket, LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs>(&e0##iter1); \
+    } \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(cc##iter1); \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_SINGLE_MMA(N) \
+  GEMV_UNROLL_ROW(GEMV_INIT_ROW_COMPLEX_MMA, N) \
+  GEMV_PROCESS_ROW_COMPLEX_SINGLE_WORK(GEMV_WORK_ROW_COMPLEX_MMA, N)
+
+#define GEMV_PROCESS_ROW_COMPLEX_ONE_MMA(N) \
+  for (; i < n##N; i += N) { \
+    GEMV_PROCESS_ROW_COMPLEX_SINGLE_MMA(N) \
+    GEMV_UNROLL_ROW_HALF(GEMV_PREDUX4_COMPLEX_MMA, (N >> 1)) \
+    GEMV_PROCESS_END_ROW_COMPLEX(N); \
+  }
+#endif
+
+#define GEMV_WORK_ROW_COMPLEX(iter, N) \
+  if (N > iter) { \
+    PLhsPacket a##iter = GEMV_LOADPACKET_ROW_COMPLEX(iter); \
+    gemv_mult_complex<ScalarPacket, PLhsPacket, RhsScalar, RhsPacket, PResPacket, ResPacket, ConjugateLhs, ConjugateRhs, RowMajor>(a##iter, b, c0##iter, c1##iter); \
+  }
+
+#define GEMV_PREDUX4_COMPLEX(iter1, iter2, iter3, N) \
+  if (N > iter1) { \
+    cc##iter1 = predux_complex<ResScalar, PResPacket, ResPacket, LhsPacket, RhsPacket>(c0##iter2, c0##iter3, c1##iter2, c1##iter3); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(cc##iter1); \
+  }
+
+#define GEMV_MULT_COMPLEX(iter1, iter2, iter3, N) \
+  if (N > iter1) { \
+    cc##iter1.scalar[0] += cj.pmul(lhs(i + iter2, j), b0); \
+    cc##iter1.scalar[1] += cj.pmul(lhs(i + iter3, j), b0); \
+  }
+
+#define GEMV_STORE_ROW_COMPLEX(iter1, iter2, iter3, N) \
+  if (N > iter1) { \
+    storeMaddData<ResScalar>(res + ((i + iter2) * resIncr), alpha, cc##iter1.scalar[0]); \
+    storeMaddData<ResScalar>(res + ((i + iter3) * resIncr), alpha, cc##iter1.scalar[1]); \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_SINGLE_NEW(N) \
+  GEMV_UNROLL_ROW(GEMV_INIT_COMPLEX, N) \
+  GEMV_PROCESS_ROW_COMPLEX_SINGLE_WORK(GEMV_WORK_ROW_COMPLEX, N)
+
+/** \internal main macro for gemv_complex_row - initialize accumulators, multiply and add inputs, predux and store results */
+#define GEMV_PROCESS_ROW_COMPLEX_ONE_NEW(N) \
+  for (; i < n##N; i += N) { \
+    GEMV_PROCESS_ROW_COMPLEX_SINGLE_NEW(N) \
+    GEMV_UNROLL_ROW_HALF(GEMV_PREDUX4_COMPLEX, (N >> 1)) \
+    GEMV_PROCESS_END_ROW_COMPLEX(N); \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_PREDUX_NEW(iter) \
+  if (GEMV_IS_COMPLEX_COMPLEX) { \
+    c0##iter = padd(c0##iter, c1##iter); \
+  } \
+  dd0 = predux(c0##iter);
+
+#if EIGEN_COMP_LLVM
+#define GEMV_PROCESS_ROW_COMPLEX_SINGLE(N) \
+  GEMV_PROCESS_ROW_COMPLEX_SINGLE_NEW(N)
+
+#define GEMV_PROCESS_ROW_COMPLEX_ONE(N) \
+  GEMV_PROCESS_ROW_COMPLEX_ONE_NEW(N)
+
+#define GEMV_PROCESS_ROW_COMPLEX_PREDUX(iter) \
+  GEMV_PROCESS_ROW_COMPLEX_PREDUX_NEW(iter)
+#else
+// gcc seems to be reading and writing registers unnecessarily to memory.
+// Use the old way for complex double until it is fixed.
+
+#define GEMV_LOADPACKET_ROW_COMPLEX_OLD(iter) \
+  lhs.template load<LhsPacket, LhsAlignment>(i + (iter), j)
+
+#define GEMV_INIT_COMPLEX_OLD(iter, N) \
+  EIGEN_UNUSED_VARIABLE(c0##iter); \
+  if (N > iter) { \
+    c1##iter = pset_zero<ResPacket>(); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(c1##iter); \
+  }
+
+#define GEMV_WORK_ROW_COMPLEX_OLD(iter, N) \
+  if (N > iter) { \
+    LhsPacket a##iter = GEMV_LOADPACKET_ROW_COMPLEX_OLD(iter); \
+    c1##iter = pcj.pmadd(a##iter, b0, c1##iter); \
+  }
+
+#define GEMV_PREDUX4_COMPLEX_OLD(iter1, iter2, iter3, N) \
+  if (N > iter1) { \
+    cc##iter1.scalar[0] = predux(c1##iter2); \
+    cc##iter1.scalar[1] = predux(c1##iter3); \
+  } else { \
+    EIGEN_UNUSED_VARIABLE(cc##iter1); \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_SINGLE_OLD(N) \
+  GEMV_UNROLL_ROW(GEMV_INIT_COMPLEX_OLD, N) \
+  j = 0; \
+  for (; j + LhsPacketSize <= cols; j += LhsPacketSize) { \
+    RhsPacket b0 = rhs2.template load<RhsPacket, Unaligned>(j); \
+    GEMV_UNROLL_ROW(GEMV_WORK_ROW_COMPLEX_OLD, N) \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_ONE_OLD(N) \
+  for (; i < n##N; i += N) { \
+    GEMV_PROCESS_ROW_COMPLEX_SINGLE_OLD(N) \
+    GEMV_UNROLL_ROW_HALF(GEMV_PREDUX4_COMPLEX_OLD, (N >> 1)) \
+    GEMV_PROCESS_END_ROW_COMPLEX(N) \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_PREDUX_OLD(iter) \
+  dd0 = predux(c1##iter);
+
+#if (__GNUC__ > 10)
+#define GEMV_PROCESS_ROW_COMPLEX_IS_NEW  1
+#else
+#define GEMV_PROCESS_ROW_COMPLEX_IS_NEW  \
+  (sizeof(Scalar) == sizeof(float)) || GEMV_IS_COMPLEX_COMPLEX
+#endif
+
+#define GEMV_PROCESS_ROW_COMPLEX_SINGLE(N) \
+  if (GEMV_PROCESS_ROW_COMPLEX_IS_NEW) { \
+    GEMV_PROCESS_ROW_COMPLEX_SINGLE_NEW(N) \
+  } else { \
+    GEMV_PROCESS_ROW_COMPLEX_SINGLE_OLD(N) \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_ONE(N) \
+  if (GEMV_PROCESS_ROW_COMPLEX_IS_NEW) { \
+    GEMV_PROCESS_ROW_COMPLEX_ONE_NEW(N) \
+  } else { \
+    GEMV_PROCESS_ROW_COMPLEX_ONE_OLD(N) \
+  }
+
+#define GEMV_PROCESS_ROW_COMPLEX_PREDUX(iter) \
+  if (GEMV_PROCESS_ROW_COMPLEX_IS_NEW) { \
+    GEMV_PROCESS_ROW_COMPLEX_PREDUX_NEW(iter) \
+  } else { \
+    GEMV_PROCESS_ROW_COMPLEX_PREDUX_OLD(iter) \
+  }
+#endif
+
+#ifdef USE_GEMV_MMA
+#define GEMV_PROCESS_ROW_COMPLEX(N) \
+  GEMV_PROCESS_ROW_COMPLEX_ONE_MMA(N)
+#else
+#define GEMV_PROCESS_ROW_COMPLEX(N) \
+  GEMV_PROCESS_ROW_COMPLEX_ONE(N)
+#endif
+
+template<typename Scalar, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, bool LhsIsReal, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, bool RhsIsReal, typename ResScalar>
+EIGEN_STRONG_INLINE void gemv_complex_row(
+    Index rows, Index cols,
+    const LhsMapper& alhs,
+    const RhsMapper& rhs,
+    ResScalar* res, Index resIncr,
+    ResScalar alpha)
+{
+    typedef gemv_traits<LhsScalar, RhsScalar> Traits;
+
+    typedef typename Traits::LhsPacket LhsPacket;
+    typedef typename Traits::RhsPacket RhsPacket;
+    typedef typename Traits::ResPacket ResPacket;
+
+    typedef typename packet_traits<Scalar>::type ScalarPacket;
+    typedef typename packet_traits<LhsScalar>::type PLhsPacket;
+    typedef typename packet_traits<ResScalar>::type PResPacket;
+    typedef gemv_traits<ResPacket, ResPacket> PTraits;
+
+    // The following copy tells the compiler that lhs's attributes are not modified outside this function
+    // This helps GCC to generate proper code.
+    LhsMapper lhs(alhs);
+    typename RhsMapper::LinearMapper rhs2 = rhs.getLinearMapper(0, 0);
+
+    eigen_internal_assert(rhs.stride() == 1);
+    conj_helper<LhsScalar, RhsScalar, ConjugateLhs, ConjugateRhs> cj;
+#if !EIGEN_COMP_LLVM
+    conj_helper<LhsPacket, RhsPacket, ConjugateLhs, ConjugateRhs> pcj;
+#endif
+
+    // TODO: fine tune the following heuristic. The rationale is that if the matrix is very large,
+    //       processing 8 rows at once might be counter productive wrt cache.
+#ifndef GCC_ONE_VECTORPAIR_BUG
+    const Index n8 = lhs.stride() * sizeof(LhsScalar) > 32000 ? (rows - 7) : (rows - 7);
+    const Index n4 = rows - 3;
+    const Index n2 = rows - 1;
+#endif
+
+    // TODO: for padded aligned inputs, we could enable aligned reads
+    enum {
+        LhsAlignment = Unaligned,
+        ResPacketSize = PTraits::ResPacketSize,
+        LhsPacketSize = PTraits::LhsPacketSize,
+        RhsPacketSize = PTraits::RhsPacketSize,
+    };
+
+    Index i = 0, j;
+    PResPacket c00, c01, c02, c03, c04, c05, c06, c07;
+    ResPacket c10, c11, c12, c13, c14, c15, c16, c17;
+#ifdef USE_GEMV_MMA
+    __vector_quad e00, e01, e02, e03, e04, e05, e06, e07;
+    GEMV_UNUSED_ROW(8, e0)
+    GEMV_UNUSED_EXTRA(1, c0)
+    GEMV_UNUSED_EXTRA(1, c1)
+#endif
+    ResScalar dd0;
+#ifndef GCC_ONE_VECTORPAIR_BUG
+    ScalarBlock<ResScalar, 2> cc0, cc1, cc2, cc3;
+#ifdef USE_GEMV_MMA
+    if (!GEMV_IS_COMPLEX_COMPLEX)
+#endif
+    {
+        GEMV_PROCESS_ROW_COMPLEX(8)
+    }
+    GEMV_PROCESS_ROW_COMPLEX(4)
+    GEMV_PROCESS_ROW_COMPLEX(2)
+#endif
+    for (; i < rows; ++i)
+    {
+        GEMV_PROCESS_ROW_COMPLEX_SINGLE(1)
+        GEMV_PROCESS_ROW_COMPLEX_PREDUX(0)
+        for (; j < cols; ++j)
+        {
+            dd0 += cj.pmul(lhs(i, j), rhs2(j));
+        }
+        res[i * resIncr] += alpha * dd0;
+    }
+}
+
+#define EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(Scalar, LhsScalar, RhsScalar) \
+template<typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
+struct general_matrix_vector_product<Index, LhsScalar, LhsMapper, ColMajor, ConjugateLhs, RhsScalar, RhsMapper, ConjugateRhs, Version> \
+{ \
+    typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar; \
+\
+    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run( \
+        Index rows, Index cols, \
+        const LhsMapper& lhs, \
+        const RhsMapper& rhs, \
+        ResScalar* res, Index resIncr, \
+        ResScalar alpha) { \
+        gemv_complex_col<Scalar, LhsScalar, LhsMapper, ConjugateLhs, sizeof(Scalar) == sizeof(LhsScalar), RhsScalar, RhsMapper, ConjugateRhs, sizeof(Scalar) == sizeof(RhsScalar), ResScalar>(rows, cols, lhs, rhs, res, resIncr, alpha); \
+    } \
+};
+
+#define EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(Scalar, LhsScalar, RhsScalar) \
+template<typename Index, typename LhsMapper, bool ConjugateLhs, typename RhsMapper, bool ConjugateRhs, int Version> \
+struct general_matrix_vector_product<Index, LhsScalar, LhsMapper, RowMajor, ConjugateLhs, RhsScalar, RhsMapper, ConjugateRhs, Version> \
+{ \
+    typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar; \
+\
+    EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run( \
+        Index rows, Index cols, \
+        const LhsMapper& lhs, \
+        const RhsMapper& rhs, \
+        ResScalar* res, Index resIncr, \
+        ResScalar alpha) { \
+        gemv_complex_row<Scalar, LhsScalar, LhsMapper, ConjugateLhs, sizeof(Scalar) == sizeof(LhsScalar), RhsScalar, RhsMapper, ConjugateRhs, sizeof(Scalar) == sizeof(RhsScalar), ResScalar>(rows, cols, lhs, rhs, res, resIncr, alpha); \
+    } \
+};
+
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(float,  float,                std::complex<float>)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(float,  std::complex<float>,  float)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(float,  std::complex<float>,  std::complex<float>)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(double, double,               std::complex<double>)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(double, std::complex<double>, double)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_COL(double, std::complex<double>, std::complex<double>)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(float,  float,                std::complex<float>)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(float,  std::complex<float>,  float)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(float,  std::complex<float>,  std::complex<float>)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(double, double,               std::complex<double>)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(double, std::complex<double>, double)
+EIGEN_POWER_GEMV_COMPLEX_SPECIALIZE_ROW(double, std::complex<double>, std::complex<double>)
+
+#endif // EIGEN_MATRIX_VECTOR_PRODUCT_ALTIVEC_H
+
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/PacketMath.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/PacketMath.h
new file mode 100644
index 0000000000000000000000000000000000000000..f12dc19ac28e62b5318c8ed9f9422402587d1db5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/PacketMath.h
@@ -0,0 +1,3218 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2016 Konstantinos Margaritis <markos@freevec.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_ALTIVEC_H
+#define EIGEN_PACKET_MATH_ALTIVEC_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 4
+#endif
+
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+
+// NOTE Altivec has 32 registers, but Eigen only accepts a value of 8 or 16
+#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS  32
+#endif
+
+typedef __vector float                   Packet4f;
+typedef __vector int                     Packet4i;
+typedef __vector unsigned int            Packet4ui;
+typedef __vector __bool int              Packet4bi;
+typedef __vector short int               Packet8s;
+typedef __vector unsigned short int      Packet8us;
+typedef __vector __bool short            Packet8bi;
+typedef __vector signed char             Packet16c;
+typedef __vector unsigned char           Packet16uc;
+typedef eigen_packet_wrapper<__vector unsigned short int,0> Packet8bf;
+
+// We don't want to write the same code all the time, but we need to reuse the constants
+// and it doesn't really work to declare them global, so we define macros instead
+#define EIGEN_DECLARE_CONST_FAST_Packet4f(NAME,X) \
+  Packet4f p4f_##NAME = {X, X, X, X}
+
+#define EIGEN_DECLARE_CONST_FAST_Packet4i(NAME,X) \
+  Packet4i p4i_##NAME = vec_splat_s32(X)
+
+#define EIGEN_DECLARE_CONST_FAST_Packet4ui(NAME,X) \
+  Packet4ui p4ui_##NAME = {X, X, X, X}
+
+#define EIGEN_DECLARE_CONST_FAST_Packet8us(NAME,X) \
+  Packet8us p8us_##NAME = {X, X, X, X, X, X, X, X}
+
+#define EIGEN_DECLARE_CONST_FAST_Packet16uc(NAME,X) \
+  Packet16uc p16uc_##NAME = {X, X, X, X, X, X, X, X, X, X, X, X, X, X, X, X}
+
+#define EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
+  Packet4f p4f_##NAME = pset1<Packet4f>(X)
+
+#define EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
+  Packet4i p4i_##NAME = pset1<Packet4i>(X)
+
+#define EIGEN_DECLARE_CONST_Packet2d(NAME,X) \
+  Packet2d p2d_##NAME = pset1<Packet2d>(X)
+
+#define EIGEN_DECLARE_CONST_Packet2l(NAME,X) \
+  Packet2l p2l_##NAME = pset1<Packet2l>(X)
+
+#define EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
+  const Packet4f p4f_##NAME = reinterpret_cast<Packet4f>(pset1<Packet4i>(X))
+
+#define DST_CHAN 1
+#define DST_CTRL(size, count, stride) (((size) << 24) | ((count) << 16) | (stride))
+#define __UNPACK_TYPE__(PACKETNAME) typename unpacket_traits<PACKETNAME>::type 
+
+// These constants are endian-agnostic
+static EIGEN_DECLARE_CONST_FAST_Packet4f(ZERO, 0); //{ 0.0, 0.0, 0.0, 0.0}
+static EIGEN_DECLARE_CONST_FAST_Packet4i(ZERO, 0); //{ 0, 0, 0, 0,}
+static EIGEN_DECLARE_CONST_FAST_Packet4i(ONE,1); //{ 1, 1, 1, 1}
+static EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS16,-16); //{ -16, -16, -16, -16}
+static EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS1,-1); //{ -1, -1, -1, -1}
+static EIGEN_DECLARE_CONST_FAST_Packet4ui(SIGN, 0x80000000u);
+static EIGEN_DECLARE_CONST_FAST_Packet4ui(PREV0DOT5, 0x3EFFFFFFu);
+static EIGEN_DECLARE_CONST_FAST_Packet8us(ONE,1); //{ 1, 1, 1, 1, 1, 1, 1, 1}
+static Packet4f p4f_MZERO = (Packet4f) vec_sl((Packet4ui)p4i_MINUS1, (Packet4ui)p4i_MINUS1); //{ 0x80000000, 0x80000000, 0x80000000, 0x80000000}
+#ifndef __VSX__
+static Packet4f p4f_ONE = vec_ctf(p4i_ONE, 0); //{ 1.0, 1.0, 1.0, 1.0}
+#endif
+
+static Packet4f  p4f_COUNTDOWN  = { 0.0, 1.0, 2.0, 3.0 };
+static Packet4i  p4i_COUNTDOWN  = { 0, 1, 2, 3 };
+static Packet8s  p8s_COUNTDOWN  = { 0, 1, 2, 3, 4, 5, 6, 7 };
+static Packet8us p8us_COUNTDOWN = { 0, 1, 2, 3, 4, 5, 6, 7 };
+
+static Packet16c  p16c_COUNTDOWN = { 0, 1, 2, 3, 4, 5, 6, 7,
+                                    8, 9, 10, 11, 12, 13, 14, 15};
+static Packet16uc p16uc_COUNTDOWN = { 0, 1, 2, 3, 4, 5, 6, 7, 
+                                    8, 9, 10, 11, 12, 13, 14, 15};
+
+static Packet16uc p16uc_REVERSE32 = { 12,13,14,15, 8,9,10,11, 4,5,6,7, 0,1,2,3 };
+static Packet16uc p16uc_REVERSE16 = { 14,15, 12,13, 10,11, 8,9, 6,7, 4,5, 2,3, 0,1 };
+#ifndef _ARCH_PWR9
+static Packet16uc p16uc_REVERSE8 = { 15,14,13,12,11,10,9,8,7,6,5,4,3,2,1,0 };
+#endif
+
+#ifdef _BIG_ENDIAN
+static Packet16uc p16uc_DUPLICATE32_HI = { 0,1,2,3, 0,1,2,3, 4,5,6,7, 4,5,6,7 };
+#endif
+static const Packet16uc p16uc_DUPLICATE16_EVEN= { 0,1 ,0,1, 4,5, 4,5, 8,9, 8,9, 12,13, 12,13 };
+static const Packet16uc p16uc_DUPLICATE16_ODD = { 2,3 ,2,3, 6,7, 6,7, 10,11, 10,11, 14,15, 14,15 };
+
+static Packet16uc p16uc_QUADRUPLICATE16_HI = { 0,1,0,1,0,1,0,1, 2,3,2,3,2,3,2,3 };
+
+static Packet16uc p16uc_MERGEE16 = { 0,1, 16,17, 4,5, 20,21, 8,9, 24,25, 12,13, 28,29 };
+static Packet16uc p16uc_MERGEO16 = { 2,3, 18,19, 6,7, 22,23, 10,11, 26,27, 14,15, 30,31 };
+#ifdef _BIG_ENDIAN
+static Packet16uc p16uc_MERGEH16 = { 0,1, 4,5, 8,9, 12,13, 16,17, 20,21, 24,25, 28,29 };
+#else
+static Packet16uc p16uc_MERGEL16 = { 2,3, 6,7, 10,11, 14,15, 18,19, 22,23, 26,27, 30,31 };
+#endif
+
+// Handle endianness properly while loading constants
+// Define global static constants:
+#ifdef _BIG_ENDIAN
+static Packet16uc p16uc_FORWARD = vec_lvsl(0, (float*)0);
+static Packet16uc p16uc_PSET32_WODD   = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
+static Packet16uc p16uc_PSET32_WEVEN  = vec_sld(p16uc_DUPLICATE32_HI, (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
+static Packet16uc p16uc_HALF64_0_16 = vec_sld((Packet16uc)p4i_ZERO, vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 3), 8);      //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
+#else
+static Packet16uc p16uc_FORWARD = p16uc_REVERSE32;
+static Packet16uc p16uc_PSET32_WODD = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 1), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
+static Packet16uc p16uc_PSET32_WEVEN = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
+static Packet16uc p16uc_HALF64_0_16 = vec_sld(vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 0), (Packet16uc)p4i_ZERO, 8);      //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
+#endif // _BIG_ENDIAN
+
+static Packet16uc p16uc_PSET64_HI = (Packet16uc) vec_mergeh((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN);     //{ 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };
+static Packet16uc p16uc_PSET64_LO = (Packet16uc) vec_mergel((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN);     //{ 8,9,10,11, 12,13,14,15, 8,9,10,11, 12,13,14,15 };
+static Packet16uc p16uc_TRANSPOSE64_HI = p16uc_PSET64_HI + p16uc_HALF64_0_16;                                         //{ 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
+static Packet16uc p16uc_TRANSPOSE64_LO = p16uc_PSET64_LO + p16uc_HALF64_0_16;                                         //{ 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};
+
+static Packet16uc p16uc_COMPLEX32_REV = vec_sld(p16uc_REVERSE32, p16uc_REVERSE32, 8);                                         //{ 4,5,6,7, 0,1,2,3, 12,13,14,15, 8,9,10,11 };
+
+#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
+  #define EIGEN_PPC_PREFETCH(ADDR) __builtin_prefetch(ADDR);
+#else
+  #define EIGEN_PPC_PREFETCH(ADDR) asm( "   dcbt [%[addr]]\n" :: [addr] "r" (ADDR) : "cc" );
+#endif
+
+#if EIGEN_COMP_LLVM
+#define LOAD_STORE_UNROLL_16 _Pragma("unroll 16")
+#else
+#define LOAD_STORE_UNROLL_16 _Pragma("GCC unroll(16)")
+#endif
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet4f type;
+  typedef Packet4f half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasAbs = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasACos = 1,
+    HasASin = 1,
+    HasATan = 1,
+    HasATanh = 1,
+    HasLog = 1,
+    HasExp = 1,
+#ifdef EIGEN_VECTORIZE_VSX
+    HasSqrt = 1,
+#if !EIGEN_COMP_CLANG
+    HasRsqrt = 1,
+#else
+    HasRsqrt = 0,
+#endif
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasRint = 1,
+#else
+    HasSqrt = 0,
+    HasRsqrt = 0,
+    HasTanh = 0,
+    HasErf = 0,
+    HasRint = 0,
+#endif
+    HasRound = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasNegate = 1,
+    HasBlend = 1
+  };
+};
+template <>
+struct packet_traits<bfloat16> : default_packet_traits {
+  typedef Packet8bf type;
+  typedef Packet8bf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasAbs = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasLog = 1,
+    HasExp = 1,
+#ifdef EIGEN_VECTORIZE_VSX
+    HasSqrt = 1,
+#if !EIGEN_COMP_CLANG
+    HasRsqrt = 1,
+#else
+    HasRsqrt = 0,
+#endif
+    HasRint = 1,
+#else
+    HasSqrt = 0,
+    HasRsqrt = 0,
+    HasRint = 0,
+#endif
+    HasTanh = 0,
+    HasErf = 0,
+    HasRound = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasNegate = 1,
+    HasBlend = 1
+  };
+};
+
+template <>
+struct packet_traits<int> : default_packet_traits {
+  typedef Packet4i type;
+  typedef Packet4i half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasAdd   = 1,
+    HasSub   = 1,
+    HasShift = 1,
+    HasMul   = 1,
+#if defined(_ARCH_PWR10) && (EIGEN_COMP_LLVM || EIGEN_GNUC_STRICT_AT_LEAST(11,0,0))
+    HasDiv   = 1,
+#else
+    HasDiv   = 0,
+#endif
+    HasBlend = 1,
+    HasCmp = 1
+  };
+};
+
+template <>
+struct packet_traits<short int> : default_packet_traits {
+  typedef Packet8s type;
+  typedef Packet8s half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasAdd  = 1,
+    HasSub  = 1,
+    HasMul  = 1,
+    HasDiv  = 0,
+    HasBlend = 1,
+    HasCmp = 1
+  };
+};
+
+template <>
+struct packet_traits<unsigned short int> : default_packet_traits {
+  typedef Packet8us type;
+  typedef Packet8us half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasAdd  = 1,
+    HasSub  = 1,
+    HasMul  = 1,
+    HasDiv  = 0,
+    HasBlend = 1,
+    HasCmp = 1
+  };
+};
+
+template <>
+struct packet_traits<signed char> : default_packet_traits {
+  typedef Packet16c type;
+  typedef Packet16c half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+
+    HasAdd  = 1,
+    HasSub  = 1,
+    HasMul  = 1,
+    HasDiv  = 0,
+    HasBlend = 1,
+    HasCmp = 1
+  };
+};
+
+template <>
+struct packet_traits<unsigned char> : default_packet_traits {
+  typedef Packet16uc type;
+  typedef Packet16uc half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+
+    HasAdd  = 1,
+    HasSub  = 1,
+    HasMul  = 1,
+    HasDiv  = 0,
+    HasBlend = 1,
+    HasCmp = 1
+  };
+};
+
+template<> struct unpacket_traits<Packet4f>
+{
+  typedef float     type;
+  typedef Packet4f  half;
+  typedef Packet4i  integer_packet;
+  enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+template<> struct unpacket_traits<Packet4i>
+{
+  typedef int       type;
+  typedef Packet4i  half;
+  enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+template<> struct unpacket_traits<Packet8s>
+{
+  typedef short int type;
+  typedef Packet8s  half;
+  enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+template<> struct unpacket_traits<Packet8us>
+{
+  typedef unsigned short int type;
+  typedef Packet8us          half;
+  enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+
+template<> struct unpacket_traits<Packet16c>
+{
+  typedef signed char type;
+  typedef Packet16c  half;
+  enum {size=16, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+template<> struct unpacket_traits<Packet16uc>
+{
+  typedef unsigned char type;
+  typedef Packet16uc  half;
+  enum {size=16, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+
+template<> struct unpacket_traits<Packet8bf>
+{
+  typedef bfloat16 type;
+  typedef Packet8bf          half;
+  enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+inline std::ostream & operator <<(std::ostream & s, const Packet16c & v)
+{
+  union {
+    Packet16c   v;
+    signed char n[16];
+  } vt;
+  vt.v = v;
+  for (int i=0; i< 16; i++)
+    s << vt.n[i] << ", ";
+  return s;
+}
+
+inline std::ostream & operator <<(std::ostream & s, const Packet16uc & v)
+{
+  union {
+    Packet16uc   v;
+    unsigned char n[16];
+  } vt;
+  vt.v = v;
+  for (int i=0; i< 16; i++)
+    s << vt.n[i] << ", ";
+  return s;
+}
+
+inline std::ostream & operator <<(std::ostream & s, const Packet4f & v)
+{
+  union {
+    Packet4f   v;
+    float n[4];
+  } vt;
+  vt.v = v;
+  s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3];
+  return s;
+}
+
+inline std::ostream & operator <<(std::ostream & s, const Packet4i & v)
+{
+  union {
+    Packet4i   v;
+    int n[4];
+  } vt;
+  vt.v = v;
+  s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3];
+  return s;
+}
+
+inline std::ostream & operator <<(std::ostream & s, const Packet4ui & v)
+{
+  union {
+    Packet4ui   v;
+    unsigned int n[4];
+  } vt;
+  vt.v = v;
+  s << vt.n[0] << ", " << vt.n[1] << ", " << vt.n[2] << ", " << vt.n[3];
+  return s;
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet pload_common(const __UNPACK_TYPE__(Packet)* from)
+{
+  // some versions of GCC throw "unused-but-set-parameter".
+  // ignoring these warnings for now.
+  EIGEN_UNUSED_VARIABLE(from);
+  EIGEN_DEBUG_ALIGNED_LOAD
+#ifdef EIGEN_VECTORIZE_VSX
+  return vec_xl(0, const_cast<__UNPACK_TYPE__(Packet)*>(from));
+#else
+  return vec_ld(0, from);
+#endif
+}
+
+// Need to define them first or we get specialization after instantiation errors
+template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from)
+{
+  return pload_common<Packet4f>(from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int*     from)
+{
+  return pload_common<Packet4i>(from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8s pload<Packet8s>(const short int* from)
+{
+  return pload_common<Packet8s>(from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8us pload<Packet8us>(const unsigned short int* from)
+{
+  return pload_common<Packet8us>(from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16c pload<Packet16c>(const signed char*     from)
+{
+  return pload_common<Packet16c>(from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16uc pload<Packet16uc>(const unsigned char*     from)
+{
+  return pload_common<Packet16uc>(from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pload<Packet8bf>(const bfloat16*     from)
+{
+  return pload_common<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE Packet pload_ignore(const __UNPACK_TYPE__(Packet)* from)
+{
+  // some versions of GCC throw "unused-but-set-parameter".
+  // ignoring these warnings for now.
+  EIGEN_UNUSED_VARIABLE(from);
+  EIGEN_DEBUG_ALIGNED_LOAD
+  // Ignore partial input memory initialized
+#if !EIGEN_COMP_LLVM
+  #pragma GCC diagnostic push
+  #pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
+#endif
+#ifdef EIGEN_VECTORIZE_VSX
+  return vec_xl(0, const_cast<__UNPACK_TYPE__(Packet)*>(from));
+#else
+  return vec_ld(0, from);
+#endif
+#if !EIGEN_COMP_LLVM
+  #pragma GCC diagnostic pop
+#endif
+}
+
+template<> EIGEN_ALWAYS_INLINE Packet8bf pload_ignore<Packet8bf>(const bfloat16*     from)
+{
+  return pload_ignore<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
+}
+
+template <typename Packet>
+EIGEN_ALWAYS_INLINE Packet pload_partial_common(const __UNPACK_TYPE__(Packet)* from, const Index n, const Index offset)
+{
+  // some versions of GCC throw "unused-but-set-parameter".
+  // ignoring these warnings for now.
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_internal_assert(n + offset <= packet_size && "number of elements plus offset will read past end of packet");
+  const Index size = sizeof(__UNPACK_TYPE__(Packet));
+#ifdef _ARCH_PWR9
+  EIGEN_UNUSED_VARIABLE(packet_size);
+  EIGEN_DEBUG_ALIGNED_LOAD
+  EIGEN_UNUSED_VARIABLE(from);
+  Packet load = vec_xl_len(const_cast<__UNPACK_TYPE__(Packet)*>(from), n * size);
+  if (offset) {
+    Packet16uc shift = pset1<Packet16uc>(offset * 8 * size);
+#ifdef _BIG_ENDIAN
+    load = Packet(vec_sro(Packet16uc(load), shift));
+#else
+    load = Packet(vec_slo(Packet16uc(load), shift));
+#endif
+  }
+  return load;
+#else
+  if (n) {
+    EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) load[packet_size];
+    unsigned char* load2 = reinterpret_cast<unsigned char *>(load + offset);
+    unsigned char* from2 = reinterpret_cast<unsigned char *>(const_cast<__UNPACK_TYPE__(Packet)*>(from));
+    Index n2 = n * size;
+    if (16 <= n2) {
+      pstoreu(load2, ploadu<Packet16uc>(from2));
+    } else {
+      memcpy((void *)load2, (void *)from2, n2);
+    }
+    return pload_ignore<Packet>(load);
+  } else {
+    return Packet(pset1<Packet16uc>(0));
+  }
+#endif
+}
+
+template<> EIGEN_ALWAYS_INLINE Packet4f pload_partial<Packet4f>(const float* from, const Index n, const Index offset)
+{
+  return pload_partial_common<Packet4f>(from, n, offset);
+}
+
+template<> EIGEN_ALWAYS_INLINE Packet4i pload_partial<Packet4i>(const int* from, const Index n, const Index offset)
+{
+  return pload_partial_common<Packet4i>(from, n, offset);
+}
+
+template<> EIGEN_ALWAYS_INLINE Packet8s pload_partial<Packet8s>(const short int* from, const Index n, const Index offset)
+{
+  return pload_partial_common<Packet8s>(from, n, offset);
+}
+
+template<> EIGEN_ALWAYS_INLINE Packet8us pload_partial<Packet8us>(const unsigned short int* from, const Index n, const Index offset)
+{
+  return pload_partial_common<Packet8us>(from, n, offset);
+}
+
+template<> EIGEN_ALWAYS_INLINE Packet8bf pload_partial<Packet8bf>(const bfloat16* from, const Index n, const Index offset)
+{
+  return pload_partial_common<Packet8us>(reinterpret_cast<const unsigned short int*>(from), n, offset);
+}
+
+template<> EIGEN_ALWAYS_INLINE Packet16c pload_partial<Packet16c>(const signed char* from, const Index n, const Index offset)
+{
+  return pload_partial_common<Packet16c>(from, n, offset);
+}
+
+template<> EIGEN_ALWAYS_INLINE Packet16uc pload_partial<Packet16uc>(const unsigned char* from, const Index n, const Index offset)
+{
+  return pload_partial_common<Packet16uc>(from, n, offset);
+}
+
+template <typename Packet>
+EIGEN_STRONG_INLINE void pstore_common(__UNPACK_TYPE__(Packet)* to, const Packet& from){
+  // some versions of GCC throw "unused-but-set-parameter" (float *to).
+  // ignoring these warnings for now.
+  EIGEN_UNUSED_VARIABLE(to);
+  EIGEN_DEBUG_ALIGNED_STORE
+#ifdef EIGEN_VECTORIZE_VSX
+  vec_xst(from, 0, to);
+#else
+  vec_st(from, 0, to);
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<float>(float*   to, const Packet4f& from)
+{
+  pstore_common<Packet4f>(to, from);
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<int>(int*       to, const Packet4i& from)
+{
+  pstore_common<Packet4i>(to, from);
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<short int>(short int*       to, const Packet8s& from)
+{
+  pstore_common<Packet8s>(to, from);
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<unsigned short int>(unsigned short int*       to, const Packet8us& from)
+{
+  pstore_common<Packet8us>(to, from);
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16*       to, const Packet8bf& from)
+{
+  pstore_common<Packet8us>(reinterpret_cast<unsigned short int*>(to), from.m_val);
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<signed char>(signed char*       to, const Packet16c& from)
+{
+  pstore_common<Packet16c>(to, from);
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<unsigned char>(unsigned char*       to, const Packet16uc& from)
+{
+  pstore_common<Packet16uc>(to, from);
+}
+
+template<typename Packet> EIGEN_ALWAYS_INLINE void pstore_partial_common(__UNPACK_TYPE__(Packet)*  to, const Packet& from, const Index n, const Index offset)
+{
+  // some versions of GCC throw "unused-but-set-parameter" (float *to).
+  // ignoring these warnings for now.
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_internal_assert(n + offset <= packet_size && "number of elements plus offset will write past end of packet");
+  const Index size = sizeof(__UNPACK_TYPE__(Packet));
+#ifdef _ARCH_PWR9
+  EIGEN_UNUSED_VARIABLE(packet_size);
+  EIGEN_UNUSED_VARIABLE(to);
+  EIGEN_DEBUG_ALIGNED_STORE
+  Packet store = from;
+  if (offset) {
+    Packet16uc shift = pset1<Packet16uc>(offset * 8 * size);
+#ifdef _BIG_ENDIAN
+    store = Packet(vec_slo(Packet16uc(store), shift));
+#else
+    store = Packet(vec_sro(Packet16uc(store), shift));
+#endif
+  }
+  vec_xst_len(store, to, n * size);
+#else
+  if (n) {
+    EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) store[packet_size];
+    pstore(store, from);
+    unsigned char* store2 = reinterpret_cast<unsigned char *>(store + offset);
+    unsigned char* to2 = reinterpret_cast<unsigned char *>(to);
+    Index n2 = n * size;
+    if (16 <= n2) {
+      pstore(to2, ploadu<Packet16uc>(store2));
+    } else {
+      memcpy((void *)to2, (void *)store2, n2);
+    }
+  }
+#endif
+}
+
+template<> EIGEN_ALWAYS_INLINE void pstore_partial<float>(float*  to, const Packet4f& from, const Index n, const Index offset)
+{
+  pstore_partial_common<Packet4f>(to, from, n, offset);
+}
+
+template<> EIGEN_ALWAYS_INLINE void pstore_partial<int>(int*  to, const Packet4i& from, const Index n, const Index offset)
+{
+  pstore_partial_common<Packet4i>(to, from, n, offset);
+}
+
+template<> EIGEN_ALWAYS_INLINE void pstore_partial<short int>(short int*  to, const Packet8s& from, const Index n, const Index offset)
+{
+  pstore_partial_common<Packet8s>(to, from, n, offset);
+}
+
+template<> EIGEN_ALWAYS_INLINE void pstore_partial<unsigned short int>(unsigned short int*  to, const Packet8us& from, const Index n, const Index offset)
+{
+  pstore_partial_common<Packet8us>(to, from, n, offset);
+}
+
+template<> EIGEN_ALWAYS_INLINE void pstore_partial<bfloat16>(bfloat16*      to, const Packet8bf& from, const Index n, const Index offset)
+{
+  pstore_partial_common<Packet8us>(reinterpret_cast<unsigned short int*>(to), from.m_val, n, offset);
+}
+
+template<> EIGEN_ALWAYS_INLINE void pstore_partial<signed char>(signed char*  to, const Packet16c& from, const Index n, const Index offset)
+{
+  pstore_partial_common<Packet16c>(to, from, n, offset);
+}
+
+template<> EIGEN_ALWAYS_INLINE void pstore_partial<unsigned char>(unsigned char*  to, const Packet16uc& from, const Index n, const Index offset)
+{
+  pstore_partial_common<Packet16uc>(to, from, n, offset);
+}
+
+template<typename Packet>
+EIGEN_STRONG_INLINE Packet pset1_size4(const __UNPACK_TYPE__(Packet)& from)
+{
+  Packet v = {from, from, from, from};
+  return v;
+}
+
+template<typename Packet>
+EIGEN_STRONG_INLINE Packet pset1_size8(const __UNPACK_TYPE__(Packet)& from)
+{
+  Packet v = {from, from, from, from, from, from, from, from};
+  return v;
+}
+
+template<typename Packet>
+EIGEN_STRONG_INLINE Packet pset1_size16(const __UNPACK_TYPE__(Packet)& from)
+{
+  Packet v = {from, from, from, from, from, from, from, from, from, from, from, from, from, from, from, from};
+  return v;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float&  from) {
+  return pset1_size4<Packet4f>(from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int&    from)   {
+  return pset1_size4<Packet4i>(from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8s pset1<Packet8s>(const short int&    from)   {
+  return pset1_size8<Packet8s>(from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8us pset1<Packet8us>(const unsigned short int&    from)   {
+  return pset1_size8<Packet8us>(from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16c pset1<Packet16c>(const signed char&    from)   {
+  return pset1_size16<Packet16c>(from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16uc pset1<Packet16uc>(const unsigned char&    from)   {
+  return pset1_size16<Packet16uc>(from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pset1frombits<Packet4f>(unsigned int from) {
+  return reinterpret_cast<Packet4f>(pset1<Packet4i>(from));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pset1<Packet8bf>(const bfloat16&    from)   {
+  return pset1_size8<Packet8us>(reinterpret_cast<const unsigned short int&>(from));
+}
+
+template<typename Packet> EIGEN_STRONG_INLINE void
+pbroadcast4_common(const __UNPACK_TYPE__(Packet) *a,
+                      Packet& a0, Packet& a1, Packet& a2, Packet& a3)
+{
+  a3 = pload<Packet>(a);
+  a0 = vec_splat(a3, 0);
+  a1 = vec_splat(a3, 1);
+  a2 = vec_splat(a3, 2);
+  a3 = vec_splat(a3, 3);
+}
+
+template<> EIGEN_STRONG_INLINE void
+pbroadcast4<Packet4f>(const float *a,
+                      Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
+{
+  pbroadcast4_common<Packet4f>(a, a0, a1, a2, a3);
+}
+template<> EIGEN_STRONG_INLINE void
+pbroadcast4<Packet4i>(const int *a,
+                      Packet4i& a0, Packet4i& a1, Packet4i& a2, Packet4i& a3)
+{
+  pbroadcast4_common<Packet4i>(a, a0, a1, a2, a3);
+}
+
+template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet pgather_common(const __UNPACK_TYPE__(Packet)* from, Index stride, const Index n = unpacket_traits<Packet>::size)
+{
+  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[unpacket_traits<Packet>::size];
+  eigen_internal_assert(n <= unpacket_traits<Packet>::size && "number of elements will gather past end of packet");
+  if (stride == 1) {
+    if (n == unpacket_traits<Packet>::size) {
+      return ploadu<Packet>(from);
+    } else {
+      return ploadu_partial<Packet>(from, n);
+    }
+  } else {
+    LOAD_STORE_UNROLL_16
+    for (Index i = 0; i < n; i++) {
+      a[i] = from[i*stride];
+    }
+    // Leave rest of the array uninitialized
+    return pload_ignore<Packet>(a);
+  }
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4f pgather<float, Packet4f>(const float* from, Index stride)
+{
+  return pgather_common<Packet4f>(from, stride);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4i pgather<int, Packet4i>(const int* from, Index stride)
+{
+  return pgather_common<Packet4i>(from, stride);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet8s pgather<short int, Packet8s>(const short int* from, Index stride)
+{
+  return pgather_common<Packet8s>(from, stride);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet8us pgather<unsigned short int, Packet8us>(const unsigned short int* from, Index stride)
+{
+  return pgather_common<Packet8us>(from, stride);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet8bf pgather<bfloat16, Packet8bf>(const bfloat16* from, Index stride)
+{
+  return pgather_common<Packet8bf>(from, stride);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet16c pgather<signed char, Packet16c>(const signed char* from, Index stride)
+{
+  return pgather_common<Packet16c>(from, stride);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet16uc pgather<unsigned char, Packet16uc>(const unsigned char* from, Index stride)
+{
+  return pgather_common<Packet16uc>(from, stride);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4f pgather_partial<float, Packet4f>(const float* from, Index stride, const Index n)
+{
+  return pgather_common<Packet4f>(from, stride, n);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4i pgather_partial<int, Packet4i>(const int* from, Index stride, const Index n)
+{
+  return pgather_common<Packet4i>(from, stride, n);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet8s pgather_partial<short int, Packet8s>(const short int* from, Index stride, const Index n)
+{
+  return pgather_common<Packet8s>(from, stride, n);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet8us pgather_partial<unsigned short int, Packet8us>(const unsigned short int* from, Index stride, const Index n)
+{
+  return pgather_common<Packet8us>(from, stride, n);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet8bf pgather_partial<bfloat16, Packet8bf>(const bfloat16* from, Index stride, const Index n)
+{
+  return pgather_common<Packet8bf>(from, stride, n);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet16c pgather_partial<signed char, Packet16c>(const signed char* from, Index stride, const Index n)
+{
+  return pgather_common<Packet16c>(from, stride, n);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet16uc pgather_partial<unsigned char, Packet16uc>(const unsigned char* from, Index stride, const Index n)
+{
+  return pgather_common<Packet16uc>(from, stride, n);
+}
+
+template<typename Packet> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_common(__UNPACK_TYPE__(Packet)* to, const Packet& from, Index stride, const Index n = unpacket_traits<Packet>::size)
+{
+  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) a[unpacket_traits<Packet>::size];
+  eigen_internal_assert(n <= unpacket_traits<Packet>::size && "number of elements will scatter past end of packet");
+  if (stride == 1) {
+    if (n == unpacket_traits<Packet>::size) {
+      return pstoreu(to, from);
+    } else {
+      return pstoreu_partial(to, from, n);
+    }
+  } else {
+    pstore<__UNPACK_TYPE__(Packet)>(a, from);
+    LOAD_STORE_UNROLL_16
+    for (Index i = 0; i < n; i++) {
+      to[i*stride] = a[i];
+    }
+  }
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
+{
+  pscatter_common<Packet4f>(to, from, stride);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<int, Packet4i>(int* to, const Packet4i& from, Index stride)
+{
+  pscatter_common<Packet4i>(to, from, stride);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<short int, Packet8s>(short int* to, const Packet8s& from, Index stride)
+{
+  pscatter_common<Packet8s>(to, from, stride);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<unsigned short int, Packet8us>(unsigned short int* to, const Packet8us& from, Index stride)
+{
+  pscatter_common<Packet8us>(to, from, stride);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<bfloat16, Packet8bf>(bfloat16* to, const Packet8bf& from, Index stride)
+{
+  pscatter_common<Packet8bf>(to, from, stride);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<signed char, Packet16c>(signed char* to, const Packet16c& from, Index stride)
+{
+  pscatter_common<Packet16c>(to, from, stride);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<unsigned char, Packet16uc>(unsigned char* to, const Packet16uc& from, Index stride)
+{
+  pscatter_common<Packet16uc>(to, from, stride);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<float, Packet4f>(float* to, const Packet4f& from, Index stride, const Index n)
+{
+  pscatter_common<Packet4f>(to, from, stride, n);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<int, Packet4i>(int* to, const Packet4i& from, Index stride, const Index n)
+{
+  pscatter_common<Packet4i>(to, from, stride, n);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<short int, Packet8s>(short int* to, const Packet8s& from, Index stride, const Index n)
+{
+  pscatter_common<Packet8s>(to, from, stride, n);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<unsigned short int, Packet8us>(unsigned short int* to, const Packet8us& from, Index stride, const Index n)
+{
+  pscatter_common<Packet8us>(to, from, stride, n);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<bfloat16, Packet8bf>(bfloat16* to, const Packet8bf& from, Index stride, const Index n)
+{
+  pscatter_common<Packet8bf>(to, from, stride, n);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<signed char, Packet16c>(signed char* to, const Packet16c& from, Index stride, const Index n)
+{
+  pscatter_common<Packet16c>(to, from, stride, n);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<unsigned char, Packet16uc>(unsigned char* to, const Packet16uc& from, Index stride, const Index n)
+{
+  pscatter_common<Packet16uc>(to, from, stride, n);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f   plset<Packet4f>(const float&     a) { return pset1<Packet4f>(a) + p4f_COUNTDOWN;  }
+template<> EIGEN_STRONG_INLINE Packet4i   plset<Packet4i>(const int&       a) { return pset1<Packet4i>(a) + p4i_COUNTDOWN;  }
+template<> EIGEN_STRONG_INLINE Packet8s   plset<Packet8s>(const short int& a) { return pset1<Packet8s>(a) + p8s_COUNTDOWN; }
+template<> EIGEN_STRONG_INLINE Packet8us  plset<Packet8us>(const unsigned short int& a) { return pset1<Packet8us>(a) + p8us_COUNTDOWN; }
+template<> EIGEN_STRONG_INLINE Packet16c  plset<Packet16c>(const signed char& a)   { return pset1<Packet16c>(a) + p16c_COUNTDOWN; }
+template<> EIGEN_STRONG_INLINE Packet16uc plset<Packet16uc>(const unsigned char& a)   { return pset1<Packet16uc>(a) + p16uc_COUNTDOWN; }
+
+template<> EIGEN_STRONG_INLINE Packet4f   padd<Packet4f>  (const Packet4f&   a, const Packet4f&   b) { return a + b; }
+template<> EIGEN_STRONG_INLINE Packet4i   padd<Packet4i>  (const Packet4i&   a, const Packet4i&   b) { return a + b; }
+template<> EIGEN_STRONG_INLINE Packet4ui   padd<Packet4ui>  (const Packet4ui&   a, const Packet4ui&   b) { return a + b; }
+template<> EIGEN_STRONG_INLINE Packet8s   padd<Packet8s>  (const Packet8s&   a, const Packet8s&   b) { return a + b; }
+template<> EIGEN_STRONG_INLINE Packet8us  padd<Packet8us> (const Packet8us&  a, const Packet8us&  b) { return a + b; }
+template<> EIGEN_STRONG_INLINE Packet16c  padd<Packet16c> (const Packet16c&  a, const Packet16c&  b) { return a + b; }
+template<> EIGEN_STRONG_INLINE Packet16uc padd<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return a + b; }
+
+template<> EIGEN_STRONG_INLINE Packet4f   psub<Packet4f>  (const Packet4f&   a, const Packet4f&   b) { return a - b; }
+template<> EIGEN_STRONG_INLINE Packet4i   psub<Packet4i>  (const Packet4i&   a, const Packet4i&   b) { return a - b; }
+template<> EIGEN_STRONG_INLINE Packet8s   psub<Packet8s>  (const Packet8s&   a, const Packet8s&   b) { return a - b; }
+template<> EIGEN_STRONG_INLINE Packet8us  psub<Packet8us> (const Packet8us&  a, const Packet8us&  b) { return a - b; }
+template<> EIGEN_STRONG_INLINE Packet16c  psub<Packet16c> (const Packet16c&  a, const Packet16c&  b) { return a - b; }
+template<> EIGEN_STRONG_INLINE Packet16uc psub<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return a - b; }
+
+template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a)
+{
+#ifdef __POWER8_VECTOR__
+  return vec_neg(a);
+#else
+  return vec_xor(a, p4f_MZERO);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet16c pnegate(const Packet16c& a)
+{
+#ifdef __POWER8_VECTOR__
+  return vec_neg(a);
+#else
+  return reinterpret_cast<Packet16c>(p4i_ZERO) - a;
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8s pnegate(const Packet8s& a)
+{
+#ifdef __POWER8_VECTOR__
+  return vec_neg(a);
+#else
+  return reinterpret_cast<Packet8s>(p4i_ZERO) - a;
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a)
+{
+#ifdef __POWER8_VECTOR__
+  return vec_neg(a);
+#else
+  return p4i_ZERO - a;
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE Packet4f   pmul<Packet4f>  (const Packet4f&   a, const Packet4f&   b) { return vec_madd(a,b, p4f_MZERO); }
+template<> EIGEN_STRONG_INLINE Packet4i   pmul<Packet4i>  (const Packet4i&   a, const Packet4i&   b) { return a * b; }
+template<> EIGEN_STRONG_INLINE Packet8s   pmul<Packet8s>  (const Packet8s&   a, const Packet8s&   b) { return vec_mul(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8us  pmul<Packet8us> (const Packet8us&  a, const Packet8us&  b) { return vec_mul(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16c  pmul<Packet16c> (const Packet16c&  a, const Packet16c&  b) { return vec_mul(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16uc pmul<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vec_mul(a,b); }
+
+
+template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+#ifndef __VSX__  // VSX actually provides a div instruction
+  Packet4f t, y_0, y_1;
+
+  // Altivec does not offer a divide instruction, we have to do a reciprocal approximation
+  y_0 = vec_re(b);
+
+  // Do one Newton-Raphson iteration to get the needed accuracy
+  t   = vec_nmsub(y_0, b, p4f_ONE);
+  y_1 = vec_madd(y_0, t, y_0);
+
+  return vec_madd(a, y_1, p4f_MZERO);
+#else
+  return vec_div(a, b);
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& a, const Packet4i& b)
+{
+#if defined(_ARCH_PWR10) && (EIGEN_COMP_LLVM || EIGEN_GNUC_STRICT_AT_LEAST(11,0,0))
+  return vec_div(a, b);
+#else
+  EIGEN_UNUSED_VARIABLE(a);
+  EIGEN_UNUSED_VARIABLE(b);
+  eigen_assert(false && "packet integer division are not supported by AltiVec");
+  return pset1<Packet4i>(0);
+#endif
+}
+
+// for some weird raisons, it has to be overloaded for packet of integers
+template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vec_madd(a,b,c); }
+template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return a*b + c; }
+template<> EIGEN_STRONG_INLINE Packet8s pmadd(const Packet8s& a, const Packet8s& b, const Packet8s& c) { return vec_madd(a,b,c); }
+template<> EIGEN_STRONG_INLINE Packet8us pmadd(const Packet8us& a, const Packet8us& b, const Packet8us& c) { return vec_madd(a,b,c); }
+
+#ifdef EIGEN_VECTORIZE_VSX
+template<> EIGEN_STRONG_INLINE Packet4f pmsub(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vec_msub(a,b,c); }
+template<> EIGEN_STRONG_INLINE Packet4f pnmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vec_nmsub(a,b,c); }
+template<> EIGEN_STRONG_INLINE Packet4f pnmsub(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vec_nmadd(a,b,c); }
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+  #ifdef EIGEN_VECTORIZE_VSX
+  // NOTE: about 10% slower than vec_min, but consistent with std::min and SSE regarding NaN
+  Packet4f ret;
+  __asm__ ("xvcmpgesp %x0,%x1,%x2\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
+  return ret;
+  #else
+  return vec_min(a, b);
+  #endif
+}
+template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_min(a, b); }
+template<> EIGEN_STRONG_INLINE Packet8s pmin<Packet8s>(const Packet8s& a, const Packet8s& b) { return vec_min(a, b); }
+template<> EIGEN_STRONG_INLINE Packet8us pmin<Packet8us>(const Packet8us& a, const Packet8us& b) { return vec_min(a, b); }
+template<> EIGEN_STRONG_INLINE Packet16c pmin<Packet16c>(const Packet16c& a, const Packet16c& b) { return vec_min(a, b); }
+template<> EIGEN_STRONG_INLINE Packet16uc pmin<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vec_min(a, b); }
+
+
+template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+  #ifdef EIGEN_VECTORIZE_VSX
+  // NOTE: about 10% slower than vec_max, but consistent with std::max and SSE regarding NaN
+  Packet4f ret;
+  __asm__ ("xvcmpgtsp %x0,%x2,%x1\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
+  return ret;
+  #else
+  return vec_max(a, b);
+  #endif
+}
+template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_max(a, b); }
+template<> EIGEN_STRONG_INLINE Packet8s pmax<Packet8s>(const Packet8s& a, const Packet8s& b) { return vec_max(a, b); }
+template<> EIGEN_STRONG_INLINE Packet8us pmax<Packet8us>(const Packet8us& a, const Packet8us& b) { return vec_max(a, b); }
+template<> EIGEN_STRONG_INLINE Packet16c pmax<Packet16c>(const Packet16c& a, const Packet16c& b) { return vec_max(a, b); }
+template<> EIGEN_STRONG_INLINE Packet16uc pmax<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vec_max(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pcmp_le(const Packet4f& a, const Packet4f& b) { return reinterpret_cast<Packet4f>(vec_cmple(a,b)); }
+// To fix bug with vec_cmplt on older versions
+#ifdef EIGEN_VECTORIZE_VSX
+template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt(const Packet4f& a, const Packet4f& b) { return reinterpret_cast<Packet4f>(vec_cmplt(a,b)); }
+#endif
+template<> EIGEN_STRONG_INLINE Packet4f pcmp_eq(const Packet4f& a, const Packet4f& b) { return reinterpret_cast<Packet4f>(vec_cmpeq(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt_or_nan(const Packet4f& a, const Packet4f& b) {
+  Packet4f c = reinterpret_cast<Packet4f>(vec_cmpge(a,b));
+  return vec_nor(c,c);
+}
+
+#ifdef EIGEN_VECTORIZE_VSX
+template<> EIGEN_STRONG_INLINE Packet4i pcmp_le(const Packet4i& a, const Packet4i& b) { return reinterpret_cast<Packet4i>(vec_cmple(a,b)); }
+#endif
+template<> EIGEN_STRONG_INLINE Packet4i pcmp_lt(const Packet4i& a, const Packet4i& b) { return reinterpret_cast<Packet4i>(vec_cmplt(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4i pcmp_eq(const Packet4i& a, const Packet4i& b) { return reinterpret_cast<Packet4i>(vec_cmpeq(a,b)); }
+#ifdef EIGEN_VECTORIZE_VSX
+template<> EIGEN_STRONG_INLINE Packet8s pcmp_le(const Packet8s& a, const Packet8s& b) { return reinterpret_cast<Packet8s>(vec_cmple(a,b)); }
+#endif
+template<> EIGEN_STRONG_INLINE Packet8s pcmp_lt(const Packet8s& a, const Packet8s& b) { return reinterpret_cast<Packet8s>(vec_cmplt(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet8s pcmp_eq(const Packet8s& a, const Packet8s& b) { return reinterpret_cast<Packet8s>(vec_cmpeq(a,b)); }
+#ifdef EIGEN_VECTORIZE_VSX
+template<> EIGEN_STRONG_INLINE Packet8us pcmp_le(const Packet8us& a, const Packet8us& b) { return reinterpret_cast<Packet8us>(vec_cmple(a,b)); }
+#endif
+template<> EIGEN_STRONG_INLINE Packet8us pcmp_lt(const Packet8us& a, const Packet8us& b) { return reinterpret_cast<Packet8us>(vec_cmplt(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet8us pcmp_eq(const Packet8us& a, const Packet8us& b) { return reinterpret_cast<Packet8us>(vec_cmpeq(a,b)); }
+#ifdef EIGEN_VECTORIZE_VSX
+template<> EIGEN_STRONG_INLINE Packet16c pcmp_le(const Packet16c& a, const Packet16c& b) { return reinterpret_cast<Packet16c>(vec_cmple(a,b)); }
+#endif
+template<> EIGEN_STRONG_INLINE Packet16c pcmp_lt(const Packet16c& a, const Packet16c& b) { return reinterpret_cast<Packet16c>(vec_cmplt(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet16c pcmp_eq(const Packet16c& a, const Packet16c& b) { return reinterpret_cast<Packet16c>(vec_cmpeq(a,b)); }
+#ifdef EIGEN_VECTORIZE_VSX
+template<> EIGEN_STRONG_INLINE Packet16uc pcmp_le(const Packet16uc& a, const Packet16uc& b) { return reinterpret_cast<Packet16uc>(vec_cmple(a,b)); }
+#endif
+template<> EIGEN_STRONG_INLINE Packet16uc pcmp_lt(const Packet16uc& a, const Packet16uc& b) { return reinterpret_cast<Packet16uc>(vec_cmplt(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet16uc pcmp_eq(const Packet16uc& a, const Packet16uc& b) { return reinterpret_cast<Packet16uc>(vec_cmpeq(a,b)); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_and(a, b); }
+template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_and(a, b); }
+template<> EIGEN_STRONG_INLINE Packet4ui pand<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vec_and(a, b); }
+template<> EIGEN_STRONG_INLINE Packet8us pand<Packet8us>(const Packet8us& a, const Packet8us& b) { return vec_and(a, b); }
+template<> EIGEN_STRONG_INLINE Packet8bf pand<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  return pand<Packet8us>(a, b);
+}
+
+
+template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_or(a, b); }
+template<> EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_or(a, b); }
+template<> EIGEN_STRONG_INLINE Packet8s por<Packet8s>(const Packet8s& a, const Packet8s& b) { return vec_or(a, b); }
+template<> EIGEN_STRONG_INLINE Packet8us por<Packet8us>(const Packet8us& a, const Packet8us& b) { return vec_or(a, b); }
+template<> EIGEN_STRONG_INLINE Packet8bf por<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  return por<Packet8us>(a, b);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_xor(a, b); }
+template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_xor(a, b); }
+template<> EIGEN_STRONG_INLINE Packet8us pxor<Packet8us>(const Packet8us& a, const Packet8us& b) { return vec_xor(a, b); }
+template<> EIGEN_STRONG_INLINE Packet8bf pxor<Packet8bf>(const Packet8bf& a, const Packet8bf& b) { 
+  return pxor<Packet8us>(a, b);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_andc(a, b); }
+template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_andc(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pselect(const Packet4f& mask, const Packet4f& a, const Packet4f& b) {
+  return vec_sel(b, a, reinterpret_cast<Packet4ui>(mask));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a)
+{
+    Packet4f t = vec_add(reinterpret_cast<Packet4f>(vec_or(vec_and(reinterpret_cast<Packet4ui>(a), p4ui_SIGN), p4ui_PREV0DOT5)), a);
+    Packet4f res;
+
+#ifdef EIGEN_VECTORIZE_VSX
+    __asm__("xvrspiz %x0, %x1\n\t"
+        : "=&wa" (res)
+        : "wa" (t));
+#else
+    __asm__("vrfiz %0, %1\n\t"
+        : "=v" (res)
+        : "v" (t));
+#endif
+
+    return res;
+}
+template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const  Packet4f& a) { return vec_ceil(a); }
+template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) { return vec_floor(a); }
+#ifdef EIGEN_VECTORIZE_VSX
+template<> EIGEN_STRONG_INLINE Packet4f print<Packet4f>(const Packet4f& a)
+{
+    Packet4f res;
+
+    __asm__("xvrspic %x0, %x1\n\t"
+        : "=&wa" (res)
+        : "wa" (a));
+
+    return res;
+}
+#endif
+
+template<typename Packet> EIGEN_STRONG_INLINE Packet ploadu_common(const __UNPACK_TYPE__(Packet)* from)
+{
+  EIGEN_DEBUG_ALIGNED_LOAD
+#if defined(EIGEN_VECTORIZE_VSX) || !defined(_BIG_ENDIAN)
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return vec_xl(0, const_cast<__UNPACK_TYPE__(Packet)*>(from));
+#else
+  Packet16uc MSQ, LSQ;
+  Packet16uc mask;
+  MSQ = vec_ld(0, (unsigned char *)from);          // most significant quadword
+  LSQ = vec_ld(15, (unsigned char *)from);         // least significant quadword
+  mask = vec_lvsl(0, from);                        // create the permute mask
+  //TODO: Add static_cast here
+  return (Packet) vec_perm(MSQ, LSQ, mask);           // align the data
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from)
+{
+  return ploadu_common<Packet4f>(from);
+}
+template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from)
+{
+  return ploadu_common<Packet4i>(from);
+}
+template<> EIGEN_STRONG_INLINE Packet8s ploadu<Packet8s>(const short int* from)
+{
+  return ploadu_common<Packet8s>(from);
+}
+template<> EIGEN_STRONG_INLINE Packet8us ploadu<Packet8us>(const unsigned short int* from)
+{
+  return ploadu_common<Packet8us>(from);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf ploadu<Packet8bf>(const bfloat16* from)
+{
+  return ploadu_common<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
+}
+template<> EIGEN_STRONG_INLINE Packet16c ploadu<Packet16c>(const signed char* from)
+{
+  return ploadu_common<Packet16c>(from);
+}
+template<> EIGEN_STRONG_INLINE Packet16uc ploadu<Packet16uc>(const unsigned char* from)
+{
+  return ploadu_common<Packet16uc>(from);
+}
+
+template<typename Packet> EIGEN_ALWAYS_INLINE Packet ploadu_partial_common(const __UNPACK_TYPE__(Packet)* from, const Index n, const Index offset)
+{
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_internal_assert(n + offset <= packet_size && "number of elements plus offset will read past end of packet");
+  const Index size = sizeof(__UNPACK_TYPE__(Packet));
+#ifdef _ARCH_PWR9
+  EIGEN_UNUSED_VARIABLE(packet_size);
+  EIGEN_DEBUG_ALIGNED_LOAD
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  Packet load = vec_xl_len(const_cast<__UNPACK_TYPE__(Packet)*>(from), n * size);
+  if (offset) {
+    Packet16uc shift = pset1<Packet16uc>(offset * 8 * size);
+#ifdef _BIG_ENDIAN
+    load = Packet(vec_sro(Packet16uc(load), shift));
+#else
+    load = Packet(vec_slo(Packet16uc(load), shift));
+#endif
+  }
+  return load;
+#else
+  if (n) {
+    EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) load[packet_size];
+    unsigned char* load2 = reinterpret_cast<unsigned char *>(load + offset);
+    unsigned char* from2 = reinterpret_cast<unsigned char *>(const_cast<__UNPACK_TYPE__(Packet)*>(from));
+    Index n2 = n * size;
+    if (16 <= n2) {
+      pstoreu(load2, ploadu<Packet16uc>(from2));
+    } else {
+      memcpy((void *)load2, (void *)from2, n2);
+    }
+    return pload_ignore<Packet>(load);
+  } else {
+    return Packet(pset1<Packet16uc>(0));
+  }
+#endif
+}
+
+template<> EIGEN_ALWAYS_INLINE Packet4f ploadu_partial<Packet4f>(const float* from, const Index n, const Index offset)
+{
+  return ploadu_partial_common<Packet4f>(from, n, offset);
+}
+template<> EIGEN_ALWAYS_INLINE Packet4i ploadu_partial<Packet4i>(const int* from, const Index n, const Index offset)
+{
+  return ploadu_partial_common<Packet4i>(from, n, offset);
+}
+template<> EIGEN_ALWAYS_INLINE Packet8s ploadu_partial<Packet8s>(const short int* from, const Index n, const Index offset)
+{
+  return ploadu_partial_common<Packet8s>(from, n, offset);
+}
+template<> EIGEN_ALWAYS_INLINE Packet8us ploadu_partial<Packet8us>(const unsigned short int* from, const Index n, const Index offset)
+{
+  return ploadu_partial_common<Packet8us>(from, n, offset);
+}
+template<> EIGEN_ALWAYS_INLINE Packet8bf ploadu_partial<Packet8bf>(const bfloat16* from, const Index n, const Index offset)
+{
+  return ploadu_partial_common<Packet8us>(reinterpret_cast<const unsigned short int*>(from), n, offset);
+}
+template<> EIGEN_ALWAYS_INLINE Packet16c ploadu_partial<Packet16c>(const signed char* from, const Index n, const Index offset)
+{
+  return ploadu_partial_common<Packet16c>(from, n, offset);
+}
+template<> EIGEN_ALWAYS_INLINE Packet16uc ploadu_partial<Packet16uc>(const unsigned char* from, const Index n, const Index offset)
+{
+  return ploadu_partial_common<Packet16uc>(from, n, offset);
+}
+
+template<typename Packet> EIGEN_STRONG_INLINE Packet ploaddup_common(const __UNPACK_TYPE__(Packet)*   from)
+{
+  Packet p;
+  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet>(from);
+  else                                  p = ploadu<Packet>(from);
+  return vec_mergeh(p, p);
+}
+template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float*   from)
+{
+  return ploaddup_common<Packet4f>(from);
+}
+template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int*     from)
+{
+  return ploaddup_common<Packet4i>(from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8s ploaddup<Packet8s>(const short int*     from)
+{
+  Packet8s p;
+  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet8s>(from);
+  else                                  p = ploadu<Packet8s>(from);
+  return vec_mergeh(p, p);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8us ploaddup<Packet8us>(const unsigned short int*     from)
+{
+  Packet8us p;
+  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet8us>(from);
+  else                                  p = ploadu<Packet8us>(from);
+  return vec_mergeh(p, p);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8s ploadquad<Packet8s>(const short int*     from)
+{
+  Packet8s p;
+  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet8s>(from);
+  else                                  p = ploadu<Packet8s>(from);
+  return vec_perm(p, p, p16uc_QUADRUPLICATE16_HI);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8us ploadquad<Packet8us>(const unsigned short int*     from)
+{
+  Packet8us p;
+  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet8us>(from);
+  else                                  p = ploadu<Packet8us>(from);
+  return vec_perm(p, p, p16uc_QUADRUPLICATE16_HI);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf ploadquad<Packet8bf>(const bfloat16*     from)
+{
+  return ploadquad<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
+}
+
+template<> EIGEN_STRONG_INLINE Packet16c ploaddup<Packet16c>(const signed char*     from)
+{
+  Packet16c p;
+  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet16c>(from);
+  else                                  p = ploadu<Packet16c>(from);
+  return vec_mergeh(p, p);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16uc ploaddup<Packet16uc>(const unsigned char*     from)
+{
+  Packet16uc p;
+  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet16uc>(from);
+  else                                  p = ploadu<Packet16uc>(from);
+  return vec_mergeh(p, p);
+}
+
+template<typename Packet> EIGEN_STRONG_INLINE void pstoreu_common(__UNPACK_TYPE__(Packet)*  to, const Packet& from)
+{
+  EIGEN_DEBUG_UNALIGNED_STORE
+#if defined(EIGEN_VECTORIZE_VSX) || !defined(_BIG_ENDIAN)
+  vec_xst(from, 0, to);
+#else
+  // Taken from http://developer.apple.com/hardwaredrivers/ve/alignment.html
+  // Warning: not thread safe!
+  Packet16uc MSQ, LSQ, edges;
+  Packet16uc edgeAlign, align;
+
+  MSQ = vec_ld(0, (unsigned char *)to);                     // most significant quadword
+  LSQ = vec_ld(15, (unsigned char *)to);                    // least significant quadword
+  edgeAlign = vec_lvsl(0, to);                              // permute map to extract edges
+  edges=vec_perm(LSQ,MSQ,edgeAlign);                        // extract the edges
+  align = vec_lvsr( 0, to );                                // permute map to misalign data
+  MSQ = vec_perm(edges,(Packet16uc)from,align);             // misalign the data (MSQ)
+  LSQ = vec_perm((Packet16uc)from,edges,align);             // misalign the data (LSQ)
+  vec_st( LSQ, 15, (unsigned char *)to );                   // Store the LSQ part first
+  vec_st( MSQ, 0, (unsigned char *)to );                   // Store the MSQ part second
+#endif
+}
+template<> EIGEN_STRONG_INLINE void pstoreu<float>(float*  to, const Packet4f& from)
+{
+  pstoreu_common<Packet4f>(to, from);
+}
+template<> EIGEN_STRONG_INLINE void pstoreu<int>(int*      to, const Packet4i& from)
+{
+  pstoreu_common<Packet4i>(to, from);
+}
+template<> EIGEN_STRONG_INLINE void pstoreu<short int>(short int*      to, const Packet8s& from)
+{
+  pstoreu_common<Packet8s>(to, from);
+}
+template<> EIGEN_STRONG_INLINE void pstoreu<unsigned short int>(unsigned short int*      to, const Packet8us& from)
+{
+  pstoreu_common<Packet8us>(to, from);
+}
+template<> EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16*      to, const Packet8bf& from)
+{
+  pstoreu_common<Packet8us>(reinterpret_cast<unsigned short int*>(to), from.m_val);
+}
+template<> EIGEN_STRONG_INLINE void pstoreu<signed char>(signed char*      to, const Packet16c& from)
+{
+  pstoreu_common<Packet16c>(to, from);
+}
+template<> EIGEN_STRONG_INLINE void pstoreu<unsigned char>(unsigned char*      to, const Packet16uc& from)
+{
+  pstoreu_common<Packet16uc>(to, from);
+}
+
+template<typename Packet> EIGEN_ALWAYS_INLINE void pstoreu_partial_common(__UNPACK_TYPE__(Packet)*  to, const Packet& from, const Index n, const Index offset)
+{
+  const Index packet_size = unpacket_traits<Packet>::size;
+  eigen_internal_assert(n + offset <= packet_size && "number of elements plus offset will write past end of packet");
+  const Index size = sizeof(__UNPACK_TYPE__(Packet));
+#ifdef _ARCH_PWR9
+  EIGEN_UNUSED_VARIABLE(packet_size);
+  EIGEN_DEBUG_UNALIGNED_STORE
+  Packet store = from;
+  if (offset) {
+    Packet16uc shift = pset1<Packet16uc>(offset * 8 * size);
+#ifdef _BIG_ENDIAN
+    store = Packet(vec_slo(Packet16uc(store), shift));
+#else
+    store = Packet(vec_sro(Packet16uc(store), shift));
+#endif
+  }
+  vec_xst_len(store, to, n * size);
+#else
+  if (n) {
+    EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) store[packet_size];
+    pstore(store, from);
+    unsigned char* store2 = reinterpret_cast<unsigned char *>(store + offset);
+    unsigned char* to2 = reinterpret_cast<unsigned char *>(to);
+    Index n2 = n * size;
+    if (16 <= n2) {
+      pstoreu(to2, ploadu<Packet16uc>(store2));
+    } else {
+      memcpy((void *)to2, (void *)store2, n2);
+    }
+  }
+#endif
+}
+
+template<> EIGEN_ALWAYS_INLINE void pstoreu_partial<float>(float*  to, const Packet4f& from, const Index n, const Index offset)
+{
+  pstoreu_partial_common<Packet4f>(to, from, n, offset);
+}
+template<> EIGEN_ALWAYS_INLINE void pstoreu_partial<int>(int*  to, const Packet4i& from, const Index n, const Index offset)
+{
+  pstoreu_partial_common<Packet4i>(to, from, n, offset);
+}
+template<> EIGEN_ALWAYS_INLINE void pstoreu_partial<short int>(short int*  to, const Packet8s& from, const Index n, const Index offset)
+{
+  pstoreu_partial_common<Packet8s>(to, from, n, offset);
+}
+template<> EIGEN_ALWAYS_INLINE void pstoreu_partial<unsigned short int>(unsigned short int*  to, const Packet8us& from, const Index n, const Index offset)
+{
+  pstoreu_partial_common<Packet8us>(to, from, n, offset);
+}
+template<> EIGEN_ALWAYS_INLINE void pstoreu_partial<bfloat16>(bfloat16*      to, const Packet8bf& from, const Index n, const Index offset)
+{
+  pstoreu_partial_common<Packet8us>(reinterpret_cast<unsigned short int*>(to), from, n, offset);
+}
+template<> EIGEN_ALWAYS_INLINE void pstoreu_partial<signed char>(signed char*  to, const Packet16c& from, const Index n, const Index offset)
+{
+  pstoreu_partial_common<Packet16c>(to, from, n, offset);
+}
+template<> EIGEN_ALWAYS_INLINE void pstoreu_partial<unsigned char>(unsigned char*  to, const Packet16uc& from, const Index n, const Index offset)
+{
+  pstoreu_partial_common<Packet16uc>(to, from, n, offset);
+}
+
+template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr)    { EIGEN_PPC_PREFETCH(addr); }
+template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*     addr)    { EIGEN_PPC_PREFETCH(addr); }
+
+template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { EIGEN_ALIGN16 float x; vec_ste(a, 0, &x); return x; }
+template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { EIGEN_ALIGN16 int   x; vec_ste(a, 0, &x); return x; }
+
+template<typename Packet> EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) pfirst_common(const Packet& a) {
+  EIGEN_ALIGN16 __UNPACK_TYPE__(Packet) x;
+  vec_ste(a, 0, &x);
+  return x;
+}
+
+template<> EIGEN_STRONG_INLINE short int pfirst<Packet8s>(const Packet8s& a) {
+  return pfirst_common<Packet8s>(a);
+}
+
+template<> EIGEN_STRONG_INLINE unsigned short int pfirst<Packet8us>(const Packet8us& a) {
+  return pfirst_common<Packet8us>(a);
+}
+
+template<> EIGEN_STRONG_INLINE signed char pfirst<Packet16c>(const Packet16c& a)
+{
+  return pfirst_common<Packet16c>(a);
+}
+
+template<> EIGEN_STRONG_INLINE unsigned char pfirst<Packet16uc>(const Packet16uc& a)
+{
+  return pfirst_common<Packet16uc>(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a)
+{
+  return reinterpret_cast<Packet4f>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
+}
+template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a)
+{
+  return reinterpret_cast<Packet4i>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
+}
+template<> EIGEN_STRONG_INLINE Packet8s preverse(const Packet8s& a)
+{
+  return reinterpret_cast<Packet8s>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE16));
+}
+template<> EIGEN_STRONG_INLINE Packet8us preverse(const Packet8us& a)
+{
+  return reinterpret_cast<Packet8us>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE16));
+}
+template<> EIGEN_STRONG_INLINE Packet16c preverse(const Packet16c& a)
+{
+#ifdef _ARCH_PWR9
+  return vec_revb(a);
+#else
+  return vec_perm(a, a, p16uc_REVERSE8);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet16uc preverse(const Packet16uc& a)
+{
+#ifdef _ARCH_PWR9
+  return vec_revb(a);
+#else
+  return vec_perm(a, a, p16uc_REVERSE8);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet8bf preverse(const Packet8bf& a)
+{
+  return preverse<Packet8us>(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) { return vec_abs(a); }
+template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) { return vec_abs(a); }
+template<> EIGEN_STRONG_INLINE Packet8s pabs(const Packet8s& a) { return vec_abs(a); }
+template<> EIGEN_STRONG_INLINE Packet8us pabs(const Packet8us& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet16c pabs(const Packet16c& a) { return vec_abs(a); }
+template<> EIGEN_STRONG_INLINE Packet16uc pabs(const Packet16uc& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet8bf  pabs(const Packet8bf& a) {
+  EIGEN_DECLARE_CONST_FAST_Packet8us(abs_mask,0x7FFF);
+  return pand<Packet8us>(p8us_abs_mask, a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf psignbit(const Packet8bf& a) { return vec_sra(a.m_val, vec_splat_u16(15)); }
+template<> EIGEN_STRONG_INLINE Packet4f  psignbit(const Packet4f&  a) { return  (Packet4f)vec_sra((Packet4i)a, vec_splats((unsigned int)(31))); }
+
+template<int N> EIGEN_STRONG_INLINE Packet4i parithmetic_shift_right(const Packet4i& a)
+{ return vec_sra(a,reinterpret_cast<Packet4ui>(pset1<Packet4i>(N))); }
+template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_right(const Packet4i& a)
+{ return vec_sr(a,reinterpret_cast<Packet4ui>(pset1<Packet4i>(N))); }
+template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_left(const Packet4i& a)
+{ return vec_sl(a,reinterpret_cast<Packet4ui>(pset1<Packet4i>(N))); }
+template<int N> EIGEN_STRONG_INLINE Packet4f plogical_shift_left(const Packet4f& a)
+{
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
+  Packet4ui r = vec_sl(reinterpret_cast<Packet4ui>(a), p4ui_mask);
+  return reinterpret_cast<Packet4f>(r);
+}
+
+template<int N> EIGEN_STRONG_INLINE Packet4f plogical_shift_right(const Packet4f& a)
+{
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
+  Packet4ui r = vec_sr(reinterpret_cast<Packet4ui>(a), p4ui_mask);
+  return reinterpret_cast<Packet4f>(r);
+}
+
+template<int N> EIGEN_STRONG_INLINE Packet4ui plogical_shift_right(const Packet4ui& a)
+{
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
+  return vec_sr(a, p4ui_mask);
+}
+
+template<int N> EIGEN_STRONG_INLINE Packet4ui plogical_shift_left(const Packet4ui& a)
+{
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(mask, N);
+  return vec_sl(a, p4ui_mask);
+}
+
+template<int N> EIGEN_STRONG_INLINE Packet8us plogical_shift_left(const Packet8us& a)
+{
+  const EIGEN_DECLARE_CONST_FAST_Packet8us(mask, N);
+  return vec_sl(a, p8us_mask);
+}
+template<int N> EIGEN_STRONG_INLINE Packet8us plogical_shift_right(const Packet8us& a)
+{
+  const EIGEN_DECLARE_CONST_FAST_Packet8us(mask, N);
+  return vec_sr(a, p8us_mask);
+}
+
+EIGEN_STRONG_INLINE Packet4f Bf16ToF32Even(const Packet8bf& bf){
+  return plogical_shift_left<16>(reinterpret_cast<Packet4f>(bf.m_val));
+}
+
+EIGEN_STRONG_INLINE Packet4f Bf16ToF32Odd(const Packet8bf& bf){
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(high_mask, 0xFFFF0000);
+  return pand<Packet4f>(
+    reinterpret_cast<Packet4f>(bf.m_val),
+    reinterpret_cast<Packet4f>(p4ui_high_mask)
+  );
+}
+
+EIGEN_ALWAYS_INLINE Packet8us pmerge(Packet4ui even, Packet4ui odd) {
+#ifdef _BIG_ENDIAN
+  return vec_perm(reinterpret_cast<Packet8us>(odd), reinterpret_cast<Packet8us>(even), p16uc_MERGEO16);
+#else
+  return vec_perm(reinterpret_cast<Packet8us>(even), reinterpret_cast<Packet8us>(odd), p16uc_MERGEE16);
+#endif
+}
+
+// Simple interleaving of bool masks, prevents true values from being
+// converted to NaNs.
+EIGEN_STRONG_INLINE Packet8bf F32ToBf16Bool(Packet4f even, Packet4f odd) {
+  return pmerge(reinterpret_cast<Packet4ui>(even), reinterpret_cast<Packet4ui>(odd));
+}
+
+//#define SUPPORT_BF16_SUBNORMALS
+
+#ifndef __VEC_CLASS_FP_NAN
+#define __VEC_CLASS_FP_NAN (1<<6)
+#endif
+
+#if defined(SUPPORT_BF16_SUBNORMALS) && !defined(__VEC_CLASS_FP_SUBNORMAL)
+#define __VEC_CLASS_FP_SUBNORMAL_P (1<<1)
+#define __VEC_CLASS_FP_SUBNORMAL_N (1<<0)
+
+#define __VEC_CLASS_FP_SUBNORMAL (__VEC_CLASS_FP_SUBNORMAL_P | __VEC_CLASS_FP_SUBNORMAL_N)
+#endif
+
+EIGEN_STRONG_INLINE Packet8bf F32ToBf16(Packet4f p4f){
+#ifdef _ARCH_PWR10
+  return reinterpret_cast<Packet8us>(__builtin_vsx_xvcvspbf16(reinterpret_cast<Packet16uc>(p4f)));
+#else
+  Packet4ui input = reinterpret_cast<Packet4ui>(p4f);
+  Packet4ui lsb = plogical_shift_right<16>(input);
+  lsb = pand<Packet4ui>(lsb, reinterpret_cast<Packet4ui>(p4i_ONE));
+
+  EIGEN_DECLARE_CONST_FAST_Packet4ui(BIAS,0x7FFFu);
+  Packet4ui rounding_bias = padd<Packet4ui>(lsb, p4ui_BIAS);
+  input = padd<Packet4ui>(input, rounding_bias);
+
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(nan, 0x7FC00000);
+#ifdef _ARCH_PWR9
+  Packet4bi nan_selector = vec_test_data_class(p4f, __VEC_CLASS_FP_NAN);
+  input = vec_sel(input, p4ui_nan, nan_selector);
+
+#ifdef SUPPORT_BF16_SUBNORMALS
+  Packet4bi subnormal_selector = vec_test_data_class(p4f, __VEC_CLASS_FP_SUBNORMAL);
+  input = vec_sel(input, reinterpret_cast<Packet4ui>(p4f), subnormal_selector);
+#endif
+#else
+#ifdef SUPPORT_BF16_SUBNORMALS
+  //Test NaN and Subnormal
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(exp_mask, 0x7F800000);
+  Packet4ui exp = pand<Packet4ui>(p4ui_exp_mask, reinterpret_cast<Packet4ui>(p4f));
+
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(mantissa_mask, 0x7FFFFF);
+  Packet4ui mantissa = pand<Packet4ui>(p4ui_mantissa_mask, reinterpret_cast<Packet4ui>(p4f));
+
+  Packet4bi is_max_exp = vec_cmpeq(exp, p4ui_exp_mask);
+  Packet4bi is_mant_zero = vec_cmpeq(mantissa, reinterpret_cast<Packet4ui>(p4i_ZERO));
+
+  Packet4ui nan_selector = pandnot<Packet4ui>(
+      reinterpret_cast<Packet4ui>(is_max_exp),
+      reinterpret_cast<Packet4ui>(is_mant_zero)
+  );
+
+  Packet4bi is_zero_exp = vec_cmpeq(exp, reinterpret_cast<Packet4ui>(p4i_ZERO));
+
+  Packet4ui subnormal_selector = pandnot<Packet4ui>(
+      reinterpret_cast<Packet4ui>(is_zero_exp),
+      reinterpret_cast<Packet4ui>(is_mant_zero)
+  );
+
+  input = vec_sel(input, p4ui_nan, nan_selector);
+  input = vec_sel(input, reinterpret_cast<Packet4ui>(p4f), subnormal_selector);
+#else
+  //Test only NaN
+  Packet4bi nan_selector = vec_cmpeq(p4f, p4f);
+
+  input = vec_sel(p4ui_nan, input, nan_selector);
+#endif
+#endif
+
+  input = plogical_shift_right<16>(input);
+  return reinterpret_cast<Packet8us>(input);
+#endif
+}
+
+#ifdef _BIG_ENDIAN
+/**
+ * Pack the high portion of two float Packets into one bfloat16 Packet
+ *
+ * @param lohi to expect either a low & high OR odd & even order
+ */
+template<bool lohi>
+EIGEN_ALWAYS_INLINE Packet8bf Bf16PackHigh(Packet4f lo, Packet4f hi)
+{
+  if (lohi) {
+    return vec_perm(reinterpret_cast<Packet8us>(lo), reinterpret_cast<Packet8us>(hi), p16uc_MERGEH16);
+  } else {
+    return vec_perm(reinterpret_cast<Packet8us>(hi), reinterpret_cast<Packet8us>(lo), p16uc_MERGEE16);
+  }
+}
+
+/**
+ * Pack the low portion of two float Packets into one bfloat16 Packet
+ *
+ * @param lohi to expect either a low & high OR odd & even order
+ */
+template<bool lohi>
+EIGEN_ALWAYS_INLINE Packet8bf Bf16PackLow(Packet4f lo, Packet4f hi)
+{
+  if (lohi) {
+    return vec_pack(reinterpret_cast<Packet4ui>(lo), reinterpret_cast<Packet4ui>(hi));
+  } else {
+    return vec_perm(reinterpret_cast<Packet8us>(hi), reinterpret_cast<Packet8us>(lo), p16uc_MERGEO16);
+  }
+}
+#else
+template<bool lohi>
+EIGEN_ALWAYS_INLINE Packet8bf Bf16PackLow(Packet4f hi, Packet4f lo)
+{
+  if (lohi) {
+    return vec_pack(reinterpret_cast<Packet4ui>(hi), reinterpret_cast<Packet4ui>(lo));
+  } else {
+    return vec_perm(reinterpret_cast<Packet8us>(hi), reinterpret_cast<Packet8us>(lo), p16uc_MERGEE16);
+  }
+}
+
+template<bool lohi>
+EIGEN_ALWAYS_INLINE Packet8bf Bf16PackHigh(Packet4f hi, Packet4f lo)
+{
+  if (lohi) {
+    return vec_perm(reinterpret_cast<Packet8us>(hi), reinterpret_cast<Packet8us>(lo), p16uc_MERGEL16);
+  } else {
+    return vec_perm(reinterpret_cast<Packet8us>(hi), reinterpret_cast<Packet8us>(lo), p16uc_MERGEO16);
+  }
+}
+#endif
+
+/**
+ * Convert and pack two float Packets into one bfloat16 Packet
+ *
+ * @param lohi to expect either a low & high OR odd & even order
+ */
+template<bool lohi = true>
+EIGEN_ALWAYS_INLINE Packet8bf F32ToBf16Two(Packet4f lo, Packet4f hi)
+{
+  Packet8us p4f = Bf16PackHigh<lohi>(lo, hi);
+  Packet8us p4f2 = Bf16PackLow<lohi>(lo, hi);
+
+  Packet8us lsb = pand<Packet8us>(p4f, p8us_ONE);
+  EIGEN_DECLARE_CONST_FAST_Packet8us(BIAS,0x7FFFu);
+  lsb = padd<Packet8us>(lsb, p8us_BIAS);
+  lsb = padd<Packet8us>(lsb, p4f2);
+
+  Packet8bi rounding_bias = vec_cmplt(lsb, p4f2);
+  Packet8us input = psub<Packet8us>(p4f, reinterpret_cast<Packet8us>(rounding_bias));
+
+#ifdef _ARCH_PWR9
+  Packet4bi nan_selector_lo = vec_test_data_class(lo, __VEC_CLASS_FP_NAN);
+  Packet4bi nan_selector_hi = vec_test_data_class(hi, __VEC_CLASS_FP_NAN);
+  Packet8us nan_selector = Bf16PackLow<lohi>(reinterpret_cast<Packet4f>(nan_selector_lo), reinterpret_cast<Packet4f>(nan_selector_hi));
+
+  input = vec_sel(input, p8us_BIAS, nan_selector);
+
+#ifdef SUPPORT_BF16_SUBNORMALS
+  Packet4bi subnormal_selector_lo = vec_test_data_class(lo, __VEC_CLASS_FP_SUBNORMAL);
+  Packet4bi subnormal_selector_hi = vec_test_data_class(hi, __VEC_CLASS_FP_SUBNORMAL);
+  Packet8us subnormal_selector = Bf16PackLow<lohi>(reinterpret_cast<Packet4f>(subnormal_selector_lo), reinterpret_cast<Packet4f>(subnormal_selector_hi));
+
+  input = vec_sel(input, reinterpret_cast<Packet8us>(p4f), subnormal_selector);
+#endif
+#else
+#ifdef SUPPORT_BF16_SUBNORMALS
+  //Test NaN and Subnormal
+  const EIGEN_DECLARE_CONST_FAST_Packet8us(exp_mask, 0x7F80);
+  Packet8us exp = pand<Packet8us>(p8us_exp_mask, p4f);
+
+  const EIGEN_DECLARE_CONST_FAST_Packet8us(mantissa_mask, 0x7Fu);
+  Packet8us mantissa = pand<Packet8us>(p8us_mantissa_mask, p4f);
+
+  Packet8bi is_max_exp = vec_cmpeq(exp, p8us_exp_mask);
+  Packet8bi is_mant_zero = vec_cmpeq(mantissa, reinterpret_cast<Packet8us>(p4i_ZERO));
+
+  Packet8us nan_selector = pandnot<Packet8us>(
+      reinterpret_cast<Packet8us>(is_max_exp),
+      reinterpret_cast<Packet8us>(is_mant_zero)
+  );
+
+  Packet8bi is_zero_exp = vec_cmpeq(exp, reinterpret_cast<Packet8us>(p4i_ZERO));
+
+  Packet8us subnormal_selector = pandnot<Packet8us>(
+      reinterpret_cast<Packet8us>(is_zero_exp),
+      reinterpret_cast<Packet8us>(is_mant_zero)
+  );
+
+  // Using BIAS as NaN (since any or all of the last 7 bits can be set)
+  input = vec_sel(input, p8us_BIAS, nan_selector);
+  input = vec_sel(input, reinterpret_cast<Packet8us>(p4f), subnormal_selector);
+#else
+  //Test only NaN
+  Packet4bi nan_selector_lo = vec_cmpeq(lo, lo);
+  Packet4bi nan_selector_hi = vec_cmpeq(hi, hi);
+  Packet8us nan_selector = Bf16PackLow<lohi>(reinterpret_cast<Packet4f>(nan_selector_lo), reinterpret_cast<Packet4f>(nan_selector_hi));
+
+  input = vec_sel(p8us_BIAS, input, nan_selector);
+#endif
+#endif
+
+  return input;
+}
+
+/**
+ * Convert and pack two float Packets into one bfloat16 Packet - low & high order
+ */
+EIGEN_STRONG_INLINE Packet8bf F32ToBf16Both(Packet4f lo, Packet4f hi)
+{
+#ifdef _ARCH_PWR10
+  Packet8bf fp16_0 = F32ToBf16(lo);
+  Packet8bf fp16_1 = F32ToBf16(hi);
+  return vec_pack(reinterpret_cast<Packet4ui>(fp16_0.m_val), reinterpret_cast<Packet4ui>(fp16_1.m_val));
+#else
+  return F32ToBf16Two(lo, hi);
+#endif
+}
+
+/**
+ * Convert and pack two float Packets into one bfloat16 Packet - odd & even order
+ */
+EIGEN_STRONG_INLINE Packet8bf F32ToBf16(Packet4f even, Packet4f odd){
+#ifdef _ARCH_PWR10
+  return pmerge(reinterpret_cast<Packet4ui>(F32ToBf16(even).m_val), reinterpret_cast<Packet4ui>(F32ToBf16(odd).m_val));
+#else
+  return F32ToBf16Two<false>(even, odd);
+#endif
+}
+#define BF16_TO_F32_UNARY_OP_WRAPPER(OP, A) \
+  Packet4f a_even = Bf16ToF32Even(A);\
+  Packet4f a_odd = Bf16ToF32Odd(A);\
+  Packet4f op_even = OP(a_even);\
+  Packet4f op_odd = OP(a_odd);\
+  return F32ToBf16(op_even, op_odd);\
+
+#define BF16_TO_F32_BINARY_OP_WRAPPER(OP, A, B) \
+  Packet4f a_even = Bf16ToF32Even(A);\
+  Packet4f a_odd = Bf16ToF32Odd(A);\
+  Packet4f b_even = Bf16ToF32Even(B);\
+  Packet4f b_odd = Bf16ToF32Odd(B);\
+  Packet4f op_even = OP(a_even, b_even);\
+  Packet4f op_odd = OP(a_odd, b_odd);\
+  return F32ToBf16(op_even, op_odd);\
+
+#define BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(OP, A, B) \
+  Packet4f a_even = Bf16ToF32Even(A);\
+  Packet4f a_odd = Bf16ToF32Odd(A);\
+  Packet4f b_even = Bf16ToF32Even(B);\
+  Packet4f b_odd = Bf16ToF32Odd(B);\
+  Packet4f op_even = OP(a_even, b_even);\
+  Packet4f op_odd = OP(a_odd, b_odd);\
+  return F32ToBf16Bool(op_even, op_odd);\
+
+template<> EIGEN_STRONG_INLINE Packet8bf padd<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER(padd<Packet4f>, a, b);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pmul<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER(pmul<Packet4f>, a, b);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pdiv<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER(pdiv<Packet4f>, a, b);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pnegate<Packet8bf>(const Packet8bf& a) {
+  EIGEN_DECLARE_CONST_FAST_Packet8us(neg_mask,0x8000);
+  return pxor<Packet8us>(p8us_neg_mask, a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf psub<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER(psub<Packet4f>, a, b);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pexp<Packet8bf> (const Packet8bf& a){
+  BF16_TO_F32_UNARY_OP_WRAPPER(pexp_float, a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pldexp<Packet4f>(const Packet4f& a, const Packet4f& exponent) {
+  return pldexp_generic(a,exponent);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf pldexp<Packet8bf> (const Packet8bf& a, const Packet8bf& exponent){
+  BF16_TO_F32_BINARY_OP_WRAPPER(pldexp<Packet4f>, a, exponent);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pfrexp<Packet4f>(const Packet4f& a, Packet4f& exponent) {
+  return pfrexp_generic(a,exponent);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf pfrexp<Packet8bf> (const Packet8bf& a, Packet8bf& e){
+  Packet4f a_even = Bf16ToF32Even(a);
+  Packet4f a_odd = Bf16ToF32Odd(a);
+  Packet4f e_even;
+  Packet4f e_odd;
+  Packet4f op_even = pfrexp<Packet4f>(a_even, e_even);
+  Packet4f op_odd = pfrexp<Packet4f>(a_odd, e_odd);
+  e = F32ToBf16(e_even, e_odd);
+  return F32ToBf16(op_even, op_odd);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf psin<Packet8bf> (const Packet8bf& a){
+  BF16_TO_F32_UNARY_OP_WRAPPER(psin_float, a);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf pcos<Packet8bf> (const Packet8bf& a){
+  BF16_TO_F32_UNARY_OP_WRAPPER(pcos_float, a);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf plog<Packet8bf> (const Packet8bf& a){
+  BF16_TO_F32_UNARY_OP_WRAPPER(plog_float, a);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf pfloor<Packet8bf> (const Packet8bf& a){
+  BF16_TO_F32_UNARY_OP_WRAPPER(pfloor<Packet4f>, a);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf pceil<Packet8bf> (const Packet8bf& a){
+  BF16_TO_F32_UNARY_OP_WRAPPER(pceil<Packet4f>, a);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf pround<Packet8bf> (const Packet8bf& a){
+  BF16_TO_F32_UNARY_OP_WRAPPER(pround<Packet4f>, a);
+}
+#ifdef EIGEN_VECTORIZE_VSX
+template<> EIGEN_STRONG_INLINE Packet8bf print<Packet8bf> (const Packet8bf& a){
+  BF16_TO_F32_UNARY_OP_WRAPPER(print<Packet4f>, a);
+}
+#endif
+template<> EIGEN_STRONG_INLINE Packet8bf pmadd(const Packet8bf& a, const Packet8bf& b, const Packet8bf& c) {
+  Packet4f a_even = Bf16ToF32Even(a);
+  Packet4f a_odd = Bf16ToF32Odd(a);
+  Packet4f b_even = Bf16ToF32Even(b);
+  Packet4f b_odd = Bf16ToF32Odd(b);
+  Packet4f c_even = Bf16ToF32Even(c);
+  Packet4f c_odd = Bf16ToF32Odd(c);
+  Packet4f pmadd_even = pmadd<Packet4f>(a_even, b_even, c_even);
+  Packet4f pmadd_odd = pmadd<Packet4f>(a_odd, b_odd, c_odd);
+  return F32ToBf16(pmadd_even, pmadd_odd);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pmin<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER(pmin<Packet4f>, a, b);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pmax<Packet8bf>(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER(pmax<Packet4f>, a, b);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pcmp_lt(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_lt<Packet4f>, a, b);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf pcmp_lt_or_nan(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_lt_or_nan<Packet4f>, a, b);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf pcmp_le(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_le<Packet4f>, a, b);
+}
+template<> EIGEN_STRONG_INLINE Packet8bf pcmp_eq(const Packet8bf& a, const Packet8bf& b) {
+  BF16_TO_F32_BINARY_OP_WRAPPER_BOOL(pcmp_eq<Packet4f>, a, b);
+}
+
+template<> EIGEN_STRONG_INLINE bfloat16 pfirst(const Packet8bf& a) {
+  return Eigen::bfloat16_impl::raw_uint16_to_bfloat16((pfirst<Packet8us>(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf ploaddup<Packet8bf>(const  bfloat16*     from)
+{
+  return ploaddup<Packet8us>(reinterpret_cast<const unsigned short int*>(from));
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf plset<Packet8bf>(const bfloat16& a) {
+  bfloat16 countdown[8] = { bfloat16(0), bfloat16(1), bfloat16(2), bfloat16(3),
+                            bfloat16(4), bfloat16(5), bfloat16(6), bfloat16(7) };
+  return padd<Packet8bf>(pset1<Packet8bf>(a), pload<Packet8bf>(countdown));
+}
+
+template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
+{
+  Packet4f b, sum;
+  b   = vec_sld(a, a, 8);
+  sum = a + b;
+  b   = vec_sld(sum, sum, 4);
+  sum += b;
+  return pfirst(sum);
+}
+
+template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a)
+{
+  Packet4i sum;
+  sum = vec_sums(a, p4i_ZERO);
+#ifdef _BIG_ENDIAN
+  sum = vec_sld(sum, p4i_ZERO, 12);
+#else
+  sum = vec_sld(p4i_ZERO, sum, 4);
+#endif
+  return pfirst(sum);
+}
+
+template<> EIGEN_STRONG_INLINE bfloat16 predux<Packet8bf>(const Packet8bf& a)
+{
+  float redux_even = predux<Packet4f>(Bf16ToF32Even(a));
+  float redux_odd  = predux<Packet4f>(Bf16ToF32Odd(a));
+  float f32_result = redux_even + redux_odd;
+  return bfloat16(f32_result);
+}
+template<typename Packet> EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) predux_size8(const Packet& a)
+{
+  union{
+    Packet v;
+    __UNPACK_TYPE__(Packet) n[8];
+  } vt;
+  vt.v = a;
+
+  EIGEN_ALIGN16 int first_loader[4] = { vt.n[0], vt.n[1], vt.n[2], vt.n[3] };
+  EIGEN_ALIGN16 int second_loader[4] = { vt.n[4], vt.n[5], vt.n[6], vt.n[7] };
+  Packet4i first_half  = pload<Packet4i>(first_loader);
+  Packet4i second_half = pload<Packet4i>(second_loader);
+
+  return static_cast<__UNPACK_TYPE__(Packet)>(predux(first_half) + predux(second_half));
+}
+
+template<> EIGEN_STRONG_INLINE short int predux<Packet8s>(const Packet8s& a)
+{
+  return predux_size8<Packet8s>(a);
+}
+
+template<> EIGEN_STRONG_INLINE unsigned short int predux<Packet8us>(const Packet8us& a)
+{
+  return predux_size8<Packet8us>(a);
+}
+
+template<typename Packet> EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) predux_size16(const Packet& a)
+{
+  union{
+    Packet v;
+    __UNPACK_TYPE__(Packet) n[16];
+  } vt;
+  vt.v = a;
+
+  EIGEN_ALIGN16 int first_loader[4] = { vt.n[0], vt.n[1], vt.n[2], vt.n[3] };
+  EIGEN_ALIGN16 int second_loader[4] = { vt.n[4], vt.n[5], vt.n[6], vt.n[7] };
+  EIGEN_ALIGN16 int third_loader[4] = { vt.n[8], vt.n[9], vt.n[10], vt.n[11] };
+  EIGEN_ALIGN16 int fourth_loader[4] = { vt.n[12], vt.n[13], vt.n[14], vt.n[15] };
+
+  Packet4i first_quarter = pload<Packet4i>(first_loader);
+  Packet4i second_quarter = pload<Packet4i>(second_loader);
+  Packet4i third_quarter = pload<Packet4i>(third_loader);
+  Packet4i fourth_quarter = pload<Packet4i>(fourth_loader);
+
+  return static_cast<__UNPACK_TYPE__(Packet)>(predux(first_quarter) + predux(second_quarter)
+		                  + predux(third_quarter) + predux(fourth_quarter));
+}
+
+template<> EIGEN_STRONG_INLINE signed char predux<Packet16c>(const Packet16c& a)
+{
+  return predux_size16<Packet16c>(a);
+}
+
+template<> EIGEN_STRONG_INLINE unsigned char predux<Packet16uc>(const Packet16uc& a)
+{
+  return predux_size16<Packet16uc>(a);
+}
+
+// Other reduction functions:
+// mul
+template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
+{
+  Packet4f prod;
+  prod = pmul(a, vec_sld(a, a, 8));
+  return pfirst(pmul(prod, vec_sld(prod, prod, 4)));
+}
+
+template<> EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a)
+{
+  EIGEN_ALIGN16 int aux[4];
+  pstore(aux, a);
+  return aux[0] * aux[1] * aux[2] * aux[3];
+}
+
+template<> EIGEN_STRONG_INLINE short int predux_mul<Packet8s>(const Packet8s& a)
+{
+  Packet8s pair, quad, octo;
+
+  pair = vec_mul(a, vec_sld(a, a, 8));
+  quad = vec_mul(pair, vec_sld(pair, pair, 4));
+  octo = vec_mul(quad, vec_sld(quad, quad, 2));
+
+  return pfirst(octo);
+}
+
+template<> EIGEN_STRONG_INLINE unsigned short int predux_mul<Packet8us>(const Packet8us& a)
+{
+  Packet8us pair, quad, octo;
+
+  pair = vec_mul(a, vec_sld(a, a, 8));
+  quad = vec_mul(pair, vec_sld(pair, pair, 4));
+  octo = vec_mul(quad, vec_sld(quad, quad, 2));
+
+  return pfirst(octo);
+}
+
+template<> EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet8bf>(const Packet8bf& a)
+{
+  float redux_even = predux_mul<Packet4f>(Bf16ToF32Even(a));
+  float redux_odd  = predux_mul<Packet4f>(Bf16ToF32Odd(a));
+  float f32_result = redux_even * redux_odd;
+  return bfloat16(f32_result);
+}
+
+
+template<> EIGEN_STRONG_INLINE signed char predux_mul<Packet16c>(const Packet16c& a)
+{
+  Packet16c pair, quad, octo, result;
+
+  pair = vec_mul(a, vec_sld(a, a, 8));
+  quad = vec_mul(pair, vec_sld(pair, pair, 4));
+  octo = vec_mul(quad, vec_sld(quad, quad, 2));
+  result = vec_mul(octo, vec_sld(octo, octo, 1));
+
+  return pfirst(result);
+}
+
+template<> EIGEN_STRONG_INLINE unsigned char predux_mul<Packet16uc>(const Packet16uc& a)
+{
+  Packet16uc pair, quad, octo, result;
+
+  pair = vec_mul(a, vec_sld(a, a, 8));
+  quad = vec_mul(pair, vec_sld(pair, pair, 4));
+  octo = vec_mul(quad, vec_sld(quad, quad, 2));
+  result = vec_mul(octo, vec_sld(octo, octo, 1));
+
+  return pfirst(result);
+}
+
+// min
+template<typename Packet> EIGEN_STRONG_INLINE
+__UNPACK_TYPE__(Packet) predux_min4(const Packet& a)
+{
+  Packet b, res;
+  b = vec_min(a, vec_sld(a, a, 8));
+  res = vec_min(b, vec_sld(b, b, 4));
+  return pfirst(res);
+}
+
+
+template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
+{
+  return predux_min4<Packet4f>(a);
+}
+
+template<> EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a)
+{
+  return predux_min4<Packet4i>(a);
+}
+
+template<> EIGEN_STRONG_INLINE bfloat16 predux_min<Packet8bf>(const Packet8bf& a)
+{
+  float redux_even = predux_min<Packet4f>(Bf16ToF32Even(a));
+  float redux_odd  = predux_min<Packet4f>(Bf16ToF32Odd(a));
+  float f32_result = (std::min)(redux_even, redux_odd);
+  return bfloat16(f32_result);
+}
+
+template<> EIGEN_STRONG_INLINE short int predux_min<Packet8s>(const Packet8s& a)
+{
+  Packet8s pair, quad, octo;
+  
+  //pair = { Min(a0,a4), Min(a1,a5), Min(a2,a6), Min(a3,a7) }
+  pair = vec_min(a, vec_sld(a, a, 8)); 
+
+  //quad = { Min(a0, a4, a2, a6), Min(a1, a5, a3, a7) }
+  quad = vec_min(pair, vec_sld(pair, pair, 4));
+
+  //octo = { Min(a0, a4, a2, a6, a1, a5, a3, a7) }
+  octo = vec_min(quad, vec_sld(quad, quad, 2));
+  return pfirst(octo);
+}
+
+template<> EIGEN_STRONG_INLINE unsigned short int predux_min<Packet8us>(const Packet8us& a)
+{
+  Packet8us pair, quad, octo;
+  
+  //pair = { Min(a0,a4), Min(a1,a5), Min(a2,a6), Min(a3,a7) }
+  pair = vec_min(a, vec_sld(a, a, 8)); 
+
+  //quad = { Min(a0, a4, a2, a6), Min(a1, a5, a3, a7) }
+  quad = vec_min(pair, vec_sld(pair, pair, 4));
+
+  //octo = { Min(a0, a4, a2, a6, a1, a5, a3, a7) }
+  octo = vec_min(quad, vec_sld(quad, quad, 2));
+  return pfirst(octo);
+}
+
+template<> EIGEN_STRONG_INLINE signed char predux_min<Packet16c>(const Packet16c& a)
+{
+  Packet16c pair, quad, octo, result;
+
+  pair = vec_min(a, vec_sld(a, a, 8));
+  quad = vec_min(pair, vec_sld(pair, pair, 4));
+  octo = vec_min(quad, vec_sld(quad, quad, 2));
+  result = vec_min(octo, vec_sld(octo, octo, 1));
+
+  return pfirst(result);
+}
+
+template<> EIGEN_STRONG_INLINE unsigned char predux_min<Packet16uc>(const Packet16uc& a)
+{
+  Packet16uc pair, quad, octo, result;
+
+  pair = vec_min(a, vec_sld(a, a, 8));
+  quad = vec_min(pair, vec_sld(pair, pair, 4));
+  octo = vec_min(quad, vec_sld(quad, quad, 2));
+  result = vec_min(octo, vec_sld(octo, octo, 1));
+
+  return pfirst(result);
+}
+// max
+template<typename Packet> EIGEN_STRONG_INLINE __UNPACK_TYPE__(Packet) predux_max4(const Packet& a)
+{
+  Packet b, res;
+  b = vec_max(a, vec_sld(a, a, 8));
+  res = vec_max(b, vec_sld(b, b, 4));
+  return pfirst(res);
+}
+
+template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
+{
+  return predux_max4<Packet4f>(a);
+}
+
+template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a)
+{
+  return predux_max4<Packet4i>(a);
+}
+
+template<> EIGEN_STRONG_INLINE bfloat16 predux_max<Packet8bf>(const Packet8bf& a)
+{
+  float redux_even = predux_max<Packet4f>(Bf16ToF32Even(a));
+  float redux_odd  = predux_max<Packet4f>(Bf16ToF32Odd(a));
+  float f32_result = (std::max)(redux_even, redux_odd);
+  return bfloat16(f32_result);
+}
+
+template<> EIGEN_STRONG_INLINE short int predux_max<Packet8s>(const Packet8s& a)
+{
+  Packet8s pair, quad, octo;
+  
+  //pair = { Max(a0,a4), Max(a1,a5), Max(a2,a6), Max(a3,a7) }
+  pair = vec_max(a, vec_sld(a, a, 8)); 
+
+  //quad = { Max(a0, a4, a2, a6), Max(a1, a5, a3, a7) }
+  quad = vec_max(pair, vec_sld(pair, pair, 4));
+
+  //octo = { Max(a0, a4, a2, a6, a1, a5, a3, a7) }
+  octo = vec_max(quad, vec_sld(quad, quad, 2));
+  return pfirst(octo);
+}
+
+template<> EIGEN_STRONG_INLINE unsigned short int predux_max<Packet8us>(const Packet8us& a)
+{
+  Packet8us pair, quad, octo;
+  
+  //pair = { Max(a0,a4), Max(a1,a5), Max(a2,a6), Max(a3,a7) }
+  pair = vec_max(a, vec_sld(a, a, 8)); 
+
+  //quad = { Max(a0, a4, a2, a6), Max(a1, a5, a3, a7) }
+  quad = vec_max(pair, vec_sld(pair, pair, 4));
+
+  //octo = { Max(a0, a4, a2, a6, a1, a5, a3, a7) }
+  octo = vec_max(quad, vec_sld(quad, quad, 2));
+  return pfirst(octo);
+}
+
+template<> EIGEN_STRONG_INLINE signed char predux_max<Packet16c>(const Packet16c& a)
+{
+  Packet16c pair, quad, octo, result;
+
+  pair = vec_max(a, vec_sld(a, a, 8));
+  quad = vec_max(pair, vec_sld(pair, pair, 4));
+  octo = vec_max(quad, vec_sld(quad, quad, 2));
+  result = vec_max(octo, vec_sld(octo, octo, 1));
+
+  return pfirst(result);
+}
+
+template<> EIGEN_STRONG_INLINE unsigned char predux_max<Packet16uc>(const Packet16uc& a)
+{
+  Packet16uc pair, quad, octo, result;
+
+  pair = vec_max(a, vec_sld(a, a, 8));
+  quad = vec_max(pair, vec_sld(pair, pair, 4));
+  octo = vec_max(quad, vec_sld(quad, quad, 2));
+  result = vec_max(octo, vec_sld(octo, octo, 1));
+
+  return pfirst(result);
+}
+
+template<> EIGEN_STRONG_INLINE bool predux_any(const Packet4f& x)
+{
+  return vec_any_ne(x, pzero(x));
+}
+
+template <typename T> EIGEN_DEVICE_FUNC inline void
+ptranpose_common(PacketBlock<T,4>& kernel){
+  T t0, t1, t2, t3;
+  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
+  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
+  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
+  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet4f,4>& kernel) {
+  ptranpose_common<Packet4f>(kernel);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet4i,4>& kernel) {
+  ptranpose_common<Packet4i>(kernel);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet8s,4>& kernel) {
+  Packet8s t0, t1, t2, t3;
+  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
+  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
+  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
+  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet8us,4>& kernel) {
+  Packet8us t0, t1, t2, t3;
+  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
+  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
+  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
+  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet8bf,4>& kernel) {
+  Packet8us t0, t1, t2, t3;
+
+  t0 = vec_mergeh(kernel.packet[0].m_val, kernel.packet[2].m_val);
+  t1 = vec_mergel(kernel.packet[0].m_val, kernel.packet[2].m_val);
+  t2 = vec_mergeh(kernel.packet[1].m_val, kernel.packet[3].m_val);
+  t3 = vec_mergel(kernel.packet[1].m_val, kernel.packet[3].m_val);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet16c,4>& kernel) {
+  Packet16c t0, t1, t2, t3;
+  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
+  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
+  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
+  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet16uc,4>& kernel) {
+  Packet16uc t0, t1, t2, t3;
+  t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
+  t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
+  t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
+  t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet8s,8>& kernel) {
+  Packet8s v[8], sum[8];
+
+  v[0] = vec_mergeh(kernel.packet[0], kernel.packet[4]);
+  v[1] = vec_mergel(kernel.packet[0], kernel.packet[4]);
+  v[2] = vec_mergeh(kernel.packet[1], kernel.packet[5]);
+  v[3] = vec_mergel(kernel.packet[1], kernel.packet[5]);
+  v[4] = vec_mergeh(kernel.packet[2], kernel.packet[6]);
+  v[5] = vec_mergel(kernel.packet[2], kernel.packet[6]);
+  v[6] = vec_mergeh(kernel.packet[3], kernel.packet[7]);
+  v[7] = vec_mergel(kernel.packet[3], kernel.packet[7]);
+  sum[0] = vec_mergeh(v[0], v[4]);
+  sum[1] = vec_mergel(v[0], v[4]);
+  sum[2] = vec_mergeh(v[1], v[5]);
+  sum[3] = vec_mergel(v[1], v[5]);
+  sum[4] = vec_mergeh(v[2], v[6]);
+  sum[5] = vec_mergel(v[2], v[6]);
+  sum[6] = vec_mergeh(v[3], v[7]);
+  sum[7] = vec_mergel(v[3], v[7]);
+
+  kernel.packet[0] = vec_mergeh(sum[0], sum[4]);
+  kernel.packet[1] = vec_mergel(sum[0], sum[4]);
+  kernel.packet[2] = vec_mergeh(sum[1], sum[5]);
+  kernel.packet[3] = vec_mergel(sum[1], sum[5]);
+  kernel.packet[4] = vec_mergeh(sum[2], sum[6]);
+  kernel.packet[5] = vec_mergel(sum[2], sum[6]);
+  kernel.packet[6] = vec_mergeh(sum[3], sum[7]);
+  kernel.packet[7] = vec_mergel(sum[3], sum[7]);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet8us,8>& kernel) {
+  Packet8us v[8], sum[8];
+
+  v[0] = vec_mergeh(kernel.packet[0], kernel.packet[4]);
+  v[1] = vec_mergel(kernel.packet[0], kernel.packet[4]);
+  v[2] = vec_mergeh(kernel.packet[1], kernel.packet[5]);
+  v[3] = vec_mergel(kernel.packet[1], kernel.packet[5]);
+  v[4] = vec_mergeh(kernel.packet[2], kernel.packet[6]);
+  v[5] = vec_mergel(kernel.packet[2], kernel.packet[6]);
+  v[6] = vec_mergeh(kernel.packet[3], kernel.packet[7]);
+  v[7] = vec_mergel(kernel.packet[3], kernel.packet[7]);
+  sum[0] = vec_mergeh(v[0], v[4]);
+  sum[1] = vec_mergel(v[0], v[4]);
+  sum[2] = vec_mergeh(v[1], v[5]);
+  sum[3] = vec_mergel(v[1], v[5]);
+  sum[4] = vec_mergeh(v[2], v[6]);
+  sum[5] = vec_mergel(v[2], v[6]);
+  sum[6] = vec_mergeh(v[3], v[7]);
+  sum[7] = vec_mergel(v[3], v[7]);
+
+  kernel.packet[0] = vec_mergeh(sum[0], sum[4]);
+  kernel.packet[1] = vec_mergel(sum[0], sum[4]);
+  kernel.packet[2] = vec_mergeh(sum[1], sum[5]);
+  kernel.packet[3] = vec_mergel(sum[1], sum[5]);
+  kernel.packet[4] = vec_mergeh(sum[2], sum[6]);
+  kernel.packet[5] = vec_mergel(sum[2], sum[6]);
+  kernel.packet[6] = vec_mergeh(sum[3], sum[7]);
+  kernel.packet[7] = vec_mergel(sum[3], sum[7]);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet8bf,8>& kernel) {
+  Packet8bf v[8], sum[8];
+
+  v[0] = vec_mergeh(kernel.packet[0].m_val, kernel.packet[4].m_val);
+  v[1] = vec_mergel(kernel.packet[0].m_val, kernel.packet[4].m_val);
+  v[2] = vec_mergeh(kernel.packet[1].m_val, kernel.packet[5].m_val);
+  v[3] = vec_mergel(kernel.packet[1].m_val, kernel.packet[5].m_val);
+  v[4] = vec_mergeh(kernel.packet[2].m_val, kernel.packet[6].m_val);
+  v[5] = vec_mergel(kernel.packet[2].m_val, kernel.packet[6].m_val);
+  v[6] = vec_mergeh(kernel.packet[3].m_val, kernel.packet[7].m_val);
+  v[7] = vec_mergel(kernel.packet[3].m_val, kernel.packet[7].m_val);
+  sum[0] = vec_mergeh(v[0].m_val, v[4].m_val);
+  sum[1] = vec_mergel(v[0].m_val, v[4].m_val);
+  sum[2] = vec_mergeh(v[1].m_val, v[5].m_val);
+  sum[3] = vec_mergel(v[1].m_val, v[5].m_val);
+  sum[4] = vec_mergeh(v[2].m_val, v[6].m_val);
+  sum[5] = vec_mergel(v[2].m_val, v[6].m_val);
+  sum[6] = vec_mergeh(v[3].m_val, v[7].m_val);
+  sum[7] = vec_mergel(v[3].m_val, v[7].m_val);
+
+  kernel.packet[0] = vec_mergeh(sum[0].m_val, sum[4].m_val);
+  kernel.packet[1] = vec_mergel(sum[0].m_val, sum[4].m_val);
+  kernel.packet[2] = vec_mergeh(sum[1].m_val, sum[5].m_val);
+  kernel.packet[3] = vec_mergel(sum[1].m_val, sum[5].m_val);
+  kernel.packet[4] = vec_mergeh(sum[2].m_val, sum[6].m_val);
+  kernel.packet[5] = vec_mergel(sum[2].m_val, sum[6].m_val);
+  kernel.packet[6] = vec_mergeh(sum[3].m_val, sum[7].m_val);
+  kernel.packet[7] = vec_mergel(sum[3].m_val, sum[7].m_val);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet16c,16>& kernel) {
+  Packet16c step1[16], step2[16], step3[16];
+
+  step1[0] = vec_mergeh(kernel.packet[0], kernel.packet[8]);
+  step1[1] = vec_mergel(kernel.packet[0], kernel.packet[8]);
+  step1[2] = vec_mergeh(kernel.packet[1], kernel.packet[9]);
+  step1[3] = vec_mergel(kernel.packet[1], kernel.packet[9]);
+  step1[4] = vec_mergeh(kernel.packet[2], kernel.packet[10]);
+  step1[5] = vec_mergel(kernel.packet[2], kernel.packet[10]);
+  step1[6] = vec_mergeh(kernel.packet[3], kernel.packet[11]);
+  step1[7] = vec_mergel(kernel.packet[3], kernel.packet[11]);
+  step1[8] = vec_mergeh(kernel.packet[4], kernel.packet[12]);
+  step1[9] = vec_mergel(kernel.packet[4], kernel.packet[12]);
+  step1[10] = vec_mergeh(kernel.packet[5], kernel.packet[13]);
+  step1[11] = vec_mergel(kernel.packet[5], kernel.packet[13]);
+  step1[12] = vec_mergeh(kernel.packet[6], kernel.packet[14]);
+  step1[13] = vec_mergel(kernel.packet[6], kernel.packet[14]);
+  step1[14] = vec_mergeh(kernel.packet[7], kernel.packet[15]);
+  step1[15] = vec_mergel(kernel.packet[7], kernel.packet[15]);
+
+  step2[0]  = vec_mergeh(step1[0], step1[8]);
+  step2[1]  = vec_mergel(step1[0], step1[8]);
+  step2[2]  = vec_mergeh(step1[1], step1[9]);
+  step2[3]  = vec_mergel(step1[1], step1[9]);
+  step2[4]  = vec_mergeh(step1[2], step1[10]);
+  step2[5]  = vec_mergel(step1[2], step1[10]);
+  step2[6]  = vec_mergeh(step1[3], step1[11]);
+  step2[7]  = vec_mergel(step1[3], step1[11]);
+  step2[8]  = vec_mergeh(step1[4], step1[12]);
+  step2[9]  = vec_mergel(step1[4], step1[12]);
+  step2[10] = vec_mergeh(step1[5], step1[13]);
+  step2[11] = vec_mergel(step1[5], step1[13]);
+  step2[12] = vec_mergeh(step1[6], step1[14]);
+  step2[13] = vec_mergel(step1[6], step1[14]);
+  step2[14] = vec_mergeh(step1[7], step1[15]);
+  step2[15] = vec_mergel(step1[7], step1[15]);
+
+  step3[0]  = vec_mergeh(step2[0], step2[8]);
+  step3[1]  = vec_mergel(step2[0], step2[8]);
+  step3[2]  = vec_mergeh(step2[1], step2[9]);
+  step3[3]  = vec_mergel(step2[1], step2[9]);
+  step3[4]  = vec_mergeh(step2[2], step2[10]);
+  step3[5]  = vec_mergel(step2[2], step2[10]);
+  step3[6]  = vec_mergeh(step2[3], step2[11]);
+  step3[7]  = vec_mergel(step2[3], step2[11]);
+  step3[8]  = vec_mergeh(step2[4], step2[12]);
+  step3[9]  = vec_mergel(step2[4], step2[12]);
+  step3[10] = vec_mergeh(step2[5], step2[13]);
+  step3[11] = vec_mergel(step2[5], step2[13]);
+  step3[12] = vec_mergeh(step2[6], step2[14]);
+  step3[13] = vec_mergel(step2[6], step2[14]);
+  step3[14] = vec_mergeh(step2[7], step2[15]);
+  step3[15] = vec_mergel(step2[7], step2[15]);
+
+  kernel.packet[0]  = vec_mergeh(step3[0], step3[8]);
+  kernel.packet[1]  = vec_mergel(step3[0], step3[8]);
+  kernel.packet[2]  = vec_mergeh(step3[1], step3[9]);
+  kernel.packet[3]  = vec_mergel(step3[1], step3[9]);
+  kernel.packet[4]  = vec_mergeh(step3[2], step3[10]);
+  kernel.packet[5]  = vec_mergel(step3[2], step3[10]);
+  kernel.packet[6]  = vec_mergeh(step3[3], step3[11]);
+  kernel.packet[7]  = vec_mergel(step3[3], step3[11]);
+  kernel.packet[8]  = vec_mergeh(step3[4], step3[12]);
+  kernel.packet[9]  = vec_mergel(step3[4], step3[12]);
+  kernel.packet[10] = vec_mergeh(step3[5], step3[13]);
+  kernel.packet[11] = vec_mergel(step3[5], step3[13]);
+  kernel.packet[12] = vec_mergeh(step3[6], step3[14]);
+  kernel.packet[13] = vec_mergel(step3[6], step3[14]);
+  kernel.packet[14] = vec_mergeh(step3[7], step3[15]);
+  kernel.packet[15] = vec_mergel(step3[7], step3[15]);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet16uc,16>& kernel) {
+  Packet16uc step1[16], step2[16], step3[16];
+
+  step1[0] = vec_mergeh(kernel.packet[0], kernel.packet[8]);
+  step1[1] = vec_mergel(kernel.packet[0], kernel.packet[8]);
+  step1[2] = vec_mergeh(kernel.packet[1], kernel.packet[9]);
+  step1[3] = vec_mergel(kernel.packet[1], kernel.packet[9]);
+  step1[4] = vec_mergeh(kernel.packet[2], kernel.packet[10]);
+  step1[5] = vec_mergel(kernel.packet[2], kernel.packet[10]);
+  step1[6] = vec_mergeh(kernel.packet[3], kernel.packet[11]);
+  step1[7] = vec_mergel(kernel.packet[3], kernel.packet[11]);
+  step1[8] = vec_mergeh(kernel.packet[4], kernel.packet[12]);
+  step1[9] = vec_mergel(kernel.packet[4], kernel.packet[12]);
+  step1[10] = vec_mergeh(kernel.packet[5], kernel.packet[13]);
+  step1[11] = vec_mergel(kernel.packet[5], kernel.packet[13]);
+  step1[12] = vec_mergeh(kernel.packet[6], kernel.packet[14]);
+  step1[13] = vec_mergel(kernel.packet[6], kernel.packet[14]);
+  step1[14] = vec_mergeh(kernel.packet[7], kernel.packet[15]);
+  step1[15] = vec_mergel(kernel.packet[7], kernel.packet[15]);
+
+  step2[0]  = vec_mergeh(step1[0], step1[8]);
+  step2[1]  = vec_mergel(step1[0], step1[8]);
+  step2[2]  = vec_mergeh(step1[1], step1[9]);
+  step2[3]  = vec_mergel(step1[1], step1[9]);
+  step2[4]  = vec_mergeh(step1[2], step1[10]);
+  step2[5]  = vec_mergel(step1[2], step1[10]);
+  step2[6]  = vec_mergeh(step1[3], step1[11]);
+  step2[7]  = vec_mergel(step1[3], step1[11]);
+  step2[8]  = vec_mergeh(step1[4], step1[12]);
+  step2[9]  = vec_mergel(step1[4], step1[12]);
+  step2[10] = vec_mergeh(step1[5], step1[13]);
+  step2[11] = vec_mergel(step1[5], step1[13]);
+  step2[12] = vec_mergeh(step1[6], step1[14]);
+  step2[13] = vec_mergel(step1[6], step1[14]);
+  step2[14] = vec_mergeh(step1[7], step1[15]);
+  step2[15] = vec_mergel(step1[7], step1[15]);
+
+  step3[0]  = vec_mergeh(step2[0], step2[8]);
+  step3[1]  = vec_mergel(step2[0], step2[8]);
+  step3[2]  = vec_mergeh(step2[1], step2[9]);
+  step3[3]  = vec_mergel(step2[1], step2[9]);
+  step3[4]  = vec_mergeh(step2[2], step2[10]);
+  step3[5]  = vec_mergel(step2[2], step2[10]);
+  step3[6]  = vec_mergeh(step2[3], step2[11]);
+  step3[7]  = vec_mergel(step2[3], step2[11]);
+  step3[8]  = vec_mergeh(step2[4], step2[12]);
+  step3[9]  = vec_mergel(step2[4], step2[12]);
+  step3[10] = vec_mergeh(step2[5], step2[13]);
+  step3[11] = vec_mergel(step2[5], step2[13]);
+  step3[12] = vec_mergeh(step2[6], step2[14]);
+  step3[13] = vec_mergel(step2[6], step2[14]);
+  step3[14] = vec_mergeh(step2[7], step2[15]);
+  step3[15] = vec_mergel(step2[7], step2[15]);
+
+  kernel.packet[0]  = vec_mergeh(step3[0], step3[8]);
+  kernel.packet[1]  = vec_mergel(step3[0], step3[8]);
+  kernel.packet[2]  = vec_mergeh(step3[1], step3[9]);
+  kernel.packet[3]  = vec_mergel(step3[1], step3[9]);
+  kernel.packet[4]  = vec_mergeh(step3[2], step3[10]);
+  kernel.packet[5]  = vec_mergel(step3[2], step3[10]);
+  kernel.packet[6]  = vec_mergeh(step3[3], step3[11]);
+  kernel.packet[7]  = vec_mergel(step3[3], step3[11]);
+  kernel.packet[8]  = vec_mergeh(step3[4], step3[12]);
+  kernel.packet[9]  = vec_mergel(step3[4], step3[12]);
+  kernel.packet[10] = vec_mergeh(step3[5], step3[13]);
+  kernel.packet[11] = vec_mergel(step3[5], step3[13]);
+  kernel.packet[12] = vec_mergeh(step3[6], step3[14]);
+  kernel.packet[13] = vec_mergel(step3[6], step3[14]);
+  kernel.packet[14] = vec_mergeh(step3[7], step3[15]);
+  kernel.packet[15] = vec_mergel(step3[7], step3[15]);
+}
+
+template<typename Packet> EIGEN_STRONG_INLINE
+Packet pblend4(const Selector<4>& ifPacket, const Packet& thenPacket, const Packet& elsePacket) {
+  Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3] };
+  Packet4ui mask = reinterpret_cast<Packet4ui>(pnegate(reinterpret_cast<Packet4i>(select)));
+  return vec_sel(elsePacket, thenPacket, mask);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i pblend(const Selector<4>& ifPacket, const Packet4i& thenPacket, const Packet4i& elsePacket) {
+  return pblend4<Packet4i>(ifPacket, thenPacket, elsePacket);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket, const Packet4f& elsePacket) {
+  return pblend4<Packet4f>(ifPacket, thenPacket, elsePacket);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8s pblend(const Selector<8>& ifPacket, const Packet8s& thenPacket, const Packet8s& elsePacket) {
+  Packet8us select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3],
+                       ifPacket.select[4], ifPacket.select[5], ifPacket.select[6], ifPacket.select[7] };
+  Packet8us mask = reinterpret_cast<Packet8us>(pnegate(reinterpret_cast<Packet8s>(select)));
+  Packet8s result = vec_sel(elsePacket, thenPacket, mask);
+  return result;
+}
+
+template<> EIGEN_STRONG_INLINE Packet8us pblend(const Selector<8>& ifPacket, const Packet8us& thenPacket, const Packet8us& elsePacket) {
+  Packet8us select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3],
+                       ifPacket.select[4], ifPacket.select[5], ifPacket.select[6], ifPacket.select[7] };
+  Packet8us mask = reinterpret_cast<Packet8us>(pnegate(reinterpret_cast<Packet8s>(select)));
+  return vec_sel(elsePacket, thenPacket, mask);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pblend(const Selector<8>& ifPacket, const Packet8bf& thenPacket, const Packet8bf& elsePacket) {
+  return pblend<Packet8us>(ifPacket, thenPacket, elsePacket);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16c pblend(const Selector<16>& ifPacket, const Packet16c& thenPacket, const Packet16c& elsePacket) {
+  Packet16uc select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3],
+                       ifPacket.select[4], ifPacket.select[5], ifPacket.select[6], ifPacket.select[7],
+                       ifPacket.select[8], ifPacket.select[9], ifPacket.select[10], ifPacket.select[11],
+                       ifPacket.select[12], ifPacket.select[13], ifPacket.select[14], ifPacket.select[15] };
+
+  Packet16uc mask = reinterpret_cast<Packet16uc>(pnegate(reinterpret_cast<Packet16c>(select)));
+  return vec_sel(elsePacket, thenPacket, mask);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16uc pblend(const Selector<16>& ifPacket, const Packet16uc& thenPacket, const Packet16uc& elsePacket) {
+  Packet16uc select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3],
+                       ifPacket.select[4], ifPacket.select[5], ifPacket.select[6], ifPacket.select[7],
+                       ifPacket.select[8], ifPacket.select[9], ifPacket.select[10], ifPacket.select[11],
+                       ifPacket.select[12], ifPacket.select[13], ifPacket.select[14], ifPacket.select[15] };
+
+  Packet16uc mask = reinterpret_cast<Packet16uc>(pnegate(reinterpret_cast<Packet16c>(select)));
+  return vec_sel(elsePacket, thenPacket, mask);
+}
+
+
+//---------- double ----------
+#ifdef EIGEN_VECTORIZE_VSX
+typedef __vector double              Packet2d;
+typedef __vector unsigned long long  Packet2ul;
+typedef __vector long long           Packet2l;
+#if EIGEN_COMP_CLANG
+typedef Packet2ul                    Packet2bl;
+#else
+typedef __vector __bool long         Packet2bl;
+#endif
+
+static Packet2l  p2l_ZERO = reinterpret_cast<Packet2l>(p4i_ZERO);
+static Packet2ul p2ul_SIGN = { 0x8000000000000000ull, 0x8000000000000000ull };
+static Packet2ul p2ul_PREV0DOT5 = { 0x3FDFFFFFFFFFFFFFull, 0x3FDFFFFFFFFFFFFFull };
+static Packet2d  p2d_ONE  = { 1.0, 1.0 };
+static Packet2d  p2d_ZERO = reinterpret_cast<Packet2d>(p4f_ZERO);
+static Packet2d  p2d_MZERO = { numext::bit_cast<double>(0x8000000000000000ull),
+                               numext::bit_cast<double>(0x8000000000000000ull) };
+
+#ifdef _BIG_ENDIAN
+static Packet2d p2d_COUNTDOWN = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(p2d_ZERO), reinterpret_cast<Packet4f>(p2d_ONE), 8));
+#else
+static Packet2d p2d_COUNTDOWN = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(p2d_ONE), reinterpret_cast<Packet4f>(p2d_ZERO), 8));
+#endif
+
+template<int index> Packet2d vec_splat_dbl(Packet2d& a)
+{
+  return vec_splat(a, index);
+}
+
+template<> struct packet_traits<double> : default_packet_traits
+{
+  typedef Packet2d type;
+  typedef Packet2d half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size=2,
+
+    HasAdd  = 1,
+    HasSub  = 1,
+    HasMul  = 1,
+    HasDiv  = 1,
+    HasMin  = 1,
+    HasMax  = 1,
+    HasAbs  = 1,
+    HasSin  = 0,
+    HasCos  = 0,
+    HasATan = 0,
+    HasLog  = 0,
+    HasExp  = 1,
+    HasSqrt = 1,
+#if !EIGEN_COMP_CLANG
+    HasRsqrt = 1,
+#else
+    HasRsqrt = 0,
+#endif
+    HasRound = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasRint = 1,
+    HasNegate = 1,
+    HasBlend = 1
+  };
+};
+
+template<> struct unpacket_traits<Packet2d> { typedef double type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet2d half; };
+
+inline std::ostream & operator <<(std::ostream & s, const Packet2l & v)
+{
+  union {
+    Packet2l   v;
+    int64_t n[2];
+  } vt;
+  vt.v = v;
+  s << vt.n[0] << ", " << vt.n[1];
+  return s;
+}
+
+inline std::ostream & operator <<(std::ostream & s, const Packet2d & v)
+{
+  union {
+    Packet2d   v;
+    double n[2];
+  } vt;
+  vt.v = v;
+  s << vt.n[0] << ", " << vt.n[1];
+  return s;
+}
+
+// Need to define them first or we get specialization after instantiation errors
+template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from)
+{
+  EIGEN_DEBUG_ALIGNED_LOAD
+  return vec_xl(0, const_cast<double *>(from)); // cast needed by Clang
+}
+
+template<> EIGEN_ALWAYS_INLINE Packet2d pload_partial<Packet2d>(const double* from, const Index n, const Index offset)
+{
+  return pload_partial_common<Packet2d>(from, n, offset);
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<double>(double*   to, const Packet2d& from)
+{
+  EIGEN_DEBUG_ALIGNED_STORE
+  vec_xst(from, 0, to);
+}
+
+template<> EIGEN_ALWAYS_INLINE void pstore_partial<double>(double*  to, const Packet2d& from, const Index n, const Index offset)
+{
+  pstore_partial_common<Packet2d>(to, from, n, offset);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double&  from) {
+  Packet2d v = {from, from};
+  return v;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pset1frombits<Packet2d>(unsigned long from) {
+  Packet2l v = {static_cast<long long>(from), static_cast<long long>(from)};
+  return reinterpret_cast<Packet2d>(v);
+}
+
+template<> EIGEN_STRONG_INLINE void
+pbroadcast4<Packet2d>(const double *a,
+                      Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
+{
+  //This way is faster than vec_splat (at least for doubles in Power 9)
+  a0 = pset1<Packet2d>(a[0]);
+  a1 = pset1<Packet2d>(a[1]);
+  a2 = pset1<Packet2d>(a[2]);
+  a3 = pset1<Packet2d>(a[3]);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet2d pgather<double, Packet2d>(const double* from, Index stride)
+{
+  return pgather_common<Packet2d>(from, stride);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet2d pgather_partial<double, Packet2d>(const double* from, Index stride, const Index n)
+{
+  return pgather_common<Packet2d>(from, stride, n);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride)
+{
+  pscatter_common<Packet2d>(to, from, stride);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pscatter_partial<double, Packet2d>(double* to, const Packet2d& from, Index stride, const Index n)
+{
+  pscatter_common<Packet2d>(to, from, stride, n);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) { return pset1<Packet2d>(a) + p2d_COUNTDOWN; }
+
+template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return a + b; }
+
+template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return a - b; }
+
+template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a)
+{
+#ifdef __POWER8_VECTOR__
+  return vec_neg(a);
+#else
+  return vec_xor(a, p2d_MZERO);
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_madd(a,b,p2d_MZERO); }
+template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_div(a,b); }
+
+// for some weird raisons, it has to be overloaded for packet of integers
+template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vec_madd(a, b, c); }
+template<> EIGEN_STRONG_INLINE Packet2d pmsub(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vec_msub(a,b,c); }
+template<> EIGEN_STRONG_INLINE Packet2d pnmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vec_nmsub(a,b,c); }
+template<> EIGEN_STRONG_INLINE Packet2d pnmsub(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vec_nmadd(a,b,c); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b)
+{
+  // NOTE: about 10% slower than vec_min, but consistent with std::min and SSE regarding NaN
+  Packet2d ret;
+  __asm__ ("xvcmpgedp %x0,%x1,%x2\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
+  return ret;
+ }
+
+template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b)
+{
+  // NOTE: about 10% slower than vec_max, but consistent with std::max and SSE regarding NaN
+  Packet2d ret;
+  __asm__ ("xvcmpgtdp %x0,%x2,%x1\n\txxsel %x0,%x1,%x2,%x0" : "=&wa" (ret) : "wa" (a), "wa" (b));
+  return ret;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pcmp_le(const Packet2d& a, const Packet2d& b) { return reinterpret_cast<Packet2d>(vec_cmple(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt(const Packet2d& a, const Packet2d& b) { return reinterpret_cast<Packet2d>(vec_cmplt(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet2d pcmp_eq(const Packet2d& a, const Packet2d& b) { return reinterpret_cast<Packet2d>(vec_cmpeq(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt_or_nan(const Packet2d& a, const Packet2d& b) {
+  Packet2d c = reinterpret_cast<Packet2d>(vec_cmpge(a,b));
+  return vec_nor(c,c);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_or(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_xor(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, vec_nor(b, b)); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a)
+{
+    Packet2d t = vec_add(reinterpret_cast<Packet2d>(vec_or(vec_and(reinterpret_cast<Packet2ul>(a), p2ul_SIGN), p2ul_PREV0DOT5)), a);
+    Packet2d res;
+
+    __asm__("xvrdpiz %x0, %x1\n\t"
+        : "=&wa" (res)
+        : "wa" (t));
+
+    return res;
+}
+template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const  Packet2d& a) { return vec_ceil(a); }
+template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) { return vec_floor(a); }
+template<> EIGEN_STRONG_INLINE Packet2d print<Packet2d>(const Packet2d& a)
+{
+    Packet2d res;
+
+    __asm__("xvrdpic %x0, %x1\n\t"
+        : "=&wa" (res)
+        : "wa" (a));
+
+    return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from)
+{
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return vec_xl(0, const_cast<double*>(from));
+}
+
+template<> EIGEN_ALWAYS_INLINE Packet2d ploadu_partial<Packet2d>(const double* from, const Index n, const Index offset)
+{
+  return ploadu_partial_common<Packet2d>(from, n, offset);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double*   from)
+{
+  Packet2d p;
+  if((std::ptrdiff_t(from) % 16) == 0)  p = pload<Packet2d>(from);
+  else                                  p = ploadu<Packet2d>(from);
+  return vec_splat_dbl<0>(p);
+}
+
+template<> EIGEN_STRONG_INLINE void pstoreu<double>(double*  to, const Packet2d& from)
+{
+  EIGEN_DEBUG_UNALIGNED_STORE
+  vec_xst(from, 0, to);
+}
+
+template<> EIGEN_ALWAYS_INLINE void pstoreu_partial<double>(double*  to, const Packet2d& from, const Index n, const Index offset)
+{
+  pstoreu_partial_common<Packet2d>(to, from, n, offset);
+}
+
+template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { EIGEN_PPC_PREFETCH(addr); }
+
+template<> EIGEN_STRONG_INLINE double  pfirst<Packet2d>(const Packet2d& a) { EIGEN_ALIGN16 double x[2]; pstore<double>(x, a); return x[0]; }
+
+template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a)
+{
+  return vec_sld(a, a, 8);
+}
+template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) { return vec_abs(a); }
+#ifdef __POWER8_VECTOR__
+template<> EIGEN_STRONG_INLINE Packet2d psignbit(const Packet2d&  a) { return (Packet2d)vec_sra((Packet2l)a, vec_splats((unsigned long long)(63))); }
+#else
+#ifdef _BIG_ENDIAN
+static Packet16uc p16uc_DUPSIGN = { 0,0,0,0, 0,0,0,0, 8,8,8,8, 8,8,8,8 };
+#else
+static Packet16uc p16uc_DUPSIGN = { 7,7,7,7, 7,7,7,7, 15,15,15,15, 15,15,15,15 };
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet2d psignbit(const Packet2d&  a)
+{
+  Packet16c tmp = vec_sra(reinterpret_cast<Packet16c>(a), vec_splats((unsigned char)(7)));
+  return reinterpret_cast<Packet2d>(vec_perm(tmp, tmp, p16uc_DUPSIGN));
+}
+#endif
+
+template<> inline Packet2l pcast<Packet2d, Packet2l>(const Packet2d& x);
+
+template<> inline Packet2d pcast<Packet2l, Packet2d>(const Packet2l& x);
+
+// Packet2l shifts.
+// For POWER8 we simply use vec_sr/l. 
+//
+// Things are more complicated for POWER7. There is actually a
+// vec_xxsxdi intrinsic but it is not supported by some gcc versions.
+// So we need to shift by N % 32 and rearrage bytes.
+#ifdef __POWER8_VECTOR__
+
+template<int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_left(const Packet2l& a) {
+  const Packet2ul shift = { N, N };
+  return vec_sl(a, shift); 
+}
+
+template<int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_right(const Packet2l& a) {
+  const Packet2ul shift = { N, N };
+  return vec_sr(a, shift); 
+}
+
+#else
+
+// Shifts [A, B, C, D] to [B, 0, D, 0].
+// Used to implement left shifts for Packet2l.
+EIGEN_ALWAYS_INLINE Packet4i shift_even_left(const Packet4i& a) {
+  static const Packet16uc perm = {
+      0x14, 0x15, 0x16, 0x17, 0x00, 0x01, 0x02, 0x03, 
+      0x1c, 0x1d, 0x1e, 0x1f, 0x08, 0x09, 0x0a, 0x0b };
+  #ifdef  _BIG_ENDIAN
+    return vec_perm(p4i_ZERO, a, perm);
+  #else
+    return vec_perm(a, p4i_ZERO, perm);
+  #endif
+}
+
+// Shifts [A, B, C, D] to [0, A, 0, C].
+// Used to implement right shifts for Packet2l.
+EIGEN_ALWAYS_INLINE Packet4i shift_odd_right(const Packet4i& a) {
+  static const Packet16uc perm = {
+      0x04, 0x05, 0x06, 0x07, 0x10, 0x11, 0x12, 0x13, 
+      0x0c, 0x0d, 0x0e, 0x0f, 0x18, 0x19, 0x1a, 0x1b };
+  #ifdef  _BIG_ENDIAN
+    return vec_perm(p4i_ZERO, a, perm);
+  #else
+    return vec_perm(a, p4i_ZERO, perm);
+  #endif
+}
+
+template<int N, typename EnableIf = void>
+struct plogical_shift_left_impl;
+
+template<int N>
+struct plogical_shift_left_impl<N, std::enable_if_t<(N < 32) && (N >= 0)>> {
+  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
+    static const unsigned n = static_cast<unsigned>(N);
+    const Packet4ui shift = {n, n, n, n};
+    const Packet4i ai = reinterpret_cast<Packet4i>(a);
+    static const unsigned m = static_cast<unsigned>(32 - N);
+    const Packet4ui shift_right = {m, m, m, m};
+    const Packet4i out_hi = vec_sl(ai, shift);
+    const Packet4i out_lo = shift_even_left(vec_sr(ai, shift_right));
+    return reinterpret_cast<Packet2l>(por<Packet4i>(out_hi, out_lo));
+  }
+};
+
+template<int N>
+struct plogical_shift_left_impl<N, std::enable_if_t<(N >= 32)>> {
+  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
+    static const unsigned m = static_cast<unsigned>(N - 32);
+    const Packet4ui shift = {m, m, m, m};
+    const Packet4i ai = reinterpret_cast<Packet4i>(a);
+    return reinterpret_cast<Packet2l>(shift_even_left(vec_sl(ai, shift)));
+  }
+};
+
+template<int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_left(const Packet2l& a) {
+  return plogical_shift_left_impl<N>::run(a); 
+}
+
+template<int N, typename EnableIf = void>
+struct plogical_shift_right_impl;
+
+template<int N>
+struct plogical_shift_right_impl<N, std::enable_if_t<(N < 32) && (N >= 0)>> {
+  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
+    static const unsigned n = static_cast<unsigned>(N);
+    const Packet4ui shift = {n, n, n, n};
+    const Packet4i ai = reinterpret_cast<Packet4i>(a);
+    static const unsigned m = static_cast<unsigned>(32 - N);
+    const Packet4ui shift_left = {m, m, m, m};
+    const Packet4i out_lo = vec_sr(ai, shift);
+    const Packet4i out_hi = shift_odd_right(vec_sl(ai, shift_left));
+    return reinterpret_cast<Packet2l>(por<Packet4i>(out_hi, out_lo));
+  }
+};
+
+template<int N>
+struct plogical_shift_right_impl<N, std::enable_if_t<(N >= 32)>> {
+  static EIGEN_STRONG_INLINE Packet2l run(const Packet2l& a) {
+    static const unsigned m = static_cast<unsigned>(N - 32);
+    const Packet4ui shift = {m, m, m, m};
+    const Packet4i ai = reinterpret_cast<Packet4i>(a);
+    return reinterpret_cast<Packet2l>(shift_odd_right(vec_sr(ai, shift)));
+  }
+};
+
+template<int N>
+EIGEN_STRONG_INLINE Packet2l plogical_shift_right(const Packet2l& a) {
+  return plogical_shift_right_impl<N>::run(a); 
+}
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet2d pldexp<Packet2d>(const Packet2d& a, const Packet2d& exponent) {
+  // Clamp exponent to [-2099, 2099]
+  const Packet2d max_exponent = pset1<Packet2d>(2099.0);
+  const Packet2l e = pcast<Packet2d, Packet2l>(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
+
+  // Split 2^e into four factors and multiply:
+  const Packet2l  bias = { 1023, 1023 };
+  Packet2l b = plogical_shift_right<2>(e);  // floor(e/4)
+  Packet2d c = reinterpret_cast<Packet2d>(plogical_shift_left<52>(b + bias));
+  Packet2d out = pmul(pmul(pmul(a, c), c), c); // a * 2^(3b)
+  b = psub(psub(psub(e, b), b), b);  // e - 3b
+  c = reinterpret_cast<Packet2d>(plogical_shift_left<52>(b + bias)); // 2^(e - 3b)
+  out = pmul(out, c); // a * 2^e
+  return out;
+}
+
+
+// Extract exponent without existence of Packet2l.
+template<>
+EIGEN_STRONG_INLINE  
+Packet2d pfrexp_generic_get_biased_exponent(const Packet2d& a) {
+  return pcast<Packet2l, Packet2d>(plogical_shift_right<52>(reinterpret_cast<Packet2l>(pabs(a))));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pfrexp<Packet2d> (const Packet2d& a, Packet2d& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
+{
+  Packet2d b, sum;
+  b   = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4f>(a), reinterpret_cast<Packet4f>(a), 8));
+  sum = a + b;
+  return pfirst<Packet2d>(sum);
+}
+
+// Other reduction functions:
+// mul
+template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
+{
+  return pfirst(pmul(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(a), reinterpret_cast<Packet4ui>(a), 8))));
+}
+
+// min
+template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a)
+{
+  return pfirst(pmin(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(a), reinterpret_cast<Packet4ui>(a), 8))));
+}
+
+// max
+template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a)
+{
+  return pfirst(pmax(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4ui>(a), reinterpret_cast<Packet4ui>(a), 8))));
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet2d,2>& kernel) {
+  Packet2d t0, t1;
+  t0 = vec_mergeh(kernel.packet[0], kernel.packet[1]);
+  t1 = vec_mergel(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[0] = t0;
+  kernel.packet[1] = t1;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, const Packet2d& thenPacket, const Packet2d& elsePacket) {
+  Packet2l select = { ifPacket.select[0], ifPacket.select[1] };
+  Packet2ul mask = reinterpret_cast<Packet2ul>(pnegate(reinterpret_cast<Packet2l>(select)));
+  return vec_sel(elsePacket, thenPacket, mask);
+}
+
+
+#endif // __VSX__
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_PACKET_MATH_ALTIVEC_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/TypeCasting.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/TypeCasting.h
new file mode 100644
index 0000000000000000000000000000000000000000..bda63d85496976cd93765ceb80a6b5f5348208d4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/AltiVec/TypeCasting.h
@@ -0,0 +1,178 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2019 Rasmus Munk Larsen <rmlarsen@google.com>
+// Copyright (C) 2023 Chip Kerchner (chip.kerchner@ibm.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_ALTIVEC_H
+#define EIGEN_TYPE_CASTING_ALTIVEC_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <>
+struct type_casting_traits<float, int> {
+  enum {
+    VectorizedCast = 1,
+    SrcCoeffRatio = 1,
+    TgtCoeffRatio = 1
+  };
+};
+
+template <>
+struct type_casting_traits<int, float> {
+  enum {
+    VectorizedCast = 1,
+    SrcCoeffRatio = 1,
+    TgtCoeffRatio = 1
+  };
+};
+
+template <>
+struct type_casting_traits<bfloat16, unsigned short int> {
+  enum {
+    VectorizedCast = 1,
+    SrcCoeffRatio = 1,
+    TgtCoeffRatio = 1
+  };
+};
+
+template <>
+struct type_casting_traits<unsigned short int, bfloat16> {
+  enum {
+    VectorizedCast = 1,
+    SrcCoeffRatio = 1,
+    TgtCoeffRatio = 1
+  };
+};
+
+template<> EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) {
+  return vec_cts(a,0);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4ui pcast<Packet4f, Packet4ui>(const Packet4f& a) {
+  return vec_ctu(a,0);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) {
+  return vec_ctf(a,0);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4ui, Packet4f>(const Packet4ui& a) {
+  return vec_ctf(a,0);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8us pcast<Packet8bf, Packet8us>(const Packet8bf& a) {
+  Packet4f float_even = Bf16ToF32Even(a);
+  Packet4f float_odd = Bf16ToF32Odd(a);
+  Packet4ui int_even = pcast<Packet4f, Packet4ui>(float_even);
+  Packet4ui int_odd = pcast<Packet4f, Packet4ui>(float_odd);
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(low_mask, 0x0000FFFF);
+  Packet4ui low_even = pand<Packet4ui>(int_even, p4ui_low_mask);
+  Packet4ui low_odd = pand<Packet4ui>(int_odd, p4ui_low_mask);
+
+  //Check values that are bigger than USHRT_MAX (0xFFFF)
+  Packet4bi overflow_selector;
+  if(vec_any_gt(int_even, p4ui_low_mask)){
+    overflow_selector = vec_cmpgt(int_even, p4ui_low_mask);
+    low_even = vec_sel(low_even, p4ui_low_mask, overflow_selector);
+  }
+  if(vec_any_gt(int_odd, p4ui_low_mask)){
+    overflow_selector = vec_cmpgt(int_odd, p4ui_low_mask);
+    low_odd = vec_sel(low_even, p4ui_low_mask, overflow_selector);
+  }
+
+  return pmerge(low_even, low_odd);
+}
+
+template<> EIGEN_STRONG_INLINE Packet8bf pcast<Packet8us, Packet8bf>(const Packet8us& a) {
+  //short -> int -> float -> bfloat16
+  const EIGEN_DECLARE_CONST_FAST_Packet4ui(low_mask, 0x0000FFFF);
+  Packet4ui int_cast = reinterpret_cast<Packet4ui>(a);
+  Packet4ui int_even = pand<Packet4ui>(int_cast, p4ui_low_mask);
+  Packet4ui int_odd = plogical_shift_right<16>(int_cast);
+  Packet4f float_even = pcast<Packet4ui, Packet4f>(int_even);
+  Packet4f float_odd = pcast<Packet4ui, Packet4f>(int_odd);
+  return F32ToBf16(float_even, float_odd);
+}
+
+template <>
+struct type_casting_traits<bfloat16, float> {
+  enum {
+    VectorizedCast = 1,
+    SrcCoeffRatio = 1,
+    TgtCoeffRatio = 2
+  };
+};
+
+template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet8bf, Packet4f>(const Packet8bf& a) {
+  Packet8us z = pset1<Packet8us>(0);
+#ifdef _BIG_ENDIAN
+  return reinterpret_cast<Packet4f>(vec_mergeh(a.m_val, z));
+#else
+  return reinterpret_cast<Packet4f>(vec_mergeh(z, a.m_val));
+#endif
+}
+
+template <>
+struct type_casting_traits<float, bfloat16> {
+  enum {
+    VectorizedCast = 1,
+    SrcCoeffRatio = 2,
+    TgtCoeffRatio = 1
+  };
+};
+
+template<> EIGEN_STRONG_INLINE Packet8bf pcast<Packet4f, Packet8bf>(const Packet4f& a, const Packet4f &b) {
+  return F32ToBf16Both(a, b);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i,Packet4f>(const Packet4f& a) {
+  return reinterpret_cast<Packet4i>(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f,Packet4i>(const Packet4i& a) {
+  return reinterpret_cast<Packet4f>(a);
+}
+
+#ifdef EIGEN_VECTORIZE_VSX
+// VSX support varies between different compilers and even different
+// versions of the same compiler.  For gcc version >= 4.9.3, we can use
+// vec_cts to efficiently convert Packet2d to Packet2l.  Otherwise, use
+// a slow version that works with older compilers.
+// Update: apparently vec_cts/vec_ctf intrinsics for 64-bit doubles
+// are buggy, https://gcc.gnu.org/bugzilla/show_bug.cgi?id=70963
+template<>
+inline Packet2l pcast<Packet2d, Packet2l>(const Packet2d& x) {
+#if EIGEN_GNUC_STRICT_AT_LEAST(7,1,0)
+  return vec_cts(x, 0);    // TODO: check clang version.
+#else
+  double tmp[2];
+  memcpy(tmp, &x, sizeof(tmp));
+  Packet2l l = { static_cast<long long>(tmp[0]),
+                 static_cast<long long>(tmp[1]) };
+  return l;
+#endif
+}
+
+template<>
+inline Packet2d pcast<Packet2l, Packet2d>(const Packet2l& x) {
+  unsigned long long tmp[2];
+  memcpy(tmp, &x, sizeof(tmp));
+  Packet2d d = { static_cast<double>(tmp[0]),
+                 static_cast<double>(tmp[1]) };
+  return d;
+}
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TYPE_CASTING_ALTIVEC_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/BFloat16.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/BFloat16.h
new file mode 100644
index 0000000000000000000000000000000000000000..d2137d46f6f90a9a4f25da6f2bd3902f4588c098
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/BFloat16.h
@@ -0,0 +1,863 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef EIGEN_BFLOAT16_H
+#define EIGEN_BFLOAT16_H
+
+#include "../../InternalHeaderCheck.h"
+
+#if defined(EIGEN_HAS_HIP_BF16)
+// When compiling with GPU support, the "hip_bfloat16" base class as well as
+// some other routines are defined in the GPU compiler header files
+// (hip_bfloat16.h), and they are not tagged constexpr
+// As a consequence, we get compile failures when compiling Eigen with
+// GPU support. Hence the need to disable EIGEN_CONSTEXPR when building
+// Eigen with GPU support
+  #pragma push_macro("EIGEN_CONSTEXPR")
+  #undef EIGEN_CONSTEXPR
+  #define EIGEN_CONSTEXPR
+#endif
+
+#define BF16_PACKET_FUNCTION(PACKET_F, PACKET_BF16, METHOD)         \
+  template <>                                                       \
+  EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED  \
+  PACKET_BF16 METHOD<PACKET_BF16>(const PACKET_BF16& _x) {          \
+    return F32ToBf16(METHOD<PACKET_F>(Bf16ToF32(_x)));              \
+  }
+
+// Only use HIP GPU bf16 in kernels
+#if defined(EIGEN_HAS_HIP_BF16) && defined(EIGEN_GPU_COMPILE_PHASE)
+#define EIGEN_USE_HIP_BF16
+#endif
+
+namespace Eigen {
+
+struct bfloat16;
+
+namespace numext {
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bit_cast<Eigen::bfloat16, uint16_t>(const uint16_t& src);
+
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC uint16_t bit_cast<uint16_t, Eigen::bfloat16>(const Eigen::bfloat16& src);
+}  // namespace numext
+namespace bfloat16_impl {
+
+#if defined(EIGEN_USE_HIP_BF16)
+
+struct __bfloat16_raw : public hip_bfloat16 {
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw() {}
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw(hip_bfloat16 hb) : hip_bfloat16(hb) {}
+  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw(unsigned short raw) : hip_bfloat16(raw) {}
+};
+
+#else
+
+// Make our own __bfloat16_raw definition.
+struct __bfloat16_raw {
+#if defined(EIGEN_HAS_HIP_BF16) && !defined(EIGEN_GPU_COMPILE_PHASE)
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw() {}
+#else
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw() : value(0) {}
+#endif
+  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw(unsigned short raw) : value(raw) {}
+  unsigned short value;
+};
+
+#endif // defined(EIGEN_USE_HIP_BF16)
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw raw_uint16_to_bfloat16(unsigned short value);
+template <bool AssumeArgumentIsNormalOrInfinityOrZero>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne(float ff);
+// Forward declarations of template specializations, to avoid Visual C++ 2019 errors, saying:
+// > error C2908: explicit specialization; 'float_to_bfloat16_rtne' has already been instantiated
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<false>(float ff);
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<true>(float ff);
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float bfloat16_to_float(__bfloat16_raw h);
+
+struct bfloat16_base : public __bfloat16_raw {
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16_base() {}
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16_base(const __bfloat16_raw& h) : __bfloat16_raw(h) {}
+};
+
+} // namespace bfloat16_impl
+
+// Class definition.
+struct bfloat16 : public bfloat16_impl::bfloat16_base {
+
+  typedef bfloat16_impl::__bfloat16_raw __bfloat16_raw;
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16() {}
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(const __bfloat16_raw& h) : bfloat16_impl::bfloat16_base(h) {}
+
+  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(bool b)
+      : bfloat16_impl::bfloat16_base(bfloat16_impl::raw_uint16_to_bfloat16(b ? 0x3f80 : 0)) {}
+
+  template<class T>
+  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(T val)
+      : bfloat16_impl::bfloat16_base(bfloat16_impl::float_to_bfloat16_rtne<internal::is_integral<T>::value>(static_cast<float>(val))) {}
+
+  explicit EIGEN_DEVICE_FUNC bfloat16(float f)
+      : bfloat16_impl::bfloat16_base(bfloat16_impl::float_to_bfloat16_rtne<false>(f)) {}
+
+  // Following the convention of numpy, converting between complex and
+  // float will lead to loss of imag value.
+  template<typename RealScalar>
+  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR bfloat16(const std::complex<RealScalar>& val)
+      : bfloat16_impl::bfloat16_base(bfloat16_impl::float_to_bfloat16_rtne<false>(static_cast<float>(val.real()))) {}
+
+  EIGEN_DEVICE_FUNC operator float() const {  // NOLINT: Allow implicit conversion to float, because it is lossless.
+    return bfloat16_impl::bfloat16_to_float(*this);
+  }
+};
+
+// TODO(majnemer): Get rid of this once we can rely on C++17 inline variables do
+// solve the ODR issue.
+namespace bfloat16_impl {
+template <typename = void>
+struct numeric_limits_bfloat16_impl {
+  static EIGEN_CONSTEXPR const bool is_specialized = true;
+  static EIGEN_CONSTEXPR const bool is_signed = true;
+  static EIGEN_CONSTEXPR const bool is_integer = false;
+  static EIGEN_CONSTEXPR const bool is_exact = false;
+  static EIGEN_CONSTEXPR const bool has_infinity = true;
+  static EIGEN_CONSTEXPR const bool has_quiet_NaN = true;
+  static EIGEN_CONSTEXPR const bool has_signaling_NaN = true;
+  static EIGEN_CONSTEXPR const std::float_denorm_style has_denorm = std::denorm_present;
+  static EIGEN_CONSTEXPR const bool has_denorm_loss = false;
+  static EIGEN_CONSTEXPR const std::float_round_style round_style = std::numeric_limits<float>::round_style;
+  static EIGEN_CONSTEXPR const bool is_iec559 = true;
+  // The C++ standard defines this as "true if the set of values representable
+  // by the type is finite." BFloat16 has finite precision.
+  static EIGEN_CONSTEXPR const bool is_bounded = true;
+  static EIGEN_CONSTEXPR const bool is_modulo = false;
+  static EIGEN_CONSTEXPR const int digits = 8;
+  static EIGEN_CONSTEXPR const int digits10 = 2;
+  static EIGEN_CONSTEXPR const int max_digits10 = 4;
+  static EIGEN_CONSTEXPR const int radix = std::numeric_limits<float>::radix;
+  static EIGEN_CONSTEXPR const int min_exponent = std::numeric_limits<float>::min_exponent;
+  static EIGEN_CONSTEXPR const int min_exponent10 = std::numeric_limits<float>::min_exponent10;
+  static EIGEN_CONSTEXPR const int max_exponent = std::numeric_limits<float>::max_exponent;
+  static EIGEN_CONSTEXPR const int max_exponent10 = std::numeric_limits<float>::max_exponent10;
+  static EIGEN_CONSTEXPR const bool traps = std::numeric_limits<float>::traps;
+  // IEEE754: "The implementer shall choose how tininess is detected, but shall
+  // detect tininess in the same way for all operations in radix two"
+  static EIGEN_CONSTEXPR const bool tinyness_before = std::numeric_limits<float>::tinyness_before;
+
+  static EIGEN_CONSTEXPR Eigen::bfloat16 (min)() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x0080); }
+  static EIGEN_CONSTEXPR Eigen::bfloat16 lowest() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0xff7f); }
+  static EIGEN_CONSTEXPR Eigen::bfloat16 (max)() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7f7f); }
+  static EIGEN_CONSTEXPR Eigen::bfloat16 epsilon() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x3c00); }
+  static EIGEN_CONSTEXPR Eigen::bfloat16 round_error() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x3f00); }
+  static EIGEN_CONSTEXPR Eigen::bfloat16 infinity() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7f80); }
+  static EIGEN_CONSTEXPR Eigen::bfloat16 quiet_NaN() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7fc0); }
+  static EIGEN_CONSTEXPR Eigen::bfloat16 signaling_NaN() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x7fa0); }
+  static EIGEN_CONSTEXPR Eigen::bfloat16 denorm_min() { return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(0x0001); }
+};
+
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::is_specialized;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::is_signed;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::is_integer;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::is_exact;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::has_infinity;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::has_quiet_NaN;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::has_signaling_NaN;
+template<typename T>
+EIGEN_CONSTEXPR const std::float_denorm_style numeric_limits_bfloat16_impl<T>::has_denorm;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::has_denorm_loss;
+template<typename T>
+EIGEN_CONSTEXPR const std::float_round_style numeric_limits_bfloat16_impl<T>::round_style;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::is_iec559;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::is_bounded;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::is_modulo;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::digits;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::digits10;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::max_digits10;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::radix;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::min_exponent;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::min_exponent10;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::max_exponent;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_bfloat16_impl<T>::max_exponent10;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::traps;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_bfloat16_impl<T>::tinyness_before;
+}  // end namespace bfloat16_impl
+}  // end namespace Eigen
+
+namespace std {
+// If std::numeric_limits<T> is specialized, should also specialize
+// std::numeric_limits<const T>, std::numeric_limits<volatile T>, and
+// std::numeric_limits<const volatile T>
+// https://stackoverflow.com/a/16519653/
+template<>
+class numeric_limits<Eigen::bfloat16> : public Eigen::bfloat16_impl::numeric_limits_bfloat16_impl<> {};
+template<>
+class numeric_limits<const Eigen::bfloat16> : public numeric_limits<Eigen::bfloat16> {};
+template<>
+class numeric_limits<volatile Eigen::bfloat16> : public numeric_limits<Eigen::bfloat16> {};
+template<>
+class numeric_limits<const volatile Eigen::bfloat16> : public numeric_limits<Eigen::bfloat16> {};
+}  // end namespace std
+
+namespace Eigen {
+
+namespace bfloat16_impl {
+
+// We need to distinguish ‘clang as the CUDA compiler’ from ‘clang as the host compiler,
+// invoked by NVCC’ (e.g. on MacOS). The former needs to see both host and device implementation
+// of the functions, while the latter can only deal with one of them.
+#if !defined(EIGEN_HAS_NATIVE_BF16) || (EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC) // Emulate support for bfloat16 floats
+
+#if EIGEN_COMP_CLANG && defined(EIGEN_CUDACC)
+// We need to provide emulated *host-side* BF16 operators for clang.
+#pragma push_macro("EIGEN_DEVICE_FUNC")
+#undef EIGEN_DEVICE_FUNC
+#if (defined(EIGEN_HAS_GPU_BF16) && defined(EIGEN_HAS_NATIVE_BF16))
+#define EIGEN_DEVICE_FUNC __host__
+#else // both host and device need emulated ops.
+#define EIGEN_DEVICE_FUNC __host__ __device__
+#endif
+#endif
+
+// Definitions for CPUs, mostly working through conversion
+// to/from fp32.
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator + (const bfloat16& a, const bfloat16& b) {
+  return bfloat16(float(a) + float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator + (const bfloat16& a, const int& b) {
+  return bfloat16(float(a) + static_cast<float>(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator + (const int& a, const bfloat16& b) {
+  return bfloat16(static_cast<float>(a) + float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator * (const bfloat16& a, const bfloat16& b) {
+  return bfloat16(float(a) * float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator - (const bfloat16& a, const bfloat16& b) {
+  return bfloat16(float(a) - float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator / (const bfloat16& a, const bfloat16& b) {
+  return bfloat16(float(a) / float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator - (const bfloat16& a) {
+  numext::uint16_t x = numext::bit_cast<uint16_t>(a) ^ 0x8000;
+  return numext::bit_cast<bfloat16>(x);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator += (bfloat16& a, const bfloat16& b) {
+  a = bfloat16(float(a) + float(b));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator *= (bfloat16& a, const bfloat16& b) {
+  a = bfloat16(float(a) * float(b));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator -= (bfloat16& a, const bfloat16& b) {
+  a = bfloat16(float(a) - float(b));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16& operator /= (bfloat16& a, const bfloat16& b) {
+  a = bfloat16(float(a) / float(b));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator++(bfloat16& a) {
+  a += bfloat16(1);
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator--(bfloat16& a) {
+  a -= bfloat16(1);
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator++(bfloat16& a, int) {
+  bfloat16 original_value = a;
+  ++a;
+  return original_value;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator--(bfloat16& a, int) {
+  bfloat16 original_value = a;
+  --a;
+  return original_value;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator == (const bfloat16& a, const bfloat16& b) {
+  return numext::equal_strict(float(a),float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator != (const bfloat16& a, const bfloat16& b) {
+  return numext::not_equal_strict(float(a), float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator < (const bfloat16& a, const bfloat16& b) {
+  return float(a) < float(b);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator <= (const bfloat16& a, const bfloat16& b) {
+  return float(a) <= float(b);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator > (const bfloat16& a, const bfloat16& b) {
+  return float(a) > float(b);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator >= (const bfloat16& a, const bfloat16& b) {
+  return float(a) >= float(b);
+}
+
+#if EIGEN_COMP_CLANG && defined(EIGEN_CUDACC)
+#pragma pop_macro("EIGEN_DEVICE_FUNC")
+#endif
+#endif  // Emulate support for bfloat16 floats
+
+// Division by an index. Do it in full float precision to avoid accuracy
+// issues in converting the denominator to bfloat16.
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 operator / (const bfloat16& a, Index b) {
+  return bfloat16(static_cast<float>(a) / static_cast<float>(b));
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw truncate_to_bfloat16(const float v) {
+#if defined(EIGEN_USE_HIP_BF16)
+  return __bfloat16_raw(__bfloat16_raw::round_to_bfloat16(v, __bfloat16_raw::truncate));
+#else
+  __bfloat16_raw output;
+  if (numext::isnan EIGEN_NOT_A_MACRO(v)) {
+    output.value = std::signbit(v) ? 0xFFC0: 0x7FC0;
+    return output;
+  }
+  output.value = static_cast<numext::uint16_t>(numext::bit_cast<numext::uint32_t>(v) >> 16);
+  return output;
+#endif
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __bfloat16_raw raw_uint16_to_bfloat16(numext::uint16_t value) {
+#if defined(EIGEN_USE_HIP_BF16)
+  __bfloat16_raw bf;
+  bf.data = value;
+  return bf;
+#else
+  return __bfloat16_raw(value);
+#endif
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR numext::uint16_t raw_bfloat16_as_uint16(const __bfloat16_raw& bf) {
+#if defined(EIGEN_USE_HIP_BF16)
+  return bf.data;
+#else
+  return bf.value;
+#endif
+}
+
+// float_to_bfloat16_rtne template specialization that does not make any
+// assumption about the value of its function argument (ff).
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<false>(float ff) {
+#if defined(EIGEN_USE_HIP_BF16)
+  return __bfloat16_raw(__bfloat16_raw::round_to_bfloat16(ff));
+#else
+  __bfloat16_raw output;
+
+  if (numext::isnan EIGEN_NOT_A_MACRO(ff)) {
+    // If the value is a NaN, squash it to a qNaN with msb of fraction set,
+    // this makes sure after truncation we don't end up with an inf.
+    //
+    // qNaN magic: All exponent bits set + most significant bit of fraction
+    // set.
+    output.value = std::signbit(ff) ? 0xFFC0: 0x7FC0;
+  } else {
+    // Fast rounding algorithm that rounds a half value to nearest even. This
+    // reduces expected error when we convert a large number of floats. Here
+    // is how it works:
+    //
+    // Definitions:
+    // To convert a float 32 to bfloat16, a float 32 can be viewed as 32 bits
+    // with the following tags:
+    //
+    // Sign |  Exp (8 bits) | Frac (23 bits)
+    //  S     EEEEEEEE         FFFFFFLRTTTTTTTTTTTTTTT
+    //
+    //  S: Sign bit.
+    //  E: Exponent bits.
+    //  F: First 6 bits of fraction.
+    //  L: Least significant bit of resulting bfloat16 if we truncate away the
+    //  rest of the float32. This is also the 7th bit of fraction
+    //  R: Rounding bit, 8th bit of fraction.
+    //  T: Sticky bits, rest of fraction, 15 bits.
+    //
+    // To round half to nearest even, there are 3 cases where we want to round
+    // down (simply truncate the result of the bits away, which consists of
+    // rounding bit and sticky bits) and two cases where we want to round up
+    // (truncate then add one to the result).
+    //
+    // The fast converting algorithm simply adds lsb (L) to 0x7fff (15 bits of
+    // 1s) as the rounding bias, adds the rounding bias to the input, then
+    // truncates the last 16 bits away.
+    //
+    // To understand how it works, we can analyze this algorithm case by case:
+    //
+    // 1. L = 0, R = 0:
+    //   Expect: round down, this is less than half value.
+    //
+    //   Algorithm:
+    //   - Rounding bias: 0x7fff + 0 = 0x7fff
+    //   - Adding rounding bias to input may create any carry, depending on
+    //   whether there is any value set to 1 in T bits.
+    //   - R may be set to 1 if there is a carry.
+    //   - L remains 0.
+    //   - Note that this case also handles Inf and -Inf, where all fraction
+    //   bits, including L, R and Ts are all 0. The output remains Inf after
+    //   this algorithm.
+    //
+    // 2. L = 1, R = 0:
+    //   Expect: round down, this is less than half value.
+    //
+    //   Algorithm:
+    //   - Rounding bias: 0x7fff + 1 = 0x8000
+    //   - Adding rounding bias to input doesn't change sticky bits but
+    //   adds 1 to rounding bit.
+    //   - L remains 1.
+    //
+    // 3. L = 0, R = 1, all of T are 0:
+    //   Expect: round down, this is exactly at half, the result is already
+    //   even (L=0).
+    //
+    //   Algorithm:
+    //   - Rounding bias: 0x7fff + 0 = 0x7fff
+    //   - Adding rounding bias to input sets all sticky bits to 1, but
+    //   doesn't create a carry.
+    //   - R remains 1.
+    //   - L remains 0.
+    //
+    // 4. L = 1, R = 1:
+    //   Expect: round up, this is exactly at half, the result needs to be
+    //   round to the next even number.
+    //
+    //   Algorithm:
+    //   - Rounding bias: 0x7fff + 1 = 0x8000
+    //   - Adding rounding bias to input doesn't change sticky bits, but
+    //   creates a carry from rounding bit.
+    //   - The carry sets L to 0, creates another carry bit and propagate
+    //   forward to F bits.
+    //   - If all the F bits are 1, a carry then propagates to the exponent
+    //   bits, which then creates the minimum value with the next exponent
+    //   value. Note that we won't have the case where exponents are all 1,
+    //   since that's either a NaN (handled in the other if condition) or inf
+    //   (handled in case 1).
+    //
+    // 5. L = 0, R = 1, any of T is 1:
+    //   Expect: round up, this is greater than half.
+    //
+    //   Algorithm:
+    //   - Rounding bias: 0x7fff + 0 = 0x7fff
+    //   - Adding rounding bias to input creates a carry from sticky bits,
+    //   sets rounding bit to 0, then create another carry.
+    //   - The second carry sets L to 1.
+    //
+    // Examples:
+    //
+    //  Exact half value that is already even:
+    //    Input:
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
+    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
+    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 1 0     1000000000000000
+    //
+    //     This falls into case 3. We truncate the rest of 16 bits and no
+    //     carry is created into F and L:
+    //
+    //    Output:
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
+    //     S     E E E E E E E E      F F F F F F L
+    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 1 0
+    //
+    //  Exact half value, round to next even number:
+    //    Input:
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
+    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
+    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 0 1     1000000000000000
+    //
+    //     This falls into case 4. We create a carry from R and T,
+    //     which then propagates into L and F:
+    //
+    //    Output:
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
+    //     S     E E E E E E E E      F F F F F F L
+    //     0     0 0 0 0 0 0 0 0      0 0 0 0 0 1 0
+    //
+    //
+    //  Max denormal value round to min normal value:
+    //    Input:
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
+    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
+    //     0     0 0 0 0 0 0 0 0      1 1 1 1 1 1 1     1111111111111111
+    //
+    //     This falls into case 4. We create a carry from R and T,
+    //     propagate into L and F, which then propagates into exponent
+    //     bits:
+    //
+    //    Output:
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
+    //     S     E E E E E E E E      F F F F F F L
+    //     0     0 0 0 0 0 0 0 1      0 0 0 0 0 0 0
+    //
+    //  Max normal value round to Inf:
+    //    Input:
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit) | Frac (last 16 bit)
+    //     S     E E E E E E E E      F F F F F F L     RTTTTTTTTTTTTTTT
+    //     0     1 1 1 1 1 1 1 0      1 1 1 1 1 1 1     1111111111111111
+    //
+    //     This falls into case 4. We create a carry from R and T,
+    //     propagate into L and F, which then propagates into exponent
+    //     bits:
+    //
+    //    Sign |  Exp (8 bit)     | Frac (first 7 bit)
+    //     S     E E E E E E E E      F F F F F F L
+    //     0     1 1 1 1 1 1 1 1      0 0 0 0 0 0 0
+
+    // At this point, ff must be either a normal float, or +/-infinity.
+    output = float_to_bfloat16_rtne<true>(ff);
+  }
+  return output;
+#endif
+}
+
+// float_to_bfloat16_rtne template specialization that assumes that its function
+// argument (ff) is either a normal floating point number, or +/-infinity, or
+// zero. Used to improve the runtime performance of conversion from an integer
+// type to bfloat16.
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __bfloat16_raw float_to_bfloat16_rtne<true>(float ff) {
+#if defined(EIGEN_USE_HIP_BF16)
+    return __bfloat16_raw(__bfloat16_raw::round_to_bfloat16(ff));
+#else
+    numext::uint32_t input = numext::bit_cast<numext::uint32_t>(ff);
+    __bfloat16_raw output;
+
+    // Least significant bit of resulting bfloat.
+    numext::uint32_t lsb = (input >> 16) & 1;
+    numext::uint32_t rounding_bias = 0x7fff + lsb;
+    input += rounding_bias;
+    output.value = static_cast<numext::uint16_t>(input >> 16);
+    return output;
+#endif
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float bfloat16_to_float(__bfloat16_raw h) {
+#if defined(EIGEN_USE_HIP_BF16)
+    return static_cast<float>(h);
+#else
+    return numext::bit_cast<float>(static_cast<numext::uint32_t>(h.value) << 16);
+#endif
+}
+
+// --- standard functions ---
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isinf)(const bfloat16& a) {
+  EIGEN_USING_STD(isinf);
+#if defined(EIGEN_USE_HIP_BF16)
+  return (isinf)(a); // Uses HIP hip_bfloat16 isinf operator
+#else
+  return (isinf)(float(a));
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isnan)(const bfloat16& a) {
+  EIGEN_USING_STD(isnan);
+#if defined(EIGEN_USE_HIP_BF16)
+  return (isnan)(a); // Uses HIP hip_bfloat16 isnan operator
+#else
+  return (isnan)(float(a));
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isfinite)(const bfloat16& a) {
+  return !(isinf EIGEN_NOT_A_MACRO (a)) && !(isnan EIGEN_NOT_A_MACRO (a));
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 abs(const bfloat16& a) {
+  numext::uint16_t x = numext::bit_cast<numext::uint16_t>(a) & 0x7FFF;
+  return numext::bit_cast<bfloat16>(x);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 exp(const bfloat16& a) {
+  return bfloat16(::expf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 expm1(const bfloat16& a) {
+  return bfloat16(numext::expm1(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log(const bfloat16& a) {
+  return bfloat16(::logf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log1p(const bfloat16& a) {
+  return bfloat16(numext::log1p(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log10(const bfloat16& a) {
+  return bfloat16(::log10f(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 log2(const bfloat16& a) {
+  return bfloat16(static_cast<float>(EIGEN_LOG2E) * ::logf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 sqrt(const bfloat16& a) {
+  return bfloat16(::sqrtf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 pow(const bfloat16& a, const bfloat16& b) {
+  return bfloat16(::powf(float(a), float(b)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 atan2(const bfloat16& a, const bfloat16& b) {
+  return bfloat16(::atan2f(float(a), float(b)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 sin(const bfloat16& a) {
+  return bfloat16(::sinf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 cos(const bfloat16& a) {
+  return bfloat16(::cosf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 tan(const bfloat16& a) {
+  return bfloat16(::tanf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 asin(const bfloat16& a) {
+  return bfloat16(::asinf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 acos(const bfloat16& a) {
+  return bfloat16(::acosf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 atan(const bfloat16& a) {
+  return bfloat16(::atanf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 sinh(const bfloat16& a) {
+  return bfloat16(::sinhf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 cosh(const bfloat16& a) {
+  return bfloat16(::coshf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 tanh(const bfloat16& a) {
+  return bfloat16(::tanhf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 asinh(const bfloat16& a) {
+  return bfloat16(::asinhf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 acosh(const bfloat16& a) {
+  return bfloat16(::acoshf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 atanh(const bfloat16& a) {
+  return bfloat16(::atanhf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 floor(const bfloat16& a) {
+  return bfloat16(::floorf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 ceil(const bfloat16& a) {
+  return bfloat16(::ceilf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 rint(const bfloat16& a) {
+  return bfloat16(::rintf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 round(const bfloat16& a) {
+  return bfloat16(::roundf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 fmod(const bfloat16& a, const bfloat16& b) {
+  return bfloat16(::fmodf(float(a), float(b)));
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 (min)(const bfloat16& a, const bfloat16& b) {
+  const float f1 = static_cast<float>(a);
+  const float f2 = static_cast<float>(b);
+  return f2 < f1 ? b : a;
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 (max)(const bfloat16& a, const bfloat16& b) {
+  const float f1 = static_cast<float>(a);
+  const float f2 = static_cast<float>(b);
+  return f1 < f2 ? b : a;
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 fmin(const bfloat16& a, const bfloat16& b) {
+  const float f1 = static_cast<float>(a);
+  const float f2 = static_cast<float>(b);
+  return bfloat16(::fminf(f1, f2));
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bfloat16 fmax(const bfloat16& a, const bfloat16& b) {
+  const float f1 = static_cast<float>(a);
+  const float f2 = static_cast<float>(b);
+  return bfloat16(::fmaxf(f1, f2));
+}
+
+#ifndef EIGEN_NO_IO
+EIGEN_ALWAYS_INLINE std::ostream& operator << (std::ostream& os, const bfloat16& v) {
+  os << static_cast<float>(v);
+  return os;
+}
+#endif
+
+} // namespace bfloat16_impl
+
+namespace internal {
+
+template<>
+struct random_default_impl<bfloat16, false, false>
+{
+  static inline bfloat16 run(const bfloat16& x, const bfloat16& y)
+  {
+    return x + (y-x) * bfloat16(float(std::rand()) / float(RAND_MAX));
+  }
+  static inline bfloat16 run()
+  {
+    return run(bfloat16(-1.f), bfloat16(1.f));
+  }
+};
+
+template<> struct is_arithmetic<bfloat16> { enum { value = true }; };
+
+} // namespace internal
+
+template<> struct NumTraits<Eigen::bfloat16>
+    : GenericNumTraits<Eigen::bfloat16>
+{
+  enum {
+    IsSigned = true,
+    IsInteger = false,
+    IsComplex = false,
+    RequireInitialization = false
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 epsilon() {
+    return bfloat16_impl::raw_uint16_to_bfloat16(0x3c00);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 dummy_precision() {
+    return bfloat16_impl::raw_uint16_to_bfloat16(0x3D4D);  // bfloat16(5e-2f);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 highest() {
+    return bfloat16_impl::raw_uint16_to_bfloat16(0x7F7F);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 lowest() {
+    return bfloat16_impl::raw_uint16_to_bfloat16(0xFF7F);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 infinity() {
+    return bfloat16_impl::raw_uint16_to_bfloat16(0x7f80);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::bfloat16 quiet_NaN() {
+    return bfloat16_impl::raw_uint16_to_bfloat16(0x7fc0);
+  }
+};
+
+} // namespace Eigen
+
+
+#if defined(EIGEN_HAS_HIP_BF16)
+  #pragma pop_macro("EIGEN_CONSTEXPR")
+#endif
+
+namespace Eigen {
+namespace numext {
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+bool (isnan)(const Eigen::bfloat16& h) {
+  return (bfloat16_impl::isnan)(h);
+}
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+bool (isinf)(const Eigen::bfloat16& h) {
+  return (bfloat16_impl::isinf)(h);
+}
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+bool (isfinite)(const Eigen::bfloat16& h) {
+  return (bfloat16_impl::isfinite)(h);
+}
+
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::bfloat16 bit_cast<Eigen::bfloat16, uint16_t>(const uint16_t& src) {
+  return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(src);
+}
+
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC uint16_t bit_cast<uint16_t, Eigen::bfloat16>(const Eigen::bfloat16& src) {
+  return Eigen::bfloat16_impl::raw_bfloat16_as_uint16(src);
+}
+
+}  // namespace numext
+}  // namespace Eigen
+
+#if EIGEN_HAS_STD_HASH
+namespace std {
+template <>
+struct hash<Eigen::bfloat16> {
+  EIGEN_STRONG_INLINE std::size_t operator()(const Eigen::bfloat16& a) const {
+    return static_cast<std::size_t>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(a));
+  }
+};
+} // namespace std
+#endif
+
+// Add the missing shfl* intrinsics.
+// The __shfl* functions are only valid on HIP or _CUDA_ARCH_ >= 300.
+//   CUDA defines them for (__CUDA_ARCH__ >= 300 || !defined(__CUDA_ARCH__))
+//
+// HIP and CUDA prior to SDK 9.0 define
+//    __shfl, __shfl_up, __shfl_down, __shfl_xor for int and float
+// CUDA since 9.0 deprecates those and instead defines
+//    __shfl_sync, __shfl_up_sync, __shfl_down_sync, __shfl_xor_sync,
+//    with native support for __half and __nv_bfloat16
+//
+// Note that the following are __device__ - only functions.
+#if defined(EIGEN_HIPCC)
+
+#if defined(EIGEN_HAS_HIP_BF16)
+
+__device__ EIGEN_STRONG_INLINE Eigen::bfloat16 __shfl(Eigen::bfloat16 var, int srcLane, int width=warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::bfloat16>(static_cast<Eigen::numext::uint16_t>(__shfl(ivar, srcLane, width)));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::bfloat16 __shfl_up(Eigen::bfloat16 var, unsigned int delta, int width=warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::bfloat16>(static_cast<Eigen::numext::uint16_t>(__shfl_up(ivar, delta, width)));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::bfloat16 __shfl_down(Eigen::bfloat16 var, unsigned int delta, int width=warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::bfloat16>(static_cast<Eigen::numext::uint16_t>(__shfl_down(ivar, delta, width)));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::bfloat16 __shfl_xor(Eigen::bfloat16 var, int laneMask, int width=warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::bfloat16>(static_cast<Eigen::numext::uint16_t>(__shfl_xor(ivar, laneMask, width)));
+}
+
+#endif // HIP
+
+#endif // __shfl*
+
+#if defined(EIGEN_HIPCC)
+EIGEN_STRONG_INLINE __device__ Eigen::bfloat16 __ldg(const Eigen::bfloat16* ptr) {
+  return Eigen::bfloat16_impl::raw_uint16_to_bfloat16(__ldg(Eigen::numext::bit_cast<const Eigen::numext::uint16_t*>(ptr)));
+}
+#endif // __ldg
+
+#endif // EIGEN_BFLOAT16_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/ConjHelper.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/ConjHelper.h
new file mode 100644
index 0000000000000000000000000000000000000000..6b5afe3126f748b3cb20b939a75bf4493b593ce2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/ConjHelper.h
@@ -0,0 +1,119 @@
+
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARCH_CONJ_HELPER_H
+#define EIGEN_ARCH_CONJ_HELPER_H
+
+#define EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(PACKET_CPLX, PACKET_REAL)      \
+  template <>                                                           \
+  struct conj_helper<PACKET_REAL, PACKET_CPLX, false, false> {          \
+    EIGEN_STRONG_INLINE PACKET_CPLX pmadd(const PACKET_REAL& x,         \
+                                          const PACKET_CPLX& y,         \
+                                          const PACKET_CPLX& c) const { \
+      return padd(c, this->pmul(x, y));                                 \
+    }                                                                   \
+    EIGEN_STRONG_INLINE PACKET_CPLX pmul(const PACKET_REAL& x,          \
+                                         const PACKET_CPLX& y) const {  \
+      return PACKET_CPLX(Eigen::internal::pmul<PACKET_REAL>(x, y.v));   \
+    }                                                                   \
+  };                                                                    \
+                                                                        \
+  template <>                                                           \
+  struct conj_helper<PACKET_CPLX, PACKET_REAL, false, false> {          \
+    EIGEN_STRONG_INLINE PACKET_CPLX pmadd(const PACKET_CPLX& x,         \
+                                          const PACKET_REAL& y,         \
+                                          const PACKET_CPLX& c) const { \
+      return padd(c, this->pmul(x, y));                                 \
+    }                                                                   \
+    EIGEN_STRONG_INLINE PACKET_CPLX pmul(const PACKET_CPLX& x,          \
+                                         const PACKET_REAL& y) const {  \
+      return PACKET_CPLX(Eigen::internal::pmul<PACKET_REAL>(x.v, y));   \
+    }                                                                   \
+  };
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+template<bool Conjugate> struct conj_if;
+
+template<> struct conj_if<true> {
+  template<typename T>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& x) const { return numext::conj(x); }
+  template<typename T>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T pconj(const T& x) const { return internal::pconj(x); }
+};
+
+template<> struct conj_if<false> {
+  template<typename T>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& operator()(const T& x) const { return x; }
+  template<typename T>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& pconj(const T& x) const { return x; }
+};
+
+// Generic Implementation, assume scalars since the packet-version is
+// specialized below.
+template<typename LhsType, typename RhsType, bool ConjLhs, bool ConjRhs>
+struct conj_helper {
+  typedef typename ScalarBinaryOpTraits<LhsType, RhsType>::ReturnType ResultType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType
+  pmadd(const LhsType& x, const RhsType& y, const ResultType& c) const
+  { return this->pmul(x, y) + c; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType
+  pmul(const LhsType& x, const RhsType& y) const
+  { return conj_if<ConjLhs>()(x) * conj_if<ConjRhs>()(y); }
+};
+
+template<typename LhsScalar, typename RhsScalar>
+struct conj_helper<LhsScalar, RhsScalar, true, true> {
+  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar>::ReturnType ResultType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType
+  pmadd(const LhsScalar& x, const RhsScalar& y, const ResultType& c) const
+  { return this->pmul(x, y) + c; }
+
+  // We save a conjuation by using the identity conj(a)*conj(b) = conj(a*b).
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType
+  pmul(const LhsScalar& x, const RhsScalar& y) const
+  { return numext::conj(x * y); }
+};
+
+// Implementation with equal type, use packet operations.
+template<typename Packet, bool ConjLhs, bool ConjRhs>
+struct conj_helper<Packet, Packet, ConjLhs, ConjRhs>
+{
+  typedef Packet ResultType;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmadd(const Packet& x, const Packet& y, const Packet& c) const
+  { return Eigen::internal::pmadd(conj_if<ConjLhs>().pconj(x), conj_if<ConjRhs>().pconj(y), c); }
+
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmul(const Packet& x, const Packet& y) const
+  { return Eigen::internal::pmul(conj_if<ConjLhs>().pconj(x), conj_if<ConjRhs>().pconj(y)); }
+};
+
+template<typename Packet>
+struct conj_helper<Packet, Packet, true, true>
+{
+  typedef Packet ResultType;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmadd(const Packet& x, const Packet& y, const Packet& c) const
+  { return Eigen::internal::pmadd(pconj(x), pconj(y), c); }
+  // We save a conjuation by using the identity conj(a)*conj(b) = conj(a*b).
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet pmul(const Packet& x, const Packet& y) const
+  { return pconj(Eigen::internal::pmul(x, y)); }
+};
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_ARCH_CONJ_HELPER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..2739d9a205078b338b0d4f3925d37cadbedf8638
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctions.h
@@ -0,0 +1,2210 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007 Julien Pommier
+// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
+// Copyright (C) 2009-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* The exp and log functions of this file initially come from
+ * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
+ */
+
+#ifndef EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_H
+#define EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+// Creates a Scalar integer type with same bit-width.
+template<typename T> struct make_integer;
+template<> struct make_integer<float>    { typedef numext::int32_t type; };
+template<> struct make_integer<double>   { typedef numext::int64_t type; };
+template<> struct make_integer<half>     { typedef numext::int16_t type; };
+template<> struct make_integer<bfloat16> { typedef numext::int16_t type; };
+
+template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+Packet pfrexp_generic_get_biased_exponent(const Packet& a) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
+  static constexpr int mantissa_bits = numext::numeric_limits<Scalar>::digits - 1;
+  return pcast<PacketI, Packet>(plogical_shift_right<mantissa_bits>(preinterpret<PacketI>(pabs(a))));
+}
+
+// Safely applies frexp, correctly handles denormals.
+// Assumes IEEE floating point format.
+template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+Packet pfrexp_generic(const Packet& a, Packet& exponent) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename make_unsigned<typename make_integer<Scalar>::type>::type ScalarUI;
+  static constexpr int
+    TotalBits = sizeof(Scalar) * CHAR_BIT,
+    MantissaBits = numext::numeric_limits<Scalar>::digits - 1,
+    ExponentBits = TotalBits - MantissaBits - 1;
+
+  EIGEN_CONSTEXPR ScalarUI scalar_sign_mantissa_mask =
+      ~(((ScalarUI(1) << ExponentBits) - ScalarUI(1)) << MantissaBits); // ~0x7f800000
+  const Packet sign_mantissa_mask = pset1frombits<Packet>(static_cast<ScalarUI>(scalar_sign_mantissa_mask));
+  const Packet half = pset1<Packet>(Scalar(0.5));
+  const Packet zero = pzero(a);
+  const Packet normal_min = pset1<Packet>((numext::numeric_limits<Scalar>::min)()); // Minimum normal value, 2^-126
+
+  // To handle denormals, normalize by multiplying by 2^(int(MantissaBits)+1).
+  const Packet is_denormal = pcmp_lt(pabs(a), normal_min);
+  EIGEN_CONSTEXPR ScalarUI scalar_normalization_offset = ScalarUI(MantissaBits + 1); // 24
+  // The following cannot be constexpr because bfloat16(uint16_t) is not constexpr.
+  const Scalar scalar_normalization_factor = Scalar(ScalarUI(1) << int(scalar_normalization_offset)); // 2^24
+  const Packet normalization_factor = pset1<Packet>(scalar_normalization_factor);
+  const Packet normalized_a = pselect(is_denormal, pmul(a, normalization_factor), a);
+
+  // Determine exponent offset: -126 if normal, -126-24 if denormal
+  const Scalar scalar_exponent_offset = -Scalar((ScalarUI(1)<<(ExponentBits-1)) - ScalarUI(2)); // -126
+  Packet exponent_offset = pset1<Packet>(scalar_exponent_offset);
+  const Packet normalization_offset = pset1<Packet>(-Scalar(scalar_normalization_offset)); // -24
+  exponent_offset = pselect(is_denormal, padd(exponent_offset, normalization_offset), exponent_offset);
+
+  // Determine exponent and mantissa from normalized_a.
+  exponent = pfrexp_generic_get_biased_exponent(normalized_a);
+  // Zero, Inf and NaN return 'a' unmodified, exponent is zero
+  // (technically the exponent is unspecified for inf/NaN, but GCC/Clang set it to zero)
+  const Scalar scalar_non_finite_exponent = Scalar((ScalarUI(1) << ExponentBits) - ScalarUI(1));  // 255
+  const Packet non_finite_exponent = pset1<Packet>(scalar_non_finite_exponent);
+  const Packet is_zero_or_not_finite = por(pcmp_eq(a, zero), pcmp_eq(exponent, non_finite_exponent));
+  const Packet m = pselect(is_zero_or_not_finite, a, por(pand(normalized_a, sign_mantissa_mask), half));
+  exponent = pselect(is_zero_or_not_finite, zero, padd(exponent, exponent_offset));
+  return m;
+}
+
+// Safely applies ldexp, correctly handles overflows, underflows and denormals.
+// Assumes IEEE floating point format.
+template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+Packet pldexp_generic(const Packet& a, const Packet& exponent) {
+  // We want to return a * 2^exponent, allowing for all possible integer
+  // exponents without overflowing or underflowing in intermediate
+  // computations.
+  //
+  // Since 'a' and the output can be denormal, the maximum range of 'exponent'
+  // to consider for a float is:
+  //   -255-23 -> 255+23
+  // Below -278 any finite float 'a' will become zero, and above +278 any
+  // finite float will become inf, including when 'a' is the smallest possible 
+  // denormal.
+  //
+  // Unfortunately, 2^(278) cannot be represented using either one or two
+  // finite normal floats, so we must split the scale factor into at least
+  // three parts. It turns out to be faster to split 'exponent' into four
+  // factors, since [exponent>>2] is much faster to compute that [exponent/3].
+  //
+  // Set e = min(max(exponent, -278), 278);
+  //     b = floor(e/4);
+  //   out = ((((a * 2^(b)) * 2^(b)) * 2^(b)) * 2^(e-3*b))
+  //
+  // This will avoid any intermediate overflows and correctly handle 0, inf,
+  // NaN cases.
+  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename unpacket_traits<PacketI>::type ScalarI;
+  static constexpr int
+    TotalBits = sizeof(Scalar) * CHAR_BIT,
+    MantissaBits = numext::numeric_limits<Scalar>::digits - 1,
+    ExponentBits = TotalBits - MantissaBits - 1;
+
+  const Packet max_exponent = pset1<Packet>(Scalar((ScalarI(1)<<ExponentBits) + ScalarI(MantissaBits - 1)));  // 278
+  const PacketI bias = pset1<PacketI>((ScalarI(1)<<(ExponentBits-1)) - ScalarI(1));  // 127
+  const PacketI e = pcast<Packet, PacketI>(pmin(pmax(exponent, pnegate(max_exponent)), max_exponent));
+  PacketI b = parithmetic_shift_right<2>(e); // floor(e/4);
+  Packet c = preinterpret<Packet>(plogical_shift_left<MantissaBits>(padd(b, bias)));  // 2^b
+  Packet out = pmul(pmul(pmul(a, c), c), c);  // a * 2^(3b)
+  b = psub(psub(psub(e, b), b), b); // e - 3b
+  c = preinterpret<Packet>(plogical_shift_left<MantissaBits>(padd(b, bias)));  // 2^(e-3*b)
+  out = pmul(out, c);
+  return out;
+}
+
+// Explicitly multiplies
+//    a * (2^e)
+// clamping e to the range
+// [NumTraits<Scalar>::min_exponent()-2, NumTraits<Scalar>::max_exponent()]
+//
+// This is approx 7x faster than pldexp_impl, but will prematurely over/underflow
+// if 2^e doesn't fit into a normal floating-point Scalar.
+//
+// Assumes IEEE floating point format
+template<typename Packet>
+struct pldexp_fast_impl {
+  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename unpacket_traits<PacketI>::type ScalarI;
+  static constexpr int
+    TotalBits = sizeof(Scalar) * CHAR_BIT,
+    MantissaBits = numext::numeric_limits<Scalar>::digits - 1,
+    ExponentBits = TotalBits - MantissaBits - 1;
+
+  static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+  Packet run(const Packet& a, const Packet& exponent) {
+    const Packet bias = pset1<Packet>(Scalar((ScalarI(1)<<(ExponentBits-1)) - ScalarI(1)));  // 127
+    const Packet limit = pset1<Packet>(Scalar((ScalarI(1)<<ExponentBits) - ScalarI(1)));     // 255
+    // restrict biased exponent between 0 and 255 for float.
+    const PacketI e = pcast<Packet, PacketI>(pmin(pmax(padd(exponent, bias), pzero(limit)), limit)); // exponent + 127
+    // return a * (2^e)
+    return pmul(a, preinterpret<Packet>(plogical_shift_left<MantissaBits>(e)));
+  }
+};
+
+// Natural or base 2 logarithm.
+// Computes log(x) as log(2^e * m) = C*e + log(m), where the constant C =log(2)
+// and m is in the range [sqrt(1/2),sqrt(2)). In this range, the logarithm can
+// be easily approximated by a polynomial centered on m=1 for stability.
+// TODO(gonnet): Further reduce the interval allowing for lower-degree
+//               polynomial interpolants -> ... -> profit!
+template <typename Packet, bool base2>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet plog_impl_float(const Packet _x)
+{
+  const Packet cst_1              = pset1<Packet>(1.0f);
+  const Packet cst_minus_inf      = pset1frombits<Packet>(static_cast<Eigen::numext::uint32_t>(0xff800000u));
+  const Packet cst_pos_inf        = pset1frombits<Packet>(static_cast<Eigen::numext::uint32_t>(0x7f800000u));
+
+  const Packet cst_cephes_SQRTHF = pset1<Packet>(0.707106781186547524f);
+  Packet e, x;
+  // extract significant in the range [0.5,1) and exponent
+  x = pfrexp(_x,e);
+
+  // part2: Shift the inputs from the range [0.5,1) to [sqrt(1/2),sqrt(2))
+  // and shift by -1. The values are then centered around 0, which improves
+  // the stability of the polynomial evaluation.
+  //   if( x < SQRTHF ) {
+  //     e -= 1;
+  //     x = x + x - 1.0;
+  //   } else { x = x - 1.0; }
+  Packet mask = pcmp_lt(x, cst_cephes_SQRTHF);
+  Packet tmp = pand(x, mask);
+  x = psub(x, cst_1);
+  e = psub(e, pand(cst_1, mask));
+  x = padd(x, tmp);
+
+  // Polynomial coefficients for rational (3,3) r(x) = p(x)/q(x)
+  // approximating log(1+x) on [sqrt(0.5)-1;sqrt(2)-1].
+  const Packet cst_p1 = pset1<Packet>(1.0000000190281136f);
+  const Packet cst_p2 = pset1<Packet>(1.0000000190281063f);
+  const Packet cst_p3 = pset1<Packet>(0.18256296349849254f);
+  const Packet cst_q1 = pset1<Packet>(1.4999999999999927f);
+  const Packet cst_q2 = pset1<Packet>(0.59923249590823520f);
+  const Packet cst_q3 = pset1<Packet>(0.049616247954120038f);
+
+  Packet p = pmadd(x, cst_p3, cst_p2);
+  p = pmadd(x, p, cst_p1);
+  p = pmul(x, p);
+  Packet q = pmadd(x, cst_q3, cst_q2);
+  q = pmadd(x, q, cst_q1);
+  q = pmadd(x, q, cst_1);
+  x = pdiv(p, q);
+
+  // Add the logarithm of the exponent back to the result of the interpolation.
+  if (base2) {
+    const Packet cst_log2e = pset1<Packet>(static_cast<float>(EIGEN_LOG2E));
+    x = pmadd(x, cst_log2e, e);
+  } else {
+    const Packet cst_ln2 = pset1<Packet>(static_cast<float>(EIGEN_LN2));
+    x = pmadd(e, cst_ln2, x);
+  }
+
+  Packet invalid_mask = pcmp_lt_or_nan(_x, pzero(_x));
+  Packet iszero_mask  = pcmp_eq(_x,pzero(_x));
+  Packet pos_inf_mask = pcmp_eq(_x,cst_pos_inf);
+  // Filter out invalid inputs, i.e.:
+  //  - negative arg will be NAN
+  //  - 0 will be -INF
+  //  - +INF will be +INF
+  return pselect(iszero_mask, cst_minus_inf,
+                              por(pselect(pos_inf_mask,cst_pos_inf,x), invalid_mask));
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet plog_float(const Packet _x)
+{
+  return plog_impl_float<Packet, /* base2 */ false>(_x);
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet plog2_float(const Packet _x)
+{
+  return plog_impl_float<Packet, /* base2 */ true>(_x);
+}
+
+/* Returns the base e (2.718...) or base 2 logarithm of x.
+ * The argument is separated into its exponent and fractional parts.
+ * The logarithm of the fraction in the interval [sqrt(1/2), sqrt(2)],
+ * is approximated by
+ *
+ *     log(1+x) = x - 0.5 x**2 + x**3 P(x)/Q(x).
+ *
+ * for more detail see: http://www.netlib.org/cephes/
+ */
+template <typename Packet, bool base2>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet plog_impl_double(const Packet _x)
+{
+  Packet x = _x;
+
+  const Packet cst_1              = pset1<Packet>(1.0);
+  const Packet cst_neg_half       = pset1<Packet>(-0.5);
+  const Packet cst_minus_inf      = pset1frombits<Packet>( static_cast<uint64_t>(0xfff0000000000000ull));
+  const Packet cst_pos_inf        = pset1frombits<Packet>( static_cast<uint64_t>(0x7ff0000000000000ull));
+
+
+ // Polynomial Coefficients for log(1+x) = x - x**2/2 + x**3 P(x)/Q(x)
+ //                             1/sqrt(2) <= x < sqrt(2)
+  const Packet cst_cephes_SQRTHF = pset1<Packet>(0.70710678118654752440E0);
+  const Packet cst_cephes_log_p0 = pset1<Packet>(1.01875663804580931796E-4);
+  const Packet cst_cephes_log_p1 = pset1<Packet>(4.97494994976747001425E-1);
+  const Packet cst_cephes_log_p2 = pset1<Packet>(4.70579119878881725854E0);
+  const Packet cst_cephes_log_p3 = pset1<Packet>(1.44989225341610930846E1);
+  const Packet cst_cephes_log_p4 = pset1<Packet>(1.79368678507819816313E1);
+  const Packet cst_cephes_log_p5 = pset1<Packet>(7.70838733755885391666E0);
+
+  const Packet cst_cephes_log_q0 = pset1<Packet>(1.0);
+  const Packet cst_cephes_log_q1 = pset1<Packet>(1.12873587189167450590E1);
+  const Packet cst_cephes_log_q2 = pset1<Packet>(4.52279145837532221105E1);
+  const Packet cst_cephes_log_q3 = pset1<Packet>(8.29875266912776603211E1);
+  const Packet cst_cephes_log_q4 = pset1<Packet>(7.11544750618563894466E1);
+  const Packet cst_cephes_log_q5 = pset1<Packet>(2.31251620126765340583E1);
+
+  Packet e;
+  // extract significant in the range [0.5,1) and exponent
+  x = pfrexp(x,e);
+  
+  // Shift the inputs from the range [0.5,1) to [sqrt(1/2),sqrt(2))
+  // and shift by -1. The values are then centered around 0, which improves
+  // the stability of the polynomial evaluation.
+  //   if( x < SQRTHF ) {
+  //     e -= 1;
+  //     x = x + x - 1.0;
+  //   } else { x = x - 1.0; }
+  Packet mask = pcmp_lt(x, cst_cephes_SQRTHF);
+  Packet tmp = pand(x, mask);
+  x = psub(x, cst_1);
+  e = psub(e, pand(cst_1, mask));
+  x = padd(x, tmp);
+
+  Packet x2 = pmul(x, x);
+  Packet x3 = pmul(x2, x);
+
+  // Evaluate the polynomial approximant , probably to improve instruction-level parallelism.
+  // y = x - 0.5*x^2 + x^3 * polevl( x, P, 5 ) / p1evl( x, Q, 5 ) );
+  Packet y, y1, y_;
+  y  = pmadd(cst_cephes_log_p0, x, cst_cephes_log_p1);
+  y1 = pmadd(cst_cephes_log_p3, x, cst_cephes_log_p4);
+  y  = pmadd(y, x, cst_cephes_log_p2);
+  y1 = pmadd(y1, x, cst_cephes_log_p5);
+  y_ = pmadd(y, x3, y1);
+
+  y  = pmadd(cst_cephes_log_q0, x, cst_cephes_log_q1);
+  y1 = pmadd(cst_cephes_log_q3, x, cst_cephes_log_q4);
+  y  = pmadd(y, x, cst_cephes_log_q2);
+  y1 = pmadd(y1, x, cst_cephes_log_q5);
+  y  = pmadd(y, x3, y1);
+
+  y_ = pmul(y_, x3);
+  y  = pdiv(y_, y);
+
+  y = pmadd(cst_neg_half, x2, y);
+  x = padd(x, y);
+
+  // Add the logarithm of the exponent back to the result of the interpolation.
+  if (base2) {
+    const Packet cst_log2e = pset1<Packet>(static_cast<double>(EIGEN_LOG2E));
+    x = pmadd(x, cst_log2e, e);
+  } else {
+    const Packet cst_ln2 = pset1<Packet>(static_cast<double>(EIGEN_LN2));
+    x = pmadd(e, cst_ln2, x);
+  }
+
+  Packet invalid_mask = pcmp_lt_or_nan(_x, pzero(_x));
+  Packet iszero_mask  = pcmp_eq(_x,pzero(_x));
+  Packet pos_inf_mask = pcmp_eq(_x,cst_pos_inf);
+  // Filter out invalid inputs, i.e.:
+  //  - negative arg will be NAN
+  //  - 0 will be -INF
+  //  - +INF will be +INF
+  return pselect(iszero_mask, cst_minus_inf,
+                              por(pselect(pos_inf_mask,cst_pos_inf,x), invalid_mask));
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet plog_double(const Packet _x)
+{
+  return plog_impl_double<Packet, /* base2 */ false>(_x);
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet plog2_double(const Packet _x)
+{
+  return plog_impl_double<Packet, /* base2 */ true>(_x);
+}
+
+/** \internal \returns log(1 + x) computed using W. Kahan's formula.
+    See: http://www.plunk.org/~hatch/rightway.php
+ */
+template<typename Packet>
+Packet generic_plog1p(const Packet& x)
+{
+  typedef typename unpacket_traits<Packet>::type ScalarType;
+  const Packet one = pset1<Packet>(ScalarType(1));
+  Packet xp1 = padd(x, one);
+  Packet small_mask = pcmp_eq(xp1, one);
+  Packet log1 = plog(xp1);
+  Packet inf_mask = pcmp_eq(xp1, log1);
+  Packet log_large = pmul(x, pdiv(log1, psub(xp1, one)));
+  return pselect(por(small_mask, inf_mask), x, log_large);
+}
+
+/** \internal \returns exp(x)-1 computed using W. Kahan's formula.
+    See: http://www.plunk.org/~hatch/rightway.php
+ */
+template<typename Packet>
+Packet generic_expm1(const Packet& x)
+{
+  typedef typename unpacket_traits<Packet>::type ScalarType;
+  const Packet one = pset1<Packet>(ScalarType(1));
+  const Packet neg_one = pset1<Packet>(ScalarType(-1));
+  Packet u = pexp(x);
+  Packet one_mask = pcmp_eq(u, one);
+  Packet u_minus_one = psub(u, one);
+  Packet neg_one_mask = pcmp_eq(u_minus_one, neg_one);
+  Packet logu = plog(u);
+  // The following comparison is to catch the case where
+  // exp(x) = +inf. It is written in this way to avoid having
+  // to form the constant +inf, which depends on the packet
+  // type.
+  Packet pos_inf_mask = pcmp_eq(logu, u);
+  Packet expm1 = pmul(u_minus_one, pdiv(x, logu));
+  expm1 = pselect(pos_inf_mask, u, expm1);
+  return pselect(one_mask,
+                 x,
+                 pselect(neg_one_mask,
+                         neg_one,
+                         expm1));
+}
+
+
+// Exponential function. Works by writing "x = m*log(2) + r" where
+// "m = floor(x/log(2)+1/2)" and "r" is the remainder. The result is then
+// "exp(x) = 2^m*exp(r)" where exp(r) is in the range [-1,1).
+// exp(r) is computed using a 6th order minimax polynomial approximation.
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pexp_float(const Packet _x)
+{
+  const Packet cst_zero   = pset1<Packet>(0.0f);
+  const Packet cst_one    = pset1<Packet>(1.0f);
+  const Packet cst_half   = pset1<Packet>(0.5f);
+  const Packet cst_exp_hi = pset1<Packet>(88.723f);
+  const Packet cst_exp_lo = pset1<Packet>(-104.f);
+
+  const Packet cst_cephes_LOG2EF = pset1<Packet>(1.44269504088896341f);
+  const Packet cst_p2 = pset1<Packet>(0.49999988079071044921875f);
+  const Packet cst_p3 = pset1<Packet>(0.16666518151760101318359375f);
+  const Packet cst_p4 = pset1<Packet>(4.166965186595916748046875e-2f);
+  const Packet cst_p5 = pset1<Packet>(8.36894474923610687255859375e-3f);
+  const Packet cst_p6 = pset1<Packet>(1.37449637986719608306884765625e-3f);
+
+  // Clamp x.
+  Packet zero_mask = pcmp_lt(_x, cst_exp_lo);
+  Packet x = pmin(_x, cst_exp_hi);
+
+  // Express exp(x) as exp(m*ln(2) + r), start by extracting
+  // m = floor(x/ln(2) + 0.5).
+  Packet m = pfloor(pmadd(x, cst_cephes_LOG2EF, cst_half));
+
+  // Get r = x - m*ln(2). If no FMA instructions are available, m*ln(2) is
+  // subtracted out in two parts, m*C1+m*C2 = m*ln(2), to avoid accumulating
+  // truncation errors.
+  const Packet cst_cephes_exp_C1 = pset1<Packet>(-0.693359375f);
+  const Packet cst_cephes_exp_C2 = pset1<Packet>(2.12194440e-4f);
+  Packet r = pmadd(m, cst_cephes_exp_C1, x);
+  r = pmadd(m, cst_cephes_exp_C2, r);
+
+  // Evaluate the 6th order polynomial approximation to exp(r)
+  // with r in the interval [-ln(2)/2;ln(2)/2].
+  const Packet r2 = pmul(r, r);
+  Packet p_even = pmadd(r2, cst_p6, cst_p4);
+  const Packet p_odd = pmadd(r2, cst_p5, cst_p3);
+  p_even = pmadd(r2, p_even, cst_p2);
+  const Packet p_low = padd(r, cst_one);
+  Packet y = pmadd(r, p_odd, p_even);
+  y = pmadd(r2, y, p_low);
+
+  // Return 2^m * exp(r).
+  // TODO: replace pldexp with faster implementation since y in [-1, 1).
+  return pselect(zero_mask, cst_zero, pmax(pldexp(y,m), _x));
+}
+
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pexp_double(const Packet _x)
+{
+  Packet x = _x;
+  const Packet cst_zero = pset1<Packet>(0.0);
+  const Packet cst_1 = pset1<Packet>(1.0);
+  const Packet cst_2 = pset1<Packet>(2.0);
+  const Packet cst_half = pset1<Packet>(0.5);
+
+  const Packet cst_exp_hi = pset1<Packet>(709.784);
+  const Packet cst_exp_lo = pset1<Packet>(-709.784);
+
+  const Packet cst_cephes_LOG2EF = pset1<Packet>(1.4426950408889634073599);
+  const Packet cst_cephes_exp_p0 = pset1<Packet>(1.26177193074810590878e-4);
+  const Packet cst_cephes_exp_p1 = pset1<Packet>(3.02994407707441961300e-2);
+  const Packet cst_cephes_exp_p2 = pset1<Packet>(9.99999999999999999910e-1);
+  const Packet cst_cephes_exp_q0 = pset1<Packet>(3.00198505138664455042e-6);
+  const Packet cst_cephes_exp_q1 = pset1<Packet>(2.52448340349684104192e-3);
+  const Packet cst_cephes_exp_q2 = pset1<Packet>(2.27265548208155028766e-1);
+  const Packet cst_cephes_exp_q3 = pset1<Packet>(2.00000000000000000009e0);
+  const Packet cst_cephes_exp_C1 = pset1<Packet>(0.693145751953125);
+  const Packet cst_cephes_exp_C2 = pset1<Packet>(1.42860682030941723212e-6);
+
+  Packet tmp, fx;
+
+  // clamp x
+  Packet zero_mask = pcmp_lt(_x, cst_exp_lo);
+  x = pmin(x, cst_exp_hi);
+  // Express exp(x) as exp(g + n*log(2)).
+  fx = pmadd(cst_cephes_LOG2EF, x, cst_half);
+
+  // Get the integer modulus of log(2), i.e. the "n" described above.
+  fx = pfloor(fx);
+
+  // Get the remainder modulo log(2), i.e. the "g" described above. Subtract
+  // n*log(2) out in two steps, i.e. n*C1 + n*C2, C1+C2=log2 to get the last
+  // digits right.
+  tmp = pmul(fx, cst_cephes_exp_C1);
+  Packet z = pmul(fx, cst_cephes_exp_C2);
+  x = psub(x, tmp);
+  x = psub(x, z);
+
+  Packet x2 = pmul(x, x);
+
+  // Evaluate the numerator polynomial of the rational interpolant.
+  Packet px = cst_cephes_exp_p0;
+  px = pmadd(px, x2, cst_cephes_exp_p1);
+  px = pmadd(px, x2, cst_cephes_exp_p2);
+  px = pmul(px, x);
+
+  // Evaluate the denominator polynomial of the rational interpolant.
+  Packet qx = cst_cephes_exp_q0;
+  qx = pmadd(qx, x2, cst_cephes_exp_q1);
+  qx = pmadd(qx, x2, cst_cephes_exp_q2);
+  qx = pmadd(qx, x2, cst_cephes_exp_q3);
+
+  // I don't really get this bit, copied from the SSE2 routines, so...
+  // TODO(gonnet): Figure out what is going on here, perhaps find a better
+  // rational interpolant?
+  x = pdiv(px, psub(qx, px));
+  x = pmadd(cst_2, x, cst_1);
+
+  // Construct the result 2^n * exp(g) = e * x. The max is used to catch
+  // non-finite values in the input.
+  // TODO: replace pldexp with faster implementation since x in [-1, 1).
+  return pselect(zero_mask, cst_zero, pmax(pldexp(x,fx), _x));
+}
+
+// The following code is inspired by the following stack-overflow answer:
+//   https://stackoverflow.com/questions/30463616/payne-hanek-algorithm-implementation-in-c/30465751#30465751
+// It has been largely optimized:
+//  - By-pass calls to frexp.
+//  - Aligned loads of required 96 bits of 2/pi. This is accomplished by
+//    (1) balancing the mantissa and exponent to the required bits of 2/pi are
+//    aligned on 8-bits, and (2) replicating the storage of the bits of 2/pi.
+//  - Avoid a branch in rounding and extraction of the remaining fractional part.
+// Overall, I measured a speed up higher than x2 on x86-64.
+inline float trig_reduce_huge (float xf, Eigen::numext::int32_t *quadrant)
+{
+  using Eigen::numext::int32_t;
+  using Eigen::numext::uint32_t;
+  using Eigen::numext::int64_t;
+  using Eigen::numext::uint64_t;
+
+  const double pio2_62 = 3.4061215800865545e-19;    // pi/2 * 2^-62
+  const uint64_t zero_dot_five = uint64_t(1) << 61; // 0.5 in 2.62-bit fixed-point format
+
+  // 192 bits of 2/pi for Payne-Hanek reduction
+  // Bits are introduced by packet of 8 to enable aligned reads.
+  static const uint32_t two_over_pi [] = 
+  {
+    0x00000028, 0x000028be, 0x0028be60, 0x28be60db,
+    0xbe60db93, 0x60db9391, 0xdb939105, 0x9391054a,
+    0x91054a7f, 0x054a7f09, 0x4a7f09d5, 0x7f09d5f4,
+    0x09d5f47d, 0xd5f47d4d, 0xf47d4d37, 0x7d4d3770,
+    0x4d377036, 0x377036d8, 0x7036d8a5, 0x36d8a566,
+    0xd8a5664f, 0xa5664f10, 0x664f10e4, 0x4f10e410,
+    0x10e41000, 0xe4100000
+  };
+  
+  uint32_t xi = numext::bit_cast<uint32_t>(xf);
+  // Below, -118 = -126 + 8.
+  //   -126 is to get the exponent,
+  //   +8 is to enable alignment of 2/pi's bits on 8 bits.
+  // This is possible because the fractional part of x as only 24 meaningful bits.
+  uint32_t e = (xi >> 23) - 118;
+  // Extract the mantissa and shift it to align it wrt the exponent
+  xi = ((xi & 0x007fffffu)| 0x00800000u) << (e & 0x7);
+
+  uint32_t i = e >> 3;
+  uint32_t twoopi_1  = two_over_pi[i-1];
+  uint32_t twoopi_2  = two_over_pi[i+3];
+  uint32_t twoopi_3  = two_over_pi[i+7];
+
+  // Compute x * 2/pi in 2.62-bit fixed-point format.
+  uint64_t p;
+  p = uint64_t(xi) * twoopi_3;
+  p = uint64_t(xi) * twoopi_2 + (p >> 32);
+  p = (uint64_t(xi * twoopi_1) << 32) + p;
+
+  // Round to nearest: add 0.5 and extract integral part.
+  uint64_t q = (p + zero_dot_five) >> 62;
+  *quadrant = int(q);
+  // Now it remains to compute "r = x - q*pi/2" with high accuracy,
+  // since we have p=x/(pi/2) with high accuracy, we can more efficiently compute r as:
+  //   r = (p-q)*pi/2,
+  // where the product can be be carried out with sufficient accuracy using double precision.
+  p -= q<<62;
+  return float(double(int64_t(p)) * pio2_62);
+}
+
+template<bool ComputeSine,typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+#if EIGEN_COMP_GNUC_STRICT
+__attribute__((optimize("-fno-unsafe-math-optimizations")))
+#endif
+Packet psincos_float(const Packet& _x)
+{
+  typedef typename unpacket_traits<Packet>::integer_packet PacketI;
+
+  const Packet  cst_2oPI            = pset1<Packet>(0.636619746685028076171875f); // 2/PI
+  const Packet  cst_rounding_magic  = pset1<Packet>(12582912); // 2^23 for rounding
+  const PacketI csti_1              = pset1<PacketI>(1);
+  const Packet  cst_sign_mask       = pset1frombits<Packet>(static_cast<Eigen::numext::uint32_t>(0x80000000u));
+
+  Packet x = pabs(_x);
+
+  // Scale x by 2/Pi to find x's octant.
+  Packet y = pmul(x, cst_2oPI);
+
+  // Rounding trick to find nearest integer:
+  Packet y_round = padd(y, cst_rounding_magic);
+  EIGEN_OPTIMIZATION_BARRIER(y_round)
+  PacketI y_int = preinterpret<PacketI>(y_round); // last 23 digits represent integer (if abs(x)<2^24)
+  y = psub(y_round, cst_rounding_magic); // nearest integer to x * (2/pi)
+
+  // Subtract y * Pi/2 to reduce x to the interval -Pi/4 <= x <= +Pi/4
+  // using "Extended precision modular arithmetic"
+  #if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD)
+  // This version requires true FMA for high accuracy
+  // It provides a max error of 1ULP up to (with absolute_error < 5.9605e-08):
+  const float huge_th = ComputeSine ? 117435.992f : 71476.0625f;
+  x = pmadd(y, pset1<Packet>(-1.57079601287841796875f), x);
+  x = pmadd(y, pset1<Packet>(-3.1391647326017846353352069854736328125e-07f), x);
+  x = pmadd(y, pset1<Packet>(-5.390302529957764765544681040410068817436695098876953125e-15f), x);
+  #else
+  // Without true FMA, the previous set of coefficients maintain 1ULP accuracy
+  // up to x<15.7 (for sin), but accuracy is immediately lost for x>15.7.
+  // We thus use one more iteration to maintain 2ULPs up to reasonably large inputs.
+
+  // The following set of coefficients maintain 1ULP up to 9.43 and 14.16 for sin and cos respectively.
+  // and 2 ULP up to:
+  const float huge_th = ComputeSine ? 25966.f : 18838.f;
+  x = pmadd(y, pset1<Packet>(-1.5703125), x); // = 0xbfc90000
+  EIGEN_OPTIMIZATION_BARRIER(x)
+  x = pmadd(y, pset1<Packet>(-0.000483989715576171875), x); // = 0xb9fdc000
+  EIGEN_OPTIMIZATION_BARRIER(x)
+  x = pmadd(y, pset1<Packet>(1.62865035235881805419921875e-07), x); // = 0x342ee000
+  x = pmadd(y, pset1<Packet>(5.5644315544167710640977020375430583953857421875e-11), x); // = 0x2e74b9ee
+
+  // For the record, the following set of coefficients maintain 2ULP up
+  // to a slightly larger range:
+  // const float huge_th = ComputeSine ? 51981.f : 39086.125f;
+  // but it slightly fails to maintain 1ULP for two values of sin below pi.
+  // x = pmadd(y, pset1<Packet>(-3.140625/2.), x);
+  // x = pmadd(y, pset1<Packet>(-0.00048351287841796875), x);
+  // x = pmadd(y, pset1<Packet>(-3.13855707645416259765625e-07), x);
+  // x = pmadd(y, pset1<Packet>(-6.0771006282767103812147979624569416046142578125e-11), x);
+
+  // For the record, with only 3 iterations it is possible to maintain
+  // 1 ULP up to 3PI (maybe more) and 2ULP up to 255.
+  // The coefficients are: 0xbfc90f80, 0xb7354480, 0x2e74b9ee
+  #endif
+
+  if(predux_any(pcmp_le(pset1<Packet>(huge_th),pabs(_x))))
+  {
+    const int PacketSize = unpacket_traits<Packet>::size;
+    EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) float vals[PacketSize];
+    EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) float x_cpy[PacketSize];
+    EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) Eigen::numext::int32_t y_int2[PacketSize];
+    pstoreu(vals, pabs(_x));
+    pstoreu(x_cpy, x);
+    pstoreu(y_int2, y_int);
+    for(int k=0; k<PacketSize;++k)
+    {
+      float val = vals[k];
+      if(val>=huge_th && (numext::isfinite)(val))
+        x_cpy[k] = trig_reduce_huge(val,&y_int2[k]);
+    }
+    x = ploadu<Packet>(x_cpy);
+    y_int = ploadu<PacketI>(y_int2);
+  }
+
+  // Compute the sign to apply to the polynomial.
+  // sin: sign = second_bit(y_int) xor signbit(_x)
+  // cos: sign = second_bit(y_int+1)
+  Packet sign_bit = ComputeSine ? pxor(_x, preinterpret<Packet>(plogical_shift_left<30>(y_int)))
+                                : preinterpret<Packet>(plogical_shift_left<30>(padd(y_int,csti_1)));
+  sign_bit = pand(sign_bit, cst_sign_mask); // clear all but left most bit
+
+  // Get the polynomial selection mask from the second bit of y_int
+  // We'll calculate both (sin and cos) polynomials and then select from the two.
+  Packet poly_mask = preinterpret<Packet>(pcmp_eq(pand(y_int, csti_1), pzero(y_int)));
+
+  Packet x2 = pmul(x,x);
+
+  // Evaluate the cos(x) polynomial. (-Pi/4 <= x <= Pi/4)
+  Packet y1 =        pset1<Packet>(2.4372266125283204019069671630859375e-05f);
+  y1 = pmadd(y1, x2, pset1<Packet>(-0.00138865201734006404876708984375f     ));
+  y1 = pmadd(y1, x2, pset1<Packet>(0.041666619479656219482421875f           ));
+  y1 = pmadd(y1, x2, pset1<Packet>(-0.5f));
+  y1 = pmadd(y1, x2, pset1<Packet>(1.f));
+
+  // Evaluate the sin(x) polynomial. (Pi/4 <= x <= Pi/4)
+  // octave/matlab code to compute those coefficients:
+  //    x = (0:0.0001:pi/4)';
+  //    A = [x.^3 x.^5 x.^7];
+  //    w = ((1.-(x/(pi/4)).^2).^5)*2000+1;         # weights trading relative accuracy
+  //    c = (A'*diag(w)*A)\(A'*diag(w)*(sin(x)-x)); # weighted LS, linear coeff forced to 1
+  //    printf('%.64f\n %.64f\n%.64f\n', c(3), c(2), c(1))
+  //
+  Packet y2 =        pset1<Packet>(-0.0001959234114083702898469196984621021329076029360294342041015625f);
+  y2 = pmadd(y2, x2, pset1<Packet>( 0.0083326873655616851693794799871284340042620897293090820312500000f));
+  y2 = pmadd(y2, x2, pset1<Packet>(-0.1666666203982298255503735617821803316473960876464843750000000000f));
+  y2 = pmul(y2, x2);
+  y2 = pmadd(y2, x, x);
+
+  // Select the correct result from the two polynomials.
+  y = ComputeSine ? pselect(poly_mask,y2,y1)
+                  : pselect(poly_mask,y1,y2);
+
+  // Update the sign and filter huge inputs
+  return pxor(y, sign_bit);
+}
+
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet psin_float(const Packet& x)
+{
+  return psincos_float<true>(x);
+}
+
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pcos_float(const Packet& x)
+{
+  return psincos_float<false>(x);
+}
+
+// Generic implementation of acos(x).
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pacos_float(const Packet& x_in) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static_assert(std::is_same<Scalar, float>::value, "Scalar type must be float");
+
+  const Packet cst_one = pset1<Packet>(Scalar(1));
+  const Packet cst_pi = pset1<Packet>(Scalar(EIGEN_PI));
+  const Packet p6 = pset1<Packet>(Scalar(2.36423197202384471893310546875e-3));
+  const Packet p5 = pset1<Packet>(Scalar(-1.1368644423782825469970703125e-2));
+  const Packet p4 = pset1<Packet>(Scalar(2.717843465507030487060546875e-2));
+  const Packet p3 = pset1<Packet>(Scalar(-4.8969544470310211181640625e-2));
+  const Packet p2 = pset1<Packet>(Scalar(8.8804088532924652099609375e-2));
+  const Packet p1 = pset1<Packet>(Scalar(-0.214591205120086669921875));
+  const Packet p0 = pset1<Packet>(Scalar(1.57079637050628662109375));
+
+  // For x in [0:1], we approximate acos(x)/sqrt(1-x), which is a smooth
+  // function, by a 6'th order polynomial.
+  // For x in [-1:0) we use that acos(-x) = pi - acos(x).
+  const Packet neg_mask = psignbit(x_in);
+  const Packet abs_x = pabs(x_in);
+
+  // Evaluate the polynomial using Horner's rule:
+  //   P(x) = p0 + x * (p1 +  x * (p2 + ... (p5 + x * p6)) ... ) .
+  // We evaluate even and odd terms independently to increase
+  // instruction level parallelism.
+  Packet x2 = pmul(x_in,x_in);
+  Packet p_even = pmadd(p6, x2, p4);
+  Packet p_odd = pmadd(p5, x2, p3);
+  p_even = pmadd(p_even, x2, p2);
+  p_odd = pmadd(p_odd, x2, p1);
+  p_even = pmadd(p_even, x2, p0);
+  Packet p = pmadd(p_odd, abs_x, p_even);
+
+  // The polynomial approximates acos(x)/sqrt(1-x), so
+  // multiply by sqrt(1-x) to get acos(x).
+  // Conveniently returns NaN for arguments outside [-1:1].
+  Packet denom = psqrt(psub(cst_one, abs_x));
+  Packet result = pmul(denom, p);
+  // Undo mapping for negative arguments.
+  return pselect(neg_mask, psub(cst_pi, result), result);
+}
+
+// Generic implementation of asin(x).
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pasin_float(const Packet& x_in) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static_assert(std::is_same<Scalar, float>::value, "Scalar type must be float");
+
+  constexpr float kPiOverTwo = static_cast<float>(EIGEN_PI / 2);
+
+  const Packet cst_half = pset1<Packet>(0.5f);
+  const Packet cst_one = pset1<Packet>(1.0f);
+  const Packet cst_two = pset1<Packet>(2.0f);
+  const Packet cst_pi_over_two = pset1<Packet>(kPiOverTwo);
+  // For |x| < 0.5 approximate asin(x)/x by an 8th order polynomial with
+  // even terms only.
+  const Packet p9 = pset1<Packet>(5.08838854730129241943359375e-2f);
+  const Packet p7 = pset1<Packet>(3.95139865577220916748046875e-2f);
+  const Packet p5 = pset1<Packet>(7.550220191478729248046875e-2f);
+  const Packet p3 = pset1<Packet>(0.16664917767047882080078125f);
+  const Packet p1 = pset1<Packet>(1.00000011920928955078125f);
+
+  const Packet abs_x = pabs(x_in);
+  const Packet sign_mask = pandnot(x_in, abs_x);
+  const Packet invalid_mask = pcmp_lt(cst_one, abs_x);
+
+  // For arguments |x| > 0.5, we map x back to [0:0.5] using
+  // the transformation x_large = sqrt(0.5*(1-x)), and use the
+  // identity
+  //   asin(x) = pi/2 - 2 * asin( sqrt( 0.5 * (1 - x)))
+
+  const Packet x_large = psqrt(pnmadd(cst_half, abs_x, cst_half));
+  const Packet large_mask = pcmp_lt(cst_half, abs_x);
+  const Packet x = pselect(large_mask, x_large, abs_x);
+  const Packet x2 = pmul(x, x);
+
+  // Compute polynomial.
+  // x * (p1 + x^2*(p3 + x^2*(p5 + x^2*(p7 + x^2*p9))))
+
+  Packet p = pmadd(p9, x2, p7);
+  p = pmadd(p, x2, p5);
+  p = pmadd(p, x2, p3);
+  p = pmadd(p, x2, p1);
+  p = pmul(p, x);
+
+  const Packet p_large = pnmadd(cst_two, p, cst_pi_over_two);
+  p = pselect(large_mask, p_large, p);
+  // Flip the sign for negative arguments.
+  p = pxor(p, sign_mask);
+  // Return NaN for arguments outside [-1:1].
+  return por(invalid_mask, p);
+}
+
+// Computes elementwise atan(x) for x in [-1:1] with 2 ulp accuracy.
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet patan_reduced_float(const Packet& x) {
+  const Packet q0 = pset1<Packet>(-0.3333314359188079833984375f);
+  const Packet q2 = pset1<Packet>(0.19993579387664794921875f);
+  const Packet q4 = pset1<Packet>(-0.14209578931331634521484375f);
+  const Packet q6 = pset1<Packet>(0.1066047251224517822265625f);
+  const Packet q8 = pset1<Packet>(-7.5408883392810821533203125e-2f);
+  const Packet q10 = pset1<Packet>(4.3082617223262786865234375e-2f);
+  const Packet q12 = pset1<Packet>(-1.62907354533672332763671875e-2f);
+  const Packet q14 = pset1<Packet>(2.90188402868807315826416015625e-3f);
+
+  // Approximate atan(x) by a polynomial of the form
+  //   P(x) = x + x^3 * Q(x^2),
+  // where Q(x^2) is a 7th order polynomial in x^2.
+  // We evaluate even and odd terms in x^2 in parallel
+  // to take advantage of instruction level parallelism
+  // and hardware with multiple FMA units.
+
+  // note: if x == -0, this returns +0
+  const Packet x2 = pmul(x, x);
+  const Packet x4 = pmul(x2, x2);
+  Packet q_odd = pmadd(q14, x4, q10);
+  Packet q_even = pmadd(q12, x4, q8);
+  q_odd = pmadd(q_odd, x4, q6);
+  q_even = pmadd(q_even, x4, q4);
+  q_odd = pmadd(q_odd, x4, q2);
+  q_even = pmadd(q_even, x4, q0);
+  const Packet q = pmadd(q_odd, x2, q_even);
+  return pmadd(q, pmul(x, x2), x);
+}
+
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet patan_float(const Packet& x_in) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static_assert(std::is_same<Scalar, float>::value, "Scalar type must be float");
+
+  constexpr float kPiOverTwo = static_cast<float>(EIGEN_PI / 2);
+
+  const Packet cst_signmask = pset1<Packet>(-0.0f);
+  const Packet cst_one = pset1<Packet>(1.0f);
+  const Packet cst_pi_over_two = pset1<Packet>(kPiOverTwo);
+
+  //   "Large": For |x| > 1, use atan(1/x) = sign(x)*pi/2 - atan(x).
+  //   "Small": For |x| <= 1, approximate atan(x) directly by a polynomial
+  //            calculated using Sollya.
+
+  const Packet abs_x = pabs(x_in);
+  const Packet x_signmask = pand(x_in, cst_signmask);
+  const Packet large_mask = pcmp_lt(cst_one, abs_x);
+  const Packet x = pselect(large_mask, preciprocal(abs_x), abs_x);
+  const Packet p = patan_reduced_float(x);
+  // Apply transformations according to the range reduction masks.
+  Packet result = pselect(large_mask, psub(cst_pi_over_two, p), p);
+  // Return correct sign
+  return pxor(result, x_signmask);
+}
+
+// Computes elementwise atan(x) for x in [-tan(pi/8):tan(pi/8)]
+// with 2 ulp accuracy.
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet
+patan_reduced_double(const Packet& x) {
+  const Packet q0 =
+      pset1<Packet>(-0.33333333333330028569463365784031338989734649658203);
+  const Packet q2 =
+      pset1<Packet>(0.199999999990664090177006073645316064357757568359375);
+  const Packet q4 =
+      pset1<Packet>(-0.142857141937123677255527809393242932856082916259766);
+  const Packet q6 =
+      pset1<Packet>(0.111111065991039953404495577160560060292482376098633);
+  const Packet q8 =
+      pset1<Packet>(-9.0907812986129224452902519715280504897236824035645e-2);
+  const Packet q10 =
+      pset1<Packet>(7.6900542950704739442180368769186316058039665222168e-2);
+  const Packet q12 =
+      pset1<Packet>(-6.6410112986494976294871150912513257935643196105957e-2);
+  const Packet q14 =
+      pset1<Packet>(5.6920144995467943094258345126945641823112964630127e-2);
+  const Packet q16 =
+      pset1<Packet>(-4.3577020814990513608577771265117917209863662719727e-2);
+  const Packet q18 =
+      pset1<Packet>(2.1244050233624342527427586446719942614436149597168e-2);
+
+  // Approximate atan(x) on [0:tan(pi/8)] by a polynomial of the form
+  //   P(x) = x + x^3 * Q(x^2),
+  // where Q(x^2) is a 9th order polynomial in x^2.
+  // We evaluate even and odd terms in x^2 in parallel
+  // to take advantage of instruction level parallelism
+  // and hardware with multiple FMA units.
+  const Packet x2 = pmul(x, x);
+  const Packet x4 = pmul(x2, x2);
+  Packet q_odd = pmadd(q18, x4, q14);
+  Packet q_even = pmadd(q16, x4, q12);
+  q_odd = pmadd(q_odd, x4, q10);
+  q_even = pmadd(q_even, x4, q8);
+  q_odd = pmadd(q_odd, x4, q6);
+  q_even = pmadd(q_even, x4, q4);
+  q_odd = pmadd(q_odd, x4, q2);
+  q_even = pmadd(q_even, x4, q0);
+  const Packet p = pmadd(q_odd, x2, q_even);
+  return pmadd(p, pmul(x, x2), x);
+}
+
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet patan_double(const Packet& x_in) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static_assert(std::is_same<Scalar, double>::value, "Scalar type must be double");
+
+  constexpr double kPiOverTwo = static_cast<double>(EIGEN_PI / 2);
+  constexpr double kPiOverFour = static_cast<double>(EIGEN_PI / 4);
+  constexpr double kTanPiOverEight = 0.4142135623730950488016887;
+  constexpr double kTan3PiOverEight = 2.4142135623730950488016887;
+
+  const Packet cst_signmask = pset1<Packet>(-0.0);
+  const Packet cst_one = pset1<Packet>(1.0);
+  const Packet cst_pi_over_two = pset1<Packet>(kPiOverTwo);
+  const Packet cst_pi_over_four = pset1<Packet>(kPiOverFour);
+  const Packet cst_large = pset1<Packet>(kTan3PiOverEight);
+  const Packet cst_medium = pset1<Packet>(kTanPiOverEight);
+
+  // Use the same range reduction strategy (to [0:tan(pi/8)]) as the
+  // Cephes library:
+  //   "Large": For x >= tan(3*pi/8), use atan(1/x) = pi/2 - atan(x).
+  //   "Medium": For x in [tan(pi/8) : tan(3*pi/8)),
+  //             use atan(x) = pi/4 + atan((x-1)/(x+1)).
+  //   "Small": For x < tan(pi/8), approximate atan(x) directly by a polynomial
+  //            calculated using Sollya.
+
+  const Packet abs_x = pabs(x_in);
+  const Packet x_signmask = pand(x_in, cst_signmask);
+  const Packet large_mask = pcmp_lt(cst_large, abs_x);
+  const Packet medium_mask = pandnot(pcmp_lt(cst_medium, abs_x), large_mask);
+
+  Packet x = abs_x;
+  x = pselect(large_mask, preciprocal(abs_x), x);
+  x = pselect(medium_mask, pdiv(psub(abs_x, cst_one), padd(abs_x, cst_one)), x);
+
+  // Compute approximation of p ~= atan(x') where x' is the argument reduced to
+  // [0:tan(pi/8)].
+  Packet p = patan_reduced_double(x);
+
+  // Apply transformations according to the range reduction masks.
+  p = pselect(large_mask, psub(cst_pi_over_two, p), p);
+  p = pselect(medium_mask, padd(cst_pi_over_four, p), p);
+  // Return the correct sign
+  return pxor(p, x_signmask);
+}
+
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet patanh_float(const Packet& x) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static_assert(std::is_same<Scalar, float>::value, "Scalar type must be float");
+  const Packet half = pset1<Packet>(0.5f);
+  const Packet x_gt_half = pcmp_le(half, pabs(x));
+  // For |x| in [0:0.5] we use a polynomial approximation of the form
+  // P(x) = x + x^3*(c3 + x^2 * (c5 + x^2 * (... x^2 * c11) ... )).
+  const Packet C3 = pset1<Packet>(0.3333373963832855224609375f);
+  const Packet C5 = pset1<Packet>(0.1997792422771453857421875f);
+  const Packet C7 = pset1<Packet>(0.14672131836414337158203125f);
+  const Packet C9 = pset1<Packet>(8.2311116158962249755859375e-2f);
+  const Packet C11 = pset1<Packet>(0.1819281280040740966796875f);
+  const Packet x2 = pmul(x,x);
+  Packet p = pmadd(C11, x2, C9);
+  p = pmadd(x2, p, C7);
+  p = pmadd(x2, p, C5);
+  p = pmadd(x2, p, C3);
+  p = pmadd(pmul(x,x2), p, x);
+
+  // For |x| in ]0.5:1.0] we use atanh = 0.5*ln((1+x)/(1-x));
+  const Packet one = pset1<Packet>(1.0f);
+  Packet r = pdiv(padd(one, x), psub(one, x));
+  r = pmul(half, plog(r));
+  return pselect(x_gt_half, r, p);
+}
+
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pdiv_complex(const Packet& x, const Packet& y) {
+  typedef typename unpacket_traits<Packet>::as_real RealPacket;
+  // In the following we annotate the code for the case where the inputs
+  // are a pair length-2 SIMD vectors representing a single pair of complex
+  // numbers x = a + i*b, y = c + i*d.
+  const RealPacket y_abs = pabs(y.v);  // |c|, |d|
+  const RealPacket y_abs_flip = pcplxflip(Packet(y_abs)).v; // |d|, |c|
+  const RealPacket y_max = pmax(y_abs, y_abs_flip); // max(|c|, |d|), max(|c|, |d|)
+  const RealPacket y_scaled = pdiv(y.v, y_max);  // c / max(|c|, |d|), d / max(|c|, |d|)
+  // Compute scaled denominator.
+  const RealPacket y_scaled_sq = pmul(y_scaled, y_scaled); // c'**2, d'**2
+  const RealPacket denom = padd(y_scaled_sq, pcplxflip(Packet(y_scaled_sq)).v);
+  Packet result_scaled = pmul(x, pconj(Packet(y_scaled)));  // a * c' + b * d', -a * d + b * c
+  // Divide elementwise by denom.
+  result_scaled = Packet(pdiv(result_scaled.v, denom));
+  // Rescale result
+  return Packet(pdiv(result_scaled.v, y_max));
+}
+
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet psqrt_complex(const Packet& a) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  typedef typename Scalar::value_type RealScalar;
+  typedef typename unpacket_traits<Packet>::as_real RealPacket;
+
+  // Computes the principal sqrt of the complex numbers in the input.
+  //
+  // For example, for packets containing 2 complex numbers stored in interleaved format
+  //    a = [a0, a1] = [x0, y0, x1, y1],
+  // where x0 = real(a0), y0 = imag(a0) etc., this function returns
+  //    b = [b0, b1] = [u0, v0, u1, v1],
+  // such that b0^2 = a0, b1^2 = a1.
+  //
+  // To derive the formula for the complex square roots, let's consider the equation for
+  // a single complex square root of the number x + i*y. We want to find real numbers
+  // u and v such that
+  //    (u + i*v)^2 = x + i*y  <=>
+  //    u^2 - v^2 + i*2*u*v = x + i*v.
+  // By equating the real and imaginary parts we get:
+  //    u^2 - v^2 = x
+  //    2*u*v = y.
+  //
+  // For x >= 0, this has the numerically stable solution
+  //    u = sqrt(0.5 * (x + sqrt(x^2 + y^2)))
+  //    v = 0.5 * (y / u)
+  // and for x < 0,
+  //    v = sign(y) * sqrt(0.5 * (-x + sqrt(x^2 + y^2)))
+  //    u = 0.5 * (y / v)
+  //
+  //  To avoid unnecessary over- and underflow, we compute sqrt(x^2 + y^2) as
+  //     l = max(|x|, |y|) * sqrt(1 + (min(|x|, |y|) / max(|x|, |y|))^2) ,
+
+  // In the following, without lack of generality, we have annotated the code, assuming
+  // that the input is a packet of 2 complex numbers.
+  //
+  // Step 1. Compute l = [l0, l0, l1, l1], where
+  //    l0 = sqrt(x0^2 + y0^2),  l1 = sqrt(x1^2 + y1^2)
+  // To avoid over- and underflow, we use the stable formula for each hypotenuse
+  //    l0 = (min0 == 0 ? max0 : max0 * sqrt(1 + (min0/max0)**2)),
+  // where max0 = max(|x0|, |y0|), min0 = min(|x0|, |y0|), and similarly for l1.
+
+  RealPacket a_abs = pabs(a.v);           // [|x0|, |y0|, |x1|, |y1|]
+  RealPacket a_abs_flip = pcplxflip(Packet(a_abs)).v; // [|y0|, |x0|, |y1|, |x1|]
+  RealPacket a_max = pmax(a_abs, a_abs_flip);
+  RealPacket a_min = pmin(a_abs, a_abs_flip);
+  RealPacket a_min_zero_mask = pcmp_eq(a_min, pzero(a_min));
+  RealPacket a_max_zero_mask = pcmp_eq(a_max, pzero(a_max));
+  RealPacket r = pdiv(a_min, a_max);
+  const RealPacket cst_one  = pset1<RealPacket>(RealScalar(1));
+  RealPacket l = pmul(a_max, psqrt(padd(cst_one, pmul(r, r))));  // [l0, l0, l1, l1]
+  // Set l to a_max if a_min is zero.
+  l = pselect(a_min_zero_mask, a_max, l);
+
+  // Step 2. Compute [rho0, *, rho1, *], where
+  // rho0 = sqrt(0.5 * (l0 + |x0|)), rho1 =  sqrt(0.5 * (l1 + |x1|))
+  // We don't care about the imaginary parts computed here. They will be overwritten later.
+  const RealPacket cst_half = pset1<RealPacket>(RealScalar(0.5));
+  Packet rho;
+  rho.v = psqrt(pmul(cst_half, padd(a_abs, l)));
+
+  // Step 3. Compute [rho0, eta0, rho1, eta1], where
+  // eta0 = (y0 / l0) / 2, and eta1 = (y1 / l1) / 2.
+  // set eta = 0 of input is 0 + i0.
+  RealPacket eta = pandnot(pmul(cst_half, pdiv(a.v, pcplxflip(rho).v)), a_max_zero_mask);
+  RealPacket real_mask = peven_mask(a.v);
+  Packet positive_real_result;
+  // Compute result for inputs with positive real part.
+  positive_real_result.v = pselect(real_mask, rho.v, eta);
+
+  // Step 4. Compute solution for inputs with negative real part:
+  //         [|eta0|, sign(y0)*rho0, |eta1|, sign(y1)*rho1]
+  const RealPacket cst_imag_sign_mask =
+      pset1<Packet>(Scalar(RealScalar(0.0), RealScalar(-0.0))).v;
+  RealPacket imag_signs = pand(a.v, cst_imag_sign_mask);
+  Packet negative_real_result;
+  // Notice that rho is positive, so taking it's absolute value is a noop.
+  negative_real_result.v = por(pabs(pcplxflip(positive_real_result).v), imag_signs);
+
+  // Step 5. Select solution branch based on the sign of the real parts.
+  Packet negative_real_mask;
+  negative_real_mask.v = pcmp_lt(pand(real_mask, a.v), pzero(a.v));
+  negative_real_mask.v = por(negative_real_mask.v, pcplxflip(negative_real_mask).v);
+  Packet result = pselect(negative_real_mask, negative_real_result, positive_real_result);
+
+  // Step 6. Handle special cases for infinities:
+  // * If z is (x,+∞), the result is (+∞,+∞) even if x is NaN
+  // * If z is (x,-∞), the result is (+∞,-∞) even if x is NaN
+  // * If z is (-∞,y), the result is (0*|y|,+∞) for finite or NaN y
+  // * If z is (+∞,y), the result is (+∞,0*|y|) for finite or NaN y
+  const RealPacket cst_pos_inf = pset1<RealPacket>(NumTraits<RealScalar>::infinity());
+  Packet is_inf;
+  is_inf.v = pcmp_eq(a_abs, cst_pos_inf);
+  Packet is_real_inf;
+  is_real_inf.v = pand(is_inf.v, real_mask);
+  is_real_inf = por(is_real_inf, pcplxflip(is_real_inf));
+  // prepare packet of (+∞,0*|y|) or (0*|y|,+∞), depending on the sign of the infinite real part.
+  Packet real_inf_result;
+  real_inf_result.v = pmul(a_abs, pset1<Packet>(Scalar(RealScalar(1.0), RealScalar(0.0))).v);
+  real_inf_result.v = pselect(negative_real_mask.v, pcplxflip(real_inf_result).v, real_inf_result.v);
+  // prepare packet of (+∞,+∞) or (+∞,-∞), depending on the sign of the infinite imaginary part.
+  Packet is_imag_inf;
+  is_imag_inf.v = pandnot(is_inf.v, real_mask);
+  is_imag_inf = por(is_imag_inf, pcplxflip(is_imag_inf));
+  Packet imag_inf_result;
+  imag_inf_result.v = por(pand(cst_pos_inf, real_mask), pandnot(a.v, real_mask));
+  // unless otherwise specified, if either the real or imaginary component is nan, the entire result is nan
+  Packet result_is_nan = pisnan(result);
+  result = por(result_is_nan, result);
+
+  return pselect(is_imag_inf, imag_inf_result, pselect(is_real_inf, real_inf_result, result));
+}
+
+
+template <typename Packet>
+struct psign_impl<Packet, std::enable_if_t<!NumTraits<typename unpacket_traits<Packet>::type>::IsComplex &&
+                                           !NumTraits<typename unpacket_traits<Packet>::type>::IsInteger>> {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a) {
+    using Scalar = typename unpacket_traits<Packet>::type;
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    const Packet cst_zero = pzero(a);
+
+    const Packet abs_a = pabs(a);
+    const Packet sign_mask = pandnot(a, abs_a);
+    const Packet nonzero_mask = pcmp_lt(cst_zero, abs_a);
+
+    return pselect(nonzero_mask, por(sign_mask, cst_one), abs_a);
+  }
+};
+
+template <typename Packet>
+struct psign_impl<Packet, std::enable_if_t<!NumTraits<typename unpacket_traits<Packet>::type>::IsComplex &&
+                                           NumTraits<typename unpacket_traits<Packet>::type>::IsSigned &&
+                                           NumTraits<typename unpacket_traits<Packet>::type>::IsInteger>> {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a) {
+    using Scalar = typename unpacket_traits<Packet>::type;
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    const Packet cst_minus_one = pset1<Packet>(Scalar(-1));
+    const Packet cst_zero = pzero(a);
+
+    const Packet positive_mask = pcmp_lt(cst_zero, a);
+    const Packet positive = pand(positive_mask, cst_one);
+    const Packet negative_mask = pcmp_lt(a, cst_zero);
+    const Packet negative = pand(negative_mask, cst_minus_one);
+
+    return por(positive, negative);
+  }
+};
+
+template <typename Packet>
+struct psign_impl<Packet, std::enable_if_t<!NumTraits<typename unpacket_traits<Packet>::type>::IsComplex &&
+                                           !NumTraits<typename unpacket_traits<Packet>::type>::IsSigned &&
+                                           NumTraits<typename unpacket_traits<Packet>::type>::IsInteger>> {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a) {
+    using Scalar = typename unpacket_traits<Packet>::type;
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    const Packet cst_zero = pzero(a);
+
+    const Packet zero_mask = pcmp_eq(cst_zero, a);
+    return pandnot(cst_one, zero_mask);
+  }
+};
+
+// \internal \returns the the sign of a complex number z, defined as z / abs(z).
+template <typename Packet>
+struct psign_impl<Packet, std::enable_if_t<NumTraits<typename unpacket_traits<Packet>::type>::IsComplex &&
+                                           unpacket_traits<Packet>::vectorizable>> {
+  static EIGEN_DEVICE_FUNC inline Packet run(const Packet& a) {
+    typedef typename unpacket_traits<Packet>::type Scalar;
+    typedef typename Scalar::value_type RealScalar;
+    typedef typename unpacket_traits<Packet>::as_real RealPacket;
+
+    // Step 1. Compute (for each element z = x + i*y in a)
+    //     l = abs(z) = sqrt(x^2 + y^2).
+    // To avoid over- and underflow, we use the stable formula for each hypotenuse
+    //    l = (zmin == 0 ? zmax : zmax * sqrt(1 + (zmin/zmax)**2)),
+    // where zmax = max(|x|, |y|), zmin = min(|x|, |y|),
+    RealPacket a_abs = pabs(a.v);
+    RealPacket a_abs_flip = pcplxflip(Packet(a_abs)).v;
+    RealPacket a_max = pmax(a_abs, a_abs_flip);
+    RealPacket a_min = pmin(a_abs, a_abs_flip);
+    RealPacket a_min_zero_mask = pcmp_eq(a_min, pzero(a_min));
+    RealPacket a_max_zero_mask = pcmp_eq(a_max, pzero(a_max));
+    RealPacket r = pdiv(a_min, a_max);
+    const RealPacket cst_one = pset1<RealPacket>(RealScalar(1));
+    RealPacket l = pmul(a_max, psqrt(padd(cst_one, pmul(r, r))));  // [l0, l0, l1, l1]
+    // Set l to a_max if a_min is zero, since the roundtrip sqrt(a_max^2) may be
+    // lossy.
+    l = pselect(a_min_zero_mask, a_max, l);
+    // Step 2 compute a / abs(a).
+    RealPacket sign_as_real = pandnot(pdiv(a.v, l), a_max_zero_mask);
+    Packet sign;
+    sign.v = sign_as_real;
+    return sign;
+  }
+};
+
+// TODO(rmlarsen): The following set of utilities for double word arithmetic
+// should perhaps be refactored as a separate file, since it would be generally
+// useful for special function implementation etc. Writing the algorithms in
+// terms if a double word type would also make the code more readable.
+
+// This function splits x into the nearest integer n and fractional part r,
+// such that x = n + r holds exactly.
+template<typename Packet>
+EIGEN_STRONG_INLINE
+void absolute_split(const Packet& x, Packet& n, Packet& r) {
+  n = pround(x);
+  r = psub(x, n);
+}
+
+// This function computes the sum {s, r}, such that x + y = s_hi + s_lo
+// holds exactly, and s_hi = fl(x+y), if |x| >= |y|.
+template<typename Packet>
+EIGEN_STRONG_INLINE
+void fast_twosum(const Packet& x, const Packet& y, Packet& s_hi, Packet& s_lo) {
+  s_hi = padd(x, y);
+  const Packet t = psub(s_hi, x);
+  s_lo = psub(y, t);
+}
+
+#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+// This function implements the extended precision product of
+// a pair of floating point numbers. Given {x, y}, it computes the pair
+// {p_hi, p_lo} such that x * y = p_hi + p_lo holds exactly and
+// p_hi = fl(x * y).
+template<typename Packet>
+EIGEN_STRONG_INLINE
+void twoprod(const Packet& x, const Packet& y,
+             Packet& p_hi, Packet& p_lo) {
+  p_hi = pmul(x, y);
+  p_lo = pmsub(x, y, p_hi);
+}
+
+#else
+
+// This function implements the Veltkamp splitting. Given a floating point
+// number x it returns the pair {x_hi, x_lo} such that x_hi + x_lo = x holds
+// exactly and that half of the significant of x fits in x_hi.
+// This is Algorithm 3 from Jean-Michel Muller, "Elementary Functions",
+// 3rd edition, Birkh\"auser, 2016.
+template<typename Packet>
+EIGEN_STRONG_INLINE
+void veltkamp_splitting(const Packet& x, Packet& x_hi, Packet& x_lo) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  EIGEN_CONSTEXPR int shift = (NumTraits<Scalar>::digits() + 1) / 2;
+  const Scalar shift_scale = Scalar(uint64_t(1) << shift);  // Scalar constructor not necessarily constexpr.
+  const Packet gamma = pmul(pset1<Packet>(shift_scale + Scalar(1)), x);
+  Packet rho = psub(x, gamma);
+  x_hi = padd(rho, gamma);
+  x_lo = psub(x, x_hi);
+}
+
+// This function implements Dekker's algorithm for products x * y.
+// Given floating point numbers {x, y} computes the pair
+// {p_hi, p_lo} such that x * y = p_hi + p_lo holds exactly and
+// p_hi = fl(x * y).
+template<typename Packet>
+EIGEN_STRONG_INLINE
+void twoprod(const Packet& x, const Packet& y,
+             Packet& p_hi, Packet& p_lo) {
+  Packet x_hi, x_lo, y_hi, y_lo;
+  veltkamp_splitting(x, x_hi, x_lo);
+  veltkamp_splitting(y, y_hi, y_lo);
+
+  p_hi = pmul(x, y);
+  p_lo = pmadd(x_hi, y_hi, pnegate(p_hi));
+  p_lo = pmadd(x_hi, y_lo, p_lo);
+  p_lo = pmadd(x_lo, y_hi, p_lo);
+  p_lo = pmadd(x_lo, y_lo, p_lo);
+}
+
+#endif  // EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+
+
+// This function implements Dekker's algorithm for the addition
+// of two double word numbers represented by {x_hi, x_lo} and {y_hi, y_lo}.
+// It returns the result as a pair {s_hi, s_lo} such that
+// x_hi + x_lo + y_hi + y_lo = s_hi + s_lo holds exactly.
+// This is Algorithm 5 from Jean-Michel Muller, "Elementary Functions",
+// 3rd edition, Birkh\"auser, 2016.
+template<typename Packet>
+EIGEN_STRONG_INLINE
+  void twosum(const Packet& x_hi, const Packet& x_lo,
+              const Packet& y_hi, const Packet& y_lo,
+              Packet& s_hi, Packet& s_lo) {
+  const Packet x_greater_mask = pcmp_lt(pabs(y_hi), pabs(x_hi));
+  Packet r_hi_1, r_lo_1;
+  fast_twosum(x_hi, y_hi,r_hi_1, r_lo_1);
+  Packet r_hi_2, r_lo_2;
+  fast_twosum(y_hi, x_hi,r_hi_2, r_lo_2);
+  const Packet r_hi = pselect(x_greater_mask, r_hi_1, r_hi_2);
+
+  const Packet s1 = padd(padd(y_lo, r_lo_1), x_lo);
+  const Packet s2 = padd(padd(x_lo, r_lo_2), y_lo);
+  const Packet s = pselect(x_greater_mask, s1, s2);
+
+  fast_twosum(r_hi, s, s_hi, s_lo);
+}
+
+// This is a version of twosum for double word numbers,
+// which assumes that |x_hi| >= |y_hi|.
+template<typename Packet>
+EIGEN_STRONG_INLINE
+  void fast_twosum(const Packet& x_hi, const Packet& x_lo,
+              const Packet& y_hi, const Packet& y_lo,
+              Packet& s_hi, Packet& s_lo) {
+  Packet r_hi, r_lo;
+  fast_twosum(x_hi, y_hi, r_hi, r_lo);
+  const Packet s = padd(padd(y_lo, r_lo), x_lo);
+  fast_twosum(r_hi, s, s_hi, s_lo);
+}
+
+// This is a version of twosum for adding a floating point number x to
+// double word number {y_hi, y_lo} number, with the assumption
+// that |x| >= |y_hi|.
+template<typename Packet>
+EIGEN_STRONG_INLINE
+void fast_twosum(const Packet& x,
+                 const Packet& y_hi, const Packet& y_lo,
+                 Packet& s_hi, Packet& s_lo) {
+  Packet r_hi, r_lo;
+  fast_twosum(x, y_hi, r_hi, r_lo);
+  const Packet s = padd(y_lo, r_lo);
+  fast_twosum(r_hi, s, s_hi, s_lo);
+}
+
+// This function implements the multiplication of a double word
+// number represented by {x_hi, x_lo} by a floating point number y.
+// It returns the result as a pair {p_hi, p_lo} such that
+// (x_hi + x_lo) * y = p_hi + p_lo hold with a relative error
+// of less than 2*2^{-2p}, where p is the number of significand bit
+// in the floating point type.
+// This is Algorithm 7 from Jean-Michel Muller, "Elementary Functions",
+// 3rd edition, Birkh\"auser, 2016.
+template<typename Packet>
+EIGEN_STRONG_INLINE
+void twoprod(const Packet& x_hi, const Packet& x_lo, const Packet& y,
+             Packet& p_hi, Packet& p_lo) {
+  Packet c_hi, c_lo1;
+  twoprod(x_hi, y, c_hi, c_lo1);
+  const Packet c_lo2 = pmul(x_lo, y);
+  Packet t_hi, t_lo1;
+  fast_twosum(c_hi, c_lo2, t_hi, t_lo1);
+  const Packet t_lo2 = padd(t_lo1, c_lo1);
+  fast_twosum(t_hi, t_lo2, p_hi, p_lo);
+}
+
+// This function implements the multiplication of two double word
+// numbers represented by {x_hi, x_lo} and {y_hi, y_lo}.
+// It returns the result as a pair {p_hi, p_lo} such that
+// (x_hi + x_lo) * (y_hi + y_lo) = p_hi + p_lo holds with a relative error
+// of less than 2*2^{-2p}, where p is the number of significand bit
+// in the floating point type.
+template<typename Packet>
+EIGEN_STRONG_INLINE
+void twoprod(const Packet& x_hi, const Packet& x_lo,
+             const Packet& y_hi, const Packet& y_lo,
+             Packet& p_hi, Packet& p_lo) {
+  Packet p_hi_hi, p_hi_lo;
+  twoprod(x_hi, x_lo, y_hi, p_hi_hi, p_hi_lo);
+  Packet p_lo_hi, p_lo_lo;
+  twoprod(x_hi, x_lo, y_lo, p_lo_hi, p_lo_lo);
+  fast_twosum(p_hi_hi, p_hi_lo, p_lo_hi, p_lo_lo, p_hi, p_lo);
+}
+
+// This function implements the division of double word {x_hi, x_lo}
+// by float y. This is Algorithm 15 from "Tight and rigourous error bounds
+// for basic building blocks of double-word arithmetic", Joldes, Muller, & Popescu,
+// 2017. https://hal.archives-ouvertes.fr/hal-01351529
+template <typename Packet>
+void doubleword_div_fp(const Packet& x_hi, const Packet& x_lo, const Packet& y,
+                           Packet& z_hi, Packet& z_lo) {
+  const Packet t_hi = pdiv(x_hi, y);
+  Packet pi_hi, pi_lo;
+  twoprod(t_hi, y, pi_hi, pi_lo);
+  const Packet delta_hi = psub(x_hi, pi_hi);
+  const Packet delta_t = psub(delta_hi, pi_lo);
+  const Packet delta = padd(delta_t, x_lo);
+  const Packet t_lo = pdiv(delta, y);
+  fast_twosum(t_hi, t_lo, z_hi, z_lo);
+}
+
+// This function computes log2(x) and returns the result as a double word.
+template <typename Scalar>
+struct accurate_log2 {
+  template <typename Packet>
+  EIGEN_STRONG_INLINE
+  void operator()(const Packet& x, Packet& log2_x_hi, Packet& log2_x_lo) {
+    log2_x_hi = plog2(x);
+    log2_x_lo = pzero(x);
+  }
+};
+
+// This specialization uses a more accurate algorithm to compute log2(x) for
+// floats in [1/sqrt(2);sqrt(2)] with a relative accuracy of ~6.42e-10.
+// This additional accuracy is needed to counter the error-magnification
+// inherent in multiplying by a potentially large exponent in pow(x,y).
+// The minimax polynomial used was calculated using the Sollya tool.
+// See sollya.org.
+template <>
+struct accurate_log2<float> {
+  template <typename Packet>
+  EIGEN_STRONG_INLINE
+  void operator()(const Packet& z, Packet& log2_x_hi, Packet& log2_x_lo) {
+    // The function log(1+x)/x is approximated in the interval
+    // [1/sqrt(2)-1;sqrt(2)-1] by a degree 10 polynomial of the form
+    //  Q(x) = (C0 + x * (C1 + x * (C2 + x * (C3 + x * P(x))))),
+    // where the degree 6 polynomial P(x) is evaluated in single precision,
+    // while the remaining 4 terms of Q(x), as well as the final multiplication by x
+    // to reconstruct log(1+x) are evaluated in extra precision using
+    // double word arithmetic. C0 through C3 are extra precise constants
+    // stored as double words.
+    //
+    // The polynomial coefficients were calculated using Sollya commands:
+    // > n = 10;
+    // > f = log2(1+x)/x;
+    // > interval = [sqrt(0.5)-1;sqrt(2)-1];
+    // > p = fpminimax(f,n,[|double,double,double,double,single...|],interval,relative,floating);
+    
+    const Packet p6 = pset1<Packet>( 9.703654795885e-2f);
+    const Packet p5 = pset1<Packet>(-0.1690667718648f);
+    const Packet p4 = pset1<Packet>( 0.1720575392246f);
+    const Packet p3 = pset1<Packet>(-0.1789081543684f);
+    const Packet p2 = pset1<Packet>( 0.2050433009862f);
+    const Packet p1 = pset1<Packet>(-0.2404672354459f);
+    const Packet p0 = pset1<Packet>( 0.2885761857032f);
+
+    const Packet C3_hi = pset1<Packet>(-0.360674142838f);
+    const Packet C3_lo = pset1<Packet>(-6.13283912543e-09f);
+    const Packet C2_hi = pset1<Packet>(0.480897903442f);
+    const Packet C2_lo = pset1<Packet>(-1.44861207474e-08f);
+    const Packet C1_hi = pset1<Packet>(-0.721347510815f);
+    const Packet C1_lo = pset1<Packet>(-4.84483164698e-09f);
+    const Packet C0_hi = pset1<Packet>(1.44269502163f);
+    const Packet C0_lo = pset1<Packet>(2.01711713999e-08f);
+    const Packet one = pset1<Packet>(1.0f);
+
+    const Packet x = psub(z, one);
+    // Evaluate P(x) in working precision.
+    // We evaluate it in multiple parts to improve instruction level
+    // parallelism.
+    Packet x2 = pmul(x,x);
+    Packet p_even = pmadd(p6, x2, p4);
+    p_even = pmadd(p_even, x2, p2);
+    p_even = pmadd(p_even, x2, p0);
+    Packet p_odd = pmadd(p5, x2, p3);
+    p_odd = pmadd(p_odd, x2, p1);
+    Packet p = pmadd(p_odd, x, p_even);
+
+    // Now evaluate the low-order tems of Q(x) in double word precision.
+    // In the following, due to the alternating signs and the fact that
+    // |x| < sqrt(2)-1, we can assume that |C*_hi| >= q_i, and use
+    // fast_twosum instead of the slower twosum.
+    Packet q_hi, q_lo;
+    Packet t_hi, t_lo;
+    // C3 + x * p(x)
+    twoprod(p, x, t_hi, t_lo);
+    fast_twosum(C3_hi, C3_lo, t_hi, t_lo, q_hi, q_lo);
+    // C2 + x * p(x)
+    twoprod(q_hi, q_lo, x, t_hi, t_lo);
+    fast_twosum(C2_hi, C2_lo, t_hi, t_lo, q_hi, q_lo);
+    // C1 + x * p(x)
+    twoprod(q_hi, q_lo, x, t_hi, t_lo);
+    fast_twosum(C1_hi, C1_lo, t_hi, t_lo, q_hi, q_lo);
+    // C0 + x * p(x)
+    twoprod(q_hi, q_lo, x, t_hi, t_lo);
+    fast_twosum(C0_hi, C0_lo, t_hi, t_lo, q_hi, q_lo);
+
+    // log(z) ~= x * Q(x)
+    twoprod(q_hi, q_lo, x, log2_x_hi, log2_x_lo);
+  }
+};
+
+// This specialization uses a more accurate algorithm to compute log2(x) for
+// floats in [1/sqrt(2);sqrt(2)] with a relative accuracy of ~1.27e-18.
+// This additional accuracy is needed to counter the error-magnification
+// inherent in multiplying by a potentially large exponent in pow(x,y).
+// The minimax polynomial used was calculated using the Sollya tool.
+// See sollya.org.
+
+template <>
+struct accurate_log2<double> {
+  template <typename Packet>
+  EIGEN_STRONG_INLINE
+  void operator()(const Packet& x, Packet& log2_x_hi, Packet& log2_x_lo) {
+    // We use a transformation of variables:
+    //    r = c * (x-1) / (x+1),
+    // such that
+    //    log2(x) = log2((1 + r/c) / (1 - r/c)) = f(r).
+    // The function f(r) can be approximated well using an odd polynomial
+    // of the form
+    //   P(r) = ((Q(r^2) * r^2 + C) * r^2 + 1) * r,
+    // For the implementation of log2<double> here, Q is of degree 6 with
+    // coefficient represented in working precision (double), while C is a
+    // constant represented in extra precision as a double word to achieve
+    // full accuracy.
+    //
+    // The polynomial coefficients were computed by the Sollya script:
+    //
+    // c = 2 / log(2);
+    // trans = c * (x-1)/(x+1);
+    // itrans = (1+x/c)/(1-x/c);
+    // interval=[trans(sqrt(0.5)); trans(sqrt(2))];
+    // print(interval);
+    // f = log2(itrans(x));
+    // p=fpminimax(f,[|1,3,5,7,9,11,13,15,17|],[|1,DD,double...|],interval,relative,floating);
+    const Packet q12 = pset1<Packet>(2.87074255468000586e-9);
+    const Packet q10 = pset1<Packet>(2.38957980901884082e-8);
+    const Packet q8 = pset1<Packet>(2.31032094540014656e-7);
+    const Packet q6 = pset1<Packet>(2.27279857398537278e-6);
+    const Packet q4 = pset1<Packet>(2.31271023278625638e-5);
+    const Packet q2 = pset1<Packet>(2.47556738444535513e-4);
+    const Packet q0 = pset1<Packet>(2.88543873228900172e-3);
+    const Packet C_hi = pset1<Packet>(0.0400377511598501157);
+    const Packet C_lo = pset1<Packet>(-4.77726582251425391e-19);
+    const Packet one = pset1<Packet>(1.0);
+
+    const Packet cst_2_log2e_hi = pset1<Packet>(2.88539008177792677);
+    const Packet cst_2_log2e_lo = pset1<Packet>(4.07660016854549667e-17);
+    // c * (x - 1)
+    Packet t_hi, t_lo;
+    // t = c * (x-1)
+    twoprod(cst_2_log2e_hi, cst_2_log2e_lo, psub(x, one), t_hi, t_lo);
+    // r = c * (x-1) / (x+1),
+    Packet r_hi, r_lo;
+    doubleword_div_fp(t_hi, t_lo, padd(x, one), r_hi, r_lo);
+
+    // r2 = r * r
+    Packet r2_hi, r2_lo;
+    twoprod(r_hi, r_lo, r_hi, r_lo, r2_hi, r2_lo);
+    // r4 = r2 * r2
+    Packet r4_hi, r4_lo;
+    twoprod(r2_hi, r2_lo, r2_hi, r2_lo, r4_hi, r4_lo);
+
+    // Evaluate Q(r^2) in working precision. We evaluate it in two parts
+    // (even and odd in r^2) to improve instruction level parallelism.
+    Packet q_even = pmadd(q12, r4_hi, q8);
+    Packet q_odd = pmadd(q10, r4_hi, q6);
+    q_even = pmadd(q_even, r4_hi, q4);
+    q_odd = pmadd(q_odd, r4_hi, q2);
+    q_even = pmadd(q_even, r4_hi, q0);
+    Packet q = pmadd(q_odd, r2_hi, q_even);
+
+    // Now evaluate the low order terms of P(x) in double word precision.
+    // In the following, due to the increasing magnitude of the coefficients
+    // and r being constrained to [-0.5, 0.5] we can use fast_twosum instead
+    // of the slower twosum.
+    // Q(r^2) * r^2
+    Packet p_hi, p_lo;
+    twoprod(r2_hi, r2_lo, q, p_hi, p_lo);
+    // Q(r^2) * r^2 + C
+    Packet p1_hi, p1_lo;
+    fast_twosum(C_hi, C_lo, p_hi, p_lo, p1_hi, p1_lo);
+    // (Q(r^2) * r^2 + C) * r^2
+    Packet p2_hi, p2_lo;
+    twoprod(r2_hi, r2_lo, p1_hi, p1_lo, p2_hi, p2_lo);
+    // ((Q(r^2) * r^2 + C) * r^2 + 1)
+    Packet p3_hi, p3_lo;
+    fast_twosum(one, p2_hi, p2_lo, p3_hi, p3_lo);
+
+    // log(z) ~= ((Q(r^2) * r^2 + C) * r^2 + 1) * r
+    twoprod(p3_hi, p3_lo, r_hi, r_lo, log2_x_hi, log2_x_lo);
+  }
+};
+
+// This function computes exp2(x) (i.e. 2**x).
+template <typename Scalar>
+struct fast_accurate_exp2 {
+  template <typename Packet>
+  EIGEN_STRONG_INLINE
+  Packet operator()(const Packet& x) {
+    // TODO(rmlarsen): Add a pexp2 packetop.
+    return pexp(pmul(pset1<Packet>(Scalar(EIGEN_LN2)), x));
+  }
+};
+
+// This specialization uses a faster algorithm to compute exp2(x) for floats
+// in [-0.5;0.5] with a relative accuracy of 1 ulp.
+// The minimax polynomial used was calculated using the Sollya tool.
+// See sollya.org.
+template <>
+struct fast_accurate_exp2<float> {
+  template <typename Packet>
+  EIGEN_STRONG_INLINE
+  Packet operator()(const Packet& x) {
+    // This function approximates exp2(x) by a degree 6 polynomial of the form
+    // Q(x) = 1 + x * (C + x * P(x)), where the degree 4 polynomial P(x) is evaluated in
+    // single precision, and the remaining steps are evaluated with extra precision using
+    // double word arithmetic. C is an extra precise constant stored as a double word.
+    //
+    // The polynomial coefficients were calculated using Sollya commands:
+    // > n = 6;
+    // > f = 2^x;
+    // > interval = [-0.5;0.5];
+    // > p = fpminimax(f,n,[|1,double,single...|],interval,relative,floating);
+
+    const Packet p4 = pset1<Packet>(1.539513905e-4f);
+    const Packet p3 = pset1<Packet>(1.340007293e-3f);
+    const Packet p2 = pset1<Packet>(9.618283249e-3f);
+    const Packet p1 = pset1<Packet>(5.550328270e-2f);
+    const Packet p0 = pset1<Packet>(0.2402264923f);
+
+    const Packet C_hi = pset1<Packet>(0.6931471825f);
+    const Packet C_lo = pset1<Packet>(2.36836577e-08f);
+    const Packet one = pset1<Packet>(1.0f);
+
+    // Evaluate P(x) in working precision.
+    // We evaluate even and odd parts of the polynomial separately
+    // to gain some instruction level parallelism.
+    Packet x2 = pmul(x,x);
+    Packet p_even = pmadd(p4, x2, p2);
+    Packet p_odd = pmadd(p3, x2, p1);
+    p_even = pmadd(p_even, x2, p0);
+    Packet p = pmadd(p_odd, x, p_even);
+
+    // Evaluate the remaining terms of Q(x) with extra precision using
+    // double word arithmetic.
+    Packet p_hi, p_lo;
+    // x * p(x)
+    twoprod(p, x, p_hi, p_lo);
+    // C + x * p(x)
+    Packet q1_hi, q1_lo;
+    twosum(p_hi, p_lo, C_hi, C_lo, q1_hi, q1_lo);
+    // x * (C + x * p(x))
+    Packet q2_hi, q2_lo;
+    twoprod(q1_hi, q1_lo, x, q2_hi, q2_lo);
+    // 1 + x * (C + x * p(x))
+    Packet q3_hi, q3_lo;
+    // Since |q2_hi| <= sqrt(2)-1 < 1, we can use fast_twosum
+    // for adding it to unity here.
+    fast_twosum(one, q2_hi, q3_hi, q3_lo);
+    return padd(q3_hi, padd(q2_lo, q3_lo));
+  }
+};
+
+// in [-0.5;0.5] with a relative accuracy of 1 ulp.
+// The minimax polynomial used was calculated using the Sollya tool.
+// See sollya.org.
+template <>
+struct fast_accurate_exp2<double> {
+  template <typename Packet>
+  EIGEN_STRONG_INLINE
+  Packet operator()(const Packet& x) {
+    // This function approximates exp2(x) by a degree 10 polynomial of the form
+    // Q(x) = 1 + x * (C + x * P(x)), where the degree 8 polynomial P(x) is evaluated in
+    // single precision, and the remaining steps are evaluated with extra precision using
+    // double word arithmetic. C is an extra precise constant stored as a double word.
+    //
+    // The polynomial coefficients were calculated using Sollya commands:
+    // > n = 11;
+    // > f = 2^x;
+    // > interval = [-0.5;0.5];
+    // > p = fpminimax(f,n,[|1,DD,double...|],interval,relative,floating);
+
+    const Packet p9 = pset1<Packet>(4.431642109085495276e-10);
+    const Packet p8 = pset1<Packet>(7.073829923303358410e-9);
+    const Packet p7 = pset1<Packet>(1.017822306737031311e-7);
+    const Packet p6 = pset1<Packet>(1.321543498017646657e-6);
+    const Packet p5 = pset1<Packet>(1.525273342728892877e-5);
+    const Packet p4 = pset1<Packet>(1.540353045780084423e-4);
+    const Packet p3 = pset1<Packet>(1.333355814685869807e-3);
+    const Packet p2 = pset1<Packet>(9.618129107593478832e-3);
+    const Packet p1 = pset1<Packet>(5.550410866481961247e-2);
+    const Packet p0 = pset1<Packet>(0.240226506959101332);
+    const Packet C_hi = pset1<Packet>(0.693147180559945286); 
+    const Packet C_lo = pset1<Packet>(4.81927865669806721e-17);
+    const Packet one = pset1<Packet>(1.0);
+
+    // Evaluate P(x) in working precision.
+    // We evaluate even and odd parts of the polynomial separately
+    // to gain some instruction level parallelism.
+    Packet x2 = pmul(x,x);
+    Packet p_even = pmadd(p8, x2, p6);
+    Packet p_odd = pmadd(p9, x2, p7);
+    p_even = pmadd(p_even, x2, p4);
+    p_odd = pmadd(p_odd, x2, p5);
+    p_even = pmadd(p_even, x2, p2);
+    p_odd = pmadd(p_odd, x2, p3);
+    p_even = pmadd(p_even, x2, p0);
+    p_odd = pmadd(p_odd, x2, p1);
+    Packet p = pmadd(p_odd, x, p_even);
+
+    // Evaluate the remaining terms of Q(x) with extra precision using
+    // double word arithmetic.
+    Packet p_hi, p_lo;
+    // x * p(x)
+    twoprod(p, x, p_hi, p_lo);
+    // C + x * p(x)
+    Packet q1_hi, q1_lo;
+    twosum(p_hi, p_lo, C_hi, C_lo, q1_hi, q1_lo);
+    // x * (C + x * p(x))
+    Packet q2_hi, q2_lo;
+    twoprod(q1_hi, q1_lo, x, q2_hi, q2_lo);
+    // 1 + x * (C + x * p(x))
+    Packet q3_hi, q3_lo;
+    // Since |q2_hi| <= sqrt(2)-1 < 1, we can use fast_twosum
+    // for adding it to unity here.
+    fast_twosum(one, q2_hi, q3_hi, q3_lo);
+    return padd(q3_hi, padd(q2_lo, q3_lo));
+  }
+};
+
+// This function implements the non-trivial case of pow(x,y) where x is
+// positive and y is (possibly) non-integer.
+// Formally, pow(x,y) = exp2(y * log2(x)), where exp2(x) is shorthand for 2^x.
+// TODO(rmlarsen): We should probably add this as a packet up 'ppow', to make it
+// easier to specialize or turn off for specific types and/or backends.x
+template <typename Packet>
+EIGEN_STRONG_INLINE Packet generic_pow_impl(const Packet& x, const Packet& y) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  // Split x into exponent e_x and mantissa m_x.
+  Packet e_x;
+  Packet m_x = pfrexp(x, e_x);
+
+  // Adjust m_x to lie in [1/sqrt(2):sqrt(2)] to minimize absolute error in log2(m_x).
+  EIGEN_CONSTEXPR Scalar sqrt_half = Scalar(0.70710678118654752440);
+  const Packet m_x_scale_mask = pcmp_lt(m_x, pset1<Packet>(sqrt_half));
+  m_x = pselect(m_x_scale_mask, pmul(pset1<Packet>(Scalar(2)), m_x), m_x);
+  e_x = pselect(m_x_scale_mask, psub(e_x, pset1<Packet>(Scalar(1))), e_x);
+
+  // Compute log2(m_x) with 6 extra bits of accuracy.
+  Packet rx_hi, rx_lo;
+  accurate_log2<Scalar>()(m_x, rx_hi, rx_lo);
+
+  // Compute the two terms {y * e_x, y * r_x} in f = y * log2(x) with doubled
+  // precision using double word arithmetic.
+  Packet f1_hi, f1_lo, f2_hi, f2_lo;
+  twoprod(e_x, y, f1_hi, f1_lo);
+  twoprod(rx_hi, rx_lo, y, f2_hi, f2_lo);
+  // Sum the two terms in f using double word arithmetic. We know
+  // that |e_x| > |log2(m_x)|, except for the case where e_x==0.
+  // This means that we can use fast_twosum(f1,f2).
+  // In the case e_x == 0, e_x * y = f1 = 0, so we don't lose any
+  // accuracy by violating the assumption of fast_twosum, because
+  // it's a no-op.
+  Packet f_hi, f_lo;
+  fast_twosum(f1_hi, f1_lo, f2_hi, f2_lo, f_hi, f_lo);
+
+  // Split f into integer and fractional parts.
+  Packet n_z, r_z;
+  absolute_split(f_hi, n_z, r_z);
+  r_z = padd(r_z, f_lo);
+  Packet n_r;
+  absolute_split(r_z, n_r, r_z);
+  n_z = padd(n_z, n_r);
+
+  // We now have an accurate split of f = n_z + r_z and can compute
+  //   x^y = 2**{n_z + r_z) = exp2(r_z) * 2**{n_z}.
+  // Since r_z is in [-0.5;0.5], we compute the first factor to high accuracy
+  // using a specialized algorithm. Multiplication by the second factor can
+  // be done exactly using pldexp(), since it is an integer power of 2.
+  const Packet e_r = fast_accurate_exp2<Scalar>()(r_z);
+  return pldexp(e_r, n_z);
+}
+
+// Generic implementation of pow(x,y).
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet generic_pow(const Packet& x, const Packet& y) {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+
+  const Packet cst_pos_inf = pset1<Packet>(NumTraits<Scalar>::infinity());
+  const Packet cst_neg_inf = pset1<Packet>(-NumTraits<Scalar>::infinity());
+  const Packet cst_zero = pset1<Packet>(Scalar(0));
+  const Packet cst_one = pset1<Packet>(Scalar(1));
+  const Packet cst_nan = pset1<Packet>(NumTraits<Scalar>::quiet_NaN());
+
+  const Packet abs_x = pabs(x);
+  // Predicates for sign and magnitude of x.
+  const Packet abs_x_is_zero = pcmp_eq(abs_x, cst_zero);
+  const Packet x_has_signbit = psignbit(x);
+  const Packet x_is_neg = pandnot(x_has_signbit, abs_x_is_zero);
+  const Packet x_is_neg_zero = pand(x_has_signbit, abs_x_is_zero);
+  const Packet abs_x_is_inf = pcmp_eq(abs_x, cst_pos_inf);
+  const Packet abs_x_is_one = pcmp_eq(abs_x, cst_one);
+  const Packet abs_x_is_gt_one = pcmp_lt(cst_one, abs_x);
+  const Packet abs_x_is_lt_one = pcmp_lt(abs_x, cst_one);
+  const Packet x_is_one = pandnot(abs_x_is_one, x_is_neg);
+  const Packet x_is_neg_one = pand(abs_x_is_one, x_is_neg);
+  const Packet x_is_nan = pisnan(x);
+
+  // Predicates for sign and magnitude of y.
+  const Packet abs_y = pabs(y);
+  const Packet y_is_one = pcmp_eq(y, cst_one);
+  const Packet abs_y_is_zero = pcmp_eq(abs_y, cst_zero);
+  const Packet y_is_neg = pcmp_lt(y, cst_zero);
+  const Packet y_is_pos = pandnot(ptrue(y), por(abs_y_is_zero, y_is_neg));
+  const Packet y_is_nan = pisnan(y);
+  const Packet abs_y_is_inf = pcmp_eq(abs_y, cst_pos_inf);
+  EIGEN_CONSTEXPR Scalar huge_exponent =
+      (NumTraits<Scalar>::max_exponent() * Scalar(EIGEN_LN2)) / NumTraits<Scalar>::epsilon();
+  const Packet abs_y_is_huge = pcmp_le(pset1<Packet>(huge_exponent), pabs(y));
+
+  // Predicates for whether y is integer and/or even.
+  const Packet y_is_int = pcmp_eq(pfloor(y), y);
+  const Packet y_div_2 = pmul(y, pset1<Packet>(Scalar(0.5)));
+  const Packet y_is_even = pcmp_eq(pround(y_div_2), y_div_2);
+
+  // Predicates encoding special cases for the value of pow(x,y)
+  const Packet invalid_negative_x = pandnot(pandnot(pandnot(x_is_neg, abs_x_is_inf), y_is_int), abs_y_is_inf);
+  const Packet pow_is_nan = por(invalid_negative_x, por(x_is_nan, y_is_nan));
+  const Packet pow_is_one =
+      por(por(x_is_one, abs_y_is_zero), pand(x_is_neg_one, por(abs_y_is_inf, pandnot(y_is_even, invalid_negative_x))));
+  const Packet pow_is_zero = por(por(por(pand(abs_x_is_zero, y_is_pos), pand(abs_x_is_inf, y_is_neg)),
+                                     pand(pand(abs_x_is_lt_one, abs_y_is_huge), y_is_pos)),
+                                 pand(pand(abs_x_is_gt_one, abs_y_is_huge), y_is_neg));
+  const Packet pow_is_inf = por(por(por(pand(abs_x_is_zero, y_is_neg), pand(abs_x_is_inf, y_is_pos)),
+                                    pand(pand(abs_x_is_lt_one, abs_y_is_huge), y_is_neg)),
+                                pand(pand(abs_x_is_gt_one, abs_y_is_huge), y_is_pos));
+  const Packet pow_is_neg_zero = pand(pandnot(y_is_int, y_is_even),
+                                      por(pand(y_is_neg, pand(abs_x_is_inf, x_is_neg)), pand(y_is_pos, x_is_neg_zero)));
+  const Packet inf_val =
+      pselect(pandnot(pand(por(pand(abs_x_is_inf, x_is_neg), pand(x_is_neg_zero, y_is_neg)), y_is_int), y_is_even),
+              cst_neg_inf, cst_pos_inf);
+  // General computation of pow(x,y) for positive x or negative x and integer y.
+  const Packet negate_pow_abs = pandnot(x_is_neg, y_is_even);
+  const Packet pow_abs = generic_pow_impl(abs_x, y);
+  return pselect(y_is_one, x,
+                 pselect(pow_is_one, cst_one,
+                         pselect(pow_is_nan, cst_nan,
+                                 pselect(pow_is_inf, inf_val,
+                                         pselect(pow_is_neg_zero, pnegate(cst_zero),
+                                                 pselect(pow_is_zero, cst_zero,
+                                                         pselect(negate_pow_abs, pnegate(pow_abs), pow_abs)))))));
+}
+
+/* polevl (modified for Eigen)
+ *
+ *      Evaluate polynomial
+ *
+ *
+ *
+ * SYNOPSIS:
+ *
+ * int N;
+ * Scalar x, y, coef[N+1];
+ *
+ * y = polevl<decltype(x), N>( x, coef);
+ *
+ *
+ *
+ * DESCRIPTION:
+ *
+ * Evaluates polynomial of degree N:
+ *
+ *                     2          N
+ * y  =  C  + C x + C x  +...+ C x
+ *        0    1     2          N
+ *
+ * Coefficients are stored in reverse order:
+ *
+ * coef[0] = C  , ..., coef[N] = C  .
+ *            N                   0
+ *
+ *  The function p1evl() assumes that coef[N] = 1.0 and is
+ * omitted from the array.  Its calling arguments are
+ * otherwise the same as polevl().
+ *
+ *
+ * The Eigen implementation is templatized.  For best speed, store
+ * coef as a const array (constexpr), e.g.
+ *
+ * const double coef[] = {1.0, 2.0, 3.0, ...};
+ *
+ */
+template <typename Packet, int N>
+struct ppolevl {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const typename unpacket_traits<Packet>::type coeff[]) {
+    EIGEN_STATIC_ASSERT((N > 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
+    return pmadd(ppolevl<Packet, N-1>::run(x, coeff), x, pset1<Packet>(coeff[N]));
+  }
+};
+
+template <typename Packet>
+struct ppolevl<Packet, 0> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const typename unpacket_traits<Packet>::type coeff[]) {
+    EIGEN_UNUSED_VARIABLE(x);
+    return pset1<Packet>(coeff[0]);
+  }
+};
+
+/* chbevl (modified for Eigen)
+ *
+ *     Evaluate Chebyshev series
+ *
+ *
+ *
+ * SYNOPSIS:
+ *
+ * int N;
+ * Scalar x, y, coef[N], chebevl();
+ *
+ * y = chbevl( x, coef, N );
+ *
+ *
+ *
+ * DESCRIPTION:
+ *
+ * Evaluates the series
+ *
+ *        N-1
+ *         - '
+ *  y  =   >   coef[i] T (x/2)
+ *         -            i
+ *        i=0
+ *
+ * of Chebyshev polynomials Ti at argument x/2.
+ *
+ * Coefficients are stored in reverse order, i.e. the zero
+ * order term is last in the array.  Note N is the number of
+ * coefficients, not the order.
+ *
+ * If coefficients are for the interval a to b, x must
+ * have been transformed to x -> 2(2x - b - a)/(b-a) before
+ * entering the routine.  This maps x from (a, b) to (-1, 1),
+ * over which the Chebyshev polynomials are defined.
+ *
+ * If the coefficients are for the inverted interval, in
+ * which (a, b) is mapped to (1/b, 1/a), the transformation
+ * required is x -> 2(2ab/x - b - a)/(b-a).  If b is infinity,
+ * this becomes x -> 4a/x - 1.
+ *
+ *
+ *
+ * SPEED:
+ *
+ * Taking advantage of the recurrence properties of the
+ * Chebyshev polynomials, the routine requires one more
+ * addition per loop than evaluating a nested polynomial of
+ * the same degree.
+ *
+ */
+
+template <typename Packet, int N>
+struct pchebevl {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE Packet run(Packet x, const typename unpacket_traits<Packet>::type coef[]) {
+    typedef typename unpacket_traits<Packet>::type Scalar;
+    Packet b0 = pset1<Packet>(coef[0]);
+    Packet b1 = pset1<Packet>(static_cast<Scalar>(0.f));
+    Packet b2;
+
+    for (int i = 1; i < N; i++) {
+      b2 = b1;
+      b1 = b0;
+      b0 = psub(pmadd(x, b1, pset1<Packet>(coef[i])), b2);
+    }
+
+    return pmul(pset1<Packet>(static_cast<Scalar>(0.5f)), psub(b0, b2));
+  }
+};
+
+namespace unary_pow {
+
+template <typename ScalarExponent, bool IsInteger = NumTraits<ScalarExponent>::IsInteger>
+struct exponent_helper {
+  using safe_abs_type = ScalarExponent;
+  static constexpr ScalarExponent one_half = ScalarExponent(0.5);
+  // these routines assume that exp is an integer stored as a floating point type
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ScalarExponent safe_abs(const ScalarExponent& exp) {
+    return numext::abs(exp);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool is_odd(const ScalarExponent& exp) {
+    eigen_assert(((numext::isfinite)(exp) && exp == numext::floor(exp)) && "exp must be an integer");
+    ScalarExponent exp_div_2 = exp * one_half;
+    ScalarExponent floor_exp_div_2 = numext::floor(exp_div_2);
+    return exp_div_2 != floor_exp_div_2;
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ScalarExponent floor_div_two(const ScalarExponent& exp) {
+    ScalarExponent exp_div_2 = exp * one_half;
+    return numext::floor(exp_div_2);
+  }
+};
+
+template <typename ScalarExponent>
+struct exponent_helper<ScalarExponent, true> {
+  // if `exp` is a signed integer type, cast it to its unsigned counterpart to safely store its absolute value
+  // consider the (rare) case where `exp` is an int32_t: abs(-2147483648) != 2147483648
+  using safe_abs_type = typename numext::get_integer_by_size<sizeof(ScalarExponent)>::unsigned_type;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE safe_abs_type safe_abs(const ScalarExponent& exp) {
+    ScalarExponent mask = numext::signbit(exp);
+    safe_abs_type result = safe_abs_type(exp ^ mask);
+    return result + safe_abs_type(ScalarExponent(1) & mask);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool is_odd(const safe_abs_type& exp) {
+    return exp % safe_abs_type(2) != safe_abs_type(0);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE safe_abs_type floor_div_two(const safe_abs_type& exp) {
+    return exp >> safe_abs_type(1);
+  }
+};
+
+template <typename Packet, typename ScalarExponent,
+          bool ReciprocateIfExponentIsNegative =
+              !NumTraits<typename unpacket_traits<Packet>::type>::IsInteger && NumTraits<ScalarExponent>::IsSigned>
+struct reciprocate {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const ScalarExponent& exponent) {
+    using Scalar = typename unpacket_traits<Packet>::type;
+    const Packet cst_pos_one = pset1<Packet>(Scalar(1));
+    return exponent < 0 ? pdiv(cst_pos_one, x) : x;
+  }
+};
+
+template <typename Packet, typename ScalarExponent>
+struct reciprocate<Packet, ScalarExponent, false> {
+  // pdiv not defined, nor necessary for integer base types
+  // if the exponent is unsigned, then the exponent cannot be negative
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const ScalarExponent&) { return x; }
+};
+
+template <typename Packet, typename ScalarExponent>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet int_pow(const Packet& x, const ScalarExponent& exponent) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+  using ExponentHelper = exponent_helper<ScalarExponent>;
+  using AbsExponentType = typename ExponentHelper::safe_abs_type;
+  const Packet cst_pos_one = pset1<Packet>(Scalar(1));
+  if (exponent == ScalarExponent(0)) return cst_pos_one;
+
+  Packet result = reciprocate<Packet, ScalarExponent>::run(x, exponent);
+  Packet y = cst_pos_one;
+  AbsExponentType m = ExponentHelper::safe_abs(exponent);
+
+  while (m > 1) {
+    bool odd = ExponentHelper::is_odd(m);
+    if (odd) y = pmul(y, result);
+    result = pmul(result, result);
+    m = ExponentHelper::floor_div_two(m);
+  }
+
+  return pmul(y, result);
+}
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet gen_pow(const Packet& x,
+                                                            const typename unpacket_traits<Packet>::type& exponent) {
+  const Packet exponent_packet = pset1<Packet>(exponent);
+  return generic_pow_impl(x, exponent_packet);
+}
+
+template <typename Packet, typename ScalarExponent>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet handle_nonint_nonint_errors(const Packet& x, const Packet& powx,
+                                                                                const ScalarExponent& exponent) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+
+  // non-integer base and exponent case
+
+  const Scalar pos_zero = Scalar(0);
+  const Scalar all_ones = ptrue<Scalar>(Scalar());
+  const Scalar pos_one = Scalar(1);
+  const Scalar pos_inf = NumTraits<Scalar>::infinity();
+
+  const Packet cst_pos_zero = pzero(x);
+  const Packet cst_pos_one = pset1<Packet>(pos_one);
+  const Packet cst_pos_inf = pset1<Packet>(pos_inf);
+
+  const bool exponent_is_not_fin = !(numext::isfinite)(exponent);
+  const bool exponent_is_neg = exponent < ScalarExponent(0);
+  const bool exponent_is_pos = exponent > ScalarExponent(0);
+
+  const Packet exp_is_not_fin = pset1<Packet>(exponent_is_not_fin ? all_ones : pos_zero);
+  const Packet exp_is_neg = pset1<Packet>(exponent_is_neg ? all_ones : pos_zero);
+  const Packet exp_is_pos = pset1<Packet>(exponent_is_pos ? all_ones : pos_zero);
+  const Packet exp_is_inf = pand(exp_is_not_fin, por(exp_is_neg, exp_is_pos));
+  const Packet exp_is_nan = pandnot(exp_is_not_fin, por(exp_is_neg, exp_is_pos));
+
+  const Packet x_is_le_zero = pcmp_le(x, cst_pos_zero);
+  const Packet x_is_ge_zero = pcmp_le(cst_pos_zero, x);
+  const Packet x_is_zero = pand(x_is_le_zero, x_is_ge_zero);
+
+  const Packet abs_x = pabs(x);
+  const Packet abs_x_is_le_one = pcmp_le(abs_x, cst_pos_one);
+  const Packet abs_x_is_ge_one = pcmp_le(cst_pos_one, abs_x);
+  const Packet abs_x_is_inf = pcmp_eq(abs_x, cst_pos_inf);
+  const Packet abs_x_is_one = pand(abs_x_is_le_one, abs_x_is_ge_one);
+
+  Packet pow_is_inf_if_exp_is_neg = por(x_is_zero, pand(abs_x_is_le_one, exp_is_inf));
+  Packet pow_is_inf_if_exp_is_pos = por(abs_x_is_inf, pand(abs_x_is_ge_one, exp_is_inf));
+  Packet pow_is_one = pand(abs_x_is_one, por(exp_is_inf, x_is_ge_zero));
+
+  Packet result = powx;
+  result = por(x_is_le_zero, result);
+  result = pselect(pow_is_inf_if_exp_is_neg, pand(cst_pos_inf, exp_is_neg), result);
+  result = pselect(pow_is_inf_if_exp_is_pos, pand(cst_pos_inf, exp_is_pos), result);
+  result = por(exp_is_nan, result);
+  result = pselect(pow_is_one, cst_pos_one, result);
+  return result;
+}
+
+template <typename Packet, typename ScalarExponent,
+          std::enable_if_t<NumTraits<typename unpacket_traits<Packet>::type>::IsSigned, bool> = true>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet handle_negative_exponent(const Packet& x, const ScalarExponent& exponent) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+
+  // singed integer base, signed integer exponent case
+
+  // This routine handles negative exponents.
+  // The return value is either 0, 1, or -1.
+
+  const Scalar pos_zero = Scalar(0);
+  const Scalar all_ones = ptrue<Scalar>(Scalar());
+  const Scalar pos_one = Scalar(1);
+
+  const Packet cst_pos_one = pset1<Packet>(pos_one);
+
+  const bool exponent_is_odd = exponent % ScalarExponent(2) != ScalarExponent(0);
+
+  const Packet exp_is_odd = pset1<Packet>(exponent_is_odd ? all_ones : pos_zero);
+
+  const Packet abs_x = pabs(x);
+  const Packet abs_x_is_one = pcmp_eq(abs_x, cst_pos_one);
+
+  Packet result = pselect(exp_is_odd, x, abs_x);
+  result = pand(abs_x_is_one, result);
+  return result;
+}
+
+template <typename Packet, typename ScalarExponent,
+          std::enable_if_t<!NumTraits<typename unpacket_traits<Packet>::type>::IsSigned, bool> = true>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet handle_negative_exponent(const Packet& x, const ScalarExponent&) {
+  using Scalar = typename unpacket_traits<Packet>::type;
+
+  // unsigned integer base, signed integer exponent case
+
+  // This routine handles negative exponents.
+  // The return value is either 0 or 1
+
+  const Scalar pos_one = Scalar(1);
+
+  const Packet cst_pos_one = pset1<Packet>(pos_one);
+
+  const Packet x_is_one = pcmp_eq(x, cst_pos_one);
+
+  return pand(x_is_one, x);
+}
+
+
+}  // end namespace unary_pow
+
+template <typename Packet, typename ScalarExponent,
+          bool BaseIsIntegerType = NumTraits<typename unpacket_traits<Packet>::type>::IsInteger,
+          bool ExponentIsIntegerType = NumTraits<ScalarExponent>::IsInteger,
+          bool ExponentIsSigned = NumTraits<ScalarExponent>::IsSigned>
+struct unary_pow_impl;
+
+template <typename Packet, typename ScalarExponent, bool ExponentIsSigned>
+struct unary_pow_impl<Packet, ScalarExponent, false, false, ExponentIsSigned> {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const ScalarExponent& exponent) {
+    const bool exponent_is_integer = (numext::isfinite)(exponent) && numext::round(exponent) == exponent;
+    if (exponent_is_integer) {
+      return unary_pow::int_pow(x, exponent);
+    } else {
+      Packet result = unary_pow::gen_pow(x, exponent);
+      result = unary_pow::handle_nonint_nonint_errors(x, result, exponent);
+      return result;
+    }
+  }
+};
+
+template <typename Packet, typename ScalarExponent, bool ExponentIsSigned>
+struct unary_pow_impl<Packet, ScalarExponent, false, true, ExponentIsSigned> {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const ScalarExponent& exponent) {
+    return unary_pow::int_pow(x, exponent);
+  }
+};
+
+template <typename Packet, typename ScalarExponent>
+struct unary_pow_impl<Packet, ScalarExponent, true, true, true> {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const ScalarExponent& exponent) {
+    if (exponent < ScalarExponent(0)) {
+      return unary_pow::handle_negative_exponent(x, exponent);
+    } else {
+      return unary_pow::int_pow(x, exponent);
+    }
+  }
+};
+
+template <typename Packet, typename ScalarExponent>
+struct unary_pow_impl<Packet, ScalarExponent, true, true, false> {
+  typedef typename unpacket_traits<Packet>::type Scalar;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet run(const Packet& x, const ScalarExponent& exponent) {
+    return unary_pow::int_pow(x, exponent);
+  }
+};
+
+} // end namespace internal
+} // end namespace Eigen
+
+#endif // EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
new file mode 100644
index 0000000000000000000000000000000000000000..dc08efaa71808361039f93e1985197cc7d846848
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/GenericPacketMathFunctionsFwd.h
@@ -0,0 +1,171 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2019 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_FWD_H
+#define EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_FWD_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+// Forward declarations of the generic math functions
+// implemented in GenericPacketMathFunctions.h
+// This is needed to workaround a circular dependency.
+
+/***************************************************************************
+ * Some generic implementations to be used by implementors
+***************************************************************************/
+
+/** Default implementation of pfrexp.
+  * It is expected to be called by implementers of template<> pfrexp.
+  */
+template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+Packet pfrexp_generic(const Packet& a, Packet& exponent);
+
+// Extracts the biased exponent value from Packet p, and casts the results to
+// a floating-point Packet type. Used by pfrexp_generic. Override this if
+// there is no unpacket_traits<Packet>::integer_packet.
+template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+Packet pfrexp_generic_get_biased_exponent(const Packet& p);
+
+/** Default implementation of pldexp.
+  * It is expected to be called by implementers of template<> pldexp.
+  */
+template<typename Packet> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+Packet pldexp_generic(const Packet& a, const Packet& exponent);
+
+/** \internal \returns log(x) for single precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet plog_float(const Packet _x);
+
+/** \internal \returns log2(x) for single precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet plog2_float(const Packet _x);
+
+/** \internal \returns log(x) for single precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet plog_double(const Packet _x);
+
+/** \internal \returns log2(x) for single precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet plog2_double(const Packet _x);
+
+/** \internal \returns log(1 + x) */
+template<typename Packet>
+Packet generic_plog1p(const Packet& x);
+
+/** \internal \returns exp(x)-1 */
+template<typename Packet>
+Packet generic_expm1(const Packet& x);
+
+/** \internal \returns exp(x) for single precision float */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pexp_float(const Packet _x);
+
+/** \internal \returns exp(x) for double precision real numbers */
+template <typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pexp_double(const Packet _x);
+
+/** \internal \returns sin(x) for single precision float */
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet psin_float(const Packet& x);
+
+/** \internal \returns cos(x) for single precision float */
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pcos_float(const Packet& x);
+
+/** \internal \returns asin(x) for single precision float */
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pasin_float(const Packet& x);
+
+/** \internal \returns acos(x) for single precision float */
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pacos_float(const Packet& x);
+
+/** \internal \returns atan(x) for single precision float */
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet patan_float(const Packet& x);
+
+/** \internal \returns atan(x) for double precision float */
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet patan_double(const Packet& x);
+
+/** \internal \returns atanh(x) for single precision float */
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet patanh_float(const Packet& x);
+
+/** \internal \returns sqrt(x) for complex types */
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet psqrt_complex(const Packet& a);
+
+/** \internal \returns x / y for complex types */
+template<typename Packet>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet pdiv_complex(const Packet& x, const Packet& y);
+
+template <typename Packet, int N> struct ppolevl;
+
+// Macros for instantiating these generic functions for different backends.
+#define EIGEN_PACKET_FUNCTION(METHOD, SCALAR, PACKET)                                             \
+  template <>                                                                                     \
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_UNUSED PACKET p##METHOD<PACKET>(const PACKET& _x) { \
+    return p##METHOD##_##SCALAR(_x);                                                              \
+  }
+
+#define EIGEN_FLOAT_PACKET_FUNCTION(METHOD, PACKET) EIGEN_PACKET_FUNCTION(METHOD, float, PACKET)
+#define EIGEN_DOUBLE_PACKET_FUNCTION(METHOD, PACKET) EIGEN_PACKET_FUNCTION(METHOD, double, PACKET)
+
+#define EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(PACKET)                                     \
+  EIGEN_FLOAT_PACKET_FUNCTION(sin, PACKET)                                                     \
+  EIGEN_FLOAT_PACKET_FUNCTION(cos, PACKET)                                                     \
+  EIGEN_FLOAT_PACKET_FUNCTION(asin, PACKET)                                                    \
+  EIGEN_FLOAT_PACKET_FUNCTION(acos, PACKET)                                                    \
+  EIGEN_FLOAT_PACKET_FUNCTION(atan, PACKET)                                                    \
+  EIGEN_FLOAT_PACKET_FUNCTION(atanh, PACKET)                                                   \
+  EIGEN_FLOAT_PACKET_FUNCTION(log, PACKET)                                                     \
+  EIGEN_FLOAT_PACKET_FUNCTION(log2, PACKET)                                                    \
+  EIGEN_FLOAT_PACKET_FUNCTION(exp, PACKET)                                                     \
+  template <>                                                                                  \
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_UNUSED PACKET pexpm1<PACKET>(const PACKET& _x) { \
+    return internal::generic_expm1(_x);                                                        \
+  }                                                                                            \
+  template <>                                                                                  \
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_UNUSED PACKET plog1p<PACKET>(const PACKET& _x) { \
+    return internal::generic_plog1p(_x);                                                       \
+  }                                                                                            \
+  template <>                                                                                  \
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_UNUSED PACKET ptanh<PACKET>(const PACKET& _x) {  \
+    return internal::generic_fast_tanh_float(_x);                                              \
+  }
+
+#define EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_DOUBLE(PACKET) \
+  EIGEN_DOUBLE_PACKET_FUNCTION(atan, PACKET)                \
+  EIGEN_DOUBLE_PACKET_FUNCTION(log, PACKET)                 \
+  EIGEN_DOUBLE_PACKET_FUNCTION(log2, PACKET)                \
+  EIGEN_DOUBLE_PACKET_FUNCTION(exp, PACKET)
+
+} // end namespace internal
+} // end namespace Eigen
+
+#endif // EIGEN_ARCH_GENERIC_PACKET_MATH_FUNCTIONS_FWD_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/Half.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/Half.h
new file mode 100644
index 0000000000000000000000000000000000000000..17ce135818b7d1b80a7ce5e1c245201f5262df00
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/Half.h
@@ -0,0 +1,1062 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+//
+// The conversion routines are Copyright (c) Fabian Giesen, 2016.
+// The original license follows:
+//
+// Copyright (c) Fabian Giesen, 2016
+// All rights reserved.
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted.
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+// Standard 16-bit float type, mostly useful for GPUs. Defines a new
+// type Eigen::half (inheriting either from CUDA's or HIP's __half struct) with
+// operator overloads such that it behaves basically as an arithmetic
+// type. It will be quite slow on CPUs (so it is recommended to stay
+// in fp32 for CPUs, except for simple parameter conversions, I/O
+// to disk and the likes), but fast on GPUs.
+
+
+#ifndef EIGEN_HALF_H
+#define EIGEN_HALF_H
+
+#include "../../InternalHeaderCheck.h"
+
+#if defined(EIGEN_HAS_GPU_FP16) || defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+// When compiling with GPU support, the "__half_raw" base class as well as
+// some other routines are defined in the GPU compiler header files
+// (cuda_fp16.h, hip_fp16.h), and they are not tagged constexpr
+// As a consequence, we get compile failures when compiling Eigen with
+// GPU support. Hence the need to disable EIGEN_CONSTEXPR when building
+// Eigen with GPU support
+  #pragma push_macro("EIGEN_CONSTEXPR")
+  #undef EIGEN_CONSTEXPR
+  #define EIGEN_CONSTEXPR
+#endif
+
+#define F16_PACKET_FUNCTION(PACKET_F, PACKET_F16, METHOD)           \
+  template <>                                                       \
+  EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_UNUSED                \
+  PACKET_F16 METHOD<PACKET_F16>(const PACKET_F16& _x) {             \
+    return float2half(METHOD<PACKET_F>(half2float(_x)));            \
+  }
+
+namespace Eigen {
+
+struct half;
+
+namespace half_impl {
+
+// We want to use the __half_raw struct from the HIP header file only during the device compile phase.
+// This is required because of a quirk in the way TensorFlow GPU builds are done.
+// When compiling TensorFlow source code with GPU support, files that
+//  * contain GPU kernels (i.e. *.cu.cc files) are compiled via hipcc
+//  * do not contain GPU kernels ( i.e. *.cc files) are compiled via gcc (typically)
+//
+// Tensorflow uses the Eigen::half type as its FP16 type, and there are functions that
+//  * are defined in a file that gets compiled via hipcc AND
+//  * have Eigen::half as a pass-by-value argument AND
+//  * are called in a file that gets compiled via gcc
+//
+// In the scenario described above the caller and callee will see different versions
+// of the Eigen::half base class __half_raw, and they will be compiled by different compilers
+//
+// There appears to be an ABI mismatch between gcc and clang (which is called by hipcc) that results in
+// the callee getting corrupted values for the Eigen::half argument.
+//
+// Making the host side compile phase of hipcc use the same Eigen::half impl, as the gcc compile, resolves
+// this error, and hence the following convoluted #if condition
+#if !defined(EIGEN_HAS_GPU_FP16) || !defined(EIGEN_GPU_COMPILE_PHASE)
+// Make our own __half_raw definition that is similar to CUDA's.
+struct __half_raw {
+#if (defined(EIGEN_HAS_GPU_FP16) && !defined(EIGEN_GPU_COMPILE_PHASE))
+  // Eigen::half can be used as the datatype for shared memory declarations (in Eigen and TF)
+  // The element type for shared memory cannot have non-trivial constructors
+  // and hence the following special casing (which skips the zero-initilization).
+  // Note that this check gets done even in the host compilation phase, and
+  // hence the need for this
+  EIGEN_DEVICE_FUNC __half_raw() {}
+#else
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __half_raw() : x(0) {}
+#endif
+#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __half_raw(numext::uint16_t raw) : x(numext::bit_cast<__fp16>(raw)) {
+  }
+  __fp16 x;
+#else
+  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __half_raw(numext::uint16_t raw) : x(raw) {}
+  numext::uint16_t x;
+#endif
+};
+
+#elif defined(EIGEN_HAS_HIP_FP16)
+  // Nothing to do here
+  // HIP fp16 header file has a definition for __half_raw
+#elif defined(EIGEN_HAS_CUDA_FP16)
+  #if EIGEN_CUDA_SDK_VER < 90000
+    // In CUDA < 9.0, __half is the equivalent of CUDA 9's __half_raw
+    typedef __half __half_raw;
+  #endif // defined(EIGEN_HAS_CUDA_FP16)
+#elif defined(SYCL_DEVICE_ONLY)
+  typedef cl::sycl::half __half_raw;
+#endif
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __half_raw raw_uint16_to_half(numext::uint16_t x);
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half_raw float_to_half_rtne(float ff);
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half_raw h);
+
+struct half_base : public __half_raw {
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half_base() {}
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half_base(const __half_raw& h) : __half_raw(h) {}
+
+#if defined(EIGEN_HAS_GPU_FP16)
+ #if defined(EIGEN_HAS_HIP_FP16)
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half_base(const __half& h) { x = __half_as_ushort(h); }
+ #elif defined(EIGEN_HAS_CUDA_FP16)
+  #if EIGEN_CUDA_SDK_VER >= 90000
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half_base(const __half& h) : __half_raw(*(__half_raw*)&h) {}
+  #endif
+ #endif
+#endif
+};
+
+} // namespace half_impl
+
+// Class definition.
+struct half : public half_impl::half_base {
+
+  // Writing this out as separate #if-else blocks to make the code easier to follow
+  // The same applies to most #if-else blocks in this file
+#if !defined(EIGEN_HAS_GPU_FP16) || !defined(EIGEN_GPU_COMPILE_PHASE)
+  // Use the same base class for the following two scenarios
+  // * when compiling without GPU support enabled
+  // * during host compile phase when compiling with GPU support enabled
+  typedef half_impl::__half_raw __half_raw;
+#elif defined(EIGEN_HAS_HIP_FP16)
+  // Nothing to do here
+  // HIP fp16 header file has a definition for __half_raw
+#elif defined(EIGEN_HAS_CUDA_FP16)
+  // Note that EIGEN_CUDA_SDK_VER is set to 0 even when compiling with HIP, so
+  // (EIGEN_CUDA_SDK_VER < 90000) is true even for HIP!  So keeping this within
+  // #if defined(EIGEN_HAS_CUDA_FP16) is needed
+  #if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER < 90000
+    typedef half_impl::__half_raw __half_raw;
+  #endif
+#endif
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half() {}
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half(const __half_raw& h) : half_impl::half_base(h) {}
+
+#if defined(EIGEN_HAS_GPU_FP16)
+ #if defined(EIGEN_HAS_HIP_FP16)
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half(const __half& h) : half_impl::half_base(h) {}
+ #elif defined(EIGEN_HAS_CUDA_FP16)
+  #if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER >= 90000
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half(const __half& h) : half_impl::half_base(h) {}
+  #endif
+ #endif
+#endif
+
+
+  explicit EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR half(bool b)
+      : half_impl::half_base(half_impl::raw_uint16_to_half(b ? 0x3c00 : 0)) {}
+  template<class T>
+  explicit EIGEN_DEVICE_FUNC half(T val)
+      : half_impl::half_base(half_impl::float_to_half_rtne(static_cast<float>(val))) {}
+  explicit EIGEN_DEVICE_FUNC half(float f)
+      : half_impl::half_base(half_impl::float_to_half_rtne(f)) {}
+
+  // Following the convention of numpy, converting between complex and
+  // float will lead to loss of imag value.
+  template<typename RealScalar>
+  explicit EIGEN_DEVICE_FUNC half(std::complex<RealScalar> c)
+      : half_impl::half_base(half_impl::float_to_half_rtne(static_cast<float>(c.real()))) {}
+
+   EIGEN_DEVICE_FUNC operator float() const {  // NOLINT: Allow implicit conversion to float, because it is lossless.
+    return half_impl::half_to_float(*this);
+  }
+
+#if defined(EIGEN_HAS_GPU_FP16) && !defined(EIGEN_GPU_COMPILE_PHASE)
+  EIGEN_DEVICE_FUNC operator __half() const {
+    ::__half_raw hr;
+    hr.x = x;
+    return __half(hr);
+  }
+#endif
+};
+
+// TODO(majnemer): Get rid of this once we can rely on C++17 inline variables do
+// solve the ODR issue.
+namespace half_impl {
+template <typename = void>
+struct numeric_limits_half_impl {
+  static EIGEN_CONSTEXPR const bool is_specialized = true;
+  static EIGEN_CONSTEXPR const bool is_signed = true;
+  static EIGEN_CONSTEXPR const bool is_integer = false;
+  static EIGEN_CONSTEXPR const bool is_exact = false;
+  static EIGEN_CONSTEXPR const bool has_infinity = true;
+  static EIGEN_CONSTEXPR const bool has_quiet_NaN = true;
+  static EIGEN_CONSTEXPR const bool has_signaling_NaN = true;
+  static EIGEN_CONSTEXPR const std::float_denorm_style has_denorm = std::denorm_present;
+  static EIGEN_CONSTEXPR const bool has_denorm_loss = false;
+  static EIGEN_CONSTEXPR const std::float_round_style round_style = std::round_to_nearest;
+  static EIGEN_CONSTEXPR const bool is_iec559 = true;
+  // The C++ standard defines this as "true if the set of values representable
+  // by the type is finite." Half has finite precision.
+  static EIGEN_CONSTEXPR const bool is_bounded = true;
+  static EIGEN_CONSTEXPR const bool is_modulo = false;
+  static EIGEN_CONSTEXPR const int digits = 11;
+  static EIGEN_CONSTEXPR const int digits10 = 3;      // according to http://half.sourceforge.net/structstd_1_1numeric__limits_3_01half__float_1_1half_01_4.html
+  static EIGEN_CONSTEXPR const int max_digits10 = 5;  // according to http://half.sourceforge.net/structstd_1_1numeric__limits_3_01half__float_1_1half_01_4.html
+  static EIGEN_CONSTEXPR const int radix = std::numeric_limits<float>::radix;
+  static EIGEN_CONSTEXPR const int min_exponent = -13;
+  static EIGEN_CONSTEXPR const int min_exponent10 = -4;
+  static EIGEN_CONSTEXPR const int max_exponent = 16;
+  static EIGEN_CONSTEXPR const int max_exponent10 = 4;
+  static EIGEN_CONSTEXPR const bool traps = std::numeric_limits<float>::traps;
+  // IEEE754: "The implementer shall choose how tininess is detected, but shall
+  // detect tininess in the same way for all operations in radix two"
+  static EIGEN_CONSTEXPR const bool tinyness_before = std::numeric_limits<float>::tinyness_before;
+
+  static EIGEN_CONSTEXPR Eigen::half (min)() { return Eigen::half_impl::raw_uint16_to_half(0x0400); }
+  static EIGEN_CONSTEXPR Eigen::half lowest() { return Eigen::half_impl::raw_uint16_to_half(0xfbff); }
+  static EIGEN_CONSTEXPR Eigen::half (max)() { return Eigen::half_impl::raw_uint16_to_half(0x7bff); }
+  static EIGEN_CONSTEXPR Eigen::half epsilon() { return Eigen::half_impl::raw_uint16_to_half(0x1400); }
+  static EIGEN_CONSTEXPR Eigen::half round_error() { return Eigen::half_impl::raw_uint16_to_half(0x3800); }
+  static EIGEN_CONSTEXPR Eigen::half infinity() { return Eigen::half_impl::raw_uint16_to_half(0x7c00); }
+  static EIGEN_CONSTEXPR Eigen::half quiet_NaN() { return Eigen::half_impl::raw_uint16_to_half(0x7e00); }
+  static EIGEN_CONSTEXPR Eigen::half signaling_NaN() { return Eigen::half_impl::raw_uint16_to_half(0x7d00); }
+  static EIGEN_CONSTEXPR Eigen::half denorm_min() { return Eigen::half_impl::raw_uint16_to_half(0x0001); }
+};
+
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_half_impl<T>::is_specialized;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_half_impl<T>::is_signed;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_half_impl<T>::is_integer;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_half_impl<T>::is_exact;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_half_impl<T>::has_infinity;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_half_impl<T>::has_quiet_NaN;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_half_impl<T>::has_signaling_NaN;
+template<typename T>
+EIGEN_CONSTEXPR const std::float_denorm_style numeric_limits_half_impl<T>::has_denorm;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_half_impl<T>::has_denorm_loss;
+template<typename T>
+EIGEN_CONSTEXPR const std::float_round_style numeric_limits_half_impl<T>::round_style;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_half_impl<T>::is_iec559;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_half_impl<T>::is_bounded;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_half_impl<T>::is_modulo;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_half_impl<T>::digits;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_half_impl<T>::digits10;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_half_impl<T>::max_digits10;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_half_impl<T>::radix;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_half_impl<T>::min_exponent;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_half_impl<T>::min_exponent10;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_half_impl<T>::max_exponent;
+template<typename T>
+EIGEN_CONSTEXPR const int numeric_limits_half_impl<T>::max_exponent10;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_half_impl<T>::traps;
+template<typename T>
+EIGEN_CONSTEXPR const bool numeric_limits_half_impl<T>::tinyness_before;
+}  // end namespace half_impl
+}  // end namespace Eigen
+
+namespace std {
+// If std::numeric_limits<T> is specialized, should also specialize
+// std::numeric_limits<const T>, std::numeric_limits<volatile T>, and
+// std::numeric_limits<const volatile T>
+// https://stackoverflow.com/a/16519653/
+template<>
+class numeric_limits<Eigen::half> : public Eigen::half_impl::numeric_limits_half_impl<> {};
+template<>
+class numeric_limits<const Eigen::half> : public numeric_limits<Eigen::half> {};
+template<>
+class numeric_limits<volatile Eigen::half> : public numeric_limits<Eigen::half> {};
+template<>
+class numeric_limits<const volatile Eigen::half> : public numeric_limits<Eigen::half> {};
+}  // end namespace std
+
+namespace Eigen {
+
+namespace half_impl {
+
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && \
+     EIGEN_CUDA_ARCH >= 530) ||                                  \
+    (defined(EIGEN_HAS_HIP_FP16) && defined(HIP_DEVICE_COMPILE))
+// Note: We deliberately do *not* define this to 1 even if we have Arm's native
+// fp16 type since GPU halfs are rather different from native CPU halfs.
+// TODO: Rename to something like EIGEN_HAS_NATIVE_GPU_FP16
+#define EIGEN_HAS_NATIVE_FP16
+#endif
+
+// Intrinsics for native fp16 support. Note that on current hardware,
+// these are no faster than fp32 arithmetic (you need to use the half2
+// versions to get the ALU speed increased), but you do save the
+// conversion steps back and forth.
+
+#if defined(EIGEN_HAS_NATIVE_FP16)
+EIGEN_STRONG_INLINE __device__ half operator + (const half& a, const half& b) {
+#if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER >= 90000
+  return __hadd(::__half(a), ::__half(b));
+#else
+  return __hadd(a, b);
+#endif
+}
+EIGEN_STRONG_INLINE __device__ half operator * (const half& a, const half& b) {
+  return __hmul(a, b);
+}
+EIGEN_STRONG_INLINE __device__ half operator - (const half& a, const half& b) {
+  return __hsub(a, b);
+}
+EIGEN_STRONG_INLINE __device__ half operator / (const half& a, const half& b) {
+#if defined(EIGEN_CUDA_SDK_VER) && EIGEN_CUDA_SDK_VER >= 90000
+  return __hdiv(a, b);
+#else
+  float num = __half2float(a);
+  float denom = __half2float(b);
+  return __float2half(num / denom);
+#endif
+}
+EIGEN_STRONG_INLINE __device__ half operator - (const half& a) {
+  return __hneg(a);
+}
+EIGEN_STRONG_INLINE __device__ half& operator += (half& a, const half& b) {
+  a = a + b;
+  return a;
+}
+EIGEN_STRONG_INLINE __device__ half& operator *= (half& a, const half& b) {
+  a = a * b;
+  return a;
+}
+EIGEN_STRONG_INLINE __device__ half& operator -= (half& a, const half& b) {
+  a = a - b;
+  return a;
+}
+EIGEN_STRONG_INLINE __device__ half& operator /= (half& a, const half& b) {
+  a = a / b;
+  return a;
+}
+EIGEN_STRONG_INLINE __device__ bool operator == (const half& a, const half& b) {
+  return __heq(a, b);
+}
+EIGEN_STRONG_INLINE __device__ bool operator != (const half& a, const half& b) {
+  return __hne(a, b);
+}
+EIGEN_STRONG_INLINE __device__ bool operator < (const half& a, const half& b) {
+  return __hlt(a, b);
+}
+EIGEN_STRONG_INLINE __device__ bool operator <= (const half& a, const half& b) {
+  return __hle(a, b);
+}
+EIGEN_STRONG_INLINE __device__ bool operator > (const half& a, const half& b) {
+  return __hgt(a, b);
+}
+EIGEN_STRONG_INLINE __device__ bool operator >= (const half& a, const half& b) {
+  return __hge(a, b);
+}
+#endif
+
+#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC) && !defined(EIGEN_GPU_COMPILE_PHASE)
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator + (const half& a, const half& b) {
+  return half(vaddh_f16(a.x, b.x));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator * (const half& a, const half& b) {
+  return half(vmulh_f16(a.x, b.x));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a, const half& b) {
+  return half(vsubh_f16(a.x, b.x));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator / (const half& a, const half& b) {
+  return half(vdivh_f16(a.x, b.x));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a) {
+  return half(vnegh_f16(a.x));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator += (half& a, const half& b) {
+  a = half(vaddh_f16(a.x, b.x));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator *= (half& a, const half& b) {
+  a = half(vmulh_f16(a.x, b.x));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator -= (half& a, const half& b) {
+  a = half(vsubh_f16(a.x, b.x));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator /= (half& a, const half& b) {
+  a = half(vdivh_f16(a.x, b.x));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator == (const half& a, const half& b) {
+  return vceqh_f16(a.x, b.x);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator != (const half& a, const half& b) {
+  return !vceqh_f16(a.x, b.x);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator < (const half& a, const half& b) {
+  return vclth_f16(a.x, b.x);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator <= (const half& a, const half& b) {
+  return vcleh_f16(a.x, b.x);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator > (const half& a, const half& b) {
+  return vcgth_f16(a.x, b.x);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator >= (const half& a, const half& b) {
+  return vcgeh_f16(a.x, b.x);
+}
+// We need to distinguish ‘clang as the CUDA compiler’ from ‘clang as the host compiler,
+// invoked by NVCC’ (e.g. on MacOS). The former needs to see both host and device implementation
+// of the functions, while the latter can only deal with one of them.
+#elif !defined(EIGEN_HAS_NATIVE_FP16) || (EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC) // Emulate support for half floats
+
+#if EIGEN_COMP_CLANG && defined(EIGEN_GPUCC)
+// We need to provide emulated *host-side* FP16 operators for clang.
+#pragma push_macro("EIGEN_DEVICE_FUNC")
+#undef EIGEN_DEVICE_FUNC
+#if defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_HAS_NATIVE_FP16)
+#define EIGEN_DEVICE_FUNC __host__
+#else // both host and device need emulated ops.
+#define EIGEN_DEVICE_FUNC __host__ __device__
+#endif
+#endif
+
+// Definitions for CPUs and older HIP+CUDA, mostly working through conversion
+// to/from fp32.
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator + (const half& a, const half& b) {
+  return half(float(a) + float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator * (const half& a, const half& b) {
+  return half(float(a) * float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a, const half& b) {
+  return half(float(a) - float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator / (const half& a, const half& b) {
+  return half(float(a) / float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator - (const half& a) {
+  half result;
+  result.x = a.x ^ 0x8000;
+  return result;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator += (half& a, const half& b) {
+  a = half(float(a) + float(b));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator *= (half& a, const half& b) {
+  a = half(float(a) * float(b));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator -= (half& a, const half& b) {
+  a = half(float(a) - float(b));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half& operator /= (half& a, const half& b) {
+  a = half(float(a) / float(b));
+  return a;
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator == (const half& a, const half& b) {
+  return numext::equal_strict(float(a),float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator != (const half& a, const half& b) {
+  return numext::not_equal_strict(float(a), float(b));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator < (const half& a, const half& b) {
+  return float(a) < float(b);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator <= (const half& a, const half& b) {
+  return float(a) <= float(b);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator > (const half& a, const half& b) {
+  return float(a) > float(b);
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool operator >= (const half& a, const half& b) {
+  return float(a) >= float(b);
+}
+
+#if EIGEN_COMP_CLANG && defined(EIGEN_GPUCC)
+#pragma pop_macro("EIGEN_DEVICE_FUNC")
+#endif
+#endif  // Emulate support for half floats
+
+// Division by an index. Do it in full float precision to avoid accuracy
+// issues in converting the denominator to half.
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator / (const half& a, Index b) {
+  return half(static_cast<float>(a) / static_cast<float>(b));
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator++(half& a) {
+  a += half(1);
+  return a;
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator--(half& a) {
+  a -= half(1);
+  return a;
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator++(half& a, int) {
+  half original_value = a;
+  ++a;
+  return original_value;
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half operator--(half& a, int) {
+  half original_value = a;
+  --a;
+  return original_value;
+}
+
+// Conversion routines, including fallbacks for the host or older CUDA.
+// Note that newer Intel CPUs (Haswell or newer) have vectorized versions of
+// these in hardware. If we need more performance on older/other CPUs, they are
+// also possible to vectorize directly.
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR __half_raw raw_uint16_to_half(numext::uint16_t x) {
+  // We cannot simply do a "return __half_raw(x)" here, because __half_raw is union type
+  // in the hip_fp16 header file, and that will trigger a compile error
+  // On the other hand, having anything but a return statement also triggers a compile error
+  // because this is constexpr function.
+  // Fortunately, since we need to disable EIGEN_CONSTEXPR for GPU anyway, we can get out
+  // of this catch22 by having separate bodies for GPU / non GPU
+#if defined(EIGEN_HAS_GPU_FP16)
+   __half_raw h;
+   h.x = x;
+  return h;
+#else
+  return __half_raw(x);
+#endif
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC numext::uint16_t raw_half_as_uint16(const __half_raw& h) {
+  // HIP/CUDA/Default have a member 'x' of type uint16_t.
+  // For ARM64 native half, the member 'x' is of type __fp16, so we need to bit-cast.
+  // For SYCL, cl::sycl::half is _Float16, so cast directly.
+#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+  return numext::bit_cast<numext::uint16_t>(h.x);
+#elif defined(SYCL_DEVICE_ONLY)
+  return numext::bit_cast<numext::uint16_t>(h);
+#else
+  return h.x;
+#endif
+}
+
+union float32_bits {
+  unsigned int u;
+  float f;
+};
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC __half_raw float_to_half_rtne(float ff) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
+  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+  __half tmp_ff = __float2half(ff);
+  return *(__half_raw*)&tmp_ff;
+
+#elif defined(EIGEN_HAS_FP16_C)
+  __half_raw h;
+  #if EIGEN_COMP_MSVC
+    // MSVC does not have scalar instructions.
+    h.x =_mm_extract_epi16(_mm_cvtps_ph(_mm_set_ss(ff), 0), 0);
+  #else
+    h.x = _cvtss_sh(ff, 0);
+  #endif
+  return h;
+
+#elif defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+  __half_raw h;
+  h.x = static_cast<__fp16>(ff);
+  return h;
+
+#else
+  float32_bits f; f.f = ff;
+
+  const float32_bits f32infty = { 255 << 23 };
+  const float32_bits f16max = { (127 + 16) << 23 };
+  const float32_bits denorm_magic = { ((127 - 15) + (23 - 10) + 1) << 23 };
+  unsigned int sign_mask = 0x80000000u;
+  __half_raw o;
+  o.x = static_cast<numext::uint16_t>(0x0u);
+
+  unsigned int sign = f.u & sign_mask;
+  f.u ^= sign;
+
+  // NOTE all the integer compares in this function can be safely
+  // compiled into signed compares since all operands are below
+  // 0x80000000. Important if you want fast straight SSE2 code
+  // (since there's no unsigned PCMPGTD).
+
+  if (f.u >= f16max.u) {  // result is Inf or NaN (all exponent bits set)
+    o.x = (f.u > f32infty.u) ? 0x7e00 : 0x7c00; // NaN->qNaN and Inf->Inf
+  } else {  // (De)normalized number or zero
+    if (f.u < (113 << 23)) {  // resulting FP16 is subnormal or zero
+      // use a magic value to align our 10 mantissa bits at the bottom of
+      // the float. as long as FP addition is round-to-nearest-even this
+      // just works.
+      f.f += denorm_magic.f;
+
+      // and one integer subtract of the bias later, we have our final float!
+      o.x = static_cast<numext::uint16_t>(f.u - denorm_magic.u);
+    } else {
+      unsigned int mant_odd = (f.u >> 13) & 1; // resulting mantissa is odd
+
+      // update exponent, rounding bias part 1
+      // Equivalent to `f.u += ((unsigned int)(15 - 127) << 23) + 0xfff`, but
+      // without arithmetic overflow.
+      f.u += 0xc8000fffU;
+      // rounding bias part 2
+      f.u += mant_odd;
+      // take the bits!
+      o.x = static_cast<numext::uint16_t>(f.u >> 13);
+    }
+  }
+
+  o.x |= static_cast<numext::uint16_t>(sign >> 16);
+  return o;
+#endif
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC float half_to_float(__half_raw h) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
+  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+  return __half2float(h);
+#elif defined(EIGEN_HAS_FP16_C)
+  #if EIGEN_COMP_MSVC
+    // MSVC does not have scalar instructions.
+    return _mm_cvtss_f32(_mm_cvtph_ps(_mm_set1_epi16(h.x)));
+  #else
+    return _cvtsh_ss(h.x);
+  #endif
+#elif defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+  return static_cast<float>(h.x);
+#else
+  const float32_bits magic = { 113 << 23 };
+  const unsigned int shifted_exp = 0x7c00 << 13; // exponent mask after shift
+  float32_bits o;
+
+  o.u = (h.x & 0x7fff) << 13;             // exponent/mantissa bits
+  unsigned int exp = shifted_exp & o.u;   // just the exponent
+  o.u += (127 - 15) << 23;                // exponent adjust
+
+  // handle exponent special cases
+  if (exp == shifted_exp) {     // Inf/NaN?
+    o.u += (128 - 16) << 23;    // extra exp adjust
+  } else if (exp == 0) {        // Zero/Denormal?
+    o.u += 1 << 23;             // extra exp adjust
+    o.f -= magic.f;             // renormalize
+  }
+
+  o.u |= (h.x & 0x8000) << 16;    // sign bit
+  return o.f;
+#endif
+}
+
+// --- standard functions ---
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isinf)(const half& a) {
+#ifdef EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC
+  return (numext::bit_cast<numext::uint16_t>(a.x) & 0x7fff) == 0x7c00;
+#else
+  return (a.x & 0x7fff) == 0x7c00;
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isnan)(const half& a) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530) || \
+  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+  return __hisnan(a);
+#elif defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+  return (numext::bit_cast<numext::uint16_t>(a.x) & 0x7fff) > 0x7c00;
+#else
+  return (a.x & 0x7fff) > 0x7c00;
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool (isfinite)(const half& a) {
+  return !(isinf EIGEN_NOT_A_MACRO (a)) && !(isnan EIGEN_NOT_A_MACRO (a));
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half abs(const half& a) {
+#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+  return half(vabsh_f16(a.x));
+#else
+  half result;
+  result.x = a.x & 0x7FFF;
+  return result;
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half exp(const half& a) {
+#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 530) || \
+  defined(EIGEN_HIP_DEVICE_COMPILE)
+  return half(hexp(a));
+#else
+   return half(::expf(float(a)));
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half expm1(const half& a) {
+  return half(numext::expm1(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log(const half& a) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && EIGEN_CUDA_SDK_VER >= 80000 && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530) || \
+  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+  return half(::hlog(a));
+#else
+  return half(::logf(float(a)));
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log1p(const half& a) {
+  return half(numext::log1p(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log10(const half& a) {
+  return half(::log10f(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half log2(const half& a) {
+  return half(static_cast<float>(EIGEN_LOG2E) * ::logf(float(a)));
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half sqrt(const half& a) {
+#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 530) || \
+  defined(EIGEN_HIP_DEVICE_COMPILE)
+  return half(hsqrt(a));
+#else
+    return half(::sqrtf(float(a)));
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half pow(const half& a, const half& b) {
+  return half(::powf(float(a), float(b)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half atan2(const half& a, const half& b) {
+  return half(::atan2f(float(a), float(b)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half sin(const half& a) {
+  return half(::sinf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half cos(const half& a) {
+  return half(::cosf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half tan(const half& a) {
+  return half(::tanf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half tanh(const half& a) {
+  return half(::tanhf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half asin(const half& a) {
+  return half(::asinf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half acos(const half& a) {
+  return half(::acosf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half atan(const half& a) {
+  return half(::atanf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half atanh(const half& a) {
+  return half(::atanhf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half floor(const half& a) {
+#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 300) || \
+  defined(EIGEN_HIP_DEVICE_COMPILE)
+  return half(hfloor(a));
+#else
+  return half(::floorf(float(a)));
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half ceil(const half& a) {
+#if (EIGEN_CUDA_SDK_VER >= 80000 && defined EIGEN_CUDA_ARCH && EIGEN_CUDA_ARCH >= 300) || \
+  defined(EIGEN_HIP_DEVICE_COMPILE)
+  return half(hceil(a));
+#else
+  return half(::ceilf(float(a)));
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half rint(const half& a) {
+  return half(::rintf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half round(const half& a) {
+  return half(::roundf(float(a)));
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half fmod(const half& a, const half& b) {
+  return half(::fmodf(float(a), float(b)));
+}
+
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half (min)(const half& a, const half& b) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530) || \
+  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+  return __hlt(b, a) ? b : a;
+#else
+  const float f1 = static_cast<float>(a);
+  const float f2 = static_cast<float>(b);
+  return f2 < f1 ? b : a;
+#endif
+}
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC half (max)(const half& a, const half& b) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530) || \
+  (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+  return __hlt(a, b) ? b : a;
+#else
+  const float f1 = static_cast<float>(a);
+  const float f2 = static_cast<float>(b);
+  return f1 < f2 ? b : a;
+#endif
+}
+
+#ifndef EIGEN_NO_IO
+EIGEN_ALWAYS_INLINE std::ostream& operator << (std::ostream& os, const half& v) {
+  os << static_cast<float>(v);
+  return os;
+}
+#endif
+
+} // end namespace half_impl
+
+// import Eigen::half_impl::half into Eigen namespace
+// using half_impl::half;
+
+namespace internal {
+
+template<>
+struct random_default_impl<half, false, false>
+{
+  static inline half run(const half& x, const half& y)
+  {
+    return x + (y-x) * half(float(std::rand()) / float(RAND_MAX));
+  }
+  static inline half run()
+  {
+    return run(half(-1.f), half(1.f));
+  }
+};
+
+template<> struct is_arithmetic<half> { enum { value = true }; };
+
+} // end namespace internal
+
+template<> struct NumTraits<Eigen::half>
+    : GenericNumTraits<Eigen::half>
+{
+  enum {
+    IsSigned = true,
+    IsInteger = false,
+    IsComplex = false,
+    RequireInitialization = false
+  };
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half epsilon() {
+    return half_impl::raw_uint16_to_half(0x0800);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half dummy_precision() {
+    return half_impl::raw_uint16_to_half(0x211f); //  Eigen::half(1e-2f);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half highest() {
+    return half_impl::raw_uint16_to_half(0x7bff);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half lowest() {
+    return half_impl::raw_uint16_to_half(0xfbff);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half infinity() {
+    return half_impl::raw_uint16_to_half(0x7c00);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR static EIGEN_STRONG_INLINE Eigen::half quiet_NaN() {
+    return half_impl::raw_uint16_to_half(0x7e00);
+  }
+};
+
+} // end namespace Eigen
+
+#if defined(EIGEN_HAS_GPU_FP16) || defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+  #pragma pop_macro("EIGEN_CONSTEXPR")
+#endif
+
+namespace Eigen {
+namespace numext {
+
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isnan)(const Eigen::half& h) {
+  return (half_impl::isnan)(h);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isinf)(const Eigen::half& h) {
+  return (half_impl::isinf)(h);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isfinite)(const Eigen::half& h) {
+  return (half_impl::isfinite)(h);
+}
+
+#endif
+
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC Eigen::half bit_cast<Eigen::half, uint16_t>(const uint16_t& src) {
+  return Eigen::half(Eigen::half_impl::raw_uint16_to_half(src));
+}
+
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC uint16_t bit_cast<uint16_t, Eigen::half>(const Eigen::half& src) {
+  return Eigen::half_impl::raw_half_as_uint16(src);
+}
+
+}  // namespace numext
+}  // namespace Eigen
+
+// Add the missing shfl* intrinsics.
+// The __shfl* functions are only valid on HIP or _CUDA_ARCH_ >= 300.
+//   CUDA defines them for (__CUDA_ARCH__ >= 300 || !defined(__CUDA_ARCH__))
+//
+// HIP and CUDA prior to SDK 9.0 define
+//    __shfl, __shfl_up, __shfl_down, __shfl_xor for int and float
+// CUDA since 9.0 deprecates those and instead defines
+//    __shfl_sync, __shfl_up_sync, __shfl_down_sync, __shfl_xor_sync,
+//    with native support for __half and __nv_bfloat16
+//
+// Note that the following are __device__ - only functions.
+#if (defined(EIGEN_CUDACC) && (!defined(EIGEN_CUDA_ARCH) || EIGEN_CUDA_ARCH >= 300)) \
+    || defined(EIGEN_HIPCC)
+
+#if defined(EIGEN_HAS_CUDA_FP16) && EIGEN_CUDA_SDK_VER >= 90000
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_sync(unsigned mask, Eigen::half var, int srcLane, int width=warpSize) {
+  const __half h = var;
+  return static_cast<Eigen::half>(__shfl_sync(mask, h, srcLane, width));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_up_sync(unsigned mask, Eigen::half var, unsigned int delta, int width=warpSize) {
+  const __half h = var;
+  return static_cast<Eigen::half>(__shfl_up_sync(mask, h, delta, width));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_down_sync(unsigned mask, Eigen::half var, unsigned int delta, int width=warpSize) {
+  const __half h = var;
+  return static_cast<Eigen::half>(__shfl_down_sync(mask, h, delta, width));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_xor_sync(unsigned mask, Eigen::half var, int laneMask, int width=warpSize) {
+  const __half h = var;
+  return static_cast<Eigen::half>(__shfl_xor_sync(mask, h, laneMask, width));
+}
+
+#else // HIP or CUDA SDK < 9.0
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl(Eigen::half var, int srcLane, int width=warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl(ivar, srcLane, width)));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_up(Eigen::half var, unsigned int delta, int width=warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl_up(ivar, delta, width)));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_down(Eigen::half var, unsigned int delta, int width=warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl_down(ivar, delta, width)));
+}
+
+__device__ EIGEN_STRONG_INLINE Eigen::half __shfl_xor(Eigen::half var, int laneMask, int width=warpSize) {
+  const int ivar = static_cast<int>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(var));
+  return Eigen::numext::bit_cast<Eigen::half>(static_cast<Eigen::numext::uint16_t>(__shfl_xor(ivar, laneMask, width)));
+}
+
+#endif // HIP vs CUDA
+#endif // __shfl*
+
+// ldg() has an overload for __half_raw, but we also need one for Eigen::half.
+#if (defined(EIGEN_CUDACC) && (!defined(EIGEN_CUDA_ARCH) || EIGEN_CUDA_ARCH >= 350)) \
+    || defined(EIGEN_HIPCC)
+EIGEN_STRONG_INLINE __device__ Eigen::half __ldg(const Eigen::half* ptr) {
+  return Eigen::half_impl::raw_uint16_to_half(__ldg(reinterpret_cast<const Eigen::numext::uint16_t*>(ptr)));
+}
+#endif // __ldg
+
+#if EIGEN_HAS_STD_HASH
+namespace std {
+template <>
+struct hash<Eigen::half> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE std::size_t operator()(const Eigen::half& a) const {
+    return static_cast<std::size_t>(Eigen::numext::bit_cast<Eigen::numext::uint16_t>(a));
+  }
+};
+} // end namespace std
+#endif
+
+namespace Eigen {
+namespace internal {
+
+template <>
+struct cast_impl<float, half> {
+  EIGEN_DEVICE_FUNC
+  static inline half run(const float& a) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
+    (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+    return __float2half(a);
+#else
+    return half(a);
+#endif
+  }
+};
+
+template <>
+struct cast_impl<int, half> {
+  EIGEN_DEVICE_FUNC
+  static inline half run(const int& a) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
+    (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+    return __float2half(static_cast<float>(a));
+#else
+    return half(static_cast<float>(a));
+#endif
+  }
+};
+
+template <>
+struct cast_impl<half, float> {
+  EIGEN_DEVICE_FUNC
+  static inline float run(const half& a) {
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
+    (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+    return __half2float(a);
+#else
+    return static_cast<float>(a);
+#endif
+  }
+};
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif // EIGEN_HALF_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/Settings.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/Settings.h
new file mode 100644
index 0000000000000000000000000000000000000000..a5c3ada4c7fc54fd250a020c7d4fe3487cd1b1c5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/Default/Settings.h
@@ -0,0 +1,49 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+
+/* All the parameters defined in this file can be specialized in the
+ * architecture specific files, and/or by the user.
+ * More to come... */
+
+#ifndef EIGEN_DEFAULT_SETTINGS_H
+#define EIGEN_DEFAULT_SETTINGS_H
+
+/** Defines the maximal loop size to enable meta unrolling of loops.
+  * Note that the value here is expressed in Eigen's own notion of "number of FLOPS",
+  * it does not correspond to the number of iterations or the number of instructions
+  */
+#ifndef EIGEN_UNROLLING_LIMIT
+#define EIGEN_UNROLLING_LIMIT 110
+#endif
+
+/** Defines the threshold between a "small" and a "large" matrix.
+  * This threshold is mainly used to select the proper product implementation.
+  */
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+/** Defines the maximal width of the blocks used in the triangular product and solver
+  * for vectors (level 2 blas xTRMV and xTRSV). The default is 8.
+  */
+#ifndef EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH
+#define EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH 8
+#endif
+
+
+/** Defines the default number of registers available for that architecture.
+  * Currently it must be 8 or 16. Other values will fail.
+  */
+#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 8
+#endif
+
+#endif // EIGEN_DEFAULT_SETTINGS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/GPU/Complex.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/GPU/Complex.h
new file mode 100644
index 0000000000000000000000000000000000000000..4e4cdd1ac666b45648bc4525e646d20de2773f5b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/GPU/Complex.h
@@ -0,0 +1,271 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+// Copyright (C) 2021 C. Antonio Sanchez <cantonios@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_GPU_H
+#define EIGEN_COMPLEX_GPU_H
+
+// Many std::complex methods such as operator+, operator-, operator* and
+// operator/ are not constexpr. Due to this, GCC and older versions of clang do
+// not treat them as device functions and thus Eigen functors making use of
+// these operators fail to compile. Here, we manually specialize these
+// operators and functors for complex types when building for CUDA to enable
+// their use on-device.
+//
+// NOTES:
+//  - Compound assignment operators +=,-=,*=,/=(Scalar) will not work on device,
+//    since they are already specialized in the standard. Using them will result
+//    in silent kernel failures.
+//  - Compiling with MSVC and using +=,-=,*=,/=(std::complex<Scalar>) will lead
+//    to duplicate definition errors, since these are already specialized in
+//    Visual Studio's <complex> header (contrary to the standard).  This is
+//    preferable to removing such definitions, which will lead to silent kernel
+//    failures.
+//  - Compiling with ICC requires defining _USE_COMPLEX_SPECIALIZATION_ prior
+//    to the first inclusion of <complex>.
+
+#if defined(EIGEN_GPUCC) && defined(EIGEN_GPU_COMPILE_PHASE)
+    
+// ICC already specializes std::complex<float> and std::complex<double>
+// operators, preventing us from making them device functions here.
+// This will lead to silent runtime errors if the operators are used on device.
+//
+// To allow std::complex operator use on device, define _OVERRIDE_COMPLEX_SPECIALIZATION_
+// prior to first inclusion of <complex>.  This prevents ICC from adding
+// its own specializations, so our custom ones below can be used instead.
+#if !(EIGEN_COMP_ICC && defined(_USE_COMPLEX_SPECIALIZATION_))
+
+// Import Eigen's internal operator specializations.
+#define EIGEN_USING_STD_COMPLEX_OPERATORS           \
+  using Eigen::complex_operator_detail::operator+;  \
+  using Eigen::complex_operator_detail::operator-;  \
+  using Eigen::complex_operator_detail::operator*;  \
+  using Eigen::complex_operator_detail::operator/;  \
+  using Eigen::complex_operator_detail::operator+=; \
+  using Eigen::complex_operator_detail::operator-=; \
+  using Eigen::complex_operator_detail::operator*=; \
+  using Eigen::complex_operator_detail::operator/=; \
+  using Eigen::complex_operator_detail::operator==; \
+  using Eigen::complex_operator_detail::operator!=;
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// Specialized std::complex overloads.
+namespace complex_operator_detail {
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+std::complex<T> complex_multiply(const std::complex<T>& a, const std::complex<T>& b) {
+  const T a_real = numext::real(a);
+  const T a_imag = numext::imag(a);
+  const T b_real = numext::real(b);
+  const T b_imag = numext::imag(b);
+  return std::complex<T>(
+      a_real * b_real - a_imag * b_imag,
+      a_imag * b_real + a_real * b_imag);
+}
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+std::complex<T> complex_divide_fast(const std::complex<T>& a, const std::complex<T>& b) {
+  const T a_real = numext::real(a);
+  const T a_imag = numext::imag(a);
+  const T b_real = numext::real(b);
+  const T b_imag = numext::imag(b);
+  const T norm = (b_real * b_real + b_imag * b_imag);
+  return std::complex<T>((a_real * b_real + a_imag * b_imag) / norm,
+                          (a_imag * b_real - a_real * b_imag) / norm);
+}
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+std::complex<T> complex_divide_stable(const std::complex<T>& a, const std::complex<T>& b) {
+  const T a_real = numext::real(a);
+  const T a_imag = numext::imag(a);
+  const T b_real = numext::real(b);
+  const T b_imag = numext::imag(b);
+  // Smith's complex division (https://arxiv.org/pdf/1210.4539.pdf),
+  // guards against over/under-flow.
+  const bool scale_imag = numext::abs(b_imag) <= numext::abs(b_real);
+  const T rscale = scale_imag ? T(1) : b_real / b_imag;
+  const T iscale = scale_imag ? b_imag / b_real : T(1);
+  const T denominator = b_real * rscale + b_imag * iscale;
+  return std::complex<T>((a_real * rscale + a_imag * iscale) / denominator, 
+                         (a_imag * rscale - a_real * iscale) / denominator);
+}
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+std::complex<T> complex_divide(const std::complex<T>& a, const std::complex<T>& b) {
+#if EIGEN_FAST_MATH
+  return complex_divide_fast(a, b);
+#else
+  return complex_divide_stable(a, b);
+#endif
+}
+
+// NOTE: We cannot specialize compound assignment operators with Scalar T,
+//         (i.e.  operator@=(const T&), for @=+,-,*,/)
+//       since they are already specialized for float/double/long double within
+//       the standard <complex> header. We also do not specialize the stream
+//       operators.
+#define EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS(T)                                    \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T> operator+(const std::complex<T>& a) { return a; }                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T> operator-(const std::complex<T>& a) {                                           \
+  return std::complex<T>(-numext::real(a), -numext::imag(a));                                   \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T> operator+(const std::complex<T>& a, const std::complex<T>& b) {                 \
+  return std::complex<T>(numext::real(a) + numext::real(b), numext::imag(a) + numext::imag(b)); \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T> operator+(const std::complex<T>& a, const T& b) {                               \
+  return std::complex<T>(numext::real(a) + b, numext::imag(a));                                 \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T> operator+(const T& a, const std::complex<T>& b) {                               \
+  return std::complex<T>(a + numext::real(b), numext::imag(b));                                 \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T> operator-(const std::complex<T>& a, const std::complex<T>& b) {                 \
+  return std::complex<T>(numext::real(a) - numext::real(b), numext::imag(a) - numext::imag(b)); \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T> operator-(const std::complex<T>& a, const T& b) {                               \
+  return std::complex<T>(numext::real(a) - b, numext::imag(a));                                 \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T> operator-(const T& a, const std::complex<T>& b) {                               \
+  return std::complex<T>(a - numext::real(b), -numext::imag(b));                                \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T> operator*(const std::complex<T>& a, const std::complex<T>& b) {                 \
+  return complex_multiply(a, b);                                                                \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T> operator*(const std::complex<T>& a, const T& b) {                               \
+  return std::complex<T>(numext::real(a) * b, numext::imag(a) * b);                             \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T> operator*(const T& a, const std::complex<T>& b) {                               \
+  return std::complex<T>(a * numext::real(b), a * numext::imag(b));                             \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T> operator/(const std::complex<T>& a, const std::complex<T>& b) {                 \
+  return complex_divide(a, b);                                                                  \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T> operator/(const std::complex<T>& a, const T& b) {                               \
+  return std::complex<T>(numext::real(a) / b, numext::imag(a) / b);                             \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T> operator/(const T& a, const std::complex<T>& b) {                               \
+  return complex_divide(std::complex<T>(a, 0), b);                                              \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T>& operator+=(std::complex<T>& a, const std::complex<T>& b) {                     \
+  numext::real_ref(a) += numext::real(b);                                                       \
+  numext::imag_ref(a) += numext::imag(b);                                                       \
+  return a;                                                                                     \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T>& operator-=(std::complex<T>& a, const std::complex<T>& b) {                     \
+  numext::real_ref(a) -= numext::real(b);                                                       \
+  numext::imag_ref(a) -= numext::imag(b);                                                       \
+  return a;                                                                                     \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T>& operator*=(std::complex<T>& a, const std::complex<T>& b) {                     \
+  a = complex_multiply(a, b);                                                                   \
+  return a;                                                                                     \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+std::complex<T>& operator/=(std::complex<T>& a, const std::complex<T>& b) {                     \
+  a = complex_divide(a, b);                                                                     \
+  return  a;                                                                                    \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+bool operator==(const std::complex<T>& a, const std::complex<T>& b) {                           \
+  return numext::real(a) == numext::real(b) && numext::imag(a) == numext::imag(b);              \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+bool operator==(const std::complex<T>& a, const T& b) {                                         \
+  return numext::real(a) == b && numext::imag(a) == 0;                                          \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+bool operator==(const T& a, const std::complex<T>& b) {                                         \
+  return a  == numext::real(b) && 0 == numext::imag(b);                                         \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+bool operator!=(const std::complex<T>& a, const std::complex<T>& b) {                           \
+  return !(a == b);                                                                             \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+bool operator!=(const std::complex<T>& a, const T& b) {                                         \
+  return !(a == b);                                                                             \
+}                                                                                               \
+                                                                                                \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE                                                           \
+bool operator!=(const T& a, const std::complex<T>& b) {                                         \
+  return !(a == b);                                                                             \
+}
+
+// Do not specialize for long double, since that reduces to double on device.
+EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS(float)
+EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS(double)
+
+#undef EIGEN_CREATE_STD_COMPLEX_OPERATOR_SPECIALIZATIONS
+
+  
+}  // namespace complex_operator_detail
+
+EIGEN_USING_STD_COMPLEX_OPERATORS
+
+namespace numext {
+EIGEN_USING_STD_COMPLEX_OPERATORS
+}  // namespace numext
+
+namespace internal {
+EIGEN_USING_STD_COMPLEX_OPERATORS
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // !(EIGEN_COMP_ICC && _USE_COMPLEX_SPECIALIZATION_)
+
+#endif  // EIGEN_GPUCC && EIGEN_GPU_COMPILE_PHASE
+
+#endif  // EIGEN_COMPLEX_GPU_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/GPU/MathFunctions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/GPU/MathFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..ad61e95cbf6956cbe373c5f17110a1491f685d82
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/GPU/MathFunctions.h
@@ -0,0 +1,105 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATH_FUNCTIONS_GPU_H
+#define EIGEN_MATH_FUNCTIONS_GPU_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Make sure this is only available when targeting a GPU: we don't want to
+// introduce conflicts between these packet_traits definitions and the ones
+// we'll use on the host side (SSE, AVX, ...)
+#if defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 plog<float4>(const float4& a)
+{
+  return make_float4(logf(a.x), logf(a.y), logf(a.z), logf(a.w));
+}
+
+template<>  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 plog<double2>(const double2& a)
+{
+  using ::log;
+  return make_double2(log(a.x), log(a.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 plog1p<float4>(const float4& a)
+{
+  return make_float4(log1pf(a.x), log1pf(a.y), log1pf(a.z), log1pf(a.w));
+}
+
+template<>  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 plog1p<double2>(const double2& a)
+{
+  return make_double2(log1p(a.x), log1p(a.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 pexp<float4>(const float4& a)
+{
+  return make_float4(expf(a.x), expf(a.y), expf(a.z), expf(a.w));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 pexp<double2>(const double2& a)
+{
+  using ::exp;
+  return make_double2(exp(a.x), exp(a.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 pexpm1<float4>(const float4& a)
+{
+  return make_float4(expm1f(a.x), expm1f(a.y), expm1f(a.z), expm1f(a.w));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 pexpm1<double2>(const double2& a)
+{
+  return make_double2(expm1(a.x), expm1(a.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 psqrt<float4>(const float4& a)
+{
+  return make_float4(sqrtf(a.x), sqrtf(a.y), sqrtf(a.z), sqrtf(a.w));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 psqrt<double2>(const double2& a)
+{
+  using ::sqrt;
+  return make_double2(sqrt(a.x), sqrt(a.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+float4 prsqrt<float4>(const float4& a)
+{
+  return make_float4(rsqrtf(a.x), rsqrtf(a.y), rsqrtf(a.z), rsqrtf(a.w));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+double2 prsqrt<double2>(const double2& a)
+{
+  return make_double2(rsqrt(a.x), rsqrt(a.y));
+}
+
+
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATH_FUNCTIONS_GPU_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/GPU/PacketMath.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/GPU/PacketMath.h
new file mode 100644
index 0000000000000000000000000000000000000000..a04c5634d585cbfb9248fb0473855c2cfed94386
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/GPU/PacketMath.h
@@ -0,0 +1,1708 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_GPU_H
+#define EIGEN_PACKET_MATH_GPU_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Read-only data cached load available.
+#if defined(EIGEN_HIP_DEVICE_COMPILE) || (defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 350)
+#define EIGEN_GPU_HAS_LDG 1
+#endif
+
+// FP16 math available.
+#if (defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 530)
+#define EIGEN_CUDA_HAS_FP16_ARITHMETIC 1
+#endif
+
+#if defined(EIGEN_HIP_DEVICE_COMPILE) || defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
+#define EIGEN_GPU_HAS_FP16_ARITHMETIC 1
+#endif
+
+// Make sure this is only available when targeting a GPU: we don't want to
+// introduce conflicts between these packet_traits definitions and the ones
+// we'll use on the host side (SSE, AVX, ...)
+#if defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
+
+template<> struct is_arithmetic<float4>  { enum { value = true }; };
+template<> struct is_arithmetic<double2> { enum { value = true }; };
+
+template<> struct packet_traits<float> : default_packet_traits
+{
+  typedef float4 type;
+  typedef float4 half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size=4,
+
+    HasDiv  = 1,
+    HasSin  = 0,
+    HasCos  = 0,
+    HasLog  = 1,
+    HasExp  = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasLGamma = 1,
+    HasDiGamma = 1,
+    HasZeta = 1,
+    HasPolygamma = 1,
+    HasErf = 1,
+    HasErfc = 1,
+    HasNdtri = 1,
+    HasBessel = 1,
+    HasIGamma = 1,
+    HasIGammaDerA = 1,
+    HasGammaSampleDerAlpha = 1,
+    HasIGammac = 1,
+    HasBetaInc = 1,
+
+    HasBlend = 0,
+    HasFloor = 1,
+  };
+};
+
+template<> struct packet_traits<double> : default_packet_traits
+{
+  typedef double2 type;
+  typedef double2 half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size=2,
+
+    HasDiv  = 1,
+    HasLog  = 1,
+    HasExp  = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasLGamma = 1,
+    HasDiGamma = 1,
+    HasZeta = 1,
+    HasPolygamma = 1,
+    HasErf = 1,
+    HasErfc = 1,
+    HasNdtri = 1,
+    HasBessel = 1,
+    HasIGamma = 1,
+    HasIGammaDerA = 1,
+    HasGammaSampleDerAlpha = 1,
+    HasIGammac = 1,
+    HasBetaInc = 1,
+
+    HasBlend = 0,
+    HasFloor = 1,
+  };
+};
+
+
+template<> struct unpacket_traits<float4>  { typedef float  type; enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef float4 half; };
+template<> struct unpacket_traits<double2> { typedef double type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef double2 half; };
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pset1<float4>(const float&  from) {
+  return make_float4(from, from, from, from);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pset1<double2>(const double& from) {
+  return make_double2(from, from);
+}
+
+// We need to distinguish ‘clang as the CUDA compiler’ from ‘clang as the host compiler,
+// invoked by NVCC’ (e.g. on MacOS). The former needs to see both host and device implementation
+// of the functions, while the latter can only deal with one of them.
+#if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIPCC) || (defined(EIGEN_CUDACC) && EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_and(const float& a,
+                                                        const float& b) {
+  return __int_as_float(__float_as_int(a) & __float_as_int(b));
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_and(const double& a,
+                                                         const double& b) {
+  return __longlong_as_double(__double_as_longlong(a) &
+                              __double_as_longlong(b));
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_or(const float& a,
+                                                       const float& b) {
+  return __int_as_float(__float_as_int(a) | __float_as_int(b));
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_or(const double& a,
+                                                        const double& b) {
+  return __longlong_as_double(__double_as_longlong(a) |
+                              __double_as_longlong(b));
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_xor(const float& a,
+                                                        const float& b) {
+  return __int_as_float(__float_as_int(a) ^ __float_as_int(b));
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_xor(const double& a,
+                                                         const double& b) {
+  return __longlong_as_double(__double_as_longlong(a) ^
+                              __double_as_longlong(b));
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float bitwise_andnot(const float& a,
+                                                           const float& b) {
+  return __int_as_float(__float_as_int(a) & ~__float_as_int(b));
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double bitwise_andnot(const double& a,
+                                                            const double& b) {
+  return __longlong_as_double(__double_as_longlong(a) &
+                              ~__double_as_longlong(b));
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float eq_mask(const float& a,
+                                                    const float& b) {
+  return __int_as_float(a == b ? 0xffffffffu : 0u);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double eq_mask(const double& a,
+                                                     const double& b) {
+  return __longlong_as_double(a == b ? 0xffffffffffffffffull : 0ull);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float lt_mask(const float& a,
+                                                    const float& b) {
+  return __int_as_float(a < b ? 0xffffffffu : 0u);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double lt_mask(const double& a,
+                                                     const double& b) {
+  return __longlong_as_double(a < b ? 0xffffffffffffffffull : 0ull);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float le_mask(const float& a,
+                                                    const float& b) {
+  return __int_as_float(a <= b ? 0xffffffffu : 0u);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double le_mask(const double& a,
+                                                     const double& b) {
+  return __longlong_as_double(a <= b ? 0xffffffffffffffffull : 0ull);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pand<float4>(const float4& a,
+                                                          const float4& b) {
+  return make_float4(bitwise_and(a.x, b.x), bitwise_and(a.y, b.y),
+                     bitwise_and(a.z, b.z), bitwise_and(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pand<double2>(const double2& a,
+                                                            const double2& b) {
+  return make_double2(bitwise_and(a.x, b.x), bitwise_and(a.y, b.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 por<float4>(const float4& a,
+                                                         const float4& b) {
+  return make_float4(bitwise_or(a.x, b.x), bitwise_or(a.y, b.y),
+                     bitwise_or(a.z, b.z), bitwise_or(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 por<double2>(const double2& a,
+                                                           const double2& b) {
+  return make_double2(bitwise_or(a.x, b.x), bitwise_or(a.y, b.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pxor<float4>(const float4& a,
+                                                          const float4& b) {
+  return make_float4(bitwise_xor(a.x, b.x), bitwise_xor(a.y, b.y),
+                     bitwise_xor(a.z, b.z), bitwise_xor(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pxor<double2>(const double2& a,
+                                                            const double2& b) {
+  return make_double2(bitwise_xor(a.x, b.x), bitwise_xor(a.y, b.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pandnot<float4>(const float4& a,
+                                                             const float4& b) {
+  return make_float4(bitwise_andnot(a.x, b.x), bitwise_andnot(a.y, b.y),
+                     bitwise_andnot(a.z, b.z), bitwise_andnot(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pandnot<double2>(const double2& a, const double2& b) {
+  return make_double2(bitwise_andnot(a.x, b.x), bitwise_andnot(a.y, b.y));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcmp_eq<float4>(const float4& a,
+                                                             const float4& b) {
+  return make_float4(eq_mask(a.x, b.x), eq_mask(a.y, b.y), eq_mask(a.z, b.z),
+                     eq_mask(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcmp_lt<float4>(const float4& a,
+                                                             const float4& b) {
+  return make_float4(lt_mask(a.x, b.x), lt_mask(a.y, b.y), lt_mask(a.z, b.z),
+                     lt_mask(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcmp_le<float4>(const float4& a,
+                                                             const float4& b) {
+  return make_float4(le_mask(a.x, b.x), le_mask(a.y, b.y), le_mask(a.z, b.z),
+                     le_mask(a.w, b.w));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pcmp_eq<double2>(const double2& a, const double2& b) {
+  return make_double2(eq_mask(a.x, b.x), eq_mask(a.y, b.y));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pcmp_lt<double2>(const double2& a, const double2& b) {
+  return make_double2(lt_mask(a.x, b.x), lt_mask(a.y, b.y));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2
+pcmp_le<double2>(const double2& a, const double2& b) {
+  return make_double2(le_mask(a.x, b.x), le_mask(a.y, b.y));
+}
+#endif // defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIPCC) || (defined(EIGEN_CUDACC) && EIGEN_COMP_CLANG && !EIGEN_COMP_NVCC)
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 plset<float4>(const float& a) {
+  return make_float4(a, a+1, a+2, a+3);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 plset<double2>(const double& a) {
+  return make_double2(a, a+1);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 padd<float4>(const float4& a, const float4& b) {
+  return make_float4(a.x+b.x, a.y+b.y, a.z+b.z, a.w+b.w);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 padd<double2>(const double2& a, const double2& b) {
+  return make_double2(a.x+b.x, a.y+b.y);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 psub<float4>(const float4& a, const float4& b) {
+  return make_float4(a.x-b.x, a.y-b.y, a.z-b.z, a.w-b.w);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 psub<double2>(const double2& a, const double2& b) {
+  return make_double2(a.x-b.x, a.y-b.y);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pnegate(const float4& a) {
+  return make_float4(-a.x, -a.y, -a.z, -a.w);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pnegate(const double2& a) {
+  return make_double2(-a.x, -a.y);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pconj(const float4& a) { return a; }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pconj(const double2& a) { return a; }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmul<float4>(const float4& a, const float4& b) {
+  return make_float4(a.x*b.x, a.y*b.y, a.z*b.z, a.w*b.w);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmul<double2>(const double2& a, const double2& b) {
+  return make_double2(a.x*b.x, a.y*b.y);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pdiv<float4>(const float4& a, const float4& b) {
+  return make_float4(a.x/b.x, a.y/b.y, a.z/b.z, a.w/b.w);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pdiv<double2>(const double2& a, const double2& b) {
+  return make_double2(a.x/b.x, a.y/b.y);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmin<float4>(const float4& a, const float4& b) {
+  return make_float4(fminf(a.x, b.x), fminf(a.y, b.y), fminf(a.z, b.z), fminf(a.w, b.w));
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmin<double2>(const double2& a, const double2& b) {
+  return make_double2(fmin(a.x, b.x), fmin(a.y, b.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pmax<float4>(const float4& a, const float4& b) {
+  return make_float4(fmaxf(a.x, b.x), fmaxf(a.y, b.y), fmaxf(a.z, b.z), fmaxf(a.w, b.w));
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pmax<double2>(const double2& a, const double2& b) {
+  return make_double2(fmax(a.x, b.x), fmax(a.y, b.y));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pload<float4>(const float* from) {
+  return *reinterpret_cast<const float4*>(from);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 pload<double2>(const double* from) {
+  return *reinterpret_cast<const double2*>(from);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 ploadu<float4>(const float* from) {
+  return make_float4(from[0], from[1], from[2], from[3]);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 ploadu<double2>(const double* from) {
+  return make_double2(from[0], from[1]);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 ploaddup<float4>(const float*   from) {
+  return make_float4(from[0], from[0], from[1], from[1]);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE double2 ploaddup<double2>(const double*  from) {
+  return make_double2(from[0], from[0]);
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<float>(float*   to, const float4& from) {
+  *reinterpret_cast<float4*>(to) = from;
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<double>(double* to, const double2& from) {
+  *reinterpret_cast<double2*>(to) = from;
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<float>(float*  to, const float4& from) {
+  to[0] = from.x;
+  to[1] = from.y;
+  to[2] = from.z;
+  to[3] = from.w;
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const double2& from) {
+  to[0] = from.x;
+  to[1] = from.y;
+}
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float4 ploadt_ro<float4, Aligned>(const float* from) {
+#if defined(EIGEN_GPU_HAS_LDG)
+  return __ldg(reinterpret_cast<const float4*>(from));
+#else
+  return make_float4(from[0], from[1], from[2], from[3]);
+#endif
+}
+template<>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double2 ploadt_ro<double2, Aligned>(const double* from) {
+#if defined(EIGEN_GPU_HAS_LDG)
+  return __ldg(reinterpret_cast<const double2*>(from));
+#else
+  return make_double2(from[0], from[1]);
+#endif
+}
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float4 ploadt_ro<float4, Unaligned>(const float* from) {
+#if defined(EIGEN_GPU_HAS_LDG)
+  return make_float4(__ldg(from+0), __ldg(from+1), __ldg(from+2), __ldg(from+3));
+#else
+  return make_float4(from[0], from[1], from[2], from[3]);
+#endif
+}
+template<>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double2 ploadt_ro<double2, Unaligned>(const double* from) {
+#if defined(EIGEN_GPU_HAS_LDG)
+  return make_double2(__ldg(from+0), __ldg(from+1));
+#else
+  return make_double2(from[0], from[1]);
+#endif
+}
+
+template<> EIGEN_DEVICE_FUNC inline float4 pgather<float, float4>(const float* from, Index stride) {
+  return make_float4(from[0*stride], from[1*stride], from[2*stride], from[3*stride]);
+}
+
+template<> EIGEN_DEVICE_FUNC inline double2 pgather<double, double2>(const double* from, Index stride) {
+  return make_double2(from[0*stride], from[1*stride]);
+}
+
+template<> EIGEN_DEVICE_FUNC inline void pscatter<float, float4>(float* to, const float4& from, Index stride) {
+  to[stride*0] = from.x;
+  to[stride*1] = from.y;
+  to[stride*2] = from.z;
+  to[stride*3] = from.w;
+}
+template<> EIGEN_DEVICE_FUNC inline void pscatter<double, double2>(double* to, const double2& from, Index stride) {
+  to[stride*0] = from.x;
+  to[stride*1] = from.y;
+}
+
+template<> EIGEN_DEVICE_FUNC inline float  pfirst<float4>(const float4& a) {
+  return a.x;
+}
+template<> EIGEN_DEVICE_FUNC inline double pfirst<double2>(const double2& a) {
+  return a.x;
+}
+
+template<> EIGEN_DEVICE_FUNC inline float  predux<float4>(const float4& a) {
+  return a.x + a.y + a.z + a.w;
+}
+template<> EIGEN_DEVICE_FUNC inline double predux<double2>(const double2& a) {
+  return a.x + a.y;
+}
+
+template<> EIGEN_DEVICE_FUNC inline float  predux_max<float4>(const float4& a) {
+  return fmaxf(fmaxf(a.x, a.y), fmaxf(a.z, a.w));
+}
+template<> EIGEN_DEVICE_FUNC inline double predux_max<double2>(const double2& a) {
+  return fmax(a.x, a.y);
+}
+
+template<> EIGEN_DEVICE_FUNC inline float  predux_min<float4>(const float4& a) {
+  return fminf(fminf(a.x, a.y), fminf(a.z, a.w));
+}
+template<> EIGEN_DEVICE_FUNC inline double predux_min<double2>(const double2& a) {
+  return fmin(a.x, a.y);
+}
+
+template<> EIGEN_DEVICE_FUNC inline float  predux_mul<float4>(const float4& a) {
+  return a.x * a.y * a.z * a.w;
+}
+template<> EIGEN_DEVICE_FUNC inline double predux_mul<double2>(const double2& a) {
+  return a.x * a.y;
+}
+
+template<> EIGEN_DEVICE_FUNC inline float4  pabs<float4>(const float4& a) {
+  return make_float4(fabsf(a.x), fabsf(a.y), fabsf(a.z), fabsf(a.w));
+}
+template<> EIGEN_DEVICE_FUNC inline double2 pabs<double2>(const double2& a) {
+  return make_double2(fabs(a.x), fabs(a.y));
+}
+
+template<> EIGEN_DEVICE_FUNC inline float4  pfloor<float4>(const float4& a) {
+  return make_float4(floorf(a.x), floorf(a.y), floorf(a.z), floorf(a.w));
+}
+template<> EIGEN_DEVICE_FUNC inline double2 pfloor<double2>(const double2& a) {
+  return make_double2(floor(a.x), floor(a.y));
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<float4,4>& kernel) {
+  float tmp = kernel.packet[0].y;
+  kernel.packet[0].y = kernel.packet[1].x;
+  kernel.packet[1].x = tmp;
+
+  tmp = kernel.packet[0].z;
+  kernel.packet[0].z = kernel.packet[2].x;
+  kernel.packet[2].x = tmp;
+
+  tmp = kernel.packet[0].w;
+  kernel.packet[0].w = kernel.packet[3].x;
+  kernel.packet[3].x = tmp;
+
+  tmp = kernel.packet[1].z;
+  kernel.packet[1].z = kernel.packet[2].y;
+  kernel.packet[2].y = tmp;
+
+  tmp = kernel.packet[1].w;
+  kernel.packet[1].w = kernel.packet[3].y;
+  kernel.packet[3].y = tmp;
+
+  tmp = kernel.packet[2].w;
+  kernel.packet[2].w = kernel.packet[3].z;
+  kernel.packet[3].z = tmp;
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<double2,2>& kernel) {
+  double tmp = kernel.packet[0].y;
+  kernel.packet[0].y = kernel.packet[1].x;
+  kernel.packet[1].x = tmp;
+}
+
+#endif // defined(EIGEN_GPUCC) && defined(EIGEN_USE_GPU)
+
+// Half-packet functions are not available on the host for CUDA 9.0-9.2, only
+// on device. There is no benefit to using them on the host anyways, since they are
+// emulated.
+#if (defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)) && defined(EIGEN_GPU_COMPILE_PHASE)
+
+typedef ulonglong2 Packet4h2;
+template<> struct unpacket_traits<Packet4h2> { typedef Eigen::half type; enum {size=8, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet4h2 half; };
+template<> struct is_arithmetic<Packet4h2> { enum { value = true }; };
+
+template<> struct unpacket_traits<half2> { typedef Eigen::half type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef half2 half; };
+template<> struct is_arithmetic<half2> { enum { value = true }; };
+
+template<> struct packet_traits<Eigen::half> : default_packet_traits
+{
+  typedef Packet4h2 type;
+  typedef Packet4h2 half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size=8,
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasDiv    = 1,
+    HasSqrt   = 1,
+    HasRsqrt  = 1,
+    HasExp    = 1,
+    HasExpm1  = 1,
+    HasLog    = 1,
+    HasLog1p  = 1
+  };
+};
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pset1<half2>(const Eigen::half& from) {
+  return __half2half2(from);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
+pset1<Packet4h2>(const Eigen::half& from) {
+  Packet4h2 r;
+  half2* p_alias = reinterpret_cast<half2*>(&r);
+  p_alias[0] = pset1<half2>(from);
+  p_alias[1] = pset1<half2>(from);
+  p_alias[2] = pset1<half2>(from);
+  p_alias[3] = pset1<half2>(from);
+  return r;
+}
+
+namespace {
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pload(const Eigen::half* from) {
+  return *reinterpret_cast<const half2*>(from);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ploadu(const Eigen::half* from) {
+  return __halves2half2(from[0], from[1]);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ploaddup(const Eigen::half*  from) {
+  return __halves2half2(from[0], from[0]);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore(Eigen::half* to,
+                                                  const half2& from) {
+  *reinterpret_cast<half2*>(to) = from;
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu(Eigen::half* to,
+                                                   const half2& from) {
+  to[0] = __low2half(from);
+  to[1] = __high2half(from);
+}
+
+
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE half2 ploadt_ro_aligned(
+    const Eigen::half* from) {
+#if defined(EIGEN_GPU_HAS_LDG)
+  // Input is guaranteed to be properly aligned.
+  return __ldg(reinterpret_cast<const half2*>(from));
+#else
+  return __halves2half2(*(from+0), *(from+1));
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE half2 ploadt_ro_unaligned(
+    const Eigen::half* from) {
+#if defined(EIGEN_GPU_HAS_LDG)
+  return __halves2half2(__ldg(from+0), __ldg(from+1));
+#else
+  return __halves2half2(*(from+0), *(from+1));
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pgather(const Eigen::half* from,
+                                                    Index stride) {
+  return __halves2half2(from[0*stride], from[1*stride]);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter(
+    Eigen::half* to, const half2& from, Index stride) {
+  to[stride*0] = __low2half(from);
+  to[stride*1] = __high2half(from);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half pfirst(const half2& a) {
+  return __low2half(a);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pabs(const half2& a) {
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half result1 = half_impl::raw_uint16_to_half(a1.x & 0x7FFF);
+  half result2 = half_impl::raw_uint16_to_half(a2.x & 0x7FFF);
+  return __halves2half2(result1, result2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 ptrue(const half2& /*a*/) {
+  half true_half = half_impl::raw_uint16_to_half(0xffffu);
+  return pset1<half2>(true_half);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pzero(const half2& /*a*/) {
+  half false_half = half_impl::raw_uint16_to_half(0x0000u);
+  return pset1<half2>(false_half);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
+ptranspose(PacketBlock<half2,2>& kernel) {
+  __half a1 = __low2half(kernel.packet[0]);
+  __half a2 = __high2half(kernel.packet[0]);
+  __half b1 = __low2half(kernel.packet[1]);
+  __half b2 = __high2half(kernel.packet[1]);
+  kernel.packet[0] = __halves2half2(a1, b1);
+  kernel.packet[1] = __halves2half2(a2, b2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plset(const Eigen::half& a) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __halves2half2(a, __hadd(a, __float2half(1.0f)));
+#else
+  float f = __half2float(a) + 1.0f;
+  return __halves2half2(a, __float2half(f));
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pselect(const half2& mask,
+                                                    const half2& a,
+                                                    const half2& b) {
+  half mask_low = __low2half(mask);
+  half mask_high = __high2half(mask);
+  half result_low = mask_low == half(0) ? __low2half(b) : __low2half(a);
+  half result_high = mask_high == half(0) ? __high2half(b) : __high2half(a);
+  return __halves2half2(result_low, result_high);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcmp_eq(const half2& a,
+                                                    const half2& b) {
+  half true_half = half_impl::raw_uint16_to_half(0xffffu);
+  half false_half = half_impl::raw_uint16_to_half(0x0000u);
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half b1 = __low2half(b);
+  half b2 = __high2half(b);
+  half eq1 = __half2float(a1) == __half2float(b1) ? true_half : false_half;
+  half eq2 = __half2float(a2) == __half2float(b2) ? true_half : false_half;
+  return __halves2half2(eq1, eq2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcmp_lt(const half2& a,
+                                                    const half2& b) {
+  half true_half = half_impl::raw_uint16_to_half(0xffffu);
+  half false_half = half_impl::raw_uint16_to_half(0x0000u);
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half b1 = __low2half(b);
+  half b2 = __high2half(b);
+  half eq1 = __half2float(a1) < __half2float(b1) ? true_half : false_half;
+  half eq2 = __half2float(a2) < __half2float(b2) ? true_half : false_half;
+  return __halves2half2(eq1, eq2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcmp_le(const half2& a,
+                                                    const half2& b) {
+  half true_half = half_impl::raw_uint16_to_half(0xffffu);
+  half false_half = half_impl::raw_uint16_to_half(0x0000u);
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half b1 = __low2half(b);
+  half b2 = __high2half(b);
+  half eq1 = __half2float(a1) <= __half2float(b1) ? true_half : false_half;
+  half eq2 = __half2float(a2) <= __half2float(b2) ? true_half : false_half;
+  return __halves2half2(eq1, eq2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pand(const half2& a,
+                                                 const half2& b) {
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half b1 = __low2half(b);
+  half b2 = __high2half(b);
+  half result1 = half_impl::raw_uint16_to_half(a1.x & b1.x);
+  half result2 = half_impl::raw_uint16_to_half(a2.x & b2.x);
+  return __halves2half2(result1, result2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 por(const half2& a,
+                                                const half2& b) {
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half b1 = __low2half(b);
+  half b2 = __high2half(b);
+  half result1 = half_impl::raw_uint16_to_half(a1.x | b1.x);
+  half result2 = half_impl::raw_uint16_to_half(a2.x | b2.x);
+  return __halves2half2(result1, result2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pxor(const half2& a,
+                                                 const half2& b) {
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half b1 = __low2half(b);
+  half b2 = __high2half(b);
+  half result1 = half_impl::raw_uint16_to_half(a1.x ^ b1.x);
+  half result2 = half_impl::raw_uint16_to_half(a2.x ^ b2.x);
+  return __halves2half2(result1, result2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pandnot(const half2& a,
+                                                    const half2& b) {
+  half a1 = __low2half(a);
+  half a2 = __high2half(a);
+  half b1 = __low2half(b);
+  half b2 = __high2half(b);
+  half result1 = half_impl::raw_uint16_to_half(a1.x & ~b1.x);
+  half result2 = half_impl::raw_uint16_to_half(a2.x & ~b2.x);
+  return __halves2half2(result1, result2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 padd(const half2& a,
+                                                 const half2& b) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hadd2(a, b);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float r1 = a1 + b1;
+  float r2 = a2 + b2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 psub(const half2& a,
+                                                 const half2& b) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hsub2(a, b);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float r1 = a1 - b1;
+  float r2 = a2 - b2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pnegate(const half2& a) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hneg2(a);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  return __floats2half2_rn(-a1, -a2);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pconj(const half2& a) { return a; }
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmul(const half2& a,
+                                                 const half2& b) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hmul2(a, b);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float r1 = a1 * b1;
+  float r2 = a2 * b2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmadd(const half2& a,
+                                                  const half2& b,
+                                                  const half2& c) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+   return __hfma2(a, b, c);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float c1 = __low2float(c);
+  float c2 = __high2float(c);
+  float r1 = a1 * b1 + c1;
+  float r2 = a2 * b2 + c2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pdiv(const half2& a,
+                                                 const half2& b) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __h2div(a, b);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float r1 = a1 / b1;
+  float r2 = a2 / b2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmin(const half2& a,
+                                                 const half2& b) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  __half r1 = a1 < b1 ? __low2half(a) : __low2half(b);
+  __half r2 = a2 < b2 ? __high2half(a) : __high2half(b);
+  return __halves2half2(r1, r2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmax(const half2& a,
+                                                 const half2& b) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  __half r1 = a1 > b1 ? __low2half(a) : __low2half(b);
+  __half r2 = a2 > b2 ? __high2half(a) : __high2half(b);
+  return __halves2half2(r1, r2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux(const half2& a) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hadd(__low2half(a), __high2half(a));
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  return Eigen::half(__float2half(a1 + a2));
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_max(const half2& a) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  __half first = __low2half(a);
+  __half second = __high2half(a);
+  return __hgt(first, second) ? first : second;
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  return a1 > a2 ? __low2half(a) : __high2half(a);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_min(const half2& a) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  __half first = __low2half(a);
+  __half second = __high2half(a);
+  return __hlt(first, second) ? first : second;
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  return a1 < a2 ? __low2half(a) : __high2half(a);
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_mul(const half2& a) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hmul(__low2half(a), __high2half(a));
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  return Eigen::half(__float2half(a1 * a2));
+#endif
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plog1p(const half2& a) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float r1 = log1pf(a1);
+  float r2 = log1pf(a2);
+  return __floats2half2_rn(r1, r2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pexpm1(const half2& a) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float r1 = expm1f(a1);
+  float r2 = expm1f(a2);
+  return __floats2half2_rn(r1, r2);
+}
+
+#if (EIGEN_CUDA_SDK_VER >= 80000 && defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)) || \
+  defined(EIGEN_HIP_DEVICE_COMPILE)
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+half2 plog(const half2& a) {
+  return h2log(a);
+}
+
+ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+half2 pexp(const half2& a) {
+  return h2exp(a);
+}
+
+ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+half2 psqrt(const half2& a) {
+  return h2sqrt(a);
+}
+
+ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+half2 prsqrt(const half2& a) {
+  return h2rsqrt(a);
+}
+
+#else
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 plog(const half2& a) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float r1 = logf(a1);
+  float r2 = logf(a2);
+  return __floats2half2_rn(r1, r2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pexp(const half2& a) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float r1 = expf(a1);
+  float r2 = expf(a2);
+  return __floats2half2_rn(r1, r2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 psqrt(const half2& a) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float r1 = sqrtf(a1);
+  float r2 = sqrtf(a2);
+  return __floats2half2_rn(r1, r2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 prsqrt(const half2& a) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float r1 = rsqrtf(a1);
+  float r2 = rsqrtf(a2);
+  return __floats2half2_rn(r1, r2);
+}
+#endif
+} // namespace
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
+pload<Packet4h2>(const Eigen::half* from) {
+  return *reinterpret_cast<const Packet4h2*>(from);
+}
+
+// unaligned load;
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
+ploadu<Packet4h2>(const Eigen::half* from) {
+  Packet4h2 r;
+  half2* p_alias = reinterpret_cast<half2*>(&r);
+  p_alias[0] = ploadu(from + 0);
+  p_alias[1] = ploadu(from + 2);
+  p_alias[2] = ploadu(from + 4);
+  p_alias[3] = ploadu(from + 6);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
+ploaddup<Packet4h2>(const Eigen::half* from) {
+  Packet4h2 r;
+  half2* p_alias = reinterpret_cast<half2*>(&r);
+  p_alias[0] = ploaddup(from + 0);
+  p_alias[1] = ploaddup(from + 1);
+  p_alias[2] = ploaddup(from + 2);
+  p_alias[3] = ploaddup(from + 3);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstore<Eigen::half>(
+    Eigen::half* to, const Packet4h2& from) {
+  *reinterpret_cast<Packet4h2*>(to) = from;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(
+    Eigen::half* to, const Packet4h2& from) {
+  const half2* from_alias = reinterpret_cast<const half2*>(&from);
+  pstoreu(to + 0,from_alias[0]);
+  pstoreu(to + 2,from_alias[1]);
+  pstoreu(to + 4,from_alias[2]);
+  pstoreu(to + 6,from_alias[3]);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4h2
+ploadt_ro<Packet4h2, Aligned>(const Eigen::half* from) {
+#if defined(EIGEN_GPU_HAS_LDG)
+  Packet4h2 r;
+  r = __ldg(reinterpret_cast<const Packet4h2*>(from));
+  return r;
+#else
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  r_alias[0] = ploadt_ro_aligned(from + 0);
+  r_alias[1] = ploadt_ro_aligned(from + 2);
+  r_alias[2] = ploadt_ro_aligned(from + 4);
+  r_alias[3] = ploadt_ro_aligned(from + 6);
+  return r;
+#endif
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet4h2
+ploadt_ro<Packet4h2, Unaligned>(const Eigen::half* from) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  r_alias[0] = ploadt_ro_unaligned(from + 0);
+  r_alias[1] = ploadt_ro_unaligned(from + 2);
+  r_alias[2] = ploadt_ro_unaligned(from + 4);
+  r_alias[3] = ploadt_ro_unaligned(from + 6);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
+pgather<Eigen::half, Packet4h2>(const Eigen::half* from, Index stride) {
+  Packet4h2 r;
+  half2* p_alias = reinterpret_cast<half2*>(&r);
+  p_alias[0] = __halves2half2(from[0 * stride], from[1 * stride]);
+  p_alias[1] = __halves2half2(from[2 * stride], from[3 * stride]);
+  p_alias[2] = __halves2half2(from[4 * stride], from[5 * stride]);
+  p_alias[3] = __halves2half2(from[6 * stride], from[7 * stride]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet4h2>(
+    Eigen::half* to, const Packet4h2& from, Index stride) {
+  const half2* from_alias = reinterpret_cast<const half2*>(&from);
+  pscatter(to + stride * 0, from_alias[0], stride);
+  pscatter(to + stride * 2, from_alias[1], stride);
+  pscatter(to + stride * 4, from_alias[2], stride);
+  pscatter(to + stride * 6, from_alias[3], stride);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half pfirst<Packet4h2>(
+    const Packet4h2& a) {
+  return pfirst(*(reinterpret_cast<const half2*>(&a)));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pabs<Packet4h2>(
+    const Packet4h2& a) {
+  Packet4h2 r;
+  half2* p_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  p_alias[0] = pabs(a_alias[0]);
+  p_alias[1] = pabs(a_alias[1]);
+  p_alias[2] = pabs(a_alias[2]);
+  p_alias[3] = pabs(a_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 ptrue<Packet4h2>(
+    const Packet4h2& /*a*/) {
+  half true_half = half_impl::raw_uint16_to_half(0xffffu);
+  return pset1<Packet4h2>(true_half);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pzero<Packet4h2>(const Packet4h2& /*a*/) {
+  half false_half = half_impl::raw_uint16_to_half(0x0000u);
+  return pset1<Packet4h2>(false_half);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose_double(
+    double* d_row0, double* d_row1, double* d_row2, double* d_row3,
+    double* d_row4, double* d_row5, double* d_row6, double* d_row7) {
+  double d_tmp;
+  d_tmp = d_row0[1];
+  d_row0[1] = d_row4[0];
+  d_row4[0] = d_tmp;
+
+  d_tmp = d_row1[1];
+  d_row1[1] = d_row5[0];
+  d_row5[0] = d_tmp;
+
+  d_tmp = d_row2[1];
+  d_row2[1] = d_row6[0];
+  d_row6[0] = d_tmp;
+
+  d_tmp = d_row3[1];
+  d_row3[1] = d_row7[0];
+  d_row7[0] = d_tmp;
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose_half2(
+    half2* f_row0, half2* f_row1, half2* f_row2, half2* f_row3) {
+  half2 f_tmp;
+  f_tmp = f_row0[1];
+  f_row0[1] = f_row2[0];
+  f_row2[0] = f_tmp;
+
+  f_tmp = f_row1[1];
+  f_row1[1] = f_row3[0];
+  f_row3[0] = f_tmp;
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
+ptranspose_half(half2& f0, half2& f1) {
+  __half a1 = __low2half(f0);
+  __half a2 = __high2half(f0);
+  __half b1 = __low2half(f1);
+  __half b2 = __high2half(f1);
+  f0 = __halves2half2(a1, b1);
+  f1 = __halves2half2(a2, b2);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
+ptranspose(PacketBlock<Packet4h2,8>& kernel) {
+  double* d_row0 = reinterpret_cast<double*>(&kernel.packet[0]);
+  double* d_row1 = reinterpret_cast<double*>(&kernel.packet[1]);
+  double* d_row2 = reinterpret_cast<double*>(&kernel.packet[2]);
+  double* d_row3 = reinterpret_cast<double*>(&kernel.packet[3]);
+  double* d_row4 = reinterpret_cast<double*>(&kernel.packet[4]);
+  double* d_row5 = reinterpret_cast<double*>(&kernel.packet[5]);
+  double* d_row6 = reinterpret_cast<double*>(&kernel.packet[6]);
+  double* d_row7 = reinterpret_cast<double*>(&kernel.packet[7]);
+  ptranspose_double(d_row0, d_row1, d_row2, d_row3,
+                    d_row4, d_row5, d_row6, d_row7);
+
+
+  half2* f_row0 = reinterpret_cast<half2*>(d_row0);
+  half2* f_row1 = reinterpret_cast<half2*>(d_row1);
+  half2* f_row2 = reinterpret_cast<half2*>(d_row2);
+  half2* f_row3 = reinterpret_cast<half2*>(d_row3);
+  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
+  ptranspose_half(f_row0[0], f_row1[0]);
+  ptranspose_half(f_row0[1], f_row1[1]);
+  ptranspose_half(f_row2[0], f_row3[0]);
+  ptranspose_half(f_row2[1], f_row3[1]);
+
+  f_row0 = reinterpret_cast<half2*>(d_row0 + 1);
+  f_row1 = reinterpret_cast<half2*>(d_row1 + 1);
+  f_row2 = reinterpret_cast<half2*>(d_row2 + 1);
+  f_row3 = reinterpret_cast<half2*>(d_row3 + 1);
+  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
+  ptranspose_half(f_row0[0], f_row1[0]);
+  ptranspose_half(f_row0[1], f_row1[1]);
+  ptranspose_half(f_row2[0], f_row3[0]);
+  ptranspose_half(f_row2[1], f_row3[1]);
+
+  f_row0 = reinterpret_cast<half2*>(d_row4);
+  f_row1 = reinterpret_cast<half2*>(d_row5);
+  f_row2 = reinterpret_cast<half2*>(d_row6);
+  f_row3 = reinterpret_cast<half2*>(d_row7);
+  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
+  ptranspose_half(f_row0[0], f_row1[0]);
+  ptranspose_half(f_row0[1], f_row1[1]);
+  ptranspose_half(f_row2[0], f_row3[0]);
+  ptranspose_half(f_row2[1], f_row3[1]);
+
+  f_row0 = reinterpret_cast<half2*>(d_row4 + 1);
+  f_row1 = reinterpret_cast<half2*>(d_row5 + 1);
+  f_row2 = reinterpret_cast<half2*>(d_row6 + 1);
+  f_row3 = reinterpret_cast<half2*>(d_row7 + 1);
+  ptranspose_half2(f_row0, f_row1, f_row2, f_row3);
+  ptranspose_half(f_row0[0], f_row1[0]);
+  ptranspose_half(f_row0[1], f_row1[1]);
+  ptranspose_half(f_row2[0], f_row3[0]);
+  ptranspose_half(f_row2[1], f_row3[1]);
+
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
+plset<Packet4h2>(const Eigen::half& a) {
+#if defined(EIGEN_HIP_DEVICE_COMPILE)
+
+  Packet4h2 r;
+  half2* p_alias = reinterpret_cast<half2*>(&r);
+  p_alias[0] = __halves2half2(a, __hadd(a, __float2half(1.0f)));
+  p_alias[1] = __halves2half2(__hadd(a, __float2half(2.0f)),
+                              __hadd(a, __float2half(3.0f)));
+  p_alias[2] = __halves2half2(__hadd(a, __float2half(4.0f)),
+                              __hadd(a, __float2half(5.0f)));
+  p_alias[3] = __halves2half2(__hadd(a, __float2half(6.0f)),
+                              __hadd(a, __float2half(7.0f)));
+  return r;
+#elif defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+
+  half2 b = pset1<half2>(a);
+  half2 c;
+  half2 half_offset0 = __halves2half2(__float2half(0.0f),__float2half(2.0f));
+  half2 half_offset1 = __halves2half2(__float2half(4.0f),__float2half(6.0f));
+
+  c = __hadd2(b, half_offset0);
+  r_alias[0] = plset(__low2half(c));
+  r_alias[1] = plset(__high2half(c));
+
+  c = __hadd2(b, half_offset1);
+  r_alias[2] = plset(__low2half(c));
+  r_alias[3] = plset(__high2half(c));
+
+  return r;
+
+#else
+  float f = __half2float(a);
+  Packet4h2 r;
+  half2* p_alias = reinterpret_cast<half2*>(&r);
+  p_alias[0] = __halves2half2(a, __float2half(f + 1.0f));
+  p_alias[1] = __halves2half2(__float2half(f + 2.0f), __float2half(f + 3.0f));
+  p_alias[2] = __halves2half2(__float2half(f + 4.0f), __float2half(f + 5.0f));
+  p_alias[3] = __halves2half2(__float2half(f + 6.0f), __float2half(f + 7.0f));
+  return r;
+#endif
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
+pselect<Packet4h2>(const Packet4h2& mask, const Packet4h2& a,
+                   const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* mask_alias = reinterpret_cast<const half2*>(&mask);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pselect(mask_alias[0], a_alias[0], b_alias[0]);
+  r_alias[1] = pselect(mask_alias[1], a_alias[1], b_alias[1]);
+  r_alias[2] = pselect(mask_alias[2], a_alias[2], b_alias[2]);
+  r_alias[3] = pselect(mask_alias[3], a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
+pcmp_eq<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pcmp_eq(a_alias[0], b_alias[0]);
+  r_alias[1] = pcmp_eq(a_alias[1], b_alias[1]);
+  r_alias[2] = pcmp_eq(a_alias[2], b_alias[2]);
+  r_alias[3] = pcmp_eq(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
+pcmp_lt<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pcmp_lt(a_alias[0], b_alias[0]);
+  r_alias[1] = pcmp_lt(a_alias[1], b_alias[1]);
+  r_alias[2] = pcmp_lt(a_alias[2], b_alias[2]);
+  r_alias[3] = pcmp_lt(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
+pcmp_le<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pcmp_le(a_alias[0], b_alias[0]);
+  r_alias[1] = pcmp_le(a_alias[1], b_alias[1]);
+  r_alias[2] = pcmp_le(a_alias[2], b_alias[2]);
+  r_alias[3] = pcmp_le(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pand<Packet4h2>(
+    const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pand(a_alias[0], b_alias[0]);
+  r_alias[1] = pand(a_alias[1], b_alias[1]);
+  r_alias[2] = pand(a_alias[2], b_alias[2]);
+  r_alias[3] = pand(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 por<Packet4h2>(
+    const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = por(a_alias[0], b_alias[0]);
+  r_alias[1] = por(a_alias[1], b_alias[1]);
+  r_alias[2] = por(a_alias[2], b_alias[2]);
+  r_alias[3] = por(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pxor<Packet4h2>(
+    const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pxor(a_alias[0], b_alias[0]);
+  r_alias[1] = pxor(a_alias[1], b_alias[1]);
+  r_alias[2] = pxor(a_alias[2], b_alias[2]);
+  r_alias[3] = pxor(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
+pandnot<Packet4h2>(const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pandnot(a_alias[0], b_alias[0]);
+  r_alias[1] = pandnot(a_alias[1], b_alias[1]);
+  r_alias[2] = pandnot(a_alias[2], b_alias[2]);
+  r_alias[3] = pandnot(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 padd<Packet4h2>(
+    const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = padd(a_alias[0], b_alias[0]);
+  r_alias[1] = padd(a_alias[1], b_alias[1]);
+  r_alias[2] = padd(a_alias[2], b_alias[2]);
+  r_alias[3] = padd(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 psub<Packet4h2>(
+    const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = psub(a_alias[0], b_alias[0]);
+  r_alias[1] = psub(a_alias[1], b_alias[1]);
+  r_alias[2] = psub(a_alias[2], b_alias[2]);
+  r_alias[3] = psub(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pnegate(const Packet4h2& a) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  r_alias[0] = pnegate(a_alias[0]);
+  r_alias[1] = pnegate(a_alias[1]);
+  r_alias[2] = pnegate(a_alias[2]);
+  r_alias[3] = pnegate(a_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pconj(const Packet4h2& a) {
+  return a;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmul<Packet4h2>(
+    const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pmul(a_alias[0], b_alias[0]);
+  r_alias[1] = pmul(a_alias[1], b_alias[1]);
+  r_alias[2] = pmul(a_alias[2], b_alias[2]);
+  r_alias[3] = pmul(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmadd<Packet4h2>(
+    const Packet4h2& a, const Packet4h2& b, const Packet4h2& c) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  const half2* c_alias = reinterpret_cast<const half2*>(&c);
+  r_alias[0] = pmadd(a_alias[0], b_alias[0], c_alias[0]);
+  r_alias[1] = pmadd(a_alias[1], b_alias[1], c_alias[1]);
+  r_alias[2] = pmadd(a_alias[2], b_alias[2], c_alias[2]);
+  r_alias[3] = pmadd(a_alias[3], b_alias[3], c_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pdiv<Packet4h2>(
+    const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pdiv(a_alias[0], b_alias[0]);
+  r_alias[1] = pdiv(a_alias[1], b_alias[1]);
+  r_alias[2] = pdiv(a_alias[2], b_alias[2]);
+  r_alias[3] = pdiv(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmin<Packet4h2>(
+    const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pmin(a_alias[0], b_alias[0]);
+  r_alias[1] = pmin(a_alias[1], b_alias[1]);
+  r_alias[2] = pmin(a_alias[2], b_alias[2]);
+  r_alias[3] = pmin(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pmax<Packet4h2>(
+    const Packet4h2& a, const Packet4h2& b) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  const half2* b_alias = reinterpret_cast<const half2*>(&b);
+  r_alias[0] = pmax(a_alias[0], b_alias[0]);
+  r_alias[1] = pmax(a_alias[1], b_alias[1]);
+  r_alias[2] = pmax(a_alias[2], b_alias[2]);
+  r_alias[3] = pmax(a_alias[3], b_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux<Packet4h2>(
+    const Packet4h2& a) {
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+
+  return predux(a_alias[0]) + predux(a_alias[1]) +
+         predux(a_alias[2]) + predux(a_alias[3]);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_max<Packet4h2>(
+    const Packet4h2& a) {
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  half2 m0 = __halves2half2(predux_max(a_alias[0]),
+                            predux_max(a_alias[1]));
+  half2 m1 = __halves2half2(predux_max(a_alias[2]),
+                            predux_max(a_alias[3]));
+  __half first  = predux_max(m0);
+  __half second = predux_max(m1);
+#if defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
+  return (__hgt(first, second) ? first : second);
+#else
+  float ffirst  = __half2float(first);
+  float fsecond = __half2float(second);
+  return (ffirst > fsecond)? first: second;
+#endif
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_min<Packet4h2>(
+    const Packet4h2& a) {
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  half2 m0 = __halves2half2(predux_min(a_alias[0]),
+                            predux_min(a_alias[1]));
+  half2 m1 = __halves2half2(predux_min(a_alias[2]),
+                            predux_min(a_alias[3]));
+  __half first  = predux_min(m0);
+  __half second = predux_min(m1);
+#if defined(EIGEN_CUDA_HAS_FP16_ARITHMETIC)
+  return (__hlt(first, second) ? first : second);
+#else
+  float ffirst  = __half2float(first);
+  float fsecond = __half2float(second);
+  return (ffirst < fsecond)? first: second;
+#endif
+}
+
+// likely overflow/underflow
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet4h2>(
+    const Packet4h2& a) {
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  return predux_mul(pmul(pmul(a_alias[0], a_alias[1]),
+                                       pmul(a_alias[2], a_alias[3])));
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
+plog1p<Packet4h2>(const Packet4h2& a) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  r_alias[0] = plog1p(a_alias[0]);
+  r_alias[1] = plog1p(a_alias[1]);
+  r_alias[2] = plog1p(a_alias[2]);
+  r_alias[3] = plog1p(a_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
+pexpm1<Packet4h2>(const Packet4h2& a) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  r_alias[0] = pexpm1(a_alias[0]);
+  r_alias[1] = pexpm1(a_alias[1]);
+  r_alias[2] = pexpm1(a_alias[2]);
+  r_alias[3] = pexpm1(a_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 plog<Packet4h2>(const Packet4h2& a) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  r_alias[0] = plog(a_alias[0]);
+  r_alias[1] = plog(a_alias[1]);
+  r_alias[2] = plog(a_alias[2]);
+  r_alias[3] = plog(a_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pexp<Packet4h2>(const Packet4h2& a) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  r_alias[0] = pexp(a_alias[0]);
+  r_alias[1] = pexp(a_alias[1]);
+  r_alias[2] = pexp(a_alias[2]);
+  r_alias[3] = pexp(a_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 psqrt<Packet4h2>(const Packet4h2& a) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  r_alias[0] = psqrt(a_alias[0]);
+  r_alias[1] = psqrt(a_alias[1]);
+  r_alias[2] = psqrt(a_alias[2]);
+  r_alias[3] = psqrt(a_alias[3]);
+  return r;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2
+prsqrt<Packet4h2>(const Packet4h2& a) {
+  Packet4h2 r;
+  half2* r_alias = reinterpret_cast<half2*>(&r);
+  const half2* a_alias = reinterpret_cast<const half2*>(&a);
+  r_alias[0] = prsqrt(a_alias[0]);
+  r_alias[1] = prsqrt(a_alias[1]);
+  r_alias[2] = prsqrt(a_alias[2]);
+  r_alias[3] = prsqrt(a_alias[3]);
+  return r;
+}
+
+// The following specialized padd, pmul, pdiv, pmin, pmax, pset1 are needed for
+// the implementation of GPU half reduction.
+template<>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 padd<half2>(const half2& a,
+                                                        const half2& b) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hadd2(a, b);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float r1 = a1 + b1;
+  float r2 = a2 + b2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmul<half2>(const half2& a,
+                                                        const half2& b) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __hmul2(a, b);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float r1 = a1 * b1;
+  float r2 = a2 * b2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pdiv<half2>(const half2& a,
+                                                        const half2& b) {
+#if defined(EIGEN_GPU_HAS_FP16_ARITHMETIC)
+  return __h2div(a, b);
+#else
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  float r1 = a1 / b1;
+  float r2 = a2 / b2;
+  return __floats2half2_rn(r1, r2);
+#endif
+}
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmin<half2>(const half2& a,
+                                                        const half2& b) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  __half r1 = a1 < b1 ? __low2half(a) : __low2half(b);
+  __half r2 = a2 < b2 ? __high2half(a) : __high2half(b);
+  return __halves2half2(r1, r2);
+}
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pmax<half2>(const half2& a,
+                                                        const half2& b) {
+  float a1 = __low2float(a);
+  float a2 = __high2float(a);
+  float b1 = __low2float(b);
+  float b2 = __high2float(b);
+  __half r1 = a1 > b1 ? __low2half(a) : __low2half(b);
+  __half r2 = a2 > b2 ? __high2half(a) : __high2half(b);
+  return __halves2half2(r1, r2);
+}
+
+#endif // (defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)) && defined(EIGEN_GPU_COMPILE_PHASE)
+
+#undef EIGEN_GPU_HAS_LDG
+#undef EIGEN_CUDA_HAS_FP16_ARITHMETIC
+#undef EIGEN_GPU_HAS_FP16_ARITHMETIC
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+
+#endif // EIGEN_PACKET_MATH_GPU_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/GPU/Tuple.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/GPU/Tuple.h
new file mode 100644
index 0000000000000000000000000000000000000000..e223ca1d2c13abe5bb6f8443e6ba26ca1f78bd23
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/GPU/Tuple.h
@@ -0,0 +1,302 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2021 The Eigen Team
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TUPLE_GPU
+#define EIGEN_TUPLE_GPU
+
+#include <type_traits>
+#include <utility>
+
+// This is a replacement of std::tuple that can be used in device code.
+
+namespace Eigen {
+namespace internal {
+namespace tuple_impl {
+
+// Internal tuple implementation.
+template<size_t N, typename... Types>
+class TupleImpl;
+
+// Generic recursive tuple.
+template<size_t N, typename T1, typename... Ts>
+class TupleImpl<N, T1, Ts...> {
+ public:
+  // Tuple may contain Eigen types.
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW
+  
+  // Default constructor, enable if all types are default-constructible.
+  template<typename U1 = T1, typename EnableIf = std::enable_if_t<
+      std::is_default_constructible<U1>::value
+      && reduce_all<std::is_default_constructible<Ts>::value...>::value
+    >>
+  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC
+  TupleImpl() : head_{}, tail_{} {}
+ 
+  // Element constructor.
+  template<typename U1, typename... Us, 
+           // Only enable if...
+           typename EnableIf = std::enable_if_t<
+              // the number of input arguments match, and ...
+              sizeof...(Us) == sizeof...(Ts) && (
+                // this does not look like a copy/move constructor.
+                N > 1 || std::is_convertible<U1, T1>::value)
+           >>
+  EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC
+  TupleImpl(U1&& arg1, Us&&... args) 
+    : head_(std::forward<U1>(arg1)), tail_(std::forward<Us>(args)...) {}
+ 
+  // The first stored value. 
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  T1& head() {
+    return head_;
+  }
+  
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  const T1& head() const {
+    return head_;
+  }
+  
+  // The tail values.
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  TupleImpl<N-1, Ts...>& tail() {
+    return tail_;
+  }
+  
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  const TupleImpl<N-1, Ts...>& tail() const {
+    return tail_;
+  }
+  
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void swap(TupleImpl& other) {
+    using numext::swap;
+    swap(head_, other.head_);
+    swap(tail_, other.tail_);
+  }
+  
+  template<typename... UTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  TupleImpl& operator=(const TupleImpl<N, UTypes...>& other) {
+    head_ = other.head_;
+    tail_ = other.tail_;
+    return *this;
+  }
+  
+  template<typename... UTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  TupleImpl& operator=(TupleImpl<N, UTypes...>&& other) {
+    head_ = std::move(other.head_);
+    tail_ = std::move(other.tail_);
+    return *this;
+  }
+  
+ private:
+  // Allow related tuples to reference head_/tail_.
+  template<size_t M, typename... UTypes>
+  friend class TupleImpl;
+ 
+  T1 head_;
+  TupleImpl<N-1, Ts...> tail_;
+};
+
+// Empty tuple specialization.
+template<>
+class TupleImpl<size_t(0)> {};
+
+template<typename TupleType>
+struct is_tuple : std::false_type {};
+
+template<typename... Types>
+struct is_tuple< TupleImpl<sizeof...(Types), Types...> > : std::true_type {};
+
+// Gets an element from a tuple.
+template<size_t Idx, typename T1, typename... Ts>
+struct tuple_get_impl {
+  using TupleType = TupleImpl<sizeof...(Ts) + 1, T1, Ts...>;
+  using ReturnType = typename tuple_get_impl<Idx - 1, Ts...>::ReturnType;
+  
+  static EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  ReturnType& run(TupleType& tuple) {
+    return tuple_get_impl<Idx-1, Ts...>::run(tuple.tail());
+  }
+
+  static EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  const ReturnType& run(const TupleType& tuple) {
+    return tuple_get_impl<Idx-1, Ts...>::run(tuple.tail());
+  }
+};
+
+// Base case, getting the head element.
+template<typename T1, typename... Ts>
+struct tuple_get_impl<0, T1, Ts...> {
+  using TupleType = TupleImpl<sizeof...(Ts) + 1, T1, Ts...>;
+  using ReturnType = T1;
+
+  static EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  T1& run(TupleType& tuple) {
+    return tuple.head();
+  }
+
+  static EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  const T1& run(const TupleType& tuple) {
+    return tuple.head();
+  }
+};
+
+// Concatenates N Tuples.
+template<size_t NTuples, typename... Tuples>
+struct tuple_cat_impl;
+
+template<size_t NTuples, size_t N1, typename... Args1, size_t N2, typename... Args2, typename... Tuples>
+struct tuple_cat_impl<NTuples, TupleImpl<N1, Args1...>, TupleImpl<N2, Args2...>, Tuples...> {
+  using TupleType1 = TupleImpl<N1, Args1...>;
+  using TupleType2 = TupleImpl<N2, Args2...>;
+  using MergedTupleType = TupleImpl<N1 + N2, Args1..., Args2...>;
+  
+  using ReturnType = typename tuple_cat_impl<NTuples-1, MergedTupleType, Tuples...>::ReturnType;
+  
+  // Uses the index sequences to extract and merge elements from tuple1 and tuple2,
+  // then recursively calls again.
+  template<typename Tuple1, size_t... I1s, typename Tuple2, size_t... I2s, typename... MoreTuples>
+  static EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  ReturnType run(Tuple1&& tuple1, std::index_sequence<I1s...>,
+                 Tuple2&& tuple2, std::index_sequence<I2s...>,
+                 MoreTuples&&... tuples) {
+    return tuple_cat_impl<NTuples-1, MergedTupleType, Tuples...>::run(
+        MergedTupleType(tuple_get_impl<I1s, Args1...>::run(std::forward<Tuple1>(tuple1))...,
+                        tuple_get_impl<I2s, Args2...>::run(std::forward<Tuple2>(tuple2))...),
+        std::forward<MoreTuples>(tuples)...);
+  }
+  
+  // Concatenates the first two tuples.
+  template<typename Tuple1, typename Tuple2, typename... MoreTuples>
+  static EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  ReturnType run(Tuple1&& tuple1, Tuple2&& tuple2, MoreTuples&&... tuples) {
+    return run(std::forward<Tuple1>(tuple1), std::make_index_sequence<N1>{},
+               std::forward<Tuple2>(tuple2), std::make_index_sequence<N2>{},
+               std::forward<MoreTuples>(tuples)...);
+  }
+};
+
+// Base case with a single tuple.
+template<size_t N, typename... Args>
+struct tuple_cat_impl<1, TupleImpl<N, Args...> > { 
+  using ReturnType = TupleImpl<N, Args...>;
+  
+  template<typename Tuple1>
+  static EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  ReturnType run(Tuple1&& tuple1) {
+    return tuple1;
+  }
+};
+
+// Special case of no tuples.
+template<>
+struct tuple_cat_impl<0> { 
+  using ReturnType = TupleImpl<0>;
+  static EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  ReturnType run() {return ReturnType{}; }
+};
+
+// For use in make_tuple, unwraps a reference_wrapper.
+template <typename T>
+struct unwrap_reference_wrapper { using type = T; };
+ 
+template <typename T>
+struct unwrap_reference_wrapper<std::reference_wrapper<T> > { using type = T&; };
+
+// For use in make_tuple, decays a type and unwraps a reference_wrapper.
+template <typename T>
+struct unwrap_decay {
+  using type = typename unwrap_reference_wrapper<typename std::decay<T>::type>::type;
+};
+
+/**
+ * Utility for determining a tuple's size.
+ */
+template<typename Tuple>
+struct tuple_size;
+
+template<typename... Types >
+struct tuple_size< TupleImpl<sizeof...(Types), Types...> > : std::integral_constant<size_t, sizeof...(Types)> {};
+
+/**
+ * Gets an element of a tuple.
+ * \tparam Idx index of the element.
+ * \tparam Types ... tuple element types.
+ * \param tuple the tuple.
+ * \return a reference to the desired element.
+ */
+template<size_t Idx, typename... Types>
+EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const typename tuple_get_impl<Idx, Types...>::ReturnType&
+get(const TupleImpl<sizeof...(Types), Types...>& tuple) {
+  return tuple_get_impl<Idx, Types...>::run(tuple);
+}
+
+template<size_t Idx, typename... Types>
+EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename tuple_get_impl<Idx, Types...>::ReturnType&
+get(TupleImpl<sizeof...(Types), Types...>& tuple) {
+  return tuple_get_impl<Idx, Types...>::run(tuple);
+}
+
+/**
+ * Concatenate multiple tuples.
+ * \param tuples ... list of tuples.
+ * \return concatenated tuple.
+ */
+template<typename... Tuples,
+          typename EnableIf = std::enable_if_t<
+            internal::reduce_all<
+              is_tuple<typename std::decay<Tuples>::type>::value...>::value>>
+EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename tuple_cat_impl<sizeof...(Tuples), typename std::decay<Tuples>::type...>::ReturnType
+tuple_cat(Tuples&&... tuples) {
+  return tuple_cat_impl<sizeof...(Tuples), typename std::decay<Tuples>::type...>::run(std::forward<Tuples>(tuples)...);
+}
+
+/**
+ * Tie arguments together into a tuple.
+ */
+template <typename... Args, typename ReturnType = TupleImpl<sizeof...(Args), Args&...> >
+EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+ReturnType tie(Args&... args) EIGEN_NOEXCEPT {
+    return ReturnType{args...};
+}
+
+/**
+ * Create a tuple of l-values with the supplied arguments.
+ */
+template <typename... Args, typename ReturnType = TupleImpl<sizeof...(Args), typename unwrap_decay<Args>::type...> >
+EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+ReturnType make_tuple(Args&&... args) {
+  return ReturnType{std::forward<Args>(args)...};
+}
+
+/**
+ * Forward a set of arguments as a tuple.
+ */
+template <typename... Args>
+EIGEN_CONSTEXPR EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+TupleImpl<sizeof...(Args), Args...> forward_as_tuple(Args&&... args) {
+  return TupleImpl<sizeof...(Args), Args...>(std::forward<Args>(args)...);
+}
+
+/**
+ * Alternative to std::tuple that can be used on device.
+ */
+template<typename... Types>
+using tuple = TupleImpl<sizeof...(Types), Types...>;
+
+}  // namespace tuple_impl
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_TUPLE_GPU
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/GPU/TypeCasting.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/GPU/TypeCasting.h
new file mode 100644
index 0000000000000000000000000000000000000000..6e8ba27c4f07f77915a11dc016dd05b90d09338f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/GPU/TypeCasting.h
@@ -0,0 +1,81 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_GPU_H
+#define EIGEN_TYPE_CASTING_GPU_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#if (defined(EIGEN_HAS_CUDA_FP16) && defined(EIGEN_CUDA_ARCH) && EIGEN_CUDA_ARCH >= 300) || \
+    (defined(EIGEN_HAS_HIP_FP16) && defined(EIGEN_HIP_DEVICE_COMPILE))
+
+template <>
+struct type_casting_traits<Eigen::half, float> {
+  enum {
+    VectorizedCast = 1,
+    SrcCoeffRatio = 1,
+    TgtCoeffRatio = 2
+  };
+};
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcast<half2, float4>(const half2& a, const half2& b) {
+  float2 r1 = __half22float2(a);
+  float2 r2 = __half22float2(b);
+  return make_float4(r1.x, r1.y, r2.x, r2.y);
+}
+
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4h2 pcast<float4, Packet4h2>(const float4& a, const float4& b) {
+  Packet4h2 r;
+  half2* r_alias=reinterpret_cast<half2*>(&r);
+  r_alias[0]=__floats2half2_rn(a.x,a.y);
+  r_alias[1]=__floats2half2_rn(a.z,a.w);
+  r_alias[2]=__floats2half2_rn(b.x,b.y);
+  r_alias[3]=__floats2half2_rn(b.z,b.w);
+  return r;
+}
+
+template <>
+struct type_casting_traits<float, Eigen::half> {
+  enum {
+    VectorizedCast = 1,
+    SrcCoeffRatio = 2,
+    TgtCoeffRatio = 1
+  };
+};
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float4 pcast<Packet4h2, float4>(const Packet4h2& a) {
+  // Simply discard the second half of the input
+  float4 r;
+  const half2* a_alias=reinterpret_cast<const half2*>(&a);
+  float2 r1 = __half22float2(a_alias[0]);
+  float2 r2 = __half22float2(a_alias[1]);
+  r.x=static_cast<float>(r1.x);
+  r.y=static_cast<float>(r1.y);
+  r.z=static_cast<float>(r2.x);
+  r.w=static_cast<float>(r2.y);
+  return r;
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE half2 pcast<float4, half2>(const float4& a) {
+  // Simply discard the second half of the input
+  return __floats2half2_rn(a.x, a.y);
+}
+
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TYPE_CASTING_GPU_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/HIP/hcc/math_constants.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/HIP/hcc/math_constants.h
new file mode 100644
index 0000000000000000000000000000000000000000..25375a0a420aaee8d7dd9fb6ebc88eca4ee42adc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/HIP/hcc/math_constants.h
@@ -0,0 +1,23 @@
+/*
+ * math_constants.h - 
+ *  HIP equivalent of the CUDA header of the same name
+ */
+
+#ifndef __MATH_CONSTANTS_H__
+#define __MATH_CONSTANTS_H__
+
+/* single precision constants */
+
+#define HIPRT_INF_F        __int_as_float(0x7f800000)
+#define HIPRT_NAN_F        __int_as_float(0x7fffffff)
+#define HIPRT_MIN_DENORM_F __int_as_float(0x00000001)
+#define HIPRT_MAX_NORMAL_F __int_as_float(0x7f7fffff)
+#define HIPRT_NEG_ZERO_F   __int_as_float(0x80000000)
+#define HIPRT_ZERO_F       0.0f
+#define HIPRT_ONE_F        1.0f
+
+/* double precision constants */
+#define HIPRT_INF          __hiloint2double(0x7ff00000, 0x00000000)
+#define HIPRT_NAN          __hiloint2double(0xfff80000, 0x00000000)
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/HVX/GeneralBlockPanelKernel.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/HVX/GeneralBlockPanelKernel.h
new file mode 100644
index 0000000000000000000000000000000000000000..51f37fa414815361de8a95f1c95faef007cfa07e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/HVX/GeneralBlockPanelKernel.h
@@ -0,0 +1,46 @@
+#ifndef EIGEN_HVX_GENERAL_BLOCK_KERNEL_H
+#define EIGEN_HVX_GENERAL_BLOCK_KERNEL_H
+
+// Only support 128B HVX now.
+// Floating-point operations are only supported since V68.
+#if defined __HVX__ && (__HVX_LENGTH__ == 128) && __HVX_ARCH__ >= 68
+
+namespace Eigen {
+namespace internal {
+
+template <bool ConjLhs_, bool ConjRhs_, int PacketSize_>
+class gebp_traits<float, float, ConjLhs_, ConjRhs_, Architecture::Target,
+                  PacketSize_>
+    : public gebp_traits<float, float, ConjLhs_, ConjRhs_,
+                         Architecture::Generic, PacketSize_> {
+ public:
+  typedef Packet32qf AccPacket;
+
+  EIGEN_STRONG_INLINE void initAcc(Packet32qf& p) { p = pzero<Packet32qf>(p); }
+
+  template <typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const Packet32f& a, const Packet32f& b,
+                                Packet32qf& c, Packet32f& /*tmp*/,
+                                const LaneIdType&) const {
+    c = pmadd_f32_to_qf32(a, b, c);
+  }
+
+  template <typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const Packet32f& a,
+                                const QuadPacket<Packet32f>& b, Packet32qf& c,
+                                Packet32f& tmp, const LaneIdType& lane) const {
+    madd(a, b.get(lane), c, tmp, lane);
+  }
+
+  EIGEN_STRONG_INLINE void acc(const Packet32qf& c, const Packet32f& alpha,
+                               Packet32f& r) const {
+    r = pmadd_qf32_to_f32(c, alpha, r);
+  }
+};
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // __HVX__ && (__HVX_LENGTH__ == 128) && __HVX_ARCH__ >= 68
+
+#endif  // EIGEN_HVX_GENERAL_BLOCK_KERNEL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/HVX/PacketMath.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/HVX/PacketMath.h
new file mode 100644
index 0000000000000000000000000000000000000000..cc8722fdda908c4a816609b314064d56c71d63b0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/HVX/PacketMath.h
@@ -0,0 +1,548 @@
+
+#ifndef EIGEN_HVX_PACKET_MATH_H
+#define EIGEN_HVX_PACKET_MATH_H
+
+// Only support 128B HVX now.
+// Floating-point operations are supported only since V68.
+#if defined __HVX__ && (__HVX_LENGTH__ == 128) && __HVX_ARCH__ >= 68
+
+// All the floating-point operations do not support IEEE standard.
+// From HVX document:
+//   There is no concept of infinity or NaN. QFloat saturates to maximum
+//   exponent with maximum positive or minimum negative significand.
+
+#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
+#endif
+
+namespace Eigen {
+namespace internal {
+
+EIGEN_STRONG_INLINE HVX_Vector HVX_load(const void* mem) {
+  return *((HVX_Vector*)mem);
+}
+
+EIGEN_STRONG_INLINE HVX_Vector HVX_loadu(const void* mem) {
+  return *((HVX_UVector*)mem);
+}
+
+EIGEN_STRONG_INLINE void HVX_store(void* mem, HVX_Vector v) {
+  *((HVX_Vector*)mem) = v;
+}
+
+EIGEN_STRONG_INLINE void HVX_storeu(void* mem, HVX_Vector v) {
+  *((HVX_UVector*)mem) = v;
+}
+
+// Hexagon compiler uses same HVX_Vector to represent all HVX vector types.
+// Wrap different vector type (float32, int32, etc) to different class with
+// explicit constructor and casting back-and-force to HVX_Vector.
+template <int unique_id>
+class HVXPacket {
+ public:
+  HVXPacket() = default;
+  static HVXPacket Create(HVX_Vector v) { return HVXPacket(v); }
+  HVX_Vector Get() const { return m_val; }
+
+ private:
+  explicit HVXPacket(HVX_Vector v) : m_val(v) {}
+  HVX_Vector m_val = Q6_V_vzero();
+};
+
+typedef HVXPacket<0> Packet32f;   // float32
+typedef HVXPacket<1> Packet32qf;  // qfloat32
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet32f type;
+  typedef Packet32f half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 32,
+  };
+};
+
+template <>
+struct unpacket_traits<Packet32f> {
+  typedef float type;
+  typedef Packet32f half;
+  enum {
+    size = 32,
+    alignment = Aligned128,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+// float32 operations.
+template <>
+EIGEN_STRONG_INLINE Packet32f pset1<Packet32f>(const float& from) {
+  union {
+    float f;
+    int32_t i;
+  } u;
+  u.f = from;
+  return Packet32f::Create(Q6_V_vsplat_R(u.i));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f pload<Packet32f>(const float* from) {
+  return Packet32f::Create(HVX_load(from));
+}
+template <>
+EIGEN_STRONG_INLINE Packet32f ploadu<Packet32f>(const float* from) {
+  return Packet32f::Create(HVX_loadu(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet32f& from) {
+  HVX_store(to, from.Get());
+}
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet32f& from) {
+  HVX_storeu(to, from.Get());
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f pmul<Packet32f>(const Packet32f& a,
+                                              const Packet32f& b) {
+  return Packet32f::Create(
+      Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(a.Get(), b.Get())));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f padd<Packet32f>(const Packet32f& a,
+                                              const Packet32f& b) {
+  return Packet32f::Create(
+      Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_VsfVsf(a.Get(), b.Get())));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f psub<Packet32f>(const Packet32f& a,
+                                              const Packet32f& b) {
+  return Packet32f::Create(
+      Q6_Vsf_equals_Vqf32(Q6_Vqf32_vsub_VsfVsf(a.Get(), b.Get())));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f pnegate(const Packet32f& a) {
+  return psub(Packet32f::Create(Q6_V_vzero()), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f pcmp_le(const Packet32f& a, const Packet32f& b) {
+  HVX_Vector v_true = Q6_Vb_vsplat_R(0xff);
+  HVX_VectorPred pred = Q6_Q_vcmp_gt_VsfVsf(a.Get(), b.Get());
+  return Packet32f::Create(Q6_V_vmux_QVV(pred, Q6_V_vzero(), v_true));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f pcmp_eq(const Packet32f& a, const Packet32f& b) {
+  HVX_Vector v_true = Q6_Vb_vsplat_R(0xff);
+  HVX_VectorPred pred = Q6_Q_vcmp_eq_VwVw(a.Get(), b.Get());
+  return Packet32f::Create(Q6_V_vmux_QVV(pred, v_true, Q6_V_vzero()));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f pcmp_lt(const Packet32f& a, const Packet32f& b) {
+  HVX_Vector v_true = Q6_Vb_vsplat_R(0xff);
+  HVX_VectorPred pred = Q6_Q_vcmp_gt_VsfVsf(b.Get(), a.Get());
+  return Packet32f::Create(Q6_V_vmux_QVV(pred, v_true, Q6_V_vzero()));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f pcmp_lt_or_nan(const Packet32f& a,
+                                             const Packet32f& b) {
+  HVX_Vector v_true = Q6_Vb_vsplat_R(0xff);
+  HVX_VectorPred pred = Q6_Q_vcmp_gt_VsfVsf(b.Get(), a.Get());
+  return Packet32f::Create(Q6_V_vmux_QVV(pred, v_true, Q6_V_vzero()));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f pabs(const Packet32f& a) {
+  HVX_VectorPred pred = Q6_Q_vcmp_gt_VsfVsf(a.Get(), Q6_V_vzero());
+  return Packet32f::Create(Q6_V_vmux_QVV(pred, a.Get(), pnegate(a).Get()));
+}
+
+template <>
+EIGEN_STRONG_INLINE float pfirst(const Packet32f& a) {
+  float vsf[32] __attribute__((aligned(128)));
+  pstore(vsf, a);
+  return vsf[0];
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet32f, 4>& kernel) {
+  // Shuffle the 32-bit lanes.
+  HVX_VectorPair v_0_1_0 =
+      Q6_W_vshuff_VVR(kernel.packet[1].Get(), kernel.packet[0].Get(), -4);
+  HVX_VectorPair v_0_3_2 =
+      Q6_W_vshuff_VVR(kernel.packet[3].Get(), kernel.packet[2].Get(), -4);
+
+  // Shuffle the 64-bit lanes.
+  HVX_VectorPair v_1_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_3_2),
+                                           HEXAGON_HVX_GET_V0(v_0_1_0), -8);
+  HVX_VectorPair v_1_3_2 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_3_2),
+                                           HEXAGON_HVX_GET_V1(v_0_1_0), -8);
+
+  kernel.packet[0] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_1_1_0));
+  kernel.packet[1] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_1_1_0));
+  kernel.packet[2] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_1_3_2));
+  kernel.packet[3] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_1_3_2));
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet32f, 32>& kernel) {
+  // Shuffle the 32-bit lanes.
+  HVX_VectorPair v_0_1_0 =
+      Q6_W_vshuff_VVR(kernel.packet[1].Get(), kernel.packet[0].Get(), -4);
+  HVX_VectorPair v_0_3_2 =
+      Q6_W_vshuff_VVR(kernel.packet[3].Get(), kernel.packet[2].Get(), -4);
+  HVX_VectorPair v_0_5_4 =
+      Q6_W_vshuff_VVR(kernel.packet[5].Get(), kernel.packet[4].Get(), -4);
+  HVX_VectorPair v_0_7_6 =
+      Q6_W_vshuff_VVR(kernel.packet[7].Get(), kernel.packet[6].Get(), -4);
+  HVX_VectorPair v_0_9_8 =
+      Q6_W_vshuff_VVR(kernel.packet[9].Get(), kernel.packet[8].Get(), -4);
+  HVX_VectorPair v_0_11_10 =
+      Q6_W_vshuff_VVR(kernel.packet[11].Get(), kernel.packet[10].Get(), -4);
+  HVX_VectorPair v_0_13_12 =
+      Q6_W_vshuff_VVR(kernel.packet[13].Get(), kernel.packet[12].Get(), -4);
+  HVX_VectorPair v_0_15_14 =
+      Q6_W_vshuff_VVR(kernel.packet[15].Get(), kernel.packet[14].Get(), -4);
+  HVX_VectorPair v_0_17_16 =
+      Q6_W_vshuff_VVR(kernel.packet[17].Get(), kernel.packet[16].Get(), -4);
+  HVX_VectorPair v_0_19_18 =
+      Q6_W_vshuff_VVR(kernel.packet[19].Get(), kernel.packet[18].Get(), -4);
+  HVX_VectorPair v_0_21_20 =
+      Q6_W_vshuff_VVR(kernel.packet[21].Get(), kernel.packet[20].Get(), -4);
+  HVX_VectorPair v_0_23_22 =
+      Q6_W_vshuff_VVR(kernel.packet[23].Get(), kernel.packet[22].Get(), -4);
+  HVX_VectorPair v_0_25_24 =
+      Q6_W_vshuff_VVR(kernel.packet[25].Get(), kernel.packet[24].Get(), -4);
+  HVX_VectorPair v_0_27_26 =
+      Q6_W_vshuff_VVR(kernel.packet[27].Get(), kernel.packet[26].Get(), -4);
+  HVX_VectorPair v_0_29_28 =
+      Q6_W_vshuff_VVR(kernel.packet[29].Get(), kernel.packet[28].Get(), -4);
+  HVX_VectorPair v_0_31_30 =
+      Q6_W_vshuff_VVR(kernel.packet[31].Get(), kernel.packet[30].Get(), -4);
+
+  // Shuffle the 64-bit lanes.
+  HVX_VectorPair v_1_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_3_2),
+                                           HEXAGON_HVX_GET_V0(v_0_1_0), -8);
+  HVX_VectorPair v_1_3_2 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_3_2),
+                                           HEXAGON_HVX_GET_V1(v_0_1_0), -8);
+  HVX_VectorPair v_1_5_4 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_7_6),
+                                           HEXAGON_HVX_GET_V0(v_0_5_4), -8);
+  HVX_VectorPair v_1_7_6 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_7_6),
+                                           HEXAGON_HVX_GET_V1(v_0_5_4), -8);
+  HVX_VectorPair v_1_9_8 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_11_10),
+                                           HEXAGON_HVX_GET_V0(v_0_9_8), -8);
+  HVX_VectorPair v_1_11_10 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_11_10),
+                                             HEXAGON_HVX_GET_V1(v_0_9_8), -8);
+  HVX_VectorPair v_1_13_12 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_15_14),
+                                             HEXAGON_HVX_GET_V0(v_0_13_12), -8);
+  HVX_VectorPair v_1_15_14 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_15_14),
+                                             HEXAGON_HVX_GET_V1(v_0_13_12), -8);
+  HVX_VectorPair v_1_17_16 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_19_18),
+                                             HEXAGON_HVX_GET_V0(v_0_17_16), -8);
+  HVX_VectorPair v_1_19_18 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_19_18),
+                                             HEXAGON_HVX_GET_V1(v_0_17_16), -8);
+  HVX_VectorPair v_1_21_20 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_23_22),
+                                             HEXAGON_HVX_GET_V0(v_0_21_20), -8);
+  HVX_VectorPair v_1_23_22 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_23_22),
+                                             HEXAGON_HVX_GET_V1(v_0_21_20), -8);
+  HVX_VectorPair v_1_25_24 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_27_26),
+                                             HEXAGON_HVX_GET_V0(v_0_25_24), -8);
+  HVX_VectorPair v_1_27_26 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_27_26),
+                                             HEXAGON_HVX_GET_V1(v_0_25_24), -8);
+  HVX_VectorPair v_1_29_28 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_31_30),
+                                             HEXAGON_HVX_GET_V0(v_0_29_28), -8);
+  HVX_VectorPair v_1_31_30 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_31_30),
+                                             HEXAGON_HVX_GET_V1(v_0_29_28), -8);
+
+  // Shuffle the 128-bit lanes.
+  v_0_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_5_4),
+                            HEXAGON_HVX_GET_V0(v_1_1_0), -16);
+  v_0_3_2 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_5_4),
+                            HEXAGON_HVX_GET_V1(v_1_1_0), -16);
+  v_0_5_4 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_7_6),
+                            HEXAGON_HVX_GET_V0(v_1_3_2), -16);
+  v_0_7_6 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_7_6),
+                            HEXAGON_HVX_GET_V1(v_1_3_2), -16);
+  v_0_9_8 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_13_12),
+                            HEXAGON_HVX_GET_V0(v_1_9_8), -16);
+  v_0_11_10 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_13_12),
+                              HEXAGON_HVX_GET_V1(v_1_9_8), -16);
+  v_0_13_12 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_15_14),
+                              HEXAGON_HVX_GET_V0(v_1_11_10), -16);
+  v_0_15_14 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_15_14),
+                              HEXAGON_HVX_GET_V1(v_1_11_10), -16);
+  v_0_17_16 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_21_20),
+                              HEXAGON_HVX_GET_V0(v_1_17_16), -16);
+  v_0_19_18 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_21_20),
+                              HEXAGON_HVX_GET_V1(v_1_17_16), -16);
+  v_0_21_20 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_23_22),
+                              HEXAGON_HVX_GET_V0(v_1_19_18), -16);
+  v_0_23_22 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_23_22),
+                              HEXAGON_HVX_GET_V1(v_1_19_18), -16);
+  v_0_25_24 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_29_28),
+                              HEXAGON_HVX_GET_V0(v_1_25_24), -16);
+  v_0_27_26 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_29_28),
+                              HEXAGON_HVX_GET_V1(v_1_25_24), -16);
+  v_0_29_28 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_31_30),
+                              HEXAGON_HVX_GET_V0(v_1_27_26), -16);
+  v_0_31_30 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_31_30),
+                              HEXAGON_HVX_GET_V1(v_1_27_26), -16);
+
+  // Shuffle the 256-bit lanes.
+  v_1_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_9_8),
+                            HEXAGON_HVX_GET_V0(v_0_1_0), -32);
+  v_1_3_2 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_9_8),
+                            HEXAGON_HVX_GET_V1(v_0_1_0), -32);
+  v_1_5_4 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_11_10),
+                            HEXAGON_HVX_GET_V0(v_0_3_2), -32);
+  v_1_7_6 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_11_10),
+                            HEXAGON_HVX_GET_V1(v_0_3_2), -32);
+  v_1_9_8 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_13_12),
+                            HEXAGON_HVX_GET_V0(v_0_5_4), -32);
+  v_1_11_10 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_13_12),
+                              HEXAGON_HVX_GET_V1(v_0_5_4), -32);
+  v_1_13_12 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_15_14),
+                              HEXAGON_HVX_GET_V0(v_0_7_6), -32);
+  v_1_15_14 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_15_14),
+                              HEXAGON_HVX_GET_V1(v_0_7_6), -32);
+  v_1_17_16 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_25_24),
+                              HEXAGON_HVX_GET_V0(v_0_17_16), -32);
+  v_1_19_18 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_25_24),
+                              HEXAGON_HVX_GET_V1(v_0_17_16), -32);
+  v_1_21_20 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_27_26),
+                              HEXAGON_HVX_GET_V0(v_0_19_18), -32);
+  v_1_23_22 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_27_26),
+                              HEXAGON_HVX_GET_V1(v_0_19_18), -32);
+  v_1_25_24 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_29_28),
+                              HEXAGON_HVX_GET_V0(v_0_21_20), -32);
+  v_1_27_26 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_29_28),
+                              HEXAGON_HVX_GET_V1(v_0_21_20), -32);
+  v_1_29_28 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_0_31_30),
+                              HEXAGON_HVX_GET_V0(v_0_23_22), -32);
+  v_1_31_30 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_0_31_30),
+                              HEXAGON_HVX_GET_V1(v_0_23_22), -32);
+
+  // Shuffle the 512-bit lanes.
+  v_0_1_0 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_17_16),
+                            HEXAGON_HVX_GET_V0(v_1_1_0), -64);
+  v_0_3_2 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_17_16),
+                            HEXAGON_HVX_GET_V1(v_1_1_0), -64);
+  v_0_5_4 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_19_18),
+                            HEXAGON_HVX_GET_V0(v_1_3_2), -64);
+  v_0_7_6 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_19_18),
+                            HEXAGON_HVX_GET_V1(v_1_3_2), -64);
+  v_0_9_8 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_21_20),
+                            HEXAGON_HVX_GET_V0(v_1_5_4), -64);
+  v_0_11_10 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_21_20),
+                              HEXAGON_HVX_GET_V1(v_1_5_4), -64);
+  v_0_13_12 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_23_22),
+                              HEXAGON_HVX_GET_V0(v_1_7_6), -64);
+  v_0_15_14 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_23_22),
+                              HEXAGON_HVX_GET_V1(v_1_7_6), -64);
+  v_0_17_16 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_25_24),
+                              HEXAGON_HVX_GET_V0(v_1_9_8), -64);
+  v_0_19_18 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_25_24),
+                              HEXAGON_HVX_GET_V1(v_1_9_8), -64);
+  v_0_21_20 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_27_26),
+                              HEXAGON_HVX_GET_V0(v_1_11_10), -64);
+  v_0_23_22 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_27_26),
+                              HEXAGON_HVX_GET_V1(v_1_11_10), -64);
+  v_0_25_24 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_29_28),
+                              HEXAGON_HVX_GET_V0(v_1_13_12), -64);
+  v_0_27_26 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_29_28),
+                              HEXAGON_HVX_GET_V1(v_1_13_12), -64);
+  v_0_29_28 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(v_1_31_30),
+                              HEXAGON_HVX_GET_V0(v_1_15_14), -64);
+  v_0_31_30 = Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V1(v_1_31_30),
+                              HEXAGON_HVX_GET_V1(v_1_15_14), -64);
+
+  kernel.packet[0] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_1_0));
+  kernel.packet[1] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_1_0));
+  kernel.packet[2] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_3_2));
+  kernel.packet[3] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_3_2));
+  kernel.packet[4] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_5_4));
+  kernel.packet[5] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_5_4));
+  kernel.packet[6] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_7_6));
+  kernel.packet[7] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_7_6));
+  kernel.packet[8] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_9_8));
+  kernel.packet[9] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_9_8));
+  kernel.packet[10] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_11_10));
+  kernel.packet[11] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_11_10));
+  kernel.packet[12] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_13_12));
+  kernel.packet[13] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_13_12));
+  kernel.packet[14] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_15_14));
+  kernel.packet[15] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_15_14));
+  kernel.packet[16] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_17_16));
+  kernel.packet[17] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_17_16));
+  kernel.packet[18] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_19_18));
+  kernel.packet[19] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_19_18));
+  kernel.packet[20] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_21_20));
+  kernel.packet[21] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_21_20));
+  kernel.packet[22] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_23_22));
+  kernel.packet[23] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_23_22));
+  kernel.packet[24] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_25_24));
+  kernel.packet[25] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_25_24));
+  kernel.packet[26] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_27_26));
+  kernel.packet[27] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_27_26));
+  kernel.packet[28] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_29_28));
+  kernel.packet[29] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_29_28));
+  kernel.packet[30] = Packet32f::Create(HEXAGON_HVX_GET_V0(v_0_31_30));
+  kernel.packet[31] = Packet32f::Create(HEXAGON_HVX_GET_V1(v_0_31_30));
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux<Packet32f>(const Packet32f& a) {
+  HVX_Vector vsum_4 = Q6_Vqf32_vadd_VsfVsf(Q6_V_vror_VR(a.Get(), 4), a.Get());
+  HVX_Vector vsum_8 = Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_vror_VR(vsum_4, 8), vsum_4);
+  HVX_Vector vsum_16 =
+      Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_vror_VR(vsum_8, 16), vsum_8);
+  HVX_Vector vsum_32 =
+      Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_vror_VR(vsum_16, 32), vsum_16);
+  HVX_Vector vsum_64 =
+      Q6_Vqf32_vadd_Vqf32Vqf32(Q6_V_vror_VR(vsum_32, 64), vsum_32);
+  return pfirst(Packet32f::Create(Q6_Vsf_equals_Vqf32(vsum_64)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f ploaddup(const float* from) {
+  HVX_Vector load = HVX_loadu(from);
+  HVX_VectorPair dup = Q6_W_vshuff_VVR(load, load, -4);
+  return Packet32f::Create(HEXAGON_HVX_GET_V0(dup));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f ploadquad(const float* from) {
+  HVX_Vector load = HVX_loadu(from);
+  HVX_VectorPair dup = Q6_W_vshuff_VVR(load, load, -4);
+  HVX_VectorPair quad =
+      Q6_W_vshuff_VVR(HEXAGON_HVX_GET_V0(dup), HEXAGON_HVX_GET_V0(dup), -8);
+  return Packet32f::Create(HEXAGON_HVX_GET_V0(quad));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f preverse(const Packet32f& a) {
+  HVX_Vector delta = Q6_Vb_vsplat_R(0x7c);
+  return Packet32f::Create(Q6_V_vdelta_VV(a.Get(), delta));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f pmin(const Packet32f& a, const Packet32f& b) {
+  return Packet32f::Create(Q6_Vsf_vmin_VsfVsf(a.Get(), b.Get()));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f pmax(const Packet32f& a, const Packet32f& b) {
+  return Packet32f::Create(Q6_Vsf_vmax_VsfVsf(a.Get(), b.Get()));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f pand(const Packet32f& a, const Packet32f& b) {
+  return Packet32f::Create(a.Get() & b.Get());
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f por(const Packet32f& a, const Packet32f& b) {
+  return Packet32f::Create(a.Get() | b.Get());
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f pxor(const Packet32f& a, const Packet32f& b) {
+  return Packet32f::Create(a.Get() ^ b.Get());
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f pnot(const Packet32f& a) {
+  return Packet32f::Create(~a.Get());
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32f pselect(const Packet32f& mask, const Packet32f& a,
+                                      const Packet32f& b) {
+  HVX_VectorPred pred = Q6_Q_vcmp_eq_VwVw(mask.Get(), Q6_V_vzero());
+  return Packet32f::Create(Q6_V_vmux_QVV(pred, b.Get(), a.Get()));
+}
+
+template <typename Op>
+EIGEN_STRONG_INLINE float predux_generic(const Packet32f& a, Op op) {
+  Packet32f vredux_4 = op(Packet32f::Create(Q6_V_vror_VR(a.Get(), 4)), a);
+  Packet32f vredux_8 =
+      op(Packet32f::Create(Q6_V_vror_VR(vredux_4.Get(), 8)), vredux_4);
+  Packet32f vredux_16 =
+      op(Packet32f::Create(Q6_V_vror_VR(vredux_8.Get(), 16)), vredux_8);
+  Packet32f vredux_32 =
+      op(Packet32f::Create(Q6_V_vror_VR(vredux_16.Get(), 32)), vredux_16);
+  Packet32f vredux_64 =
+      op(Packet32f::Create(Q6_V_vror_VR(vredux_32.Get(), 64)), vredux_32);
+  return pfirst(vredux_64);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max(const Packet32f& a) {
+  return predux_generic(a, pmax<Packet32f>);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min(const Packet32f& a) {
+  return predux_generic(a, pmin<Packet32f>);
+}
+
+template <>
+EIGEN_STRONG_INLINE bool predux_any(const Packet32f& a) {
+  return predux_generic(a, por<Packet32f>) != 0.0f;
+}
+
+static const float index_vsf[32] __attribute__((aligned(128))) = {
+    0,  1,  2,  3,  4,  5,  6,  7,  8,  9,  10, 11, 12, 13, 14, 15,
+    16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31};
+
+template <>
+EIGEN_STRONG_INLINE Packet32f plset(const float& a) {
+  return padd(pload<Packet32f>(index_vsf), pset1<Packet32f>(a));
+}
+
+// qfloat32 operations.
+template <>
+EIGEN_STRONG_INLINE Packet32qf pzero<Packet32qf>(const Packet32qf&) {
+  return Packet32qf::Create(Q6_V_vzero());
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32qf pmul<Packet32qf>(const Packet32qf& a,
+                                                const Packet32qf& b) {
+  return Packet32qf::Create(Q6_Vqf32_vmpy_Vqf32Vqf32(a.Get(), b.Get()));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet32qf padd<Packet32qf>(const Packet32qf& a,
+                                                const Packet32qf& b) {
+  return Packet32qf::Create(Q6_Vqf32_vadd_Vqf32Vqf32(a.Get(), b.Get()));
+}
+
+// Mixed float32 and qfloat32 operations.
+EIGEN_STRONG_INLINE Packet32qf pmadd_f32_to_qf32(const Packet32f& a,
+                                                 const Packet32f& b,
+                                                 const Packet32qf& c) {
+  return Packet32qf::Create(Q6_Vqf32_vadd_Vqf32Vqf32(
+      Q6_Vqf32_vmpy_VsfVsf(a.Get(), b.Get()), c.Get()));
+}
+
+EIGEN_STRONG_INLINE Packet32f pmadd_qf32_to_f32(const Packet32qf& a,
+                                                const Packet32f& b,
+                                                const Packet32f& c) {
+  return Packet32f::Create(Q6_Vsf_equals_Vqf32(Q6_Vqf32_vadd_Vqf32Vsf(
+      Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(a.Get()), b.Get()), c.Get())));
+}
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // __HVX__ && (__HVX_LENGTH__ == 128) && __HVX_ARCH__ >= 68
+
+#endif  // EIGEN_HVX_PACKET_MATH_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/MSA/Complex.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/MSA/Complex.h
new file mode 100644
index 0000000000000000000000000000000000000000..83239c0a7cf277889c51fe882d4daaac43d34f05
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/MSA/Complex.h
@@ -0,0 +1,645 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Wave Computing, Inc.
+// Written by:
+//   Chris Larsen
+//   Alexey Frunze (afrunze@wavecomp.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_MSA_H
+#define EIGEN_COMPLEX_MSA_H
+
+#include <iostream>
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+//---------- float ----------
+struct Packet2cf {
+  EIGEN_STRONG_INLINE Packet2cf() {
+  }
+  EIGEN_STRONG_INLINE explicit Packet2cf(const std::complex<float>& a,
+                                         const std::complex<float>& b) {
+    Packet4f t = { std::real(a), std::imag(a), std::real(b), std::imag(b) };
+    v = t;
+  }
+  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {
+  }
+  EIGEN_STRONG_INLINE Packet2cf(const Packet2cf& a) : v(a.v) {
+  }
+  EIGEN_STRONG_INLINE Packet2cf& operator=(const Packet2cf& b) {
+    v = b.v;
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf conjugate(void) const {
+    return Packet2cf((Packet4f)__builtin_msa_bnegi_d((v2u64)v, 63));
+  }
+  EIGEN_STRONG_INLINE Packet2cf& operator*=(const Packet2cf& b) {
+    Packet4f v1, v2;
+
+    // Get the real values of a | a1_re | a1_re | a2_re | a2_re |
+    v1 = (Packet4f)__builtin_msa_ilvev_w((v4i32)v, (v4i32)v);
+    // Get the imag values of a | a1_im | a1_im | a2_im | a2_im |
+    v2 = (Packet4f)__builtin_msa_ilvod_w((v4i32)v, (v4i32)v);
+    // Multiply the real a with b
+    v1 = pmul(v1, b.v);
+    // Multiply the imag a with b
+    v2 = pmul(v2, b.v);
+    // Conjugate v2
+    v2 = Packet2cf(v2).conjugate().v;
+    // Swap real/imag elements in v2.
+    v2 = (Packet4f)__builtin_msa_shf_w((v4i32)v2, EIGEN_MSA_SHF_I8(1, 0, 3, 2));
+    // Add and return the result
+    v = padd(v1, v2);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator*(const Packet2cf& b) const {
+    return Packet2cf(*this) *= b;
+  }
+  EIGEN_STRONG_INLINE Packet2cf& operator+=(const Packet2cf& b) {
+    v = padd(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator+(const Packet2cf& b) const {
+    return Packet2cf(*this) += b;
+  }
+  EIGEN_STRONG_INLINE Packet2cf& operator-=(const Packet2cf& b) {
+    v = psub(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator-(const Packet2cf& b) const {
+    return Packet2cf(*this) -= b;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator/(const Packet2cf& b) const {
+    return pdiv_complex(Packet2cf(*this), b);
+  }
+  EIGEN_STRONG_INLINE Packet2cf& operator/=(const Packet2cf& b) {
+    *this = Packet2cf(*this) / b;
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet2cf operator-(void) const {
+    return Packet2cf(pnegate(v));
+  }
+
+  Packet4f v;
+};
+
+inline std::ostream& operator<<(std::ostream& os, const Packet2cf& value) {
+  os << "[ (" << value.v[0] << ", " << value.v[1]
+     << "i),"
+        "  ("
+     << value.v[2] << ", " << value.v[3] << "i) ]";
+  return os;
+}
+
+template <>
+struct packet_traits<std::complex<float> > : default_packet_traits {
+  typedef Packet2cf type;
+  typedef Packet2cf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0,
+    HasBlend = 1
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2cf> {
+  typedef std::complex<float> type;
+  enum { size = 2, alignment = Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false };
+  typedef Packet2cf half;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from) {
+  EIGEN_MSA_DEBUG;
+
+  float f0 = from.real(), f1 = from.imag();
+  Packet4f v0 = { f0, f0, f0, f0 };
+  Packet4f v1 = { f1, f1, f1, f1 };
+  return Packet2cf((Packet4f)__builtin_msa_ilvr_w((Packet4i)v1, (Packet4i)v0));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a + b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a - b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) {
+  EIGEN_MSA_DEBUG;
+
+  return -a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a.conjugate();
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a * b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pand<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet2cf(pand(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf por<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet2cf(por(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pxor<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet2cf(pxor(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet2cf(pandnot(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>((const float*)from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>((const float*)from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) {
+  EIGEN_MSA_DEBUG;
+
+  return pset1<Packet2cf>(*from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<float> >(std::complex<float>* to,
+                                                      const Packet2cf& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_STORE pstore<float>((float*)to, from.v);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float>* to,
+                                                       const Packet2cf& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_STORE pstoreu<float>((float*)to, from.v);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(
+    const std::complex<float>* from, Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet2cf(from[0 * stride], from[1 * stride]);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to,
+                                                                       const Packet2cf& from,
+                                                                       Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  *to = std::complex<float>(from.v[0], from.v[1]);
+  to += stride;
+  *to = std::complex<float>(from.v[2], from.v[3]);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float>* addr) {
+  EIGEN_MSA_DEBUG;
+
+  prefetch(reinterpret_cast<const float*>(addr));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a) {
+  EIGEN_MSA_DEBUG;
+
+  return std::complex<float>(a.v[0], a.v[1]);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet2cf((Packet4f)__builtin_msa_shf_w((v4i32)a.v, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& a) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet2cf((Packet4f)__builtin_msa_shf_w((v4i32)a.v, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4f value = (Packet4f)preverse((Packet2d)a.v);
+  value += a.v;
+  return std::complex<float>(value[0], value[1]);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a) {
+  EIGEN_MSA_DEBUG;
+
+  return std::complex<float>((a.v[0] * a.v[2]) - (a.v[1] * a.v[3]),
+                             (a.v[0] * a.v[3]) + (a.v[1] * a.v[2]));
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf, Packet4f)
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a / b;
+}
+
+inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet2cf, 2>& value) {
+  os << "[ " << value.packet[0] << ", " << std::endl << "  " << value.packet[1] << " ]";
+  return os;
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2cf, 2>& kernel) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4f tmp =
+      (Packet4f)__builtin_msa_ilvl_d((v2i64)kernel.packet[1].v, (v2i64)kernel.packet[0].v);
+  kernel.packet[0].v =
+      (Packet4f)__builtin_msa_ilvr_d((v2i64)kernel.packet[1].v, (v2i64)kernel.packet[0].v);
+  kernel.packet[1].v = tmp;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket,
+                                     const Packet2cf& elsePacket) {
+  return (Packet2cf)(Packet4f)pblend<Packet2d>(ifPacket, (Packet2d)thenPacket.v,
+                                               (Packet2d)elsePacket.v);
+}
+
+//---------- double ----------
+
+struct Packet1cd {
+  EIGEN_STRONG_INLINE Packet1cd() {
+  }
+  EIGEN_STRONG_INLINE explicit Packet1cd(const std::complex<double>& a) {
+    v[0] = std::real(a);
+    v[1] = std::imag(a);
+  }
+  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {
+  }
+  EIGEN_STRONG_INLINE Packet1cd(const Packet1cd& a) : v(a.v) {
+  }
+  EIGEN_STRONG_INLINE Packet1cd& operator=(const Packet1cd& b) {
+    v = b.v;
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd conjugate(void) const {
+    static const v2u64 p2ul_CONJ_XOR = { 0x0, 0x8000000000000000 };
+    return (Packet1cd)pxor(v, (Packet2d)p2ul_CONJ_XOR);
+  }
+  EIGEN_STRONG_INLINE Packet1cd& operator*=(const Packet1cd& b) {
+    Packet2d v1, v2;
+
+    // Get the real values of a | a1_re | a1_re
+    v1 = (Packet2d)__builtin_msa_ilvev_d((v2i64)v, (v2i64)v);
+    // Get the imag values of a | a1_im | a1_im
+    v2 = (Packet2d)__builtin_msa_ilvod_d((v2i64)v, (v2i64)v);
+    // Multiply the real a with b
+    v1 = pmul(v1, b.v);
+    // Multiply the imag a with b
+    v2 = pmul(v2, b.v);
+    // Conjugate v2
+    v2 = Packet1cd(v2).conjugate().v;
+    // Swap real/imag elements in v2.
+    v2 = (Packet2d)__builtin_msa_shf_w((v4i32)v2, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
+    // Add and return the result
+    v = padd(v1, v2);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator*(const Packet1cd& b) const {
+    return Packet1cd(*this) *= b;
+  }
+  EIGEN_STRONG_INLINE Packet1cd& operator+=(const Packet1cd& b) {
+    v = padd(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator+(const Packet1cd& b) const {
+    return Packet1cd(*this) += b;
+  }
+  EIGEN_STRONG_INLINE Packet1cd& operator-=(const Packet1cd& b) {
+    v = psub(v, b.v);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator-(const Packet1cd& b) const {
+    return Packet1cd(*this) -= b;
+  }
+  EIGEN_STRONG_INLINE Packet1cd& operator/=(const Packet1cd& b) {
+    *this *= b.conjugate();
+    Packet2d s = pmul<Packet2d>(b.v, b.v);
+    s = padd(s, preverse<Packet2d>(s));
+    v = pdiv(v, s);
+    return *this;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator/(const Packet1cd& b) const {
+    return Packet1cd(*this) /= b;
+  }
+  EIGEN_STRONG_INLINE Packet1cd operator-(void) const {
+    return Packet1cd(pnegate(v));
+  }
+
+  Packet2d v;
+};
+
+inline std::ostream& operator<<(std::ostream& os, const Packet1cd& value) {
+  os << "[ (" << value.v[0] << ", " << value.v[1] << "i) ]";
+  return os;
+}
+
+template <>
+struct packet_traits<std::complex<double> > : default_packet_traits {
+  typedef Packet1cd type;
+  typedef Packet1cd half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 0,
+    size = 1,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasNegate = 1,
+    HasAbs = 0,
+    HasAbs2 = 0,
+    HasMin = 0,
+    HasMax = 0,
+    HasSetLinear = 0
+  };
+};
+
+template <>
+struct unpacket_traits<Packet1cd> {
+  typedef std::complex<double> type;
+  enum { size = 1, alignment = Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false };
+  typedef Packet1cd half;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pload<Packet1cd>(const std::complex<double>* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet1cd(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a + b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a - b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) {
+  EIGEN_MSA_DEBUG;
+
+  return -a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a.conjugate();
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a * b;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pand<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet1cd(pand(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd por<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet1cd(por(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pxor<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet1cd(pxor(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet1cd(pandnot(a.v, b.v));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) {
+  EIGEN_MSA_DEBUG;
+
+  return pset1<Packet1cd>(*from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<std::complex<double> >(std::complex<double>* to,
+                                                       const Packet1cd& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_STORE pstore<double>((double*)to, from.v);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double>* to,
+                                                        const Packet1cd& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_STORE pstoreu<double>((double*)to, from.v);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double>* addr) {
+  EIGEN_MSA_DEBUG;
+
+  prefetch(reinterpret_cast<const double*>(addr));
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(
+    const std::complex<double>* from, Index stride __attribute__((unused))) {
+  EIGEN_MSA_DEBUG;
+
+  Packet1cd res;
+  res.v[0] = std::real(from[0]);
+  res.v[1] = std::imag(from[0]);
+  return res;
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to,
+                                                                        const Packet1cd& from,
+                                                                        Index stride
+                                                                        __attribute__((unused))) {
+  EIGEN_MSA_DEBUG;
+
+  pstore(to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a) {
+  EIGEN_MSA_DEBUG;
+
+  return std::complex<double>(a.v[0], a.v[1]);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) {
+  EIGEN_MSA_DEBUG;
+
+  return pfirst(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) {
+  EIGEN_MSA_DEBUG;
+
+  return pfirst(a);
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd, Packet2d)
+
+template <>
+EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b) {
+  EIGEN_MSA_DEBUG;
+
+  return a / b;
+}
+
+EIGEN_STRONG_INLINE Packet1cd pcplxflip /*<Packet1cd>*/ (const Packet1cd& x) {
+  EIGEN_MSA_DEBUG;
+
+  return Packet1cd(preverse(Packet2d(x.v)));
+}
+
+inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet1cd, 2>& value) {
+  os << "[ " << value.packet[0] << ", " << std::endl << "  " << value.packet[1] << " ]";
+  return os;
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd, 2>& kernel) {
+  EIGEN_MSA_DEBUG;
+
+  Packet2d v1, v2;
+
+  v1 = (Packet2d)__builtin_msa_ilvev_d((v2i64)kernel.packet[0].v, (v2i64)kernel.packet[1].v);
+  // Get the imag values of a
+  v2 = (Packet2d)__builtin_msa_ilvod_d((v2i64)kernel.packet[0].v, (v2i64)kernel.packet[1].v);
+
+  kernel.packet[0].v = v1;
+  kernel.packet[1].v = v2;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPLEX_MSA_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/MSA/MathFunctions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/MSA/MathFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..59320413ffc92c4da368df17c4ea0db9ff1a00ce
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/MSA/MathFunctions.h
@@ -0,0 +1,389 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007 Julien Pommier
+// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
+// Copyright (C) 2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Copyright (C) 2018 Wave Computing, Inc.
+// Written by:
+//   Chris Larsen
+//   Alexey Frunze (afrunze@wavecomp.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* The sin, cos, exp, and log functions of this file come from
+ * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
+ */
+
+/* The tanh function of this file is an adaptation of
+ * template<typename T> T generic_fast_tanh_float(const T&)
+ * from MathFunctionsImpl.h.
+ */
+
+#ifndef EIGEN_MATH_FUNCTIONS_MSA_H
+#define EIGEN_MATH_FUNCTIONS_MSA_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f
+plog<Packet4f>(const Packet4f& _x) {
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_SQRTHF, 0.707106781186547524f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p0, 7.0376836292e-2f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p1, -1.1514610310e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p2, 1.1676998740e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p3, -1.2420140846e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p4, +1.4249322787e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p5, -1.6668057665e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p6, +2.0000714765e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p7, -2.4999993993e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p8, +3.3333331174e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_q1, -2.12194440e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_log_q2, 0.693359375f);
+  static EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
+  static EIGEN_DECLARE_CONST_Packet4f(1, 1.0f);
+
+  // Convert negative argument into NAN (quiet negative, to be specific).
+  Packet4f zero = (Packet4f)__builtin_msa_ldi_w(0);
+  Packet4i neg_mask = __builtin_msa_fclt_w(_x, zero);
+  Packet4i zero_mask = __builtin_msa_fceq_w(_x, zero);
+  Packet4f non_neg_x_or_nan = padd(_x, (Packet4f)neg_mask);  // Add 0.0 or NAN.
+  Packet4f x = non_neg_x_or_nan;
+
+  // Extract exponent from x = mantissa * 2**exponent, where 1.0 <= mantissa < 2.0.
+  // N.B. the exponent is one less of what frexpf() would return.
+  Packet4i e_int = __builtin_msa_ftint_s_w(__builtin_msa_flog2_w(x));
+  // Multiply x by 2**(-exponent-1) to get 0.5 <= x < 1.0 as from frexpf().
+  x = __builtin_msa_fexp2_w(x, (Packet4i)__builtin_msa_nori_b((v16u8)e_int, 0));
+
+  /*
+     if (x < SQRTHF) {
+       x = x + x - 1.0;
+     } else {
+       e += 1;
+       x = x - 1.0;
+     }
+  */
+  Packet4f xx = padd(x, x);
+  Packet4i ge_mask = __builtin_msa_fcle_w(p4f_cephes_SQRTHF, x);
+  e_int = psub(e_int, ge_mask);
+  x = (Packet4f)__builtin_msa_bsel_v((v16u8)ge_mask, (v16u8)xx, (v16u8)x);
+  x = psub(x, p4f_1);
+  Packet4f e = __builtin_msa_ffint_s_w(e_int);
+
+  Packet4f x2 = pmul(x, x);
+  Packet4f x3 = pmul(x2, x);
+
+  Packet4f y, y1, y2;
+  y = pmadd(p4f_cephes_log_p0, x, p4f_cephes_log_p1);
+  y1 = pmadd(p4f_cephes_log_p3, x, p4f_cephes_log_p4);
+  y2 = pmadd(p4f_cephes_log_p6, x, p4f_cephes_log_p7);
+  y = pmadd(y, x, p4f_cephes_log_p2);
+  y1 = pmadd(y1, x, p4f_cephes_log_p5);
+  y2 = pmadd(y2, x, p4f_cephes_log_p8);
+  y = pmadd(y, x3, y1);
+  y = pmadd(y, x3, y2);
+  y = pmul(y, x3);
+
+  y = pmadd(e, p4f_cephes_log_q1, y);
+  x = __builtin_msa_fmsub_w(x, x2, p4f_half);
+  x = padd(x, y);
+  x = pmadd(e, p4f_cephes_log_q2, x);
+
+  // x is now the logarithm result candidate. We still need to handle the
+  // extreme arguments of zero and positive infinity, though.
+  // N.B. if the argument is +INFINITY, x is NAN because the polynomial terms
+  // contain infinities of both signs (see the coefficients and code above).
+  // INFINITY - INFINITY is NAN.
+
+  // If the argument is +INFINITY, make it the new result candidate.
+  // To achieve that we choose the smaller of the result candidate and the
+  // argument.
+  // This is correct for all finite pairs of values (the logarithm is smaller
+  // than the argument).
+  // This is also correct in the special case when the argument is +INFINITY
+  // and the result candidate is NAN. This is because the fmin.df instruction
+  // prefers non-NANs to NANs.
+  x = __builtin_msa_fmin_w(x, non_neg_x_or_nan);
+
+  // If the argument is zero (including -0.0), the result becomes -INFINITY.
+  Packet4i neg_infs = __builtin_msa_slli_w(zero_mask, 23);
+  x = (Packet4f)__builtin_msa_bsel_v((v16u8)zero_mask, (v16u8)x, (v16u8)neg_infs);
+
+  return x;
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f
+pexp<Packet4f>(const Packet4f& _x) {
+  // Limiting single-precision pexp's argument to [-128, +128] lets pexp
+  // reach 0 and INFINITY naturally.
+  static EIGEN_DECLARE_CONST_Packet4f(exp_lo, -128.0f);
+  static EIGEN_DECLARE_CONST_Packet4f(exp_hi, +128.0f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_LOG2EF, 1.44269504088896341f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C1, 0.693359375f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C2, -2.12194440e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p0, 1.9875691500e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p1, 1.3981999507e-3f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p2, 8.3334519073e-3f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p3, 4.1665795894e-2f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p4, 1.6666665459e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p5, 5.0000001201e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
+  static EIGEN_DECLARE_CONST_Packet4f(1, 1.0f);
+
+  Packet4f x = _x;
+
+  // Clamp x.
+  x = (Packet4f)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_w(x, p4f_exp_lo), (v16u8)x,
+                                     (v16u8)p4f_exp_lo);
+  x = (Packet4f)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_w(p4f_exp_hi, x), (v16u8)x,
+                                     (v16u8)p4f_exp_hi);
+
+  // Round to nearest integer by adding 0.5 (with x's sign) and truncating.
+  Packet4f x2_add = (Packet4f)__builtin_msa_binsli_w((v4u32)p4f_half, (v4u32)x, 0);
+  Packet4f x2 = pmadd(x, p4f_cephes_LOG2EF, x2_add);
+  Packet4i x2_int = __builtin_msa_ftrunc_s_w(x2);
+  Packet4f x2_int_f = __builtin_msa_ffint_s_w(x2_int);
+
+  x = __builtin_msa_fmsub_w(x, x2_int_f, p4f_cephes_exp_C1);
+  x = __builtin_msa_fmsub_w(x, x2_int_f, p4f_cephes_exp_C2);
+
+  Packet4f z = pmul(x, x);
+
+  Packet4f y = p4f_cephes_exp_p0;
+  y = pmadd(y, x, p4f_cephes_exp_p1);
+  y = pmadd(y, x, p4f_cephes_exp_p2);
+  y = pmadd(y, x, p4f_cephes_exp_p3);
+  y = pmadd(y, x, p4f_cephes_exp_p4);
+  y = pmadd(y, x, p4f_cephes_exp_p5);
+  y = pmadd(y, z, x);
+  y = padd(y, p4f_1);
+
+  // y *= 2**exponent.
+  y = __builtin_msa_fexp2_w(y, x2_int);
+
+  return y;
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f
+ptanh<Packet4f>(const Packet4f& _x) {
+  static EIGEN_DECLARE_CONST_Packet4f(tanh_tiny, 1e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(tanh_hi, 9.0f);
+  // The monomial coefficients of the numerator polynomial (odd).
+  static EIGEN_DECLARE_CONST_Packet4f(alpha_1, 4.89352455891786e-3f);
+  static EIGEN_DECLARE_CONST_Packet4f(alpha_3, 6.37261928875436e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(alpha_5, 1.48572235717979e-5f);
+  static EIGEN_DECLARE_CONST_Packet4f(alpha_7, 5.12229709037114e-8f);
+  static EIGEN_DECLARE_CONST_Packet4f(alpha_9, -8.60467152213735e-11f);
+  static EIGEN_DECLARE_CONST_Packet4f(alpha_11, 2.00018790482477e-13f);
+  static EIGEN_DECLARE_CONST_Packet4f(alpha_13, -2.76076847742355e-16f);
+  // The monomial coefficients of the denominator polynomial (even).
+  static EIGEN_DECLARE_CONST_Packet4f(beta_0, 4.89352518554385e-3f);
+  static EIGEN_DECLARE_CONST_Packet4f(beta_2, 2.26843463243900e-3f);
+  static EIGEN_DECLARE_CONST_Packet4f(beta_4, 1.18534705686654e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(beta_6, 1.19825839466702e-6f);
+
+  Packet4f x = pabs(_x);
+  Packet4i tiny_mask = __builtin_msa_fclt_w(x, p4f_tanh_tiny);
+
+  // Clamp the inputs to the range [-9, 9] since anything outside
+  // this range is -/+1.0f in single-precision.
+  x = (Packet4f)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_w(p4f_tanh_hi, x), (v16u8)x,
+                                     (v16u8)p4f_tanh_hi);
+
+  // Since the polynomials are odd/even, we need x**2.
+  Packet4f x2 = pmul(x, x);
+
+  // Evaluate the numerator polynomial p.
+  Packet4f p = pmadd(x2, p4f_alpha_13, p4f_alpha_11);
+  p = pmadd(x2, p, p4f_alpha_9);
+  p = pmadd(x2, p, p4f_alpha_7);
+  p = pmadd(x2, p, p4f_alpha_5);
+  p = pmadd(x2, p, p4f_alpha_3);
+  p = pmadd(x2, p, p4f_alpha_1);
+  p = pmul(x, p);
+
+  // Evaluate the denominator polynomial q.
+  Packet4f q = pmadd(x2, p4f_beta_6, p4f_beta_4);
+  q = pmadd(x2, q, p4f_beta_2);
+  q = pmadd(x2, q, p4f_beta_0);
+
+  // Divide the numerator by the denominator.
+  p = pdiv(p, q);
+
+  // Reinstate the sign.
+  p = (Packet4f)__builtin_msa_binsli_w((v4u32)p, (v4u32)_x, 0);
+
+  // When the argument is very small in magnitude it's more accurate to just return it.
+  p = (Packet4f)__builtin_msa_bsel_v((v16u8)tiny_mask, (v16u8)p, (v16u8)_x);
+
+  return p;
+}
+
+template <bool sine>
+Packet4f psincos_inner_msa_float(const Packet4f& _x) {
+  static EIGEN_DECLARE_CONST_Packet4f(sincos_max_arg, 13176795.0f);  // Approx. (2**24) / (4/Pi).
+  static EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP1, -0.78515625f);
+  static EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP2, -2.4187564849853515625e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(minus_cephes_DP3, -3.77489497744594108e-8f);
+  static EIGEN_DECLARE_CONST_Packet4f(sincof_p0, -1.9515295891e-4f);
+  static EIGEN_DECLARE_CONST_Packet4f(sincof_p1, 8.3321608736e-3f);
+  static EIGEN_DECLARE_CONST_Packet4f(sincof_p2, -1.6666654611e-1f);
+  static EIGEN_DECLARE_CONST_Packet4f(coscof_p0, 2.443315711809948e-5f);
+  static EIGEN_DECLARE_CONST_Packet4f(coscof_p1, -1.388731625493765e-3f);
+  static EIGEN_DECLARE_CONST_Packet4f(coscof_p2, 4.166664568298827e-2f);
+  static EIGEN_DECLARE_CONST_Packet4f(cephes_FOPI, 1.27323954473516f);  // 4/Pi.
+  static EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
+  static EIGEN_DECLARE_CONST_Packet4f(1, 1.0f);
+
+  Packet4f x = pabs(_x);
+
+  // Translate infinite arguments into NANs.
+  Packet4f zero_or_nan_if_inf = psub(_x, _x);
+  x = padd(x, zero_or_nan_if_inf);
+  // Prevent sin/cos from generating values larger than 1.0 in magnitude
+  // for very large arguments by setting x to 0.0.
+  Packet4i small_or_nan_mask = __builtin_msa_fcult_w(x, p4f_sincos_max_arg);
+  x = pand(x, (Packet4f)small_or_nan_mask);
+
+  // Scale x by 4/Pi to find x's octant.
+  Packet4f y = pmul(x, p4f_cephes_FOPI);
+  // Get the octant. We'll reduce x by this number of octants or by one more than it.
+  Packet4i y_int = __builtin_msa_ftrunc_s_w(y);
+  // x's from even-numbered octants will translate to octant 0: [0, +Pi/4].
+  // x's from odd-numbered octants will translate to octant -1: [-Pi/4, 0].
+  // Adjustment for odd-numbered octants: octant = (octant + 1) & (~1).
+  Packet4i y_int1 = __builtin_msa_addvi_w(y_int, 1);
+  Packet4i y_int2 = (Packet4i)__builtin_msa_bclri_w((Packet4ui)y_int1, 0); // bclri = bit-clear
+  y = __builtin_msa_ffint_s_w(y_int2);
+
+  // Compute the sign to apply to the polynomial.
+  Packet4i sign_mask = sine ? pxor(__builtin_msa_slli_w(y_int1, 29), (Packet4i)_x)
+                            : __builtin_msa_slli_w(__builtin_msa_addvi_w(y_int, 3), 29);
+
+  // Get the polynomial selection mask.
+  // We'll calculate both (sin and cos) polynomials and then select from the two.
+  Packet4i poly_mask = __builtin_msa_ceqi_w(__builtin_msa_slli_w(y_int2, 30), 0);
+
+  // Reduce x by y octants to get: -Pi/4 <= x <= +Pi/4.
+  // The magic pass: "Extended precision modular arithmetic"
+  // x = ((x - y * DP1) - y * DP2) - y * DP3
+  Packet4f tmp1 = pmul(y, p4f_minus_cephes_DP1);
+  Packet4f tmp2 = pmul(y, p4f_minus_cephes_DP2);
+  Packet4f tmp3 = pmul(y, p4f_minus_cephes_DP3);
+  x = padd(x, tmp1);
+  x = padd(x, tmp2);
+  x = padd(x, tmp3);
+
+  // Evaluate the cos(x) polynomial.
+  y = p4f_coscof_p0;
+  Packet4f z = pmul(x, x);
+  y = pmadd(y, z, p4f_coscof_p1);
+  y = pmadd(y, z, p4f_coscof_p2);
+  y = pmul(y, z);
+  y = pmul(y, z);
+  y = __builtin_msa_fmsub_w(y, z, p4f_half);
+  y = padd(y, p4f_1);
+
+  // Evaluate the sin(x) polynomial.
+  Packet4f y2 = p4f_sincof_p0;
+  y2 = pmadd(y2, z, p4f_sincof_p1);
+  y2 = pmadd(y2, z, p4f_sincof_p2);
+  y2 = pmul(y2, z);
+  y2 = pmadd(y2, x, x);
+
+  // Select the correct result from the two polynomials.
+  y = sine ? (Packet4f)__builtin_msa_bsel_v((v16u8)poly_mask, (v16u8)y, (v16u8)y2)
+           : (Packet4f)__builtin_msa_bsel_v((v16u8)poly_mask, (v16u8)y2, (v16u8)y);
+
+  // Update the sign.
+  sign_mask = pxor(sign_mask, (Packet4i)y);
+  y = (Packet4f)__builtin_msa_binsli_w((v4u32)y, (v4u32)sign_mask, 0); // binsli = bit-insert-left
+  return y;
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f
+psin<Packet4f>(const Packet4f& x) {
+  return psincos_inner_msa_float</* sine */ true>(x);
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f
+pcos<Packet4f>(const Packet4f& x) {
+  return psincos_inner_msa_float</* sine */ false>(x);
+}
+
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet2d
+pexp<Packet2d>(const Packet2d& _x) {
+  // Limiting double-precision pexp's argument to [-1024, +1024] lets pexp
+  // reach 0 and INFINITY naturally.
+  static EIGEN_DECLARE_CONST_Packet2d(exp_lo, -1024.0);
+  static EIGEN_DECLARE_CONST_Packet2d(exp_hi, +1024.0);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_LOG2EF, 1.4426950408889634073599);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C1, 0.693145751953125);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C2, 1.42860682030941723212e-6);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p0, 1.26177193074810590878e-4);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p1, 3.02994407707441961300e-2);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p2, 9.99999999999999999910e-1);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q0, 3.00198505138664455042e-6);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q1, 2.52448340349684104192e-3);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q2, 2.27265548208155028766e-1);
+  static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q3, 2.00000000000000000009e0);
+  static EIGEN_DECLARE_CONST_Packet2d(half, 0.5);
+  static EIGEN_DECLARE_CONST_Packet2d(1, 1.0);
+  static EIGEN_DECLARE_CONST_Packet2d(2, 2.0);
+
+  Packet2d x = _x;
+
+  // Clamp x.
+  x = (Packet2d)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_d(x, p2d_exp_lo), (v16u8)x,
+                                     (v16u8)p2d_exp_lo);
+  x = (Packet2d)__builtin_msa_bsel_v((v16u8)__builtin_msa_fclt_d(p2d_exp_hi, x), (v16u8)x,
+                                     (v16u8)p2d_exp_hi);
+
+  // Round to nearest integer by adding 0.5 (with x's sign) and truncating.
+  Packet2d x2_add = (Packet2d)__builtin_msa_binsli_d((v2u64)p2d_half, (v2u64)x, 0);
+  Packet2d x2 = pmadd(x, p2d_cephes_LOG2EF, x2_add);
+  Packet2l x2_long = __builtin_msa_ftrunc_s_d(x2);
+  Packet2d x2_long_d = __builtin_msa_ffint_s_d(x2_long);
+
+  x = __builtin_msa_fmsub_d(x, x2_long_d, p2d_cephes_exp_C1);
+  x = __builtin_msa_fmsub_d(x, x2_long_d, p2d_cephes_exp_C2);
+
+  x2 = pmul(x, x);
+
+  Packet2d px = p2d_cephes_exp_p0;
+  px = pmadd(px, x2, p2d_cephes_exp_p1);
+  px = pmadd(px, x2, p2d_cephes_exp_p2);
+  px = pmul(px, x);
+
+  Packet2d qx = p2d_cephes_exp_q0;
+  qx = pmadd(qx, x2, p2d_cephes_exp_q1);
+  qx = pmadd(qx, x2, p2d_cephes_exp_q2);
+  qx = pmadd(qx, x2, p2d_cephes_exp_q3);
+
+  x = pdiv(px, psub(qx, px));
+  x = pmadd(p2d_2, x, p2d_1);
+
+  // x *= 2**exponent.
+  x = __builtin_msa_fexp2_d(x, x2_long);
+
+  return x;
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_MSA_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/MSA/PacketMath.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/MSA/PacketMath.h
new file mode 100644
index 0000000000000000000000000000000000000000..4e6bcdf411d6e03abb17f0db7dcdbde3dbeb82f5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/MSA/PacketMath.h
@@ -0,0 +1,1232 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Wave Computing, Inc.
+// Written by:
+//   Chris Larsen
+//   Alexey Frunze (afrunze@wavecomp.com)
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_MSA_H
+#define EIGEN_PACKET_MATH_MSA_H
+
+#include <iostream>
+#include <string>
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+
+#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
+#endif
+
+#if 0
+#define EIGEN_MSA_DEBUG                                                             \
+  static bool firstTime = true;                                                     \
+  do {                                                                              \
+    if (firstTime) {                                                                \
+      std::cout << __FILE__ << ':' << __LINE__ << ':' << __FUNCTION__ << std::endl; \
+      firstTime = false;                                                            \
+    }                                                                               \
+  } while (0)
+#else
+#define EIGEN_MSA_DEBUG
+#endif
+
+#define EIGEN_MSA_SHF_I8(a, b, c, d) (((d) << 6) | ((c) << 4) | ((b) << 2) | (a))
+
+typedef v4f32 Packet4f;
+typedef v4i32 Packet4i;
+typedef v4u32 Packet4ui;
+
+#define EIGEN_DECLARE_CONST_Packet4f(NAME, X) const Packet4f p4f_##NAME = { X, X, X, X }
+#define EIGEN_DECLARE_CONST_Packet4i(NAME, X) const Packet4i p4i_##NAME = { X, X, X, X }
+#define EIGEN_DECLARE_CONST_Packet4ui(NAME, X) const Packet4ui p4ui_##NAME = { X, X, X, X }
+
+inline std::ostream& operator<<(std::ostream& os, const Packet4f& value) {
+  os << "[ " << value[0] << ", " << value[1] << ", " << value[2] << ", " << value[3] << " ]";
+  return os;
+}
+
+inline std::ostream& operator<<(std::ostream& os, const Packet4i& value) {
+  os << "[ " << value[0] << ", " << value[1] << ", " << value[2] << ", " << value[3] << " ]";
+  return os;
+}
+
+inline std::ostream& operator<<(std::ostream& os, const Packet4ui& value) {
+  os << "[ " << value[0] << ", " << value[1] << ", " << value[2] << ", " << value[3] << " ]";
+  return os;
+}
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet4f type;
+  typedef Packet4f half;  // Packet2f intrinsics not implemented yet
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+    // FIXME check the Has*
+    HasDiv = 1,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasLog = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasRound = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasBlend = 1
+  };
+};
+
+template <>
+struct packet_traits<int32_t> : default_packet_traits {
+  typedef Packet4i type;
+  typedef Packet4i half;  // Packet2i intrinsics not implemented yet
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+    // FIXME check the Has*
+    HasDiv = 1,
+    HasBlend = 1
+  };
+};
+
+template <>
+struct unpacket_traits<Packet4f> {
+  typedef float type;
+  enum { size = 4, alignment = Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false };
+  typedef Packet4f half;
+};
+
+template <>
+struct unpacket_traits<Packet4i> {
+  typedef int32_t type;
+  enum { size = 4, alignment = Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false };
+  typedef Packet4i half;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4f v = { from, from, from, from };
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int32_t& from) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fill_w(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pload1<Packet4f>(const float* from) {
+  EIGEN_MSA_DEBUG;
+
+  float f = *from;
+  Packet4f v = { f, f, f, f };
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pload1<Packet4i>(const int32_t* from) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fill_w(*from);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fadd_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_addv_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a) {
+  EIGEN_MSA_DEBUG;
+
+  static const Packet4f countdown = { 0.0f, 1.0f, 2.0f, 3.0f };
+  return padd(pset1<Packet4f>(a), countdown);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int32_t& a) {
+  EIGEN_MSA_DEBUG;
+
+  static const Packet4i countdown = { 0, 1, 2, 3 };
+  return padd(pset1<Packet4i>(a), countdown);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fsub_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_subv_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4f)__builtin_msa_bnegi_w((v4u32)a, 31);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_addvi_w((v4i32)__builtin_msa_nori_b((v16u8)a, 0), 1);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fmul_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_mulv_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fdiv_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_div_s_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fmadd_w(c, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) {
+  EIGEN_MSA_DEBUG;
+
+  // Use "asm" construct to avoid __builtin_msa_maddv_w GNU C bug.
+  Packet4i value = c;
+  __asm__("maddv.w %w[value], %w[a], %w[b]\n"
+          // Outputs
+          : [value] "+f"(value)
+          // Inputs
+          : [a] "f"(a), [b] "f"(b));
+  return value;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4f)__builtin_msa_and_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4i)__builtin_msa_and_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4f)__builtin_msa_or_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4i)__builtin_msa_or_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4f)__builtin_msa_xor_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4i)__builtin_msa_xor_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+  return pand(a, (Packet4f)__builtin_msa_xori_b((v16u8)b, 255));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return pand(a, (Packet4i)__builtin_msa_xori_b((v16u8)b, 255));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  // This prefers numbers to NaNs.
+  return __builtin_msa_fmin_w(a, b);
+#else
+  // This prefers NaNs to numbers.
+  Packet4i aNaN = __builtin_msa_fcun_w(a, a);
+  Packet4i aMinOrNaN = por(__builtin_msa_fclt_w(a, b), aNaN);
+  return (Packet4f)__builtin_msa_bsel_v((v16u8)aMinOrNaN, (v16u8)b, (v16u8)a);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_min_s_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  // This prefers numbers to NaNs.
+  return __builtin_msa_fmax_w(a, b);
+#else
+  // This prefers NaNs to numbers.
+  Packet4i aNaN = __builtin_msa_fcun_w(a, a);
+  Packet4i aMaxOrNaN = por(__builtin_msa_fclt_w(b, a), aNaN);
+  return (Packet4f)__builtin_msa_bsel_v((v16u8)aMaxOrNaN, (v16u8)b, (v16u8)a);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_max_s_w(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_LOAD return (Packet4f)__builtin_msa_ld_w(const_cast<float*>(from), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int32_t* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_LOAD return __builtin_msa_ld_w(const_cast<int32_t*>(from), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet4f)__builtin_msa_ld_w(const_cast<float*>(from), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int32_t* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet4i)__builtin_msa_ld_w(const_cast<int32_t*>(from), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from) {
+  EIGEN_MSA_DEBUG;
+
+  float f0 = from[0], f1 = from[1];
+  Packet4f v0 = { f0, f0, f0, f0 };
+  Packet4f v1 = { f1, f1, f1, f1 };
+  return (Packet4f)__builtin_msa_ilvr_d((v2i64)v1, (v2i64)v0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int32_t* from) {
+  EIGEN_MSA_DEBUG;
+
+  int32_t i0 = from[0], i1 = from[1];
+  Packet4i v0 = { i0, i0, i0, i0 };
+  Packet4i v1 = { i1, i1, i1, i1 };
+  return (Packet4i)__builtin_msa_ilvr_d((v2i64)v1, (v2i64)v0);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_STORE __builtin_msa_st_w((Packet4i)from, to, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<int32_t>(int32_t* to, const Packet4i& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_STORE __builtin_msa_st_w(from, to, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_STORE __builtin_msa_st_w((Packet4i)from, to, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<int32_t>(int32_t* to, const Packet4i& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_STORE __builtin_msa_st_w(from, to, 0);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  float f = *from;
+  Packet4f v = { f, f, f, f };
+  v[1] = from[stride];
+  v[2] = from[2 * stride];
+  v[3] = from[3 * stride];
+  return v;
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet4i pgather<int32_t, Packet4i>(const int32_t* from, Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  int32_t i = *from;
+  Packet4i v = { i, i, i, i };
+  v[1] = from[stride];
+  v[2] = from[2 * stride];
+  v[3] = from[3 * stride];
+  return v;
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from,
+                                                        Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  *to = from[0];
+  to += stride;
+  *to = from[1];
+  to += stride;
+  *to = from[2];
+  to += stride;
+  *to = from[3];
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<int32_t, Packet4i>(int32_t* to, const Packet4i& from,
+                                                          Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  *to = from[0];
+  to += stride;
+  *to = from[1];
+  to += stride;
+  *to = from[2];
+  to += stride;
+  *to = from[3];
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) {
+  EIGEN_MSA_DEBUG;
+
+  __builtin_prefetch(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<int32_t>(const int32_t* addr) {
+  EIGEN_MSA_DEBUG;
+
+  __builtin_prefetch(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE int32_t pfirst<Packet4i>(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4f)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(3, 2, 1, 0));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(3, 2, 1, 0));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet4f)__builtin_msa_bclri_w((v4u32)a, 31);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4i zero = __builtin_msa_ldi_w(0);
+  return __builtin_msa_add_a_w(zero, a);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4f s = padd(a, (Packet4f)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
+  s = padd(s, (Packet4f)__builtin_msa_shf_w((v4i32)s, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+  return s[0];
+}
+
+
+template <>
+EIGEN_STRONG_INLINE int32_t predux<Packet4i>(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4i s = padd(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
+  s = padd(s, __builtin_msa_shf_w(s, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+  return s[0];
+}
+
+// Other reduction functions:
+// mul
+template <>
+EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4f p = pmul(a, (Packet4f)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
+  p = pmul(p, (Packet4f)__builtin_msa_shf_w((v4i32)p, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+  return p[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE int32_t predux_mul<Packet4i>(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4i p = pmul(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
+  p = pmul(p, __builtin_msa_shf_w(p, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+  return p[0];
+}
+
+// min
+template <>
+EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  // Swap 64-bit halves of a.
+  Packet4f swapped = (Packet4f)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
+#if !EIGEN_FAST_MATH
+  // Detect presence of NaNs from pairs a[0]-a[2] and a[1]-a[3] as two 32-bit
+  // masks of all zeroes/ones in low 64 bits.
+  v16u8 unord = (v16u8)__builtin_msa_fcun_w(a, swapped);
+  // Combine the two masks into one: 64 ones if no NaNs, otherwise 64 zeroes.
+  unord = (v16u8)__builtin_msa_ceqi_d((v2i64)unord, 0);
+#endif
+  // Continue with min computation.
+  Packet4f v = __builtin_msa_fmin_w(a, swapped);
+  v = __builtin_msa_fmin_w(
+      v, (Packet4f)__builtin_msa_shf_w((Packet4i)v, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+#if !EIGEN_FAST_MATH
+  // Based on the mask select between v and 4 qNaNs.
+  v16u8 qnans = (v16u8)__builtin_msa_fill_w(0x7FC00000);
+  v = (Packet4f)__builtin_msa_bsel_v(unord, qnans, (v16u8)v);
+#endif
+  return v[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE int32_t predux_min<Packet4i>(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4i m = pmin(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
+  m = pmin(m, __builtin_msa_shf_w(m, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+  return m[0];
+}
+
+// max
+template <>
+EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  // Swap 64-bit halves of a.
+  Packet4f swapped = (Packet4f)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
+#if !EIGEN_FAST_MATH
+  // Detect presence of NaNs from pairs a[0]-a[2] and a[1]-a[3] as two 32-bit
+  // masks of all zeroes/ones in low 64 bits.
+  v16u8 unord = (v16u8)__builtin_msa_fcun_w(a, swapped);
+  // Combine the two masks into one: 64 ones if no NaNs, otherwise 64 zeroes.
+  unord = (v16u8)__builtin_msa_ceqi_d((v2i64)unord, 0);
+#endif
+  // Continue with max computation.
+  Packet4f v = __builtin_msa_fmax_w(a, swapped);
+  v = __builtin_msa_fmax_w(
+      v, (Packet4f)__builtin_msa_shf_w((Packet4i)v, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+#if !EIGEN_FAST_MATH
+  // Based on the mask select between v and 4 qNaNs.
+  v16u8 qnans = (v16u8)__builtin_msa_fill_w(0x7FC00000);
+  v = (Packet4f)__builtin_msa_bsel_v(unord, qnans, (v16u8)v);
+#endif
+  return v[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE int32_t predux_max<Packet4i>(const Packet4i& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet4i m = pmax(a, __builtin_msa_shf_w(a, EIGEN_MSA_SHF_I8(2, 3, 0, 1)));
+  m = pmax(m, __builtin_msa_shf_w(m, EIGEN_MSA_SHF_I8(1, 0, 3, 2)));
+  return m[0];
+}
+
+inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet4f, 4>& value) {
+  os << "[ " << value.packet[0] << "," << std::endl
+     << "  " << value.packet[1] << "," << std::endl
+     << "  " << value.packet[2] << "," << std::endl
+     << "  " << value.packet[3] << " ]";
+  return os;
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4f, 4>& kernel) {
+  EIGEN_MSA_DEBUG;
+
+  v4i32 tmp1, tmp2, tmp3, tmp4;
+
+  tmp1 = __builtin_msa_ilvr_w((v4i32)kernel.packet[1], (v4i32)kernel.packet[0]);
+  tmp2 = __builtin_msa_ilvr_w((v4i32)kernel.packet[3], (v4i32)kernel.packet[2]);
+  tmp3 = __builtin_msa_ilvl_w((v4i32)kernel.packet[1], (v4i32)kernel.packet[0]);
+  tmp4 = __builtin_msa_ilvl_w((v4i32)kernel.packet[3], (v4i32)kernel.packet[2]);
+
+  kernel.packet[0] = (Packet4f)__builtin_msa_ilvr_d((v2i64)tmp2, (v2i64)tmp1);
+  kernel.packet[1] = (Packet4f)__builtin_msa_ilvod_d((v2i64)tmp2, (v2i64)tmp1);
+  kernel.packet[2] = (Packet4f)__builtin_msa_ilvr_d((v2i64)tmp4, (v2i64)tmp3);
+  kernel.packet[3] = (Packet4f)__builtin_msa_ilvod_d((v2i64)tmp4, (v2i64)tmp3);
+}
+
+inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet4i, 4>& value) {
+  os << "[ " << value.packet[0] << "," << std::endl
+     << "  " << value.packet[1] << "," << std::endl
+     << "  " << value.packet[2] << "," << std::endl
+     << "  " << value.packet[3] << " ]";
+  return os;
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4i, 4>& kernel) {
+  EIGEN_MSA_DEBUG;
+
+  v4i32 tmp1, tmp2, tmp3, tmp4;
+
+  tmp1 = __builtin_msa_ilvr_w(kernel.packet[1], kernel.packet[0]);
+  tmp2 = __builtin_msa_ilvr_w(kernel.packet[3], kernel.packet[2]);
+  tmp3 = __builtin_msa_ilvl_w(kernel.packet[1], kernel.packet[0]);
+  tmp4 = __builtin_msa_ilvl_w(kernel.packet[3], kernel.packet[2]);
+
+  kernel.packet[0] = (Packet4i)__builtin_msa_ilvr_d((v2i64)tmp2, (v2i64)tmp1);
+  kernel.packet[1] = (Packet4i)__builtin_msa_ilvod_d((v2i64)tmp2, (v2i64)tmp1);
+  kernel.packet[2] = (Packet4i)__builtin_msa_ilvr_d((v2i64)tmp4, (v2i64)tmp3);
+  kernel.packet[3] = (Packet4i)__builtin_msa_ilvod_d((v2i64)tmp4, (v2i64)tmp3);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fsqrt_w(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f prsqrt(const Packet4f& a) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  return __builtin_msa_frsqrt_w(a);
+#else
+  Packet4f ones = __builtin_msa_ffint_s_w(__builtin_msa_ldi_w(1));
+  return pdiv(ones, psqrt(a));
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) {
+  Packet4f v = a;
+  int32_t old_mode, new_mode;
+  asm volatile(
+      "cfcmsa  %[old_mode], $1\n"
+      "ori     %[new_mode], %[old_mode], 3\n"  // 3 = round towards -INFINITY.
+      "ctcmsa  $1, %[new_mode]\n"
+      "frint.w %w[v], %w[v]\n"
+      "ctcmsa  $1, %[old_mode]\n"
+      :  // outputs
+      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
+      [v] "+f"(v)
+      :  // inputs
+      :  // clobbers
+  );
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a) {
+  Packet4f v = a;
+  int32_t old_mode, new_mode;
+  asm volatile(
+      "cfcmsa  %[old_mode], $1\n"
+      "ori     %[new_mode], %[old_mode], 3\n"
+      "xori    %[new_mode], %[new_mode], 1\n"  // 2 = round towards +INFINITY.
+      "ctcmsa  $1, %[new_mode]\n"
+      "frint.w %w[v], %w[v]\n"
+      "ctcmsa  $1, %[old_mode]\n"
+      :  // outputs
+      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
+      [v] "+f"(v)
+      :  // inputs
+      :  // clobbers
+  );
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a) {
+  Packet4f v = a;
+  int32_t old_mode, new_mode;
+  asm volatile(
+      "cfcmsa  %[old_mode], $1\n"
+      "ori     %[new_mode], %[old_mode], 3\n"
+      "xori    %[new_mode], %[new_mode], 3\n"  // 0 = round to nearest, ties to even.
+      "ctcmsa  $1, %[new_mode]\n"
+      "frint.w %w[v], %w[v]\n"
+      "ctcmsa  $1, %[old_mode]\n"
+      :  // outputs
+      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
+      [v] "+f"(v)
+      :  // inputs
+      :  // clobbers
+  );
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket,
+                                    const Packet4f& elsePacket) {
+  Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2],
+                       ifPacket.select[3] };
+  Packet4i mask = __builtin_msa_ceqi_w((Packet4i)select, 0);
+  return (Packet4f)__builtin_msa_bsel_v((v16u8)mask, (v16u8)thenPacket, (v16u8)elsePacket);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pblend(const Selector<4>& ifPacket, const Packet4i& thenPacket,
+                                    const Packet4i& elsePacket) {
+  Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2],
+                       ifPacket.select[3] };
+  Packet4i mask = __builtin_msa_ceqi_w((Packet4i)select, 0);
+  return (Packet4i)__builtin_msa_bsel_v((v16u8)mask, (v16u8)thenPacket, (v16u8)elsePacket);
+}
+
+//---------- double ----------
+
+typedef v2f64 Packet2d;
+typedef v2i64 Packet2l;
+typedef v2u64 Packet2ul;
+
+#define EIGEN_DECLARE_CONST_Packet2d(NAME, X) const Packet2d p2d_##NAME = { X, X }
+#define EIGEN_DECLARE_CONST_Packet2l(NAME, X) const Packet2l p2l_##NAME = { X, X }
+#define EIGEN_DECLARE_CONST_Packet2ul(NAME, X) const Packet2ul p2ul_##NAME = { X, X }
+
+inline std::ostream& operator<<(std::ostream& os, const Packet2d& value) {
+  os << "[ " << value[0] << ", " << value[1] << " ]";
+  return os;
+}
+
+inline std::ostream& operator<<(std::ostream& os, const Packet2l& value) {
+  os << "[ " << value[0] << ", " << value[1] << " ]";
+  return os;
+}
+
+inline std::ostream& operator<<(std::ostream& os, const Packet2ul& value) {
+  os << "[ " << value[0] << ", " << value[1] << " ]";
+  return os;
+}
+
+template <>
+struct packet_traits<double> : default_packet_traits {
+  typedef Packet2d type;
+  typedef Packet2d half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+    // FIXME check the Has*
+    HasDiv = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasRound = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasBlend = 1
+  };
+};
+
+template <>
+struct unpacket_traits<Packet2d> {
+  typedef double type;
+  enum { size = 2, alignment = Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false };
+  typedef Packet2d half;
+};
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) {
+  EIGEN_MSA_DEBUG;
+
+  Packet2d value = { from, from };
+  return value;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fadd_d(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) {
+  EIGEN_MSA_DEBUG;
+
+  static const Packet2d countdown = { 0.0, 1.0 };
+  return padd(pset1<Packet2d>(a), countdown);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fsub_d(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet2d)__builtin_msa_bnegi_d((v2u64)a, 63);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fmul_d(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fdiv_d(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fmadd_d(c, a, b);
+}
+
+// Logical Operations are not supported for float, so we have to reinterpret casts using MSA
+// intrinsics
+template <>
+EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet2d)__builtin_msa_and_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet2d)__builtin_msa_or_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet2d)__builtin_msa_xor_v((v16u8)a, (v16u8)b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+  return pand(a, (Packet2d)__builtin_msa_xori_b((v16u8)b, 255));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet2d)__builtin_msa_ld_d(const_cast<double*>(from), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  // This prefers numbers to NaNs.
+  return __builtin_msa_fmin_d(a, b);
+#else
+  // This prefers NaNs to numbers.
+  v2i64 aNaN = __builtin_msa_fcun_d(a, a);
+  v2i64 aMinOrNaN = por(__builtin_msa_fclt_d(a, b), aNaN);
+  return (Packet2d)__builtin_msa_bsel_v((v16u8)aMinOrNaN, (v16u8)b, (v16u8)a);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  // This prefers numbers to NaNs.
+  return __builtin_msa_fmax_d(a, b);
+#else
+  // This prefers NaNs to numbers.
+  v2i64 aNaN = __builtin_msa_fcun_d(a, a);
+  v2i64 aMaxOrNaN = por(__builtin_msa_fclt_d(b, a), aNaN);
+  return (Packet2d)__builtin_msa_bsel_v((v16u8)aMaxOrNaN, (v16u8)b, (v16u8)a);
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_LOAD return (Packet2d)__builtin_msa_ld_d(const_cast<double*>(from), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from) {
+  EIGEN_MSA_DEBUG;
+
+  Packet2d value = { *from, *from };
+  return value;
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_ALIGNED_STORE __builtin_msa_st_d((v2i64)from, to, 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) {
+  EIGEN_MSA_DEBUG;
+
+  EIGEN_DEBUG_UNALIGNED_STORE __builtin_msa_st_d((v2i64)from, to, 0);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  Packet2d value;
+  value[0] = *from;
+  from += stride;
+  value[1] = *from;
+  return value;
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from,
+                                                         Index stride) {
+  EIGEN_MSA_DEBUG;
+
+  *to = from[0];
+  to += stride;
+  *to = from[1];
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) {
+  EIGEN_MSA_DEBUG;
+
+  __builtin_prefetch(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  return a[0];
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet2d)__builtin_msa_shf_w((v4i32)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  return (Packet2d)__builtin_msa_bclri_d((v2u64)a, 63);
+}
+
+template <>
+EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet2d s = padd(a, preverse(a));
+  return s[0];
+}
+
+// Other reduction functions:
+// mul
+template <>
+EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  Packet2d p = pmul(a, preverse(a));
+  return p[0];
+}
+
+// min
+template <>
+EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  Packet2d swapped = (Packet2d)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
+  Packet2d v = __builtin_msa_fmin_d(a, swapped);
+  return v[0];
+#else
+  double a0 = a[0], a1 = a[1];
+  return ((numext::isnan)(a0) || a0 < a1) ? a0 : a1;
+#endif
+}
+
+// max
+template <>
+EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  Packet2d swapped = (Packet2d)__builtin_msa_shf_w((Packet4i)a, EIGEN_MSA_SHF_I8(2, 3, 0, 1));
+  Packet2d v = __builtin_msa_fmax_d(a, swapped);
+  return v[0];
+#else
+  double a0 = a[0], a1 = a[1];
+  return ((numext::isnan)(a0) || a0 > a1) ? a0 : a1;
+#endif
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d psqrt(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+  return __builtin_msa_fsqrt_d(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d prsqrt(const Packet2d& a) {
+  EIGEN_MSA_DEBUG;
+
+#if EIGEN_FAST_MATH
+  return __builtin_msa_frsqrt_d(a);
+#else
+  Packet2d ones = __builtin_msa_ffint_s_d(__builtin_msa_ldi_d(1));
+  return pdiv(ones, psqrt(a));
+#endif
+}
+
+inline std::ostream& operator<<(std::ostream& os, const PacketBlock<Packet2d, 2>& value) {
+  os << "[ " << value.packet[0] << "," << std::endl << "  " << value.packet[1] << " ]";
+  return os;
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2d, 2>& kernel) {
+  EIGEN_MSA_DEBUG;
+
+  Packet2d trn1 = (Packet2d)__builtin_msa_ilvev_d((v2i64)kernel.packet[1], (v2i64)kernel.packet[0]);
+  Packet2d trn2 = (Packet2d)__builtin_msa_ilvod_d((v2i64)kernel.packet[1], (v2i64)kernel.packet[0]);
+  kernel.packet[0] = trn1;
+  kernel.packet[1] = trn2;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) {
+  Packet2d v = a;
+  int32_t old_mode, new_mode;
+  asm volatile(
+      "cfcmsa  %[old_mode], $1\n"
+      "ori     %[new_mode], %[old_mode], 3\n"  // 3 = round towards -INFINITY.
+      "ctcmsa  $1, %[new_mode]\n"
+      "frint.d %w[v], %w[v]\n"
+      "ctcmsa  $1, %[old_mode]\n"
+      :  // outputs
+      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
+      [v] "+f"(v)
+      :  // inputs
+      :  // clobbers
+  );
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a) {
+  Packet2d v = a;
+  int32_t old_mode, new_mode;
+  asm volatile(
+      "cfcmsa  %[old_mode], $1\n"
+      "ori     %[new_mode], %[old_mode], 3\n"
+      "xori    %[new_mode], %[new_mode], 1\n"  // 2 = round towards +INFINITY.
+      "ctcmsa  $1, %[new_mode]\n"
+      "frint.d %w[v], %w[v]\n"
+      "ctcmsa  $1, %[old_mode]\n"
+      :  // outputs
+      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
+      [v] "+f"(v)
+      :  // inputs
+      :  // clobbers
+  );
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a) {
+  Packet2d v = a;
+  int32_t old_mode, new_mode;
+  asm volatile(
+      "cfcmsa  %[old_mode], $1\n"
+      "ori     %[new_mode], %[old_mode], 3\n"
+      "xori    %[new_mode], %[new_mode], 3\n"  // 0 = round to nearest, ties to even.
+      "ctcmsa  $1, %[new_mode]\n"
+      "frint.d %w[v], %w[v]\n"
+      "ctcmsa  $1, %[old_mode]\n"
+      :  // outputs
+      [old_mode] "=r"(old_mode), [new_mode] "=r"(new_mode),
+      [v] "+f"(v)
+      :  // inputs
+      :  // clobbers
+  );
+  return v;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, const Packet2d& thenPacket,
+                                    const Packet2d& elsePacket) {
+  Packet2ul select = { ifPacket.select[0], ifPacket.select[1] };
+  Packet2l mask = __builtin_msa_ceqi_d((Packet2l)select, 0);
+  return (Packet2d)__builtin_msa_bsel_v((v16u8)mask, (v16u8)thenPacket, (v16u8)elsePacket);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_MSA_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/Complex.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/Complex.h
new file mode 100644
index 0000000000000000000000000000000000000000..97f41169bb036f5505ebd75ebd09c8ba9ec15dd7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/Complex.h
@@ -0,0 +1,565 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Konstantinos Margaritis <markos@freevec.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_NEON_H
+#define EIGEN_COMPLEX_NEON_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+inline uint32x4_t p4ui_CONJ_XOR()
+{
+// See bug 1325, clang fails to call vld1q_u64.
+#if EIGEN_COMP_CLANG || EIGEN_COMP_CASTXML
+  uint32x4_t ret = { 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
+  return ret;
+#else
+  static const uint32_t conj_XOR_DATA[] = { 0x00000000, 0x80000000, 0x00000000, 0x80000000 };
+  return vld1q_u32( conj_XOR_DATA );
+#endif
+}
+
+inline uint32x2_t p2ui_CONJ_XOR()
+{
+  static const uint32_t conj_XOR_DATA[] = { 0x00000000, 0x80000000 };
+  return vld1_u32( conj_XOR_DATA );
+}
+
+//---------- float ----------
+
+struct Packet1cf
+{
+  EIGEN_STRONG_INLINE Packet1cf() {}
+  EIGEN_STRONG_INLINE explicit Packet1cf(const Packet2f& a) : v(a) {}
+  Packet2f v;
+};
+struct Packet2cf
+{
+  EIGEN_STRONG_INLINE Packet2cf() {}
+  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {}
+  Packet4f v;
+};
+
+template<> struct packet_traits<std::complex<float> > : default_packet_traits
+{
+  typedef Packet2cf type;
+  typedef Packet1cf half;
+  enum
+  {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasAdd       = 1,
+    HasSub       = 1,
+    HasMul       = 1,
+    HasDiv       = 1,
+    HasNegate    = 1,
+    HasSqrt      = 1,
+    HasAbs       = 0,
+    HasAbs2      = 0,
+    HasMin       = 0,
+    HasMax       = 0,
+    HasSetLinear = 0
+  };
+};
+
+template<> struct unpacket_traits<Packet1cf>
+{
+  typedef std::complex<float> type;
+  typedef Packet1cf half;
+  typedef Packet2f as_real;
+  enum
+  {
+    size = 1,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet2cf>
+{
+  typedef std::complex<float> type;
+  typedef Packet1cf half;
+  typedef Packet4f as_real;
+  enum
+  {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template<> EIGEN_STRONG_INLINE Packet1cf pcast<float,Packet1cf>(const float& a)
+{ return Packet1cf(vset_lane_f32(a, vdup_n_f32(0.f), 0)); }
+template<> EIGEN_STRONG_INLINE Packet2cf pcast<Packet2f,Packet2cf>(const Packet2f& a)
+{ return Packet2cf(vreinterpretq_f32_u64(vmovl_u32(vreinterpret_u32_f32(a)))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cf pset1<Packet1cf>(const std::complex<float>& from)
+{ return Packet1cf(vld1_f32(reinterpret_cast<const float*>(&from))); }
+template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>& from)
+{
+  const float32x2_t r64 = vld1_f32(reinterpret_cast<const float*>(&from));
+  return Packet2cf(vcombine_f32(r64, r64));
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cf padd<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
+{ return Packet1cf(padd<Packet2f>(a.v, b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
+{ return Packet2cf(padd<Packet4f>(a.v, b.v)); }
+
+template<> EIGEN_STRONG_INLINE Packet1cf psub<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
+{ return Packet1cf(psub<Packet2f>(a.v, b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
+{ return Packet2cf(psub<Packet4f>(a.v, b.v)); }
+
+template<> EIGEN_STRONG_INLINE Packet1cf pnegate(const Packet1cf& a) { return Packet1cf(pnegate<Packet2f>(a.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) { return Packet2cf(pnegate<Packet4f>(a.v)); }
+
+template<> EIGEN_STRONG_INLINE Packet1cf pconj(const Packet1cf& a)
+{
+  const Packet2ui b = Packet2ui(vreinterpret_u32_f32(a.v));
+  return Packet1cf(vreinterpret_f32_u32(veor_u32(b, p2ui_CONJ_XOR())));
+}
+template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a)
+{
+  const Packet4ui b = Packet4ui(vreinterpretq_u32_f32(a.v));
+  return Packet2cf(vreinterpretq_f32_u32(veorq_u32(b, p4ui_CONJ_XOR())));
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cf pmul<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
+{
+  Packet2f v1, v2;
+
+  // Get the real values of a | a1_re | a1_re |
+  v1 = vdup_lane_f32(a.v, 0);
+  // Get the imag values of a | a1_im | a1_im |
+  v2 = vdup_lane_f32(a.v, 1);
+  // Multiply the real a with b
+  v1 = vmul_f32(v1, b.v);
+  // Multiply the imag a with b
+  v2 = vmul_f32(v2, b.v);
+  // Conjugate v2
+  v2 = vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(v2), p2ui_CONJ_XOR()));
+  // Swap real/imag elements in v2.
+  v2 = vrev64_f32(v2);
+  // Add and return the result
+  return Packet1cf(vadd_f32(v1, v2));
+}
+template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
+{
+  Packet4f v1, v2;
+
+  // Get the real values of a | a1_re | a1_re | a2_re | a2_re |
+  v1 = vcombine_f32(vdup_lane_f32(vget_low_f32(a.v), 0), vdup_lane_f32(vget_high_f32(a.v), 0));
+  // Get the imag values of a | a1_im | a1_im | a2_im | a2_im |
+  v2 = vcombine_f32(vdup_lane_f32(vget_low_f32(a.v), 1), vdup_lane_f32(vget_high_f32(a.v), 1));
+  // Multiply the real a with b
+  v1 = vmulq_f32(v1, b.v);
+  // Multiply the imag a with b
+  v2 = vmulq_f32(v2, b.v);
+  // Conjugate v2
+  v2 = vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(v2), p4ui_CONJ_XOR()));
+  // Swap real/imag elements in v2.
+  v2 = vrev64q_f32(v2);
+  // Add and return the result
+  return Packet2cf(vaddq_f32(v1, v2));
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cf pcmp_eq(const Packet1cf& a, const Packet1cf& b)
+{
+  // Compare real and imaginary parts of a and b to get the mask vector:
+  // [re(a[0])==re(b[0]), im(a[0])==im(b[0])]
+  Packet2f eq = pcmp_eq<Packet2f>(a.v, b.v);
+  // Swap real/imag elements in the mask in to get:
+  // [im(a[0])==im(b[0]), re(a[0])==re(b[0])]
+  Packet2f eq_swapped = vrev64_f32(eq);
+  // Return re(a)==re(b) && im(a)==im(b) by computing bitwise AND of eq and eq_swapped
+  return Packet1cf(pand<Packet2f>(eq, eq_swapped));
+}
+template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b)
+{
+  // Compare real and imaginary parts of a and b to get the mask vector:
+  // [re(a[0])==re(b[0]), im(a[0])==im(b[0]), re(a[1])==re(b[1]), im(a[1])==im(b[1])]
+  Packet4f eq = pcmp_eq<Packet4f>(a.v, b.v);
+  // Swap real/imag elements in the mask in to get:
+  // [im(a[0])==im(b[0]), re(a[0])==re(b[0]), im(a[1])==im(b[1]), re(a[1])==re(b[1])]
+  Packet4f eq_swapped = vrev64q_f32(eq);
+  // Return re(a)==re(b) && im(a)==im(b) by computing bitwise AND of eq and eq_swapped
+  return Packet2cf(pand<Packet4f>(eq, eq_swapped));
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cf pand<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
+{ return Packet1cf(vreinterpret_f32_u32(vand_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v)))); }
+template<> EIGEN_STRONG_INLINE Packet2cf pand<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
+{ return Packet2cf(vreinterpretq_f32_u32(vandq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v)))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cf por<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
+{ return Packet1cf(vreinterpret_f32_u32(vorr_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v)))); }
+template<> EIGEN_STRONG_INLINE Packet2cf por<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
+{ return Packet2cf(vreinterpretq_f32_u32(vorrq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v)))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cf pxor<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
+{ return Packet1cf(vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v)))); }
+template<> EIGEN_STRONG_INLINE Packet2cf pxor<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
+{ return Packet2cf(vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v)))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cf pandnot<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
+{ return Packet1cf(vreinterpret_f32_u32(vbic_u32(vreinterpret_u32_f32(a.v), vreinterpret_u32_f32(b.v)))); }
+template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
+{ return Packet2cf(vreinterpretq_f32_u32(vbicq_u32(vreinterpretq_u32_f32(a.v), vreinterpretq_u32_f32(b.v)))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cf pload<Packet1cf>(const std::complex<float>* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return Packet1cf(pload<Packet2f>((const float*)from)); }
+template<> EIGEN_STRONG_INLINE Packet2cf pload<Packet2cf>(const std::complex<float>* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>(reinterpret_cast<const float*>(from))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cf ploadu<Packet1cf>(const std::complex<float>* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cf(ploadu<Packet2f>((const float*)from)); }
+template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>(reinterpret_cast<const float*>(from))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cf ploaddup<Packet1cf>(const std::complex<float>* from)
+{ return pset1<Packet1cf>(*from); }
+template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from)
+{ return pset1<Packet2cf>(*from); }
+
+template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *to, const Packet1cf& from)
+{ EIGEN_DEBUG_ALIGNED_STORE pstore((float*)to, from.v); }
+template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *to, const Packet2cf& from)
+{ EIGEN_DEBUG_ALIGNED_STORE pstore(reinterpret_cast<float*>(to), from.v); }
+
+template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *to, const Packet1cf& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE pstoreu((float*)to, from.v); }
+template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *to, const Packet2cf& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE pstoreu(reinterpret_cast<float*>(to), from.v); }
+
+template<> EIGEN_DEVICE_FUNC inline Packet1cf pgather<std::complex<float>, Packet1cf>(
+    const std::complex<float>* from, Index stride)
+{
+  const Packet2f tmp = vdup_n_f32(std::real(from[0*stride]));
+  return Packet1cf(vset_lane_f32(std::imag(from[0*stride]), tmp, 1));
+}
+template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(
+    const std::complex<float>* from, Index stride)
+{
+  Packet4f res = vdupq_n_f32(std::real(from[0*stride]));
+  res = vsetq_lane_f32(std::imag(from[0*stride]), res, 1);
+  res = vsetq_lane_f32(std::real(from[1*stride]), res, 2);
+  res = vsetq_lane_f32(std::imag(from[1*stride]), res, 3);
+  return Packet2cf(res);
+}
+
+template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet1cf>(
+    std::complex<float>* to, const Packet1cf& from, Index stride)
+{ to[stride*0] = std::complex<float>(vget_lane_f32(from.v, 0), vget_lane_f32(from.v, 1)); }
+template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(
+    std::complex<float>* to, const Packet2cf& from, Index stride)
+{
+  to[stride*0] = std::complex<float>(vgetq_lane_f32(from.v, 0), vgetq_lane_f32(from.v, 1));
+  to[stride*1] = std::complex<float>(vgetq_lane_f32(from.v, 2), vgetq_lane_f32(from.v, 3));
+}
+
+template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> *addr)
+{ EIGEN_ARM_PREFETCH(reinterpret_cast<const float*>(addr)); }
+
+template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet1cf>(const Packet1cf& a)
+{
+  EIGEN_ALIGN16 std::complex<float> x;
+  vst1_f32(reinterpret_cast<float*>(&x), a.v);
+  return x;
+}
+template<> EIGEN_STRONG_INLINE std::complex<float> pfirst<Packet2cf>(const Packet2cf& a)
+{
+  EIGEN_ALIGN16 std::complex<float> x[2];
+  vst1q_f32(reinterpret_cast<float*>(x), a.v);
+  return x[0];
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cf preverse(const Packet1cf& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a)
+{ return Packet2cf(vcombine_f32(vget_high_f32(a.v), vget_low_f32(a.v))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cf pcplxflip<Packet1cf>(const Packet1cf& a)
+{ return Packet1cf(vrev64_f32(a.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& a)
+{ return Packet2cf(vrev64q_f32(a.v)); }
+
+template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet1cf>(const Packet1cf& a)
+{
+  std::complex<float> s;
+  vst1_f32((float *)&s, a.v);
+  return s;
+}
+template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
+{
+  std::complex<float> s;
+  vst1_f32(reinterpret_cast<float*>(&s), vadd_f32(vget_low_f32(a.v), vget_high_f32(a.v)));
+  return s;
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet1cf>(const Packet1cf& a)
+{
+  std::complex<float> s;
+  vst1_f32((float *)&s, a.v);
+  return s;
+}
+template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a)
+{
+  float32x2_t a1, a2, v1, v2, prod;
+  std::complex<float> s;
+
+  a1 = vget_low_f32(a.v);
+  a2 = vget_high_f32(a.v);
+   // Get the real values of a | a1_re | a1_re | a2_re | a2_re |
+  v1 = vdup_lane_f32(a1, 0);
+  // Get the real values of a | a1_im | a1_im | a2_im | a2_im |
+  v2 = vdup_lane_f32(a1, 1);
+  // Multiply the real a with b
+  v1 = vmul_f32(v1, a2);
+  // Multiply the imag a with b
+  v2 = vmul_f32(v2, a2);
+  // Conjugate v2
+  v2 = vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(v2), p2ui_CONJ_XOR()));
+  // Swap real/imag elements in v2.
+  v2 = vrev64_f32(v2);
+  // Add v1, v2
+  prod = vadd_f32(v1, v2);
+
+  vst1_f32(reinterpret_cast<float*>(&s), prod);
+
+  return s;
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cf,Packet2f)
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
+
+template<> EIGEN_STRONG_INLINE Packet1cf pdiv<Packet1cf>(const Packet1cf& a, const Packet1cf& b)
+{
+  return pdiv_complex(a, b);
+}
+template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
+{
+  return pdiv_complex(a, b);
+}
+
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet1cf, 1>& /*kernel*/) {}
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet2cf, 2>& kernel)
+{
+  Packet4f tmp = vcombine_f32(vget_high_f32(kernel.packet[0].v), vget_high_f32(kernel.packet[1].v));
+  kernel.packet[0].v = vcombine_f32(vget_low_f32(kernel.packet[0].v), vget_low_f32(kernel.packet[1].v));
+  kernel.packet[1].v = tmp;
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cf psqrt<Packet1cf>(const Packet1cf& a) {
+  return psqrt_complex<Packet1cf>(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a) {
+  return psqrt_complex<Packet2cf>(a);
+}
+
+//---------- double ----------
+#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
+
+// See bug 1325, clang fails to call vld1q_u64.
+#if EIGEN_COMP_CLANG || EIGEN_COMP_CASTXML || EIGEN_COMP_CPE
+  static uint64x2_t p2ul_CONJ_XOR = {0x0, 0x8000000000000000};
+#else
+  const uint64_t  p2ul_conj_XOR_DATA[] = { 0x0, 0x8000000000000000 };
+  static uint64x2_t p2ul_CONJ_XOR = vld1q_u64( p2ul_conj_XOR_DATA );
+#endif
+
+struct Packet1cd
+{
+  EIGEN_STRONG_INLINE Packet1cd() {}
+  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
+  Packet2d v;
+};
+
+template<> struct packet_traits<std::complex<double> >  : default_packet_traits
+{
+  typedef Packet1cd type;
+  typedef Packet1cd half;
+  enum
+  {
+    Vectorizable = 1,
+    AlignedOnScalar = 0,
+    size = 1,
+
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasDiv    = 1,
+    HasNegate = 1,
+    HasSqrt   = 1,
+    HasAbs    = 0,
+    HasAbs2   = 0,
+    HasMin    = 0,
+    HasMax    = 0,
+    HasSetLinear = 0
+  };
+};
+
+template<> struct unpacket_traits<Packet1cd>
+{
+  typedef std::complex<double> type;
+  typedef Packet1cd half;
+  typedef Packet2d as_real;
+  enum
+  {
+    size=1,
+    alignment=Aligned16,
+    vectorizable=true,
+    masked_load_available=false,
+    masked_store_available=false
+  };
+};
+
+template<> EIGEN_STRONG_INLINE Packet1cd pload<Packet1cd>(const std::complex<double>* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>(reinterpret_cast<const double*>(from))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>(reinterpret_cast<const double*>(from))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>& from)
+{
+  /* here we really have to use unaligned loads :( */
+  return ploadu<Packet1cd>(&from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
+{ return Packet1cd(padd<Packet2d>(a.v, b.v)); }
+
+template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
+{ return Packet1cd(psub<Packet2d>(a.v, b.v)); }
+
+template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a)
+{ return Packet1cd(pnegate<Packet2d>(a.v)); }
+
+template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a)
+{ return Packet1cd(vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a.v), p2ul_CONJ_XOR))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
+{
+  Packet2d v1, v2;
+
+  // Get the real values of a
+  v1 = vdupq_lane_f64(vget_low_f64(a.v), 0);
+  // Get the imag values of a
+  v2 = vdupq_lane_f64(vget_high_f64(a.v), 0);
+  // Multiply the real a with b
+  v1 = vmulq_f64(v1, b.v);
+  // Multiply the imag a with b
+  v2 = vmulq_f64(v2, b.v);
+  // Conjugate v2
+  v2 = vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(v2), p2ul_CONJ_XOR));
+  // Swap real/imag elements in v2.
+  v2 = preverse<Packet2d>(v2);
+  // Add and return the result
+  return Packet1cd(vaddq_f64(v1, v2));
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b)
+{
+  // Compare real and imaginary parts of a and b to get the mask vector:
+  // [re(a)==re(b), im(a)==im(b)]
+  Packet2d eq = pcmp_eq<Packet2d>(a.v, b.v);
+  // Swap real/imag elements in the mask in to get:
+  // [im(a)==im(b), re(a)==re(b)]
+  Packet2d eq_swapped = vreinterpretq_f64_u32(vrev64q_u32(vreinterpretq_u32_f64(eq)));
+  // Return re(a)==re(b) & im(a)==im(b) by computing bitwise AND of eq and eq_swapped
+  return Packet1cd(pand<Packet2d>(eq, eq_swapped));
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cd pand<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
+{ return Packet1cd(vreinterpretq_f64_u64(vandq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cd por<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
+{ return Packet1cd(vreinterpretq_f64_u64(vorrq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cd pxor<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
+{ return Packet1cd(vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
+{ return Packet1cd(vreinterpretq_f64_u64(vbicq_u64(vreinterpretq_u64_f64(a.v),vreinterpretq_u64_f64(b.v)))); }
+
+template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from)
+{ return pset1<Packet1cd>(*from); }
+
+template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *to, const Packet1cd& from)
+{ EIGEN_DEBUG_ALIGNED_STORE pstore(reinterpret_cast<double*>(to), from.v); }
+
+template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *to, const Packet1cd& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE pstoreu(reinterpret_cast<double*>(to), from.v); }
+
+template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> *addr)
+{ EIGEN_ARM_PREFETCH(reinterpret_cast<const double*>(addr)); }
+
+template<> EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(
+    const std::complex<double>* from, Index stride)
+{
+  Packet2d res = pset1<Packet2d>(0.0);
+  res = vsetq_lane_f64(std::real(from[0*stride]), res, 0);
+  res = vsetq_lane_f64(std::imag(from[0*stride]), res, 1);
+  return Packet1cd(res);
+}
+
+template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(
+    std::complex<double>* to, const Packet1cd& from, Index stride)
+{ to[stride*0] = std::complex<double>(vgetq_lane_f64(from.v, 0), vgetq_lane_f64(from.v, 1)); }
+
+template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet1cd>(const Packet1cd& a)
+{
+  EIGEN_ALIGN16 std::complex<double> res;
+  pstore<std::complex<double> >(&res, a);
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
+
+template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a) { return pfirst(a); }
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
+
+template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
+{
+  return pdiv_complex(a, b);
+}
+
+EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)
+{ return Packet1cd(preverse(Packet2d(x.v))); }
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd,2>& kernel)
+{
+  Packet2d tmp = vcombine_f64(vget_high_f64(kernel.packet[0].v), vget_high_f64(kernel.packet[1].v));
+  kernel.packet[0].v = vcombine_f64(vget_low_f64(kernel.packet[0].v), vget_low_f64(kernel.packet[1].v));
+  kernel.packet[1].v = tmp;
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cd psqrt<Packet1cd>(const Packet1cd& a) {
+  return psqrt_complex<Packet1cd>(a);
+}
+
+#endif // EIGEN_ARCH_ARM64
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_COMPLEX_NEON_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
new file mode 100644
index 0000000000000000000000000000000000000000..ff1646d51a01c61bdec0e0fccdf907b3a32e6b2d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/GeneralBlockPanelKernel.h
@@ -0,0 +1,256 @@
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+#if EIGEN_ARCH_ARM && EIGEN_COMP_CLANG
+
+// Clang seems to excessively spill registers in the GEBP kernel on 32-bit arm.
+// Here we specialize gebp_traits to eliminate these register spills.
+// See #2138.
+template<>
+struct gebp_traits <float,float,false,false,Architecture::NEON,GEBPPacketFull>
+ : gebp_traits<float,float,false,false,Architecture::Generic,GEBPPacketFull>
+{
+  EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
+  { 
+    // This volatile inline ASM both acts as a barrier to prevent reordering,
+    // as well as enforces strict register use.
+    asm volatile(
+      "vmla.f32 %q[r], %q[c], %q[alpha]"
+      : [r] "+w" (r)
+      : [c] "w" (c),
+        [alpha] "w" (alpha)
+      : );
+  }
+
+  template <typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const Packet4f& a, const Packet4f& b,
+                                Packet4f& c, Packet4f&,
+                                const LaneIdType&) const {
+    acc(a, b, c);
+  }
+  
+  template <typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const Packet4f& a, const QuadPacket<Packet4f>& b,
+                                Packet4f& c, Packet4f& tmp,
+                                const LaneIdType& lane) const {
+    madd(a, b.get(lane), c, tmp, lane);
+  }
+};
+
+#endif // EIGEN_ARCH_ARM && EIGEN_COMP_CLANG
+
+#if EIGEN_ARCH_ARM64
+
+#ifndef EIGEN_NEON_GEBP_NR
+#define EIGEN_NEON_GEBP_NR 8
+#endif
+
+template<>
+struct gebp_traits <float,float,false,false,Architecture::NEON,GEBPPacketFull>
+ : gebp_traits<float,float,false,false,Architecture::Generic,GEBPPacketFull>
+{
+  typedef float RhsPacket;
+  typedef float32x4_t RhsPacketx4;
+  enum { nr = EIGEN_NEON_GEBP_NR };
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const {
+    dest = *b;
+  }
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
+  {
+    dest = vld1q_f32(b);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacket& dest) const
+  {
+    dest = *b;
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
+  {}
+
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
+  {
+    loadRhs(b,dest);
+  }
+
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<0>&) const
+  {
+    c = vfmaq_n_f32(c, a, b);
+  }
+  // NOTE: Template parameter inference failed when compiled with Android NDK:
+  // "candidate template ignored: could not match 'FixedInt<N>' against 'Eigen::internal::FixedInt<0>".
+
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<0>&) const
+  { madd_helper<0>(a, b, c); }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<1>&) const
+  { madd_helper<1>(a, b, c); }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<2>&) const
+  { madd_helper<2>(a, b, c); }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<3>&) const
+  { madd_helper<3>(a, b, c); }
+
+ private:
+  template<int LaneID>
+  EIGEN_STRONG_INLINE void madd_helper(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c) const
+  {
+    #if EIGEN_GNUC_STRICT_LESS_THAN(9,0,0)
+    // 1. workaround gcc issue https://gcc.gnu.org/bugzilla/show_bug.cgi?id=89101
+    //    vfmaq_laneq_f32 is implemented through a costly dup, which was fixed in gcc9
+    // 2. workaround the gcc register split problem on arm64-neon
+         if(LaneID==0)  asm("fmla %0.4s, %1.4s, %2.s[0]\n" : "+w" (c) : "w" (a), "w" (b) :  );
+    else if(LaneID==1)  asm("fmla %0.4s, %1.4s, %2.s[1]\n" : "+w" (c) : "w" (a), "w" (b) :  );
+    else if(LaneID==2)  asm("fmla %0.4s, %1.4s, %2.s[2]\n" : "+w" (c) : "w" (a), "w" (b) :  );
+    else if(LaneID==3)  asm("fmla %0.4s, %1.4s, %2.s[3]\n" : "+w" (c) : "w" (a), "w" (b) :  );
+    #else
+    c = vfmaq_laneq_f32(c, a, b, LaneID);
+    #endif
+  }
+};
+
+
+template<>
+struct gebp_traits <double,double,false,false,Architecture::NEON>
+ : gebp_traits<double,double,false,false,Architecture::Generic>
+{
+  typedef double RhsPacket;
+  enum { nr = EIGEN_NEON_GEBP_NR };
+  struct RhsPacketx4 {
+    float64x2_t B_0, B_1;
+  };
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const
+  {
+    dest = *b;
+  }
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
+  {
+    dest.B_0 = vld1q_f64(b);
+    dest.B_1 = vld1q_f64(b+2);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacket& dest) const
+  {
+    loadRhs(b,dest);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
+  {}
+
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
+  {
+    loadRhs(b,dest);
+  }
+
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<0>&) const
+  {
+    c = vfmaq_n_f64(c, a, b);
+  }
+
+  // NOTE: Template parameter inference failed when compiled with Android NDK:
+  // "candidate template ignored: could not match 'FixedInt<N>' against 'Eigen::internal::FixedInt<0>".
+
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<0>&) const
+  { madd_helper<0>(a, b, c); }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<1>&) const
+  { madd_helper<1>(a, b, c); }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<2>&) const
+  { madd_helper<2>(a, b, c); }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<3>&) const
+  { madd_helper<3>(a, b, c); }
+
+ private:
+  template <int LaneID>
+  EIGEN_STRONG_INLINE void madd_helper(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c) const
+  {
+    #if EIGEN_GNUC_STRICT_LESS_THAN(9,0,0)
+    // 1. workaround gcc issue https://gcc.gnu.org/bugzilla/show_bug.cgi?id=89101
+    //    vfmaq_laneq_f64 is implemented through a costly dup, which was fixed in gcc9
+    // 2. workaround the gcc register split problem on arm64-neon
+         if(LaneID==0)  asm("fmla %0.2d, %1.2d, %2.d[0]\n" : "+w" (c) : "w" (a), "w" (b.B_0) :  );
+    else if(LaneID==1)  asm("fmla %0.2d, %1.2d, %2.d[1]\n" : "+w" (c) : "w" (a), "w" (b.B_0) :  );
+    else if(LaneID==2)  asm("fmla %0.2d, %1.2d, %2.d[0]\n" : "+w" (c) : "w" (a), "w" (b.B_1) :  );
+    else if(LaneID==3)  asm("fmla %0.2d, %1.2d, %2.d[1]\n" : "+w" (c) : "w" (a), "w" (b.B_1) :  );
+    #else
+         if(LaneID==0) c = vfmaq_laneq_f64(c, a, b.B_0, 0);
+    else if(LaneID==1) c = vfmaq_laneq_f64(c, a, b.B_0, 1);
+    else if(LaneID==2) c = vfmaq_laneq_f64(c, a, b.B_1, 0);
+    else if(LaneID==3) c = vfmaq_laneq_f64(c, a, b.B_1, 1);
+    #endif
+  }
+};
+
+// The register at operand 3 of fmla for data type half must be v0~v15, the compiler may not
+// allocate a required register for the '%2' of inline asm 'fmla %0.8h, %1.8h, %2.h[id]',
+// so inline assembly can't be used here to advoid the bug that vfmaq_lane_f16 is implemented
+// through a costly dup in gcc compiler.
+#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC && EIGEN_COMP_CLANG
+
+template<>
+struct gebp_traits <half,half,false,false,Architecture::NEON>
+ : gebp_traits<half,half,false,false,Architecture::Generic>
+{
+  typedef half RhsPacket;
+  typedef float16x4_t RhsPacketx4;
+  typedef float16x4_t PacketHalf;
+  enum { nr = EIGEN_NEON_GEBP_NR };
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacket& dest) const
+  {
+    dest = *b;
+  }
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
+  {
+    dest = vld1_f16((const __fp16 *)b);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacket& dest) const
+  {
+    dest = *b;
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
+  {}
+
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar*, RhsPacket&) const
+  {
+    // If LHS is a Packet8h, we cannot correctly mimic a ploadquad of the RHS
+    // using a single scalar value.
+    eigen_assert(false && "Cannot loadRhsQuad for a scalar RHS.");
+  }
+
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<0>&) const
+  {
+    c = vfmaq_n_f16(c, a, b);
+  }
+  EIGEN_STRONG_INLINE void madd(const PacketHalf& a, const RhsPacket& b, PacketHalf& c, RhsPacket& /*tmp*/, const FixedInt<0>&) const
+  {
+    c = vfma_n_f16(c, a, b);
+  }
+
+  // NOTE: Template parameter inference failed when compiled with Android NDK:
+  // "candidate template ignored: could not match 'FixedInt<N>' against 'Eigen::internal::FixedInt<0>".
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<0>&) const
+  { madd_helper<0>(a, b, c); }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<1>&) const
+  { madd_helper<1>(a, b, c); }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<2>&) const
+  { madd_helper<2>(a, b, c); }
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c, RhsPacket& /*tmp*/, const FixedInt<3>&) const
+  { madd_helper<3>(a, b, c); }
+ private:
+  template<int LaneID>
+  EIGEN_STRONG_INLINE void madd_helper(const LhsPacket& a, const RhsPacketx4& b, AccPacket& c) const
+  {
+    c = vfmaq_lane_f16(c, a, b, LaneID);
+  }
+};
+#endif // EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC && EIGEN_COMP_CLANG
+#endif // EIGEN_ARCH_ARM64
+
+}  // namespace internal
+}  // namespace Eigen
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/MathFunctions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/MathFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..c2a821951c6488c393622b6fed90a29753853034
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/MathFunctions.h
@@ -0,0 +1,69 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATH_FUNCTIONS_NEON_H
+#define EIGEN_MATH_FUNCTIONS_NEON_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet2f)
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet4f)
+
+#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+Packet4hf ptanh<Packet4hf>(const Packet4hf& x) {
+  // Convert to float, call the float ptanh, and then convert back.
+  return vcvt_f16_f32(ptanh<Packet4f>(vcvt_f32_f16(x)));
+}
+
+template <>
+EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+Packet8hf ptanh<Packet8hf>(const Packet8hf& x) {
+  // Convert each 4 halfs to float, call the float ptanh, and then convert back.
+  return vcombine_f16(
+    vcvt_f16_f32(ptanh<Packet4f>(vcvt_f32_f16(vget_low_f16(x)))),
+    vcvt_f16_f32(ptanh<Packet4f>(vcvt_high_f32_f16(x))));
+}
+#endif // EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, psin)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pcos)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, plog)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, pexp)
+BF16_PACKET_FUNCTION(Packet4f, Packet4bf, ptanh)
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pfrexp(const Packet4bf& a, Packet4bf& exponent) {
+  Packet4f fexponent;
+  const Packet4bf out = F32ToBf16(pfrexp<Packet4f>(Bf16ToF32(a), fexponent));
+  exponent = F32ToBf16(fexponent);
+  return out;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4bf pldexp(const Packet4bf& a, const Packet4bf& exponent) {
+  return F32ToBf16(pldexp<Packet4f>(Bf16ToF32(a), Bf16ToF32(exponent)));
+}
+
+//---------- double ----------
+
+#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_DOUBLE(Packet2d)
+
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATH_FUNCTIONS_NEON_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/PacketMath.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/PacketMath.h
new file mode 100644
index 0000000000000000000000000000000000000000..638dea45d8ffdddf8cecd40d4bc89b2130834cb6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/PacketMath.h
@@ -0,0 +1,4660 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Konstantinos Margaritis <markos@freevec.org>
+// Heavily based on Gael's SSE version.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_NEON_H
+#define EIGEN_PACKET_MATH_NEON_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+
+#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#if EIGEN_ARCH_ARM64
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
+#else
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 16
+#endif
+#endif
+
+#if EIGEN_COMP_MSVC_STRICT
+
+// In MSVC's arm_neon.h header file, all NEON vector types
+// are aliases to the same underlying type __n128.
+// We thus have to wrap them to make them different C++ types.
+// (See also bug 1428)
+typedef eigen_packet_wrapper<float32x2_t,0>  Packet2f;
+typedef eigen_packet_wrapper<float32x4_t,1>  Packet4f;
+typedef eigen_packet_wrapper<int32_t    ,2>  Packet4c;
+typedef eigen_packet_wrapper<int8x8_t   ,3>  Packet8c;
+typedef eigen_packet_wrapper<int8x16_t  ,4>  Packet16c;
+typedef eigen_packet_wrapper<uint32_t   ,5>  Packet4uc;
+typedef eigen_packet_wrapper<uint8x8_t  ,6>  Packet8uc;
+typedef eigen_packet_wrapper<uint8x16_t ,7>  Packet16uc;
+typedef eigen_packet_wrapper<int16x4_t  ,8>  Packet4s;
+typedef eigen_packet_wrapper<int16x8_t  ,9>  Packet8s;
+typedef eigen_packet_wrapper<uint16x4_t ,10> Packet4us;
+typedef eigen_packet_wrapper<uint16x8_t ,11> Packet8us;
+typedef eigen_packet_wrapper<int32x2_t  ,12> Packet2i;
+typedef eigen_packet_wrapper<int32x4_t  ,13> Packet4i;
+typedef eigen_packet_wrapper<uint32x2_t ,14> Packet2ui;
+typedef eigen_packet_wrapper<uint32x4_t ,15> Packet4ui;
+typedef eigen_packet_wrapper<int64x2_t  ,16> Packet2l;
+typedef eigen_packet_wrapper<uint64x2_t ,17> Packet2ul;
+
+EIGEN_ALWAYS_INLINE Packet4f make_packet4f(float a, float b, float c, float d) {
+  float from[4] = {a, b, c, d};
+  return vld1q_f32(from);
+}
+
+EIGEN_ALWAYS_INLINE Packet2f make_packet2f(float a, float b) {
+  float from[2] = {a, b};
+  return vld1_f32(from);
+}
+
+#else
+
+typedef float32x2_t                          Packet2f;
+typedef float32x4_t                          Packet4f;
+typedef eigen_packet_wrapper<int32_t    ,2>  Packet4c;
+typedef int8x8_t                             Packet8c;
+typedef int8x16_t                            Packet16c;
+typedef eigen_packet_wrapper<uint32_t   ,5>  Packet4uc;
+typedef uint8x8_t                            Packet8uc;
+typedef uint8x16_t                           Packet16uc;
+typedef int16x4_t                            Packet4s;
+typedef int16x8_t                            Packet8s;
+typedef uint16x4_t                           Packet4us;
+typedef uint16x8_t                           Packet8us;
+typedef int32x2_t                            Packet2i;
+typedef int32x4_t                            Packet4i;
+typedef uint32x2_t                           Packet2ui;
+typedef uint32x4_t                           Packet4ui;
+typedef int64x2_t                            Packet2l;
+typedef uint64x2_t                           Packet2ul;
+
+EIGEN_ALWAYS_INLINE Packet4f make_packet4f(float a, float b, float c, float d) { return Packet4f{a, b, c, d}; }
+EIGEN_ALWAYS_INLINE Packet2f make_packet2f(float a, float b) { return Packet2f{a, b}; }
+
+#endif // EIGEN_COMP_MSVC_STRICT
+
+EIGEN_STRONG_INLINE Packet4f shuffle1(const Packet4f& m, int mask){
+  const float* a = reinterpret_cast<const float*>(&m);
+  Packet4f res = make_packet4f(*(a + (mask & 3)), *(a + ((mask >> 2) & 3)), *(a + ((mask >> 4) & 3 )), *(a + ((mask >> 6) & 3)));
+  return res;
+}
+
+// fuctionally equivalent to _mm_shuffle_ps in SSE when interleave
+// == false (i.e. shuffle<false>(m, n, mask) equals _mm_shuffle_ps(m, n, mask)),
+// interleave m and n when interleave == true. Currently used in LU/arch/InverseSize4.h
+// to enable a shared implementation for fast inversion of matrices of size 4. 
+template<bool interleave> 
+EIGEN_STRONG_INLINE Packet4f shuffle2(const Packet4f &m, const Packet4f &n, int mask)
+{
+  const float* a = reinterpret_cast<const float*>(&m);
+  const float* b = reinterpret_cast<const float*>(&n);
+  Packet4f res = make_packet4f(*(a + (mask & 3)), *(a + ((mask >> 2) & 3)), *(b + ((mask >> 4) & 3)), *(b + ((mask >> 6) & 3)));
+  return res;
+}
+
+template<> 
+EIGEN_STRONG_INLINE Packet4f shuffle2<true>(const Packet4f &m, const Packet4f &n, int mask) 
+{
+  const float* a = reinterpret_cast<const float*>(&m);
+  const float* b = reinterpret_cast<const float*>(&n);
+  Packet4f res = make_packet4f(*(a + (mask & 3)), *(b + ((mask >> 2) & 3)), *(a + ((mask >> 4) & 3)), *(b + ((mask >> 6) & 3)));
+  return res;
+}
+
+EIGEN_STRONG_INLINE static int eigen_neon_shuffle_mask(int p, int q, int r, int s) {return ((s)<<6|(r)<<4|(q)<<2|(p));}
+
+EIGEN_STRONG_INLINE Packet4f vec4f_swizzle1(const Packet4f& a, int p, int q, int r, int s)
+{ 
+  return shuffle1(a, eigen_neon_shuffle_mask(p, q, r, s));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_swizzle2(const Packet4f& a, const Packet4f& b, int p, int q, int r, int s)
+{ 
+  return shuffle2<false>(a,b,eigen_neon_shuffle_mask(p, q, r, s));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_movelh(const Packet4f& a, const Packet4f& b)
+{
+  return shuffle2<false>(a,b,eigen_neon_shuffle_mask(0, 1, 0, 1));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_movehl(const Packet4f& a, const Packet4f& b)
+{
+  return shuffle2<false>(b,a,eigen_neon_shuffle_mask(2, 3, 2, 3));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_unpacklo(const Packet4f& a, const Packet4f& b)
+{
+  return shuffle2<true>(a,b,eigen_neon_shuffle_mask(0, 0, 1, 1));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_unpackhi(const Packet4f& a, const Packet4f& b)
+{
+  return shuffle2<true>(a,b,eigen_neon_shuffle_mask(2, 2, 3, 3));
+}
+#define vec4f_duplane(a, p) \
+  Packet4f(vdupq_lane_f32(vget_low_f32(a), p))
+
+#define EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
+  const Packet4f p4f_##NAME = pset1<Packet4f>(X)
+
+#define EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
+  const Packet4f p4f_##NAME = vreinterpretq_f32_u32(pset1<int32_t>(X))
+
+#define EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
+  const Packet4i p4i_##NAME = pset1<Packet4i>(X)
+
+#if EIGEN_ARCH_ARM64 && EIGEN_COMP_GNUC
+  // __builtin_prefetch tends to do nothing on ARM64 compilers because the
+  // prefetch instructions there are too detailed for __builtin_prefetch to map
+  // meaningfully to them.
+  #define EIGEN_ARM_PREFETCH(ADDR)  __asm__ __volatile__("prfm pldl1keep, [%[addr]]\n" ::[addr] "r"(ADDR) : );
+#elif EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
+  #define EIGEN_ARM_PREFETCH(ADDR) __builtin_prefetch(ADDR);
+#elif defined __pld
+  #define EIGEN_ARM_PREFETCH(ADDR) __pld(ADDR)
+#elif EIGEN_ARCH_ARM
+  #define EIGEN_ARM_PREFETCH(ADDR) __asm__ __volatile__ ("pld [%[addr]]\n" :: [addr] "r" (ADDR) : );
+#else
+  // by default no explicit prefetching
+  #define EIGEN_ARM_PREFETCH(ADDR)
+#endif
+
+template <>
+struct packet_traits<float> : default_packet_traits
+{
+  typedef Packet4f type;
+  typedef Packet2f half;
+  enum
+  {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasCmp       = 1,
+    HasAdd       = 1,
+    HasSub       = 1,
+    HasShift     = 1,
+    HasMul       = 1,
+    HasNegate    = 1,
+    HasAbs       = 1,
+    HasArg       = 0,
+    HasAbs2      = 1,
+    HasAbsDiff   = 1,
+    HasMin       = 1,
+    HasMax       = 1,
+    HasConj      = 1,
+    HasSetLinear = 1,
+    HasBlend     = 0,
+
+    HasDiv   = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasRint = 1,
+
+    HasSin  = EIGEN_FAST_MATH,
+    HasCos  = EIGEN_FAST_MATH,
+    HasACos  = 1,
+    HasASin  = 1,
+    HasATan  = 1,
+    HasATanh = 1,
+    HasLog  = 1,
+    HasExp  = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf  = EIGEN_FAST_MATH,
+    HasBessel = 0,  // Issues with accuracy.
+    HasNdtri = 0
+  };
+};
+
+template <>
+struct packet_traits<int8_t> : default_packet_traits
+{
+  typedef Packet16c type;
+  typedef Packet8c half;
+  enum
+  {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+
+    HasCmp       = 1,
+    HasAdd       = 1,
+    HasSub       = 1,
+    HasShift     = 1,
+    HasMul       = 1,
+    HasNegate    = 1,
+    HasAbs       = 1,
+    HasAbsDiff   = 1,
+    HasArg       = 0,
+    HasAbs2      = 1,
+    HasMin       = 1,
+    HasMax       = 1,
+    HasConj      = 1,
+    HasSetLinear = 1,
+    HasBlend     = 0
+  };
+};
+
+template <>
+struct packet_traits<uint8_t> : default_packet_traits
+{
+  typedef Packet16uc type;
+  typedef Packet8uc half;
+  enum
+  {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 16,
+
+    HasCmp       = 1,
+    HasAdd       = 1,
+    HasSub       = 1,
+    HasShift     = 1,
+    HasMul       = 1,
+    HasNegate    = 0,
+    HasAbs       = 1,
+    HasAbsDiff   = 1,
+    HasArg       = 0,
+    HasAbs2      = 1,
+    HasMin       = 1,
+    HasMax       = 1,
+    HasConj      = 1,
+    HasSetLinear = 1,
+    HasBlend     = 0,
+
+    HasSqrt = 1
+  };
+};
+
+template <>
+struct packet_traits<int16_t> : default_packet_traits
+{
+  typedef Packet8s type;
+  typedef Packet4s half;
+  enum
+  {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasCmp       = 1,
+    HasAdd       = 1,
+    HasSub       = 1,
+    HasShift     = 1,
+    HasMul       = 1,
+    HasNegate    = 1,
+    HasAbs       = 1,
+    HasAbsDiff   = 1,
+    HasArg       = 0,
+    HasAbs2      = 1,
+    HasMin       = 1,
+    HasMax       = 1,
+    HasConj      = 1,
+    HasSetLinear = 1,
+    HasBlend     = 0
+  };
+};
+
+template <>
+struct packet_traits<uint16_t> : default_packet_traits
+{
+  typedef Packet8us type;
+  typedef Packet4us half;
+  enum
+  {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasCmp       = 1,
+    HasAdd       = 1,
+    HasSub       = 1,
+    HasShift     = 1,
+    HasMul       = 1,
+    HasNegate    = 0,
+    HasAbs       = 1,
+    HasAbsDiff   = 1,
+    HasArg       = 0,
+    HasAbs2      = 1,
+    HasMin       = 1,
+    HasMax       = 1,
+    HasConj      = 1,
+    HasSetLinear = 1,
+    HasBlend     = 0,
+    HasSqrt = 1
+  };
+};
+
+template <>
+struct packet_traits<int32_t> : default_packet_traits
+{
+  typedef Packet4i type;
+  typedef Packet2i half;
+  enum
+  {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasCmp       = 1,
+    HasAdd       = 1,
+    HasSub       = 1,
+    HasShift     = 1,
+    HasMul       = 1,
+    HasNegate    = 1,
+    HasAbs       = 1,
+    HasArg       = 0,
+    HasAbs2      = 1,
+    HasAbsDiff   = 1,
+    HasMin       = 1,
+    HasMax       = 1,
+    HasConj      = 1,
+    HasSetLinear = 1,
+    HasBlend     = 0
+  };
+};
+
+template <>
+struct packet_traits<uint32_t> : default_packet_traits
+{
+  typedef Packet4ui type;
+  typedef Packet2ui half;
+  enum
+  {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasCmp       = 1,
+    HasAdd       = 1,
+    HasSub       = 1,
+    HasShift     = 1,
+    HasMul       = 1,
+    HasNegate    = 0,
+    HasAbs       = 1,
+    HasArg       = 0,
+    HasAbs2      = 1,
+    HasAbsDiff   = 1,
+    HasMin       = 1,
+    HasMax       = 1,
+    HasConj      = 1,
+    HasSetLinear = 1,
+    HasBlend     = 0,
+
+    HasSqrt = 1
+  };
+};
+
+template <>
+struct packet_traits<int64_t> : default_packet_traits
+{
+  typedef Packet2l type;
+  typedef Packet2l half;
+  enum
+  {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasCmp       = 1,
+    HasAdd       = 1,
+    HasSub       = 1,
+    HasShift     = 1,
+    HasMul       = 1,
+    HasNegate    = 1,
+    HasAbs       = 1,
+    HasArg       = 0,
+    HasAbs2      = 1,
+    HasAbsDiff   = 1,
+    HasMin       = 1,
+    HasMax       = 1,
+    HasConj      = 1,
+    HasSetLinear = 1,
+    HasBlend     = 0
+  };
+};
+
+template <>
+struct packet_traits<uint64_t> : default_packet_traits
+{
+  typedef Packet2ul type;
+  typedef Packet2ul half;
+  enum
+  {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasCmp       = 1,
+    HasAdd       = 1,
+    HasSub       = 1,
+    HasShift     = 1,
+    HasMul       = 1,
+    HasNegate    = 0,
+    HasAbs       = 1,
+    HasArg       = 0,
+    HasAbs2      = 1,
+    HasAbsDiff   = 1,
+    HasMin       = 1,
+    HasMax       = 1,
+    HasConj      = 1,
+    HasSetLinear = 1,
+    HasBlend     = 0
+  };
+};
+
+template<> struct unpacket_traits<Packet2f>
+{
+  typedef float type;
+  typedef Packet2f half;
+  typedef Packet2i integer_packet;
+  enum
+  {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet4f>
+{
+  typedef float type;
+  typedef Packet2f half;
+  typedef Packet4i integer_packet;
+  enum
+  {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet4c>
+{
+  typedef int8_t type;
+  typedef Packet4c half;
+  enum
+  {
+    size = 4,
+    alignment = Unaligned,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet8c>
+{
+  typedef int8_t type;
+  typedef Packet4c half;
+  enum
+  {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet16c>
+{
+  typedef int8_t type;
+  typedef Packet8c half;
+  enum
+  {
+    size = 16,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet4uc>
+{
+  typedef uint8_t type;
+  typedef Packet4uc half;
+  enum
+  {
+    size = 4,
+    alignment = Unaligned,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet8uc>
+{
+  typedef uint8_t type;
+  typedef Packet4uc half;
+  enum
+  {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet16uc>
+{
+  typedef uint8_t type;
+  typedef Packet8uc half;
+  enum
+  {
+    size = 16,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false};
+};
+template<> struct unpacket_traits<Packet4s>
+{
+  typedef int16_t type;
+  typedef Packet4s half;
+  enum
+  {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet8s>
+{
+  typedef int16_t type;
+  typedef Packet4s half;
+  enum
+  {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet4us>
+{
+  typedef uint16_t type;
+  typedef Packet4us half;
+  enum
+  {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet8us>
+{
+  typedef uint16_t type;
+  typedef Packet4us half;
+  enum
+  {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet2i>
+{
+  typedef int32_t type;
+  typedef Packet2i half;
+  enum
+  {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet4i>
+{
+  typedef int32_t type;
+  typedef Packet2i half;
+  enum
+  {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet2ui>
+{
+  typedef uint32_t type;
+  typedef Packet2ui half;
+  enum
+  {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet4ui>
+{
+  typedef uint32_t type;
+  typedef Packet2ui half;
+  enum
+  {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet2l>
+{
+  typedef int64_t type;
+  typedef Packet2l half;
+  enum
+  {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+template<> struct unpacket_traits<Packet2ul>
+{
+  typedef uint64_t type;
+  typedef Packet2ul half;
+  enum
+  {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template<> EIGEN_STRONG_INLINE Packet2f pset1<Packet2f>(const float& from) { return vdup_n_f32(from); }
+template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from) { return vdupq_n_f32(from); }
+template<> EIGEN_STRONG_INLINE Packet4c pset1<Packet4c>(const int8_t& from)
+{ return vget_lane_s32(vreinterpret_s32_s8(vdup_n_s8(from)), 0); }
+template<> EIGEN_STRONG_INLINE Packet8c pset1<Packet8c>(const int8_t& from) { return vdup_n_s8(from); }
+template<> EIGEN_STRONG_INLINE Packet16c pset1<Packet16c>(const int8_t& from) { return vdupq_n_s8(from); }
+template<> EIGEN_STRONG_INLINE Packet4uc pset1<Packet4uc>(const uint8_t& from)
+{ return vget_lane_u32(vreinterpret_u32_u8(vdup_n_u8(from)), 0); }
+template<> EIGEN_STRONG_INLINE Packet8uc pset1<Packet8uc>(const uint8_t& from) { return vdup_n_u8(from); }
+template<> EIGEN_STRONG_INLINE Packet16uc pset1<Packet16uc>(const uint8_t& from) { return vdupq_n_u8(from); }
+template<> EIGEN_STRONG_INLINE Packet4s pset1<Packet4s>(const int16_t& from) { return vdup_n_s16(from); }
+template<> EIGEN_STRONG_INLINE Packet8s pset1<Packet8s>(const int16_t& from) { return vdupq_n_s16(from); }
+template<> EIGEN_STRONG_INLINE Packet4us pset1<Packet4us>(const uint16_t& from) { return vdup_n_u16(from); }
+template<> EIGEN_STRONG_INLINE Packet8us pset1<Packet8us>(const uint16_t& from) { return vdupq_n_u16(from); }
+template<> EIGEN_STRONG_INLINE Packet2i pset1<Packet2i>(const int32_t& from) { return vdup_n_s32(from); }
+template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int32_t& from) { return vdupq_n_s32(from); }
+template<> EIGEN_STRONG_INLINE Packet2ui pset1<Packet2ui>(const uint32_t& from) { return vdup_n_u32(from); }
+template<> EIGEN_STRONG_INLINE Packet4ui pset1<Packet4ui>(const uint32_t& from) { return vdupq_n_u32(from); }
+template<> EIGEN_STRONG_INLINE Packet2l pset1<Packet2l>(const int64_t& from) { return vdupq_n_s64(from); }
+template<> EIGEN_STRONG_INLINE Packet2ul pset1<Packet2ul>(const uint64_t& from) { return vdupq_n_u64(from); }
+
+template<> EIGEN_STRONG_INLINE Packet2f pset1frombits<Packet2f>(uint32_t from)
+{ return vreinterpret_f32_u32(vdup_n_u32(from)); }
+template<> EIGEN_STRONG_INLINE Packet4f pset1frombits<Packet4f>(uint32_t from)
+{ return vreinterpretq_f32_u32(vdupq_n_u32(from)); }
+
+template<> EIGEN_STRONG_INLINE Packet2f plset<Packet2f>(const float& a)
+{
+  const float c[] = {0.0f,1.0f};
+  return vadd_f32(pset1<Packet2f>(a), vld1_f32(c));
+}
+template<> EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a)
+{
+  const float c[] = {0.0f,1.0f,2.0f,3.0f};
+  return vaddq_f32(pset1<Packet4f>(a), vld1q_f32(c));
+}
+template<> EIGEN_STRONG_INLINE Packet4c plset<Packet4c>(const int8_t& a)
+{ return vget_lane_s32(vreinterpret_s32_s8(vadd_s8(vreinterpret_s8_u32(vdup_n_u32(0x03020100)), vdup_n_s8(a))), 0); }
+template<> EIGEN_STRONG_INLINE Packet8c plset<Packet8c>(const int8_t& a)
+{
+  const int8_t c[] = {0,1,2,3,4,5,6,7};
+  return vadd_s8(pset1<Packet8c>(a), vld1_s8(c));
+}
+template<> EIGEN_STRONG_INLINE Packet16c plset<Packet16c>(const int8_t& a)
+{
+  const int8_t c[] = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15};
+  return vaddq_s8(pset1<Packet16c>(a), vld1q_s8(c));
+}
+template<> EIGEN_STRONG_INLINE Packet4uc plset<Packet4uc>(const uint8_t& a)
+{ return vget_lane_u32(vreinterpret_u32_u8(vadd_u8(vreinterpret_u8_u32(vdup_n_u32(0x03020100)), vdup_n_u8(a))), 0); }
+template<> EIGEN_STRONG_INLINE Packet8uc plset<Packet8uc>(const uint8_t& a)
+{
+  const uint8_t c[] = {0,1,2,3,4,5,6,7};
+  return vadd_u8(pset1<Packet8uc>(a), vld1_u8(c));
+}
+template<> EIGEN_STRONG_INLINE Packet16uc plset<Packet16uc>(const uint8_t& a)
+{
+  const uint8_t c[] = {0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15};
+  return vaddq_u8(pset1<Packet16uc>(a), vld1q_u8(c));
+}
+template<> EIGEN_STRONG_INLINE Packet4s plset<Packet4s>(const int16_t& a)
+{
+  const int16_t c[] = {0,1,2,3};
+  return vadd_s16(pset1<Packet4s>(a), vld1_s16(c));
+}
+template<> EIGEN_STRONG_INLINE Packet4us plset<Packet4us>(const uint16_t& a)
+{
+  const uint16_t c[] = {0,1,2,3};
+  return vadd_u16(pset1<Packet4us>(a), vld1_u16(c));
+}
+template<> EIGEN_STRONG_INLINE Packet8s plset<Packet8s>(const int16_t& a)
+{
+  const int16_t c[] = {0,1,2,3,4,5,6,7};
+  return vaddq_s16(pset1<Packet8s>(a), vld1q_s16(c));
+}
+template<> EIGEN_STRONG_INLINE Packet8us plset<Packet8us>(const uint16_t& a)
+{
+  const uint16_t c[] = {0,1,2,3,4,5,6,7};
+  return vaddq_u16(pset1<Packet8us>(a), vld1q_u16(c));
+}
+template<> EIGEN_STRONG_INLINE Packet2i plset<Packet2i>(const int32_t& a)
+{
+  const int32_t c[] = {0,1};
+  return vadd_s32(pset1<Packet2i>(a), vld1_s32(c));
+}
+template<> EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int32_t& a)
+{
+  const int32_t c[] = {0,1,2,3};
+  return vaddq_s32(pset1<Packet4i>(a), vld1q_s32(c));
+}
+template<> EIGEN_STRONG_INLINE Packet2ui plset<Packet2ui>(const uint32_t& a)
+{
+  const uint32_t c[] = {0,1};
+  return vadd_u32(pset1<Packet2ui>(a), vld1_u32(c));
+}
+template<> EIGEN_STRONG_INLINE Packet4ui plset<Packet4ui>(const uint32_t& a)
+{
+  const uint32_t c[] = {0,1,2,3};
+  return vaddq_u32(pset1<Packet4ui>(a), vld1q_u32(c));
+}
+template<> EIGEN_STRONG_INLINE Packet2l plset<Packet2l>(const int64_t& a)
+{
+  const int64_t c[] = {0,1};
+  return vaddq_s64(pset1<Packet2l>(a), vld1q_s64(c));
+}
+template<> EIGEN_STRONG_INLINE Packet2ul plset<Packet2ul>(const uint64_t& a)
+{
+  const uint64_t c[] = {0,1};
+  return vaddq_u64(pset1<Packet2ul>(a), vld1q_u64(c));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2f padd<Packet2f>(const Packet2f& a, const Packet2f& b) { return vadd_f32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) { return vaddq_f32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4c padd<Packet4c>(const Packet4c& a, const Packet4c& b)
+{
+  return vget_lane_s32(vreinterpret_s32_s8(vadd_s8(
+      vreinterpret_s8_s32(vdup_n_s32(a)),
+      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8c padd<Packet8c>(const Packet8c& a, const Packet8c& b) { return vadd_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16c padd<Packet16c>(const Packet16c& a, const Packet16c& b) { return vaddq_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4uc padd<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
+{
+  return vget_lane_u32(vreinterpret_u32_u8(vadd_u8(
+      vreinterpret_u8_u32(vdup_n_u32(a)),
+      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8uc padd<Packet8uc>(const Packet8uc& a, const Packet8uc& b) { return vadd_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16uc padd<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vaddq_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4s padd<Packet4s>(const Packet4s& a, const Packet4s& b) { return vadd_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8s padd<Packet8s>(const Packet8s& a, const Packet8s& b) { return vaddq_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4us padd<Packet4us>(const Packet4us& a, const Packet4us& b) { return vadd_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8us padd<Packet8us>(const Packet8us& a, const Packet8us& b) { return vaddq_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2i padd<Packet2i>(const Packet2i& a, const Packet2i& b) { return vadd_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) { return vaddq_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ui padd<Packet2ui>(const Packet2ui& a, const Packet2ui& b) { return vadd_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui padd<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vaddq_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2l padd<Packet2l>(const Packet2l& a, const Packet2l& b) { return vaddq_s64(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ul padd<Packet2ul>(const Packet2ul& a, const Packet2ul& b) { return vaddq_u64(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet2f psub<Packet2f>(const Packet2f& a, const Packet2f& b) { return vsub_f32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) { return vsubq_f32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4c psub<Packet4c>(const Packet4c& a, const Packet4c& b)
+{
+  return vget_lane_s32(vreinterpret_s32_s8(vsub_s8(
+      vreinterpret_s8_s32(vdup_n_s32(a)),
+      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8c psub<Packet8c>(const Packet8c& a, const Packet8c& b) { return vsub_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16c psub<Packet16c>(const Packet16c& a, const Packet16c& b) { return vsubq_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4uc psub<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
+{
+  return vget_lane_u32(vreinterpret_u32_u8(vsub_u8(
+      vreinterpret_u8_u32(vdup_n_u32(a)),
+      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8uc psub<Packet8uc>(const Packet8uc& a, const Packet8uc& b) { return vsub_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16uc psub<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vsubq_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4s psub<Packet4s>(const Packet4s& a, const Packet4s& b) { return vsub_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8s psub<Packet8s>(const Packet8s& a, const Packet8s& b) { return vsubq_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4us psub<Packet4us>(const Packet4us& a, const Packet4us& b) { return vsub_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8us psub<Packet8us>(const Packet8us& a, const Packet8us& b) { return vsubq_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2i psub<Packet2i>(const Packet2i& a, const Packet2i& b) { return vsub_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) { return vsubq_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ui psub<Packet2ui>(const Packet2ui& a, const Packet2ui& b) { return vsub_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui psub<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vsubq_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2l psub<Packet2l>(const Packet2l& a, const Packet2l& b) { return vsubq_s64(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ul psub<Packet2ul>(const Packet2ul& a, const Packet2ul& b) { return vsubq_u64(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet2f pxor<Packet2f>(const Packet2f& a, const Packet2f& b);
+template<> EIGEN_STRONG_INLINE Packet2f paddsub<Packet2f>(const Packet2f& a, const Packet2f & b) {
+  Packet2f mask = make_packet2f(numext::bit_cast<float>(0x80000000u), 0.0f);
+  return padd(a, pxor(mask, b));
+}
+template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b);
+template<> EIGEN_STRONG_INLINE Packet4f paddsub<Packet4f>(const Packet4f& a, const Packet4f& b) {
+  Packet4f mask = make_packet4f(numext::bit_cast<float>(0x80000000u), 0.0f, numext::bit_cast<float>(0x80000000u), 0.0f);
+  return padd(a, pxor(mask, b));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2f pnegate(const Packet2f& a) { return vneg_f32(a); }
+template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a) { return vnegq_f32(a); }
+template<> EIGEN_STRONG_INLINE Packet4c pnegate(const Packet4c& a)
+{ return vget_lane_s32(vreinterpret_s32_s8(vneg_s8(vreinterpret_s8_s32(vdup_n_s32(a)))), 0); }
+template<> EIGEN_STRONG_INLINE Packet8c pnegate(const Packet8c& a) { return vneg_s8(a); }
+template<> EIGEN_STRONG_INLINE Packet16c pnegate(const Packet16c& a) { return vnegq_s8(a); }
+template<> EIGEN_STRONG_INLINE Packet4s pnegate(const Packet4s& a) { return vneg_s16(a); }
+template<> EIGEN_STRONG_INLINE Packet8s pnegate(const Packet8s& a) { return vnegq_s16(a); }
+template<> EIGEN_STRONG_INLINE Packet2i pnegate(const Packet2i& a) { return vneg_s32(a); }
+template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return vnegq_s32(a); }
+template<> EIGEN_STRONG_INLINE Packet2l pnegate(const Packet2l& a) {
+#if EIGEN_ARCH_ARM64
+  return vnegq_s64(a);
+#else
+  return vcombine_s64(
+      vdup_n_s64(-vgetq_lane_s64(a, 0)),
+      vdup_n_s64(-vgetq_lane_s64(a, 1)));
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet2f pconj(const Packet2f& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet4c pconj(const Packet4c& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet8c pconj(const Packet8c& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet16c pconj(const Packet16c& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet4uc pconj(const Packet4uc& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet8uc pconj(const Packet8uc& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet16uc pconj(const Packet16uc& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet4s pconj(const Packet4s& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet8s pconj(const Packet8s& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet4us pconj(const Packet4us& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet8us pconj(const Packet8us& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet2i pconj(const Packet2i& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet2ui pconj(const Packet2ui& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet4ui pconj(const Packet4ui& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet2l pconj(const Packet2l& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet2ul pconj(const Packet2ul& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE Packet2f pmul<Packet2f>(const Packet2f& a, const Packet2f& b) { return vmul_f32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return vmulq_f32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4c pmul<Packet4c>(const Packet4c& a, const Packet4c& b)
+{
+  return vget_lane_s32(vreinterpret_s32_s8(vmul_s8(
+      vreinterpret_s8_s32(vdup_n_s32(a)),
+      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8c pmul<Packet8c>(const Packet8c& a, const Packet8c& b) { return vmul_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16c pmul<Packet16c>(const Packet16c& a, const Packet16c& b) { return vmulq_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4uc pmul<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
+{
+  return vget_lane_u32(vreinterpret_u32_u8(vmul_u8(
+      vreinterpret_u8_u32(vdup_n_u32(a)),
+      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8uc pmul<Packet8uc>(const Packet8uc& a, const Packet8uc& b) { return vmul_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16uc pmul<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vmulq_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4s pmul<Packet4s>(const Packet4s& a, const Packet4s& b) { return vmul_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8s pmul<Packet8s>(const Packet8s& a, const Packet8s& b) { return vmulq_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4us pmul<Packet4us>(const Packet4us& a, const Packet4us& b) { return vmul_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8us pmul<Packet8us>(const Packet8us& a, const Packet8us& b) { return vmulq_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2i pmul<Packet2i>(const Packet2i& a, const Packet2i& b) { return vmul_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) { return vmulq_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ui pmul<Packet2ui>(const Packet2ui& a, const Packet2ui& b) { return vmul_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui pmul<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vmulq_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2l pmul<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return vcombine_s64(
+    vdup_n_s64(vgetq_lane_s64(a, 0)*vgetq_lane_s64(b, 0)),
+    vdup_n_s64(vgetq_lane_s64(a, 1)*vgetq_lane_s64(b, 1)));
+}
+template<> EIGEN_STRONG_INLINE Packet2ul pmul<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return vcombine_u64(
+    vdup_n_u64(vgetq_lane_u64(a, 0)*vgetq_lane_u64(b, 0)),
+    vdup_n_u64(vgetq_lane_u64(a, 1)*vgetq_lane_u64(b, 1)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2f pdiv<Packet2f>(const Packet2f& a, const Packet2f& b)
+{
+#if EIGEN_ARCH_ARM64
+  return vdiv_f32(a,b);
+#else
+  Packet2f inv, restep, div;
+
+  // NEON does not offer a divide instruction, we have to do a reciprocal approximation
+  // However NEON in contrast to other SIMD engines (AltiVec/SSE), offers
+  // a reciprocal estimate AND a reciprocal step -which saves a few instructions
+  // vrecpeq_f32() returns an estimate to 1/b, which we will finetune with
+  // Newton-Raphson and vrecpsq_f32()
+  inv = vrecpe_f32(b);
+
+  // This returns a differential, by which we will have to multiply inv to get a better
+  // approximation of 1/b.
+  restep = vrecps_f32(b, inv);
+  inv = vmul_f32(restep, inv);
+
+  // Finally, multiply a by 1/b and get the wanted result of the division.
+  div = vmul_f32(a, inv);
+
+  return div;
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+#if EIGEN_ARCH_ARM64
+  return vdivq_f32(a,b);
+#else
+  Packet4f inv, restep, div;
+
+  // NEON does not offer a divide instruction, we have to do a reciprocal approximation
+  // However NEON in contrast to other SIMD engines (AltiVec/SSE), offers
+  // a reciprocal estimate AND a reciprocal step -which saves a few instructions
+  // vrecpeq_f32() returns an estimate to 1/b, which we will finetune with
+  // Newton-Raphson and vrecpsq_f32()
+  inv = vrecpeq_f32(b);
+
+  // This returns a differential, by which we will have to multiply inv to get a better
+  // approximation of 1/b.
+  restep = vrecpsq_f32(b, inv);
+  inv = vmulq_f32(restep, inv);
+
+  // Finally, multiply a by 1/b and get the wanted result of the division.
+  div = vmulq_f32(a, inv);
+
+  return div;
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet4c pdiv<Packet4c>(const Packet4c& /*a*/, const Packet4c& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet4c>(0);
+}
+template<> EIGEN_STRONG_INLINE Packet8c pdiv<Packet8c>(const Packet8c& /*a*/, const Packet8c& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet8c>(0);
+}
+template<> EIGEN_STRONG_INLINE Packet16c pdiv<Packet16c>(const Packet16c& /*a*/, const Packet16c& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet16c>(0);
+}
+template<> EIGEN_STRONG_INLINE Packet4uc pdiv<Packet4uc>(const Packet4uc& /*a*/, const Packet4uc& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet4uc>(0);
+}
+template<> EIGEN_STRONG_INLINE Packet8uc pdiv<Packet8uc>(const Packet8uc& /*a*/, const Packet8uc& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet8uc>(0);
+}
+template<> EIGEN_STRONG_INLINE Packet16uc pdiv<Packet16uc>(const Packet16uc& /*a*/, const Packet16uc& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet16uc>(0);
+}
+template<> EIGEN_STRONG_INLINE Packet4s pdiv<Packet4s>(const Packet4s& /*a*/, const Packet4s& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet4s>(0);
+}
+template<> EIGEN_STRONG_INLINE Packet8s pdiv<Packet8s>(const Packet8s& /*a*/, const Packet8s& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet8s>(0);
+}
+template<> EIGEN_STRONG_INLINE Packet4us pdiv<Packet4us>(const Packet4us& /*a*/, const Packet4us& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet4us>(0);
+}
+template<> EIGEN_STRONG_INLINE Packet8us pdiv<Packet8us>(const Packet8us& /*a*/, const Packet8us& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet8us>(0);
+}
+template<> EIGEN_STRONG_INLINE Packet2i pdiv<Packet2i>(const Packet2i& /*a*/, const Packet2i& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet2i>(0);
+}
+template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& /*a*/, const Packet4i& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet4i>(0);
+}
+template<> EIGEN_STRONG_INLINE Packet2ui pdiv<Packet2ui>(const Packet2ui& /*a*/, const Packet2ui& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet2ui>(0);
+}
+template<> EIGEN_STRONG_INLINE Packet4ui pdiv<Packet4ui>(const Packet4ui& /*a*/, const Packet4ui& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet4ui>(0);
+}
+template<> EIGEN_STRONG_INLINE Packet2l pdiv<Packet2l>(const Packet2l& /*a*/, const Packet2l& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet2l>(0LL);
+}
+template<> EIGEN_STRONG_INLINE Packet2ul pdiv<Packet2ul>(const Packet2ul& /*a*/, const Packet2ul& /*b*/)
+{
+  eigen_assert(false && "packet integer division are not supported by NEON");
+  return pset1<Packet2ul>(0ULL);
+}
+
+
+#ifdef __ARM_FEATURE_FMA
+template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c)
+{ return vfmaq_f32(c,a,b); }
+template<> EIGEN_STRONG_INLINE Packet2f pmadd(const Packet2f& a, const Packet2f& b, const Packet2f& c)
+{ return vfma_f32(c,a,b); }
+#else
+template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c)
+{
+  return vmlaq_f32(c,a,b);
+}
+template<> EIGEN_STRONG_INLINE Packet2f pmadd(const Packet2f& a, const Packet2f& b, const Packet2f& c)
+{
+  return vmla_f32(c,a,b);
+}
+#endif
+
+// No FMA instruction for int, so use MLA unconditionally.
+template<> EIGEN_STRONG_INLINE Packet4c pmadd(const Packet4c& a, const Packet4c& b, const Packet4c& c)
+{
+  return vget_lane_s32(vreinterpret_s32_s8(vmla_s8(
+      vreinterpret_s8_s32(vdup_n_s32(c)),
+      vreinterpret_s8_s32(vdup_n_s32(a)),
+      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8c pmadd(const Packet8c& a, const Packet8c& b, const Packet8c& c)
+{ return vmla_s8(c,a,b); }
+template<> EIGEN_STRONG_INLINE Packet16c pmadd(const Packet16c& a, const Packet16c& b, const Packet16c& c)
+{ return vmlaq_s8(c,a,b); }
+template<> EIGEN_STRONG_INLINE Packet4uc pmadd(const Packet4uc& a, const Packet4uc& b, const Packet4uc& c)
+{
+  return vget_lane_u32(vreinterpret_u32_u8(vmla_u8(
+      vreinterpret_u8_u32(vdup_n_u32(c)),
+      vreinterpret_u8_u32(vdup_n_u32(a)),
+      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8uc pmadd(const Packet8uc& a, const Packet8uc& b, const Packet8uc& c)
+{ return vmla_u8(c,a,b); }
+template<> EIGEN_STRONG_INLINE Packet16uc pmadd(const Packet16uc& a, const Packet16uc& b, const Packet16uc& c)
+{ return vmlaq_u8(c,a,b); }
+template<> EIGEN_STRONG_INLINE Packet4s pmadd(const Packet4s& a, const Packet4s& b, const Packet4s& c)
+{ return vmla_s16(c,a,b); }
+template<> EIGEN_STRONG_INLINE Packet8s pmadd(const Packet8s& a, const Packet8s& b, const Packet8s& c)
+{ return vmlaq_s16(c,a,b); }
+template<> EIGEN_STRONG_INLINE Packet4us pmadd(const Packet4us& a, const Packet4us& b, const Packet4us& c)
+{ return vmla_u16(c,a,b); }
+template<> EIGEN_STRONG_INLINE Packet8us pmadd(const Packet8us& a, const Packet8us& b, const Packet8us& c)
+{ return vmlaq_u16(c,a,b); }
+template<> EIGEN_STRONG_INLINE Packet2i pmadd(const Packet2i& a, const Packet2i& b, const Packet2i& c)
+{ return vmla_s32(c,a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c)
+{ return vmlaq_s32(c,a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ui pmadd(const Packet2ui& a, const Packet2ui& b, const Packet2ui& c)
+{ return vmla_u32(c,a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui pmadd(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c)
+{ return vmlaq_u32(c,a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet2f pabsdiff<Packet2f>(const Packet2f& a, const Packet2f& b)
+{ return vabd_f32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4f pabsdiff<Packet4f>(const Packet4f& a, const Packet4f& b)
+{ return vabdq_f32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4c pabsdiff<Packet4c>(const Packet4c& a, const Packet4c& b)
+{
+  return vget_lane_s32(vreinterpret_s32_s8(vabd_s8(
+      vreinterpret_s8_s32(vdup_n_s32(a)),
+      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8c pabsdiff<Packet8c>(const Packet8c& a, const Packet8c& b)
+{ return vabd_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16c pabsdiff<Packet16c>(const Packet16c& a, const Packet16c& b)
+{ return vabdq_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4uc pabsdiff<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
+{
+  return vget_lane_u32(vreinterpret_u32_u8(vabd_u8(
+      vreinterpret_u8_u32(vdup_n_u32(a)),
+      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8uc pabsdiff<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
+{ return vabd_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16uc pabsdiff<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
+{ return vabdq_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4s pabsdiff<Packet4s>(const Packet4s& a, const Packet4s& b)
+{ return vabd_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8s pabsdiff<Packet8s>(const Packet8s& a, const Packet8s& b)
+{ return vabdq_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4us pabsdiff<Packet4us>(const Packet4us& a, const Packet4us& b)
+{ return vabd_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8us pabsdiff<Packet8us>(const Packet8us& a, const Packet8us& b)
+{ return vabdq_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2i pabsdiff<Packet2i>(const Packet2i& a, const Packet2i& b)
+{ return vabd_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i pabsdiff<Packet4i>(const Packet4i& a, const Packet4i& b)
+{ return vabdq_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ui pabsdiff<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
+{ return vabd_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui pabsdiff<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
+{ return vabdq_u32(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet2f pmin<Packet2f>(const Packet2f& a, const Packet2f& b) { return vmin_f32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) { return vminq_f32(a,b); }
+
+#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
+// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8 systems).
+template<> EIGEN_STRONG_INLINE Packet4f pmin<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) { return vminnmq_f32(a, b); }
+template<> EIGEN_STRONG_INLINE Packet2f pmin<PropagateNumbers, Packet2f>(const Packet2f& a, const Packet2f& b) { return vminnm_f32(a, b); }
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet4f pmin<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) { return pmin<Packet4f>(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet2f pmin<PropagateNaN, Packet2f>(const Packet2f& a, const Packet2f& b) { return pmin<Packet2f>(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet4c pmin<Packet4c>(const Packet4c& a, const Packet4c& b)
+{
+  return vget_lane_s32(vreinterpret_s32_s8(vmin_s8(
+      vreinterpret_s8_s32(vdup_n_s32(a)),
+      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8c pmin<Packet8c>(const Packet8c& a, const Packet8c& b) { return vmin_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16c pmin<Packet16c>(const Packet16c& a, const Packet16c& b) { return vminq_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4uc pmin<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
+{
+  return vget_lane_u32(vreinterpret_u32_u8(vmin_u8(
+      vreinterpret_u8_u32(vdup_n_u32(a)),
+      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8uc pmin<Packet8uc>(const Packet8uc& a, const Packet8uc& b) { return vmin_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16uc pmin<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vminq_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4s pmin<Packet4s>(const Packet4s& a, const Packet4s& b) { return vmin_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8s pmin<Packet8s>(const Packet8s& a, const Packet8s& b) { return vminq_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4us pmin<Packet4us>(const Packet4us& a, const Packet4us& b) { return vmin_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8us pmin<Packet8us>(const Packet8us& a, const Packet8us& b) { return vminq_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2i pmin<Packet2i>(const Packet2i& a, const Packet2i& b) { return vmin_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) { return vminq_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ui pmin<Packet2ui>(const Packet2ui& a, const Packet2ui& b) { return vmin_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui pmin<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vminq_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2l pmin<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return vcombine_s64(
+      vdup_n_s64((std::min)(vgetq_lane_s64(a, 0), vgetq_lane_s64(b, 0))),
+      vdup_n_s64((std::min)(vgetq_lane_s64(a, 1), vgetq_lane_s64(b, 1))));
+}
+template<> EIGEN_STRONG_INLINE Packet2ul pmin<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return vcombine_u64(
+      vdup_n_u64((std::min)(vgetq_lane_u64(a, 0), vgetq_lane_u64(b, 0))),
+      vdup_n_u64((std::min)(vgetq_lane_u64(a, 1), vgetq_lane_u64(b, 1))));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2f pmax<Packet2f>(const Packet2f& a, const Packet2f& b) { return vmax_f32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) { return vmaxq_f32(a,b); }
+
+#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
+// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8 systems).
+template<> EIGEN_STRONG_INLINE Packet4f pmax<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) { return vmaxnmq_f32(a, b); }
+template<> EIGEN_STRONG_INLINE Packet2f pmax<PropagateNumbers, Packet2f>(const Packet2f& a, const Packet2f& b) { return vmaxnm_f32(a, b); }
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet4f pmax<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) { return pmax<Packet4f>(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet2f pmax<PropagateNaN, Packet2f>(const Packet2f& a, const Packet2f& b) { return pmax<Packet2f>(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet4c pmax<Packet4c>(const Packet4c& a, const Packet4c& b)
+{
+  return vget_lane_s32(vreinterpret_s32_s8(vmax_s8(
+      vreinterpret_s8_s32(vdup_n_s32(a)),
+      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8c pmax<Packet8c>(const Packet8c& a, const Packet8c& b) { return vmax_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16c pmax<Packet16c>(const Packet16c& a, const Packet16c& b) { return vmaxq_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4uc pmax<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
+{
+  return vget_lane_u32(vreinterpret_u32_u8(vmax_u8(
+      vreinterpret_u8_u32(vdup_n_u32(a)),
+      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8uc pmax<Packet8uc>(const Packet8uc& a, const Packet8uc& b) { return vmax_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16uc pmax<Packet16uc>(const Packet16uc& a, const Packet16uc& b) { return vmaxq_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4s pmax<Packet4s>(const Packet4s& a, const Packet4s& b) { return vmax_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8s pmax<Packet8s>(const Packet8s& a, const Packet8s& b) { return vmaxq_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4us pmax<Packet4us>(const Packet4us& a, const Packet4us& b) { return vmax_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8us pmax<Packet8us>(const Packet8us& a, const Packet8us& b) { return vmaxq_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2i pmax<Packet2i>(const Packet2i& a, const Packet2i& b) { return vmax_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) { return vmaxq_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ui pmax<Packet2ui>(const Packet2ui& a, const Packet2ui& b) { return vmax_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui pmax<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return vmaxq_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2l pmax<Packet2l>(const Packet2l& a, const Packet2l& b) {
+  return vcombine_s64(
+      vdup_n_s64((std::max)(vgetq_lane_s64(a, 0), vgetq_lane_s64(b, 0))),
+      vdup_n_s64((std::max)(vgetq_lane_s64(a, 1), vgetq_lane_s64(b, 1))));
+}
+template<> EIGEN_STRONG_INLINE Packet2ul pmax<Packet2ul>(const Packet2ul& a, const Packet2ul& b) {
+  return vcombine_u64(
+      vdup_n_u64((std::max)(vgetq_lane_u64(a, 0), vgetq_lane_u64(b, 0))),
+      vdup_n_u64((std::max)(vgetq_lane_u64(a, 1), vgetq_lane_u64(b, 1))));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2f pcmp_le<Packet2f>(const Packet2f& a, const Packet2f& b)
+{ return vreinterpret_f32_u32(vcle_f32(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4f pcmp_le<Packet4f>(const Packet4f& a, const Packet4f& b)
+{ return vreinterpretq_f32_u32(vcleq_f32(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4c pcmp_le<Packet4c>(const Packet4c& a, const Packet4c& b)
+{
+  return vget_lane_s32(vreinterpret_s32_u8(vcle_s8(
+      vreinterpret_s8_s32(vdup_n_s32(a)),
+      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8c pcmp_le<Packet8c>(const Packet8c& a, const Packet8c& b)
+{ return vreinterpret_s8_u8(vcle_s8(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet16c pcmp_le<Packet16c>(const Packet16c& a, const Packet16c& b)
+{ return vreinterpretq_s8_u8(vcleq_s8(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4uc pcmp_le<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
+{
+  return vget_lane_u32(vreinterpret_u32_u8(vcle_u8(
+      vreinterpret_u8_u32(vdup_n_u32(a)),
+      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8uc pcmp_le<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
+{ return vcle_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16uc pcmp_le<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
+{ return vcleq_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4s pcmp_le<Packet4s>(const Packet4s& a, const Packet4s& b)
+{ return vreinterpret_s16_u16(vcle_s16(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet8s pcmp_le<Packet8s>(const Packet8s& a, const Packet8s& b)
+{ return vreinterpretq_s16_u16(vcleq_s16(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4us pcmp_le<Packet4us>(const Packet4us& a, const Packet4us& b)
+{ return vcle_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8us pcmp_le<Packet8us>(const Packet8us& a, const Packet8us& b)
+{ return vcleq_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2i pcmp_le<Packet2i>(const Packet2i& a, const Packet2i& b)
+{ return vreinterpret_s32_u32(vcle_s32(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4i pcmp_le<Packet4i>(const Packet4i& a, const Packet4i& b)
+{ return vreinterpretq_s32_u32(vcleq_s32(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet2ui pcmp_le<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
+{ return vcle_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui pcmp_le<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
+{ return vcleq_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2l pcmp_le<Packet2l>(const Packet2l& a, const Packet2l& b)
+{
+#if EIGEN_ARCH_ARM64
+  return vreinterpretq_s64_u64(vcleq_s64(a,b));
+#else
+  return vcombine_s64(
+      vdup_n_s64(vgetq_lane_s64(a, 0) <= vgetq_lane_s64(b, 0) ? numext::int64_t(-1) : 0),
+      vdup_n_s64(vgetq_lane_s64(a, 1) <= vgetq_lane_s64(b, 1) ? numext::int64_t(-1) : 0));
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet2ul pcmp_le<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
+{
+#if EIGEN_ARCH_ARM64
+  return vcleq_u64(a,b);
+#else
+  return vcombine_u64(
+      vdup_n_u64(vgetq_lane_u64(a, 0) <= vgetq_lane_u64(b, 0) ? numext::uint64_t(-1) : 0),
+      vdup_n_u64(vgetq_lane_u64(a, 1) <= vgetq_lane_u64(b, 1) ? numext::uint64_t(-1) : 0));
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet2f pcmp_lt<Packet2f>(const Packet2f& a, const Packet2f& b)
+{ return vreinterpret_f32_u32(vclt_f32(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt<Packet4f>(const Packet4f& a, const Packet4f& b)
+{ return vreinterpretq_f32_u32(vcltq_f32(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4c pcmp_lt<Packet4c>(const Packet4c& a, const Packet4c& b)
+{
+  return vget_lane_s32(vreinterpret_s32_u8(vclt_s8(
+      vreinterpret_s8_s32(vdup_n_s32(a)),
+      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8c pcmp_lt<Packet8c>(const Packet8c& a, const Packet8c& b)
+{ return vreinterpret_s8_u8(vclt_s8(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet16c pcmp_lt<Packet16c>(const Packet16c& a, const Packet16c& b)
+{ return vreinterpretq_s8_u8(vcltq_s8(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4uc pcmp_lt<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
+{
+  return vget_lane_u32(vreinterpret_u32_u8(vclt_u8(
+      vreinterpret_u8_u32(vdup_n_u32(a)),
+      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8uc pcmp_lt<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
+{ return vclt_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16uc pcmp_lt<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
+{ return vcltq_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4s pcmp_lt<Packet4s>(const Packet4s& a, const Packet4s& b)
+{ return vreinterpret_s16_u16(vclt_s16(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet8s pcmp_lt<Packet8s>(const Packet8s& a, const Packet8s& b)
+{ return vreinterpretq_s16_u16(vcltq_s16(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4us pcmp_lt<Packet4us>(const Packet4us& a, const Packet4us& b)
+{ return vclt_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8us pcmp_lt<Packet8us>(const Packet8us& a, const Packet8us& b)
+{ return vcltq_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2i pcmp_lt<Packet2i>(const Packet2i& a, const Packet2i& b)
+{ return vreinterpret_s32_u32(vclt_s32(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4i pcmp_lt<Packet4i>(const Packet4i& a, const Packet4i& b)
+{ return vreinterpretq_s32_u32(vcltq_s32(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet2ui pcmp_lt<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
+{ return vclt_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui pcmp_lt<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
+{ return vcltq_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2l pcmp_lt<Packet2l>(const Packet2l& a, const Packet2l& b)
+{
+#if EIGEN_ARCH_ARM64
+  return vreinterpretq_s64_u64(vcltq_s64(a,b));
+#else
+  return vcombine_s64(
+      vdup_n_s64(vgetq_lane_s64(a, 0) < vgetq_lane_s64(b, 0) ? numext::int64_t(-1) : 0),
+      vdup_n_s64(vgetq_lane_s64(a, 1) < vgetq_lane_s64(b, 1) ? numext::int64_t(-1) : 0));
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet2ul pcmp_lt<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
+{
+#if EIGEN_ARCH_ARM64
+  return vcltq_u64(a,b);
+#else
+  return vcombine_u64(
+      vdup_n_u64(vgetq_lane_u64(a, 0) < vgetq_lane_u64(b, 0) ? numext::uint64_t(-1) : 0),
+      vdup_n_u64(vgetq_lane_u64(a, 1) < vgetq_lane_u64(b, 1) ? numext::uint64_t(-1) : 0));
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet2f pcmp_eq<Packet2f>(const Packet2f& a, const Packet2f& b)
+{ return vreinterpret_f32_u32(vceq_f32(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4f pcmp_eq<Packet4f>(const Packet4f& a, const Packet4f& b)
+{ return vreinterpretq_f32_u32(vceqq_f32(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4c pcmp_eq<Packet4c>(const Packet4c& a, const Packet4c& b)
+{
+  return vget_lane_s32(vreinterpret_s32_u8(vceq_s8(
+      vreinterpret_s8_s32(vdup_n_s32(a)),
+      vreinterpret_s8_s32(vdup_n_s32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8c pcmp_eq<Packet8c>(const Packet8c& a, const Packet8c& b)
+{ return vreinterpret_s8_u8(vceq_s8(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet16c pcmp_eq<Packet16c>(const Packet16c& a, const Packet16c& b)
+{ return vreinterpretq_s8_u8(vceqq_s8(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4uc pcmp_eq<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
+{
+  return vget_lane_u32(vreinterpret_u32_u8(vceq_u8(
+      vreinterpret_u8_u32(vdup_n_u32(a)),
+      vreinterpret_u8_u32(vdup_n_u32(b)))), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8uc pcmp_eq<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
+{ return vceq_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16uc pcmp_eq<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
+{ return vceqq_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4s pcmp_eq<Packet4s>(const Packet4s& a, const Packet4s& b)
+{ return vreinterpret_s16_u16(vceq_s16(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet8s pcmp_eq<Packet8s>(const Packet8s& a, const Packet8s& b)
+{ return vreinterpretq_s16_u16(vceqq_s16(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4us pcmp_eq<Packet4us>(const Packet4us& a, const Packet4us& b)
+{ return vceq_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8us pcmp_eq<Packet8us>(const Packet8us& a, const Packet8us& b)
+{ return vceqq_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2i pcmp_eq<Packet2i>(const Packet2i& a, const Packet2i& b)
+{ return vreinterpret_s32_u32(vceq_s32(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet4i pcmp_eq<Packet4i>(const Packet4i& a, const Packet4i& b)
+{ return vreinterpretq_s32_u32(vceqq_s32(a,b)); }
+template<> EIGEN_STRONG_INLINE Packet2ui pcmp_eq<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
+{ return vceq_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui pcmp_eq<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
+{ return vceqq_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2l pcmp_eq<Packet2l>(const Packet2l& a, const Packet2l& b)
+{
+#if EIGEN_ARCH_ARM64
+  return vreinterpretq_s64_u64(vceqq_s64(a,b));
+#else
+  return vcombine_s64(
+      vdup_n_s64(vgetq_lane_s64(a, 0) == vgetq_lane_s64(b, 0) ? numext::int64_t(-1) : 0),
+      vdup_n_s64(vgetq_lane_s64(a, 1) == vgetq_lane_s64(b, 1) ? numext::int64_t(-1) : 0));
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet2ul pcmp_eq<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
+{
+#if EIGEN_ARCH_ARM64
+  return vceqq_u64(a,b);
+#else
+  return vcombine_u64(
+      vdup_n_u64(vgetq_lane_u64(a, 0) == vgetq_lane_u64(b, 0) ? numext::uint64_t(-1) : 0),
+      vdup_n_u64(vgetq_lane_u64(a, 1) == vgetq_lane_u64(b, 1) ? numext::uint64_t(-1) : 0));
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet2f pcmp_lt_or_nan<Packet2f>(const Packet2f& a, const Packet2f& b)
+{ return vreinterpret_f32_u32(vmvn_u32(vcge_f32(a,b))); }
+template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt_or_nan<Packet4f>(const Packet4f& a, const Packet4f& b)
+{ return vreinterpretq_f32_u32(vmvnq_u32(vcgeq_f32(a,b))); }
+
+// Logical Operations are not supported for float, so we have to reinterpret casts using NEON intrinsics
+template<> EIGEN_STRONG_INLINE Packet2f pand<Packet2f>(const Packet2f& a, const Packet2f& b)
+{ return vreinterpret_f32_u32(vand_u32(vreinterpret_u32_f32(a),vreinterpret_u32_f32(b))); }
+template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b)
+{ return vreinterpretq_f32_u32(vandq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); }
+template<> EIGEN_STRONG_INLINE Packet4c pand<Packet4c>(const Packet4c& a, const Packet4c& b)
+{ return a & b; }
+template<> EIGEN_STRONG_INLINE Packet8c pand<Packet8c>(const Packet8c& a, const Packet8c& b)
+{ return vand_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16c pand<Packet16c>(const Packet16c& a, const Packet16c& b)
+{ return vandq_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4uc pand<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
+{ return a & b; }
+template<> EIGEN_STRONG_INLINE Packet8uc pand<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
+{ return vand_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16uc pand<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
+{ return vandq_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4s pand<Packet4s>(const Packet4s& a, const Packet4s& b) { return vand_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8s pand<Packet8s>(const Packet8s& a, const Packet8s& b) { return vandq_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4us pand<Packet4us>(const Packet4us& a, const Packet4us& b)
+{ return vand_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8us pand<Packet8us>(const Packet8us& a, const Packet8us& b)
+{ return vandq_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2i pand<Packet2i>(const Packet2i& a, const Packet2i& b) { return vand_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return vandq_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ui pand<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
+{ return vand_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui pand<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
+{ return vandq_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2l pand<Packet2l>(const Packet2l& a, const Packet2l& b) { return vandq_s64(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ul pand<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
+{ return vandq_u64(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet2f por<Packet2f>(const Packet2f& a, const Packet2f& b)
+{ return vreinterpret_f32_u32(vorr_u32(vreinterpret_u32_f32(a),vreinterpret_u32_f32(b))); }
+template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b)
+{ return vreinterpretq_f32_u32(vorrq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); }
+template<> EIGEN_STRONG_INLINE Packet4c por<Packet4c>(const Packet4c& a, const Packet4c& b)
+{ return a | b; }
+template<> EIGEN_STRONG_INLINE Packet8c por<Packet8c>(const Packet8c& a, const Packet8c& b) { return vorr_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16c por<Packet16c>(const Packet16c& a, const Packet16c& b)
+{ return vorrq_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4uc por<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
+{ return a | b; }
+template<> EIGEN_STRONG_INLINE Packet8uc por<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
+{ return vorr_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16uc por<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
+{ return vorrq_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4s por<Packet4s>(const Packet4s& a, const Packet4s& b)
+{ return vorr_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8s por<Packet8s>(const Packet8s& a, const Packet8s& b)
+{ return vorrq_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4us por<Packet4us>(const Packet4us& a, const Packet4us& b)
+{ return vorr_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8us por<Packet8us>(const Packet8us& a, const Packet8us& b)
+{ return vorrq_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2i por<Packet2i>(const Packet2i& a, const Packet2i& b) { return vorr_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) { return vorrq_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ui por<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
+{ return vorr_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui por<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
+{ return vorrq_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2l por<Packet2l>(const Packet2l& a, const Packet2l& b)
+{ return vorrq_s64(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ul por<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
+{ return vorrq_u64(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet2f pxor<Packet2f>(const Packet2f& a, const Packet2f& b)
+{ return vreinterpret_f32_u32(veor_u32(vreinterpret_u32_f32(a),vreinterpret_u32_f32(b))); }
+template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b)
+{ return vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); }
+template<> EIGEN_STRONG_INLINE Packet4c pxor<Packet4c>(const Packet4c& a, const Packet4c& b)
+{ return a ^ b; }
+template<> EIGEN_STRONG_INLINE Packet8c pxor<Packet8c>(const Packet8c& a, const Packet8c& b)
+{ return veor_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16c pxor<Packet16c>(const Packet16c& a, const Packet16c& b)
+{ return veorq_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4uc pxor<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
+{ return a ^ b; }
+template<> EIGEN_STRONG_INLINE Packet8uc pxor<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
+{ return veor_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16uc pxor<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
+{ return veorq_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4s pxor<Packet4s>(const Packet4s& a, const Packet4s& b) { return veor_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8s pxor<Packet8s>(const Packet8s& a, const Packet8s& b) { return veorq_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4us pxor<Packet4us>(const Packet4us& a, const Packet4us& b)
+{ return veor_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8us pxor<Packet8us>(const Packet8us& a, const Packet8us& b)
+{ return veorq_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2i pxor<Packet2i>(const Packet2i& a, const Packet2i& b) { return veor_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) { return veorq_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ui pxor<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
+{ return veor_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui pxor<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
+{ return veorq_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2l pxor<Packet2l>(const Packet2l& a, const Packet2l& b)
+{ return veorq_s64(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ul pxor<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
+{ return veorq_u64(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet2f pandnot<Packet2f>(const Packet2f& a, const Packet2f& b)
+{ return vreinterpret_f32_u32(vbic_u32(vreinterpret_u32_f32(a),vreinterpret_u32_f32(b))); }
+template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b)
+{ return vreinterpretq_f32_u32(vbicq_u32(vreinterpretq_u32_f32(a),vreinterpretq_u32_f32(b))); }
+template<> EIGEN_STRONG_INLINE Packet4c pandnot<Packet4c>(const Packet4c& a, const Packet4c& b)
+{ return a & ~b; }
+template<> EIGEN_STRONG_INLINE Packet8c pandnot<Packet8c>(const Packet8c& a, const Packet8c& b) { return vbic_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16c pandnot<Packet16c>(const Packet16c& a, const Packet16c& b) { return vbicq_s8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4uc pandnot<Packet4uc>(const Packet4uc& a, const Packet4uc& b)
+{ return a & ~b; }
+template<> EIGEN_STRONG_INLINE Packet8uc pandnot<Packet8uc>(const Packet8uc& a, const Packet8uc& b)
+{ return vbic_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet16uc pandnot<Packet16uc>(const Packet16uc& a, const Packet16uc& b)
+{ return vbicq_u8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4s pandnot<Packet4s>(const Packet4s& a, const Packet4s& b)
+{ return vbic_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8s pandnot<Packet8s>(const Packet8s& a, const Packet8s& b)
+{ return vbicq_s16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4us pandnot<Packet4us>(const Packet4us& a, const Packet4us& b)
+{ return vbic_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet8us pandnot<Packet8us>(const Packet8us& a, const Packet8us& b)
+{ return vbicq_u16(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2i pandnot<Packet2i>(const Packet2i& a, const Packet2i& b)
+{ return vbic_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b)
+{ return vbicq_s32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ui pandnot<Packet2ui>(const Packet2ui& a, const Packet2ui& b)
+{ return vbic_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui pandnot<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
+{ return vbicq_u32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2l pandnot<Packet2l>(const Packet2l& a, const Packet2l& b)
+{ return vbicq_s64(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2ul pandnot<Packet2ul>(const Packet2ul& a, const Packet2ul& b)
+{ return vbicq_u64(a,b); }
+
+
+template<int N> EIGEN_STRONG_INLINE Packet4c parithmetic_shift_right(Packet4c& a)
+{ return vget_lane_s32(vreinterpret_s32_s8(vshr_n_s8(vreinterpret_s8_s32(vdup_n_s32(a)), N)), 0); }
+template<int N> EIGEN_STRONG_INLINE Packet8c parithmetic_shift_right(Packet8c a) { return vshr_n_s8(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet16c parithmetic_shift_right(Packet16c a) { return vshrq_n_s8(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4uc parithmetic_shift_right(Packet4uc& a)
+{ return vget_lane_u32(vreinterpret_u32_u8(vshr_n_u8(vreinterpret_u8_u32(vdup_n_u32(a)), N)), 0); }
+template<int N> EIGEN_STRONG_INLINE Packet8uc parithmetic_shift_right(Packet8uc a) { return vshr_n_u8(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet16uc parithmetic_shift_right(Packet16uc a) { return vshrq_n_u8(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4s parithmetic_shift_right(Packet4s a) { return vshr_n_s16(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet8s parithmetic_shift_right(Packet8s a) { return vshrq_n_s16(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4us parithmetic_shift_right(Packet4us a) { return vshr_n_u16(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet8us parithmetic_shift_right(Packet8us a) { return vshrq_n_u16(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet2i parithmetic_shift_right(Packet2i a) { return vshr_n_s32(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4i parithmetic_shift_right(Packet4i a) { return vshrq_n_s32(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet2ui parithmetic_shift_right(Packet2ui a) { return vshr_n_u32(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4ui parithmetic_shift_right(Packet4ui a) { return vshrq_n_u32(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet2l parithmetic_shift_right(Packet2l a) { return vshrq_n_s64(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet2ul parithmetic_shift_right(Packet2ul a) { return vshrq_n_u64(a,N); }
+
+template<int N> EIGEN_STRONG_INLINE Packet4c plogical_shift_right(Packet4c& a)
+{ return vget_lane_s32(vreinterpret_s32_u8(vshr_n_u8(vreinterpret_u8_s32(vdup_n_s32(a)), N)), 0); }
+template<int N> EIGEN_STRONG_INLINE Packet8c plogical_shift_right(Packet8c a)
+{ return vreinterpret_s8_u8(vshr_n_u8(vreinterpret_u8_s8(a),N)); }
+template<int N> EIGEN_STRONG_INLINE Packet16c plogical_shift_right(Packet16c a)
+{ return vreinterpretq_s8_u8(vshrq_n_u8(vreinterpretq_u8_s8(a),N)); }
+template<int N> EIGEN_STRONG_INLINE Packet4uc plogical_shift_right(Packet4uc& a)
+{ return vget_lane_u32(vreinterpret_u32_s8(vshr_n_s8(vreinterpret_s8_u32(vdup_n_u32(a)), N)), 0); }
+template<int N> EIGEN_STRONG_INLINE Packet8uc plogical_shift_right(Packet8uc a) { return vshr_n_u8(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet16uc plogical_shift_right(Packet16uc a) { return vshrq_n_u8(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4s plogical_shift_right(Packet4s a)
+{ return vreinterpret_s16_u16(vshr_n_u16(vreinterpret_u16_s16(a),N)); }
+template<int N> EIGEN_STRONG_INLINE Packet8s plogical_shift_right(Packet8s a)
+{ return vreinterpretq_s16_u16(vshrq_n_u16(vreinterpretq_u16_s16(a),N)); }
+template<int N> EIGEN_STRONG_INLINE Packet4us plogical_shift_right(Packet4us a) { return vshr_n_u16(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet8us plogical_shift_right(Packet8us a) { return vshrq_n_u16(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet2i plogical_shift_right(Packet2i a)
+{ return vreinterpret_s32_u32(vshr_n_u32(vreinterpret_u32_s32(a),N)); }
+template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_right(Packet4i a)
+{ return vreinterpretq_s32_u32(vshrq_n_u32(vreinterpretq_u32_s32(a),N)); }
+template<int N> EIGEN_STRONG_INLINE Packet2ui plogical_shift_right(Packet2ui a) { return vshr_n_u32(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4ui plogical_shift_right(Packet4ui a) { return vshrq_n_u32(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet2l plogical_shift_right(Packet2l a)
+{ return vreinterpretq_s64_u64(vshrq_n_u64(vreinterpretq_u64_s64(a),N)); }
+template<int N> EIGEN_STRONG_INLINE Packet2ul plogical_shift_right(Packet2ul a) { return vshrq_n_u64(a,N); }
+
+template<int N> EIGEN_STRONG_INLINE Packet4c plogical_shift_left(Packet4c& a)
+{ return vget_lane_s32(vreinterpret_s32_s8(vshl_n_s8(vreinterpret_s8_s32(vdup_n_s32(a)), N)), 0); }
+template<int N> EIGEN_STRONG_INLINE Packet8c plogical_shift_left(Packet8c a) { return vshl_n_s8(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet16c plogical_shift_left(Packet16c a) { return vshlq_n_s8(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4uc plogical_shift_left(Packet4uc& a)
+{ return vget_lane_u32(vreinterpret_u32_u8(vshl_n_u8(vreinterpret_u8_u32(vdup_n_u32(a)), N)), 0); }
+template<int N> EIGEN_STRONG_INLINE Packet8uc plogical_shift_left(Packet8uc a) { return vshl_n_u8(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet16uc plogical_shift_left(Packet16uc a) { return vshlq_n_u8(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4s plogical_shift_left(Packet4s a) { return vshl_n_s16(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet8s plogical_shift_left(Packet8s a) { return vshlq_n_s16(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4us plogical_shift_left(Packet4us a) { return vshl_n_u16(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet8us plogical_shift_left(Packet8us a) { return vshlq_n_u16(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet2i plogical_shift_left(Packet2i a) { return vshl_n_s32(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_left(Packet4i a) { return vshlq_n_s32(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet2ui plogical_shift_left(Packet2ui a) { return vshl_n_u32(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4ui plogical_shift_left(Packet4ui a) { return vshlq_n_u32(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet2l plogical_shift_left(Packet2l a) { return vshlq_n_s64(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet2ul plogical_shift_left(Packet2ul a) { return vshlq_n_u64(a,N); }
+
+template<> EIGEN_STRONG_INLINE Packet2f pload<Packet2f>(const float* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_f32(from); }
+template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_f32(from); }
+template<> EIGEN_STRONG_INLINE Packet4c pload<Packet4c>(const int8_t* from)
+{
+  Packet4c res;
+  memcpy(&res, from, sizeof(Packet4c));
+  return res;
+}
+template<> EIGEN_STRONG_INLINE Packet8c pload<Packet8c>(const int8_t* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_s8(from); }
+template<> EIGEN_STRONG_INLINE Packet16c pload<Packet16c>(const int8_t* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s8(from); }
+template<> EIGEN_STRONG_INLINE Packet4uc pload<Packet4uc>(const uint8_t* from)
+{
+  Packet4uc res;
+  memcpy(&res, from, sizeof(Packet4uc));
+  return res;
+}
+template<> EIGEN_STRONG_INLINE Packet8uc pload<Packet8uc>(const uint8_t* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_u8(from); }
+template<> EIGEN_STRONG_INLINE Packet16uc pload<Packet16uc>(const uint8_t* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u8(from); }
+template<> EIGEN_STRONG_INLINE Packet4s pload<Packet4s>(const int16_t* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_s16(from); }
+template<> EIGEN_STRONG_INLINE Packet8s pload<Packet8s>(const int16_t* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s16(from); }
+template<> EIGEN_STRONG_INLINE Packet4us pload<Packet4us>(const uint16_t* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_u16(from); }
+template<> EIGEN_STRONG_INLINE Packet8us pload<Packet8us>(const uint16_t* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u16(from); }
+template<> EIGEN_STRONG_INLINE Packet2i pload<Packet2i>(const int32_t* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_s32(from); }
+template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int32_t* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s32(from); }
+template<> EIGEN_STRONG_INLINE Packet2ui pload<Packet2ui>(const uint32_t* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1_u32(from); }
+template<> EIGEN_STRONG_INLINE Packet4ui pload<Packet4ui>(const uint32_t* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u32(from); }
+template<> EIGEN_STRONG_INLINE Packet2l pload<Packet2l>(const int64_t* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_s64(from); }
+template<> EIGEN_STRONG_INLINE Packet2ul pload<Packet2ul>(const uint64_t* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_u64(from); }
+
+template<> EIGEN_STRONG_INLINE Packet2f ploadu<Packet2f>(const float* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_f32(from); }
+template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_f32(from); }
+template<> EIGEN_STRONG_INLINE Packet4c ploadu<Packet4c>(const int8_t* from)
+{
+  Packet4c res;
+  memcpy(&res, from, sizeof(Packet4c));
+  return res;
+}
+template<> EIGEN_STRONG_INLINE Packet8c ploadu<Packet8c>(const int8_t* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_s8(from); }
+template<> EIGEN_STRONG_INLINE Packet16c ploadu<Packet16c>(const int8_t* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s8(from); }
+template<> EIGEN_STRONG_INLINE Packet4uc ploadu<Packet4uc>(const uint8_t* from)
+{
+  Packet4uc res;
+  memcpy(&res, from, sizeof(Packet4uc));
+  return res;
+}
+template<> EIGEN_STRONG_INLINE Packet8uc ploadu<Packet8uc>(const uint8_t* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_u8(from); }
+template<> EIGEN_STRONG_INLINE Packet16uc ploadu<Packet16uc>(const uint8_t* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u8(from); }
+template<> EIGEN_STRONG_INLINE Packet4s ploadu<Packet4s>(const int16_t* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_s16(from); }
+template<> EIGEN_STRONG_INLINE Packet8s ploadu<Packet8s>(const int16_t* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s16(from); }
+template<> EIGEN_STRONG_INLINE Packet4us ploadu<Packet4us>(const uint16_t* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_u16(from); }
+template<> EIGEN_STRONG_INLINE Packet8us ploadu<Packet8us>(const uint16_t* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u16(from); }
+template<> EIGEN_STRONG_INLINE Packet2i ploadu<Packet2i>(const int32_t* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_s32(from); }
+template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int32_t* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s32(from); }
+template<> EIGEN_STRONG_INLINE Packet2ui ploadu<Packet2ui>(const uint32_t* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1_u32(from); }
+template<> EIGEN_STRONG_INLINE Packet4ui ploadu<Packet4ui>(const uint32_t* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u32(from); }
+template<> EIGEN_STRONG_INLINE Packet2l ploadu<Packet2l>(const int64_t* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_s64(from); }
+template<> EIGEN_STRONG_INLINE Packet2ul ploadu<Packet2ul>(const uint64_t* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_u64(from); }
+
+template<> EIGEN_STRONG_INLINE Packet2f ploaddup<Packet2f>(const float* from)
+{ return vld1_dup_f32(from); }
+template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from)
+{ return vcombine_f32(vld1_dup_f32(from), vld1_dup_f32(from+1)); }
+template<> EIGEN_STRONG_INLINE Packet4c ploaddup<Packet4c>(const int8_t* from)
+{
+  const int8x8_t a = vreinterpret_s8_s32(vdup_n_s32(pload<Packet4c>(from)));
+  return vget_lane_s32(vreinterpret_s32_s8(vzip_s8(a,a).val[0]), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8c ploaddup<Packet8c>(const int8_t* from)
+{
+  const int8x8_t a = vld1_s8(from);
+  return vzip_s8(a,a).val[0];
+}
+template<> EIGEN_STRONG_INLINE Packet16c ploaddup<Packet16c>(const int8_t* from)
+{
+  const int8x8_t a = vld1_s8(from);
+  const int8x8x2_t b = vzip_s8(a,a);
+  return vcombine_s8(b.val[0], b.val[1]);
+}
+template<> EIGEN_STRONG_INLINE Packet4uc ploaddup<Packet4uc>(const uint8_t* from)
+{
+  const uint8x8_t a = vreinterpret_u8_u32(vdup_n_u32(pload<Packet4uc>(from)));
+  return vget_lane_u32(vreinterpret_u32_u8(vzip_u8(a,a).val[0]), 0);
+}
+template<> EIGEN_STRONG_INLINE Packet8uc ploaddup<Packet8uc>(const uint8_t* from)
+{
+  const uint8x8_t a = vld1_u8(from);
+  return vzip_u8(a,a).val[0];
+}
+template<> EIGEN_STRONG_INLINE Packet16uc ploaddup<Packet16uc>(const uint8_t* from)
+{
+  const uint8x8_t a = vld1_u8(from);
+  const uint8x8x2_t b = vzip_u8(a,a);
+  return vcombine_u8(b.val[0], b.val[1]);
+}
+template<> EIGEN_STRONG_INLINE Packet4s ploaddup<Packet4s>(const int16_t* from)
+{
+  return vreinterpret_s16_u32(vzip_u32(vreinterpret_u32_s16(vld1_dup_s16(from)),
+      vreinterpret_u32_s16(vld1_dup_s16(from+1))).val[0]);
+}
+template<> EIGEN_STRONG_INLINE Packet8s ploaddup<Packet8s>(const int16_t* from)
+{
+  const int16x4_t a = vld1_s16(from);
+  const int16x4x2_t b = vzip_s16(a,a);
+  return vcombine_s16(b.val[0], b.val[1]);
+}
+template<> EIGEN_STRONG_INLINE Packet4us ploaddup<Packet4us>(const uint16_t* from)
+{
+  return vreinterpret_u16_u32(vzip_u32(vreinterpret_u32_u16(vld1_dup_u16(from)),
+      vreinterpret_u32_u16(vld1_dup_u16(from+1))).val[0]);
+}
+template<> EIGEN_STRONG_INLINE Packet8us ploaddup<Packet8us>(const uint16_t* from)
+{
+  const uint16x4_t a = vld1_u16(from);
+  const uint16x4x2_t b = vzip_u16(a,a);
+  return vcombine_u16(b.val[0], b.val[1]);
+}
+template<> EIGEN_STRONG_INLINE Packet2i ploaddup<Packet2i>(const int32_t* from)
+{ return vld1_dup_s32(from); }
+template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int32_t* from)
+{ return vcombine_s32(vld1_dup_s32(from), vld1_dup_s32(from+1)); }
+template<> EIGEN_STRONG_INLINE Packet2ui ploaddup<Packet2ui>(const uint32_t* from)
+{ return vld1_dup_u32(from); }
+template<> EIGEN_STRONG_INLINE Packet4ui ploaddup<Packet4ui>(const uint32_t* from)
+{ return vcombine_u32(vld1_dup_u32(from), vld1_dup_u32(from+1)); }
+template<> EIGEN_STRONG_INLINE Packet2l ploaddup<Packet2l>(const int64_t* from)
+{ return vld1q_dup_s64(from); }
+template<> EIGEN_STRONG_INLINE Packet2ul ploaddup<Packet2ul>(const uint64_t* from)
+{ return vld1q_dup_u64(from); }
+
+template<> EIGEN_STRONG_INLINE Packet4f ploadquad<Packet4f>(const float* from) { return vld1q_dup_f32(from); }
+template<> EIGEN_STRONG_INLINE Packet4c ploadquad<Packet4c>(const int8_t* from)
+{ return vget_lane_s32(vreinterpret_s32_s8(vld1_dup_s8(from)), 0); }
+template<> EIGEN_STRONG_INLINE Packet8c ploadquad<Packet8c>(const int8_t* from)
+{
+  return vreinterpret_s8_u32(vzip_u32(
+      vreinterpret_u32_s8(vld1_dup_s8(from)),
+      vreinterpret_u32_s8(vld1_dup_s8(from+1))).val[0]);
+}
+template<> EIGEN_STRONG_INLINE Packet16c ploadquad<Packet16c>(const int8_t* from)
+{
+  const int8x8_t a = vreinterpret_s8_u32(vzip_u32(
+      vreinterpret_u32_s8(vld1_dup_s8(from)),
+      vreinterpret_u32_s8(vld1_dup_s8(from+1))).val[0]);
+  const int8x8_t b = vreinterpret_s8_u32(vzip_u32(
+      vreinterpret_u32_s8(vld1_dup_s8(from+2)),
+      vreinterpret_u32_s8(vld1_dup_s8(from+3))).val[0]);
+  return vcombine_s8(a,b);
+}
+template<> EIGEN_STRONG_INLINE Packet4uc ploadquad<Packet4uc>(const uint8_t* from)
+{ return vget_lane_u32(vreinterpret_u32_u8(vld1_dup_u8(from)), 0); }
+template<> EIGEN_STRONG_INLINE Packet8uc ploadquad<Packet8uc>(const uint8_t* from)
+{
+  return vreinterpret_u8_u32(vzip_u32(
+      vreinterpret_u32_u8(vld1_dup_u8(from)),
+      vreinterpret_u32_u8(vld1_dup_u8(from+1))).val[0]);
+}
+template<> EIGEN_STRONG_INLINE Packet16uc ploadquad<Packet16uc>(const uint8_t* from)
+{
+  const uint8x8_t a = vreinterpret_u8_u32(vzip_u32(
+      vreinterpret_u32_u8(vld1_dup_u8(from)),
+      vreinterpret_u32_u8(vld1_dup_u8(from+1))).val[0]);
+  const uint8x8_t b = vreinterpret_u8_u32(vzip_u32(
+      vreinterpret_u32_u8(vld1_dup_u8(from+2)),
+      vreinterpret_u32_u8(vld1_dup_u8(from+3))).val[0]);
+  return vcombine_u8(a,b);
+}
+template<> EIGEN_STRONG_INLINE Packet8s ploadquad<Packet8s>(const int16_t* from)
+{ return vcombine_s16(vld1_dup_s16(from), vld1_dup_s16(from+1)); }
+template<> EIGEN_STRONG_INLINE Packet8us ploadquad<Packet8us>(const uint16_t* from)
+{ return vcombine_u16(vld1_dup_u16(from), vld1_dup_u16(from+1)); }
+template<> EIGEN_STRONG_INLINE Packet4i ploadquad<Packet4i>(const int32_t* from) { return vld1q_dup_s32(from); }
+template<> EIGEN_STRONG_INLINE Packet4ui ploadquad<Packet4ui>(const uint32_t* from) { return vld1q_dup_u32(from); }
+
+template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet2f& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1_f32(to,from); }
+template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1q_f32(to,from); }
+template<> EIGEN_STRONG_INLINE void pstore<int8_t>(int8_t* to, const Packet4c& from)
+{ memcpy(to, &from, sizeof(from)); }
+template<> EIGEN_STRONG_INLINE void pstore<int8_t>(int8_t* to, const Packet8c& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1_s8(to,from); }
+template<> EIGEN_STRONG_INLINE void pstore<int8_t>(int8_t* to, const Packet16c& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1q_s8(to,from); }
+template<> EIGEN_STRONG_INLINE void pstore<uint8_t>(uint8_t* to, const Packet4uc& from)
+{ memcpy(to, &from, sizeof(from)); }
+template<> EIGEN_STRONG_INLINE void pstore<uint8_t>(uint8_t* to, const Packet8uc& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1_u8(to,from); }
+template<> EIGEN_STRONG_INLINE void pstore<uint8_t>(uint8_t* to, const Packet16uc& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1q_u8(to,from); }
+template<> EIGEN_STRONG_INLINE void pstore<int16_t>(int16_t* to, const Packet4s& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1_s16(to,from); }
+template<> EIGEN_STRONG_INLINE void pstore<int16_t>(int16_t* to, const Packet8s& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1q_s16(to,from); }
+template<> EIGEN_STRONG_INLINE void pstore<uint16_t>(uint16_t* to, const Packet4us& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1_u16(to,from); }
+template<> EIGEN_STRONG_INLINE void pstore<uint16_t>(uint16_t* to, const Packet8us& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1q_u16(to,from); }
+template<> EIGEN_STRONG_INLINE void pstore<int32_t>(int32_t* to, const Packet2i& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1_s32(to,from); }
+template<> EIGEN_STRONG_INLINE void pstore<int32_t>(int32_t* to, const Packet4i& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1q_s32(to,from); }
+template<> EIGEN_STRONG_INLINE void pstore<uint32_t>(uint32_t* to, const Packet2ui& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1_u32(to,from); }
+template<> EIGEN_STRONG_INLINE void pstore<uint32_t>(uint32_t* to, const Packet4ui& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1q_u32(to,from); }
+template<> EIGEN_STRONG_INLINE void pstore<int64_t>(int64_t* to, const Packet2l& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1q_s64(to,from); }
+template<> EIGEN_STRONG_INLINE void pstore<uint64_t>(uint64_t* to, const Packet2ul& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1q_u64(to,from); }
+
+template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet2f& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1_f32(to,from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_f32(to,from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<int8_t>(int8_t* to, const Packet4c& from)
+{ memcpy(to, &from, sizeof(from)); }
+template<> EIGEN_STRONG_INLINE void pstoreu<int8_t>(int8_t* to, const Packet8c& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1_s8(to,from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<int8_t>(int8_t* to, const Packet16c& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_s8(to,from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<uint8_t>(uint8_t* to, const Packet4uc& from)
+{ memcpy(to, &from, sizeof(from)); }
+template<> EIGEN_STRONG_INLINE void pstoreu<uint8_t>(uint8_t* to, const Packet8uc& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1_u8(to,from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<uint8_t>(uint8_t* to, const Packet16uc& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_u8(to,from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<int16_t>(int16_t* to, const Packet4s& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1_s16(to,from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<int16_t>(int16_t* to, const Packet8s& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_s16(to,from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<uint16_t>(uint16_t* to, const Packet4us& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1_u16(to,from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<uint16_t>(uint16_t* to, const Packet8us& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_u16(to,from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<int32_t>(int32_t* to, const Packet2i& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1_s32(to,from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<int32_t>(int32_t* to, const Packet4i& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_s32(to,from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<uint32_t>(uint32_t* to, const Packet2ui& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1_u32(to,from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<uint32_t>(uint32_t* to, const Packet4ui& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_u32(to,from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<int64_t>(int64_t* to, const Packet2l& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_s64(to,from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<uint64_t>(uint64_t* to, const Packet2ul& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_u64(to,from); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2f pgather<float, Packet2f>(const float* from, Index stride)
+{
+  Packet2f res = vld1_dup_f32(from);
+  res = vld1_lane_f32(from + 1*stride, res, 1);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4f pgather<float, Packet4f>(const float* from, Index stride)
+{
+  Packet4f res = vld1q_dup_f32(from);
+  res = vld1q_lane_f32(from + 1*stride, res, 1);
+  res = vld1q_lane_f32(from + 2*stride, res, 2);
+  res = vld1q_lane_f32(from + 3*stride, res, 3);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4c pgather<int8_t, Packet4c>(const int8_t* from, Index stride)
+{
+  Packet4c res;
+  for (int i = 0; i != 4; i++)
+    reinterpret_cast<int8_t*>(&res)[i] = *(from + i * stride);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8c pgather<int8_t, Packet8c>(const int8_t* from, Index stride)
+{
+  Packet8c res = vld1_dup_s8(from);
+  res = vld1_lane_s8(from + 1*stride, res, 1);
+  res = vld1_lane_s8(from + 2*stride, res, 2);
+  res = vld1_lane_s8(from + 3*stride, res, 3);
+  res = vld1_lane_s8(from + 4*stride, res, 4);
+  res = vld1_lane_s8(from + 5*stride, res, 5);
+  res = vld1_lane_s8(from + 6*stride, res, 6);
+  res = vld1_lane_s8(from + 7*stride, res, 7);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16c pgather<int8_t, Packet16c>(const int8_t* from, Index stride)
+{
+  Packet16c res = vld1q_dup_s8(from);
+  res = vld1q_lane_s8(from + 1*stride, res, 1);
+  res = vld1q_lane_s8(from + 2*stride, res, 2);
+  res = vld1q_lane_s8(from + 3*stride, res, 3);
+  res = vld1q_lane_s8(from + 4*stride, res, 4);
+  res = vld1q_lane_s8(from + 5*stride, res, 5);
+  res = vld1q_lane_s8(from + 6*stride, res, 6);
+  res = vld1q_lane_s8(from + 7*stride, res, 7);
+  res = vld1q_lane_s8(from + 8*stride, res, 8);
+  res = vld1q_lane_s8(from + 9*stride, res, 9);
+  res = vld1q_lane_s8(from + 10*stride, res, 10);
+  res = vld1q_lane_s8(from + 11*stride, res, 11);
+  res = vld1q_lane_s8(from + 12*stride, res, 12);
+  res = vld1q_lane_s8(from + 13*stride, res, 13);
+  res = vld1q_lane_s8(from + 14*stride, res, 14);
+  res = vld1q_lane_s8(from + 15*stride, res, 15);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4uc pgather<uint8_t, Packet4uc>(const uint8_t* from, Index stride)
+{
+  Packet4uc res;
+  for (int i = 0; i != 4; i++)
+    reinterpret_cast<uint8_t*>(&res)[i] = *(from + i * stride);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8uc pgather<uint8_t, Packet8uc>(const uint8_t* from, Index stride)
+{
+  Packet8uc res = vld1_dup_u8(from);
+  res = vld1_lane_u8(from + 1*stride, res, 1);
+  res = vld1_lane_u8(from + 2*stride, res, 2);
+  res = vld1_lane_u8(from + 3*stride, res, 3);
+  res = vld1_lane_u8(from + 4*stride, res, 4);
+  res = vld1_lane_u8(from + 5*stride, res, 5);
+  res = vld1_lane_u8(from + 6*stride, res, 6);
+  res = vld1_lane_u8(from + 7*stride, res, 7);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16uc pgather<uint8_t, Packet16uc>(const uint8_t* from, Index stride)
+{
+  Packet16uc res = vld1q_dup_u8(from);
+  res = vld1q_lane_u8(from + 1*stride, res, 1);
+  res = vld1q_lane_u8(from + 2*stride, res, 2);
+  res = vld1q_lane_u8(from + 3*stride, res, 3);
+  res = vld1q_lane_u8(from + 4*stride, res, 4);
+  res = vld1q_lane_u8(from + 5*stride, res, 5);
+  res = vld1q_lane_u8(from + 6*stride, res, 6);
+  res = vld1q_lane_u8(from + 7*stride, res, 7);
+  res = vld1q_lane_u8(from + 8*stride, res, 8);
+  res = vld1q_lane_u8(from + 9*stride, res, 9);
+  res = vld1q_lane_u8(from + 10*stride, res, 10);
+  res = vld1q_lane_u8(from + 11*stride, res, 11);
+  res = vld1q_lane_u8(from + 12*stride, res, 12);
+  res = vld1q_lane_u8(from + 13*stride, res, 13);
+  res = vld1q_lane_u8(from + 14*stride, res, 14);
+  res = vld1q_lane_u8(from + 15*stride, res, 15);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4s pgather<int16_t, Packet4s>(const int16_t* from, Index stride)
+{
+  Packet4s res = vld1_dup_s16(from);
+  res = vld1_lane_s16(from + 1*stride, res, 1);
+  res = vld1_lane_s16(from + 2*stride, res, 2);
+  res = vld1_lane_s16(from + 3*stride, res, 3);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8s pgather<int16_t, Packet8s>(const int16_t* from, Index stride)
+{
+  Packet8s res = vld1q_dup_s16(from);
+  res = vld1q_lane_s16(from + 1*stride, res, 1);
+  res = vld1q_lane_s16(from + 2*stride, res, 2);
+  res = vld1q_lane_s16(from + 3*stride, res, 3);
+  res = vld1q_lane_s16(from + 4*stride, res, 4);
+  res = vld1q_lane_s16(from + 5*stride, res, 5);
+  res = vld1q_lane_s16(from + 6*stride, res, 6);
+  res = vld1q_lane_s16(from + 7*stride, res, 7);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4us pgather<uint16_t, Packet4us>(const uint16_t* from, Index stride)
+{
+  Packet4us res = vld1_dup_u16(from);
+  res = vld1_lane_u16(from + 1*stride, res, 1);
+  res = vld1_lane_u16(from + 2*stride, res, 2);
+  res = vld1_lane_u16(from + 3*stride, res, 3);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8us pgather<uint16_t, Packet8us>(const uint16_t* from, Index stride)
+{
+  Packet8us res = vld1q_dup_u16(from);
+  res = vld1q_lane_u16(from + 1*stride, res, 1);
+  res = vld1q_lane_u16(from + 2*stride, res, 2);
+  res = vld1q_lane_u16(from + 3*stride, res, 3);
+  res = vld1q_lane_u16(from + 4*stride, res, 4);
+  res = vld1q_lane_u16(from + 5*stride, res, 5);
+  res = vld1q_lane_u16(from + 6*stride, res, 6);
+  res = vld1q_lane_u16(from + 7*stride, res, 7);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2i pgather<int32_t, Packet2i>(const int32_t* from, Index stride)
+{
+  Packet2i res = vld1_dup_s32(from);
+  res = vld1_lane_s32(from + 1*stride, res, 1);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4i pgather<int32_t, Packet4i>(const int32_t* from, Index stride)
+{
+  Packet4i res = vld1q_dup_s32(from);
+  res = vld1q_lane_s32(from + 1*stride, res, 1);
+  res = vld1q_lane_s32(from + 2*stride, res, 2);
+  res = vld1q_lane_s32(from + 3*stride, res, 3);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ui pgather<uint32_t, Packet2ui>(const uint32_t* from, Index stride)
+{
+  Packet2ui res = vld1_dup_u32(from);
+  res = vld1_lane_u32(from + 1*stride, res, 1);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4ui pgather<uint32_t, Packet4ui>(const uint32_t* from, Index stride)
+{
+  Packet4ui res = vld1q_dup_u32(from);
+  res = vld1q_lane_u32(from + 1*stride, res, 1);
+  res = vld1q_lane_u32(from + 2*stride, res, 2);
+  res = vld1q_lane_u32(from + 3*stride, res, 3);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2l pgather<int64_t, Packet2l>(const int64_t* from, Index stride)
+{
+  Packet2l res = vld1q_dup_s64(from);
+  res = vld1q_lane_s64(from + 1*stride, res, 1);
+  return res;
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ul pgather<uint64_t, Packet2ul>(const uint64_t* from, Index stride)
+{
+  Packet2ul res = vld1q_dup_u64(from);
+  res = vld1q_lane_u64(from + 1*stride, res, 1);
+  return res;
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<float, Packet2f>(float* to, const Packet2f& from, Index stride)
+{
+  vst1_lane_f32(to + stride*0, from, 0);
+  vst1_lane_f32(to + stride*1, from, 1);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
+{
+  vst1q_lane_f32(to + stride*0, from, 0);
+  vst1q_lane_f32(to + stride*1, from, 1);
+  vst1q_lane_f32(to + stride*2, from, 2);
+  vst1q_lane_f32(to + stride*3, from, 3);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int8_t, Packet4c>(int8_t* to, const Packet4c& from, Index stride)
+{
+  for (int i = 0; i != 4; i++)
+    *(to + i * stride) = reinterpret_cast<const int8_t*>(&from)[i];
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int8_t, Packet8c>(int8_t* to, const Packet8c& from, Index stride)
+{
+  vst1_lane_s8(to + stride*0, from, 0);
+  vst1_lane_s8(to + stride*1, from, 1);
+  vst1_lane_s8(to + stride*2, from, 2);
+  vst1_lane_s8(to + stride*3, from, 3);
+  vst1_lane_s8(to + stride*4, from, 4);
+  vst1_lane_s8(to + stride*5, from, 5);
+  vst1_lane_s8(to + stride*6, from, 6);
+  vst1_lane_s8(to + stride*7, from, 7);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int8_t, Packet16c>(int8_t* to, const Packet16c& from, Index stride)
+{
+  vst1q_lane_s8(to + stride*0, from, 0);
+  vst1q_lane_s8(to + stride*1, from, 1);
+  vst1q_lane_s8(to + stride*2, from, 2);
+  vst1q_lane_s8(to + stride*3, from, 3);
+  vst1q_lane_s8(to + stride*4, from, 4);
+  vst1q_lane_s8(to + stride*5, from, 5);
+  vst1q_lane_s8(to + stride*6, from, 6);
+  vst1q_lane_s8(to + stride*7, from, 7);
+  vst1q_lane_s8(to + stride*8, from, 8);
+  vst1q_lane_s8(to + stride*9, from, 9);
+  vst1q_lane_s8(to + stride*10, from, 10);
+  vst1q_lane_s8(to + stride*11, from, 11);
+  vst1q_lane_s8(to + stride*12, from, 12);
+  vst1q_lane_s8(to + stride*13, from, 13);
+  vst1q_lane_s8(to + stride*14, from, 14);
+  vst1q_lane_s8(to + stride*15, from, 15);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint8_t, Packet4uc>(uint8_t* to, const Packet4uc& from, Index stride)
+{
+  for (int i = 0; i != 4; i++)
+    *(to + i * stride) = reinterpret_cast<const uint8_t*>(&from)[i];
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint8_t, Packet8uc>(uint8_t* to, const Packet8uc& from, Index stride)
+{
+  vst1_lane_u8(to + stride*0, from, 0);
+  vst1_lane_u8(to + stride*1, from, 1);
+  vst1_lane_u8(to + stride*2, from, 2);
+  vst1_lane_u8(to + stride*3, from, 3);
+  vst1_lane_u8(to + stride*4, from, 4);
+  vst1_lane_u8(to + stride*5, from, 5);
+  vst1_lane_u8(to + stride*6, from, 6);
+  vst1_lane_u8(to + stride*7, from, 7);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint8_t, Packet16uc>(uint8_t* to, const Packet16uc& from, Index stride)
+{
+  vst1q_lane_u8(to + stride*0, from, 0);
+  vst1q_lane_u8(to + stride*1, from, 1);
+  vst1q_lane_u8(to + stride*2, from, 2);
+  vst1q_lane_u8(to + stride*3, from, 3);
+  vst1q_lane_u8(to + stride*4, from, 4);
+  vst1q_lane_u8(to + stride*5, from, 5);
+  vst1q_lane_u8(to + stride*6, from, 6);
+  vst1q_lane_u8(to + stride*7, from, 7);
+  vst1q_lane_u8(to + stride*8, from, 8);
+  vst1q_lane_u8(to + stride*9, from, 9);
+  vst1q_lane_u8(to + stride*10, from, 10);
+  vst1q_lane_u8(to + stride*11, from, 11);
+  vst1q_lane_u8(to + stride*12, from, 12);
+  vst1q_lane_u8(to + stride*13, from, 13);
+  vst1q_lane_u8(to + stride*14, from, 14);
+  vst1q_lane_u8(to + stride*15, from, 15);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int16_t, Packet4s>(int16_t* to, const Packet4s& from, Index stride)
+{
+  vst1_lane_s16(to + stride*0, from, 0);
+  vst1_lane_s16(to + stride*1, from, 1);
+  vst1_lane_s16(to + stride*2, from, 2);
+  vst1_lane_s16(to + stride*3, from, 3);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int16_t, Packet8s>(int16_t* to, const Packet8s& from, Index stride)
+{
+  vst1q_lane_s16(to + stride*0, from, 0);
+  vst1q_lane_s16(to + stride*1, from, 1);
+  vst1q_lane_s16(to + stride*2, from, 2);
+  vst1q_lane_s16(to + stride*3, from, 3);
+  vst1q_lane_s16(to + stride*4, from, 4);
+  vst1q_lane_s16(to + stride*5, from, 5);
+  vst1q_lane_s16(to + stride*6, from, 6);
+  vst1q_lane_s16(to + stride*7, from, 7);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint16_t, Packet4us>(uint16_t* to, const Packet4us& from, Index stride)
+{
+  vst1_lane_u16(to + stride*0, from, 0);
+  vst1_lane_u16(to + stride*1, from, 1);
+  vst1_lane_u16(to + stride*2, from, 2);
+  vst1_lane_u16(to + stride*3, from, 3);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint16_t, Packet8us>(uint16_t* to, const Packet8us& from, Index stride)
+{
+  vst1q_lane_u16(to + stride*0, from, 0);
+  vst1q_lane_u16(to + stride*1, from, 1);
+  vst1q_lane_u16(to + stride*2, from, 2);
+  vst1q_lane_u16(to + stride*3, from, 3);
+  vst1q_lane_u16(to + stride*4, from, 4);
+  vst1q_lane_u16(to + stride*5, from, 5);
+  vst1q_lane_u16(to + stride*6, from, 6);
+  vst1q_lane_u16(to + stride*7, from, 7);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int32_t, Packet2i>(int32_t* to, const Packet2i& from, Index stride)
+{
+  vst1_lane_s32(to + stride*0, from, 0);
+  vst1_lane_s32(to + stride*1, from, 1);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int32_t, Packet4i>(int32_t* to, const Packet4i& from, Index stride)
+{
+  vst1q_lane_s32(to + stride*0, from, 0);
+  vst1q_lane_s32(to + stride*1, from, 1);
+  vst1q_lane_s32(to + stride*2, from, 2);
+  vst1q_lane_s32(to + stride*3, from, 3);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint32_t, Packet2ui>(uint32_t* to, const Packet2ui& from, Index stride)
+{
+  vst1_lane_u32(to + stride*0, from, 0);
+  vst1_lane_u32(to + stride*1, from, 1);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint32_t, Packet4ui>(uint32_t* to, const Packet4ui& from, Index stride)
+{
+  vst1q_lane_u32(to + stride*0, from, 0);
+  vst1q_lane_u32(to + stride*1, from, 1);
+  vst1q_lane_u32(to + stride*2, from, 2);
+  vst1q_lane_u32(to + stride*3, from, 3);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<int64_t, Packet2l>(int64_t* to, const Packet2l& from, Index stride)
+{
+  vst1q_lane_s64(to + stride*0, from, 0);
+  vst1q_lane_s64(to + stride*1, from, 1);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<uint64_t, Packet2ul>(uint64_t* to, const Packet2ul& from, Index stride)
+{
+  vst1q_lane_u64(to + stride*0, from, 0);
+  vst1q_lane_u64(to + stride*1, from, 1);
+}
+
+template<> EIGEN_STRONG_INLINE void prefetch<float>(const float* addr) { EIGEN_ARM_PREFETCH(addr); }
+template<> EIGEN_STRONG_INLINE void prefetch<int8_t>(const int8_t* addr) { EIGEN_ARM_PREFETCH(addr); }
+template<> EIGEN_STRONG_INLINE void prefetch<uint8_t>(const uint8_t* addr) { EIGEN_ARM_PREFETCH(addr); }
+template<> EIGEN_STRONG_INLINE void prefetch<int16_t>(const int16_t* addr) { EIGEN_ARM_PREFETCH(addr); }
+template<> EIGEN_STRONG_INLINE void prefetch<uint16_t>(const uint16_t* addr) { EIGEN_ARM_PREFETCH(addr); }
+template<> EIGEN_STRONG_INLINE void prefetch<int32_t>(const int32_t* addr) { EIGEN_ARM_PREFETCH(addr); }
+template<> EIGEN_STRONG_INLINE void prefetch<uint32_t>(const uint32_t* addr) { EIGEN_ARM_PREFETCH(addr); }
+template<> EIGEN_STRONG_INLINE void prefetch<int64_t>(const int64_t* addr) { EIGEN_ARM_PREFETCH(addr); }
+template<> EIGEN_STRONG_INLINE void prefetch<uint64_t>(const uint64_t* addr) { EIGEN_ARM_PREFETCH(addr); }
+
+template<> EIGEN_STRONG_INLINE float pfirst<Packet2f>(const Packet2f& a) { return vget_lane_f32(a,0); }
+template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { return vgetq_lane_f32(a,0); }
+template<> EIGEN_STRONG_INLINE int8_t pfirst<Packet4c>(const Packet4c& a) { return static_cast<int8_t>(a & 0xff); }
+template<> EIGEN_STRONG_INLINE int8_t pfirst<Packet8c>(const Packet8c& a) { return vget_lane_s8(a,0); }
+template<> EIGEN_STRONG_INLINE int8_t pfirst<Packet16c>(const Packet16c& a) { return vgetq_lane_s8(a,0); }
+template<> EIGEN_STRONG_INLINE uint8_t pfirst<Packet4uc>(const Packet4uc& a) { return static_cast<uint8_t>(a & 0xff); }
+template<> EIGEN_STRONG_INLINE uint8_t pfirst<Packet8uc>(const Packet8uc& a) { return vget_lane_u8(a,0); }
+template<> EIGEN_STRONG_INLINE uint8_t pfirst<Packet16uc>(const Packet16uc& a) { return vgetq_lane_u8(a,0); }
+template<> EIGEN_STRONG_INLINE int16_t pfirst<Packet4s>(const Packet4s& a) { return vget_lane_s16(a,0); }
+template<> EIGEN_STRONG_INLINE int16_t pfirst<Packet8s>(const Packet8s& a) { return vgetq_lane_s16(a,0); }
+template<> EIGEN_STRONG_INLINE uint16_t pfirst<Packet4us>(const Packet4us& a) { return vget_lane_u16(a,0); }
+template<> EIGEN_STRONG_INLINE uint16_t pfirst<Packet8us>(const Packet8us& a) { return vgetq_lane_u16(a,0); }
+template<> EIGEN_STRONG_INLINE int32_t pfirst<Packet2i>(const Packet2i& a) { return vget_lane_s32(a,0); }
+template<> EIGEN_STRONG_INLINE int32_t pfirst<Packet4i>(const Packet4i& a) { return vgetq_lane_s32(a,0); }
+template<> EIGEN_STRONG_INLINE uint32_t pfirst<Packet2ui>(const Packet2ui& a) { return vget_lane_u32(a,0); }
+template<> EIGEN_STRONG_INLINE uint32_t pfirst<Packet4ui>(const Packet4ui& a) { return vgetq_lane_u32(a,0); }
+template<> EIGEN_STRONG_INLINE int64_t pfirst<Packet2l>(const Packet2l& a) { return vgetq_lane_s64(a,0); }
+template<> EIGEN_STRONG_INLINE uint64_t pfirst<Packet2ul>(const Packet2ul& a) { return vgetq_lane_u64(a,0); }
+
+template<> EIGEN_STRONG_INLINE Packet2f preverse(const Packet2f& a) { return vrev64_f32(a); }
+template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a)
+{
+  const float32x4_t a_r64 = vrev64q_f32(a);
+  return vcombine_f32(vget_high_f32(a_r64), vget_low_f32(a_r64));
+}
+template<> EIGEN_STRONG_INLINE Packet4c preverse(const Packet4c& a)
+{ return vget_lane_s32(vreinterpret_s32_s8(vrev64_s8(vreinterpret_s8_s32(vdup_n_s32(a)))), 0); }
+template<> EIGEN_STRONG_INLINE Packet8c preverse(const Packet8c& a) { return vrev64_s8(a); }
+template<> EIGEN_STRONG_INLINE Packet16c preverse(const Packet16c& a)
+{
+  const int8x16_t a_r64 = vrev64q_s8(a);
+  return vcombine_s8(vget_high_s8(a_r64), vget_low_s8(a_r64));
+}
+template<> EIGEN_STRONG_INLINE Packet4uc preverse(const Packet4uc& a)
+{ return vget_lane_u32(vreinterpret_u32_u8(vrev64_u8(vreinterpret_u8_u32(vdup_n_u32(a)))), 0); }
+template<> EIGEN_STRONG_INLINE Packet8uc preverse(const Packet8uc& a) { return vrev64_u8(a); }
+template<> EIGEN_STRONG_INLINE Packet16uc preverse(const Packet16uc& a)
+{
+  const uint8x16_t a_r64 = vrev64q_u8(a);
+  return vcombine_u8(vget_high_u8(a_r64), vget_low_u8(a_r64));
+}
+template<> EIGEN_STRONG_INLINE Packet4s preverse(const Packet4s& a) { return vrev64_s16(a); }
+template<> EIGEN_STRONG_INLINE Packet8s preverse(const Packet8s& a)
+{
+  const int16x8_t a_r64 = vrev64q_s16(a);
+  return vcombine_s16(vget_high_s16(a_r64), vget_low_s16(a_r64));
+}
+template<> EIGEN_STRONG_INLINE Packet4us preverse(const Packet4us& a) { return vrev64_u16(a); }
+template<> EIGEN_STRONG_INLINE Packet8us preverse(const Packet8us& a)
+{
+  const uint16x8_t a_r64 = vrev64q_u16(a);
+  return vcombine_u16(vget_high_u16(a_r64), vget_low_u16(a_r64));
+}
+template<> EIGEN_STRONG_INLINE Packet2i preverse(const Packet2i& a) { return vrev64_s32(a); }
+template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a)
+{
+  const int32x4_t a_r64 = vrev64q_s32(a);
+  return vcombine_s32(vget_high_s32(a_r64), vget_low_s32(a_r64));
+}
+template<> EIGEN_STRONG_INLINE Packet2ui preverse(const Packet2ui& a) { return vrev64_u32(a); }
+template<> EIGEN_STRONG_INLINE Packet4ui preverse(const Packet4ui& a)
+{
+  const uint32x4_t a_r64 = vrev64q_u32(a);
+  return vcombine_u32(vget_high_u32(a_r64), vget_low_u32(a_r64));
+}
+template<> EIGEN_STRONG_INLINE Packet2l preverse(const Packet2l& a)
+{ return vcombine_s64(vget_high_s64(a), vget_low_s64(a)); }
+template<> EIGEN_STRONG_INLINE Packet2ul preverse(const Packet2ul& a)
+{ return vcombine_u64(vget_high_u64(a), vget_low_u64(a)); }
+
+template<> EIGEN_STRONG_INLINE Packet2f pabs(const Packet2f& a) { return vabs_f32(a); }
+template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a) { return vabsq_f32(a); }
+template<> EIGEN_STRONG_INLINE Packet4c pabs<Packet4c>(const Packet4c& a)
+{ return vget_lane_s32(vreinterpret_s32_s8(vabs_s8(vreinterpret_s8_s32(vdup_n_s32(a)))), 0); }
+template<> EIGEN_STRONG_INLINE Packet8c pabs(const Packet8c& a) { return vabs_s8(a); }
+template<> EIGEN_STRONG_INLINE Packet16c pabs(const Packet16c& a) { return vabsq_s8(a); }
+template<> EIGEN_STRONG_INLINE Packet4uc pabs(const Packet4uc& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet8uc pabs(const Packet8uc& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet16uc pabs(const Packet16uc& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet4s pabs(const Packet4s& a) { return vabs_s16(a); }
+template<> EIGEN_STRONG_INLINE Packet8s pabs(const Packet8s& a) { return vabsq_s16(a); }
+template<> EIGEN_STRONG_INLINE Packet4us pabs(const Packet4us& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet8us pabs(const Packet8us& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet2i pabs(const Packet2i& a) { return vabs_s32(a); }
+template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a) { return vabsq_s32(a); }
+template<> EIGEN_STRONG_INLINE Packet2ui pabs(const Packet2ui& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet4ui pabs(const Packet4ui& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet2l pabs(const Packet2l& a) {
+#if EIGEN_ARCH_ARM64
+  return vabsq_s64(a);
+#else
+  return vcombine_s64(
+      vdup_n_s64((std::abs)(vgetq_lane_s64(a, 0))),
+      vdup_n_s64((std::abs)(vgetq_lane_s64(a, 1))));
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet2ul pabs(const Packet2ul& a) { return a; }
+
+template <>
+EIGEN_STRONG_INLINE Packet2f psignbit(const Packet2f& a) {
+  return vreinterpret_f32_s32(vshr_n_s32(vreinterpret_s32_f32(a), 31));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f psignbit(const Packet4f& a) {
+  return vreinterpretq_f32_s32(vshrq_n_s32(vreinterpretq_s32_f32(a), 31));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2f pfrexp<Packet2f>(const Packet2f& a, Packet2f& exponent)
+{ return pfrexp_generic(a,exponent); }
+template<> EIGEN_STRONG_INLINE Packet4f pfrexp<Packet4f>(const Packet4f& a, Packet4f& exponent)
+{ return pfrexp_generic(a,exponent); }
+
+template<> EIGEN_STRONG_INLINE Packet2f pldexp<Packet2f>(const Packet2f& a, const Packet2f& exponent)
+{ return pldexp_generic(a,exponent); }
+template<> EIGEN_STRONG_INLINE Packet4f pldexp<Packet4f>(const Packet4f& a, const Packet4f& exponent)
+{ return pldexp_generic(a,exponent); }
+
+#if EIGEN_ARCH_ARM64
+template<> EIGEN_STRONG_INLINE float predux<Packet2f>(const Packet2f& a) { return vaddv_f32(a); }
+template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a) { return vaddvq_f32(a); }
+#else
+template<> EIGEN_STRONG_INLINE float predux<Packet2f>(const Packet2f& a) { return vget_lane_f32(vpadd_f32(a,a), 0); }
+template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
+{
+  const float32x2_t sum = vadd_f32(vget_low_f32(a), vget_high_f32(a));
+  return vget_lane_f32(vpadd_f32(sum, sum), 0);
+}
+#endif
+template<> EIGEN_STRONG_INLINE int8_t predux<Packet4c>(const Packet4c& a)
+{
+  const int8x8_t a_dup = vreinterpret_s8_s32(vdup_n_s32(a));
+  int8x8_t sum = vpadd_s8(a_dup, a_dup);
+  sum = vpadd_s8(sum, sum);
+  return vget_lane_s8(sum, 0);
+}
+#if EIGEN_ARCH_ARM64
+template<> EIGEN_STRONG_INLINE int8_t predux<Packet8c>(const Packet8c& a) { return vaddv_s8(a); }
+template<> EIGEN_STRONG_INLINE int8_t predux<Packet16c>(const Packet16c& a) { return vaddvq_s8(a); }
+#else
+template<> EIGEN_STRONG_INLINE int8_t predux<Packet8c>(const Packet8c& a)
+{
+  int8x8_t sum = vpadd_s8(a,a);
+  sum = vpadd_s8(sum, sum);
+  sum = vpadd_s8(sum, sum);
+  return vget_lane_s8(sum, 0);
+}
+template<> EIGEN_STRONG_INLINE int8_t predux<Packet16c>(const Packet16c& a)
+{
+  int8x8_t sum = vadd_s8(vget_low_s8(a), vget_high_s8(a));
+  sum = vpadd_s8(sum, sum);
+  sum = vpadd_s8(sum, sum);
+  sum = vpadd_s8(sum, sum);
+  return vget_lane_s8(sum, 0);
+}
+#endif
+template<> EIGEN_STRONG_INLINE uint8_t predux<Packet4uc>(const Packet4uc& a)
+{
+  const uint8x8_t a_dup = vreinterpret_u8_u32(vdup_n_u32(a));
+  uint8x8_t sum = vpadd_u8(a_dup, a_dup);
+  sum = vpadd_u8(sum, sum);
+  return vget_lane_u8(sum, 0);
+}
+#if EIGEN_ARCH_ARM64
+template<> EIGEN_STRONG_INLINE uint8_t predux<Packet8uc>(const Packet8uc& a) { return vaddv_u8(a); }
+template<> EIGEN_STRONG_INLINE uint8_t predux<Packet16uc>(const Packet16uc& a) { return vaddvq_u8(a); }
+template<> EIGEN_STRONG_INLINE int16_t predux<Packet4s>(const Packet4s& a) { return vaddv_s16(a); }
+template<> EIGEN_STRONG_INLINE int16_t predux<Packet8s>(const Packet8s& a) { return vaddvq_s16(a); }
+template<> EIGEN_STRONG_INLINE uint16_t predux<Packet4us>(const Packet4us& a) { return vaddv_u16(a); }
+template<> EIGEN_STRONG_INLINE uint16_t predux<Packet8us>(const Packet8us& a) { return vaddvq_u16(a); }
+template<> EIGEN_STRONG_INLINE int32_t predux<Packet2i>(const Packet2i& a) { return vaddv_s32(a); }
+template<> EIGEN_STRONG_INLINE int32_t predux<Packet4i>(const Packet4i& a) { return vaddvq_s32(a); }
+template<> EIGEN_STRONG_INLINE uint32_t predux<Packet2ui>(const Packet2ui& a) { return vaddv_u32(a); }
+template<> EIGEN_STRONG_INLINE uint32_t predux<Packet4ui>(const Packet4ui& a) { return vaddvq_u32(a); }
+template<> EIGEN_STRONG_INLINE int64_t predux<Packet2l>(const Packet2l& a) { return vaddvq_s64(a); }
+template<> EIGEN_STRONG_INLINE uint64_t predux<Packet2ul>(const Packet2ul& a) { return vaddvq_u64(a); }
+#else
+template<> EIGEN_STRONG_INLINE uint8_t predux<Packet8uc>(const Packet8uc& a)
+{
+  uint8x8_t sum = vpadd_u8(a,a);
+  sum = vpadd_u8(sum, sum);
+  sum = vpadd_u8(sum, sum);
+  return vget_lane_u8(sum, 0);
+}
+template<> EIGEN_STRONG_INLINE uint8_t predux<Packet16uc>(const Packet16uc& a)
+{
+  uint8x8_t sum = vadd_u8(vget_low_u8(a), vget_high_u8(a));
+  sum = vpadd_u8(sum, sum);
+  sum = vpadd_u8(sum, sum);
+  sum = vpadd_u8(sum, sum);
+  return vget_lane_u8(sum, 0);
+}
+template<> EIGEN_STRONG_INLINE int16_t predux<Packet4s>(const Packet4s& a)
+{
+  const int16x4_t sum = vpadd_s16(a,a);
+  return vget_lane_s16(vpadd_s16(sum, sum), 0);
+}
+template<> EIGEN_STRONG_INLINE int16_t predux<Packet8s>(const Packet8s& a)
+{
+  int16x4_t sum = vadd_s16(vget_low_s16(a), vget_high_s16(a));
+  sum = vpadd_s16(sum, sum);
+  sum = vpadd_s16(sum, sum);
+  return vget_lane_s16(sum, 0);
+}
+template<> EIGEN_STRONG_INLINE uint16_t predux<Packet4us>(const Packet4us& a)
+{
+  const uint16x4_t sum = vpadd_u16(a,a);
+  return vget_lane_u16(vpadd_u16(sum, sum), 0);
+}
+template<> EIGEN_STRONG_INLINE uint16_t predux<Packet8us>(const Packet8us& a)
+{
+  uint16x4_t sum = vadd_u16(vget_low_u16(a), vget_high_u16(a));
+  sum = vpadd_u16(sum, sum);
+  sum = vpadd_u16(sum, sum);
+  return vget_lane_u16(sum, 0);
+}
+template<> EIGEN_STRONG_INLINE int32_t predux<Packet2i>(const Packet2i& a) { return vget_lane_s32(vpadd_s32(a,a), 0); }
+template<> EIGEN_STRONG_INLINE int32_t predux<Packet4i>(const Packet4i& a)
+{
+  const int32x2_t sum = vadd_s32(vget_low_s32(a), vget_high_s32(a));
+  return vget_lane_s32(vpadd_s32(sum, sum), 0);
+}
+template<> EIGEN_STRONG_INLINE uint32_t predux<Packet2ui>(const Packet2ui& a) { return vget_lane_u32(vpadd_u32(a,a), 0); }
+template<> EIGEN_STRONG_INLINE uint32_t predux<Packet4ui>(const Packet4ui& a)
+{
+  const uint32x2_t sum = vadd_u32(vget_low_u32(a), vget_high_u32(a));
+  return vget_lane_u32(vpadd_u32(sum, sum), 0);
+}
+template<> EIGEN_STRONG_INLINE int64_t predux<Packet2l>(const Packet2l& a)
+{ return vgetq_lane_s64(a, 0) + vgetq_lane_s64(a, 1); }
+template<> EIGEN_STRONG_INLINE uint64_t predux<Packet2ul>(const Packet2ul& a)
+{ return vgetq_lane_u64(a, 0) + vgetq_lane_u64(a, 1); }
+#endif
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4c predux_half_dowto4(const Packet8c& a)
+{
+  return vget_lane_s32(vreinterpret_s32_s8(vadd_s8(a,
+      vreinterpret_s8_s32(vrev64_s32(vreinterpret_s32_s8(a))))), 0);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8c predux_half_dowto4(const Packet16c& a)
+{ return vadd_s8(vget_high_s8(a), vget_low_s8(a)); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4uc predux_half_dowto4(const Packet8uc& a)
+{
+  return vget_lane_u32(vreinterpret_u32_u8(vadd_u8(a,
+      vreinterpret_u8_u32(vrev64_u32(vreinterpret_u32_u8(a))))), 0);
+}
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8uc predux_half_dowto4(const Packet16uc& a)
+{ return vadd_u8(vget_high_u8(a), vget_low_u8(a)); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4s predux_half_dowto4(const Packet8s& a)
+{ return vadd_s16(vget_high_s16(a), vget_low_s16(a)); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4us predux_half_dowto4(const Packet8us& a)
+{ return vadd_u16(vget_high_u16(a), vget_low_u16(a)); }
+
+// Other reduction functions:
+// mul
+template<> EIGEN_STRONG_INLINE float predux_mul<Packet2f>(const Packet2f& a)
+{ return vget_lane_f32(a, 0) * vget_lane_f32(a, 1); }
+template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
+{ return predux_mul<Packet2f>(vmul_f32(vget_low_f32(a), vget_high_f32(a))); }
+template<> EIGEN_STRONG_INLINE int8_t predux_mul<Packet4c>(const Packet4c& a)
+{
+  int8x8_t prod = vreinterpret_s8_s32(vdup_n_s32(a));
+  prod = vmul_s8(prod, vrev16_s8(prod));
+  return vget_lane_s8(prod, 0) * vget_lane_s8(prod, 2);
+}
+template<> EIGEN_STRONG_INLINE int8_t predux_mul<Packet8c>(const Packet8c& a)
+{
+  int8x8_t prod = vmul_s8(a, vrev16_s8(a));
+  prod = vmul_s8(prod, vrev32_s8(prod));
+  return vget_lane_s8(prod, 0) * vget_lane_s8(prod, 4);
+}
+template<> EIGEN_STRONG_INLINE int8_t predux_mul<Packet16c>(const Packet16c& a)
+{ return predux_mul<Packet8c>(vmul_s8(vget_low_s8(a), vget_high_s8(a))); }
+template<> EIGEN_STRONG_INLINE uint8_t predux_mul<Packet4uc>(const Packet4uc& a)
+{
+  uint8x8_t prod = vreinterpret_u8_u32(vdup_n_u32(a));
+  prod = vmul_u8(prod, vrev16_u8(prod));
+  return vget_lane_u8(prod, 0) * vget_lane_u8(prod, 2);
+}
+template<> EIGEN_STRONG_INLINE uint8_t predux_mul<Packet8uc>(const Packet8uc& a)
+{
+  uint8x8_t prod = vmul_u8(a, vrev16_u8(a));
+  prod = vmul_u8(prod, vrev32_u8(prod));
+  return vget_lane_u8(prod, 0) * vget_lane_u8(prod, 4);
+}
+template<> EIGEN_STRONG_INLINE uint8_t predux_mul<Packet16uc>(const Packet16uc& a)
+{ return predux_mul<Packet8uc>(vmul_u8(vget_low_u8(a), vget_high_u8(a))); }
+template<> EIGEN_STRONG_INLINE int16_t predux_mul<Packet4s>(const Packet4s& a)
+{
+  const int16x4_t prod = vmul_s16(a, vrev32_s16(a));
+  return vget_lane_s16(prod, 0) * vget_lane_s16(prod, 2);
+}
+template<> EIGEN_STRONG_INLINE int16_t predux_mul<Packet8s>(const Packet8s& a)
+{
+  int16x4_t prod;
+
+  // Get the product of a_lo * a_hi -> |a1*a5|a2*a6|a3*a7|a4*a8|
+  prod = vmul_s16(vget_low_s16(a), vget_high_s16(a));
+  // Swap and multiply |a1*a5*a2*a6|a3*a7*a4*a8|
+  prod = vmul_s16(prod, vrev32_s16(prod));
+  // Multiply |a1*a5*a2*a6*a3*a7*a4*a8|
+  return vget_lane_s16(prod, 0) * vget_lane_s16(prod, 2);
+}
+template<> EIGEN_STRONG_INLINE uint16_t predux_mul<Packet4us>(const Packet4us& a)
+{
+  const uint16x4_t prod = vmul_u16(a, vrev32_u16(a));
+  return vget_lane_u16(prod, 0) * vget_lane_u16(prod, 2);
+}
+template<> EIGEN_STRONG_INLINE uint16_t predux_mul<Packet8us>(const Packet8us& a)
+{
+  uint16x4_t prod;
+
+  // Get the product of a_lo * a_hi -> |a1*a5|a2*a6|a3*a7|a4*a8|
+  prod = vmul_u16(vget_low_u16(a), vget_high_u16(a));
+  // Swap and multiply |a1*a5*a2*a6|a3*a7*a4*a8|
+  prod = vmul_u16(prod, vrev32_u16(prod));
+  // Multiply |a1*a5*a2*a6*a3*a7*a4*a8|
+  return vget_lane_u16(prod, 0) * vget_lane_u16(prod, 2);
+}
+template<> EIGEN_STRONG_INLINE int32_t predux_mul<Packet2i>(const Packet2i& a)
+{ return vget_lane_s32(a, 0) * vget_lane_s32(a, 1); }
+template<> EIGEN_STRONG_INLINE int32_t predux_mul<Packet4i>(const Packet4i& a)
+{ return predux_mul<Packet2i>(vmul_s32(vget_low_s32(a), vget_high_s32(a))); }
+template<> EIGEN_STRONG_INLINE uint32_t predux_mul<Packet2ui>(const Packet2ui& a)
+{ return vget_lane_u32(a, 0) * vget_lane_u32(a, 1); }
+template<> EIGEN_STRONG_INLINE uint32_t predux_mul<Packet4ui>(const Packet4ui& a)
+{ return predux_mul<Packet2ui>(vmul_u32(vget_low_u32(a), vget_high_u32(a))); }
+template<> EIGEN_STRONG_INLINE int64_t predux_mul<Packet2l>(const Packet2l& a)
+{ return vgetq_lane_s64(a, 0) * vgetq_lane_s64(a, 1); }
+template<> EIGEN_STRONG_INLINE uint64_t predux_mul<Packet2ul>(const Packet2ul& a)
+{ return vgetq_lane_u64(a, 0) * vgetq_lane_u64(a, 1); }
+
+// min
+#if EIGEN_ARCH_ARM64
+template<> EIGEN_STRONG_INLINE float predux_min<Packet2f>(const Packet2f& a) { return vminv_f32(a); }
+template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a) { return vminvq_f32(a); }
+#else
+template<> EIGEN_STRONG_INLINE float predux_min<Packet2f>(const Packet2f& a)
+{ return vget_lane_f32(vpmin_f32(a,a), 0); }
+template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
+{
+  const float32x2_t min = vmin_f32(vget_low_f32(a), vget_high_f32(a));
+  return vget_lane_f32(vpmin_f32(min, min), 0);
+}
+#endif
+template<> EIGEN_STRONG_INLINE int8_t predux_min<Packet4c>(const Packet4c& a)
+{
+  const int8x8_t a_dup = vreinterpret_s8_s32(vdup_n_s32(a));
+  int8x8_t min = vpmin_s8(a_dup, a_dup);
+  min = vpmin_s8(min, min);
+  return vget_lane_s8(min, 0);
+}
+#if EIGEN_ARCH_ARM64
+template<> EIGEN_STRONG_INLINE int8_t predux_min<Packet8c>(const Packet8c& a) { return vminv_s8(a); }
+template<> EIGEN_STRONG_INLINE int8_t predux_min<Packet16c>(const Packet16c& a) { return vminvq_s8(a); }
+#else
+template<> EIGEN_STRONG_INLINE int8_t predux_min<Packet8c>(const Packet8c& a)
+{
+  int8x8_t min = vpmin_s8(a,a);
+  min = vpmin_s8(min, min);
+  min = vpmin_s8(min, min);
+  return vget_lane_s8(min, 0);
+}
+template<> EIGEN_STRONG_INLINE int8_t predux_min<Packet16c>(const Packet16c& a)
+{
+  int8x8_t min = vmin_s8(vget_low_s8(a), vget_high_s8(a));
+  min = vpmin_s8(min, min);
+  min = vpmin_s8(min, min);
+  min = vpmin_s8(min, min);
+  return vget_lane_s8(min, 0);
+}
+#endif
+template<> EIGEN_STRONG_INLINE uint8_t predux_min<Packet4uc>(const Packet4uc& a)
+{
+  const uint8x8_t a_dup = vreinterpret_u8_u32(vdup_n_u32(a));
+  uint8x8_t min = vpmin_u8(a_dup, a_dup);
+  min = vpmin_u8(min, min);
+  return vget_lane_u8(min, 0);
+}
+#if EIGEN_ARCH_ARM64
+template<> EIGEN_STRONG_INLINE uint8_t predux_min<Packet8uc>(const Packet8uc& a) { return vminv_u8(a); }
+template<> EIGEN_STRONG_INLINE uint8_t predux_min<Packet16uc>(const Packet16uc& a) { return vminvq_u8(a); }
+template<> EIGEN_STRONG_INLINE int16_t predux_min<Packet4s>(const Packet4s& a) { return vminv_s16(a); }
+template<> EIGEN_STRONG_INLINE int16_t predux_min<Packet8s>(const Packet8s& a) { return vminvq_s16(a); }
+template<> EIGEN_STRONG_INLINE uint16_t predux_min<Packet4us>(const Packet4us& a) { return vminv_u16(a); }
+template<> EIGEN_STRONG_INLINE uint16_t predux_min<Packet8us>(const Packet8us& a) { return vminvq_u16(a); }
+template<> EIGEN_STRONG_INLINE int32_t predux_min<Packet2i>(const Packet2i& a) { return vminv_s32(a); }
+template<> EIGEN_STRONG_INLINE int32_t predux_min<Packet4i>(const Packet4i& a) { return vminvq_s32(a); }
+template<> EIGEN_STRONG_INLINE uint32_t predux_min<Packet2ui>(const Packet2ui& a) { return vminv_u32(a); }
+template<> EIGEN_STRONG_INLINE uint32_t predux_min<Packet4ui>(const Packet4ui& a) { return vminvq_u32(a); }
+#else
+template<> EIGEN_STRONG_INLINE uint8_t predux_min<Packet8uc>(const Packet8uc& a)
+{
+  uint8x8_t min = vpmin_u8(a,a);
+  min = vpmin_u8(min, min);
+  min = vpmin_u8(min, min);
+  return vget_lane_u8(min, 0);
+}
+template<> EIGEN_STRONG_INLINE uint8_t predux_min<Packet16uc>(const Packet16uc& a)
+{
+  uint8x8_t min = vmin_u8(vget_low_u8(a), vget_high_u8(a));
+  min = vpmin_u8(min, min);
+  min = vpmin_u8(min, min);
+  min = vpmin_u8(min, min);
+  return vget_lane_u8(min, 0);
+}
+template<> EIGEN_STRONG_INLINE int16_t predux_min<Packet4s>(const Packet4s& a)
+{
+  const int16x4_t min = vpmin_s16(a,a);
+  return vget_lane_s16(vpmin_s16(min, min), 0);
+}
+template<> EIGEN_STRONG_INLINE int16_t predux_min<Packet8s>(const Packet8s& a)
+{
+  int16x4_t min = vmin_s16(vget_low_s16(a), vget_high_s16(a));
+  min = vpmin_s16(min, min);
+  min = vpmin_s16(min, min);
+  return vget_lane_s16(min, 0);
+}
+template<> EIGEN_STRONG_INLINE uint16_t predux_min<Packet4us>(const Packet4us& a)
+{
+  const uint16x4_t min = vpmin_u16(a,a);
+  return vget_lane_u16(vpmin_u16(min, min), 0);
+}
+template<> EIGEN_STRONG_INLINE uint16_t predux_min<Packet8us>(const Packet8us& a)
+{
+  uint16x4_t min = vmin_u16(vget_low_u16(a), vget_high_u16(a));
+  min = vpmin_u16(min, min);
+  min = vpmin_u16(min, min);
+  return vget_lane_u16(min, 0);
+}
+template<> EIGEN_STRONG_INLINE int32_t predux_min<Packet2i>(const Packet2i& a)
+{ return vget_lane_s32(vpmin_s32(a,a), 0); }
+template<> EIGEN_STRONG_INLINE int32_t predux_min<Packet4i>(const Packet4i& a)
+{
+  const int32x2_t min = vmin_s32(vget_low_s32(a), vget_high_s32(a));
+  return vget_lane_s32(vpmin_s32(min, min), 0);
+}
+template<> EIGEN_STRONG_INLINE uint32_t predux_min<Packet2ui>(const Packet2ui& a)
+{ return vget_lane_u32(vpmin_u32(a,a), 0); }
+template<> EIGEN_STRONG_INLINE uint32_t predux_min<Packet4ui>(const Packet4ui& a)
+{
+  const uint32x2_t min = vmin_u32(vget_low_u32(a), vget_high_u32(a));
+  return vget_lane_u32(vpmin_u32(min, min), 0);
+}
+#endif
+template<> EIGEN_STRONG_INLINE int64_t predux_min<Packet2l>(const Packet2l& a)
+{ return (std::min)(vgetq_lane_s64(a, 0), vgetq_lane_s64(a, 1)); }
+template<> EIGEN_STRONG_INLINE uint64_t predux_min<Packet2ul>(const Packet2ul& a)
+{ return (std::min)(vgetq_lane_u64(a, 0), vgetq_lane_u64(a, 1)); }
+
+// max
+#if EIGEN_ARCH_ARM64
+template<> EIGEN_STRONG_INLINE float predux_max<Packet2f>(const Packet2f& a) { return vmaxv_f32(a); }
+template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a) { return vmaxvq_f32(a); }
+#else
+template<> EIGEN_STRONG_INLINE float predux_max<Packet2f>(const Packet2f& a)
+{ return vget_lane_f32(vpmax_f32(a,a), 0); }
+template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
+{
+  const float32x2_t max = vmax_f32(vget_low_f32(a), vget_high_f32(a));
+  return vget_lane_f32(vpmax_f32(max, max), 0);
+}
+#endif
+template<> EIGEN_STRONG_INLINE int8_t predux_max<Packet4c>(const Packet4c& a)
+{
+  const int8x8_t a_dup = vreinterpret_s8_s32(vdup_n_s32(a));
+  int8x8_t max = vpmax_s8(a_dup, a_dup);
+  max = vpmax_s8(max, max);
+  return vget_lane_s8(max, 0);
+}
+#if EIGEN_ARCH_ARM64
+template<> EIGEN_STRONG_INLINE int8_t predux_max<Packet8c>(const Packet8c& a) { return vmaxv_s8(a); }
+template<> EIGEN_STRONG_INLINE int8_t predux_max<Packet16c>(const Packet16c& a) { return vmaxvq_s8(a); }
+#else
+template<> EIGEN_STRONG_INLINE int8_t predux_max<Packet8c>(const Packet8c& a)
+{
+  int8x8_t max = vpmax_s8(a,a);
+  max = vpmax_s8(max, max);
+  max = vpmax_s8(max, max);
+  return vget_lane_s8(max, 0);
+}
+template<> EIGEN_STRONG_INLINE int8_t predux_max<Packet16c>(const Packet16c& a)
+{
+  int8x8_t max = vmax_s8(vget_low_s8(a), vget_high_s8(a));
+  max = vpmax_s8(max, max);
+  max = vpmax_s8(max, max);
+  max = vpmax_s8(max, max);
+  return vget_lane_s8(max, 0);
+}
+#endif
+template<> EIGEN_STRONG_INLINE uint8_t predux_max<Packet4uc>(const Packet4uc& a)
+{
+  const uint8x8_t a_dup = vreinterpret_u8_u32(vdup_n_u32(a));
+  uint8x8_t max = vpmax_u8(a_dup, a_dup);
+  max = vpmax_u8(max, max);
+  return vget_lane_u8(max, 0);
+}
+#if EIGEN_ARCH_ARM64
+template<> EIGEN_STRONG_INLINE uint8_t predux_max<Packet8uc>(const Packet8uc& a) { return vmaxv_u8(a); }
+template<> EIGEN_STRONG_INLINE uint8_t predux_max<Packet16uc>(const Packet16uc& a) { return vmaxvq_u8(a); }
+template<> EIGEN_STRONG_INLINE int16_t predux_max<Packet4s>(const Packet4s& a) { return vmaxv_s16(a); }
+template<> EIGEN_STRONG_INLINE int16_t predux_max<Packet8s>(const Packet8s& a) { return vmaxvq_s16(a); }
+template<> EIGEN_STRONG_INLINE uint16_t predux_max<Packet4us>(const Packet4us& a) { return vmaxv_u16(a); }
+template<> EIGEN_STRONG_INLINE uint16_t predux_max<Packet8us>(const Packet8us& a) { return vmaxvq_u16(a); }
+template<> EIGEN_STRONG_INLINE int32_t predux_max<Packet2i>(const Packet2i& a) { return vmaxv_s32(a); }
+template<> EIGEN_STRONG_INLINE int32_t predux_max<Packet4i>(const Packet4i& a) { return vmaxvq_s32(a); }
+template<> EIGEN_STRONG_INLINE uint32_t predux_max<Packet2ui>(const Packet2ui& a) { return vmaxv_u32(a); }
+template<> EIGEN_STRONG_INLINE uint32_t predux_max<Packet4ui>(const Packet4ui& a) { return vmaxvq_u32(a); }
+#else
+template<> EIGEN_STRONG_INLINE uint8_t predux_max<Packet8uc>(const Packet8uc& a)
+{
+  uint8x8_t max = vpmax_u8(a,a);
+  max = vpmax_u8(max, max);
+  max = vpmax_u8(max, max);
+  return vget_lane_u8(max, 0);
+}
+template<> EIGEN_STRONG_INLINE uint8_t predux_max<Packet16uc>(const Packet16uc& a)
+{
+  uint8x8_t max = vmax_u8(vget_low_u8(a), vget_high_u8(a));
+  max = vpmax_u8(max, max);
+  max = vpmax_u8(max, max);
+  max = vpmax_u8(max, max);
+  return vget_lane_u8(max, 0);
+}
+template<> EIGEN_STRONG_INLINE int16_t predux_max<Packet4s>(const Packet4s& a)
+{
+  const int16x4_t max = vpmax_s16(a,a);
+  return vget_lane_s16(vpmax_s16(max, max), 0);
+}
+template<> EIGEN_STRONG_INLINE int16_t predux_max<Packet8s>(const Packet8s& a)
+{
+  int16x4_t max = vmax_s16(vget_low_s16(a), vget_high_s16(a));
+  max = vpmax_s16(max, max);
+  max = vpmax_s16(max, max);
+  return vget_lane_s16(max, 0);
+}
+template<> EIGEN_STRONG_INLINE uint16_t predux_max<Packet4us>(const Packet4us& a)
+{
+  const uint16x4_t max = vpmax_u16(a,a);
+  return vget_lane_u16(vpmax_u16(max, max), 0);
+}
+template<> EIGEN_STRONG_INLINE uint16_t predux_max<Packet8us>(const Packet8us& a)
+{
+  uint16x4_t max = vmax_u16(vget_low_u16(a), vget_high_u16(a));
+  max = vpmax_u16(max, max);
+  max = vpmax_u16(max, max);
+  return vget_lane_u16(max, 0);
+}
+template<> EIGEN_STRONG_INLINE int32_t predux_max<Packet2i>(const Packet2i& a)
+{ return vget_lane_s32(vpmax_s32(a,a), 0); }
+template<> EIGEN_STRONG_INLINE int32_t predux_max<Packet4i>(const Packet4i& a)
+{
+  const int32x2_t max = vmax_s32(vget_low_s32(a), vget_high_s32(a));
+  return vget_lane_s32(vpmax_s32(max, max), 0);
+}
+template<> EIGEN_STRONG_INLINE uint32_t predux_max<Packet2ui>(const Packet2ui& a)
+{ return vget_lane_u32(vpmax_u32(a,a), 0); }
+template<> EIGEN_STRONG_INLINE uint32_t predux_max<Packet4ui>(const Packet4ui& a)
+{
+  const uint32x2_t max = vmax_u32(vget_low_u32(a), vget_high_u32(a));
+  return vget_lane_u32(vpmax_u32(max, max), 0);
+}
+#endif
+template<> EIGEN_STRONG_INLINE int64_t predux_max<Packet2l>(const Packet2l& a)
+{ return (std::max)(vgetq_lane_s64(a, 0), vgetq_lane_s64(a, 1)); }
+template<> EIGEN_STRONG_INLINE uint64_t predux_max<Packet2ul>(const Packet2ul& a)
+{ return (std::max)(vgetq_lane_u64(a, 0), vgetq_lane_u64(a, 1)); }
+
+template<> EIGEN_STRONG_INLINE bool predux_any(const Packet4f& x)
+{
+  uint32x2_t tmp = vorr_u32(vget_low_u32( vreinterpretq_u32_f32(x)),
+                            vget_high_u32(vreinterpretq_u32_f32(x)));
+  return vget_lane_u32(vpmax_u32(tmp, tmp), 0);
+}
+
+// Helpers for ptranspose.
+namespace detail {
+  
+template<typename Packet>
+void zip_in_place(Packet& p1, Packet& p2);
+
+template<>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet2f>(Packet2f& p1, Packet2f& p2) {
+  const float32x2x2_t tmp = vzip_f32(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet4f>(Packet4f& p1, Packet4f& p2) {
+  const float32x4x2_t tmp = vzipq_f32(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet8c>(Packet8c& p1, Packet8c& p2) {
+  const int8x8x2_t tmp = vzip_s8(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet16c>(Packet16c& p1, Packet16c& p2) {
+  const int8x16x2_t tmp = vzipq_s8(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet8uc>(Packet8uc& p1, Packet8uc& p2) {
+  const uint8x8x2_t tmp = vzip_u8(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet16uc>(Packet16uc& p1, Packet16uc& p2) {
+  const uint8x16x2_t tmp = vzipq_u8(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet2i>(Packet2i& p1, Packet2i& p2) {
+  const int32x2x2_t tmp = vzip_s32(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet4i>(Packet4i& p1, Packet4i& p2) {
+  const int32x4x2_t tmp = vzipq_s32(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet2ui>(Packet2ui& p1, Packet2ui& p2) {
+  const uint32x2x2_t tmp = vzip_u32(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet4ui>(Packet4ui& p1, Packet4ui& p2) {
+  const uint32x4x2_t tmp = vzipq_u32(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet4s>(Packet4s& p1, Packet4s& p2) {
+  const int16x4x2_t tmp = vzip_s16(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet8s>(Packet8s& p1, Packet8s& p2) {
+  const int16x8x2_t tmp = vzipq_s16(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet4us>(Packet4us& p1, Packet4us& p2) {
+  const uint16x4x2_t tmp = vzip_u16(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template<>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet8us>(Packet8us& p1, Packet8us& p2) {
+  const uint16x8x2_t tmp = vzipq_u16(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+
+template<typename Packet>
+EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 2>& kernel) {
+  zip_in_place(kernel.packet[0], kernel.packet[1]);
+}
+
+template<typename Packet>
+EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 4>& kernel) {
+  zip_in_place(kernel.packet[0], kernel.packet[2]);
+  zip_in_place(kernel.packet[1], kernel.packet[3]);
+  zip_in_place(kernel.packet[0], kernel.packet[1]);
+  zip_in_place(kernel.packet[2], kernel.packet[3]);
+}
+
+template<typename Packet>
+EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 8>& kernel) {
+  zip_in_place(kernel.packet[0], kernel.packet[4]);
+  zip_in_place(kernel.packet[1], kernel.packet[5]);
+  zip_in_place(kernel.packet[2], kernel.packet[6]);
+  zip_in_place(kernel.packet[3], kernel.packet[7]);
+
+  zip_in_place(kernel.packet[0], kernel.packet[2]);
+  zip_in_place(kernel.packet[1], kernel.packet[3]);
+  zip_in_place(kernel.packet[4], kernel.packet[6]);
+  zip_in_place(kernel.packet[5], kernel.packet[7]);
+  
+  zip_in_place(kernel.packet[0], kernel.packet[1]);
+  zip_in_place(kernel.packet[2], kernel.packet[3]);
+  zip_in_place(kernel.packet[4], kernel.packet[5]);
+  zip_in_place(kernel.packet[6], kernel.packet[7]);
+}
+
+template<typename Packet>
+EIGEN_ALWAYS_INLINE void ptranspose_impl(PacketBlock<Packet, 16>& kernel) {
+  EIGEN_UNROLL_LOOP
+  for (int i=0; i<4; ++i) {
+    const int m = (1 << i);
+    EIGEN_UNROLL_LOOP
+    for (int j=0; j<m; ++j) {
+      const int n = (1 << (3-i));
+      EIGEN_UNROLL_LOOP
+      for (int k=0; k<n; ++k) {
+        const int idx = 2*j*n+k;
+        zip_in_place(kernel.packet[idx], kernel.packet[idx + n]);
+      }
+    }
+  }
+}
+
+} // namespace detail
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2f, 2>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4f, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4c, 4>& kernel)
+{
+  const int8x8_t a = vreinterpret_s8_s32(vset_lane_s32(kernel.packet[2], vdup_n_s32(kernel.packet[0]), 1));
+  const int8x8_t b = vreinterpret_s8_s32(vset_lane_s32(kernel.packet[3], vdup_n_s32(kernel.packet[1]), 1));
+
+  const int8x8x2_t zip8 = vzip_s8(a,b);
+  const int16x4x2_t zip16 = vzip_s16(vreinterpret_s16_s8(zip8.val[0]), vreinterpret_s16_s8(zip8.val[1]));
+
+  kernel.packet[0] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[0]), 0);
+  kernel.packet[1] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[0]), 1);
+  kernel.packet[2] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[1]), 0);
+  kernel.packet[3] = vget_lane_s32(vreinterpret_s32_s16(zip16.val[1]), 1);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8c, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8c, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16c, 16>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16c, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16c, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4uc, 4>& kernel)
+{
+  const uint8x8_t a = vreinterpret_u8_u32(vset_lane_u32(kernel.packet[2], vdup_n_u32(kernel.packet[0]), 1));
+  const uint8x8_t b = vreinterpret_u8_u32(vset_lane_u32(kernel.packet[3], vdup_n_u32(kernel.packet[1]), 1));
+
+  const uint8x8x2_t zip8 = vzip_u8(a,b);
+  const uint16x4x2_t zip16 = vzip_u16(vreinterpret_u16_u8(zip8.val[0]), vreinterpret_u16_u8(zip8.val[1]));
+
+  kernel.packet[0] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[0]), 0);
+  kernel.packet[1] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[0]), 1);
+  kernel.packet[2] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[1]), 0);
+  kernel.packet[3] = vget_lane_u32(vreinterpret_u32_u16(zip16.val[1]), 1);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8uc, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8uc, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16uc, 16>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16uc, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet16uc, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4s, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8s, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8s, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4us, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8us, 8>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8us, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2i, 2>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4i, 4>& kernel) {
+    detail::ptranspose_impl(kernel);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2ui, 2>& kernel) {
+  detail::zip_in_place(kernel.packet[0], kernel.packet[1]);
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4ui, 4>& kernel) {
+  detail::ptranspose_impl(kernel);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
+ptranspose(PacketBlock<Packet2l, 2>& kernel)
+{
+#if EIGEN_ARCH_ARM64
+  const int64x2_t tmp1 = vzip1q_s64(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[1] = vzip2q_s64(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[0] = tmp1;
+#else
+  const int64x1_t tmp[2][2] = {
+    { vget_low_s64(kernel.packet[0]), vget_high_s64(kernel.packet[0]) },
+    { vget_low_s64(kernel.packet[1]), vget_high_s64(kernel.packet[1]) }
+  };
+
+  kernel.packet[0] = vcombine_s64(tmp[0][0], tmp[1][0]);
+  kernel.packet[1] = vcombine_s64(tmp[0][1], tmp[1][1]);
+#endif
+}
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
+ptranspose(PacketBlock<Packet2ul, 2>& kernel)
+{
+#if EIGEN_ARCH_ARM64
+  const uint64x2_t tmp1 = vzip1q_u64(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[1] = vzip2q_u64(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[0] = tmp1;
+#else
+  const uint64x1_t tmp[2][2] = {
+    { vget_low_u64(kernel.packet[0]), vget_high_u64(kernel.packet[0]) },
+    { vget_low_u64(kernel.packet[1]), vget_high_u64(kernel.packet[1]) }
+  };
+
+  kernel.packet[0] = vcombine_u64(tmp[0][0], tmp[1][0]);
+  kernel.packet[1] = vcombine_u64(tmp[0][1], tmp[1][1]);
+#endif
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2f pselect( const Packet2f& mask, const Packet2f& a, const Packet2f& b)
+{ return vbsl_f32(vreinterpret_u32_f32(mask), a, b); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4f pselect(const Packet4f& mask, const Packet4f& a, const Packet4f& b)
+{ return vbslq_f32(vreinterpretq_u32_f32(mask), a, b); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8c pselect(const Packet8c& mask, const Packet8c& a, const Packet8c& b)
+{ return vbsl_s8(vreinterpret_u8_s8(mask), a, b); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16c pselect(const Packet16c& mask, const Packet16c& a, const Packet16c& b)
+{ return vbslq_s8(vreinterpretq_u8_s8(mask), a, b); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8uc pselect(const Packet8uc& mask, const Packet8uc& a, const Packet8uc& b)
+{ return vbsl_u8(mask, a, b); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet16uc pselect(const Packet16uc& mask, const Packet16uc& a, const Packet16uc& b)
+{ return vbslq_u8(mask, a, b); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4s pselect(const Packet4s& mask, const Packet4s& a, const Packet4s& b)
+{ return vbsl_s16(vreinterpret_u16_s16(mask), a, b); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8s pselect(const Packet8s& mask, const Packet8s& a, const Packet8s& b)
+{ return vbslq_s16(vreinterpretq_u16_s16(mask), a, b); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4us pselect(const Packet4us& mask, const Packet4us& a, const Packet4us& b)
+{ return vbsl_u16(mask, a, b); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8us pselect(const Packet8us& mask, const Packet8us& a, const Packet8us& b)
+{ return vbslq_u16(mask, a, b); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2i pselect(const Packet2i& mask, const Packet2i& a, const Packet2i& b)
+{ return vbsl_s32(vreinterpret_u32_s32(mask), a, b); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4i pselect(const Packet4i& mask, const Packet4i& a, const Packet4i& b)
+{ return vbslq_s32(vreinterpretq_u32_s32(mask), a, b); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ui pselect(const Packet2ui& mask, const Packet2ui& a, const Packet2ui& b)
+{ return vbsl_u32(mask, a, b); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4ui pselect(const Packet4ui& mask, const Packet4ui& a, const Packet4ui& b)
+{ return vbslq_u32(mask, a, b); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2l pselect(const Packet2l& mask, const Packet2l& a, const Packet2l& b)
+{ return vbslq_s64(vreinterpretq_u64_s64(mask), a, b); }
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2ul pselect(const Packet2ul& mask, const Packet2ul& a, const Packet2ul& b)
+{ return vbslq_u64(mask, a, b); }
+
+// Use armv8 rounding intinsics if available.
+#if EIGEN_ARCH_ARMV8
+template<> EIGEN_STRONG_INLINE Packet2f print<Packet2f>(const Packet2f& a)
+{ return vrndn_f32(a); }
+
+template<> EIGEN_STRONG_INLINE Packet4f print<Packet4f>(const Packet4f& a)
+{ return vrndnq_f32(a); }
+
+template<> EIGEN_STRONG_INLINE Packet2f pfloor<Packet2f>(const Packet2f& a)
+{ return vrndm_f32(a); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a)
+{ return vrndmq_f32(a); }
+
+template<> EIGEN_STRONG_INLINE Packet2f pceil<Packet2f>(const Packet2f& a)
+{ return vrndp_f32(a); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a)
+{ return vrndpq_f32(a); }
+
+#else
+
+template<> EIGEN_STRONG_INLINE Packet4f print(const Packet4f& a) {
+  // Adds and subtracts signum(a) * 2^23 to force rounding.
+  const Packet4f limit = pset1<Packet4f>(static_cast<float>(1<<23));
+  const Packet4f abs_a = pabs(a);
+  Packet4f r = padd(abs_a, limit);
+  // Don't compile-away addition and subtraction.
+  EIGEN_OPTIMIZATION_BARRIER(r);
+  r = psub(r, limit);
+  // If greater than limit, simply return a.  Otherwise, account for sign.
+  r = pselect(pcmp_lt(abs_a, limit),
+              pselect(pcmp_lt(a, pzero(a)), pnegate(r), r), a);
+  return r;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2f print(const Packet2f& a) {
+  // Adds and subtracts signum(a) * 2^23 to force rounding.
+  const Packet2f limit = pset1<Packet2f>(static_cast<float>(1<<23));
+  const Packet2f abs_a = pabs(a);
+  Packet2f r = padd(abs_a, limit);
+  // Don't compile-away addition and subtraction.
+  EIGEN_OPTIMIZATION_BARRIER(r);
+  r = psub(r, limit);
+  // If greater than limit, simply return a.  Otherwise, account for sign.
+  r = pselect(pcmp_lt(abs_a, limit),
+              pselect(pcmp_lt(a, pzero(a)), pnegate(r), r), a);
+  return r;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a)
+{
+  const Packet4f cst_1 = pset1<Packet4f>(1.0f);
+  Packet4f tmp  = print<Packet4f>(a);
+  // If greater, subtract one.
+  Packet4f mask = pcmp_lt(a, tmp);
+  mask = pand(mask, cst_1);
+  return psub(tmp, mask);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2f pfloor<Packet2f>(const Packet2f& a)
+{
+  const Packet2f cst_1 = pset1<Packet2f>(1.0f);
+  Packet2f tmp  = print<Packet2f>(a);
+  // If greater, subtract one.
+  Packet2f mask = pcmp_lt(a, tmp);
+  mask = pand(mask, cst_1);
+  return psub(tmp, mask);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a)
+{
+  const Packet4f cst_1 = pset1<Packet4f>(1.0f);
+  Packet4f tmp  = print<Packet4f>(a);
+  // If smaller, add one.
+  Packet4f mask = pcmp_lt(tmp, a);
+  mask = pand(mask, cst_1);
+  return padd(tmp, mask);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2f pceil<Packet2f>(const Packet2f& a)
+{
+  const Packet2f cst_1 = pset1<Packet2f>(1.0);
+  Packet2f tmp  = print<Packet2f>(a);
+  // If smaller, add one.
+  Packet2f mask = pcmp_lt(tmp, a);
+  mask = pand(mask, cst_1);
+  return padd(tmp, mask);
+}
+
+#endif
+
+/**
+ * Computes the integer square root
+ * @remarks The calculation is performed using an algorithm which iterates through each binary digit of the result
+ *   and tests whether setting that digit to 1 would cause the square of the value to be greater than the argument
+ *   value. The algorithm is described in detail here: http://ww1.microchip.com/downloads/en/AppNotes/91040a.pdf .
+ */
+template<> EIGEN_STRONG_INLINE Packet4uc psqrt(const Packet4uc& a) {
+  uint8x8_t x = vreinterpret_u8_u32(vdup_n_u32(a));
+  uint8x8_t res = vdup_n_u8(0);
+  uint8x8_t add = vdup_n_u8(0x8);
+  for (int i = 0; i < 4; i++)
+  {
+    const uint8x8_t temp = vorr_u8(res, add);
+    res = vbsl_u8(vcge_u8(x, vmul_u8(temp, temp)), temp, res);
+    add = vshr_n_u8(add, 1);
+  }
+  return vget_lane_u32(vreinterpret_u32_u8(res), 0);
+}
+/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
+template<> EIGEN_STRONG_INLINE Packet8uc psqrt(const Packet8uc& a) {
+  uint8x8_t res = vdup_n_u8(0);
+  uint8x8_t add = vdup_n_u8(0x8);
+  for (int i = 0; i < 4; i++)
+  {
+    const uint8x8_t temp = vorr_u8(res, add);
+    res = vbsl_u8(vcge_u8(a, vmul_u8(temp, temp)), temp, res);
+    add = vshr_n_u8(add, 1);
+  }
+  return res;
+}
+/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
+template<> EIGEN_STRONG_INLINE Packet16uc psqrt(const Packet16uc& a) {
+  uint8x16_t res = vdupq_n_u8(0);
+  uint8x16_t add = vdupq_n_u8(0x8);
+  for (int i = 0; i < 4; i++)
+  {
+    const uint8x16_t temp = vorrq_u8(res, add);
+    res = vbslq_u8(vcgeq_u8(a, vmulq_u8(temp, temp)), temp, res);
+    add = vshrq_n_u8(add, 1);
+  }
+  return res;
+}
+/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
+template<> EIGEN_STRONG_INLINE Packet4us psqrt(const Packet4us& a) {
+  uint16x4_t res = vdup_n_u16(0);
+  uint16x4_t add = vdup_n_u16(0x80);
+  for (int i = 0; i < 8; i++)
+  {
+    const uint16x4_t temp = vorr_u16(res, add);
+    res = vbsl_u16(vcge_u16(a, vmul_u16(temp, temp)), temp, res);
+    add = vshr_n_u16(add, 1);
+  }
+  return res;
+}
+/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
+template<> EIGEN_STRONG_INLINE Packet8us psqrt(const Packet8us& a) {
+  uint16x8_t res = vdupq_n_u16(0);
+  uint16x8_t add = vdupq_n_u16(0x80);
+  for (int i = 0; i < 8; i++)
+  {
+    const uint16x8_t temp = vorrq_u16(res, add);
+    res = vbslq_u16(vcgeq_u16(a, vmulq_u16(temp, temp)), temp, res);
+    add = vshrq_n_u16(add, 1);
+  }
+  return res;
+}
+/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
+template<> EIGEN_STRONG_INLINE Packet2ui psqrt(const Packet2ui& a) {
+  uint32x2_t res = vdup_n_u32(0);
+  uint32x2_t add = vdup_n_u32(0x8000);
+  for (int i = 0; i < 16; i++)
+  {
+    const uint32x2_t temp = vorr_u32(res, add);
+    res = vbsl_u32(vcge_u32(a, vmul_u32(temp, temp)), temp, res);
+    add = vshr_n_u32(add, 1);
+  }
+  return res;
+}
+/// @copydoc Eigen::internal::psqrt(const Packet4uc& a)
+template<> EIGEN_STRONG_INLINE Packet4ui psqrt(const Packet4ui& a) {
+  uint32x4_t res = vdupq_n_u32(0);
+  uint32x4_t add = vdupq_n_u32(0x8000);
+  for (int i = 0; i < 16; i++)
+  {
+    const uint32x4_t temp = vorrq_u32(res, add);
+    res = vbslq_u32(vcgeq_u32(a, vmulq_u32(temp, temp)), temp, res);
+    add = vshrq_n_u32(add, 1);
+  }
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f prsqrt(const Packet4f& a) {
+  // Do Newton iterations for 1/sqrt(x).
+  return generic_rsqrt_newton_step<Packet4f, /*Steps=*/2>::run(a, vrsqrteq_f32(a));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2f prsqrt(const Packet2f& a) {
+  // Compute approximate reciprocal sqrt.
+  return generic_rsqrt_newton_step<Packet2f, /*Steps=*/2>::run(a, vrsqrte_f32(a));
+}
+
+// Unfortunately vsqrt_f32 is only available for A64.
+#if EIGEN_ARCH_ARM64
+template<> EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& _x){return vsqrtq_f32(_x);}
+template<> EIGEN_STRONG_INLINE Packet2f psqrt(const Packet2f& _x){return vsqrt_f32(_x); }
+#else
+template<> EIGEN_STRONG_INLINE Packet4f psqrt(const Packet4f& a) {
+  return generic_sqrt_newton_step<Packet4f>::run(a, prsqrt(a));
+}
+template<> EIGEN_STRONG_INLINE Packet2f psqrt(const Packet2f& a) {
+  return generic_sqrt_newton_step<Packet2f>::run(a, prsqrt(a));
+}
+#endif
+
+//---------- bfloat16 ----------
+// TODO: Add support for native armv8.6-a bfloat16_t
+
+// TODO: Guard if we have native bfloat16 support
+typedef eigen_packet_wrapper<uint16x4_t, 19> Packet4bf;
+
+template<> struct is_arithmetic<Packet4bf> { enum { value = true }; };
+
+template<> struct packet_traits<bfloat16> : default_packet_traits
+{
+  typedef Packet4bf type;
+  typedef Packet4bf half;
+  enum
+  {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasCmp       = 1,
+    HasAdd       = 1,
+    HasSub       = 1,
+    HasShift     = 1,
+    HasMul       = 1,
+    HasNegate    = 1,
+    HasAbs       = 1,
+    HasArg       = 0,
+    HasAbs2      = 1,
+    HasAbsDiff   = 1,
+    HasMin       = 1,
+    HasMax       = 1,
+    HasConj      = 1,
+    HasSetLinear = 1,
+    HasBlend     = 0,
+    HasDiv       = 1,
+    HasFloor     = 1,
+    HasCeil      = 1,
+    HasRint      = 1,
+
+    HasSin  = EIGEN_FAST_MATH,
+    HasCos  = EIGEN_FAST_MATH,
+    HasLog  = 1,
+    HasExp  = 1,
+    HasSqrt = 0,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf  = EIGEN_FAST_MATH,
+    HasBessel = 0,  // Issues with accuracy.
+    HasNdtri = 0
+  };
+};
+
+template<> struct unpacket_traits<Packet4bf>
+{
+  typedef bfloat16 type;
+  typedef Packet4bf half;
+  enum
+  {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+namespace detail {  
+template<>
+EIGEN_ALWAYS_INLINE void zip_in_place<Packet4bf>(Packet4bf& p1, Packet4bf& p2) {
+  const uint16x4x2_t tmp = vzip_u16(p1, p2);
+  p1 = tmp.val[0];
+  p2 = tmp.val[1];
+}
+} // namespace detail
+
+EIGEN_STRONG_INLINE Packet4bf F32ToBf16(const Packet4f& p)
+{
+  // See the scalar implementation in BFloat16.h for a comprehensible explanation
+  // of this fast rounding algorithm
+  Packet4ui input = Packet4ui(vreinterpretq_u32_f32(p));
+
+  // lsb = (input >> 16) & 1
+  Packet4ui lsb =  vandq_u32(vshrq_n_u32(input, 16), vdupq_n_u32(1));
+
+  // rounding_bias = 0x7fff + lsb
+  Packet4ui rounding_bias = vaddq_u32(lsb, vdupq_n_u32(0x7fff));
+
+  // input += rounding_bias
+  input = vaddq_u32(input, rounding_bias);
+
+  // input = input >> 16
+  input = vshrq_n_u32(input, 16);
+
+  // Replace float-nans by bfloat16-nans, that is 0x7fc0
+  const Packet4ui bf16_nan = vdupq_n_u32(0x7fc0);
+  const Packet4ui mask = vceqq_f32(p, p);
+  input = vbslq_u32(mask, input, bf16_nan);
+
+  // output = static_cast<uint16_t>(input)
+  return vmovn_u32(input);
+}
+
+EIGEN_STRONG_INLINE Packet4f Bf16ToF32(const Packet4bf& p)
+{
+  return Packet4f(vreinterpretq_f32_u32(vshlq_n_u32(vmovl_u16(p), 16)));
+}
+
+EIGEN_STRONG_INLINE Packet4bf F32MaskToBf16Mask(const Packet4f& p) {
+  return vmovn_u32(vreinterpretq_u32_f32(p));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pset1<Packet4bf>(const bfloat16& from) {
+  return Packet4bf(pset1<Packet4us>(from.value));
+}
+
+template<> EIGEN_STRONG_INLINE bfloat16 pfirst<Packet4bf>(const Packet4bf& from) {
+  return bfloat16_impl::raw_uint16_to_bfloat16(static_cast<uint16_t>(pfirst<Packet4us>(Packet4us(from))));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pload<Packet4bf>(const bfloat16* from)
+{
+  return Packet4bf(pload<Packet4us>(reinterpret_cast<const uint16_t*>(from)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf ploadu<Packet4bf>(const bfloat16* from)
+{
+  return Packet4bf(ploadu<Packet4us>(reinterpret_cast<const uint16_t*>(from)));
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<bfloat16>(bfloat16* to, const Packet4bf& from)
+{
+  EIGEN_DEBUG_ALIGNED_STORE vst1_u16(reinterpret_cast<uint16_t*>(to), from);
+}
+
+template<> EIGEN_STRONG_INLINE void pstoreu<bfloat16>(bfloat16* to, const Packet4bf& from)
+{
+  EIGEN_DEBUG_UNALIGNED_STORE vst1_u16(reinterpret_cast<uint16_t*>(to), from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf ploaddup<Packet4bf>(const bfloat16* from)
+{
+  return Packet4bf(ploaddup<Packet4us>(reinterpret_cast<const uint16_t*>(from)));
+}
+
+template <> EIGEN_STRONG_INLINE Packet4bf pabs(const Packet4bf& a) {
+  return F32ToBf16(pabs<Packet4f>(Bf16ToF32(a)));
+}
+
+template <> EIGEN_STRONG_INLINE Packet4bf pmin<PropagateNumbers, Packet4bf>(const Packet4bf &a,
+                                                                            const Packet4bf &b)
+{
+  return F32ToBf16(pmin<PropagateNumbers, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+template <> EIGEN_STRONG_INLINE Packet4bf pmin<PropagateNaN, Packet4bf>(const Packet4bf &a,
+                                                                        const Packet4bf &b)
+{
+  return F32ToBf16(pmin<PropagateNaN, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <> EIGEN_STRONG_INLINE Packet4bf pmin<Packet4bf>(const Packet4bf &a,
+                                                          const Packet4bf &b)
+{
+  return F32ToBf16(pmin<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <> EIGEN_STRONG_INLINE Packet4bf pmax<PropagateNumbers, Packet4bf>(const Packet4bf &a,
+                                                                            const Packet4bf &b)
+{
+  return F32ToBf16(pmax<PropagateNumbers, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+template <> EIGEN_STRONG_INLINE Packet4bf pmax<PropagateNaN, Packet4bf>(const Packet4bf &a,
+                                                                        const Packet4bf &b)
+{
+  return F32ToBf16(pmax<PropagateNaN, Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template <> EIGEN_STRONG_INLINE Packet4bf pmax<Packet4bf>(const Packet4bf &a,
+                                                          const Packet4bf &b)
+{
+  return F32ToBf16(pmax<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf plset<Packet4bf>(const bfloat16& a)
+{
+  return F32ToBf16(plset<Packet4f>(static_cast<float>(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf por(const Packet4bf& a,const Packet4bf& b) {
+  return Packet4bf(por<Packet4us>(Packet4us(a), Packet4us(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pxor(const Packet4bf& a,const Packet4bf& b) {
+  return Packet4bf(pxor<Packet4us>(Packet4us(a), Packet4us(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pand(const Packet4bf& a,const Packet4bf& b) {
+  return Packet4bf(pand<Packet4us>(Packet4us(a), Packet4us(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pandnot(const Packet4bf& a,const Packet4bf& b) {
+  return Packet4bf(pandnot<Packet4us>(Packet4us(a), Packet4us(b)));
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4bf pselect(const Packet4bf& mask, const Packet4bf& a,
+                                                      const Packet4bf& b)
+{
+  return Packet4bf(pselect<Packet4us>(Packet4us(mask), Packet4us(a), Packet4us(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf print<Packet4bf>(const Packet4bf& a)
+{
+  return F32ToBf16(print<Packet4f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pfloor<Packet4bf>(const Packet4bf& a)
+{
+  return F32ToBf16(pfloor<Packet4f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pceil<Packet4bf>(const Packet4bf& a)
+{
+  return F32ToBf16(pceil<Packet4f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pconj(const Packet4bf& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE Packet4bf padd<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32ToBf16(padd<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf psub<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32ToBf16(psub<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pmul<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32ToBf16(pmul<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pdiv<Packet4bf>(const Packet4bf& a, const Packet4bf& b) {
+  return F32ToBf16(pdiv<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<>
+EIGEN_STRONG_INLINE Packet4bf pgather<bfloat16, Packet4bf>(const bfloat16* from, Index stride)
+{
+  return Packet4bf(pgather<uint16_t, Packet4us>(reinterpret_cast<const uint16_t*>(from), stride));
+}
+
+template<>
+EIGEN_STRONG_INLINE void pscatter<bfloat16, Packet4bf>(bfloat16* to, const Packet4bf& from, Index stride)
+{
+  pscatter<uint16_t, Packet4us>(reinterpret_cast<uint16_t*>(to), Packet4us(from), stride);
+}
+
+template<> EIGEN_STRONG_INLINE bfloat16 predux<Packet4bf>(const Packet4bf& a)
+{
+  return static_cast<bfloat16>(predux<Packet4f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE bfloat16 predux_max<Packet4bf>(const Packet4bf& a)
+{
+  return static_cast<bfloat16>(predux_max<Packet4f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE bfloat16 predux_min<Packet4bf>(const Packet4bf& a)
+{
+  return static_cast<bfloat16>(predux_min<Packet4f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE bfloat16 predux_mul<Packet4bf>(const Packet4bf& a)
+{
+  return static_cast<bfloat16>(predux_mul<Packet4f>(Bf16ToF32(a)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf preverse<Packet4bf>(const Packet4bf& a)
+{
+  return Packet4bf(preverse<Packet4us>(Packet4us(a)));
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4bf, 4>& kernel)
+{
+  detail::ptranspose_impl(kernel);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pabsdiff<Packet4bf>(const Packet4bf& a, const Packet4bf& b)
+{
+  return F32ToBf16(pabsdiff<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pcmp_eq<Packet4bf>(const Packet4bf& a, const Packet4bf& b)
+{
+  return F32MaskToBf16Mask(pcmp_eq<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pcmp_lt<Packet4bf>(const Packet4bf& a, const Packet4bf& b)
+{
+  return F32MaskToBf16Mask(pcmp_lt<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pcmp_lt_or_nan<Packet4bf>(const Packet4bf& a, const Packet4bf& b)
+{
+  return F32MaskToBf16Mask(pcmp_lt_or_nan<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pcmp_le<Packet4bf>(const Packet4bf& a, const Packet4bf& b)
+{
+  return F32MaskToBf16Mask(pcmp_le<Packet4f>(Bf16ToF32(a), Bf16ToF32(b)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4bf pnegate<Packet4bf>(const Packet4bf& a)
+{
+  return Packet4bf(pxor<Packet4us>(Packet4us(a), pset1<Packet4us>(static_cast<uint16_t>(0x8000))));
+}
+
+//---------- double ----------
+
+// Clang 3.5 in the iOS toolchain has an ICE triggered by NEON intrisics for double.
+// Confirmed at least with __apple_build_version__ = 6000054.
+#if EIGEN_COMP_CLANGAPPLE
+// Let's hope that by the time __apple_build_version__ hits the 601* range, the bug will be fixed.
+// https://gist.github.com/yamaya/2924292 suggests that the 3 first digits are only updated with
+// major toolchain updates.
+#define EIGEN_APPLE_DOUBLE_NEON_BUG (EIGEN_COMP_CLANGAPPLE < 6010000)
+#else
+#define EIGEN_APPLE_DOUBLE_NEON_BUG 0
+#endif
+
+#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
+
+#if EIGEN_COMP_GNUC
+// Bug 907: workaround missing declarations of the following two functions in the ADK
+// Defining these functions as templates ensures that if these intrinsics are
+// already defined in arm_neon.h, then our workaround doesn't cause a conflict
+// and has lower priority in overload resolution.
+// This doesn't work with MSVC though, since the function names are macros.
+template <typename T> uint64x2_t vreinterpretq_u64_f64(T a) { return (uint64x2_t) a; }
+
+template <typename T> float64x2_t vreinterpretq_f64_u64(T a) { return (float64x2_t) a; }
+#endif
+
+#if EIGEN_COMP_MSVC_STRICT
+typedef eigen_packet_wrapper<float64x2_t, 18> Packet2d;
+typedef eigen_packet_wrapper<float64x1_t, 19> Packet1d;
+
+EIGEN_ALWAYS_INLINE Packet2d make_packet2d(double a, double b) {
+  double from[2] = {a, b};
+  return vld1q_f64(from);
+}
+
+#else
+typedef float64x2_t Packet2d;
+typedef float64x1_t Packet1d;
+
+EIGEN_ALWAYS_INLINE Packet2d make_packet2d(double a, double b) { return Packet2d{a, b}; }
+#endif
+
+
+// fuctionally equivalent to _mm_shuffle_pd in SSE (i.e. shuffle(m, n, mask) equals _mm_shuffle_pd(m,n,mask))
+// Currently used in LU/arch/InverseSize4.h to enable a shared implementation
+// for fast inversion of matrices of size 4.
+EIGEN_STRONG_INLINE Packet2d shuffle(const Packet2d& m, const Packet2d& n, int mask)
+{
+  const double* a = reinterpret_cast<const double*>(&m);
+  const double* b = reinterpret_cast<const double*>(&n);
+  Packet2d res = make_packet2d(*(a + (mask & 1)), *(b + ((mask >> 1) & 1)));
+  return res;
+}
+
+EIGEN_STRONG_INLINE Packet2d vec2d_swizzle2(const Packet2d& a, const Packet2d& b, int mask)
+{
+  return shuffle(a, b, mask);
+}
+EIGEN_STRONG_INLINE Packet2d vec2d_unpacklo(const Packet2d& a,const Packet2d& b)
+{
+  return shuffle(a, b, 0);
+}
+EIGEN_STRONG_INLINE Packet2d vec2d_unpackhi(const Packet2d& a,const Packet2d& b)
+{
+  return shuffle(a, b, 3);
+}
+#define vec2d_duplane(a, p) \
+  Packet2d(vdupq_laneq_f64(a, p))
+
+template<> struct packet_traits<double>  : default_packet_traits
+{
+  typedef Packet2d type;
+  typedef Packet2d half;
+  enum
+  {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasCmp       = 1,
+    HasAdd       = 1,
+    HasSub       = 1,
+    HasShift     = 1,
+    HasMul       = 1,
+    HasNegate    = 1,
+    HasAbs       = 1,
+    HasArg       = 0,
+    HasAbs2      = 1,
+    HasAbsDiff   = 1,
+    HasMin       = 1,
+    HasMax       = 1,
+    HasConj      = 1,
+    HasSetLinear = 1,
+    HasBlend     = 0,
+
+    HasDiv   = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasRint = 1,
+
+#if EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
+    HasExp  = 1,
+    HasLog  = 1,
+    HasATan = 1,
+#endif
+    HasSin  = 0,
+    HasCos  = 0,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasTanh = 0,
+    HasErf  = 0
+  };
+};
+
+template<> struct unpacket_traits<Packet2d>
+{
+  typedef double type;
+  typedef Packet2d half;
+  typedef Packet2l integer_packet;
+  enum
+  {
+    size = 2,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double&  from) { return vdupq_n_f64(from); }
+
+template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a)
+{
+  const double c[] = {0.0,1.0};
+  return vaddq_f64(pset1<Packet2d>(a), vld1q_f64(c));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return vaddq_f64(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return vsubq_f64(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& , const Packet2d& );
+template<> EIGEN_STRONG_INLINE Packet2d paddsub<Packet2d>(const Packet2d& a, const Packet2d& b){
+  const Packet2d mask = make_packet2d(numext::bit_cast<double>(0x8000000000000000ull), 0.0);
+  return padd(a, pxor(mask, b));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return vnegq_f64(a); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return vmulq_f64(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return vdivq_f64(a,b); }
+
+#ifdef __ARM_FEATURE_FMA
+// See bug 936. See above comment about FMA for float.
+template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c)
+{ return vfmaq_f64(c,a,b); }
+#else
+template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c)
+{ return vmlaq_f64(c,a,b); }
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) { return vminq_f64(a,b); }
+
+#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
+// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8 systems).
+template<> EIGEN_STRONG_INLINE Packet2d pmin<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) { return vminnmq_f64(a, b); }
+template<> EIGEN_STRONG_INLINE Packet2d pmax<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) { return vmaxnmq_f64(a, b); }
+
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet2d pmin<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) { return pmin<Packet2d>(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) { return vmaxq_f64(a,b); }
+
+
+template<> EIGEN_STRONG_INLINE Packet2d pmax<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) { return pmax<Packet2d>(a, b); }
+
+// Logical Operations are not supported for float, so we have to reinterpret casts using NEON intrinsics
+template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b)
+{ return vreinterpretq_f64_u64(vandq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); }
+
+template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b)
+{ return vreinterpretq_f64_u64(vorrq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b)
+{ return vreinterpretq_f64_u64(veorq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b)
+{ return vreinterpretq_f64_u64(vbicq_u64(vreinterpretq_u64_f64(a),vreinterpretq_u64_f64(b))); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pcmp_le(const Packet2d& a, const Packet2d& b)
+{ return vreinterpretq_f64_u64(vcleq_f64(a,b)); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt(const Packet2d& a, const Packet2d& b)
+{ return vreinterpretq_f64_u64(vcltq_f64(a,b)); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt_or_nan(const Packet2d& a, const Packet2d& b)
+{ return vreinterpretq_f64_u32(vmvnq_u32(vreinterpretq_u32_u64(vcgeq_f64(a,b)))); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pcmp_eq(const Packet2d& a, const Packet2d& b)
+{ return vreinterpretq_f64_u64(vceqq_f64(a,b)); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return vld1q_f64(from); }
+
+template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_f64(from); }
+
+template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double* from) { return vld1q_dup_f64(from); }
+template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from)
+{ EIGEN_DEBUG_ALIGNED_STORE vst1q_f64(to,from); }
+
+template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from)
+{ EIGEN_DEBUG_UNALIGNED_STORE vst1q_f64(to,from); }
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2d pgather<double, Packet2d>(const double* from, Index stride)
+{
+  Packet2d res = pset1<Packet2d>(0.0);
+  res = vld1q_lane_f64(from + 0*stride, res, 0);
+  res = vld1q_lane_f64(from + 1*stride, res, 1);
+  return res;
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride)
+{
+  vst1q_lane_f64(to + stride*0, from, 0);
+  vst1q_lane_f64(to + stride*1, from, 1);
+}
+
+template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { EIGEN_ARM_PREFETCH(addr); }
+
+// FIXME only store the 2 first elements ?
+template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { return vgetq_lane_f64(a,0); }
+
+template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a)
+{ return vcombine_f64(vget_high_f64(a), vget_low_f64(a)); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a) { return vabsq_f64(a); }
+
+template <>
+EIGEN_STRONG_INLINE Packet2d psignbit(const Packet2d& a) {
+  return vreinterpretq_f64_s64(vshrq_n_s64(vreinterpretq_s64_f64(a), 63));
+}
+
+template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
+{ return vaddvq_f64(a); }
+
+// Other reduction functions:
+// mul
+#if EIGEN_COMP_CLANGAPPLE
+template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
+{ return (vget_low_f64(a) * vget_high_f64(a))[0]; }
+#else
+template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
+{ return vget_lane_f64(vmul_f64(vget_low_f64(a), vget_high_f64(a)), 0); }
+#endif
+
+// min
+template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a)
+{ return vminvq_f64(a); }
+
+// max
+template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a)
+{ return vmaxvq_f64(a); }
+
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void
+ptranspose(PacketBlock<Packet2d, 2>& kernel)
+{
+  const float64x2_t tmp1 = vzip1q_f64(kernel.packet[0], kernel.packet[1]);
+  const float64x2_t tmp2 = vzip2q_f64(kernel.packet[0], kernel.packet[1]);
+
+  kernel.packet[0] = tmp1;
+  kernel.packet[1] = tmp2;
+}
+
+template<> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet2d pselect( const Packet2d& mask, const Packet2d& a, const Packet2d& b)
+{ return vbslq_f64(vreinterpretq_u64_f64(mask), a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet2d print<Packet2d>(const Packet2d& a)
+{ return vrndnq_f64(a); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a)
+{ return vrndmq_f64(a); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a)
+{ return vrndpq_f64(a); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pldexp<Packet2d>(const Packet2d& a, const Packet2d& exponent)
+{ return pldexp_generic(a, exponent); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pfrexp<Packet2d>(const Packet2d& a, Packet2d& exponent)
+{ return pfrexp_generic(a,exponent); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pset1frombits<Packet2d>(uint64_t from)
+{ return vreinterpretq_f64_u64(vdupq_n_u64(from)); }
+
+template<> EIGEN_STRONG_INLINE Packet2d prsqrt(const Packet2d& a) {
+  // Do Newton iterations for 1/sqrt(x).
+  return generic_rsqrt_newton_step<Packet2d, /*Steps=*/3>::run(a, vrsqrteq_f64(a));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d psqrt(const Packet2d& _x){ return vsqrtq_f64(_x); }
+
+#endif // EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG
+
+// Do we have an fp16 types and supporting Neon intrinsics?
+#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+typedef float16x4_t Packet4hf;
+typedef float16x8_t Packet8hf;
+
+template <>
+struct packet_traits<Eigen::half> : default_packet_traits {
+  typedef Packet8hf type;
+  typedef Packet4hf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 8,
+
+    HasCmp = 1,
+    HasCast = 1,
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasAbsDiff = 0,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 1,
+    HasBlend = 0,
+    HasInsert = 1,
+    HasReduxp = 1,
+    HasDiv = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasRint = 1,
+    HasSin = 0,
+    HasCos = 0,
+    HasLog = 0,
+    HasExp = 0,
+    HasTanh = packet_traits<float>::HasTanh,  // tanh<half> calls tanh<float>
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasErf = EIGEN_FAST_MATH,
+    HasBessel = 0,  // Issues with accuracy.
+    HasNdtri = 0
+  };
+};
+
+template <>
+struct unpacket_traits<Packet4hf> {
+  typedef Eigen::half type;
+  typedef Packet4hf half;
+  enum {
+    size = 4,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+struct unpacket_traits<Packet8hf> {
+  typedef Eigen::half type;
+  typedef Packet4hf half;
+  enum {
+    size = 8,
+    alignment = Aligned16,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf predux_half_dowto4<Packet8hf>(const Packet8hf& a) {
+  return vadd_f16(vget_low_f16(a), vget_high_f16(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pset1<Packet8hf>(const Eigen::half& from) {
+  return vdupq_n_f16(from.x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pset1<Packet4hf>(const Eigen::half& from) {
+  return vdup_n_f16(from.x);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf plset<Packet8hf>(const Eigen::half& a) {
+  const float16_t f[] = {0, 1, 2, 3, 4, 5, 6, 7};
+  Packet8hf countdown = vld1q_f16(f);
+  return vaddq_f16(pset1<Packet8hf>(a), countdown);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf plset<Packet4hf>(const Eigen::half& a) {
+  const float16_t f[] = {0, 1, 2, 3};
+  Packet4hf countdown = vld1_f16(f);
+  return vadd_f16(pset1<Packet4hf>(a), countdown);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf padd<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vaddq_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf padd<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vadd_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf psub<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vsubq_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf psub<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vsub_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pnegate(const Packet8hf& a) {
+  return vnegq_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pnegate(const Packet4hf& a) {
+  return vneg_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pconj(const Packet8hf& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pconj(const Packet4hf& a) {
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pmul<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vmulq_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pmul<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vmul_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pdiv<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vdivq_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pdiv<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vdiv_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pmadd(const Packet8hf& a, const Packet8hf& b, const Packet8hf& c) {
+  return vfmaq_f16(c, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pmadd(const Packet4hf& a, const Packet4hf& b, const Packet4hf& c) {
+  return vfma_f16(c, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pmin<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vminq_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pmin<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vmin_f16(a, b);
+}
+
+#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
+// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8 systems).
+template<> EIGEN_STRONG_INLINE Packet4hf pmin<PropagateNumbers, Packet4hf>(const Packet4hf& a, const Packet4hf& b) { return vminnm_f16(a, b); }
+template<> EIGEN_STRONG_INLINE Packet8hf pmin<PropagateNumbers, Packet8hf>(const Packet8hf& a, const Packet8hf& b) { return vminnmq_f16(a, b); }
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet4hf pmin<PropagateNaN, Packet4hf>(const Packet4hf& a, const Packet4hf& b) { return pmin<Packet4hf>(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet8hf pmin<PropagateNaN, Packet8hf>(const Packet8hf& a, const Packet8hf& b) { return pmin<Packet8hf>(a, b); }
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pmax<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vmaxq_f16(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pmax<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vmax_f16(a, b);
+}
+
+#ifdef __ARM_FEATURE_NUMERIC_MAXMIN
+// numeric max and min are only available if ARM_FEATURE_NUMERIC_MAXMIN is defined (which can only be the case for Armv8 systems).
+template<> EIGEN_STRONG_INLINE Packet4hf pmax<PropagateNumbers, Packet4hf>(const Packet4hf& a, const Packet4hf& b) { return vmaxnm_f16(a, b); }
+template<> EIGEN_STRONG_INLINE Packet8hf pmax<PropagateNumbers, Packet8hf>(const Packet8hf& a, const Packet8hf& b) { return vmaxnmq_f16(a, b); }
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet4hf pmax<PropagateNaN, Packet4hf>(const Packet4hf& a, const Packet4hf& b) { return pmax<Packet4hf>(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet8hf pmax<PropagateNaN, Packet8hf>(const Packet8hf& a, const Packet8hf& b) { return pmax<Packet8hf>(a, b); }
+
+#define EIGEN_MAKE_ARM_FP16_CMP_8(name)                                               \
+  template <>                                                                         \
+  EIGEN_STRONG_INLINE Packet8hf pcmp_##name(const Packet8hf& a, const Packet8hf& b) { \
+    return vreinterpretq_f16_u16(vc##name##q_f16(a, b));                              \
+  }
+
+#define EIGEN_MAKE_ARM_FP16_CMP_4(name)                                               \
+  template <>                                                                         \
+  EIGEN_STRONG_INLINE Packet4hf pcmp_##name(const Packet4hf& a, const Packet4hf& b) { \
+    return vreinterpret_f16_u16(vc##name##_f16(a, b));                                \
+  }
+
+EIGEN_MAKE_ARM_FP16_CMP_8(eq)
+EIGEN_MAKE_ARM_FP16_CMP_8(lt)
+EIGEN_MAKE_ARM_FP16_CMP_8(le)
+
+EIGEN_MAKE_ARM_FP16_CMP_4(eq)
+EIGEN_MAKE_ARM_FP16_CMP_4(lt)
+EIGEN_MAKE_ARM_FP16_CMP_4(le)
+
+#undef EIGEN_MAKE_ARM_FP16_CMP_8
+#undef EIGEN_MAKE_ARM_FP16_CMP_4
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pcmp_lt_or_nan<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vreinterpretq_f16_u16(vmvnq_u16(vcgeq_f16(a, b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pcmp_lt_or_nan<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vreinterpret_f16_u16(vmvn_u16(vcge_f16(a, b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf print<Packet8hf>(const Packet8hf& a)
+{ return vrndnq_f16(a); }
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf print<Packet4hf>(const Packet4hf& a)
+{ return vrndn_f16(a); }
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pfloor<Packet8hf>(const Packet8hf& a)
+{ return vrndmq_f16(a); }
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pfloor<Packet4hf>(const Packet4hf& a)
+{ return vrndm_f16(a); }
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pceil<Packet8hf>(const Packet8hf& a)
+{ return vrndpq_f16(a); }
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pceil<Packet4hf>(const Packet4hf& a)
+{ return vrndp_f16(a); }
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf psqrt<Packet8hf>(const Packet8hf& a) {
+  return vsqrtq_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf psqrt<Packet4hf>(const Packet4hf& a) {
+  return vsqrt_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pand<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vreinterpretq_f16_u16(vandq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pand<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vreinterpret_f16_u16(vand_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf por<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vreinterpretq_f16_u16(vorrq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf por<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vreinterpret_f16_u16(vorr_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pxor<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vreinterpretq_f16_u16(veorq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pxor<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vreinterpret_f16_u16(veor_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pandnot<Packet8hf>(const Packet8hf& a, const Packet8hf& b) {
+  return vreinterpretq_f16_u16(vbicq_u16(vreinterpretq_u16_f16(a), vreinterpretq_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pandnot<Packet4hf>(const Packet4hf& a, const Packet4hf& b) {
+  return vreinterpret_f16_u16(vbic_u16(vreinterpret_u16_f16(a), vreinterpret_u16_f16(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pload<Packet8hf>(const Eigen::half* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1q_f16(reinterpret_cast<const float16_t*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pload<Packet4hf>(const Eigen::half* from) {
+  EIGEN_DEBUG_ALIGNED_LOAD return vld1_f16(reinterpret_cast<const float16_t*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf ploadu<Packet8hf>(const Eigen::half* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1q_f16(reinterpret_cast<const float16_t*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf ploadu<Packet4hf>(const Eigen::half* from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD return vld1_f16(reinterpret_cast<const float16_t*>(from));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf ploaddup<Packet8hf>(const Eigen::half* from) {
+  Packet8hf packet;
+  packet[0] = from[0].x;
+  packet[1] = from[0].x;
+  packet[2] = from[1].x;
+  packet[3] = from[1].x;
+  packet[4] = from[2].x;
+  packet[5] = from[2].x;
+  packet[6] = from[3].x;
+  packet[7] = from[3].x;
+  return packet;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf ploaddup<Packet4hf>(const Eigen::half* from) {
+  float16x4_t packet;
+  float16_t* tmp;
+  tmp = (float16_t*)&packet;
+  tmp[0] = from[0].x;
+  tmp[1] = from[0].x;
+  tmp[2] = from[1].x;
+  tmp[3] = from[1].x;
+  return packet;
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf ploadquad<Packet8hf>(const Eigen::half* from) {
+  Packet4hf lo, hi;
+  lo = vld1_dup_f16(reinterpret_cast<const float16_t*>(from));
+  hi = vld1_dup_f16(reinterpret_cast<const float16_t*>(from+1));
+  return vcombine_f16(lo, hi);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pinsertfirst(const Packet8hf& a, Eigen::half b) { return vsetq_lane_f16(b.x, a, 0); }
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pinsertfirst(const Packet4hf& a, Eigen::half b) { return vset_lane_f16(b.x, a, 0); }
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pselect(const Packet8hf& mask, const Packet8hf& a, const Packet8hf& b) {
+  return vbslq_f16(vreinterpretq_u16_f16(mask), a, b);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pselect(const Packet4hf& mask, const Packet4hf& a, const Packet4hf& b) {
+  return vbsl_f16(vreinterpret_u16_f16(mask), a, b);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pinsertlast(const Packet8hf& a, Eigen::half b) { return vsetq_lane_f16(b.x, a, 7); }
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pinsertlast(const Packet4hf& a, Eigen::half b) { return vset_lane_f16(b.x, a, 3); }
+
+template <>
+EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet8hf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1q_f16(reinterpret_cast<float16_t*>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet4hf& from) {
+  EIGEN_DEBUG_ALIGNED_STORE vst1_f16(reinterpret_cast<float16_t*>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet8hf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1q_f16(reinterpret_cast<float16_t*>(to), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet4hf& from) {
+  EIGEN_DEBUG_UNALIGNED_STORE vst1_f16(reinterpret_cast<float16_t*>(to), from);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet8hf pgather<Eigen::half, Packet8hf>(const Eigen::half* from, Index stride) {
+  Packet8hf res = pset1<Packet8hf>(Eigen::half(0.f));
+  res = vsetq_lane_f16(from[0 * stride].x, res, 0);
+  res = vsetq_lane_f16(from[1 * stride].x, res, 1);
+  res = vsetq_lane_f16(from[2 * stride].x, res, 2);
+  res = vsetq_lane_f16(from[3 * stride].x, res, 3);
+  res = vsetq_lane_f16(from[4 * stride].x, res, 4);
+  res = vsetq_lane_f16(from[5 * stride].x, res, 5);
+  res = vsetq_lane_f16(from[6 * stride].x, res, 6);
+  res = vsetq_lane_f16(from[7 * stride].x, res, 7);
+  return res;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet4hf pgather<Eigen::half, Packet4hf>(const Eigen::half* from, Index stride) {
+  Packet4hf res = pset1<Packet4hf>(Eigen::half(0.f));
+  res = vset_lane_f16(from[0 * stride].x, res, 0);
+  res = vset_lane_f16(from[1 * stride].x, res, 1);
+  res = vset_lane_f16(from[2 * stride].x, res, 2);
+  res = vset_lane_f16(from[3 * stride].x, res, 3);
+  return res;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet8hf>(Eigen::half* to, const Packet8hf& from, Index stride) {
+  to[stride * 0].x = vgetq_lane_f16(from, 0);
+  to[stride * 1].x = vgetq_lane_f16(from, 1);
+  to[stride * 2].x = vgetq_lane_f16(from, 2);
+  to[stride * 3].x = vgetq_lane_f16(from, 3);
+  to[stride * 4].x = vgetq_lane_f16(from, 4);
+  to[stride * 5].x = vgetq_lane_f16(from, 5);
+  to[stride * 6].x = vgetq_lane_f16(from, 6);
+  to[stride * 7].x = vgetq_lane_f16(from, 7);
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet4hf>(Eigen::half* to, const Packet4hf& from, Index stride) {
+  to[stride * 0].x = vget_lane_f16(from, 0);
+  to[stride * 1].x = vget_lane_f16(from, 1);
+  to[stride * 2].x = vget_lane_f16(from, 2);
+  to[stride * 3].x = vget_lane_f16(from, 3);
+}
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<Eigen::half>(const Eigen::half* addr) {
+  EIGEN_ARM_PREFETCH(addr);
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half pfirst<Packet8hf>(const Packet8hf& a) {
+  float16_t x[8];
+  vst1q_f16(x, a);
+  Eigen::half h;
+  h.x = x[0];
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half pfirst<Packet4hf>(const Packet4hf& a) {
+  float16_t x[4];
+  vst1_f16(x, a);
+  Eigen::half h;
+  h.x = x[0];
+  return h;
+}
+
+template<> EIGEN_STRONG_INLINE Packet8hf preverse(const Packet8hf& a) {
+  float16x4_t a_lo, a_hi;
+  Packet8hf a_r64;
+
+  a_r64 = vrev64q_f16(a);
+  a_lo = vget_low_f16(a_r64);
+  a_hi = vget_high_f16(a_r64);
+  return vcombine_f16(a_hi, a_lo);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf preverse<Packet4hf>(const Packet4hf& a) {
+  return vrev64_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8hf pabs<Packet8hf>(const Packet8hf& a) {
+  return vabsq_f16(a);
+}
+
+template<>
+EIGEN_STRONG_INLINE Packet8hf psignbit(const Packet8hf& a) {
+  return vreinterpretq_f16_s16(vshrq_n_s16(vreinterpretq_s16_f16(a), 15));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf pabs<Packet4hf>(const Packet4hf& a) {
+  return vabs_f16(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4hf psignbit(const Packet4hf& a) {
+  return vreinterpret_f16_s16( vshr_n_s16( vreinterpret_s16_f16(a), 15)); 
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux<Packet8hf>(const Packet8hf& a) {
+  float16x4_t a_lo, a_hi, sum;
+
+  a_lo = vget_low_f16(a);
+  a_hi = vget_high_f16(a);
+  sum = vpadd_f16(a_lo, a_hi);
+  sum = vpadd_f16(sum, sum);
+  sum = vpadd_f16(sum, sum);
+
+  Eigen::half h;
+  h.x = vget_lane_f16(sum, 0);
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux<Packet4hf>(const Packet4hf& a) {
+  float16x4_t sum;
+
+  sum = vpadd_f16(a, a);
+  sum = vpadd_f16(sum, sum);
+  Eigen::half h;
+  h.x = vget_lane_f16(sum, 0);
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet8hf>(const Packet8hf& a) {
+  float16x4_t a_lo, a_hi, prod;
+
+  a_lo = vget_low_f16(a);
+  a_hi = vget_high_f16(a);
+  prod = vmul_f16(a_lo, a_hi);
+  prod = vmul_f16(prod, vrev64_f16(prod));
+
+  Eigen::half h;
+  h.x = vmulh_f16(vget_lane_f16(prod, 0), vget_lane_f16(prod, 1));
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_mul<Packet4hf>(const Packet4hf& a) {
+  float16x4_t prod;
+  prod = vmul_f16(a, vrev64_f16(a));
+  Eigen::half h;
+  h.x = vmulh_f16(vget_lane_f16(prod, 0), vget_lane_f16(prod, 1));
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_min<Packet8hf>(const Packet8hf& a) {
+  Eigen::half h;
+  h.x = vminvq_f16(a);
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_min<Packet4hf>(const Packet4hf& a) {
+  Eigen::half h;
+  h.x = vminv_f16(a);
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_max<Packet8hf>(const Packet8hf& a) {
+  Eigen::half h;
+  h.x = vmaxvq_f16(a);
+  return h;
+}
+
+template <>
+EIGEN_STRONG_INLINE Eigen::half predux_max<Packet4hf>(const Packet4hf& a) {
+  Eigen::half h;
+  h.x = vmaxv_f16(a);
+  return h;
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8hf, 4>& kernel)
+{
+  const float16x8x2_t zip16_1 = vzipq_f16(kernel.packet[0], kernel.packet[1]);
+  const float16x8x2_t zip16_2 = vzipq_f16(kernel.packet[2], kernel.packet[3]);
+
+  const float32x4x2_t zip32_1 = vzipq_f32(vreinterpretq_f32_f16(zip16_1.val[0]), vreinterpretq_f32_f16(zip16_2.val[0]));
+  const float32x4x2_t zip32_2 = vzipq_f32(vreinterpretq_f32_f16(zip16_1.val[1]), vreinterpretq_f32_f16(zip16_2.val[1]));
+
+  kernel.packet[0] = vreinterpretq_f16_f32(zip32_1.val[0]);
+  kernel.packet[1] = vreinterpretq_f16_f32(zip32_1.val[1]);
+  kernel.packet[2] = vreinterpretq_f16_f32(zip32_2.val[0]);
+  kernel.packet[3] = vreinterpretq_f16_f32(zip32_2.val[1]);
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet4hf, 4>& kernel) {
+  EIGEN_ALIGN16 float16x4x4_t tmp_x4;
+  float16_t* tmp = (float16_t*)&kernel;
+  tmp_x4 = vld4_f16(tmp);
+
+  kernel.packet[0] = tmp_x4.val[0];
+  kernel.packet[1] = tmp_x4.val[1];
+  kernel.packet[2] = tmp_x4.val[2];
+  kernel.packet[3] = tmp_x4.val[3];
+}
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet8hf, 8>& kernel) {
+  float16x8x2_t T_1[4];
+
+  T_1[0] = vuzpq_f16(kernel.packet[0], kernel.packet[1]);
+  T_1[1] = vuzpq_f16(kernel.packet[2], kernel.packet[3]);
+  T_1[2] = vuzpq_f16(kernel.packet[4], kernel.packet[5]);
+  T_1[3] = vuzpq_f16(kernel.packet[6], kernel.packet[7]);
+
+  float16x8x2_t T_2[4];
+  T_2[0] = vuzpq_f16(T_1[0].val[0], T_1[1].val[0]);
+  T_2[1] = vuzpq_f16(T_1[0].val[1], T_1[1].val[1]);
+  T_2[2] = vuzpq_f16(T_1[2].val[0], T_1[3].val[0]);
+  T_2[3] = vuzpq_f16(T_1[2].val[1], T_1[3].val[1]);
+
+  float16x8x2_t T_3[4];
+  T_3[0] = vuzpq_f16(T_2[0].val[0], T_2[2].val[0]);
+  T_3[1] = vuzpq_f16(T_2[0].val[1], T_2[2].val[1]);
+  T_3[2] = vuzpq_f16(T_2[1].val[0], T_2[3].val[0]);
+  T_3[3] = vuzpq_f16(T_2[1].val[1], T_2[3].val[1]);
+
+  kernel.packet[0] = T_3[0].val[0];
+  kernel.packet[1] = T_3[2].val[0];
+  kernel.packet[2] = T_3[1].val[0];
+  kernel.packet[3] = T_3[3].val[0];
+  kernel.packet[4] = T_3[0].val[1];
+  kernel.packet[5] = T_3[2].val[1];
+  kernel.packet[6] = T_3[1].val[1];
+  kernel.packet[7] = T_3[3].val[1];
+}
+#endif // end EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_PACKET_MATH_NEON_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/TypeCasting.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/TypeCasting.h
new file mode 100644
index 0000000000000000000000000000000000000000..834fcf53ec4d7e7eb3d46674398e890e90bea77c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/TypeCasting.h
@@ -0,0 +1,1622 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Rasmus Munk Larsen <rmlarsen@google.com>
+// Copyright (C) 2020 Antonio Sanchez <cantonios@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_NEON_H
+#define EIGEN_TYPE_CASTING_NEON_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+
+//==============================================================================
+// preinterpret (truncation operations)
+//==============================================================================
+
+template <>
+EIGEN_STRONG_INLINE Packet8c preinterpret<Packet8c, Packet16c>(const Packet16c& a) {
+  return Packet8c(vget_low_s8(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c preinterpret<Packet4c, Packet8c>(const Packet8c& a) {
+  return Packet4c(vget_lane_s32(vreinterpret_s32_s8(a), 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c preinterpret<Packet4c, Packet16c>(const Packet16c& a) {
+  return preinterpret<Packet4c>(preinterpret<Packet8c>(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet8uc preinterpret<Packet8uc, Packet16uc>(const Packet16uc& a) {
+  return Packet8uc(vget_low_u8(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc preinterpret<Packet4uc, Packet8uc>(const Packet8uc& a) {
+  return Packet4uc(vget_lane_u32(vreinterpret_u32_u8(a), 0));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc preinterpret<Packet4uc, Packet16uc>(const Packet16uc& a) {
+  return preinterpret<Packet4uc>(preinterpret<Packet8uc>(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4s preinterpret<Packet4s, Packet8s>(const Packet8s& a) {
+  return Packet4s(vget_low_s16(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4us preinterpret<Packet4us, Packet8us>(const Packet8us& a) {
+  return Packet4us(vget_low_u16(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2i preinterpret<Packet2i, Packet4i>(const Packet4i& a) {
+  return Packet2i(vget_low_s32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui preinterpret<Packet2ui, Packet4ui>(const Packet4ui& a) {
+  return Packet2ui(vget_low_u32(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2f preinterpret<Packet2f, Packet4f>(const Packet4f& a) {
+  return Packet2f(vget_low_f32(a));
+}
+
+//==============================================================================
+// preinterpret
+//==============================================================================
+template <>
+EIGEN_STRONG_INLINE Packet2f preinterpret<Packet2f, Packet2i>(const Packet2i& a) {
+  return Packet2f(vreinterpret_f32_s32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f preinterpret<Packet2f, Packet2ui>(const Packet2ui& a) {
+  return Packet2f(vreinterpret_f32_u32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4i>(const Packet4i& a) {
+  return Packet4f(vreinterpretq_f32_s32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet4ui>(const Packet4ui& a) {
+  return Packet4f(vreinterpretq_f32_u32(a));
+}
+
+
+template <>
+EIGEN_STRONG_INLINE Packet4c preinterpret<Packet4c, Packet4uc>(const Packet4uc& a) {
+  return static_cast<Packet4c>(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c preinterpret<Packet8c, Packet8uc>(const Packet8uc& a) {
+  return Packet8c(vreinterpret_s8_u8(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16c preinterpret<Packet16c, Packet16uc>(const Packet16uc& a) {
+  return Packet16c(vreinterpretq_s8_u8(a));
+}
+
+
+template <>
+EIGEN_STRONG_INLINE Packet4uc preinterpret<Packet4uc, Packet4c>(const Packet4c& a) {
+  return static_cast<Packet4uc>(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc preinterpret<Packet8uc, Packet8c>(const Packet8c& a) {
+  return Packet8uc(vreinterpret_u8_s8(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet16uc preinterpret<Packet16uc, Packet16c>(const Packet16c& a) {
+  return Packet16uc(vreinterpretq_u8_s8(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4s preinterpret<Packet4s, Packet4us>(const Packet4us& a) {
+  return Packet4s(vreinterpret_s16_u16(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s preinterpret<Packet8s, Packet8us>(const Packet8us& a) {
+  return Packet8s(vreinterpretq_s16_u16(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us preinterpret<Packet4us, Packet4s>(const Packet4s& a) {
+  return Packet4us(vreinterpret_u16_s16(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us preinterpret<Packet8us, Packet8s>(const Packet8s& a) {
+  return Packet8us(vreinterpretq_u16_s16(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2i preinterpret<Packet2i, Packet2f>(const Packet2f& a) {
+  return Packet2i(vreinterpret_s32_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i preinterpret<Packet2i, Packet2ui>(const Packet2ui& a) {
+  return Packet2i(vreinterpret_s32_u32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4f>(const Packet4f& a) {
+  return Packet4i(vreinterpretq_s32_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4ui>(const Packet4ui& a) {
+  return Packet4i(vreinterpretq_s32_u32(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2ui preinterpret<Packet2ui, Packet2f>(const Packet2f& a) {
+  return Packet2ui(vreinterpret_u32_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui preinterpret<Packet2ui, Packet2i>(const Packet2i& a) {
+  return Packet2ui(vreinterpret_u32_s32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4f>(const Packet4f& a) {
+  return Packet4ui(vreinterpretq_u32_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4i>(const Packet4i& a) {
+  return Packet4ui(vreinterpretq_u32_s32(a));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet2l preinterpret<Packet2l, Packet2ul>(const Packet2ul& a) {
+  return Packet2l(vreinterpretq_s64_u64(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul preinterpret<Packet2ul, Packet2l>(const Packet2l& a) {
+  return Packet2ul(vreinterpretq_u64_s64(a));
+}
+
+//==============================================================================
+// pcast, SrcType = float
+//==============================================================================
+
+
+template <>
+struct type_casting_traits<float, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+struct type_casting_traits<float, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+// If float64 exists, first convert to that to keep as much precision as possible.
+#if EIGEN_ARCH_ARM64
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet4f, Packet2l>(const Packet4f& a) {
+  // Discard second half of input.
+  return vcvtq_s64_f64(vcvt_f64_f32(vget_low_f32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet2f, Packet2l>(const Packet2f& a) {
+  return vcvtq_s64_f64(vcvt_f64_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet4f, Packet2ul>(const Packet4f& a) {
+  // Discard second half of input.
+  return vcvtq_u64_f64(vcvt_f64_f32(vget_low_f32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet2f, Packet2ul>(const Packet2f& a) {
+  return vcvtq_u64_f64(vcvt_f64_f32(a));
+}
+#else
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet4f, Packet2l>(const Packet4f& a) {
+  // Discard second half of input.
+  return vmovl_s32(vget_low_s32(vcvtq_s32_f32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet2f, Packet2l>(const Packet2f& a) {
+  return vmovl_s32(vcvt_s32_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet4f, Packet2ul>(const Packet4f& a) {
+  // Discard second half of input.
+  return vmovl_u32(vget_low_u32(vcvtq_u32_f32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet2f, Packet2ul>(const Packet2f& a) {
+  // Discard second half of input.
+  return vmovl_u32(vcvt_u32_f32(a));
+}
+#endif  // EIGEN_ARCH_ARM64
+
+template <>
+struct type_casting_traits<float, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) {
+  return vcvtq_s32_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet2f, Packet2i>(const Packet2f& a) {
+  return vcvt_s32_f32(a);
+}
+
+template <>
+struct type_casting_traits<float, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet4f, Packet4ui>(const Packet4f& a) {
+  return vcvtq_u32_f32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet2f, Packet2ui>(const Packet2f& a) {
+  return vcvt_u32_f32(a);
+}
+
+template <>
+struct type_casting_traits<float, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet4f, Packet8s>(const Packet4f& a, const Packet4f& b) {
+  return vcombine_s16(vmovn_s32(vcvtq_s32_f32(a)), vmovn_s32(vcvtq_s32_f32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet4f, Packet4s>(const Packet4f& a) {
+  return vmovn_s32(vcvtq_s32_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet2f, Packet4s>(const Packet2f& a, const Packet2f& b) {
+  return vmovn_s32(vcombine_s32(vcvt_s32_f32(a), vcvt_s32_f32(b)));
+}
+
+template <>
+struct type_casting_traits<float, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet4f, Packet8us>(const Packet4f& a, const Packet4f& b) {
+  return vcombine_u16(vmovn_u32(vcvtq_u32_f32(a)), vmovn_u32(vcvtq_u32_f32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet4f, Packet4us>(const Packet4f& a) {
+  return vmovn_u32(vcvtq_u32_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet2f, Packet4us>(const Packet2f& a, const Packet2f& b) {
+  return vmovn_u32(vcombine_u32(vcvt_u32_f32(a), vcvt_u32_f32(b)));
+}
+
+template <>
+struct type_casting_traits<float, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet4f, Packet16c>(const Packet4f& a, const Packet4f& b, const Packet4f& c,
+                                                         const Packet4f& d) {
+  const int16x8_t ab_s16 = pcast<Packet4f, Packet8s>(a, b);
+  const int16x8_t cd_s16 = pcast<Packet4f, Packet8s>(c, d);
+  return vcombine_s8(vmovn_s16(ab_s16), vmovn_s16(cd_s16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet4f, Packet8c>(const Packet4f& a, const Packet4f& b) {
+  const int16x8_t ab_s16 = pcast<Packet4f, Packet8s>(a, b);
+  return vmovn_s16(ab_s16);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet2f, Packet8c>(const Packet2f& a, const Packet2f& b, const Packet2f& c,
+                                                       const Packet2f& d) {
+  const int16x4_t ab_s16 = pcast<Packet2f, Packet4s>(a, b);
+  const int16x4_t cd_s16 = pcast<Packet2f, Packet4s>(c, d);
+  return vmovn_s16(vcombine_s16(ab_s16, cd_s16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet4f, Packet4c>(const Packet4f& a) {
+  const int32x4_t a_s32x4 = vcvtq_s32_f32(a);
+  const int16x4_t a_s16x4 = vmovn_s32(a_s32x4);
+  const int16x8_t aa_s16x8 = vcombine_s16(a_s16x4, a_s16x4);
+  const int8x8_t aa_s8x8 = vmovn_s16(aa_s16x8);
+  return vget_lane_s32(vreinterpret_s32_s8(aa_s8x8), 0);
+}
+
+template <>
+struct type_casting_traits<float, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet4f, Packet16uc>(const Packet4f& a, const Packet4f& b, const Packet4f& c,
+                                                           const Packet4f& d) {
+  return preinterpret<Packet16uc>(pcast<Packet4f, Packet16c>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet4f, Packet8uc>(const Packet4f& a, const Packet4f& b) {
+  return preinterpret<Packet8uc>(pcast<Packet4f, Packet8c>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet2f, Packet8uc>(const Packet2f& a, const Packet2f& b, const Packet2f& c,
+                                                         const Packet2f& d) {
+  return preinterpret<Packet8uc>(pcast<Packet2f, Packet8c>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet4f, Packet4uc>(const Packet4f& a) {
+  return static_cast<Packet4uc>(pcast<Packet4f, Packet4c>(a));
+}
+
+//==============================================================================
+// pcast, SrcType = int8_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::int8_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet16c, Packet4f>(const Packet16c& a) {
+  // Discard all but first 4 bytes.
+  return vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(a)))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4c, Packet4f>(const Packet4c& a) {
+  return vcvtq_f32_s32(vmovl_s16(vget_low_s16(vmovl_s8(vreinterpret_s8_s32(vdup_n_s32(a))))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet8c, Packet2f>(const Packet8c& a) {
+  // Discard all but first 2 bytes.
+  return vcvt_f32_s32(vget_low_s32(vmovl_s16(vget_low_s16(vmovl_s8(a)))));
+}
+
+template <>
+struct type_casting_traits<numext::int8_t, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet16c, Packet2l>(const Packet16c& a) {
+  // Discard all but first two bytes.
+  return vmovl_s32(vget_low_s32(vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(a))))));
+}
+
+template <>
+struct type_casting_traits<numext::int8_t, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet16c, Packet2ul>(const Packet16c& a) {
+  return preinterpret<Packet2ul>(pcast<Packet16c, Packet2l>(a));
+}
+
+template <>
+struct type_casting_traits<numext::int8_t, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet16c, Packet4i>(const Packet16c& a) {
+  // Discard all but first 4 bytes.
+  return vmovl_s16(vget_low_s16(vmovl_s8(vget_low_s8(a))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet8c, Packet4i>(const Packet8c& a) {
+  return vmovl_s16(vget_low_s16(vmovl_s8(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet4c, Packet4i>(const Packet4c& a) {
+  return pcast<Packet8c, Packet4i>(vreinterpret_s8_s32(vdup_n_s32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet8c, Packet2i>(const Packet8c& a) {
+  // Discard all but first 2 bytes.
+  return vget_low_s32(vmovl_s16(vget_low_s16(vmovl_s8(a))));
+}
+
+
+template <>
+struct type_casting_traits<numext::int8_t, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet16c, Packet4ui>(const Packet16c& a) {
+  return preinterpret<Packet4ui>(pcast<Packet16c, Packet4i>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet8c, Packet2ui>(const Packet8c& a) {
+  return preinterpret<Packet2ui>(pcast<Packet8c, Packet2i>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet4c, Packet4ui>(const Packet4c& a) {
+  return preinterpret<Packet4ui>(pcast<Packet4c, Packet4i>(a));
+}
+
+template <>
+struct type_casting_traits<numext::int8_t, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet16c, Packet8s>(const Packet16c& a) {
+  // Discard second half of input.
+  return vmovl_s8(vget_low_s8(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet8c, Packet8s>(const Packet8c& a) {
+  return vmovl_s8(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet8c, Packet4s>(const Packet8c& a) {
+  // Discard second half of input.
+  return vget_low_s16(vmovl_s8(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet4c, Packet4s>(const Packet4c& a) {
+  return pcast<Packet8c, Packet4s>(vreinterpret_s8_s32(vdup_n_s32(a)));
+}
+
+template <>
+struct type_casting_traits<numext::int8_t, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet16c, Packet8us>(const Packet16c& a) {
+  return preinterpret<Packet8us>(pcast<Packet16c, Packet8s>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet8c, Packet8us>(const Packet8c& a) {
+  return preinterpret<Packet8us>(pcast<Packet8c, Packet8s>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet8c, Packet4us>(const Packet8c& a) {
+  return preinterpret<Packet4us>(pcast<Packet8c, Packet4s>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet4c, Packet4us>(const Packet4c& a) {
+  return preinterpret<Packet4us>(pcast<Packet4c, Packet4s>(a));
+}
+
+
+//==============================================================================
+// pcast, SrcType = uint8_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::uint8_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet16uc, Packet4f>(const Packet16uc& a) {
+  // Discard all but first 4 bytes.
+  return vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a)))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4uc, Packet4f>(const Packet4uc& a) {
+  return vcvtq_f32_u32(vmovl_u16(vget_low_u16(vmovl_u8(vreinterpret_u8_u32(vdup_n_u32(a))))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet8uc, Packet2f>(const Packet8uc& a) {
+  // Discard all but first 2 bytes.
+  return vcvt_f32_u32(vget_low_u32(vmovl_u16(vget_low_u16(vmovl_u8(a)))));
+}
+
+template <>
+struct type_casting_traits<numext::uint8_t, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet16uc, Packet2ul>(const Packet16uc& a) {
+  // Discard all but first two bytes.
+  return vmovl_u32(vget_low_u32(vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a))))));
+}
+
+template <>
+struct type_casting_traits<numext::uint8_t, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet16uc, Packet2l>(const Packet16uc& a) {
+  return preinterpret<Packet2l>(pcast<Packet16uc, Packet2ul>(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint8_t, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet16uc, Packet4ui>(const Packet16uc& a) {
+  // Discard all but first 4 bytes.
+  return vmovl_u16(vget_low_u16(vmovl_u8(vget_low_u8(a))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet8uc, Packet4ui>(const Packet8uc& a) {
+  return vmovl_u16(vget_low_u16(vmovl_u8(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet8uc, Packet2ui>(const Packet8uc& a) {
+  // Discard all but first 2 bytes.
+  return vget_low_u32(vmovl_u16(vget_low_u16(vmovl_u8(a))));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet4uc, Packet4ui>(const Packet4uc& a) {
+  return pcast<Packet8uc, Packet4ui>(vreinterpret_u8_u32(vdup_n_u32(a)));
+}
+
+template <>
+struct type_casting_traits<numext::uint8_t, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet16uc, Packet4i>(const Packet16uc& a) {
+  return preinterpret<Packet4i>(pcast<Packet16uc, Packet4ui>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet8uc, Packet2i>(const Packet8uc& a) {
+  return preinterpret<Packet2i>(pcast<Packet8uc, Packet2ui>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet4uc, Packet4i>(const Packet4uc& a) {
+  return preinterpret<Packet4i>(pcast<Packet4uc, Packet4ui>(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint8_t, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet16uc, Packet8us>(const Packet16uc& a) {
+  // Discard second half of input.
+  return vmovl_u8(vget_low_u8(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet8uc, Packet8us>(const Packet8uc& a) {
+  return vmovl_u8(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet4uc, Packet4us>(const Packet4uc& a) {
+  return vget_low_u16(vmovl_u8(vreinterpret_u8_u32(vdup_n_u32(a))));
+}
+
+
+template <>
+struct type_casting_traits<numext::uint8_t, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet16uc, Packet8s>(const Packet16uc& a) {
+  return preinterpret<Packet8s>(pcast<Packet16uc, Packet8us>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet8uc, Packet8s>(const Packet8uc& a) {
+  return preinterpret<Packet8s>(pcast<Packet8uc, Packet8us>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet4uc, Packet4s>(const Packet4uc& a) {
+  return preinterpret<Packet4s>(pcast<Packet4uc, Packet4us>(a));
+}
+
+
+//==============================================================================
+// pcast, SrcType = int16_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::int16_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet8s, Packet4f>(const Packet8s& a) {
+  // Discard second half of input.
+  return vcvtq_f32_s32(vmovl_s16(vget_low_s16(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4s, Packet4f>(const Packet4s& a) {
+  return vcvtq_f32_s32(vmovl_s16(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet4s, Packet2f>(const Packet4s& a) {
+  // Discard second half of input.
+  return vcvt_f32_s32(vget_low_s32(vmovl_s16(a)));
+}
+
+template <>
+struct type_casting_traits<numext::int16_t, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet8s, Packet2l>(const Packet8s& a) {
+  // Discard all but first two values.
+  return vmovl_s32(vget_low_s32(vmovl_s16(vget_low_s16(a))));
+}
+
+template <>
+struct type_casting_traits<numext::int16_t, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet8s, Packet2ul>(const Packet8s& a) {
+  return preinterpret<Packet2ul>(pcast<Packet8s, Packet2l>(a));
+}
+
+template <>
+struct type_casting_traits<numext::int16_t, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet8s, Packet4i>(const Packet8s& a) {
+  // Discard second half of input.
+  return vmovl_s16(vget_low_s16(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet4s, Packet4i>(const Packet4s& a) {
+  return vmovl_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet4s, Packet2i>(const Packet4s& a) {
+  // Discard second half of input.
+  return vget_low_s32(vmovl_s16(a));
+}
+
+template <>
+struct type_casting_traits<numext::int16_t, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet8s, Packet4ui>(const Packet8s& a) {
+  return preinterpret<Packet4ui>(pcast<Packet8s, Packet4i>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet4s, Packet4ui>(const Packet4s& a) {
+  return preinterpret<Packet4ui>(pcast<Packet4s, Packet4i>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet4s, Packet2ui>(const Packet4s& a) {
+  return preinterpret<Packet2ui>(pcast<Packet4s, Packet2i>(a));
+}
+
+
+template <>
+struct type_casting_traits<numext::int16_t, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet8s, Packet16c>(const Packet8s& a, const Packet8s& b) {
+  return vcombine_s8(vmovn_s16(a), vmovn_s16(b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet8s, Packet8c>(const Packet8s& a) {
+  return vmovn_s16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet4s, Packet8c>(const Packet4s& a, const Packet4s& b) {
+  return vmovn_s16(vcombine_s16(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet4s, Packet4c>(const Packet4s& a) {
+  const int8x8_t aa_s8x8 = pcast<Packet4s, Packet8c>(a, a);
+  return vget_lane_s32(vreinterpret_s32_s8(aa_s8x8), 0);
+}
+
+template <>
+struct type_casting_traits<numext::int16_t, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet8s, Packet16uc>(const Packet8s& a, const Packet8s& b) {
+  return preinterpret<Packet16uc>(pcast<Packet8s, Packet16c>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet8s, Packet8uc>(const Packet8s& a) {
+  return preinterpret<Packet8uc>(pcast<Packet8s, Packet8c>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet4s, Packet8uc>(const Packet4s& a, const Packet4s& b) {
+  return preinterpret<Packet8uc>(pcast<Packet4s, Packet8c>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet4s, Packet4uc>(const Packet4s& a) {
+  return static_cast<Packet4uc>(pcast<Packet4s, Packet4c>(a));
+}
+
+//==============================================================================
+// pcast, SrcType = uint16_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::uint16_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet8us, Packet4f>(const Packet8us& a) {
+  // Discard second half of input.
+  return vcvtq_f32_u32(vmovl_u16(vget_low_u16(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4us, Packet4f>(const Packet4us& a) {
+  return vcvtq_f32_u32(vmovl_u16(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet4us, Packet2f>(const Packet4us& a) {
+  // Discard second half of input.
+  return vcvt_f32_u32(vget_low_u32(vmovl_u16(a)));
+}
+
+template <>
+struct type_casting_traits<numext::uint16_t, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet8us, Packet2ul>(const Packet8us& a) {
+  // Discard all but first two values.
+  return vmovl_u32(vget_low_u32(vmovl_u16(vget_low_u16(a))));
+}
+
+template <>
+struct type_casting_traits<numext::uint16_t, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet8us, Packet2l>(const Packet8us& a) {
+  return preinterpret<Packet2l>(pcast<Packet8us, Packet2ul>(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint16_t, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet8us, Packet4ui>(const Packet8us& a) {
+  // Discard second half of input.
+  return vmovl_u16(vget_low_u16(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet4us, Packet4ui>(const Packet4us& a) {
+  return vmovl_u16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet4us, Packet2ui>(const Packet4us& a) {
+  // Discard second half of input.
+  return vget_low_u32(vmovl_u16(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint16_t, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet8us, Packet4i>(const Packet8us& a) {
+  return preinterpret<Packet4i>(pcast<Packet8us, Packet4ui>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet4us, Packet4i>(const Packet4us& a) {
+  return preinterpret<Packet4i>(pcast<Packet4us, Packet4ui>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet4us, Packet2i>(const Packet4us& a) {
+  return preinterpret<Packet2i>(pcast<Packet4us, Packet2ui>(a));
+}
+
+
+template <>
+struct type_casting_traits<numext::uint16_t, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet8us, Packet16uc>(const Packet8us& a, const Packet8us& b) {
+  return vcombine_u8(vmovn_u16(a), vmovn_u16(b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet8us, Packet8uc>(const Packet8us& a) {
+  return vmovn_u16(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet4us, Packet8uc>(const Packet4us& a, const Packet4us& b) {
+  return vmovn_u16(vcombine_u16(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet4us, Packet4uc>(const Packet4us& a) {
+  uint8x8_t aa_u8x8 = pcast<Packet4us, Packet8uc>(a, a);
+  return vget_lane_u32(vreinterpret_u32_u8(aa_u8x8), 0);
+}
+
+template <>
+struct type_casting_traits<numext::uint16_t, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet8us, Packet16c>(const Packet8us& a, const Packet8us& b) {
+  return preinterpret<Packet16c>(pcast<Packet8us, Packet16uc>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet8us, Packet8c>(const Packet8us& a) {
+  return preinterpret<Packet8c>(pcast<Packet8us, Packet8uc>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet4us, Packet8c>(const Packet4us& a, const Packet4us& b) {
+  return preinterpret<Packet8c>(pcast<Packet4us, Packet8uc>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet4us, Packet4c>(const Packet4us& a) {
+  return static_cast<Packet4c>(pcast<Packet4us, Packet4uc>(a));
+}
+
+//==============================================================================
+// pcast, SrcType = int32_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::int32_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) {
+  return vcvtq_f32_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet2i, Packet2f>(const Packet2i& a) {
+  return vcvt_f32_s32(a);
+}
+
+template <>
+struct type_casting_traits<numext::int32_t, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet4i, Packet2l>(const Packet4i& a) {
+  // Discard second half of input.
+  return vmovl_s32(vget_low_s32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet2i, Packet2l>(const Packet2i& a) {
+  return vmovl_s32(a);
+}
+
+template <>
+struct type_casting_traits<numext::int32_t, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet4i, Packet2ul>(const Packet4i& a) {
+  return preinterpret<Packet2ul>(pcast<Packet4i, Packet2l>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet2i, Packet2ul>(const Packet2i& a) {
+  return preinterpret<Packet2ul>(pcast<Packet2i, Packet2l>(a));
+}
+
+
+template <>
+struct type_casting_traits<numext::int32_t, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet4i, Packet8s>(const Packet4i& a, const Packet4i& b) {
+  return vcombine_s16(vmovn_s32(a), vmovn_s32(b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet4i, Packet4s>(const Packet4i& a) {
+  return vmovn_s32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet2i, Packet4s>(const Packet2i& a, const Packet2i& b) {
+  return vmovn_s32(vcombine_s32(a, b));
+}
+
+template <>
+struct type_casting_traits<numext::int32_t, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet4i, Packet8us>(const Packet4i& a, const Packet4i& b) {
+  return vcombine_u16(vmovn_u32(vreinterpretq_u32_s32(a)), vmovn_u32(vreinterpretq_u32_s32(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet4i, Packet4us>(const Packet4i& a) {
+  return vmovn_u32(vreinterpretq_u32_s32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet2i, Packet4us>(const Packet2i& a, const Packet2i& b) {
+  return vmovn_u32(vreinterpretq_u32_s32(vcombine_s32(a, b)));
+}
+
+template <>
+struct type_casting_traits<numext::int32_t, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet4i, Packet16c>(const Packet4i& a, const Packet4i& b, const Packet4i& c,
+                                                         const Packet4i& d) {
+  const int16x8_t ab_s16 = pcast<Packet4i, Packet8s>(a, b);
+  const int16x8_t cd_s16 = pcast<Packet4i, Packet8s>(c, d);
+  return vcombine_s8(vmovn_s16(ab_s16), vmovn_s16(cd_s16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet4i, Packet8c>(const Packet4i& a, const Packet4i& b) {
+  const int16x8_t ab_s16 = pcast<Packet4i, Packet8s>(a, b);
+  return vmovn_s16(ab_s16);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet2i, Packet8c>(const Packet2i& a, const Packet2i& b, const Packet2i& c,
+                                                       const Packet2i& d) {
+  const int16x4_t ab_s16 = vmovn_s32(vcombine_s32(a, b));
+  const int16x4_t cd_s16 = vmovn_s32(vcombine_s32(c, d));
+  return vmovn_s16(vcombine_s16(ab_s16, cd_s16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet4i, Packet4c>(const Packet4i& a) {
+  const int16x4_t a_s16x4 = vmovn_s32(a);
+  const int16x8_t aa_s16x8 = vcombine_s16(a_s16x4, a_s16x4);
+  const int8x8_t aa_s8x8 = vmovn_s16(aa_s16x8);
+  return vget_lane_s32(vreinterpret_s32_s8(aa_s8x8), 0);
+}
+
+template <>
+struct type_casting_traits<numext::int32_t, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet4i, Packet16uc>(const Packet4i& a, const Packet4i& b, const Packet4i& c,
+                                                           const Packet4i& d) {
+  return preinterpret<Packet16uc>(pcast<Packet4i, Packet16c>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet4i, Packet8uc>(const Packet4i& a, const Packet4i& b) {
+  return preinterpret<Packet8uc>(pcast<Packet4i, Packet8c>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet2i, Packet8uc>(const Packet2i& a, const Packet2i& b, const Packet2i& c,
+                                                         const Packet2i& d) {
+  return preinterpret<Packet8uc>(pcast<Packet2i, Packet8c>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet4i, Packet4uc>(const Packet4i& a) {
+  return static_cast<Packet4uc>(pcast<Packet4i, Packet4c>(a));
+}
+
+//==============================================================================
+// pcast, SrcType = uint32_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::uint32_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet4ui, Packet4f>(const Packet4ui& a) {
+  return vcvtq_f32_u32(a);
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet2ui, Packet2f>(const Packet2ui& a) {
+  return vcvt_f32_u32(a);
+}
+
+template <>
+struct type_casting_traits<numext::uint32_t, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet4ui, Packet2ul>(const Packet4ui& a) {
+  // Discard second half of input.
+  return vmovl_u32(vget_low_u32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet2ui, Packet2ul>(const Packet2ui& a) {
+  return vmovl_u32(a);
+}
+
+template <>
+struct type_casting_traits<numext::uint32_t, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet4ui, Packet2l>(const Packet4ui& a) {
+  return preinterpret<Packet2l>(pcast<Packet4ui, Packet2ul>(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet2ui, Packet2l>(const Packet2ui& a) {
+  return preinterpret<Packet2l>(pcast<Packet2ui, Packet2ul>(a));
+}
+
+
+template <>
+struct type_casting_traits<numext::uint32_t, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet4ui, Packet8us>(const Packet4ui& a, const Packet4ui& b) {
+  return vcombine_u16(vmovn_u32(a), vmovn_u32(b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet2ui, Packet4us>(const Packet2ui& a, const Packet2ui& b) {
+  return vmovn_u32(vcombine_u32(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet4ui, Packet4us>(const Packet4ui& a) {
+  return vmovn_u32(a);
+}
+
+template <>
+struct type_casting_traits<numext::uint32_t, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet4ui, Packet8s>(const Packet4ui& a, const Packet4ui& b) {
+  return preinterpret<Packet8s>(pcast<Packet4ui, Packet8us>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet2ui, Packet4s>(const Packet2ui& a, const Packet2ui& b) {
+  return preinterpret<Packet4s>(pcast<Packet2ui, Packet4us>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet4ui, Packet4s>(const Packet4ui& a) {
+  return preinterpret<Packet4s>(pcast<Packet4ui, Packet4us>(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint32_t, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet4ui, Packet16uc>(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c,
+                                                            const Packet4ui& d) {
+  const uint16x8_t ab_u16 = vcombine_u16(vmovn_u32(a), vmovn_u32(b));
+  const uint16x8_t cd_u16 = vcombine_u16(vmovn_u32(c), vmovn_u32(d));
+  return vcombine_u8(vmovn_u16(ab_u16), vmovn_u16(cd_u16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet4ui, Packet8uc>(const Packet4ui& a, const Packet4ui& b) {
+  const uint16x8_t ab_u16 = vcombine_u16(vmovn_u32(a), vmovn_u32(b));
+  return vmovn_u16(ab_u16);
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet2ui, Packet8uc>(const Packet2ui& a, const Packet2ui& b, const Packet2ui& c,
+                                                          const Packet2ui& d) {
+  const uint16x4_t ab_u16 = vmovn_u32(vcombine_u32(a, b));
+  const uint16x4_t cd_u16 = vmovn_u32(vcombine_u32(c, d));
+  return vmovn_u16(vcombine_u16(ab_u16, cd_u16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet4ui, Packet4uc>(const Packet4ui& a) {
+  const uint16x4_t a_u16x4 = vmovn_u32(a);
+  const uint16x8_t aa_u16x8 = vcombine_u16(a_u16x4, a_u16x4);
+  const uint8x8_t aa_u8x8 = vmovn_u16(aa_u16x8);
+  return vget_lane_u32(vreinterpret_u32_u8(aa_u8x8), 0);
+}
+
+template <>
+struct type_casting_traits<numext::uint32_t, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet4ui, Packet16c>(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c,
+                                                          const Packet4ui& d) {
+  return preinterpret<Packet16c>(pcast<Packet4ui, Packet16uc>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet4ui, Packet8c>(const Packet4ui& a, const Packet4ui& b) {
+  return preinterpret<Packet8c>(pcast<Packet4ui, Packet8uc>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet2ui, Packet8c>(const Packet2ui& a, const Packet2ui& b, const Packet2ui& c,
+                                                        const Packet2ui& d) {
+  return preinterpret<Packet8c>(pcast<Packet2ui, Packet8uc>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet4ui, Packet4c>(const Packet4ui& a) {
+  return static_cast<Packet4c>(pcast<Packet4ui, Packet4uc>(a));
+}
+
+//==============================================================================
+// pcast, SrcType = int64_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::int64_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet2l, Packet4f>(const Packet2l& a, const Packet2l& b) {
+  return vcvtq_f32_s32(vcombine_s32(vmovn_s64(a), vmovn_s64(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet2l, Packet2f>(const Packet2l& a) {
+  return vcvt_f32_s32(vmovn_s64(a));
+}
+
+
+template <>
+struct type_casting_traits<numext::int64_t, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet2l, Packet4i>(const Packet2l& a, const Packet2l& b) {
+  return vcombine_s32(vmovn_s64(a), vmovn_s64(b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet2l, Packet2i>(const Packet2l& a) {
+  return vmovn_s64(a);
+}
+
+template <>
+struct type_casting_traits<numext::int64_t, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet2l, Packet4ui>(const Packet2l& a, const Packet2l& b) {
+  return vcombine_u32(vmovn_u64(vreinterpretq_u64_s64(a)), vmovn_u64(vreinterpretq_u64_s64(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet2l, Packet2ui>(const Packet2l& a) {
+  return vmovn_u64(vreinterpretq_u64_s64(a));
+}
+
+template <>
+struct type_casting_traits<numext::int64_t, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet2l, Packet8s>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                       const Packet2l& d) {
+  const int32x4_t ab_s32 = pcast<Packet2l, Packet4i>(a, b);
+  const int32x4_t cd_s32 = pcast<Packet2l, Packet4i>(c, d);
+  return vcombine_s16(vmovn_s32(ab_s32), vmovn_s32(cd_s32));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet2l, Packet4s>(const Packet2l& a, const Packet2l& b) {
+  const int32x4_t ab_s32 = pcast<Packet2l, Packet4i>(a, b);
+  return vmovn_s32(ab_s32);
+}
+
+template <>
+struct type_casting_traits<numext::int64_t, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet2l, Packet8us>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                         const Packet2l& d) {
+  return preinterpret<Packet8us>(pcast<Packet2l, Packet8s>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet2l, Packet4us>(const Packet2l& a, const Packet2l& b) {
+  return preinterpret<Packet4us>(pcast<Packet2l, Packet4s>(a, b));
+}
+
+template <>
+struct type_casting_traits<numext::int64_t, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet2l, Packet16c>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                         const Packet2l& d, const Packet2l& e, const Packet2l& f,
+                                                         const Packet2l& g, const Packet2l& h) {
+  const int16x8_t abcd_s16 = pcast<Packet2l, Packet8s>(a, b, c, d);
+  const int16x8_t efgh_s16 = pcast<Packet2l, Packet8s>(e, f, g, h);
+  return vcombine_s8(vmovn_s16(abcd_s16), vmovn_s16(efgh_s16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet2l, Packet8c>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                       const Packet2l& d) {
+  const int16x8_t abcd_s16 = pcast<Packet2l, Packet8s>(a, b, c, d);
+  return vmovn_s16(abcd_s16);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet2l, Packet4c>(const Packet2l& a, const Packet2l& b) {
+  const int16x4_t ab_s16 = pcast<Packet2l, Packet4s>(a, b);
+  const int16x8_t abab_s16 = vcombine_s16(ab_s16, ab_s16);
+  const int8x8_t abab_s8 = vmovn_s16(abab_s16);
+  return vget_lane_s32(vreinterpret_s32_s8(abab_s8), 0);
+}
+
+template <>
+struct type_casting_traits<numext::int64_t, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet2l, Packet16uc>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                           const Packet2l& d, const Packet2l& e, const Packet2l& f,
+                                                           const Packet2l& g, const Packet2l& h) {
+  const uint16x8_t abcd_u16 = pcast<Packet2l, Packet8us>(a, b, c, d);
+  const uint16x8_t efgh_u16 = pcast<Packet2l, Packet8us>(e, f, g, h);
+  return vcombine_u8(vmovn_u16(abcd_u16), vmovn_u16(efgh_u16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet2l, Packet8uc>(const Packet2l& a, const Packet2l& b, const Packet2l& c,
+                                                         const Packet2l& d) {
+  return preinterpret<Packet8uc>(pcast<Packet2l, Packet8c>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet2l, Packet4uc>(const Packet2l& a, const Packet2l& b) {
+  return static_cast<Packet4uc>(pcast<Packet2l, Packet4c>(a, b));
+}
+
+//==============================================================================
+// pcast, SrcType = uint64_t
+//==============================================================================
+template <>
+struct type_casting_traits<numext::uint64_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet2ul, Packet4f>(const Packet2ul& a, const Packet2ul& b) {
+  return vcvtq_f32_u32(vcombine_u32(vmovn_u64(a), vmovn_u64(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet2ul, Packet2f>(const Packet2ul& a) {
+  return vcvt_f32_u32(vmovn_u64(a));
+}
+
+
+template <>
+struct type_casting_traits<numext::uint64_t, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet2ul, Packet4ui>(const Packet2ul& a, const Packet2ul& b) {
+  return vcombine_u32(vmovn_u64(a), vmovn_u64(b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet2ul, Packet2ui>(const Packet2ul& a) {
+  return vmovn_u64(a);
+}
+
+template <>
+struct type_casting_traits<numext::uint64_t, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet2ul, Packet4i>(const Packet2ul& a, const Packet2ul& b) {
+  return preinterpret<Packet4i>(pcast<Packet2ul, Packet4ui>(a, b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet2ul, Packet2i>(const Packet2ul& a) {
+  return preinterpret<Packet2i>(pcast<Packet2ul, Packet2ui>(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint64_t, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet2ul, Packet8us>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                          const Packet2ul& d) {
+  const uint16x4_t ab_u16 = vmovn_u32(vcombine_u32(vmovn_u64(a), vmovn_u64(b)));
+  const uint16x4_t cd_u16 = vmovn_u32(vcombine_u32(vmovn_u64(c), vmovn_u64(d)));
+  return vcombine_u16(ab_u16, cd_u16);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet2ul, Packet4us>(const Packet2ul& a, const Packet2ul& b) {
+  return vmovn_u32(vcombine_u32(vmovn_u64(a), vmovn_u64(b)));
+}
+
+template <>
+struct type_casting_traits<numext::uint64_t, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet2ul, Packet8s>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                        const Packet2ul& d) {
+  return preinterpret<Packet8s>(pcast<Packet2ul, Packet8us>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet2ul, Packet4s>(const Packet2ul& a, const Packet2ul& b) {
+  return preinterpret<Packet4s>(pcast<Packet2ul, Packet4us>(a, b));
+}
+
+template <>
+struct type_casting_traits<numext::uint64_t, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet2ul, Packet16uc>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                            const Packet2ul& d, const Packet2ul& e, const Packet2ul& f,
+                                                            const Packet2ul& g, const Packet2ul& h) {
+  const uint16x8_t abcd_u16 = pcast<Packet2ul, Packet8us>(a, b, c, d);
+  const uint16x8_t efgh_u16 = pcast<Packet2ul, Packet8us>(e, f, g, h);
+  return vcombine_u8(vmovn_u16(abcd_u16), vmovn_u16(efgh_u16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet2ul, Packet8uc>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                          const Packet2ul& d) {
+  const uint16x8_t abcd_u16 = pcast<Packet2ul, Packet8us>(a, b, c, d);
+  return vmovn_u16(abcd_u16);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet2ul, Packet4uc>(const Packet2ul& a, const Packet2ul& b) {
+  const uint16x4_t ab_u16 = pcast<Packet2ul, Packet4us>(a, b);
+  const uint16x8_t abab_u16 = vcombine_u16(ab_u16, ab_u16);
+  const uint8x8_t abab_u8 = vmovn_u16(abab_u16);
+  return vget_lane_u32(vreinterpret_u32_u8(abab_u8), 0);
+}
+
+template <>
+struct type_casting_traits<numext::uint64_t, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet2ul, Packet16c>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                          const Packet2ul& d, const Packet2ul& e, const Packet2ul& f,
+                                                          const Packet2ul& g, const Packet2ul& h) {
+  return preinterpret<Packet16c>(pcast<Packet2ul, Packet16uc>(a, b, c, d, e, f, g, h));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet2ul, Packet8c>(const Packet2ul& a, const Packet2ul& b, const Packet2ul& c,
+                                                        const Packet2ul& d) {
+  return preinterpret<Packet8c>(pcast<Packet2ul, Packet8uc>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet2ul, Packet4c>(const Packet2ul& a, const Packet2ul& b) {
+  return static_cast<Packet4c>(pcast<Packet2ul, Packet4uc>(a, b));
+}
+
+#if EIGEN_ARCH_ARM64
+
+//==============================================================================
+// pcast/preinterpret, Double
+//==============================================================================
+
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet2l>(const Packet2l& a) {
+  return Packet2d(vreinterpretq_f64_s64(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet2ul>(const Packet2ul& a) {
+  return Packet2d(vreinterpretq_f64_u64(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2l preinterpret<Packet2l, Packet2d>(const Packet2d& a) {
+  return Packet2l(vreinterpretq_s64_f64(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ul preinterpret<Packet2ul, Packet2d>(const Packet2d& a) {
+  return Packet2ul(vreinterpretq_u64_f64(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet4i>(const Packet4i& a) {
+  return Packet2d(vreinterpretq_f64_s32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet2d>(const Packet2d& a) {
+  return Packet4i(vreinterpretq_s32_f64(a));
+}
+
+
+template <>
+struct type_casting_traits<double, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet2d, Packet4f>(const Packet2d& a, const Packet2d& b) {
+  return vcombine_f32(vcvt_f32_f64(a), vcvt_f32_f64(b));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2f pcast<Packet2d, Packet2f>(const Packet2d& a) {
+  return vcvt_f32_f64(a);
+}
+
+template <>
+struct type_casting_traits<double, numext::int64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2l pcast<Packet2d, Packet2l>(const Packet2d& a) {
+  return vcvtq_s64_f64(a);
+}
+
+template <>
+struct type_casting_traits<double, numext::uint64_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2ul pcast<Packet2d, Packet2ul>(const Packet2d& a) {
+  return vcvtq_u64_f64(a);
+}
+
+template <>
+struct type_casting_traits<double, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4i pcast<Packet2d, Packet4i>(const Packet2d& a, const Packet2d& b) {
+  return vcombine_s32(vmovn_s64(vcvtq_s64_f64(a)), vmovn_s64(vcvtq_s64_f64(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2i pcast<Packet2d, Packet2i>(const Packet2d& a) {
+  return vmovn_s64(vcvtq_s64_f64(a));
+}
+
+template <>
+struct type_casting_traits<double, numext::uint32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet4ui pcast<Packet2d, Packet4ui>(const Packet2d& a, const Packet2d& b) {
+  return vcombine_u32(vmovn_u64(vcvtq_u64_f64(a)), vmovn_u64(vcvtq_u64_f64(b)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2ui pcast<Packet2d, Packet2ui>(const Packet2d& a) {
+  return vmovn_u64(vcvtq_u64_f64(a));
+}
+
+template <>
+struct type_casting_traits<double, numext::int16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8s pcast<Packet2d, Packet8s>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                       const Packet2d& d) {
+  const int32x4_t ab_s32 = pcast<Packet2d, Packet4i>(a, b);
+  const int32x4_t cd_s32 = pcast<Packet2d, Packet4i>(c, d);
+  return vcombine_s16(vmovn_s32(ab_s32), vmovn_s32(cd_s32));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4s pcast<Packet2d, Packet4s>(const Packet2d& a, const Packet2d& b) {
+  const int32x4_t ab_s32 = pcast<Packet2d, Packet4i>(a, b);
+  return vmovn_s32(ab_s32);
+}
+
+template <>
+struct type_casting_traits<double, numext::uint16_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 4, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet8us pcast<Packet2d, Packet8us>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                         const Packet2d& d) {
+  return preinterpret<Packet8us>(pcast<Packet2d, Packet8s>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4us pcast<Packet2d, Packet4us>(const Packet2d& a, const Packet2d& b) {
+  return preinterpret<Packet4us>(pcast<Packet2d, Packet4s>(a, b));
+}
+
+template <>
+struct type_casting_traits<double, numext::int8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16c pcast<Packet2d, Packet16c>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                         const Packet2d& d, const Packet2d& e, const Packet2d& f,
+                                                         const Packet2d& g, const Packet2d& h) {
+  const int16x8_t abcd_s16 = pcast<Packet2d, Packet8s>(a, b, c, d);
+  const int16x8_t efgh_s16 = pcast<Packet2d, Packet8s>(e, f, g, h);
+  return vcombine_s8(vmovn_s16(abcd_s16), vmovn_s16(efgh_s16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8c pcast<Packet2d, Packet8c>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                       const Packet2d& d) {
+  const int16x8_t abcd_s16 = pcast<Packet2d, Packet8s>(a, b, c, d);
+  return vmovn_s16(abcd_s16);
+}
+template <>
+EIGEN_STRONG_INLINE Packet4c pcast<Packet2d, Packet4c>(const Packet2d& a, const Packet2d& b) {
+  const int32x4_t ab_s32 = pcast<Packet2d, Packet4i>(a, b);
+  return pcast<Packet4i, Packet4c>(ab_s32);
+}
+
+template <>
+struct type_casting_traits<double, numext::uint8_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 8, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet16uc pcast<Packet2d, Packet16uc>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                           const Packet2d& d, const Packet2d& e, const Packet2d& f,
+                                                           const Packet2d& g, const Packet2d& h) {
+  const uint16x8_t abcd_u16 = pcast<Packet2d, Packet8us>(a, b, c, d);
+  const uint16x8_t efgh_u16 = pcast<Packet2d, Packet8us>(e, f, g, h);
+  return vcombine_u8(vmovn_u16(abcd_u16), vmovn_u16(efgh_u16));
+}
+template <>
+EIGEN_STRONG_INLINE Packet8uc pcast<Packet2d, Packet8uc>(const Packet2d& a, const Packet2d& b, const Packet2d& c,
+                                                           const Packet2d& d) {
+  return preinterpret<Packet8uc>(pcast<Packet2d, Packet8c>(a, b, c, d));
+}
+template <>
+EIGEN_STRONG_INLINE Packet4uc pcast<Packet2d, Packet4uc>(const Packet2d& a, const Packet2d& b) {
+  return static_cast<Packet4uc>(pcast<Packet2d, Packet4c>(a, b));
+}
+
+template <>
+struct type_casting_traits<float, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet4f, Packet2d>(const Packet4f& a) {
+  // Discard second-half of input.
+  return vcvt_f64_f32(vget_low_f32(a));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet2f, Packet2d>(const Packet2f& a) {
+  return vcvt_f64_f32(a);
+}
+
+template <>
+struct type_casting_traits<numext::int8_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet16c, Packet2d>(const Packet16c& a) {
+  // Discard all but first two values.
+  // MSVC defines most intrinsics as macros, so we need to do this in two lines for portability.
+  Packet2f tmp = pcast<Packet8c, Packet2f>(vget_low_s8(a));
+  return vcvt_f64_f32(tmp);
+}
+
+template <>
+struct type_casting_traits<numext::uint8_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 8 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet16uc, Packet2d>(const Packet16uc& a) {
+  // Discard all but first two values.
+  Packet2f tmp = pcast<Packet8uc, Packet2f>(vget_low_u8(a));
+  return vcvt_f64_f32(tmp);
+}
+
+template <>
+struct type_casting_traits<numext::int16_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet8s, Packet2d>(const Packet8s& a) {
+  // Discard all but first two values.
+  Packet2f tmp = pcast<Packet4s, Packet2f>(vget_low_s16(a));
+  return vcvt_f64_f32(tmp);
+}
+
+template <>
+struct type_casting_traits<numext::uint16_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 4 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet8us, Packet2d>(const Packet8us& a) {
+  // Discard all but first two values.
+  Packet2f tmp = pcast<Packet4us, Packet2f>(vget_low_u16(a));
+  return vcvt_f64_f32(tmp);
+}
+
+template <>
+struct type_casting_traits<numext::int32_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet4i, Packet2d>(const Packet4i& a) {
+  // Discard second half of input.
+  return vcvtq_f64_s64(vmovl_s32(vget_low_s32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet2i, Packet2d>(const Packet2i& a) {
+  return vcvtq_f64_s64(vmovl_s32(a));
+}
+
+template <>
+struct type_casting_traits<numext::uint32_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet4ui, Packet2d>(const Packet4ui& a) {
+  // Discard second half of input.
+  return vcvtq_f64_u64(vmovl_u32(vget_low_u32(a)));
+}
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet2ui, Packet2d>(const Packet2ui& a) {
+  return vcvtq_f64_u64(vmovl_u32(a));
+}
+
+template <>
+struct type_casting_traits<numext::int64_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet2l, Packet2d>(const Packet2l& a) {
+  return vcvtq_f64_s64(a);
+}
+
+template <>
+struct type_casting_traits<numext::uint64_t, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+template <>
+EIGEN_STRONG_INLINE Packet2d pcast<Packet2ul, Packet2d>(const Packet2ul& a) {
+  return vcvtq_f64_u64(a);
+}
+
+#endif  // EIGEN_ARCH_ARM64
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TYPE_CASTING_NEON_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/UnaryFunctors.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/UnaryFunctors.h
new file mode 100644
index 0000000000000000000000000000000000000000..67f9dcff0d30bbe1d437464bb65a850143004ad3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/NEON/UnaryFunctors.h
@@ -0,0 +1,63 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NEON_UNARY_FUNCTORS_H
+#define EIGEN_NEON_UNARY_FUNCTORS_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#if EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+/** \internal
+  * \brief Template specialization of the logistic function for Eigen::half.
+  */
+template <>
+struct scalar_logistic_op<Eigen::half> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Eigen::half operator()(const Eigen::half& x) const {
+    // Convert to float and call scalar_logistic_op<float>.
+    const scalar_logistic_op<float> float_op;
+    return Eigen::half(float_op(float(x)));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Eigen::half packetOp(const Eigen::half& x) const {
+    return this->operator()(x);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Packet4hf packetOp(const Packet4hf& x) const {
+    const scalar_logistic_op<float> float_op;
+    return vcvt_f16_f32(float_op.packetOp(vcvt_f32_f16(x)));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Packet8hf packetOp(const Packet8hf& x) const {
+    const scalar_logistic_op<float> float_op;
+    return vcombine_f16(
+      vcvt_f16_f32(float_op.packetOp(vcvt_f32_f16(vget_low_f16(x)))),
+      vcvt_f16_f32(float_op.packetOp(vcvt_high_f32_f16(x))));
+  }
+};
+
+template<>
+struct functor_traits<scalar_logistic_op<Eigen::half>> {
+  enum {
+    Cost = functor_traits<scalar_logistic_op<float>>::Cost,
+    PacketAccess = functor_traits<scalar_logistic_op<float>>::PacketAccess,
+  };
+};
+#endif  // EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_NEON_UNARY_FUNCTORS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SSE/Complex.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SSE/Complex.h
new file mode 100644
index 0000000000000000000000000000000000000000..366daa757f4f31e15fcc9700fe65c5748e248407
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SSE/Complex.h
@@ -0,0 +1,330 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_SSE_H
+#define EIGEN_COMPLEX_SSE_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+//---------- float ----------
+struct Packet2cf
+{
+  EIGEN_STRONG_INLINE Packet2cf() {}
+  EIGEN_STRONG_INLINE explicit Packet2cf(const __m128& a) : v(a) {}
+  Packet4f v;
+};
+
+// Use the packet_traits defined in AVX/PacketMath.h instead if we're going
+// to leverage AVX instructions.
+#ifndef EIGEN_VECTORIZE_AVX
+template<> struct packet_traits<std::complex<float> >  : default_packet_traits
+{
+  typedef Packet2cf type;
+  typedef Packet2cf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasDiv    = 1,
+    HasNegate = 1,
+    HasSqrt   = 1,
+    HasAbs    = 0,
+    HasAbs2   = 0,
+    HasMin    = 0,
+    HasMax    = 0,
+    HasSetLinear = 0,
+    HasBlend  = 1
+  };
+};
+#endif
+
+template<> struct unpacket_traits<Packet2cf> {
+  typedef std::complex<float> type;
+  typedef Packet2cf half;
+  typedef Packet4f as_real;
+  enum {
+    size=2,
+    alignment=Aligned16,
+    vectorizable=true,
+    masked_load_available=false,
+    masked_store_available=false
+  };
+};
+
+template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_add_ps(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_sub_ps(a.v,b.v)); }
+
+template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a)
+{
+  const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000,0x80000000,0x80000000,0x80000000));
+  return Packet2cf(_mm_xor_ps(a.v,mask));
+}
+template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a)
+{
+  const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x00000000,0x80000000,0x00000000,0x80000000));
+  return Packet2cf(_mm_xor_ps(a.v,mask));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
+{
+  #ifdef EIGEN_VECTORIZE_SSE3
+  return Packet2cf(_mm_addsub_ps(_mm_mul_ps(_mm_moveldup_ps(a.v), b.v),
+                                 _mm_mul_ps(_mm_movehdup_ps(a.v),
+                                            vec4f_swizzle1(b.v, 1, 0, 3, 2))));
+//   return Packet2cf(_mm_addsub_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 0, 0, 2, 2), b.v),
+//                                  _mm_mul_ps(vec4f_swizzle1(a.v, 1, 1, 3, 3),
+//                                             vec4f_swizzle1(b.v, 1, 0, 3, 2))));
+  #else
+  const __m128 mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000,0x00000000,0x80000000,0x00000000));
+  return Packet2cf(_mm_add_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 0, 0, 2, 2), b.v),
+                              _mm_xor_ps(_mm_mul_ps(vec4f_swizzle1(a.v, 1, 1, 3, 3),
+                                                    vec4f_swizzle1(b.v, 1, 0, 3, 2)), mask)));
+  #endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf ptrue  <Packet2cf>(const Packet2cf& a) { return Packet2cf(ptrue(Packet4f(a.v))); }
+template<> EIGEN_STRONG_INLINE Packet2cf pand   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_and_ps(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf por    <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_or_ps(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf pxor   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_xor_ps(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(_mm_andnot_ps(b.v,a.v)); }
+
+template<> EIGEN_STRONG_INLINE Packet2cf pload <Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>(&numext::real_ref(*from))); }
+template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>(&numext::real_ref(*from))); }
+
+template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>&  from)
+{
+  const float re = std::real(from);
+  const float im = std::imag(from);
+  return Packet2cf(_mm_set_ps(im, re, im, re));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>* from) { return pset1<Packet2cf>(*from); }
+
+template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *   to, const Packet2cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore(&numext::real_ref(*to), Packet4f(from.v)); }
+template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *   to, const Packet2cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu(&numext::real_ref(*to), Packet4f(from.v)); }
+
+
+template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, Index stride)
+{
+  return Packet2cf(_mm_set_ps(std::imag(from[1*stride]), std::real(from[1*stride]),
+                              std::imag(from[0*stride]), std::real(from[0*stride])));
+}
+
+template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, Index stride)
+{
+  to[stride*0] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 0)),
+                                     _mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 1)));
+  to[stride*1] = std::complex<float>(_mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 2)),
+                                     _mm_cvtss_f32(_mm_shuffle_ps(from.v, from.v, 3)));
+}
+
+template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> *   addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
+
+template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet2cf>(const Packet2cf& a)
+{
+  alignas(alignof(__m64)) std::complex<float> res;
+  _mm_storel_pi((__m64*)&res, a.v);
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a) { return Packet2cf(_mm_castpd_ps(preverse(Packet2d(_mm_castps_pd(a.v))))); }
+
+template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
+{
+  return pfirst(Packet2cf(_mm_add_ps(a.v, _mm_movehl_ps(a.v,a.v))));
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a)
+{
+  return pfirst(pmul(a, Packet2cf(_mm_movehl_ps(a.v,a.v))));
+}
+
+EIGEN_STRONG_INLINE Packet2cf pcplxflip/* <Packet2cf> */(const Packet2cf& x)
+{
+  return Packet2cf(vec4f_swizzle1(x.v, 1, 0, 3, 2));
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
+
+template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
+{
+  return pdiv_complex(a, b);
+}
+
+//---------- double ----------
+struct Packet1cd
+{
+  EIGEN_STRONG_INLINE Packet1cd() {}
+  EIGEN_STRONG_INLINE explicit Packet1cd(const __m128d& a) : v(a) {}
+  Packet2d v;
+};
+
+// Use the packet_traits defined in AVX/PacketMath.h instead if we're going
+// to leverage AVX instructions.
+#ifndef EIGEN_VECTORIZE_AVX
+template<> struct packet_traits<std::complex<double> >  : default_packet_traits
+{
+  typedef Packet1cd type;
+  typedef Packet1cd half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 0,
+    size = 1,
+
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasDiv    = 1,
+    HasNegate = 1,
+    HasSqrt   = 1,
+    HasAbs    = 0,
+    HasAbs2   = 0,
+    HasMin    = 0,
+    HasMax    = 0,
+    HasSetLinear = 0
+  };
+};
+#endif
+
+template<> struct unpacket_traits<Packet1cd> {
+  typedef std::complex<double> type;
+  typedef Packet1cd half;
+  typedef Packet2d as_real;
+  enum {
+    size=1,
+    alignment=Aligned16,
+    vectorizable=true,
+    masked_load_available=false,
+    masked_store_available=false
+  };
+};
+
+template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_add_pd(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_sub_pd(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate(Packet2d(a.v))); }
+template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a)
+{
+  const __m128d mask = _mm_castsi128_pd(_mm_set_epi32(0x80000000,0x0,0x0,0x0));
+  return Packet1cd(_mm_xor_pd(a.v,mask));
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
+{
+  #ifdef EIGEN_VECTORIZE_SSE3
+  return Packet1cd(_mm_addsub_pd(_mm_mul_pd(_mm_movedup_pd(a.v), b.v),
+                                 _mm_mul_pd(vec2d_swizzle1(a.v, 1, 1),
+                                            vec2d_swizzle1(b.v, 1, 0))));
+  #else
+  const __m128d mask = _mm_castsi128_pd(_mm_set_epi32(0x0,0x0,0x80000000,0x0));
+  return Packet1cd(_mm_add_pd(_mm_mul_pd(vec2d_swizzle1(a.v, 0, 0), b.v),
+                              _mm_xor_pd(_mm_mul_pd(vec2d_swizzle1(a.v, 1, 1),
+                                                    vec2d_swizzle1(b.v, 1, 0)), mask)));
+  #endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cd ptrue  <Packet1cd>(const Packet1cd& a) { return Packet1cd(ptrue(Packet2d(a.v))); }
+template<> EIGEN_STRONG_INLINE Packet1cd pand   <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_and_pd(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet1cd por    <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_or_pd(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet1cd pxor   <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_xor_pd(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet1cd pandnot<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(_mm_andnot_pd(b.v,a.v)); }
+
+// FIXME force unaligned load, this is a temporary fix
+template<> EIGEN_STRONG_INLINE Packet1cd pload <Packet1cd>(const std::complex<double>* from)
+{ EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from)); }
+template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from)
+{ EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from)); }
+template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>&  from)
+{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); }
+
+template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>* from) { return pset1<Packet1cd>(*from); }
+
+// FIXME force unaligned store, this is a temporary fix
+template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, Packet2d(from.v)); }
+template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, Packet2d(from.v)); }
+
+template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> *   addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
+
+template<> EIGEN_STRONG_INLINE std::complex<double>  pfirst<Packet1cd>(const Packet1cd& a)
+{
+  EIGEN_ALIGN16 double res[2];
+  _mm_store_pd(res, a.v);
+  return std::complex<double>(res[0],res[1]);
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a)
+{
+  return pfirst(a);
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a)
+{
+  return pfirst(a);
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
+
+template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
+{
+  return pdiv_complex(a, b);
+}
+
+EIGEN_STRONG_INLINE Packet1cd pcplxflip/* <Packet1cd> */(const Packet1cd& x)
+{
+  return Packet1cd(preverse(Packet2d(x.v)));
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet2cf,2>& kernel) {
+  __m128d w1 = _mm_castps_pd(kernel.packet[0].v);
+  __m128d w2 = _mm_castps_pd(kernel.packet[1].v);
+
+  __m128 tmp = _mm_castpd_ps(_mm_unpackhi_pd(w1, w2));
+  kernel.packet[0].v = _mm_castpd_ps(_mm_unpacklo_pd(w1, w2));
+  kernel.packet[1].v = tmp;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b)
+{
+  __m128 eq = _mm_cmpeq_ps(a.v, b.v);
+  return Packet2cf(pand<Packet4f>(eq, vec4f_swizzle1(eq, 1, 0, 3, 2)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b)
+{
+  __m128d eq = _mm_cmpeq_pd(a.v, b.v);
+  return Packet1cd(pand<Packet2d>(eq, vec2d_swizzle1(eq, 1, 0)));
+}
+
+template<>  EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket, const Packet2cf& elsePacket) {
+  __m128d result = pblend<Packet2d>(ifPacket, _mm_castps_pd(thenPacket.v), _mm_castps_pd(elsePacket.v));
+  return Packet2cf(_mm_castpd_ps(result));
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cd psqrt<Packet1cd>(const Packet1cd& a) {
+  return psqrt_complex<Packet1cd>(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf psqrt<Packet2cf>(const Packet2cf& a) {
+  return psqrt_complex<Packet2cf>(a);
+}
+
+} // end namespace internal
+} // end namespace Eigen
+
+#endif // EIGEN_COMPLEX_SSE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SSE/MathFunctions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SSE/MathFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..f0ddbe6cfa5c78bf0f75967f83afb63f758c7524
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SSE/MathFunctions.h
@@ -0,0 +1,84 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007 Julien Pommier
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* The sin and cos and functions of this file come from
+ * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
+ */
+
+#ifndef EIGEN_MATH_FUNCTIONS_SSE_H
+#define EIGEN_MATH_FUNCTIONS_SSE_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_FLOAT(Packet4f)
+EIGEN_INSTANTIATE_GENERIC_MATH_FUNCS_DOUBLE(Packet2d)
+
+// Notice that for newer processors, it is counterproductive to use Newton
+// iteration for square root. In particular, Skylake and Zen2 processors
+// have approximately doubled throughput of the _mm_sqrt_ps instruction
+// compared to their predecessors.
+template<>EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet4f psqrt<Packet4f>(const Packet4f& x) { return _mm_sqrt_ps(x); }
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet2d psqrt<Packet2d>(const Packet2d& x) { return _mm_sqrt_pd(x); }
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet16b psqrt<Packet16b>(const Packet16b& x) { return x; }
+
+#if EIGEN_FAST_MATH
+// Even on Skylake, using Newton iteration is a win for reciprocal square root.
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_UNUSED
+Packet4f prsqrt<Packet4f>(const Packet4f& x) {
+  return generic_rsqrt_newton_step<Packet4f, /*Steps=*/1>::run(x, _mm_rsqrt_ps(x));
+}
+
+#ifdef EIGEN_VECTORIZE_FMA
+// Trying to speed up reciprocal using Newton-Raphson is counterproductive
+// unless FMA is available. Without FMA pdiv(pset1<Packet>(Scalar(1),a)) is
+// 30% faster.
+template<> EIGEN_STRONG_INLINE Packet4f preciprocal<Packet4f>(const Packet4f& x) {
+  return generic_reciprocal_newton_step<Packet4f, /*Steps=*/1>::run(x, _mm_rcp_ps(x));
+}
+#endif
+
+#endif
+
+} // end namespace internal
+
+namespace numext {
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+float sqrt(const float &x)
+{
+  return internal::pfirst(internal::Packet4f(_mm_sqrt_ss(_mm_set_ss(x))));
+}
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+double sqrt(const double &x)
+{
+#if EIGEN_COMP_GNUC_STRICT
+  // This works around a GCC bug generating poor code for _mm_sqrt_pd
+  // See https://gitlab.com/libeigen/eigen/commit/8dca9f97e38970
+  return internal::pfirst(internal::Packet2d(__builtin_ia32_sqrtsd(_mm_set_sd(x))));
+#else
+  return internal::pfirst(internal::Packet2d(_mm_sqrt_pd(_mm_set_sd(x))));
+#endif
+}
+
+} // end namespace numex
+
+} // end namespace Eigen
+
+#endif // EIGEN_MATH_FUNCTIONS_SSE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SSE/PacketMath.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SSE/PacketMath.h
new file mode 100644
index 0000000000000000000000000000000000000000..6240f38dda0dc6e2b37fe3cbfa9ec6aed3189929
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SSE/PacketMath.h
@@ -0,0 +1,1876 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_SSE_H
+#define EIGEN_PACKET_MATH_SSE_H
+
+#include <cstdint>
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+#if !defined(EIGEN_VECTORIZE_AVX) && !defined(EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS)
+// 32 bits =>  8 registers
+// 64 bits => 16 registers
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS (2*sizeof(void*))
+#endif
+
+#ifdef EIGEN_VECTORIZE_FMA
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+#endif
+
+#if ((defined EIGEN_VECTORIZE_AVX) && (EIGEN_COMP_GNUC_STRICT || EIGEN_COMP_MINGW || EIGEN_COMP_LCC) && (__GXX_ABI_VERSION < 1004)) || EIGEN_OS_QNX
+// With GCC's default ABI version, a __m128 or __m256 are the same types and therefore we cannot
+// have overloads for both types without linking error.
+// One solution is to increase ABI version using -fabi-version=4 (or greater).
+// Otherwise, we workaround this inconvenience by wrapping 128bit types into the following helper
+// structure:
+typedef eigen_packet_wrapper<__m128>  Packet4f;
+typedef eigen_packet_wrapper<__m128d> Packet2d;
+#else
+typedef __m128  Packet4f;
+typedef __m128d Packet2d;
+#endif
+
+typedef eigen_packet_wrapper<__m128i, 0> Packet4i;
+typedef eigen_packet_wrapper<__m128i, 1> Packet16b;
+typedef eigen_packet_wrapper<__m128i, 4> Packet4ui;
+
+template<> struct is_arithmetic<__m128>  { enum { value = true }; };
+template<> struct is_arithmetic<__m128i> { enum { value = true }; };
+template<> struct is_arithmetic<__m128d> { enum { value = true }; };
+template<> struct is_arithmetic<Packet4i>  { enum { value = true }; };
+// Note that `Packet4ui` uses the underlying type `__m128i`, which is
+// interpreted as a vector of _signed_ `int32`s, which breaks some arithmetic
+// operations used in `GenericPacketMath.h`.
+template<> struct is_arithmetic<Packet4ui> { enum { value = false }; };
+template<> struct is_arithmetic<Packet16b>  { enum { value = true }; };
+
+template<int p, int q, int r, int s>
+struct shuffle_mask{
+ enum { mask = (s)<<6|(r)<<4|(q)<<2|(p) };
+};
+
+// TODO: change the implementation of all swizzle* ops from macro to template,
+#define vec4f_swizzle1(v,p,q,r,s) \
+  Packet4f(_mm_castsi128_ps(_mm_shuffle_epi32( _mm_castps_si128(v), (shuffle_mask<p,q,r,s>::mask))))
+
+#define vec4i_swizzle1(v,p,q,r,s) \
+  Packet4i(_mm_shuffle_epi32( v, (shuffle_mask<p,q,r,s>::mask)))
+
+#define vec4ui_swizzle1(v, p, q, r, s) \
+  Packet4ui(vec4i_swizzle1(v,p,q,r,s))
+
+#define vec2d_swizzle1(v,p,q) \
+  Packet2d(_mm_castsi128_pd(_mm_shuffle_epi32( _mm_castpd_si128(v), (shuffle_mask<2*p,2*p+1,2*q,2*q+1>::mask))))
+
+#define vec4f_swizzle2(a,b,p,q,r,s) \
+  Packet4f(_mm_shuffle_ps( (a), (b), (shuffle_mask<p,q,r,s>::mask)))
+
+#define vec4i_swizzle2(a,b,p,q,r,s) \
+  Packet4i(_mm_castps_si128( (_mm_shuffle_ps( _mm_castsi128_ps(a), _mm_castsi128_ps(b), (shuffle_mask<p,q,r,s>::mask)))))
+
+#define vec4ui_swizzle2(a,b,p,q,r,s) \
+  Packet4i(vec4i_swizzle2(a,b,p,q,r,s))
+
+EIGEN_STRONG_INLINE Packet4f vec4f_movelh(const Packet4f& a, const Packet4f& b)
+{
+  return Packet4f(_mm_movelh_ps(a,b));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_movehl(const Packet4f& a, const Packet4f& b)
+{
+  return Packet4f(_mm_movehl_ps(a,b));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_unpacklo(const Packet4f& a, const Packet4f& b)
+{
+  return Packet4f(_mm_unpacklo_ps(a,b));
+}
+EIGEN_STRONG_INLINE Packet4f vec4f_unpackhi(const Packet4f& a, const Packet4f& b)
+{
+  return Packet4f(_mm_unpackhi_ps(a,b));
+}
+#define vec4f_duplane(a,p) \
+  vec4f_swizzle2(a,a,p,p,p,p)
+
+#define vec2d_swizzle2(a,b,mask) \
+  Packet2d(_mm_shuffle_pd(a,b,mask))
+
+EIGEN_STRONG_INLINE Packet2d vec2d_unpacklo(const Packet2d& a, const Packet2d& b)
+{
+  return Packet2d(_mm_unpacklo_pd(a,b));
+}
+EIGEN_STRONG_INLINE Packet2d vec2d_unpackhi(const Packet2d& a, const Packet2d& b)
+{
+  return Packet2d(_mm_unpackhi_pd(a,b));
+}
+#define vec2d_duplane(a,p) \
+  vec2d_swizzle2(a,a,(p<<1)|p)
+
+#define EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
+  const Packet4f p4f_##NAME = pset1<Packet4f>(X)
+
+#define EIGEN_DECLARE_CONST_Packet2d(NAME,X) \
+  const Packet2d p2d_##NAME = pset1<Packet2d>(X)
+
+#define EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
+  const Packet4f p4f_##NAME = pset1frombits<Packet4f>(X)
+
+#define EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
+  const Packet4i p4i_##NAME = pset1<Packet4i>(X)
+
+#define EIGEN_DECLARE_CONST_Packet4ui(NAME, X) const Packet4ui p4ui_##NAME = pset1<Packet4ui>(X)
+
+// Use the packet_traits defined in AVX/PacketMath.h instead if we're going
+// to leverage AVX instructions.
+#ifndef EIGEN_VECTORIZE_AVX
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet4f type;
+  typedef Packet4f half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasCmp  = 1,
+    HasDiv = 1,
+    HasReciprocal = EIGEN_FAST_MATH,
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasACos = 1,
+    HasASin = 1,
+    HasATan = 1,
+    HasATanh = 1,
+    HasLog = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasNdtri = 1,
+    HasExp = 1,
+    HasBessel = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH,
+    HasBlend = 1,
+    HasCeil = 1,
+    HasFloor = 1,
+#ifdef EIGEN_VECTORIZE_SSE4_1
+    HasRound = 1,
+#endif
+    HasRint = 1,
+    HasSign = 0   // The manually vectorized version is slightly slower for SSE.
+  };
+};
+template <>
+struct packet_traits<double> : default_packet_traits {
+  typedef Packet2d type;
+  typedef Packet2d half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size=2,
+
+    HasCmp  = 1,
+    HasDiv  = 1,
+    HasLog  = 1,
+    HasExp  = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasATan = 1,
+    HasBlend = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+#ifdef EIGEN_VECTORIZE_SSE4_1
+    HasRound = 1,
+#endif
+    HasRint = 1
+  };
+};
+template<> struct packet_traits<int>    : default_packet_traits
+{
+  typedef Packet4i type;
+  typedef Packet4i half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    HasCmp = 1,
+    HasDiv=1,
+    size=4,
+
+    HasShift = 1,
+    HasBlend = 1
+  };
+};
+template<> struct packet_traits<uint32_t> : default_packet_traits
+{
+  typedef Packet4ui type;
+  typedef Packet4ui half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasDiv = 0,
+    HasNegate = 0,
+    HasSqrt = 0,
+    HasCmp = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasShift = 1,
+    HasBlend = 1
+  };
+};
+#endif
+template<> struct packet_traits<bool> : default_packet_traits
+{
+  typedef Packet16b type;
+  typedef Packet16b half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size=16,
+
+    HasAdd       = 1,
+    HasSub       = 1,
+    HasCmp       = 1, // note -- only pcmp_eq is defined
+    HasShift     = 0,
+    HasMul       = 1,
+    HasNegate    = 1,
+    HasAbs       = 0,
+    HasAbs2      = 0,
+    HasMin       = 0,
+    HasMax       = 0,
+    HasConj      = 0,
+    HasSqrt      = 1,
+    HasSign      = 0   // Don't try to vectorize psign<bool> = identity.
+  };
+};
+
+template<> struct unpacket_traits<Packet4f> {
+  typedef float     type;
+  typedef Packet4f  half;
+  typedef Packet4i  integer_packet;
+  enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+template<> struct unpacket_traits<Packet2d> {
+  typedef double    type;
+  typedef Packet2d  half;
+  enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+template<> struct unpacket_traits<Packet4i> {
+  typedef int       type;
+  typedef Packet4i  half;
+  enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+template<> struct unpacket_traits<Packet4ui> {
+  typedef uint32_t type;
+  typedef Packet4ui half;
+  enum {size = 4, alignment = Aligned16, vectorizable = true, masked_load_available = false, masked_store_available = false};
+};
+template<> struct unpacket_traits<Packet16b> {
+  typedef bool       type;
+  typedef Packet16b  half;
+  enum {size=16, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
+};
+
+#ifndef EIGEN_VECTORIZE_AVX
+template<> struct scalar_div_cost<float,true> { enum { value = 7 }; };
+template<> struct scalar_div_cost<double,true> { enum { value = 8 }; };
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float&  from) { return _mm_set_ps1(from); }
+template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) { return _mm_set1_pd(from); }
+template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int&    from) { return _mm_set1_epi32(from); }
+template<> EIGEN_STRONG_INLINE Packet4ui pset1<Packet4ui>(const uint32_t& from) { return _mm_set1_epi32(numext::bit_cast<int32_t>(from)); }
+template<> EIGEN_STRONG_INLINE Packet16b pset1<Packet16b>(const bool&    from) { return _mm_set1_epi8(static_cast<char>(from)); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pset1frombits<Packet4f>(unsigned int from) { return _mm_castsi128_ps(pset1<Packet4i>(from)); }
+template<> EIGEN_STRONG_INLINE Packet2d pset1frombits<Packet2d>(uint64_t from) { return _mm_castsi128_pd(_mm_set1_epi64x(from)); }
+
+template<> EIGEN_STRONG_INLINE Packet4f peven_mask(const Packet4f& /*a*/) { return _mm_castsi128_ps(_mm_set_epi32(0, -1, 0, -1)); }
+template<> EIGEN_STRONG_INLINE Packet4i peven_mask(const Packet4i& /*a*/) { return _mm_set_epi32(0, -1, 0, -1); }
+template<> EIGEN_STRONG_INLINE Packet4ui peven_mask(const Packet4ui& /*a*/) { return _mm_set_epi32(0, -1, 0, -1); }
+template<> EIGEN_STRONG_INLINE Packet2d peven_mask(const Packet2d& /*a*/) { return _mm_castsi128_pd(_mm_set_epi32(0, 0, -1, -1)); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pzero(const Packet4f& /*a*/) { return _mm_setzero_ps(); }
+template<> EIGEN_STRONG_INLINE Packet2d pzero(const Packet2d& /*a*/) { return _mm_setzero_pd(); }
+template<> EIGEN_STRONG_INLINE Packet4i pzero(const Packet4i& /*a*/) { return _mm_setzero_si128(); }
+template<> EIGEN_STRONG_INLINE Packet4ui pzero(const Packet4ui& /*a*/) { return _mm_setzero_si128(); }
+
+// GCC generates a shufps instruction for _mm_set1_ps/_mm_load1_ps instead of the more efficient pshufd instruction.
+// However, using inrinsics for pset1 makes gcc to generate crappy code in some cases (see bug 203)
+// Using inline assembly is also not an option because then gcc fails to reorder properly the instructions.
+// Therefore, we introduced the pload1 functions to be used in product kernels for which bug 203 does not apply.
+// Also note that with AVX, we want it to generate a vbroadcastss.
+#if EIGEN_COMP_GNUC_STRICT && (!defined __AVX__)
+template<> EIGEN_STRONG_INLINE Packet4f pload1<Packet4f>(const float *from) {
+  return vec4f_swizzle1(_mm_load_ss(from),0,0,0,0);
+}
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet4f plset<Packet4f>(const float& a) { return _mm_add_ps(pset1<Packet4f>(a), _mm_set_ps(3,2,1,0)); }
+template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) { return _mm_add_pd(pset1<Packet2d>(a),_mm_set_pd(1,0)); }
+template<> EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int& a) { return _mm_add_epi32(pset1<Packet4i>(a),_mm_set_epi32(3,2,1,0)); }
+template<> EIGEN_STRONG_INLINE Packet4ui plset<Packet4ui>(const uint32_t& a) { return _mm_add_epi32(pset1<Packet4ui>(a), _mm_set_epi32(3, 2, 1, 0)); }
+
+template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_add_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_add_pd(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_add_epi32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui padd<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return _mm_add_epi32(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet16b padd<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_or_si128(a,b); }
+
+template<typename Packet> EIGEN_STRONG_INLINE Packet padds(const Packet& a, const Packet& b);
+template<> EIGEN_STRONG_INLINE Packet4f padds<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_add_ss(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2d padds<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_add_sd(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_sub_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_sub_pd(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_sub_epi32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui psub<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return _mm_sub_epi32(a, b); }
+template<> EIGEN_STRONG_INLINE Packet16b psub<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_xor_si128(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b);
+template<> EIGEN_STRONG_INLINE Packet4f paddsub<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+#ifdef EIGEN_VECTORIZE_SSE3
+  return _mm_addsub_ps(a,b);
+#else
+  const Packet4f mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000,0x0,0x80000000,0x0));
+  return padd(a, pxor(mask, b));
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& , const Packet2d& );
+template<> EIGEN_STRONG_INLINE Packet2d paddsub<Packet2d>(const Packet2d& a, const Packet2d& b) 
+{
+#ifdef EIGEN_VECTORIZE_SSE3
+  return _mm_addsub_pd(a,b); 
+#else
+  const Packet2d mask = _mm_castsi128_pd(_mm_setr_epi32(0x0,0x80000000,0x0,0x0)); 
+  return padd(a, pxor(mask, b));
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a)
+{
+  const Packet4f mask = _mm_castsi128_ps(_mm_setr_epi32(0x80000000,0x80000000,0x80000000,0x80000000));
+  return _mm_xor_ps(a,mask);
+}
+template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a)
+{
+  const Packet2d mask = _mm_castsi128_pd(_mm_setr_epi32(0x0,0x80000000,0x0,0x80000000));
+  return _mm_xor_pd(a,mask);
+}
+template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a)
+{
+  return psub(Packet4i(_mm_setr_epi32(0,0,0,0)), a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet16b pnegate(const Packet16b& a)
+{
+  return psub(pset1<Packet16b>(false), a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pconj(const Packet4f& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_mul_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_mul_pd(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b)
+{
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_mullo_epi32(a,b);
+#else
+  // this version is slightly faster than 4 scalar products
+  return vec4i_swizzle1(
+            vec4i_swizzle2(
+              _mm_mul_epu32(a,b),
+              _mm_mul_epu32(vec4i_swizzle1(a,1,0,3,2),
+                            vec4i_swizzle1(b,1,0,3,2)),
+              0,2,0,2),
+            0,2,1,3);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet4ui pmul<Packet4ui>(const Packet4ui& a, const Packet4ui& b)
+{
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_mullo_epi32(a,b);
+#else
+  // this version is slightly faster than 4 scalar products
+  return vec4ui_swizzle1(
+            vec4ui_swizzle2(
+              _mm_mul_epu32(a,b),
+              _mm_mul_epu32(vec4ui_swizzle1(a,1,0,3,2),
+                            vec4ui_swizzle1(b,1,0,3,2)),
+              0,2,0,2),
+            0,2,1,3);
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet16b pmul<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_and_si128(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_div_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_div_pd(a,b); }
+
+template <>
+EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& a,
+                                            const Packet4i& b) {
+#ifdef EIGEN_VECTORIZE_AVX
+  return _mm256_cvttpd_epi32(
+      _mm256_div_pd(_mm256_cvtepi32_pd(a), _mm256_cvtepi32_pd(b)));
+#else
+  __m128i q_lo = _mm_cvttpd_epi32(_mm_div_pd(_mm_cvtepi32_pd(a), _mm_cvtepi32_pd(b)));
+  __m128i q_hi =
+      _mm_cvttpd_epi32(_mm_div_pd(_mm_cvtepi32_pd(vec4i_swizzle1(a, 2, 3, 0, 1)),
+                                 _mm_cvtepi32_pd(vec4i_swizzle1(b, 2, 3, 0, 1))));
+  return vec4i_swizzle1(_mm_unpacklo_epi32(q_lo, q_hi), 0, 2, 1, 3);
+#endif
+}
+
+
+// for some weird raisons, it has to be overloaded for packet of integers
+template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return padd(pmul(a,b), c); }
+template<> EIGEN_STRONG_INLINE Packet4ui pmadd(const Packet4ui& a, const Packet4ui& b, const Packet4ui& c) { return padd(pmul(a, b), c); }
+#ifdef EIGEN_VECTORIZE_FMA
+template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return _mm_fmadd_ps(a,b,c); }
+template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return _mm_fmadd_pd(a,b,c); }
+template<> EIGEN_STRONG_INLINE Packet4f pmsub(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return _mm_fmsub_ps(a,b,c); }
+template<> EIGEN_STRONG_INLINE Packet2d pmsub(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return _mm_fmsub_pd(a,b,c); }
+template<> EIGEN_STRONG_INLINE Packet4f pnmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return _mm_fnmadd_ps(a,b,c); }
+template<> EIGEN_STRONG_INLINE Packet2d pnmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return _mm_fnmadd_pd(a,b,c); }
+template<> EIGEN_STRONG_INLINE Packet4f pnmsub(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return _mm_fnmsub_ps(a,b,c); }
+template<> EIGEN_STRONG_INLINE Packet2d pnmsub(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return _mm_fnmsub_pd(a,b,c); }
+
+template<typename Packet> EIGEN_STRONG_INLINE Packet pmadds(const Packet& a, const Packet& b, const Packet& c);
+template<> EIGEN_STRONG_INLINE Packet4f pmadds<Packet4f>(const Packet4f& a, const Packet4f& b, const Packet4f& c) { return _mm_fmadd_ss(a,b,c); }
+template<> EIGEN_STRONG_INLINE Packet2d pmadds<Packet2d>(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return _mm_fmadd_sd(a,b,c); }
+#endif
+
+#ifdef EIGEN_VECTORIZE_SSE4_1
+template<> EIGEN_DEVICE_FUNC inline Packet4f pselect(const Packet4f& mask, const Packet4f& a, const Packet4f& b) {
+  return _mm_blendv_ps(b,a,mask);
+}
+
+template<> EIGEN_DEVICE_FUNC inline Packet4i pselect(const Packet4i& mask, const Packet4i& a, const Packet4i& b) {
+  return _mm_castps_si128(_mm_blendv_ps(_mm_castsi128_ps(b),_mm_castsi128_ps(a),_mm_castsi128_ps(mask)));
+}
+
+template<> EIGEN_DEVICE_FUNC inline Packet4ui pselect(const Packet4ui& mask, const Packet4ui& a, const Packet4ui& b) {
+  return _mm_castps_si128(_mm_blendv_ps(_mm_castsi128_ps(b),_mm_castsi128_ps(a),_mm_castsi128_ps(mask)));
+}
+
+template<> EIGEN_DEVICE_FUNC inline Packet2d pselect(const Packet2d& mask, const Packet2d& a, const Packet2d& b) {  return _mm_blendv_pd(b,a,mask); }
+
+template<> EIGEN_DEVICE_FUNC inline Packet16b pselect(const Packet16b& mask, const Packet16b& a, const Packet16b& b) {
+  return _mm_blendv_epi8(b,a,mask);
+}
+#else
+template<> EIGEN_DEVICE_FUNC inline Packet16b pselect(const Packet16b& mask, const Packet16b& a, const Packet16b& b) {
+  Packet16b a_part = _mm_and_si128(mask, a);
+  Packet16b b_part = _mm_andnot_si128(mask, b);
+  return _mm_or_si128(a_part, b_part);
+}
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet4i ptrue<Packet4i>(const Packet4i& a) { return _mm_cmpeq_epi32(a, a); }
+template<> EIGEN_STRONG_INLINE Packet16b ptrue<Packet16b>(const Packet16b& a) { return _mm_cmpeq_epi8(a, a); }
+template<> EIGEN_STRONG_INLINE Packet4f
+ptrue<Packet4f>(const Packet4f& a) {
+  Packet4i b = _mm_castps_si128(a);
+  return _mm_castsi128_ps(_mm_cmpeq_epi32(b, b));
+}
+template<> EIGEN_STRONG_INLINE Packet2d
+ptrue<Packet2d>(const Packet2d& a) {
+  Packet4i b = _mm_castpd_si128(a);
+  return _mm_castsi128_pd(_mm_cmpeq_epi32(b, b));
+}
+
+
+template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_and_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_and_pd(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_and_si128(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui pand<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return _mm_and_si128(a, b); }
+template<> EIGEN_STRONG_INLINE Packet16b pand<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_and_si128(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_or_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_or_pd(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_or_si128(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui por<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return _mm_or_si128(a, b); }
+template<> EIGEN_STRONG_INLINE Packet16b por<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_or_si128(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_xor_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_xor_pd(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_xor_si128(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui pxor<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return _mm_xor_si128(a, b); }
+template<> EIGEN_STRONG_INLINE Packet16b pxor<Packet16b>(const Packet16b& a, const Packet16b& b) { return _mm_xor_si128(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) { return _mm_andnot_ps(b,a); }
+template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) { return _mm_andnot_pd(b,a); }
+template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) { return _mm_andnot_si128(b,a); }
+template<> EIGEN_STRONG_INLINE Packet4ui pandnot<Packet4ui>(const Packet4ui& a, const Packet4ui& b) { return _mm_andnot_si128(b, a); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pcmp_le(const Packet4f& a, const Packet4f& b) { return _mm_cmple_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt(const Packet4f& a, const Packet4f& b) { return _mm_cmplt_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4f pcmp_lt_or_nan(const Packet4f& a, const Packet4f& b) { return _mm_cmpnge_ps(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4f pcmp_eq(const Packet4f& a, const Packet4f& b) { return _mm_cmpeq_ps(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pcmp_le(const Packet2d& a, const Packet2d& b) { return _mm_cmple_pd(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt(const Packet2d& a, const Packet2d& b) { return _mm_cmplt_pd(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2d pcmp_lt_or_nan(const Packet2d& a, const Packet2d& b) { return _mm_cmpnge_pd(a,b); }
+template<> EIGEN_STRONG_INLINE Packet2d pcmp_eq(const Packet2d& a, const Packet2d& b) { return _mm_cmpeq_pd(a,b); }
+
+template<> EIGEN_STRONG_INLINE Packet4i pcmp_lt(const Packet4i& a, const Packet4i& b) { return _mm_cmplt_epi32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i pcmp_eq(const Packet4i& a, const Packet4i& b) { return _mm_cmpeq_epi32(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4ui pcmp_eq(const Packet4ui& a, const Packet4ui& b) { return _mm_cmpeq_epi32(a, b); }
+template<> EIGEN_STRONG_INLINE Packet16b pcmp_eq(const Packet16b& a, const Packet16b& b) { return _mm_cmpeq_epi8(a,b); }
+template<> EIGEN_STRONG_INLINE Packet4i pcmp_le(const Packet4i& a, const Packet4i& b) { return por(pcmp_lt(a,b), pcmp_eq(a,b)); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6,3,0)
+// There appears to be a bug in GCC, by which the optimizer may
+// flip the argument order in calls to _mm_min_ps, so we have to
+// resort to inline ASM here. This is supposed to be fixed in gcc6.3,
+// see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
+#ifdef EIGEN_VECTORIZE_AVX
+  Packet4f res;
+  asm("vminps %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
+#else
+  Packet4f res = b;
+  asm("minps %[a], %[res]" : [res] "+x" (res) : [a] "x" (a));
+#endif
+  return res;
+#else
+  // Arguments are reversed to match NaN propagation behavior of std::min.
+  return _mm_min_ps(b, a);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6,3,0)
+// There appears to be a bug in GCC, by which the optimizer may
+// flip the argument order in calls to _mm_min_pd, so we have to
+// resort to inline ASM here. This is supposed to be fixed in gcc6.3,
+// see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
+#ifdef EIGEN_VECTORIZE_AVX
+  Packet2d res;
+  asm("vminpd %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
+#else
+  Packet2d res = b;
+  asm("minpd %[a], %[res]" : [res] "+x" (res) : [a] "x" (a));
+#endif
+  return res;
+#else
+  // Arguments are reversed to match NaN propagation behavior of std::min.
+  return _mm_min_pd(b, a);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b)
+{
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_min_epi32(a,b);
+#else
+  // after some bench, this version *is* faster than a scalar implementation
+  Packet4i mask = _mm_cmplt_epi32(a,b);
+  return _mm_or_si128(_mm_and_si128(mask,a),_mm_andnot_si128(mask,b));
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet4ui pmin<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_min_epu32(a, b);
+#else
+  return padd((Packet4ui)pmin((Packet4i)psub(a, pset1<Packet4ui>(0x80000000UL)),
+                              (Packet4i)psub(b, pset1<Packet4ui>(0x80000000UL))),
+              pset1<Packet4ui>(0x80000000UL));
+#endif
+}
+
+
+template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6,3,0)
+// There appears to be a bug in GCC, by which the optimizer may
+// flip the argument order in calls to _mm_max_ps, so we have to
+// resort to inline ASM here. This is supposed to be fixed in gcc6.3,
+// see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
+#ifdef EIGEN_VECTORIZE_AVX
+  Packet4f res;
+  asm("vmaxps %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
+#else
+  Packet4f res = b;
+  asm("maxps %[a], %[res]" : [res] "+x" (res) : [a] "x" (a));
+#endif
+  return res;
+#else
+  // Arguments are reversed to match NaN propagation behavior of std::max.
+  return _mm_max_ps(b, a);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) {
+#if EIGEN_GNUC_STRICT_LESS_THAN(6,3,0)
+// There appears to be a bug in GCC, by which the optimizer may
+// flip the argument order in calls to _mm_max_pd, so we have to
+// resort to inline ASM here. This is supposed to be fixed in gcc6.3,
+// see also: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=72867
+#ifdef EIGEN_VECTORIZE_AVX
+  Packet2d res;
+  asm("vmaxpd %[a], %[b], %[res]" : [res] "=x" (res) : [a] "x" (a), [b] "x" (b));
+#else
+  Packet2d res = b;
+  asm("maxpd %[a], %[res]" : [res] "+x" (res) : [a] "x" (a));
+#endif
+  return res;
+#else
+  // Arguments are reversed to match NaN propagation behavior of std::max.
+  return _mm_max_pd(b, a);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b)
+{
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_max_epi32(a,b);
+#else
+  // after some bench, this version *is* faster than a scalar implementation
+  Packet4i mask = _mm_cmpgt_epi32(a,b);
+  return _mm_or_si128(_mm_and_si128(mask,a),_mm_andnot_si128(mask,b));
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet4ui pmax<Packet4ui>(const Packet4ui& a, const Packet4ui& b) {
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_max_epu32(a, b);
+#else
+  return padd((Packet4ui)pmax((Packet4i)psub(a, pset1<Packet4ui>(0x80000000UL)),
+                              (Packet4i)psub(b, pset1<Packet4ui>(0x80000000UL))),
+              pset1<Packet4ui>(0x80000000UL));
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet4ui pcmp_lt(const Packet4ui& a, const Packet4ui& b) {
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return pxor(pcmp_eq(a, pmax(a, b)), ptrue(a));
+#else
+  return (Packet4ui)pcmp_lt((Packet4i)psub(a, pset1<Packet4ui>(0x80000000UL)),
+                            (Packet4i)psub(b, pset1<Packet4ui>(0x80000000UL)));
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet4ui pcmp_le(const Packet4ui& a, const Packet4ui& b) {
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return pcmp_eq(a, pmin(a, b));
+#else
+  return (Packet4ui)pcmp_le((Packet4i)psub(a, pset1<Packet4ui>(0x80000000UL)),
+                            (Packet4i)psub(b, pset1<Packet4ui>(0x80000000UL)));
+#endif
+}
+
+template <typename Packet, typename Op>
+EIGEN_STRONG_INLINE Packet pminmax_propagate_numbers(const Packet& a, const Packet& b, Op op) {
+  // In this implementation, we take advantage of the fact that pmin/pmax for SSE
+  // always return a if either a or b is NaN.
+  Packet not_nan_mask_a = pcmp_eq(a, a);
+  Packet m = op(a, b);
+  return pselect<Packet>(not_nan_mask_a, m, b);
+}
+
+template <typename Packet, typename Op>
+EIGEN_STRONG_INLINE Packet pminmax_propagate_nan(const Packet& a, const Packet& b, Op op) {
+  // In this implementation, we take advantage of the fact that pmin/pmax for SSE
+  // always return a if either a or b is NaN.
+  Packet not_nan_mask_a = pcmp_eq(a, a);
+  Packet m = op(b, a);
+  return pselect<Packet>(not_nan_mask_a, m, a);
+}
+
+// Add specializations for min/max with prescribed NaN progation.
+template<>
+EIGEN_STRONG_INLINE Packet4f pmin<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return pminmax_propagate_numbers(a, b, pmin<Packet4f>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet2d pmin<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return pminmax_propagate_numbers(a, b, pmin<Packet2d>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet4f pmax<PropagateNumbers, Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return pminmax_propagate_numbers(a, b, pmax<Packet4f>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet2d pmax<PropagateNumbers, Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return pminmax_propagate_numbers(a, b, pmax<Packet2d>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet4f pmin<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return pminmax_propagate_nan(a, b, pmin<Packet4f>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet2d pmin<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return pminmax_propagate_nan(a, b, pmin<Packet2d>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet4f pmax<PropagateNaN, Packet4f>(const Packet4f& a, const Packet4f& b) {
+  return pminmax_propagate_nan(a, b, pmax<Packet4f>);
+}
+template<>
+EIGEN_STRONG_INLINE Packet2d pmax<PropagateNaN, Packet2d>(const Packet2d& a, const Packet2d& b) {
+  return pminmax_propagate_nan(a, b, pmax<Packet2d>);
+}
+
+template<int N> EIGEN_STRONG_INLINE Packet4i parithmetic_shift_right(const Packet4i& a) { return _mm_srai_epi32(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_right   (const Packet4i& a) { return _mm_srli_epi32(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4i plogical_shift_left    (const Packet4i& a) { return _mm_slli_epi32(a,N); }
+
+template<int N> EIGEN_STRONG_INLINE Packet4ui parithmetic_shift_right(const Packet4ui& a) { return _mm_srli_epi32(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4ui plogical_shift_right   (const Packet4ui& a) { return _mm_srli_epi32(a,N); }
+template<int N> EIGEN_STRONG_INLINE Packet4ui plogical_shift_left    (const Packet4ui& a) { return _mm_slli_epi32(a,N); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pabs(const Packet4f& a)
+{
+  const Packet4f mask = _mm_castsi128_ps(_mm_setr_epi32(0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF,0x7FFFFFFF));
+  return _mm_and_ps(a,mask);
+}
+template<> EIGEN_STRONG_INLINE Packet2d pabs(const Packet2d& a)
+{
+  const Packet2d mask = _mm_castsi128_pd(_mm_setr_epi32(0xFFFFFFFF,0x7FFFFFFF,0xFFFFFFFF,0x7FFFFFFF));
+  return _mm_and_pd(a,mask);
+}
+template<> EIGEN_STRONG_INLINE Packet4i pabs(const Packet4i& a)
+{
+#ifdef EIGEN_VECTORIZE_SSSE3
+  return _mm_abs_epi32(a);
+#else
+  Packet4i aux = _mm_srai_epi32(a,31);
+  return _mm_sub_epi32(_mm_xor_si128(a,aux),aux);
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet4ui pabs(const Packet4ui& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE Packet4f psignbit(const Packet4f& a) { return _mm_castsi128_ps(_mm_srai_epi32(_mm_castps_si128(a), 31)); }
+template<> EIGEN_STRONG_INLINE Packet2d psignbit(const Packet2d& a)
+{
+  Packet4f tmp = psignbit<Packet4f>(_mm_castpd_ps(a));
+#ifdef EIGEN_VECTORIZE_AVX
+  return _mm_castps_pd(_mm_permute_ps(tmp, (shuffle_mask<1, 1, 3, 3>::mask)));
+#else
+  return _mm_castps_pd(_mm_shuffle_ps(tmp, tmp, (shuffle_mask<1, 1, 3, 3>::mask)));
+#endif // EIGEN_VECTORIZE_AVX
+}
+template<> EIGEN_STRONG_INLINE Packet4ui  psignbit(const Packet4ui& a)  { return pzero(a); }
+
+#ifdef EIGEN_VECTORIZE_SSE4_1
+template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a)
+{
+  // Unfortunately _mm_round_ps doesn't have a rounding mode to implement numext::round.
+  const Packet4f mask = pset1frombits<Packet4f>(0x80000000u);
+  const Packet4f prev0dot5 = pset1frombits<Packet4f>(0x3EFFFFFFu);
+  return _mm_round_ps(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a)
+{
+  const Packet2d mask = _mm_castsi128_pd(_mm_set_epi64x(0x8000000000000000ull, 0x8000000000000000ull));
+  const Packet2d prev0dot5 = _mm_castsi128_pd(_mm_set_epi64x(0x3FDFFFFFFFFFFFFFull, 0x3FDFFFFFFFFFFFFFull));
+  return _mm_round_pd(padd(por(pand(a, mask), prev0dot5), a), _MM_FROUND_TO_ZERO);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f print<Packet4f>(const Packet4f& a) { return _mm_round_ps(a, _MM_FROUND_CUR_DIRECTION); }
+template<> EIGEN_STRONG_INLINE Packet2d print<Packet2d>(const Packet2d& a) { return _mm_round_pd(a, _MM_FROUND_CUR_DIRECTION); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a) { return _mm_ceil_ps(a); }
+template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a) { return _mm_ceil_pd(a); }
+
+template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a) { return _mm_floor_ps(a); }
+template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) { return _mm_floor_pd(a); }
+#else
+template<> EIGEN_STRONG_INLINE Packet4f print(const Packet4f& a) {
+  // Adds and subtracts signum(a) * 2^23 to force rounding.
+  const Packet4f limit = pset1<Packet4f>(static_cast<float>(1<<23));
+  const Packet4f abs_a = pabs(a);
+  Packet4f r = padd(abs_a, limit);
+  // Don't compile-away addition and subtraction.
+  EIGEN_OPTIMIZATION_BARRIER(r);
+  r = psub(r, limit);
+  // If greater than limit, simply return a.  Otherwise, account for sign.
+  r = pselect(pcmp_lt(abs_a, limit),
+              pselect(pcmp_lt(a, pzero(a)), pnegate(r), r), a);
+  return r;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d print(const Packet2d& a) {
+  // Adds and subtracts signum(a) * 2^52 to force rounding.
+  const Packet2d limit = pset1<Packet2d>(static_cast<double>(1ull<<52));
+  const Packet2d abs_a = pabs(a);
+  Packet2d r = padd(abs_a, limit);
+  // Don't compile-away addition and subtraction.
+  EIGEN_OPTIMIZATION_BARRIER(r);
+  r = psub(r, limit);
+  // If greater than limit, simply return a.  Otherwise, account for sign.
+  r = pselect(pcmp_lt(abs_a, limit),
+              pselect(pcmp_lt(a, pzero(a)), pnegate(r), r), a);
+  return r;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a)
+{
+  const Packet4f cst_1 = pset1<Packet4f>(1.0f);
+  Packet4f tmp  = print<Packet4f>(a);
+  // If greater, subtract one.
+  Packet4f mask = _mm_cmpgt_ps(tmp, a);
+  mask = pand(mask, cst_1);
+  return psub(tmp, mask);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a)
+{
+  const Packet2d cst_1 = pset1<Packet2d>(1.0);
+  Packet2d tmp  = print<Packet2d>(a);
+  // If greater, subtract one.
+  Packet2d mask = _mm_cmpgt_pd(tmp, a);
+  mask = pand(mask, cst_1);
+  return psub(tmp, mask);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const Packet4f& a)
+{
+  const Packet4f cst_1 = pset1<Packet4f>(1.0f);
+  Packet4f tmp  = print<Packet4f>(a);
+  // If smaller, add one.
+  Packet4f mask = _mm_cmplt_ps(tmp, a);
+  mask = pand(mask, cst_1);
+  return padd(tmp, mask);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const Packet2d& a)
+{
+  const Packet2d cst_1 = pset1<Packet2d>(1.0);
+  Packet2d tmp  = print<Packet2d>(a);
+  // If smaller, add one.
+  Packet2d mask = _mm_cmplt_pd(tmp, a);
+  mask = pand(mask, cst_1);
+  return padd(tmp, mask);
+}
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float*   from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_ps(from); }
+template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double*  from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_pd(from); }
+template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int*     from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_si128(reinterpret_cast<const __m128i*>(from)); }
+template<> EIGEN_STRONG_INLINE Packet4ui pload<Packet4ui>(const uint32_t* from) { EIGEN_DEBUG_ALIGNED_LOAD return _mm_load_si128(reinterpret_cast<const __m128i*>(from)); }
+template<> EIGEN_STRONG_INLINE Packet16b pload<Packet16b>(const bool*     from) { EIGEN_DEBUG_ALIGNED_LOAD return  _mm_load_si128(reinterpret_cast<const __m128i*>(from)); }
+
+#if EIGEN_COMP_MSVC
+  template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float*  from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return _mm_loadu_ps(from);
+}
+#else
+// NOTE: with the code below, MSVC's compiler crashes!
+
+template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f>(const float* from)
+{
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return _mm_loadu_ps(from);
+}
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double* from)
+{
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return _mm_loadu_pd(from);
+}
+template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int* from)
+{
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
+}
+template<> EIGEN_STRONG_INLINE Packet4ui ploadu<Packet4ui>(const uint32_t* from)
+{
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
+}
+template<> EIGEN_STRONG_INLINE Packet16b ploadu<Packet16b>(const bool*     from) {
+  EIGEN_DEBUG_UNALIGNED_LOAD
+  return _mm_loadu_si128(reinterpret_cast<const __m128i*>(from));
+}
+
+// Load lower part of packet zero extending.
+template<typename Packet> EIGEN_STRONG_INLINE Packet ploadl(const typename unpacket_traits<Packet>::type* from);
+template<> EIGEN_STRONG_INLINE Packet4f ploadl<Packet4f>(const float*  from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm_castpd_ps(_mm_load_sd(reinterpret_cast<const double*>(from))); }
+template<> EIGEN_STRONG_INLINE Packet2d ploadl<Packet2d>(const double* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm_load_sd(from); }
+
+// Load scalar
+template<typename Packet> EIGEN_STRONG_INLINE Packet ploads(const typename unpacket_traits<Packet>::type* from);
+template<> EIGEN_STRONG_INLINE Packet4f ploads<Packet4f>(const float*  from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm_load_ss(from); }
+template<> EIGEN_STRONG_INLINE Packet2d ploads<Packet2d>(const double* from) { EIGEN_DEBUG_UNALIGNED_LOAD return _mm_load_sd(from); }
+
+template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float*   from)
+{
+  return vec4f_swizzle1(_mm_castpd_ps(_mm_load_sd(reinterpret_cast<const double*>(from))), 0, 0, 1, 1);
+}
+template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double*  from)
+{ return pset1<Packet2d>(from[0]); }
+template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int*     from)
+{
+  Packet4i tmp;
+  tmp = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(from));
+  return vec4i_swizzle1(tmp, 0, 0, 1, 1);
+}
+template<> EIGEN_STRONG_INLINE Packet4ui ploaddup<Packet4ui>(const uint32_t* from)
+{
+  Packet4ui tmp;
+  tmp = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(from));
+  return vec4ui_swizzle1(tmp, 0, 0, 1, 1);
+}
+
+// Loads 8 bools from memory and returns the packet
+// {b0, b0, b1, b1, b2, b2, b3, b3, b4, b4, b5, b5, b6, b6, b7, b7}
+template<> EIGEN_STRONG_INLINE Packet16b ploaddup<Packet16b>(const bool*     from)
+{
+  __m128i tmp = _mm_castpd_si128(pload1<Packet2d>(reinterpret_cast<const double*>(from)));
+  return  _mm_unpacklo_epi8(tmp, tmp);
+}
+
+// Loads 4 bools from memory and returns the packet
+// {b0, b0  b0, b0, b1, b1, b1, b1, b2, b2, b2, b2, b3, b3, b3, b3}
+template<> EIGEN_STRONG_INLINE Packet16b
+ploadquad<Packet16b>(const bool* from) {
+  __m128i tmp = _mm_castps_si128(pload1<Packet4f>(reinterpret_cast<const float*>(from)));
+  tmp = _mm_unpacklo_epi8(tmp, tmp);
+  return  _mm_unpacklo_epi16(tmp, tmp);
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<float>(float*   to, const Packet4f& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_ps(to, from); }
+template<> EIGEN_STRONG_INLINE void pstore<double>(double* to, const Packet2d& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_pd(to, from); }
+template<> EIGEN_STRONG_INLINE void pstore<int>(int*       to, const Packet4i& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_si128(reinterpret_cast<__m128i*>(to), from); }
+template<> EIGEN_STRONG_INLINE void pstore<uint32_t>(uint32_t* to, const Packet4ui& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_si128(reinterpret_cast<__m128i*>(to), from); }
+template<> EIGEN_STRONG_INLINE void pstore<bool>(bool*     to, const Packet16b& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_store_si128(reinterpret_cast<__m128i*>(to), from); }
+
+template<> EIGEN_STRONG_INLINE void pstoreu<double>(double* to, const Packet2d& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_pd(to, from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<float>(float*   to, const Packet4f& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_ps(to, from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<int>(int*       to, const Packet4i& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<uint32_t>(uint32_t* to, const Packet4ui& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<bool>(bool*     to, const Packet16b& from) { EIGEN_DEBUG_ALIGNED_STORE _mm_storeu_si128(reinterpret_cast<__m128i*>(to), from); }
+
+template<typename Scalar, typename Packet> EIGEN_STRONG_INLINE void pstorel(Scalar* to, const Packet& from);
+template<> EIGEN_STRONG_INLINE void pstorel(float*   to, const Packet4f& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm_storel_pi(reinterpret_cast<__m64*>(to), from); }
+template<> EIGEN_STRONG_INLINE void pstorel(double*  to, const Packet2d& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm_storel_pd(to, from); }
+
+template<typename Scalar, typename Packet> EIGEN_STRONG_INLINE void pstores(Scalar* to, const Packet& from);
+template<> EIGEN_STRONG_INLINE void pstores(float*   to, const Packet4f& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm_store_ss(to, from); }
+template<> EIGEN_STRONG_INLINE void pstores(double*  to, const Packet2d& from) { EIGEN_DEBUG_UNALIGNED_STORE _mm_store_sd(to, from); }
+
+template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride)
+{
+ return _mm_set_ps(from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
+}
+template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride)
+{
+ return _mm_set_pd(from[1*stride], from[0*stride]);
+}
+template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, Index stride)
+{
+ return _mm_set_epi32(from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
+}
+template<> EIGEN_DEVICE_FUNC inline Packet4ui pgather<uint32_t, Packet4ui>(const uint32_t* from, Index stride)
+{
+  return _mm_set_epi32(numext::bit_cast<int32_t>(from[3 * stride]), numext::bit_cast<int32_t>(from[2 * stride]),
+                       numext::bit_cast<int32_t>(from[1 * stride]), numext::bit_cast<int32_t>(from[0 * stride]));
+}
+
+template<> EIGEN_DEVICE_FUNC inline Packet16b pgather<bool, Packet16b>(const bool* from, Index stride)
+{
+  return _mm_set_epi8(from[15*stride], from[14*stride], from[13*stride], from[12*stride],
+                      from[11*stride], from[10*stride], from[9*stride], from[8*stride],
+                      from[7*stride], from[6*stride], from[5*stride], from[4*stride],
+                      from[3*stride], from[2*stride], from[1*stride], from[0*stride]);
+}
+
+template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
+{
+  to[stride*0] = _mm_cvtss_f32(from);
+  to[stride*1] = _mm_cvtss_f32(_mm_shuffle_ps(from, from, 1));
+  to[stride*2] = _mm_cvtss_f32(_mm_shuffle_ps(from, from, 2));
+  to[stride*3] = _mm_cvtss_f32(_mm_shuffle_ps(from, from, 3));
+}
+template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride)
+{
+  to[stride*0] = _mm_cvtsd_f64(from);
+  to[stride*1] = _mm_cvtsd_f64(_mm_shuffle_pd(from, from, 1));
+}
+template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, Index stride)
+{
+  to[stride*0] = _mm_cvtsi128_si32(from);
+  to[stride*1] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 1));
+  to[stride*2] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 2));
+  to[stride*3] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 3));
+}
+template<> EIGEN_DEVICE_FUNC inline void pscatter<uint32_t, Packet4ui>(uint32_t* to, const Packet4ui& from, Index stride)
+{
+  to[stride * 0] = numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(from));
+  to[stride * 1] = numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(_mm_shuffle_epi32(from, 1)));
+  to[stride * 2] = numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(_mm_shuffle_epi32(from, 2)));
+  to[stride * 3] = numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(_mm_shuffle_epi32(from, 3)));
+}
+template<> EIGEN_DEVICE_FUNC inline void pscatter<bool, Packet16b>(bool* to, const Packet16b& from, Index stride)
+{
+  to[4*stride*0] = _mm_cvtsi128_si32(from);
+  to[4*stride*1] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 1));
+  to[4*stride*2] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 2));
+  to[4*stride*3] = _mm_cvtsi128_si32(_mm_shuffle_epi32(from, 3));
+}
+
+
+// some compilers might be tempted to perform multiple moves instead of using a vector path.
+template<> EIGEN_STRONG_INLINE void pstore1<Packet4f>(float* to, const float& a)
+{
+  Packet4f pa = _mm_set_ss(a);
+  pstore(to, Packet4f(vec4f_swizzle1(pa,0,0,0,0)));
+}
+// some compilers might be tempted to perform multiple moves instead of using a vector path.
+template<> EIGEN_STRONG_INLINE void pstore1<Packet2d>(double* to, const double& a)
+{
+  Packet2d pa = _mm_set_sd(a);
+  pstore(to, Packet2d(vec2d_swizzle1(pa,0,0)));
+}
+
+#if EIGEN_COMP_PGI && EIGEN_COMP_PGI < 1900
+typedef const void * SsePrefetchPtrType;
+#else
+typedef const char * SsePrefetchPtrType;
+#endif
+
+#ifndef EIGEN_VECTORIZE_AVX
+template<> EIGEN_STRONG_INLINE void prefetch<float>(const float*   addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
+template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
+template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*       addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
+template<> EIGEN_STRONG_INLINE void prefetch<uint32_t>(const uint32_t*       addr) { _mm_prefetch((SsePrefetchPtrType)(addr), _MM_HINT_T0); }
+#endif
+
+#if EIGEN_COMP_MSVC_STRICT && EIGEN_OS_WIN64
+// The temporary variable fixes an internal compilation error in vs <= 2008 and a wrong-result bug in vs 2010
+// Direct of the struct members fixed bug #62.
+template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { return a.m128_f32[0]; }
+template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { return a.m128d_f64[0]; }
+template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { int x = _mm_cvtsi128_si32(a); return x; }
+template<> EIGEN_STRONG_INLINE uint32_t    pfirst<Packet4ui>(const Packet4ui& a) { uint32_t x = numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(a)); return x; }
+#elif EIGEN_COMP_MSVC_STRICT
+// The temporary variable fixes an internal compilation error in vs <= 2008 and a wrong-result bug in vs 2010
+template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { float x = _mm_cvtss_f32(a); return x; }
+template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { double x = _mm_cvtsd_f64(a); return x; }
+template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { int x = _mm_cvtsi128_si32(a); return x; }
+template<> EIGEN_STRONG_INLINE uint32_t pfirst<Packet4ui>(const Packet4ui& a) { uint32_t x = numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(a)); return x; }
+#else
+template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { return _mm_cvtss_f32(a); }
+template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { return _mm_cvtsd_f64(a); }
+template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { return _mm_cvtsi128_si32(a); }
+template<> EIGEN_STRONG_INLINE uint32_t pfirst<Packet4ui>(const Packet4ui& a) { return numext::bit_cast<uint32_t>(_mm_cvtsi128_si32(a)); }
+#endif
+template<> EIGEN_STRONG_INLINE bool   pfirst<Packet16b>(const Packet16b& a) { int x = _mm_cvtsi128_si32(a); return static_cast<bool>(x & 1); }
+
+template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a) { return _mm_shuffle_ps(a,a,0x1B); }
+template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a) { return _mm_shuffle_pd(a,a,0x1); }
+template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a) { return _mm_shuffle_epi32(a,0x1B); }
+template<> EIGEN_STRONG_INLINE Packet4ui preverse(const Packet4ui& a) { return _mm_shuffle_epi32(a, 0x1B); }
+template<> EIGEN_STRONG_INLINE Packet16b preverse(const Packet16b& a) {
+#ifdef EIGEN_VECTORIZE_SSSE3
+  __m128i mask = _mm_set_epi8(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15);
+  return _mm_shuffle_epi8(a, mask);
+#else
+  Packet16b tmp = _mm_shuffle_epi32(a, _MM_SHUFFLE(0, 1, 2, 3));
+  tmp = _mm_shufflehi_epi16(_mm_shufflelo_epi16(tmp, _MM_SHUFFLE(2, 3, 0, 1)), _MM_SHUFFLE(2, 3, 0, 1));
+  return _mm_or_si128(_mm_slli_epi16(tmp, 8), _mm_srli_epi16(tmp, 8));
+#endif
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pfrexp<Packet4f>(const Packet4f& a, Packet4f& exponent) {
+  return pfrexp_generic(a,exponent);
+}
+
+// Extract exponent without existence of Packet2l.
+template<>
+EIGEN_STRONG_INLINE  
+Packet2d pfrexp_generic_get_biased_exponent(const Packet2d& a) {
+  const Packet2d cst_exp_mask  = pset1frombits<Packet2d>(static_cast<uint64_t>(0x7ff0000000000000ull));
+  __m128i a_expo = _mm_srli_epi64(_mm_castpd_si128(pand(a, cst_exp_mask)), 52);
+  return _mm_cvtepi32_pd(vec4i_swizzle1(a_expo, 0, 2, 1, 3));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pfrexp<Packet2d>(const Packet2d& a, Packet2d& exponent) {
+  return pfrexp_generic(a, exponent);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pldexp<Packet4f>(const Packet4f& a, const Packet4f& exponent) {
+  return pldexp_generic(a,exponent);
+}
+
+// We specialize pldexp here, since the generic implementation uses Packet2l, which is not well
+// supported by SSE, and has more range than is needed for exponents.
+template<> EIGEN_STRONG_INLINE Packet2d pldexp<Packet2d>(const Packet2d& a, const Packet2d& exponent) {
+  // Clamp exponent to [-2099, 2099]
+  const Packet2d max_exponent = pset1<Packet2d>(2099.0);
+  const Packet2d e = pmin(pmax(exponent, pnegate(max_exponent)), max_exponent);
+
+  // Convert e to integer and swizzle to low-order bits.
+  const Packet4i ei = vec4i_swizzle1(_mm_cvtpd_epi32(e), 0, 3, 1, 3);
+
+  // Split 2^e into four factors and multiply:
+  const Packet4i bias = _mm_set_epi32(0, 1023, 0, 1023);
+  Packet4i b = parithmetic_shift_right<2>(ei);  // floor(e/4)
+  Packet2d c = _mm_castsi128_pd(_mm_slli_epi64(padd(b, bias), 52));  // 2^b
+  Packet2d out = pmul(pmul(pmul(a, c), c), c); // a * 2^(3b)
+  b = psub(psub(psub(ei, b), b), b);  // e - 3b
+  c = _mm_castsi128_pd(_mm_slli_epi64(padd(b, bias), 52));  // 2^(e - 3b)
+  out = pmul(out, c);  // a * 2^e
+  return out;
+}
+
+// with AVX, the default implementations based on pload1 are faster
+#ifndef __AVX__
+template<> EIGEN_STRONG_INLINE void
+pbroadcast4<Packet4f>(const float *a,
+                      Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
+{
+  a3 = pload<Packet4f>(a);
+  a0 = vec4f_swizzle1(a3, 0,0,0,0);
+  a1 = vec4f_swizzle1(a3, 1,1,1,1);
+  a2 = vec4f_swizzle1(a3, 2,2,2,2);
+  a3 = vec4f_swizzle1(a3, 3,3,3,3);
+}
+template<> EIGEN_STRONG_INLINE void
+pbroadcast4<Packet2d>(const double *a,
+                      Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
+{
+#ifdef EIGEN_VECTORIZE_SSE3
+  a0 = _mm_loaddup_pd(a+0);
+  a1 = _mm_loaddup_pd(a+1);
+  a2 = _mm_loaddup_pd(a+2);
+  a3 = _mm_loaddup_pd(a+3);
+#else
+  a1 = pload<Packet2d>(a);
+  a0 = vec2d_swizzle1(a1, 0,0);
+  a1 = vec2d_swizzle1(a1, 1,1);
+  a3 = pload<Packet2d>(a+2);
+  a2 = vec2d_swizzle1(a3, 0,0);
+  a3 = vec2d_swizzle1(a3, 1,1);
+#endif
+}
+#endif
+
+EIGEN_STRONG_INLINE void punpackp(Packet4f* vecs)
+{
+  vecs[1] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0x55));
+  vecs[2] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0xAA));
+  vecs[3] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0xFF));
+  vecs[0] = _mm_castsi128_ps(_mm_shuffle_epi32(_mm_castps_si128(vecs[0]), 0x00));
+}
+
+template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
+{
+  // Disable SSE3 _mm_hadd_pd that is extremely slow on all existing Intel's architectures
+  // (from Nehalem to Haswell)
+  // #ifdef EIGEN_VECTORIZE_SSE3
+  //   Packet4f tmp = _mm_add_ps(a, vec4f_swizzle1(a,2,3,2,3));
+  //   return pfirst<Packet4f>(_mm_hadd_ps(tmp, tmp));
+  // #else
+  Packet4f tmp = _mm_add_ps(a, _mm_movehl_ps(a,a));
+  return pfirst<Packet4f>(_mm_add_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1)));
+  // #endif
+}
+
+template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
+{
+  // Disable SSE3 _mm_hadd_pd that is extremely slow on all existing Intel's architectures
+  // (from Nehalem to Haswell)
+  // #ifdef EIGEN_VECTORIZE_SSE3
+  //   return pfirst<Packet2d>(_mm_hadd_pd(a, a));
+  // #else
+  return pfirst<Packet2d>(_mm_add_sd(a, _mm_unpackhi_pd(a,a)));
+  // #endif
+}
+
+#ifdef EIGEN_VECTORIZE_SSSE3
+template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a)
+{
+  Packet4i tmp0 = _mm_hadd_epi32(a,a);
+  return pfirst<Packet4i>(_mm_hadd_epi32(tmp0,tmp0));
+}
+template<> EIGEN_STRONG_INLINE uint32_t predux<Packet4ui>(const Packet4ui& a)
+{
+  Packet4ui tmp0 = _mm_hadd_epi32(a, a);
+  return pfirst<Packet4ui>(_mm_hadd_epi32(tmp0, tmp0));
+}
+
+#else
+template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a)
+{
+  Packet4i tmp = _mm_add_epi32(a, _mm_unpackhi_epi64(a,a));
+  return pfirst(tmp) + pfirst<Packet4i>(_mm_shuffle_epi32(tmp, 1));
+}
+template<> EIGEN_STRONG_INLINE uint32_t predux<Packet4ui>(const Packet4ui& a) {
+  Packet4ui tmp = _mm_add_epi32(a, _mm_unpackhi_epi64(a, a));
+  return pfirst(tmp) + pfirst<Packet4ui>(_mm_shuffle_epi32(tmp, 1));
+}
+#endif
+
+template<> EIGEN_STRONG_INLINE bool predux<Packet16b>(const Packet16b& a) {
+  Packet4i tmp = _mm_or_si128(a, _mm_unpackhi_epi64(a,a));
+  return (pfirst(tmp) != 0) || (pfirst<Packet4i>(_mm_shuffle_epi32(tmp, 1)) != 0);
+}
+
+// Other reduction functions:
+
+
+// mul
+template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
+{
+  Packet4f tmp = _mm_mul_ps(a, _mm_movehl_ps(a,a));
+  return pfirst<Packet4f>(_mm_mul_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1)));
+}
+template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
+{
+  return pfirst<Packet2d>(_mm_mul_sd(a, _mm_unpackhi_pd(a,a)));
+}
+template<> EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a)
+{
+  // after some experiments, it is seems this is the fastest way to implement it
+  // for GCC (eg., reusing pmul is very slow !)
+  // TODO try to call _mm_mul_epu32 directly
+  EIGEN_ALIGN16 int aux[4];
+  pstore(aux, a);
+  return  (aux[0] * aux[1]) * (aux[2] * aux[3]);
+}
+template<> EIGEN_STRONG_INLINE uint32_t predux_mul<Packet4ui>(const Packet4ui& a)
+{
+  // after some experiments, it is seems this is the fastest way to implement it
+  // for GCC (eg., reusing pmul is very slow !)
+  // TODO try to call _mm_mul_epu32 directly
+  EIGEN_ALIGN16 uint32_t aux[4];
+  pstore(aux, a);
+  return  (aux[0] * aux[1]) * (aux[2] * aux[3]);
+}
+
+template<> EIGEN_STRONG_INLINE bool predux_mul<Packet16b>(const Packet16b& a) {
+  Packet4i tmp = _mm_and_si128(a, _mm_unpackhi_epi64(a,a));
+  return ((pfirst<Packet4i>(tmp) == 0x01010101) &&
+          (pfirst<Packet4i>(_mm_shuffle_epi32(tmp, 1)) == 0x01010101));
+}
+
+// min
+template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
+{
+  Packet4f tmp = _mm_min_ps(a, _mm_movehl_ps(a,a));
+  return pfirst<Packet4f>(_mm_min_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1)));
+}
+template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a)
+{
+  return pfirst<Packet2d>(_mm_min_sd(a, _mm_unpackhi_pd(a,a)));
+}
+template<> EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a)
+{
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  Packet4i tmp = _mm_min_epi32(a, _mm_shuffle_epi32(a, _MM_SHUFFLE(0,0,3,2)));
+  return pfirst<Packet4i>(_mm_min_epi32(tmp,_mm_shuffle_epi32(tmp, 1)));
+#else
+  // after some experiments, it is seems this is the fastest way to implement it
+  // for GCC (eg., it does not like using std::min after the pstore !!)
+  EIGEN_ALIGN16 int aux[4];
+  pstore(aux, a);
+  int aux0 = aux[0]<aux[1] ? aux[0] : aux[1];
+  int aux2 = aux[2]<aux[3] ? aux[2] : aux[3];
+  return aux0<aux2 ? aux0 : aux2;
+#endif // EIGEN_VECTORIZE_SSE4_1
+}
+template<> EIGEN_STRONG_INLINE uint32_t predux_min<Packet4ui>(const Packet4ui& a)
+{
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  Packet4ui tmp = _mm_min_epu32(a, _mm_shuffle_epi32(a, _MM_SHUFFLE(0,0,3,2)));
+  return pfirst<Packet4ui>(_mm_min_epu32(tmp,_mm_shuffle_epi32(tmp, 1)));
+#else
+  // after some experiments, it is seems this is the fastest way to implement it
+  // for GCC (eg., it does not like using std::min after the pstore !!)
+  EIGEN_ALIGN16 uint32_t aux[4];
+  pstore(aux, a);
+  uint32_t aux0 = aux[0]<aux[1] ? aux[0] : aux[1];
+  uint32_t aux2 = aux[2]<aux[3] ? aux[2] : aux[3];
+  return aux0<aux2 ? aux0 : aux2;
+#endif // EIGEN_VECTORIZE_SSE4_1
+}
+
+// max
+template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
+{
+  Packet4f tmp = _mm_max_ps(a, _mm_movehl_ps(a,a));
+  return pfirst<Packet4f>(_mm_max_ss(tmp, _mm_shuffle_ps(tmp,tmp, 1)));
+}
+template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a)
+{
+  return pfirst<Packet2d>(_mm_max_sd(a, _mm_unpackhi_pd(a,a)));
+}
+template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a)
+{
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  Packet4i tmp = _mm_max_epi32(a, _mm_shuffle_epi32(a, _MM_SHUFFLE(0,0,3,2)));
+  return pfirst<Packet4i>(_mm_max_epi32(tmp,_mm_shuffle_epi32(tmp, 1)));
+#else
+  // after some experiments, it is seems this is the fastest way to implement it
+  // for GCC (eg., it does not like using std::min after the pstore !!)
+  EIGEN_ALIGN16 int aux[4];
+  pstore(aux, a);
+  int aux0 = aux[0]>aux[1] ? aux[0] : aux[1];
+  int aux2 = aux[2]>aux[3] ? aux[2] : aux[3];
+  return aux0>aux2 ? aux0 : aux2;
+#endif // EIGEN_VECTORIZE_SSE4_1
+}
+template<> EIGEN_STRONG_INLINE uint32_t predux_max<Packet4ui>(const Packet4ui& a)
+{
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  Packet4ui tmp = _mm_max_epu32(a, _mm_shuffle_epi32(a, _MM_SHUFFLE(0,0,3,2)));
+  return pfirst<Packet4ui>(_mm_max_epu32(tmp,_mm_shuffle_epi32(tmp, 1)));
+#else
+  // after some experiments, it is seems this is the fastest way to implement it
+  // for GCC (eg., it does not like using std::min after the pstore !!)
+  EIGEN_ALIGN16 uint32_t aux[4];
+  pstore(aux, a);
+  uint32_t aux0 = aux[0]>aux[1] ? aux[0] : aux[1];
+  uint32_t aux2 = aux[2]>aux[3] ? aux[2] : aux[3];
+  return aux0>aux2 ? aux0 : aux2;
+#endif // EIGEN_VECTORIZE_SSE4_1
+}
+
+// not needed yet
+// template<> EIGEN_STRONG_INLINE bool predux_all(const Packet4f& x)
+// {
+//   return _mm_movemask_ps(x) == 0xF;
+// }
+
+template<> EIGEN_STRONG_INLINE bool predux_any(const Packet4f& x)
+{
+  return _mm_movemask_ps(x) != 0x0;
+}
+
+template<> EIGEN_STRONG_INLINE bool predux_any(const Packet4i& x)
+{
+  return _mm_movemask_ps(_mm_castsi128_ps(x)) != 0x0;
+}
+template<> EIGEN_STRONG_INLINE bool predux_any(const Packet4ui& x)
+{
+  return _mm_movemask_ps(_mm_castsi128_ps(x)) != 0x0;
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet4f,4>& kernel) {
+  _MM_TRANSPOSE4_PS(kernel.packet[0], kernel.packet[1], kernel.packet[2], kernel.packet[3]);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet2d,2>& kernel) {
+  __m128d tmp = _mm_unpackhi_pd(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[0] = _mm_unpacklo_pd(kernel.packet[0], kernel.packet[1]);
+  kernel.packet[1] = tmp;
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet4i,4>& kernel) {
+  __m128i T0 = _mm_unpacklo_epi32(kernel.packet[0], kernel.packet[1]);
+  __m128i T1 = _mm_unpacklo_epi32(kernel.packet[2], kernel.packet[3]);
+  __m128i T2 = _mm_unpackhi_epi32(kernel.packet[0], kernel.packet[1]);
+  __m128i T3 = _mm_unpackhi_epi32(kernel.packet[2], kernel.packet[3]);
+
+  kernel.packet[0] = _mm_unpacklo_epi64(T0, T1);
+  kernel.packet[1] = _mm_unpackhi_epi64(T0, T1);
+  kernel.packet[2] = _mm_unpacklo_epi64(T2, T3);
+  kernel.packet[3] = _mm_unpackhi_epi64(T2, T3);
+}
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4ui, 4>& kernel) {
+  ptranspose((PacketBlock<Packet4i, 4>&)kernel);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet16b,4>& kernel) {
+  __m128i T0 =  _mm_unpacklo_epi8(kernel.packet[0], kernel.packet[1]);
+  __m128i T1 =  _mm_unpackhi_epi8(kernel.packet[0], kernel.packet[1]);
+  __m128i T2 =  _mm_unpacklo_epi8(kernel.packet[2], kernel.packet[3]);
+  __m128i T3 =  _mm_unpackhi_epi8(kernel.packet[2], kernel.packet[3]);
+  kernel.packet[0] = _mm_unpacklo_epi16(T0, T2);
+  kernel.packet[1] = _mm_unpackhi_epi16(T0, T2);
+  kernel.packet[2] = _mm_unpacklo_epi16(T1, T3);
+  kernel.packet[3] = _mm_unpackhi_epi16(T1, T3);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet16b,16>& kernel) {
+  // If we number the elements in the input thus:
+  // kernel.packet[ 0] = {00, 01, 02, 03, 04, 05, 06, 07, 08, 09, 0a, 0b, 0c, 0d, 0e, 0f}
+  // kernel.packet[ 1] = {10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 1a, 1b, 1c, 1d, 1e, 1f}
+  // ...
+  // kernel.packet[15] = {f0, f1, f2, f3, f4, f5, f6, f7, f8, f9, fa, fb, fc, fd, fe, ff},
+  //
+  // the desired output is:
+  // kernel.packet[ 0] = {00, 10, 20, 30, 40, 50, 60, 70, 80, 90, a0, b0, c0, d0, e0, f0}
+  // kernel.packet[ 1] = {01, 11, 21, 31, 41, 51, 61, 71, 81, 91, a1, b1, c1, d1, e1, f1}
+  // ...
+  // kernel.packet[15] = {0f, 1f, 2f, 3f, 4f, 5f, 6f, 7f, 8f, 9f, af, bf, cf, df, ef, ff},
+  __m128i t0 =  _mm_unpacklo_epi8(kernel.packet[0], kernel.packet[1]); // 00 10 01 11 02 12 03 13 04 14 05 15 06 16 07 17
+  __m128i t1 =  _mm_unpackhi_epi8(kernel.packet[0], kernel.packet[1]); // 08 18 09 19 0a 1a 0b 1b 0c 1c 0d 1d 0e 1e 0f 1f
+  __m128i t2 =  _mm_unpacklo_epi8(kernel.packet[2], kernel.packet[3]); // 20 30 21 31 22 32 ...                     27 37
+  __m128i t3 =  _mm_unpackhi_epi8(kernel.packet[2], kernel.packet[3]); // 28 38 29 39 2a 3a ...                     2f 3f
+  __m128i t4 =  _mm_unpacklo_epi8(kernel.packet[4], kernel.packet[5]); // 40 50 41 51 42 52                         47 57
+  __m128i t5 =  _mm_unpackhi_epi8(kernel.packet[4], kernel.packet[5]); // 48 58 49 59 4a 5a
+  __m128i t6 =  _mm_unpacklo_epi8(kernel.packet[6], kernel.packet[7]);
+  __m128i t7 =  _mm_unpackhi_epi8(kernel.packet[6], kernel.packet[7]);
+  __m128i t8 =  _mm_unpacklo_epi8(kernel.packet[8], kernel.packet[9]);
+  __m128i t9 =  _mm_unpackhi_epi8(kernel.packet[8], kernel.packet[9]);
+  __m128i ta =  _mm_unpacklo_epi8(kernel.packet[10], kernel.packet[11]);
+  __m128i tb =  _mm_unpackhi_epi8(kernel.packet[10], kernel.packet[11]);
+  __m128i tc =  _mm_unpacklo_epi8(kernel.packet[12], kernel.packet[13]);
+  __m128i td =  _mm_unpackhi_epi8(kernel.packet[12], kernel.packet[13]);
+  __m128i te =  _mm_unpacklo_epi8(kernel.packet[14], kernel.packet[15]);
+  __m128i tf =  _mm_unpackhi_epi8(kernel.packet[14], kernel.packet[15]);
+
+  __m128i s0 =  _mm_unpacklo_epi16(t0, t2); // 00 10 20 30 01 11 21 31 02 12 22 32 03 13 23 33
+  __m128i s1 =  _mm_unpackhi_epi16(t0, t2); // 04 14 24 34
+  __m128i s2 =  _mm_unpacklo_epi16(t1, t3); // 08 18 28 38 ...
+  __m128i s3 =  _mm_unpackhi_epi16(t1, t3); // 0c 1c 2c 3c ...
+  __m128i s4 =  _mm_unpacklo_epi16(t4, t6); // 40 50 60 70 41 51 61 71 42 52 62 72 43 53 63 73
+  __m128i s5 =  _mm_unpackhi_epi16(t4, t6); // 44 54 64 74 ...
+  __m128i s6 =  _mm_unpacklo_epi16(t5, t7);
+  __m128i s7 =  _mm_unpackhi_epi16(t5, t7);
+  __m128i s8 =  _mm_unpacklo_epi16(t8, ta);
+  __m128i s9 =  _mm_unpackhi_epi16(t8, ta);
+  __m128i sa =  _mm_unpacklo_epi16(t9, tb);
+  __m128i sb =  _mm_unpackhi_epi16(t9, tb);
+  __m128i sc =  _mm_unpacklo_epi16(tc, te);
+  __m128i sd =  _mm_unpackhi_epi16(tc, te);
+  __m128i se =  _mm_unpacklo_epi16(td, tf);
+  __m128i sf =  _mm_unpackhi_epi16(td, tf);
+
+  __m128i u0 =  _mm_unpacklo_epi32(s0, s4); // 00 10 20 30 40 50 60 70 01 11 21 31 41 51 61 71
+  __m128i u1 =  _mm_unpackhi_epi32(s0, s4); // 02 12 22 32 42 52 62 72 03 13 23 33 43 53 63 73
+  __m128i u2 =  _mm_unpacklo_epi32(s1, s5);
+  __m128i u3 =  _mm_unpackhi_epi32(s1, s5);
+  __m128i u4 =  _mm_unpacklo_epi32(s2, s6);
+  __m128i u5 =  _mm_unpackhi_epi32(s2, s6);
+  __m128i u6 =  _mm_unpacklo_epi32(s3, s7);
+  __m128i u7 =  _mm_unpackhi_epi32(s3, s7);
+  __m128i u8 =  _mm_unpacklo_epi32(s8, sc);
+  __m128i u9 =  _mm_unpackhi_epi32(s8, sc);
+  __m128i ua =  _mm_unpacklo_epi32(s9, sd);
+  __m128i ub =  _mm_unpackhi_epi32(s9, sd);
+  __m128i uc =  _mm_unpacklo_epi32(sa, se);
+  __m128i ud =  _mm_unpackhi_epi32(sa, se);
+  __m128i ue =  _mm_unpacklo_epi32(sb, sf);
+  __m128i uf =  _mm_unpackhi_epi32(sb, sf);
+
+  kernel.packet[0]  = _mm_unpacklo_epi64(u0, u8);
+  kernel.packet[1]  = _mm_unpackhi_epi64(u0, u8);
+  kernel.packet[2]  = _mm_unpacklo_epi64(u1, u9);
+  kernel.packet[3]  = _mm_unpackhi_epi64(u1, u9);
+  kernel.packet[4]  = _mm_unpacklo_epi64(u2, ua);
+  kernel.packet[5]  = _mm_unpackhi_epi64(u2, ua);
+  kernel.packet[6]  = _mm_unpacklo_epi64(u3, ub);
+  kernel.packet[7]  = _mm_unpackhi_epi64(u3, ub);
+  kernel.packet[8]  = _mm_unpacklo_epi64(u4, uc);
+  kernel.packet[9]  = _mm_unpackhi_epi64(u4, uc);
+  kernel.packet[10] = _mm_unpacklo_epi64(u5, ud);
+  kernel.packet[11] = _mm_unpackhi_epi64(u5, ud);
+  kernel.packet[12] = _mm_unpacklo_epi64(u6, ue);
+  kernel.packet[13] = _mm_unpackhi_epi64(u6, ue);
+  kernel.packet[14] = _mm_unpacklo_epi64(u7, uf);
+  kernel.packet[15] = _mm_unpackhi_epi64(u7, uf);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i pblend(const Selector<4>& ifPacket, const Packet4i& thenPacket, const Packet4i& elsePacket) {
+  const __m128i zero = _mm_setzero_si128();
+  const __m128i select = _mm_set_epi32(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
+  __m128i false_mask = _mm_cmpeq_epi32(select, zero);
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_blendv_epi8(thenPacket, elsePacket, false_mask);
+#else
+  return _mm_or_si128(_mm_andnot_si128(false_mask, thenPacket), _mm_and_si128(false_mask, elsePacket));
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet4ui pblend(const Selector<4>& ifPacket, const Packet4ui& thenPacket,
+                                    const Packet4ui& elsePacket) {
+  return (Packet4ui)pblend(ifPacket, (Packet4i)thenPacket, (Packet4i)elsePacket);
+}
+template<> EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket, const Packet4f& elsePacket) {
+  const __m128 zero = _mm_setzero_ps();
+  const __m128 select = _mm_set_ps(ifPacket.select[3], ifPacket.select[2], ifPacket.select[1], ifPacket.select[0]);
+  __m128 false_mask = _mm_cmpeq_ps(select, zero);
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_blendv_ps(thenPacket, elsePacket, false_mask);
+#else
+  return _mm_or_ps(_mm_andnot_ps(false_mask, thenPacket), _mm_and_ps(false_mask, elsePacket));
+#endif
+}
+template<> EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, const Packet2d& thenPacket, const Packet2d& elsePacket) {
+  const __m128d zero = _mm_setzero_pd();
+  const __m128d select = _mm_set_pd(ifPacket.select[1], ifPacket.select[0]);
+  __m128d false_mask = _mm_cmpeq_pd(select, zero);
+#ifdef EIGEN_VECTORIZE_SSE4_1
+  return _mm_blendv_pd(thenPacket, elsePacket, false_mask);
+#else
+  return _mm_or_pd(_mm_andnot_pd(false_mask, thenPacket), _mm_and_pd(false_mask, elsePacket));
+#endif
+}
+
+// Scalar path for pmadd with FMA to ensure consistency with vectorized path.
+#ifdef EIGEN_VECTORIZE_FMA
+template<> EIGEN_STRONG_INLINE float pmadd(const float& a, const float& b, const float& c) {
+  return ::fmaf(a,b,c);
+}
+template<> EIGEN_STRONG_INLINE double pmadd(const double& a, const double& b, const double& c) {
+  return ::fma(a,b,c);
+}
+template<> EIGEN_STRONG_INLINE float pmsub(const float& a, const float& b, const float& c) {
+  return ::fmaf(a,b,-c);
+}
+template<> EIGEN_STRONG_INLINE double pmsub(const double& a, const double& b, const double& c) {
+  return ::fma(a,b,-c);
+}
+template<> EIGEN_STRONG_INLINE float pnmadd(const float& a, const float& b, const float& c) {
+  return ::fmaf(-a,b,c);
+}
+template<> EIGEN_STRONG_INLINE double pnmadd(const double& a, const double& b, const double& c) {
+  return ::fma(-a,b,c);
+}
+template<> EIGEN_STRONG_INLINE float pnmsub(const float& a, const float& b, const float& c) {
+  return ::fmaf(-a,b,-c);
+}
+template<> EIGEN_STRONG_INLINE double pnmsub(const double& a, const double& b, const double& c) {
+  return ::fma(-a,b,-c);
+}
+#endif
+
+#ifdef EIGEN_VECTORIZE_SSE4_1
+// Helpers for half->float and float->half conversions.
+// Currently only used by the AVX code.
+EIGEN_STRONG_INLINE __m128i half2floatsse(__m128i h) {
+  __m128i input = _mm_cvtepu16_epi32(h);
+
+  // Direct vectorization of half_to_float, C parts in the comments.
+  __m128i shifted_exp = _mm_set1_epi32(0x7c00 << 13);
+  // o.u = (h.x & 0x7fff) << 13; // exponent/mantissa bits
+  __m128i ou = _mm_slli_epi32(_mm_and_si128(input, _mm_set1_epi32(0x7fff)), 13);
+  // exp = shifted_exp & o.u;   // just the exponent
+  __m128i exp = _mm_and_si128(ou, shifted_exp);
+  // o.u += (127 - 15) << 23;
+  ou = _mm_add_epi32(ou, _mm_set1_epi32((127 - 15) << 23));
+
+  // Inf/NaN?
+  __m128i naninf_mask = _mm_cmpeq_epi32(exp, shifted_exp);
+  // Inf/NaN adjust
+  __m128i naninf_adj =
+      _mm_and_si128(_mm_set1_epi32((128 - 16) << 23), naninf_mask);
+  // extra exp adjust for  Inf/NaN
+  ou = _mm_add_epi32(ou, naninf_adj);
+
+  // Zero/Denormal?
+  __m128i zeroden_mask = _mm_cmpeq_epi32(exp, _mm_setzero_si128());
+  __m128i zeroden_adj = _mm_and_si128(zeroden_mask, _mm_set1_epi32(1 << 23));
+  // o.u += 1 << 23;
+  ou = _mm_add_epi32(ou, zeroden_adj);
+  // magic.u = 113 << 23
+  __m128i magic = _mm_and_si128(zeroden_mask, _mm_set1_epi32(113 << 23));
+  // o.f -= magic.f
+  ou = _mm_castps_si128(
+      _mm_sub_ps(_mm_castsi128_ps(ou), _mm_castsi128_ps(magic)));
+
+  __m128i sign =
+      _mm_slli_epi32(_mm_and_si128(input, _mm_set1_epi32(0x8000)), 16);
+  // o.u |= (h.x & 0x8000) << 16;    // sign bit
+  ou = _mm_or_si128(ou, sign);
+  // return o.f;
+  // We are actually returning uint version, to make
+  // _mm256_insertf128_si256 work.
+  return ou;
+}
+
+EIGEN_STRONG_INLINE __m128i float2half(__m128 f) {
+  __m128i o = _mm_setzero_si128();
+
+  // unsigned int sign_mask = 0x80000000u;
+  __m128i sign = _mm_set1_epi32(0x80000000u);
+  // unsigned int sign = f.u & sign_mask;
+  sign = _mm_and_si128(sign, _mm_castps_si128(f));
+  // f.u ^= sign;
+  f = _mm_xor_ps(f, _mm_castsi128_ps(sign));
+
+  __m128i fu = _mm_castps_si128(f);
+
+  __m128i f16max = _mm_set1_epi32((127 + 16) << 23);
+  __m128i f32infty = _mm_set1_epi32(255 << 23);
+  // if (f.u >= f16max.u) // result is Inf or NaN (all exponent bits set)
+  // there is no _mm_cmpge_epi32, so use lt and swap operands
+  __m128i infnan_mask = _mm_cmplt_epi32(f16max, _mm_castps_si128(f));
+  __m128i inf_mask = _mm_cmpgt_epi32(_mm_castps_si128(f), f32infty);
+  __m128i nan_mask = _mm_andnot_si128(inf_mask, infnan_mask);
+  __m128i inf_value = _mm_and_si128(inf_mask, _mm_set1_epi32(0x7e00));
+  __m128i nan_value = _mm_and_si128(nan_mask, _mm_set1_epi32(0x7c00));
+  // o.x = (f.u > f32infty.u) ? 0x7e00 : 0x7c00; // NaN->qNaN and Inf->Inf
+  __m128i naninf_value = _mm_or_si128(inf_value, nan_value);
+
+  __m128i denorm_magic = _mm_set1_epi32(((127 - 15) + (23 - 10) + 1) << 23);
+  __m128i subnorm_mask =
+      _mm_cmplt_epi32(_mm_castps_si128(f), _mm_set1_epi32(113 << 23));
+  //  f.f += denorm_magic.f;
+  f = _mm_add_ps(f, _mm_castsi128_ps(denorm_magic));
+  // f.u - denorm_magic.u
+  o = _mm_sub_epi32(_mm_castps_si128(f), denorm_magic);
+  o = _mm_and_si128(o, subnorm_mask);
+  // Correct result for inf/nan/zero/subnormal, 0 otherwise
+  o = _mm_or_si128(o, naninf_value);
+
+  __m128i mask = _mm_or_si128(infnan_mask, subnorm_mask);
+  o = _mm_and_si128(o, mask);
+
+  // mant_odd = (f.u >> 13) & 1;
+  __m128i mand_odd = _mm_and_si128(_mm_srli_epi32(fu, 13), _mm_set1_epi32(0x1));
+  // f.u += 0xc8000fffU;
+  fu = _mm_add_epi32(fu, _mm_set1_epi32(0xc8000fffU));
+  // f.u += mant_odd;
+  fu = _mm_add_epi32(fu, mand_odd);
+  fu = _mm_andnot_si128(mask, fu);
+  // f.u >> 13
+  fu = _mm_srli_epi32(fu, 13);
+  o = _mm_or_si128(fu, o);
+
+  // o.x |= static_cast<numext::uint16_t>(sign >> 16);
+  o = _mm_or_si128(o, _mm_srli_epi32(sign, 16));
+
+  // 16 bit values
+  return _mm_and_si128(o, _mm_set1_epi32(0xffff));
+}
+#endif
+
+// Packet math for Eigen::half
+// Disable the following code since it's broken on too many platforms / compilers.
+//#elif defined(EIGEN_VECTORIZE_SSE) && (!EIGEN_ARCH_x86_64) && (!EIGEN_COMP_MSVC)
+#if 0
+
+typedef struct {
+  __m64 x;
+} Packet4h;
+
+
+template<> struct is_arithmetic<Packet4h> { enum { value = true }; };
+
+template <>
+struct packet_traits<Eigen::half> : default_packet_traits {
+  typedef Packet4h type;
+  // There is no half-size packet for Packet4h.
+  typedef Packet4h half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasDiv    = 1,
+    HasNegate = 0,
+    HasAbs    = 0,
+    HasAbs2   = 0,
+    HasMin    = 0,
+    HasMax    = 0,
+    HasConj   = 0,
+    HasSetLinear = 0,
+    HasSqrt = 0,
+    HasRsqrt = 0,
+    HasExp = 0,
+    HasLog = 0,
+    HasBlend = 0
+  };
+};
+
+
+template<> struct unpacket_traits<Packet4h> { typedef Eigen::half type; enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet4h half; };
+
+template<> EIGEN_STRONG_INLINE Packet4h pset1<Packet4h>(const Eigen::half& from) {
+  Packet4h result;
+  result.x = _mm_set1_pi16(from.x);
+  return result;
+}
+
+template<> EIGEN_STRONG_INLINE Eigen::half pfirst<Packet4h>(const Packet4h& from) {
+  return half_impl::raw_uint16_to_half(static_cast<unsigned short>(_mm_cvtsi64_si32(from.x)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4h pconj(const Packet4h& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE Packet4h padd<Packet4h>(const Packet4h& a, const Packet4h& b) {
+  __int64_t a64 = _mm_cvtm64_si64(a.x);
+  __int64_t b64 = _mm_cvtm64_si64(b.x);
+
+  Eigen::half h[4];
+
+  Eigen::half ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64));
+  Eigen::half hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64));
+  h[0] = ha + hb;
+  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
+  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 16));
+  h[1] = ha + hb;
+  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
+  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 32));
+  h[2] = ha + hb;
+  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
+  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 48));
+  h[3] = ha + hb;
+  Packet4h result;
+  result.x = _mm_set_pi16(h[3].x, h[2].x, h[1].x, h[0].x);
+  return result;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4h psub<Packet4h>(const Packet4h& a, const Packet4h& b) {
+  __int64_t a64 = _mm_cvtm64_si64(a.x);
+  __int64_t b64 = _mm_cvtm64_si64(b.x);
+
+  Eigen::half h[4];
+
+  Eigen::half ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64));
+  Eigen::half hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64));
+  h[0] = ha - hb;
+  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
+  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 16));
+  h[1] = ha - hb;
+  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
+  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 32));
+  h[2] = ha - hb;
+  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
+  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 48));
+  h[3] = ha - hb;
+  Packet4h result;
+  result.x = _mm_set_pi16(h[3].x, h[2].x, h[1].x, h[0].x);
+  return result;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4h pmul<Packet4h>(const Packet4h& a, const Packet4h& b) {
+  __int64_t a64 = _mm_cvtm64_si64(a.x);
+  __int64_t b64 = _mm_cvtm64_si64(b.x);
+
+  Eigen::half h[4];
+
+  Eigen::half ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64));
+  Eigen::half hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64));
+  h[0] = ha * hb;
+  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
+  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 16));
+  h[1] = ha * hb;
+  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
+  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 32));
+  h[2] = ha * hb;
+  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
+  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 48));
+  h[3] = ha * hb;
+  Packet4h result;
+  result.x = _mm_set_pi16(h[3].x, h[2].x, h[1].x, h[0].x);
+  return result;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4h pdiv<Packet4h>(const Packet4h& a, const Packet4h& b) {
+  __int64_t a64 = _mm_cvtm64_si64(a.x);
+  __int64_t b64 = _mm_cvtm64_si64(b.x);
+
+  Eigen::half h[4];
+
+  Eigen::half ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64));
+  Eigen::half hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64));
+  h[0] = ha / hb;
+  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
+  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 16));
+  h[1] = ha / hb;
+  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
+  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 32));
+  h[2] = ha / hb;
+  ha = half_impl::raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
+  hb = half_impl::raw_uint16_to_half(static_cast<unsigned short>(b64 >> 48));
+  h[3] = ha / hb;
+  Packet4h result;
+  result.x = _mm_set_pi16(h[3].x, h[2].x, h[1].x, h[0].x);
+  return result;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4h pload<Packet4h>(const Eigen::half* from) {
+  Packet4h result;
+  result.x = _mm_cvtsi64_m64(*reinterpret_cast<const __int64_t*>(from));
+  return result;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4h ploadu<Packet4h>(const Eigen::half* from) {
+  Packet4h result;
+  result.x = _mm_cvtsi64_m64(*reinterpret_cast<const __int64_t*>(from));
+  return result;
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<Eigen::half>(Eigen::half* to, const Packet4h& from) {
+  __int64_t r = _mm_cvtm64_si64(from.x);
+  *(reinterpret_cast<__int64_t*>(to)) = r;
+}
+
+template<> EIGEN_STRONG_INLINE void pstoreu<Eigen::half>(Eigen::half* to, const Packet4h& from) {
+  __int64_t r = _mm_cvtm64_si64(from.x);
+  *(reinterpret_cast<__int64_t*>(to)) = r;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4h
+ploadquad<Packet4h>(const Eigen::half* from) {
+  return pset1<Packet4h>(*from);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4h pgather<Eigen::half, Packet4h>(const Eigen::half* from, Index stride)
+{
+  Packet4h result;
+  result.x = _mm_set_pi16(from[3*stride].x, from[2*stride].x, from[1*stride].x, from[0*stride].x);
+  return result;
+}
+
+template<> EIGEN_STRONG_INLINE void pscatter<Eigen::half, Packet4h>(Eigen::half* to, const Packet4h& from, Index stride)
+{
+  __int64_t a = _mm_cvtm64_si64(from.x);
+  to[stride*0].x = static_cast<unsigned short>(a);
+  to[stride*1].x = static_cast<unsigned short>(a >> 16);
+  to[stride*2].x = static_cast<unsigned short>(a >> 32);
+  to[stride*3].x = static_cast<unsigned short>(a >> 48);
+}
+
+EIGEN_STRONG_INLINE void
+ptranspose(PacketBlock<Packet4h,4>& kernel) {
+  __m64 T0 = _mm_unpacklo_pi16(kernel.packet[0].x, kernel.packet[1].x);
+  __m64 T1 = _mm_unpacklo_pi16(kernel.packet[2].x, kernel.packet[3].x);
+  __m64 T2 = _mm_unpackhi_pi16(kernel.packet[0].x, kernel.packet[1].x);
+  __m64 T3 = _mm_unpackhi_pi16(kernel.packet[2].x, kernel.packet[3].x);
+
+  kernel.packet[0].x = _mm_unpacklo_pi32(T0, T1);
+  kernel.packet[1].x = _mm_unpackhi_pi32(T0, T1);
+  kernel.packet[2].x = _mm_unpacklo_pi32(T2, T3);
+  kernel.packet[3].x = _mm_unpackhi_pi32(T2, T3);
+}
+
+#endif
+
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#if EIGEN_COMP_PGI && EIGEN_COMP_PGI < 1900
+// PGI++ does not define the following intrinsics in C++ mode.
+static inline __m128  _mm_castpd_ps   (__m128d x) { return reinterpret_cast<__m128&>(x);  }
+static inline __m128i _mm_castpd_si128(__m128d x) { return reinterpret_cast<__m128i&>(x); }
+static inline __m128d _mm_castps_pd   (__m128  x) { return reinterpret_cast<__m128d&>(x); }
+static inline __m128i _mm_castps_si128(__m128  x) { return reinterpret_cast<__m128i&>(x); }
+static inline __m128  _mm_castsi128_ps(__m128i x) { return reinterpret_cast<__m128&>(x);  }
+static inline __m128d _mm_castsi128_pd(__m128i x) { return reinterpret_cast<__m128d&>(x); }
+#endif
+
+#endif // EIGEN_PACKET_MATH_SSE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SSE/TypeCasting.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SSE/TypeCasting.h
new file mode 100644
index 0000000000000000000000000000000000000000..bb28170307eb4dec45ed81d4a9e8ee334b4a3887
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SSE/TypeCasting.h
@@ -0,0 +1,177 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_SSE_H
+#define EIGEN_TYPE_CASTING_SSE_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_VECTORIZE_AVX
+template<> struct type_casting_traits<float, bool> : vectorized_type_casting_traits<float, bool> {};
+template<> struct type_casting_traits<bool, float> : vectorized_type_casting_traits<bool, float> {};
+
+template<> struct type_casting_traits<float, int> : vectorized_type_casting_traits<float, int> {};
+template<> struct type_casting_traits<int, float> : vectorized_type_casting_traits<int, float> {};
+
+template<> struct type_casting_traits<float, double> : vectorized_type_casting_traits<float, double> {};
+template<> struct type_casting_traits<double, float> : vectorized_type_casting_traits<double, float> {};
+
+template<> struct type_casting_traits<double, int> : vectorized_type_casting_traits<double, int> {};
+template<> struct type_casting_traits<int, double> : vectorized_type_casting_traits<int, double> {};
+#endif
+
+template <>
+EIGEN_STRONG_INLINE Packet16b pcast<Packet4f, Packet16b>(const Packet4f& a,
+                                                         const Packet4f& b,
+                                                         const Packet4f& c,
+                                                         const Packet4f& d) {
+  __m128 zero = pzero(a);
+  __m128 nonzero_a = _mm_cmpneq_ps(a, zero);
+  __m128 nonzero_b = _mm_cmpneq_ps(b, zero);
+  __m128 nonzero_c = _mm_cmpneq_ps(c, zero);
+  __m128 nonzero_d = _mm_cmpneq_ps(d, zero);
+  __m128i ab_bytes = _mm_packs_epi32(_mm_castps_si128(nonzero_a), _mm_castps_si128(nonzero_b));
+  __m128i cd_bytes = _mm_packs_epi32(_mm_castps_si128(nonzero_c), _mm_castps_si128(nonzero_d));
+  __m128i merged = _mm_packs_epi16(ab_bytes, cd_bytes);
+  return _mm_and_si128(merged, _mm_set1_epi8(1));
+}
+
+template <>
+EIGEN_STRONG_INLINE Packet4f pcast<Packet16b, Packet4f>(const Packet16b& a) {
+  const __m128 cst_one = _mm_set_ps1(1.0f);
+  #ifdef EIGEN_VECTORIZE_SSE4_1
+  __m128i a_extended = _mm_cvtepi8_epi32(a);
+  __m128i abcd = _mm_cmpeq_epi32(a_extended, _mm_setzero_si128());
+  #else
+  __m128i abcd_efhg_ijkl_mnop = _mm_cmpeq_epi8(a, _mm_setzero_si128());
+  __m128i aabb_ccdd_eeff_gghh = _mm_unpacklo_epi8(abcd_efhg_ijkl_mnop, abcd_efhg_ijkl_mnop);
+  __m128i abcd = _mm_unpacklo_epi8(aabb_ccdd_eeff_gghh, aabb_ccdd_eeff_gghh);
+  #endif
+  __m128 result = _mm_andnot_ps(_mm_castsi128_ps(abcd), cst_one);
+  return result;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i pcast<Packet4f, Packet4i>(const Packet4f& a) {
+  return _mm_cvttps_epi32(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i pcast<Packet2d, Packet4i>(const Packet2d& a, const Packet2d& b) {
+  return _mm_castps_si128(_mm_shuffle_ps(_mm_castsi128_ps(_mm_cvttpd_epi32(a)),
+                                         _mm_castsi128_ps(_mm_cvttpd_epi32(b)),
+                                         (1 << 2) | (1 << 6)));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4i, Packet4f>(const Packet4i& a) {
+  return _mm_cvtepi32_ps(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet2d, Packet4f>(const Packet2d& a, const Packet2d& b) {
+  return _mm_shuffle_ps(_mm_cvtpd_ps(a), _mm_cvtpd_ps(b), (1 << 2) | (1 << 6));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pcast<Packet4i, Packet2d>(const Packet4i& a) {
+  // Simply discard the second half of the input
+  return _mm_cvtepi32_pd(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pcast<Packet4f, Packet2d>(const Packet4f& a) {
+  // Simply discard the second half of the input
+  return _mm_cvtps_pd(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d, Packet4f>(const Packet4f& a) {
+  return _mm_castps_pd(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f, Packet2d>(const Packet2d& a) {
+  return _mm_castpd_ps(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i,Packet4f>(const Packet4f& a) {
+  return _mm_castps_si128(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f preinterpret<Packet4f,Packet4i>(const Packet4i& a) {
+  return _mm_castsi128_ps(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d preinterpret<Packet2d,Packet4i>(const Packet4i& a) {
+  return _mm_castsi128_pd(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i,Packet2d>(const Packet2d& a) {
+  return _mm_castpd_si128(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4ui preinterpret<Packet4ui, Packet4i>(const Packet4i& a) {
+  return Packet4ui(a);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i preinterpret<Packet4i, Packet4ui>(const Packet4ui& a) {
+  return Packet4i(a);
+}
+// Disable the following code since it's broken on too many platforms / compilers.
+//#elif defined(EIGEN_VECTORIZE_SSE) && (!EIGEN_ARCH_x86_64) && (!EIGEN_COMP_MSVC)
+#if 0
+
+template <>
+struct type_casting_traits<Eigen::half, float> {
+  enum {
+    VectorizedCast = 1,
+    SrcCoeffRatio = 1,
+    TgtCoeffRatio = 1
+  };
+};
+
+template<> EIGEN_STRONG_INLINE Packet4f pcast<Packet4h, Packet4f>(const Packet4h& a) {
+  __int64_t a64 = _mm_cvtm64_si64(a.x);
+  Eigen::half h = raw_uint16_to_half(static_cast<unsigned short>(a64));
+  float f1 = static_cast<float>(h);
+  h = raw_uint16_to_half(static_cast<unsigned short>(a64 >> 16));
+  float f2 = static_cast<float>(h);
+  h = raw_uint16_to_half(static_cast<unsigned short>(a64 >> 32));
+  float f3 = static_cast<float>(h);
+  h = raw_uint16_to_half(static_cast<unsigned short>(a64 >> 48));
+  float f4 = static_cast<float>(h);
+  return _mm_set_ps(f4, f3, f2, f1);
+}
+
+template <>
+struct type_casting_traits<float, Eigen::half> {
+  enum {
+    VectorizedCast = 1,
+    SrcCoeffRatio = 1,
+    TgtCoeffRatio = 1
+  };
+};
+
+template<> EIGEN_STRONG_INLINE Packet4h pcast<Packet4f, Packet4h>(const Packet4f& a) {
+  EIGEN_ALIGN16 float aux[4];
+  pstore(aux, a);
+  Eigen::half h0(aux[0]);
+  Eigen::half h1(aux[1]);
+  Eigen::half h2(aux[2]);
+  Eigen::half h3(aux[3]);
+
+  Packet4h result;
+  result.x = _mm_set_pi16(h3.x, h2.x, h1.x, h0.x);
+  return result;
+}
+
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TYPE_CASTING_SSE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SVE/MathFunctions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SVE/MathFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..8b588b1e09571bb4128865aa366b71c97dd0372e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SVE/MathFunctions.h
@@ -0,0 +1,46 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2020, Arm Limited and Contributors
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATH_FUNCTIONS_SVE_H
+#define EIGEN_MATH_FUNCTIONS_SVE_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pexp<PacketXf>(const PacketXf& x) {
+  return pexp_float(x);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf plog<PacketXf>(const PacketXf& x) {
+  return plog_float(x);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf psin<PacketXf>(const PacketXf& x) {
+  return psin_float(x);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pcos<PacketXf>(const PacketXf& x) {
+  return pcos_float(x);
+}
+
+// Hyperbolic Tangent function.
+template <>
+EIGEN_STRONG_INLINE PacketXf ptanh<PacketXf>(const PacketXf& x) {
+  return internal::generic_fast_tanh_float(x);
+}
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_SVE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SVE/PacketMath.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SVE/PacketMath.h
new file mode 100644
index 0000000000000000000000000000000000000000..a2f292fc1b306b4e4e7f33330e8fa0da9473d3d6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SVE/PacketMath.h
@@ -0,0 +1,752 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2020, Arm Limited and Contributors
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_SVE_H
+#define EIGEN_PACKET_MATH_SVE_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen
+{
+namespace internal
+{
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 8
+#endif
+
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS 32
+
+template <typename Scalar, int SVEVectorLength>
+struct sve_packet_size_selector {
+  enum { size = SVEVectorLength / (sizeof(Scalar) * CHAR_BIT) };
+};
+
+/********************************* int32 **************************************/
+typedef svint32_t PacketXi __attribute__((arm_sve_vector_bits(EIGEN_ARM64_SVE_VL)));
+
+template <>
+struct packet_traits<numext::int32_t> : default_packet_traits {
+  typedef PacketXi type;
+  typedef PacketXi half;  // Half not implemented yet
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = sve_packet_size_selector<numext::int32_t, EIGEN_ARM64_SVE_VL>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0  // Not implemented in SVE
+  };
+};
+
+template <>
+struct unpacket_traits<PacketXi> {
+  typedef numext::int32_t type;
+  typedef PacketXi half;  // Half not yet implemented
+  enum {
+    size = sve_packet_size_selector<numext::int32_t, EIGEN_ARM64_SVE_VL>::size,
+    alignment = Aligned64,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE void prefetch<numext::int32_t>(const numext::int32_t* addr)
+{
+  svprfw(svptrue_b32(), addr, SV_PLDL1KEEP);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pset1<PacketXi>(const numext::int32_t& from)
+{
+  return svdup_n_s32(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi plset<PacketXi>(const numext::int32_t& a)
+{
+  numext::int32_t c[packet_traits<numext::int32_t>::size];
+  for (int i = 0; i < packet_traits<numext::int32_t>::size; i++) c[i] = i;
+  return svadd_s32_z(svptrue_b32(), pset1<PacketXi>(a), svld1_s32(svptrue_b32(), c));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi padd<PacketXi>(const PacketXi& a, const PacketXi& b)
+{
+  return svadd_s32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi psub<PacketXi>(const PacketXi& a, const PacketXi& b)
+{
+  return svsub_s32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pnegate(const PacketXi& a)
+{
+  return svneg_s32_z(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pconj(const PacketXi& a)
+{
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pmul<PacketXi>(const PacketXi& a, const PacketXi& b)
+{
+  return svmul_s32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pdiv<PacketXi>(const PacketXi& a, const PacketXi& b)
+{
+  return svdiv_s32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pmadd(const PacketXi& a, const PacketXi& b, const PacketXi& c)
+{
+  return svmla_s32_z(svptrue_b32(), c, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pmin<PacketXi>(const PacketXi& a, const PacketXi& b)
+{
+  return svmin_s32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pmax<PacketXi>(const PacketXi& a, const PacketXi& b)
+{
+  return svmax_s32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pcmp_le<PacketXi>(const PacketXi& a, const PacketXi& b)
+{
+  return svdup_n_s32_z(svcmple_s32(svptrue_b32(), a, b), 0xffffffffu);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pcmp_lt<PacketXi>(const PacketXi& a, const PacketXi& b)
+{
+  return svdup_n_s32_z(svcmplt_s32(svptrue_b32(), a, b), 0xffffffffu);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pcmp_eq<PacketXi>(const PacketXi& a, const PacketXi& b)
+{
+  return svdup_n_s32_z(svcmpeq_s32(svptrue_b32(), a, b), 0xffffffffu);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi ptrue<PacketXi>(const PacketXi& /*a*/)
+{
+  return svdup_n_s32_z(svptrue_b32(), 0xffffffffu);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pzero<PacketXi>(const PacketXi& /*a*/)
+{
+  return svdup_n_s32_z(svptrue_b32(), 0);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pand<PacketXi>(const PacketXi& a, const PacketXi& b)
+{
+  return svand_s32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi por<PacketXi>(const PacketXi& a, const PacketXi& b)
+{
+  return svorr_s32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pxor<PacketXi>(const PacketXi& a, const PacketXi& b)
+{
+  return sveor_s32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pandnot<PacketXi>(const PacketXi& a, const PacketXi& b)
+{
+  return svbic_s32_z(svptrue_b32(), a, b);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE PacketXi parithmetic_shift_right(PacketXi a)
+{
+  return svasrd_n_s32_z(svptrue_b32(), a, N);
+}
+
+template <int N>
+EIGEN_STRONG_INLINE PacketXi plogical_shift_right(PacketXi a)
+{
+  return svreinterpret_s32_u32(svlsr_n_u32_z(svptrue_b32(), svreinterpret_u32_s32(a), N));
+}
+
+template <int N>
+EIGEN_STRONG_INLINE PacketXi plogical_shift_left(PacketXi a)
+{
+  return svlsl_n_s32_z(svptrue_b32(), a, N);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pload<PacketXi>(const numext::int32_t* from)
+{
+  EIGEN_DEBUG_ALIGNED_LOAD return svld1_s32(svptrue_b32(), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi ploadu<PacketXi>(const numext::int32_t* from)
+{
+  EIGEN_DEBUG_UNALIGNED_LOAD return svld1_s32(svptrue_b32(), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi ploaddup<PacketXi>(const numext::int32_t* from)
+{
+  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
+  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
+  return svld1_gather_u32index_s32(svptrue_b32(), from, indices);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi ploadquad<PacketXi>(const numext::int32_t* from)
+{
+  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
+  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
+  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a0, a0, a1, a1, a1, a1, ...}
+  return svld1_gather_u32index_s32(svptrue_b32(), from, indices);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<numext::int32_t>(numext::int32_t* to, const PacketXi& from)
+{
+  EIGEN_DEBUG_ALIGNED_STORE svst1_s32(svptrue_b32(), to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<numext::int32_t>(numext::int32_t* to, const PacketXi& from)
+{
+  EIGEN_DEBUG_UNALIGNED_STORE svst1_s32(svptrue_b32(), to, from);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline PacketXi pgather<numext::int32_t, PacketXi>(const numext::int32_t* from, Index stride)
+{
+  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
+  svint32_t indices = svindex_s32(0, stride);
+  return svld1_gather_s32index_s32(svptrue_b32(), from, indices);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<numext::int32_t, PacketXi>(numext::int32_t* to, const PacketXi& from, Index stride)
+{
+  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
+  svint32_t indices = svindex_s32(0, stride);
+  svst1_scatter_s32index_s32(svptrue_b32(), to, indices, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t pfirst<PacketXi>(const PacketXi& a)
+{
+  // svlasta returns the first element if all predicate bits are 0
+  return svlasta_s32(svpfalse_b(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi preverse(const PacketXi& a)
+{
+  return svrev_s32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pabs(const PacketXi& a)
+{
+  return svabs_s32_z(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux<PacketXi>(const PacketXi& a)
+{
+  return static_cast<numext::int32_t>(svaddv_s32(svptrue_b32(), a));
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux_mul<PacketXi>(const PacketXi& a)
+{
+  EIGEN_STATIC_ASSERT((EIGEN_ARM64_SVE_VL % 128 == 0),
+                      EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT);
+
+  // Multiply the vector by its reverse
+  svint32_t prod = svmul_s32_z(svptrue_b32(), a, svrev_s32(a));
+  svint32_t half_prod;
+
+  // Extract the high half of the vector. Depending on the VL more reductions need to be done
+  if (EIGEN_ARM64_SVE_VL >= 2048) {
+    half_prod = svtbl_s32(prod, svindex_u32(32, 1));
+    prod = svmul_s32_z(svptrue_b32(), prod, half_prod);
+  }
+  if (EIGEN_ARM64_SVE_VL >= 1024) {
+    half_prod = svtbl_s32(prod, svindex_u32(16, 1));
+    prod = svmul_s32_z(svptrue_b32(), prod, half_prod);
+  }
+  if (EIGEN_ARM64_SVE_VL >= 512) {
+    half_prod = svtbl_s32(prod, svindex_u32(8, 1));
+    prod = svmul_s32_z(svptrue_b32(), prod, half_prod);
+  }
+  if (EIGEN_ARM64_SVE_VL >= 256) {
+    half_prod = svtbl_s32(prod, svindex_u32(4, 1));
+    prod = svmul_s32_z(svptrue_b32(), prod, half_prod);
+  }
+  // Last reduction
+  half_prod = svtbl_s32(prod, svindex_u32(2, 1));
+  prod = svmul_s32_z(svptrue_b32(), prod, half_prod);
+
+  // The reduction is done to the first element.
+  return pfirst<PacketXi>(prod);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux_min<PacketXi>(const PacketXi& a)
+{
+  return svminv_s32(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE numext::int32_t predux_max<PacketXi>(const PacketXi& a)
+{
+  return svmaxv_s32(svptrue_b32(), a);
+}
+
+template <int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<PacketXi, N>& kernel) {
+  int buffer[packet_traits<numext::int32_t>::size * N] = {0};
+  int i = 0;
+
+  PacketXi stride_index = svindex_s32(0, N);
+
+  for (i = 0; i < N; i++) {
+    svst1_scatter_s32index_s32(svptrue_b32(), buffer + i, stride_index, kernel.packet[i]);
+  }
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] = svld1_s32(svptrue_b32(), buffer + i * packet_traits<numext::int32_t>::size);
+  }
+}
+
+/********************************* float32 ************************************/
+
+typedef svfloat32_t PacketXf __attribute__((arm_sve_vector_bits(EIGEN_ARM64_SVE_VL)));
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef PacketXf type;
+  typedef PacketXf half;
+
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = sve_packet_size_selector<float, EIGEN_ARM64_SVE_VL>::size,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasShift = 1,
+    HasMul = 1,
+    HasNegate = 1,
+    HasAbs = 1,
+    HasArg = 0,
+    HasAbs2 = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasConj = 1,
+    HasSetLinear = 0,
+    HasBlend = 0,
+    HasReduxp = 0,  // Not implemented in SVE
+
+    HasDiv = 1,
+    HasFloor = 1,
+
+    HasSin = EIGEN_FAST_MATH,
+    HasCos = EIGEN_FAST_MATH,
+    HasLog = 1,
+    HasExp = 1,
+    HasSqrt = 0,
+    HasTanh = EIGEN_FAST_MATH,
+    HasErf = EIGEN_FAST_MATH
+  };
+};
+
+template <>
+struct unpacket_traits<PacketXf> {
+  typedef float type;
+  typedef PacketXf half;  // Half not yet implemented
+  typedef PacketXi integer_packet;
+
+  enum {
+    size = sve_packet_size_selector<float, EIGEN_ARM64_SVE_VL>::size,
+    alignment = Aligned64,
+    vectorizable = true,
+    masked_load_available = false,
+    masked_store_available = false
+  };
+};
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pset1<PacketXf>(const float& from)
+{
+  return svdup_n_f32(from);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pset1frombits<PacketXf>(numext::uint32_t from)
+{
+  return svreinterpret_f32_u32(svdup_n_u32_z(svptrue_b32(), from));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf plset<PacketXf>(const float& a)
+{
+  float c[packet_traits<float>::size];
+  for (int i = 0; i < packet_traits<float>::size; i++) c[i] = i;
+  return svadd_f32_z(svptrue_b32(), pset1<PacketXf>(a), svld1_f32(svptrue_b32(), c));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf padd<PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svadd_f32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf psub<PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svsub_f32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pnegate(const PacketXf& a)
+{
+  return svneg_f32_z(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pconj(const PacketXf& a)
+{
+  return a;
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmul<PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svmul_f32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pdiv<PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svdiv_f32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmadd(const PacketXf& a, const PacketXf& b, const PacketXf& c)
+{
+  return svmla_f32_z(svptrue_b32(), c, a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmin<PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svmin_f32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmin<PropagateNaN, PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return pmin<PacketXf>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmin<PropagateNumbers, PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svminnm_f32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmax<PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svmax_f32_z(svptrue_b32(), a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmax<PropagateNaN, PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return pmax<PacketXf>(a, b);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pmax<PropagateNumbers, PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svmaxnm_f32_z(svptrue_b32(), a, b);
+}
+
+// Float comparisons in SVE return svbool (predicate). Use svdup to set active
+// lanes to 1 (0xffffffffu) and inactive lanes to 0.
+template <>
+EIGEN_STRONG_INLINE PacketXf pcmp_le<PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svreinterpret_f32_u32(svdup_n_u32_z(svcmple_f32(svptrue_b32(), a, b), 0xffffffffu));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pcmp_lt<PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svreinterpret_f32_u32(svdup_n_u32_z(svcmplt_f32(svptrue_b32(), a, b), 0xffffffffu));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pcmp_eq<PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svreinterpret_f32_u32(svdup_n_u32_z(svcmpeq_f32(svptrue_b32(), a, b), 0xffffffffu));
+}
+
+// Do a predicate inverse (svnot_b_z) on the predicate resulted from the
+// greater/equal comparison (svcmpge_f32). Then fill a float vector with the
+// active elements.
+template <>
+EIGEN_STRONG_INLINE PacketXf pcmp_lt_or_nan<PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svreinterpret_f32_u32(svdup_n_u32_z(svnot_b_z(svptrue_b32(), svcmpge_f32(svptrue_b32(), a, b)), 0xffffffffu));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pfloor<PacketXf>(const PacketXf& a)
+{
+  return svrintm_f32_z(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf ptrue<PacketXf>(const PacketXf& /*a*/)
+{
+  return svreinterpret_f32_u32(svdup_n_u32_z(svptrue_b32(), 0xffffffffu));
+}
+
+// Logical Operations are not supported for float, so reinterpret casts
+template <>
+EIGEN_STRONG_INLINE PacketXf pand<PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svreinterpret_f32_u32(svand_u32_z(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf por<PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svreinterpret_f32_u32(svorr_u32_z(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pxor<PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svreinterpret_f32_u32(sveor_u32_z(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pandnot<PacketXf>(const PacketXf& a, const PacketXf& b)
+{
+  return svreinterpret_f32_u32(svbic_u32_z(svptrue_b32(), svreinterpret_u32_f32(a), svreinterpret_u32_f32(b)));
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pload<PacketXf>(const float* from)
+{
+  EIGEN_DEBUG_ALIGNED_LOAD return svld1_f32(svptrue_b32(), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf ploadu<PacketXf>(const float* from)
+{
+  EIGEN_DEBUG_UNALIGNED_LOAD return svld1_f32(svptrue_b32(), from);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf ploaddup<PacketXf>(const float* from)
+{
+  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
+  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
+  return svld1_gather_u32index_f32(svptrue_b32(), from, indices);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf ploadquad<PacketXf>(const float* from)
+{
+  svuint32_t indices = svindex_u32(0, 1);  // index {base=0, base+step=1, base+step*2, ...}
+  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a1, a1, a2, a2, ...}
+  indices = svzip1_u32(indices, indices);  // index in the format {a0, a0, a0, a0, a1, a1, a1, a1, ...}
+  return svld1_gather_u32index_f32(svptrue_b32(), from, indices);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstore<float>(float* to, const PacketXf& from)
+{
+  EIGEN_DEBUG_ALIGNED_STORE svst1_f32(svptrue_b32(), to, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const PacketXf& from)
+{
+  EIGEN_DEBUG_UNALIGNED_STORE svst1_f32(svptrue_b32(), to, from);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline PacketXf pgather<float, PacketXf>(const float* from, Index stride)
+{
+  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
+  svint32_t indices = svindex_s32(0, stride);
+  return svld1_gather_s32index_f32(svptrue_b32(), from, indices);
+}
+
+template <>
+EIGEN_DEVICE_FUNC inline void pscatter<float, PacketXf>(float* to, const PacketXf& from, Index stride)
+{
+  // Indice format: {base=0, base+stride, base+stride*2, base+stride*3, ...}
+  svint32_t indices = svindex_s32(0, stride);
+  svst1_scatter_s32index_f32(svptrue_b32(), to, indices, from);
+}
+
+template <>
+EIGEN_STRONG_INLINE float pfirst<PacketXf>(const PacketXf& a)
+{
+  // svlasta returns the first element if all predicate bits are 0
+  return svlasta_f32(svpfalse_b(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf preverse(const PacketXf& a)
+{
+  return svrev_f32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pabs(const PacketXf& a)
+{
+  return svabs_f32_z(svptrue_b32(), a);
+}
+
+// TODO(tellenbach): Should this go into MathFunctions.h? If so, change for 
+// all vector extensions and the generic version.
+template <>
+EIGEN_STRONG_INLINE PacketXf pfrexp<PacketXf>(const PacketXf& a, PacketXf& exponent)
+{
+  return pfrexp_generic(a, exponent);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux<PacketXf>(const PacketXf& a)
+{
+  return svaddv_f32(svptrue_b32(), a);
+}
+
+// Other reduction functions:
+// mul
+// Only works for SVE Vls multiple of 128
+template <>
+EIGEN_STRONG_INLINE float predux_mul<PacketXf>(const PacketXf& a)
+{
+  EIGEN_STATIC_ASSERT((EIGEN_ARM64_SVE_VL % 128 == 0),
+                      EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT);
+  // Multiply the vector by its reverse
+  svfloat32_t prod = svmul_f32_z(svptrue_b32(), a, svrev_f32(a));
+  svfloat32_t half_prod;
+
+  // Extract the high half of the vector. Depending on the VL more reductions need to be done
+  if (EIGEN_ARM64_SVE_VL >= 2048) {
+    half_prod = svtbl_f32(prod, svindex_u32(32, 1));
+    prod = svmul_f32_z(svptrue_b32(), prod, half_prod);
+  }
+  if (EIGEN_ARM64_SVE_VL >= 1024) {
+    half_prod = svtbl_f32(prod, svindex_u32(16, 1));
+    prod = svmul_f32_z(svptrue_b32(), prod, half_prod);
+  }
+  if (EIGEN_ARM64_SVE_VL >= 512) {
+    half_prod = svtbl_f32(prod, svindex_u32(8, 1));
+    prod = svmul_f32_z(svptrue_b32(), prod, half_prod);
+  }
+  if (EIGEN_ARM64_SVE_VL >= 256) {
+    half_prod = svtbl_f32(prod, svindex_u32(4, 1));
+    prod = svmul_f32_z(svptrue_b32(), prod, half_prod);
+  }
+  // Last reduction
+  half_prod = svtbl_f32(prod, svindex_u32(2, 1));
+  prod = svmul_f32_z(svptrue_b32(), prod, half_prod);
+
+  // The reduction is done to the first element.
+  return pfirst<PacketXf>(prod);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_min<PacketXf>(const PacketXf& a)
+{
+  return svminv_f32(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE float predux_max<PacketXf>(const PacketXf& a)
+{
+  return svmaxv_f32(svptrue_b32(), a);
+}
+
+template<int N>
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<PacketXf, N>& kernel)
+{
+  float buffer[packet_traits<float>::size * N] = {0};
+  int i = 0;
+
+  PacketXi stride_index = svindex_s32(0, N);
+
+  for (i = 0; i < N; i++) {
+    svst1_scatter_s32index_f32(svptrue_b32(), buffer + i, stride_index, kernel.packet[i]);
+  }
+
+  for (i = 0; i < N; i++) {
+    kernel.packet[i] = svld1_f32(svptrue_b32(), buffer + i * packet_traits<float>::size);
+  }
+}
+
+template<>
+EIGEN_STRONG_INLINE PacketXf pldexp<PacketXf>(const PacketXf& a, const PacketXf& exponent)
+{
+  return pldexp_generic(a, exponent);
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_SVE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SVE/TypeCasting.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SVE/TypeCasting.h
new file mode 100644
index 0000000000000000000000000000000000000000..1067a418df0cb4c87ed342d859d89f1f673525c1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SVE/TypeCasting.h
@@ -0,0 +1,51 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2020, Arm Limited and Contributors
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TYPE_CASTING_SVE_H
+#define EIGEN_TYPE_CASTING_SVE_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+template <>
+struct type_casting_traits<float, numext::int32_t> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+struct type_casting_traits<numext::int32_t, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+EIGEN_STRONG_INLINE PacketXf pcast<PacketXi, PacketXf>(const PacketXi& a) {
+  return svcvt_f32_s32_z(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi pcast<PacketXf, PacketXi>(const PacketXf& a) {
+  return svcvt_s32_f32_z(svptrue_b32(), a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXf preinterpret<PacketXf, PacketXi>(const PacketXi& a) {
+  return svreinterpret_f32_s32(a);
+}
+
+template <>
+EIGEN_STRONG_INLINE PacketXi preinterpret<PacketXi, PacketXf>(const PacketXf& a) {
+  return svreinterpret_s32_f32(a);
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#endif // EIGEN_TYPE_CASTING_SVE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SYCL/InteropHeaders.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SYCL/InteropHeaders.h
new file mode 100644
index 0000000000000000000000000000000000000000..30c4980d366d0b6948771f2c4062304a353251f4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SYCL/InteropHeaders.h
@@ -0,0 +1,233 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************
+ * InteropHeaders.h
+ *
+ * \brief:
+ *  InteropHeaders
+ *
+ *****************************************************************/
+
+#ifndef EIGEN_INTEROP_HEADERS_SYCL_H
+#define EIGEN_INTEROP_HEADERS_SYCL_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+#if !defined(EIGEN_DONT_VECTORIZE_SYCL)
+
+namespace internal {
+
+template <int has_blend, int lengths>
+struct sycl_packet_traits : default_packet_traits {
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = lengths,
+    HasDiv = 1,
+    HasLog = 1,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasSin = 1,
+    HasCos = 1,
+    HasTan = 1,
+    HasASin = 1,
+    HasACos = 1,
+    HasATan = 1,
+    HasSinh = 1,
+    HasCosh = 1,
+    HasTanh = 1,
+    HasLGamma = 0,
+    HasDiGamma = 0,
+    HasZeta = 0,
+    HasPolygamma = 0,
+    HasErf = 0,
+    HasErfc = 0,
+    HasNdtri = 0,
+    HasIGamma = 0,
+    HasIGammac = 0,
+    HasBetaInc = 0,
+    HasBlend = has_blend,
+    // This flag is used to indicate whether packet comparison is supported.
+    // pcmp_eq, pcmp_lt and pcmp_le should be defined for it to be true.
+    HasCmp = 1,
+    HasMax = 1,
+    HasMin = 1,
+    HasMul = 1,
+    HasAdd = 1,
+    HasFloor = 1,
+    HasRound = 1,
+    HasRint = 1,
+    HasLog1p = 1,
+    HasExpm1 = 1,
+    HasCeil = 1,
+  };
+};
+
+#ifdef SYCL_DEVICE_ONLY
+#define SYCL_PACKET_TRAITS(packet_type, has_blend, unpacket_type, lengths) \
+  template <>                                                              \
+  struct packet_traits<unpacket_type>                                      \
+      : sycl_packet_traits<has_blend, lengths> {                           \
+    typedef packet_type type;                                              \
+    typedef packet_type half;                                              \
+  };
+
+SYCL_PACKET_TRAITS(cl::sycl::cl_float4, 1, float, 4)
+SYCL_PACKET_TRAITS(cl::sycl::cl_float4, 1, const float, 4)
+SYCL_PACKET_TRAITS(cl::sycl::cl_double2, 0, double, 2)
+SYCL_PACKET_TRAITS(cl::sycl::cl_double2, 0, const double, 2)
+#undef SYCL_PACKET_TRAITS
+
+// Make sure this is only available when targeting a GPU: we don't want to
+// introduce conflicts between these packet_traits definitions and the ones
+// we'll use on the host side (SSE, AVX, ...)
+#define SYCL_ARITHMETIC(packet_type)  \
+  template <>                         \
+  struct is_arithmetic<packet_type> { \
+    enum { value = true };            \
+  };
+SYCL_ARITHMETIC(cl::sycl::cl_float4)
+SYCL_ARITHMETIC(cl::sycl::cl_double2)
+#undef SYCL_ARITHMETIC
+
+#define SYCL_UNPACKET_TRAITS(packet_type, unpacket_type, lengths)        \
+  template <>                                                            \
+  struct unpacket_traits<packet_type> {                                  \
+    typedef unpacket_type type;                                          \
+    enum { size = lengths, vectorizable = true, alignment = Aligned16 }; \
+    typedef packet_type half;                                            \
+  };
+SYCL_UNPACKET_TRAITS(cl::sycl::cl_float4, float, 4)
+SYCL_UNPACKET_TRAITS(cl::sycl::cl_double2, double, 2)
+
+#undef SYCL_UNPACKET_TRAITS
+#endif
+
+}  // end namespace internal
+
+#endif
+
+namespace TensorSycl {
+namespace internal {
+
+template <typename PacketReturnType, int PacketSize>
+struct PacketWrapper;
+// This function should never get called on the device
+#ifndef SYCL_DEVICE_ONLY
+template <typename PacketReturnType, int PacketSize>
+struct PacketWrapper {
+  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type
+      Scalar;
+  template <typename Index>
+  EIGEN_DEVICE_FUNC static Scalar scalarize(Index, PacketReturnType &) {
+    eigen_assert(false && "THERE IS NO PACKETIZE VERSION FOR  THE CHOSEN TYPE");
+    abort();
+  }
+  EIGEN_DEVICE_FUNC static PacketReturnType convert_to_packet_type(Scalar in,
+                                                                   Scalar) {
+    return ::Eigen::internal::template plset<PacketReturnType>(in);
+  }
+  EIGEN_DEVICE_FUNC static void set_packet(PacketReturnType, Scalar *) {
+    eigen_assert(false && "THERE IS NO PACKETIZE VERSION FOR  THE CHOSEN TYPE");
+    abort();
+  }
+};
+
+#elif defined(SYCL_DEVICE_ONLY)
+template <typename PacketReturnType>
+struct PacketWrapper<PacketReturnType, 4> {
+  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type
+      Scalar;
+  template <typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Scalar scalarize(Index index, PacketReturnType &in) {
+    switch (index) {
+      case 0:
+        return in.x();
+      case 1:
+        return in.y();
+      case 2:
+        return in.z();
+      case 3:
+        return in.w();
+      default:
+      //INDEX MUST BE BETWEEN 0 and 3.There is no abort function in SYCL kernel. so we cannot use abort here. 
+      // The code will never reach here
+      __builtin_unreachable();
+    }
+    __builtin_unreachable();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType convert_to_packet_type(
+      Scalar in, Scalar other) {
+    return PacketReturnType(in, other, other, other);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void set_packet(PacketReturnType &lhs, Scalar *rhs) {
+    lhs = PacketReturnType(rhs[0], rhs[1], rhs[2], rhs[3]);
+  }
+};
+
+template <typename PacketReturnType>
+struct PacketWrapper<PacketReturnType, 1> {
+  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type
+      Scalar;
+  template <typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Scalar scalarize(Index, PacketReturnType &in) {
+    return in;
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType convert_to_packet_type(Scalar in,
+                                                                   Scalar) {
+    return PacketReturnType(in);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void set_packet(PacketReturnType &lhs, Scalar *rhs) {
+    lhs = rhs[0];
+  }
+};
+
+template <typename PacketReturnType>
+struct PacketWrapper<PacketReturnType, 2> {
+  typedef typename ::Eigen::internal::unpacket_traits<PacketReturnType>::type
+      Scalar;
+  template <typename Index>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static Scalar scalarize(Index index, PacketReturnType &in) {
+    switch (index) {
+      case 0:
+        return in.x();
+      case 1:
+        return in.y();
+      default:
+        //INDEX MUST BE BETWEEN 0 and 1.There is no abort function in SYCL kernel. so we cannot use abort here. 
+      // The code will never reach here
+        __builtin_unreachable();
+    }
+    __builtin_unreachable();
+  }
+  
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static PacketReturnType convert_to_packet_type(
+      Scalar in, Scalar other) {
+    return PacketReturnType(in, other);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE static void set_packet(PacketReturnType &lhs, Scalar *rhs) {
+    lhs = PacketReturnType(rhs[0], rhs[1]);
+  }
+};
+
+#endif
+
+}  // end namespace internal
+}  // end namespace TensorSycl
+}  // end namespace Eigen
+
+#endif  // EIGEN_INTEROP_HEADERS_SYCL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SYCL/MathFunctions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SYCL/MathFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..9eb46bb89f608db3220d240d70a82c8c6b2b590f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SYCL/MathFunctions.h
@@ -0,0 +1,303 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************
+ * MathFunctions.h
+ *
+ * \brief:
+ *  MathFunctions
+ *
+ *****************************************************************/
+
+#ifndef EIGEN_MATH_FUNCTIONS_SYCL_H
+#define EIGEN_MATH_FUNCTIONS_SYCL_H
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Make sure this is only available when targeting a GPU: we don't want to
+// introduce conflicts between these packet_traits definitions and the ones
+// we'll use on the host side (SSE, AVX, ...)
+#if defined(SYCL_DEVICE_ONLY)
+#define SYCL_PLOG(packet_type)                                         \
+  template <>                                                          \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type plog<packet_type>( \
+      const packet_type& a) {                                          \
+    return cl::sycl::log(a);                                           \
+  }
+
+SYCL_PLOG(cl::sycl::cl_float4)
+SYCL_PLOG(cl::sycl::cl_double2)
+#undef SYCL_PLOG
+
+#define SYCL_PLOG1P(packet_type)                                         \
+  template <>                                                            \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type plog1p<packet_type>( \
+      const packet_type& a) {                                            \
+    return cl::sycl::log1p(a);                                           \
+  }
+
+SYCL_PLOG1P(cl::sycl::cl_float4)
+SYCL_PLOG1P(cl::sycl::cl_double2)
+#undef SYCL_PLOG1P
+
+#define SYCL_PLOG10(packet_type)                                         \
+  template <>                                                            \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type plog10<packet_type>( \
+      const packet_type& a) {                                            \
+    return cl::sycl::log10(a);                                           \
+  }
+
+SYCL_PLOG10(cl::sycl::cl_float4)
+SYCL_PLOG10(cl::sycl::cl_double2)
+#undef SYCL_PLOG10
+
+#define SYCL_PEXP(packet_type)                                         \
+  template <>                                                          \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pexp<packet_type>( \
+      const packet_type& a) {                                          \
+    return cl::sycl::exp(a);                                           \
+  }
+
+SYCL_PEXP(cl::sycl::cl_float4)
+SYCL_PEXP(cl::sycl::cl_float)
+SYCL_PEXP(cl::sycl::cl_double2)
+#undef SYCL_PEXP
+
+#define SYCL_PEXPM1(packet_type)                                         \
+  template <>                                                            \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pexpm1<packet_type>( \
+      const packet_type& a) {                                            \
+    return cl::sycl::expm1(a);                                           \
+  }
+
+SYCL_PEXPM1(cl::sycl::cl_float4)
+SYCL_PEXPM1(cl::sycl::cl_double2)
+#undef SYCL_PEXPM1
+
+#define SYCL_PSQRT(packet_type)                                         \
+  template <>                                                           \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type psqrt<packet_type>( \
+      const packet_type& a) {                                           \
+    return cl::sycl::sqrt(a);                                           \
+  }
+
+SYCL_PSQRT(cl::sycl::cl_float4)
+SYCL_PSQRT(cl::sycl::cl_double2)
+#undef SYCL_PSQRT
+
+#define SYCL_PRSQRT(packet_type)                                         \
+  template <>                                                            \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type prsqrt<packet_type>( \
+      const packet_type& a) {                                            \
+    return cl::sycl::rsqrt(a);                                           \
+  }
+
+SYCL_PRSQRT(cl::sycl::cl_float4)
+SYCL_PRSQRT(cl::sycl::cl_double2)
+#undef SYCL_PRSQRT
+
+/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
+#define SYCL_PSIN(packet_type)                                         \
+  template <>                                                          \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type psin<packet_type>( \
+      const packet_type& a) {                                          \
+    return cl::sycl::sin(a);                                           \
+  }
+
+SYCL_PSIN(cl::sycl::cl_float4)
+SYCL_PSIN(cl::sycl::cl_double2)
+#undef SYCL_PSIN
+
+/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
+#define SYCL_PCOS(packet_type)                                         \
+  template <>                                                          \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pcos<packet_type>( \
+      const packet_type& a) {                                          \
+    return cl::sycl::cos(a);                                           \
+  }
+
+SYCL_PCOS(cl::sycl::cl_float4)
+SYCL_PCOS(cl::sycl::cl_double2)
+#undef SYCL_PCOS
+
+/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
+#define SYCL_PTAN(packet_type)                                         \
+  template <>                                                          \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type ptan<packet_type>( \
+      const packet_type& a) {                                          \
+    return cl::sycl::tan(a);                                           \
+  }
+
+SYCL_PTAN(cl::sycl::cl_float4)
+SYCL_PTAN(cl::sycl::cl_double2)
+#undef SYCL_PTAN
+
+/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
+#define SYCL_PASIN(packet_type)                                         \
+  template <>                                                           \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pasin<packet_type>( \
+      const packet_type& a) {                                           \
+    return cl::sycl::asin(a);                                           \
+  }
+
+SYCL_PASIN(cl::sycl::cl_float4)
+SYCL_PASIN(cl::sycl::cl_double2)
+#undef SYCL_PASIN
+
+/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
+#define SYCL_PACOS(packet_type)                                         \
+  template <>                                                           \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pacos<packet_type>( \
+      const packet_type& a) {                                           \
+    return cl::sycl::acos(a);                                           \
+  }
+
+SYCL_PACOS(cl::sycl::cl_float4)
+SYCL_PACOS(cl::sycl::cl_double2)
+#undef SYCL_PACOS
+
+/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
+#define SYCL_PATAN(packet_type)                                         \
+  template <>                                                           \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type patan<packet_type>( \
+      const packet_type& a) {                                           \
+    return cl::sycl::atan(a);                                           \
+  }
+
+SYCL_PATAN(cl::sycl::cl_float4)
+SYCL_PATAN(cl::sycl::cl_double2)
+#undef SYCL_PATAN
+
+/** \internal \returns the hyperbolic sine of \a a (coeff-wise) */
+#define SYCL_PSINH(packet_type)                                         \
+  template <>                                                           \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type psinh<packet_type>( \
+      const packet_type& a) {                                           \
+    return cl::sycl::sinh(a);                                           \
+  }
+
+SYCL_PSINH(cl::sycl::cl_float4)
+SYCL_PSINH(cl::sycl::cl_double2)
+#undef SYCL_PSINH
+
+/** \internal \returns the hyperbolic cosine of \a a (coeff-wise) */
+#define SYCL_PCOSH(packet_type)                                         \
+  template <>                                                           \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pcosh<packet_type>( \
+      const packet_type& a) {                                           \
+    return cl::sycl::cosh(a);                                           \
+  }
+
+SYCL_PCOSH(cl::sycl::cl_float4)
+SYCL_PCOSH(cl::sycl::cl_double2)
+#undef SYCL_PCOSH
+
+/** \internal \returns the hyperbolic tan of \a a (coeff-wise) */
+#define SYCL_PTANH(packet_type)                                         \
+  template <>                                                           \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type ptanh<packet_type>( \
+      const packet_type& a) {                                           \
+    return cl::sycl::tanh(a);                                           \
+  }
+
+SYCL_PTANH(cl::sycl::cl_float4)
+SYCL_PTANH(cl::sycl::cl_double2)
+#undef SYCL_PTANH
+
+#define SYCL_PCEIL(packet_type)                                         \
+  template <>                                                           \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pceil<packet_type>( \
+      const packet_type& a) {                                           \
+    return cl::sycl::ceil(a);                                           \
+  }
+
+SYCL_PCEIL(cl::sycl::cl_float4)
+SYCL_PCEIL(cl::sycl::cl_double2)
+#undef SYCL_PCEIL
+
+#define SYCL_PROUND(packet_type)                                         \
+  template <>                                                            \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pround<packet_type>( \
+      const packet_type& a) {                                            \
+    return cl::sycl::round(a);                                           \
+  }
+
+SYCL_PROUND(cl::sycl::cl_float4)
+SYCL_PROUND(cl::sycl::cl_double2)
+#undef SYCL_PROUND
+
+#define SYCL_PRINT(packet_type)                                         \
+  template <>                                                           \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type print<packet_type>( \
+      const packet_type& a) {                                           \
+    return cl::sycl::rint(a);                                           \
+  }
+
+SYCL_PRINT(cl::sycl::cl_float4)
+SYCL_PRINT(cl::sycl::cl_double2)
+#undef SYCL_PRINT
+
+#define SYCL_FLOOR(packet_type)                                          \
+  template <>                                                            \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pfloor<packet_type>( \
+      const packet_type& a) {                                            \
+    return cl::sycl::floor(a);                                           \
+  }
+
+SYCL_FLOOR(cl::sycl::cl_float4)
+SYCL_FLOOR(cl::sycl::cl_double2)
+#undef SYCL_FLOOR
+
+#define SYCL_PMIN(packet_type, expr)                                   \
+  template <>                                                          \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pmin<packet_type>( \
+      const packet_type& a, const packet_type& b) {                    \
+    return expr;                                                       \
+  }
+
+SYCL_PMIN(cl::sycl::cl_float4, cl::sycl::fmin(a, b))
+SYCL_PMIN(cl::sycl::cl_double2, cl::sycl::fmin(a, b))
+#undef SYCL_PMIN
+
+#define SYCL_PMAX(packet_type, expr)                                   \
+  template <>                                                          \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pmax<packet_type>( \
+      const packet_type& a, const packet_type& b) {                    \
+    return expr;                                                       \
+  }
+
+SYCL_PMAX(cl::sycl::cl_float4, cl::sycl::fmax(a, b))
+SYCL_PMAX(cl::sycl::cl_double2, cl::sycl::fmax(a, b))
+#undef SYCL_PMAX
+
+#define SYCL_PLDEXP(packet_type)                                             \
+  template <>                                                                \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type pldexp(                  \
+      const packet_type& a, const packet_type& exponent) {                   \
+    return cl::sycl::ldexp(                                                  \
+        a, exponent.template convert<cl::sycl::cl_int,                       \
+                                     cl::sycl::rounding_mode::automatic>()); \
+  }
+
+SYCL_PLDEXP(cl::sycl::cl_float4)
+SYCL_PLDEXP(cl::sycl::cl_double2)
+#undef SYCL_PLDEXP
+
+#endif
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_SYCL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SYCL/PacketMath.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SYCL/PacketMath.h
new file mode 100644
index 0000000000000000000000000000000000000000..57495a1aebb0d964349551cf4b7a9d7d5682f99e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SYCL/PacketMath.h
@@ -0,0 +1,370 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************
+ * PacketMath.h
+ *
+ * \brief:
+ *  PacketMath
+ *
+ *****************************************************************/
+
+#ifndef EIGEN_PACKET_MATH_SYCL_H
+#define EIGEN_PACKET_MATH_SYCL_H
+#include <type_traits>
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+#ifdef SYCL_DEVICE_ONLY
+#define SYCL_PLOAD(packet_type, AlignedType)                          \
+  template <>                                                         \
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type                   \
+      pload##AlignedType<packet_type>(                                \
+          const typename unpacket_traits<packet_type>::type* from) {  \
+   auto ptr = cl::sycl::address_space_cast<cl::sycl::access::address_space::generic_space, cl::sycl::access::decorated::no>(from);\
+    packet_type res{};                                                \
+    res.load(0, ptr);                                     \
+    return res;                                                       \
+  }
+
+SYCL_PLOAD(cl::sycl::cl_float4, u)
+SYCL_PLOAD(cl::sycl::cl_float4, )
+SYCL_PLOAD(cl::sycl::cl_double2, u)
+SYCL_PLOAD(cl::sycl::cl_double2, )
+
+#undef SYCL_PLOAD
+
+#define SYCL_PSTORE(scalar, packet_type, alignment)             \
+  template <>                                                   \
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void pstore##alignment( \
+      scalar* to, const packet_type& from) {                    \
+    auto ptr = cl::sycl::address_space_cast<cl::sycl::access::address_space::generic_space, cl::sycl::access::decorated::no>(to);\
+    from.store(0, ptr);                               \
+  }
+
+SYCL_PSTORE(float, cl::sycl::cl_float4, )
+SYCL_PSTORE(float, cl::sycl::cl_float4, u)
+SYCL_PSTORE(double, cl::sycl::cl_double2, )
+SYCL_PSTORE(double, cl::sycl::cl_double2, u)
+
+#undef SYCL_PSTORE
+
+#define SYCL_PSET1(packet_type)                                         \
+  template <>                                                           \
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type pset1<packet_type>( \
+      const typename unpacket_traits<packet_type>::type& from) {        \
+    return packet_type(from);                                           \
+  }
+
+// global space
+SYCL_PSET1(cl::sycl::cl_float4)
+SYCL_PSET1(cl::sycl::cl_double2)
+
+#undef SYCL_PSET1
+
+template <typename packet_type>
+struct get_base_packet {
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type
+  get_ploaddup(sycl_multi_pointer) {}
+
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type
+  get_pgather(sycl_multi_pointer, Index) {}
+};
+
+template <>
+struct get_base_packet<cl::sycl::cl_float4> {
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_float4 get_ploaddup(
+      sycl_multi_pointer from) {
+    return cl::sycl::cl_float4(from[0], from[0], from[1], from[1]);
+  }
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_float4 get_pgather(
+      sycl_multi_pointer from, Index stride) {
+    return cl::sycl::cl_float4(from[0 * stride], from[1 * stride],
+                               from[2 * stride], from[3 * stride]);
+  }
+
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void set_pscatter(
+      sycl_multi_pointer to, const cl::sycl::cl_float4& from, Index stride) {
+    auto tmp = stride;
+    to[0] = from.x();
+    to[tmp] = from.y();
+    to[tmp += stride] = from.z();
+    to[tmp += stride] = from.w();
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_float4 set_plset(
+      const float& a) {
+    return cl::sycl::cl_float4(static_cast<float>(a), static_cast<float>(a + 1),
+                               static_cast<float>(a + 2),
+                               static_cast<float>(a + 3));
+  }
+};
+
+template <>
+struct get_base_packet<cl::sycl::cl_double2> {
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_double2
+  get_ploaddup(const sycl_multi_pointer from) {
+    return cl::sycl::cl_double2(from[0], from[0]);
+  }
+
+  template <typename sycl_multi_pointer, typename Index>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_double2 get_pgather(
+      const sycl_multi_pointer from, Index stride) {
+    return cl::sycl::cl_double2(from[0 * stride], from[1 * stride]);
+  }
+
+  template <typename sycl_multi_pointer>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void set_pscatter(
+      sycl_multi_pointer to, const cl::sycl::cl_double2& from, Index stride) {
+    to[0] = from.x();
+    to[stride] = from.y();
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE cl::sycl::cl_double2 set_plset(
+      const double& a) {
+    return cl::sycl::cl_double2(static_cast<double>(a),
+                                static_cast<double>(a + 1));
+  }
+};
+
+#define SYCL_PLOAD_DUP_SPECILIZE(packet_type)                              \
+  template <>                                                              \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type ploaddup<packet_type>( \
+      const typename unpacket_traits<packet_type>::type* from) {           \
+    return get_base_packet<packet_type>::get_ploaddup(from);               \
+  }
+
+SYCL_PLOAD_DUP_SPECILIZE(cl::sycl::cl_float4)
+SYCL_PLOAD_DUP_SPECILIZE(cl::sycl::cl_double2)
+
+#undef SYCL_PLOAD_DUP_SPECILIZE
+
+#define SYCL_PLSET(packet_type)                                         \
+  template <>                                                           \
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type plset<packet_type>( \
+      const typename unpacket_traits<packet_type>::type& a) {           \
+    return get_base_packet<packet_type>::set_plset(a);                  \
+  }
+SYCL_PLSET(cl::sycl::cl_float4)
+SYCL_PLSET(cl::sycl::cl_double2)
+
+#undef SYCL_PLSET
+
+#define SYCL_PGATHER_SPECILIZE(scalar, packet_type)                            \
+  template <>                                                                  \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE packet_type                            \
+  pgather<scalar, packet_type>(                                                \
+      const typename unpacket_traits<packet_type>::type* from, Index stride) { \
+    return get_base_packet<packet_type>::get_pgather(from, stride);            \
+  }
+
+SYCL_PGATHER_SPECILIZE(float, cl::sycl::cl_float4)
+SYCL_PGATHER_SPECILIZE(double, cl::sycl::cl_double2)
+
+#undef SYCL_PGATHER_SPECILIZE
+
+#define SYCL_PSCATTER_SPECILIZE(scalar, packet_type)                        \
+  template <>                                                               \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void pscatter<scalar, packet_type>( \
+      typename unpacket_traits<packet_type>::type * to,                     \
+      const packet_type& from, Index stride) {                              \
+    get_base_packet<packet_type>::set_pscatter(to, from, stride);           \
+  }
+
+SYCL_PSCATTER_SPECILIZE(float, cl::sycl::cl_float4)
+SYCL_PSCATTER_SPECILIZE(double, cl::sycl::cl_double2)
+
+#undef SYCL_PSCATTER_SPECILIZE
+
+#define SYCL_PMAD(packet_type)                                            \
+  template <>                                                             \
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE packet_type pmadd(                \
+      const packet_type& a, const packet_type& b, const packet_type& c) { \
+    return cl::sycl::mad(a, b, c);                                        \
+  }
+
+SYCL_PMAD(cl::sycl::cl_float4)
+SYCL_PMAD(cl::sycl::cl_double2)
+#undef SYCL_PMAD
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float pfirst<cl::sycl::cl_float4>(
+    const cl::sycl::cl_float4& a) {
+  return a.x();
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double pfirst<cl::sycl::cl_double2>(
+    const cl::sycl::cl_double2& a) {
+  return a.x();
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux<cl::sycl::cl_float4>(
+    const cl::sycl::cl_float4& a) {
+  return a.x() + a.y() + a.z() + a.w();
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux<cl::sycl::cl_double2>(
+    const cl::sycl::cl_double2& a) {
+  return a.x() + a.y();
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux_max<cl::sycl::cl_float4>(
+    const cl::sycl::cl_float4& a) {
+  return cl::sycl::fmax(cl::sycl::fmax(a.x(), a.y()),
+                        cl::sycl::fmax(a.z(), a.w()));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux_max<cl::sycl::cl_double2>(
+    const cl::sycl::cl_double2& a) {
+  return cl::sycl::fmax(a.x(), a.y());
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux_min<cl::sycl::cl_float4>(
+    const cl::sycl::cl_float4& a) {
+  return cl::sycl::fmin(cl::sycl::fmin(a.x(), a.y()),
+                        cl::sycl::fmin(a.z(), a.w()));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux_min<cl::sycl::cl_double2>(
+    const cl::sycl::cl_double2& a) {
+  return cl::sycl::fmin(a.x(), a.y());
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE float predux_mul<cl::sycl::cl_float4>(
+    const cl::sycl::cl_float4& a) {
+  return a.x() * a.y() * a.z() * a.w();
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE double predux_mul<cl::sycl::cl_double2>(
+    const cl::sycl::cl_double2& a) {
+  return a.x() * a.y();
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_float4
+pabs<cl::sycl::cl_float4>(const cl::sycl::cl_float4& a) {
+  return cl::sycl::cl_float4(cl::sycl::fabs(a.x()), cl::sycl::fabs(a.y()),
+                             cl::sycl::fabs(a.z()), cl::sycl::fabs(a.w()));
+}
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_double2
+pabs<cl::sycl::cl_double2>(const cl::sycl::cl_double2& a) {
+  return cl::sycl::cl_double2(cl::sycl::fabs(a.x()), cl::sycl::fabs(a.y()));
+}
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet sycl_pcmp_le(const Packet &a,
+                                                          const Packet &b) {
+  return (a <= b).template as<Packet>();
+}
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet sycl_pcmp_lt(const Packet &a,
+                                                          const Packet &b) {
+  return (a < b).template as<Packet>();
+}
+
+template <typename Packet>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet sycl_pcmp_eq(const Packet &a,
+                                                          const Packet &b) {
+  return (a == b).template as<Packet>();
+}
+
+#define SYCL_PCMP(OP, TYPE)                                                    \
+  template <>                                                                  \
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE TYPE pcmp_##OP<TYPE>(const TYPE &a,    \
+                                                             const TYPE &b) {  \
+    return sycl_pcmp_##OP<TYPE>(a, b);                                         \
+  }
+
+SYCL_PCMP(le, cl::sycl::cl_float4)
+SYCL_PCMP(lt, cl::sycl::cl_float4)
+SYCL_PCMP(eq, cl::sycl::cl_float4)
+SYCL_PCMP(le, cl::sycl::cl_double2)
+SYCL_PCMP(lt, cl::sycl::cl_double2)
+SYCL_PCMP(eq, cl::sycl::cl_double2)
+#undef SYCL_PCMP
+
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void ptranspose(
+    PacketBlock<cl::sycl::cl_float4, 4>& kernel) {
+  float tmp = kernel.packet[0].y();
+  kernel.packet[0].y() = kernel.packet[1].x();
+  kernel.packet[1].x() = tmp;
+
+  tmp = kernel.packet[0].z();
+  kernel.packet[0].z() = kernel.packet[2].x();
+  kernel.packet[2].x() = tmp;
+
+  tmp = kernel.packet[0].w();
+  kernel.packet[0].w() = kernel.packet[3].x();
+  kernel.packet[3].x() = tmp;
+
+  tmp = kernel.packet[1].z();
+  kernel.packet[1].z() = kernel.packet[2].y();
+  kernel.packet[2].y() = tmp;
+
+  tmp = kernel.packet[1].w();
+  kernel.packet[1].w() = kernel.packet[3].y();
+  kernel.packet[3].y() = tmp;
+
+  tmp = kernel.packet[2].w();
+  kernel.packet[2].w() = kernel.packet[3].z();
+  kernel.packet[3].z() = tmp;
+}
+
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void ptranspose(
+    PacketBlock<cl::sycl::cl_double2, 2>& kernel) {
+  double tmp = kernel.packet[0].y();
+  kernel.packet[0].y() = kernel.packet[1].x();
+  kernel.packet[1].x() = tmp;
+}
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_float4 pblend(
+    const Selector<unpacket_traits<cl::sycl::cl_float4>::size>& ifPacket,
+    const cl::sycl::cl_float4& thenPacket,
+    const cl::sycl::cl_float4& elsePacket) {
+  cl::sycl::cl_int4 condition(
+      ifPacket.select[0] ? 0 : -1, ifPacket.select[1] ? 0 : -1,
+      ifPacket.select[2] ? 0 : -1, ifPacket.select[3] ? 0 : -1);
+  return cl::sycl::select(thenPacket, elsePacket, condition);
+}
+
+template <>
+inline cl::sycl::cl_double2 pblend(
+    const Selector<unpacket_traits<cl::sycl::cl_double2>::size>& ifPacket,
+    const cl::sycl::cl_double2& thenPacket,
+    const cl::sycl::cl_double2& elsePacket) {
+  cl::sycl::cl_long2 condition(ifPacket.select[0] ? 0 : -1,
+                               ifPacket.select[1] ? 0 : -1);
+  return cl::sycl::select(thenPacket, elsePacket, condition);
+}
+#endif  // SYCL_DEVICE_ONLY
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_PACKET_MATH_SYCL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SYCL/TypeCasting.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SYCL/TypeCasting.h
new file mode 100644
index 0000000000000000000000000000000000000000..f6f057b32de9181d8b062eb90ea45b9a864315d8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/SYCL/TypeCasting.h
@@ -0,0 +1,87 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Mehdi Goli    Codeplay Software Ltd.
+// Ralph Potter  Codeplay Software Ltd.
+// Luke Iwanski  Codeplay Software Ltd.
+// Contact: <eigen@codeplay.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*****************************************************************
+ * TypeCasting.h
+ *
+ * \brief:
+ *  TypeCasting
+ *
+ *****************************************************************/
+
+#ifndef EIGEN_TYPE_CASTING_SYCL_H
+#define EIGEN_TYPE_CASTING_SYCL_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+#ifdef SYCL_DEVICE_ONLY
+template <>
+struct type_casting_traits<float, int> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_int4
+pcast<cl::sycl::cl_float4, cl::sycl::cl_int4>(const cl::sycl::cl_float4& a) {
+  return a
+      .template convert<cl::sycl::cl_int, cl::sycl::rounding_mode::automatic>();
+}
+
+template <>
+struct type_casting_traits<int, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 1 };
+};
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_float4
+pcast<cl::sycl::cl_int4, cl::sycl::cl_float4>(const cl::sycl::cl_int4& a) {
+  return a.template convert<cl::sycl::cl_float,
+                            cl::sycl::rounding_mode::automatic>();
+}
+
+template <>
+struct type_casting_traits<double, float> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 };
+};
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_float4
+pcast<cl::sycl::cl_double2, cl::sycl::cl_float4>(
+    const cl::sycl::cl_double2& a, const cl::sycl::cl_double2& b) {
+  auto a1 = a.template convert<cl::sycl::cl_float,
+                               cl::sycl::rounding_mode::automatic>();
+  auto b1 = b.template convert<cl::sycl::cl_float,
+                               cl::sycl::rounding_mode::automatic>();
+  return cl::sycl::cl_float4(a1.x(), a1.y(), b1.x(), b1.y());
+}
+
+template <>
+struct type_casting_traits<float, double> {
+  enum { VectorizedCast = 1, SrcCoeffRatio = 1, TgtCoeffRatio = 2 };
+};
+
+template <>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE cl::sycl::cl_double2
+pcast<cl::sycl::cl_float4, cl::sycl::cl_double2>(const cl::sycl::cl_float4& a) {
+  // Simply discard the second half of the input
+  return cl::sycl::cl_double2(a.x(), a.y());
+}
+
+#endif
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_TYPE_CASTING_SYCL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/ZVector/Complex.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/ZVector/Complex.h
new file mode 100644
index 0000000000000000000000000000000000000000..7f22e5cae20e19a52f3c50137370ae097d540b23
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/ZVector/Complex.h
@@ -0,0 +1,426 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX32_ZVECTOR_H
+#define EIGEN_COMPLEX32_ZVECTOR_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+static Packet4ui  p4ui_CONJ_XOR = { 0x00000000, 0x80000000, 0x00000000, 0x80000000 }; //vec_mergeh((Packet4ui)p4i_ZERO, (Packet4ui)p4f_MZERO);
+#endif
+
+static Packet2ul  p2ul_CONJ_XOR1 = (Packet2ul) vec_sld((Packet4ui) p2d_ZERO_, (Packet4ui) p2l_ZERO, 8);//{ 0x8000000000000000, 0x0000000000000000 };
+static Packet2ul  p2ul_CONJ_XOR2 = (Packet2ul) vec_sld((Packet4ui) p2l_ZERO,  (Packet4ui) p2d_ZERO_, 8);//{ 0x8000000000000000, 0x0000000000000000 };
+
+struct Packet1cd
+{
+  EIGEN_STRONG_INLINE Packet1cd() {}
+  EIGEN_STRONG_INLINE explicit Packet1cd(const Packet2d& a) : v(a) {}
+  Packet2d v;
+};
+
+struct Packet2cf
+{
+  EIGEN_STRONG_INLINE Packet2cf() {}
+  EIGEN_STRONG_INLINE explicit Packet2cf(const Packet4f& a) : v(a) {}
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
+  union {
+    Packet4f v;
+    Packet1cd cd[2];
+  };
+#else
+  Packet4f v;
+#endif
+};
+
+template<> struct packet_traits<std::complex<float> >  : default_packet_traits
+{
+  typedef Packet2cf type;
+  typedef Packet2cf half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 2,
+
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasDiv    = 1,
+    HasNegate = 1,
+    HasAbs    = 0,
+    HasAbs2   = 0,
+    HasMin    = 0,
+    HasMax    = 0,
+    HasBlend  = 1,
+    HasSetLinear = 0
+  };
+};
+
+
+template<> struct packet_traits<std::complex<double> >  : default_packet_traits
+{
+  typedef Packet1cd type;
+  typedef Packet1cd half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 1,
+
+    HasAdd    = 1,
+    HasSub    = 1,
+    HasMul    = 1,
+    HasDiv    = 1,
+    HasNegate = 1,
+    HasAbs    = 0,
+    HasAbs2   = 0,
+    HasMin    = 0,
+    HasMax    = 0,
+    HasSetLinear = 0
+  };
+};
+
+template<> struct unpacket_traits<Packet2cf> {
+  typedef std::complex<float>  type;
+  enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
+  typedef Packet2cf half;
+  typedef Packet4f as_real;
+};
+template<> struct unpacket_traits<Packet1cd> {
+  typedef std::complex<double> type;
+  enum {size=1, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false};
+  typedef Packet1cd half;
+  typedef Packet2d as_real;
+};
+
+/* Forward declaration */
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel);
+
+/* complex<double> first */
+template<> EIGEN_STRONG_INLINE Packet1cd pload <Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_ALIGNED_LOAD return Packet1cd(pload<Packet2d>((const double*)from)); }
+template<> EIGEN_STRONG_INLINE Packet1cd ploadu<Packet1cd>(const std::complex<double>* from) { EIGEN_DEBUG_UNALIGNED_LOAD return Packet1cd(ploadu<Packet2d>((const double*)from)); }
+template<> EIGEN_STRONG_INLINE void pstore <std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((double*)to, from.v); }
+template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<double> *   to, const Packet1cd& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((double*)to, from.v); }
+
+template<> EIGEN_STRONG_INLINE Packet1cd pset1<Packet1cd>(const std::complex<double>&  from)
+{ /* here we really have to use unaligned loads :( */ return ploadu<Packet1cd>(&from); }
+
+template<> EIGEN_DEVICE_FUNC inline Packet1cd pgather<std::complex<double>, Packet1cd>(const std::complex<double>* from, Index stride EIGEN_UNUSED)
+{
+  return pload<Packet1cd>(from);
+}
+template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet1cd>(std::complex<double>* to, const Packet1cd& from, Index stride EIGEN_UNUSED)
+{
+  pstore<std::complex<double> >(to, from);
+}
+template<> EIGEN_STRONG_INLINE Packet1cd padd<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v + b.v); }
+template<> EIGEN_STRONG_INLINE Packet1cd psub<Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(a.v - b.v); }
+template<> EIGEN_STRONG_INLINE Packet1cd pnegate(const Packet1cd& a) { return Packet1cd(pnegate(Packet2d(a.v))); }
+template<> EIGEN_STRONG_INLINE Packet1cd pconj(const Packet1cd& a) { return Packet1cd((Packet2d)vec_xor((Packet2d)a.v, (Packet2d)p2ul_CONJ_XOR2)); }
+template<> EIGEN_STRONG_INLINE Packet1cd pmul<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
+{
+  Packet2d a_re, a_im, v1, v2;
+
+  // Permute and multiply the real parts of a and b
+  a_re = vec_perm(a.v, a.v, p16uc_PSET64_HI);
+  // Get the imaginary parts of a
+  a_im = vec_perm(a.v, a.v, p16uc_PSET64_LO);
+  // multiply a_re * b
+  v1 = vec_madd(a_re, b.v, p2d_ZERO);
+  // multiply a_im * b and get the conjugate result
+  v2 = vec_madd(a_im, b.v, p2d_ZERO);
+  v2 = (Packet2d) vec_sld((Packet4ui)v2, (Packet4ui)v2, 8);
+  v2 = (Packet2d) vec_xor((Packet2d)v2, (Packet2d) p2ul_CONJ_XOR1);
+
+  return Packet1cd(v1 + v2);
+}
+template<> EIGEN_STRONG_INLINE Packet1cd pand    <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_and(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet1cd por     <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_or(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet1cd pxor    <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_xor(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet1cd pandnot <Packet1cd>(const Packet1cd& a, const Packet1cd& b) { return Packet1cd(vec_and(a.v, vec_nor(b.v,b.v))); }
+template<> EIGEN_STRONG_INLINE Packet1cd ploaddup<Packet1cd>(const std::complex<double>*     from) {  return pset1<Packet1cd>(*from); }
+template<> EIGEN_STRONG_INLINE Packet1cd pcmp_eq(const Packet1cd& a, const Packet1cd& b) {
+  Packet2d eq = vec_cmpeq (a.v, b.v);
+  Packet2d tmp = { eq[1], eq[0] };
+  return (Packet1cd)pand<Packet2d>(eq, tmp);
+}
+
+template<> EIGEN_STRONG_INLINE void prefetch<std::complex<double> >(const std::complex<double> *   addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
+
+template<> EIGEN_STRONG_INLINE std::complex<double>  pfirst<Packet1cd>(const Packet1cd& a)
+{
+  EIGEN_ALIGN16 std::complex<double> res;
+  pstore<std::complex<double> >(&res, a);
+
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet1cd preverse(const Packet1cd& a) { return a; }
+template<> EIGEN_STRONG_INLINE std::complex<double> predux<Packet1cd>(const Packet1cd& a)
+{
+  return pfirst(a);
+}
+template<> EIGEN_STRONG_INLINE std::complex<double> predux_mul<Packet1cd>(const Packet1cd& a)
+{
+  return pfirst(a);
+}
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet1cd,Packet2d)
+
+template<> EIGEN_STRONG_INLINE Packet1cd pdiv<Packet1cd>(const Packet1cd& a, const Packet1cd& b)
+{
+  return pdiv_complex(a, b);
+}
+
+EIGEN_STRONG_INLINE Packet1cd pcplxflip/*<Packet1cd>*/(const Packet1cd& x)
+{
+  return Packet1cd(preverse(Packet2d(x.v)));
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet1cd,2>& kernel)
+{
+  Packet2d tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
+  kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
+  kernel.packet[0].v = tmp;
+}
+
+/* complex<float> follows */
+template<> EIGEN_STRONG_INLINE Packet2cf pload <Packet2cf>(const std::complex<float>* from)  { EIGEN_DEBUG_ALIGNED_LOAD return Packet2cf(pload<Packet4f>((const float*)from)); }
+template<> EIGEN_STRONG_INLINE Packet2cf ploadu<Packet2cf>(const std::complex<float>* from)  { EIGEN_DEBUG_UNALIGNED_LOAD return Packet2cf(ploadu<Packet4f>((const float*)from)); }
+template<> EIGEN_STRONG_INLINE void pstore <std::complex<float> >(std::complex<float> *     to, const Packet2cf& from) { EIGEN_DEBUG_ALIGNED_STORE pstore((float*)to, from.v); }
+template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<float> >(std::complex<float> *     to, const Packet2cf& from) { EIGEN_DEBUG_UNALIGNED_STORE pstoreu((float*)to, from.v); }
+
+template<> EIGEN_STRONG_INLINE std::complex<float>  pfirst<Packet2cf>(const Packet2cf& a)
+{
+  EIGEN_ALIGN16 std::complex<float> res[2];
+  pstore<std::complex<float> >(res, a);
+
+  return res[0];
+}
+
+
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
+template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>&  from)
+{
+  Packet2cf res;
+  res.cd[0] = Packet1cd(vec_ld2f((const float *)&from));
+  res.cd[1] = res.cd[0];
+  return res;
+}
+#else
+template<> EIGEN_STRONG_INLINE Packet2cf pset1<Packet2cf>(const std::complex<float>&  from)
+{
+  Packet2cf res;
+  if((std::ptrdiff_t(&from) % 16) == 0)
+    res.v = pload<Packet4f>((const float *)&from);
+  else
+    res.v = ploadu<Packet4f>((const float *)&from);
+  res.v = vec_perm(res.v, res.v, p16uc_PSET64_HI);
+  return res;
+}
+#endif
+
+template<> EIGEN_DEVICE_FUNC inline Packet2cf pgather<std::complex<float>, Packet2cf>(const std::complex<float>* from, Index stride)
+{
+  EIGEN_ALIGN16 std::complex<float> af[2];
+  af[0] = from[0*stride];
+  af[1] = from[1*stride];
+  return pload<Packet2cf>(af);
+}
+template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<float>, Packet2cf>(std::complex<float>* to, const Packet2cf& from, Index stride)
+{
+  EIGEN_ALIGN16 std::complex<float> af[2];
+  pstore<std::complex<float> >((std::complex<float> *) af, from);
+  to[0*stride] = af[0];
+  to[1*stride] = af[1];
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf padd<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(padd<Packet4f>(a.v, b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf psub<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(psub<Packet4f>(a.v, b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf pnegate(const Packet2cf& a) { return Packet2cf(pnegate(Packet4f(a.v))); }
+
+template<> EIGEN_STRONG_INLINE Packet2cf pand   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pand<Packet4f>(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf por    <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(por<Packet4f>(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf pxor   <Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pxor<Packet4f>(a.v,b.v)); }
+template<> EIGEN_STRONG_INLINE Packet2cf pandnot<Packet2cf>(const Packet2cf& a, const Packet2cf& b) { return Packet2cf(pandnot<Packet4f>(a.v,b.v)); }
+
+template<> EIGEN_STRONG_INLINE Packet2cf ploaddup<Packet2cf>(const std::complex<float>*      from) {  return pset1<Packet2cf>(*from); }
+
+template<> EIGEN_STRONG_INLINE void prefetch<std::complex<float> >(const std::complex<float> *     addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
+
+
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
+
+template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b) {
+  Packet4f eq = pcmp_eq<Packet4f> (a.v, b.v);
+  Packet2cf res;
+  Packet2d tmp1 = { eq.v4f[0][1], eq.v4f[0][0] };
+  Packet2d tmp2 = { eq.v4f[1][1], eq.v4f[1][0] };
+  res.v.v4f[0] = pand<Packet2d>(eq.v4f[0], tmp1);
+  res.v.v4f[1] = pand<Packet2d>(eq.v4f[1], tmp2);
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a)
+{
+  Packet2cf res;
+  res.v.v4f[0] = pconj(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[0]))).v;
+  res.v.v4f[1] = pconj(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[1]))).v;
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
+{
+  Packet2cf res;
+  res.v.v4f[0] = pmul(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[0])), Packet1cd(reinterpret_cast<Packet2d>(b.v.v4f[0]))).v;
+  res.v.v4f[1] = pmul(Packet1cd(reinterpret_cast<Packet2d>(a.v.v4f[1])), Packet1cd(reinterpret_cast<Packet2d>(b.v.v4f[1]))).v;
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a)
+{
+  Packet2cf res;
+  res.cd[0] = a.cd[1];
+  res.cd[1] = a.cd[0];
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
+{
+  std::complex<float> res;
+  Packet1cd b = padd<Packet1cd>(a.cd[0], a.cd[1]);
+  vec_st2f(b.v, (float*)&res);
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a)
+{
+  std::complex<float> res;
+  Packet1cd b = pmul<Packet1cd>(a.cd[0], a.cd[1]);
+  vec_st2f(b.v, (float*)&res);
+  return res;
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
+
+template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
+{
+  return pdiv_complex(a, b);
+}
+
+EIGEN_STRONG_INLINE Packet2cf pcplxflip/*<Packet2cf>*/(const Packet2cf& x)
+{
+  Packet2cf res;
+  res.cd[0] = pcplxflip(x.cd[0]);
+  res.cd[1] = pcplxflip(x.cd[1]);
+  return res;
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel)
+{
+  Packet1cd tmp = kernel.packet[0].cd[1];
+  kernel.packet[0].cd[1] = kernel.packet[1].cd[0];
+  kernel.packet[1].cd[0] = tmp;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket, const Packet2cf& elsePacket) {
+  Packet2cf result;
+  const Selector<4> ifPacket4 = { ifPacket.select[0], ifPacket.select[0], ifPacket.select[1], ifPacket.select[1] };
+  result.v = pblend<Packet4f>(ifPacket4, thenPacket.v, elsePacket.v);
+  return result;
+}
+#else
+template<> EIGEN_STRONG_INLINE Packet2cf pcmp_eq(const Packet2cf& a, const Packet2cf& b) {
+  Packet4f eq = vec_cmpeq (a.v, b.v);
+  Packet4f tmp = { eq[1], eq[0], eq[3], eq[2] };
+  return (Packet2cf)pand<Packet4f>(eq, tmp);
+}
+template<> EIGEN_STRONG_INLINE Packet2cf pconj(const Packet2cf& a) { return Packet2cf(pxor<Packet4f>(a.v, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR))); }
+template<> EIGEN_STRONG_INLINE Packet2cf pmul<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
+{
+  Packet4f a_re, a_im, prod, prod_im;
+
+  // Permute and multiply the real parts of a and b
+  a_re = vec_perm(a.v, a.v, p16uc_PSET32_WODD);
+  
+  // Get the imaginary parts of a
+  a_im = vec_perm(a.v, a.v, p16uc_PSET32_WEVEN);
+
+  // multiply a_im * b and get the conjugate result
+  prod_im = a_im * b.v;
+  prod_im = pxor<Packet4f>(prod_im, reinterpret_cast<Packet4f>(p4ui_CONJ_XOR));
+  // permute back to a proper order
+  prod_im = vec_perm(prod_im, prod_im, p16uc_COMPLEX32_REV);
+
+  // multiply a_re * b, add prod_im
+  prod = pmadd<Packet4f>(a_re, b.v, prod_im);
+ 
+  return Packet2cf(prod);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf preverse(const Packet2cf& a)
+{
+  Packet4f rev_a;
+  rev_a = vec_perm(a.v, a.v, p16uc_COMPLEX32_REV2);
+  return Packet2cf(rev_a);
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<float> predux<Packet2cf>(const Packet2cf& a)
+{
+  Packet4f b;
+  b = vec_sld(a.v, a.v, 8);
+  b = padd<Packet4f>(a.v, b);
+  return pfirst<Packet2cf>(Packet2cf(b));
+}
+
+template<> EIGEN_STRONG_INLINE std::complex<float> predux_mul<Packet2cf>(const Packet2cf& a)
+{
+  Packet4f b;
+  Packet2cf prod;
+  b = vec_sld(a.v, a.v, 8);
+  prod = pmul<Packet2cf>(a, Packet2cf(b));
+
+  return pfirst<Packet2cf>(prod);
+}
+
+EIGEN_MAKE_CONJ_HELPER_CPLX_REAL(Packet2cf,Packet4f)
+
+template<> EIGEN_STRONG_INLINE Packet2cf pdiv<Packet2cf>(const Packet2cf& a, const Packet2cf& b)
+{
+  return pdiv_complex(a, b);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf pcplxflip<Packet2cf>(const Packet2cf& x)
+{
+  return Packet2cf(vec_perm(x.v, x.v, p16uc_COMPLEX32_REV));
+}
+
+EIGEN_STRONG_INLINE void ptranspose(PacketBlock<Packet2cf,2>& kernel)
+{
+  Packet4f tmp = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_HI);
+  kernel.packet[1].v = vec_perm(kernel.packet[0].v, kernel.packet[1].v, p16uc_TRANSPOSE64_LO);
+  kernel.packet[0].v = tmp;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2cf pblend(const Selector<2>& ifPacket, const Packet2cf& thenPacket, const Packet2cf& elsePacket) {
+  Packet2cf result;
+  result.v = reinterpret_cast<Packet4f>(pblend<Packet2d>(ifPacket, reinterpret_cast<Packet2d>(thenPacket.v), reinterpret_cast<Packet2d>(elsePacket.v)));
+  return result;
+}
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_COMPLEX32_ZVECTOR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/ZVector/MathFunctions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/ZVector/MathFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..1f2da26442bba168835b44fb42acb81c249f0886
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/ZVector/MathFunctions.h
@@ -0,0 +1,235 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007 Julien Pommier
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* The sin, cos, exp, and log functions of this file come from
+ * Julien Pommier's sse math library: http://gruntthepeon.free.fr/ssemath/
+ */
+
+#ifndef EIGEN_MATH_FUNCTIONS_ZVECTOR_H
+#define EIGEN_MATH_FUNCTIONS_ZVECTOR_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+static EIGEN_DECLARE_CONST_Packet4f(1 , 1.0f);
+static EIGEN_DECLARE_CONST_Packet4f(half, 0.5f);
+static EIGEN_DECLARE_CONST_Packet4i(0x7f, 0x7f);
+static EIGEN_DECLARE_CONST_Packet4i(23, 23);
+
+static EIGEN_DECLARE_CONST_Packet4f_FROM_INT(inv_mant_mask, ~0x7f800000);
+
+/* the smallest non denormalized float number */
+static EIGEN_DECLARE_CONST_Packet4f_FROM_INT(min_norm_pos,  0x00800000);
+static EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_inf,     0xff800000); // -1.f/0.f
+static EIGEN_DECLARE_CONST_Packet4f_FROM_INT(minus_nan,     0xffffffff);
+  
+/* natural logarithm computed for 4 simultaneous float
+  return NaN for x <= 0
+*/
+static EIGEN_DECLARE_CONST_Packet4f(cephes_SQRTHF, 0.707106781186547524f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p0, 7.0376836292E-2f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p1, - 1.1514610310E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p2, 1.1676998740E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p3, - 1.2420140846E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p4, + 1.4249322787E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p5, - 1.6668057665E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p6, + 2.0000714765E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p7, - 2.4999993993E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_p8, + 3.3333331174E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_q1, -2.12194440e-4f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_log_q2, 0.693359375f);
+
+static EIGEN_DECLARE_CONST_Packet4f(exp_hi,  88.3762626647950f);
+static EIGEN_DECLARE_CONST_Packet4f(exp_lo, -88.3762626647949f);
+
+static EIGEN_DECLARE_CONST_Packet4f(cephes_LOG2EF, 1.44269504088896341f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C1, 0.693359375f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_C2, -2.12194440e-4f);
+
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p0, 1.9875691500E-4f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p1, 1.3981999507E-3f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p2, 8.3334519073E-3f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p3, 4.1665795894E-2f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p4, 1.6666665459E-1f);
+static EIGEN_DECLARE_CONST_Packet4f(cephes_exp_p5, 5.0000001201E-1f);
+#endif
+
+static EIGEN_DECLARE_CONST_Packet2d(1 , 1.0);
+static EIGEN_DECLARE_CONST_Packet2d(2 , 2.0);
+static EIGEN_DECLARE_CONST_Packet2d(half, 0.5);
+
+static EIGEN_DECLARE_CONST_Packet2d(exp_hi,  709.437);
+static EIGEN_DECLARE_CONST_Packet2d(exp_lo, -709.436139303);
+
+static EIGEN_DECLARE_CONST_Packet2d(cephes_LOG2EF, 1.4426950408889634073599);
+
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p0, 1.26177193074810590878e-4);
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p1, 3.02994407707441961300e-2);
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_p2, 9.99999999999999999910e-1);
+
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q0, 3.00198505138664455042e-6);
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q1, 2.52448340349684104192e-3);
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q2, 2.27265548208155028766e-1);
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_q3, 2.00000000000000000009e0);
+
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C1, 0.693145751953125);
+static EIGEN_DECLARE_CONST_Packet2d(cephes_exp_C2, 1.42860682030941723212e-6);
+
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet2d pexp<Packet2d>(const Packet2d& _x)
+{
+  Packet2d x = _x;
+
+  Packet2d tmp, fx;
+  Packet2l emm0;
+
+  // clamp x
+  x = pmax(pmin(x, p2d_exp_hi), p2d_exp_lo);
+  /* express exp(x) as exp(g + n*log(2)) */
+  fx = pmadd(p2d_cephes_LOG2EF, x, p2d_half);
+
+  fx = vec_floor(fx);
+
+  tmp = pmul(fx, p2d_cephes_exp_C1);
+  Packet2d z = pmul(fx, p2d_cephes_exp_C2);
+  x = psub(x, tmp);
+  x = psub(x, z);
+
+  Packet2d x2 = pmul(x,x);
+
+  Packet2d px = p2d_cephes_exp_p0;
+  px = pmadd(px, x2, p2d_cephes_exp_p1);
+  px = pmadd(px, x2, p2d_cephes_exp_p2);
+  px = pmul (px, x);
+
+  Packet2d qx = p2d_cephes_exp_q0;
+  qx = pmadd(qx, x2, p2d_cephes_exp_q1);
+  qx = pmadd(qx, x2, p2d_cephes_exp_q2);
+  qx = pmadd(qx, x2, p2d_cephes_exp_q3);
+
+  x = pdiv(px,psub(qx,px));
+  x = pmadd(p2d_2,x,p2d_1);
+
+  // build 2^n
+  emm0 = vec_ctsl(fx, 0);
+
+  static const Packet2l p2l_1023 = { 1023, 1023 };
+  static const Packet2ul p2ul_52 = { 52, 52 };
+
+  emm0 = emm0 + p2l_1023;
+  emm0 = emm0 << reinterpret_cast<Packet2l>(p2ul_52);
+
+  // Altivec's max & min operators just drop silent NaNs. Check NaNs in 
+  // inputs and return them unmodified.
+  Packet2ul isnumber_mask = reinterpret_cast<Packet2ul>(vec_cmpeq(_x, _x));
+  return vec_sel(_x, pmax(pmul(x, reinterpret_cast<Packet2d>(emm0)), _x),
+                 isnumber_mask);
+}
+
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet4f pexp<Packet4f>(const Packet4f& _x)
+{
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+  Packet4f x = _x;
+
+  Packet4f tmp, fx;
+  Packet4i emm0;
+
+  // clamp x
+  x = pmax(pmin(x, p4f_exp_hi), p4f_exp_lo);
+
+  // express exp(x) as exp(g + n*log(2))
+  fx = pmadd(x, p4f_cephes_LOG2EF, p4f_half);
+
+  fx = pfloor(fx);
+
+  tmp = pmul(fx, p4f_cephes_exp_C1);
+  Packet4f z = pmul(fx, p4f_cephes_exp_C2);
+  x = psub(x, tmp);
+  x = psub(x, z);
+
+  z = pmul(x,x);
+
+  Packet4f y = p4f_cephes_exp_p0;
+  y = pmadd(y, x, p4f_cephes_exp_p1);
+  y = pmadd(y, x, p4f_cephes_exp_p2);
+  y = pmadd(y, x, p4f_cephes_exp_p3);
+  y = pmadd(y, x, p4f_cephes_exp_p4);
+  y = pmadd(y, x, p4f_cephes_exp_p5);
+  y = pmadd(y, z, x);
+  y = padd(y, p4f_1);
+
+  // build 2^n
+  emm0 = (Packet4i){ (int)fx[0], (int)fx[1], (int)fx[2], (int)fx[3] };
+  emm0 = emm0 + p4i_0x7f;
+  emm0 = emm0 << reinterpret_cast<Packet4i>(p4i_23);
+
+  return pmax(pmul(y, reinterpret_cast<Packet4f>(emm0)), _x);
+#else
+  Packet4f res;
+  res.v4f[0] = pexp<Packet2d>(_x.v4f[0]);
+  res.v4f[1] = pexp<Packet2d>(_x.v4f[1]);
+  return res;
+#endif
+}
+
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet2d psqrt<Packet2d>(const Packet2d& x)
+{
+  return vec_sqrt(x);
+}
+
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet4f psqrt<Packet4f>(const Packet4f& x)
+{
+  Packet4f res;
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+  res = vec_sqrt(x);
+#else
+  res.v4f[0] = psqrt<Packet2d>(x.v4f[0]);
+  res.v4f[1] = psqrt<Packet2d>(x.v4f[1]);
+#endif
+  return res;
+}
+
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet2d prsqrt<Packet2d>(const Packet2d& x) {
+  return pset1<Packet2d>(1.0) / psqrt<Packet2d>(x);
+}
+
+template<> EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
+Packet4f prsqrt<Packet4f>(const Packet4f& x) {
+  Packet4f res;
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+  res = pset1<Packet4f>(1.0) / psqrt<Packet4f>(x);
+#else
+  res.v4f[0] = prsqrt<Packet2d>(x.v4f[0]);
+  res.v4f[1] = prsqrt<Packet2d>(x.v4f[1]);
+#endif
+  return res;
+}
+
+// Hyperbolic Tangent function.
+template <>
+EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS Packet4f
+ptanh<Packet4f>(const Packet4f& x) {
+  return internal::generic_fast_tanh_float(x);
+}
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_MATH_FUNCTIONS_ZVECTOR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/ZVector/PacketMath.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/ZVector/PacketMath.h
new file mode 100644
index 0000000000000000000000000000000000000000..892e3a1beb9115e58e382f3675921efb1720573d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/arch/ZVector/PacketMath.h
@@ -0,0 +1,1059 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Konstantinos Margaritis <markos@freevec.org>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PACKET_MATH_ZVECTOR_H
+#define EIGEN_PACKET_MATH_ZVECTOR_H
+
+#include "../../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+#ifndef EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD
+#define EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD 16
+#endif
+
+#ifndef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#define EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+#endif
+
+#ifndef EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS
+#define EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS  32
+#endif
+
+typedef __vector int                 Packet4i;
+typedef __vector unsigned int        Packet4ui;
+typedef __vector __bool int          Packet4bi;
+typedef __vector short int           Packet8i;
+typedef __vector unsigned char       Packet16uc;
+typedef __vector double              Packet2d;
+typedef __vector unsigned long long  Packet2ul;
+typedef __vector long long           Packet2l;
+
+// Z14 has builtin support for float vectors
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+typedef __vector float               Packet4f;
+#else
+typedef struct {
+	Packet2d  v4f[2];
+} Packet4f;
+#endif
+
+typedef union {
+  numext::int32_t   i[4];
+  numext::uint32_t ui[4];
+  numext::int64_t   l[2];
+  numext::uint64_t ul[2];
+  double    d[2];
+  float     f[4];
+  Packet4i  v4i;
+  Packet4ui v4ui;
+  Packet2l  v2l;
+  Packet2ul v2ul;
+  Packet2d  v2d;
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+  Packet4f  v4f;
+#endif
+} Packet;
+
+// We don't want to write the same code all the time, but we need to reuse the constants
+// and it doesn't really work to declare them global, so we define macros instead
+
+#define EIGEN_DECLARE_CONST_FAST_Packet4i(NAME,X) \
+  Packet4i p4i_##NAME = reinterpret_cast<Packet4i>(vec_splat_s32(X))
+
+#define EIGEN_DECLARE_CONST_FAST_Packet2d(NAME,X) \
+  Packet2d p2d_##NAME = reinterpret_cast<Packet2d>(vec_splat_s64(X))
+
+#define EIGEN_DECLARE_CONST_FAST_Packet2l(NAME,X) \
+  Packet2l p2l_##NAME = reinterpret_cast<Packet2l>(vec_splat_s64(X))
+
+#define EIGEN_DECLARE_CONST_Packet4i(NAME,X) \
+  Packet4i p4i_##NAME = pset1<Packet4i>(X)
+
+#define EIGEN_DECLARE_CONST_Packet2d(NAME,X) \
+  Packet2d p2d_##NAME = pset1<Packet2d>(X)
+
+#define EIGEN_DECLARE_CONST_Packet2l(NAME,X) \
+  Packet2l p2l_##NAME = pset1<Packet2l>(X)
+
+// These constants are endian-agnostic
+static EIGEN_DECLARE_CONST_FAST_Packet4i(ZERO, 0); //{ 0, 0, 0, 0,}
+static EIGEN_DECLARE_CONST_FAST_Packet4i(ONE, 1); //{ 1, 1, 1, 1}
+
+static EIGEN_DECLARE_CONST_FAST_Packet2d(ZERO, 0);
+static EIGEN_DECLARE_CONST_FAST_Packet2l(ZERO, 0);
+static EIGEN_DECLARE_CONST_FAST_Packet2l(ONE, 1);
+
+static Packet2d p2d_ONE = { 1.0, 1.0 };
+static Packet2d p2d_ZERO_ = { numext::bit_cast<double>(0x8000000000000000ull),
+                              numext::bit_cast<double>(0x8000000000000000ull) };
+
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+#define EIGEN_DECLARE_CONST_FAST_Packet4f(NAME,X) \
+  Packet4f p4f_##NAME = reinterpret_cast<Packet4f>(vec_splat_s32(X))
+
+#define EIGEN_DECLARE_CONST_Packet4f(NAME,X) \
+  Packet4f p4f_##NAME = pset1<Packet4f>(X)
+
+#define EIGEN_DECLARE_CONST_Packet4f_FROM_INT(NAME,X) \
+  const Packet4f p4f_##NAME = reinterpret_cast<Packet4f>(pset1<Packet4i>(X))
+
+static EIGEN_DECLARE_CONST_FAST_Packet4f(ZERO, 0); //{ 0.0, 0.0, 0.0, 0.0}
+static EIGEN_DECLARE_CONST_FAST_Packet4i(MINUS1,-1); //{ -1, -1, -1, -1}
+static Packet4f p4f_MZERO = { 0x80000000, 0x80000000, 0x80000000, 0x80000000};
+#endif
+
+static Packet4i p4i_COUNTDOWN = { 0, 1, 2, 3 };
+static Packet4f p4f_COUNTDOWN = { 0.0, 1.0, 2.0, 3.0 };
+static Packet2d p2d_COUNTDOWN = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet16uc>(p2d_ZERO), reinterpret_cast<Packet16uc>(p2d_ONE), 8));
+
+static Packet16uc p16uc_PSET64_HI = { 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };
+static Packet16uc p16uc_DUPLICATE32_HI = { 0,1,2,3, 0,1,2,3, 4,5,6,7, 4,5,6,7 };
+
+// Mask alignment
+#define EIGEN_MASK_ALIGNMENT	0xfffffffffffffff0
+
+#define EIGEN_ALIGNED_PTR(x)	((std::ptrdiff_t)(x) & EIGEN_MASK_ALIGNMENT)
+
+// Handle endianness properly while loading constants
+// Define global static constants:
+
+static Packet16uc p16uc_FORWARD =   { 0,1,2,3, 4,5,6,7, 8,9,10,11, 12,13,14,15 };
+static Packet16uc p16uc_REVERSE32 = { 12,13,14,15, 8,9,10,11, 4,5,6,7, 0,1,2,3 };
+static Packet16uc p16uc_REVERSE64 = { 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
+
+static Packet16uc p16uc_PSET32_WODD   = vec_sld((Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 0), (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 2), 8);//{ 0,1,2,3, 0,1,2,3, 8,9,10,11, 8,9,10,11 };
+static Packet16uc p16uc_PSET32_WEVEN  = vec_sld(p16uc_DUPLICATE32_HI, (Packet16uc) vec_splat((Packet4ui)p16uc_FORWARD, 3), 8);//{ 4,5,6,7, 4,5,6,7, 12,13,14,15, 12,13,14,15 };
+/*static Packet16uc p16uc_HALF64_0_16 = vec_sld((Packet16uc)p4i_ZERO, vec_splat((Packet16uc) vec_abs(p4i_MINUS16), 3), 8);      //{ 0,0,0,0, 0,0,0,0, 16,16,16,16, 16,16,16,16};
+
+static Packet16uc p16uc_PSET64_HI = (Packet16uc) vec_mergeh((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN);     //{ 0,1,2,3, 4,5,6,7, 0,1,2,3, 4,5,6,7 };*/
+static Packet16uc p16uc_PSET64_LO = (Packet16uc) vec_mergel((Packet4ui)p16uc_PSET32_WODD, (Packet4ui)p16uc_PSET32_WEVEN);     //{ 8,9,10,11, 12,13,14,15, 8,9,10,11, 12,13,14,15 };
+/*static Packet16uc p16uc_TRANSPOSE64_HI = vec_add(p16uc_PSET64_HI, p16uc_HALF64_0_16);                                         //{ 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
+static Packet16uc p16uc_TRANSPOSE64_LO = vec_add(p16uc_PSET64_LO, p16uc_HALF64_0_16);                                         //{ 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};*/
+static Packet16uc p16uc_TRANSPOSE64_HI = { 0,1,2,3, 4,5,6,7, 16,17,18,19, 20,21,22,23};
+static Packet16uc p16uc_TRANSPOSE64_LO = { 8,9,10,11, 12,13,14,15, 24,25,26,27, 28,29,30,31};
+
+static Packet16uc p16uc_COMPLEX32_REV = vec_sld(p16uc_REVERSE32, p16uc_REVERSE32, 8);                                         //{ 4,5,6,7, 0,1,2,3, 12,13,14,15, 8,9,10,11 };
+
+static Packet16uc p16uc_COMPLEX32_REV2 = vec_sld(p16uc_FORWARD, p16uc_FORWARD, 8);                                            //{ 8,9,10,11, 12,13,14,15, 0,1,2,3, 4,5,6,7 };
+
+
+#if EIGEN_HAS_BUILTIN(__builtin_prefetch) || EIGEN_COMP_GNUC
+  #define EIGEN_ZVECTOR_PREFETCH(ADDR) __builtin_prefetch(ADDR);
+#else
+  #define EIGEN_ZVECTOR_PREFETCH(ADDR) asm( "   pfd [%[addr]]\n" :: [addr] "r" (ADDR) : "cc" );
+#endif
+
+template<> struct packet_traits<int>    : default_packet_traits
+{
+  typedef Packet4i type;
+  typedef Packet4i half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasAdd  = 1,
+    HasSub  = 1,
+    HasMul  = 1,
+    HasDiv  = 1,
+    HasBlend = 1
+  };
+};
+
+template <>
+struct packet_traits<float> : default_packet_traits {
+  typedef Packet4f type;
+  typedef Packet4f half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size = 4,
+
+    HasAdd = 1,
+    HasSub = 1,
+    HasMul = 1,
+    HasDiv = 1,
+    HasMin = 1,
+    HasMax = 1,
+    HasAbs = 1,
+    HasSin = 0,
+    HasCos = 0,
+    HasLog = 0,
+    HasExp = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasTanh = 1,
+    HasErf = 1,
+    HasRound = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasNegate = 1,
+    HasBlend = 1
+  };
+};
+
+template<> struct packet_traits<double> : default_packet_traits
+{
+  typedef Packet2d type;
+  typedef Packet2d half;
+  enum {
+    Vectorizable = 1,
+    AlignedOnScalar = 1,
+    size=2,
+
+    HasAdd  = 1,
+    HasSub  = 1,
+    HasMul  = 1,
+    HasDiv  = 1,
+    HasMin  = 1,
+    HasMax  = 1,
+    HasAbs  = 1,
+    HasSin  = 0,
+    HasCos  = 0,
+    HasLog  = 0,
+    HasExp  = 1,
+    HasSqrt = 1,
+    HasRsqrt = 1,
+    HasRound = 1,
+    HasFloor = 1,
+    HasCeil = 1,
+    HasNegate = 1,
+    HasBlend = 1
+  };
+};
+
+template<> struct unpacket_traits<Packet4i> { typedef int    type; enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet4i half; };
+template<> struct unpacket_traits<Packet4f> { typedef float  type; enum {size=4, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet4f half; };
+template<> struct unpacket_traits<Packet2d> { typedef double type; enum {size=2, alignment=Aligned16, vectorizable=true, masked_load_available=false, masked_store_available=false}; typedef Packet2d half; };
+
+/* Forward declaration */
+EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet4f,4>& kernel);
+ 
+inline std::ostream & operator <<(std::ostream & s, const Packet4i & v)
+{
+  Packet vt;
+  vt.v4i = v;
+  s << vt.i[0] << ", " << vt.i[1] << ", " << vt.i[2] << ", " << vt.i[3];
+  return s;
+}
+
+inline std::ostream & operator <<(std::ostream & s, const Packet4ui & v)
+{
+  Packet vt;
+  vt.v4ui = v;
+  s << vt.ui[0] << ", " << vt.ui[1] << ", " << vt.ui[2] << ", " << vt.ui[3];
+  return s;
+}
+
+inline std::ostream & operator <<(std::ostream & s, const Packet2l & v)
+{
+  Packet vt;
+  vt.v2l = v;
+  s << vt.l[0] << ", " << vt.l[1];
+  return s;
+}
+
+inline std::ostream & operator <<(std::ostream & s, const Packet2ul & v)
+{
+  Packet vt;
+  vt.v2ul = v;
+  s << vt.ul[0] << ", " << vt.ul[1] ;
+  return s;
+}
+
+inline std::ostream & operator <<(std::ostream & s, const Packet2d & v)
+{
+  Packet vt;
+  vt.v2d = v;
+  s << vt.d[0] << ", " << vt.d[1];
+  return s;
+}
+
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ >= 12)
+inline std::ostream & operator <<(std::ostream & s, const Packet4f & v)
+{
+  Packet vt;
+  vt.v4f = v;
+  s << vt.f[0] << ", " << vt.f[1] << ", " << vt.f[2] << ", " << vt.f[3];
+  return s;
+}
+#endif
+
+template<> EIGEN_STRONG_INLINE Packet4i pload<Packet4i>(const int*     from)
+{
+  // FIXME: No intrinsic yet
+  EIGEN_DEBUG_ALIGNED_LOAD
+  Packet *vfrom;
+  vfrom = (Packet *) from;
+  return vfrom->v4i;
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d pload<Packet2d>(const double* from)
+{
+  // FIXME: No intrinsic yet
+  EIGEN_DEBUG_ALIGNED_LOAD
+  Packet *vfrom;
+  vfrom = (Packet *) from;
+  return vfrom->v2d;
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<int>(int*       to, const Packet4i& from)
+{
+  // FIXME: No intrinsic yet
+  EIGEN_DEBUG_ALIGNED_STORE
+  Packet *vto;
+  vto = (Packet *) to;
+  vto->v4i = from;
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<double>(double*   to, const Packet2d& from)
+{
+  // FIXME: No intrinsic yet
+  EIGEN_DEBUG_ALIGNED_STORE
+  Packet *vto;
+  vto = (Packet *) to;
+  vto->v2d = from;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i pset1<Packet4i>(const int&    from)
+{
+  return vec_splats(from);
+}
+template<> EIGEN_STRONG_INLINE Packet2d pset1<Packet2d>(const double& from) {
+  return vec_splats(from);
+}
+
+template<> EIGEN_STRONG_INLINE void
+pbroadcast4<Packet4i>(const int *a,
+                      Packet4i& a0, Packet4i& a1, Packet4i& a2, Packet4i& a3)
+{
+  a3 = pload<Packet4i>(a);
+  a0 = vec_splat(a3, 0);
+  a1 = vec_splat(a3, 1);
+  a2 = vec_splat(a3, 2);
+  a3 = vec_splat(a3, 3);
+}
+
+template<> EIGEN_STRONG_INLINE void
+pbroadcast4<Packet2d>(const double *a,
+                      Packet2d& a0, Packet2d& a1, Packet2d& a2, Packet2d& a3)
+{
+  a1 = pload<Packet2d>(a);
+  a0 = vec_splat(a1, 0);
+  a1 = vec_splat(a1, 1);
+  a3 = pload<Packet2d>(a+2);
+  a2 = vec_splat(a3, 0);
+  a3 = vec_splat(a3, 1);
+}
+
+template<> EIGEN_DEVICE_FUNC inline Packet4i pgather<int, Packet4i>(const int* from, Index stride)
+{
+  EIGEN_ALIGN16 int ai[4];
+  ai[0] = from[0*stride];
+  ai[1] = from[1*stride];
+  ai[2] = from[2*stride];
+  ai[3] = from[3*stride];
+ return pload<Packet4i>(ai);
+}
+
+template<> EIGEN_DEVICE_FUNC inline Packet2d pgather<double, Packet2d>(const double* from, Index stride)
+{
+  EIGEN_ALIGN16 double af[2];
+  af[0] = from[0*stride];
+  af[1] = from[1*stride];
+ return pload<Packet2d>(af);
+}
+
+template<> EIGEN_DEVICE_FUNC inline void pscatter<int, Packet4i>(int* to, const Packet4i& from, Index stride)
+{
+  EIGEN_ALIGN16 int ai[4];
+  pstore<int>((int *)ai, from);
+  to[0*stride] = ai[0];
+  to[1*stride] = ai[1];
+  to[2*stride] = ai[2];
+  to[3*stride] = ai[3];
+}
+
+template<> EIGEN_DEVICE_FUNC inline void pscatter<double, Packet2d>(double* to, const Packet2d& from, Index stride)
+{
+  EIGEN_ALIGN16 double af[2];
+  pstore<double>(af, from);
+  to[0*stride] = af[0];
+  to[1*stride] = af[1];
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i padd<Packet4i>(const Packet4i& a, const Packet4i& b) { return (a + b); }
+template<> EIGEN_STRONG_INLINE Packet2d padd<Packet2d>(const Packet2d& a, const Packet2d& b) { return (a + b); }
+
+template<> EIGEN_STRONG_INLINE Packet4i psub<Packet4i>(const Packet4i& a, const Packet4i& b) { return (a - b); }
+template<> EIGEN_STRONG_INLINE Packet2d psub<Packet2d>(const Packet2d& a, const Packet2d& b) { return (a - b); }
+
+template<> EIGEN_STRONG_INLINE Packet4i pmul<Packet4i>(const Packet4i& a, const Packet4i& b) { return (a * b); }
+template<> EIGEN_STRONG_INLINE Packet2d pmul<Packet2d>(const Packet2d& a, const Packet2d& b) { return (a * b); }
+
+template<> EIGEN_STRONG_INLINE Packet4i pdiv<Packet4i>(const Packet4i& a, const Packet4i& b) { return (a / b); }
+template<> EIGEN_STRONG_INLINE Packet2d pdiv<Packet2d>(const Packet2d& a, const Packet2d& b) { return (a / b); }
+
+template<> EIGEN_STRONG_INLINE Packet4i pnegate(const Packet4i& a) { return (-a); }
+template<> EIGEN_STRONG_INLINE Packet2d pnegate(const Packet2d& a) { return (-a); }
+
+template<> EIGEN_STRONG_INLINE Packet4i pconj(const Packet4i& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet2d pconj(const Packet2d& a) { return a; }
+
+template<> EIGEN_STRONG_INLINE Packet4i pmadd(const Packet4i& a, const Packet4i& b, const Packet4i& c) { return padd<Packet4i>(pmul<Packet4i>(a, b), c); }
+template<> EIGEN_STRONG_INLINE Packet2d pmadd(const Packet2d& a, const Packet2d& b, const Packet2d& c) { return vec_madd(a, b, c); }
+
+template<> EIGEN_STRONG_INLINE Packet4i plset<Packet4i>(const int& a)    { return padd<Packet4i>(pset1<Packet4i>(a), p4i_COUNTDOWN); }
+template<> EIGEN_STRONG_INLINE Packet2d plset<Packet2d>(const double& a) { return padd<Packet2d>(pset1<Packet2d>(a), p2d_COUNTDOWN); }
+
+template<> EIGEN_STRONG_INLINE Packet4i pmin<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_min(a, b); }
+template<> EIGEN_STRONG_INLINE Packet2d pmin<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_min(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet4i pmax<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_max(a, b); }
+template<> EIGEN_STRONG_INLINE Packet2d pmax<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_max(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet4i pand<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_and(a, b); }
+template<> EIGEN_STRONG_INLINE Packet2d pand<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet4i por<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_or(a, b); }
+template<> EIGEN_STRONG_INLINE Packet2d por<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_or(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet4i pxor<Packet4i>(const Packet4i& a, const Packet4i& b) { return vec_xor(a, b); }
+template<> EIGEN_STRONG_INLINE Packet2d pxor<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_xor(a, b); }
+
+template<> EIGEN_STRONG_INLINE Packet4i pandnot<Packet4i>(const Packet4i& a, const Packet4i& b) { return pand<Packet4i>(a, vec_nor(b, b)); }
+template<> EIGEN_STRONG_INLINE Packet2d pandnot<Packet2d>(const Packet2d& a, const Packet2d& b) { return vec_and(a, vec_nor(b, b)); }
+
+template<> EIGEN_STRONG_INLINE Packet2d pround<Packet2d>(const Packet2d& a) { return vec_round(a); }
+template<> EIGEN_STRONG_INLINE Packet2d pceil<Packet2d>(const  Packet2d& a) { return vec_ceil(a); }
+template<> EIGEN_STRONG_INLINE Packet2d pfloor<Packet2d>(const Packet2d& a) { return vec_floor(a); }
+
+template<> EIGEN_STRONG_INLINE Packet4i ploadu<Packet4i>(const int*       from) { return pload<Packet4i>(from); }
+template<> EIGEN_STRONG_INLINE Packet2d ploadu<Packet2d>(const double*    from) { return pload<Packet2d>(from); }
+
+
+template<> EIGEN_STRONG_INLINE Packet4i ploaddup<Packet4i>(const int*     from)
+{
+  Packet4i p = pload<Packet4i>(from);
+  return vec_perm(p, p, p16uc_DUPLICATE32_HI);
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d ploaddup<Packet2d>(const double*   from)
+{
+  Packet2d p = pload<Packet2d>(from);
+  return vec_perm(p, p, p16uc_PSET64_HI);
+}
+
+template<> EIGEN_STRONG_INLINE void pstoreu<int>(int*        to, const Packet4i& from) { pstore<int>(to, from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<double>(double*  to, const Packet2d& from) { pstore<double>(to, from); }
+
+template<> EIGEN_STRONG_INLINE void prefetch<int>(const int*       addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
+template<> EIGEN_STRONG_INLINE void prefetch<double>(const double* addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
+
+template<> EIGEN_STRONG_INLINE int    pfirst<Packet4i>(const Packet4i& a) { EIGEN_ALIGN16 int    x[4]; pstore(x, a); return x[0]; }
+template<> EIGEN_STRONG_INLINE double pfirst<Packet2d>(const Packet2d& a) { EIGEN_ALIGN16 double x[2]; pstore(x, a); return x[0]; }
+
+template<> EIGEN_STRONG_INLINE Packet4i preverse(const Packet4i& a)
+{
+  return reinterpret_cast<Packet4i>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
+}
+
+template<> EIGEN_STRONG_INLINE Packet2d preverse(const Packet2d& a)
+{
+  return reinterpret_cast<Packet2d>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE64));
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i pabs<Packet4i>(const Packet4i& a) { return vec_abs(a); }
+template<> EIGEN_STRONG_INLINE Packet2d pabs<Packet2d>(const Packet2d& a) { return vec_abs(a); }
+
+template<> EIGEN_STRONG_INLINE int predux<Packet4i>(const Packet4i& a)
+{
+  Packet4i b, sum;
+  b   = vec_sld(a, a, 8);
+  sum = padd<Packet4i>(a, b);
+  b   = vec_sld(sum, sum, 4);
+  sum = padd<Packet4i>(sum, b);
+  return pfirst(sum);
+}
+
+template<> EIGEN_STRONG_INLINE double predux<Packet2d>(const Packet2d& a)
+{
+  Packet2d b, sum;
+  b   = reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8));
+  sum = padd<Packet2d>(a, b);
+  return pfirst(sum);
+}
+
+// Other reduction functions:
+// mul
+template<> EIGEN_STRONG_INLINE int predux_mul<Packet4i>(const Packet4i& a)
+{
+  EIGEN_ALIGN16 int aux[4];
+  pstore(aux, a);
+  return aux[0] * aux[1] * aux[2] * aux[3];
+}
+
+template<> EIGEN_STRONG_INLINE double predux_mul<Packet2d>(const Packet2d& a)
+{
+  return pfirst(pmul(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8))));
+}
+
+// min
+template<> EIGEN_STRONG_INLINE int predux_min<Packet4i>(const Packet4i& a)
+{
+  Packet4i b, res;
+  b   = pmin<Packet4i>(a, vec_sld(a, a, 8));
+  res = pmin<Packet4i>(b, vec_sld(b, b, 4));
+  return pfirst(res);
+}
+
+template<> EIGEN_STRONG_INLINE double predux_min<Packet2d>(const Packet2d& a)
+{
+  return pfirst(pmin<Packet2d>(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8))));
+}
+
+// max
+template<> EIGEN_STRONG_INLINE int predux_max<Packet4i>(const Packet4i& a)
+{
+  Packet4i b, res;
+  b = pmax<Packet4i>(a, vec_sld(a, a, 8));
+  res = pmax<Packet4i>(b, vec_sld(b, b, 4));
+  return pfirst(res);
+}
+
+// max
+template<> EIGEN_STRONG_INLINE double predux_max<Packet2d>(const Packet2d& a)
+{
+  return pfirst(pmax<Packet2d>(a, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(a), reinterpret_cast<Packet4i>(a), 8))));
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet4i,4>& kernel) {
+  Packet4i t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
+  Packet4i t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
+  Packet4i t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
+  Packet4i t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet2d,2>& kernel) {
+  Packet2d t0 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_HI);
+  Packet2d t1 = vec_perm(kernel.packet[0], kernel.packet[1], p16uc_TRANSPOSE64_LO);
+  kernel.packet[0] = t0;
+  kernel.packet[1] = t1;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4i pblend(const Selector<4>& ifPacket, const Packet4i& thenPacket, const Packet4i& elsePacket) {
+  Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3] };
+  Packet4ui mask = vec_cmpeq(select, reinterpret_cast<Packet4ui>(p4i_ONE));
+  return vec_sel(elsePacket, thenPacket, mask);
+}
+
+
+template<> EIGEN_STRONG_INLINE Packet2d pblend(const Selector<2>& ifPacket, const Packet2d& thenPacket, const Packet2d& elsePacket) {
+  Packet2ul select = { ifPacket.select[0], ifPacket.select[1] };
+  Packet2ul mask = vec_cmpeq(select, reinterpret_cast<Packet2ul>(p2l_ONE));
+  return vec_sel(elsePacket, thenPacket, mask);
+}
+
+/* z13 has no vector float support so we emulate that with double
+   z14 has proper vector float support.
+*/
+#if !defined(__ARCH__) || (defined(__ARCH__) && __ARCH__ < 12)
+/* Helper function to simulate a vec_splat_packet4f
+ */
+template<int element> EIGEN_STRONG_INLINE Packet4f vec_splat_packet4f(const Packet4f&   from)
+{
+  Packet4f splat;
+  switch (element) {
+  case 0:
+    splat.v4f[0] = vec_splat(from.v4f[0], 0);
+    splat.v4f[1] = splat.v4f[0];
+    break;
+  case 1:
+    splat.v4f[0] = vec_splat(from.v4f[0], 1);
+    splat.v4f[1] = splat.v4f[0];
+    break;
+  case 2:
+    splat.v4f[0] = vec_splat(from.v4f[1], 0);
+    splat.v4f[1] = splat.v4f[0];
+    break;
+  case 3:
+    splat.v4f[0] = vec_splat(from.v4f[1], 1);
+    splat.v4f[1] = splat.v4f[0];
+    break;
+  }
+  return splat;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float*   from)
+{
+  // FIXME: No intrinsic yet
+  EIGEN_DEBUG_ALIGNED_LOAD
+  Packet4f vfrom;
+  vfrom.v4f[0] = vec_ld2f(&from[0]);
+  vfrom.v4f[1] = vec_ld2f(&from[2]);
+  return vfrom;
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<float>(float*   to, const Packet4f& from)
+{
+  // FIXME: No intrinsic yet
+  EIGEN_DEBUG_ALIGNED_STORE
+  vec_st2f(from.v4f[0], &to[0]);
+  vec_st2f(from.v4f[1], &to[2]);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float&    from)
+{
+  Packet4f to;
+  to.v4f[0] = pset1<Packet2d>(static_cast<const double&>(from));
+  to.v4f[1] = to.v4f[0];
+  return to;
+}
+
+template<> EIGEN_STRONG_INLINE void
+pbroadcast4<Packet4f>(const float *a,
+                      Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
+{
+  a3 = pload<Packet4f>(a);
+  a0 = vec_splat_packet4f<0>(a3);
+  a1 = vec_splat_packet4f<1>(a3);
+  a2 = vec_splat_packet4f<2>(a3);
+  a3 = vec_splat_packet4f<3>(a3);
+}
+
+template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride)
+{
+  EIGEN_ALIGN16 float ai[4];
+  ai[0] = from[0*stride];
+  ai[1] = from[1*stride];
+  ai[2] = from[2*stride];
+  ai[3] = from[3*stride];
+ return pload<Packet4f>(ai);
+}
+
+template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
+{
+  EIGEN_ALIGN16 float ai[4];
+  pstore<float>((float *)ai, from);
+  to[0*stride] = ai[0];
+  to[1*stride] = ai[1];
+  to[2*stride] = ai[2];
+  to[3*stride] = ai[3];
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+  Packet4f c;
+  c.v4f[0] = a.v4f[0] + b.v4f[0];
+  c.v4f[1] = a.v4f[1] + b.v4f[1];
+  return c;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+  Packet4f c;
+  c.v4f[0] = a.v4f[0] - b.v4f[0];
+  c.v4f[1] = a.v4f[1] - b.v4f[1];
+  return c;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+  Packet4f c;
+  c.v4f[0] = a.v4f[0] * b.v4f[0];
+  c.v4f[1] = a.v4f[1] * b.v4f[1];
+  return c;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+  Packet4f c;
+  c.v4f[0] = a.v4f[0] / b.v4f[0];
+  c.v4f[1] = a.v4f[1] / b.v4f[1];
+  return c;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pnegate(const Packet4f& a)
+{
+  Packet4f c;
+  c.v4f[0] = -a.v4f[0];
+  c.v4f[1] = -a.v4f[1];
+  return c;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pmadd(const Packet4f& a, const Packet4f& b, const Packet4f& c)
+{
+  Packet4f res;
+  res.v4f[0] = vec_madd(a.v4f[0], b.v4f[0], c.v4f[0]);
+  res.v4f[1] = vec_madd(a.v4f[1], b.v4f[1], c.v4f[1]);
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+  Packet4f res;
+  res.v4f[0] = pmin(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pmin(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+  Packet4f res;
+  res.v4f[0] = pmax(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pmax(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+  Packet4f res;
+  res.v4f[0] = pand(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pand(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+  Packet4f res;
+  res.v4f[0] = por(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = por(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+  Packet4f res;
+  res.v4f[0] = pxor(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pxor(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+  Packet4f res;
+  res.v4f[0] = pandnot(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pandnot(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f>(const Packet4f& a)
+{
+  Packet4f res;
+  res.v4f[0] = vec_round(a.v4f[0]);
+  res.v4f[1] = vec_round(a.v4f[1]);
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>(const  Packet4f& a)
+{
+  Packet4f res;
+  res.v4f[0] = vec_ceil(a.v4f[0]);
+  res.v4f[1] = vec_ceil(a.v4f[1]);
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f>(const Packet4f& a)
+{
+  Packet4f res;
+  res.v4f[0] = vec_floor(a.v4f[0]);
+  res.v4f[1] = vec_floor(a.v4f[1]);
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float*    from)
+{
+  Packet4f p = pload<Packet4f>(from);
+  p.v4f[1] = vec_splat(p.v4f[0], 1);
+  p.v4f[0] = vec_splat(p.v4f[0], 0);
+  return p;
+}
+
+template<> EIGEN_STRONG_INLINE float  pfirst<Packet4f>(const Packet4f& a) { EIGEN_ALIGN16 float x[2]; vec_st2f(a.v4f[0], &x[0]); return x[0]; }
+
+template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a)
+{
+  Packet4f rev;
+  rev.v4f[0] = preverse<Packet2d>(a.v4f[1]);
+  rev.v4f[1] = preverse<Packet2d>(a.v4f[0]);
+  return rev;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pabs<Packet4f>(const Packet4f& a)
+{
+  Packet4f res;
+  res.v4f[0] = pabs(a.v4f[0]);
+  res.v4f[1] = pabs(a.v4f[1]);
+  return res;
+}
+
+template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
+{
+  Packet2d sum;
+  sum = padd<Packet2d>(a.v4f[0], a.v4f[1]);
+  double first = predux<Packet2d>(sum);
+  return static_cast<float>(first);
+}
+
+template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
+{
+  // Return predux_mul<Packet2d> of the subvectors product
+  return static_cast<float>(pfirst(predux_mul(pmul(a.v4f[0], a.v4f[1]))));
+}
+
+template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
+{
+  Packet2d b, res;
+  b   = pmin<Packet2d>(a.v4f[0], a.v4f[1]);
+  res = pmin<Packet2d>(b, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(b), reinterpret_cast<Packet4i>(b), 8)));
+  return static_cast<float>(pfirst(res));
+}
+
+template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
+{
+  Packet2d b, res;
+  b   = pmax<Packet2d>(a.v4f[0], a.v4f[1]);
+  res = pmax<Packet2d>(b, reinterpret_cast<Packet2d>(vec_sld(reinterpret_cast<Packet4i>(b), reinterpret_cast<Packet4i>(b), 8)));
+  return static_cast<float>(pfirst(res));
+}
+
+/* Split the Packet4f PacketBlock into 4 Packet2d PacketBlocks and transpose each one
+ */
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet4f,4>& kernel) {
+  PacketBlock<Packet2d,2> t0,t1,t2,t3;
+  // copy top-left 2x2 Packet2d block
+  t0.packet[0] = kernel.packet[0].v4f[0];
+  t0.packet[1] = kernel.packet[1].v4f[0];
+
+  // copy top-right 2x2 Packet2d block
+  t1.packet[0] = kernel.packet[0].v4f[1];
+  t1.packet[1] = kernel.packet[1].v4f[1];
+
+  // copy bottom-left 2x2 Packet2d block
+  t2.packet[0] = kernel.packet[2].v4f[0];
+  t2.packet[1] = kernel.packet[3].v4f[0];
+
+  // copy bottom-right 2x2 Packet2d block
+  t3.packet[0] = kernel.packet[2].v4f[1];
+  t3.packet[1] = kernel.packet[3].v4f[1];
+
+  // Transpose all 2x2 blocks
+  ptranspose(t0);
+  ptranspose(t1);
+  ptranspose(t2);
+  ptranspose(t3);
+
+  // Copy back transposed blocks, but exchange t1 and t2 due to transposition
+  kernel.packet[0].v4f[0] = t0.packet[0];
+  kernel.packet[0].v4f[1] = t2.packet[0];
+  kernel.packet[1].v4f[0] = t0.packet[1];
+  kernel.packet[1].v4f[1] = t2.packet[1];
+  kernel.packet[2].v4f[0] = t1.packet[0];
+  kernel.packet[2].v4f[1] = t3.packet[0];
+  kernel.packet[3].v4f[0] = t1.packet[1];
+  kernel.packet[3].v4f[1] = t3.packet[1];
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket, const Packet4f& elsePacket) {
+  Packet2ul select_hi = { ifPacket.select[0], ifPacket.select[1] };
+  Packet2ul select_lo = { ifPacket.select[2], ifPacket.select[3] };
+  Packet2ul mask_hi = vec_cmpeq(select_hi, reinterpret_cast<Packet2ul>(p2l_ONE));
+  Packet2ul mask_lo = vec_cmpeq(select_lo, reinterpret_cast<Packet2ul>(p2l_ONE));
+  Packet4f result;
+  result.v4f[0] = vec_sel(elsePacket.v4f[0], thenPacket.v4f[0], mask_hi);
+  result.v4f[1] = vec_sel(elsePacket.v4f[1], thenPacket.v4f[1], mask_lo);
+  return result;
+}
+
+template<> Packet4f EIGEN_STRONG_INLINE pcmp_le<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+  Packet4f res;
+  res.v4f[0] = pcmp_le(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pcmp_le(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template<> Packet4f EIGEN_STRONG_INLINE pcmp_lt<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+  Packet4f res;
+  res.v4f[0] = pcmp_lt(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pcmp_lt(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+template<> Packet4f EIGEN_STRONG_INLINE pcmp_eq<Packet4f>(const Packet4f& a, const Packet4f& b)
+{
+  Packet4f res;
+  res.v4f[0] = pcmp_eq(a.v4f[0], b.v4f[0]);
+  res.v4f[1] = pcmp_eq(a.v4f[1], b.v4f[1]);
+  return res;
+}
+
+#else
+template<> EIGEN_STRONG_INLINE Packet4f pload<Packet4f>(const float* from)
+{
+  // FIXME: No intrinsic yet
+  EIGEN_DEBUG_ALIGNED_LOAD
+  Packet *vfrom;
+  vfrom = (Packet *) from;
+  return vfrom->v4f;
+}
+
+template<> EIGEN_STRONG_INLINE void pstore<float>(float* to, const Packet4f& from)
+{
+  // FIXME: No intrinsic yet
+  EIGEN_DEBUG_ALIGNED_STORE
+  Packet *vto;
+  vto = (Packet *) to;
+  vto->v4f = from;
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pset1<Packet4f>(const float& from)
+{
+  return vec_splats(from);
+}
+
+template<> EIGEN_STRONG_INLINE void
+pbroadcast4<Packet4f>(const float *a,
+                      Packet4f& a0, Packet4f& a1, Packet4f& a2, Packet4f& a3)
+{
+  a3 = pload<Packet4f>(a);
+  a0 = vec_splat(a3, 0);
+  a1 = vec_splat(a3, 1);
+  a2 = vec_splat(a3, 2);
+  a3 = vec_splat(a3, 3);
+}
+
+template<> EIGEN_DEVICE_FUNC inline Packet4f pgather<float, Packet4f>(const float* from, Index stride)
+{
+  EIGEN_ALIGN16 float af[4];
+  af[0] = from[0*stride];
+  af[1] = from[1*stride];
+  af[2] = from[2*stride];
+  af[3] = from[3*stride];
+ return pload<Packet4f>(af);
+}
+
+template<> EIGEN_DEVICE_FUNC inline void pscatter<float, Packet4f>(float* to, const Packet4f& from, Index stride)
+{
+  EIGEN_ALIGN16 float af[4];
+  pstore<float>((float*)af, from);
+  to[0*stride] = af[0];
+  to[1*stride] = af[1];
+  to[2*stride] = af[2];
+  to[3*stride] = af[3];
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f padd<Packet4f>(const Packet4f& a, const Packet4f& b) { return (a + b); }
+template<> EIGEN_STRONG_INLINE Packet4f psub<Packet4f>(const Packet4f& a, const Packet4f& b) { return (a - b); }
+template<> EIGEN_STRONG_INLINE Packet4f pmul<Packet4f>(const Packet4f& a, const Packet4f& b) { return (a * b); }
+template<> EIGEN_STRONG_INLINE Packet4f pdiv<Packet4f>(const Packet4f& a, const Packet4f& b) { return (a / b); }
+template<> EIGEN_STRONG_INLINE Packet4f pnegate<Packet4f>(const Packet4f& a) { return (-a); }
+template<> EIGEN_STRONG_INLINE Packet4f pconj<Packet4f>  (const Packet4f& a) { return a; }
+template<> EIGEN_STRONG_INLINE Packet4f pmadd<Packet4f>  (const Packet4f& a, const Packet4f& b, const Packet4f& c) { return vec_madd(a, b, c); }
+template<> EIGEN_STRONG_INLINE Packet4f pmin<Packet4f>   (const Packet4f& a, const Packet4f& b) { return vec_min(a, b); }
+template<> EIGEN_STRONG_INLINE Packet4f pmax<Packet4f>   (const Packet4f& a, const Packet4f& b) { return vec_max(a, b); }
+template<> EIGEN_STRONG_INLINE Packet4f pand<Packet4f>   (const Packet4f& a, const Packet4f& b) { return vec_and(a, b); }
+template<> EIGEN_STRONG_INLINE Packet4f por<Packet4f>    (const Packet4f& a, const Packet4f& b) { return vec_or(a, b); }
+template<> EIGEN_STRONG_INLINE Packet4f pxor<Packet4f>   (const Packet4f& a, const Packet4f& b) { return vec_xor(a, b); }
+template<> EIGEN_STRONG_INLINE Packet4f pandnot<Packet4f>(const Packet4f& a, const Packet4f& b) { return vec_and(a, vec_nor(b, b)); }
+template<> EIGEN_STRONG_INLINE Packet4f pround<Packet4f> (const Packet4f& a) { return vec_round(a); }
+template<> EIGEN_STRONG_INLINE Packet4f pceil<Packet4f>  (const Packet4f& a) { return vec_ceil(a); }
+template<> EIGEN_STRONG_INLINE Packet4f pfloor<Packet4f> (const Packet4f& a) { return vec_floor(a); }
+template<> EIGEN_STRONG_INLINE Packet4f pabs<Packet4f>   (const Packet4f& a) { return vec_abs(a); }
+template<> EIGEN_STRONG_INLINE float pfirst<Packet4f>(const Packet4f& a) { EIGEN_ALIGN16 float x[4]; pstore(x, a); return x[0]; }
+
+template<> EIGEN_STRONG_INLINE Packet4f ploaddup<Packet4f>(const float* from)
+{
+  Packet4f p = pload<Packet4f>(from);
+  return vec_perm(p, p, p16uc_DUPLICATE32_HI);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f preverse(const Packet4f& a)
+{
+  return reinterpret_cast<Packet4f>(vec_perm(reinterpret_cast<Packet16uc>(a), reinterpret_cast<Packet16uc>(a), p16uc_REVERSE32));
+}
+
+template<> EIGEN_STRONG_INLINE float predux<Packet4f>(const Packet4f& a)
+{
+  Packet4f b, sum;
+  b   = vec_sld(a, a, 8);
+  sum = padd<Packet4f>(a, b);
+  b   = vec_sld(sum, sum, 4);
+  sum = padd<Packet4f>(sum, b);
+  return pfirst(sum);
+}
+
+// Other reduction functions:
+// mul
+template<> EIGEN_STRONG_INLINE float predux_mul<Packet4f>(const Packet4f& a)
+{
+  Packet4f prod;
+  prod = pmul(a, vec_sld(a, a, 8));
+  return pfirst(pmul(prod, vec_sld(prod, prod, 4)));
+}
+
+// min
+template<> EIGEN_STRONG_INLINE float predux_min<Packet4f>(const Packet4f& a)
+{
+  Packet4f b, res;
+  b   = pmin<Packet4f>(a, vec_sld(a, a, 8));
+  res = pmin<Packet4f>(b, vec_sld(b, b, 4));
+  return pfirst(res);
+}
+
+// max
+template<> EIGEN_STRONG_INLINE float predux_max<Packet4f>(const Packet4f& a)
+{
+  Packet4f b, res;
+  b = pmax<Packet4f>(a, vec_sld(a, a, 8));
+  res = pmax<Packet4f>(b, vec_sld(b, b, 4));
+  return pfirst(res);
+}
+
+EIGEN_DEVICE_FUNC inline void
+ptranspose(PacketBlock<Packet4f,4>& kernel) {
+  Packet4f t0 = vec_mergeh(kernel.packet[0], kernel.packet[2]);
+  Packet4f t1 = vec_mergel(kernel.packet[0], kernel.packet[2]);
+  Packet4f t2 = vec_mergeh(kernel.packet[1], kernel.packet[3]);
+  Packet4f t3 = vec_mergel(kernel.packet[1], kernel.packet[3]);
+  kernel.packet[0] = vec_mergeh(t0, t2);
+  kernel.packet[1] = vec_mergel(t0, t2);
+  kernel.packet[2] = vec_mergeh(t1, t3);
+  kernel.packet[3] = vec_mergel(t1, t3);
+}
+
+template<> EIGEN_STRONG_INLINE Packet4f pblend(const Selector<4>& ifPacket, const Packet4f& thenPacket, const Packet4f& elsePacket) {
+  Packet4ui select = { ifPacket.select[0], ifPacket.select[1], ifPacket.select[2], ifPacket.select[3] };
+  Packet4ui mask = vec_cmpeq(select, reinterpret_cast<Packet4ui>(p4i_ONE));
+  return vec_sel(elsePacket, thenPacket, mask);
+}
+
+#endif
+
+template<> EIGEN_STRONG_INLINE void prefetch<float>(const float*   addr) { EIGEN_ZVECTOR_PREFETCH(addr); }
+template<> EIGEN_STRONG_INLINE Packet4f ploadu<Packet4f> (const float* from) { return pload<Packet4f>(from); }
+template<> EIGEN_STRONG_INLINE void pstoreu<float>(float* to, const Packet4f& from) { pstore<float>(to, from); }
+template<> EIGEN_STRONG_INLINE Packet4f plset<Packet4f>  (const float& a)  { return padd<Packet4f>(pset1<Packet4f>(a), p4f_COUNTDOWN); }
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_PACKET_MATH_ZVECTOR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/AssignmentFunctors.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/AssignmentFunctors.h
new file mode 100644
index 0000000000000000000000000000000000000000..ca08991829755b26c7c8dc6b54919145b094cfa6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/AssignmentFunctors.h
@@ -0,0 +1,173 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ASSIGNMENT_FUNCTORS_H
+#define EIGEN_ASSIGNMENT_FUNCTORS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+  
+/** \internal
+  * \brief Template functor for scalar/packet assignment
+  *
+  */
+template<typename DstScalar,typename SrcScalar> struct assign_op {
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a = b; }
+
+  template<int Alignment, typename Packet>
+  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
+  { internal::pstoret<DstScalar,Packet,Alignment>(a,b); }
+};
+
+// Empty overload for void type (used by PermutationMatrix)
+template<typename DstScalar> struct assign_op<DstScalar,void> {};
+
+template<typename DstScalar,typename SrcScalar>
+struct functor_traits<assign_op<DstScalar,SrcScalar> > {
+  enum {
+    Cost = NumTraits<DstScalar>::ReadCost,
+    PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::Vectorizable && packet_traits<SrcScalar>::Vectorizable
+  };
+};
+
+/** \internal
+  * \brief Template functor for scalar/packet assignment with addition
+  *
+  */
+template<typename DstScalar,typename SrcScalar> struct add_assign_op {
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a += b; }
+
+  template<int Alignment, typename Packet>
+  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
+  { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::padd(internal::ploadt<Packet,Alignment>(a),b)); }
+};
+template<typename DstScalar,typename SrcScalar>
+struct functor_traits<add_assign_op<DstScalar,SrcScalar> > {
+  enum {
+    Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::AddCost,
+    PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasAdd
+  };
+};
+
+/** \internal
+  * \brief Template functor for scalar/packet assignment with subtraction
+  *
+  */
+template<typename DstScalar,typename SrcScalar> struct sub_assign_op {
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a -= b; }
+
+  template<int Alignment, typename Packet>
+  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
+  { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::psub(internal::ploadt<Packet,Alignment>(a),b)); }
+};
+template<typename DstScalar,typename SrcScalar>
+struct functor_traits<sub_assign_op<DstScalar,SrcScalar> > {
+  enum {
+    Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::AddCost,
+    PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasSub
+  };
+};
+
+/** \internal
+  * \brief Template functor for scalar/packet assignment with multiplication
+  *
+  */
+template<typename DstScalar, typename SrcScalar=DstScalar>
+struct mul_assign_op {
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a *= b; }
+
+  template<int Alignment, typename Packet>
+  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
+  { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::pmul(internal::ploadt<Packet,Alignment>(a),b)); }
+};
+template<typename DstScalar, typename SrcScalar>
+struct functor_traits<mul_assign_op<DstScalar,SrcScalar> > {
+  enum {
+    Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::MulCost,
+    PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasMul
+  };
+};
+
+/** \internal
+  * \brief Template functor for scalar/packet assignment with diviving
+  *
+  */
+template<typename DstScalar, typename SrcScalar=DstScalar> struct div_assign_op {
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(DstScalar& a, const SrcScalar& b) const { a /= b; }
+
+  template<int Alignment, typename Packet>
+  EIGEN_STRONG_INLINE void assignPacket(DstScalar* a, const Packet& b) const
+  { internal::pstoret<DstScalar,Packet,Alignment>(a,internal::pdiv(internal::ploadt<Packet,Alignment>(a),b)); }
+};
+template<typename DstScalar, typename SrcScalar>
+struct functor_traits<div_assign_op<DstScalar,SrcScalar> > {
+  enum {
+    Cost = NumTraits<DstScalar>::ReadCost + NumTraits<DstScalar>::MulCost,
+    PacketAccess = is_same<DstScalar,SrcScalar>::value && packet_traits<DstScalar>::HasDiv
+  };
+};
+
+/** \internal
+  * \brief Template functor for scalar/packet assignment with swapping
+  *
+  * It works as follow. For a non-vectorized evaluation loop, we have:
+  *   for(i) func(A.coeffRef(i), B.coeff(i));
+  * where B is a SwapWrapper expression. The trick is to make SwapWrapper::coeff behaves like a non-const coeffRef.
+  * Actually, SwapWrapper might not even be needed since even if B is a plain expression, since it has to be writable
+  * B.coeff already returns a const reference to the underlying scalar value.
+  * 
+  * The case of a vectorized loop is more tricky:
+  *   for(i,j) func.assignPacket<A_Align>(&A.coeffRef(i,j), B.packet<B_Align>(i,j));
+  * Here, B must be a SwapWrapper whose packet function actually returns a proxy object holding a Scalar*,
+  * the actual alignment and Packet type.
+  *
+  */
+template<typename Scalar> struct swap_assign_op {
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void assignCoeff(Scalar& a, const Scalar& b) const
+  {
+#ifdef EIGEN_GPUCC
+    // FIXME is there some kind of cuda::swap?
+    Scalar t=b; const_cast<Scalar&>(b)=a; a=t;
+#else
+    using std::swap;
+    swap(a,const_cast<Scalar&>(b));
+#endif
+  }
+};
+template<typename Scalar>
+struct functor_traits<swap_assign_op<Scalar> > {
+  enum {
+    Cost = 3 * NumTraits<Scalar>::ReadCost,
+    PacketAccess = 
+    #if defined(EIGEN_VECTORIZE_AVX) && (EIGEN_CLANG_STRICT_LESS_THAN(8,0,0) || EIGEN_COMP_CLANGAPPLE)
+    // This is a partial workaround for a bug in clang generating bad code
+    // when mixing 256/512 bits loads and 128 bits moves.
+    // See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1684
+    //     https://bugs.llvm.org/show_bug.cgi?id=40815
+    0
+    #else
+    packet_traits<Scalar>::Vectorizable
+    #endif
+  };
+};
+
+} // namespace internal
+
+} // namespace Eigen
+
+#endif // EIGEN_ASSIGNMENT_FUNCTORS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/BinaryFunctors.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/BinaryFunctors.h
new file mode 100644
index 0000000000000000000000000000000000000000..cd8ae9ee5ea28e3d044248274612d19470f97cb2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/BinaryFunctors.h
@@ -0,0 +1,763 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BINARY_FUNCTORS_H
+#define EIGEN_BINARY_FUNCTORS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+//---------- associative binary functors ----------
+
+template<typename Arg1, typename Arg2>
+struct binary_op_base
+{
+  typedef Arg1 first_argument_type;
+  typedef Arg2 second_argument_type;
+};
+
+/** \internal
+  * \brief Template functor to compute the sum of two scalars
+  *
+  * \sa class CwiseBinaryOp, MatrixBase::operator+, class VectorwiseOp, DenseBase::sum()
+  */
+template<typename LhsScalar,typename RhsScalar>
+struct scalar_sum_op : binary_op_base<LhsScalar,RhsScalar>
+{
+  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_sum_op>::ReturnType result_type;
+#ifdef EIGEN_SCALAR_BINARY_OP_PLUGIN
+  scalar_sum_op() {
+    EIGEN_SCALAR_BINARY_OP_PLUGIN
+  }
+#endif
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a + b; }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const
+  { return internal::padd(a,b); }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const
+  { return internal::predux(a); }
+};
+template<typename LhsScalar,typename RhsScalar>
+struct functor_traits<scalar_sum_op<LhsScalar,RhsScalar> > {
+  enum {
+    Cost = (int(NumTraits<LhsScalar>::AddCost) + int(NumTraits<RhsScalar>::AddCost)) / 2, // rough estimate!
+    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasAdd && packet_traits<RhsScalar>::HasAdd
+    // TODO vectorize mixed sum
+  };
+};
+
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool scalar_sum_op<bool,bool>::operator() (const bool& a, const bool& b) const { return a || b; }
+
+
+/** \internal
+  * \brief Template functor to compute the product of two scalars
+  *
+  * \sa class CwiseBinaryOp, Cwise::operator*(), class VectorwiseOp, MatrixBase::redux()
+  */
+template<typename LhsScalar,typename RhsScalar>
+struct scalar_product_op  : binary_op_base<LhsScalar,RhsScalar>
+{
+  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_product_op>::ReturnType result_type;
+#ifdef EIGEN_SCALAR_BINARY_OP_PLUGIN
+  scalar_product_op() {
+    EIGEN_SCALAR_BINARY_OP_PLUGIN
+  }
+#endif
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a * b; }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const
+  { return internal::pmul(a,b); }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const
+  { return internal::predux_mul(a); }
+};
+template<typename LhsScalar,typename RhsScalar>
+struct functor_traits<scalar_product_op<LhsScalar,RhsScalar> > {
+  enum {
+    Cost = (int(NumTraits<LhsScalar>::MulCost) + int(NumTraits<RhsScalar>::MulCost))/2, // rough estimate!
+    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasMul && packet_traits<RhsScalar>::HasMul
+    // TODO vectorize mixed product
+  };
+};
+
+template<>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool scalar_product_op<bool,bool>::operator() (const bool& a, const bool& b) const { return a && b; }
+
+
+/** \internal
+  * \brief Template functor to compute the conjugate product of two scalars
+  *
+  * This is a short cut for conj(x) * y which is needed for optimization purpose; in Eigen2 support mode, this becomes x * conj(y)
+  */
+template<typename LhsScalar,typename RhsScalar>
+struct scalar_conj_product_op  : binary_op_base<LhsScalar,RhsScalar>
+{
+
+  enum {
+    Conj = NumTraits<LhsScalar>::IsComplex
+  };
+
+  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_conj_product_op>::ReturnType result_type;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const
+  { return conj_helper<LhsScalar,RhsScalar,Conj,false>().pmul(a,b); }
+
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const
+  { return conj_helper<Packet,Packet,Conj,false>().pmul(a,b); }
+};
+template<typename LhsScalar,typename RhsScalar>
+struct functor_traits<scalar_conj_product_op<LhsScalar,RhsScalar> > {
+  enum {
+    Cost = NumTraits<LhsScalar>::MulCost,
+    PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMul
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the min of two scalars
+  *
+  * \sa class CwiseBinaryOp, MatrixBase::cwiseMin, class VectorwiseOp, MatrixBase::minCoeff()
+  */
+template<typename LhsScalar,typename RhsScalar, int NaNPropagation>
+struct scalar_min_op : binary_op_base<LhsScalar,RhsScalar>
+{
+  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_min_op>::ReturnType result_type;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const {
+    return internal::pmin<NaNPropagation>(a, b);
+  }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const
+  {
+    return internal::pmin<NaNPropagation>(a,b);
+  }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const
+  {
+    return internal::predux_min<NaNPropagation>(a);
+  }
+};
+
+template<typename LhsScalar,typename RhsScalar, int NaNPropagation>
+struct functor_traits<scalar_min_op<LhsScalar,RhsScalar, NaNPropagation> > {
+  enum {
+    Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
+    PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMin
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the max of two scalars
+  *
+  * \sa class CwiseBinaryOp, MatrixBase::cwiseMax, class VectorwiseOp, MatrixBase::maxCoeff()
+  */
+template<typename LhsScalar,typename RhsScalar, int NaNPropagation>
+struct scalar_max_op : binary_op_base<LhsScalar,RhsScalar>
+{
+  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_max_op>::ReturnType result_type;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const {
+    return internal::pmax<NaNPropagation>(a,b);
+  }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const
+  {
+    return internal::pmax<NaNPropagation>(a,b);
+  }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type predux(const Packet& a) const
+  {
+    return internal::predux_max<NaNPropagation>(a);
+  }
+};
+
+template<typename LhsScalar,typename RhsScalar, int NaNPropagation>
+struct functor_traits<scalar_max_op<LhsScalar,RhsScalar, NaNPropagation> > {
+  enum {
+    Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
+    PacketAccess = internal::is_same<LhsScalar, RhsScalar>::value && packet_traits<LhsScalar>::HasMax
+  };
+};
+
+/** \internal
+ * \brief Template functors for comparison of two scalars
+ * \todo Implement packet-comparisons
+ */
+template <typename LhsScalar, typename RhsScalar, ComparisonName cmp,
+          bool UseTypedComparators = false>
+struct scalar_cmp_op;
+
+template <typename LhsScalar, typename RhsScalar, ComparisonName cmp, bool UseTypedComparators>
+struct functor_traits<scalar_cmp_op<LhsScalar, RhsScalar, cmp, UseTypedComparators>> {
+  enum {
+    Cost = (NumTraits<LhsScalar>::AddCost + NumTraits<RhsScalar>::AddCost) / 2,
+    PacketAccess = (UseTypedComparators || is_same<LhsScalar, bool>::value) && is_same<LhsScalar, RhsScalar>::value &&
+                   packet_traits<LhsScalar>::HasCmp
+  };
+};
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+struct typed_cmp_helper {
+  static constexpr bool SameType = is_same<LhsScalar, RhsScalar>::value;
+  static constexpr bool IsNumeric = is_arithmetic<typename NumTraits<LhsScalar>::Real>::value;
+  static constexpr bool UseTyped = UseTypedComparators && SameType && IsNumeric;
+  using type = typename conditional<UseTyped, LhsScalar, bool>::type;
+};
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+using cmp_return_t = typename typed_cmp_helper<LhsScalar, RhsScalar, UseTypedComparators>::type;
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+struct scalar_cmp_op<LhsScalar, RhsScalar, cmp_EQ, UseTypedComparators> : binary_op_base<LhsScalar, RhsScalar> {
+  using result_type = cmp_return_t<LhsScalar, RhsScalar, UseTypedComparators>;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a == b ? result_type(1) : result_type(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(result_type(1));
+    return pand(pcmp_eq(a, b), cst_one);
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+struct scalar_cmp_op<LhsScalar, RhsScalar, cmp_LT, UseTypedComparators> : binary_op_base<LhsScalar, RhsScalar> {
+  using result_type = cmp_return_t<LhsScalar, RhsScalar, UseTypedComparators>;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a < b ? result_type(1) : result_type(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(result_type(1));
+    return pand(pcmp_lt(a, b), cst_one);
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+struct scalar_cmp_op<LhsScalar, RhsScalar, cmp_LE, UseTypedComparators> : binary_op_base<LhsScalar, RhsScalar> {
+  using result_type = cmp_return_t<LhsScalar, RhsScalar, UseTypedComparators>;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a <= b ? result_type(1) : result_type(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(result_type(1));
+    return pand(cst_one, pcmp_le(a, b));
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+struct scalar_cmp_op<LhsScalar, RhsScalar, cmp_GT, UseTypedComparators> : binary_op_base<LhsScalar, RhsScalar> {
+  using result_type = cmp_return_t<LhsScalar, RhsScalar, UseTypedComparators>;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a > b ? result_type(1) : result_type(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(result_type(1));
+    return pand(cst_one, pcmp_lt(b, a));
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+struct scalar_cmp_op<LhsScalar, RhsScalar, cmp_GE, UseTypedComparators> : binary_op_base<LhsScalar, RhsScalar> {
+  using result_type = cmp_return_t<LhsScalar, RhsScalar, UseTypedComparators>;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a >= b ? result_type(1) : result_type(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(result_type(1));
+    return pand(cst_one, pcmp_le(b, a));
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+struct scalar_cmp_op<LhsScalar, RhsScalar, cmp_UNORD, UseTypedComparators> : binary_op_base<LhsScalar, RhsScalar> {
+  using result_type = cmp_return_t<LhsScalar, RhsScalar, UseTypedComparators>;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return !(a <= b || b <= a) ? result_type(1) : result_type(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(result_type(1));
+    return pandnot(cst_one, por(pcmp_le(a, b), pcmp_le(b, a)));
+  }
+};
+
+template <typename LhsScalar, typename RhsScalar, bool UseTypedComparators>
+struct scalar_cmp_op<LhsScalar, RhsScalar, cmp_NEQ, UseTypedComparators> : binary_op_base<LhsScalar, RhsScalar> {
+  using result_type = cmp_return_t<LhsScalar, RhsScalar, UseTypedComparators>;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator()(const LhsScalar& a, const RhsScalar& b) const {
+    return a != b ? result_type(1) : result_type(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(result_type(1));
+    return pandnot(cst_one, pcmp_eq(a, b));
+  }
+};
+
+/** \internal
+  * \brief Template functor to compute the hypot of two \b positive \b and \b real scalars
+  *
+  * \sa MatrixBase::stableNorm(), class Redux
+  */
+template<typename Scalar>
+struct scalar_hypot_op<Scalar,Scalar> : binary_op_base<Scalar,Scalar>
+{
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar &x, const Scalar &y) const
+  {
+    // This functor is used by hypotNorm only for which it is faster to first apply abs
+    // on all coefficients prior to reduction through hypot.
+    // This way we avoid calling abs on positive and real entries, and this also permits
+    // to seamlessly handle complexes. Otherwise we would have to handle both real and complexes
+    // through the same functor...
+    return internal::positive_real_hypot(x,y);
+  }
+};
+template<typename Scalar>
+struct functor_traits<scalar_hypot_op<Scalar,Scalar> > {
+  enum
+  {
+    Cost = 3 * NumTraits<Scalar>::AddCost +
+           2 * NumTraits<Scalar>::MulCost +
+           2 * scalar_div_cost<Scalar,false>::value,
+    PacketAccess = false
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the pow of two scalars
+  * See the specification of pow in https://en.cppreference.com/w/cpp/numeric/math/pow
+  */
+template<typename Scalar, typename Exponent>
+struct scalar_pow_op  : binary_op_base<Scalar,Exponent>
+{
+  typedef typename ScalarBinaryOpTraits<Scalar,Exponent,scalar_pow_op>::ReturnType result_type;
+#ifdef EIGEN_SCALAR_BINARY_OP_PLUGIN
+  scalar_pow_op() {
+    typedef Scalar LhsScalar;
+    typedef Exponent RhsScalar;
+    EIGEN_SCALAR_BINARY_OP_PLUGIN
+  }
+#endif
+
+  EIGEN_DEVICE_FUNC
+  inline result_type operator() (const Scalar& a, const Exponent& b) const { return numext::pow(a, b); }
+
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
+  {
+    return generic_pow(a,b);
+  }
+};
+
+template<typename Scalar, typename Exponent>
+struct functor_traits<scalar_pow_op<Scalar,Exponent> > {
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = (!NumTraits<Scalar>::IsComplex && !NumTraits<Scalar>::IsInteger &&
+                    packet_traits<Scalar>::HasExp && packet_traits<Scalar>::HasLog &&
+                    packet_traits<Scalar>::HasRound && packet_traits<Scalar>::HasCmp &&
+                    // Temporarily disable packet access for half/bfloat16 until
+                    // accuracy is improved.
+                    !is_same<Scalar, half>::value && !is_same<Scalar, bfloat16>::value
+                    )
+  };
+};
+
+//---------- non associative binary functors ----------
+
+/** \internal
+  * \brief Template functor to compute the difference of two scalars
+  *
+  * \sa class CwiseBinaryOp, MatrixBase::operator-
+  */
+template<typename LhsScalar,typename RhsScalar>
+struct scalar_difference_op : binary_op_base<LhsScalar,RhsScalar>
+{
+  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_difference_op>::ReturnType result_type;
+#ifdef EIGEN_SCALAR_BINARY_OP_PLUGIN
+  scalar_difference_op() {
+    EIGEN_SCALAR_BINARY_OP_PLUGIN
+  }
+#endif
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a - b; }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
+  { return internal::psub(a,b); }
+};
+template<typename LhsScalar,typename RhsScalar>
+struct functor_traits<scalar_difference_op<LhsScalar,RhsScalar> > {
+  enum {
+    Cost = (int(NumTraits<LhsScalar>::AddCost) + int(NumTraits<RhsScalar>::AddCost)) / 2,
+    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasSub && packet_traits<RhsScalar>::HasSub
+  };
+};
+
+template <typename Packet, bool IsInteger = NumTraits<typename unpacket_traits<Packet>::type>::IsInteger>
+struct maybe_raise_div_by_zero {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Packet x) {
+    EIGEN_UNUSED_VARIABLE(x);
+  }
+};
+
+#ifndef EIGEN_GPU_COMPILE_PHASE
+template <typename Packet>
+struct maybe_raise_div_by_zero<Packet, true> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Packet x) {
+    if (EIGEN_PREDICT_FALSE(predux_any(pcmp_eq(x, pzero(x))))) {
+      // Use volatile variables to force a division by zero, which will
+      // result in the default platform behaviour (usually SIGFPE).
+      volatile typename unpacket_traits<Packet>::type zero = 0;
+      volatile typename unpacket_traits<Packet>::type val = 1;
+      val = val / zero;
+    }
+  }
+};
+#endif
+
+/** \internal
+  * \brief Template functor to compute the quotient of two scalars
+  *
+  * \sa class CwiseBinaryOp, Cwise::operator/()
+  */
+template<typename LhsScalar,typename RhsScalar>
+struct scalar_quotient_op  : binary_op_base<LhsScalar,RhsScalar>
+{
+  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_quotient_op>::ReturnType result_type;
+#ifdef EIGEN_SCALAR_BINARY_OP_PLUGIN
+  scalar_quotient_op() {
+    EIGEN_SCALAR_BINARY_OP_PLUGIN
+  }
+#endif
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return a / b; }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const {
+    maybe_raise_div_by_zero<Packet>::run(b);
+    return internal::pdiv(a,b);
+  }
+};
+template<typename LhsScalar,typename RhsScalar>
+struct functor_traits<scalar_quotient_op<LhsScalar,RhsScalar> > {
+  typedef typename scalar_quotient_op<LhsScalar,RhsScalar>::result_type result_type;
+  enum {
+    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasDiv && packet_traits<RhsScalar>::HasDiv,
+    Cost = scalar_div_cost<result_type,PacketAccess>::value
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the and of two scalars as if they were booleans
+  *
+  * \sa class CwiseBinaryOp, ArrayBase::operator&&
+  */
+template <typename Scalar>
+struct scalar_boolean_and_op {
+  using result_type = Scalar;
+  // `false` any value `a` that satisfies `a == Scalar(0)`
+  // `true` is the complement of `false`
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a, const Scalar& b) const {
+    return (a != Scalar(0)) && (b != Scalar(0)) ? Scalar(1) : Scalar(0);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    // and(a,b) == !or(!a,!b)
+    Packet not_a = pcmp_eq(a, pzero(a));
+    Packet not_b = pcmp_eq(b, pzero(b));
+    Packet a_nand_b = por(not_a, not_b);
+    return pandnot(cst_one, a_nand_b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_boolean_and_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasCmp };
+};
+
+/** \internal
+  * \brief Template functor to compute the or of two scalars as if they were booleans
+  *
+  * \sa class CwiseBinaryOp, ArrayBase::operator||
+  */
+template <typename Scalar>
+struct scalar_boolean_or_op {
+  using result_type = Scalar;
+  // `false` any value `a` that satisfies `a == Scalar(0)`
+  // `true` is the complement of `false`
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a, const Scalar& b) const {
+    return (a != Scalar(0)) || (b != Scalar(0)) ? Scalar(1) : Scalar(0);
+  }
+  template <typename Packet>
+  EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    // if or(a,b) == 0, then a == 0 and b == 0
+    // or(a,b) == !nor(a,b)
+    Packet a_nor_b = pcmp_eq(por(a, b), pzero(a));
+    return pandnot(cst_one, a_nor_b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_boolean_or_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasCmp };
+};
+
+/** \internal
+ * \brief Template functor to compute the xor of two scalars as if they were booleans
+ *
+ * \sa class CwiseBinaryOp, ArrayBase::operator^
+ */
+template <typename Scalar>
+struct scalar_boolean_xor_op {
+  using result_type = Scalar;
+  // `false` any value `a` that satisfies `a == Scalar(0)`
+  // `true` is the complement of `false`
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a, const Scalar& b) const {
+    return (a != Scalar(0)) != (b != Scalar(0)) ? Scalar(1) : Scalar(0);
+  }
+  template <typename Packet>
+  EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    // xor(a,b) == xor(!a,!b)
+    Packet not_a = pcmp_eq(a, pzero(a));
+    Packet not_b = pcmp_eq(b, pzero(b));
+    Packet a_xor_b = pxor(not_a, not_b);
+    return pand(cst_one, a_xor_b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_boolean_xor_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasCmp };
+};
+
+template <typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
+struct bitwise_binary_impl {
+  static constexpr size_t Size = sizeof(Scalar);
+  using uint_t = typename numext::get_integer_by_size<Size>::unsigned_type;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_and(const Scalar& a, const Scalar& b) {
+    uint_t a_as_uint = numext::bit_cast<uint_t, Scalar>(a);
+    uint_t b_as_uint = numext::bit_cast<uint_t, Scalar>(b);
+    uint_t result = a_as_uint & b_as_uint;
+    return numext::bit_cast<Scalar, uint_t>(result);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_or(const Scalar& a, const Scalar& b) {
+    uint_t a_as_uint = numext::bit_cast<uint_t, Scalar>(a);
+    uint_t b_as_uint = numext::bit_cast<uint_t, Scalar>(b);
+    uint_t result = a_as_uint | b_as_uint;
+    return numext::bit_cast<Scalar, uint_t>(result);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_xor(const Scalar& a, const Scalar& b) {
+    uint_t a_as_uint = numext::bit_cast<uint_t, Scalar>(a);
+    uint_t b_as_uint = numext::bit_cast<uint_t, Scalar>(b);
+    uint_t result = a_as_uint ^ b_as_uint;
+    return numext::bit_cast<Scalar, uint_t>(result);
+  }
+};
+
+template <typename Scalar>
+struct bitwise_binary_impl<Scalar, true> {
+  using Real = typename NumTraits<Scalar>::Real;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_and(const Scalar& a, const Scalar& b) {
+    Real real_result = bitwise_binary_impl<Real>::run_and(numext::real(a), numext::real(b));
+    Real imag_result = bitwise_binary_impl<Real>::run_and(numext::imag(a), numext::imag(b));
+    return Scalar(real_result, imag_result);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_or(const Scalar& a, const Scalar& b) {
+    Real real_result = bitwise_binary_impl<Real>::run_or(numext::real(a), numext::real(b));
+    Real imag_result = bitwise_binary_impl<Real>::run_or(numext::imag(a), numext::imag(b));
+    return Scalar(real_result, imag_result);
+  }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_xor(const Scalar& a, const Scalar& b) {
+    Real real_result = bitwise_binary_impl<Real>::run_xor(numext::real(a), numext::real(b));
+    Real imag_result = bitwise_binary_impl<Real>::run_xor(numext::imag(a), numext::imag(b));
+    return Scalar(real_result, imag_result);
+  }
+};
+
+/** \internal
+  * \brief Template functor to compute the bitwise and of two scalars
+  *
+  * \sa class CwiseBinaryOp, ArrayBase::operator&
+  */
+template <typename Scalar>
+struct scalar_bitwise_and_op {
+  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::RequireInitialization,
+                      BITWISE OPERATIONS MAY ONLY BE PERFORMED ON PLAIN DATA TYPES)
+  EIGEN_STATIC_ASSERT((!internal::is_same<Scalar, bool>::value), DONT USE BITWISE OPS ON BOOLEAN TYPES)
+  using result_type = Scalar;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a, const Scalar& b) const {
+    return bitwise_binary_impl<Scalar>::run_and(a, b);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    return pand(a, b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bitwise_and_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = true };
+};
+
+/** \internal
+  * \brief Template functor to compute the bitwise or of two scalars
+  *
+  * \sa class CwiseBinaryOp, ArrayBase::operator|
+  */
+template <typename Scalar>
+struct scalar_bitwise_or_op {
+  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::RequireInitialization,
+                      BITWISE OPERATIONS MAY ONLY BE PERFORMED ON PLAIN DATA TYPES)
+  EIGEN_STATIC_ASSERT((!internal::is_same<Scalar, bool>::value), DONT USE BITWISE OPS ON BOOLEAN TYPES)
+  using result_type = Scalar;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a, const Scalar& b) const {
+    return bitwise_binary_impl<Scalar>::run_or(a, b);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    return por(a, b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bitwise_or_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = true };
+};
+
+/** \internal
+  * \brief Template functor to compute the bitwise xor of two scalars
+  *
+  * \sa class CwiseBinaryOp, ArrayBase::operator^
+  */
+template <typename Scalar>
+struct scalar_bitwise_xor_op {
+  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::RequireInitialization,
+                      BITWISE OPERATIONS MAY ONLY BE PERFORMED ON PLAIN DATA TYPES)
+  EIGEN_STATIC_ASSERT((!internal::is_same<Scalar, bool>::value), DONT USE BITWISE OPS ON BOOLEAN TYPES)
+  using result_type = Scalar;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a, const Scalar& b) const {
+    return bitwise_binary_impl<Scalar>::run_xor(a, b);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b) const {
+    return pxor(a, b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bitwise_xor_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = true };
+};
+
+/** \internal
+  * \brief Template functor to compute the absolute difference of two scalars
+  *
+  * \sa class CwiseBinaryOp, MatrixBase::absolute_difference
+  */
+template<typename LhsScalar,typename RhsScalar>
+struct scalar_absolute_difference_op : binary_op_base<LhsScalar,RhsScalar>
+{
+  typedef typename ScalarBinaryOpTraits<LhsScalar,RhsScalar,scalar_absolute_difference_op>::ReturnType result_type;
+#ifdef EIGEN_SCALAR_BINARY_OP_PLUGIN
+  scalar_absolute_difference_op() {
+    EIGEN_SCALAR_BINARY_OP_PLUGIN
+  }
+#endif
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const LhsScalar& a, const RhsScalar& b) const
+  { return numext::absdiff(a,b); }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a, const Packet& b) const
+  { return internal::pabsdiff(a,b); }
+};
+template<typename LhsScalar,typename RhsScalar>
+struct functor_traits<scalar_absolute_difference_op<LhsScalar,RhsScalar> > {
+  enum {
+    Cost = (NumTraits<LhsScalar>::AddCost+NumTraits<RhsScalar>::AddCost)/2,
+    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<LhsScalar>::HasAbsDiff
+  };
+};
+
+
+template <typename LhsScalar, typename RhsScalar>
+struct scalar_atan2_op {
+  using Scalar = LhsScalar;
+
+  static constexpr bool Enable = is_same<LhsScalar, RhsScalar>::value && !NumTraits<Scalar>::IsInteger && !NumTraits<Scalar>::IsComplex;
+  EIGEN_STATIC_ASSERT(Enable, "LhsScalar and RhsScalar must be the same non-integer, non-complex type")
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const Scalar& y, const Scalar& x) const {
+    return numext::atan2(y, x);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& y, const Packet& x) const {
+    return internal::patan2(y, x);
+  }
+};
+
+template<typename LhsScalar,typename RhsScalar>
+    struct functor_traits<scalar_atan2_op<LhsScalar, RhsScalar>> {
+  using Scalar = LhsScalar;
+  enum {
+    PacketAccess = is_same<LhsScalar,RhsScalar>::value && packet_traits<Scalar>::HasATan && packet_traits<Scalar>::HasDiv && !NumTraits<Scalar>::IsInteger && !NumTraits<Scalar>::IsComplex,
+    Cost = int(scalar_div_cost<Scalar, PacketAccess>::value) + int(functor_traits<scalar_atan_op<Scalar>>::Cost)
+  };
+};
+
+//---------- binary functors bound to a constant, thus appearing as a unary functor ----------
+
+// The following two classes permits to turn any binary functor into a unary one with one argument bound to a constant value.
+// They are analogues to std::binder1st/binder2nd but with the following differences:
+//  - they are compatible with packetOp
+//  - they are portable across C++ versions (the std::binder* are deprecated in C++11)
+template<typename BinaryOp> struct bind1st_op : BinaryOp {
+
+  typedef typename BinaryOp::first_argument_type  first_argument_type;
+  typedef typename BinaryOp::second_argument_type second_argument_type;
+  typedef typename BinaryOp::result_type          result_type;
+
+  EIGEN_DEVICE_FUNC explicit bind1st_op(const first_argument_type &val) : m_value(val) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const second_argument_type& b) const { return BinaryOp::operator()(m_value,b); }
+
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& b) const
+  { return BinaryOp::packetOp(internal::pset1<Packet>(m_value), b); }
+
+  first_argument_type m_value;
+};
+template<typename BinaryOp> struct functor_traits<bind1st_op<BinaryOp> > : functor_traits<BinaryOp> {};
+
+
+template<typename BinaryOp> struct bind2nd_op : BinaryOp {
+
+  typedef typename BinaryOp::first_argument_type  first_argument_type;
+  typedef typename BinaryOp::second_argument_type second_argument_type;
+  typedef typename BinaryOp::result_type          result_type;
+
+  EIGEN_DEVICE_FUNC explicit bind2nd_op(const second_argument_type &val) : m_value(val) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const first_argument_type& a) const { return BinaryOp::operator()(a,m_value); }
+
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
+  { return BinaryOp::packetOp(a,internal::pset1<Packet>(m_value)); }
+
+  second_argument_type m_value;
+};
+template<typename BinaryOp> struct functor_traits<bind2nd_op<BinaryOp> > : functor_traits<BinaryOp> {};
+
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_BINARY_FUNCTORS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/NullaryFunctors.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/NullaryFunctors.h
new file mode 100644
index 0000000000000000000000000000000000000000..4943d87565e6fd70cd66362d863e5fa5ab0770a1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/NullaryFunctors.h
@@ -0,0 +1,223 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_NULLARY_FUNCTORS_H
+#define EIGEN_NULLARY_FUNCTORS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename Scalar>
+struct scalar_constant_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_constant_op(const scalar_constant_op& other) : m_other(other.m_other) { }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_constant_op(const Scalar& other) : m_other(other) { }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() () const { return m_other; }
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const PacketType packetOp() const { return internal::pset1<PacketType>(m_other); }
+  const Scalar m_other;
+};
+template<typename Scalar>
+struct functor_traits<scalar_constant_op<Scalar> >
+{ enum { Cost = 0 /* as the constant value should be loaded in register only once for the whole expression */,
+         PacketAccess = packet_traits<Scalar>::Vectorizable, IsRepeatable = true }; };
+
+template<typename Scalar> struct scalar_identity_op {
+  template<typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType row, IndexType col) const { return row==col ? Scalar(1) : Scalar(0); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_identity_op<Scalar> >
+{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = false, IsRepeatable = true }; };
+
+template <typename Scalar, bool IsInteger> struct linspaced_op_impl;
+
+template <typename Scalar>
+struct linspaced_op_impl<Scalar,/*IsInteger*/false>
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  EIGEN_DEVICE_FUNC linspaced_op_impl(const Scalar& low, const Scalar& high, Index num_steps) :
+    m_low(low), m_high(high), m_size1(num_steps==1 ? 1 : num_steps-1), m_step(num_steps==1 ? Scalar() : Scalar((high-low)/RealScalar(num_steps-1))),
+    m_flip(numext::abs(high)<numext::abs(low))
+  {}
+
+  template<typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType i) const {
+    if(m_flip)
+      return (i==0)? m_low : Scalar(m_high - RealScalar(m_size1-i)*m_step);
+    else
+      return (i==m_size1)? m_high : Scalar(m_low + RealScalar(i)*m_step);
+  }
+
+  template<typename Packet, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(IndexType i) const
+  {
+    // Principle:
+    // [low, ..., low] + ( [step, ..., step] * ( [i, ..., i] + [0, ..., size] ) )
+    if(m_flip)
+    {
+      Packet pi = plset<Packet>(Scalar(i-m_size1));
+      Packet res = padd(pset1<Packet>(m_high), pmul(pset1<Packet>(m_step), pi));
+      if (EIGEN_PREDICT_TRUE(i != 0)) return res;
+      Packet mask = pcmp_lt(pset1<Packet>(0), plset<Packet>(0));
+      return pselect<Packet>(mask, res, pset1<Packet>(m_low));
+    }
+    else
+    {
+      Packet pi = plset<Packet>(Scalar(i));
+      Packet res = padd(pset1<Packet>(m_low), pmul(pset1<Packet>(m_step), pi));
+      if(EIGEN_PREDICT_TRUE(i != m_size1-unpacket_traits<Packet>::size+1)) return res;
+      Packet mask = pcmp_lt(plset<Packet>(0), pset1<Packet>(unpacket_traits<Packet>::size-1));
+      return pselect<Packet>(mask, res, pset1<Packet>(m_high));
+    }
+  }
+
+  const Scalar m_low;
+  const Scalar m_high;
+  const Index m_size1;
+  const Scalar m_step;
+  const bool m_flip;
+};
+
+template <typename Scalar>
+struct linspaced_op_impl<Scalar,/*IsInteger*/true>
+{
+  EIGEN_DEVICE_FUNC linspaced_op_impl(const Scalar& low, const Scalar& high, Index num_steps) :
+    m_low(low),
+    m_multiplier((high-low)/convert_index<Scalar>(num_steps<=1 ? 1 : num_steps-1)),
+    m_divisor(convert_index<Scalar>((high>=low?num_steps:-num_steps)+(high-low))/((numext::abs(high-low)+1)==0?1:(numext::abs(high-low)+1))),
+    m_use_divisor(num_steps>1 && (numext::abs(high-low)+1)<num_steps)
+  {}
+
+  template<typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const Scalar operator() (IndexType i) const
+  {
+    if(m_use_divisor) return m_low + convert_index<Scalar>(i)/m_divisor;
+    else              return m_low + convert_index<Scalar>(i)*m_multiplier;
+  }
+
+  const Scalar m_low;
+  const Scalar m_multiplier;
+  const Scalar m_divisor;
+  const bool m_use_divisor;
+};
+
+// ----- Linspace functor ----------------------------------------------------------------
+
+// Forward declaration (we default to random access which does not really give
+// us a speed gain when using packet access but it allows to use the functor in
+// nested expressions).
+template <typename Scalar> struct linspaced_op;
+template <typename Scalar> struct functor_traits< linspaced_op<Scalar> >
+{
+  enum
+  {
+    Cost = 1,
+    PacketAccess =   (!NumTraits<Scalar>::IsInteger) && packet_traits<Scalar>::HasSetLinear && packet_traits<Scalar>::HasBlend,
+                  /*&& ((!NumTraits<Scalar>::IsInteger) || packet_traits<Scalar>::HasDiv),*/ // <- vectorization for integer is currently disabled
+    IsRepeatable = true
+  };
+};
+template <typename Scalar> struct linspaced_op
+{
+  EIGEN_DEVICE_FUNC linspaced_op(const Scalar& low, const Scalar& high, Index num_steps)
+    : impl((num_steps==1 ? high : low),high,num_steps)
+  {}
+
+  template<typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (IndexType i) const { return impl(i); }
+
+  template<typename Packet,typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(IndexType i) const { return impl.template packetOp<Packet>(i); }
+
+  // This proxy object handles the actual required temporaries and the different
+  // implementations (integer vs. floating point).
+  const linspaced_op_impl<Scalar,NumTraits<Scalar>::IsInteger> impl;
+};
+
+template <typename Scalar>
+struct equalspaced_op {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  EIGEN_DEVICE_FUNC equalspaced_op(const Scalar& start, const Scalar& step) : m_start(start), m_step(step) {}
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator()(IndexType i) const {
+    return m_start + m_step * static_cast<Scalar>(i);
+  }
+  template <typename Packet, typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(IndexType i) const {
+    const Packet cst_start = pset1<Packet>(m_start);
+    const Packet cst_step = pset1<Packet>(m_step);
+    const Packet cst_lin0 = plset<Packet>(Scalar(0));
+    const Packet cst_offset = pmadd(cst_lin0, cst_step, cst_start);
+
+    Packet i_packet = pset1<Packet>(static_cast<Scalar>(i));
+    return pmadd(i_packet, cst_step, cst_offset);
+  }
+  const Scalar m_start;
+  const Scalar m_step;
+};
+
+template <typename Scalar>
+struct functor_traits<equalspaced_op<Scalar> > {
+  enum {
+    Cost = NumTraits<Scalar>::AddCost + NumTraits<Scalar>::MulCost,
+    PacketAccess =
+        packet_traits<Scalar>::HasSetLinear && packet_traits<Scalar>::HasMul && packet_traits<Scalar>::HasAdd,
+    IsRepeatable = true
+  };
+};
+
+// Linear access is automatically determined from the operator() prototypes available for the given functor.
+// If it exposes an operator()(i,j), then we assume the i and j coefficients are required independently
+// and linear access is not possible. In all other cases, linear access is enabled.
+// Users should not have to deal with this structure.
+template<typename Functor> struct functor_has_linear_access { enum { ret = !has_binary_operator<Functor>::value }; };
+
+// For unreliable compilers, let's specialize the has_*ary_operator
+// helpers so that at least built-in nullary functors work fine.
+#if !( EIGEN_COMP_MSVC || EIGEN_COMP_GNUC || (EIGEN_COMP_ICC>=1600))
+template<typename Scalar,typename IndexType>
+struct has_nullary_operator<scalar_constant_op<Scalar>,IndexType> { enum { value = 1}; };
+template<typename Scalar,typename IndexType>
+struct has_unary_operator<scalar_constant_op<Scalar>,IndexType> { enum { value = 0}; };
+template<typename Scalar,typename IndexType>
+struct has_binary_operator<scalar_constant_op<Scalar>,IndexType> { enum { value = 0}; };
+
+template<typename Scalar,typename IndexType>
+struct has_nullary_operator<scalar_identity_op<Scalar>,IndexType> { enum { value = 0}; };
+template<typename Scalar,typename IndexType>
+struct has_unary_operator<scalar_identity_op<Scalar>,IndexType> { enum { value = 0}; };
+template<typename Scalar,typename IndexType>
+struct has_binary_operator<scalar_identity_op<Scalar>,IndexType> { enum { value = 1}; };
+
+template<typename Scalar,typename IndexType>
+struct has_nullary_operator<linspaced_op<Scalar>,IndexType> { enum { value = 0}; };
+template<typename Scalar,typename IndexType>
+struct has_unary_operator<linspaced_op<Scalar>,IndexType> { enum { value = 1}; };
+template<typename Scalar,typename IndexType>
+struct has_binary_operator<linspaced_op<Scalar>,IndexType> { enum { value = 0}; };
+
+template<typename Scalar,typename IndexType>
+struct has_nullary_operator<scalar_random_op<Scalar>,IndexType> { enum { value = 1}; };
+template<typename Scalar,typename IndexType>
+struct has_unary_operator<scalar_random_op<Scalar>,IndexType> { enum { value = 0}; };
+template<typename Scalar,typename IndexType>
+struct has_binary_operator<scalar_random_op<Scalar>,IndexType> { enum { value = 0}; };
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_NULLARY_FUNCTORS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/StlFunctors.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/StlFunctors.h
new file mode 100644
index 0000000000000000000000000000000000000000..b8b842bcc526c1cbbd0dac01406b7370dc85119f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/StlFunctors.h
@@ -0,0 +1,126 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STL_FUNCTORS_H
+#define EIGEN_STL_FUNCTORS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+// default functor traits for STL functors:
+
+template<typename T>
+struct functor_traits<std::multiplies<T> >
+{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::divides<T> >
+{ enum { Cost = NumTraits<T>::MulCost, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::plus<T> >
+{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::minus<T> >
+{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::negate<T> >
+{ enum { Cost = NumTraits<T>::AddCost, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::logical_or<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::logical_and<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::logical_not<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::greater<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::less<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::greater_equal<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::less_equal<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::equal_to<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+template<typename T>
+struct functor_traits<std::not_equal_to<T> >
+{ enum { Cost = 1, PacketAccess = false }; };
+
+#if (EIGEN_COMP_CXXVER < 17)
+// std::unary_negate is deprecated since c++17 and will be removed in c++20
+template<typename T>
+struct functor_traits<std::unary_negate<T> >
+{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; };
+
+// std::binary_negate is deprecated since c++17 and will be removed in c++20
+template<typename T>
+struct functor_traits<std::binary_negate<T> >
+{ enum { Cost = 1 + functor_traits<T>::Cost, PacketAccess = false }; };
+#endif
+
+#ifdef EIGEN_STDEXT_SUPPORT
+
+template<typename T0,typename T1>
+struct functor_traits<std::project1st<T0,T1> >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+template<typename T0,typename T1>
+struct functor_traits<std::project2nd<T0,T1> >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+template<typename T0,typename T1>
+struct functor_traits<std::select2nd<std::pair<T0,T1> > >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+template<typename T0,typename T1>
+struct functor_traits<std::select1st<std::pair<T0,T1> > >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+template<typename T0,typename T1>
+struct functor_traits<std::unary_compose<T0,T1> >
+{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost, PacketAccess = false }; };
+
+template<typename T0,typename T1,typename T2>
+struct functor_traits<std::binary_compose<T0,T1,T2> >
+{ enum { Cost = functor_traits<T0>::Cost + functor_traits<T1>::Cost + functor_traits<T2>::Cost, PacketAccess = false }; };
+
+#endif // EIGEN_STDEXT_SUPPORT
+
+// allow to add new functors and specializations of functor_traits from outside Eigen.
+// this macro is really needed because functor_traits must be specialized after it is declared but before it is used...
+#ifdef EIGEN_FUNCTORS_PLUGIN
+#include EIGEN_FUNCTORS_PLUGIN
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_STL_FUNCTORS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/TernaryFunctors.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/TernaryFunctors.h
new file mode 100644
index 0000000000000000000000000000000000000000..8f9492ba9dbdbd4bb7722324ff7acdaf5b142500
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/TernaryFunctors.h
@@ -0,0 +1,49 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Eugene Brevdo <ebrevdo@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TERNARY_FUNCTORS_H
+#define EIGEN_TERNARY_FUNCTORS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+//---------- associative ternary functors ----------
+
+template <typename ThenScalar, typename ElseScalar, typename ConditionScalar>
+struct scalar_boolean_select_op {
+  static constexpr bool ThenElseAreSame = is_same<ThenScalar, ElseScalar>::value;
+  EIGEN_STATIC_ASSERT(ThenElseAreSame, THEN AND ELSE MUST BE SAME TYPE)
+  using Scalar = ThenScalar;
+  using result_type = Scalar;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const ThenScalar& a, const ElseScalar& b, const ConditionScalar& cond) const {
+    return cond == ConditionScalar(0) ? b : a;
+  }
+  template <typename Packet>
+      EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a, const Packet& b, const Packet& cond) const {
+    return pselect(pcmp_eq(cond, pzero(cond)), b, a);
+  }
+};
+
+template <typename ThenScalar, typename ElseScalar, typename ConditionScalar>
+    struct functor_traits<scalar_boolean_select_op<ThenScalar, ElseScalar, ConditionScalar>> {
+  using Scalar = ThenScalar;
+  enum {
+    Cost = 1,
+    PacketAccess = is_same<ThenScalar, ElseScalar>::value && is_same<ConditionScalar, Scalar>::value && packet_traits<Scalar>::HasCmp
+  };
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TERNARY_FUNCTORS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/UnaryFunctors.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/UnaryFunctors.h
new file mode 100644
index 0000000000000000000000000000000000000000..fcd81c18676c82b0f64ebcc57b54fc309bdd5648
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/functors/UnaryFunctors.h
@@ -0,0 +1,1278 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_UNARY_FUNCTORS_H
+#define EIGEN_UNARY_FUNCTORS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+  * \brief Template functor to compute the opposite of a scalar
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::operator-
+  */
+template<typename Scalar> struct scalar_opposite_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return -a; }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
+  { return internal::pnegate(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_opposite_op<Scalar> >
+{ enum {
+    Cost = NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasNegate };
+};
+
+/** \internal
+  * \brief Template functor to compute the absolute value of a scalar
+  *
+  * \sa class CwiseUnaryOp, Cwise::abs
+  */
+template<typename Scalar> struct scalar_abs_op {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::abs(a); }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
+  { return internal::pabs(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_abs_op<Scalar> >
+{
+  enum {
+    Cost = NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasAbs
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the score of a scalar, to chose a pivot
+  *
+  * \sa class CwiseUnaryOp
+  */
+template<typename Scalar> struct scalar_score_coeff_op : scalar_abs_op<Scalar>
+{
+  typedef void Score_is_abs;
+};
+template<typename Scalar>
+struct functor_traits<scalar_score_coeff_op<Scalar> > : functor_traits<scalar_abs_op<Scalar> > {};
+
+/* Avoid recomputing abs when we know the score and they are the same. Not a true Eigen functor.  */
+template<typename Scalar, typename=void> struct abs_knowing_score
+{
+  typedef typename NumTraits<Scalar>::Real result_type;
+  template<typename Score>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a, const Score&) const { return numext::abs(a); }
+};
+template<typename Scalar> struct abs_knowing_score<Scalar, typename scalar_score_coeff_op<Scalar>::Score_is_abs>
+{
+  typedef typename NumTraits<Scalar>::Real result_type;
+  template<typename Scal>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scal&, const result_type& a) const { return a; }
+};
+
+/** \internal
+  * \brief Template functor to compute the squared absolute value of a scalar
+  *
+  * \sa class CwiseUnaryOp, Cwise::abs2
+  */
+template<typename Scalar> struct scalar_abs2_op {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::abs2(a); }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
+  { return internal::pmul(a,a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_abs2_op<Scalar> >
+{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasAbs2 }; };
+
+/** \internal
+  * \brief Template functor to compute the conjugate of a complex value
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::conjugate()
+  */
+template<typename Scalar> struct scalar_conjugate_op {
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::conj(a); }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const { return internal::pconj(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_conjugate_op<Scalar> >
+{
+  enum {
+    Cost = 0,
+    // Yes the cost is zero even for complexes because in most cases for which
+    // the cost is used, conjugation turns to be a no-op. Some examples:
+    //   cost(a*conj(b)) == cost(a*b)
+    //   cost(a+conj(b)) == cost(a+b)
+    //   <etc.
+    // If we don't set it to zero, then:
+    //   A.conjugate().lazyProduct(B.conjugate())
+    // will bake its operands. We definitely don't want that!
+    PacketAccess = packet_traits<Scalar>::HasConj
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the phase angle of a complex
+  *
+  * \sa class CwiseUnaryOp, Cwise::arg
+  */
+template<typename Scalar> struct scalar_arg_op {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const result_type operator() (const Scalar& a) const { return numext::arg(a); }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
+  { return internal::parg(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_arg_op<Scalar> >
+{
+  enum {
+    Cost = NumTraits<Scalar>::IsComplex ? 5 * NumTraits<Scalar>::MulCost : NumTraits<Scalar>::AddCost,
+    PacketAccess = packet_traits<Scalar>::HasArg
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the complex argument, returned as a complex type
+  *
+  * \sa class CwiseUnaryOp, Cwise::carg
+  */
+template <typename Scalar>
+struct scalar_carg_op {
+  using result_type = Scalar;
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const Scalar operator()(const Scalar& a) const { return Scalar(numext::arg(a)); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const {
+    return pcarg(a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_carg_op<Scalar>> {
+  using RealScalar = typename NumTraits<Scalar>::Real;
+  enum { Cost = functor_traits<scalar_atan2_op<RealScalar>>::Cost, PacketAccess = packet_traits<RealScalar>::HasATan };
+};
+
+/** \internal
+  * \brief Template functor to cast a scalar to another type
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::cast()
+  */
+template<typename Scalar, typename NewType>
+struct scalar_cast_op {
+  typedef NewType result_type;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const NewType operator() (const Scalar& a) const { return cast<Scalar, NewType>(a); }
+};
+
+template<typename Scalar, typename NewType>
+struct functor_traits<scalar_cast_op<Scalar,NewType> >
+{ enum { Cost = is_same<Scalar, NewType>::value ? 0 : NumTraits<NewType>::AddCost, PacketAccess = false }; };
+
+/** \internal
+ * `core_cast_op` serves to distinguish the vectorized implementation from that of the legacy `scalar_cast_op` for backwards
+ * compatibility. The manner in which packet ops are handled is defined by the specialized unary_evaluator:
+ * `unary_evaluator<CwiseUnaryOp<core_cast_op<SrcType, DstType>, ArgType>, IndexBased>` in CoreEvaluators.h
+ * Otherwise, the non-vectorized behavior is identical to that of `scalar_cast_op`
+ */
+template <typename SrcType, typename DstType>
+struct core_cast_op : scalar_cast_op<SrcType, DstType> {};
+
+template <typename SrcType, typename DstType>
+struct functor_traits<core_cast_op<SrcType, DstType>> {
+  using CastingTraits = type_casting_traits<SrcType, DstType>;
+  enum {
+    Cost = is_same<SrcType, DstType>::value ? 0 : NumTraits<DstType>::AddCost,
+    PacketAccess = CastingTraits::VectorizedCast && (CastingTraits::SrcCoeffRatio <= 8)
+  };
+};
+
+/** \internal
+  * \brief Template functor to arithmetically shift a scalar right by a number of bits
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::shift_right()
+  */
+template<typename Scalar, int N>
+struct scalar_shift_right_op {
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const
+  { return a >> N; }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
+  { return internal::parithmetic_shift_right<N>(a); }
+};
+template<typename Scalar, int N>
+struct functor_traits<scalar_shift_right_op<Scalar,N> >
+{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasShift }; };
+
+/** \internal
+  * \brief Template functor to logically shift a scalar left by a number of bits
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::shift_left()
+  */
+template<typename Scalar, int N>
+struct scalar_shift_left_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const
+  { return a << N; }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet packetOp(const Packet& a) const
+  { return internal::plogical_shift_left<N>(a); }
+};
+template<typename Scalar, int N>
+struct functor_traits<scalar_shift_left_op<Scalar,N> >
+{ enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasShift }; };
+
+/** \internal
+  * \brief Template functor to extract the real part of a complex
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::real()
+  */
+template<typename Scalar>
+struct scalar_real_op {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::real(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_real_op<Scalar> >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+/** \internal
+  * \brief Template functor to extract the imaginary part of a complex
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::imag()
+  */
+template<typename Scalar>
+struct scalar_imag_op {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const { return numext::imag(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_imag_op<Scalar> >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+/** \internal
+  * \brief Template functor to extract the real part of a complex as a reference
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::real()
+  */
+template<typename Scalar>
+struct scalar_real_ref_op {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::real_ref(*const_cast<Scalar*>(&a)); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_real_ref_op<Scalar> >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+/** \internal
+  * \brief Template functor to extract the imaginary part of a complex as a reference
+  *
+  * \sa class CwiseUnaryOp, MatrixBase::imag()
+  */
+template<typename Scalar>
+struct scalar_imag_ref_op {
+  typedef typename NumTraits<Scalar>::Real result_type;
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE result_type& operator() (const Scalar& a) const { return numext::imag_ref(*const_cast<Scalar*>(&a)); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_imag_ref_op<Scalar> >
+{ enum { Cost = 0, PacketAccess = false }; };
+
+/** \internal
+  *
+  * \brief Template functor to compute the exponential of a scalar
+  *
+  * \sa class CwiseUnaryOp, Cwise::exp()
+  */
+template<typename Scalar> struct scalar_exp_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return internal::pexp(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pexp(a); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_exp_op<Scalar> > {
+  enum {
+    PacketAccess = packet_traits<Scalar>::HasExp,
+    // The following numbers are based on the AVX implementation.
+#ifdef EIGEN_VECTORIZE_FMA
+    // Haswell can issue 2 add/mul/madd per cycle.
+    Cost =
+    (sizeof(Scalar) == 4
+     // float: 8 pmadd, 4 pmul, 2 padd/psub, 6 other
+     ? (8 * NumTraits<Scalar>::AddCost + 6 * NumTraits<Scalar>::MulCost)
+     // double: 7 pmadd, 5 pmul, 3 padd/psub, 1 div,  13 other
+     : (14 * NumTraits<Scalar>::AddCost +
+        6 * NumTraits<Scalar>::MulCost +
+        scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value))
+#else
+    Cost =
+    (sizeof(Scalar) == 4
+     // float: 7 pmadd, 6 pmul, 4 padd/psub, 10 other
+     ? (21 * NumTraits<Scalar>::AddCost + 13 * NumTraits<Scalar>::MulCost)
+     // double: 7 pmadd, 5 pmul, 3 padd/psub, 1 div,  13 other
+     : (23 * NumTraits<Scalar>::AddCost +
+        12 * NumTraits<Scalar>::MulCost +
+        scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value))
+#endif
+  };
+};
+
+/** \internal
+  *
+  * \brief Template functor to compute the exponential of a scalar - 1.
+  *
+  * \sa class CwiseUnaryOp, ArrayBase::expm1()
+  */
+template<typename Scalar> struct scalar_expm1_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::expm1(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pexpm1(a); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_expm1_op<Scalar> > {
+  enum {
+    PacketAccess = packet_traits<Scalar>::HasExpm1,
+    Cost = functor_traits<scalar_exp_op<Scalar> >::Cost // TODO measure cost of expm1
+  };
+};
+
+/** \internal
+  *
+  * \brief Template functor to compute the logarithm of a scalar
+  *
+  * \sa class CwiseUnaryOp, ArrayBase::log()
+  */
+template<typename Scalar> struct scalar_log_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::log(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog(a); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_log_op<Scalar> > {
+  enum {
+    PacketAccess = packet_traits<Scalar>::HasLog,
+    Cost =
+    (PacketAccess
+     // The following numbers are based on the AVX implementation.
+#ifdef EIGEN_VECTORIZE_FMA
+     // 8 pmadd, 6 pmul, 8 padd/psub, 16 other, can issue 2 add/mul/madd per cycle.
+     ? (20 * NumTraits<Scalar>::AddCost + 7 * NumTraits<Scalar>::MulCost)
+#else
+     // 8 pmadd, 6 pmul, 8 padd/psub, 20 other
+     ? (36 * NumTraits<Scalar>::AddCost + 14 * NumTraits<Scalar>::MulCost)
+#endif
+     // Measured cost of std::log.
+     : sizeof(Scalar)==4 ? 40 : 85)
+  };
+};
+
+/** \internal
+  *
+  * \brief Template functor to compute the logarithm of 1 plus a scalar value
+  *
+  * \sa class CwiseUnaryOp, ArrayBase::log1p()
+  */
+template<typename Scalar> struct scalar_log1p_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::log1p(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog1p(a); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_log1p_op<Scalar> > {
+  enum {
+    PacketAccess = packet_traits<Scalar>::HasLog1p,
+    Cost = functor_traits<scalar_log_op<Scalar> >::Cost // TODO measure cost of log1p
+  };
+};
+
+/** \internal
+  *
+  * \brief Template functor to compute the base-10 logarithm of a scalar
+  *
+  * \sa class CwiseUnaryOp, Cwise::log10()
+  */
+template<typename Scalar> struct scalar_log10_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { EIGEN_USING_STD(log10) return log10(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog10(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_log10_op<Scalar> >
+{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasLog10 }; };
+
+/** \internal
+  *
+  * \brief Template functor to compute the base-2 logarithm of a scalar
+  *
+  * \sa class CwiseUnaryOp, Cwise::log2()
+  */
+template<typename Scalar> struct scalar_log2_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return Scalar(EIGEN_LOG2E) * numext::log(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::plog2(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_log2_op<Scalar> >
+{ enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasLog }; };
+
+/** \internal
+  * \brief Template functor to compute the square root of a scalar
+  * \sa class CwiseUnaryOp, Cwise::sqrt()
+  */
+template<typename Scalar> struct scalar_sqrt_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::sqrt(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psqrt(a); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_sqrt_op<Scalar> > {
+  enum {
+#if EIGEN_FAST_MATH
+    // The following numbers are based on the AVX implementation.
+    Cost = (sizeof(Scalar) == 8 ? 28
+                                // 4 pmul, 1 pmadd, 3 other
+                                : (3 * NumTraits<Scalar>::AddCost +
+                                   5 * NumTraits<Scalar>::MulCost)),
+#else
+    // The following numbers are based on min VSQRT throughput on Haswell.
+    Cost = (sizeof(Scalar) == 8 ? 28 : 14),
+#endif
+    PacketAccess = packet_traits<Scalar>::HasSqrt
+  };
+};
+
+// Boolean specialization to eliminate -Wimplicit-conversion-floating-point-to-bool warnings.
+template<> struct scalar_sqrt_op<bool> {
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator() (const bool& a) const { return a; }
+  template <typename Packet>
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return a; }
+};
+template <>
+struct functor_traits<scalar_sqrt_op<bool> > {
+  enum { Cost = 1, PacketAccess = packet_traits<bool>::Vectorizable };
+};
+
+/** \internal
+  * \brief Template functor to compute the reciprocal square root of a scalar
+  * \sa class CwiseUnaryOp, Cwise::rsqrt()
+  */
+template<typename Scalar> struct scalar_rsqrt_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::rsqrt(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::prsqrt(a); }
+};
+
+template<typename Scalar>
+struct functor_traits<scalar_rsqrt_op<Scalar> >
+{ enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasRsqrt
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the cosine of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::cos()
+  */
+template<typename Scalar> struct scalar_cos_op {
+  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return numext::cos(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pcos(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_cos_op<Scalar> >
+{
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasCos
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the sine of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::sin()
+  */
+template<typename Scalar> struct scalar_sin_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::sin(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psin(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_sin_op<Scalar> >
+{
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasSin
+  };
+};
+
+
+/** \internal
+  * \brief Template functor to compute the tan of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::tan()
+  */
+template<typename Scalar> struct scalar_tan_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::tan(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::ptan(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_tan_op<Scalar> >
+{
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasTan
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the arc cosine of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::acos()
+  */
+template<typename Scalar> struct scalar_acos_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::acos(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pacos(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_acos_op<Scalar> >
+{
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasACos
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the arc sine of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::asin()
+  */
+template<typename Scalar> struct scalar_asin_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::asin(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pasin(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_asin_op<Scalar> >
+{
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasASin
+  };
+};
+
+
+/** \internal
+  * \brief Template functor to compute the atan of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::atan()
+  */
+template<typename Scalar> struct scalar_atan_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::atan(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::patan(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_atan_op<Scalar> >
+{
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasATan
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the tanh of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::tanh()
+  */
+template <typename Scalar>
+struct scalar_tanh_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::tanh(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& x) const { return ptanh(x); }
+};
+
+template <typename Scalar>
+struct functor_traits<scalar_tanh_op<Scalar> > {
+  enum {
+    PacketAccess = packet_traits<Scalar>::HasTanh,
+    Cost = ( (EIGEN_FAST_MATH && is_same<Scalar,float>::value)
+// The following numbers are based on the AVX implementation,
+#ifdef EIGEN_VECTORIZE_FMA
+                // Haswell can issue 2 add/mul/madd per cycle.
+                // 9 pmadd, 2 pmul, 1 div, 2 other
+                ? (2 * NumTraits<Scalar>::AddCost +
+                   6 * NumTraits<Scalar>::MulCost +
+                   scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value)
+#else
+                ? (11 * NumTraits<Scalar>::AddCost +
+                   11 * NumTraits<Scalar>::MulCost +
+                   scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value)
+#endif
+                // This number assumes a naive implementation of tanh
+                : (6 * NumTraits<Scalar>::AddCost +
+                   3 * NumTraits<Scalar>::MulCost +
+                   2 * scalar_div_cost<Scalar,packet_traits<Scalar>::HasDiv>::value +
+                   functor_traits<scalar_exp_op<Scalar> >::Cost))
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the atanh of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::atanh()
+  */
+template <typename Scalar>
+struct scalar_atanh_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::atanh(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& x) const { return patanh(x); }
+};
+
+template <typename Scalar>
+struct functor_traits<scalar_atanh_op<Scalar> > {
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasATanh
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the sinh of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::sinh()
+  */
+template<typename Scalar> struct scalar_sinh_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::sinh(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::psinh(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_sinh_op<Scalar> >
+{
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasSinh
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the asinh of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::asinh()
+  */
+template <typename Scalar>
+struct scalar_asinh_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::asinh(a); }
+};
+
+template <typename Scalar>
+struct functor_traits<scalar_asinh_op<Scalar> > {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false };
+};
+
+/** \internal
+  * \brief Template functor to compute the cosh of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::cosh()
+  */
+template<typename Scalar> struct scalar_cosh_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const { return numext::cosh(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pcosh(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_cosh_op<Scalar> >
+{
+  enum {
+    Cost = 5 * NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasCosh
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the acosh of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::acosh()
+  */
+template <typename Scalar>
+struct scalar_acosh_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator()(const Scalar& a) const { return numext::acosh(a); }
+};
+
+template <typename Scalar>
+struct functor_traits<scalar_acosh_op<Scalar> > {
+  enum { Cost = 5 * NumTraits<Scalar>::MulCost, PacketAccess = false };
+};
+
+/** \internal
+  * \brief Template functor to compute the inverse of a scalar
+  * \sa class CwiseUnaryOp, Cwise::inverse()
+  */
+template<typename Scalar>
+struct scalar_inverse_op {
+  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return Scalar(1)/a; }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
+  { return internal::preciprocal(a); }
+};
+template <typename Scalar>
+struct functor_traits<scalar_inverse_op<Scalar> > {
+  enum {
+    PacketAccess = packet_traits<Scalar>::HasDiv,
+    // If packet_traits<Scalar>::HasReciprocal then the Estimated cost is that
+    // of computing an approximation plus a single Newton-Raphson step, which
+    // consists of 1 pmul + 1 pmadd.
+    Cost = (packet_traits<Scalar>::HasReciprocal
+                ? 4 * NumTraits<Scalar>::MulCost
+                : scalar_div_cost<Scalar, PacketAccess>::value)
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the square of a scalar
+  * \sa class CwiseUnaryOp, Cwise::square()
+  */
+template<typename Scalar>
+struct scalar_square_op {
+  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a*a; }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
+  { return internal::pmul(a,a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_square_op<Scalar> >
+{ enum { Cost = NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
+
+// Boolean specialization to avoid -Wint-in-bool-context warnings on GCC.
+template<>
+struct scalar_square_op<bool> {
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator() (const bool& a) const { return a; }
+  template<typename Packet>
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
+  { return a; }
+};
+template<>
+struct functor_traits<scalar_square_op<bool> >
+{ enum { Cost = 0, PacketAccess = packet_traits<bool>::Vectorizable }; };
+
+/** \internal
+  * \brief Template functor to compute the cube of a scalar
+  * \sa class CwiseUnaryOp, Cwise::cube()
+  */
+template<typename Scalar>
+struct scalar_cube_op {
+  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a*a*a; }
+  template<typename Packet>
+  EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
+  { return internal::pmul(a,pmul(a,a)); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_cube_op<Scalar> >
+{ enum { Cost = 2*NumTraits<Scalar>::MulCost, PacketAccess = packet_traits<Scalar>::HasMul }; };
+
+// Boolean specialization to avoid -Wint-in-bool-context warnings on GCC.
+template<>
+struct scalar_cube_op<bool> {
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline bool operator() (const bool& a) const { return a; }
+  template<typename Packet>
+  EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline const Packet packetOp(const Packet& a) const
+  { return a; }
+};
+template<>
+struct functor_traits<scalar_cube_op<bool> >
+{ enum { Cost = 0, PacketAccess = packet_traits<bool>::Vectorizable }; };
+
+/** \internal
+  * \brief Template functor to compute the rounded value of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::round()
+  */
+template<typename Scalar> struct scalar_round_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::round(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pround(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_round_op<Scalar> >
+{
+  enum {
+    Cost = NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasRound || NumTraits<Scalar>::IsInteger
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the floor of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::floor()
+  */
+template<typename Scalar> struct scalar_floor_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::floor(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pfloor(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_floor_op<Scalar> >
+{
+  enum {
+    Cost = NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasFloor || NumTraits<Scalar>::IsInteger
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the rounded (with current rounding mode)  value of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::rint()
+  */
+template<typename Scalar> struct scalar_rint_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::rint(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::print(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_rint_op<Scalar> >
+{
+  enum {
+    Cost = NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasRint || NumTraits<Scalar>::IsInteger
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the ceil of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::ceil()
+  */
+template<typename Scalar> struct scalar_ceil_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar operator() (const Scalar& a) const { return numext::ceil(a); }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const { return internal::pceil(a); }
+};
+template<typename Scalar>
+struct functor_traits<scalar_ceil_op<Scalar> >
+{
+  enum {
+    Cost = NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasCeil || NumTraits<Scalar>::IsInteger
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute whether a scalar is NaN
+  * \sa class CwiseUnaryOp, ArrayBase::isnan()
+  */
+template<typename Scalar, bool UseTypedPredicate=false>
+struct scalar_isnan_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool operator() (const Scalar& a) const {
+#if defined(SYCL_DEVICE_ONLY)
+    return numext::isnan(a);
+#else
+    return numext::isnan EIGEN_NOT_A_MACRO (a);
+#endif
+  }
+};
+
+
+template<typename Scalar>
+struct scalar_isnan_op<Scalar, true> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator() (const Scalar& a) const {
+#if defined(SYCL_DEVICE_ONLY)
+    return (numext::isnan(a) ? ptrue(a) : pzero(a));
+#else
+    return (numext::isnan EIGEN_NOT_A_MACRO (a)  ? ptrue(a) : pzero(a));
+#endif
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return pisnan(a);
+  }
+};
+
+template<typename Scalar, bool UseTypedPredicate>
+struct functor_traits<scalar_isnan_op<Scalar, UseTypedPredicate> >
+{
+  enum {
+    Cost = NumTraits<Scalar>::MulCost,
+    PacketAccess = packet_traits<Scalar>::HasCmp && UseTypedPredicate
+  };
+};
+
+/** \internal
+  * \brief Template functor to check whether a scalar is +/-inf
+  * \sa class CwiseUnaryOp, ArrayBase::isinf()
+  */
+template<typename Scalar> struct scalar_isinf_op {
+  typedef bool result_type;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const {
+#if defined(SYCL_DEVICE_ONLY)
+    return numext::isinf(a);
+#else
+    return (numext::isinf)(a);
+#endif
+  }
+};
+template<typename Scalar>
+struct functor_traits<scalar_isinf_op<Scalar> >
+{
+  enum {
+    Cost = NumTraits<Scalar>::MulCost,
+    PacketAccess = false
+  };
+};
+
+/** \internal
+  * \brief Template functor to check whether a scalar has a finite value
+  * \sa class CwiseUnaryOp, ArrayBase::isfinite()
+  */
+template<typename Scalar> struct scalar_isfinite_op {
+  typedef bool result_type;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const Scalar& a) const {
+#if defined(SYCL_DEVICE_ONLY)
+    return numext::isfinite(a);
+#else
+    return (numext::isfinite)(a);
+#endif
+  }
+};
+template<typename Scalar>
+struct functor_traits<scalar_isfinite_op<Scalar> >
+{
+  enum {
+    Cost = NumTraits<Scalar>::MulCost,
+    PacketAccess = false
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the logical not of a scalar as if it were a boolean
+  *
+  * \sa class CwiseUnaryOp, ArrayBase::operator!
+  */
+template <typename Scalar>
+struct scalar_boolean_not_op {
+  using result_type = Scalar;
+  // `false` any value `a` that satisfies `a == Scalar(0)`
+  // `true` is the complement of `false`
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const {
+    return a == Scalar(0) ? Scalar(1) : Scalar(0);
+  }
+  template <typename Packet>
+  EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    const Packet cst_one = pset1<Packet>(Scalar(1));
+    Packet not_a = pcmp_eq(a, pzero(a));
+    return pand(not_a, cst_one);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_boolean_not_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = packet_traits<Scalar>::HasCmp };
+};
+
+template <typename Scalar, bool IsComplex = NumTraits<Scalar>::IsComplex>
+struct bitwise_unary_impl {
+  static constexpr size_t Size = sizeof(Scalar);
+  using uint_t = typename numext::get_integer_by_size<Size>::unsigned_type;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_not(const Scalar& a) {
+    uint_t a_as_uint = numext::bit_cast<uint_t, Scalar>(a);
+    uint_t result = ~a_as_uint;
+    return numext::bit_cast<Scalar, uint_t>(result);
+  }
+};
+
+template <typename Scalar>
+struct bitwise_unary_impl<Scalar, true> {
+  using Real = typename NumTraits<Scalar>::Real;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar run_not(const Scalar& a) {
+    Real real_result = bitwise_unary_impl<Real>::run_not(numext::real(a));
+    Real imag_result = bitwise_unary_impl<Real>::run_not(numext::imag(a));
+    return Scalar(real_result, imag_result);
+  }
+};
+
+/** \internal
+  * \brief Template functor to compute the bitwise not of a scalar
+  *
+  * \sa class CwiseUnaryOp, ArrayBase::operator~
+ */
+template <typename Scalar>
+struct scalar_bitwise_not_op {
+  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::RequireInitialization, BITWISE OPERATIONS MAY ONLY BE PERFORMED ON PLAIN DATA TYPES)
+  EIGEN_STATIC_ASSERT((!internal::is_same<Scalar, bool>::value), DONT USE BITWISE OPS ON BOOLEAN TYPES)
+  using result_type = Scalar;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const {
+    return bitwise_unary_impl<Scalar>::run_not(a);
+  }
+  template <typename Packet>
+  EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    return pandnot(ptrue(a), a);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_bitwise_not_op<Scalar>> {
+  enum { Cost = NumTraits<Scalar>::AddCost, PacketAccess = true };
+};
+
+/** \internal
+  * \brief Template functor to compute the signum of a scalar
+  * \sa class CwiseUnaryOp, Cwise::sign()
+  */
+template<typename Scalar>
+struct scalar_sign_op {
+  EIGEN_DEVICE_FUNC inline const Scalar operator() (const Scalar& a) const
+  {
+    return numext::sign(a);
+  }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC inline Packet packetOp(const Packet& a) const {
+    return internal::psign(a);
+  }
+};
+
+template<typename Scalar>
+struct functor_traits<scalar_sign_op<Scalar> >
+{ enum {
+    Cost =
+        NumTraits<Scalar>::IsComplex
+        ? ( 8*NumTraits<Scalar>::MulCost  ) // roughly
+        : ( 3*NumTraits<Scalar>::AddCost),
+    PacketAccess = packet_traits<Scalar>::HasSign && packet_traits<Scalar>::Vectorizable
+  };
+};
+
+/** \internal
+  * \brief Template functor to compute the logistic function of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::logistic()
+  */
+template <typename T>
+struct scalar_logistic_op {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& x) const {
+    return packetOp(x);
+  }
+
+  template <typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Packet packetOp(const Packet& x) const {
+    const Packet one = pset1<Packet>(T(1));
+    const Packet inf = pset1<Packet>(NumTraits<T>::infinity());
+    const Packet e = pexp(x);
+    const Packet inf_mask = pcmp_eq(e, inf);
+    return pselect(inf_mask, one, pdiv(e, padd(one, e)));
+  }
+};
+
+// TODO(rmlarsen): Enable the following on host when integer_packet is defined
+// for the relevant packet types.
+#ifdef EIGEN_GPU_CC
+
+/** \internal
+  * \brief Template specialization of the logistic function for float.
+  * Computes S(x) = exp(x) / (1 + exp(x)), where exp(x) is implemented
+  * using an algorithm partly adopted from the implementation of
+  * pexp_float. See the individual steps described in the code below.
+  * Note that compared to pexp, we use an additional outer multiplicative
+  * range reduction step using the identity exp(x) = exp(x/2)^2.
+  * This prevert us from having to call ldexp on values that could produce
+  * a denormal result, which allows us to call the faster implementation in
+  * pldexp_fast_impl<Packet>::run(p, m).
+  * The final squaring, however, doubles the error bound on the final
+  * approximation. Exhaustive testing shows that we have a worst case error
+  * of 4.5 ulps (compared to computing S(x) in double precision), which is
+  * acceptable.
+  */
+template <>
+struct scalar_logistic_op<float> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE float operator()(const float& x) const {
+    // Truncate at the first point where the interpolant is exactly one.
+    const float cst_exp_hi = 16.6355324f;
+    const float e = numext::exp(numext::mini(x, cst_exp_hi));
+    return e / (1.0f + e);
+  }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet
+  packetOp(const Packet& _x) const {
+    const Packet cst_zero = pset1<Packet>(0.0f);
+    const Packet cst_one = pset1<Packet>(1.0f);
+    const Packet cst_half = pset1<Packet>(0.5f);
+    // Truncate at the first point where the interpolant is exactly one.
+    const Packet cst_exp_hi = pset1<Packet>(16.6355324f);
+    const Packet cst_exp_lo = pset1<Packet>(-104.f);
+
+    // Clamp x to the non-trivial range where S(x). Outside this
+    // interval the correctly rounded value of S(x) is either zero
+    // or one.
+    Packet zero_mask = pcmp_lt(_x, cst_exp_lo);
+    Packet x = pmin(_x, cst_exp_hi);
+
+    // 1. Multiplicative range reduction:
+    // Reduce the range of x by a factor of 2. This avoids having
+    // to compute exp(x) accurately where the result is a denormalized
+    // value.
+    x = pmul(x, cst_half);
+
+    // 2. Subtractive range reduction:
+    // Express exp(x) as exp(m*ln(2) + r) = 2^m*exp(r), start by extracting
+    // m = floor(x/ln(2) + 0.5), such that x = m*ln(2) + r.
+    const Packet cst_cephes_LOG2EF = pset1<Packet>(1.44269504088896341f);
+    Packet m = pfloor(pmadd(x, cst_cephes_LOG2EF, cst_half));
+    // Get r = x - m*ln(2). We use a trick from Cephes where the term
+    // m*ln(2) is subtracted out in two parts, m*C1+m*C2 = m*ln(2),
+    // to avoid accumulating truncation errors.
+    const Packet cst_cephes_exp_C1 = pset1<Packet>(-0.693359375f);
+    const Packet cst_cephes_exp_C2 = pset1<Packet>(2.12194440e-4f);
+    Packet r = pmadd(m, cst_cephes_exp_C1, x);
+    r = pmadd(m, cst_cephes_exp_C2, r);
+
+    // 3. Compute an approximation to exp(r) using a degree 5 minimax polynomial.
+    // We compute even and odd terms separately to increase instruction level
+    // parallelism.
+    Packet r2 = pmul(r, r);
+    const Packet cst_p2 = pset1<Packet>(0.49999141693115234375f);
+    const Packet cst_p3 = pset1<Packet>(0.16666877269744873046875f);
+    const Packet cst_p4 = pset1<Packet>(4.1898667812347412109375e-2f);
+    const Packet cst_p5 = pset1<Packet>(8.33471305668354034423828125e-3f);
+
+    const Packet p_even = pmadd(r2, cst_p4, cst_p2);
+    const Packet p_odd = pmadd(r2, cst_p5, cst_p3);
+    const Packet p_low = padd(r, cst_one);
+    Packet p = pmadd(r, p_odd, p_even);
+    p = pmadd(r2, p, p_low);
+
+    // 4. Undo subtractive range reduction exp(m*ln(2) + r) = 2^m * exp(r).
+    Packet e = pldexp_fast_impl<Packet>::run(p, m);
+
+    // 5. Undo multiplicative range reduction by using exp(r) = exp(r/2)^2.
+    e = pmul(e, e);
+
+    // Return exp(x) / (1 + exp(x))
+    return pselect(zero_mask, cst_zero, pdiv(e, padd(cst_one, e)));
+  }
+};
+#endif  // #ifndef EIGEN_GPU_COMPILE_PHASE
+
+
+template <typename T>
+struct functor_traits<scalar_logistic_op<T> > {
+  enum {
+    // The cost estimate for float here here is for the common(?) case where
+    // all arguments are greater than -9.
+    Cost = scalar_div_cost<T, packet_traits<T>::HasDiv>::value +
+           (internal::is_same<T, float>::value
+                ? NumTraits<T>::AddCost * 15 + NumTraits<T>::MulCost * 11
+                : NumTraits<T>::AddCost * 2 +
+                      functor_traits<scalar_exp_op<T> >::Cost),
+    PacketAccess =
+        packet_traits<T>::HasAdd && packet_traits<T>::HasDiv &&
+        (internal::is_same<T, float>::value
+             ? packet_traits<T>::HasMul && packet_traits<T>::HasMax &&
+                   packet_traits<T>::HasMin
+             : packet_traits<T>::HasNegate && packet_traits<T>::HasExp)
+  };
+};
+
+template <typename Scalar, typename ExponentScalar, 
+          bool IsBaseInteger = NumTraits<Scalar>::IsInteger,
+          bool IsExponentInteger = NumTraits<ExponentScalar>::IsInteger,
+          bool IsBaseComplex = NumTraits<Scalar>::IsComplex,
+          bool IsExponentComplex = NumTraits<ExponentScalar>::IsComplex>
+struct scalar_unary_pow_op {
+  typedef typename internal::promote_scalar_arg<
+      Scalar, ExponentScalar,
+      internal::has_ReturnType<ScalarBinaryOpTraits<Scalar,ExponentScalar,scalar_unary_pow_op> >::value>::type PromotedExponent;
+  typedef typename ScalarBinaryOpTraits<Scalar, PromotedExponent, scalar_unary_pow_op>::ReturnType result_type;
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_unary_pow_op(const ExponentScalar& exponent) : m_exponent(exponent) {}
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator()(const Scalar& a) const {
+    EIGEN_USING_STD(pow);
+    return static_cast<result_type>(pow(a, m_exponent));
+  }
+
+ private:
+  const ExponentScalar m_exponent;
+  scalar_unary_pow_op() {}
+};
+
+template <typename T>
+constexpr int exponent_digits() {
+  return CHAR_BIT * sizeof(T) - NumTraits<T>::digits() - NumTraits<T>::IsSigned;
+}
+
+template<typename From, typename To>
+struct is_floating_exactly_representable {
+  // TODO(rmlarsen): Add radix to NumTraits and enable this check.
+  // (NumTraits<To>::radix == NumTraits<From>::radix) &&
+  static constexpr bool value = (exponent_digits<To>() >= exponent_digits<From>() &&
+                                  NumTraits<To>::digits() >= NumTraits<From>::digits());
+};
+
+
+// Specialization for real, non-integer types, non-complex types.
+template <typename Scalar, typename ExponentScalar>
+struct scalar_unary_pow_op<Scalar, ExponentScalar, false, false, false, false> {
+  template <bool IsExactlyRepresentable = is_floating_exactly_representable<ExponentScalar, Scalar>::value>
+  std::enable_if_t<IsExactlyRepresentable, void> check_is_representable() const {}
+
+  // Issue a deprecation warning if we do a narrowing conversion on the exponent.
+  template <bool IsExactlyRepresentable = is_floating_exactly_representable<ExponentScalar, Scalar>::value>
+  EIGEN_DEPRECATED std::enable_if_t<!IsExactlyRepresentable, void> check_is_representable() const {}
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+      scalar_unary_pow_op(const ExponentScalar& exponent) : m_exponent(static_cast<Scalar>(exponent)) {
+    check_is_representable();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const {
+    EIGEN_USING_STD(pow);
+    return static_cast<Scalar>(pow(a, m_exponent));
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    return unary_pow_impl<Packet, Scalar>::run(a, m_exponent);
+  }
+
+ private:
+  const Scalar m_exponent;
+  scalar_unary_pow_op() {}
+};
+
+template <typename Scalar, typename ExponentScalar, bool BaseIsInteger>
+struct scalar_unary_pow_op<Scalar, ExponentScalar, BaseIsInteger, true, false, false> {
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE scalar_unary_pow_op(const ExponentScalar& exponent) : m_exponent(exponent) {}
+  // TODO: error handling logic for complex^real_integer
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar operator()(const Scalar& a) const {
+    return unary_pow_impl<Scalar, ExponentScalar>::run(a, m_exponent);
+  }
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet packetOp(const Packet& a) const {
+    return unary_pow_impl<Packet, ExponentScalar>::run(a, m_exponent);
+  }
+
+ private:
+  const ExponentScalar m_exponent;
+  scalar_unary_pow_op() {}
+};
+
+template <typename Scalar, typename ExponentScalar>
+struct functor_traits<scalar_unary_pow_op<Scalar, ExponentScalar>> {
+  enum {
+    GenPacketAccess = functor_traits<scalar_pow_op<Scalar, ExponentScalar>>::PacketAccess,
+    IntPacketAccess = !NumTraits<Scalar>::IsComplex && packet_traits<Scalar>::HasMul && (packet_traits<Scalar>::HasDiv || NumTraits<Scalar>::IsInteger) && packet_traits<Scalar>::HasCmp,
+    PacketAccess = NumTraits<ExponentScalar>::IsInteger ? IntPacketAccess : (IntPacketAccess && GenPacketAccess),
+    Cost = functor_traits<scalar_pow_op<Scalar, ExponentScalar>>::Cost
+  };
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_FUNCTORS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralBlockPanelKernel.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralBlockPanelKernel.h
new file mode 100644
index 0000000000000000000000000000000000000000..862efc611788d64bedfe635de6cd205506831dbb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralBlockPanelKernel.h
@@ -0,0 +1,3302 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERAL_BLOCK_PANEL_H
+#define EIGEN_GENERAL_BLOCK_PANEL_H
+
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+enum GEBPPacketSizeType {
+  GEBPPacketFull = 0,
+  GEBPPacketHalf,
+  GEBPPacketQuarter
+};
+
+template<typename LhsScalar_, typename RhsScalar_, bool ConjLhs_=false, bool ConjRhs_=false, int Arch=Architecture::Target, int PacketSize_=GEBPPacketFull>
+class gebp_traits;
+
+
+/** \internal \returns b if a<=0, and returns a otherwise. */
+inline std::ptrdiff_t manage_caching_sizes_helper(std::ptrdiff_t a, std::ptrdiff_t b)
+{
+  return a<=0 ? b : a;
+}
+
+#if defined(EIGEN_DEFAULT_L1_CACHE_SIZE)
+#define EIGEN_SET_DEFAULT_L1_CACHE_SIZE(val) EIGEN_DEFAULT_L1_CACHE_SIZE
+#else
+#define EIGEN_SET_DEFAULT_L1_CACHE_SIZE(val) val
+#endif // defined(EIGEN_DEFAULT_L1_CACHE_SIZE)
+
+#if defined(EIGEN_DEFAULT_L2_CACHE_SIZE)
+#define EIGEN_SET_DEFAULT_L2_CACHE_SIZE(val) EIGEN_DEFAULT_L2_CACHE_SIZE
+#else
+#define EIGEN_SET_DEFAULT_L2_CACHE_SIZE(val) val
+#endif // defined(EIGEN_DEFAULT_L2_CACHE_SIZE)
+
+#if defined(EIGEN_DEFAULT_L3_CACHE_SIZE)
+#define EIGEN_SET_DEFAULT_L3_CACHE_SIZE(val) EIGEN_DEFAULT_L3_CACHE_SIZE
+#else
+#define EIGEN_SET_DEFAULT_L3_CACHE_SIZE(val) val
+#endif // defined(EIGEN_DEFAULT_L3_CACHE_SIZE)
+  
+#if EIGEN_ARCH_i386_OR_x86_64
+const std::ptrdiff_t defaultL1CacheSize = EIGEN_SET_DEFAULT_L1_CACHE_SIZE(32*1024);
+const std::ptrdiff_t defaultL2CacheSize = EIGEN_SET_DEFAULT_L2_CACHE_SIZE(256*1024);
+const std::ptrdiff_t defaultL3CacheSize = EIGEN_SET_DEFAULT_L3_CACHE_SIZE(2*1024*1024);
+#elif EIGEN_ARCH_PPC
+const std::ptrdiff_t defaultL1CacheSize = EIGEN_SET_DEFAULT_L1_CACHE_SIZE(64*1024);
+#ifdef _ARCH_PWR10
+const std::ptrdiff_t defaultL2CacheSize = EIGEN_SET_DEFAULT_L2_CACHE_SIZE(2*1024*1024);
+const std::ptrdiff_t defaultL3CacheSize = EIGEN_SET_DEFAULT_L3_CACHE_SIZE(8*1024*1024);
+#else
+const std::ptrdiff_t defaultL2CacheSize = EIGEN_SET_DEFAULT_L2_CACHE_SIZE(512*1024);
+const std::ptrdiff_t defaultL3CacheSize = EIGEN_SET_DEFAULT_L3_CACHE_SIZE(4*1024*1024);
+#endif
+#else
+const std::ptrdiff_t defaultL1CacheSize = EIGEN_SET_DEFAULT_L1_CACHE_SIZE(16*1024);
+const std::ptrdiff_t defaultL2CacheSize = EIGEN_SET_DEFAULT_L2_CACHE_SIZE(512*1024);
+const std::ptrdiff_t defaultL3CacheSize = EIGEN_SET_DEFAULT_L3_CACHE_SIZE(512*1024);
+#endif
+
+#undef EIGEN_SET_DEFAULT_L1_CACHE_SIZE
+#undef EIGEN_SET_DEFAULT_L2_CACHE_SIZE
+#undef EIGEN_SET_DEFAULT_L3_CACHE_SIZE
+
+/** \internal */
+struct CacheSizes {
+  CacheSizes(): m_l1(-1),m_l2(-1),m_l3(-1) {
+    int l1CacheSize, l2CacheSize, l3CacheSize;
+    queryCacheSizes(l1CacheSize, l2CacheSize, l3CacheSize);
+    m_l1 = manage_caching_sizes_helper(l1CacheSize, defaultL1CacheSize);
+    m_l2 = manage_caching_sizes_helper(l2CacheSize, defaultL2CacheSize);
+    m_l3 = manage_caching_sizes_helper(l3CacheSize, defaultL3CacheSize);
+  }
+
+  std::ptrdiff_t m_l1;
+  std::ptrdiff_t m_l2;
+  std::ptrdiff_t m_l3;
+};
+
+/** \internal */
+inline void manage_caching_sizes(Action action, std::ptrdiff_t* l1, std::ptrdiff_t* l2, std::ptrdiff_t* l3)
+{
+  static CacheSizes m_cacheSizes;
+
+  if(action==SetAction)
+  {
+    // set the cpu cache size and cache all block sizes from a global cache size in byte
+    eigen_internal_assert(l1!=0 && l2!=0);
+    m_cacheSizes.m_l1 = *l1;
+    m_cacheSizes.m_l2 = *l2;
+    m_cacheSizes.m_l3 = *l3;
+  }
+  else if(action==GetAction)
+  {
+    eigen_internal_assert(l1!=0 && l2!=0);
+    *l1 = m_cacheSizes.m_l1;
+    *l2 = m_cacheSizes.m_l2;
+    *l3 = m_cacheSizes.m_l3;
+  }
+  else
+  {
+    eigen_internal_assert(false);
+  }
+}
+
+/* Helper for computeProductBlockingSizes.
+ *
+ * Given a m x k times k x n matrix product of scalar types \c LhsScalar and \c RhsScalar,
+ * this function computes the blocking size parameters along the respective dimensions
+ * for matrix products and related algorithms. The blocking sizes depends on various
+ * parameters:
+ * - the L1 and L2 cache sizes,
+ * - the register level blocking sizes defined by gebp_traits,
+ * - the number of scalars that fit into a packet (when vectorization is enabled).
+ *
+ * \sa setCpuCacheSizes */
+
+template<typename LhsScalar, typename RhsScalar, int KcFactor, typename Index>
+void evaluateProductBlockingSizesHeuristic(Index& k, Index& m, Index& n, Index num_threads = 1)
+{
+  typedef gebp_traits<LhsScalar,RhsScalar> Traits;
+
+  // Explanations:
+  // Let's recall that the product algorithms form mc x kc vertical panels A' on the lhs and
+  // kc x nc blocks B' on the rhs. B' has to fit into L2/L3 cache. Moreover, A' is processed
+  // per mr x kc horizontal small panels where mr is the blocking size along the m dimension
+  // at the register level. This small horizontal panel has to stay within L1 cache.
+  std::ptrdiff_t l1, l2, l3;
+  manage_caching_sizes(GetAction, &l1, &l2, &l3);
+  #ifdef EIGEN_VECTORIZE_AVX512
+  // We need to find a rationale for that, but without this adjustment,
+  // performance with AVX512 is pretty bad, like -20% slower.
+  // One reason is that with increasing packet-size, the blocking size k
+  // has to become pretty small if we want that 1 lhs panel fit within L1.
+  // For instance, with the 3pX4 kernel and double, the size of the lhs+rhs panels are:
+  //   k*(3*64 + 4*8) Bytes, with l1=32kBytes, and k%8=0, we have k=144.
+  // This is quite small for a good reuse of the accumulation registers.
+  l1 *= 4;
+  #endif
+
+  if (num_threads > 1) {
+    typedef typename Traits::ResScalar ResScalar;
+    enum {
+      kdiv = KcFactor * (Traits::mr * sizeof(LhsScalar) + Traits::nr * sizeof(RhsScalar)),
+      ksub = Traits::mr * Traits::nr * sizeof(ResScalar),
+      kr = 8,
+      mr = Traits::mr,
+      nr = Traits::nr
+    };
+    // Increasing k gives us more time to prefetch the content of the "C"
+    // registers. However once the latency is hidden there is no point in
+    // increasing the value of k, so we'll cap it at 320 (value determined
+    // experimentally).
+    // To avoid that k vanishes, we make k_cache at least as big as kr
+    const Index k_cache = numext::maxi<Index>(kr, (numext::mini<Index>)((l1-ksub)/kdiv, 320));
+    if (k_cache < k) {
+      k = k_cache - (k_cache % kr);
+      eigen_internal_assert(k > 0);
+    }
+
+    const Index n_cache = (l2-l1) / (nr * sizeof(RhsScalar) * k);
+    const Index n_per_thread = numext::div_ceil(n, num_threads);
+    if (n_cache <= n_per_thread) {
+      // Don't exceed the capacity of the l2 cache.
+      eigen_internal_assert(n_cache >= static_cast<Index>(nr));
+      n = n_cache - (n_cache % nr);
+      eigen_internal_assert(n > 0);
+    } else {
+      n = (numext::mini<Index>)(n, (n_per_thread + nr - 1) - ((n_per_thread + nr - 1) % nr));
+    }
+
+    if (l3 > l2) {
+      // l3 is shared between all cores, so we'll give each thread its own chunk of l3.
+      const Index m_cache = (l3-l2) / (sizeof(LhsScalar) * k * num_threads);
+      const Index m_per_thread = numext::div_ceil(m, num_threads);
+      if(m_cache < m_per_thread && m_cache >= static_cast<Index>(mr)) {
+        m = m_cache - (m_cache % mr);
+        eigen_internal_assert(m > 0);
+      } else {
+        m = (numext::mini<Index>)(m, (m_per_thread + mr - 1) - ((m_per_thread + mr - 1) % mr));
+      }
+    }
+  }
+  else {
+    // In unit tests we do not want to use extra large matrices,
+    // so we reduce the cache size to check the blocking strategy is not flawed
+#ifdef EIGEN_DEBUG_SMALL_PRODUCT_BLOCKS
+    l1 = 9*1024;
+    l2 = 32*1024;
+    l3 = 512*1024;
+#endif
+
+    // Early return for small problems because the computation below are time consuming for small problems.
+    // Perhaps it would make more sense to consider k*n*m??
+    // Note that for very tiny problem, this function should be bypassed anyway
+    // because we use the coefficient-based implementation for them.
+    if((numext::maxi)(k,(numext::maxi)(m,n))<48)
+      return;
+
+    typedef typename Traits::ResScalar ResScalar;
+    enum {
+      k_peeling = 8,
+      k_div = KcFactor * (Traits::mr * sizeof(LhsScalar) + Traits::nr * sizeof(RhsScalar)),
+      k_sub = Traits::mr * Traits::nr * sizeof(ResScalar)
+    };
+
+    // ---- 1st level of blocking on L1, yields kc ----
+
+    // Blocking on the third dimension (i.e., k) is chosen so that an horizontal panel
+    // of size mr x kc of the lhs plus a vertical panel of kc x nr of the rhs both fits within L1 cache.
+    // We also include a register-level block of the result (mx x nr).
+    // (In an ideal world only the lhs panel would stay in L1)
+    // Moreover, kc has to be a multiple of 8 to be compatible with loop peeling, leading to a maximum blocking size of:
+    const Index max_kc = numext::maxi<Index>(((l1-k_sub)/k_div) & (~(k_peeling-1)),1);
+    const Index old_k = k;
+    if(k>max_kc)
+    {
+      // We are really blocking on the third dimension:
+      // -> reduce blocking size to make sure the last block is as large as possible
+      //    while keeping the same number of sweeps over the result.
+      k = (k%max_kc)==0 ? max_kc
+                        : max_kc - k_peeling * ((max_kc-1-(k%max_kc))/(k_peeling*(k/max_kc+1)));
+
+      eigen_internal_assert(((old_k/k) == (old_k/max_kc)) && "the number of sweeps has to remain the same");
+    }
+
+    // ---- 2nd level of blocking on max(L2,L3), yields nc ----
+
+    // TODO find a reliable way to get the actual amount of cache per core to use for 2nd level blocking, that is:
+    //      actual_l2 = max(l2, l3/nb_core_sharing_l3)
+    // The number below is quite conservative: it is better to underestimate the cache size rather than overestimating it)
+    // For instance, it corresponds to 6MB of L3 shared among 4 cores.
+    #ifdef EIGEN_DEBUG_SMALL_PRODUCT_BLOCKS
+    const Index actual_l2 = l3;
+    #else
+    const Index actual_l2 = 1572864; // == 1.5 MB
+    #endif
+
+    // Here, nc is chosen such that a block of kc x nc of the rhs fit within half of L2.
+    // The second half is implicitly reserved to access the result and lhs coefficients.
+    // When k<max_kc, then nc can arbitrarily growth. In practice, it seems to be fruitful
+    // to limit this growth: we bound nc to growth by a factor x1.5.
+    // However, if the entire lhs block fit within L1, then we are not going to block on the rows at all,
+    // and it becomes fruitful to keep the packed rhs blocks in L1 if there is enough remaining space.
+    Index max_nc;
+    const Index lhs_bytes = m * k * sizeof(LhsScalar);
+    const Index remaining_l1 = l1- k_sub - lhs_bytes;
+    if(remaining_l1 >= Index(Traits::nr*sizeof(RhsScalar))*k)
+    {
+      // L1 blocking
+      max_nc = remaining_l1 / (k*sizeof(RhsScalar));
+    }
+    else
+    {
+      // L2 blocking
+      max_nc = (3*actual_l2)/(2*2*max_kc*sizeof(RhsScalar));
+    }
+    // WARNING Below, we assume that Traits::nr is a power of two.
+    Index nc = numext::mini<Index>(actual_l2/(2*k*sizeof(RhsScalar)), max_nc) & (~(Traits::nr-1));
+    if(n>nc)
+    {
+      // We are really blocking over the columns:
+      // -> reduce blocking size to make sure the last block is as large as possible
+      //    while keeping the same number of sweeps over the packed lhs.
+      //    Here we allow one more sweep if this gives us a perfect match, thus the commented "-1"
+      n = (n%nc)==0 ? nc
+                    : (nc - Traits::nr * ((nc/*-1*/-(n%nc))/(Traits::nr*(n/nc+1))));
+    }
+    else if(old_k==k)
+    {
+      // So far, no blocking at all, i.e., kc==k, and nc==n.
+      // In this case, let's perform a blocking over the rows such that the packed lhs data is kept in cache L1/L2
+      // TODO: part of this blocking strategy is now implemented within the kernel itself, so the L1-based heuristic here should be obsolete.
+      Index problem_size = k*n*sizeof(LhsScalar);
+      Index actual_lm = actual_l2;
+      Index max_mc = m;
+      if(problem_size<=1024)
+      {
+        // problem is small enough to keep in L1
+        // Let's choose m such that lhs's block fit in 1/3 of L1
+        actual_lm = l1;
+      }
+      else if(l3!=0 && problem_size<=32768)
+      {
+        // we have both L2 and L3, and problem is small enough to be kept in L2
+        // Let's choose m such that lhs's block fit in 1/3 of L2
+        actual_lm = l2;
+        max_mc = (numext::mini<Index>)(576,max_mc);
+      }
+      Index mc = (numext::mini<Index>)(actual_lm/(3*k*sizeof(LhsScalar)), max_mc);
+      if (mc > Traits::mr) mc -= mc % Traits::mr;
+      else if (mc==0) return;
+      m = (m%mc)==0 ? mc
+                    : (mc - Traits::mr * ((mc/*-1*/-(m%mc))/(Traits::mr*(m/mc+1))));
+    }
+  }
+}
+
+template <typename Index>
+inline bool useSpecificBlockingSizes(Index& k, Index& m, Index& n)
+{
+#ifdef EIGEN_TEST_SPECIFIC_BLOCKING_SIZES
+  if (EIGEN_TEST_SPECIFIC_BLOCKING_SIZES) {
+    k = numext::mini<Index>(k, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_K);
+    m = numext::mini<Index>(m, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_M);
+    n = numext::mini<Index>(n, EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_N);
+    return true;
+  }
+#else
+  EIGEN_UNUSED_VARIABLE(k)
+  EIGEN_UNUSED_VARIABLE(m)
+  EIGEN_UNUSED_VARIABLE(n)
+#endif
+  return false;
+}
+
+/** \brief Computes the blocking parameters for a m x k times k x n matrix product
+  *
+  * \param[in,out] k Input: the third dimension of the product. Output: the blocking size along the same dimension.
+  * \param[in,out] m Input: the number of rows of the left hand side. Output: the blocking size along the same dimension.
+  * \param[in,out] n Input: the number of columns of the right hand side. Output: the blocking size along the same dimension.
+  *
+  * Given a m x k times k x n matrix product of scalar types \c LhsScalar and \c RhsScalar,
+  * this function computes the blocking size parameters along the respective dimensions
+  * for matrix products and related algorithms.
+  *
+  * The blocking size parameters may be evaluated:
+  *   - either by a heuristic based on cache sizes;
+  *   - or using fixed prescribed values (for testing purposes).
+  *
+  * \sa setCpuCacheSizes */
+
+template<typename LhsScalar, typename RhsScalar, int KcFactor, typename Index>
+void computeProductBlockingSizes(Index& k, Index& m, Index& n, Index num_threads = 1)
+{
+  if (!useSpecificBlockingSizes(k, m, n)) {
+    evaluateProductBlockingSizesHeuristic<LhsScalar, RhsScalar, KcFactor, Index>(k, m, n, num_threads);
+  }
+}
+
+template<typename LhsScalar, typename RhsScalar, typename Index>
+inline void computeProductBlockingSizes(Index& k, Index& m, Index& n, Index num_threads = 1)
+{
+  computeProductBlockingSizes<LhsScalar,RhsScalar,1,Index>(k, m, n, num_threads);
+}
+
+template <typename RhsPacket, typename RhsPacketx4, int registers_taken>
+struct RhsPanelHelper {
+ private:
+  static constexpr int remaining_registers = (std::max)(int(EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS) - registers_taken, 0);
+ public:
+  typedef std::conditional_t<remaining_registers>=4, RhsPacketx4, RhsPacket> type;
+};
+
+template <typename Packet>
+struct QuadPacket
+{
+  Packet B_0, B1, B2, B3;
+  const Packet& get(const FixedInt<0>&) const { return B_0; }
+  const Packet& get(const FixedInt<1>&) const { return B1; }
+  const Packet& get(const FixedInt<2>&) const { return B2; }
+  const Packet& get(const FixedInt<3>&) const { return B3; }
+};
+
+template <int N, typename T1, typename T2, typename T3>
+struct packet_conditional { typedef T3 type; };
+
+template <typename T1, typename T2, typename T3>
+struct packet_conditional<GEBPPacketFull, T1, T2, T3> { typedef T1 type; };
+
+template <typename T1, typename T2, typename T3>
+struct packet_conditional<GEBPPacketHalf, T1, T2, T3> { typedef T2 type; };
+
+#define PACKET_DECL_COND_POSTFIX(postfix, name, packet_size)       \
+  typedef typename packet_conditional<packet_size,                 \
+                                      typename packet_traits<name ## Scalar>::type, \
+                                      typename packet_traits<name ## Scalar>::half, \
+                                      typename unpacket_traits<typename packet_traits<name ## Scalar>::half>::half>::type \
+  name ## Packet ## postfix
+
+#define PACKET_DECL_COND(name, packet_size)                        \
+  typedef typename packet_conditional<packet_size,                 \
+                                      typename packet_traits<name ## Scalar>::type, \
+                                      typename packet_traits<name ## Scalar>::half, \
+                                      typename unpacket_traits<typename packet_traits<name ## Scalar>::half>::half>::type \
+  name ## Packet
+
+#define PACKET_DECL_COND_SCALAR_POSTFIX(postfix, packet_size)      \
+  typedef typename packet_conditional<packet_size,                 \
+                                      typename packet_traits<Scalar>::type, \
+                                      typename packet_traits<Scalar>::half, \
+                                      typename unpacket_traits<typename packet_traits<Scalar>::half>::half>::type \
+  ScalarPacket ## postfix
+
+#define PACKET_DECL_COND_SCALAR(packet_size)                       \
+  typedef typename packet_conditional<packet_size,                 \
+                                      typename packet_traits<Scalar>::type, \
+                                      typename packet_traits<Scalar>::half, \
+                                      typename unpacket_traits<typename packet_traits<Scalar>::half>::half>::type \
+  ScalarPacket
+
+/* Vectorization logic
+ *  real*real: unpack rhs to constant packets, ...
+ * 
+ *  cd*cd : unpack rhs to (b_r,b_r), (b_i,b_i), mul to get (a_r b_r,a_i b_r) (a_r b_i,a_i b_i),
+ *          storing each res packet into two packets (2x2),
+ *          at the end combine them: swap the second and addsub them 
+ *  cf*cf : same but with 2x4 blocks
+ *  cplx*real : unpack rhs to constant packets, ...
+ *  real*cplx : load lhs as (a0,a0,a1,a1), and mul as usual
+ */
+template<typename LhsScalar_, typename RhsScalar_, bool ConjLhs_, bool ConjRhs_, int Arch, int PacketSize_>
+class gebp_traits
+{
+public:
+  typedef LhsScalar_ LhsScalar;
+  typedef RhsScalar_ RhsScalar;
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+
+  PACKET_DECL_COND_POSTFIX(_, Lhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Rhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Res, PacketSize_);
+
+  enum {
+    ConjLhs = ConjLhs_,
+    ConjRhs = ConjRhs_,
+    Vectorizable = unpacket_traits<LhsPacket_>::vectorizable && unpacket_traits<RhsPacket_>::vectorizable,
+    LhsPacketSize = Vectorizable ? unpacket_traits<LhsPacket_>::size : 1,
+    RhsPacketSize = Vectorizable ? unpacket_traits<RhsPacket_>::size : 1,
+    ResPacketSize = Vectorizable ? unpacket_traits<ResPacket_>::size : 1,
+    
+    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
+
+    // register block size along the N direction must be 1 or 4
+    nr = 4,
+
+    // register block size along the M direction (currently, this one cannot be modified)
+    default_mr = (plain_enum_min(16, NumberOfRegisters)/2/nr)*LhsPacketSize,
+#if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD) && !defined(EIGEN_VECTORIZE_ALTIVEC) && !defined(EIGEN_VECTORIZE_VSX) \
+    && ((!EIGEN_COMP_MSVC) || (EIGEN_COMP_MSVC>=1914))
+    // we assume 16 registers or more
+    // See bug 992, if the scalar type is not vectorizable but that EIGEN_HAS_SINGLE_INSTRUCTION_MADD is defined,
+    // then using 3*LhsPacketSize triggers non-implemented paths in syrk.
+    // Bug 1515: MSVC prior to v19.14 yields to register spilling.
+    mr = Vectorizable ? 3*LhsPacketSize : default_mr,
+#else
+    mr = default_mr,
+#endif
+    
+    LhsProgress = LhsPacketSize,
+    RhsProgress = 1
+  };
+
+
+  typedef std::conditional_t<Vectorizable,LhsPacket_,LhsScalar> LhsPacket;
+  typedef std::conditional_t<Vectorizable,RhsPacket_,RhsScalar> RhsPacket;
+  typedef std::conditional_t<Vectorizable,ResPacket_,ResScalar> ResPacket;
+  typedef LhsPacket LhsPacket4Packing;
+
+  typedef QuadPacket<RhsPacket> RhsPacketx4;
+  typedef ResPacket AccPacket;
+  
+  EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
+  {
+    p = pset1<ResPacket>(ResScalar(0));
+  }
+
+  template<typename RhsPacketType>
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketType& dest) const
+  {
+    dest = pset1<RhsPacketType>(*b);
+  }
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
+  {
+    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
+  }
+
+  template<typename RhsPacketType>
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacketType& dest) const
+  {
+    loadRhs(b, dest);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
+  {
+  }
+
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
+  {
+    dest = ploadquad<RhsPacket>(b);
+  }
+
+  template<typename LhsPacketType>
+  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacketType& dest) const
+  {
+    dest = pload<LhsPacketType>(a);
+  }
+
+  template<typename LhsPacketType>
+  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const
+  {
+    dest = ploadu<LhsPacketType>(a);
+  }
+
+  template<typename LhsPacketType, typename RhsPacketType, typename AccPacketType, typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp, const LaneIdType&) const
+  {
+    conj_helper<LhsPacketType,RhsPacketType,ConjLhs,ConjRhs> cj;
+    // It would be a lot cleaner to call pmadd all the time. Unfortunately if we
+    // let gcc allocate the register in which to store the result of the pmul
+    // (in the case where there is no FMA) gcc fails to figure out how to avoid
+    // spilling register.
+#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+    EIGEN_UNUSED_VARIABLE(tmp);
+    c = cj.pmadd(a,b,c);
+#else
+    tmp = b; tmp = cj.pmul(a,tmp); c = padd(c,tmp);
+#endif
+  }
+
+  template<typename LhsPacketType, typename AccPacketType, typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp, const LaneIdType& lane) const
+  {
+    madd(a, b.get(lane), c, tmp, lane);
+  }
+
+  EIGEN_STRONG_INLINE void acc(const AccPacket& c, const ResPacket& alpha, ResPacket& r) const
+  {
+    r = pmadd(c,alpha,r);
+  }
+  
+  template<typename ResPacketHalf>
+  EIGEN_STRONG_INLINE void acc(const ResPacketHalf& c, const ResPacketHalf& alpha, ResPacketHalf& r) const
+  {
+    r = pmadd(c,alpha,r);
+  }
+
+};
+
+template<typename RealScalar, bool ConjLhs_, int Arch, int PacketSize_>
+class gebp_traits<std::complex<RealScalar>, RealScalar, ConjLhs_, false, Arch, PacketSize_>
+{
+public:
+  typedef std::complex<RealScalar> LhsScalar;
+  typedef RealScalar RhsScalar;
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+
+  PACKET_DECL_COND_POSTFIX(_, Lhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Rhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Res, PacketSize_);
+
+  enum {
+    ConjLhs = ConjLhs_,
+    ConjRhs = false,
+    Vectorizable = unpacket_traits<LhsPacket_>::vectorizable && unpacket_traits<RhsPacket_>::vectorizable,
+    LhsPacketSize = Vectorizable ? unpacket_traits<LhsPacket_>::size : 1,
+    RhsPacketSize = Vectorizable ? unpacket_traits<RhsPacket_>::size : 1,
+    ResPacketSize = Vectorizable ? unpacket_traits<ResPacket_>::size : 1,
+    
+    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
+    nr = 4,
+#if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD) && !defined(EIGEN_VECTORIZE_ALTIVEC) && !defined(EIGEN_VECTORIZE_VSX)
+    // we assume 16 registers
+    mr = 3*LhsPacketSize,
+#else
+    mr = (plain_enum_min(16, NumberOfRegisters)/2/nr)*LhsPacketSize,
+#endif
+
+    LhsProgress = LhsPacketSize,
+    RhsProgress = 1
+  };
+
+  typedef std::conditional_t<Vectorizable,LhsPacket_,LhsScalar> LhsPacket;
+  typedef std::conditional_t<Vectorizable,RhsPacket_,RhsScalar> RhsPacket;
+  typedef std::conditional_t<Vectorizable,ResPacket_,ResScalar> ResPacket;
+  typedef LhsPacket LhsPacket4Packing;
+
+  typedef QuadPacket<RhsPacket> RhsPacketx4;
+
+  typedef ResPacket AccPacket;
+
+  EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
+  {
+    p = pset1<ResPacket>(ResScalar(0));
+  }
+
+  template<typename RhsPacketType>
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketType& dest) const
+  {
+    dest = pset1<RhsPacketType>(*b);
+  }
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
+  {
+    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
+  }
+
+  template<typename RhsPacketType>
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacketType& dest) const
+  {
+    loadRhs(b, dest);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
+  {}
+  
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
+  {
+    loadRhsQuad_impl(b,dest, std::conditional_t<RhsPacketSize==16,true_type,false_type>());
+  }
+
+  EIGEN_STRONG_INLINE void loadRhsQuad_impl(const RhsScalar* b, RhsPacket& dest, const true_type&) const
+  {
+    // FIXME we can do better!
+    // what we want here is a ploadheight
+    RhsScalar tmp[4] = {b[0],b[0],b[1],b[1]};
+    dest = ploadquad<RhsPacket>(tmp);
+  }
+
+  EIGEN_STRONG_INLINE void loadRhsQuad_impl(const RhsScalar* b, RhsPacket& dest, const false_type&) const
+  {
+    eigen_internal_assert(RhsPacketSize<=8);
+    dest = pset1<RhsPacket>(*b);
+  }
+
+  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
+  {
+    dest = pload<LhsPacket>(a);
+  }
+
+  template<typename LhsPacketType>
+  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const
+  {
+    dest = ploadu<LhsPacketType>(a);
+  }
+
+  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType, typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp, const LaneIdType&) const
+  {
+    madd_impl(a, b, c, tmp, std::conditional_t<Vectorizable,true_type,false_type>());
+  }
+
+  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType>
+  EIGEN_STRONG_INLINE void madd_impl(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp, const true_type&) const
+  {
+#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+    EIGEN_UNUSED_VARIABLE(tmp);
+    c.v = pmadd(a.v,b,c.v);
+#else
+    tmp = b; tmp = pmul(a.v,tmp); c.v = padd(c.v,tmp);
+#endif
+  }
+
+  EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const
+  {
+    c += a * b;
+  }
+
+  template<typename LhsPacketType, typename AccPacketType, typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp, const LaneIdType& lane) const
+  {
+    madd(a, b.get(lane), c, tmp, lane);
+  }
+
+  template <typename ResPacketType, typename AccPacketType>
+  EIGEN_STRONG_INLINE void acc(const AccPacketType& c, const ResPacketType& alpha, ResPacketType& r) const
+  {
+    conj_helper<ResPacketType,ResPacketType,ConjLhs,false> cj;
+    r = cj.pmadd(c,alpha,r);
+  }
+
+protected:
+};
+
+template<typename Packet>
+struct DoublePacket
+{
+  Packet first;
+  Packet second;
+};
+
+template<typename Packet>
+DoublePacket<Packet> padd(const DoublePacket<Packet> &a, const DoublePacket<Packet> &b)
+{
+  DoublePacket<Packet> res;
+  res.first  = padd(a.first, b.first);
+  res.second = padd(a.second,b.second);
+  return res;
+}
+
+// note that for DoublePacket<RealPacket> the "4" in "downto4"
+// corresponds to the number of complexes, so it means "8"
+// it terms of real coefficients.
+
+template<typename Packet>
+const DoublePacket<Packet>&
+predux_half_dowto4(const DoublePacket<Packet> &a,
+                   std::enable_if_t<unpacket_traits<Packet>::size<=8>* = 0)
+{
+  return a;
+}
+
+template<typename Packet>
+DoublePacket<typename unpacket_traits<Packet>::half>
+predux_half_dowto4(const DoublePacket<Packet> &a,
+                   std::enable_if_t<unpacket_traits<Packet>::size==16>* = 0)
+{
+  // yes, that's pretty hackish :(
+  DoublePacket<typename unpacket_traits<Packet>::half> res;
+  typedef std::complex<typename unpacket_traits<Packet>::type> Cplx;
+  typedef typename packet_traits<Cplx>::type CplxPacket;
+  res.first  = predux_half_dowto4(CplxPacket(a.first)).v;
+  res.second = predux_half_dowto4(CplxPacket(a.second)).v;
+  return res;
+}
+
+// same here, "quad" actually means "8" in terms of real coefficients
+template<typename Scalar, typename RealPacket>
+void loadQuadToDoublePacket(const Scalar* b, DoublePacket<RealPacket>& dest,
+                            std::enable_if_t<unpacket_traits<RealPacket>::size<=8>* = 0)
+{
+  dest.first  = pset1<RealPacket>(numext::real(*b));
+  dest.second = pset1<RealPacket>(numext::imag(*b));
+}
+
+template<typename Scalar, typename RealPacket>
+void loadQuadToDoublePacket(const Scalar* b, DoublePacket<RealPacket>& dest,
+                            std::enable_if_t<unpacket_traits<RealPacket>::size==16>* = 0)
+{
+  // yes, that's pretty hackish too :(
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  RealScalar r[4] = {numext::real(b[0]), numext::real(b[0]), numext::real(b[1]), numext::real(b[1])};
+  RealScalar i[4] = {numext::imag(b[0]), numext::imag(b[0]), numext::imag(b[1]), numext::imag(b[1])};
+  dest.first  = ploadquad<RealPacket>(r);
+  dest.second = ploadquad<RealPacket>(i);
+}
+
+
+template<typename Packet> struct unpacket_traits<DoublePacket<Packet> > {
+  typedef DoublePacket<typename unpacket_traits<Packet>::half> half;
+  enum{
+    size = 2 * unpacket_traits<Packet>::size
+  };
+};
+// template<typename Packet>
+// DoublePacket<Packet> pmadd(const DoublePacket<Packet> &a, const DoublePacket<Packet> &b)
+// {
+//   DoublePacket<Packet> res;
+//   res.first  = padd(a.first, b.first);
+//   res.second = padd(a.second,b.second);
+//   return res;
+// }
+
+template<typename RealScalar, bool ConjLhs_, bool ConjRhs_, int Arch, int PacketSize_>
+class gebp_traits<std::complex<RealScalar>, std::complex<RealScalar>, ConjLhs_, ConjRhs_, Arch, PacketSize_ >
+{
+public:
+  typedef std::complex<RealScalar>  Scalar;
+  typedef std::complex<RealScalar>  LhsScalar;
+  typedef std::complex<RealScalar>  RhsScalar;
+  typedef std::complex<RealScalar>  ResScalar;
+  
+  PACKET_DECL_COND_POSTFIX(_, Lhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Rhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Res, PacketSize_);
+  PACKET_DECL_COND(Real, PacketSize_);
+  PACKET_DECL_COND_SCALAR(PacketSize_);
+
+  enum {
+    ConjLhs = ConjLhs_,
+    ConjRhs = ConjRhs_,
+    Vectorizable = unpacket_traits<RealPacket>::vectorizable
+                && unpacket_traits<ScalarPacket>::vectorizable,
+    ResPacketSize   = Vectorizable ? unpacket_traits<ResPacket_>::size : 1,
+    LhsPacketSize = Vectorizable ? unpacket_traits<LhsPacket_>::size : 1,
+    RhsPacketSize = Vectorizable ? unpacket_traits<RhsScalar>::size : 1,
+    RealPacketSize  = Vectorizable ? unpacket_traits<RealPacket>::size : 1,
+
+    // FIXME: should depend on NumberOfRegisters
+    nr = 4,
+    mr = ResPacketSize,
+
+    LhsProgress = ResPacketSize,
+    RhsProgress = 1
+  };
+  
+  typedef DoublePacket<RealPacket>                 DoublePacketType;
+
+  typedef std::conditional_t<Vectorizable,ScalarPacket,Scalar> LhsPacket4Packing;
+  typedef std::conditional_t<Vectorizable,RealPacket,  Scalar> LhsPacket;
+  typedef std::conditional_t<Vectorizable,DoublePacketType,Scalar> RhsPacket;
+  typedef std::conditional_t<Vectorizable,ScalarPacket,Scalar> ResPacket;
+  typedef std::conditional_t<Vectorizable,DoublePacketType,Scalar> AccPacket;
+
+  // this actually holds 8 packets!
+  typedef QuadPacket<RhsPacket> RhsPacketx4;
+  
+  EIGEN_STRONG_INLINE void initAcc(Scalar& p) { p = Scalar(0); }
+
+  EIGEN_STRONG_INLINE void initAcc(DoublePacketType& p)
+  {
+    p.first   = pset1<RealPacket>(RealScalar(0));
+    p.second  = pset1<RealPacket>(RealScalar(0));
+  }
+
+  // Scalar path
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, ScalarPacket& dest) const
+  {
+    dest = pset1<ScalarPacket>(*b);
+  }
+
+  // Vectorized path
+  template<typename RealPacketType>
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, DoublePacket<RealPacketType>& dest) const
+  {
+    dest.first  = pset1<RealPacketType>(numext::real(*b));
+    dest.second = pset1<RealPacketType>(numext::imag(*b));
+  }
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
+  {
+    loadRhs(b, dest.B_0);
+    loadRhs(b + 1, dest.B1);
+    loadRhs(b + 2, dest.B2);
+    loadRhs(b + 3, dest.B3);
+  }
+
+  // Scalar path
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, ScalarPacket& dest) const
+  {
+    loadRhs(b, dest);
+  }
+
+  // Vectorized path
+  template<typename RealPacketType>
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, DoublePacket<RealPacketType>& dest) const
+  {
+    loadRhs(b, dest);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const {}
+  
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, ResPacket& dest) const
+  {
+    loadRhs(b,dest);
+  }
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, DoublePacketType& dest) const
+  {
+    loadQuadToDoublePacket(b,dest);
+  }
+
+  // nothing special here
+  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
+  {
+    dest = pload<LhsPacket>((const typename unpacket_traits<LhsPacket>::type*)(a));
+  }
+
+  template<typename LhsPacketType>
+  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const
+  {
+    dest = ploadu<LhsPacketType>((const typename unpacket_traits<LhsPacketType>::type*)(a));
+  }
+
+  template<typename LhsPacketType, typename RhsPacketType, typename ResPacketType, typename TmpType, typename LaneIdType>
+  EIGEN_STRONG_INLINE
+  std::enable_if_t<!is_same<RhsPacketType,RhsPacketx4>::value>
+  madd(const LhsPacketType& a, const RhsPacketType& b, DoublePacket<ResPacketType>& c, TmpType& /*tmp*/, const LaneIdType&) const
+  {
+    c.first   = padd(pmul(a,b.first), c.first);
+    c.second  = padd(pmul(a,b.second),c.second);
+  }
+
+  template<typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacket& a, const RhsPacket& b, ResPacket& c, RhsPacket& /*tmp*/, const LaneIdType&) const
+  {
+    c = cj.pmadd(a,b,c);
+  }
+
+  template<typename LhsPacketType, typename AccPacketType, typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp, const LaneIdType& lane) const
+  {
+    madd(a, b.get(lane), c, tmp, lane);
+  }
+  
+  EIGEN_STRONG_INLINE void acc(const Scalar& c, const Scalar& alpha, Scalar& r) const { r += alpha * c; }
+  
+  template<typename RealPacketType, typename ResPacketType>
+  EIGEN_STRONG_INLINE void acc(const DoublePacket<RealPacketType>& c, const ResPacketType& alpha, ResPacketType& r) const
+  {
+    // assemble c
+    ResPacketType tmp;
+    if((!ConjLhs)&&(!ConjRhs))
+    {
+      tmp = pcplxflip(pconj(ResPacketType(c.second)));
+      tmp = padd(ResPacketType(c.first),tmp);
+    }
+    else if((!ConjLhs)&&(ConjRhs))
+    {
+      tmp = pconj(pcplxflip(ResPacketType(c.second)));
+      tmp = padd(ResPacketType(c.first),tmp);
+    }
+    else if((ConjLhs)&&(!ConjRhs))
+    {
+      tmp = pcplxflip(ResPacketType(c.second));
+      tmp = padd(pconj(ResPacketType(c.first)),tmp);
+    }
+    else if((ConjLhs)&&(ConjRhs))
+    {
+      tmp = pcplxflip(ResPacketType(c.second));
+      tmp = psub(pconj(ResPacketType(c.first)),tmp);
+    }
+    
+    r = pmadd(tmp,alpha,r);
+  }
+
+protected:
+  conj_helper<LhsScalar,RhsScalar,ConjLhs,ConjRhs> cj;
+};
+
+template<typename RealScalar, bool ConjRhs_, int Arch, int PacketSize_>
+class gebp_traits<RealScalar, std::complex<RealScalar>, false, ConjRhs_, Arch, PacketSize_ >
+{
+public:
+  typedef std::complex<RealScalar>  Scalar;
+  typedef RealScalar  LhsScalar;
+  typedef Scalar      RhsScalar;
+  typedef Scalar      ResScalar;
+
+  PACKET_DECL_COND_POSTFIX(_, Lhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Rhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Res, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Real, PacketSize_);
+  PACKET_DECL_COND_SCALAR_POSTFIX(_, PacketSize_);
+
+#undef PACKET_DECL_COND_SCALAR_POSTFIX
+#undef PACKET_DECL_COND_POSTFIX
+#undef PACKET_DECL_COND_SCALAR
+#undef PACKET_DECL_COND
+
+  enum {
+    ConjLhs = false,
+    ConjRhs = ConjRhs_,
+    Vectorizable = unpacket_traits<RealPacket_>::vectorizable
+                && unpacket_traits<ScalarPacket_>::vectorizable,
+    LhsPacketSize = Vectorizable ? unpacket_traits<LhsPacket_>::size : 1,
+    RhsPacketSize = Vectorizable ? unpacket_traits<RhsPacket_>::size : 1,
+    ResPacketSize = Vectorizable ? unpacket_traits<ResPacket_>::size : 1,
+    
+    NumberOfRegisters = EIGEN_ARCH_DEFAULT_NUMBER_OF_REGISTERS,
+    // FIXME: should depend on NumberOfRegisters
+    nr = 4,
+    mr = (plain_enum_min(16, NumberOfRegisters)/2/nr)*ResPacketSize,
+
+    LhsProgress = ResPacketSize,
+    RhsProgress = 1
+  };
+
+  typedef std::conditional_t<Vectorizable,LhsPacket_,LhsScalar> LhsPacket;
+  typedef std::conditional_t<Vectorizable,RhsPacket_,RhsScalar> RhsPacket;
+  typedef std::conditional_t<Vectorizable,ResPacket_,ResScalar> ResPacket;
+  typedef LhsPacket LhsPacket4Packing;
+  typedef QuadPacket<RhsPacket> RhsPacketx4;
+  typedef ResPacket AccPacket;
+
+  EIGEN_STRONG_INLINE void initAcc(AccPacket& p)
+  {
+    p = pset1<ResPacket>(ResScalar(0));
+  }
+
+  template<typename RhsPacketType>
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketType& dest) const
+  {
+    dest = pset1<RhsPacketType>(*b);
+  }
+
+  EIGEN_STRONG_INLINE void loadRhs(const RhsScalar* b, RhsPacketx4& dest) const
+  {
+    pbroadcast4(b, dest.B_0, dest.B1, dest.B2, dest.B3);
+  }
+
+  template<typename RhsPacketType>
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar* b, RhsPacketType& dest) const
+  {
+    loadRhs(b, dest);
+  }
+
+  EIGEN_STRONG_INLINE void updateRhs(const RhsScalar*, RhsPacketx4&) const
+  {}
+
+  EIGEN_STRONG_INLINE void loadLhs(const LhsScalar* a, LhsPacket& dest) const
+  {
+    dest = ploaddup<LhsPacket>(a);
+  }
+  
+  EIGEN_STRONG_INLINE void loadRhsQuad(const RhsScalar* b, RhsPacket& dest) const
+  {
+    dest = ploadquad<RhsPacket>(b);
+  }
+
+  template<typename LhsPacketType>
+  EIGEN_STRONG_INLINE void loadLhsUnaligned(const LhsScalar* a, LhsPacketType& dest) const
+  {
+    dest = ploaddup<LhsPacketType>(a);
+  }
+
+  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType, typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp, const LaneIdType&) const
+  {
+    madd_impl(a, b, c, tmp, std::conditional_t<Vectorizable,true_type,false_type>());
+  }
+
+  template <typename LhsPacketType, typename RhsPacketType, typename AccPacketType>
+  EIGEN_STRONG_INLINE void madd_impl(const LhsPacketType& a, const RhsPacketType& b, AccPacketType& c, RhsPacketType& tmp, const true_type&) const
+  {
+#ifdef EIGEN_HAS_SINGLE_INSTRUCTION_MADD
+    EIGEN_UNUSED_VARIABLE(tmp);
+    c.v = pmadd(a,b.v,c.v);
+#else
+    tmp = b; tmp.v = pmul(a,tmp.v); c = padd(c,tmp);
+#endif
+    
+  }
+
+  EIGEN_STRONG_INLINE void madd_impl(const LhsScalar& a, const RhsScalar& b, ResScalar& c, RhsScalar& /*tmp*/, const false_type&) const
+  {
+    c += a * b;
+  }
+
+  template<typename LhsPacketType, typename AccPacketType, typename LaneIdType>
+  EIGEN_STRONG_INLINE void madd(const LhsPacketType& a, const RhsPacketx4& b, AccPacketType& c, RhsPacket& tmp, const LaneIdType& lane) const
+  {
+    madd(a, b.get(lane), c, tmp, lane);
+  }
+
+  template <typename ResPacketType, typename AccPacketType>
+  EIGEN_STRONG_INLINE void acc(const AccPacketType& c, const ResPacketType& alpha, ResPacketType& r) const
+  {
+    conj_helper<ResPacketType,ResPacketType,false,ConjRhs> cj;
+    r = cj.pmadd(alpha,c,r);
+  }
+
+protected:
+
+};
+
+/* optimized General packed Block * packed Panel product kernel
+ *
+ * Mixing type logic: C += A * B
+ *  |  A  |  B  | comments
+ *  |real |cplx | no vectorization yet, would require to pack A with duplication
+ *  |cplx |real | easy vectorization
+ */
+template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct gebp_kernel
+{
+  typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs,Architecture::Target> Traits;
+  typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs,Architecture::Target,GEBPPacketHalf> HalfTraits;
+  typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs,Architecture::Target,GEBPPacketQuarter> QuarterTraits;
+  
+  typedef typename Traits::ResScalar ResScalar;
+  typedef typename Traits::LhsPacket LhsPacket;
+  typedef typename Traits::RhsPacket RhsPacket;
+  typedef typename Traits::ResPacket ResPacket;
+  typedef typename Traits::AccPacket AccPacket;
+  typedef typename Traits::RhsPacketx4 RhsPacketx4;
+
+  typedef typename RhsPanelHelper<RhsPacket, RhsPacketx4, 15>::type RhsPanel15;
+  typedef typename RhsPanelHelper<RhsPacket, RhsPacketx4, 27>::type RhsPanel27;
+
+  typedef gebp_traits<RhsScalar,LhsScalar,ConjugateRhs,ConjugateLhs,Architecture::Target> SwappedTraits;
+
+  typedef typename SwappedTraits::ResScalar SResScalar;
+  typedef typename SwappedTraits::LhsPacket SLhsPacket;
+  typedef typename SwappedTraits::RhsPacket SRhsPacket;
+  typedef typename SwappedTraits::ResPacket SResPacket;
+  typedef typename SwappedTraits::AccPacket SAccPacket;
+
+  typedef typename HalfTraits::LhsPacket LhsPacketHalf;
+  typedef typename HalfTraits::RhsPacket RhsPacketHalf;
+  typedef typename HalfTraits::ResPacket ResPacketHalf;
+  typedef typename HalfTraits::AccPacket AccPacketHalf;
+
+  typedef typename QuarterTraits::LhsPacket LhsPacketQuarter;
+  typedef typename QuarterTraits::RhsPacket RhsPacketQuarter;
+  typedef typename QuarterTraits::ResPacket ResPacketQuarter;
+  typedef typename QuarterTraits::AccPacket AccPacketQuarter;
+
+  typedef typename DataMapper::LinearMapper LinearMapper;
+
+  enum {
+    Vectorizable  = Traits::Vectorizable,
+    LhsProgress   = Traits::LhsProgress,
+    LhsProgressHalf      = HalfTraits::LhsProgress,
+    LhsProgressQuarter   = QuarterTraits::LhsProgress,
+    RhsProgress   = Traits::RhsProgress,
+    RhsProgressHalf      = HalfTraits::RhsProgress,
+    RhsProgressQuarter   = QuarterTraits::RhsProgress,
+    ResPacketSize = Traits::ResPacketSize
+  };
+
+  EIGEN_DONT_INLINE
+  void operator()(const DataMapper& res, const LhsScalar* blockA, const RhsScalar* blockB,
+                  Index rows, Index depth, Index cols, ResScalar alpha,
+                  Index strideA=-1, Index strideB=-1, Index offsetA=0, Index offsetB=0);
+};
+
+template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs,
+int SwappedLhsProgress = gebp_traits<RhsScalar,LhsScalar,ConjugateRhs,ConjugateLhs,Architecture::Target>::LhsProgress>
+struct last_row_process_16_packets
+{
+  typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs,Architecture::Target> Traits;
+  typedef gebp_traits<RhsScalar,LhsScalar,ConjugateRhs,ConjugateLhs,Architecture::Target> SwappedTraits;
+
+  typedef typename Traits::ResScalar ResScalar;
+  typedef typename SwappedTraits::LhsPacket SLhsPacket;
+  typedef typename SwappedTraits::RhsPacket SRhsPacket;
+  typedef typename SwappedTraits::ResPacket SResPacket;
+  typedef typename SwappedTraits::AccPacket SAccPacket;
+
+  EIGEN_STRONG_INLINE void operator()(const DataMapper& res, SwappedTraits &straits, const LhsScalar* blA,
+                  const RhsScalar* blB, Index depth, const Index endk, Index i, Index j2,
+                  ResScalar alpha, SAccPacket &C0)
+    {
+      EIGEN_UNUSED_VARIABLE(res);
+      EIGEN_UNUSED_VARIABLE(straits);
+      EIGEN_UNUSED_VARIABLE(blA);
+      EIGEN_UNUSED_VARIABLE(blB);
+      EIGEN_UNUSED_VARIABLE(depth);
+      EIGEN_UNUSED_VARIABLE(endk);
+      EIGEN_UNUSED_VARIABLE(i);
+      EIGEN_UNUSED_VARIABLE(j2);
+      EIGEN_UNUSED_VARIABLE(alpha);
+      EIGEN_UNUSED_VARIABLE(C0);
+    }
+};
+
+
+template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+struct last_row_process_16_packets<LhsScalar, RhsScalar, Index, DataMapper,  mr,  nr, ConjugateLhs,  ConjugateRhs, 16> {
+  typedef gebp_traits<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs,Architecture::Target> Traits;
+  typedef gebp_traits<RhsScalar,LhsScalar,ConjugateRhs,ConjugateLhs,Architecture::Target> SwappedTraits;
+
+  typedef typename Traits::ResScalar ResScalar;
+  typedef typename SwappedTraits::LhsPacket SLhsPacket;
+  typedef typename SwappedTraits::RhsPacket SRhsPacket;
+  typedef typename SwappedTraits::ResPacket SResPacket;
+  typedef typename SwappedTraits::AccPacket SAccPacket;
+
+  EIGEN_STRONG_INLINE void operator()(const DataMapper& res, SwappedTraits &straits, const LhsScalar* blA,
+                  const RhsScalar* blB, Index depth, const Index endk, Index i, Index j2,
+                  ResScalar alpha, SAccPacket &C0)
+  {
+    typedef typename unpacket_traits<typename unpacket_traits<SResPacket>::half>::half SResPacketQuarter;
+    typedef typename unpacket_traits<typename unpacket_traits<SLhsPacket>::half>::half SLhsPacketQuarter;
+    typedef typename unpacket_traits<typename unpacket_traits<SRhsPacket>::half>::half SRhsPacketQuarter;
+    typedef typename unpacket_traits<typename unpacket_traits<SAccPacket>::half>::half SAccPacketQuarter;
+
+    SResPacketQuarter R = res.template gatherPacket<SResPacketQuarter>(i, j2);
+    SResPacketQuarter alphav = pset1<SResPacketQuarter>(alpha);
+
+    if (depth - endk > 0)
+      {
+	// We have to handle the last row(s) of the rhs, which
+	// correspond to a half-packet
+	SAccPacketQuarter c0 = predux_half_dowto4(predux_half_dowto4(C0));
+
+	for (Index kk = endk; kk < depth; kk++)
+	  {
+	    SLhsPacketQuarter a0;
+	    SRhsPacketQuarter b0;
+	    straits.loadLhsUnaligned(blB, a0);
+	    straits.loadRhs(blA, b0);
+	    straits.madd(a0,b0,c0,b0, fix<0>);
+	    blB += SwappedTraits::LhsProgress/4;
+	    blA += 1;
+	  }
+	straits.acc(c0, alphav, R);
+      }
+    else
+      {
+	straits.acc(predux_half_dowto4(predux_half_dowto4(C0)), alphav, R);
+      }
+    res.scatterPacket(i, j2, R);
+  }
+};
+
+template<int nr, Index LhsProgress, Index RhsProgress, typename LhsScalar, typename RhsScalar, typename ResScalar, typename AccPacket, typename LhsPacket, typename RhsPacket, typename ResPacket, typename GEBPTraits, typename LinearMapper, typename DataMapper>
+struct lhs_process_one_packet
+{
+  typedef typename GEBPTraits::RhsPacketx4 RhsPacketx4;
+
+  EIGEN_STRONG_INLINE void peeled_kc_onestep(Index K, const LhsScalar* blA, const RhsScalar* blB, GEBPTraits traits, LhsPacket *A0, RhsPacketx4 *rhs_panel, RhsPacket *T0, AccPacket *C0, AccPacket *C1, AccPacket *C2, AccPacket *C3)
+  {
+    EIGEN_ASM_COMMENT("begin step of gebp micro kernel 1X4");
+    EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!");
+    traits.loadLhs(&blA[(0+1*K)*LhsProgress], *A0);
+    traits.loadRhs(&blB[(0+4*K)*RhsProgress], *rhs_panel);
+    traits.madd(*A0, *rhs_panel, *C0, *T0, fix<0>);
+    traits.madd(*A0, *rhs_panel, *C1, *T0, fix<1>);
+    traits.madd(*A0, *rhs_panel, *C2, *T0, fix<2>);
+    traits.madd(*A0, *rhs_panel, *C3, *T0, fix<3>);
+    #if EIGEN_GNUC_STRICT_AT_LEAST(6,0,0) && defined(EIGEN_VECTORIZE_SSE) && !(EIGEN_COMP_LCC)
+    __asm__  ("" : "+x,m" (*A0));
+    #endif
+    EIGEN_ASM_COMMENT("end step of gebp micro kernel 1X4");
+  }
+
+  EIGEN_STRONG_INLINE void operator()(
+    const DataMapper& res, const LhsScalar* blockA, const RhsScalar* blockB, ResScalar alpha,
+    Index peelStart, Index peelEnd, Index strideA, Index strideB, Index offsetA, Index offsetB,
+    int prefetch_res_offset, Index peeled_kc, Index pk, Index cols, Index depth, Index packet_cols4)
+  {
+    GEBPTraits traits;
+    Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0;
+    // loops on each largest micro horizontal panel of lhs
+    // (LhsProgress x depth)
+    for(Index i=peelStart; i<peelEnd; i+=LhsProgress)
+    {
+#if EIGEN_ARCH_ARM64
+      EIGEN_IF_CONSTEXPR(nr>=8) {
+      for(Index j2=0; j2<packet_cols8; j2+=8)
+      {
+        const LhsScalar* blA = &blockA[i*strideA+offsetA*(LhsProgress)];
+        prefetch(&blA[0]);
+
+        // gets res block as register
+        AccPacket C0, C1, C2, C3, C4, C5, C6, C7;
+        traits.initAcc(C0);
+        traits.initAcc(C1);
+        traits.initAcc(C2);
+        traits.initAcc(C3);
+        traits.initAcc(C4);
+        traits.initAcc(C5);
+        traits.initAcc(C6);
+        traits.initAcc(C7);
+
+        LinearMapper r0 = res.getLinearMapper(i, j2 + 0);
+        LinearMapper r1 = res.getLinearMapper(i, j2 + 1);
+        LinearMapper r2 = res.getLinearMapper(i, j2 + 2);
+        LinearMapper r3 = res.getLinearMapper(i, j2 + 3);
+        LinearMapper r4 = res.getLinearMapper(i, j2 + 4);
+        LinearMapper r5 = res.getLinearMapper(i, j2 + 5);
+        LinearMapper r6 = res.getLinearMapper(i, j2 + 6);
+        LinearMapper r7 = res.getLinearMapper(i, j2 + 7);
+        r0.prefetch(prefetch_res_offset);
+        r1.prefetch(prefetch_res_offset);
+        r2.prefetch(prefetch_res_offset);
+        r3.prefetch(prefetch_res_offset);
+        r4.prefetch(prefetch_res_offset);
+        r5.prefetch(prefetch_res_offset);
+        r6.prefetch(prefetch_res_offset);
+        r7.prefetch(prefetch_res_offset);
+        const RhsScalar* blB = &blockB[j2*strideB+offsetB*8];
+        prefetch(&blB[0]);
+
+        LhsPacket A0;
+        for(Index k=0; k<peeled_kc; k+=pk)
+        {
+            RhsPacketx4 rhs_panel;
+            RhsPacket T0;
+#define EIGEN_GEBGP_ONESTEP(K)                                                \
+            do {                                                              \
+                EIGEN_ASM_COMMENT("begin step of gebp micro kernel 1pX8");    \
+                traits.loadLhs(&blA[(0 + 1 * K) * LhsProgress], A0);          \
+                traits.loadRhs(&blB[(0 + 8 * K) * RhsProgress], rhs_panel);   \
+                traits.madd(A0, rhs_panel, C0, T0, fix<0>);                   \
+                traits.updateRhs(&blB[(1 + 8 * K) * RhsProgress], rhs_panel); \
+                traits.madd(A0, rhs_panel, C1, T0, fix<1>);                   \
+                traits.updateRhs(&blB[(2 + 8 * K) * RhsProgress], rhs_panel); \
+                traits.madd(A0, rhs_panel, C2, T0, fix<2>);                   \
+                traits.updateRhs(&blB[(3 + 8 * K) * RhsProgress], rhs_panel); \
+                traits.madd(A0, rhs_panel, C3, T0, fix<3>);                   \
+                traits.loadRhs(&blB[(4 + 8 * K) * RhsProgress], rhs_panel);   \
+                traits.madd(A0, rhs_panel, C4, T0, fix<0>);                   \
+                traits.updateRhs(&blB[(5 + 8 * K) * RhsProgress], rhs_panel); \
+                traits.madd(A0, rhs_panel, C5, T0, fix<1>);                   \
+                traits.updateRhs(&blB[(6 + 8 * K) * RhsProgress], rhs_panel); \
+                traits.madd(A0, rhs_panel, C6, T0, fix<2>);                   \
+                traits.updateRhs(&blB[(7 + 8 * K) * RhsProgress], rhs_panel); \
+                traits.madd(A0, rhs_panel, C7, T0, fix<3>);                   \
+                EIGEN_ASM_COMMENT("end step of gebp micro kernel 1pX8");      \
+            } while (false)
+
+            EIGEN_ASM_COMMENT("begin gebp micro kernel 1pX8");
+
+            EIGEN_GEBGP_ONESTEP(0);
+            EIGEN_GEBGP_ONESTEP(1);
+            EIGEN_GEBGP_ONESTEP(2);
+            EIGEN_GEBGP_ONESTEP(3);
+            EIGEN_GEBGP_ONESTEP(4);
+            EIGEN_GEBGP_ONESTEP(5);
+            EIGEN_GEBGP_ONESTEP(6);
+            EIGEN_GEBGP_ONESTEP(7);
+
+            blB += pk*8*RhsProgress;
+            blA += pk*(1*LhsProgress);
+
+            EIGEN_ASM_COMMENT("end gebp micro kernel 1pX8");
+          }
+          // process remaining peeled loop
+          for(Index k=peeled_kc; k<depth; k++)
+          {
+            RhsPacketx4 rhs_panel;
+            RhsPacket T0;
+            EIGEN_GEBGP_ONESTEP(0);
+            blB += 8*RhsProgress;
+            blA += 1*LhsProgress;
+          }
+
+#undef EIGEN_GEBGP_ONESTEP
+
+          ResPacket R0, R1;
+          ResPacket alphav = pset1<ResPacket>(alpha);
+
+          R0 = r0.template loadPacket<ResPacket>(0);
+          R1 = r1.template loadPacket<ResPacket>(0);
+          traits.acc(C0, alphav, R0);
+          traits.acc(C1, alphav, R1);
+          r0.storePacket(0, R0);
+          r1.storePacket(0, R1);
+
+          R0 = r2.template loadPacket<ResPacket>(0);
+          R1 = r3.template loadPacket<ResPacket>(0);
+          traits.acc(C2,  alphav, R0);
+          traits.acc(C3,  alphav, R1);
+          r2.storePacket(0, R0);
+          r3.storePacket(0, R1);
+
+          R0 = r4.template loadPacket<ResPacket>(0);
+          R1 = r5.template loadPacket<ResPacket>(0);
+          traits.acc(C4,  alphav, R0);
+          traits.acc(C5,  alphav, R1);
+          r4.storePacket(0, R0);
+          r5.storePacket(0, R1);
+
+          R0 = r6.template loadPacket<ResPacket>(0);
+          R1 = r7.template loadPacket<ResPacket>(0);
+          traits.acc(C6, alphav, R0);
+          traits.acc(C7, alphav, R1);
+          r6.storePacket(0, R0);
+          r7.storePacket(0, R1);
+      }
+      }
+#endif
+      
+      // loops on each largest micro vertical panel of rhs (depth * nr)
+      for(Index j2=packet_cols8; j2<packet_cols4; j2+=4)
+      {
+        // We select a LhsProgress x nr micro block of res
+        // which is entirely stored into 1 x nr registers.
+
+        const LhsScalar* blA = &blockA[i*strideA+offsetA*(LhsProgress)];
+        prefetch(&blA[0]);
+
+        // gets res block as register
+        AccPacket C0, C1, C2, C3;
+        traits.initAcc(C0);
+        traits.initAcc(C1);
+        traits.initAcc(C2);
+        traits.initAcc(C3);
+        // To improve instruction pipelining, let's double the accumulation registers:
+        //  even k will accumulate in C*, while odd k will accumulate in D*.
+        // This trick is crutial to get good performance with FMA, otherwise it is 
+        // actually faster to perform separated MUL+ADD because of a naturally
+        // better instruction-level parallelism.
+        AccPacket D0, D1, D2, D3;
+        traits.initAcc(D0);
+        traits.initAcc(D1);
+        traits.initAcc(D2);
+        traits.initAcc(D3);
+
+        LinearMapper r0 = res.getLinearMapper(i, j2 + 0);
+        LinearMapper r1 = res.getLinearMapper(i, j2 + 1);
+        LinearMapper r2 = res.getLinearMapper(i, j2 + 2);
+        LinearMapper r3 = res.getLinearMapper(i, j2 + 3);
+
+        r0.prefetch(prefetch_res_offset);
+        r1.prefetch(prefetch_res_offset);
+        r2.prefetch(prefetch_res_offset);
+        r3.prefetch(prefetch_res_offset);
+
+        // performs "inner" products
+        const RhsScalar* blB = &blockB[j2*strideB+offsetB*4];
+        prefetch(&blB[0]);
+        LhsPacket A0, A1;
+
+        for(Index k=0; k<peeled_kc; k+=pk)
+        {
+          EIGEN_ASM_COMMENT("begin gebp micro kernel 1/half/quarterX4");
+          RhsPacketx4 rhs_panel;
+          RhsPacket T0;
+
+          internal::prefetch(blB+(48+0));
+          peeled_kc_onestep(0, blA, blB, traits, &A0, &rhs_panel, &T0, &C0, &C1, &C2, &C3);
+          peeled_kc_onestep(1, blA, blB, traits, &A1, &rhs_panel, &T0, &D0, &D1, &D2, &D3);
+          peeled_kc_onestep(2, blA, blB, traits, &A0, &rhs_panel, &T0, &C0, &C1, &C2, &C3);
+          peeled_kc_onestep(3, blA, blB, traits, &A1, &rhs_panel, &T0, &D0, &D1, &D2, &D3);
+          internal::prefetch(blB+(48+16));
+          peeled_kc_onestep(4, blA, blB, traits, &A0, &rhs_panel, &T0, &C0, &C1, &C2, &C3);
+          peeled_kc_onestep(5, blA, blB, traits, &A1, &rhs_panel, &T0, &D0, &D1, &D2, &D3);
+          peeled_kc_onestep(6, blA, blB, traits, &A0, &rhs_panel, &T0, &C0, &C1, &C2, &C3);
+          peeled_kc_onestep(7, blA, blB, traits, &A1, &rhs_panel, &T0, &D0, &D1, &D2, &D3);
+
+          blB += pk*4*RhsProgress;
+          blA += pk*LhsProgress;
+
+          EIGEN_ASM_COMMENT("end gebp micro kernel 1/half/quarterX4");
+        }
+        C0 = padd(C0,D0);
+        C1 = padd(C1,D1);
+        C2 = padd(C2,D2);
+        C3 = padd(C3,D3);
+
+        // process remaining peeled loop
+        for(Index k=peeled_kc; k<depth; k++)
+        {
+          RhsPacketx4 rhs_panel;
+          RhsPacket T0;
+          peeled_kc_onestep(0, blA, blB, traits, &A0, &rhs_panel, &T0, &C0, &C1, &C2, &C3);
+          blB += 4*RhsProgress;
+          blA += LhsProgress;
+        }
+
+        ResPacket R0, R1;
+        ResPacket alphav = pset1<ResPacket>(alpha);
+
+        R0 = r0.template loadPacket<ResPacket>(0);
+        R1 = r1.template loadPacket<ResPacket>(0);
+        traits.acc(C0, alphav, R0);
+        traits.acc(C1,  alphav, R1);
+        r0.storePacket(0, R0);
+        r1.storePacket(0, R1);
+
+        R0 = r2.template loadPacket<ResPacket>(0);
+        R1 = r3.template loadPacket<ResPacket>(0);
+        traits.acc(C2,  alphav, R0);
+        traits.acc(C3,  alphav, R1);
+        r2.storePacket(0, R0);
+        r3.storePacket(0, R1);
+      }
+
+      // Deal with remaining columns of the rhs
+      for(Index j2=packet_cols4; j2<cols; j2++)
+      {
+        // One column at a time
+        const LhsScalar* blA = &blockA[i*strideA+offsetA*(LhsProgress)];
+        prefetch(&blA[0]);
+
+        // gets res block as register
+        AccPacket C0;
+        traits.initAcc(C0);
+
+        LinearMapper r0 = res.getLinearMapper(i, j2);
+
+        // performs "inner" products
+        const RhsScalar* blB = &blockB[j2*strideB+offsetB];
+        LhsPacket A0;
+
+        for(Index k= 0; k<peeled_kc; k+=pk)
+        {
+          EIGEN_ASM_COMMENT("begin gebp micro kernel 1/half/quarterX1");
+          RhsPacket B_0;
+
+#define EIGEN_GEBGP_ONESTEP(K)                                          \
+	      do {                                                      \
+		EIGEN_ASM_COMMENT("begin step of gebp micro kernel 1/half/quarterX1"); \
+		EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
+    /* FIXME: why unaligned???? */ \
+		traits.loadLhsUnaligned(&blA[(0+1*K)*LhsProgress], A0); \
+		traits.loadRhs(&blB[(0+K)*RhsProgress], B_0);		\
+		traits.madd(A0, B_0, C0, B_0, fix<0>);				\
+		EIGEN_ASM_COMMENT("end step of gebp micro kernel 1/half/quarterX1"); \
+	      } while(false);
+
+          EIGEN_GEBGP_ONESTEP(0);
+          EIGEN_GEBGP_ONESTEP(1);
+          EIGEN_GEBGP_ONESTEP(2);
+          EIGEN_GEBGP_ONESTEP(3);
+          EIGEN_GEBGP_ONESTEP(4);
+          EIGEN_GEBGP_ONESTEP(5);
+          EIGEN_GEBGP_ONESTEP(6);
+          EIGEN_GEBGP_ONESTEP(7);
+
+          blB += pk*RhsProgress;
+          blA += pk*LhsProgress;
+
+          EIGEN_ASM_COMMENT("end gebp micro kernel 1/half/quarterX1");
+        }
+
+        // process remaining peeled loop
+        for(Index k=peeled_kc; k<depth; k++)
+        {
+          RhsPacket B_0;
+          EIGEN_GEBGP_ONESTEP(0);
+          blB += RhsProgress;
+          blA += LhsProgress;
+        }
+#undef EIGEN_GEBGP_ONESTEP
+        ResPacket R0;
+        ResPacket alphav = pset1<ResPacket>(alpha);
+        R0 = r0.template loadPacket<ResPacket>(0);
+        traits.acc(C0, alphav, R0);
+        r0.storePacket(0, R0);
+      }
+    }
+  }
+};
+
+template<int nr, Index LhsProgress, Index RhsProgress, typename LhsScalar, typename RhsScalar, typename ResScalar, typename AccPacket, typename LhsPacket, typename RhsPacket, typename ResPacket, typename GEBPTraits, typename LinearMapper, typename DataMapper>
+struct lhs_process_fraction_of_packet : lhs_process_one_packet<nr, LhsProgress, RhsProgress, LhsScalar, RhsScalar, ResScalar, AccPacket, LhsPacket, RhsPacket, ResPacket, GEBPTraits, LinearMapper, DataMapper>
+{
+
+EIGEN_STRONG_INLINE void peeled_kc_onestep(Index K, const LhsScalar* blA, const RhsScalar* blB, GEBPTraits traits, LhsPacket *A0, RhsPacket *B_0, RhsPacket *B1, RhsPacket *B2, RhsPacket *B3, AccPacket *C0, AccPacket *C1, AccPacket *C2, AccPacket *C3)
+  {
+        EIGEN_ASM_COMMENT("begin step of gebp micro kernel 1X4");
+        EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!");
+        traits.loadLhsUnaligned(&blA[(0+1*K)*(LhsProgress)], *A0);
+        traits.broadcastRhs(&blB[(0+4*K)*RhsProgress], *B_0, *B1, *B2, *B3);
+        traits.madd(*A0, *B_0, *C0, *B_0);
+        traits.madd(*A0, *B1,  *C1, *B1);
+        traits.madd(*A0, *B2,  *C2, *B2);
+        traits.madd(*A0, *B3,  *C3, *B3);
+        EIGEN_ASM_COMMENT("end step of gebp micro kernel 1X4");
+  }
+};
+
+template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs, bool ConjugateRhs>
+EIGEN_DONT_INLINE
+void gebp_kernel<LhsScalar,RhsScalar,Index,DataMapper,mr,nr,ConjugateLhs,ConjugateRhs>
+  ::operator()(const DataMapper& res, const LhsScalar* blockA, const RhsScalar* blockB,
+               Index rows, Index depth, Index cols, ResScalar alpha,
+               Index strideA, Index strideB, Index offsetA, Index offsetB)
+  {
+    Traits traits;
+    SwappedTraits straits;
+    
+    if(strideA==-1) strideA = depth;
+    if(strideB==-1) strideB = depth;
+    conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
+    Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0;
+    Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0;
+    const Index peeled_mc3 = mr>=3*Traits::LhsProgress ? (rows/(3*LhsProgress))*(3*LhsProgress) : 0;
+    const Index peeled_mc2 = mr>=2*Traits::LhsProgress ? peeled_mc3+((rows-peeled_mc3)/(2*LhsProgress))*(2*LhsProgress) : 0;
+    const Index peeled_mc1 = mr>=1*Traits::LhsProgress ? peeled_mc2+((rows-peeled_mc2)/(1*LhsProgress))*(1*LhsProgress) : 0;
+    const Index peeled_mc_half = mr>=LhsProgressHalf ? peeled_mc1+((rows-peeled_mc1)/(LhsProgressHalf))*(LhsProgressHalf) : 0;
+    const Index peeled_mc_quarter = mr>=LhsProgressQuarter ? peeled_mc_half+((rows-peeled_mc_half)/(LhsProgressQuarter))*(LhsProgressQuarter) : 0;
+    enum { pk = 8 }; // NOTE Such a large peeling factor is important for large matrices (~ +5% when >1000 on Haswell)
+    const Index peeled_kc  = depth & ~(pk-1);
+    const int prefetch_res_offset = 32/sizeof(ResScalar);    
+//     const Index depth2     = depth & ~1;
+
+    //---------- Process 3 * LhsProgress rows at once ----------
+    // This corresponds to 3*LhsProgress x nr register blocks.
+    // Usually, make sense only with FMA
+    if(mr>=3*Traits::LhsProgress)
+    {
+      // Here, the general idea is to loop on each largest micro horizontal panel of the lhs (3*Traits::LhsProgress x depth)
+      // and on each largest micro vertical panel of the rhs (depth * nr).
+      // Blocking sizes, i.e., 'depth' has been computed so that the micro horizontal panel of the lhs fit in L1.
+      // However, if depth is too small, we can extend the number of rows of these horizontal panels.
+      // This actual number of rows is computed as follow:
+      const Index l1 = defaultL1CacheSize; // in Bytes, TODO, l1 should be passed to this function.
+      // The max(1, ...) here is needed because we may be using blocking params larger than what our known l1 cache size
+      // suggests we should be using: either because our known l1 cache size is inaccurate (e.g. on Android, we can only guess),
+      // or because we are testing specific blocking sizes.
+      const Index actual_panel_rows = (3*LhsProgress) * std::max<Index>(1,( (l1 - sizeof(ResScalar)*mr*nr - depth*nr*sizeof(RhsScalar)) / (depth * sizeof(LhsScalar) * 3*LhsProgress) ));
+      for(Index i1=0; i1<peeled_mc3; i1+=actual_panel_rows)
+      {
+        const Index actual_panel_end = (std::min)(i1+actual_panel_rows, peeled_mc3);
+#if EIGEN_ARCH_ARM64
+        EIGEN_IF_CONSTEXPR(nr>=8) {
+        for(Index j2=0; j2<packet_cols8; j2+=8)
+        {
+          for(Index i=i1; i<actual_panel_end; i+=3*LhsProgress)
+          {
+            const LhsScalar* blA = &blockA[i*strideA+offsetA*(3*LhsProgress)];
+            prefetch(&blA[0]);
+            // gets res block as register
+            AccPacket C0, C1, C2, C3, C4, C5, C6, C7,
+                      C8, C9, C10, C11, C12, C13, C14, C15,
+                      C16, C17, C18, C19, C20, C21, C22, C23;
+            traits.initAcc(C0);  traits.initAcc(C1);  traits.initAcc(C2);  traits.initAcc(C3);
+            traits.initAcc(C4);  traits.initAcc(C5);  traits.initAcc(C6);  traits.initAcc(C7);
+            traits.initAcc(C8);  traits.initAcc(C9);  traits.initAcc(C10); traits.initAcc(C11);
+            traits.initAcc(C12);  traits.initAcc(C13);  traits.initAcc(C14);  traits.initAcc(C15);
+            traits.initAcc(C16);  traits.initAcc(C17);  traits.initAcc(C18);  traits.initAcc(C19);
+            traits.initAcc(C20);  traits.initAcc(C21);  traits.initAcc(C22); traits.initAcc(C23);
+
+            LinearMapper r0 = res.getLinearMapper(i, j2 + 0);
+            LinearMapper r1 = res.getLinearMapper(i, j2 + 1);
+            LinearMapper r2 = res.getLinearMapper(i, j2 + 2);
+            LinearMapper r3 = res.getLinearMapper(i, j2 + 3);
+            LinearMapper r4 = res.getLinearMapper(i, j2 + 4);
+            LinearMapper r5 = res.getLinearMapper(i, j2 + 5);
+            LinearMapper r6 = res.getLinearMapper(i, j2 + 6);
+            LinearMapper r7 = res.getLinearMapper(i, j2 + 7);
+
+            r0.prefetch(0);
+            r1.prefetch(0);
+            r2.prefetch(0);
+            r3.prefetch(0);
+            r4.prefetch(0);
+            r5.prefetch(0);
+            r6.prefetch(0);
+            r7.prefetch(0);
+
+            // performs "inner" products
+            const RhsScalar* blB = &blockB[j2*strideB+offsetB*8];
+            prefetch(&blB[0]);
+            LhsPacket A0, A1;
+            for(Index k=0; k<peeled_kc; k+=pk)
+            {
+              EIGEN_ASM_COMMENT("begin gebp micro kernel 3pX8");
+              // 27 registers are taken (24 for acc, 3 for lhs).
+              RhsPanel27 rhs_panel;
+              RhsPacket T0;
+              LhsPacket A2;
+            #if EIGEN_ARCH_ARM64 && defined(EIGEN_VECTORIZE_NEON) && EIGEN_GNUC_STRICT_LESS_THAN(9,0,0)
+            // see http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1633
+            // without this workaround A0, A1, and A2 are loaded in the same register,
+            // which is not good for pipelining
+            #define EIGEN_GEBP_3Px8_REGISTER_ALLOC_WORKAROUND __asm__  ("" : "+w,m" (A0), "+w,m" (A1), "+w,m" (A2));
+            #else
+            #define EIGEN_GEBP_3Px8_REGISTER_ALLOC_WORKAROUND
+            #endif
+
+#define EIGEN_GEBP_ONESTEP(K)                                                         \
+            do {                                                                      \
+                EIGEN_ASM_COMMENT("begin step of gebp micro kernel 3pX8");            \
+                traits.loadLhs(&blA[(0 + 3 * K) * LhsProgress], A0);                  \
+                traits.loadLhs(&blA[(1 + 3 * K) * LhsProgress], A1);                  \
+                traits.loadLhs(&blA[(2 + 3 * K) * LhsProgress], A2);                  \
+                EIGEN_GEBP_3Px8_REGISTER_ALLOC_WORKAROUND                             \
+                traits.loadRhs(blB + (0 + 8 * K) * Traits::RhsProgress, rhs_panel);   \
+                traits.madd(A0, rhs_panel, C0, T0, fix<0>);                           \
+                traits.madd(A1, rhs_panel, C8, T0, fix<0>);                           \
+                traits.madd(A2, rhs_panel, C16, T0, fix<0>);                          \
+                traits.updateRhs(blB + (1 + 8 * K) * Traits::RhsProgress, rhs_panel); \
+                traits.madd(A0, rhs_panel, C1, T0, fix<1>);                           \
+                traits.madd(A1, rhs_panel, C9, T0, fix<1>);                           \
+                traits.madd(A2, rhs_panel, C17, T0, fix<1>);                          \
+                traits.updateRhs(blB + (2 + 8 * K) * Traits::RhsProgress, rhs_panel); \
+                traits.madd(A0, rhs_panel, C2, T0, fix<2>);                           \
+                traits.madd(A1, rhs_panel, C10, T0, fix<2>);                          \
+                traits.madd(A2, rhs_panel, C18, T0, fix<2>);                          \
+                traits.updateRhs(blB + (3 + 8 * K) * Traits::RhsProgress, rhs_panel); \
+                traits.madd(A0, rhs_panel, C3, T0, fix<3>);                           \
+                traits.madd(A1, rhs_panel, C11, T0, fix<3>);                          \
+                traits.madd(A2, rhs_panel, C19, T0, fix<3>);                          \
+                traits.loadRhs(blB + (4 + 8 * K) * Traits::RhsProgress, rhs_panel);   \
+                traits.madd(A0, rhs_panel, C4, T0, fix<0>);                           \
+                traits.madd(A1, rhs_panel, C12, T0, fix<0>);                          \
+                traits.madd(A2, rhs_panel, C20, T0, fix<0>);                          \
+                traits.updateRhs(blB + (5 + 8 * K) * Traits::RhsProgress, rhs_panel); \
+                traits.madd(A0, rhs_panel, C5, T0, fix<1>);                           \
+                traits.madd(A1, rhs_panel, C13, T0, fix<1>);                          \
+                traits.madd(A2, rhs_panel, C21, T0, fix<1>);                          \
+                traits.updateRhs(blB + (6 + 8 * K) * Traits::RhsProgress, rhs_panel); \
+                traits.madd(A0, rhs_panel, C6, T0, fix<2>);                           \
+                traits.madd(A1, rhs_panel, C14, T0, fix<2>);                          \
+                traits.madd(A2, rhs_panel, C22, T0, fix<2>);                          \
+                traits.updateRhs(blB + (7 + 8 * K) * Traits::RhsProgress, rhs_panel); \
+                traits.madd(A0, rhs_panel, C7, T0, fix<3>);                           \
+                traits.madd(A1, rhs_panel, C15, T0, fix<3>);                          \
+                traits.madd(A2, rhs_panel, C23, T0, fix<3>);                          \
+                EIGEN_ASM_COMMENT("end step of gebp micro kernel 3pX8");              \
+            } while (false)
+
+                EIGEN_GEBP_ONESTEP(0);
+                EIGEN_GEBP_ONESTEP(1);
+                EIGEN_GEBP_ONESTEP(2);
+                EIGEN_GEBP_ONESTEP(3);
+                EIGEN_GEBP_ONESTEP(4);
+                EIGEN_GEBP_ONESTEP(5);
+                EIGEN_GEBP_ONESTEP(6);
+                EIGEN_GEBP_ONESTEP(7);
+
+                blB += pk * 8 * RhsProgress;
+                blA += pk * 3 * Traits::LhsProgress;
+                EIGEN_ASM_COMMENT("end gebp micro kernel 3pX8");
+            }
+
+            // process remaining peeled loop
+            for (Index k = peeled_kc; k < depth; k++)
+            {
+
+                RhsPanel27 rhs_panel;
+                RhsPacket T0;
+                LhsPacket A2;
+                EIGEN_GEBP_ONESTEP(0);
+                blB += 8 * RhsProgress;
+                blA += 3 * Traits::LhsProgress;
+            }
+
+            #undef EIGEN_GEBP_ONESTEP
+
+            ResPacket R0, R1, R2;
+            ResPacket alphav = pset1<ResPacket>(alpha);
+
+            R0 = r0.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R1 = r0.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            R2 = r0.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
+            traits.acc(C0, alphav, R0);
+            traits.acc(C8, alphav, R1);
+            traits.acc(C16, alphav, R2);
+            r0.storePacket(0 * Traits::ResPacketSize, R0);
+            r0.storePacket(1 * Traits::ResPacketSize, R1);
+            r0.storePacket(2 * Traits::ResPacketSize, R2);
+
+            R0 = r1.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R1 = r1.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            R2 = r1.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
+            traits.acc(C1, alphav, R0);
+            traits.acc(C9, alphav, R1);
+            traits.acc(C17, alphav, R2);
+            r1.storePacket(0 * Traits::ResPacketSize, R0);
+            r1.storePacket(1 * Traits::ResPacketSize, R1);
+            r1.storePacket(2 * Traits::ResPacketSize, R2);
+
+            R0 = r2.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R1 = r2.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            R2 = r2.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
+            traits.acc(C2, alphav, R0);
+            traits.acc(C10, alphav, R1);
+            traits.acc(C18, alphav, R2);
+            r2.storePacket(0 * Traits::ResPacketSize, R0);
+            r2.storePacket(1 * Traits::ResPacketSize, R1);
+            r2.storePacket(2 * Traits::ResPacketSize, R2);
+
+            R0 = r3.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R1 = r3.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            R2 = r3.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
+            traits.acc(C3, alphav, R0);
+            traits.acc(C11, alphav, R1);
+            traits.acc(C19, alphav, R2);
+            r3.storePacket(0 * Traits::ResPacketSize, R0);
+            r3.storePacket(1 * Traits::ResPacketSize, R1);
+            r3.storePacket(2 * Traits::ResPacketSize, R2);
+
+            R0 = r4.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R1 = r4.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            R2 = r4.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
+            traits.acc(C4, alphav, R0);
+            traits.acc(C12, alphav, R1);
+            traits.acc(C20, alphav, R2);
+            r4.storePacket(0 * Traits::ResPacketSize, R0);
+            r4.storePacket(1 * Traits::ResPacketSize, R1);
+            r4.storePacket(2 * Traits::ResPacketSize, R2);
+
+            R0 = r5.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R1 = r5.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            R2 = r5.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
+            traits.acc(C5, alphav, R0);
+            traits.acc(C13, alphav, R1);
+            traits.acc(C21, alphav, R2);
+            r5.storePacket(0 * Traits::ResPacketSize, R0);
+            r5.storePacket(1 * Traits::ResPacketSize, R1);
+            r5.storePacket(2 * Traits::ResPacketSize, R2);
+
+            R0 = r6.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R1 = r6.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            R2 = r6.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
+            traits.acc(C6, alphav, R0);
+            traits.acc(C14, alphav, R1);
+            traits.acc(C22, alphav, R2);
+            r6.storePacket(0 * Traits::ResPacketSize, R0);
+            r6.storePacket(1 * Traits::ResPacketSize, R1);
+            r6.storePacket(2 * Traits::ResPacketSize, R2);
+
+            R0 = r7.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R1 = r7.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            R2 = r7.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
+            traits.acc(C7, alphav, R0);
+            traits.acc(C15, alphav, R1);
+            traits.acc(C23, alphav, R2);
+            r7.storePacket(0 * Traits::ResPacketSize, R0);
+            r7.storePacket(1 * Traits::ResPacketSize, R1);
+            r7.storePacket(2 * Traits::ResPacketSize, R2);
+          }
+        }
+        }
+#endif
+        for(Index j2=packet_cols8; j2<packet_cols4; j2+=4)
+        {
+          for(Index i=i1; i<actual_panel_end; i+=3*LhsProgress)
+          {
+          
+          // We selected a 3*Traits::LhsProgress x nr micro block of res which is entirely
+          // stored into 3 x nr registers.
+          
+          const LhsScalar* blA = &blockA[i*strideA+offsetA*(3*LhsProgress)];
+          prefetch(&blA[0]);
+
+          // gets res block as register
+          AccPacket C0, C1, C2,  C3,
+                    C4, C5, C6,  C7,
+                    C8, C9, C10, C11;
+          traits.initAcc(C0);  traits.initAcc(C1);  traits.initAcc(C2);  traits.initAcc(C3);
+          traits.initAcc(C4);  traits.initAcc(C5);  traits.initAcc(C6);  traits.initAcc(C7);
+          traits.initAcc(C8);  traits.initAcc(C9);  traits.initAcc(C10); traits.initAcc(C11);
+
+          LinearMapper r0 = res.getLinearMapper(i, j2 + 0);
+          LinearMapper r1 = res.getLinearMapper(i, j2 + 1);
+          LinearMapper r2 = res.getLinearMapper(i, j2 + 2);
+          LinearMapper r3 = res.getLinearMapper(i, j2 + 3);
+
+          r0.prefetch(0);
+          r1.prefetch(0);
+          r2.prefetch(0);
+          r3.prefetch(0);
+
+          // performs "inner" products
+          const RhsScalar* blB = &blockB[j2*strideB+offsetB*4];
+          prefetch(&blB[0]);
+          LhsPacket A0, A1;
+
+          for(Index k=0; k<peeled_kc; k+=pk)
+          {
+            EIGEN_ASM_COMMENT("begin gebp micro kernel 3pX4");
+            // 15 registers are taken (12 for acc, 3 for lhs).
+            RhsPanel15 rhs_panel;
+            RhsPacket T0;
+            LhsPacket A2;
+            #if EIGEN_ARCH_ARM64 && defined(EIGEN_VECTORIZE_NEON) && EIGEN_GNUC_STRICT_LESS_THAN(9,0,0)
+            // see http://eigen.tuxfamily.org/bz/show_bug.cgi?id=1633
+            // without this workaround A0, A1, and A2 are loaded in the same register,
+            // which is not good for pipelining
+            #define EIGEN_GEBP_3PX4_REGISTER_ALLOC_WORKAROUND __asm__  ("" : "+w,m" (A0), "+w,m" (A1), "+w,m" (A2));
+            #else
+            #define EIGEN_GEBP_3PX4_REGISTER_ALLOC_WORKAROUND
+            #endif
+#define EIGEN_GEBP_ONESTEP(K)                                                     \
+            do {                                                                  \
+              EIGEN_ASM_COMMENT("begin step of gebp micro kernel 3pX4");          \
+              EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
+              internal::prefetch(blA + (3 * K + 16) * LhsProgress);               \
+              if (EIGEN_ARCH_ARM || EIGEN_ARCH_MIPS) {                            \
+                internal::prefetch(blB + (4 * K + 16) * RhsProgress);             \
+              } /* Bug 953 */                                                     \
+              traits.loadLhs(&blA[(0 + 3 * K) * LhsProgress], A0);                \
+              traits.loadLhs(&blA[(1 + 3 * K) * LhsProgress], A1);                \
+              traits.loadLhs(&blA[(2 + 3 * K) * LhsProgress], A2);                \
+              EIGEN_GEBP_3PX4_REGISTER_ALLOC_WORKAROUND \
+              traits.loadRhs(blB + (0+4*K) * Traits::RhsProgress, rhs_panel);     \
+              traits.madd(A0, rhs_panel, C0, T0, fix<0>);                         \
+              traits.madd(A1, rhs_panel, C4, T0, fix<0>);                         \
+              traits.madd(A2, rhs_panel, C8, T0, fix<0>);                         \
+              traits.updateRhs(blB + (1+4*K) * Traits::RhsProgress, rhs_panel);   \
+              traits.madd(A0, rhs_panel, C1, T0, fix<1>);                         \
+              traits.madd(A1, rhs_panel, C5, T0, fix<1>);                         \
+              traits.madd(A2, rhs_panel, C9, T0, fix<1>);                         \
+              traits.updateRhs(blB + (2+4*K) * Traits::RhsProgress, rhs_panel);   \
+              traits.madd(A0, rhs_panel, C2, T0, fix<2>);                         \
+              traits.madd(A1, rhs_panel, C6, T0, fix<2>);                         \
+              traits.madd(A2, rhs_panel, C10, T0, fix<2>);                        \
+              traits.updateRhs(blB + (3+4*K) * Traits::RhsProgress, rhs_panel);   \
+              traits.madd(A0, rhs_panel, C3, T0, fix<3>);                         \
+              traits.madd(A1, rhs_panel, C7, T0, fix<3>);                         \
+              traits.madd(A2, rhs_panel, C11, T0, fix<3>);                        \
+              EIGEN_ASM_COMMENT("end step of gebp micro kernel 3pX4");            \
+            } while (false)
+
+            internal::prefetch(blB);
+            EIGEN_GEBP_ONESTEP(0);
+            EIGEN_GEBP_ONESTEP(1);
+            EIGEN_GEBP_ONESTEP(2);
+            EIGEN_GEBP_ONESTEP(3);
+            EIGEN_GEBP_ONESTEP(4);
+            EIGEN_GEBP_ONESTEP(5);
+            EIGEN_GEBP_ONESTEP(6);
+            EIGEN_GEBP_ONESTEP(7);
+
+            blB += pk*4*RhsProgress;
+            blA += pk*3*Traits::LhsProgress;
+
+            EIGEN_ASM_COMMENT("end gebp micro kernel 3pX4");
+          }
+          // process remaining peeled loop
+          for(Index k=peeled_kc; k<depth; k++)
+          {
+            RhsPanel15 rhs_panel;
+            RhsPacket T0;
+            LhsPacket A2;
+            EIGEN_GEBP_ONESTEP(0);
+            blB += 4*RhsProgress;
+            blA += 3*Traits::LhsProgress;
+          }
+
+#undef EIGEN_GEBP_ONESTEP
+
+          ResPacket R0, R1, R2;
+          ResPacket alphav = pset1<ResPacket>(alpha);
+
+          R0 = r0.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+          R1 = r0.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+          R2 = r0.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
+          traits.acc(C0, alphav, R0);
+          traits.acc(C4, alphav, R1);
+          traits.acc(C8, alphav, R2);
+          r0.storePacket(0 * Traits::ResPacketSize, R0);
+          r0.storePacket(1 * Traits::ResPacketSize, R1);
+          r0.storePacket(2 * Traits::ResPacketSize, R2);
+
+          R0 = r1.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+          R1 = r1.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+          R2 = r1.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
+          traits.acc(C1, alphav, R0);
+          traits.acc(C5, alphav, R1);
+          traits.acc(C9, alphav, R2);
+          r1.storePacket(0 * Traits::ResPacketSize, R0);
+          r1.storePacket(1 * Traits::ResPacketSize, R1);
+          r1.storePacket(2 * Traits::ResPacketSize, R2);
+
+          R0 = r2.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+          R1 = r2.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+          R2 = r2.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
+          traits.acc(C2, alphav, R0);
+          traits.acc(C6, alphav, R1);
+          traits.acc(C10, alphav, R2);
+          r2.storePacket(0 * Traits::ResPacketSize, R0);
+          r2.storePacket(1 * Traits::ResPacketSize, R1);
+          r2.storePacket(2 * Traits::ResPacketSize, R2);
+
+          R0 = r3.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+          R1 = r3.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+          R2 = r3.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
+          traits.acc(C3, alphav, R0);
+          traits.acc(C7, alphav, R1);
+          traits.acc(C11, alphav, R2);
+          r3.storePacket(0 * Traits::ResPacketSize, R0);
+          r3.storePacket(1 * Traits::ResPacketSize, R1);
+          r3.storePacket(2 * Traits::ResPacketSize, R2);          
+          }
+        }
+
+        // Deal with remaining columns of the rhs
+        for(Index j2=packet_cols4; j2<cols; j2++)
+        {
+          for(Index i=i1; i<actual_panel_end; i+=3*LhsProgress)
+          {
+          // One column at a time
+          const LhsScalar* blA = &blockA[i*strideA+offsetA*(3*Traits::LhsProgress)];
+          prefetch(&blA[0]);
+
+          // gets res block as register
+          AccPacket C0, C4, C8;
+          traits.initAcc(C0);
+          traits.initAcc(C4);
+          traits.initAcc(C8);
+
+          LinearMapper r0 = res.getLinearMapper(i, j2);
+          r0.prefetch(0);
+
+          // performs "inner" products
+          const RhsScalar* blB = &blockB[j2*strideB+offsetB];
+          LhsPacket A0, A1, A2;
+          
+          for(Index k=0; k<peeled_kc; k+=pk)
+          {
+            EIGEN_ASM_COMMENT("begin gebp micro kernel 3pX1");
+            RhsPacket B_0;
+#define EIGEN_GEBGP_ONESTEP(K)                                                    \
+            do {                                                                  \
+              EIGEN_ASM_COMMENT("begin step of gebp micro kernel 3pX1");          \
+              EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
+              traits.loadLhs(&blA[(0 + 3 * K) * LhsProgress], A0);                \
+              traits.loadLhs(&blA[(1 + 3 * K) * LhsProgress], A1);                \
+              traits.loadLhs(&blA[(2 + 3 * K) * LhsProgress], A2);                \
+              traits.loadRhs(&blB[(0 + K) * RhsProgress], B_0);                   \
+              traits.madd(A0, B_0, C0, B_0, fix<0>);                              \
+              traits.madd(A1, B_0, C4, B_0, fix<0>);                              \
+              traits.madd(A2, B_0, C8, B_0, fix<0>);                              \
+              EIGEN_ASM_COMMENT("end step of gebp micro kernel 3pX1");            \
+            } while (false)
+
+            EIGEN_GEBGP_ONESTEP(0);
+            EIGEN_GEBGP_ONESTEP(1);
+            EIGEN_GEBGP_ONESTEP(2);
+            EIGEN_GEBGP_ONESTEP(3);
+            EIGEN_GEBGP_ONESTEP(4);
+            EIGEN_GEBGP_ONESTEP(5);
+            EIGEN_GEBGP_ONESTEP(6);
+            EIGEN_GEBGP_ONESTEP(7);
+
+            blB += int(pk) * int(RhsProgress);
+            blA += int(pk) * 3 * int(Traits::LhsProgress);
+
+            EIGEN_ASM_COMMENT("end gebp micro kernel 3pX1");
+          }
+
+          // process remaining peeled loop
+          for(Index k=peeled_kc; k<depth; k++)
+          {
+            RhsPacket B_0;
+            EIGEN_GEBGP_ONESTEP(0);
+            blB += RhsProgress;
+            blA += 3*Traits::LhsProgress;
+          }
+#undef EIGEN_GEBGP_ONESTEP
+          ResPacket R0, R1, R2;
+          ResPacket alphav = pset1<ResPacket>(alpha);
+
+          R0 = r0.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+          R1 = r0.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+          R2 = r0.template loadPacket<ResPacket>(2 * Traits::ResPacketSize);
+          traits.acc(C0, alphav, R0);
+          traits.acc(C4, alphav, R1);
+          traits.acc(C8, alphav, R2);
+          r0.storePacket(0 * Traits::ResPacketSize, R0);
+          r0.storePacket(1 * Traits::ResPacketSize, R1);
+          r0.storePacket(2 * Traits::ResPacketSize, R2);          
+          }
+        }
+      }
+    }
+
+    //---------- Process 2 * LhsProgress rows at once ----------
+    if(mr>=2*Traits::LhsProgress)
+    {
+      const Index l1 = defaultL1CacheSize; // in Bytes, TODO, l1 should be passed to this function.
+      // The max(1, ...) here is needed because we may be using blocking params larger than what our known l1 cache size
+      // suggests we should be using: either because our known l1 cache size is inaccurate (e.g. on Android, we can only guess),
+      // or because we are testing specific blocking sizes.
+      Index actual_panel_rows = (2*LhsProgress) * std::max<Index>(1,( (l1 - sizeof(ResScalar)*mr*nr - depth*nr*sizeof(RhsScalar)) / (depth * sizeof(LhsScalar) * 2*LhsProgress) ));
+
+      for(Index i1=peeled_mc3; i1<peeled_mc2; i1+=actual_panel_rows)
+      {
+        Index actual_panel_end = (std::min)(i1+actual_panel_rows, peeled_mc2);
+#if EIGEN_ARCH_ARM64
+        EIGEN_IF_CONSTEXPR(nr>=8) {
+        for(Index j2=0; j2<packet_cols8; j2+=8)
+        {
+          for(Index i=i1; i<actual_panel_end; i+=2*LhsProgress)
+          {
+            const LhsScalar* blA = &blockA[i*strideA+offsetA*(2*Traits::LhsProgress)];
+            prefetch(&blA[0]);
+
+            AccPacket C0, C1, C2, C3, C4, C5, C6, C7,
+                      C8, C9, C10, C11, C12, C13, C14, C15;
+            traits.initAcc(C0); traits.initAcc(C1); traits.initAcc(C2); traits.initAcc(C3);
+            traits.initAcc(C4); traits.initAcc(C5); traits.initAcc(C6); traits.initAcc(C7);
+            traits.initAcc(C8); traits.initAcc(C9); traits.initAcc(C10); traits.initAcc(C11);
+            traits.initAcc(C12); traits.initAcc(C13); traits.initAcc(C14); traits.initAcc(C15);
+
+            LinearMapper r0 = res.getLinearMapper(i, j2 + 0);
+            LinearMapper r1 = res.getLinearMapper(i, j2 + 1);
+            LinearMapper r2 = res.getLinearMapper(i, j2 + 2);
+            LinearMapper r3 = res.getLinearMapper(i, j2 + 3);
+            LinearMapper r4 = res.getLinearMapper(i, j2 + 4);
+            LinearMapper r5 = res.getLinearMapper(i, j2 + 5);
+            LinearMapper r6 = res.getLinearMapper(i, j2 + 6);
+            LinearMapper r7 = res.getLinearMapper(i, j2 + 7);
+            r0.prefetch(prefetch_res_offset);
+            r1.prefetch(prefetch_res_offset);
+            r2.prefetch(prefetch_res_offset);
+            r3.prefetch(prefetch_res_offset);
+            r4.prefetch(prefetch_res_offset);
+            r5.prefetch(prefetch_res_offset);
+            r6.prefetch(prefetch_res_offset);
+            r7.prefetch(prefetch_res_offset);
+
+            const RhsScalar* blB = &blockB[j2*strideB+offsetB*8];
+            prefetch(&blB[0]);
+            LhsPacket A0, A1;
+            for(Index k=0; k<peeled_kc; k+=pk)
+            {
+              RhsPacketx4 rhs_panel;
+              RhsPacket T0;
+              // NOTE: the begin/end asm comments below work around bug 935!
+              // but they are not enough for gcc>=6 without FMA (bug 1637)
+              #if EIGEN_GNUC_STRICT_AT_LEAST(6,0,0) && defined(EIGEN_VECTORIZE_SSE)
+                #define EIGEN_GEBP_2Px8_SPILLING_WORKAROUND __asm__  ("" : [a0] "+x,m" (A0),[a1] "+x,m" (A1));
+              #else
+                #define EIGEN_GEBP_2Px8_SPILLING_WORKAROUND
+              #endif
+#define EIGEN_GEBGP_ONESTEP(K)                                                \
+            do {                                                              \
+              EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX8");      \
+              traits.loadLhs(&blA[(0 + 2 * K) * LhsProgress], A0);            \
+              traits.loadLhs(&blA[(1 + 2 * K) * LhsProgress], A1);            \
+              traits.loadRhs(&blB[(0 + 8 * K) * RhsProgress], rhs_panel);     \
+              traits.madd(A0, rhs_panel, C0, T0, fix<0>);                     \
+              traits.madd(A1, rhs_panel, C8, T0, fix<0>);                     \
+              traits.updateRhs(&blB[(1 + 8 * K) * RhsProgress], rhs_panel);   \
+              traits.madd(A0, rhs_panel, C1, T0, fix<1>);                     \
+              traits.madd(A1, rhs_panel, C9, T0, fix<1>);                     \
+              traits.updateRhs(&blB[(2 + 8 * K) * RhsProgress], rhs_panel);   \
+              traits.madd(A0, rhs_panel, C2, T0, fix<2>);                     \
+              traits.madd(A1, rhs_panel, C10, T0, fix<2>);                    \
+              traits.updateRhs(&blB[(3 + 8 * K) * RhsProgress], rhs_panel);   \
+              traits.madd(A0, rhs_panel, C3, T0, fix<3>);                     \
+              traits.madd(A1, rhs_panel, C11, T0, fix<3>);                    \
+              traits.loadRhs(&blB[(4 + 8 * K) * RhsProgress], rhs_panel);     \
+              traits.madd(A0, rhs_panel, C4, T0, fix<0>);                     \
+              traits.madd(A1, rhs_panel, C12, T0, fix<0>);                    \
+              traits.updateRhs(&blB[(5 + 8 * K) * RhsProgress], rhs_panel);   \
+              traits.madd(A0, rhs_panel, C5, T0, fix<1>);                     \
+              traits.madd(A1, rhs_panel, C13, T0, fix<1>);                    \
+              traits.updateRhs(&blB[(6 + 8 * K) * RhsProgress], rhs_panel);   \
+              traits.madd(A0, rhs_panel, C6, T0, fix<2>);                     \
+              traits.madd(A1, rhs_panel, C14, T0, fix<2>);                    \
+              traits.updateRhs(&blB[(7 + 8 * K) * RhsProgress], rhs_panel);   \
+              traits.madd(A0, rhs_panel, C7, T0, fix<3>);                     \
+              traits.madd(A1, rhs_panel, C15, T0, fix<3>);                    \
+              EIGEN_GEBP_2Px8_SPILLING_WORKAROUND                             \
+              EIGEN_ASM_COMMENT("end step of gebp micro kernel 2pX8");        \
+            } while (false)
+
+              EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX8");
+
+              EIGEN_GEBGP_ONESTEP(0);
+              EIGEN_GEBGP_ONESTEP(1);
+              EIGEN_GEBGP_ONESTEP(2);
+              EIGEN_GEBGP_ONESTEP(3);
+              EIGEN_GEBGP_ONESTEP(4);
+              EIGEN_GEBGP_ONESTEP(5);
+              EIGEN_GEBGP_ONESTEP(6);
+              EIGEN_GEBGP_ONESTEP(7);
+
+              blB += pk*8*RhsProgress;
+              blA += pk*(2*Traits::LhsProgress);
+
+              EIGEN_ASM_COMMENT("end gebp micro kernel 2pX8");
+            }
+            // process remaining peeled loop
+            for(Index k=peeled_kc; k<depth; k++)
+            {
+              RhsPacketx4 rhs_panel;
+              RhsPacket T0;
+              EIGEN_GEBGP_ONESTEP(0);
+              blB += 8*RhsProgress;
+              blA += 2*Traits::LhsProgress;
+            }
+
+#undef EIGEN_GEBGP_ONESTEP
+
+            ResPacket R0, R1, R2, R3;
+            ResPacket alphav = pset1<ResPacket>(alpha);
+
+            R0 = r0.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R1 = r0.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            R2 = r1.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R3 = r1.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            traits.acc(C0, alphav, R0);
+            traits.acc(C8, alphav, R1);
+            traits.acc(C1, alphav, R2);
+            traits.acc(C9, alphav, R3);
+            r0.storePacket(0 * Traits::ResPacketSize, R0);
+            r0.storePacket(1 * Traits::ResPacketSize, R1);
+            r1.storePacket(0 * Traits::ResPacketSize, R2);
+            r1.storePacket(1 * Traits::ResPacketSize, R3);
+
+            R0 = r2.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R1 = r2.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            R2 = r3.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R3 = r3.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            traits.acc(C2,  alphav, R0);
+            traits.acc(C10,  alphav, R1);
+            traits.acc(C3,  alphav, R2);
+            traits.acc(C11,  alphav, R3);
+            r2.storePacket(0 * Traits::ResPacketSize, R0);
+            r2.storePacket(1 * Traits::ResPacketSize, R1);
+            r3.storePacket(0 * Traits::ResPacketSize, R2);
+            r3.storePacket(1 * Traits::ResPacketSize, R3);
+
+            R0 = r4.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R1 = r4.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            R2 = r5.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R3 = r5.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            traits.acc(C4,  alphav, R0);
+            traits.acc(C12,  alphav, R1);
+            traits.acc(C5,  alphav, R2);
+            traits.acc(C13,  alphav, R3);
+            r4.storePacket(0 * Traits::ResPacketSize, R0);
+            r4.storePacket(1 * Traits::ResPacketSize, R1);
+            r5.storePacket(0 * Traits::ResPacketSize, R2);
+            r5.storePacket(1 * Traits::ResPacketSize, R3);
+
+            R0 = r6.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R1 = r6.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            R2 = r7.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+            R3 = r7.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+            traits.acc(C6,  alphav, R0);
+            traits.acc(C14,  alphav, R1);
+            traits.acc(C7,  alphav, R2);
+            traits.acc(C15,  alphav, R3);
+            r6.storePacket(0 * Traits::ResPacketSize, R0);
+            r6.storePacket(1 * Traits::ResPacketSize, R1);
+            r7.storePacket(0 * Traits::ResPacketSize, R2);
+            r7.storePacket(1 * Traits::ResPacketSize, R3);
+          }
+        }
+        }
+#endif
+        for(Index j2=packet_cols8; j2<packet_cols4; j2+=4)
+        {
+          for(Index i=i1; i<actual_panel_end; i+=2*LhsProgress)
+          {
+          
+          // We selected a 2*Traits::LhsProgress x nr micro block of res which is entirely
+          // stored into 2 x nr registers.
+          
+          const LhsScalar* blA = &blockA[i*strideA+offsetA*(2*Traits::LhsProgress)];
+          prefetch(&blA[0]);
+
+          // gets res block as register
+          AccPacket C0, C1, C2, C3,
+                    C4, C5, C6, C7;
+          traits.initAcc(C0); traits.initAcc(C1); traits.initAcc(C2); traits.initAcc(C3);
+          traits.initAcc(C4); traits.initAcc(C5); traits.initAcc(C6); traits.initAcc(C7);
+
+          LinearMapper r0 = res.getLinearMapper(i, j2 + 0);
+          LinearMapper r1 = res.getLinearMapper(i, j2 + 1);
+          LinearMapper r2 = res.getLinearMapper(i, j2 + 2);
+          LinearMapper r3 = res.getLinearMapper(i, j2 + 3);
+
+          r0.prefetch(prefetch_res_offset);
+          r1.prefetch(prefetch_res_offset);
+          r2.prefetch(prefetch_res_offset);
+          r3.prefetch(prefetch_res_offset);
+
+          // performs "inner" products
+          const RhsScalar* blB = &blockB[j2*strideB+offsetB*4];
+          prefetch(&blB[0]);
+          LhsPacket A0, A1;
+
+          for(Index k=0; k<peeled_kc; k+=pk)
+          {
+            EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX4");
+            RhsPacketx4 rhs_panel;
+            RhsPacket T0;
+
+          // NOTE: the begin/end asm comments below work around bug 935!
+          // but they are not enough for gcc>=6 without FMA (bug 1637)
+          #if EIGEN_GNUC_STRICT_AT_LEAST(6,0,0) && defined(EIGEN_VECTORIZE_SSE) && !(EIGEN_COMP_LCC)
+            #define EIGEN_GEBP_2PX4_SPILLING_WORKAROUND __asm__  ("" : [a0] "+x,m" (A0),[a1] "+x,m" (A1));
+          #else
+            #define EIGEN_GEBP_2PX4_SPILLING_WORKAROUND
+          #endif
+#define EIGEN_GEBGP_ONESTEP(K)                                            \
+            do {                                                          \
+              EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX4");  \
+              traits.loadLhs(&blA[(0 + 2 * K) * LhsProgress], A0);        \
+              traits.loadLhs(&blA[(1 + 2 * K) * LhsProgress], A1);        \
+              traits.loadRhs(&blB[(0 + 4 * K) * RhsProgress], rhs_panel); \
+              traits.madd(A0, rhs_panel, C0, T0, fix<0>);                 \
+              traits.madd(A1, rhs_panel, C4, T0, fix<0>);                 \
+              traits.madd(A0, rhs_panel, C1, T0, fix<1>);                 \
+              traits.madd(A1, rhs_panel, C5, T0, fix<1>);                 \
+              traits.madd(A0, rhs_panel, C2, T0, fix<2>);                 \
+              traits.madd(A1, rhs_panel, C6, T0, fix<2>);                 \
+              traits.madd(A0, rhs_panel, C3, T0, fix<3>);                 \
+              traits.madd(A1, rhs_panel, C7, T0, fix<3>);                 \
+              EIGEN_GEBP_2PX4_SPILLING_WORKAROUND                         \
+              EIGEN_ASM_COMMENT("end step of gebp micro kernel 2pX4");    \
+            } while (false)
+
+            internal::prefetch(blB+(48+0));
+            EIGEN_GEBGP_ONESTEP(0);
+            EIGEN_GEBGP_ONESTEP(1);
+            EIGEN_GEBGP_ONESTEP(2);
+            EIGEN_GEBGP_ONESTEP(3);
+            internal::prefetch(blB+(48+16));
+            EIGEN_GEBGP_ONESTEP(4);
+            EIGEN_GEBGP_ONESTEP(5);
+            EIGEN_GEBGP_ONESTEP(6);
+            EIGEN_GEBGP_ONESTEP(7);
+
+            blB += pk*4*RhsProgress;
+            blA += pk*(2*Traits::LhsProgress);
+
+            EIGEN_ASM_COMMENT("end gebp micro kernel 2pX4");
+          }
+          // process remaining peeled loop
+          for(Index k=peeled_kc; k<depth; k++)
+          {
+            RhsPacketx4 rhs_panel;
+            RhsPacket T0;
+            EIGEN_GEBGP_ONESTEP(0);
+            blB += 4*RhsProgress;
+            blA += 2*Traits::LhsProgress;
+          }
+#undef EIGEN_GEBGP_ONESTEP
+
+          ResPacket R0, R1, R2, R3;
+          ResPacket alphav = pset1<ResPacket>(alpha);
+
+          R0 = r0.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+          R1 = r0.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+          R2 = r1.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+          R3 = r1.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+          traits.acc(C0, alphav, R0);
+          traits.acc(C4, alphav, R1);
+          traits.acc(C1, alphav, R2);
+          traits.acc(C5, alphav, R3);
+          r0.storePacket(0 * Traits::ResPacketSize, R0);
+          r0.storePacket(1 * Traits::ResPacketSize, R1);
+          r1.storePacket(0 * Traits::ResPacketSize, R2);
+          r1.storePacket(1 * Traits::ResPacketSize, R3);
+
+          R0 = r2.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+          R1 = r2.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+          R2 = r3.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+          R3 = r3.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+          traits.acc(C2,  alphav, R0);
+          traits.acc(C6,  alphav, R1);
+          traits.acc(C3,  alphav, R2);
+          traits.acc(C7,  alphav, R3);
+          r2.storePacket(0 * Traits::ResPacketSize, R0);
+          r2.storePacket(1 * Traits::ResPacketSize, R1);
+          r3.storePacket(0 * Traits::ResPacketSize, R2);
+          r3.storePacket(1 * Traits::ResPacketSize, R3);
+          }
+        }
+      
+        // Deal with remaining columns of the rhs
+        for(Index j2=packet_cols4; j2<cols; j2++)
+        {
+          for(Index i=i1; i<actual_panel_end; i+=2*LhsProgress)
+          {
+          // One column at a time
+          const LhsScalar* blA = &blockA[i*strideA+offsetA*(2*Traits::LhsProgress)];
+          prefetch(&blA[0]);
+
+          // gets res block as register
+          AccPacket C0, C4;
+          traits.initAcc(C0);
+          traits.initAcc(C4);
+
+          LinearMapper r0 = res.getLinearMapper(i, j2);
+          r0.prefetch(prefetch_res_offset);
+
+          // performs "inner" products
+          const RhsScalar* blB = &blockB[j2*strideB+offsetB];
+          LhsPacket A0, A1;
+
+          for(Index k=0; k<peeled_kc; k+=pk)
+          {
+            EIGEN_ASM_COMMENT("begin gebp micro kernel 2pX1");
+            RhsPacket B_0, B1;
+        
+#define EIGEN_GEBGP_ONESTEP(K) \
+            do {                                                                  \
+              EIGEN_ASM_COMMENT("begin step of gebp micro kernel 2pX1");          \
+              EIGEN_ASM_COMMENT("Note: these asm comments work around bug 935!"); \
+              traits.loadLhs(&blA[(0+2*K)*LhsProgress], A0);                      \
+              traits.loadLhs(&blA[(1+2*K)*LhsProgress], A1);                      \
+              traits.loadRhs(&blB[(0+K)*RhsProgress], B_0);                       \
+              traits.madd(A0, B_0, C0, B1, fix<0>);                               \
+              traits.madd(A1, B_0, C4, B_0, fix<0>);                              \
+              EIGEN_ASM_COMMENT("end step of gebp micro kernel 2pX1");            \
+            } while(false)
+        
+            EIGEN_GEBGP_ONESTEP(0);
+            EIGEN_GEBGP_ONESTEP(1);
+            EIGEN_GEBGP_ONESTEP(2);
+            EIGEN_GEBGP_ONESTEP(3);
+            EIGEN_GEBGP_ONESTEP(4);
+            EIGEN_GEBGP_ONESTEP(5);
+            EIGEN_GEBGP_ONESTEP(6);
+            EIGEN_GEBGP_ONESTEP(7);
+
+            blB += int(pk) * int(RhsProgress);
+            blA += int(pk) * 2 * int(Traits::LhsProgress);
+
+            EIGEN_ASM_COMMENT("end gebp micro kernel 2pX1");
+          }
+
+          // process remaining peeled loop
+          for(Index k=peeled_kc; k<depth; k++)
+          {
+            RhsPacket B_0, B1;
+            EIGEN_GEBGP_ONESTEP(0);
+            blB += RhsProgress;
+            blA += 2*Traits::LhsProgress;
+          }
+#undef EIGEN_GEBGP_ONESTEP
+          ResPacket R0, R1;
+          ResPacket alphav = pset1<ResPacket>(alpha);
+
+          R0 = r0.template loadPacket<ResPacket>(0 * Traits::ResPacketSize);
+          R1 = r0.template loadPacket<ResPacket>(1 * Traits::ResPacketSize);
+          traits.acc(C0, alphav, R0);
+          traits.acc(C4, alphav, R1);
+          r0.storePacket(0 * Traits::ResPacketSize, R0);
+          r0.storePacket(1 * Traits::ResPacketSize, R1);
+          }
+        }
+      }
+    }
+    //---------- Process 1 * LhsProgress rows at once ----------
+    if(mr>=1*Traits::LhsProgress)
+    {
+      lhs_process_one_packet<nr, LhsProgress, RhsProgress, LhsScalar, RhsScalar, ResScalar, AccPacket, LhsPacket, RhsPacket, ResPacket, Traits, LinearMapper, DataMapper> p;
+      p(res, blockA, blockB, alpha, peeled_mc2, peeled_mc1, strideA, strideB, offsetA, offsetB, prefetch_res_offset, peeled_kc, pk, cols, depth, packet_cols4);
+    }
+    //---------- Process LhsProgressHalf rows at once ----------
+    if((LhsProgressHalf < LhsProgress) && mr>=LhsProgressHalf)
+    {
+      lhs_process_fraction_of_packet<nr, LhsProgressHalf, RhsProgressHalf, LhsScalar, RhsScalar, ResScalar, AccPacketHalf, LhsPacketHalf, RhsPacketHalf, ResPacketHalf, HalfTraits, LinearMapper, DataMapper> p;
+      p(res, blockA, blockB, alpha, peeled_mc1, peeled_mc_half, strideA, strideB, offsetA, offsetB, prefetch_res_offset, peeled_kc, pk, cols, depth, packet_cols4);
+    }
+    //---------- Process LhsProgressQuarter rows at once ----------
+    if((LhsProgressQuarter < LhsProgressHalf) && mr>=LhsProgressQuarter)
+    {
+      lhs_process_fraction_of_packet<nr, LhsProgressQuarter, RhsProgressQuarter, LhsScalar, RhsScalar, ResScalar, AccPacketQuarter, LhsPacketQuarter, RhsPacketQuarter, ResPacketQuarter, QuarterTraits, LinearMapper, DataMapper> p;
+      p(res, blockA, blockB, alpha, peeled_mc_half, peeled_mc_quarter, strideA, strideB, offsetA, offsetB, prefetch_res_offset, peeled_kc, pk, cols, depth, packet_cols4);
+    }
+    //---------- Process remaining rows, 1 at once ----------
+    if(peeled_mc_quarter<rows)
+    {
+#if EIGEN_ARCH_ARM64
+      EIGEN_IF_CONSTEXPR(nr>=8) {
+      // loop on each panel of the rhs
+      for(Index j2=0; j2<packet_cols8; j2+=8)
+      {
+        // loop on each row of the lhs (1*LhsProgress x depth)
+        for(Index i=peeled_mc_quarter; i<rows; i+=1)
+        {
+          const LhsScalar* blA = &blockA[i*strideA+offsetA];
+          prefetch(&blA[0]);
+          // gets a 1 x 1 res block as registers
+          ResScalar C0(0),C1(0),C2(0),C3(0),C4(0),C5(0),C6(0),C7(0);
+          const RhsScalar* blB = &blockB[j2*strideB+offsetB*8];
+          for(Index k=0; k<depth; k++)
+          {
+            LhsScalar A0 = blA[k];
+            RhsScalar B_0;
+
+            B_0 = blB[0];
+            C0 = cj.pmadd(A0, B_0, C0);
+
+            B_0 = blB[1];
+            C1 = cj.pmadd(A0, B_0, C1);
+
+            B_0 = blB[2];
+            C2 = cj.pmadd(A0, B_0, C2);
+
+            B_0 = blB[3];
+            C3 = cj.pmadd(A0, B_0, C3);
+
+            B_0 = blB[4];
+            C4 = cj.pmadd(A0, B_0, C4);
+
+            B_0 = blB[5];
+            C5 = cj.pmadd(A0, B_0, C5);
+
+            B_0 = blB[6];
+            C6 = cj.pmadd(A0, B_0, C6);
+
+            B_0 = blB[7];
+            C7 = cj.pmadd(A0, B_0, C7);
+
+            blB += 8;
+          }
+          res(i, j2 + 0) += alpha * C0;
+          res(i, j2 + 1) += alpha * C1;
+          res(i, j2 + 2) += alpha * C2;
+          res(i, j2 + 3) += alpha * C3;
+          res(i, j2 + 4) += alpha * C4;
+          res(i, j2 + 5) += alpha * C5;
+          res(i, j2 + 6) += alpha * C6;
+          res(i, j2 + 7) += alpha * C7;
+        }
+      }
+      }
+#endif
+
+      for(Index j2=packet_cols8; j2<packet_cols4; j2+=4)
+      {
+        // loop on each row of the lhs (1*LhsProgress x depth)
+        for(Index i=peeled_mc_quarter; i<rows; i+=1)
+        {
+          const LhsScalar* blA = &blockA[i*strideA+offsetA];
+          prefetch(&blA[0]);
+          const RhsScalar* blB = &blockB[j2*strideB+offsetB*4];
+
+          // If LhsProgress is 8 or 16, it assumes that there is a
+          // half or quarter packet, respectively, of the same size as
+          // nr (which is currently 4) for the return type.
+          const int SResPacketHalfSize = unpacket_traits<typename unpacket_traits<SResPacket>::half>::size;
+          const int SResPacketQuarterSize = unpacket_traits<typename unpacket_traits<typename unpacket_traits<SResPacket>::half>::half>::size;
+          // The following code assumes we can load SRhsPacket in such a way that
+          // it multiplies blocks of 4 elements in SLhsPacket.  This is not the
+          // case for some customized kernels (i.e. NEON fp16).  If the assumption
+          // fails, drop down to the scalar path.
+          constexpr bool kCanLoadSRhsQuad = (unpacket_traits<SLhsPacket>::size < 4) || (unpacket_traits<SRhsPacket>::size % (unpacket_traits<SLhsPacket>::size / 4)) == 0;
+          if (kCanLoadSRhsQuad && 
+              (SwappedTraits::LhsProgress % 4) == 0 &&
+              (SwappedTraits::LhsProgress<=16) &&
+              (SwappedTraits::LhsProgress!=8  || SResPacketHalfSize==nr) &&
+              (SwappedTraits::LhsProgress!=16 || SResPacketQuarterSize==nr))
+          {
+            SAccPacket C0, C1, C2, C3;
+            straits.initAcc(C0);
+            straits.initAcc(C1);
+            straits.initAcc(C2);
+            straits.initAcc(C3);
+
+            const Index spk   = (std::max)(1,SwappedTraits::LhsProgress/4);
+            const Index endk  = (depth/spk)*spk;
+            const Index endk4 = (depth/(spk*4))*(spk*4);
+
+            Index k=0;
+            for(; k<endk4; k+=4*spk)
+            {
+              SLhsPacket A0,A1;
+              SRhsPacket B_0,B_1;
+
+              straits.loadLhsUnaligned(blB+0*SwappedTraits::LhsProgress, A0);
+              straits.loadLhsUnaligned(blB+1*SwappedTraits::LhsProgress, A1);
+
+              straits.loadRhsQuad(blA+0*spk, B_0);
+              straits.loadRhsQuad(blA+1*spk, B_1);
+              straits.madd(A0,B_0,C0,B_0, fix<0>);
+              straits.madd(A1,B_1,C1,B_1, fix<0>);
+
+              straits.loadLhsUnaligned(blB+2*SwappedTraits::LhsProgress, A0);
+              straits.loadLhsUnaligned(blB+3*SwappedTraits::LhsProgress, A1);
+              straits.loadRhsQuad(blA+2*spk, B_0);
+              straits.loadRhsQuad(blA+3*spk, B_1);
+              straits.madd(A0,B_0,C2,B_0, fix<0>);
+              straits.madd(A1,B_1,C3,B_1, fix<0>);
+
+              blB += 4*SwappedTraits::LhsProgress;
+              blA += 4*spk;
+            }
+            C0 = padd(padd(C0,C1),padd(C2,C3));
+            for(; k<endk; k+=spk)
+            {
+              SLhsPacket A0;
+              SRhsPacket B_0;
+
+              straits.loadLhsUnaligned(blB, A0);
+              straits.loadRhsQuad(blA, B_0);
+              straits.madd(A0,B_0,C0,B_0, fix<0>);
+
+              blB += SwappedTraits::LhsProgress;
+              blA += spk;
+            }
+            if(SwappedTraits::LhsProgress==8)
+            {
+              // Special case where we have to first reduce the accumulation register C0
+              typedef std::conditional_t<SwappedTraits::LhsProgress>=8,typename unpacket_traits<SResPacket>::half,SResPacket> SResPacketHalf;
+              typedef std::conditional_t<SwappedTraits::LhsProgress>=8,typename unpacket_traits<SLhsPacket>::half,SLhsPacket> SLhsPacketHalf;
+              typedef std::conditional_t<SwappedTraits::LhsProgress>=8,typename unpacket_traits<SRhsPacket>::half,SRhsPacket> SRhsPacketHalf;
+              typedef std::conditional_t<SwappedTraits::LhsProgress>=8,typename unpacket_traits<SAccPacket>::half,SAccPacket> SAccPacketHalf;
+
+              SResPacketHalf R = res.template gatherPacket<SResPacketHalf>(i, j2);
+              SResPacketHalf alphav = pset1<SResPacketHalf>(alpha);
+
+              if(depth-endk>0)
+              {
+                // We have to handle the last row of the rhs which corresponds to a half-packet
+                SLhsPacketHalf a0;
+                SRhsPacketHalf b0;
+                straits.loadLhsUnaligned(blB, a0);
+                straits.loadRhs(blA, b0);
+                SAccPacketHalf c0 = predux_half_dowto4(C0);
+                straits.madd(a0,b0,c0,b0, fix<0>);
+                straits.acc(c0, alphav, R);
+              }
+              else
+              {
+                straits.acc(predux_half_dowto4(C0), alphav, R);
+              }
+              res.scatterPacket(i, j2, R);
+            }
+            else if (SwappedTraits::LhsProgress==16)
+            {
+              // Special case where we have to first reduce the
+              // accumulation register C0. We specialize the block in
+              // template form, so that LhsProgress < 16 paths don't
+              // fail to compile
+              last_row_process_16_packets<LhsScalar, RhsScalar, Index, DataMapper, mr, nr, ConjugateLhs, ConjugateRhs> p;
+	            p(res, straits, blA, blB, depth, endk, i, j2,alpha, C0);
+            }
+            else
+            {
+              SResPacket R = res.template gatherPacket<SResPacket>(i, j2);
+              SResPacket alphav = pset1<SResPacket>(alpha);
+              straits.acc(C0, alphav, R);
+              res.scatterPacket(i, j2, R);
+            }
+          }
+          else // scalar path
+          {
+            // get a 1 x 4 res block as registers
+            ResScalar C0(0), C1(0), C2(0), C3(0);
+
+            for(Index k=0; k<depth; k++)
+            {
+              LhsScalar A0;
+              RhsScalar B_0, B_1;
+
+              A0 = blA[k];
+
+              B_0 = blB[0];
+              B_1 = blB[1];
+              C0 = cj.pmadd(A0,B_0,C0);
+              C1 = cj.pmadd(A0,B_1,C1);
+
+              B_0 = blB[2];
+              B_1 = blB[3];
+              C2 = cj.pmadd(A0,B_0,C2);
+              C3 = cj.pmadd(A0,B_1,C3);
+
+              blB += 4;
+            }
+            res(i, j2 + 0) += alpha * C0;
+            res(i, j2 + 1) += alpha * C1;
+            res(i, j2 + 2) += alpha * C2;
+            res(i, j2 + 3) += alpha * C3;
+          }
+        }
+      }
+      // remaining columns
+      for(Index j2=packet_cols4; j2<cols; j2++)
+      {
+        // loop on each row of the lhs (1*LhsProgress x depth)
+        for(Index i=peeled_mc_quarter; i<rows; i+=1)
+        {
+          const LhsScalar* blA = &blockA[i*strideA+offsetA];
+          prefetch(&blA[0]);
+          // gets a 1 x 1 res block as registers
+          ResScalar C0(0);
+          const RhsScalar* blB = &blockB[j2*strideB+offsetB];
+          for(Index k=0; k<depth; k++)
+          {
+            LhsScalar A0 = blA[k];
+            RhsScalar B_0 = blB[k];
+            C0 = cj.pmadd(A0, B_0, C0);
+          }
+          res(i, j2) += alpha * C0;
+        }
+      }
+    }
+  }
+
+
+// pack a block of the lhs
+// The traversal is as follow (mr==4):
+//   0  4  8 12 ...
+//   1  5  9 13 ...
+//   2  6 10 14 ...
+//   3  7 11 15 ...
+//
+//  16 20 24 28 ...
+//  17 21 25 29 ...
+//  18 22 26 30 ...
+//  19 23 27 31 ...
+//
+//  32 33 34 35 ...
+//  36 36 38 39 ...
+template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
+{
+  typedef typename DataMapper::LinearMapper LinearMapper;
+  EIGEN_DONT_INLINE void operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
+};
+
+template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, ColMajor, Conjugate, PanelMode>
+  ::operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+{
+  typedef typename unpacket_traits<Packet>::half HalfPacket;
+  typedef typename unpacket_traits<typename unpacket_traits<Packet>::half>::half QuarterPacket;
+  enum { PacketSize = unpacket_traits<Packet>::size,
+         HalfPacketSize = unpacket_traits<HalfPacket>::size,
+         QuarterPacketSize = unpacket_traits<QuarterPacket>::size,
+         HasHalf = (int)HalfPacketSize < (int)PacketSize,
+         HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize};
+
+  EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS");
+  EIGEN_UNUSED_VARIABLE(stride);
+  EIGEN_UNUSED_VARIABLE(offset);
+  eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
+  eigen_assert( ((Pack1%PacketSize)==0 && Pack1<=4*PacketSize) || (Pack1<=4) );
+  conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
+  Index count = 0;
+
+  const Index peeled_mc3 = Pack1>=3*PacketSize ? (rows/(3*PacketSize))*(3*PacketSize) : 0;
+  const Index peeled_mc2 = Pack1>=2*PacketSize ? peeled_mc3+((rows-peeled_mc3)/(2*PacketSize))*(2*PacketSize) : 0;
+  const Index peeled_mc1 = Pack1>=1*PacketSize ? peeled_mc2+((rows-peeled_mc2)/(1*PacketSize))*(1*PacketSize) : 0;
+  const Index peeled_mc_half = Pack1>=HalfPacketSize ? peeled_mc1+((rows-peeled_mc1)/(HalfPacketSize))*(HalfPacketSize) : 0;
+  const Index peeled_mc_quarter = Pack1>=QuarterPacketSize ? (rows/(QuarterPacketSize))*(QuarterPacketSize) : 0;
+  const Index last_lhs_progress = rows > peeled_mc_quarter ? (rows - peeled_mc_quarter) & ~1 : 0;
+  const Index peeled_mc0 = Pack2>=PacketSize ? peeled_mc_quarter
+                         : Pack2>1 && last_lhs_progress ? (rows/last_lhs_progress)*last_lhs_progress : 0;
+
+  Index i=0;
+
+  // Pack 3 packets
+  if(Pack1>=3*PacketSize)
+  {
+    for(; i<peeled_mc3; i+=3*PacketSize)
+    {
+      if(PanelMode) count += (3*PacketSize) * offset;
+
+      for(Index k=0; k<depth; k++)
+      {
+        Packet A, B, C;
+        A = lhs.template loadPacket<Packet>(i+0*PacketSize, k);
+        B = lhs.template loadPacket<Packet>(i+1*PacketSize, k);
+        C = lhs.template loadPacket<Packet>(i+2*PacketSize, k);
+        pstore(blockA+count, cj.pconj(A)); count+=PacketSize;
+        pstore(blockA+count, cj.pconj(B)); count+=PacketSize;
+        pstore(blockA+count, cj.pconj(C)); count+=PacketSize;
+      }
+      if(PanelMode) count += (3*PacketSize) * (stride-offset-depth);
+    }
+  }
+  // Pack 2 packets
+  if(Pack1>=2*PacketSize)
+  {
+    for(; i<peeled_mc2; i+=2*PacketSize)
+    {
+      if(PanelMode) count += (2*PacketSize) * offset;
+
+      for(Index k=0; k<depth; k++)
+      {
+        Packet A, B;
+        A = lhs.template loadPacket<Packet>(i+0*PacketSize, k);
+        B = lhs.template loadPacket<Packet>(i+1*PacketSize, k);
+        pstore(blockA+count, cj.pconj(A)); count+=PacketSize;
+        pstore(blockA+count, cj.pconj(B)); count+=PacketSize;
+      }
+      if(PanelMode) count += (2*PacketSize) * (stride-offset-depth);
+    }
+  }
+  // Pack 1 packets
+  if(Pack1>=1*PacketSize)
+  {
+    for(; i<peeled_mc1; i+=1*PacketSize)
+    {
+      if(PanelMode) count += (1*PacketSize) * offset;
+
+      for(Index k=0; k<depth; k++)
+      {
+        Packet A;
+        A = lhs.template loadPacket<Packet>(i+0*PacketSize, k);
+        pstore(blockA+count, cj.pconj(A));
+        count+=PacketSize;
+      }
+      if(PanelMode) count += (1*PacketSize) * (stride-offset-depth);
+    }
+  }
+  // Pack half packets
+  if(HasHalf && Pack1>=HalfPacketSize)
+  {
+    for(; i<peeled_mc_half; i+=HalfPacketSize)
+    {
+      if(PanelMode) count += (HalfPacketSize) * offset;
+
+      for(Index k=0; k<depth; k++)
+      {
+        HalfPacket A;
+        A = lhs.template loadPacket<HalfPacket>(i+0*(HalfPacketSize), k);
+        pstoreu(blockA+count, cj.pconj(A));
+        count+=HalfPacketSize;
+      }
+      if(PanelMode) count += (HalfPacketSize) * (stride-offset-depth);
+    }
+  }
+  // Pack quarter packets
+  if(HasQuarter && Pack1>=QuarterPacketSize)
+  {
+    for(; i<peeled_mc_quarter; i+=QuarterPacketSize)
+    {
+      if(PanelMode) count += (QuarterPacketSize) * offset;
+
+      for(Index k=0; k<depth; k++)
+      {
+        QuarterPacket A;
+        A = lhs.template loadPacket<QuarterPacket>(i+0*(QuarterPacketSize), k);
+        pstoreu(blockA+count, cj.pconj(A));
+        count+=QuarterPacketSize;
+      }
+      if(PanelMode) count += (QuarterPacketSize) * (stride-offset-depth);
+    }
+  }
+  // Pack2 may be *smaller* than PacketSize—that happens for
+  // products like real * complex, where we have to go half the
+  // progress on the lhs in order to duplicate those operands to
+  // address both real & imaginary parts on the rhs. This portion will
+  // pack those half ones until they match the number expected on the
+  // last peeling loop at this point (for the rhs).
+  if(Pack2<PacketSize && Pack2>1)
+  {
+    for(; i<peeled_mc0; i+=last_lhs_progress)
+    {
+      if(PanelMode) count += last_lhs_progress * offset;
+
+      for(Index k=0; k<depth; k++)
+        for(Index w=0; w<last_lhs_progress; w++)
+          blockA[count++] = cj(lhs(i+w, k));
+
+      if(PanelMode) count += last_lhs_progress * (stride-offset-depth);
+    }
+  }
+  // Pack scalars
+  for(; i<rows; i++)
+  {
+    if(PanelMode) count += offset;
+    for(Index k=0; k<depth; k++)
+      blockA[count++] = cj(lhs(i, k));
+    if(PanelMode) count += (stride-offset-depth);
+  }
+}
+
+template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+struct gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
+{
+  typedef typename DataMapper::LinearMapper LinearMapper;
+  EIGEN_DONT_INLINE void operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride=0, Index offset=0);
+};
+
+template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, bool Conjugate, bool PanelMode>
+EIGEN_DONT_INLINE void gemm_pack_lhs<Scalar, Index, DataMapper, Pack1, Pack2, Packet, RowMajor, Conjugate, PanelMode>
+  ::operator()(Scalar* blockA, const DataMapper& lhs, Index depth, Index rows, Index stride, Index offset)
+{
+  typedef typename unpacket_traits<Packet>::half HalfPacket;
+  typedef typename unpacket_traits<typename unpacket_traits<Packet>::half>::half QuarterPacket;
+  enum { PacketSize = unpacket_traits<Packet>::size,
+         HalfPacketSize = unpacket_traits<HalfPacket>::size,
+         QuarterPacketSize = unpacket_traits<QuarterPacket>::size,
+         HasHalf = (int)HalfPacketSize < (int)PacketSize,
+         HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize};
+
+  EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK LHS");
+  EIGEN_UNUSED_VARIABLE(stride);
+  EIGEN_UNUSED_VARIABLE(offset);
+  eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
+  conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
+  Index count = 0;
+  bool gone_half = false, gone_quarter = false, gone_last = false;
+
+  Index i = 0;
+  Index pack = Pack1;
+  Index psize = PacketSize;
+  while(pack>0)
+  {
+    Index remaining_rows = rows-i;
+    Index peeled_mc = gone_last ? Pack2>1 ? (rows/pack)*pack : 0 : i+(remaining_rows/pack)*pack;
+    Index starting_pos = i;
+    for(; i<peeled_mc; i+=pack)
+    {
+      if(PanelMode) count += pack * offset;
+
+      Index k=0;
+      if(pack>=psize && psize >= QuarterPacketSize)
+      {
+        const Index peeled_k = (depth/psize)*psize;
+        for(; k<peeled_k; k+=psize)
+        {
+          for (Index m = 0; m < pack; m += psize)
+          {
+            if (psize == PacketSize) {
+              PacketBlock<Packet> kernel;
+              for (Index p = 0; p < psize; ++p) kernel.packet[p] = lhs.template loadPacket<Packet>(i+p+m, k);
+              ptranspose(kernel);
+              for (Index p = 0; p < psize; ++p) pstore(blockA+count+m+(pack)*p, cj.pconj(kernel.packet[p]));
+            } else if (HasHalf && psize == HalfPacketSize) {
+              gone_half = true;
+              PacketBlock<HalfPacket> kernel_half;
+              for (Index p = 0; p < psize; ++p) kernel_half.packet[p] = lhs.template loadPacket<HalfPacket>(i+p+m, k);
+              ptranspose(kernel_half);
+              for (Index p = 0; p < psize; ++p) pstore(blockA+count+m+(pack)*p, cj.pconj(kernel_half.packet[p]));
+            } else if (HasQuarter && psize == QuarterPacketSize) {
+              gone_quarter = true;
+              PacketBlock<QuarterPacket> kernel_quarter;
+              for (Index p = 0; p < psize; ++p) kernel_quarter.packet[p] = lhs.template loadPacket<QuarterPacket>(i+p+m, k);
+              ptranspose(kernel_quarter);
+              for (Index p = 0; p < psize; ++p) pstore(blockA+count+m+(pack)*p, cj.pconj(kernel_quarter.packet[p]));
+	    }
+          }
+          count += psize*pack;
+        }
+      }
+
+      for(; k<depth; k++)
+      {
+        Index w=0;
+        for(; w<pack-3; w+=4)
+        {
+          Scalar a(cj(lhs(i+w+0, k))),
+                 b(cj(lhs(i+w+1, k))),
+                 c(cj(lhs(i+w+2, k))),
+                 d(cj(lhs(i+w+3, k)));
+          blockA[count++] = a;
+          blockA[count++] = b;
+          blockA[count++] = c;
+          blockA[count++] = d;
+        }
+        if(pack%4)
+          for(;w<pack;++w)
+            blockA[count++] = cj(lhs(i+w, k));
+      }
+
+      if(PanelMode) count += pack * (stride-offset-depth);
+    }
+
+    pack -= psize;
+    Index left = rows - i;
+    if (pack <= 0) {
+      if (!gone_last &&
+          (starting_pos == i || left >= psize/2 || left >= psize/4) &&
+          ((psize/2 == HalfPacketSize && HasHalf && !gone_half) ||
+           (psize/2 == QuarterPacketSize && HasQuarter && !gone_quarter))) {
+        psize /= 2;
+        pack = psize;
+        continue;
+      }
+      // Pack2 may be *smaller* than PacketSize—that happens for
+      // products like real * complex, where we have to go half the
+      // progress on the lhs in order to duplicate those operands to
+      // address both real & imaginary parts on the rhs. This portion will
+      // pack those half ones until they match the number expected on the
+      // last peeling loop at this point (for the rhs).
+      if (Pack2 < PacketSize && !gone_last) {
+        gone_last = true;
+        psize = pack = left & ~1;
+      }
+    }
+  }
+
+  for(; i<rows; i++)
+  {
+    if(PanelMode) count += offset;
+    for(Index k=0; k<depth; k++)
+      blockA[count++] = cj(lhs(i, k));
+    if(PanelMode) count += (stride-offset-depth);
+  }
+}
+
+// copy a complete panel of the rhs
+// this version is optimized for column major matrices
+// The traversal order is as follow: (nr==4):
+//  0  1  2  3   12 13 14 15   24 27
+//  4  5  6  7   16 17 18 19   25 28
+//  8  9 10 11   20 21 22 23   26 29
+//  .  .  .  .    .  .  .  .    .  .
+template<typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<Scalar, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
+{
+  typedef typename packet_traits<Scalar>::type Packet;
+  typedef typename DataMapper::LinearMapper LinearMapper;
+  enum { PacketSize = packet_traits<Scalar>::size };
+  EIGEN_DONT_INLINE void operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0);
+};
+
+template<typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+EIGEN_DONT_INLINE void gemm_pack_rhs<Scalar, Index, DataMapper, nr, ColMajor, Conjugate, PanelMode>
+  ::operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride, Index offset)
+{
+  EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS COLMAJOR");
+  EIGEN_UNUSED_VARIABLE(stride);
+  EIGEN_UNUSED_VARIABLE(offset);
+  eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
+  conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
+  Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0;
+  Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0;
+  Index count = 0;
+  const Index peeled_k = (depth/PacketSize)*PacketSize;
+
+#if EIGEN_ARCH_ARM64
+  EIGEN_IF_CONSTEXPR(nr>=8)
+  {
+    for(Index j2=0; j2<packet_cols8; j2+=8)
+    {
+      // skip what we have before
+      if(PanelMode) count += 8 * offset;
+      const LinearMapper dm0 = rhs.getLinearMapper(0, j2 + 0);
+      const LinearMapper dm1 = rhs.getLinearMapper(0, j2 + 1);
+      const LinearMapper dm2 = rhs.getLinearMapper(0, j2 + 2);
+      const LinearMapper dm3 = rhs.getLinearMapper(0, j2 + 3);
+      const LinearMapper dm4 = rhs.getLinearMapper(0, j2 + 4);
+      const LinearMapper dm5 = rhs.getLinearMapper(0, j2 + 5);
+      const LinearMapper dm6 = rhs.getLinearMapper(0, j2 + 6);
+      const LinearMapper dm7 = rhs.getLinearMapper(0, j2 + 7);
+      Index k = 0;
+      if (PacketSize % 2 == 0 && PacketSize <= 8) // 2 4 8
+      {
+        for (; k < peeled_k; k += PacketSize)
+        {
+          if (PacketSize == 2)
+          {
+            PacketBlock<Packet, PacketSize==2 ?2:PacketSize> kernel0, kernel1, kernel2, kernel3;
+            kernel0.packet[0%PacketSize] = dm0.template loadPacket<Packet>(k);
+            kernel0.packet[1%PacketSize] = dm1.template loadPacket<Packet>(k);
+            kernel1.packet[0%PacketSize] = dm2.template loadPacket<Packet>(k);
+            kernel1.packet[1%PacketSize] = dm3.template loadPacket<Packet>(k);
+            kernel2.packet[0%PacketSize] = dm4.template loadPacket<Packet>(k);
+            kernel2.packet[1%PacketSize] = dm5.template loadPacket<Packet>(k);
+            kernel3.packet[0%PacketSize] = dm6.template loadPacket<Packet>(k);
+            kernel3.packet[1%PacketSize] = dm7.template loadPacket<Packet>(k);
+            ptranspose(kernel0);
+            ptranspose(kernel1);
+            ptranspose(kernel2);
+            ptranspose(kernel3);
+
+            pstoreu(blockB + count + 0 * PacketSize, cj.pconj(kernel0.packet[0 % PacketSize]));
+            pstoreu(blockB + count + 1 * PacketSize, cj.pconj(kernel1.packet[0 % PacketSize]));
+            pstoreu(blockB + count + 2 * PacketSize, cj.pconj(kernel2.packet[0 % PacketSize]));
+            pstoreu(blockB + count + 3 * PacketSize, cj.pconj(kernel3.packet[0 % PacketSize]));
+
+            pstoreu(blockB + count + 4 * PacketSize, cj.pconj(kernel0.packet[1 % PacketSize]));
+            pstoreu(blockB + count + 5 * PacketSize, cj.pconj(kernel1.packet[1 % PacketSize]));
+            pstoreu(blockB + count + 6 * PacketSize, cj.pconj(kernel2.packet[1 % PacketSize]));
+            pstoreu(blockB + count + 7 * PacketSize, cj.pconj(kernel3.packet[1 % PacketSize]));
+            count+=8*PacketSize;
+          }
+          else if (PacketSize == 4)
+          {
+            PacketBlock<Packet, PacketSize == 4?4:PacketSize> kernel0, kernel1;
+
+            kernel0.packet[0%PacketSize] = dm0.template loadPacket<Packet>(k);
+            kernel0.packet[1%PacketSize] = dm1.template loadPacket<Packet>(k);
+            kernel0.packet[2%PacketSize] = dm2.template loadPacket<Packet>(k);
+            kernel0.packet[3%PacketSize] = dm3.template loadPacket<Packet>(k);
+            kernel1.packet[0%PacketSize] = dm4.template loadPacket<Packet>(k);
+            kernel1.packet[1%PacketSize] = dm5.template loadPacket<Packet>(k);
+            kernel1.packet[2%PacketSize] = dm6.template loadPacket<Packet>(k);
+            kernel1.packet[3%PacketSize] = dm7.template loadPacket<Packet>(k);
+            ptranspose(kernel0);
+            ptranspose(kernel1);
+
+            pstoreu(blockB+count+0*PacketSize, cj.pconj(kernel0.packet[0%PacketSize]));
+            pstoreu(blockB+count+1*PacketSize, cj.pconj(kernel1.packet[0%PacketSize]));
+            pstoreu(blockB+count+2*PacketSize, cj.pconj(kernel0.packet[1%PacketSize]));
+            pstoreu(blockB+count+3*PacketSize, cj.pconj(kernel1.packet[1%PacketSize]));
+            pstoreu(blockB+count+4*PacketSize, cj.pconj(kernel0.packet[2%PacketSize]));
+            pstoreu(blockB+count+5*PacketSize, cj.pconj(kernel1.packet[2%PacketSize]));
+            pstoreu(blockB+count+6*PacketSize, cj.pconj(kernel0.packet[3%PacketSize]));
+            pstoreu(blockB+count+7*PacketSize, cj.pconj(kernel1.packet[3%PacketSize]));
+            count+=8*PacketSize;
+          }
+          else if (PacketSize == 8)
+          {
+            PacketBlock<Packet, PacketSize==8?8:PacketSize> kernel0;
+
+            kernel0.packet[0%PacketSize] = dm0.template loadPacket<Packet>(k);
+            kernel0.packet[1%PacketSize] = dm1.template loadPacket<Packet>(k);
+            kernel0.packet[2%PacketSize] = dm2.template loadPacket<Packet>(k);
+            kernel0.packet[3%PacketSize] = dm3.template loadPacket<Packet>(k);
+            kernel0.packet[4%PacketSize] = dm4.template loadPacket<Packet>(k);
+            kernel0.packet[5%PacketSize] = dm5.template loadPacket<Packet>(k);
+            kernel0.packet[6%PacketSize] = dm6.template loadPacket<Packet>(k);
+            kernel0.packet[7%PacketSize] = dm7.template loadPacket<Packet>(k);
+            ptranspose(kernel0);
+
+            pstoreu(blockB+count+0*PacketSize, cj.pconj(kernel0.packet[0%PacketSize]));
+            pstoreu(blockB+count+1*PacketSize, cj.pconj(kernel0.packet[1%PacketSize]));
+            pstoreu(blockB+count+2*PacketSize, cj.pconj(kernel0.packet[2%PacketSize]));
+            pstoreu(blockB+count+3*PacketSize, cj.pconj(kernel0.packet[3%PacketSize]));
+            pstoreu(blockB+count+4*PacketSize, cj.pconj(kernel0.packet[4%PacketSize]));
+            pstoreu(blockB+count+5*PacketSize, cj.pconj(kernel0.packet[5%PacketSize]));
+            pstoreu(blockB+count+6*PacketSize, cj.pconj(kernel0.packet[6%PacketSize]));
+            pstoreu(blockB+count+7*PacketSize, cj.pconj(kernel0.packet[7%PacketSize]));
+            count+=8*PacketSize;
+          }
+        }
+      }
+
+      for(; k<depth; k++)
+      {
+        blockB[count+0] = cj(dm0(k));
+        blockB[count+1] = cj(dm1(k));
+        blockB[count+2] = cj(dm2(k));
+        blockB[count+3] = cj(dm3(k));
+        blockB[count+4] = cj(dm4(k));
+        blockB[count+5] = cj(dm5(k));
+        blockB[count+6] = cj(dm6(k));
+        blockB[count+7] = cj(dm7(k));
+        count += 8;
+      }
+      // skip what we have after
+      if(PanelMode) count += 8 * (stride-offset-depth);
+    }
+  }
+#endif
+  
+  EIGEN_IF_CONSTEXPR(nr>=4)
+  {
+    for(Index j2=packet_cols8; j2<packet_cols4; j2+=4)
+    {
+      // skip what we have before
+      if(PanelMode) count += 4 * offset;
+      const LinearMapper dm0 = rhs.getLinearMapper(0, j2 + 0);
+      const LinearMapper dm1 = rhs.getLinearMapper(0, j2 + 1);
+      const LinearMapper dm2 = rhs.getLinearMapper(0, j2 + 2);
+      const LinearMapper dm3 = rhs.getLinearMapper(0, j2 + 3);
+
+      Index k=0;
+      if((PacketSize%4)==0) // TODO enable vectorized transposition for PacketSize==2 ??
+      {
+        for(; k<peeled_k; k+=PacketSize) {
+          PacketBlock<Packet,(PacketSize%4)==0?4:PacketSize> kernel;
+          kernel.packet[0           ] = dm0.template loadPacket<Packet>(k);
+          kernel.packet[1%PacketSize] = dm1.template loadPacket<Packet>(k);
+          kernel.packet[2%PacketSize] = dm2.template loadPacket<Packet>(k);
+          kernel.packet[3%PacketSize] = dm3.template loadPacket<Packet>(k);
+          ptranspose(kernel);
+          pstoreu(blockB+count+0*PacketSize, cj.pconj(kernel.packet[0]));
+          pstoreu(blockB+count+1*PacketSize, cj.pconj(kernel.packet[1%PacketSize]));
+          pstoreu(blockB+count+2*PacketSize, cj.pconj(kernel.packet[2%PacketSize]));
+          pstoreu(blockB+count+3*PacketSize, cj.pconj(kernel.packet[3%PacketSize]));
+          count+=4*PacketSize;
+        }
+      }
+      for(; k<depth; k++)
+      {
+        blockB[count+0] = cj(dm0(k));
+        blockB[count+1] = cj(dm1(k));
+        blockB[count+2] = cj(dm2(k));
+        blockB[count+3] = cj(dm3(k));
+        count += 4;
+      }
+      // skip what we have after
+      if(PanelMode) count += 4 * (stride-offset-depth);
+    }
+  }
+
+  // copy the remaining columns one at a time (nr==1)
+  for(Index j2=packet_cols4; j2<cols; ++j2)
+  {
+    if(PanelMode) count += offset;
+    const LinearMapper dm0 = rhs.getLinearMapper(0, j2);
+    for(Index k=0; k<depth; k++)
+    {
+      blockB[count] = cj(dm0(k));
+      count += 1;
+    }
+    if(PanelMode) count += (stride-offset-depth);
+  }
+}
+
+// this version is optimized for row major matrices
+template<typename Scalar, typename Index, typename DataMapper, int nr, bool Conjugate, bool PanelMode>
+struct gemm_pack_rhs<Scalar, Index, DataMapper, nr, RowMajor, Conjugate, PanelMode>
+{
+  typedef typename packet_traits<Scalar>::type Packet;
+  typedef typename unpacket_traits<Packet>::half HalfPacket;
+  typedef typename unpacket_traits<typename unpacket_traits<Packet>::half>::half QuarterPacket;
+  typedef typename DataMapper::LinearMapper LinearMapper;
+  enum { PacketSize = packet_traits<Scalar>::size,
+         HalfPacketSize = unpacket_traits<HalfPacket>::size,
+		 QuarterPacketSize = unpacket_traits<QuarterPacket>::size};
+  EIGEN_DONT_INLINE void operator()(Scalar* blockB, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0)
+  {
+    EIGEN_ASM_COMMENT("EIGEN PRODUCT PACK RHS ROWMAJOR");
+    EIGEN_UNUSED_VARIABLE(stride);
+    EIGEN_UNUSED_VARIABLE(offset);
+    eigen_assert(((!PanelMode) && stride==0 && offset==0) || (PanelMode && stride>=depth && offset<=stride));
+    const bool HasHalf = (int)HalfPacketSize < (int)PacketSize;
+    const bool HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize;
+    conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
+    Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0;
+    Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0;
+    Index count = 0;
+
+#if EIGEN_ARCH_ARM64
+    EIGEN_IF_CONSTEXPR(nr>=8)
+    {
+      for(Index j2=0; j2<packet_cols8; j2+=8)
+      {
+        // skip what we have before
+        if(PanelMode) count += 8 * offset;
+        for(Index k=0; k<depth; k++)
+        {
+          if (PacketSize==8) {
+            Packet A = rhs.template loadPacket<Packet>(k, j2);
+            pstoreu(blockB+count, cj.pconj(A));
+            count += PacketSize;
+          } else if (PacketSize==4) {
+            Packet A = rhs.template loadPacket<Packet>(k, j2);
+            Packet B = rhs.template loadPacket<Packet>(k, j2 + 4);
+            pstoreu(blockB+count, cj.pconj(A));
+            pstoreu(blockB+count+PacketSize, cj.pconj(B));
+            count += 2*PacketSize;
+          } else {
+            const LinearMapper dm0 = rhs.getLinearMapper(k, j2);
+            blockB[count+0] = cj(dm0(0));
+            blockB[count+1] = cj(dm0(1));
+            blockB[count+2] = cj(dm0(2));
+            blockB[count+3] = cj(dm0(3));
+            blockB[count+4] = cj(dm0(4));
+            blockB[count+5] = cj(dm0(5));
+            blockB[count+6] = cj(dm0(6));
+            blockB[count+7] = cj(dm0(7));
+            count += 8;
+          }
+        }
+        // skip what we have after
+        if(PanelMode) count += 8 * (stride-offset-depth);
+      }
+    }
+#endif
+    
+    if(nr>=4)
+    {
+      for(Index j2=packet_cols8; j2<packet_cols4; j2+=4)
+      {
+        // skip what we have before
+        if(PanelMode) count += 4 * offset;
+        for(Index k=0; k<depth; k++)
+        {
+          if (PacketSize==4) {
+            Packet A = rhs.template loadPacket<Packet>(k, j2);
+            pstoreu(blockB+count, cj.pconj(A));
+            count += PacketSize;
+          } else if (HasHalf && HalfPacketSize==4) {
+            HalfPacket A = rhs.template loadPacket<HalfPacket>(k, j2);
+            pstoreu(blockB+count, cj.pconj(A));
+            count += HalfPacketSize;
+          } else if (HasQuarter && QuarterPacketSize==4) {
+            QuarterPacket A = rhs.template loadPacket<QuarterPacket>(k, j2);
+            pstoreu(blockB+count, cj.pconj(A));
+            count += QuarterPacketSize;
+          } else {
+            const LinearMapper dm0 = rhs.getLinearMapper(k, j2);
+            blockB[count+0] = cj(dm0(0));
+            blockB[count+1] = cj(dm0(1));
+            blockB[count+2] = cj(dm0(2));
+            blockB[count+3] = cj(dm0(3));
+            count += 4;
+          }
+        }
+        // skip what we have after
+        if(PanelMode) count += 4 * (stride-offset-depth);
+      }
+    }
+    // copy the remaining columns one at a time (nr==1)
+    for(Index j2=packet_cols4; j2<cols; ++j2)
+    {
+      if(PanelMode) count += offset;
+      for(Index k=0; k<depth; k++)
+      {
+        blockB[count] = cj(rhs(k, j2));
+        count += 1;
+      }
+      if(PanelMode) count += stride-offset-depth;
+    }
+  }
+};
+
+} // end namespace internal
+
+/** \returns the currently set level 1 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.
+  * \sa setCpuCacheSize */
+inline std::ptrdiff_t l1CacheSize()
+{
+  std::ptrdiff_t l1, l2, l3;
+  internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
+  return l1;
+}
+
+/** \returns the currently set level 2 cpu cache size (in bytes) used to estimate the ideal blocking size parameters.
+  * \sa setCpuCacheSize */
+inline std::ptrdiff_t l2CacheSize()
+{
+  std::ptrdiff_t l1, l2, l3;
+  internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
+  return l2;
+}
+
+/** \returns the currently set level 3 cpu cache size (in bytes) used to estimate the ideal blocking size paramete\
+rs.
+* \sa setCpuCacheSize */
+inline std::ptrdiff_t l3CacheSize()
+{
+  std::ptrdiff_t l1, l2, l3;
+  internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
+  return l3;
+}
+
+/** Set the cpu L1 and L2 cache sizes (in bytes).
+  * These values are use to adjust the size of the blocks
+  * for the algorithms working per blocks.
+  *
+  * \sa computeProductBlockingSizes */
+inline void setCpuCacheSizes(std::ptrdiff_t l1, std::ptrdiff_t l2, std::ptrdiff_t l3)
+{
+  internal::manage_caching_sizes(SetAction, &l1, &l2, &l3);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_GENERAL_BLOCK_PANEL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixMatrix.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixMatrix.h
new file mode 100644
index 0000000000000000000000000000000000000000..9f2a53da16376f9a9bd7ee54a95ebf89beba675e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixMatrix.h
@@ -0,0 +1,514 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERAL_MATRIX_MATRIX_H
+#define EIGEN_GENERAL_MATRIX_MATRIX_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename LhsScalar_, typename RhsScalar_> class level3_blocking;
+
+/* Specialization for a row-major destination matrix => simple transposition of the product */
+template<
+  typename Index,
+  typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
+  typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
+  int ResInnerStride>
+struct general_matrix_matrix_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,RowMajor,ResInnerStride>
+{
+  typedef gebp_traits<RhsScalar,LhsScalar> Traits;
+
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+  static EIGEN_STRONG_INLINE void run(
+    Index rows, Index cols, Index depth,
+    const LhsScalar* lhs, Index lhsStride,
+    const RhsScalar* rhs, Index rhsStride,
+    ResScalar* res, Index resIncr, Index resStride,
+    ResScalar alpha,
+    level3_blocking<RhsScalar,LhsScalar>& blocking,
+    GemmParallelInfo<Index>* info = 0)
+  {
+    // transpose the product such that the result is column major
+    general_matrix_matrix_product<Index,
+      RhsScalar, RhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateRhs,
+      LhsScalar, LhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateLhs,
+      ColMajor,ResInnerStride>
+    ::run(cols,rows,depth,rhs,rhsStride,lhs,lhsStride,res,resIncr,resStride,alpha,blocking,info);
+  }
+};
+
+/*  Specialization for a col-major destination matrix
+ *    => Blocking algorithm following Goto's paper */
+template<
+  typename Index,
+  typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
+  typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
+  int ResInnerStride>
+struct general_matrix_matrix_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride>
+{
+
+typedef gebp_traits<LhsScalar,RhsScalar> Traits;
+
+typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+static void run(Index rows, Index cols, Index depth,
+  const LhsScalar* _lhs, Index lhsStride,
+  const RhsScalar* _rhs, Index rhsStride,
+  ResScalar* _res, Index resIncr, Index resStride,
+  ResScalar alpha,
+  level3_blocking<LhsScalar,RhsScalar>& blocking,
+  GemmParallelInfo<Index>* info = 0)
+{
+  typedef const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> LhsMapper;
+  typedef const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> RhsMapper;
+  typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor,Unaligned,ResInnerStride> ResMapper;
+  LhsMapper lhs(_lhs, lhsStride);
+  RhsMapper rhs(_rhs, rhsStride);
+  ResMapper res(_res, resStride, resIncr);
+
+  Index kc = blocking.kc();                   // cache block size along the K direction
+  Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
+  Index nc = (std::min)(cols,blocking.nc());  // cache block size along the N direction
+
+  gemm_pack_lhs<LhsScalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder> pack_lhs;
+  gemm_pack_rhs<RhsScalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
+  gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp;
+
+#ifdef EIGEN_HAS_OPENMP
+  if(info)
+  {
+    // this is the parallel version!
+    int tid = omp_get_thread_num();
+    int threads = omp_get_num_threads();
+
+    LhsScalar* blockA = blocking.blockA();
+    eigen_internal_assert(blockA!=0);
+
+    std::size_t sizeB = kc*nc;
+    ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, 0);
+
+    // For each horizontal panel of the rhs, and corresponding vertical panel of the lhs...
+    for(Index k=0; k<depth; k+=kc)
+    {
+      const Index actual_kc = (std::min)(k+kc,depth)-k; // => rows of B', and cols of the A'
+
+      // In order to reduce the chance that a thread has to wait for the other,
+      // let's start by packing B'.
+      pack_rhs(blockB, rhs.getSubMapper(k,0), actual_kc, nc);
+
+      // Pack A_k to A' in a parallel fashion:
+      // each thread packs the sub block A_k,i to A'_i where i is the thread id.
+
+      // However, before copying to A'_i, we have to make sure that no other thread is still using it,
+      // i.e., we test that info[tid].users equals 0.
+      // Then, we set info[tid].users to the number of threads to mark that all other threads are going to use it.
+      while(info[tid].users!=0) {}
+      info[tid].users = threads;
+
+      pack_lhs(blockA+info[tid].lhs_start*actual_kc, lhs.getSubMapper(info[tid].lhs_start,k), actual_kc, info[tid].lhs_length);
+
+      // Notify the other threads that the part A'_i is ready to go.
+      info[tid].sync = k;
+
+      // Computes C_i += A' * B' per A'_i
+      for(int shift=0; shift<threads; ++shift)
+      {
+        int i = (tid+shift)%threads;
+
+        // At this point we have to make sure that A'_i has been updated by the thread i,
+        // we use testAndSetOrdered to mimic a volatile access.
+        // However, no need to wait for the B' part which has been updated by the current thread!
+        if (shift>0) {
+          while(info[i].sync!=k) {
+          }
+        }
+
+        gebp(res.getSubMapper(info[i].lhs_start, 0), blockA+info[i].lhs_start*actual_kc, blockB, info[i].lhs_length, actual_kc, nc, alpha);
+      }
+
+      // Then keep going as usual with the remaining B'
+      for(Index j=nc; j<cols; j+=nc)
+      {
+        const Index actual_nc = (std::min)(j+nc,cols)-j;
+
+        // pack B_k,j to B'
+        pack_rhs(blockB, rhs.getSubMapper(k,j), actual_kc, actual_nc);
+
+        // C_j += A' * B'
+        gebp(res.getSubMapper(0, j), blockA, blockB, rows, actual_kc, actual_nc, alpha);
+      }
+
+      // Release all the sub blocks A'_i of A' for the current thread,
+      // i.e., we simply decrement the number of users by 1
+      for(Index i=0; i<threads; ++i)
+        info[i].users -= 1;
+    }
+  }
+  else
+#endif // EIGEN_HAS_OPENMP
+  {
+    EIGEN_UNUSED_VARIABLE(info);
+
+    // this is the sequential version!
+    std::size_t sizeA = kc*mc;
+    std::size_t sizeB = kc*nc;
+
+    ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, sizeA, blocking.blockA());
+    ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, blocking.blockB());
+
+    const bool pack_rhs_once = mc!=rows && kc==depth && nc==cols;
+
+    // For each horizontal panel of the rhs, and corresponding panel of the lhs...
+    for(Index i2=0; i2<rows; i2+=mc)
+    {
+      const Index actual_mc = (std::min)(i2+mc,rows)-i2;
+
+      for(Index k2=0; k2<depth; k2+=kc)
+      {
+        const Index actual_kc = (std::min)(k2+kc,depth)-k2;
+
+        // OK, here we have selected one horizontal panel of rhs and one vertical panel of lhs.
+        // => Pack lhs's panel into a sequential chunk of memory (L2/L3 caching)
+        // Note that this panel will be read as many times as the number of blocks in the rhs's
+        // horizontal panel which is, in practice, a very low number.
+        pack_lhs(blockA, lhs.getSubMapper(i2,k2), actual_kc, actual_mc);
+
+        // For each kc x nc block of the rhs's horizontal panel...
+        for(Index j2=0; j2<cols; j2+=nc)
+        {
+          const Index actual_nc = (std::min)(j2+nc,cols)-j2;
+
+          // We pack the rhs's block into a sequential chunk of memory (L2 caching)
+          // Note that this block will be read a very high number of times, which is equal to the number of
+          // micro horizontal panel of the large rhs's panel (e.g., rows/12 times).
+          if((!pack_rhs_once) || i2==0)
+            pack_rhs(blockB, rhs.getSubMapper(k2,j2), actual_kc, actual_nc);
+
+          // Everything is packed, we can now call the panel * block kernel:
+          gebp(res.getSubMapper(i2, j2), blockA, blockB, actual_mc, actual_kc, actual_nc, alpha);
+        }
+      }
+    }
+  }
+}
+
+};
+
+/*********************************************************************************
+*  Specialization of generic_product_impl for "large" GEMM, i.e.,
+*  implementation of the high level wrapper to general_matrix_matrix_product
+**********************************************************************************/
+
+template<typename Scalar, typename Index, typename Gemm, typename Lhs, typename Rhs, typename Dest, typename BlockingType>
+struct gemm_functor
+{
+  gemm_functor(const Lhs& lhs, const Rhs& rhs, Dest& dest, const Scalar& actualAlpha, BlockingType& blocking)
+    : m_lhs(lhs), m_rhs(rhs), m_dest(dest), m_actualAlpha(actualAlpha), m_blocking(blocking)
+  {}
+
+  void initParallelSession(Index num_threads) const
+  {
+    m_blocking.initParallel(m_lhs.rows(), m_rhs.cols(), m_lhs.cols(), num_threads);
+    m_blocking.allocateA();
+  }
+
+  void operator() (Index row, Index rows, Index col=0, Index cols=-1, GemmParallelInfo<Index>* info=0) const
+  {
+    if(cols==-1)
+      cols = m_rhs.cols();
+
+    Gemm::run(rows, cols, m_lhs.cols(),
+              &m_lhs.coeffRef(row,0), m_lhs.outerStride(),
+              &m_rhs.coeffRef(0,col), m_rhs.outerStride(),
+              (Scalar*)&(m_dest.coeffRef(row,col)), m_dest.innerStride(), m_dest.outerStride(),
+              m_actualAlpha, m_blocking, info);
+  }
+
+  typedef typename Gemm::Traits Traits;
+
+  protected:
+    const Lhs& m_lhs;
+    const Rhs& m_rhs;
+    Dest& m_dest;
+    Scalar m_actualAlpha;
+    BlockingType& m_blocking;
+};
+
+template<int StorageOrder, typename LhsScalar, typename RhsScalar, int MaxRows, int MaxCols, int MaxDepth, int KcFactor=1,
+bool FiniteAtCompileTime = MaxRows!=Dynamic && MaxCols!=Dynamic && MaxDepth != Dynamic> class gemm_blocking_space;
+
+template<typename LhsScalar_, typename RhsScalar_>
+class level3_blocking
+{
+    typedef LhsScalar_ LhsScalar;
+    typedef RhsScalar_ RhsScalar;
+
+  protected:
+    LhsScalar* m_blockA;
+    RhsScalar* m_blockB;
+
+    Index m_mc;
+    Index m_nc;
+    Index m_kc;
+
+  public:
+
+    level3_blocking()
+      : m_blockA(0), m_blockB(0), m_mc(0), m_nc(0), m_kc(0)
+    {}
+
+    inline Index mc() const { return m_mc; }
+    inline Index nc() const { return m_nc; }
+    inline Index kc() const { return m_kc; }
+
+    inline LhsScalar* blockA() { return m_blockA; }
+    inline RhsScalar* blockB() { return m_blockB; }
+};
+
+template<int StorageOrder, typename LhsScalar_, typename RhsScalar_, int MaxRows, int MaxCols, int MaxDepth, int KcFactor>
+class gemm_blocking_space<StorageOrder,LhsScalar_,RhsScalar_,MaxRows, MaxCols, MaxDepth, KcFactor, true /* == FiniteAtCompileTime */>
+  : public level3_blocking<
+      std::conditional_t<StorageOrder==RowMajor,RhsScalar_,LhsScalar_>,
+      std::conditional_t<StorageOrder==RowMajor,LhsScalar_,RhsScalar_>>
+{
+    enum {
+      Transpose = StorageOrder==RowMajor,
+      ActualRows = Transpose ? MaxCols : MaxRows,
+      ActualCols = Transpose ? MaxRows : MaxCols
+    };
+    typedef std::conditional_t<Transpose,RhsScalar_,LhsScalar_> LhsScalar;
+    typedef std::conditional_t<Transpose,LhsScalar_,RhsScalar_> RhsScalar;
+    enum {
+      SizeA = ActualRows * MaxDepth,
+      SizeB = ActualCols * MaxDepth
+    };
+
+#if EIGEN_MAX_STATIC_ALIGN_BYTES >= EIGEN_DEFAULT_ALIGN_BYTES
+    EIGEN_ALIGN_MAX LhsScalar m_staticA[SizeA];
+    EIGEN_ALIGN_MAX RhsScalar m_staticB[SizeB];
+#else
+    EIGEN_ALIGN_MAX char m_staticA[SizeA * sizeof(LhsScalar) + EIGEN_DEFAULT_ALIGN_BYTES-1];
+    EIGEN_ALIGN_MAX char m_staticB[SizeB * sizeof(RhsScalar) + EIGEN_DEFAULT_ALIGN_BYTES-1];
+#endif
+
+  public:
+
+    gemm_blocking_space(Index /*rows*/, Index /*cols*/, Index /*depth*/, Index /*num_threads*/, bool /*full_rows = false*/)
+    {
+      this->m_mc = ActualRows;
+      this->m_nc = ActualCols;
+      this->m_kc = MaxDepth;
+#if EIGEN_MAX_STATIC_ALIGN_BYTES >= EIGEN_DEFAULT_ALIGN_BYTES
+      this->m_blockA = m_staticA;
+      this->m_blockB = m_staticB;
+#else
+      this->m_blockA = reinterpret_cast<LhsScalar*>((std::uintptr_t(m_staticA) + (EIGEN_DEFAULT_ALIGN_BYTES-1)) & ~std::size_t(EIGEN_DEFAULT_ALIGN_BYTES-1));
+      this->m_blockB = reinterpret_cast<RhsScalar*>((std::uintptr_t(m_staticB) + (EIGEN_DEFAULT_ALIGN_BYTES-1)) & ~std::size_t(EIGEN_DEFAULT_ALIGN_BYTES-1));
+#endif
+    }
+
+    void initParallel(Index, Index, Index, Index)
+    {}
+
+    inline void allocateA() {}
+    inline void allocateB() {}
+    inline void allocateAll() {}
+};
+
+template<int StorageOrder, typename LhsScalar_, typename RhsScalar_, int MaxRows, int MaxCols, int MaxDepth, int KcFactor>
+class gemm_blocking_space<StorageOrder,LhsScalar_,RhsScalar_,MaxRows, MaxCols, MaxDepth, KcFactor, false>
+  : public level3_blocking<
+      std::conditional_t<StorageOrder==RowMajor,RhsScalar_,LhsScalar_>,
+      std::conditional_t<StorageOrder==RowMajor,LhsScalar_,RhsScalar_>>
+{
+    enum {
+      Transpose = StorageOrder==RowMajor
+    };
+    typedef std::conditional_t<Transpose,RhsScalar_,LhsScalar_> LhsScalar;
+    typedef std::conditional_t<Transpose,LhsScalar_,RhsScalar_> RhsScalar;
+
+    Index m_sizeA;
+    Index m_sizeB;
+
+  public:
+
+    gemm_blocking_space(Index rows, Index cols, Index depth, Index num_threads, bool l3_blocking)
+    {
+      this->m_mc = Transpose ? cols : rows;
+      this->m_nc = Transpose ? rows : cols;
+      this->m_kc = depth;
+
+      if(l3_blocking)
+      {
+        computeProductBlockingSizes<LhsScalar,RhsScalar,KcFactor>(this->m_kc, this->m_mc, this->m_nc, num_threads);
+      }
+      else  // no l3 blocking
+      {
+        Index n = this->m_nc;
+        computeProductBlockingSizes<LhsScalar,RhsScalar,KcFactor>(this->m_kc, this->m_mc, n, num_threads);
+      }
+
+      m_sizeA = this->m_mc * this->m_kc;
+      m_sizeB = this->m_kc * this->m_nc;
+    }
+
+    void initParallel(Index rows, Index cols, Index depth, Index num_threads)
+    {
+      this->m_mc = Transpose ? cols : rows;
+      this->m_nc = Transpose ? rows : cols;
+      this->m_kc = depth;
+
+      eigen_internal_assert(this->m_blockA==0 && this->m_blockB==0);
+      Index m = this->m_mc;
+      computeProductBlockingSizes<LhsScalar,RhsScalar,KcFactor>(this->m_kc, m, this->m_nc, num_threads);
+      m_sizeA = this->m_mc * this->m_kc;
+      m_sizeB = this->m_kc * this->m_nc;
+    }
+
+    void allocateA()
+    {
+      if(this->m_blockA==0)
+        this->m_blockA = aligned_new<LhsScalar>(m_sizeA);
+    }
+
+    void allocateB()
+    {
+      if(this->m_blockB==0)
+        this->m_blockB = aligned_new<RhsScalar>(m_sizeB);
+    }
+
+    void allocateAll()
+    {
+      allocateA();
+      allocateB();
+    }
+
+    ~gemm_blocking_space()
+    {
+      aligned_delete(this->m_blockA, m_sizeA);
+      aligned_delete(this->m_blockB, m_sizeB);
+    }
+};
+
+} // end namespace internal
+
+namespace internal {
+
+template<typename Lhs, typename Rhs>
+struct generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct>
+  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,GemmProduct> >
+{
+  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
+  typedef typename Lhs::Scalar LhsScalar;
+  typedef typename Rhs::Scalar RhsScalar;
+
+  typedef internal::blas_traits<Lhs> LhsBlasTraits;
+  typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+  typedef internal::remove_all_t<ActualLhsType> ActualLhsTypeCleaned;
+
+  typedef internal::blas_traits<Rhs> RhsBlasTraits;
+  typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+  typedef internal::remove_all_t<ActualRhsType> ActualRhsTypeCleaned;
+
+  enum {
+    MaxDepthAtCompileTime = min_size_prefer_fixed(Lhs::MaxColsAtCompileTime, Rhs::MaxRowsAtCompileTime)
+  };
+
+  typedef generic_product_impl<Lhs,Rhs,DenseShape,DenseShape,CoeffBasedProductMode> lazyproduct;
+
+  template<typename Dst>
+  static void evalTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    // See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=404 for a discussion and helper program
+    // to determine the following heuristic.
+    // EIGEN_GEMM_TO_COEFFBASED_THRESHOLD is typically defined to 20 in GeneralProduct.h,
+    // unless it has been specialized by the user or for a given architecture.
+    // Note that the condition rhs.rows()>0 was required because lazy product is (was?) not happy with empty inputs.
+    // I'm not sure it is still required.
+    if((rhs.rows()+dst.rows()+dst.cols())<EIGEN_GEMM_TO_COEFFBASED_THRESHOLD && rhs.rows()>0)
+      lazyproduct::eval_dynamic(dst, lhs, rhs, internal::assign_op<typename Dst::Scalar,Scalar>());
+    else
+    {
+      dst.setZero();
+      scaleAndAddTo(dst, lhs, rhs, Scalar(1));
+    }
+  }
+
+  template<typename Dst>
+  static void addTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    if((rhs.rows()+dst.rows()+dst.cols())<EIGEN_GEMM_TO_COEFFBASED_THRESHOLD && rhs.rows()>0)
+      lazyproduct::eval_dynamic(dst, lhs, rhs, internal::add_assign_op<typename Dst::Scalar,Scalar>());
+    else
+      scaleAndAddTo(dst,lhs, rhs, Scalar(1));
+  }
+
+  template<typename Dst>
+  static void subTo(Dst& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    if((rhs.rows()+dst.rows()+dst.cols())<EIGEN_GEMM_TO_COEFFBASED_THRESHOLD && rhs.rows()>0)
+      lazyproduct::eval_dynamic(dst, lhs, rhs, internal::sub_assign_op<typename Dst::Scalar,Scalar>());
+    else
+      scaleAndAddTo(dst, lhs, rhs, Scalar(-1));
+  }
+
+  template<typename Dest>
+  static void scaleAndAddTo(Dest& dst, const Lhs& a_lhs, const Rhs& a_rhs, const Scalar& alpha)
+  {
+    eigen_assert(dst.rows()==a_lhs.rows() && dst.cols()==a_rhs.cols());
+    if(a_lhs.cols()==0 || a_lhs.rows()==0 || a_rhs.cols()==0)
+      return;
+
+    if (dst.cols() == 1)
+    {
+      // Fallback to GEMV if either the lhs or rhs is a runtime vector
+      typename Dest::ColXpr dst_vec(dst.col(0));
+      return internal::generic_product_impl<Lhs,typename Rhs::ConstColXpr,DenseShape,DenseShape,GemvProduct>
+        ::scaleAndAddTo(dst_vec, a_lhs, a_rhs.col(0), alpha);
+    }
+    else if (dst.rows() == 1)
+    {
+      // Fallback to GEMV if either the lhs or rhs is a runtime vector
+      typename Dest::RowXpr dst_vec(dst.row(0));
+      return internal::generic_product_impl<typename Lhs::ConstRowXpr,Rhs,DenseShape,DenseShape,GemvProduct>
+        ::scaleAndAddTo(dst_vec, a_lhs.row(0), a_rhs, alpha);
+    }
+
+    add_const_on_value_type_t<ActualLhsType> lhs = LhsBlasTraits::extract(a_lhs);
+    add_const_on_value_type_t<ActualRhsType> rhs = RhsBlasTraits::extract(a_rhs);
+
+    Scalar actualAlpha = combine_scalar_factors(alpha, a_lhs, a_rhs);
+
+    typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,LhsScalar,RhsScalar,
+            Dest::MaxRowsAtCompileTime,Dest::MaxColsAtCompileTime,MaxDepthAtCompileTime> BlockingType;
+
+    typedef internal::gemm_functor<
+      Scalar, Index,
+      internal::general_matrix_matrix_product<
+        Index,
+        LhsScalar, (ActualLhsTypeCleaned::Flags&RowMajorBit) ? RowMajor : ColMajor, bool(LhsBlasTraits::NeedToConjugate),
+        RhsScalar, (ActualRhsTypeCleaned::Flags&RowMajorBit) ? RowMajor : ColMajor, bool(RhsBlasTraits::NeedToConjugate),
+        (Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,
+        Dest::InnerStrideAtCompileTime>,
+      ActualLhsTypeCleaned, ActualRhsTypeCleaned, Dest, BlockingType> GemmFunctor;
+
+    BlockingType blocking(dst.rows(), dst.cols(), lhs.cols(), 1, true);
+    internal::parallelize_gemm<(Dest::MaxRowsAtCompileTime>32 || Dest::MaxRowsAtCompileTime==Dynamic)>
+        (GemmFunctor(lhs, rhs, dst, actualAlpha, blocking), a_lhs.rows(), a_rhs.cols(), a_lhs.cols(), Dest::Flags&RowMajorBit);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_GENERAL_MATRIX_MATRIX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
new file mode 100644
index 0000000000000000000000000000000000000000..716f2ca7848d560ce1955834685dd1d652616054
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular.h
@@ -0,0 +1,319 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H
+#define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjLhs, bool ConjRhs>
+struct selfadjoint_rank1_update;
+
+namespace internal {
+
+/**********************************************************************
+* This file implements a general A * B product while
+* evaluating only one triangular part of the product.
+* This is a more general version of self adjoint product (C += A A^T)
+* as the level 3 SYRK Blas routine.
+**********************************************************************/
+
+// forward declarations (defined at the end of this file)
+template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs, int ResInnerStride, int UpLo>
+struct tribb_kernel;
+  
+/* Optimized matrix-matrix product evaluating only one triangular half */
+template <typename Index,
+          typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
+          typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
+                              int ResStorageOrder, int ResInnerStride, int  UpLo, int Version = Specialized>
+struct general_matrix_matrix_triangular_product;
+
+// as usual if the result is row major => we transpose the product
+template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
+                          typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
+                          int ResInnerStride, int  UpLo, int Version>
+struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,RowMajor,ResInnerStride,UpLo,Version>
+{
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* lhs, Index lhsStride,
+                                      const RhsScalar* rhs, Index rhsStride, ResScalar* res, Index resIncr, Index resStride,
+                                      const ResScalar& alpha, level3_blocking<RhsScalar,LhsScalar>& blocking)
+  {
+    general_matrix_matrix_triangular_product<Index,
+        RhsScalar, RhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateRhs,
+        LhsScalar, LhsStorageOrder==RowMajor ? ColMajor : RowMajor, ConjugateLhs,
+        ColMajor, ResInnerStride, UpLo==Lower?Upper:Lower>
+      ::run(size,depth,rhs,rhsStride,lhs,lhsStride,res,resIncr,resStride,alpha,blocking);
+  }
+};
+
+template <typename Index, typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
+                          typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
+                          int ResInnerStride, int  UpLo, int Version>
+struct general_matrix_matrix_triangular_product<Index,LhsScalar,LhsStorageOrder,ConjugateLhs,RhsScalar,RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,UpLo,Version>
+{
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const LhsScalar* _lhs, Index lhsStride,
+                                      const RhsScalar* _rhs, Index rhsStride,
+                                      ResScalar* _res, Index resIncr, Index resStride,
+                                      const ResScalar& alpha, level3_blocking<LhsScalar,RhsScalar>& blocking)
+  {
+    typedef gebp_traits<LhsScalar,RhsScalar> Traits;
+
+    typedef const_blas_data_mapper<LhsScalar, Index, LhsStorageOrder> LhsMapper;
+    typedef const_blas_data_mapper<RhsScalar, Index, RhsStorageOrder> RhsMapper;
+    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
+    LhsMapper lhs(_lhs,lhsStride);
+    RhsMapper rhs(_rhs,rhsStride);
+    ResMapper res(_res, resStride, resIncr);
+
+    Index kc = blocking.kc();
+    Index mc = (std::min)(size,blocking.mc());
+
+    // !!! mc must be a multiple of nr:
+    if(mc > Traits::nr)
+      mc = (mc/Traits::nr)*Traits::nr;
+
+    std::size_t sizeA = kc*mc;
+    std::size_t sizeB = kc*size;
+
+    ei_declare_aligned_stack_constructed_variable(LhsScalar, blockA, sizeA, blocking.blockA());
+    ei_declare_aligned_stack_constructed_variable(RhsScalar, blockB, sizeB, blocking.blockB());
+
+    gemm_pack_lhs<LhsScalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder> pack_lhs;
+    gemm_pack_rhs<RhsScalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
+    gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp;
+    tribb_kernel<LhsScalar, RhsScalar, Index, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs, ResInnerStride, UpLo> sybb;
+
+    for(Index k2=0; k2<depth; k2+=kc)
+    {
+      const Index actual_kc = (std::min)(k2+kc,depth)-k2;
+
+      // note that the actual rhs is the transpose/adjoint of mat
+      pack_rhs(blockB, rhs.getSubMapper(k2,0), actual_kc, size);
+
+      for(Index i2=0; i2<size; i2+=mc)
+      {
+        const Index actual_mc = (std::min)(i2+mc,size)-i2;
+
+        pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
+
+        // the selected actual_mc * size panel of res is split into three different part:
+        //  1 - before the diagonal => processed with gebp or skipped
+        //  2 - the actual_mc x actual_mc symmetric block => processed with a special kernel
+        //  3 - after the diagonal => processed with gebp or skipped
+        if (UpLo==Lower)
+          gebp(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc,
+               (std::min)(size,i2), alpha, -1, -1, 0, 0);
+
+        sybb(_res+resStride*i2 + resIncr*i2, resIncr, resStride, blockA, blockB + actual_kc*i2, actual_mc, actual_kc, alpha);
+
+        if (UpLo==Upper)
+        {
+          Index j2 = i2+actual_mc;
+          gebp(res.getSubMapper(i2, j2), blockA, blockB+actual_kc*j2, actual_mc,
+               actual_kc, (std::max)(Index(0), size-j2), alpha, -1, -1, 0, 0);
+        }
+      }
+    }
+  }
+};
+
+// Optimized packed Block * packed Block product kernel evaluating only one given triangular part
+// This kernel is built on top of the gebp kernel:
+// - the current destination block is processed per panel of actual_mc x BlockSize
+//   where BlockSize is set to the minimal value allowing gebp to be as fast as possible
+// - then, as usual, each panel is split into three parts along the diagonal,
+//   the sub blocks above and below the diagonal are processed as usual,
+//   while the triangular block overlapping the diagonal is evaluated into a
+//   small temporary buffer which is then accumulated into the result using a
+//   triangular traversal.
+template<typename LhsScalar, typename RhsScalar, typename Index, int mr, int nr, bool ConjLhs, bool ConjRhs, int ResInnerStride, int UpLo>
+struct tribb_kernel
+{
+  typedef gebp_traits<LhsScalar,RhsScalar,ConjLhs,ConjRhs> Traits;
+  typedef typename Traits::ResScalar ResScalar;
+
+  enum {
+    BlockSize  = meta_least_common_multiple<plain_enum_max(mr, nr), plain_enum_min(mr,nr)>::ret
+  };
+  void operator()(ResScalar* _res, Index resIncr, Index resStride, const LhsScalar* blockA, const RhsScalar* blockB, Index size, Index depth, const ResScalar& alpha)
+  {
+    typedef blas_data_mapper<ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
+    typedef blas_data_mapper<ResScalar, Index, ColMajor, Unaligned> BufferMapper;
+    ResMapper res(_res, resStride, resIncr);
+    gebp_kernel<LhsScalar, RhsScalar, Index, ResMapper, mr, nr, ConjLhs, ConjRhs> gebp_kernel1;
+    gebp_kernel<LhsScalar, RhsScalar, Index, BufferMapper, mr, nr, ConjLhs, ConjRhs> gebp_kernel2;
+
+    Matrix<ResScalar,BlockSize,BlockSize,ColMajor> buffer((internal::constructor_without_unaligned_array_assert()));
+
+    // let's process the block per panel of actual_mc x BlockSize,
+    // again, each is split into three parts, etc.
+    for (Index j=0; j<size; j+=BlockSize)
+    {
+      Index actualBlockSize = std::min<Index>(BlockSize,size - j);
+      const RhsScalar* actual_b = blockB+j*depth;
+
+      if(UpLo==Upper)
+        gebp_kernel1(res.getSubMapper(0, j), blockA, actual_b, j, depth, actualBlockSize, alpha,
+                     -1, -1, 0, 0);
+      
+      // selfadjoint micro block
+      {
+        Index i = j;
+        buffer.setZero();
+        // 1 - apply the kernel on the temporary buffer
+        gebp_kernel2(BufferMapper(buffer.data(), BlockSize), blockA+depth*i, actual_b, actualBlockSize, depth, actualBlockSize, alpha,
+                     -1, -1, 0, 0);
+
+        // 2 - triangular accumulation
+        for(Index j1=0; j1<actualBlockSize; ++j1)
+        {
+          typename ResMapper::LinearMapper r = res.getLinearMapper(i,j+j1);
+          for(Index i1=UpLo==Lower ? j1 : 0;
+              UpLo==Lower ? i1<actualBlockSize : i1<=j1; ++i1)
+            r(i1) += buffer(i1,j1);
+        }
+      }
+
+      if(UpLo==Lower)
+      {
+        Index i = j+actualBlockSize;
+        gebp_kernel1(res.getSubMapper(i, j), blockA+depth*i, actual_b, size-i, 
+                     depth, actualBlockSize, alpha, -1, -1, 0, 0);
+      }
+    }
+  }
+};
+
+} // end namespace internal
+
+// high level API
+
+template<typename MatrixType, typename ProductType, int UpLo, bool IsOuterProduct>
+struct general_product_to_triangular_selector;
+
+
+template<typename MatrixType, typename ProductType, int UpLo>
+struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,true>
+{
+  static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha, bool beta)
+  {
+    typedef typename MatrixType::Scalar Scalar;
+    
+    typedef internal::remove_all_t<typename ProductType::LhsNested> Lhs;
+    typedef internal::blas_traits<Lhs> LhsBlasTraits;
+    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs;
+    typedef internal::remove_all_t<ActualLhs> ActualLhs_;
+    internal::add_const_on_value_type_t<ActualLhs> actualLhs = LhsBlasTraits::extract(prod.lhs());
+    
+    typedef internal::remove_all_t<typename ProductType::RhsNested> Rhs;
+    typedef internal::blas_traits<Rhs> RhsBlasTraits;
+    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs;
+    typedef internal::remove_all_t<ActualRhs> ActualRhs_;
+    internal::add_const_on_value_type_t<ActualRhs> actualRhs = RhsBlasTraits::extract(prod.rhs());
+
+    Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived());
+
+    if(!beta)
+      mat.template triangularView<UpLo>().setZero();
+
+    enum {
+      StorageOrder = (internal::traits<MatrixType>::Flags&RowMajorBit) ? RowMajor : ColMajor,
+      UseLhsDirectly = ActualLhs_::InnerStrideAtCompileTime==1,
+      UseRhsDirectly = ActualRhs_::InnerStrideAtCompileTime==1
+    };
+    
+    internal::gemv_static_vector_if<Scalar,Lhs::SizeAtCompileTime,Lhs::MaxSizeAtCompileTime,!UseLhsDirectly> static_lhs;
+    ei_declare_aligned_stack_constructed_variable(Scalar, actualLhsPtr, actualLhs.size(),
+      (UseLhsDirectly ? const_cast<Scalar*>(actualLhs.data()) : static_lhs.data()));
+    if(!UseLhsDirectly) Map<typename ActualLhs_::PlainObject>(actualLhsPtr, actualLhs.size()) = actualLhs;
+    
+    internal::gemv_static_vector_if<Scalar,Rhs::SizeAtCompileTime,Rhs::MaxSizeAtCompileTime,!UseRhsDirectly> static_rhs;
+    ei_declare_aligned_stack_constructed_variable(Scalar, actualRhsPtr, actualRhs.size(),
+      (UseRhsDirectly ? const_cast<Scalar*>(actualRhs.data()) : static_rhs.data()));
+    if(!UseRhsDirectly) Map<typename ActualRhs_::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
+    
+    
+    selfadjoint_rank1_update<Scalar,Index,StorageOrder,UpLo,
+                              LhsBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex,
+                              RhsBlasTraits::NeedToConjugate && NumTraits<Scalar>::IsComplex>
+          ::run(actualLhs.size(), mat.data(), mat.outerStride(), actualLhsPtr, actualRhsPtr, actualAlpha);
+  }
+};
+
+template<typename MatrixType, typename ProductType, int UpLo>
+struct general_product_to_triangular_selector<MatrixType,ProductType,UpLo,false>
+{
+  static void run(MatrixType& mat, const ProductType& prod, const typename MatrixType::Scalar& alpha, bool beta)
+  {
+    typedef internal::remove_all_t<typename ProductType::LhsNested> Lhs;
+    typedef internal::blas_traits<Lhs> LhsBlasTraits;
+    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhs;
+    typedef internal::remove_all_t<ActualLhs> ActualLhs_;
+    internal::add_const_on_value_type_t<ActualLhs> actualLhs = LhsBlasTraits::extract(prod.lhs());
+    
+    typedef internal::remove_all_t<typename ProductType::RhsNested> Rhs;
+    typedef internal::blas_traits<Rhs> RhsBlasTraits;
+    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhs;
+    typedef internal::remove_all_t<ActualRhs> ActualRhs_;
+    internal::add_const_on_value_type_t<ActualRhs> actualRhs = RhsBlasTraits::extract(prod.rhs());
+
+    typename ProductType::Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(prod.lhs().derived()) * RhsBlasTraits::extractScalarFactor(prod.rhs().derived());
+
+    if(!beta)
+      mat.template triangularView<UpLo>().setZero();
+
+    enum {
+      IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0,
+      LhsIsRowMajor = ActualLhs_::Flags&RowMajorBit ? 1 : 0,
+      RhsIsRowMajor = ActualRhs_::Flags&RowMajorBit ? 1 : 0,
+      SkipDiag = (UpLo&(UnitDiag|ZeroDiag))!=0
+    };
+
+    Index size = mat.cols();
+    if(SkipDiag)
+      size--;
+    Index depth = actualLhs.cols();
+
+    typedef internal::gemm_blocking_space<IsRowMajor ? RowMajor : ColMajor,typename Lhs::Scalar,typename Rhs::Scalar,
+          MatrixType::MaxColsAtCompileTime, MatrixType::MaxColsAtCompileTime, ActualRhs_::MaxColsAtCompileTime> BlockingType;
+
+    BlockingType blocking(size, size, depth, 1, false);
+
+    internal::general_matrix_matrix_triangular_product<Index,
+      typename Lhs::Scalar, LhsIsRowMajor ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
+      typename Rhs::Scalar, RhsIsRowMajor ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
+      IsRowMajor ? RowMajor : ColMajor, MatrixType::InnerStrideAtCompileTime, UpLo&(Lower|Upper)>
+      ::run(size, depth,
+            &actualLhs.coeffRef(SkipDiag&&(UpLo&Lower)==Lower ? 1 : 0,0), actualLhs.outerStride(),
+            &actualRhs.coeffRef(0,SkipDiag&&(UpLo&Upper)==Upper ? 1 : 0), actualRhs.outerStride(),
+            mat.data() + (SkipDiag ? (bool(IsRowMajor) != ((UpLo&Lower)==Lower) ? mat.innerStride() : mat.outerStride() ) : 0),
+            mat.innerStride(), mat.outerStride(), actualAlpha, blocking);
+  }
+};
+
+template<typename MatrixType, unsigned int UpLo>
+template<typename ProductType>
+EIGEN_DEVICE_FUNC TriangularView<MatrixType,UpLo>& TriangularViewImpl<MatrixType,UpLo,Dense>::_assignProduct(const ProductType& prod, const Scalar& alpha, bool beta)
+{
+  EIGEN_STATIC_ASSERT((UpLo&UnitDiag)==0, WRITING_TO_TRIANGULAR_PART_WITH_UNIT_DIAGONAL_IS_NOT_SUPPORTED);
+  eigen_assert(derived().nestedExpression().rows() == prod.rows() && derived().cols() == prod.cols());
+
+  general_product_to_triangular_selector<MatrixType, ProductType, UpLo, internal::traits<ProductType>::InnerSize==1>::run(derived().nestedExpression().const_cast_derived(), prod, alpha, beta);
+
+  return derived();
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
new file mode 100644
index 0000000000000000000000000000000000000000..45ad5da12670f717a09adcef3f6f4d5c3987b6c1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h
@@ -0,0 +1,147 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   Level 3 BLAS SYRK/HERK implementation.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_BLAS_H
+#define EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_BLAS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Index, typename Scalar, int AStorageOrder, bool ConjugateA, int ResStorageOrder, int UpLo>
+struct general_matrix_matrix_rankupdate :
+       general_matrix_matrix_triangular_product<
+         Index,Scalar,AStorageOrder,ConjugateA,Scalar,AStorageOrder,ConjugateA,ResStorageOrder,1,UpLo,BuiltIn> {};
+
+
+// try to go to BLAS specialization
+#define EIGEN_BLAS_RANKUPDATE_SPECIALIZE(Scalar) \
+template <typename Index, int LhsStorageOrder, bool ConjugateLhs, \
+                          int RhsStorageOrder, bool ConjugateRhs, int  UpLo> \
+struct general_matrix_matrix_triangular_product<Index,Scalar,LhsStorageOrder,ConjugateLhs, \
+               Scalar,RhsStorageOrder,ConjugateRhs,ColMajor,1,UpLo,Specialized> { \
+  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const Scalar* lhs, Index lhsStride, \
+                          const Scalar* rhs, Index rhsStride, Scalar* res, Index resIncr, Index resStride, Scalar alpha, level3_blocking<Scalar, Scalar>& blocking) \
+  { \
+    if ( lhs==rhs && ((UpLo&(Lower|Upper))==UpLo) ) { \
+      general_matrix_matrix_rankupdate<Index,Scalar,LhsStorageOrder,ConjugateLhs,ColMajor,UpLo> \
+      ::run(size,depth,lhs,lhsStride,rhs,rhsStride,res,resStride,alpha,blocking); \
+    } else { \
+      general_matrix_matrix_triangular_product<Index, \
+        Scalar, LhsStorageOrder, ConjugateLhs, \
+        Scalar, RhsStorageOrder, ConjugateRhs, \
+        ColMajor, 1, UpLo, BuiltIn> \
+      ::run(size,depth,lhs,lhsStride,rhs,rhsStride,res,resIncr,resStride,alpha,blocking); \
+    } \
+  } \
+};
+
+EIGEN_BLAS_RANKUPDATE_SPECIALIZE(double)
+EIGEN_BLAS_RANKUPDATE_SPECIALIZE(float)
+// TODO handle complex cases
+// EIGEN_BLAS_RANKUPDATE_SPECIALIZE(dcomplex)
+// EIGEN_BLAS_RANKUPDATE_SPECIALIZE(scomplex)
+
+// SYRK for float/double
+#define EIGEN_BLAS_RANKUPDATE_R(EIGTYPE, BLASTYPE, BLASFUNC) \
+template <typename Index, int AStorageOrder, bool ConjugateA, int  UpLo> \
+struct general_matrix_matrix_rankupdate<Index,EIGTYPE,AStorageOrder,ConjugateA,ColMajor,UpLo> { \
+  enum { \
+    IsLower = (UpLo&Lower) == Lower, \
+    LowUp = IsLower ? Lower : Upper, \
+    conjA = ((AStorageOrder==ColMajor) && ConjugateA) ? 1 : 0 \
+  }; \
+  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const EIGTYPE* lhs, Index lhsStride, \
+                          const EIGTYPE* /*rhs*/, Index /*rhsStride*/, EIGTYPE* res, Index resStride, EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
+  { \
+  /* typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs;*/ \
+\
+   BlasIndex lda=convert_index<BlasIndex>(lhsStride), ldc=convert_index<BlasIndex>(resStride), n=convert_index<BlasIndex>(size), k=convert_index<BlasIndex>(depth); \
+   char uplo=((IsLower) ? 'L' : 'U'), trans=((AStorageOrder==RowMajor) ? 'T':'N'); \
+   EIGTYPE beta(1); \
+   BLASFUNC(&uplo, &trans, &n, &k, (const BLASTYPE*)&numext::real_ref(alpha), lhs, &lda, (const BLASTYPE*)&numext::real_ref(beta), res, &ldc); \
+  } \
+};
+
+// HERK for complex data
+#define EIGEN_BLAS_RANKUPDATE_C(EIGTYPE, BLASTYPE, RTYPE, BLASFUNC) \
+template <typename Index, int AStorageOrder, bool ConjugateA, int  UpLo> \
+struct general_matrix_matrix_rankupdate<Index,EIGTYPE,AStorageOrder,ConjugateA,ColMajor,UpLo> { \
+  enum { \
+    IsLower = (UpLo&Lower) == Lower, \
+    LowUp = IsLower ? Lower : Upper, \
+    conjA = (((AStorageOrder==ColMajor) && ConjugateA) || ((AStorageOrder==RowMajor) && !ConjugateA)) ? 1 : 0 \
+  }; \
+  static EIGEN_STRONG_INLINE void run(Index size, Index depth,const EIGTYPE* lhs, Index lhsStride, \
+                          const EIGTYPE* /*rhs*/, Index /*rhsStride*/, EIGTYPE* res, Index resStride, EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
+  { \
+   typedef Matrix<EIGTYPE, Dynamic, Dynamic, AStorageOrder> MatrixType; \
+\
+   BlasIndex lda=convert_index<BlasIndex>(lhsStride), ldc=convert_index<BlasIndex>(resStride), n=convert_index<BlasIndex>(size), k=convert_index<BlasIndex>(depth); \
+   char uplo=((IsLower) ? 'L' : 'U'), trans=((AStorageOrder==RowMajor) ? 'C':'N'); \
+   RTYPE alpha_, beta_; \
+   const EIGTYPE* a_ptr; \
+\
+   alpha_ = alpha.real(); \
+   beta_ = 1.0; \
+/* Copy with conjugation in some cases*/ \
+   MatrixType a; \
+   if (conjA) { \
+     Map<const MatrixType, 0, OuterStride<> > mapA(lhs,n,k,OuterStride<>(lhsStride)); \
+     a = mapA.conjugate(); \
+     lda = a.outerStride(); \
+     a_ptr = a.data(); \
+   } else a_ptr=lhs; \
+   BLASFUNC(&uplo, &trans, &n, &k, &alpha_, (BLASTYPE*)a_ptr, &lda, &beta_, (BLASTYPE*)res, &ldc); \
+  } \
+};
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_RANKUPDATE_R(double, double, dsyrk)
+EIGEN_BLAS_RANKUPDATE_R(float,  float,  ssyrk)
+#else
+EIGEN_BLAS_RANKUPDATE_R(double, double, dsyrk_)
+EIGEN_BLAS_RANKUPDATE_R(float,  float,  ssyrk_)
+#endif
+
+// TODO hanlde complex cases
+// EIGEN_BLAS_RANKUPDATE_C(dcomplex, double, double, zherk_)
+// EIGEN_BLAS_RANKUPDATE_C(scomplex, float,  float, cherk_)
+
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_GENERAL_MATRIX_MATRIX_TRIANGULAR_BLAS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
new file mode 100644
index 0000000000000000000000000000000000000000..490fe67c5051f0082f988478e81b9816582eb5b5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixMatrix_BLAS.h
@@ -0,0 +1,126 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   General matrix-matrix product functionality based on ?GEMM.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_GENERAL_MATRIX_MATRIX_BLAS_H
+#define EIGEN_GENERAL_MATRIX_MATRIX_BLAS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/**********************************************************************
+* This file implements general matrix-matrix multiplication using BLAS
+* gemm function via partial specialization of
+* general_matrix_matrix_product::run(..) method for float, double,
+* std::complex<float> and std::complex<double> types
+**********************************************************************/
+
+// gemm specialization
+
+#define GEMM_SPECIALIZATION(EIGTYPE, EIGPREFIX, BLASTYPE, BLASFUNC) \
+template< \
+  typename Index, \
+  int LhsStorageOrder, bool ConjugateLhs, \
+  int RhsStorageOrder, bool ConjugateRhs> \
+struct general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor,1> \
+{ \
+typedef gebp_traits<EIGTYPE,EIGTYPE> Traits; \
+\
+static void run(Index rows, Index cols, Index depth, \
+  const EIGTYPE* _lhs, Index lhsStride, \
+  const EIGTYPE* _rhs, Index rhsStride, \
+  EIGTYPE* res, Index resIncr, Index resStride, \
+  EIGTYPE alpha, \
+  level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/, \
+  GemmParallelInfo<Index>* /*info = 0*/) \
+{ \
+  using std::conj; \
+\
+  EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
+  eigen_assert(resIncr == 1); \
+  char transa, transb; \
+  BlasIndex m, n, k, lda, ldb, ldc; \
+  const EIGTYPE *a, *b; \
+  EIGTYPE beta(1); \
+  MatrixX##EIGPREFIX a_tmp, b_tmp; \
+\
+/* Set transpose options */ \
+  transa = (LhsStorageOrder==RowMajor) ? ((ConjugateLhs) ? 'C' : 'T') : 'N'; \
+  transb = (RhsStorageOrder==RowMajor) ? ((ConjugateRhs) ? 'C' : 'T') : 'N'; \
+\
+/* Set m, n, k */ \
+  m = convert_index<BlasIndex>(rows);  \
+  n = convert_index<BlasIndex>(cols);  \
+  k = convert_index<BlasIndex>(depth); \
+\
+/* Set lda, ldb, ldc */ \
+  lda = convert_index<BlasIndex>(lhsStride); \
+  ldb = convert_index<BlasIndex>(rhsStride); \
+  ldc = convert_index<BlasIndex>(resStride); \
+\
+/* Set a, b, c */ \
+  if ((LhsStorageOrder==ColMajor) && (ConjugateLhs)) { \
+    Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,m,k,OuterStride<>(lhsStride)); \
+    a_tmp = lhs.conjugate(); \
+    a = a_tmp.data(); \
+    lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
+  } else a = _lhs; \
+\
+  if ((RhsStorageOrder==ColMajor) && (ConjugateRhs)) { \
+    Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,k,n,OuterStride<>(rhsStride)); \
+    b_tmp = rhs.conjugate(); \
+    b = b_tmp.data(); \
+    ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
+  } else b = _rhs; \
+\
+  BLASFUNC(&transa, &transb, &m, &n, &k, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
+}};
+
+#ifdef EIGEN_USE_MKL
+GEMM_SPECIALIZATION(double,   d,  double, dgemm)
+GEMM_SPECIALIZATION(float,    f,  float,  sgemm)
+GEMM_SPECIALIZATION(dcomplex, cd, MKL_Complex16, zgemm)
+GEMM_SPECIALIZATION(scomplex, cf, MKL_Complex8,  cgemm)
+#else
+GEMM_SPECIALIZATION(double,   d,  double, dgemm_)
+GEMM_SPECIALIZATION(float,    f,  float,  sgemm_)
+GEMM_SPECIALIZATION(dcomplex, cd, double, zgemm_)
+GEMM_SPECIALIZATION(scomplex, cf, float,  cgemm_)
+#endif
+
+} // end namespase internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_GENERAL_MATRIX_MATRIX_BLAS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixVector.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixVector.h
new file mode 100644
index 0000000000000000000000000000000000000000..e4c6f23491055378dc240481d581d3d957d920c4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixVector.h
@@ -0,0 +1,524 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERAL_MATRIX_VECTOR_H
+#define EIGEN_GENERAL_MATRIX_VECTOR_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+enum GEMVPacketSizeType {
+  GEMVPacketFull = 0,
+  GEMVPacketHalf,
+  GEMVPacketQuarter
+};
+
+template <int N, typename T1, typename T2, typename T3>
+struct gemv_packet_cond { typedef T3 type; };
+
+template <typename T1, typename T2, typename T3>
+struct gemv_packet_cond<GEMVPacketFull, T1, T2, T3> { typedef T1 type; };
+
+template <typename T1, typename T2, typename T3>
+struct gemv_packet_cond<GEMVPacketHalf, T1, T2, T3> { typedef T2 type; };
+
+template<typename LhsScalar, typename RhsScalar, int PacketSize_=GEMVPacketFull>
+class gemv_traits
+{
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+
+#define PACKET_DECL_COND_POSTFIX(postfix, name, packet_size)                        \
+  typedef typename gemv_packet_cond<packet_size,                                  \
+                                    typename packet_traits<name ## Scalar>::type, \
+                                    typename packet_traits<name ## Scalar>::half, \
+                                    typename unpacket_traits<typename packet_traits<name ## Scalar>::half>::half>::type \
+  name ## Packet ## postfix
+
+  PACKET_DECL_COND_POSTFIX(_, Lhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Rhs, PacketSize_);
+  PACKET_DECL_COND_POSTFIX(_, Res, PacketSize_);
+#undef PACKET_DECL_COND_POSTFIX
+
+public:
+  enum {
+        Vectorizable = unpacket_traits<LhsPacket_>::vectorizable &&
+        unpacket_traits<RhsPacket_>::vectorizable &&
+        int(unpacket_traits<LhsPacket_>::size)==int(unpacket_traits<RhsPacket_>::size),
+        LhsPacketSize = Vectorizable ? unpacket_traits<LhsPacket_>::size : 1,
+        RhsPacketSize = Vectorizable ? unpacket_traits<RhsPacket_>::size : 1,
+        ResPacketSize = Vectorizable ? unpacket_traits<ResPacket_>::size : 1
+  };
+
+  typedef std::conditional_t<Vectorizable,LhsPacket_,LhsScalar> LhsPacket;
+  typedef std::conditional_t<Vectorizable,RhsPacket_,RhsScalar> RhsPacket;
+  typedef std::conditional_t<Vectorizable,ResPacket_,ResScalar> ResPacket;
+};
+
+
+/* Optimized col-major matrix * vector product:
+ * This algorithm processes the matrix per vertical panels,
+ * which are then processed horizontally per chunck of 8*PacketSize x 1 vertical segments.
+ *
+ * Mixing type logic: C += alpha * A * B
+ *  |  A  |  B  |alpha| comments
+ *  |real |cplx |cplx | no vectorization
+ *  |real |cplx |real | alpha is converted to a cplx when calling the run function, no vectorization
+ *  |cplx |real |cplx | invalid, the caller has to do tmp: = A * B; C += alpha*tmp
+ *  |cplx |real |real | optimal case, vectorization possible via real-cplx mul
+ *
+ * The same reasoning apply for the transposed case.
+ */
+template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
+struct general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>
+{
+  typedef gemv_traits<LhsScalar,RhsScalar> Traits;
+  typedef gemv_traits<LhsScalar,RhsScalar,GEMVPacketHalf> HalfTraits;
+  typedef gemv_traits<LhsScalar,RhsScalar,GEMVPacketQuarter> QuarterTraits;
+
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+
+  typedef typename Traits::LhsPacket LhsPacket;
+  typedef typename Traits::RhsPacket RhsPacket;
+  typedef typename Traits::ResPacket ResPacket;
+
+  typedef typename HalfTraits::LhsPacket LhsPacketHalf;
+  typedef typename HalfTraits::RhsPacket RhsPacketHalf;
+  typedef typename HalfTraits::ResPacket ResPacketHalf;
+
+  typedef typename QuarterTraits::LhsPacket LhsPacketQuarter;
+  typedef typename QuarterTraits::RhsPacket RhsPacketQuarter;
+  typedef typename QuarterTraits::ResPacket ResPacketQuarter;
+
+EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run(
+  Index rows, Index cols,
+  const LhsMapper& lhs,
+  const RhsMapper& rhs,
+        ResScalar* res, Index resIncr,
+  RhsScalar alpha);
+};
+
+template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
+EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>::run(
+  Index rows, Index cols,
+  const LhsMapper& alhs,
+  const RhsMapper& rhs,
+        ResScalar* res, Index resIncr,
+  RhsScalar alpha)
+{
+  EIGEN_UNUSED_VARIABLE(resIncr);
+  eigen_internal_assert(resIncr==1);
+
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate propoer code.
+  LhsMapper lhs(alhs);
+
+  conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
+  conj_helper<LhsPacket,RhsPacket,ConjugateLhs,ConjugateRhs> pcj;
+  conj_helper<LhsPacketHalf,RhsPacketHalf,ConjugateLhs,ConjugateRhs> pcj_half;
+  conj_helper<LhsPacketQuarter,RhsPacketQuarter,ConjugateLhs,ConjugateRhs> pcj_quarter;
+
+  const Index lhsStride = lhs.stride();
+  // TODO: for padded aligned inputs, we could enable aligned reads
+  enum { LhsAlignment = Unaligned,
+         ResPacketSize = Traits::ResPacketSize,
+         ResPacketSizeHalf = HalfTraits::ResPacketSize,
+         ResPacketSizeQuarter = QuarterTraits::ResPacketSize,
+         LhsPacketSize = Traits::LhsPacketSize,
+         HasHalf = (int)ResPacketSizeHalf < (int)ResPacketSize,
+         HasQuarter = (int)ResPacketSizeQuarter < (int)ResPacketSizeHalf
+  };
+
+  const Index n8 = rows-8*ResPacketSize+1;
+  const Index n4 = rows-4*ResPacketSize+1;
+  const Index n3 = rows-3*ResPacketSize+1;
+  const Index n2 = rows-2*ResPacketSize+1;
+  const Index n1 = rows-1*ResPacketSize+1;
+  const Index n_half = rows-1*ResPacketSizeHalf+1;
+  const Index n_quarter = rows-1*ResPacketSizeQuarter+1;
+
+  // TODO: improve the following heuristic:
+  const Index block_cols = cols<128 ? cols : (lhsStride*sizeof(LhsScalar)<32000?16:4);
+  ResPacket palpha = pset1<ResPacket>(alpha);
+  ResPacketHalf palpha_half = pset1<ResPacketHalf>(alpha);
+  ResPacketQuarter palpha_quarter = pset1<ResPacketQuarter>(alpha);
+
+  for(Index j2=0; j2<cols; j2+=block_cols)
+  {
+    Index jend = numext::mini(j2+block_cols,cols);
+    Index i=0;
+    for(; i<n8; i+=ResPacketSize*8)
+    {
+      ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
+                c1 = pset1<ResPacket>(ResScalar(0)),
+                c2 = pset1<ResPacket>(ResScalar(0)),
+                c3 = pset1<ResPacket>(ResScalar(0)),
+                c4 = pset1<ResPacket>(ResScalar(0)),
+                c5 = pset1<ResPacket>(ResScalar(0)),
+                c6 = pset1<ResPacket>(ResScalar(0)),
+                c7 = pset1<ResPacket>(ResScalar(0));
+
+      for(Index j=j2; j<jend; j+=1)
+      {
+        RhsPacket b0 = pset1<RhsPacket>(rhs(j,0));
+        c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*0,j),b0,c0);
+        c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*1,j),b0,c1);
+        c2 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*2,j),b0,c2);
+        c3 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*3,j),b0,c3);
+        c4 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*4,j),b0,c4);
+        c5 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*5,j),b0,c5);
+        c6 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*6,j),b0,c6);
+        c7 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*7,j),b0,c7);
+      }
+      pstoreu(res+i+ResPacketSize*0, pmadd(c0,palpha,ploadu<ResPacket>(res+i+ResPacketSize*0)));
+      pstoreu(res+i+ResPacketSize*1, pmadd(c1,palpha,ploadu<ResPacket>(res+i+ResPacketSize*1)));
+      pstoreu(res+i+ResPacketSize*2, pmadd(c2,palpha,ploadu<ResPacket>(res+i+ResPacketSize*2)));
+      pstoreu(res+i+ResPacketSize*3, pmadd(c3,palpha,ploadu<ResPacket>(res+i+ResPacketSize*3)));
+      pstoreu(res+i+ResPacketSize*4, pmadd(c4,palpha,ploadu<ResPacket>(res+i+ResPacketSize*4)));
+      pstoreu(res+i+ResPacketSize*5, pmadd(c5,palpha,ploadu<ResPacket>(res+i+ResPacketSize*5)));
+      pstoreu(res+i+ResPacketSize*6, pmadd(c6,palpha,ploadu<ResPacket>(res+i+ResPacketSize*6)));
+      pstoreu(res+i+ResPacketSize*7, pmadd(c7,palpha,ploadu<ResPacket>(res+i+ResPacketSize*7)));
+    }
+    if(i<n4)
+    {
+      ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
+                c1 = pset1<ResPacket>(ResScalar(0)),
+                c2 = pset1<ResPacket>(ResScalar(0)),
+                c3 = pset1<ResPacket>(ResScalar(0));
+
+      for(Index j=j2; j<jend; j+=1)
+      {
+        RhsPacket b0 = pset1<RhsPacket>(rhs(j,0));
+        c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*0,j),b0,c0);
+        c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*1,j),b0,c1);
+        c2 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*2,j),b0,c2);
+        c3 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*3,j),b0,c3);
+      }
+      pstoreu(res+i+ResPacketSize*0, pmadd(c0,palpha,ploadu<ResPacket>(res+i+ResPacketSize*0)));
+      pstoreu(res+i+ResPacketSize*1, pmadd(c1,palpha,ploadu<ResPacket>(res+i+ResPacketSize*1)));
+      pstoreu(res+i+ResPacketSize*2, pmadd(c2,palpha,ploadu<ResPacket>(res+i+ResPacketSize*2)));
+      pstoreu(res+i+ResPacketSize*3, pmadd(c3,palpha,ploadu<ResPacket>(res+i+ResPacketSize*3)));
+
+      i+=ResPacketSize*4;
+    }
+    if(i<n3)
+    {
+      ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
+                c1 = pset1<ResPacket>(ResScalar(0)),
+                c2 = pset1<ResPacket>(ResScalar(0));
+
+      for(Index j=j2; j<jend; j+=1)
+      {
+        RhsPacket b0 = pset1<RhsPacket>(rhs(j,0));
+        c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*0,j),b0,c0);
+        c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*1,j),b0,c1);
+        c2 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*2,j),b0,c2);
+      }
+      pstoreu(res+i+ResPacketSize*0, pmadd(c0,palpha,ploadu<ResPacket>(res+i+ResPacketSize*0)));
+      pstoreu(res+i+ResPacketSize*1, pmadd(c1,palpha,ploadu<ResPacket>(res+i+ResPacketSize*1)));
+      pstoreu(res+i+ResPacketSize*2, pmadd(c2,palpha,ploadu<ResPacket>(res+i+ResPacketSize*2)));
+
+      i+=ResPacketSize*3;
+    }
+    if(i<n2)
+    {
+      ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
+                c1 = pset1<ResPacket>(ResScalar(0));
+
+      for(Index j=j2; j<jend; j+=1)
+      {
+        RhsPacket b0 = pset1<RhsPacket>(rhs(j,0));
+        c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*0,j),b0,c0);
+        c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+LhsPacketSize*1,j),b0,c1);
+      }
+      pstoreu(res+i+ResPacketSize*0, pmadd(c0,palpha,ploadu<ResPacket>(res+i+ResPacketSize*0)));
+      pstoreu(res+i+ResPacketSize*1, pmadd(c1,palpha,ploadu<ResPacket>(res+i+ResPacketSize*1)));
+      i+=ResPacketSize*2;
+    }
+    if(i<n1)
+    {
+      ResPacket c0 = pset1<ResPacket>(ResScalar(0));
+      for(Index j=j2; j<jend; j+=1)
+      {
+        RhsPacket b0 = pset1<RhsPacket>(rhs(j,0));
+        c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+0,j),b0,c0);
+      }
+      pstoreu(res+i+ResPacketSize*0, pmadd(c0,palpha,ploadu<ResPacket>(res+i+ResPacketSize*0)));
+      i+=ResPacketSize;
+    }
+    if(HasHalf && i<n_half)
+    {
+      ResPacketHalf c0 = pset1<ResPacketHalf>(ResScalar(0));
+      for(Index j=j2; j<jend; j+=1)
+      {
+        RhsPacketHalf b0 = pset1<RhsPacketHalf>(rhs(j,0));
+        c0 = pcj_half.pmadd(lhs.template load<LhsPacketHalf,LhsAlignment>(i+0,j),b0,c0);
+      }
+      pstoreu(res+i+ResPacketSizeHalf*0, pmadd(c0,palpha_half,ploadu<ResPacketHalf>(res+i+ResPacketSizeHalf*0)));
+      i+=ResPacketSizeHalf;
+    }
+    if(HasQuarter && i<n_quarter)
+    {
+      ResPacketQuarter c0 = pset1<ResPacketQuarter>(ResScalar(0));
+      for(Index j=j2; j<jend; j+=1)
+      {
+        RhsPacketQuarter b0 = pset1<RhsPacketQuarter>(rhs(j,0));
+        c0 = pcj_quarter.pmadd(lhs.template load<LhsPacketQuarter,LhsAlignment>(i+0,j),b0,c0);
+      }
+      pstoreu(res+i+ResPacketSizeQuarter*0, pmadd(c0,palpha_quarter,ploadu<ResPacketQuarter>(res+i+ResPacketSizeQuarter*0)));
+      i+=ResPacketSizeQuarter;
+    }
+    for(;i<rows;++i)
+    {
+      ResScalar c0(0);
+      for(Index j=j2; j<jend; j+=1)
+        c0 += cj.pmul(lhs(i,j), rhs(j,0));
+      res[i] += alpha*c0;
+    }
+  }
+}
+
+/* Optimized row-major matrix * vector product:
+ * This algorithm processes 4 rows at once that allows to both reduce
+ * the number of load/stores of the result by a factor 4 and to reduce
+ * the instruction dependency. Moreover, we know that all bands have the
+ * same alignment pattern.
+ *
+ * Mixing type logic:
+ *  - alpha is always a complex (or converted to a complex)
+ *  - no vectorization
+ */
+template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
+struct general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>
+{
+  typedef gemv_traits<LhsScalar,RhsScalar> Traits;
+  typedef gemv_traits<LhsScalar,RhsScalar,GEMVPacketHalf> HalfTraits;
+  typedef gemv_traits<LhsScalar,RhsScalar,GEMVPacketQuarter> QuarterTraits;
+
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+
+  typedef typename Traits::LhsPacket LhsPacket;
+  typedef typename Traits::RhsPacket RhsPacket;
+  typedef typename Traits::ResPacket ResPacket;
+
+  typedef typename HalfTraits::LhsPacket LhsPacketHalf;
+  typedef typename HalfTraits::RhsPacket RhsPacketHalf;
+  typedef typename HalfTraits::ResPacket ResPacketHalf;
+
+  typedef typename QuarterTraits::LhsPacket LhsPacketQuarter;
+  typedef typename QuarterTraits::RhsPacket RhsPacketQuarter;
+  typedef typename QuarterTraits::ResPacket ResPacketQuarter;
+
+EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE static void run(
+  Index rows, Index cols,
+  const LhsMapper& lhs,
+  const RhsMapper& rhs,
+        ResScalar* res, Index resIncr,
+  ResScalar alpha);
+};
+
+template<typename Index, typename LhsScalar, typename LhsMapper, bool ConjugateLhs, typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version>
+EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE void general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjugateLhs,RhsScalar,RhsMapper,ConjugateRhs,Version>::run(
+  Index rows, Index cols,
+  const LhsMapper& alhs,
+  const RhsMapper& rhs,
+  ResScalar* res, Index resIncr,
+  ResScalar alpha)
+{
+  // The following copy tells the compiler that lhs's attributes are not modified outside this function
+  // This helps GCC to generate propoer code.
+  LhsMapper lhs(alhs);
+
+  eigen_internal_assert(rhs.stride()==1);
+  conj_helper<LhsScalar,RhsScalar,ConjugateLhs,ConjugateRhs> cj;
+  conj_helper<LhsPacket,RhsPacket,ConjugateLhs,ConjugateRhs> pcj;
+  conj_helper<LhsPacketHalf,RhsPacketHalf,ConjugateLhs,ConjugateRhs> pcj_half;
+  conj_helper<LhsPacketQuarter,RhsPacketQuarter,ConjugateLhs,ConjugateRhs> pcj_quarter;
+
+  // TODO: fine tune the following heuristic. The rationale is that if the matrix is very large,
+  //       processing 8 rows at once might be counter productive wrt cache.
+  const Index n8 = lhs.stride()*sizeof(LhsScalar)>32000 ? 0 : rows-7;
+  const Index n4 = rows-3;
+  const Index n2 = rows-1;
+
+  // TODO: for padded aligned inputs, we could enable aligned reads
+  enum { LhsAlignment = Unaligned,
+         ResPacketSize = Traits::ResPacketSize,
+         ResPacketSizeHalf = HalfTraits::ResPacketSize,
+         ResPacketSizeQuarter = QuarterTraits::ResPacketSize,
+         LhsPacketSize = Traits::LhsPacketSize,
+         LhsPacketSizeHalf = HalfTraits::LhsPacketSize,
+         LhsPacketSizeQuarter = QuarterTraits::LhsPacketSize,
+         HasHalf = (int)ResPacketSizeHalf < (int)ResPacketSize,
+         HasQuarter = (int)ResPacketSizeQuarter < (int)ResPacketSizeHalf
+  };
+
+  const Index fullColBlockEnd = LhsPacketSize * (cols / LhsPacketSize);
+  const Index halfColBlockEnd = LhsPacketSizeHalf * (cols / LhsPacketSizeHalf);
+  const Index quarterColBlockEnd = LhsPacketSizeQuarter * (cols / LhsPacketSizeQuarter);
+
+  Index i=0;
+  for(; i<n8; i+=8)
+  {
+    ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
+              c1 = pset1<ResPacket>(ResScalar(0)),
+              c2 = pset1<ResPacket>(ResScalar(0)),
+              c3 = pset1<ResPacket>(ResScalar(0)),
+              c4 = pset1<ResPacket>(ResScalar(0)),
+              c5 = pset1<ResPacket>(ResScalar(0)),
+              c6 = pset1<ResPacket>(ResScalar(0)),
+              c7 = pset1<ResPacket>(ResScalar(0));
+
+    for (Index j = 0; j < fullColBlockEnd; j += LhsPacketSize)
+    {
+      RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j,0);
+
+      c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+0,j),b0,c0);
+      c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+1,j),b0,c1);
+      c2 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+2,j),b0,c2);
+      c3 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+3,j),b0,c3);
+      c4 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+4,j),b0,c4);
+      c5 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+5,j),b0,c5);
+      c6 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+6,j),b0,c6);
+      c7 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+7,j),b0,c7);
+    }
+    ResScalar cc0 = predux(c0);
+    ResScalar cc1 = predux(c1);
+    ResScalar cc2 = predux(c2);
+    ResScalar cc3 = predux(c3);
+    ResScalar cc4 = predux(c4);
+    ResScalar cc5 = predux(c5);
+    ResScalar cc6 = predux(c6);
+    ResScalar cc7 = predux(c7);
+
+    for (Index j = fullColBlockEnd; j < cols; ++j)
+    {
+      RhsScalar b0 = rhs(j,0);
+
+      cc0 += cj.pmul(lhs(i+0,j), b0);
+      cc1 += cj.pmul(lhs(i+1,j), b0);
+      cc2 += cj.pmul(lhs(i+2,j), b0);
+      cc3 += cj.pmul(lhs(i+3,j), b0);
+      cc4 += cj.pmul(lhs(i+4,j), b0);
+      cc5 += cj.pmul(lhs(i+5,j), b0);
+      cc6 += cj.pmul(lhs(i+6,j), b0);
+      cc7 += cj.pmul(lhs(i+7,j), b0);
+    }
+    res[(i+0)*resIncr] += alpha*cc0;
+    res[(i+1)*resIncr] += alpha*cc1;
+    res[(i+2)*resIncr] += alpha*cc2;
+    res[(i+3)*resIncr] += alpha*cc3;
+    res[(i+4)*resIncr] += alpha*cc4;
+    res[(i+5)*resIncr] += alpha*cc5;
+    res[(i+6)*resIncr] += alpha*cc6;
+    res[(i+7)*resIncr] += alpha*cc7;
+  }
+  for(; i<n4; i+=4)
+  {
+    ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
+              c1 = pset1<ResPacket>(ResScalar(0)),
+              c2 = pset1<ResPacket>(ResScalar(0)),
+              c3 = pset1<ResPacket>(ResScalar(0));
+
+    for (Index j = 0; j < fullColBlockEnd; j += LhsPacketSize)
+    {
+      RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j,0);
+
+      c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+0,j),b0,c0);
+      c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+1,j),b0,c1);
+      c2 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+2,j),b0,c2);
+      c3 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+3,j),b0,c3);
+    }
+    ResScalar cc0 = predux(c0);
+    ResScalar cc1 = predux(c1);
+    ResScalar cc2 = predux(c2);
+    ResScalar cc3 = predux(c3);
+
+    for(Index j = fullColBlockEnd; j < cols; ++j)
+    {
+      RhsScalar b0 = rhs(j,0);
+
+      cc0 += cj.pmul(lhs(i+0,j), b0);
+      cc1 += cj.pmul(lhs(i+1,j), b0);
+      cc2 += cj.pmul(lhs(i+2,j), b0);
+      cc3 += cj.pmul(lhs(i+3,j), b0);
+    }
+    res[(i+0)*resIncr] += alpha*cc0;
+    res[(i+1)*resIncr] += alpha*cc1;
+    res[(i+2)*resIncr] += alpha*cc2;
+    res[(i+3)*resIncr] += alpha*cc3;
+  }
+  for(; i<n2; i+=2)
+  {
+    ResPacket c0 = pset1<ResPacket>(ResScalar(0)),
+              c1 = pset1<ResPacket>(ResScalar(0));
+
+    for (Index j = 0; j < fullColBlockEnd; j += LhsPacketSize)
+    {
+      RhsPacket b0 = rhs.template load<RhsPacket, Unaligned>(j,0);
+
+      c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+0,j),b0,c0);
+      c1 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i+1,j),b0,c1);
+    }
+    ResScalar cc0 = predux(c0);
+    ResScalar cc1 = predux(c1);
+
+    for(Index j = fullColBlockEnd; j < cols; ++j)
+    {
+      RhsScalar b0 = rhs(j,0);
+
+      cc0 += cj.pmul(lhs(i+0,j), b0);
+      cc1 += cj.pmul(lhs(i+1,j), b0);
+    }
+    res[(i+0)*resIncr] += alpha*cc0;
+    res[(i+1)*resIncr] += alpha*cc1;
+  }
+  for(; i<rows; ++i)
+  {
+    ResPacket c0 = pset1<ResPacket>(ResScalar(0));
+    ResPacketHalf c0_h = pset1<ResPacketHalf>(ResScalar(0));
+    ResPacketQuarter c0_q = pset1<ResPacketQuarter>(ResScalar(0));
+
+    for (Index j = 0; j < fullColBlockEnd; j += LhsPacketSize)
+    {
+      RhsPacket b0 = rhs.template load<RhsPacket,Unaligned>(j,0);
+      c0 = pcj.pmadd(lhs.template load<LhsPacket,LhsAlignment>(i,j),b0,c0);
+    }
+    ResScalar cc0 = predux(c0);
+    if (HasHalf) {
+      for (Index j = fullColBlockEnd; j < halfColBlockEnd; j += LhsPacketSizeHalf)
+        {
+          RhsPacketHalf b0 = rhs.template load<RhsPacketHalf,Unaligned>(j,0);
+          c0_h = pcj_half.pmadd(lhs.template load<LhsPacketHalf,LhsAlignment>(i,j),b0,c0_h);
+        }
+      cc0 += predux(c0_h);
+    }
+    if (HasQuarter) {
+      for (Index j = halfColBlockEnd; j < quarterColBlockEnd; j += LhsPacketSizeQuarter)
+        {
+          RhsPacketQuarter b0 = rhs.template load<RhsPacketQuarter,Unaligned>(j,0);
+          c0_q = pcj_quarter.pmadd(lhs.template load<LhsPacketQuarter,LhsAlignment>(i,j),b0,c0_q);
+        }
+      cc0 += predux(c0_q);
+    }
+    for (Index j = quarterColBlockEnd; j < cols; ++j)
+    {
+      cc0 += cj.pmul(lhs(i,j), rhs(j,0));
+    }
+    res[i*resIncr] += alpha*cc0;
+  }
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_GENERAL_MATRIX_VECTOR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
new file mode 100644
index 0000000000000000000000000000000000000000..f77e2e47bea1e7aab5b04980824d7bf502e4b6b9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/GeneralMatrixVector_BLAS.h
@@ -0,0 +1,138 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   General matrix-vector product functionality based on ?GEMV.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_GENERAL_MATRIX_VECTOR_BLAS_H
+#define EIGEN_GENERAL_MATRIX_VECTOR_BLAS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/**********************************************************************
+* This file implements general matrix-vector multiplication using BLAS
+* gemv function via partial specialization of
+* general_matrix_vector_product::run(..) method for float, double,
+* std::complex<float> and std::complex<double> types
+**********************************************************************/
+
+// gemv specialization
+
+template<typename Index, typename LhsScalar, int StorageOrder, bool ConjugateLhs, typename RhsScalar, bool ConjugateRhs>
+struct general_matrix_vector_product_gemv;
+
+#define EIGEN_BLAS_GEMV_SPECIALIZE(Scalar) \
+template<typename Index, bool ConjugateLhs, bool ConjugateRhs> \
+struct general_matrix_vector_product<Index,Scalar,const_blas_data_mapper<Scalar,Index,ColMajor>,ColMajor,ConjugateLhs,Scalar,const_blas_data_mapper<Scalar,Index,RowMajor>,ConjugateRhs,Specialized> { \
+static void run( \
+  Index rows, Index cols, \
+  const const_blas_data_mapper<Scalar,Index,ColMajor> &lhs, \
+  const const_blas_data_mapper<Scalar,Index,RowMajor> &rhs, \
+  Scalar* res, Index resIncr, Scalar alpha) \
+{ \
+  if (ConjugateLhs) { \
+    general_matrix_vector_product<Index,Scalar,const_blas_data_mapper<Scalar,Index,ColMajor>,ColMajor,ConjugateLhs,Scalar,const_blas_data_mapper<Scalar,Index,RowMajor>,ConjugateRhs,BuiltIn>::run( \
+      rows, cols, lhs, rhs, res, resIncr, alpha); \
+  } else { \
+    general_matrix_vector_product_gemv<Index,Scalar,ColMajor,ConjugateLhs,Scalar,ConjugateRhs>::run( \
+      rows, cols, lhs.data(), lhs.stride(), rhs.data(), rhs.stride(), res, resIncr, alpha); \
+  } \
+} \
+}; \
+template<typename Index, bool ConjugateLhs, bool ConjugateRhs> \
+struct general_matrix_vector_product<Index,Scalar,const_blas_data_mapper<Scalar,Index,RowMajor>,RowMajor,ConjugateLhs,Scalar,const_blas_data_mapper<Scalar,Index,ColMajor>,ConjugateRhs,Specialized> { \
+static void run( \
+  Index rows, Index cols, \
+  const const_blas_data_mapper<Scalar,Index,RowMajor> &lhs, \
+  const const_blas_data_mapper<Scalar,Index,ColMajor> &rhs, \
+  Scalar* res, Index resIncr, Scalar alpha) \
+{ \
+    general_matrix_vector_product_gemv<Index,Scalar,RowMajor,ConjugateLhs,Scalar,ConjugateRhs>::run( \
+      rows, cols, lhs.data(), lhs.stride(), rhs.data(), rhs.stride(), res, resIncr, alpha); \
+} \
+}; \
+
+EIGEN_BLAS_GEMV_SPECIALIZE(double)
+EIGEN_BLAS_GEMV_SPECIALIZE(float)
+EIGEN_BLAS_GEMV_SPECIALIZE(dcomplex)
+EIGEN_BLAS_GEMV_SPECIALIZE(scomplex)
+
+#define EIGEN_BLAS_GEMV_SPECIALIZATION(EIGTYPE,BLASTYPE,BLASFUNC) \
+template<typename Index, int LhsStorageOrder, bool ConjugateLhs, bool ConjugateRhs> \
+struct general_matrix_vector_product_gemv<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,ConjugateRhs> \
+{ \
+typedef Matrix<EIGTYPE,Dynamic,1,ColMajor> GEMVVector;\
+\
+static void run( \
+  Index rows, Index cols, \
+  const EIGTYPE* lhs, Index lhsStride, \
+  const EIGTYPE* rhs, Index rhsIncr, \
+  EIGTYPE* res, Index resIncr, EIGTYPE alpha) \
+{ \
+  BlasIndex m=convert_index<BlasIndex>(rows), n=convert_index<BlasIndex>(cols), \
+            lda=convert_index<BlasIndex>(lhsStride), incx=convert_index<BlasIndex>(rhsIncr), incy=convert_index<BlasIndex>(resIncr); \
+  const EIGTYPE beta(1); \
+  const EIGTYPE *x_ptr; \
+  char trans=(LhsStorageOrder==ColMajor) ? 'N' : (ConjugateLhs) ? 'C' : 'T'; \
+  if (LhsStorageOrder==RowMajor) { \
+    m = convert_index<BlasIndex>(cols); \
+    n = convert_index<BlasIndex>(rows); \
+  }\
+  GEMVVector x_tmp; \
+  if (ConjugateRhs) { \
+    Map<const GEMVVector, 0, InnerStride<> > map_x(rhs,cols,1,InnerStride<>(incx)); \
+    x_tmp=map_x.conjugate(); \
+    x_ptr=x_tmp.data(); \
+    incx=1; \
+  } else x_ptr=rhs; \
+  BLASFUNC(&trans, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)lhs, &lda, (const BLASTYPE*)x_ptr, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &incy); \
+}\
+};
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_GEMV_SPECIALIZATION(double,   double, dgemv)
+EIGEN_BLAS_GEMV_SPECIALIZATION(float,    float,  sgemv)
+EIGEN_BLAS_GEMV_SPECIALIZATION(dcomplex, MKL_Complex16, zgemv)
+EIGEN_BLAS_GEMV_SPECIALIZATION(scomplex, MKL_Complex8 , cgemv)
+#else
+EIGEN_BLAS_GEMV_SPECIALIZATION(double,   double, dgemv_)
+EIGEN_BLAS_GEMV_SPECIALIZATION(float,    float,  sgemv_)
+EIGEN_BLAS_GEMV_SPECIALIZATION(dcomplex, double, zgemv_)
+EIGEN_BLAS_GEMV_SPECIALIZATION(scomplex, float,  cgemv_)
+#endif
+
+} // end namespase internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_GENERAL_MATRIX_VECTOR_BLAS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/Parallelizer.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/Parallelizer.h
new file mode 100644
index 0000000000000000000000000000000000000000..da4affb80b387cb1a8d763d16af46f3270e93507
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/Parallelizer.h
@@ -0,0 +1,173 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PARALLELIZER_H
+#define EIGEN_PARALLELIZER_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal */
+inline void manage_multi_threading(Action action, int* v)
+{
+  static int m_maxThreads = -1;
+  EIGEN_UNUSED_VARIABLE(m_maxThreads)
+
+  if(action==SetAction)
+  {
+    eigen_internal_assert(v!=0);
+    m_maxThreads = *v;
+  }
+  else if(action==GetAction)
+  {
+    eigen_internal_assert(v!=0);
+    #ifdef EIGEN_HAS_OPENMP
+    if(m_maxThreads>0)
+      *v = m_maxThreads;
+    else
+      *v = omp_get_max_threads();
+    #else
+    *v = 1;
+    #endif
+  }
+  else
+  {
+    eigen_internal_assert(false);
+  }
+}
+
+}
+
+/** Must be call first when calling Eigen from multiple threads */
+inline void initParallel()
+{
+  int nbt;
+  internal::manage_multi_threading(GetAction, &nbt);
+  std::ptrdiff_t l1, l2, l3;
+  internal::manage_caching_sizes(GetAction, &l1, &l2, &l3);
+}
+
+/** \returns the max number of threads reserved for Eigen
+  * \sa setNbThreads */
+inline int nbThreads()
+{
+  int ret;
+  internal::manage_multi_threading(GetAction, &ret);
+  return ret;
+}
+
+/** Sets the max number of threads reserved for Eigen
+  * \sa nbThreads */
+inline void setNbThreads(int v)
+{
+  internal::manage_multi_threading(SetAction, &v);
+}
+
+namespace internal {
+
+template<typename Index> struct GemmParallelInfo
+{
+
+#ifdef EIGEN_HAS_OPENMP
+  GemmParallelInfo() : sync(-1), users(0), lhs_start(0), lhs_length(0) {}
+  std::atomic<Index> sync;
+  std::atomic<int> users;
+#else
+  GemmParallelInfo() : lhs_start(0), lhs_length(0) {}
+#endif
+
+  Index lhs_start;
+  Index lhs_length;
+};
+
+template<bool Condition, typename Functor, typename Index>
+void parallelize_gemm(const Functor& func, Index rows, Index cols, Index depth, bool transpose)
+{
+  // TODO when EIGEN_USE_BLAS is defined,
+  // we should still enable OMP for other scalar types
+  // Without C++11, we have to disable GEMM's parallelization on
+  // non x86 architectures because there volatile is not enough for our purpose.
+  // See bug 1572.
+#if (! defined(EIGEN_HAS_OPENMP)) || defined(EIGEN_USE_BLAS)
+  // FIXME the transpose variable is only needed to properly split
+  // the matrix product when multithreading is enabled. This is a temporary
+  // fix to support row-major destination matrices. This whole
+  // parallelizer mechanism has to be redesigned anyway.
+  EIGEN_UNUSED_VARIABLE(depth);
+  EIGEN_UNUSED_VARIABLE(transpose);
+  func(0,rows, 0,cols);
+#else
+
+  // Dynamically check whether we should enable or disable OpenMP.
+  // The conditions are:
+  // - the max number of threads we can create is greater than 1
+  // - we are not already in a parallel code
+  // - the sizes are large enough
+
+  // compute the maximal number of threads from the size of the product:
+  // This first heuristic takes into account that the product kernel is fully optimized when working with nr columns at once.
+  Index size = transpose ? rows : cols;
+  Index pb_max_threads = std::max<Index>(1,size / Functor::Traits::nr);
+
+  // compute the maximal number of threads from the total amount of work:
+  double work = static_cast<double>(rows) * static_cast<double>(cols) *
+      static_cast<double>(depth);
+  double kMinTaskSize = 50000;  // FIXME improve this heuristic.
+  pb_max_threads = std::max<Index>(1, std::min<Index>(pb_max_threads, static_cast<Index>( work / kMinTaskSize ) ));
+
+  // compute the number of threads we are going to use
+  Index threads = std::min<Index>(nbThreads(), pb_max_threads);
+
+  // if multi-threading is explicitly disabled, not useful, or if we already are in a parallel session,
+  // then abort multi-threading
+  // FIXME omp_get_num_threads()>1 only works for openmp, what if the user does not use openmp?
+  if((!Condition) || (threads==1) || (omp_get_num_threads()>1))
+    return func(0,rows, 0,cols);
+
+  Eigen::initParallel();
+  func.initParallelSession(threads);
+
+  if(transpose)
+    std::swap(rows,cols);
+
+  ei_declare_aligned_stack_constructed_variable(GemmParallelInfo<Index>,info,threads,0);
+
+  #pragma omp parallel num_threads(threads)
+  {
+    Index i = omp_get_thread_num();
+    // Note that the actual number of threads might be lower than the number of request ones.
+    Index actual_threads = omp_get_num_threads();
+
+    Index blockCols = (cols / actual_threads) & ~Index(0x3);
+    Index blockRows = (rows / actual_threads);
+    blockRows = (blockRows/Functor::Traits::mr)*Functor::Traits::mr;
+
+    Index r0 = i*blockRows;
+    Index actualBlockRows = (i+1==actual_threads) ? rows-r0 : blockRows;
+
+    Index c0 = i*blockCols;
+    Index actualBlockCols = (i+1==actual_threads) ? cols-c0 : blockCols;
+
+    info[i].lhs_start = r0;
+    info[i].lhs_length = actualBlockRows;
+
+    if(transpose) func(c0, actualBlockCols, 0, rows, info);
+    else          func(0, rows, c0, actualBlockCols, info);
+  }
+#endif
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_PARALLELIZER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointMatrixMatrix.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
new file mode 100644
index 0000000000000000000000000000000000000000..c7bb44596eb47227edf46ad34e645bc8f8d9965e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointMatrixMatrix.h
@@ -0,0 +1,546 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELFADJOINT_MATRIX_MATRIX_H
+#define EIGEN_SELFADJOINT_MATRIX_MATRIX_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+// pack a selfadjoint block diagonal for use with the gebp_kernel
+template<typename Scalar, typename Index, int Pack1, int Pack2_dummy, int StorageOrder>
+struct symm_pack_lhs
+{
+  template<int BlockRows> inline
+  void pack(Scalar* blockA, const const_blas_data_mapper<Scalar,Index,StorageOrder>& lhs, Index cols, Index i, Index& count)
+  {
+    // normal copy
+    for(Index k=0; k<i; k++)
+      for(Index w=0; w<BlockRows; w++)
+        blockA[count++] = lhs(i+w,k);           // normal
+    // symmetric copy
+    Index h = 0;
+    for(Index k=i; k<i+BlockRows; k++)
+    {
+      for(Index w=0; w<h; w++)
+        blockA[count++] = numext::conj(lhs(k, i+w)); // transposed
+
+      blockA[count++] = numext::real(lhs(k,k));   // real (diagonal)
+
+      for(Index w=h+1; w<BlockRows; w++)
+        blockA[count++] = lhs(i+w, k);          // normal
+      ++h;
+    }
+    // transposed copy
+    for(Index k=i+BlockRows; k<cols; k++)
+      for(Index w=0; w<BlockRows; w++)
+        blockA[count++] = numext::conj(lhs(k, i+w)); // transposed
+  }
+  void operator()(Scalar* blockA, const Scalar* _lhs, Index lhsStride, Index cols, Index rows)
+  {
+    typedef typename unpacket_traits<typename packet_traits<Scalar>::type>::half HalfPacket;
+    typedef typename unpacket_traits<typename unpacket_traits<typename packet_traits<Scalar>::type>::half>::half QuarterPacket;
+    enum { PacketSize = packet_traits<Scalar>::size,
+           HalfPacketSize = unpacket_traits<HalfPacket>::size,
+           QuarterPacketSize = unpacket_traits<QuarterPacket>::size,
+           HasHalf = (int)HalfPacketSize < (int)PacketSize,
+           HasQuarter = (int)QuarterPacketSize < (int)HalfPacketSize};
+
+    const_blas_data_mapper<Scalar,Index,StorageOrder> lhs(_lhs,lhsStride);
+    Index count = 0;
+    //Index peeled_mc3 = (rows/Pack1)*Pack1;
+    
+    const Index peeled_mc3 = Pack1>=3*PacketSize ? (rows/(3*PacketSize))*(3*PacketSize) : 0;
+    const Index peeled_mc2 = Pack1>=2*PacketSize ? peeled_mc3+((rows-peeled_mc3)/(2*PacketSize))*(2*PacketSize) : 0;
+    const Index peeled_mc1 = Pack1>=1*PacketSize ? peeled_mc2+((rows-peeled_mc2)/(1*PacketSize))*(1*PacketSize) : 0;
+    const Index peeled_mc_half = Pack1>=HalfPacketSize ? peeled_mc1+((rows-peeled_mc1)/(HalfPacketSize))*(HalfPacketSize) : 0;
+    const Index peeled_mc_quarter = Pack1>=QuarterPacketSize ? peeled_mc_half+((rows-peeled_mc_half)/(QuarterPacketSize))*(QuarterPacketSize) : 0;
+    
+    if(Pack1>=3*PacketSize)
+      for(Index i=0; i<peeled_mc3; i+=3*PacketSize)
+        pack<3*PacketSize>(blockA, lhs, cols, i, count);
+    
+    if(Pack1>=2*PacketSize)
+      for(Index i=peeled_mc3; i<peeled_mc2; i+=2*PacketSize)
+        pack<2*PacketSize>(blockA, lhs, cols, i, count);
+    
+    if(Pack1>=1*PacketSize)
+      for(Index i=peeled_mc2; i<peeled_mc1; i+=1*PacketSize)
+        pack<1*PacketSize>(blockA, lhs, cols, i, count);
+
+    if(HasHalf && Pack1>=HalfPacketSize)
+      for(Index i=peeled_mc1; i<peeled_mc_half; i+=HalfPacketSize)
+        pack<HalfPacketSize>(blockA, lhs, cols, i, count);
+
+    if(HasQuarter && Pack1>=QuarterPacketSize)
+      for(Index i=peeled_mc_half; i<peeled_mc_quarter; i+=QuarterPacketSize)
+        pack<QuarterPacketSize>(blockA, lhs, cols, i, count);
+
+    // do the same with mr==1
+    for(Index i=peeled_mc_quarter; i<rows; i++)
+    {
+      for(Index k=0; k<i; k++)
+        blockA[count++] = lhs(i, k);                   // normal
+
+      blockA[count++] = numext::real(lhs(i, i));       // real (diagonal)
+
+      for(Index k=i+1; k<cols; k++)
+        blockA[count++] = numext::conj(lhs(k, i));     // transposed
+    }
+  }
+};
+
+template<typename Scalar, typename Index, int nr, int StorageOrder>
+struct symm_pack_rhs
+{
+  enum { PacketSize = packet_traits<Scalar>::size };
+  void operator()(Scalar* blockB, const Scalar* _rhs, Index rhsStride, Index rows, Index cols, Index k2)
+  {
+    Index end_k = k2 + rows;
+    Index count = 0;
+    const_blas_data_mapper<Scalar,Index,StorageOrder> rhs(_rhs,rhsStride);
+    Index packet_cols8 = nr>=8 ? (cols/8) * 8 : 0;
+    Index packet_cols4 = nr>=4 ? (cols/4) * 4 : 0;
+
+    // first part: normal case
+    for(Index j2=0; j2<k2; j2+=nr)
+    {
+      for(Index k=k2; k<end_k; k++)
+      {
+        blockB[count+0] = rhs(k,j2+0);
+        blockB[count+1] = rhs(k,j2+1);
+        if (nr>=4)
+        {
+          blockB[count+2] = rhs(k,j2+2);
+          blockB[count+3] = rhs(k,j2+3);
+        }
+        if (nr>=8)
+        {
+          blockB[count+4] = rhs(k,j2+4);
+          blockB[count+5] = rhs(k,j2+5);
+          blockB[count+6] = rhs(k,j2+6);
+          blockB[count+7] = rhs(k,j2+7);
+        }
+        count += nr;
+      }
+    }
+
+    // second part: diagonal block
+    Index end8 = nr>=8 ? (std::min)(k2+rows,packet_cols8) : k2;
+    if(nr>=8)
+    {
+      for(Index j2=k2; j2<end8; j2+=8)
+      {
+        // again we can split vertically in three different parts (transpose, symmetric, normal)
+        // transpose
+        for(Index k=k2; k<j2; k++)
+        {
+          blockB[count+0] = numext::conj(rhs(j2+0,k));
+          blockB[count+1] = numext::conj(rhs(j2+1,k));
+          blockB[count+2] = numext::conj(rhs(j2+2,k));
+          blockB[count+3] = numext::conj(rhs(j2+3,k));
+          blockB[count+4] = numext::conj(rhs(j2+4,k));
+          blockB[count+5] = numext::conj(rhs(j2+5,k));
+          blockB[count+6] = numext::conj(rhs(j2+6,k));
+          blockB[count+7] = numext::conj(rhs(j2+7,k));
+          count += 8;
+        }
+        // symmetric
+        Index h = 0;
+        for(Index k=j2; k<j2+8; k++)
+        {
+          // normal
+          for (Index w=0 ; w<h; ++w)
+            blockB[count+w] = rhs(k,j2+w);
+
+          blockB[count+h] = numext::real(rhs(k,k));
+
+          // transpose
+          for (Index w=h+1 ; w<8; ++w)
+            blockB[count+w] = numext::conj(rhs(j2+w,k));
+          count += 8;
+          ++h;
+        }
+        // normal
+        for(Index k=j2+8; k<end_k; k++)
+        {
+          blockB[count+0] = rhs(k,j2+0);
+          blockB[count+1] = rhs(k,j2+1);
+          blockB[count+2] = rhs(k,j2+2);
+          blockB[count+3] = rhs(k,j2+3);
+          blockB[count+4] = rhs(k,j2+4);
+          blockB[count+5] = rhs(k,j2+5);
+          blockB[count+6] = rhs(k,j2+6);
+          blockB[count+7] = rhs(k,j2+7);
+          count += 8;
+        }
+      }
+    }
+    if(nr>=4)
+    {
+      for(Index j2=end8; j2<(std::min)(k2+rows,packet_cols4); j2+=4)
+      {
+        // again we can split vertically in three different parts (transpose, symmetric, normal)
+        // transpose
+        for(Index k=k2; k<j2; k++)
+        {
+          blockB[count+0] = numext::conj(rhs(j2+0,k));
+          blockB[count+1] = numext::conj(rhs(j2+1,k));
+          blockB[count+2] = numext::conj(rhs(j2+2,k));
+          blockB[count+3] = numext::conj(rhs(j2+3,k));
+          count += 4;
+        }
+        // symmetric
+        Index h = 0;
+        for(Index k=j2; k<j2+4; k++)
+        {
+          // normal
+          for (Index w=0 ; w<h; ++w)
+            blockB[count+w] = rhs(k,j2+w);
+
+          blockB[count+h] = numext::real(rhs(k,k));
+
+          // transpose
+          for (Index w=h+1 ; w<4; ++w)
+            blockB[count+w] = numext::conj(rhs(j2+w,k));
+          count += 4;
+          ++h;
+        }
+        // normal
+        for(Index k=j2+4; k<end_k; k++)
+        {
+          blockB[count+0] = rhs(k,j2+0);
+          blockB[count+1] = rhs(k,j2+1);
+          blockB[count+2] = rhs(k,j2+2);
+          blockB[count+3] = rhs(k,j2+3);
+          count += 4;
+        }
+      }
+    }
+
+    // third part: transposed
+    if(nr>=8)
+    {
+      for(Index j2=k2+rows; j2<packet_cols8; j2+=8)
+      {
+        for(Index k=k2; k<end_k; k++)
+        {
+          blockB[count+0] = numext::conj(rhs(j2+0,k));
+          blockB[count+1] = numext::conj(rhs(j2+1,k));
+          blockB[count+2] = numext::conj(rhs(j2+2,k));
+          blockB[count+3] = numext::conj(rhs(j2+3,k));
+          blockB[count+4] = numext::conj(rhs(j2+4,k));
+          blockB[count+5] = numext::conj(rhs(j2+5,k));
+          blockB[count+6] = numext::conj(rhs(j2+6,k));
+          blockB[count+7] = numext::conj(rhs(j2+7,k));
+          count += 8;
+        }
+      }
+    }
+    if(nr>=4)
+    {
+      for(Index j2=(std::max)(packet_cols8,k2+rows); j2<packet_cols4; j2+=4)
+      {
+        for(Index k=k2; k<end_k; k++)
+        {
+          blockB[count+0] = numext::conj(rhs(j2+0,k));
+          blockB[count+1] = numext::conj(rhs(j2+1,k));
+          blockB[count+2] = numext::conj(rhs(j2+2,k));
+          blockB[count+3] = numext::conj(rhs(j2+3,k));
+          count += 4;
+        }
+      }
+    }
+
+    // copy the remaining columns one at a time (=> the same with nr==1)
+    for(Index j2=packet_cols4; j2<cols; ++j2)
+    {
+      // transpose
+      Index half = (std::min)(end_k,j2);
+      for(Index k=k2; k<half; k++)
+      {
+        blockB[count] = numext::conj(rhs(j2,k));
+        count += 1;
+      }
+
+      if(half==j2 && half<k2+rows)
+      {
+        blockB[count] = numext::real(rhs(j2,j2));
+        count += 1;
+      }
+      else
+        half--;
+
+      // normal
+      for(Index k=half+1; k<k2+rows; k++)
+      {
+        blockB[count] = rhs(k,j2);
+        count += 1;
+      }
+    }
+  }
+};
+
+/* Optimized selfadjoint matrix * matrix (_SYMM) product built on top of
+ * the general matrix matrix product.
+ */
+template <typename Scalar, typename Index,
+          int LhsStorageOrder, bool LhsSelfAdjoint, bool ConjugateLhs,
+          int RhsStorageOrder, bool RhsSelfAdjoint, bool ConjugateRhs,
+          int ResStorageOrder, int ResInnerStride>
+struct product_selfadjoint_matrix;
+
+template <typename Scalar, typename Index,
+          int LhsStorageOrder, bool LhsSelfAdjoint, bool ConjugateLhs,
+          int RhsStorageOrder, bool RhsSelfAdjoint, bool ConjugateRhs,
+          int ResInnerStride>
+struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,LhsSelfAdjoint,ConjugateLhs, RhsStorageOrder,RhsSelfAdjoint,ConjugateRhs,RowMajor,ResInnerStride>
+{
+
+  static EIGEN_STRONG_INLINE void run(
+    Index rows, Index cols,
+    const Scalar* lhs, Index lhsStride,
+    const Scalar* rhs, Index rhsStride,
+    Scalar* res,       Index resIncr, Index resStride,
+    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
+  {
+    product_selfadjoint_matrix<Scalar, Index,
+      logical_xor(RhsSelfAdjoint,RhsStorageOrder==RowMajor) ? ColMajor : RowMajor,
+      RhsSelfAdjoint, NumTraits<Scalar>::IsComplex && logical_xor(RhsSelfAdjoint, ConjugateRhs),
+      logical_xor(LhsSelfAdjoint,LhsStorageOrder==RowMajor) ? ColMajor : RowMajor,
+      LhsSelfAdjoint, NumTraits<Scalar>::IsComplex && logical_xor(LhsSelfAdjoint, ConjugateLhs),
+      ColMajor,ResInnerStride>
+      ::run(cols, rows,  rhs, rhsStride,  lhs, lhsStride,  res, resIncr, resStride,  alpha, blocking);
+  }
+};
+
+template <typename Scalar, typename Index,
+          int LhsStorageOrder, bool ConjugateLhs,
+          int RhsStorageOrder, bool ConjugateRhs,
+          int ResInnerStride>
+struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs, RhsStorageOrder,false,ConjugateRhs,ColMajor,ResInnerStride>
+{
+
+  static EIGEN_DONT_INLINE void run(
+    Index rows, Index cols,
+    const Scalar* _lhs, Index lhsStride,
+    const Scalar* _rhs, Index rhsStride,
+    Scalar* res,        Index resIncr, Index resStride,
+    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
+};
+
+template <typename Scalar, typename Index,
+          int LhsStorageOrder, bool ConjugateLhs,
+          int RhsStorageOrder, bool ConjugateRhs,
+          int ResInnerStride>
+EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,true,ConjugateLhs, RhsStorageOrder,false,ConjugateRhs,ColMajor,ResInnerStride>::run(
+    Index rows, Index cols,
+    const Scalar* _lhs, Index lhsStride,
+    const Scalar* _rhs, Index rhsStride,
+    Scalar* _res,       Index resIncr, Index resStride,
+    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
+  {
+    Index size = rows;
+
+    typedef gebp_traits<Scalar,Scalar> Traits;
+
+    typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
+    typedef const_blas_data_mapper<Scalar, Index, (LhsStorageOrder == RowMajor) ? ColMajor : RowMajor> LhsTransposeMapper;
+    typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper;
+    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
+    LhsMapper lhs(_lhs,lhsStride);
+    LhsTransposeMapper lhs_transpose(_lhs,lhsStride);
+    RhsMapper rhs(_rhs,rhsStride);
+    ResMapper res(_res, resStride, resIncr);
+
+    Index kc = blocking.kc();                   // cache block size along the K direction
+    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
+    // kc must be smaller than mc
+    kc = (std::min)(kc,mc);
+    std::size_t sizeA = kc*mc;
+    std::size_t sizeB = kc*cols;
+    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
+    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
+
+    gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
+    symm_pack_lhs<Scalar, Index, Traits::mr, Traits::LhsProgress, LhsStorageOrder> pack_lhs;
+    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder> pack_rhs;
+    gemm_pack_lhs<Scalar, Index, LhsTransposeMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder==RowMajor?ColMajor:RowMajor, true> pack_lhs_transposed;
+
+    for(Index k2=0; k2<size; k2+=kc)
+    {
+      const Index actual_kc = (std::min)(k2+kc,size)-k2;
+
+      // we have selected one row panel of rhs and one column panel of lhs
+      // pack rhs's panel into a sequential chunk of memory
+      // and expand each coeff to a constant packet for further reuse
+      pack_rhs(blockB, rhs.getSubMapper(k2,0), actual_kc, cols);
+
+      // the select lhs's panel has to be split in three different parts:
+      //  1 - the transposed panel above the diagonal block => transposed packed copy
+      //  2 - the diagonal block => special packed copy
+      //  3 - the panel below the diagonal block => generic packed copy
+      for(Index i2=0; i2<k2; i2+=mc)
+      {
+        const Index actual_mc = (std::min)(i2+mc,k2)-i2;
+        // transposed packed copy
+        pack_lhs_transposed(blockA, lhs_transpose.getSubMapper(i2, k2), actual_kc, actual_mc);
+
+        gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
+      }
+      // the block diagonal
+      {
+        const Index actual_mc = (std::min)(k2+kc,size)-k2;
+        // symmetric packed copy
+        pack_lhs(blockA, &lhs(k2,k2), lhsStride, actual_kc, actual_mc);
+
+        gebp_kernel(res.getSubMapper(k2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
+      }
+
+      for(Index i2=k2+kc; i2<size; i2+=mc)
+      {
+        const Index actual_mc = (std::min)(i2+mc,size)-i2;
+        gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder,false>()
+          (blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
+
+        gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
+      }
+    }
+  }
+
+// matrix * selfadjoint product
+template <typename Scalar, typename Index,
+          int LhsStorageOrder, bool ConjugateLhs,
+          int RhsStorageOrder, bool ConjugateRhs,
+          int ResInnerStride>
+struct product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLhs, RhsStorageOrder,true,ConjugateRhs,ColMajor,ResInnerStride>
+{
+
+  static EIGEN_DONT_INLINE void run(
+    Index rows, Index cols,
+    const Scalar* _lhs, Index lhsStride,
+    const Scalar* _rhs, Index rhsStride,
+    Scalar* res,        Index resIncr, Index resStride,
+    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
+};
+
+template <typename Scalar, typename Index,
+          int LhsStorageOrder, bool ConjugateLhs,
+          int RhsStorageOrder, bool ConjugateRhs,
+          int ResInnerStride>
+EIGEN_DONT_INLINE void product_selfadjoint_matrix<Scalar,Index,LhsStorageOrder,false,ConjugateLhs, RhsStorageOrder,true,ConjugateRhs,ColMajor,ResInnerStride>::run(
+    Index rows, Index cols,
+    const Scalar* _lhs, Index lhsStride,
+    const Scalar* _rhs, Index rhsStride,
+    Scalar* _res,       Index resIncr, Index resStride,
+    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
+  {
+    Index size = cols;
+
+    typedef gebp_traits<Scalar,Scalar> Traits;
+
+    typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
+    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
+    LhsMapper lhs(_lhs,lhsStride);
+    ResMapper res(_res,resStride, resIncr);
+
+    Index kc = blocking.kc();                   // cache block size along the K direction
+    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
+    std::size_t sizeA = kc*mc;
+    std::size_t sizeB = kc*cols;
+    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
+    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
+
+    gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
+    gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder> pack_lhs;
+    symm_pack_rhs<Scalar, Index, Traits::nr,RhsStorageOrder> pack_rhs;
+
+    for(Index k2=0; k2<size; k2+=kc)
+    {
+      const Index actual_kc = (std::min)(k2+kc,size)-k2;
+
+      pack_rhs(blockB, _rhs, rhsStride, actual_kc, cols, k2);
+
+      // => GEPP
+      for(Index i2=0; i2<rows; i2+=mc)
+      {
+        const Index actual_mc = (std::min)(i2+mc,rows)-i2;
+        pack_lhs(blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);
+
+        gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, alpha);
+      }
+    }
+  }
+
+} // end namespace internal
+
+/***************************************************************************
+* Wrapper to product_selfadjoint_matrix
+***************************************************************************/
+
+namespace internal {
+  
+template<typename Lhs, int LhsMode, typename Rhs, int RhsMode>
+struct selfadjoint_product_impl<Lhs,LhsMode,false,Rhs,RhsMode,false>
+{
+  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
+  
+  typedef internal::blas_traits<Lhs> LhsBlasTraits;
+  typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+  typedef internal::blas_traits<Rhs> RhsBlasTraits;
+  typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+  
+  enum {
+    LhsIsUpper = (LhsMode&(Upper|Lower))==Upper,
+    LhsIsSelfAdjoint = (LhsMode&SelfAdjoint)==SelfAdjoint,
+    RhsIsUpper = (RhsMode&(Upper|Lower))==Upper,
+    RhsIsSelfAdjoint = (RhsMode&SelfAdjoint)==SelfAdjoint
+  };
+  
+  template<typename Dest>
+  static void run(Dest &dst, const Lhs &a_lhs, const Rhs &a_rhs, const Scalar& alpha)
+  {
+    eigen_assert(dst.rows()==a_lhs.rows() && dst.cols()==a_rhs.cols());
+
+    add_const_on_value_type_t<ActualLhsType> lhs = LhsBlasTraits::extract(a_lhs);
+    add_const_on_value_type_t<ActualRhsType> rhs = RhsBlasTraits::extract(a_rhs);
+
+    Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs)
+                               * RhsBlasTraits::extractScalarFactor(a_rhs);
+
+    typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar,
+              Lhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxColsAtCompileTime,1> BlockingType;
+
+    BlockingType blocking(lhs.rows(), rhs.cols(), lhs.cols(), 1, false);
+
+    internal::product_selfadjoint_matrix<Scalar, Index,
+      internal::logical_xor(LhsIsUpper, internal::traits<Lhs>::Flags &RowMajorBit) ? RowMajor : ColMajor, LhsIsSelfAdjoint,
+      NumTraits<Scalar>::IsComplex && internal::logical_xor(LhsIsUpper, bool(LhsBlasTraits::NeedToConjugate)),
+      internal::logical_xor(RhsIsUpper, internal::traits<Rhs>::Flags &RowMajorBit) ? RowMajor : ColMajor, RhsIsSelfAdjoint,
+      NumTraits<Scalar>::IsComplex && internal::logical_xor(RhsIsUpper, bool(RhsBlasTraits::NeedToConjugate)),
+      internal::traits<Dest>::Flags&RowMajorBit  ? RowMajor : ColMajor,
+      Dest::InnerStrideAtCompileTime>
+      ::run(
+        lhs.rows(), rhs.cols(),                 // sizes
+        &lhs.coeffRef(0,0), lhs.outerStride(),  // lhs info
+        &rhs.coeffRef(0,0), rhs.outerStride(),  // rhs info
+        &dst.coeffRef(0,0), dst.innerStride(), dst.outerStride(),  // result info
+        actualAlpha, blocking                   // alpha
+      );
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SELFADJOINT_MATRIX_MATRIX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
new file mode 100644
index 0000000000000000000000000000000000000000..0e371da6b8602a4f0c9d0954ca41a9882168f996
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointMatrixMatrix_BLAS.h
@@ -0,0 +1,297 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+//
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   Self adjoint matrix * matrix product functionality based on ?SYMM/?HEMM.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_SELFADJOINT_MATRIX_MATRIX_BLAS_H
+#define EIGEN_SELFADJOINT_MATRIX_MATRIX_BLAS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+
+/* Optimized selfadjoint matrix * matrix (?SYMM/?HEMM) product */
+
+#define EIGEN_BLAS_SYMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
+template <typename Index, \
+          int LhsStorageOrder, bool ConjugateLhs, \
+          int RhsStorageOrder, bool ConjugateRhs> \
+struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,true,ConjugateLhs,RhsStorageOrder,false,ConjugateRhs,ColMajor,1> \
+{\
+\
+  static void run( \
+    Index rows, Index cols, \
+    const EIGTYPE* _lhs, Index lhsStride, \
+    const EIGTYPE* _rhs, Index rhsStride, \
+    EIGTYPE* res,        Index resIncr, Index resStride, \
+    EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
+  { \
+    EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
+    eigen_assert(resIncr == 1); \
+    char side='L', uplo='L'; \
+    BlasIndex m, n, lda, ldb, ldc; \
+    const EIGTYPE *a, *b; \
+    EIGTYPE beta(1); \
+    MatrixX##EIGPREFIX b_tmp; \
+\
+/* Set transpose options */ \
+/* Set m, n, k */ \
+    m = convert_index<BlasIndex>(rows);  \
+    n = convert_index<BlasIndex>(cols);  \
+\
+/* Set lda, ldb, ldc */ \
+    lda = convert_index<BlasIndex>(lhsStride); \
+    ldb = convert_index<BlasIndex>(rhsStride); \
+    ldc = convert_index<BlasIndex>(resStride); \
+\
+/* Set a, b, c */ \
+    if (LhsStorageOrder==RowMajor) uplo='U'; \
+    a = _lhs; \
+\
+    if (RhsStorageOrder==RowMajor) { \
+      Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \
+      b_tmp = rhs.adjoint(); \
+      b = b_tmp.data(); \
+      ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
+    } else b = _rhs; \
+\
+    BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
+\
+  } \
+};
+
+
+#define EIGEN_BLAS_HEMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
+template <typename Index, \
+          int LhsStorageOrder, bool ConjugateLhs, \
+          int RhsStorageOrder, bool ConjugateRhs> \
+struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,true,ConjugateLhs,RhsStorageOrder,false,ConjugateRhs,ColMajor,1> \
+{\
+  static void run( \
+    Index rows, Index cols, \
+    const EIGTYPE* _lhs, Index lhsStride, \
+    const EIGTYPE* _rhs, Index rhsStride, \
+    EIGTYPE* res,        Index resIncr, Index resStride, \
+    EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
+  { \
+    EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
+    eigen_assert(resIncr == 1); \
+    char side='L', uplo='L'; \
+    BlasIndex m, n, lda, ldb, ldc; \
+    const EIGTYPE *a, *b; \
+    EIGTYPE beta(1); \
+    MatrixX##EIGPREFIX b_tmp; \
+    Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> a_tmp; \
+\
+/* Set transpose options */ \
+/* Set m, n, k */ \
+    m = convert_index<BlasIndex>(rows); \
+    n = convert_index<BlasIndex>(cols); \
+\
+/* Set lda, ldb, ldc */ \
+    lda = convert_index<BlasIndex>(lhsStride); \
+    ldb = convert_index<BlasIndex>(rhsStride); \
+    ldc = convert_index<BlasIndex>(resStride); \
+\
+/* Set a, b, c */ \
+    if (((LhsStorageOrder==ColMajor) && ConjugateLhs) || ((LhsStorageOrder==RowMajor) && (!ConjugateLhs))) { \
+      Map<const Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder>, 0, OuterStride<> > lhs(_lhs,m,m,OuterStride<>(lhsStride)); \
+      a_tmp = lhs.conjugate(); \
+      a = a_tmp.data(); \
+      lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
+    } else a = _lhs; \
+    if (LhsStorageOrder==RowMajor) uplo='U'; \
+\
+    if (RhsStorageOrder==ColMajor && (!ConjugateRhs)) { \
+       b = _rhs; } \
+    else { \
+      if (RhsStorageOrder==ColMajor && ConjugateRhs) { \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,m,n,OuterStride<>(rhsStride)); \
+        b_tmp = rhs.conjugate(); \
+      } else \
+      if (ConjugateRhs) { \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \
+        b_tmp = rhs.adjoint(); \
+      } else { \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > rhs(_rhs,n,m,OuterStride<>(rhsStride)); \
+        b_tmp = rhs.transpose(); \
+      } \
+      b = b_tmp.data(); \
+      ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
+    } \
+\
+    BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
+\
+  } \
+};
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_SYMM_L(double, double, d, dsymm)
+EIGEN_BLAS_SYMM_L(float, float, f, ssymm)
+EIGEN_BLAS_HEMM_L(dcomplex, MKL_Complex16, cd, zhemm)
+EIGEN_BLAS_HEMM_L(scomplex, MKL_Complex8, cf, chemm)
+#else
+EIGEN_BLAS_SYMM_L(double, double, d, dsymm_)
+EIGEN_BLAS_SYMM_L(float, float, f, ssymm_)
+EIGEN_BLAS_HEMM_L(dcomplex, double, cd, zhemm_)
+EIGEN_BLAS_HEMM_L(scomplex, float, cf, chemm_)
+#endif
+
+/* Optimized matrix * selfadjoint matrix (?SYMM/?HEMM) product */
+
+#define EIGEN_BLAS_SYMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
+template <typename Index, \
+          int LhsStorageOrder, bool ConjugateLhs, \
+          int RhsStorageOrder, bool ConjugateRhs> \
+struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,false,ConjugateLhs,RhsStorageOrder,true,ConjugateRhs,ColMajor,1> \
+{\
+\
+  static void run( \
+    Index rows, Index cols, \
+    const EIGTYPE* _lhs, Index lhsStride, \
+    const EIGTYPE* _rhs, Index rhsStride, \
+    EIGTYPE* res,        Index resIncr, Index resStride, \
+    EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
+  { \
+    EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
+    eigen_assert(resIncr == 1); \
+    char side='R', uplo='L'; \
+    BlasIndex m, n, lda, ldb, ldc; \
+    const EIGTYPE *a, *b; \
+    EIGTYPE beta(1); \
+    MatrixX##EIGPREFIX b_tmp; \
+\
+/* Set m, n, k */ \
+    m = convert_index<BlasIndex>(rows);  \
+    n = convert_index<BlasIndex>(cols);  \
+\
+/* Set lda, ldb, ldc */ \
+    lda = convert_index<BlasIndex>(rhsStride); \
+    ldb = convert_index<BlasIndex>(lhsStride); \
+    ldc = convert_index<BlasIndex>(resStride); \
+\
+/* Set a, b, c */ \
+    if (RhsStorageOrder==RowMajor) uplo='U'; \
+    a = _rhs; \
+\
+    if (LhsStorageOrder==RowMajor) { \
+      Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,n,m,OuterStride<>(rhsStride)); \
+      b_tmp = lhs.adjoint(); \
+      b = b_tmp.data(); \
+      ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
+    } else b = _lhs; \
+\
+    BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
+\
+  } \
+};
+
+
+#define EIGEN_BLAS_HEMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
+template <typename Index, \
+          int LhsStorageOrder, bool ConjugateLhs, \
+          int RhsStorageOrder, bool ConjugateRhs> \
+struct product_selfadjoint_matrix<EIGTYPE,Index,LhsStorageOrder,false,ConjugateLhs,RhsStorageOrder,true,ConjugateRhs,ColMajor,1> \
+{\
+  static void run( \
+    Index rows, Index cols, \
+    const EIGTYPE* _lhs, Index lhsStride, \
+    const EIGTYPE* _rhs, Index rhsStride, \
+    EIGTYPE* res,        Index resIncr, Index resStride, \
+    EIGTYPE alpha, level3_blocking<EIGTYPE, EIGTYPE>& /*blocking*/) \
+  { \
+    EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
+    eigen_assert(resIncr == 1); \
+    char side='R', uplo='L'; \
+    BlasIndex m, n, lda, ldb, ldc; \
+    const EIGTYPE *a, *b; \
+    EIGTYPE beta(1); \
+    MatrixX##EIGPREFIX b_tmp; \
+    Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> a_tmp; \
+\
+/* Set m, n, k */ \
+    m = convert_index<BlasIndex>(rows); \
+    n = convert_index<BlasIndex>(cols); \
+\
+/* Set lda, ldb, ldc */ \
+    lda = convert_index<BlasIndex>(rhsStride); \
+    ldb = convert_index<BlasIndex>(lhsStride); \
+    ldc = convert_index<BlasIndex>(resStride); \
+\
+/* Set a, b, c */ \
+    if (((RhsStorageOrder==ColMajor) && ConjugateRhs) || ((RhsStorageOrder==RowMajor) && (!ConjugateRhs))) { \
+      Map<const Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder>, 0, OuterStride<> > rhs(_rhs,n,n,OuterStride<>(rhsStride)); \
+      a_tmp = rhs.conjugate(); \
+      a = a_tmp.data(); \
+      lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
+    } else a = _rhs; \
+    if (RhsStorageOrder==RowMajor) uplo='U'; \
+\
+    if (LhsStorageOrder==ColMajor && (!ConjugateLhs)) { \
+       b = _lhs; } \
+    else { \
+      if (LhsStorageOrder==ColMajor && ConjugateLhs) { \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,m,n,OuterStride<>(lhsStride)); \
+        b_tmp = lhs.conjugate(); \
+      } else \
+      if (ConjugateLhs) { \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,n,m,OuterStride<>(lhsStride)); \
+        b_tmp = lhs.adjoint(); \
+      } else { \
+        Map<const MatrixX##EIGPREFIX, 0, OuterStride<> > lhs(_lhs,n,m,OuterStride<>(lhsStride)); \
+        b_tmp = lhs.transpose(); \
+      } \
+      b = b_tmp.data(); \
+      ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
+    } \
+\
+    BLASFUNC(&side, &uplo, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)b, &ldb, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &ldc); \
+  } \
+};
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_SYMM_R(double, double, d, dsymm)
+EIGEN_BLAS_SYMM_R(float, float, f, ssymm)
+EIGEN_BLAS_HEMM_R(dcomplex, MKL_Complex16, cd, zhemm)
+EIGEN_BLAS_HEMM_R(scomplex, MKL_Complex8, cf, chemm)
+#else
+EIGEN_BLAS_SYMM_R(double, double, d, dsymm_)
+EIGEN_BLAS_SYMM_R(float, float, f, ssymm_)
+EIGEN_BLAS_HEMM_R(dcomplex, double, cd, zhemm_)
+EIGEN_BLAS_HEMM_R(scomplex, float, cf, chemm_)
+#endif
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SELFADJOINT_MATRIX_MATRIX_BLAS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointMatrixVector.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointMatrixVector.h
new file mode 100644
index 0000000000000000000000000000000000000000..a62b6b5b5d29966931e5e1430cdc8876bcc20180
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointMatrixVector.h
@@ -0,0 +1,264 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELFADJOINT_MATRIX_VECTOR_H
+#define EIGEN_SELFADJOINT_MATRIX_VECTOR_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/* Optimized selfadjoint matrix * vector product:
+ * This algorithm processes 2 columns at once that allows to both reduce
+ * the number of load/stores of the result by a factor 2 and to reduce
+ * the instruction dependency.
+ */
+
+template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version=Specialized>
+struct selfadjoint_matrix_vector_product;
+
+template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version>
+struct selfadjoint_matrix_vector_product
+
+{
+static EIGEN_DONT_INLINE EIGEN_DEVICE_FUNC
+void run(
+  Index size,
+  const Scalar*  lhs, Index lhsStride,
+  const Scalar*  rhs,
+  Scalar* res,
+  Scalar alpha);
+};
+
+template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs, int Version>
+EIGEN_DONT_INLINE EIGEN_DEVICE_FUNC
+void selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,Version>::run(
+  Index size,
+  const Scalar*  lhs, Index lhsStride,
+  const Scalar*  rhs,
+  Scalar* res,
+  Scalar alpha)
+{
+  typedef typename packet_traits<Scalar>::type Packet;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  const Index PacketSize = sizeof(Packet)/sizeof(Scalar);
+
+  enum {
+    IsRowMajor = StorageOrder==RowMajor ? 1 : 0,
+    IsLower = UpLo == Lower ? 1 : 0,
+    FirstTriangular = IsRowMajor == IsLower
+  };
+
+  conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex && logical_xor(ConjugateLhs,  IsRowMajor), ConjugateRhs> cj0;
+  conj_helper<Scalar,Scalar,NumTraits<Scalar>::IsComplex && logical_xor(ConjugateLhs, !IsRowMajor), ConjugateRhs> cj1;
+  conj_helper<RealScalar,Scalar,false, ConjugateRhs> cjd;
+
+  conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && logical_xor(ConjugateLhs,  IsRowMajor), ConjugateRhs> pcj0;
+  conj_helper<Packet,Packet,NumTraits<Scalar>::IsComplex && logical_xor(ConjugateLhs, !IsRowMajor), ConjugateRhs> pcj1;
+
+  Scalar cjAlpha = ConjugateRhs ? numext::conj(alpha) : alpha;
+
+  Index bound = numext::maxi(Index(0), size-8) & 0xfffffffe;
+  if (FirstTriangular)
+    bound = size - bound;
+
+  for (Index j=FirstTriangular ? bound : 0;
+       j<(FirstTriangular ? size : bound);j+=2)
+  {
+    const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride;
+    const Scalar* EIGEN_RESTRICT A1 = lhs + (j+1)*lhsStride;
+
+    Scalar t0 = cjAlpha * rhs[j];
+    Packet ptmp0 = pset1<Packet>(t0);
+    Scalar t1 = cjAlpha * rhs[j+1];
+    Packet ptmp1 = pset1<Packet>(t1);
+
+    Scalar t2(0);
+    Packet ptmp2 = pset1<Packet>(t2);
+    Scalar t3(0);
+    Packet ptmp3 = pset1<Packet>(t3);
+
+    Index starti = FirstTriangular ? 0 : j+2;
+    Index endi   = FirstTriangular ? j : size;
+    Index alignedStart = (starti) + internal::first_default_aligned(&res[starti], endi-starti);
+    Index alignedEnd = alignedStart + ((endi-alignedStart)/(PacketSize))*(PacketSize);
+
+    res[j]   += cjd.pmul(numext::real(A0[j]), t0);
+    res[j+1] += cjd.pmul(numext::real(A1[j+1]), t1);
+    if(FirstTriangular)
+    {
+      res[j]   += cj0.pmul(A1[j],   t1);
+      t3       += cj1.pmul(A1[j],   rhs[j]);
+    }
+    else
+    {
+      res[j+1] += cj0.pmul(A0[j+1],t0);
+      t2 += cj1.pmul(A0[j+1], rhs[j+1]);
+    }
+
+    for (Index i=starti; i<alignedStart; ++i)
+    {
+      res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i],t1);
+      t2 += cj1.pmul(A0[i], rhs[i]);
+      t3 += cj1.pmul(A1[i], rhs[i]);
+    }
+    // Yes this an optimization for gcc 4.3 and 4.4 (=> huge speed up)
+    // gcc 4.2 does this optimization automatically.
+    const Scalar* EIGEN_RESTRICT a0It  = A0  + alignedStart;
+    const Scalar* EIGEN_RESTRICT a1It  = A1  + alignedStart;
+    const Scalar* EIGEN_RESTRICT rhsIt = rhs + alignedStart;
+          Scalar* EIGEN_RESTRICT resIt = res + alignedStart;
+    for (Index i=alignedStart; i<alignedEnd; i+=PacketSize)
+    {
+      Packet A0i = ploadu<Packet>(a0It);  a0It  += PacketSize;
+      Packet A1i = ploadu<Packet>(a1It);  a1It  += PacketSize;
+      Packet Bi  = ploadu<Packet>(rhsIt); rhsIt += PacketSize; // FIXME should be aligned in most cases
+      Packet Xi  = pload <Packet>(resIt);
+
+      Xi    = pcj0.pmadd(A0i,ptmp0, pcj0.pmadd(A1i,ptmp1,Xi));
+      ptmp2 = pcj1.pmadd(A0i,  Bi, ptmp2);
+      ptmp3 = pcj1.pmadd(A1i,  Bi, ptmp3);
+      pstore(resIt,Xi); resIt += PacketSize;
+    }
+    for (Index i=alignedEnd; i<endi; i++)
+    {
+      res[i] += cj0.pmul(A0[i], t0) + cj0.pmul(A1[i],t1);
+      t2 += cj1.pmul(A0[i], rhs[i]);
+      t3 += cj1.pmul(A1[i], rhs[i]);
+    }
+
+    res[j]   += alpha * (t2 + predux(ptmp2));
+    res[j+1] += alpha * (t3 + predux(ptmp3));
+  }
+  for (Index j=FirstTriangular ? 0 : bound;j<(FirstTriangular ? bound : size);j++)
+  {
+    const Scalar* EIGEN_RESTRICT A0 = lhs + j*lhsStride;
+
+    Scalar t1 = cjAlpha * rhs[j];
+    Scalar t2(0);
+    res[j] += cjd.pmul(numext::real(A0[j]), t1);
+    for (Index i=FirstTriangular ? 0 : j+1; i<(FirstTriangular ? j : size); i++)
+    {
+      res[i] += cj0.pmul(A0[i], t1);
+      t2 += cj1.pmul(A0[i], rhs[i]);
+    }
+    res[j] += alpha * t2;
+  }
+}
+
+} // end namespace internal 
+
+/***************************************************************************
+* Wrapper to product_selfadjoint_vector
+***************************************************************************/
+
+namespace internal {
+
+template<typename Lhs, int LhsMode, typename Rhs>
+struct selfadjoint_product_impl<Lhs,LhsMode,false,Rhs,0,true>
+{
+  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
+  
+  typedef internal::blas_traits<Lhs> LhsBlasTraits;
+  typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+  typedef internal::remove_all_t<ActualLhsType> ActualLhsTypeCleaned;
+  
+  typedef internal::blas_traits<Rhs> RhsBlasTraits;
+  typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+  typedef internal::remove_all_t<ActualRhsType> ActualRhsTypeCleaned;
+
+  enum { LhsUpLo = LhsMode&(Upper|Lower) };
+
+  template<typename Dest>
+  static EIGEN_DEVICE_FUNC
+  void run(Dest& dest, const Lhs &a_lhs, const Rhs &a_rhs, const Scalar& alpha)
+  {
+    typedef typename Dest::Scalar ResScalar;
+    typedef typename Rhs::Scalar RhsScalar;
+    typedef Map<Matrix<ResScalar,Dynamic,1>, plain_enum_min(AlignedMax,internal::packet_traits<ResScalar>::size)> MappedDest;
+    
+    eigen_assert(dest.rows()==a_lhs.rows() && dest.cols()==a_rhs.cols());
+
+    add_const_on_value_type_t<ActualLhsType> lhs = LhsBlasTraits::extract(a_lhs);
+    add_const_on_value_type_t<ActualRhsType> rhs = RhsBlasTraits::extract(a_rhs);
+
+    Scalar actualAlpha = alpha * LhsBlasTraits::extractScalarFactor(a_lhs)
+                               * RhsBlasTraits::extractScalarFactor(a_rhs);
+
+    enum {
+      EvalToDest = (Dest::InnerStrideAtCompileTime==1),
+      UseRhs = (ActualRhsTypeCleaned::InnerStrideAtCompileTime==1)
+    };
+    
+    internal::gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,!EvalToDest> static_dest;
+    internal::gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::MaxSizeAtCompileTime,!UseRhs> static_rhs;
+
+    ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
+                                                  EvalToDest ? dest.data() : static_dest.data());
+                                                  
+    ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,rhs.size(),
+        UseRhs ? const_cast<RhsScalar*>(rhs.data()) : static_rhs.data());
+    
+    if(!EvalToDest)
+    {
+      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      Index size = dest.size();
+      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      #endif
+      MappedDest(actualDestPtr, dest.size()) = dest;
+    }
+      
+    if(!UseRhs)
+    {
+      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      Index size = rhs.size();
+      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      #endif
+      Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, rhs.size()) = rhs;
+    }
+      
+      
+    internal::selfadjoint_matrix_vector_product<Scalar, Index, (internal::traits<ActualLhsTypeCleaned>::Flags&RowMajorBit) ? RowMajor : ColMajor,
+                                                int(LhsUpLo), bool(LhsBlasTraits::NeedToConjugate), bool(RhsBlasTraits::NeedToConjugate)>::run
+      (
+        lhs.rows(),                             // size
+        &lhs.coeffRef(0,0),  lhs.outerStride(), // lhs info
+        actualRhsPtr,                           // rhs info
+        actualDestPtr,                          // result info
+        actualAlpha                             // scale factor
+      );
+    
+    if(!EvalToDest)
+      dest = MappedDest(actualDestPtr, dest.size());
+  }
+};
+
+template<typename Lhs, typename Rhs, int RhsMode>
+struct selfadjoint_product_impl<Lhs,0,true,Rhs,RhsMode,false>
+{
+  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
+  enum { RhsUpLo = RhsMode&(Upper|Lower)  };
+
+  template<typename Dest>
+  static void run(Dest& dest, const Lhs &a_lhs, const Rhs &a_rhs, const Scalar& alpha)
+  {
+    // let's simply transpose the product
+    Transpose<Dest> destT(dest);
+    selfadjoint_product_impl<Transpose<const Rhs>, int(RhsUpLo)==Upper ? Lower : Upper, false,
+                             Transpose<const Lhs>, 0, true>::run(destT, a_rhs.transpose(), a_lhs.transpose(), alpha);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SELFADJOINT_MATRIX_VECTOR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
new file mode 100644
index 0000000000000000000000000000000000000000..99a8ccdfff5bbcba5e6b725ad0f93d8b0a8533f6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointMatrixVector_BLAS.h
@@ -0,0 +1,120 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   Selfadjoint matrix-vector product functionality based on ?SYMV/HEMV.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_SELFADJOINT_MATRIX_VECTOR_BLAS_H
+#define EIGEN_SELFADJOINT_MATRIX_VECTOR_BLAS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/**********************************************************************
+* This file implements selfadjoint matrix-vector multiplication using BLAS
+**********************************************************************/
+
+// symv/hemv specialization
+
+template<typename Scalar, typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs>
+struct selfadjoint_matrix_vector_product_symv :
+  selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,BuiltIn> {};
+
+#define EIGEN_BLAS_SYMV_SPECIALIZE(Scalar) \
+template<typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs> \
+struct selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,Specialized> { \
+static void run( \
+  Index size, const Scalar*  lhs, Index lhsStride, \
+  const Scalar* _rhs, Scalar* res, Scalar alpha) { \
+    enum {\
+      IsColMajor = StorageOrder==ColMajor \
+    }; \
+    if (IsColMajor == ConjugateLhs) {\
+      selfadjoint_matrix_vector_product<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs,BuiltIn>::run( \
+        size, lhs, lhsStride, _rhs, res, alpha);  \
+    } else {\
+      selfadjoint_matrix_vector_product_symv<Scalar,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs>::run( \
+        size, lhs, lhsStride, _rhs, res, alpha);  \
+    }\
+  } \
+}; \
+
+EIGEN_BLAS_SYMV_SPECIALIZE(double)
+EIGEN_BLAS_SYMV_SPECIALIZE(float)
+EIGEN_BLAS_SYMV_SPECIALIZE(dcomplex)
+EIGEN_BLAS_SYMV_SPECIALIZE(scomplex)
+
+#define EIGEN_BLAS_SYMV_SPECIALIZATION(EIGTYPE,BLASTYPE,BLASFUNC) \
+template<typename Index, int StorageOrder, int UpLo, bool ConjugateLhs, bool ConjugateRhs> \
+struct selfadjoint_matrix_vector_product_symv<EIGTYPE,Index,StorageOrder,UpLo,ConjugateLhs,ConjugateRhs> \
+{ \
+typedef Matrix<EIGTYPE,Dynamic,1,ColMajor> SYMVVector;\
+\
+static void run( \
+Index size, const EIGTYPE*  lhs, Index lhsStride, \
+const EIGTYPE* _rhs, EIGTYPE* res, EIGTYPE alpha) \
+{ \
+  enum {\
+    IsRowMajor = StorageOrder==RowMajor ? 1 : 0, \
+    IsLower = UpLo == Lower ? 1 : 0 \
+  }; \
+  BlasIndex n=convert_index<BlasIndex>(size), lda=convert_index<BlasIndex>(lhsStride), incx=1, incy=1; \
+  EIGTYPE beta(1); \
+  const EIGTYPE *x_ptr; \
+  char uplo=(IsRowMajor) ? (IsLower ? 'U' : 'L') : (IsLower ? 'L' : 'U'); \
+  SYMVVector x_tmp; \
+  if (ConjugateRhs) { \
+    Map<const SYMVVector, 0 > map_x(_rhs,size,1); \
+    x_tmp=map_x.conjugate(); \
+    x_ptr=x_tmp.data(); \
+  } else x_ptr=_rhs; \
+  BLASFUNC(&uplo, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)lhs, &lda, (const BLASTYPE*)x_ptr, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)res, &incy); \
+}\
+};
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_SYMV_SPECIALIZATION(double,   double, dsymv)
+EIGEN_BLAS_SYMV_SPECIALIZATION(float,    float,  ssymv)
+EIGEN_BLAS_SYMV_SPECIALIZATION(dcomplex, MKL_Complex16, zhemv)
+EIGEN_BLAS_SYMV_SPECIALIZATION(scomplex, MKL_Complex8,  chemv)
+#else
+EIGEN_BLAS_SYMV_SPECIALIZATION(double,   double, dsymv_)
+EIGEN_BLAS_SYMV_SPECIALIZATION(float,    float,  ssymv_)
+EIGEN_BLAS_SYMV_SPECIALIZATION(dcomplex, double, zhemv_)
+EIGEN_BLAS_SYMV_SPECIALIZATION(scomplex, float,  chemv_)
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SELFADJOINT_MATRIX_VECTOR_BLAS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointProduct.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointProduct.h
new file mode 100644
index 0000000000000000000000000000000000000000..4cbc1f71406c93ac70a2f678ecbaa8c3c5e1dd90
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointProduct.h
@@ -0,0 +1,135 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELFADJOINT_PRODUCT_H
+#define EIGEN_SELFADJOINT_PRODUCT_H
+
+/**********************************************************************
+* This file implements a self adjoint product: C += A A^T updating only
+* half of the selfadjoint matrix C.
+* It corresponds to the level 3 SYRK and level 2 SYR Blas routines.
+**********************************************************************/
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+
+template<typename Scalar, typename Index, int UpLo, bool ConjLhs, bool ConjRhs>
+struct selfadjoint_rank1_update<Scalar,Index,ColMajor,UpLo,ConjLhs,ConjRhs>
+{
+  static void run(Index size, Scalar* mat, Index stride, const Scalar* vecX, const Scalar* vecY, const Scalar& alpha)
+  {
+    internal::conj_if<ConjRhs> cj;
+    typedef Map<const Matrix<Scalar,Dynamic,1> > OtherMap;
+    typedef std::conditional_t<ConjLhs,typename OtherMap::ConjugateReturnType,const OtherMap&> ConjLhsType;
+    for (Index i=0; i<size; ++i)
+    {
+      Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i+(UpLo==Lower ? i : 0), (UpLo==Lower ? size-i : (i+1)))
+          += (alpha * cj(vecY[i])) * ConjLhsType(OtherMap(vecX+(UpLo==Lower ? i : 0),UpLo==Lower ? size-i : (i+1)));
+    }
+  }
+};
+
+template<typename Scalar, typename Index, int UpLo, bool ConjLhs, bool ConjRhs>
+struct selfadjoint_rank1_update<Scalar,Index,RowMajor,UpLo,ConjLhs,ConjRhs>
+{
+  static void run(Index size, Scalar* mat, Index stride, const Scalar* vecX, const Scalar* vecY, const Scalar& alpha)
+  {
+    selfadjoint_rank1_update<Scalar,Index,ColMajor,UpLo==Lower?Upper:Lower,ConjRhs,ConjLhs>::run(size,mat,stride,vecY,vecX,alpha);
+  }
+};
+
+template<typename MatrixType, typename OtherType, int UpLo, bool OtherIsVector = OtherType::IsVectorAtCompileTime>
+struct selfadjoint_product_selector;
+
+template<typename MatrixType, typename OtherType, int UpLo>
+struct selfadjoint_product_selector<MatrixType,OtherType,UpLo,true>
+{
+  static void run(MatrixType& mat, const OtherType& other, const typename MatrixType::Scalar& alpha)
+  {
+    typedef typename MatrixType::Scalar Scalar;
+    typedef internal::blas_traits<OtherType> OtherBlasTraits;
+    typedef typename OtherBlasTraits::DirectLinearAccessType ActualOtherType;
+    typedef internal::remove_all_t<ActualOtherType> ActualOtherType_;
+    internal::add_const_on_value_type_t<ActualOtherType> actualOther = OtherBlasTraits::extract(other.derived());
+
+    Scalar actualAlpha = alpha * OtherBlasTraits::extractScalarFactor(other.derived());
+
+    enum {
+      StorageOrder = (internal::traits<MatrixType>::Flags&RowMajorBit) ? RowMajor : ColMajor,
+      UseOtherDirectly = ActualOtherType_::InnerStrideAtCompileTime==1
+    };
+    internal::gemv_static_vector_if<Scalar,OtherType::SizeAtCompileTime,OtherType::MaxSizeAtCompileTime,!UseOtherDirectly> static_other;
+
+    ei_declare_aligned_stack_constructed_variable(Scalar, actualOtherPtr, other.size(),
+      (UseOtherDirectly ? const_cast<Scalar*>(actualOther.data()) : static_other.data()));
+      
+    if(!UseOtherDirectly)
+      Map<typename ActualOtherType_::PlainObject>(actualOtherPtr, actualOther.size()) = actualOther;
+    
+    selfadjoint_rank1_update<Scalar,Index,StorageOrder,UpLo,
+                              OtherBlasTraits::NeedToConjugate  && NumTraits<Scalar>::IsComplex,
+                            (!OtherBlasTraits::NeedToConjugate) && NumTraits<Scalar>::IsComplex>
+          ::run(other.size(), mat.data(), mat.outerStride(), actualOtherPtr, actualOtherPtr, actualAlpha);
+  }
+};
+
+template<typename MatrixType, typename OtherType, int UpLo>
+struct selfadjoint_product_selector<MatrixType,OtherType,UpLo,false>
+{
+  static void run(MatrixType& mat, const OtherType& other, const typename MatrixType::Scalar& alpha)
+  {
+    typedef typename MatrixType::Scalar Scalar;
+    typedef internal::blas_traits<OtherType> OtherBlasTraits;
+    typedef typename OtherBlasTraits::DirectLinearAccessType ActualOtherType;
+    typedef internal::remove_all_t<ActualOtherType> ActualOtherType_;
+    internal::add_const_on_value_type_t<ActualOtherType> actualOther = OtherBlasTraits::extract(other.derived());
+
+    Scalar actualAlpha = alpha * OtherBlasTraits::extractScalarFactor(other.derived());
+
+    enum {
+      IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0,
+      OtherIsRowMajor = ActualOtherType_::Flags&RowMajorBit ? 1 : 0
+    };
+
+    Index size = mat.cols();
+    Index depth = actualOther.cols();
+
+    typedef internal::gemm_blocking_space<IsRowMajor ? RowMajor : ColMajor,Scalar,Scalar,
+              MatrixType::MaxColsAtCompileTime, MatrixType::MaxColsAtCompileTime, ActualOtherType_::MaxColsAtCompileTime> BlockingType;
+
+    BlockingType blocking(size, size, depth, 1, false);
+
+
+    internal::general_matrix_matrix_triangular_product<Index,
+      Scalar, OtherIsRowMajor ? RowMajor : ColMajor,   OtherBlasTraits::NeedToConjugate  && NumTraits<Scalar>::IsComplex,
+      Scalar, OtherIsRowMajor ? ColMajor : RowMajor, (!OtherBlasTraits::NeedToConjugate) && NumTraits<Scalar>::IsComplex,
+      IsRowMajor ? RowMajor : ColMajor, MatrixType::InnerStrideAtCompileTime, UpLo>
+      ::run(size, depth,
+            actualOther.data(), actualOther.outerStride(), actualOther.data(), actualOther.outerStride(),
+            mat.data(), mat.innerStride(), mat.outerStride(), actualAlpha, blocking);
+  }
+};
+
+// high level API
+
+template<typename MatrixType, unsigned int UpLo>
+template<typename DerivedU>
+EIGEN_DEVICE_FUNC SelfAdjointView<MatrixType,UpLo>& SelfAdjointView<MatrixType,UpLo>
+::rankUpdate(const MatrixBase<DerivedU>& u, const Scalar& alpha)
+{
+  selfadjoint_product_selector<MatrixType,DerivedU,UpLo>::run(_expression().const_cast_derived(), u.derived(), alpha);
+
+  return *this;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SELFADJOINT_PRODUCT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointRank2Update.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointRank2Update.h
new file mode 100644
index 0000000000000000000000000000000000000000..fb199ad75bb20ab8b578c193ba531b5ed32a6c9f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/SelfadjointRank2Update.h
@@ -0,0 +1,95 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELFADJOINTRANK2UPTADE_H
+#define EIGEN_SELFADJOINTRANK2UPTADE_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/* Optimized selfadjoint matrix += alpha * uv' + conj(alpha)*vu'
+ * It corresponds to the Level2 syr2 BLAS routine
+ */
+
+template<typename Scalar, typename Index, typename UType, typename VType, int UpLo>
+struct selfadjoint_rank2_update_selector;
+
+template<typename Scalar, typename Index, typename UType, typename VType>
+struct selfadjoint_rank2_update_selector<Scalar,Index,UType,VType,Lower>
+{
+  static EIGEN_DEVICE_FUNC
+  void run(Scalar* mat, Index stride, const UType& u, const VType& v, const Scalar& alpha)
+  {
+    const Index size = u.size();
+    for (Index i=0; i<size; ++i)
+    {
+      Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i+i, size-i) +=
+                        (numext::conj(alpha) * numext::conj(u.coeff(i))) * v.tail(size-i)
+                      + (alpha * numext::conj(v.coeff(i))) * u.tail(size-i);
+    }
+  }
+};
+
+template<typename Scalar, typename Index, typename UType, typename VType>
+struct selfadjoint_rank2_update_selector<Scalar,Index,UType,VType,Upper>
+{
+  static void run(Scalar* mat, Index stride, const UType& u, const VType& v, const Scalar& alpha)
+  {
+    const Index size = u.size();
+    for (Index i=0; i<size; ++i)
+      Map<Matrix<Scalar,Dynamic,1> >(mat+stride*i, i+1) +=
+                        (numext::conj(alpha)  * numext::conj(u.coeff(i))) * v.head(i+1)
+                      + (alpha * numext::conj(v.coeff(i))) * u.head(i+1);
+  }
+};
+
+template<bool Cond, typename T>
+using conj_expr_if = std::conditional<!Cond, const T&, CwiseUnaryOp<scalar_conjugate_op<typename traits<T>::Scalar>,T>>;
+
+} // end namespace internal
+
+template<typename MatrixType, unsigned int UpLo>
+template<typename DerivedU, typename DerivedV>
+EIGEN_DEVICE_FUNC SelfAdjointView<MatrixType,UpLo>& SelfAdjointView<MatrixType,UpLo>
+::rankUpdate(const MatrixBase<DerivedU>& u, const MatrixBase<DerivedV>& v, const Scalar& alpha)
+{
+  typedef internal::blas_traits<DerivedU> UBlasTraits;
+  typedef typename UBlasTraits::DirectLinearAccessType ActualUType;
+  typedef internal::remove_all_t<ActualUType> ActualUType_;
+  internal::add_const_on_value_type_t<ActualUType> actualU = UBlasTraits::extract(u.derived());
+
+  typedef internal::blas_traits<DerivedV> VBlasTraits;
+  typedef typename VBlasTraits::DirectLinearAccessType ActualVType;
+  typedef internal::remove_all_t<ActualVType> ActualVType_;
+  internal::add_const_on_value_type_t<ActualVType> actualV = VBlasTraits::extract(v.derived());
+
+  // If MatrixType is row major, then we use the routine for lower triangular in the upper triangular case and
+  // vice versa, and take the complex conjugate of all coefficients and vector entries.
+
+  enum { IsRowMajor = (internal::traits<MatrixType>::Flags&RowMajorBit) ? 1 : 0 };
+  Scalar actualAlpha = alpha * UBlasTraits::extractScalarFactor(u.derived())
+                             * numext::conj(VBlasTraits::extractScalarFactor(v.derived()));
+  if (IsRowMajor)
+    actualAlpha = numext::conj(actualAlpha);
+
+  typedef internal::remove_all_t<typename internal::conj_expr_if<int(IsRowMajor) ^ int(UBlasTraits::NeedToConjugate), ActualUType_>::type> UType;
+  typedef internal::remove_all_t<typename internal::conj_expr_if<int(IsRowMajor) ^ int(VBlasTraits::NeedToConjugate), ActualVType_>::type> VType;
+  internal::selfadjoint_rank2_update_selector<Scalar, Index, UType, VType,
+    (IsRowMajor ? int(UpLo==Upper ? Lower : Upper) : UpLo)>
+    ::run(_expression().const_cast_derived().data(),_expression().outerStride(),UType(actualU),VType(actualV),actualAlpha);
+
+  return *this;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SELFADJOINTRANK2UPTADE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularMatrixMatrix.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularMatrixMatrix.h
new file mode 100644
index 0000000000000000000000000000000000000000..80c98dd0d72e226c81038d49c601b1c9edc0ea9e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularMatrixMatrix.h
@@ -0,0 +1,480 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRIANGULAR_MATRIX_MATRIX_H
+#define EIGEN_TRIANGULAR_MATRIX_MATRIX_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+// template<typename Scalar, int mr, int StorageOrder, bool Conjugate, int Mode>
+// struct gemm_pack_lhs_triangular
+// {
+//   Matrix<Scalar,mr,mr,
+//   void operator()(Scalar* blockA, const EIGEN_RESTRICT Scalar* lhs_, int lhsStride, int depth, int rows)
+//   {
+//     conj_if<NumTraits<Scalar>::IsComplex && Conjugate> cj;
+//     const_blas_data_mapper<Scalar, StorageOrder> lhs(lhs_,lhsStride);
+//     int count = 0;
+//     const int peeled_mc = (rows/mr)*mr;
+//     for(int i=0; i<peeled_mc; i+=mr)
+//     {
+//       for(int k=0; k<depth; k++)
+//         for(int w=0; w<mr; w++)
+//           blockA[count++] = cj(lhs(i+w, k));
+//     }
+//     for(int i=peeled_mc; i<rows; i++)
+//     {
+//       for(int k=0; k<depth; k++)
+//         blockA[count++] = cj(lhs(i, k));
+//     }
+//   }
+// };
+
+/* Optimized triangular matrix * matrix (_TRMM++) product built on top of
+ * the general matrix matrix product.
+ */
+template <typename Scalar, typename Index,
+          int Mode, bool LhsIsTriangular,
+          int LhsStorageOrder, bool ConjugateLhs,
+          int RhsStorageOrder, bool ConjugateRhs,
+          int ResStorageOrder, int ResInnerStride,
+          int Version = Specialized>
+struct product_triangular_matrix_matrix;
+
+template <typename Scalar, typename Index,
+          int Mode, bool LhsIsTriangular,
+          int LhsStorageOrder, bool ConjugateLhs,
+          int RhsStorageOrder, bool ConjugateRhs,
+          int ResInnerStride, int Version>
+struct product_triangular_matrix_matrix<Scalar,Index,Mode,LhsIsTriangular,
+                                           LhsStorageOrder,ConjugateLhs,
+                                           RhsStorageOrder,ConjugateRhs,RowMajor,ResInnerStride,Version>
+{
+  static EIGEN_STRONG_INLINE void run(
+    Index rows, Index cols, Index depth,
+    const Scalar* lhs, Index lhsStride,
+    const Scalar* rhs, Index rhsStride,
+    Scalar* res,       Index resIncr, Index resStride,
+    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
+  {
+    product_triangular_matrix_matrix<Scalar, Index,
+      (Mode&(UnitDiag|ZeroDiag)) | ((Mode&Upper) ? Lower : Upper),
+      (!LhsIsTriangular),
+      RhsStorageOrder==RowMajor ? ColMajor : RowMajor,
+      ConjugateRhs,
+      LhsStorageOrder==RowMajor ? ColMajor : RowMajor,
+      ConjugateLhs,
+      ColMajor, ResInnerStride>
+      ::run(cols, rows, depth, rhs, rhsStride, lhs, lhsStride, res, resIncr, resStride, alpha, blocking);
+  }
+};
+
+// implements col-major += alpha * op(triangular) * op(general)
+template <typename Scalar, typename Index, int Mode,
+          int LhsStorageOrder, bool ConjugateLhs,
+          int RhsStorageOrder, bool ConjugateRhs,
+          int ResInnerStride, int Version>
+struct product_triangular_matrix_matrix<Scalar,Index,Mode,true,
+                                           LhsStorageOrder,ConjugateLhs,
+                                           RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,Version>
+{
+  
+  typedef gebp_traits<Scalar,Scalar> Traits;
+  enum {
+    SmallPanelWidth   = 2 * plain_enum_max(Traits::mr, Traits::nr),
+    IsLower = (Mode&Lower) == Lower,
+    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1
+  };
+
+  static EIGEN_DONT_INLINE void run(
+    Index _rows, Index _cols, Index _depth,
+    const Scalar* lhs_, Index lhsStride,
+    const Scalar* rhs_, Index rhsStride,
+    Scalar* res,        Index resIncr, Index resStride,
+    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
+};
+
+template <typename Scalar, typename Index, int Mode,
+          int LhsStorageOrder, bool ConjugateLhs,
+          int RhsStorageOrder, bool ConjugateRhs,
+          int ResInnerStride, int Version>
+EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,true,
+                                                        LhsStorageOrder,ConjugateLhs,
+                                                        RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,Version>::run(
+    Index _rows, Index _cols, Index _depth,
+    const Scalar* lhs_, Index lhsStride,
+    const Scalar* rhs_, Index rhsStride,
+    Scalar* res_,       Index resIncr, Index resStride,
+    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
+  {
+    // strip zeros
+    Index diagSize  = (std::min)(_rows,_depth);
+    Index rows      = IsLower ? _rows : diagSize;
+    Index depth     = IsLower ? diagSize : _depth;
+    Index cols      = _cols;
+    
+    typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
+    typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper;
+    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
+    LhsMapper lhs(lhs_,lhsStride);
+    RhsMapper rhs(rhs_,rhsStride);
+    ResMapper res(res_, resStride, resIncr);
+
+    Index kc = blocking.kc();                   // cache block size along the K direction
+    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
+    // The small panel size must not be larger than blocking size.
+    // Usually this should never be the case because SmallPanelWidth^2 is very small
+    // compared to L2 cache size, but let's be safe:
+    Index panelWidth = (std::min)(Index(SmallPanelWidth),(std::min)(kc,mc));
+
+    std::size_t sizeA = kc*mc;
+    std::size_t sizeB = kc*cols;
+
+    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
+    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
+
+    // To work around an "error: member reference base type 'Matrix<...>
+    // (Eigen::internal::constructor_without_unaligned_array_assert (*)())' is
+    // not a structure or union" compilation error in nvcc (tested V8.0.61),
+    // create a dummy internal::constructor_without_unaligned_array_assert
+    // object to pass to the Matrix constructor.
+    internal::constructor_without_unaligned_array_assert a;
+    Matrix<Scalar,SmallPanelWidth,SmallPanelWidth,LhsStorageOrder> triangularBuffer(a);
+    triangularBuffer.setZero();
+    if((Mode&ZeroDiag)==ZeroDiag)
+      triangularBuffer.diagonal().setZero();
+    else
+      triangularBuffer.diagonal().setOnes();
+
+    gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
+    gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder> pack_lhs;
+    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder> pack_rhs;
+
+    for(Index k2=IsLower ? depth : 0;
+        IsLower ? k2>0 : k2<depth;
+        IsLower ? k2-=kc : k2+=kc)
+    {
+      Index actual_kc = (std::min)(IsLower ? k2 : depth-k2, kc);
+      Index actual_k2 = IsLower ? k2-actual_kc : k2;
+
+      // align blocks with the end of the triangular part for trapezoidal lhs
+      if((!IsLower)&&(k2<rows)&&(k2+actual_kc>rows))
+      {
+        actual_kc = rows-k2;
+        k2 = k2+actual_kc-kc;
+      }
+
+      pack_rhs(blockB, rhs.getSubMapper(actual_k2,0), actual_kc, cols);
+
+      // the selected lhs's panel has to be split in three different parts:
+      //  1 - the part which is zero => skip it
+      //  2 - the diagonal block => special kernel
+      //  3 - the dense panel below (lower case) or above (upper case) the diagonal block => GEPP
+
+      // the block diagonal, if any:
+      if(IsLower || actual_k2<rows)
+      {
+        // for each small vertical panels of lhs
+        for (Index k1=0; k1<actual_kc; k1+=panelWidth)
+        {
+          Index actualPanelWidth = std::min<Index>(actual_kc-k1, panelWidth);
+          Index lengthTarget = IsLower ? actual_kc-k1-actualPanelWidth : k1;
+          Index startBlock   = actual_k2+k1;
+          Index blockBOffset = k1;
+
+          // => GEBP with the micro triangular block
+          // The trick is to pack this micro block while filling the opposite triangular part with zeros.
+          // To this end we do an extra triangular copy to a small temporary buffer
+          for (Index k=0;k<actualPanelWidth;++k)
+          {
+            if (SetDiag)
+              triangularBuffer.coeffRef(k,k) = lhs(startBlock+k,startBlock+k);
+            for (Index i=IsLower ? k+1 : 0; IsLower ? i<actualPanelWidth : i<k; ++i)
+              triangularBuffer.coeffRef(i,k) = lhs(startBlock+i,startBlock+k);
+          }
+          pack_lhs(blockA, LhsMapper(triangularBuffer.data(), triangularBuffer.outerStride()), actualPanelWidth, actualPanelWidth);
+
+          gebp_kernel(res.getSubMapper(startBlock, 0), blockA, blockB,
+                      actualPanelWidth, actualPanelWidth, cols, alpha,
+                      actualPanelWidth, actual_kc, 0, blockBOffset);
+
+          // GEBP with remaining micro panel
+          if (lengthTarget>0)
+          {
+            Index startTarget  = IsLower ? actual_k2+k1+actualPanelWidth : actual_k2;
+
+            pack_lhs(blockA, lhs.getSubMapper(startTarget,startBlock), actualPanelWidth, lengthTarget);
+
+            gebp_kernel(res.getSubMapper(startTarget, 0), blockA, blockB,
+                        lengthTarget, actualPanelWidth, cols, alpha,
+                        actualPanelWidth, actual_kc, 0, blockBOffset);
+          }
+        }
+      }
+      // the part below (lower case) or above (upper case) the diagonal => GEPP
+      {
+        Index start = IsLower ? k2 : 0;
+        Index end   = IsLower ? rows : (std::min)(actual_k2,rows);
+        for(Index i2=start; i2<end; i2+=mc)
+        {
+          const Index actual_mc = (std::min)(i2+mc,end)-i2;
+          gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr,Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder,false>()
+            (blockA, lhs.getSubMapper(i2, actual_k2), actual_kc, actual_mc);
+
+          gebp_kernel(res.getSubMapper(i2, 0), blockA, blockB, actual_mc,
+                      actual_kc, cols, alpha, -1, -1, 0, 0);
+        }
+      }
+    }
+  }
+
+// implements col-major += alpha * op(general) * op(triangular)
+template <typename Scalar, typename Index, int Mode,
+          int LhsStorageOrder, bool ConjugateLhs,
+          int RhsStorageOrder, bool ConjugateRhs,
+          int ResInnerStride, int Version>
+struct product_triangular_matrix_matrix<Scalar,Index,Mode,false,
+                                        LhsStorageOrder,ConjugateLhs,
+                                        RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,Version>
+{
+  typedef gebp_traits<Scalar,Scalar> Traits;
+  enum {
+    SmallPanelWidth   = plain_enum_max(Traits::mr, Traits::nr),
+    IsLower = (Mode&Lower) == Lower,
+    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1
+  };
+
+  static EIGEN_DONT_INLINE void run(
+    Index _rows, Index _cols, Index _depth,
+    const Scalar* lhs_, Index lhsStride,
+    const Scalar* rhs_, Index rhsStride,
+    Scalar* res,        Index resIncr, Index resStride,
+    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking);
+};
+
+template <typename Scalar, typename Index, int Mode,
+          int LhsStorageOrder, bool ConjugateLhs,
+          int RhsStorageOrder, bool ConjugateRhs,
+          int ResInnerStride, int Version>
+EIGEN_DONT_INLINE void product_triangular_matrix_matrix<Scalar,Index,Mode,false,
+                                                        LhsStorageOrder,ConjugateLhs,
+                                                        RhsStorageOrder,ConjugateRhs,ColMajor,ResInnerStride,Version>::run(
+    Index _rows, Index _cols, Index _depth,
+    const Scalar* lhs_, Index lhsStride,
+    const Scalar* rhs_, Index rhsStride,
+    Scalar* res_,       Index resIncr, Index resStride,
+    const Scalar& alpha, level3_blocking<Scalar,Scalar>& blocking)
+  {
+    const Index PacketBytes = packet_traits<Scalar>::size*sizeof(Scalar);
+    // strip zeros
+    Index diagSize  = (std::min)(_cols,_depth);
+    Index rows      = _rows;
+    Index depth     = IsLower ? _depth : diagSize;
+    Index cols      = IsLower ? diagSize : _cols;
+    
+    typedef const_blas_data_mapper<Scalar, Index, LhsStorageOrder> LhsMapper;
+    typedef const_blas_data_mapper<Scalar, Index, RhsStorageOrder> RhsMapper;
+    typedef blas_data_mapper<typename Traits::ResScalar, Index, ColMajor, Unaligned, ResInnerStride> ResMapper;
+    LhsMapper lhs(lhs_,lhsStride);
+    RhsMapper rhs(rhs_,rhsStride);
+    ResMapper res(res_, resStride, resIncr);
+
+    Index kc = blocking.kc();                   // cache block size along the K direction
+    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
+
+    std::size_t sizeA = kc*mc;
+    std::size_t sizeB = kc*cols+EIGEN_MAX_ALIGN_BYTES/sizeof(Scalar);
+
+    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
+    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
+
+    internal::constructor_without_unaligned_array_assert a;
+    Matrix<Scalar,SmallPanelWidth,SmallPanelWidth,RhsStorageOrder> triangularBuffer(a);
+    triangularBuffer.setZero();
+    if((Mode&ZeroDiag)==ZeroDiag)
+      triangularBuffer.diagonal().setZero();
+    else
+      triangularBuffer.diagonal().setOnes();
+
+    gebp_kernel<Scalar, Scalar, Index, ResMapper, Traits::mr, Traits::nr, ConjugateLhs, ConjugateRhs> gebp_kernel;
+    gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, LhsStorageOrder> pack_lhs;
+    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder> pack_rhs;
+    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr,RhsStorageOrder,false,true> pack_rhs_panel;
+
+    for(Index k2=IsLower ? 0 : depth;
+        IsLower ? k2<depth  : k2>0;
+        IsLower ? k2+=kc   : k2-=kc)
+    {
+      Index actual_kc = (std::min)(IsLower ? depth-k2 : k2, kc);
+      Index actual_k2 = IsLower ? k2 : k2-actual_kc;
+
+      // align blocks with the end of the triangular part for trapezoidal rhs
+      if(IsLower && (k2<cols) && (actual_k2+actual_kc>cols))
+      {
+        actual_kc = cols-k2;
+        k2 = actual_k2 + actual_kc - kc;
+      }
+
+      // remaining size
+      Index rs = IsLower ? (std::min)(cols,actual_k2) : cols - k2;
+      // size of the triangular part
+      Index ts = (IsLower && actual_k2>=cols) ? 0 : actual_kc;
+
+      Scalar* geb = blockB+ts*ts;
+      geb = geb + internal::first_aligned<PacketBytes>(geb,PacketBytes/sizeof(Scalar));
+
+      pack_rhs(geb, rhs.getSubMapper(actual_k2,IsLower ? 0 : k2), actual_kc, rs);
+
+      // pack the triangular part of the rhs padding the unrolled blocks with zeros
+      if(ts>0)
+      {
+        for (Index j2=0; j2<actual_kc; j2+=SmallPanelWidth)
+        {
+          Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth);
+          Index actual_j2 = actual_k2 + j2;
+          Index panelOffset = IsLower ? j2+actualPanelWidth : 0;
+          Index panelLength = IsLower ? actual_kc-j2-actualPanelWidth : j2;
+          // general part
+          pack_rhs_panel(blockB+j2*actual_kc,
+                         rhs.getSubMapper(actual_k2+panelOffset, actual_j2),
+                         panelLength, actualPanelWidth,
+                         actual_kc, panelOffset);
+
+          // append the triangular part via a temporary buffer
+          for (Index j=0;j<actualPanelWidth;++j)
+          {
+            if (SetDiag)
+              triangularBuffer.coeffRef(j,j) = rhs(actual_j2+j,actual_j2+j);
+            for (Index k=IsLower ? j+1 : 0; IsLower ? k<actualPanelWidth : k<j; ++k)
+              triangularBuffer.coeffRef(k,j) = rhs(actual_j2+k,actual_j2+j);
+          }
+
+          pack_rhs_panel(blockB+j2*actual_kc,
+                         RhsMapper(triangularBuffer.data(), triangularBuffer.outerStride()),
+                         actualPanelWidth, actualPanelWidth,
+                         actual_kc, j2);
+        }
+      }
+
+      for (Index i2=0; i2<rows; i2+=mc)
+      {
+        const Index actual_mc = (std::min)(mc,rows-i2);
+        pack_lhs(blockA, lhs.getSubMapper(i2, actual_k2), actual_kc, actual_mc);
+
+        // triangular kernel
+        if(ts>0)
+        {
+          for (Index j2=0; j2<actual_kc; j2+=SmallPanelWidth)
+          {
+            Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth);
+            Index panelLength = IsLower ? actual_kc-j2 : j2+actualPanelWidth;
+            Index blockOffset = IsLower ? j2 : 0;
+
+            gebp_kernel(res.getSubMapper(i2, actual_k2 + j2),
+                        blockA, blockB+j2*actual_kc,
+                        actual_mc, panelLength, actualPanelWidth,
+                        alpha,
+                        actual_kc, actual_kc,  // strides
+                        blockOffset, blockOffset);// offsets
+          }
+        }
+        gebp_kernel(res.getSubMapper(i2, IsLower ? 0 : k2),
+                    blockA, geb, actual_mc, actual_kc, rs,
+                    alpha,
+                    -1, -1, 0, 0);
+      }
+    }
+  }
+
+/***************************************************************************
+* Wrapper to product_triangular_matrix_matrix
+***************************************************************************/
+
+} // end namespace internal
+
+namespace internal {
+template<int Mode, bool LhsIsTriangular, typename Lhs, typename Rhs>
+struct triangular_product_impl<Mode,LhsIsTriangular,Lhs,false,Rhs,false>
+{
+  template<typename Dest> static void run(Dest& dst, const Lhs &a_lhs, const Rhs &a_rhs, const typename Dest::Scalar& alpha)
+  {
+    typedef typename Lhs::Scalar  LhsScalar;
+    typedef typename Rhs::Scalar  RhsScalar;
+    typedef typename Dest::Scalar Scalar;
+    
+    typedef internal::blas_traits<Lhs> LhsBlasTraits;
+    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+    typedef internal::remove_all_t<ActualLhsType> ActualLhsTypeCleaned;
+    typedef internal::blas_traits<Rhs> RhsBlasTraits;
+    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+    typedef internal::remove_all_t<ActualRhsType> ActualRhsTypeCleaned;
+
+    internal::add_const_on_value_type_t<ActualLhsType> lhs = LhsBlasTraits::extract(a_lhs);
+    internal::add_const_on_value_type_t<ActualRhsType> rhs = RhsBlasTraits::extract(a_rhs);
+
+    // Empty product, return early.  Otherwise, we get `nullptr` use errors below when we try to access
+    // coeffRef(0,0).
+    if (lhs.size() == 0 || rhs.size() == 0) {
+      return;
+    }
+
+    LhsScalar lhs_alpha = LhsBlasTraits::extractScalarFactor(a_lhs);
+    RhsScalar rhs_alpha = RhsBlasTraits::extractScalarFactor(a_rhs);
+    Scalar actualAlpha = alpha * lhs_alpha * rhs_alpha;
+
+    typedef internal::gemm_blocking_space<(Dest::Flags&RowMajorBit) ? RowMajor : ColMajor,Scalar,Scalar,
+              Lhs::MaxRowsAtCompileTime, Rhs::MaxColsAtCompileTime, Lhs::MaxColsAtCompileTime,4> BlockingType;
+
+    enum { IsLower = (Mode&Lower) == Lower };
+    Index stripedRows  = ((!LhsIsTriangular) || (IsLower))  ? lhs.rows() : (std::min)(lhs.rows(),lhs.cols());
+    Index stripedCols  = ((LhsIsTriangular)  || (!IsLower)) ? rhs.cols() : (std::min)(rhs.cols(),rhs.rows());
+    Index stripedDepth = LhsIsTriangular ? ((!IsLower) ? lhs.cols() : (std::min)(lhs.cols(),lhs.rows()))
+                                         : ((IsLower)  ? rhs.rows() : (std::min)(rhs.rows(),rhs.cols()));
+
+    BlockingType blocking(stripedRows, stripedCols, stripedDepth, 1, false);
+
+    internal::product_triangular_matrix_matrix<Scalar, Index,
+      Mode, LhsIsTriangular,
+      (internal::traits<ActualLhsTypeCleaned>::Flags&RowMajorBit) ? RowMajor : ColMajor, LhsBlasTraits::NeedToConjugate,
+      (internal::traits<ActualRhsTypeCleaned>::Flags&RowMajorBit) ? RowMajor : ColMajor, RhsBlasTraits::NeedToConjugate,
+      (internal::traits<Dest          >::Flags&RowMajorBit) ? RowMajor : ColMajor, Dest::InnerStrideAtCompileTime>
+      ::run(
+        stripedRows, stripedCols, stripedDepth,   // sizes
+        &lhs.coeffRef(0,0), lhs.outerStride(),    // lhs info
+        &rhs.coeffRef(0,0), rhs.outerStride(),    // rhs info
+        &dst.coeffRef(0,0), dst.innerStride(), dst.outerStride(),    // result info
+        actualAlpha, blocking
+      );
+
+    // Apply correction if the diagonal is unit and a scalar factor was nested:
+    if ((Mode&UnitDiag)==UnitDiag)
+    {
+      if (LhsIsTriangular && !numext::is_exactly_one(lhs_alpha))
+      {
+        Index diagSize = (std::min)(lhs.rows(),lhs.cols());
+        dst.topRows(diagSize) -= ((lhs_alpha-LhsScalar(1))*a_rhs).topRows(diagSize);
+      }
+      else if ((!LhsIsTriangular) && !numext::is_exactly_one(rhs_alpha))
+      {
+        Index diagSize = (std::min)(rhs.rows(),rhs.cols());
+        dst.leftCols(diagSize) -= (rhs_alpha-RhsScalar(1))*a_lhs.leftCols(diagSize);
+      }
+    }
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRIANGULAR_MATRIX_MATRIX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
new file mode 100644
index 0000000000000000000000000000000000000000..1eb57d3d69ebc6e76e854970afee8f5c1bb2dcae
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularMatrixMatrix_BLAS.h
@@ -0,0 +1,319 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   Triangular matrix * matrix product functionality based on ?TRMM.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_TRIANGULAR_MATRIX_MATRIX_BLAS_H
+#define EIGEN_TRIANGULAR_MATRIX_MATRIX_BLAS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+
+template <typename Scalar, typename Index,
+          int Mode, bool LhsIsTriangular,
+          int LhsStorageOrder, bool ConjugateLhs,
+          int RhsStorageOrder, bool ConjugateRhs,
+          int ResStorageOrder>
+struct product_triangular_matrix_matrix_trmm :
+       product_triangular_matrix_matrix<Scalar,Index,Mode,
+          LhsIsTriangular,LhsStorageOrder,ConjugateLhs,
+          RhsStorageOrder, ConjugateRhs, ResStorageOrder, 1, BuiltIn> {};
+
+
+// try to go to BLAS specialization
+#define EIGEN_BLAS_TRMM_SPECIALIZE(Scalar, LhsIsTriangular) \
+template <typename Index, int Mode, \
+          int LhsStorageOrder, bool ConjugateLhs, \
+          int RhsStorageOrder, bool ConjugateRhs> \
+struct product_triangular_matrix_matrix<Scalar,Index, Mode, LhsIsTriangular, \
+           LhsStorageOrder,ConjugateLhs, RhsStorageOrder,ConjugateRhs,ColMajor,1,Specialized> { \
+  static inline void run(Index _rows, Index _cols, Index _depth, const Scalar* _lhs, Index lhsStride,\
+    const Scalar* _rhs, Index rhsStride, Scalar* res, Index resIncr, Index resStride, Scalar alpha, level3_blocking<Scalar,Scalar>& blocking) { \
+      EIGEN_ONLY_USED_FOR_DEBUG(resIncr); \
+      eigen_assert(resIncr == 1); \
+      product_triangular_matrix_matrix_trmm<Scalar,Index,Mode, \
+        LhsIsTriangular,LhsStorageOrder,ConjugateLhs, \
+        RhsStorageOrder, ConjugateRhs, ColMajor>::run( \
+          _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, resStride, alpha, blocking); \
+  } \
+};
+
+EIGEN_BLAS_TRMM_SPECIALIZE(double, true)
+EIGEN_BLAS_TRMM_SPECIALIZE(double, false)
+EIGEN_BLAS_TRMM_SPECIALIZE(dcomplex, true)
+EIGEN_BLAS_TRMM_SPECIALIZE(dcomplex, false)
+EIGEN_BLAS_TRMM_SPECIALIZE(float, true)
+EIGEN_BLAS_TRMM_SPECIALIZE(float, false)
+EIGEN_BLAS_TRMM_SPECIALIZE(scomplex, true)
+EIGEN_BLAS_TRMM_SPECIALIZE(scomplex, false)
+
+// implements col-major += alpha * op(triangular) * op(general)
+#define EIGEN_BLAS_TRMM_L(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
+template <typename Index, int Mode, \
+          int LhsStorageOrder, bool ConjugateLhs, \
+          int RhsStorageOrder, bool ConjugateRhs> \
+struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,true, \
+         LhsStorageOrder,ConjugateLhs,RhsStorageOrder,ConjugateRhs,ColMajor> \
+{ \
+  enum { \
+    IsLower = (Mode&Lower) == Lower, \
+    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
+    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
+    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
+    LowUp = IsLower ? Lower : Upper, \
+    conjA = ((LhsStorageOrder==ColMajor) && ConjugateLhs) ? 1 : 0 \
+  }; \
+\
+  static void run( \
+    Index _rows, Index _cols, Index _depth, \
+    const EIGTYPE* _lhs, Index lhsStride, \
+    const EIGTYPE* _rhs, Index rhsStride, \
+    EIGTYPE* res,        Index resStride, \
+    EIGTYPE alpha, level3_blocking<EIGTYPE,EIGTYPE>& blocking) \
+  { \
+   Index diagSize  = (std::min)(_rows,_depth); \
+   Index rows      = IsLower ? _rows : diagSize; \
+   Index depth     = IsLower ? diagSize : _depth; \
+   Index cols      = _cols; \
+\
+   typedef Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> MatrixLhs; \
+   typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs; \
+\
+/* Non-square case - doesn't fit to BLAS ?TRMM. Fall to default triangular product or call BLAS ?GEMM*/ \
+   if (rows != depth) { \
+\
+     /* FIXME handle mkl_domain_get_max_threads */ \
+     /*int nthr = mkl_domain_get_max_threads(EIGEN_BLAS_DOMAIN_BLAS);*/ int nthr = 1;\
+\
+     if (((nthr==1) && (((std::max)(rows,depth)-diagSize)/(double)diagSize < 0.5))) { \
+     /* Most likely no benefit to call TRMM or GEMM from BLAS */ \
+       product_triangular_matrix_matrix<EIGTYPE,Index,Mode,true, \
+       LhsStorageOrder,ConjugateLhs, RhsStorageOrder, ConjugateRhs, ColMajor, 1, BuiltIn>::run( \
+           _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, 1, resStride, alpha, blocking); \
+     /*std::cout << "TRMM_L: A is not square! Go to Eigen TRMM implementation!\n";*/ \
+     } else { \
+     /* Make sense to call GEMM */ \
+       Map<const MatrixLhs, 0, OuterStride<> > lhsMap(_lhs,rows,depth,OuterStride<>(lhsStride)); \
+       MatrixLhs aa_tmp=lhsMap.template triangularView<Mode>(); \
+       BlasIndex aStride = convert_index<BlasIndex>(aa_tmp.outerStride()); \
+       gemm_blocking_space<ColMajor,EIGTYPE,EIGTYPE,Dynamic,Dynamic,Dynamic> gemm_blocking(_rows,_cols,_depth, 1, true); \
+       general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor,1>::run( \
+       rows, cols, depth, aa_tmp.data(), aStride, _rhs, rhsStride, res, 1, resStride, alpha, gemm_blocking, 0); \
+\
+     /*std::cout << "TRMM_L: A is not square! Go to BLAS GEMM implementation! " << nthr<<" \n";*/ \
+     } \
+     return; \
+   } \
+   char side = 'L', transa, uplo, diag = 'N'; \
+   EIGTYPE *b; \
+   const EIGTYPE *a; \
+   BlasIndex m, n, lda, ldb; \
+\
+/* Set m, n */ \
+   m = convert_index<BlasIndex>(diagSize); \
+   n = convert_index<BlasIndex>(cols); \
+\
+/* Set trans */ \
+   transa = (LhsStorageOrder==RowMajor) ? ((ConjugateLhs) ? 'C' : 'T') : 'N'; \
+\
+/* Set b, ldb */ \
+   Map<const MatrixRhs, 0, OuterStride<> > rhs(_rhs,depth,cols,OuterStride<>(rhsStride)); \
+   MatrixX##EIGPREFIX b_tmp; \
+\
+   if (ConjugateRhs) b_tmp = rhs.conjugate(); else b_tmp = rhs; \
+   b = b_tmp.data(); \
+   ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
+\
+/* Set uplo */ \
+   uplo = IsLower ? 'L' : 'U'; \
+   if (LhsStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
+/* Set a, lda */ \
+   Map<const MatrixLhs, 0, OuterStride<> > lhs(_lhs,rows,depth,OuterStride<>(lhsStride)); \
+   MatrixLhs a_tmp; \
+\
+   if ((conjA!=0) || (SetDiag==0)) { \
+     if (conjA) a_tmp = lhs.conjugate(); else a_tmp = lhs; \
+     if (IsZeroDiag) \
+       a_tmp.diagonal().setZero(); \
+     else if (IsUnitDiag) \
+       a_tmp.diagonal().setOnes();\
+     a = a_tmp.data(); \
+     lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
+   } else { \
+     a = _lhs; \
+     lda = convert_index<BlasIndex>(lhsStride); \
+   } \
+   /*std::cout << "TRMM_L: A is square! Go to BLAS TRMM implementation! \n";*/ \
+/* call ?trmm*/ \
+   BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)b, &ldb); \
+\
+/* Add op(a_triangular)*b into res*/ \
+   Map<MatrixX##EIGPREFIX, 0, OuterStride<> > res_tmp(res,rows,cols,OuterStride<>(resStride)); \
+   res_tmp=res_tmp+b_tmp; \
+  } \
+};
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_TRMM_L(double, double, d, dtrmm)
+EIGEN_BLAS_TRMM_L(dcomplex, MKL_Complex16, cd, ztrmm)
+EIGEN_BLAS_TRMM_L(float, float, f, strmm)
+EIGEN_BLAS_TRMM_L(scomplex, MKL_Complex8, cf, ctrmm)
+#else
+EIGEN_BLAS_TRMM_L(double, double, d, dtrmm_)
+EIGEN_BLAS_TRMM_L(dcomplex, double, cd, ztrmm_)
+EIGEN_BLAS_TRMM_L(float, float, f, strmm_)
+EIGEN_BLAS_TRMM_L(scomplex, float, cf, ctrmm_)
+#endif
+
+// implements col-major += alpha * op(general) * op(triangular)
+#define EIGEN_BLAS_TRMM_R(EIGTYPE, BLASTYPE, EIGPREFIX, BLASFUNC) \
+template <typename Index, int Mode, \
+          int LhsStorageOrder, bool ConjugateLhs, \
+          int RhsStorageOrder, bool ConjugateRhs> \
+struct product_triangular_matrix_matrix_trmm<EIGTYPE,Index,Mode,false, \
+         LhsStorageOrder,ConjugateLhs,RhsStorageOrder,ConjugateRhs,ColMajor> \
+{ \
+  enum { \
+    IsLower = (Mode&Lower) == Lower, \
+    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
+    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
+    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
+    LowUp = IsLower ? Lower : Upper, \
+    conjA = ((RhsStorageOrder==ColMajor) && ConjugateRhs) ? 1 : 0 \
+  }; \
+\
+  static void run( \
+    Index _rows, Index _cols, Index _depth, \
+    const EIGTYPE* _lhs, Index lhsStride, \
+    const EIGTYPE* _rhs, Index rhsStride, \
+    EIGTYPE* res,        Index resStride, \
+    EIGTYPE alpha, level3_blocking<EIGTYPE,EIGTYPE>& blocking) \
+  { \
+   Index diagSize  = (std::min)(_cols,_depth); \
+   Index rows      = _rows; \
+   Index depth     = IsLower ? _depth : diagSize; \
+   Index cols      = IsLower ? diagSize : _cols; \
+\
+   typedef Matrix<EIGTYPE, Dynamic, Dynamic, LhsStorageOrder> MatrixLhs; \
+   typedef Matrix<EIGTYPE, Dynamic, Dynamic, RhsStorageOrder> MatrixRhs; \
+\
+/* Non-square case - doesn't fit to BLAS ?TRMM. Fall to default triangular product or call BLAS ?GEMM*/ \
+   if (cols != depth) { \
+\
+     int nthr = 1 /*mkl_domain_get_max_threads(EIGEN_BLAS_DOMAIN_BLAS)*/; \
+\
+     if ((nthr==1) && (((std::max)(cols,depth)-diagSize)/(double)diagSize < 0.5)) { \
+     /* Most likely no benefit to call TRMM or GEMM from BLAS*/ \
+       product_triangular_matrix_matrix<EIGTYPE,Index,Mode,false, \
+       LhsStorageOrder,ConjugateLhs, RhsStorageOrder, ConjugateRhs, ColMajor, 1, BuiltIn>::run( \
+           _rows, _cols, _depth, _lhs, lhsStride, _rhs, rhsStride, res, 1, resStride, alpha, blocking); \
+       /*std::cout << "TRMM_R: A is not square! Go to Eigen TRMM implementation!\n";*/ \
+     } else { \
+     /* Make sense to call GEMM */ \
+       Map<const MatrixRhs, 0, OuterStride<> > rhsMap(_rhs,depth,cols, OuterStride<>(rhsStride)); \
+       MatrixRhs aa_tmp=rhsMap.template triangularView<Mode>(); \
+       BlasIndex aStride = convert_index<BlasIndex>(aa_tmp.outerStride()); \
+       gemm_blocking_space<ColMajor,EIGTYPE,EIGTYPE,Dynamic,Dynamic,Dynamic> gemm_blocking(_rows,_cols,_depth, 1, true); \
+       general_matrix_matrix_product<Index,EIGTYPE,LhsStorageOrder,ConjugateLhs,EIGTYPE,RhsStorageOrder,ConjugateRhs,ColMajor,1>::run( \
+       rows, cols, depth, _lhs, lhsStride, aa_tmp.data(), aStride, res, 1, resStride, alpha, gemm_blocking, 0); \
+\
+     /*std::cout << "TRMM_R: A is not square! Go to BLAS GEMM implementation! " << nthr<<" \n";*/ \
+     } \
+     return; \
+   } \
+   char side = 'R', transa, uplo, diag = 'N'; \
+   EIGTYPE *b; \
+   const EIGTYPE *a; \
+   BlasIndex m, n, lda, ldb; \
+\
+/* Set m, n */ \
+   m = convert_index<BlasIndex>(rows); \
+   n = convert_index<BlasIndex>(diagSize); \
+\
+/* Set trans */ \
+   transa = (RhsStorageOrder==RowMajor) ? ((ConjugateRhs) ? 'C' : 'T') : 'N'; \
+\
+/* Set b, ldb */ \
+   Map<const MatrixLhs, 0, OuterStride<> > lhs(_lhs,rows,depth,OuterStride<>(lhsStride)); \
+   MatrixX##EIGPREFIX b_tmp; \
+\
+   if (ConjugateLhs) b_tmp = lhs.conjugate(); else b_tmp = lhs; \
+   b = b_tmp.data(); \
+   ldb = convert_index<BlasIndex>(b_tmp.outerStride()); \
+\
+/* Set uplo */ \
+   uplo = IsLower ? 'L' : 'U'; \
+   if (RhsStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
+/* Set a, lda */ \
+   Map<const MatrixRhs, 0, OuterStride<> > rhs(_rhs,depth,cols, OuterStride<>(rhsStride)); \
+   MatrixRhs a_tmp; \
+\
+   if ((conjA!=0) || (SetDiag==0)) { \
+     if (conjA) a_tmp = rhs.conjugate(); else a_tmp = rhs; \
+     if (IsZeroDiag) \
+       a_tmp.diagonal().setZero(); \
+     else if (IsUnitDiag) \
+       a_tmp.diagonal().setOnes();\
+     a = a_tmp.data(); \
+     lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
+   } else { \
+     a = _rhs; \
+     lda = convert_index<BlasIndex>(rhsStride); \
+   } \
+   /*std::cout << "TRMM_R: A is square! Go to BLAS TRMM implementation! \n";*/ \
+/* call ?trmm*/ \
+   BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)b, &ldb); \
+\
+/* Add op(a_triangular)*b into res*/ \
+   Map<MatrixX##EIGPREFIX, 0, OuterStride<> > res_tmp(res,rows,cols,OuterStride<>(resStride)); \
+   res_tmp=res_tmp+b_tmp; \
+  } \
+};
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_TRMM_R(double, double, d, dtrmm)
+EIGEN_BLAS_TRMM_R(dcomplex, MKL_Complex16, cd, ztrmm)
+EIGEN_BLAS_TRMM_R(float, float, f, strmm)
+EIGEN_BLAS_TRMM_R(scomplex, MKL_Complex8, cf, ctrmm)
+#else
+EIGEN_BLAS_TRMM_R(double, double, d, dtrmm_)
+EIGEN_BLAS_TRMM_R(dcomplex, double, cd, ztrmm_)
+EIGEN_BLAS_TRMM_R(float, float, f, strmm_)
+EIGEN_BLAS_TRMM_R(scomplex, float, cf, ctrmm_)
+#endif
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRIANGULAR_MATRIX_MATRIX_BLAS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularMatrixVector.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularMatrixVector.h
new file mode 100644
index 0000000000000000000000000000000000000000..ce7550c360a42520e0c7c967ac12ca96dc5828a5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularMatrixVector.h
@@ -0,0 +1,346 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRIANGULARMATRIXVECTOR_H
+#define EIGEN_TRIANGULARMATRIXVECTOR_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int StorageOrder, int Version=Specialized>
+struct triangular_matrix_vector_product;
+
+template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version>
+struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,ColMajor,Version>
+{
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+  static constexpr bool IsLower = ((Mode & Lower) == Lower);
+  static constexpr bool HasUnitDiag = (Mode & UnitDiag) == UnitDiag;
+  static constexpr bool HasZeroDiag = (Mode & ZeroDiag) == ZeroDiag;
+  static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
+                                    const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr,
+                                    const RhsScalar& alpha);
+};
+
+template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int Version>
+EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,ColMajor,Version>
+  ::run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
+        const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const RhsScalar& alpha)
+  {
+    static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
+    Index size = (std::min)(_rows,_cols);
+    Index rows = IsLower ? _rows : (std::min)(_rows,_cols);
+    Index cols = IsLower ? (std::min)(_rows,_cols) : _cols;
+
+    typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,ColMajor>, 0, OuterStride<> > LhsMap;
+    const LhsMap lhs(_lhs,rows,cols,OuterStride<>(lhsStride));
+    typename conj_expr_if<ConjLhs,LhsMap>::type cjLhs(lhs);
+
+    typedef Map<const Matrix<RhsScalar,Dynamic,1>, 0, InnerStride<> > RhsMap;
+    const RhsMap rhs(_rhs,cols,InnerStride<>(rhsIncr));
+    typename conj_expr_if<ConjRhs,RhsMap>::type cjRhs(rhs);
+
+    typedef Map<Matrix<ResScalar,Dynamic,1> > ResMap;
+    ResMap res(_res,rows);
+
+    typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper;
+    typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper;
+
+    for (Index pi=0; pi<size; pi+=PanelWidth)
+    {
+      Index actualPanelWidth = (std::min)(PanelWidth, size-pi);
+      for (Index k=0; k<actualPanelWidth; ++k)
+      {
+        Index i = pi + k;
+        Index s = IsLower ? ((HasUnitDiag||HasZeroDiag) ? i+1 : i ) : pi;
+        Index r = IsLower ? actualPanelWidth-k : k+1;
+        if ((!(HasUnitDiag||HasZeroDiag)) || (--r)>0)
+          res.segment(s,r) += (alpha * cjRhs.coeff(i)) * cjLhs.col(i).segment(s,r);
+        if (HasUnitDiag)
+          res.coeffRef(i) += alpha * cjRhs.coeff(i);
+      }
+      Index r = IsLower ? rows - pi - actualPanelWidth : pi;
+      if (r>0)
+      {
+        Index s = IsLower ? pi+actualPanelWidth : 0;
+        general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs,BuiltIn>::run(
+            r, actualPanelWidth,
+            LhsMapper(&lhs.coeffRef(s,pi), lhsStride),
+            RhsMapper(&rhs.coeffRef(pi), rhsIncr),
+            &res.coeffRef(s), resIncr, alpha);
+      }
+    }
+    if((!IsLower) && cols>size)
+    {
+      general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs>::run(
+          rows, cols-size,
+          LhsMapper(&lhs.coeffRef(0,size), lhsStride),
+          RhsMapper(&rhs.coeffRef(size), rhsIncr),
+          _res, resIncr, alpha);
+    }
+  }
+
+template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs,int Version>
+struct triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,RowMajor,Version>
+{
+  typedef typename ScalarBinaryOpTraits<LhsScalar, RhsScalar>::ReturnType ResScalar;
+  static constexpr bool IsLower = ((Mode & Lower) == Lower);
+  static constexpr bool HasUnitDiag = (Mode & UnitDiag) == UnitDiag;
+  static constexpr bool HasZeroDiag = (Mode & ZeroDiag) == ZeroDiag;
+  static EIGEN_DONT_INLINE void run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
+                                    const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr,
+                                    const ResScalar& alpha);
+};
+
+template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs,int Version>
+EIGEN_DONT_INLINE void triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,RowMajor,Version>
+  ::run(Index _rows, Index _cols, const LhsScalar* _lhs, Index lhsStride,
+        const RhsScalar* _rhs, Index rhsIncr, ResScalar* _res, Index resIncr, const ResScalar& alpha)
+  {
+    static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
+    Index diagSize = (std::min)(_rows,_cols);
+    Index rows = IsLower ? _rows : diagSize;
+    Index cols = IsLower ? diagSize : _cols;
+
+    typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,RowMajor>, 0, OuterStride<> > LhsMap;
+    const LhsMap lhs(_lhs,rows,cols,OuterStride<>(lhsStride));
+    typename conj_expr_if<ConjLhs,LhsMap>::type cjLhs(lhs);
+
+    typedef Map<const Matrix<RhsScalar,Dynamic,1> > RhsMap;
+    const RhsMap rhs(_rhs,cols);
+    typename conj_expr_if<ConjRhs,RhsMap>::type cjRhs(rhs);
+
+    typedef Map<Matrix<ResScalar,Dynamic,1>, 0, InnerStride<> > ResMap;
+    ResMap res(_res,rows,InnerStride<>(resIncr));
+
+    typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper;
+    typedef const_blas_data_mapper<RhsScalar,Index,RowMajor> RhsMapper;
+
+    for (Index pi=0; pi<diagSize; pi+=PanelWidth)
+    {
+      Index actualPanelWidth = (std::min)(PanelWidth, diagSize-pi);
+      for (Index k=0; k<actualPanelWidth; ++k)
+      {
+        Index i = pi + k;
+        Index s = IsLower ? pi  : ((HasUnitDiag||HasZeroDiag) ? i+1 : i);
+        Index r = IsLower ? k+1 : actualPanelWidth-k;
+        if ((!(HasUnitDiag||HasZeroDiag)) || (--r)>0)
+          res.coeffRef(i) += alpha * (cjLhs.row(i).segment(s,r).cwiseProduct(cjRhs.segment(s,r).transpose())).sum();
+        if (HasUnitDiag)
+          res.coeffRef(i) += alpha * cjRhs.coeff(i);
+      }
+      Index r = IsLower ? pi : cols - pi - actualPanelWidth;
+      if (r>0)
+      {
+        Index s = IsLower ? 0 : pi + actualPanelWidth;
+        general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs,BuiltIn>::run(
+            actualPanelWidth, r,
+            LhsMapper(&lhs.coeffRef(pi,s), lhsStride),
+            RhsMapper(&rhs.coeffRef(s), rhsIncr),
+            &res.coeffRef(pi), resIncr, alpha);
+      }
+    }
+    if(IsLower && rows>diagSize)
+    {
+      general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,ConjLhs,RhsScalar,RhsMapper,ConjRhs>::run(
+            rows-diagSize, cols,
+            LhsMapper(&lhs.coeffRef(diagSize,0), lhsStride),
+            RhsMapper(&rhs.coeffRef(0), rhsIncr),
+            &res.coeffRef(diagSize), resIncr, alpha);
+    }
+  }
+
+/***************************************************************************
+* Wrapper to product_triangular_vector
+***************************************************************************/
+
+template<int Mode,int StorageOrder>
+struct trmv_selector;
+
+} // end namespace internal
+
+namespace internal {
+
+template<int Mode, typename Lhs, typename Rhs>
+struct triangular_product_impl<Mode,true,Lhs,false,Rhs,true>
+{
+  template<typename Dest> static void run(Dest& dst, const Lhs &lhs, const Rhs &rhs, const typename Dest::Scalar& alpha)
+  {
+    eigen_assert(dst.rows()==lhs.rows() && dst.cols()==rhs.cols());
+  
+    internal::trmv_selector<Mode,(int(internal::traits<Lhs>::Flags)&RowMajorBit) ? RowMajor : ColMajor>::run(lhs, rhs, dst, alpha);
+  }
+};
+
+template<int Mode, typename Lhs, typename Rhs>
+struct triangular_product_impl<Mode,false,Lhs,true,Rhs,false>
+{
+  template<typename Dest> static void run(Dest& dst, const Lhs &lhs, const Rhs &rhs, const typename Dest::Scalar& alpha)
+  {
+    eigen_assert(dst.rows()==lhs.rows() && dst.cols()==rhs.cols());
+
+    Transpose<Dest> dstT(dst);
+    internal::trmv_selector<(Mode & (UnitDiag|ZeroDiag)) | ((Mode & Lower) ? Upper : Lower),
+                            (int(internal::traits<Rhs>::Flags)&RowMajorBit) ? ColMajor : RowMajor>
+            ::run(rhs.transpose(),lhs.transpose(), dstT, alpha);
+  }
+};
+
+} // end namespace internal
+
+namespace internal {
+
+// TODO: find a way to factorize this piece of code with gemv_selector since the logic is exactly the same.
+
+template<int Mode> struct trmv_selector<Mode,ColMajor>
+{
+  template<typename Lhs, typename Rhs, typename Dest>
+  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
+  {
+    typedef typename Lhs::Scalar      LhsScalar;
+    typedef typename Rhs::Scalar      RhsScalar;
+    typedef typename Dest::Scalar     ResScalar;
+
+    typedef internal::blas_traits<Lhs> LhsBlasTraits;
+    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+    typedef internal::blas_traits<Rhs> RhsBlasTraits;
+    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+    constexpr int Alignment = (std::min)(int(AlignedMax), int(internal::packet_traits<ResScalar>::size));
+
+    typedef Map<Matrix<ResScalar,Dynamic,1>, Alignment> MappedDest;
+
+    add_const_on_value_type_t<ActualLhsType> actualLhs = LhsBlasTraits::extract(lhs);
+    add_const_on_value_type_t<ActualRhsType> actualRhs = RhsBlasTraits::extract(rhs);
+
+    LhsScalar lhs_alpha = LhsBlasTraits::extractScalarFactor(lhs);
+    RhsScalar rhs_alpha = RhsBlasTraits::extractScalarFactor(rhs);
+    ResScalar actualAlpha = alpha * lhs_alpha * rhs_alpha;
+
+    // FIXME find a way to allow an inner stride on the result if packet_traits<Scalar>::size==1
+    // on, the other hand it is good for the cache to pack the vector anyways...
+    constexpr bool EvalToDestAtCompileTime = Dest::InnerStrideAtCompileTime==1;
+    constexpr bool ComplexByReal = (NumTraits<LhsScalar>::IsComplex) && (!NumTraits<RhsScalar>::IsComplex);
+    constexpr bool MightCannotUseDest = (Dest::InnerStrideAtCompileTime!=1) || ComplexByReal;
+
+    gemv_static_vector_if<ResScalar,Dest::SizeAtCompileTime,Dest::MaxSizeAtCompileTime,MightCannotUseDest> static_dest;
+
+    bool alphaIsCompatible = (!ComplexByReal) || numext::is_exactly_zero(numext::imag(actualAlpha));
+    bool evalToDest = EvalToDestAtCompileTime && alphaIsCompatible;
+
+    RhsScalar compatibleAlpha = get_factor<ResScalar,RhsScalar>::run(actualAlpha);
+
+    ei_declare_aligned_stack_constructed_variable(ResScalar,actualDestPtr,dest.size(),
+                                                  evalToDest ? dest.data() : static_dest.data());
+
+    if(!evalToDest)
+    {
+      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      Index size = dest.size();
+      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      #endif
+      if(!alphaIsCompatible)
+      {
+        MappedDest(actualDestPtr, dest.size()).setZero();
+        compatibleAlpha = RhsScalar(1);
+      }
+      else
+        MappedDest(actualDestPtr, dest.size()) = dest;
+    }
+
+    internal::triangular_matrix_vector_product
+      <Index,Mode,
+       LhsScalar, LhsBlasTraits::NeedToConjugate,
+       RhsScalar, RhsBlasTraits::NeedToConjugate,
+       ColMajor>
+      ::run(actualLhs.rows(),actualLhs.cols(),
+            actualLhs.data(),actualLhs.outerStride(),
+            actualRhs.data(),actualRhs.innerStride(),
+            actualDestPtr,1,compatibleAlpha);
+
+    if (!evalToDest)
+    {
+      if(!alphaIsCompatible)
+        dest += actualAlpha * MappedDest(actualDestPtr, dest.size());
+      else
+        dest = MappedDest(actualDestPtr, dest.size());
+    }
+
+    if ( ((Mode&UnitDiag)==UnitDiag) && !numext::is_exactly_one(lhs_alpha) )
+    {
+      Index diagSize = (std::min)(lhs.rows(),lhs.cols());
+      dest.head(diagSize) -= (lhs_alpha-LhsScalar(1))*rhs.head(diagSize);
+    }
+  }
+};
+
+template<int Mode> struct trmv_selector<Mode,RowMajor>
+{
+  template<typename Lhs, typename Rhs, typename Dest>
+  static void run(const Lhs &lhs, const Rhs &rhs, Dest& dest, const typename Dest::Scalar& alpha)
+  {
+    typedef typename Lhs::Scalar      LhsScalar;
+    typedef typename Rhs::Scalar      RhsScalar;
+    typedef typename Dest::Scalar     ResScalar;
+    
+    typedef internal::blas_traits<Lhs> LhsBlasTraits;
+    typedef typename LhsBlasTraits::DirectLinearAccessType ActualLhsType;
+    typedef internal::blas_traits<Rhs> RhsBlasTraits;
+    typedef typename RhsBlasTraits::DirectLinearAccessType ActualRhsType;
+    typedef internal::remove_all_t<ActualRhsType> ActualRhsTypeCleaned;
+
+    std::add_const_t<ActualLhsType> actualLhs = LhsBlasTraits::extract(lhs);
+    std::add_const_t<ActualRhsType> actualRhs = RhsBlasTraits::extract(rhs);
+
+    LhsScalar lhs_alpha = LhsBlasTraits::extractScalarFactor(lhs);
+    RhsScalar rhs_alpha = RhsBlasTraits::extractScalarFactor(rhs);
+    ResScalar actualAlpha = alpha * lhs_alpha * rhs_alpha;
+
+    constexpr bool DirectlyUseRhs = ActualRhsTypeCleaned::InnerStrideAtCompileTime==1;
+
+    gemv_static_vector_if<RhsScalar,ActualRhsTypeCleaned::SizeAtCompileTime,ActualRhsTypeCleaned::MaxSizeAtCompileTime,!DirectlyUseRhs> static_rhs;
+
+    ei_declare_aligned_stack_constructed_variable(RhsScalar,actualRhsPtr,actualRhs.size(),
+        DirectlyUseRhs ? const_cast<RhsScalar*>(actualRhs.data()) : static_rhs.data());
+
+    if(!DirectlyUseRhs)
+    {
+      #ifdef EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      Index size = actualRhs.size();
+      EIGEN_DENSE_STORAGE_CTOR_PLUGIN
+      #endif
+      Map<typename ActualRhsTypeCleaned::PlainObject>(actualRhsPtr, actualRhs.size()) = actualRhs;
+    }
+
+    internal::triangular_matrix_vector_product
+      <Index,Mode,
+       LhsScalar, LhsBlasTraits::NeedToConjugate,
+       RhsScalar, RhsBlasTraits::NeedToConjugate,
+       RowMajor>
+      ::run(actualLhs.rows(),actualLhs.cols(),
+            actualLhs.data(),actualLhs.outerStride(),
+            actualRhsPtr,1,
+            dest.data(),dest.innerStride(),
+            actualAlpha);
+
+    if ( ((Mode&UnitDiag)==UnitDiag) && !numext::is_exactly_one(lhs_alpha) )
+    {
+      Index diagSize = (std::min)(lhs.rows(),lhs.cols());
+      dest.head(diagSize) -= (lhs_alpha-LhsScalar(1))*rhs.head(diagSize);
+    }
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRIANGULARMATRIXVECTOR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
new file mode 100644
index 0000000000000000000000000000000000000000..7a4d59eea706266e970b9a08c0d7692e7e658e52
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularMatrixVector_BLAS.h
@@ -0,0 +1,257 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   Triangular matrix-vector product functionality based on ?TRMV.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_TRIANGULAR_MATRIX_VECTOR_BLAS_H
+#define EIGEN_TRIANGULAR_MATRIX_VECTOR_BLAS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/**********************************************************************
+* This file implements triangular matrix-vector multiplication using BLAS
+**********************************************************************/
+
+// trmv/hemv specialization
+
+template<typename Index, int Mode, typename LhsScalar, bool ConjLhs, typename RhsScalar, bool ConjRhs, int StorageOrder>
+struct triangular_matrix_vector_product_trmv :
+  triangular_matrix_vector_product<Index,Mode,LhsScalar,ConjLhs,RhsScalar,ConjRhs,StorageOrder,BuiltIn> {};
+
+#define EIGEN_BLAS_TRMV_SPECIALIZE(Scalar) \
+template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
+struct triangular_matrix_vector_product<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,ColMajor,Specialized> { \
+ static void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \
+                                     const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \
+      triangular_matrix_vector_product_trmv<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,ColMajor>::run( \
+        _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
+  } \
+}; \
+template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
+struct triangular_matrix_vector_product<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,RowMajor,Specialized> { \
+ static void run(Index _rows, Index _cols, const Scalar* _lhs, Index lhsStride, \
+                                     const Scalar* _rhs, Index rhsIncr, Scalar* _res, Index resIncr, Scalar alpha) { \
+      triangular_matrix_vector_product_trmv<Index,Mode,Scalar,ConjLhs,Scalar,ConjRhs,RowMajor>::run( \
+        _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
+  } \
+};
+
+EIGEN_BLAS_TRMV_SPECIALIZE(double)
+EIGEN_BLAS_TRMV_SPECIALIZE(float)
+EIGEN_BLAS_TRMV_SPECIALIZE(dcomplex)
+EIGEN_BLAS_TRMV_SPECIALIZE(scomplex)
+
+// implements col-major: res += alpha * op(triangular) * vector
+#define EIGEN_BLAS_TRMV_CM(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX, BLASPOSTFIX) \
+template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
+struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,ColMajor> { \
+  enum { \
+    IsLower = (Mode&Lower) == Lower, \
+    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
+    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
+    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
+    LowUp = IsLower ? Lower : Upper \
+  }; \
+ static void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
+                 const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
+ { \
+   if (ConjLhs || IsZeroDiag) { \
+     triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,ColMajor,BuiltIn>::run( \
+       _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
+     return; \
+   }\
+   Index size = (std::min)(_rows,_cols); \
+   Index rows = IsLower ? _rows : size; \
+   Index cols = IsLower ? size : _cols; \
+\
+   typedef VectorX##EIGPREFIX VectorRhs; \
+   EIGTYPE *x, *y;\
+\
+/* Set x*/ \
+   Map<const VectorRhs, 0, InnerStride<> > rhs(_rhs,cols,InnerStride<>(rhsIncr)); \
+   VectorRhs x_tmp; \
+   if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
+   x = x_tmp.data(); \
+\
+/* Square part handling */\
+\
+   char trans, uplo, diag; \
+   BlasIndex m, n, lda, incx, incy; \
+   EIGTYPE const *a; \
+   EIGTYPE beta(1); \
+\
+/* Set m, n */ \
+   n = convert_index<BlasIndex>(size); \
+   lda = convert_index<BlasIndex>(lhsStride); \
+   incx = 1; \
+   incy = convert_index<BlasIndex>(resIncr); \
+\
+/* Set uplo, trans and diag*/ \
+   trans = 'N'; \
+   uplo = IsLower ? 'L' : 'U'; \
+   diag = IsUnitDiag ? 'U' : 'N'; \
+\
+/* call ?TRMV*/ \
+   BLASPREFIX##trmv##BLASPOSTFIX(&uplo, &trans, &diag, &n, (const BLASTYPE*)_lhs, &lda, (BLASTYPE*)x, &incx); \
+\
+/* Add op(a_tr)rhs into res*/ \
+   BLASPREFIX##axpy##BLASPOSTFIX(&n, (const BLASTYPE*)&numext::real_ref(alpha),(const BLASTYPE*)x, &incx, (BLASTYPE*)_res, &incy); \
+/* Non-square case - doesn't fit to BLAS ?TRMV. Fall to default triangular product*/ \
+   if (size<(std::max)(rows,cols)) { \
+     if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
+     x = x_tmp.data(); \
+     if (size<rows) { \
+       y = _res + size*resIncr; \
+       a = _lhs + size; \
+       m = convert_index<BlasIndex>(rows-size); \
+       n = convert_index<BlasIndex>(size); \
+     } \
+     else { \
+       x += size; \
+       y = _res; \
+       a = _lhs + size*lda; \
+       m = convert_index<BlasIndex>(size); \
+       n = convert_index<BlasIndex>(cols-size); \
+     } \
+     BLASPREFIX##gemv##BLASPOSTFIX(&trans, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)x, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)y, &incy); \
+   } \
+  } \
+};
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_TRMV_CM(double,   double, d,  d,)
+EIGEN_BLAS_TRMV_CM(dcomplex, MKL_Complex16, cd, z,)
+EIGEN_BLAS_TRMV_CM(float,    float,  f,  s,)
+EIGEN_BLAS_TRMV_CM(scomplex, MKL_Complex8,  cf, c,)
+#else
+EIGEN_BLAS_TRMV_CM(double,   double, d,  d, _)
+EIGEN_BLAS_TRMV_CM(dcomplex, double, cd, z, _)
+EIGEN_BLAS_TRMV_CM(float,    float,  f,  s, _)
+EIGEN_BLAS_TRMV_CM(scomplex, float,  cf, c, _)
+#endif
+
+// implements row-major: res += alpha * op(triangular) * vector
+#define EIGEN_BLAS_TRMV_RM(EIGTYPE, BLASTYPE, EIGPREFIX, BLASPREFIX, BLASPOSTFIX) \
+template<typename Index, int Mode, bool ConjLhs, bool ConjRhs> \
+struct triangular_matrix_vector_product_trmv<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,RowMajor> { \
+  enum { \
+    IsLower = (Mode&Lower) == Lower, \
+    SetDiag = (Mode&(ZeroDiag|UnitDiag)) ? 0 : 1, \
+    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
+    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
+    LowUp = IsLower ? Lower : Upper \
+  }; \
+ static void run(Index _rows, Index _cols, const EIGTYPE* _lhs, Index lhsStride, \
+                 const EIGTYPE* _rhs, Index rhsIncr, EIGTYPE* _res, Index resIncr, EIGTYPE alpha) \
+ { \
+   if (IsZeroDiag) { \
+     triangular_matrix_vector_product<Index,Mode,EIGTYPE,ConjLhs,EIGTYPE,ConjRhs,RowMajor,BuiltIn>::run( \
+       _rows, _cols, _lhs, lhsStride, _rhs, rhsIncr, _res, resIncr, alpha); \
+     return; \
+   }\
+   Index size = (std::min)(_rows,_cols); \
+   Index rows = IsLower ? _rows : size; \
+   Index cols = IsLower ? size : _cols; \
+\
+   typedef VectorX##EIGPREFIX VectorRhs; \
+   EIGTYPE *x, *y;\
+\
+/* Set x*/ \
+   Map<const VectorRhs, 0, InnerStride<> > rhs(_rhs,cols,InnerStride<>(rhsIncr)); \
+   VectorRhs x_tmp; \
+   if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
+   x = x_tmp.data(); \
+\
+/* Square part handling */\
+\
+   char trans, uplo, diag; \
+   BlasIndex m, n, lda, incx, incy; \
+   EIGTYPE const *a; \
+   EIGTYPE beta(1); \
+\
+/* Set m, n */ \
+   n = convert_index<BlasIndex>(size); \
+   lda = convert_index<BlasIndex>(lhsStride); \
+   incx = 1; \
+   incy = convert_index<BlasIndex>(resIncr); \
+\
+/* Set uplo, trans and diag*/ \
+   trans = ConjLhs ? 'C' : 'T'; \
+   uplo = IsLower ? 'U' : 'L'; \
+   diag = IsUnitDiag ? 'U' : 'N'; \
+\
+/* call ?TRMV*/ \
+   BLASPREFIX##trmv##BLASPOSTFIX(&uplo, &trans, &diag, &n, (const BLASTYPE*)_lhs, &lda, (BLASTYPE*)x, &incx); \
+\
+/* Add op(a_tr)rhs into res*/ \
+   BLASPREFIX##axpy##BLASPOSTFIX(&n, (const BLASTYPE*)&numext::real_ref(alpha),(const BLASTYPE*)x, &incx, (BLASTYPE*)_res, &incy); \
+/* Non-square case - doesn't fit to BLAS ?TRMV. Fall to default triangular product*/ \
+   if (size<(std::max)(rows,cols)) { \
+     if (ConjRhs) x_tmp = rhs.conjugate(); else x_tmp = rhs; \
+     x = x_tmp.data(); \
+     if (size<rows) { \
+       y = _res + size*resIncr; \
+       a = _lhs + size*lda; \
+       m = convert_index<BlasIndex>(rows-size); \
+       n = convert_index<BlasIndex>(size); \
+     } \
+     else { \
+       x += size; \
+       y = _res; \
+       a = _lhs + size; \
+       m = convert_index<BlasIndex>(size); \
+       n = convert_index<BlasIndex>(cols-size); \
+     } \
+     BLASPREFIX##gemv##BLASPOSTFIX(&trans, &n, &m, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (const BLASTYPE*)x, &incx, (const BLASTYPE*)&numext::real_ref(beta), (BLASTYPE*)y, &incy); \
+   } \
+  } \
+};
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_TRMV_RM(double,   double, d,  d,)
+EIGEN_BLAS_TRMV_RM(dcomplex, MKL_Complex16, cd, z,)
+EIGEN_BLAS_TRMV_RM(float,    float,  f,  s,)
+EIGEN_BLAS_TRMV_RM(scomplex, MKL_Complex8,  cf, c,)
+#else
+EIGEN_BLAS_TRMV_RM(double,   double, d,  d,_)
+EIGEN_BLAS_TRMV_RM(dcomplex, double, cd, z,_)
+EIGEN_BLAS_TRMV_RM(float,    float,  f,  s,_)
+EIGEN_BLAS_TRMV_RM(scomplex, float,  cf, c,_)
+#endif
+
+} // end namespase internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRIANGULAR_MATRIX_VECTOR_BLAS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularSolverMatrix.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularSolverMatrix.h
new file mode 100644
index 0000000000000000000000000000000000000000..22b4a7f2daf855827329ba3feea0e6d80321fddc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularSolverMatrix.h
@@ -0,0 +1,449 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Modifications Copyright (C) 2022 Intel Corporation
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRIANGULAR_SOLVER_MATRIX_H
+#define EIGEN_TRIANGULAR_SOLVER_MATRIX_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder,int OtherInnerStride, bool Specialized>
+struct trsmKernelL {
+  // Generic Implementation of triangular solve for triangular matrix on left and multiple rhs.
+  // Handles non-packed matrices.
+  static void kernel(
+    Index size, Index otherSize,
+    const Scalar* _tri, Index triStride,
+    Scalar* _other, Index otherIncr, Index otherStride);
+};
+
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder,int OtherInnerStride, bool Specialized>
+struct trsmKernelR {
+  // Generic Implementation of triangular solve for triangular matrix on right and multiple lhs.
+  // Handles non-packed matrices.
+  static void kernel(
+    Index size, Index otherSize,
+    const Scalar* _tri, Index triStride,
+    Scalar* _other, Index otherIncr, Index otherStride);
+};
+
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder,int OtherInnerStride, bool Specialized>
+EIGEN_STRONG_INLINE void trsmKernelL<Scalar, Index, Mode, Conjugate, TriStorageOrder, OtherInnerStride, Specialized>::kernel(
+    Index size, Index otherSize,
+    const Scalar* _tri, Index triStride,
+    Scalar* _other, Index otherIncr, Index otherStride)
+  {
+    typedef const_blas_data_mapper<Scalar, Index, TriStorageOrder> TriMapper;
+    typedef blas_data_mapper<Scalar, Index, ColMajor, Unaligned, OtherInnerStride> OtherMapper;
+    TriMapper tri(_tri, triStride);
+    OtherMapper other(_other, otherStride, otherIncr);
+
+    enum { IsLower = (Mode&Lower) == Lower };
+    conj_if<Conjugate> conj;
+
+    // tr solve
+    for (Index k=0; k<size; ++k)
+    {
+      // TODO write a small kernel handling this (can be shared with trsv)
+      Index i  = IsLower ? k : -k-1;
+      Index rs = size - k - 1; // remaining size
+      Index s  = TriStorageOrder==RowMajor ? (IsLower ? 0 : i+1)
+        :  IsLower ? i+1 : i-rs;
+
+      Scalar a = (Mode & UnitDiag) ? Scalar(1) : Scalar(1)/conj(tri(i,i));
+      for (Index j=0; j<otherSize; ++j)
+      {
+        if (TriStorageOrder==RowMajor)
+        {
+          Scalar b(0);
+          const Scalar* l = &tri(i,s);
+          typename OtherMapper::LinearMapper r = other.getLinearMapper(s,j);
+          for (Index i3=0; i3<k; ++i3)
+            b += conj(l[i3]) * r(i3);
+
+          other(i,j) = (other(i,j) - b)*a;
+        }
+        else
+        {
+          Scalar& otherij = other(i,j);
+          otherij *= a;
+          Scalar b = otherij;
+          typename OtherMapper::LinearMapper r = other.getLinearMapper(s,j);
+          typename TriMapper::LinearMapper l = tri.getLinearMapper(s,i);
+          for (Index i3=0;i3<rs;++i3)
+            r(i3) -= b * conj(l(i3));
+        }
+      }
+    }
+  }
+
+
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride, bool Specialized>
+EIGEN_STRONG_INLINE void trsmKernelR<Scalar, Index, Mode, Conjugate, TriStorageOrder, OtherInnerStride, Specialized>::kernel(
+    Index size, Index otherSize,
+    const Scalar* _tri, Index triStride,
+    Scalar* _other, Index otherIncr, Index otherStride)
+{
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef blas_data_mapper<Scalar, Index, ColMajor, Unaligned, OtherInnerStride> LhsMapper;
+  typedef const_blas_data_mapper<Scalar, Index, TriStorageOrder> RhsMapper;
+  LhsMapper lhs(_other, otherStride, otherIncr);
+  RhsMapper rhs(_tri, triStride);
+
+  enum {
+    RhsStorageOrder   = TriStorageOrder,
+    IsLower = (Mode&Lower) == Lower
+  };
+  conj_if<Conjugate> conj;
+
+  for (Index k=0; k<size; ++k)
+  {
+    Index j = IsLower ? size-k-1 : k;
+
+    typename LhsMapper::LinearMapper r = lhs.getLinearMapper(0,j);
+    for (Index k3=0; k3<k; ++k3)
+    {
+      Scalar b = conj(rhs(IsLower ? j+1+k3 : k3,j));
+      typename LhsMapper::LinearMapper a = lhs.getLinearMapper(0,IsLower ? j+1+k3 : k3);
+      for (Index i=0; i<otherSize; ++i)
+                    r(i) -= a(i) * b;
+    }
+    if((Mode & UnitDiag)==0)
+    {
+      Scalar inv_rjj = RealScalar(1)/conj(rhs(j,j));
+      for (Index i=0; i<otherSize; ++i)
+        r(i) *= inv_rjj;
+    }
+  }
+}
+
+
+// if the rhs is row major, let's transpose the product
+template <typename Scalar, typename Index, int Side, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
+struct triangular_solve_matrix<Scalar,Index,Side,Mode,Conjugate,TriStorageOrder,RowMajor,OtherInnerStride>
+{
+  static void run(
+    Index size, Index cols,
+    const Scalar*  tri, Index triStride,
+    Scalar* _other, Index otherIncr, Index otherStride,
+    level3_blocking<Scalar,Scalar>& blocking)
+  {
+    triangular_solve_matrix<
+      Scalar, Index, Side==OnTheLeft?OnTheRight:OnTheLeft,
+      (Mode&UnitDiag) | ((Mode&Upper) ? Lower : Upper),
+      NumTraits<Scalar>::IsComplex && Conjugate,
+      TriStorageOrder==RowMajor ? ColMajor : RowMajor, ColMajor, OtherInnerStride>
+      ::run(size, cols, tri, triStride, _other, otherIncr, otherStride, blocking);
+  }
+};
+
+/* Optimized triangular solver with multiple right hand side and the triangular matrix on the left
+ */
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder,int OtherInnerStride>
+struct triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor,OtherInnerStride>
+{
+  static EIGEN_DONT_INLINE void run(
+    Index size, Index otherSize,
+    const Scalar* _tri, Index triStride,
+    Scalar* _other, Index otherIncr, Index otherStride,
+    level3_blocking<Scalar,Scalar>& blocking);
+};
+
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
+EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor,OtherInnerStride>::run(
+    Index size, Index otherSize,
+    const Scalar* _tri, Index triStride,
+    Scalar* _other, Index otherIncr, Index otherStride,
+    level3_blocking<Scalar,Scalar>& blocking)
+  {
+    Index cols = otherSize;
+
+    std::ptrdiff_t l1, l2, l3;
+    manage_caching_sizes(GetAction, &l1, &l2, &l3);
+
+#if defined(EIGEN_VECTORIZE_AVX512) && EIGEN_USE_AVX512_TRSM_L_KERNELS && EIGEN_ENABLE_AVX512_NOCOPY_TRSM_L_CUTOFFS
+    EIGEN_IF_CONSTEXPR( (OtherInnerStride == 1 &&
+                       (std::is_same<Scalar,float>::value ||
+                        std::is_same<Scalar,double>::value)) ) {
+      // Very rough cutoffs to determine when to call trsm w/o packing
+      // For small problem sizes trsmKernel compiled with clang is generally faster.
+      // TODO: Investigate better heuristics for cutoffs.
+      double L2Cap = 0.5; // 50% of L2 size
+      if (size < avx512_trsm_cutoff<Scalar>(l2, cols, L2Cap)) {
+        trsmKernelL<Scalar, Index, Mode, Conjugate, TriStorageOrder, 1, /*Specialized=*/true>::kernel(
+          size, cols, _tri, triStride, _other, 1, otherStride);
+        return;
+      }
+    }
+#endif
+
+    typedef const_blas_data_mapper<Scalar, Index, TriStorageOrder> TriMapper;
+    typedef blas_data_mapper<Scalar, Index, ColMajor, Unaligned, OtherInnerStride> OtherMapper;
+    TriMapper tri(_tri, triStride);
+    OtherMapper other(_other, otherStride, otherIncr);
+
+    typedef gebp_traits<Scalar,Scalar> Traits;
+
+    enum {
+      SmallPanelWidth   = plain_enum_max(Traits::mr, Traits::nr),
+      IsLower = (Mode&Lower) == Lower
+    };
+
+    Index kc = blocking.kc();                   // cache block size along the K direction
+    Index mc = (std::min)(size,blocking.mc());  // cache block size along the M direction
+
+    std::size_t sizeA = kc*mc;
+    std::size_t sizeB = kc*cols;
+
+    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
+    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
+
+    gebp_kernel<Scalar, Scalar, Index, OtherMapper, Traits::mr, Traits::nr, Conjugate, false> gebp_kernel;
+    gemm_pack_lhs<Scalar, Index, TriMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, TriStorageOrder> pack_lhs;
+    gemm_pack_rhs<Scalar, Index, OtherMapper, Traits::nr, ColMajor, false, true> pack_rhs;
+
+    // the goal here is to subdivise the Rhs panels such that we keep some cache
+    // coherence when accessing the rhs elements
+    Index subcols = cols>0 ? l2/(4 * sizeof(Scalar) * std::max<Index>(otherStride,size)) : 0;
+    subcols = std::max<Index>((subcols/Traits::nr)*Traits::nr, Traits::nr);
+
+    for(Index k2=IsLower ? 0 : size;
+        IsLower ? k2<size : k2>0;
+        IsLower ? k2+=kc : k2-=kc)
+    {
+      const Index actual_kc = (std::min)(IsLower ? size-k2 : k2, kc);
+
+      // We have selected and packed a big horizontal panel R1 of rhs. Let B be the packed copy of this panel,
+      // and R2 the remaining part of rhs. The corresponding vertical panel of lhs is split into
+      // A11 (the triangular part) and A21 the remaining rectangular part.
+      // Then the high level algorithm is:
+      //  - B = R1                    => general block copy (done during the next step)
+      //  - R1 = A11^-1 B             => tricky part
+      //  - update B from the new R1  => actually this has to be performed continuously during the above step
+      //  - R2 -= A21 * B             => GEPP
+
+      // The tricky part: compute R1 = A11^-1 B while updating B from R1
+      // The idea is to split A11 into multiple small vertical panels.
+      // Each panel can be split into a small triangular part T1k which is processed without optimization,
+      // and the remaining small part T2k which is processed using gebp with appropriate block strides
+      for(Index j2=0; j2<cols; j2+=subcols)
+      {
+        Index actual_cols = (std::min)(cols-j2,subcols);
+        // for each small vertical panels [T1k^T, T2k^T]^T of lhs
+        for (Index k1=0; k1<actual_kc; k1+=SmallPanelWidth)
+        {
+          Index actualPanelWidth = std::min<Index>(actual_kc-k1, SmallPanelWidth);
+          // tr solve
+          {
+            Index i  = IsLower ? k2+k1 : k2-k1;
+#if defined(EIGEN_VECTORIZE_AVX512) && EIGEN_USE_AVX512_TRSM_L_KERNELS
+            EIGEN_IF_CONSTEXPR( (OtherInnerStride == 1 &&
+                                 (std::is_same<Scalar,float>::value ||
+                                  std::is_same<Scalar,double>::value)) ) {
+              i  = IsLower ? k2 + k1: k2 - k1 - actualPanelWidth;
+            }
+#endif
+            trsmKernelL<Scalar, Index, Mode, Conjugate, TriStorageOrder, OtherInnerStride, /*Specialized=*/true>::kernel(
+              actualPanelWidth, actual_cols,
+              _tri + i + (i)*triStride, triStride,
+              _other + i*OtherInnerStride + j2*otherStride, otherIncr, otherStride);
+          }
+
+          Index lengthTarget = actual_kc-k1-actualPanelWidth;
+          Index startBlock   = IsLower ? k2+k1 : k2-k1-actualPanelWidth;
+          Index blockBOffset = IsLower ? k1 : lengthTarget;
+
+          // update the respective rows of B from other
+          pack_rhs(blockB+actual_kc*j2, other.getSubMapper(startBlock,j2), actualPanelWidth, actual_cols, actual_kc, blockBOffset);
+
+          // GEBP
+          if (lengthTarget>0)
+          {
+            Index startTarget  = IsLower ? k2+k1+actualPanelWidth : k2-actual_kc;
+
+            pack_lhs(blockA, tri.getSubMapper(startTarget,startBlock), actualPanelWidth, lengthTarget);
+
+            gebp_kernel(other.getSubMapper(startTarget,j2), blockA, blockB+actual_kc*j2, lengthTarget, actualPanelWidth, actual_cols, Scalar(-1),
+                        actualPanelWidth, actual_kc, 0, blockBOffset);
+          }
+        }
+      }
+
+      // R2 -= A21 * B => GEPP
+      {
+        Index start = IsLower ? k2+kc : 0;
+        Index end   = IsLower ? size : k2-kc;
+        for(Index i2=start; i2<end; i2+=mc)
+        {
+          const Index actual_mc = (std::min)(mc,end-i2);
+          if (actual_mc>0)
+          {
+            pack_lhs(blockA, tri.getSubMapper(i2, IsLower ? k2 : k2-kc), actual_kc, actual_mc);
+
+            gebp_kernel(other.getSubMapper(i2, 0), blockA, blockB, actual_mc, actual_kc, cols, Scalar(-1), -1, -1, 0, 0);
+          }
+        }
+      }
+    }
+  }
+
+/* Optimized triangular solver with multiple left hand sides and the triangular matrix on the right
+ */
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
+struct triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor,OtherInnerStride>
+{
+  static EIGEN_DONT_INLINE void run(
+    Index size, Index otherSize,
+    const Scalar* _tri, Index triStride,
+    Scalar* _other, Index otherIncr, Index otherStride,
+    level3_blocking<Scalar,Scalar>& blocking);
+};
+
+template <typename Scalar, typename Index, int Mode, bool Conjugate, int TriStorageOrder, int OtherInnerStride>
+EIGEN_DONT_INLINE void triangular_solve_matrix<Scalar,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor,OtherInnerStride>::run(
+    Index size, Index otherSize,
+    const Scalar* _tri, Index triStride,
+    Scalar* _other, Index otherIncr, Index otherStride,
+    level3_blocking<Scalar,Scalar>& blocking)
+  {
+    Index rows = otherSize;
+
+#if defined(EIGEN_VECTORIZE_AVX512) && EIGEN_USE_AVX512_TRSM_R_KERNELS && EIGEN_ENABLE_AVX512_NOCOPY_TRSM_R_CUTOFFS
+    EIGEN_IF_CONSTEXPR( (OtherInnerStride == 1 &&
+                 (std::is_same<Scalar,float>::value ||
+                  std::is_same<Scalar,double>::value)) ) {
+      // TODO: Investigate better heuristics for cutoffs.
+      std::ptrdiff_t l1, l2, l3;
+      manage_caching_sizes(GetAction, &l1, &l2, &l3);
+      double L2Cap = 0.5; // 50% of L2 size
+      if (size < avx512_trsm_cutoff<Scalar>(l2, rows, L2Cap)) {
+        trsmKernelR<Scalar, Index, Mode, Conjugate, TriStorageOrder, OtherInnerStride, /*Specialized=*/true>::
+          kernel(size, rows, _tri, triStride, _other, 1, otherStride);
+        return;
+      }
+    }
+#endif
+
+    typedef blas_data_mapper<Scalar, Index, ColMajor, Unaligned, OtherInnerStride> LhsMapper;
+    typedef const_blas_data_mapper<Scalar, Index, TriStorageOrder> RhsMapper;
+    LhsMapper lhs(_other, otherStride, otherIncr);
+    RhsMapper rhs(_tri, triStride);
+
+    typedef gebp_traits<Scalar,Scalar> Traits;
+    enum {
+      RhsStorageOrder   = TriStorageOrder,
+      SmallPanelWidth   = plain_enum_max(Traits::mr, Traits::nr),
+      IsLower = (Mode&Lower) == Lower
+    };
+
+    Index kc = blocking.kc();                   // cache block size along the K direction
+    Index mc = (std::min)(rows,blocking.mc());  // cache block size along the M direction
+
+    std::size_t sizeA = kc*mc;
+    std::size_t sizeB = kc*size;
+
+    ei_declare_aligned_stack_constructed_variable(Scalar, blockA, sizeA, blocking.blockA());
+    ei_declare_aligned_stack_constructed_variable(Scalar, blockB, sizeB, blocking.blockB());
+
+    gebp_kernel<Scalar, Scalar, Index, LhsMapper, Traits::mr, Traits::nr, false, Conjugate> gebp_kernel;
+    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr, RhsStorageOrder> pack_rhs;
+    gemm_pack_rhs<Scalar, Index, RhsMapper, Traits::nr, RhsStorageOrder,false,true> pack_rhs_panel;
+    gemm_pack_lhs<Scalar, Index, LhsMapper, Traits::mr, Traits::LhsProgress, typename Traits::LhsPacket4Packing, ColMajor, false, true> pack_lhs_panel;
+
+    for(Index k2=IsLower ? size : 0;
+        IsLower ? k2>0 : k2<size;
+        IsLower ? k2-=kc : k2+=kc)
+    {
+      const Index actual_kc = (std::min)(IsLower ? k2 : size-k2, kc);
+      Index actual_k2 = IsLower ? k2-actual_kc : k2 ;
+
+      Index startPanel = IsLower ? 0 : k2+actual_kc;
+      Index rs = IsLower ? actual_k2 : size - actual_k2 - actual_kc;
+      Scalar* geb = blockB+actual_kc*actual_kc;
+
+      if (rs>0) pack_rhs(geb, rhs.getSubMapper(actual_k2,startPanel), actual_kc, rs);
+
+      // triangular packing (we only pack the panels off the diagonal,
+      // neglecting the blocks overlapping the diagonal
+      {
+        for (Index j2=0; j2<actual_kc; j2+=SmallPanelWidth)
+        {
+          Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth);
+          Index actual_j2 = actual_k2 + j2;
+          Index panelOffset = IsLower ? j2+actualPanelWidth : 0;
+          Index panelLength = IsLower ? actual_kc-j2-actualPanelWidth : j2;
+
+          if (panelLength>0)
+          pack_rhs_panel(blockB+j2*actual_kc,
+                         rhs.getSubMapper(actual_k2+panelOffset, actual_j2),
+                         panelLength, actualPanelWidth,
+                         actual_kc, panelOffset);
+        }
+      }
+
+      for(Index i2=0; i2<rows; i2+=mc)
+      {
+        const Index actual_mc = (std::min)(mc,rows-i2);
+
+        // triangular solver kernel
+        {
+          // for each small block of the diagonal (=> vertical panels of rhs)
+          for (Index j2 = IsLower
+                      ? (actual_kc - ((actual_kc%SmallPanelWidth) ? Index(actual_kc%SmallPanelWidth)
+                                                                  : Index(SmallPanelWidth)))
+                      : 0;
+               IsLower ? j2>=0 : j2<actual_kc;
+               IsLower ? j2-=SmallPanelWidth : j2+=SmallPanelWidth)
+          {
+            Index actualPanelWidth = std::min<Index>(actual_kc-j2, SmallPanelWidth);
+            Index absolute_j2 = actual_k2 + j2;
+            Index panelOffset = IsLower ? j2+actualPanelWidth : 0;
+            Index panelLength = IsLower ? actual_kc - j2 - actualPanelWidth : j2;
+
+            // GEBP
+            if(panelLength>0)
+            {
+              gebp_kernel(lhs.getSubMapper(i2,absolute_j2),
+                          blockA, blockB+j2*actual_kc,
+                          actual_mc, panelLength, actualPanelWidth,
+                          Scalar(-1),
+                          actual_kc, actual_kc, // strides
+                          panelOffset, panelOffset); // offsets
+            }
+
+            {
+              // unblocked triangular solve
+              trsmKernelR<Scalar, Index, Mode, Conjugate, TriStorageOrder, OtherInnerStride, /*Specialized=*/true>::
+                kernel(actualPanelWidth, actual_mc,
+                            _tri + absolute_j2 + absolute_j2*triStride, triStride,
+                            _other + i2*OtherInnerStride + absolute_j2*otherStride, otherIncr, otherStride);
+            }
+            // pack the just computed part of lhs to A
+            pack_lhs_panel(blockA, lhs.getSubMapper(i2,absolute_j2),
+                           actualPanelWidth, actual_mc,
+                           actual_kc, j2);
+          }
+        }
+
+        if (rs>0)
+          gebp_kernel(lhs.getSubMapper(i2, startPanel), blockA, geb,
+                      actual_mc, actual_kc, rs, Scalar(-1),
+                      -1, -1, 0, 0);
+      }
+    }
+  }
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRIANGULAR_SOLVER_MATRIX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
new file mode 100644
index 0000000000000000000000000000000000000000..2b633887ab7d2c4f88859a51fca32557dec7a7f6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularSolverMatrix_BLAS.h
@@ -0,0 +1,169 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to BLAS F77
+ *   Triangular matrix * matrix product functionality based on ?TRMM.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_TRIANGULAR_SOLVER_MATRIX_BLAS_H
+#define EIGEN_TRIANGULAR_SOLVER_MATRIX_BLAS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// implements LeftSide op(triangular)^-1 * general
+#define EIGEN_BLAS_TRSM_L(EIGTYPE, BLASTYPE, BLASFUNC) \
+template <typename Index, int Mode, bool Conjugate, int TriStorageOrder> \
+struct triangular_solve_matrix<EIGTYPE,Index,OnTheLeft,Mode,Conjugate,TriStorageOrder,ColMajor,1> \
+{ \
+  enum { \
+    IsLower = (Mode&Lower) == Lower, \
+    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
+    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
+    conjA = ((TriStorageOrder==ColMajor) && Conjugate) ? 1 : 0 \
+  }; \
+  static void run( \
+      Index size, Index otherSize, \
+      const EIGTYPE* _tri, Index triStride, \
+      EIGTYPE* _other, Index otherIncr, Index otherStride, level3_blocking<EIGTYPE,EIGTYPE>& /*blocking*/) \
+  { \
+   EIGEN_ONLY_USED_FOR_DEBUG(otherIncr); \
+   eigen_assert(otherIncr == 1); \
+   BlasIndex m = convert_index<BlasIndex>(size), n = convert_index<BlasIndex>(otherSize), lda, ldb; \
+   char side = 'L', uplo, diag='N', transa; \
+   /* Set alpha_ */ \
+   EIGTYPE alpha(1); \
+   ldb = convert_index<BlasIndex>(otherStride);\
+\
+   const EIGTYPE *a; \
+/* Set trans */ \
+   transa = (TriStorageOrder==RowMajor) ? ((Conjugate) ? 'C' : 'T') : 'N'; \
+/* Set uplo */ \
+   uplo = IsLower ? 'L' : 'U'; \
+   if (TriStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
+/* Set a, lda */ \
+   typedef Matrix<EIGTYPE, Dynamic, Dynamic, TriStorageOrder> MatrixTri; \
+   Map<const MatrixTri, 0, OuterStride<> > tri(_tri,size,size,OuterStride<>(triStride)); \
+   MatrixTri a_tmp; \
+\
+   if (conjA) { \
+     a_tmp = tri.conjugate(); \
+     a = a_tmp.data(); \
+     lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
+   } else { \
+     a = _tri; \
+     lda = convert_index<BlasIndex>(triStride); \
+   } \
+   if (IsUnitDiag) diag='U'; \
+/* call ?trsm*/ \
+   BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)_other, &ldb); \
+ } \
+};
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_TRSM_L(double,   double, dtrsm)
+EIGEN_BLAS_TRSM_L(dcomplex, MKL_Complex16, ztrsm)
+EIGEN_BLAS_TRSM_L(float,    float,  strsm)
+EIGEN_BLAS_TRSM_L(scomplex, MKL_Complex8, ctrsm)
+#else
+EIGEN_BLAS_TRSM_L(double,   double, dtrsm_)
+EIGEN_BLAS_TRSM_L(dcomplex, double, ztrsm_)
+EIGEN_BLAS_TRSM_L(float,    float,  strsm_)
+EIGEN_BLAS_TRSM_L(scomplex, float,  ctrsm_)
+#endif
+
+// implements RightSide general * op(triangular)^-1
+#define EIGEN_BLAS_TRSM_R(EIGTYPE, BLASTYPE, BLASFUNC) \
+template <typename Index, int Mode, bool Conjugate, int TriStorageOrder> \
+struct triangular_solve_matrix<EIGTYPE,Index,OnTheRight,Mode,Conjugate,TriStorageOrder,ColMajor,1> \
+{ \
+  enum { \
+    IsLower = (Mode&Lower) == Lower, \
+    IsUnitDiag  = (Mode&UnitDiag) ? 1 : 0, \
+    IsZeroDiag  = (Mode&ZeroDiag) ? 1 : 0, \
+    conjA = ((TriStorageOrder==ColMajor) && Conjugate) ? 1 : 0 \
+  }; \
+  static void run( \
+      Index size, Index otherSize, \
+      const EIGTYPE* _tri, Index triStride, \
+      EIGTYPE* _other, Index otherIncr, Index otherStride, level3_blocking<EIGTYPE,EIGTYPE>& /*blocking*/) \
+  { \
+   EIGEN_ONLY_USED_FOR_DEBUG(otherIncr); \
+   eigen_assert(otherIncr == 1); \
+   BlasIndex m = convert_index<BlasIndex>(otherSize), n = convert_index<BlasIndex>(size), lda, ldb; \
+   char side = 'R', uplo, diag='N', transa; \
+   /* Set alpha_ */ \
+   EIGTYPE alpha(1); \
+   ldb = convert_index<BlasIndex>(otherStride);\
+\
+   const EIGTYPE *a; \
+/* Set trans */ \
+   transa = (TriStorageOrder==RowMajor) ? ((Conjugate) ? 'C' : 'T') : 'N'; \
+/* Set uplo */ \
+   uplo = IsLower ? 'L' : 'U'; \
+   if (TriStorageOrder==RowMajor) uplo = (uplo == 'L') ? 'U' : 'L'; \
+/* Set a, lda */ \
+   typedef Matrix<EIGTYPE, Dynamic, Dynamic, TriStorageOrder> MatrixTri; \
+   Map<const MatrixTri, 0, OuterStride<> > tri(_tri,size,size,OuterStride<>(triStride)); \
+   MatrixTri a_tmp; \
+\
+   if (conjA) { \
+     a_tmp = tri.conjugate(); \
+     a = a_tmp.data(); \
+     lda = convert_index<BlasIndex>(a_tmp.outerStride()); \
+   } else { \
+     a = _tri; \
+     lda = convert_index<BlasIndex>(triStride); \
+   } \
+   if (IsUnitDiag) diag='U'; \
+/* call ?trsm*/ \
+   BLASFUNC(&side, &uplo, &transa, &diag, &m, &n, (const BLASTYPE*)&numext::real_ref(alpha), (const BLASTYPE*)a, &lda, (BLASTYPE*)_other, &ldb); \
+   /*std::cout << "TRMS_L specialization!\n";*/ \
+ } \
+};
+
+#ifdef EIGEN_USE_MKL
+EIGEN_BLAS_TRSM_R(double,   double, dtrsm)
+EIGEN_BLAS_TRSM_R(dcomplex, MKL_Complex16, ztrsm)
+EIGEN_BLAS_TRSM_R(float,    float,  strsm)
+EIGEN_BLAS_TRSM_R(scomplex, MKL_Complex8,  ctrsm)
+#else
+EIGEN_BLAS_TRSM_R(double,   double, dtrsm_)
+EIGEN_BLAS_TRSM_R(dcomplex, double, ztrsm_)
+EIGEN_BLAS_TRSM_R(float,    float,  strsm_)
+EIGEN_BLAS_TRSM_R(scomplex, float,  ctrsm_)
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRIANGULAR_SOLVER_MATRIX_BLAS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularSolverVector.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularSolverVector.h
new file mode 100644
index 0000000000000000000000000000000000000000..b8fbb5bcc0feafdbf880020f2b33ee4ed51f0b80
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/products/TriangularSolverVector.h
@@ -0,0 +1,149 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRIANGULAR_SOLVER_VECTOR_H
+#define EIGEN_TRIANGULAR_SOLVER_VECTOR_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool Conjugate, int StorageOrder>
+struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheRight, Mode, Conjugate, StorageOrder>
+{
+  static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs)
+  {
+    triangular_solve_vector<LhsScalar,RhsScalar,Index,OnTheLeft,
+        ((Mode&Upper)==Upper ? Lower : Upper) | (Mode&UnitDiag),
+        Conjugate,StorageOrder==RowMajor?ColMajor:RowMajor
+      >::run(size, _lhs, lhsStride, rhs);
+  }
+};
+
+// forward and backward substitution, row-major, rhs is a vector
+template<typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool Conjugate>
+struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode, Conjugate, RowMajor>
+{
+  enum {
+    IsLower = ((Mode&Lower)==Lower)
+  };
+  static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs)
+  {
+    typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,RowMajor>, 0, OuterStride<> > LhsMap;
+    const LhsMap lhs(_lhs,size,size,OuterStride<>(lhsStride));
+
+    typedef const_blas_data_mapper<LhsScalar,Index,RowMajor> LhsMapper;
+    typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper;
+
+    std::conditional_t<
+                  Conjugate,
+                  const CwiseUnaryOp<typename internal::scalar_conjugate_op<LhsScalar>,LhsMap>,
+                  const LhsMap&> cjLhs(lhs);
+    static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
+    for(Index pi=IsLower ? 0 : size;
+        IsLower ? pi<size : pi>0;
+        IsLower ? pi+=PanelWidth : pi-=PanelWidth)
+    {
+      Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth);
+
+      Index r = IsLower ? pi : size - pi; // remaining size
+      if (r > 0)
+      {
+        // let's directly call the low level product function because:
+        // 1 - it is faster to compile
+        // 2 - it is slightly faster at runtime
+        Index startRow = IsLower ? pi : pi-actualPanelWidth;
+        Index startCol = IsLower ? 0 : pi;
+
+        general_matrix_vector_product<Index,LhsScalar,LhsMapper,RowMajor,Conjugate,RhsScalar,RhsMapper,false>::run(
+          actualPanelWidth, r,
+          LhsMapper(&lhs.coeffRef(startRow,startCol), lhsStride),
+          RhsMapper(rhs + startCol, 1),
+          rhs + startRow, 1,
+          RhsScalar(-1));
+      }
+
+      for(Index k=0; k<actualPanelWidth; ++k)
+      {
+        Index i = IsLower ? pi+k : pi-k-1;
+        Index s = IsLower ? pi   : i+1;
+        if (k>0)
+          rhs[i] -= (cjLhs.row(i).segment(s,k).transpose().cwiseProduct(Map<const Matrix<RhsScalar,Dynamic,1> >(rhs+s,k))).sum();
+
+        if((!(Mode & UnitDiag)) && !is_identically_zero(rhs[i]))
+          rhs[i] /= cjLhs(i,i);
+      }
+    }
+  }
+};
+
+// forward and backward substitution, column-major, rhs is a vector
+template<typename LhsScalar, typename RhsScalar, typename Index, int Mode, bool Conjugate>
+struct triangular_solve_vector<LhsScalar, RhsScalar, Index, OnTheLeft, Mode, Conjugate, ColMajor>
+{
+  enum {
+    IsLower = ((Mode&Lower)==Lower)
+  };
+  static void run(Index size, const LhsScalar* _lhs, Index lhsStride, RhsScalar* rhs)
+  {
+    typedef Map<const Matrix<LhsScalar,Dynamic,Dynamic,ColMajor>, 0, OuterStride<> > LhsMap;
+    const LhsMap lhs(_lhs,size,size,OuterStride<>(lhsStride));
+    typedef const_blas_data_mapper<LhsScalar,Index,ColMajor> LhsMapper;
+    typedef const_blas_data_mapper<RhsScalar,Index,ColMajor> RhsMapper;
+    std::conditional_t<Conjugate,
+                            const CwiseUnaryOp<typename internal::scalar_conjugate_op<LhsScalar>,LhsMap>,
+                            const LhsMap&
+                           > cjLhs(lhs);
+    static const Index PanelWidth = EIGEN_TUNE_TRIANGULAR_PANEL_WIDTH;
+
+    for(Index pi=IsLower ? 0 : size;
+        IsLower ? pi<size : pi>0;
+        IsLower ? pi+=PanelWidth : pi-=PanelWidth)
+    {
+      Index actualPanelWidth = (std::min)(IsLower ? size - pi : pi, PanelWidth);
+      Index startBlock = IsLower ? pi : pi-actualPanelWidth;
+      Index endBlock = IsLower ? pi + actualPanelWidth : 0;
+
+      for(Index k=0; k<actualPanelWidth; ++k)
+      {
+        Index i = IsLower ? pi+k : pi-k-1;
+        if(!is_identically_zero(rhs[i]))
+        {
+          if(!(Mode & UnitDiag))
+            rhs[i] /= cjLhs.coeff(i,i);
+
+          Index r = actualPanelWidth - k - 1; // remaining size
+          Index s = IsLower ? i+1 : i-r;
+          if (r>0)
+            Map<Matrix<RhsScalar,Dynamic,1> >(rhs+s,r) -= rhs[i] * cjLhs.col(i).segment(s,r);
+        }
+      }
+      Index r = IsLower ? size - endBlock : startBlock; // remaining size
+      if (r > 0)
+      {
+        // let's directly call the low level product function because:
+        // 1 - it is faster to compile
+        // 2 - it is slightly faster at runtime
+        general_matrix_vector_product<Index,LhsScalar,LhsMapper,ColMajor,Conjugate,RhsScalar,RhsMapper,false>::run(
+            r, actualPanelWidth,
+            LhsMapper(&lhs.coeffRef(endBlock,startBlock), lhsStride),
+            RhsMapper(rhs+startBlock, 1),
+            rhs+endBlock, 1, RhsScalar(-1));
+      }
+    }
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRIANGULAR_SOLVER_VECTOR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Assert.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Assert.h
new file mode 100644
index 0000000000000000000000000000000000000000..f8ba6329779f0f6b257c953b442b1ee67ee610ea
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Assert.h
@@ -0,0 +1,166 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2022, The Eigen authors.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CORE_UTIL_ASSERT_H
+#define EIGEN_CORE_UTIL_ASSERT_H
+
+// Eigen custom assert function.
+//
+// The combination of Eigen's relative includes and cassert's `assert` function
+// (or any usage of the __FILE__ macro) can lead to ODR issues:
+// a header included using different relative paths in two different TUs will
+// have two different token-for-token definitions, since __FILE__ is expanded
+// as an in-line string with different values.  Normally this would be
+// harmless - the linker would just choose one definition. However, it breaks
+// with C++20 modules when functions in different modules have different
+// definitions.
+//
+// To get around this, we need to use __builtin_FILE() when available, which is
+// considered a single token, and thus satisfies the ODR.
+
+// Only define eigen_plain_assert if we are debugging, and either
+//  - we are not compiling for GPU, or
+//  - gpu debugging is enabled.
+#if !defined(EIGEN_NO_DEBUG) && (!defined(EIGEN_GPU_COMPILE_PHASE) || !defined(EIGEN_NO_DEBUG_GPU))
+
+#include <cassert>
+
+#ifndef EIGEN_USE_CUSTOM_PLAIN_ASSERT
+// Disable new custom asserts by default for now.
+#define EIGEN_USE_CUSTOM_PLAIN_ASSERT  0
+#endif
+
+#if EIGEN_USE_CUSTOM_PLAIN_ASSERT
+
+#ifndef EIGEN_HAS_BUILTIN_FILE
+// Clang can check if __builtin_FILE() is supported.
+// GCC > 5, MSVC 2019 14.26 (1926) all have __builtin_FILE().
+// 
+// For NVCC, it's more complicated.  Through trial-and-error:
+//   - nvcc+gcc supports __builtin_FILE() on host, and on device after CUDA 11.
+//   - nvcc+msvc supports __builtin_FILE() only after CUDA 11.
+#if (EIGEN_HAS_BUILTIN(__builtin_FILE) && (EIGEN_COMP_CLANG || !defined(EIGEN_CUDA_ARCH))) || \
+    (EIGEN_GNUC_STRICT_AT_LEAST(5, 0, 0) && (EIGEN_COMP_NVCC >= 110000 || !defined(EIGEN_CUDA_ARCH))) || \
+    (EIGEN_COMP_MSVC >= 1926 && (!EIGEN_COMP_NVCC || EIGEN_COMP_NVCC >= 110000))
+#define EIGEN_HAS_BUILTIN_FILE 1
+#else
+#define EIGEN_HAS_BUILTIN_FILE 0
+#endif
+#endif  // EIGEN_HAS_BUILTIN_FILE
+
+#if EIGEN_HAS_BUILTIN_FILE
+#  define EIGEN_BUILTIN_FILE __builtin_FILE()
+#  define EIGEN_BUILTIN_LINE __builtin_LINE()
+#else
+// Default (potentially unsafe) values.
+#  define EIGEN_BUILTIN_FILE __FILE__
+#  define EIGEN_BUILTIN_LINE __LINE__
+#endif
+
+// Use __PRETTY_FUNCTION__ when available, since it is more descriptive, as
+// __builtin_FUNCTION() only returns the undecorated function name.
+// This should still be okay ODR-wise since it is a compiler-specific fixed
+// value.  Mixing compilers will likely lead to ODR violations anyways.
+#if EIGEN_COMP_MSVC
+#  define EIGEN_BUILTIN_FUNCTION __FUNCSIG__
+#elif EIGEN_COMP_GNUC
+#  define EIGEN_BUILTIN_FUNCTION __PRETTY_FUNCTION__
+#else
+#  define EIGEN_BUILTIN_FUNCTION __func__
+#endif
+
+namespace Eigen {
+namespace internal {
+
+// Generic default assert handler.
+template<typename EnableIf = void, typename... EmptyArgs>
+struct assert_handler_impl {
+  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE
+  static inline void run(const char* expression, const char* file, unsigned line, const char* function) {
+#ifdef EIGEN_GPU_COMPILE_PHASE
+    // GPU device code doesn't allow stderr or abort, so use printf and raise an
+    // illegal instruction exception to trigger a kernel failure.
+#ifndef EIGEN_NO_IO
+    printf("Assertion failed at %s:%u in %s: %s\n",
+      file == nullptr ? "<file>" : file,
+      line,
+      function == nullptr ? "<function>" : function,
+      expression);
+#endif
+    __trap();
+      
+#else  // EIGEN_GPU_COMPILE_PHASE
+
+    // Print to stderr and abort, as specified in <cassert>.
+#ifndef EIGEN_NO_IO
+    fprintf(stderr, "Assertion failed at %s:%u in %s: %s\n",
+      file == nullptr ? "<file>" : file,
+      line,
+      function == nullptr ? "<function>" : function,
+      expression);
+#endif
+    std::abort();
+    
+#endif  // EIGEN_GPU_COMPILE_PHASE
+  }
+};
+
+// Use POSIX __assert_fail handler when available.
+//
+// This allows us to integrate with systems that have custom handlers.
+//
+// NOTE: this handler is not always available on all POSIX systems (otherwise
+// we could simply test for __unix__ or similar).  The handler function name
+// seems to depend on the specific toolchain implementation, and differs between
+// compilers, platforms, OSes, etc.  Hence, we detect support via SFINAE.
+template<typename... EmptyArgs>
+struct assert_handler_impl<
+    void_t<decltype(__assert_fail(
+        (const char*)nullptr,         // expression
+        (const char*)nullptr,         // file
+        0,                            // line
+        (const char*)nullptr,         // function
+        std::declval<EmptyArgs>()...  // Empty substitution required for SFINAE.
+    ))>, EmptyArgs... > {
+  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE
+  static inline void run(const char* expression, const char* file, unsigned line, const char* function) {
+    // GCC requires this call to be dependent on the template parameters.
+    __assert_fail(expression, file, line, function, std::declval<EmptyArgs>()...);
+  }
+};
+
+EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE
+inline void __assert_handler(const char* expression, const char* file, unsigned line, const char* function) {
+  assert_handler_impl<>::run(expression, file, line, function);
+}
+
+}  // namespace internal
+}  // namespace Eigen
+
+#define eigen_plain_assert(expression)                     \
+  (EIGEN_PREDICT_FALSE(!(expression)) ?                    \
+    Eigen::internal::__assert_handler(#expression,         \
+                                      EIGEN_BUILTIN_FILE,  \
+                                      EIGEN_BUILTIN_LINE,  \
+                                      EIGEN_BUILTIN_FUNCTION) : (void)0)
+
+#else  // EIGEN_USE_CUSTOM_PLAIN_ASSERT
+
+// Use regular assert.
+#define eigen_plain_assert(condition) assert(condition)
+
+#endif  // EIGEN_USE_CUSTOM_PLAIN_ASSERT
+
+#else   // EIGEN_NO_DEBUG
+
+#define eigen_plain_assert(condition) ((void)0)
+
+#endif  // EIGEN_NO_DEBUG
+
+#endif  // EIGEN_CORE_UTIL_ASSERT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/BlasUtil.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/BlasUtil.h
new file mode 100644
index 0000000000000000000000000000000000000000..b0217a905e8d6c6372c78f18dac7b0ddb4d663e1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/BlasUtil.h
@@ -0,0 +1,647 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BLASUTIL_H
+#define EIGEN_BLASUTIL_H
+
+// This file contains many lightweight helper classes used to
+// implement and control fast level 2 and level 3 BLAS-like routines.
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// forward declarations
+template<typename LhsScalar, typename RhsScalar, typename Index, typename DataMapper, int mr, int nr, bool ConjugateLhs=false, bool ConjugateRhs=false>
+struct gebp_kernel;
+
+template<typename Scalar, typename Index, typename DataMapper, int nr, int StorageOrder, bool Conjugate = false, bool PanelMode=false>
+struct gemm_pack_rhs;
+
+template<typename Scalar, typename Index, typename DataMapper, int Pack1, int Pack2, typename Packet, int StorageOrder, bool Conjugate = false, bool PanelMode = false>
+struct gemm_pack_lhs;
+
+template<
+  typename Index,
+  typename LhsScalar, int LhsStorageOrder, bool ConjugateLhs,
+  typename RhsScalar, int RhsStorageOrder, bool ConjugateRhs,
+  int ResStorageOrder, int ResInnerStride>
+struct general_matrix_matrix_product;
+
+template<typename Index,
+         typename LhsScalar, typename LhsMapper, int LhsStorageOrder, bool ConjugateLhs,
+         typename RhsScalar, typename RhsMapper, bool ConjugateRhs, int Version=Specialized>
+struct general_matrix_vector_product;
+
+template<typename From,typename To> struct get_factor {
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE To run(const From& x) { return To(x); }
+};
+
+template<typename Scalar> struct get_factor<Scalar,typename NumTraits<Scalar>::Real> {
+  EIGEN_DEVICE_FUNC
+  static EIGEN_STRONG_INLINE typename NumTraits<Scalar>::Real run(const Scalar& x) { return numext::real(x); }
+};
+
+
+template<typename Scalar, typename Index>
+class BlasVectorMapper {
+  public:
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE BlasVectorMapper(Scalar *data) : m_data(data) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i) const {
+    return m_data[i];
+  }
+  template <typename Packet, int AlignmentType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet load(Index i) const {
+    return ploadt<Packet, AlignmentType>(m_data + i);
+  }
+
+  template <typename Packet>
+  EIGEN_DEVICE_FUNC bool aligned(Index i) const {
+    return (std::uintptr_t(m_data+i)%sizeof(Packet))==0;
+  }
+
+  protected:
+  Scalar* m_data;
+};
+
+template<typename Scalar, typename Index, int AlignmentType, int Incr=1>
+class BlasLinearMapper;
+
+template<typename Scalar, typename Index, int AlignmentType>
+class BlasLinearMapper<Scalar,Index,AlignmentType>
+{
+public:
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE BlasLinearMapper(Scalar *data, Index incr=1)
+    : m_data(data)
+  {
+    EIGEN_ONLY_USED_FOR_DEBUG(incr);
+    eigen_assert(incr==1);
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void prefetch(Index i) const {
+    internal::prefetch(&operator()(i));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar& operator()(Index i) const {
+    return m_data[i];
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i) const {
+    return ploadt<PacketType, AlignmentType>(m_data + i);
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacketPartial(Index i, Index n, Index offset = 0) const {
+    return ploadt_partial<PacketType, AlignmentType>(m_data + i, n, offset);
+  }
+
+  template<typename PacketType, int AlignmentT>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType load(Index i) const {
+    return ploadt<PacketType, AlignmentT>(m_data + i);
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, const PacketType &p) const {
+    pstoret<Scalar, PacketType, AlignmentType>(m_data + i, p);
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketPartial(Index i, const PacketType &p, Index n, Index offset = 0) const {
+    pstoret_partial<Scalar, PacketType, AlignmentType>(m_data + i, p, n, offset);
+  }
+
+protected:
+  Scalar *m_data;
+};
+
+// Lightweight helper class to access matrix coefficients.
+template<typename Scalar, typename Index, int StorageOrder, int AlignmentType = Unaligned, int Incr = 1>
+class blas_data_mapper;
+
+// TMP to help PacketBlock store implementation.
+// There's currently no known use case for PacketBlock load.
+// The default implementation assumes ColMajor order.
+// It always store each packet sequentially one `stride` apart.
+template<typename Index, typename Scalar, typename Packet, int n, int idx, int StorageOrder>
+struct PacketBlockManagement
+{
+  PacketBlockManagement<Index, Scalar, Packet, n, idx - 1, StorageOrder> pbm;
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar *to, const Index stride, Index i, Index j, const PacketBlock<Packet, n> &block) const {
+    pbm.store(to, stride, i, j, block);
+    pstoreu<Scalar>(to + i + (j + idx)*stride, block.packet[idx]);
+  }
+};
+
+// PacketBlockManagement specialization to take care of RowMajor order without ifs.
+template<typename Index, typename Scalar, typename Packet, int n, int idx>
+struct PacketBlockManagement<Index, Scalar, Packet, n, idx, RowMajor>
+{
+  PacketBlockManagement<Index, Scalar, Packet, n, idx - 1, RowMajor> pbm;
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar *to, const Index stride, Index i, Index j, const PacketBlock<Packet, n> &block) const {
+    pbm.store(to, stride, i, j, block);
+    pstoreu<Scalar>(to + j + (i + idx)*stride, block.packet[idx]);
+  }
+};
+
+template<typename Index, typename Scalar, typename Packet, int n, int StorageOrder>
+struct PacketBlockManagement<Index, Scalar, Packet, n, -1, StorageOrder>
+{
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar *to, const Index stride, Index i, Index j, const PacketBlock<Packet, n> &block) const {
+    EIGEN_UNUSED_VARIABLE(to);
+    EIGEN_UNUSED_VARIABLE(stride);
+    EIGEN_UNUSED_VARIABLE(i);
+    EIGEN_UNUSED_VARIABLE(j);
+    EIGEN_UNUSED_VARIABLE(block);
+  }
+};
+
+template<typename Index, typename Scalar, typename Packet, int n>
+struct PacketBlockManagement<Index, Scalar, Packet, n, -1, RowMajor>
+{
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(Scalar *to, const Index stride, Index i, Index j, const PacketBlock<Packet, n> &block) const {
+    EIGEN_UNUSED_VARIABLE(to);
+    EIGEN_UNUSED_VARIABLE(stride);
+    EIGEN_UNUSED_VARIABLE(i);
+    EIGEN_UNUSED_VARIABLE(j);
+    EIGEN_UNUSED_VARIABLE(block);
+  }
+};
+
+template<typename Scalar, typename Index, int StorageOrder, int AlignmentType>
+class blas_data_mapper<Scalar,Index,StorageOrder,AlignmentType,1>
+{
+public:
+  typedef BlasLinearMapper<Scalar, Index, AlignmentType> LinearMapper;
+  typedef blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType> SubMapper;
+  typedef BlasVectorMapper<Scalar, Index> VectorMapper;
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE blas_data_mapper(Scalar* data, Index stride, Index incr=1)
+   : m_data(data), m_stride(stride)
+  {
+    EIGEN_ONLY_USED_FOR_DEBUG(incr);
+    eigen_assert(incr==1);
+  }
+
+  EIGEN_DEVICE_FUNC  EIGEN_ALWAYS_INLINE SubMapper
+  getSubMapper(Index i, Index j) const {
+    return SubMapper(&operator()(i, j), m_stride);
+  }
+
+  EIGEN_DEVICE_FUNC  EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const {
+    return LinearMapper(&operator()(i, j));
+  }
+
+  EIGEN_DEVICE_FUNC  EIGEN_ALWAYS_INLINE VectorMapper getVectorMapper(Index i, Index j) const {
+    return VectorMapper(&operator()(i, j));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void prefetch(Index i, Index j) const {
+    internal::prefetch(&operator()(i, j));
+  }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_ALWAYS_INLINE Scalar& operator()(Index i, Index j) const {
+    return m_data[StorageOrder==RowMajor ? j + i*m_stride : i + j*m_stride];
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i, Index j) const {
+    return ploadt<PacketType, AlignmentType>(&operator()(i, j));
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacketPartial(Index i, Index j, Index n, Index offset = 0) const {
+    return ploadt_partial<PacketType, AlignmentType>(&operator()(i, j), n, offset);
+  }
+
+  template <typename PacketT, int AlignmentT>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT load(Index i, Index j) const {
+    return ploadt<PacketT, AlignmentT>(&operator()(i, j));
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, Index j, const PacketType &p) const {
+    pstoret<Scalar, PacketType, AlignmentType>(&operator()(i, j), p);
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketPartial(Index i, Index j, const PacketType &p, Index n, Index offset = 0) const {
+    pstoret_partial<Scalar, PacketType, AlignmentType>(&operator()(i, j), p, n, offset);
+  }
+
+  template<typename SubPacket>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void scatterPacket(Index i, Index j, const SubPacket &p) const {
+    pscatter<Scalar, SubPacket>(&operator()(i, j), p, m_stride);
+  }
+
+  template<typename SubPacket>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE SubPacket gatherPacket(Index i, Index j) const {
+    return pgather<Scalar, SubPacket>(&operator()(i, j), m_stride);
+  }
+
+  EIGEN_DEVICE_FUNC const Index stride() const { return m_stride; }
+  EIGEN_DEVICE_FUNC const Index incr() const { return 1; }
+  EIGEN_DEVICE_FUNC const Scalar* data() const { return m_data; }
+
+  EIGEN_DEVICE_FUNC Index firstAligned(Index size) const {
+    if (std::uintptr_t(m_data)%sizeof(Scalar)) {
+      return -1;
+    }
+    return internal::first_default_aligned(m_data, size);
+  }
+
+  template<typename SubPacket, int n>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketBlock(Index i, Index j, const PacketBlock<SubPacket, n> &block) const {
+    PacketBlockManagement<Index, Scalar, SubPacket, n, n-1, StorageOrder> pbm;
+    pbm.store(m_data, m_stride, i, j, block);
+  }
+protected:
+  Scalar* EIGEN_RESTRICT m_data;
+  const Index m_stride;
+};
+
+// Implementation of non-natural increment (i.e. inner-stride != 1)
+// The exposed API is not complete yet compared to the Incr==1 case
+// because some features makes less sense in this case.
+template<typename Scalar, typename Index, int AlignmentType, int Incr>
+class BlasLinearMapper
+{
+public:
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE BlasLinearMapper(Scalar *data,Index incr) : m_data(data), m_incr(incr) {}
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void prefetch(int i) const {
+    internal::prefetch(&operator()(i));
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar& operator()(Index i) const {
+    return m_data[i*m_incr.value()];
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i) const {
+    return pgather<Scalar,PacketType>(m_data + i*m_incr.value(), m_incr.value());
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacketPartial(Index i, Index n, Index /*offset*/ = 0) const {
+    return pgather_partial<Scalar,PacketType>(m_data + i*m_incr.value(), m_incr.value(), n);
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, const PacketType &p) const {
+    pscatter<Scalar, PacketType>(m_data + i*m_incr.value(), p, m_incr.value());
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketPartial(Index i, const PacketType &p, Index n, Index /*offset*/ = 0) const {
+    pscatter_partial<Scalar, PacketType>(m_data + i*m_incr.value(), p, m_incr.value(), n);
+  }
+
+protected:
+  Scalar *m_data;
+  const internal::variable_if_dynamic<Index,Incr> m_incr;
+};
+
+template<typename Scalar, typename Index, int StorageOrder, int AlignmentType,int Incr>
+class blas_data_mapper
+{
+public:
+  typedef BlasLinearMapper<Scalar, Index, AlignmentType,Incr> LinearMapper;
+  typedef blas_data_mapper SubMapper;
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE blas_data_mapper(Scalar* data, Index stride, Index incr) : m_data(data), m_stride(stride), m_incr(incr) {}
+
+  EIGEN_DEVICE_FUNC  EIGEN_ALWAYS_INLINE SubMapper
+  getSubMapper(Index i, Index j) const {
+    return SubMapper(&operator()(i, j), m_stride, m_incr.value());
+  }
+
+  EIGEN_DEVICE_FUNC  EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const {
+    return LinearMapper(&operator()(i, j), m_incr.value());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void prefetch(Index i, Index j) const {
+    internal::prefetch(&operator()(i, j));
+  }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_ALWAYS_INLINE Scalar& operator()(Index i, Index j) const {
+    return m_data[StorageOrder==RowMajor ? j*m_incr.value() + i*m_stride : i*m_incr.value() + j*m_stride];
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacket(Index i, Index j) const {
+    return pgather<Scalar,PacketType>(&operator()(i, j),m_incr.value());
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketType loadPacketPartial(Index i, Index j, Index n, Index /*offset*/ = 0) const {
+    return pgather_partial<Scalar,PacketType>(&operator()(i, j),m_incr.value(),n);
+  }
+
+  template <typename PacketT, int AlignmentT>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE PacketT load(Index i, Index j) const {
+    return pgather<Scalar,PacketT>(&operator()(i, j),m_incr.value());
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacket(Index i, Index j, const PacketType &p) const {
+    pscatter<Scalar, PacketType>(&operator()(i, j), p, m_incr.value());
+  }
+
+  template<typename PacketType>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketPartial(Index i, Index j, const PacketType &p, Index n, Index /*offset*/ = 0) const {
+    pscatter_partial<Scalar, PacketType>(&operator()(i, j), p, m_incr.value(), n);
+  }
+
+  template<typename SubPacket>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void scatterPacket(Index i, Index j, const SubPacket &p) const {
+    pscatter<Scalar, SubPacket>(&operator()(i, j), p, m_stride);
+  }
+
+  template<typename SubPacket>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE SubPacket gatherPacket(Index i, Index j) const {
+    return pgather<Scalar, SubPacket>(&operator()(i, j), m_stride);
+  }
+
+  // storePacketBlock_helper defines a way to access values inside the PacketBlock, this is essentially required by the Complex types.
+  template<typename SubPacket, typename Scalar_, int n, int idx>
+  struct storePacketBlock_helper
+  {
+    storePacketBlock_helper<SubPacket, Scalar_, n, idx-1> spbh;
+    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>* sup, Index i, Index j, const PacketBlock<SubPacket, n>& block) const {
+      spbh.store(sup, i,j,block);
+      sup->template storePacket<SubPacket>(i, j+idx, block.packet[idx]);
+    }
+  };
+
+  template<typename SubPacket, int n, int idx>
+  struct storePacketBlock_helper<SubPacket, std::complex<float>, n, idx>
+  {
+    storePacketBlock_helper<SubPacket, std::complex<float>, n, idx-1> spbh;
+    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>* sup, Index i, Index j, const PacketBlock<SubPacket, n>& block) const {
+      spbh.store(sup,i,j,block);
+      sup->template storePacket<SubPacket>(i, j+idx, block.packet[idx]);
+    }
+  };
+
+  template<typename SubPacket, int n, int idx>
+  struct storePacketBlock_helper<SubPacket, std::complex<double>, n, idx>
+  {
+    storePacketBlock_helper<SubPacket, std::complex<double>, n, idx-1> spbh;
+    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>* sup, Index i, Index j, const PacketBlock<SubPacket, n>& block) const {
+      spbh.store(sup,i,j,block);
+      for(int l = 0; l < unpacket_traits<SubPacket>::size; l++)
+      {
+        std::complex<double> *v = &sup->operator()(i+l, j+idx);
+        v->real(block.packet[idx].v[2*l+0]);
+        v->imag(block.packet[idx].v[2*l+1]);
+      }
+    }
+  };
+
+  template<typename SubPacket, typename Scalar_, int n>
+  struct storePacketBlock_helper<SubPacket, Scalar_, n, -1>
+  {
+    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>*, Index, Index, const PacketBlock<SubPacket, n>& ) const {
+    }
+  };
+
+  template<typename SubPacket, int n>
+  struct storePacketBlock_helper<SubPacket, std::complex<float>, n, -1>
+  {
+    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>*, Index, Index, const PacketBlock<SubPacket, n>& ) const {
+    }
+  };
+
+  template<typename SubPacket, int n>
+  struct storePacketBlock_helper<SubPacket, std::complex<double>, n, -1>
+  {
+    EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void store(const blas_data_mapper<Scalar, Index, StorageOrder, AlignmentType, Incr>*, Index, Index, const PacketBlock<SubPacket, n>& ) const {
+    }
+  };
+  // This function stores a PacketBlock on m_data, this approach is really quite slow compare to Incr=1 and should be avoided when possible.
+  template<typename SubPacket, int n>
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void storePacketBlock(Index i, Index j, const PacketBlock<SubPacket, n>&block) const {
+    storePacketBlock_helper<SubPacket, Scalar, n, n-1> spb;
+    spb.store(this, i,j,block);
+  }
+
+  EIGEN_DEVICE_FUNC const Index stride() const { return m_stride; }
+  EIGEN_DEVICE_FUNC const Index incr() const { return m_incr.value(); }
+  EIGEN_DEVICE_FUNC Scalar* data() const { return m_data; }
+protected:
+  Scalar* EIGEN_RESTRICT m_data;
+  const Index m_stride;
+  const internal::variable_if_dynamic<Index,Incr> m_incr;
+};
+
+// lightweight helper class to access matrix coefficients (const version)
+template<typename Scalar, typename Index, int StorageOrder>
+class const_blas_data_mapper : public blas_data_mapper<const Scalar, Index, StorageOrder> {
+  public:
+  typedef const_blas_data_mapper<Scalar, Index, StorageOrder> SubMapper;
+
+  EIGEN_ALWAYS_INLINE const_blas_data_mapper(const Scalar *data, Index stride) : blas_data_mapper<const Scalar, Index, StorageOrder>(data, stride) {}
+
+  EIGEN_ALWAYS_INLINE SubMapper getSubMapper(Index i, Index j) const {
+    return SubMapper(&(this->operator()(i, j)), this->m_stride);
+  }
+};
+
+
+/* Helper class to analyze the factors of a Product expression.
+ * In particular it allows to pop out operator-, scalar multiples,
+ * and conjugate */
+template<typename XprType> struct blas_traits
+{
+  typedef typename traits<XprType>::Scalar Scalar;
+  typedef const XprType& ExtractType;
+  typedef XprType ExtractType_;
+  enum {
+    IsComplex = NumTraits<Scalar>::IsComplex,
+    IsTransposed = false,
+    NeedToConjugate = false,
+    HasUsableDirectAccess = (    (int(XprType::Flags)&DirectAccessBit)
+                              && (   bool(XprType::IsVectorAtCompileTime)
+                                  || int(inner_stride_at_compile_time<XprType>::ret) == 1)
+                             ) ?  1 : 0,
+    HasScalarFactor = false
+  };
+  typedef std::conditional_t<bool(HasUsableDirectAccess),
+    ExtractType,
+    typename ExtractType_::PlainObject
+    > DirectLinearAccessType;
+  EIGEN_DEVICE_FUNC static inline EIGEN_DEVICE_FUNC ExtractType extract(const XprType& x) { return x; }
+  EIGEN_DEVICE_FUNC static inline EIGEN_DEVICE_FUNC const Scalar extractScalarFactor(const XprType&) { return Scalar(1); }
+};
+
+// pop conjugate
+template<typename Scalar, typename NestedXpr>
+struct blas_traits<CwiseUnaryOp<scalar_conjugate_op<Scalar>, NestedXpr> >
+ : blas_traits<NestedXpr>
+{
+  typedef blas_traits<NestedXpr> Base;
+  typedef CwiseUnaryOp<scalar_conjugate_op<Scalar>, NestedXpr> XprType;
+  typedef typename Base::ExtractType ExtractType;
+
+  enum {
+    IsComplex = NumTraits<Scalar>::IsComplex,
+    NeedToConjugate = Base::NeedToConjugate ? 0 : IsComplex
+  };
+  EIGEN_DEVICE_FUNC static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); }
+  EIGEN_DEVICE_FUNC static inline Scalar extractScalarFactor(const XprType& x) { return conj(Base::extractScalarFactor(x.nestedExpression())); }
+};
+
+// pop scalar multiple
+template<typename Scalar, typename NestedXpr, typename Plain>
+struct blas_traits<CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain>, NestedXpr> >
+ : blas_traits<NestedXpr>
+{
+  enum {
+    HasScalarFactor = true
+  };
+  typedef blas_traits<NestedXpr> Base;
+  typedef CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain>, NestedXpr> XprType;
+  typedef typename Base::ExtractType ExtractType;
+  EIGEN_DEVICE_FUNC static inline EIGEN_DEVICE_FUNC ExtractType extract(const XprType& x) { return Base::extract(x.rhs()); }
+  EIGEN_DEVICE_FUNC static inline EIGEN_DEVICE_FUNC Scalar extractScalarFactor(const XprType& x)
+  { return x.lhs().functor().m_other * Base::extractScalarFactor(x.rhs()); }
+};
+template<typename Scalar, typename NestedXpr, typename Plain>
+struct blas_traits<CwiseBinaryOp<scalar_product_op<Scalar>, NestedXpr, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain> > >
+ : blas_traits<NestedXpr>
+{
+  enum {
+    HasScalarFactor = true
+  };
+  typedef blas_traits<NestedXpr> Base;
+  typedef CwiseBinaryOp<scalar_product_op<Scalar>, NestedXpr, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain> > XprType;
+  typedef typename Base::ExtractType ExtractType;
+  EIGEN_DEVICE_FUNC static inline ExtractType extract(const XprType& x) { return Base::extract(x.lhs()); }
+  EIGEN_DEVICE_FUNC static inline Scalar extractScalarFactor(const XprType& x)
+  { return Base::extractScalarFactor(x.lhs()) * x.rhs().functor().m_other; }
+};
+template<typename Scalar, typename Plain1, typename Plain2>
+struct blas_traits<CwiseBinaryOp<scalar_product_op<Scalar>, const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain1>,
+                                                            const CwiseNullaryOp<scalar_constant_op<Scalar>,Plain2> > >
+ : blas_traits<CwiseNullaryOp<scalar_constant_op<Scalar>,Plain1> >
+{};
+
+// pop opposite
+template<typename Scalar, typename NestedXpr>
+struct blas_traits<CwiseUnaryOp<scalar_opposite_op<Scalar>, NestedXpr> >
+ : blas_traits<NestedXpr>
+{
+  enum {
+    HasScalarFactor = true
+  };
+  typedef blas_traits<NestedXpr> Base;
+  typedef CwiseUnaryOp<scalar_opposite_op<Scalar>, NestedXpr> XprType;
+  typedef typename Base::ExtractType ExtractType;
+  EIGEN_DEVICE_FUNC static inline ExtractType extract(const XprType& x) { return Base::extract(x.nestedExpression()); }
+  EIGEN_DEVICE_FUNC static inline Scalar extractScalarFactor(const XprType& x)
+  { return - Base::extractScalarFactor(x.nestedExpression()); }
+};
+
+// pop/push transpose
+template<typename NestedXpr>
+struct blas_traits<Transpose<NestedXpr> >
+ : blas_traits<NestedXpr>
+{
+  typedef typename NestedXpr::Scalar Scalar;
+  typedef blas_traits<NestedXpr> Base;
+  typedef Transpose<NestedXpr> XprType;
+  typedef Transpose<const typename Base::ExtractType_>  ExtractType; // const to get rid of a compile error; anyway blas traits are only used on the RHS
+  typedef Transpose<const typename Base::ExtractType_> ExtractType_;
+  typedef std::conditional_t<bool(Base::HasUsableDirectAccess),
+    ExtractType,
+    typename ExtractType::PlainObject
+    > DirectLinearAccessType;
+  enum {
+    IsTransposed = Base::IsTransposed ? 0 : 1
+  };
+  EIGEN_DEVICE_FUNC static inline ExtractType extract(const XprType& x) { return ExtractType(Base::extract(x.nestedExpression())); }
+  EIGEN_DEVICE_FUNC static inline Scalar extractScalarFactor(const XprType& x) { return Base::extractScalarFactor(x.nestedExpression()); }
+};
+
+template<typename T>
+struct blas_traits<const T>
+     : blas_traits<T>
+{};
+
+template<typename T, bool HasUsableDirectAccess=blas_traits<T>::HasUsableDirectAccess>
+struct extract_data_selector {
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static const typename T::Scalar* run(const T& m)
+  {
+    return blas_traits<T>::extract(m).data();
+  }
+};
+
+template<typename T>
+struct extract_data_selector<T,false> {
+  EIGEN_DEVICE_FUNC static typename T::Scalar* run(const T&) { return 0; }
+};
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE const typename T::Scalar* extract_data(const T& m)
+{
+  return extract_data_selector<T>::run(m);
+}
+
+/**
+ * \c combine_scalar_factors extracts and multiplies factors from GEMM and GEMV products.
+ * There is a specialization for booleans
+ */
+template<typename ResScalar, typename Lhs, typename Rhs>
+struct combine_scalar_factors_impl
+{
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static ResScalar run(const Lhs& lhs, const Rhs& rhs)
+  {
+    return blas_traits<Lhs>::extractScalarFactor(lhs) * blas_traits<Rhs>::extractScalarFactor(rhs);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static ResScalar run(const ResScalar& alpha, const Lhs& lhs, const Rhs& rhs)
+  {
+    return alpha * blas_traits<Lhs>::extractScalarFactor(lhs) * blas_traits<Rhs>::extractScalarFactor(rhs);
+  }
+};
+template<typename Lhs, typename Rhs>
+struct combine_scalar_factors_impl<bool, Lhs, Rhs>
+{
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static bool run(const Lhs& lhs, const Rhs& rhs)
+  {
+    return blas_traits<Lhs>::extractScalarFactor(lhs) && blas_traits<Rhs>::extractScalarFactor(rhs);
+  }
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE static bool run(const bool& alpha, const Lhs& lhs, const Rhs& rhs)
+  {
+    return alpha && blas_traits<Lhs>::extractScalarFactor(lhs) && blas_traits<Rhs>::extractScalarFactor(rhs);
+  }
+};
+
+template<typename ResScalar, typename Lhs, typename Rhs>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE ResScalar combine_scalar_factors(const ResScalar& alpha, const Lhs& lhs, const Rhs& rhs)
+{
+  return combine_scalar_factors_impl<ResScalar,Lhs,Rhs>::run(alpha, lhs, rhs);
+}
+template<typename ResScalar, typename Lhs, typename Rhs>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE ResScalar combine_scalar_factors(const Lhs& lhs, const Rhs& rhs)
+{
+  return combine_scalar_factors_impl<ResScalar,Lhs,Rhs>::run(lhs, rhs);
+}
+
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_BLASUTIL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/ConfigureVectorization.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/ConfigureVectorization.h
new file mode 100644
index 0000000000000000000000000000000000000000..29e43d5916c61287495dd018307e272fe98f6599
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/ConfigureVectorization.h
@@ -0,0 +1,515 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2018 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2020, Arm Limited and Contributors
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CONFIGURE_VECTORIZATION_H
+#define EIGEN_CONFIGURE_VECTORIZATION_H
+
+//------------------------------------------------------------------------------------------
+// Static and dynamic alignment control
+//
+// The main purpose of this section is to define EIGEN_MAX_ALIGN_BYTES and EIGEN_MAX_STATIC_ALIGN_BYTES
+// as the maximal boundary in bytes on which dynamically and statically allocated data may be alignment respectively.
+// The values of EIGEN_MAX_ALIGN_BYTES and EIGEN_MAX_STATIC_ALIGN_BYTES can be specified by the user. If not,
+// a default value is automatically computed based on architecture, compiler, and OS.
+//
+// This section also defines macros EIGEN_ALIGN_TO_BOUNDARY(N) and the shortcuts EIGEN_ALIGN{8,16,32,_MAX}
+// to be used to declare statically aligned buffers.
+//------------------------------------------------------------------------------------------
+
+
+/* EIGEN_ALIGN_TO_BOUNDARY(n) forces data to be n-byte aligned. This is used to satisfy SIMD requirements.
+ * However, we do that EVEN if vectorization (EIGEN_VECTORIZE) is disabled,
+ * so that vectorization doesn't affect binary compatibility.
+ *
+ * If we made alignment depend on whether or not EIGEN_VECTORIZE is defined, it would be impossible to link
+ * vectorized and non-vectorized code.
+ */
+#if (defined EIGEN_CUDACC)
+  #define EIGEN_ALIGN_TO_BOUNDARY(n) __align__(n)
+  #define EIGEN_ALIGNOF(x) __alignof(x)
+#else
+  #define EIGEN_ALIGN_TO_BOUNDARY(n) alignas(n)
+  #define EIGEN_ALIGNOF(x) alignof(x)
+#endif
+
+// If the user explicitly disable vectorization, then we also disable alignment
+#if defined(EIGEN_DONT_VECTORIZE)
+  #if defined(EIGEN_GPUCC)
+    // GPU code is always vectorized and requires memory alignment for
+    // statically allocated buffers.
+    #define EIGEN_IDEAL_MAX_ALIGN_BYTES 16
+  #else
+    #define EIGEN_IDEAL_MAX_ALIGN_BYTES 0
+  #endif
+#elif defined(__AVX512F__)
+  // 64 bytes static alignment is preferred only if really required
+  #define EIGEN_IDEAL_MAX_ALIGN_BYTES 64
+#elif defined(__AVX__)
+  // 32 bytes static alignment is preferred only if really required
+  #define EIGEN_IDEAL_MAX_ALIGN_BYTES 32
+#elif defined __HVX__ && (__HVX_LENGTH__ == 128)
+  #define EIGEN_IDEAL_MAX_ALIGN_BYTES 128
+#else
+  #define EIGEN_IDEAL_MAX_ALIGN_BYTES 16
+#endif
+
+
+// EIGEN_MIN_ALIGN_BYTES defines the minimal value for which the notion of explicit alignment makes sense
+#define EIGEN_MIN_ALIGN_BYTES 16
+
+// Defined the boundary (in bytes) on which the data needs to be aligned. Note
+// that unless EIGEN_ALIGN is defined and not equal to 0, the data may not be
+// aligned at all regardless of the value of this #define.
+
+#if (defined(EIGEN_DONT_ALIGN_STATICALLY) || defined(EIGEN_DONT_ALIGN))  && defined(EIGEN_MAX_STATIC_ALIGN_BYTES) && EIGEN_MAX_STATIC_ALIGN_BYTES>0
+#error EIGEN_MAX_STATIC_ALIGN_BYTES and EIGEN_DONT_ALIGN[_STATICALLY] are both defined with EIGEN_MAX_STATIC_ALIGN_BYTES!=0. Use EIGEN_MAX_STATIC_ALIGN_BYTES=0 as a synonym of EIGEN_DONT_ALIGN_STATICALLY.
+#endif
+
+// EIGEN_DONT_ALIGN_STATICALLY and EIGEN_DONT_ALIGN are deprecated
+// They imply EIGEN_MAX_STATIC_ALIGN_BYTES=0
+#if defined(EIGEN_DONT_ALIGN_STATICALLY) || defined(EIGEN_DONT_ALIGN)
+  #ifdef EIGEN_MAX_STATIC_ALIGN_BYTES
+    #undef EIGEN_MAX_STATIC_ALIGN_BYTES
+  #endif
+  #define EIGEN_MAX_STATIC_ALIGN_BYTES 0
+#endif
+
+#ifndef EIGEN_MAX_STATIC_ALIGN_BYTES
+
+  // Try to automatically guess what is the best default value for EIGEN_MAX_STATIC_ALIGN_BYTES
+
+  // 16 byte alignment is only useful for vectorization. Since it affects the ABI, we need to enable
+  // 16 byte alignment on all platforms where vectorization might be enabled. In theory we could always
+  // enable alignment, but it can be a cause of problems on some platforms, so we just disable it in
+  // certain common platform (compiler+architecture combinations) to avoid these problems.
+  // Only static alignment is really problematic (relies on nonstandard compiler extensions),
+  // try to keep heap alignment even when we have to disable static alignment.
+  #if EIGEN_COMP_GNUC && !(EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM_OR_ARM64 || EIGEN_ARCH_PPC || EIGEN_ARCH_IA64 || EIGEN_ARCH_MIPS)
+  #define EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT 1
+  #else
+  #define EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT 0
+  #endif
+
+  // static alignment is completely disabled with GCC 3, Sun Studio, and QCC/QNX
+  #if !EIGEN_GCC_AND_ARCH_DOESNT_WANT_STACK_ALIGNMENT \
+  && !EIGEN_COMP_SUNCC \
+  && !EIGEN_OS_QNX
+    #define EIGEN_ARCH_WANTS_STACK_ALIGNMENT 1
+  #else
+    #define EIGEN_ARCH_WANTS_STACK_ALIGNMENT 0
+  #endif
+
+  #if EIGEN_ARCH_WANTS_STACK_ALIGNMENT
+    #define EIGEN_MAX_STATIC_ALIGN_BYTES EIGEN_IDEAL_MAX_ALIGN_BYTES
+  #else
+    #define EIGEN_MAX_STATIC_ALIGN_BYTES 0
+  #endif
+
+#endif
+
+// If EIGEN_MAX_ALIGN_BYTES is defined, then it is considered as an upper bound for EIGEN_MAX_STATIC_ALIGN_BYTES
+#if defined(EIGEN_MAX_ALIGN_BYTES) && EIGEN_MAX_ALIGN_BYTES<EIGEN_MAX_STATIC_ALIGN_BYTES
+#undef EIGEN_MAX_STATIC_ALIGN_BYTES
+#define EIGEN_MAX_STATIC_ALIGN_BYTES EIGEN_MAX_ALIGN_BYTES
+#endif
+
+#if EIGEN_MAX_STATIC_ALIGN_BYTES==0 && !defined(EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT)
+  #define EIGEN_DISABLE_UNALIGNED_ARRAY_ASSERT
+#endif
+
+// At this stage, EIGEN_MAX_STATIC_ALIGN_BYTES>0 is the true test whether we want to align arrays on the stack or not.
+// It takes into account both the user choice to explicitly enable/disable alignment (by setting EIGEN_MAX_STATIC_ALIGN_BYTES)
+// and the architecture config (EIGEN_ARCH_WANTS_STACK_ALIGNMENT).
+// Henceforth, only EIGEN_MAX_STATIC_ALIGN_BYTES should be used.
+
+
+// Shortcuts to EIGEN_ALIGN_TO_BOUNDARY
+#define EIGEN_ALIGN8  EIGEN_ALIGN_TO_BOUNDARY(8)
+#define EIGEN_ALIGN16 EIGEN_ALIGN_TO_BOUNDARY(16)
+#define EIGEN_ALIGN32 EIGEN_ALIGN_TO_BOUNDARY(32)
+#define EIGEN_ALIGN64 EIGEN_ALIGN_TO_BOUNDARY(64)
+#if EIGEN_MAX_STATIC_ALIGN_BYTES>0
+#define EIGEN_ALIGN_MAX EIGEN_ALIGN_TO_BOUNDARY(EIGEN_MAX_STATIC_ALIGN_BYTES)
+#else
+#define EIGEN_ALIGN_MAX
+#endif
+
+
+// Dynamic alignment control
+
+#if defined(EIGEN_DONT_ALIGN) && defined(EIGEN_MAX_ALIGN_BYTES) && EIGEN_MAX_ALIGN_BYTES>0
+#error EIGEN_MAX_ALIGN_BYTES and EIGEN_DONT_ALIGN are both defined with EIGEN_MAX_ALIGN_BYTES!=0. Use EIGEN_MAX_ALIGN_BYTES=0 as a synonym of EIGEN_DONT_ALIGN.
+#endif
+
+#ifdef EIGEN_DONT_ALIGN
+  #ifdef EIGEN_MAX_ALIGN_BYTES
+    #undef EIGEN_MAX_ALIGN_BYTES
+  #endif
+  #define EIGEN_MAX_ALIGN_BYTES 0
+#elif !defined(EIGEN_MAX_ALIGN_BYTES)
+  #define EIGEN_MAX_ALIGN_BYTES EIGEN_IDEAL_MAX_ALIGN_BYTES
+#endif
+
+#if EIGEN_IDEAL_MAX_ALIGN_BYTES > EIGEN_MAX_ALIGN_BYTES
+#define EIGEN_DEFAULT_ALIGN_BYTES EIGEN_IDEAL_MAX_ALIGN_BYTES
+#else
+#define EIGEN_DEFAULT_ALIGN_BYTES EIGEN_MAX_ALIGN_BYTES
+#endif
+
+
+#ifndef EIGEN_UNALIGNED_VECTORIZE
+#define EIGEN_UNALIGNED_VECTORIZE 1
+#endif
+
+//----------------------------------------------------------------------
+
+// if alignment is disabled, then disable vectorization. Note: EIGEN_MAX_ALIGN_BYTES is the proper check, it takes into
+// account both the user's will (EIGEN_MAX_ALIGN_BYTES,EIGEN_DONT_ALIGN) and our own platform checks
+#if EIGEN_MAX_ALIGN_BYTES==0
+  #ifndef EIGEN_DONT_VECTORIZE
+    #define EIGEN_DONT_VECTORIZE
+  #endif
+#endif
+
+
+// The following (except #include <malloc.h> and _M_IX86_FP ??) can likely be
+// removed as gcc 4.1 and msvc 2008 are not supported anyways.
+#if EIGEN_COMP_MSVC
+  #include <malloc.h> // for _aligned_malloc -- need it regardless of whether vectorization is enabled
+  // a user reported that in 64-bit mode, MSVC doesn't care to define _M_IX86_FP.
+  #if (defined(_M_IX86_FP) && (_M_IX86_FP >= 2)) || EIGEN_ARCH_x86_64
+    #define EIGEN_SSE2_ON_MSVC_2008_OR_LATER
+  #endif
+#else
+  #if defined(__SSE2__)
+    #define EIGEN_SSE2_ON_NON_MSVC
+  #endif
+#endif
+
+#if !(defined(EIGEN_DONT_VECTORIZE) || defined(EIGEN_GPUCC))
+
+  #if defined (EIGEN_SSE2_ON_NON_MSVC) || defined(EIGEN_SSE2_ON_MSVC_2008_OR_LATER)
+
+    // Defines symbols for compile-time detection of which instructions are
+    // used.
+    // EIGEN_VECTORIZE_YY is defined if and only if the instruction set YY is used
+    #define EIGEN_VECTORIZE
+    #define EIGEN_VECTORIZE_SSE
+    #define EIGEN_VECTORIZE_SSE2
+
+    // Detect sse3/ssse3/sse4:
+    // gcc and icc defines __SSE3__, ...
+    // there is no way to know about this on msvc. You can define EIGEN_VECTORIZE_SSE* if you
+    // want to force the use of those instructions with msvc.
+    #ifdef __SSE3__
+      #define EIGEN_VECTORIZE_SSE3
+    #endif
+    #ifdef __SSSE3__
+      #define EIGEN_VECTORIZE_SSSE3
+    #endif
+    #ifdef __SSE4_1__
+      #define EIGEN_VECTORIZE_SSE4_1
+    #endif
+    #ifdef __SSE4_2__
+      #define EIGEN_VECTORIZE_SSE4_2
+    #endif
+    #ifdef __AVX__
+      #ifndef EIGEN_USE_SYCL 
+        #define EIGEN_VECTORIZE_AVX
+      #endif
+      #define EIGEN_VECTORIZE_SSE3
+      #define EIGEN_VECTORIZE_SSSE3
+      #define EIGEN_VECTORIZE_SSE4_1
+      #define EIGEN_VECTORIZE_SSE4_2
+    #endif
+    #ifdef __AVX2__
+      #ifndef EIGEN_USE_SYCL 
+        #define EIGEN_VECTORIZE_AVX2
+        #define EIGEN_VECTORIZE_AVX
+      #endif
+      #define EIGEN_VECTORIZE_SSE3
+      #define EIGEN_VECTORIZE_SSSE3
+      #define EIGEN_VECTORIZE_SSE4_1
+      #define EIGEN_VECTORIZE_SSE4_2
+    #endif
+    #if defined(__FMA__) || (EIGEN_COMP_MSVC && defined(__AVX2__))
+      // MSVC does not expose a switch dedicated for FMA
+      // For MSVC, AVX2 => FMA
+      #define EIGEN_VECTORIZE_FMA
+    #endif
+    #if defined(__AVX512F__)
+      #ifndef EIGEN_VECTORIZE_FMA
+      #if EIGEN_COMP_GNUC
+      #error Please add -mfma to your compiler flags: compiling with -mavx512f alone without SSE/AVX FMA is not supported (bug 1638).
+      #else
+      #error Please enable FMA in your compiler flags (e.g. -mfma): compiling with AVX512 alone without SSE/AVX FMA is not supported (bug 1638).
+      #endif
+      #endif
+      #ifndef EIGEN_USE_SYCL
+        #define EIGEN_VECTORIZE_AVX512
+        #define EIGEN_VECTORIZE_AVX2
+        #define EIGEN_VECTORIZE_AVX
+      #endif
+      #define EIGEN_VECTORIZE_FMA
+      #define EIGEN_VECTORIZE_SSE3
+      #define EIGEN_VECTORIZE_SSSE3
+      #define EIGEN_VECTORIZE_SSE4_1
+      #define EIGEN_VECTORIZE_SSE4_2
+      #ifndef EIGEN_USE_SYCL
+        #ifdef __AVX512DQ__
+          #define EIGEN_VECTORIZE_AVX512DQ
+        #endif
+        #ifdef __AVX512ER__
+          #define EIGEN_VECTORIZE_AVX512ER
+        #endif
+        #ifdef __AVX512BF16__
+          #define EIGEN_VECTORIZE_AVX512BF16
+        #endif
+        #ifdef __AVX512FP16__
+          #ifdef __AVX512VL__
+            #define EIGEN_VECTORIZE_AVX512FP16
+          #else
+            #if EIGEN_COMP_GNUC
+              #error Please add -mavx512vl to your compiler flags: compiling with -mavx512fp16 alone without AVX512-VL is not supported.
+            #else
+              #error Please enable AVX512-VL in your compiler flags (e.g. -mavx512vl): compiling with AVX512-FP16 alone without AVX512-VL is not supported.
+            #endif
+          #endif 
+        #endif
+      #endif
+    #endif
+
+    // Disable AVX support on broken xcode versions
+    #if ( EIGEN_COMP_CLANGAPPLE == 11000033 ) && ( __MAC_OS_X_VERSION_MIN_REQUIRED == 101500 )
+      // A nasty bug in the clang compiler shipped with xcode in a common compilation situation
+      // when XCode 11.0 and Mac deployment target macOS 10.15 is https://trac.macports.org/ticket/58776#no1
+      #ifdef EIGEN_VECTORIZE_AVX
+        #undef EIGEN_VECTORIZE_AVX
+        #warning "Disabling AVX support: clang compiler shipped with XCode 11.[012] generates broken assembly with -macosx-version-min=10.15 and AVX enabled. "
+        #ifdef EIGEN_VECTORIZE_AVX2
+          #undef EIGEN_VECTORIZE_AVX2
+        #endif
+        #ifdef EIGEN_VECTORIZE_FMA
+          #undef EIGEN_VECTORIZE_FMA
+        #endif
+        #ifdef EIGEN_VECTORIZE_AVX512
+          #undef EIGEN_VECTORIZE_AVX512
+        #endif
+        #ifdef EIGEN_VECTORIZE_AVX512DQ
+          #undef EIGEN_VECTORIZE_AVX512DQ
+        #endif
+        #ifdef EIGEN_VECTORIZE_AVX512ER
+          #undef EIGEN_VECTORIZE_AVX512ER
+        #endif
+      #endif
+      // NOTE: Confirmed test failures in XCode 11.0, and XCode 11.2 with  -macosx-version-min=10.15 and AVX
+      // NOTE using -macosx-version-min=10.15 with Xcode 11.0 results in runtime segmentation faults in many tests, 11.2 produce core dumps in 3 tests
+      // NOTE using -macosx-version-min=10.14 produces functioning and passing tests in all cases
+      // NOTE __clang_version__ "11.0.0 (clang-1100.0.33.8)"  XCode 11.0 <- Produces many segfault and core dumping tests
+      //                                                                    with  -macosx-version-min=10.15 and AVX
+      // NOTE __clang_version__ "11.0.0 (clang-1100.0.33.12)" XCode 11.2 <- Produces 3 core dumping tests with  
+      //                                                                    -macosx-version-min=10.15 and AVX
+    #endif
+
+    // include files
+
+    // This extern "C" works around a MINGW-w64 compilation issue
+    // https://sourceforge.net/tracker/index.php?func=detail&aid=3018394&group_id=202880&atid=983354
+    // In essence, intrin.h is included by windows.h and also declares intrinsics (just as emmintrin.h etc. below do).
+    // However, intrin.h uses an extern "C" declaration, and g++ thus complains of duplicate declarations
+    // with conflicting linkage.  The linkage for intrinsics doesn't matter, but at that stage the compiler doesn't know;
+    // so, to avoid compile errors when windows.h is included after Eigen/Core, ensure intrinsics are extern "C" here too.
+    // notice that since these are C headers, the extern "C" is theoretically needed anyways.
+    extern "C" {
+      // In theory we should only include immintrin.h and not the other *mmintrin.h header files directly.
+      // Doing so triggers some issues with ICC. However old gcc versions seems to not have this file, thus:
+      #if EIGEN_COMP_ICC >= 1110 || EIGEN_COMP_EMSCRIPTEN
+        #include <immintrin.h>
+      #else
+        #include <mmintrin.h>
+        #include <emmintrin.h>
+        #include <xmmintrin.h>
+        #ifdef  EIGEN_VECTORIZE_SSE3
+        #include <pmmintrin.h>
+        #endif
+        #ifdef EIGEN_VECTORIZE_SSSE3
+        #include <tmmintrin.h>
+        #endif
+        #ifdef EIGEN_VECTORIZE_SSE4_1
+        #include <smmintrin.h>
+        #endif
+        #ifdef EIGEN_VECTORIZE_SSE4_2
+        #include <nmmintrin.h>
+        #endif
+        #if defined(EIGEN_VECTORIZE_AVX) || defined(EIGEN_VECTORIZE_AVX512)
+        #include <immintrin.h>
+        #endif
+      #endif
+    } // end extern "C"
+
+  #elif defined(__VSX__) && !defined(__APPLE__)
+
+    #define EIGEN_VECTORIZE
+    #define EIGEN_VECTORIZE_VSX 1
+    #include <altivec.h>
+    // We need to #undef all these ugly tokens defined in <altivec.h>
+    // => use __vector instead of vector
+    #undef bool
+    #undef vector
+    #undef pixel
+
+  #elif defined __ALTIVEC__
+
+    #define EIGEN_VECTORIZE
+    #define EIGEN_VECTORIZE_ALTIVEC
+    #include <altivec.h>
+    // We need to #undef all these ugly tokens defined in <altivec.h>
+    // => use __vector instead of vector
+    #undef bool
+    #undef vector
+    #undef pixel
+
+  #elif ((defined  __ARM_NEON) || (defined __ARM_NEON__)) && !(defined EIGEN_ARM64_USE_SVE)
+
+    #define EIGEN_VECTORIZE
+    #define EIGEN_VECTORIZE_NEON
+    #include <arm_neon.h>
+
+  // We currently require SVE to be enabled explicitly via EIGEN_ARM64_USE_SVE and
+  // will not select the backend automatically
+  #elif (defined __ARM_FEATURE_SVE) && (defined EIGEN_ARM64_USE_SVE)
+
+    #define EIGEN_VECTORIZE
+    #define EIGEN_VECTORIZE_SVE
+    #include <arm_sve.h>
+
+    // Since we depend on knowing SVE vector lengths at compile-time, we need
+    // to ensure a fixed lengths is set
+    #if defined __ARM_FEATURE_SVE_BITS
+      #define EIGEN_ARM64_SVE_VL __ARM_FEATURE_SVE_BITS
+    #else
+#error "Eigen requires a fixed SVE lector length but EIGEN_ARM64_SVE_VL is not set."
+#endif
+
+#elif (defined __s390x__ && defined __VEC__)
+
+#define EIGEN_VECTORIZE
+#define EIGEN_VECTORIZE_ZVECTOR
+#include <vecintrin.h>
+
+#elif defined __mips_msa
+
+// Limit MSA optimizations to little-endian CPUs for now.
+// TODO: Perhaps, eventually support MSA optimizations on big-endian CPUs?
+#if defined(__BYTE_ORDER__) && (__BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)
+#if defined(__LP64__)
+#define EIGEN_MIPS_64
+#else
+#define EIGEN_MIPS_32
+#endif
+#define EIGEN_VECTORIZE
+#define EIGEN_VECTORIZE_MSA
+#include <msa.h>
+#endif
+
+#elif defined __HVX__ && (__HVX_LENGTH__ == 128)
+
+#define EIGEN_VECTORIZE
+#define EIGEN_VECTORIZE_HVX
+#include <hexagon_types.h>
+
+#endif
+#endif
+
+// Following the Arm ACLE arm_neon.h should also include arm_fp16.h but not all
+// compilers seem to follow this. We therefore include it explicitly.
+// See also: https://bugs.llvm.org/show_bug.cgi?id=47955
+#if defined(EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC)
+  #include <arm_fp16.h>
+#endif
+
+#if defined(__F16C__) && !defined(EIGEN_GPUCC) && (!EIGEN_COMP_CLANG_STRICT || EIGEN_CLANG_STRICT_AT_LEAST(3,8,0))
+  // We can use the optimized fp16 to float and float to fp16 conversion routines
+  #define EIGEN_HAS_FP16_C
+
+  #if EIGEN_COMP_GNUC
+    // Make sure immintrin.h is included, even if e.g. vectorization is
+    // explicitly disabled (see also issue #2395).
+    // Note that FP16C intrinsics for gcc and clang are included by immintrin.h,
+    // as opposed to emmintrin.h as suggested by Intel:
+    // https://software.intel.com/sites/landingpage/IntrinsicsGuide/#othertechs=FP16C&expand=1711
+    #include <immintrin.h>
+  #endif
+#endif
+
+#if defined EIGEN_CUDACC
+  #define EIGEN_VECTORIZE_GPU
+  #include <vector_types.h>
+  #if EIGEN_CUDA_SDK_VER >= 70500
+    #define EIGEN_HAS_CUDA_FP16
+  #endif
+#endif
+
+#if defined(EIGEN_HAS_CUDA_FP16)
+  #include <cuda_runtime_api.h>
+  #include <cuda_fp16.h>
+#endif
+
+#if defined(EIGEN_HIPCC)
+  #define EIGEN_VECTORIZE_GPU
+  #include <hip/hip_vector_types.h>
+  #define EIGEN_HAS_HIP_FP16
+  #include <hip/hip_fp16.h>
+  #define EIGEN_HAS_HIP_BF16
+  #include <hip/hip_bfloat16.h>
+#endif
+
+
+/** \brief Namespace containing all symbols from the %Eigen library. */
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+inline static const char *SimdInstructionSetsInUse(void) {
+#if defined(EIGEN_VECTORIZE_AVX512)
+  return "AVX512, FMA, AVX2, AVX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
+#elif defined(EIGEN_VECTORIZE_AVX)
+  return "AVX SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
+#elif defined(EIGEN_VECTORIZE_SSE4_2)
+  return "SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2";
+#elif defined(EIGEN_VECTORIZE_SSE4_1)
+  return "SSE, SSE2, SSE3, SSSE3, SSE4.1";
+#elif defined(EIGEN_VECTORIZE_SSSE3)
+  return "SSE, SSE2, SSE3, SSSE3";
+#elif defined(EIGEN_VECTORIZE_SSE3)
+  return "SSE, SSE2, SSE3";
+#elif defined(EIGEN_VECTORIZE_SSE2)
+  return "SSE, SSE2";
+#elif defined(EIGEN_VECTORIZE_ALTIVEC)
+  return "AltiVec";
+#elif defined(EIGEN_VECTORIZE_VSX)
+  return "VSX";
+#elif defined(EIGEN_VECTORIZE_NEON)
+  return "ARM NEON";
+#elif defined(EIGEN_VECTORIZE_SVE)
+  return "ARM SVE";
+#elif defined(EIGEN_VECTORIZE_ZVECTOR)
+  return "S390X ZVECTOR";
+#elif defined(EIGEN_VECTORIZE_MSA)
+  return "MIPS MSA";
+#else
+  return "None";
+#endif
+}
+
+} // end namespace Eigen
+
+
+#endif // EIGEN_CONFIGURE_VECTORIZATION_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Constants.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Constants.h
new file mode 100644
index 0000000000000000000000000000000000000000..04f7af482737fd83f66457220a8662342c496302
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Constants.h
@@ -0,0 +1,571 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2007-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2020, Arm Limited and Contributors
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CONSTANTS_H
+#define EIGEN_CONSTANTS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** This value means that a positive quantity (e.g., a size) is not known at compile-time, and that instead the value is
+  * stored in some runtime variable.
+  *
+  * Changing the value of Dynamic breaks the ABI, as Dynamic is often used as a template parameter for Matrix.
+  */
+const int Dynamic = -1;
+
+/** This value means that a signed quantity (e.g., a signed index) is not known at compile-time, and that instead its value
+  * has to be specified at runtime.
+  */
+const int DynamicIndex = 0xffffff;
+
+/** This value means that the increment to go from one value to another in a sequence is not constant for each step.
+  */
+const int UndefinedIncr = 0xfffffe;
+
+/** This value means +Infinity; it is currently used only as the p parameter to MatrixBase::lpNorm<int>().
+  * The value Infinity there means the L-infinity norm.
+  */
+const int Infinity = -1;
+
+/** This value means that the cost to evaluate an expression coefficient is either very expensive or
+  * cannot be known at compile time.
+  *
+  * This value has to be positive to (1) simplify cost computation, and (2) allow to distinguish between a very expensive and very very expensive expressions.
+  * It thus must also be large enough to make sure unrolling won't happen and that sub expressions will be evaluated, but not too large to avoid overflow.
+  */
+const int HugeCost = 10000;
+
+/** \defgroup flags Flags
+  * \ingroup Core_Module
+  *
+  * These are the possible bits which can be OR'ed to constitute the flags of a matrix or
+  * expression.
+  *
+  * It is important to note that these flags are a purely compile-time notion. They are a compile-time property of
+  * an expression type, implemented as enum's. They are not stored in memory at runtime, and they do not incur any
+  * runtime overhead.
+  *
+  * \sa MatrixBase::Flags
+  */
+
+/** \ingroup flags
+  *
+  * for a matrix, this means that the storage order is row-major.
+  * If this bit is not set, the storage order is column-major.
+  * For an expression, this determines the storage order of
+  * the matrix created by evaluation of that expression.
+  * \sa \blank  \ref TopicStorageOrders */
+const unsigned int RowMajorBit = 0x1;
+
+/** \ingroup flags
+  * means the expression should be evaluated by the calling expression */
+const unsigned int EvalBeforeNestingBit = 0x2;
+
+/** \ingroup flags
+  * \deprecated
+  * means the expression should be evaluated before any assignment */
+EIGEN_DEPRECATED
+const unsigned int EvalBeforeAssigningBit = 0x4; // FIXME deprecated
+
+/** \ingroup flags
+  *
+  * Short version: means the expression might be vectorized
+  *
+  * Long version: means that the coefficients can be handled by packets
+  * and start at a memory location whose alignment meets the requirements
+  * of the present CPU architecture for optimized packet access. In the fixed-size
+  * case, there is the additional condition that it be possible to access all the
+  * coefficients by packets (this implies the requirement that the size be a multiple of 16 bytes,
+  * and that any nontrivial strides don't break the alignment). In the dynamic-size case,
+  * there is no such condition on the total size and strides, so it might not be possible to access
+  * all coeffs by packets.
+  *
+  * \note This bit can be set regardless of whether vectorization is actually enabled.
+  *       To check for actual vectorizability, see \a ActualPacketAccessBit.
+  */
+const unsigned int PacketAccessBit = 0x8;
+
+#ifdef EIGEN_VECTORIZE
+/** \ingroup flags
+  *
+  * If vectorization is enabled (EIGEN_VECTORIZE is defined) this constant
+  * is set to the value \a PacketAccessBit.
+  *
+  * If vectorization is not enabled (EIGEN_VECTORIZE is not defined) this constant
+  * is set to the value 0.
+  */
+const unsigned int ActualPacketAccessBit = PacketAccessBit;
+#else
+const unsigned int ActualPacketAccessBit = 0x0;
+#endif
+
+/** \ingroup flags
+  *
+  * Short version: means the expression can be seen as 1D vector.
+  *
+  * Long version: means that one can access the coefficients
+  * of this expression by coeff(int), and coeffRef(int) in the case of a lvalue expression. These
+  * index-based access methods are guaranteed
+  * to not have to do any runtime computation of a (row, col)-pair from the index, so that it
+  * is guaranteed that whenever it is available, index-based access is at least as fast as
+  * (row,col)-based access. Expressions for which that isn't possible don't have the LinearAccessBit.
+  *
+  * If both PacketAccessBit and LinearAccessBit are set, then the
+  * packets of this expression can be accessed by packet(int), and writePacket(int) in the case of a
+  * lvalue expression.
+  *
+  * Typically, all vector expressions have the LinearAccessBit, but there is one exception:
+  * Product expressions don't have it, because it would be troublesome for vectorization, even when the
+  * Product is a vector expression. Thus, vector Product expressions allow index-based coefficient access but
+  * not index-based packet access, so they don't have the LinearAccessBit.
+  */
+const unsigned int LinearAccessBit = 0x10;
+
+/** \ingroup flags
+  *
+  * Means the expression has a coeffRef() method, i.e. is writable as its individual coefficients are directly addressable.
+  * This rules out read-only expressions.
+  *
+  * Note that DirectAccessBit and LvalueBit are mutually orthogonal, as there are examples of expression having one but not
+  * the other:
+  *   \li writable expressions that don't have a very simple memory layout as a strided array, have LvalueBit but not DirectAccessBit
+  *   \li Map-to-const expressions, for example Map<const Matrix>, have DirectAccessBit but not LvalueBit
+  *
+  * Expressions having LvalueBit also have their coeff() method returning a const reference instead of returning a new value.
+  */
+const unsigned int LvalueBit = 0x20;
+
+/** \ingroup flags
+  *
+  * Means that the underlying array of coefficients can be directly accessed as a plain strided array. The memory layout
+  * of the array of coefficients must be exactly the natural one suggested by rows(), cols(),
+  * outerStride(), innerStride(), and the RowMajorBit. This rules out expressions such as Diagonal, whose coefficients,
+  * though referencable, do not have such a regular memory layout.
+  *
+  * See the comment on LvalueBit for an explanation of how LvalueBit and DirectAccessBit are mutually orthogonal.
+  */
+const unsigned int DirectAccessBit = 0x40;
+
+/** \deprecated \ingroup flags
+  *
+  * means the first coefficient packet is guaranteed to be aligned.
+  * An expression cannot have the AlignedBit without the PacketAccessBit flag.
+  * In other words, this means we are allow to perform an aligned packet access to the first element regardless
+  * of the expression kind:
+  * \code
+  * expression.packet<Aligned>(0);
+  * \endcode
+  */
+EIGEN_DEPRECATED const unsigned int AlignedBit = 0x80;
+
+const unsigned int NestByRefBit = 0x100;
+
+/** \ingroup flags
+  *
+  * for an expression, this means that the storage order
+  * can be either row-major or column-major.
+  * The precise choice will be decided at evaluation time or when
+  * combined with other expressions.
+  * \sa \blank  \ref RowMajorBit, \ref TopicStorageOrders */
+const unsigned int NoPreferredStorageOrderBit = 0x200;
+
+/** \ingroup flags
+  *
+  * Means that the underlying coefficients can be accessed through pointers to the sparse (un)compressed storage format,
+  * that is, the expression provides:
+  * \code
+    inline const Scalar* valuePtr() const;
+    inline const Index* innerIndexPtr() const;
+    inline const Index* outerIndexPtr() const;
+    inline const Index* innerNonZeroPtr() const;
+    \endcode
+  */
+const unsigned int CompressedAccessBit = 0x400;
+
+
+// list of flags that are inherited by default
+const unsigned int HereditaryBits = RowMajorBit
+                                  | EvalBeforeNestingBit;
+
+/** \defgroup enums Enumerations
+  * \ingroup Core_Module
+  *
+  * Various enumerations used in %Eigen. Many of these are used as template parameters.
+  */
+
+/** \ingroup enums
+  * Enum containing possible values for the \c Mode or \c UpLo parameter of
+  * MatrixBase::selfadjointView() and MatrixBase::triangularView(), and selfadjoint solvers. */
+enum UpLoType {
+  /** View matrix as a lower triangular matrix. */
+  Lower=0x1,                      
+  /** View matrix as an upper triangular matrix. */
+  Upper=0x2,                      
+  /** %Matrix has ones on the diagonal; to be used in combination with #Lower or #Upper. */
+  UnitDiag=0x4, 
+  /** %Matrix has zeros on the diagonal; to be used in combination with #Lower or #Upper. */
+  ZeroDiag=0x8,
+  /** View matrix as a lower triangular matrix with ones on the diagonal. */
+  UnitLower=UnitDiag|Lower, 
+  /** View matrix as an upper triangular matrix with ones on the diagonal. */
+  UnitUpper=UnitDiag|Upper,
+  /** View matrix as a lower triangular matrix with zeros on the diagonal. */
+  StrictlyLower=ZeroDiag|Lower, 
+  /** View matrix as an upper triangular matrix with zeros on the diagonal. */
+  StrictlyUpper=ZeroDiag|Upper,
+  /** Used in BandMatrix and SelfAdjointView to indicate that the matrix is self-adjoint. */
+  SelfAdjoint=0x10,
+  /** Used to support symmetric, non-selfadjoint, complex matrices. */
+  Symmetric=0x20
+};
+
+/** \ingroup enums
+  * Enum for indicating whether a buffer is aligned or not. */
+enum AlignmentType {
+  Unaligned=0,        /**< Data pointer has no specific alignment. */
+  Aligned8=8,         /**< Data pointer is aligned on a 8 bytes boundary. */
+  Aligned16=16,       /**< Data pointer is aligned on a 16 bytes boundary. */
+  Aligned32=32,       /**< Data pointer is aligned on a 32 bytes boundary. */
+  Aligned64=64,       /**< Data pointer is aligned on a 64 bytes boundary. */
+  Aligned128=128,     /**< Data pointer is aligned on a 128 bytes boundary. */
+  AlignedMask=255,
+  Aligned=16,         /**< \deprecated Synonym for Aligned16. */
+#if EIGEN_MAX_ALIGN_BYTES==128
+  AlignedMax = Aligned128
+#elif EIGEN_MAX_ALIGN_BYTES==64
+  AlignedMax = Aligned64
+#elif EIGEN_MAX_ALIGN_BYTES==32
+  AlignedMax = Aligned32
+#elif EIGEN_MAX_ALIGN_BYTES==16
+  AlignedMax = Aligned16
+#elif EIGEN_MAX_ALIGN_BYTES==8
+  AlignedMax = Aligned8
+#elif EIGEN_MAX_ALIGN_BYTES==0
+  AlignedMax = Unaligned
+#else
+#error Invalid value for EIGEN_MAX_ALIGN_BYTES
+#endif
+};
+
+/** \ingroup enums
+  * Enum containing possible values for the \p Direction parameter of
+  * Reverse, PartialReduxExpr and VectorwiseOp. */
+enum DirectionType { 
+  /** For Reverse, all columns are reversed; 
+    * for PartialReduxExpr and VectorwiseOp, act on columns. */
+  Vertical, 
+  /** For Reverse, all rows are reversed; 
+    * for PartialReduxExpr and VectorwiseOp, act on rows. */
+  Horizontal, 
+  /** For Reverse, both rows and columns are reversed; 
+    * not used for PartialReduxExpr and VectorwiseOp. */
+  BothDirections 
+};
+
+/** \internal \ingroup enums
+  * Enum to specify how to traverse the entries of a matrix. */
+enum TraversalType {
+  /** \internal Default traversal, no vectorization, no index-based access */
+  DefaultTraversal,
+  /** \internal No vectorization, use index-based access to have only one for loop instead of 2 nested loops */
+  LinearTraversal,
+  /** \internal Equivalent to a slice vectorization for fixed-size matrices having good alignment
+    * and good size */
+  InnerVectorizedTraversal,
+  /** \internal Vectorization path using a single loop plus scalar loops for the
+    * unaligned boundaries */
+  LinearVectorizedTraversal,
+  /** \internal Generic vectorization path using one vectorized loop per row/column with some
+    * scalar loops to handle the unaligned boundaries */
+  SliceVectorizedTraversal,
+  /** \internal Special case to properly handle incompatible scalar types or other defecting cases*/
+  InvalidTraversal,
+  /** \internal Evaluate all entries at once */
+  AllAtOnceTraversal
+};
+
+/** \internal \ingroup enums
+  * Enum to specify whether to unroll loops when traversing over the entries of a matrix. */
+enum UnrollingType {
+  /** \internal Do not unroll loops. */
+  NoUnrolling,
+  /** \internal Unroll only the inner loop, but not the outer loop. */
+  InnerUnrolling,
+  /** \internal Unroll both the inner and the outer loop. If there is only one loop, 
+    * because linear traversal is used, then unroll that loop. */
+  CompleteUnrolling
+};
+
+/** \internal \ingroup enums
+  * Enum to specify whether to use the default (built-in) implementation or the specialization. */
+enum SpecializedType {
+  Specialized,
+  BuiltIn
+};
+
+/** \ingroup enums
+  * Enum containing possible values for the \p Options_ template parameter of
+  * Matrix, Array and BandMatrix. */
+enum StorageOptions {
+  /** Storage order is column major (see \ref TopicStorageOrders). */
+  ColMajor = 0,
+  /** Storage order is row major (see \ref TopicStorageOrders). */
+  RowMajor = 0x1,  // it is only a coincidence that this is equal to RowMajorBit -- don't rely on that
+  /** Align the matrix itself if it is vectorizable fixed-size */
+  AutoAlign = 0,
+  /** Don't require alignment for the matrix itself (the array of coefficients, if dynamically allocated, may still be requested to be aligned) */ // FIXME --- clarify the situation
+  DontAlign = 0x2
+};
+
+/** \ingroup enums
+  * Enum for specifying whether to apply or solve on the left or right. */
+enum SideType {
+  /** Apply transformation on the left. */
+  OnTheLeft = 1,
+  /** Apply transformation on the right. */
+  OnTheRight = 2
+};
+
+/** \ingroup enums
+ * Enum for specifying NaN-propagation behavior, e.g. for coeff-wise min/max. */
+enum NaNPropagationOptions {
+  /**  Implementation defined behavior if NaNs are present. */
+  PropagateFast = 0,
+  /**  Always propagate NaNs. */
+  PropagateNaN,
+  /**  Always propagate not-NaNs. */
+  PropagateNumbers
+};
+
+/* the following used to be written as:
+ *
+ *   struct NoChange_t {};
+ *   namespace {
+ *     EIGEN_UNUSED NoChange_t NoChange;
+ *   }
+ *
+ * on the ground that it feels dangerous to disambiguate overloaded functions on enum/integer types.  
+ * However, this leads to "variable declared but never referenced" warnings on Intel Composer XE,
+ * and we do not know how to get rid of them (bug 450).
+ */
+
+enum NoChange_t   { NoChange };
+enum Sequential_t { Sequential };
+enum Default_t    { Default };
+
+/** \internal \ingroup enums
+  * Used in AmbiVector. */
+enum AmbiVectorMode {
+  IsDense         = 0,
+  IsSparse
+};
+
+/** \ingroup enums
+  * Used as template parameter in DenseCoeffBase and MapBase to indicate 
+  * which accessors should be provided. */
+enum AccessorLevels {
+  /** Read-only access via a member function. */
+  ReadOnlyAccessors, 
+  /** Read/write access via member functions. */
+  WriteAccessors, 
+  /** Direct read-only access to the coefficients. */
+  DirectAccessors, 
+  /** Direct read/write access to the coefficients. */
+  DirectWriteAccessors
+};
+
+/** \ingroup enums
+  * Enum with options to give to various decompositions. */
+enum DecompositionOptions {
+  /** \internal Not used (meant for LDLT?). */
+  Pivoting            = 0x01, 
+  /** \internal Not used (meant for LDLT?). */
+  NoPivoting          = 0x02, 
+  /** Used in JacobiSVD to indicate that the square matrix U is to be computed. */
+  ComputeFullU        = 0x04,
+  /** Used in JacobiSVD to indicate that the thin matrix U is to be computed. */
+  ComputeThinU        = 0x08,
+  /** Used in JacobiSVD to indicate that the square matrix V is to be computed. */
+  ComputeFullV        = 0x10,
+  /** Used in JacobiSVD to indicate that the thin matrix V is to be computed. */
+  ComputeThinV        = 0x20,
+  /** Used in SelfAdjointEigenSolver and GeneralizedSelfAdjointEigenSolver to specify
+    * that only the eigenvalues are to be computed and not the eigenvectors. */
+  EigenvaluesOnly     = 0x40,
+  /** Used in SelfAdjointEigenSolver and GeneralizedSelfAdjointEigenSolver to specify
+    * that both the eigenvalues and the eigenvectors are to be computed. */
+  ComputeEigenvectors = 0x80,
+  /** \internal */
+  EigVecMask = EigenvaluesOnly | ComputeEigenvectors,
+  /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should
+    * solve the generalized eigenproblem \f$ Ax = \lambda B x \f$. */
+  Ax_lBx              = 0x100,
+  /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should
+    * solve the generalized eigenproblem \f$ ABx = \lambda x \f$. */
+  ABx_lx              = 0x200,
+  /** Used in GeneralizedSelfAdjointEigenSolver to indicate that it should
+    * solve the generalized eigenproblem \f$ BAx = \lambda x \f$. */
+  BAx_lx              = 0x400,
+  /** \internal */
+  GenEigMask = Ax_lBx | ABx_lx | BAx_lx
+};
+
+/** \ingroup enums
+  * Possible values for the \p QRPreconditioner template parameter of JacobiSVD. */
+enum QRPreconditioners {
+  /** Use a QR decomposition with column pivoting as the first step. */
+  ColPivHouseholderQRPreconditioner = 0x0,
+  /** Do not specify what is to be done if the SVD of a non-square matrix is asked for. */
+  NoQRPreconditioner = 0x40,
+  /** Use a QR decomposition without pivoting as the first step. */
+  HouseholderQRPreconditioner = 0x80,
+  /** Use a QR decomposition with full pivoting as the first step. */
+  FullPivHouseholderQRPreconditioner = 0xC0,
+  /** Used to disable the QR Preconditioner in BDCSVD. */
+  DisableQRDecomposition = NoQRPreconditioner
+};
+
+#ifdef Success
+#error The preprocessor symbol 'Success' is defined, possibly by the X11 header file X.h
+#endif
+
+/** \ingroup enums
+  * Enum for reporting the status of a computation. */
+enum ComputationInfo {
+  /** Computation was successful. */
+  Success = 0,        
+  /** The provided data did not satisfy the prerequisites. */
+  NumericalIssue = 1, 
+  /** Iterative procedure did not converge. */
+  NoConvergence = 2,
+  /** The inputs are invalid, or the algorithm has been improperly called.
+    * When assertions are enabled, such errors trigger an assert. */
+  InvalidInput = 3
+};
+
+/** \ingroup enums
+  * Enum used to specify how a particular transformation is stored in a matrix.
+  * \sa Transform, Hyperplane::transform(). */
+enum TransformTraits {
+  /** Transformation is an isometry. */
+  Isometry      = 0x1,
+  /** Transformation is an affine transformation stored as a (Dim+1)^2 matrix whose last row is 
+    * assumed to be [0 ... 0 1]. */
+  Affine        = 0x2,
+  /** Transformation is an affine transformation stored as a (Dim) x (Dim+1) matrix. */
+  AffineCompact = 0x10 | Affine,
+  /** Transformation is a general projective transformation stored as a (Dim+1)^2 matrix. */
+  Projective    = 0x20
+};
+
+/** \internal \ingroup enums
+  * Enum used to choose between implementation depending on the computer architecture. */
+namespace Architecture
+{
+  enum Type {
+    Generic = 0x0,
+    SSE = 0x1,
+    AltiVec = 0x2,
+    VSX = 0x3,
+    NEON = 0x4,
+    MSA = 0x5,
+    SVE = 0x6,
+    HVX = 0x7,
+#if defined EIGEN_VECTORIZE_SSE
+    Target = SSE
+#elif defined EIGEN_VECTORIZE_ALTIVEC
+    Target = AltiVec
+#elif defined EIGEN_VECTORIZE_VSX
+    Target = VSX
+#elif defined EIGEN_VECTORIZE_NEON
+    Target = NEON
+#elif defined EIGEN_VECTORIZE_SVE
+    Target = SVE
+#elif defined EIGEN_VECTORIZE_MSA
+    Target = MSA
+#elif defined EIGEN_VECTORIZE_HVX
+    Target = HVX
+#else
+    Target = Generic
+#endif
+  };
+}
+
+/** \internal \ingroup enums
+  * Enum used as template parameter in Product and product evaluators. */
+enum ProductImplType
+{ DefaultProduct=0, LazyProduct, AliasFreeProduct, CoeffBasedProductMode, LazyCoeffBasedProductMode, OuterProduct, InnerProduct, GemvProduct, GemmProduct };
+
+/** \internal \ingroup enums
+  * Enum used in experimental parallel implementation. */
+enum Action {GetAction, SetAction};
+
+/** The type used to identify a dense storage. */
+struct Dense {};
+
+/** The type used to identify a general sparse storage. */
+struct Sparse {};
+
+/** The type used to identify a general solver (factored) storage. */
+struct SolverStorage {};
+
+/** The type used to identify a permutation storage. */
+struct PermutationStorage {};
+
+/** The type used to identify a permutation storage. */
+struct TranspositionsStorage {};
+
+/** The type used to identify a matrix expression */
+struct MatrixXpr {};
+
+/** The type used to identify an array expression */
+struct ArrayXpr {};
+
+// An evaluator must define its shape. By default, it can be one of the following:
+struct DenseShape             { static std::string debugName() { return "DenseShape"; } };
+struct SolverShape            { static std::string debugName() { return "SolverShape"; } };
+struct HomogeneousShape       { static std::string debugName() { return "HomogeneousShape"; } };
+struct DiagonalShape          { static std::string debugName() { return "DiagonalShape"; } };
+struct SkewSymmetricShape     { static std::string debugName() { return "SkewSymmetricShape"; } };
+struct BandShape              { static std::string debugName() { return "BandShape"; } };
+struct TriangularShape        { static std::string debugName() { return "TriangularShape"; } };
+struct SelfAdjointShape       { static std::string debugName() { return "SelfAdjointShape"; } };
+struct PermutationShape       { static std::string debugName() { return "PermutationShape"; } };
+struct TranspositionsShape    { static std::string debugName() { return "TranspositionsShape"; } };
+struct SparseShape            { static std::string debugName() { return "SparseShape"; } };
+
+namespace internal {
+
+  // random access iterators based on coeff*() accessors.
+struct IndexBased {};
+
+// evaluator based on iterators to access coefficients. 
+struct IteratorBased {};
+
+/** \internal
+ * Constants for comparison functors
+ */
+enum ComparisonName : unsigned int {
+  cmp_EQ = 0,
+  cmp_LT = 1,
+  cmp_LE = 2,
+  cmp_UNORD = 3,
+  cmp_NEQ = 4,
+  cmp_GT = 5,
+  cmp_GE = 6
+};
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_CONSTANTS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/DisableStupidWarnings.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/DisableStupidWarnings.h
new file mode 100644
index 0000000000000000000000000000000000000000..bed6cddf8cf157877fdf2838a52cd17bf84091ac
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/DisableStupidWarnings.h
@@ -0,0 +1,139 @@
+#ifndef EIGEN_WARNINGS_DISABLED
+#define EIGEN_WARNINGS_DISABLED
+
+#if defined(_MSC_VER)
+  // 4100 - unreferenced formal parameter (occurred e.g. in aligned_allocator::destroy(pointer p))
+  // 4101 - unreferenced local variable
+  // 4127 - conditional expression is constant
+  // 4181 - qualifier applied to reference type ignored
+  // 4211 - nonstandard extension used : redefined extern to static
+  // 4244 - 'argument' : conversion from 'type1' to 'type2', possible loss of data
+  // 4273 - QtAlignedMalloc, inconsistent DLL linkage
+  // 4324 - structure was padded due to declspec(align())
+  // 4503 - decorated name length exceeded, name was truncated
+  // 4512 - assignment operator could not be generated
+  // 4522 - 'class' : multiple assignment operators specified
+  // 4700 - uninitialized local variable 'xyz' used
+  // 4714 - function marked as __forceinline not inlined
+  // 4717 - 'function' : recursive on all control paths, function will cause runtime stack overflow
+  // 4800 - 'type' : forcing value to bool 'true' or 'false' (performance warning)
+  #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
+    #pragma warning( push )
+  #endif
+  #pragma warning( disable : 4100 4101 4127 4181 4211 4244 4273 4324 4503 4512 4522 4700 4714 4717 4800)
+
+#elif defined __INTEL_COMPILER
+  // 2196 - routine is both "inline" and "noinline" ("noinline" assumed)
+  //        ICC 12 generates this warning even without any inline keyword, when defining class methods 'inline' i.e. inside of class body
+  //        typedef that may be a reference type.
+  // 279  - controlling expression is constant
+  //        ICC 12 generates this warning on assert(constant_expression_depending_on_template_params) and frankly this is a legitimate use case.
+  // 1684 - conversion from pointer to same-sized integral type (potential portability problem)
+  // 2259 - non-pointer conversion from "Eigen::Index={ptrdiff_t={long}}" to "int" may lose significant bits
+  #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
+    #pragma warning push
+  #endif
+  #pragma warning disable 2196 279 1684 2259
+
+#elif defined __clang__
+  #ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
+    #pragma clang diagnostic push
+  #endif
+  #if defined(__has_warning)
+    // -Wconstant-logical-operand - warning: use of logical && with constant operand; switch to bitwise & or remove constant
+    //     this is really a stupid warning as it warns on compile-time expressions involving enums
+    #if __has_warning("-Wconstant-logical-operand")
+      #pragma clang diagnostic ignored "-Wconstant-logical-operand"
+    #endif
+    #if __has_warning("-Wimplicit-int-float-conversion")
+      #pragma clang diagnostic ignored "-Wimplicit-int-float-conversion"
+    #endif
+    #if ( defined(__ALTIVEC__) || defined(__VSX__) ) && ( !defined(__STDC_VERSION__) || (__STDC_VERSION__ < 201112L) )
+      // warning: generic selections are a C11-specific feature
+      // ignoring warnings thrown at vec_ctf in Altivec/PacketMath.h
+      #if __has_warning("-Wc11-extensions")
+        #pragma clang diagnostic ignored "-Wc11-extensions"
+      #endif
+    #endif
+  #endif
+
+#elif defined __GNUC__ && !defined(__FUJITSU)
+
+  #if (!defined(EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS)) &&  (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
+    #pragma GCC diagnostic push
+  #endif
+  // g++ warns about local variables shadowing member functions, which is too strict
+  #pragma GCC diagnostic ignored "-Wshadow"
+  #if __GNUC__ == 4 && __GNUC_MINOR__ < 8
+    // Until g++-4.7 there are warnings when comparing unsigned int vs 0, even in templated functions:
+    #pragma GCC diagnostic ignored "-Wtype-limits"
+  #endif
+  #if __GNUC__>=6
+    #pragma GCC diagnostic ignored "-Wignored-attributes"
+  #endif
+  #if __GNUC__==7
+    // See: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=89325
+    #pragma GCC diagnostic ignored "-Wattributes"
+  #endif
+#endif
+
+#if defined __NVCC__
+  // MSVC 14.16 (required by CUDA 9.*) does not support the _Pragma keyword, so
+  // we instead use Microsoft's __pragma extension.
+  #if defined _MSC_VER
+    #define EIGEN_MAKE_PRAGMA(X) __pragma(#X)
+  #else
+    #define EIGEN_MAKE_PRAGMA(X) _Pragma(#X)
+  #endif
+  #if defined __NVCC_DIAG_PRAGMA_SUPPORT__
+    #define EIGEN_NV_DIAG_SUPPRESS(X) EIGEN_MAKE_PRAGMA(nv_diag_suppress X)
+  #else
+    #define EIGEN_NV_DIAG_SUPPRESS(X) EIGEN_MAKE_PRAGMA(diag_suppress X)
+  #endif
+
+  EIGEN_NV_DIAG_SUPPRESS(boolean_controlling_expr_is_constant)
+  // Disable the "statement is unreachable" message
+  EIGEN_NV_DIAG_SUPPRESS(code_is_unreachable)
+  // Disable the "dynamic initialization in unreachable code" message
+  EIGEN_NV_DIAG_SUPPRESS(initialization_not_reachable)
+  // Disable the "invalid error number" message that we get with older versions of nvcc
+  EIGEN_NV_DIAG_SUPPRESS(1222)
+  // Disable the "calling a __host__ function from a __host__ __device__ function is not allowed" messages (yes, there are many of them and they seem to change with every version of the compiler)
+  EIGEN_NV_DIAG_SUPPRESS(2527)
+  EIGEN_NV_DIAG_SUPPRESS(2529)
+  EIGEN_NV_DIAG_SUPPRESS(2651)
+  EIGEN_NV_DIAG_SUPPRESS(2653)
+  EIGEN_NV_DIAG_SUPPRESS(2668)
+  EIGEN_NV_DIAG_SUPPRESS(2669)
+  EIGEN_NV_DIAG_SUPPRESS(2670)
+  EIGEN_NV_DIAG_SUPPRESS(2671)
+  EIGEN_NV_DIAG_SUPPRESS(2735)
+  EIGEN_NV_DIAG_SUPPRESS(2737)
+  EIGEN_NV_DIAG_SUPPRESS(2739)
+  EIGEN_NV_DIAG_SUPPRESS(2885)
+  EIGEN_NV_DIAG_SUPPRESS(2888)
+  EIGEN_NV_DIAG_SUPPRESS(2976)
+  EIGEN_NV_DIAG_SUPPRESS(2979)
+  EIGEN_NV_DIAG_SUPPRESS(20011)
+  EIGEN_NV_DIAG_SUPPRESS(20014)
+  // Disable the "// __device__ annotation is ignored on a function(...) that is
+  //              explicitly defaulted on its first declaration" message.
+  // The __device__ annotation seems to actually be needed in some cases,
+  // otherwise resulting in kernel runtime errors.
+  EIGEN_NV_DIAG_SUPPRESS(2886)
+  EIGEN_NV_DIAG_SUPPRESS(2929)
+  EIGEN_NV_DIAG_SUPPRESS(2977)
+  EIGEN_NV_DIAG_SUPPRESS(20012)
+  #undef EIGEN_NV_DIAG_SUPPRESS
+  #undef EIGEN_MAKE_PRAGMA
+#endif
+
+#else
+// warnings already disabled:
+# ifndef EIGEN_WARNINGS_DISABLED_2
+#  define EIGEN_WARNINGS_DISABLED_2
+# elif defined(EIGEN_INTERNAL_DEBUGGING)
+#  error "Do not include \"DisableStupidWarnings.h\" recursively more than twice!"
+# endif
+
+#endif // not EIGEN_WARNINGS_DISABLED
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/EmulateArray.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/EmulateArray.h
new file mode 100644
index 0000000000000000000000000000000000000000..a4b1d0c215b8ec571736d31c99e60f3ab2f770c4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/EmulateArray.h
@@ -0,0 +1,286 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EMULATE_ARRAY_H
+#define EIGEN_EMULATE_ARRAY_H
+
+// CUDA doesn't support the STL containers, so we use our own instead.
+#if defined(EIGEN_GPUCC) || defined(EIGEN_AVOID_STL_ARRAY)
+
+namespace Eigen {
+template <typename T, size_t n> class array {
+
+ public:
+  typedef T value_type;
+  typedef T* iterator;
+  typedef const T* const_iterator;
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE iterator begin() { return values; }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const_iterator begin() const { return values; }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE iterator end() { return values + n; }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const_iterator end() const { return values + n; }
+
+
+#if !defined(EIGEN_GPUCC)
+  typedef std::reverse_iterator<iterator> reverse_iterator;
+  typedef std::reverse_iterator<const_iterator> const_reverse_iterator;
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE reverse_iterator rbegin() { return reverse_iterator(end());}
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const_reverse_iterator rbegin() const { return const_reverse_iterator(end()); }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE reverse_iterator rend() { return reverse_iterator(begin()); }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const_reverse_iterator rend() const { return const_reverse_iterator(begin()); }
+#endif
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE T& operator[] (size_t index) { eigen_internal_assert(index < size()); return values[index]; }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const T& operator[] (size_t index) const { eigen_internal_assert(index < size()); return values[index]; }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE T& at(size_t index) { eigen_assert(index < size()); return values[index]; }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const T& at(size_t index) const { eigen_assert(index < size()); return values[index]; }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE T& front() { return values[0]; }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const T& front() const { return values[0]; }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE T& back() { return values[n-1]; }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const T& back() const { return values[n-1]; }
+
+
+  EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
+  static std::size_t size() { return n; }
+
+  T values[n];
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array() { }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(const T& v) {
+    EIGEN_STATIC_ASSERT(n==1, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(const T& v1, const T& v2) {
+    EIGEN_STATIC_ASSERT(n==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3) {
+    EIGEN_STATIC_ASSERT(n==3, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3,
+                            const T& v4) {
+    EIGEN_STATIC_ASSERT(n==4, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+    values[3] = v4;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4,
+                            const T& v5) {
+    EIGEN_STATIC_ASSERT(n==5, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+    values[3] = v4;
+    values[4] = v5;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4,
+                            const T& v5, const T& v6) {
+    EIGEN_STATIC_ASSERT(n==6, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+    values[3] = v4;
+    values[4] = v5;
+    values[5] = v6;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(const T& v1, const T& v2, const T& v3, const T& v4,
+                            const T& v5, const T& v6, const T& v7) {
+    EIGEN_STATIC_ASSERT(n==7, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+    values[3] = v4;
+    values[4] = v5;
+    values[5] = v6;
+    values[6] = v7;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(
+      const T& v1, const T& v2, const T& v3, const T& v4,
+      const T& v5, const T& v6, const T& v7, const T& v8) {
+    EIGEN_STATIC_ASSERT(n==8, YOU_MADE_A_PROGRAMMING_MISTAKE)
+    values[0] = v1;
+    values[1] = v2;
+    values[2] = v3;
+    values[3] = v4;
+    values[4] = v5;
+    values[5] = v6;
+    values[6] = v7;
+    values[7] = v8;
+  }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array(std::initializer_list<T> l) {
+    eigen_assert(l.size() == n);
+    internal::smart_copy(l.begin(), l.end(), values);
+  }
+};
+
+
+// Specialize array for zero size
+template <typename T> class array<T, 0> {
+ public:
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE T& operator[] (size_t) {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const T& operator[] (size_t) const {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE T& front() {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const T& front() const {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE T& back() {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE const T& back() const {
+    eigen_assert(false && "Can't index a zero size array");
+    return dummy;
+  }
+
+  static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::size_t size() { return 0; }
+
+  EIGEN_DEVICE_FUNC
+  EIGEN_STRONG_INLINE array() : dummy() { }
+
+  EIGEN_DEVICE_FUNC array(std::initializer_list<T> l) : dummy() {
+    EIGEN_UNUSED_VARIABLE(l);
+    eigen_assert(l.size() == 0);
+  }
+
+ private:
+  T dummy;
+};
+
+// Comparison operator
+// Todo: implement !=, <, <=, >,  and >=
+template<class T, std::size_t N>
+EIGEN_DEVICE_FUNC bool operator==(const array<T,N>& lhs, const array<T,N>& rhs) {
+  for (std::size_t i = 0; i < N; ++i) {
+    if (lhs[i] != rhs[i]) {
+      return false;
+    }
+  }
+  return true;
+}
+
+
+namespace internal {
+template<std::size_t I_, class T, std::size_t N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T& array_get(array<T,N>& a) {
+  return a[I_];
+}
+template<std::size_t I_, class T, std::size_t N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const T& array_get(const array<T,N>& a) {
+  return a[I_];
+}
+
+template<class T, std::size_t N> struct array_size<array<T,N> > {
+  enum { value = N };
+};
+template<class T, std::size_t N> struct array_size<array<T,N>& > {
+  enum { value = N };
+};
+template<class T, std::size_t N> struct array_size<const array<T,N> > {
+  enum { value = N };
+};
+template<class T, std::size_t N> struct array_size<const array<T,N>& > {
+  enum { value = N };
+};
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#else
+
+// The compiler supports c++11, and we're not targeting cuda: use std::array as Eigen::array
+#include <array>
+
+namespace Eigen {
+
+template <typename T, std::size_t N> using array = std::array<T, N>;
+
+namespace internal {
+/* std::get is only constexpr in C++14, not yet in C++11
+ *     - libstdc++ from version 4.7 onwards has it nevertheless,
+ *                                          so use that
+ *     - libstdc++ older versions: use _M_instance directly
+ *     - libc++ all versions so far: use __elems_ directly
+ *     - all other libs: use std::get to be portable, but
+ *                       this may not be constexpr
+ */
+#if defined(__GLIBCXX__) && __GLIBCXX__ < 20120322
+#define STD_GET_ARR_HACK             a._M_instance[I_]
+#elif defined(_LIBCPP_VERSION)
+#define STD_GET_ARR_HACK             a.__elems_[I_]
+#else
+#define STD_GET_ARR_HACK             std::template get<I_, T, N>(a)
+#endif
+
+template<std::size_t I_, class T, std::size_t N> constexpr inline T&       array_get(std::array<T,N>&       a) { return (T&)       STD_GET_ARR_HACK; }
+template<std::size_t I_, class T, std::size_t N> constexpr inline T&&      array_get(std::array<T,N>&&      a) { return (T&&)      STD_GET_ARR_HACK; }
+template<std::size_t I_, class T, std::size_t N> constexpr inline T const& array_get(std::array<T,N> const& a) { return (T const&) STD_GET_ARR_HACK; }
+
+#undef STD_GET_ARR_HACK
+
+}  // end namespace internal
+}  // end namespace Eigen
+
+#endif
+
+#endif  // EIGEN_EMULATE_ARRAY_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/ForwardDeclarations.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/ForwardDeclarations.h
new file mode 100644
index 0000000000000000000000000000000000000000..8963019a9ed330953f6a328b55e42631fdca7e5e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/ForwardDeclarations.h
@@ -0,0 +1,329 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_FORWARDDECLARATIONS_H
+#define EIGEN_FORWARDDECLARATIONS_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+template<typename T> struct traits;
+
+// here we say once and for all that traits<const T> == traits<T>
+// When constness must affect traits, it has to be constness on template parameters on which T itself depends.
+// For example, traits<Map<const T> > != traits<Map<T> >, but
+//              traits<const Map<T> > == traits<Map<T> >
+template<typename T> struct traits<const T> : traits<T> {};
+
+template<typename Derived> struct has_direct_access
+{
+  enum { ret = (traits<Derived>::Flags & DirectAccessBit) ? 1 : 0 };
+};
+
+template<typename Derived> struct accessors_level
+{
+  enum { has_direct_access = (traits<Derived>::Flags & DirectAccessBit) ? 1 : 0,
+         has_write_access = (traits<Derived>::Flags & LvalueBit) ? 1 : 0,
+         value = has_direct_access ? (has_write_access ? DirectWriteAccessors : DirectAccessors)
+                                   : (has_write_access ? WriteAccessors       : ReadOnlyAccessors)
+  };
+};
+
+template<typename T> struct evaluator_traits;
+
+template< typename T> struct evaluator;
+
+} // end namespace internal
+
+template<typename T> struct NumTraits;
+
+template<typename Derived> struct EigenBase;
+template<typename Derived> class DenseBase;
+template<typename Derived> class PlainObjectBase;
+template<typename Derived, int Level> class DenseCoeffsBase;
+
+template<typename Scalar_, int Rows_, int Cols_,
+         int Options_ = AutoAlign |
+                          ( (Rows_==1 && Cols_!=1) ? Eigen::RowMajor
+                          : (Cols_==1 && Rows_!=1) ? Eigen::ColMajor
+                          : EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ),
+         int MaxRows_ = Rows_,
+         int MaxCols_ = Cols_
+> class Matrix;
+
+template<typename Derived> class MatrixBase;
+template<typename Derived> class ArrayBase;
+
+template<typename ExpressionType, unsigned int Added, unsigned int Removed> class Flagged;
+template<typename ExpressionType, template <typename> class StorageBase > class NoAlias;
+template<typename ExpressionType> class NestByValue;
+template<typename ExpressionType> class ForceAlignedAccess;
+template<typename ExpressionType> class SwapWrapper;
+
+template<typename XprType, int BlockRows=Dynamic, int BlockCols=Dynamic, bool InnerPanel = false> class Block;
+template<typename XprType, typename RowIndices, typename ColIndices> class IndexedView;
+template<typename XprType, int Rows=Dynamic, int Cols=Dynamic, int Order=0> class Reshaped;
+
+template<typename MatrixType, int Size=Dynamic> class VectorBlock;
+template<typename MatrixType> class Transpose;
+template<typename MatrixType> class Conjugate;
+template<typename NullaryOp, typename MatrixType>         class CwiseNullaryOp;
+template<typename UnaryOp,   typename MatrixType>         class CwiseUnaryOp;
+template<typename BinaryOp,  typename Lhs, typename Rhs>  class CwiseBinaryOp;
+template<typename TernaryOp, typename Arg1, typename Arg2, typename Arg3>  class CwiseTernaryOp;
+template<typename Decomposition, typename Rhstype>        class Solve;
+template<typename XprType>                                class Inverse;
+
+template<typename Lhs, typename Rhs, int Option = DefaultProduct> class Product;
+
+template<typename Derived> class DiagonalBase;
+template<typename DiagonalVectorType_> class DiagonalWrapper;
+template<typename Scalar_, int SizeAtCompileTime, int MaxSizeAtCompileTime=SizeAtCompileTime> class DiagonalMatrix;
+template<typename MatrixType, typename DiagonalType, int ProductOrder> class DiagonalProduct;
+template<typename MatrixType, int Index = 0> class Diagonal;
+template<typename Derived> class SkewSymmetricBase;
+template<typename VectorType_> class SkewSymmetricWrapper;
+template<typename Scalar_> class SkewSymmetricMatrix3;
+template<int SizeAtCompileTime, int MaxSizeAtCompileTime = SizeAtCompileTime, typename IndexType=int> class PermutationMatrix;
+template<int SizeAtCompileTime, int MaxSizeAtCompileTime = SizeAtCompileTime, typename IndexType=int> class Transpositions;
+template<typename Derived> class PermutationBase;
+template<typename Derived> class TranspositionsBase;
+template<typename IndicesType_> class PermutationWrapper;
+template<typename IndicesType_> class TranspositionsWrapper;
+
+template<typename Derived,
+         int Level = internal::accessors_level<Derived>::has_write_access ? WriteAccessors : ReadOnlyAccessors
+> class MapBase;
+template<int OuterStrideAtCompileTime, int InnerStrideAtCompileTime> class Stride;
+template<int Value = Dynamic> class InnerStride;
+template<int Value = Dynamic> class OuterStride;
+template<typename MatrixType, int MapOptions=Unaligned, typename StrideType = Stride<0,0> > class Map;
+template<typename Derived> class RefBase;
+template<typename PlainObjectType, int Options = 0,
+         typename StrideType = typename std::conditional_t<PlainObjectType::IsVectorAtCompileTime,InnerStride<1>,OuterStride<> > > class Ref;
+template<typename ViewOp,    typename MatrixType, typename StrideType = Stride<0,0>>         class CwiseUnaryView;
+
+template<typename Derived> class TriangularBase;
+template<typename MatrixType, unsigned int Mode> class TriangularView;
+template<typename MatrixType, unsigned int Mode> class SelfAdjointView;
+template<typename MatrixType> class SparseView;
+template<typename ExpressionType> class WithFormat;
+template<typename MatrixType> struct CommaInitializer;
+template<typename Derived> class ReturnByValue;
+template<typename ExpressionType> class ArrayWrapper;
+template<typename ExpressionType> class MatrixWrapper;
+template<typename Derived> class SolverBase;
+template<typename XprType> class InnerIterator;
+
+namespace internal {
+template<typename XprType> class generic_randaccess_stl_iterator;
+template<typename XprType> class pointer_based_stl_iterator;
+template<typename XprType, DirectionType Direction> class subvector_stl_iterator;
+template<typename XprType, DirectionType Direction> class subvector_stl_reverse_iterator;
+template<typename DecompositionType> struct kernel_retval_base;
+template<typename DecompositionType> struct kernel_retval;
+template<typename DecompositionType> struct image_retval_base;
+template<typename DecompositionType> struct image_retval;
+} // end namespace internal
+
+namespace internal {
+template<typename Scalar_, int Rows=Dynamic, int Cols=Dynamic, int Supers=Dynamic, int Subs=Dynamic, int Options=0> class BandMatrix;
+}
+
+namespace internal {
+template<typename Lhs, typename Rhs> struct product_type;
+
+template<bool> struct EnableIf;
+
+/** \internal
+  * \class product_evaluator
+  * Products need their own evaluator with more template arguments allowing for
+  * easier partial template specializations.
+  */
+template< typename T,
+          int ProductTag = internal::product_type<typename T::Lhs,typename T::Rhs>::ret,
+          typename LhsShape = typename evaluator_traits<typename T::Lhs>::Shape,
+          typename RhsShape = typename evaluator_traits<typename T::Rhs>::Shape,
+          typename LhsScalar = typename traits<typename T::Lhs>::Scalar,
+          typename RhsScalar = typename traits<typename T::Rhs>::Scalar
+        > struct product_evaluator;
+}
+
+template<typename Lhs, typename Rhs,
+         int ProductType = internal::product_type<Lhs,Rhs>::value>
+struct ProductReturnType;
+
+// this is a workaround for sun CC
+template<typename Lhs, typename Rhs> struct LazyProductReturnType;
+
+namespace internal {
+
+// Provides scalar/packet-wise product and product with accumulation
+// with optional conjugation of the arguments.
+template<typename LhsScalar, typename RhsScalar, bool ConjLhs=false, bool ConjRhs=false> struct conj_helper;
+
+template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_sum_op;
+template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_difference_op;
+template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_conj_product_op;
+template<typename LhsScalar,typename RhsScalar=LhsScalar, int NaNPropagation=PropagateFast> struct scalar_min_op;
+template<typename LhsScalar,typename RhsScalar=LhsScalar, int NaNPropagation=PropagateFast> struct scalar_max_op;
+template<typename Scalar> struct scalar_opposite_op;
+template<typename Scalar> struct scalar_conjugate_op;
+template<typename Scalar> struct scalar_real_op;
+template<typename Scalar> struct scalar_imag_op;
+template<typename Scalar> struct scalar_abs_op;
+template<typename Scalar> struct scalar_abs2_op;
+template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_absolute_difference_op;
+template<typename Scalar> struct scalar_sqrt_op;
+template<typename Scalar> struct scalar_rsqrt_op;
+template<typename Scalar> struct scalar_exp_op;
+template<typename Scalar> struct scalar_log_op;
+template<typename Scalar> struct scalar_cos_op;
+template<typename Scalar> struct scalar_sin_op;
+template<typename Scalar> struct scalar_acos_op;
+template<typename Scalar> struct scalar_asin_op;
+template<typename Scalar> struct scalar_tan_op;
+template<typename Scalar> struct scalar_atan_op;
+template <typename LhsScalar, typename RhsScalar = LhsScalar> struct scalar_atan2_op;
+template<typename Scalar> struct scalar_inverse_op;
+template<typename Scalar> struct scalar_square_op;
+template<typename Scalar> struct scalar_cube_op;
+template<typename Scalar, typename NewType> struct scalar_cast_op;
+template<typename Scalar> struct scalar_random_op;
+template<typename Scalar> struct scalar_constant_op;
+template<typename Scalar> struct scalar_identity_op;
+template<typename Scalar> struct scalar_sign_op;
+template <typename Scalar, typename ScalarExponent>
+struct scalar_pow_op;
+template <typename Scalar, typename ScalarExponent, bool BaseIsInteger, bool ExponentIsInteger, bool BaseIsComplex,
+          bool ExponentIsComplex>
+struct scalar_unary_pow_op;
+template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_hypot_op;
+template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_product_op;
+template<typename LhsScalar,typename RhsScalar=LhsScalar> struct scalar_quotient_op;
+// logical and bitwise operations
+template <typename Scalar> struct scalar_boolean_and_op;
+template <typename Scalar> struct scalar_boolean_or_op;
+template <typename Scalar> struct scalar_boolean_xor_op;
+template <typename Scalar> struct scalar_boolean_not_op;
+template <typename Scalar> struct scalar_bitwise_and_op;
+template <typename Scalar> struct scalar_bitwise_or_op;
+template <typename Scalar> struct scalar_bitwise_xor_op;
+template <typename Scalar> struct scalar_bitwise_not_op;
+
+// SpecialFunctions module
+template<typename Scalar> struct scalar_lgamma_op;
+template<typename Scalar> struct scalar_digamma_op;
+template<typename Scalar> struct scalar_erf_op;
+template<typename Scalar> struct scalar_erfc_op;
+template<typename Scalar> struct scalar_ndtri_op;
+template<typename Scalar> struct scalar_igamma_op;
+template<typename Scalar> struct scalar_igammac_op;
+template<typename Scalar> struct scalar_zeta_op;
+template<typename Scalar> struct scalar_betainc_op;
+
+// Bessel functions in SpecialFunctions module
+template<typename Scalar> struct scalar_bessel_i0_op;
+template<typename Scalar> struct scalar_bessel_i0e_op;
+template<typename Scalar> struct scalar_bessel_i1_op;
+template<typename Scalar> struct scalar_bessel_i1e_op;
+template<typename Scalar> struct scalar_bessel_j0_op;
+template<typename Scalar> struct scalar_bessel_y0_op;
+template<typename Scalar> struct scalar_bessel_j1_op;
+template<typename Scalar> struct scalar_bessel_y1_op;
+template<typename Scalar> struct scalar_bessel_k0_op;
+template<typename Scalar> struct scalar_bessel_k0e_op;
+template<typename Scalar> struct scalar_bessel_k1_op;
+template<typename Scalar> struct scalar_bessel_k1e_op;
+
+
+} // end namespace internal
+
+struct IOFormat;
+
+// Array module
+template<typename Scalar_, int Rows_, int Cols_,
+         int Options_ = AutoAlign |
+                          ( (Rows_==1 && Cols_!=1) ? Eigen::RowMajor
+                          : (Cols_==1 && Rows_!=1) ? Eigen::ColMajor
+                          : EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ),
+         int MaxRows_ = Rows_, int MaxCols_ = Cols_> class Array;
+template<typename ConditionMatrixType, typename ThenMatrixType, typename ElseMatrixType> class Select;
+template<typename MatrixType, typename BinaryOp, int Direction> class PartialReduxExpr;
+template<typename ExpressionType, int Direction> class VectorwiseOp;
+template<typename MatrixType,int RowFactor,int ColFactor> class Replicate;
+template<typename MatrixType, int Direction = BothDirections> class Reverse;
+
+#if defined(EIGEN_USE_LAPACKE) && defined(lapack_int)
+// Lapacke interface requires StorageIndex to be lapack_int
+typedef lapack_int DefaultPermutationIndex;
+#else
+typedef int DefaultPermutationIndex;
+#endif
+
+template<typename MatrixType, typename PermutationIndex = DefaultPermutationIndex> class FullPivLU;
+template<typename MatrixType, typename PermutationIndex = DefaultPermutationIndex> class PartialPivLU;
+namespace internal {
+template<typename MatrixType> struct inverse_impl;
+}
+template<typename MatrixType> class HouseholderQR;
+template<typename MatrixType, typename PermutationIndex = DefaultPermutationIndex> class ColPivHouseholderQR;
+template<typename MatrixType, typename PermutationIndex = DefaultPermutationIndex> class FullPivHouseholderQR;
+template<typename MatrixType, typename PermutationIndex = DefaultPermutationIndex> class CompleteOrthogonalDecomposition;
+template<typename MatrixType> class SVDBase;
+template<typename MatrixType, int Options = 0> class JacobiSVD;
+template<typename MatrixType, int Options = 0> class BDCSVD;
+template<typename MatrixType, int UpLo = Lower> class LLT;
+template<typename MatrixType, int UpLo = Lower> class LDLT;
+template<typename VectorsType, typename CoeffsType, int Side=OnTheLeft> class HouseholderSequence;
+template<typename Scalar>     class JacobiRotation;
+
+// Geometry module:
+namespace internal {
+template<typename Derived, typename OtherDerived, int Size = MatrixBase<Derived>::SizeAtCompileTime> struct cross_impl;
+}
+template<typename Derived, int Dim_> class RotationBase;
+template<typename Derived> class QuaternionBase;
+template<typename Scalar> class Rotation2D;
+template<typename Scalar> class AngleAxis;
+template<typename Scalar,int Dim> class Translation;
+template<typename Scalar,int Dim> class AlignedBox;
+template<typename Scalar, int Options = AutoAlign> class Quaternion;
+template<typename Scalar,int Dim,int Mode,int Options_=AutoAlign> class Transform;
+template <typename Scalar_, int AmbientDim_, int Options=AutoAlign> class ParametrizedLine;
+template <typename Scalar_, int AmbientDim_, int Options=AutoAlign> class Hyperplane;
+template<typename Scalar> class UniformScaling;
+template<typename MatrixType,int Direction> class Homogeneous;
+
+// Sparse module:
+template<typename Derived> class SparseMatrixBase;
+
+// MatrixFunctions module
+template<typename Derived> struct MatrixExponentialReturnValue;
+template<typename Derived> class MatrixFunctionReturnValue;
+template<typename Derived> class MatrixSquareRootReturnValue;
+template<typename Derived> class MatrixLogarithmReturnValue;
+template<typename Derived> class MatrixPowerReturnValue;
+template<typename Derived> class MatrixComplexPowerReturnValue;
+
+namespace internal {
+template <typename Scalar>
+struct stem_function
+{
+  typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar;
+  typedef ComplexScalar type(ComplexScalar, int);
+};
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_FORWARDDECLARATIONS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/IndexedViewHelper.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/IndexedViewHelper.h
new file mode 100644
index 0000000000000000000000000000000000000000..19fa45d7f17614d42e40370ff91c1910310ce943
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/IndexedViewHelper.h
@@ -0,0 +1,195 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+
+#ifndef EIGEN_INDEXED_VIEW_HELPER_H
+#define EIGEN_INDEXED_VIEW_HELPER_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+struct symbolic_last_tag {};
+}  // namespace internal
+
+namespace placeholders {
+
+typedef symbolic::SymbolExpr<internal::symbolic_last_tag> last_t;
+
+/** \var last
+  * \ingroup Core_Module
+  *
+  * Can be used as a parameter to Eigen::seq and Eigen::seqN functions to symbolically reference the last element/row/columns
+  * of the underlying vector or matrix once passed to DenseBase::operator()(const RowIndices&, const ColIndices&).
+  *
+  * This symbolic placeholder supports standard arithmetic operations.
+  *
+  * A typical usage example would be:
+  * \code
+  * using namespace Eigen;
+  * using Eigen::placeholders::last;
+  * VectorXd v(n);
+  * v(seq(2,last-2)).setOnes();
+  * \endcode
+  *
+  * \sa end
+  */
+static const last_t last;
+
+}  // namespace placeholders
+
+namespace internal {
+
+// Replace symbolic last/end "keywords" by their true runtime value
+inline Index eval_expr_given_size(Index x, Index /* size */)   { return x; }
+
+template<int N>
+FixedInt<N> eval_expr_given_size(FixedInt<N> x, Index /*size*/)   { return x; }
+
+template<typename Derived>
+Index eval_expr_given_size(const symbolic::BaseExpr<Derived> &x, Index size)
+{
+  return x.derived().eval(Eigen::placeholders::last=size-1);
+}
+
+// Extract increment/step at compile time
+template<typename T, typename EnableIf = void> struct get_compile_time_incr {
+  enum { value = UndefinedIncr };
+};
+
+// Analogue of std::get<0>(x), but tailored for our needs.
+template<typename T>
+EIGEN_CONSTEXPR Index first(const T& x) EIGEN_NOEXCEPT { return x.first(); }
+
+// IndexedViewCompatibleType/makeIndexedViewCompatible turn an arbitrary object of type T into something usable by MatrixSlice
+// The generic implementation is a no-op
+template<typename T,int XprSize,typename EnableIf=void>
+struct IndexedViewCompatibleType {
+  typedef T type;
+};
+
+template<typename T,typename Q>
+const T& makeIndexedViewCompatible(const T& x, Index /*size*/, Q) { return x; }
+
+//--------------------------------------------------------------------------------
+// Handling of a single Index
+//--------------------------------------------------------------------------------
+
+struct SingleRange {
+  enum {
+    SizeAtCompileTime = 1
+  };
+  SingleRange(Index val) : m_value(val) {}
+  Index operator[](Index) const { return m_value; }
+  static EIGEN_CONSTEXPR Index size() EIGEN_NOEXCEPT { return 1; }
+  Index first() const EIGEN_NOEXCEPT { return m_value; }
+  Index m_value;
+};
+
+template<> struct get_compile_time_incr<SingleRange> {
+  enum { value = 1 }; // 1 or 0 ??
+};
+
+// Turn a single index into something that looks like an array (i.e., that exposes a .size(), and operator[](int) methods)
+template<typename T, int XprSize>
+struct IndexedViewCompatibleType<T,XprSize,std::enable_if_t<internal::is_integral<T>::value>> {
+  // Here we could simply use Array, but maybe it's less work for the compiler to use
+  // a simpler wrapper as SingleRange
+  //typedef Eigen::Array<Index,1,1> type;
+  typedef SingleRange type;
+};
+
+template<typename T, int XprSize>
+struct IndexedViewCompatibleType<T, XprSize, std::enable_if_t<symbolic::is_symbolic<T>::value>> {
+  typedef SingleRange type;
+};
+
+
+template<typename T>
+std::enable_if_t<symbolic::is_symbolic<T>::value,SingleRange>
+makeIndexedViewCompatible(const T& id, Index size, SpecializedType) {
+  return eval_expr_given_size(id,size);
+}
+
+//--------------------------------------------------------------------------------
+// Handling of all
+//--------------------------------------------------------------------------------
+
+struct all_t { all_t() {} };
+
+// Convert a symbolic 'all' into a usable range type
+template<int XprSize>
+struct AllRange {
+  enum { SizeAtCompileTime = XprSize };
+  AllRange(Index size = XprSize) : m_size(size) {}
+  EIGEN_CONSTEXPR Index operator[](Index i) const EIGEN_NOEXCEPT { return i; }
+  EIGEN_CONSTEXPR Index size() const EIGEN_NOEXCEPT { return m_size.value(); }
+  EIGEN_CONSTEXPR Index first() const EIGEN_NOEXCEPT { return 0; }
+  variable_if_dynamic<Index,XprSize> m_size;
+};
+
+template<int XprSize>
+struct IndexedViewCompatibleType<all_t,XprSize> {
+  typedef AllRange<XprSize> type;
+};
+
+template<typename XprSizeType>
+inline AllRange<get_fixed_value<XprSizeType>::value> makeIndexedViewCompatible(all_t , XprSizeType size, SpecializedType) {
+  return AllRange<get_fixed_value<XprSizeType>::value>(size);
+}
+
+template<int Size> struct get_compile_time_incr<AllRange<Size> > {
+  enum { value = 1 };
+};
+
+} // end namespace internal
+
+namespace placeholders {
+
+typedef symbolic::AddExpr<symbolic::SymbolExpr<internal::symbolic_last_tag>,symbolic::ValueExpr<Eigen::internal::FixedInt<1> > > lastp1_t;
+typedef Eigen::internal::all_t all_t;
+
+/** \var lastp1
+  * \ingroup Core_Module
+  *
+  * Can be used as a parameter to Eigen::seq and Eigen::seqN functions to symbolically
+  * reference the last+1 element/row/columns of the underlying vector or matrix once
+  * passed to DenseBase::operator()(const RowIndices&, const ColIndices&).
+  *
+  * This symbolic placeholder supports standard arithmetic operations.
+  * It is essentially an alias to last+fix<1>.
+  *
+  * \sa last
+  */
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+static const auto lastp1 = last+fix<1>;
+#else
+// Using a FixedExpr<1> expression is important here to make sure the compiler
+// can fully optimize the computation starting indices with zero overhead.
+static const lastp1_t lastp1(last+fix<1>());
+#endif
+
+/** \var end
+  * \ingroup Core_Module
+  * \sa lastp1
+  */
+static const lastp1_t end = lastp1;
+
+/** \var all
+  * \ingroup Core_Module
+  * Can be used as a parameter to DenseBase::operator()(const RowIndices&, const ColIndices&) to index all rows or columns
+  */
+static const Eigen::internal::all_t all;
+
+} // namespace placeholders
+
+} // end namespace Eigen
+
+#endif // EIGEN_INDEXED_VIEW_HELPER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/IntegralConstant.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/IntegralConstant.h
new file mode 100644
index 0000000000000000000000000000000000000000..ea275bd98ecaf4d869ef635810a5d066588b6bd2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/IntegralConstant.h
@@ -0,0 +1,252 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+
+#ifndef EIGEN_INTEGRAL_CONSTANT_H
+#define EIGEN_INTEGRAL_CONSTANT_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<int N> class FixedInt;
+template<int N> class VariableAndFixedInt;
+
+/** \internal
+  * \class FixedInt
+  *
+  * This class embeds a compile-time integer \c N.
+  *
+  * It is similar to c++11 std::integral_constant<int,N> but with some additional features
+  * such as:
+  *  - implicit conversion to int
+  *  - arithmetic and some bitwise operators: -, +, *, /, %, &, |
+  *  - c++98/14 compatibility with fix<N> and fix<N>() syntax to define integral constants.
+  *
+  * It is strongly discouraged to directly deal with this class FixedInt. Instances are expected to
+  * be created by the user using Eigen::fix<N> or Eigen::fix<N>().
+  * \code
+  * internal::cleanup_index_type<T>::type
+  * internal::cleanup_index_type<T,DynamicKey>::type
+  * \endcode
+  * where T can a FixedInt<N>, a pointer to function FixedInt<N> (*)(), or numerous other integer-like representations.
+  * \c DynamicKey is either Dynamic (default) or DynamicIndex and used to identify true compile-time values.
+  *
+  * For convenience, you can extract the compile-time value \c N in a generic way using the following helper:
+  * \code
+  * internal::get_fixed_value<T,DefaultVal>::value
+  * \endcode
+  * that will give you \c N if T equals FixedInt<N> or FixedInt<N> (*)(), and \c DefaultVal if T does not embed any compile-time value (e.g., T==int).
+  *
+  * \sa fix<N>, class VariableAndFixedInt
+  */
+template<int N> class FixedInt
+{
+public:
+  static const int value = N;
+  EIGEN_CONSTEXPR operator int() const { return value; }
+  
+  EIGEN_CONSTEXPR
+  FixedInt() = default;
+  
+  EIGEN_CONSTEXPR
+  FixedInt(std::integral_constant<int,N>) {}
+  
+  EIGEN_CONSTEXPR
+  FixedInt( VariableAndFixedInt<N> other) {
+    #ifndef EIGEN_INTERNAL_DEBUGGING
+    EIGEN_UNUSED_VARIABLE(other);
+    #endif
+    eigen_internal_assert(int(other)==N);
+  }
+
+  EIGEN_CONSTEXPR
+  FixedInt<-N> operator-() const { return FixedInt<-N>(); }
+  
+  template<int M>
+  EIGEN_CONSTEXPR
+  FixedInt<N+M> operator+( FixedInt<M>) const { return FixedInt<N+M>(); }
+  
+  template<int M>
+  EIGEN_CONSTEXPR
+  FixedInt<N-M> operator-( FixedInt<M>) const { return FixedInt<N-M>(); }
+  
+  template<int M>
+  EIGEN_CONSTEXPR
+  FixedInt<N*M> operator*( FixedInt<M>) const { return FixedInt<N*M>(); }
+  
+  template<int M>
+  EIGEN_CONSTEXPR
+  FixedInt<N/M> operator/( FixedInt<M>) const { return FixedInt<N/M>(); }
+  
+  template<int M>
+  EIGEN_CONSTEXPR
+  FixedInt<N%M> operator%( FixedInt<M>) const { return FixedInt<N%M>(); }
+  
+  template<int M>
+  EIGEN_CONSTEXPR
+  FixedInt<N|M> operator|( FixedInt<M>) const { return FixedInt<N|M>(); }
+  
+  template<int M>
+  EIGEN_CONSTEXPR
+  FixedInt<N&M> operator&( FixedInt<M>) const { return FixedInt<N&M>(); }
+
+  // Needed in C++14 to allow fix<N>():
+  EIGEN_CONSTEXPR FixedInt operator() () const { return *this; }
+
+  VariableAndFixedInt<N> operator() (int val) const { return VariableAndFixedInt<N>(val); }
+};
+
+/** \internal
+  * \class VariableAndFixedInt
+  *
+  * This class embeds both a compile-time integer \c N and a runtime integer.
+  * Both values are supposed to be equal unless the compile-time value \c N has a special
+  * value meaning that the runtime-value should be used. Depending on the context, this special
+  * value can be either Eigen::Dynamic (for positive quantities) or Eigen::DynamicIndex (for
+  * quantities that can be negative).
+  *
+  * It is the return-type of the function Eigen::fix<N>(int), and most of the time this is the only
+  * way it is used. It is strongly discouraged to directly deal with instances of VariableAndFixedInt.
+  * Indeed, in order to write generic code, it is the responsibility of the callee to properly convert
+  * it to either a true compile-time quantity (i.e. a FixedInt<N>), or to a runtime quantity (e.g., an Index)
+  * using the following generic helper:
+  * \code
+  * internal::cleanup_index_type<T>::type
+  * internal::cleanup_index_type<T,DynamicKey>::type
+  * \endcode
+  * where T can be a template instantiation of VariableAndFixedInt or numerous other integer-like representations.
+  * \c DynamicKey is either Dynamic (default) or DynamicIndex and used to identify true compile-time values.
+  *
+  * For convenience, you can also extract the compile-time value \c N using the following helper:
+  * \code
+  * internal::get_fixed_value<T,DefaultVal>::value
+  * \endcode
+  * that will give you \c N if T equals VariableAndFixedInt<N>, and \c DefaultVal if T does not embed any compile-time value (e.g., T==int).
+  *
+  * \sa fix<N>(int), class FixedInt
+  */
+template<int N> class VariableAndFixedInt
+{
+public:
+  static const int value = N;
+  operator int() const { return m_value; }
+  VariableAndFixedInt(int val) { m_value = val; }
+protected:
+  int m_value;
+};
+
+template<typename T, int Default=Dynamic> struct get_fixed_value {
+  static const int value = Default;
+};
+
+template<int N,int Default> struct get_fixed_value<FixedInt<N>,Default> {
+  static const int value = N;
+};
+
+template<int N,int Default> struct get_fixed_value<VariableAndFixedInt<N>,Default> {
+  static const int value = N ;
+};
+
+template<typename T, int N, int Default>
+struct get_fixed_value<variable_if_dynamic<T,N>,Default> {
+  static const int value = N;
+};
+
+template<typename T> EIGEN_DEVICE_FUNC Index get_runtime_value(const T &x) { return x; }
+
+// Cleanup integer/FixedInt/VariableAndFixedInt/etc types:
+
+// By default, no cleanup:
+template<typename T, int DynamicKey=Dynamic, typename EnableIf=void> struct cleanup_index_type { typedef T type; };
+
+// Convert any integral type (e.g., short, int, unsigned int, etc.) to Eigen::Index
+template<typename T, int DynamicKey> struct cleanup_index_type<T,DynamicKey,std::enable_if_t<internal::is_integral<T>::value>> { typedef Index type; };
+
+// If VariableAndFixedInt does not match DynamicKey, then we turn it to a pure compile-time value:
+template<int N, int DynamicKey> struct cleanup_index_type<VariableAndFixedInt<N>, DynamicKey> { typedef FixedInt<N> type; };
+// If VariableAndFixedInt matches DynamicKey, then we turn it to a pure runtime-value (aka Index):
+template<int DynamicKey> struct cleanup_index_type<VariableAndFixedInt<DynamicKey>, DynamicKey> { typedef Index type; };
+
+template<int N, int DynamicKey> struct cleanup_index_type<std::integral_constant<int,N>, DynamicKey> { typedef FixedInt<N> type; };
+
+} // end namespace internal
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+template<int N>
+constexpr internal::FixedInt<N> fix{};
+
+#else // EIGEN_PARSED_BY_DOXYGEN
+
+/** \var fix<N>()
+  * \ingroup Core_Module
+  *
+  * This \em identifier permits to construct an object embedding a compile-time integer \c N.
+  *
+  * \tparam N the compile-time integer value
+  *
+  * It is typically used in conjunction with the Eigen::seq and Eigen::seqN functions to pass compile-time values to them:
+  * \code
+  * seqN(10,fix<4>,fix<-3>)   // <=> [10 7 4 1]
+  * \endcode
+  *
+  * See also the function fix(int) to pass both a compile-time and runtime value.
+  *
+  * In c++14, it is implemented as:
+  * \code
+  * template<int N> static const internal::FixedInt<N> fix{};
+  * \endcode
+  * where internal::FixedInt<N> is an internal template class similar to
+  * <a href="http://en.cppreference.com/w/cpp/types/integral_constant">\c std::integral_constant </a><tt> <int,N> </tt>
+  * Here, \c fix<N> is thus an object of type \c internal::FixedInt<N>.
+  *
+  * \sa fix<N>(int), seq, seqN
+  */
+template<int N>
+static const auto fix();
+
+/** \fn fix<N>(int)
+  * \ingroup Core_Module
+  *
+  * This function returns an object embedding both a compile-time integer \c N, and a fallback runtime value \a val.
+  *
+  * \tparam N the compile-time integer value
+  * \param  val the fallback runtime integer value
+  *
+  * This function is a more general version of the \ref fix identifier/function that can be used in template code
+  * where the compile-time value could turn out to actually mean "undefined at compile-time". For positive integers
+  * such as a size or a dimension, this case is identified by Eigen::Dynamic, whereas runtime signed integers
+  * (e.g., an increment/stride) are identified as Eigen::DynamicIndex. In such a case, the runtime value \a val
+  * will be used as a fallback.
+  *
+  * A typical use case would be:
+  * \code
+  * template<typename Derived> void foo(const MatrixBase<Derived> &mat) {
+  *   const int N = Derived::RowsAtCompileTime==Dynamic ? Dynamic : Derived::RowsAtCompileTime/2;
+  *   const int n = mat.rows()/2;
+  *   ... mat( seqN(0,fix<N>(n) ) ...;
+  * }
+  * \endcode
+  * In this example, the function Eigen::seqN knows that the second argument is expected to be a size.
+  * If the passed compile-time value N equals Eigen::Dynamic, then the proxy object returned by fix will be dissmissed, and converted to an Eigen::Index of value \c n.
+  * Otherwise, the runtime-value \c n will be dissmissed, and the returned ArithmeticSequence will be of the exact same type as <tt> seqN(0,fix<N>) </tt>.
+  *
+  * \sa fix, seqN, class ArithmeticSequence
+  */
+template<int N>
+static const auto fix(int val);
+
+#endif // EIGEN_PARSED_BY_DOXYGEN
+
+} // end namespace Eigen
+
+#endif // EIGEN_INTEGRAL_CONSTANT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/MKL_support.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/MKL_support.h
new file mode 100644
index 0000000000000000000000000000000000000000..9cf5f6f3470d8c42508e9442aff0652524fe8bbb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/MKL_support.h
@@ -0,0 +1,139 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *   Include file with common MKL declarations
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_MKL_SUPPORT_H
+#define EIGEN_MKL_SUPPORT_H
+
+#ifdef EIGEN_USE_MKL_ALL
+  #ifndef EIGEN_USE_BLAS
+    #define EIGEN_USE_BLAS
+  #endif
+  #ifndef EIGEN_USE_LAPACKE
+    #define EIGEN_USE_LAPACKE
+  #endif
+  #ifndef EIGEN_USE_MKL_VML
+    #define EIGEN_USE_MKL_VML
+  #endif
+#endif
+
+#ifdef EIGEN_USE_LAPACKE_STRICT
+  #define EIGEN_USE_LAPACKE
+#endif
+
+#if defined(EIGEN_USE_MKL_VML) && !defined(EIGEN_USE_MKL)
+  #define EIGEN_USE_MKL
+#endif
+
+
+#if defined EIGEN_USE_MKL
+#   if (!defined MKL_DIRECT_CALL) && (!defined EIGEN_MKL_NO_DIRECT_CALL)
+#       define MKL_DIRECT_CALL
+#       define MKL_DIRECT_CALL_JUST_SET
+#   endif
+#   include <mkl.h>
+/*Check IMKL version for compatibility: < 10.3 is not usable with Eigen*/
+#   ifndef INTEL_MKL_VERSION
+#       undef EIGEN_USE_MKL /* INTEL_MKL_VERSION is not even defined on older versions */
+#   elif INTEL_MKL_VERSION < 100305    /* the intel-mkl-103-release-notes say this was when the lapacke.h interface was added*/
+#       undef EIGEN_USE_MKL
+#   endif
+#   ifndef EIGEN_USE_MKL
+    /*If the MKL version is too old, undef everything*/
+#       undef   EIGEN_USE_MKL_ALL
+#       undef   EIGEN_USE_LAPACKE
+#       undef   EIGEN_USE_MKL_VML
+#       undef   EIGEN_USE_LAPACKE_STRICT
+#       undef   EIGEN_USE_LAPACKE
+#       ifdef   MKL_DIRECT_CALL_JUST_SET
+#           undef MKL_DIRECT_CALL
+#       endif
+#   endif
+#endif
+
+#if defined EIGEN_USE_MKL
+
+#define EIGEN_MKL_VML_THRESHOLD 128
+
+/* MKL_DOMAIN_BLAS, etc are defined only in 10.3 update 7 */
+/* MKL_BLAS, etc are not defined in 11.2 */
+#ifdef MKL_DOMAIN_ALL
+#define EIGEN_MKL_DOMAIN_ALL MKL_DOMAIN_ALL
+#else
+#define EIGEN_MKL_DOMAIN_ALL MKL_ALL
+#endif
+
+#ifdef MKL_DOMAIN_BLAS
+#define EIGEN_MKL_DOMAIN_BLAS MKL_DOMAIN_BLAS
+#else
+#define EIGEN_MKL_DOMAIN_BLAS MKL_BLAS
+#endif
+
+#ifdef MKL_DOMAIN_FFT
+#define EIGEN_MKL_DOMAIN_FFT MKL_DOMAIN_FFT
+#else
+#define EIGEN_MKL_DOMAIN_FFT MKL_FFT
+#endif
+
+#ifdef MKL_DOMAIN_VML
+#define EIGEN_MKL_DOMAIN_VML MKL_DOMAIN_VML
+#else
+#define EIGEN_MKL_DOMAIN_VML MKL_VML
+#endif
+
+#ifdef MKL_DOMAIN_PARDISO
+#define EIGEN_MKL_DOMAIN_PARDISO MKL_DOMAIN_PARDISO
+#else
+#define EIGEN_MKL_DOMAIN_PARDISO MKL_PARDISO
+#endif
+#endif
+
+#if defined(EIGEN_USE_BLAS) && !defined(EIGEN_USE_MKL)
+#include "../../misc/blas.h"
+#endif
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+typedef std::complex<double> dcomplex;
+typedef std::complex<float>  scomplex;
+
+#if defined(EIGEN_USE_MKL)
+typedef MKL_INT BlasIndex;
+#else
+typedef int BlasIndex;
+#endif
+
+} // end namespace Eigen
+
+
+#endif // EIGEN_MKL_SUPPORT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Macros.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Macros.h
new file mode 100644
index 0000000000000000000000000000000000000000..613a2619da1cc31d5e45db6d52eacb27883c6c5b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Macros.h
@@ -0,0 +1,1301 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MACROS_H
+#define EIGEN_MACROS_H
+#include "../InternalHeaderCheck.h"
+
+//------------------------------------------------------------------------------------------
+// Eigen version and basic defaults
+//------------------------------------------------------------------------------------------
+
+#define EIGEN_WORLD_VERSION 3
+#define EIGEN_MAJOR_VERSION 4
+#define EIGEN_MINOR_VERSION 90
+
+#define EIGEN_VERSION_AT_LEAST(x,y,z) (EIGEN_WORLD_VERSION>x || (EIGEN_WORLD_VERSION>=x && \
+                                      (EIGEN_MAJOR_VERSION>y || (EIGEN_MAJOR_VERSION>=y && \
+                                                                 EIGEN_MINOR_VERSION>=z))))
+
+#ifdef EIGEN_DEFAULT_TO_ROW_MAJOR
+#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION Eigen::RowMajor
+#else
+#define EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION Eigen::ColMajor
+#endif
+
+#ifndef EIGEN_DEFAULT_DENSE_INDEX_TYPE
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE std::ptrdiff_t
+#endif
+
+// Upperbound on the C++ version to use.
+// Expected values are 03, 11, 14, 17, etc.
+// By default, let's use an arbitrarily large C++ version.
+#ifndef EIGEN_MAX_CPP_VER
+#define EIGEN_MAX_CPP_VER 99
+#endif
+
+/** Allows to disable some optimizations which might affect the accuracy of the result.
+  * Such optimization are enabled by default, and set EIGEN_FAST_MATH to 0 to disable them.
+  * They currently include:
+  *   - single precision ArrayBase::sin() and ArrayBase::cos() for SSE and AVX vectorization.
+  */
+#ifndef EIGEN_FAST_MATH
+#define EIGEN_FAST_MATH 1
+#endif
+
+#ifndef EIGEN_STACK_ALLOCATION_LIMIT
+// 131072 == 128 KB
+#define EIGEN_STACK_ALLOCATION_LIMIT 131072
+#endif
+
+//------------------------------------------------------------------------------------------
+// Compiler identification, EIGEN_COMP_*
+//------------------------------------------------------------------------------------------
+
+/// \internal EIGEN_COMP_GNUC set to version (e.g., 951 for GCC 9.5.1) for all compilers compatible with GCC
+#ifdef __GNUC__
+  #define EIGEN_COMP_GNUC (__GNUC__*100+__GNUC_MINOR__*10+__GNUC_PATCHLEVEL__)
+#else
+  #define EIGEN_COMP_GNUC 0
+#endif
+
+/// \internal EIGEN_COMP_CLANG set to version (e.g., 372 for clang 3.7.2) if the compiler is clang
+#if defined(__clang__)
+  #define EIGEN_COMP_CLANG (__clang_major__*100+__clang_minor__*10+__clang_patchlevel__)
+#else
+  #define EIGEN_COMP_CLANG 0
+#endif
+
+/// \internal EIGEN_COMP_CLANGAPPLE set to the version number (e.g. 9000000 for AppleClang 9.0) if the compiler is AppleClang
+#if defined(__clang__) && defined(__apple_build_version__)
+  #define EIGEN_COMP_CLANGAPPLE __apple_build_version__
+#else
+  #define EIGEN_COMP_CLANGAPPLE 0
+#endif
+
+/// \internal EIGEN_COMP_CASTXML set to 1 if being preprocessed by CastXML
+#if defined(__castxml__)
+  #define EIGEN_COMP_CASTXML 1
+#else
+  #define EIGEN_COMP_CASTXML 0
+#endif
+
+/// \internal EIGEN_COMP_LLVM set to 1 if the compiler backend is llvm
+#if defined(__llvm__)
+  #define EIGEN_COMP_LLVM 1
+#else
+  #define EIGEN_COMP_LLVM 0
+#endif
+
+/// \internal EIGEN_COMP_ICC set to __INTEL_COMPILER if the compiler is Intel icc compiler, 0 otherwise
+#if defined(__INTEL_COMPILER)
+  #define EIGEN_COMP_ICC __INTEL_COMPILER
+#else
+  #define EIGEN_COMP_ICC 0
+#endif
+
+/// \internal EIGEN_COMP_CLANGICC set to __INTEL_CLANG_COMPILER if the compiler is Intel icx compiler, 0 otherwise
+#if defined(__INTEL_CLANG_COMPILER)
+  #define EIGEN_COMP_CLANGICC __INTEL_CLANG_COMPILER
+#else
+  #define EIGEN_COMP_CLANGICC 0
+#endif
+
+/// \internal EIGEN_COMP_MINGW set to 1 if the compiler is mingw
+#if defined(__MINGW32__)
+  #define EIGEN_COMP_MINGW 1
+#else
+  #define EIGEN_COMP_MINGW 0
+#endif
+
+/// \internal EIGEN_COMP_SUNCC set to 1 if the compiler is Solaris Studio
+#if defined(__SUNPRO_CC)
+  #define EIGEN_COMP_SUNCC 1
+#else
+  #define EIGEN_COMP_SUNCC 0
+#endif
+
+/// \internal EIGEN_COMP_MSVC set to _MSC_VER if the compiler is Microsoft Visual C++, 0 otherwise.
+#if defined(_MSC_VER)
+  #define EIGEN_COMP_MSVC _MSC_VER
+#else
+  #define EIGEN_COMP_MSVC 0
+#endif
+
+#if defined(__NVCC__)
+#if defined(__CUDACC_VER_MAJOR__) && (__CUDACC_VER_MAJOR__ >= 9)
+  #define EIGEN_COMP_NVCC  ((__CUDACC_VER_MAJOR__ * 10000) + (__CUDACC_VER_MINOR__ * 100))
+#elif defined(__CUDACC_VER__)
+  #define EIGEN_COMP_NVCC __CUDACC_VER__
+#else
+  #error "NVCC did not define compiler version."
+#endif
+#else
+  #define EIGEN_COMP_NVCC 0
+#endif
+
+// For the record, here is a table summarizing the possible values for EIGEN_COMP_MSVC:
+//  name        ver   MSC_VER
+//  2015        14      1900
+//  "15"        15      1900
+//  2017-14.1   15.0    1910
+//  2017-14.11  15.3    1911
+//  2017-14.12  15.5    1912
+//  2017-14.13  15.6    1913
+//  2017-14.14  15.7    1914
+//  2017        15.8    1915
+//  2017        15.9    1916
+//  2019 RTW    16.0    1920
+
+/// \internal EIGEN_COMP_MSVC_LANG set to _MSVC_LANG if the compiler is Microsoft Visual C++, 0 otherwise.
+#if defined(_MSVC_LANG)
+  #define EIGEN_COMP_MSVC_LANG _MSVC_LANG
+#else
+  #define EIGEN_COMP_MSVC_LANG 0
+#endif
+
+// For the record, here is a table summarizing the possible values for EIGEN_COMP_MSVC_LANG:
+// MSVC option                          Standard  MSVC_LANG
+// /std:c++14 (default as of VS 2019)   C++14     201402L
+// /std:c++17                           C++17     201703L
+// /std:c++latest                       >C++17    >201703L
+
+/// \internal EIGEN_COMP_MSVC_STRICT set to 1 if the compiler is really Microsoft Visual C++ and not ,e.g., ICC or clang-cl
+#if EIGEN_COMP_MSVC && !(EIGEN_COMP_ICC || EIGEN_COMP_LLVM || EIGEN_COMP_CLANG)
+  #define EIGEN_COMP_MSVC_STRICT _MSC_VER
+#else
+  #define EIGEN_COMP_MSVC_STRICT 0
+#endif
+
+/// \internal EIGEN_COMP_IBM set to xlc version if the compiler is IBM XL C++
+// XLC   version
+// 3.1   0x0301
+// 4.5   0x0405
+// 5.0   0x0500
+// 12.1  0x0C01
+#if defined(__IBMCPP__) || defined(__xlc__) || defined(__ibmxl__)
+  #define EIGEN_COMP_IBM __xlC__
+#else
+  #define EIGEN_COMP_IBM 0
+#endif
+
+/// \internal EIGEN_COMP_PGI set to PGI version if the compiler is Portland Group Compiler
+#if defined(__PGI)
+  #define EIGEN_COMP_PGI (__PGIC__*100+__PGIC_MINOR__)
+#else
+  #define EIGEN_COMP_PGI 0
+#endif
+
+/// \internal EIGEN_COMP_ARM set to 1 if the compiler is ARM Compiler
+#if defined(__CC_ARM) || defined(__ARMCC_VERSION)
+  #define EIGEN_COMP_ARM 1
+#else
+  #define EIGEN_COMP_ARM 0
+#endif
+
+/// \internal EIGEN_COMP_EMSCRIPTEN set to 1 if the compiler is Emscripten Compiler
+#if defined(__EMSCRIPTEN__)
+  #define EIGEN_COMP_EMSCRIPTEN 1
+#else
+  #define EIGEN_COMP_EMSCRIPTEN 0
+#endif
+
+/// \internal EIGEN_COMP_FCC set to FCC version if the compiler is Fujitsu Compiler (traditional mode)
+/// \note The Fujitsu C/C++ compiler uses the traditional mode based
+/// on EDG g++ 6.1 by default or if envoked with the -Nnoclang flag
+#if defined(__FUJITSU)
+  #define EIGEN_COMP_FCC (__FCC_major__*100+__FCC_minor__*10+__FCC_patchlevel__)
+#else
+  #define EIGEN_COMP_FCC 0
+#endif
+
+/// \internal EIGEN_COMP_CLANGFCC set to FCC version if the compiler is Fujitsu Compiler (Clang mode)
+/// \note The Fujitsu C/C++ compiler uses the non-traditional mode
+/// based on Clang 7.1.0 if envoked with the -Nclang flag
+#if defined(__CLANG_FUJITSU)
+  #define EIGEN_COMP_CLANGFCC (__FCC_major__*100+__FCC_minor__*10+__FCC_patchlevel__)
+#else
+  #define EIGEN_COMP_CLANGFCC 0
+#endif
+
+/// \internal EIGEN_COMP_CPE set to CPE version if the compiler is HPE Cray Compiler (GCC based)
+/// \note This is the SVE-enabled C/C++ compiler from the HPE Cray
+/// Programming Environment (CPE) based on Cray GCC 8.1
+#if defined(_CRAYC) && !defined(__clang__)
+  #define EIGEN_COMP_CPE (_RELEASE_MAJOR*100+_RELEASE_MINOR*10+_RELEASE_PATCHLEVEL)
+#else
+  #define EIGEN_COMP_CPE 0
+#endif
+
+/// \internal EIGEN_COMP_CLANGCPE set to CPE version if the compiler is HPE Cray Compiler (Clang based)
+/// \note This is the C/C++ compiler from the HPE Cray Programming
+/// Environment (CPE) based on Cray Clang 11.0 without SVE-support
+#if defined(_CRAYC) && defined(__clang__)
+  #define EIGEN_COMP_CLANGCPE (_RELEASE_MAJOR*100+_RELEASE_MINOR*10+_RELEASE_PATCHLEVEL)
+#else
+  #define EIGEN_COMP_CLANGCPE 0
+#endif
+
+/// \internal EIGEN_COMP_LCC set to 1 if the compiler is MCST-LCC (MCST eLbrus Compiler Collection)
+#if defined(__LCC__) && defined(__MCST__)
+  #define EIGEN_COMP_LCC (__LCC__*100+__LCC_MINOR__)
+#else
+  #define EIGEN_COMP_LCC 0
+#endif
+
+
+/// \internal EIGEN_COMP_GNUC_STRICT set to 1 if the compiler is really GCC and not a compatible compiler (e.g., ICC, clang, mingw, etc.)
+#if EIGEN_COMP_GNUC && !(EIGEN_COMP_CLANG || EIGEN_COMP_ICC || EIGEN_COMP_CLANGICC || EIGEN_COMP_MINGW || EIGEN_COMP_PGI || EIGEN_COMP_IBM || EIGEN_COMP_ARM || EIGEN_COMP_EMSCRIPTEN || EIGEN_COMP_FCC || EIGEN_COMP_CLANGFCC || EIGEN_COMP_CPE || EIGEN_COMP_CLANGCPE || EIGEN_COMP_LCC)
+  #define EIGEN_COMP_GNUC_STRICT 1
+#else
+  #define EIGEN_COMP_GNUC_STRICT 0
+#endif
+
+// GCC, and compilers that pretend to be it, have different version schemes, so this only makes sense to use with the real GCC.
+#if EIGEN_COMP_GNUC_STRICT
+  #define EIGEN_GNUC_STRICT_AT_LEAST(x,y,z)  ((__GNUC__ > x) || \
+                                              (__GNUC__ == x && __GNUC_MINOR__ > y) || \
+                                              (__GNUC__ == x && __GNUC_MINOR__ == y && __GNUC_PATCHLEVEL__ >= z))
+  #define EIGEN_GNUC_STRICT_LESS_THAN(x,y,z) ((__GNUC__ < x) || \
+                                              (__GNUC__ == x && __GNUC_MINOR__ < y) || \
+                                              (__GNUC__ == x && __GNUC_MINOR__ == y && __GNUC_PATCHLEVEL__ < z))
+#else
+  #define EIGEN_GNUC_STRICT_AT_LEAST(x,y,z)  0
+  #define EIGEN_GNUC_STRICT_LESS_THAN(x,y,z) 0
+#endif
+
+
+
+/// \internal EIGEN_COMP_CLANG_STRICT set to 1 if the compiler is really Clang and not a compatible compiler (e.g., AppleClang, etc.)
+#if EIGEN_COMP_CLANG && !(EIGEN_COMP_CLANGAPPLE || EIGEN_COMP_CLANGICC || EIGEN_COMP_CLANGFCC || EIGEN_COMP_CLANGCPE)
+  #define EIGEN_COMP_CLANG_STRICT 1
+#else
+  #define EIGEN_COMP_CLANG_STRICT 0
+#endif
+
+// Clang, and compilers forked from it, have different version schemes, so this only makes sense to use with the real Clang.
+#if EIGEN_COMP_CLANG_STRICT
+  #define EIGEN_CLANG_STRICT_AT_LEAST(x,y,z)  ((__clang_major__ > x) || \
+                                               (__clang_major__ == x && __clang_minor__ > y) || \
+                                               (__clang_major__ == x && __clang_minor__ == y && __clang_patchlevel__ >= z))
+  #define EIGEN_CLANG_STRICT_LESS_THAN(x,y,z) ((__clang_major__ < x) || \
+                                               (__clang_major__ == x && __clang_minor__ < y) || \
+                                               (__clang_major__ == x && __clang_minor__ == y && __clang_patchlevel__ < z))
+#else
+  #define EIGEN_CLANG_STRICT_AT_LEAST(x,y,z)  0
+  #define EIGEN_CLANG_STRICT_LESS_THAN(x,y,z) 0
+#endif
+
+//------------------------------------------------------------------------------------------
+// Architecture identification, EIGEN_ARCH_*
+//------------------------------------------------------------------------------------------
+
+
+#if defined(__x86_64__) || (defined(_M_X64) && !defined(_M_ARM64EC)) || defined(__amd64)
+  #define EIGEN_ARCH_x86_64 1
+#else
+  #define EIGEN_ARCH_x86_64 0
+#endif
+
+#if defined(__i386__) || defined(_M_IX86) || defined(_X86_) || defined(__i386)
+  #define EIGEN_ARCH_i386 1
+#else
+  #define EIGEN_ARCH_i386 0
+#endif
+
+#if EIGEN_ARCH_x86_64 || EIGEN_ARCH_i386
+  #define EIGEN_ARCH_i386_OR_x86_64 1
+#else
+  #define EIGEN_ARCH_i386_OR_x86_64 0
+#endif
+
+/// \internal EIGEN_ARCH_ARM set to 1 if the architecture is ARM
+#if defined(__arm__)
+  #define EIGEN_ARCH_ARM 1
+#else
+  #define EIGEN_ARCH_ARM 0
+#endif
+
+/// \internal EIGEN_ARCH_ARM64 set to 1 if the architecture is ARM64
+#if defined(__aarch64__) || defined(_M_ARM64) || defined(_M_ARM64EC)
+  #define EIGEN_ARCH_ARM64 1
+#else
+  #define EIGEN_ARCH_ARM64 0
+#endif
+
+/// \internal EIGEN_ARCH_ARM_OR_ARM64 set to 1 if the architecture is ARM or ARM64
+#if EIGEN_ARCH_ARM || EIGEN_ARCH_ARM64
+  #define EIGEN_ARCH_ARM_OR_ARM64 1
+#else
+  #define EIGEN_ARCH_ARM_OR_ARM64 0
+#endif
+
+/// \internal EIGEN_ARCH_ARMV8 set to 1 if the architecture is armv8 or greater.
+#if EIGEN_ARCH_ARM_OR_ARM64 && defined(__ARM_ARCH) && __ARM_ARCH >= 8
+#define EIGEN_ARCH_ARMV8 1
+#else
+#define EIGEN_ARCH_ARMV8 0
+#endif
+
+
+/// \internal EIGEN_HAS_ARM64_FP16 set to 1 if the architecture provides an IEEE
+/// compliant Arm fp16 type
+#if EIGEN_ARCH_ARM_OR_ARM64
+  #ifndef EIGEN_HAS_ARM64_FP16
+    #if defined(__ARM_FP16_FORMAT_IEEE)
+      #define EIGEN_HAS_ARM64_FP16 1
+    #else
+      #define EIGEN_HAS_ARM64_FP16 0
+    #endif
+  #endif
+#endif
+
+/// \internal EIGEN_ARCH_MIPS set to 1 if the architecture is MIPS
+#if defined(__mips__) || defined(__mips)
+  #define EIGEN_ARCH_MIPS 1
+#else
+  #define EIGEN_ARCH_MIPS 0
+#endif
+
+/// \internal EIGEN_ARCH_SPARC set to 1 if the architecture is SPARC
+#if defined(__sparc__) || defined(__sparc)
+  #define EIGEN_ARCH_SPARC 1
+#else
+  #define EIGEN_ARCH_SPARC 0
+#endif
+
+/// \internal EIGEN_ARCH_IA64 set to 1 if the architecture is Intel Itanium
+#if defined(__ia64__)
+  #define EIGEN_ARCH_IA64 1
+#else
+  #define EIGEN_ARCH_IA64 0
+#endif
+
+/// \internal EIGEN_ARCH_PPC set to 1 if the architecture is PowerPC
+#if defined(__powerpc__) || defined(__ppc__) || defined(_M_PPC) || defined(__POWERPC__)
+  #define EIGEN_ARCH_PPC 1
+#else
+  #define EIGEN_ARCH_PPC 0
+#endif
+
+
+
+//------------------------------------------------------------------------------------------
+// Operating system identification, EIGEN_OS_*
+//------------------------------------------------------------------------------------------
+
+/// \internal EIGEN_OS_UNIX set to 1 if the OS is a unix variant
+#if defined(__unix__) || defined(__unix)
+  #define EIGEN_OS_UNIX 1
+#else
+  #define EIGEN_OS_UNIX 0
+#endif
+
+/// \internal EIGEN_OS_LINUX set to 1 if the OS is based on Linux kernel
+#if defined(__linux__)
+  #define EIGEN_OS_LINUX 1
+#else
+  #define EIGEN_OS_LINUX 0
+#endif
+
+/// \internal EIGEN_OS_ANDROID set to 1 if the OS is Android
+// note: ANDROID is defined when using ndk_build, __ANDROID__ is defined when using a standalone toolchain.
+#if defined(__ANDROID__) || defined(ANDROID)
+  #define EIGEN_OS_ANDROID 1
+#else
+  #define EIGEN_OS_ANDROID 0
+#endif
+
+/// \internal EIGEN_OS_GNULINUX set to 1 if the OS is GNU Linux and not Linux-based OS (e.g., not android)
+#if defined(__gnu_linux__) && !(EIGEN_OS_ANDROID)
+  #define EIGEN_OS_GNULINUX 1
+#else
+  #define EIGEN_OS_GNULINUX 0
+#endif
+
+/// \internal EIGEN_OS_BSD set to 1 if the OS is a BSD variant
+#if defined(__FreeBSD__) || defined(__NetBSD__) || defined(__OpenBSD__) || defined(__bsdi__) || defined(__DragonFly__)
+  #define EIGEN_OS_BSD 1
+#else
+  #define EIGEN_OS_BSD 0
+#endif
+
+/// \internal EIGEN_OS_MAC set to 1 if the OS is MacOS
+#if defined(__APPLE__)
+  #define EIGEN_OS_MAC 1
+#else
+  #define EIGEN_OS_MAC 0
+#endif
+
+/// \internal EIGEN_OS_QNX set to 1 if the OS is QNX
+#if defined(__QNX__)
+  #define EIGEN_OS_QNX 1
+#else
+  #define EIGEN_OS_QNX 0
+#endif
+
+/// \internal EIGEN_OS_WIN set to 1 if the OS is Windows based
+#if defined(_WIN32)
+  #define EIGEN_OS_WIN 1
+#else
+  #define EIGEN_OS_WIN 0
+#endif
+
+/// \internal EIGEN_OS_WIN64 set to 1 if the OS is Windows 64bits
+#if defined(_WIN64)
+  #define EIGEN_OS_WIN64 1
+#else
+  #define EIGEN_OS_WIN64 0
+#endif
+
+/// \internal EIGEN_OS_WINCE set to 1 if the OS is Windows CE
+#if defined(_WIN32_WCE)
+  #define EIGEN_OS_WINCE 1
+#else
+  #define EIGEN_OS_WINCE 0
+#endif
+
+/// \internal EIGEN_OS_CYGWIN set to 1 if the OS is Windows/Cygwin
+#if defined(__CYGWIN__)
+  #define EIGEN_OS_CYGWIN 1
+#else
+  #define EIGEN_OS_CYGWIN 0
+#endif
+
+/// \internal EIGEN_OS_WIN_STRICT set to 1 if the OS is really Windows and not some variants
+#if EIGEN_OS_WIN && !( EIGEN_OS_WINCE || EIGEN_OS_CYGWIN )
+  #define EIGEN_OS_WIN_STRICT 1
+#else
+  #define EIGEN_OS_WIN_STRICT 0
+#endif
+
+/// \internal EIGEN_OS_SUN set to __SUNPRO_C if the OS is SUN
+// compiler  solaris   __SUNPRO_C
+// version   studio
+// 5.7       10        0x570
+// 5.8       11        0x580
+// 5.9       12        0x590
+// 5.10	     12.1      0x5100
+// 5.11	     12.2      0x5110
+// 5.12	     12.3      0x5120
+#if (defined(sun) || defined(__sun)) && !(defined(__SVR4) || defined(__svr4__))
+  #define EIGEN_OS_SUN __SUNPRO_C
+#else
+  #define EIGEN_OS_SUN 0
+#endif
+
+/// \internal EIGEN_OS_SOLARIS set to 1 if the OS is Solaris
+#if (defined(sun) || defined(__sun)) && (defined(__SVR4) || defined(__svr4__))
+  #define EIGEN_OS_SOLARIS 1
+#else
+  #define EIGEN_OS_SOLARIS 0
+#endif
+
+
+//------------------------------------------------------------------------------------------
+// Detect GPU compilers and architectures
+//------------------------------------------------------------------------------------------
+
+// NVCC is not supported as the target platform for HIPCC
+// Note that this also makes EIGEN_CUDACC and EIGEN_HIPCC mutually exclusive
+#if defined(__NVCC__) && defined(__HIPCC__)
+  #error "NVCC as the target platform for HIPCC is currently not supported."
+#endif
+
+#if defined(__CUDACC__) && !defined(EIGEN_NO_CUDA) && !defined(__SYCL_DEVICE_ONLY__)
+  // Means the compiler is either nvcc or clang with CUDA enabled
+  #define EIGEN_CUDACC __CUDACC__
+#endif
+
+#if defined(__CUDA_ARCH__) && !defined(EIGEN_NO_CUDA) && !defined(__SYCL_DEVICE_ONLY__)
+  // Means we are generating code for the device
+  #define EIGEN_CUDA_ARCH __CUDA_ARCH__
+#endif
+
+#if defined(EIGEN_CUDACC)
+#include <cuda.h>
+  #define EIGEN_CUDA_SDK_VER (CUDA_VERSION * 10)
+#else
+  #define EIGEN_CUDA_SDK_VER 0
+#endif
+
+#if defined(__HIPCC__) && !defined(EIGEN_NO_HIP) && !defined(__SYCL_DEVICE_ONLY__)
+  // Means the compiler is HIPCC (analogous to EIGEN_CUDACC, but for HIP)
+  #define EIGEN_HIPCC __HIPCC__
+
+  // We need to include hip_runtime.h here because it pulls in
+  // ++ hip_common.h which contains the define for  __HIP_DEVICE_COMPILE__
+  // ++ host_defines.h which contains the defines for the __host__ and __device__ macros
+  #include <hip/hip_runtime.h>
+
+  #if defined(__HIP_DEVICE_COMPILE__) && !defined(__SYCL_DEVICE_ONLY__)
+    // analogous to EIGEN_CUDA_ARCH, but for HIP
+    #define EIGEN_HIP_DEVICE_COMPILE __HIP_DEVICE_COMPILE__
+  #endif
+
+  // For HIP (ROCm 3.5 and higher), we need to explicitly set the launch_bounds attribute
+  // value to 1024. The compiler assigns a default value of 256 when the attribute is not
+  // specified. This results in failures on the HIP platform, for cases when a GPU kernel
+  // without an explicit launch_bounds attribute is called with a threads_per_block value
+  // greater than 256.
+  //
+  // This is a regression in functioanlity and is expected to be fixed within the next
+  // couple of ROCm releases (compiler will go back to using 1024 value as the default)
+  //
+  // In the meantime, we will use a "only enabled for HIP" macro to set the launch_bounds
+  // attribute.
+
+  #define EIGEN_HIP_LAUNCH_BOUNDS_1024 __launch_bounds__(1024)
+
+#endif
+
+#if !defined(EIGEN_HIP_LAUNCH_BOUNDS_1024)
+#define EIGEN_HIP_LAUNCH_BOUNDS_1024
+#endif // !defined(EIGEN_HIP_LAUNCH_BOUNDS_1024)
+
+// Unify CUDA/HIPCC
+
+#if defined(EIGEN_CUDACC) || defined(EIGEN_HIPCC)
+//
+// If either EIGEN_CUDACC or EIGEN_HIPCC is defined, then define EIGEN_GPUCC
+//
+#define EIGEN_GPUCC
+//
+// EIGEN_HIPCC implies the HIP compiler and is used to tweak Eigen code for use in HIP kernels
+// EIGEN_CUDACC implies the CUDA compiler and is used to tweak Eigen code for use in CUDA kernels
+//
+// In most cases the same tweaks are required to the Eigen code to enable in both the HIP and CUDA kernels.
+// For those cases, the corresponding code should be guarded with
+//      #if defined(EIGEN_GPUCC)
+// instead of
+//      #if defined(EIGEN_CUDACC) || defined(EIGEN_HIPCC)
+//
+// For cases where the tweak is specific to HIP, the code should be guarded with
+//      #if defined(EIGEN_HIPCC)
+//
+// For cases where the tweak is specific to CUDA, the code should be guarded with
+//      #if defined(EIGEN_CUDACC)
+//
+#endif
+
+#if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIP_DEVICE_COMPILE)
+//
+// If either EIGEN_CUDA_ARCH or EIGEN_HIP_DEVICE_COMPILE is defined, then define EIGEN_GPU_COMPILE_PHASE
+//
+#define EIGEN_GPU_COMPILE_PHASE
+//
+// GPU compilers (HIPCC, NVCC) typically do two passes over the source code,
+//   + one to compile the source for the "host" (ie CPU)
+//   + another to compile the source for the "device" (ie. GPU)
+//
+// Code that needs to enabled only during the either the "host" or "device" compilation phase
+// needs to be guarded with a macro that indicates the current compilation phase
+//
+// EIGEN_HIP_DEVICE_COMPILE implies the device compilation phase in HIP
+// EIGEN_CUDA_ARCH implies the device compilation phase in CUDA
+//
+// In most cases, the "host" / "device" specific code is the same for both HIP and CUDA
+// For those cases, the code should be guarded with
+//       #if defined(EIGEN_GPU_COMPILE_PHASE)
+// instead of
+//       #if defined(EIGEN_CUDA_ARCH) || defined(EIGEN_HIP_DEVICE_COMPILE)
+//
+// For cases where the tweak is specific to HIP, the code should be guarded with
+//      #if defined(EIGEN_HIP_DEVICE_COMPILE)
+//
+// For cases where the tweak is specific to CUDA, the code should be guarded with
+//      #if defined(EIGEN_CUDA_ARCH)
+//
+#endif
+
+/// \internal EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC set to 1 if the architecture
+/// supports Neon vector intrinsics for fp16.
+#if EIGEN_ARCH_ARM_OR_ARM64
+  #ifndef EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC
+    // Clang only supports FP16 on aarch64, and not all intrinsics are available
+    // on A32 anyways even in GCC (e.g. vdiv_f16, vsqrt_f16).
+    #if EIGEN_ARCH_ARM64 && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) && !defined(EIGEN_GPU_COMPILE_PHASE)
+      #define EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC 1
+    #else
+      #define EIGEN_HAS_ARM64_FP16_VECTOR_ARITHMETIC 0
+    #endif
+  #endif
+#endif
+
+/// \internal EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC set to 1 if the architecture
+/// supports Neon scalar intrinsics for fp16.
+#if EIGEN_ARCH_ARM_OR_ARM64
+  #ifndef EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC
+    // Clang only supports FP16 on aarch64, and not all intrinsics are available
+    // on A32 anyways, even in GCC (e.g. vceqh_f16).
+    #if EIGEN_ARCH_ARM64 && defined(__ARM_FEATURE_FP16_SCALAR_ARITHMETIC) && !defined(EIGEN_GPU_COMPILE_PHASE)
+      #define EIGEN_HAS_ARM64_FP16_SCALAR_ARITHMETIC 1
+    #endif
+  #endif
+#endif
+
+#if defined(EIGEN_USE_SYCL) && defined(__SYCL_DEVICE_ONLY__)
+// EIGEN_USE_SYCL is a user-defined macro while __SYCL_DEVICE_ONLY__ is a compiler-defined macro.
+// In most cases we want to check if both macros are defined which can be done using the define below.
+#define SYCL_DEVICE_ONLY
+#endif
+
+//------------------------------------------------------------------------------------------
+// Detect Compiler/Architecture/OS specific features
+//------------------------------------------------------------------------------------------
+
+// Cross compiler wrapper around LLVM's __has_builtin
+#ifdef __has_builtin
+#  define EIGEN_HAS_BUILTIN(x) __has_builtin(x)
+#else
+#  define EIGEN_HAS_BUILTIN(x) 0
+#endif
+
+// A Clang feature extension to determine compiler features.
+// We use it to determine 'cxx_rvalue_references'
+#ifndef __has_feature
+# define __has_feature(x) 0
+#endif
+
+// The macro EIGEN_CPLUSPLUS is a replacement for __cplusplus/_MSVC_LANG that
+// works for both platforms, indicating the C++ standard version number.
+//
+// With MSVC, without defining /Zc:__cplusplus, the __cplusplus macro will
+// report 199711L regardless of the language standard specified via /std.
+// We need to rely on _MSVC_LANG instead, which is only available after
+// VS2015.3.
+#if EIGEN_COMP_MSVC_LANG > 0
+#define EIGEN_CPLUSPLUS EIGEN_COMP_MSVC_LANG
+#elif EIGEN_COMP_MSVC >= 1900
+#define EIGEN_CPLUSPLUS 201103L
+#elif defined(__cplusplus)
+#define EIGEN_CPLUSPLUS __cplusplus
+#else
+#define EIGEN_CPLUSPLUS 0
+#endif
+
+// The macro EIGEN_COMP_CXXVER defines the c++ version expected by the compiler.
+// For instance, if compiling with gcc and -std=c++17, then EIGEN_COMP_CXXVER
+// is defined to 17.
+#if EIGEN_CPLUSPLUS >= 202002L
+  #define EIGEN_COMP_CXXVER 20
+#elif EIGEN_CPLUSPLUS >= 201703L
+  #define EIGEN_COMP_CXXVER 17
+#elif EIGEN_CPLUSPLUS >= 201402L
+  #define EIGEN_COMP_CXXVER 14
+#elif EIGEN_CPLUSPLUS >= 201103L
+  #define EIGEN_COMP_CXXVER 11
+#else
+  #define EIGEN_COMP_CXXVER 03
+#endif
+
+
+// The macros EIGEN_HAS_CXX?? defines a rough estimate of available c++ features
+// but in practice we should not rely on them but rather on the availability of
+// individual features as defined later.
+// This is why there is no EIGEN_HAS_CXX17.
+#if EIGEN_MAX_CPP_VER < 14 || EIGEN_COMP_CXXVER < 14 || \
+  (EIGEN_COMP_MSVC && EIGEN_COMP_MSVC < 1900) || \
+  (EIGEN_COMP_ICC && EIGEN_COMP_ICC < 1500) || \
+  (EIGEN_COMP_NVCC && EIGEN_COMP_NVCC < 80000) || \
+  (EIGEN_COMP_CLANG_STRICT && EIGEN_COMP_CLANG < 390) || \
+  (EIGEN_COMP_CLANGAPPLE && EIGEN_COMP_CLANGAPPLE < 9000000) || \
+  (EIGEN_COMP_GNUC_STRICT && EIGEN_COMP_GNUC < 510)
+#error This compiler appears to be too old to be supported by Eigen
+#endif
+
+// Does the compiler support C99?
+// Need to include <cmath> to make sure _GLIBCXX_USE_C99 gets defined
+#include <cmath>
+#ifndef EIGEN_HAS_C99_MATH
+#if ((defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901))       \
+  || (defined(__GNUC__) && defined(_GLIBCXX_USE_C99)) \
+  || (defined(_LIBCPP_VERSION) && !defined(_MSC_VER)) \
+  || (EIGEN_COMP_MSVC) || defined(SYCL_DEVICE_ONLY))
+  #define EIGEN_HAS_C99_MATH 1
+#else
+  #define EIGEN_HAS_C99_MATH 0
+#endif
+#endif
+
+// Does the compiler support std::hash?
+#ifndef EIGEN_HAS_STD_HASH
+// The std::hash struct is defined in C++11 but is not labelled as a __device__
+// function and is not constexpr, so cannot be used on device.
+#if !defined(EIGEN_GPU_COMPILE_PHASE)
+#define EIGEN_HAS_STD_HASH 1
+#else
+#define EIGEN_HAS_STD_HASH 0
+#endif
+#endif  // EIGEN_HAS_STD_HASH
+
+#ifndef EIGEN_HAS_STD_INVOKE_RESULT
+#if EIGEN_MAX_CPP_VER >= 17 && EIGEN_COMP_CXXVER >= 17
+#define EIGEN_HAS_STD_INVOKE_RESULT 1
+#else
+#define EIGEN_HAS_STD_INVOKE_RESULT 0
+#endif
+#endif
+
+#define EIGEN_CONSTEXPR constexpr
+
+// NOTE: the required Apple's clang version is very conservative
+//       and it could be that XCode 9 works just fine.
+// NOTE: the MSVC version is based on https://en.cppreference.com/w/cpp/compiler_support
+//       and not tested.
+// NOTE: Intel C++ Compiler Classic (icc) Version 19.0 and later supports dynamic allocation
+//       for over-aligned data, but not in a manner that is compatible with Eigen.
+//       See https://gitlab.com/libeigen/eigen/-/issues/2575
+#ifndef EIGEN_HAS_CXX17_OVERALIGN
+#if EIGEN_MAX_CPP_VER>=17 && EIGEN_COMP_CXXVER>=17 && (                 \
+           (EIGEN_COMP_MSVC >= 1912)                                    \
+        || (EIGEN_GNUC_STRICT_AT_LEAST(7,0,0))                          \
+        || (EIGEN_CLANG_STRICT_AT_LEAST(5,0,0))                         \
+        || (EIGEN_COMP_CLANGAPPLE && EIGEN_COMP_CLANGAPPLE >= 10000000) \
+      ) && !EIGEN_COMP_ICC
+#define EIGEN_HAS_CXX17_OVERALIGN 1
+#else
+#define EIGEN_HAS_CXX17_OVERALIGN 0
+#endif
+#endif
+
+#if defined(EIGEN_CUDACC)
+  // While available already with c++11, this is useful mostly starting with c++14 and relaxed constexpr rules
+  #if defined(__NVCC__)
+    // nvcc considers constexpr functions as __host__ __device__ with the option --expt-relaxed-constexpr
+    #ifdef __CUDACC_RELAXED_CONSTEXPR__
+      #define EIGEN_CONSTEXPR_ARE_DEVICE_FUNC
+    #endif
+  #elif defined(__clang__) && defined(__CUDA__) && __has_feature(cxx_relaxed_constexpr)
+    // clang++ always considers constexpr functions as implicitly __host__ __device__
+    #define EIGEN_CONSTEXPR_ARE_DEVICE_FUNC
+  #endif
+#endif
+
+// Does the compiler support the __int128 and __uint128_t extensions for 128-bit
+// integer arithmetic?
+//
+// Clang and GCC define __SIZEOF_INT128__ when these extensions are supported,
+// but we avoid using them in certain cases:
+//
+// * Building using Clang for Windows, where the Clang runtime library has
+//   128-bit support only on LP64 architectures, but Windows is LLP64.
+#ifndef EIGEN_HAS_BUILTIN_INT128
+#if defined(__SIZEOF_INT128__) && !(EIGEN_OS_WIN && EIGEN_COMP_CLANG)
+#define EIGEN_HAS_BUILTIN_INT128 1
+#else
+#define EIGEN_HAS_BUILTIN_INT128 0
+#endif
+#endif
+
+//------------------------------------------------------------------------------------------
+// Preprocessor programming helpers
+//------------------------------------------------------------------------------------------
+
+// This macro can be used to prevent from macro expansion, e.g.:
+//   std::max EIGEN_NOT_A_MACRO(a,b)
+#define EIGEN_NOT_A_MACRO
+
+#define EIGEN_DEBUG_VAR(x) std::cerr << #x << " = " << x << std::endl;
+
+// concatenate two tokens
+#define EIGEN_CAT2(a,b) a ## b
+#define EIGEN_CAT(a,b) EIGEN_CAT2(a,b)
+
+#define EIGEN_COMMA ,
+
+// convert a token to a string
+#define EIGEN_MAKESTRING2(a) #a
+#define EIGEN_MAKESTRING(a) EIGEN_MAKESTRING2(a)
+
+// EIGEN_STRONG_INLINE is a stronger version of the inline, using __forceinline on MSVC,
+// but it still doesn't use GCC's always_inline. This is useful in (common) situations where MSVC needs forceinline
+// but GCC is still doing fine with just inline.
+#ifndef EIGEN_STRONG_INLINE
+#if (EIGEN_COMP_MSVC || EIGEN_COMP_ICC) && !defined(EIGEN_GPUCC)
+#define EIGEN_STRONG_INLINE __forceinline
+#else
+#define EIGEN_STRONG_INLINE inline
+#endif
+#endif
+
+// EIGEN_ALWAYS_INLINE is the strongest, it has the effect of making the function inline and adding every possible
+// attribute to maximize inlining. This should only be used when really necessary: in particular,
+// it uses __attribute__((always_inline)) on GCC, which most of the time is useless and can severely harm compile times.
+// FIXME with the always_inline attribute,
+#if EIGEN_COMP_GNUC && !defined(SYCL_DEVICE_ONLY)
+#define EIGEN_ALWAYS_INLINE __attribute__((always_inline)) inline
+#else
+#define EIGEN_ALWAYS_INLINE EIGEN_STRONG_INLINE
+#endif
+
+#if EIGEN_COMP_GNUC
+#define EIGEN_DONT_INLINE __attribute__((noinline))
+#elif EIGEN_COMP_MSVC
+#define EIGEN_DONT_INLINE __declspec(noinline)
+#else
+#define EIGEN_DONT_INLINE
+#endif
+
+#if EIGEN_COMP_GNUC
+#define EIGEN_PERMISSIVE_EXPR __extension__
+#else
+#define EIGEN_PERMISSIVE_EXPR
+#endif
+
+// GPU stuff
+
+// Disable some features when compiling with GPU compilers (SYCL/HIPCC)
+#if defined(SYCL_DEVICE_ONLY) || defined(EIGEN_HIP_DEVICE_COMPILE)
+  // Do not try asserts on device code
+  #ifndef EIGEN_NO_DEBUG
+  #define EIGEN_NO_DEBUG
+  #endif
+
+  #ifdef EIGEN_INTERNAL_DEBUGGING
+  #undef EIGEN_INTERNAL_DEBUGGING
+  #endif
+#endif
+
+// No exceptions on device.
+#if defined(SYCL_DEVICE_ONLY) || defined(EIGEN_GPU_COMPILE_PHASE)
+  #ifdef EIGEN_EXCEPTIONS
+  #undef EIGEN_EXCEPTIONS
+  #endif
+#endif
+
+#if defined(SYCL_DEVICE_ONLY)
+  #ifndef EIGEN_DONT_VECTORIZE
+    #define EIGEN_DONT_VECTORIZE
+  #endif
+  #define EIGEN_DEVICE_FUNC __attribute__((flatten)) __attribute__((always_inline))
+// All functions callable from CUDA/HIP code must be qualified with __device__
+#elif defined(EIGEN_GPUCC)
+    #define EIGEN_DEVICE_FUNC __host__ __device__
+#else
+  #define EIGEN_DEVICE_FUNC
+#endif
+
+
+// this macro allows to get rid of linking errors about multiply defined functions.
+//  - static is not very good because it prevents definitions from different object files to be merged.
+//           So static causes the resulting linked executable to be bloated with multiple copies of the same function.
+//  - inline is not perfect either as it unwantedly hints the compiler toward inlining the function.
+#define EIGEN_DECLARE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_DEVICE_FUNC
+#define EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS EIGEN_DEVICE_FUNC inline
+
+#ifdef NDEBUG
+# ifndef EIGEN_NO_DEBUG
+#  define EIGEN_NO_DEBUG
+# endif
+#endif
+
+// eigen_assert can be overridden
+#ifndef eigen_assert
+#define eigen_assert(x) eigen_plain_assert(x)
+#endif
+
+#ifdef EIGEN_INTERNAL_DEBUGGING
+#define eigen_internal_assert(x) eigen_assert(x)
+#else
+#define eigen_internal_assert(x) ((void)0)
+#endif
+
+#if defined(EIGEN_NO_DEBUG) || (defined(EIGEN_GPU_COMPILE_PHASE) && defined(EIGEN_NO_DEBUG_GPU))
+#define EIGEN_ONLY_USED_FOR_DEBUG(x) EIGEN_UNUSED_VARIABLE(x)
+#else
+#define EIGEN_ONLY_USED_FOR_DEBUG(x)
+#endif
+
+#ifndef EIGEN_NO_DEPRECATED_WARNING
+  #if EIGEN_COMP_GNUC
+    #define EIGEN_DEPRECATED __attribute__((deprecated))
+  #elif EIGEN_COMP_MSVC
+    #define EIGEN_DEPRECATED __declspec(deprecated)
+  #else
+    #define EIGEN_DEPRECATED
+  #endif
+#else
+  #define EIGEN_DEPRECATED
+#endif
+
+#if EIGEN_COMP_GNUC
+#define EIGEN_UNUSED __attribute__((unused))
+#else
+#define EIGEN_UNUSED
+#endif
+
+#if EIGEN_COMP_GNUC
+  #define EIGEN_PRAGMA(tokens) _Pragma(#tokens)
+  #define EIGEN_DIAGNOSTICS(tokens) EIGEN_PRAGMA(GCC diagnostic tokens)
+  #define EIGEN_DIAGNOSTICS_OFF(msc, gcc) EIGEN_DIAGNOSTICS(gcc)
+#elif EIGEN_COMP_MSVC
+  #define EIGEN_PRAGMA(tokens) __pragma(tokens)
+  #define EIGEN_DIAGNOSTICS(tokens) EIGEN_PRAGMA(warning(tokens))
+  #define EIGEN_DIAGNOSTICS_OFF(msc, gcc) EIGEN_DIAGNOSTICS(msc)
+#else
+  #define EIGEN_PRAGMA(tokens)
+  #define EIGEN_DIAGNOSTICS(tokens)
+  #define EIGEN_DIAGNOSTICS_OFF(msc, gcc)
+#endif
+
+#define EIGEN_DISABLE_DEPRECATED_WARNING EIGEN_DIAGNOSTICS_OFF(disable : 4996, ignored "-Wdeprecated-declarations")
+
+// Suppresses 'unused variable' warnings.
+namespace Eigen {
+  namespace internal {
+    template<typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE constexpr void ignore_unused_variable(const T&) {}
+  }
+}
+#define EIGEN_UNUSED_VARIABLE(var) Eigen::internal::ignore_unused_variable(var);
+
+#if !defined(EIGEN_ASM_COMMENT)
+  #if EIGEN_COMP_GNUC && (EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM_OR_ARM64)
+    #define EIGEN_ASM_COMMENT(X)  __asm__("#" X)
+  #else
+    #define EIGEN_ASM_COMMENT(X)
+  #endif
+#endif
+
+
+// Acts as a barrier preventing operations involving `X` from crossing. This
+// occurs, for example, in the fast rounding trick where a magic constant is
+// added then subtracted, which is otherwise compiled away with -ffast-math.
+//
+// See bug 1674
+#if !defined(EIGEN_OPTIMIZATION_BARRIER)
+  #if EIGEN_COMP_GNUC
+    // According to https://gcc.gnu.org/onlinedocs/gcc/Constraints.html:
+    //   X: Any operand whatsoever.
+    //   r: A register operand is allowed provided that it is in a general
+    //      register.
+    //   g: Any register, memory or immediate integer operand is allowed, except
+    //      for registers that are not general registers.
+    //   w: (AArch32/AArch64) Floating point register, Advanced SIMD vector
+    //      register or SVE vector register.
+    //   x: (SSE) Any SSE register.
+    //      (AArch64) Like w, but restricted to registers 0 to 15 inclusive.
+    //   v: (PowerPC) An Altivec vector register.
+    //   wa:(PowerPC) A VSX register.
+    //
+    // "X" (uppercase) should work for all cases, though this seems to fail for
+    // some versions of GCC for arm/aarch64 with
+    //   "error: inconsistent operand constraints in an 'asm'"
+    // Clang x86_64/arm/aarch64 seems to require "g" to support both scalars and
+    // vectors, otherwise
+    //   "error: non-trivial scalar-to-vector conversion, possible invalid
+    //    constraint for vector type"
+    //
+    // GCC for ppc64le generates an internal compiler error with x/X/g.
+    // GCC for AVX generates an internal compiler error with X.
+    //
+    // Tested on icc/gcc/clang for sse, avx, avx2, avx512dq
+    //           gcc for arm, aarch64,
+    //           gcc for ppc64le,
+    // both vectors and scalars.
+    //
+    // Note that this is restricted to plain types - this will not work
+    // directly for std::complex<T>, Eigen::half, Eigen::bfloat16. For these,
+    // you will need to apply to the underlying POD type.
+    #if EIGEN_ARCH_PPC && EIGEN_COMP_GNUC_STRICT
+      // This seems to be broken on clang. Packet4f is loaded into a single
+      //   register rather than a vector, zeroing out some entries. Integer
+      //   types also generate a compile error.
+      #if EIGEN_OS_MAC
+        // General, Altivec for Apple (VSX were added in ISA v2.06):
+        #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+r,v" (X));
+      #else
+        // General, Altivec, VSX otherwise:
+        #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+r,v,wa" (X));
+      #endif
+    #elif EIGEN_ARCH_ARM_OR_ARM64
+      #ifdef __ARM_FP
+        // General, VFP or NEON.
+        // Clang doesn't like "r",
+        //    error: non-trivial scalar-to-vector conversion, possible invalid
+        //           constraint for vector typ
+        #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+g,w" (X));
+      #else
+        // Arm without VFP or NEON.
+        // "w" constraint will not compile.
+        #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+g" (X));
+      #endif
+    #elif EIGEN_ARCH_i386_OR_x86_64
+      // General, SSE.
+      #define EIGEN_OPTIMIZATION_BARRIER(X)  __asm__  ("" : "+g,x" (X));
+    #else
+      // Not implemented for other architectures.
+      #define EIGEN_OPTIMIZATION_BARRIER(X)
+    #endif
+  #else
+    // Not implemented for other compilers.
+    #define EIGEN_OPTIMIZATION_BARRIER(X)
+  #endif
+#endif
+
+#if EIGEN_COMP_MSVC
+  // NOTE MSVC often gives C4127 warnings with compiletime if statements. See bug 1362.
+  // This workaround is ugly, but it does the job.
+#  define EIGEN_CONST_CONDITIONAL(cond)  (void)0, cond
+#else
+#  define EIGEN_CONST_CONDITIONAL(cond)  cond
+#endif
+
+#ifdef EIGEN_DONT_USE_RESTRICT_KEYWORD
+  #define EIGEN_RESTRICT
+#endif
+#ifndef EIGEN_RESTRICT
+  #define EIGEN_RESTRICT __restrict
+#endif
+
+
+#ifndef EIGEN_DEFAULT_IO_FORMAT
+#ifdef EIGEN_MAKING_DOCS
+// format used in Eigen's documentation
+// needed to define it here as escaping characters in CMake add_definition's argument seems very problematic.
+#define EIGEN_DEFAULT_IO_FORMAT Eigen::IOFormat(3, 0, " ", "\n", "", "")
+#else
+#define EIGEN_DEFAULT_IO_FORMAT Eigen::IOFormat()
+#endif
+#endif
+
+// just an empty macro !
+#define EIGEN_EMPTY
+
+
+// When compiling CUDA/HIP device code with NVCC or HIPCC
+// pull in math functions from the global namespace.
+// In host mode, and when device code is compiled with clang,
+// use the std versions.
+#if (defined(EIGEN_CUDA_ARCH) && defined(__NVCC__)) || defined(EIGEN_HIP_DEVICE_COMPILE)
+  #define EIGEN_USING_STD(FUNC) using ::FUNC;
+#else
+  #define EIGEN_USING_STD(FUNC) using std::FUNC;
+#endif
+
+#if EIGEN_COMP_MSVC_STRICT && EIGEN_COMP_NVCC
+  // Wwhen compiling with NVCC, using the base operator is necessary,
+  //   otherwise we get duplicate definition errors
+  // For later MSVC versions, we require explicit operator= definition, otherwise we get
+  //   use of implicitly deleted operator errors.
+  // (cf Bugs 920, 1000, 1324, 2291)
+  #define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
+    using Base::operator =;
+#elif EIGEN_COMP_CLANG // workaround clang bug (see http://forum.kde.org/viewtopic.php?f=74&t=102653)
+  #define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
+    using Base::operator =; \
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) { Base::operator=(other); return *this; } \
+    template <typename OtherDerived> \
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const DenseBase<OtherDerived>& other) { Base::operator=(other.derived()); return *this; }
+#else
+  #define EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
+    using Base::operator =; \
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Derived& other) \
+    { \
+      Base::operator=(other); \
+      return *this; \
+    }
+#endif
+
+
+/**
+ * \internal
+ * \brief Macro to explicitly define the default copy constructor.
+ * This is necessary, because the implicit definition is deprecated if the copy-assignment is overridden.
+ */
+#define EIGEN_DEFAULT_COPY_CONSTRUCTOR(CLASS) EIGEN_DEVICE_FUNC CLASS(const CLASS&) = default;
+
+
+
+/** \internal
+ * \brief Macro to manually inherit assignment operators.
+ * This is necessary, because the implicitly defined assignment operator gets deleted when a custom operator= is defined.
+ * With C++11 or later this also default-implements the copy-constructor
+ */
+#define EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Derived)  \
+    EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Derived) \
+    EIGEN_DEFAULT_COPY_CONSTRUCTOR(Derived)
+
+/** \internal
+ * \brief Macro to manually define default constructors and destructors.
+ * This is necessary when the copy constructor is re-defined.
+ * For empty helper classes this should usually be protected, to avoid accidentally creating empty objects.
+ *
+ * Hiding the default destructor lead to problems in C++03 mode together with boost::multiprecision
+ */
+#define EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(Derived)  \
+    EIGEN_DEVICE_FUNC Derived() = default; \
+    EIGEN_DEVICE_FUNC ~Derived() = default;
+
+
+
+
+
+/**
+* Just a side note. Commenting within defines works only by documenting
+* behind the object (via '!<'). Comments cannot be multi-line and thus
+* we have these extra long lines. What is confusing doxygen over here is
+* that we use '\' and basically have a bunch of typedefs with their
+* documentation in a single line.
+**/
+
+#define EIGEN_GENERIC_PUBLIC_INTERFACE(Derived) \
+  typedef typename Eigen::internal::traits<Derived>::Scalar Scalar; /*!< \brief Numeric type, e.g. float, double, int or std::complex<float>. */ \
+  typedef typename Eigen::NumTraits<Scalar>::Real RealScalar; /*!< \brief The underlying numeric type for composed scalar types. \details In cases where Scalar is e.g. std::complex<T>, T were corresponding to RealScalar. */ \
+  typedef typename Base::CoeffReturnType CoeffReturnType; /*!< \brief The return type for coefficient access. \details Depending on whether the object allows direct coefficient access (e.g. for a MatrixXd), this type is either 'const Scalar&' or simply 'Scalar' for objects that do not allow direct coefficient access. */ \
+  typedef typename Eigen::internal::ref_selector<Derived>::type Nested; \
+  typedef typename Eigen::internal::traits<Derived>::StorageKind StorageKind; \
+  typedef typename Eigen::internal::traits<Derived>::StorageIndex StorageIndex; \
+  enum CompileTimeTraits \
+      { RowsAtCompileTime = Eigen::internal::traits<Derived>::RowsAtCompileTime, \
+        ColsAtCompileTime = Eigen::internal::traits<Derived>::ColsAtCompileTime, \
+        Flags = Eigen::internal::traits<Derived>::Flags, \
+        SizeAtCompileTime = Base::SizeAtCompileTime, \
+        MaxSizeAtCompileTime = Base::MaxSizeAtCompileTime, \
+        IsVectorAtCompileTime = Base::IsVectorAtCompileTime }; \
+  using Base::derived; \
+  using Base::const_cast_derived;
+
+
+// FIXME Maybe the EIGEN_DENSE_PUBLIC_INTERFACE could be removed as importing PacketScalar is rarely needed
+#define EIGEN_DENSE_PUBLIC_INTERFACE(Derived) \
+  EIGEN_GENERIC_PUBLIC_INTERFACE(Derived) \
+  typedef typename Base::PacketScalar PacketScalar;
+
+
+#if EIGEN_HAS_BUILTIN(__builtin_expect) || EIGEN_COMP_GNUC
+#define EIGEN_PREDICT_FALSE(x) (__builtin_expect(x, false))
+#define EIGEN_PREDICT_TRUE(x) (__builtin_expect(false || (x), true))
+#else
+#define EIGEN_PREDICT_FALSE(x) (x)
+#define EIGEN_PREDICT_TRUE(x) (x)
+#endif
+
+// the expression type of a standard coefficient wise binary operation
+#define EIGEN_CWISE_BINARY_RETURN_TYPE(LHS,RHS,OPNAME) \
+    CwiseBinaryOp< \
+      EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)< \
+          typename internal::traits<LHS>::Scalar, \
+          typename internal::traits<RHS>::Scalar \
+      >, \
+      const LHS, \
+      const RHS \
+    >
+
+#define EIGEN_MAKE_CWISE_BINARY_OP(METHOD,OPNAME) \
+  template<typename OtherDerived> \
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,OPNAME) \
+  (METHOD)(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
+  { \
+    return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,OPNAME)(derived(), other.derived()); \
+  }
+
+#define EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,TYPEA,TYPEB) \
+  (Eigen::internal::has_ReturnType<Eigen::ScalarBinaryOpTraits<TYPEA,TYPEB,EIGEN_CAT(EIGEN_CAT(Eigen::internal::scalar_,OPNAME),_op)<TYPEA,TYPEB>  > >::value)
+
+#define EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(EXPR,SCALAR,OPNAME) \
+  CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)<typename internal::traits<EXPR>::Scalar,SCALAR>, const EXPR, \
+                const typename internal::plain_constant_type<EXPR,SCALAR>::type>
+
+#define EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(SCALAR,EXPR,OPNAME) \
+  CwiseBinaryOp<EIGEN_CAT(EIGEN_CAT(internal::scalar_,OPNAME),_op)<SCALAR,typename internal::traits<EXPR>::Scalar>, \
+                const typename internal::plain_constant_type<EXPR,SCALAR>::type, const EXPR>
+
+#define EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(METHOD,OPNAME) \
+  template <typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE \
+  const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,typename internal::promote_scalar_arg<Scalar EIGEN_COMMA T EIGEN_COMMA EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,Scalar,T)>::type,OPNAME)\
+  (METHOD)(const T& scalar) const { \
+    typedef typename internal::promote_scalar_arg<Scalar,T,EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,Scalar,T)>::type PromotedT; \
+    return EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,PromotedT,OPNAME)(derived(), \
+           typename internal::plain_constant_type<Derived,PromotedT>::type(derived().rows(), derived().cols(), internal::scalar_constant_op<PromotedT>(scalar))); \
+  }
+
+#define EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(METHOD,OPNAME) \
+  template <typename T> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE friend \
+  const EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(typename internal::promote_scalar_arg<Scalar EIGEN_COMMA T EIGEN_COMMA EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,T,Scalar)>::type,Derived,OPNAME) \
+  (METHOD)(const T& scalar, const StorageBaseType& matrix) { \
+    typedef typename internal::promote_scalar_arg<Scalar,T,EIGEN_SCALAR_BINARY_SUPPORTED(OPNAME,T,Scalar)>::type PromotedT; \
+    return EIGEN_SCALAR_BINARYOP_EXPR_RETURN_TYPE(PromotedT,Derived,OPNAME)( \
+           typename internal::plain_constant_type<Derived,PromotedT>::type(matrix.derived().rows(), matrix.derived().cols(), internal::scalar_constant_op<PromotedT>(scalar)), matrix.derived()); \
+  }
+
+#define EIGEN_MAKE_SCALAR_BINARY_OP(METHOD,OPNAME) \
+  EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(METHOD,OPNAME) \
+  EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(METHOD,OPNAME)
+
+
+#if (defined(_CPPUNWIND) || defined(__EXCEPTIONS)) && !defined(EIGEN_CUDA_ARCH) && !defined(EIGEN_EXCEPTIONS) && !defined(EIGEN_USE_SYCL) && !defined(EIGEN_HIP_DEVICE_COMPILE)
+  #define EIGEN_EXCEPTIONS
+#endif
+
+
+#ifdef EIGEN_EXCEPTIONS
+#  define EIGEN_THROW_X(X) throw X
+#  define EIGEN_THROW throw
+#  define EIGEN_TRY try
+#  define EIGEN_CATCH(X) catch (X)
+#else
+#  if defined(EIGEN_CUDA_ARCH)
+#    define EIGEN_THROW_X(X) asm("trap;")
+#    define EIGEN_THROW asm("trap;")
+#  elif defined(EIGEN_HIP_DEVICE_COMPILE)
+#    define EIGEN_THROW_X(X) asm("s_trap 0")
+#    define EIGEN_THROW asm("s_trap 0")
+#  else
+#    define EIGEN_THROW_X(X) std::abort()
+#    define EIGEN_THROW std::abort()
+#  endif
+#  define EIGEN_TRY if (true)
+#  define EIGEN_CATCH(X) else
+#endif
+
+
+#define EIGEN_NOEXCEPT noexcept
+#define EIGEN_NOEXCEPT_IF(x) noexcept(x)
+#define EIGEN_NO_THROW noexcept(true)
+#define EIGEN_EXCEPTION_SPEC(X) noexcept(false)
+
+
+// The all function is used to enable a variadic version of eigen_assert which can take a parameter pack as its input.
+namespace Eigen {
+namespace internal {
+
+EIGEN_DEVICE_FUNC inline bool all(){ return true; }
+
+template<typename T, typename ...Ts>
+EIGEN_DEVICE_FUNC bool all(T t, Ts ... ts){ return t && all(ts...); }
+
+}
+}
+
+// provide override and final specifiers if they are available:
+#define EIGEN_OVERRIDE override
+#define EIGEN_FINAL final
+
+// Wrapping #pragma unroll in a macro since it is required for SYCL
+#if defined(SYCL_DEVICE_ONLY)
+  #if defined(_MSC_VER)
+    #define EIGEN_UNROLL_LOOP __pragma(unroll)
+  #else
+    #define EIGEN_UNROLL_LOOP _Pragma("unroll")
+  #endif
+#else
+  #define EIGEN_UNROLL_LOOP
+#endif
+
+// Notice: Use this macro with caution. The code in the if body should still
+// compile with C++14.
+#if defined(EIGEN_HAS_CXX17_IFCONSTEXPR)
+#define EIGEN_IF_CONSTEXPR(X) if constexpr (X)
+#else
+#define EIGEN_IF_CONSTEXPR(X) if (X)
+#endif
+
+#endif // EIGEN_MACROS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/MaxSizeVector.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/MaxSizeVector.h
new file mode 100644
index 0000000000000000000000000000000000000000..ca0e3d1b9139f74a6c6c18154d7f53006970d30a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/MaxSizeVector.h
@@ -0,0 +1,158 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_FIXEDSIZEVECTOR_H
+#define EIGEN_FIXEDSIZEVECTOR_H
+
+namespace Eigen {
+
+/** \class MaxSizeVector
+  * \ingroup Core
+  *
+  * \brief The MaxSizeVector class.
+  *
+  * The %MaxSizeVector provides a subset of std::vector functionality.
+  *
+  * The goal is to provide basic std::vector operations when using
+  * std::vector is not an option (e.g. on GPU or when compiling using
+  * FMA/AVX, as this can cause either compilation failures or illegal
+  * instruction failures).
+  *
+  * Beware: The constructors are not API compatible with these of
+  * std::vector.
+  */
+template <typename T>
+class MaxSizeVector {
+  static const size_t alignment = internal::plain_enum_max(EIGEN_ALIGNOF(T), sizeof(void*));
+ public:
+  // Construct a new MaxSizeVector, reserve n elements.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  explicit MaxSizeVector(size_t n)
+      : reserve_(n), size_(0),
+        data_(static_cast<T*>(internal::handmade_aligned_malloc(n * sizeof(T), alignment))) {
+  }
+
+  // Construct a new MaxSizeVector, reserve and resize to n.
+  // Copy the init value to all elements.
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  MaxSizeVector(size_t n, const T& init)
+      : reserve_(n), size_(n),
+        data_(static_cast<T*>(internal::handmade_aligned_malloc(n * sizeof(T), alignment))) {
+    size_t i = 0;
+    EIGEN_TRY
+    {
+      for(; i < size_; ++i) { new (&data_[i]) T(init); }
+    }
+    EIGEN_CATCH(...)
+    {
+      // Construction failed, destruct in reverse order:
+      for(; (i+1) > 0; --i) { data_[i-1].~T(); }
+      internal::handmade_aligned_free(data_);
+      EIGEN_THROW;
+    }
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  ~MaxSizeVector() {
+    for (size_t i = size_; i > 0; --i) {
+      data_[i-1].~T();
+    }
+    internal::handmade_aligned_free(data_);
+  }
+
+  void resize(size_t n) {
+    eigen_assert(n <= reserve_);
+    for (; size_ < n; ++size_) {
+      new (&data_[size_]) T;
+    }
+    for (; size_ > n; --size_) {
+      data_[size_-1].~T();
+    }
+    eigen_assert(size_ == n);
+  }
+
+  // Append new elements (up to reserved size).
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void push_back(const T& t) {
+    eigen_assert(size_ < reserve_);
+    new (&data_[size_++]) T(t);
+  }
+
+  // For C++03 compatibility this only takes one argument
+  template<class X>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void emplace_back(const X& x) {
+    eigen_assert(size_ < reserve_);
+    new (&data_[size_++]) T(x);
+  }
+
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const T& operator[] (size_t i) const {
+    eigen_assert(i < size_);
+    return data_[i];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  T& operator[] (size_t i) {
+    eigen_assert(i < size_);
+    return data_[i];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  T& back() {
+    eigen_assert(size_ > 0);
+    return data_[size_ - 1];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const T& back() const {
+    eigen_assert(size_ > 0);
+    return data_[size_ - 1];
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void pop_back() {
+    eigen_assert(size_ > 0);
+    data_[--size_].~T();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  size_t size() const { return size_; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  bool empty() const { return size_ == 0; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  T* data() { return data_; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const T* data() const { return data_; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  T* begin() { return data_; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  T* end() { return data_ + size_; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const T* begin() const { return data_; }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const T* end() const { return data_ + size_; }
+
+ private:
+  size_t reserve_;
+  size_t size_;
+  T* data_;
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_FIXEDSIZEVECTOR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Memory.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Memory.h
new file mode 100644
index 0000000000000000000000000000000000000000..f4217e261dd64013f0a95d00a4ce62a9988e23ae
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Memory.h
@@ -0,0 +1,1274 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009 Kenneth Riddile <kfriddile@yahoo.com>
+// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>
+// Copyright (C) 2010 Thomas Capricelli <orzel@freehackers.org>
+// Copyright (C) 2013 Pavel Holoborodko <pavel@holoborodko.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+
+/*****************************************************************************
+*** Platform checks for aligned malloc functions                           ***
+*****************************************************************************/
+
+#ifndef EIGEN_MEMORY_H
+#define EIGEN_MEMORY_H
+
+#ifndef EIGEN_MALLOC_ALREADY_ALIGNED
+
+// Try to determine automatically if malloc is already aligned.
+
+// On 64-bit systems, glibc's malloc returns 16-byte-aligned pointers, see:
+//   http://www.gnu.org/s/libc/manual/html_node/Aligned-Memory-Blocks.html
+// This is true at least since glibc 2.8.
+// This leaves the question how to detect 64-bit. According to this document,
+//   http://gcc.fyxm.net/summit/2003/Porting%20to%2064%20bit.pdf
+// page 114, "[The] LP64 model [...] is used by all 64-bit UNIX ports" so it's indeed
+// quite safe, at least within the context of glibc, to equate 64-bit with LP64.
+#if defined(__GLIBC__) && ((__GLIBC__>=2 && __GLIBC_MINOR__ >= 8) || __GLIBC__>2) \
+ && defined(__LP64__) && ! defined( __SANITIZE_ADDRESS__ ) && (EIGEN_DEFAULT_ALIGN_BYTES == 16)
+  #define EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED 1
+#else
+  #define EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED 0
+#endif
+
+// FreeBSD 6 seems to have 16-byte aligned malloc
+//   See http://svn.freebsd.org/viewvc/base/stable/6/lib/libc/stdlib/malloc.c?view=markup
+// FreeBSD 7 seems to have 16-byte aligned malloc except on ARM and MIPS architectures
+//   See http://svn.freebsd.org/viewvc/base/stable/7/lib/libc/stdlib/malloc.c?view=markup
+#if defined(__FreeBSD__) && !(EIGEN_ARCH_ARM || EIGEN_ARCH_MIPS) && (EIGEN_DEFAULT_ALIGN_BYTES == 16)
+  #define EIGEN_FREEBSD_MALLOC_ALREADY_ALIGNED 1
+#else
+  #define EIGEN_FREEBSD_MALLOC_ALREADY_ALIGNED 0
+#endif
+
+#if (EIGEN_OS_MAC && (EIGEN_DEFAULT_ALIGN_BYTES == 16))     \
+ || (EIGEN_OS_WIN64 && (EIGEN_DEFAULT_ALIGN_BYTES == 16))   \
+ || EIGEN_GLIBC_MALLOC_ALREADY_ALIGNED              \
+ || EIGEN_FREEBSD_MALLOC_ALREADY_ALIGNED
+  #define EIGEN_MALLOC_ALREADY_ALIGNED 1
+#else
+  #define EIGEN_MALLOC_ALREADY_ALIGNED 0
+#endif
+
+#endif
+
+#ifndef EIGEN_MALLOC_CHECK_THREAD_LOCAL
+
+// Check whether we can use the thread_local keyword to allow or disallow
+// allocating memory with per-thread granularity, by means of the
+// set_is_malloc_allowed() function.
+#ifndef EIGEN_AVOID_THREAD_LOCAL
+
+#if ((EIGEN_COMP_GNUC) || __has_feature(cxx_thread_local) || EIGEN_COMP_MSVC >= 1900) && !defined(EIGEN_GPU_COMPILE_PHASE)
+#define EIGEN_MALLOC_CHECK_THREAD_LOCAL thread_local
+#else
+#define EIGEN_MALLOC_CHECK_THREAD_LOCAL
+#endif
+
+#else // EIGEN_AVOID_THREAD_LOCAL
+#define EIGEN_MALLOC_CHECK_THREAD_LOCAL
+#endif // EIGEN_AVOID_THREAD_LOCAL
+
+#endif
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/*****************************************************************************
+*** Implementation of portable aligned versions of malloc/free/realloc     ***
+*****************************************************************************/
+
+#ifdef EIGEN_NO_MALLOC
+EIGEN_DEVICE_FUNC inline void check_that_malloc_is_allowed()
+{
+  eigen_assert(false && "heap allocation is forbidden (EIGEN_NO_MALLOC is defined)");
+}
+#elif defined EIGEN_RUNTIME_NO_MALLOC
+EIGEN_DEVICE_FUNC inline bool is_malloc_allowed_impl(bool update, bool new_value = false)
+{
+  EIGEN_MALLOC_CHECK_THREAD_LOCAL static bool value = true;
+  if (update == 1)
+    value = new_value;
+  return value;
+}
+EIGEN_DEVICE_FUNC inline bool is_malloc_allowed() { return is_malloc_allowed_impl(false); }
+EIGEN_DEVICE_FUNC inline bool set_is_malloc_allowed(bool new_value) { return is_malloc_allowed_impl(true, new_value); }
+EIGEN_DEVICE_FUNC inline void check_that_malloc_is_allowed()
+{
+  eigen_assert(is_malloc_allowed() && "heap allocation is forbidden (EIGEN_RUNTIME_NO_MALLOC is defined and g_is_malloc_allowed is false)");
+}
+#else
+EIGEN_DEVICE_FUNC inline void check_that_malloc_is_allowed()
+{}
+#endif
+
+
+EIGEN_DEVICE_FUNC
+inline void throw_std_bad_alloc()
+{
+  #ifdef EIGEN_EXCEPTIONS
+    throw std::bad_alloc();
+  #else
+    std::size_t huge = static_cast<std::size_t>(-1);
+    #if defined(EIGEN_HIPCC)
+    //
+    // calls to "::operator new" are to be treated as opaque function calls (i.e no inlining),
+    // and as a consequence the code in the #else block triggers the hipcc warning :
+    // "no overloaded function has restriction specifiers that are compatible with the ambient context"
+    //
+    // "throw_std_bad_alloc" has the EIGEN_DEVICE_FUNC attribute, so it seems that hipcc expects
+    // the same on "operator new"
+    // Reverting code back to the old version in this #if block for the hipcc compiler
+    //
+    new int[huge];
+    #else
+    void* unused = ::operator new(huge);
+    EIGEN_UNUSED_VARIABLE(unused);
+    #endif
+  #endif
+}
+
+/*****************************************************************************
+*** Implementation of handmade aligned functions                           ***
+*****************************************************************************/
+
+/* ----- Hand made implementations of aligned malloc/free and realloc ----- */
+
+/** \internal Like malloc, but the returned pointer is guaranteed to be aligned to `alignment`.
+  * Fast, but wastes `alignment` additional bytes of memory. Does not throw any exception.
+  */
+EIGEN_DEVICE_FUNC inline void* handmade_aligned_malloc(std::size_t size, std::size_t alignment = EIGEN_DEFAULT_ALIGN_BYTES)
+{
+  eigen_assert(alignment >= sizeof(void*) && alignment <= 128 && (alignment & (alignment-1)) == 0 && "Alignment must be at least sizeof(void*), less than or equal to 128, and a power of 2");
+
+  check_that_malloc_is_allowed();
+  EIGEN_USING_STD(malloc)
+  void* original = malloc(size + alignment);
+  if (original == 0) return 0;
+  uint8_t offset = static_cast<uint8_t>(alignment - (reinterpret_cast<std::size_t>(original) & (alignment - 1)));
+  void* aligned = static_cast<void*>(static_cast<uint8_t*>(original) + offset);
+  *(static_cast<uint8_t*>(aligned) - 1) = offset;
+  return aligned;
+}
+
+/** \internal Frees memory allocated with handmade_aligned_malloc */
+EIGEN_DEVICE_FUNC inline void handmade_aligned_free(void *ptr)
+{
+  if (ptr) {
+    uint8_t offset = static_cast<uint8_t>(*(static_cast<uint8_t*>(ptr) - 1));
+    void* original = static_cast<void*>(static_cast<uint8_t*>(ptr) - offset);
+
+    check_that_malloc_is_allowed();
+    EIGEN_USING_STD(free)
+    free(original);
+  }
+}
+
+/** \internal
+  * \brief Reallocates aligned memory.
+  * Since we know that our handmade version is based on std::malloc
+  * we can use std::realloc to implement efficient reallocation.
+  */
+EIGEN_DEVICE_FUNC inline void* handmade_aligned_realloc(void* ptr, std::size_t new_size, std::size_t old_size, std::size_t alignment = EIGEN_DEFAULT_ALIGN_BYTES)
+{
+  if (ptr == nullptr) return handmade_aligned_malloc(new_size, alignment);
+  uint8_t old_offset = *(static_cast<uint8_t*>(ptr) - 1);
+  void* old_original = static_cast<uint8_t*>(ptr) - old_offset;
+
+  check_that_malloc_is_allowed();
+  EIGEN_USING_STD(realloc)
+  void* original = realloc(old_original, new_size + alignment);
+  if (original == nullptr) return nullptr;
+  if (original == old_original) return ptr;
+  uint8_t offset = static_cast<uint8_t>(alignment - (reinterpret_cast<std::size_t>(original) & (alignment - 1)));
+  void* aligned = static_cast<void*>(static_cast<uint8_t*>(original) + offset);
+  if (offset != old_offset) {
+    const void* src = static_cast<const void*>(static_cast<uint8_t*>(original) + old_offset);
+    std::size_t count = (std::min)(new_size, old_size);
+    std::memmove(aligned, src, count);
+  }
+  *(static_cast<uint8_t*>(aligned) - 1) = offset;
+  return aligned;
+}
+
+/** \internal Allocates \a size bytes. The returned pointer is guaranteed to have 16 or 32 bytes alignment depending on the requirements.
+  * On allocation error, the returned pointer is null, and std::bad_alloc is thrown.
+  */
+EIGEN_DEVICE_FUNC inline void* aligned_malloc(std::size_t size)
+{
+  if (size == 0) return nullptr;
+  
+  void *result;
+  #if (EIGEN_DEFAULT_ALIGN_BYTES==0) || EIGEN_MALLOC_ALREADY_ALIGNED
+
+    check_that_malloc_is_allowed();
+    EIGEN_USING_STD(malloc)
+    result = malloc(size);
+
+    #if EIGEN_DEFAULT_ALIGN_BYTES==16
+    eigen_assert((size<16 || (std::size_t(result)%16)==0) && "System's malloc returned an unaligned pointer. Compile with EIGEN_MALLOC_ALREADY_ALIGNED=0 to fallback to handmade aligned memory allocator.");
+    #endif
+  #else
+    result = handmade_aligned_malloc(size);
+  #endif
+
+  if(!result && size)
+    throw_std_bad_alloc();
+
+  return result;
+}
+
+/** \internal Frees memory allocated with aligned_malloc. */
+EIGEN_DEVICE_FUNC inline void aligned_free(void *ptr)
+{
+  #if (EIGEN_DEFAULT_ALIGN_BYTES==0) || EIGEN_MALLOC_ALREADY_ALIGNED
+
+    if(ptr)
+      check_that_malloc_is_allowed();
+    EIGEN_USING_STD(free)
+    free(ptr);
+
+  #else
+    handmade_aligned_free(ptr);
+  #endif
+}
+
+/**
+  * \internal
+  * \brief Reallocates an aligned block of memory.
+  * \throws std::bad_alloc on allocation failure
+  */
+EIGEN_DEVICE_FUNC inline void* aligned_realloc(void *ptr, std::size_t new_size, std::size_t old_size)
+{
+  if (ptr == nullptr) return aligned_malloc(new_size);
+  if (old_size == new_size) return ptr;
+  if (new_size == 0) { aligned_free(ptr); return nullptr; }
+
+  void *result;
+#if (EIGEN_DEFAULT_ALIGN_BYTES==0) || EIGEN_MALLOC_ALREADY_ALIGNED
+  EIGEN_UNUSED_VARIABLE(old_size)
+
+  check_that_malloc_is_allowed();
+  EIGEN_USING_STD(realloc)
+  result = realloc(ptr,new_size);
+#else
+  result = handmade_aligned_realloc(ptr,new_size,old_size);
+#endif
+
+  if (!result && new_size)
+    throw_std_bad_alloc();
+
+  return result;
+}
+
+/*****************************************************************************
+*** Implementation of conditionally aligned functions                      ***
+*****************************************************************************/
+
+/** \internal Allocates \a size bytes. If Align is true, then the returned ptr is 16-byte-aligned.
+  * On allocation error, the returned pointer is null, and a std::bad_alloc is thrown.
+  */
+template<bool Align> EIGEN_DEVICE_FUNC inline void* conditional_aligned_malloc(std::size_t size)
+{
+  return aligned_malloc(size);
+}
+
+template<> EIGEN_DEVICE_FUNC inline void* conditional_aligned_malloc<false>(std::size_t size)
+{
+  if (size == 0) return nullptr;
+
+  check_that_malloc_is_allowed();
+  EIGEN_USING_STD(malloc)
+  void *result = malloc(size);
+
+  if(!result && size)
+    throw_std_bad_alloc();
+  return result;
+}
+
+/** \internal Frees memory allocated with conditional_aligned_malloc */
+template<bool Align> EIGEN_DEVICE_FUNC inline void conditional_aligned_free(void *ptr)
+{
+  aligned_free(ptr);
+}
+
+template<> EIGEN_DEVICE_FUNC inline void conditional_aligned_free<false>(void *ptr)
+{
+  if(ptr)
+    check_that_malloc_is_allowed();
+  EIGEN_USING_STD(free)
+  free(ptr);
+}
+
+template<bool Align> EIGEN_DEVICE_FUNC inline void* conditional_aligned_realloc(void* ptr, std::size_t new_size, std::size_t old_size)
+{
+  return aligned_realloc(ptr, new_size, old_size);
+}
+
+template<> EIGEN_DEVICE_FUNC inline void* conditional_aligned_realloc<false>(void* ptr, std::size_t new_size, std::size_t old_size)
+{
+  if (ptr == nullptr) return conditional_aligned_malloc<false>(new_size);
+  if (old_size == new_size) return ptr;
+  if (new_size == 0) { conditional_aligned_free<false>(ptr); return nullptr; }
+
+  check_that_malloc_is_allowed();
+  EIGEN_USING_STD(realloc)
+  return realloc(ptr, new_size);
+}
+
+/*****************************************************************************
+*** Construction/destruction of array elements                             ***
+*****************************************************************************/
+
+/** \internal Destructs the elements of an array.
+  * The \a size parameters tells on how many objects to call the destructor of T.
+  */
+template<typename T> EIGEN_DEVICE_FUNC inline void destruct_elements_of_array(T *ptr, std::size_t size)
+{
+  // always destruct an array starting from the end.
+  if(ptr)
+    while(size) ptr[--size].~T();
+}
+
+/** \internal Constructs the elements of an array.
+  * The \a size parameter tells on how many objects to call the constructor of T.
+  */
+template<typename T> EIGEN_DEVICE_FUNC inline T* default_construct_elements_of_array(T *ptr, std::size_t size)
+{
+  std::size_t i=0;
+  EIGEN_TRY
+  {
+      for (i = 0; i < size; ++i) ::new (ptr + i) T;
+  }
+  EIGEN_CATCH(...)
+  {
+    destruct_elements_of_array(ptr, i);
+    EIGEN_THROW;
+  }
+  return ptr;
+}
+
+/** \internal Copy-constructs the elements of an array.
+  * The \a size parameter tells on how many objects to copy.
+  */
+template<typename T> EIGEN_DEVICE_FUNC inline T* copy_construct_elements_of_array(T *ptr, const T* src, std::size_t size)
+{
+  std::size_t i=0;
+  EIGEN_TRY
+  {
+      for (i = 0; i < size; ++i) ::new (ptr + i) T(*(src + i));
+  }
+  EIGEN_CATCH(...)
+  {
+    destruct_elements_of_array(ptr, i);
+    EIGEN_THROW;
+  }
+  return ptr;
+}
+
+/** \internal Move-constructs the elements of an array.
+  * The \a size parameter tells on how many objects to move.
+  */
+template<typename T> EIGEN_DEVICE_FUNC inline T* move_construct_elements_of_array(T *ptr, T* src, std::size_t size)
+{
+  std::size_t i=0;
+  EIGEN_TRY
+  {
+      for (i = 0; i < size; ++i) ::new (ptr + i) T(std::move(*(src + i)));
+  }
+  EIGEN_CATCH(...)
+  {
+    destruct_elements_of_array(ptr, i);
+    EIGEN_THROW;
+  }
+  return ptr;
+}
+
+/*****************************************************************************
+*** Implementation of aligned new/delete-like functions                    ***
+*****************************************************************************/
+
+template<typename T>
+EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE void check_size_for_overflow(std::size_t size)
+{
+  if(size > std::size_t(-1) / sizeof(T))
+    throw_std_bad_alloc();
+}
+
+/** \internal Allocates \a size objects of type T. The returned pointer is guaranteed to have 16 bytes alignment.
+  * On allocation error, the returned pointer is undefined, but a std::bad_alloc is thrown.
+  * The default constructor of T is called.
+  */
+template<typename T> EIGEN_DEVICE_FUNC inline T* aligned_new(std::size_t size)
+{
+  check_size_for_overflow<T>(size);
+  T *result = static_cast<T*>(aligned_malloc(sizeof(T)*size));
+  EIGEN_TRY
+  {
+    return default_construct_elements_of_array(result, size);
+  }
+  EIGEN_CATCH(...)
+  {
+    aligned_free(result);
+    EIGEN_THROW;
+  }
+  return result;
+}
+
+template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned_new(std::size_t size)
+{
+  check_size_for_overflow<T>(size);
+  T *result = static_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size));
+  EIGEN_TRY
+  {
+    return default_construct_elements_of_array(result, size);
+  }
+  EIGEN_CATCH(...)
+  {
+    conditional_aligned_free<Align>(result);
+    EIGEN_THROW;
+  }
+  return result;
+}
+
+/** \internal Deletes objects constructed with aligned_new
+  * The \a size parameters tells on how many objects to call the destructor of T.
+  */
+template<typename T> EIGEN_DEVICE_FUNC inline void aligned_delete(T *ptr, std::size_t size)
+{
+  destruct_elements_of_array<T>(ptr, size);
+  aligned_free(ptr);
+}
+
+/** \internal Deletes objects constructed with conditional_aligned_new
+  * The \a size parameters tells on how many objects to call the destructor of T.
+  */
+template<typename T, bool Align> EIGEN_DEVICE_FUNC inline void conditional_aligned_delete(T *ptr, std::size_t size)
+{
+  destruct_elements_of_array<T>(ptr, size);
+  conditional_aligned_free<Align>(ptr);
+}
+
+template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned_realloc_new(T* pts, std::size_t new_size, std::size_t old_size)
+{
+  check_size_for_overflow<T>(new_size);
+  check_size_for_overflow<T>(old_size);
+  
+  // If elements need to be explicitly initialized, we cannot simply realloc
+  // (or memcpy) the memory block - each element needs to be reconstructed.
+  // Otherwise, objects that contain internal pointers like mpfr or
+  // AnnoyingScalar can be pointing to the wrong thing.
+  T* result = static_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*new_size));
+  EIGEN_TRY
+  {
+    // Move-construct initial elements.
+    std::size_t copy_size = (std::min)(old_size, new_size);
+    move_construct_elements_of_array(result, pts, copy_size);
+    
+    // Default-construct remaining elements.
+    if (new_size > old_size) {
+      default_construct_elements_of_array(result + copy_size, new_size - old_size);
+    }
+    
+    // Delete old elements.
+    conditional_aligned_delete<T, Align>(pts, old_size);      
+  }
+  EIGEN_CATCH(...)
+  {
+    conditional_aligned_free<Align>(result);
+    EIGEN_THROW;
+  }
+
+  return result;
+}
+
+
+template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned_new_auto(std::size_t size)
+{
+  if(size==0)
+    return 0; // short-cut. Also fixes Bug 884
+  check_size_for_overflow<T>(size);
+  T *result = static_cast<T*>(conditional_aligned_malloc<Align>(sizeof(T)*size));
+  if(NumTraits<T>::RequireInitialization)
+  {
+    EIGEN_TRY
+    {
+      default_construct_elements_of_array(result, size);
+    }
+    EIGEN_CATCH(...)
+    {
+      conditional_aligned_free<Align>(result);
+      EIGEN_THROW;
+    }
+  }
+  return result;
+}
+
+template<typename T, bool Align> EIGEN_DEVICE_FUNC inline T* conditional_aligned_realloc_new_auto(T* pts, std::size_t new_size, std::size_t old_size)
+{
+  if (NumTraits<T>::RequireInitialization) {
+    return conditional_aligned_realloc_new<T, Align>(pts, new_size, old_size);
+  }
+  
+  check_size_for_overflow<T>(new_size);
+  check_size_for_overflow<T>(old_size);
+  return static_cast<T*>(conditional_aligned_realloc<Align>(static_cast<void*>(pts), sizeof(T)*new_size, sizeof(T)*old_size));
+}
+
+template<typename T, bool Align> EIGEN_DEVICE_FUNC inline void conditional_aligned_delete_auto(T *ptr, std::size_t size)
+{
+  if(NumTraits<T>::RequireInitialization)
+    destruct_elements_of_array<T>(ptr, size);
+  conditional_aligned_free<Align>(ptr);
+}
+
+/****************************************************************************/
+
+/** \internal Returns the index of the first element of the array that is well aligned with respect to the requested \a Alignment.
+  *
+  * \tparam Alignment requested alignment in Bytes.
+  * \param array the address of the start of the array
+  * \param size the size of the array
+  *
+  * \note If no element of the array is well aligned or the requested alignment is not a multiple of a scalar,
+  * the size of the array is returned. For example with SSE, the requested alignment is typically 16-bytes. If
+  * packet size for the given scalar type is 1, then everything is considered well-aligned.
+  *
+  * \note Otherwise, if the Alignment is larger that the scalar size, we rely on the assumptions that sizeof(Scalar) is a
+  * power of 2. On the other hand, we do not assume that the array address is a multiple of sizeof(Scalar), as that fails for
+  * example with Scalar=double on certain 32-bit platforms, see bug #79.
+  *
+  * There is also the variant first_aligned(const MatrixBase&) defined in DenseCoeffsBase.h.
+  * \sa first_default_aligned()
+  */
+template<int Alignment, typename Scalar, typename Index>
+EIGEN_DEVICE_FUNC inline Index first_aligned(const Scalar* array, Index size)
+{
+  const Index ScalarSize = sizeof(Scalar);
+  const Index AlignmentSize = Alignment / ScalarSize;
+  const Index AlignmentMask = AlignmentSize-1;
+
+  if(AlignmentSize<=1)
+  {
+    // Either the requested alignment if smaller than a scalar, or it exactly match a 1 scalar
+    // so that all elements of the array have the same alignment.
+    return 0;
+  }
+  else if( (std::uintptr_t(array) & (sizeof(Scalar)-1)) || (Alignment%ScalarSize)!=0)
+  {
+    // The array is not aligned to the size of a single scalar, or the requested alignment is not a multiple of the scalar size.
+    // Consequently, no element of the array is well aligned.
+    return size;
+  }
+  else
+  {
+    Index first = (AlignmentSize - (Index((std::uintptr_t(array)/sizeof(Scalar))) & AlignmentMask)) & AlignmentMask;
+    return (first < size) ? first : size;
+  }
+}
+
+/** \internal Returns the index of the first element of the array that is well aligned with respect the largest packet requirement.
+   * \sa first_aligned(Scalar*,Index) and first_default_aligned(DenseBase<Derived>) */
+template<typename Scalar, typename Index>
+EIGEN_DEVICE_FUNC inline Index first_default_aligned(const Scalar* array, Index size)
+{
+  typedef typename packet_traits<Scalar>::type DefaultPacketType;
+  return first_aligned<unpacket_traits<DefaultPacketType>::alignment>(array, size);
+}
+
+/** \internal Returns the smallest integer multiple of \a base and greater or equal to \a size
+  */
+template<typename Index>
+inline Index first_multiple(Index size, Index base)
+{
+  return ((size+base-1)/base)*base;
+}
+
+// std::copy is much slower than memcpy, so let's introduce a smart_copy which
+// use memcpy on trivial types, i.e., on types that does not require an initialization ctor.
+template<typename T, bool UseMemcpy> struct smart_copy_helper;
+
+template<typename T> EIGEN_DEVICE_FUNC void smart_copy(const T* start, const T* end, T* target)
+{
+  smart_copy_helper<T,!NumTraits<T>::RequireInitialization>::run(start, end, target);
+}
+
+template<typename T> struct smart_copy_helper<T,true> {
+  EIGEN_DEVICE_FUNC static inline void run(const T* start, const T* end, T* target)
+  {
+    std::intptr_t size = std::intptr_t(end)-std::intptr_t(start);
+    if(size==0) return;
+    eigen_internal_assert(start!=0 && end!=0 && target!=0);
+    EIGEN_USING_STD(memcpy)
+    memcpy(target, start, size);
+  }
+};
+
+template<typename T> struct smart_copy_helper<T,false> {
+  EIGEN_DEVICE_FUNC static inline void run(const T* start, const T* end, T* target)
+  { std::copy(start, end, target); }
+};
+
+// intelligent memmove. falls back to std::memmove for POD types, uses std::copy otherwise.
+template<typename T, bool UseMemmove> struct smart_memmove_helper;
+
+template<typename T> void smart_memmove(const T* start, const T* end, T* target)
+{
+  smart_memmove_helper<T,!NumTraits<T>::RequireInitialization>::run(start, end, target);
+}
+
+template<typename T> struct smart_memmove_helper<T,true> {
+  static inline void run(const T* start, const T* end, T* target)
+  {
+    std::intptr_t size = std::intptr_t(end)-std::intptr_t(start);
+    if(size==0) return;
+    eigen_internal_assert(start!=0 && end!=0 && target!=0);
+    std::memmove(target, start, size);
+  }
+};
+
+template<typename T> struct smart_memmove_helper<T,false> {
+  static inline void run(const T* start, const T* end, T* target)
+  {
+    if (std::uintptr_t(target) < std::uintptr_t(start))
+    {
+      std::copy(start, end, target);
+    }
+    else
+    {
+      std::ptrdiff_t count = (std::ptrdiff_t(end)-std::ptrdiff_t(start)) / sizeof(T);
+      std::copy_backward(start, end, target + count);
+    }
+  }
+};
+
+template<typename T> EIGEN_DEVICE_FUNC T* smart_move(T* start, T* end, T* target)
+{
+  return std::move(start, end, target);
+}
+
+/*****************************************************************************
+*** Implementation of runtime stack allocation (falling back to malloc)    ***
+*****************************************************************************/
+
+// you can overwrite Eigen's default behavior regarding alloca by defining EIGEN_ALLOCA
+// to the appropriate stack allocation function
+#if ! defined EIGEN_ALLOCA && ! defined EIGEN_GPU_COMPILE_PHASE
+  #if EIGEN_OS_LINUX || EIGEN_OS_MAC || (defined alloca)
+    #define EIGEN_ALLOCA alloca
+  #elif EIGEN_COMP_MSVC
+    #define EIGEN_ALLOCA _alloca
+  #endif
+#endif
+
+// With clang -Oz -mthumb, alloca changes the stack pointer in a way that is
+// not allowed in Thumb2. -DEIGEN_STACK_ALLOCATION_LIMIT=0 doesn't work because
+// the compiler still emits bad code because stack allocation checks use "<=".
+// TODO: Eliminate after https://bugs.llvm.org/show_bug.cgi?id=23772
+// is fixed.
+#if defined(__clang__) && defined(__thumb__)
+  #undef EIGEN_ALLOCA
+#endif
+
+// This helper class construct the allocated memory, and takes care of destructing and freeing the handled data
+// at destruction time. In practice this helper class is mainly useful to avoid memory leak in case of exceptions.
+template<typename T> class aligned_stack_memory_handler : noncopyable
+{
+  public:
+    /* Creates a stack_memory_handler responsible for the buffer \a ptr of size \a size.
+     * Note that \a ptr can be 0 regardless of the other parameters.
+     * This constructor takes care of constructing/initializing the elements of the buffer if required by the scalar type T (see NumTraits<T>::RequireInitialization).
+     * In this case, the buffer elements will also be destructed when this handler will be destructed.
+     * Finally, if \a dealloc is true, then the pointer \a ptr is freed.
+     **/
+    EIGEN_DEVICE_FUNC
+    aligned_stack_memory_handler(T* ptr, std::size_t size, bool dealloc)
+      : m_ptr(ptr), m_size(size), m_deallocate(dealloc)
+    {
+      if(NumTraits<T>::RequireInitialization && m_ptr)
+        Eigen::internal::default_construct_elements_of_array(m_ptr, size);
+    }
+    EIGEN_DEVICE_FUNC
+    ~aligned_stack_memory_handler()
+    {
+      if(NumTraits<T>::RequireInitialization && m_ptr)
+        Eigen::internal::destruct_elements_of_array<T>(m_ptr, m_size);
+      if(m_deallocate)
+        Eigen::internal::aligned_free(m_ptr);
+    }
+  protected:
+    T* m_ptr;
+    std::size_t m_size;
+    bool m_deallocate;
+};
+
+#ifdef EIGEN_ALLOCA
+
+template<typename Xpr, int NbEvaluations,
+         bool MapExternalBuffer = nested_eval<Xpr,NbEvaluations>::Evaluate && Xpr::MaxSizeAtCompileTime==Dynamic
+         >
+struct local_nested_eval_wrapper
+{
+  static constexpr bool NeedExternalBuffer = false;
+  typedef typename Xpr::Scalar Scalar;
+  typedef typename nested_eval<Xpr,NbEvaluations>::type ObjectType;
+  ObjectType object;
+
+  EIGEN_DEVICE_FUNC
+  local_nested_eval_wrapper(const Xpr& xpr, Scalar* ptr) : object(xpr)
+  {
+    EIGEN_UNUSED_VARIABLE(ptr);
+    eigen_internal_assert(ptr==0);
+  }
+};
+
+template<typename Xpr, int NbEvaluations>
+struct local_nested_eval_wrapper<Xpr,NbEvaluations,true>
+{
+  static constexpr bool NeedExternalBuffer = true;
+  typedef typename Xpr::Scalar Scalar;
+  typedef typename plain_object_eval<Xpr>::type PlainObject;
+  typedef Map<PlainObject,EIGEN_DEFAULT_ALIGN_BYTES> ObjectType;
+  ObjectType object;
+
+  EIGEN_DEVICE_FUNC
+  local_nested_eval_wrapper(const Xpr& xpr, Scalar* ptr)
+    : object(ptr==0 ? reinterpret_cast<Scalar*>(Eigen::internal::aligned_malloc(sizeof(Scalar)*xpr.size())) : ptr, xpr.rows(), xpr.cols()),
+      m_deallocate(ptr==0)
+  {
+    if(NumTraits<Scalar>::RequireInitialization && object.data())
+      Eigen::internal::default_construct_elements_of_array(object.data(), object.size());
+    object = xpr;
+  }
+
+  EIGEN_DEVICE_FUNC
+  ~local_nested_eval_wrapper()
+  {
+    if(NumTraits<Scalar>::RequireInitialization && object.data())
+      Eigen::internal::destruct_elements_of_array(object.data(), object.size());
+    if(m_deallocate)
+      Eigen::internal::aligned_free(object.data());
+  }
+
+private:
+  bool m_deallocate;
+};
+
+#endif // EIGEN_ALLOCA
+
+template<typename T> class scoped_array : noncopyable
+{
+  T* m_ptr;
+public:
+  explicit scoped_array(std::ptrdiff_t size)
+  {
+    m_ptr = new T[size];
+  }
+  ~scoped_array()
+  {
+    delete[] m_ptr;
+  }
+  T& operator[](std::ptrdiff_t i) { return m_ptr[i]; }
+  const T& operator[](std::ptrdiff_t i) const { return m_ptr[i]; }
+  T* &ptr() { return m_ptr; }
+  const T* ptr() const { return m_ptr; }
+  operator const T*() const { return m_ptr; }
+};
+
+template<typename T> void swap(scoped_array<T> &a,scoped_array<T> &b)
+{
+  std::swap(a.ptr(),b.ptr());
+}
+
+} // end namespace internal
+
+/** \internal
+  *
+  * The macro ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) declares, allocates,
+  * and construct an aligned buffer named NAME of SIZE elements of type TYPE on the stack
+  * if the size in bytes is smaller than EIGEN_STACK_ALLOCATION_LIMIT, and if stack allocation is supported by the platform
+  * (currently, this is Linux, OSX and Visual Studio only). Otherwise the memory is allocated on the heap.
+  * The allocated buffer is automatically deleted when exiting the scope of this declaration.
+  * If BUFFER is non null, then the declared variable is simply an alias for BUFFER, and no allocation/deletion occurs.
+  * Here is an example:
+  * \code
+  * {
+  *   ei_declare_aligned_stack_constructed_variable(float,data,size,0);
+  *   // use data[0] to data[size-1]
+  * }
+  * \endcode
+  * The underlying stack allocation function can controlled with the EIGEN_ALLOCA preprocessor token.
+  *
+  * The macro ei_declare_local_nested_eval(XPR_T,XPR,N,NAME) is analogue to
+  * \code
+  *   typename internal::nested_eval<XPRT_T,N>::type NAME(XPR);
+  * \endcode
+  * with the advantage of using aligned stack allocation even if the maximal size of XPR at compile time is unknown.
+  * This is accomplished through alloca if this later is supported and if the required number of bytes
+  * is below EIGEN_STACK_ALLOCATION_LIMIT.
+  */
+#ifdef EIGEN_ALLOCA
+
+  #if EIGEN_DEFAULT_ALIGN_BYTES>0
+    // We always manually re-align the result of EIGEN_ALLOCA.
+    // If alloca is already aligned, the compiler should be smart enough to optimize away the re-alignment.
+    #define EIGEN_ALIGNED_ALLOCA(SIZE) reinterpret_cast<void*>((std::uintptr_t(EIGEN_ALLOCA(SIZE+EIGEN_DEFAULT_ALIGN_BYTES-1)) + EIGEN_DEFAULT_ALIGN_BYTES-1) & ~(std::size_t(EIGEN_DEFAULT_ALIGN_BYTES-1)))
+  #else
+    #define EIGEN_ALIGNED_ALLOCA(SIZE) EIGEN_ALLOCA(SIZE)
+  #endif
+
+  #define ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) \
+    Eigen::internal::check_size_for_overflow<TYPE>(SIZE); \
+    TYPE* NAME = (BUFFER)!=0 ? (BUFFER) \
+               : reinterpret_cast<TYPE*>( \
+                      (sizeof(TYPE)*SIZE<=EIGEN_STACK_ALLOCATION_LIMIT) ? EIGEN_ALIGNED_ALLOCA(sizeof(TYPE)*SIZE) \
+                    : Eigen::internal::aligned_malloc(sizeof(TYPE)*SIZE) );  \
+    Eigen::internal::aligned_stack_memory_handler<TYPE> EIGEN_CAT(NAME,_stack_memory_destructor)((BUFFER)==0 ? NAME : 0,SIZE,sizeof(TYPE)*SIZE>EIGEN_STACK_ALLOCATION_LIMIT)
+
+
+  #define ei_declare_local_nested_eval(XPR_T,XPR,N,NAME) \
+    Eigen::internal::local_nested_eval_wrapper<XPR_T,N> EIGEN_CAT(NAME,_wrapper)(XPR, reinterpret_cast<typename XPR_T::Scalar*>( \
+      ( (Eigen::internal::local_nested_eval_wrapper<XPR_T,N>::NeedExternalBuffer) && ((sizeof(typename XPR_T::Scalar)*XPR.size())<=EIGEN_STACK_ALLOCATION_LIMIT) ) \
+        ? EIGEN_ALIGNED_ALLOCA( sizeof(typename XPR_T::Scalar)*XPR.size() ) : 0 ) ) ; \
+    typename Eigen::internal::local_nested_eval_wrapper<XPR_T,N>::ObjectType NAME(EIGEN_CAT(NAME,_wrapper).object)
+
+#else
+
+  #define ei_declare_aligned_stack_constructed_variable(TYPE,NAME,SIZE,BUFFER) \
+    Eigen::internal::check_size_for_overflow<TYPE>(SIZE); \
+    TYPE* NAME = (BUFFER)!=0 ? BUFFER : reinterpret_cast<TYPE*>(Eigen::internal::aligned_malloc(sizeof(TYPE)*SIZE));    \
+    Eigen::internal::aligned_stack_memory_handler<TYPE> EIGEN_CAT(NAME,_stack_memory_destructor)((BUFFER)==0 ? NAME : 0,SIZE,true)
+
+
+#define ei_declare_local_nested_eval(XPR_T,XPR,N,NAME) typename Eigen::internal::nested_eval<XPR_T,N>::type NAME(XPR)
+
+#endif
+
+
+/*****************************************************************************
+*** Implementation of EIGEN_MAKE_ALIGNED_OPERATOR_NEW [_IF]                ***
+*****************************************************************************/
+
+#if EIGEN_HAS_CXX17_OVERALIGN
+
+// C++17 -> no need to bother about alignment anymore :)
+
+#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign)
+#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
+#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW
+#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar,Size)
+
+#else
+
+// HIP does not support new/delete on device.
+#if EIGEN_MAX_ALIGN_BYTES!=0 && !defined(EIGEN_HIP_DEVICE_COMPILE)
+  #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign) \
+      EIGEN_DEVICE_FUNC \
+      void* operator new(std::size_t size, const std::nothrow_t&) EIGEN_NO_THROW { \
+        EIGEN_TRY { return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size); } \
+        EIGEN_CATCH (...) { return 0; } \
+      }
+  #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign) \
+      EIGEN_DEVICE_FUNC \
+      void *operator new(std::size_t size) { \
+        return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size); \
+      } \
+      EIGEN_DEVICE_FUNC \
+      void *operator new[](std::size_t size) { \
+        return Eigen::internal::conditional_aligned_malloc<NeedsToAlign>(size); \
+      } \
+      EIGEN_DEVICE_FUNC \
+      void operator delete(void * ptr) EIGEN_NO_THROW { Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); } \
+      EIGEN_DEVICE_FUNC \
+      void operator delete[](void * ptr) EIGEN_NO_THROW { Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); } \
+      EIGEN_DEVICE_FUNC \
+      void operator delete(void * ptr, std::size_t /* sz */) EIGEN_NO_THROW { Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); } \
+      EIGEN_DEVICE_FUNC \
+      void operator delete[](void * ptr, std::size_t /* sz */) EIGEN_NO_THROW { Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); } \
+      /* in-place new and delete. since (at least afaik) there is no actual   */ \
+      /* memory allocated we can safely let the default implementation handle */ \
+      /* this particular case. */ \
+      EIGEN_DEVICE_FUNC \
+      static void *operator new(std::size_t size, void *ptr) { return ::operator new(size,ptr); } \
+      EIGEN_DEVICE_FUNC \
+      static void *operator new[](std::size_t size, void* ptr) { return ::operator new[](size,ptr); } \
+      EIGEN_DEVICE_FUNC \
+      void operator delete(void * memory, void *ptr) EIGEN_NO_THROW { return ::operator delete(memory,ptr); } \
+      EIGEN_DEVICE_FUNC \
+      void operator delete[](void * memory, void *ptr) EIGEN_NO_THROW { return ::operator delete[](memory,ptr); } \
+      /* nothrow-new (returns zero instead of std::bad_alloc) */ \
+      EIGEN_MAKE_ALIGNED_OPERATOR_NEW_NOTHROW(NeedsToAlign) \
+      EIGEN_DEVICE_FUNC \
+      void operator delete(void *ptr, const std::nothrow_t&) EIGEN_NO_THROW { \
+        Eigen::internal::conditional_aligned_free<NeedsToAlign>(ptr); \
+      } \
+      typedef void eigen_aligned_operator_new_marker_type;
+#else
+  #define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(NeedsToAlign)
+#endif
+
+#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(true)
+#define EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar,Size)                        \
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(bool(                                                             \
+        ((Size)!=Eigen::Dynamic) &&                                                                    \
+        (((EIGEN_MAX_ALIGN_BYTES>=16) && ((sizeof(Scalar)*(Size))%(EIGEN_MAX_ALIGN_BYTES  )==0)) ||    \
+         ((EIGEN_MAX_ALIGN_BYTES>=32) && ((sizeof(Scalar)*(Size))%(EIGEN_MAX_ALIGN_BYTES/2)==0)) ||    \
+         ((EIGEN_MAX_ALIGN_BYTES>=64) && ((sizeof(Scalar)*(Size))%(EIGEN_MAX_ALIGN_BYTES/4)==0))   )))
+
+#endif
+
+/****************************************************************************/
+
+/** \class aligned_allocator
+* \ingroup Core_Module
+*
+* \brief STL compatible allocator to use with types requiring a non-standard alignment.
+*
+* The memory is aligned as for dynamically aligned matrix/array types such as MatrixXd.
+* By default, it will thus provide at least 16 bytes alignment and more in following cases:
+*  - 32 bytes alignment if AVX is enabled.
+*  - 64 bytes alignment if AVX512 is enabled.
+*
+* This can be controlled using the \c EIGEN_MAX_ALIGN_BYTES macro as documented
+* \link TopicPreprocessorDirectivesPerformance there \endlink.
+*
+* Example:
+* \code
+* // Matrix4f requires 16 bytes alignment:
+* std::map< int, Matrix4f, std::less<int>,
+*           aligned_allocator<std::pair<const int, Matrix4f> > > my_map_mat4;
+* // Vector3f does not require 16 bytes alignment, no need to use Eigen's allocator:
+* std::map< int, Vector3f > my_map_vec3;
+* \endcode
+*
+* \sa \blank \ref TopicStlContainers.
+*/
+template<class T>
+class aligned_allocator : public std::allocator<T>
+{
+public:
+  typedef std::size_t     size_type;
+  typedef std::ptrdiff_t  difference_type;
+  typedef T*              pointer;
+  typedef const T*        const_pointer;
+  typedef T&              reference;
+  typedef const T&        const_reference;
+  typedef T               value_type;
+
+  template<class U>
+  struct rebind
+  {
+    typedef aligned_allocator<U> other;
+  };
+
+  aligned_allocator() : std::allocator<T>() {}
+
+  aligned_allocator(const aligned_allocator& other) : std::allocator<T>(other) {}
+
+  template<class U>
+  aligned_allocator(const aligned_allocator<U>& other) : std::allocator<T>(other) {}
+
+  ~aligned_allocator() {}
+
+  #if EIGEN_COMP_GNUC_STRICT && EIGEN_GNUC_STRICT_AT_LEAST(7,0,0)
+  // In gcc std::allocator::max_size() is bugged making gcc triggers a warning:
+  // eigen/Eigen/src/Core/util/Memory.h:189:12: warning: argument 1 value '18446744073709551612' exceeds maximum object size 9223372036854775807
+  // See https://gcc.gnu.org/bugzilla/show_bug.cgi?id=87544
+  size_type max_size() const {
+    return (std::numeric_limits<std::ptrdiff_t>::max)()/sizeof(T);
+  }
+  #endif
+
+  pointer allocate(size_type num, const void* /*hint*/ = 0)
+  {
+    internal::check_size_for_overflow<T>(num);
+    return static_cast<pointer>( internal::aligned_malloc(num * sizeof(T)) );
+  }
+
+  void deallocate(pointer p, size_type /*num*/)
+  {
+    internal::aligned_free(p);
+  }
+};
+
+//---------- Cache sizes ----------
+
+#if !defined(EIGEN_NO_CPUID)
+#  if EIGEN_COMP_GNUC && EIGEN_ARCH_i386_OR_x86_64
+#    if defined(__PIC__) && EIGEN_ARCH_i386
+       // Case for x86 with PIC
+#      define EIGEN_CPUID(abcd,func,id) \
+         __asm__ __volatile__ ("xchgl %%ebx, %k1;cpuid; xchgl %%ebx,%k1": "=a" (abcd[0]), "=&r" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "a" (func), "c" (id));
+#    elif defined(__PIC__) && EIGEN_ARCH_x86_64
+       // Case for x64 with PIC. In theory this is only a problem with recent gcc and with medium or large code model, not with the default small code model.
+       // However, we cannot detect which code model is used, and the xchg overhead is negligible anyway.
+#      define EIGEN_CPUID(abcd,func,id) \
+        __asm__ __volatile__ ("xchg{q}\t{%%}rbx, %q1; cpuid; xchg{q}\t{%%}rbx, %q1": "=a" (abcd[0]), "=&r" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "0" (func), "2" (id));
+#    else
+       // Case for x86_64 or x86 w/o PIC
+#      define EIGEN_CPUID(abcd,func,id) \
+         __asm__ __volatile__ ("cpuid": "=a" (abcd[0]), "=b" (abcd[1]), "=c" (abcd[2]), "=d" (abcd[3]) : "0" (func), "2" (id) );
+#    endif
+#  elif EIGEN_COMP_MSVC
+#    if EIGEN_ARCH_i386_OR_x86_64
+#      define EIGEN_CPUID(abcd,func,id) __cpuidex((int*)abcd,func,id)
+#    endif
+#  endif
+#endif
+
+namespace internal {
+
+#ifdef EIGEN_CPUID
+
+inline bool cpuid_is_vendor(int abcd[4], const int vendor[3])
+{
+  return abcd[1]==vendor[0] && abcd[3]==vendor[1] && abcd[2]==vendor[2];
+}
+
+inline void queryCacheSizes_intel_direct(int& l1, int& l2, int& l3)
+{
+  int abcd[4];
+  l1 = l2 = l3 = 0;
+  int cache_id = 0;
+  int cache_type = 0;
+  do {
+    abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
+    EIGEN_CPUID(abcd,0x4,cache_id);
+    cache_type  = (abcd[0] & 0x0F) >> 0;
+    if(cache_type==1||cache_type==3) // data or unified cache
+    {
+      int cache_level = (abcd[0] & 0xE0) >> 5;  // A[7:5]
+      int ways        = (abcd[1] & 0xFFC00000) >> 22; // B[31:22]
+      int partitions  = (abcd[1] & 0x003FF000) >> 12; // B[21:12]
+      int line_size   = (abcd[1] & 0x00000FFF) >>  0; // B[11:0]
+      int sets        = (abcd[2]);                    // C[31:0]
+
+      int cache_size = (ways+1) * (partitions+1) * (line_size+1) * (sets+1);
+
+      switch(cache_level)
+      {
+        case 1: l1 = cache_size; break;
+        case 2: l2 = cache_size; break;
+        case 3: l3 = cache_size; break;
+        default: break;
+      }
+    }
+    cache_id++;
+  } while(cache_type>0 && cache_id<16);
+}
+
+inline void queryCacheSizes_intel_codes(int& l1, int& l2, int& l3)
+{
+  int abcd[4];
+  abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
+  l1 = l2 = l3 = 0;
+  EIGEN_CPUID(abcd,0x00000002,0);
+  unsigned char * bytes = reinterpret_cast<unsigned char *>(abcd)+2;
+  bool check_for_p2_core2 = false;
+  for(int i=0; i<14; ++i)
+  {
+    switch(bytes[i])
+    {
+      case 0x0A: l1 = 8; break;   // 0Ah   data L1 cache, 8 KB, 2 ways, 32 byte lines
+      case 0x0C: l1 = 16; break;  // 0Ch   data L1 cache, 16 KB, 4 ways, 32 byte lines
+      case 0x0E: l1 = 24; break;  // 0Eh   data L1 cache, 24 KB, 6 ways, 64 byte lines
+      case 0x10: l1 = 16; break;  // 10h   data L1 cache, 16 KB, 4 ways, 32 byte lines (IA-64)
+      case 0x15: l1 = 16; break;  // 15h   code L1 cache, 16 KB, 4 ways, 32 byte lines (IA-64)
+      case 0x2C: l1 = 32; break;  // 2Ch   data L1 cache, 32 KB, 8 ways, 64 byte lines
+      case 0x30: l1 = 32; break;  // 30h   code L1 cache, 32 KB, 8 ways, 64 byte lines
+      case 0x60: l1 = 16; break;  // 60h   data L1 cache, 16 KB, 8 ways, 64 byte lines, sectored
+      case 0x66: l1 = 8; break;   // 66h   data L1 cache, 8 KB, 4 ways, 64 byte lines, sectored
+      case 0x67: l1 = 16; break;  // 67h   data L1 cache, 16 KB, 4 ways, 64 byte lines, sectored
+      case 0x68: l1 = 32; break;  // 68h   data L1 cache, 32 KB, 4 ways, 64 byte lines, sectored
+      case 0x1A: l2 = 96; break;   // code and data L2 cache, 96 KB, 6 ways, 64 byte lines (IA-64)
+      case 0x22: l3 = 512; break;   // code and data L3 cache, 512 KB, 4 ways (!), 64 byte lines, dual-sectored
+      case 0x23: l3 = 1024; break;   // code and data L3 cache, 1024 KB, 8 ways, 64 byte lines, dual-sectored
+      case 0x25: l3 = 2048; break;   // code and data L3 cache, 2048 KB, 8 ways, 64 byte lines, dual-sectored
+      case 0x29: l3 = 4096; break;   // code and data L3 cache, 4096 KB, 8 ways, 64 byte lines, dual-sectored
+      case 0x39: l2 = 128; break;   // code and data L2 cache, 128 KB, 4 ways, 64 byte lines, sectored
+      case 0x3A: l2 = 192; break;   // code and data L2 cache, 192 KB, 6 ways, 64 byte lines, sectored
+      case 0x3B: l2 = 128; break;   // code and data L2 cache, 128 KB, 2 ways, 64 byte lines, sectored
+      case 0x3C: l2 = 256; break;   // code and data L2 cache, 256 KB, 4 ways, 64 byte lines, sectored
+      case 0x3D: l2 = 384; break;   // code and data L2 cache, 384 KB, 6 ways, 64 byte lines, sectored
+      case 0x3E: l2 = 512; break;   // code and data L2 cache, 512 KB, 4 ways, 64 byte lines, sectored
+      case 0x40: l2 = 0; break;   // no integrated L2 cache (P6 core) or L3 cache (P4 core)
+      case 0x41: l2 = 128; break;   // code and data L2 cache, 128 KB, 4 ways, 32 byte lines
+      case 0x42: l2 = 256; break;   // code and data L2 cache, 256 KB, 4 ways, 32 byte lines
+      case 0x43: l2 = 512; break;   // code and data L2 cache, 512 KB, 4 ways, 32 byte lines
+      case 0x44: l2 = 1024; break;   // code and data L2 cache, 1024 KB, 4 ways, 32 byte lines
+      case 0x45: l2 = 2048; break;   // code and data L2 cache, 2048 KB, 4 ways, 32 byte lines
+      case 0x46: l3 = 4096; break;   // code and data L3 cache, 4096 KB, 4 ways, 64 byte lines
+      case 0x47: l3 = 8192; break;   // code and data L3 cache, 8192 KB, 8 ways, 64 byte lines
+      case 0x48: l2 = 3072; break;   // code and data L2 cache, 3072 KB, 12 ways, 64 byte lines
+      case 0x49: if(l2!=0) l3 = 4096; else {check_for_p2_core2=true; l3 = l2 = 4096;} break;// code and data L3 cache, 4096 KB, 16 ways, 64 byte lines (P4) or L2 for core2
+      case 0x4A: l3 = 6144; break;   // code and data L3 cache, 6144 KB, 12 ways, 64 byte lines
+      case 0x4B: l3 = 8192; break;   // code and data L3 cache, 8192 KB, 16 ways, 64 byte lines
+      case 0x4C: l3 = 12288; break;   // code and data L3 cache, 12288 KB, 12 ways, 64 byte lines
+      case 0x4D: l3 = 16384; break;   // code and data L3 cache, 16384 KB, 16 ways, 64 byte lines
+      case 0x4E: l2 = 6144; break;   // code and data L2 cache, 6144 KB, 24 ways, 64 byte lines
+      case 0x78: l2 = 1024; break;   // code and data L2 cache, 1024 KB, 4 ways, 64 byte lines
+      case 0x79: l2 = 128; break;   // code and data L2 cache, 128 KB, 8 ways, 64 byte lines, dual-sectored
+      case 0x7A: l2 = 256; break;   // code and data L2 cache, 256 KB, 8 ways, 64 byte lines, dual-sectored
+      case 0x7B: l2 = 512; break;   // code and data L2 cache, 512 KB, 8 ways, 64 byte lines, dual-sectored
+      case 0x7C: l2 = 1024; break;   // code and data L2 cache, 1024 KB, 8 ways, 64 byte lines, dual-sectored
+      case 0x7D: l2 = 2048; break;   // code and data L2 cache, 2048 KB, 8 ways, 64 byte lines
+      case 0x7E: l2 = 256; break;   // code and data L2 cache, 256 KB, 8 ways, 128 byte lines, sect. (IA-64)
+      case 0x7F: l2 = 512; break;   // code and data L2 cache, 512 KB, 2 ways, 64 byte lines
+      case 0x80: l2 = 512; break;   // code and data L2 cache, 512 KB, 8 ways, 64 byte lines
+      case 0x81: l2 = 128; break;   // code and data L2 cache, 128 KB, 8 ways, 32 byte lines
+      case 0x82: l2 = 256; break;   // code and data L2 cache, 256 KB, 8 ways, 32 byte lines
+      case 0x83: l2 = 512; break;   // code and data L2 cache, 512 KB, 8 ways, 32 byte lines
+      case 0x84: l2 = 1024; break;   // code and data L2 cache, 1024 KB, 8 ways, 32 byte lines
+      case 0x85: l2 = 2048; break;   // code and data L2 cache, 2048 KB, 8 ways, 32 byte lines
+      case 0x86: l2 = 512; break;   // code and data L2 cache, 512 KB, 4 ways, 64 byte lines
+      case 0x87: l2 = 1024; break;   // code and data L2 cache, 1024 KB, 8 ways, 64 byte lines
+      case 0x88: l3 = 2048; break;   // code and data L3 cache, 2048 KB, 4 ways, 64 byte lines (IA-64)
+      case 0x89: l3 = 4096; break;   // code and data L3 cache, 4096 KB, 4 ways, 64 byte lines (IA-64)
+      case 0x8A: l3 = 8192; break;   // code and data L3 cache, 8192 KB, 4 ways, 64 byte lines (IA-64)
+      case 0x8D: l3 = 3072; break;   // code and data L3 cache, 3072 KB, 12 ways, 128 byte lines (IA-64)
+
+      default: break;
+    }
+  }
+  if(check_for_p2_core2 && l2 == l3)
+    l3 = 0;
+  l1 *= 1024;
+  l2 *= 1024;
+  l3 *= 1024;
+}
+
+inline void queryCacheSizes_intel(int& l1, int& l2, int& l3, int max_std_funcs)
+{
+  if(max_std_funcs>=4)
+    queryCacheSizes_intel_direct(l1,l2,l3);
+  else if(max_std_funcs>=2)
+    queryCacheSizes_intel_codes(l1,l2,l3);
+  else
+    l1 = l2 = l3 = 0;
+}
+
+inline void queryCacheSizes_amd(int& l1, int& l2, int& l3)
+{
+  int abcd[4];
+  abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
+  
+  // First query the max supported function.
+  EIGEN_CPUID(abcd,0x80000000,0);
+  if(static_cast<numext::uint32_t>(abcd[0]) >= static_cast<numext::uint32_t>(0x80000006))
+  {
+    EIGEN_CPUID(abcd,0x80000005,0);
+    l1 = (abcd[2] >> 24) * 1024; // C[31:24] = L1 size in KB
+    abcd[0] = abcd[1] = abcd[2] = abcd[3] = 0;
+    EIGEN_CPUID(abcd,0x80000006,0);
+    l2 = (abcd[2] >> 16) * 1024; // C[31;16] = l2 cache size in KB
+    l3 = ((abcd[3] & 0xFFFC000) >> 18) * 512 * 1024; // D[31;18] = l3 cache size in 512KB
+  }
+  else
+  {
+    l1 = l2 = l3 = 0;
+  }
+}
+#endif
+
+/** \internal
+ * Queries and returns the cache sizes in Bytes of the L1, L2, and L3 data caches respectively */
+inline void queryCacheSizes(int& l1, int& l2, int& l3)
+{
+  #ifdef EIGEN_CPUID
+  int abcd[4];
+  const int GenuineIntel[] = {0x756e6547, 0x49656e69, 0x6c65746e};
+  const int AuthenticAMD[] = {0x68747541, 0x69746e65, 0x444d4163};
+  const int AMDisbetter_[] = {0x69444d41, 0x74656273, 0x21726574}; // "AMDisbetter!"
+
+  // identify the CPU vendor
+  EIGEN_CPUID(abcd,0x0,0);
+  int max_std_funcs = abcd[0];
+  if(cpuid_is_vendor(abcd,GenuineIntel))
+    queryCacheSizes_intel(l1,l2,l3,max_std_funcs);
+  else if(cpuid_is_vendor(abcd,AuthenticAMD) || cpuid_is_vendor(abcd,AMDisbetter_))
+    queryCacheSizes_amd(l1,l2,l3);
+  else
+    // by default let's use Intel's API
+    queryCacheSizes_intel(l1,l2,l3,max_std_funcs);
+
+  // here is the list of other vendors:
+//   ||cpuid_is_vendor(abcd,"VIA VIA VIA ")
+//   ||cpuid_is_vendor(abcd,"CyrixInstead")
+//   ||cpuid_is_vendor(abcd,"CentaurHauls")
+//   ||cpuid_is_vendor(abcd,"GenuineTMx86")
+//   ||cpuid_is_vendor(abcd,"TransmetaCPU")
+//   ||cpuid_is_vendor(abcd,"RiseRiseRise")
+//   ||cpuid_is_vendor(abcd,"Geode by NSC")
+//   ||cpuid_is_vendor(abcd,"SiS SiS SiS ")
+//   ||cpuid_is_vendor(abcd,"UMC UMC UMC ")
+//   ||cpuid_is_vendor(abcd,"NexGenDriven")
+  #else
+  l1 = l2 = l3 = -1;
+  #endif
+}
+
+/** \internal
+ * \returns the size in Bytes of the L1 data cache */
+inline int queryL1CacheSize()
+{
+  int l1(-1), l2, l3;
+  queryCacheSizes(l1,l2,l3);
+  return l1;
+}
+
+/** \internal
+ * \returns the size in Bytes of the L2 or L3 cache if this later is present */
+inline int queryTopLevelCacheSize()
+{
+  int l1, l2(-1), l3(-1);
+  queryCacheSizes(l1,l2,l3);
+  return (std::max)(l2,l3);
+}
+
+
+
+/** \internal
+ * This wraps C++20's std::construct_at, using placement new instead if it is not available.
+ */
+
+#if EIGEN_COMP_CXXVER >= 20
+using std::construct_at;
+#else
+template<class T, class... Args>
+EIGEN_DEVICE_FUNC T* construct_at( T* p, Args&&... args )
+{
+  return ::new (const_cast<void*>(static_cast<const volatile void*>(p)))
+    T(std::forward<Args>(args)...);
+}
+#endif
+
+/** \internal
+ * This wraps C++17's std::destroy_at.  If it's not available it calls the destructor.
+ * The wrapper is not a full replacement for C++20's std::destroy_at as it cannot
+ * be applied to std::array.
+ */
+#if EIGEN_COMP_CXXVER >= 17
+using std::destroy_at;
+#else
+template<class T>
+EIGEN_DEVICE_FUNC void destroy_at(T* p)
+{
+  p->~T();
+}
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_MEMORY_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Meta.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..ba5a5638525645dd20cc3c1e85c9b22a540d5c0e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Meta.h
@@ -0,0 +1,594 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_META_H
+#define EIGEN_META_H
+
+#include "../InternalHeaderCheck.h"
+
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+
+ #include <cfloat>
+
+ #if defined(EIGEN_CUDA_ARCH)
+  #include <math_constants.h>
+ #endif
+
+ #if defined(EIGEN_HIP_DEVICE_COMPILE)
+  #include "Eigen/src/Core/arch/HIP/hcc/math_constants.h"
+  #endif
+
+#endif
+
+// Define portable (u)int{32,64} types
+#include <cstdint>
+
+namespace Eigen {
+namespace numext {
+typedef std::uint8_t  uint8_t;
+typedef std::int8_t   int8_t;
+typedef std::uint16_t uint16_t;
+typedef std::int16_t  int16_t;
+typedef std::uint32_t uint32_t;
+typedef std::int32_t  int32_t;
+typedef std::uint64_t uint64_t;
+typedef std::int64_t  int64_t;
+
+template <size_t Size>
+struct get_integer_by_size {
+    typedef void signed_type;
+    typedef void unsigned_type;
+};
+template <>
+struct get_integer_by_size<1> {
+    typedef int8_t signed_type;
+    typedef uint8_t unsigned_type;
+};
+template <>
+struct get_integer_by_size<2> {
+    typedef int16_t signed_type;
+    typedef uint16_t unsigned_type;
+};
+template <>
+struct get_integer_by_size<4> {
+    typedef int32_t signed_type;
+    typedef uint32_t unsigned_type;
+};
+template <>
+struct get_integer_by_size<8> {
+    typedef int64_t signed_type;
+    typedef uint64_t unsigned_type;
+};
+}
+}
+
+namespace Eigen {
+
+typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE DenseIndex;
+
+/**
+ * \brief The Index type as used for the API.
+ * \details To change this, \c \#define the preprocessor symbol \c EIGEN_DEFAULT_DENSE_INDEX_TYPE.
+ * \sa \blank \ref TopicPreprocessorDirectives, StorageIndex.
+ */
+
+typedef EIGEN_DEFAULT_DENSE_INDEX_TYPE Index;
+
+namespace internal {
+
+/** \internal
+  * \file Meta.h
+  * This file contains generic metaprogramming classes which are not specifically related to Eigen.
+  * \note In case you wonder, yes we're aware that Boost already provides all these features,
+  * we however don't want to add a dependency to Boost.
+  */
+
+struct true_type {  enum { value = 1 }; };
+struct false_type { enum { value = 0 }; };
+
+template<bool Condition>
+struct bool_constant;
+
+template<>
+struct bool_constant<true> : true_type {};
+
+template<>
+struct bool_constant<false> : false_type {};
+
+// Third-party libraries rely on these.
+using std::conditional;
+using std::remove_reference;
+using std::remove_pointer;
+using std::remove_const;
+
+template<typename T> struct remove_all { typedef T type; };
+template<typename T> struct remove_all<const T>   { typedef typename remove_all<T>::type type; };
+template<typename T> struct remove_all<T const&>  { typedef typename remove_all<T>::type type; };
+template<typename T> struct remove_all<T&>        { typedef typename remove_all<T>::type type; };
+template<typename T> struct remove_all<T const*>  { typedef typename remove_all<T>::type type; };
+template<typename T> struct remove_all<T*>        { typedef typename remove_all<T>::type type; };
+
+template<typename T>
+using remove_all_t = typename remove_all<T>::type;
+
+template<typename T> struct is_arithmetic      { enum { value = false }; };
+template<> struct is_arithmetic<float>         { enum { value = true }; };
+template<> struct is_arithmetic<double>        { enum { value = true }; };
+// GPU devices treat `long double` as `double`.
+#ifndef EIGEN_GPU_COMPILE_PHASE
+template<> struct is_arithmetic<long double>   { enum { value = true }; };
+#endif
+template<> struct is_arithmetic<bool>          { enum { value = true }; };
+template<> struct is_arithmetic<char>          { enum { value = true }; };
+template<> struct is_arithmetic<signed char>   { enum { value = true }; };
+template<> struct is_arithmetic<unsigned char> { enum { value = true }; };
+template<> struct is_arithmetic<signed short>  { enum { value = true }; };
+template<> struct is_arithmetic<unsigned short>{ enum { value = true }; };
+template<> struct is_arithmetic<signed int>    { enum { value = true }; };
+template<> struct is_arithmetic<unsigned int>  { enum { value = true }; };
+template<> struct is_arithmetic<signed long>   { enum { value = true }; };
+template<> struct is_arithmetic<unsigned long> { enum { value = true }; };
+
+template<typename T, typename U> struct is_same { enum { value = 0 }; };
+template<typename T> struct is_same<T,T> { enum { value = 1 }; };
+
+template< class T >
+struct is_void : is_same<void, std::remove_const_t<T>> {};
+
+/** \internal
+  * Implementation of std::void_t for SFINAE.
+  *
+  * Pre C++17:
+  * Custom implementation.
+  *
+  * Post C++17: Uses std::void_t
+  */
+#if EIGEN_COMP_CXXVER >= 17
+using std::void_t;
+#else
+template<typename...>
+using void_t = void;
+#endif
+
+template<> struct is_arithmetic<signed long long>   { enum { value = true }; };
+template<> struct is_arithmetic<unsigned long long> { enum { value = true }; };
+using std::is_integral;
+
+using std::make_unsigned;
+
+template <typename T> struct is_const { enum { value = 0 }; };
+template <typename T> struct is_const<T const> { enum { value = 1 }; };
+
+template<typename T> struct add_const_on_value_type            { typedef const T type;  };
+template<typename T> struct add_const_on_value_type<T&>        { typedef T const& type; };
+template<typename T> struct add_const_on_value_type<T*>        { typedef T const* type; };
+template<typename T> struct add_const_on_value_type<T* const>  { typedef T const* const type; };
+template<typename T> struct add_const_on_value_type<T const* const>  { typedef T const* const type; };
+
+template<typename T>
+using add_const_on_value_type_t = typename add_const_on_value_type<T>::type;
+
+using std::is_convertible;
+
+/** \internal
+  * A base class do disable default copy ctor and copy assignment operator.
+  */
+class noncopyable
+{
+  EIGEN_DEVICE_FUNC noncopyable(const noncopyable&);
+  EIGEN_DEVICE_FUNC const noncopyable& operator=(const noncopyable&);
+protected:
+  EIGEN_DEVICE_FUNC noncopyable() {}
+  EIGEN_DEVICE_FUNC ~noncopyable() {}
+};
+
+/** \internal
+  * Provides access to the number of elements in the object of as a compile-time constant expression.
+  * It "returns" Eigen::Dynamic if the size cannot be resolved at compile-time (default).
+  *
+  * Similar to std::tuple_size, but more general.
+  *
+  * It currently supports:
+  *  - any types T defining T::SizeAtCompileTime
+  *  - plain C arrays as T[N]
+  *  - std::array (c++11)
+  *  - some internal types such as SingleRange and AllRange
+  *
+  * The second template parameter eases SFINAE-based specializations.
+  */
+template<typename T, typename EnableIf = void> struct array_size {
+  enum { value = Dynamic };
+};
+
+template<typename T> struct array_size<T, std::enable_if_t<((T::SizeAtCompileTime&0)==0)>> {
+  enum { value = T::SizeAtCompileTime };
+};
+
+template<typename T, int N> struct array_size<const T (&)[N]> {
+  enum { value = N };
+};
+template<typename T, int N> struct array_size<T (&)[N]> {
+  enum { value = N };
+};
+
+template<typename T, std::size_t N> struct array_size<const std::array<T,N> > {
+  enum { value = N };
+};
+template<typename T, std::size_t N> struct array_size<std::array<T,N> > {
+  enum { value = N };
+};
+
+
+/** \internal
+  * Analogue of the std::ssize free function.
+  * It returns the signed size of the container or view \a x of type \c T
+  *
+  * It currently supports:
+  *  - any types T defining a member T::size() const
+  *  - plain C arrays as T[N]
+  *
+  * For C++20, this function just forwards to `std::ssize`, or any ADL discoverable `ssize` function.
+  */
+#if EIGEN_COMP_CXXVER < 20 || EIGEN_GNUC_STRICT_LESS_THAN(10,0,0)
+template <typename T>
+EIGEN_CONSTEXPR auto index_list_size(const T& x) {
+  using R = std::common_type_t<std::ptrdiff_t, std::make_signed_t<decltype(x.size())>>;
+  return static_cast<R>(x.size());
+}
+
+template<typename T, std::ptrdiff_t N>
+EIGEN_CONSTEXPR std::ptrdiff_t index_list_size(const T (&)[N]) { return N; }
+#else
+template <typename T>
+EIGEN_CONSTEXPR auto index_list_size(T&& x) {
+  using std::ssize;
+  return ssize(std::forward<T>(x));
+}
+#endif // EIGEN_COMP_CXXVER
+
+/** \internal
+  * Convenient struct to get the result type of a nullary, unary, binary, or
+  * ternary functor.
+  *
+  * Pre C++17:
+  * This uses std::result_of. However, note the `type` member removes
+  * const and converts references/pointers to their corresponding value type.
+  *
+  * Post C++17: Uses std::invoke_result
+  */
+#if EIGEN_HAS_STD_INVOKE_RESULT
+template<typename T> struct result_of;
+
+template<typename F, typename... ArgTypes>
+struct result_of<F(ArgTypes...)> {
+  typedef typename std::invoke_result<F, ArgTypes...>::type type1;
+  typedef remove_all_t<type1> type;
+};
+
+template<typename F, typename... ArgTypes>
+struct invoke_result {
+  typedef typename std::invoke_result<F, ArgTypes...>::type type1;
+  typedef remove_all_t<type1> type;
+};
+#else
+template<typename T> struct result_of {
+  typedef typename std::result_of<T>::type type1;
+  typedef remove_all_t<type1> type;
+};
+
+template<typename F, typename... ArgTypes>
+struct invoke_result {
+    typedef typename result_of<F(ArgTypes...)>::type type1;
+    typedef remove_all_t<type1> type;
+};
+#endif
+
+// Reduces a sequence of bools to true if all are true, false otherwise.
+template<bool... values>
+using reduce_all = std::is_same<std::integer_sequence<bool, values..., true>,
+    std::integer_sequence<bool, true, values...> >;
+
+// Reduces a sequence of bools to true if any are true, false if all false.
+template<bool... values>
+using reduce_any = std::integral_constant<bool,
+    !std::is_same<std::integer_sequence<bool, values..., false>, std::integer_sequence<bool, false, values...> >::value>;
+
+struct meta_yes { char a[1]; };
+struct meta_no  { char a[2]; };
+
+// Check whether T::ReturnType does exist
+template <typename T>
+struct has_ReturnType
+{
+  template <typename C> static meta_yes testFunctor(C const *, typename C::ReturnType const * = 0);
+  template <typename C> static meta_no  testFunctor(...);
+
+  enum { value = sizeof(testFunctor<T>(static_cast<T*>(0))) == sizeof(meta_yes) };
+};
+
+template<typename T> const T* return_ptr();
+
+template <typename T, typename IndexType=Index>
+struct has_nullary_operator
+{
+  template <typename C> static meta_yes testFunctor(C const *,std::enable_if_t<(sizeof(return_ptr<C>()->operator()())>0)> * = 0);
+  static meta_no testFunctor(...);
+
+  enum { value = sizeof(testFunctor(static_cast<T*>(0))) == sizeof(meta_yes) };
+};
+
+template <typename T, typename IndexType=Index>
+struct has_unary_operator
+{
+  template <typename C> static meta_yes testFunctor(C const *,std::enable_if_t<(sizeof(return_ptr<C>()->operator()(IndexType(0)))>0)> * = 0);
+  static meta_no testFunctor(...);
+
+  enum { value = sizeof(testFunctor(static_cast<T*>(0))) == sizeof(meta_yes) };
+};
+
+template <typename T, typename IndexType=Index>
+struct has_binary_operator
+{
+  template <typename C> static meta_yes testFunctor(C const *,std::enable_if_t<(sizeof(return_ptr<C>()->operator()(IndexType(0),IndexType(0)))>0)> * = 0);
+  static meta_no testFunctor(...);
+
+  enum { value = sizeof(testFunctor(static_cast<T*>(0))) == sizeof(meta_yes) };
+};
+
+/** \internal In short, it computes int(sqrt(\a Y)) with \a Y an integer.
+  * Usage example: \code meta_sqrt<1023>::ret \endcode
+  */
+template<int Y,
+         int InfX = 0,
+         int SupX = ((Y==1) ? 1 : Y/2),
+         bool Done = ((SupX - InfX) <= 1 || ((SupX * SupX <= Y) && ((SupX + 1) * (SupX + 1) > Y)))>
+class meta_sqrt
+{
+    enum {
+      MidX = (InfX+SupX)/2,
+      TakeInf = MidX*MidX > Y ? 1 : 0,
+      NewInf = int(TakeInf) ? InfX : int(MidX),
+      NewSup = int(TakeInf) ? int(MidX) : SupX
+    };
+  public:
+    enum { ret = meta_sqrt<Y,NewInf,NewSup>::ret };
+};
+
+template<int Y, int InfX, int SupX>
+class meta_sqrt<Y, InfX, SupX, true> { public:  enum { ret = (SupX*SupX <= Y) ? SupX : InfX }; };
+
+
+/** \internal Computes the least common multiple of two positive integer A and B
+  * at compile-time. 
+  */
+template<int A, int B, int K=1, bool Done = ((A*K)%B)==0, bool Big=(A>=B)>
+struct meta_least_common_multiple
+{
+  enum { ret = meta_least_common_multiple<A,B,K+1>::ret };
+};
+template<int A, int B, int K, bool Done>
+struct meta_least_common_multiple<A,B,K,Done,false>
+{
+  enum { ret = meta_least_common_multiple<B,A,K>::ret };
+};
+template<int A, int B, int K>
+struct meta_least_common_multiple<A,B,K,true,true>
+{
+  enum { ret = A*K };
+};
+
+
+/** \internal determines whether the product of two numeric types is allowed and what the return type is */
+template<typename T, typename U> struct scalar_product_traits
+{
+  enum { Defined = 0 };
+};
+
+// FIXME quick workaround around current limitation of result_of
+// template<typename Scalar, typename ArgType0, typename ArgType1>
+// struct result_of<scalar_product_op<Scalar>(ArgType0,ArgType1)> {
+// typedef typename scalar_product_traits<remove_all_t<ArgType0>, remove_all_t<ArgType1>>::ReturnType type;
+// };
+
+/** \internal Obtains a POD type suitable to use as storage for an object of a size
+  * of at most Len bytes, aligned as specified by \c Align.
+  */
+template<unsigned Len, unsigned Align>
+struct aligned_storage {
+  struct type {
+    EIGEN_ALIGN_TO_BOUNDARY(Align) unsigned char data[Len];
+  };
+};
+
+} // end namespace internal
+
+template<typename T> struct NumTraits;
+
+namespace numext {
+
+#if defined(EIGEN_GPU_COMPILE_PHASE)
+template<typename T> EIGEN_DEVICE_FUNC   void swap(T &a, T &b) { T tmp = b; b = a; a = tmp; }
+#else
+template<typename T> EIGEN_STRONG_INLINE void swap(T &a, T &b) { std::swap(a,b); }
+#endif
+
+using std::numeric_limits;
+
+// Integer division with rounding up.
+// T is assumed to be an integer type with a>=0, and b>0
+template<typename T>
+EIGEN_DEVICE_FUNC
+T div_ceil(const T &a, const T &b)
+{
+  return (a+b-1) / b;
+}
+
+// Handle integer comparisons of different signedness.
+template <typename X, typename Y, bool XIsInteger = NumTraits<X>::IsInteger, bool XIsSigned = NumTraits<X>::IsSigned,
+          bool YIsInteger = NumTraits<Y>::IsInteger, bool YIsSigned = NumTraits<Y>::IsSigned>
+struct equal_strict_impl {
+  static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool run(const X& x, const Y& y) { return x == y; }
+};
+template <typename X, typename Y>
+struct equal_strict_impl<X, Y, true, false, true, true> {
+  // X is an unsigned integer
+  // Y is a signed integer
+  // if Y is non-negative, it may be represented exactly as its unsigned counterpart.
+  using UnsignedY = typename internal::make_unsigned<Y>::type;
+  static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool run(const X& x, const Y& y) {
+    return y < Y(0) ? false : (x == static_cast<UnsignedY>(y));
+  }
+};
+template <typename X, typename Y>
+struct equal_strict_impl<X, Y, true, true, true, false> {
+  // X is a signed integer
+  // Y is an unsigned integer
+  static EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool run(const X& x, const Y& y) {
+    return equal_strict_impl<Y, X>::run(y, x);
+  }
+};
+
+// The aim of the following functions is to bypass -Wfloat-equal warnings
+// when we really want a strict equality comparison on floating points.
+template<typename X, typename Y> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool equal_strict(const X& x, const Y& y) { return equal_strict_impl<X, Y>::run(x, y); }
+
+#if !defined(EIGEN_GPU_COMPILE_PHASE) || (!defined(EIGEN_CUDA_ARCH) && defined(EIGEN_CONSTEXPR_ARE_DEVICE_FUNC))
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+bool equal_strict(const float& x,const float& y) { return std::equal_to<float>()(x,y); }
+
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+bool equal_strict(const double& x,const double& y) { return std::equal_to<double>()(x,y); }
+#endif
+
+/**
+ * \internal Performs an exact comparison of x to zero, e.g. to decide whether a term can be ignored.
+ * Use this to to bypass -Wfloat-equal warnings when exact zero is what needs to be tested.
+*/
+template<typename X> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+bool is_exactly_zero(const X& x) { return equal_strict(x, typename NumTraits<X>::Literal{0}); }
+
+/**
+ * \internal Performs an exact comparison of x to one, e.g. to decide whether a factor needs to be multiplied.
+ * Use this to to bypass -Wfloat-equal warnings when exact one is what needs to be tested.
+*/
+template<typename X> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+bool is_exactly_one(const X& x) { return equal_strict(x, typename NumTraits<X>::Literal{1}); }
+
+template<typename X, typename Y> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+bool not_equal_strict(const X& x,const Y& y) { return !equal_strict_impl<X, Y>::run(x, y); }
+
+#if !defined(EIGEN_GPU_COMPILE_PHASE) || (!defined(EIGEN_CUDA_ARCH) && defined(EIGEN_CONSTEXPR_ARE_DEVICE_FUNC))
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+bool not_equal_strict(const float& x,const float& y) { return std::not_equal_to<float>()(x,y); }
+
+template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
+bool not_equal_strict(const double& x,const double& y) { return std::not_equal_to<double>()(x,y); }
+#endif
+
+} // end namespace numext
+
+namespace internal {
+
+template<typename Scalar>
+struct is_identically_zero_impl {
+  static inline bool run(const Scalar& s) {
+    return numext::is_exactly_zero(s);
+  }
+};
+
+template<typename Scalar> EIGEN_STRONG_INLINE
+bool is_identically_zero(const Scalar& s) { return is_identically_zero_impl<Scalar>::run(s); }
+
+/// \internal Returns true if its argument is of integer or enum type.
+/// FIXME this has the same purpose as `is_valid_index_type` in XprHelper.h
+template<typename A>
+constexpr bool is_int_or_enum_v = std::is_enum<A>::value || std::is_integral<A>::value;
+
+/// \internal Gets the minimum of two values which may be integers or enums
+template<typename A, typename B>
+inline constexpr int plain_enum_min(A a, B b) {
+  static_assert(is_int_or_enum_v<A>, "Argument a must be an integer or enum");
+  static_assert(is_int_or_enum_v<B>, "Argument b must be an integer or enum");
+  return ((int) a <= (int) b) ? (int) a : (int) b;
+}
+
+/// \internal Gets the maximum of two values which may be integers or enums
+template<typename A, typename B>
+inline constexpr int plain_enum_max(A a, B b) {
+  static_assert(is_int_or_enum_v<A>, "Argument a must be an integer or enum");
+  static_assert(is_int_or_enum_v<B>, "Argument b must be an integer or enum");
+  return ((int) a >= (int) b) ? (int) a : (int) b;
+}
+
+/**
+ * \internal
+ *  `min_size_prefer_dynamic` gives the min between compile-time sizes. 0 has absolute priority, followed by 1,
+ *  followed by Dynamic, followed by other finite values. The reason for giving Dynamic the priority over
+ *  finite values is that min(3, Dynamic) should be Dynamic, since that could be anything between 0 and 3.
+ */
+template<typename A, typename B>
+inline constexpr int min_size_prefer_dynamic(A a, B b) {
+  static_assert(is_int_or_enum_v<A>, "Argument a must be an integer or enum");
+  static_assert(is_int_or_enum_v<B>, "Argument b must be an integer or enum");
+  if ((int) a == 0 || (int) b == 0) return 0;
+  if ((int) a == 1 || (int) b == 1) return 1;
+  if ((int) a == Dynamic || (int) b == Dynamic) return Dynamic;
+  return plain_enum_min(a, b);
+}
+
+/**
+ * \internal
+ *  min_size_prefer_fixed is a variant of `min_size_prefer_dynamic` comparing MaxSizes. The difference is that finite values
+ *  now have priority over Dynamic, so that min(3, Dynamic) gives 3. Indeed, whatever the actual value is
+ *  (between 0 and 3), it is not more than 3.
+ */
+template<typename A, typename B>
+inline constexpr int min_size_prefer_fixed(A a, B b) {
+  static_assert(is_int_or_enum_v<A>, "Argument a must be an integer or enum");
+  static_assert(is_int_or_enum_v<B>, "Argument b must be an integer or enum");
+  if ((int) a == 0 || (int) b == 0) return 0;
+  if ((int) a == 1 || (int) b == 1) return 1;
+  if ((int) a == Dynamic && (int) b == Dynamic) return Dynamic;
+  if ((int) a == Dynamic) return (int) b;
+  if ((int) b == Dynamic) return (int) a;
+  return plain_enum_min(a, b);
+}
+
+/// \internal see `min_size_prefer_fixed`. No need for a separate variant for MaxSizes here.
+template<typename A, typename B>
+inline constexpr int max_size_prefer_dynamic(A a, B b) {
+  static_assert(is_int_or_enum_v<A>, "Argument a must be an integer or enum");
+  static_assert(is_int_or_enum_v<B>, "Argument b must be an integer or enum");
+  if ((int) a == Dynamic || (int) b == Dynamic) return Dynamic;
+  return plain_enum_max(a, b);
+}
+
+/// \internal Calculate logical XOR at compile time
+inline constexpr bool logical_xor(bool a, bool b) {
+  return a != b;
+}
+
+/// \internal Calculate logical IMPLIES at compile time
+inline constexpr bool check_implication(bool a, bool b) {
+  return !a || b;
+}
+
+/// \internal Provide fallback for std::is_constant_evaluated for pre-C++20.
+#if EIGEN_COMP_CXXVER >= 20
+using std::is_constant_evaluated;
+#else
+constexpr bool is_constant_evaluated() { return false; }
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_META_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/MoreMeta.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/MoreMeta.h
new file mode 100644
index 0000000000000000000000000000000000000000..53a9de6858703f36e649ead3c1901384bc4e0a67
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/MoreMeta.h
@@ -0,0 +1,531 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MOREMETA_H
+#define EIGEN_MOREMETA_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename... tt>
+struct type_list { constexpr static int count = sizeof...(tt); };
+
+template<typename t, typename... tt>
+struct type_list<t, tt...> { constexpr static int count = sizeof...(tt) + 1; typedef t first_type; };
+
+template<typename T, T... nn>
+struct numeric_list { constexpr static std::size_t count = sizeof...(nn); };
+
+template<typename T, T n, T... nn>
+struct numeric_list<T, n, nn...> { static constexpr std::size_t count = sizeof...(nn) + 1;
+                                   static constexpr T first_value = n; };
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+/* numeric list constructors
+ *
+ * equivalencies:
+ *     constructor                                              result
+ *     typename gen_numeric_list<int, 5>::type                  numeric_list<int, 0,1,2,3,4>
+ *     typename gen_numeric_list_reversed<int, 5>::type         numeric_list<int, 4,3,2,1,0>
+ *     typename gen_numeric_list_swapped_pair<int, 5,1,2>::type numeric_list<int, 0,2,1,3,4>
+ *     typename gen_numeric_list_repeated<int, 0, 5>::type      numeric_list<int, 0,0,0,0,0>
+ */
+
+template<typename T, std::size_t n, T start = 0, T... ii> struct gen_numeric_list                     : gen_numeric_list<T, n-1, start, start + n-1, ii...> {};
+template<typename T, T start, T... ii>                    struct gen_numeric_list<T, 0, start, ii...> { typedef numeric_list<T, ii...> type; };
+
+template<typename T, std::size_t n, T start = 0, T... ii> struct gen_numeric_list_reversed                     : gen_numeric_list_reversed<T, n-1, start, ii..., start + n-1> {};
+template<typename T, T start, T... ii>                    struct gen_numeric_list_reversed<T, 0, start, ii...> { typedef numeric_list<T, ii...> type; };
+
+template<typename T, std::size_t n, T a, T b, T start = 0, T... ii> struct gen_numeric_list_swapped_pair                           : gen_numeric_list_swapped_pair<T, n-1, a, b, start, (start + n-1) == a ? b : ((start + n-1) == b ? a : (start + n-1)), ii...> {};
+template<typename T, T a, T b, T start, T... ii>                    struct gen_numeric_list_swapped_pair<T, 0, a, b, start, ii...> { typedef numeric_list<T, ii...> type; };
+
+template<typename T, std::size_t n, T V, T... nn> struct gen_numeric_list_repeated                 : gen_numeric_list_repeated<T, n-1, V, V, nn...> {};
+template<typename T, T V, T... nn>                struct gen_numeric_list_repeated<T, 0, V, nn...> { typedef numeric_list<T, nn...> type; };
+
+/* list manipulation: concatenate */
+
+template<class a, class b> struct concat;
+
+template<typename... as, typename... bs> struct concat<type_list<as...>,       type_list<bs...>>        { typedef type_list<as..., bs...> type; };
+template<typename T, T... as, T... bs>   struct concat<numeric_list<T, as...>, numeric_list<T, bs...> > { typedef numeric_list<T, as..., bs...> type; };
+
+template<typename... p> struct mconcat;
+template<typename a>                             struct mconcat<a>           { typedef a type; };
+template<typename a, typename b>                 struct mconcat<a, b>        : concat<a, b> {};
+template<typename a, typename b, typename... cs> struct mconcat<a, b, cs...> : concat<a, typename mconcat<b, cs...>::type> {};
+
+/* list manipulation: extract slices */
+
+template<int n, typename x> struct take;
+template<int n, typename a, typename... as> struct take<n, type_list<a, as...>> : concat<type_list<a>, typename take<n-1, type_list<as...>>::type> {};
+template<int n>                             struct take<n, type_list<>>         { typedef type_list<> type; };
+template<typename a, typename... as>        struct take<0, type_list<a, as...>> { typedef type_list<> type; };
+template<>                                  struct take<0, type_list<>>         { typedef type_list<> type; };
+
+template<typename T, int n, T a, T... as> struct take<n, numeric_list<T, a, as...>> : concat<numeric_list<T, a>, typename take<n-1, numeric_list<T, as...>>::type> {};
+// XXX The following breaks in gcc-11, and is invalid anyways.
+// template<typename T, int n>               struct take<n, numeric_list<T>>           { typedef numeric_list<T> type; };
+template<typename T, T a, T... as>        struct take<0, numeric_list<T, a, as...>> { typedef numeric_list<T> type; };
+template<typename T>                      struct take<0, numeric_list<T>>           { typedef numeric_list<T> type; };
+
+template<typename T, int n, T... ii>      struct h_skip_helper_numeric;
+template<typename T, int n, T i, T... ii> struct h_skip_helper_numeric<T, n, i, ii...> : h_skip_helper_numeric<T, n-1, ii...> {};
+template<typename T, T i, T... ii>        struct h_skip_helper_numeric<T, 0, i, ii...> { typedef numeric_list<T, i, ii...> type; };
+template<typename T, int n>               struct h_skip_helper_numeric<T, n>           { typedef numeric_list<T> type; };
+template<typename T>                      struct h_skip_helper_numeric<T, 0>           { typedef numeric_list<T> type; };
+
+template<int n, typename... tt>             struct h_skip_helper_type;
+template<int n, typename t, typename... tt> struct h_skip_helper_type<n, t, tt...> : h_skip_helper_type<n-1, tt...> {};
+template<typename t, typename... tt>        struct h_skip_helper_type<0, t, tt...> { typedef type_list<t, tt...> type; };
+template<int n>                             struct h_skip_helper_type<n>           { typedef type_list<> type; };
+template<>                                  struct h_skip_helper_type<0>           { typedef type_list<> type; };
+#endif //not EIGEN_PARSED_BY_DOXYGEN
+
+template<int n>
+struct h_skip {
+  template<typename T, T... ii>
+  constexpr static EIGEN_STRONG_INLINE typename h_skip_helper_numeric<T, n, ii...>::type helper(numeric_list<T, ii...>) { return typename h_skip_helper_numeric<T, n, ii...>::type(); }
+  template<typename... tt>
+  constexpr static EIGEN_STRONG_INLINE typename h_skip_helper_type<n, tt...>::type helper(type_list<tt...>) { return typename h_skip_helper_type<n, tt...>::type(); }
+};
+
+template<int n, typename a> struct skip { typedef decltype(h_skip<n>::helper(a())) type; };
+
+template<int start, int count, typename a> struct slice : take<count, typename skip<start, a>::type> {};
+
+/* list manipulation: retrieve single element from list */
+
+template<int n, typename x> struct get;
+
+template<int n, typename a, typename... as>               struct get<n, type_list<a, as...>>   : get<n-1, type_list<as...>> {};
+template<typename a, typename... as>                      struct get<0, type_list<a, as...>>   { typedef a type; };
+
+template<typename T, int n, T a, T... as>                        struct get<n, numeric_list<T, a, as...>>   : get<n-1, numeric_list<T, as...>> {};
+template<typename T, T a, T... as>                               struct get<0, numeric_list<T, a, as...>>   { constexpr static T value = a; };
+
+template<std::size_t n, typename T, T a, T... as> constexpr T       array_get(const numeric_list<T, a, as...>&) {
+   return get<(int)n, numeric_list<T, a, as...>>::value;
+}
+
+/* always get type, regardless of dummy; good for parameter pack expansion */
+
+template<typename T, T dummy, typename t> struct id_numeric  { typedef t type; };
+template<typename dummy, typename t>      struct id_type     { typedef t type; };
+
+/* equality checking, flagged version */
+
+template<typename a, typename b> struct is_same_gf : is_same<a, b> { constexpr static int global_flags = 0; };
+
+/* apply_op to list */
+
+template<
+  bool from_left, // false
+  template<typename, typename> class op,
+  typename additional_param,
+  typename... values
+>
+struct h_apply_op_helper                                        { typedef type_list<typename op<values, additional_param>::type...> type; };
+template<
+  template<typename, typename> class op,
+  typename additional_param,
+  typename... values
+>
+struct h_apply_op_helper<true, op, additional_param, values...> { typedef type_list<typename op<additional_param, values>::type...> type; };
+
+template<
+  bool from_left,
+  template<typename, typename> class op,
+  typename additional_param
+>
+struct h_apply_op
+{
+  template<typename... values>
+  constexpr static typename h_apply_op_helper<from_left, op, additional_param, values...>::type helper(type_list<values...>)
+  { return typename h_apply_op_helper<from_left, op, additional_param, values...>::type(); }
+};
+
+template<
+  template<typename, typename> class op,
+  typename additional_param,
+  typename a
+>
+struct apply_op_from_left { typedef decltype(h_apply_op<true, op, additional_param>::helper(a())) type; };
+
+template<
+  template<typename, typename> class op,
+  typename additional_param,
+  typename a
+>
+struct apply_op_from_right { typedef decltype(h_apply_op<false, op, additional_param>::helper(a())) type; };
+
+/* see if an element is in a list */
+
+template<
+  template<typename, typename> class test,
+  typename check_against,
+  typename h_list,
+  bool last_check_positive = false
+>
+struct contained_in_list;
+
+template<
+  template<typename, typename> class test,
+  typename check_against,
+  typename h_list
+>
+struct contained_in_list<test, check_against, h_list, true>
+{
+  constexpr static bool value = true;
+};
+
+template<
+  template<typename, typename> class test,
+  typename check_against,
+  typename a,
+  typename... as
+>
+struct contained_in_list<test, check_against, type_list<a, as...>, false> : contained_in_list<test, check_against, type_list<as...>, test<check_against, a>::value> {};
+
+template<
+  template<typename, typename> class test,
+  typename check_against,
+  typename... empty
+>
+struct contained_in_list<test, check_against, type_list<empty...>, false> { constexpr static bool value = false; };
+
+/* see if an element is in a list and check for global flags */
+
+template<
+  template<typename, typename> class test,
+  typename check_against,
+  typename h_list,
+  int default_flags = 0,
+  bool last_check_positive = false,
+  int last_check_flags = default_flags
+>
+struct contained_in_list_gf;
+
+template<
+  template<typename, typename> class test,
+  typename check_against,
+  typename h_list,
+  int default_flags,
+  int last_check_flags
+>
+struct contained_in_list_gf<test, check_against, h_list, default_flags, true, last_check_flags>
+{
+  constexpr static bool value = true;
+  constexpr static int global_flags = last_check_flags;
+};
+
+template<
+  template<typename, typename> class test,
+  typename check_against,
+  typename a,
+  typename... as,
+  int default_flags,
+  int last_check_flags
+>
+struct contained_in_list_gf<test, check_against, type_list<a, as...>, default_flags, false, last_check_flags> : contained_in_list_gf<test, check_against, type_list<as...>, default_flags, test<check_against, a>::value, test<check_against, a>::global_flags> {};
+
+template<
+  template<typename, typename> class test,
+  typename check_against,
+  typename... empty,
+  int default_flags,
+  int last_check_flags
+>
+struct contained_in_list_gf<test, check_against, type_list<empty...>, default_flags, false, last_check_flags> { constexpr static bool value = false; constexpr static int global_flags = default_flags; };
+
+/* generic reductions */
+
+template<
+  typename Reducer,
+  typename... Ts
+> struct reduce;
+
+template<
+  typename Reducer
+> struct reduce<Reducer>
+{
+  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE int run() { return Reducer::Identity; }
+};
+
+template<
+  typename Reducer,
+  typename A
+> struct reduce<Reducer, A>
+{
+  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE A run(A a) { return a; }
+};
+
+template<
+  typename Reducer,
+  typename A,
+  typename... Ts
+> struct reduce<Reducer, A, Ts...>
+{
+  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE auto run(A a, Ts... ts) -> decltype(Reducer::run(a, reduce<Reducer, Ts...>::run(ts...))) {
+    return Reducer::run(a, reduce<Reducer, Ts...>::run(ts...));
+  }
+};
+
+/* generic binary operations */
+
+struct sum_op           {
+  template<typename A, typename B> EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a + b)   { return a + b;   }
+  static constexpr int Identity = 0;
+};
+struct product_op       {
+  template<typename A, typename B> EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a * b)   { return a * b;   }
+  static constexpr int Identity = 1;
+};
+
+struct logical_and_op   { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a && b)  { return a && b;  } };
+struct logical_or_op    { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a || b)  { return a || b;  } };
+
+struct equal_op         { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a == b)  { return a == b;  } };
+struct not_equal_op     { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a != b)  { return a != b;  } };
+struct lesser_op        { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a < b)   { return a < b;   } };
+struct lesser_equal_op  { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a <= b)  { return a <= b;  } };
+struct greater_op       { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a > b)   { return a > b;   } };
+struct greater_equal_op { template<typename A, typename B> constexpr static EIGEN_STRONG_INLINE auto run(A a, B b) -> decltype(a >= b)  { return a >= b;  } };
+
+/* generic unary operations */
+
+struct not_op                { template<typename A> constexpr static EIGEN_STRONG_INLINE auto run(A a) -> decltype(!a)      { return !a;      } };
+struct negation_op           { template<typename A> constexpr static EIGEN_STRONG_INLINE auto run(A a) -> decltype(-a)      { return -a;      } };
+struct greater_equal_zero_op { template<typename A> constexpr static EIGEN_STRONG_INLINE auto run(A a) -> decltype(a >= 0)  { return a >= 0;  } };
+
+
+/* reductions for lists */
+
+// using auto -> return value spec makes ICC 13.0 and 13.1 crash here, so we have to hack it
+// together in front... (13.0 doesn't work with array_prod/array_reduce/... anyway, but 13.1
+// does...
+template<typename... Ts>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE decltype(reduce<product_op, Ts...>::run((*((Ts*)0))...)) arg_prod(Ts... ts)
+{
+  return reduce<product_op, Ts...>::run(ts...);
+}
+
+template<typename... Ts>
+constexpr EIGEN_STRONG_INLINE decltype(reduce<sum_op, Ts...>::run((*((Ts*)0))...)) arg_sum(Ts... ts)
+{
+  return reduce<sum_op, Ts...>::run(ts...);
+}
+
+/* reverse arrays */
+
+template<typename Array, int... n>
+constexpr EIGEN_STRONG_INLINE Array h_array_reverse(Array arr, numeric_list<int, n...>)
+{
+  return {{array_get<sizeof...(n) - n - 1>(arr)...}};
+}
+
+template<typename T, std::size_t N>
+constexpr EIGEN_STRONG_INLINE array<T, N> array_reverse(array<T, N> arr)
+{
+  return h_array_reverse(arr, typename gen_numeric_list<int, N>::type());
+}
+
+
+/* generic array reductions */
+
+// can't reuse standard reduce() interface above because Intel's Compiler
+// *really* doesn't like it, so we just reimplement the stuff
+// (start from N - 1 and work down to 0 because specialization for
+// n == N - 1 also doesn't work in Intel's compiler, so it goes into
+// an infinite loop)
+template<typename Reducer, typename T, std::size_t N, std::size_t n = N - 1>
+struct h_array_reduce {
+  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE auto run(array<T, N> arr, T identity) -> decltype(Reducer::run(h_array_reduce<Reducer, T, N, n - 1>::run(arr, identity), array_get<n>(arr)))
+  {
+    return Reducer::run(h_array_reduce<Reducer, T, N, n - 1>::run(arr, identity), array_get<n>(arr));
+  }
+};
+
+template<typename Reducer, typename T, std::size_t N>
+struct h_array_reduce<Reducer, T, N, 0>
+{
+  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE T run(const array<T, N>& arr, T)
+  {
+    return array_get<0>(arr);
+  }
+};
+
+template<typename Reducer, typename T>
+struct h_array_reduce<Reducer, T, 0>
+{
+  EIGEN_DEVICE_FUNC constexpr static EIGEN_STRONG_INLINE T run(const array<T, 0>&, T identity)
+  {
+    return identity;
+  }
+};
+
+template<typename Reducer, typename T, std::size_t N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE auto array_reduce(const array<T, N>& arr, T identity) -> decltype(h_array_reduce<Reducer, T, N>::run(arr, identity))
+{
+  return h_array_reduce<Reducer, T, N>::run(arr, identity);
+}
+
+/* standard array reductions */
+
+template<typename T, std::size_t N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE auto array_sum(const array<T, N>& arr) -> decltype(array_reduce<sum_op, T, N>(arr, static_cast<T>(0)))
+{
+  return array_reduce<sum_op, T, N>(arr, static_cast<T>(0));
+}
+
+template<typename T, std::size_t N>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE auto array_prod(const array<T, N>& arr) -> decltype(array_reduce<product_op, T, N>(arr, static_cast<T>(1)))
+{
+  return array_reduce<product_op, T, N>(arr, static_cast<T>(1));
+}
+
+template<typename t>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE t array_prod(const std::vector<t>& a) {
+  eigen_assert(a.size() > 0);
+  t prod = 1;
+  for (size_t i = 0; i < a.size(); ++i) { prod *= a[i]; }
+  return prod;
+}
+
+/* zip an array */
+
+template<typename Op, typename A, typename B, std::size_t N, int... n>
+constexpr EIGEN_STRONG_INLINE array<decltype(Op::run(A(), B())),N> h_array_zip(array<A, N> a, array<B, N> b, numeric_list<int, n...>)
+{
+  return array<decltype(Op::run(A(), B())),N>{{ Op::run(array_get<n>(a), array_get<n>(b))... }};
+}
+
+template<typename Op, typename A, typename B, std::size_t N>
+constexpr EIGEN_STRONG_INLINE array<decltype(Op::run(A(), B())),N> array_zip(array<A, N> a, array<B, N> b)
+{
+  return h_array_zip<Op>(a, b, typename gen_numeric_list<int, N>::type());
+}
+
+/* zip an array and reduce the result */
+
+template<typename Reducer, typename Op, typename A, typename B, std::size_t N, int... n>
+constexpr EIGEN_STRONG_INLINE auto h_array_zip_and_reduce(array<A, N> a, array<B, N> b, numeric_list<int, n...>) -> decltype(reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A(), B()))>::type...>::run(Op::run(array_get<n>(a), array_get<n>(b))...))
+{
+  return reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A(), B()))>::type...>::run(Op::run(array_get<n>(a), array_get<n>(b))...);
+}
+
+template<typename Reducer, typename Op, typename A, typename B, std::size_t N>
+constexpr EIGEN_STRONG_INLINE auto array_zip_and_reduce(array<A, N> a, array<B, N> b) -> decltype(h_array_zip_and_reduce<Reducer, Op, A, B, N>(a, b, typename gen_numeric_list<int, N>::type()))
+{
+  return h_array_zip_and_reduce<Reducer, Op, A, B, N>(a, b, typename gen_numeric_list<int, N>::type());
+}
+
+/* apply stuff to an array */
+
+template<typename Op, typename A, std::size_t N, int... n>
+constexpr EIGEN_STRONG_INLINE array<decltype(Op::run(A())),N> h_array_apply(array<A, N> a, numeric_list<int, n...>)
+{
+  return array<decltype(Op::run(A())),N>{{ Op::run(array_get<n>(a))... }};
+}
+
+template<typename Op, typename A, std::size_t N>
+constexpr EIGEN_STRONG_INLINE array<decltype(Op::run(A())),N> array_apply(array<A, N> a)
+{
+  return h_array_apply<Op>(a, typename gen_numeric_list<int, N>::type());
+}
+
+/* apply stuff to an array and reduce */
+
+template<typename Reducer, typename Op, typename A, std::size_t N, int... n>
+constexpr EIGEN_STRONG_INLINE auto h_array_apply_and_reduce(array<A, N> arr, numeric_list<int, n...>) -> decltype(reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A()))>::type...>::run(Op::run(array_get<n>(arr))...))
+{
+  return reduce<Reducer, typename id_numeric<int,n,decltype(Op::run(A()))>::type...>::run(Op::run(array_get<n>(arr))...);
+}
+
+template<typename Reducer, typename Op, typename A, std::size_t N>
+constexpr EIGEN_STRONG_INLINE auto array_apply_and_reduce(array<A, N> a) -> decltype(h_array_apply_and_reduce<Reducer, Op, A, N>(a, typename gen_numeric_list<int, N>::type()))
+{
+  return h_array_apply_and_reduce<Reducer, Op, A, N>(a, typename gen_numeric_list<int, N>::type());
+}
+
+/* repeat a value n times (and make an array out of it
+ * usage:
+ *   array<int, 16> = repeat<16>(42);
+ */
+
+template<int n>
+struct h_repeat
+{
+  template<typename t, int... ii>
+  constexpr static EIGEN_STRONG_INLINE array<t, n> run(t v, numeric_list<int, ii...>)
+  {
+    return {{ typename id_numeric<int, ii, t>::type(v)... }};
+  }
+};
+
+template<int n, typename t>
+constexpr array<t, n> repeat(t v) { return h_repeat<n>::run(v, typename gen_numeric_list<int, n>::type()); }
+
+/* instantiate a class by a C-style array */
+template<class InstType, typename ArrType, std::size_t N, bool Reverse, typename... Ps>
+struct h_instantiate_by_c_array;
+
+template<class InstType, typename ArrType, std::size_t N, typename... Ps>
+struct h_instantiate_by_c_array<InstType, ArrType, N, false, Ps...>
+{
+  static InstType run(ArrType* arr, Ps... args)
+  {
+    return h_instantiate_by_c_array<InstType, ArrType, N - 1, false, Ps..., ArrType>::run(arr + 1, args..., arr[0]);
+  }
+};
+
+template<class InstType, typename ArrType, std::size_t N, typename... Ps>
+struct h_instantiate_by_c_array<InstType, ArrType, N, true, Ps...>
+{
+  static InstType run(ArrType* arr, Ps... args)
+  {
+    return h_instantiate_by_c_array<InstType, ArrType, N - 1, false, ArrType, Ps...>::run(arr + 1, arr[0], args...);
+  }
+};
+
+template<class InstType, typename ArrType, typename... Ps>
+struct h_instantiate_by_c_array<InstType, ArrType, 0, false, Ps...>
+{
+  static InstType run(ArrType* arr, Ps... args)
+  {
+    (void)arr;
+    return InstType(args...);
+  }
+};
+
+template<class InstType, typename ArrType, typename... Ps>
+struct h_instantiate_by_c_array<InstType, ArrType, 0, true, Ps...>
+{
+  static InstType run(ArrType* arr, Ps... args)
+  {
+    (void)arr;
+    return InstType(args...);
+  }
+};
+
+template<class InstType, typename ArrType, std::size_t N, bool Reverse = false>
+InstType instantiate_by_c_array(ArrType* arr)
+{
+  return h_instantiate_by_c_array<InstType, ArrType, N, Reverse>::run(arr);
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_MOREMETA_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/ReenableStupidWarnings.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/ReenableStupidWarnings.h
new file mode 100644
index 0000000000000000000000000000000000000000..7021e6d3cb63ece54c553d3f098421461b77278a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/ReenableStupidWarnings.h
@@ -0,0 +1,39 @@
+#ifdef EIGEN_WARNINGS_DISABLED_2
+// "DisableStupidWarnings.h" was included twice recursively: Do not re-enable warnings yet!
+#  undef EIGEN_WARNINGS_DISABLED_2
+
+#elif defined(EIGEN_WARNINGS_DISABLED)
+#undef EIGEN_WARNINGS_DISABLED
+
+#ifndef EIGEN_PERMANENTLY_DISABLE_STUPID_WARNINGS
+  #ifdef _MSC_VER
+    #pragma warning( pop )
+  #elif defined __INTEL_COMPILER
+    #pragma warning pop
+  #elif defined __clang__
+    #pragma clang diagnostic pop
+  #elif defined __GNUC__  &&  !defined(__FUJITSU) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6))
+    #pragma GCC diagnostic pop
+  #endif
+
+  #if defined __NVCC__
+//    Don't re-enable the diagnostic messages, as it turns out these messages need
+//    to be disabled at the point of the template instantiation (i.e the user code)
+//    otherwise they'll be triggered by nvcc.
+//    #define EIGEN_MAKE_PRAGMA(X) _Pragma(#X)
+//    #if __NVCC_DIAG_PRAGMA_SUPPORT__
+//      #define EIGEN_NV_DIAG_DEFAULT(X) EIGEN_MAKE_PRAGMA(nv_diag_default X)
+//    #else
+//      #define EIGEN_NV_DIAG_DEFAULT(X) EIGEN_MAKE_PRAGMA(diag_default X)
+//    #endif
+//    EIGEN_NV_DIAG_DEFAULT(code_is_unreachable)
+//    EIGEN_NV_DIAG_DEFAULT(initialization_not_reachable)
+//    EIGEN_NV_DIAG_DEFAULT(2651)
+//    EIGEN_NV_DIAG_DEFAULT(2653)
+//    #undef EIGEN_NV_DIAG_DEFAULT
+//    #undef EIGEN_MAKE_PRAGMA
+  #endif
+
+#endif
+
+#endif // EIGEN_WARNINGS_DISABLED
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/ReshapedHelper.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/ReshapedHelper.h
new file mode 100644
index 0000000000000000000000000000000000000000..6daea0305f356d27ea8bb3f8844bb9cbaeb6a958
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/ReshapedHelper.h
@@ -0,0 +1,52 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+
+#ifndef EIGEN_RESHAPED_HELPER_H
+#define EIGEN_RESHAPED_HELPER_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+enum AutoSize_t   { AutoSize };
+const int AutoOrder = 2;
+
+namespace internal {
+
+template<typename SizeType,typename OtherSize, int TotalSize>
+struct get_compiletime_reshape_size {
+  enum { value = get_fixed_value<SizeType>::value };
+};
+
+template<typename SizeType>
+Index get_runtime_reshape_size(SizeType size, Index /*other*/, Index /*total*/) {
+  return internal::get_runtime_value(size);
+}
+
+template<typename OtherSize, int TotalSize>
+struct get_compiletime_reshape_size<AutoSize_t,OtherSize,TotalSize> {
+  enum {
+    other_size = get_fixed_value<OtherSize>::value,
+    value = (TotalSize==Dynamic || other_size==Dynamic) ? Dynamic : TotalSize / other_size };
+};
+
+inline Index get_runtime_reshape_size(AutoSize_t /*size*/, Index other, Index total) {
+  return total/other;
+}
+
+constexpr inline int get_compiletime_reshape_order(int flags, int order) {
+  return order == AutoOrder ? flags & RowMajorBit : order;
+}
+
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_RESHAPED_HELPER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Serializer.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Serializer.h
new file mode 100644
index 0000000000000000000000000000000000000000..cbfc04acbabb53e6f634d2a8a8b170d0bb5086b2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/Serializer.h
@@ -0,0 +1,220 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2021 The Eigen Team
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SERIALIZER_H
+#define EIGEN_SERIALIZER_H
+
+#include <type_traits>
+
+// The Serializer class encodes data into a memory buffer so it can be later
+// reconstructed. This is mainly used to send objects back-and-forth between
+// the CPU and GPU.
+
+namespace Eigen {
+
+/**
+ * Serializes an object to a memory buffer.
+ * 
+ * Useful for transferring data (e.g. back-and-forth to a device).
+ */
+template<typename T, typename EnableIf = void>
+class Serializer;
+
+// Specialization for POD types.
+template<typename T>
+class Serializer<T, typename std::enable_if_t<
+                      std::is_trivial<T>::value 
+                      && std::is_standard_layout<T>::value>> {
+ public:
+ 
+  /**
+   * Determines the required size of the serialization buffer for a value.
+   * 
+   * \param value the value to serialize.
+   * \return the required size.
+   */
+  EIGEN_DEVICE_FUNC size_t size(const T& value) const {
+    return sizeof(value);
+  }
+  
+  /**
+   * Serializes a value to a byte buffer.
+   * \param dest the destination buffer; if this is nullptr, does nothing.
+   * \param end the end of the destination buffer.
+   * \param value the value to serialize.
+   * \return the next memory address past the end of the serialized data.
+   */
+  EIGEN_DEVICE_FUNC uint8_t* serialize(uint8_t* dest, uint8_t* end, const T& value) {
+    if (EIGEN_PREDICT_FALSE(dest == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(dest + sizeof(value) > end)) return nullptr;
+    EIGEN_USING_STD(memcpy)
+    memcpy(dest, &value, sizeof(value));
+    return dest + sizeof(value);
+  }
+  
+  /**
+   * Deserializes a value from a byte buffer.
+   * \param src the source buffer; if this is nullptr, does nothing.
+   * \param end the end of the source buffer.
+   * \param value the value to populate.
+   * \return the next unprocessed memory address; nullptr if parsing errors are detected.
+   */
+  EIGEN_DEVICE_FUNC const uint8_t* deserialize(const uint8_t* src, const uint8_t* end, T& value) const {
+    if (EIGEN_PREDICT_FALSE(src == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(src + sizeof(value) > end)) return nullptr;
+    EIGEN_USING_STD(memcpy)
+    memcpy(&value, src, sizeof(value));
+    return src + sizeof(value);
+  }
+};
+
+// Specialization for DenseBase.
+// Serializes [rows, cols, data...].
+template<typename Derived>
+class Serializer<DenseBase<Derived>, void> {
+ public:
+  typedef typename Derived::Scalar Scalar;
+  
+  struct Header {
+    typename Derived::Index rows;
+    typename Derived::Index cols;
+  };
+  
+  EIGEN_DEVICE_FUNC size_t size(const Derived& value) const {
+    return sizeof(Header) + sizeof(Scalar) * value.size();
+  }
+  
+  EIGEN_DEVICE_FUNC uint8_t* serialize(uint8_t* dest, uint8_t* end, const Derived& value) {
+    if (EIGEN_PREDICT_FALSE(dest == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(dest + size(value) > end)) return nullptr;
+    const size_t header_bytes = sizeof(Header);
+    const size_t data_bytes = sizeof(Scalar) * value.size();
+    Header header = {value.rows(), value.cols()};
+    EIGEN_USING_STD(memcpy)
+    memcpy(dest, &header, header_bytes);
+    dest += header_bytes;
+    memcpy(dest, value.data(), data_bytes);
+    return dest + data_bytes;
+  }
+  
+  EIGEN_DEVICE_FUNC const uint8_t* deserialize(const uint8_t* src, const uint8_t* end, Derived& value) const {
+    if (EIGEN_PREDICT_FALSE(src == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(src + sizeof(Header) > end)) return nullptr;
+    const size_t header_bytes = sizeof(Header);
+    Header header;
+    EIGEN_USING_STD(memcpy)
+    memcpy(&header, src, header_bytes);
+    src += header_bytes;
+    const size_t data_bytes = sizeof(Scalar) * header.rows * header.cols;
+    if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr;
+    value.resize(header.rows, header.cols);
+    memcpy(value.data(), src, data_bytes);
+    return src + data_bytes;
+  }
+};
+
+template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+class Serializer<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > : public
+  Serializer<DenseBase<Matrix<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > > {};
+  
+template<typename Scalar, int Rows, int Cols, int Options, int MaxRows, int MaxCols>
+class Serializer<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > : public
+  Serializer<DenseBase<Array<Scalar, Rows, Cols, Options, MaxRows, MaxCols> > > {};
+  
+namespace internal {
+ 
+// Recursive serialization implementation helper.
+template<size_t N, typename... Types>
+struct serialize_impl;
+
+template<size_t N, typename T1, typename... Ts>
+struct serialize_impl<N, T1, Ts...> {
+  using Serializer = Eigen::Serializer<typename std::decay<T1>::type>;
+  
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  size_t serialize_size(const T1& value, const Ts&... args) {
+    Serializer serializer;
+    size_t size = serializer.size(value);
+    return size + serialize_impl<N-1, Ts...>::serialize_size(args...);
+  }
+  
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  uint8_t* serialize(uint8_t* dest, uint8_t* end, const T1& value, const Ts&... args) {
+    Serializer serializer;
+    dest = serializer.serialize(dest, end, value);
+    return serialize_impl<N-1, Ts...>::serialize(dest, end, args...);
+  }
+  
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const uint8_t* deserialize(const uint8_t* src, const uint8_t* end, T1& value, Ts&... args) {
+    Serializer serializer;
+    src = serializer.deserialize(src, end, value);
+    return serialize_impl<N-1, Ts...>::deserialize(src, end, args...);
+  }
+};
+
+// Base case.
+template<>
+struct serialize_impl<0> {
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  size_t serialize_size() { return 0; }
+  
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  uint8_t* serialize(uint8_t* dest, uint8_t* /*end*/) { return dest; }
+  
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  const uint8_t* deserialize(const uint8_t* src, const uint8_t* /*end*/) { return src; }
+};
+
+}  // namespace internal
+
+
+/**
+ * Determine the buffer size required to serialize a set of values.
+ * 
+ * \param args ... arguments to serialize in sequence.
+ * \return the total size of the required buffer.
+ */
+template<typename... Args>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+size_t serialize_size(const Args&... args) {
+  return internal::serialize_impl<sizeof...(args), Args...>::serialize_size(args...);
+}
+
+/**
+ * Serialize a set of values to the byte buffer.
+ * 
+ * \param dest output byte buffer; if this is nullptr, does nothing.
+ * \param end the end of the output byte buffer.
+ * \param args ... arguments to serialize in sequence.
+ * \return the next address after all serialized values.
+ */
+template<typename... Args>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+uint8_t* serialize(uint8_t* dest, uint8_t* end, const Args&... args) {
+  return internal::serialize_impl<sizeof...(args), Args...>::serialize(dest, end, args...);
+}
+
+/**
+ * Deserialize a set of values from the byte buffer.
+ * 
+ * \param src input byte buffer; if this is nullptr, does nothing.
+ * \param end the end of input byte buffer.
+ * \param args ... arguments to deserialize in sequence.
+ * \return the next address after all parsed values; nullptr if parsing errors are detected.
+ */
+template<typename... Args>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const uint8_t* deserialize(const uint8_t* src, const uint8_t* end, Args&... args) {
+  return internal::serialize_impl<sizeof...(args), Args...>::deserialize(src, end, args...);
+}
+
+}  // namespace Eigen
+
+#endif // EIGEN_SERIALIZER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/StaticAssert.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/StaticAssert.h
new file mode 100644
index 0000000000000000000000000000000000000000..c938eb800e5ff48778e84ad912cec66da20935f1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/StaticAssert.h
@@ -0,0 +1,115 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STATIC_ASSERT_H
+#define EIGEN_STATIC_ASSERT_H
+
+/* Some notes on Eigen's static assertion mechanism:
+ *
+ *  - in EIGEN_STATIC_ASSERT(CONDITION,MSG) the parameter CONDITION must be a compile time boolean
+ *    expression, and MSG an enum listed in struct internal::static_assertion<true>
+ *
+ *  - currently EIGEN_STATIC_ASSERT can only be used in function scope
+ *
+ */
+
+#ifndef EIGEN_STATIC_ASSERT
+#ifndef EIGEN_NO_STATIC_ASSERT
+
+#define EIGEN_STATIC_ASSERT(X,MSG) static_assert(X,#MSG);
+
+#else // EIGEN_NO_STATIC_ASSERT
+
+#define EIGEN_STATIC_ASSERT(CONDITION,MSG)
+
+#endif // EIGEN_NO_STATIC_ASSERT
+#endif // EIGEN_STATIC_ASSERT
+
+// static assertion failing if the type \a TYPE is not a vector type
+#define EIGEN_STATIC_ASSERT_VECTOR_ONLY(TYPE) \
+  EIGEN_STATIC_ASSERT(TYPE::IsVectorAtCompileTime, \
+                      YOU_TRIED_CALLING_A_VECTOR_METHOD_ON_A_MATRIX)
+
+// static assertion failing if the type \a TYPE is not fixed-size
+#define EIGEN_STATIC_ASSERT_FIXED_SIZE(TYPE) \
+  EIGEN_STATIC_ASSERT(TYPE::SizeAtCompileTime!=Eigen::Dynamic, \
+                      YOU_CALLED_A_FIXED_SIZE_METHOD_ON_A_DYNAMIC_SIZE_MATRIX_OR_VECTOR)
+
+// static assertion failing if the type \a TYPE is not dynamic-size
+#define EIGEN_STATIC_ASSERT_DYNAMIC_SIZE(TYPE) \
+  EIGEN_STATIC_ASSERT(TYPE::SizeAtCompileTime==Eigen::Dynamic, \
+                      YOU_CALLED_A_DYNAMIC_SIZE_METHOD_ON_A_FIXED_SIZE_MATRIX_OR_VECTOR)
+
+// static assertion failing if the type \a TYPE is not a vector type of the given size
+#define EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(TYPE, SIZE) \
+  EIGEN_STATIC_ASSERT(TYPE::IsVectorAtCompileTime && TYPE::SizeAtCompileTime==SIZE, \
+                      THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE)
+
+// static assertion failing if the type \a TYPE is not a vector type of the given size
+#define EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(TYPE, ROWS, COLS) \
+  EIGEN_STATIC_ASSERT(TYPE::RowsAtCompileTime==ROWS && TYPE::ColsAtCompileTime==COLS, \
+                      THIS_METHOD_IS_ONLY_FOR_MATRICES_OF_A_SPECIFIC_SIZE)
+
+// static assertion failing if the two vector expression types are not compatible (same fixed-size or dynamic size)
+#define EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(TYPE0,TYPE1) \
+  EIGEN_STATIC_ASSERT( \
+      (int(TYPE0::SizeAtCompileTime)==Eigen::Dynamic \
+    || int(TYPE1::SizeAtCompileTime)==Eigen::Dynamic \
+    || int(TYPE0::SizeAtCompileTime)==int(TYPE1::SizeAtCompileTime)),\
+    YOU_MIXED_VECTORS_OF_DIFFERENT_SIZES)
+
+#define EIGEN_PREDICATE_SAME_MATRIX_SIZE(TYPE0,TYPE1) \
+     ( \
+        (int(Eigen::internal::size_of_xpr_at_compile_time<TYPE0>::ret)==0 && int(Eigen::internal::size_of_xpr_at_compile_time<TYPE1>::ret)==0) \
+    || (\
+          (int(TYPE0::RowsAtCompileTime)==Eigen::Dynamic \
+        || int(TYPE1::RowsAtCompileTime)==Eigen::Dynamic \
+        || int(TYPE0::RowsAtCompileTime)==int(TYPE1::RowsAtCompileTime)) \
+      &&  (int(TYPE0::ColsAtCompileTime)==Eigen::Dynamic \
+        || int(TYPE1::ColsAtCompileTime)==Eigen::Dynamic \
+        || int(TYPE0::ColsAtCompileTime)==int(TYPE1::ColsAtCompileTime))\
+       ) \
+     )
+
+#define EIGEN_STATIC_ASSERT_NON_INTEGER(TYPE) \
+    EIGEN_STATIC_ASSERT(!Eigen::NumTraits<TYPE>::IsInteger, THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES)
+
+
+// static assertion failing if it is guaranteed at compile-time that the two matrix expression types have different sizes
+#define EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(TYPE0,TYPE1) \
+  EIGEN_STATIC_ASSERT( \
+     EIGEN_PREDICATE_SAME_MATRIX_SIZE(TYPE0,TYPE1),\
+    YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES)
+
+#define EIGEN_STATIC_ASSERT_SIZE_1x1(TYPE) \
+      EIGEN_STATIC_ASSERT((TYPE::RowsAtCompileTime == 1 || TYPE::RowsAtCompileTime == Eigen::Dynamic) && \
+                          (TYPE::ColsAtCompileTime == 1 || TYPE::ColsAtCompileTime == Eigen::Dynamic), \
+                          THIS_METHOD_IS_ONLY_FOR_1x1_EXPRESSIONS)
+
+#define EIGEN_STATIC_ASSERT_LVALUE(Derived) \
+      EIGEN_STATIC_ASSERT(Eigen::internal::is_lvalue<Derived>::value, \
+                          THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY)
+
+#define EIGEN_STATIC_ASSERT_ARRAYXPR(Derived) \
+      EIGEN_STATIC_ASSERT((Eigen::internal::is_same<typename Eigen::internal::traits<Derived>::XprKind, ArrayXpr>::value), \
+                          THIS_METHOD_IS_ONLY_FOR_ARRAYS_NOT_MATRICES)
+
+#define EIGEN_STATIC_ASSERT_SAME_XPR_KIND(Derived1, Derived2) \
+      EIGEN_STATIC_ASSERT((Eigen::internal::is_same<typename Eigen::internal::traits<Derived1>::XprKind, \
+                                             typename Eigen::internal::traits<Derived2>::XprKind \
+                                            >::value), \
+                          YOU_CANNOT_MIX_ARRAYS_AND_MATRICES)
+
+// Check that a cost value is positive, and that is stay within a reasonable range
+// TODO this check could be enabled for internal debugging only
+#define EIGEN_INTERNAL_CHECK_COST_VALUE(C) \
+      EIGEN_STATIC_ASSERT((C)>=0 && (C)<=HugeCost*HugeCost, EIGEN_INTERNAL_ERROR_PLEASE_FILE_A_BUG_REPORT__INVALID_COST_VALUE);
+
+#endif // EIGEN_STATIC_ASSERT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/SymbolicIndex.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/SymbolicIndex.h
new file mode 100644
index 0000000000000000000000000000000000000000..3b19185e67bcf77c6489d42651083444e6ed6926
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/SymbolicIndex.h
@@ -0,0 +1,262 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SYMBOLIC_INDEX_H
+#define EIGEN_SYMBOLIC_INDEX_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \namespace Eigen::symbolic
+  * \ingroup Core_Module
+  *
+  * This namespace defines a set of classes and functions to build and evaluate symbolic expressions of scalar type Index.
+  * Here is a simple example:
+  *
+  * \code
+  * // First step, defines symbols:
+  * struct x_tag {};  static const symbolic::SymbolExpr<x_tag> x;
+  * struct y_tag {};  static const symbolic::SymbolExpr<y_tag> y;
+  * struct z_tag {};  static const symbolic::SymbolExpr<z_tag> z;
+  *
+  * // Defines an expression:
+  * auto expr = (x+3)/y+z;
+  *
+  * // And evaluate it: (c++14)
+  * std::cout << expr.eval(x=6,y=3,z=-13) << "\n";
+  *
+  * \endcode
+  *
+  * It is currently only used internally to define and manipulate the
+  * Eigen::placeholders::last and Eigen::placeholders::lastp1 symbols in
+  * Eigen::seq and Eigen::seqN.
+  *
+  */
+namespace symbolic {
+
+template<typename Tag> class Symbol;
+template<typename Arg0> class NegateExpr;
+template<typename Arg1,typename Arg2> class AddExpr;
+template<typename Arg1,typename Arg2> class ProductExpr;
+template<typename Arg1,typename Arg2> class QuotientExpr;
+
+// A simple wrapper around an integral value to provide the eval method.
+// We could also use a free-function symbolic_eval...
+template<typename IndexType=Index>
+class ValueExpr {
+public:
+  ValueExpr(IndexType val) : m_value(val) {}
+  template<typename T>
+  IndexType eval_impl(const T&) const { return m_value; }
+protected:
+  IndexType m_value;
+};
+
+// Specialization for compile-time value,
+// It is similar to ValueExpr(N) but this version helps the compiler to generate better code.
+template<int N>
+class ValueExpr<internal::FixedInt<N> > {
+public:
+  ValueExpr() {}
+  template<typename T>
+  EIGEN_CONSTEXPR Index eval_impl(const T&) const { return N; }
+};
+
+
+/** \class BaseExpr
+  * \ingroup Core_Module
+  * Common base class of any symbolic expressions
+  */
+template<typename Derived>
+class BaseExpr
+{
+public:
+  const Derived& derived() const { return *static_cast<const Derived*>(this); }
+
+  /** Evaluate the expression given the \a values of the symbols.
+    *
+    * \param values defines the values of the symbols, it can either be a SymbolValue or a std::tuple of SymbolValue
+    *               as constructed by SymbolExpr::operator= operator.
+    *
+    */
+  template<typename T>
+  Index eval(const T& values) const { return derived().eval_impl(values); }
+
+  template<typename... Types>
+  Index eval(Types&&... values) const { return derived().eval_impl(std::make_tuple(values...)); }
+
+  NegateExpr<Derived> operator-() const { return NegateExpr<Derived>(derived()); }
+
+  AddExpr<Derived,ValueExpr<> > operator+(Index b) const
+  { return AddExpr<Derived,ValueExpr<> >(derived(),  b); }
+  AddExpr<Derived,ValueExpr<> > operator-(Index a) const
+  { return AddExpr<Derived,ValueExpr<> >(derived(), -a); }
+  ProductExpr<Derived,ValueExpr<> > operator*(Index a) const
+  { return ProductExpr<Derived,ValueExpr<> >(derived(),a); }
+  QuotientExpr<Derived,ValueExpr<> > operator/(Index a) const
+  { return QuotientExpr<Derived,ValueExpr<> >(derived(),a); }
+
+  friend AddExpr<Derived,ValueExpr<> > operator+(Index a, const BaseExpr& b)
+  { return AddExpr<Derived,ValueExpr<> >(b.derived(), a); }
+  friend AddExpr<NegateExpr<Derived>,ValueExpr<> > operator-(Index a, const BaseExpr& b)
+  { return AddExpr<NegateExpr<Derived>,ValueExpr<> >(-b.derived(), a); }
+  friend ProductExpr<ValueExpr<>,Derived> operator*(Index a, const BaseExpr& b)
+  { return ProductExpr<ValueExpr<>,Derived>(a,b.derived()); }
+  friend QuotientExpr<ValueExpr<>,Derived> operator/(Index a, const BaseExpr& b)
+  { return QuotientExpr<ValueExpr<>,Derived>(a,b.derived()); }
+
+  template<int N>
+  AddExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator+(internal::FixedInt<N>) const
+  { return AddExpr<Derived,ValueExpr<internal::FixedInt<N> > >(derived(), ValueExpr<internal::FixedInt<N> >()); }
+  template<int N>
+  AddExpr<Derived,ValueExpr<internal::FixedInt<-N> > > operator-(internal::FixedInt<N>) const
+  { return AddExpr<Derived,ValueExpr<internal::FixedInt<-N> > >(derived(), ValueExpr<internal::FixedInt<-N> >()); }
+  template<int N>
+  ProductExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator*(internal::FixedInt<N>) const
+  { return ProductExpr<Derived,ValueExpr<internal::FixedInt<N> > >(derived(),ValueExpr<internal::FixedInt<N> >()); }
+  template<int N>
+  QuotientExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator/(internal::FixedInt<N>) const
+  { return QuotientExpr<Derived,ValueExpr<internal::FixedInt<N> > >(derived(),ValueExpr<internal::FixedInt<N> >()); }
+
+  template<int N>
+  friend AddExpr<Derived,ValueExpr<internal::FixedInt<N> > > operator+(internal::FixedInt<N>, const BaseExpr& b)
+  { return AddExpr<Derived,ValueExpr<internal::FixedInt<N> > >(b.derived(), ValueExpr<internal::FixedInt<N> >()); }
+  template<int N>
+  friend AddExpr<NegateExpr<Derived>,ValueExpr<internal::FixedInt<N> > > operator-(internal::FixedInt<N>, const BaseExpr& b)
+  { return AddExpr<NegateExpr<Derived>,ValueExpr<internal::FixedInt<N> > >(-b.derived(), ValueExpr<internal::FixedInt<N> >()); }
+  template<int N>
+  friend ProductExpr<ValueExpr<internal::FixedInt<N> >,Derived> operator*(internal::FixedInt<N>, const BaseExpr& b)
+  { return ProductExpr<ValueExpr<internal::FixedInt<N> >,Derived>(ValueExpr<internal::FixedInt<N> >(),b.derived()); }
+  template<int N>
+  friend QuotientExpr<ValueExpr<internal::FixedInt<N> >,Derived> operator/(internal::FixedInt<N>, const BaseExpr& b)
+  { return QuotientExpr<ValueExpr<internal::FixedInt<N> > ,Derived>(ValueExpr<internal::FixedInt<N> >(),b.derived()); }
+
+
+  template<typename OtherDerived>
+  AddExpr<Derived,OtherDerived> operator+(const BaseExpr<OtherDerived> &b) const
+  { return AddExpr<Derived,OtherDerived>(derived(),  b.derived()); }
+
+  template<typename OtherDerived>
+  AddExpr<Derived,NegateExpr<OtherDerived> > operator-(const BaseExpr<OtherDerived> &b) const
+  { return AddExpr<Derived,NegateExpr<OtherDerived> >(derived(), -b.derived()); }
+
+  template<typename OtherDerived>
+  ProductExpr<Derived,OtherDerived> operator*(const BaseExpr<OtherDerived> &b) const
+  { return ProductExpr<Derived,OtherDerived>(derived(), b.derived()); }
+
+  template<typename OtherDerived>
+  QuotientExpr<Derived,OtherDerived> operator/(const BaseExpr<OtherDerived> &b) const
+  { return QuotientExpr<Derived,OtherDerived>(derived(), b.derived()); }
+};
+
+template<typename T>
+struct is_symbolic {
+  // BaseExpr has no conversion ctor, so we only have to check whether T can be statically cast to its base class BaseExpr<T>.
+  enum { value = internal::is_convertible<T,BaseExpr<T> >::value };
+};
+
+/** Represents the actual value of a symbol identified by its tag
+  *
+  * It is the return type of SymbolValue::operator=, and most of the time this is only way it is used.
+  */
+template<typename Tag>
+class SymbolValue
+{
+public:
+  /** Default constructor from the value \a val */
+  SymbolValue(Index val) : m_value(val) {}
+
+  /** \returns the stored value of the symbol */
+  Index value() const { return m_value; }
+protected:
+  Index m_value;
+};
+
+/** Expression of a symbol uniquely identified by the template parameter type \c tag */
+template<typename tag>
+class SymbolExpr : public BaseExpr<SymbolExpr<tag> >
+{
+public:
+  /** Alias to the template parameter \c tag */
+  typedef tag Tag;
+
+  SymbolExpr() {}
+
+  /** Associate the value \a val to the given symbol \c *this, uniquely identified by its \c Tag.
+    *
+    * The returned object should be passed to ExprBase::eval() to evaluate a given expression with this specified runtime-time value.
+    */
+  SymbolValue<Tag> operator=(Index val) const {
+    return SymbolValue<Tag>(val);
+  }
+
+  Index eval_impl(const SymbolValue<Tag> &values) const { return values.value(); }
+
+  // C++14 versions suitable for multiple symbols
+  template<typename... Types>
+  Index eval_impl(const std::tuple<Types...>& values) const { return std::get<SymbolValue<Tag> >(values).value(); }
+};
+
+template<typename Arg0>
+class NegateExpr : public BaseExpr<NegateExpr<Arg0> >
+{
+public:
+  NegateExpr(const Arg0& arg0) : m_arg0(arg0) {}
+
+  template<typename T>
+  Index eval_impl(const T& values) const { return -m_arg0.eval_impl(values); }
+protected:
+  Arg0 m_arg0;
+};
+
+template<typename Arg0, typename Arg1>
+class AddExpr : public BaseExpr<AddExpr<Arg0,Arg1> >
+{
+public:
+  AddExpr(const Arg0& arg0, const Arg1& arg1) : m_arg0(arg0), m_arg1(arg1) {}
+
+  template<typename T>
+  Index eval_impl(const T& values) const { return m_arg0.eval_impl(values) + m_arg1.eval_impl(values); }
+protected:
+  Arg0 m_arg0;
+  Arg1 m_arg1;
+};
+
+template<typename Arg0, typename Arg1>
+class ProductExpr : public BaseExpr<ProductExpr<Arg0,Arg1> >
+{
+public:
+  ProductExpr(const Arg0& arg0, const Arg1& arg1) : m_arg0(arg0), m_arg1(arg1) {}
+
+  template<typename T>
+  Index eval_impl(const T& values) const { return m_arg0.eval_impl(values) * m_arg1.eval_impl(values); }
+protected:
+  Arg0 m_arg0;
+  Arg1 m_arg1;
+};
+
+template<typename Arg0, typename Arg1>
+class QuotientExpr : public BaseExpr<QuotientExpr<Arg0,Arg1> >
+{
+public:
+  QuotientExpr(const Arg0& arg0, const Arg1& arg1) : m_arg0(arg0), m_arg1(arg1) {}
+
+  template<typename T>
+  Index eval_impl(const T& values) const { return m_arg0.eval_impl(values) / m_arg1.eval_impl(values); }
+protected:
+  Arg0 m_arg0;
+  Arg1 m_arg1;
+};
+
+} // end namespace symbolic
+
+} // end namespace Eigen
+
+#endif // EIGEN_SYMBOLIC_INDEX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/XprHelper.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/XprHelper.h
new file mode 100644
index 0000000000000000000000000000000000000000..d8ac1b3406960cfafe65de2811326bc548cae03c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Core/util/XprHelper.h
@@ -0,0 +1,906 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_XPRHELPER_H
+#define EIGEN_XPRHELPER_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+
+// useful for unsigned / signed integer comparisons when idx is intended to be non-negative
+template <typename IndexType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename make_unsigned<IndexType>::type returnUnsignedIndexValue(
+    const IndexType& idx) {
+  EIGEN_STATIC_ASSERT((NumTraits<IndexType>::IsInteger), THIS FUNCTION IS FOR INTEGER TYPES)
+  eigen_internal_assert(idx >= 0 && "Index value is negative and target type is unsigned");
+  using UnsignedType = typename make_unsigned<IndexType>::type;
+  return static_cast<UnsignedType>(idx);
+}
+
+template <typename IndexDest, typename IndexSrc, 
+          bool IndexDestIsInteger = NumTraits<IndexDest>::IsInteger,
+          bool IndexDestIsSigned = NumTraits<IndexDest>::IsSigned,
+          bool IndexSrcIsInteger = NumTraits<IndexSrc>::IsInteger,
+          bool IndexSrcIsSigned = NumTraits<IndexSrc>::IsSigned>
+struct convert_index_impl {
+  static inline EIGEN_DEVICE_FUNC IndexDest run(const IndexSrc& idx) {
+    eigen_internal_assert(idx <= NumTraits<IndexDest>::highest() && "Index value is too big for target type");
+    return static_cast<IndexDest>(idx);
+  }
+};
+template <typename IndexDest, typename IndexSrc>
+struct convert_index_impl<IndexDest, IndexSrc, true, true, true, false> {
+  // IndexDest is a signed integer
+  // IndexSrc is an unsigned integer
+  static inline EIGEN_DEVICE_FUNC IndexDest run(const IndexSrc& idx) {
+    eigen_internal_assert(idx <= returnUnsignedIndexValue(NumTraits<IndexDest>::highest()) &&
+                          "Index value is too big for target type");
+    return static_cast<IndexDest>(idx);
+  }
+};
+template <typename IndexDest, typename IndexSrc>
+struct convert_index_impl<IndexDest, IndexSrc, true, false, true, true> {
+  // IndexDest is an unsigned integer
+  // IndexSrc is a signed integer
+  static inline EIGEN_DEVICE_FUNC IndexDest run(const IndexSrc& idx) {
+    eigen_internal_assert(returnUnsignedIndexValue(idx) <= NumTraits<IndexDest>::highest() &&
+                          "Index value is too big for target type");
+    return static_cast<IndexDest>(idx);
+  }
+};
+
+template <typename IndexDest, typename IndexSrc>
+EIGEN_DEVICE_FUNC inline IndexDest convert_index(const IndexSrc& idx) {
+  return convert_index_impl<IndexDest, IndexSrc>::run(idx);
+}
+
+// true if T can be considered as an integral index (i.e., and integral type or enum)
+template<typename T> struct is_valid_index_type
+{
+  enum { value = internal::is_integral<T>::value || std::is_enum<T>::value
+  };
+};
+
+// true if both types are not valid index types
+template<typename RowIndices, typename ColIndices>
+struct valid_indexed_view_overload {
+  enum { value = !(internal::is_valid_index_type<RowIndices>::value && internal::is_valid_index_type<ColIndices>::value) };
+};
+
+// promote_scalar_arg is an helper used in operation between an expression and a scalar, like:
+//    expression * scalar
+// Its role is to determine how the type T of the scalar operand should be promoted given the scalar type ExprScalar of the given expression.
+// The IsSupported template parameter must be provided by the caller as: internal::has_ReturnType<ScalarBinaryOpTraits<ExprScalar,T,op> >::value using the proper order for ExprScalar and T.
+// Then the logic is as follows:
+//  - if the operation is natively supported as defined by IsSupported, then the scalar type is not promoted, and T is returned.
+//  - otherwise, NumTraits<ExprScalar>::Literal is returned if T is implicitly convertible to NumTraits<ExprScalar>::Literal AND that this does not imply a float to integer conversion.
+//  - otherwise, ExprScalar is returned if T is implicitly convertible to ExprScalar AND that this does not imply a float to integer conversion.
+//  - In all other cases, the promoted type is not defined, and the respective operation is thus invalid and not available (SFINAE).
+template<typename ExprScalar,typename T, bool IsSupported>
+struct promote_scalar_arg;
+
+template<typename S,typename T>
+struct promote_scalar_arg<S,T,true>
+{
+  typedef T type;
+};
+
+// Recursively check safe conversion to PromotedType, and then ExprScalar if they are different.
+template<typename ExprScalar,typename T,typename PromotedType,
+  bool ConvertibleToLiteral = internal::is_convertible<T,PromotedType>::value,
+  bool IsSafe = NumTraits<T>::IsInteger || !NumTraits<PromotedType>::IsInteger>
+struct promote_scalar_arg_unsupported;
+
+// Start recursion with NumTraits<ExprScalar>::Literal
+template<typename S,typename T>
+struct promote_scalar_arg<S,T,false> : promote_scalar_arg_unsupported<S,T,typename NumTraits<S>::Literal> {};
+
+// We found a match!
+template<typename S,typename T, typename PromotedType>
+struct promote_scalar_arg_unsupported<S,T,PromotedType,true,true>
+{
+  typedef PromotedType type;
+};
+
+// No match, but no real-to-integer issues, and ExprScalar and current PromotedType are different,
+// so let's try to promote to ExprScalar
+template<typename ExprScalar,typename T, typename PromotedType>
+struct promote_scalar_arg_unsupported<ExprScalar,T,PromotedType,false,true>
+   : promote_scalar_arg_unsupported<ExprScalar,T,ExprScalar>
+{};
+
+// Unsafe real-to-integer, let's stop.
+template<typename S,typename T, typename PromotedType, bool ConvertibleToLiteral>
+struct promote_scalar_arg_unsupported<S,T,PromotedType,ConvertibleToLiteral,false> {};
+
+// T is not even convertible to ExprScalar, let's stop.
+template<typename S,typename T>
+struct promote_scalar_arg_unsupported<S,T,S,false,true> {};
+
+//classes inheriting no_assignment_operator don't generate a default operator=.
+class no_assignment_operator
+{
+  private:
+    no_assignment_operator& operator=(const no_assignment_operator&);
+  protected:
+    EIGEN_DEFAULT_COPY_CONSTRUCTOR(no_assignment_operator)
+    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(no_assignment_operator)
+};
+
+/** \internal return the index type with the largest number of bits */
+template<typename I1, typename I2>
+struct promote_index_type
+{
+  typedef std::conditional_t<(sizeof(I1)<sizeof(I2)), I2, I1> type;
+};
+
+/** \internal If the template parameter Value is Dynamic, this class is just a wrapper around a T variable that
+  * can be accessed using value() and setValue().
+  * Otherwise, this class is an empty structure and value() just returns the template parameter Value.
+  */
+template<typename T, int Value> class variable_if_dynamic
+{
+  public:
+    EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(variable_if_dynamic)
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamic(T v) { EIGEN_ONLY_USED_FOR_DEBUG(v); eigen_assert(v == T(Value)); }
+    EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    T value() { return T(Value); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    operator T() const { return T(Value); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    void setValue(T v) const { EIGEN_ONLY_USED_FOR_DEBUG(v); eigen_assert(v == T(Value)); }
+};
+
+template<typename T> class variable_if_dynamic<T, Dynamic>
+{
+    T m_value;
+  public:
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamic(T value = 0) EIGEN_NO_THROW : m_value(value) {}
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T value() const { return m_value; }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE operator T() const { return m_value; }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void setValue(T value) { m_value = value; }
+};
+
+/** \internal like variable_if_dynamic but for DynamicIndex
+  */
+template<typename T, int Value> class variable_if_dynamicindex
+{
+  public:
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamicindex(T v) { EIGEN_ONLY_USED_FOR_DEBUG(v); eigen_assert(v == T(Value)); }
+    EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+    T value() { return T(Value); }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+    void setValue(T) {}
+};
+
+template<typename T> class variable_if_dynamicindex<T, DynamicIndex>
+{
+    T m_value;
+    EIGEN_DEVICE_FUNC variable_if_dynamicindex() { eigen_assert(false); }
+  public:
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit variable_if_dynamicindex(T value) : m_value(value) {}
+    EIGEN_DEVICE_FUNC T EIGEN_STRONG_INLINE value() const { return m_value; }
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void setValue(T value) { m_value = value; }
+};
+
+template<typename T> struct functor_traits
+{
+  enum
+  {
+    Cost = 10,
+    PacketAccess = false,
+    IsRepeatable = false
+  };
+};
+
+template<typename T> struct packet_traits;
+
+template<typename T> struct unpacket_traits;
+
+template<int Size, typename PacketType,
+         bool Stop = Size==Dynamic || (Size%unpacket_traits<PacketType>::size)==0 || is_same<PacketType,typename unpacket_traits<PacketType>::half>::value>
+struct find_best_packet_helper;
+
+template< int Size, typename PacketType>
+struct find_best_packet_helper<Size,PacketType,true>
+{
+  typedef PacketType type;
+};
+
+template<int Size, typename PacketType>
+struct find_best_packet_helper<Size,PacketType,false>
+{
+  typedef typename find_best_packet_helper<Size,typename unpacket_traits<PacketType>::half>::type type;
+};
+
+template<typename T, int Size>
+struct find_best_packet
+{
+  typedef typename find_best_packet_helper<Size,typename packet_traits<T>::type>::type type;
+};
+
+template <int Size, typename PacketType,
+          bool Stop = (Size == unpacket_traits<PacketType>::size) ||
+                      is_same<PacketType, typename unpacket_traits<PacketType>::half>::value>
+struct find_packet_by_size_helper;
+template <int Size, typename PacketType>
+struct find_packet_by_size_helper<Size, PacketType, true> {
+  using type = PacketType;
+};
+template <int Size, typename PacketType>
+struct find_packet_by_size_helper<Size, PacketType, false> {
+  using type = typename find_packet_by_size_helper<Size, typename unpacket_traits<PacketType>::half>::type;
+};
+
+template <typename T, int Size>
+struct find_packet_by_size {
+  using type = typename find_packet_by_size_helper<Size, typename packet_traits<T>::type>::type;
+  static constexpr bool value = (Size == unpacket_traits<type>::size);
+};
+template <typename T>
+struct find_packet_by_size<T, 1> {
+  using type = typename unpacket_traits<T>::type;
+  static constexpr bool value = (unpacket_traits<type>::size == 1);
+};
+
+#if EIGEN_MAX_STATIC_ALIGN_BYTES>0
+constexpr inline int compute_default_alignment_helper(int ArrayBytes, int AlignmentBytes) {
+  if((ArrayBytes % AlignmentBytes) == 0) {
+    return AlignmentBytes;
+  } else if (EIGEN_MIN_ALIGN_BYTES<AlignmentBytes) {
+    return compute_default_alignment_helper(ArrayBytes, AlignmentBytes/2);
+  } else {
+    return 0;
+  }
+}
+#else
+// If static alignment is disabled, no need to bother.
+// This also avoids a division by zero
+constexpr inline int compute_default_alignment_helper(int ArrayBytes, int AlignmentBytes) {
+  EIGEN_UNUSED_VARIABLE(ArrayBytes);
+  EIGEN_UNUSED_VARIABLE(AlignmentBytes);
+  return 0;
+}
+#endif
+
+template<typename T, int Size> struct compute_default_alignment {
+  enum { value = compute_default_alignment_helper(Size*sizeof(T), EIGEN_MAX_STATIC_ALIGN_BYTES) };
+};
+
+template<typename T> struct compute_default_alignment<T,Dynamic> {
+  enum { value = EIGEN_MAX_ALIGN_BYTES };
+};
+
+template<typename Scalar_, int Rows_, int Cols_,
+         int Options_ = AutoAlign |
+                          ( (Rows_==1 && Cols_!=1) ? RowMajor
+                          : (Cols_==1 && Rows_!=1) ? ColMajor
+                          : EIGEN_DEFAULT_MATRIX_STORAGE_ORDER_OPTION ),
+         int MaxRows_ = Rows_,
+         int MaxCols_ = Cols_
+> class make_proper_matrix_type
+{
+    enum {
+      IsColVector = Cols_==1 && Rows_!=1,
+      IsRowVector = Rows_==1 && Cols_!=1,
+      Options = IsColVector ? (Options_ | ColMajor) & ~RowMajor
+              : IsRowVector ? (Options_ | RowMajor) & ~ColMajor
+              : Options_
+    };
+  public:
+    typedef Matrix<Scalar_, Rows_, Cols_, Options, MaxRows_, MaxCols_> type;
+};
+
+constexpr inline unsigned compute_matrix_flags(int Options) {
+  unsigned row_major_bit = Options&RowMajor ? RowMajorBit : 0;
+  // FIXME currently we still have to handle DirectAccessBit at the expression level to handle DenseCoeffsBase<>
+  // and then propagate this information to the evaluator's flags.
+  // However, I (Gael) think that DirectAccessBit should only matter at the evaluation stage.
+  return DirectAccessBit | LvalueBit | NestByRefBit | row_major_bit;
+}
+
+constexpr inline int size_at_compile_time(int rows, int cols) {
+  if (rows == 0 || cols == 0) return 0;
+  if (rows == Dynamic || cols == Dynamic) return Dynamic;
+  return rows * cols;
+}
+
+template<typename XprType> struct size_of_xpr_at_compile_time
+{
+  enum { ret = size_at_compile_time(traits<XprType>::RowsAtCompileTime, traits<XprType>::ColsAtCompileTime) };
+};
+
+/* plain_matrix_type : the difference from eval is that plain_matrix_type is always a plain matrix type,
+ * whereas eval is a const reference in the case of a matrix
+ */
+
+template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct plain_matrix_type;
+template<typename T, typename BaseClassType, int Flags> struct plain_matrix_type_dense;
+template<typename T> struct plain_matrix_type<T,Dense>
+{
+  typedef typename plain_matrix_type_dense<T,typename traits<T>::XprKind, traits<T>::Flags>::type type;
+};
+template<typename T> struct plain_matrix_type<T,DiagonalShape>
+{
+  typedef typename T::PlainObject type;
+};
+
+template<typename T> struct plain_matrix_type<T,SkewSymmetricShape>
+{
+  typedef typename T::PlainObject type;
+};
+
+template<typename T, int Flags> struct plain_matrix_type_dense<T,MatrixXpr,Flags>
+{
+  typedef Matrix<typename traits<T>::Scalar,
+                traits<T>::RowsAtCompileTime,
+                traits<T>::ColsAtCompileTime,
+                AutoAlign | (Flags&RowMajorBit ? RowMajor : ColMajor),
+                traits<T>::MaxRowsAtCompileTime,
+                traits<T>::MaxColsAtCompileTime
+          > type;
+};
+
+template<typename T, int Flags> struct plain_matrix_type_dense<T,ArrayXpr,Flags>
+{
+  typedef Array<typename traits<T>::Scalar,
+                traits<T>::RowsAtCompileTime,
+                traits<T>::ColsAtCompileTime,
+                AutoAlign | (Flags&RowMajorBit ? RowMajor : ColMajor),
+                traits<T>::MaxRowsAtCompileTime,
+                traits<T>::MaxColsAtCompileTime
+          > type;
+};
+
+/* eval : the return type of eval(). For matrices, this is just a const reference
+ * in order to avoid a useless copy
+ */
+
+template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct eval;
+
+template<typename T> struct eval<T,Dense>
+{
+  typedef typename plain_matrix_type<T>::type type;
+//   typedef typename T::PlainObject type;
+//   typedef T::Matrix<typename traits<T>::Scalar,
+//                 traits<T>::RowsAtCompileTime,
+//                 traits<T>::ColsAtCompileTime,
+//                 AutoAlign | (traits<T>::Flags&RowMajorBit ? RowMajor : ColMajor),
+//                 traits<T>::MaxRowsAtCompileTime,
+//                 traits<T>::MaxColsAtCompileTime
+//           > type;
+};
+
+template<typename T> struct eval<T,DiagonalShape>
+{
+  typedef typename plain_matrix_type<T>::type type;
+};
+
+template<typename T> struct eval<T,SkewSymmetricShape>
+{
+  typedef typename plain_matrix_type<T>::type type;
+};
+
+// for matrices, no need to evaluate, just use a const reference to avoid a useless copy
+template<typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+struct eval<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>, Dense>
+{
+  typedef const Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>& type;
+};
+
+template<typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+struct eval<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>, Dense>
+{
+  typedef const Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>& type;
+};
+
+
+/* similar to plain_matrix_type, but using the evaluator's Flags */
+template<typename T, typename StorageKind = typename traits<T>::StorageKind> struct plain_object_eval;
+
+template<typename T>
+struct plain_object_eval<T,Dense>
+{
+  typedef typename plain_matrix_type_dense<T,typename traits<T>::XprKind, evaluator<T>::Flags>::type type;
+};
+
+
+/* plain_matrix_type_column_major : same as plain_matrix_type but guaranteed to be column-major
+ */
+template<typename T> struct plain_matrix_type_column_major
+{
+  enum { Rows = traits<T>::RowsAtCompileTime,
+         Cols = traits<T>::ColsAtCompileTime,
+         MaxRows = traits<T>::MaxRowsAtCompileTime,
+         MaxCols = traits<T>::MaxColsAtCompileTime
+  };
+  typedef Matrix<typename traits<T>::Scalar,
+                Rows,
+                Cols,
+                (MaxRows==1&&MaxCols!=1) ? RowMajor : ColMajor,
+                MaxRows,
+                MaxCols
+          > type;
+};
+
+/* plain_matrix_type_row_major : same as plain_matrix_type but guaranteed to be row-major
+ */
+template<typename T> struct plain_matrix_type_row_major
+{
+  enum { Rows = traits<T>::RowsAtCompileTime,
+         Cols = traits<T>::ColsAtCompileTime,
+         MaxRows = traits<T>::MaxRowsAtCompileTime,
+         MaxCols = traits<T>::MaxColsAtCompileTime
+  };
+  typedef Matrix<typename traits<T>::Scalar,
+                Rows,
+                Cols,
+                (MaxCols==1&&MaxRows!=1) ? ColMajor : RowMajor,
+                MaxRows,
+                MaxCols
+          > type;
+};
+
+/** \internal The reference selector for template expressions. The idea is that we don't
+  * need to use references for expressions since they are light weight proxy
+  * objects which should generate no copying overhead. */
+template <typename T>
+struct ref_selector
+{
+  typedef std::conditional_t<
+    bool(traits<T>::Flags & NestByRefBit),
+    T const&,
+    const T
+  > type;
+
+  typedef std::conditional_t<
+    bool(traits<T>::Flags & NestByRefBit),
+    T &,
+    T
+  > non_const_type;
+};
+
+/** \internal Adds the const qualifier on the value-type of T2 if and only if T1 is a const type */
+template<typename T1, typename T2>
+struct transfer_constness
+{
+  typedef std::conditional_t<
+    bool(internal::is_const<T1>::value),
+    add_const_on_value_type_t<T2>,
+    T2
+  > type;
+};
+
+
+// However, we still need a mechanism to detect whether an expression which is evaluated multiple time
+// has to be evaluated into a temporary.
+// That's the purpose of this new nested_eval helper:
+/** \internal Determines how a given expression should be nested when evaluated multiple times.
+  * For example, when you do a * (b+c), Eigen will determine how the expression b+c should be
+  * evaluated into the bigger product expression. The choice is between nesting the expression b+c as-is, or
+  * evaluating that expression b+c into a temporary variable d, and nest d so that the resulting expression is
+  * a*d. Evaluating can be beneficial for example if every coefficient access in the resulting expression causes
+  * many coefficient accesses in the nested expressions -- as is the case with matrix product for example.
+  *
+  * \tparam T the type of the expression being nested.
+  * \tparam n the number of coefficient accesses in the nested expression for each coefficient access in the bigger expression.
+  * \tparam PlainObject the type of the temporary if needed.
+  */
+template<typename T, int n, typename PlainObject = typename plain_object_eval<T>::type> struct nested_eval
+{
+  enum {
+    ScalarReadCost = NumTraits<typename traits<T>::Scalar>::ReadCost,
+    CoeffReadCost = evaluator<T>::CoeffReadCost,  // NOTE What if an evaluator evaluate itself into a temporary?
+                                                  //      Then CoeffReadCost will be small (e.g., 1) but we still have to evaluate, especially if n>1.
+                                                  //      This situation is already taken care by the EvalBeforeNestingBit flag, which is turned ON
+                                                  //      for all evaluator creating a temporary. This flag is then propagated by the parent evaluators.
+                                                  //      Another solution could be to count the number of temps?
+    NAsInteger = n == Dynamic ? HugeCost : n,
+    CostEval   = (NAsInteger+1) * ScalarReadCost + CoeffReadCost,
+    CostNoEval = NAsInteger * CoeffReadCost,
+    Evaluate = (int(evaluator<T>::Flags) & EvalBeforeNestingBit) || (int(CostEval) < int(CostNoEval))
+  };
+
+  typedef std::conditional_t<Evaluate, PlainObject, typename ref_selector<T>::type> type;
+};
+
+template<typename T>
+EIGEN_DEVICE_FUNC
+inline T* const_cast_ptr(const T* ptr)
+{
+  return const_cast<T*>(ptr);
+}
+
+template<typename Derived, typename XprKind = typename traits<Derived>::XprKind>
+struct dense_xpr_base
+{
+  /* dense_xpr_base should only ever be used on dense expressions, thus falling either into the MatrixXpr or into the ArrayXpr cases */
+};
+
+template<typename Derived>
+struct dense_xpr_base<Derived, MatrixXpr>
+{
+  typedef MatrixBase<Derived> type;
+};
+
+template<typename Derived>
+struct dense_xpr_base<Derived, ArrayXpr>
+{
+  typedef ArrayBase<Derived> type;
+};
+
+template<typename Derived, typename XprKind = typename traits<Derived>::XprKind, typename StorageKind = typename traits<Derived>::StorageKind>
+struct generic_xpr_base;
+
+template<typename Derived, typename XprKind>
+struct generic_xpr_base<Derived, XprKind, Dense>
+{
+  typedef typename dense_xpr_base<Derived,XprKind>::type type;
+};
+
+template<typename XprType, typename CastType> struct cast_return_type
+{
+  typedef typename XprType::Scalar CurrentScalarType;
+  typedef remove_all_t<CastType> CastType_;
+  typedef typename CastType_::Scalar NewScalarType;
+  typedef std::conditional_t<is_same<CurrentScalarType,NewScalarType>::value,
+                              const XprType&,CastType> type;
+};
+
+template <typename A, typename B> struct promote_storage_type;
+
+template <typename A> struct promote_storage_type<A,A>
+{
+  typedef A ret;
+};
+template <typename A> struct promote_storage_type<A, const A>
+{
+  typedef A ret;
+};
+template <typename A> struct promote_storage_type<const A, A>
+{
+  typedef A ret;
+};
+
+/** \internal Specify the "storage kind" of applying a coefficient-wise
+  * binary operations between two expressions of kinds A and B respectively.
+  * The template parameter Functor permits to specialize the resulting storage kind wrt to
+  * the functor.
+  * The default rules are as follows:
+  * \code
+  * A      op A      -> A
+  * A      op dense  -> dense
+  * dense  op B      -> dense
+  * sparse op dense  -> sparse
+  * dense  op sparse -> sparse
+  * \endcode
+  */
+template <typename A, typename B, typename Functor> struct cwise_promote_storage_type;
+
+template <typename A, typename Functor>                   struct cwise_promote_storage_type<A,A,Functor>                                      { typedef A      ret; };
+template <typename Functor>                               struct cwise_promote_storage_type<Dense,Dense,Functor>                              { typedef Dense  ret; };
+template <typename A, typename Functor>                   struct cwise_promote_storage_type<A,Dense,Functor>                                  { typedef Dense  ret; };
+template <typename B, typename Functor>                   struct cwise_promote_storage_type<Dense,B,Functor>                                  { typedef Dense  ret; };
+template <typename Functor>                               struct cwise_promote_storage_type<Sparse,Dense,Functor>                             { typedef Sparse ret; };
+template <typename Functor>                               struct cwise_promote_storage_type<Dense,Sparse,Functor>                             { typedef Sparse ret; };
+
+template <typename LhsKind, typename RhsKind, int LhsOrder, int RhsOrder> struct cwise_promote_storage_order {
+  enum { value = LhsOrder };
+};
+
+template <typename LhsKind, int LhsOrder, int RhsOrder>   struct cwise_promote_storage_order<LhsKind,Sparse,LhsOrder,RhsOrder>                { enum { value = RhsOrder }; };
+template <typename RhsKind, int LhsOrder, int RhsOrder>   struct cwise_promote_storage_order<Sparse,RhsKind,LhsOrder,RhsOrder>                { enum { value = LhsOrder }; };
+template <int Order>                                      struct cwise_promote_storage_order<Sparse,Sparse,Order,Order>                       { enum { value = Order }; };
+
+
+/** \internal Specify the "storage kind" of multiplying an expression of kind A with kind B.
+  * The template parameter ProductTag permits to specialize the resulting storage kind wrt to
+  * some compile-time properties of the product: GemmProduct, GemvProduct, OuterProduct, InnerProduct.
+  * The default rules are as follows:
+  * \code
+  *  K * K            -> K
+  *  dense * K        -> dense
+  *  K * dense        -> dense
+  *  diag * K         -> K
+  *  K * diag         -> K
+  *  Perm * K         -> K
+  * K * Perm          -> K
+  * \endcode
+  */
+template <typename A, typename B, int ProductTag> struct product_promote_storage_type;
+
+template <typename A, int ProductTag> struct product_promote_storage_type<A,                  A,                  ProductTag> { typedef A     ret;};
+template <int ProductTag>             struct product_promote_storage_type<Dense,              Dense,              ProductTag> { typedef Dense ret;};
+template <typename A, int ProductTag> struct product_promote_storage_type<A,                  Dense,              ProductTag> { typedef Dense ret; };
+template <typename B, int ProductTag> struct product_promote_storage_type<Dense,              B,                  ProductTag> { typedef Dense ret; };
+
+template <typename A, int ProductTag> struct product_promote_storage_type<A,                  DiagonalShape,      ProductTag> { typedef A ret; };
+template <typename B, int ProductTag> struct product_promote_storage_type<DiagonalShape,      B,                  ProductTag> { typedef B ret; };
+template <int ProductTag>             struct product_promote_storage_type<Dense,              DiagonalShape,      ProductTag> { typedef Dense ret; };
+template <int ProductTag>             struct product_promote_storage_type<DiagonalShape,      Dense,              ProductTag> { typedef Dense ret; };
+
+template <typename A, int ProductTag> struct product_promote_storage_type<A,                  SkewSymmetricShape, ProductTag> { typedef A ret; };
+template <typename B, int ProductTag> struct product_promote_storage_type<SkewSymmetricShape, B,                  ProductTag> { typedef B ret; };
+template <int ProductTag>             struct product_promote_storage_type<Dense,              SkewSymmetricShape, ProductTag> { typedef Dense ret; };
+template <int ProductTag>             struct product_promote_storage_type<SkewSymmetricShape, Dense,              ProductTag> { typedef Dense ret; };
+template <int ProductTag>             struct product_promote_storage_type<SkewSymmetricShape, SkewSymmetricShape, ProductTag> { typedef Dense ret; };
+
+template <typename A, int ProductTag> struct product_promote_storage_type<A,                  PermutationStorage, ProductTag> { typedef A ret; };
+template <typename B, int ProductTag> struct product_promote_storage_type<PermutationStorage, B,                  ProductTag> { typedef B ret; };
+template <int ProductTag>             struct product_promote_storage_type<Dense,              PermutationStorage, ProductTag> { typedef Dense ret; };
+template <int ProductTag>             struct product_promote_storage_type<PermutationStorage, Dense,              ProductTag> { typedef Dense ret; };
+
+/** \internal gives the plain matrix or array type to store a row/column/diagonal of a matrix type.
+  * \tparam Scalar optional parameter allowing to pass a different scalar type than the one of the MatrixType.
+  */
+template<typename ExpressionType, typename Scalar = typename ExpressionType::Scalar>
+struct plain_row_type
+{
+  typedef Matrix<Scalar, 1, ExpressionType::ColsAtCompileTime,
+                 int(ExpressionType::PlainObject::Options) | int(RowMajor), 1, ExpressionType::MaxColsAtCompileTime> MatrixRowType;
+  typedef Array<Scalar, 1, ExpressionType::ColsAtCompileTime,
+                 int(ExpressionType::PlainObject::Options) | int(RowMajor), 1, ExpressionType::MaxColsAtCompileTime> ArrayRowType;
+
+  typedef std::conditional_t<
+    is_same< typename traits<ExpressionType>::XprKind, MatrixXpr >::value,
+    MatrixRowType,
+    ArrayRowType
+  > type;
+};
+
+template<typename ExpressionType, typename Scalar = typename ExpressionType::Scalar>
+struct plain_col_type
+{
+  typedef Matrix<Scalar, ExpressionType::RowsAtCompileTime, 1,
+                 ExpressionType::PlainObject::Options & ~RowMajor, ExpressionType::MaxRowsAtCompileTime, 1> MatrixColType;
+  typedef Array<Scalar, ExpressionType::RowsAtCompileTime, 1,
+                 ExpressionType::PlainObject::Options & ~RowMajor, ExpressionType::MaxRowsAtCompileTime, 1> ArrayColType;
+
+  typedef std::conditional_t<
+    is_same< typename traits<ExpressionType>::XprKind, MatrixXpr >::value,
+    MatrixColType,
+    ArrayColType
+  > type;
+};
+
+template<typename ExpressionType, typename Scalar = typename ExpressionType::Scalar>
+struct plain_diag_type
+{
+  enum { diag_size = internal::min_size_prefer_dynamic(ExpressionType::RowsAtCompileTime, ExpressionType::ColsAtCompileTime),
+         max_diag_size = min_size_prefer_fixed(ExpressionType::MaxRowsAtCompileTime,
+                                               ExpressionType::MaxColsAtCompileTime)
+  };
+  typedef Matrix<Scalar, diag_size, 1, ExpressionType::PlainObject::Options & ~RowMajor, max_diag_size, 1> MatrixDiagType;
+  typedef Array<Scalar, diag_size, 1, ExpressionType::PlainObject::Options & ~RowMajor, max_diag_size, 1> ArrayDiagType;
+
+  typedef std::conditional_t<
+    is_same< typename traits<ExpressionType>::XprKind, MatrixXpr >::value,
+    MatrixDiagType,
+    ArrayDiagType
+  > type;
+};
+
+template<typename Expr,typename Scalar = typename Expr::Scalar>
+struct plain_constant_type
+{
+  enum { Options = (traits<Expr>::Flags&RowMajorBit)?RowMajor:0 };
+
+  typedef Array<Scalar,  traits<Expr>::RowsAtCompileTime,   traits<Expr>::ColsAtCompileTime,
+                Options, traits<Expr>::MaxRowsAtCompileTime,traits<Expr>::MaxColsAtCompileTime> array_type;
+
+  typedef Matrix<Scalar,  traits<Expr>::RowsAtCompileTime,   traits<Expr>::ColsAtCompileTime,
+                 Options, traits<Expr>::MaxRowsAtCompileTime,traits<Expr>::MaxColsAtCompileTime> matrix_type;
+
+  typedef CwiseNullaryOp<scalar_constant_op<Scalar>, const std::conditional_t<is_same< typename traits<Expr>::XprKind, MatrixXpr >::value, matrix_type, array_type> > type;
+};
+
+template<typename ExpressionType>
+struct is_lvalue
+{
+  enum { value = (!bool(is_const<ExpressionType>::value)) &&
+                 bool(traits<ExpressionType>::Flags & LvalueBit) };
+};
+
+template<typename T> struct is_diagonal
+{ enum { ret = false }; };
+
+template<typename T> struct is_diagonal<DiagonalBase<T> >
+{ enum { ret = true }; };
+
+template<typename T> struct is_diagonal<DiagonalWrapper<T> >
+{ enum { ret = true }; };
+
+template<typename T, int S> struct is_diagonal<DiagonalMatrix<T,S> >
+{ enum { ret = true }; };
+
+
+template<typename T> struct is_identity
+{ enum { value = false }; };
+
+template<typename T> struct is_identity<CwiseNullaryOp<internal::scalar_identity_op<typename T::Scalar>, T> >
+{ enum { value = true }; };
+
+
+template<typename S1, typename S2> struct glue_shapes;
+template<> struct glue_shapes<DenseShape,TriangularShape> { typedef TriangularShape type;  };
+
+template<typename T1, typename T2>
+struct possibly_same_dense {
+  enum { value = has_direct_access<T1>::ret && has_direct_access<T2>::ret && is_same<typename T1::Scalar,typename T2::Scalar>::value };
+};
+
+template<typename T1, typename T2>
+EIGEN_DEVICE_FUNC
+bool is_same_dense(const T1 &mat1, const T2 &mat2, std::enable_if_t<possibly_same_dense<T1,T2>::value> * = 0)
+{
+  return (mat1.data()==mat2.data()) && (mat1.innerStride()==mat2.innerStride()) && (mat1.outerStride()==mat2.outerStride());
+}
+
+template<typename T1, typename T2>
+EIGEN_DEVICE_FUNC
+bool is_same_dense(const T1 &, const T2 &, std::enable_if_t<!possibly_same_dense<T1,T2>::value> * = 0)
+{
+  return false;
+}
+
+// Internal helper defining the cost of a scalar division for the type T.
+// The default heuristic can be specialized for each scalar type and architecture.
+template<typename T,bool Vectorized=false,typename EnableIf = void>
+struct scalar_div_cost {
+  enum { value = 8*NumTraits<T>::MulCost };
+};
+
+template<typename T,bool Vectorized>
+struct scalar_div_cost<std::complex<T>, Vectorized> {
+  enum { value = 2*scalar_div_cost<T>::value
+               + 6*NumTraits<T>::MulCost
+               + 3*NumTraits<T>::AddCost
+  };
+};
+
+
+template<bool Vectorized>
+struct scalar_div_cost<signed long,Vectorized, std::conditional_t<sizeof(long)==8,void,false_type>> { enum { value = 24 }; };
+template<bool Vectorized>
+struct scalar_div_cost<unsigned long,Vectorized, std::conditional_t<sizeof(long)==8,void,false_type>> { enum { value = 21 }; };
+
+
+#ifdef EIGEN_DEBUG_ASSIGN
+std::string demangle_traversal(int t)
+{
+  if(t==DefaultTraversal) return "DefaultTraversal";
+  if(t==LinearTraversal) return "LinearTraversal";
+  if(t==InnerVectorizedTraversal) return "InnerVectorizedTraversal";
+  if(t==LinearVectorizedTraversal) return "LinearVectorizedTraversal";
+  if(t==SliceVectorizedTraversal) return "SliceVectorizedTraversal";
+  return "?";
+}
+std::string demangle_unrolling(int t)
+{
+  if(t==NoUnrolling) return "NoUnrolling";
+  if(t==InnerUnrolling) return "InnerUnrolling";
+  if(t==CompleteUnrolling) return "CompleteUnrolling";
+  return "?";
+}
+std::string demangle_flags(int f)
+{
+  std::string res;
+  if(f&RowMajorBit)                 res += " | RowMajor";
+  if(f&PacketAccessBit)             res += " | Packet";
+  if(f&LinearAccessBit)             res += " | Linear";
+  if(f&LvalueBit)                   res += " | Lvalue";
+  if(f&DirectAccessBit)             res += " | Direct";
+  if(f&NestByRefBit)                res += " | NestByRef";
+  if(f&NoPreferredStorageOrderBit)  res += " | NoPreferredStorageOrderBit";
+
+  return res;
+}
+#endif
+
+} // end namespace internal
+
+
+/** \class ScalarBinaryOpTraits
+  * \ingroup Core_Module
+  *
+  * \brief Determines whether the given binary operation of two numeric types is allowed and what the scalar return type is.
+  *
+  * This class permits to control the scalar return type of any binary operation performed on two different scalar types through (partial) template specializations.
+  *
+  * For instance, let \c U1, \c U2 and \c U3 be three user defined scalar types for which most operations between instances of \c U1 and \c U2 returns an \c U3.
+  * You can let %Eigen knows that by defining:
+    \code
+    template<typename BinaryOp>
+    struct ScalarBinaryOpTraits<U1,U2,BinaryOp> { typedef U3 ReturnType;  };
+    template<typename BinaryOp>
+    struct ScalarBinaryOpTraits<U2,U1,BinaryOp> { typedef U3 ReturnType;  };
+    \endcode
+  * You can then explicitly disable some particular operations to get more explicit error messages:
+    \code
+    template<>
+    struct ScalarBinaryOpTraits<U1,U2,internal::scalar_max_op<U1,U2> > {};
+    \endcode
+  * Or customize the return type for individual operation:
+    \code
+    template<>
+    struct ScalarBinaryOpTraits<U1,U2,internal::scalar_sum_op<U1,U2> > { typedef U1 ReturnType; };
+    \endcode
+  *
+  * By default, the following generic combinations are supported:
+  <table class="manual">
+  <tr><th>ScalarA</th><th>ScalarB</th><th>BinaryOp</th><th>ReturnType</th><th>Note</th></tr>
+  <tr            ><td>\c T </td><td>\c T </td><td>\c * </td><td>\c T </td><td></td></tr>
+  <tr class="alt"><td>\c NumTraits<T>::Real </td><td>\c T </td><td>\c * </td><td>\c T </td><td>Only if \c NumTraits<T>::IsComplex </td></tr>
+  <tr            ><td>\c T </td><td>\c NumTraits<T>::Real </td><td>\c * </td><td>\c T </td><td>Only if \c NumTraits<T>::IsComplex </td></tr>
+  </table>
+  *
+  * \sa CwiseBinaryOp
+  */
+template<typename ScalarA, typename ScalarB, typename BinaryOp=internal::scalar_product_op<ScalarA,ScalarB> >
+struct ScalarBinaryOpTraits
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  // for backward compatibility, use the hints given by the (deprecated) internal::scalar_product_traits class.
+  : internal::scalar_product_traits<ScalarA,ScalarB>
+#endif // EIGEN_PARSED_BY_DOXYGEN
+{};
+
+template<typename T, typename BinaryOp>
+struct ScalarBinaryOpTraits<T,T,BinaryOp>
+{
+  typedef T ReturnType;
+};
+
+template <typename T, typename BinaryOp>
+struct ScalarBinaryOpTraits<T, typename NumTraits<std::enable_if_t<NumTraits<T>::IsComplex,T>>::Real, BinaryOp>
+{
+  typedef T ReturnType;
+};
+template <typename T, typename BinaryOp>
+struct ScalarBinaryOpTraits<typename NumTraits<std::enable_if_t<NumTraits<T>::IsComplex,T>>::Real, T, BinaryOp>
+{
+  typedef T ReturnType;
+};
+
+// For Matrix * Permutation
+template<typename T, typename BinaryOp>
+struct ScalarBinaryOpTraits<T,void,BinaryOp>
+{
+  typedef T ReturnType;
+};
+
+// For Permutation * Matrix
+template<typename T, typename BinaryOp>
+struct ScalarBinaryOpTraits<void,T,BinaryOp>
+{
+  typedef T ReturnType;
+};
+
+// for Permutation*Permutation
+template<typename BinaryOp>
+struct ScalarBinaryOpTraits<void,void,BinaryOp>
+{
+  typedef void ReturnType;
+};
+
+// We require Lhs and Rhs to have "compatible" scalar types.
+// It is tempting to always allow mixing different types but remember that this is often impossible in the vectorized paths.
+// So allowing mixing different types gives very unexpected errors when enabling vectorization, when the user tries to
+// add together a float matrix and a double matrix.
+#define EIGEN_CHECK_BINARY_COMPATIBILIY(BINOP,LHS,RHS) \
+  EIGEN_STATIC_ASSERT((Eigen::internal::has_ReturnType<ScalarBinaryOpTraits<LHS, RHS,BINOP> >::value), \
+    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+} // end namespace Eigen
+
+#endif // EIGEN_XPRHELPER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/ComplexEigenSolver.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/ComplexEigenSolver.h
new file mode 100644
index 0000000000000000000000000000000000000000..1cfc0ca869fb8a39fb119f51232dcca399e7f364
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/ComplexEigenSolver.h
@@ -0,0 +1,343 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Claire Maurice
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_EIGEN_SOLVER_H
+#define EIGEN_COMPLEX_EIGEN_SOLVER_H
+
+#include "./ComplexSchur.h"
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+  *
+  *
+  * \class ComplexEigenSolver
+  *
+  * \brief Computes eigenvalues and eigenvectors of general complex matrices
+  *
+  * \tparam MatrixType_ the type of the matrix of which we are
+  * computing the eigendecomposition; this is expected to be an
+  * instantiation of the Matrix class template.
+  *
+  * The eigenvalues and eigenvectors of a matrix \f$ A \f$ are scalars
+  * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda v
+  * \f$.  If \f$ D \f$ is a diagonal matrix with the eigenvalues on
+  * the diagonal, and \f$ V \f$ is a matrix with the eigenvectors as
+  * its columns, then \f$ A V = V D \f$. The matrix \f$ V \f$ is
+  * almost always invertible, in which case we have \f$ A = V D V^{-1}
+  * \f$. This is called the eigendecomposition.
+  *
+  * The main function in this class is compute(), which computes the
+  * eigenvalues and eigenvectors of a given function. The
+  * documentation for that function contains an example showing the
+  * main features of the class.
+  *
+  * \sa class EigenSolver, class SelfAdjointEigenSolver
+  */
+template<typename MatrixType_> class ComplexEigenSolver
+{
+  public:
+
+    /** \brief Synonym for the template parameter \p MatrixType_. */
+    typedef MatrixType_ MatrixType;
+
+    enum {
+      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      Options = MatrixType::Options,
+      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+
+    /** \brief Scalar type for matrices of type #MatrixType. */
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+
+    /** \brief Complex scalar type for #MatrixType.
+      *
+      * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
+      * \c float or \c double) and just \c Scalar if #Scalar is
+      * complex.
+      */
+    typedef std::complex<RealScalar> ComplexScalar;
+
+    /** \brief Type for vector of eigenvalues as returned by eigenvalues().
+      *
+      * This is a column vector with entries of type #ComplexScalar.
+      * The length of the vector is the size of #MatrixType.
+      */
+    typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options&(~RowMajor), MaxColsAtCompileTime, 1> EigenvalueType;
+
+    /** \brief Type for matrix of eigenvectors as returned by eigenvectors().
+      *
+      * This is a square matrix with entries of type #ComplexScalar.
+      * The size is the same as the size of #MatrixType.
+      */
+    typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> EigenvectorType;
+
+    /** \brief Default constructor.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via compute().
+      */
+    ComplexEigenSolver()
+            : m_eivec(),
+              m_eivalues(),
+              m_schur(),
+              m_isInitialized(false),
+              m_eigenvectorsOk(false),
+              m_matX()
+    {}
+
+    /** \brief Default Constructor with memory preallocation
+      *
+      * Like the default constructor but with preallocation of the internal data
+      * according to the specified problem \a size.
+      * \sa ComplexEigenSolver()
+      */
+    explicit ComplexEigenSolver(Index size)
+            : m_eivec(size, size),
+              m_eivalues(size),
+              m_schur(size),
+              m_isInitialized(false),
+              m_eigenvectorsOk(false),
+              m_matX(size, size)
+    {}
+
+    /** \brief Constructor; computes eigendecomposition of given matrix.
+      *
+      * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
+      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
+      *    eigenvalues are computed; if false, only the eigenvalues are
+      *    computed.
+      *
+      * This constructor calls compute() to compute the eigendecomposition.
+      */
+    template<typename InputType>
+    explicit ComplexEigenSolver(const EigenBase<InputType>& matrix, bool computeEigenvectors = true)
+            : m_eivec(matrix.rows(),matrix.cols()),
+              m_eivalues(matrix.cols()),
+              m_schur(matrix.rows()),
+              m_isInitialized(false),
+              m_eigenvectorsOk(false),
+              m_matX(matrix.rows(),matrix.cols())
+    {
+      compute(matrix.derived(), computeEigenvectors);
+    }
+
+    /** \brief Returns the eigenvectors of given matrix.
+      *
+      * \returns  A const reference to the matrix whose columns are the eigenvectors.
+      *
+      * \pre Either the constructor
+      * ComplexEigenSolver(const MatrixType& matrix, bool) or the member
+      * function compute(const MatrixType& matrix, bool) has been called before
+      * to compute the eigendecomposition of a matrix, and
+      * \p computeEigenvectors was set to true (the default).
+      *
+      * This function returns a matrix whose columns are the eigenvectors. Column
+      * \f$ k \f$ is an eigenvector corresponding to eigenvalue number \f$ k
+      * \f$ as returned by eigenvalues().  The eigenvectors are normalized to
+      * have (Euclidean) norm equal to one. The matrix returned by this
+      * function is the matrix \f$ V \f$ in the eigendecomposition \f$ A = V D
+      * V^{-1} \f$, if it exists.
+      *
+      * Example: \include ComplexEigenSolver_eigenvectors.cpp
+      * Output: \verbinclude ComplexEigenSolver_eigenvectors.out
+      */
+    const EigenvectorType& eigenvectors() const
+    {
+      eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
+      eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+      return m_eivec;
+    }
+
+    /** \brief Returns the eigenvalues of given matrix.
+      *
+      * \returns A const reference to the column vector containing the eigenvalues.
+      *
+      * \pre Either the constructor
+      * ComplexEigenSolver(const MatrixType& matrix, bool) or the member
+      * function compute(const MatrixType& matrix, bool) has been called before
+      * to compute the eigendecomposition of a matrix.
+      *
+      * This function returns a column vector containing the
+      * eigenvalues. Eigenvalues are repeated according to their
+      * algebraic multiplicity, so there are as many eigenvalues as
+      * rows in the matrix. The eigenvalues are not sorted in any particular
+      * order.
+      *
+      * Example: \include ComplexEigenSolver_eigenvalues.cpp
+      * Output: \verbinclude ComplexEigenSolver_eigenvalues.out
+      */
+    const EigenvalueType& eigenvalues() const
+    {
+      eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
+      return m_eivalues;
+    }
+
+    /** \brief Computes eigendecomposition of given matrix.
+      *
+      * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
+      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
+      *    eigenvalues are computed; if false, only the eigenvalues are
+      *    computed.
+      * \returns    Reference to \c *this
+      *
+      * This function computes the eigenvalues of the complex matrix \p matrix.
+      * The eigenvalues() function can be used to retrieve them.  If
+      * \p computeEigenvectors is true, then the eigenvectors are also computed
+      * and can be retrieved by calling eigenvectors().
+      *
+      * The matrix is first reduced to Schur form using the
+      * ComplexSchur class. The Schur decomposition is then used to
+      * compute the eigenvalues and eigenvectors.
+      *
+      * The cost of the computation is dominated by the cost of the
+      * Schur decomposition, which is \f$ O(n^3) \f$ where \f$ n \f$
+      * is the size of the matrix.
+      *
+      * Example: \include ComplexEigenSolver_compute.cpp
+      * Output: \verbinclude ComplexEigenSolver_compute.out
+      */
+    template<typename InputType>
+    ComplexEigenSolver& compute(const EigenBase<InputType>& matrix, bool computeEigenvectors = true);
+
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful, \c NoConvergence otherwise.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "ComplexEigenSolver is not initialized.");
+      return m_schur.info();
+    }
+
+    /** \brief Sets the maximum number of iterations allowed. */
+    ComplexEigenSolver& setMaxIterations(Index maxIters)
+    {
+      m_schur.setMaxIterations(maxIters);
+      return *this;
+    }
+
+    /** \brief Returns the maximum number of iterations. */
+    Index getMaxIterations()
+    {
+      return m_schur.getMaxIterations();
+    }
+
+  protected:
+
+    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+    EigenvectorType m_eivec;
+    EigenvalueType m_eivalues;
+    ComplexSchur<MatrixType> m_schur;
+    bool m_isInitialized;
+    bool m_eigenvectorsOk;
+    EigenvectorType m_matX;
+
+  private:
+    void doComputeEigenvectors(RealScalar matrixnorm);
+    void sortEigenvalues(bool computeEigenvectors);
+};
+
+
+template<typename MatrixType>
+template<typename InputType>
+ComplexEigenSolver<MatrixType>& 
+ComplexEigenSolver<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeEigenvectors)
+{
+  // this code is inspired from Jampack
+  eigen_assert(matrix.cols() == matrix.rows());
+
+  // Do a complex Schur decomposition, A = U T U^*
+  // The eigenvalues are on the diagonal of T.
+  m_schur.compute(matrix.derived(), computeEigenvectors);
+
+  if(m_schur.info() == Success)
+  {
+    m_eivalues = m_schur.matrixT().diagonal();
+    if(computeEigenvectors)
+      doComputeEigenvectors(m_schur.matrixT().norm());
+    sortEigenvalues(computeEigenvectors);
+  }
+
+  m_isInitialized = true;
+  m_eigenvectorsOk = computeEigenvectors;
+  return *this;
+}
+
+
+template<typename MatrixType>
+void ComplexEigenSolver<MatrixType>::doComputeEigenvectors(RealScalar matrixnorm)
+{
+  const Index n = m_eivalues.size();
+
+  matrixnorm = numext::maxi(matrixnorm,(std::numeric_limits<RealScalar>::min)());
+
+  // Compute X such that T = X D X^(-1), where D is the diagonal of T.
+  // The matrix X is unit triangular.
+  m_matX = EigenvectorType::Zero(n, n);
+  for(Index k=n-1 ; k>=0 ; k--)
+  {
+    m_matX.coeffRef(k,k) = ComplexScalar(1.0,0.0);
+    // Compute X(i,k) using the (i,k) entry of the equation X T = D X
+    for(Index i=k-1 ; i>=0 ; i--)
+    {
+      m_matX.coeffRef(i,k) = -m_schur.matrixT().coeff(i,k);
+      if(k-i-1>0)
+        m_matX.coeffRef(i,k) -= (m_schur.matrixT().row(i).segment(i+1,k-i-1) * m_matX.col(k).segment(i+1,k-i-1)).value();
+      ComplexScalar z = m_schur.matrixT().coeff(i,i) - m_schur.matrixT().coeff(k,k);
+      if(z==ComplexScalar(0))
+      {
+        // If the i-th and k-th eigenvalue are equal, then z equals 0.
+        // Use a small value instead, to prevent division by zero.
+        numext::real_ref(z) = NumTraits<RealScalar>::epsilon() * matrixnorm;
+      }
+      m_matX.coeffRef(i,k) = m_matX.coeff(i,k) / z;
+    }
+  }
+
+  // Compute V as V = U X; now A = U T U^* = U X D X^(-1) U^* = V D V^(-1)
+  m_eivec.noalias() = m_schur.matrixU() * m_matX;
+  // .. and normalize the eigenvectors
+  for(Index k=0 ; k<n ; k++)
+  {
+    m_eivec.col(k).normalize();
+  }
+}
+
+
+template<typename MatrixType>
+void ComplexEigenSolver<MatrixType>::sortEigenvalues(bool computeEigenvectors)
+{
+  const Index n =  m_eivalues.size();
+  for (Index i=0; i<n; i++)
+  {
+    Index k;
+    m_eivalues.cwiseAbs().tail(n-i).minCoeff(&k);
+    if (k != 0)
+    {
+      k += i;
+      std::swap(m_eivalues[k],m_eivalues[i]);
+      if(computeEigenvectors)
+	m_eivec.col(i).swap(m_eivec.col(k));
+    }
+  }
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_COMPLEX_EIGEN_SOLVER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/ComplexSchur.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/ComplexSchur.h
new file mode 100644
index 0000000000000000000000000000000000000000..80a28fb3985018070f0e58505b1f4296474b6170
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/ComplexSchur.h
@@ -0,0 +1,464 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Claire Maurice
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLEX_SCHUR_H
+#define EIGEN_COMPLEX_SCHUR_H
+
+#include "./HessenbergDecomposition.h"
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+template<typename MatrixType, bool IsComplex> struct complex_schur_reduce_to_hessenberg;
+}
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+  *
+  *
+  * \class ComplexSchur
+  *
+  * \brief Performs a complex Schur decomposition of a real or complex square matrix
+  *
+  * \tparam MatrixType_ the type of the matrix of which we are
+  * computing the Schur decomposition; this is expected to be an
+  * instantiation of the Matrix class template.
+  *
+  * Given a real or complex square matrix A, this class computes the
+  * Schur decomposition: \f$ A = U T U^*\f$ where U is a unitary
+  * complex matrix, and T is a complex upper triangular matrix.  The
+  * diagonal of the matrix T corresponds to the eigenvalues of the
+  * matrix A.
+  *
+  * Call the function compute() to compute the Schur decomposition of
+  * a given matrix. Alternatively, you can use the 
+  * ComplexSchur(const MatrixType&, bool) constructor which computes
+  * the Schur decomposition at construction time. Once the
+  * decomposition is computed, you can use the matrixU() and matrixT()
+  * functions to retrieve the matrices U and V in the decomposition.
+  *
+  * \note This code is inspired from Jampack
+  *
+  * \sa class RealSchur, class EigenSolver, class ComplexEigenSolver
+  */
+template<typename MatrixType_> class ComplexSchur
+{
+  public:
+    typedef MatrixType_ MatrixType;
+    enum {
+      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      Options = MatrixType::Options,
+      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+
+    /** \brief Scalar type for matrices of type \p MatrixType_. */
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+
+    /** \brief Complex scalar type for \p MatrixType_.
+      *
+      * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
+      * \c float or \c double) and just \c Scalar if #Scalar is
+      * complex.
+      */
+    typedef std::complex<RealScalar> ComplexScalar;
+
+    /** \brief Type for the matrices in the Schur decomposition.
+      *
+      * This is a square matrix with entries of type #ComplexScalar. 
+      * The size is the same as the size of \p MatrixType_.
+      */
+    typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> ComplexMatrixType;
+
+    /** \brief Default constructor.
+      *
+      * \param [in] size  Positive integer, size of the matrix whose Schur decomposition will be computed.
+      *
+      * The default constructor is useful in cases in which the user
+      * intends to perform decompositions via compute().  The \p size
+      * parameter is only used as a hint. It is not an error to give a
+      * wrong \p size, but it may impair performance.
+      *
+      * \sa compute() for an example.
+      */
+    explicit ComplexSchur(Index size = RowsAtCompileTime==Dynamic ? 1 : RowsAtCompileTime)
+      : m_matT(size,size),
+        m_matU(size,size),
+        m_hess(size),
+        m_isInitialized(false),
+        m_matUisUptodate(false),
+        m_maxIters(-1)
+    {}
+
+    /** \brief Constructor; computes Schur decomposition of given matrix. 
+      * 
+      * \param[in]  matrix    Square matrix whose Schur decomposition is to be computed.
+      * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
+      *
+      * This constructor calls compute() to compute the Schur decomposition.
+      *
+      * \sa matrixT() and matrixU() for examples.
+      */
+    template<typename InputType>
+    explicit ComplexSchur(const EigenBase<InputType>& matrix, bool computeU = true)
+      : m_matT(matrix.rows(),matrix.cols()),
+        m_matU(matrix.rows(),matrix.cols()),
+        m_hess(matrix.rows()),
+        m_isInitialized(false),
+        m_matUisUptodate(false),
+        m_maxIters(-1)
+    {
+      compute(matrix.derived(), computeU);
+    }
+
+    /** \brief Returns the unitary matrix in the Schur decomposition. 
+      *
+      * \returns A const reference to the matrix U.
+      *
+      * It is assumed that either the constructor
+      * ComplexSchur(const MatrixType& matrix, bool computeU) or the
+      * member function compute(const MatrixType& matrix, bool computeU)
+      * has been called before to compute the Schur decomposition of a
+      * matrix, and that \p computeU was set to true (the default
+      * value).
+      *
+      * Example: \include ComplexSchur_matrixU.cpp
+      * Output: \verbinclude ComplexSchur_matrixU.out
+      */
+    const ComplexMatrixType& matrixU() const
+    {
+      eigen_assert(m_isInitialized && "ComplexSchur is not initialized.");
+      eigen_assert(m_matUisUptodate && "The matrix U has not been computed during the ComplexSchur decomposition.");
+      return m_matU;
+    }
+
+    /** \brief Returns the triangular matrix in the Schur decomposition. 
+      *
+      * \returns A const reference to the matrix T.
+      *
+      * It is assumed that either the constructor
+      * ComplexSchur(const MatrixType& matrix, bool computeU) or the
+      * member function compute(const MatrixType& matrix, bool computeU)
+      * has been called before to compute the Schur decomposition of a
+      * matrix.
+      *
+      * Note that this function returns a plain square matrix. If you want to reference
+      * only the upper triangular part, use:
+      * \code schur.matrixT().triangularView<Upper>() \endcode 
+      *
+      * Example: \include ComplexSchur_matrixT.cpp
+      * Output: \verbinclude ComplexSchur_matrixT.out
+      */
+    const ComplexMatrixType& matrixT() const
+    {
+      eigen_assert(m_isInitialized && "ComplexSchur is not initialized.");
+      return m_matT;
+    }
+
+    /** \brief Computes Schur decomposition of given matrix. 
+      * 
+      * \param[in]  matrix  Square matrix whose Schur decomposition is to be computed.
+      * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
+
+      * \returns    Reference to \c *this
+      *
+      * The Schur decomposition is computed by first reducing the
+      * matrix to Hessenberg form using the class
+      * HessenbergDecomposition. The Hessenberg matrix is then reduced
+      * to triangular form by performing QR iterations with a single
+      * shift. The cost of computing the Schur decomposition depends
+      * on the number of iterations; as a rough guide, it may be taken
+      * on the number of iterations; as a rough guide, it may be taken
+      * to be \f$25n^3\f$ complex flops, or \f$10n^3\f$ complex flops
+      * if \a computeU is false.
+      *
+      * Example: \include ComplexSchur_compute.cpp
+      * Output: \verbinclude ComplexSchur_compute.out
+      *
+      * \sa compute(const MatrixType&, bool, Index)
+      */
+    template<typename InputType>
+    ComplexSchur& compute(const EigenBase<InputType>& matrix, bool computeU = true);
+    
+    /** \brief Compute Schur decomposition from a given Hessenberg matrix
+     *  \param[in] matrixH Matrix in Hessenberg form H
+     *  \param[in] matrixQ orthogonal matrix Q that transform a matrix A to H : A = Q H Q^T
+     *  \param computeU Computes the matriX U of the Schur vectors
+     * \return Reference to \c *this
+     * 
+     *  This routine assumes that the matrix is already reduced in Hessenberg form matrixH
+     *  using either the class HessenbergDecomposition or another mean. 
+     *  It computes the upper quasi-triangular matrix T of the Schur decomposition of H
+     *  When computeU is true, this routine computes the matrix U such that 
+     *  A = U T U^T =  (QZ) T (QZ)^T = Q H Q^T where A is the initial matrix
+     * 
+     * NOTE Q is referenced if computeU is true; so, if the initial orthogonal matrix
+     * is not available, the user should give an identity matrix (Q.setIdentity())
+     * 
+     * \sa compute(const MatrixType&, bool)
+     */
+    template<typename HessMatrixType, typename OrthMatrixType>
+    ComplexSchur& computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ,  bool computeU=true);
+
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful, \c NoConvergence otherwise.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "ComplexSchur is not initialized.");
+      return m_info;
+    }
+
+    /** \brief Sets the maximum number of iterations allowed. 
+      *
+      * If not specified by the user, the maximum number of iterations is m_maxIterationsPerRow times the size
+      * of the matrix.
+      */
+    ComplexSchur& setMaxIterations(Index maxIters)
+    {
+      m_maxIters = maxIters;
+      return *this;
+    }
+
+    /** \brief Returns the maximum number of iterations. */
+    Index getMaxIterations()
+    {
+      return m_maxIters;
+    }
+
+    /** \brief Maximum number of iterations per row.
+      *
+      * If not otherwise specified, the maximum number of iterations is this number times the size of the
+      * matrix. It is currently set to 30.
+      */
+    static const int m_maxIterationsPerRow = 30;
+
+  protected:
+    ComplexMatrixType m_matT, m_matU;
+    HessenbergDecomposition<MatrixType> m_hess;
+    ComputationInfo m_info;
+    bool m_isInitialized;
+    bool m_matUisUptodate;
+    Index m_maxIters;
+
+  private:  
+    bool subdiagonalEntryIsNeglegible(Index i);
+    ComplexScalar computeShift(Index iu, Index iter);
+    void reduceToTriangularForm(bool computeU);
+    friend struct internal::complex_schur_reduce_to_hessenberg<MatrixType, NumTraits<Scalar>::IsComplex>;
+};
+
+/** If m_matT(i+1,i) is negligible in floating point arithmetic
+  * compared to m_matT(i,i) and m_matT(j,j), then set it to zero and
+  * return true, else return false. */
+template<typename MatrixType>
+inline bool ComplexSchur<MatrixType>::subdiagonalEntryIsNeglegible(Index i)
+{
+  RealScalar d = numext::norm1(m_matT.coeff(i,i)) + numext::norm1(m_matT.coeff(i+1,i+1));
+  RealScalar sd = numext::norm1(m_matT.coeff(i+1,i));
+  if (internal::isMuchSmallerThan(sd, d, NumTraits<RealScalar>::epsilon()))
+  {
+    m_matT.coeffRef(i+1,i) = ComplexScalar(0);
+    return true;
+  }
+  return false;
+}
+
+
+/** Compute the shift in the current QR iteration. */
+template<typename MatrixType>
+typename ComplexSchur<MatrixType>::ComplexScalar ComplexSchur<MatrixType>::computeShift(Index iu, Index iter)
+{
+  using std::abs;
+  if (iter == 10 || iter == 20) 
+  {
+    // exceptional shift, taken from http://www.netlib.org/eispack/comqr.f
+    return abs(numext::real(m_matT.coeff(iu,iu-1))) + abs(numext::real(m_matT.coeff(iu-1,iu-2)));
+  }
+
+  // compute the shift as one of the eigenvalues of t, the 2x2
+  // diagonal block on the bottom of the active submatrix
+  Matrix<ComplexScalar,2,2> t = m_matT.template block<2,2>(iu-1,iu-1);
+  RealScalar normt = t.cwiseAbs().sum();
+  t /= normt;     // the normalization by sf is to avoid under/overflow
+
+  ComplexScalar b = t.coeff(0,1) * t.coeff(1,0);
+  ComplexScalar c = t.coeff(0,0) - t.coeff(1,1);
+  ComplexScalar disc = sqrt(c*c + RealScalar(4)*b);
+  ComplexScalar det = t.coeff(0,0) * t.coeff(1,1) - b;
+  ComplexScalar trace = t.coeff(0,0) + t.coeff(1,1);
+  ComplexScalar eival1 = (trace + disc) / RealScalar(2);
+  ComplexScalar eival2 = (trace - disc) / RealScalar(2);
+  RealScalar eival1_norm = numext::norm1(eival1);
+  RealScalar eival2_norm = numext::norm1(eival2);
+  // A division by zero can only occur if eival1==eival2==0.
+  // In this case, det==0, and all we have to do is checking that eival2_norm!=0
+  if(eival1_norm > eival2_norm)
+    eival2 = det / eival1;
+  else if(!numext::is_exactly_zero(eival2_norm))
+    eival1 = det / eival2;
+
+  // choose the eigenvalue closest to the bottom entry of the diagonal
+  if(numext::norm1(eival1-t.coeff(1,1)) < numext::norm1(eival2-t.coeff(1,1)))
+    return normt * eival1;
+  else
+    return normt * eival2;
+}
+
+
+template<typename MatrixType>
+template<typename InputType>
+ComplexSchur<MatrixType>& ComplexSchur<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeU)
+{
+  m_matUisUptodate = false;
+  eigen_assert(matrix.cols() == matrix.rows());
+
+  if(matrix.cols() == 1)
+  {
+    m_matT = matrix.derived().template cast<ComplexScalar>();
+    if(computeU)  m_matU = ComplexMatrixType::Identity(1,1);
+    m_info = Success;
+    m_isInitialized = true;
+    m_matUisUptodate = computeU;
+    return *this;
+  }
+
+  internal::complex_schur_reduce_to_hessenberg<MatrixType, NumTraits<Scalar>::IsComplex>::run(*this, matrix.derived(), computeU);
+  computeFromHessenberg(m_matT, m_matU, computeU);
+  return *this;
+}
+
+template<typename MatrixType>
+template<typename HessMatrixType, typename OrthMatrixType>
+ComplexSchur<MatrixType>& ComplexSchur<MatrixType>::computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ, bool computeU)
+{
+  m_matT = matrixH;
+  if(computeU)
+    m_matU = matrixQ;
+  reduceToTriangularForm(computeU);
+  return *this;
+}
+namespace internal {
+
+/* Reduce given matrix to Hessenberg form */
+template<typename MatrixType, bool IsComplex>
+struct complex_schur_reduce_to_hessenberg
+{
+  // this is the implementation for the case IsComplex = true
+  static void run(ComplexSchur<MatrixType>& _this, const MatrixType& matrix, bool computeU)
+  {
+    _this.m_hess.compute(matrix);
+    _this.m_matT = _this.m_hess.matrixH();
+    if(computeU)  _this.m_matU = _this.m_hess.matrixQ();
+  }
+};
+
+template<typename MatrixType>
+struct complex_schur_reduce_to_hessenberg<MatrixType, false>
+{
+  static void run(ComplexSchur<MatrixType>& _this, const MatrixType& matrix, bool computeU)
+  {
+    typedef typename ComplexSchur<MatrixType>::ComplexScalar ComplexScalar;
+
+    // Note: m_hess is over RealScalar; m_matT and m_matU is over ComplexScalar
+    _this.m_hess.compute(matrix);
+    _this.m_matT = _this.m_hess.matrixH().template cast<ComplexScalar>();
+    if(computeU)  
+    {
+      // This may cause an allocation which seems to be avoidable
+      MatrixType Q = _this.m_hess.matrixQ(); 
+      _this.m_matU = Q.template cast<ComplexScalar>();
+    }
+  }
+};
+
+} // end namespace internal
+
+// Reduce the Hessenberg matrix m_matT to triangular form by QR iteration.
+template<typename MatrixType>
+void ComplexSchur<MatrixType>::reduceToTriangularForm(bool computeU)
+{  
+  Index maxIters = m_maxIters;
+  if (maxIters == -1)
+    maxIters = m_maxIterationsPerRow * m_matT.rows();
+
+  // The matrix m_matT is divided in three parts. 
+  // Rows 0,...,il-1 are decoupled from the rest because m_matT(il,il-1) is zero. 
+  // Rows il,...,iu is the part we are working on (the active submatrix).
+  // Rows iu+1,...,end are already brought in triangular form.
+  Index iu = m_matT.cols() - 1;
+  Index il;
+  Index iter = 0; // number of iterations we are working on the (iu,iu) element
+  Index totalIter = 0; // number of iterations for whole matrix
+
+  while(true)
+  {
+    // find iu, the bottom row of the active submatrix
+    while(iu > 0)
+    {
+      if(!subdiagonalEntryIsNeglegible(iu-1)) break;
+      iter = 0;
+      --iu;
+    }
+
+    // if iu is zero then we are done; the whole matrix is triangularized
+    if(iu==0) break;
+
+    // if we spent too many iterations, we give up
+    iter++;
+    totalIter++;
+    if(totalIter > maxIters) break;
+
+    // find il, the top row of the active submatrix
+    il = iu-1;
+    while(il > 0 && !subdiagonalEntryIsNeglegible(il-1))
+    {
+      --il;
+    }
+
+    /* perform the QR step using Givens rotations. The first rotation
+       creates a bulge; the (il+2,il) element becomes nonzero. This
+       bulge is chased down to the bottom of the active submatrix. */
+
+    ComplexScalar shift = computeShift(iu, iter);
+    JacobiRotation<ComplexScalar> rot;
+    rot.makeGivens(m_matT.coeff(il,il) - shift, m_matT.coeff(il+1,il));
+    m_matT.rightCols(m_matT.cols()-il).applyOnTheLeft(il, il+1, rot.adjoint());
+    m_matT.topRows((std::min)(il+2,iu)+1).applyOnTheRight(il, il+1, rot);
+    if(computeU) m_matU.applyOnTheRight(il, il+1, rot);
+
+    for(Index i=il+1 ; i<iu ; i++)
+    {
+      rot.makeGivens(m_matT.coeffRef(i,i-1), m_matT.coeffRef(i+1,i-1), &m_matT.coeffRef(i,i-1));
+      m_matT.coeffRef(i+1,i-1) = ComplexScalar(0);
+      m_matT.rightCols(m_matT.cols()-i).applyOnTheLeft(i, i+1, rot.adjoint());
+      m_matT.topRows((std::min)(i+2,iu)+1).applyOnTheRight(i, i+1, rot);
+      if(computeU) m_matU.applyOnTheRight(i, i+1, rot);
+    }
+  }
+
+  if(totalIter <= maxIters)
+    m_info = Success;
+  else
+    m_info = NoConvergence;
+
+  m_isInitialized = true;
+  m_matUisUptodate = computeU;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_COMPLEX_SCHUR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
new file mode 100644
index 0000000000000000000000000000000000000000..144eb2a89c47479054ebab21f8fd1feb56cc8fb8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/ComplexSchur_LAPACKE.h
@@ -0,0 +1,93 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *    Complex Schur needed to complex unsymmetrical eigenvalues/eigenvectors.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_COMPLEX_SCHUR_LAPACKE_H
+#define EIGEN_COMPLEX_SCHUR_LAPACKE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \internal Specialization for the data types supported by LAPACKe */
+
+#define EIGEN_LAPACKE_SCHUR_COMPLEX(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX, LAPACKE_PREFIX_U, EIGCOLROW, LAPACKE_COLROW) \
+template<> template<typename InputType> inline \
+ComplexSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
+ComplexSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const EigenBase<InputType>& matrix, bool computeU) \
+{ \
+  typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> MatrixType; \
+  typedef MatrixType::RealScalar RealScalar; \
+  typedef std::complex<RealScalar> ComplexScalar; \
+\
+  eigen_assert(matrix.cols() == matrix.rows()); \
+\
+  m_matUisUptodate = false; \
+  if(matrix.cols() == 1) \
+  { \
+    m_matT = matrix.derived().template cast<ComplexScalar>(); \
+    if(computeU)  m_matU = ComplexMatrixType::Identity(1,1); \
+      m_info = Success; \
+      m_isInitialized = true; \
+      m_matUisUptodate = computeU; \
+      return *this; \
+  } \
+  lapack_int n = internal::convert_index<lapack_int>(matrix.cols()), sdim, info; \
+  lapack_int matrix_order = LAPACKE_COLROW; \
+  char jobvs, sort='N'; \
+  LAPACK_##LAPACKE_PREFIX_U##_SELECT1 select = 0; \
+  jobvs = (computeU) ? 'V' : 'N'; \
+  m_matU.resize(n, n); \
+  lapack_int ldvs  = internal::convert_index<lapack_int>(m_matU.outerStride()); \
+  m_matT = matrix; \
+  lapack_int lda = internal::convert_index<lapack_int>(m_matT.outerStride()); \
+  Matrix<EIGTYPE, Dynamic, Dynamic> w; \
+  w.resize(n, 1);\
+  info = LAPACKE_##LAPACKE_PREFIX##gees( matrix_order, jobvs, sort, select, n, (LAPACKE_TYPE*)m_matT.data(), lda, &sdim, (LAPACKE_TYPE*)w.data(), (LAPACKE_TYPE*)m_matU.data(), ldvs ); \
+  if(info == 0) \
+    m_info = Success; \
+  else \
+    m_info = NoConvergence; \
+\
+  m_isInitialized = true; \
+  m_matUisUptodate = computeU; \
+  return *this; \
+\
+}
+
+EIGEN_LAPACKE_SCHUR_COMPLEX(dcomplex, lapack_complex_double, z, Z, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_LAPACKE_SCHUR_COMPLEX(scomplex, lapack_complex_float,  c, C, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_LAPACKE_SCHUR_COMPLEX(dcomplex, lapack_complex_double, z, Z, RowMajor, LAPACK_ROW_MAJOR)
+EIGEN_LAPACKE_SCHUR_COMPLEX(scomplex, lapack_complex_float,  c, C, RowMajor, LAPACK_ROW_MAJOR)
+
+} // end namespace Eigen
+
+#endif // EIGEN_COMPLEX_SCHUR_LAPACKE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/EigenSolver.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/EigenSolver.h
new file mode 100644
index 0000000000000000000000000000000000000000..f6ff140fb81c895839fca790b12c89d5f0a9399e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/EigenSolver.h
@@ -0,0 +1,624 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EIGENSOLVER_H
+#define EIGEN_EIGENSOLVER_H
+
+#include "./RealSchur.h"
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+  *
+  *
+  * \class EigenSolver
+  *
+  * \brief Computes eigenvalues and eigenvectors of general matrices
+  *
+  * \tparam MatrixType_ the type of the matrix of which we are computing the
+  * eigendecomposition; this is expected to be an instantiation of the Matrix
+  * class template. Currently, only real matrices are supported.
+  *
+  * The eigenvalues and eigenvectors of a matrix \f$ A \f$ are scalars
+  * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda v \f$.  If
+  * \f$ D \f$ is a diagonal matrix with the eigenvalues on the diagonal, and
+  * \f$ V \f$ is a matrix with the eigenvectors as its columns, then \f$ A V =
+  * V D \f$. The matrix \f$ V \f$ is almost always invertible, in which case we
+  * have \f$ A = V D V^{-1} \f$. This is called the eigendecomposition.
+  *
+  * The eigenvalues and eigenvectors of a matrix may be complex, even when the
+  * matrix is real. However, we can choose real matrices \f$ V \f$ and \f$ D
+  * \f$ satisfying \f$ A V = V D \f$, just like the eigendecomposition, if the
+  * matrix \f$ D \f$ is not required to be diagonal, but if it is allowed to
+  * have blocks of the form
+  * \f[ \begin{bmatrix} u & v \\ -v & u \end{bmatrix} \f]
+  * (where \f$ u \f$ and \f$ v \f$ are real numbers) on the diagonal.  These
+  * blocks correspond to complex eigenvalue pairs \f$ u \pm iv \f$. We call
+  * this variant of the eigendecomposition the pseudo-eigendecomposition.
+  *
+  * Call the function compute() to compute the eigenvalues and eigenvectors of
+  * a given matrix. Alternatively, you can use the 
+  * EigenSolver(const MatrixType&, bool) constructor which computes the
+  * eigenvalues and eigenvectors at construction time. Once the eigenvalue and
+  * eigenvectors are computed, they can be retrieved with the eigenvalues() and
+  * eigenvectors() functions. The pseudoEigenvalueMatrix() and
+  * pseudoEigenvectors() methods allow the construction of the
+  * pseudo-eigendecomposition.
+  *
+  * The documentation for EigenSolver(const MatrixType&, bool) contains an
+  * example of the typical use of this class.
+  *
+  * \note The implementation is adapted from
+  * <a href="http://math.nist.gov/javanumerics/jama/">JAMA</a> (public domain).
+  * Their code is based on EISPACK.
+  *
+  * \sa MatrixBase::eigenvalues(), class ComplexEigenSolver, class SelfAdjointEigenSolver
+  */
+template<typename MatrixType_> class EigenSolver
+{
+  public:
+
+    /** \brief Synonym for the template parameter \p MatrixType_. */
+    typedef MatrixType_ MatrixType;
+
+    enum {
+      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      Options = MatrixType::Options,
+      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+
+    /** \brief Scalar type for matrices of type #MatrixType. */
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+
+    /** \brief Complex scalar type for #MatrixType. 
+      *
+      * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
+      * \c float or \c double) and just \c Scalar if #Scalar is
+      * complex.
+      */
+    typedef std::complex<RealScalar> ComplexScalar;
+
+    /** \brief Type for vector of eigenvalues as returned by eigenvalues(). 
+      *
+      * This is a column vector with entries of type #ComplexScalar.
+      * The length of the vector is the size of #MatrixType.
+      */
+    typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> EigenvalueType;
+
+    /** \brief Type for matrix of eigenvectors as returned by eigenvectors(). 
+      *
+      * This is a square matrix with entries of type #ComplexScalar. 
+      * The size is the same as the size of #MatrixType.
+      */
+    typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> EigenvectorsType;
+
+    /** \brief Default constructor.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via EigenSolver::compute(const MatrixType&, bool).
+      *
+      * \sa compute() for an example.
+      */
+    EigenSolver() : m_eivec(), m_eivalues(), m_isInitialized(false), m_eigenvectorsOk(false), m_realSchur(), m_matT(), m_tmp() {}
+
+    /** \brief Default constructor with memory preallocation
+      *
+      * Like the default constructor but with preallocation of the internal data
+      * according to the specified problem \a size.
+      * \sa EigenSolver()
+      */
+    explicit EigenSolver(Index size)
+      : m_eivec(size, size),
+        m_eivalues(size),
+        m_isInitialized(false),
+        m_eigenvectorsOk(false),
+        m_realSchur(size),
+        m_matT(size, size), 
+        m_tmp(size)
+    {}
+
+    /** \brief Constructor; computes eigendecomposition of given matrix. 
+      * 
+      * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
+      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
+      *    eigenvalues are computed; if false, only the eigenvalues are
+      *    computed. 
+      *
+      * This constructor calls compute() to compute the eigenvalues
+      * and eigenvectors.
+      *
+      * Example: \include EigenSolver_EigenSolver_MatrixType.cpp
+      * Output: \verbinclude EigenSolver_EigenSolver_MatrixType.out
+      *
+      * \sa compute()
+      */
+    template<typename InputType>
+    explicit EigenSolver(const EigenBase<InputType>& matrix, bool computeEigenvectors = true)
+      : m_eivec(matrix.rows(), matrix.cols()),
+        m_eivalues(matrix.cols()),
+        m_isInitialized(false),
+        m_eigenvectorsOk(false),
+        m_realSchur(matrix.cols()),
+        m_matT(matrix.rows(), matrix.cols()), 
+        m_tmp(matrix.cols())
+    {
+      compute(matrix.derived(), computeEigenvectors);
+    }
+
+    /** \brief Returns the eigenvectors of given matrix. 
+      *
+      * \returns  %Matrix whose columns are the (possibly complex) eigenvectors.
+      *
+      * \pre Either the constructor 
+      * EigenSolver(const MatrixType&,bool) or the member function
+      * compute(const MatrixType&, bool) has been called before, and
+      * \p computeEigenvectors was set to true (the default).
+      *
+      * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding
+      * to eigenvalue number \f$ k \f$ as returned by eigenvalues().  The
+      * eigenvectors are normalized to have (Euclidean) norm equal to one. The
+      * matrix returned by this function is the matrix \f$ V \f$ in the
+      * eigendecomposition \f$ A = V D V^{-1} \f$, if it exists.
+      *
+      * Example: \include EigenSolver_eigenvectors.cpp
+      * Output: \verbinclude EigenSolver_eigenvectors.out
+      *
+      * \sa eigenvalues(), pseudoEigenvectors()
+      */
+    EigenvectorsType eigenvectors() const;
+
+    /** \brief Returns the pseudo-eigenvectors of given matrix. 
+      *
+      * \returns  Const reference to matrix whose columns are the pseudo-eigenvectors.
+      *
+      * \pre Either the constructor 
+      * EigenSolver(const MatrixType&,bool) or the member function
+      * compute(const MatrixType&, bool) has been called before, and
+      * \p computeEigenvectors was set to true (the default).
+      *
+      * The real matrix \f$ V \f$ returned by this function and the
+      * block-diagonal matrix \f$ D \f$ returned by pseudoEigenvalueMatrix()
+      * satisfy \f$ AV = VD \f$.
+      *
+      * Example: \include EigenSolver_pseudoEigenvectors.cpp
+      * Output: \verbinclude EigenSolver_pseudoEigenvectors.out
+      *
+      * \sa pseudoEigenvalueMatrix(), eigenvectors()
+      */
+    const MatrixType& pseudoEigenvectors() const
+    {
+      eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
+      eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+      return m_eivec;
+    }
+
+    /** \brief Returns the block-diagonal matrix in the pseudo-eigendecomposition.
+      *
+      * \returns  A block-diagonal matrix.
+      *
+      * \pre Either the constructor 
+      * EigenSolver(const MatrixType&,bool) or the member function
+      * compute(const MatrixType&, bool) has been called before.
+      *
+      * The matrix \f$ D \f$ returned by this function is real and
+      * block-diagonal. The blocks on the diagonal are either 1-by-1 or 2-by-2
+      * blocks of the form
+      * \f$ \begin{bmatrix} u & v \\ -v & u \end{bmatrix} \f$.
+      * These blocks are not sorted in any particular order.
+      * The matrix \f$ D \f$ and the matrix \f$ V \f$ returned by
+      * pseudoEigenvectors() satisfy \f$ AV = VD \f$.
+      *
+      * \sa pseudoEigenvectors() for an example, eigenvalues()
+      */
+    MatrixType pseudoEigenvalueMatrix() const;
+
+    /** \brief Returns the eigenvalues of given matrix. 
+      *
+      * \returns A const reference to the column vector containing the eigenvalues.
+      *
+      * \pre Either the constructor 
+      * EigenSolver(const MatrixType&,bool) or the member function
+      * compute(const MatrixType&, bool) has been called before.
+      *
+      * The eigenvalues are repeated according to their algebraic multiplicity,
+      * so there are as many eigenvalues as rows in the matrix. The eigenvalues 
+      * are not sorted in any particular order.
+      *
+      * Example: \include EigenSolver_eigenvalues.cpp
+      * Output: \verbinclude EigenSolver_eigenvalues.out
+      *
+      * \sa eigenvectors(), pseudoEigenvalueMatrix(),
+      *     MatrixBase::eigenvalues()
+      */
+    const EigenvalueType& eigenvalues() const
+    {
+      eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
+      return m_eivalues;
+    }
+
+    /** \brief Computes eigendecomposition of given matrix. 
+      * 
+      * \param[in]  matrix  Square matrix whose eigendecomposition is to be computed.
+      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
+      *    eigenvalues are computed; if false, only the eigenvalues are
+      *    computed. 
+      * \returns    Reference to \c *this
+      *
+      * This function computes the eigenvalues of the real matrix \p matrix.
+      * The eigenvalues() function can be used to retrieve them.  If 
+      * \p computeEigenvectors is true, then the eigenvectors are also computed
+      * and can be retrieved by calling eigenvectors().
+      *
+      * The matrix is first reduced to real Schur form using the RealSchur
+      * class. The Schur decomposition is then used to compute the eigenvalues
+      * and eigenvectors.
+      *
+      * The cost of the computation is dominated by the cost of the
+      * Schur decomposition, which is very approximately \f$ 25n^3 \f$
+      * (where \f$ n \f$ is the size of the matrix) if \p computeEigenvectors 
+      * is true, and \f$ 10n^3 \f$ if \p computeEigenvectors is false.
+      *
+      * This method reuses of the allocated data in the EigenSolver object.
+      *
+      * Example: \include EigenSolver_compute.cpp
+      * Output: \verbinclude EigenSolver_compute.out
+      */
+    template<typename InputType>
+    EigenSolver& compute(const EigenBase<InputType>& matrix, bool computeEigenvectors = true);
+
+    /** \returns NumericalIssue if the input contains INF or NaN values or overflow occurred. Returns Success otherwise. */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
+      return m_info;
+    }
+
+    /** \brief Sets the maximum number of iterations allowed. */
+    EigenSolver& setMaxIterations(Index maxIters)
+    {
+      m_realSchur.setMaxIterations(maxIters);
+      return *this;
+    }
+
+    /** \brief Returns the maximum number of iterations. */
+    Index getMaxIterations()
+    {
+      return m_realSchur.getMaxIterations();
+    }
+
+  private:
+    void doComputeEigenvectors();
+
+  protected:
+    
+    static void check_template_parameters()
+    {
+      EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar);
+      EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL);
+    }
+    
+    MatrixType m_eivec;
+    EigenvalueType m_eivalues;
+    bool m_isInitialized;
+    bool m_eigenvectorsOk;
+    ComputationInfo m_info;
+    RealSchur<MatrixType> m_realSchur;
+    MatrixType m_matT;
+
+    typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType;
+    ColumnVectorType m_tmp;
+};
+
+template<typename MatrixType>
+MatrixType EigenSolver<MatrixType>::pseudoEigenvalueMatrix() const
+{
+  eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
+  const RealScalar precision = RealScalar(2)*NumTraits<RealScalar>::epsilon();
+  Index n = m_eivalues.rows();
+  MatrixType matD = MatrixType::Zero(n,n);
+  for (Index i=0; i<n; ++i)
+  {
+    if (internal::isMuchSmallerThan(numext::imag(m_eivalues.coeff(i)), numext::real(m_eivalues.coeff(i)), precision))
+      matD.coeffRef(i,i) = numext::real(m_eivalues.coeff(i));
+    else
+    {
+      matD.template block<2,2>(i,i) <<  numext::real(m_eivalues.coeff(i)), numext::imag(m_eivalues.coeff(i)),
+                                       -numext::imag(m_eivalues.coeff(i)), numext::real(m_eivalues.coeff(i));
+      ++i;
+    }
+  }
+  return matD;
+}
+
+template<typename MatrixType>
+typename EigenSolver<MatrixType>::EigenvectorsType EigenSolver<MatrixType>::eigenvectors() const
+{
+  eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
+  eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+  const RealScalar precision = RealScalar(2)*NumTraits<RealScalar>::epsilon();
+  Index n = m_eivec.cols();
+  EigenvectorsType matV(n,n);
+  for (Index j=0; j<n; ++j)
+  {
+    if (internal::isMuchSmallerThan(numext::imag(m_eivalues.coeff(j)), numext::real(m_eivalues.coeff(j)), precision) || j+1==n)
+    {
+      // we have a real eigen value
+      matV.col(j) = m_eivec.col(j).template cast<ComplexScalar>();
+      matV.col(j).normalize();
+    }
+    else
+    {
+      // we have a pair of complex eigen values
+      for (Index i=0; i<n; ++i)
+      {
+        matV.coeffRef(i,j)   = ComplexScalar(m_eivec.coeff(i,j),  m_eivec.coeff(i,j+1));
+        matV.coeffRef(i,j+1) = ComplexScalar(m_eivec.coeff(i,j), -m_eivec.coeff(i,j+1));
+      }
+      matV.col(j).normalize();
+      matV.col(j+1).normalize();
+      ++j;
+    }
+  }
+  return matV;
+}
+
+template<typename MatrixType>
+template<typename InputType>
+EigenSolver<MatrixType>& 
+EigenSolver<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeEigenvectors)
+{
+  check_template_parameters();
+  
+  using std::sqrt;
+  using std::abs;
+  using numext::isfinite;
+  eigen_assert(matrix.cols() == matrix.rows());
+
+  // Reduce to real Schur form.
+  m_realSchur.compute(matrix.derived(), computeEigenvectors);
+  
+  m_info = m_realSchur.info();
+
+  if (m_info == Success)
+  {
+    m_matT = m_realSchur.matrixT();
+    if (computeEigenvectors)
+      m_eivec = m_realSchur.matrixU();
+  
+    // Compute eigenvalues from matT
+    m_eivalues.resize(matrix.cols());
+    Index i = 0;
+    while (i < matrix.cols()) 
+    {
+      if (i == matrix.cols() - 1 || m_matT.coeff(i+1, i) == Scalar(0)) 
+      {
+        m_eivalues.coeffRef(i) = m_matT.coeff(i, i);
+        if(!(isfinite)(m_eivalues.coeffRef(i)))
+        {
+          m_isInitialized = true;
+          m_eigenvectorsOk = false;
+          m_info = NumericalIssue;
+          return *this;
+        }
+        ++i;
+      }
+      else
+      {
+        Scalar p = Scalar(0.5) * (m_matT.coeff(i, i) - m_matT.coeff(i+1, i+1));
+        Scalar z;
+        // Compute z = sqrt(abs(p * p + m_matT.coeff(i+1, i) * m_matT.coeff(i, i+1)));
+        // without overflow
+        {
+          Scalar t0 = m_matT.coeff(i+1, i);
+          Scalar t1 = m_matT.coeff(i, i+1);
+          Scalar maxval = numext::maxi<Scalar>(abs(p),numext::maxi<Scalar>(abs(t0),abs(t1)));
+          t0 /= maxval;
+          t1 /= maxval;
+          Scalar p0 = p/maxval;
+          z = maxval * sqrt(abs(p0 * p0 + t0 * t1));
+        }
+        
+        m_eivalues.coeffRef(i)   = ComplexScalar(m_matT.coeff(i+1, i+1) + p, z);
+        m_eivalues.coeffRef(i+1) = ComplexScalar(m_matT.coeff(i+1, i+1) + p, -z);
+        if(!((isfinite)(m_eivalues.coeffRef(i)) && (isfinite)(m_eivalues.coeffRef(i+1))))
+        {
+          m_isInitialized = true;
+          m_eigenvectorsOk = false;
+          m_info = NumericalIssue;
+          return *this;
+        }
+        i += 2;
+      }
+    }
+    
+    // Compute eigenvectors.
+    if (computeEigenvectors)
+      doComputeEigenvectors();
+  }
+
+  m_isInitialized = true;
+  m_eigenvectorsOk = computeEigenvectors;
+
+  return *this;
+}
+
+
+template<typename MatrixType>
+void EigenSolver<MatrixType>::doComputeEigenvectors()
+{
+  using std::abs;
+  const Index size = m_eivec.cols();
+  const Scalar eps = NumTraits<Scalar>::epsilon();
+
+  // inefficient! this is already computed in RealSchur
+  Scalar norm(0);
+  for (Index j = 0; j < size; ++j)
+  {
+    norm += m_matT.row(j).segment((std::max)(j-1,Index(0)), size-(std::max)(j-1,Index(0))).cwiseAbs().sum();
+  }
+  
+  // Backsubstitute to find vectors of upper triangular form
+  if (norm == Scalar(0))
+  {
+    return;
+  }
+
+  for (Index n = size-1; n >= 0; n--)
+  {
+    Scalar p = m_eivalues.coeff(n).real();
+    Scalar q = m_eivalues.coeff(n).imag();
+
+    // Scalar vector
+    if (q == Scalar(0))
+    {
+      Scalar lastr(0), lastw(0);
+      Index l = n;
+
+      m_matT.coeffRef(n,n) = Scalar(1);
+      for (Index i = n-1; i >= 0; i--)
+      {
+        Scalar w = m_matT.coeff(i,i) - p;
+        Scalar r = m_matT.row(i).segment(l,n-l+1).dot(m_matT.col(n).segment(l, n-l+1));
+
+        if (m_eivalues.coeff(i).imag() < Scalar(0))
+        {
+          lastw = w;
+          lastr = r;
+        }
+        else
+        {
+          l = i;
+          if (m_eivalues.coeff(i).imag() == Scalar(0))
+          {
+            if (w != Scalar(0))
+              m_matT.coeffRef(i,n) = -r / w;
+            else
+              m_matT.coeffRef(i,n) = -r / (eps * norm);
+          }
+          else // Solve real equations
+          {
+            Scalar x = m_matT.coeff(i,i+1);
+            Scalar y = m_matT.coeff(i+1,i);
+            Scalar denom = (m_eivalues.coeff(i).real() - p) * (m_eivalues.coeff(i).real() - p) + m_eivalues.coeff(i).imag() * m_eivalues.coeff(i).imag();
+            Scalar t = (x * lastr - lastw * r) / denom;
+            m_matT.coeffRef(i,n) = t;
+            if (abs(x) > abs(lastw))
+              m_matT.coeffRef(i+1,n) = (-r - w * t) / x;
+            else
+              m_matT.coeffRef(i+1,n) = (-lastr - y * t) / lastw;
+          }
+
+          // Overflow control
+          Scalar t = abs(m_matT.coeff(i,n));
+          if ((eps * t) * t > Scalar(1))
+            m_matT.col(n).tail(size-i) /= t;
+        }
+      }
+    }
+    else if (q < Scalar(0) && n > 0) // Complex vector
+    {
+      Scalar lastra(0), lastsa(0), lastw(0);
+      Index l = n-1;
+
+      // Last vector component imaginary so matrix is triangular
+      if (abs(m_matT.coeff(n,n-1)) > abs(m_matT.coeff(n-1,n)))
+      {
+        m_matT.coeffRef(n-1,n-1) = q / m_matT.coeff(n,n-1);
+        m_matT.coeffRef(n-1,n) = -(m_matT.coeff(n,n) - p) / m_matT.coeff(n,n-1);
+      }
+      else
+      {
+        ComplexScalar cc = ComplexScalar(Scalar(0),-m_matT.coeff(n-1,n)) / ComplexScalar(m_matT.coeff(n-1,n-1)-p,q);
+        m_matT.coeffRef(n-1,n-1) = numext::real(cc);
+        m_matT.coeffRef(n-1,n) = numext::imag(cc);
+      }
+      m_matT.coeffRef(n,n-1) = Scalar(0);
+      m_matT.coeffRef(n,n) = Scalar(1);
+      for (Index i = n-2; i >= 0; i--)
+      {
+        Scalar ra = m_matT.row(i).segment(l, n-l+1).dot(m_matT.col(n-1).segment(l, n-l+1));
+        Scalar sa = m_matT.row(i).segment(l, n-l+1).dot(m_matT.col(n).segment(l, n-l+1));
+        Scalar w = m_matT.coeff(i,i) - p;
+
+        if (m_eivalues.coeff(i).imag() < Scalar(0))
+        {
+          lastw = w;
+          lastra = ra;
+          lastsa = sa;
+        }
+        else
+        {
+          l = i;
+          if (m_eivalues.coeff(i).imag() == RealScalar(0))
+          {
+            ComplexScalar cc = ComplexScalar(-ra,-sa) / ComplexScalar(w,q);
+            m_matT.coeffRef(i,n-1) = numext::real(cc);
+            m_matT.coeffRef(i,n) = numext::imag(cc);
+          }
+          else
+          {
+            // Solve complex equations
+            Scalar x = m_matT.coeff(i,i+1);
+            Scalar y = m_matT.coeff(i+1,i);
+            Scalar vr = (m_eivalues.coeff(i).real() - p) * (m_eivalues.coeff(i).real() - p) + m_eivalues.coeff(i).imag() * m_eivalues.coeff(i).imag() - q * q;
+            Scalar vi = (m_eivalues.coeff(i).real() - p) * Scalar(2) * q;
+            if ((vr == Scalar(0)) && (vi == Scalar(0)))
+              vr = eps * norm * (abs(w) + abs(q) + abs(x) + abs(y) + abs(lastw));
+
+            ComplexScalar cc = ComplexScalar(x*lastra-lastw*ra+q*sa,x*lastsa-lastw*sa-q*ra) / ComplexScalar(vr,vi);
+            m_matT.coeffRef(i,n-1) = numext::real(cc);
+            m_matT.coeffRef(i,n) = numext::imag(cc);
+            if (abs(x) > (abs(lastw) + abs(q)))
+            {
+              m_matT.coeffRef(i+1,n-1) = (-ra - w * m_matT.coeff(i,n-1) + q * m_matT.coeff(i,n)) / x;
+              m_matT.coeffRef(i+1,n) = (-sa - w * m_matT.coeff(i,n) - q * m_matT.coeff(i,n-1)) / x;
+            }
+            else
+            {
+              cc = ComplexScalar(-lastra-y*m_matT.coeff(i,n-1),-lastsa-y*m_matT.coeff(i,n)) / ComplexScalar(lastw,q);
+              m_matT.coeffRef(i+1,n-1) = numext::real(cc);
+              m_matT.coeffRef(i+1,n) = numext::imag(cc);
+            }
+          }
+
+          // Overflow control
+          Scalar t = numext::maxi<Scalar>(abs(m_matT.coeff(i,n-1)),abs(m_matT.coeff(i,n)));
+          if ((eps * t) * t > Scalar(1))
+            m_matT.block(i, n-1, size-i, 2) /= t;
+
+        }
+      }
+      
+      // We handled a pair of complex conjugate eigenvalues, so need to skip them both
+      n--;
+    }
+    else
+    {
+      eigen_assert(0 && "Internal bug in EigenSolver (INF or NaN has not been detected)"); // this should not happen
+    }
+  }
+
+  // Back transformation to get eigenvectors of original matrix
+  for (Index j = size-1; j >= 0; j--)
+  {
+    m_tmp.noalias() = m_eivec.leftCols(j+1) * m_matT.col(j).segment(0, j+1);
+    m_eivec.col(j) = m_tmp;
+  }
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_EIGENSOLVER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
new file mode 100644
index 0000000000000000000000000000000000000000..d62c41114da73e4bcb9fed0b5c311618f29f514c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/GeneralizedEigenSolver.h
@@ -0,0 +1,414 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
+// Copyright (C) 2016 Tobias Wood <tobias@spinicist.org.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERALIZEDEIGENSOLVER_H
+#define EIGEN_GENERALIZEDEIGENSOLVER_H
+
+#include "./RealQZ.h"
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+  *
+  *
+  * \class GeneralizedEigenSolver
+  *
+  * \brief Computes the generalized eigenvalues and eigenvectors of a pair of general matrices
+  *
+  * \tparam MatrixType_ the type of the matrices of which we are computing the
+  * eigen-decomposition; this is expected to be an instantiation of the Matrix
+  * class template. Currently, only real matrices are supported.
+  *
+  * The generalized eigenvalues and eigenvectors of a matrix pair \f$ A \f$ and \f$ B \f$ are scalars
+  * \f$ \lambda \f$ and vectors \f$ v \f$ such that \f$ Av = \lambda Bv \f$.  If
+  * \f$ D \f$ is a diagonal matrix with the eigenvalues on the diagonal, and
+  * \f$ V \f$ is a matrix with the eigenvectors as its columns, then \f$ A V =
+  * B V D \f$. The matrix \f$ V \f$ is almost always invertible, in which case we
+  * have \f$ A = B V D V^{-1} \f$. This is called the generalized eigen-decomposition.
+  *
+  * The generalized eigenvalues and eigenvectors of a matrix pair may be complex, even when the
+  * matrices are real. Moreover, the generalized eigenvalue might be infinite if the matrix B is
+  * singular. To workaround this difficulty, the eigenvalues are provided as a pair of complex \f$ \alpha \f$
+  * and real \f$ \beta \f$ such that: \f$ \lambda_i = \alpha_i / \beta_i \f$. If \f$ \beta_i \f$ is (nearly) zero,
+  * then one can consider the well defined left eigenvalue \f$ \mu = \beta_i / \alpha_i\f$ such that:
+  * \f$ \mu_i A v_i = B v_i \f$, or even \f$ \mu_i u_i^T A  = u_i^T B \f$ where \f$ u_i \f$ is
+  * called the left eigenvector.
+  *
+  * Call the function compute() to compute the generalized eigenvalues and eigenvectors of
+  * a given matrix pair. Alternatively, you can use the
+  * GeneralizedEigenSolver(const MatrixType&, const MatrixType&, bool) constructor which computes the
+  * eigenvalues and eigenvectors at construction time. Once the eigenvalue and
+  * eigenvectors are computed, they can be retrieved with the eigenvalues() and
+  * eigenvectors() functions.
+  *
+  * Here is an usage example of this class:
+  * Example: \include GeneralizedEigenSolver.cpp
+  * Output: \verbinclude GeneralizedEigenSolver.out
+  *
+  * \sa MatrixBase::eigenvalues(), class ComplexEigenSolver, class SelfAdjointEigenSolver
+  */
+template<typename MatrixType_> class GeneralizedEigenSolver
+{
+  public:
+
+    /** \brief Synonym for the template parameter \p MatrixType_. */
+    typedef MatrixType_ MatrixType;
+
+    enum {
+      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      Options = MatrixType::Options,
+      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+
+    /** \brief Scalar type for matrices of type #MatrixType. */
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+
+    /** \brief Complex scalar type for #MatrixType. 
+      *
+      * This is \c std::complex<Scalar> if #Scalar is real (e.g.,
+      * \c float or \c double) and just \c Scalar if #Scalar is
+      * complex.
+      */
+    typedef std::complex<RealScalar> ComplexScalar;
+
+    /** \brief Type for vector of real scalar values eigenvalues as returned by betas().
+      *
+      * This is a column vector with entries of type #Scalar.
+      * The length of the vector is the size of #MatrixType.
+      */
+    typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> VectorType;
+
+    /** \brief Type for vector of complex scalar values eigenvalues as returned by alphas().
+      *
+      * This is a column vector with entries of type #ComplexScalar.
+      * The length of the vector is the size of #MatrixType.
+      */
+    typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ComplexVectorType;
+
+    /** \brief Expression type for the eigenvalues as returned by eigenvalues().
+      */
+    typedef CwiseBinaryOp<internal::scalar_quotient_op<ComplexScalar,Scalar>,ComplexVectorType,VectorType> EigenvalueType;
+
+    /** \brief Type for matrix of eigenvectors as returned by eigenvectors(). 
+      *
+      * This is a square matrix with entries of type #ComplexScalar. 
+      * The size is the same as the size of #MatrixType.
+      */
+    typedef Matrix<ComplexScalar, RowsAtCompileTime, ColsAtCompileTime, Options, MaxRowsAtCompileTime, MaxColsAtCompileTime> EigenvectorsType;
+
+    /** \brief Default constructor.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via EigenSolver::compute(const MatrixType&, bool).
+      *
+      * \sa compute() for an example.
+      */
+    GeneralizedEigenSolver()
+      : m_eivec(),
+        m_alphas(),
+        m_betas(),
+        m_computeEigenvectors(false),
+        m_isInitialized(false),
+        m_realQZ()
+    {}
+
+    /** \brief Default constructor with memory preallocation
+      *
+      * Like the default constructor but with preallocation of the internal data
+      * according to the specified problem \a size.
+      * \sa GeneralizedEigenSolver()
+      */
+    explicit GeneralizedEigenSolver(Index size)
+      : m_eivec(size, size),
+        m_alphas(size),
+        m_betas(size),
+        m_computeEigenvectors(false),
+        m_isInitialized(false),
+        m_realQZ(size),
+        m_tmp(size)
+    {}
+
+    /** \brief Constructor; computes the generalized eigendecomposition of given matrix pair.
+      * 
+      * \param[in]  A  Square matrix whose eigendecomposition is to be computed.
+      * \param[in]  B  Square matrix whose eigendecomposition is to be computed.
+      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
+      *    eigenvalues are computed; if false, only the eigenvalues are computed.
+      *
+      * This constructor calls compute() to compute the generalized eigenvalues
+      * and eigenvectors.
+      *
+      * \sa compute()
+      */
+    GeneralizedEigenSolver(const MatrixType& A, const MatrixType& B, bool computeEigenvectors = true)
+      : m_eivec(A.rows(), A.cols()),
+        m_alphas(A.cols()),
+        m_betas(A.cols()),
+        m_computeEigenvectors(false),
+        m_isInitialized(false),
+        m_realQZ(A.cols()),
+        m_tmp(A.cols())
+    {
+      compute(A, B, computeEigenvectors);
+    }
+
+    /* \brief Returns the computed generalized eigenvectors.
+      *
+      * \returns  %Matrix whose columns are the (possibly complex) right eigenvectors.
+      * i.e. the eigenvectors that solve (A - l*B)x = 0. The ordering matches the eigenvalues.
+      *
+      * \pre Either the constructor 
+      * GeneralizedEigenSolver(const MatrixType&,const MatrixType&, bool) or the member function
+      * compute(const MatrixType&, const MatrixType& bool) has been called before, and
+      * \p computeEigenvectors was set to true (the default).
+      *
+      * \sa eigenvalues()
+      */
+    EigenvectorsType eigenvectors() const {
+      eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute eigenvectors");
+      eigen_assert(m_computeEigenvectors && "Eigenvectors for GeneralizedEigenSolver were not calculated");
+      return m_eivec;
+    }
+
+    /** \brief Returns an expression of the computed generalized eigenvalues.
+      *
+      * \returns An expression of the column vector containing the eigenvalues.
+      *
+      * It is a shortcut for \code this->alphas().cwiseQuotient(this->betas()); \endcode
+      * Not that betas might contain zeros. It is therefore not recommended to use this function,
+      * but rather directly deal with the alphas and betas vectors.
+      *
+      * \pre Either the constructor 
+      * GeneralizedEigenSolver(const MatrixType&,const MatrixType&,bool) or the member function
+      * compute(const MatrixType&,const MatrixType&,bool) has been called before.
+      *
+      * The eigenvalues are repeated according to their algebraic multiplicity,
+      * so there are as many eigenvalues as rows in the matrix. The eigenvalues 
+      * are not sorted in any particular order.
+      *
+      * \sa alphas(), betas(), eigenvectors()
+      */
+    EigenvalueType eigenvalues() const
+    {
+      eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute eigenvalues.");
+      return EigenvalueType(m_alphas,m_betas);
+    }
+
+    /** \returns A const reference to the vectors containing the alpha values
+      *
+      * This vector permits to reconstruct the j-th eigenvalues as alphas(i)/betas(j).
+      *
+      * \sa betas(), eigenvalues() */
+    const ComplexVectorType& alphas() const
+    {
+      eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute alphas.");
+      return m_alphas;
+    }
+
+    /** \returns A const reference to the vectors containing the beta values
+      *
+      * This vector permits to reconstruct the j-th eigenvalues as alphas(i)/betas(j).
+      *
+      * \sa alphas(), eigenvalues() */
+    const VectorType& betas() const
+    {
+      eigen_assert(info() == Success && "GeneralizedEigenSolver failed to compute betas.");
+      return m_betas;
+    }
+
+    /** \brief Computes generalized eigendecomposition of given matrix.
+      * 
+      * \param[in]  A  Square matrix whose eigendecomposition is to be computed.
+      * \param[in]  B  Square matrix whose eigendecomposition is to be computed.
+      * \param[in]  computeEigenvectors  If true, both the eigenvectors and the
+      *    eigenvalues are computed; if false, only the eigenvalues are
+      *    computed. 
+      * \returns    Reference to \c *this
+      *
+      * This function computes the eigenvalues of the real matrix \p matrix.
+      * The eigenvalues() function can be used to retrieve them.  If 
+      * \p computeEigenvectors is true, then the eigenvectors are also computed
+      * and can be retrieved by calling eigenvectors().
+      *
+      * The matrix is first reduced to real generalized Schur form using the RealQZ
+      * class. The generalized Schur decomposition is then used to compute the eigenvalues
+      * and eigenvectors.
+      *
+      * The cost of the computation is dominated by the cost of the
+      * generalized Schur decomposition.
+      *
+      * This method reuses of the allocated data in the GeneralizedEigenSolver object.
+      */
+    GeneralizedEigenSolver& compute(const MatrixType& A, const MatrixType& B, bool computeEigenvectors = true);
+
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "EigenSolver is not initialized.");
+      return m_realQZ.info();
+    }
+
+    /** Sets the maximal number of iterations allowed.
+    */
+    GeneralizedEigenSolver& setMaxIterations(Index maxIters)
+    {
+      m_realQZ.setMaxIterations(maxIters);
+      return *this;
+    }
+
+  protected:
+
+    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+    EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL)
+
+    EigenvectorsType m_eivec;
+    ComplexVectorType m_alphas;
+    VectorType m_betas;
+    bool m_computeEigenvectors;
+    bool m_isInitialized;
+    RealQZ<MatrixType> m_realQZ;
+    ComplexVectorType m_tmp;
+};
+
+template<typename MatrixType>
+GeneralizedEigenSolver<MatrixType>&
+GeneralizedEigenSolver<MatrixType>::compute(const MatrixType& A, const MatrixType& B, bool computeEigenvectors)
+{
+  using std::sqrt;
+  using std::abs;
+  eigen_assert(A.cols() == A.rows() && B.cols() == A.rows() && B.cols() == B.rows());
+  Index size = A.cols();
+  // Reduce to generalized real Schur form:
+  // A = Q S Z and B = Q T Z
+  m_realQZ.compute(A, B, computeEigenvectors);
+  if (m_realQZ.info() == Success)
+  {
+    // Resize storage
+    m_alphas.resize(size);
+    m_betas.resize(size);
+    if (computeEigenvectors)
+    {
+      m_eivec.resize(size,size);
+      m_tmp.resize(size);
+    }
+
+    // Aliases:
+    Map<VectorType> v(reinterpret_cast<Scalar*>(m_tmp.data()), size);
+    ComplexVectorType &cv = m_tmp;
+    const MatrixType &mS = m_realQZ.matrixS();
+    const MatrixType &mT = m_realQZ.matrixT();
+
+    Index i = 0;
+    while (i < size)
+    {
+      if (i == size - 1 || mS.coeff(i+1, i) == Scalar(0))
+      {
+        // Real eigenvalue
+        m_alphas.coeffRef(i) = mS.diagonal().coeff(i);
+        m_betas.coeffRef(i)  = mT.diagonal().coeff(i);
+        if (computeEigenvectors)
+        {
+          v.setConstant(Scalar(0.0));
+          v.coeffRef(i) = Scalar(1.0);
+          // For singular eigenvalues do nothing more
+          if(abs(m_betas.coeffRef(i)) >= (std::numeric_limits<RealScalar>::min)())
+          {
+            // Non-singular eigenvalue
+            const Scalar alpha = real(m_alphas.coeffRef(i));
+            const Scalar beta = m_betas.coeffRef(i);
+            for (Index j = i-1; j >= 0; j--)
+            {
+              const Index st = j+1;
+              const Index sz = i-j;
+              if (j > 0 && mS.coeff(j, j-1) != Scalar(0))
+              {
+                // 2x2 block
+                Matrix<Scalar, 2, 1> rhs = (alpha*mT.template block<2,Dynamic>(j-1,st,2,sz) - beta*mS.template block<2,Dynamic>(j-1,st,2,sz)) .lazyProduct( v.segment(st,sz) );
+                Matrix<Scalar, 2, 2> lhs = beta * mS.template block<2,2>(j-1,j-1) - alpha * mT.template block<2,2>(j-1,j-1);
+                v.template segment<2>(j-1) = lhs.partialPivLu().solve(rhs);
+                j--;
+              }
+              else
+              {
+                v.coeffRef(j) = -v.segment(st,sz).transpose().cwiseProduct(beta*mS.block(j,st,1,sz) - alpha*mT.block(j,st,1,sz)).sum() / (beta*mS.coeffRef(j,j) - alpha*mT.coeffRef(j,j));
+              }
+            }
+          }
+          m_eivec.col(i).real().noalias() = m_realQZ.matrixZ().transpose() * v;
+          m_eivec.col(i).real().normalize();
+          m_eivec.col(i).imag().setConstant(0);
+        }
+        ++i;
+      }
+      else
+      {
+        // We need to extract the generalized eigenvalues of the pair of a general 2x2 block S and a positive diagonal 2x2 block T
+        // Then taking beta=T_00*T_11, we can avoid any division, and alpha is the eigenvalues of A = (U^-1 * S * U) * diag(T_11,T_00):
+
+        // T =  [a 0]
+        //      [0 b]
+        RealScalar a = mT.diagonal().coeff(i),
+                   b = mT.diagonal().coeff(i+1);
+        const RealScalar beta = m_betas.coeffRef(i) = m_betas.coeffRef(i+1) = a*b;
+
+        // ^^ NOTE: using diagonal()(i) instead of coeff(i,i) workarounds a MSVC bug.
+        Matrix<RealScalar,2,2> S2 = mS.template block<2,2>(i,i) * Matrix<Scalar,2,1>(b,a).asDiagonal();
+
+        Scalar p = Scalar(0.5) * (S2.coeff(0,0) - S2.coeff(1,1));
+        Scalar z = sqrt(abs(p * p + S2.coeff(1,0) * S2.coeff(0,1)));
+        const ComplexScalar alpha = ComplexScalar(S2.coeff(1,1) + p, (beta > 0) ? z : -z);
+        m_alphas.coeffRef(i)   = conj(alpha);
+        m_alphas.coeffRef(i+1) = alpha;
+
+        if (computeEigenvectors) {
+          // Compute eigenvector in position (i+1) and then position (i) is just the conjugate
+          cv.setZero();
+          cv.coeffRef(i+1) = Scalar(1.0);
+          // here, the "static_cast" workaound expression template issues.
+          cv.coeffRef(i) = -(static_cast<Scalar>(beta*mS.coeffRef(i,i+1)) - alpha*mT.coeffRef(i,i+1))
+                          / (static_cast<Scalar>(beta*mS.coeffRef(i,i))   - alpha*mT.coeffRef(i,i));
+          for (Index j = i-1; j >= 0; j--)
+          {
+            const Index st = j+1;
+            const Index sz = i+1-j;
+            if (j > 0 && mS.coeff(j, j-1) != Scalar(0))
+            {
+              // 2x2 block
+              Matrix<ComplexScalar, 2, 1> rhs = (alpha*mT.template block<2,Dynamic>(j-1,st,2,sz) - beta*mS.template block<2,Dynamic>(j-1,st,2,sz)) .lazyProduct( cv.segment(st,sz) );
+              Matrix<ComplexScalar, 2, 2> lhs = beta * mS.template block<2,2>(j-1,j-1) - alpha * mT.template block<2,2>(j-1,j-1);
+              cv.template segment<2>(j-1) = lhs.partialPivLu().solve(rhs);
+              j--;
+            } else {
+              cv.coeffRef(j) =  cv.segment(st,sz).transpose().cwiseProduct(beta*mS.block(j,st,1,sz) - alpha*mT.block(j,st,1,sz)).sum()
+                              / (alpha*mT.coeffRef(j,j) - static_cast<Scalar>(beta*mS.coeffRef(j,j)));
+            }
+          }
+          m_eivec.col(i+1).noalias() = (m_realQZ.matrixZ().transpose() * cv);
+          m_eivec.col(i+1).normalize();
+          m_eivec.col(i) = m_eivec.col(i+1).conjugate();
+        }
+        i += 2;
+      }
+    }
+  }
+  m_computeEigenvectors = computeEigenvectors;
+  m_isInitialized = true;
+  return *this;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_GENERALIZEDEIGENSOLVER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
new file mode 100644
index 0000000000000000000000000000000000000000..dab66caabd066b60283fc36ba524ead5c00a3fcb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h
@@ -0,0 +1,228 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H
+#define EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H
+
+#include "./Tridiagonalization.h"
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+  *
+  *
+  * \class GeneralizedSelfAdjointEigenSolver
+  *
+  * \brief Computes eigenvalues and eigenvectors of the generalized selfadjoint eigen problem
+  *
+  * \tparam MatrixType_ the type of the matrix of which we are computing the
+  * eigendecomposition; this is expected to be an instantiation of the Matrix
+  * class template.
+  *
+  * This class solves the generalized eigenvalue problem
+  * \f$ Av = \lambda Bv \f$. In this case, the matrix \f$ A \f$ should be
+  * selfadjoint and the matrix \f$ B \f$ should be positive definite.
+  *
+  * Only the \b lower \b triangular \b part of the input matrix is referenced.
+  *
+  * Call the function compute() to compute the eigenvalues and eigenvectors of
+  * a given matrix. Alternatively, you can use the
+  * GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int)
+  * constructor which computes the eigenvalues and eigenvectors at construction time.
+  * Once the eigenvalue and eigenvectors are computed, they can be retrieved with the eigenvalues()
+  * and eigenvectors() functions.
+  *
+  * The documentation for GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int)
+  * contains an example of the typical use of this class.
+  *
+  * \sa class SelfAdjointEigenSolver, class EigenSolver, class ComplexEigenSolver
+  */
+template<typename MatrixType_>
+class GeneralizedSelfAdjointEigenSolver : public SelfAdjointEigenSolver<MatrixType_>
+{
+    typedef SelfAdjointEigenSolver<MatrixType_> Base;
+  public:
+
+    typedef MatrixType_ MatrixType;
+
+    /** \brief Default constructor for fixed-size matrices.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via compute(). This constructor
+      * can only be used if \p MatrixType_ is a fixed-size matrix; use
+      * GeneralizedSelfAdjointEigenSolver(Index) for dynamic-size matrices.
+      */
+    GeneralizedSelfAdjointEigenSolver() : Base() {}
+
+    /** \brief Constructor, pre-allocates memory for dynamic-size matrices.
+      *
+      * \param [in]  size  Positive integer, size of the matrix whose
+      * eigenvalues and eigenvectors will be computed.
+      *
+      * This constructor is useful for dynamic-size matrices, when the user
+      * intends to perform decompositions via compute(). The \p size
+      * parameter is only used as a hint. It is not an error to give a wrong
+      * \p size, but it may impair performance.
+      *
+      * \sa compute() for an example
+      */
+    explicit GeneralizedSelfAdjointEigenSolver(Index size)
+        : Base(size)
+    {}
+
+    /** \brief Constructor; computes generalized eigendecomposition of given matrix pencil.
+      *
+      * \param[in]  matA  Selfadjoint matrix in matrix pencil.
+      *                   Only the lower triangular part of the matrix is referenced.
+      * \param[in]  matB  Positive-definite matrix in matrix pencil.
+      *                   Only the lower triangular part of the matrix is referenced.
+      * \param[in]  options A or-ed set of flags {#ComputeEigenvectors,#EigenvaluesOnly} | {#Ax_lBx,#ABx_lx,#BAx_lx}.
+      *                     Default is #ComputeEigenvectors|#Ax_lBx.
+      *
+      * This constructor calls compute(const MatrixType&, const MatrixType&, int)
+      * to compute the eigenvalues and (if requested) the eigenvectors of the
+      * generalized eigenproblem \f$ Ax = \lambda B x \f$ with \a matA the
+      * selfadjoint matrix \f$ A \f$ and \a matB the positive definite matrix
+      * \f$ B \f$. Each eigenvector \f$ x \f$ satisfies the property
+      * \f$ x^* B x = 1 \f$. The eigenvectors are computed if
+      * \a options contains ComputeEigenvectors.
+      *
+      * In addition, the two following variants can be solved via \p options:
+      * - \c ABx_lx: \f$ ABx = \lambda x \f$
+      * - \c BAx_lx: \f$ BAx = \lambda x \f$
+      *
+      * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.cpp
+      * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType2.out
+      *
+      * \sa compute(const MatrixType&, const MatrixType&, int)
+      */
+    GeneralizedSelfAdjointEigenSolver(const MatrixType& matA, const MatrixType& matB,
+                                      int options = ComputeEigenvectors|Ax_lBx)
+      : Base(matA.cols())
+    {
+      compute(matA, matB, options);
+    }
+
+    /** \brief Computes generalized eigendecomposition of given matrix pencil.
+      *
+      * \param[in]  matA  Selfadjoint matrix in matrix pencil.
+      *                   Only the lower triangular part of the matrix is referenced.
+      * \param[in]  matB  Positive-definite matrix in matrix pencil.
+      *                   Only the lower triangular part of the matrix is referenced.
+      * \param[in]  options A or-ed set of flags {#ComputeEigenvectors,#EigenvaluesOnly} | {#Ax_lBx,#ABx_lx,#BAx_lx}.
+      *                     Default is #ComputeEigenvectors|#Ax_lBx.
+      *
+      * \returns    Reference to \c *this
+      *
+      * According to \p options, this function computes eigenvalues and (if requested)
+      * the eigenvectors of one of the following three generalized eigenproblems:
+      * - \c Ax_lBx: \f$ Ax = \lambda B x \f$
+      * - \c ABx_lx: \f$ ABx = \lambda x \f$
+      * - \c BAx_lx: \f$ BAx = \lambda x \f$
+      * with \a matA the selfadjoint matrix \f$ A \f$ and \a matB the positive definite
+      * matrix \f$ B \f$.
+      * In addition, each eigenvector \f$ x \f$ satisfies the property \f$ x^* B x = 1 \f$.
+      *
+      * The eigenvalues() function can be used to retrieve
+      * the eigenvalues. If \p options contains ComputeEigenvectors, then the
+      * eigenvectors are also computed and can be retrieved by calling
+      * eigenvectors().
+      *
+      * The implementation uses LLT to compute the Cholesky decomposition
+      * \f$ B = LL^* \f$ and computes the classical eigendecomposition
+      * of the selfadjoint matrix \f$ L^{-1} A (L^*)^{-1} \f$ if \p options contains Ax_lBx
+      * and of \f$ L^{*} A L \f$ otherwise. This solves the
+      * generalized eigenproblem, because any solution of the generalized
+      * eigenproblem \f$ Ax = \lambda B x \f$ corresponds to a solution
+      * \f$ L^{-1} A (L^*)^{-1} (L^* x) = \lambda (L^* x) \f$ of the
+      * eigenproblem for \f$ L^{-1} A (L^*)^{-1} \f$. Similar statements
+      * can be made for the two other variants.
+      *
+      * Example: \include SelfAdjointEigenSolver_compute_MatrixType2.cpp
+      * Output: \verbinclude SelfAdjointEigenSolver_compute_MatrixType2.out
+      *
+      * \sa GeneralizedSelfAdjointEigenSolver(const MatrixType&, const MatrixType&, int)
+      */
+    GeneralizedSelfAdjointEigenSolver& compute(const MatrixType& matA, const MatrixType& matB,
+                                               int options = ComputeEigenvectors|Ax_lBx);
+
+  protected:
+
+};
+
+
+template<typename MatrixType>
+GeneralizedSelfAdjointEigenSolver<MatrixType>& GeneralizedSelfAdjointEigenSolver<MatrixType>::
+compute(const MatrixType& matA, const MatrixType& matB, int options)
+{
+  eigen_assert(matA.cols()==matA.rows() && matB.rows()==matA.rows() && matB.cols()==matB.rows());
+  eigen_assert((options&~(EigVecMask|GenEigMask))==0
+          && (options&EigVecMask)!=EigVecMask
+          && ((options&GenEigMask)==0 || (options&GenEigMask)==Ax_lBx
+           || (options&GenEigMask)==ABx_lx || (options&GenEigMask)==BAx_lx)
+          && "invalid option parameter");
+
+  bool computeEigVecs = ((options&EigVecMask)==0) || ((options&EigVecMask)==ComputeEigenvectors);
+
+  // Compute the cholesky decomposition of matB = L L' = U'U
+  LLT<MatrixType> cholB(matB);
+
+  int type = (options&GenEigMask);
+  if(type==0)
+    type = Ax_lBx;
+
+  if(type==Ax_lBx)
+  {
+    // compute C = inv(L) A inv(L')
+    MatrixType matC = matA.template selfadjointView<Lower>();
+    cholB.matrixL().template solveInPlace<OnTheLeft>(matC);
+    cholB.matrixU().template solveInPlace<OnTheRight>(matC);
+
+    Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly );
+
+    // transform back the eigen vectors: evecs = inv(U) * evecs
+    if(computeEigVecs)
+      cholB.matrixU().solveInPlace(Base::m_eivec);
+  }
+  else if(type==ABx_lx)
+  {
+    // compute C = L' A L
+    MatrixType matC = matA.template selfadjointView<Lower>();
+    matC = matC * cholB.matrixL();
+    matC = cholB.matrixU() * matC;
+
+    Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly);
+
+    // transform back the eigen vectors: evecs = inv(U) * evecs
+    if(computeEigVecs)
+      cholB.matrixU().solveInPlace(Base::m_eivec);
+  }
+  else if(type==BAx_lx)
+  {
+    // compute C = L' A L
+    MatrixType matC = matA.template selfadjointView<Lower>();
+    matC = matC * cholB.matrixL();
+    matC = cholB.matrixU() * matC;
+
+    Base::compute(matC, computeEigVecs ? ComputeEigenvectors : EigenvaluesOnly);
+
+    // transform back the eigen vectors: evecs = L * evecs
+    if(computeEigVecs)
+      Base::m_eivec = cholB.matrixL() * Base::m_eivec;
+  }
+
+  return *this;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_GENERALIZEDSELFADJOINTEIGENSOLVER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/HessenbergDecomposition.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/HessenbergDecomposition.h
new file mode 100644
index 0000000000000000000000000000000000000000..fafab990ea46862e301a11a69438e5c872cfd07f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/HessenbergDecomposition.h
@@ -0,0 +1,376 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_HESSENBERGDECOMPOSITION_H
+#define EIGEN_HESSENBERGDECOMPOSITION_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+  
+template<typename MatrixType> struct HessenbergDecompositionMatrixHReturnType;
+template<typename MatrixType>
+struct traits<HessenbergDecompositionMatrixHReturnType<MatrixType> >
+{
+  typedef MatrixType ReturnType;
+};
+
+}
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+  *
+  *
+  * \class HessenbergDecomposition
+  *
+  * \brief Reduces a square matrix to Hessenberg form by an orthogonal similarity transformation
+  *
+  * \tparam MatrixType_ the type of the matrix of which we are computing the Hessenberg decomposition
+  *
+  * This class performs an Hessenberg decomposition of a matrix \f$ A \f$. In
+  * the real case, the Hessenberg decomposition consists of an orthogonal
+  * matrix \f$ Q \f$ and a Hessenberg matrix \f$ H \f$ such that \f$ A = Q H
+  * Q^T \f$. An orthogonal matrix is a matrix whose inverse equals its
+  * transpose (\f$ Q^{-1} = Q^T \f$). A Hessenberg matrix has zeros below the
+  * subdiagonal, so it is almost upper triangular. The Hessenberg decomposition
+  * of a complex matrix is \f$ A = Q H Q^* \f$ with \f$ Q \f$ unitary (that is,
+  * \f$ Q^{-1} = Q^* \f$).
+  *
+  * Call the function compute() to compute the Hessenberg decomposition of a
+  * given matrix. Alternatively, you can use the
+  * HessenbergDecomposition(const MatrixType&) constructor which computes the
+  * Hessenberg decomposition at construction time. Once the decomposition is
+  * computed, you can use the matrixH() and matrixQ() functions to construct
+  * the matrices H and Q in the decomposition.
+  *
+  * The documentation for matrixH() contains an example of the typical use of
+  * this class.
+  *
+  * \sa class ComplexSchur, class Tridiagonalization, \ref QR_Module "QR Module"
+  */
+template<typename MatrixType_> class HessenbergDecomposition
+{
+  public:
+
+    /** \brief Synonym for the template parameter \p MatrixType_. */
+    typedef MatrixType_ MatrixType;
+
+    enum {
+      Size = MatrixType::RowsAtCompileTime,
+      SizeMinusOne = Size == Dynamic ? Dynamic : Size - 1,
+      Options = MatrixType::Options,
+      MaxSize = MatrixType::MaxRowsAtCompileTime,
+      MaxSizeMinusOne = MaxSize == Dynamic ? Dynamic : MaxSize - 1
+    };
+
+    /** \brief Scalar type for matrices of type #MatrixType. */
+    typedef typename MatrixType::Scalar Scalar;
+    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+
+    /** \brief Type for vector of Householder coefficients.
+      *
+      * This is column vector with entries of type #Scalar. The length of the
+      * vector is one less than the size of #MatrixType, if it is a fixed-side
+      * type.
+      */
+    typedef Matrix<Scalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> CoeffVectorType;
+
+    /** \brief Return type of matrixQ() */
+    typedef HouseholderSequence<MatrixType,internal::remove_all_t<typename CoeffVectorType::ConjugateReturnType>> HouseholderSequenceType;
+    
+    typedef internal::HessenbergDecompositionMatrixHReturnType<MatrixType> MatrixHReturnType;
+
+    /** \brief Default constructor; the decomposition will be computed later.
+      *
+      * \param [in] size  The size of the matrix whose Hessenberg decomposition will be computed.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via compute().  The \p size parameter is only
+      * used as a hint. It is not an error to give a wrong \p size, but it may
+      * impair performance.
+      *
+      * \sa compute() for an example.
+      */
+    explicit HessenbergDecomposition(Index size = Size==Dynamic ? 2 : Size)
+      : m_matrix(size,size),
+        m_temp(size),
+        m_isInitialized(false)
+    {
+      if(size>1)
+        m_hCoeffs.resize(size-1);
+    }
+
+    /** \brief Constructor; computes Hessenberg decomposition of given matrix.
+      *
+      * \param[in]  matrix  Square matrix whose Hessenberg decomposition is to be computed.
+      *
+      * This constructor calls compute() to compute the Hessenberg
+      * decomposition.
+      *
+      * \sa matrixH() for an example.
+      */
+    template<typename InputType>
+    explicit HessenbergDecomposition(const EigenBase<InputType>& matrix)
+      : m_matrix(matrix.derived()),
+        m_temp(matrix.rows()),
+        m_isInitialized(false)
+    {
+      if(matrix.rows()<2)
+      {
+        m_isInitialized = true;
+        return;
+      }
+      m_hCoeffs.resize(matrix.rows()-1,1);
+      _compute(m_matrix, m_hCoeffs, m_temp);
+      m_isInitialized = true;
+    }
+
+    /** \brief Computes Hessenberg decomposition of given matrix.
+      *
+      * \param[in]  matrix  Square matrix whose Hessenberg decomposition is to be computed.
+      * \returns    Reference to \c *this
+      *
+      * The Hessenberg decomposition is computed by bringing the columns of the
+      * matrix successively in the required form using Householder reflections
+      * (see, e.g., Algorithm 7.4.2 in Golub \& Van Loan, <i>%Matrix
+      * Computations</i>). The cost is \f$ 10n^3/3 \f$ flops, where \f$ n \f$
+      * denotes the size of the given matrix.
+      *
+      * This method reuses of the allocated data in the HessenbergDecomposition
+      * object.
+      *
+      * Example: \include HessenbergDecomposition_compute.cpp
+      * Output: \verbinclude HessenbergDecomposition_compute.out
+      */
+    template<typename InputType>
+    HessenbergDecomposition& compute(const EigenBase<InputType>& matrix)
+    {
+      m_matrix = matrix.derived();
+      if(matrix.rows()<2)
+      {
+        m_isInitialized = true;
+        return *this;
+      }
+      m_hCoeffs.resize(matrix.rows()-1,1);
+      _compute(m_matrix, m_hCoeffs, m_temp);
+      m_isInitialized = true;
+      return *this;
+    }
+
+    /** \brief Returns the Householder coefficients.
+      *
+      * \returns a const reference to the vector of Householder coefficients
+      *
+      * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
+      * or the member function compute(const MatrixType&) has been called
+      * before to compute the Hessenberg decomposition of a matrix.
+      *
+      * The Householder coefficients allow the reconstruction of the matrix
+      * \f$ Q \f$ in the Hessenberg decomposition from the packed data.
+      *
+      * \sa packedMatrix(), \ref Householder_Module "Householder module"
+      */
+    const CoeffVectorType& householderCoefficients() const
+    {
+      eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
+      return m_hCoeffs;
+    }
+
+    /** \brief Returns the internal representation of the decomposition
+      *
+      *	\returns a const reference to a matrix with the internal representation
+      *	         of the decomposition.
+      *
+      * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
+      * or the member function compute(const MatrixType&) has been called
+      * before to compute the Hessenberg decomposition of a matrix.
+      *
+      * The returned matrix contains the following information:
+      *  - the upper part and lower sub-diagonal represent the Hessenberg matrix H
+      *  - the rest of the lower part contains the Householder vectors that, combined with
+      *    Householder coefficients returned by householderCoefficients(),
+      *    allows to reconstruct the matrix Q as
+      *       \f$ Q = H_{N-1} \ldots H_1 H_0 \f$.
+      *    Here, the matrices \f$ H_i \f$ are the Householder transformations
+      *       \f$ H_i = (I - h_i v_i v_i^T) \f$
+      *    where \f$ h_i \f$ is the \f$ i \f$th Householder coefficient and
+      *    \f$ v_i \f$ is the Householder vector defined by
+      *       \f$ v_i = [ 0, \ldots, 0, 1, M(i+2,i), \ldots, M(N-1,i) ]^T \f$
+      *    with M the matrix returned by this function.
+      *
+      * See LAPACK for further details on this packed storage.
+      *
+      * Example: \include HessenbergDecomposition_packedMatrix.cpp
+      * Output: \verbinclude HessenbergDecomposition_packedMatrix.out
+      *
+      * \sa householderCoefficients()
+      */
+    const MatrixType& packedMatrix() const
+    {
+      eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
+      return m_matrix;
+    }
+
+    /** \brief Reconstructs the orthogonal matrix Q in the decomposition
+      *
+      * \returns object representing the matrix Q
+      *
+      * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
+      * or the member function compute(const MatrixType&) has been called
+      * before to compute the Hessenberg decomposition of a matrix.
+      *
+      * This function returns a light-weight object of template class
+      * HouseholderSequence. You can either apply it directly to a matrix or
+      * you can convert it to a matrix of type #MatrixType.
+      *
+      * \sa matrixH() for an example, class HouseholderSequence
+      */
+    HouseholderSequenceType matrixQ() const
+    {
+      eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
+      return HouseholderSequenceType(m_matrix, m_hCoeffs.conjugate())
+             .setLength(m_matrix.rows() - 1)
+             .setShift(1);
+    }
+
+    /** \brief Constructs the Hessenberg matrix H in the decomposition
+      *
+      * \returns expression object representing the matrix H
+      *
+      * \pre Either the constructor HessenbergDecomposition(const MatrixType&)
+      * or the member function compute(const MatrixType&) has been called
+      * before to compute the Hessenberg decomposition of a matrix.
+      *
+      * The object returned by this function constructs the Hessenberg matrix H
+      * when it is assigned to a matrix or otherwise evaluated. The matrix H is
+      * constructed from the packed matrix as returned by packedMatrix(): The
+      * upper part (including the subdiagonal) of the packed matrix contains
+      * the matrix H. It may sometimes be better to directly use the packed
+      * matrix instead of constructing the matrix H.
+      *
+      * Example: \include HessenbergDecomposition_matrixH.cpp
+      * Output: \verbinclude HessenbergDecomposition_matrixH.out
+      *
+      * \sa matrixQ(), packedMatrix()
+      */
+    MatrixHReturnType matrixH() const
+    {
+      eigen_assert(m_isInitialized && "HessenbergDecomposition is not initialized.");
+      return MatrixHReturnType(*this);
+    }
+
+  private:
+
+    typedef Matrix<Scalar, 1, Size, int(Options) | int(RowMajor), 1, MaxSize> VectorType;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    static void _compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp);
+
+  protected:
+    MatrixType m_matrix;
+    CoeffVectorType m_hCoeffs;
+    VectorType m_temp;
+    bool m_isInitialized;
+};
+
+/** \internal
+  * Performs a tridiagonal decomposition of \a matA in place.
+  *
+  * \param matA the input selfadjoint matrix
+  * \param hCoeffs returned Householder coefficients
+  *
+  * The result is written in the lower triangular part of \a matA.
+  *
+  * Implemented from Golub's "%Matrix Computations", algorithm 8.3.1.
+  *
+  * \sa packedMatrix()
+  */
+template<typename MatrixType>
+void HessenbergDecomposition<MatrixType>::_compute(MatrixType& matA, CoeffVectorType& hCoeffs, VectorType& temp)
+{
+  eigen_assert(matA.rows()==matA.cols());
+  Index n = matA.rows();
+  temp.resize(n);
+  for (Index i = 0; i<n-1; ++i)
+  {
+    // let's consider the vector v = i-th column starting at position i+1
+    Index remainingSize = n-i-1;
+    RealScalar beta;
+    Scalar h;
+    matA.col(i).tail(remainingSize).makeHouseholderInPlace(h, beta);
+    matA.col(i).coeffRef(i+1) = beta;
+    hCoeffs.coeffRef(i) = h;
+
+    // Apply similarity transformation to remaining columns,
+    // i.e., compute A = H A H'
+
+    // A = H A
+    matA.bottomRightCorner(remainingSize, remainingSize)
+        .applyHouseholderOnTheLeft(matA.col(i).tail(remainingSize-1), h, &temp.coeffRef(0));
+
+    // A = A H'
+    matA.rightCols(remainingSize)
+        .applyHouseholderOnTheRight(matA.col(i).tail(remainingSize-1), numext::conj(h), &temp.coeffRef(0));
+  }
+}
+
+namespace internal {
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+  *
+  *
+  * \brief Expression type for return value of HessenbergDecomposition::matrixH()
+  *
+  * \tparam MatrixType type of matrix in the Hessenberg decomposition
+  *
+  * Objects of this type represent the Hessenberg matrix in the Hessenberg
+  * decomposition of some matrix. The object holds a reference to the
+  * HessenbergDecomposition class until the it is assigned or evaluated for
+  * some other reason (the reference should remain valid during the life time
+  * of this object). This class is the return type of
+  * HessenbergDecomposition::matrixH(); there is probably no other use for this
+  * class.
+  */
+template<typename MatrixType> struct HessenbergDecompositionMatrixHReturnType
+: public ReturnByValue<HessenbergDecompositionMatrixHReturnType<MatrixType> >
+{
+  public:
+    /** \brief Constructor.
+      *
+      * \param[in] hess  Hessenberg decomposition
+      */
+    HessenbergDecompositionMatrixHReturnType(const HessenbergDecomposition<MatrixType>& hess) : m_hess(hess) { }
+
+    /** \brief Hessenberg matrix in decomposition.
+      *
+      * \param[out] result  Hessenberg matrix in decomposition \p hess which
+      *                     was passed to the constructor
+      */
+    template <typename ResultType>
+    inline void evalTo(ResultType& result) const
+    {
+      result = m_hess.packedMatrix();
+      Index n = result.rows();
+      if (n>2)
+        result.bottomLeftCorner(n-2, n-2).template triangularView<Lower>().setZero();
+    }
+
+    Index rows() const { return m_hess.packedMatrix().rows(); }
+    Index cols() const { return m_hess.packedMatrix().cols(); }
+
+  protected:
+    const HessenbergDecomposition<MatrixType>& m_hess;
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_HESSENBERGDECOMPOSITION_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..374cbd45571c50f267fa965c32a79c0c659323e2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_EIGENVALUES_MODULE_H
+#error "Please include Eigen/Eigenvalues instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
new file mode 100644
index 0000000000000000000000000000000000000000..c8df26083ff3ea537abaf3e9de1c7b34c5780225
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/MatrixBaseEigenvalues.h
@@ -0,0 +1,160 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MATRIXBASEEIGENVALUES_H
+#define EIGEN_MATRIXBASEEIGENVALUES_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename Derived, bool IsComplex>
+struct eigenvalues_selector
+{
+  // this is the implementation for the case IsComplex = true
+  static inline typename MatrixBase<Derived>::EigenvaluesReturnType const
+  run(const MatrixBase<Derived>& m)
+  {
+    typedef typename Derived::PlainObject PlainObject;
+    PlainObject m_eval(m);
+    return ComplexEigenSolver<PlainObject>(m_eval, false).eigenvalues();
+  }
+};
+
+template<typename Derived>
+struct eigenvalues_selector<Derived, false>
+{
+  static inline typename MatrixBase<Derived>::EigenvaluesReturnType const
+  run(const MatrixBase<Derived>& m)
+  {
+    typedef typename Derived::PlainObject PlainObject;
+    PlainObject m_eval(m);
+    return EigenSolver<PlainObject>(m_eval, false).eigenvalues();
+  }
+};
+
+} // end namespace internal
+
+/** \brief Computes the eigenvalues of a matrix 
+  * \returns Column vector containing the eigenvalues.
+  *
+  * \eigenvalues_module
+  * This function computes the eigenvalues with the help of the EigenSolver
+  * class (for real matrices) or the ComplexEigenSolver class (for complex
+  * matrices). 
+  *
+  * The eigenvalues are repeated according to their algebraic multiplicity,
+  * so there are as many eigenvalues as rows in the matrix.
+  *
+  * The SelfAdjointView class provides a better algorithm for selfadjoint
+  * matrices.
+  *
+  * Example: \include MatrixBase_eigenvalues.cpp
+  * Output: \verbinclude MatrixBase_eigenvalues.out
+  *
+  * \sa EigenSolver::eigenvalues(), ComplexEigenSolver::eigenvalues(),
+  *     SelfAdjointView::eigenvalues()
+  */
+template<typename Derived>
+inline typename MatrixBase<Derived>::EigenvaluesReturnType
+MatrixBase<Derived>::eigenvalues() const
+{
+  return internal::eigenvalues_selector<Derived, NumTraits<Scalar>::IsComplex>::run(derived());
+}
+
+/** \brief Computes the eigenvalues of a matrix
+  * \returns Column vector containing the eigenvalues.
+  *
+  * \eigenvalues_module
+  * This function computes the eigenvalues with the help of the
+  * SelfAdjointEigenSolver class.  The eigenvalues are repeated according to
+  * their algebraic multiplicity, so there are as many eigenvalues as rows in
+  * the matrix.
+  *
+  * Example: \include SelfAdjointView_eigenvalues.cpp
+  * Output: \verbinclude SelfAdjointView_eigenvalues.out
+  *
+  * \sa SelfAdjointEigenSolver::eigenvalues(), MatrixBase::eigenvalues()
+  */
+template<typename MatrixType, unsigned int UpLo> 
+EIGEN_DEVICE_FUNC inline typename SelfAdjointView<MatrixType, UpLo>::EigenvaluesReturnType
+SelfAdjointView<MatrixType, UpLo>::eigenvalues() const
+{
+  PlainObject thisAsMatrix(*this);
+  return SelfAdjointEigenSolver<PlainObject>(thisAsMatrix, false).eigenvalues();
+}
+
+
+
+/** \brief Computes the L2 operator norm
+  * \returns Operator norm of the matrix.
+  *
+  * \eigenvalues_module
+  * This function computes the L2 operator norm of a matrix, which is also
+  * known as the spectral norm. The norm of a matrix \f$ A \f$ is defined to be
+  * \f[ \|A\|_2 = \max_x \frac{\|Ax\|_2}{\|x\|_2} \f]
+  * where the maximum is over all vectors and the norm on the right is the
+  * Euclidean vector norm. The norm equals the largest singular value, which is
+  * the square root of the largest eigenvalue of the positive semi-definite
+  * matrix \f$ A^*A \f$.
+  *
+  * The current implementation uses the eigenvalues of \f$ A^*A \f$, as computed
+  * by SelfAdjointView::eigenvalues(), to compute the operator norm of a
+  * matrix.  The SelfAdjointView class provides a better algorithm for
+  * selfadjoint matrices.
+  *
+  * Example: \include MatrixBase_operatorNorm.cpp
+  * Output: \verbinclude MatrixBase_operatorNorm.out
+  *
+  * \sa SelfAdjointView::eigenvalues(), SelfAdjointView::operatorNorm()
+  */
+template<typename Derived>
+inline typename MatrixBase<Derived>::RealScalar
+MatrixBase<Derived>::operatorNorm() const
+{
+  using std::sqrt;
+  typename Derived::PlainObject m_eval(derived());
+  // FIXME if it is really guaranteed that the eigenvalues are already sorted,
+  // then we don't need to compute a maxCoeff() here, comparing the 1st and last ones is enough.
+  return sqrt((m_eval*m_eval.adjoint())
+                 .eval()
+		 .template selfadjointView<Lower>()
+		 .eigenvalues()
+		 .maxCoeff()
+		 );
+}
+
+/** \brief Computes the L2 operator norm
+  * \returns Operator norm of the matrix.
+  *
+  * \eigenvalues_module
+  * This function computes the L2 operator norm of a self-adjoint matrix. For a
+  * self-adjoint matrix, the operator norm is the largest eigenvalue.
+  *
+  * The current implementation uses the eigenvalues of the matrix, as computed
+  * by eigenvalues(), to compute the operator norm of the matrix.
+  *
+  * Example: \include SelfAdjointView_operatorNorm.cpp
+  * Output: \verbinclude SelfAdjointView_operatorNorm.out
+  *
+  * \sa eigenvalues(), MatrixBase::operatorNorm()
+  */
+template<typename MatrixType, unsigned int UpLo>
+EIGEN_DEVICE_FUNC inline typename SelfAdjointView<MatrixType, UpLo>::RealScalar
+SelfAdjointView<MatrixType, UpLo>::operatorNorm() const
+{
+  return eigenvalues().cwiseAbs().maxCoeff();
+}
+
+} // end namespace Eigen
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/RealQZ.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/RealQZ.h
new file mode 100644
index 0000000000000000000000000000000000000000..545918fa88dd840dd2436fa1b636bd3458afda04
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/RealQZ.h
@@ -0,0 +1,659 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Alexey Korepanov <kaikaikai@yandex.ru>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REAL_QZ_H
+#define EIGEN_REAL_QZ_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+  /** \eigenvalues_module \ingroup Eigenvalues_Module
+   *
+   *
+   * \class RealQZ
+   *
+   * \brief Performs a real QZ decomposition of a pair of square matrices
+   *
+   * \tparam MatrixType_ the type of the matrix of which we are computing the
+   * real QZ decomposition; this is expected to be an instantiation of the
+   * Matrix class template.
+   *
+   * Given a real square matrices A and B, this class computes the real QZ
+   * decomposition: \f$ A = Q S Z \f$, \f$ B = Q T Z \f$ where Q and Z are
+   * real orthogonal matrixes, T is upper-triangular matrix, and S is upper
+   * quasi-triangular matrix. An orthogonal matrix is a matrix whose
+   * inverse is equal to its transpose, \f$ U^{-1} = U^T \f$. A quasi-triangular
+   * matrix is a block-triangular matrix whose diagonal consists of 1-by-1
+   * blocks and 2-by-2 blocks where further reduction is impossible due to
+   * complex eigenvalues. 
+   *
+   * The eigenvalues of the pencil \f$ A - z B \f$ can be obtained from
+   * 1x1 and 2x2 blocks on the diagonals of S and T.
+   *
+   * Call the function compute() to compute the real QZ decomposition of a
+   * given pair of matrices. Alternatively, you can use the 
+   * RealQZ(const MatrixType& B, const MatrixType& B, bool computeQZ)
+   * constructor which computes the real QZ decomposition at construction
+   * time. Once the decomposition is computed, you can use the matrixS(),
+   * matrixT(), matrixQ() and matrixZ() functions to retrieve the matrices
+   * S, T, Q and Z in the decomposition. If computeQZ==false, some time
+   * is saved by not computing matrices Q and Z.
+   *
+   * Example: \include RealQZ_compute.cpp
+   * Output: \include RealQZ_compute.out
+   *
+   * \note The implementation is based on the algorithm in "Matrix Computations"
+   * by Gene H. Golub and Charles F. Van Loan, and a paper "An algorithm for
+   * generalized eigenvalue problems" by C.B.Moler and G.W.Stewart.
+   *
+   * \sa class RealSchur, class ComplexSchur, class EigenSolver, class ComplexEigenSolver
+   */
+
+  template<typename MatrixType_> class RealQZ
+  {
+    public:
+      typedef MatrixType_ MatrixType;
+      enum {
+        RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+        ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+        Options = MatrixType::Options,
+        MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+        MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+      };
+      typedef typename MatrixType::Scalar Scalar;
+      typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar;
+      typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+
+      typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> EigenvalueType;
+      typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType;
+
+      /** \brief Default constructor.
+       *
+       * \param [in] size  Positive integer, size of the matrix whose QZ decomposition will be computed.
+       *
+       * The default constructor is useful in cases in which the user intends to
+       * perform decompositions via compute().  The \p size parameter is only
+       * used as a hint. It is not an error to give a wrong \p size, but it may
+       * impair performance.
+       *
+       * \sa compute() for an example.
+       */
+      explicit RealQZ(Index size = RowsAtCompileTime==Dynamic ? 1 : RowsAtCompileTime) :
+        m_S(size, size),
+        m_T(size, size),
+        m_Q(size, size),
+        m_Z(size, size),
+        m_workspace(size*2),
+        m_maxIters(400),
+        m_isInitialized(false),
+        m_computeQZ(true)
+      {}
+
+      /** \brief Constructor; computes real QZ decomposition of given matrices
+       * 
+       * \param[in]  A          Matrix A.
+       * \param[in]  B          Matrix B.
+       * \param[in]  computeQZ  If false, A and Z are not computed.
+       *
+       * This constructor calls compute() to compute the QZ decomposition.
+       */
+      RealQZ(const MatrixType& A, const MatrixType& B, bool computeQZ = true) :
+        m_S(A.rows(),A.cols()),
+        m_T(A.rows(),A.cols()),
+        m_Q(A.rows(),A.cols()),
+        m_Z(A.rows(),A.cols()),
+        m_workspace(A.rows()*2),
+        m_maxIters(400),
+        m_isInitialized(false),
+        m_computeQZ(true)
+      {
+        compute(A, B, computeQZ);
+      }
+
+      /** \brief Returns matrix Q in the QZ decomposition. 
+       *
+       * \returns A const reference to the matrix Q.
+       */
+      const MatrixType& matrixQ() const {
+        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
+        eigen_assert(m_computeQZ && "The matrices Q and Z have not been computed during the QZ decomposition.");
+        return m_Q;
+      }
+
+      /** \brief Returns matrix Z in the QZ decomposition. 
+       *
+       * \returns A const reference to the matrix Z.
+       */
+      const MatrixType& matrixZ() const {
+        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
+        eigen_assert(m_computeQZ && "The matrices Q and Z have not been computed during the QZ decomposition.");
+        return m_Z;
+      }
+
+      /** \brief Returns matrix S in the QZ decomposition. 
+       *
+       * \returns A const reference to the matrix S.
+       */
+      const MatrixType& matrixS() const {
+        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
+        return m_S;
+      }
+
+      /** \brief Returns matrix S in the QZ decomposition. 
+       *
+       * \returns A const reference to the matrix S.
+       */
+      const MatrixType& matrixT() const {
+        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
+        return m_T;
+      }
+
+      /** \brief Computes QZ decomposition of given matrix. 
+       * 
+       * \param[in]  A          Matrix A.
+       * \param[in]  B          Matrix B.
+       * \param[in]  computeQZ  If false, A and Z are not computed.
+       * \returns    Reference to \c *this
+       */
+      RealQZ& compute(const MatrixType& A, const MatrixType& B, bool computeQZ = true);
+
+      /** \brief Reports whether previous computation was successful.
+       *
+       * \returns \c Success if computation was successful, \c NoConvergence otherwise.
+       */
+      ComputationInfo info() const
+      {
+        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
+        return m_info;
+      }
+
+      /** \brief Returns number of performed QR-like iterations.
+      */
+      Index iterations() const
+      {
+        eigen_assert(m_isInitialized && "RealQZ is not initialized.");
+        return m_global_iter;
+      }
+
+      /** Sets the maximal number of iterations allowed to converge to one eigenvalue
+       * or decouple the problem.
+      */
+      RealQZ& setMaxIterations(Index maxIters)
+      {
+        m_maxIters = maxIters;
+        return *this;
+      }
+
+    private:
+
+      MatrixType m_S, m_T, m_Q, m_Z;
+      Matrix<Scalar,Dynamic,1> m_workspace;
+      ComputationInfo m_info;
+      Index m_maxIters;
+      bool m_isInitialized;
+      bool m_computeQZ;
+      Scalar m_normOfT, m_normOfS;
+      Index m_global_iter;
+
+      typedef Matrix<Scalar,3,1> Vector3s;
+      typedef Matrix<Scalar,2,1> Vector2s;
+      typedef Matrix<Scalar,2,2> Matrix2s;
+      typedef JacobiRotation<Scalar> JRs;
+
+      void hessenbergTriangular();
+      void computeNorms();
+      Index findSmallSubdiagEntry(Index iu);
+      Index findSmallDiagEntry(Index f, Index l);
+      void splitOffTwoRows(Index i);
+      void pushDownZero(Index z, Index f, Index l);
+      void step(Index f, Index l, Index iter);
+
+  }; // RealQZ
+
+  /** \internal Reduces S and T to upper Hessenberg - triangular form */
+  template<typename MatrixType>
+    void RealQZ<MatrixType>::hessenbergTriangular()
+    {
+
+      const Index dim = m_S.cols();
+
+      // perform QR decomposition of T, overwrite T with R, save Q
+      HouseholderQR<MatrixType> qrT(m_T);
+      m_T = qrT.matrixQR();
+      m_T.template triangularView<StrictlyLower>().setZero();
+      m_Q = qrT.householderQ();
+      // overwrite S with Q* S
+      m_S.applyOnTheLeft(m_Q.adjoint());
+      // init Z as Identity
+      if (m_computeQZ)
+        m_Z = MatrixType::Identity(dim,dim);
+      // reduce S to upper Hessenberg with Givens rotations
+      for (Index j=0; j<=dim-3; j++) {
+        for (Index i=dim-1; i>=j+2; i--) {
+          JRs G;
+          // kill S(i,j)
+          if(!numext::is_exactly_zero(m_S.coeff(i, j)))
+          {
+            G.makeGivens(m_S.coeff(i-1,j), m_S.coeff(i,j), &m_S.coeffRef(i-1, j));
+            m_S.coeffRef(i,j) = Scalar(0.0);
+            m_S.rightCols(dim-j-1).applyOnTheLeft(i-1,i,G.adjoint());
+            m_T.rightCols(dim-i+1).applyOnTheLeft(i-1,i,G.adjoint());
+            // update Q
+            if (m_computeQZ)
+              m_Q.applyOnTheRight(i-1,i,G);
+          }
+          // kill T(i,i-1)
+          if(!numext::is_exactly_zero(m_T.coeff(i, i - 1)))
+          {
+            G.makeGivens(m_T.coeff(i,i), m_T.coeff(i,i-1), &m_T.coeffRef(i,i));
+            m_T.coeffRef(i,i-1) = Scalar(0.0);
+            m_S.applyOnTheRight(i,i-1,G);
+            m_T.topRows(i).applyOnTheRight(i,i-1,G);
+            // update Z
+            if (m_computeQZ)
+              m_Z.applyOnTheLeft(i,i-1,G.adjoint());
+          }
+        }
+      }
+    }
+
+  /** \internal Computes vector L1 norms of S and T when in Hessenberg-Triangular form already */
+  template<typename MatrixType>
+    inline void RealQZ<MatrixType>::computeNorms()
+    {
+      const Index size = m_S.cols();
+      m_normOfS = Scalar(0.0);
+      m_normOfT = Scalar(0.0);
+      for (Index j = 0; j < size; ++j)
+      {
+        m_normOfS += m_S.col(j).segment(0, (std::min)(size,j+2)).cwiseAbs().sum();
+        m_normOfT += m_T.row(j).segment(j, size - j).cwiseAbs().sum();
+      }
+    }
+
+
+  /** \internal Look for single small sub-diagonal element S(res, res-1) and return res (or 0) */
+  template<typename MatrixType>
+    inline Index RealQZ<MatrixType>::findSmallSubdiagEntry(Index iu)
+    {
+      using std::abs;
+      Index res = iu;
+      while (res > 0)
+      {
+        Scalar s = abs(m_S.coeff(res-1,res-1)) + abs(m_S.coeff(res,res));
+        if (numext::is_exactly_zero(s))
+          s = m_normOfS;
+        if (abs(m_S.coeff(res,res-1)) < NumTraits<Scalar>::epsilon() * s)
+          break;
+        res--;
+      }
+      return res;
+    }
+
+  /** \internal Look for single small diagonal element T(res, res) for res between f and l, and return res (or f-1)  */
+  template<typename MatrixType>
+    inline Index RealQZ<MatrixType>::findSmallDiagEntry(Index f, Index l)
+    {
+      using std::abs;
+      Index res = l;
+      while (res >= f) {
+        if (abs(m_T.coeff(res,res)) <= NumTraits<Scalar>::epsilon() * m_normOfT)
+          break;
+        res--;
+      }
+      return res;
+    }
+
+  /** \internal decouple 2x2 diagonal block in rows i, i+1 if eigenvalues are real */
+  template<typename MatrixType>
+    inline void RealQZ<MatrixType>::splitOffTwoRows(Index i)
+    {
+      using std::abs;
+      using std::sqrt;
+      const Index dim=m_S.cols();
+      if (numext::is_exactly_zero(abs(m_S.coeff(i + 1, i))))
+        return;
+      Index j = findSmallDiagEntry(i,i+1);
+      if (j==i-1)
+      {
+        // block of (S T^{-1})
+        Matrix2s STi = m_T.template block<2,2>(i,i).template triangularView<Upper>().
+          template solve<OnTheRight>(m_S.template block<2,2>(i,i));
+        Scalar p = Scalar(0.5)*(STi(0,0)-STi(1,1));
+        Scalar q = p*p + STi(1,0)*STi(0,1);
+        if (q>=0) {
+          Scalar z = sqrt(q);
+          // one QR-like iteration for ABi - lambda I
+          // is enough - when we know exact eigenvalue in advance,
+          // convergence is immediate
+          JRs G;
+          if (p>=0)
+            G.makeGivens(p + z, STi(1,0));
+          else
+            G.makeGivens(p - z, STi(1,0));
+          m_S.rightCols(dim-i).applyOnTheLeft(i,i+1,G.adjoint());
+          m_T.rightCols(dim-i).applyOnTheLeft(i,i+1,G.adjoint());
+          // update Q
+          if (m_computeQZ)
+            m_Q.applyOnTheRight(i,i+1,G);
+
+          G.makeGivens(m_T.coeff(i+1,i+1), m_T.coeff(i+1,i));
+          m_S.topRows(i+2).applyOnTheRight(i+1,i,G);
+          m_T.topRows(i+2).applyOnTheRight(i+1,i,G);
+          // update Z
+          if (m_computeQZ)
+            m_Z.applyOnTheLeft(i+1,i,G.adjoint());
+
+          m_S.coeffRef(i+1,i) = Scalar(0.0);
+          m_T.coeffRef(i+1,i) = Scalar(0.0);
+        }
+      }
+      else
+      {
+        pushDownZero(j,i,i+1);
+      }
+    }
+
+  /** \internal use zero in T(z,z) to zero S(l,l-1), working in block f..l */
+  template<typename MatrixType>
+    inline void RealQZ<MatrixType>::pushDownZero(Index z, Index f, Index l)
+    {
+      JRs G;
+      const Index dim = m_S.cols();
+      for (Index zz=z; zz<l; zz++)
+      {
+        // push 0 down
+        Index firstColS = zz>f ? (zz-1) : zz;
+        G.makeGivens(m_T.coeff(zz, zz+1), m_T.coeff(zz+1, zz+1));
+        m_S.rightCols(dim-firstColS).applyOnTheLeft(zz,zz+1,G.adjoint());
+        m_T.rightCols(dim-zz).applyOnTheLeft(zz,zz+1,G.adjoint());
+        m_T.coeffRef(zz+1,zz+1) = Scalar(0.0);
+        // update Q
+        if (m_computeQZ)
+          m_Q.applyOnTheRight(zz,zz+1,G);
+        // kill S(zz+1, zz-1)
+        if (zz>f)
+        {
+          G.makeGivens(m_S.coeff(zz+1, zz), m_S.coeff(zz+1,zz-1));
+          m_S.topRows(zz+2).applyOnTheRight(zz, zz-1,G);
+          m_T.topRows(zz+1).applyOnTheRight(zz, zz-1,G);
+          m_S.coeffRef(zz+1,zz-1) = Scalar(0.0);
+          // update Z
+          if (m_computeQZ)
+            m_Z.applyOnTheLeft(zz,zz-1,G.adjoint());
+        }
+      }
+      // finally kill S(l,l-1)
+      G.makeGivens(m_S.coeff(l,l), m_S.coeff(l,l-1));
+      m_S.applyOnTheRight(l,l-1,G);
+      m_T.applyOnTheRight(l,l-1,G);
+      m_S.coeffRef(l,l-1)=Scalar(0.0);
+      // update Z
+      if (m_computeQZ)
+        m_Z.applyOnTheLeft(l,l-1,G.adjoint());
+    }
+
+  /** \internal QR-like iterative step for block f..l */
+  template<typename MatrixType>
+    inline void RealQZ<MatrixType>::step(Index f, Index l, Index iter)
+    {
+      using std::abs;
+      const Index dim = m_S.cols();
+
+      // x, y, z
+      Scalar x, y, z;
+      if (iter==10)
+      {
+        // Wilkinson ad hoc shift
+        const Scalar
+          a11=m_S.coeff(f+0,f+0), a12=m_S.coeff(f+0,f+1),
+          a21=m_S.coeff(f+1,f+0), a22=m_S.coeff(f+1,f+1), a32=m_S.coeff(f+2,f+1),
+          b12=m_T.coeff(f+0,f+1),
+          b11i=Scalar(1.0)/m_T.coeff(f+0,f+0),
+          b22i=Scalar(1.0)/m_T.coeff(f+1,f+1),
+          a87=m_S.coeff(l-1,l-2),
+          a98=m_S.coeff(l-0,l-1),
+          b77i=Scalar(1.0)/m_T.coeff(l-2,l-2),
+          b88i=Scalar(1.0)/m_T.coeff(l-1,l-1);
+        Scalar ss = abs(a87*b77i) + abs(a98*b88i),
+               lpl = Scalar(1.5)*ss,
+               ll = ss*ss;
+        x = ll + a11*a11*b11i*b11i - lpl*a11*b11i + a12*a21*b11i*b22i
+          - a11*a21*b12*b11i*b11i*b22i;
+        y = a11*a21*b11i*b11i - lpl*a21*b11i + a21*a22*b11i*b22i 
+          - a21*a21*b12*b11i*b11i*b22i;
+        z = a21*a32*b11i*b22i;
+      }
+      else if (iter==16)
+      {
+        // another exceptional shift
+        x = m_S.coeff(f,f)/m_T.coeff(f,f)-m_S.coeff(l,l)/m_T.coeff(l,l) + m_S.coeff(l,l-1)*m_T.coeff(l-1,l) /
+          (m_T.coeff(l-1,l-1)*m_T.coeff(l,l));
+        y = m_S.coeff(f+1,f)/m_T.coeff(f,f);
+        z = 0;
+      }
+      else if (iter>23 && !(iter%8))
+      {
+        // extremely exceptional shift
+        x = internal::random<Scalar>(-1.0,1.0);
+        y = internal::random<Scalar>(-1.0,1.0);
+        z = internal::random<Scalar>(-1.0,1.0);
+      }
+      else
+      {
+        // Compute the shifts: (x,y,z,0...) = (AB^-1 - l1 I) (AB^-1 - l2 I) e1
+        // where l1 and l2 are the eigenvalues of the 2x2 matrix C = U V^-1 where
+        // U and V are 2x2 bottom right sub matrices of A and B. Thus:
+        //  = AB^-1AB^-1 + l1 l2 I - (l1+l2)(AB^-1)
+        //  = AB^-1AB^-1 + det(M) - tr(M)(AB^-1)
+        // Since we are only interested in having x, y, z with a correct ratio, we have:
+        const Scalar
+          a11 = m_S.coeff(f,f),     a12 = m_S.coeff(f,f+1),
+          a21 = m_S.coeff(f+1,f),   a22 = m_S.coeff(f+1,f+1),
+                                    a32 = m_S.coeff(f+2,f+1),
+
+          a88 = m_S.coeff(l-1,l-1), a89 = m_S.coeff(l-1,l),
+          a98 = m_S.coeff(l,l-1),   a99 = m_S.coeff(l,l),
+
+          b11 = m_T.coeff(f,f),     b12 = m_T.coeff(f,f+1),
+                                    b22 = m_T.coeff(f+1,f+1),
+
+          b88 = m_T.coeff(l-1,l-1), b89 = m_T.coeff(l-1,l),
+                                    b99 = m_T.coeff(l,l);
+
+        x = ( (a88/b88 - a11/b11)*(a99/b99 - a11/b11) - (a89/b99)*(a98/b88) + (a98/b88)*(b89/b99)*(a11/b11) ) * (b11/a21)
+          + a12/b22 - (a11/b11)*(b12/b22);
+        y = (a22/b22-a11/b11) - (a21/b11)*(b12/b22) - (a88/b88-a11/b11) - (a99/b99-a11/b11) + (a98/b88)*(b89/b99);
+        z = a32/b22;
+      }
+
+      JRs G;
+
+      for (Index k=f; k<=l-2; k++)
+      {
+        // variables for Householder reflections
+        Vector2s essential2;
+        Scalar tau, beta;
+
+        Vector3s hr(x,y,z);
+
+        // Q_k to annihilate S(k+1,k-1) and S(k+2,k-1)
+        hr.makeHouseholderInPlace(tau, beta);
+        essential2 = hr.template bottomRows<2>();
+        Index fc=(std::max)(k-1,Index(0));  // first col to update
+        m_S.template middleRows<3>(k).rightCols(dim-fc).applyHouseholderOnTheLeft(essential2, tau, m_workspace.data());
+        m_T.template middleRows<3>(k).rightCols(dim-fc).applyHouseholderOnTheLeft(essential2, tau, m_workspace.data());
+        if (m_computeQZ)
+          m_Q.template middleCols<3>(k).applyHouseholderOnTheRight(essential2, tau, m_workspace.data());
+        if (k>f)
+          m_S.coeffRef(k+2,k-1) = m_S.coeffRef(k+1,k-1) = Scalar(0.0);
+
+        // Z_{k1} to annihilate T(k+2,k+1) and T(k+2,k)
+        hr << m_T.coeff(k+2,k+2),m_T.coeff(k+2,k),m_T.coeff(k+2,k+1);
+        hr.makeHouseholderInPlace(tau, beta);
+        essential2 = hr.template bottomRows<2>();
+        {
+          Index lr = (std::min)(k+4,dim); // last row to update
+          Map<Matrix<Scalar,Dynamic,1> > tmp(m_workspace.data(),lr);
+          // S
+          tmp = m_S.template middleCols<2>(k).topRows(lr) * essential2;
+          tmp += m_S.col(k+2).head(lr);
+          m_S.col(k+2).head(lr) -= tau*tmp;
+          m_S.template middleCols<2>(k).topRows(lr) -= (tau*tmp) * essential2.adjoint();
+          // T
+          tmp = m_T.template middleCols<2>(k).topRows(lr) * essential2;
+          tmp += m_T.col(k+2).head(lr);
+          m_T.col(k+2).head(lr) -= tau*tmp;
+          m_T.template middleCols<2>(k).topRows(lr) -= (tau*tmp) * essential2.adjoint();
+        }
+        if (m_computeQZ)
+        {
+          // Z
+          Map<Matrix<Scalar,1,Dynamic> > tmp(m_workspace.data(),dim);
+          tmp = essential2.adjoint()*(m_Z.template middleRows<2>(k));
+          tmp += m_Z.row(k+2);
+          m_Z.row(k+2) -= tau*tmp;
+          m_Z.template middleRows<2>(k) -= essential2 * (tau*tmp);
+        }
+        m_T.coeffRef(k+2,k) = m_T.coeffRef(k+2,k+1) = Scalar(0.0);
+
+        // Z_{k2} to annihilate T(k+1,k)
+        G.makeGivens(m_T.coeff(k+1,k+1), m_T.coeff(k+1,k));
+        m_S.applyOnTheRight(k+1,k,G);
+        m_T.applyOnTheRight(k+1,k,G);
+        // update Z
+        if (m_computeQZ)
+          m_Z.applyOnTheLeft(k+1,k,G.adjoint());
+        m_T.coeffRef(k+1,k) = Scalar(0.0);
+
+        // update x,y,z
+        x = m_S.coeff(k+1,k);
+        y = m_S.coeff(k+2,k);
+        if (k < l-2)
+          z = m_S.coeff(k+3,k);
+      } // loop over k
+
+      // Q_{n-1} to annihilate y = S(l,l-2)
+      G.makeGivens(x,y);
+      m_S.applyOnTheLeft(l-1,l,G.adjoint());
+      m_T.applyOnTheLeft(l-1,l,G.adjoint());
+      if (m_computeQZ)
+        m_Q.applyOnTheRight(l-1,l,G);
+      m_S.coeffRef(l,l-2) = Scalar(0.0);
+
+      // Z_{n-1} to annihilate T(l,l-1)
+      G.makeGivens(m_T.coeff(l,l),m_T.coeff(l,l-1));
+      m_S.applyOnTheRight(l,l-1,G);
+      m_T.applyOnTheRight(l,l-1,G);
+      if (m_computeQZ)
+        m_Z.applyOnTheLeft(l,l-1,G.adjoint());
+      m_T.coeffRef(l,l-1) = Scalar(0.0);
+    }
+
+  template<typename MatrixType>
+    RealQZ<MatrixType>& RealQZ<MatrixType>::compute(const MatrixType& A_in, const MatrixType& B_in, bool computeQZ)
+    {
+
+      const Index dim = A_in.cols();
+
+      eigen_assert (A_in.rows()==dim && A_in.cols()==dim 
+          && B_in.rows()==dim && B_in.cols()==dim 
+          && "Need square matrices of the same dimension");
+
+      m_isInitialized = true;
+      m_computeQZ = computeQZ;
+      m_S = A_in; m_T = B_in;
+      m_workspace.resize(dim*2);
+      m_global_iter = 0;
+
+      // entrance point: hessenberg triangular decomposition
+      hessenbergTriangular();
+      // compute L1 vector norms of T, S into m_normOfS, m_normOfT
+      computeNorms();
+
+      Index l = dim-1, 
+            f, 
+            local_iter = 0;
+
+      while (l>0 && local_iter<m_maxIters)
+      {
+        f = findSmallSubdiagEntry(l);
+        // now rows and columns f..l (including) decouple from the rest of the problem
+        if (f>0) m_S.coeffRef(f,f-1) = Scalar(0.0);
+        if (f == l) // One root found
+        {
+          l--;
+          local_iter = 0;
+        }
+        else if (f == l-1) // Two roots found
+        {
+          splitOffTwoRows(f);
+          l -= 2;
+          local_iter = 0;
+        }
+        else // No convergence yet
+        {
+          // if there's zero on diagonal of T, we can isolate an eigenvalue with Givens rotations
+          Index z = findSmallDiagEntry(f,l);
+          if (z>=f)
+          {
+            // zero found
+            pushDownZero(z,f,l);
+          }
+          else
+          {
+            // We are sure now that S.block(f,f, l-f+1,l-f+1) is underuced upper-Hessenberg 
+            // and T.block(f,f, l-f+1,l-f+1) is invertible uper-triangular, which allows to
+            // apply a QR-like iteration to rows and columns f..l.
+            step(f,l, local_iter);
+            local_iter++;
+            m_global_iter++;
+          }
+        }
+      }
+      // check if we converged before reaching iterations limit
+      m_info = (local_iter<m_maxIters) ? Success : NoConvergence;
+
+      // For each non triangular 2x2 diagonal block of S,
+      //    reduce the respective 2x2 diagonal block of T to positive diagonal form using 2x2 SVD.
+      // This step is not mandatory for QZ, but it does help further extraction of eigenvalues/eigenvectors,
+      // and is in par with Lapack/Matlab QZ.
+      if(m_info==Success)
+      {
+        for(Index i=0; i<dim-1; ++i)
+        {
+          if(!numext::is_exactly_zero(m_S.coeff(i + 1, i)))
+          {
+            JacobiRotation<Scalar> j_left, j_right;
+            internal::real_2x2_jacobi_svd(m_T, i, i+1, &j_left, &j_right);
+
+            // Apply resulting Jacobi rotations
+            m_S.applyOnTheLeft(i,i+1,j_left);
+            m_S.applyOnTheRight(i,i+1,j_right);
+            m_T.applyOnTheLeft(i,i+1,j_left);
+            m_T.applyOnTheRight(i,i+1,j_right);
+            m_T(i+1,i) = m_T(i,i+1) = Scalar(0);
+
+            if(m_computeQZ) {
+              m_Q.applyOnTheRight(i,i+1,j_left.transpose());
+              m_Z.applyOnTheLeft(i,i+1,j_right.transpose());
+            }
+
+            i++;
+          }
+        }
+      }
+
+      return *this;
+    } // end compute
+
+} // end namespace Eigen
+
+#endif //EIGEN_REAL_QZ
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/RealSchur.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/RealSchur.h
new file mode 100644
index 0000000000000000000000000000000000000000..98176668a13251ec36148b595a61e01267f41501
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/RealSchur.h
@@ -0,0 +1,560 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010,2012 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REAL_SCHUR_H
+#define EIGEN_REAL_SCHUR_H
+
+#include "./HessenbergDecomposition.h"
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+  *
+  *
+  * \class RealSchur
+  *
+  * \brief Performs a real Schur decomposition of a square matrix
+  *
+  * \tparam MatrixType_ the type of the matrix of which we are computing the
+  * real Schur decomposition; this is expected to be an instantiation of the
+  * Matrix class template.
+  *
+  * Given a real square matrix A, this class computes the real Schur
+  * decomposition: \f$ A = U T U^T \f$ where U is a real orthogonal matrix and
+  * T is a real quasi-triangular matrix. An orthogonal matrix is a matrix whose
+  * inverse is equal to its transpose, \f$ U^{-1} = U^T \f$. A quasi-triangular
+  * matrix is a block-triangular matrix whose diagonal consists of 1-by-1
+  * blocks and 2-by-2 blocks with complex eigenvalues. The eigenvalues of the
+  * blocks on the diagonal of T are the same as the eigenvalues of the matrix
+  * A, and thus the real Schur decomposition is used in EigenSolver to compute
+  * the eigendecomposition of a matrix.
+  *
+  * Call the function compute() to compute the real Schur decomposition of a
+  * given matrix. Alternatively, you can use the RealSchur(const MatrixType&, bool)
+  * constructor which computes the real Schur decomposition at construction
+  * time. Once the decomposition is computed, you can use the matrixU() and
+  * matrixT() functions to retrieve the matrices U and T in the decomposition.
+  *
+  * The documentation of RealSchur(const MatrixType&, bool) contains an example
+  * of the typical use of this class.
+  *
+  * \note The implementation is adapted from
+  * <a href="http://math.nist.gov/javanumerics/jama/">JAMA</a> (public domain).
+  * Their code is based on EISPACK.
+  *
+  * \sa class ComplexSchur, class EigenSolver, class ComplexEigenSolver
+  */
+template<typename MatrixType_> class RealSchur
+{
+  public:
+    typedef MatrixType_ MatrixType;
+    enum {
+      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      Options = MatrixType::Options,
+      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+    typedef typename MatrixType::Scalar Scalar;
+    typedef std::complex<typename NumTraits<Scalar>::Real> ComplexScalar;
+    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+
+    typedef Matrix<ComplexScalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> EigenvalueType;
+    typedef Matrix<Scalar, ColsAtCompileTime, 1, Options & ~RowMajor, MaxColsAtCompileTime, 1> ColumnVectorType;
+
+    /** \brief Default constructor.
+      *
+      * \param [in] size  Positive integer, size of the matrix whose Schur decomposition will be computed.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via compute().  The \p size parameter is only
+      * used as a hint. It is not an error to give a wrong \p size, but it may
+      * impair performance.
+      *
+      * \sa compute() for an example.
+      */
+    explicit RealSchur(Index size = RowsAtCompileTime==Dynamic ? 1 : RowsAtCompileTime)
+            : m_matT(size, size),
+              m_matU(size, size),
+              m_workspaceVector(size),
+              m_hess(size),
+              m_isInitialized(false),
+              m_matUisUptodate(false),
+              m_maxIters(-1)
+    { }
+
+    /** \brief Constructor; computes real Schur decomposition of given matrix. 
+      * 
+      * \param[in]  matrix    Square matrix whose Schur decomposition is to be computed.
+      * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
+      *
+      * This constructor calls compute() to compute the Schur decomposition.
+      *
+      * Example: \include RealSchur_RealSchur_MatrixType.cpp
+      * Output: \verbinclude RealSchur_RealSchur_MatrixType.out
+      */
+    template<typename InputType>
+    explicit RealSchur(const EigenBase<InputType>& matrix, bool computeU = true)
+            : m_matT(matrix.rows(),matrix.cols()),
+              m_matU(matrix.rows(),matrix.cols()),
+              m_workspaceVector(matrix.rows()),
+              m_hess(matrix.rows()),
+              m_isInitialized(false),
+              m_matUisUptodate(false),
+              m_maxIters(-1)
+    {
+      compute(matrix.derived(), computeU);
+    }
+
+    /** \brief Returns the orthogonal matrix in the Schur decomposition. 
+      *
+      * \returns A const reference to the matrix U.
+      *
+      * \pre Either the constructor RealSchur(const MatrixType&, bool) or the
+      * member function compute(const MatrixType&, bool) has been called before
+      * to compute the Schur decomposition of a matrix, and \p computeU was set
+      * to true (the default value).
+      *
+      * \sa RealSchur(const MatrixType&, bool) for an example
+      */
+    const MatrixType& matrixU() const
+    {
+      eigen_assert(m_isInitialized && "RealSchur is not initialized.");
+      eigen_assert(m_matUisUptodate && "The matrix U has not been computed during the RealSchur decomposition.");
+      return m_matU;
+    }
+
+    /** \brief Returns the quasi-triangular matrix in the Schur decomposition. 
+      *
+      * \returns A const reference to the matrix T.
+      *
+      * \pre Either the constructor RealSchur(const MatrixType&, bool) or the
+      * member function compute(const MatrixType&, bool) has been called before
+      * to compute the Schur decomposition of a matrix.
+      *
+      * \sa RealSchur(const MatrixType&, bool) for an example
+      */
+    const MatrixType& matrixT() const
+    {
+      eigen_assert(m_isInitialized && "RealSchur is not initialized.");
+      return m_matT;
+    }
+  
+    /** \brief Computes Schur decomposition of given matrix. 
+      * 
+      * \param[in]  matrix    Square matrix whose Schur decomposition is to be computed.
+      * \param[in]  computeU  If true, both T and U are computed; if false, only T is computed.
+      * \returns    Reference to \c *this
+      *
+      * The Schur decomposition is computed by first reducing the matrix to
+      * Hessenberg form using the class HessenbergDecomposition. The Hessenberg
+      * matrix is then reduced to triangular form by performing Francis QR
+      * iterations with implicit double shift. The cost of computing the Schur
+      * decomposition depends on the number of iterations; as a rough guide, it
+      * may be taken to be \f$25n^3\f$ flops if \a computeU is true and
+      * \f$10n^3\f$ flops if \a computeU is false.
+      *
+      * Example: \include RealSchur_compute.cpp
+      * Output: \verbinclude RealSchur_compute.out
+      *
+      * \sa compute(const MatrixType&, bool, Index)
+      */
+    template<typename InputType>
+    RealSchur& compute(const EigenBase<InputType>& matrix, bool computeU = true);
+
+    /** \brief Computes Schur decomposition of a Hessenberg matrix H = Z T Z^T
+     *  \param[in] matrixH Matrix in Hessenberg form H
+     *  \param[in] matrixQ orthogonal matrix Q that transform a matrix A to H : A = Q H Q^T
+     *  \param computeU Computes the matriX U of the Schur vectors
+     * \return Reference to \c *this
+     * 
+     *  This routine assumes that the matrix is already reduced in Hessenberg form matrixH
+     *  using either the class HessenbergDecomposition or another mean. 
+     *  It computes the upper quasi-triangular matrix T of the Schur decomposition of H
+     *  When computeU is true, this routine computes the matrix U such that 
+     *  A = U T U^T =  (QZ) T (QZ)^T = Q H Q^T where A is the initial matrix
+     * 
+     * NOTE Q is referenced if computeU is true; so, if the initial orthogonal matrix
+     * is not available, the user should give an identity matrix (Q.setIdentity())
+     * 
+     * \sa compute(const MatrixType&, bool)
+     */
+    template<typename HessMatrixType, typename OrthMatrixType>
+    RealSchur& computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ,  bool computeU);
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful, \c NoConvergence otherwise.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "RealSchur is not initialized.");
+      return m_info;
+    }
+
+    /** \brief Sets the maximum number of iterations allowed. 
+      *
+      * If not specified by the user, the maximum number of iterations is m_maxIterationsPerRow times the size
+      * of the matrix.
+      */
+    RealSchur& setMaxIterations(Index maxIters)
+    {
+      m_maxIters = maxIters;
+      return *this;
+    }
+
+    /** \brief Returns the maximum number of iterations. */
+    Index getMaxIterations()
+    {
+      return m_maxIters;
+    }
+
+    /** \brief Maximum number of iterations per row.
+      *
+      * If not otherwise specified, the maximum number of iterations is this number times the size of the
+      * matrix. It is currently set to 40.
+      */
+    static const int m_maxIterationsPerRow = 40;
+
+  private:
+    
+    MatrixType m_matT;
+    MatrixType m_matU;
+    ColumnVectorType m_workspaceVector;
+    HessenbergDecomposition<MatrixType> m_hess;
+    ComputationInfo m_info;
+    bool m_isInitialized;
+    bool m_matUisUptodate;
+    Index m_maxIters;
+
+    typedef Matrix<Scalar,3,1> Vector3s;
+
+    Scalar computeNormOfT();
+    Index findSmallSubdiagEntry(Index iu, const Scalar& considerAsZero);
+    void splitOffTwoRows(Index iu, bool computeU, const Scalar& exshift);
+    void computeShift(Index iu, Index iter, Scalar& exshift, Vector3s& shiftInfo);
+    void initFrancisQRStep(Index il, Index iu, const Vector3s& shiftInfo, Index& im, Vector3s& firstHouseholderVector);
+    void performFrancisQRStep(Index il, Index im, Index iu, bool computeU, const Vector3s& firstHouseholderVector, Scalar* workspace);
+};
+
+
+template<typename MatrixType>
+template<typename InputType>
+RealSchur<MatrixType>& RealSchur<MatrixType>::compute(const EigenBase<InputType>& matrix, bool computeU)
+{
+  const Scalar considerAsZero = (std::numeric_limits<Scalar>::min)();
+
+  eigen_assert(matrix.cols() == matrix.rows());
+  Index maxIters = m_maxIters;
+  if (maxIters == -1)
+    maxIters = m_maxIterationsPerRow * matrix.rows();
+
+  Scalar scale = matrix.derived().cwiseAbs().maxCoeff();
+  if(scale<considerAsZero)
+  {
+    m_matT.setZero(matrix.rows(),matrix.cols());
+    if(computeU)
+      m_matU.setIdentity(matrix.rows(),matrix.cols());
+    m_info = Success;
+    m_isInitialized = true;
+    m_matUisUptodate = computeU;
+    return *this;
+  }
+
+  // Step 1. Reduce to Hessenberg form
+  m_hess.compute(matrix.derived()/scale);
+
+  // Step 2. Reduce to real Schur form
+  // Note: we copy m_hess.matrixQ() into m_matU here and not in computeFromHessenberg
+  //       to be able to pass our working-space buffer for the Householder to Dense evaluation.
+  m_workspaceVector.resize(matrix.cols());
+  if(computeU)
+    m_hess.matrixQ().evalTo(m_matU, m_workspaceVector);
+  computeFromHessenberg(m_hess.matrixH(), m_matU, computeU);
+
+  m_matT *= scale;
+  
+  return *this;
+}
+template<typename MatrixType>
+template<typename HessMatrixType, typename OrthMatrixType>
+RealSchur<MatrixType>& RealSchur<MatrixType>::computeFromHessenberg(const HessMatrixType& matrixH, const OrthMatrixType& matrixQ,  bool computeU)
+{
+  using std::abs;
+
+  m_matT = matrixH;
+  m_workspaceVector.resize(m_matT.cols());
+  if(computeU && !internal::is_same_dense(m_matU,matrixQ))
+    m_matU = matrixQ;
+  
+  Index maxIters = m_maxIters;
+  if (maxIters == -1)
+    maxIters = m_maxIterationsPerRow * matrixH.rows();
+  Scalar* workspace = &m_workspaceVector.coeffRef(0);
+
+  // The matrix m_matT is divided in three parts. 
+  // Rows 0,...,il-1 are decoupled from the rest because m_matT(il,il-1) is zero. 
+  // Rows il,...,iu is the part we are working on (the active window).
+  // Rows iu+1,...,end are already brought in triangular form.
+  Index iu = m_matT.cols() - 1;
+  Index iter = 0;      // iteration count for current eigenvalue
+  Index totalIter = 0; // iteration count for whole matrix
+  Scalar exshift(0);   // sum of exceptional shifts
+  Scalar norm = computeNormOfT();
+  // sub-diagonal entries smaller than considerAsZero will be treated as zero.
+  // We use eps^2 to enable more precision in small eigenvalues.
+  Scalar considerAsZero = numext::maxi<Scalar>( norm * numext::abs2(NumTraits<Scalar>::epsilon()),
+                                                (std::numeric_limits<Scalar>::min)() );
+
+  if(!numext::is_exactly_zero(norm))
+  {
+    while (iu >= 0)
+    {
+      Index il = findSmallSubdiagEntry(iu,considerAsZero);
+
+      // Check for convergence
+      if (il == iu) // One root found
+      {
+        m_matT.coeffRef(iu,iu) = m_matT.coeff(iu,iu) + exshift;
+        if (iu > 0)
+          m_matT.coeffRef(iu, iu-1) = Scalar(0);
+        iu--;
+        iter = 0;
+      }
+      else if (il == iu-1) // Two roots found
+      {
+        splitOffTwoRows(iu, computeU, exshift);
+        iu -= 2;
+        iter = 0;
+      }
+      else // No convergence yet
+      {
+        // The firstHouseholderVector vector has to be initialized to something to get rid of a silly GCC warning (-O1 -Wall -DNDEBUG )
+        Vector3s firstHouseholderVector = Vector3s::Zero(), shiftInfo;
+        computeShift(iu, iter, exshift, shiftInfo);
+        iter = iter + 1;
+        totalIter = totalIter + 1;
+        if (totalIter > maxIters) break;
+        Index im;
+        initFrancisQRStep(il, iu, shiftInfo, im, firstHouseholderVector);
+        performFrancisQRStep(il, im, iu, computeU, firstHouseholderVector, workspace);
+      }
+    }
+  }
+  if(totalIter <= maxIters)
+    m_info = Success;
+  else
+    m_info = NoConvergence;
+
+  m_isInitialized = true;
+  m_matUisUptodate = computeU;
+  return *this;
+}
+
+/** \internal Computes and returns vector L1 norm of T */
+template<typename MatrixType>
+inline typename MatrixType::Scalar RealSchur<MatrixType>::computeNormOfT()
+{
+  const Index size = m_matT.cols();
+  // FIXME to be efficient the following would requires a triangular reduxion code
+  // Scalar norm = m_matT.upper().cwiseAbs().sum() 
+  //               + m_matT.bottomLeftCorner(size-1,size-1).diagonal().cwiseAbs().sum();
+  Scalar norm(0);
+  for (Index j = 0; j < size; ++j)
+    norm += m_matT.col(j).segment(0, (std::min)(size,j+2)).cwiseAbs().sum();
+  return norm;
+}
+
+/** \internal Look for single small sub-diagonal element and returns its index */
+template<typename MatrixType>
+inline Index RealSchur<MatrixType>::findSmallSubdiagEntry(Index iu, const Scalar& considerAsZero)
+{
+  using std::abs;
+  Index res = iu;
+  while (res > 0)
+  {
+    Scalar s = abs(m_matT.coeff(res-1,res-1)) + abs(m_matT.coeff(res,res));
+
+    s = numext::maxi<Scalar>(s * NumTraits<Scalar>::epsilon(), considerAsZero);
+    
+    if (abs(m_matT.coeff(res,res-1)) <= s)
+      break;
+    res--;
+  }
+  return res;
+}
+
+/** \internal Update T given that rows iu-1 and iu decouple from the rest. */
+template<typename MatrixType>
+inline void RealSchur<MatrixType>::splitOffTwoRows(Index iu, bool computeU, const Scalar& exshift)
+{
+  using std::sqrt;
+  using std::abs;
+  const Index size = m_matT.cols();
+
+  // The eigenvalues of the 2x2 matrix [a b; c d] are 
+  // trace +/- sqrt(discr/4) where discr = tr^2 - 4*det, tr = a + d, det = ad - bc
+  Scalar p = Scalar(0.5) * (m_matT.coeff(iu-1,iu-1) - m_matT.coeff(iu,iu));
+  Scalar q = p * p + m_matT.coeff(iu,iu-1) * m_matT.coeff(iu-1,iu);   // q = tr^2 / 4 - det = discr/4
+  m_matT.coeffRef(iu,iu) += exshift;
+  m_matT.coeffRef(iu-1,iu-1) += exshift;
+
+  if (q >= Scalar(0)) // Two real eigenvalues
+  {
+    Scalar z = sqrt(abs(q));
+    JacobiRotation<Scalar> rot;
+    if (p >= Scalar(0))
+      rot.makeGivens(p + z, m_matT.coeff(iu, iu-1));
+    else
+      rot.makeGivens(p - z, m_matT.coeff(iu, iu-1));
+
+    m_matT.rightCols(size-iu+1).applyOnTheLeft(iu-1, iu, rot.adjoint());
+    m_matT.topRows(iu+1).applyOnTheRight(iu-1, iu, rot);
+    m_matT.coeffRef(iu, iu-1) = Scalar(0); 
+    if (computeU)
+      m_matU.applyOnTheRight(iu-1, iu, rot);
+  }
+
+  if (iu > 1) 
+    m_matT.coeffRef(iu-1, iu-2) = Scalar(0);
+}
+
+/** \internal Form shift in shiftInfo, and update exshift if an exceptional shift is performed. */
+template<typename MatrixType>
+inline void RealSchur<MatrixType>::computeShift(Index iu, Index iter, Scalar& exshift, Vector3s& shiftInfo)
+{
+  using std::sqrt;
+  using std::abs;
+  shiftInfo.coeffRef(0) = m_matT.coeff(iu,iu);
+  shiftInfo.coeffRef(1) = m_matT.coeff(iu-1,iu-1);
+  shiftInfo.coeffRef(2) = m_matT.coeff(iu,iu-1) * m_matT.coeff(iu-1,iu);
+
+  // Wilkinson's original ad hoc shift
+  if (iter == 10)
+  {
+    exshift += shiftInfo.coeff(0);
+    for (Index i = 0; i <= iu; ++i)
+      m_matT.coeffRef(i,i) -= shiftInfo.coeff(0);
+    Scalar s = abs(m_matT.coeff(iu,iu-1)) + abs(m_matT.coeff(iu-1,iu-2));
+    shiftInfo.coeffRef(0) = Scalar(0.75) * s;
+    shiftInfo.coeffRef(1) = Scalar(0.75) * s;
+    shiftInfo.coeffRef(2) = Scalar(-0.4375) * s * s;
+  }
+
+  // MATLAB's new ad hoc shift
+  if (iter == 30)
+  {
+    Scalar s = (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
+    s = s * s + shiftInfo.coeff(2);
+    if (s > Scalar(0))
+    {
+      s = sqrt(s);
+      if (shiftInfo.coeff(1) < shiftInfo.coeff(0))
+        s = -s;
+      s = s + (shiftInfo.coeff(1) - shiftInfo.coeff(0)) / Scalar(2.0);
+      s = shiftInfo.coeff(0) - shiftInfo.coeff(2) / s;
+      exshift += s;
+      for (Index i = 0; i <= iu; ++i)
+        m_matT.coeffRef(i,i) -= s;
+      shiftInfo.setConstant(Scalar(0.964));
+    }
+  }
+}
+
+/** \internal Compute index im at which Francis QR step starts and the first Householder vector. */
+template<typename MatrixType>
+inline void RealSchur<MatrixType>::initFrancisQRStep(Index il, Index iu, const Vector3s& shiftInfo, Index& im, Vector3s& firstHouseholderVector)
+{
+  using std::abs;
+  Vector3s& v = firstHouseholderVector; // alias to save typing
+
+  for (im = iu-2; im >= il; --im)
+  {
+    const Scalar Tmm = m_matT.coeff(im,im);
+    const Scalar r = shiftInfo.coeff(0) - Tmm;
+    const Scalar s = shiftInfo.coeff(1) - Tmm;
+    v.coeffRef(0) = (r * s - shiftInfo.coeff(2)) / m_matT.coeff(im+1,im) + m_matT.coeff(im,im+1);
+    v.coeffRef(1) = m_matT.coeff(im+1,im+1) - Tmm - r - s;
+    v.coeffRef(2) = m_matT.coeff(im+2,im+1);
+    if (im == il) {
+      break;
+    }
+    const Scalar lhs = m_matT.coeff(im,im-1) * (abs(v.coeff(1)) + abs(v.coeff(2)));
+    const Scalar rhs = v.coeff(0) * (abs(m_matT.coeff(im-1,im-1)) + abs(Tmm) + abs(m_matT.coeff(im+1,im+1)));
+    if (abs(lhs) < NumTraits<Scalar>::epsilon() * rhs)
+      break;
+  }
+}
+
+/** \internal Perform a Francis QR step involving rows il:iu and columns im:iu. */
+template<typename MatrixType>
+inline void RealSchur<MatrixType>::performFrancisQRStep(Index il, Index im, Index iu, bool computeU, const Vector3s& firstHouseholderVector, Scalar* workspace)
+{
+  eigen_assert(im >= il);
+  eigen_assert(im <= iu-2);
+
+  const Index size = m_matT.cols();
+
+  for (Index k = im; k <= iu-2; ++k)
+  {
+    bool firstIteration = (k == im);
+
+    Vector3s v;
+    if (firstIteration)
+      v = firstHouseholderVector;
+    else
+      v = m_matT.template block<3,1>(k,k-1);
+
+    Scalar tau, beta;
+    Matrix<Scalar, 2, 1> ess;
+    v.makeHouseholder(ess, tau, beta);
+    
+    if (!numext::is_exactly_zero(beta)) // if v is not zero
+    {
+      if (firstIteration && k > il)
+        m_matT.coeffRef(k,k-1) = -m_matT.coeff(k,k-1);
+      else if (!firstIteration)
+        m_matT.coeffRef(k,k-1) = beta;
+
+      // These Householder transformations form the O(n^3) part of the algorithm
+      m_matT.block(k, k, 3, size-k).applyHouseholderOnTheLeft(ess, tau, workspace);
+      m_matT.block(0, k, (std::min)(iu,k+3) + 1, 3).applyHouseholderOnTheRight(ess, tau, workspace);
+      if (computeU)
+        m_matU.block(0, k, size, 3).applyHouseholderOnTheRight(ess, tau, workspace);
+    }
+  }
+
+  Matrix<Scalar, 2, 1> v = m_matT.template block<2,1>(iu-1, iu-2);
+  Scalar tau, beta;
+  Matrix<Scalar, 1, 1> ess;
+  v.makeHouseholder(ess, tau, beta);
+
+  if (!numext::is_exactly_zero(beta)) // if v is not zero
+  {
+    m_matT.coeffRef(iu-1, iu-2) = beta;
+    m_matT.block(iu-1, iu-1, 2, size-iu+1).applyHouseholderOnTheLeft(ess, tau, workspace);
+    m_matT.block(0, iu-1, iu+1, 2).applyHouseholderOnTheRight(ess, tau, workspace);
+    if (computeU)
+      m_matU.block(0, iu-1, size, 2).applyHouseholderOnTheRight(ess, tau, workspace);
+  }
+
+  // clean up pollution due to round-off errors
+  for (Index i = im+2; i <= iu; ++i)
+  {
+    m_matT.coeffRef(i,i-2) = Scalar(0);
+    if (i > im+2)
+      m_matT.coeffRef(i,i-3) = Scalar(0);
+  }
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_REAL_SCHUR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
new file mode 100644
index 0000000000000000000000000000000000000000..0a6ed2174631f7a45f99ff1271ca877fb62facba
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/RealSchur_LAPACKE.h
@@ -0,0 +1,79 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *    Real Schur needed to real unsymmetrical eigenvalues/eigenvectors.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_REAL_SCHUR_LAPACKE_H
+#define EIGEN_REAL_SCHUR_LAPACKE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \internal Specialization for the data types supported by LAPACKe */
+
+#define EIGEN_LAPACKE_SCHUR_REAL(EIGTYPE, LAPACKE_TYPE, LAPACKE_PREFIX, LAPACKE_PREFIX_U, EIGCOLROW, LAPACKE_COLROW) \
+template<> template<typename InputType> inline \
+RealSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
+RealSchur<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const EigenBase<InputType>& matrix, bool computeU) \
+{ \
+  eigen_assert(matrix.cols() == matrix.rows()); \
+\
+  lapack_int n = internal::convert_index<lapack_int>(matrix.cols()), sdim, info; \
+  lapack_int matrix_order = LAPACKE_COLROW; \
+  char jobvs, sort='N'; \
+  LAPACK_##LAPACKE_PREFIX_U##_SELECT2 select = 0; \
+  jobvs = (computeU) ? 'V' : 'N'; \
+  m_matU.resize(n, n); \
+  lapack_int ldvs  = internal::convert_index<lapack_int>(m_matU.outerStride()); \
+  m_matT = matrix; \
+  lapack_int lda = internal::convert_index<lapack_int>(m_matT.outerStride()); \
+  Matrix<EIGTYPE, Dynamic, Dynamic> wr, wi; \
+  wr.resize(n, 1); wi.resize(n, 1); \
+  info = LAPACKE_##LAPACKE_PREFIX##gees( matrix_order, jobvs, sort, select, n, (LAPACKE_TYPE*)m_matT.data(), lda, &sdim, (LAPACKE_TYPE*)wr.data(), (LAPACKE_TYPE*)wi.data(), (LAPACKE_TYPE*)m_matU.data(), ldvs ); \
+  if(info == 0) \
+    m_info = Success; \
+  else \
+    m_info = NoConvergence; \
+\
+  m_isInitialized = true; \
+  m_matUisUptodate = computeU; \
+  return *this; \
+\
+}
+
+EIGEN_LAPACKE_SCHUR_REAL(double,   double, d, D, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_LAPACKE_SCHUR_REAL(float,    float,  s, S, ColMajor, LAPACK_COL_MAJOR)
+EIGEN_LAPACKE_SCHUR_REAL(double,   double, d, D, RowMajor, LAPACK_ROW_MAJOR)
+EIGEN_LAPACKE_SCHUR_REAL(float,    float,  s, S, RowMajor, LAPACK_ROW_MAJOR)
+
+} // end namespace Eigen
+
+#endif // EIGEN_REAL_SCHUR_LAPACKE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
new file mode 100644
index 0000000000000000000000000000000000000000..88e05bd7371718b0ae85f127f4eb45a976e09ba7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver.h
@@ -0,0 +1,905 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SELFADJOINTEIGENSOLVER_H
+#define EIGEN_SELFADJOINTEIGENSOLVER_H
+
+#include "./Tridiagonalization.h"
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+template<typename MatrixType_>
+class GeneralizedSelfAdjointEigenSolver;
+
+namespace internal {
+template<typename SolverType,int Size,bool IsComplex> struct direct_selfadjoint_eigenvalues;
+
+template<typename MatrixType, typename DiagType, typename SubDiagType>
+EIGEN_DEVICE_FUNC
+ComputationInfo computeFromTridiagonal_impl(DiagType& diag, SubDiagType& subdiag, const Index maxIterations, bool computeEigenvectors, MatrixType& eivec);
+}
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+  *
+  *
+  * \class SelfAdjointEigenSolver
+  *
+  * \brief Computes eigenvalues and eigenvectors of selfadjoint matrices
+  *
+  * \tparam MatrixType_ the type of the matrix of which we are computing the
+  * eigendecomposition; this is expected to be an instantiation of the Matrix
+  * class template.
+  *
+  * A matrix \f$ A \f$ is selfadjoint if it equals its adjoint. For real
+  * matrices, this means that the matrix is symmetric: it equals its
+  * transpose. This class computes the eigenvalues and eigenvectors of a
+  * selfadjoint matrix. These are the scalars \f$ \lambda \f$ and vectors
+  * \f$ v \f$ such that \f$ Av = \lambda v \f$.  The eigenvalues of a
+  * selfadjoint matrix are always real. If \f$ D \f$ is a diagonal matrix with
+  * the eigenvalues on the diagonal, and \f$ V \f$ is a matrix with the
+  * eigenvectors as its columns, then \f$ A = V D V^{-1} \f$. This is called the
+  * eigendecomposition.
+  *
+  * For a selfadjoint matrix, \f$ V \f$ is unitary, meaning its inverse is equal
+  * to its adjoint, \f$ V^{-1} = V^{\dagger} \f$. If \f$ A \f$ is real, then
+  * \f$ V \f$ is also real and therefore orthogonal, meaning its inverse is
+  * equal to its transpose, \f$ V^{-1} = V^T \f$.
+  *
+  * The algorithm exploits the fact that the matrix is selfadjoint, making it
+  * faster and more accurate than the general purpose eigenvalue algorithms
+  * implemented in EigenSolver and ComplexEigenSolver.
+  *
+  * Only the \b lower \b triangular \b part of the input matrix is referenced.
+  *
+  * Call the function compute() to compute the eigenvalues and eigenvectors of
+  * a given matrix. Alternatively, you can use the
+  * SelfAdjointEigenSolver(const MatrixType&, int) constructor which computes
+  * the eigenvalues and eigenvectors at construction time. Once the eigenvalue
+  * and eigenvectors are computed, they can be retrieved with the eigenvalues()
+  * and eigenvectors() functions.
+  *
+  * The documentation for SelfAdjointEigenSolver(const MatrixType&, int)
+  * contains an example of the typical use of this class.
+  *
+  * To solve the \em generalized eigenvalue problem \f$ Av = \lambda Bv \f$ and
+  * the likes, see the class GeneralizedSelfAdjointEigenSolver.
+  *
+  * \sa MatrixBase::eigenvalues(), class EigenSolver, class ComplexEigenSolver
+  */
+template<typename MatrixType_> class SelfAdjointEigenSolver
+{
+  public:
+
+    typedef MatrixType_ MatrixType;
+    enum {
+      Size = MatrixType::RowsAtCompileTime,
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      Options = MatrixType::Options,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+    
+    /** \brief Scalar type for matrices of type \p MatrixType_. */
+    typedef typename MatrixType::Scalar Scalar;
+    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+    
+    typedef Matrix<Scalar,Size,Size,ColMajor,MaxColsAtCompileTime,MaxColsAtCompileTime> EigenvectorsType;
+
+    /** \brief Real scalar type for \p MatrixType_.
+      *
+      * This is just \c Scalar if #Scalar is real (e.g., \c float or
+      * \c double), and the type of the real part of \c Scalar if #Scalar is
+      * complex.
+      */
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    
+    friend struct internal::direct_selfadjoint_eigenvalues<SelfAdjointEigenSolver,Size,NumTraits<Scalar>::IsComplex>;
+
+    /** \brief Type for vector of eigenvalues as returned by eigenvalues().
+      *
+      * This is a column vector with entries of type #RealScalar.
+      * The length of the vector is the size of \p MatrixType_.
+      */
+    typedef typename internal::plain_col_type<MatrixType, Scalar>::type VectorType;
+    typedef typename internal::plain_col_type<MatrixType, RealScalar>::type RealVectorType;
+    typedef Tridiagonalization<MatrixType> TridiagonalizationType;
+    typedef typename TridiagonalizationType::SubDiagonalType SubDiagonalType;
+
+    /** \brief Default constructor for fixed-size matrices.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via compute(). This constructor
+      * can only be used if \p MatrixType_ is a fixed-size matrix; use
+      * SelfAdjointEigenSolver(Index) for dynamic-size matrices.
+      *
+      * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver.cpp
+      * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver.out
+      */
+    EIGEN_DEVICE_FUNC
+    SelfAdjointEigenSolver()
+        : m_eivec(),
+          m_workspace(),
+          m_eivalues(),
+          m_subdiag(),
+          m_hcoeffs(),
+          m_info(InvalidInput),
+          m_isInitialized(false),
+          m_eigenvectorsOk(false)
+    { }
+
+    /** \brief Constructor, pre-allocates memory for dynamic-size matrices.
+      *
+      * \param [in]  size  Positive integer, size of the matrix whose
+      * eigenvalues and eigenvectors will be computed.
+      *
+      * This constructor is useful for dynamic-size matrices, when the user
+      * intends to perform decompositions via compute(). The \p size
+      * parameter is only used as a hint. It is not an error to give a wrong
+      * \p size, but it may impair performance.
+      *
+      * \sa compute() for an example
+      */
+    EIGEN_DEVICE_FUNC
+    explicit SelfAdjointEigenSolver(Index size)
+        : m_eivec(size, size),
+          m_workspace(size),
+          m_eivalues(size),
+          m_subdiag(size > 1 ? size - 1 : 1),
+          m_hcoeffs(size > 1 ? size - 1 : 1),
+          m_isInitialized(false),
+          m_eigenvectorsOk(false)
+    {}
+
+    /** \brief Constructor; computes eigendecomposition of given matrix.
+      *
+      * \param[in]  matrix  Selfadjoint matrix whose eigendecomposition is to
+      *    be computed. Only the lower triangular part of the matrix is referenced.
+      * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
+      *
+      * This constructor calls compute(const MatrixType&, int) to compute the
+      * eigenvalues of the matrix \p matrix. The eigenvectors are computed if
+      * \p options equals #ComputeEigenvectors.
+      *
+      * Example: \include SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType.cpp
+      * Output: \verbinclude SelfAdjointEigenSolver_SelfAdjointEigenSolver_MatrixType.out
+      *
+      * \sa compute(const MatrixType&, int)
+      */
+    template<typename InputType>
+    EIGEN_DEVICE_FUNC
+    explicit SelfAdjointEigenSolver(const EigenBase<InputType>& matrix, int options = ComputeEigenvectors)
+      : m_eivec(matrix.rows(), matrix.cols()),
+        m_workspace(matrix.cols()),
+        m_eivalues(matrix.cols()),
+        m_subdiag(matrix.rows() > 1 ? matrix.rows() - 1 : 1),
+        m_hcoeffs(matrix.cols() > 1 ? matrix.cols() - 1 : 1),
+        m_isInitialized(false),
+        m_eigenvectorsOk(false)
+    {
+      compute(matrix.derived(), options);
+    }
+
+    /** \brief Computes eigendecomposition of given matrix.
+      *
+      * \param[in]  matrix  Selfadjoint matrix whose eigendecomposition is to
+      *    be computed. Only the lower triangular part of the matrix is referenced.
+      * \param[in]  options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
+      * \returns    Reference to \c *this
+      *
+      * This function computes the eigenvalues of \p matrix.  The eigenvalues()
+      * function can be used to retrieve them.  If \p options equals #ComputeEigenvectors,
+      * then the eigenvectors are also computed and can be retrieved by
+      * calling eigenvectors().
+      *
+      * This implementation uses a symmetric QR algorithm. The matrix is first
+      * reduced to tridiagonal form using the Tridiagonalization class. The
+      * tridiagonal matrix is then brought to diagonal form with implicit
+      * symmetric QR steps with Wilkinson shift. Details can be found in
+      * Section 8.3 of Golub \& Van Loan, <i>%Matrix Computations</i>.
+      *
+      * The cost of the computation is about \f$ 9n^3 \f$ if the eigenvectors
+      * are required and \f$ 4n^3/3 \f$ if they are not required.
+      *
+      * This method reuses the memory in the SelfAdjointEigenSolver object that
+      * was allocated when the object was constructed, if the size of the
+      * matrix does not change.
+      *
+      * Example: \include SelfAdjointEigenSolver_compute_MatrixType.cpp
+      * Output: \verbinclude SelfAdjointEigenSolver_compute_MatrixType.out
+      *
+      * \sa SelfAdjointEigenSolver(const MatrixType&, int)
+      */
+    template<typename InputType>
+    EIGEN_DEVICE_FUNC
+    SelfAdjointEigenSolver& compute(const EigenBase<InputType>& matrix, int options = ComputeEigenvectors);
+    
+    /** \brief Computes eigendecomposition of given matrix using a closed-form algorithm
+      *
+      * This is a variant of compute(const MatrixType&, int options) which
+      * directly solves the underlying polynomial equation.
+      * 
+      * Currently only 2x2 and 3x3 matrices for which the sizes are known at compile time are supported (e.g., Matrix3d).
+      * 
+      * This method is usually significantly faster than the QR iterative algorithm
+      * but it might also be less accurate. It is also worth noting that
+      * for 3x3 matrices it involves trigonometric operations which are
+      * not necessarily available for all scalar types.
+      * 
+      * For the 3x3 case, we observed the following worst case relative error regarding the eigenvalues:
+      *   - double: 1e-8
+      *   - float:  1e-3
+      *
+      * \sa compute(const MatrixType&, int options)
+      */
+    EIGEN_DEVICE_FUNC
+    SelfAdjointEigenSolver& computeDirect(const MatrixType& matrix, int options = ComputeEigenvectors);
+
+    /**
+      *\brief Computes the eigen decomposition from a tridiagonal symmetric matrix
+      *
+      * \param[in] diag The vector containing the diagonal of the matrix.
+      * \param[in] subdiag The subdiagonal of the matrix.
+      * \param[in] options Can be #ComputeEigenvectors (default) or #EigenvaluesOnly.
+      * \returns Reference to \c *this
+      *
+      * This function assumes that the matrix has been reduced to tridiagonal form.
+      *
+      * \sa compute(const MatrixType&, int) for more information
+      */
+    SelfAdjointEigenSolver& computeFromTridiagonal(const RealVectorType& diag, const SubDiagonalType& subdiag , int options=ComputeEigenvectors);
+
+    /** \brief Returns the eigenvectors of given matrix.
+      *
+      * \returns  A const reference to the matrix whose columns are the eigenvectors.
+      *
+      * \pre The eigenvectors have been computed before.
+      *
+      * Column \f$ k \f$ of the returned matrix is an eigenvector corresponding
+      * to eigenvalue number \f$ k \f$ as returned by eigenvalues().  The
+      * eigenvectors are normalized to have (Euclidean) norm equal to one. If
+      * this object was used to solve the eigenproblem for the selfadjoint
+      * matrix \f$ A \f$, then the matrix returned by this function is the
+      * matrix \f$ V \f$ in the eigendecomposition \f$ A = V D V^{-1} \f$.
+      *
+      * For a selfadjoint matrix, \f$ V \f$ is unitary, meaning its inverse is equal
+      * to its adjoint, \f$ V^{-1} = V^{\dagger} \f$. If \f$ A \f$ is real, then
+      * \f$ V \f$ is also real and therefore orthogonal, meaning its inverse is
+      * equal to its transpose, \f$ V^{-1} = V^T \f$.
+      *
+      * Example: \include SelfAdjointEigenSolver_eigenvectors.cpp
+      * Output: \verbinclude SelfAdjointEigenSolver_eigenvectors.out
+      *
+      * \sa eigenvalues()
+      */
+    EIGEN_DEVICE_FUNC
+    const EigenvectorsType& eigenvectors() const
+    {
+      eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
+      eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+      return m_eivec;
+    }
+
+    /** \brief Returns the eigenvalues of given matrix.
+      *
+      * \returns A const reference to the column vector containing the eigenvalues.
+      *
+      * \pre The eigenvalues have been computed before.
+      *
+      * The eigenvalues are repeated according to their algebraic multiplicity,
+      * so there are as many eigenvalues as rows in the matrix. The eigenvalues
+      * are sorted in increasing order.
+      *
+      * Example: \include SelfAdjointEigenSolver_eigenvalues.cpp
+      * Output: \verbinclude SelfAdjointEigenSolver_eigenvalues.out
+      *
+      * \sa eigenvectors(), MatrixBase::eigenvalues()
+      */
+    EIGEN_DEVICE_FUNC
+    const RealVectorType& eigenvalues() const
+    {
+      eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
+      return m_eivalues;
+    }
+
+    /** \brief Computes the positive-definite square root of the matrix.
+      *
+      * \returns the positive-definite square root of the matrix
+      *
+      * \pre The eigenvalues and eigenvectors of a positive-definite matrix
+      * have been computed before.
+      *
+      * The square root of a positive-definite matrix \f$ A \f$ is the
+      * positive-definite matrix whose square equals \f$ A \f$. This function
+      * uses the eigendecomposition \f$ A = V D V^{-1} \f$ to compute the
+      * square root as \f$ A^{1/2} = V D^{1/2} V^{-1} \f$.
+      *
+      * Example: \include SelfAdjointEigenSolver_operatorSqrt.cpp
+      * Output: \verbinclude SelfAdjointEigenSolver_operatorSqrt.out
+      *
+      * \sa operatorInverseSqrt(), <a href="unsupported/group__MatrixFunctions__Module.html">MatrixFunctions Module</a>
+      */
+    EIGEN_DEVICE_FUNC
+    MatrixType operatorSqrt() const
+    {
+      eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
+      eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+      return m_eivec * m_eivalues.cwiseSqrt().asDiagonal() * m_eivec.adjoint();
+    }
+
+    /** \brief Computes the inverse square root of the matrix.
+      *
+      * \returns the inverse positive-definite square root of the matrix
+      *
+      * \pre The eigenvalues and eigenvectors of a positive-definite matrix
+      * have been computed before.
+      *
+      * This function uses the eigendecomposition \f$ A = V D V^{-1} \f$ to
+      * compute the inverse square root as \f$ V D^{-1/2} V^{-1} \f$. This is
+      * cheaper than first computing the square root with operatorSqrt() and
+      * then its inverse with MatrixBase::inverse().
+      *
+      * Example: \include SelfAdjointEigenSolver_operatorInverseSqrt.cpp
+      * Output: \verbinclude SelfAdjointEigenSolver_operatorInverseSqrt.out
+      *
+      * \sa operatorSqrt(), MatrixBase::inverse(), <a href="unsupported/group__MatrixFunctions__Module.html">MatrixFunctions Module</a>
+      */
+    EIGEN_DEVICE_FUNC
+    MatrixType operatorInverseSqrt() const
+    {
+      eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
+      eigen_assert(m_eigenvectorsOk && "The eigenvectors have not been computed together with the eigenvalues.");
+      return m_eivec * m_eivalues.cwiseInverse().cwiseSqrt().asDiagonal() * m_eivec.adjoint();
+    }
+
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful, \c NoConvergence otherwise.
+      */
+    EIGEN_DEVICE_FUNC
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "SelfAdjointEigenSolver is not initialized.");
+      return m_info;
+    }
+
+    /** \brief Maximum number of iterations.
+      *
+      * The algorithm terminates if it does not converge within m_maxIterations * n iterations, where n
+      * denotes the size of the matrix. This value is currently set to 30 (copied from LAPACK).
+      */
+    static const int m_maxIterations = 30;
+
+  protected:
+    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+    EigenvectorsType m_eivec;
+    VectorType m_workspace;
+    RealVectorType m_eivalues;
+    typename TridiagonalizationType::SubDiagonalType m_subdiag;
+    typename TridiagonalizationType::CoeffVectorType m_hcoeffs;
+    ComputationInfo m_info;
+    bool m_isInitialized;
+    bool m_eigenvectorsOk;
+};
+
+namespace internal {
+/** \internal
+  *
+  * \eigenvalues_module \ingroup Eigenvalues_Module
+  *
+  * Performs a QR step on a tridiagonal symmetric matrix represented as a
+  * pair of two vectors \a diag and \a subdiag.
+  *
+  * \param diag the diagonal part of the input selfadjoint tridiagonal matrix
+  * \param subdiag the sub-diagonal part of the input selfadjoint tridiagonal matrix
+  * \param start starting index of the submatrix to work on
+  * \param end last+1 index of the submatrix to work on
+  * \param matrixQ pointer to the column-major matrix holding the eigenvectors, can be 0
+  * \param n size of the input matrix
+  *
+  * For compilation efficiency reasons, this procedure does not use eigen expression
+  * for its arguments.
+  *
+  * Implemented from Golub's "Matrix Computations", algorithm 8.3.2:
+  * "implicit symmetric QR step with Wilkinson shift"
+  */
+template<int StorageOrder,typename RealScalar, typename Scalar, typename Index>
+EIGEN_DEVICE_FUNC
+static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index start, Index end, Scalar* matrixQ, Index n);
+}
+
+template<typename MatrixType>
+template<typename InputType>
+EIGEN_DEVICE_FUNC
+SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>
+::compute(const EigenBase<InputType>& a_matrix, int options)
+{
+  const InputType &matrix(a_matrix.derived());
+
+  EIGEN_USING_STD(abs);
+  eigen_assert(matrix.cols() == matrix.rows());
+  eigen_assert((options&~(EigVecMask|GenEigMask))==0
+          && (options&EigVecMask)!=EigVecMask
+          && "invalid option parameter");
+  bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors;
+  Index n = matrix.cols();
+  m_eivalues.resize(n,1);
+
+  if(n==1)
+  {
+    m_eivec = matrix;
+    m_eivalues.coeffRef(0,0) = numext::real(m_eivec.coeff(0,0));
+    if(computeEigenvectors)
+      m_eivec.setOnes(n,n);
+    m_info = Success;
+    m_isInitialized = true;
+    m_eigenvectorsOk = computeEigenvectors;
+    return *this;
+  }
+
+  // declare some aliases
+  RealVectorType& diag = m_eivalues;
+  EigenvectorsType& mat = m_eivec;
+
+  // map the matrix coefficients to [-1:1] to avoid over- and underflow.
+  mat = matrix.template triangularView<Lower>();
+  RealScalar scale = mat.cwiseAbs().maxCoeff();
+  if(numext::is_exactly_zero(scale)) scale = RealScalar(1);
+  mat.template triangularView<Lower>() /= scale;
+  m_subdiag.resize(n-1);
+  m_hcoeffs.resize(n-1);
+  internal::tridiagonalization_inplace(mat, diag, m_subdiag, m_hcoeffs, m_workspace, computeEigenvectors);
+
+  m_info = internal::computeFromTridiagonal_impl(diag, m_subdiag, m_maxIterations, computeEigenvectors, m_eivec);
+  
+  // scale back the eigen values
+  m_eivalues *= scale;
+
+  m_isInitialized = true;
+  m_eigenvectorsOk = computeEigenvectors;
+  return *this;
+}
+
+template<typename MatrixType>
+SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>
+::computeFromTridiagonal(const RealVectorType& diag, const SubDiagonalType& subdiag , int options)
+{
+  //TODO : Add an option to scale the values beforehand
+  bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors;
+
+  m_eivalues = diag;
+  m_subdiag = subdiag;
+  if (computeEigenvectors)
+  {
+    m_eivec.setIdentity(diag.size(), diag.size());
+  }
+  m_info = internal::computeFromTridiagonal_impl(m_eivalues, m_subdiag, m_maxIterations, computeEigenvectors, m_eivec);
+
+  m_isInitialized = true;
+  m_eigenvectorsOk = computeEigenvectors;
+  return *this;
+}
+
+namespace internal {
+/**
+  * \internal
+  * \brief Compute the eigendecomposition from a tridiagonal matrix
+  *
+  * \param[in,out] diag : On input, the diagonal of the matrix, on output the eigenvalues
+  * \param[in,out] subdiag : The subdiagonal part of the matrix (entries are modified during the decomposition)
+  * \param[in] maxIterations : the maximum number of iterations
+  * \param[in] computeEigenvectors : whether the eigenvectors have to be computed or not
+  * \param[out] eivec : The matrix to store the eigenvectors if computeEigenvectors==true. Must be allocated on input.
+  * \returns \c Success or \c NoConvergence
+  */
+template<typename MatrixType, typename DiagType, typename SubDiagType>
+EIGEN_DEVICE_FUNC
+ComputationInfo computeFromTridiagonal_impl(DiagType& diag, SubDiagType& subdiag, const Index maxIterations, bool computeEigenvectors, MatrixType& eivec)
+{
+  ComputationInfo info;
+  typedef typename MatrixType::Scalar Scalar;
+
+  Index n = diag.size();
+  Index end = n-1;
+  Index start = 0;
+  Index iter = 0; // total number of iterations
+  
+  typedef typename DiagType::RealScalar RealScalar;
+  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
+  const RealScalar precision_inv = RealScalar(1)/NumTraits<RealScalar>::epsilon();
+  while (end>0)
+  {
+    for (Index i = start; i<end; ++i) {
+      if (numext::abs(subdiag[i]) < considerAsZero) {
+        subdiag[i] = RealScalar(0);
+      } else {
+        // abs(subdiag[i]) <= epsilon * sqrt(abs(diag[i]) + abs(diag[i+1]))
+        // Scaled to prevent underflows.
+        const RealScalar scaled_subdiag = precision_inv * subdiag[i];
+        if (scaled_subdiag * scaled_subdiag <= (numext::abs(diag[i])+numext::abs(diag[i+1]))) {
+          subdiag[i] = RealScalar(0);
+        }
+      }
+    }
+
+    // find the largest unreduced block at the end of the matrix.
+    while (end>0 && numext::is_exactly_zero(subdiag[end - 1]))
+    {
+      end--;
+    }
+    if (end<=0)
+      break;
+
+    // if we spent too many iterations, we give up
+    iter++;
+    if(iter > maxIterations * n) break;
+
+    start = end - 1;
+    while (start>0 && !numext::is_exactly_zero(subdiag[start - 1]))
+      start--;
+
+    internal::tridiagonal_qr_step<MatrixType::Flags&RowMajorBit ? RowMajor : ColMajor>(diag.data(), subdiag.data(), start, end, computeEigenvectors ? eivec.data() : (Scalar*)0, n);
+  }
+  if (iter <= maxIterations * n)
+    info = Success;
+  else
+    info = NoConvergence;
+
+  // Sort eigenvalues and corresponding vectors.
+  // TODO make the sort optional ?
+  // TODO use a better sort algorithm !!
+  if (info == Success)
+  {
+    for (Index i = 0; i < n-1; ++i)
+    {
+      Index k;
+      diag.segment(i,n-i).minCoeff(&k);
+      if (k > 0)
+      {
+        numext::swap(diag[i], diag[k+i]);
+        if(computeEigenvectors)
+          eivec.col(i).swap(eivec.col(k+i));
+      }
+    }
+  }
+  return info;
+}
+  
+template<typename SolverType,int Size,bool IsComplex> struct direct_selfadjoint_eigenvalues
+{
+  EIGEN_DEVICE_FUNC
+  static inline void run(SolverType& eig, const typename SolverType::MatrixType& A, int options)
+  { eig.compute(A,options); }
+};
+
+template<typename SolverType> struct direct_selfadjoint_eigenvalues<SolverType,3,false>
+{
+  typedef typename SolverType::MatrixType MatrixType;
+  typedef typename SolverType::RealVectorType VectorType;
+  typedef typename SolverType::Scalar Scalar;
+  typedef typename SolverType::EigenvectorsType EigenvectorsType;
+  
+
+  /** \internal
+   * Computes the roots of the characteristic polynomial of \a m.
+   * For numerical stability m.trace() should be near zero and to avoid over- or underflow m should be normalized.
+   */
+  EIGEN_DEVICE_FUNC
+  static inline void computeRoots(const MatrixType& m, VectorType& roots)
+  {
+    EIGEN_USING_STD(sqrt)
+    EIGEN_USING_STD(atan2)
+    EIGEN_USING_STD(cos)
+    EIGEN_USING_STD(sin)
+    const Scalar s_inv3 = Scalar(1)/Scalar(3);
+    const Scalar s_sqrt3 = sqrt(Scalar(3));
+
+    // The characteristic equation is x^3 - c2*x^2 + c1*x - c0 = 0.  The
+    // eigenvalues are the roots to this equation, all guaranteed to be
+    // real-valued, because the matrix is symmetric.
+    Scalar c0 = m(0,0)*m(1,1)*m(2,2) + Scalar(2)*m(1,0)*m(2,0)*m(2,1) - m(0,0)*m(2,1)*m(2,1) - m(1,1)*m(2,0)*m(2,0) - m(2,2)*m(1,0)*m(1,0);
+    Scalar c1 = m(0,0)*m(1,1) - m(1,0)*m(1,0) + m(0,0)*m(2,2) - m(2,0)*m(2,0) + m(1,1)*m(2,2) - m(2,1)*m(2,1);
+    Scalar c2 = m(0,0) + m(1,1) + m(2,2);
+
+    // Construct the parameters used in classifying the roots of the equation
+    // and in solving the equation for the roots in closed form.
+    Scalar c2_over_3 = c2*s_inv3;
+    Scalar a_over_3 = (c2*c2_over_3 - c1)*s_inv3;
+    a_over_3 = numext::maxi(a_over_3, Scalar(0));
+
+    Scalar half_b = Scalar(0.5)*(c0 + c2_over_3*(Scalar(2)*c2_over_3*c2_over_3 - c1));
+
+    Scalar q = a_over_3*a_over_3*a_over_3 - half_b*half_b;
+    q = numext::maxi(q, Scalar(0));
+
+    // Compute the eigenvalues by solving for the roots of the polynomial.
+    Scalar rho = sqrt(a_over_3);
+    Scalar theta = atan2(sqrt(q),half_b)*s_inv3;  // since sqrt(q) > 0, atan2 is in [0, pi] and theta is in [0, pi/3]
+    Scalar cos_theta = cos(theta);
+    Scalar sin_theta = sin(theta);
+    // roots are already sorted, since cos is monotonically decreasing on [0, pi]
+    roots(0) = c2_over_3 - rho*(cos_theta + s_sqrt3*sin_theta); // == 2*rho*cos(theta+2pi/3)
+    roots(1) = c2_over_3 - rho*(cos_theta - s_sqrt3*sin_theta); // == 2*rho*cos(theta+ pi/3)
+    roots(2) = c2_over_3 + Scalar(2)*rho*cos_theta;
+  }
+
+  EIGEN_DEVICE_FUNC
+  static inline bool extract_kernel(MatrixType& mat, Ref<VectorType> res, Ref<VectorType> representative)
+  {
+    EIGEN_USING_STD(abs);
+    EIGEN_USING_STD(sqrt);
+    Index i0;
+    // Find non-zero column i0 (by construction, there must exist a non zero coefficient on the diagonal):
+    mat.diagonal().cwiseAbs().maxCoeff(&i0);
+    // mat.col(i0) is a good candidate for an orthogonal vector to the current eigenvector,
+    // so let's save it:
+    representative = mat.col(i0);
+    Scalar n0, n1;
+    VectorType c0, c1;
+    n0 = (c0 = representative.cross(mat.col((i0+1)%3))).squaredNorm();
+    n1 = (c1 = representative.cross(mat.col((i0+2)%3))).squaredNorm();
+    if(n0>n1) res = c0/sqrt(n0);
+    else      res = c1/sqrt(n1);
+
+    return true;
+  }
+
+  EIGEN_DEVICE_FUNC
+  static inline void run(SolverType& solver, const MatrixType& mat, int options)
+  {
+    eigen_assert(mat.cols() == 3 && mat.cols() == mat.rows());
+    eigen_assert((options&~(EigVecMask|GenEigMask))==0
+            && (options&EigVecMask)!=EigVecMask
+            && "invalid option parameter");
+    bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors;
+    
+    EigenvectorsType& eivecs = solver.m_eivec;
+    VectorType& eivals = solver.m_eivalues;
+  
+    // Shift the matrix to the mean eigenvalue and map the matrix coefficients to [-1:1] to avoid over- and underflow.
+    Scalar shift = mat.trace() / Scalar(3);
+    // TODO Avoid this copy. Currently it is necessary to suppress bogus values when determining maxCoeff and for computing the eigenvectors later
+    MatrixType scaledMat = mat.template selfadjointView<Lower>();
+    scaledMat.diagonal().array() -= shift;
+    Scalar scale = scaledMat.cwiseAbs().maxCoeff();
+    if(scale > 0) scaledMat /= scale;   // TODO for scale==0 we could save the remaining operations
+
+    // compute the eigenvalues
+    computeRoots(scaledMat,eivals);
+
+    // compute the eigenvectors
+    if(computeEigenvectors)
+    {
+      if((eivals(2)-eivals(0))<=Eigen::NumTraits<Scalar>::epsilon())
+      {
+        // All three eigenvalues are numerically the same
+        eivecs.setIdentity();
+      }
+      else
+      {
+        MatrixType tmp;
+        tmp = scaledMat;
+
+        // Compute the eigenvector of the most distinct eigenvalue
+        Scalar d0 = eivals(2) - eivals(1);
+        Scalar d1 = eivals(1) - eivals(0);
+        Index k(0), l(2);
+        if(d0 > d1)
+        {
+          numext::swap(k,l);
+          d0 = d1;
+        }
+
+        // Compute the eigenvector of index k
+        {
+          tmp.diagonal().array () -= eivals(k);
+          // By construction, 'tmp' is of rank 2, and its kernel corresponds to the respective eigenvector.
+          extract_kernel(tmp, eivecs.col(k), eivecs.col(l));
+        }
+
+        // Compute eigenvector of index l
+        if(d0<=2*Eigen::NumTraits<Scalar>::epsilon()*d1)
+        {
+          // If d0 is too small, then the two other eigenvalues are numerically the same,
+          // and thus we only have to ortho-normalize the near orthogonal vector we saved above.
+          eivecs.col(l) -= eivecs.col(k).dot(eivecs.col(l))*eivecs.col(l);
+          eivecs.col(l).normalize();
+        }
+        else
+        {
+          tmp = scaledMat;
+          tmp.diagonal().array () -= eivals(l);
+
+          VectorType dummy;
+          extract_kernel(tmp, eivecs.col(l), dummy);
+        }
+
+        // Compute last eigenvector from the other two
+        eivecs.col(1) = eivecs.col(2).cross(eivecs.col(0)).normalized();
+      }
+    }
+
+    // Rescale back to the original size.
+    eivals *= scale;
+    eivals.array() += shift;
+    
+    solver.m_info = Success;
+    solver.m_isInitialized = true;
+    solver.m_eigenvectorsOk = computeEigenvectors;
+  }
+};
+
+// 2x2 direct eigenvalues decomposition, code from Hauke Heibel
+template<typename SolverType> 
+struct direct_selfadjoint_eigenvalues<SolverType,2,false>
+{
+  typedef typename SolverType::MatrixType MatrixType;
+  typedef typename SolverType::RealVectorType VectorType;
+  typedef typename SolverType::Scalar Scalar;
+  typedef typename SolverType::EigenvectorsType EigenvectorsType;
+  
+  EIGEN_DEVICE_FUNC
+  static inline void computeRoots(const MatrixType& m, VectorType& roots)
+  {
+    EIGEN_USING_STD(sqrt);
+    const Scalar t0 = Scalar(0.5) * sqrt( numext::abs2(m(0,0)-m(1,1)) + Scalar(4)*numext::abs2(m(1,0)));
+    const Scalar t1 = Scalar(0.5) * (m(0,0) + m(1,1));
+    roots(0) = t1 - t0;
+    roots(1) = t1 + t0;
+  }
+  
+  EIGEN_DEVICE_FUNC
+  static inline void run(SolverType& solver, const MatrixType& mat, int options)
+  {
+    EIGEN_USING_STD(sqrt);
+    EIGEN_USING_STD(abs);
+    
+    eigen_assert(mat.cols() == 2 && mat.cols() == mat.rows());
+    eigen_assert((options&~(EigVecMask|GenEigMask))==0
+            && (options&EigVecMask)!=EigVecMask
+            && "invalid option parameter");
+    bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors;
+    
+    EigenvectorsType& eivecs = solver.m_eivec;
+    VectorType& eivals = solver.m_eivalues;
+  
+    // Shift the matrix to the mean eigenvalue and map the matrix coefficients to [-1:1] to avoid over- and underflow.
+    Scalar shift = mat.trace() / Scalar(2);
+    MatrixType scaledMat = mat;
+    scaledMat.coeffRef(0,1) = mat.coeff(1,0);
+    scaledMat.diagonal().array() -= shift;
+    Scalar scale = scaledMat.cwiseAbs().maxCoeff();
+    if(scale > Scalar(0))
+      scaledMat /= scale;
+
+    // Compute the eigenvalues
+    computeRoots(scaledMat,eivals);
+
+    // compute the eigen vectors
+    if(computeEigenvectors)
+    {
+      if((eivals(1)-eivals(0))<=abs(eivals(1))*Eigen::NumTraits<Scalar>::epsilon())
+      {
+        eivecs.setIdentity();
+      }
+      else
+      {
+        scaledMat.diagonal().array () -= eivals(1);
+        Scalar a2 = numext::abs2(scaledMat(0,0));
+        Scalar c2 = numext::abs2(scaledMat(1,1));
+        Scalar b2 = numext::abs2(scaledMat(1,0));
+        if(a2>c2)
+        {
+          eivecs.col(1) << -scaledMat(1,0), scaledMat(0,0);
+          eivecs.col(1) /= sqrt(a2+b2);
+        }
+        else
+        {
+          eivecs.col(1) << -scaledMat(1,1), scaledMat(1,0);
+          eivecs.col(1) /= sqrt(c2+b2);
+        }
+
+        eivecs.col(0) << eivecs.col(1).unitOrthogonal();
+      }
+    }
+
+    // Rescale back to the original size.
+    eivals *= scale;
+    eivals.array() += shift;
+
+    solver.m_info = Success;
+    solver.m_isInitialized = true;
+    solver.m_eigenvectorsOk = computeEigenvectors;
+  }
+};
+
+}
+
+template<typename MatrixType>
+EIGEN_DEVICE_FUNC
+SelfAdjointEigenSolver<MatrixType>& SelfAdjointEigenSolver<MatrixType>
+::computeDirect(const MatrixType& matrix, int options)
+{
+  internal::direct_selfadjoint_eigenvalues<SelfAdjointEigenSolver,Size,NumTraits<Scalar>::IsComplex>::run(*this,matrix,options);
+  return *this;
+}
+
+namespace internal {
+
+// Francis implicit QR step.
+template<int StorageOrder,typename RealScalar, typename Scalar, typename Index>
+EIGEN_DEVICE_FUNC
+static void tridiagonal_qr_step(RealScalar* diag, RealScalar* subdiag, Index start, Index end, Scalar* matrixQ, Index n)
+{
+  // Wilkinson Shift.
+  RealScalar td = (diag[end-1] - diag[end])*RealScalar(0.5);
+  RealScalar e = subdiag[end-1];
+  // Note that thanks to scaling, e^2 or td^2 cannot overflow, however they can still
+  // underflow thus leading to inf/NaN values when using the following commented code:
+  //   RealScalar e2 = numext::abs2(subdiag[end-1]);
+  //   RealScalar mu = diag[end] - e2 / (td + (td>0 ? 1 : -1) * sqrt(td*td + e2));
+  // This explain the following, somewhat more complicated, version:
+  RealScalar mu = diag[end];
+  if(numext::is_exactly_zero(td)) {
+    mu -= numext::abs(e);
+  } else if (!numext::is_exactly_zero(e)) {
+    const RealScalar e2 = numext::abs2(e);
+    const RealScalar h = numext::hypot(td,e);
+    if(numext::is_exactly_zero(e2)) {
+      mu -= e / ((td + (td>RealScalar(0) ? h : -h)) / e);
+    } else {
+      mu -= e2 / (td + (td>RealScalar(0) ? h : -h)); 
+    }
+  }
+
+  RealScalar x = diag[start] - mu;
+  RealScalar z = subdiag[start];
+  // If z ever becomes zero, the Givens rotation will be the identity and
+  // z will stay zero for all future iterations.
+  for (Index k = start; k < end && !numext::is_exactly_zero(z); ++k)
+  {
+    JacobiRotation<RealScalar> rot;
+    rot.makeGivens(x, z);
+
+    // do T = G' T G
+    RealScalar sdk = rot.s() * diag[k] + rot.c() * subdiag[k];
+    RealScalar dkp1 = rot.s() * subdiag[k] + rot.c() * diag[k+1];
+
+    diag[k] = rot.c() * (rot.c() * diag[k] - rot.s() * subdiag[k]) - rot.s() * (rot.c() * subdiag[k] - rot.s() * diag[k+1]);
+    diag[k+1] = rot.s() * sdk + rot.c() * dkp1;
+    subdiag[k] = rot.c() * sdk - rot.s() * dkp1;
+    
+    if (k > start)
+      subdiag[k - 1] = rot.c() * subdiag[k-1] - rot.s() * z;
+
+    // "Chasing the bulge" to return to triangular form.
+    x = subdiag[k];
+    if (k < end - 1)
+    {
+      z = -rot.s() * subdiag[k+1];
+      subdiag[k + 1] = rot.c() * subdiag[k+1];
+    }
+    
+    // apply the givens rotation to the unit matrix Q = Q * G
+    if (matrixQ)
+    {
+      // FIXME if StorageOrder == RowMajor this operation is not very efficient
+      Map<Matrix<Scalar,Dynamic,Dynamic,StorageOrder> > q(matrixQ,n,n);
+      q.applyOnTheRight(k,k+1,rot);
+    }
+  }
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SELFADJOINTEIGENSOLVER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
new file mode 100644
index 0000000000000000000000000000000000000000..b24de67d4632357ba68473f5e2103855f18e0639
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h
@@ -0,0 +1,89 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *    Self-adjoint eigenvalues/eigenvectors.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_SAEIGENSOLVER_LAPACKE_H
+#define EIGEN_SAEIGENSOLVER_LAPACKE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \internal Specialization for the data types supported by LAPACKe */
+
+#define EIGEN_LAPACKE_EIG_SELFADJ_2(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, EIGCOLROW ) \
+template<> template<typename InputType> inline \
+SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >& \
+SelfAdjointEigenSolver<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW> >::compute(const EigenBase<InputType>& matrix, int options) \
+{ \
+  eigen_assert(matrix.cols() == matrix.rows()); \
+  eigen_assert((options&~(EigVecMask|GenEigMask))==0 \
+          && (options&EigVecMask)!=EigVecMask \
+          && "invalid option parameter"); \
+  bool computeEigenvectors = (options&ComputeEigenvectors)==ComputeEigenvectors; \
+  lapack_int n = internal::convert_index<lapack_int>(matrix.cols()), lda, info; \
+  m_eivalues.resize(n,1); \
+  m_subdiag.resize(n-1); \
+  m_eivec = matrix; \
+\
+  if(n==1) \
+  { \
+    m_eivalues.coeffRef(0,0) = numext::real(m_eivec.coeff(0,0)); \
+    if(computeEigenvectors) m_eivec.setOnes(n,n); \
+    m_info = Success; \
+    m_isInitialized = true; \
+    m_eigenvectorsOk = computeEigenvectors; \
+    return *this; \
+  } \
+\
+  lda = internal::convert_index<lapack_int>(m_eivec.outerStride()); \
+  char jobz, uplo='L'/*, range='A'*/; \
+  jobz = computeEigenvectors ? 'V' : 'N'; \
+\
+  info = LAPACKE_##LAPACKE_NAME( LAPACK_COL_MAJOR, jobz, uplo, n, (LAPACKE_TYPE*)m_eivec.data(), lda, (LAPACKE_RTYPE*)m_eivalues.data() ); \
+  m_info = (info==0) ? Success : NoConvergence; \
+  m_isInitialized = true; \
+  m_eigenvectorsOk = computeEigenvectors; \
+  return *this; \
+}
+
+#define EIGEN_LAPACKE_EIG_SELFADJ(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME )              \
+        EIGEN_LAPACKE_EIG_SELFADJ_2(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, ColMajor )  \
+        EIGEN_LAPACKE_EIG_SELFADJ_2(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_NAME, RowMajor ) 
+
+EIGEN_LAPACKE_EIG_SELFADJ(double,   double,                double, dsyev)
+EIGEN_LAPACKE_EIG_SELFADJ(float,    float,                 float,  ssyev)
+EIGEN_LAPACKE_EIG_SELFADJ(dcomplex, lapack_complex_double, double, zheev)
+EIGEN_LAPACKE_EIG_SELFADJ(scomplex, lapack_complex_float,  float,  cheev)
+
+} // end namespace Eigen
+
+#endif // EIGEN_SAEIGENSOLVER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/Tridiagonalization.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/Tridiagonalization.h
new file mode 100644
index 0000000000000000000000000000000000000000..3de9818493ed6db619a13f9861e11ceea3cf4bc9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Eigenvalues/Tridiagonalization.h
@@ -0,0 +1,564 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRIDIAGONALIZATION_H
+#define EIGEN_TRIDIAGONALIZATION_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename MatrixType> struct TridiagonalizationMatrixTReturnType;
+template<typename MatrixType>
+struct traits<TridiagonalizationMatrixTReturnType<MatrixType> >
+  : public traits<typename MatrixType::PlainObject>
+{
+  typedef typename MatrixType::PlainObject ReturnType; // FIXME shall it be a BandMatrix?
+  enum { Flags = 0 };
+};
+
+template<typename MatrixType, typename CoeffVectorType>
+EIGEN_DEVICE_FUNC
+void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs);
+}
+
+/** \eigenvalues_module \ingroup Eigenvalues_Module
+  *
+  *
+  * \class Tridiagonalization
+  *
+  * \brief Tridiagonal decomposition of a selfadjoint matrix
+  *
+  * \tparam MatrixType_ the type of the matrix of which we are computing the
+  * tridiagonal decomposition; this is expected to be an instantiation of the
+  * Matrix class template.
+  *
+  * This class performs a tridiagonal decomposition of a selfadjoint matrix \f$ A \f$ such that:
+  * \f$ A = Q T Q^* \f$ where \f$ Q \f$ is unitary and \f$ T \f$ a real symmetric tridiagonal matrix.
+  *
+  * A tridiagonal matrix is a matrix which has nonzero elements only on the
+  * main diagonal and the first diagonal below and above it. The Hessenberg
+  * decomposition of a selfadjoint matrix is in fact a tridiagonal
+  * decomposition. This class is used in SelfAdjointEigenSolver to compute the
+  * eigenvalues and eigenvectors of a selfadjoint matrix.
+  *
+  * Call the function compute() to compute the tridiagonal decomposition of a
+  * given matrix. Alternatively, you can use the Tridiagonalization(const MatrixType&)
+  * constructor which computes the tridiagonal Schur decomposition at
+  * construction time. Once the decomposition is computed, you can use the
+  * matrixQ() and matrixT() functions to retrieve the matrices Q and T in the
+  * decomposition.
+  *
+  * The documentation of Tridiagonalization(const MatrixType&) contains an
+  * example of the typical use of this class.
+  *
+  * \sa class HessenbergDecomposition, class SelfAdjointEigenSolver
+  */
+template<typename MatrixType_> class Tridiagonalization
+{
+  public:
+
+    /** \brief Synonym for the template parameter \p MatrixType_. */
+    typedef MatrixType_ MatrixType;
+
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+
+    enum {
+      Size = MatrixType::RowsAtCompileTime,
+      SizeMinusOne = Size == Dynamic ? Dynamic : (Size > 1 ? Size - 1 : 1),
+      Options = MatrixType::Options,
+      MaxSize = MatrixType::MaxRowsAtCompileTime,
+      MaxSizeMinusOne = MaxSize == Dynamic ? Dynamic : (MaxSize > 1 ? MaxSize - 1 : 1)
+    };
+
+    typedef Matrix<Scalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> CoeffVectorType;
+    typedef typename internal::plain_col_type<MatrixType, RealScalar>::type DiagonalType;
+    typedef Matrix<RealScalar, SizeMinusOne, 1, Options & ~RowMajor, MaxSizeMinusOne, 1> SubDiagonalType;
+    typedef internal::remove_all_t<typename MatrixType::RealReturnType> MatrixTypeRealView;
+    typedef internal::TridiagonalizationMatrixTReturnType<MatrixTypeRealView> MatrixTReturnType;
+
+    typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
+              internal::add_const_on_value_type_t<typename Diagonal<const MatrixType>::RealReturnType>,
+              const Diagonal<const MatrixType>
+            > DiagonalReturnType;
+
+    typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
+              internal::add_const_on_value_type_t<typename Diagonal<const MatrixType, -1>::RealReturnType>,
+              const Diagonal<const MatrixType, -1>
+            > SubDiagonalReturnType;
+
+    /** \brief Return type of matrixQ() */
+    typedef HouseholderSequence<MatrixType,internal::remove_all_t<typename CoeffVectorType::ConjugateReturnType>> HouseholderSequenceType;
+
+    /** \brief Default constructor.
+      *
+      * \param [in]  size  Positive integer, size of the matrix whose tridiagonal
+      * decomposition will be computed.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via compute().  The \p size parameter is only
+      * used as a hint. It is not an error to give a wrong \p size, but it may
+      * impair performance.
+      *
+      * \sa compute() for an example.
+      */
+    explicit Tridiagonalization(Index size = Size==Dynamic ? 2 : Size)
+      : m_matrix(size,size),
+        m_hCoeffs(size > 1 ? size-1 : 1),
+        m_isInitialized(false)
+    {}
+
+    /** \brief Constructor; computes tridiagonal decomposition of given matrix.
+      *
+      * \param[in]  matrix  Selfadjoint matrix whose tridiagonal decomposition
+      * is to be computed.
+      *
+      * This constructor calls compute() to compute the tridiagonal decomposition.
+      *
+      * Example: \include Tridiagonalization_Tridiagonalization_MatrixType.cpp
+      * Output: \verbinclude Tridiagonalization_Tridiagonalization_MatrixType.out
+      */
+    template<typename InputType>
+    explicit Tridiagonalization(const EigenBase<InputType>& matrix)
+      : m_matrix(matrix.derived()),
+        m_hCoeffs(matrix.cols() > 1 ? matrix.cols()-1 : 1),
+        m_isInitialized(false)
+    {
+      internal::tridiagonalization_inplace(m_matrix, m_hCoeffs);
+      m_isInitialized = true;
+    }
+
+    /** \brief Computes tridiagonal decomposition of given matrix.
+      *
+      * \param[in]  matrix  Selfadjoint matrix whose tridiagonal decomposition
+      * is to be computed.
+      * \returns    Reference to \c *this
+      *
+      * The tridiagonal decomposition is computed by bringing the columns of
+      * the matrix successively in the required form using Householder
+      * reflections. The cost is \f$ 4n^3/3 \f$ flops, where \f$ n \f$ denotes
+      * the size of the given matrix.
+      *
+      * This method reuses of the allocated data in the Tridiagonalization
+      * object, if the size of the matrix does not change.
+      *
+      * Example: \include Tridiagonalization_compute.cpp
+      * Output: \verbinclude Tridiagonalization_compute.out
+      */
+    template<typename InputType>
+    Tridiagonalization& compute(const EigenBase<InputType>& matrix)
+    {
+      m_matrix = matrix.derived();
+      m_hCoeffs.resize(matrix.rows()-1, 1);
+      internal::tridiagonalization_inplace(m_matrix, m_hCoeffs);
+      m_isInitialized = true;
+      return *this;
+    }
+
+    /** \brief Returns the Householder coefficients.
+      *
+      * \returns a const reference to the vector of Householder coefficients
+      *
+      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+      * the member function compute(const MatrixType&) has been called before
+      * to compute the tridiagonal decomposition of a matrix.
+      *
+      * The Householder coefficients allow the reconstruction of the matrix
+      * \f$ Q \f$ in the tridiagonal decomposition from the packed data.
+      *
+      * Example: \include Tridiagonalization_householderCoefficients.cpp
+      * Output: \verbinclude Tridiagonalization_householderCoefficients.out
+      *
+      * \sa packedMatrix(), \ref Householder_Module "Householder module"
+      */
+    inline CoeffVectorType householderCoefficients() const
+    {
+      eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+      return m_hCoeffs;
+    }
+
+    /** \brief Returns the internal representation of the decomposition
+      *
+      *	\returns a const reference to a matrix with the internal representation
+      *	         of the decomposition.
+      *
+      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+      * the member function compute(const MatrixType&) has been called before
+      * to compute the tridiagonal decomposition of a matrix.
+      *
+      * The returned matrix contains the following information:
+      *  - the strict upper triangular part is equal to the input matrix A.
+      *  - the diagonal and lower sub-diagonal represent the real tridiagonal
+      *    symmetric matrix T.
+      *  - the rest of the lower part contains the Householder vectors that,
+      *    combined with Householder coefficients returned by
+      *    householderCoefficients(), allows to reconstruct the matrix Q as
+      *       \f$ Q = H_{N-1} \ldots H_1 H_0 \f$.
+      *    Here, the matrices \f$ H_i \f$ are the Householder transformations
+      *       \f$ H_i = (I - h_i v_i v_i^T) \f$
+      *    where \f$ h_i \f$ is the \f$ i \f$th Householder coefficient and
+      *    \f$ v_i \f$ is the Householder vector defined by
+      *       \f$ v_i = [ 0, \ldots, 0, 1, M(i+2,i), \ldots, M(N-1,i) ]^T \f$
+      *    with M the matrix returned by this function.
+      *
+      * See LAPACK for further details on this packed storage.
+      *
+      * Example: \include Tridiagonalization_packedMatrix.cpp
+      * Output: \verbinclude Tridiagonalization_packedMatrix.out
+      *
+      * \sa householderCoefficients()
+      */
+    inline const MatrixType& packedMatrix() const
+    {
+      eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+      return m_matrix;
+    }
+
+    /** \brief Returns the unitary matrix Q in the decomposition
+      *
+      * \returns object representing the matrix Q
+      *
+      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+      * the member function compute(const MatrixType&) has been called before
+      * to compute the tridiagonal decomposition of a matrix.
+      *
+      * This function returns a light-weight object of template class
+      * HouseholderSequence. You can either apply it directly to a matrix or
+      * you can convert it to a matrix of type #MatrixType.
+      *
+      * \sa Tridiagonalization(const MatrixType&) for an example,
+      *     matrixT(), class HouseholderSequence
+      */
+    HouseholderSequenceType matrixQ() const
+    {
+      eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+      return HouseholderSequenceType(m_matrix, m_hCoeffs.conjugate())
+             .setLength(m_matrix.rows() - 1)
+             .setShift(1);
+    }
+
+    /** \brief Returns an expression of the tridiagonal matrix T in the decomposition
+      *
+      * \returns expression object representing the matrix T
+      *
+      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+      * the member function compute(const MatrixType&) has been called before
+      * to compute the tridiagonal decomposition of a matrix.
+      *
+      * Currently, this function can be used to extract the matrix T from internal
+      * data and copy it to a dense matrix object. In most cases, it may be
+      * sufficient to directly use the packed matrix or the vector expressions
+      * returned by diagonal() and subDiagonal() instead of creating a new
+      * dense copy matrix with this function.
+      *
+      * \sa Tridiagonalization(const MatrixType&) for an example,
+      * matrixQ(), packedMatrix(), diagonal(), subDiagonal()
+      */
+    MatrixTReturnType matrixT() const
+    {
+      eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+      return MatrixTReturnType(m_matrix.real());
+    }
+
+    /** \brief Returns the diagonal of the tridiagonal matrix T in the decomposition.
+      *
+      * \returns expression representing the diagonal of T
+      *
+      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+      * the member function compute(const MatrixType&) has been called before
+      * to compute the tridiagonal decomposition of a matrix.
+      *
+      * Example: \include Tridiagonalization_diagonal.cpp
+      * Output: \verbinclude Tridiagonalization_diagonal.out
+      *
+      * \sa matrixT(), subDiagonal()
+      */
+    DiagonalReturnType diagonal() const;
+
+    /** \brief Returns the subdiagonal of the tridiagonal matrix T in the decomposition.
+      *
+      * \returns expression representing the subdiagonal of T
+      *
+      * \pre Either the constructor Tridiagonalization(const MatrixType&) or
+      * the member function compute(const MatrixType&) has been called before
+      * to compute the tridiagonal decomposition of a matrix.
+      *
+      * \sa diagonal() for an example, matrixT()
+      */
+    SubDiagonalReturnType subDiagonal() const;
+
+  protected:
+
+    MatrixType m_matrix;
+    CoeffVectorType m_hCoeffs;
+    bool m_isInitialized;
+};
+
+template<typename MatrixType>
+typename Tridiagonalization<MatrixType>::DiagonalReturnType
+Tridiagonalization<MatrixType>::diagonal() const
+{
+  eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+  return m_matrix.diagonal().real();
+}
+
+template<typename MatrixType>
+typename Tridiagonalization<MatrixType>::SubDiagonalReturnType
+Tridiagonalization<MatrixType>::subDiagonal() const
+{
+  eigen_assert(m_isInitialized && "Tridiagonalization is not initialized.");
+  return m_matrix.template diagonal<-1>().real();
+}
+
+namespace internal {
+
+/** \internal
+  * Performs a tridiagonal decomposition of the selfadjoint matrix \a matA in-place.
+  *
+  * \param[in,out] matA On input the selfadjoint matrix. Only the \b lower triangular part is referenced.
+  *                     On output, the strict upper part is left unchanged, and the lower triangular part
+  *                     represents the T and Q matrices in packed format has detailed below.
+  * \param[out]    hCoeffs returned Householder coefficients (see below)
+  *
+  * On output, the tridiagonal selfadjoint matrix T is stored in the diagonal
+  * and lower sub-diagonal of the matrix \a matA.
+  * The unitary matrix Q is represented in a compact way as a product of
+  * Householder reflectors \f$ H_i \f$ such that:
+  *       \f$ Q = H_{N-1} \ldots H_1 H_0 \f$.
+  * The Householder reflectors are defined as
+  *       \f$ H_i = (I - h_i v_i v_i^T) \f$
+  * where \f$ h_i = hCoeffs[i]\f$ is the \f$ i \f$th Householder coefficient and
+  * \f$ v_i \f$ is the Householder vector defined by
+  *       \f$ v_i = [ 0, \ldots, 0, 1, matA(i+2,i), \ldots, matA(N-1,i) ]^T \f$.
+  *
+  * Implemented from Golub's "Matrix Computations", algorithm 8.3.1.
+  *
+  * \sa Tridiagonalization::packedMatrix()
+  */
+template<typename MatrixType, typename CoeffVectorType>
+EIGEN_DEVICE_FUNC
+void tridiagonalization_inplace(MatrixType& matA, CoeffVectorType& hCoeffs)
+{
+  using numext::conj;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  Index n = matA.rows();
+  eigen_assert(n==matA.cols());
+  eigen_assert(n==hCoeffs.size()+1 || n==1);
+
+  for (Index i = 0; i<n-1; ++i)
+  {
+    Index remainingSize = n-i-1;
+    RealScalar beta;
+    Scalar h;
+    matA.col(i).tail(remainingSize).makeHouseholderInPlace(h, beta);
+
+    // Apply similarity transformation to remaining columns,
+    // i.e., A = H A H' where H = I - h v v' and v = matA.col(i).tail(n-i-1)
+    matA.col(i).coeffRef(i+1) = 1;
+
+    hCoeffs.tail(n-i-1).noalias() = (matA.bottomRightCorner(remainingSize,remainingSize).template selfadjointView<Lower>()
+                                  * (conj(h) * matA.col(i).tail(remainingSize)));
+
+    hCoeffs.tail(n-i-1) += (conj(h)*RealScalar(-0.5)*(hCoeffs.tail(remainingSize).dot(matA.col(i).tail(remainingSize)))) * matA.col(i).tail(n-i-1);
+
+    matA.bottomRightCorner(remainingSize, remainingSize).template selfadjointView<Lower>()
+      .rankUpdate(matA.col(i).tail(remainingSize), hCoeffs.tail(remainingSize), Scalar(-1));
+
+    matA.col(i).coeffRef(i+1) = beta;
+    hCoeffs.coeffRef(i) = h;
+  }
+}
+
+// forward declaration, implementation at the end of this file
+template<typename MatrixType,
+         int Size=MatrixType::ColsAtCompileTime,
+         bool IsComplex=NumTraits<typename MatrixType::Scalar>::IsComplex>
+struct tridiagonalization_inplace_selector;
+
+/** \brief Performs a full tridiagonalization in place
+  *
+  * \param[in,out]  mat  On input, the selfadjoint matrix whose tridiagonal
+  *    decomposition is to be computed. Only the lower triangular part referenced.
+  *    The rest is left unchanged. On output, the orthogonal matrix Q
+  *    in the decomposition if \p extractQ is true.
+  * \param[out]  diag  The diagonal of the tridiagonal matrix T in the
+  *    decomposition.
+  * \param[out]  subdiag  The subdiagonal of the tridiagonal matrix T in
+  *    the decomposition.
+  * \param[in]  extractQ  If true, the orthogonal matrix Q in the
+  *    decomposition is computed and stored in \p mat.
+  *
+  * Computes the tridiagonal decomposition of the selfadjoint matrix \p mat in place
+  * such that \f$ mat = Q T Q^* \f$ where \f$ Q \f$ is unitary and \f$ T \f$ a real
+  * symmetric tridiagonal matrix.
+  *
+  * The tridiagonal matrix T is passed to the output parameters \p diag and \p subdiag. If
+  * \p extractQ is true, then the orthogonal matrix Q is passed to \p mat. Otherwise the lower
+  * part of the matrix \p mat is destroyed.
+  *
+  * The vectors \p diag and \p subdiag are not resized. The function
+  * assumes that they are already of the correct size. The length of the
+  * vector \p diag should equal the number of rows in \p mat, and the
+  * length of the vector \p subdiag should be one left.
+  *
+  * This implementation contains an optimized path for 3-by-3 matrices
+  * which is especially useful for plane fitting.
+  *
+  * \note Currently, it requires two temporary vectors to hold the intermediate
+  * Householder coefficients, and to reconstruct the matrix Q from the Householder
+  * reflectors.
+  *
+  * Example (this uses the same matrix as the example in
+  *    Tridiagonalization::Tridiagonalization(const MatrixType&)):
+  *    \include Tridiagonalization_decomposeInPlace.cpp
+  * Output: \verbinclude Tridiagonalization_decomposeInPlace.out
+  *
+  * \sa class Tridiagonalization
+  */
+template<typename MatrixType, typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType, typename WorkSpaceType>
+EIGEN_DEVICE_FUNC
+void tridiagonalization_inplace(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag,
+                                CoeffVectorType& hcoeffs, WorkSpaceType& workspace, bool extractQ)
+{
+  eigen_assert(mat.cols()==mat.rows() && diag.size()==mat.rows() && subdiag.size()==mat.rows()-1);
+  tridiagonalization_inplace_selector<MatrixType>::run(mat, diag, subdiag, hcoeffs, workspace, extractQ);
+}
+
+/** \internal
+  * General full tridiagonalization
+  */
+template<typename MatrixType, int Size, bool IsComplex>
+struct tridiagonalization_inplace_selector
+{
+  typedef typename Tridiagonalization<MatrixType>::HouseholderSequenceType HouseholderSequenceType;
+  template<typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType, typename WorkSpaceType>
+  static EIGEN_DEVICE_FUNC
+      void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag, CoeffVectorType& hCoeffs, WorkSpaceType& workspace, bool extractQ)
+  {
+    tridiagonalization_inplace(mat, hCoeffs);
+    diag = mat.diagonal().real();
+    subdiag = mat.template diagonal<-1>().real();
+    if (extractQ) {
+      HouseholderSequenceType(mat, hCoeffs.conjugate())
+          .setLength(mat.rows() - 1)
+          .setShift(1)
+          .evalTo(mat, workspace);
+    }
+  }
+};
+
+/** \internal
+  * Specialization for 3x3 real matrices.
+  * Especially useful for plane fitting.
+  */
+template<typename MatrixType>
+struct tridiagonalization_inplace_selector<MatrixType,3,false>
+{
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+
+  template<typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType, typename WorkSpaceType>
+  static void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType& subdiag, CoeffVectorType&, WorkSpaceType&, bool extractQ)
+  {
+    using std::sqrt;
+    const RealScalar tol = (std::numeric_limits<RealScalar>::min)();
+    diag[0] = mat(0,0);
+    RealScalar v1norm2 = numext::abs2(mat(2,0));
+    if(v1norm2 <= tol)
+    {
+      diag[1] = mat(1,1);
+      diag[2] = mat(2,2);
+      subdiag[0] = mat(1,0);
+      subdiag[1] = mat(2,1);
+      if (extractQ)
+        mat.setIdentity();
+    }
+    else
+    {
+      RealScalar beta = sqrt(numext::abs2(mat(1,0)) + v1norm2);
+      RealScalar invBeta = RealScalar(1)/beta;
+      Scalar m01 = mat(1,0) * invBeta;
+      Scalar m02 = mat(2,0) * invBeta;
+      Scalar q = RealScalar(2)*m01*mat(2,1) + m02*(mat(2,2) - mat(1,1));
+      diag[1] = mat(1,1) + m02*q;
+      diag[2] = mat(2,2) - m02*q;
+      subdiag[0] = beta;
+      subdiag[1] = mat(2,1) - m01 * q;
+      if (extractQ)
+      {
+        mat << 1,   0,    0,
+               0, m01,  m02,
+               0, m02, -m01;
+      }
+    }
+  }
+};
+
+/** \internal
+  * Trivial specialization for 1x1 matrices
+  */
+template<typename MatrixType, bool IsComplex>
+struct tridiagonalization_inplace_selector<MatrixType,1,IsComplex>
+{
+  typedef typename MatrixType::Scalar Scalar;
+
+  template<typename DiagonalType, typename SubDiagonalType, typename CoeffVectorType, typename WorkSpaceType>
+  static EIGEN_DEVICE_FUNC
+  void run(MatrixType& mat, DiagonalType& diag, SubDiagonalType&, CoeffVectorType&, WorkSpaceType&, bool extractQ)
+  {
+    diag(0,0) = numext::real(mat(0,0));
+    if(extractQ)
+      mat(0,0) = Scalar(1);
+  }
+};
+
+/** \internal
+  * \eigenvalues_module \ingroup Eigenvalues_Module
+  *
+  * \brief Expression type for return value of Tridiagonalization::matrixT()
+  *
+  * \tparam MatrixType type of underlying dense matrix
+  */
+template<typename MatrixType> struct TridiagonalizationMatrixTReturnType
+: public ReturnByValue<TridiagonalizationMatrixTReturnType<MatrixType> >
+{
+  public:
+    /** \brief Constructor.
+      *
+      * \param[in] mat The underlying dense matrix
+      */
+    TridiagonalizationMatrixTReturnType(const MatrixType& mat) : m_matrix(mat) { }
+
+    template <typename ResultType>
+    inline void evalTo(ResultType& result) const
+    {
+      result.setZero();
+      result.template diagonal<1>() = m_matrix.template diagonal<-1>().conjugate();
+      result.diagonal() = m_matrix.diagonal();
+      result.template diagonal<-1>() = m_matrix.template diagonal<-1>();
+    }
+
+    EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows(); }
+    EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
+
+  protected:
+    typename MatrixType::Nested m_matrix;
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRIDIAGONALIZATION_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/AlignedBox.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/AlignedBox.h
new file mode 100644
index 0000000000000000000000000000000000000000..a8248171406ee4a2c64d325b83d38be7d66f0ba3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/AlignedBox.h
@@ -0,0 +1,488 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// Function void Eigen::AlignedBox::transform(const Transform& transform)
+// is provided under the following license agreement:
+//
+// Software License Agreement (BSD License)
+//
+// Copyright (c) 2011-2014, Willow Garage, Inc.
+// Copyright (c) 2014-2015, Open Source Robotics Foundation
+// All rights reserved.
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions
+// are met:
+//
+//  * Redistributions of source code must retain the above copyright
+//    notice, this list of conditions and the following disclaimer.
+//  * Redistributions in binary form must reproduce the above
+//    copyright notice, this list of conditions and the following
+//    disclaimer in the documentation and/or other materials provided
+//    with the distribution.
+//  * Neither the name of Open Source Robotics Foundation nor the names of its
+//    contributors may be used to endorse or promote products derived
+//    from this software without specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+// FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+// COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+// INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+// BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+// LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+// CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+// LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
+
+#ifndef EIGEN_ALIGNEDBOX_H
+#define EIGEN_ALIGNEDBOX_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  *
+  * \class AlignedBox
+  *
+  * \brief An axis aligned box
+  *
+  * \tparam Scalar_ the type of the scalar coefficients
+  * \tparam AmbientDim_ the dimension of the ambient space, can be a compile time value or Dynamic.
+  *
+  * This class represents an axis aligned box as a pair of the minimal and maximal corners.
+  * \warning The result of most methods is undefined when applied to an empty box. You can check for empty boxes using isEmpty().
+  * \sa alignedboxtypedefs
+  */
+template <typename Scalar_, int AmbientDim_>
+class AlignedBox
+{
+public:
+EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar_,AmbientDim_)
+  enum { AmbientDimAtCompileTime = AmbientDim_ };
+  typedef Scalar_                                   Scalar;
+  typedef NumTraits<Scalar>                         ScalarTraits;
+  typedef Eigen::Index                              Index; ///< \deprecated since Eigen 3.3
+  typedef typename ScalarTraits::Real               RealScalar;
+  typedef typename ScalarTraits::NonInteger         NonInteger;
+  typedef Matrix<Scalar,AmbientDimAtCompileTime,1>  VectorType;
+  typedef CwiseBinaryOp<internal::scalar_sum_op<Scalar>, const VectorType, const VectorType> VectorTypeSum;
+
+  /** Define constants to name the corners of a 1D, 2D or 3D axis aligned bounding box */
+  enum CornerType
+  {
+    /** 1D names @{ */
+    Min=0, Max=1,
+    /** @} */
+
+    /** Identifier for 2D corner @{ */
+    BottomLeft=0, BottomRight=1,
+    TopLeft=2, TopRight=3,
+    /** @} */
+
+    /** Identifier for 3D corner  @{ */
+    BottomLeftFloor=0, BottomRightFloor=1,
+    TopLeftFloor=2, TopRightFloor=3,
+    BottomLeftCeil=4, BottomRightCeil=5,
+    TopLeftCeil=6, TopRightCeil=7
+    /** @} */
+  };
+
+
+  /** Default constructor initializing a null box. */
+  EIGEN_DEVICE_FUNC inline AlignedBox()
+  { if (EIGEN_CONST_CONDITIONAL(AmbientDimAtCompileTime!=Dynamic)) setEmpty(); }
+
+  /** Constructs a null box with \a _dim the dimension of the ambient space. */
+  EIGEN_DEVICE_FUNC inline explicit AlignedBox(Index _dim) : m_min(_dim), m_max(_dim)
+  { setEmpty(); }
+
+  /** Constructs a box with extremities \a _min and \a _max.
+   * \warning If either component of \a _min is larger than the same component of \a _max, the constructed box is empty. */
+  template<typename OtherVectorType1, typename OtherVectorType2>
+  EIGEN_DEVICE_FUNC inline AlignedBox(const OtherVectorType1& _min, const OtherVectorType2& _max) : m_min(_min), m_max(_max) {}
+
+  /** Constructs a box containing a single point \a p. */
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC inline explicit AlignedBox(const MatrixBase<Derived>& p) : m_min(p), m_max(m_min)
+  { }
+
+  EIGEN_DEVICE_FUNC ~AlignedBox() {}
+
+  /** \returns the dimension in which the box holds */
+  EIGEN_DEVICE_FUNC inline Index dim() const { return AmbientDimAtCompileTime==Dynamic ? m_min.size() : Index(AmbientDimAtCompileTime); }
+
+  /** \deprecated use isEmpty() */
+  EIGEN_DEVICE_FUNC inline bool isNull() const { return isEmpty(); }
+
+  /** \deprecated use setEmpty() */
+  EIGEN_DEVICE_FUNC inline void setNull() { setEmpty(); }
+
+  /** \returns true if the box is empty.
+   * \sa setEmpty */
+  EIGEN_DEVICE_FUNC inline bool isEmpty() const { return (m_min.array() > m_max.array()).any(); }
+
+  /** Makes \c *this an empty box.
+   * \sa isEmpty */
+  EIGEN_DEVICE_FUNC inline void setEmpty()
+  {
+    m_min.setConstant( ScalarTraits::highest() );
+    m_max.setConstant( ScalarTraits::lowest() );
+  }
+
+  /** \returns the minimal corner */
+  EIGEN_DEVICE_FUNC inline const VectorType& (min)() const { return m_min; }
+  /** \returns a non const reference to the minimal corner */
+  EIGEN_DEVICE_FUNC inline VectorType& (min)() { return m_min; }
+  /** \returns the maximal corner */
+  EIGEN_DEVICE_FUNC inline const VectorType& (max)() const { return m_max; }
+  /** \returns a non const reference to the maximal corner */
+  EIGEN_DEVICE_FUNC inline VectorType& (max)() { return m_max; }
+
+  /** \returns the center of the box */
+  EIGEN_DEVICE_FUNC inline const EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(VectorTypeSum, RealScalar, quotient)
+  center() const
+  { return (m_min+m_max)/RealScalar(2); }
+
+  /** \returns the lengths of the sides of the bounding box.
+    * Note that this function does not get the same
+    * result for integral or floating scalar types: see
+    */
+  EIGEN_DEVICE_FUNC inline const CwiseBinaryOp< internal::scalar_difference_op<Scalar,Scalar>, const VectorType, const VectorType> sizes() const
+  { return m_max - m_min; }
+
+  /** \returns the volume of the bounding box */
+  EIGEN_DEVICE_FUNC inline Scalar volume() const
+  { return sizes().prod(); }
+
+  /** \returns an expression for the bounding box diagonal vector
+    * if the length of the diagonal is needed: diagonal().norm()
+    * will provide it.
+    */
+  EIGEN_DEVICE_FUNC inline CwiseBinaryOp< internal::scalar_difference_op<Scalar,Scalar>, const VectorType, const VectorType> diagonal() const
+  { return sizes(); }
+
+  /** \returns the vertex of the bounding box at the corner defined by
+    * the corner-id corner. It works only for a 1D, 2D or 3D bounding box.
+    * For 1D bounding boxes corners are named by 2 enum constants:
+    * BottomLeft and BottomRight.
+    * For 2D bounding boxes, corners are named by 4 enum constants:
+    * BottomLeft, BottomRight, TopLeft, TopRight.
+    * For 3D bounding boxes, the following names are added:
+    * BottomLeftCeil, BottomRightCeil, TopLeftCeil, TopRightCeil.
+    */
+  EIGEN_DEVICE_FUNC inline VectorType corner(CornerType corner) const
+  {
+    EIGEN_STATIC_ASSERT(AmbientDim_ <= 3, THIS_METHOD_IS_ONLY_FOR_VECTORS_OF_A_SPECIFIC_SIZE);
+
+    VectorType res;
+
+    Index mult = 1;
+    for(Index d=0; d<dim(); ++d)
+    {
+      if( mult & corner ) res[d] = m_max[d];
+      else                res[d] = m_min[d];
+      mult *= 2;
+    }
+    return res;
+  }
+
+  /** \returns a random point inside the bounding box sampled with
+   * a uniform distribution */
+  EIGEN_DEVICE_FUNC inline VectorType sample() const
+  {
+    VectorType r(dim());
+    for(Index d=0; d<dim(); ++d)
+    {
+      if(!ScalarTraits::IsInteger)
+      {
+        r[d] = m_min[d] + (m_max[d]-m_min[d])
+             * internal::random<Scalar>(Scalar(0), Scalar(1));
+      }
+      else
+        r[d] = internal::random(m_min[d], m_max[d]);
+    }
+    return r;
+  }
+
+  /** \returns true if the point \a p is inside the box \c *this. */
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC inline bool contains(const MatrixBase<Derived>& p) const
+  {
+    typename internal::nested_eval<Derived,2>::type p_n(p.derived());
+    return (m_min.array()<=p_n.array()).all() && (p_n.array()<=m_max.array()).all();
+  }
+
+  /** \returns true if the box \a b is entirely inside the box \c *this. */
+  EIGEN_DEVICE_FUNC inline bool contains(const AlignedBox& b) const
+  { return (m_min.array()<=(b.min)().array()).all() && ((b.max)().array()<=m_max.array()).all(); }
+
+  /** \returns true if the box \a b is intersecting the box \c *this.
+   * \sa intersection, clamp */
+  EIGEN_DEVICE_FUNC inline bool intersects(const AlignedBox& b) const
+  { return (m_min.array()<=(b.max)().array()).all() && ((b.min)().array()<=m_max.array()).all(); }
+
+  /** Extends \c *this such that it contains the point \a p and returns a reference to \c *this.
+   * \sa extend(const AlignedBox&) */
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC inline AlignedBox& extend(const MatrixBase<Derived>& p)
+  {
+    typename internal::nested_eval<Derived,2>::type p_n(p.derived());
+    m_min = m_min.cwiseMin(p_n);
+    m_max = m_max.cwiseMax(p_n);
+    return *this;
+  }
+
+  /** Extends \c *this such that it contains the box \a b and returns a reference to \c *this.
+   * \sa merged, extend(const MatrixBase&) */
+  EIGEN_DEVICE_FUNC inline AlignedBox& extend(const AlignedBox& b)
+  {
+    m_min = m_min.cwiseMin(b.m_min);
+    m_max = m_max.cwiseMax(b.m_max);
+    return *this;
+  }
+
+  /** Clamps \c *this by the box \a b and returns a reference to \c *this.
+   * \note If the boxes don't intersect, the resulting box is empty.
+   * \sa intersection(), intersects() */
+  EIGEN_DEVICE_FUNC inline AlignedBox& clamp(const AlignedBox& b)
+  {
+    m_min = m_min.cwiseMax(b.m_min);
+    m_max = m_max.cwiseMin(b.m_max);
+    return *this;
+  }
+
+  /** Returns an AlignedBox that is the intersection of \a b and \c *this
+   * \note If the boxes don't intersect, the resulting box is empty.
+   * \sa intersects(), clamp, contains()  */
+  EIGEN_DEVICE_FUNC inline AlignedBox intersection(const AlignedBox& b) const
+  {return AlignedBox(m_min.cwiseMax(b.m_min), m_max.cwiseMin(b.m_max)); }
+
+  /** Returns an AlignedBox that is the union of \a b and \c *this.
+   * \note Merging with an empty box may result in a box bigger than \c *this.
+   * \sa extend(const AlignedBox&) */
+  EIGEN_DEVICE_FUNC inline AlignedBox merged(const AlignedBox& b) const
+  { return AlignedBox(m_min.cwiseMin(b.m_min), m_max.cwiseMax(b.m_max)); }
+
+  /** Translate \c *this by the vector \a t and returns a reference to \c *this. */
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC inline AlignedBox& translate(const MatrixBase<Derived>& a_t)
+  {
+    const typename internal::nested_eval<Derived,2>::type t(a_t.derived());
+    m_min += t;
+    m_max += t;
+    return *this;
+  }
+
+  /** \returns a copy of \c *this translated by the vector \a t. */
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC inline AlignedBox translated(const MatrixBase<Derived>& a_t) const
+  {
+    AlignedBox result(m_min, m_max);
+    result.translate(a_t);
+    return result;
+  }
+
+  /** \returns the squared distance between the point \a p and the box \c *this,
+    * and zero if \a p is inside the box.
+    * \sa exteriorDistance(const MatrixBase&), squaredExteriorDistance(const AlignedBox&)
+    */
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC inline Scalar squaredExteriorDistance(const MatrixBase<Derived>& p) const;
+
+  /** \returns the squared distance between the boxes \a b and \c *this,
+    * and zero if the boxes intersect.
+    * \sa exteriorDistance(const AlignedBox&), squaredExteriorDistance(const MatrixBase&)
+    */
+  EIGEN_DEVICE_FUNC inline Scalar squaredExteriorDistance(const AlignedBox& b) const;
+
+  /** \returns the distance between the point \a p and the box \c *this,
+    * and zero if \a p is inside the box.
+    * \sa squaredExteriorDistance(const MatrixBase&), exteriorDistance(const AlignedBox&)
+    */
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC inline NonInteger exteriorDistance(const MatrixBase<Derived>& p) const
+  { EIGEN_USING_STD(sqrt) return sqrt(NonInteger(squaredExteriorDistance(p))); }
+
+  /** \returns the distance between the boxes \a b and \c *this,
+    * and zero if the boxes intersect.
+    * \sa squaredExteriorDistance(const AlignedBox&), exteriorDistance(const MatrixBase&)
+    */
+  EIGEN_DEVICE_FUNC inline NonInteger exteriorDistance(const AlignedBox& b) const
+  { EIGEN_USING_STD(sqrt) return sqrt(NonInteger(squaredExteriorDistance(b))); }
+
+  /**
+   * Specialization of transform for pure translation.
+   */
+  template<int Mode, int Options>
+  EIGEN_DEVICE_FUNC inline void transform(
+      const typename Transform<Scalar, AmbientDimAtCompileTime, Mode, Options>::TranslationType& translation)
+  {
+    this->translate(translation);
+  }
+
+  /**
+   * Transforms this box by \a transform and recomputes it to
+   * still be an axis-aligned box.
+   *
+   * \note This method is provided under BSD license (see the top of this file).
+   */
+  template<int Mode, int Options>
+  EIGEN_DEVICE_FUNC inline void transform(const Transform<Scalar, AmbientDimAtCompileTime, Mode, Options>& transform)
+  {
+    // Only Affine and Isometry transforms are currently supported.
+    EIGEN_STATIC_ASSERT(Mode == Affine || Mode == AffineCompact || Mode == Isometry, THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS);
+
+    // Method adapted from FCL src/shape/geometric_shapes_utility.cpp#computeBV<AABB, Box>(...)
+    // https://github.com/flexible-collision-library/fcl/blob/fcl-0.4/src/shape/geometric_shapes_utility.cpp#L292
+    //
+    // Here's a nice explanation why it works: https://zeuxcg.org/2010/10/17/aabb-from-obb-with-component-wise-abs/
+
+    // two times rotated extent
+    const VectorType rotated_extent_2 = transform.linear().cwiseAbs() * sizes();
+    // two times new center
+    const VectorType rotated_center_2 = transform.linear() * (this->m_max + this->m_min) +
+        Scalar(2) * transform.translation();
+
+    this->m_max = (rotated_center_2 + rotated_extent_2) / Scalar(2);
+    this->m_min = (rotated_center_2 - rotated_extent_2) / Scalar(2);
+  }
+
+  /**
+   * \returns a copy of \c *this transformed by \a transform and recomputed to
+   * still be an axis-aligned box.
+   */
+  template<int Mode, int Options>
+  EIGEN_DEVICE_FUNC AlignedBox transformed(const Transform<Scalar, AmbientDimAtCompileTime, Mode, Options>& transform) const
+  {
+    AlignedBox result(m_min, m_max);
+    result.transform(transform);
+    return result;
+  }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<AlignedBox,
+           AlignedBox<NewScalarType,AmbientDimAtCompileTime> >::type cast() const
+  {
+    return typename internal::cast_return_type<AlignedBox,
+                    AlignedBox<NewScalarType,AmbientDimAtCompileTime> >::type(*this);
+  }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType>
+  EIGEN_DEVICE_FUNC inline explicit AlignedBox(const AlignedBox<OtherScalarType,AmbientDimAtCompileTime>& other)
+  {
+    m_min = (other.min)().template cast<Scalar>();
+    m_max = (other.max)().template cast<Scalar>();
+  }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  EIGEN_DEVICE_FUNC bool isApprox(const AlignedBox& other, const RealScalar& prec = ScalarTraits::dummy_precision()) const
+  { return m_min.isApprox(other.m_min, prec) && m_max.isApprox(other.m_max, prec); }
+
+protected:
+
+  VectorType m_min, m_max;
+};
+
+
+
+template<typename Scalar,int AmbientDim>
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline Scalar AlignedBox<Scalar,AmbientDim>::squaredExteriorDistance(const MatrixBase<Derived>& a_p) const
+{
+  typename internal::nested_eval<Derived,2*AmbientDim>::type p(a_p.derived());
+  Scalar dist2(0);
+  Scalar aux;
+  for (Index k=0; k<dim(); ++k)
+  {
+    if( m_min[k] > p[k] )
+    {
+      aux = m_min[k] - p[k];
+      dist2 += aux*aux;
+    }
+    else if( p[k] > m_max[k] )
+    {
+      aux = p[k] - m_max[k];
+      dist2 += aux*aux;
+    }
+  }
+  return dist2;
+}
+
+template<typename Scalar,int AmbientDim>
+EIGEN_DEVICE_FUNC inline Scalar AlignedBox<Scalar,AmbientDim>::squaredExteriorDistance(const AlignedBox& b) const
+{
+  Scalar dist2(0);
+  Scalar aux;
+  for (Index k=0; k<dim(); ++k)
+  {
+    if( m_min[k] > b.m_max[k] )
+    {
+      aux = m_min[k] - b.m_max[k];
+      dist2 += aux*aux;
+    }
+    else if( b.m_min[k] > m_max[k] )
+    {
+      aux = b.m_min[k] - m_max[k];
+      dist2 += aux*aux;
+    }
+  }
+  return dist2;
+}
+
+/** \defgroup alignedboxtypedefs Global aligned box typedefs
+  *
+  * \ingroup Geometry_Module
+  *
+  * Eigen defines several typedef shortcuts for most common aligned box types.
+  *
+  * The general patterns are the following:
+  *
+  * \c AlignedBoxSizeType where \c Size can be \c 1, \c 2,\c 3,\c 4 for fixed size boxes or \c X for dynamic size,
+  * and where \c Type can be \c i for integer, \c f for float, \c d for double.
+  *
+  * For example, \c AlignedBox3d is a fixed-size 3x3 aligned box type of doubles, and \c AlignedBoxXf is a dynamic-size aligned box of floats.
+  *
+  * \sa class AlignedBox
+  */
+
+#define EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix)    \
+/** \ingroup alignedboxtypedefs */                                 \
+typedef AlignedBox<Type, Size>   AlignedBox##SizeSuffix##TypeSuffix;
+
+#define EIGEN_MAKE_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
+EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 1, 1) \
+EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 2, 2) \
+EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 3, 3) \
+EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, 4, 4) \
+EIGEN_MAKE_TYPEDEFS(Type, TypeSuffix, Dynamic, X)
+
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(int,                  i)
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(float,                f)
+EIGEN_MAKE_TYPEDEFS_ALL_SIZES(double,               d)
+
+#undef EIGEN_MAKE_TYPEDEFS_ALL_SIZES
+#undef EIGEN_MAKE_TYPEDEFS
+
+} // end namespace Eigen
+
+#endif // EIGEN_ALIGNEDBOX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/AngleAxis.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/AngleAxis.h
new file mode 100644
index 0000000000000000000000000000000000000000..c23a908ea821490d2791ba62bf8b9e0ec9e8fd09
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/AngleAxis.h
@@ -0,0 +1,249 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ANGLEAXIS_H
+#define EIGEN_ANGLEAXIS_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \class AngleAxis
+  *
+  * \brief Represents a 3D rotation as a rotation angle around an arbitrary 3D axis
+  *
+  * \param Scalar_ the scalar type, i.e., the type of the coefficients.
+  *
+  * \warning When setting up an AngleAxis object, the axis vector \b must \b be \b normalized.
+  *
+  * The following two typedefs are provided for convenience:
+  * \li \c AngleAxisf for \c float
+  * \li \c AngleAxisd for \c double
+  *
+  * Combined with MatrixBase::Unit{X,Y,Z}, AngleAxis can be used to easily
+  * mimic Euler-angles. Here is an example:
+  * \include AngleAxis_mimic_euler.cpp
+  * Output: \verbinclude AngleAxis_mimic_euler.out
+  *
+  * \note This class is not aimed to be used to store a rotation transformation,
+  * but rather to make easier the creation of other rotation (Quaternion, rotation Matrix)
+  * and transformation objects.
+  *
+  * \sa class Quaternion, class Transform, MatrixBase::UnitX()
+  */
+
+namespace internal {
+template<typename Scalar_> struct traits<AngleAxis<Scalar_> >
+{
+  typedef Scalar_ Scalar;
+};
+}
+
+template<typename Scalar_>
+class AngleAxis : public RotationBase<AngleAxis<Scalar_>,3>
+{
+  typedef RotationBase<AngleAxis<Scalar_>,3> Base;
+
+public:
+
+  using Base::operator*;
+
+  enum { Dim = 3 };
+  /** the scalar type of the coefficients */
+  typedef Scalar_ Scalar;
+  typedef Matrix<Scalar,3,3> Matrix3;
+  typedef Matrix<Scalar,3,1> Vector3;
+  typedef Quaternion<Scalar> QuaternionType;
+
+protected:
+
+  Vector3 m_axis;
+  Scalar m_angle;
+
+public:
+
+  /** Default constructor without initialization. */
+  EIGEN_DEVICE_FUNC AngleAxis() {}
+  /** Constructs and initialize the angle-axis rotation from an \a angle in radian
+    * and an \a axis which \b must \b be \b normalized.
+    *
+    * \warning If the \a axis vector is not normalized, then the angle-axis object
+    *          represents an invalid rotation. */
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC 
+  inline AngleAxis(const Scalar& angle, const MatrixBase<Derived>& axis) : m_axis(axis), m_angle(angle) {}
+  /** Constructs and initialize the angle-axis rotation from a quaternion \a q.
+    * This function implicitly normalizes the quaternion \a q.
+    */
+  template<typename QuatDerived> 
+  EIGEN_DEVICE_FUNC inline explicit AngleAxis(const QuaternionBase<QuatDerived>& q) { *this = q; }
+  /** Constructs and initialize the angle-axis rotation from a 3x3 rotation matrix. */
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC inline explicit AngleAxis(const MatrixBase<Derived>& m) { *this = m; }
+
+  /** \returns the value of the rotation angle in radian */
+  EIGEN_DEVICE_FUNC Scalar angle() const { return m_angle; }
+  /** \returns a read-write reference to the stored angle in radian */
+  EIGEN_DEVICE_FUNC Scalar& angle() { return m_angle; }
+
+  /** \returns the rotation axis */
+  EIGEN_DEVICE_FUNC const Vector3& axis() const { return m_axis; }
+  /** \returns a read-write reference to the stored rotation axis.
+    *
+    * \warning The rotation axis must remain a \b unit vector.
+    */
+  EIGEN_DEVICE_FUNC Vector3& axis() { return m_axis; }
+
+  /** Concatenates two rotations */
+  EIGEN_DEVICE_FUNC inline QuaternionType operator* (const AngleAxis& other) const
+  { return QuaternionType(*this) * QuaternionType(other); }
+
+  /** Concatenates two rotations */
+  EIGEN_DEVICE_FUNC inline QuaternionType operator* (const QuaternionType& other) const
+  { return QuaternionType(*this) * other; }
+
+  /** Concatenates two rotations */
+  friend EIGEN_DEVICE_FUNC inline QuaternionType operator* (const QuaternionType& a, const AngleAxis& b)
+  { return a * QuaternionType(b); }
+
+  /** \returns the inverse rotation, i.e., an angle-axis with opposite rotation angle */
+  EIGEN_DEVICE_FUNC AngleAxis inverse() const
+  { return AngleAxis(-m_angle, m_axis); }
+
+  template<class QuatDerived>
+  EIGEN_DEVICE_FUNC AngleAxis& operator=(const QuaternionBase<QuatDerived>& q);
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC AngleAxis& operator=(const MatrixBase<Derived>& m);
+
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC AngleAxis& fromRotationMatrix(const MatrixBase<Derived>& m);
+  EIGEN_DEVICE_FUNC Matrix3 toRotationMatrix(void) const;
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<AngleAxis,AngleAxis<NewScalarType> >::type cast() const
+  { return typename internal::cast_return_type<AngleAxis,AngleAxis<NewScalarType> >::type(*this); }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType>
+  EIGEN_DEVICE_FUNC inline explicit AngleAxis(const AngleAxis<OtherScalarType>& other)
+  {
+    m_axis = other.axis().template cast<Scalar>();
+    m_angle = Scalar(other.angle());
+  }
+
+  EIGEN_DEVICE_FUNC static inline const AngleAxis Identity() { return AngleAxis(Scalar(0), Vector3::UnitX()); }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  EIGEN_DEVICE_FUNC bool isApprox(const AngleAxis& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
+  { return m_axis.isApprox(other.m_axis, prec) && internal::isApprox(m_angle,other.m_angle, prec); }
+};
+
+/** \ingroup Geometry_Module
+  * single precision angle-axis type */
+typedef AngleAxis<float> AngleAxisf;
+/** \ingroup Geometry_Module
+  * double precision angle-axis type */
+typedef AngleAxis<double> AngleAxisd;
+
+/** Set \c *this from a \b unit quaternion.
+  *
+  * The resulting axis is normalized, and the computed angle is in the [0,pi] range.
+  * 
+  * This function implicitly normalizes the quaternion \a q.
+  */
+template<typename Scalar>
+template<typename QuatDerived>
+EIGEN_DEVICE_FUNC AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const QuaternionBase<QuatDerived>& q)
+{
+  EIGEN_USING_STD(atan2)
+  EIGEN_USING_STD(abs)
+  Scalar n = q.vec().norm();
+  if(n<NumTraits<Scalar>::epsilon())
+    n = q.vec().stableNorm();
+
+  if (n != Scalar(0))
+  {
+    m_angle = Scalar(2)*atan2(n, abs(q.w()));
+    if(q.w() < Scalar(0))
+      n = -n;
+    m_axis  = q.vec() / n;
+  }
+  else
+  {
+    m_angle = Scalar(0);
+    m_axis << Scalar(1), Scalar(0), Scalar(0);
+  }
+  return *this;
+}
+
+/** Set \c *this from a 3x3 rotation matrix \a mat.
+  */
+template<typename Scalar>
+template<typename Derived>
+EIGEN_DEVICE_FUNC AngleAxis<Scalar>& AngleAxis<Scalar>::operator=(const MatrixBase<Derived>& mat)
+{
+  // Since a direct conversion would not be really faster,
+  // let's use the robust Quaternion implementation:
+  return *this = QuaternionType(mat);
+}
+
+/**
+* \brief Sets \c *this from a 3x3 rotation matrix.
+**/
+template<typename Scalar>
+template<typename Derived>
+EIGEN_DEVICE_FUNC AngleAxis<Scalar>& AngleAxis<Scalar>::fromRotationMatrix(const MatrixBase<Derived>& mat)
+{
+  return *this = QuaternionType(mat);
+}
+
+/** Constructs and \returns an equivalent 3x3 rotation matrix.
+  */
+template<typename Scalar>
+typename AngleAxis<Scalar>::Matrix3
+EIGEN_DEVICE_FUNC AngleAxis<Scalar>::toRotationMatrix(void) const
+{
+  EIGEN_USING_STD(sin)
+  EIGEN_USING_STD(cos)
+  Matrix3 res;
+  Vector3 sin_axis  = sin(m_angle) * m_axis;
+  Scalar c = cos(m_angle);
+  Vector3 cos1_axis = (Scalar(1)-c) * m_axis;
+
+  Scalar tmp;
+  tmp = cos1_axis.x() * m_axis.y();
+  res.coeffRef(0,1) = tmp - sin_axis.z();
+  res.coeffRef(1,0) = tmp + sin_axis.z();
+
+  tmp = cos1_axis.x() * m_axis.z();
+  res.coeffRef(0,2) = tmp + sin_axis.y();
+  res.coeffRef(2,0) = tmp - sin_axis.y();
+
+  tmp = cos1_axis.y() * m_axis.z();
+  res.coeffRef(1,2) = tmp - sin_axis.x();
+  res.coeffRef(2,1) = tmp + sin_axis.x();
+
+  res.diagonal() = (cos1_axis.cwiseProduct(m_axis)).array() + c;
+
+  return res;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_ANGLEAXIS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/EulerAngles.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/EulerAngles.h
new file mode 100644
index 0000000000000000000000000000000000000000..b9cce671c997e50aade45373a4ce60f5339b93c0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/EulerAngles.h
@@ -0,0 +1,222 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2023 Juraj Oršulić, University of Zagreb <juraj.orsulic@fer.hr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EULERANGLES_H
+#define EIGEN_EULERANGLES_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  *
+  * \returns the canonical Euler-angles of the rotation matrix \c *this using the convention defined by the triplet (\a a0,\a a1,\a a2)
+  *
+  * Each of the three parameters \a a0,\a a1,\a a2 represents the respective rotation axis as an integer in {0,1,2}.
+  * For instance, in:
+  * \code Vector3f ea = mat.eulerAngles(2, 0, 2); \endcode
+  * "2" represents the z axis and "0" the x axis, etc. The returned angles are such that
+  * we have the following equality:
+  * \code
+  * mat == AngleAxisf(ea[0], Vector3f::UnitZ())
+  *      * AngleAxisf(ea[1], Vector3f::UnitX())
+  *      * AngleAxisf(ea[2], Vector3f::UnitZ()); \endcode
+  * This corresponds to the right-multiply conventions (with right hand side frames).
+  *
+  * For Tait-Bryan angle configurations (a0 != a2), the returned angles are in the ranges [-pi:pi]x[-pi/2:pi/2]x[-pi:pi].
+  * For proper Euler angle configurations (a0 == a2), the returned angles are in the ranges [-pi:pi]x[0:pi]x[-pi:pi].
+  *
+  * The approach used is also described here: https://d3cw3dd2w32x2b.cloudfront.net/wp-content/uploads/2012/07/euler-angles.pdf
+  *
+  * \sa class AngleAxis
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline Matrix<typename MatrixBase<Derived>::Scalar,3,1>
+MatrixBase<Derived>::canonicalEulerAngles(Index a0, Index a1, Index a2) const
+{
+  /* Implemented from Graphics Gems IV */
+  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Derived, 3, 3)
+
+  Matrix<Scalar, 3, 1> res;
+
+  const Index odd = ((a0 + 1) % 3 == a1) ? 0 : 1;
+  const Index i = a0;
+  const Index j = (a0 + 1 + odd) % 3;
+  const Index k = (a0 + 2 - odd) % 3;
+
+  if (a0 == a2)
+  {
+    // Proper Euler angles (same first and last axis).
+    // The i, j, k indices enable addressing the input matrix as the XYX archetype matrix (see Graphics Gems IV),
+    // where e.g. coeff(k, i) means third column, first row in the XYX archetype matrix:
+    //  c2      s2s1              s2c1
+    //  s2s3   -c2s1s3 + c1c3    -c2c1s3 - s1c3
+    // -s2c3    c2s1c3 + c1s3     c2c1c3 - s1s3
+
+    // Note: s2 is always positive.
+    Scalar s2 = numext::hypot(coeff(j, i), coeff(k, i));
+    if (odd)
+    {
+      res[0] = numext::atan2(coeff(j, i), coeff(k, i));
+      // s2 is always positive, so res[1] will be within the canonical [0, pi] range
+      res[1] = numext::atan2(s2, coeff(i, i));
+    }
+    else
+    {
+      // In the !odd case, signs of all three angles are flipped at the very end. To keep the solution within the canonical range,
+      // we flip the solution and make res[1] always negative here (since s2 is always positive, -atan2(s2, c2) will always be negative).
+      // The final flip at the end due to !odd will thus make res[1] positive and canonical.
+      // NB: in the general case, there are two correct solutions, but only one is canonical. For proper Euler angles,
+      // flipping from one solution to the other involves flipping the sign of the second angle res[1] and adding/subtracting pi
+      // to the first and third angles. The addition/subtraction of pi to the first angle res[0] is handled here by flipping
+      // the signs of arguments to atan2, while the calculation of the third angle does not need special adjustment since
+      // it uses the adjusted res[0] as the input and produces a correct result.
+      res[0] = numext::atan2(-coeff(j, i), -coeff(k, i));
+      res[1] = -numext::atan2(s2, coeff(i, i));
+    }
+
+    // With a=(0,1,0), we have i=0; j=1; k=2, and after computing the first two angles,
+    // we can compute their respective rotation, and apply its inverse to M. Since the result must
+    // be a rotation around x, we have:
+    //
+    //  c2  s1.s2 c1.s2                   1  0   0
+    //  0   c1    -s1       *    M    =   0  c3  s3
+    //  -s2 s1.c2 c1.c2                   0 -s3  c3
+    //
+    //  Thus:  m11.c1 - m21.s1 = c3  &   m12.c1 - m22.s1 = s3
+
+    Scalar s1 = numext::sin(res[0]);
+    Scalar c1 = numext::cos(res[0]);
+    res[2] = numext::atan2(c1 * coeff(j, k) - s1 * coeff(k, k), c1 * coeff(j, j) - s1 * coeff(k, j));
+  }
+  else
+  {
+    // Tait-Bryan angles (all three axes are different; typically used for yaw-pitch-roll calculations).
+    // The i, j, k indices enable addressing the input matrix as the XYZ archetype matrix (see Graphics Gems IV),
+    // where e.g. coeff(k, i) means third column, first row in the XYZ archetype matrix:
+    //  c2c3    s2s1c3 - c1s3     s2c1c3 + s1s3
+    //  c2s3    s2s1s3 + c1c3     s2c1s3 - s1c3
+    // -s2      c2s1              c2c1
+
+    res[0] = numext::atan2(coeff(j, k), coeff(k, k));
+
+    Scalar c2 = numext::hypot(coeff(i, i), coeff(i, j));
+    // c2 is always positive, so the following atan2 will always return a result in the correct canonical middle angle range [-pi/2, pi/2]
+    res[1] = numext::atan2(-coeff(i, k), c2);
+
+    Scalar s1 = numext::sin(res[0]);
+    Scalar c1 = numext::cos(res[0]);
+    res[2] = numext::atan2(s1 * coeff(k, i) - c1 * coeff(j, i), c1 * coeff(j, j) - s1 * coeff(k, j));
+  }
+  if (!odd)
+  {
+    res = -res;
+  }
+
+  return res;
+}
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  *
+  * \returns the Euler-angles of the rotation matrix \c *this using the convention defined by the triplet (\a a0,\a a1,\a a2)
+  *
+  * NB: The returned angles are in non-canonical ranges [0:pi]x[-pi:pi]x[-pi:pi]. For canonical Tait-Bryan/proper Euler ranges, use canonicalEulerAngles.
+  *
+  * \sa MatrixBase::canonicalEulerAngles
+  * \sa class AngleAxis
+  */
+template<typename Derived>
+EIGEN_DEPRECATED EIGEN_DEVICE_FUNC inline Matrix<typename MatrixBase<Derived>::Scalar,3,1>
+MatrixBase<Derived>::eulerAngles(Index a0, Index a1, Index a2) const
+{
+  /* Implemented from Graphics Gems IV */
+  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Derived, 3, 3)
+
+  Matrix<Scalar, 3, 1> res;
+
+  const Index odd = ((a0 + 1) % 3 == a1) ? 0 : 1;
+  const Index i = a0;
+  const Index j = (a0 + 1 + odd) % 3;
+  const Index k = (a0 + 2 - odd) % 3;
+
+  if (a0 == a2)
+  {
+    res[0] = numext::atan2(coeff(j, i), coeff(k, i));
+    if ((odd && res[0] < Scalar(0)) || ((!odd) && res[0] > Scalar(0)))
+    {
+      if (res[0] > Scalar(0))
+      {
+        res[0] -= Scalar(EIGEN_PI);
+      }
+      else
+      {
+        res[0] += Scalar(EIGEN_PI);
+      }
+
+      Scalar s2 = numext::hypot(coeff(j, i), coeff(k, i));
+      res[1] = -numext::atan2(s2, coeff(i, i));
+    }
+    else
+    {
+      Scalar s2 = numext::hypot(coeff(j, i), coeff(k, i));
+      res[1] = numext::atan2(s2, coeff(i, i));
+    }
+
+    // With a=(0,1,0), we have i=0; j=1; k=2, and after computing the first two angles,
+    // we can compute their respective rotation, and apply its inverse to M. Since the result must
+    // be a rotation around x, we have:
+    //
+    //  c2  s1.s2 c1.s2                   1  0   0
+    //  0   c1    -s1       *    M    =   0  c3  s3
+    //  -s2 s1.c2 c1.c2                   0 -s3  c3
+    //
+    //  Thus:  m11.c1 - m21.s1 = c3  &   m12.c1 - m22.s1 = s3
+
+    Scalar s1 = numext::sin(res[0]);
+    Scalar c1 = numext::cos(res[0]);
+    res[2] = numext::atan2(c1 * coeff(j, k) - s1 * coeff(k, k), c1 * coeff(j, j) - s1 * coeff(k, j));
+  }
+  else
+  {
+    res[0] = numext::atan2(coeff(j, k), coeff(k, k));
+    Scalar c2 = numext::hypot(coeff(i, i), coeff(i, j));
+    if ((odd && res[0] < Scalar(0)) || ((!odd) && res[0] > Scalar(0)))
+    {
+      if (res[0] > Scalar(0))
+      {
+        res[0] -= Scalar(EIGEN_PI);
+      }
+      else
+      {
+        res[0] += Scalar(EIGEN_PI);
+      }
+      res[1] = numext::atan2(-coeff(i, k), -c2);
+    }
+    else
+    {
+      res[1] = numext::atan2(-coeff(i, k), c2);
+    }
+    Scalar s1 = numext::sin(res[0]);
+    Scalar c1 = numext::cos(res[0]);
+    res[2] = numext::atan2(s1 * coeff(k, i) - c1 * coeff(j, i), c1 * coeff(j, j) - s1 * coeff(k, j));
+  }
+  if (!odd)
+  {
+    res = -res;
+  }
+
+  return res;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_EULERANGLES_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Homogeneous.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Homogeneous.h
new file mode 100644
index 0000000000000000000000000000000000000000..538cf83e6c82f5f79a8ee248f4c424f405d7a18e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Homogeneous.h
@@ -0,0 +1,503 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_HOMOGENEOUS_H
+#define EIGEN_HOMOGENEOUS_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \class Homogeneous
+  *
+  * \brief Expression of one (or a set of) homogeneous vector(s)
+  *
+  * \param MatrixType the type of the object in which we are making homogeneous
+  *
+  * This class represents an expression of one (or a set of) homogeneous vector(s).
+  * It is the return type of MatrixBase::homogeneous() and most of the time
+  * this is the only way it is used.
+  *
+  * \sa MatrixBase::homogeneous()
+  */
+
+namespace internal {
+
+template<typename MatrixType,int Direction>
+struct traits<Homogeneous<MatrixType,Direction> >
+ : traits<MatrixType>
+{
+  typedef typename traits<MatrixType>::StorageKind StorageKind;
+  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
+  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_;
+  enum {
+    RowsPlusOne = (MatrixType::RowsAtCompileTime != Dynamic) ?
+                  int(MatrixType::RowsAtCompileTime) + 1 : Dynamic,
+    ColsPlusOne = (MatrixType::ColsAtCompileTime != Dynamic) ?
+                  int(MatrixType::ColsAtCompileTime) + 1 : Dynamic,
+    RowsAtCompileTime = Direction==Vertical  ?  RowsPlusOne : MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = Direction==Horizontal ? ColsPlusOne : MatrixType::ColsAtCompileTime,
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+    TmpFlags = MatrixTypeNested_::Flags & HereditaryBits,
+    Flags = ColsAtCompileTime==1 ? (TmpFlags & ~RowMajorBit)
+          : RowsAtCompileTime==1 ? (TmpFlags | RowMajorBit)
+          : TmpFlags
+  };
+};
+
+template<typename MatrixType,typename Lhs> struct homogeneous_left_product_impl;
+template<typename MatrixType,typename Rhs> struct homogeneous_right_product_impl;
+
+} // end namespace internal
+
+template<typename MatrixType,int Direction_> class Homogeneous
+  : public MatrixBase<Homogeneous<MatrixType,Direction_> >, internal::no_assignment_operator
+{
+  public:
+
+    typedef MatrixType NestedExpression;
+    enum { Direction = Direction_ };
+
+    typedef MatrixBase<Homogeneous> Base;
+    EIGEN_DENSE_PUBLIC_INTERFACE(Homogeneous)
+
+    EIGEN_DEVICE_FUNC explicit inline Homogeneous(const MatrixType& matrix)
+      : m_matrix(matrix)
+    {}
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index rows() const EIGEN_NOEXCEPT { return m_matrix.rows() + (int(Direction)==Vertical   ? 1 : 0); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols() + (int(Direction)==Horizontal ? 1 : 0); }
+
+    EIGEN_DEVICE_FUNC const NestedExpression& nestedExpression() const { return m_matrix; }
+
+    template<typename Rhs>
+    EIGEN_DEVICE_FUNC inline const Product<Homogeneous,Rhs>
+    operator* (const MatrixBase<Rhs>& rhs) const
+    {
+      eigen_assert(int(Direction)==Horizontal);
+      return Product<Homogeneous,Rhs>(*this,rhs.derived());
+    }
+
+    template<typename Lhs> friend
+    EIGEN_DEVICE_FUNC inline const Product<Lhs,Homogeneous>
+    operator* (const MatrixBase<Lhs>& lhs, const Homogeneous& rhs)
+    {
+      eigen_assert(int(Direction)==Vertical);
+      return Product<Lhs,Homogeneous>(lhs.derived(),rhs);
+    }
+
+    template<typename Scalar, int Dim, int Mode, int Options> friend
+    EIGEN_DEVICE_FUNC inline const Product<Transform<Scalar,Dim,Mode,Options>, Homogeneous >
+    operator* (const Transform<Scalar,Dim,Mode,Options>& lhs, const Homogeneous& rhs)
+    {
+      eigen_assert(int(Direction)==Vertical);
+      return Product<Transform<Scalar,Dim,Mode,Options>, Homogeneous>(lhs,rhs);
+    }
+
+    template<typename Func>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::result_of<Func(Scalar,Scalar)>::type
+    redux(const Func& func) const
+    {
+      return func(m_matrix.redux(func), Scalar(1));
+    }
+
+  protected:
+    typename MatrixType::Nested m_matrix;
+};
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \returns a vector expression that is one longer than the vector argument, with the value 1 symbolically appended as the last coefficient.
+  *
+  * This can be used to convert affine coordinates to homogeneous coordinates.
+  *
+  * \only_for_vectors
+  *
+  * Example: \include MatrixBase_homogeneous.cpp
+  * Output: \verbinclude MatrixBase_homogeneous.out
+  *
+  * \sa VectorwiseOp::homogeneous(), class Homogeneous
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::HomogeneousReturnType
+MatrixBase<Derived>::homogeneous() const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  return HomogeneousReturnType(derived());
+}
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \returns an expression where the value 1 is symbolically appended as the final coefficient to each column (or row) of the matrix.
+  *
+  * This can be used to convert affine coordinates to homogeneous coordinates.
+  *
+  * Example: \include VectorwiseOp_homogeneous.cpp
+  * Output: \verbinclude VectorwiseOp_homogeneous.out
+  *
+  * \sa MatrixBase::homogeneous(), class Homogeneous */
+template<typename ExpressionType, int Direction>
+EIGEN_DEVICE_FUNC inline Homogeneous<ExpressionType,Direction>
+VectorwiseOp<ExpressionType,Direction>::homogeneous() const
+{
+  return HomogeneousReturnType(_expression());
+}
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \brief homogeneous normalization
+  *
+  * \returns a vector expression of the N-1 first coefficients of \c *this divided by that last coefficient.
+  *
+  * This can be used to convert homogeneous coordinates to affine coordinates.
+  *
+  * It is essentially a shortcut for:
+  * \code
+    this->head(this->size()-1)/this->coeff(this->size()-1);
+    \endcode
+  *
+  * Example: \include MatrixBase_hnormalized.cpp
+  * Output: \verbinclude MatrixBase_hnormalized.out
+  *
+  * \sa VectorwiseOp::hnormalized() */
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline const typename MatrixBase<Derived>::HNormalizedReturnType
+MatrixBase<Derived>::hnormalized() const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+  return ConstStartMinusOne(derived(),0,0,
+    ColsAtCompileTime==1?size()-1:1,
+    ColsAtCompileTime==1?1:size()-1) / coeff(size()-1);
+}
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \brief column or row-wise homogeneous normalization
+  *
+  * \returns an expression of the first N-1 coefficients of each column (or row) of \c *this divided by the last coefficient of each column (or row).
+  *
+  * This can be used to convert homogeneous coordinates to affine coordinates.
+  *
+  * It is conceptually equivalent to calling MatrixBase::hnormalized() to each column (or row) of \c *this.
+  *
+  * Example: \include DirectionWise_hnormalized.cpp
+  * Output: \verbinclude DirectionWise_hnormalized.out
+  *
+  * \sa MatrixBase::hnormalized() */
+template<typename ExpressionType, int Direction>
+EIGEN_DEVICE_FUNC inline const typename VectorwiseOp<ExpressionType,Direction>::HNormalizedReturnType
+VectorwiseOp<ExpressionType,Direction>::hnormalized() const
+{
+  return HNormalized_Block(_expression(),0,0,
+      Direction==Vertical   ? _expression().rows()-1 : _expression().rows(),
+      Direction==Horizontal ? _expression().cols()-1 : _expression().cols()).cwiseQuotient(
+      Replicate<HNormalized_Factors,
+                Direction==Vertical   ? HNormalized_SizeMinusOne : 1,
+                Direction==Horizontal ? HNormalized_SizeMinusOne : 1>
+        (HNormalized_Factors(_expression(),
+          Direction==Vertical    ? _expression().rows()-1:0,
+          Direction==Horizontal  ? _expression().cols()-1:0,
+          Direction==Vertical    ? 1 : _expression().rows(),
+          Direction==Horizontal  ? 1 : _expression().cols()),
+         Direction==Vertical   ? _expression().rows()-1 : 1,
+         Direction==Horizontal ? _expression().cols()-1 : 1));
+}
+
+namespace internal {
+
+template<typename MatrixOrTransformType>
+struct take_matrix_for_product
+{
+  typedef MatrixOrTransformType type;
+  EIGEN_DEVICE_FUNC static const type& run(const type &x) { return x; }
+};
+
+template<typename Scalar, int Dim, int Mode,int Options>
+struct take_matrix_for_product<Transform<Scalar, Dim, Mode, Options> >
+{
+  typedef Transform<Scalar, Dim, Mode, Options> TransformType;
+  typedef std::add_const_t<typename TransformType::ConstAffinePart> type;
+  EIGEN_DEVICE_FUNC static type run (const TransformType& x) { return x.affine(); }
+};
+
+template<typename Scalar, int Dim, int Options>
+struct take_matrix_for_product<Transform<Scalar, Dim, Projective, Options> >
+{
+  typedef Transform<Scalar, Dim, Projective, Options> TransformType;
+  typedef typename TransformType::MatrixType type;
+  EIGEN_DEVICE_FUNC static const type& run (const TransformType& x) { return x.matrix(); }
+};
+
+template<typename MatrixType,typename Lhs>
+struct traits<homogeneous_left_product_impl<Homogeneous<MatrixType,Vertical>,Lhs> >
+{
+  typedef typename take_matrix_for_product<Lhs>::type LhsMatrixType;
+  typedef remove_all_t<MatrixType> MatrixTypeCleaned;
+  typedef remove_all_t<LhsMatrixType> LhsMatrixTypeCleaned;
+  typedef typename make_proper_matrix_type<
+                 typename traits<MatrixTypeCleaned>::Scalar,
+                 LhsMatrixTypeCleaned::RowsAtCompileTime,
+                 MatrixTypeCleaned::ColsAtCompileTime,
+                 MatrixTypeCleaned::PlainObject::Options,
+                 LhsMatrixTypeCleaned::MaxRowsAtCompileTime,
+                 MatrixTypeCleaned::MaxColsAtCompileTime>::type ReturnType;
+};
+
+template<typename MatrixType,typename Lhs>
+struct homogeneous_left_product_impl<Homogeneous<MatrixType,Vertical>,Lhs>
+  : public ReturnByValue<homogeneous_left_product_impl<Homogeneous<MatrixType,Vertical>,Lhs> >
+{
+  typedef typename traits<homogeneous_left_product_impl>::LhsMatrixType LhsMatrixType;
+  typedef remove_all_t<LhsMatrixType> LhsMatrixTypeCleaned;
+  typedef remove_all_t<typename LhsMatrixTypeCleaned::Nested> LhsMatrixTypeNested;
+  EIGEN_DEVICE_FUNC homogeneous_left_product_impl(const Lhs& lhs, const MatrixType& rhs)
+    : m_lhs(take_matrix_for_product<Lhs>::run(lhs)),
+      m_rhs(rhs)
+  {}
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  inline Index rows() const EIGEN_NOEXCEPT { return m_lhs.rows(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  inline Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
+
+  template<typename Dest> EIGEN_DEVICE_FUNC void evalTo(Dest& dst) const
+  {
+    // FIXME investigate how to allow lazy evaluation of this product when possible
+    dst = Block<const LhsMatrixTypeNested,
+              LhsMatrixTypeNested::RowsAtCompileTime,
+              LhsMatrixTypeNested::ColsAtCompileTime==Dynamic?Dynamic:LhsMatrixTypeNested::ColsAtCompileTime-1>
+            (m_lhs,0,0,m_lhs.rows(),m_lhs.cols()-1) * m_rhs;
+    dst += m_lhs.col(m_lhs.cols()-1).rowwise()
+            .template replicate<MatrixType::ColsAtCompileTime>(m_rhs.cols());
+  }
+
+  typename LhsMatrixTypeCleaned::Nested m_lhs;
+  typename MatrixType::Nested m_rhs;
+};
+
+template<typename MatrixType,typename Rhs>
+struct traits<homogeneous_right_product_impl<Homogeneous<MatrixType,Horizontal>,Rhs> >
+{
+  typedef typename make_proper_matrix_type<typename traits<MatrixType>::Scalar,
+                 MatrixType::RowsAtCompileTime,
+                 Rhs::ColsAtCompileTime,
+                 MatrixType::PlainObject::Options,
+                 MatrixType::MaxRowsAtCompileTime,
+                 Rhs::MaxColsAtCompileTime>::type ReturnType;
+};
+
+template<typename MatrixType,typename Rhs>
+struct homogeneous_right_product_impl<Homogeneous<MatrixType,Horizontal>,Rhs>
+  : public ReturnByValue<homogeneous_right_product_impl<Homogeneous<MatrixType,Horizontal>,Rhs> >
+{
+  typedef remove_all_t<typename Rhs::Nested> RhsNested;
+  EIGEN_DEVICE_FUNC homogeneous_right_product_impl(const MatrixType& lhs, const Rhs& rhs)
+    : m_lhs(lhs), m_rhs(rhs)
+  {}
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return m_lhs.rows(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
+
+  template<typename Dest> EIGEN_DEVICE_FUNC void evalTo(Dest& dst) const
+  {
+    // FIXME investigate how to allow lazy evaluation of this product when possible
+    dst = m_lhs * Block<const RhsNested,
+                        RhsNested::RowsAtCompileTime==Dynamic?Dynamic:RhsNested::RowsAtCompileTime-1,
+                        RhsNested::ColsAtCompileTime>
+            (m_rhs,0,0,m_rhs.rows()-1,m_rhs.cols());
+    dst += m_rhs.row(m_rhs.rows()-1).colwise()
+            .template replicate<MatrixType::RowsAtCompileTime>(m_lhs.rows());
+  }
+
+  typename MatrixType::Nested m_lhs;
+  typename Rhs::Nested m_rhs;
+};
+
+template<typename ArgType,int Direction>
+struct evaluator_traits<Homogeneous<ArgType,Direction> >
+{
+  typedef typename storage_kind_to_evaluator_kind<typename ArgType::StorageKind>::Kind Kind;
+  typedef HomogeneousShape Shape;
+};
+
+template<> struct AssignmentKind<DenseShape,HomogeneousShape> { typedef Dense2Dense Kind; };
+
+
+template<typename ArgType,int Direction>
+struct unary_evaluator<Homogeneous<ArgType,Direction>, IndexBased>
+  : evaluator<typename Homogeneous<ArgType,Direction>::PlainObject >
+{
+  typedef Homogeneous<ArgType,Direction> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  EIGEN_DEVICE_FUNC explicit unary_evaluator(const XprType& op)
+    : Base(), m_temp(op)
+  {
+    internal::construct_at<Base>(this, m_temp);
+  }
+
+protected:
+  PlainObject m_temp;
+};
+
+// dense = homogeneous
+template< typename DstXprType, typename ArgType, typename Scalar>
+struct Assignment<DstXprType, Homogeneous<ArgType,Vertical>, internal::assign_op<Scalar,typename ArgType::Scalar>, Dense2Dense>
+{
+  typedef Homogeneous<ArgType,Vertical> SrcXprType;
+  EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename ArgType::Scalar> &)
+  {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+      dst.resize(dstRows, dstCols);
+
+    dst.template topRows<ArgType::RowsAtCompileTime>(src.nestedExpression().rows()) = src.nestedExpression();
+    dst.row(dst.rows()-1).setOnes();
+  }
+};
+
+// dense = homogeneous
+template< typename DstXprType, typename ArgType, typename Scalar>
+struct Assignment<DstXprType, Homogeneous<ArgType,Horizontal>, internal::assign_op<Scalar,typename ArgType::Scalar>, Dense2Dense>
+{
+  typedef Homogeneous<ArgType,Horizontal> SrcXprType;
+  EIGEN_DEVICE_FUNC static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename ArgType::Scalar> &)
+  {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+      dst.resize(dstRows, dstCols);
+
+    dst.template leftCols<ArgType::ColsAtCompileTime>(src.nestedExpression().cols()) = src.nestedExpression();
+    dst.col(dst.cols()-1).setOnes();
+  }
+};
+
+template<typename LhsArg, typename Rhs, int ProductTag>
+struct generic_product_impl<Homogeneous<LhsArg,Horizontal>, Rhs, HomogeneousShape, DenseShape, ProductTag>
+{
+  template<typename Dest>
+  EIGEN_DEVICE_FUNC static void evalTo(Dest& dst, const Homogeneous<LhsArg,Horizontal>& lhs, const Rhs& rhs)
+  {
+    homogeneous_right_product_impl<Homogeneous<LhsArg,Horizontal>, Rhs>(lhs.nestedExpression(), rhs).evalTo(dst);
+  }
+};
+
+template<typename Lhs,typename Rhs>
+struct homogeneous_right_product_refactoring_helper
+{
+  enum {
+    Dim  = Lhs::ColsAtCompileTime,
+    Rows = Lhs::RowsAtCompileTime
+  };
+  typedef typename Rhs::template ConstNRowsBlockXpr<Dim>::Type          LinearBlockConst;
+  typedef std::remove_const_t<LinearBlockConst>                 LinearBlock;
+  typedef typename Rhs::ConstRowXpr                                     ConstantColumn;
+  typedef Replicate<const ConstantColumn,Rows,1>                        ConstantBlock;
+  typedef Product<Lhs,LinearBlock,LazyProduct>                          LinearProduct;
+  typedef CwiseBinaryOp<internal::scalar_sum_op<typename Lhs::Scalar,typename Rhs::Scalar>, const LinearProduct, const ConstantBlock> Xpr;
+};
+
+template<typename Lhs, typename Rhs, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, HomogeneousShape, DenseShape>
+ : public evaluator<typename homogeneous_right_product_refactoring_helper<typename Lhs::NestedExpression,Rhs>::Xpr>
+{
+  typedef Product<Lhs, Rhs, LazyProduct> XprType;
+  typedef homogeneous_right_product_refactoring_helper<typename Lhs::NestedExpression,Rhs> helper;
+  typedef typename helper::ConstantBlock ConstantBlock;
+  typedef typename helper::Xpr RefactoredXpr;
+  typedef evaluator<RefactoredXpr> Base;
+
+  EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
+    : Base(  xpr.lhs().nestedExpression() .lazyProduct(  xpr.rhs().template topRows<helper::Dim>(xpr.lhs().nestedExpression().cols()) )
+            + ConstantBlock(xpr.rhs().row(xpr.rhs().rows()-1),xpr.lhs().rows(), 1) )
+  {}
+};
+
+template<typename Lhs, typename RhsArg, int ProductTag>
+struct generic_product_impl<Lhs, Homogeneous<RhsArg,Vertical>, DenseShape, HomogeneousShape, ProductTag>
+{
+  template<typename Dest>
+  EIGEN_DEVICE_FUNC static void evalTo(Dest& dst, const Lhs& lhs, const Homogeneous<RhsArg,Vertical>& rhs)
+  {
+    homogeneous_left_product_impl<Homogeneous<RhsArg,Vertical>, Lhs>(lhs, rhs.nestedExpression()).evalTo(dst);
+  }
+};
+
+// TODO: the following specialization is to address a regression from 3.2 to 3.3
+// In the future, this path should be optimized.
+template<typename Lhs, typename RhsArg, int ProductTag>
+struct generic_product_impl<Lhs, Homogeneous<RhsArg,Vertical>, TriangularShape, HomogeneousShape, ProductTag>
+{
+  template<typename Dest>
+  static void evalTo(Dest& dst, const Lhs& lhs, const Homogeneous<RhsArg,Vertical>& rhs)
+  {
+    dst.noalias() = lhs * rhs.eval();
+  }
+};
+
+template<typename Lhs,typename Rhs>
+struct homogeneous_left_product_refactoring_helper
+{
+  enum {
+    Dim = Rhs::RowsAtCompileTime,
+    Cols = Rhs::ColsAtCompileTime
+  };
+  typedef typename Lhs::template ConstNColsBlockXpr<Dim>::Type          LinearBlockConst;
+  typedef std::remove_const_t<LinearBlockConst>                 LinearBlock;
+  typedef typename Lhs::ConstColXpr                                     ConstantColumn;
+  typedef Replicate<const ConstantColumn,1,Cols>                        ConstantBlock;
+  typedef Product<LinearBlock,Rhs,LazyProduct>                          LinearProduct;
+  typedef CwiseBinaryOp<internal::scalar_sum_op<typename Lhs::Scalar,typename Rhs::Scalar>, const LinearProduct, const ConstantBlock> Xpr;
+};
+
+template<typename Lhs, typename Rhs, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, LazyProduct>, ProductTag, DenseShape, HomogeneousShape>
+ : public evaluator<typename homogeneous_left_product_refactoring_helper<Lhs,typename Rhs::NestedExpression>::Xpr>
+{
+  typedef Product<Lhs, Rhs, LazyProduct> XprType;
+  typedef homogeneous_left_product_refactoring_helper<Lhs,typename Rhs::NestedExpression> helper;
+  typedef typename helper::ConstantBlock ConstantBlock;
+  typedef typename helper::Xpr RefactoredXpr;
+  typedef evaluator<RefactoredXpr> Base;
+
+  EIGEN_DEVICE_FUNC explicit product_evaluator(const XprType& xpr)
+    : Base(   xpr.lhs().template leftCols<helper::Dim>(xpr.rhs().nestedExpression().rows()) .lazyProduct( xpr.rhs().nestedExpression() )
+            + ConstantBlock(xpr.lhs().col(xpr.lhs().cols()-1),1,xpr.rhs().cols()) )
+  {}
+};
+
+template<typename Scalar, int Dim, int Mode,int Options, typename RhsArg, int ProductTag>
+struct generic_product_impl<Transform<Scalar,Dim,Mode,Options>, Homogeneous<RhsArg,Vertical>, DenseShape, HomogeneousShape, ProductTag>
+{
+  typedef Transform<Scalar,Dim,Mode,Options> TransformType;
+  template<typename Dest>
+  EIGEN_DEVICE_FUNC static void evalTo(Dest& dst, const TransformType& lhs, const Homogeneous<RhsArg,Vertical>& rhs)
+  {
+    homogeneous_left_product_impl<Homogeneous<RhsArg,Vertical>, TransformType>(lhs, rhs.nestedExpression()).evalTo(dst);
+  }
+};
+
+template<typename ExpressionType, int Side, bool Transposed>
+struct permutation_matrix_product<ExpressionType, Side, Transposed, HomogeneousShape>
+  : public permutation_matrix_product<ExpressionType, Side, Transposed, DenseShape>
+{};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_HOMOGENEOUS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Hyperplane.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Hyperplane.h
new file mode 100644
index 0000000000000000000000000000000000000000..ad6aae9ef52d39d30b2f112daf2ed35ec059772b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Hyperplane.h
@@ -0,0 +1,284 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_HYPERPLANE_H
+#define EIGEN_HYPERPLANE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \class Hyperplane
+  *
+  * \brief A hyperplane
+  *
+  * A hyperplane is an affine subspace of dimension n-1 in a space of dimension n.
+  * For example, a hyperplane in a plane is a line; a hyperplane in 3-space is a plane.
+  *
+  * \tparam Scalar_ the scalar type, i.e., the type of the coefficients
+  * \tparam AmbientDim_ the dimension of the ambient space, can be a compile time value or Dynamic.
+  *             Notice that the dimension of the hyperplane is AmbientDim_-1.
+  *
+  * This class represents an hyperplane as the zero set of the implicit equation
+  * \f$ n \cdot x + d = 0 \f$ where \f$ n \f$ is a unit normal vector of the plane (linear part)
+  * and \f$ d \f$ is the distance (offset) to the origin.
+  */
+template <typename Scalar_, int AmbientDim_, int Options_>
+class Hyperplane
+{
+public:
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar_,AmbientDim_==Dynamic ? Dynamic : AmbientDim_+1)
+  enum {
+    AmbientDimAtCompileTime = AmbientDim_,
+    Options = Options_
+  };
+  typedef Scalar_ Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+  typedef Matrix<Scalar,AmbientDimAtCompileTime,1> VectorType;
+  typedef Matrix<Scalar,Index(AmbientDimAtCompileTime)==Dynamic
+                        ? Dynamic
+                        : Index(AmbientDimAtCompileTime)+1,1,Options> Coefficients;
+  typedef Block<Coefficients,AmbientDimAtCompileTime,1> NormalReturnType;
+  typedef const Block<const Coefficients,AmbientDimAtCompileTime,1> ConstNormalReturnType;
+
+  /** Default constructor without initialization */
+  EIGEN_DEVICE_FUNC inline Hyperplane() {}
+  
+  template<int OtherOptions>
+  EIGEN_DEVICE_FUNC Hyperplane(const Hyperplane<Scalar,AmbientDimAtCompileTime,OtherOptions>& other)
+   : m_coeffs(other.coeffs())
+  {}
+
+  /** Constructs a dynamic-size hyperplane with \a _dim the dimension
+    * of the ambient space */
+  EIGEN_DEVICE_FUNC inline explicit Hyperplane(Index _dim) : m_coeffs(_dim+1) {}
+
+  /** Construct a plane from its normal \a n and a point \a e onto the plane.
+    * \warning the vector normal is assumed to be normalized.
+    */
+  EIGEN_DEVICE_FUNC inline Hyperplane(const VectorType& n, const VectorType& e)
+    : m_coeffs(n.size()+1)
+  {
+    normal() = n;
+    offset() = -n.dot(e);
+  }
+
+  /** Constructs a plane from its normal \a n and distance to the origin \a d
+    * such that the algebraic equation of the plane is \f$ n \cdot x + d = 0 \f$.
+    * \warning the vector normal is assumed to be normalized.
+    */
+  EIGEN_DEVICE_FUNC inline Hyperplane(const VectorType& n, const Scalar& d)
+    : m_coeffs(n.size()+1)
+  {
+    normal() = n;
+    offset() = d;
+  }
+
+  /** Constructs a hyperplane passing through the two points. If the dimension of the ambient space
+    * is greater than 2, then there isn't uniqueness, so an arbitrary choice is made.
+    */
+  EIGEN_DEVICE_FUNC static inline Hyperplane Through(const VectorType& p0, const VectorType& p1)
+  {
+    Hyperplane result(p0.size());
+    result.normal() = (p1 - p0).unitOrthogonal();
+    result.offset() = -p0.dot(result.normal());
+    return result;
+  }
+
+  /** Constructs a hyperplane passing through the three points. The dimension of the ambient space
+    * is required to be exactly 3.
+    */
+  EIGEN_DEVICE_FUNC static inline Hyperplane Through(const VectorType& p0, const VectorType& p1, const VectorType& p2)
+  {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 3)
+    Hyperplane result(p0.size());
+    VectorType v0(p2 - p0), v1(p1 - p0);
+    result.normal() = v0.cross(v1);
+    RealScalar norm = result.normal().norm();
+    if(norm <= v0.norm() * v1.norm() * NumTraits<RealScalar>::epsilon())
+    {
+      Matrix<Scalar,2,3> m; m << v0.transpose(), v1.transpose();
+      JacobiSVD<Matrix<Scalar,2,3>, ComputeFullV> svd(m);
+      result.normal() = svd.matrixV().col(2);
+    }
+    else
+      result.normal() /= norm;
+    result.offset() = -p0.dot(result.normal());
+    return result;
+  }
+
+  /** Constructs a hyperplane passing through the parametrized line \a parametrized.
+    * If the dimension of the ambient space is greater than 2, then there isn't uniqueness,
+    * so an arbitrary choice is made.
+    */
+  // FIXME to be consistent with the rest this could be implemented as a static Through function ??
+  EIGEN_DEVICE_FUNC explicit Hyperplane(const ParametrizedLine<Scalar, AmbientDimAtCompileTime>& parametrized)
+  {
+    normal() = parametrized.direction().unitOrthogonal();
+    offset() = -parametrized.origin().dot(normal());
+  }
+
+  EIGEN_DEVICE_FUNC ~Hyperplane() {}
+
+  /** \returns the dimension in which the plane holds */
+  EIGEN_DEVICE_FUNC inline Index dim() const { return AmbientDimAtCompileTime==Dynamic ? m_coeffs.size()-1 : Index(AmbientDimAtCompileTime); }
+
+  /** normalizes \c *this */
+  EIGEN_DEVICE_FUNC void normalize(void)
+  {
+    m_coeffs /= normal().norm();
+  }
+
+  /** \returns the signed distance between the plane \c *this and a point \a p.
+    * \sa absDistance()
+    */
+  EIGEN_DEVICE_FUNC inline Scalar signedDistance(const VectorType& p) const { return normal().dot(p) + offset(); }
+
+  /** \returns the absolute distance between the plane \c *this and a point \a p.
+    * \sa signedDistance()
+    */
+  EIGEN_DEVICE_FUNC inline Scalar absDistance(const VectorType& p) const { return numext::abs(signedDistance(p)); }
+
+  /** \returns the projection of a point \a p onto the plane \c *this.
+    */
+  EIGEN_DEVICE_FUNC inline VectorType projection(const VectorType& p) const { return p - signedDistance(p) * normal(); }
+
+  /** \returns a constant reference to the unit normal vector of the plane, which corresponds
+    * to the linear part of the implicit equation.
+    */
+  EIGEN_DEVICE_FUNC inline ConstNormalReturnType normal() const { return ConstNormalReturnType(m_coeffs,0,0,dim(),1); }
+
+  /** \returns a non-constant reference to the unit normal vector of the plane, which corresponds
+    * to the linear part of the implicit equation.
+    */
+  EIGEN_DEVICE_FUNC inline NormalReturnType normal() { return NormalReturnType(m_coeffs,0,0,dim(),1); }
+
+  /** \returns the distance to the origin, which is also the "constant term" of the implicit equation
+    * \warning the vector normal is assumed to be normalized.
+    */
+  EIGEN_DEVICE_FUNC inline const Scalar& offset() const { return m_coeffs.coeff(dim()); }
+
+  /** \returns a non-constant reference to the distance to the origin, which is also the constant part
+    * of the implicit equation */
+  EIGEN_DEVICE_FUNC inline Scalar& offset() { return m_coeffs(dim()); }
+
+  /** \returns a constant reference to the coefficients c_i of the plane equation:
+    * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$
+    */
+  EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs; }
+
+  /** \returns a non-constant reference to the coefficients c_i of the plane equation:
+    * \f$ c_0*x_0 + ... + c_{d-1}*x_{d-1} + c_d = 0 \f$
+    */
+  EIGEN_DEVICE_FUNC inline Coefficients& coeffs() { return m_coeffs; }
+
+  /** \returns the intersection of *this with \a other.
+    *
+    * \warning The ambient space must be a plane, i.e. have dimension 2, so that \c *this and \a other are lines.
+    *
+    * \note If \a other is approximately parallel to *this, this method will return any point on *this.
+    */
+  EIGEN_DEVICE_FUNC VectorType intersection(const Hyperplane& other) const
+  {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2)
+    Scalar det = coeffs().coeff(0) * other.coeffs().coeff(1) - coeffs().coeff(1) * other.coeffs().coeff(0);
+    // since the line equations ax+by=c are normalized with a^2+b^2=1, the following tests
+    // whether the two lines are approximately parallel.
+    if(internal::isMuchSmallerThan(det, Scalar(1)))
+    {   // special case where the two lines are approximately parallel. Pick any point on the first line.
+        if(numext::abs(coeffs().coeff(1))>numext::abs(coeffs().coeff(0)))
+            return VectorType(coeffs().coeff(1), -coeffs().coeff(2)/coeffs().coeff(1)-coeffs().coeff(0));
+        else
+            return VectorType(-coeffs().coeff(2)/coeffs().coeff(0)-coeffs().coeff(1), coeffs().coeff(0));
+    }
+    else
+    {   // general case
+        Scalar invdet = Scalar(1) / det;
+        return VectorType(invdet*(coeffs().coeff(1)*other.coeffs().coeff(2)-other.coeffs().coeff(1)*coeffs().coeff(2)),
+                          invdet*(other.coeffs().coeff(0)*coeffs().coeff(2)-coeffs().coeff(0)*other.coeffs().coeff(2)));
+    }
+  }
+
+  /** Applies the transformation matrix \a mat to \c *this and returns a reference to \c *this.
+    *
+    * \param mat the Dim x Dim transformation matrix
+    * \param traits specifies whether the matrix \a mat represents an #Isometry
+    *               or a more generic #Affine transformation. The default is #Affine.
+    */
+  template<typename XprType>
+  EIGEN_DEVICE_FUNC inline Hyperplane& transform(const MatrixBase<XprType>& mat, TransformTraits traits = Affine)
+  {
+    if (traits==Affine)
+    {
+      normal() = mat.inverse().transpose() * normal();
+      m_coeffs /= normal().norm();
+    }
+    else if (traits==Isometry)
+      normal() = mat * normal();
+    else
+    {
+      eigen_assert(0 && "invalid traits value in Hyperplane::transform()");
+    }
+    return *this;
+  }
+
+  /** Applies the transformation \a t to \c *this and returns a reference to \c *this.
+    *
+    * \param t the transformation of dimension Dim
+    * \param traits specifies whether the transformation \a t represents an #Isometry
+    *               or a more generic #Affine transformation. The default is #Affine.
+    *               Other kind of transformations are not supported.
+    */
+  template<int TrOptions>
+  EIGEN_DEVICE_FUNC inline Hyperplane& transform(const Transform<Scalar,AmbientDimAtCompileTime,Affine,TrOptions>& t,
+                                TransformTraits traits = Affine)
+  {
+    transform(t.linear(), traits);
+    offset() -= normal().dot(t.translation());
+    return *this;
+  }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Hyperplane,
+           Hyperplane<NewScalarType,AmbientDimAtCompileTime,Options> >::type cast() const
+  {
+    return typename internal::cast_return_type<Hyperplane,
+                    Hyperplane<NewScalarType,AmbientDimAtCompileTime,Options> >::type(*this);
+  }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType,int OtherOptions>
+  EIGEN_DEVICE_FUNC inline explicit Hyperplane(const Hyperplane<OtherScalarType,AmbientDimAtCompileTime,OtherOptions>& other)
+  { m_coeffs = other.coeffs().template cast<Scalar>(); }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  template<int OtherOptions>
+  EIGEN_DEVICE_FUNC bool isApprox(const Hyperplane<Scalar,AmbientDimAtCompileTime,OtherOptions>& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
+  { return m_coeffs.isApprox(other.m_coeffs, prec); }
+
+protected:
+
+  Coefficients m_coeffs;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_HYPERPLANE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..a1159a3c69cfec90be8ebe9a63c1d8fd7dd2f666
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_GEOMETRY_MODULE_H
+#error "Please include Eigen/Geometry instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/OrthoMethods.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/OrthoMethods.h
new file mode 100644
index 0000000000000000000000000000000000000000..fbf020d3010ef1e6423942fb95635dbbd1210a02
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/OrthoMethods.h
@@ -0,0 +1,281 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ORTHOMETHODS_H
+#define EIGEN_ORTHOMETHODS_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+// Vector3 version (default)
+template<typename Derived, typename OtherDerived, int Size>
+struct cross_impl
+{
+  typedef typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar;
+  typedef Matrix<Scalar,MatrixBase<Derived>::RowsAtCompileTime,MatrixBase<Derived>::ColsAtCompileTime> return_type;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  return_type run(const MatrixBase<Derived>& first, const MatrixBase<OtherDerived>& second)
+  {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived,3)
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,3)
+
+    // Note that there is no need for an expression here since the compiler
+    // optimize such a small temporary very well (even within a complex expression)
+    typename internal::nested_eval<Derived,2>::type lhs(first.derived());
+    typename internal::nested_eval<OtherDerived,2>::type rhs(second.derived());
+    return return_type(
+      numext::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)),
+      numext::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)),
+      numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0))
+    );
+  }
+};
+
+// Vector2 version
+template<typename Derived, typename OtherDerived>
+struct cross_impl<Derived, OtherDerived, 2>
+{
+  typedef typename ScalarBinaryOpTraits<typename internal::traits<Derived>::Scalar,typename internal::traits<OtherDerived>::Scalar>::ReturnType Scalar;
+  typedef Scalar return_type;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  return_type run(const MatrixBase<Derived>& first, const MatrixBase<OtherDerived>& second)
+  {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived,2);
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,2);
+    typename internal::nested_eval<Derived,2>::type lhs(first.derived());
+    typename internal::nested_eval<OtherDerived,2>::type rhs(second.derived());
+    return numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0));
+  }
+};
+
+} // end namespace internal
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \returns the cross product of \c *this and \a other. This is either a scalar for size-2 vectors or a size-3 vector for size-3 vectors.
+  *
+  * This method is implemented for two different cases: between vectors of fixed size 2 and between vectors of fixed size 3.
+  * 
+  * For vectors of size 3, the output is simply the traditional cross product.
+  *
+  * For vectors of size 2, the output is a scalar.
+  * Given vectors \f$ v = \begin{bmatrix} v_1 & v_2 \end{bmatrix} \f$ and \f$ w = \begin{bmatrix} w_1 & w_2 \end{bmatrix} \f$,
+  * the result is simply \f$ v\times w = \overline{v_1 w_2 - v_2 w_1} = \text{conj}\left|\begin{smallmatrix} v_1 & w_1 \\ v_2 & w_2 \end{smallmatrix}\right| \f$;
+  * or, to put it differently, it is the third coordinate of the cross product of \f$ \begin{bmatrix} v_1 & v_2 & v_3 \end{bmatrix} \f$ and \f$ \begin{bmatrix} w_1 & w_2 & w_3 \end{bmatrix} \f$.
+  * For real-valued inputs, the result can be interpreted as the signed area of a parallelogram spanned by the two vectors.
+  * 
+  * \note With complex numbers, the cross product is implemented as
+  * \f$ (\mathbf{a}+i\mathbf{b}) \times (\mathbf{c}+i\mathbf{d}) = (\mathbf{a} \times \mathbf{c} - \mathbf{b} \times \mathbf{d}) - i(\mathbf{a} \times \mathbf{d} + \mathbf{b} \times \mathbf{c})\f$
+  * 
+  * \sa MatrixBase::cross3()
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename internal::cross_impl<Derived, OtherDerived>::return_type
+#else
+inline std::conditional_t<SizeAtCompileTime==2, Scalar, PlainObject>
+#endif
+MatrixBase<Derived>::cross(const MatrixBase<OtherDerived>& other) const
+{
+  return internal::cross_impl<Derived, OtherDerived>::run(*this, other);
+}
+
+namespace internal {
+
+template< int Arch,typename VectorLhs,typename VectorRhs,
+          typename Scalar = typename VectorLhs::Scalar,
+          bool Vectorizable = bool((VectorLhs::Flags&VectorRhs::Flags)&PacketAccessBit)>
+struct cross3_impl {
+  EIGEN_DEVICE_FUNC static inline typename internal::plain_matrix_type<VectorLhs>::type
+  run(const VectorLhs& lhs, const VectorRhs& rhs)
+  {
+    return typename internal::plain_matrix_type<VectorLhs>::type(
+      numext::conj(lhs.coeff(1) * rhs.coeff(2) - lhs.coeff(2) * rhs.coeff(1)),
+      numext::conj(lhs.coeff(2) * rhs.coeff(0) - lhs.coeff(0) * rhs.coeff(2)),
+      numext::conj(lhs.coeff(0) * rhs.coeff(1) - lhs.coeff(1) * rhs.coeff(0)),
+      0
+    );
+  }
+};
+
+}
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \returns the cross product of \c *this and \a other using only the x, y, and z coefficients
+  *
+  * The size of \c *this and \a other must be four. This function is especially useful
+  * when using 4D vectors instead of 3D ones to get advantage of SSE/AltiVec vectorization.
+  *
+  * \sa MatrixBase::cross()
+  */
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC inline typename MatrixBase<Derived>::PlainObject
+MatrixBase<Derived>::cross3(const MatrixBase<OtherDerived>& other) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived,4)
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,4)
+
+  typedef typename internal::nested_eval<Derived,2>::type DerivedNested;
+  typedef typename internal::nested_eval<OtherDerived,2>::type OtherDerivedNested;
+  DerivedNested lhs(derived());
+  OtherDerivedNested rhs(other.derived());
+
+  return internal::cross3_impl<Architecture::Target,
+                        internal::remove_all_t<DerivedNested>,
+                        internal::remove_all_t<OtherDerivedNested>>::run(lhs,rhs);
+}
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \returns a matrix expression of the cross product of each column or row
+  * of the referenced expression with the \a other vector.
+  *
+  * The referenced matrix must have one dimension equal to 3.
+  * The result matrix has the same dimensions than the referenced one.
+  *
+  * \sa MatrixBase::cross() */
+template<typename ExpressionType, int Direction>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC 
+const typename VectorwiseOp<ExpressionType,Direction>::CrossReturnType
+VectorwiseOp<ExpressionType,Direction>::cross(const MatrixBase<OtherDerived>& other) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,3)
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+  
+  typename internal::nested_eval<ExpressionType,2>::type mat(_expression());
+  typename internal::nested_eval<OtherDerived,2>::type vec(other.derived());
+
+  CrossReturnType res(_expression().rows(),_expression().cols());
+  if(Direction==Vertical)
+  {
+    eigen_assert(CrossReturnType::RowsAtCompileTime==3 && "the matrix must have exactly 3 rows");
+    res.row(0) = (mat.row(1) * vec.coeff(2) - mat.row(2) * vec.coeff(1)).conjugate();
+    res.row(1) = (mat.row(2) * vec.coeff(0) - mat.row(0) * vec.coeff(2)).conjugate();
+    res.row(2) = (mat.row(0) * vec.coeff(1) - mat.row(1) * vec.coeff(0)).conjugate();
+  }
+  else
+  {
+    eigen_assert(CrossReturnType::ColsAtCompileTime==3 && "the matrix must have exactly 3 columns");
+    res.col(0) = (mat.col(1) * vec.coeff(2) - mat.col(2) * vec.coeff(1)).conjugate();
+    res.col(1) = (mat.col(2) * vec.coeff(0) - mat.col(0) * vec.coeff(2)).conjugate();
+    res.col(2) = (mat.col(0) * vec.coeff(1) - mat.col(1) * vec.coeff(0)).conjugate();
+  }
+  return res;
+}
+
+namespace internal {
+
+template<typename Derived, int Size = Derived::SizeAtCompileTime>
+struct unitOrthogonal_selector
+{
+  typedef typename plain_matrix_type<Derived>::type VectorType;
+  typedef typename traits<Derived>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Matrix<Scalar,2,1> Vector2;
+  EIGEN_DEVICE_FUNC
+  static inline VectorType run(const Derived& src)
+  {
+    VectorType perp = VectorType::Zero(src.size());
+    Index maxi = 0;
+    Index sndi = 0;
+    src.cwiseAbs().maxCoeff(&maxi);
+    if (maxi==0)
+      sndi = 1;
+    RealScalar invnm = RealScalar(1)/(Vector2() << src.coeff(sndi),src.coeff(maxi)).finished().norm();
+    perp.coeffRef(maxi) = -numext::conj(src.coeff(sndi)) * invnm;
+    perp.coeffRef(sndi) =  numext::conj(src.coeff(maxi)) * invnm;
+
+    return perp;
+   }
+};
+
+template<typename Derived>
+struct unitOrthogonal_selector<Derived,3>
+{
+  typedef typename plain_matrix_type<Derived>::type VectorType;
+  typedef typename traits<Derived>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  EIGEN_DEVICE_FUNC
+  static inline VectorType run(const Derived& src)
+  {
+    VectorType perp;
+    /* Let us compute the crossed product of *this with a vector
+     * that is not too close to being colinear to *this.
+     */
+
+    /* unless the x and y coords are both close to zero, we can
+     * simply take ( -y, x, 0 ) and normalize it.
+     */
+    if((!isMuchSmallerThan(src.x(), src.z()))
+    || (!isMuchSmallerThan(src.y(), src.z())))
+    {
+      RealScalar invnm = RealScalar(1)/src.template head<2>().norm();
+      perp.coeffRef(0) = -numext::conj(src.y())*invnm;
+      perp.coeffRef(1) = numext::conj(src.x())*invnm;
+      perp.coeffRef(2) = 0;
+    }
+    /* if both x and y are close to zero, then the vector is close
+     * to the z-axis, so it's far from colinear to the x-axis for instance.
+     * So we take the crossed product with (1,0,0) and normalize it.
+     */
+    else
+    {
+      RealScalar invnm = RealScalar(1)/src.template tail<2>().norm();
+      perp.coeffRef(0) = 0;
+      perp.coeffRef(1) = -numext::conj(src.z())*invnm;
+      perp.coeffRef(2) = numext::conj(src.y())*invnm;
+    }
+
+    return perp;
+   }
+};
+
+template<typename Derived>
+struct unitOrthogonal_selector<Derived,2>
+{
+  typedef typename plain_matrix_type<Derived>::type VectorType;
+  EIGEN_DEVICE_FUNC
+  static inline VectorType run(const Derived& src)
+  { return VectorType(-numext::conj(src.y()), numext::conj(src.x())).normalized(); }
+};
+
+} // end namespace internal
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \returns a unit vector which is orthogonal to \c *this
+  *
+  * The size of \c *this must be at least 2. If the size is exactly 2,
+  * then the returned vector is a counter clock wise rotation of \c *this, i.e., (-y,x).normalized().
+  *
+  * \sa cross()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC typename MatrixBase<Derived>::PlainObject
+MatrixBase<Derived>::unitOrthogonal() const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return internal::unitOrthogonal_selector<Derived>::run(derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_ORTHOMETHODS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/ParametrizedLine.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/ParametrizedLine.h
new file mode 100644
index 0000000000000000000000000000000000000000..757692243f61f9b8d258a15854f7ab518f91b095
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/ParametrizedLine.h
@@ -0,0 +1,234 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PARAMETRIZEDLINE_H
+#define EIGEN_PARAMETRIZEDLINE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \class ParametrizedLine
+  *
+  * \brief A parametrized line
+  *
+  * A parametrized line is defined by an origin point \f$ \mathbf{o} \f$ and a unit
+  * direction vector \f$ \mathbf{d} \f$ such that the line corresponds to
+  * the set \f$ l(t) = \mathbf{o} + t \mathbf{d} \f$, \f$ t \in \mathbf{R} \f$.
+  *
+  * \tparam Scalar_ the scalar type, i.e., the type of the coefficients
+  * \tparam AmbientDim_ the dimension of the ambient space, can be a compile time value or Dynamic.
+  */
+template <typename Scalar_, int AmbientDim_, int Options_>
+class ParametrizedLine
+{
+public:
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar_,AmbientDim_)
+  enum {
+    AmbientDimAtCompileTime = AmbientDim_,
+    Options = Options_
+  };
+  typedef Scalar_ Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+  typedef Matrix<Scalar,AmbientDimAtCompileTime,1,Options> VectorType;
+
+  /** Default constructor without initialization */
+  EIGEN_DEVICE_FUNC inline ParametrizedLine() {}
+  
+  template<int OtherOptions>
+  EIGEN_DEVICE_FUNC ParametrizedLine(const ParametrizedLine<Scalar,AmbientDimAtCompileTime,OtherOptions>& other)
+   : m_origin(other.origin()), m_direction(other.direction())
+  {}
+
+  /** Constructs a dynamic-size line with \a _dim the dimension
+    * of the ambient space */
+  EIGEN_DEVICE_FUNC inline explicit ParametrizedLine(Index _dim) : m_origin(_dim), m_direction(_dim) {}
+
+  /** Initializes a parametrized line of direction \a direction and origin \a origin.
+    * \warning the vector direction is assumed to be normalized.
+    */
+  EIGEN_DEVICE_FUNC ParametrizedLine(const VectorType& origin, const VectorType& direction)
+    : m_origin(origin), m_direction(direction) {}
+
+  template <int OtherOptions>
+  EIGEN_DEVICE_FUNC explicit ParametrizedLine(const Hyperplane<Scalar_, AmbientDim_, OtherOptions>& hyperplane);
+
+  /** Constructs a parametrized line going from \a p0 to \a p1. */
+  EIGEN_DEVICE_FUNC static inline ParametrizedLine Through(const VectorType& p0, const VectorType& p1)
+  { return ParametrizedLine(p0, (p1-p0).normalized()); }
+
+  EIGEN_DEVICE_FUNC ~ParametrizedLine() {}
+
+  /** \returns the dimension in which the line holds */
+  EIGEN_DEVICE_FUNC inline Index dim() const { return m_direction.size(); }
+
+  EIGEN_DEVICE_FUNC const VectorType& origin() const { return m_origin; }
+  EIGEN_DEVICE_FUNC VectorType& origin() { return m_origin; }
+
+  EIGEN_DEVICE_FUNC const VectorType& direction() const { return m_direction; }
+  EIGEN_DEVICE_FUNC VectorType& direction() { return m_direction; }
+
+  /** \returns the squared distance of a point \a p to its projection onto the line \c *this.
+    * \sa distance()
+    */
+  EIGEN_DEVICE_FUNC RealScalar squaredDistance(const VectorType& p) const
+  {
+    VectorType diff = p - origin();
+    return (diff - direction().dot(diff) * direction()).squaredNorm();
+  }
+  /** \returns the distance of a point \a p to its projection onto the line \c *this.
+    * \sa squaredDistance()
+    */
+  EIGEN_DEVICE_FUNC RealScalar distance(const VectorType& p) const { EIGEN_USING_STD(sqrt) return sqrt(squaredDistance(p)); }
+
+  /** \returns the projection of a point \a p onto the line \c *this. */
+  EIGEN_DEVICE_FUNC VectorType projection(const VectorType& p) const
+  { return origin() + direction().dot(p-origin()) * direction(); }
+
+  EIGEN_DEVICE_FUNC VectorType pointAt(const Scalar& t) const;
+  
+  template <int OtherOptions>
+  EIGEN_DEVICE_FUNC Scalar intersectionParameter(const Hyperplane<Scalar_, AmbientDim_, OtherOptions>& hyperplane) const;
+
+  template <int OtherOptions>
+  EIGEN_DEVICE_FUNC Scalar intersection(const Hyperplane<Scalar_, AmbientDim_, OtherOptions>& hyperplane) const;
+
+  template <int OtherOptions>
+  EIGEN_DEVICE_FUNC VectorType intersectionPoint(const Hyperplane<Scalar_, AmbientDim_, OtherOptions>& hyperplane) const;
+
+  /** Applies the transformation matrix \a mat to \c *this and returns a reference to \c *this.
+    *
+    * \param mat the Dim x Dim transformation matrix
+    * \param traits specifies whether the matrix \a mat represents an #Isometry
+    *               or a more generic #Affine transformation. The default is #Affine.
+    */
+  template<typename XprType>
+  EIGEN_DEVICE_FUNC inline ParametrizedLine& transform(const MatrixBase<XprType>& mat, TransformTraits traits = Affine)
+  {
+    if (traits==Affine)
+      direction() = (mat * direction()).normalized();
+    else if (traits==Isometry)
+      direction() = mat * direction();
+    else
+    {
+      eigen_assert(0 && "invalid traits value in ParametrizedLine::transform()");
+    }
+    origin() = mat * origin();
+    return *this;
+  }
+
+  /** Applies the transformation \a t to \c *this and returns a reference to \c *this.
+    *
+    * \param t the transformation of dimension Dim
+    * \param traits specifies whether the transformation \a t represents an #Isometry
+    *               or a more generic #Affine transformation. The default is #Affine.
+    *               Other kind of transformations are not supported.
+    */
+  template<int TrOptions>
+  EIGEN_DEVICE_FUNC inline ParametrizedLine& transform(const Transform<Scalar,AmbientDimAtCompileTime,Affine,TrOptions>& t,
+                                                       TransformTraits traits = Affine)
+  {
+    transform(t.linear(), traits);
+    origin() += t.translation();
+    return *this;
+  }
+
+/** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<ParametrizedLine,
+           ParametrizedLine<NewScalarType,AmbientDimAtCompileTime,Options> >::type cast() const
+  {
+    return typename internal::cast_return_type<ParametrizedLine,
+                    ParametrizedLine<NewScalarType,AmbientDimAtCompileTime,Options> >::type(*this);
+  }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType,int OtherOptions>
+  EIGEN_DEVICE_FUNC inline explicit ParametrizedLine(const ParametrizedLine<OtherScalarType,AmbientDimAtCompileTime,OtherOptions>& other)
+  {
+    m_origin = other.origin().template cast<Scalar>();
+    m_direction = other.direction().template cast<Scalar>();
+  }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  EIGEN_DEVICE_FUNC bool isApprox(const ParametrizedLine& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
+  { return m_origin.isApprox(other.m_origin, prec) && m_direction.isApprox(other.m_direction, prec); }
+
+protected:
+
+  VectorType m_origin, m_direction;
+};
+
+/** Constructs a parametrized line from a 2D hyperplane
+  *
+  * \warning the ambient space must have dimension 2 such that the hyperplane actually describes a line
+  */
+template <typename Scalar_, int AmbientDim_, int Options_>
+template <int OtherOptions>
+EIGEN_DEVICE_FUNC inline ParametrizedLine<Scalar_, AmbientDim_,Options_>::ParametrizedLine(const Hyperplane<Scalar_, AmbientDim_,OtherOptions>& hyperplane)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(VectorType, 2)
+  direction() = hyperplane.normal().unitOrthogonal();
+  origin() = -hyperplane.normal()*hyperplane.offset();
+}
+
+/** \returns the point at \a t along this line
+  */
+template <typename Scalar_, int AmbientDim_, int Options_>
+EIGEN_DEVICE_FUNC inline typename ParametrizedLine<Scalar_, AmbientDim_,Options_>::VectorType
+ParametrizedLine<Scalar_, AmbientDim_,Options_>::pointAt(const Scalar_& t) const
+{
+  return origin() + (direction()*t); 
+}
+
+/** \returns the parameter value of the intersection between \c *this and the given \a hyperplane
+  */
+template <typename Scalar_, int AmbientDim_, int Options_>
+template <int OtherOptions>
+EIGEN_DEVICE_FUNC inline Scalar_ ParametrizedLine<Scalar_, AmbientDim_,Options_>::intersectionParameter(const Hyperplane<Scalar_, AmbientDim_, OtherOptions>& hyperplane) const
+{
+  return -(hyperplane.offset()+hyperplane.normal().dot(origin()))
+          / hyperplane.normal().dot(direction());
+}
+
+
+/** \deprecated use intersectionParameter()
+  * \returns the parameter value of the intersection between \c *this and the given \a hyperplane
+  */
+template <typename Scalar_, int AmbientDim_, int Options_>
+template <int OtherOptions>
+EIGEN_DEVICE_FUNC inline Scalar_ ParametrizedLine<Scalar_, AmbientDim_,Options_>::intersection(const Hyperplane<Scalar_, AmbientDim_, OtherOptions>& hyperplane) const
+{
+  return intersectionParameter(hyperplane);
+}
+
+/** \returns the point of the intersection between \c *this and the given hyperplane
+  */
+template <typename Scalar_, int AmbientDim_, int Options_>
+template <int OtherOptions>
+EIGEN_DEVICE_FUNC inline typename ParametrizedLine<Scalar_, AmbientDim_,Options_>::VectorType
+ParametrizedLine<Scalar_, AmbientDim_,Options_>::intersectionPoint(const Hyperplane<Scalar_, AmbientDim_, OtherOptions>& hyperplane) const
+{
+  return pointAt(intersectionParameter(hyperplane));
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_PARAMETRIZEDLINE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Quaternion.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Quaternion.h
new file mode 100644
index 0000000000000000000000000000000000000000..3413a5197337e5d15b77e8c1212039c34f9ba8c9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Quaternion.h
@@ -0,0 +1,876 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Mathieu Gautier <mathieu.gautier@cea.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_QUATERNION_H
+#define EIGEN_QUATERNION_H
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+
+/***************************************************************************
+* Definition of QuaternionBase<Derived>
+* The implementation is at the end of the file
+***************************************************************************/
+
+namespace internal {
+template<typename Other,
+         int OtherRows=Other::RowsAtCompileTime,
+         int OtherCols=Other::ColsAtCompileTime>
+struct quaternionbase_assign_impl;
+}
+
+/** \geometry_module \ingroup Geometry_Module
+  * \class QuaternionBase
+  * \brief Base class for quaternion expressions
+  * \tparam Derived derived type (CRTP)
+  * \sa class Quaternion
+  */
+template<class Derived>
+class QuaternionBase : public RotationBase<Derived, 3>
+{
+ public:
+  typedef RotationBase<Derived, 3> Base;
+
+  using Base::operator*;
+  using Base::derived;
+
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  typedef typename internal::traits<Derived>::Coefficients Coefficients;
+  typedef typename Coefficients::CoeffReturnType CoeffReturnType;
+  typedef std::conditional_t<bool(internal::traits<Derived>::Flags&LvalueBit),
+                                  Scalar&, CoeffReturnType> NonConstCoeffReturnType;
+
+
+  enum {
+    Flags = Eigen::internal::traits<Derived>::Flags
+  };
+
+ // typedef typename Matrix<Scalar,4,1> Coefficients;
+  /** the type of a 3D vector */
+  typedef Matrix<Scalar,3,1> Vector3;
+  /** the equivalent rotation matrix type */
+  typedef Matrix<Scalar,3,3> Matrix3;
+  /** the equivalent angle-axis type */
+  typedef AngleAxis<Scalar> AngleAxisType;
+
+
+
+  /** \returns the \c x coefficient */
+  EIGEN_DEVICE_FUNC inline CoeffReturnType x() const { return this->derived().coeffs().coeff(0); }
+  /** \returns the \c y coefficient */
+  EIGEN_DEVICE_FUNC inline CoeffReturnType y() const { return this->derived().coeffs().coeff(1); }
+  /** \returns the \c z coefficient */
+  EIGEN_DEVICE_FUNC inline CoeffReturnType z() const { return this->derived().coeffs().coeff(2); }
+  /** \returns the \c w coefficient */
+  EIGEN_DEVICE_FUNC inline CoeffReturnType w() const { return this->derived().coeffs().coeff(3); }
+
+  /** \returns a reference to the \c x coefficient (if Derived is a non-const lvalue) */
+  EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType x() { return this->derived().coeffs().x(); }
+  /** \returns a reference to the \c y coefficient (if Derived is a non-const lvalue) */
+  EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType y() { return this->derived().coeffs().y(); }
+  /** \returns a reference to the \c z coefficient (if Derived is a non-const lvalue) */
+  EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType z() { return this->derived().coeffs().z(); }
+  /** \returns a reference to the \c w coefficient (if Derived is a non-const lvalue) */
+  EIGEN_DEVICE_FUNC inline NonConstCoeffReturnType w() { return this->derived().coeffs().w(); }
+
+  /** \returns a read-only vector expression of the imaginary part (x,y,z) */
+  EIGEN_DEVICE_FUNC inline const VectorBlock<const Coefficients,3> vec() const { return coeffs().template head<3>(); }
+
+  /** \returns a vector expression of the imaginary part (x,y,z) */
+  EIGEN_DEVICE_FUNC inline VectorBlock<Coefficients,3> vec() { return coeffs().template head<3>(); }
+
+  /** \returns a read-only vector expression of the coefficients (x,y,z,w) */
+  EIGEN_DEVICE_FUNC inline const typename internal::traits<Derived>::Coefficients& coeffs() const { return derived().coeffs(); }
+
+  /** \returns a vector expression of the coefficients (x,y,z,w) */
+  EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Coefficients& coeffs() { return derived().coeffs(); }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE QuaternionBase<Derived>& operator=(const QuaternionBase<Derived>& other);
+  template<class OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const QuaternionBase<OtherDerived>& other);
+
+// disabled this copy operator as it is giving very strange compilation errors when compiling
+// test_stdvector with GCC 4.4.2. This looks like a GCC bug though, so feel free to re-enable it if it's
+// useful; however notice that we already have the templated operator= above and e.g. in MatrixBase
+// we didn't have to add, in addition to templated operator=, such a non-templated copy operator.
+//  Derived& operator=(const QuaternionBase& other)
+//  { return operator=<Derived>(other); }
+
+  EIGEN_DEVICE_FUNC Derived& operator=(const AngleAxisType& aa);
+  template<class OtherDerived> EIGEN_DEVICE_FUNC Derived& operator=(const MatrixBase<OtherDerived>& m);
+
+  /** \returns a quaternion representing an identity rotation
+    * \sa MatrixBase::Identity()
+    */
+  EIGEN_DEVICE_FUNC static inline Quaternion<Scalar> Identity() { return Quaternion<Scalar>(Scalar(1), Scalar(0), Scalar(0), Scalar(0)); }
+
+  /** \sa QuaternionBase::Identity(), MatrixBase::setIdentity()
+    */
+  EIGEN_DEVICE_FUNC inline QuaternionBase& setIdentity() { coeffs() << Scalar(0), Scalar(0), Scalar(0), Scalar(1); return *this; }
+
+  /** \returns the squared norm of the quaternion's coefficients
+    * \sa QuaternionBase::norm(), MatrixBase::squaredNorm()
+    */
+  EIGEN_DEVICE_FUNC inline Scalar squaredNorm() const { return coeffs().squaredNorm(); }
+
+  /** \returns the norm of the quaternion's coefficients
+    * \sa QuaternionBase::squaredNorm(), MatrixBase::norm()
+    */
+  EIGEN_DEVICE_FUNC inline Scalar norm() const { return coeffs().norm(); }
+
+  /** Normalizes the quaternion \c *this
+    * \sa normalized(), MatrixBase::normalize() */
+  EIGEN_DEVICE_FUNC inline void normalize() { coeffs().normalize(); }
+  /** \returns a normalized copy of \c *this
+    * \sa normalize(), MatrixBase::normalized() */
+  EIGEN_DEVICE_FUNC inline Quaternion<Scalar> normalized() const { return Quaternion<Scalar>(coeffs().normalized()); }
+
+    /** \returns the dot product of \c *this and \a other
+    * Geometrically speaking, the dot product of two unit quaternions
+    * corresponds to the cosine of half the angle between the two rotations.
+    * \sa angularDistance()
+    */
+  template<class OtherDerived> EIGEN_DEVICE_FUNC inline Scalar dot(const QuaternionBase<OtherDerived>& other) const { return coeffs().dot(other.coeffs()); }
+
+  template<class OtherDerived> EIGEN_DEVICE_FUNC Scalar angularDistance(const QuaternionBase<OtherDerived>& other) const;
+
+  /** \returns an equivalent 3x3 rotation matrix */
+  EIGEN_DEVICE_FUNC inline Matrix3 toRotationMatrix() const;
+
+  /** \returns the quaternion which transform \a a into \a b through a rotation */
+  template<typename Derived1, typename Derived2>
+  EIGEN_DEVICE_FUNC Derived& setFromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b);
+
+  template<class OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Quaternion<Scalar> operator* (const QuaternionBase<OtherDerived>& q) const;
+  template<class OtherDerived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator*= (const QuaternionBase<OtherDerived>& q);
+
+  /** \returns the quaternion describing the inverse rotation */
+  EIGEN_DEVICE_FUNC Quaternion<Scalar> inverse() const;
+
+  /** \returns the conjugated quaternion */
+  EIGEN_DEVICE_FUNC Quaternion<Scalar> conjugate() const;
+
+  template<class OtherDerived> EIGEN_DEVICE_FUNC Quaternion<Scalar> slerp(const Scalar& t, const QuaternionBase<OtherDerived>& other) const;
+
+  /** \returns true if each coefficients of \c *this and \a other are all exactly equal.
+    * \warning When using floating point scalar values you probably should rather use a
+    *          fuzzy comparison such as isApprox()
+    * \sa isApprox(), operator!= */
+  template<class OtherDerived>
+  EIGEN_DEVICE_FUNC inline bool operator==(const QuaternionBase<OtherDerived>& other) const
+  { return coeffs() == other.coeffs(); }
+
+  /** \returns true if at least one pair of coefficients of \c *this and \a other are not exactly equal to each other.
+    * \warning When using floating point scalar values you probably should rather use a
+    *          fuzzy comparison such as isApprox()
+    * \sa isApprox(), operator== */
+  template<class OtherDerived>
+  EIGEN_DEVICE_FUNC inline bool operator!=(const QuaternionBase<OtherDerived>& other) const
+  { return coeffs() != other.coeffs(); }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  template<class OtherDerived>
+  EIGEN_DEVICE_FUNC bool isApprox(const QuaternionBase<OtherDerived>& other, const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const
+  { return coeffs().isApprox(other.coeffs(), prec); }
+
+  /** return the result vector of \a v through the rotation*/
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Vector3 _transformVector(const Vector3& v) const;
+
+  #ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Derived,Quaternion<NewScalarType> >::type cast() const;
+
+  #else
+
+  template<typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline
+  std::enable_if_t<internal::is_same<Scalar,NewScalarType>::value,const Derived&> cast() const
+  {
+    return derived();
+  }
+
+  template<typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline
+  std::enable_if_t<!internal::is_same<Scalar,NewScalarType>::value,Quaternion<NewScalarType> > cast() const
+  {
+    return Quaternion<NewScalarType>(coeffs().template cast<NewScalarType>());
+  }
+  #endif
+
+#ifndef EIGEN_NO_IO
+  friend std::ostream& operator<<(std::ostream& s, const QuaternionBase<Derived>& q) {
+    s << q.x() << "i + " << q.y() << "j + " << q.z() << "k" << " + " << q.w();
+    return s;
+  }
+#endif
+
+#ifdef EIGEN_QUATERNIONBASE_PLUGIN
+# include EIGEN_QUATERNIONBASE_PLUGIN
+#endif
+protected:
+  EIGEN_DEFAULT_COPY_CONSTRUCTOR(QuaternionBase)
+  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(QuaternionBase)
+};
+
+/***************************************************************************
+* Definition/implementation of Quaternion<Scalar>
+***************************************************************************/
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \class Quaternion
+  *
+  * \brief The quaternion class used to represent 3D orientations and rotations
+  *
+  * \tparam Scalar_ the scalar type, i.e., the type of the coefficients
+  * \tparam Options_ controls the memory alignment of the coefficients. Can be \# AutoAlign or \# DontAlign. Default is AutoAlign.
+  *
+  * This class represents a quaternion \f$ w+xi+yj+zk \f$ that is a convenient representation of
+  * orientations and rotations of objects in three dimensions. Compared to other representations
+  * like Euler angles or 3x3 matrices, quaternions offer the following advantages:
+  * \li \b compact storage (4 scalars)
+  * \li \b efficient to compose (28 flops),
+  * \li \b stable spherical interpolation
+  *
+  * The following two typedefs are provided for convenience:
+  * \li \c Quaternionf for \c float
+  * \li \c Quaterniond for \c double
+  *
+  * \warning Operations interpreting the quaternion as rotation have undefined behavior if the quaternion is not normalized.
+  *
+  * \sa  class AngleAxis, class Transform
+  */
+
+namespace internal {
+template<typename Scalar_,int Options_>
+struct traits<Quaternion<Scalar_,Options_> >
+{
+  typedef Quaternion<Scalar_,Options_> PlainObject;
+  typedef Scalar_ Scalar;
+  typedef Matrix<Scalar_,4,1,Options_> Coefficients;
+  enum{
+    Alignment = internal::traits<Coefficients>::Alignment,
+    Flags = LvalueBit
+  };
+};
+}
+
+template<typename Scalar_, int Options_>
+class Quaternion : public QuaternionBase<Quaternion<Scalar_,Options_> >
+{
+public:
+  typedef QuaternionBase<Quaternion<Scalar_,Options_> > Base;
+  enum { NeedsAlignment = internal::traits<Quaternion>::Alignment>0 };
+
+  typedef Scalar_ Scalar;
+
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Quaternion)
+  using Base::operator*=;
+
+  typedef typename internal::traits<Quaternion>::Coefficients Coefficients;
+  typedef typename Base::AngleAxisType AngleAxisType;
+
+  /** Default constructor leaving the quaternion uninitialized. */
+  EIGEN_DEVICE_FUNC inline Quaternion() {}
+
+  /** Constructs and initializes the quaternion \f$ w+xi+yj+zk \f$ from
+    * its four coefficients \a w, \a x, \a y and \a z.
+    *
+    * \warning Note the order of the arguments: the real \a w coefficient first,
+    * while internally the coefficients are stored in the following order:
+    * [\c x, \c y, \c z, \c w]
+    */
+  EIGEN_DEVICE_FUNC inline Quaternion(const Scalar& w, const Scalar& x, const Scalar& y, const Scalar& z) : m_coeffs(x, y, z, w){}
+
+  /** Constructs and initializes a quaternion from its real part as a scalar,
+   *  and its imaginary part as a 3-vector [\c x, \c y, \c z]
+   */
+  template <typename Derived>
+  EIGEN_DEVICE_FUNC inline Quaternion(const Scalar& w, const Eigen::MatrixBase<Derived>& vec)
+      : m_coeffs(vec.x(), vec.y(), vec.z(), w) {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Derived, 3);
+  }
+
+  /** Constructs and initialize a quaternion from the array data */
+  EIGEN_DEVICE_FUNC explicit inline Quaternion(const Scalar* data) : m_coeffs(data) {}
+
+  /** Copy constructor */
+  template<class Derived> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Quaternion(const QuaternionBase<Derived>& other) { this->Base::operator=(other); }
+
+  /** Constructs and initializes a quaternion from the angle-axis \a aa */
+  EIGEN_DEVICE_FUNC explicit inline Quaternion(const AngleAxisType& aa) { *this = aa; }
+
+  /** Constructs and initializes a quaternion from either:
+    *  - a rotation matrix expression,
+    *  - a 4D vector expression representing quaternion coefficients in the order [\c x, \c y, \c z, \c w].
+    */
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC explicit inline Quaternion(const MatrixBase<Derived>& other) { *this = other; }
+
+  /** Explicit copy constructor with scalar conversion */
+  template<typename OtherScalar, int OtherOptions>
+  EIGEN_DEVICE_FUNC explicit inline Quaternion(const Quaternion<OtherScalar, OtherOptions>& other)
+  { m_coeffs = other.coeffs().template cast<Scalar>(); }
+
+  // We define a copy constructor, which means we don't get an implicit move constructor or assignment operator.
+  /** Default move constructor */
+  EIGEN_DEVICE_FUNC inline Quaternion(Quaternion&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_constructible<Scalar>::value)
+    : m_coeffs(std::move(other.coeffs()))
+  {}
+
+  /** Default move assignment operator */
+  EIGEN_DEVICE_FUNC Quaternion& operator=(Quaternion&& other) EIGEN_NOEXCEPT_IF(std::is_nothrow_move_assignable<Scalar>::value)
+  {
+    m_coeffs = std::move(other.coeffs());
+    return *this;
+  }
+
+  EIGEN_DEVICE_FUNC static Quaternion UnitRandom();
+
+  template<typename Derived1, typename Derived2>
+  EIGEN_DEVICE_FUNC static Quaternion FromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b);
+
+  EIGEN_DEVICE_FUNC inline Coefficients& coeffs() { return m_coeffs;}
+  EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs;}
+
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF(bool(NeedsAlignment))
+
+#ifdef EIGEN_QUATERNION_PLUGIN
+# include EIGEN_QUATERNION_PLUGIN
+#endif
+
+protected:
+  Coefficients m_coeffs;
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  EIGEN_STATIC_ASSERT( (Options_ & DontAlign) == Options_,
+                       INVALID_MATRIX_TEMPLATE_PARAMETERS)
+#endif
+};
+
+/** \ingroup Geometry_Module
+  * single precision quaternion type */
+typedef Quaternion<float> Quaternionf;
+/** \ingroup Geometry_Module
+  * double precision quaternion type */
+typedef Quaternion<double> Quaterniond;
+
+/***************************************************************************
+* Specialization of Map<Quaternion<Scalar>>
+***************************************************************************/
+
+namespace internal {
+  template<typename Scalar_, int Options_>
+  struct traits<Map<Quaternion<Scalar_>, Options_> > : traits<Quaternion<Scalar_, (int(Options_)&Aligned)==Aligned ? AutoAlign : DontAlign> >
+  {
+    typedef Map<Matrix<Scalar_,4,1>, Options_> Coefficients;
+  };
+}
+
+namespace internal {
+  template<typename Scalar_, int Options_>
+  struct traits<Map<const Quaternion<Scalar_>, Options_> > : traits<Quaternion<Scalar_, (int(Options_)&Aligned)==Aligned ? AutoAlign : DontAlign> >
+  {
+    typedef Map<const Matrix<Scalar_,4,1>, Options_> Coefficients;
+    typedef traits<Quaternion<Scalar_, (int(Options_)&Aligned)==Aligned ? AutoAlign : DontAlign> > TraitsBase;
+    enum {
+      Flags = TraitsBase::Flags & ~LvalueBit
+    };
+  };
+}
+
+/** \ingroup Geometry_Module
+  * \brief Quaternion expression mapping a constant memory buffer
+  *
+  * \tparam Scalar_ the type of the Quaternion coefficients
+  * \tparam Options_ see class Map
+  *
+  * This is a specialization of class Map for Quaternion. This class allows to view
+  * a 4 scalar memory buffer as an Eigen's Quaternion object.
+  *
+  * \sa class Map, class Quaternion, class QuaternionBase
+  */
+template<typename Scalar_, int Options_>
+class Map<const Quaternion<Scalar_>, Options_ >
+  : public QuaternionBase<Map<const Quaternion<Scalar_>, Options_> >
+{
+  public:
+    typedef QuaternionBase<Map<const Quaternion<Scalar_>, Options_> > Base;
+
+    typedef Scalar_ Scalar;
+    typedef typename internal::traits<Map>::Coefficients Coefficients;
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)
+    using Base::operator*=;
+
+    /** Constructs a Mapped Quaternion object from the pointer \a coeffs
+      *
+      * The pointer \a coeffs must reference the four coefficients of Quaternion in the following order:
+      * \code *coeffs == {x, y, z, w} \endcode
+      *
+      * If the template parameter Options_ is set to #Aligned, then the pointer coeffs must be aligned. */
+    EIGEN_DEVICE_FUNC explicit EIGEN_STRONG_INLINE Map(const Scalar* coeffs) : m_coeffs(coeffs) {}
+
+    EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs;}
+
+  protected:
+    const Coefficients m_coeffs;
+};
+
+/** \ingroup Geometry_Module
+  * \brief Expression of a quaternion from a memory buffer
+  *
+  * \tparam Scalar_ the type of the Quaternion coefficients
+  * \tparam Options_ see class Map
+  *
+  * This is a specialization of class Map for Quaternion. This class allows to view
+  * a 4 scalar memory buffer as an Eigen's  Quaternion object.
+  *
+  * \sa class Map, class Quaternion, class QuaternionBase
+  */
+template<typename Scalar_, int Options_>
+class Map<Quaternion<Scalar_>, Options_ >
+  : public QuaternionBase<Map<Quaternion<Scalar_>, Options_> >
+{
+  public:
+    typedef QuaternionBase<Map<Quaternion<Scalar_>, Options_> > Base;
+
+    typedef Scalar_ Scalar;
+    typedef typename internal::traits<Map>::Coefficients Coefficients;
+    EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Map)
+    using Base::operator*=;
+
+    /** Constructs a Mapped Quaternion object from the pointer \a coeffs
+      *
+      * The pointer \a coeffs must reference the four coefficients of Quaternion in the following order:
+      * \code *coeffs == {x, y, z, w} \endcode
+      *
+      * If the template parameter Options_ is set to #Aligned, then the pointer coeffs must be aligned. */
+    EIGEN_DEVICE_FUNC explicit EIGEN_STRONG_INLINE Map(Scalar* coeffs) : m_coeffs(coeffs) {}
+
+    EIGEN_DEVICE_FUNC inline Coefficients& coeffs() { return m_coeffs; }
+    EIGEN_DEVICE_FUNC inline const Coefficients& coeffs() const { return m_coeffs; }
+
+  protected:
+    Coefficients m_coeffs;
+};
+
+/** \ingroup Geometry_Module
+  * Map an unaligned array of single precision scalars as a quaternion */
+typedef Map<Quaternion<float>, 0>         QuaternionMapf;
+/** \ingroup Geometry_Module
+  * Map an unaligned array of double precision scalars as a quaternion */
+typedef Map<Quaternion<double>, 0>        QuaternionMapd;
+/** \ingroup Geometry_Module
+  * Map a 16-byte aligned array of single precision scalars as a quaternion */
+typedef Map<Quaternion<float>, Aligned>   QuaternionMapAlignedf;
+/** \ingroup Geometry_Module
+  * Map a 16-byte aligned array of double precision scalars as a quaternion */
+typedef Map<Quaternion<double>, Aligned>  QuaternionMapAlignedd;
+
+/***************************************************************************
+* Implementation of QuaternionBase methods
+***************************************************************************/
+
+// Generic Quaternion * Quaternion product
+// This product can be specialized for a given architecture via the Arch template argument.
+namespace internal {
+template<int Arch, class Derived1, class Derived2, typename Scalar> struct quat_product
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Quaternion<Scalar> run(const QuaternionBase<Derived1>& a, const QuaternionBase<Derived2>& b){
+    return Quaternion<Scalar>
+    (
+      a.w() * b.w() - a.x() * b.x() - a.y() * b.y() - a.z() * b.z(),
+      a.w() * b.x() + a.x() * b.w() + a.y() * b.z() - a.z() * b.y(),
+      a.w() * b.y() + a.y() * b.w() + a.z() * b.x() - a.x() * b.z(),
+      a.w() * b.z() + a.z() * b.w() + a.x() * b.y() - a.y() * b.x()
+    );
+  }
+};
+}
+
+/** \returns the concatenation of two rotations as a quaternion-quaternion product */
+template <class Derived>
+template <class OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Quaternion<typename internal::traits<Derived>::Scalar>
+QuaternionBase<Derived>::operator* (const QuaternionBase<OtherDerived>& other) const
+{
+  EIGEN_STATIC_ASSERT((internal::is_same<typename Derived::Scalar, typename OtherDerived::Scalar>::value),
+   YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+  return internal::quat_product<Architecture::Target, Derived, OtherDerived,
+                         typename internal::traits<Derived>::Scalar>::run(*this, other);
+}
+
+/** \sa operator*(Quaternion) */
+template <class Derived>
+template <class OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& QuaternionBase<Derived>::operator*= (const QuaternionBase<OtherDerived>& other)
+{
+  derived() = derived() * other.derived();
+  return derived();
+}
+
+/** Rotation of a vector by a quaternion.
+  * \remarks If the quaternion is used to rotate several points (>1)
+  * then it is much more efficient to first convert it to a 3x3 Matrix.
+  * Comparison of the operation cost for n transformations:
+  *   - Quaternion2:    30n
+  *   - Via a Matrix3: 24 + 15n
+  */
+template <class Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename QuaternionBase<Derived>::Vector3
+QuaternionBase<Derived>::_transformVector(const Vector3& v) const
+{
+    // Note that this algorithm comes from the optimization by hand
+    // of the conversion to a Matrix followed by a Matrix/Vector product.
+    // It appears to be much faster than the common algorithm found
+    // in the literature (30 versus 39 flops). It also requires two
+    // Vector3 as temporaries.
+    Vector3 uv = this->vec().cross(v);
+    uv += uv;
+    return v + this->w() * uv + this->vec().cross(uv);
+}
+
+template<class Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE QuaternionBase<Derived>& QuaternionBase<Derived>::operator=(const QuaternionBase<Derived>& other)
+{
+  coeffs() = other.coeffs();
+  return derived();
+}
+
+template<class Derived>
+template<class OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& QuaternionBase<Derived>::operator=(const QuaternionBase<OtherDerived>& other)
+{
+  coeffs() = other.coeffs();
+  return derived();
+}
+
+/** Set \c *this from an angle-axis \a aa and returns a reference to \c *this
+  */
+template<class Derived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& QuaternionBase<Derived>::operator=(const AngleAxisType& aa)
+{
+  EIGEN_USING_STD(cos)
+  EIGEN_USING_STD(sin)
+  Scalar ha = Scalar(0.5)*aa.angle(); // Scalar(0.5) to suppress precision loss warnings
+  this->w() = cos(ha);
+  this->vec() = sin(ha) * aa.axis();
+  return derived();
+}
+
+/** Set \c *this from the expression \a xpr:
+  *   - if \a xpr is a 4x1 vector, then \a xpr is assumed to be a quaternion
+  *   - if \a xpr is a 3x3 matrix, then \a xpr is assumed to be rotation matrix
+  *     and \a xpr is converted to a quaternion
+  */
+
+template<class Derived>
+template<class MatrixDerived>
+EIGEN_DEVICE_FUNC inline Derived& QuaternionBase<Derived>::operator=(const MatrixBase<MatrixDerived>& xpr)
+{
+  EIGEN_STATIC_ASSERT((internal::is_same<typename Derived::Scalar, typename MatrixDerived::Scalar>::value),
+   YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+  internal::quaternionbase_assign_impl<MatrixDerived>::run(*this, xpr.derived());
+  return derived();
+}
+
+/** Convert the quaternion to a 3x3 rotation matrix. The quaternion is required to
+  * be normalized, otherwise the result is undefined.
+  */
+template<class Derived>
+EIGEN_DEVICE_FUNC inline typename QuaternionBase<Derived>::Matrix3
+QuaternionBase<Derived>::toRotationMatrix(void) const
+{
+  // NOTE if inlined, then gcc 4.2 and 4.4 get rid of the temporary (not gcc 4.3 !!)
+  // if not inlined then the cost of the return by value is huge ~ +35%,
+  // however, not inlining this function is an order of magnitude slower, so
+  // it has to be inlined, and so the return by value is not an issue
+  Matrix3 res;
+
+  const Scalar tx  = Scalar(2)*this->x();
+  const Scalar ty  = Scalar(2)*this->y();
+  const Scalar tz  = Scalar(2)*this->z();
+  const Scalar twx = tx*this->w();
+  const Scalar twy = ty*this->w();
+  const Scalar twz = tz*this->w();
+  const Scalar txx = tx*this->x();
+  const Scalar txy = ty*this->x();
+  const Scalar txz = tz*this->x();
+  const Scalar tyy = ty*this->y();
+  const Scalar tyz = tz*this->y();
+  const Scalar tzz = tz*this->z();
+
+  res.coeffRef(0,0) = Scalar(1)-(tyy+tzz);
+  res.coeffRef(0,1) = txy-twz;
+  res.coeffRef(0,2) = txz+twy;
+  res.coeffRef(1,0) = txy+twz;
+  res.coeffRef(1,1) = Scalar(1)-(txx+tzz);
+  res.coeffRef(1,2) = tyz-twx;
+  res.coeffRef(2,0) = txz-twy;
+  res.coeffRef(2,1) = tyz+twx;
+  res.coeffRef(2,2) = Scalar(1)-(txx+tyy);
+
+  return res;
+}
+
+/** Sets \c *this to be a quaternion representing a rotation between
+  * the two arbitrary vectors \a a and \a b. In other words, the built
+  * rotation represent a rotation sending the line of direction \a a
+  * to the line of direction \a b, both lines passing through the origin.
+  *
+  * \returns a reference to \c *this.
+  *
+  * Note that the two input vectors do \b not have to be normalized, and
+  * do not need to have the same norm.
+  */
+template<class Derived>
+template<typename Derived1, typename Derived2>
+EIGEN_DEVICE_FUNC inline Derived& QuaternionBase<Derived>::setFromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b)
+{
+  EIGEN_USING_STD(sqrt)
+  Vector3 v0 = a.normalized();
+  Vector3 v1 = b.normalized();
+  Scalar c = v1.dot(v0);
+
+  // if dot == -1, vectors are nearly opposites
+  // => accurately compute the rotation axis by computing the
+  //    intersection of the two planes. This is done by solving:
+  //       x^T v0 = 0
+  //       x^T v1 = 0
+  //    under the constraint:
+  //       ||x|| = 1
+  //    which yields a singular value problem
+  if (c < Scalar(-1)+NumTraits<Scalar>::dummy_precision())
+  {
+    c = numext::maxi(c,Scalar(-1));
+    Matrix<Scalar,2,3> m; m << v0.transpose(), v1.transpose();
+    JacobiSVD<Matrix<Scalar,2,3>, ComputeFullV> svd(m);
+    Vector3 axis = svd.matrixV().col(2);
+
+    Scalar w2 = (Scalar(1)+c)*Scalar(0.5);
+    this->w() = sqrt(w2);
+    this->vec() = axis * sqrt(Scalar(1) - w2);
+    return derived();
+  }
+  Vector3 axis = v0.cross(v1);
+  Scalar s = sqrt((Scalar(1)+c)*Scalar(2));
+  Scalar invs = Scalar(1)/s;
+  this->vec() = axis * invs;
+  this->w() = s * Scalar(0.5);
+
+  return derived();
+}
+
+/** \returns a random unit quaternion following a uniform distribution law on SO(3)
+  *
+  * \note The implementation is based on http://planning.cs.uiuc.edu/node198.html
+  */
+template<typename Scalar, int Options>
+EIGEN_DEVICE_FUNC Quaternion<Scalar,Options> Quaternion<Scalar,Options>::UnitRandom()
+{
+  EIGEN_USING_STD(sqrt)
+  EIGEN_USING_STD(sin)
+  EIGEN_USING_STD(cos)
+  const Scalar u1 = internal::random<Scalar>(0, 1),
+               u2 = internal::random<Scalar>(0, 2*EIGEN_PI),
+               u3 = internal::random<Scalar>(0, 2*EIGEN_PI);
+  const Scalar a = sqrt(Scalar(1) - u1),
+               b = sqrt(u1);
+  return Quaternion (a * sin(u2), a * cos(u2), b * sin(u3), b * cos(u3));
+}
+
+
+/** Returns a quaternion representing a rotation between
+  * the two arbitrary vectors \a a and \a b. In other words, the built
+  * rotation represent a rotation sending the line of direction \a a
+  * to the line of direction \a b, both lines passing through the origin.
+  *
+  * \returns resulting quaternion
+  *
+  * Note that the two input vectors do \b not have to be normalized, and
+  * do not need to have the same norm.
+  */
+template<typename Scalar, int Options>
+template<typename Derived1, typename Derived2>
+EIGEN_DEVICE_FUNC Quaternion<Scalar,Options> Quaternion<Scalar,Options>::FromTwoVectors(const MatrixBase<Derived1>& a, const MatrixBase<Derived2>& b)
+{
+    Quaternion quat;
+    quat.setFromTwoVectors(a, b);
+    return quat;
+}
+
+
+/** \returns the multiplicative inverse of \c *this
+  * Note that in most cases, i.e., if you simply want the opposite rotation,
+  * and/or the quaternion is normalized, then it is enough to use the conjugate.
+  *
+  * \sa QuaternionBase::conjugate()
+  */
+template <class Derived>
+EIGEN_DEVICE_FUNC inline Quaternion<typename internal::traits<Derived>::Scalar> QuaternionBase<Derived>::inverse() const
+{
+  // FIXME should this function be called multiplicativeInverse and conjugate() be called inverse() or opposite()  ??
+  Scalar n2 = this->squaredNorm();
+  if (n2 > Scalar(0))
+    return Quaternion<Scalar>(conjugate().coeffs() / n2);
+  else
+  {
+    // return an invalid result to flag the error
+    return Quaternion<Scalar>(Coefficients::Zero());
+  }
+}
+
+// Generic conjugate of a Quaternion
+namespace internal {
+template<int Arch, class Derived, typename Scalar> struct quat_conj
+{
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE Quaternion<Scalar> run(const QuaternionBase<Derived>& q){
+    return Quaternion<Scalar>(q.w(),-q.x(),-q.y(),-q.z());
+  }
+};
+}
+                         
+/** \returns the conjugate of the \c *this which is equal to the multiplicative inverse
+  * if the quaternion is normalized.
+  * The conjugate of a quaternion represents the opposite rotation.
+  *
+  * \sa Quaternion2::inverse()
+  */
+template <class Derived>
+EIGEN_DEVICE_FUNC inline Quaternion<typename internal::traits<Derived>::Scalar>
+QuaternionBase<Derived>::conjugate() const
+{
+  return internal::quat_conj<Architecture::Target, Derived,
+                         typename internal::traits<Derived>::Scalar>::run(*this);
+                         
+}
+
+/** \returns the angle (in radian) between two rotations
+  * \sa dot()
+  */
+template <class Derived>
+template <class OtherDerived>
+EIGEN_DEVICE_FUNC inline typename internal::traits<Derived>::Scalar
+QuaternionBase<Derived>::angularDistance(const QuaternionBase<OtherDerived>& other) const
+{
+  EIGEN_USING_STD(atan2)
+  Quaternion<Scalar> d = (*this) * other.conjugate();
+  return Scalar(2) * atan2( d.vec().norm(), numext::abs(d.w()) );
+}
+
+ 
+    
+/** \returns the spherical linear interpolation between the two quaternions
+  * \c *this and \a other at the parameter \a t in [0;1].
+  * 
+  * This represents an interpolation for a constant motion between \c *this and \a other,
+  * see also http://en.wikipedia.org/wiki/Slerp.
+  */
+template <class Derived>
+template <class OtherDerived>
+EIGEN_DEVICE_FUNC Quaternion<typename internal::traits<Derived>::Scalar>
+QuaternionBase<Derived>::slerp(const Scalar& t, const QuaternionBase<OtherDerived>& other) const
+{
+  EIGEN_USING_STD(acos)
+  EIGEN_USING_STD(sin)
+  const Scalar one = Scalar(1) - NumTraits<Scalar>::epsilon();
+  Scalar d = this->dot(other);
+  Scalar absD = numext::abs(d);
+
+  Scalar scale0;
+  Scalar scale1;
+
+  if(absD>=one)
+  {
+    scale0 = Scalar(1) - t;
+    scale1 = t;
+  }
+  else
+  {
+    // theta is the angle between the 2 quaternions
+    Scalar theta = acos(absD);
+    Scalar sinTheta = sin(theta);
+
+    scale0 = sin( ( Scalar(1) - t ) * theta) / sinTheta;
+    scale1 = sin( ( t * theta) ) / sinTheta;
+  }
+  if(d<Scalar(0)) scale1 = -scale1;
+
+  return Quaternion<Scalar>(scale0 * coeffs() + scale1 * other.coeffs());
+}
+
+namespace internal {
+
+// set from a rotation matrix
+template<typename Other>
+struct quaternionbase_assign_impl<Other,3,3>
+{
+  typedef typename Other::Scalar Scalar;
+  template<class Derived> EIGEN_DEVICE_FUNC static inline void run(QuaternionBase<Derived>& q, const Other& a_mat)
+  {
+    const typename internal::nested_eval<Other,2>::type mat(a_mat);
+    EIGEN_USING_STD(sqrt)
+    // This algorithm comes from  "Quaternion Calculus and Fast Animation",
+    // Ken Shoemake, 1987 SIGGRAPH course notes
+    Scalar t = mat.trace();
+    if (t > Scalar(0))
+    {
+      t = sqrt(t + Scalar(1.0));
+      q.w() = Scalar(0.5)*t;
+      t = Scalar(0.5)/t;
+      q.x() = (mat.coeff(2,1) - mat.coeff(1,2)) * t;
+      q.y() = (mat.coeff(0,2) - mat.coeff(2,0)) * t;
+      q.z() = (mat.coeff(1,0) - mat.coeff(0,1)) * t;
+    }
+    else
+    {
+      Index i = 0;
+      if (mat.coeff(1,1) > mat.coeff(0,0))
+        i = 1;
+      if (mat.coeff(2,2) > mat.coeff(i,i))
+        i = 2;
+      Index j = (i+1)%3;
+      Index k = (j+1)%3;
+
+      t = sqrt(mat.coeff(i,i)-mat.coeff(j,j)-mat.coeff(k,k) + Scalar(1.0));
+      q.coeffs().coeffRef(i) = Scalar(0.5) * t;
+      t = Scalar(0.5)/t;
+      q.w() = (mat.coeff(k,j)-mat.coeff(j,k))*t;
+      q.coeffs().coeffRef(j) = (mat.coeff(j,i)+mat.coeff(i,j))*t;
+      q.coeffs().coeffRef(k) = (mat.coeff(k,i)+mat.coeff(i,k))*t;
+    }
+  }
+};
+
+// set from a vector of coefficients assumed to be a quaternion
+template<typename Other>
+struct quaternionbase_assign_impl<Other,4,1>
+{
+  typedef typename Other::Scalar Scalar;
+  template<class Derived> EIGEN_DEVICE_FUNC static inline void run(QuaternionBase<Derived>& q, const Other& vec)
+  {
+    q.coeffs() = vec;
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_QUATERNION_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Rotation2D.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Rotation2D.h
new file mode 100644
index 0000000000000000000000000000000000000000..aa7f8635dfe52cc8913aab4705c0d6d57c46f205
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Rotation2D.h
@@ -0,0 +1,201 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ROTATION2D_H
+#define EIGEN_ROTATION2D_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \class Rotation2D
+  *
+  * \brief Represents a rotation/orientation in a 2 dimensional space.
+  *
+  * \tparam Scalar_ the scalar type, i.e., the type of the coefficients
+  *
+  * This class is equivalent to a single scalar representing a counter clock wise rotation
+  * as a single angle in radian. It provides some additional features such as the automatic
+  * conversion from/to a 2x2 rotation matrix. Moreover this class aims to provide a similar
+  * interface to Quaternion in order to facilitate the writing of generic algorithms
+  * dealing with rotations.
+  *
+  * \sa class Quaternion, class Transform
+  */
+
+namespace internal {
+
+template<typename Scalar_> struct traits<Rotation2D<Scalar_> >
+{
+  typedef Scalar_ Scalar;
+};
+} // end namespace internal
+
+template<typename Scalar_>
+class Rotation2D : public RotationBase<Rotation2D<Scalar_>,2>
+{
+  typedef RotationBase<Rotation2D<Scalar_>,2> Base;
+
+public:
+
+  using Base::operator*;
+
+  enum { Dim = 2 };
+  /** the scalar type of the coefficients */
+  typedef Scalar_ Scalar;
+  typedef Matrix<Scalar,2,1> Vector2;
+  typedef Matrix<Scalar,2,2> Matrix2;
+
+protected:
+
+  Scalar m_angle;
+
+public:
+
+  /** Construct a 2D counter clock wise rotation from the angle \a a in radian. */
+  EIGEN_DEVICE_FUNC explicit inline Rotation2D(const Scalar& a) : m_angle(a) {}
+  
+  /** Default constructor wihtout initialization. The represented rotation is undefined. */
+  EIGEN_DEVICE_FUNC Rotation2D() {}
+
+  /** Construct a 2D rotation from a 2x2 rotation matrix \a mat.
+    *
+    * \sa fromRotationMatrix()
+    */
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC explicit Rotation2D(const MatrixBase<Derived>& m)
+  {
+    fromRotationMatrix(m.derived());
+  }
+
+  /** \returns the rotation angle */
+  EIGEN_DEVICE_FUNC inline Scalar angle() const { return m_angle; }
+
+  /** \returns a read-write reference to the rotation angle */
+  EIGEN_DEVICE_FUNC inline Scalar& angle() { return m_angle; }
+  
+  /** \returns the rotation angle in [0,2pi] */
+  EIGEN_DEVICE_FUNC inline Scalar smallestPositiveAngle() const {
+    Scalar tmp = numext::fmod(m_angle,Scalar(2*EIGEN_PI));
+    return tmp<Scalar(0) ? tmp + Scalar(2*EIGEN_PI) : tmp;
+  }
+  
+  /** \returns the rotation angle in [-pi,pi] */
+  EIGEN_DEVICE_FUNC inline Scalar smallestAngle() const {
+    Scalar tmp = numext::fmod(m_angle,Scalar(2*EIGEN_PI));
+    if(tmp>Scalar(EIGEN_PI))       tmp -= Scalar(2*EIGEN_PI);
+    else if(tmp<-Scalar(EIGEN_PI)) tmp += Scalar(2*EIGEN_PI);
+    return tmp;
+  }
+
+  /** \returns the inverse rotation */
+  EIGEN_DEVICE_FUNC inline Rotation2D inverse() const { return Rotation2D(-m_angle); }
+
+  /** Concatenates two rotations */
+  EIGEN_DEVICE_FUNC inline Rotation2D operator*(const Rotation2D& other) const
+  { return Rotation2D(m_angle + other.m_angle); }
+
+  /** Concatenates two rotations */
+  EIGEN_DEVICE_FUNC inline Rotation2D& operator*=(const Rotation2D& other)
+  { m_angle += other.m_angle; return *this; }
+
+  /** Applies the rotation to a 2D vector */
+  EIGEN_DEVICE_FUNC Vector2 operator* (const Vector2& vec) const
+  { return toRotationMatrix() * vec; }
+  
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC Rotation2D& fromRotationMatrix(const MatrixBase<Derived>& m);
+  EIGEN_DEVICE_FUNC Matrix2 toRotationMatrix() const;
+
+  /** Set \c *this from a 2x2 rotation matrix \a mat.
+    * In other words, this function extract the rotation angle from the rotation matrix.
+    *
+    * This method is an alias for fromRotationMatrix()
+    *
+    * \sa fromRotationMatrix()
+    */
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC Rotation2D& operator=(const  MatrixBase<Derived>& m)
+  { return fromRotationMatrix(m.derived()); }
+
+  /** \returns the spherical interpolation between \c *this and \a other using
+    * parameter \a t. It is in fact equivalent to a linear interpolation.
+    */
+  EIGEN_DEVICE_FUNC inline Rotation2D slerp(const Scalar& t, const Rotation2D& other) const
+  {
+    Scalar dist = Rotation2D(other.m_angle-m_angle).smallestAngle();
+    return Rotation2D(m_angle + dist*t);
+  }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Rotation2D,Rotation2D<NewScalarType> >::type cast() const
+  { return typename internal::cast_return_type<Rotation2D,Rotation2D<NewScalarType> >::type(*this); }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType>
+  EIGEN_DEVICE_FUNC inline explicit Rotation2D(const Rotation2D<OtherScalarType>& other)
+  {
+    m_angle = Scalar(other.angle());
+  }
+
+  EIGEN_DEVICE_FUNC static inline Rotation2D Identity() { return Rotation2D(0); }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  EIGEN_DEVICE_FUNC bool isApprox(const Rotation2D& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
+  { return internal::isApprox(m_angle,other.m_angle, prec); }
+  
+};
+
+/** \ingroup Geometry_Module
+  * single precision 2D rotation type */
+typedef Rotation2D<float> Rotation2Df;
+/** \ingroup Geometry_Module
+  * double precision 2D rotation type */
+typedef Rotation2D<double> Rotation2Dd;
+
+/** Set \c *this from a 2x2 rotation matrix \a mat.
+  * In other words, this function extract the rotation angle
+  * from the rotation matrix.
+  */
+template<typename Scalar>
+template<typename Derived>
+EIGEN_DEVICE_FUNC Rotation2D<Scalar>& Rotation2D<Scalar>::fromRotationMatrix(const MatrixBase<Derived>& mat)
+{
+  EIGEN_USING_STD(atan2)
+  EIGEN_STATIC_ASSERT(Derived::RowsAtCompileTime==2 && Derived::ColsAtCompileTime==2,YOU_MADE_A_PROGRAMMING_MISTAKE)
+  m_angle = atan2(mat.coeff(1,0), mat.coeff(0,0));
+  return *this;
+}
+
+/** Constructs and \returns an equivalent 2x2 rotation matrix.
+  */
+template<typename Scalar>
+typename Rotation2D<Scalar>::Matrix2
+EIGEN_DEVICE_FUNC Rotation2D<Scalar>::toRotationMatrix(void) const
+{
+  EIGEN_USING_STD(sin)
+  EIGEN_USING_STD(cos)
+  Scalar sinA = sin(m_angle);
+  Scalar cosA = cos(m_angle);
+  return (Matrix2() << cosA, -sinA, sinA, cosA).finished();
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_ROTATION2D_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/RotationBase.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/RotationBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..f21277f41b9823e36c8704752733f22542de31c1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/RotationBase.h
@@ -0,0 +1,208 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ROTATIONBASE_H
+#define EIGEN_ROTATIONBASE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+// forward declaration
+namespace internal {
+template<typename RotationDerived, typename MatrixType, bool IsVector=MatrixType::IsVectorAtCompileTime>
+struct rotation_base_generic_product_selector;
+}
+
+/** \class RotationBase
+  *
+  * \brief Common base class for compact rotation representations
+  *
+  * \tparam Derived is the derived type, i.e., a rotation type
+  * \tparam Dim_ the dimension of the space
+  */
+template<typename Derived, int Dim_>
+class RotationBase
+{
+  public:
+    enum { Dim = Dim_ };
+    /** the scalar type of the coefficients */
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+
+    /** corresponding linear transformation matrix type */
+    typedef Matrix<Scalar,Dim,Dim> RotationMatrixType;
+    typedef Matrix<Scalar,Dim,1> VectorType;
+
+  public:
+    EIGEN_DEVICE_FUNC inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
+    EIGEN_DEVICE_FUNC inline Derived& derived() { return *static_cast<Derived*>(this); }
+
+    /** \returns an equivalent rotation matrix */
+    EIGEN_DEVICE_FUNC inline RotationMatrixType toRotationMatrix() const { return derived().toRotationMatrix(); }
+
+    /** \returns an equivalent rotation matrix 
+      * This function is added to be conform with the Transform class' naming scheme.
+      */
+    EIGEN_DEVICE_FUNC inline RotationMatrixType matrix() const { return derived().toRotationMatrix(); }
+
+    /** \returns the inverse rotation */
+    EIGEN_DEVICE_FUNC inline Derived inverse() const { return derived().inverse(); }
+
+    /** \returns the concatenation of the rotation \c *this with a translation \a t */
+    EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Isometry> operator*(const Translation<Scalar,Dim>& t) const
+    { return Transform<Scalar,Dim,Isometry>(*this) * t; }
+
+    /** \returns the concatenation of the rotation \c *this with a uniform scaling \a s */
+    EIGEN_DEVICE_FUNC inline RotationMatrixType operator*(const UniformScaling<Scalar>& s) const
+    { return toRotationMatrix() * s.factor(); }
+
+    /** \returns the concatenation of the rotation \c *this with a generic expression \a e
+      * \a e can be:
+      *  - a DimxDim linear transformation matrix
+      *  - a DimxDim diagonal matrix (axis aligned scaling)
+      *  - a vector of size Dim
+      */
+    template<typename OtherDerived>
+    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE typename internal::rotation_base_generic_product_selector<Derived,OtherDerived,OtherDerived::IsVectorAtCompileTime>::ReturnType
+    operator*(const EigenBase<OtherDerived>& e) const
+    { return internal::rotation_base_generic_product_selector<Derived,OtherDerived>::run(derived(), e.derived()); }
+
+    /** \returns the concatenation of a linear transformation \a l with the rotation \a r */
+    template<typename OtherDerived> friend
+    EIGEN_DEVICE_FUNC inline RotationMatrixType operator*(const EigenBase<OtherDerived>& l, const Derived& r)
+    { return l.derived() * r.toRotationMatrix(); }
+
+    /** \returns the concatenation of a scaling \a l with the rotation \a r */
+    EIGEN_DEVICE_FUNC friend inline Transform<Scalar,Dim,Affine> operator*(const DiagonalMatrix<Scalar,Dim>& l, const Derived& r)
+    { 
+      Transform<Scalar,Dim,Affine> res(r);
+      res.linear().applyOnTheLeft(l);
+      return res;
+    }
+
+    /** \returns the concatenation of the rotation \c *this with a transformation \a t */
+    template<int Mode, int Options>
+    EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode> operator*(const Transform<Scalar,Dim,Mode,Options>& t) const
+    { return toRotationMatrix() * t; }
+
+    template<typename OtherVectorType>
+    EIGEN_DEVICE_FUNC inline VectorType _transformVector(const OtherVectorType& v) const
+    { return toRotationMatrix() * v; }
+};
+
+namespace internal {
+
+// implementation of the generic product rotation * matrix
+template<typename RotationDerived, typename MatrixType>
+struct rotation_base_generic_product_selector<RotationDerived,MatrixType,false>
+{
+  enum { Dim = RotationDerived::Dim };
+  typedef Matrix<typename RotationDerived::Scalar,Dim,Dim> ReturnType;
+  EIGEN_DEVICE_FUNC static inline ReturnType run(const RotationDerived& r, const MatrixType& m)
+  { return r.toRotationMatrix() * m; }
+};
+
+template<typename RotationDerived, typename Scalar, int Dim, int MaxDim>
+struct rotation_base_generic_product_selector< RotationDerived, DiagonalMatrix<Scalar,Dim,MaxDim>, false >
+{
+  typedef Transform<Scalar,Dim,Affine> ReturnType;
+  EIGEN_DEVICE_FUNC static inline ReturnType run(const RotationDerived& r, const DiagonalMatrix<Scalar,Dim,MaxDim>& m)
+  {
+    ReturnType res(r);
+    res.linear() *= m;
+    return res;
+  }
+};
+
+template<typename RotationDerived,typename OtherVectorType>
+struct rotation_base_generic_product_selector<RotationDerived,OtherVectorType,true>
+{
+  enum { Dim = RotationDerived::Dim };
+  typedef Matrix<typename RotationDerived::Scalar,Dim,1> ReturnType;
+  EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE ReturnType run(const RotationDerived& r, const OtherVectorType& v)
+  {
+    return r._transformVector(v);
+  }
+};
+
+} // end namespace internal
+
+/** \geometry_module
+  *
+  * \brief Constructs a Dim x Dim rotation matrix from the rotation \a r
+  */
+template<typename Scalar_, int Rows_, int Cols_, int Storage_, int MaxRows_, int MaxCols_>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC Matrix<Scalar_, Rows_, Cols_, Storage_, MaxRows_, MaxCols_>
+::Matrix(const RotationBase<OtherDerived,ColsAtCompileTime>& r)
+{
+  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim))
+  *this = r.toRotationMatrix();
+}
+
+/** \geometry_module
+  *
+  * \brief Set a Dim x Dim rotation matrix from the rotation \a r
+  */
+template<typename Scalar_, int Rows_, int Cols_, int Storage_, int MaxRows_, int MaxCols_>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC Matrix<Scalar_, Rows_, Cols_, Storage_, MaxRows_, MaxCols_>&
+Matrix<Scalar_, Rows_, Cols_, Storage_, MaxRows_, MaxCols_>
+::operator=(const RotationBase<OtherDerived,ColsAtCompileTime>& r)
+{
+  EIGEN_STATIC_ASSERT_MATRIX_SPECIFIC_SIZE(Matrix,int(OtherDerived::Dim),int(OtherDerived::Dim))
+  return *this = r.toRotationMatrix();
+}
+
+namespace internal {
+
+/** \internal
+  *
+  * Helper function to return an arbitrary rotation object to a rotation matrix.
+  *
+  * \tparam Scalar the numeric type of the matrix coefficients
+  * \tparam Dim the dimension of the current space
+  *
+  * It returns a Dim x Dim fixed size matrix.
+  *
+  * Default specializations are provided for:
+  *   - any scalar type (2D),
+  *   - any matrix expression,
+  *   - any type based on RotationBase (e.g., Quaternion, AngleAxis, Rotation2D)
+  *
+  * Currently toRotationMatrix is only used by Transform.
+  *
+  * \sa class Transform, class Rotation2D, class Quaternion, class AngleAxis
+  */
+template<typename Scalar, int Dim>
+EIGEN_DEVICE_FUNC static inline Matrix<Scalar,2,2> toRotationMatrix(const Scalar& s)
+{
+  EIGEN_STATIC_ASSERT(Dim==2,YOU_MADE_A_PROGRAMMING_MISTAKE)
+  return Rotation2D<Scalar>(s).toRotationMatrix();
+}
+
+template<typename Scalar, int Dim, typename OtherDerived>
+EIGEN_DEVICE_FUNC static inline Matrix<Scalar,Dim,Dim> toRotationMatrix(const RotationBase<OtherDerived,Dim>& r)
+{
+  return r.toRotationMatrix();
+}
+
+template<typename Scalar, int Dim, typename OtherDerived>
+EIGEN_DEVICE_FUNC static inline const MatrixBase<OtherDerived>& toRotationMatrix(const MatrixBase<OtherDerived>& mat)
+{
+  EIGEN_STATIC_ASSERT(OtherDerived::RowsAtCompileTime==Dim && OtherDerived::ColsAtCompileTime==Dim,
+    YOU_MADE_A_PROGRAMMING_MISTAKE)
+  return mat;
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_ROTATIONBASE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Scaling.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Scaling.h
new file mode 100644
index 0000000000000000000000000000000000000000..8bcdce63414dc3122607a54dd6265e3dcfbd968b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Scaling.h
@@ -0,0 +1,195 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SCALING_H
+#define EIGEN_SCALING_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \class UniformScaling
+  *
+  * \brief Represents a generic uniform scaling transformation
+  *
+  * \tparam Scalar_ the scalar type, i.e., the type of the coefficients.
+  *
+  * This class represent a uniform scaling transformation. It is the return
+  * type of Scaling(Scalar), and most of the time this is the only way it
+  * is used. In particular, this class is not aimed to be used to store a scaling transformation,
+  * but rather to make easier the constructions and updates of Transform objects.
+  *
+  * To represent an axis aligned scaling, use the DiagonalMatrix class.
+  *
+  * \sa Scaling(), class DiagonalMatrix, MatrixBase::asDiagonal(), class Translation, class Transform
+  */
+
+namespace internal
+{
+  // This helper helps nvcc+MSVC to properly parse this file.
+  // See bug 1412.
+  template <typename Scalar, int Dim, int Mode>
+  struct uniformscaling_times_affine_returntype
+  {
+    enum
+    {
+      NewMode = int(Mode) == int(Isometry) ? Affine : Mode
+    };
+    typedef Transform <Scalar, Dim, NewMode> type;
+  };
+}
+
+template<typename Scalar_>
+class UniformScaling
+{
+public:
+  /** the scalar type of the coefficients */
+  typedef Scalar_ Scalar;
+
+protected:
+
+  Scalar m_factor;
+
+public:
+
+  /** Default constructor without initialization. */
+  UniformScaling() {}
+  /** Constructs and initialize a uniform scaling transformation */
+  explicit inline UniformScaling(const Scalar& s) : m_factor(s) {}
+
+  inline const Scalar& factor() const { return m_factor; }
+  inline Scalar& factor() { return m_factor; }
+
+  /** Concatenates two uniform scaling */
+  inline UniformScaling operator* (const UniformScaling& other) const
+  { return UniformScaling(m_factor * other.factor()); }
+
+  /** Concatenates a uniform scaling and a translation */
+  template<int Dim>
+  inline Transform<Scalar,Dim,Affine> operator* (const Translation<Scalar,Dim>& t) const;
+
+  /** Concatenates a uniform scaling and an affine transformation */
+  template<int Dim, int Mode, int Options>
+  inline typename
+	internal::uniformscaling_times_affine_returntype<Scalar,Dim,Mode>::type
+	operator* (const Transform<Scalar, Dim, Mode, Options>& t) const
+  {
+    typename internal::uniformscaling_times_affine_returntype<Scalar,Dim,Mode>::type res = t;
+    res.prescale(factor());
+    return res;
+  }
+
+  /** Concatenates a uniform scaling and a linear transformation matrix */
+  // TODO returns an expression
+  template<typename Derived>
+  inline typename Eigen::internal::plain_matrix_type<Derived>::type operator* (const MatrixBase<Derived>& other) const
+  { return other * m_factor; }
+
+  template<typename Derived,int Dim>
+  inline Matrix<Scalar,Dim,Dim> operator*(const RotationBase<Derived,Dim>& r) const
+  { return r.toRotationMatrix() * m_factor; }
+
+  /** \returns the inverse scaling */
+  inline UniformScaling inverse() const
+  { return UniformScaling(Scalar(1)/m_factor); }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  inline UniformScaling<NewScalarType> cast() const
+  { return UniformScaling<NewScalarType>(NewScalarType(m_factor)); }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType>
+  inline explicit UniformScaling(const UniformScaling<OtherScalarType>& other)
+  { m_factor = Scalar(other.factor()); }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  bool isApprox(const UniformScaling& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
+  { return internal::isApprox(m_factor, other.factor(), prec); }
+
+};
+
+/** \addtogroup Geometry_Module */
+//@{
+
+/** Concatenates a linear transformation matrix and a uniform scaling
+  * \relates UniformScaling
+  */
+// NOTE this operator is defined in MatrixBase and not as a friend function
+// of UniformScaling to fix an internal crash of Intel's ICC
+template<typename Derived,typename Scalar>
+EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(Derived,Scalar,product)
+operator*(const MatrixBase<Derived>& matrix, const UniformScaling<Scalar>& s)
+{ return matrix.derived() * s.factor(); }
+
+/** Constructs a uniform scaling from scale factor \a s */
+inline UniformScaling<float> Scaling(float s) { return UniformScaling<float>(s); }
+/** Constructs a uniform scaling from scale factor \a s */
+inline UniformScaling<double> Scaling(double s) { return UniformScaling<double>(s); }
+/** Constructs a uniform scaling from scale factor \a s */
+template<typename RealScalar>
+inline UniformScaling<std::complex<RealScalar> > Scaling(const std::complex<RealScalar>& s)
+{ return UniformScaling<std::complex<RealScalar> >(s); }
+
+/** Constructs a 2D axis aligned scaling */
+template<typename Scalar>
+inline DiagonalMatrix<Scalar,2> Scaling(const Scalar& sx, const Scalar& sy)
+{ return DiagonalMatrix<Scalar,2>(sx, sy); }
+/** Constructs a 3D axis aligned scaling */
+template<typename Scalar>
+inline DiagonalMatrix<Scalar,3> Scaling(const Scalar& sx, const Scalar& sy, const Scalar& sz)
+{ return DiagonalMatrix<Scalar,3>(sx, sy, sz); }
+
+/** Constructs an axis aligned scaling expression from vector expression \a coeffs
+  * This is an alias for coeffs.asDiagonal()
+  */
+template<typename Derived>
+inline const DiagonalWrapper<const Derived> Scaling(const MatrixBase<Derived>& coeffs)
+{ return coeffs.asDiagonal(); }
+
+/** Constructs an axis aligned scaling expression from vector \a coeffs when passed as an rvalue reference */
+template<typename Derived>
+inline typename DiagonalWrapper<const Derived>::PlainObject Scaling(MatrixBase<Derived>&& coeffs)
+{ return typename DiagonalWrapper<const Derived>::PlainObject(std::move(coeffs.derived())); }
+
+/** \deprecated */
+typedef DiagonalMatrix<float, 2> AlignedScaling2f;
+/** \deprecated */
+typedef DiagonalMatrix<double,2> AlignedScaling2d;
+/** \deprecated */
+typedef DiagonalMatrix<float, 3> AlignedScaling3f;
+/** \deprecated */
+typedef DiagonalMatrix<double,3> AlignedScaling3d;
+//@}
+
+template<typename Scalar>
+template<int Dim>
+inline Transform<Scalar,Dim,Affine>
+UniformScaling<Scalar>::operator* (const Translation<Scalar,Dim>& t) const
+{
+  Transform<Scalar,Dim,Affine> res;
+  res.matrix().setZero();
+  res.linear().diagonal().fill(factor());
+  res.translation() = factor() * t.vector();
+  res(Dim,Dim) = Scalar(1);
+  return res;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SCALING_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Transform.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Transform.h
new file mode 100644
index 0000000000000000000000000000000000000000..fd0ae7e6c1dab3d04a304eb1338f0640ba251f6b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Transform.h
@@ -0,0 +1,1570 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2010 Hauke Heibel <hauke.heibel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRANSFORM_H
+#define EIGEN_TRANSFORM_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename Transform>
+struct transform_traits
+{
+  enum
+  {
+    Dim = Transform::Dim,
+    HDim = Transform::HDim,
+    Mode = Transform::Mode,
+    IsProjective = (int(Mode)==int(Projective))
+  };
+};
+
+template< typename TransformType,
+          typename MatrixType,
+          int Case = transform_traits<TransformType>::IsProjective ? 0
+                   : int(MatrixType::RowsAtCompileTime) == int(transform_traits<TransformType>::HDim) ? 1
+                   : 2,
+          int RhsCols = MatrixType::ColsAtCompileTime>
+struct transform_right_product_impl;
+
+template< typename Other,
+          int Mode,
+          int Options,
+          int Dim,
+          int HDim,
+          int OtherRows=Other::RowsAtCompileTime,
+          int OtherCols=Other::ColsAtCompileTime>
+struct transform_left_product_impl;
+
+template< typename Lhs,
+          typename Rhs,
+          bool AnyProjective =
+            transform_traits<Lhs>::IsProjective ||
+            transform_traits<Rhs>::IsProjective>
+struct transform_transform_product_impl;
+
+template< typename Other,
+          int Mode,
+          int Options,
+          int Dim,
+          int HDim,
+          int OtherRows=Other::RowsAtCompileTime,
+          int OtherCols=Other::ColsAtCompileTime>
+struct transform_construct_from_matrix;
+
+template<typename TransformType> struct transform_take_affine_part;
+
+template<typename Scalar_, int Dim_, int Mode_, int Options_>
+struct traits<Transform<Scalar_,Dim_,Mode_,Options_> >
+{
+  typedef Scalar_ Scalar;
+  typedef Eigen::Index StorageIndex;
+  typedef Dense StorageKind;
+  enum {
+    Dim1 = Dim_==Dynamic ? Dim_ : Dim_ + 1,
+    RowsAtCompileTime = Mode_==Projective ? Dim1 : Dim_,
+    ColsAtCompileTime = Dim1,
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+    Flags = 0
+  };
+};
+
+template<int Mode> struct transform_make_affine;
+
+} // end namespace internal
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \class Transform
+  *
+  * \brief Represents an homogeneous transformation in a N dimensional space
+  *
+  * \tparam Scalar_ the scalar type, i.e., the type of the coefficients
+  * \tparam Dim_ the dimension of the space
+  * \tparam Mode_ the type of the transformation. Can be:
+  *              - #Affine: the transformation is stored as a (Dim+1)^2 matrix,
+  *                         where the last row is assumed to be [0 ... 0 1].
+  *              - #AffineCompact: the transformation is stored as a (Dim)x(Dim+1) matrix.
+  *              - #Projective: the transformation is stored as a (Dim+1)^2 matrix
+  *                             without any assumption.
+  *              - #Isometry: same as #Affine with the additional assumption that
+  *                           the linear part represents a rotation. This assumption is exploited
+  *                           to speed up some functions such as inverse() and rotation().
+  * \tparam Options_ has the same meaning as in class Matrix. It allows to specify DontAlign and/or RowMajor.
+  *                  These Options are passed directly to the underlying matrix type.
+  *
+  * The homography is internally represented and stored by a matrix which
+  * is available through the matrix() method. To understand the behavior of
+  * this class you have to think a Transform object as its internal
+  * matrix representation. The chosen convention is right multiply:
+  *
+  * \code v' = T * v \endcode
+  *
+  * Therefore, an affine transformation matrix M is shaped like this:
+  *
+  * \f$ \left( \begin{array}{cc}
+  * linear & translation\\
+  * 0 ... 0 & 1
+  * \end{array} \right) \f$
+  *
+  * Note that for a projective transformation the last row can be anything,
+  * and then the interpretation of different parts might be slightly different.
+  *
+  * However, unlike a plain matrix, the Transform class provides many features
+  * simplifying both its assembly and usage. In particular, it can be composed
+  * with any other transformations (Transform,Translation,RotationBase,DiagonalMatrix)
+  * and can be directly used to transform implicit homogeneous vectors. All these
+  * operations are handled via the operator*. For the composition of transformations,
+  * its principle consists to first convert the right/left hand sides of the product
+  * to a compatible (Dim+1)^2 matrix and then perform a pure matrix product.
+  * Of course, internally, operator* tries to perform the minimal number of operations
+  * according to the nature of each terms. Likewise, when applying the transform
+  * to points, the latters are automatically promoted to homogeneous vectors
+  * before doing the matrix product. The conventions to homogeneous representations
+  * are performed as follow:
+  *
+  * \b Translation t (Dim)x(1):
+  * \f$ \left( \begin{array}{cc}
+  * I & t \\
+  * 0\,...\,0 & 1
+  * \end{array} \right) \f$
+  *
+  * \b Rotation R (Dim)x(Dim):
+  * \f$ \left( \begin{array}{cc}
+  * R & 0\\
+  * 0\,...\,0 & 1
+  * \end{array} \right) \f$
+  *<!--
+  * \b Linear \b Matrix L (Dim)x(Dim):
+  * \f$ \left( \begin{array}{cc}
+  * L & 0\\
+  * 0\,...\,0 & 1
+  * \end{array} \right) \f$
+  *
+  * \b Affine \b Matrix A (Dim)x(Dim+1):
+  * \f$ \left( \begin{array}{c}
+  * A\\
+  * 0\,...\,0\,1
+  * \end{array} \right) \f$
+  *-->
+  * \b Scaling \b DiagonalMatrix S (Dim)x(Dim):
+  * \f$ \left( \begin{array}{cc}
+  * S & 0\\
+  * 0\,...\,0 & 1
+  * \end{array} \right) \f$
+  *
+  * \b Column \b point v (Dim)x(1):
+  * \f$ \left( \begin{array}{c}
+  * v\\
+  * 1
+  * \end{array} \right) \f$
+  *
+  * \b Set \b of \b column \b points V1...Vn (Dim)x(n):
+  * \f$ \left( \begin{array}{ccc}
+  * v_1 & ... & v_n\\
+  * 1 & ... & 1
+  * \end{array} \right) \f$
+  *
+  * The concatenation of a Transform object with any kind of other transformation
+  * always returns a Transform object.
+  *
+  * A little exception to the "as pure matrix product" rule is the case of the
+  * transformation of non homogeneous vectors by an affine transformation. In
+  * that case the last matrix row can be ignored, and the product returns non
+  * homogeneous vectors.
+  *
+  * Since, for instance, a Dim x Dim matrix is interpreted as a linear transformation,
+  * it is not possible to directly transform Dim vectors stored in a Dim x Dim matrix.
+  * The solution is either to use a Dim x Dynamic matrix or explicitly request a
+  * vector transformation by making the vector homogeneous:
+  * \code
+  * m' = T * m.colwise().homogeneous();
+  * \endcode
+  * Note that there is zero overhead.
+  *
+  * Conversion methods from/to Qt's QMatrix and QTransform are available if the
+  * preprocessor token EIGEN_QT_SUPPORT is defined.
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_TRANSFORM_PLUGIN.
+  *
+  * \sa class Matrix, class Quaternion
+  */
+template<typename Scalar_, int Dim_, int Mode_, int Options_>
+class Transform
+{
+public:
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar_,Dim_==Dynamic ? Dynamic : (Dim_+1)*(Dim_+1))
+  enum {
+    Mode = Mode_,
+    Options = Options_,
+    Dim = Dim_,     ///< space dimension in which the transformation holds
+    HDim = Dim_+1,  ///< size of a respective homogeneous vector
+    Rows = int(Mode)==(AffineCompact) ? Dim : HDim
+  };
+  /** the scalar type of the coefficients */
+  typedef Scalar_ Scalar;
+  typedef Eigen::Index StorageIndex;
+  typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+  /** type of the matrix used to represent the transformation */
+  typedef typename internal::make_proper_matrix_type<Scalar,Rows,HDim,Options>::type MatrixType;
+  /** constified MatrixType */
+  typedef const MatrixType ConstMatrixType;
+  /** type of the matrix used to represent the linear part of the transformation */
+  typedef Matrix<Scalar,Dim,Dim,Options> LinearMatrixType;
+  /** type of read/write reference to the linear part of the transformation */
+  typedef Block<MatrixType,Dim,Dim,int(Mode)==(AffineCompact) && (int(Options)&RowMajor)==0> LinearPart;
+  /** type of read reference to the linear part of the transformation */
+  typedef const Block<ConstMatrixType,Dim,Dim,int(Mode)==(AffineCompact) && (int(Options)&RowMajor)==0> ConstLinearPart;
+  /** type of read/write reference to the affine part of the transformation */
+  typedef std::conditional_t<int(Mode)==int(AffineCompact),
+                              MatrixType&,
+                              Block<MatrixType,Dim,HDim> > AffinePart;
+  /** type of read reference to the affine part of the transformation */
+  typedef std::conditional_t<int(Mode)==int(AffineCompact),
+                              const MatrixType&,
+                              const Block<const MatrixType,Dim,HDim> > ConstAffinePart;
+  /** type of a vector */
+  typedef Matrix<Scalar,Dim,1> VectorType;
+  /** type of a read/write reference to the translation part of the rotation */
+  typedef Block<MatrixType,Dim,1,!(internal::traits<MatrixType>::Flags & RowMajorBit)> TranslationPart;
+  /** type of a read reference to the translation part of the rotation */
+  typedef const Block<ConstMatrixType,Dim,1,!(internal::traits<MatrixType>::Flags & RowMajorBit)> ConstTranslationPart;
+  /** corresponding translation type */
+  typedef Translation<Scalar,Dim> TranslationType;
+
+  // this intermediate enum is needed to avoid an ICE with gcc 3.4 and 4.0
+  enum { TransformTimeDiagonalMode = ((Mode==int(Isometry))?Affine:int(Mode)) };
+  /** The return type of the product between a diagonal matrix and a transform */
+  typedef Transform<Scalar,Dim,TransformTimeDiagonalMode> TransformTimeDiagonalReturnType;
+
+protected:
+
+  MatrixType m_matrix;
+
+public:
+
+  /** Default constructor without initialization of the meaningful coefficients.
+    * If Mode==Affine or Mode==Isometry, then the last row is set to [0 ... 0 1] */
+  EIGEN_DEVICE_FUNC inline Transform()
+  {
+    check_template_params();
+    internal::transform_make_affine<(int(Mode)==Affine || int(Mode)==Isometry) ? Affine : AffineCompact>::run(m_matrix);
+  }
+
+  EIGEN_DEVICE_FUNC inline explicit Transform(const TranslationType& t)
+  {
+    check_template_params();
+    *this = t;
+  }
+  EIGEN_DEVICE_FUNC inline explicit Transform(const UniformScaling<Scalar>& s)
+  {
+    check_template_params();
+    *this = s;
+  }
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC inline explicit Transform(const RotationBase<Derived, Dim>& r)
+  {
+    check_template_params();
+    *this = r;
+  }
+
+  typedef internal::transform_take_affine_part<Transform> take_affine_part;
+
+  /** Constructs and initializes a transformation from a Dim^2 or a (Dim+1)^2 matrix. */
+  template<typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline explicit Transform(const EigenBase<OtherDerived>& other)
+  {
+    EIGEN_STATIC_ASSERT((internal::is_same<Scalar,typename OtherDerived::Scalar>::value),
+      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY);
+
+    check_template_params();
+    internal::transform_construct_from_matrix<OtherDerived,Mode,Options,Dim,HDim>::run(this, other.derived());
+  }
+
+  /** Set \c *this from a Dim^2 or (Dim+1)^2 matrix. */
+  template<typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline Transform& operator=(const EigenBase<OtherDerived>& other)
+  {
+    EIGEN_STATIC_ASSERT((internal::is_same<Scalar,typename OtherDerived::Scalar>::value),
+      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY);
+
+    internal::transform_construct_from_matrix<OtherDerived,Mode,Options,Dim,HDim>::run(this, other.derived());
+    return *this;
+  }
+
+  template<int OtherOptions>
+  EIGEN_DEVICE_FUNC inline Transform(const Transform<Scalar,Dim,Mode,OtherOptions>& other)
+  {
+    check_template_params();
+    // only the options change, we can directly copy the matrices
+    m_matrix = other.matrix();
+  }
+
+  template<int OtherMode,int OtherOptions>
+  EIGEN_DEVICE_FUNC inline Transform(const Transform<Scalar,Dim,OtherMode,OtherOptions>& other)
+  {
+    check_template_params();
+    // prevent conversions as:
+    // Affine | AffineCompact | Isometry = Projective
+    EIGEN_STATIC_ASSERT(internal::check_implication(OtherMode==int(Projective), Mode==int(Projective)),
+                        YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION)
+
+    // prevent conversions as:
+    // Isometry = Affine | AffineCompact
+    EIGEN_STATIC_ASSERT(internal::check_implication(OtherMode==int(Affine)||OtherMode==int(AffineCompact), Mode!=int(Isometry)),
+                        YOU_PERFORMED_AN_INVALID_TRANSFORMATION_CONVERSION)
+
+    enum { ModeIsAffineCompact = Mode == int(AffineCompact),
+           OtherModeIsAffineCompact = OtherMode == int(AffineCompact)
+    };
+
+    if(EIGEN_CONST_CONDITIONAL(ModeIsAffineCompact == OtherModeIsAffineCompact))
+    {
+      // We need the block expression because the code is compiled for all
+      // combinations of transformations and will trigger a compile time error
+      // if one tries to assign the matrices directly
+      m_matrix.template block<Dim,Dim+1>(0,0) = other.matrix().template block<Dim,Dim+1>(0,0);
+      makeAffine();
+    }
+    else if(EIGEN_CONST_CONDITIONAL(OtherModeIsAffineCompact))
+    {
+      typedef typename Transform<Scalar,Dim,OtherMode,OtherOptions>::MatrixType OtherMatrixType;
+      internal::transform_construct_from_matrix<OtherMatrixType,Mode,Options,Dim,HDim>::run(this, other.matrix());
+    }
+    else
+    {
+      // here we know that Mode == AffineCompact and OtherMode != AffineCompact.
+      // if OtherMode were Projective, the static assert above would already have caught it.
+      // So the only possibility is that OtherMode == Affine
+      linear() = other.linear();
+      translation() = other.translation();
+    }
+  }
+
+  template<typename OtherDerived>
+  EIGEN_DEVICE_FUNC Transform(const ReturnByValue<OtherDerived>& other)
+  {
+    check_template_params();
+    other.evalTo(*this);
+  }
+
+  template<typename OtherDerived>
+  EIGEN_DEVICE_FUNC Transform& operator=(const ReturnByValue<OtherDerived>& other)
+  {
+    other.evalTo(*this);
+    return *this;
+  }
+
+  #ifdef EIGEN_QT_SUPPORT
+  #if (QT_VERSION < QT_VERSION_CHECK(6, 0, 0))
+  inline Transform(const QMatrix& other);
+  inline Transform& operator=(const QMatrix& other);
+  inline QMatrix toQMatrix(void) const;
+  #endif
+  inline Transform(const QTransform& other);
+  inline Transform& operator=(const QTransform& other);
+  inline QTransform toQTransform(void) const;
+  #endif
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return int(Mode)==int(Projective) ? m_matrix.cols() : (m_matrix.cols()-1); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_matrix.cols(); }
+
+  /** shortcut for m_matrix(row,col);
+    * \sa MatrixBase::operator(Index,Index) const */
+  EIGEN_DEVICE_FUNC inline Scalar operator() (Index row, Index col) const { return m_matrix(row,col); }
+  /** shortcut for m_matrix(row,col);
+    * \sa MatrixBase::operator(Index,Index) */
+  EIGEN_DEVICE_FUNC inline Scalar& operator() (Index row, Index col) { return m_matrix(row,col); }
+
+  /** \returns a read-only expression of the transformation matrix */
+  EIGEN_DEVICE_FUNC inline const MatrixType& matrix() const { return m_matrix; }
+  /** \returns a writable expression of the transformation matrix */
+  EIGEN_DEVICE_FUNC inline MatrixType& matrix() { return m_matrix; }
+
+  /** \returns a read-only expression of the linear part of the transformation */
+  EIGEN_DEVICE_FUNC inline ConstLinearPart linear() const { return ConstLinearPart(m_matrix,0,0); }
+  /** \returns a writable expression of the linear part of the transformation */
+  EIGEN_DEVICE_FUNC inline LinearPart linear() { return LinearPart(m_matrix,0,0); }
+
+  /** \returns a read-only expression of the Dim x HDim affine part of the transformation */
+  EIGEN_DEVICE_FUNC inline ConstAffinePart affine() const { return take_affine_part::run(m_matrix); }
+  /** \returns a writable expression of the Dim x HDim affine part of the transformation */
+  EIGEN_DEVICE_FUNC inline AffinePart affine() { return take_affine_part::run(m_matrix); }
+
+  /** \returns a read-only expression of the translation vector of the transformation */
+  EIGEN_DEVICE_FUNC inline ConstTranslationPart translation() const { return ConstTranslationPart(m_matrix,0,Dim); }
+  /** \returns a writable expression of the translation vector of the transformation */
+  EIGEN_DEVICE_FUNC inline TranslationPart translation() { return TranslationPart(m_matrix,0,Dim); }
+
+  /** \returns an expression of the product between the transform \c *this and a matrix expression \a other.
+    *
+    * The right-hand-side \a other can be either:
+    * \li an homogeneous vector of size Dim+1,
+    * \li a set of homogeneous vectors of size Dim+1 x N,
+    * \li a transformation matrix of size Dim+1 x Dim+1.
+    *
+    * Moreover, if \c *this represents an affine transformation (i.e., Mode!=Projective), then \a other can also be:
+    * \li a point of size Dim (computes: \code this->linear() * other + this->translation()\endcode),
+    * \li a set of N points as a Dim x N matrix (computes: \code (this->linear() * other).colwise() + this->translation()\endcode),
+    *
+    * In all cases, the return type is a matrix or vector of same sizes as the right-hand-side \a other.
+    *
+    * If you want to interpret \a other as a linear or affine transformation, then first convert it to a Transform<> type,
+    * or do your own cooking.
+    *
+    * Finally, if you want to apply Affine transformations to vectors, then explicitly apply the linear part only:
+    * \code
+    * Affine3f A;
+    * Vector3f v1, v2;
+    * v2 = A.linear() * v1;
+    * \endcode
+    *
+    */
+  // note: this function is defined here because some compilers cannot find the respective declaration
+  template<typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const typename internal::transform_right_product_impl<Transform, OtherDerived>::ResultType
+  operator * (const EigenBase<OtherDerived> &other) const
+  { return internal::transform_right_product_impl<Transform, OtherDerived>::run(*this,other.derived()); }
+
+  /** \returns the product expression of a transformation matrix \a a times a transform \a b
+    *
+    * The left hand side \a other can be either:
+    * \li a linear transformation matrix of size Dim x Dim,
+    * \li an affine transformation matrix of size Dim x Dim+1,
+    * \li a general transformation matrix of size Dim+1 x Dim+1.
+    */
+  template<typename OtherDerived> friend
+  EIGEN_DEVICE_FUNC inline const typename internal::transform_left_product_impl<OtherDerived,Mode,Options,Dim_,Dim_+1>::ResultType
+    operator * (const EigenBase<OtherDerived> &a, const Transform &b)
+  { return internal::transform_left_product_impl<OtherDerived,Mode,Options,Dim,HDim>::run(a.derived(),b); }
+
+  /** \returns The product expression of a transform \a a times a diagonal matrix \a b
+    *
+    * The rhs diagonal matrix is interpreted as an affine scaling transformation. The
+    * product results in a Transform of the same type (mode) as the lhs only if the lhs
+    * mode is no isometry. In that case, the returned transform is an affinity.
+    */
+  template<typename DiagonalDerived>
+  EIGEN_DEVICE_FUNC inline const TransformTimeDiagonalReturnType
+    operator * (const DiagonalBase<DiagonalDerived> &b) const
+  {
+    TransformTimeDiagonalReturnType res(*this);
+    res.linearExt() *= b;
+    return res;
+  }
+
+  /** \returns The product expression of a diagonal matrix \a a times a transform \a b
+    *
+    * The lhs diagonal matrix is interpreted as an affine scaling transformation. The
+    * product results in a Transform of the same type (mode) as the lhs only if the lhs
+    * mode is no isometry. In that case, the returned transform is an affinity.
+    */
+  template<typename DiagonalDerived>
+  EIGEN_DEVICE_FUNC friend inline TransformTimeDiagonalReturnType
+    operator * (const DiagonalBase<DiagonalDerived> &a, const Transform &b)
+  {
+    TransformTimeDiagonalReturnType res;
+    res.linear().noalias() = a*b.linear();
+    res.translation().noalias() = a*b.translation();
+    if (EIGEN_CONST_CONDITIONAL(Mode!=int(AffineCompact)))
+      res.matrix().row(Dim) = b.matrix().row(Dim);
+    return res;
+  }
+
+  template<typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline Transform& operator*=(const EigenBase<OtherDerived>& other) { return *this = *this * other; }
+
+  /** Concatenates two transformations */
+  EIGEN_DEVICE_FUNC inline const Transform operator * (const Transform& other) const
+  {
+    return internal::transform_transform_product_impl<Transform,Transform>::run(*this,other);
+  }
+
+  #if EIGEN_COMP_ICC
+private:
+  // this intermediate structure permits to workaround a bug in ICC 11:
+  //   error: template instantiation resulted in unexpected function type of "Eigen::Transform<double, 3, 32, 0>
+  //             (const Eigen::Transform<double, 3, 2, 0> &) const"
+  //  (the meaning of a name may have changed since the template declaration -- the type of the template is:
+  // "Eigen::internal::transform_transform_product_impl<Eigen::Transform<double, 3, 32, 0>,
+  //     Eigen::Transform<double, 3, Mode, Options>, <expression>>::ResultType (const Eigen::Transform<double, 3, Mode, Options> &) const")
+  //
+  template<int OtherMode,int OtherOptions> struct icc_11_workaround
+  {
+    typedef internal::transform_transform_product_impl<Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> > ProductType;
+    typedef typename ProductType::ResultType ResultType;
+  };
+
+public:
+  /** Concatenates two different transformations */
+  template<int OtherMode,int OtherOptions>
+  inline typename icc_11_workaround<OtherMode,OtherOptions>::ResultType
+    operator * (const Transform<Scalar,Dim,OtherMode,OtherOptions>& other) const
+  {
+    typedef typename icc_11_workaround<OtherMode,OtherOptions>::ProductType ProductType;
+    return ProductType::run(*this,other);
+  }
+  #else
+  /** Concatenates two different transformations */
+  template<int OtherMode,int OtherOptions>
+  EIGEN_DEVICE_FUNC inline typename internal::transform_transform_product_impl<Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> >::ResultType
+    operator * (const Transform<Scalar,Dim,OtherMode,OtherOptions>& other) const
+  {
+    return internal::transform_transform_product_impl<Transform,Transform<Scalar,Dim,OtherMode,OtherOptions> >::run(*this,other);
+  }
+  #endif
+
+  /** \sa MatrixBase::setIdentity() */
+  EIGEN_DEVICE_FUNC void setIdentity() { m_matrix.setIdentity(); }
+
+  /**
+   * \brief Returns an identity transformation.
+   * \todo In the future this function should be returning a Transform expression.
+   */
+  EIGEN_DEVICE_FUNC static const Transform Identity()
+  {
+    return Transform(MatrixType::Identity());
+  }
+
+  template<typename OtherDerived>
+  EIGEN_DEVICE_FUNC
+  inline Transform& scale(const MatrixBase<OtherDerived> &other);
+
+  template<typename OtherDerived>
+  EIGEN_DEVICE_FUNC
+  inline Transform& prescale(const MatrixBase<OtherDerived> &other);
+
+  EIGEN_DEVICE_FUNC inline Transform& scale(const Scalar& s);
+  EIGEN_DEVICE_FUNC inline Transform& prescale(const Scalar& s);
+
+  template<typename OtherDerived>
+  EIGEN_DEVICE_FUNC
+  inline Transform& translate(const MatrixBase<OtherDerived> &other);
+
+  template<typename OtherDerived>
+  EIGEN_DEVICE_FUNC
+  inline Transform& pretranslate(const MatrixBase<OtherDerived> &other);
+
+  template<typename RotationType>
+  EIGEN_DEVICE_FUNC
+  inline Transform& rotate(const RotationType& rotation);
+
+  template<typename RotationType>
+  EIGEN_DEVICE_FUNC
+  inline Transform& prerotate(const RotationType& rotation);
+
+  EIGEN_DEVICE_FUNC Transform& shear(const Scalar& sx, const Scalar& sy);
+  EIGEN_DEVICE_FUNC Transform& preshear(const Scalar& sx, const Scalar& sy);
+
+  EIGEN_DEVICE_FUNC inline Transform& operator=(const TranslationType& t);
+
+  EIGEN_DEVICE_FUNC
+  inline Transform& operator*=(const TranslationType& t) { return translate(t.vector()); }
+
+  EIGEN_DEVICE_FUNC inline Transform operator*(const TranslationType& t) const;
+
+  EIGEN_DEVICE_FUNC
+  inline Transform& operator=(const UniformScaling<Scalar>& t);
+
+  EIGEN_DEVICE_FUNC
+  inline Transform& operator*=(const UniformScaling<Scalar>& s) { return scale(s.factor()); }
+
+  EIGEN_DEVICE_FUNC
+  inline TransformTimeDiagonalReturnType operator*(const UniformScaling<Scalar>& s) const
+  {
+    TransformTimeDiagonalReturnType res = *this;
+    res.scale(s.factor());
+    return res;
+  }
+
+  EIGEN_DEVICE_FUNC
+  inline Transform& operator*=(const DiagonalMatrix<Scalar,Dim>& s) { linearExt() *= s; return *this; }
+
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC inline Transform& operator=(const RotationBase<Derived,Dim>& r);
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC inline Transform& operator*=(const RotationBase<Derived,Dim>& r) { return rotate(r.toRotationMatrix()); }
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC inline Transform operator*(const RotationBase<Derived,Dim>& r) const;
+
+  typedef std::conditional_t<int(Mode)==Isometry,ConstLinearPart,const LinearMatrixType> RotationReturnType;
+  EIGEN_DEVICE_FUNC RotationReturnType rotation() const;
+
+  template<typename RotationMatrixType, typename ScalingMatrixType>
+  EIGEN_DEVICE_FUNC
+  void computeRotationScaling(RotationMatrixType *rotation, ScalingMatrixType *scaling) const;
+  template<typename ScalingMatrixType, typename RotationMatrixType>
+  EIGEN_DEVICE_FUNC
+  void computeScalingRotation(ScalingMatrixType *scaling, RotationMatrixType *rotation) const;
+
+  template<typename PositionDerived, typename OrientationType, typename ScaleDerived>
+  EIGEN_DEVICE_FUNC
+  Transform& fromPositionOrientationScale(const MatrixBase<PositionDerived> &position,
+    const OrientationType& orientation, const MatrixBase<ScaleDerived> &scale);
+
+  EIGEN_DEVICE_FUNC
+  inline Transform inverse(TransformTraits traits = (TransformTraits)Mode) const;
+
+  /** \returns a const pointer to the column major internal matrix */
+  EIGEN_DEVICE_FUNC const Scalar* data() const { return m_matrix.data(); }
+  /** \returns a non-const pointer to the column major internal matrix */
+  EIGEN_DEVICE_FUNC Scalar* data() { return m_matrix.data(); }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Transform,Transform<NewScalarType,Dim,Mode,Options> >::type cast() const
+  { return typename internal::cast_return_type<Transform,Transform<NewScalarType,Dim,Mode,Options> >::type(*this); }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType>
+  EIGEN_DEVICE_FUNC inline explicit Transform(const Transform<OtherScalarType,Dim,Mode,Options>& other)
+  {
+    check_template_params();
+    m_matrix = other.matrix().template cast<Scalar>();
+  }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  EIGEN_DEVICE_FUNC bool isApprox(const Transform& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
+  { return m_matrix.isApprox(other.m_matrix, prec); }
+
+  /** Sets the last row to [0 ... 0 1]
+    */
+  EIGEN_DEVICE_FUNC void makeAffine()
+  {
+    internal::transform_make_affine<int(Mode)>::run(m_matrix);
+  }
+
+  /** \internal
+    * \returns the Dim x Dim linear part if the transformation is affine,
+    *          and the HDim x Dim part for projective transformations.
+    */
+  EIGEN_DEVICE_FUNC inline Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,Dim> linearExt()
+  { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,Dim>(0,0); }
+  /** \internal
+    * \returns the Dim x Dim linear part if the transformation is affine,
+    *          and the HDim x Dim part for projective transformations.
+    */
+  EIGEN_DEVICE_FUNC inline const Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,Dim> linearExt() const
+  { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,Dim>(0,0); }
+
+  /** \internal
+    * \returns the translation part if the transformation is affine,
+    *          and the last column for projective transformations.
+    */
+  EIGEN_DEVICE_FUNC inline Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,1> translationExt()
+  { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,1>(0,Dim); }
+  /** \internal
+    * \returns the translation part if the transformation is affine,
+    *          and the last column for projective transformations.
+    */
+  EIGEN_DEVICE_FUNC inline const Block<MatrixType,int(Mode)==int(Projective)?HDim:Dim,1> translationExt() const
+  { return m_matrix.template block<int(Mode)==int(Projective)?HDim:Dim,1>(0,Dim); }
+
+
+  #ifdef EIGEN_TRANSFORM_PLUGIN
+  #include EIGEN_TRANSFORM_PLUGIN
+  #endif
+
+protected:
+  #ifndef EIGEN_PARSED_BY_DOXYGEN
+    EIGEN_DEVICE_FUNC static EIGEN_STRONG_INLINE void check_template_params()
+    {
+      EIGEN_STATIC_ASSERT((Options & (DontAlign|RowMajor)) == Options, INVALID_MATRIX_TEMPLATE_PARAMETERS)
+    }
+  #endif
+
+};
+
+/** \ingroup Geometry_Module */
+typedef Transform<float,2,Isometry> Isometry2f;
+/** \ingroup Geometry_Module */
+typedef Transform<float,3,Isometry> Isometry3f;
+/** \ingroup Geometry_Module */
+typedef Transform<double,2,Isometry> Isometry2d;
+/** \ingroup Geometry_Module */
+typedef Transform<double,3,Isometry> Isometry3d;
+
+/** \ingroup Geometry_Module */
+typedef Transform<float,2,Affine> Affine2f;
+/** \ingroup Geometry_Module */
+typedef Transform<float,3,Affine> Affine3f;
+/** \ingroup Geometry_Module */
+typedef Transform<double,2,Affine> Affine2d;
+/** \ingroup Geometry_Module */
+typedef Transform<double,3,Affine> Affine3d;
+
+/** \ingroup Geometry_Module */
+typedef Transform<float,2,AffineCompact> AffineCompact2f;
+/** \ingroup Geometry_Module */
+typedef Transform<float,3,AffineCompact> AffineCompact3f;
+/** \ingroup Geometry_Module */
+typedef Transform<double,2,AffineCompact> AffineCompact2d;
+/** \ingroup Geometry_Module */
+typedef Transform<double,3,AffineCompact> AffineCompact3d;
+
+/** \ingroup Geometry_Module */
+typedef Transform<float,2,Projective> Projective2f;
+/** \ingroup Geometry_Module */
+typedef Transform<float,3,Projective> Projective3f;
+/** \ingroup Geometry_Module */
+typedef Transform<double,2,Projective> Projective2d;
+/** \ingroup Geometry_Module */
+typedef Transform<double,3,Projective> Projective3d;
+
+/**************************
+*** Optional QT support ***
+**************************/
+
+#ifdef EIGEN_QT_SUPPORT
+
+#if (QT_VERSION < QT_VERSION_CHECK(6, 0, 0))
+/** Initializes \c *this from a QMatrix assuming the dimension is 2.
+  *
+  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
+  */
+template<typename Scalar, int Dim, int Mode,int Options>
+Transform<Scalar,Dim,Mode,Options>::Transform(const QMatrix& other)
+{
+  check_template_params();
+  *this = other;
+}
+
+/** Set \c *this from a QMatrix assuming the dimension is 2.
+  *
+  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
+  */
+template<typename Scalar, int Dim, int Mode,int Options>
+Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const QMatrix& other)
+{
+  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  if (EIGEN_CONST_CONDITIONAL(Mode == int(AffineCompact)))
+    m_matrix << other.m11(), other.m21(), other.dx(),
+                other.m12(), other.m22(), other.dy();
+  else
+    m_matrix << other.m11(), other.m21(), other.dx(),
+                other.m12(), other.m22(), other.dy(),
+                0, 0, 1;
+  return *this;
+}
+
+/** \returns a QMatrix from \c *this assuming the dimension is 2.
+  *
+  * \warning this conversion might loss data if \c *this is not affine
+  *
+  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+QMatrix Transform<Scalar,Dim,Mode,Options>::toQMatrix(void) const
+{
+  check_template_params();
+  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  return QMatrix(m_matrix.coeff(0,0), m_matrix.coeff(1,0),
+                 m_matrix.coeff(0,1), m_matrix.coeff(1,1),
+                 m_matrix.coeff(0,2), m_matrix.coeff(1,2));
+}
+#endif
+
+/** Initializes \c *this from a QTransform assuming the dimension is 2.
+  *
+  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
+  */
+template<typename Scalar, int Dim, int Mode,int Options>
+Transform<Scalar,Dim,Mode,Options>::Transform(const QTransform& other)
+{
+  check_template_params();
+  *this = other;
+}
+
+/** Set \c *this from a QTransform assuming the dimension is 2.
+  *
+  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const QTransform& other)
+{
+  check_template_params();
+  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  if (EIGEN_CONST_CONDITIONAL(Mode == int(AffineCompact)))
+    m_matrix << other.m11(), other.m21(), other.dx(),
+                other.m12(), other.m22(), other.dy();
+  else
+    m_matrix << other.m11(), other.m21(), other.dx(),
+                other.m12(), other.m22(), other.dy(),
+                other.m13(), other.m23(), other.m33();
+  return *this;
+}
+
+/** \returns a QTransform from \c *this assuming the dimension is 2.
+  *
+  * This function is available only if the token EIGEN_QT_SUPPORT is defined.
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+QTransform Transform<Scalar,Dim,Mode,Options>::toQTransform(void) const
+{
+  EIGEN_STATIC_ASSERT(Dim==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  if (EIGEN_CONST_CONDITIONAL(Mode == int(AffineCompact)))
+    return QTransform(m_matrix.coeff(0,0), m_matrix.coeff(1,0),
+                      m_matrix.coeff(0,1), m_matrix.coeff(1,1),
+                      m_matrix.coeff(0,2), m_matrix.coeff(1,2));
+  else
+    return QTransform(m_matrix.coeff(0,0), m_matrix.coeff(1,0), m_matrix.coeff(2,0),
+                      m_matrix.coeff(0,1), m_matrix.coeff(1,1), m_matrix.coeff(2,1),
+                      m_matrix.coeff(0,2), m_matrix.coeff(1,2), m_matrix.coeff(2,2));
+}
+#endif
+
+/*********************
+*** Procedural API ***
+*********************/
+
+/** Applies on the right the non uniform scale transformation represented
+  * by the vector \a other to \c *this and returns a reference to \c *this.
+  * \sa prescale()
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
+Transform<Scalar,Dim,Mode,Options>::scale(const MatrixBase<OtherDerived> &other)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
+  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
+  linearExt().noalias() = (linearExt() * other.asDiagonal());
+  return *this;
+}
+
+/** Applies on the right a uniform scale of a factor \a c to \c *this
+  * and returns a reference to \c *this.
+  * \sa prescale(Scalar)
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::scale(const Scalar& s)
+{
+  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
+  linearExt() *= s;
+  return *this;
+}
+
+/** Applies on the left the non uniform scale transformation represented
+  * by the vector \a other to \c *this and returns a reference to \c *this.
+  * \sa scale()
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
+Transform<Scalar,Dim,Mode,Options>::prescale(const MatrixBase<OtherDerived> &other)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
+  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
+  affine().noalias() = (other.asDiagonal() * affine());
+  return *this;
+}
+
+/** Applies on the left a uniform scale of a factor \a c to \c *this
+  * and returns a reference to \c *this.
+  * \sa scale(Scalar)
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::prescale(const Scalar& s)
+{
+  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
+  m_matrix.template topRows<Dim>() *= s;
+  return *this;
+}
+
+/** Applies on the right the translation matrix represented by the vector \a other
+  * to \c *this and returns a reference to \c *this.
+  * \sa pretranslate()
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
+Transform<Scalar,Dim,Mode,Options>::translate(const MatrixBase<OtherDerived> &other)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
+  translationExt() += linearExt() * other;
+  return *this;
+}
+
+/** Applies on the left the translation matrix represented by the vector \a other
+  * to \c *this and returns a reference to \c *this.
+  * \sa translate()
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
+Transform<Scalar,Dim,Mode,Options>::pretranslate(const MatrixBase<OtherDerived> &other)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(OtherDerived,int(Dim))
+  if(EIGEN_CONST_CONDITIONAL(int(Mode)==int(Projective)))
+    affine() += other * m_matrix.row(Dim);
+  else
+    translation() += other;
+  return *this;
+}
+
+/** Applies on the right the rotation represented by the rotation \a rotation
+  * to \c *this and returns a reference to \c *this.
+  *
+  * The template parameter \a RotationType is the type of the rotation which
+  * must be known by internal::toRotationMatrix<>.
+  *
+  * Natively supported types includes:
+  *   - any scalar (2D),
+  *   - a Dim x Dim matrix expression,
+  *   - a Quaternion (3D),
+  *   - a AngleAxis (3D)
+  *
+  * This mechanism is easily extendable to support user types such as Euler angles,
+  * or a pair of Quaternion for 4D rotations.
+  *
+  * \sa rotate(Scalar), class Quaternion, class AngleAxis, prerotate(RotationType)
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+template<typename RotationType>
+EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
+Transform<Scalar,Dim,Mode,Options>::rotate(const RotationType& rotation)
+{
+  linearExt() *= internal::toRotationMatrix<Scalar,Dim>(rotation);
+  return *this;
+}
+
+/** Applies on the left the rotation represented by the rotation \a rotation
+  * to \c *this and returns a reference to \c *this.
+  *
+  * See rotate() for further details.
+  *
+  * \sa rotate()
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+template<typename RotationType>
+EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
+Transform<Scalar,Dim,Mode,Options>::prerotate(const RotationType& rotation)
+{
+  m_matrix.template block<Dim,HDim>(0,0) = internal::toRotationMatrix<Scalar,Dim>(rotation)
+                                         * m_matrix.template block<Dim,HDim>(0,0);
+  return *this;
+}
+
+/** Applies on the right the shear transformation represented
+  * by the vector \a other to \c *this and returns a reference to \c *this.
+  * \warning 2D only.
+  * \sa preshear()
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
+Transform<Scalar,Dim,Mode,Options>::shear(const Scalar& sx, const Scalar& sy)
+{
+  EIGEN_STATIC_ASSERT(int(Dim)==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
+  VectorType tmp = linear().col(0)*sy + linear().col(1);
+  linear() << linear().col(0) + linear().col(1)*sx, tmp;
+  return *this;
+}
+
+/** Applies on the left the shear transformation represented
+  * by the vector \a other to \c *this and returns a reference to \c *this.
+  * \warning 2D only.
+  * \sa shear()
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
+Transform<Scalar,Dim,Mode,Options>::preshear(const Scalar& sx, const Scalar& sy)
+{
+  EIGEN_STATIC_ASSERT(int(Dim)==2, YOU_MADE_A_PROGRAMMING_MISTAKE)
+  EIGEN_STATIC_ASSERT(Mode!=int(Isometry), THIS_METHOD_IS_ONLY_FOR_SPECIFIC_TRANSFORMATIONS)
+  m_matrix.template block<Dim,HDim>(0,0) = LinearMatrixType(1, sx, sy, 1) * m_matrix.template block<Dim,HDim>(0,0);
+  return *this;
+}
+
+/******************************************************
+*** Scaling, Translation and Rotation compatibility ***
+******************************************************/
+
+template<typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const TranslationType& t)
+{
+  linear().setIdentity();
+  translation() = t.vector();
+  makeAffine();
+  return *this;
+}
+
+template<typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options> Transform<Scalar,Dim,Mode,Options>::operator*(const TranslationType& t) const
+{
+  Transform res = *this;
+  res.translate(t.vector());
+  return res;
+}
+
+template<typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const UniformScaling<Scalar>& s)
+{
+  m_matrix.setZero();
+  linear().diagonal().fill(s.factor());
+  makeAffine();
+  return *this;
+}
+
+template<typename Scalar, int Dim, int Mode, int Options>
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options>& Transform<Scalar,Dim,Mode,Options>::operator=(const RotationBase<Derived,Dim>& r)
+{
+  linear() = internal::toRotationMatrix<Scalar,Dim>(r);
+  translation().setZero();
+  makeAffine();
+  return *this;
+}
+
+template<typename Scalar, int Dim, int Mode, int Options>
+template<typename Derived>
+EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode,Options> Transform<Scalar,Dim,Mode,Options>::operator*(const RotationBase<Derived,Dim>& r) const
+{
+  Transform res = *this;
+  res.rotate(r.derived());
+  return res;
+}
+
+/************************
+*** Special functions ***
+************************/
+
+namespace internal {
+template<int Mode> struct transform_rotation_impl {
+  template<typename TransformType>
+  EIGEN_DEVICE_FUNC static inline
+  const typename TransformType::LinearMatrixType run(const TransformType& t)
+  {
+    typedef typename TransformType::LinearMatrixType LinearMatrixType;
+    LinearMatrixType result;
+    t.computeRotationScaling(&result, (LinearMatrixType*)0);
+    return result;
+  }
+};
+template<> struct transform_rotation_impl<Isometry> {
+  template<typename TransformType>
+  EIGEN_DEVICE_FUNC static inline
+  typename TransformType::ConstLinearPart run(const TransformType& t)
+  {
+    return t.linear();
+  }
+};
+}
+/** \returns the rotation part of the transformation
+  *
+  * If Mode==Isometry, then this method is an alias for linear(),
+  * otherwise it calls computeRotationScaling() to extract the rotation
+  * through a SVD decomposition.
+  *
+  * \svd_module
+  *
+  * \sa computeRotationScaling(), computeScalingRotation(), class SVD
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC
+typename Transform<Scalar,Dim,Mode,Options>::RotationReturnType
+Transform<Scalar,Dim,Mode,Options>::rotation() const
+{
+  return internal::transform_rotation_impl<Mode>::run(*this);
+}
+
+
+/** decomposes the linear part of the transformation as a product rotation x scaling, the scaling being
+  * not necessarily positive.
+  *
+  * If either pointer is zero, the corresponding computation is skipped.
+  *
+  *
+  *
+  * \svd_module
+  *
+  * \sa computeScalingRotation(), rotation(), class SVD
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+template<typename RotationMatrixType, typename ScalingMatrixType>
+EIGEN_DEVICE_FUNC void Transform<Scalar,Dim,Mode,Options>::computeRotationScaling(RotationMatrixType *rotation, ScalingMatrixType *scaling) const
+{
+  // Note that JacobiSVD is faster than BDCSVD for small matrices.
+  JacobiSVD<LinearMatrixType, ComputeFullU | ComputeFullV> svd(linear());
+
+  Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant() < Scalar(0) ? Scalar(-1) : Scalar(1); // so x has absolute value 1
+  VectorType sv(svd.singularValues());
+  sv.coeffRef(Dim-1) *= x;
+  if(scaling) *scaling = svd.matrixV() * sv.asDiagonal() * svd.matrixV().adjoint();
+  if(rotation)
+  {
+    LinearMatrixType m(svd.matrixU());
+    m.col(Dim-1) *= x;
+    *rotation = m * svd.matrixV().adjoint();
+  }
+}
+
+/** decomposes the linear part of the transformation as a product scaling x rotation, the scaling being
+  * not necessarily positive.
+  *
+  * If either pointer is zero, the corresponding computation is skipped.
+  *
+  *
+  *
+  * \svd_module
+  *
+  * \sa computeRotationScaling(), rotation(), class SVD
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+template<typename ScalingMatrixType, typename RotationMatrixType>
+EIGEN_DEVICE_FUNC void Transform<Scalar,Dim,Mode,Options>::computeScalingRotation(ScalingMatrixType *scaling, RotationMatrixType *rotation) const
+{
+  // Note that JacobiSVD is faster than BDCSVD for small matrices.
+  JacobiSVD<LinearMatrixType, ComputeFullU | ComputeFullV> svd(linear());
+
+  Scalar x = (svd.matrixU() * svd.matrixV().adjoint()).determinant() < Scalar(0) ? Scalar(-1) : Scalar(1); // so x has absolute value 1
+  VectorType sv(svd.singularValues());
+  sv.coeffRef(Dim-1) *= x;
+  if(scaling) *scaling = svd.matrixU() * sv.asDiagonal() * svd.matrixU().adjoint();
+  if(rotation)
+  {
+    LinearMatrixType m(svd.matrixU());
+    m.col(Dim-1) *= x;
+    *rotation = m * svd.matrixV().adjoint();
+  }
+}
+
+/** Convenient method to set \c *this from a position, orientation and scale
+  * of a 3D object.
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+template<typename PositionDerived, typename OrientationType, typename ScaleDerived>
+EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>&
+Transform<Scalar,Dim,Mode,Options>::fromPositionOrientationScale(const MatrixBase<PositionDerived> &position,
+  const OrientationType& orientation, const MatrixBase<ScaleDerived> &scale)
+{
+  linear() = internal::toRotationMatrix<Scalar,Dim>(orientation);
+  linear() *= scale.asDiagonal();
+  translation() = position;
+  makeAffine();
+  return *this;
+}
+
+namespace internal {
+
+template<int Mode>
+struct transform_make_affine
+{
+  template<typename MatrixType>
+  EIGEN_DEVICE_FUNC static void run(MatrixType &mat)
+  {
+    static const int Dim = MatrixType::ColsAtCompileTime-1;
+    mat.template block<1,Dim>(Dim,0).setZero();
+    mat.coeffRef(Dim,Dim) = typename MatrixType::Scalar(1);
+  }
+};
+
+template<>
+struct transform_make_affine<AffineCompact>
+{
+  template<typename MatrixType> EIGEN_DEVICE_FUNC static void run(MatrixType &) { }
+};
+
+// selector needed to avoid taking the inverse of a 3x4 matrix
+template<typename TransformType, int Mode=TransformType::Mode>
+struct projective_transform_inverse
+{
+  EIGEN_DEVICE_FUNC static inline void run(const TransformType&, TransformType&)
+  {}
+};
+
+template<typename TransformType>
+struct projective_transform_inverse<TransformType, Projective>
+{
+  EIGEN_DEVICE_FUNC static inline void run(const TransformType& m, TransformType& res)
+  {
+    res.matrix() = m.matrix().inverse();
+  }
+};
+
+} // end namespace internal
+
+
+/**
+  *
+  * \returns the inverse transformation according to some given knowledge
+  * on \c *this.
+  *
+  * \param hint allows to optimize the inversion process when the transformation
+  * is known to be not a general transformation (optional). The possible values are:
+  *  - #Projective if the transformation is not necessarily affine, i.e., if the
+  *    last row is not guaranteed to be [0 ... 0 1]
+  *  - #Affine if the last row can be assumed to be [0 ... 0 1]
+  *  - #Isometry if the transformation is only a concatenations of translations
+  *    and rotations.
+  *  The default is the template class parameter \c Mode.
+  *
+  * \warning unless \a traits is always set to NoShear or NoScaling, this function
+  * requires the generic inverse method of MatrixBase defined in the LU module. If
+  * you forget to include this module, then you will get hard to debug linking errors.
+  *
+  * \sa MatrixBase::inverse()
+  */
+template<typename Scalar, int Dim, int Mode, int Options>
+EIGEN_DEVICE_FUNC Transform<Scalar,Dim,Mode,Options>
+Transform<Scalar,Dim,Mode,Options>::inverse(TransformTraits hint) const
+{
+  Transform res;
+  if (hint == Projective)
+  {
+    internal::projective_transform_inverse<Transform>::run(*this, res);
+  }
+  else
+  {
+    if (hint == Isometry)
+    {
+      res.matrix().template topLeftCorner<Dim,Dim>() = linear().transpose();
+    }
+    else if(hint&Affine)
+    {
+      res.matrix().template topLeftCorner<Dim,Dim>() = linear().inverse();
+    }
+    else
+    {
+      eigen_assert(false && "Invalid transform traits in Transform::Inverse");
+    }
+    // translation and remaining parts
+    res.matrix().template topRightCorner<Dim,1>()
+      = - res.matrix().template topLeftCorner<Dim,Dim>() * translation();
+    res.makeAffine(); // we do need this, because in the beginning res is uninitialized
+  }
+  return res;
+}
+
+namespace internal {
+
+/*****************************************************
+*** Specializations of take affine part            ***
+*****************************************************/
+
+template<typename TransformType> struct transform_take_affine_part {
+  typedef typename TransformType::MatrixType MatrixType;
+  typedef typename TransformType::AffinePart AffinePart;
+  typedef typename TransformType::ConstAffinePart ConstAffinePart;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE AffinePart run(MatrixType& m)
+  { return m.template block<TransformType::Dim,TransformType::HDim>(0,0); }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ConstAffinePart run(const MatrixType& m)
+  { return m.template block<TransformType::Dim,TransformType::HDim>(0,0); }
+};
+
+template<typename Scalar, int Dim, int Options>
+struct transform_take_affine_part<Transform<Scalar,Dim,AffineCompact, Options> > {
+  typedef typename Transform<Scalar,Dim,AffineCompact,Options>::MatrixType MatrixType;
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE MatrixType& run(MatrixType& m) { return m; }
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const MatrixType& run(const MatrixType& m) { return m; }
+};
+
+/*****************************************************
+*** Specializations of construct from matrix       ***
+*****************************************************/
+
+template<typename Other, int Mode, int Options, int Dim, int HDim>
+struct transform_construct_from_matrix<Other, Mode,Options,Dim,HDim, Dim,Dim>
+{
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Transform<typename Other::Scalar,Dim,Mode,Options> *transform, const Other& other)
+  {
+    transform->linear() = other;
+    transform->translation().setZero();
+    transform->makeAffine();
+  }
+};
+
+template<typename Other, int Mode, int Options, int Dim, int HDim>
+struct transform_construct_from_matrix<Other, Mode,Options,Dim,HDim, Dim,HDim>
+{
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Transform<typename Other::Scalar,Dim,Mode,Options> *transform, const Other& other)
+  {
+    transform->affine() = other;
+    transform->makeAffine();
+  }
+};
+
+template<typename Other, int Mode, int Options, int Dim, int HDim>
+struct transform_construct_from_matrix<Other, Mode,Options,Dim,HDim, HDim,HDim>
+{
+  static  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Transform<typename Other::Scalar,Dim,Mode,Options> *transform, const Other& other)
+  { transform->matrix() = other; }
+};
+
+template<typename Other, int Options, int Dim, int HDim>
+struct transform_construct_from_matrix<Other, AffineCompact,Options,Dim,HDim, HDim,HDim>
+{
+  static  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(Transform<typename Other::Scalar,Dim,AffineCompact,Options> *transform, const Other& other)
+  { transform->matrix() = other.template block<Dim,HDim>(0,0); }
+};
+
+/**********************************************************
+***   Specializations of operator* with rhs EigenBase   ***
+**********************************************************/
+
+template<int LhsMode,int RhsMode>
+struct transform_product_result
+{
+  enum
+  {
+    Mode =
+      (LhsMode == (int)Projective    || RhsMode == (int)Projective    ) ? Projective :
+      (LhsMode == (int)Affine        || RhsMode == (int)Affine        ) ? Affine :
+      (LhsMode == (int)AffineCompact || RhsMode == (int)AffineCompact ) ? AffineCompact :
+      (LhsMode == (int)Isometry      || RhsMode == (int)Isometry      ) ? Isometry : Projective
+  };
+};
+
+template< typename TransformType, typename MatrixType, int RhsCols>
+struct transform_right_product_impl< TransformType, MatrixType, 0, RhsCols>
+{
+  typedef typename MatrixType::PlainObject ResultType;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other)
+  {
+    return T.matrix() * other;
+  }
+};
+
+template< typename TransformType, typename MatrixType, int RhsCols>
+struct transform_right_product_impl< TransformType, MatrixType, 1, RhsCols>
+{
+  enum {
+    Dim = TransformType::Dim,
+    HDim = TransformType::HDim,
+    OtherRows = MatrixType::RowsAtCompileTime,
+    OtherCols = MatrixType::ColsAtCompileTime
+  };
+
+  typedef typename MatrixType::PlainObject ResultType;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other)
+  {
+    EIGEN_STATIC_ASSERT(OtherRows==HDim, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES);
+
+    typedef Block<ResultType, Dim, OtherCols, int(MatrixType::RowsAtCompileTime)==Dim> TopLeftLhs;
+
+    ResultType res(other.rows(),other.cols());
+    TopLeftLhs(res, 0, 0, Dim, other.cols()).noalias() = T.affine() * other;
+    res.row(OtherRows-1) = other.row(OtherRows-1);
+
+    return res;
+  }
+};
+
+template< typename TransformType, typename MatrixType, int RhsCols>
+struct transform_right_product_impl< TransformType, MatrixType, 2, RhsCols>
+{
+  enum {
+    Dim = TransformType::Dim,
+    HDim = TransformType::HDim,
+    OtherRows = MatrixType::RowsAtCompileTime,
+    OtherCols = MatrixType::ColsAtCompileTime
+  };
+
+  typedef typename MatrixType::PlainObject ResultType;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other)
+  {
+    EIGEN_STATIC_ASSERT(OtherRows==Dim, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES);
+
+    typedef Block<ResultType, Dim, OtherCols, true> TopLeftLhs;
+    ResultType res(Replicate<typename TransformType::ConstTranslationPart, 1, OtherCols>(T.translation(),1,other.cols()));
+    TopLeftLhs(res, 0, 0, Dim, other.cols()).noalias() += T.linear() * other;
+
+    return res;
+  }
+};
+
+template< typename TransformType, typename MatrixType >
+struct transform_right_product_impl< TransformType, MatrixType, 2, 1> // rhs is a vector of size Dim
+{
+  typedef typename TransformType::MatrixType TransformMatrix;
+  enum {
+    Dim = TransformType::Dim,
+    HDim = TransformType::HDim,
+    OtherRows = MatrixType::RowsAtCompileTime,
+    WorkingRows = plain_enum_min(TransformMatrix::RowsAtCompileTime, HDim)
+  };
+
+  typedef typename MatrixType::PlainObject ResultType;
+
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ResultType run(const TransformType& T, const MatrixType& other)
+  {
+    EIGEN_STATIC_ASSERT(OtherRows==Dim, YOU_MIXED_MATRICES_OF_DIFFERENT_SIZES);
+
+    Matrix<typename ResultType::Scalar, Dim+1, 1> rhs;
+    rhs.template head<Dim>() = other; rhs[Dim] = typename ResultType::Scalar(1);
+    Matrix<typename ResultType::Scalar, WorkingRows, 1> res(T.matrix() * rhs);
+    return res.template head<Dim>();
+  }
+};
+
+/**********************************************************
+***   Specializations of operator* with lhs EigenBase   ***
+**********************************************************/
+
+// generic HDim x HDim matrix * T => Projective
+template<typename Other,int Mode, int Options, int Dim, int HDim>
+struct transform_left_product_impl<Other,Mode,Options,Dim,HDim, HDim,HDim>
+{
+  typedef Transform<typename Other::Scalar,Dim,Mode,Options> TransformType;
+  typedef typename TransformType::MatrixType MatrixType;
+  typedef Transform<typename Other::Scalar,Dim,Projective,Options> ResultType;
+  static ResultType run(const Other& other,const TransformType& tr)
+  { return ResultType(other * tr.matrix()); }
+};
+
+// generic HDim x HDim matrix * AffineCompact => Projective
+template<typename Other, int Options, int Dim, int HDim>
+struct transform_left_product_impl<Other,AffineCompact,Options,Dim,HDim, HDim,HDim>
+{
+  typedef Transform<typename Other::Scalar,Dim,AffineCompact,Options> TransformType;
+  typedef typename TransformType::MatrixType MatrixType;
+  typedef Transform<typename Other::Scalar,Dim,Projective,Options> ResultType;
+  static ResultType run(const Other& other,const TransformType& tr)
+  {
+    ResultType res;
+    res.matrix().noalias() = other.template block<HDim,Dim>(0,0) * tr.matrix();
+    res.matrix().col(Dim) += other.col(Dim);
+    return res;
+  }
+};
+
+// affine matrix * T
+template<typename Other,int Mode, int Options, int Dim, int HDim>
+struct transform_left_product_impl<Other,Mode,Options,Dim,HDim, Dim,HDim>
+{
+  typedef Transform<typename Other::Scalar,Dim,Mode,Options> TransformType;
+  typedef typename TransformType::MatrixType MatrixType;
+  typedef TransformType ResultType;
+  static ResultType run(const Other& other,const TransformType& tr)
+  {
+    ResultType res;
+    res.affine().noalias() = other * tr.matrix();
+    res.matrix().row(Dim) = tr.matrix().row(Dim);
+    return res;
+  }
+};
+
+// affine matrix * AffineCompact
+template<typename Other, int Options, int Dim, int HDim>
+struct transform_left_product_impl<Other,AffineCompact,Options,Dim,HDim, Dim,HDim>
+{
+  typedef Transform<typename Other::Scalar,Dim,AffineCompact,Options> TransformType;
+  typedef typename TransformType::MatrixType MatrixType;
+  typedef TransformType ResultType;
+  static ResultType run(const Other& other,const TransformType& tr)
+  {
+    ResultType res;
+    res.matrix().noalias() = other.template block<Dim,Dim>(0,0) * tr.matrix();
+    res.translation() += other.col(Dim);
+    return res;
+  }
+};
+
+// linear matrix * T
+template<typename Other,int Mode, int Options, int Dim, int HDim>
+struct transform_left_product_impl<Other,Mode,Options,Dim,HDim, Dim,Dim>
+{
+  typedef Transform<typename Other::Scalar,Dim,Mode,Options> TransformType;
+  typedef typename TransformType::MatrixType MatrixType;
+  typedef TransformType ResultType;
+  static ResultType run(const Other& other, const TransformType& tr)
+  {
+    TransformType res;
+    if(Mode!=int(AffineCompact))
+      res.matrix().row(Dim) = tr.matrix().row(Dim);
+    res.matrix().template topRows<Dim>().noalias()
+      = other * tr.matrix().template topRows<Dim>();
+    return res;
+  }
+};
+
+/**********************************************************
+*** Specializations of operator* with another Transform ***
+**********************************************************/
+
+template<typename Scalar, int Dim, int LhsMode, int LhsOptions, int RhsMode, int RhsOptions>
+struct transform_transform_product_impl<Transform<Scalar,Dim,LhsMode,LhsOptions>,Transform<Scalar,Dim,RhsMode,RhsOptions>,false >
+{
+  enum { ResultMode = transform_product_result<LhsMode,RhsMode>::Mode };
+  typedef Transform<Scalar,Dim,LhsMode,LhsOptions> Lhs;
+  typedef Transform<Scalar,Dim,RhsMode,RhsOptions> Rhs;
+  typedef Transform<Scalar,Dim,ResultMode,LhsOptions> ResultType;
+  static ResultType run(const Lhs& lhs, const Rhs& rhs)
+  {
+    ResultType res;
+    res.linear() = lhs.linear() * rhs.linear();
+    res.translation() = lhs.linear() * rhs.translation() + lhs.translation();
+    res.makeAffine();
+    return res;
+  }
+};
+
+template<typename Scalar, int Dim, int LhsMode, int LhsOptions, int RhsMode, int RhsOptions>
+struct transform_transform_product_impl<Transform<Scalar,Dim,LhsMode,LhsOptions>,Transform<Scalar,Dim,RhsMode,RhsOptions>,true >
+{
+  typedef Transform<Scalar,Dim,LhsMode,LhsOptions> Lhs;
+  typedef Transform<Scalar,Dim,RhsMode,RhsOptions> Rhs;
+  typedef Transform<Scalar,Dim,Projective> ResultType;
+  static ResultType run(const Lhs& lhs, const Rhs& rhs)
+  {
+    return ResultType( lhs.matrix() * rhs.matrix() );
+  }
+};
+
+template<typename Scalar, int Dim, int LhsOptions, int RhsOptions>
+struct transform_transform_product_impl<Transform<Scalar,Dim,AffineCompact,LhsOptions>,Transform<Scalar,Dim,Projective,RhsOptions>,true >
+{
+  typedef Transform<Scalar,Dim,AffineCompact,LhsOptions> Lhs;
+  typedef Transform<Scalar,Dim,Projective,RhsOptions> Rhs;
+  typedef Transform<Scalar,Dim,Projective> ResultType;
+  static ResultType run(const Lhs& lhs, const Rhs& rhs)
+  {
+    ResultType res;
+    res.matrix().template topRows<Dim>() = lhs.matrix() * rhs.matrix();
+    res.matrix().row(Dim) = rhs.matrix().row(Dim);
+    return res;
+  }
+};
+
+template<typename Scalar, int Dim, int LhsOptions, int RhsOptions>
+struct transform_transform_product_impl<Transform<Scalar,Dim,Projective,LhsOptions>,Transform<Scalar,Dim,AffineCompact,RhsOptions>,true >
+{
+  typedef Transform<Scalar,Dim,Projective,LhsOptions> Lhs;
+  typedef Transform<Scalar,Dim,AffineCompact,RhsOptions> Rhs;
+  typedef Transform<Scalar,Dim,Projective> ResultType;
+  static ResultType run(const Lhs& lhs, const Rhs& rhs)
+  {
+    ResultType res(lhs.matrix().template leftCols<Dim>() * rhs.matrix());
+    res.matrix().col(Dim) += lhs.matrix().col(Dim);
+    return res;
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRANSFORM_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Translation.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Translation.h
new file mode 100644
index 0000000000000000000000000000000000000000..dd0adbad6f03977244b7b25d2ffb0b43aa6a7ccb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Translation.h
@@ -0,0 +1,204 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_TRANSLATION_H
+#define EIGEN_TRANSLATION_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \geometry_module \ingroup Geometry_Module
+  *
+  * \class Translation
+  *
+  * \brief Represents a translation transformation
+  *
+  * \tparam Scalar_ the scalar type, i.e., the type of the coefficients.
+  * \tparam Dim_ the  dimension of the space, can be a compile time value or Dynamic
+  *
+  * \note This class is not aimed to be used to store a translation transformation,
+  * but rather to make easier the constructions and updates of Transform objects.
+  *
+  * \sa class Scaling, class Transform
+  */
+template<typename Scalar_, int Dim_>
+class Translation
+{
+public:
+  EIGEN_MAKE_ALIGNED_OPERATOR_NEW_IF_VECTORIZABLE_FIXED_SIZE(Scalar_,Dim_)
+  /** dimension of the space */
+  enum { Dim = Dim_ };
+  /** the scalar type of the coefficients */
+  typedef Scalar_ Scalar;
+  /** corresponding vector type */
+  typedef Matrix<Scalar,Dim,1> VectorType;
+  /** corresponding linear transformation matrix type */
+  typedef Matrix<Scalar,Dim,Dim> LinearMatrixType;
+  /** corresponding affine transformation type */
+  typedef Transform<Scalar,Dim,Affine> AffineTransformType;
+  /** corresponding isometric transformation type */
+  typedef Transform<Scalar,Dim,Isometry> IsometryTransformType;
+
+protected:
+
+  VectorType m_coeffs;
+
+public:
+
+  /** Default constructor without initialization. */
+  EIGEN_DEVICE_FUNC Translation() {}
+  /**  */
+  EIGEN_DEVICE_FUNC inline Translation(const Scalar& sx, const Scalar& sy)
+  {
+    eigen_assert(Dim==2);
+    m_coeffs.x() = sx;
+    m_coeffs.y() = sy;
+  }
+  /**  */
+  EIGEN_DEVICE_FUNC inline Translation(const Scalar& sx, const Scalar& sy, const Scalar& sz)
+  {
+    eigen_assert(Dim==3);
+    m_coeffs.x() = sx;
+    m_coeffs.y() = sy;
+    m_coeffs.z() = sz;
+  }
+  /** Constructs and initialize the translation transformation from a vector of translation coefficients */
+  EIGEN_DEVICE_FUNC explicit inline Translation(const VectorType& vector) : m_coeffs(vector) {}
+
+  /** \brief Returns the x-translation by value. **/
+  EIGEN_DEVICE_FUNC inline Scalar x() const { return m_coeffs.x(); }
+  /** \brief Returns the y-translation by value. **/
+  EIGEN_DEVICE_FUNC inline Scalar y() const { return m_coeffs.y(); }
+  /** \brief Returns the z-translation by value. **/
+  EIGEN_DEVICE_FUNC inline Scalar z() const { return m_coeffs.z(); }
+
+  /** \brief Returns the x-translation as a reference. **/
+  EIGEN_DEVICE_FUNC inline Scalar& x() { return m_coeffs.x(); }
+  /** \brief Returns the y-translation as a reference. **/
+  EIGEN_DEVICE_FUNC inline Scalar& y() { return m_coeffs.y(); }
+  /** \brief Returns the z-translation as a reference. **/
+  EIGEN_DEVICE_FUNC inline Scalar& z() { return m_coeffs.z(); }
+
+  EIGEN_DEVICE_FUNC const VectorType& vector() const { return m_coeffs; }
+  EIGEN_DEVICE_FUNC VectorType& vector() { return m_coeffs; }
+
+  EIGEN_DEVICE_FUNC const VectorType& translation() const { return m_coeffs; }
+  EIGEN_DEVICE_FUNC VectorType& translation() { return m_coeffs; }
+
+  /** Concatenates two translation */
+  EIGEN_DEVICE_FUNC inline Translation operator* (const Translation& other) const
+  { return Translation(m_coeffs + other.m_coeffs); }
+
+  /** Concatenates a translation and a uniform scaling */
+  EIGEN_DEVICE_FUNC inline AffineTransformType operator* (const UniformScaling<Scalar>& other) const;
+
+  /** Concatenates a translation and a linear transformation */
+  template<typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline AffineTransformType operator* (const EigenBase<OtherDerived>& linear) const;
+
+  /** Concatenates a translation and a rotation */
+  template<typename Derived>
+  EIGEN_DEVICE_FUNC inline IsometryTransformType operator*(const RotationBase<Derived,Dim>& r) const
+  { return *this * IsometryTransformType(r); }
+
+  /** \returns the concatenation of a linear transformation \a l with the translation \a t */
+  // its a nightmare to define a templated friend function outside its declaration
+  template<typename OtherDerived> friend
+  EIGEN_DEVICE_FUNC inline AffineTransformType operator*(const EigenBase<OtherDerived>& linear, const Translation& t)
+  {
+    AffineTransformType res;
+    res.matrix().setZero();
+    res.linear() = linear.derived();
+    res.translation() = linear.derived() * t.m_coeffs;
+    res.matrix().row(Dim).setZero();
+    res(Dim,Dim) = Scalar(1);
+    return res;
+  }
+
+  /** Concatenates a translation and a transformation */
+  template<int Mode, int Options>
+  EIGEN_DEVICE_FUNC inline Transform<Scalar,Dim,Mode> operator* (const Transform<Scalar,Dim,Mode,Options>& t) const
+  {
+    Transform<Scalar,Dim,Mode> res = t;
+    res.pretranslate(m_coeffs);
+    return res;
+  }
+
+  /** Applies translation to vector */
+  template<typename Derived>
+  inline std::enable_if_t<Derived::IsVectorAtCompileTime,VectorType>
+  operator* (const MatrixBase<Derived>& vec) const
+  { return m_coeffs + vec.derived(); }
+
+  /** \returns the inverse translation (opposite) */
+  Translation inverse() const { return Translation(-m_coeffs); }
+
+  static const Translation Identity() { return Translation(VectorType::Zero()); }
+
+  /** \returns \c *this with scalar type casted to \a NewScalarType
+    *
+    * Note that if \a NewScalarType is equal to the current scalar type of \c *this
+    * then this function smartly returns a const reference to \c *this.
+    */
+  template<typename NewScalarType>
+  EIGEN_DEVICE_FUNC inline typename internal::cast_return_type<Translation,Translation<NewScalarType,Dim> >::type cast() const
+  { return typename internal::cast_return_type<Translation,Translation<NewScalarType,Dim> >::type(*this); }
+
+  /** Copy constructor with scalar type conversion */
+  template<typename OtherScalarType>
+  EIGEN_DEVICE_FUNC inline explicit Translation(const Translation<OtherScalarType,Dim>& other)
+  { m_coeffs = other.vector().template cast<Scalar>(); }
+
+  /** \returns \c true if \c *this is approximately equal to \a other, within the precision
+    * determined by \a prec.
+    *
+    * \sa MatrixBase::isApprox() */
+  EIGEN_DEVICE_FUNC bool isApprox(const Translation& other, const typename NumTraits<Scalar>::Real& prec = NumTraits<Scalar>::dummy_precision()) const
+  { return m_coeffs.isApprox(other.m_coeffs, prec); }
+
+};
+
+/** \addtogroup Geometry_Module */
+//@{
+typedef Translation<float, 2> Translation2f;
+typedef Translation<double,2> Translation2d;
+typedef Translation<float, 3> Translation3f;
+typedef Translation<double,3> Translation3d;
+//@}
+
+template<typename Scalar, int Dim>
+EIGEN_DEVICE_FUNC inline typename Translation<Scalar,Dim>::AffineTransformType
+Translation<Scalar,Dim>::operator* (const UniformScaling<Scalar>& other) const
+{
+  AffineTransformType res;
+  res.matrix().setZero();
+  res.linear().diagonal().fill(other.factor());
+  res.translation() = m_coeffs;
+  res(Dim,Dim) = Scalar(1);
+  return res;
+}
+
+template<typename Scalar, int Dim>
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC inline typename Translation<Scalar,Dim>::AffineTransformType
+Translation<Scalar,Dim>::operator* (const EigenBase<OtherDerived>& linear) const
+{
+  AffineTransformType res;
+  res.matrix().setZero();
+  res.linear() = linear.derived();
+  res.translation() = m_coeffs;
+  res.matrix().row(Dim).setZero();
+  res(Dim,Dim) = Scalar(1);
+  return res;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_TRANSLATION_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Umeyama.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Umeyama.h
new file mode 100644
index 0000000000000000000000000000000000000000..2da26355d5568dcdc10bf6af9689620f892f09cf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/Umeyama.h
@@ -0,0 +1,170 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_UMEYAMA_H
+#define EIGEN_UMEYAMA_H
+
+// This file requires the user to include 
+// * Eigen/Core
+// * Eigen/LU 
+// * Eigen/SVD
+// * Eigen/Array
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+// These helpers are required since it allows to use mixed types as parameters
+// for the Umeyama. The problem with mixed parameters is that the return type
+// cannot trivially be deduced when float and double types are mixed.
+namespace internal {
+
+// Compile time return type deduction for different MatrixBase types.
+// Different means here different alignment and parameters but the same underlying
+// real scalar type.
+template<typename MatrixType, typename OtherMatrixType>
+struct umeyama_transform_matrix_type
+{
+  enum {
+    MinRowsAtCompileTime = internal::min_size_prefer_dynamic(MatrixType::RowsAtCompileTime, OtherMatrixType::RowsAtCompileTime),
+
+    // When possible we want to choose some small fixed size value since the result
+    // is likely to fit on the stack. So here, min_size_prefer_dynamic is not what we want.
+    HomogeneousDimension = int(MinRowsAtCompileTime) == Dynamic ? Dynamic : int(MinRowsAtCompileTime)+1
+  };
+
+  typedef Matrix<typename traits<MatrixType>::Scalar,
+    HomogeneousDimension,
+    HomogeneousDimension,
+    AutoAlign | (traits<MatrixType>::Flags & RowMajorBit ? RowMajor : ColMajor),
+    HomogeneousDimension,
+    HomogeneousDimension
+  > type;
+};
+
+}
+
+#endif
+
+/**
+* \geometry_module \ingroup Geometry_Module
+*
+* \brief Returns the transformation between two point sets.
+*
+* The algorithm is based on:
+* "Least-squares estimation of transformation parameters between two point patterns",
+* Shinji Umeyama, PAMI 1991, DOI: 10.1109/34.88573
+*
+* It estimates parameters \f$ c, \mathbf{R}, \f$ and \f$ \mathbf{t} \f$ such that
+* \f{align*}
+*   \frac{1}{n} \sum_{i=1}^n \vert\vert y_i - (c\mathbf{R}x_i + \mathbf{t}) \vert\vert_2^2
+* \f}
+* is minimized.
+*
+* The algorithm is based on the analysis of the covariance matrix
+* \f$ \Sigma_{\mathbf{x}\mathbf{y}} \in \mathbb{R}^{d \times d} \f$
+* of the input point sets \f$ \mathbf{x} \f$ and \f$ \mathbf{y} \f$ where 
+* \f$d\f$ is corresponding to the dimension (which is typically small).
+* The analysis is involving the SVD having a complexity of \f$O(d^3)\f$
+* though the actual computational effort lies in the covariance
+* matrix computation which has an asymptotic lower bound of \f$O(dm)\f$ when 
+* the input point sets have dimension \f$d \times m\f$.
+*
+* Currently the method is working only for floating point matrices.
+*
+* \todo Should the return type of umeyama() become a Transform?
+*
+* \param src Source points \f$ \mathbf{x} = \left( x_1, \hdots, x_n \right) \f$.
+* \param dst Destination points \f$ \mathbf{y} = \left( y_1, \hdots, y_n \right) \f$.
+* \param with_scaling Sets \f$ c=1 \f$ when <code>false</code> is passed.
+* \return The homogeneous transformation 
+* \f{align*}
+*   T = \begin{bmatrix} c\mathbf{R} & \mathbf{t} \\ \mathbf{0} & 1 \end{bmatrix}
+* \f}
+* minimizing the residual above. This transformation is always returned as an 
+* Eigen::Matrix.
+*/
+template <typename Derived, typename OtherDerived>
+typename internal::umeyama_transform_matrix_type<Derived, OtherDerived>::type
+umeyama(const MatrixBase<Derived>& src, const MatrixBase<OtherDerived>& dst, bool with_scaling = true)
+{
+  typedef typename internal::umeyama_transform_matrix_type<Derived, OtherDerived>::type TransformationMatrixType;
+  typedef typename internal::traits<TransformationMatrixType>::Scalar Scalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsComplex, NUMERIC_TYPE_MUST_BE_REAL)
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename internal::traits<OtherDerived>::Scalar>::value),
+    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  enum { Dimension = internal::min_size_prefer_dynamic(Derived::RowsAtCompileTime, OtherDerived::RowsAtCompileTime) };
+
+  typedef Matrix<Scalar, Dimension, 1> VectorType;
+  typedef Matrix<Scalar, Dimension, Dimension> MatrixType;
+  typedef typename internal::plain_matrix_type_row_major<Derived>::type RowMajorMatrixType;
+
+  const Index m = src.rows(); // dimension
+  const Index n = src.cols(); // number of measurements
+
+  // required for demeaning ...
+  const RealScalar one_over_n = RealScalar(1) / static_cast<RealScalar>(n);
+
+  // computation of mean
+  const VectorType src_mean = src.rowwise().sum() * one_over_n;
+  const VectorType dst_mean = dst.rowwise().sum() * one_over_n;
+
+  // demeaning of src and dst points
+  const RowMajorMatrixType src_demean = src.colwise() - src_mean;
+  const RowMajorMatrixType dst_demean = dst.colwise() - dst_mean;
+
+  // Eq. (38)
+  const MatrixType sigma = one_over_n * dst_demean * src_demean.transpose();
+
+  JacobiSVD<MatrixType, ComputeFullU | ComputeFullV> svd(sigma);
+
+  // Initialize the resulting transformation with an identity matrix...
+  TransformationMatrixType Rt = TransformationMatrixType::Identity(m+1,m+1);
+
+  // Eq. (39)
+  VectorType S = VectorType::Ones(m);
+
+  if  ( svd.matrixU().determinant() * svd.matrixV().determinant() < 0 ) {
+    Index tmp = m - 1;  
+    S(tmp) = -1;
+  }
+
+  // Eq. (40) and (43)
+  Rt.block(0,0,m,m).noalias() = svd.matrixU() * S.asDiagonal() * svd.matrixV().transpose();
+
+  if (with_scaling)
+  {
+    // Eq. (36)-(37)
+    const Scalar src_var = src_demean.rowwise().squaredNorm().sum() * one_over_n;
+
+    // Eq. (42)
+    const Scalar c = Scalar(1)/src_var * svd.singularValues().dot(S);
+
+    // Eq. (41)
+    Rt.col(m).head(m) = dst_mean;
+    Rt.col(m).head(m).noalias() -= c*Rt.topLeftCorner(m,m)*src_mean;
+    Rt.block(0,0,m,m) *= c;
+  }
+  else
+  {
+    Rt.col(m).head(m) = dst_mean;
+    Rt.col(m).head(m).noalias() -= Rt.topLeftCorner(m,m)*src_mean;
+  }
+
+  return Rt;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_UMEYAMA_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/arch/Geometry_SIMD.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/arch/Geometry_SIMD.h
new file mode 100644
index 0000000000000000000000000000000000000000..bd91949769780bd2583938d644be91599537e1d5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Geometry/arch/Geometry_SIMD.h
@@ -0,0 +1,170 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Rohit Garg <rpg.314@gmail.com>
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GEOMETRY_SIMD_H
+#define EIGEN_GEOMETRY_SIMD_H
+
+#include "../InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+template<class Derived, class OtherDerived>
+struct quat_product<Architecture::Target, Derived, OtherDerived, float>
+{
+  enum {
+    AAlignment = traits<Derived>::Alignment,
+    BAlignment = traits<OtherDerived>::Alignment,
+    ResAlignment = traits<Quaternion<float> >::Alignment
+  };
+  static inline Quaternion<float> run(const QuaternionBase<Derived>& _a, const QuaternionBase<OtherDerived>& _b)
+  {
+    evaluator<typename Derived::Coefficients> ae(_a.coeffs());
+    evaluator<typename OtherDerived::Coefficients> be(_b.coeffs());
+    Quaternion<float> res;
+    const float neg_zero = numext::bit_cast<float>(0x80000000u);
+    const float arr[4] = {0.f, 0.f, 0.f, neg_zero};
+    const Packet4f mask = ploadu<Packet4f>(arr);
+    Packet4f a = ae.template packet<AAlignment,Packet4f>(0);
+    Packet4f b = be.template packet<BAlignment,Packet4f>(0);
+    Packet4f s1 = pmul(vec4f_swizzle1(a,1,2,0,2),vec4f_swizzle1(b,2,0,1,2));
+    Packet4f s2 = pmul(vec4f_swizzle1(a,3,3,3,1),vec4f_swizzle1(b,0,1,2,1));
+    pstoret<float,Packet4f,ResAlignment>(
+              &res.x(),
+              padd(psub(pmul(a,vec4f_swizzle1(b,3,3,3,3)),
+                                    pmul(vec4f_swizzle1(a,2,0,1,0),
+                                               vec4f_swizzle1(b,1,2,0,0))),
+                         pxor(mask,padd(s1,s2))));
+    
+    return res;
+  }
+};
+
+template<class Derived>
+struct quat_conj<Architecture::Target, Derived, float>
+{
+  enum {
+    ResAlignment = traits<Quaternion<float> >::Alignment
+  };
+  static inline Quaternion<float> run(const QuaternionBase<Derived>& q)
+  {
+    evaluator<typename Derived::Coefficients> qe(q.coeffs());
+    Quaternion<float> res;
+    const float neg_zero = numext::bit_cast<float>(0x80000000u);
+    const float arr[4] = {neg_zero, neg_zero, neg_zero,0.f};
+    const Packet4f mask = ploadu<Packet4f>(arr);
+    pstoret<float,Packet4f,ResAlignment>(&res.x(), pxor(mask, qe.template packet<traits<Derived>::Alignment,Packet4f>(0)));
+    return res;
+  }
+};
+
+
+template<typename VectorLhs,typename VectorRhs>
+struct cross3_impl<Architecture::Target,VectorLhs,VectorRhs,float,true>
+{
+  enum {
+    ResAlignment = traits<typename plain_matrix_type<VectorLhs>::type>::Alignment
+  };
+  static inline typename plain_matrix_type<VectorLhs>::type
+  run(const VectorLhs& lhs, const VectorRhs& rhs)
+  {
+    evaluator<VectorLhs> lhs_eval(lhs);
+    evaluator<VectorRhs> rhs_eval(rhs);
+    Packet4f a = lhs_eval.template packet<traits<VectorLhs>::Alignment,Packet4f>(0);
+    Packet4f b = rhs_eval.template packet<traits<VectorRhs>::Alignment,Packet4f>(0);
+    Packet4f mul1 = pmul(vec4f_swizzle1(a,1,2,0,3),vec4f_swizzle1(b,2,0,1,3));
+    Packet4f mul2 = pmul(vec4f_swizzle1(a,2,0,1,3),vec4f_swizzle1(b,1,2,0,3));
+    typename plain_matrix_type<VectorLhs>::type res;
+    pstoret<float,Packet4f,ResAlignment>(&res.x(),psub(mul1,mul2));
+    return res;
+  }
+};
+
+
+
+#if (defined EIGEN_VECTORIZE_SSE) || (EIGEN_ARCH_ARM64)
+
+template<class Derived, class OtherDerived>
+struct quat_product<Architecture::Target, Derived, OtherDerived, double>
+{
+  enum {
+    BAlignment = traits<OtherDerived>::Alignment,
+    ResAlignment = traits<Quaternion<double> >::Alignment
+  };
+
+  static inline Quaternion<double> run(const QuaternionBase<Derived>& _a, const QuaternionBase<OtherDerived>& _b)
+  {
+  Quaternion<double> res;
+
+  evaluator<typename Derived::Coefficients> ae(_a.coeffs());
+  evaluator<typename OtherDerived::Coefficients> be(_b.coeffs());
+
+  const double* a = _a.coeffs().data();
+  Packet2d b_xy = be.template packet<BAlignment,Packet2d>(0);
+  Packet2d b_zw = be.template packet<BAlignment,Packet2d>(2);
+  Packet2d a_xx = pset1<Packet2d>(a[0]);
+  Packet2d a_yy = pset1<Packet2d>(a[1]);
+  Packet2d a_zz = pset1<Packet2d>(a[2]);
+  Packet2d a_ww = pset1<Packet2d>(a[3]);
+
+  // two temporaries:
+  Packet2d t1, t2;
+
+  /*
+   * t1 = ww*xy + yy*zw
+   * t2 = zz*xy - xx*zw
+   * res.xy = t1 +/- swap(t2)
+   */
+  t1 = padd(pmul(a_ww, b_xy), pmul(a_yy, b_zw));
+  t2 = psub(pmul(a_zz, b_xy), pmul(a_xx, b_zw));
+  pstoret<double,Packet2d,ResAlignment>(&res.x(), paddsub(t1, preverse(t2)));
+  
+  /*
+   * t1 = ww*zw - yy*xy
+   * t2 = zz*zw + xx*xy
+   * res.zw = t1 -/+ swap(t2) = swap( swap(t1) +/- t2)
+   */
+  t1 = psub(pmul(a_ww, b_zw), pmul(a_yy, b_xy));
+  t2 = padd(pmul(a_zz, b_zw), pmul(a_xx, b_xy));
+  pstoret<double,Packet2d,ResAlignment>(&res.z(), preverse(paddsub(preverse(t1), t2)));
+
+  return res;
+}
+};
+
+template<class Derived>
+struct quat_conj<Architecture::Target, Derived, double>
+{
+  enum {
+    ResAlignment = traits<Quaternion<double> >::Alignment
+  };
+  static inline Quaternion<double> run(const QuaternionBase<Derived>& q)
+  {
+    evaluator<typename Derived::Coefficients> qe(q.coeffs());
+    Quaternion<double> res;
+    const double neg_zero = numext::bit_cast<double>(0x8000000000000000ull);
+    const double arr1[2] = {neg_zero, neg_zero};
+    const double arr2[2] = {neg_zero,  0.0};
+    const Packet2d mask0 = ploadu<Packet2d>(arr1);
+    const Packet2d mask2 = ploadu<Packet2d>(arr2);
+    pstoret<double,Packet2d,ResAlignment>(&res.x(), pxor(mask0, qe.template packet<traits<Derived>::Alignment,Packet2d>(0)));
+    pstoret<double,Packet2d,ResAlignment>(&res.z(), pxor(mask2, qe.template packet<traits<Derived>::Alignment,Packet2d>(2)));
+    return res;
+  }
+};
+
+#endif // end EIGEN_VECTORIZE_SSE_OR_EIGEN_ARCH_ARM64
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_GEOMETRY_SIMD_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Householder/BlockHouseholder.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Householder/BlockHouseholder.h
new file mode 100644
index 0000000000000000000000000000000000000000..a5c809544ca17bc86c10e28b997cb4d5129134bf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Householder/BlockHouseholder.h
@@ -0,0 +1,112 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Vincent Lejeune
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BLOCK_HOUSEHOLDER_H
+#define EIGEN_BLOCK_HOUSEHOLDER_H
+
+// This file contains some helper function to deal with block householder reflectors
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+  
+/** \internal */
+// template<typename TriangularFactorType,typename VectorsType,typename CoeffsType>
+// void make_block_householder_triangular_factor(TriangularFactorType& triFactor, const VectorsType& vectors, const CoeffsType& hCoeffs)
+// {
+//   typedef typename VectorsType::Scalar Scalar;
+//   const Index nbVecs = vectors.cols();
+//   eigen_assert(triFactor.rows() == nbVecs && triFactor.cols() == nbVecs && vectors.rows()>=nbVecs);
+// 
+//   for(Index i = 0; i < nbVecs; i++)
+//   {
+//     Index rs = vectors.rows() - i;
+//     // Warning, note that hCoeffs may alias with vectors.
+//     // It is then necessary to copy it before modifying vectors(i,i). 
+//     typename CoeffsType::Scalar h = hCoeffs(i);
+//     // This hack permits to pass trough nested Block<> and Transpose<> expressions.
+//     Scalar *Vii_ptr = const_cast<Scalar*>(vectors.data() + vectors.outerStride()*i + vectors.innerStride()*i);
+//     Scalar Vii = *Vii_ptr;
+//     *Vii_ptr = Scalar(1);
+//     triFactor.col(i).head(i).noalias() = -h * vectors.block(i, 0, rs, i).adjoint()
+//                                        * vectors.col(i).tail(rs);
+//     *Vii_ptr = Vii;
+//     // FIXME add .noalias() once the triangular product can work inplace
+//     triFactor.col(i).head(i) = triFactor.block(0,0,i,i).template triangularView<Upper>()
+//                              * triFactor.col(i).head(i);
+//     triFactor(i,i) = hCoeffs(i);
+//   }
+// }
+
+/** \internal */
+// This variant avoid modifications in vectors
+template<typename TriangularFactorType,typename VectorsType,typename CoeffsType>
+void make_block_householder_triangular_factor(TriangularFactorType& triFactor, const VectorsType& vectors, const CoeffsType& hCoeffs)
+{
+  const Index nbVecs = vectors.cols();
+  eigen_assert(triFactor.rows() == nbVecs && triFactor.cols() == nbVecs && vectors.rows()>=nbVecs);
+
+  for(Index i = nbVecs-1; i >=0 ; --i)
+  {
+    Index rs = vectors.rows() - i - 1;
+    Index rt = nbVecs-i-1;
+
+    if(rt>0)
+    {
+      triFactor.row(i).tail(rt).noalias() = -hCoeffs(i) * vectors.col(i).tail(rs).adjoint()
+                                                        * vectors.bottomRightCorner(rs, rt).template triangularView<UnitLower>();
+            
+      // FIXME use the following line with .noalias() once the triangular product can work inplace
+      // triFactor.row(i).tail(rt) = triFactor.row(i).tail(rt) * triFactor.bottomRightCorner(rt,rt).template triangularView<Upper>();
+      for(Index j=nbVecs-1; j>i; --j)
+      {
+        typename TriangularFactorType::Scalar z = triFactor(i,j);
+        triFactor(i,j) = z * triFactor(j,j);
+        if(nbVecs-j-1>0)
+          triFactor.row(i).tail(nbVecs-j-1) += z * triFactor.row(j).tail(nbVecs-j-1);
+      }
+      
+    }
+    triFactor(i,i) = hCoeffs(i);
+  }
+}
+
+/** \internal
+  * if forward then perform   mat = H0 * H1 * H2 * mat
+  * otherwise perform         mat = H2 * H1 * H0 * mat
+  */
+template<typename MatrixType,typename VectorsType,typename CoeffsType>
+void apply_block_householder_on_the_left(MatrixType& mat, const VectorsType& vectors, const CoeffsType& hCoeffs, bool forward)
+{
+  enum { TFactorSize = VectorsType::ColsAtCompileTime };
+  Index nbVecs = vectors.cols();
+  Matrix<typename MatrixType::Scalar, TFactorSize, TFactorSize, RowMajor> T(nbVecs,nbVecs);
+  
+  if(forward) make_block_householder_triangular_factor(T, vectors, hCoeffs);
+  else        make_block_householder_triangular_factor(T, vectors, hCoeffs.conjugate());  
+  const TriangularView<const VectorsType, UnitLower> V(vectors);
+
+  // A -= V T V^* A
+  Matrix<typename MatrixType::Scalar,VectorsType::ColsAtCompileTime,MatrixType::ColsAtCompileTime,
+         (VectorsType::MaxColsAtCompileTime==1 && MatrixType::MaxColsAtCompileTime!=1)?RowMajor:ColMajor,
+         VectorsType::MaxColsAtCompileTime,MatrixType::MaxColsAtCompileTime> tmp = V.adjoint() * mat;
+  // FIXME add .noalias() once the triangular product can work inplace
+  if(forward) tmp = T.template triangularView<Upper>()           * tmp;
+  else        tmp = T.template triangularView<Upper>().adjoint() * tmp;
+  mat.noalias() -= V * tmp;
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_BLOCK_HOUSEHOLDER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Householder/Householder.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Householder/Householder.h
new file mode 100644
index 0000000000000000000000000000000000000000..855b752d9e0f8fe907f93ed3309cb561c7290737
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Householder/Householder.h
@@ -0,0 +1,178 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_HOUSEHOLDER_H
+#define EIGEN_HOUSEHOLDER_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+template<int n> struct decrement_size
+{
+  enum {
+    ret = n==Dynamic ? n : n-1
+  };
+};
+}
+
+/** Computes the elementary reflector H such that:
+  * \f$ H *this = [ beta 0 ... 0]^T \f$
+  * where the transformation H is:
+  * \f$ H = I - tau v v^*\f$
+  * and the vector v is:
+  * \f$ v^T = [1 essential^T] \f$
+  *
+  * The essential part of the vector \c v is stored in *this.
+  * 
+  * On output:
+  * \param tau the scaling factor of the Householder transformation
+  * \param beta the result of H * \c *this
+  *
+  * \sa MatrixBase::makeHouseholder(), MatrixBase::applyHouseholderOnTheLeft(),
+  *     MatrixBase::applyHouseholderOnTheRight()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC
+void MatrixBase<Derived>::makeHouseholderInPlace(Scalar& tau, RealScalar& beta)
+{
+  VectorBlock<Derived, internal::decrement_size<Base::SizeAtCompileTime>::ret> essentialPart(derived(), 1, size()-1);
+  makeHouseholder(essentialPart, tau, beta);
+}
+
+/** Computes the elementary reflector H such that:
+  * \f$ H *this = [ beta 0 ... 0]^T \f$
+  * where the transformation H is:
+  * \f$ H = I - tau v v^*\f$
+  * and the vector v is:
+  * \f$ v^T = [1 essential^T] \f$
+  *
+  * On output:
+  * \param essential the essential part of the vector \c v
+  * \param tau the scaling factor of the Householder transformation
+  * \param beta the result of H * \c *this
+  *
+  * \sa MatrixBase::makeHouseholderInPlace(), MatrixBase::applyHouseholderOnTheLeft(),
+  *     MatrixBase::applyHouseholderOnTheRight()
+  */
+template<typename Derived>
+template<typename EssentialPart>
+EIGEN_DEVICE_FUNC
+void MatrixBase<Derived>::makeHouseholder(
+  EssentialPart& essential,
+  Scalar& tau,
+  RealScalar& beta) const
+{
+  using numext::sqrt;
+  using numext::conj;
+  
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(EssentialPart)
+  VectorBlock<const Derived, EssentialPart::SizeAtCompileTime> tail(derived(), 1, size()-1);
+  
+  RealScalar tailSqNorm = size()==1 ? RealScalar(0) : tail.squaredNorm();
+  Scalar c0 = coeff(0);
+  const RealScalar tol = (std::numeric_limits<RealScalar>::min)();
+
+  if(tailSqNorm <= tol && numext::abs2(numext::imag(c0))<=tol)
+  {
+    tau = RealScalar(0);
+    beta = numext::real(c0);
+    essential.setZero();
+  }
+  else
+  {
+    beta = sqrt(numext::abs2(c0) + tailSqNorm);
+    if (numext::real(c0)>=RealScalar(0))
+      beta = -beta;
+    essential = tail / (c0 - beta);
+    tau = conj((beta - c0) / beta);
+  }
+}
+
+/** Apply the elementary reflector H given by
+  * \f$ H = I - tau v v^*\f$
+  * with
+  * \f$ v^T = [1 essential^T] \f$
+  * from the left to a vector or matrix.
+  *
+  * On input:
+  * \param essential the essential part of the vector \c v
+  * \param tau the scaling factor of the Householder transformation
+  * \param workspace a pointer to working space with at least
+  *                  this->cols() entries
+  *
+  * \sa MatrixBase::makeHouseholder(), MatrixBase::makeHouseholderInPlace(), 
+  *     MatrixBase::applyHouseholderOnTheRight()
+  */
+template<typename Derived>
+template<typename EssentialPart>
+EIGEN_DEVICE_FUNC
+void MatrixBase<Derived>::applyHouseholderOnTheLeft(
+  const EssentialPart& essential,
+  const Scalar& tau,
+  Scalar* workspace)
+{
+  if(rows() == 1)
+  {
+    *this *= Scalar(1)-tau;
+  }
+  else if(!numext::is_exactly_zero(tau))
+  {
+    Map<typename internal::plain_row_type<PlainObject>::type> tmp(workspace,cols());
+    Block<Derived, EssentialPart::SizeAtCompileTime, Derived::ColsAtCompileTime> bottom(derived(), 1, 0, rows()-1, cols());
+    tmp.noalias() = essential.adjoint() * bottom;
+    tmp += this->row(0);
+    this->row(0) -= tau * tmp;
+    bottom.noalias() -= tau * essential * tmp;
+  }
+}
+
+/** Apply the elementary reflector H given by
+  * \f$ H = I - tau v v^*\f$
+  * with
+  * \f$ v^T = [1 essential^T] \f$
+  * from the right to a vector or matrix.
+  *
+  * On input:
+  * \param essential the essential part of the vector \c v
+  * \param tau the scaling factor of the Householder transformation
+  * \param workspace a pointer to working space with at least
+  *                  this->rows() entries
+  *
+  * \sa MatrixBase::makeHouseholder(), MatrixBase::makeHouseholderInPlace(), 
+  *     MatrixBase::applyHouseholderOnTheLeft()
+  */
+template<typename Derived>
+template<typename EssentialPart>
+EIGEN_DEVICE_FUNC
+void MatrixBase<Derived>::applyHouseholderOnTheRight(
+  const EssentialPart& essential,
+  const Scalar& tau,
+  Scalar* workspace)
+{
+  if(cols() == 1)
+  {
+    *this *= Scalar(1)-tau;
+  }
+  else if(!numext::is_exactly_zero(tau))
+  {
+    Map<typename internal::plain_col_type<PlainObject>::type> tmp(workspace,rows());
+    Block<Derived, Derived::RowsAtCompileTime, EssentialPart::SizeAtCompileTime> right(derived(), 0, 1, rows(), cols()-1);
+    tmp.noalias() = right * essential;
+    tmp += this->col(0);
+    this->col(0) -= tau * tmp;
+    right.noalias() -= tau * tmp * essential.adjoint();
+  }
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_HOUSEHOLDER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Householder/HouseholderSequence.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Householder/HouseholderSequence.h
new file mode 100644
index 0000000000000000000000000000000000000000..41fef641d5b541b1c00c6b9a5091fb41be14c81f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Householder/HouseholderSequence.h
@@ -0,0 +1,560 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_HOUSEHOLDER_SEQUENCE_H
+#define EIGEN_HOUSEHOLDER_SEQUENCE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup Householder_Module
+  * \householder_module
+  * \class HouseholderSequence
+  * \brief Sequence of Householder reflections acting on subspaces with decreasing size
+  * \tparam VectorsType type of matrix containing the Householder vectors
+  * \tparam CoeffsType  type of vector containing the Householder coefficients
+  * \tparam Side        either OnTheLeft (the default) or OnTheRight
+  *
+  * This class represents a product sequence of Householder reflections where the first Householder reflection
+  * acts on the whole space, the second Householder reflection leaves the one-dimensional subspace spanned by
+  * the first unit vector invariant, the third Householder reflection leaves the two-dimensional subspace
+  * spanned by the first two unit vectors invariant, and so on up to the last reflection which leaves all but
+  * one dimensions invariant and acts only on the last dimension. Such sequences of Householder reflections
+  * are used in several algorithms to zero out certain parts of a matrix. Indeed, the methods
+  * HessenbergDecomposition::matrixQ(), Tridiagonalization::matrixQ(), HouseholderQR::householderQ(),
+  * and ColPivHouseholderQR::householderQ() all return a %HouseholderSequence.
+  *
+  * More precisely, the class %HouseholderSequence represents an \f$ n \times n \f$ matrix \f$ H \f$ of the
+  * form \f$ H = \prod_{i=0}^{n-1} H_i \f$ where the i-th Householder reflection is \f$ H_i = I - h_i v_i
+  * v_i^* \f$. The i-th Householder coefficient \f$ h_i \f$ is a scalar and the i-th Householder vector \f$
+  * v_i \f$ is a vector of the form
+  * \f[
+  * v_i = [\underbrace{0, \ldots, 0}_{i-1\mbox{ zeros}}, 1, \underbrace{*, \ldots,*}_{n-i\mbox{ arbitrary entries}} ].
+  * \f]
+  * The last \f$ n-i \f$ entries of \f$ v_i \f$ are called the essential part of the Householder vector.
+  *
+  * Typical usages are listed below, where H is a HouseholderSequence:
+  * \code
+  * A.applyOnTheRight(H);             // A = A * H
+  * A.applyOnTheLeft(H);              // A = H * A
+  * A.applyOnTheRight(H.adjoint());   // A = A * H^*
+  * A.applyOnTheLeft(H.adjoint());    // A = H^* * A
+  * MatrixXd Q = H;                   // conversion to a dense matrix
+  * \endcode
+  * In addition to the adjoint, you can also apply the inverse (=adjoint), the transpose, and the conjugate operators.
+  *
+  * See the documentation for HouseholderSequence(const VectorsType&, const CoeffsType&) for an example.
+  *
+  * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
+  */
+
+namespace internal {
+
+template<typename VectorsType, typename CoeffsType, int Side>
+struct traits<HouseholderSequence<VectorsType,CoeffsType,Side> >
+{
+  typedef typename VectorsType::Scalar Scalar;
+  typedef typename VectorsType::StorageIndex StorageIndex;
+  typedef typename VectorsType::StorageKind StorageKind;
+  enum {
+    RowsAtCompileTime = Side==OnTheLeft ? traits<VectorsType>::RowsAtCompileTime
+                                        : traits<VectorsType>::ColsAtCompileTime,
+    ColsAtCompileTime = RowsAtCompileTime,
+    MaxRowsAtCompileTime = Side==OnTheLeft ? traits<VectorsType>::MaxRowsAtCompileTime
+                                           : traits<VectorsType>::MaxColsAtCompileTime,
+    MaxColsAtCompileTime = MaxRowsAtCompileTime,
+    Flags = 0
+  };
+};
+
+struct HouseholderSequenceShape {};
+
+template<typename VectorsType, typename CoeffsType, int Side>
+struct evaluator_traits<HouseholderSequence<VectorsType,CoeffsType,Side> >
+  : public evaluator_traits_base<HouseholderSequence<VectorsType,CoeffsType,Side> >
+{
+  typedef HouseholderSequenceShape Shape;
+};
+
+template<typename VectorsType, typename CoeffsType, int Side>
+struct hseq_side_dependent_impl
+{
+  typedef Block<const VectorsType, Dynamic, 1> EssentialVectorType;
+  typedef HouseholderSequence<VectorsType, CoeffsType, OnTheLeft> HouseholderSequenceType;
+  static EIGEN_DEVICE_FUNC inline const EssentialVectorType essentialVector(const HouseholderSequenceType& h, Index k)
+  {
+    Index start = k+1+h.m_shift;
+    return Block<const VectorsType,Dynamic,1>(h.m_vectors, start, k, h.rows()-start, 1);
+  }
+};
+
+template<typename VectorsType, typename CoeffsType>
+struct hseq_side_dependent_impl<VectorsType, CoeffsType, OnTheRight>
+{
+  typedef Transpose<Block<const VectorsType, 1, Dynamic> > EssentialVectorType;
+  typedef HouseholderSequence<VectorsType, CoeffsType, OnTheRight> HouseholderSequenceType;
+  static inline const EssentialVectorType essentialVector(const HouseholderSequenceType& h, Index k)
+  {
+    Index start = k+1+h.m_shift;
+    return Block<const VectorsType,1,Dynamic>(h.m_vectors, k, start, 1, h.rows()-start).transpose();
+  }
+};
+
+template<typename OtherScalarType, typename MatrixType> struct matrix_type_times_scalar_type
+{
+  typedef typename ScalarBinaryOpTraits<OtherScalarType, typename MatrixType::Scalar>::ReturnType
+    ResultScalar;
+  typedef Matrix<ResultScalar, MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime,
+                 0, MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime> Type;
+};
+
+} // end namespace internal
+
+template<typename VectorsType, typename CoeffsType, int Side> class HouseholderSequence
+  : public EigenBase<HouseholderSequence<VectorsType,CoeffsType,Side> >
+{
+    typedef typename internal::hseq_side_dependent_impl<VectorsType,CoeffsType,Side>::EssentialVectorType EssentialVectorType;
+
+  public:
+    enum {
+      RowsAtCompileTime = internal::traits<HouseholderSequence>::RowsAtCompileTime,
+      ColsAtCompileTime = internal::traits<HouseholderSequence>::ColsAtCompileTime,
+      MaxRowsAtCompileTime = internal::traits<HouseholderSequence>::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = internal::traits<HouseholderSequence>::MaxColsAtCompileTime
+    };
+    typedef typename internal::traits<HouseholderSequence>::Scalar Scalar;
+
+    typedef HouseholderSequence<
+      std::conditional_t<NumTraits<Scalar>::IsComplex,
+        internal::remove_all_t<typename VectorsType::ConjugateReturnType>,
+        VectorsType>,
+      std::conditional_t<NumTraits<Scalar>::IsComplex,
+        internal::remove_all_t<typename CoeffsType::ConjugateReturnType>,
+        CoeffsType>,
+      Side
+    > ConjugateReturnType;
+
+    typedef HouseholderSequence<
+      VectorsType,
+      std::conditional_t<NumTraits<Scalar>::IsComplex,
+        internal::remove_all_t<typename CoeffsType::ConjugateReturnType>,
+        CoeffsType>,
+      Side
+    > AdjointReturnType;
+
+    typedef HouseholderSequence<
+      std::conditional_t<NumTraits<Scalar>::IsComplex,
+        internal::remove_all_t<typename VectorsType::ConjugateReturnType>,
+        VectorsType>,
+      CoeffsType,
+      Side
+    > TransposeReturnType;
+
+    typedef HouseholderSequence<
+      std::add_const_t<VectorsType>,
+      std::add_const_t<CoeffsType>,
+      Side
+    > ConstHouseholderSequence;
+
+    /** \brief Constructor.
+      * \param[in]  v      %Matrix containing the essential parts of the Householder vectors
+      * \param[in]  h      Vector containing the Householder coefficients
+      *
+      * Constructs the Householder sequence with coefficients given by \p h and vectors given by \p v. The
+      * i-th Householder coefficient \f$ h_i \f$ is given by \p h(i) and the essential part of the i-th
+      * Householder vector \f$ v_i \f$ is given by \p v(k,i) with \p k > \p i (the subdiagonal part of the
+      * i-th column). If \p v has fewer columns than rows, then the Householder sequence contains as many
+      * Householder reflections as there are columns.
+      *
+      * \note The %HouseholderSequence object stores \p v and \p h by reference.
+      *
+      * Example: \include HouseholderSequence_HouseholderSequence.cpp
+      * Output: \verbinclude HouseholderSequence_HouseholderSequence.out
+      *
+      * \sa setLength(), setShift()
+      */
+    EIGEN_DEVICE_FUNC
+    HouseholderSequence(const VectorsType& v, const CoeffsType& h)
+      : m_vectors(v), m_coeffs(h), m_reverse(false), m_length(v.diagonalSize()),
+        m_shift(0)
+    {
+    }
+
+    /** \brief Copy constructor. */
+    EIGEN_DEVICE_FUNC
+    HouseholderSequence(const HouseholderSequence& other)
+      : m_vectors(other.m_vectors),
+        m_coeffs(other.m_coeffs),
+        m_reverse(other.m_reverse),
+        m_length(other.m_length),
+        m_shift(other.m_shift)
+    {
+    }
+
+    /** \brief Number of rows of transformation viewed as a matrix.
+      * \returns Number of rows
+      * \details This equals the dimension of the space that the transformation acts on.
+      */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    Index rows() const EIGEN_NOEXCEPT { return Side==OnTheLeft ? m_vectors.rows() : m_vectors.cols(); }
+
+    /** \brief Number of columns of transformation viewed as a matrix.
+      * \returns Number of columns
+      * \details This equals the dimension of the space that the transformation acts on.
+      */
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    Index cols() const EIGEN_NOEXCEPT { return rows(); }
+
+    /** \brief Essential part of a Householder vector.
+      * \param[in]  k  Index of Householder reflection
+      * \returns    Vector containing non-trivial entries of k-th Householder vector
+      *
+      * This function returns the essential part of the Householder vector \f$ v_i \f$. This is a vector of
+      * length \f$ n-i \f$ containing the last \f$ n-i \f$ entries of the vector
+      * \f[
+      * v_i = [\underbrace{0, \ldots, 0}_{i-1\mbox{ zeros}}, 1, \underbrace{*, \ldots,*}_{n-i\mbox{ arbitrary entries}} ].
+      * \f]
+      * The index \f$ i \f$ equals \p k + shift(), corresponding to the k-th column of the matrix \p v
+      * passed to the constructor.
+      *
+      * \sa setShift(), shift()
+      */
+    EIGEN_DEVICE_FUNC
+    const EssentialVectorType essentialVector(Index k) const
+    {
+      eigen_assert(k >= 0 && k < m_length);
+      return internal::hseq_side_dependent_impl<VectorsType,CoeffsType,Side>::essentialVector(*this, k);
+    }
+
+    /** \brief %Transpose of the Householder sequence. */
+    TransposeReturnType transpose() const
+    {
+      return TransposeReturnType(m_vectors.conjugate(), m_coeffs)
+              .setReverseFlag(!m_reverse)
+              .setLength(m_length)
+              .setShift(m_shift);
+    }
+
+    /** \brief Complex conjugate of the Householder sequence. */
+    ConjugateReturnType conjugate() const
+    {
+      return ConjugateReturnType(m_vectors.conjugate(), m_coeffs.conjugate())
+             .setReverseFlag(m_reverse)
+             .setLength(m_length)
+             .setShift(m_shift);
+    }
+
+    /** \returns an expression of the complex conjugate of \c *this if Cond==true,
+     *           returns \c *this otherwise.
+     */
+    template<bool Cond>
+    EIGEN_DEVICE_FUNC
+    inline std::conditional_t<Cond,ConjugateReturnType,ConstHouseholderSequence>
+    conjugateIf() const
+    {
+      typedef std::conditional_t<Cond,ConjugateReturnType,ConstHouseholderSequence> ReturnType;
+      return ReturnType(m_vectors.template conjugateIf<Cond>(), m_coeffs.template conjugateIf<Cond>());
+    }
+
+    /** \brief Adjoint (conjugate transpose) of the Householder sequence. */
+    AdjointReturnType adjoint() const
+    {
+      return AdjointReturnType(m_vectors, m_coeffs.conjugate())
+              .setReverseFlag(!m_reverse)
+              .setLength(m_length)
+              .setShift(m_shift);
+    }
+
+    /** \brief Inverse of the Householder sequence (equals the adjoint). */
+    AdjointReturnType inverse() const { return adjoint(); }
+
+    /** \internal */
+    template<typename DestType>
+    inline EIGEN_DEVICE_FUNC
+    void evalTo(DestType& dst) const
+    {
+      Matrix<Scalar, DestType::RowsAtCompileTime, 1,
+             AutoAlign|ColMajor, DestType::MaxRowsAtCompileTime, 1> workspace(rows());
+      evalTo(dst, workspace);
+    }
+
+    /** \internal */
+    template<typename Dest, typename Workspace>
+    EIGEN_DEVICE_FUNC
+    void evalTo(Dest& dst, Workspace& workspace) const
+    {
+      workspace.resize(rows());
+      Index vecs = m_length;
+      if(internal::is_same_dense(dst,m_vectors))
+      {
+        // in-place
+        dst.diagonal().setOnes();
+        dst.template triangularView<StrictlyUpper>().setZero();
+        for(Index k = vecs-1; k >= 0; --k)
+        {
+          Index cornerSize = rows() - k - m_shift;
+          if(m_reverse)
+            dst.bottomRightCorner(cornerSize, cornerSize)
+               .applyHouseholderOnTheRight(essentialVector(k), m_coeffs.coeff(k), workspace.data());
+          else
+            dst.bottomRightCorner(cornerSize, cornerSize)
+               .applyHouseholderOnTheLeft(essentialVector(k), m_coeffs.coeff(k), workspace.data());
+
+          // clear the off diagonal vector
+          dst.col(k).tail(rows()-k-1).setZero();
+        }
+        // clear the remaining columns if needed
+        for(Index k = 0; k<cols()-vecs ; ++k)
+          dst.col(k).tail(rows()-k-1).setZero();
+      }
+      else if(m_length>BlockSize)
+      {
+        dst.setIdentity(rows(), rows());
+        if(m_reverse)
+          applyThisOnTheLeft(dst,workspace,true);
+        else
+          applyThisOnTheLeft(dst,workspace,true);
+      }
+      else
+      {
+        dst.setIdentity(rows(), rows());
+        for(Index k = vecs-1; k >= 0; --k)
+        {
+          Index cornerSize = rows() - k - m_shift;
+          if(m_reverse)
+            dst.bottomRightCorner(cornerSize, cornerSize)
+               .applyHouseholderOnTheRight(essentialVector(k), m_coeffs.coeff(k), workspace.data());
+          else
+            dst.bottomRightCorner(cornerSize, cornerSize)
+               .applyHouseholderOnTheLeft(essentialVector(k), m_coeffs.coeff(k), workspace.data());
+        }
+      }
+    }
+
+    /** \internal */
+    template<typename Dest> inline void applyThisOnTheRight(Dest& dst) const
+    {
+      Matrix<Scalar,1,Dest::RowsAtCompileTime,RowMajor,1,Dest::MaxRowsAtCompileTime> workspace(dst.rows());
+      applyThisOnTheRight(dst, workspace);
+    }
+
+    /** \internal */
+    template<typename Dest, typename Workspace>
+    inline void applyThisOnTheRight(Dest& dst, Workspace& workspace) const
+    {
+      workspace.resize(dst.rows());
+      for(Index k = 0; k < m_length; ++k)
+      {
+        Index actual_k = m_reverse ? m_length-k-1 : k;
+        dst.rightCols(rows()-m_shift-actual_k)
+           .applyHouseholderOnTheRight(essentialVector(actual_k), m_coeffs.coeff(actual_k), workspace.data());
+      }
+    }
+
+    /** \internal */
+    template<typename Dest> inline void applyThisOnTheLeft(Dest& dst, bool inputIsIdentity = false) const
+    {
+      Matrix<Scalar,1,Dest::ColsAtCompileTime,RowMajor,1,Dest::MaxColsAtCompileTime> workspace;
+      applyThisOnTheLeft(dst, workspace, inputIsIdentity);
+    }
+
+    /** \internal */
+    template<typename Dest, typename Workspace>
+    inline void applyThisOnTheLeft(Dest& dst, Workspace& workspace, bool inputIsIdentity = false) const
+    {
+      if(inputIsIdentity && m_reverse)
+        inputIsIdentity = false;
+      // if the entries are large enough, then apply the reflectors by block
+      if(m_length>=BlockSize && dst.cols()>1)
+      {
+        // Make sure we have at least 2 useful blocks, otherwise it is point-less:
+        Index blockSize = m_length<Index(2*BlockSize) ? (m_length+1)/2 : Index(BlockSize);
+        for(Index i = 0; i < m_length; i+=blockSize)
+        {
+          Index end = m_reverse ? (std::min)(m_length,i+blockSize) : m_length-i;
+          Index k = m_reverse ? i : (std::max)(Index(0),end-blockSize);
+          Index bs = end-k;
+          Index start = k + m_shift;
+
+          typedef Block<internal::remove_all_t<VectorsType>,Dynamic,Dynamic> SubVectorsType;
+          SubVectorsType sub_vecs1(m_vectors.const_cast_derived(), Side==OnTheRight ? k : start,
+                                                                   Side==OnTheRight ? start : k,
+                                                                   Side==OnTheRight ? bs : m_vectors.rows()-start,
+                                                                   Side==OnTheRight ? m_vectors.cols()-start : bs);
+          std::conditional_t<Side==OnTheRight, Transpose<SubVectorsType>, SubVectorsType&> sub_vecs(sub_vecs1);
+
+          Index dstRows  = rows()-m_shift-k;
+
+          if (inputIsIdentity)
+          {
+            Block<Dest, Dynamic, Dynamic> sub_dst = dst.bottomRightCorner(dstRows, dstRows);
+            apply_block_householder_on_the_left(sub_dst, sub_vecs, m_coeffs.segment(k, bs), !m_reverse);
+          }
+          else
+          {
+            auto sub_dst = dst.bottomRows(dstRows);
+            apply_block_householder_on_the_left(sub_dst, sub_vecs, m_coeffs.segment(k, bs), !m_reverse);
+          }
+        }
+      }
+      else
+      {
+        workspace.resize(dst.cols());
+        for(Index k = 0; k < m_length; ++k)
+        {
+          Index actual_k = m_reverse ? k : m_length-k-1;
+          Index dstRows = rows()-m_shift-actual_k;
+
+          if (inputIsIdentity)
+          {
+            Block<Dest, Dynamic, Dynamic> sub_dst = dst.bottomRightCorner(dstRows, dstRows);
+            sub_dst.applyHouseholderOnTheLeft(essentialVector(actual_k), m_coeffs.coeff(actual_k), workspace.data());
+          }
+          else
+          {
+            auto sub_dst = dst.bottomRows(dstRows);
+            sub_dst.applyHouseholderOnTheLeft(essentialVector(actual_k), m_coeffs.coeff(actual_k), workspace.data());
+          }
+        }
+      }
+    }
+
+    /** \brief Computes the product of a Householder sequence with a matrix.
+      * \param[in]  other  %Matrix being multiplied.
+      * \returns    Expression object representing the product.
+      *
+      * This function computes \f$ HM \f$ where \f$ H \f$ is the Householder sequence represented by \p *this
+      * and \f$ M \f$ is the matrix \p other.
+      */
+    template<typename OtherDerived>
+    typename internal::matrix_type_times_scalar_type<Scalar, OtherDerived>::Type operator*(const MatrixBase<OtherDerived>& other) const
+    {
+      typename internal::matrix_type_times_scalar_type<Scalar, OtherDerived>::Type
+        res(other.template cast<typename internal::matrix_type_times_scalar_type<Scalar,OtherDerived>::ResultScalar>());
+      applyThisOnTheLeft(res, internal::is_identity<OtherDerived>::value && res.rows()==res.cols());
+      return res;
+    }
+
+    template<typename VectorsType_, typename CoeffsType_, int Side_> friend struct internal::hseq_side_dependent_impl;
+
+    /** \brief Sets the length of the Householder sequence.
+      * \param [in]  length  New value for the length.
+      *
+      * By default, the length \f$ n \f$ of the Householder sequence \f$ H = H_0 H_1 \ldots H_{n-1} \f$ is set
+      * to the number of columns of the matrix \p v passed to the constructor, or the number of rows if that
+      * is smaller. After this function is called, the length equals \p length.
+      *
+      * \sa length()
+      */
+    EIGEN_DEVICE_FUNC
+    HouseholderSequence& setLength(Index length)
+    {
+      m_length = length;
+      return *this;
+    }
+
+    /** \brief Sets the shift of the Householder sequence.
+      * \param [in]  shift  New value for the shift.
+      *
+      * By default, a %HouseholderSequence object represents \f$ H = H_0 H_1 \ldots H_{n-1} \f$ and the i-th
+      * column of the matrix \p v passed to the constructor corresponds to the i-th Householder
+      * reflection. After this function is called, the object represents \f$ H = H_{\mathrm{shift}}
+      * H_{\mathrm{shift}+1} \ldots H_{n-1} \f$ and the i-th column of \p v corresponds to the (shift+i)-th
+      * Householder reflection.
+      *
+      * \sa shift()
+      */
+    EIGEN_DEVICE_FUNC
+    HouseholderSequence& setShift(Index shift)
+    {
+      m_shift = shift;
+      return *this;
+    }
+
+    EIGEN_DEVICE_FUNC
+    Index length() const { return m_length; }  /**< \brief Returns the length of the Householder sequence. */
+
+    EIGEN_DEVICE_FUNC
+    Index shift() const { return m_shift; }    /**< \brief Returns the shift of the Householder sequence. */
+
+    /* Necessary for .adjoint() and .conjugate() */
+    template <typename VectorsType2, typename CoeffsType2, int Side2> friend class HouseholderSequence;
+
+  protected:
+
+    /** \internal
+      * \brief Sets the reverse flag.
+      * \param [in]  reverse  New value of the reverse flag.
+      *
+      * By default, the reverse flag is not set. If the reverse flag is set, then this object represents
+      * \f$ H^r = H_{n-1} \ldots H_1 H_0 \f$ instead of \f$ H = H_0 H_1 \ldots H_{n-1} \f$.
+      * \note For real valued HouseholderSequence this is equivalent to transposing \f$ H \f$.
+      *
+      * \sa reverseFlag(), transpose(), adjoint()
+      */
+    HouseholderSequence& setReverseFlag(bool reverse)
+    {
+      m_reverse = reverse;
+      return *this;
+    }
+
+    bool reverseFlag() const { return m_reverse; }     /**< \internal \brief Returns the reverse flag. */
+
+    typename VectorsType::Nested m_vectors;
+    typename CoeffsType::Nested m_coeffs;
+    bool m_reverse;
+    Index m_length;
+    Index m_shift;
+    enum { BlockSize = 48 };
+};
+
+/** \brief Computes the product of a matrix with a Householder sequence.
+  * \param[in]  other  %Matrix being multiplied.
+  * \param[in]  h      %HouseholderSequence being multiplied.
+  * \returns    Expression object representing the product.
+  *
+  * This function computes \f$ MH \f$ where \f$ M \f$ is the matrix \p other and \f$ H \f$ is the
+  * Householder sequence represented by \p h.
+  */
+template<typename OtherDerived, typename VectorsType, typename CoeffsType, int Side>
+typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar,OtherDerived>::Type operator*(const MatrixBase<OtherDerived>& other, const HouseholderSequence<VectorsType,CoeffsType,Side>& h)
+{
+  typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar,OtherDerived>::Type
+    res(other.template cast<typename internal::matrix_type_times_scalar_type<typename VectorsType::Scalar,OtherDerived>::ResultScalar>());
+  h.applyThisOnTheRight(res);
+  return res;
+}
+
+/** \ingroup Householder_Module \householder_module
+  * \brief Convenience function for constructing a Householder sequence.
+  * \returns A HouseholderSequence constructed from the specified arguments.
+  */
+template<typename VectorsType, typename CoeffsType>
+HouseholderSequence<VectorsType,CoeffsType> householderSequence(const VectorsType& v, const CoeffsType& h)
+{
+  return HouseholderSequence<VectorsType,CoeffsType,OnTheLeft>(v, h);
+}
+
+/** \ingroup Householder_Module \householder_module
+  * \brief Convenience function for constructing a Householder sequence.
+  * \returns A HouseholderSequence constructed from the specified arguments.
+  * \details This function differs from householderSequence() in that the template argument \p OnTheSide of
+  * the constructed HouseholderSequence is set to OnTheRight, instead of the default OnTheLeft.
+  */
+template<typename VectorsType, typename CoeffsType>
+HouseholderSequence<VectorsType,CoeffsType,OnTheRight> rightHouseholderSequence(const VectorsType& v, const CoeffsType& h)
+{
+  return HouseholderSequence<VectorsType,CoeffsType,OnTheRight>(v, h);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_HOUSEHOLDER_SEQUENCE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Householder/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Householder/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..70de89bf2dd1bd4aca112137c582e1ecca09fa3d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Householder/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_HOUSEHOLDER_MODULE_H
+#error "Please include Eigen/Householder instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
new file mode 100644
index 0000000000000000000000000000000000000000..d2d55b714529f1fc19f11da3c162609d350ba202
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/BasicPreconditioners.h
@@ -0,0 +1,228 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BASIC_PRECONDITIONERS_H
+#define EIGEN_BASIC_PRECONDITIONERS_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief A preconditioner based on the digonal entries
+  *
+  * This class allows to approximately solve for A.x = b problems assuming A is a diagonal matrix.
+  * In other words, this preconditioner neglects all off diagonal entries and, in Eigen's language, solves for:
+    \code
+    A.diagonal().asDiagonal() . x = b
+    \endcode
+  *
+  * \tparam Scalar_ the type of the scalar.
+  *
+  * \implsparsesolverconcept
+  *
+  * This preconditioner is suitable for both selfadjoint and general problems.
+  * The diagonal entries are pre-inverted and stored into a dense vector.
+  *
+  * \note A variant that has yet to be implemented would attempt to preserve the norm of each column.
+  *
+  * \sa class LeastSquareDiagonalPreconditioner, class ConjugateGradient
+  */
+template <typename Scalar_>
+class DiagonalPreconditioner
+{
+    typedef Scalar_ Scalar;
+    typedef Matrix<Scalar,Dynamic,1> Vector;
+  public:
+    typedef typename Vector::StorageIndex StorageIndex;
+    enum {
+      ColsAtCompileTime = Dynamic,
+      MaxColsAtCompileTime = Dynamic
+    };
+
+    DiagonalPreconditioner() : m_isInitialized(false) {}
+
+    template<typename MatType>
+    explicit DiagonalPreconditioner(const MatType& mat) : m_invdiag(mat.cols())
+    {
+      compute(mat);
+    }
+
+    EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_invdiag.size(); }
+    EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_invdiag.size(); }
+
+    template<typename MatType>
+    DiagonalPreconditioner& analyzePattern(const MatType& )
+    {
+      return *this;
+    }
+
+    template<typename MatType>
+    DiagonalPreconditioner& factorize(const MatType& mat)
+    {
+      m_invdiag.resize(mat.cols());
+      for(int j=0; j<mat.outerSize(); ++j)
+      {
+        typename MatType::InnerIterator it(mat,j);
+        while(it && it.index()!=j) ++it;
+        if(it && it.index()==j && it.value()!=Scalar(0))
+          m_invdiag(j) = Scalar(1)/it.value();
+        else
+          m_invdiag(j) = Scalar(1);
+      }
+      m_isInitialized = true;
+      return *this;
+    }
+
+    template<typename MatType>
+    DiagonalPreconditioner& compute(const MatType& mat)
+    {
+      return factorize(mat);
+    }
+
+    /** \internal */
+    template<typename Rhs, typename Dest>
+    void _solve_impl(const Rhs& b, Dest& x) const
+    {
+      x = m_invdiag.array() * b.array() ;
+    }
+
+    template<typename Rhs> inline const Solve<DiagonalPreconditioner, Rhs>
+    solve(const MatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_isInitialized && "DiagonalPreconditioner is not initialized.");
+      eigen_assert(m_invdiag.size()==b.rows()
+                && "DiagonalPreconditioner::solve(): invalid number of rows of the right hand side matrix b");
+      return Solve<DiagonalPreconditioner, Rhs>(*this, b.derived());
+    }
+
+    ComputationInfo info() { return Success; }
+
+  protected:
+    Vector m_invdiag;
+    bool m_isInitialized;
+};
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief Jacobi preconditioner for LeastSquaresConjugateGradient
+  *
+  * This class allows to approximately solve for A' A x  = A' b problems assuming A' A is a diagonal matrix.
+  * In other words, this preconditioner neglects all off diagonal entries and, in Eigen's language, solves for:
+    \code
+    (A.adjoint() * A).diagonal().asDiagonal() * x = b
+    \endcode
+  *
+  * \tparam Scalar_ the type of the scalar.
+  *
+  * \implsparsesolverconcept
+  *
+  * The diagonal entries are pre-inverted and stored into a dense vector.
+  *
+  * \sa class LeastSquaresConjugateGradient, class DiagonalPreconditioner
+  */
+template <typename Scalar_>
+class LeastSquareDiagonalPreconditioner : public DiagonalPreconditioner<Scalar_>
+{
+    typedef Scalar_ Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef DiagonalPreconditioner<Scalar_> Base;
+    using Base::m_invdiag;
+  public:
+
+    LeastSquareDiagonalPreconditioner() : Base() {}
+
+    template<typename MatType>
+    explicit LeastSquareDiagonalPreconditioner(const MatType& mat) : Base()
+    {
+      compute(mat);
+    }
+
+    template<typename MatType>
+    LeastSquareDiagonalPreconditioner& analyzePattern(const MatType& )
+    {
+      return *this;
+    }
+
+    template<typename MatType>
+    LeastSquareDiagonalPreconditioner& factorize(const MatType& mat)
+    {
+      // Compute the inverse squared-norm of each column of mat
+      m_invdiag.resize(mat.cols());
+      if(MatType::IsRowMajor)
+      {
+        m_invdiag.setZero();
+        for(Index j=0; j<mat.outerSize(); ++j)
+        {
+          for(typename MatType::InnerIterator it(mat,j); it; ++it)
+            m_invdiag(it.index()) += numext::abs2(it.value());
+        }
+        for(Index j=0; j<mat.cols(); ++j)
+          if(numext::real(m_invdiag(j))>RealScalar(0))
+            m_invdiag(j) = RealScalar(1)/numext::real(m_invdiag(j));
+      }
+      else
+      {
+        for(Index j=0; j<mat.outerSize(); ++j)
+        {
+          RealScalar sum = mat.col(j).squaredNorm();
+          if(sum>RealScalar(0))
+            m_invdiag(j) = RealScalar(1)/sum;
+          else
+            m_invdiag(j) = RealScalar(1);
+        }
+      }
+      Base::m_isInitialized = true;
+      return *this;
+    }
+
+    template<typename MatType>
+    LeastSquareDiagonalPreconditioner& compute(const MatType& mat)
+    {
+      return factorize(mat);
+    }
+
+    ComputationInfo info() { return Success; }
+
+  protected:
+};
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief A naive preconditioner which approximates any matrix as the identity matrix
+  *
+  * \implsparsesolverconcept
+  *
+  * \sa class DiagonalPreconditioner
+  */
+class IdentityPreconditioner
+{
+  public:
+
+    IdentityPreconditioner() {}
+
+    template<typename MatrixType>
+    explicit IdentityPreconditioner(const MatrixType& ) {}
+
+    template<typename MatrixType>
+    IdentityPreconditioner& analyzePattern(const MatrixType& ) { return *this; }
+
+    template<typename MatrixType>
+    IdentityPreconditioner& factorize(const MatrixType& ) { return *this; }
+
+    template<typename MatrixType>
+    IdentityPreconditioner& compute(const MatrixType& ) { return *this; }
+
+    template<typename Rhs>
+    inline const Rhs& solve(const Rhs& b) const { return b; }
+
+    ComputationInfo info() { return Success; }
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_BASIC_PRECONDITIONERS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
new file mode 100644
index 0000000000000000000000000000000000000000..76195c70cb8589239b2f959082b849cc12b61df4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/BiCGSTAB.h
@@ -0,0 +1,214 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BICGSTAB_H
+#define EIGEN_BICGSTAB_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \internal Low-level bi conjugate gradient stabilized algorithm
+  * \param mat The matrix A
+  * \param rhs The right hand side vector b
+  * \param x On input and initial solution, on output the computed solution.
+  * \param precond A preconditioner being able to efficiently solve for an
+  *                approximation of Ax=b (regardless of b)
+  * \param iters On input the max number of iteration, on output the number of performed iterations.
+  * \param tol_error On input the tolerance error, on output an estimation of the relative error.
+  * \return false in the case of numerical issue, for example a break down of BiCGSTAB. 
+  */
+template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+bool bicgstab(const MatrixType& mat, const Rhs& rhs, Dest& x,
+              const Preconditioner& precond, Index& iters,
+              typename Dest::RealScalar& tol_error)
+{
+  using std::sqrt;
+  using std::abs;
+  typedef typename Dest::RealScalar RealScalar;
+  typedef typename Dest::Scalar Scalar;
+  typedef Matrix<Scalar,Dynamic,1> VectorType;
+  RealScalar tol = tol_error;
+  Index maxIters = iters;
+
+  Index n = mat.cols();
+  VectorType r  = rhs - mat * x;
+  VectorType r0 = r;
+  
+  RealScalar r0_sqnorm = r0.squaredNorm();
+  RealScalar rhs_sqnorm = rhs.squaredNorm();
+  if(rhs_sqnorm == 0)
+  {
+    x.setZero();
+    return true;
+  }
+  Scalar rho    (1);
+  Scalar alpha  (1);
+  Scalar w      (1);
+  
+  VectorType v = VectorType::Zero(n), p = VectorType::Zero(n);
+  VectorType y(n),  z(n);
+  VectorType kt(n), ks(n);
+
+  VectorType s(n), t(n);
+
+  RealScalar tol2 = tol*tol*rhs_sqnorm;
+  RealScalar eps2 = NumTraits<Scalar>::epsilon()*NumTraits<Scalar>::epsilon();
+  Index i = 0;
+  Index restarts = 0;
+
+  while ( r.squaredNorm() > tol2 && i<maxIters )
+  {
+    Scalar rho_old = rho;
+
+    rho = r0.dot(r);
+    if (abs(rho) < eps2*r0_sqnorm)
+    {
+      // The new residual vector became too orthogonal to the arbitrarily chosen direction r0
+      // Let's restart with a new r0:
+      r  = rhs - mat * x;
+      r0 = r;
+      rho = r0_sqnorm = r.squaredNorm();
+      if(restarts++ == 0)
+        i = 0;
+    }
+    Scalar beta = (rho/rho_old) * (alpha / w);
+    p = r + beta * (p - w * v);
+    
+    y = precond.solve(p);
+    
+    v.noalias() = mat * y;
+
+    alpha = rho / r0.dot(v);
+    s = r - alpha * v;
+
+    z = precond.solve(s);
+    t.noalias() = mat * z;
+
+    RealScalar tmp = t.squaredNorm();
+    if(tmp>RealScalar(0))
+      w = t.dot(s) / tmp;
+    else
+      w = Scalar(0);
+    x += alpha * y + w * z;
+    r = s - w * t;
+    ++i;
+  }
+  tol_error = sqrt(r.squaredNorm()/rhs_sqnorm);
+  iters = i;
+  return true; 
+}
+
+}
+
+template< typename MatrixType_,
+          typename Preconditioner_ = DiagonalPreconditioner<typename MatrixType_::Scalar> >
+class BiCGSTAB;
+
+namespace internal {
+
+template< typename MatrixType_, typename Preconditioner_>
+struct traits<BiCGSTAB<MatrixType_,Preconditioner_> >
+{
+  typedef MatrixType_ MatrixType;
+  typedef Preconditioner_ Preconditioner;
+};
+
+}
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief A bi conjugate gradient stabilized solver for sparse square problems
+  *
+  * This class allows to solve for A.x = b sparse linear problems using a bi conjugate gradient
+  * stabilized algorithm. The vectors x and b can be either dense or sparse.
+  *
+  * \tparam MatrixType_ the type of the sparse matrix A, can be a dense or a sparse matrix.
+  * \tparam Preconditioner_ the type of the preconditioner. Default is DiagonalPreconditioner
+  *
+  * \implsparsesolverconcept
+  *
+  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
+  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
+  * and NumTraits<Scalar>::epsilon() for the tolerance.
+  * 
+  * The tolerance corresponds to the relative residual error: |Ax-b|/|b|
+  * 
+  * \b Performance: when using sparse matrices, best performance is achied for a row-major sparse matrix format.
+  * Moreover, in this case multi-threading can be exploited if the user code is compiled with OpenMP enabled.
+  * See \ref TopicMultiThreading for details.
+  * 
+  * This class can be used as the direct solver classes. Here is a typical usage example:
+  * \include BiCGSTAB_simple.cpp
+  * 
+  * By default the iterations start with x=0 as an initial guess of the solution.
+  * One can control the start using the solveWithGuess() method.
+  * 
+  * BiCGSTAB can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
+  *
+  * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+  */
+template< typename MatrixType_, typename Preconditioner_>
+class BiCGSTAB : public IterativeSolverBase<BiCGSTAB<MatrixType_,Preconditioner_> >
+{
+  typedef IterativeSolverBase<BiCGSTAB> Base;
+  using Base::matrix;
+  using Base::m_error;
+  using Base::m_iterations;
+  using Base::m_info;
+  using Base::m_isInitialized;
+public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef Preconditioner_ Preconditioner;
+
+public:
+
+  /** Default constructor. */
+  BiCGSTAB() : Base() {}
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+    * 
+    * This constructor is a shortcut for the default constructor followed
+    * by a call to compute().
+    * 
+    * \warning this class stores a reference to the matrix A as well as some
+    * precomputed values that depend on it. Therefore, if \a A is changed
+    * this class becomes invalid. Call compute() to update it with the new
+    * matrix A, or modify a copy of A.
+    */
+  template<typename MatrixDerived>
+  explicit BiCGSTAB(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
+
+  ~BiCGSTAB() {}
+
+  /** \internal */
+  template<typename Rhs,typename Dest>
+  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
+  {    
+    m_iterations = Base::maxIterations();
+    m_error = Base::m_tolerance;
+    
+    bool ret = internal::bicgstab(matrix(), b, x, Base::m_preconditioner, m_iterations, m_error);
+
+    m_info = (!ret) ? NumericalIssue
+           : m_error <= Base::m_tolerance ? Success
+           : NoConvergence;
+  }
+
+protected:
+
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_BICGSTAB_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
new file mode 100644
index 0000000000000000000000000000000000000000..5a7dbc7eead8171639bfa57eb48f1334afeb7211
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/ConjugateGradient.h
@@ -0,0 +1,229 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CONJUGATE_GRADIENT_H
+#define EIGEN_CONJUGATE_GRADIENT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \internal Low-level conjugate gradient algorithm
+  * \param mat The matrix A
+  * \param rhs The right hand side vector b
+  * \param x On input and initial solution, on output the computed solution.
+  * \param precond A preconditioner being able to efficiently solve for an
+  *                approximation of Ax=b (regardless of b)
+  * \param iters On input the max number of iteration, on output the number of performed iterations.
+  * \param tol_error On input the tolerance error, on output an estimation of the relative error.
+  */
+template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+EIGEN_DONT_INLINE
+void conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
+                        const Preconditioner& precond, Index& iters,
+                        typename Dest::RealScalar& tol_error)
+{
+  typedef typename Dest::RealScalar RealScalar;
+  typedef typename Dest::Scalar Scalar;
+  typedef Matrix<Scalar,Dynamic,1> VectorType;
+  
+  RealScalar tol = tol_error;
+  Index maxIters = iters;
+  
+  Index n = mat.cols();
+
+  VectorType residual = rhs - mat * x; //initial residual
+
+  RealScalar rhsNorm2 = rhs.squaredNorm();
+  if(rhsNorm2 == 0) 
+  {
+    x.setZero();
+    iters = 0;
+    tol_error = 0;
+    return;
+  }
+  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
+  RealScalar threshold = numext::maxi(RealScalar(tol*tol*rhsNorm2),considerAsZero);
+  RealScalar residualNorm2 = residual.squaredNorm();
+  if (residualNorm2 < threshold)
+  {
+    iters = 0;
+    tol_error = numext::sqrt(residualNorm2 / rhsNorm2);
+    return;
+  }
+
+  VectorType p(n);
+  p = precond.solve(residual);      // initial search direction
+
+  VectorType z(n), tmp(n);
+  RealScalar absNew = numext::real(residual.dot(p));  // the square of the absolute value of r scaled by invM
+  Index i = 0;
+  while(i < maxIters)
+  {
+    tmp.noalias() = mat * p;                    // the bottleneck of the algorithm
+
+    Scalar alpha = absNew / p.dot(tmp);         // the amount we travel on dir
+    x += alpha * p;                             // update solution
+    residual -= alpha * tmp;                    // update residual
+    
+    residualNorm2 = residual.squaredNorm();
+    if(residualNorm2 < threshold)
+      break;
+    
+    z = precond.solve(residual);                // approximately solve for "A z = residual"
+
+    RealScalar absOld = absNew;
+    absNew = numext::real(residual.dot(z));     // update the absolute value of r
+    RealScalar beta = absNew / absOld;          // calculate the Gram-Schmidt value used to create the new search direction
+    p = z + beta * p;                           // update search direction
+    i++;
+  }
+  tol_error = numext::sqrt(residualNorm2 / rhsNorm2);
+  iters = i;
+}
+
+}
+
+template< typename MatrixType_, int UpLo_=Lower,
+          typename Preconditioner_ = DiagonalPreconditioner<typename MatrixType_::Scalar> >
+class ConjugateGradient;
+
+namespace internal {
+
+template< typename MatrixType_, int UpLo_, typename Preconditioner_>
+struct traits<ConjugateGradient<MatrixType_,UpLo_,Preconditioner_> >
+{
+  typedef MatrixType_ MatrixType;
+  typedef Preconditioner_ Preconditioner;
+};
+
+}
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief A conjugate gradient solver for sparse (or dense) self-adjoint problems
+  *
+  * This class allows to solve for A.x = b linear problems using an iterative conjugate gradient algorithm.
+  * The matrix A must be selfadjoint. The matrix A and the vectors x and b can be either dense or sparse.
+  *
+  * \tparam MatrixType_ the type of the matrix A, can be a dense or a sparse matrix.
+  * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower,
+  *               \c Upper, or \c Lower|Upper in which the full matrix entries will be considered.
+  *               Default is \c Lower, best performance is \c Lower|Upper.
+  * \tparam Preconditioner_ the type of the preconditioner. Default is DiagonalPreconditioner
+  *
+  * \implsparsesolverconcept
+  *
+  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
+  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
+  * and NumTraits<Scalar>::epsilon() for the tolerance.
+  * 
+  * The tolerance corresponds to the relative residual error: |Ax-b|/|b|
+  * 
+  * \b Performance: Even though the default value of \c UpLo_ is \c Lower, significantly higher performance is
+  * achieved when using a complete matrix and \b Lower|Upper as the \a UpLo_ template parameter. Moreover, in this
+  * case multi-threading can be exploited if the user code is compiled with OpenMP enabled.
+  * See \ref TopicMultiThreading for details.
+  * 
+  * This class can be used as the direct solver classes. Here is a typical usage example:
+    \code
+    int n = 10000;
+    VectorXd x(n), b(n);
+    SparseMatrix<double> A(n,n);
+    // fill A and b
+    ConjugateGradient<SparseMatrix<double>, Lower|Upper> cg;
+    cg.compute(A);
+    x = cg.solve(b);
+    std::cout << "#iterations:     " << cg.iterations() << std::endl;
+    std::cout << "estimated error: " << cg.error()      << std::endl;
+    // update b, and solve again
+    x = cg.solve(b);
+    \endcode
+  * 
+  * By default the iterations start with x=0 as an initial guess of the solution.
+  * One can control the start using the solveWithGuess() method.
+  * 
+  * ConjugateGradient can also be used in a matrix-free context, see the following \link MatrixfreeSolverExample example \endlink.
+  *
+  * \sa class LeastSquaresConjugateGradient, class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+  */
+template< typename MatrixType_, int UpLo_, typename Preconditioner_>
+class ConjugateGradient : public IterativeSolverBase<ConjugateGradient<MatrixType_,UpLo_,Preconditioner_> >
+{
+  typedef IterativeSolverBase<ConjugateGradient> Base;
+  using Base::matrix;
+  using Base::m_error;
+  using Base::m_iterations;
+  using Base::m_info;
+  using Base::m_isInitialized;
+public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef Preconditioner_ Preconditioner;
+
+  enum {
+    UpLo = UpLo_
+  };
+
+public:
+
+  /** Default constructor. */
+  ConjugateGradient() : Base() {}
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+    * 
+    * This constructor is a shortcut for the default constructor followed
+    * by a call to compute().
+    * 
+    * \warning this class stores a reference to the matrix A as well as some
+    * precomputed values that depend on it. Therefore, if \a A is changed
+    * this class becomes invalid. Call compute() to update it with the new
+    * matrix A, or modify a copy of A.
+    */
+  template<typename MatrixDerived>
+  explicit ConjugateGradient(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
+
+  ~ConjugateGradient() {}
+
+  /** \internal */
+  template<typename Rhs,typename Dest>
+  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
+  {
+    typedef typename Base::MatrixWrapper MatrixWrapper;
+    typedef typename Base::ActualMatrixType ActualMatrixType;
+    enum {
+      TransposeInput  =   (!MatrixWrapper::MatrixFree)
+                      &&  (UpLo==(Lower|Upper))
+                      &&  (!MatrixType::IsRowMajor)
+                      &&  (!NumTraits<Scalar>::IsComplex)
+    };
+    typedef std::conditional_t<TransposeInput,Transpose<const ActualMatrixType>, ActualMatrixType const&> RowMajorWrapper;
+    EIGEN_STATIC_ASSERT(internal::check_implication(MatrixWrapper::MatrixFree,UpLo==(Lower|Upper)),MATRIX_FREE_CONJUGATE_GRADIENT_IS_COMPATIBLE_WITH_UPPER_UNION_LOWER_MODE_ONLY);
+    typedef std::conditional_t<UpLo==(Lower|Upper),
+                                    RowMajorWrapper,
+                                    typename MatrixWrapper::template ConstSelfAdjointViewReturnType<UpLo>::Type
+                                   > SelfAdjointWrapper;
+
+    m_iterations = Base::maxIterations();
+    m_error = Base::m_tolerance;
+
+    RowMajorWrapper row_mat(matrix());
+    internal::conjugate_gradient(SelfAdjointWrapper(row_mat), b, x, Base::m_preconditioner, m_iterations, m_error);
+    m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
+  }
+
+protected:
+
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_CONJUGATE_GRADIENT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
new file mode 100644
index 0000000000000000000000000000000000000000..122aea2b892cdb3d9a8674adef6cf2eb5b0dc57f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/IncompleteCholesky.h
@@ -0,0 +1,395 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_INCOMPLETE_CHOlESKY_H
+#define EIGEN_INCOMPLETE_CHOlESKY_H
+
+#include <vector>
+#include <list>
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+/**
+  * \brief Modified Incomplete Cholesky with dual threshold
+  *
+  * References : C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with
+  *              Limited memory, SIAM J. Sci. Comput.  21(1), pp. 24-45, 1999
+  *
+  * \tparam Scalar the scalar type of the input matrices
+  * \tparam UpLo_ The triangular part that will be used for the computations. It can be Lower
+    *               or Upper. Default is Lower.
+  * \tparam OrderingType_ The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is AMDOrdering<int>.
+  *
+  * \implsparsesolverconcept
+  *
+  * It performs the following incomplete factorization: \f$ S P A P' S \approx L L' \f$
+  * where L is a lower triangular factor, S is a diagonal scaling matrix, and P is a
+  * fill-in reducing permutation as computed by the ordering method.
+  *
+  * \b Shifting \b strategy: Let \f$ B = S P A P' S \f$  be the scaled matrix on which the factorization is carried out,
+  * and \f$ \beta \f$ be the minimum value of the diagonal. If \f$ \beta > 0 \f$ then, the factorization is directly performed
+  * on the matrix B. Otherwise, the factorization is performed on the shifted matrix \f$ B + (\sigma+|\beta| I \f$ where
+  * \f$ \sigma \f$ is the initial shift value as returned and set by setInitialShift() method. The default value is \f$ \sigma = 10^{-3} \f$.
+  * If the factorization fails, then the shift in doubled until it succeed or a maximum of ten attempts. If it still fails, as returned by
+  * the info() method, then you can either increase the initial shift, or better use another preconditioning technique.
+  *
+  */
+template <typename Scalar, int UpLo_ = Lower, typename OrderingType_ = AMDOrdering<int> >
+class IncompleteCholesky : public SparseSolverBase<IncompleteCholesky<Scalar,UpLo_,OrderingType_> >
+{
+  protected:
+    typedef SparseSolverBase<IncompleteCholesky<Scalar,UpLo_,OrderingType_> > Base;
+    using Base::m_isInitialized;
+  public:
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef OrderingType_ OrderingType;
+    typedef typename OrderingType::PermutationType PermutationType;
+    typedef typename PermutationType::StorageIndex StorageIndex;
+    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> FactorType;
+    typedef Matrix<Scalar,Dynamic,1> VectorSx;
+    typedef Matrix<RealScalar,Dynamic,1> VectorRx;
+    typedef Matrix<StorageIndex,Dynamic, 1> VectorIx;
+    typedef std::vector<std::list<StorageIndex> > VectorList;
+    enum { UpLo = UpLo_ };
+    enum {
+      ColsAtCompileTime = Dynamic,
+      MaxColsAtCompileTime = Dynamic
+    };
+  public:
+
+    /** Default constructor leaving the object in a partly non-initialized stage.
+      *
+      * You must call compute() or the pair analyzePattern()/factorize() to make it valid.
+      *
+      * \sa IncompleteCholesky(const MatrixType&)
+      */
+    IncompleteCholesky() : m_initialShift(1e-3),m_analysisIsOk(false),m_factorizationIsOk(false) {}
+
+    /** Constructor computing the incomplete factorization for the given matrix \a matrix.
+      */
+    template<typename MatrixType>
+    IncompleteCholesky(const MatrixType& matrix) : m_initialShift(1e-3),m_analysisIsOk(false),m_factorizationIsOk(false)
+    {
+      compute(matrix);
+    }
+
+    /** \returns number of rows of the factored matrix */
+    EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_L.rows(); }
+
+    /** \returns number of columns of the factored matrix */
+    EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_L.cols(); }
+
+
+    /** \brief Reports whether previous computation was successful.
+      *
+      * It triggers an assertion if \c *this has not been initialized through the respective constructor,
+      * or a call to compute() or analyzePattern().
+      *
+      * \returns \c Success if computation was successful,
+      *          \c NumericalIssue if the matrix appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "IncompleteCholesky is not initialized.");
+      return m_info;
+    }
+
+    /** \brief Set the initial shift parameter \f$ \sigma \f$.
+      */
+    void setInitialShift(RealScalar shift) { m_initialShift = shift; }
+
+    /** \brief Computes the fill reducing permutation vector using the sparsity pattern of \a mat
+      */
+    template<typename MatrixType>
+    void analyzePattern(const MatrixType& mat)
+    {
+      OrderingType ord;
+      PermutationType pinv;
+      ord(mat.template selfadjointView<UpLo>(), pinv);
+      if(pinv.size()>0) m_perm = pinv.inverse();
+      else              m_perm.resize(0);
+      m_L.resize(mat.rows(), mat.cols());
+      m_analysisIsOk = true;
+      m_isInitialized = true;
+      m_info = Success;
+    }
+
+    /** \brief Performs the numerical factorization of the input matrix \a mat
+      *
+      * The method analyzePattern() or compute() must have been called beforehand
+      * with a matrix having the same pattern.
+      *
+      * \sa compute(), analyzePattern()
+      */
+    template<typename MatrixType>
+    void factorize(const MatrixType& mat);
+
+    /** Computes or re-computes the incomplete Cholesky factorization of the input matrix \a mat
+      *
+      * It is a shortcut for a sequential call to the analyzePattern() and factorize() methods.
+      *
+      * \sa analyzePattern(), factorize()
+      */
+    template<typename MatrixType>
+    void compute(const MatrixType& mat)
+    {
+      analyzePattern(mat);
+      factorize(mat);
+    }
+
+    // internal
+    template<typename Rhs, typename Dest>
+    void _solve_impl(const Rhs& b, Dest& x) const
+    {
+      eigen_assert(m_factorizationIsOk && "factorize() should be called first");
+      if (m_perm.rows() == b.rows())  x = m_perm * b;
+      else                            x = b;
+      x = m_scale.asDiagonal() * x;
+      x = m_L.template triangularView<Lower>().solve(x);
+      x = m_L.adjoint().template triangularView<Upper>().solve(x);
+      x = m_scale.asDiagonal() * x;
+      if (m_perm.rows() == b.rows())
+        x = m_perm.inverse() * x;
+    }
+
+    /** \returns the sparse lower triangular factor L */
+    const FactorType& matrixL() const { eigen_assert(m_factorizationIsOk && "factorize() should be called first"); return m_L; }
+
+    /** \returns a vector representing the scaling factor S */
+    const VectorRx& scalingS() const { eigen_assert(m_factorizationIsOk && "factorize() should be called first"); return m_scale; }
+
+    /** \returns the fill-in reducing permutation P (can be empty for a natural ordering) */
+    const PermutationType& permutationP() const { eigen_assert(m_analysisIsOk && "analyzePattern() should be called first"); return m_perm; }
+
+  protected:
+    FactorType m_L;              // The lower part stored in CSC
+    VectorRx m_scale;            // The vector for scaling the matrix
+    RealScalar m_initialShift;   // The initial shift parameter
+    bool m_analysisIsOk;
+    bool m_factorizationIsOk;
+    ComputationInfo m_info;
+    PermutationType m_perm;
+
+  private:
+    inline void updateList(Ref<const VectorIx> colPtr, Ref<VectorIx> rowIdx, Ref<VectorSx> vals, const Index& col, const Index& jk, VectorIx& firstElt, VectorList& listCol);
+};
+
+// Based on the following paper:
+//   C-J. Lin and J. J. Moré, Incomplete Cholesky Factorizations with
+//   Limited memory, SIAM J. Sci. Comput.  21(1), pp. 24-45, 1999
+//   http://ftp.mcs.anl.gov/pub/tech_reports/reports/P682.pdf
+template<typename Scalar, int UpLo_, typename OrderingType>
+template<typename MatrixType_>
+void IncompleteCholesky<Scalar,UpLo_, OrderingType>::factorize(const MatrixType_& mat)
+{
+  using std::sqrt;
+  eigen_assert(m_analysisIsOk && "analyzePattern() should be called first");
+
+  // Dropping strategy : Keep only the p largest elements per column, where p is the number of elements in the column of the original matrix. Other strategies will be added
+
+  // Apply the fill-reducing permutation computed in analyzePattern()
+  if (m_perm.rows() == mat.rows() ) // To detect the null permutation
+  {
+    // The temporary is needed to make sure that the diagonal entry is properly sorted
+    FactorType tmp(mat.rows(), mat.cols());
+    tmp = mat.template selfadjointView<UpLo_>().twistedBy(m_perm);
+    m_L.template selfadjointView<Lower>() = tmp.template selfadjointView<Lower>();
+  }
+  else
+  {
+    m_L.template selfadjointView<Lower>() = mat.template selfadjointView<UpLo_>();
+  }
+
+  Index n = m_L.cols();
+  Index nnz = m_L.nonZeros();
+  Map<VectorSx> vals(m_L.valuePtr(), nnz);         //values
+  Map<VectorIx> rowIdx(m_L.innerIndexPtr(), nnz);  //Row indices
+  Map<VectorIx> colPtr( m_L.outerIndexPtr(), n+1); // Pointer to the beginning of each row
+  VectorIx firstElt(n-1); // for each j, points to the next entry in vals that will be used in the factorization
+  VectorList listCol(n);  // listCol(j) is a linked list of columns to update column j
+  VectorSx col_vals(n);   // Store a  nonzero values in each column
+  VectorIx col_irow(n);   // Row indices of nonzero elements in each column
+  VectorIx col_pattern(n);
+  col_pattern.fill(-1);
+  StorageIndex col_nnz;
+
+
+  // Computes the scaling factors
+  m_scale.resize(n);
+  m_scale.setZero();
+  for (Index j = 0; j < n; j++)
+    for (Index k = colPtr[j]; k < colPtr[j+1]; k++)
+    {
+      m_scale(j) += numext::abs2(vals(k));
+      if(rowIdx[k]!=j)
+        m_scale(rowIdx[k]) += numext::abs2(vals(k));
+    }
+
+  m_scale = m_scale.cwiseSqrt().cwiseSqrt();
+
+  for (Index j = 0; j < n; ++j)
+    if(m_scale(j)>(std::numeric_limits<RealScalar>::min)())
+      m_scale(j) = RealScalar(1)/m_scale(j);
+    else
+      m_scale(j) = 1;
+
+  // TODO disable scaling if not needed, i.e., if it is roughly uniform? (this will make solve() faster)
+
+  // Scale and compute the shift for the matrix
+  RealScalar mindiag = NumTraits<RealScalar>::highest();
+  for (Index j = 0; j < n; j++)
+  {
+    for (Index k = colPtr[j]; k < colPtr[j+1]; k++)
+      vals[k] *= (m_scale(j)*m_scale(rowIdx[k]));
+    eigen_internal_assert(rowIdx[colPtr[j]]==j && "IncompleteCholesky: only the lower triangular part must be stored");
+    mindiag = numext::mini(numext::real(vals[colPtr[j]]), mindiag);
+  }
+
+  FactorType L_save = m_L;
+
+  RealScalar shift = 0;
+  if(mindiag <= RealScalar(0.))
+    shift = m_initialShift - mindiag;
+
+  m_info = NumericalIssue;
+
+  // Try to perform the incomplete factorization using the current shift
+  int iter = 0;
+  do
+  {
+    // Apply the shift to the diagonal elements of the matrix
+    for (Index j = 0; j < n; j++)
+      vals[colPtr[j]] += shift;
+
+    // jki version of the Cholesky factorization
+    Index j=0;
+    for (; j < n; ++j)
+    {
+      // Left-looking factorization of the j-th column
+      // First, load the j-th column into col_vals
+      Scalar diag = vals[colPtr[j]];  // It is assumed that only the lower part is stored
+      col_nnz = 0;
+      for (Index i = colPtr[j] + 1; i < colPtr[j+1]; i++)
+      {
+        StorageIndex l = rowIdx[i];
+        col_vals(col_nnz) = vals[i];
+        col_irow(col_nnz) = l;
+        col_pattern(l) = col_nnz;
+        col_nnz++;
+      }
+      {
+        typename std::list<StorageIndex>::iterator k;
+        // Browse all previous columns that will update column j
+        for(k = listCol[j].begin(); k != listCol[j].end(); k++)
+        {
+          Index jk = firstElt(*k); // First element to use in the column
+          eigen_internal_assert(rowIdx[jk]==j);
+          Scalar v_j_jk = numext::conj(vals[jk]);
+
+          jk += 1;
+          for (Index i = jk; i < colPtr[*k+1]; i++)
+          {
+            StorageIndex l = rowIdx[i];
+            if(col_pattern[l]<0)
+            {
+              col_vals(col_nnz) = vals[i] * v_j_jk;
+              col_irow[col_nnz] = l;
+              col_pattern(l) = col_nnz;
+              col_nnz++;
+            }
+            else
+              col_vals(col_pattern[l]) -= vals[i] * v_j_jk;
+          }
+          updateList(colPtr,rowIdx,vals, *k, jk, firstElt, listCol);
+        }
+      }
+
+      // Scale the current column
+      if(numext::real(diag) <= 0)
+      {
+        if(++iter>=10)
+          return;
+
+        // increase shift
+        shift = numext::maxi(m_initialShift,RealScalar(2)*shift);
+        // restore m_L, col_pattern, and listCol
+        vals = Map<const VectorSx>(L_save.valuePtr(), nnz);
+        rowIdx = Map<const VectorIx>(L_save.innerIndexPtr(), nnz);
+        colPtr = Map<const VectorIx>(L_save.outerIndexPtr(), n+1);
+        col_pattern.fill(-1);
+        for(Index i=0; i<n; ++i)
+          listCol[i].clear();
+
+        break;
+      }
+
+      RealScalar rdiag = sqrt(numext::real(diag));
+      vals[colPtr[j]] = rdiag;
+      for (Index k = 0; k<col_nnz; ++k)
+      {
+        Index i = col_irow[k];
+        //Scale
+        col_vals(k) /= rdiag;
+        //Update the remaining diagonals with col_vals
+        vals[colPtr[i]] -= numext::abs2(col_vals(k));
+      }
+      // Select the largest p elements
+      // p is the original number of elements in the column (without the diagonal)
+      Index p = colPtr[j+1] - colPtr[j] - 1 ;
+      Ref<VectorSx> cvals = col_vals.head(col_nnz);
+      Ref<VectorIx> cirow = col_irow.head(col_nnz);
+      internal::QuickSplit(cvals,cirow, p);
+      // Insert the largest p elements in the matrix
+      Index cpt = 0;
+      for (Index i = colPtr[j]+1; i < colPtr[j+1]; i++)
+      {
+        vals[i] = col_vals(cpt);
+        rowIdx[i] = col_irow(cpt);
+        // restore col_pattern:
+        col_pattern(col_irow(cpt)) = -1;
+        cpt++;
+      }
+      // Get the first smallest row index and put it after the diagonal element
+      Index jk = colPtr(j)+1;
+      updateList(colPtr,rowIdx,vals,j,jk,firstElt,listCol);
+    }
+
+    if(j==n)
+    {
+      m_factorizationIsOk = true;
+      m_info = Success;
+    }
+  } while(m_info!=Success);
+}
+
+template<typename Scalar, int UpLo_, typename OrderingType>
+inline void IncompleteCholesky<Scalar,UpLo_, OrderingType>::updateList(Ref<const VectorIx> colPtr, Ref<VectorIx> rowIdx, Ref<VectorSx> vals, const Index& col, const Index& jk, VectorIx& firstElt, VectorList& listCol)
+{
+  if (jk < colPtr(col+1) )
+  {
+    Index p = colPtr(col+1) - jk;
+    Index minpos;
+    rowIdx.segment(jk,p).minCoeff(&minpos);
+    minpos += jk;
+    if (rowIdx(minpos) != rowIdx(jk))
+    {
+      //Swap
+      std::swap(rowIdx(jk),rowIdx(minpos));
+      std::swap(vals(jk),vals(minpos));
+    }
+    firstElt(col) = internal::convert_index<StorageIndex,Index>(jk);
+    listCol[rowIdx(jk)].push_back(internal::convert_index<StorageIndex,Index>(col));
+  }
+}
+
+} // end namespace Eigen
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
new file mode 100644
index 0000000000000000000000000000000000000000..44f25fcdfba871047aa4897cf43f2cd665c4b6f4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
@@ -0,0 +1,455 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_INCOMPLETE_LUT_H
+#define EIGEN_INCOMPLETE_LUT_H
+
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+  * Compute a quick-sort split of a vector
+  * On output, the vector row is permuted such that its elements satisfy
+  * abs(row(i)) >= abs(row(ncut)) if i<ncut
+  * abs(row(i)) <= abs(row(ncut)) if i>ncut
+  * \param row The vector of values
+  * \param ind The array of index for the elements in @p row
+  * \param ncut  The number of largest elements to keep
+  **/
+template <typename VectorV, typename VectorI>
+Index QuickSplit(VectorV &row, VectorI &ind, Index ncut)
+{
+  typedef typename VectorV::RealScalar RealScalar;
+  using std::swap;
+  using std::abs;
+  Index mid;
+  Index n = row.size(); /* length of the vector */
+  Index first, last ;
+
+  ncut--; /* to fit the zero-based indices */
+  first = 0;
+  last = n-1;
+  if (ncut < first || ncut > last ) return 0;
+
+  do {
+    mid = first;
+    RealScalar abskey = abs(row(mid));
+    for (Index j = first + 1; j <= last; j++) {
+      if ( abs(row(j)) > abskey) {
+        ++mid;
+        swap(row(mid), row(j));
+        swap(ind(mid), ind(j));
+      }
+    }
+    /* Interchange for the pivot element */
+    swap(row(mid), row(first));
+    swap(ind(mid), ind(first));
+
+    if (mid > ncut) last = mid - 1;
+    else if (mid < ncut ) first = mid + 1;
+  } while (mid != ncut );
+
+  return 0; /* mid is equal to ncut */
+}
+
+}// end namespace internal
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \class IncompleteLUT
+  * \brief Incomplete LU factorization with dual-threshold strategy
+  *
+  * \implsparsesolverconcept
+  *
+  * During the numerical factorization, two dropping rules are used :
+  *  1) any element whose magnitude is less than some tolerance is dropped.
+  *    This tolerance is obtained by multiplying the input tolerance @p droptol
+  *    by the average magnitude of all the original elements in the current row.
+  *  2) After the elimination of the row, only the @p fill largest elements in
+  *    the L part and the @p fill largest elements in the U part are kept
+  *    (in addition to the diagonal element ). Note that @p fill is computed from
+  *    the input parameter @p fillfactor which is used the ratio to control the fill_in
+  *    relatively to the initial number of nonzero elements.
+  *
+  * The two extreme cases are when @p droptol=0 (to keep all the @p fill*2 largest elements)
+  * and when @p fill=n/2 with @p droptol being different to zero.
+  *
+  * References : Yousef Saad, ILUT: A dual threshold incomplete LU factorization,
+  *              Numerical Linear Algebra with Applications, 1(4), pp 387-402, 1994.
+  *
+  * NOTE : The following implementation is derived from the ILUT implementation
+  * in the SPARSKIT package, Copyright (C) 2005, the Regents of the University of Minnesota
+  *  released under the terms of the GNU LGPL:
+  *    http://www-users.cs.umn.edu/~saad/software/SPARSKIT/README
+  * However, Yousef Saad gave us permission to relicense his ILUT code to MPL2.
+  * See the Eigen mailing list archive, thread: ILUT, date: July 8, 2012:
+  *   http://listengine.tuxfamily.org/lists.tuxfamily.org/eigen/2012/07/msg00064.html
+  * alternatively, on GMANE:
+  *   http://comments.gmane.org/gmane.comp.lib.eigen/3302
+  */
+template <typename Scalar_, typename StorageIndex_ = int>
+class IncompleteLUT : public SparseSolverBase<IncompleteLUT<Scalar_, StorageIndex_> >
+{
+  protected:
+    typedef SparseSolverBase<IncompleteLUT> Base;
+    using Base::m_isInitialized;
+  public:
+    typedef Scalar_ Scalar;
+    typedef StorageIndex_ StorageIndex;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+    typedef Matrix<Scalar,Dynamic,1> Vector;
+    typedef Matrix<StorageIndex,Dynamic,1> VectorI;
+    typedef SparseMatrix<Scalar,RowMajor,StorageIndex> FactorType;
+
+    enum {
+      ColsAtCompileTime = Dynamic,
+      MaxColsAtCompileTime = Dynamic
+    };
+
+  public:
+
+    IncompleteLUT()
+      : m_droptol(NumTraits<Scalar>::dummy_precision()), m_fillfactor(10),
+        m_analysisIsOk(false), m_factorizationIsOk(false)
+    {}
+
+    template<typename MatrixType>
+    explicit IncompleteLUT(const MatrixType& mat, const RealScalar& droptol=NumTraits<Scalar>::dummy_precision(), int fillfactor = 10)
+      : m_droptol(droptol),m_fillfactor(fillfactor),
+        m_analysisIsOk(false),m_factorizationIsOk(false)
+    {
+      eigen_assert(fillfactor != 0);
+      compute(mat);
+    }
+
+    EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return m_lu.rows(); }
+
+    EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return m_lu.cols(); }
+
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful,
+      *          \c NumericalIssue if the matrix.appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "IncompleteLUT is not initialized.");
+      return m_info;
+    }
+
+    template<typename MatrixType>
+    void analyzePattern(const MatrixType& amat);
+
+    template<typename MatrixType>
+    void factorize(const MatrixType& amat);
+
+    /**
+      * Compute an incomplete LU factorization with dual threshold on the matrix mat
+      * No pivoting is done in this version
+      *
+      **/
+    template<typename MatrixType>
+    IncompleteLUT& compute(const MatrixType& amat)
+    {
+      analyzePattern(amat);
+      factorize(amat);
+      return *this;
+    }
+
+    void setDroptol(const RealScalar& droptol);
+    void setFillfactor(int fillfactor);
+
+    template<typename Rhs, typename Dest>
+    void _solve_impl(const Rhs& b, Dest& x) const
+    {
+      x = m_Pinv * b;
+      x = m_lu.template triangularView<UnitLower>().solve(x);
+      x = m_lu.template triangularView<Upper>().solve(x);
+      x = m_P * x;
+    }
+
+protected:
+
+    /** keeps off-diagonal entries; drops diagonal entries */
+    struct keep_diag {
+      inline bool operator() (const Index& row, const Index& col, const Scalar&) const
+      {
+        return row!=col;
+      }
+    };
+
+protected:
+
+    FactorType m_lu;
+    RealScalar m_droptol;
+    int m_fillfactor;
+    bool m_analysisIsOk;
+    bool m_factorizationIsOk;
+    ComputationInfo m_info;
+    PermutationMatrix<Dynamic,Dynamic,StorageIndex> m_P;     // Fill-reducing permutation
+    PermutationMatrix<Dynamic,Dynamic,StorageIndex> m_Pinv;  // Inverse permutation
+};
+
+/**
+ * Set control parameter droptol
+ *  \param droptol   Drop any element whose magnitude is less than this tolerance
+ **/
+template<typename Scalar, typename StorageIndex>
+void IncompleteLUT<Scalar,StorageIndex>::setDroptol(const RealScalar& droptol)
+{
+  this->m_droptol = droptol;
+}
+
+/**
+ * Set control parameter fillfactor
+ * \param fillfactor  This is used to compute the  number @p fill_in of largest elements to keep on each row.
+ **/
+template<typename Scalar, typename StorageIndex>
+void IncompleteLUT<Scalar,StorageIndex>::setFillfactor(int fillfactor)
+{
+  this->m_fillfactor = fillfactor;
+}
+
+template <typename Scalar, typename StorageIndex>
+template<typename MatrixType_>
+void IncompleteLUT<Scalar,StorageIndex>::analyzePattern(const MatrixType_& amat)
+{
+  // Compute the Fill-reducing permutation
+  // Since ILUT does not perform any numerical pivoting,
+  // it is highly preferable to keep the diagonal through symmetric permutations.
+  // To this end, let's symmetrize the pattern and perform AMD on it.
+  SparseMatrix<Scalar,ColMajor, StorageIndex> mat1 = amat;
+  SparseMatrix<Scalar,ColMajor, StorageIndex> mat2 = amat.transpose();
+  // FIXME for a matrix with nearly symmetric pattern, mat2+mat1 is the appropriate choice.
+  //       on the other hand for a really non-symmetric pattern, mat2*mat1 should be preferred...
+  SparseMatrix<Scalar,ColMajor, StorageIndex> AtA = mat2 + mat1;
+  AMDOrdering<StorageIndex> ordering;
+  ordering(AtA,m_P);
+  m_Pinv  = m_P.inverse(); // cache the inverse permutation
+  m_analysisIsOk = true;
+  m_factorizationIsOk = false;
+  m_isInitialized = true;
+}
+
+template <typename Scalar, typename StorageIndex>
+template<typename MatrixType_>
+void IncompleteLUT<Scalar,StorageIndex>::factorize(const MatrixType_& amat)
+{
+  using std::sqrt;
+  using std::swap;
+  using std::abs;
+  using internal::convert_index;
+
+  eigen_assert((amat.rows() == amat.cols()) && "The factorization should be done on a square matrix");
+  Index n = amat.cols();  // Size of the matrix
+  m_lu.resize(n,n);
+  // Declare Working vectors and variables
+  Vector u(n) ;     // real values of the row -- maximum size is n --
+  VectorI ju(n);   // column position of the values in u -- maximum size  is n
+  VectorI jr(n);   // Indicate the position of the nonzero elements in the vector u -- A zero location is indicated by -1
+
+  // Apply the fill-reducing permutation
+  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+  SparseMatrix<Scalar,RowMajor, StorageIndex> mat;
+  mat = amat.twistedBy(m_Pinv);
+
+  // Initialization
+  jr.fill(-1);
+  ju.fill(0);
+  u.fill(0);
+
+  // number of largest elements to keep in each row:
+  Index fill_in = (amat.nonZeros()*m_fillfactor)/n + 1;
+  if (fill_in > n) fill_in = n;
+
+  // number of largest nonzero elements to keep in the L and the U part of the current row:
+  Index nnzL = fill_in/2;
+  Index nnzU = nnzL;
+  m_lu.reserve(n * (nnzL + nnzU + 1));
+
+  // global loop over the rows of the sparse matrix
+  for (Index ii = 0; ii < n; ii++)
+  {
+    // 1 - copy the lower and the upper part of the row i of mat in the working vector u
+
+    Index sizeu = 1; // number of nonzero elements in the upper part of the current row
+    Index sizel = 0; // number of nonzero elements in the lower part of the current row
+    ju(ii)    = convert_index<StorageIndex>(ii);
+    u(ii)     = 0;
+    jr(ii)    = convert_index<StorageIndex>(ii);
+    RealScalar rownorm = 0;
+
+    typename FactorType::InnerIterator j_it(mat, ii); // Iterate through the current row ii
+    for (; j_it; ++j_it)
+    {
+      Index k = j_it.index();
+      if (k < ii)
+      {
+        // copy the lower part
+        ju(sizel) = convert_index<StorageIndex>(k);
+        u(sizel) = j_it.value();
+        jr(k) = convert_index<StorageIndex>(sizel);
+        ++sizel;
+      }
+      else if (k == ii)
+      {
+        u(ii) = j_it.value();
+      }
+      else
+      {
+        // copy the upper part
+        Index jpos = ii + sizeu;
+        ju(jpos) = convert_index<StorageIndex>(k);
+        u(jpos) = j_it.value();
+        jr(k) = convert_index<StorageIndex>(jpos);
+        ++sizeu;
+      }
+      rownorm += numext::abs2(j_it.value());
+    }
+
+    // 2 - detect possible zero row
+    if(rownorm==0)
+    {
+      m_info = NumericalIssue;
+      return;
+    }
+    // Take the 2-norm of the current row as a relative tolerance
+    rownorm = sqrt(rownorm);
+
+    // 3 - eliminate the previous nonzero rows
+    Index jj = 0;
+    Index len = 0;
+    while (jj < sizel)
+    {
+      // In order to eliminate in the correct order,
+      // we must select first the smallest column index among  ju(jj:sizel)
+      Index k;
+      Index minrow = ju.segment(jj,sizel-jj).minCoeff(&k); // k is relative to the segment
+      k += jj;
+      if (minrow != ju(jj))
+      {
+        // swap the two locations
+        Index j = ju(jj);
+        swap(ju(jj), ju(k));
+        jr(minrow) = convert_index<StorageIndex>(jj);
+        jr(j) = convert_index<StorageIndex>(k);
+        swap(u(jj), u(k));
+      }
+      // Reset this location
+      jr(minrow) = -1;
+
+      // Start elimination
+      typename FactorType::InnerIterator ki_it(m_lu, minrow);
+      while (ki_it && ki_it.index() < minrow) ++ki_it;
+      eigen_internal_assert(ki_it && ki_it.col()==minrow);
+      Scalar fact = u(jj) / ki_it.value();
+
+      // drop too small elements
+      if(abs(fact) <= m_droptol)
+      {
+        jj++;
+        continue;
+      }
+
+      // linear combination of the current row ii and the row minrow
+      ++ki_it;
+      for (; ki_it; ++ki_it)
+      {
+        Scalar prod = fact * ki_it.value();
+        Index j     = ki_it.index();
+        Index jpos  = jr(j);
+        if (jpos == -1) // fill-in element
+        {
+          Index newpos;
+          if (j >= ii) // dealing with the upper part
+          {
+            newpos = ii + sizeu;
+            sizeu++;
+            eigen_internal_assert(sizeu<=n);
+          }
+          else // dealing with the lower part
+          {
+            newpos = sizel;
+            sizel++;
+            eigen_internal_assert(sizel<=ii);
+          }
+          ju(newpos) = convert_index<StorageIndex>(j);
+          u(newpos) = -prod;
+          jr(j) = convert_index<StorageIndex>(newpos);
+        }
+        else
+          u(jpos) -= prod;
+      }
+      // store the pivot element
+      u(len)  = fact;
+      ju(len) = convert_index<StorageIndex>(minrow);
+      ++len;
+
+      jj++;
+    } // end of the elimination on the row ii
+
+    // reset the upper part of the pointer jr to zero
+    for(Index k = 0; k <sizeu; k++) jr(ju(ii+k)) = -1;
+
+    // 4 - partially sort and insert the elements in the m_lu matrix
+
+    // sort the L-part of the row
+    sizel = len;
+    len = (std::min)(sizel, nnzL);
+    typename Vector::SegmentReturnType ul(u.segment(0, sizel));
+    typename VectorI::SegmentReturnType jul(ju.segment(0, sizel));
+    internal::QuickSplit(ul, jul, len);
+
+    // store the largest m_fill elements of the L part
+    m_lu.startVec(ii);
+    for(Index k = 0; k < len; k++)
+      m_lu.insertBackByOuterInnerUnordered(ii,ju(k)) = u(k);
+
+    // store the diagonal element
+    // apply a shifting rule to avoid zero pivots (we are doing an incomplete factorization)
+    if (u(ii) == Scalar(0))
+      u(ii) = sqrt(m_droptol) * rownorm;
+    m_lu.insertBackByOuterInnerUnordered(ii, ii) = u(ii);
+
+    // sort the U-part of the row
+    // apply the dropping rule first
+    len = 0;
+    for(Index k = 1; k < sizeu; k++)
+    {
+      if(abs(u(ii+k)) > m_droptol * rownorm )
+      {
+        ++len;
+        u(ii + len)  = u(ii + k);
+        ju(ii + len) = ju(ii + k);
+      }
+    }
+    sizeu = len + 1; // +1 to take into account the diagonal element
+    len = (std::min)(sizeu, nnzU);
+    typename Vector::SegmentReturnType uu(u.segment(ii+1, sizeu-1));
+    typename VectorI::SegmentReturnType juu(ju.segment(ii+1, sizeu-1));
+    internal::QuickSplit(uu, juu, len);
+
+    // store the largest elements of the U part
+    for(Index k = ii + 1; k < ii + len; k++)
+      m_lu.insertBackByOuterInnerUnordered(ii,ju(k)) = u(k);
+  }
+  m_lu.finalize();
+  m_lu.makeCompressed();
+
+  m_factorizationIsOk = true;
+  m_info = Success;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_INCOMPLETE_LUT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..b657e84de267e64729e011b341493719cf973200
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
+#error "Please include Eigen/IterativeLinearSolvers instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..49829d018fae14fef540a564e8bb76acf70fc4f8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/IterativeSolverBase.h
@@ -0,0 +1,449 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ITERATIVE_SOLVER_BASE_H
+#define EIGEN_ITERATIVE_SOLVER_BASE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename MatrixType>
+struct is_ref_compatible_impl
+{
+private:
+  template <typename T0>
+  struct any_conversion
+  {
+    template <typename T> any_conversion(const volatile T&);
+    template <typename T> any_conversion(T&);
+  };
+  struct yes {int a[1];};
+  struct no  {int a[2];};
+
+  template<typename T>
+  static yes test(const Ref<const T>&, int);
+  template<typename T>
+  static no  test(any_conversion<T>, ...);
+
+public:
+  static MatrixType ms_from;
+  enum { value = sizeof(test<MatrixType>(ms_from, 0))==sizeof(yes) };
+};
+
+template<typename MatrixType>
+struct is_ref_compatible
+{
+  enum { value = is_ref_compatible_impl<remove_all_t<MatrixType>>::value };
+};
+
+template<typename MatrixType, bool MatrixFree = !internal::is_ref_compatible<MatrixType>::value>
+class generic_matrix_wrapper;
+
+// We have an explicit matrix at hand, compatible with Ref<>
+template<typename MatrixType>
+class generic_matrix_wrapper<MatrixType,false>
+{
+public:
+  typedef Ref<const MatrixType> ActualMatrixType;
+  template<int UpLo> struct ConstSelfAdjointViewReturnType {
+    typedef typename ActualMatrixType::template ConstSelfAdjointViewReturnType<UpLo>::Type Type;
+  };
+
+  enum {
+    MatrixFree = false
+  };
+
+  generic_matrix_wrapper()
+    : m_dummy(0,0), m_matrix(m_dummy)
+  {}
+
+  template<typename InputType>
+  generic_matrix_wrapper(const InputType &mat)
+    : m_matrix(mat)
+  {}
+
+  const ActualMatrixType& matrix() const
+  {
+    return m_matrix;
+  }
+
+  template<typename MatrixDerived>
+  void grab(const EigenBase<MatrixDerived> &mat)
+  {
+    internal::destroy_at(&m_matrix);
+    internal::construct_at(&m_matrix, mat.derived());
+  }
+
+  void grab(const Ref<const MatrixType> &mat)
+  {
+    if(&(mat.derived()) != &m_matrix)
+    {
+      internal::destroy_at(&m_matrix);
+      internal::construct_at(&m_matrix, mat);
+    }
+  }
+
+protected:
+  MatrixType m_dummy; // used to default initialize the Ref<> object
+  ActualMatrixType m_matrix;
+};
+
+// MatrixType is not compatible with Ref<> -> matrix-free wrapper
+template<typename MatrixType>
+class generic_matrix_wrapper<MatrixType,true>
+{
+public:
+  typedef MatrixType ActualMatrixType;
+  template<int UpLo> struct ConstSelfAdjointViewReturnType
+  {
+    typedef ActualMatrixType Type;
+  };
+
+  enum {
+    MatrixFree = true
+  };
+
+  generic_matrix_wrapper()
+    : mp_matrix(0)
+  {}
+
+  generic_matrix_wrapper(const MatrixType &mat)
+    : mp_matrix(&mat)
+  {}
+
+  const ActualMatrixType& matrix() const
+  {
+    return *mp_matrix;
+  }
+
+  void grab(const MatrixType &mat)
+  {
+    mp_matrix = &mat;
+  }
+
+protected:
+  const ActualMatrixType *mp_matrix;
+};
+
+}
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief Base class for linear iterative solvers
+  *
+  * \sa class SimplicialCholesky, DiagonalPreconditioner, IdentityPreconditioner
+  */
+template< typename Derived>
+class IterativeSolverBase : public SparseSolverBase<Derived>
+{
+protected:
+  typedef SparseSolverBase<Derived> Base;
+  using Base::m_isInitialized;
+
+public:
+  typedef typename internal::traits<Derived>::MatrixType MatrixType;
+  typedef typename internal::traits<Derived>::Preconditioner Preconditioner;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef typename MatrixType::RealScalar RealScalar;
+
+  enum {
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+
+public:
+
+  using Base::derived;
+
+  /** Default constructor. */
+  IterativeSolverBase()
+  {
+    init();
+  }
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+    *
+    * This constructor is a shortcut for the default constructor followed
+    * by a call to compute().
+    *
+    * \warning this class stores a reference to the matrix A as well as some
+    * precomputed values that depend on it. Therefore, if \a A is changed
+    * this class becomes invalid. Call compute() to update it with the new
+    * matrix A, or modify a copy of A.
+    */
+  template<typename MatrixDerived>
+  explicit IterativeSolverBase(const EigenBase<MatrixDerived>& A)
+    : m_matrixWrapper(A.derived())
+  {
+    init();
+    compute(matrix());
+  }
+
+
+  IterativeSolverBase(IterativeSolverBase&&) = default;
+
+  ~IterativeSolverBase() {}
+
+  /** Initializes the iterative solver for the sparsity pattern of the matrix \a A for further solving \c Ax=b problems.
+    *
+    * Currently, this function mostly calls analyzePattern on the preconditioner. In the future
+    * we might, for instance, implement column reordering for faster matrix vector products.
+    */
+  template<typename MatrixDerived>
+  Derived& analyzePattern(const EigenBase<MatrixDerived>& A)
+  {
+    grab(A.derived());
+    m_preconditioner.analyzePattern(matrix());
+    m_isInitialized = true;
+    m_analysisIsOk = true;
+    m_info = m_preconditioner.info();
+    return derived();
+  }
+
+  /** Initializes the iterative solver with the numerical values of the matrix \a A for further solving \c Ax=b problems.
+    *
+    * Currently, this function mostly calls factorize on the preconditioner.
+    *
+    * \warning this class stores a reference to the matrix A as well as some
+    * precomputed values that depend on it. Therefore, if \a A is changed
+    * this class becomes invalid. Call compute() to update it with the new
+    * matrix A, or modify a copy of A.
+    */
+  template<typename MatrixDerived>
+  Derived& factorize(const EigenBase<MatrixDerived>& A)
+  {
+    eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+    grab(A.derived());
+    m_preconditioner.factorize(matrix());
+    m_factorizationIsOk = true;
+    m_info = m_preconditioner.info();
+    return derived();
+  }
+
+  /** Initializes the iterative solver with the matrix \a A for further solving \c Ax=b problems.
+    *
+    * Currently, this function mostly initializes/computes the preconditioner. In the future
+    * we might, for instance, implement column reordering for faster matrix vector products.
+    *
+    * \warning this class stores a reference to the matrix A as well as some
+    * precomputed values that depend on it. Therefore, if \a A is changed
+    * this class becomes invalid. Call compute() to update it with the new
+    * matrix A, or modify a copy of A.
+    */
+  template<typename MatrixDerived>
+  Derived& compute(const EigenBase<MatrixDerived>& A)
+  {
+    grab(A.derived());
+    m_preconditioner.compute(matrix());
+    m_isInitialized = true;
+    m_analysisIsOk = true;
+    m_factorizationIsOk = true;
+    m_info = m_preconditioner.info();
+    return derived();
+  }
+
+  /** \internal */
+  EIGEN_CONSTEXPR Index rows() const EIGEN_NOEXCEPT { return matrix().rows(); }
+
+  /** \internal */
+  EIGEN_CONSTEXPR Index cols() const EIGEN_NOEXCEPT { return matrix().cols(); }
+
+  /** \returns the tolerance threshold used by the stopping criteria.
+    * \sa setTolerance()
+    */
+  RealScalar tolerance() const { return m_tolerance; }
+
+  /** Sets the tolerance threshold used by the stopping criteria.
+    *
+    * This value is used as an upper bound to the relative residual error: |Ax-b|/|b|.
+    * The default value is the machine precision given by NumTraits<Scalar>::epsilon()
+    */
+  Derived& setTolerance(const RealScalar& tolerance)
+  {
+    m_tolerance = tolerance;
+    return derived();
+  }
+
+  /** \returns a read-write reference to the preconditioner for custom configuration. */
+  Preconditioner& preconditioner() { return m_preconditioner; }
+
+  /** \returns a read-only reference to the preconditioner. */
+  const Preconditioner& preconditioner() const { return m_preconditioner; }
+
+  /** \returns the max number of iterations.
+    * It is either the value set by setMaxIterations or, by default,
+    * twice the number of columns of the matrix.
+    */
+  Index maxIterations() const
+  {
+    return (m_maxIterations<0) ? 2*matrix().cols() : m_maxIterations;
+  }
+
+  /** Sets the max number of iterations.
+    * Default is twice the number of columns of the matrix.
+    */
+  Derived& setMaxIterations(Index maxIters)
+  {
+    m_maxIterations = maxIters;
+    return derived();
+  }
+
+  /** \returns the number of iterations performed during the last solve */
+  Index iterations() const
+  {
+    eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized.");
+    return m_iterations;
+  }
+
+  /** \returns the tolerance error reached during the last solve.
+    * It is a close approximation of the true relative residual error |Ax-b|/|b|.
+    */
+  RealScalar error() const
+  {
+    eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized.");
+    return m_error;
+  }
+
+  /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A
+    * and \a x0 as an initial solution.
+    *
+    * \sa solve(), compute()
+    */
+  template<typename Rhs,typename Guess>
+  inline const SolveWithGuess<Derived, Rhs, Guess>
+  solveWithGuess(const MatrixBase<Rhs>& b, const Guess& x0) const
+  {
+    eigen_assert(m_isInitialized && "Solver is not initialized.");
+    eigen_assert(derived().rows()==b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
+    return SolveWithGuess<Derived, Rhs, Guess>(derived(), b.derived(), x0);
+  }
+
+  /** \returns Success if the iterations converged, and NoConvergence otherwise. */
+  ComputationInfo info() const
+  {
+    eigen_assert(m_isInitialized && "IterativeSolverBase is not initialized.");
+    return m_info;
+  }
+
+  /** \internal */
+  template<typename Rhs, typename DestDerived>
+  void _solve_with_guess_impl(const Rhs& b, SparseMatrixBase<DestDerived> &aDest) const
+  {
+    eigen_assert(rows()==b.rows());
+
+    Index rhsCols = b.cols();
+    Index size = b.rows();
+    DestDerived& dest(aDest.derived());
+    typedef typename DestDerived::Scalar DestScalar;
+    Eigen::Matrix<DestScalar,Dynamic,1> tb(size);
+    Eigen::Matrix<DestScalar,Dynamic,1> tx(cols());
+    // We do not directly fill dest because sparse expressions have to be free of aliasing issue.
+    // For non square least-square problems, b and dest might not have the same size whereas they might alias each-other.
+    typename DestDerived::PlainObject tmp(cols(),rhsCols);
+    ComputationInfo global_info = Success;
+    for(Index k=0; k<rhsCols; ++k)
+    {
+      tb = b.col(k);
+      tx = dest.col(k);
+      derived()._solve_vector_with_guess_impl(tb,tx);
+      tmp.col(k) = tx.sparseView(0);
+
+      // The call to _solve_vector_with_guess_impl updates m_info, so if it failed for a previous column
+      // we need to restore it to the worst value.
+      if(m_info==NumericalIssue)
+        global_info = NumericalIssue;
+      else if(m_info==NoConvergence)
+        global_info = NoConvergence;
+    }
+    m_info = global_info;
+    dest.swap(tmp);
+  }
+
+  template<typename Rhs, typename DestDerived>
+  std::enable_if_t<Rhs::ColsAtCompileTime!=1 && DestDerived::ColsAtCompileTime!=1>
+  _solve_with_guess_impl(const Rhs& b, MatrixBase<DestDerived> &aDest) const
+  {
+    eigen_assert(rows()==b.rows());
+
+    Index rhsCols = b.cols();
+    DestDerived& dest(aDest.derived());
+    ComputationInfo global_info = Success;
+    for(Index k=0; k<rhsCols; ++k)
+    {
+      typename DestDerived::ColXpr xk(dest,k);
+      typename Rhs::ConstColXpr bk(b,k);
+      derived()._solve_vector_with_guess_impl(bk,xk);
+
+      // The call to _solve_vector_with_guess updates m_info, so if it failed for a previous column
+      // we need to restore it to the worst value.
+      if(m_info==NumericalIssue)
+        global_info = NumericalIssue;
+      else if(m_info==NoConvergence)
+        global_info = NoConvergence;
+    }
+    m_info = global_info;
+  }
+
+  template<typename Rhs, typename DestDerived>
+  std::enable_if_t<Rhs::ColsAtCompileTime==1 || DestDerived::ColsAtCompileTime==1>
+  _solve_with_guess_impl(const Rhs& b, MatrixBase<DestDerived> &dest) const
+  {
+    derived()._solve_vector_with_guess_impl(b,dest.derived());
+  }
+
+  /** \internal default initial guess = 0 */
+  template<typename Rhs,typename Dest>
+  void _solve_impl(const Rhs& b, Dest& x) const
+  {
+    x.setZero();
+    derived()._solve_with_guess_impl(b,x);
+  }
+
+protected:
+  void init()
+  {
+    m_isInitialized = false;
+    m_analysisIsOk = false;
+    m_factorizationIsOk = false;
+    m_maxIterations = -1;
+    m_tolerance = NumTraits<Scalar>::epsilon();
+  }
+
+  typedef internal::generic_matrix_wrapper<MatrixType> MatrixWrapper;
+  typedef typename MatrixWrapper::ActualMatrixType ActualMatrixType;
+
+  const ActualMatrixType& matrix() const
+  {
+    return m_matrixWrapper.matrix();
+  }
+
+  template<typename InputType>
+  void grab(const InputType &A)
+  {
+    m_matrixWrapper.grab(A);
+  }
+
+  MatrixWrapper m_matrixWrapper;
+  Preconditioner m_preconditioner;
+
+  Index m_maxIterations;
+  RealScalar m_tolerance;
+
+  mutable RealScalar m_error;
+  mutable Index m_iterations;
+  mutable ComputationInfo m_info;
+  mutable bool m_analysisIsOk, m_factorizationIsOk;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_ITERATIVE_SOLVER_BASE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
new file mode 100644
index 0000000000000000000000000000000000000000..a76f3f87a76719f028879521223f82e239155cb4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/LeastSquareConjugateGradient.h
@@ -0,0 +1,200 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H
+#define EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \internal Low-level conjugate gradient algorithm for least-square problems
+  * \param mat The matrix A
+  * \param rhs The right hand side vector b
+  * \param x On input and initial solution, on output the computed solution.
+  * \param precond A preconditioner being able to efficiently solve for an
+  *                approximation of A'Ax=b (regardless of b)
+  * \param iters On input the max number of iteration, on output the number of performed iterations.
+  * \param tol_error On input the tolerance error, on output an estimation of the relative error.
+  */
+template<typename MatrixType, typename Rhs, typename Dest, typename Preconditioner>
+EIGEN_DONT_INLINE
+void least_square_conjugate_gradient(const MatrixType& mat, const Rhs& rhs, Dest& x,
+                                     const Preconditioner& precond, Index& iters,
+                                     typename Dest::RealScalar& tol_error)
+{
+  using std::sqrt;
+  using std::abs;
+  typedef typename Dest::RealScalar RealScalar;
+  typedef typename Dest::Scalar Scalar;
+  typedef Matrix<Scalar,Dynamic,1> VectorType;
+  
+  RealScalar tol = tol_error;
+  Index maxIters = iters;
+  
+  Index m = mat.rows(), n = mat.cols();
+
+  VectorType residual        = rhs - mat * x;
+  VectorType normal_residual = mat.adjoint() * residual;
+
+  RealScalar rhsNorm2 = (mat.adjoint()*rhs).squaredNorm();
+  if(rhsNorm2 == 0) 
+  {
+    x.setZero();
+    iters = 0;
+    tol_error = 0;
+    return;
+  }
+  RealScalar threshold = tol*tol*rhsNorm2;
+  RealScalar residualNorm2 = normal_residual.squaredNorm();
+  if (residualNorm2 < threshold)
+  {
+    iters = 0;
+    tol_error = sqrt(residualNorm2 / rhsNorm2);
+    return;
+  }
+  
+  VectorType p(n);
+  p = precond.solve(normal_residual);                         // initial search direction
+
+  VectorType z(n), tmp(m);
+  RealScalar absNew = numext::real(normal_residual.dot(p));  // the square of the absolute value of r scaled by invM
+  Index i = 0;
+  while(i < maxIters)
+  {
+    tmp.noalias() = mat * p;
+
+    Scalar alpha = absNew / tmp.squaredNorm();      // the amount we travel on dir
+    x += alpha * p;                                 // update solution
+    residual -= alpha * tmp;                        // update residual
+    normal_residual.noalias() = mat.adjoint() * residual;     // update residual of the normal equation
+    
+    residualNorm2 = normal_residual.squaredNorm();
+    if(residualNorm2 < threshold)
+      break;
+    
+    z = precond.solve(normal_residual);             // approximately solve for "A'A z = normal_residual"
+
+    RealScalar absOld = absNew;
+    absNew = numext::real(normal_residual.dot(z));  // update the absolute value of r
+    RealScalar beta = absNew / absOld;              // calculate the Gram-Schmidt value used to create the new search direction
+    p = z + beta * p;                               // update search direction
+    i++;
+  }
+  tol_error = sqrt(residualNorm2 / rhsNorm2);
+  iters = i;
+}
+
+}
+
+template< typename MatrixType_,
+          typename Preconditioner_ = LeastSquareDiagonalPreconditioner<typename MatrixType_::Scalar> >
+class LeastSquaresConjugateGradient;
+
+namespace internal {
+
+template< typename MatrixType_, typename Preconditioner_>
+struct traits<LeastSquaresConjugateGradient<MatrixType_,Preconditioner_> >
+{
+  typedef MatrixType_ MatrixType;
+  typedef Preconditioner_ Preconditioner;
+};
+
+}
+
+/** \ingroup IterativeLinearSolvers_Module
+  * \brief A conjugate gradient solver for sparse (or dense) least-square problems
+  *
+  * This class solves for the least-squares solution to A x = b using an iterative conjugate gradient algorithm.
+  * The matrix A can be non symmetric and rectangular, but the matrix A' A should be positive-definite to guaranty stability.
+  * Otherwise, the SparseLU or SparseQR classes might be preferable.
+  * The matrix A and the vectors x and b can be either dense or sparse.
+  *
+  * \tparam MatrixType_ the type of the matrix A, can be a dense or a sparse matrix.
+  * \tparam Preconditioner_ the type of the preconditioner. Default is LeastSquareDiagonalPreconditioner
+  *
+  * \implsparsesolverconcept
+  * 
+  * The maximal number of iterations and tolerance value can be controlled via the setMaxIterations()
+  * and setTolerance() methods. The defaults are the size of the problem for the maximal number of iterations
+  * and NumTraits<Scalar>::epsilon() for the tolerance.
+  * 
+  * This class can be used as the direct solver classes. Here is a typical usage example:
+    \code
+    int m=1000000, n = 10000;
+    VectorXd x(n), b(m);
+    SparseMatrix<double> A(m,n);
+    // fill A and b
+    LeastSquaresConjugateGradient<SparseMatrix<double> > lscg;
+    lscg.compute(A);
+    x = lscg.solve(b);
+    std::cout << "#iterations:     " << lscg.iterations() << std::endl;
+    std::cout << "estimated error: " << lscg.error()      << std::endl;
+    // update b, and solve again
+    x = lscg.solve(b);
+    \endcode
+  * 
+  * By default the iterations start with x=0 as an initial guess of the solution.
+  * One can control the start using the solveWithGuess() method.
+  * 
+  * \sa class ConjugateGradient, SparseLU, SparseQR
+  */
+template< typename MatrixType_, typename Preconditioner_>
+class LeastSquaresConjugateGradient : public IterativeSolverBase<LeastSquaresConjugateGradient<MatrixType_,Preconditioner_> >
+{
+  typedef IterativeSolverBase<LeastSquaresConjugateGradient> Base;
+  using Base::matrix;
+  using Base::m_error;
+  using Base::m_iterations;
+  using Base::m_info;
+  using Base::m_isInitialized;
+public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef Preconditioner_ Preconditioner;
+
+public:
+
+  /** Default constructor. */
+  LeastSquaresConjugateGradient() : Base() {}
+
+  /** Initialize the solver with matrix \a A for further \c Ax=b solving.
+    * 
+    * This constructor is a shortcut for the default constructor followed
+    * by a call to compute().
+    * 
+    * \warning this class stores a reference to the matrix A as well as some
+    * precomputed values that depend on it. Therefore, if \a A is changed
+    * this class becomes invalid. Call compute() to update it with the new
+    * matrix A, or modify a copy of A.
+    */
+  template<typename MatrixDerived>
+  explicit LeastSquaresConjugateGradient(const EigenBase<MatrixDerived>& A) : Base(A.derived()) {}
+
+  ~LeastSquaresConjugateGradient() {}
+
+  /** \internal */
+  template<typename Rhs,typename Dest>
+  void _solve_vector_with_guess_impl(const Rhs& b, Dest& x) const
+  {
+    m_iterations = Base::maxIterations();
+    m_error = Base::m_tolerance;
+
+    internal::least_square_conjugate_gradient(matrix(), b, x, Base::m_preconditioner, m_iterations, m_error);
+    m_info = m_error <= Base::m_tolerance ? Success : NoConvergence;
+  }
+
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_LEAST_SQUARE_CONJUGATE_GRADIENT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
new file mode 100644
index 0000000000000000000000000000000000000000..bb56db3cc7291d043b7bdf6687f4a7802c1f49b8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/IterativeLinearSolvers/SolveWithGuess.h
@@ -0,0 +1,119 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SOLVEWITHGUESS_H
+#define EIGEN_SOLVEWITHGUESS_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template<typename Decomposition, typename RhsType, typename GuessType> class SolveWithGuess;
+
+/** \class SolveWithGuess
+  * \ingroup IterativeLinearSolvers_Module
+  *
+  * \brief Pseudo expression representing a solving operation
+  *
+  * \tparam Decomposition the type of the matrix or decomposion object
+  * \tparam Rhstype the type of the right-hand side
+  *
+  * This class represents an expression of A.solve(B)
+  * and most of the time this is the only way it is used.
+  *
+  */
+namespace internal {
+
+
+template<typename Decomposition, typename RhsType, typename GuessType>
+struct traits<SolveWithGuess<Decomposition, RhsType, GuessType> >
+  : traits<Solve<Decomposition,RhsType> >
+{};
+
+}
+
+
+template<typename Decomposition, typename RhsType, typename GuessType>
+class SolveWithGuess : public internal::generic_xpr_base<SolveWithGuess<Decomposition,RhsType,GuessType>, MatrixXpr, typename internal::traits<RhsType>::StorageKind>::type
+{
+public:
+  typedef typename internal::traits<SolveWithGuess>::Scalar Scalar;
+  typedef typename internal::traits<SolveWithGuess>::PlainObject PlainObject;
+  typedef typename internal::generic_xpr_base<SolveWithGuess<Decomposition,RhsType,GuessType>, MatrixXpr, typename internal::traits<RhsType>::StorageKind>::type Base;
+  typedef typename internal::ref_selector<SolveWithGuess>::type Nested;
+
+  SolveWithGuess(const Decomposition &dec, const RhsType &rhs, const GuessType &guess)
+    : m_dec(dec), m_rhs(rhs), m_guess(guess)
+  {}
+
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  Index rows() const EIGEN_NOEXCEPT { return m_dec.cols(); }
+  EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+  Index cols() const EIGEN_NOEXCEPT { return m_rhs.cols(); }
+
+  EIGEN_DEVICE_FUNC const Decomposition& dec()   const { return m_dec; }
+  EIGEN_DEVICE_FUNC const RhsType&       rhs()   const { return m_rhs; }
+  EIGEN_DEVICE_FUNC const GuessType&     guess() const { return m_guess; }
+
+protected:
+  const Decomposition &m_dec;
+  const RhsType       &m_rhs;
+  const GuessType     &m_guess;
+
+private:
+  Scalar coeff(Index row, Index col) const;
+  Scalar coeff(Index i) const;
+};
+
+namespace internal {
+
+// Evaluator of SolveWithGuess -> eval into a temporary
+template<typename Decomposition, typename RhsType, typename GuessType>
+struct evaluator<SolveWithGuess<Decomposition,RhsType, GuessType> >
+  : public evaluator<typename SolveWithGuess<Decomposition,RhsType,GuessType>::PlainObject>
+{
+  typedef SolveWithGuess<Decomposition,RhsType,GuessType> SolveType;
+  typedef typename SolveType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  evaluator(const SolveType& solve)
+    : m_result(solve.rows(), solve.cols())
+  {
+    internal::construct_at<Base>(this, m_result);
+    m_result = solve.guess();
+    solve.dec()._solve_with_guess_impl(solve.rhs(), m_result);
+  }
+
+protected:
+  PlainObject m_result;
+};
+
+// Specialization for "dst = dec.solveWithGuess(rhs)"
+// NOTE we need to specialize it for Dense2Dense to avoid ambiguous specialization error and a Sparse2Sparse specialization must exist somewhere
+template<typename DstXprType, typename DecType, typename RhsType, typename GuessType, typename Scalar>
+struct Assignment<DstXprType, SolveWithGuess<DecType,RhsType,GuessType>, internal::assign_op<Scalar,Scalar>, Dense2Dense>
+{
+  typedef SolveWithGuess<DecType,RhsType,GuessType> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
+  {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+      dst.resize(dstRows, dstCols);
+
+    dst = src.guess();
+    src.dec()._solve_with_guess_impl(src.rhs(), dst/*, src.guess()*/);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SOLVEWITHGUESS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Jacobi/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Jacobi/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..b17b1f27bb438bb9bf588e7d2d395f25a86a814c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Jacobi/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_JACOBI_MODULE_H
+#error "Please include Eigen/Jacobi instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Jacobi/Jacobi.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Jacobi/Jacobi.h
new file mode 100644
index 0000000000000000000000000000000000000000..5d0f27637decc84cb08b910ab8955db044501880
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/Jacobi/Jacobi.h
@@ -0,0 +1,483 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_JACOBI_H
+#define EIGEN_JACOBI_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup Jacobi_Module
+  * \jacobi_module
+  * \class JacobiRotation
+  * \brief Rotation given by a cosine-sine pair.
+  *
+  * This class represents a Jacobi or Givens rotation.
+  * This is a 2D rotation in the plane \c J of angle \f$ \theta \f$ defined by
+  * its cosine \c c and sine \c s as follow:
+  * \f$ J = \left ( \begin{array}{cc} c & \overline s \\ -s  & \overline c \end{array} \right ) \f$
+  *
+  * You can apply the respective counter-clockwise rotation to a column vector \c v by
+  * applying its adjoint on the left: \f$ v = J^* v \f$ that translates to the following Eigen code:
+  * \code
+  * v.applyOnTheLeft(J.adjoint());
+  * \endcode
+  *
+  * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
+  */
+template<typename Scalar> class JacobiRotation
+{
+  public:
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    /** Default constructor without any initialization. */
+    EIGEN_DEVICE_FUNC
+    JacobiRotation() {}
+
+    /** Construct a planar rotation from a cosine-sine pair (\a c, \c s). */
+    EIGEN_DEVICE_FUNC
+    JacobiRotation(const Scalar& c, const Scalar& s) : m_c(c), m_s(s) {}
+
+    EIGEN_DEVICE_FUNC Scalar& c() { return m_c; }
+    EIGEN_DEVICE_FUNC Scalar c() const { return m_c; }
+    EIGEN_DEVICE_FUNC Scalar& s() { return m_s; }
+    EIGEN_DEVICE_FUNC Scalar s() const { return m_s; }
+
+    /** Concatenates two planar rotation */
+    EIGEN_DEVICE_FUNC
+    JacobiRotation operator*(const JacobiRotation& other)
+    {
+      using numext::conj;
+      return JacobiRotation(m_c * other.m_c - conj(m_s) * other.m_s,
+                            conj(m_c * conj(other.m_s) + conj(m_s) * conj(other.m_c)));
+    }
+
+    /** Returns the transposed transformation */
+    EIGEN_DEVICE_FUNC
+    JacobiRotation transpose() const { using numext::conj; return JacobiRotation(m_c, -conj(m_s)); }
+
+    /** Returns the adjoint transformation */
+    EIGEN_DEVICE_FUNC
+    JacobiRotation adjoint() const { using numext::conj; return JacobiRotation(conj(m_c), -m_s); }
+
+    template<typename Derived>
+    EIGEN_DEVICE_FUNC
+    bool makeJacobi(const MatrixBase<Derived>&, Index p, Index q);
+    EIGEN_DEVICE_FUNC
+    bool makeJacobi(const RealScalar& x, const Scalar& y, const RealScalar& z);
+
+    EIGEN_DEVICE_FUNC
+    void makeGivens(const Scalar& p, const Scalar& q, Scalar* r=0);
+
+  protected:
+    EIGEN_DEVICE_FUNC
+    void makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::true_type);
+    EIGEN_DEVICE_FUNC
+    void makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::false_type);
+
+    Scalar m_c, m_s;
+};
+
+/** Makes \c *this as a Jacobi rotation \a J such that applying \a J on both the right and left sides of the selfadjoint 2x2 matrix
+  * \f$ B = \left ( \begin{array}{cc} x & y \\ \overline y & z \end{array} \right )\f$ yields a diagonal matrix \f$ A = J^* B J \f$
+  *
+  * \sa MatrixBase::makeJacobi(const MatrixBase<Derived>&, Index, Index), MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
+  */
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+bool JacobiRotation<Scalar>::makeJacobi(const RealScalar& x, const Scalar& y, const RealScalar& z)
+{
+  using std::sqrt;
+  using std::abs;
+
+  RealScalar deno = RealScalar(2)*abs(y);
+  if(deno < (std::numeric_limits<RealScalar>::min)())
+  {
+    m_c = Scalar(1);
+    m_s = Scalar(0);
+    return false;
+  }
+  else
+  {
+    RealScalar tau = (x-z)/deno;
+    RealScalar w = sqrt(numext::abs2(tau) + RealScalar(1));
+    RealScalar t;
+    if(tau>RealScalar(0))
+    {
+      t = RealScalar(1) / (tau + w);
+    }
+    else
+    {
+      t = RealScalar(1) / (tau - w);
+    }
+    RealScalar sign_t = t > RealScalar(0) ? RealScalar(1) : RealScalar(-1);
+    RealScalar n = RealScalar(1) / sqrt(numext::abs2(t)+RealScalar(1));
+    m_s = - sign_t * (numext::conj(y) / abs(y)) * abs(t) * n;
+    m_c = n;
+    return true;
+  }
+}
+
+/** Makes \c *this as a Jacobi rotation \c J such that applying \a J on both the right and left sides of the 2x2 selfadjoint matrix
+  * \f$ B = \left ( \begin{array}{cc} \text{this}_{pp} & \text{this}_{pq} \\ (\text{this}_{pq})^* & \text{this}_{qq} \end{array} \right )\f$ yields
+  * a diagonal matrix \f$ A = J^* B J \f$
+  *
+  * Example: \include Jacobi_makeJacobi.cpp
+  * Output: \verbinclude Jacobi_makeJacobi.out
+  *
+  * \sa JacobiRotation::makeJacobi(RealScalar, Scalar, RealScalar), MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
+  */
+template<typename Scalar>
+template<typename Derived>
+EIGEN_DEVICE_FUNC
+inline bool JacobiRotation<Scalar>::makeJacobi(const MatrixBase<Derived>& m, Index p, Index q)
+{
+  return makeJacobi(numext::real(m.coeff(p,p)), m.coeff(p,q), numext::real(m.coeff(q,q)));
+}
+
+/** Makes \c *this as a Givens rotation \c G such that applying \f$ G^* \f$ to the left of the vector
+  * \f$ V = \left ( \begin{array}{c} p \\ q \end{array} \right )\f$ yields:
+  * \f$ G^* V = \left ( \begin{array}{c} r \\ 0 \end{array} \right )\f$.
+  *
+  * The value of \a r is returned if \a r is not null (the default is null).
+  * Also note that G is built such that the cosine is always real.
+  *
+  * Example: \include Jacobi_makeGivens.cpp
+  * Output: \verbinclude Jacobi_makeGivens.out
+  *
+  * This function implements the continuous Givens rotation generation algorithm
+  * found in Anderson (2000), Discontinuous Plane Rotations and the Symmetric Eigenvalue Problem.
+  * LAPACK Working Note 150, University of Tennessee, UT-CS-00-454, December 4, 2000.
+  *
+  * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
+  */
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* r)
+{
+  makeGivens(p, q, r, std::conditional_t<NumTraits<Scalar>::IsComplex, internal::true_type, internal::false_type>());
+}
+
+
+// specialization for complexes
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::true_type)
+{
+  using std::sqrt;
+  using std::abs;
+  using numext::conj;
+
+  if(q==Scalar(0))
+  {
+    m_c = numext::real(p)<0 ? Scalar(-1) : Scalar(1);
+    m_s = 0;
+    if(r) *r = m_c * p;
+  }
+  else if(p==Scalar(0))
+  {
+    m_c = 0;
+    m_s = -q/abs(q);
+    if(r) *r = abs(q);
+  }
+  else
+  {
+    RealScalar p1 = numext::norm1(p);
+    RealScalar q1 = numext::norm1(q);
+    if(p1>=q1)
+    {
+      Scalar ps = p / p1;
+      RealScalar p2 = numext::abs2(ps);
+      Scalar qs = q / p1;
+      RealScalar q2 = numext::abs2(qs);
+
+      RealScalar u = sqrt(RealScalar(1) + q2/p2);
+      if(numext::real(p)<RealScalar(0))
+        u = -u;
+
+      m_c = Scalar(1)/u;
+      m_s = -qs*conj(ps)*(m_c/p2);
+      if(r) *r = p * u;
+    }
+    else
+    {
+      Scalar ps = p / q1;
+      RealScalar p2 = numext::abs2(ps);
+      Scalar qs = q / q1;
+      RealScalar q2 = numext::abs2(qs);
+
+      RealScalar u = q1 * sqrt(p2 + q2);
+      if(numext::real(p)<RealScalar(0))
+        u = -u;
+
+      p1 = abs(p);
+      ps = p/p1;
+      m_c = p1/u;
+      m_s = -conj(ps) * (q/u);
+      if(r) *r = ps * u;
+    }
+  }
+}
+
+// specialization for reals
+template<typename Scalar>
+EIGEN_DEVICE_FUNC
+void JacobiRotation<Scalar>::makeGivens(const Scalar& p, const Scalar& q, Scalar* r, internal::false_type)
+{
+  using std::sqrt;
+  using std::abs;
+  if(numext::is_exactly_zero(q))
+  {
+    m_c = p<Scalar(0) ? Scalar(-1) : Scalar(1);
+    m_s = Scalar(0);
+    if(r) *r = abs(p);
+  }
+  else if(numext::is_exactly_zero(p))
+  {
+    m_c = Scalar(0);
+    m_s = q<Scalar(0) ? Scalar(1) : Scalar(-1);
+    if(r) *r = abs(q);
+  }
+  else if(abs(p) > abs(q))
+  {
+    Scalar t = q/p;
+    Scalar u = sqrt(Scalar(1) + numext::abs2(t));
+    if(p<Scalar(0))
+      u = -u;
+    m_c = Scalar(1)/u;
+    m_s = -t * m_c;
+    if(r) *r = p * u;
+  }
+  else
+  {
+    Scalar t = p/q;
+    Scalar u = sqrt(Scalar(1) + numext::abs2(t));
+    if(q<Scalar(0))
+      u = -u;
+    m_s = -Scalar(1)/u;
+    m_c = -t * m_s;
+    if(r) *r = q * u;
+  }
+
+}
+
+/****************************************************************************************
+*   Implementation of MatrixBase methods
+****************************************************************************************/
+
+namespace internal {
+/** \jacobi_module
+  * Applies the clock wise 2D rotation \a j to the set of 2D vectors of coordinates \a x and \a y:
+  * \f$ \left ( \begin{array}{cc} x \\ y \end{array} \right )  =  J \left ( \begin{array}{cc} x \\ y \end{array} \right ) \f$
+  *
+  * \sa MatrixBase::applyOnTheLeft(), MatrixBase::applyOnTheRight()
+  */
+template<typename VectorX, typename VectorY, typename OtherScalar>
+EIGEN_DEVICE_FUNC
+void apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x, DenseBase<VectorY>& xpr_y, const JacobiRotation<OtherScalar>& j);
+}
+
+/** \jacobi_module
+  * Applies the rotation in the plane \a j to the rows \a p and \a q of \c *this, i.e., it computes B = J * B,
+  * with \f$ B = \left ( \begin{array}{cc} \text{*this.row}(p) \\ \text{*this.row}(q) \end{array} \right ) \f$.
+  *
+  * \sa class JacobiRotation, MatrixBase::applyOnTheRight(), internal::apply_rotation_in_the_plane()
+  */
+template<typename Derived>
+template<typename OtherScalar>
+EIGEN_DEVICE_FUNC
+inline void MatrixBase<Derived>::applyOnTheLeft(Index p, Index q, const JacobiRotation<OtherScalar>& j)
+{
+  RowXpr x(this->row(p));
+  RowXpr y(this->row(q));
+  internal::apply_rotation_in_the_plane(x, y, j);
+}
+
+/** \ingroup Jacobi_Module
+  * Applies the rotation in the plane \a j to the columns \a p and \a q of \c *this, i.e., it computes B = B * J
+  * with \f$ B = \left ( \begin{array}{cc} \text{*this.col}(p) & \text{*this.col}(q) \end{array} \right ) \f$.
+  *
+  * \sa class JacobiRotation, MatrixBase::applyOnTheLeft(), internal::apply_rotation_in_the_plane()
+  */
+template<typename Derived>
+template<typename OtherScalar>
+EIGEN_DEVICE_FUNC
+inline void MatrixBase<Derived>::applyOnTheRight(Index p, Index q, const JacobiRotation<OtherScalar>& j)
+{
+  ColXpr x(this->col(p));
+  ColXpr y(this->col(q));
+  internal::apply_rotation_in_the_plane(x, y, j.transpose());
+}
+
+namespace internal {
+
+template<typename Scalar, typename OtherScalar,
+         int SizeAtCompileTime, int MinAlignment, bool Vectorizable>
+struct apply_rotation_in_the_plane_selector
+{
+  static EIGEN_DEVICE_FUNC
+  inline void run(Scalar *x, Index incrx, Scalar *y, Index incry, Index size, OtherScalar c, OtherScalar s)
+  {
+    for(Index i=0; i<size; ++i)
+    {
+      Scalar xi = *x;
+      Scalar yi = *y;
+      *x =  c * xi + numext::conj(s) * yi;
+      *y = -s * xi + numext::conj(c) * yi;
+      x += incrx;
+      y += incry;
+    }
+  }
+};
+
+template<typename Scalar, typename OtherScalar,
+         int SizeAtCompileTime, int MinAlignment>
+struct apply_rotation_in_the_plane_selector<Scalar,OtherScalar,SizeAtCompileTime,MinAlignment,true /* vectorizable */>
+{
+  static inline void run(Scalar *x, Index incrx, Scalar *y, Index incry, Index size, OtherScalar c, OtherScalar s)
+  {
+    typedef typename packet_traits<Scalar>::type Packet;
+    typedef typename packet_traits<OtherScalar>::type OtherPacket;
+
+    constexpr int RequiredAlignment =
+        (std::max)(unpacket_traits<Packet>::alignment, unpacket_traits<OtherPacket>::alignment);
+    constexpr Index PacketSize = packet_traits<Scalar>::size;
+
+    /*** dynamic-size vectorized paths ***/
+    if(size >= 2 * PacketSize && SizeAtCompileTime == Dynamic && ((incrx == 1 && incry == 1) || PacketSize == 1))
+    {
+      // both vectors are sequentially stored in memory => vectorization
+      constexpr Index Peeling = 2;
+
+      Index alignedStart = internal::first_default_aligned(y, size);
+      Index alignedEnd = alignedStart + ((size-alignedStart)/PacketSize)*PacketSize;
+
+      const OtherPacket pc = pset1<OtherPacket>(c);
+      const OtherPacket ps = pset1<OtherPacket>(s);
+      conj_helper<OtherPacket,Packet,NumTraits<OtherScalar>::IsComplex,false> pcj;
+      conj_helper<OtherPacket,Packet,false,false> pm;
+
+      for(Index i=0; i<alignedStart; ++i)
+      {
+        Scalar xi = x[i];
+        Scalar yi = y[i];
+        x[i] =  c * xi + numext::conj(s) * yi;
+        y[i] = -s * xi + numext::conj(c) * yi;
+      }
+
+      Scalar* EIGEN_RESTRICT px = x + alignedStart;
+      Scalar* EIGEN_RESTRICT py = y + alignedStart;
+
+      if(internal::first_default_aligned(x, size)==alignedStart)
+      {
+        for(Index i=alignedStart; i<alignedEnd; i+=PacketSize)
+        {
+          Packet xi = pload<Packet>(px);
+          Packet yi = pload<Packet>(py);
+          pstore(px, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
+          pstore(py, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
+          px += PacketSize;
+          py += PacketSize;
+        }
+      }
+      else
+      {
+        Index peelingEnd = alignedStart + ((size-alignedStart)/(Peeling*PacketSize))*(Peeling*PacketSize);
+        for(Index i=alignedStart; i<peelingEnd; i+=Peeling*PacketSize)
+        {
+          Packet xi   = ploadu<Packet>(px);
+          Packet xi1  = ploadu<Packet>(px+PacketSize);
+          Packet yi   = pload <Packet>(py);
+          Packet yi1  = pload <Packet>(py+PacketSize);
+          pstoreu(px, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
+          pstoreu(px+PacketSize, padd(pm.pmul(pc,xi1),pcj.pmul(ps,yi1)));
+          pstore (py, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
+          pstore (py+PacketSize, psub(pcj.pmul(pc,yi1),pm.pmul(ps,xi1)));
+          px += Peeling*PacketSize;
+          py += Peeling*PacketSize;
+        }
+        if(alignedEnd!=peelingEnd)
+        {
+          Packet xi = ploadu<Packet>(x+peelingEnd);
+          Packet yi = pload <Packet>(y+peelingEnd);
+          pstoreu(x+peelingEnd, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
+          pstore (y+peelingEnd, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
+        }
+      }
+
+      for(Index i=alignedEnd; i<size; ++i)
+      {
+        Scalar xi = x[i];
+        Scalar yi = y[i];
+        x[i] =  c * xi + numext::conj(s) * yi;
+        y[i] = -s * xi + numext::conj(c) * yi;
+      }
+    }
+
+    /*** fixed-size vectorized path ***/
+    else if(SizeAtCompileTime != Dynamic && MinAlignment >= RequiredAlignment)
+    {
+      const OtherPacket pc = pset1<OtherPacket>(c);
+      const OtherPacket ps = pset1<OtherPacket>(s);
+      conj_helper<OtherPacket,Packet,NumTraits<OtherScalar>::IsComplex,false> pcj;
+      conj_helper<OtherPacket,Packet,false,false> pm;
+      Scalar* EIGEN_RESTRICT px = x;
+      Scalar* EIGEN_RESTRICT py = y;
+      for(Index i=0; i<size; i+=PacketSize)
+      {
+        Packet xi = pload<Packet>(px);
+        Packet yi = pload<Packet>(py);
+        pstore(px, padd(pm.pmul(pc,xi),pcj.pmul(ps,yi)));
+        pstore(py, psub(pcj.pmul(pc,yi),pm.pmul(ps,xi)));
+        px += PacketSize;
+        py += PacketSize;
+      }
+    }
+
+    /*** non-vectorized path ***/
+    else
+    {
+      apply_rotation_in_the_plane_selector<Scalar,OtherScalar,SizeAtCompileTime,MinAlignment,false>::run(x,incrx,y,incry,size,c,s);
+    }
+  }
+};
+
+template<typename VectorX, typename VectorY, typename OtherScalar>
+EIGEN_DEVICE_FUNC
+void inline apply_rotation_in_the_plane(DenseBase<VectorX>& xpr_x, DenseBase<VectorY>& xpr_y, const JacobiRotation<OtherScalar>& j)
+{
+  typedef typename VectorX::Scalar Scalar;
+  constexpr bool Vectorizable = (int(evaluator<VectorX>::Flags) & int(evaluator<VectorY>::Flags) & PacketAccessBit) &&
+                                (int(packet_traits<Scalar>::size) == int(packet_traits<OtherScalar>::size));
+
+  eigen_assert(xpr_x.size() == xpr_y.size());
+  Index size = xpr_x.size();
+  Index incrx = xpr_x.derived().innerStride();
+  Index incry = xpr_y.derived().innerStride();
+
+  Scalar* EIGEN_RESTRICT x = &xpr_x.derived().coeffRef(0);
+  Scalar* EIGEN_RESTRICT y = &xpr_y.derived().coeffRef(0);
+
+  OtherScalar c = j.c();
+  OtherScalar s = j.s();
+  if (numext::is_exactly_one(c) && numext::is_exactly_zero(s))
+    return;
+
+  constexpr int Alignment = (std::min)(int(evaluator<VectorX>::Alignment), int(evaluator<VectorY>::Alignment));
+  apply_rotation_in_the_plane_selector<Scalar, OtherScalar, VectorX::SizeAtCompileTime, Alignment, Vectorizable>::run(
+      x, incrx, y, incry, size, c, s);
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_JACOBI_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/KLUSupport/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/KLUSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..eb1d67159f28b62387fe7eebbd81c79abc223a1d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/KLUSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_KLUSUPPORT_MODULE_H
+#error "Please include Eigen/KLUSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/KLUSupport/KLUSupport.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/KLUSupport/KLUSupport.h
new file mode 100644
index 0000000000000000000000000000000000000000..bfe2f669ed41664aa075f789515b557fbc6a21b7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/KLUSupport/KLUSupport.h
@@ -0,0 +1,360 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Kyle Macfarlan <kyle.macfarlan@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_KLUSUPPORT_H
+#define EIGEN_KLUSUPPORT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/* TODO extract L, extract U, compute det, etc... */
+
+/** \ingroup KLUSupport_Module
+  * \brief A sparse LU factorization and solver based on KLU
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a LU factorization
+  * using the KLU library. The sparse matrix A must be squared and full rank.
+  * The vectors or matrices X and B can be either dense or sparse.
+  *
+  * \warning The input matrix A should be in a \b compressed and \b column-major form.
+  * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  *
+  * \implsparsesolverconcept
+  *
+  * \sa \ref TutorialSparseSolverConcept, class UmfPackLU, class SparseLU
+  */
+
+
+inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, double B [ ], klu_common *Common, double) {
+   return klu_solve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), B, Common);
+}
+
+inline int klu_solve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, std::complex<double>B[], klu_common *Common, std::complex<double>) {
+   return klu_z_solve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), &numext::real_ref(B[0]), Common);
+}
+
+inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, double B[], klu_common *Common, double) {
+   return klu_tsolve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), B, Common);
+}
+
+inline int klu_tsolve(klu_symbolic *Symbolic, klu_numeric *Numeric, Index ldim, Index nrhs, std::complex<double>B[], klu_common *Common, std::complex<double>) {
+   return klu_z_tsolve(Symbolic, Numeric, internal::convert_index<int>(ldim), internal::convert_index<int>(nrhs), &numext::real_ref(B[0]), 0, Common);
+}
+
+inline klu_numeric* klu_factor(int Ap [ ], int Ai [ ], double Ax [ ], klu_symbolic *Symbolic, klu_common *Common, double) {
+   return klu_factor(Ap, Ai, Ax, Symbolic, Common);
+}
+
+inline klu_numeric* klu_factor(int Ap[], int Ai[], std::complex<double> Ax[], klu_symbolic *Symbolic, klu_common *Common, std::complex<double>) {
+   return klu_z_factor(Ap, Ai, &numext::real_ref(Ax[0]), Symbolic, Common);
+}
+
+
+template<typename MatrixType_>
+class KLU : public SparseSolverBase<KLU<MatrixType_> >
+{
+  protected:
+    typedef SparseSolverBase<KLU<MatrixType_> > Base;
+    using Base::m_isInitialized;
+  public:
+    using Base::_solve_impl;
+    typedef MatrixType_ MatrixType;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    typedef Matrix<Scalar,Dynamic,1> Vector;
+    typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
+    typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
+    typedef SparseMatrix<Scalar> LUMatrixType;
+    typedef SparseMatrix<Scalar,ColMajor,int> KLUMatrixType;
+    typedef Ref<const KLUMatrixType, StandardCompressedFormat> KLUMatrixRef;
+    enum {
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+
+  public:
+
+    KLU()
+      : m_dummy(0,0), mp_matrix(m_dummy)
+    {
+      init();
+    }
+
+    template<typename InputMatrixType>
+    explicit KLU(const InputMatrixType& matrix)
+      : mp_matrix(matrix)
+    {
+      init();
+      compute(matrix);
+    }
+
+    ~KLU()
+    {
+      if(m_symbolic) klu_free_symbolic(&m_symbolic,&m_common);
+      if(m_numeric)  klu_free_numeric(&m_numeric,&m_common);
+    }
+
+    EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return mp_matrix.rows(); }
+    EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return mp_matrix.cols(); }
+
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful,
+      *          \c NumericalIssue if the matrix.appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+#if 0 // not implemented yet
+    inline const LUMatrixType& matrixL() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_l;
+    }
+
+    inline const LUMatrixType& matrixU() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_u;
+    }
+
+    inline const IntColVectorType& permutationP() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_p;
+    }
+
+    inline const IntRowVectorType& permutationQ() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_q;
+    }
+#endif
+    /** Computes the sparse Cholesky decomposition of \a matrix
+     *  Note that the matrix should be column-major, and in compressed format for best performance.
+     *  \sa SparseMatrix::makeCompressed().
+     */
+    template<typename InputMatrixType>
+    void compute(const InputMatrixType& matrix)
+    {
+      if(m_symbolic) klu_free_symbolic(&m_symbolic, &m_common);
+      if(m_numeric)  klu_free_numeric(&m_numeric, &m_common);
+      grab(matrix.derived());
+      analyzePattern_impl();
+      factorize_impl();
+    }
+
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      *
+      * \sa factorize(), compute()
+      */
+    template<typename InputMatrixType>
+    void analyzePattern(const InputMatrixType& matrix)
+    {
+      if(m_symbolic) klu_free_symbolic(&m_symbolic, &m_common);
+      if(m_numeric)  klu_free_numeric(&m_numeric, &m_common);
+
+      grab(matrix.derived());
+
+      analyzePattern_impl();
+    }
+
+
+    /** Provides access to the control settings array used by KLU.
+      *
+      * See KLU documentation for details.
+      */
+    inline const klu_common& kluCommon() const
+    {
+      return m_common;
+    }
+
+    /** Provides access to the control settings array used by UmfPack.
+      *
+      * If this array contains NaN's, the default values are used.
+      *
+      * See KLU documentation for details.
+      */
+    inline klu_common& kluCommon()
+    {
+      return m_common;
+    }
+
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed.
+      *
+      * \sa analyzePattern(), compute()
+      */
+    template<typename InputMatrixType>
+    void factorize(const InputMatrixType& matrix)
+    {
+      eigen_assert(m_analysisIsOk && "KLU: you must first call analyzePattern()");
+      if(m_numeric)
+        klu_free_numeric(&m_numeric,&m_common);
+
+      grab(matrix.derived());
+
+      factorize_impl();
+    }
+
+    /** \internal */
+    template<typename BDerived,typename XDerived>
+    bool _solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const;
+
+#if 0 // not implemented yet
+    Scalar determinant() const;
+
+    void extractData() const;
+#endif
+
+  protected:
+
+    void init()
+    {
+      m_info                  = InvalidInput;
+      m_isInitialized         = false;
+      m_numeric               = 0;
+      m_symbolic              = 0;
+      m_extractedDataAreDirty = true;
+
+      klu_defaults(&m_common);
+    }
+
+    void analyzePattern_impl()
+    {
+      m_info = InvalidInput;
+      m_analysisIsOk = false;
+      m_factorizationIsOk = false;
+      m_symbolic = klu_analyze(internal::convert_index<int>(mp_matrix.rows()),
+                                     const_cast<StorageIndex*>(mp_matrix.outerIndexPtr()), const_cast<StorageIndex*>(mp_matrix.innerIndexPtr()),
+                                     &m_common);
+      if (m_symbolic) {
+         m_isInitialized = true;
+         m_info = Success;
+         m_analysisIsOk = true;
+         m_extractedDataAreDirty = true;
+      }
+    }
+
+    void factorize_impl()
+    {
+
+      m_numeric = klu_factor(const_cast<StorageIndex*>(mp_matrix.outerIndexPtr()), const_cast<StorageIndex*>(mp_matrix.innerIndexPtr()), const_cast<Scalar*>(mp_matrix.valuePtr()),
+                                    m_symbolic, &m_common, Scalar());
+
+
+      m_info = m_numeric ? Success : NumericalIssue;
+      m_factorizationIsOk = m_numeric ? 1 : 0;
+      m_extractedDataAreDirty = true;
+    }
+
+    template<typename MatrixDerived>
+    void grab(const EigenBase<MatrixDerived> &A)
+    {
+      internal::destroy_at(&mp_matrix);
+      internal::construct_at(&mp_matrix, A.derived());
+    }
+
+    void grab(const KLUMatrixRef &A)
+    {
+      if(&(A.derived()) != &mp_matrix)
+      {
+        internal::destroy_at(&mp_matrix);
+        internal::construct_at(&mp_matrix, A);
+      }
+    }
+
+    // cached data to reduce reallocation, etc.
+#if 0 // not implemented yet
+    mutable LUMatrixType m_l;
+    mutable LUMatrixType m_u;
+    mutable IntColVectorType m_p;
+    mutable IntRowVectorType m_q;
+#endif
+
+    KLUMatrixType m_dummy;
+    KLUMatrixRef mp_matrix;
+
+    klu_numeric* m_numeric;
+    klu_symbolic* m_symbolic;
+    klu_common m_common;
+    mutable ComputationInfo m_info;
+    int m_factorizationIsOk;
+    int m_analysisIsOk;
+    mutable bool m_extractedDataAreDirty;
+
+  private:
+    KLU(const KLU& ) { }
+};
+
+#if 0 // not implemented yet
+template<typename MatrixType>
+void KLU<MatrixType>::extractData() const
+{
+  if (m_extractedDataAreDirty)
+  {
+     eigen_assert(false && "KLU: extractData Not Yet Implemented");
+
+    // get size of the data
+    int lnz, unz, rows, cols, nz_udiag;
+    umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar());
+
+    // allocate data
+    m_l.resize(rows,(std::min)(rows,cols));
+    m_l.resizeNonZeros(lnz);
+
+    m_u.resize((std::min)(rows,cols),cols);
+    m_u.resizeNonZeros(unz);
+
+    m_p.resize(rows);
+    m_q.resize(cols);
+
+    // extract
+    umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(),
+                        m_u.outerIndexPtr(), m_u.innerIndexPtr(), m_u.valuePtr(),
+                        m_p.data(), m_q.data(), 0, 0, 0, m_numeric);
+
+    m_extractedDataAreDirty = false;
+  }
+}
+
+template<typename MatrixType>
+typename KLU<MatrixType>::Scalar KLU<MatrixType>::determinant() const
+{
+  eigen_assert(false && "KLU: extractData Not Yet Implemented");
+  return Scalar();
+}
+#endif
+
+template<typename MatrixType>
+template<typename BDerived,typename XDerived>
+bool KLU<MatrixType>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const
+{
+  Index rhsCols = b.cols();
+  EIGEN_STATIC_ASSERT((XDerived::Flags&RowMajorBit)==0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()");
+
+  x = b;
+  int info = klu_solve(m_symbolic, m_numeric, b.rows(), rhsCols, x.const_cast_derived().data(), const_cast<klu_common*>(&m_common), Scalar());
+
+  m_info = info!=0 ? Success : NumericalIssue;
+  return true;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_KLUSUPPORT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/Determinant.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/Determinant.h
new file mode 100644
index 0000000000000000000000000000000000000000..80e695d12cd817db7923199257d9a273ea7f23a9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/Determinant.h
@@ -0,0 +1,119 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_DETERMINANT_H
+#define EIGEN_DETERMINANT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename Derived>
+EIGEN_DEVICE_FUNC
+inline const typename Derived::Scalar bruteforce_det3_helper
+(const MatrixBase<Derived>& matrix, int a, int b, int c)
+{
+  return matrix.coeff(0,a)
+         * (matrix.coeff(1,b) * matrix.coeff(2,c) - matrix.coeff(1,c) * matrix.coeff(2,b));
+}
+
+template<typename Derived,
+         int DeterminantType = Derived::RowsAtCompileTime
+> struct determinant_impl
+{
+  static inline typename traits<Derived>::Scalar run(const Derived& m)
+  {
+    if(Derived::ColsAtCompileTime==Dynamic && m.rows()==0)
+      return typename traits<Derived>::Scalar(1);
+    return m.partialPivLu().determinant();
+  }
+};
+
+template<typename Derived> struct determinant_impl<Derived, 1>
+{
+  static inline EIGEN_DEVICE_FUNC
+  typename traits<Derived>::Scalar run(const Derived& m)
+  {
+    return m.coeff(0,0);
+  }
+};
+
+template<typename Derived> struct determinant_impl<Derived, 2>
+{
+  static inline EIGEN_DEVICE_FUNC
+  typename traits<Derived>::Scalar run(const Derived& m)
+  {
+    return m.coeff(0,0) * m.coeff(1,1) - m.coeff(1,0) * m.coeff(0,1);
+  }
+};
+
+template<typename Derived> struct determinant_impl<Derived, 3>
+{
+  static inline EIGEN_DEVICE_FUNC
+  typename traits<Derived>::Scalar run(const Derived& m)
+  {
+    return bruteforce_det3_helper(m,0,1,2)
+          - bruteforce_det3_helper(m,1,0,2)
+          + bruteforce_det3_helper(m,2,0,1);
+  }
+};
+
+template<typename Derived> struct determinant_impl<Derived, 4>
+{
+  typedef typename traits<Derived>::Scalar Scalar;
+  static EIGEN_DEVICE_FUNC
+  Scalar run(const Derived& m)
+  {
+    Scalar d2_01 = det2(m, 0, 1);
+    Scalar d2_02 = det2(m, 0, 2);
+    Scalar d2_03 = det2(m, 0, 3);
+    Scalar d2_12 = det2(m, 1, 2);
+    Scalar d2_13 = det2(m, 1, 3);
+    Scalar d2_23 = det2(m, 2, 3);
+    Scalar d3_0 = det3(m, 1,d2_23, 2,d2_13, 3,d2_12);
+    Scalar d3_1 = det3(m, 0,d2_23, 2,d2_03, 3,d2_02);
+    Scalar d3_2 = det3(m, 0,d2_13, 1,d2_03, 3,d2_01);
+    Scalar d3_3 = det3(m, 0,d2_12, 1,d2_02, 2,d2_01);
+    return internal::pmadd(static_cast<Scalar>(-m(0,3)),d3_0, static_cast<Scalar>(m(1,3)*d3_1)) +
+           internal::pmadd(static_cast<Scalar>(-m(2,3)),d3_2, static_cast<Scalar>(m(3,3)*d3_3));
+  }
+protected:
+  static EIGEN_DEVICE_FUNC
+  Scalar det2(const Derived& m, Index i0, Index i1)
+  {
+    return m(i0,0) * m(i1,1) - m(i1,0) * m(i0,1);
+  }
+
+  static EIGEN_DEVICE_FUNC
+  Scalar det3(const Derived& m, Index i0, const Scalar& d0, Index i1, const Scalar& d1, Index i2, const Scalar& d2)
+  {
+    return internal::pmadd(m(i0,2), d0, internal::pmadd(static_cast<Scalar>(-m(i1,2)), d1, static_cast<Scalar>(m(i2,2)*d2)));
+  }
+};
+
+} // end namespace internal
+
+/** \lu_module
+  *
+  * \returns the determinant of this matrix
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC
+inline typename internal::traits<Derived>::Scalar MatrixBase<Derived>::determinant() const
+{
+  eigen_assert(rows() == cols());
+  typedef typename internal::nested_eval<Derived,Base::RowsAtCompileTime>::type Nested;
+  return internal::determinant_impl<internal::remove_all_t<Nested>>::run(derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_DETERMINANT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/FullPivLU.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/FullPivLU.h
new file mode 100644
index 0000000000000000000000000000000000000000..d02dcc10f29a745673bbfab0bbc4a14daf6eb48d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/FullPivLU.h
@@ -0,0 +1,877 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LU_H
+#define EIGEN_LU_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template<typename MatrixType_, typename PermutationIndex_> struct traits<FullPivLU<MatrixType_, PermutationIndex_> >
+ : traits<MatrixType_>
+{
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef PermutationIndex_ StorageIndex;
+  enum { Flags = 0 };
+};
+
+} // end namespace internal
+
+/** \ingroup LU_Module
+  *
+  * \class FullPivLU
+  *
+  * \brief LU decomposition of a matrix with complete pivoting, and related features
+  *
+  * \tparam MatrixType_ the type of the matrix of which we are computing the LU decomposition
+  *
+  * This class represents a LU decomposition of any matrix, with complete pivoting: the matrix A is
+  * decomposed as \f$ A = P^{-1} L U Q^{-1} \f$ where L is unit-lower-triangular, U is
+  * upper-triangular, and P and Q are permutation matrices. This is a rank-revealing LU
+  * decomposition. The eigenvalues (diagonal coefficients) of U are sorted in such a way that any
+  * zeros are at the end.
+  *
+  * This decomposition provides the generic approach to solving systems of linear equations, computing
+  * the rank, invertibility, inverse, kernel, and determinant.
+  *
+  * This LU decomposition is very stable and well tested with large matrices. However there are use cases where the SVD
+  * decomposition is inherently more stable and/or flexible. For example, when computing the kernel of a matrix,
+  * working with the SVD allows to select the smallest singular values of the matrix, something that
+  * the LU decomposition doesn't see.
+  *
+  * The data of the LU decomposition can be directly accessed through the methods matrixLU(),
+  * permutationP(), permutationQ().
+  *
+  * As an example, here is how the original matrix can be retrieved:
+  * \include class_FullPivLU.cpp
+  * Output: \verbinclude class_FullPivLU.out
+  *
+  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+  *
+  * \sa MatrixBase::fullPivLu(), MatrixBase::determinant(), MatrixBase::inverse()
+  */
+template<typename MatrixType_, typename PermutationIndex_> class FullPivLU
+  : public SolverBase<FullPivLU<MatrixType_, PermutationIndex_> >
+{
+  public:
+    typedef MatrixType_ MatrixType;
+    typedef SolverBase<FullPivLU> Base;
+    friend class SolverBase<FullPivLU>;
+
+    EIGEN_GENERIC_PUBLIC_INTERFACE(FullPivLU)
+    enum {
+      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+    using PermutationIndex = PermutationIndex_;
+    typedef typename internal::plain_row_type<MatrixType, PermutationIndex>::type IntRowVectorType;
+    typedef typename internal::plain_col_type<MatrixType, PermutationIndex>::type IntColVectorType;
+    typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime, PermutationIndex> PermutationQType;
+    typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime, PermutationIndex> PermutationPType;
+    typedef typename MatrixType::PlainObject PlainObject;
+
+    /**
+      * \brief Default Constructor.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via LU::compute(const MatrixType&).
+      */
+    FullPivLU();
+
+    /** \brief Default Constructor with memory preallocation
+      *
+      * Like the default constructor but with preallocation of the internal data
+      * according to the specified problem \a size.
+      * \sa FullPivLU()
+      */
+    FullPivLU(Index rows, Index cols);
+
+    /** Constructor.
+      *
+      * \param matrix the matrix of which to compute the LU decomposition.
+      *               It is required to be nonzero.
+      */
+    template<typename InputType>
+    explicit FullPivLU(const EigenBase<InputType>& matrix);
+
+    /** \brief Constructs a LU factorization from a given matrix
+      *
+      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
+      *
+      * \sa FullPivLU(const EigenBase&)
+      */
+    template<typename InputType>
+    explicit FullPivLU(EigenBase<InputType>& matrix);
+
+    /** Computes the LU decomposition of the given matrix.
+      *
+      * \param matrix the matrix of which to compute the LU decomposition.
+      *               It is required to be nonzero.
+      *
+      * \returns a reference to *this
+      */
+    template<typename InputType>
+    FullPivLU& compute(const EigenBase<InputType>& matrix) {
+      m_lu = matrix.derived();
+      computeInPlace();
+      return *this;
+    }
+
+    /** \returns the LU decomposition matrix: the upper-triangular part is U, the
+      * unit-lower-triangular part is L (at least for square matrices; in the non-square
+      * case, special care is needed, see the documentation of class FullPivLU).
+      *
+      * \sa matrixL(), matrixU()
+      */
+    inline const MatrixType& matrixLU() const
+    {
+      eigen_assert(m_isInitialized && "LU is not initialized.");
+      return m_lu;
+    }
+
+    /** \returns the number of nonzero pivots in the LU decomposition.
+      * Here nonzero is meant in the exact sense, not in a fuzzy sense.
+      * So that notion isn't really intrinsically interesting, but it is
+      * still useful when implementing algorithms.
+      *
+      * \sa rank()
+      */
+    inline Index nonzeroPivots() const
+    {
+      eigen_assert(m_isInitialized && "LU is not initialized.");
+      return m_nonzero_pivots;
+    }
+
+    /** \returns the absolute value of the biggest pivot, i.e. the biggest
+      *          diagonal coefficient of U.
+      */
+    RealScalar maxPivot() const { return m_maxpivot; }
+
+    /** \returns the permutation matrix P
+      *
+      * \sa permutationQ()
+      */
+    EIGEN_DEVICE_FUNC inline const PermutationPType& permutationP() const
+    {
+      eigen_assert(m_isInitialized && "LU is not initialized.");
+      return m_p;
+    }
+
+    /** \returns the permutation matrix Q
+      *
+      * \sa permutationP()
+      */
+    inline const PermutationQType& permutationQ() const
+    {
+      eigen_assert(m_isInitialized && "LU is not initialized.");
+      return m_q;
+    }
+
+    /** \returns the kernel of the matrix, also called its null-space. The columns of the returned matrix
+      * will form a basis of the kernel.
+      *
+      * \note If the kernel has dimension zero, then the returned matrix is a column-vector filled with zeros.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      *
+      * Example: \include FullPivLU_kernel.cpp
+      * Output: \verbinclude FullPivLU_kernel.out
+      *
+      * \sa image()
+      */
+    inline const internal::kernel_retval<FullPivLU> kernel() const
+    {
+      eigen_assert(m_isInitialized && "LU is not initialized.");
+      return internal::kernel_retval<FullPivLU>(*this);
+    }
+
+    /** \returns the image of the matrix, also called its column-space. The columns of the returned matrix
+      * will form a basis of the image (column-space).
+      *
+      * \param originalMatrix the original matrix, of which *this is the LU decomposition.
+      *                       The reason why it is needed to pass it here, is that this allows
+      *                       a large optimization, as otherwise this method would need to reconstruct it
+      *                       from the LU decomposition.
+      *
+      * \note If the image has dimension zero, then the returned matrix is a column-vector filled with zeros.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      *
+      * Example: \include FullPivLU_image.cpp
+      * Output: \verbinclude FullPivLU_image.out
+      *
+      * \sa kernel()
+      */
+    inline const internal::image_retval<FullPivLU>
+      image(const MatrixType& originalMatrix) const
+    {
+      eigen_assert(m_isInitialized && "LU is not initialized.");
+      return internal::image_retval<FullPivLU>(*this, originalMatrix);
+    }
+
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
+    /** \return a solution x to the equation Ax=b, where A is the matrix of which
+      * *this is the LU decomposition.
+      *
+      * \param b the right-hand-side of the equation to solve. Can be a vector or a matrix,
+      *          the only requirement in order for the equation to make sense is that
+      *          b.rows()==A.rows(), where A is the matrix of which *this is the LU decomposition.
+      *
+      * \returns a solution.
+      *
+      * \note_about_checking_solutions
+      *
+      * \note_about_arbitrary_choice_of_solution
+      * \note_about_using_kernel_to_study_multiple_solutions
+      *
+      * Example: \include FullPivLU_solve.cpp
+      * Output: \verbinclude FullPivLU_solve.out
+      *
+      * \sa TriangularView::solve(), kernel(), inverse()
+      */
+    template<typename Rhs>
+    inline const Solve<FullPivLU, Rhs>
+    solve(const MatrixBase<Rhs>& b) const;
+    #endif
+
+    /** \returns an estimate of the reciprocal condition number of the matrix of which \c *this is
+        the LU decomposition.
+      */
+    inline RealScalar rcond() const
+    {
+      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
+      return internal::rcond_estimate_helper(m_l1_norm, *this);
+    }
+
+    /** \returns the determinant of the matrix of which
+      * *this is the LU decomposition. It has only linear complexity
+      * (that is, O(n) where n is the dimension of the square matrix)
+      * as the LU decomposition has already been computed.
+      *
+      * \note This is only for square matrices.
+      *
+      * \note For fixed-size matrices of size up to 4, MatrixBase::determinant() offers
+      *       optimized paths.
+      *
+      * \warning a determinant can be very big or small, so for matrices
+      * of large enough dimension, there is a risk of overflow/underflow.
+      *
+      * \sa MatrixBase::determinant()
+      */
+    typename internal::traits<MatrixType>::Scalar determinant() const;
+
+    /** Allows to prescribe a threshold to be used by certain methods, such as rank(),
+      * who need to determine when pivots are to be considered nonzero. This is not used for the
+      * LU decomposition itself.
+      *
+      * When it needs to get the threshold value, Eigen calls threshold(). By default, this
+      * uses a formula to automatically determine a reasonable threshold.
+      * Once you have called the present method setThreshold(const RealScalar&),
+      * your value is used instead.
+      *
+      * \param threshold The new value to use as the threshold.
+      *
+      * A pivot will be considered nonzero if its absolute value is strictly greater than
+      *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
+      * where maxpivot is the biggest pivot.
+      *
+      * If you want to come back to the default behavior, call setThreshold(Default_t)
+      */
+    FullPivLU& setThreshold(const RealScalar& threshold)
+    {
+      m_usePrescribedThreshold = true;
+      m_prescribedThreshold = threshold;
+      return *this;
+    }
+
+    /** Allows to come back to the default behavior, letting Eigen use its default formula for
+      * determining the threshold.
+      *
+      * You should pass the special object Eigen::Default as parameter here.
+      * \code lu.setThreshold(Eigen::Default); \endcode
+      *
+      * See the documentation of setThreshold(const RealScalar&).
+      */
+    FullPivLU& setThreshold(Default_t)
+    {
+      m_usePrescribedThreshold = false;
+      return *this;
+    }
+
+    /** Returns the threshold that will be used by certain methods such as rank().
+      *
+      * See the documentation of setThreshold(const RealScalar&).
+      */
+    RealScalar threshold() const
+    {
+      eigen_assert(m_isInitialized || m_usePrescribedThreshold);
+      return m_usePrescribedThreshold ? m_prescribedThreshold
+      // this formula comes from experimenting (see "LU precision tuning" thread on the list)
+      // and turns out to be identical to Higham's formula used already in LDLt.
+          : NumTraits<Scalar>::epsilon() * RealScalar(m_lu.diagonalSize());
+    }
+
+    /** \returns the rank of the matrix of which *this is the LU decomposition.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      */
+    inline Index rank() const
+    {
+      using std::abs;
+      eigen_assert(m_isInitialized && "LU is not initialized.");
+      RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold();
+      Index result = 0;
+      for(Index i = 0; i < m_nonzero_pivots; ++i)
+        result += (abs(m_lu.coeff(i,i)) > premultiplied_threshold);
+      return result;
+    }
+
+    /** \returns the dimension of the kernel of the matrix of which *this is the LU decomposition.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      */
+    inline Index dimensionOfKernel() const
+    {
+      eigen_assert(m_isInitialized && "LU is not initialized.");
+      return cols() - rank();
+    }
+
+    /** \returns true if the matrix of which *this is the LU decomposition represents an injective
+      *          linear map, i.e. has trivial kernel; false otherwise.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      */
+    inline bool isInjective() const
+    {
+      eigen_assert(m_isInitialized && "LU is not initialized.");
+      return rank() == cols();
+    }
+
+    /** \returns true if the matrix of which *this is the LU decomposition represents a surjective
+      *          linear map; false otherwise.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      */
+    inline bool isSurjective() const
+    {
+      eigen_assert(m_isInitialized && "LU is not initialized.");
+      return rank() == rows();
+    }
+
+    /** \returns true if the matrix of which *this is the LU decomposition is invertible.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      */
+    inline bool isInvertible() const
+    {
+      eigen_assert(m_isInitialized && "LU is not initialized.");
+      return isInjective() && (m_lu.rows() == m_lu.cols());
+    }
+
+    /** \returns the inverse of the matrix of which *this is the LU decomposition.
+      *
+      * \note If this matrix is not invertible, the returned matrix has undefined coefficients.
+      *       Use isInvertible() to first determine whether this matrix is invertible.
+      *
+      * \sa MatrixBase::inverse()
+      */
+    inline const Inverse<FullPivLU> inverse() const
+    {
+      eigen_assert(m_isInitialized && "LU is not initialized.");
+      eigen_assert(m_lu.rows() == m_lu.cols() && "You can't take the inverse of a non-square matrix!");
+      return Inverse<FullPivLU>(*this);
+    }
+
+    MatrixType reconstructedMatrix() const;
+
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index rows() const EIGEN_NOEXCEPT { return m_lu.rows(); }
+    EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR
+    inline Index cols() const EIGEN_NOEXCEPT { return m_lu.cols(); }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename RhsType, typename DstType>
+    void _solve_impl(const RhsType &rhs, DstType &dst) const;
+
+    template<bool Conjugate, typename RhsType, typename DstType>
+    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
+    #endif
+
+  protected:
+
+    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+    void computeInPlace();
+
+    MatrixType m_lu;
+    PermutationPType m_p;
+    PermutationQType m_q;
+    IntColVectorType m_rowsTranspositions;
+    IntRowVectorType m_colsTranspositions;
+    Index m_nonzero_pivots;
+    RealScalar m_l1_norm;
+    RealScalar m_maxpivot, m_prescribedThreshold;
+    signed char m_det_pq;
+    bool m_isInitialized, m_usePrescribedThreshold;
+};
+
+template<typename MatrixType, typename PermutationIndex>
+FullPivLU<MatrixType, PermutationIndex>::FullPivLU()
+  : m_isInitialized(false), m_usePrescribedThreshold(false)
+{
+}
+
+template<typename MatrixType, typename PermutationIndex>
+FullPivLU<MatrixType, PermutationIndex>::FullPivLU(Index rows, Index cols)
+  : m_lu(rows, cols),
+    m_p(rows),
+    m_q(cols),
+    m_rowsTranspositions(rows),
+    m_colsTranspositions(cols),
+    m_isInitialized(false),
+    m_usePrescribedThreshold(false)
+{
+}
+
+template<typename MatrixType, typename PermutationIndex>
+template<typename InputType>
+FullPivLU<MatrixType, PermutationIndex>::FullPivLU(const EigenBase<InputType>& matrix)
+  : m_lu(matrix.rows(), matrix.cols()),
+    m_p(matrix.rows()),
+    m_q(matrix.cols()),
+    m_rowsTranspositions(matrix.rows()),
+    m_colsTranspositions(matrix.cols()),
+    m_isInitialized(false),
+    m_usePrescribedThreshold(false)
+{
+  compute(matrix.derived());
+}
+
+template<typename MatrixType, typename PermutationIndex>
+template<typename InputType>
+FullPivLU<MatrixType, PermutationIndex>::FullPivLU(EigenBase<InputType>& matrix)
+  : m_lu(matrix.derived()),
+    m_p(matrix.rows()),
+    m_q(matrix.cols()),
+    m_rowsTranspositions(matrix.rows()),
+    m_colsTranspositions(matrix.cols()),
+    m_isInitialized(false),
+    m_usePrescribedThreshold(false)
+{
+  computeInPlace();
+}
+
+template<typename MatrixType, typename PermutationIndex>
+void FullPivLU<MatrixType, PermutationIndex>::computeInPlace()
+{
+  eigen_assert(m_lu.rows()<=NumTraits<PermutationIndex>::highest() && m_lu.cols()<=NumTraits<PermutationIndex>::highest());
+
+  m_l1_norm = m_lu.cwiseAbs().colwise().sum().maxCoeff();
+
+  const Index size = m_lu.diagonalSize();
+  const Index rows = m_lu.rows();
+  const Index cols = m_lu.cols();
+
+  // will store the transpositions, before we accumulate them at the end.
+  // can't accumulate on-the-fly because that will be done in reverse order for the rows.
+  m_rowsTranspositions.resize(m_lu.rows());
+  m_colsTranspositions.resize(m_lu.cols());
+  Index number_of_transpositions = 0; // number of NONTRIVIAL transpositions, i.e. m_rowsTranspositions[i]!=i
+
+  m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
+  m_maxpivot = RealScalar(0);
+
+  for(Index k = 0; k < size; ++k)
+  {
+    // First, we need to find the pivot.
+
+    // biggest coefficient in the remaining bottom-right corner (starting at row k, col k)
+    Index row_of_biggest_in_corner, col_of_biggest_in_corner;
+    typedef internal::scalar_score_coeff_op<Scalar> Scoring;
+    typedef typename Scoring::result_type Score;
+    Score biggest_in_corner;
+    biggest_in_corner = m_lu.bottomRightCorner(rows-k, cols-k)
+                        .unaryExpr(Scoring())
+                        .maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner);
+    row_of_biggest_in_corner += k; // correct the values! since they were computed in the corner,
+    col_of_biggest_in_corner += k; // need to add k to them.
+
+    if(numext::is_exactly_zero(biggest_in_corner))
+    {
+      // before exiting, make sure to initialize the still uninitialized transpositions
+      // in a sane state without destroying what we already have.
+      m_nonzero_pivots = k;
+      for(Index i = k; i < size; ++i)
+      {
+        m_rowsTranspositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
+        m_colsTranspositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
+      }
+      break;
+    }
+
+    RealScalar abs_pivot = internal::abs_knowing_score<Scalar>()(m_lu(row_of_biggest_in_corner, col_of_biggest_in_corner), biggest_in_corner);
+    if(abs_pivot > m_maxpivot) m_maxpivot = abs_pivot;
+
+    // Now that we've found the pivot, we need to apply the row/col swaps to
+    // bring it to the location (k,k).
+
+    m_rowsTranspositions.coeffRef(k) = internal::convert_index<StorageIndex>(row_of_biggest_in_corner);
+    m_colsTranspositions.coeffRef(k) = internal::convert_index<StorageIndex>(col_of_biggest_in_corner);
+    if(k != row_of_biggest_in_corner) {
+      m_lu.row(k).swap(m_lu.row(row_of_biggest_in_corner));
+      ++number_of_transpositions;
+    }
+    if(k != col_of_biggest_in_corner) {
+      m_lu.col(k).swap(m_lu.col(col_of_biggest_in_corner));
+      ++number_of_transpositions;
+    }
+
+    // Now that the pivot is at the right location, we update the remaining
+    // bottom-right corner by Gaussian elimination.
+
+    if(k<rows-1)
+      m_lu.col(k).tail(rows-k-1) /= m_lu.coeff(k,k);
+    if(k<size-1)
+      m_lu.block(k+1,k+1,rows-k-1,cols-k-1).noalias() -= m_lu.col(k).tail(rows-k-1) * m_lu.row(k).tail(cols-k-1);
+  }
+
+  // the main loop is over, we still have to accumulate the transpositions to find the
+  // permutations P and Q
+
+  m_p.setIdentity(rows);
+  for(Index k = size-1; k >= 0; --k)
+    m_p.applyTranspositionOnTheRight(k, m_rowsTranspositions.coeff(k));
+
+  m_q.setIdentity(cols);
+  for(Index k = 0; k < size; ++k)
+    m_q.applyTranspositionOnTheRight(k, m_colsTranspositions.coeff(k));
+
+  m_det_pq = (number_of_transpositions%2) ? -1 : 1;
+
+  m_isInitialized = true;
+}
+
+template<typename MatrixType, typename PermutationIndex>
+typename internal::traits<MatrixType>::Scalar FullPivLU<MatrixType, PermutationIndex>::determinant() const
+{
+  eigen_assert(m_isInitialized && "LU is not initialized.");
+  eigen_assert(m_lu.rows() == m_lu.cols() && "You can't take the determinant of a non-square matrix!");
+  return Scalar(m_det_pq) * Scalar(m_lu.diagonal().prod());
+}
+
+/** \returns the matrix represented by the decomposition,
+ * i.e., it returns the product: \f$ P^{-1} L U Q^{-1} \f$.
+ * This function is provided for debug purposes. */
+template<typename MatrixType, typename PermutationIndex>
+MatrixType FullPivLU<MatrixType, PermutationIndex>::reconstructedMatrix() const
+{
+  eigen_assert(m_isInitialized && "LU is not initialized.");
+  const Index smalldim = (std::min)(m_lu.rows(), m_lu.cols());
+  // LU
+  MatrixType res(m_lu.rows(),m_lu.cols());
+  // FIXME the .toDenseMatrix() should not be needed...
+  res = m_lu.leftCols(smalldim)
+            .template triangularView<UnitLower>().toDenseMatrix()
+      * m_lu.topRows(smalldim)
+            .template triangularView<Upper>().toDenseMatrix();
+
+  // P^{-1}(LU)
+  res = m_p.inverse() * res;
+
+  // (P^{-1}LU)Q^{-1}
+  res = res * m_q.inverse();
+
+  return res;
+}
+
+/********* Implementation of kernel() **************************************************/
+
+namespace internal {
+template<typename MatrixType_, typename PermutationIndex_>
+struct kernel_retval<FullPivLU<MatrixType_, PermutationIndex_> >
+  : kernel_retval_base<FullPivLU<MatrixType_, PermutationIndex_> >
+{
+  using DecompositionType = FullPivLU<MatrixType_, PermutationIndex_>;
+  EIGEN_MAKE_KERNEL_HELPERS(DecompositionType)
+
+  enum { MaxSmallDimAtCompileTime = min_size_prefer_fixed(
+              MatrixType::MaxColsAtCompileTime,
+              MatrixType::MaxRowsAtCompileTime)
+  };
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    using std::abs;
+    const Index cols = dec().matrixLU().cols(), dimker = cols - rank();
+    if(dimker == 0)
+    {
+      // The Kernel is just {0}, so it doesn't have a basis properly speaking, but let's
+      // avoid crashing/asserting as that depends on floating point calculations. Let's
+      // just return a single column vector filled with zeros.
+      dst.setZero();
+      return;
+    }
+
+    /* Let us use the following lemma:
+      *
+      * Lemma: If the matrix A has the LU decomposition PAQ = LU,
+      * then Ker A = Q(Ker U).
+      *
+      * Proof: trivial: just keep in mind that P, Q, L are invertible.
+      */
+
+    /* Thus, all we need to do is to compute Ker U, and then apply Q.
+      *
+      * U is upper triangular, with eigenvalues sorted so that any zeros appear at the end.
+      * Thus, the diagonal of U ends with exactly
+      * dimKer zero's. Let us use that to construct dimKer linearly
+      * independent vectors in Ker U.
+      */
+
+    Matrix<Index, Dynamic, 1, 0, MaxSmallDimAtCompileTime, 1> pivots(rank());
+    RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold();
+    Index p = 0;
+    for(Index i = 0; i < dec().nonzeroPivots(); ++i)
+      if(abs(dec().matrixLU().coeff(i,i)) > premultiplied_threshold)
+        pivots.coeffRef(p++) = i;
+    eigen_internal_assert(p == rank());
+
+    // we construct a temporaty trapezoid matrix m, by taking the U matrix and
+    // permuting the rows and cols to bring the nonnegligible pivots to the top of
+    // the main diagonal. We need that to be able to apply our triangular solvers.
+    // FIXME when we get triangularView-for-rectangular-matrices, this can be simplified
+    Matrix<typename MatrixType::Scalar, Dynamic, Dynamic, MatrixType::Options,
+           MaxSmallDimAtCompileTime, MatrixType::MaxColsAtCompileTime>
+      m(dec().matrixLU().block(0, 0, rank(), cols));
+    for(Index i = 0; i < rank(); ++i)
+    {
+      if(i) m.row(i).head(i).setZero();
+      m.row(i).tail(cols-i) = dec().matrixLU().row(pivots.coeff(i)).tail(cols-i);
+    }
+    m.block(0, 0, rank(), rank());
+    m.block(0, 0, rank(), rank()).template triangularView<StrictlyLower>().setZero();
+    for(Index i = 0; i < rank(); ++i)
+      m.col(i).swap(m.col(pivots.coeff(i)));
+
+    // ok, we have our trapezoid matrix, we can apply the triangular solver.
+    // notice that the math behind this suggests that we should apply this to the
+    // negative of the RHS, but for performance we just put the negative sign elsewhere, see below.
+    m.topLeftCorner(rank(), rank())
+     .template triangularView<Upper>().solveInPlace(
+        m.topRightCorner(rank(), dimker)
+      );
+
+    // now we must undo the column permutation that we had applied!
+    for(Index i = rank()-1; i >= 0; --i)
+      m.col(i).swap(m.col(pivots.coeff(i)));
+
+    // see the negative sign in the next line, that's what we were talking about above.
+    for(Index i = 0; i < rank(); ++i) dst.row(dec().permutationQ().indices().coeff(i)) = -m.row(i).tail(dimker);
+    for(Index i = rank(); i < cols; ++i) dst.row(dec().permutationQ().indices().coeff(i)).setZero();
+    for(Index k = 0; k < dimker; ++k) dst.coeffRef(dec().permutationQ().indices().coeff(rank()+k), k) = Scalar(1);
+  }
+};
+
+/***** Implementation of image() *****************************************************/
+
+template<typename MatrixType_, typename PermutationIndex_>
+struct image_retval<FullPivLU<MatrixType_, PermutationIndex_> >
+  : image_retval_base<FullPivLU<MatrixType_, PermutationIndex_> >
+{
+  using DecompositionType = FullPivLU<MatrixType_, PermutationIndex_>;
+  EIGEN_MAKE_IMAGE_HELPERS(DecompositionType)
+
+  enum { MaxSmallDimAtCompileTime = min_size_prefer_fixed(
+              MatrixType::MaxColsAtCompileTime,
+              MatrixType::MaxRowsAtCompileTime)
+  };
+
+  template<typename Dest> void evalTo(Dest& dst) const
+  {
+    using std::abs;
+    if(rank() == 0)
+    {
+      // The Image is just {0}, so it doesn't have a basis properly speaking, but let's
+      // avoid crashing/asserting as that depends on floating point calculations. Let's
+      // just return a single column vector filled with zeros.
+      dst.setZero();
+      return;
+    }
+
+    Matrix<Index, Dynamic, 1, 0, MaxSmallDimAtCompileTime, 1> pivots(rank());
+    RealScalar premultiplied_threshold = dec().maxPivot() * dec().threshold();
+    Index p = 0;
+    for(Index i = 0; i < dec().nonzeroPivots(); ++i)
+      if(abs(dec().matrixLU().coeff(i,i)) > premultiplied_threshold)
+        pivots.coeffRef(p++) = i;
+    eigen_internal_assert(p == rank());
+
+    for(Index i = 0; i < rank(); ++i)
+      dst.col(i) = originalMatrix().col(dec().permutationQ().indices().coeff(pivots.coeff(i)));
+  }
+};
+
+/***** Implementation of solve() *****************************************************/
+
+} // end namespace internal
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename MatrixType_, typename PermutationIndex_>
+template<typename RhsType, typename DstType>
+void FullPivLU<MatrixType_, PermutationIndex_>::_solve_impl(const RhsType &rhs, DstType &dst) const
+{
+  /* The decomposition PAQ = LU can be rewritten as A = P^{-1} L U Q^{-1}.
+  * So we proceed as follows:
+  * Step 1: compute c = P * rhs.
+  * Step 2: replace c by the solution x to Lx = c. Exists because L is invertible.
+  * Step 3: replace c by the solution x to Ux = c. May or may not exist.
+  * Step 4: result = Q * c;
+  */
+
+  const Index rows = this->rows(),
+              cols = this->cols(),
+              nonzero_pivots = this->rank();
+  const Index smalldim = (std::min)(rows, cols);
+
+  if(nonzero_pivots == 0)
+  {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(rhs.rows(), rhs.cols());
+
+  // Step 1
+  c = permutationP() * rhs;
+
+  // Step 2
+  m_lu.topLeftCorner(smalldim,smalldim)
+      .template triangularView<UnitLower>()
+      .solveInPlace(c.topRows(smalldim));
+  if(rows>cols)
+    c.bottomRows(rows-cols) -= m_lu.bottomRows(rows-cols) * c.topRows(cols);
+
+  // Step 3
+  m_lu.topLeftCorner(nonzero_pivots, nonzero_pivots)
+      .template triangularView<Upper>()
+      .solveInPlace(c.topRows(nonzero_pivots));
+
+  // Step 4
+  for(Index i = 0; i < nonzero_pivots; ++i)
+    dst.row(permutationQ().indices().coeff(i)) = c.row(i);
+  for(Index i = nonzero_pivots; i < m_lu.cols(); ++i)
+    dst.row(permutationQ().indices().coeff(i)).setZero();
+}
+
+template<typename MatrixType_, typename PermutationIndex_>
+template<bool Conjugate, typename RhsType, typename DstType>
+void FullPivLU<MatrixType_, PermutationIndex_>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
+{
+  /* The decomposition PAQ = LU can be rewritten as A = P^{-1} L U Q^{-1},
+   * and since permutations are real and unitary, we can write this
+   * as   A^T = Q U^T L^T P,
+   * So we proceed as follows:
+   * Step 1: compute c = Q^T rhs.
+   * Step 2: replace c by the solution x to U^T x = c. May or may not exist.
+   * Step 3: replace c by the solution x to L^T x = c.
+   * Step 4: result = P^T c.
+   * If Conjugate is true, replace "^T" by "^*" above.
+   */
+
+  const Index rows = this->rows(), cols = this->cols(),
+    nonzero_pivots = this->rank();
+  const Index smalldim = (std::min)(rows, cols);
+
+  if(nonzero_pivots == 0)
+  {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(rhs.rows(), rhs.cols());
+
+  // Step 1
+  c = permutationQ().inverse() * rhs;
+
+  // Step 2
+  m_lu.topLeftCorner(nonzero_pivots, nonzero_pivots)
+      .template triangularView<Upper>()
+      .transpose()
+      .template conjugateIf<Conjugate>()
+      .solveInPlace(c.topRows(nonzero_pivots));
+
+  // Step 3
+  m_lu.topLeftCorner(smalldim, smalldim)
+      .template triangularView<UnitLower>()
+      .transpose()
+      .template conjugateIf<Conjugate>()
+      .solveInPlace(c.topRows(smalldim));
+
+  // Step 4
+  PermutationPType invp = permutationP().inverse().eval();
+  for(Index i = 0; i < smalldim; ++i)
+    dst.row(invp.indices().coeff(i)) = c.row(i);
+  for(Index i = smalldim; i < rows; ++i)
+    dst.row(invp.indices().coeff(i)).setZero();
+}
+
+#endif
+
+namespace internal {
+
+
+/***** Implementation of inverse() *****************************************************/
+template<typename DstXprType, typename MatrixType, typename PermutationIndex>
+struct Assignment<DstXprType, Inverse<FullPivLU<MatrixType, PermutationIndex> >, internal::assign_op<typename DstXprType::Scalar,typename FullPivLU<MatrixType, PermutationIndex>::Scalar>, Dense2Dense>
+{
+  typedef FullPivLU<MatrixType, PermutationIndex> LuType;
+  typedef Inverse<LuType> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename MatrixType::Scalar> &)
+  {
+    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
+  }
+};
+} // end namespace internal
+
+/******* MatrixBase methods *****************************************************************/
+
+/** \lu_module
+  *
+  * \return the full-pivoting LU decomposition of \c *this.
+  *
+  * \sa class FullPivLU
+  */
+template<typename Derived>
+template<typename PermutationIndex>
+inline const FullPivLU<typename MatrixBase<Derived>::PlainObject, PermutationIndex>
+MatrixBase<Derived>::fullPivLu() const
+{
+  return FullPivLU<PlainObject, PermutationIndex>(eval());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_LU_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..f346b17957ca853aa92bcd767394bcfd88144d15
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_LU_MODULE_H
+#error "Please include Eigen/LU instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/InverseImpl.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/InverseImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..bcfe7031e4202b13bf93e334794f2859e42a4355
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/InverseImpl.h
@@ -0,0 +1,434 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_INVERSE_IMPL_H
+#define EIGEN_INVERSE_IMPL_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/**********************************
+*** General case implementation ***
+**********************************/
+
+template<typename MatrixType, typename ResultType, int Size = MatrixType::RowsAtCompileTime>
+struct compute_inverse
+{
+  EIGEN_DEVICE_FUNC
+  static inline void run(const MatrixType& matrix, ResultType& result)
+  {
+    result = matrix.partialPivLu().inverse();
+  }
+};
+
+template<typename MatrixType, typename ResultType, int Size = MatrixType::RowsAtCompileTime>
+struct compute_inverse_and_det_with_check { /* nothing! general case not supported. */ };
+
+/****************************
+*** Size 1 implementation ***
+****************************/
+
+template<typename MatrixType, typename ResultType>
+struct compute_inverse<MatrixType, ResultType, 1>
+{
+  EIGEN_DEVICE_FUNC
+  static inline void run(const MatrixType& matrix, ResultType& result)
+  {
+    typedef typename MatrixType::Scalar Scalar;
+    internal::evaluator<MatrixType> matrixEval(matrix);
+    result.coeffRef(0,0) = Scalar(1) / matrixEval.coeff(0,0);
+  }
+};
+
+template<typename MatrixType, typename ResultType>
+struct compute_inverse_and_det_with_check<MatrixType, ResultType, 1>
+{
+  EIGEN_DEVICE_FUNC
+  static inline void run(
+    const MatrixType& matrix,
+    const typename MatrixType::RealScalar& absDeterminantThreshold,
+    ResultType& result,
+    typename ResultType::Scalar& determinant,
+    bool& invertible
+  )
+  {
+    using std::abs;
+    determinant = matrix.coeff(0,0);
+    invertible = abs(determinant) > absDeterminantThreshold;
+    if(invertible) result.coeffRef(0,0) = typename ResultType::Scalar(1) / determinant;
+  }
+};
+
+/****************************
+*** Size 2 implementation ***
+****************************/
+
+template<typename MatrixType, typename ResultType>
+EIGEN_DEVICE_FUNC 
+inline void compute_inverse_size2_helper(
+    const MatrixType& matrix, const typename ResultType::Scalar& invdet,
+    ResultType& result)
+{
+  typename ResultType::Scalar temp = matrix.coeff(0,0);
+  result.coeffRef(0,0) =  matrix.coeff(1,1) * invdet;
+  result.coeffRef(1,0) = -matrix.coeff(1,0) * invdet;
+  result.coeffRef(0,1) = -matrix.coeff(0,1) * invdet;
+  result.coeffRef(1,1) =  temp * invdet;
+}
+
+template<typename MatrixType, typename ResultType>
+struct compute_inverse<MatrixType, ResultType, 2>
+{
+  EIGEN_DEVICE_FUNC
+  static inline void run(const MatrixType& matrix, ResultType& result)
+  {
+    typedef typename ResultType::Scalar Scalar;
+    const Scalar invdet = typename MatrixType::Scalar(1) / matrix.determinant();
+    compute_inverse_size2_helper(matrix, invdet, result);
+  }
+};
+
+template<typename MatrixType, typename ResultType>
+struct compute_inverse_and_det_with_check<MatrixType, ResultType, 2>
+{
+  EIGEN_DEVICE_FUNC
+  static inline void run(
+    const MatrixType& matrix,
+    const typename MatrixType::RealScalar& absDeterminantThreshold,
+    ResultType& inverse,
+    typename ResultType::Scalar& determinant,
+    bool& invertible
+  )
+  {
+    using std::abs;
+    typedef typename ResultType::Scalar Scalar;
+    determinant = matrix.determinant();
+    invertible = abs(determinant) > absDeterminantThreshold;
+    if(!invertible) return;
+    const Scalar invdet = Scalar(1) / determinant;
+    compute_inverse_size2_helper(matrix, invdet, inverse);
+  }
+};
+
+/****************************
+*** Size 3 implementation ***
+****************************/
+
+template<typename MatrixType, int i, int j>
+EIGEN_DEVICE_FUNC 
+inline typename MatrixType::Scalar cofactor_3x3(const MatrixType& m)
+{
+  enum {
+    i1 = (i+1) % 3,
+    i2 = (i+2) % 3,
+    j1 = (j+1) % 3,
+    j2 = (j+2) % 3
+  };
+  return m.coeff(i1, j1) * m.coeff(i2, j2)
+       - m.coeff(i1, j2) * m.coeff(i2, j1);
+}
+
+template<typename MatrixType, typename ResultType>
+EIGEN_DEVICE_FUNC
+inline void compute_inverse_size3_helper(
+    const MatrixType& matrix,
+    const typename ResultType::Scalar& invdet,
+    const Matrix<typename ResultType::Scalar,3,1>& cofactors_col0,
+    ResultType& result)
+{
+  // Compute cofactors in a way that avoids aliasing issues.
+  typedef typename ResultType::Scalar Scalar;
+  const Scalar c01 = cofactor_3x3<MatrixType,0,1>(matrix) * invdet;
+  const Scalar c11 = cofactor_3x3<MatrixType,1,1>(matrix) * invdet;
+  const Scalar c02 = cofactor_3x3<MatrixType,0,2>(matrix) * invdet;
+  result.coeffRef(1,2) =  cofactor_3x3<MatrixType,2,1>(matrix) * invdet;
+  result.coeffRef(2,1) =  cofactor_3x3<MatrixType,1,2>(matrix) * invdet;
+  result.coeffRef(2,2) =  cofactor_3x3<MatrixType,2,2>(matrix) * invdet;
+  result.coeffRef(1,0) =  c01;
+  result.coeffRef(1,1) =  c11;
+  result.coeffRef(2,0) =  c02;  
+  result.row(0) = cofactors_col0 * invdet;
+}
+
+template<typename MatrixType, typename ResultType>
+struct compute_inverse<MatrixType, ResultType, 3>
+{
+  EIGEN_DEVICE_FUNC
+  static inline void run(const MatrixType& matrix, ResultType& result)
+  {
+    typedef typename ResultType::Scalar Scalar;
+    Matrix<typename MatrixType::Scalar,3,1> cofactors_col0;
+    cofactors_col0.coeffRef(0) =  cofactor_3x3<MatrixType,0,0>(matrix);
+    cofactors_col0.coeffRef(1) =  cofactor_3x3<MatrixType,1,0>(matrix);
+    cofactors_col0.coeffRef(2) =  cofactor_3x3<MatrixType,2,0>(matrix);
+    const Scalar det = (cofactors_col0.cwiseProduct(matrix.col(0))).sum();
+    const Scalar invdet = Scalar(1) / det;
+    compute_inverse_size3_helper(matrix, invdet, cofactors_col0, result);
+  }
+};
+
+template<typename MatrixType, typename ResultType>
+struct compute_inverse_and_det_with_check<MatrixType, ResultType, 3>
+{
+  EIGEN_DEVICE_FUNC
+  static inline void run(
+    const MatrixType& matrix,
+    const typename MatrixType::RealScalar& absDeterminantThreshold,
+    ResultType& inverse,
+    typename ResultType::Scalar& determinant,
+    bool& invertible
+  )
+  {
+    typedef typename ResultType::Scalar Scalar;
+    Matrix<Scalar,3,1> cofactors_col0;
+    cofactors_col0.coeffRef(0) =  cofactor_3x3<MatrixType,0,0>(matrix);
+    cofactors_col0.coeffRef(1) =  cofactor_3x3<MatrixType,1,0>(matrix);
+    cofactors_col0.coeffRef(2) =  cofactor_3x3<MatrixType,2,0>(matrix);
+    determinant = (cofactors_col0.cwiseProduct(matrix.col(0))).sum();
+    invertible = Eigen::numext::abs(determinant) > absDeterminantThreshold;
+    if(!invertible) return;
+    const Scalar invdet = Scalar(1) / determinant;
+    compute_inverse_size3_helper(matrix, invdet, cofactors_col0, inverse);
+  }
+};
+
+/****************************
+*** Size 4 implementation ***
+****************************/
+
+template<typename Derived>
+EIGEN_DEVICE_FUNC 
+inline const typename Derived::Scalar general_det3_helper
+(const MatrixBase<Derived>& matrix, int i1, int i2, int i3, int j1, int j2, int j3)
+{
+  return matrix.coeff(i1,j1)
+         * (matrix.coeff(i2,j2) * matrix.coeff(i3,j3) - matrix.coeff(i2,j3) * matrix.coeff(i3,j2));
+}
+
+template<typename MatrixType, int i, int j>
+EIGEN_DEVICE_FUNC 
+inline typename MatrixType::Scalar cofactor_4x4(const MatrixType& matrix)
+{
+  enum {
+    i1 = (i+1) % 4,
+    i2 = (i+2) % 4,
+    i3 = (i+3) % 4,
+    j1 = (j+1) % 4,
+    j2 = (j+2) % 4,
+    j3 = (j+3) % 4
+  };
+  return general_det3_helper(matrix, i1, i2, i3, j1, j2, j3)
+       + general_det3_helper(matrix, i2, i3, i1, j1, j2, j3)
+       + general_det3_helper(matrix, i3, i1, i2, j1, j2, j3);
+}
+
+template<int Arch, typename Scalar, typename MatrixType, typename ResultType>
+struct compute_inverse_size4
+{
+  EIGEN_DEVICE_FUNC
+  static void run(const MatrixType& matrix, ResultType& result)
+  {
+    result.coeffRef(0,0) =  cofactor_4x4<MatrixType,0,0>(matrix);
+    result.coeffRef(1,0) = -cofactor_4x4<MatrixType,0,1>(matrix);
+    result.coeffRef(2,0) =  cofactor_4x4<MatrixType,0,2>(matrix);
+    result.coeffRef(3,0) = -cofactor_4x4<MatrixType,0,3>(matrix);
+    result.coeffRef(0,2) =  cofactor_4x4<MatrixType,2,0>(matrix);
+    result.coeffRef(1,2) = -cofactor_4x4<MatrixType,2,1>(matrix);
+    result.coeffRef(2,2) =  cofactor_4x4<MatrixType,2,2>(matrix);
+    result.coeffRef(3,2) = -cofactor_4x4<MatrixType,2,3>(matrix);
+    result.coeffRef(0,1) = -cofactor_4x4<MatrixType,1,0>(matrix);
+    result.coeffRef(1,1) =  cofactor_4x4<MatrixType,1,1>(matrix);
+    result.coeffRef(2,1) = -cofactor_4x4<MatrixType,1,2>(matrix);
+    result.coeffRef(3,1) =  cofactor_4x4<MatrixType,1,3>(matrix);
+    result.coeffRef(0,3) = -cofactor_4x4<MatrixType,3,0>(matrix);
+    result.coeffRef(1,3) =  cofactor_4x4<MatrixType,3,1>(matrix);
+    result.coeffRef(2,3) = -cofactor_4x4<MatrixType,3,2>(matrix);
+    result.coeffRef(3,3) =  cofactor_4x4<MatrixType,3,3>(matrix);
+    result /= (matrix.col(0).cwiseProduct(result.row(0).transpose())).sum();
+  }
+};
+
+template<typename MatrixType, typename ResultType>
+struct compute_inverse<MatrixType, ResultType, 4>
+ : compute_inverse_size4<Architecture::Target, typename MatrixType::Scalar,
+                            MatrixType, ResultType>
+{
+};
+
+template<typename MatrixType, typename ResultType>
+struct compute_inverse_and_det_with_check<MatrixType, ResultType, 4>
+{
+  EIGEN_DEVICE_FUNC
+  static inline void run(
+    const MatrixType& matrix,
+    const typename MatrixType::RealScalar& absDeterminantThreshold,
+    ResultType& inverse,
+    typename ResultType::Scalar& determinant,
+    bool& invertible
+  )
+  {
+    using std::abs;
+    determinant = matrix.determinant();
+    invertible = abs(determinant) > absDeterminantThreshold;
+    if(invertible && extract_data(matrix) != extract_data(inverse)) {
+      compute_inverse<MatrixType, ResultType>::run(matrix, inverse);
+    }
+    else if(invertible) {
+      MatrixType matrix_t = matrix;
+      compute_inverse<MatrixType, ResultType>::run(matrix_t, inverse);
+    }
+  }
+};
+
+/*************************
+*** MatrixBase methods ***
+*************************/
+
+} // end namespace internal
+
+namespace internal {
+
+// Specialization for "dense = dense_xpr.inverse()"
+template<typename DstXprType, typename XprType>
+struct Assignment<DstXprType, Inverse<XprType>, internal::assign_op<typename DstXprType::Scalar,typename XprType::Scalar>, Dense2Dense>
+{
+  typedef Inverse<XprType> SrcXprType;
+  EIGEN_DEVICE_FUNC
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename XprType::Scalar> &)
+  {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+      dst.resize(dstRows, dstCols);
+    
+    const int Size = plain_enum_min(XprType::ColsAtCompileTime, DstXprType::ColsAtCompileTime);
+    EIGEN_ONLY_USED_FOR_DEBUG(Size);
+    eigen_assert(( (Size<=1) || (Size>4) || (extract_data(src.nestedExpression())!=extract_data(dst)))
+              && "Aliasing problem detected in inverse(), you need to do inverse().eval() here.");
+
+    typedef typename internal::nested_eval<XprType,XprType::ColsAtCompileTime>::type  ActualXprType;
+    typedef internal::remove_all_t<ActualXprType>                        ActualXprTypeCleanded;
+    
+    ActualXprType actual_xpr(src.nestedExpression());
+    
+    compute_inverse<ActualXprTypeCleanded, DstXprType>::run(actual_xpr, dst);
+  }
+};
+
+  
+} // end namespace internal
+
+/** \lu_module
+  *
+  * \returns the matrix inverse of this matrix.
+  *
+  * For small fixed sizes up to 4x4, this method uses cofactors.
+  * In the general case, this method uses class PartialPivLU.
+  *
+  * \note This matrix must be invertible, otherwise the result is undefined. If you need an
+  * invertibility check, do the following:
+  * \li for fixed sizes up to 4x4, use computeInverseAndDetWithCheck().
+  * \li for the general case, use class FullPivLU.
+  *
+  * Example: \include MatrixBase_inverse.cpp
+  * Output: \verbinclude MatrixBase_inverse.out
+  *
+  * \sa computeInverseAndDetWithCheck()
+  */
+template<typename Derived>
+EIGEN_DEVICE_FUNC
+inline const Inverse<Derived> MatrixBase<Derived>::inverse() const
+{
+  EIGEN_STATIC_ASSERT(!NumTraits<Scalar>::IsInteger,THIS_FUNCTION_IS_NOT_FOR_INTEGER_NUMERIC_TYPES)
+  eigen_assert(rows() == cols());
+  return Inverse<Derived>(derived());
+}
+
+/** \lu_module
+  *
+  * Computation of matrix inverse and determinant, with invertibility check.
+  *
+  * This is only for fixed-size square matrices of size up to 4x4.
+  *
+  * Notice that it will trigger a copy of input matrix when trying to do the inverse in place.
+  *
+  * \param inverse Reference to the matrix in which to store the inverse.
+  * \param determinant Reference to the variable in which to store the determinant.
+  * \param invertible Reference to the bool variable in which to store whether the matrix is invertible.
+  * \param absDeterminantThreshold Optional parameter controlling the invertibility check.
+  *                                The matrix will be declared invertible if the absolute value of its
+  *                                determinant is greater than this threshold.
+  *
+  * Example: \include MatrixBase_computeInverseAndDetWithCheck.cpp
+  * Output: \verbinclude MatrixBase_computeInverseAndDetWithCheck.out
+  *
+  * \sa inverse(), computeInverseWithCheck()
+  */
+template<typename Derived>
+template<typename ResultType>
+inline void MatrixBase<Derived>::computeInverseAndDetWithCheck(
+    ResultType& inverse,
+    typename ResultType::Scalar& determinant,
+    bool& invertible,
+    const RealScalar& absDeterminantThreshold
+  ) const
+{
+  // i'd love to put some static assertions there, but SFINAE means that they have no effect...
+  eigen_assert(rows() == cols());
+  // for 2x2, it's worth giving a chance to avoid evaluating.
+  // for larger sizes, evaluating has negligible cost and limits code size.
+  typedef std::conditional_t<
+    RowsAtCompileTime == 2,
+    internal::remove_all_t<typename internal::nested_eval<Derived, 2>::type>,
+    PlainObject
+  > MatrixType;
+  internal::compute_inverse_and_det_with_check<MatrixType, ResultType>::run
+    (derived(), absDeterminantThreshold, inverse, determinant, invertible);
+}
+
+/** \lu_module
+  *
+  * Computation of matrix inverse, with invertibility check.
+  *
+  * This is only for fixed-size square matrices of size up to 4x4.
+  *
+  * Notice that it will trigger a copy of input matrix when trying to do the inverse in place.
+  *
+  * \param inverse Reference to the matrix in which to store the inverse.
+  * \param invertible Reference to the bool variable in which to store whether the matrix is invertible.
+  * \param absDeterminantThreshold Optional parameter controlling the invertibility check.
+  *                                The matrix will be declared invertible if the absolute value of its
+  *                                determinant is greater than this threshold.
+  *
+  * Example: \include MatrixBase_computeInverseWithCheck.cpp
+  * Output: \verbinclude MatrixBase_computeInverseWithCheck.out
+  *
+  * \sa inverse(), computeInverseAndDetWithCheck()
+  */
+template<typename Derived>
+template<typename ResultType>
+inline void MatrixBase<Derived>::computeInverseWithCheck(
+    ResultType& inverse,
+    bool& invertible,
+    const RealScalar& absDeterminantThreshold
+  ) const
+{
+  Scalar determinant;
+  // i'd love to put some static assertions there, but SFINAE means that they have no effect...
+  eigen_assert(rows() == cols());
+  computeInverseAndDetWithCheck(inverse,determinant,invertible,absDeterminantThreshold);
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_INVERSE_IMPL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/PartialPivLU.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/PartialPivLU.h
new file mode 100644
index 0000000000000000000000000000000000000000..bee74379482412055f38c9257e73c3972bf3220b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/PartialPivLU.h
@@ -0,0 +1,623 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2006-2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PARTIALLU_H
+#define EIGEN_PARTIALLU_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template<typename MatrixType_, typename PermutationIndex_> struct traits<PartialPivLU<MatrixType_, PermutationIndex_> >
+ : traits<MatrixType_>
+{
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef PermutationIndex_ StorageIndex;
+  typedef traits<MatrixType_> BaseTraits;
+  enum {
+    Flags = BaseTraits::Flags & RowMajorBit,
+    CoeffReadCost = Dynamic
+  };
+};
+
+template<typename T,typename Derived>
+struct enable_if_ref;
+// {
+//   typedef Derived type;
+// };
+
+template<typename T,typename Derived>
+struct enable_if_ref<Ref<T>,Derived> {
+  typedef Derived type;
+};
+
+} // end namespace internal
+
+/** \ingroup LU_Module
+  *
+  * \class PartialPivLU
+  *
+  * \brief LU decomposition of a matrix with partial pivoting, and related features
+  *
+  * \tparam MatrixType_ the type of the matrix of which we are computing the LU decomposition
+  *
+  * This class represents a LU decomposition of a \b square \b invertible matrix, with partial pivoting: the matrix A
+  * is decomposed as A = PLU where L is unit-lower-triangular, U is upper-triangular, and P
+  * is a permutation matrix.
+  *
+  * Typically, partial pivoting LU decomposition is only considered numerically stable for square invertible
+  * matrices. Thus LAPACK's dgesv and dgesvx require the matrix to be square and invertible. The present class
+  * does the same. It will assert that the matrix is square, but it won't (actually it can't) check that the
+  * matrix is invertible: it is your task to check that you only use this decomposition on invertible matrices.
+  *
+  * The guaranteed safe alternative, working for all matrices, is the full pivoting LU decomposition, provided
+  * by class FullPivLU.
+  *
+  * This is \b not a rank-revealing LU decomposition. Many features are intentionally absent from this class,
+  * such as rank computation. If you need these features, use class FullPivLU.
+  *
+  * This LU decomposition is suitable to invert invertible matrices. It is what MatrixBase::inverse() uses
+  * in the general case.
+  * On the other hand, it is \b not suitable to determine whether a given matrix is invertible.
+  *
+  * The data of the LU decomposition can be directly accessed through the methods matrixLU(), permutationP().
+  *
+  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+  *
+  * \sa MatrixBase::partialPivLu(), MatrixBase::determinant(), MatrixBase::inverse(), MatrixBase::computeInverse(), class FullPivLU
+  */
+template<typename MatrixType_, typename PermutationIndex_> class PartialPivLU
+  : public SolverBase<PartialPivLU<MatrixType_, PermutationIndex_> >
+{
+  public:
+
+    typedef MatrixType_ MatrixType;
+    typedef SolverBase<PartialPivLU> Base;
+    friend class SolverBase<PartialPivLU>;
+
+    EIGEN_GENERIC_PUBLIC_INTERFACE(PartialPivLU)
+    enum {
+      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+    using PermutationIndex = PermutationIndex_;
+    typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime, PermutationIndex> PermutationType;
+    typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime, PermutationIndex> TranspositionType;
+    typedef typename MatrixType::PlainObject PlainObject;
+
+    /**
+      * \brief Default Constructor.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via PartialPivLU::compute(const MatrixType&).
+      */
+    PartialPivLU();
+
+    /** \brief Default Constructor with memory preallocation
+      *
+      * Like the default constructor but with preallocation of the internal data
+      * according to the specified problem \a size.
+      * \sa PartialPivLU()
+      */
+    explicit PartialPivLU(Index size);
+
+    /** Constructor.
+      *
+      * \param matrix the matrix of which to compute the LU decomposition.
+      *
+      * \warning The matrix should have full rank (e.g. if it's square, it should be invertible).
+      * If you need to deal with non-full rank, use class FullPivLU instead.
+      */
+    template<typename InputType>
+    explicit PartialPivLU(const EigenBase<InputType>& matrix);
+
+    /** Constructor for \link InplaceDecomposition inplace decomposition \endlink
+      *
+      * \param matrix the matrix of which to compute the LU decomposition.
+      *
+      * \warning The matrix should have full rank (e.g. if it's square, it should be invertible).
+      * If you need to deal with non-full rank, use class FullPivLU instead.
+      */
+    template<typename InputType>
+    explicit PartialPivLU(EigenBase<InputType>& matrix);
+
+    template<typename InputType>
+    PartialPivLU& compute(const EigenBase<InputType>& matrix) {
+      m_lu = matrix.derived();
+      compute();
+      return *this;
+    }
+
+    /** \returns the LU decomposition matrix: the upper-triangular part is U, the
+      * unit-lower-triangular part is L (at least for square matrices; in the non-square
+      * case, special care is needed, see the documentation of class FullPivLU).
+      *
+      * \sa matrixL(), matrixU()
+      */
+    inline const MatrixType& matrixLU() const
+    {
+      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
+      return m_lu;
+    }
+
+    /** \returns the permutation matrix P.
+      */
+    inline const PermutationType& permutationP() const
+    {
+      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
+      return m_p;
+    }
+
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
+    /** This method returns the solution x to the equation Ax=b, where A is the matrix of which
+      * *this is the LU decomposition.
+      *
+      * \param b the right-hand-side of the equation to solve. Can be a vector or a matrix,
+      *          the only requirement in order for the equation to make sense is that
+      *          b.rows()==A.rows(), where A is the matrix of which *this is the LU decomposition.
+      *
+      * \returns the solution.
+      *
+      * Example: \include PartialPivLU_solve.cpp
+      * Output: \verbinclude PartialPivLU_solve.out
+      *
+      * Since this PartialPivLU class assumes anyway that the matrix A is invertible, the solution
+      * theoretically exists and is unique regardless of b.
+      *
+      * \sa TriangularView::solve(), inverse(), computeInverse()
+      */
+    template<typename Rhs>
+    inline const Solve<PartialPivLU, Rhs>
+    solve(const MatrixBase<Rhs>& b) const;
+    #endif
+
+    /** \returns an estimate of the reciprocal condition number of the matrix of which \c *this is
+        the LU decomposition.
+      */
+    inline RealScalar rcond() const
+    {
+      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
+      return internal::rcond_estimate_helper(m_l1_norm, *this);
+    }
+
+    /** \returns the inverse of the matrix of which *this is the LU decomposition.
+      *
+      * \warning The matrix being decomposed here is assumed to be invertible. If you need to check for
+      *          invertibility, use class FullPivLU instead.
+      *
+      * \sa MatrixBase::inverse(), LU::inverse()
+      */
+    inline const Inverse<PartialPivLU> inverse() const
+    {
+      eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
+      return Inverse<PartialPivLU>(*this);
+    }
+
+    /** \returns the determinant of the matrix of which
+      * *this is the LU decomposition. It has only linear complexity
+      * (that is, O(n) where n is the dimension of the square matrix)
+      * as the LU decomposition has already been computed.
+      *
+      * \note For fixed-size matrices of size up to 4, MatrixBase::determinant() offers
+      *       optimized paths.
+      *
+      * \warning a determinant can be very big or small, so for matrices
+      * of large enough dimension, there is a risk of overflow/underflow.
+      *
+      * \sa MatrixBase::determinant()
+      */
+    Scalar determinant() const;
+
+    MatrixType reconstructedMatrix() const;
+
+    EIGEN_CONSTEXPR inline Index rows() const EIGEN_NOEXCEPT { return m_lu.rows(); }
+    EIGEN_CONSTEXPR inline Index cols() const EIGEN_NOEXCEPT { return m_lu.cols(); }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename RhsType, typename DstType>
+    EIGEN_DEVICE_FUNC
+    void _solve_impl(const RhsType &rhs, DstType &dst) const {
+     /* The decomposition PA = LU can be rewritten as A = P^{-1} L U.
+      * So we proceed as follows:
+      * Step 1: compute c = Pb.
+      * Step 2: replace c by the solution x to Lx = c.
+      * Step 3: replace c by the solution x to Ux = c.
+      */
+
+      // Step 1
+      dst = permutationP() * rhs;
+
+      // Step 2
+      m_lu.template triangularView<UnitLower>().solveInPlace(dst);
+
+      // Step 3
+      m_lu.template triangularView<Upper>().solveInPlace(dst);
+    }
+
+    template<bool Conjugate, typename RhsType, typename DstType>
+    EIGEN_DEVICE_FUNC
+    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const {
+     /* The decomposition PA = LU can be rewritten as A^T = U^T L^T P.
+      * So we proceed as follows:
+      * Step 1: compute c as the solution to L^T c = b
+      * Step 2: replace c by the solution x to U^T x = c.
+      * Step 3: update  c = P^-1 c.
+      */
+
+      eigen_assert(rhs.rows() == m_lu.cols());
+
+      // Step 1
+      dst = m_lu.template triangularView<Upper>().transpose()
+                .template conjugateIf<Conjugate>().solve(rhs);
+      // Step 2
+      m_lu.template triangularView<UnitLower>().transpose()
+          .template conjugateIf<Conjugate>().solveInPlace(dst);
+      // Step 3
+      dst = permutationP().transpose() * dst;
+    }
+    #endif
+
+  protected:
+
+    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+    void compute();
+
+    MatrixType m_lu;
+    PermutationType m_p;
+    TranspositionType m_rowsTranspositions;
+    RealScalar m_l1_norm;
+    signed char m_det_p;
+    bool m_isInitialized;
+};
+
+template<typename MatrixType, typename PermutationIndex>
+PartialPivLU<MatrixType, PermutationIndex>::PartialPivLU()
+  : m_lu(),
+    m_p(),
+    m_rowsTranspositions(),
+    m_l1_norm(0),
+    m_det_p(0),
+    m_isInitialized(false)
+{
+}
+
+template<typename MatrixType, typename PermutationIndex>
+PartialPivLU<MatrixType, PermutationIndex>::PartialPivLU(Index size)
+  : m_lu(size, size),
+    m_p(size),
+    m_rowsTranspositions(size),
+    m_l1_norm(0),
+    m_det_p(0),
+    m_isInitialized(false)
+{
+}
+
+template<typename MatrixType, typename PermutationIndex>
+template<typename InputType>
+PartialPivLU<MatrixType, PermutationIndex>::PartialPivLU(const EigenBase<InputType>& matrix)
+  : m_lu(matrix.rows(),matrix.cols()),
+    m_p(matrix.rows()),
+    m_rowsTranspositions(matrix.rows()),
+    m_l1_norm(0),
+    m_det_p(0),
+    m_isInitialized(false)
+{
+  compute(matrix.derived());
+}
+
+template<typename MatrixType, typename PermutationIndex>
+template<typename InputType>
+PartialPivLU<MatrixType, PermutationIndex>::PartialPivLU(EigenBase<InputType>& matrix)
+  : m_lu(matrix.derived()),
+    m_p(matrix.rows()),
+    m_rowsTranspositions(matrix.rows()),
+    m_l1_norm(0),
+    m_det_p(0),
+    m_isInitialized(false)
+{
+  compute();
+}
+
+namespace internal {
+
+/** \internal This is the blocked version of fullpivlu_unblocked() */
+template<typename Scalar, int StorageOrder, typename PivIndex, int SizeAtCompileTime=Dynamic>
+struct partial_lu_impl
+{
+  static constexpr int UnBlockedBound = 16;
+  static constexpr bool UnBlockedAtCompileTime = SizeAtCompileTime!=Dynamic && SizeAtCompileTime<=UnBlockedBound;
+  static constexpr int ActualSizeAtCompileTime = UnBlockedAtCompileTime ? SizeAtCompileTime : Dynamic;
+  // Remaining rows and columns at compile-time:
+  static constexpr int RRows = SizeAtCompileTime==2 ? 1 : Dynamic;
+  static constexpr int RCols = SizeAtCompileTime==2 ? 1 : Dynamic;
+  typedef Matrix<Scalar, ActualSizeAtCompileTime, ActualSizeAtCompileTime, StorageOrder> MatrixType;
+  typedef Ref<MatrixType> MatrixTypeRef;
+  typedef Ref<Matrix<Scalar, Dynamic, Dynamic, StorageOrder> > BlockType;
+  typedef typename MatrixType::RealScalar RealScalar;
+
+  /** \internal performs the LU decomposition in-place of the matrix \a lu
+    * using an unblocked algorithm.
+    *
+    * In addition, this function returns the row transpositions in the
+    * vector \a row_transpositions which must have a size equal to the number
+    * of columns of the matrix \a lu, and an integer \a nb_transpositions
+    * which returns the actual number of transpositions.
+    *
+    * \returns The index of the first pivot which is exactly zero if any, or a negative number otherwise.
+    */
+  static Index unblocked_lu(MatrixTypeRef& lu, PivIndex* row_transpositions, PivIndex& nb_transpositions)
+  {
+    typedef scalar_score_coeff_op<Scalar> Scoring;
+    typedef typename Scoring::result_type Score;
+    const Index rows = lu.rows();
+    const Index cols = lu.cols();
+    const Index size = (std::min)(rows,cols);
+    // For small compile-time matrices it is worth processing the last row separately:
+    //  speedup: +100% for 2x2, +10% for others.
+    const Index endk = UnBlockedAtCompileTime ? size-1 : size;
+    nb_transpositions = 0;
+    Index first_zero_pivot = -1;
+    for(Index k = 0; k < endk; ++k)
+    {
+      int rrows = internal::convert_index<int>(rows-k-1);
+      int rcols = internal::convert_index<int>(cols-k-1);
+
+      Index row_of_biggest_in_col;
+      Score biggest_in_corner
+        = lu.col(k).tail(rows-k).unaryExpr(Scoring()).maxCoeff(&row_of_biggest_in_col);
+      row_of_biggest_in_col += k;
+
+      row_transpositions[k] = PivIndex(row_of_biggest_in_col);
+
+      if(!numext::is_exactly_zero(biggest_in_corner))
+      {
+        if(k != row_of_biggest_in_col)
+        {
+          lu.row(k).swap(lu.row(row_of_biggest_in_col));
+          ++nb_transpositions;
+        }
+
+        lu.col(k).tail(fix<RRows>(rrows)) /= lu.coeff(k,k);
+      }
+      else if(first_zero_pivot==-1)
+      {
+        // the pivot is exactly zero, we record the index of the first pivot which is exactly 0,
+        // and continue the factorization such we still have A = PLU
+        first_zero_pivot = k;
+      }
+
+      if(k<rows-1)
+        lu.bottomRightCorner(fix<RRows>(rrows),fix<RCols>(rcols)).noalias() -= lu.col(k).tail(fix<RRows>(rrows)) * lu.row(k).tail(fix<RCols>(rcols));
+    }
+
+    // special handling of the last entry
+    if(UnBlockedAtCompileTime)
+    {
+      Index k = endk;
+      row_transpositions[k] = PivIndex(k);
+      if (numext::is_exactly_zero(Scoring()(lu(k, k))) && first_zero_pivot == -1)
+        first_zero_pivot = k;
+    }
+
+    return first_zero_pivot;
+  }
+
+  /** \internal performs the LU decomposition in-place of the matrix represented
+    * by the variables \a rows, \a cols, \a lu_data, and \a lu_stride using a
+    * recursive, blocked algorithm.
+    *
+    * In addition, this function returns the row transpositions in the
+    * vector \a row_transpositions which must have a size equal to the number
+    * of columns of the matrix \a lu, and an integer \a nb_transpositions
+    * which returns the actual number of transpositions.
+    *
+    * \returns The index of the first pivot which is exactly zero if any, or a negative number otherwise.
+    *
+    * \note This very low level interface using pointers, etc. is to:
+    *   1 - reduce the number of instantiations to the strict minimum
+    *   2 - avoid infinite recursion of the instantiations with Block<Block<Block<...> > >
+    */
+  static Index blocked_lu(Index rows, Index cols, Scalar* lu_data, Index luStride, PivIndex* row_transpositions, PivIndex& nb_transpositions, Index maxBlockSize=256)
+  {
+    MatrixTypeRef lu = MatrixType::Map(lu_data,rows, cols, OuterStride<>(luStride));
+
+    const Index size = (std::min)(rows,cols);
+
+    // if the matrix is too small, no blocking:
+    if(UnBlockedAtCompileTime || size<=UnBlockedBound)
+    {
+      return unblocked_lu(lu, row_transpositions, nb_transpositions);
+    }
+
+    // automatically adjust the number of subdivisions to the size
+    // of the matrix so that there is enough sub blocks:
+    Index blockSize;
+    {
+      blockSize = size/8;
+      blockSize = (blockSize/16)*16;
+      blockSize = (std::min)((std::max)(blockSize,Index(8)), maxBlockSize);
+    }
+
+    nb_transpositions = 0;
+    Index first_zero_pivot = -1;
+    for(Index k = 0; k < size; k+=blockSize)
+    {
+      Index bs = (std::min)(size-k,blockSize); // actual size of the block
+      Index trows = rows - k - bs; // trailing rows
+      Index tsize = size - k - bs; // trailing size
+
+      // partition the matrix:
+      //                          A00 | A01 | A02
+      // lu  = A_0 | A_1 | A_2 =  A10 | A11 | A12
+      //                          A20 | A21 | A22
+      BlockType A_0 = lu.block(0,0,rows,k);
+      BlockType A_2 = lu.block(0,k+bs,rows,tsize);
+      BlockType A11 = lu.block(k,k,bs,bs);
+      BlockType A12 = lu.block(k,k+bs,bs,tsize);
+      BlockType A21 = lu.block(k+bs,k,trows,bs);
+      BlockType A22 = lu.block(k+bs,k+bs,trows,tsize);
+
+      PivIndex nb_transpositions_in_panel;
+      // recursively call the blocked LU algorithm on [A11^T A21^T]^T
+      // with a very small blocking size:
+      Index ret = blocked_lu(trows+bs, bs, &lu.coeffRef(k,k), luStride,
+                   row_transpositions+k, nb_transpositions_in_panel, 16);
+      if(ret>=0 && first_zero_pivot==-1)
+        first_zero_pivot = k+ret;
+
+      nb_transpositions += nb_transpositions_in_panel;
+      // update permutations and apply them to A_0
+      for(Index i=k; i<k+bs; ++i)
+      {
+        Index piv = (row_transpositions[i] += internal::convert_index<PivIndex>(k));
+        A_0.row(i).swap(A_0.row(piv));
+      }
+
+      if(trows)
+      {
+        // apply permutations to A_2
+        for(Index i=k;i<k+bs; ++i)
+          A_2.row(i).swap(A_2.row(row_transpositions[i]));
+
+        // A12 = A11^-1 A12
+        A11.template triangularView<UnitLower>().solveInPlace(A12);
+
+        A22.noalias() -= A21 * A12;
+      }
+    }
+    return first_zero_pivot;
+  }
+};
+
+/** \internal performs the LU decomposition with partial pivoting in-place.
+  */
+template<typename MatrixType, typename TranspositionType>
+void partial_lu_inplace(MatrixType& lu, TranspositionType& row_transpositions, typename TranspositionType::StorageIndex& nb_transpositions)
+{
+  // Special-case of zero matrix.
+  if (lu.rows() == 0 || lu.cols() == 0) {
+    nb_transpositions = 0;
+    return;
+  }
+  eigen_assert(lu.cols() == row_transpositions.size());
+  eigen_assert(row_transpositions.size() < 2 || (&row_transpositions.coeffRef(1)-&row_transpositions.coeffRef(0)) == 1);
+
+  partial_lu_impl
+    < typename MatrixType::Scalar, MatrixType::Flags&RowMajorBit?RowMajor:ColMajor,
+      typename TranspositionType::StorageIndex,
+      internal::min_size_prefer_fixed(MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime)>
+    ::blocked_lu(lu.rows(), lu.cols(), &lu.coeffRef(0,0), lu.outerStride(), &row_transpositions.coeffRef(0), nb_transpositions);
+}
+
+} // end namespace internal
+
+template<typename MatrixType, typename PermutationIndex>
+void PartialPivLU<MatrixType, PermutationIndex>::compute()
+{
+  eigen_assert(m_lu.rows()<NumTraits<PermutationIndex>::highest());
+
+  if(m_lu.cols()>0)
+    m_l1_norm = m_lu.cwiseAbs().colwise().sum().maxCoeff();
+  else
+    m_l1_norm = RealScalar(0);
+
+  eigen_assert(m_lu.rows() == m_lu.cols() && "PartialPivLU is only for square (and moreover invertible) matrices");
+  const Index size = m_lu.rows();
+
+  m_rowsTranspositions.resize(size);
+
+  typename TranspositionType::StorageIndex nb_transpositions;
+  internal::partial_lu_inplace(m_lu, m_rowsTranspositions, nb_transpositions);
+  m_det_p = (nb_transpositions%2) ? -1 : 1;
+
+  m_p = m_rowsTranspositions;
+
+  m_isInitialized = true;
+}
+
+template<typename MatrixType, typename PermutationIndex>
+typename PartialPivLU<MatrixType, PermutationIndex>::Scalar PartialPivLU<MatrixType, PermutationIndex>::determinant() const
+{
+  eigen_assert(m_isInitialized && "PartialPivLU is not initialized.");
+  return Scalar(m_det_p) * m_lu.diagonal().prod();
+}
+
+/** \returns the matrix represented by the decomposition,
+ * i.e., it returns the product: P^{-1} L U.
+ * This function is provided for debug purpose. */
+template<typename MatrixType, typename PermutationIndex>
+MatrixType PartialPivLU<MatrixType, PermutationIndex>::reconstructedMatrix() const
+{
+  eigen_assert(m_isInitialized && "LU is not initialized.");
+  // LU
+  MatrixType res = m_lu.template triangularView<UnitLower>().toDenseMatrix()
+                 * m_lu.template triangularView<Upper>();
+
+  // P^{-1}(LU)
+  res = m_p.inverse() * res;
+
+  return res;
+}
+
+/***** Implementation details *****************************************************/
+
+namespace internal {
+
+/***** Implementation of inverse() *****************************************************/
+template<typename DstXprType, typename MatrixType, typename PermutationIndex>
+struct Assignment<DstXprType, Inverse<PartialPivLU<MatrixType, PermutationIndex> >, internal::assign_op<typename DstXprType::Scalar,typename PartialPivLU<MatrixType, PermutationIndex>::Scalar>, Dense2Dense>
+{
+  typedef PartialPivLU<MatrixType, PermutationIndex> LuType;
+  typedef Inverse<LuType> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename LuType::Scalar> &)
+  {
+    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
+  }
+};
+} // end namespace internal
+
+/******** MatrixBase methods *******/
+
+/** \lu_module
+  *
+  * \return the partial-pivoting LU decomposition of \c *this.
+  *
+  * \sa class PartialPivLU
+  */
+template<typename Derived>
+template<typename PermutationIndex>
+inline const PartialPivLU<typename MatrixBase<Derived>::PlainObject, PermutationIndex>
+MatrixBase<Derived>::partialPivLu() const
+{
+  return PartialPivLU<PlainObject, PermutationIndex>(eval());
+}
+
+/** \lu_module
+  *
+  * Synonym of partialPivLu().
+  *
+  * \return the partial-pivoting LU decomposition of \c *this.
+  *
+  * \sa class PartialPivLU
+  */
+template<typename Derived>
+template<typename PermutationIndex>
+inline const PartialPivLU<typename MatrixBase<Derived>::PlainObject, PermutationIndex>
+MatrixBase<Derived>::lu() const
+{
+  return PartialPivLU<PlainObject, PermutationIndex>(eval());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_PARTIALLU_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/PartialPivLU_LAPACKE.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/PartialPivLU_LAPACKE.h
new file mode 100644
index 0000000000000000000000000000000000000000..b636442398788f35ef6945686a9d741fbad5f8df
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/PartialPivLU_LAPACKE.h
@@ -0,0 +1,96 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *     LU decomposition with partial pivoting based on LAPACKE_?getrf function.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_PARTIALLU_LAPACK_H
+#define EIGEN_PARTIALLU_LAPACK_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+namespace lapacke_helpers {
+// -------------------------------------------------------------------------------------------------------------------
+//        Generic lapacke partial lu implementation that converts arguments and dispatches to the function above
+// -------------------------------------------------------------------------------------------------------------------
+
+template<typename Scalar, int StorageOrder>
+struct lapacke_partial_lu {
+  /** \internal performs the LU decomposition in-place of the matrix represented */
+  static lapack_int blocked_lu(Index rows, Index cols, Scalar* lu_data, Index luStride, lapack_int* row_transpositions,
+  lapack_int& nb_transpositions, lapack_int maxBlockSize=256)
+  {
+    EIGEN_UNUSED_VARIABLE(maxBlockSize);
+    // Set up parameters for getrf
+    lapack_int matrix_order = StorageOrder==RowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR;
+    lapack_int lda = to_lapack(luStride);
+    Scalar* a = lu_data;
+    lapack_int* ipiv = row_transpositions;
+    lapack_int m = to_lapack(rows);
+    lapack_int n = to_lapack(cols);
+    nb_transpositions = 0;
+
+    lapack_int info = getrf(matrix_order, m, n, to_lapack(a), lda, ipiv );
+    eigen_assert(info >= 0);
+
+    for(int i=0; i<m; i++) {
+      ipiv[i]--;
+      if (ipiv[i] != i) nb_transpositions++;
+    }
+    lapack_int first_zero_pivot = info;
+    return first_zero_pivot;
+  }
+};
+} // end namespace lapacke_helpers
+
+/*
+ * Here, we just put the generic implementation from lapacke_partial_lu into a partial specialization of the partial_lu_impl
+ * type. This specialization is more specialized than the generic implementations that Eigen implements, so if the
+ * Scalar type matches they will be chosen.
+ */
+#define EIGEN_LAPACKE_PARTIAL_LU(EIGTYPE) \
+template<int StorageOrder>                \
+struct partial_lu_impl<EIGTYPE, StorageOrder, lapack_int, Dynamic> : public lapacke_helpers::lapacke_partial_lu<EIGTYPE, StorageOrder> {};
+
+EIGEN_LAPACKE_PARTIAL_LU(double)
+EIGEN_LAPACKE_PARTIAL_LU(float)
+EIGEN_LAPACKE_PARTIAL_LU(std::complex<double>)
+EIGEN_LAPACKE_PARTIAL_LU(std::complex<float>)
+
+#undef EIGEN_LAPACKE_PARTIAL_LU
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_PARTIALLU_LAPACK_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/arch/InverseSize4.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/arch/InverseSize4.h
new file mode 100644
index 0000000000000000000000000000000000000000..25f4601e71675de19eacf0b830df35e390aa9373
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/LU/arch/InverseSize4.h
@@ -0,0 +1,365 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2001 Intel Corporation
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+//
+// The algorithm below is a reimplementation of former \src\LU\Inverse_SSE.h using PacketMath.
+// inv(M) = M#/|M|, where inv(M), M# and |M| denote the inverse of M,
+// adjugate of M and determinant of M respectively. M# is computed block-wise
+// using specific formulae. For proof, see:
+// https://lxjk.github.io/2017/09/03/Fast-4x4-Matrix-Inverse-with-SSE-SIMD-Explained.html
+// Variable names are adopted from \src\LU\Inverse_SSE.h.
+//
+// The SSE code for the 4x4 float and double matrix inverse in former (deprecated) \src\LU\Inverse_SSE.h
+// comes from the following Intel's library:
+// http://software.intel.com/en-us/articles/optimized-matrix-library-for-use-with-the-intel-pentiumr-4-processors-sse2-instructions/
+//
+// Here is the respective copyright and license statement:
+//
+//   Copyright (c) 2001 Intel Corporation.
+//
+// Permition is granted to use, copy, distribute and prepare derivative works
+// of this library for any purpose and without fee, provided, that the above
+// copyright notice and this statement appear in all copies.
+// Intel makes no representations about the suitability of this software for
+// any purpose, and specifically disclaims all warranties.
+// See LEGAL.TXT for all the legal information.
+//
+// TODO: Unify implementations of different data types (i.e. float and double).
+#ifndef EIGEN_INVERSE_SIZE_4_H
+#define EIGEN_INVERSE_SIZE_4_H
+
+#include "../InternalHeaderCheck.h"
+
+#if EIGEN_COMP_GNUC_STRICT
+// These routines requires bit manipulation of the sign, which is not compatible
+// with fastmath.
+#pragma GCC push_options
+#pragma GCC optimize ("no-fast-math")
+#endif
+
+namespace Eigen
+{
+namespace internal
+{
+template <typename MatrixType, typename ResultType>
+struct compute_inverse_size4<Architecture::Target, float, MatrixType, ResultType>
+{
+  enum
+  {
+    MatrixAlignment = traits<MatrixType>::Alignment,
+    ResultAlignment = traits<ResultType>::Alignment,
+    StorageOrdersMatch = (MatrixType::Flags & RowMajorBit) == (ResultType::Flags & RowMajorBit)
+  };
+  typedef std::conditional_t<(MatrixType::Flags & LinearAccessBit), MatrixType const &, typename MatrixType::PlainObject> ActualMatrixType;
+
+  static void run(const MatrixType &mat, ResultType &result)
+  {
+    ActualMatrixType matrix(mat);
+
+    const float* data = matrix.data();
+    const Index stride = matrix.innerStride();
+    Packet4f L1 = ploadt<Packet4f,MatrixAlignment>(data);
+    Packet4f L2 = ploadt<Packet4f,MatrixAlignment>(data + stride*4);
+    Packet4f L3 = ploadt<Packet4f,MatrixAlignment>(data + stride*8);
+    Packet4f L4 = ploadt<Packet4f,MatrixAlignment>(data + stride*12);
+
+    // Four 2x2 sub-matrices of the input matrix
+    // input = [[A, B],
+    //          [C, D]]
+    Packet4f A, B, C, D;
+
+    if (!StorageOrdersMatch)
+    {
+      A = vec4f_unpacklo(L1, L2);
+      B = vec4f_unpacklo(L3, L4);
+      C = vec4f_unpackhi(L1, L2);
+      D = vec4f_unpackhi(L3, L4);
+    }
+    else
+    {
+      A = vec4f_movelh(L1, L2);
+      B = vec4f_movehl(L2, L1);
+      C = vec4f_movelh(L3, L4);
+      D = vec4f_movehl(L4, L3);
+    }
+
+    Packet4f AB, DC;
+
+    // AB = A# * B, where A# denotes the adjugate of A, and * denotes matrix product.
+    AB = pmul(vec4f_swizzle2(A, A, 3, 3, 0, 0), B);
+    AB = psub(AB, pmul(vec4f_swizzle2(A, A, 1, 1, 2, 2), vec4f_swizzle2(B, B, 2, 3, 0, 1)));
+
+    // DC = D#*C
+    DC = pmul(vec4f_swizzle2(D, D, 3, 3, 0, 0), C);
+    DC = psub(DC, pmul(vec4f_swizzle2(D, D, 1, 1, 2, 2), vec4f_swizzle2(C, C, 2, 3, 0, 1)));
+
+    // determinants of the sub-matrices
+    Packet4f dA, dB, dC, dD;
+
+    dA = pmul(vec4f_swizzle2(A, A, 3, 3, 1, 1), A);
+    dA = psub(dA, vec4f_movehl(dA, dA));
+
+    dB = pmul(vec4f_swizzle2(B, B, 3, 3, 1, 1), B);
+    dB = psub(dB, vec4f_movehl(dB, dB));
+
+    dC = pmul(vec4f_swizzle2(C, C, 3, 3, 1, 1), C);
+    dC = psub(dC, vec4f_movehl(dC, dC));
+
+    dD = pmul(vec4f_swizzle2(D, D, 3, 3, 1, 1), D);
+    dD = psub(dD, vec4f_movehl(dD, dD));
+
+    Packet4f d, d1, d2;
+
+    d = pmul(vec4f_swizzle2(DC, DC, 0, 2, 1, 3), AB);
+    d = padd(d, vec4f_movehl(d, d));
+    d = padd(d, vec4f_swizzle2(d, d, 1, 0, 0, 0));
+    d1 = pmul(dA, dD);
+    d2 = pmul(dB, dC);
+
+    // determinant of the input matrix, det = |A||D| + |B||C| - trace(A#*B*D#*C)
+    Packet4f det = vec4f_duplane(psub(padd(d1, d2), d), 0);
+
+    // reciprocal of the determinant of the input matrix, rd = 1/det
+    Packet4f rd = preciprocal(det);
+
+    // Four sub-matrices of the inverse
+    Packet4f iA, iB, iC, iD;
+
+    // iD = D*|A| - C*A#*B
+    iD = pmul(vec4f_swizzle2(C, C, 0, 0, 2, 2), vec4f_movelh(AB, AB));
+    iD = padd(iD, pmul(vec4f_swizzle2(C, C, 1, 1, 3, 3), vec4f_movehl(AB, AB)));
+    iD = psub(pmul(D, vec4f_duplane(dA, 0)), iD);
+
+    // iA = A*|D| - B*D#*C
+    iA = pmul(vec4f_swizzle2(B, B, 0, 0, 2, 2), vec4f_movelh(DC, DC));
+    iA = padd(iA, pmul(vec4f_swizzle2(B, B, 1, 1, 3, 3), vec4f_movehl(DC, DC)));
+    iA = psub(pmul(A, vec4f_duplane(dD, 0)), iA);
+
+    // iB = C*|B| - D * (A#B)# = C*|B| - D*B#*A
+    iB = pmul(D, vec4f_swizzle2(AB, AB, 3, 0, 3, 0));
+    iB = psub(iB, pmul(vec4f_swizzle2(D, D, 1, 0, 3, 2), vec4f_swizzle2(AB, AB, 2, 1, 2, 1)));
+    iB = psub(pmul(C, vec4f_duplane(dB, 0)), iB);
+
+    // iC = B*|C| - A * (D#C)# = B*|C| - A*C#*D
+    iC = pmul(A, vec4f_swizzle2(DC, DC, 3, 0, 3, 0));
+    iC = psub(iC, pmul(vec4f_swizzle2(A, A, 1, 0, 3, 2), vec4f_swizzle2(DC, DC, 2, 1, 2, 1)));
+    iC = psub(pmul(B, vec4f_duplane(dC, 0)), iC);
+
+    EIGEN_ALIGN_MAX const float sign_mask[4] = {0.0f, -0.0f, -0.0f, 0.0f};
+    const Packet4f p4f_sign_PNNP = pload<Packet4f>(sign_mask);
+    rd = pxor(rd, p4f_sign_PNNP);
+    iA = pmul(iA, rd);
+    iB = pmul(iB, rd);
+    iC = pmul(iC, rd);
+    iD = pmul(iD, rd);
+
+    Index res_stride = result.outerStride();
+    float *res = result.data();
+
+    pstoret<float, Packet4f, ResultAlignment>(res + 0, vec4f_swizzle2(iA, iB, 3, 1, 3, 1));
+    pstoret<float, Packet4f, ResultAlignment>(res + res_stride, vec4f_swizzle2(iA, iB, 2, 0, 2, 0));
+    pstoret<float, Packet4f, ResultAlignment>(res + 2 * res_stride, vec4f_swizzle2(iC, iD, 3, 1, 3, 1));
+    pstoret<float, Packet4f, ResultAlignment>(res + 3 * res_stride, vec4f_swizzle2(iC, iD, 2, 0, 2, 0));
+  }
+};
+
+#if !(defined EIGEN_VECTORIZE_NEON && !(EIGEN_ARCH_ARM64 && !EIGEN_APPLE_DOUBLE_NEON_BUG))
+// same algorithm as above, except that each operand is split into
+// halves for two registers to hold.
+template <typename MatrixType, typename ResultType>
+struct compute_inverse_size4<Architecture::Target, double, MatrixType, ResultType>
+{
+  enum
+  {
+    MatrixAlignment = traits<MatrixType>::Alignment,
+    ResultAlignment = traits<ResultType>::Alignment,
+    StorageOrdersMatch = (MatrixType::Flags & RowMajorBit) == (ResultType::Flags & RowMajorBit)
+  };
+  typedef std::conditional_t<(MatrixType::Flags & LinearAccessBit),
+                         MatrixType const &,
+                         typename MatrixType::PlainObject>
+      ActualMatrixType;
+
+  static void run(const MatrixType &mat, ResultType &result)
+  {
+    ActualMatrixType matrix(mat);
+
+    // Four 2x2 sub-matrices of the input matrix, each is further divided into upper and lower
+    // row e.g. A1, upper row of A, A2, lower row of A
+    // input = [[A, B],  =  [[[A1, [B1,
+    //          [C, D]]        A2], B2]],
+    //                       [[C1, [D1,
+    //                         C2], D2]]]
+
+    Packet2d A1, A2, B1, B2, C1, C2, D1, D2;
+
+    const double* data = matrix.data();
+    const Index stride = matrix.innerStride();
+    if (StorageOrdersMatch)
+    {
+      A1 = ploadt<Packet2d,MatrixAlignment>(data + stride*0);
+      B1 = ploadt<Packet2d,MatrixAlignment>(data + stride*2);
+      A2 = ploadt<Packet2d,MatrixAlignment>(data + stride*4);
+      B2 = ploadt<Packet2d,MatrixAlignment>(data + stride*6);
+      C1 = ploadt<Packet2d,MatrixAlignment>(data + stride*8);
+      D1 = ploadt<Packet2d,MatrixAlignment>(data + stride*10);
+      C2 = ploadt<Packet2d,MatrixAlignment>(data + stride*12);
+      D2 = ploadt<Packet2d,MatrixAlignment>(data + stride*14);
+    }
+    else
+    {
+      Packet2d temp;
+      A1 = ploadt<Packet2d,MatrixAlignment>(data + stride*0);
+      C1 = ploadt<Packet2d,MatrixAlignment>(data + stride*2);
+      A2 = ploadt<Packet2d,MatrixAlignment>(data + stride*4);
+      C2 = ploadt<Packet2d,MatrixAlignment>(data + stride*6);
+      temp = A1;
+      A1 = vec2d_unpacklo(A1, A2);
+      A2 = vec2d_unpackhi(temp, A2);
+
+      temp = C1;
+      C1 = vec2d_unpacklo(C1, C2);
+      C2 = vec2d_unpackhi(temp, C2);
+
+      B1 = ploadt<Packet2d,MatrixAlignment>(data + stride*8);
+      D1 = ploadt<Packet2d,MatrixAlignment>(data + stride*10);
+      B2 = ploadt<Packet2d,MatrixAlignment>(data + stride*12);
+      D2 = ploadt<Packet2d,MatrixAlignment>(data + stride*14);
+
+      temp = B1;
+      B1 = vec2d_unpacklo(B1, B2);
+      B2 = vec2d_unpackhi(temp, B2);
+
+      temp = D1;
+      D1 = vec2d_unpacklo(D1, D2);
+      D2 = vec2d_unpackhi(temp, D2);
+    }
+
+    // determinants of the sub-matrices
+    Packet2d dA, dB, dC, dD;
+
+    dA = vec2d_swizzle2(A2, A2, 1);
+    dA = pmul(A1, dA);
+    dA = psub(dA, vec2d_duplane(dA, 1));
+
+    dB = vec2d_swizzle2(B2, B2, 1);
+    dB = pmul(B1, dB);
+    dB = psub(dB, vec2d_duplane(dB, 1));
+
+    dC = vec2d_swizzle2(C2, C2, 1);
+    dC = pmul(C1, dC);
+    dC = psub(dC, vec2d_duplane(dC, 1));
+
+    dD = vec2d_swizzle2(D2, D2, 1);
+    dD = pmul(D1, dD);
+    dD = psub(dD, vec2d_duplane(dD, 1));
+
+    Packet2d DC1, DC2, AB1, AB2;
+
+    // AB = A# * B, where A# denotes the adjugate of A, and * denotes matrix product.
+    AB1 = pmul(B1, vec2d_duplane(A2, 1));
+    AB2 = pmul(B2, vec2d_duplane(A1, 0));
+    AB1 = psub(AB1, pmul(B2, vec2d_duplane(A1, 1)));
+    AB2 = psub(AB2, pmul(B1, vec2d_duplane(A2, 0)));
+
+    // DC = D#*C
+    DC1 = pmul(C1, vec2d_duplane(D2, 1));
+    DC2 = pmul(C2, vec2d_duplane(D1, 0));
+    DC1 = psub(DC1, pmul(C2, vec2d_duplane(D1, 1)));
+    DC2 = psub(DC2, pmul(C1, vec2d_duplane(D2, 0)));
+
+    Packet2d d1, d2;
+
+    // determinant of the input matrix, det = |A||D| + |B||C| - trace(A#*B*D#*C)
+    Packet2d det;
+
+    // reciprocal of the determinant of the input matrix, rd = 1/det
+    Packet2d rd;
+
+    d1 = pmul(AB1, vec2d_swizzle2(DC1, DC2, 0));
+    d2 = pmul(AB2, vec2d_swizzle2(DC1, DC2, 3));
+    rd = padd(d1, d2);
+    rd = padd(rd, vec2d_duplane(rd, 1));
+
+    d1 = pmul(dA, dD);
+    d2 = pmul(dB, dC);
+
+    det = padd(d1, d2);
+    det = psub(det, rd);
+    det = vec2d_duplane(det, 0);
+    rd = pdiv(pset1<Packet2d>(1.0), det);
+
+    // rows of four sub-matrices of the inverse
+    Packet2d iA1, iA2, iB1, iB2, iC1, iC2, iD1, iD2;
+
+    // iD = D*|A| - C*A#*B
+    iD1 = pmul(AB1, vec2d_duplane(C1, 0));
+    iD2 = pmul(AB1, vec2d_duplane(C2, 0));
+    iD1 = padd(iD1, pmul(AB2, vec2d_duplane(C1, 1)));
+    iD2 = padd(iD2, pmul(AB2, vec2d_duplane(C2, 1)));
+    dA = vec2d_duplane(dA, 0);
+    iD1 = psub(pmul(D1, dA), iD1);
+    iD2 = psub(pmul(D2, dA), iD2);
+
+    // iA = A*|D| - B*D#*C
+    iA1 = pmul(DC1, vec2d_duplane(B1, 0));
+    iA2 = pmul(DC1, vec2d_duplane(B2, 0));
+    iA1 = padd(iA1, pmul(DC2, vec2d_duplane(B1, 1)));
+    iA2 = padd(iA2, pmul(DC2, vec2d_duplane(B2, 1)));
+    dD = vec2d_duplane(dD, 0);
+    iA1 = psub(pmul(A1, dD), iA1);
+    iA2 = psub(pmul(A2, dD), iA2);
+
+    // iB = C*|B| - D * (A#B)# = C*|B| - D*B#*A
+    iB1 = pmul(D1, vec2d_swizzle2(AB2, AB1, 1));
+    iB2 = pmul(D2, vec2d_swizzle2(AB2, AB1, 1));
+    iB1 = psub(iB1, pmul(vec2d_swizzle2(D1, D1, 1), vec2d_swizzle2(AB2, AB1, 2)));
+    iB2 = psub(iB2, pmul(vec2d_swizzle2(D2, D2, 1), vec2d_swizzle2(AB2, AB1, 2)));
+    dB = vec2d_duplane(dB, 0);
+    iB1 = psub(pmul(C1, dB), iB1);
+    iB2 = psub(pmul(C2, dB), iB2);
+
+    // iC = B*|C| - A * (D#C)# = B*|C| - A*C#*D
+    iC1 = pmul(A1, vec2d_swizzle2(DC2, DC1, 1));
+    iC2 = pmul(A2, vec2d_swizzle2(DC2, DC1, 1));
+    iC1 = psub(iC1, pmul(vec2d_swizzle2(A1, A1, 1), vec2d_swizzle2(DC2, DC1, 2)));
+    iC2 = psub(iC2, pmul(vec2d_swizzle2(A2, A2, 1), vec2d_swizzle2(DC2, DC1, 2)));
+    dC = vec2d_duplane(dC, 0);
+    iC1 = psub(pmul(B1, dC), iC1);
+    iC2 = psub(pmul(B2, dC), iC2);
+
+    EIGEN_ALIGN_MAX const double sign_mask1[2] = {0.0, -0.0};
+    EIGEN_ALIGN_MAX const double sign_mask2[2] = {-0.0, 0.0};
+    const Packet2d sign_PN = pload<Packet2d>(sign_mask1);
+    const Packet2d sign_NP = pload<Packet2d>(sign_mask2);
+    d1 = pxor(rd, sign_PN);
+    d2 = pxor(rd, sign_NP);
+
+    Index res_stride = result.outerStride();
+    double *res = result.data();
+    pstoret<double, Packet2d, ResultAlignment>(res + 0, pmul(vec2d_swizzle2(iA2, iA1, 3), d1));
+    pstoret<double, Packet2d, ResultAlignment>(res + res_stride, pmul(vec2d_swizzle2(iA2, iA1, 0), d2));
+    pstoret<double, Packet2d, ResultAlignment>(res + 2, pmul(vec2d_swizzle2(iB2, iB1, 3), d1));
+    pstoret<double, Packet2d, ResultAlignment>(res + res_stride + 2, pmul(vec2d_swizzle2(iB2, iB1, 0), d2));
+    pstoret<double, Packet2d, ResultAlignment>(res + 2 * res_stride, pmul(vec2d_swizzle2(iC2, iC1, 3), d1));
+    pstoret<double, Packet2d, ResultAlignment>(res + 3 * res_stride, pmul(vec2d_swizzle2(iC2, iC1, 0), d2));
+    pstoret<double, Packet2d, ResultAlignment>(res + 2 * res_stride + 2, pmul(vec2d_swizzle2(iD2, iD1, 3), d1));
+    pstoret<double, Packet2d, ResultAlignment>(res + 3 * res_stride + 2, pmul(vec2d_swizzle2(iD2, iD1, 0), d2));
+  }
+};
+#endif
+} // namespace internal
+} // namespace Eigen
+
+#if EIGEN_COMP_GNUC_STRICT
+#pragma GCC pop_options
+#endif
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/MetisSupport/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/MetisSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..9d34825f2fa584083a0923b8f6af948e365ba280
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/MetisSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_METISSUPPORT_MODULE_H
+#error "Please include Eigen/MetisSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/MetisSupport/MetisSupport.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/MetisSupport/MetisSupport.h
new file mode 100644
index 0000000000000000000000000000000000000000..c5e143b9588238478333718453b91f1cbe855e2e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/MetisSupport/MetisSupport.h
@@ -0,0 +1,139 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+#ifndef METIS_SUPPORT_H
+#define METIS_SUPPORT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+/**
+ * Get the fill-reducing ordering from the METIS package
+ * 
+ * If A is the original matrix and Ap is the permuted matrix, 
+ * the fill-reducing permutation is defined as follows :
+ * Row (column) i of A is the matperm(i) row (column) of Ap. 
+ * WARNING: As computed by METIS, this corresponds to the vector iperm (instead of perm)
+ */
+template <typename StorageIndex>
+class MetisOrdering
+{
+public:
+  typedef PermutationMatrix<Dynamic,Dynamic,StorageIndex> PermutationType;
+  typedef Matrix<StorageIndex,Dynamic,1> IndexVector; 
+  
+  template <typename MatrixType>
+  void get_symmetrized_graph(const MatrixType& A)
+  {
+    Index m = A.cols(); 
+    eigen_assert((A.rows() == A.cols()) && "ONLY FOR SQUARED MATRICES");
+    // Get the transpose of the input matrix 
+    MatrixType At = A.transpose(); 
+    // Get the number of nonzeros elements in each row/col of At+A
+    Index TotNz = 0; 
+    IndexVector visited(m); 
+    visited.setConstant(-1); 
+    for (StorageIndex j = 0; j < m; j++)
+    {
+      // Compute the union structure of of A(j,:) and At(j,:)
+      visited(j) = j; // Do not include the diagonal element
+      // Get the nonzeros in row/column j of A
+      for (typename MatrixType::InnerIterator it(A, j); it; ++it)
+      {
+        Index idx = it.index(); // Get the row index (for column major) or column index (for row major)
+        if (visited(idx) != j ) 
+        {
+          visited(idx) = j; 
+          ++TotNz; 
+        }
+      }
+      //Get the nonzeros in row/column j of At
+      for (typename MatrixType::InnerIterator it(At, j); it; ++it)
+      {
+        Index idx = it.index(); 
+        if(visited(idx) != j)
+        {
+          visited(idx) = j; 
+          ++TotNz; 
+        }
+      }
+    }
+    // Reserve place for A + At
+    m_indexPtr.resize(m+1);
+    m_innerIndices.resize(TotNz); 
+
+    // Now compute the real adjacency list of each column/row 
+    visited.setConstant(-1); 
+    StorageIndex CurNz = 0; 
+    for (StorageIndex j = 0; j < m; j++)
+    {
+      m_indexPtr(j) = CurNz; 
+      
+      visited(j) = j; // Do not include the diagonal element
+      // Add the pattern of row/column j of A to A+At
+      for (typename MatrixType::InnerIterator it(A,j); it; ++it)
+      {
+        StorageIndex idx = it.index(); // Get the row index (for column major) or column index (for row major)
+        if (visited(idx) != j ) 
+        {
+          visited(idx) = j; 
+          m_innerIndices(CurNz) = idx; 
+          CurNz++; 
+        }
+      }
+      //Add the pattern of row/column j of At to A+At
+      for (typename MatrixType::InnerIterator it(At, j); it; ++it)
+      {
+        StorageIndex idx = it.index(); 
+        if(visited(idx) != j)
+        {
+          visited(idx) = j; 
+          m_innerIndices(CurNz) = idx; 
+          ++CurNz; 
+        }
+      }
+    }
+    m_indexPtr(m) = CurNz;    
+  }
+  
+  template <typename MatrixType>
+  void operator() (const MatrixType& A, PermutationType& matperm)
+  {
+     StorageIndex m = internal::convert_index<StorageIndex>(A.cols()); // must be StorageIndex, because it is passed by address to METIS
+     IndexVector perm(m),iperm(m); 
+    // First, symmetrize the matrix graph. 
+     get_symmetrized_graph(A); 
+     int output_error;
+     
+     // Call the fill-reducing routine from METIS 
+     output_error = METIS_NodeND(&m, m_indexPtr.data(), m_innerIndices.data(), NULL, NULL, perm.data(), iperm.data());
+     
+    if(output_error != METIS_OK) 
+    {
+      //FIXME The ordering interface should define a class of possible errors 
+     std::cerr << "ERROR WHILE CALLING THE METIS PACKAGE \n"; 
+     return; 
+    }
+    
+    // Get the fill-reducing permutation 
+    //NOTE:  If Ap is the permuted matrix then perm and iperm vectors are defined as follows 
+    // Row (column) i of Ap is the perm(i) row(column) of A, and row (column) i of A is the iperm(i) row(column) of Ap
+    
+     matperm.resize(m);
+     for (int j = 0; j < m; j++)
+       matperm.indices()(iperm(j)) = j;
+   
+  }
+  
+  protected:
+    IndexVector m_indexPtr; // Pointer to the adjacenccy list of each row/column
+    IndexVector m_innerIndices; // Adjacency list 
+};
+
+}// end namespace eigen 
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/OrderingMethods/Amd.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/OrderingMethods/Amd.h
new file mode 100644
index 0000000000000000000000000000000000000000..5bd531c6d9c73ccfca8193f3aeac400b6a5d871d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/OrderingMethods/Amd.h
@@ -0,0 +1,437 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*
+NOTE: this routine has been adapted from the CSparse library:
+
+Copyright (c) 2006, Timothy A. Davis.
+http://www.suitesparse.com
+
+The author of CSparse, Timothy A. Davis., has executed a license with Google LLC
+to permit distribution of this code and derivative works as part of Eigen under
+the Mozilla Public License v. 2.0, as stated at the top of this file.
+*/
+
+#ifndef EIGEN_SPARSE_AMD_H
+#define EIGEN_SPARSE_AMD_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+  
+template<typename T> inline T amd_flip(const T& i) { return -i-2; }
+template<typename T> inline T amd_unflip(const T& i) { return i<0 ? amd_flip(i) : i; }
+template<typename T0, typename T1> inline bool amd_marked(const T0* w, const T1& j) { return w[j]<0; }
+template<typename T0, typename T1> inline void amd_mark(const T0* w, const T1& j) { return w[j] = amd_flip(w[j]); }
+
+/* clear w */
+template<typename StorageIndex>
+static StorageIndex cs_wclear (StorageIndex mark, StorageIndex lemax, StorageIndex *w, StorageIndex n)
+{
+  StorageIndex k;
+  if(mark < 2 || (mark + lemax < 0))
+  {
+    for(k = 0; k < n; k++)
+      if(w[k] != 0)
+        w[k] = 1;
+    mark = 2;
+  }
+  return (mark);     /* at this point, w[0..n-1] < mark holds */
+}
+
+/* depth-first search and postorder of a tree rooted at node j */
+template<typename StorageIndex>
+StorageIndex cs_tdfs(StorageIndex j, StorageIndex k, StorageIndex *head, const StorageIndex *next, StorageIndex *post, StorageIndex *stack)
+{
+  StorageIndex i, p, top = 0;
+  if(!head || !next || !post || !stack) return (-1);    /* check inputs */
+  stack[0] = j;                 /* place j on the stack */
+  while (top >= 0)                /* while (stack is not empty) */
+  {
+    p = stack[top];           /* p = top of stack */
+    i = head[p];              /* i = youngest child of p */
+    if(i == -1)
+    {
+      top--;                 /* p has no unordered children left */
+      post[k++] = p;        /* node p is the kth postordered node */
+    }
+    else
+    {
+      head[p] = next[i];   /* remove i from children of p */
+      stack[++top] = i;     /* start dfs on child node i */
+    }
+  }
+  return k;
+}
+
+
+/** \internal
+  * \ingroup OrderingMethods_Module 
+  * Approximate minimum degree ordering algorithm.
+  *
+  * \param[in] C the input selfadjoint matrix stored in compressed column major format.
+  * \param[out] perm the permutation P reducing the fill-in of the input matrix \a C
+  *
+  * Note that the input matrix \a C must be complete, that is both the upper and lower parts have to be stored, as well as the diagonal entries.
+  * On exit the values of C are destroyed */
+template<typename Scalar, typename StorageIndex>
+void minimum_degree_ordering(SparseMatrix<Scalar,ColMajor,StorageIndex>& C, PermutationMatrix<Dynamic,Dynamic,StorageIndex>& perm)
+{
+  using std::sqrt;
+  
+  StorageIndex d, dk, dext, lemax = 0, e, elenk, eln, i, j, k, k1,
+                k2, k3, jlast, ln, dense, nzmax, mindeg = 0, nvi, nvj, nvk, mark, wnvi,
+                ok, nel = 0, p, p1, p2, p3, p4, pj, pk, pk1, pk2, pn, q, t, h;
+  
+  StorageIndex n = StorageIndex(C.cols());
+  dense = std::max<StorageIndex> (16, StorageIndex(10 * sqrt(double(n))));   /* find dense threshold */
+  dense = (std::min)(n-2, dense);
+  
+  StorageIndex cnz = StorageIndex(C.nonZeros());
+  perm.resize(n+1);
+  t = cnz + cnz/5 + 2*n;                 /* add elbow room to C */
+  C.resizeNonZeros(t);
+  
+  // get workspace
+  ei_declare_aligned_stack_constructed_variable(StorageIndex,W,8*(n+1),0);
+  StorageIndex* len     = W;
+  StorageIndex* nv      = W +   (n+1);
+  StorageIndex* next    = W + 2*(n+1);
+  StorageIndex* head    = W + 3*(n+1);
+  StorageIndex* elen    = W + 4*(n+1);
+  StorageIndex* degree  = W + 5*(n+1);
+  StorageIndex* w       = W + 6*(n+1);
+  StorageIndex* hhead   = W + 7*(n+1);
+  StorageIndex* last    = perm.indices().data();                              /* use P as workspace for last */
+  
+  /* --- Initialize quotient graph ---------------------------------------- */
+  StorageIndex* Cp = C.outerIndexPtr();
+  StorageIndex* Ci = C.innerIndexPtr();
+  for(k = 0; k < n; k++)
+    len[k] = Cp[k+1] - Cp[k];
+  len[n] = 0;
+  nzmax = t;
+  
+  for(i = 0; i <= n; i++)
+  {
+    head[i]   = -1;                     // degree list i is empty
+    last[i]   = -1;
+    next[i]   = -1;
+    hhead[i]  = -1;                     // hash list i is empty 
+    nv[i]     = 1;                      // node i is just one node
+    w[i]      = 1;                      // node i is alive
+    elen[i]   = 0;                      // Ek of node i is empty
+    degree[i] = len[i];                 // degree of node i
+  }
+  mark = internal::cs_wclear<StorageIndex>(0, 0, w, n);         /* clear w */
+  
+  /* --- Initialize degree lists ------------------------------------------ */
+  for(i = 0; i < n; i++)
+  {
+    bool has_diag = false;
+    for(p = Cp[i]; p<Cp[i+1]; ++p)
+      if(Ci[p]==i)
+      {
+        has_diag = true;
+        break;
+      }
+   
+    d = degree[i];
+    if(d == 1 && has_diag)           /* node i is empty */
+    {
+      elen[i] = -2;                 /* element i is dead */
+      nel++;
+      Cp[i] = -1;                   /* i is a root of assembly tree */
+      w[i] = 0;
+    }
+    else if(d > dense || !has_diag)  /* node i is dense or has no structural diagonal element */
+    {
+      nv[i] = 0;                    /* absorb i into element n */
+      elen[i] = -1;                 /* node i is dead */
+      nel++;
+      Cp[i] = amd_flip (n);
+      nv[n]++;
+    }
+    else
+    {
+      if(head[d] != -1) last[head[d]] = i;
+      next[i] = head[d];           /* put node i in degree list d */
+      head[d] = i;
+    }
+  }
+  
+  elen[n] = -2;                         /* n is a dead element */
+  Cp[n] = -1;                           /* n is a root of assembly tree */
+  w[n] = 0;                             /* n is a dead element */
+  
+  while (nel < n)                         /* while (selecting pivots) do */
+  {
+    /* --- Select node of minimum approximate degree -------------------- */
+    for(k = -1; mindeg < n && (k = head[mindeg]) == -1; mindeg++) {}
+    if(next[k] != -1) last[next[k]] = -1;
+    head[mindeg] = next[k];          /* remove k from degree list */
+    elenk = elen[k];                  /* elenk = |Ek| */
+    nvk = nv[k];                      /* # of nodes k represents */
+    nel += nvk;                        /* nv[k] nodes of A eliminated */
+    
+    /* --- Garbage collection ------------------------------------------- */
+    if(elenk > 0 && cnz + mindeg >= nzmax)
+    {
+      for(j = 0; j < n; j++)
+      {
+        if((p = Cp[j]) >= 0)      /* j is a live node or element */
+        {
+          Cp[j] = Ci[p];          /* save first entry of object */
+          Ci[p] = amd_flip (j);    /* first entry is now amd_flip(j) */
+        }
+      }
+      for(q = 0, p = 0; p < cnz; ) /* scan all of memory */
+      {
+        if((j = amd_flip (Ci[p++])) >= 0)  /* found object j */
+        {
+          Ci[q] = Cp[j];       /* restore first entry of object */
+          Cp[j] = q++;          /* new pointer to object j */
+          for(k3 = 0; k3 < len[j]-1; k3++) Ci[q++] = Ci[p++];
+        }
+      }
+      cnz = q;                       /* Ci[cnz...nzmax-1] now free */
+    }
+    
+    /* --- Construct new element ---------------------------------------- */
+    dk = 0;
+    nv[k] = -nvk;                     /* flag k as in Lk */
+    p = Cp[k];
+    pk1 = (elenk == 0) ? p : cnz;      /* do in place if elen[k] == 0 */
+    pk2 = pk1;
+    for(k1 = 1; k1 <= elenk + 1; k1++)
+    {
+      if(k1 > elenk)
+      {
+        e = k;                     /* search the nodes in k */
+        pj = p;                    /* list of nodes starts at Ci[pj]*/
+        ln = len[k] - elenk;      /* length of list of nodes in k */
+      }
+      else
+      {
+        e = Ci[p++];              /* search the nodes in e */
+        pj = Cp[e];
+        ln = len[e];              /* length of list of nodes in e */
+      }
+      for(k2 = 1; k2 <= ln; k2++)
+      {
+        i = Ci[pj++];
+        if((nvi = nv[i]) <= 0) continue; /* node i dead, or seen */
+        dk += nvi;                 /* degree[Lk] += size of node i */
+        nv[i] = -nvi;             /* negate nv[i] to denote i in Lk*/
+        Ci[pk2++] = i;            /* place i in Lk */
+        if(next[i] != -1) last[next[i]] = last[i];
+        if(last[i] != -1)         /* remove i from degree list */
+        {
+          next[last[i]] = next[i];
+        }
+        else
+        {
+          head[degree[i]] = next[i];
+        }
+      }
+      if(e != k)
+      {
+        Cp[e] = amd_flip (k);      /* absorb e into k */
+        w[e] = 0;                 /* e is now a dead element */
+      }
+    }
+    if(elenk != 0) cnz = pk2;         /* Ci[cnz...nzmax] is free */
+    degree[k] = dk;                   /* external degree of k - |Lk\i| */
+    Cp[k] = pk1;                      /* element k is in Ci[pk1..pk2-1] */
+    len[k] = pk2 - pk1;
+    elen[k] = -2;                     /* k is now an element */
+    
+    /* --- Find set differences ----------------------------------------- */
+    mark = internal::cs_wclear<StorageIndex>(mark, lemax, w, n);  /* clear w if necessary */
+    for(pk = pk1; pk < pk2; pk++)    /* scan 1: find |Le\Lk| */
+    {
+      i = Ci[pk];
+      if((eln = elen[i]) <= 0) continue;/* skip if elen[i] empty */
+      nvi = -nv[i];                      /* nv[i] was negated */
+      wnvi = mark - nvi;
+      for(p = Cp[i]; p <= Cp[i] + eln - 1; p++)  /* scan Ei */
+      {
+        e = Ci[p];
+        if(w[e] >= mark)
+        {
+          w[e] -= nvi;          /* decrement |Le\Lk| */
+        }
+        else if(w[e] != 0)        /* ensure e is a live element */
+        {
+          w[e] = degree[e] + wnvi; /* 1st time e seen in scan 1 */
+        }
+      }
+    }
+    
+    /* --- Degree update ------------------------------------------------ */
+    for(pk = pk1; pk < pk2; pk++)    /* scan2: degree update */
+    {
+      i = Ci[pk];                   /* consider node i in Lk */
+      p1 = Cp[i];
+      p2 = p1 + elen[i] - 1;
+      pn = p1;
+      for(h = 0, d = 0, p = p1; p <= p2; p++)    /* scan Ei */
+      {
+        e = Ci[p];
+        if(w[e] != 0)             /* e is an unabsorbed element */
+        {
+          dext = w[e] - mark;   /* dext = |Le\Lk| */
+          if(dext > 0)
+          {
+            d += dext;         /* sum up the set differences */
+            Ci[pn++] = e;     /* keep e in Ei */
+            h += e;            /* compute the hash of node i */
+          }
+          else
+          {
+            Cp[e] = amd_flip (k);  /* aggressive absorb. e->k */
+            w[e] = 0;             /* e is a dead element */
+          }
+        }
+      }
+      elen[i] = pn - p1 + 1;        /* elen[i] = |Ei| */
+      p3 = pn;
+      p4 = p1 + len[i];
+      for(p = p2 + 1; p < p4; p++) /* prune edges in Ai */
+      {
+        j = Ci[p];
+        if((nvj = nv[j]) <= 0) continue; /* node j dead or in Lk */
+        d += nvj;                  /* degree(i) += |j| */
+        Ci[pn++] = j;             /* place j in node list of i */
+        h += j;                    /* compute hash for node i */
+      }
+      if(d == 0)                     /* check for mass elimination */
+      {
+        Cp[i] = amd_flip (k);      /* absorb i into k */
+        nvi = -nv[i];
+        dk -= nvi;                 /* |Lk| -= |i| */
+        nvk += nvi;                /* |k| += nv[i] */
+        nel += nvi;
+        nv[i] = 0;
+        elen[i] = -1;             /* node i is dead */
+      }
+      else
+      {
+        degree[i] = std::min<StorageIndex> (degree[i], d);   /* update degree(i) */
+        Ci[pn] = Ci[p3];         /* move first node to end */
+        Ci[p3] = Ci[p1];         /* move 1st el. to end of Ei */
+        Ci[p1] = k;               /* add k as 1st element in of Ei */
+        len[i] = pn - p1 + 1;     /* new len of adj. list of node i */
+        h %= n;                    /* finalize hash of i */
+        next[i] = hhead[h];      /* place i in hash bucket */
+        hhead[h] = i;
+        last[i] = h;      /* save hash of i in last[i] */
+      }
+    }                                   /* scan2 is done */
+    degree[k] = dk;                   /* finalize |Lk| */
+    lemax = std::max<StorageIndex>(lemax, dk);
+    mark = internal::cs_wclear<StorageIndex>(mark+lemax, lemax, w, n);    /* clear w */
+    
+    /* --- Supernode detection ------------------------------------------ */
+    for(pk = pk1; pk < pk2; pk++)
+    {
+      i = Ci[pk];
+      if(nv[i] >= 0) continue;         /* skip if i is dead */
+      h = last[i];                      /* scan hash bucket of node i */
+      i = hhead[h];
+      hhead[h] = -1;                    /* hash bucket will be empty */
+      for(; i != -1 && next[i] != -1; i = next[i], mark++)
+      {
+        ln = len[i];
+        eln = elen[i];
+        for(p = Cp[i]+1; p <= Cp[i] + ln-1; p++) w[Ci[p]] = mark;
+        jlast = i;
+        for(j = next[i]; j != -1; ) /* compare i with all j */
+        {
+          ok = (len[j] == ln) && (elen[j] == eln);
+          for(p = Cp[j] + 1; ok && p <= Cp[j] + ln - 1; p++)
+          {
+            if(w[Ci[p]] != mark) ok = 0;    /* compare i and j*/
+          }
+          if(ok)                     /* i and j are identical */
+          {
+            Cp[j] = amd_flip (i);  /* absorb j into i */
+            nv[i] += nv[j];
+            nv[j] = 0;
+            elen[j] = -1;         /* node j is dead */
+            j = next[j];          /* delete j from hash bucket */
+            next[jlast] = j;
+          }
+          else
+          {
+            jlast = j;             /* j and i are different */
+            j = next[j];
+          }
+        }
+      }
+    }
+    
+    /* --- Finalize new element------------------------------------------ */
+    for(p = pk1, pk = pk1; pk < pk2; pk++)   /* finalize Lk */
+    {
+      i = Ci[pk];
+      if((nvi = -nv[i]) <= 0) continue;/* skip if i is dead */
+      nv[i] = nvi;                      /* restore nv[i] */
+      d = degree[i] + dk - nvi;         /* compute external degree(i) */
+      d = std::min<StorageIndex> (d, n - nel - nvi);
+      if(head[d] != -1) last[head[d]] = i;
+      next[i] = head[d];               /* put i back in degree list */
+      last[i] = -1;
+      head[d] = i;
+      mindeg = std::min<StorageIndex> (mindeg, d);       /* find new minimum degree */
+      degree[i] = d;
+      Ci[p++] = i;                      /* place i in Lk */
+    }
+    nv[k] = nvk;                      /* # nodes absorbed into k */
+    if((len[k] = p-pk1) == 0)         /* length of adj list of element k*/
+    {
+      Cp[k] = -1;                   /* k is a root of the tree */
+      w[k] = 0;                     /* k is now a dead element */
+    }
+    if(elenk != 0) cnz = p;           /* free unused space in Lk */
+  }
+  
+  /* --- Postordering ----------------------------------------------------- */
+  for(i = 0; i < n; i++) Cp[i] = amd_flip (Cp[i]);/* fix assembly tree */
+  for(j = 0; j <= n; j++) head[j] = -1;
+  for(j = n; j >= 0; j--)              /* place unordered nodes in lists */
+  {
+    if(nv[j] > 0) continue;          /* skip if j is an element */
+    next[j] = head[Cp[j]];          /* place j in list of its parent */
+    head[Cp[j]] = j;
+  }
+  for(e = n; e >= 0; e--)              /* place elements in lists */
+  {
+    if(nv[e] <= 0) continue;         /* skip unless e is an element */
+    if(Cp[e] != -1)
+    {
+      next[e] = head[Cp[e]];      /* place e in list of its parent */
+      head[Cp[e]] = e;
+    }
+  }
+  for(k = 0, i = 0; i <= n; i++)       /* postorder the assembly tree */
+  {
+    if(Cp[i] == -1) k = internal::cs_tdfs<StorageIndex>(i, k, head, next, perm.indices().data(), w);
+  }
+  
+  perm.indices().conservativeResize(n);
+}
+
+} // namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_AMD_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/OrderingMethods/Eigen_Colamd.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/OrderingMethods/Eigen_Colamd.h
new file mode 100644
index 0000000000000000000000000000000000000000..8e339a704a1812c368e19f445aaec7deda219eb2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/OrderingMethods/Eigen_Colamd.h
@@ -0,0 +1,1863 @@
+// // This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Desire Nuentsa Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// This file is modified from the colamd/symamd library. The copyright is below
+
+//   The authors of the code itself are Stefan I. Larimore and Timothy A.
+//   Davis (davis@cise.ufl.edu), University of Florida.  The algorithm was
+//   developed in collaboration with John Gilbert, Xerox PARC, and Esmond
+//   Ng, Oak Ridge National Laboratory.
+//
+//     Date:
+//
+//   September 8, 2003.  Version 2.3.
+//
+//     Acknowledgements:
+//
+//   This work was supported by the National Science Foundation, under
+//   grants DMS-9504974 and DMS-9803599.
+//
+//     Notice:
+//
+//   Copyright (c) 1998-2003 by the University of Florida.
+//   All Rights Reserved.
+//
+//   THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+//   EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+//
+//   Permission is hereby granted to use, copy, modify, and/or distribute
+//   this program, provided that the Copyright, this License, and the
+//   Availability of the original version is retained on all copies and made
+//   accessible to the end-user of any code or package that includes COLAMD
+//   or any modified version of COLAMD.
+//
+//     Availability:
+//
+//   The colamd/symamd library is available at
+//
+//       http://www.suitesparse.com
+
+
+#ifndef EIGEN_COLAMD_H
+#define EIGEN_COLAMD_H
+
+namespace internal {
+
+namespace Colamd {
+
+/* Ensure that debugging is turned off: */
+#ifndef COLAMD_NDEBUG
+#define COLAMD_NDEBUG
+#endif /* NDEBUG */
+
+
+/* ========================================================================== */
+/* === Knob and statistics definitions ====================================== */
+/* ========================================================================== */
+
+/* size of the knobs [ ] array.  Only knobs [0..1] are currently used. */
+const int NKnobs = 20;
+
+/* number of output statistics.  Only stats [0..6] are currently used. */
+const int NStats = 20;
+
+/* Indices into knobs and stats array. */
+enum KnobsStatsIndex {
+  /* knobs [0] and stats [0]: dense row knob and output statistic. */
+  DenseRow = 0,
+
+  /* knobs [1] and stats [1]: dense column knob and output statistic. */
+  DenseCol = 1,
+
+  /* stats [2]: memory defragmentation count output statistic */
+  DefragCount = 2,
+
+  /* stats [3]: colamd status:  zero OK, > 0 warning or notice, < 0 error */
+  Status = 3,
+
+  /* stats [4..6]: error info, or info on jumbled columns */
+  Info1 = 4,
+  Info2 = 5,
+  Info3 = 6
+};
+
+/* error codes returned in stats [3]: */
+enum Status {
+  Ok = 0,
+  OkButJumbled = 1,
+  ErrorANotPresent = -1,
+  ErrorPNotPresent = -2,
+  ErrorNrowNegative = -3,
+  ErrorNcolNegative = -4,
+  ErrorNnzNegative = -5,
+  ErrorP0Nonzero = -6,
+  ErrorATooSmall = -7,
+  ErrorColLengthNegative = -8,
+  ErrorRowIndexOutOfBounds = -9,
+  ErrorOutOfMemory = -10,
+  ErrorInternalError = -999
+};
+/* ========================================================================== */
+/* === Definitions ========================================================== */
+/* ========================================================================== */
+
+template <typename IndexType>
+IndexType ones_complement(const IndexType r) {
+  return (-(r)-1);
+}
+
+/* -------------------------------------------------------------------------- */
+const int Empty = -1;
+
+/* Row and column status */
+enum RowColumnStatus {
+  Alive = 0,
+  Dead = -1
+};
+
+/* Column status */
+enum ColumnStatus {
+  DeadPrincipal = -1,
+  DeadNonPrincipal = -2
+};
+
+/* ========================================================================== */
+/* === Colamd reporting mechanism =========================================== */
+/* ========================================================================== */
+
+// == Row and Column structures ==
+template <typename IndexType>
+struct ColStructure
+{
+  IndexType start ;   /* index for A of first row in this column, or Dead */
+  /* if column is dead */
+  IndexType length ;  /* number of rows in this column */
+  union
+  {
+    IndexType thickness ; /* number of original columns represented by this */
+    /* col, if the column is alive */
+    IndexType parent ;  /* parent in parent tree super-column structure, if */
+    /* the column is dead */
+  } shared1 ;
+  union
+  {
+    IndexType score ; /* the score used to maintain heap, if col is alive */
+    IndexType order ; /* pivot ordering of this column, if col is dead */
+  } shared2 ;
+  union
+  {
+    IndexType headhash ;  /* head of a hash bucket, if col is at the head of */
+    /* a degree list */
+    IndexType hash ;  /* hash value, if col is not in a degree list */
+    IndexType prev ;  /* previous column in degree list, if col is in a */
+    /* degree list (but not at the head of a degree list) */
+  } shared3 ;
+  union
+  {
+    IndexType degree_next ; /* next column, if col is in a degree list */
+    IndexType hash_next ;   /* next column, if col is in a hash list */
+  } shared4 ;
+
+  inline bool is_dead() const { return start < Alive; }
+
+  inline bool is_alive() const { return start >= Alive; }
+
+  inline bool is_dead_principal() const { return start == DeadPrincipal; }
+
+  inline void kill_principal() { start = DeadPrincipal; }
+
+  inline void kill_non_principal() { start = DeadNonPrincipal; }
+
+};
+
+template <typename IndexType>
+struct RowStructure
+{
+  IndexType start ;   /* index for A of first col in this row */
+  IndexType length ;  /* number of principal columns in this row */
+  union
+  {
+    IndexType degree ;  /* number of principal & non-principal columns in row */
+    IndexType p ;   /* used as a row pointer in init_rows_cols () */
+  } shared1 ;
+  union
+  {
+    IndexType mark ;  /* for computing set differences and marking dead rows*/
+    IndexType first_column ;/* first column in row (used in garbage collection) */
+  } shared2 ;
+
+  inline bool is_dead() const { return shared2.mark < Alive; }
+
+  inline bool is_alive() const { return shared2.mark >= Alive; }
+
+  inline void kill() { shared2.mark = Dead; }
+
+};
+
+/* ========================================================================== */
+/* === Colamd recommended memory size ======================================= */
+/* ========================================================================== */
+
+/*
+  The recommended length Alen of the array A passed to colamd is given by
+  the COLAMD_RECOMMENDED (nnz, n_row, n_col) macro.  It returns -1 if any
+  argument is negative.  2*nnz space is required for the row and column
+  indices of the matrix. colamd_c (n_col) + colamd_r (n_row) space is
+  required for the Col and Row arrays, respectively, which are internal to
+  colamd.  An additional n_col space is the minimal amount of "elbow room",
+  and nnz/5 more space is recommended for run time efficiency.
+
+  This macro is not needed when using symamd.
+
+  Explicit typecast to IndexType added Sept. 23, 2002, COLAMD version 2.2, to avoid
+  gcc -pedantic warning messages.
+*/
+template <typename IndexType>
+inline IndexType colamd_c(IndexType n_col)
+{ return IndexType( ((n_col) + 1) * sizeof (ColStructure<IndexType>) / sizeof (IndexType) ) ; }
+
+template <typename IndexType>
+inline IndexType  colamd_r(IndexType n_row)
+{ return IndexType(((n_row) + 1) * sizeof (RowStructure<IndexType>) / sizeof (IndexType)); }
+
+// Prototypes of non-user callable routines
+template <typename IndexType>
+static IndexType init_rows_cols (IndexType n_row, IndexType n_col, RowStructure<IndexType> Row [], ColStructure<IndexType> col [], IndexType A [], IndexType p [], IndexType stats[NStats] );
+
+template <typename IndexType>
+static void init_scoring (IndexType n_row, IndexType n_col, RowStructure<IndexType> Row [], ColStructure<IndexType> Col [], IndexType A [], IndexType head [], double knobs[NKnobs], IndexType *p_n_row2, IndexType *p_n_col2, IndexType *p_max_deg);
+
+template <typename IndexType>
+static IndexType find_ordering (IndexType n_row, IndexType n_col, IndexType Alen, RowStructure<IndexType> Row [], ColStructure<IndexType> Col [], IndexType A [], IndexType head [], IndexType n_col2, IndexType max_deg, IndexType pfree);
+
+template <typename IndexType>
+static void order_children (IndexType n_col, ColStructure<IndexType> Col [], IndexType p []);
+
+template <typename IndexType>
+static void detect_super_cols (ColStructure<IndexType> Col [], IndexType A [], IndexType head [], IndexType row_start, IndexType row_length ) ;
+
+template <typename IndexType>
+static IndexType garbage_collection (IndexType n_row, IndexType n_col, RowStructure<IndexType> Row [], ColStructure<IndexType> Col [], IndexType A [], IndexType *pfree) ;
+
+template <typename IndexType>
+static inline  IndexType clear_mark (IndexType n_row, RowStructure<IndexType> Row [] ) ;
+
+/* === No debugging ========================================================= */
+
+#define COLAMD_DEBUG0(params) ;
+#define COLAMD_DEBUG1(params) ;
+#define COLAMD_DEBUG2(params) ;
+#define COLAMD_DEBUG3(params) ;
+#define COLAMD_DEBUG4(params) ;
+
+#define COLAMD_ASSERT(expression) ((void) 0)
+
+
+/**
+ * \brief Returns the recommended value of Alen
+ *
+ * Returns recommended value of Alen for use by colamd.
+ * Returns -1 if any input argument is negative.
+ * The use of this routine or macro is optional.
+ * Note that the macro uses its arguments   more than once,
+ * so be careful for side effects, if you pass expressions as arguments to COLAMD_RECOMMENDED.
+ *
+ * \param nnz nonzeros in A
+ * \param n_row number of rows in A
+ * \param n_col number of columns in A
+ * \return recommended value of Alen for use by colamd
+ */
+template <typename IndexType>
+inline IndexType recommended ( IndexType nnz, IndexType n_row, IndexType n_col)
+{
+  if ((nnz) < 0 || (n_row) < 0 || (n_col) < 0)
+    return (-1);
+  else
+    return (2 * (nnz) + colamd_c (n_col) + colamd_r (n_row) + (n_col) + ((nnz) / 5));
+}
+
+/**
+ * \brief set default parameters  The use of this routine is optional.
+ *
+ * Colamd: rows with more than (knobs [DenseRow] * n_col)
+ * entries are removed prior to ordering.  Columns with more than
+ * (knobs [DenseCol] * n_row) entries are removed prior to
+ * ordering, and placed last in the output column ordering.
+ *
+ * DenseRow and DenseCol are defined as 0 and 1,
+ * respectively, in colamd.h.  Default values of these two knobs
+ * are both 0.5.  Currently, only knobs [0] and knobs [1] are
+ * used, but future versions may use more knobs.  If so, they will
+ * be properly set to their defaults by the future version of
+ * colamd_set_defaults, so that the code that calls colamd will
+ * not need to change, assuming that you either use
+ * colamd_set_defaults, or pass a (double *) NULL pointer as the
+ * knobs array to colamd or symamd.
+ *
+ * \param knobs parameter settings for colamd
+ */
+
+static inline void set_defaults(double knobs[NKnobs])
+{
+  /* === Local variables ================================================== */
+
+  int i ;
+
+  if (!knobs)
+  {
+    return ;      /* no knobs to initialize */
+  }
+  for (i = 0 ; i < NKnobs ; i++)
+  {
+    knobs [i] = 0 ;
+  }
+  knobs [Colamd::DenseRow] = 0.5 ;  /* ignore rows over 50% dense */
+  knobs [Colamd::DenseCol] = 0.5 ;  /* ignore columns over 50% dense */
+}
+
+/**
+ * \brief  Computes a column ordering using the column approximate minimum degree ordering
+ *
+ * Computes a column ordering (Q) of A such that P(AQ)=LU or
+ * (AQ)'AQ=LL' have less fill-in and require fewer floating point
+ * operations than factorizing the unpermuted matrix A or A'A,
+ * respectively.
+ *
+ *
+ * \param n_row number of rows in A
+ * \param n_col number of columns in A
+ * \param Alen, size of the array A
+ * \param A row indices of the matrix, of size ALen
+ * \param p column pointers of A, of size n_col+1
+ * \param knobs parameter settings for colamd
+ * \param stats colamd output statistics and error codes
+ */
+template <typename IndexType>
+static bool compute_ordering(IndexType n_row, IndexType n_col, IndexType Alen, IndexType *A, IndexType *p, double knobs[NKnobs], IndexType stats[NStats])
+{
+  /* === Local variables ================================================== */
+
+  IndexType i ;     /* loop index */
+  IndexType nnz ;     /* nonzeros in A */
+  IndexType Row_size ;    /* size of Row [], in integers */
+  IndexType Col_size ;    /* size of Col [], in integers */
+  IndexType need ;      /* minimum required length of A */
+  Colamd::RowStructure<IndexType> *Row ;   /* pointer into A of Row [0..n_row] array */
+  Colamd::ColStructure<IndexType> *Col ;   /* pointer into A of Col [0..n_col] array */
+  IndexType n_col2 ;    /* number of non-dense, non-empty columns */
+  IndexType n_row2 ;    /* number of non-dense, non-empty rows */
+  IndexType ngarbage ;    /* number of garbage collections performed */
+  IndexType max_deg ;   /* maximum row degree */
+  double default_knobs [NKnobs] ; /* default knobs array */
+
+
+  /* === Check the input arguments ======================================== */
+
+  if (!stats)
+  {
+    COLAMD_DEBUG0 (("colamd: stats not present\n")) ;
+    return (false) ;
+  }
+  for (i = 0 ; i < NStats ; i++)
+  {
+    stats [i] = 0 ;
+  }
+  stats [Colamd::Status] = Colamd::Ok ;
+  stats [Colamd::Info1] = -1 ;
+  stats [Colamd::Info2] = -1 ;
+
+  if (!A)   /* A is not present */
+  {
+    stats [Colamd::Status] = Colamd::ErrorANotPresent ;
+    COLAMD_DEBUG0 (("colamd: A not present\n")) ;
+    return (false) ;
+  }
+
+  if (!p)   /* p is not present */
+  {
+    stats [Colamd::Status] = Colamd::ErrorPNotPresent ;
+    COLAMD_DEBUG0 (("colamd: p not present\n")) ;
+    return (false) ;
+  }
+
+  if (n_row < 0)  /* n_row must be >= 0 */
+  {
+    stats [Colamd::Status] = Colamd::ErrorNrowNegative ;
+    stats [Colamd::Info1] = n_row ;
+    COLAMD_DEBUG0 (("colamd: nrow negative %d\n", n_row)) ;
+    return (false) ;
+  }
+
+  if (n_col < 0)  /* n_col must be >= 0 */
+  {
+    stats [Colamd::Status] = Colamd::ErrorNcolNegative ;
+    stats [Colamd::Info1] = n_col ;
+    COLAMD_DEBUG0 (("colamd: ncol negative %d\n", n_col)) ;
+    return (false) ;
+  }
+
+  nnz = p [n_col] ;
+  if (nnz < 0)  /* nnz must be >= 0 */
+  {
+    stats [Colamd::Status] = Colamd::ErrorNnzNegative ;
+    stats [Colamd::Info1] = nnz ;
+    COLAMD_DEBUG0 (("colamd: number of entries negative %d\n", nnz)) ;
+    return (false) ;
+  }
+
+  if (p [0] != 0)
+  {
+    stats [Colamd::Status] = Colamd::ErrorP0Nonzero ;
+    stats [Colamd::Info1] = p [0] ;
+    COLAMD_DEBUG0 (("colamd: p[0] not zero %d\n", p [0])) ;
+    return (false) ;
+  }
+
+  /* === If no knobs, set default knobs =================================== */
+
+  if (!knobs)
+  {
+    set_defaults (default_knobs) ;
+    knobs = default_knobs ;
+  }
+
+  /* === Allocate the Row and Col arrays from array A ===================== */
+
+  Col_size = colamd_c (n_col) ;
+  Row_size = colamd_r (n_row) ;
+  need = 2*nnz + n_col + Col_size + Row_size ;
+
+  if (need > Alen)
+  {
+    /* not enough space in array A to perform the ordering */
+    stats [Colamd::Status] = Colamd::ErrorATooSmall ;
+    stats [Colamd::Info1] = need ;
+    stats [Colamd::Info2] = Alen ;
+    COLAMD_DEBUG0 (("colamd: Need Alen >= %d, given only Alen = %d\n", need,Alen));
+    return (false) ;
+  }
+
+  Alen -= Col_size + Row_size ;
+  Col = (ColStructure<IndexType> *) &A [Alen] ;
+  Row = (RowStructure<IndexType> *) &A [Alen + Col_size] ;
+
+  /* === Construct the row and column data structures ===================== */
+
+  if (!Colamd::init_rows_cols (n_row, n_col, Row, Col, A, p, stats))
+  {
+    /* input matrix is invalid */
+    COLAMD_DEBUG0 (("colamd: Matrix invalid\n")) ;
+    return (false) ;
+  }
+
+  /* === Initialize scores, kill dense rows/columns ======================= */
+
+  Colamd::init_scoring (n_row, n_col, Row, Col, A, p, knobs,
+		&n_row2, &n_col2, &max_deg) ;
+
+  /* === Order the supercolumns =========================================== */
+
+  ngarbage = Colamd::find_ordering (n_row, n_col, Alen, Row, Col, A, p,
+			    n_col2, max_deg, 2*nnz) ;
+
+  /* === Order the non-principal columns ================================== */
+
+  Colamd::order_children (n_col, Col, p) ;
+
+  /* === Return statistics in stats ======================================= */
+
+  stats [Colamd::DenseRow] = n_row - n_row2 ;
+  stats [Colamd::DenseCol] = n_col - n_col2 ;
+  stats [Colamd::DefragCount] = ngarbage ;
+  COLAMD_DEBUG0 (("colamd: done.\n")) ;
+  return (true) ;
+}
+
+/* ========================================================================== */
+/* === NON-USER-CALLABLE ROUTINES: ========================================== */
+/* ========================================================================== */
+
+/* There are no user-callable routines beyond this point in the file */
+
+/* ========================================================================== */
+/* === init_rows_cols ======================================================= */
+/* ========================================================================== */
+
+/*
+  Takes the column form of the matrix in A and creates the row form of the
+  matrix.  Also, row and column attributes are stored in the Col and Row
+  structs.  If the columns are un-sorted or contain duplicate row indices,
+  this routine will also sort and remove duplicate row indices from the
+  column form of the matrix.  Returns false if the matrix is invalid,
+  true otherwise.  Not user-callable.
+*/
+template <typename IndexType>
+static IndexType init_rows_cols  /* returns true if OK, or false otherwise */
+  (
+    /* === Parameters ======================================================= */
+
+    IndexType n_row,      /* number of rows of A */
+    IndexType n_col,      /* number of columns of A */
+    RowStructure<IndexType> Row [],    /* of size n_row+1 */
+    ColStructure<IndexType> Col [],    /* of size n_col+1 */
+    IndexType A [],     /* row indices of A, of size Alen */
+    IndexType p [],     /* pointers to columns in A, of size n_col+1 */
+    IndexType stats [NStats]  /* colamd statistics */
+    )
+{
+  /* === Local variables ================================================== */
+
+  IndexType col ;     /* a column index */
+  IndexType row ;     /* a row index */
+  IndexType *cp ;     /* a column pointer */
+  IndexType *cp_end ;   /* a pointer to the end of a column */
+  IndexType *rp ;     /* a row pointer */
+  IndexType *rp_end ;   /* a pointer to the end of a row */
+  IndexType last_row ;    /* previous row */
+
+  /* === Initialize columns, and check column pointers ==================== */
+
+  for (col = 0 ; col < n_col ; col++)
+  {
+    Col [col].start = p [col] ;
+    Col [col].length = p [col+1] - p [col] ;
+
+    if ((Col [col].length) < 0) // extra parentheses to work-around gcc bug 10200
+    {
+      /* column pointers must be non-decreasing */
+      stats [Colamd::Status] = Colamd::ErrorColLengthNegative ;
+      stats [Colamd::Info1] = col ;
+      stats [Colamd::Info2] = Col [col].length ;
+      COLAMD_DEBUG0 (("colamd: col %d length %d < 0\n", col, Col [col].length)) ;
+      return (false) ;
+    }
+
+    Col [col].shared1.thickness = 1 ;
+    Col [col].shared2.score = 0 ;
+    Col [col].shared3.prev = Empty ;
+    Col [col].shared4.degree_next = Empty ;
+  }
+
+  /* p [0..n_col] no longer needed, used as "head" in subsequent routines */
+
+  /* === Scan columns, compute row degrees, and check row indices ========= */
+
+  stats [Info3] = 0 ;  /* number of duplicate or unsorted row indices*/
+
+  for (row = 0 ; row < n_row ; row++)
+  {
+    Row [row].length = 0 ;
+    Row [row].shared2.mark = -1 ;
+  }
+
+  for (col = 0 ; col < n_col ; col++)
+  {
+    last_row = -1 ;
+
+    cp = &A [p [col]] ;
+    cp_end = &A [p [col+1]] ;
+
+    while (cp < cp_end)
+    {
+      row = *cp++ ;
+
+      /* make sure row indices within range */
+      if (row < 0 || row >= n_row)
+      {
+	stats [Colamd::Status] = Colamd::ErrorRowIndexOutOfBounds ;
+	stats [Colamd::Info1] = col ;
+	stats [Colamd::Info2] = row ;
+	stats [Colamd::Info3] = n_row ;
+	COLAMD_DEBUG0 (("colamd: row %d col %d out of bounds\n", row, col)) ;
+	return (false) ;
+      }
+
+      if (row <= last_row || Row [row].shared2.mark == col)
+      {
+	/* row index are unsorted or repeated (or both), thus col */
+	/* is jumbled.  This is a notice, not an error condition. */
+	stats [Colamd::Status] = Colamd::OkButJumbled ;
+	stats [Colamd::Info1] = col ;
+	stats [Colamd::Info2] = row ;
+	(stats [Colamd::Info3]) ++ ;
+	COLAMD_DEBUG1 (("colamd: row %d col %d unsorted/duplicate\n",row,col));
+      }
+
+      if (Row [row].shared2.mark != col)
+      {
+	Row [row].length++ ;
+      }
+      else
+      {
+	/* this is a repeated entry in the column, */
+	/* it will be removed */
+	Col [col].length-- ;
+      }
+
+      /* mark the row as having been seen in this column */
+      Row [row].shared2.mark = col ;
+
+      last_row = row ;
+    }
+  }
+
+  /* === Compute row pointers ============================================= */
+
+  /* row form of the matrix starts directly after the column */
+  /* form of matrix in A */
+  Row [0].start = p [n_col] ;
+  Row [0].shared1.p = Row [0].start ;
+  Row [0].shared2.mark = -1 ;
+  for (row = 1 ; row < n_row ; row++)
+  {
+    Row [row].start = Row [row-1].start + Row [row-1].length ;
+    Row [row].shared1.p = Row [row].start ;
+    Row [row].shared2.mark = -1 ;
+  }
+
+  /* === Create row form ================================================== */
+
+  if (stats [Status] == OkButJumbled)
+  {
+    /* if cols jumbled, watch for repeated row indices */
+    for (col = 0 ; col < n_col ; col++)
+    {
+      cp = &A [p [col]] ;
+      cp_end = &A [p [col+1]] ;
+      while (cp < cp_end)
+      {
+	row = *cp++ ;
+	if (Row [row].shared2.mark != col)
+	{
+	  A [(Row [row].shared1.p)++] = col ;
+	  Row [row].shared2.mark = col ;
+	}
+      }
+    }
+  }
+  else
+  {
+    /* if cols not jumbled, we don't need the mark (this is faster) */
+    for (col = 0 ; col < n_col ; col++)
+    {
+      cp = &A [p [col]] ;
+      cp_end = &A [p [col+1]] ;
+      while (cp < cp_end)
+      {
+	A [(Row [*cp++].shared1.p)++] = col ;
+      }
+    }
+  }
+
+  /* === Clear the row marks and set row degrees ========================== */
+
+  for (row = 0 ; row < n_row ; row++)
+  {
+    Row [row].shared2.mark = 0 ;
+    Row [row].shared1.degree = Row [row].length ;
+  }
+
+  /* === See if we need to re-create columns ============================== */
+
+  if (stats [Status] == OkButJumbled)
+  {
+    COLAMD_DEBUG0 (("colamd: reconstructing column form, matrix jumbled\n")) ;
+
+
+    /* === Compute col pointers ========================================= */
+
+    /* col form of the matrix starts at A [0]. */
+    /* Note, we may have a gap between the col form and the row */
+    /* form if there were duplicate entries, if so, it will be */
+    /* removed upon the first garbage collection */
+    Col [0].start = 0 ;
+    p [0] = Col [0].start ;
+    for (col = 1 ; col < n_col ; col++)
+    {
+      /* note that the lengths here are for pruned columns, i.e. */
+      /* no duplicate row indices will exist for these columns */
+      Col [col].start = Col [col-1].start + Col [col-1].length ;
+      p [col] = Col [col].start ;
+    }
+
+    /* === Re-create col form =========================================== */
+
+    for (row = 0 ; row < n_row ; row++)
+    {
+      rp = &A [Row [row].start] ;
+      rp_end = rp + Row [row].length ;
+      while (rp < rp_end)
+      {
+	A [(p [*rp++])++] = row ;
+      }
+    }
+  }
+
+  /* === Done.  Matrix is not (or no longer) jumbled ====================== */
+
+  return (true) ;
+}
+
+
+/* ========================================================================== */
+/* === init_scoring ========================================================= */
+/* ========================================================================== */
+
+/*
+  Kills dense or empty columns and rows, calculates an initial score for
+  each column, and places all columns in the degree lists.  Not user-callable.
+*/
+template <typename IndexType>
+static void init_scoring
+  (
+    /* === Parameters ======================================================= */
+
+    IndexType n_row,      /* number of rows of A */
+    IndexType n_col,      /* number of columns of A */
+    RowStructure<IndexType> Row [],    /* of size n_row+1 */
+    ColStructure<IndexType> Col [],    /* of size n_col+1 */
+    IndexType A [],     /* column form and row form of A */
+    IndexType head [],    /* of size n_col+1 */
+    double knobs [NKnobs],/* parameters */
+    IndexType *p_n_row2,    /* number of non-dense, non-empty rows */
+    IndexType *p_n_col2,    /* number of non-dense, non-empty columns */
+    IndexType *p_max_deg    /* maximum row degree */
+    )
+{
+  /* === Local variables ================================================== */
+
+  IndexType c ;     /* a column index */
+  IndexType r, row ;    /* a row index */
+  IndexType *cp ;     /* a column pointer */
+  IndexType deg ;     /* degree of a row or column */
+  IndexType *cp_end ;   /* a pointer to the end of a column */
+  IndexType *new_cp ;   /* new column pointer */
+  IndexType col_length ;    /* length of pruned column */
+  IndexType score ;     /* current column score */
+  IndexType n_col2 ;    /* number of non-dense, non-empty columns */
+  IndexType n_row2 ;    /* number of non-dense, non-empty rows */
+  IndexType dense_row_count ; /* remove rows with more entries than this */
+  IndexType dense_col_count ; /* remove cols with more entries than this */
+  IndexType min_score ;   /* smallest column score */
+  IndexType max_deg ;   /* maximum row degree */
+  IndexType next_col ;    /* Used to add to degree list.*/
+
+
+  /* === Extract knobs ==================================================== */
+
+  dense_row_count = numext::maxi(IndexType(0), numext::mini(IndexType(knobs [Colamd::DenseRow] * n_col), n_col)) ;
+  dense_col_count = numext::maxi(IndexType(0), numext::mini(IndexType(knobs [Colamd::DenseCol] * n_row), n_row)) ;
+  COLAMD_DEBUG1 (("colamd: densecount: %d %d\n", dense_row_count, dense_col_count)) ;
+  max_deg = 0 ;
+  n_col2 = n_col ;
+  n_row2 = n_row ;
+
+  /* === Kill empty columns =============================================== */
+
+  /* Put the empty columns at the end in their natural order, so that LU */
+  /* factorization can proceed as far as possible. */
+  for (c = n_col-1 ; c >= 0 ; c--)
+  {
+    deg = Col [c].length ;
+    if (deg == 0)
+    {
+      /* this is a empty column, kill and order it last */
+      Col [c].shared2.order = --n_col2 ;
+      Col[c].kill_principal() ;
+    }
+  }
+  COLAMD_DEBUG1 (("colamd: null columns killed: %d\n", n_col - n_col2)) ;
+
+  /* === Kill dense columns =============================================== */
+
+  /* Put the dense columns at the end, in their natural order */
+  for (c = n_col-1 ; c >= 0 ; c--)
+  {
+    /* skip any dead columns */
+    if (Col[c].is_dead())
+    {
+      continue ;
+    }
+    deg = Col [c].length ;
+    if (deg > dense_col_count)
+    {
+      /* this is a dense column, kill and order it last */
+      Col [c].shared2.order = --n_col2 ;
+      /* decrement the row degrees */
+      cp = &A [Col [c].start] ;
+      cp_end = cp + Col [c].length ;
+      while (cp < cp_end)
+      {
+	Row [*cp++].shared1.degree-- ;
+      }
+      Col[c].kill_principal() ;
+    }
+  }
+  COLAMD_DEBUG1 (("colamd: Dense and null columns killed: %d\n", n_col - n_col2)) ;
+
+  /* === Kill dense and empty rows ======================================== */
+
+  for (r = 0 ; r < n_row ; r++)
+  {
+    deg = Row [r].shared1.degree ;
+    COLAMD_ASSERT (deg >= 0 && deg <= n_col) ;
+    if (deg > dense_row_count || deg == 0)
+    {
+      /* kill a dense or empty row */
+      Row[r].kill() ;
+      --n_row2 ;
+    }
+    else
+    {
+      /* keep track of max degree of remaining rows */
+      max_deg = numext::maxi(max_deg, deg) ;
+    }
+  }
+  COLAMD_DEBUG1 (("colamd: Dense and null rows killed: %d\n", n_row - n_row2)) ;
+
+  /* === Compute initial column scores ==================================== */
+
+  /* At this point the row degrees are accurate.  They reflect the number */
+  /* of "live" (non-dense) columns in each row.  No empty rows exist. */
+  /* Some "live" columns may contain only dead rows, however.  These are */
+  /* pruned in the code below. */
+
+  /* now find the initial matlab score for each column */
+  for (c = n_col-1 ; c >= 0 ; c--)
+  {
+    /* skip dead column */
+    if (Col[c].is_dead())
+    {
+      continue ;
+    }
+    score = 0 ;
+    cp = &A [Col [c].start] ;
+    new_cp = cp ;
+    cp_end = cp + Col [c].length ;
+    while (cp < cp_end)
+    {
+      /* get a row */
+      row = *cp++ ;
+      /* skip if dead */
+      if (Row[row].is_dead())
+      {
+	continue ;
+      }
+      /* compact the column */
+      *new_cp++ = row ;
+      /* add row's external degree */
+      score += Row [row].shared1.degree - 1 ;
+      /* guard against integer overflow */
+      score = numext::mini(score, n_col) ;
+    }
+    /* determine pruned column length */
+    col_length = (IndexType) (new_cp - &A [Col [c].start]) ;
+    if (col_length == 0)
+    {
+      /* a newly-made null column (all rows in this col are "dense" */
+      /* and have already been killed) */
+      COLAMD_DEBUG2 (("Newly null killed: %d\n", c)) ;
+      Col [c].shared2.order = --n_col2 ;
+      Col[c].kill_principal() ;
+    }
+    else
+    {
+      /* set column length and set score */
+      COLAMD_ASSERT (score >= 0) ;
+      COLAMD_ASSERT (score <= n_col) ;
+      Col [c].length = col_length ;
+      Col [c].shared2.score = score ;
+    }
+  }
+  COLAMD_DEBUG1 (("colamd: Dense, null, and newly-null columns killed: %d\n",
+		  n_col-n_col2)) ;
+
+  /* At this point, all empty rows and columns are dead.  All live columns */
+  /* are "clean" (containing no dead rows) and simplicial (no supercolumns */
+  /* yet).  Rows may contain dead columns, but all live rows contain at */
+  /* least one live column. */
+
+  /* === Initialize degree lists ========================================== */
+
+
+  /* clear the hash buckets */
+  for (c = 0 ; c <= n_col ; c++)
+  {
+    head [c] = Empty ;
+  }
+  min_score = n_col ;
+  /* place in reverse order, so low column indices are at the front */
+  /* of the lists.  This is to encourage natural tie-breaking */
+  for (c = n_col-1 ; c >= 0 ; c--)
+  {
+    /* only add principal columns to degree lists */
+    if (Col[c].is_alive())
+    {
+      COLAMD_DEBUG4 (("place %d score %d minscore %d ncol %d\n",
+		      c, Col [c].shared2.score, min_score, n_col)) ;
+
+      /* === Add columns score to DList =============================== */
+
+      score = Col [c].shared2.score ;
+
+      COLAMD_ASSERT (min_score >= 0) ;
+      COLAMD_ASSERT (min_score <= n_col) ;
+      COLAMD_ASSERT (score >= 0) ;
+      COLAMD_ASSERT (score <= n_col) ;
+      COLAMD_ASSERT (head [score] >= Empty) ;
+
+      /* now add this column to dList at proper score location */
+      next_col = head [score] ;
+      Col [c].shared3.prev = Empty ;
+      Col [c].shared4.degree_next = next_col ;
+
+      /* if there already was a column with the same score, set its */
+      /* previous pointer to this new column */
+      if (next_col != Empty)
+      {
+	Col [next_col].shared3.prev = c ;
+      }
+      head [score] = c ;
+
+      /* see if this score is less than current min */
+      min_score = numext::mini(min_score, score) ;
+
+
+    }
+  }
+
+
+  /* === Return number of remaining columns, and max row degree =========== */
+
+  *p_n_col2 = n_col2 ;
+  *p_n_row2 = n_row2 ;
+  *p_max_deg = max_deg ;
+}
+
+
+/* ========================================================================== */
+/* === find_ordering ======================================================== */
+/* ========================================================================== */
+
+/*
+  Order the principal columns of the supercolumn form of the matrix
+  (no supercolumns on input).  Uses a minimum approximate column minimum
+  degree ordering method.  Not user-callable.
+*/
+template <typename IndexType>
+static IndexType find_ordering /* return the number of garbage collections */
+  (
+    /* === Parameters ======================================================= */
+
+    IndexType n_row,      /* number of rows of A */
+    IndexType n_col,      /* number of columns of A */
+    IndexType Alen,     /* size of A, 2*nnz + n_col or larger */
+    RowStructure<IndexType> Row [],    /* of size n_row+1 */
+    ColStructure<IndexType> Col [],    /* of size n_col+1 */
+    IndexType A [],     /* column form and row form of A */
+    IndexType head [],    /* of size n_col+1 */
+    IndexType n_col2,     /* Remaining columns to order */
+    IndexType max_deg,    /* Maximum row degree */
+    IndexType pfree     /* index of first free slot (2*nnz on entry) */
+    )
+{
+  /* === Local variables ================================================== */
+
+  IndexType k ;     /* current pivot ordering step */
+  IndexType pivot_col ;   /* current pivot column */
+  IndexType *cp ;     /* a column pointer */
+  IndexType *rp ;     /* a row pointer */
+  IndexType pivot_row ;   /* current pivot row */
+  IndexType *new_cp ;   /* modified column pointer */
+  IndexType *new_rp ;   /* modified row pointer */
+  IndexType pivot_row_start ; /* pointer to start of pivot row */
+  IndexType pivot_row_degree ;  /* number of columns in pivot row */
+  IndexType pivot_row_length ;  /* number of supercolumns in pivot row */
+  IndexType pivot_col_score ; /* score of pivot column */
+  IndexType needed_memory ;   /* free space needed for pivot row */
+  IndexType *cp_end ;   /* pointer to the end of a column */
+  IndexType *rp_end ;   /* pointer to the end of a row */
+  IndexType row ;     /* a row index */
+  IndexType col ;     /* a column index */
+  IndexType max_score ;   /* maximum possible score */
+  IndexType cur_score ;   /* score of current column */
+  unsigned int hash ;   /* hash value for supernode detection */
+  IndexType head_column ;   /* head of hash bucket */
+  IndexType first_col ;   /* first column in hash bucket */
+  IndexType tag_mark ;    /* marker value for mark array */
+  IndexType row_mark ;    /* Row [row].shared2.mark */
+  IndexType set_difference ;  /* set difference size of row with pivot row */
+  IndexType min_score ;   /* smallest column score */
+  IndexType col_thickness ;   /* "thickness" (no. of columns in a supercol) */
+  IndexType max_mark ;    /* maximum value of tag_mark */
+  IndexType pivot_col_thickness ; /* number of columns represented by pivot col */
+  IndexType prev_col ;    /* Used by Dlist operations. */
+  IndexType next_col ;    /* Used by Dlist operations. */
+  IndexType ngarbage ;    /* number of garbage collections performed */
+
+
+  /* === Initialization and clear mark ==================================== */
+
+  max_mark = INT_MAX - n_col ;  /* INT_MAX defined in <limits.h> */
+  tag_mark = Colamd::clear_mark (n_row, Row) ;
+  min_score = 0 ;
+  ngarbage = 0 ;
+  COLAMD_DEBUG1 (("colamd: Ordering, n_col2=%d\n", n_col2)) ;
+
+  /* === Order the columns ================================================ */
+
+  for (k = 0 ; k < n_col2 ; /* 'k' is incremented below */)
+  {
+
+    /* === Select pivot column, and order it ============================ */
+
+    /* make sure degree list isn't empty */
+    COLAMD_ASSERT (min_score >= 0) ;
+    COLAMD_ASSERT (min_score <= n_col) ;
+    COLAMD_ASSERT (head [min_score] >= Empty) ;
+
+    /* get pivot column from head of minimum degree list */
+    while (min_score < n_col && head [min_score] == Empty)
+    {
+      min_score++ ;
+    }
+    pivot_col = head [min_score] ;
+    COLAMD_ASSERT (pivot_col >= 0 && pivot_col <= n_col) ;
+    next_col = Col [pivot_col].shared4.degree_next ;
+    head [min_score] = next_col ;
+    if (next_col != Empty)
+    {
+      Col [next_col].shared3.prev = Empty ;
+    }
+
+    COLAMD_ASSERT (Col[pivot_col].is_alive()) ;
+    COLAMD_DEBUG3 (("Pivot col: %d\n", pivot_col)) ;
+
+    /* remember score for defrag check */
+    pivot_col_score = Col [pivot_col].shared2.score ;
+
+    /* the pivot column is the kth column in the pivot order */
+    Col [pivot_col].shared2.order = k ;
+
+    /* increment order count by column thickness */
+    pivot_col_thickness = Col [pivot_col].shared1.thickness ;
+    k += pivot_col_thickness ;
+    COLAMD_ASSERT (pivot_col_thickness > 0) ;
+
+    /* === Garbage_collection, if necessary ============================= */
+
+    needed_memory = numext::mini(pivot_col_score, n_col - k) ;
+    if (pfree + needed_memory >= Alen)
+    {
+      pfree = Colamd::garbage_collection (n_row, n_col, Row, Col, A, &A [pfree]) ;
+      ngarbage++ ;
+      /* after garbage collection we will have enough */
+      COLAMD_ASSERT (pfree + needed_memory < Alen) ;
+      /* garbage collection has wiped out the Row[].shared2.mark array */
+      tag_mark = Colamd::clear_mark (n_row, Row) ;
+
+    }
+
+    /* === Compute pivot row pattern ==================================== */
+
+    /* get starting location for this new merged row */
+    pivot_row_start = pfree ;
+
+    /* initialize new row counts to zero */
+    pivot_row_degree = 0 ;
+
+    /* tag pivot column as having been visited so it isn't included */
+    /* in merged pivot row */
+    Col [pivot_col].shared1.thickness = -pivot_col_thickness ;
+
+    /* pivot row is the union of all rows in the pivot column pattern */
+    cp = &A [Col [pivot_col].start] ;
+    cp_end = cp + Col [pivot_col].length ;
+    while (cp < cp_end)
+    {
+      /* get a row */
+      row = *cp++ ;
+      COLAMD_DEBUG4 (("Pivot col pattern %d %d\n", Row[row].is_alive(), row)) ;
+      /* skip if row is dead */
+      if (Row[row].is_dead())
+      {
+	continue ;
+      }
+      rp = &A [Row [row].start] ;
+      rp_end = rp + Row [row].length ;
+      while (rp < rp_end)
+      {
+	/* get a column */
+	col = *rp++ ;
+	/* add the column, if alive and untagged */
+	col_thickness = Col [col].shared1.thickness ;
+	if (col_thickness > 0 && Col[col].is_alive())
+	{
+	  /* tag column in pivot row */
+	  Col [col].shared1.thickness = -col_thickness ;
+	  COLAMD_ASSERT (pfree < Alen) ;
+	  /* place column in pivot row */
+	  A [pfree++] = col ;
+	  pivot_row_degree += col_thickness ;
+	}
+      }
+    }
+
+    /* clear tag on pivot column */
+    Col [pivot_col].shared1.thickness = pivot_col_thickness ;
+    max_deg = numext::maxi(max_deg, pivot_row_degree) ;
+
+
+    /* === Kill all rows used to construct pivot row ==================== */
+
+    /* also kill pivot row, temporarily */
+    cp = &A [Col [pivot_col].start] ;
+    cp_end = cp + Col [pivot_col].length ;
+    while (cp < cp_end)
+    {
+      /* may be killing an already dead row */
+      row = *cp++ ;
+      COLAMD_DEBUG3 (("Kill row in pivot col: %d\n", row)) ;
+      Row[row].kill() ;
+    }
+
+    /* === Select a row index to use as the new pivot row =============== */
+
+    pivot_row_length = pfree - pivot_row_start ;
+    if (pivot_row_length > 0)
+    {
+      /* pick the "pivot" row arbitrarily (first row in col) */
+      pivot_row = A [Col [pivot_col].start] ;
+      COLAMD_DEBUG3 (("Pivotal row is %d\n", pivot_row)) ;
+    }
+    else
+    {
+      /* there is no pivot row, since it is of zero length */
+      pivot_row = Empty ;
+      COLAMD_ASSERT (pivot_row_length == 0) ;
+    }
+    COLAMD_ASSERT (Col [pivot_col].length > 0 || pivot_row_length == 0) ;
+
+    /* === Approximate degree computation =============================== */
+
+    /* Here begins the computation of the approximate degree.  The column */
+    /* score is the sum of the pivot row "length", plus the size of the */
+    /* set differences of each row in the column minus the pattern of the */
+    /* pivot row itself.  The column ("thickness") itself is also */
+    /* excluded from the column score (we thus use an approximate */
+    /* external degree). */
+
+    /* The time taken by the following code (compute set differences, and */
+    /* add them up) is proportional to the size of the data structure */
+    /* being scanned - that is, the sum of the sizes of each column in */
+    /* the pivot row.  Thus, the amortized time to compute a column score */
+    /* is proportional to the size of that column (where size, in this */
+    /* context, is the column "length", or the number of row indices */
+    /* in that column).  The number of row indices in a column is */
+    /* monotonically non-decreasing, from the length of the original */
+    /* column on input to colamd. */
+
+    /* === Compute set differences ====================================== */
+
+    COLAMD_DEBUG3 (("** Computing set differences phase. **\n")) ;
+
+    /* pivot row is currently dead - it will be revived later. */
+
+    COLAMD_DEBUG3 (("Pivot row: ")) ;
+    /* for each column in pivot row */
+    rp = &A [pivot_row_start] ;
+    rp_end = rp + pivot_row_length ;
+    while (rp < rp_end)
+    {
+      col = *rp++ ;
+      COLAMD_ASSERT (Col[col].is_alive() && col != pivot_col) ;
+      COLAMD_DEBUG3 (("Col: %d\n", col)) ;
+
+      /* clear tags used to construct pivot row pattern */
+      col_thickness = -Col [col].shared1.thickness ;
+      COLAMD_ASSERT (col_thickness > 0) ;
+      Col [col].shared1.thickness = col_thickness ;
+
+      /* === Remove column from degree list =========================== */
+
+      cur_score = Col [col].shared2.score ;
+      prev_col = Col [col].shared3.prev ;
+      next_col = Col [col].shared4.degree_next ;
+      COLAMD_ASSERT (cur_score >= 0) ;
+      COLAMD_ASSERT (cur_score <= n_col) ;
+      COLAMD_ASSERT (cur_score >= Empty) ;
+      if (prev_col == Empty)
+      {
+	head [cur_score] = next_col ;
+      }
+      else
+      {
+	Col [prev_col].shared4.degree_next = next_col ;
+      }
+      if (next_col != Empty)
+      {
+	Col [next_col].shared3.prev = prev_col ;
+      }
+
+      /* === Scan the column ========================================== */
+
+      cp = &A [Col [col].start] ;
+      cp_end = cp + Col [col].length ;
+      while (cp < cp_end)
+      {
+	/* get a row */
+	row = *cp++ ;
+	/* skip if dead */
+	if (Row[row].is_dead())
+	{
+	  continue ;
+	}
+  row_mark = Row [row].shared2.mark ;
+	COLAMD_ASSERT (row != pivot_row) ;
+	set_difference = row_mark - tag_mark ;
+	/* check if the row has been seen yet */
+	if (set_difference < 0)
+	{
+	  COLAMD_ASSERT (Row [row].shared1.degree <= max_deg) ;
+	  set_difference = Row [row].shared1.degree ;
+	}
+	/* subtract column thickness from this row's set difference */
+	set_difference -= col_thickness ;
+	COLAMD_ASSERT (set_difference >= 0) ;
+	/* absorb this row if the set difference becomes zero */
+	if (set_difference == 0)
+	{
+	  COLAMD_DEBUG3 (("aggressive absorption. Row: %d\n", row)) ;
+	  Row[row].kill() ;
+	}
+	else
+	{
+	  /* save the new mark */
+	  Row [row].shared2.mark = set_difference + tag_mark ;
+	}
+      }
+    }
+
+
+    /* === Add up set differences for each column ======================= */
+
+    COLAMD_DEBUG3 (("** Adding set differences phase. **\n")) ;
+
+    /* for each column in pivot row */
+    rp = &A [pivot_row_start] ;
+    rp_end = rp + pivot_row_length ;
+    while (rp < rp_end)
+    {
+      /* get a column */
+      col = *rp++ ;
+      COLAMD_ASSERT (Col[col].is_alive() && col != pivot_col) ;
+      hash = 0 ;
+      cur_score = 0 ;
+      cp = &A [Col [col].start] ;
+      /* compact the column */
+      new_cp = cp ;
+      cp_end = cp + Col [col].length ;
+
+      COLAMD_DEBUG4 (("Adding set diffs for Col: %d.\n", col)) ;
+
+      while (cp < cp_end)
+      {
+	/* get a row */
+	row = *cp++ ;
+	COLAMD_ASSERT(row >= 0 && row < n_row) ;
+	/* skip if dead */
+	if (Row [row].is_dead())
+	{
+	  continue ;
+	}
+  row_mark = Row [row].shared2.mark ;
+	COLAMD_ASSERT (row_mark > tag_mark) ;
+	/* compact the column */
+	*new_cp++ = row ;
+	/* compute hash function */
+	hash += row ;
+	/* add set difference */
+	cur_score += row_mark - tag_mark ;
+	/* integer overflow... */
+	cur_score = numext::mini(cur_score, n_col) ;
+      }
+
+      /* recompute the column's length */
+      Col [col].length = (IndexType) (new_cp - &A [Col [col].start]) ;
+
+      /* === Further mass elimination ================================= */
+
+      if (Col [col].length == 0)
+      {
+	COLAMD_DEBUG4 (("further mass elimination. Col: %d\n", col)) ;
+	/* nothing left but the pivot row in this column */
+	Col[col].kill_principal() ;
+	pivot_row_degree -= Col [col].shared1.thickness ;
+	COLAMD_ASSERT (pivot_row_degree >= 0) ;
+	/* order it */
+	Col [col].shared2.order = k ;
+	/* increment order count by column thickness */
+	k += Col [col].shared1.thickness ;
+      }
+      else
+      {
+	/* === Prepare for supercolumn detection ==================== */
+
+	COLAMD_DEBUG4 (("Preparing supercol detection for Col: %d.\n", col)) ;
+
+	/* save score so far */
+	Col [col].shared2.score = cur_score ;
+
+	/* add column to hash table, for supercolumn detection */
+	hash %= n_col + 1 ;
+
+	COLAMD_DEBUG4 ((" Hash = %d, n_col = %d.\n", hash, n_col)) ;
+	COLAMD_ASSERT (hash <= n_col) ;
+
+	head_column = head [hash] ;
+	if (head_column > Empty)
+	{
+	  /* degree list "hash" is non-empty, use prev (shared3) of */
+	  /* first column in degree list as head of hash bucket */
+	  first_col = Col [head_column].shared3.headhash ;
+	  Col [head_column].shared3.headhash = col ;
+	}
+	else
+	{
+	  /* degree list "hash" is empty, use head as hash bucket */
+	  first_col = - (head_column + 2) ;
+	  head [hash] = - (col + 2) ;
+	}
+	Col [col].shared4.hash_next = first_col ;
+
+	/* save hash function in Col [col].shared3.hash */
+	Col [col].shared3.hash = (IndexType) hash ;
+	COLAMD_ASSERT (Col[col].is_alive()) ;
+      }
+    }
+
+    /* The approximate external column degree is now computed.  */
+
+    /* === Supercolumn detection ======================================== */
+
+    COLAMD_DEBUG3 (("** Supercolumn detection phase. **\n")) ;
+
+    Colamd::detect_super_cols (Col, A, head, pivot_row_start, pivot_row_length) ;
+
+    /* === Kill the pivotal column ====================================== */
+
+    Col[pivot_col].kill_principal() ;
+
+    /* === Clear mark =================================================== */
+
+    tag_mark += (max_deg + 1) ;
+    if (tag_mark >= max_mark)
+    {
+      COLAMD_DEBUG2 (("clearing tag_mark\n")) ;
+      tag_mark = Colamd::clear_mark (n_row, Row) ;
+    }
+
+    /* === Finalize the new pivot row, and column scores ================ */
+
+    COLAMD_DEBUG3 (("** Finalize scores phase. **\n")) ;
+
+    /* for each column in pivot row */
+    rp = &A [pivot_row_start] ;
+    /* compact the pivot row */
+    new_rp = rp ;
+    rp_end = rp + pivot_row_length ;
+    while (rp < rp_end)
+    {
+      col = *rp++ ;
+      /* skip dead columns */
+      if (Col[col].is_dead())
+      {
+	continue ;
+      }
+      *new_rp++ = col ;
+      /* add new pivot row to column */
+      A [Col [col].start + (Col [col].length++)] = pivot_row ;
+
+      /* retrieve score so far and add on pivot row's degree. */
+      /* (we wait until here for this in case the pivot */
+      /* row's degree was reduced due to mass elimination). */
+      cur_score = Col [col].shared2.score + pivot_row_degree ;
+
+      /* calculate the max possible score as the number of */
+      /* external columns minus the 'k' value minus the */
+      /* columns thickness */
+      max_score = n_col - k - Col [col].shared1.thickness ;
+
+      /* make the score the external degree of the union-of-rows */
+      cur_score -= Col [col].shared1.thickness ;
+
+      /* make sure score is less or equal than the max score */
+      cur_score = numext::mini(cur_score, max_score) ;
+      COLAMD_ASSERT (cur_score >= 0) ;
+
+      /* store updated score */
+      Col [col].shared2.score = cur_score ;
+
+      /* === Place column back in degree list ========================= */
+
+      COLAMD_ASSERT (min_score >= 0) ;
+      COLAMD_ASSERT (min_score <= n_col) ;
+      COLAMD_ASSERT (cur_score >= 0) ;
+      COLAMD_ASSERT (cur_score <= n_col) ;
+      COLAMD_ASSERT (head [cur_score] >= Empty) ;
+      next_col = head [cur_score] ;
+      Col [col].shared4.degree_next = next_col ;
+      Col [col].shared3.prev = Empty ;
+      if (next_col != Empty)
+      {
+	Col [next_col].shared3.prev = col ;
+      }
+      head [cur_score] = col ;
+
+      /* see if this score is less than current min */
+      min_score = numext::mini(min_score, cur_score) ;
+
+    }
+
+    /* === Resurrect the new pivot row ================================== */
+
+    if (pivot_row_degree > 0)
+    {
+      /* update pivot row length to reflect any cols that were killed */
+      /* during super-col detection and mass elimination */
+      Row [pivot_row].start  = pivot_row_start ;
+      Row [pivot_row].length = (IndexType) (new_rp - &A[pivot_row_start]) ;
+      Row [pivot_row].shared1.degree = pivot_row_degree ;
+      Row [pivot_row].shared2.mark = 0 ;
+      /* pivot row is no longer dead */
+    }
+  }
+
+  /* === All principal columns have now been ordered ====================== */
+
+  return (ngarbage) ;
+}
+
+
+/* ========================================================================== */
+/* === order_children ======================================================= */
+/* ========================================================================== */
+
+/*
+  The find_ordering routine has ordered all of the principal columns (the
+  representatives of the supercolumns).  The non-principal columns have not
+  yet been ordered.  This routine orders those columns by walking up the
+  parent tree (a column is a child of the column which absorbed it).  The
+  final permutation vector is then placed in p [0 ... n_col-1], with p [0]
+  being the first column, and p [n_col-1] being the last.  It doesn't look
+  like it at first glance, but be assured that this routine takes time linear
+  in the number of columns.  Although not immediately obvious, the time
+  taken by this routine is O (n_col), that is, linear in the number of
+  columns.  Not user-callable.
+*/
+template <typename IndexType>
+static inline  void order_children
+(
+  /* === Parameters ======================================================= */
+
+  IndexType n_col,      /* number of columns of A */
+  ColStructure<IndexType> Col [],    /* of size n_col+1 */
+  IndexType p []      /* p [0 ... n_col-1] is the column permutation*/
+  )
+{
+  /* === Local variables ================================================== */
+
+  IndexType i ;     /* loop counter for all columns */
+  IndexType c ;     /* column index */
+  IndexType parent ;    /* index of column's parent */
+  IndexType order ;     /* column's order */
+
+  /* === Order each non-principal column ================================== */
+
+  for (i = 0 ; i < n_col ; i++)
+  {
+    /* find an un-ordered non-principal column */
+    COLAMD_ASSERT (col_is_dead(Col, i)) ;
+    if (!Col[i].is_dead_principal() && Col [i].shared2.order == Empty)
+    {
+      parent = i ;
+      /* once found, find its principal parent */
+      do
+      {
+	parent = Col [parent].shared1.parent ;
+      } while (!Col[parent].is_dead_principal()) ;
+
+      /* now, order all un-ordered non-principal columns along path */
+      /* to this parent.  collapse tree at the same time */
+      c = i ;
+      /* get order of parent */
+      order = Col [parent].shared2.order ;
+
+      do
+      {
+	COLAMD_ASSERT (Col [c].shared2.order == Empty) ;
+
+	/* order this column */
+	Col [c].shared2.order = order++ ;
+	/* collaps tree */
+	Col [c].shared1.parent = parent ;
+
+	/* get immediate parent of this column */
+	c = Col [c].shared1.parent ;
+
+	/* continue until we hit an ordered column.  There are */
+	/* guaranteed not to be anymore unordered columns */
+	/* above an ordered column */
+      } while (Col [c].shared2.order == Empty) ;
+
+      /* re-order the super_col parent to largest order for this group */
+      Col [parent].shared2.order = order ;
+    }
+  }
+
+  /* === Generate the permutation ========================================= */
+
+  for (c = 0 ; c < n_col ; c++)
+  {
+    p [Col [c].shared2.order] = c ;
+  }
+}
+
+
+/* ========================================================================== */
+/* === detect_super_cols ==================================================== */
+/* ========================================================================== */
+
+/*
+  Detects supercolumns by finding matches between columns in the hash buckets.
+  Check amongst columns in the set A [row_start ... row_start + row_length-1].
+  The columns under consideration are currently *not* in the degree lists,
+  and have already been placed in the hash buckets.
+
+  The hash bucket for columns whose hash function is equal to h is stored
+  as follows:
+
+  if head [h] is >= 0, then head [h] contains a degree list, so:
+
+  head [h] is the first column in degree bucket h.
+  Col [head [h]].headhash gives the first column in hash bucket h.
+
+  otherwise, the degree list is empty, and:
+
+  -(head [h] + 2) is the first column in hash bucket h.
+
+  For a column c in a hash bucket, Col [c].shared3.prev is NOT a "previous
+  column" pointer.  Col [c].shared3.hash is used instead as the hash number
+  for that column.  The value of Col [c].shared4.hash_next is the next column
+  in the same hash bucket.
+
+  Assuming no, or "few" hash collisions, the time taken by this routine is
+  linear in the sum of the sizes (lengths) of each column whose score has
+  just been computed in the approximate degree computation.
+  Not user-callable.
+*/
+template <typename IndexType>
+static void detect_super_cols
+(
+  /* === Parameters ======================================================= */
+
+  ColStructure<IndexType> Col [],    /* of size n_col+1 */
+  IndexType A [],     /* row indices of A */
+  IndexType head [],    /* head of degree lists and hash buckets */
+  IndexType row_start,    /* pointer to set of columns to check */
+  IndexType row_length    /* number of columns to check */
+)
+{
+  /* === Local variables ================================================== */
+
+  IndexType hash ;      /* hash value for a column */
+  IndexType *rp ;     /* pointer to a row */
+  IndexType c ;     /* a column index */
+  IndexType super_c ;   /* column index of the column to absorb into */
+  IndexType *cp1 ;      /* column pointer for column super_c */
+  IndexType *cp2 ;      /* column pointer for column c */
+  IndexType length ;    /* length of column super_c */
+  IndexType prev_c ;    /* column preceding c in hash bucket */
+  IndexType i ;     /* loop counter */
+  IndexType *rp_end ;   /* pointer to the end of the row */
+  IndexType col ;     /* a column index in the row to check */
+  IndexType head_column ;   /* first column in hash bucket or degree list */
+  IndexType first_col ;   /* first column in hash bucket */
+
+  /* === Consider each column in the row ================================== */
+
+  rp = &A [row_start] ;
+  rp_end = rp + row_length ;
+  while (rp < rp_end)
+  {
+    col = *rp++ ;
+    if (Col[col].is_dead())
+    {
+      continue ;
+    }
+
+    /* get hash number for this column */
+    hash = Col [col].shared3.hash ;
+    COLAMD_ASSERT (hash <= n_col) ;
+
+    /* === Get the first column in this hash bucket ===================== */
+
+    head_column = head [hash] ;
+    if (head_column > Empty)
+    {
+      first_col = Col [head_column].shared3.headhash ;
+    }
+    else
+    {
+      first_col = - (head_column + 2) ;
+    }
+
+    /* === Consider each column in the hash bucket ====================== */
+
+    for (super_c = first_col ; super_c != Empty ;
+	 super_c = Col [super_c].shared4.hash_next)
+    {
+      COLAMD_ASSERT (Col [super_c].is_alive()) ;
+      COLAMD_ASSERT (Col [super_c].shared3.hash == hash) ;
+      length = Col [super_c].length ;
+
+      /* prev_c is the column preceding column c in the hash bucket */
+      prev_c = super_c ;
+
+      /* === Compare super_c with all columns after it ================ */
+
+      for (c = Col [super_c].shared4.hash_next ;
+	   c != Empty ; c = Col [c].shared4.hash_next)
+      {
+	COLAMD_ASSERT (c != super_c) ;
+	COLAMD_ASSERT (Col[c].is_alive()) ;
+	COLAMD_ASSERT (Col [c].shared3.hash == hash) ;
+
+	/* not identical if lengths or scores are different */
+	if (Col [c].length != length ||
+	    Col [c].shared2.score != Col [super_c].shared2.score)
+	{
+	  prev_c = c ;
+	  continue ;
+	}
+
+	/* compare the two columns */
+	cp1 = &A [Col [super_c].start] ;
+	cp2 = &A [Col [c].start] ;
+
+	for (i = 0 ; i < length ; i++)
+	{
+	  /* the columns are "clean" (no dead rows) */
+	  COLAMD_ASSERT ( cp1->is_alive() );
+	  COLAMD_ASSERT ( cp2->is_alive() );
+	  /* row indices will same order for both supercols, */
+	  /* no gather scatter necessary */
+	  if (*cp1++ != *cp2++)
+	  {
+	    break ;
+	  }
+	}
+
+	/* the two columns are different if the for-loop "broke" */
+	if (i != length)
+	{
+	  prev_c = c ;
+	  continue ;
+	}
+
+	/* === Got it!  two columns are identical =================== */
+
+	COLAMD_ASSERT (Col [c].shared2.score == Col [super_c].shared2.score) ;
+
+	Col [super_c].shared1.thickness += Col [c].shared1.thickness ;
+	Col [c].shared1.parent = super_c ;
+	Col[c].kill_non_principal() ;
+	/* order c later, in order_children() */
+	Col [c].shared2.order = Empty ;
+	/* remove c from hash bucket */
+	Col [prev_c].shared4.hash_next = Col [c].shared4.hash_next ;
+      }
+    }
+
+    /* === Empty this hash bucket ======================================= */
+
+    if (head_column > Empty)
+    {
+      /* corresponding degree list "hash" is not empty */
+      Col [head_column].shared3.headhash = Empty ;
+    }
+    else
+    {
+      /* corresponding degree list "hash" is empty */
+      head [hash] = Empty ;
+    }
+  }
+}
+
+
+/* ========================================================================== */
+/* === garbage_collection =================================================== */
+/* ========================================================================== */
+
+/*
+  Defragments and compacts columns and rows in the workspace A.  Used when
+  all available memory has been used while performing row merging.  Returns
+  the index of the first free position in A, after garbage collection.  The
+  time taken by this routine is linear is the size of the array A, which is
+  itself linear in the number of nonzeros in the input matrix.
+  Not user-callable.
+*/
+template <typename IndexType>
+static IndexType garbage_collection  /* returns the new value of pfree */
+  (
+    /* === Parameters ======================================================= */
+
+    IndexType n_row,      /* number of rows */
+    IndexType n_col,      /* number of columns */
+    RowStructure<IndexType> Row [],    /* row info */
+    ColStructure<IndexType> Col [],    /* column info */
+    IndexType A [],     /* A [0 ... Alen-1] holds the matrix */
+    IndexType *pfree      /* &A [0] ... pfree is in use */
+    )
+{
+  /* === Local variables ================================================== */
+
+  IndexType *psrc ;     /* source pointer */
+  IndexType *pdest ;    /* destination pointer */
+  IndexType j ;     /* counter */
+  IndexType r ;     /* a row index */
+  IndexType c ;     /* a column index */
+  IndexType length ;    /* length of a row or column */
+
+  /* === Defragment the columns =========================================== */
+
+  pdest = &A[0] ;
+  for (c = 0 ; c < n_col ; c++)
+  {
+    if (Col[c].is_alive())
+    {
+      psrc = &A [Col [c].start] ;
+
+      /* move and compact the column */
+      COLAMD_ASSERT (pdest <= psrc) ;
+      Col [c].start = (IndexType) (pdest - &A [0]) ;
+      length = Col [c].length ;
+      for (j = 0 ; j < length ; j++)
+      {
+	r = *psrc++ ;
+	if (Row[r].is_alive())
+	{
+	  *pdest++ = r ;
+	}
+      }
+      Col [c].length = (IndexType) (pdest - &A [Col [c].start]) ;
+    }
+  }
+
+  /* === Prepare to defragment the rows =================================== */
+
+  for (r = 0 ; r < n_row ; r++)
+  {
+    if (Row[r].is_alive())
+    {
+      if (Row [r].length == 0)
+      {
+        /* this row is of zero length.  cannot compact it, so kill it */
+        COLAMD_DEBUG3 (("Defrag row kill\n")) ;
+        Row[r].kill() ;
+      }
+      else
+      {
+        /* save first column index in Row [r].shared2.first_column */
+        psrc = &A [Row [r].start] ;
+        Row [r].shared2.first_column = *psrc ;
+        COLAMD_ASSERT (Row[r].is_alive()) ;
+        /* flag the start of the row with the one's complement of row */
+        *psrc = ones_complement(r) ;
+
+      }
+    }
+  }
+
+  /* === Defragment the rows ============================================== */
+
+  psrc = pdest ;
+  while (psrc < pfree)
+  {
+    /* find a negative number ... the start of a row */
+    if (*psrc++ < 0)
+    {
+      psrc-- ;
+      /* get the row index */
+      r = ones_complement(*psrc) ;
+      COLAMD_ASSERT (r >= 0 && r < n_row) ;
+      /* restore first column index */
+      *psrc = Row [r].shared2.first_column ;
+      COLAMD_ASSERT (Row[r].is_alive()) ;
+
+      /* move and compact the row */
+      COLAMD_ASSERT (pdest <= psrc) ;
+      Row [r].start = (IndexType) (pdest - &A [0]) ;
+      length = Row [r].length ;
+      for (j = 0 ; j < length ; j++)
+      {
+	c = *psrc++ ;
+	if (Col[c].is_alive())
+	{
+	  *pdest++ = c ;
+	}
+      }
+      Row [r].length = (IndexType) (pdest - &A [Row [r].start]) ;
+
+    }
+  }
+  /* ensure we found all the rows */
+  COLAMD_ASSERT (debug_rows == 0) ;
+
+  /* === Return the new value of pfree ==================================== */
+
+  return ((IndexType) (pdest - &A [0])) ;
+}
+
+
+/* ========================================================================== */
+/* === clear_mark =========================================================== */
+/* ========================================================================== */
+
+/*
+  Clears the Row [].shared2.mark array, and returns the new tag_mark.
+  Return value is the new tag_mark.  Not user-callable.
+*/
+template <typename IndexType>
+static inline  IndexType clear_mark  /* return the new value for tag_mark */
+  (
+      /* === Parameters ======================================================= */
+
+    IndexType n_row,    /* number of rows in A */
+    RowStructure<IndexType> Row [] /* Row [0 ... n_row-1].shared2.mark is set to zero */
+    )
+{
+  /* === Local variables ================================================== */
+
+  IndexType r ;
+
+  for (r = 0 ; r < n_row ; r++)
+  {
+    if (Row[r].is_alive())
+    {
+      Row [r].shared2.mark = 0 ;
+    }
+  }
+  return (1) ;
+}
+
+} // namespace Colamd
+
+} // namespace internal
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/OrderingMethods/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/OrderingMethods/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..713c447947b3b4e164d110a572a731835f9d4bc9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/OrderingMethods/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_ORDERINGMETHODS_MODULE_H
+#error "Please include Eigen/OrderingMethods instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/OrderingMethods/Ordering.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/OrderingMethods/Ordering.h
new file mode 100644
index 0000000000000000000000000000000000000000..5cc4a8560427a055e2c769bc6bb6820bf1e28d34
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/OrderingMethods/Ordering.h
@@ -0,0 +1,155 @@
+ 
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012  Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ORDERING_H
+#define EIGEN_ORDERING_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+  
+#include "Eigen_Colamd.h"
+
+namespace internal {
+    
+/** \internal
+  * \ingroup OrderingMethods_Module
+  * \param[in] A the input non-symmetric matrix
+  * \param[out] symmat the symmetric pattern A^T+A from the input matrix \a A.
+  * FIXME: The values should not be considered here
+  */
+template<typename MatrixType> 
+void ordering_helper_at_plus_a(const MatrixType& A, MatrixType& symmat)
+{
+  MatrixType C;
+  C = A.transpose(); // NOTE: Could be  costly
+  for (int i = 0; i < C.rows(); i++) 
+  {
+      for (typename MatrixType::InnerIterator it(C, i); it; ++it)
+        it.valueRef() = typename MatrixType::Scalar(0);
+  }
+  symmat = C + A;
+}
+    
+}
+
+/** \ingroup OrderingMethods_Module
+  * \class AMDOrdering
+  *
+  * Functor computing the \em approximate \em minimum \em degree ordering
+  * If the matrix is not structurally symmetric, an ordering of A^T+A is computed
+  * \tparam  StorageIndex The type of indices of the matrix 
+  * \sa COLAMDOrdering
+  */
+template <typename StorageIndex>
+class AMDOrdering
+{
+  public:
+    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
+    
+    /** Compute the permutation vector from a sparse matrix
+     * This routine is much faster if the input matrix is column-major     
+     */
+    template <typename MatrixType>
+    void operator()(const MatrixType& mat, PermutationType& perm)
+    {
+      // Compute the symmetric pattern
+      SparseMatrix<typename MatrixType::Scalar, ColMajor, StorageIndex> symm;
+      internal::ordering_helper_at_plus_a(mat,symm); 
+    
+      // Call the AMD routine 
+      //m_mat.prune(keep_diag());
+      internal::minimum_degree_ordering(symm, perm);
+    }
+    
+    /** Compute the permutation with a selfadjoint matrix */
+    template <typename SrcType, unsigned int SrcUpLo> 
+    void operator()(const SparseSelfAdjointView<SrcType, SrcUpLo>& mat, PermutationType& perm)
+    { 
+      SparseMatrix<typename SrcType::Scalar, ColMajor, StorageIndex> C; C = mat;
+      
+      // Call the AMD routine 
+      // m_mat.prune(keep_diag()); //Remove the diagonal elements 
+      internal::minimum_degree_ordering(C, perm);
+    }
+};
+
+/** \ingroup OrderingMethods_Module
+  * \class NaturalOrdering
+  *
+  * Functor computing the natural ordering (identity)
+  * 
+  * \note Returns an empty permutation matrix
+  * \tparam  StorageIndex The type of indices of the matrix 
+  */
+template <typename StorageIndex>
+class NaturalOrdering
+{
+  public:
+    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
+    
+    /** Compute the permutation vector from a column-major sparse matrix */
+    template <typename MatrixType>
+    void operator()(const MatrixType& /*mat*/, PermutationType& perm)
+    {
+      perm.resize(0); 
+    }
+    
+};
+
+/** \ingroup OrderingMethods_Module
+  * \class COLAMDOrdering
+  *
+  * \tparam  StorageIndex The type of indices of the matrix 
+  * 
+  * Functor computing the \em column \em approximate \em minimum \em degree ordering 
+  * The matrix should be in column-major and \b compressed format (see SparseMatrix::makeCompressed()).
+  */
+template<typename StorageIndex>
+class COLAMDOrdering
+{
+  public:
+    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType; 
+    typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
+    
+    /** Compute the permutation vector \a perm form the sparse matrix \a mat
+      * \warning The input sparse matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
+      */
+    template <typename MatrixType>
+    void operator() (const MatrixType& mat, PermutationType& perm)
+    {
+      eigen_assert(mat.isCompressed() && "COLAMDOrdering requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to COLAMDOrdering");
+      
+      StorageIndex m = StorageIndex(mat.rows());
+      StorageIndex n = StorageIndex(mat.cols());
+      StorageIndex nnz = StorageIndex(mat.nonZeros());
+      // Get the recommended value of Alen to be used by colamd
+      StorageIndex Alen = internal::Colamd::recommended(nnz, m, n); 
+      // Set the default parameters
+      double knobs [internal::Colamd::NKnobs]; 
+      StorageIndex stats [internal::Colamd::NStats];
+      internal::Colamd::set_defaults(knobs);
+      
+      IndexVector p(n+1), A(Alen); 
+      for(StorageIndex i=0; i <= n; i++)   p(i) = mat.outerIndexPtr()[i];
+      for(StorageIndex i=0; i < nnz; i++)  A(i) = mat.innerIndexPtr()[i];
+      // Call Colamd routine to compute the ordering 
+      StorageIndex info = internal::Colamd::compute_ordering(m, n, Alen, A.data(), p.data(), knobs, stats); 
+      EIGEN_UNUSED_VARIABLE(info);
+      eigen_assert( info && "COLAMD failed " );
+      
+      perm.resize(n);
+      for (StorageIndex i = 0; i < n; i++) perm.indices()(p(i)) = i;
+    }
+};
+
+} // end namespace Eigen
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/PaStiXSupport/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/PaStiXSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..f588e50230ccce26729b4c26cd5015bd6f03c613
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/PaStiXSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_PASTIXSUPPORT_MODULE_H
+#error "Please include Eigen/PaStiXSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/PaStiXSupport/PaStiXSupport.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/PaStiXSupport/PaStiXSupport.h
new file mode 100644
index 0000000000000000000000000000000000000000..d3126b1d6b13a99ac0680eaa7262f1071ef57508
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/PaStiXSupport/PaStiXSupport.h
@@ -0,0 +1,680 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PASTIXSUPPORT_H
+#define EIGEN_PASTIXSUPPORT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+#if defined(DCOMPLEX)
+  #define PASTIX_COMPLEX  COMPLEX
+  #define PASTIX_DCOMPLEX DCOMPLEX
+#else
+  #define PASTIX_COMPLEX  std::complex<float>
+  #define PASTIX_DCOMPLEX std::complex<double>
+#endif
+
+/** \ingroup PaStiXSupport_Module
+  * \brief Interface to the PaStix solver
+  * 
+  * This class is used to solve the linear systems A.X = B via the PaStix library. 
+  * The matrix can be either real or complex, symmetric or not.
+  *
+  * \sa TutorialSparseDirectSolvers
+  */
+template<typename MatrixType_, bool IsStrSym = false> class PastixLU;
+template<typename MatrixType_, int Options> class PastixLLT;
+template<typename MatrixType_, int Options> class PastixLDLT;
+
+namespace internal
+{
+    
+  template<class Pastix> struct pastix_traits;
+
+  template<typename MatrixType_>
+  struct pastix_traits< PastixLU<MatrixType_> >
+  {
+    typedef MatrixType_ MatrixType;
+    typedef typename MatrixType_::Scalar Scalar;
+    typedef typename MatrixType_::RealScalar RealScalar;
+    typedef typename MatrixType_::StorageIndex StorageIndex;
+  };
+
+  template<typename MatrixType_, int Options>
+  struct pastix_traits< PastixLLT<MatrixType_,Options> >
+  {
+    typedef MatrixType_ MatrixType;
+    typedef typename MatrixType_::Scalar Scalar;
+    typedef typename MatrixType_::RealScalar RealScalar;
+    typedef typename MatrixType_::StorageIndex StorageIndex;
+  };
+
+  template<typename MatrixType_, int Options>
+  struct pastix_traits< PastixLDLT<MatrixType_,Options> >
+  {
+    typedef MatrixType_ MatrixType;
+    typedef typename MatrixType_::Scalar Scalar;
+    typedef typename MatrixType_::RealScalar RealScalar;
+    typedef typename MatrixType_::StorageIndex StorageIndex;
+  };
+  
+  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, float *vals, int *perm, int * invp, float *x, int nbrhs, int *iparm, double *dparm)
+  {
+    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
+    if (nbrhs == 0) {x = NULL; nbrhs=1;}
+    s_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm); 
+  }
+  
+  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, double *vals, int *perm, int * invp, double *x, int nbrhs, int *iparm, double *dparm)
+  {
+    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
+    if (nbrhs == 0) {x = NULL; nbrhs=1;}
+    d_pastix(pastix_data, pastix_comm, n, ptr, idx, vals, perm, invp, x, nbrhs, iparm, dparm); 
+  }
+  
+  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<float> *vals, int *perm, int * invp, std::complex<float> *x, int nbrhs, int *iparm, double *dparm)
+  {
+    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
+    if (nbrhs == 0) {x = NULL; nbrhs=1;}
+    c_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_COMPLEX*>(vals), perm, invp, reinterpret_cast<PASTIX_COMPLEX*>(x), nbrhs, iparm, dparm); 
+  }
+  
+  inline void eigen_pastix(pastix_data_t **pastix_data, int pastix_comm, int n, int *ptr, int *idx, std::complex<double> *vals, int *perm, int * invp, std::complex<double> *x, int nbrhs, int *iparm, double *dparm)
+  {
+    if (n == 0) { ptr = NULL; idx = NULL; vals = NULL; }
+    if (nbrhs == 0) {x = NULL; nbrhs=1;}
+    z_pastix(pastix_data, pastix_comm, n, ptr, idx, reinterpret_cast<PASTIX_DCOMPLEX*>(vals), perm, invp, reinterpret_cast<PASTIX_DCOMPLEX*>(x), nbrhs, iparm, dparm); 
+  }
+
+  // Convert the matrix  to Fortran-style Numbering
+  template <typename MatrixType>
+  void c_to_fortran_numbering (MatrixType& mat)
+  {
+    if ( !(mat.outerIndexPtr()[0]) ) 
+    { 
+      int i;
+      for(i = 0; i <= mat.rows(); ++i)
+        ++mat.outerIndexPtr()[i];
+      for(i = 0; i < mat.nonZeros(); ++i)
+        ++mat.innerIndexPtr()[i];
+    }
+  }
+  
+  // Convert to C-style Numbering
+  template <typename MatrixType>
+  void fortran_to_c_numbering (MatrixType& mat)
+  {
+    // Check the Numbering
+    if ( mat.outerIndexPtr()[0] == 1 ) 
+    { // Convert to C-style numbering
+      int i;
+      for(i = 0; i <= mat.rows(); ++i)
+        --mat.outerIndexPtr()[i];
+      for(i = 0; i < mat.nonZeros(); ++i)
+        --mat.innerIndexPtr()[i];
+    }
+  }
+}
+
+// This is the base class to interface with PaStiX functions. 
+// Users should not used this class directly. 
+template <class Derived>
+class PastixBase : public SparseSolverBase<Derived>
+{
+  protected:
+    typedef SparseSolverBase<Derived> Base;
+    using Base::derived;
+    using Base::m_isInitialized;
+  public:
+    using Base::_solve_impl;
+    
+    typedef typename internal::pastix_traits<Derived>::MatrixType MatrixType_;
+    typedef MatrixType_ MatrixType;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    typedef Matrix<Scalar,Dynamic,1> Vector;
+    typedef SparseMatrix<Scalar, ColMajor> ColSpMatrix;
+    enum {
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+    
+  public:
+    
+    PastixBase() : m_initisOk(false), m_analysisIsOk(false), m_factorizationIsOk(false), m_pastixdata(0), m_size(0)
+    {
+      init();
+    }
+    
+    ~PastixBase() 
+    {
+      clean();
+    }
+    
+    template<typename Rhs,typename Dest>
+    bool _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const;
+    
+    /** Returns a reference to the integer vector IPARM of PaStiX parameters
+      * to modify the default parameters. 
+      * The statistics related to the different phases of factorization and solve are saved here as well
+      * \sa analyzePattern() factorize()
+      */
+    Array<StorageIndex,IPARM_SIZE,1>& iparm()
+    {
+      return m_iparm; 
+    }
+    
+    /** Return a reference to a particular index parameter of the IPARM vector 
+     * \sa iparm()
+     */
+    
+    int& iparm(int idxparam)
+    {
+      return m_iparm(idxparam);
+    }
+    
+     /** Returns a reference to the double vector DPARM of PaStiX parameters 
+      * The statistics related to the different phases of factorization and solve are saved here as well
+      * \sa analyzePattern() factorize()
+      */
+    Array<double,DPARM_SIZE,1>& dparm()
+    {
+      return m_dparm; 
+    }
+    
+    
+    /** Return a reference to a particular index parameter of the DPARM vector 
+     * \sa dparm()
+     */
+    double& dparm(int idxparam)
+    {
+      return m_dparm(idxparam);
+    }
+    
+    inline Index cols() const { return m_size; }
+    inline Index rows() const { return m_size; }
+    
+     /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful,
+      *          \c NumericalIssue if the PaStiX reports a problem
+      *          \c InvalidInput if the input matrix is invalid
+      *
+      * \sa iparm()          
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+    
+  protected:
+
+    // Initialize the Pastix data structure, check the matrix
+    void init(); 
+    
+    // Compute the ordering and the symbolic factorization
+    void analyzePattern(ColSpMatrix& mat);
+    
+    // Compute the numerical factorization
+    void factorize(ColSpMatrix& mat);
+    
+    // Free all the data allocated by Pastix
+    void clean()
+    {
+      eigen_assert(m_initisOk && "The Pastix structure should be allocated first"); 
+      m_iparm(IPARM_START_TASK) = API_TASK_CLEAN;
+      m_iparm(IPARM_END_TASK) = API_TASK_CLEAN;
+      internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar*)0,
+                             m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
+    }
+    
+    void compute(ColSpMatrix& mat);
+    
+    int m_initisOk; 
+    int m_analysisIsOk;
+    int m_factorizationIsOk;
+    mutable ComputationInfo m_info; 
+    mutable pastix_data_t *m_pastixdata; // Data structure for pastix
+    mutable int m_comm; // The MPI communicator identifier
+    mutable Array<int,IPARM_SIZE,1> m_iparm; // integer vector for the input parameters
+    mutable Array<double,DPARM_SIZE,1> m_dparm; // Scalar vector for the input parameters
+    mutable Matrix<StorageIndex,Dynamic,1> m_perm;  // Permutation vector
+    mutable Matrix<StorageIndex,Dynamic,1> m_invp;  // Inverse permutation vector
+    mutable int m_size; // Size of the matrix 
+}; 
+
+ /** Initialize the PaStiX data structure. 
+   *A first call to this function fills iparm and dparm with the default PaStiX parameters
+   * \sa iparm() dparm()
+   */
+template <class Derived>
+void PastixBase<Derived>::init()
+{
+  m_size = 0; 
+  m_iparm.setZero(IPARM_SIZE);
+  m_dparm.setZero(DPARM_SIZE);
+  
+  m_iparm(IPARM_MODIFY_PARAMETER) = API_NO;
+  pastix(&m_pastixdata, MPI_COMM_WORLD,
+         0, 0, 0, 0,
+         0, 0, 0, 1, m_iparm.data(), m_dparm.data());
+  
+  m_iparm[IPARM_MATRIX_VERIFICATION] = API_NO;
+  m_iparm[IPARM_VERBOSE]             = API_VERBOSE_NOT;
+  m_iparm[IPARM_ORDERING]            = API_ORDER_SCOTCH;
+  m_iparm[IPARM_INCOMPLETE]          = API_NO;
+  m_iparm[IPARM_OOC_LIMIT]           = 2000;
+  m_iparm[IPARM_RHS_MAKING]          = API_RHS_B;
+  m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO;
+  
+  m_iparm(IPARM_START_TASK) = API_TASK_INIT;
+  m_iparm(IPARM_END_TASK) = API_TASK_INIT;
+  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, 0, 0, 0, (Scalar*)0,
+                         0, 0, 0, 0, m_iparm.data(), m_dparm.data());
+  
+  // Check the returned error
+  if(m_iparm(IPARM_ERROR_NUMBER)) {
+    m_info = InvalidInput;
+    m_initisOk = false;
+  }
+  else { 
+    m_info = Success;
+    m_initisOk = true;
+  }
+}
+
+template <class Derived>
+void PastixBase<Derived>::compute(ColSpMatrix& mat)
+{
+  eigen_assert(mat.rows() == mat.cols() && "The input matrix should be squared");
+  
+  analyzePattern(mat);  
+  factorize(mat);
+  
+  m_iparm(IPARM_MATRIX_VERIFICATION) = API_NO;
+}
+
+
+template <class Derived>
+void PastixBase<Derived>::analyzePattern(ColSpMatrix& mat)
+{                         
+  eigen_assert(m_initisOk && "The initialization of PaSTiX failed");
+  
+  // clean previous calls
+  if(m_size>0)
+    clean();
+  
+  m_size = internal::convert_index<int>(mat.rows());
+  m_perm.resize(m_size);
+  m_invp.resize(m_size);
+  
+  m_iparm(IPARM_START_TASK) = API_TASK_ORDERING;
+  m_iparm(IPARM_END_TASK) = API_TASK_ANALYSE;
+  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(),
+               mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
+  
+  // Check the returned error
+  if(m_iparm(IPARM_ERROR_NUMBER))
+  {
+    m_info = NumericalIssue;
+    m_analysisIsOk = false;
+  }
+  else
+  { 
+    m_info = Success;
+    m_analysisIsOk = true;
+  }
+}
+
+template <class Derived>
+void PastixBase<Derived>::factorize(ColSpMatrix& mat)
+{
+//   if(&m_cpyMat != &mat) m_cpyMat = mat;
+  eigen_assert(m_analysisIsOk && "The analysis phase should be called before the factorization phase");
+  m_iparm(IPARM_START_TASK) = API_TASK_NUMFACT;
+  m_iparm(IPARM_END_TASK) = API_TASK_NUMFACT;
+  m_size = internal::convert_index<int>(mat.rows());
+  
+  internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, m_size, mat.outerIndexPtr(), mat.innerIndexPtr(),
+               mat.valuePtr(), m_perm.data(), m_invp.data(), 0, 0, m_iparm.data(), m_dparm.data());
+  
+  // Check the returned error
+  if(m_iparm(IPARM_ERROR_NUMBER))
+  {
+    m_info = NumericalIssue;
+    m_factorizationIsOk = false;
+    m_isInitialized = false;
+  }
+  else
+  {
+    m_info = Success;
+    m_factorizationIsOk = true;
+    m_isInitialized = true;
+  }
+}
+
+/* Solve the system */
+template<typename Base>
+template<typename Rhs,typename Dest>
+bool PastixBase<Base>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &x) const
+{
+  eigen_assert(m_isInitialized && "The matrix should be factorized first");
+  EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,
+                     THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  int rhs = 1;
+  
+  x = b; /* on return, x is overwritten by the computed solution */
+  
+  for (int i = 0; i < b.cols(); i++){
+    m_iparm[IPARM_START_TASK]          = API_TASK_SOLVE;
+    m_iparm[IPARM_END_TASK]            = API_TASK_REFINE;
+  
+    internal::eigen_pastix(&m_pastixdata, MPI_COMM_WORLD, internal::convert_index<int>(x.rows()), 0, 0, 0,
+                           m_perm.data(), m_invp.data(), &x(0, i), rhs, m_iparm.data(), m_dparm.data());
+  }
+  
+  // Check the returned error
+  m_info = m_iparm(IPARM_ERROR_NUMBER)==0 ? Success : NumericalIssue;
+  
+  return m_iparm(IPARM_ERROR_NUMBER)==0;
+}
+
+/** \ingroup PaStiXSupport_Module
+  * \class PastixLU
+  * \brief Sparse direct LU solver based on PaStiX library
+  * 
+  * This class is used to solve the linear systems A.X = B with a supernodal LU 
+  * factorization in the PaStiX library. The matrix A should be squared and nonsingular
+  * PaStiX requires that the matrix A has a symmetric structural pattern. 
+  * This interface can symmetrize the input matrix otherwise. 
+  * The vectors or matrices X and B can be either dense or sparse.
+  * 
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam IsStrSym Indicates if the input matrix has a symmetric pattern, default is false
+  * NOTE : Note that if the analysis and factorization phase are called separately, 
+  * the input matrix will be symmetrized at each call, hence it is advised to 
+  * symmetrize the matrix in a end-user program and set \p IsStrSym to true
+  *
+  * \implsparsesolverconcept
+  *
+  * \sa \ref TutorialSparseSolverConcept, class SparseLU
+  * 
+  */
+template<typename MatrixType_, bool IsStrSym>
+class PastixLU : public PastixBase< PastixLU<MatrixType_> >
+{
+  public:
+    typedef MatrixType_ MatrixType;
+    typedef PastixBase<PastixLU<MatrixType> > Base;
+    typedef typename Base::ColSpMatrix ColSpMatrix;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    
+  public:
+    PastixLU() : Base()
+    {
+      init();
+    }
+    
+    explicit PastixLU(const MatrixType& matrix):Base()
+    {
+      init();
+      compute(matrix);
+    }
+    /** Compute the LU supernodal factorization of \p matrix. 
+      * iparm and dparm can be used to tune the PaStiX parameters. 
+      * see the PaStiX user's manual
+      * \sa analyzePattern() factorize()
+      */
+    void compute (const MatrixType& matrix)
+    {
+      m_structureIsUptodate = false;
+      ColSpMatrix temp;
+      grabMatrix(matrix, temp);
+      Base::compute(temp);
+    }
+    /** Compute the LU symbolic factorization of \p matrix using its sparsity pattern. 
+      * Several ordering methods can be used at this step. See the PaStiX user's manual. 
+      * The result of this operation can be used with successive matrices having the same pattern as \p matrix
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& matrix)
+    {
+      m_structureIsUptodate = false;
+      ColSpMatrix temp;
+      grabMatrix(matrix, temp);
+      Base::analyzePattern(temp);
+    }
+
+    /** Compute the LU supernodal factorization of \p matrix
+      * WARNING The matrix \p matrix should have the same structural pattern 
+      * as the same used in the analysis phase.
+      * \sa analyzePattern()
+      */ 
+    void factorize(const MatrixType& matrix)
+    {
+      ColSpMatrix temp;
+      grabMatrix(matrix, temp);
+      Base::factorize(temp);
+    }
+  protected:
+    
+    void init()
+    {
+      m_structureIsUptodate = false;
+      m_iparm(IPARM_SYM) = API_SYM_NO;
+      m_iparm(IPARM_FACTORIZATION) = API_FACT_LU;
+    }
+    
+    void grabMatrix(const MatrixType& matrix, ColSpMatrix& out)
+    {
+      if(IsStrSym)
+        out = matrix;
+      else
+      {
+        if(!m_structureIsUptodate)
+        {
+          // update the transposed structure
+          m_transposedStructure = matrix.transpose();
+          
+          // Set the elements of the matrix to zero 
+          for (Index j=0; j<m_transposedStructure.outerSize(); ++j) 
+            for(typename ColSpMatrix::InnerIterator it(m_transposedStructure, j); it; ++it)
+              it.valueRef() = 0.0;
+
+          m_structureIsUptodate = true;
+        }
+        
+        out = m_transposedStructure + matrix;
+      }
+      internal::c_to_fortran_numbering(out);
+    }
+    
+    using Base::m_iparm;
+    using Base::m_dparm;
+    
+    ColSpMatrix m_transposedStructure;
+    bool m_structureIsUptodate;
+};
+
+/** \ingroup PaStiXSupport_Module
+  * \class PastixLLT
+  * \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library
+  * 
+  * This class is used to solve the linear systems A.X = B via a LL^T supernodal Cholesky factorization
+  * available in the PaStiX library. The matrix A should be symmetric and positive definite
+  * WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX
+  * The vectors or matrices X and B can be either dense or sparse
+  * 
+  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX
+  *
+  * \implsparsesolverconcept
+  *
+  * \sa \ref TutorialSparseSolverConcept, class SimplicialLLT
+  */
+template<typename MatrixType_, int UpLo_>
+class PastixLLT : public PastixBase< PastixLLT<MatrixType_, UpLo_> >
+{
+  public:
+    typedef MatrixType_ MatrixType;
+    typedef PastixBase<PastixLLT<MatrixType, UpLo_> > Base;
+    typedef typename Base::ColSpMatrix ColSpMatrix;
+    
+  public:
+    enum { UpLo = UpLo_ };
+    PastixLLT() : Base()
+    {
+      init();
+    }
+    
+    explicit PastixLLT(const MatrixType& matrix):Base()
+    {
+      init();
+      compute(matrix);
+    }
+
+    /** Compute the L factor of the LL^T supernodal factorization of \p matrix 
+      * \sa analyzePattern() factorize()
+      */
+    void compute (const MatrixType& matrix)
+    {
+      ColSpMatrix temp;
+      grabMatrix(matrix, temp);
+      Base::compute(temp);
+    }
+
+     /** Compute the LL^T symbolic factorization of \p matrix using its sparsity pattern
+      * The result of this operation can be used with successive matrices having the same pattern as \p matrix
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& matrix)
+    {
+      ColSpMatrix temp;
+      grabMatrix(matrix, temp);
+      Base::analyzePattern(temp);
+    }
+      /** Compute the LL^T supernodal numerical factorization of \p matrix 
+        * \sa analyzePattern()
+        */
+    void factorize(const MatrixType& matrix)
+    {
+      ColSpMatrix temp;
+      grabMatrix(matrix, temp);
+      Base::factorize(temp);
+    }
+  protected:
+    using Base::m_iparm;
+    
+    void init()
+    {
+      m_iparm(IPARM_SYM) = API_SYM_YES;
+      m_iparm(IPARM_FACTORIZATION) = API_FACT_LLT;
+    }
+    
+    void grabMatrix(const MatrixType& matrix, ColSpMatrix& out)
+    {
+      out.resize(matrix.rows(), matrix.cols());
+      // Pastix supports only lower, column-major matrices 
+      out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>();
+      internal::c_to_fortran_numbering(out);
+    }
+};
+
+/** \ingroup PaStiXSupport_Module
+  * \class PastixLDLT
+  * \brief A sparse direct supernodal Cholesky (LLT) factorization and solver based on the PaStiX library
+  * 
+  * This class is used to solve the linear systems A.X = B via a LDL^T supernodal Cholesky factorization
+  * available in the PaStiX library. The matrix A should be symmetric and positive definite
+  * WARNING Selfadjoint complex matrices are not supported in the current version of PaStiX
+  * The vectors or matrices X and B can be either dense or sparse
+  * 
+  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo The part of the matrix to use : Lower or Upper. The default is Lower as required by PaStiX
+  *
+  * \implsparsesolverconcept
+  *
+  * \sa \ref TutorialSparseSolverConcept, class SimplicialLDLT
+  */
+template<typename MatrixType_, int UpLo_>
+class PastixLDLT : public PastixBase< PastixLDLT<MatrixType_, UpLo_> >
+{
+  public:
+    typedef MatrixType_ MatrixType;
+    typedef PastixBase<PastixLDLT<MatrixType, UpLo_> > Base;
+    typedef typename Base::ColSpMatrix ColSpMatrix;
+    
+  public:
+    enum { UpLo = UpLo_ };
+    PastixLDLT():Base()
+    {
+      init();
+    }
+    
+    explicit PastixLDLT(const MatrixType& matrix):Base()
+    {
+      init();
+      compute(matrix);
+    }
+
+    /** Compute the L and D factors of the LDL^T factorization of \p matrix 
+      * \sa analyzePattern() factorize()
+      */
+    void compute (const MatrixType& matrix)
+    {
+      ColSpMatrix temp;
+      grabMatrix(matrix, temp);
+      Base::compute(temp);
+    }
+
+    /** Compute the LDL^T symbolic factorization of \p matrix using its sparsity pattern
+      * The result of this operation can be used with successive matrices having the same pattern as \p matrix
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& matrix)
+    { 
+      ColSpMatrix temp;
+      grabMatrix(matrix, temp);
+      Base::analyzePattern(temp);
+    }
+    /** Compute the LDL^T supernodal numerical factorization of \p matrix 
+      * 
+      */
+    void factorize(const MatrixType& matrix)
+    {
+      ColSpMatrix temp;
+      grabMatrix(matrix, temp);
+      Base::factorize(temp);
+    }
+
+  protected:
+    using Base::m_iparm;
+    
+    void init()
+    {
+      m_iparm(IPARM_SYM) = API_SYM_YES;
+      m_iparm(IPARM_FACTORIZATION) = API_FACT_LDLT;
+    }
+    
+    void grabMatrix(const MatrixType& matrix, ColSpMatrix& out)
+    {
+      // Pastix supports only lower, column-major matrices 
+      out.resize(matrix.rows(), matrix.cols());
+      out.template selfadjointView<Lower>() = matrix.template selfadjointView<UpLo>();
+      internal::c_to_fortran_numbering(out);
+    }
+};
+
+} // end namespace Eigen
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/PardisoSupport/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/PardisoSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..8ef33f051ca6980a63ef882a12c7bf85fb99978b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/PardisoSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_PARDISOSUPPORT_MODULE_H
+#error "Please include Eigen/PardisoSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/PardisoSupport/PardisoSupport.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/PardisoSupport/PardisoSupport.h
new file mode 100644
index 0000000000000000000000000000000000000000..e9815e604faf71daa5d1e1bc8fa13e415f191394
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/PardisoSupport/PardisoSupport.h
@@ -0,0 +1,547 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL PARDISO
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_PARDISOSUPPORT_H
+#define EIGEN_PARDISOSUPPORT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+template<typename MatrixType_> class PardisoLU;
+template<typename MatrixType_, int Options=Upper> class PardisoLLT;
+template<typename MatrixType_, int Options=Upper> class PardisoLDLT;
+
+namespace internal
+{
+  template<typename IndexType>
+  struct pardiso_run_selector
+  {
+    static IndexType run( _MKL_DSS_HANDLE_t pt, IndexType maxfct, IndexType mnum, IndexType type, IndexType phase, IndexType n, void *a,
+                      IndexType *ia, IndexType *ja, IndexType *perm, IndexType nrhs, IndexType *iparm, IndexType msglvl, void *b, void *x)
+    {
+      IndexType error = 0;
+      ::pardiso(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
+      return error;
+    }
+  };
+  template<>
+  struct pardiso_run_selector<long long int>
+  {
+    typedef long long int IndexType;
+    static IndexType run( _MKL_DSS_HANDLE_t pt, IndexType maxfct, IndexType mnum, IndexType type, IndexType phase, IndexType n, void *a,
+                      IndexType *ia, IndexType *ja, IndexType *perm, IndexType nrhs, IndexType *iparm, IndexType msglvl, void *b, void *x)
+    {
+      IndexType error = 0;
+      ::pardiso_64(pt, &maxfct, &mnum, &type, &phase, &n, a, ia, ja, perm, &nrhs, iparm, &msglvl, b, x, &error);
+      return error;
+    }
+  };
+
+  template<class Pardiso> struct pardiso_traits;
+
+  template<typename MatrixType_>
+  struct pardiso_traits< PardisoLU<MatrixType_> >
+  {
+    typedef MatrixType_ MatrixType;
+    typedef typename MatrixType_::Scalar Scalar;
+    typedef typename MatrixType_::RealScalar RealScalar;
+    typedef typename MatrixType_::StorageIndex StorageIndex;
+  };
+
+  template<typename MatrixType_, int Options>
+  struct pardiso_traits< PardisoLLT<MatrixType_, Options> >
+  {
+    typedef MatrixType_ MatrixType;
+    typedef typename MatrixType_::Scalar Scalar;
+    typedef typename MatrixType_::RealScalar RealScalar;
+    typedef typename MatrixType_::StorageIndex StorageIndex;
+  };
+
+  template<typename MatrixType_, int Options>
+  struct pardiso_traits< PardisoLDLT<MatrixType_, Options> >
+  {
+    typedef MatrixType_ MatrixType;
+    typedef typename MatrixType_::Scalar Scalar;
+    typedef typename MatrixType_::RealScalar RealScalar;
+    typedef typename MatrixType_::StorageIndex StorageIndex;
+  };
+
+} // end namespace internal
+
+template<class Derived>
+class PardisoImpl : public SparseSolverBase<Derived>
+{
+  protected:
+    typedef SparseSolverBase<Derived> Base;
+    using Base::derived;
+    using Base::m_isInitialized;
+    
+    typedef internal::pardiso_traits<Derived> Traits;
+  public:
+    using Base::_solve_impl;
+    
+    typedef typename Traits::MatrixType MatrixType;
+    typedef typename Traits::Scalar Scalar;
+    typedef typename Traits::RealScalar RealScalar;
+    typedef typename Traits::StorageIndex StorageIndex;
+    typedef SparseMatrix<Scalar,RowMajor,StorageIndex> SparseMatrixType;
+    typedef Matrix<Scalar,Dynamic,1> VectorType;
+    typedef Matrix<StorageIndex, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
+    typedef Matrix<StorageIndex, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
+    typedef Array<StorageIndex,64,1,DontAlign> ParameterType;
+    enum {
+      ScalarIsComplex = NumTraits<Scalar>::IsComplex,
+      ColsAtCompileTime = Dynamic,
+      MaxColsAtCompileTime = Dynamic
+    };
+
+    PardisoImpl()
+      : m_analysisIsOk(false), m_factorizationIsOk(false)
+    {
+      eigen_assert((sizeof(StorageIndex) >= sizeof(_INTEGER_t) && sizeof(StorageIndex) <= 8) && "Non-supported index type");
+      m_iparm.setZero();
+      m_msglvl = 0; // No output
+      m_isInitialized = false;
+    }
+
+    ~PardisoImpl()
+    {
+      pardisoRelease();
+    }
+
+    inline Index cols() const { return m_size; }
+    inline Index rows() const { return m_size; }
+  
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful,
+      *          \c NumericalIssue if the matrix appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+
+    /** \warning for advanced usage only.
+      * \returns a reference to the parameter array controlling PARDISO.
+      * See the PARDISO manual to know how to use it. */
+    ParameterType& pardisoParameterArray()
+    {
+      return m_iparm;
+    }
+    
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      * 
+      * \sa factorize()
+      */
+    Derived& analyzePattern(const MatrixType& matrix);
+    
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
+      *
+      * \sa analyzePattern()
+      */
+    Derived& factorize(const MatrixType& matrix);
+
+    Derived& compute(const MatrixType& matrix);
+
+    template<typename Rhs,typename Dest>
+    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const;
+
+  protected:
+    void pardisoRelease()
+    {
+      if(m_isInitialized) // Factorization ran at least once
+      {
+        internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, -1, internal::convert_index<StorageIndex>(m_size),0, 0, 0, m_perm.data(), 0,
+                                                          m_iparm.data(), m_msglvl, NULL, NULL);
+        m_isInitialized = false;
+      }
+    }
+
+    void pardisoInit(int type)
+    {
+      m_type = type;
+      bool symmetric = std::abs(m_type) < 10;
+      m_iparm[0] = 1;   // No solver default
+      m_iparm[1] = 2;   // use Metis for the ordering
+      m_iparm[2] = 0;   // Reserved. Set to zero. (??Numbers of processors, value of OMP_NUM_THREADS??)
+      m_iparm[3] = 0;   // No iterative-direct algorithm
+      m_iparm[4] = 0;   // No user fill-in reducing permutation
+      m_iparm[5] = 0;   // Write solution into x, b is left unchanged
+      m_iparm[6] = 0;   // Not in use
+      m_iparm[7] = 2;   // Max numbers of iterative refinement steps
+      m_iparm[8] = 0;   // Not in use
+      m_iparm[9] = 13;  // Perturb the pivot elements with 1E-13
+      m_iparm[10] = symmetric ? 0 : 1; // Use nonsymmetric permutation and scaling MPS
+      m_iparm[11] = 0;  // Not in use
+      m_iparm[12] = symmetric ? 0 : 1;  // Maximum weighted matching algorithm is switched-off (default for symmetric).
+                                        // Try m_iparm[12] = 1 in case of inappropriate accuracy
+      m_iparm[13] = 0;  // Output: Number of perturbed pivots
+      m_iparm[14] = 0;  // Not in use
+      m_iparm[15] = 0;  // Not in use
+      m_iparm[16] = 0;  // Not in use
+      m_iparm[17] = -1; // Output: Number of nonzeros in the factor LU
+      m_iparm[18] = -1; // Output: Mflops for LU factorization
+      m_iparm[19] = 0;  // Output: Numbers of CG Iterations
+      
+      m_iparm[20] = 0;  // 1x1 pivoting
+      m_iparm[26] = 0;  // No matrix checker
+      m_iparm[27] = (sizeof(RealScalar) == 4) ? 1 : 0;
+      m_iparm[34] = 1;  // C indexing
+      m_iparm[36] = 0;  // CSR
+      m_iparm[59] = 0;  // 0 - In-Core ; 1 - Automatic switch between In-Core and Out-of-Core modes ; 2 - Out-of-Core
+      
+      memset(m_pt, 0, sizeof(m_pt));
+    }
+
+  protected:
+    // cached data to reduce reallocation, etc.
+    
+    void manageErrorCode(Index error) const
+    {
+      switch(error)
+      {
+        case 0:
+          m_info = Success;
+          break;
+        case -4:
+        case -7:
+          m_info = NumericalIssue;
+          break;
+        default:
+          m_info = InvalidInput;
+      }
+    }
+
+    mutable SparseMatrixType m_matrix;
+    mutable ComputationInfo m_info;
+    bool m_analysisIsOk, m_factorizationIsOk;
+    StorageIndex m_type, m_msglvl;
+    mutable void *m_pt[64];
+    mutable ParameterType m_iparm;
+    mutable IntColVectorType m_perm;
+    Index m_size;
+    
+};
+
+template<class Derived>
+Derived& PardisoImpl<Derived>::compute(const MatrixType& a)
+{
+  m_size = a.rows();
+  eigen_assert(a.rows() == a.cols());
+
+  pardisoRelease();
+  m_perm.setZero(m_size);
+  derived().getMatrix(a);
+  
+  Index error;
+  error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 12, internal::convert_index<StorageIndex>(m_size),
+                                                            m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
+                                                            m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
+  manageErrorCode(error);
+  m_analysisIsOk = true;
+  m_factorizationIsOk = true;
+  m_isInitialized = true;
+  return derived();
+}
+
+template<class Derived>
+Derived& PardisoImpl<Derived>::analyzePattern(const MatrixType& a)
+{
+  m_size = a.rows();
+  eigen_assert(m_size == a.cols());
+
+  pardisoRelease();
+  m_perm.setZero(m_size);
+  derived().getMatrix(a);
+  
+  Index error;
+  error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 11, internal::convert_index<StorageIndex>(m_size),
+                                                            m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
+                                                            m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
+  
+  manageErrorCode(error);
+  m_analysisIsOk = true;
+  m_factorizationIsOk = false;
+  m_isInitialized = true;
+  return derived();
+}
+
+template<class Derived>
+Derived& PardisoImpl<Derived>::factorize(const MatrixType& a)
+{
+  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+  eigen_assert(m_size == a.rows() && m_size == a.cols());
+  
+  derived().getMatrix(a);
+
+  Index error;
+  error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 22, internal::convert_index<StorageIndex>(m_size),
+                                                            m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
+                                                            m_perm.data(), 0, m_iparm.data(), m_msglvl, NULL, NULL);
+  
+  manageErrorCode(error);
+  m_factorizationIsOk = true;
+  return derived();
+}
+
+template<class Derived>
+template<typename BDerived,typename XDerived>
+void PardisoImpl<Derived>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived>& x) const
+{
+  if(m_iparm[0] == 0) // Factorization was not computed
+  {
+    m_info = InvalidInput;
+    return;
+  }
+
+  //Index n = m_matrix.rows();
+  Index nrhs = Index(b.cols());
+  eigen_assert(m_size==b.rows());
+  eigen_assert(((MatrixBase<BDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major right hand sides are not supported");
+  eigen_assert(((MatrixBase<XDerived>::Flags & RowMajorBit) == 0 || nrhs == 1) && "Row-major matrices of unknowns are not supported");
+  eigen_assert(((nrhs == 1) || b.outerStride() == b.rows()));
+
+
+//  switch (transposed) {
+//    case SvNoTrans    : m_iparm[11] = 0 ; break;
+//    case SvTranspose  : m_iparm[11] = 2 ; break;
+//    case SvAdjoint    : m_iparm[11] = 1 ; break;
+//    default:
+//      //std::cerr << "Eigen: transposition  option \"" << transposed << "\" not supported by the PARDISO backend\n";
+//      m_iparm[11] = 0;
+//  }
+
+  Scalar* rhs_ptr = const_cast<Scalar*>(b.derived().data());
+  Matrix<Scalar,Dynamic,Dynamic,ColMajor> tmp;
+  
+  // Pardiso cannot solve in-place
+  if(rhs_ptr == x.derived().data())
+  {
+    tmp = b;
+    rhs_ptr = tmp.data();
+  }
+  
+  Index error;
+  error = internal::pardiso_run_selector<StorageIndex>::run(m_pt, 1, 1, m_type, 33, internal::convert_index<StorageIndex>(m_size),
+                                                            m_matrix.valuePtr(), m_matrix.outerIndexPtr(), m_matrix.innerIndexPtr(),
+                                                            m_perm.data(), internal::convert_index<StorageIndex>(nrhs), m_iparm.data(), m_msglvl,
+                                                            rhs_ptr, x.derived().data());
+
+  manageErrorCode(error);
+}
+
+
+/** \ingroup PardisoSupport_Module
+  * \class PardisoLU
+  * \brief A sparse direct LU factorization and solver based on the PARDISO library
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
+  * using the Intel MKL PARDISO library. The sparse matrix A must be squared and invertible.
+  * The vectors or matrices X and B can be either dense or sparse.
+  *
+  * By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
+  * \code solver.pardisoParameterArray()[59] = 1; \endcode
+  *
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  *
+  * \implsparsesolverconcept
+  *
+  * \sa \ref TutorialSparseSolverConcept, class SparseLU
+  */
+template<typename MatrixType>
+class PardisoLU : public PardisoImpl< PardisoLU<MatrixType> >
+{
+  protected:
+    typedef PardisoImpl<PardisoLU> Base;
+    using Base::pardisoInit;
+    using Base::m_matrix;
+    friend class PardisoImpl< PardisoLU<MatrixType> >;
+
+  public:
+
+    typedef typename Base::Scalar Scalar;
+    typedef typename Base::RealScalar RealScalar;
+
+    using Base::compute;
+    using Base::solve;
+
+    PardisoLU()
+      : Base()
+    {
+      pardisoInit(Base::ScalarIsComplex ? 13 : 11);
+    }
+
+    explicit PardisoLU(const MatrixType& matrix)
+      : Base()
+    {
+      pardisoInit(Base::ScalarIsComplex ? 13 : 11);
+      compute(matrix);
+    }
+  protected:
+    void getMatrix(const MatrixType& matrix)
+    {
+      m_matrix = matrix;
+      m_matrix.makeCompressed();
+    }
+};
+
+/** \ingroup PardisoSupport_Module
+  * \class PardisoLLT
+  * \brief A sparse direct Cholesky (LLT) factorization and solver based on the PARDISO library
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a LL^T Cholesky factorization
+  * using the Intel MKL PARDISO library. The sparse matrix A must be selfajoint and positive definite.
+  * The vectors or matrices X and B can be either dense or sparse.
+  *
+  * By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
+  * \code solver.pardisoParameterArray()[59] = 1; \endcode
+  *
+  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo can be any bitwise combination of Upper, Lower. The default is Upper, meaning only the upper triangular part has to be used.
+  *         Upper|Lower can be used to tell both triangular parts can be used as input.
+  *
+  * \implsparsesolverconcept
+  *
+  * \sa \ref TutorialSparseSolverConcept, class SimplicialLLT
+  */
+template<typename MatrixType, int UpLo_>
+class PardisoLLT : public PardisoImpl< PardisoLLT<MatrixType,UpLo_> >
+{
+  protected:
+    typedef PardisoImpl< PardisoLLT<MatrixType,UpLo_> > Base;
+    using Base::pardisoInit;
+    using Base::m_matrix;
+    friend class PardisoImpl< PardisoLLT<MatrixType,UpLo_> >;
+
+  public:
+
+    typedef typename Base::Scalar Scalar;
+    typedef typename Base::RealScalar RealScalar;
+    typedef typename Base::StorageIndex StorageIndex;
+    enum { UpLo = UpLo_ };
+    using Base::compute;
+
+    PardisoLLT()
+      : Base()
+    {
+      pardisoInit(Base::ScalarIsComplex ? 4 : 2);
+    }
+
+    explicit PardisoLLT(const MatrixType& matrix)
+      : Base()
+    {
+      pardisoInit(Base::ScalarIsComplex ? 4 : 2);
+      compute(matrix);
+    }
+    
+  protected:
+    
+    void getMatrix(const MatrixType& matrix)
+    {
+      // PARDISO supports only upper, row-major matrices
+      PermutationMatrix<Dynamic,Dynamic,StorageIndex> p_null;
+      m_matrix.resize(matrix.rows(), matrix.cols());
+      m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
+      m_matrix.makeCompressed();
+    }
+};
+
+/** \ingroup PardisoSupport_Module
+  * \class PardisoLDLT
+  * \brief A sparse direct Cholesky (LDLT) factorization and solver based on the PARDISO library
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a LDL^T Cholesky factorization
+  * using the Intel MKL PARDISO library. The sparse matrix A is assumed to be selfajoint and positive definite.
+  * For complex matrices, A can also be symmetric only, see the \a Options template parameter.
+  * The vectors or matrices X and B can be either dense or sparse.
+  *
+  * By default, it runs in in-core mode. To enable PARDISO's out-of-core feature, set:
+  * \code solver.pardisoParameterArray()[59] = 1; \endcode
+  *
+  * \tparam MatrixType the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam Options can be any bitwise combination of Upper, Lower, and Symmetric. The default is Upper, meaning only the upper triangular part has to be used.
+  *         Symmetric can be used for symmetric, non-selfadjoint complex matrices, the default being to assume a selfadjoint matrix.
+  *         Upper|Lower can be used to tell both triangular parts can be used as input.
+  *
+  * \implsparsesolverconcept
+  *
+  * \sa \ref TutorialSparseSolverConcept, class SimplicialLDLT
+  */
+template<typename MatrixType, int Options>
+class PardisoLDLT : public PardisoImpl< PardisoLDLT<MatrixType,Options> >
+{
+  protected:
+    typedef PardisoImpl< PardisoLDLT<MatrixType,Options> > Base;
+    using Base::pardisoInit;
+    using Base::m_matrix;
+    friend class PardisoImpl< PardisoLDLT<MatrixType,Options> >;
+
+  public:
+
+    typedef typename Base::Scalar Scalar;
+    typedef typename Base::RealScalar RealScalar;
+    typedef typename Base::StorageIndex StorageIndex;
+    using Base::compute;
+    enum { UpLo = Options&(Upper|Lower) };
+
+    PardisoLDLT()
+      : Base()
+    {
+      pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2);
+    }
+
+    explicit PardisoLDLT(const MatrixType& matrix)
+      : Base()
+    {
+      pardisoInit(Base::ScalarIsComplex ? ( bool(Options&Symmetric) ? 6 : -4 ) : -2);
+      compute(matrix);
+    }
+    
+    void getMatrix(const MatrixType& matrix)
+    {
+      // PARDISO supports only upper, row-major matrices
+      PermutationMatrix<Dynamic,Dynamic,StorageIndex> p_null;
+      m_matrix.resize(matrix.rows(), matrix.cols());
+      m_matrix.template selfadjointView<Upper>() = matrix.template selfadjointView<UpLo>().twistedBy(p_null);
+      m_matrix.makeCompressed();
+    }
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_PARDISOSUPPORT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/ColPivHouseholderQR.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/ColPivHouseholderQR.h
new file mode 100644
index 0000000000000000000000000000000000000000..6855893e886bb70397c90b5438f96da2703397da
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/ColPivHouseholderQR.h
@@ -0,0 +1,680 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COLPIVOTINGHOUSEHOLDERQR_H
+#define EIGEN_COLPIVOTINGHOUSEHOLDERQR_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template<typename MatrixType_, typename PermutationIndex_> struct traits<ColPivHouseholderQR<MatrixType_, PermutationIndex_> >
+ : traits<MatrixType_>
+{
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef PermutationIndex_ PermutationIndex;
+  enum { Flags = 0 };
+};
+
+} // end namespace internal
+
+/** \ingroup QR_Module
+  *
+  * \class ColPivHouseholderQR
+  *
+  * \brief Householder rank-revealing QR decomposition of a matrix with column-pivoting
+  *
+  * \tparam MatrixType_ the type of the matrix of which we are computing the QR decomposition
+  *
+  * This class performs a rank-revealing QR decomposition of a matrix \b A into matrices \b P, \b Q and \b R
+  * such that
+  * \f[
+  *  \mathbf{A} \, \mathbf{P} = \mathbf{Q} \, \mathbf{R}
+  * \f]
+  * by using Householder transformations. Here, \b P is a permutation matrix, \b Q a unitary matrix and \b R an
+  * upper triangular matrix.
+  *
+  * This decomposition performs column pivoting in order to be rank-revealing and improve
+  * numerical stability. It is slower than HouseholderQR, and faster than FullPivHouseholderQR.
+  *
+  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+  * 
+  * \sa MatrixBase::colPivHouseholderQr()
+  */
+template<typename MatrixType_, typename PermutationIndex_> class ColPivHouseholderQR
+        : public SolverBase<ColPivHouseholderQR<MatrixType_, PermutationIndex_> >
+{
+  public:
+
+    typedef MatrixType_ MatrixType;
+    typedef SolverBase<ColPivHouseholderQR> Base;
+    friend class SolverBase<ColPivHouseholderQR>;
+    typedef PermutationIndex_ PermutationIndex;
+    EIGEN_GENERIC_PUBLIC_INTERFACE(ColPivHouseholderQR)
+
+    enum {
+      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+    typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
+    typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime, PermutationIndex> PermutationType;
+    typedef typename internal::plain_row_type<MatrixType, PermutationIndex>::type IntRowVectorType;
+    typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
+    typedef typename internal::plain_row_type<MatrixType, RealScalar>::type RealRowVectorType;
+    typedef HouseholderSequence<MatrixType,internal::remove_all_t<typename HCoeffsType::ConjugateReturnType>> HouseholderSequenceType;
+    typedef typename MatrixType::PlainObject PlainObject;
+
+private:
+    void init(Index rows, Index cols) {
+      Index diag = numext::mini(rows, cols);
+      m_hCoeffs.resize(diag);
+      m_colsPermutation.resize(cols);
+      m_colsTranspositions.resize(cols);
+      m_temp.resize(cols);
+      m_colNormsUpdated.resize(cols);
+      m_colNormsDirect.resize(cols);
+      m_isInitialized = false;
+      m_usePrescribedThreshold = false;
+    }
+
+  public:
+
+    /**
+    * \brief Default Constructor.
+    *
+    * The default constructor is useful in cases in which the user intends to
+    * perform decompositions via ColPivHouseholderQR::compute(const MatrixType&).
+    */
+    ColPivHouseholderQR()
+      : m_qr(),
+        m_hCoeffs(),
+        m_colsPermutation(),
+        m_colsTranspositions(),
+        m_temp(),
+        m_colNormsUpdated(),
+        m_colNormsDirect(),
+        m_isInitialized(false),
+        m_usePrescribedThreshold(false) {}
+
+    /** \brief Default Constructor with memory preallocation
+      *
+      * Like the default constructor but with preallocation of the internal data
+      * according to the specified problem \a size.
+      * \sa ColPivHouseholderQR()
+      */
+    ColPivHouseholderQR(Index rows, Index cols) : m_qr(rows, cols) { init(rows, cols); }
+
+    /** \brief Constructs a QR factorization from a given matrix
+      *
+      * This constructor computes the QR factorization of the matrix \a matrix by calling
+      * the method compute(). It is a short cut for:
+      *
+      * \code
+      * ColPivHouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
+      * qr.compute(matrix);
+      * \endcode
+      *
+      * \sa compute()
+      */
+    template <typename InputType>
+    explicit ColPivHouseholderQR(const EigenBase<InputType>& matrix) : m_qr(matrix.rows(), matrix.cols()) {
+      init(matrix.rows(), matrix.cols());
+      compute(matrix.derived());
+    }
+
+    /** \brief Constructs a QR factorization from a given matrix
+      *
+      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
+      *
+      * \sa ColPivHouseholderQR(const EigenBase&)
+      */
+    template <typename InputType>
+    explicit ColPivHouseholderQR(EigenBase<InputType>& matrix) : m_qr(matrix.derived()) {
+      init(matrix.rows(), matrix.cols());
+      computeInPlace();
+    }
+
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
+    /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
+      * *this is the QR decomposition, if any exists.
+      *
+      * \param b the right-hand-side of the equation to solve.
+      *
+      * \returns a solution.
+      *
+      * \note_about_checking_solutions
+      *
+      * \note_about_arbitrary_choice_of_solution
+      *
+      * Example: \include ColPivHouseholderQR_solve.cpp
+      * Output: \verbinclude ColPivHouseholderQR_solve.out
+      */
+    template<typename Rhs>
+    inline const Solve<ColPivHouseholderQR, Rhs>
+    solve(const MatrixBase<Rhs>& b) const;
+    #endif
+
+    HouseholderSequenceType householderQ() const;
+    HouseholderSequenceType matrixQ() const
+    {
+      return householderQ();
+    }
+
+    /** \returns a reference to the matrix where the Householder QR decomposition is stored
+      */
+    const MatrixType& matrixQR() const
+    {
+      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+      return m_qr;
+    }
+
+    /** \returns a reference to the matrix where the result Householder QR is stored
+     * \warning The strict lower part of this matrix contains internal values.
+     * Only the upper triangular part should be referenced. To get it, use
+     * \code matrixR().template triangularView<Upper>() \endcode
+     * For rank-deficient matrices, use
+     * \code
+     * matrixR().topLeftCorner(rank(), rank()).template triangularView<Upper>()
+     * \endcode
+     */
+    const MatrixType& matrixR() const
+    {
+      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+      return m_qr;
+    }
+
+    template<typename InputType>
+    ColPivHouseholderQR& compute(const EigenBase<InputType>& matrix);
+
+    /** \returns a const reference to the column permutation matrix */
+    const PermutationType& colsPermutation() const
+    {
+      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+      return m_colsPermutation;
+    }
+
+    /** \returns the determinant of the matrix of which
+      * *this is the QR decomposition. It has only linear complexity
+      * (that is, O(n) where n is the dimension of the square matrix)
+      * as the QR decomposition has already been computed.
+      *
+      * \note This is only for square matrices.
+      *
+      * \warning a determinant can be very big or small, so for matrices
+      * of large enough dimension, there is a risk of overflow/underflow.
+      * One way to work around that is to use logAbsDeterminant() instead.
+      *
+      * \sa absDeterminant(), logAbsDeterminant(), MatrixBase::determinant()
+      */
+    typename MatrixType::Scalar determinant() const;
+
+    /** \returns the absolute value of the determinant of the matrix of which
+      * *this is the QR decomposition. It has only linear complexity
+      * (that is, O(n) where n is the dimension of the square matrix)
+      * as the QR decomposition has already been computed.
+      *
+      * \note This is only for square matrices.
+      *
+      * \warning a determinant can be very big or small, so for matrices
+      * of large enough dimension, there is a risk of overflow/underflow.
+      * One way to work around that is to use logAbsDeterminant() instead.
+      *
+      * \sa determinant(), logAbsDeterminant(), MatrixBase::determinant()
+      */
+    typename MatrixType::RealScalar absDeterminant() const;
+
+    /** \returns the natural log of the absolute value of the determinant of the matrix of which
+      * *this is the QR decomposition. It has only linear complexity
+      * (that is, O(n) where n is the dimension of the square matrix)
+      * as the QR decomposition has already been computed.
+      *
+      * \note This is only for square matrices.
+      *
+      * \note This method is useful to work around the risk of overflow/underflow that's inherent
+      * to determinant computation.
+      *
+      * \sa determinant(), absDeterminant(), MatrixBase::determinant()
+      */
+    typename MatrixType::RealScalar logAbsDeterminant() const;
+
+    /** \returns the rank of the matrix of which *this is the QR decomposition.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      */
+    inline Index rank() const
+    {
+      using std::abs;
+      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+      RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold();
+      Index result = 0;
+      for(Index i = 0; i < m_nonzero_pivots; ++i)
+        result += (abs(m_qr.coeff(i,i)) > premultiplied_threshold);
+      return result;
+    }
+
+    /** \returns the dimension of the kernel of the matrix of which *this is the QR decomposition.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      */
+    inline Index dimensionOfKernel() const
+    {
+      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+      return cols() - rank();
+    }
+
+    /** \returns true if the matrix of which *this is the QR decomposition represents an injective
+      *          linear map, i.e. has trivial kernel; false otherwise.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      */
+    inline bool isInjective() const
+    {
+      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+      return rank() == cols();
+    }
+
+    /** \returns true if the matrix of which *this is the QR decomposition represents a surjective
+      *          linear map; false otherwise.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      */
+    inline bool isSurjective() const
+    {
+      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+      return rank() == rows();
+    }
+
+    /** \returns true if the matrix of which *this is the QR decomposition is invertible.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      */
+    inline bool isInvertible() const
+    {
+      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+      return isInjective() && isSurjective();
+    }
+
+    /** \returns the inverse of the matrix of which *this is the QR decomposition.
+      *
+      * \note If this matrix is not invertible, the returned matrix has undefined coefficients.
+      *       Use isInvertible() to first determine whether this matrix is invertible.
+      */
+    inline const Inverse<ColPivHouseholderQR> inverse() const
+    {
+      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+      return Inverse<ColPivHouseholderQR>(*this);
+    }
+
+    inline Index rows() const { return m_qr.rows(); }
+    inline Index cols() const { return m_qr.cols(); }
+
+    /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
+      *
+      * For advanced uses only.
+      */
+    const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
+
+    /** Allows to prescribe a threshold to be used by certain methods, such as rank(),
+      * who need to determine when pivots are to be considered nonzero. This is not used for the
+      * QR decomposition itself.
+      *
+      * When it needs to get the threshold value, Eigen calls threshold(). By default, this
+      * uses a formula to automatically determine a reasonable threshold.
+      * Once you have called the present method setThreshold(const RealScalar&),
+      * your value is used instead.
+      *
+      * \param threshold The new value to use as the threshold.
+      *
+      * A pivot will be considered nonzero if its absolute value is strictly greater than
+      *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
+      * where maxpivot is the biggest pivot.
+      *
+      * If you want to come back to the default behavior, call setThreshold(Default_t)
+      */
+    ColPivHouseholderQR& setThreshold(const RealScalar& threshold)
+    {
+      m_usePrescribedThreshold = true;
+      m_prescribedThreshold = threshold;
+      return *this;
+    }
+
+    /** Allows to come back to the default behavior, letting Eigen use its default formula for
+      * determining the threshold.
+      *
+      * You should pass the special object Eigen::Default as parameter here.
+      * \code qr.setThreshold(Eigen::Default); \endcode
+      *
+      * See the documentation of setThreshold(const RealScalar&).
+      */
+    ColPivHouseholderQR& setThreshold(Default_t)
+    {
+      m_usePrescribedThreshold = false;
+      return *this;
+    }
+
+    /** Returns the threshold that will be used by certain methods such as rank().
+      *
+      * See the documentation of setThreshold(const RealScalar&).
+      */
+    RealScalar threshold() const
+    {
+      eigen_assert(m_isInitialized || m_usePrescribedThreshold);
+      return m_usePrescribedThreshold ? m_prescribedThreshold
+      // this formula comes from experimenting (see "LU precision tuning" thread on the list)
+      // and turns out to be identical to Higham's formula used already in LDLt.
+                                      : NumTraits<Scalar>::epsilon() * RealScalar(m_qr.diagonalSize());
+    }
+
+    /** \returns the number of nonzero pivots in the QR decomposition.
+      * Here nonzero is meant in the exact sense, not in a fuzzy sense.
+      * So that notion isn't really intrinsically interesting, but it is
+      * still useful when implementing algorithms.
+      *
+      * \sa rank()
+      */
+    inline Index nonzeroPivots() const
+    {
+      eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+      return m_nonzero_pivots;
+    }
+
+    /** \returns the absolute value of the biggest pivot, i.e. the biggest
+      *          diagonal coefficient of R.
+      */
+    RealScalar maxPivot() const { return m_maxpivot; }
+
+    /** \brief Reports whether the QR factorization was successful.
+      *
+      * \note This function always returns \c Success. It is provided for compatibility
+      * with other factorization routines.
+      * \returns \c Success
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return Success;
+    }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename RhsType, typename DstType>
+    void _solve_impl(const RhsType &rhs, DstType &dst) const;
+
+    template<bool Conjugate, typename RhsType, typename DstType>
+    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
+    #endif
+
+  protected:
+
+    friend class CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>;
+
+    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+    void computeInPlace();
+
+    MatrixType m_qr;
+    HCoeffsType m_hCoeffs;
+    PermutationType m_colsPermutation;
+    IntRowVectorType m_colsTranspositions;
+    RowVectorType m_temp;
+    RealRowVectorType m_colNormsUpdated;
+    RealRowVectorType m_colNormsDirect;
+    bool m_isInitialized, m_usePrescribedThreshold;
+    RealScalar m_prescribedThreshold, m_maxpivot;
+    Index m_nonzero_pivots;
+    Index m_det_p;
+};
+
+template<typename MatrixType, typename PermutationIndex>
+typename MatrixType::Scalar ColPivHouseholderQR<MatrixType, PermutationIndex>::determinant() const
+{
+  eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  Scalar detQ;
+  internal::householder_determinant<HCoeffsType, Scalar, NumTraits<Scalar>::IsComplex>::run(m_hCoeffs, detQ);
+  return m_qr.diagonal().prod() * detQ * Scalar(m_det_p);
+}
+
+template<typename MatrixType, typename PermutationIndex>
+typename MatrixType::RealScalar ColPivHouseholderQR<MatrixType, PermutationIndex>::absDeterminant() const
+{
+  using std::abs;
+  eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  return abs(m_qr.diagonal().prod());
+}
+
+template<typename MatrixType, typename PermutationIndex>
+typename MatrixType::RealScalar ColPivHouseholderQR<MatrixType, PermutationIndex>::logAbsDeterminant() const
+{
+  eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  return m_qr.diagonal().cwiseAbs().array().log().sum();
+}
+
+/** Performs the QR factorization of the given matrix \a matrix. The result of
+  * the factorization is stored into \c *this, and a reference to \c *this
+  * is returned.
+  *
+  * \sa class ColPivHouseholderQR, ColPivHouseholderQR(const MatrixType&)
+  */
+template<typename MatrixType, typename PermutationIndex>
+template<typename InputType>
+ColPivHouseholderQR<MatrixType, PermutationIndex>& ColPivHouseholderQR<MatrixType, PermutationIndex>::compute(const EigenBase<InputType>& matrix)
+{
+  m_qr = matrix.derived();
+  computeInPlace();
+  return *this;
+}
+
+template<typename MatrixType, typename PermutationIndex>
+void ColPivHouseholderQR<MatrixType, PermutationIndex>::computeInPlace()
+{
+
+  eigen_assert(m_qr.cols()<=NumTraits<PermutationIndex>::highest());
+
+  using std::abs;
+
+  Index rows = m_qr.rows();
+  Index cols = m_qr.cols();
+  Index size = m_qr.diagonalSize();
+
+  m_hCoeffs.resize(size);
+
+  m_temp.resize(cols);
+
+  m_colsTranspositions.resize(m_qr.cols());
+  Index number_of_transpositions = 0;
+
+  m_colNormsUpdated.resize(cols);
+  m_colNormsDirect.resize(cols);
+  for (Index k = 0; k < cols; ++k) {
+    // colNormsDirect(k) caches the most recent directly computed norm of
+    // column k.
+    m_colNormsDirect.coeffRef(k) = m_qr.col(k).norm();
+    m_colNormsUpdated.coeffRef(k) = m_colNormsDirect.coeffRef(k);
+  }
+
+  RealScalar threshold_helper =  numext::abs2<RealScalar>(m_colNormsUpdated.maxCoeff() * NumTraits<RealScalar>::epsilon()) / RealScalar(rows);
+  RealScalar norm_downdate_threshold = numext::sqrt(NumTraits<RealScalar>::epsilon());
+
+  m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
+  m_maxpivot = RealScalar(0);
+
+  for(Index k = 0; k < size; ++k)
+  {
+    // first, we look up in our table m_colNormsUpdated which column has the biggest norm
+    Index biggest_col_index;
+    RealScalar biggest_col_sq_norm = numext::abs2(m_colNormsUpdated.tail(cols-k).maxCoeff(&biggest_col_index));
+    biggest_col_index += k;
+
+    // Track the number of meaningful pivots but do not stop the decomposition to make
+    // sure that the initial matrix is properly reproduced. See bug 941.
+    if(m_nonzero_pivots==size && biggest_col_sq_norm < threshold_helper * RealScalar(rows-k))
+      m_nonzero_pivots = k;
+
+    // apply the transposition to the columns
+    m_colsTranspositions.coeffRef(k) = static_cast<PermutationIndex>(biggest_col_index);
+    if(k != biggest_col_index) {
+      m_qr.col(k).swap(m_qr.col(biggest_col_index));
+      std::swap(m_colNormsUpdated.coeffRef(k), m_colNormsUpdated.coeffRef(biggest_col_index));
+      std::swap(m_colNormsDirect.coeffRef(k), m_colNormsDirect.coeffRef(biggest_col_index));
+      ++number_of_transpositions;
+    }
+
+    // generate the householder vector, store it below the diagonal
+    RealScalar beta;
+    m_qr.col(k).tail(rows-k).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta);
+
+    // apply the householder transformation to the diagonal coefficient
+    m_qr.coeffRef(k,k) = beta;
+
+    // remember the maximum absolute value of diagonal coefficients
+    if(abs(beta) > m_maxpivot) m_maxpivot = abs(beta);
+
+    // apply the householder transformation
+    m_qr.bottomRightCorner(rows-k, cols-k-1)
+        .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), m_hCoeffs.coeffRef(k), &m_temp.coeffRef(k+1));
+
+    // update our table of norms of the columns
+    for (Index j = k + 1; j < cols; ++j) {
+      // The following implements the stable norm downgrade step discussed in
+      // http://www.netlib.org/lapack/lawnspdf/lawn176.pdf
+      // and used in LAPACK routines xGEQPF and xGEQP3.
+      // See lines 278-297 in http://www.netlib.org/lapack/explore-html/dc/df4/sgeqpf_8f_source.html
+      if (!numext::is_exactly_zero(m_colNormsUpdated.coeffRef(j))) {
+        RealScalar temp = abs(m_qr.coeffRef(k, j)) / m_colNormsUpdated.coeffRef(j);
+        temp = (RealScalar(1) + temp) * (RealScalar(1) - temp);
+        temp = temp <  RealScalar(0) ? RealScalar(0) : temp;
+        RealScalar temp2 = temp * numext::abs2<RealScalar>(m_colNormsUpdated.coeffRef(j) /
+                                                           m_colNormsDirect.coeffRef(j));
+        if (temp2 <= norm_downdate_threshold) {
+          // The updated norm has become too inaccurate so re-compute the column
+          // norm directly.
+          m_colNormsDirect.coeffRef(j) = m_qr.col(j).tail(rows - k - 1).norm();
+          m_colNormsUpdated.coeffRef(j) = m_colNormsDirect.coeffRef(j);
+        } else {
+          m_colNormsUpdated.coeffRef(j) *= numext::sqrt(temp);
+        }
+      }
+    }
+  }
+
+  m_colsPermutation.setIdentity(cols);
+  for(Index k = 0; k < size/*m_nonzero_pivots*/; ++k)
+    m_colsPermutation.applyTranspositionOnTheRight(k, static_cast<Index>(m_colsTranspositions.coeff(k)));
+
+  m_det_p = (number_of_transpositions%2) ? -1 : 1;
+  m_isInitialized = true;
+}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename MatrixType_, typename PermutationIndex_>
+template<typename RhsType, typename DstType>
+void ColPivHouseholderQR<MatrixType_, PermutationIndex_>::_solve_impl(const RhsType &rhs, DstType &dst) const
+{
+  const Index nonzero_pivots = nonzeroPivots();
+
+  if(nonzero_pivots == 0)
+  {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(rhs);
+
+  c.applyOnTheLeft(householderQ().setLength(nonzero_pivots).adjoint() );
+
+  m_qr.topLeftCorner(nonzero_pivots, nonzero_pivots)
+      .template triangularView<Upper>()
+      .solveInPlace(c.topRows(nonzero_pivots));
+
+  for(Index i = 0; i < nonzero_pivots; ++i) dst.row(m_colsPermutation.indices().coeff(i)) = c.row(i);
+  for(Index i = nonzero_pivots; i < cols(); ++i) dst.row(m_colsPermutation.indices().coeff(i)).setZero();
+}
+
+template<typename MatrixType_, typename PermutationIndex_>
+template<bool Conjugate, typename RhsType, typename DstType>
+void ColPivHouseholderQR<MatrixType_, PermutationIndex_>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
+{
+  const Index nonzero_pivots = nonzeroPivots();
+
+  if(nonzero_pivots == 0)
+  {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(m_colsPermutation.transpose()*rhs);
+
+  m_qr.topLeftCorner(nonzero_pivots, nonzero_pivots)
+        .template triangularView<Upper>()
+        .transpose().template conjugateIf<Conjugate>()
+        .solveInPlace(c.topRows(nonzero_pivots));
+
+  dst.topRows(nonzero_pivots) = c.topRows(nonzero_pivots);
+  dst.bottomRows(rows()-nonzero_pivots).setZero();
+
+  dst.applyOnTheLeft(householderQ().setLength(nonzero_pivots).template conjugateIf<!Conjugate>() );
+}
+#endif
+
+namespace internal {
+
+template<typename DstXprType, typename MatrixType, typename PermutationIndex>
+struct Assignment<DstXprType, Inverse<ColPivHouseholderQR<MatrixType, PermutationIndex> >, internal::assign_op<typename DstXprType::Scalar,typename ColPivHouseholderQR<MatrixType, PermutationIndex>::Scalar>, Dense2Dense>
+{
+  typedef ColPivHouseholderQR<MatrixType, PermutationIndex> QrType;
+  typedef Inverse<QrType> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename QrType::Scalar> &)
+  {
+    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
+  }
+};
+
+} // end namespace internal
+
+/** \returns the matrix Q as a sequence of householder transformations.
+  * You can extract the meaningful part only by using:
+  * \code qr.householderQ().setLength(qr.nonzeroPivots()) \endcode*/
+template<typename MatrixType, typename PermutationIndex>
+typename ColPivHouseholderQR<MatrixType, PermutationIndex>::HouseholderSequenceType ColPivHouseholderQR<MatrixType, PermutationIndex>
+  ::householderQ() const
+{
+  eigen_assert(m_isInitialized && "ColPivHouseholderQR is not initialized.");
+  return HouseholderSequenceType(m_qr, m_hCoeffs.conjugate());
+}
+
+/** \return the column-pivoting Householder QR decomposition of \c *this.
+  *
+  * \sa class ColPivHouseholderQR
+  */
+template<typename Derived>
+template<typename PermutationIndexType>
+const ColPivHouseholderQR<typename MatrixBase<Derived>::PlainObject, PermutationIndexType>
+MatrixBase<Derived>::colPivHouseholderQr() const
+{
+  return ColPivHouseholderQR<PlainObject, PermutationIndexType>(eval());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_COLPIVOTINGHOUSEHOLDERQR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
new file mode 100644
index 0000000000000000000000000000000000000000..6861ff1cfe254f3068b94198097d8313f3045440
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/ColPivHouseholderQR_LAPACKE.h
@@ -0,0 +1,149 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *    Householder QR decomposition of a matrix with column pivoting based on
+ *    LAPACKE_?geqp3 function.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_COLPIVOTINGHOUSEHOLDERQR_LAPACKE_H
+#define EIGEN_COLPIVOTINGHOUSEHOLDERQR_LAPACKE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+#if defined(EIGEN_USE_LAPACKE)
+
+    template<typename Scalar>
+    inline lapack_int call_geqp3(int matrix_layout, lapack_int m, lapack_int n, Scalar* a, lapack_int lda, lapack_int* jpvt, Scalar* tau);
+    template<>
+    inline lapack_int call_geqp3(int matrix_layout, lapack_int m, lapack_int n, float* a, lapack_int lda, lapack_int* jpvt, float* tau)
+    { return LAPACKE_sgeqp3(matrix_layout, m, n, a, lda, jpvt, tau); }
+    template<>
+    inline lapack_int call_geqp3(int matrix_layout, lapack_int m, lapack_int n, double* a, lapack_int lda, lapack_int* jpvt, double* tau)
+    { return LAPACKE_dgeqp3(matrix_layout, m, n, a, lda, jpvt, tau); }
+    template<>
+    inline lapack_int call_geqp3(int matrix_layout, lapack_int m, lapack_int n, lapack_complex_float* a, lapack_int lda, lapack_int* jpvt, lapack_complex_float* tau)
+    { return LAPACKE_cgeqp3(matrix_layout, m, n, a, lda, jpvt, tau); }
+    template<>
+    inline lapack_int call_geqp3(int matrix_layout, lapack_int m, lapack_int n, lapack_complex_double* a, lapack_int lda, lapack_int* jpvt, lapack_complex_double* tau)
+    { return LAPACKE_zgeqp3(matrix_layout, m, n, a, lda, jpvt, tau); }
+
+    template <typename MatrixType>
+    struct ColPivHouseholderQR_LAPACKE_impl {
+      typedef typename MatrixType::Scalar Scalar;
+      typedef typename MatrixType::RealScalar RealScalar;
+      typedef typename internal::lapacke_helpers::translate_type_imp<Scalar>::type LapackeType;
+      static constexpr int LapackeStorage = MatrixType::IsRowMajor ? (LAPACK_ROW_MAJOR) : (LAPACK_COL_MAJOR);
+
+      typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
+      typedef PermutationMatrix<Dynamic, Dynamic, lapack_int> PermutationType;
+
+      static void run(MatrixType& qr, HCoeffsType& hCoeffs, PermutationType& colsPermutation, Index& nonzero_pivots,
+                      RealScalar& maxpivot, bool usePrescribedThreshold, RealScalar prescribedThreshold, Index& det_p,
+                      bool& isInitialized) {
+
+        isInitialized = false;
+        hCoeffs.resize(qr.diagonalSize());
+        nonzero_pivots = 0;
+        maxpivot = RealScalar(0);
+        colsPermutation.resize(qr.cols());
+        colsPermutation.indices().setZero();
+
+        lapack_int rows = internal::lapacke_helpers::to_lapack(qr.rows());
+        lapack_int cols = internal::lapacke_helpers::to_lapack(qr.cols());
+        LapackeType* qr_data = (LapackeType*)(qr.data());
+        lapack_int lda = internal::lapacke_helpers::to_lapack(qr.outerStride());
+        lapack_int* perm_data = colsPermutation.indices().data();
+        LapackeType* hCoeffs_data = (LapackeType*)(hCoeffs.data());
+
+        lapack_int info = call_geqp3(LapackeStorage, rows, cols, qr_data, lda, perm_data, hCoeffs_data);
+        if (info != 0) return;
+
+        maxpivot = qr.diagonal().cwiseAbs().maxCoeff();
+        hCoeffs.adjointInPlace();
+        RealScalar defaultThreshold = NumTraits<RealScalar>::epsilon() * RealScalar(qr.diagonalSize());
+        RealScalar threshold = usePrescribedThreshold ? prescribedThreshold : defaultThreshold;
+        RealScalar premultiplied_threshold = maxpivot * threshold;
+        nonzero_pivots = (qr.diagonal().cwiseAbs().array() > premultiplied_threshold).count();
+        colsPermutation.indices().array() -= 1;
+        det_p = colsPermutation.determinant();
+        isInitialized = true;
+      };
+
+      static void init(Index rows, Index cols, HCoeffsType& hCoeffs, PermutationType& colsPermutation,
+                              bool& usePrescribedThreshold, bool& isInitialized) {
+
+        Index diag = numext::mini(rows, cols);
+        hCoeffs.resize(diag);
+        colsPermutation.resize(cols);
+        usePrescribedThreshold = false;
+        isInitialized = false;
+      }
+    };
+
+    #define COLPIVQR_LAPACKE_COMPUTEINPLACE(EIGTYPE)                                                                 \
+    template <> inline void ColPivHouseholderQR<EIGTYPE, lapack_int>::computeInPlace() {                             \
+      ColPivHouseholderQR_LAPACKE_impl<MatrixType>::run(m_qr, m_hCoeffs, m_colsPermutation, m_nonzero_pivots,        \
+                                                        m_maxpivot, m_usePrescribedThreshold, m_prescribedThreshold, \
+                                                        m_det_p, m_isInitialized); }                                 \
+
+    #define COLPIVQR_LAPACKE_INIT(EIGTYPE)                                                                          \
+    template <> inline void ColPivHouseholderQR<EIGTYPE, lapack_int>::init(Index rows, Index cols) {                \
+      ColPivHouseholderQR_LAPACKE_impl<MatrixType>::init(rows, cols, m_hCoeffs, m_colsPermutation, m_isInitialized, \
+                                                         m_usePrescribedThreshold); }                               \
+
+    #define COLPIVQR_LAPACKE(EIGTYPE)               \
+      COLPIVQR_LAPACKE_COMPUTEINPLACE(EIGTYPE)      \
+      COLPIVQR_LAPACKE_INIT(EIGTYPE)                \
+      COLPIVQR_LAPACKE_COMPUTEINPLACE(Ref<EIGTYPE>) \
+      COLPIVQR_LAPACKE_INIT(Ref<EIGTYPE>)           \
+
+    typedef Matrix<float,                 Dynamic, Dynamic, ColMajor> MatrixXfC;
+    typedef Matrix<double,                Dynamic, Dynamic, ColMajor> MatrixXdC;
+    typedef Matrix<std::complex<float>,   Dynamic, Dynamic, ColMajor> MatrixXcfC;
+    typedef Matrix<std::complex<double>,  Dynamic, Dynamic, ColMajor> MatrixXcdC;
+    typedef Matrix<float,                 Dynamic, Dynamic, RowMajor> MatrixXfR;
+    typedef Matrix<double,                Dynamic, Dynamic, RowMajor> MatrixXdR;
+    typedef Matrix<std::complex<float>,   Dynamic, Dynamic, RowMajor> MatrixXcfR;
+    typedef Matrix<std::complex<double>,  Dynamic, Dynamic, RowMajor> MatrixXcdR;
+
+    COLPIVQR_LAPACKE(MatrixXfC)
+    COLPIVQR_LAPACKE(MatrixXdC)
+    COLPIVQR_LAPACKE(MatrixXcfC)
+    COLPIVQR_LAPACKE(MatrixXcdC)
+    COLPIVQR_LAPACKE(MatrixXfR)
+    COLPIVQR_LAPACKE(MatrixXdR)
+    COLPIVQR_LAPACKE(MatrixXcfR)
+    COLPIVQR_LAPACKE(MatrixXcdR)
+
+#endif
+}  // end namespace Eigen
+
+#endif  // EIGEN_COLPIVOTINGHOUSEHOLDERQR_LAPACKE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/CompleteOrthogonalDecomposition.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/CompleteOrthogonalDecomposition.h
new file mode 100644
index 0000000000000000000000000000000000000000..171e601154741f230d151950a44e9e1122f699c0
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+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/CompleteOrthogonalDecomposition.h
@@ -0,0 +1,651 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Rasmus Munk Larsen <rmlarsen@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPLETEORTHOGONALDECOMPOSITION_H
+#define EIGEN_COMPLETEORTHOGONALDECOMPOSITION_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename MatrixType_, typename PermutationIndex_>
+struct traits<CompleteOrthogonalDecomposition<MatrixType_, PermutationIndex_> >
+    : traits<MatrixType_> {
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef PermutationIndex_ PermutationIndex;
+  enum { Flags = 0 };
+};
+
+}  // end namespace internal
+
+/** \ingroup QR_Module
+  *
+  * \class CompleteOrthogonalDecomposition
+  *
+  * \brief Complete orthogonal decomposition (COD) of a matrix.
+  *
+  * \tparam MatrixType_ the type of the matrix of which we are computing the COD.
+  *
+  * This class performs a rank-revealing complete orthogonal decomposition of a
+  * matrix  \b A into matrices \b P, \b Q, \b T, and \b Z such that
+  * \f[
+  *  \mathbf{A} \, \mathbf{P} = \mathbf{Q} \,
+  *                     \begin{bmatrix} \mathbf{T} &  \mathbf{0} \\
+  *                                     \mathbf{0} & \mathbf{0} \end{bmatrix} \, \mathbf{Z}
+  * \f]
+  * by using Householder transformations. Here, \b P is a permutation matrix,
+  * \b Q and \b Z are unitary matrices and \b T an upper triangular matrix of
+  * size rank-by-rank. \b A may be rank deficient.
+  *
+  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+  * 
+  * \sa MatrixBase::completeOrthogonalDecomposition()
+  */
+template <typename MatrixType_, typename PermutationIndex_> class CompleteOrthogonalDecomposition
+          : public SolverBase<CompleteOrthogonalDecomposition<MatrixType_, PermutationIndex_> >
+{
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef SolverBase<CompleteOrthogonalDecomposition> Base;
+
+  template<typename Derived>
+  friend struct internal::solve_assertion;
+  typedef PermutationIndex_ PermutationIndex;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(CompleteOrthogonalDecomposition)
+  enum {
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+  typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
+  typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime, PermutationIndex>
+      PermutationType;
+  typedef typename internal::plain_row_type<MatrixType, Index>::type
+      IntRowVectorType;
+  typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
+  typedef typename internal::plain_row_type<MatrixType, RealScalar>::type
+      RealRowVectorType;
+  typedef HouseholderSequence<
+      MatrixType, internal::remove_all_t<
+                      typename HCoeffsType::ConjugateReturnType>>
+      HouseholderSequenceType;
+  typedef typename MatrixType::PlainObject PlainObject;
+
+ public:
+  /**
+   * \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via
+   * \c CompleteOrthogonalDecomposition::compute(const* MatrixType&).
+   */
+  CompleteOrthogonalDecomposition() : m_cpqr(), m_zCoeffs(), m_temp() {}
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem \a size.
+   * \sa CompleteOrthogonalDecomposition()
+   */
+  CompleteOrthogonalDecomposition(Index rows, Index cols)
+      : m_cpqr(rows, cols), m_zCoeffs((std::min)(rows, cols)), m_temp(cols) {}
+
+  /** \brief Constructs a complete orthogonal decomposition from a given
+   * matrix.
+   *
+   * This constructor computes the complete orthogonal decomposition of the
+   * matrix \a matrix by calling the method compute(). The default
+   * threshold for rank determination will be used. It is a short cut for:
+   *
+   * \code
+   * CompleteOrthogonalDecomposition<MatrixType> cod(matrix.rows(),
+   *                                                 matrix.cols());
+   * cod.setThreshold(Default);
+   * cod.compute(matrix);
+   * \endcode
+   *
+   * \sa compute()
+   */
+  template <typename InputType>
+  explicit CompleteOrthogonalDecomposition(const EigenBase<InputType>& matrix)
+      : m_cpqr(matrix.rows(), matrix.cols()),
+        m_zCoeffs((std::min)(matrix.rows(), matrix.cols())),
+        m_temp(matrix.cols())
+  {
+    compute(matrix.derived());
+  }
+
+  /** \brief Constructs a complete orthogonal decomposition from a given matrix
+    *
+    * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
+    *
+    * \sa CompleteOrthogonalDecomposition(const EigenBase&)
+    */
+  template<typename InputType>
+  explicit CompleteOrthogonalDecomposition(EigenBase<InputType>& matrix)
+    : m_cpqr(matrix.derived()),
+      m_zCoeffs((std::min)(matrix.rows(), matrix.cols())),
+      m_temp(matrix.cols())
+  {
+    computeInPlace();
+  } 
+
+  #ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** This method computes the minimum-norm solution X to a least squares
+   * problem \f[\mathrm{minimize} \|A X - B\|, \f] where \b A is the matrix of
+   * which \c *this is the complete orthogonal decomposition.
+   *
+   * \param b the right-hand sides of the problem to solve.
+   *
+   * \returns a solution.
+   *
+   */
+  template <typename Rhs>
+  inline const Solve<CompleteOrthogonalDecomposition, Rhs> solve(
+      const MatrixBase<Rhs>& b) const;
+  #endif
+
+  HouseholderSequenceType householderQ(void) const;
+  HouseholderSequenceType matrixQ(void) const { return m_cpqr.householderQ(); }
+
+  /** \returns the matrix \b Z.
+   */
+  MatrixType matrixZ() const {
+    MatrixType Z = MatrixType::Identity(m_cpqr.cols(), m_cpqr.cols());
+    applyZOnTheLeftInPlace<false>(Z);
+    return Z;
+  }
+
+  /** \returns a reference to the matrix where the complete orthogonal
+   * decomposition is stored
+   */
+  const MatrixType& matrixQTZ() const { return m_cpqr.matrixQR(); }
+
+  /** \returns a reference to the matrix where the complete orthogonal
+   * decomposition is stored.
+   * \warning The strict lower part and \code cols() - rank() \endcode right
+   * columns of this matrix contains internal values.
+   * Only the upper triangular part should be referenced. To get it, use
+   * \code matrixT().template triangularView<Upper>() \endcode
+   * For rank-deficient matrices, use
+   * \code
+   * matrixT().topLeftCorner(rank(), rank()).template triangularView<Upper>()
+   * \endcode
+   */
+  const MatrixType& matrixT() const { return m_cpqr.matrixQR(); }
+
+  template <typename InputType>
+  CompleteOrthogonalDecomposition& compute(const EigenBase<InputType>& matrix) {
+    // Compute the column pivoted QR factorization A P = Q R.
+    m_cpqr.compute(matrix);
+    computeInPlace();
+    return *this;
+  }
+
+  /** \returns a const reference to the column permutation matrix */
+  const PermutationType& colsPermutation() const {
+    return m_cpqr.colsPermutation();
+  }
+
+    /** \returns the determinant of the matrix of which
+   * *this is the complete orthogonal decomposition. It has only linear
+   * complexity (that is, O(n) where n is the dimension of the square matrix)
+   * as the complete orthogonal decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \warning a determinant can be very big or small, so for matrices
+   * of large enough dimension, there is a risk of overflow/underflow.
+   * One way to work around that is to use logAbsDeterminant() instead.
+   *
+   * \sa absDeterminant(), logAbsDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::Scalar determinant() const;
+
+  /** \returns the absolute value of the determinant of the matrix of which
+   * *this is the complete orthogonal decomposition. It has only linear
+   * complexity (that is, O(n) where n is the dimension of the square matrix)
+   * as the complete orthogonal decomposition has already been computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \warning a determinant can be very big or small, so for matrices
+   * of large enough dimension, there is a risk of overflow/underflow.
+   * One way to work around that is to use logAbsDeterminant() instead.
+   *
+   * \sa determinant(), logAbsDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::RealScalar absDeterminant() const;
+
+  /** \returns the natural log of the absolute value of the determinant of the
+   * matrix of which *this is the complete orthogonal decomposition. It has
+   * only linear complexity (that is, O(n) where n is the dimension of the
+   * square matrix) as the complete orthogonal decomposition has already been
+   * computed.
+   *
+   * \note This is only for square matrices.
+   *
+   * \note This method is useful to work around the risk of overflow/underflow
+   * that's inherent to determinant computation.
+   *
+   * \sa determinant(), absDeterminant(), MatrixBase::determinant()
+   */
+  typename MatrixType::RealScalar logAbsDeterminant() const;
+
+  /** \returns the rank of the matrix of which *this is the complete orthogonal
+   * decomposition.
+   *
+   * \note This method has to determine which pivots should be considered
+   * nonzero. For that, it uses the threshold value that you can control by
+   * calling setThreshold(const RealScalar&).
+   */
+  inline Index rank() const { return m_cpqr.rank(); }
+
+  /** \returns the dimension of the kernel of the matrix of which *this is the
+   * complete orthogonal decomposition.
+   *
+   * \note This method has to determine which pivots should be considered
+   * nonzero. For that, it uses the threshold value that you can control by
+   * calling setThreshold(const RealScalar&).
+   */
+  inline Index dimensionOfKernel() const { return m_cpqr.dimensionOfKernel(); }
+
+  /** \returns true if the matrix of which *this is the decomposition represents
+   * an injective linear map, i.e. has trivial kernel; false otherwise.
+   *
+   * \note This method has to determine which pivots should be considered
+   * nonzero. For that, it uses the threshold value that you can control by
+   * calling setThreshold(const RealScalar&).
+   */
+  inline bool isInjective() const { return m_cpqr.isInjective(); }
+
+  /** \returns true if the matrix of which *this is the decomposition represents
+   * a surjective linear map; false otherwise.
+   *
+   * \note This method has to determine which pivots should be considered
+   * nonzero. For that, it uses the threshold value that you can control by
+   * calling setThreshold(const RealScalar&).
+   */
+  inline bool isSurjective() const { return m_cpqr.isSurjective(); }
+
+  /** \returns true if the matrix of which *this is the complete orthogonal
+   * decomposition is invertible.
+   *
+   * \note This method has to determine which pivots should be considered
+   * nonzero. For that, it uses the threshold value that you can control by
+   * calling setThreshold(const RealScalar&).
+   */
+  inline bool isInvertible() const { return m_cpqr.isInvertible(); }
+
+  /** \returns the pseudo-inverse of the matrix of which *this is the complete
+   * orthogonal decomposition.
+   * \warning: Do not compute \c this->pseudoInverse()*rhs to solve a linear systems.
+   * It is more efficient and numerically stable to call \c this->solve(rhs).
+   */
+  inline const Inverse<CompleteOrthogonalDecomposition> pseudoInverse() const
+  {
+    eigen_assert(m_cpqr.m_isInitialized && "CompleteOrthogonalDecomposition is not initialized.");
+    return Inverse<CompleteOrthogonalDecomposition>(*this);
+  }
+
+  inline Index rows() const { return m_cpqr.rows(); }
+  inline Index cols() const { return m_cpqr.cols(); }
+
+  /** \returns a const reference to the vector of Householder coefficients used
+   * to represent the factor \c Q.
+   *
+   * For advanced uses only.
+   */
+  inline const HCoeffsType& hCoeffs() const { return m_cpqr.hCoeffs(); }
+
+  /** \returns a const reference to the vector of Householder coefficients
+   * used to represent the factor \c Z.
+   *
+   * For advanced uses only.
+   */
+  const HCoeffsType& zCoeffs() const { return m_zCoeffs; }
+
+  /** Allows to prescribe a threshold to be used by certain methods, such as
+   * rank(), who need to determine when pivots are to be considered nonzero.
+   * Most be called before calling compute().
+   *
+   * When it needs to get the threshold value, Eigen calls threshold(). By
+   * default, this uses a formula to automatically determine a reasonable
+   * threshold. Once you have called the present method
+   * setThreshold(const RealScalar&), your value is used instead.
+   *
+   * \param threshold The new value to use as the threshold.
+   *
+   * A pivot will be considered nonzero if its absolute value is strictly
+   * greater than
+   *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
+   * where maxpivot is the biggest pivot.
+   *
+   * If you want to come back to the default behavior, call
+   * setThreshold(Default_t)
+   */
+  CompleteOrthogonalDecomposition& setThreshold(const RealScalar& threshold) {
+    m_cpqr.setThreshold(threshold);
+    return *this;
+  }
+
+  /** Allows to come back to the default behavior, letting Eigen use its default
+   * formula for determining the threshold.
+   *
+   * You should pass the special object Eigen::Default as parameter here.
+   * \code qr.setThreshold(Eigen::Default); \endcode
+   *
+   * See the documentation of setThreshold(const RealScalar&).
+   */
+  CompleteOrthogonalDecomposition& setThreshold(Default_t) {
+    m_cpqr.setThreshold(Default);
+    return *this;
+  }
+
+  /** Returns the threshold that will be used by certain methods such as rank().
+   *
+   * See the documentation of setThreshold(const RealScalar&).
+   */
+  RealScalar threshold() const { return m_cpqr.threshold(); }
+
+  /** \returns the number of nonzero pivots in the complete orthogonal
+   * decomposition. Here nonzero is meant in the exact sense, not in a
+   * fuzzy sense. So that notion isn't really intrinsically interesting,
+   * but it is still useful when implementing algorithms.
+   *
+   * \sa rank()
+   */
+  inline Index nonzeroPivots() const { return m_cpqr.nonzeroPivots(); }
+
+  /** \returns the absolute value of the biggest pivot, i.e. the biggest
+   *          diagonal coefficient of R.
+   */
+  inline RealScalar maxPivot() const { return m_cpqr.maxPivot(); }
+
+  /** \brief Reports whether the complete orthogonal decomposition was
+   * successful.
+   *
+   * \note This function always returns \c Success. It is provided for
+   * compatibility
+   * with other factorization routines.
+   * \returns \c Success
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_cpqr.m_isInitialized && "Decomposition is not initialized.");
+    return Success;
+  }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename RhsType, typename DstType>
+  void _solve_impl(const RhsType& rhs, DstType& dst) const;
+
+  template<bool Conjugate, typename RhsType, typename DstType>
+  void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
+#endif
+
+ protected:
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  template<bool Transpose_, typename Rhs>
+  void _check_solve_assertion(const Rhs& b) const {
+      EIGEN_ONLY_USED_FOR_DEBUG(b);
+      eigen_assert(m_cpqr.m_isInitialized && "CompleteOrthogonalDecomposition is not initialized.");
+      eigen_assert((Transpose_?derived().cols():derived().rows())==b.rows() && "CompleteOrthogonalDecomposition::solve(): invalid number of rows of the right hand side matrix b");
+  }
+
+  void computeInPlace();
+
+  /** Overwrites \b rhs with \f$ \mathbf{Z} * \mathbf{rhs} \f$ or
+   *  \f$ \mathbf{\overline Z} * \mathbf{rhs} \f$ if \c Conjugate 
+   *  is set to \c true.
+   */
+  template <bool Conjugate, typename Rhs>
+  void applyZOnTheLeftInPlace(Rhs& rhs) const;
+
+  /** Overwrites \b rhs with \f$ \mathbf{Z}^* * \mathbf{rhs} \f$.
+   */
+  template <typename Rhs>
+  void applyZAdjointOnTheLeftInPlace(Rhs& rhs) const;
+
+  ColPivHouseholderQR<MatrixType, PermutationIndex> m_cpqr;
+  HCoeffsType m_zCoeffs;
+  RowVectorType m_temp;
+};
+
+template <typename MatrixType, typename PermutationIndex>
+typename MatrixType::Scalar
+CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::determinant() const {
+  return m_cpqr.determinant();
+}
+
+template <typename MatrixType, typename PermutationIndex>
+typename MatrixType::RealScalar
+CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::absDeterminant() const {
+  return m_cpqr.absDeterminant();
+}
+
+template <typename MatrixType, typename PermutationIndex>
+typename MatrixType::RealScalar
+CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::logAbsDeterminant() const {
+  return m_cpqr.logAbsDeterminant();
+}
+
+/** Performs the complete orthogonal decomposition of the given matrix \a
+ * matrix. The result of the factorization is stored into \c *this, and a
+ * reference to \c *this is returned.
+ *
+ * \sa class CompleteOrthogonalDecomposition,
+ * CompleteOrthogonalDecomposition(const MatrixType&)
+ */
+template <typename MatrixType, typename PermutationIndex>
+void CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::computeInPlace()
+{
+  eigen_assert(m_cpqr.cols() <= NumTraits<PermutationIndex>::highest());
+
+  const Index rank = m_cpqr.rank();
+  const Index cols = m_cpqr.cols();
+  const Index rows = m_cpqr.rows();
+  m_zCoeffs.resize((std::min)(rows, cols));
+  m_temp.resize(cols);
+
+  if (rank < cols) {
+    // We have reduced the (permuted) matrix to the form
+    //   [R11 R12]
+    //   [ 0  R22]
+    // where R11 is r-by-r (r = rank) upper triangular, R12 is
+    // r-by-(n-r), and R22 is empty or the norm of R22 is negligible.
+    // We now compute the complete orthogonal decomposition by applying
+    // Householder transformations from the right to the upper trapezoidal
+    // matrix X = [R11 R12] to zero out R12 and obtain the factorization
+    // [R11 R12] = [T11 0] * Z, where T11 is r-by-r upper triangular and
+    // Z = Z(0) * Z(1) ... Z(r-1) is an n-by-n orthogonal matrix.
+    // We store the data representing Z in R12 and m_zCoeffs.
+    for (Index k = rank - 1; k >= 0; --k) {
+      if (k != rank - 1) {
+        // Given the API for Householder reflectors, it is more convenient if
+        // we swap the leading parts of columns k and r-1 (zero-based) to form
+        // the matrix X_k = [X(0:k, k), X(0:k, r:n)]
+        m_cpqr.m_qr.col(k).head(k + 1).swap(
+            m_cpqr.m_qr.col(rank - 1).head(k + 1));
+      }
+      // Construct Householder reflector Z(k) to zero out the last row of X_k,
+      // i.e. choose Z(k) such that
+      // [X(k, k), X(k, r:n)] * Z(k) = [beta, 0, .., 0].
+      RealScalar beta;
+      m_cpqr.m_qr.row(k)
+          .tail(cols - rank + 1)
+          .makeHouseholderInPlace(m_zCoeffs(k), beta);
+      m_cpqr.m_qr(k, rank - 1) = beta;
+      if (k > 0) {
+        // Apply Z(k) to the first k rows of X_k
+        m_cpqr.m_qr.topRightCorner(k, cols - rank + 1)
+            .applyHouseholderOnTheRight(
+                m_cpqr.m_qr.row(k).tail(cols - rank).adjoint(), m_zCoeffs(k),
+                &m_temp(0));
+      }
+      if (k != rank - 1) {
+        // Swap X(0:k,k) back to its proper location.
+        m_cpqr.m_qr.col(k).head(k + 1).swap(
+            m_cpqr.m_qr.col(rank - 1).head(k + 1));
+      }
+    }
+  }
+}
+
+template <typename MatrixType, typename PermutationIndex>
+template <bool Conjugate, typename Rhs>
+void CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::applyZOnTheLeftInPlace(
+    Rhs& rhs) const {
+  const Index cols = this->cols();
+  const Index nrhs = rhs.cols();
+  const Index rank = this->rank();
+  Matrix<typename Rhs::Scalar, Dynamic, 1> temp((std::max)(cols, nrhs));
+  for (Index k = rank-1; k >= 0; --k) {
+    if (k != rank - 1) {
+      rhs.row(k).swap(rhs.row(rank - 1));
+    }
+    rhs.middleRows(rank - 1, cols - rank + 1)
+        .applyHouseholderOnTheLeft(
+            matrixQTZ().row(k).tail(cols - rank).transpose().template conjugateIf<!Conjugate>(), zCoeffs().template conjugateIf<Conjugate>()(k),
+            &temp(0));
+    if (k != rank - 1) {
+      rhs.row(k).swap(rhs.row(rank - 1));
+    }
+  }
+}
+
+template <typename MatrixType, typename PermutationIndex>
+template <typename Rhs>
+void CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::applyZAdjointOnTheLeftInPlace(
+    Rhs& rhs) const {
+  const Index cols = this->cols();
+  const Index nrhs = rhs.cols();
+  const Index rank = this->rank();
+  Matrix<typename Rhs::Scalar, Dynamic, 1> temp((std::max)(cols, nrhs));
+  for (Index k = 0; k < rank; ++k) {
+    if (k != rank - 1) {
+      rhs.row(k).swap(rhs.row(rank - 1));
+    }
+    rhs.middleRows(rank - 1, cols - rank + 1)
+        .applyHouseholderOnTheLeft(
+            matrixQTZ().row(k).tail(cols - rank).adjoint(), zCoeffs()(k),
+            &temp(0));
+    if (k != rank - 1) {
+      rhs.row(k).swap(rhs.row(rank - 1));
+    }
+  }
+}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename MatrixType_, typename PermutationIndex_>
+template <typename RhsType, typename DstType>
+void CompleteOrthogonalDecomposition<MatrixType_, PermutationIndex_>::_solve_impl(
+    const RhsType& rhs, DstType& dst) const {
+  const Index rank = this->rank();
+  if (rank == 0) {
+    dst.setZero();
+    return;
+  }
+
+  // Compute c = Q^* * rhs
+  typename RhsType::PlainObject c(rhs);
+  c.applyOnTheLeft(matrixQ().setLength(rank).adjoint());
+
+  // Solve T z = c(1:rank, :)
+  dst.topRows(rank) = matrixT()
+                          .topLeftCorner(rank, rank)
+                          .template triangularView<Upper>()
+                          .solve(c.topRows(rank));
+
+  const Index cols = this->cols();
+  if (rank < cols) {
+    // Compute y = Z^* * [ z ]
+    //                   [ 0 ]
+    dst.bottomRows(cols - rank).setZero();
+    applyZAdjointOnTheLeftInPlace(dst);
+  }
+
+  // Undo permutation to get x = P^{-1} * y.
+  dst = colsPermutation() * dst;
+}
+
+template<typename MatrixType_, typename PermutationIndex_>
+template<bool Conjugate, typename RhsType, typename DstType>
+void CompleteOrthogonalDecomposition<MatrixType_, PermutationIndex_>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
+{
+  const Index rank = this->rank();
+
+  if (rank == 0) {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(colsPermutation().transpose()*rhs);
+
+  if (rank < cols()) {
+    applyZOnTheLeftInPlace<!Conjugate>(c);
+  }
+
+  matrixT().topLeftCorner(rank, rank)
+           .template triangularView<Upper>()
+           .transpose().template conjugateIf<Conjugate>()
+           .solveInPlace(c.topRows(rank));
+
+  dst.topRows(rank) = c.topRows(rank);
+  dst.bottomRows(rows()-rank).setZero();
+
+  dst.applyOnTheLeft(householderQ().setLength(rank).template conjugateIf<!Conjugate>() );
+}
+#endif
+
+namespace internal {
+
+template<typename MatrixType, typename PermutationIndex>
+struct traits<Inverse<CompleteOrthogonalDecomposition<MatrixType, PermutationIndex> > >
+  : traits<typename Transpose<typename MatrixType::PlainObject>::PlainObject>
+{
+  enum { Flags = 0 };
+};
+
+template<typename DstXprType, typename MatrixType, typename PermutationIndex>
+struct Assignment<DstXprType, Inverse<CompleteOrthogonalDecomposition<MatrixType, PermutationIndex> >, internal::assign_op<typename DstXprType::Scalar,typename CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::Scalar>, Dense2Dense>
+{
+  typedef CompleteOrthogonalDecomposition<MatrixType, PermutationIndex> CodType;
+  typedef Inverse<CodType> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename CodType::Scalar> &)
+  {
+    typedef Matrix<typename CodType::Scalar, CodType::RowsAtCompileTime, CodType::RowsAtCompileTime, 0, CodType::MaxRowsAtCompileTime, CodType::MaxRowsAtCompileTime> IdentityMatrixType;
+    dst = src.nestedExpression().solve(IdentityMatrixType::Identity(src.cols(), src.cols()));
+  }
+};
+
+} // end namespace internal
+
+/** \returns the matrix Q as a sequence of householder transformations */
+template <typename MatrixType, typename PermutationIndex>
+typename CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::HouseholderSequenceType
+CompleteOrthogonalDecomposition<MatrixType, PermutationIndex>::householderQ() const {
+  return m_cpqr.householderQ();
+}
+
+/** \return the complete orthogonal decomposition of \c *this.
+  *
+  * \sa class CompleteOrthogonalDecomposition
+  */
+template <typename Derived>
+template <typename PermutationIndex>
+const CompleteOrthogonalDecomposition<typename MatrixBase<Derived>::PlainObject, PermutationIndex>
+MatrixBase<Derived>::completeOrthogonalDecomposition() const {
+  return CompleteOrthogonalDecomposition<PlainObject>(eval());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMPLETEORTHOGONALDECOMPOSITION_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/FullPivHouseholderQR.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/FullPivHouseholderQR.h
new file mode 100644
index 0000000000000000000000000000000000000000..588f917fd2100a8b457be4ee4c541970e6215ec1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/FullPivHouseholderQR.h
@@ -0,0 +1,738 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H
+#define EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename MatrixType_, typename PermutationIndex_> struct traits<FullPivHouseholderQR<MatrixType_, PermutationIndex_> >
+ : traits<MatrixType_>
+{
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef PermutationIndex_ PermutationIndex;
+  enum { Flags = 0 };
+};
+
+template<typename MatrixType, typename PermutationIndex> struct FullPivHouseholderQRMatrixQReturnType;
+
+template<typename MatrixType, typename PermutationIndex>
+struct traits<FullPivHouseholderQRMatrixQReturnType<MatrixType, PermutationIndex> >
+{
+  typedef typename MatrixType::PlainObject ReturnType;
+};
+
+} // end namespace internal
+
+/** \ingroup QR_Module
+  *
+  * \class FullPivHouseholderQR
+  *
+  * \brief Householder rank-revealing QR decomposition of a matrix with full pivoting
+  *
+  * \tparam MatrixType_ the type of the matrix of which we are computing the QR decomposition
+  *
+  * This class performs a rank-revealing QR decomposition of a matrix \b A into matrices \b P, \b P', \b Q and \b R
+  * such that 
+  * \f[
+  *  \mathbf{P} \, \mathbf{A} \, \mathbf{P}' = \mathbf{Q} \, \mathbf{R}
+  * \f]
+  * by using Householder transformations. Here, \b P and \b P' are permutation matrices, \b Q a unitary matrix 
+  * and \b R an upper triangular matrix.
+  *
+  * This decomposition performs a very prudent full pivoting in order to be rank-revealing and achieve optimal
+  * numerical stability. The trade-off is that it is slower than HouseholderQR and ColPivHouseholderQR.
+  *
+  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+  * 
+  * \sa MatrixBase::fullPivHouseholderQr()
+  */
+template<typename MatrixType_, typename PermutationIndex_> class FullPivHouseholderQR
+        : public SolverBase<FullPivHouseholderQR<MatrixType_, PermutationIndex_> >
+{
+  public:
+
+    typedef MatrixType_ MatrixType;
+    typedef SolverBase<FullPivHouseholderQR> Base;
+    friend class SolverBase<FullPivHouseholderQR>;
+    typedef PermutationIndex_ PermutationIndex;
+    EIGEN_GENERIC_PUBLIC_INTERFACE(FullPivHouseholderQR)
+    
+    enum {
+      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+    typedef internal::FullPivHouseholderQRMatrixQReturnType<MatrixType, PermutationIndex> MatrixQReturnType;
+    typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
+    typedef Matrix<PermutationIndex, 1,
+                   internal::min_size_prefer_dynamic(ColsAtCompileTime,RowsAtCompileTime), RowMajor, 1,
+                   internal::min_size_prefer_fixed(MaxColsAtCompileTime, MaxRowsAtCompileTime)> IntDiagSizeVectorType;
+    typedef PermutationMatrix<ColsAtCompileTime, MaxColsAtCompileTime, PermutationIndex> PermutationType;
+    typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
+    typedef typename internal::plain_col_type<MatrixType>::type ColVectorType;
+    typedef typename MatrixType::PlainObject PlainObject;
+
+    /** \brief Default Constructor.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via FullPivHouseholderQR::compute(const MatrixType&).
+      */
+    FullPivHouseholderQR()
+      : m_qr(),
+        m_hCoeffs(),
+        m_rows_transpositions(),
+        m_cols_transpositions(),
+        m_cols_permutation(),
+        m_temp(),
+        m_isInitialized(false),
+        m_usePrescribedThreshold(false) {}
+
+    /** \brief Default Constructor with memory preallocation
+      *
+      * Like the default constructor but with preallocation of the internal data
+      * according to the specified problem \a size.
+      * \sa FullPivHouseholderQR()
+      */
+    FullPivHouseholderQR(Index rows, Index cols)
+      : m_qr(rows, cols),
+        m_hCoeffs((std::min)(rows,cols)),
+        m_rows_transpositions((std::min)(rows,cols)),
+        m_cols_transpositions((std::min)(rows,cols)),
+        m_cols_permutation(cols),
+        m_temp(cols),
+        m_isInitialized(false),
+        m_usePrescribedThreshold(false) {}
+
+    /** \brief Constructs a QR factorization from a given matrix
+      *
+      * This constructor computes the QR factorization of the matrix \a matrix by calling
+      * the method compute(). It is a short cut for:
+      * 
+      * \code
+      * FullPivHouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
+      * qr.compute(matrix);
+      * \endcode
+      * 
+      * \sa compute()
+      */
+    template<typename InputType>
+    explicit FullPivHouseholderQR(const EigenBase<InputType>& matrix)
+      : m_qr(matrix.rows(), matrix.cols()),
+        m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
+        m_rows_transpositions((std::min)(matrix.rows(), matrix.cols())),
+        m_cols_transpositions((std::min)(matrix.rows(), matrix.cols())),
+        m_cols_permutation(matrix.cols()),
+        m_temp(matrix.cols()),
+        m_isInitialized(false),
+        m_usePrescribedThreshold(false)
+    {
+      compute(matrix.derived());
+    }
+
+    /** \brief Constructs a QR factorization from a given matrix
+      *
+      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c MatrixType is a Eigen::Ref.
+      *
+      * \sa FullPivHouseholderQR(const EigenBase&)
+      */
+    template<typename InputType>
+    explicit FullPivHouseholderQR(EigenBase<InputType>& matrix)
+      : m_qr(matrix.derived()),
+        m_hCoeffs((std::min)(matrix.rows(), matrix.cols())),
+        m_rows_transpositions((std::min)(matrix.rows(), matrix.cols())),
+        m_cols_transpositions((std::min)(matrix.rows(), matrix.cols())),
+        m_cols_permutation(matrix.cols()),
+        m_temp(matrix.cols()),
+        m_isInitialized(false),
+        m_usePrescribedThreshold(false)
+    {
+      computeInPlace();
+    }
+
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
+    /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
+      * \c *this is the QR decomposition.
+      *
+      * \param b the right-hand-side of the equation to solve.
+      *
+      * \returns the exact or least-square solution if the rank is greater or equal to the number of columns of A,
+      * and an arbitrary solution otherwise.
+      *
+      * \note_about_checking_solutions
+      *
+      * \note_about_arbitrary_choice_of_solution
+      *
+      * Example: \include FullPivHouseholderQR_solve.cpp
+      * Output: \verbinclude FullPivHouseholderQR_solve.out
+      */
+    template<typename Rhs>
+    inline const Solve<FullPivHouseholderQR, Rhs>
+    solve(const MatrixBase<Rhs>& b) const;
+    #endif
+
+    /** \returns Expression object representing the matrix Q
+      */
+    MatrixQReturnType matrixQ(void) const;
+
+    /** \returns a reference to the matrix where the Householder QR decomposition is stored
+      */
+    const MatrixType& matrixQR() const
+    {
+      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+      return m_qr;
+    }
+
+    template<typename InputType>
+    FullPivHouseholderQR& compute(const EigenBase<InputType>& matrix);
+
+    /** \returns a const reference to the column permutation matrix */
+    const PermutationType& colsPermutation() const
+    {
+      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+      return m_cols_permutation;
+    }
+
+    /** \returns a const reference to the vector of indices representing the rows transpositions */
+    const IntDiagSizeVectorType& rowsTranspositions() const
+    {
+      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+      return m_rows_transpositions;
+    }
+
+    /** \returns the determinant of the matrix of which
+      * *this is the QR decomposition. It has only linear complexity
+      * (that is, O(n) where n is the dimension of the square matrix)
+      * as the QR decomposition has already been computed.
+      *
+      * \note This is only for square matrices.
+      *
+      * \warning a determinant can be very big or small, so for matrices
+      * of large enough dimension, there is a risk of overflow/underflow.
+      * One way to work around that is to use logAbsDeterminant() instead.
+      *
+      * \sa absDeterminant(), logAbsDeterminant(), MatrixBase::determinant()
+      */
+    typename MatrixType::Scalar determinant() const;
+
+    /** \returns the absolute value of the determinant of the matrix of which
+      * *this is the QR decomposition. It has only linear complexity
+      * (that is, O(n) where n is the dimension of the square matrix)
+      * as the QR decomposition has already been computed.
+      *
+      * \note This is only for square matrices.
+      *
+      * \warning a determinant can be very big or small, so for matrices
+      * of large enough dimension, there is a risk of overflow/underflow.
+      * One way to work around that is to use logAbsDeterminant() instead.
+      *
+      * \sa determinant(), logAbsDeterminant(), MatrixBase::determinant()
+      */
+    typename MatrixType::RealScalar absDeterminant() const;
+
+    /** \returns the natural log of the absolute value of the determinant of the matrix of which
+      * *this is the QR decomposition. It has only linear complexity
+      * (that is, O(n) where n is the dimension of the square matrix)
+      * as the QR decomposition has already been computed.
+      *
+      * \note This is only for square matrices.
+      *
+      * \note This method is useful to work around the risk of overflow/underflow that's inherent
+      * to determinant computation.
+      *
+      * \sa determinant(), absDeterminant(), MatrixBase::determinant()
+      */
+    typename MatrixType::RealScalar logAbsDeterminant() const;
+
+    /** \returns the rank of the matrix of which *this is the QR decomposition.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      */
+    inline Index rank() const
+    {
+      using std::abs;
+      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+      RealScalar premultiplied_threshold = abs(m_maxpivot) * threshold();
+      Index result = 0;
+      for(Index i = 0; i < m_nonzero_pivots; ++i)
+        result += (abs(m_qr.coeff(i,i)) > premultiplied_threshold);
+      return result;
+    }
+
+    /** \returns the dimension of the kernel of the matrix of which *this is the QR decomposition.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      */
+    inline Index dimensionOfKernel() const
+    {
+      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+      return cols() - rank();
+    }
+
+    /** \returns true if the matrix of which *this is the QR decomposition represents an injective
+      *          linear map, i.e. has trivial kernel; false otherwise.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      */
+    inline bool isInjective() const
+    {
+      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+      return rank() == cols();
+    }
+
+    /** \returns true if the matrix of which *this is the QR decomposition represents a surjective
+      *          linear map; false otherwise.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      */
+    inline bool isSurjective() const
+    {
+      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+      return rank() == rows();
+    }
+
+    /** \returns true if the matrix of which *this is the QR decomposition is invertible.
+      *
+      * \note This method has to determine which pivots should be considered nonzero.
+      *       For that, it uses the threshold value that you can control by calling
+      *       setThreshold(const RealScalar&).
+      */
+    inline bool isInvertible() const
+    {
+      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+      return isInjective() && isSurjective();
+    }
+
+    /** \returns the inverse of the matrix of which *this is the QR decomposition.
+      *
+      * \note If this matrix is not invertible, the returned matrix has undefined coefficients.
+      *       Use isInvertible() to first determine whether this matrix is invertible.
+      */
+    inline const Inverse<FullPivHouseholderQR> inverse() const
+    {
+      eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+      return Inverse<FullPivHouseholderQR>(*this);
+    }
+
+    inline Index rows() const { return m_qr.rows(); }
+    inline Index cols() const { return m_qr.cols(); }
+    
+    /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
+      * 
+      * For advanced uses only.
+      */
+    const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
+
+    /** Allows to prescribe a threshold to be used by certain methods, such as rank(),
+      * who need to determine when pivots are to be considered nonzero. This is not used for the
+      * QR decomposition itself.
+      *
+      * When it needs to get the threshold value, Eigen calls threshold(). By default, this
+      * uses a formula to automatically determine a reasonable threshold.
+      * Once you have called the present method setThreshold(const RealScalar&),
+      * your value is used instead.
+      *
+      * \param threshold The new value to use as the threshold.
+      *
+      * A pivot will be considered nonzero if its absolute value is strictly greater than
+      *  \f$ \vert pivot \vert \leqslant threshold \times \vert maxpivot \vert \f$
+      * where maxpivot is the biggest pivot.
+      *
+      * If you want to come back to the default behavior, call setThreshold(Default_t)
+      */
+    FullPivHouseholderQR& setThreshold(const RealScalar& threshold)
+    {
+      m_usePrescribedThreshold = true;
+      m_prescribedThreshold = threshold;
+      return *this;
+    }
+
+    /** Allows to come back to the default behavior, letting Eigen use its default formula for
+      * determining the threshold.
+      *
+      * You should pass the special object Eigen::Default as parameter here.
+      * \code qr.setThreshold(Eigen::Default); \endcode
+      *
+      * See the documentation of setThreshold(const RealScalar&).
+      */
+    FullPivHouseholderQR& setThreshold(Default_t)
+    {
+      m_usePrescribedThreshold = false;
+      return *this;
+    }
+
+    /** Returns the threshold that will be used by certain methods such as rank().
+      *
+      * See the documentation of setThreshold(const RealScalar&).
+      */
+    RealScalar threshold() const
+    {
+      eigen_assert(m_isInitialized || m_usePrescribedThreshold);
+      return m_usePrescribedThreshold ? m_prescribedThreshold
+      // this formula comes from experimenting (see "LU precision tuning" thread on the list)
+      // and turns out to be identical to Higham's formula used already in LDLt.
+                                      : NumTraits<Scalar>::epsilon() * RealScalar(m_qr.diagonalSize());
+    }
+
+    /** \returns the number of nonzero pivots in the QR decomposition.
+      * Here nonzero is meant in the exact sense, not in a fuzzy sense.
+      * So that notion isn't really intrinsically interesting, but it is
+      * still useful when implementing algorithms.
+      *
+      * \sa rank()
+      */
+    inline Index nonzeroPivots() const
+    {
+      eigen_assert(m_isInitialized && "LU is not initialized.");
+      return m_nonzero_pivots;
+    }
+
+    /** \returns the absolute value of the biggest pivot, i.e. the biggest
+      *          diagonal coefficient of U.
+      */
+    RealScalar maxPivot() const { return m_maxpivot; }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename RhsType, typename DstType>
+    void _solve_impl(const RhsType &rhs, DstType &dst) const;
+
+    template<bool Conjugate, typename RhsType, typename DstType>
+    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
+    #endif
+
+  protected:
+
+    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+    void computeInPlace();
+
+    MatrixType m_qr;
+    HCoeffsType m_hCoeffs;
+    IntDiagSizeVectorType m_rows_transpositions;
+    IntDiagSizeVectorType m_cols_transpositions;
+    PermutationType m_cols_permutation;
+    RowVectorType m_temp;
+    bool m_isInitialized, m_usePrescribedThreshold;
+    RealScalar m_prescribedThreshold, m_maxpivot;
+    Index m_nonzero_pivots;
+    RealScalar m_precision;
+    Index m_det_p;
+};
+
+template<typename MatrixType, typename PermutationIndex>
+typename MatrixType::Scalar FullPivHouseholderQR<MatrixType, PermutationIndex>::determinant() const
+{
+  eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  Scalar detQ;
+  internal::householder_determinant<HCoeffsType, Scalar, NumTraits<Scalar>::IsComplex>::run(m_hCoeffs, detQ);
+  return m_qr.diagonal().prod() * detQ * Scalar(m_det_p);
+}
+
+template<typename MatrixType, typename PermutationIndex>
+typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType, PermutationIndex>::absDeterminant() const
+{
+  using std::abs;
+  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  return abs(m_qr.diagonal().prod());
+}
+
+template<typename MatrixType, typename PermutationIndex>
+typename MatrixType::RealScalar FullPivHouseholderQR<MatrixType, PermutationIndex>::logAbsDeterminant() const
+{
+  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  return m_qr.diagonal().cwiseAbs().array().log().sum();
+}
+
+/** Performs the QR factorization of the given matrix \a matrix. The result of
+  * the factorization is stored into \c *this, and a reference to \c *this
+  * is returned.
+  *
+  * \sa class FullPivHouseholderQR, FullPivHouseholderQR(const MatrixType&)
+  */
+template<typename MatrixType, typename PermutationIndex>
+template<typename InputType>
+FullPivHouseholderQR<MatrixType, PermutationIndex>& FullPivHouseholderQR<MatrixType, PermutationIndex>::compute(const EigenBase<InputType>& matrix)
+{
+  m_qr = matrix.derived();
+  computeInPlace();
+  return *this;
+}
+
+template<typename MatrixType, typename PermutationIndex>
+void FullPivHouseholderQR<MatrixType, PermutationIndex>::computeInPlace()
+{
+  eigen_assert(m_qr.cols() <= NumTraits<PermutationIndex>::highest());
+  using std::abs;
+  Index rows = m_qr.rows();
+  Index cols = m_qr.cols();
+  Index size = (std::min)(rows,cols);
+
+  
+  m_hCoeffs.resize(size);
+
+  m_temp.resize(cols);
+
+  m_precision = NumTraits<Scalar>::epsilon() * RealScalar(size);
+
+  m_rows_transpositions.resize(size);
+  m_cols_transpositions.resize(size);
+  Index number_of_transpositions = 0;
+
+  RealScalar biggest(0);
+
+  m_nonzero_pivots = size; // the generic case is that in which all pivots are nonzero (invertible case)
+  m_maxpivot = RealScalar(0);
+
+  for (Index k = 0; k < size; ++k)
+  {
+    Index row_of_biggest_in_corner, col_of_biggest_in_corner;
+    typedef internal::scalar_score_coeff_op<Scalar> Scoring;
+    typedef typename Scoring::result_type Score;
+
+    Score score = m_qr.bottomRightCorner(rows-k, cols-k)
+                      .unaryExpr(Scoring())
+                      .maxCoeff(&row_of_biggest_in_corner, &col_of_biggest_in_corner);
+    row_of_biggest_in_corner += k;
+    col_of_biggest_in_corner += k;
+    RealScalar biggest_in_corner = internal::abs_knowing_score<Scalar>()(m_qr(row_of_biggest_in_corner, col_of_biggest_in_corner), score);
+    if(k==0) biggest = biggest_in_corner;
+
+    // if the corner is negligible, then we have less than full rank, and we can finish early
+    if(internal::isMuchSmallerThan(biggest_in_corner, biggest, m_precision))
+    {
+      m_nonzero_pivots = k;
+      for(Index i = k; i < size; i++)
+      {
+        m_rows_transpositions.coeffRef(i) = internal::convert_index<PermutationIndex>(i);
+        m_cols_transpositions.coeffRef(i) = internal::convert_index<PermutationIndex>(i);
+        m_hCoeffs.coeffRef(i) = Scalar(0);
+      }
+      break;
+    }
+
+    m_rows_transpositions.coeffRef(k) = internal::convert_index<PermutationIndex>(row_of_biggest_in_corner);
+    m_cols_transpositions.coeffRef(k) = internal::convert_index<PermutationIndex>(col_of_biggest_in_corner);
+    if(k != row_of_biggest_in_corner) {
+      m_qr.row(k).tail(cols-k).swap(m_qr.row(row_of_biggest_in_corner).tail(cols-k));
+      ++number_of_transpositions;
+    }
+    if(k != col_of_biggest_in_corner) {
+      m_qr.col(k).swap(m_qr.col(col_of_biggest_in_corner));
+      ++number_of_transpositions;
+    }
+
+    RealScalar beta;
+    m_qr.col(k).tail(rows-k).makeHouseholderInPlace(m_hCoeffs.coeffRef(k), beta);
+    m_qr.coeffRef(k,k) = beta;
+
+    // remember the maximum absolute value of diagonal coefficients
+    if(abs(beta) > m_maxpivot) m_maxpivot = abs(beta);
+
+    m_qr.bottomRightCorner(rows-k, cols-k-1)
+        .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), m_hCoeffs.coeffRef(k), &m_temp.coeffRef(k+1));
+  }
+
+  m_cols_permutation.setIdentity(cols);
+  for(Index k = 0; k < size; ++k)
+    m_cols_permutation.applyTranspositionOnTheRight(k, m_cols_transpositions.coeff(k));
+
+  m_det_p = (number_of_transpositions%2) ? -1 : 1;
+  m_isInitialized = true;
+}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename MatrixType_, typename PermutationIndex_>
+template<typename RhsType, typename DstType>
+void FullPivHouseholderQR<MatrixType_, PermutationIndex_>::_solve_impl(const RhsType &rhs, DstType &dst) const
+{
+  const Index l_rank = rank();
+
+  // FIXME introduce nonzeroPivots() and use it here. and more generally,
+  // make the same improvements in this dec as in FullPivLU.
+  if(l_rank==0)
+  {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(rhs);
+
+  Matrix<typename RhsType::Scalar,1,RhsType::ColsAtCompileTime> temp(rhs.cols());
+  for (Index k = 0; k < l_rank; ++k)
+  {
+    Index remainingSize = rows()-k;
+    c.row(k).swap(c.row(m_rows_transpositions.coeff(k)));
+    c.bottomRightCorner(remainingSize, rhs.cols())
+      .applyHouseholderOnTheLeft(m_qr.col(k).tail(remainingSize-1),
+                               m_hCoeffs.coeff(k), &temp.coeffRef(0));
+  }
+
+  m_qr.topLeftCorner(l_rank, l_rank)
+      .template triangularView<Upper>()
+      .solveInPlace(c.topRows(l_rank));
+
+  for(Index i = 0; i < l_rank; ++i) dst.row(m_cols_permutation.indices().coeff(i)) = c.row(i);
+  for(Index i = l_rank; i < cols(); ++i) dst.row(m_cols_permutation.indices().coeff(i)).setZero();
+}
+
+template<typename MatrixType_, typename PermutationIndex_>
+template<bool Conjugate, typename RhsType, typename DstType>
+void FullPivHouseholderQR<MatrixType_, PermutationIndex_>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
+{
+  const Index l_rank = rank();
+
+  if(l_rank == 0)
+  {
+    dst.setZero();
+    return;
+  }
+
+  typename RhsType::PlainObject c(m_cols_permutation.transpose()*rhs);
+
+  m_qr.topLeftCorner(l_rank, l_rank)
+         .template triangularView<Upper>()
+         .transpose().template conjugateIf<Conjugate>()
+         .solveInPlace(c.topRows(l_rank));
+
+  dst.topRows(l_rank) = c.topRows(l_rank);
+  dst.bottomRows(rows()-l_rank).setZero();
+
+  Matrix<Scalar, 1, DstType::ColsAtCompileTime> temp(dst.cols());
+  const Index size = (std::min)(rows(), cols());
+  for (Index k = size-1; k >= 0; --k)
+  {
+    Index remainingSize = rows()-k;
+
+    dst.bottomRightCorner(remainingSize, dst.cols())
+       .applyHouseholderOnTheLeft(m_qr.col(k).tail(remainingSize-1).template conjugateIf<!Conjugate>(),
+                                  m_hCoeffs.template conjugateIf<Conjugate>().coeff(k), &temp.coeffRef(0));
+
+    dst.row(k).swap(dst.row(m_rows_transpositions.coeff(k)));
+  }
+}
+#endif
+
+namespace internal {
+  
+template<typename DstXprType, typename MatrixType, typename PermutationIndex>
+struct Assignment<DstXprType, Inverse<FullPivHouseholderQR<MatrixType, PermutationIndex> >, internal::assign_op<typename DstXprType::Scalar,typename FullPivHouseholderQR<MatrixType, PermutationIndex>::Scalar>, Dense2Dense>
+{
+  typedef FullPivHouseholderQR<MatrixType, PermutationIndex> QrType;
+  typedef Inverse<QrType> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename QrType::Scalar> &)
+  {    
+    dst = src.nestedExpression().solve(MatrixType::Identity(src.rows(), src.cols()));
+  }
+};
+
+/** \ingroup QR_Module
+  *
+  * \brief Expression type for return value of FullPivHouseholderQR::matrixQ()
+  *
+  * \tparam MatrixType type of underlying dense matrix
+  */
+template<typename MatrixType, typename PermutationIndex> struct FullPivHouseholderQRMatrixQReturnType
+  : public ReturnByValue<FullPivHouseholderQRMatrixQReturnType<MatrixType, PermutationIndex> >
+{
+public:
+  typedef typename FullPivHouseholderQR<MatrixType, PermutationIndex>::IntDiagSizeVectorType IntDiagSizeVectorType;
+  typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
+  typedef Matrix<typename MatrixType::Scalar, 1, MatrixType::RowsAtCompileTime, RowMajor, 1,
+                 MatrixType::MaxRowsAtCompileTime> WorkVectorType;
+
+  FullPivHouseholderQRMatrixQReturnType(const MatrixType&       qr,
+                                        const HCoeffsType&      hCoeffs,
+                                        const IntDiagSizeVectorType& rowsTranspositions)
+    : m_qr(qr),
+      m_hCoeffs(hCoeffs),
+      m_rowsTranspositions(rowsTranspositions)
+  {}
+
+  template <typename ResultType>
+  void evalTo(ResultType& result) const
+  {
+    const Index rows = m_qr.rows();
+    WorkVectorType workspace(rows);
+    evalTo(result, workspace);
+  }
+
+  template <typename ResultType>
+  void evalTo(ResultType& result, WorkVectorType& workspace) const
+  {
+    using numext::conj;
+    // compute the product H'_0 H'_1 ... H'_n-1,
+    // where H_k is the k-th Householder transformation I - h_k v_k v_k'
+    // and v_k is the k-th Householder vector [1,m_qr(k+1,k), m_qr(k+2,k), ...]
+    const Index rows = m_qr.rows();
+    const Index cols = m_qr.cols();
+    const Index size = (std::min)(rows, cols);
+    workspace.resize(rows);
+    result.setIdentity(rows, rows);
+    for (Index k = size-1; k >= 0; k--)
+    {
+      result.block(k, k, rows-k, rows-k)
+            .applyHouseholderOnTheLeft(m_qr.col(k).tail(rows-k-1), conj(m_hCoeffs.coeff(k)), &workspace.coeffRef(k));
+      result.row(k).swap(result.row(m_rowsTranspositions.coeff(k)));
+    }
+  }
+
+  Index rows() const { return m_qr.rows(); }
+  Index cols() const { return m_qr.rows(); }
+
+protected:
+  typename MatrixType::Nested m_qr;
+  typename HCoeffsType::Nested m_hCoeffs;
+  typename IntDiagSizeVectorType::Nested m_rowsTranspositions;
+};
+
+// template<typename MatrixType>
+// struct evaluator<FullPivHouseholderQRMatrixQReturnType<MatrixType> >
+//  : public evaluator<ReturnByValue<FullPivHouseholderQRMatrixQReturnType<MatrixType> > >
+// {};
+
+} // end namespace internal
+
+template<typename MatrixType, typename PermutationIndex>
+inline typename FullPivHouseholderQR<MatrixType, PermutationIndex>::MatrixQReturnType FullPivHouseholderQR<MatrixType, PermutationIndex>::matrixQ() const
+{
+  eigen_assert(m_isInitialized && "FullPivHouseholderQR is not initialized.");
+  return MatrixQReturnType(m_qr, m_hCoeffs, m_rows_transpositions);
+}
+
+/** \return the full-pivoting Householder QR decomposition of \c *this.
+  *
+  * \sa class FullPivHouseholderQR
+  */
+template<typename Derived>
+template<typename PermutationIndex>
+const FullPivHouseholderQR<typename MatrixBase<Derived>::PlainObject, PermutationIndex>
+MatrixBase<Derived>::fullPivHouseholderQr() const
+{
+  return FullPivHouseholderQR<PlainObject, PermutationIndex>(eval());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_FULLPIVOTINGHOUSEHOLDERQR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/HouseholderQR.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/HouseholderQR.h
new file mode 100644
index 0000000000000000000000000000000000000000..abfefd1e1bfce84a751507e6fac456d84d3cb916
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/HouseholderQR.h
@@ -0,0 +1,534 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2010 Vincent Lejeune
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_QR_H
+#define EIGEN_QR_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+template<typename MatrixType_> struct traits<HouseholderQR<MatrixType_> >
+ : traits<MatrixType_>
+{
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef int StorageIndex;
+  enum { Flags = 0 };
+};
+
+} // end namespace internal
+
+/** \ingroup QR_Module
+  *
+  *
+  * \class HouseholderQR
+  *
+  * \brief Householder QR decomposition of a matrix
+  *
+  * \tparam MatrixType_ the type of the matrix of which we are computing the QR decomposition
+  *
+  * This class performs a QR decomposition of a matrix \b A into matrices \b Q and \b R
+  * such that 
+  * \f[
+  *  \mathbf{A} = \mathbf{Q} \, \mathbf{R}
+  * \f]
+  * by using Householder transformations. Here, \b Q a unitary matrix and \b R an upper triangular matrix.
+  * The result is stored in a compact way compatible with LAPACK.
+  *
+  * Note that no pivoting is performed. This is \b not a rank-revealing decomposition.
+  * If you want that feature, use FullPivHouseholderQR or ColPivHouseholderQR instead.
+  *
+  * This Householder QR decomposition is faster, but less numerically stable and less feature-full than
+  * FullPivHouseholderQR or ColPivHouseholderQR.
+  *
+  * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+  *
+  * \sa MatrixBase::householderQr()
+  */
+template<typename MatrixType_> class HouseholderQR
+        : public SolverBase<HouseholderQR<MatrixType_> >
+{
+  public:
+
+    typedef MatrixType_ MatrixType;
+    typedef SolverBase<HouseholderQR> Base;
+    friend class SolverBase<HouseholderQR>;
+
+    EIGEN_GENERIC_PUBLIC_INTERFACE(HouseholderQR)
+    enum {
+      MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+    typedef Matrix<Scalar, RowsAtCompileTime, RowsAtCompileTime, (MatrixType::Flags&RowMajorBit) ? RowMajor : ColMajor, MaxRowsAtCompileTime, MaxRowsAtCompileTime> MatrixQType;
+    typedef typename internal::plain_diag_type<MatrixType>::type HCoeffsType;
+    typedef typename internal::plain_row_type<MatrixType>::type RowVectorType;
+    typedef HouseholderSequence<MatrixType,internal::remove_all_t<typename HCoeffsType::ConjugateReturnType>> HouseholderSequenceType;
+
+    /**
+      * \brief Default Constructor.
+      *
+      * The default constructor is useful in cases in which the user intends to
+      * perform decompositions via HouseholderQR::compute(const MatrixType&).
+      */
+    HouseholderQR() : m_qr(), m_hCoeffs(), m_temp(), m_isInitialized(false) {}
+
+    /** \brief Default Constructor with memory preallocation
+      *
+      * Like the default constructor but with preallocation of the internal data
+      * according to the specified problem \a size.
+      * \sa HouseholderQR()
+      */
+    HouseholderQR(Index rows, Index cols)
+      : m_qr(rows, cols),
+        m_hCoeffs((std::min)(rows,cols)),
+        m_temp(cols),
+        m_isInitialized(false) {}
+
+    /** \brief Constructs a QR factorization from a given matrix
+      *
+      * This constructor computes the QR factorization of the matrix \a matrix by calling
+      * the method compute(). It is a short cut for:
+      * 
+      * \code
+      * HouseholderQR<MatrixType> qr(matrix.rows(), matrix.cols());
+      * qr.compute(matrix);
+      * \endcode
+      * 
+      * \sa compute()
+      */
+    template<typename InputType>
+    explicit HouseholderQR(const EigenBase<InputType>& matrix)
+      : m_qr(matrix.rows(), matrix.cols()),
+        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
+        m_temp(matrix.cols()),
+        m_isInitialized(false)
+    {
+      compute(matrix.derived());
+    }
+
+
+    /** \brief Constructs a QR factorization from a given matrix
+      *
+      * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
+      * \c MatrixType is a Eigen::Ref.
+      *
+      * \sa HouseholderQR(const EigenBase&)
+      */
+    template<typename InputType>
+    explicit HouseholderQR(EigenBase<InputType>& matrix)
+      : m_qr(matrix.derived()),
+        m_hCoeffs((std::min)(matrix.rows(),matrix.cols())),
+        m_temp(matrix.cols()),
+        m_isInitialized(false)
+    {
+      computeInPlace();
+    }
+
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
+    /** This method finds a solution x to the equation Ax=b, where A is the matrix of which
+      * *this is the QR decomposition, if any exists.
+      *
+      * \param b the right-hand-side of the equation to solve.
+      *
+      * \returns a solution.
+      *
+      * \note_about_checking_solutions
+      *
+      * \note_about_arbitrary_choice_of_solution
+      *
+      * Example: \include HouseholderQR_solve.cpp
+      * Output: \verbinclude HouseholderQR_solve.out
+      */
+    template<typename Rhs>
+    inline const Solve<HouseholderQR, Rhs>
+    solve(const MatrixBase<Rhs>& b) const;
+    #endif
+
+    /** This method returns an expression of the unitary matrix Q as a sequence of Householder transformations.
+      *
+      * The returned expression can directly be used to perform matrix products. It can also be assigned to a dense Matrix object.
+      * Here is an example showing how to recover the full or thin matrix Q, as well as how to perform matrix products using operator*:
+      *
+      * Example: \include HouseholderQR_householderQ.cpp
+      * Output: \verbinclude HouseholderQR_householderQ.out
+      */
+    HouseholderSequenceType householderQ() const
+    {
+      eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
+      return HouseholderSequenceType(m_qr, m_hCoeffs.conjugate());
+    }
+
+    /** \returns a reference to the matrix where the Householder QR decomposition is stored
+      * in a LAPACK-compatible way.
+      */
+    const MatrixType& matrixQR() const
+    {
+        eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
+        return m_qr;
+    }
+
+    template<typename InputType>
+    HouseholderQR& compute(const EigenBase<InputType>& matrix) {
+      m_qr = matrix.derived();
+      computeInPlace();
+      return *this;
+    }
+
+    /** \returns the determinant of the matrix of which
+      * *this is the QR decomposition. It has only linear complexity
+      * (that is, O(n) where n is the dimension of the square matrix)
+      * as the QR decomposition has already been computed.
+      *
+      * \note This is only for square matrices.
+      *
+      * \warning a determinant can be very big or small, so for matrices
+      * of large enough dimension, there is a risk of overflow/underflow.
+      * One way to work around that is to use logAbsDeterminant() instead.
+      *
+      * \sa absDeterminant(), logAbsDeterminant(), MatrixBase::determinant()
+      */
+    typename MatrixType::Scalar determinant() const;
+
+    /** \returns the absolute value of the determinant of the matrix of which
+      * *this is the QR decomposition. It has only linear complexity
+      * (that is, O(n) where n is the dimension of the square matrix)
+      * as the QR decomposition has already been computed.
+      *
+      * \note This is only for square matrices.
+      *
+      * \warning a determinant can be very big or small, so for matrices
+      * of large enough dimension, there is a risk of overflow/underflow.
+      * One way to work around that is to use logAbsDeterminant() instead.
+      *
+      * \sa determinant(), logAbsDeterminant(), MatrixBase::determinant()
+      */
+    typename MatrixType::RealScalar absDeterminant() const;
+
+    /** \returns the natural log of the absolute value of the determinant of the matrix of which
+      * *this is the QR decomposition. It has only linear complexity
+      * (that is, O(n) where n is the dimension of the square matrix)
+      * as the QR decomposition has already been computed.
+      *
+      * \note This is only for square matrices.
+      *
+      * \note This method is useful to work around the risk of overflow/underflow that's inherent
+      * to determinant computation.
+      *
+      * \sa determinant(), absDeterminant(), MatrixBase::determinant()
+      */
+    typename MatrixType::RealScalar logAbsDeterminant() const;
+
+    inline Index rows() const { return m_qr.rows(); }
+    inline Index cols() const { return m_qr.cols(); }
+
+    /** \returns a const reference to the vector of Householder coefficients used to represent the factor \c Q.
+      * 
+      * For advanced uses only.
+      */
+    const HCoeffsType& hCoeffs() const { return m_hCoeffs; }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename RhsType, typename DstType>
+    void _solve_impl(const RhsType &rhs, DstType &dst) const;
+
+    template<bool Conjugate, typename RhsType, typename DstType>
+    void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
+    #endif
+
+  protected:
+
+    EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+    void computeInPlace();
+
+    MatrixType m_qr;
+    HCoeffsType m_hCoeffs;
+    RowVectorType m_temp;
+    bool m_isInitialized;
+};
+
+namespace internal {
+
+/** \internal */
+template<typename HCoeffs, typename Scalar, bool IsComplex>
+struct householder_determinant
+{
+  static void run(const HCoeffs& hCoeffs, Scalar& out_det)
+  {
+    out_det = Scalar(1);
+    Index size = hCoeffs.rows();
+    for (Index i = 0; i < size; i ++)
+    {
+      // For each valid reflection Q_n,
+      // det(Q_n) = - conj(h_n) / h_n
+      // where h_n is the Householder coefficient.
+      if (hCoeffs(i) != Scalar(0))
+        out_det *= - numext::conj(hCoeffs(i)) / hCoeffs(i);
+    }
+  }
+};
+
+/** \internal */
+template<typename HCoeffs, typename Scalar>
+struct householder_determinant<HCoeffs, Scalar, false>
+{
+  static void run(const HCoeffs& hCoeffs, Scalar& out_det)
+  {
+    bool negated = false;
+    Index size = hCoeffs.rows();
+    for (Index i = 0; i < size; i ++)
+    {
+      // Each valid reflection negates the determinant.
+      if (hCoeffs(i) != Scalar(0))
+        negated ^= true;
+    }
+    out_det = negated ? Scalar(-1) : Scalar(1);
+  }
+};
+
+} // end namespace internal
+
+template<typename MatrixType>
+typename MatrixType::Scalar HouseholderQR<MatrixType>::determinant() const
+{
+  eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  Scalar detQ;
+  internal::householder_determinant<HCoeffsType, Scalar, NumTraits<Scalar>::IsComplex>::run(m_hCoeffs, detQ);
+  return m_qr.diagonal().prod() * detQ;
+}
+
+template<typename MatrixType>
+typename MatrixType::RealScalar HouseholderQR<MatrixType>::absDeterminant() const
+{
+  using std::abs;
+  eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  return abs(m_qr.diagonal().prod());
+}
+
+template<typename MatrixType>
+typename MatrixType::RealScalar HouseholderQR<MatrixType>::logAbsDeterminant() const
+{
+  eigen_assert(m_isInitialized && "HouseholderQR is not initialized.");
+  eigen_assert(m_qr.rows() == m_qr.cols() && "You can't take the determinant of a non-square matrix!");
+  return m_qr.diagonal().cwiseAbs().array().log().sum();
+}
+
+namespace internal {
+
+/** \internal */
+template<typename MatrixQR, typename HCoeffs>
+void householder_qr_inplace_unblocked(MatrixQR& mat, HCoeffs& hCoeffs, typename MatrixQR::Scalar* tempData = 0)
+{
+  typedef typename MatrixQR::Scalar Scalar;
+  typedef typename MatrixQR::RealScalar RealScalar;
+  Index rows = mat.rows();
+  Index cols = mat.cols();
+  Index size = (std::min)(rows,cols);
+
+  eigen_assert(hCoeffs.size() == size);
+
+  typedef Matrix<Scalar,MatrixQR::ColsAtCompileTime,1> TempType;
+  TempType tempVector;
+  if(tempData==0)
+  {
+    tempVector.resize(cols);
+    tempData = tempVector.data();
+  }
+
+  for(Index k = 0; k < size; ++k)
+  {
+    Index remainingRows = rows - k;
+    Index remainingCols = cols - k - 1;
+
+    RealScalar beta;
+    mat.col(k).tail(remainingRows).makeHouseholderInPlace(hCoeffs.coeffRef(k), beta);
+    mat.coeffRef(k,k) = beta;
+
+    // apply H to remaining part of m_qr from the left
+    mat.bottomRightCorner(remainingRows, remainingCols)
+        .applyHouseholderOnTheLeft(mat.col(k).tail(remainingRows-1), hCoeffs.coeffRef(k), tempData+k+1);
+  }
+}
+
+// TODO: add a corresponding public API for updating a QR factorization
+/** \internal
+ * Basically a modified copy of @c Eigen::internal::householder_qr_inplace_unblocked that
+ * performs a rank-1 update of the QR matrix in compact storage. This function assumes, that
+ * the first @c k-1 columns of the matrix @c mat contain the QR decomposition of \f$A^N\f$ up to
+ * column k-1. Then the QR decomposition of the k-th column (given by @c newColumn) is computed by
+ * applying the k-1 Householder projectors on it and finally compute the projector \f$H_k\f$ of
+ * it. On exit the matrix @c mat and the vector @c hCoeffs contain the QR decomposition of the
+ * first k columns of \f$A^N\f$. The \a tempData argument must point to at least mat.cols() scalars.  */
+template <typename MatrixQR, typename HCoeffs, typename VectorQR>
+void householder_qr_inplace_update(MatrixQR& mat, HCoeffs& hCoeffs, const VectorQR& newColumn,
+                                   typename MatrixQR::Index k, typename MatrixQR::Scalar* tempData) {
+  typedef typename MatrixQR::Index Index;
+  typedef typename MatrixQR::RealScalar RealScalar;
+  Index rows = mat.rows();
+
+  eigen_assert(k < mat.cols());
+  eigen_assert(k < rows);
+  eigen_assert(hCoeffs.size() == mat.cols());
+  eigen_assert(newColumn.size() == rows);
+  eigen_assert(tempData);
+
+  // Store new column in mat at column k
+  mat.col(k) = newColumn;
+  // Apply H = H_1...H_{k-1} on newColumn (skip if k=0)
+  for (Index i = 0; i < k; ++i) {
+    Index remainingRows = rows - i;
+    mat.col(k)
+        .tail(remainingRows)
+        .applyHouseholderOnTheLeft(mat.col(i).tail(remainingRows - 1), hCoeffs.coeffRef(i), tempData + i + 1);
+  }
+  // Construct Householder projector in-place in column k
+  RealScalar beta;
+  mat.col(k).tail(rows - k).makeHouseholderInPlace(hCoeffs.coeffRef(k), beta);
+  mat.coeffRef(k, k) = beta;
+}
+
+/** \internal */
+template<typename MatrixQR, typename HCoeffs,
+  typename MatrixQRScalar = typename MatrixQR::Scalar,
+  bool InnerStrideIsOne = (MatrixQR::InnerStrideAtCompileTime == 1 && HCoeffs::InnerStrideAtCompileTime == 1)>
+struct householder_qr_inplace_blocked
+{
+  // This is specialized for LAPACK-supported Scalar types in HouseholderQR_LAPACKE.h
+  static void run(MatrixQR& mat, HCoeffs& hCoeffs, Index maxBlockSize=32,
+      typename MatrixQR::Scalar* tempData = 0)
+  {
+    typedef typename MatrixQR::Scalar Scalar;
+    typedef Block<MatrixQR,Dynamic,Dynamic> BlockType;
+
+    Index rows = mat.rows();
+    Index cols = mat.cols();
+    Index size = (std::min)(rows, cols);
+
+    typedef Matrix<Scalar,Dynamic,1,ColMajor,MatrixQR::MaxColsAtCompileTime,1> TempType;
+    TempType tempVector;
+    if(tempData==0)
+    {
+      tempVector.resize(cols);
+      tempData = tempVector.data();
+    }
+
+    Index blockSize = (std::min)(maxBlockSize,size);
+
+    Index k = 0;
+    for (k = 0; k < size; k += blockSize)
+    {
+      Index bs = (std::min)(size-k,blockSize);  // actual size of the block
+      Index tcols = cols - k - bs;              // trailing columns
+      Index brows = rows-k;                     // rows of the block
+
+      // partition the matrix:
+      //        A00 | A01 | A02
+      // mat  = A10 | A11 | A12
+      //        A20 | A21 | A22
+      // and performs the qr dec of [A11^T A12^T]^T
+      // and update [A21^T A22^T]^T using level 3 operations.
+      // Finally, the algorithm continue on A22
+
+      BlockType A11_21 = mat.block(k,k,brows,bs);
+      Block<HCoeffs,Dynamic,1> hCoeffsSegment = hCoeffs.segment(k,bs);
+
+      householder_qr_inplace_unblocked(A11_21, hCoeffsSegment, tempData);
+
+      if(tcols)
+      {
+        BlockType A21_22 = mat.block(k,k+bs,brows,tcols);
+        apply_block_householder_on_the_left(A21_22,A11_21,hCoeffsSegment, false); // false == backward
+      }
+    }
+  }
+};
+
+} // end namespace internal
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename MatrixType_>
+template<typename RhsType, typename DstType>
+void HouseholderQR<MatrixType_>::_solve_impl(const RhsType &rhs, DstType &dst) const
+{
+  const Index rank = (std::min)(rows(), cols());
+
+  typename RhsType::PlainObject c(rhs);
+
+  c.applyOnTheLeft(householderQ().setLength(rank).adjoint() );
+
+  m_qr.topLeftCorner(rank, rank)
+      .template triangularView<Upper>()
+      .solveInPlace(c.topRows(rank));
+
+  dst.topRows(rank) = c.topRows(rank);
+  dst.bottomRows(cols()-rank).setZero();
+}
+
+template<typename MatrixType_>
+template<bool Conjugate, typename RhsType, typename DstType>
+void HouseholderQR<MatrixType_>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
+{
+  const Index rank = (std::min)(rows(), cols());
+
+  typename RhsType::PlainObject c(rhs);
+
+  m_qr.topLeftCorner(rank, rank)
+      .template triangularView<Upper>()
+      .transpose().template conjugateIf<Conjugate>()
+      .solveInPlace(c.topRows(rank));
+
+  dst.topRows(rank) = c.topRows(rank);
+  dst.bottomRows(rows()-rank).setZero();
+
+  dst.applyOnTheLeft(householderQ().setLength(rank).template conjugateIf<!Conjugate>() );
+}
+#endif
+
+/** Performs the QR factorization of the given matrix \a matrix. The result of
+  * the factorization is stored into \c *this, and a reference to \c *this
+  * is returned.
+  *
+  * \sa class HouseholderQR, HouseholderQR(const MatrixType&)
+  */
+template<typename MatrixType>
+void HouseholderQR<MatrixType>::computeInPlace()
+{
+  Index rows = m_qr.rows();
+  Index cols = m_qr.cols();
+  Index size = (std::min)(rows,cols);
+
+  m_hCoeffs.resize(size);
+
+  m_temp.resize(cols);
+
+  internal::householder_qr_inplace_blocked<MatrixType, HCoeffsType>::run(m_qr, m_hCoeffs, 48, m_temp.data());
+
+  m_isInitialized = true;
+}
+
+/** \return the Householder QR decomposition of \c *this.
+  *
+  * \sa class HouseholderQR
+  */
+template<typename Derived>
+const HouseholderQR<typename MatrixBase<Derived>::PlainObject>
+MatrixBase<Derived>::householderQr() const
+{
+  return HouseholderQR<PlainObject>(eval());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_QR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/HouseholderQR_LAPACKE.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/HouseholderQR_LAPACKE.h
new file mode 100644
index 0000000000000000000000000000000000000000..57c2f6ab3a30076b651126cd512fe30dd8fd0f67
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/HouseholderQR_LAPACKE.h
@@ -0,0 +1,77 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *    Householder QR decomposition of a matrix w/o pivoting based on
+ *    LAPACKE_?geqrf function.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_QR_LAPACKE_H
+#define EIGEN_QR_LAPACKE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+namespace lapacke_helpers {
+
+template<typename MatrixQR, typename HCoeffs>
+struct lapacke_hqr
+{
+  static void run(MatrixQR& mat, HCoeffs& hCoeffs, Index = 32, typename MatrixQR::Scalar* = 0)
+  {
+    lapack_int m = to_lapack(mat.rows());
+    lapack_int n = to_lapack(mat.cols());
+    lapack_int lda = to_lapack(mat.outerStride());
+    lapack_int matrix_order = lapack_storage_of(mat);
+    geqrf(matrix_order, m, n, to_lapack(mat.data()), lda, to_lapack(hCoeffs.data()));
+    hCoeffs.adjointInPlace();
+  }
+};
+
+}
+
+/** \internal Specialization for the data types supported by LAPACKe */
+#define EIGEN_LAPACKE_HH_QR(EIGTYPE) \
+template<typename MatrixQR, typename HCoeffs> \
+struct householder_qr_inplace_blocked<MatrixQR, HCoeffs, EIGTYPE, true> : public lapacke_helpers::lapacke_hqr<MatrixQR, HCoeffs> {};
+
+EIGEN_LAPACKE_HH_QR(double)
+EIGEN_LAPACKE_HH_QR(float)
+EIGEN_LAPACKE_HH_QR(std::complex<double>)
+EIGEN_LAPACKE_HH_QR(std::complex<float>)
+
+#undef EIGEN_LAPACKE_HH_QR
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_QR_LAPACKE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..bf8df01c39873a4255255301dfc3c2702354d000
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/QR/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_QR_MODULE_H
+#error "Please include Eigen/QR instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SPQRSupport/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SPQRSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..8d94ba4b609ee15bb114d205fc4b8425cbba8b43
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SPQRSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SPQRSUPPORT_MODULE_H
+#error "Please include Eigen/SPQRSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
new file mode 100644
index 0000000000000000000000000000000000000000..36e8ead47445728966ba12cf0c9cccc19b83494f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SPQRSupport/SuiteSparseQRSupport.h
@@ -0,0 +1,337 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SUITESPARSEQRSUPPORT_H
+#define EIGEN_SUITESPARSEQRSUPPORT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+  
+  template<typename MatrixType> class SPQR; 
+  template<typename SPQRType> struct SPQRMatrixQReturnType; 
+  template<typename SPQRType> struct SPQRMatrixQTransposeReturnType; 
+  template <typename SPQRType, typename Derived> struct SPQR_QProduct;
+  namespace internal {
+    template <typename SPQRType> struct traits<SPQRMatrixQReturnType<SPQRType> >
+    {
+      typedef typename SPQRType::MatrixType ReturnType;
+    };
+    template <typename SPQRType> struct traits<SPQRMatrixQTransposeReturnType<SPQRType> >
+    {
+      typedef typename SPQRType::MatrixType ReturnType;
+    };
+    template <typename SPQRType, typename Derived> struct traits<SPQR_QProduct<SPQRType, Derived> >
+    {
+      typedef typename Derived::PlainObject ReturnType;
+    };
+  } // End namespace internal
+  
+/**
+  * \ingroup SPQRSupport_Module
+  * \class SPQR
+  * \brief Sparse QR factorization based on SuiteSparseQR library
+  *
+  * This class is used to perform a multithreaded and multifrontal rank-revealing QR decomposition
+  * of sparse matrices. The result is then used to solve linear leasts_square systems.
+  * Clearly, a QR factorization is returned such that A*P = Q*R where :
+  *
+  * P is the column permutation. Use colsPermutation() to get it.
+  *
+  * Q is the orthogonal matrix represented as Householder reflectors.
+  * Use matrixQ() to get an expression and matrixQ().transpose() to get the transpose.
+  * You can then apply it to a vector.
+  *
+  * R is the sparse triangular factor. Use matrixQR() to get it as SparseMatrix.
+  * NOTE : The Index type of R is always SuiteSparse_long. You can get it with SPQR::Index
+  *
+  * \tparam MatrixType_ The type of the sparse matrix A, must be a column-major SparseMatrix<>
+  *
+  * \implsparsesolverconcept
+  *
+  *
+  */
+template<typename MatrixType_>
+class SPQR : public SparseSolverBase<SPQR<MatrixType_> >
+{
+  protected:
+    typedef SparseSolverBase<SPQR<MatrixType_> > Base;
+    using Base::m_isInitialized;
+  public:
+    typedef typename MatrixType_::Scalar Scalar;
+    typedef typename MatrixType_::RealScalar RealScalar;
+    typedef SuiteSparse_long StorageIndex ;
+    typedef SparseMatrix<Scalar, ColMajor, StorageIndex> MatrixType;
+    typedef Map<PermutationMatrix<Dynamic, Dynamic, StorageIndex> > PermutationType;
+    enum {
+      ColsAtCompileTime = Dynamic,
+      MaxColsAtCompileTime = Dynamic
+    };
+  public:
+    SPQR() 
+      : m_analysisIsOk(false),
+        m_factorizationIsOk(false),
+        m_isRUpToDate(false),
+        m_ordering(SPQR_ORDERING_DEFAULT),
+        m_allow_tol(SPQR_DEFAULT_TOL),
+        m_tolerance (NumTraits<Scalar>::epsilon()),
+        m_cR(0),
+        m_E(0),
+        m_H(0),
+        m_HPinv(0),
+        m_HTau(0),
+        m_useDefaultThreshold(true)
+    { 
+      cholmod_l_start(&m_cc);
+    }
+    
+    explicit SPQR(const MatrixType_& matrix)
+      : m_analysisIsOk(false),
+        m_factorizationIsOk(false),
+        m_isRUpToDate(false),
+        m_ordering(SPQR_ORDERING_DEFAULT),
+        m_allow_tol(SPQR_DEFAULT_TOL),
+        m_tolerance (NumTraits<Scalar>::epsilon()),
+        m_cR(0),
+        m_E(0),
+        m_H(0),
+        m_HPinv(0),
+        m_HTau(0),
+        m_useDefaultThreshold(true)
+    {
+      cholmod_l_start(&m_cc);
+      compute(matrix);
+    }
+    
+    ~SPQR()
+    {
+      SPQR_free();
+      cholmod_l_finish(&m_cc);
+    }
+    void SPQR_free()
+    {
+      cholmod_l_free_sparse(&m_H, &m_cc);
+      cholmod_l_free_sparse(&m_cR, &m_cc);
+      cholmod_l_free_dense(&m_HTau, &m_cc);
+      std::free(m_E);
+      std::free(m_HPinv);
+    }
+
+    void compute(const MatrixType_& matrix)
+    {
+      if(m_isInitialized) SPQR_free();
+
+      MatrixType mat(matrix);
+      
+      /* Compute the default threshold as in MatLab, see:
+       * Tim Davis, "Algorithm 915, SuiteSparseQR: Multifrontal Multithreaded Rank-Revealing
+       * Sparse QR Factorization, ACM Trans. on Math. Soft. 38(1), 2011, Page 8:3 
+       */
+      RealScalar pivotThreshold = m_tolerance;
+      if(m_useDefaultThreshold) 
+      {
+        RealScalar max2Norm = 0.0;
+        for (int j = 0; j < mat.cols(); j++) max2Norm = numext::maxi(max2Norm, mat.col(j).norm());
+        if(numext::is_exactly_zero(max2Norm))
+          max2Norm = RealScalar(1);
+        pivotThreshold = 20 * (mat.rows() + mat.cols()) * max2Norm * NumTraits<RealScalar>::epsilon();
+      }
+      cholmod_sparse A; 
+      A = viewAsCholmod(mat);
+      m_rows = matrix.rows();
+      Index col = matrix.cols();
+      m_rank = SuiteSparseQR<Scalar>(m_ordering, pivotThreshold, col, &A, 
+                             &m_cR, &m_E, &m_H, &m_HPinv, &m_HTau, &m_cc);
+
+      if (!m_cR)
+      {
+        m_info = NumericalIssue;
+        m_isInitialized = false;
+        return;
+      }
+      m_info = Success;
+      m_isInitialized = true;
+      m_isRUpToDate = false;
+    }
+    /** 
+     * Get the number of rows of the input matrix and the Q matrix
+     */
+    inline Index rows() const {return m_rows; }
+    
+    /** 
+     * Get the number of columns of the input matrix. 
+     */
+    inline Index cols() const { return m_cR->ncol; }
+    
+    template<typename Rhs, typename Dest>
+    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
+    {
+      eigen_assert(m_isInitialized && " The QR factorization should be computed first, call compute()");
+      eigen_assert(b.cols()==1 && "This method is for vectors only");
+
+      //Compute Q^T * b
+      typename Dest::PlainObject y, y2;
+      y = matrixQ().transpose() * b;
+      
+      // Solves with the triangular matrix R
+      Index rk = this->rank();
+      y2 = y;
+      y.resize((std::max)(cols(),Index(y.rows())),y.cols());
+      y.topRows(rk) = this->matrixR().topLeftCorner(rk, rk).template triangularView<Upper>().solve(y2.topRows(rk));
+
+      // Apply the column permutation 
+      // colsPermutation() performs a copy of the permutation,
+      // so let's apply it manually:
+      for(Index i = 0; i < rk; ++i) dest.row(m_E[i]) = y.row(i);
+      for(Index i = rk; i < cols(); ++i) dest.row(m_E[i]).setZero();
+      
+//       y.bottomRows(y.rows()-rk).setZero();
+//       dest = colsPermutation() * y.topRows(cols());
+      
+      m_info = Success;
+    }
+    
+    /** \returns the sparse triangular factor R. It is a sparse matrix
+     */
+    const MatrixType matrixR() const
+    {
+      eigen_assert(m_isInitialized && " The QR factorization should be computed first, call compute()");
+      if(!m_isRUpToDate) {
+        m_R = viewAsEigen<Scalar,ColMajor, typename MatrixType::StorageIndex>(*m_cR);
+        m_isRUpToDate = true;
+      }
+      return m_R;
+    }
+    /// Get an expression of the matrix Q
+    SPQRMatrixQReturnType<SPQR> matrixQ() const
+    {
+      return SPQRMatrixQReturnType<SPQR>(*this);
+    }
+    /// Get the permutation that was applied to columns of A
+    PermutationType colsPermutation() const
+    { 
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return PermutationType(m_E, m_cR->ncol);
+    }
+    /**
+     * Gets the rank of the matrix. 
+     * It should be equal to matrixQR().cols if the matrix is full-rank
+     */
+    Index rank() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_cc.SPQR_istat[4];
+    }
+    /// Set the fill-reducing ordering method to be used
+    void setSPQROrdering(int ord) { m_ordering = ord;}
+    /// Set the tolerance tol to treat columns with 2-norm < =tol as zero
+    void setPivotThreshold(const RealScalar& tol)
+    {
+      m_useDefaultThreshold = false;
+      m_tolerance = tol;
+    }
+    
+    /** \returns a pointer to the SPQR workspace */
+    cholmod_common *cholmodCommon() const { return &m_cc; }
+    
+    
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful,
+      *          \c NumericalIssue if the sparse QR can not be computed
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+  protected:
+    bool m_analysisIsOk;
+    bool m_factorizationIsOk;
+    mutable bool m_isRUpToDate;
+    mutable ComputationInfo m_info;
+    int m_ordering; // Ordering method to use, see SPQR's manual
+    int m_allow_tol; // Allow to use some tolerance during numerical factorization.
+    RealScalar m_tolerance; // treat columns with 2-norm below this tolerance as zero
+    mutable cholmod_sparse *m_cR = nullptr; // The sparse R factor in cholmod format
+    mutable MatrixType m_R; // The sparse matrix R in Eigen format
+    mutable StorageIndex *m_E = nullptr; // The permutation applied to columns
+    mutable cholmod_sparse *m_H = nullptr;  //The householder vectors
+    mutable StorageIndex *m_HPinv = nullptr; // The row permutation of H
+    mutable cholmod_dense *m_HTau = nullptr; // The Householder coefficients
+    mutable Index m_rank; // The rank of the matrix
+    mutable cholmod_common m_cc; // Workspace and parameters
+    bool m_useDefaultThreshold;     // Use default threshold
+    Index m_rows;
+    template<typename ,typename > friend struct SPQR_QProduct;
+};
+
+template <typename SPQRType, typename Derived>
+struct SPQR_QProduct : ReturnByValue<SPQR_QProduct<SPQRType,Derived> >
+{
+  typedef typename SPQRType::Scalar Scalar;
+  typedef typename SPQRType::StorageIndex StorageIndex;
+  //Define the constructor to get reference to argument types
+  SPQR_QProduct(const SPQRType& spqr, const Derived& other, bool transpose) : m_spqr(spqr),m_other(other),m_transpose(transpose) {}
+  
+  inline Index rows() const { return m_transpose ? m_spqr.rows() : m_spqr.cols(); }
+  inline Index cols() const { return m_other.cols(); }
+  // Assign to a vector
+  template<typename ResType>
+  void evalTo(ResType& res) const
+  {
+    cholmod_dense y_cd;
+    cholmod_dense *x_cd; 
+    int method = m_transpose ? SPQR_QTX : SPQR_QX; 
+    cholmod_common *cc = m_spqr.cholmodCommon();
+    y_cd = viewAsCholmod(m_other.const_cast_derived());
+    x_cd = SuiteSparseQR_qmult<Scalar>(method, m_spqr.m_H, m_spqr.m_HTau, m_spqr.m_HPinv, &y_cd, cc);
+    res = Matrix<Scalar,ResType::RowsAtCompileTime,ResType::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x), x_cd->nrow, x_cd->ncol);
+    cholmod_l_free_dense(&x_cd, cc);
+  }
+  const SPQRType& m_spqr; 
+  const Derived& m_other; 
+  bool m_transpose; 
+  
+};
+template<typename SPQRType>
+struct SPQRMatrixQReturnType{
+  
+  SPQRMatrixQReturnType(const SPQRType& spqr) : m_spqr(spqr) {}
+  template<typename Derived>
+  SPQR_QProduct<SPQRType, Derived> operator*(const MatrixBase<Derived>& other)
+  {
+    return SPQR_QProduct<SPQRType,Derived>(m_spqr,other.derived(),false);
+  }
+  SPQRMatrixQTransposeReturnType<SPQRType> adjoint() const
+  {
+    return SPQRMatrixQTransposeReturnType<SPQRType>(m_spqr);
+  }
+  // To use for operations with the transpose of Q
+  SPQRMatrixQTransposeReturnType<SPQRType> transpose() const
+  {
+    return SPQRMatrixQTransposeReturnType<SPQRType>(m_spqr);
+  }
+  const SPQRType& m_spqr;
+};
+
+template<typename SPQRType>
+struct SPQRMatrixQTransposeReturnType{
+  SPQRMatrixQTransposeReturnType(const SPQRType& spqr) : m_spqr(spqr) {}
+  template<typename Derived>
+  SPQR_QProduct<SPQRType,Derived> operator*(const MatrixBase<Derived>& other)
+  {
+    return SPQR_QProduct<SPQRType,Derived>(m_spqr,other.derived(), true);
+  }
+  const SPQRType& m_spqr;
+};
+
+}// End namespace Eigen
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/BDCSVD.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/BDCSVD.h
new file mode 100644
index 0000000000000000000000000000000000000000..bb1d3db10f293e1a7623839c4fa7c0c12a33183b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/BDCSVD.h
@@ -0,0 +1,1494 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// We used the "A Divide-And-Conquer Algorithm for the Bidiagonal SVD"
+// research report written by Ming Gu and Stanley C.Eisenstat
+// The code variable names correspond to the names they used in their
+// report
+//
+// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
+// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
+// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
+// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr>
+// Copyright (C) 2013 Jitse Niesen <jitse@maths.leeds.ac.uk>
+// Copyright (C) 2014-2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BDCSVD_H
+#define EIGEN_BDCSVD_H
+// #define EIGEN_BDCSVD_DEBUG_VERBOSE
+// #define EIGEN_BDCSVD_SANITY_CHECKS
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+#undef eigen_internal_assert
+#define eigen_internal_assert(X) assert(X);
+#endif
+
+#include "./InternalHeaderCheck.h"
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+#include <iostream>
+#endif
+
+namespace Eigen {
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+IOFormat bdcsvdfmt(8, 0, ", ", "\n", "  [", "]");
+#endif
+
+template <typename MatrixType_, int Options>
+class BDCSVD;
+
+namespace internal {
+
+template <typename MatrixType_, int Options>
+struct traits<BDCSVD<MatrixType_, Options> > : svd_traits<MatrixType_, Options> {
+  typedef MatrixType_ MatrixType;
+};
+
+template <typename MatrixType, int Options>
+struct allocate_small_svd {
+  static void run(JacobiSVD<MatrixType, Options>& smallSvd, Index rows, Index cols, unsigned int computationOptions) {
+    (void)computationOptions;
+    smallSvd = JacobiSVD<MatrixType, Options>(rows, cols);
+  }
+};
+
+EIGEN_DIAGNOSTICS(push)
+EIGEN_DISABLE_DEPRECATED_WARNING
+
+template <typename MatrixType>
+struct allocate_small_svd<MatrixType, 0> {
+  static void run(JacobiSVD<MatrixType>& smallSvd, Index rows, Index cols, unsigned int computationOptions) {
+    smallSvd = JacobiSVD<MatrixType>(rows, cols, computationOptions);
+  }
+};
+
+EIGEN_DIAGNOSTICS(pop)
+
+} // end namespace internal
+
+/** \ingroup SVD_Module
+ *
+ *
+ * \class BDCSVD
+ *
+ * \brief class Bidiagonal Divide and Conquer SVD
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the SVD decomposition
+ *
+ * \tparam Options_ this optional parameter allows one to specify options for computing unitaries \a U and \a V.
+ *                  Possible values are #ComputeThinU, #ComputeThinV, #ComputeFullU, #ComputeFullV, and
+ *                  #DisableQRDecomposition. It is not possible to request both the thin and full version of \a U or
+ *                  \a V. By default, unitaries are not computed. BDCSVD uses R-Bidiagonalization to improve
+ *                  performance on tall and wide matrices. For backwards compatility, the option
+ *                  #DisableQRDecomposition can be used to disable this optimization.
+ *
+ * This class first reduces the input matrix to bi-diagonal form using class UpperBidiagonalization,
+ * and then performs a divide-and-conquer diagonalization. Small blocks are diagonalized using class JacobiSVD.
+ * You can control the switching size with the setSwitchSize() method, default is 16.
+ * For small matrice (<16), it is thus preferable to directly use JacobiSVD. For larger ones, BDCSVD is highly
+ * recommended and can several order of magnitude faster.
+ *
+ * \warning this algorithm is unlikely to provide accurate result when compiled with unsafe math optimizations.
+ * For instance, this concerns Intel's compiler (ICC), which performs such optimization by default unless
+ * you compile with the \c -fp-model \c precise option. Likewise, the \c -ffast-math option of GCC or clang will
+ * significantly degrade the accuracy.
+ *
+ * \sa class JacobiSVD
+ */
+template <typename MatrixType_, int Options_>
+class BDCSVD : public SVDBase<BDCSVD<MatrixType_, Options_> > {
+  typedef SVDBase<BDCSVD> Base;
+
+public:
+  using Base::rows;
+  using Base::cols;
+  using Base::diagSize;
+  using Base::computeU;
+  using Base::computeV;
+
+  typedef MatrixType_ MatrixType;
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::RealScalar RealScalar;
+  typedef typename NumTraits<RealScalar>::Literal Literal;
+  typedef typename Base::Index Index;
+  enum {
+    Options = Options_,
+    QRDecomposition = Options & internal::QRPreconditionerBits,
+    ComputationOptions = Options & internal::ComputationOptionsBits,
+    RowsAtCompileTime = Base::RowsAtCompileTime,
+    ColsAtCompileTime = Base::ColsAtCompileTime,
+    DiagSizeAtCompileTime = Base::DiagSizeAtCompileTime,
+    MaxRowsAtCompileTime = Base::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = Base::MaxColsAtCompileTime,
+    MaxDiagSizeAtCompileTime = Base::MaxDiagSizeAtCompileTime,
+    MatrixOptions = Base::MatrixOptions
+  };
+
+  typedef typename Base::MatrixUType MatrixUType;
+  typedef typename Base::MatrixVType MatrixVType;
+  typedef typename Base::SingularValuesType SingularValuesType;
+
+  typedef Matrix<Scalar, Dynamic, Dynamic, ColMajor> MatrixX;
+  typedef Matrix<RealScalar, Dynamic, Dynamic, ColMajor> MatrixXr;
+  typedef Matrix<RealScalar, Dynamic, 1> VectorType;
+  typedef Array<RealScalar, Dynamic, 1> ArrayXr;
+  typedef Array<Index,1,Dynamic> ArrayXi;
+  typedef Ref<ArrayXr> ArrayRef;
+  typedef Ref<ArrayXi> IndicesRef;
+
+  /** \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via BDCSVD::compute(const MatrixType&).
+   */
+  BDCSVD() : m_algoswap(16), m_isTranspose(false), m_compU(false), m_compV(false), m_numIters(0)
+  {}
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem size and \a Options template parameter.
+   * \sa BDCSVD()
+   */
+  BDCSVD(Index rows, Index cols) : m_algoswap(16), m_numIters(0) {
+    allocate(rows, cols, internal::get_computation_options(Options));
+  }
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem size and the \a computationOptions.
+   *
+   * One \b cannot request unitiaries using both the \a Options template parameter
+   * and the constructor. If possible, prefer using the \a Options template parameter.
+   *
+   * \param computationOptions specifification for computing Thin/Full unitaries U/V
+   * \sa BDCSVD()
+   *
+   * \deprecated Will be removed in the next major Eigen version. Options should
+   * be specified in the \a Options template parameter.
+   */
+  EIGEN_DEPRECATED
+  BDCSVD(Index rows, Index cols, unsigned int computationOptions) : m_algoswap(16), m_numIters(0) {
+    internal::check_svd_options_assertions<MatrixType, Options>(computationOptions, rows, cols);
+    allocate(rows, cols, computationOptions);
+  }
+
+  /** \brief Constructor performing the decomposition of given matrix, using the custom options specified
+   *         with the \a Options template paramter.
+   *
+   * \param matrix the matrix to decompose
+   */
+  BDCSVD(const MatrixType& matrix) : m_algoswap(16), m_numIters(0) {
+    compute_impl(matrix, internal::get_computation_options(Options));
+  }
+
+  /** \brief Constructor performing the decomposition of given matrix using specified options
+   *         for computing unitaries.
+   *
+   *  One \b cannot request unitiaries using both the \a Options template parameter
+   *  and the constructor. If possible, prefer using the \a Options template parameter.
+   *
+   * \param matrix the matrix to decompose
+   * \param computationOptions specifification for computing Thin/Full unitaries U/V
+   *
+   * \deprecated Will be removed in the next major Eigen version. Options should
+   * be specified in the \a Options template parameter.
+   */
+  EIGEN_DEPRECATED
+  BDCSVD(const MatrixType& matrix, unsigned int computationOptions) : m_algoswap(16), m_numIters(0) {
+    internal::check_svd_options_assertions<MatrixType, Options>(computationOptions, matrix.rows(), matrix.cols());
+    compute_impl(matrix, computationOptions);
+  }
+
+  ~BDCSVD() {}
+
+  /** \brief Method performing the decomposition of given matrix. Computes Thin/Full unitaries U/V if specified
+   *         using the \a Options template parameter or the class constructor.
+   *
+   * \param matrix the matrix to decompose
+   */
+  BDCSVD& compute(const MatrixType& matrix) { return compute_impl(matrix, m_computationOptions); }
+  
+  /** \brief Method performing the decomposition of given matrix, as specified by
+   *         the `computationOptions` parameter.
+   *
+   * \param matrix the matrix to decompose
+   * \param computationOptions specify whether to compute Thin/Full unitaries U/V
+   * 
+   * \deprecated Will be removed in the next major Eigen version. Options should
+   * be specified in the \a Options template parameter.
+   */
+  EIGEN_DEPRECATED
+  BDCSVD& compute(const MatrixType& matrix, unsigned int computationOptions) {
+    internal::check_svd_options_assertions<MatrixType, Options>(computationOptions, matrix.rows(), matrix.cols());
+    return compute_impl(matrix, computationOptions);
+  }
+
+  void setSwitchSize(int s)
+  {
+    eigen_assert(s>=3 && "BDCSVD the size of the algo switch has to be at least 3.");
+    m_algoswap = s;
+  }
+
+private:
+  BDCSVD& compute_impl(const MatrixType& matrix, unsigned int computationOptions);
+  void divide(Index firstCol, Index lastCol, Index firstRowW, Index firstColW, Index shift);
+  void computeSVDofM(Index firstCol, Index n, MatrixXr& U, VectorType& singVals, MatrixXr& V);
+  void computeSingVals(const ArrayRef& col0, const ArrayRef& diag, const IndicesRef& perm, VectorType& singVals, ArrayRef shifts, ArrayRef mus);
+  void perturbCol0(const ArrayRef& col0, const ArrayRef& diag, const IndicesRef& perm, const VectorType& singVals, const ArrayRef& shifts, const ArrayRef& mus, ArrayRef zhat);
+  void computeSingVecs(const ArrayRef& zhat, const ArrayRef& diag, const IndicesRef& perm, const VectorType& singVals, const ArrayRef& shifts, const ArrayRef& mus, MatrixXr& U, MatrixXr& V);
+  void deflation43(Index firstCol, Index shift, Index i, Index size);
+  void deflation44(Index firstColu , Index firstColm, Index firstRowW, Index firstColW, Index i, Index j, Index size);
+  void deflation(Index firstCol, Index lastCol, Index k, Index firstRowW, Index firstColW, Index shift);
+  template<typename HouseholderU, typename HouseholderV, typename NaiveU, typename NaiveV>
+  void copyUV(const HouseholderU &householderU, const HouseholderV &householderV, const NaiveU &naiveU, const NaiveV &naivev);
+  void structured_update(Block<MatrixXr,Dynamic,Dynamic> A, const MatrixXr &B, Index n1);
+  static RealScalar secularEq(RealScalar x, const ArrayRef& col0, const ArrayRef& diag, const IndicesRef &perm, const ArrayRef& diagShifted, RealScalar shift);
+  template <typename SVDType>
+  void computeBaseCase(SVDType& svd, Index n, Index firstCol, Index firstRowW, Index firstColW, Index shift);
+
+ protected:
+  void allocate(Index rows, Index cols, unsigned int computationOptions);
+  MatrixXr m_naiveU, m_naiveV;
+  MatrixXr m_computed;
+  Index m_nRec;
+  ArrayXr m_workspace;
+  ArrayXi m_workspaceI;
+  int m_algoswap;
+  bool m_isTranspose, m_compU, m_compV, m_useQrDecomp;
+  JacobiSVD<MatrixType, ComputationOptions> smallSvd;
+  HouseholderQR<MatrixX> qrDecomp;
+  internal::UpperBidiagonalization<MatrixX> bid;
+  MatrixX copyWorkspace;
+  MatrixX reducedTriangle;
+
+  using Base::m_computationOptions;
+  using Base::m_computeThinU;
+  using Base::m_computeThinV;
+  using Base::m_info;
+  using Base::m_isInitialized;
+  using Base::m_matrixU;
+  using Base::m_matrixV;
+  using Base::m_nonzeroSingularValues;
+  using Base::m_singularValues;
+
+ public:
+  int m_numIters;
+};  // end class BDCSVD
+
+// Method to allocate and initialize matrix and attributes
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::allocate(Index rows, Index cols, unsigned int computationOptions) {
+  if (Base::allocate(rows, cols, computationOptions))
+    return;
+
+  if (cols < m_algoswap)
+    internal::allocate_small_svd<MatrixType, ComputationOptions>::run(smallSvd, rows, cols, computationOptions);
+
+  m_computed = MatrixXr::Zero(diagSize() + 1, diagSize() );
+  m_compU = computeV();
+  m_compV = computeU();
+  m_isTranspose = (cols > rows);
+  if (m_isTranspose)
+    std::swap(m_compU, m_compV);
+
+  // kMinAspectRatio is the crossover point that determines if we perform R-Bidiagonalization
+  // or bidiagonalize the input matrix directly.
+  // It is based off of LAPACK's dgesdd routine, which uses 11.0/6.0
+  // we use a larger scalar to prevent a regression for relatively square matrices.
+  constexpr Index kMinAspectRatio = 4;
+  constexpr bool disableQrDecomp = static_cast<int>(QRDecomposition) == static_cast<int>(DisableQRDecomposition);
+  m_useQrDecomp = !disableQrDecomp && ((rows / kMinAspectRatio > cols) || (cols / kMinAspectRatio > rows));
+  if (m_useQrDecomp) {
+    qrDecomp = HouseholderQR<MatrixX>((std::max)(rows, cols), (std::min)(rows, cols));
+    reducedTriangle = MatrixX(diagSize(), diagSize());
+  }
+
+  copyWorkspace = MatrixX(m_isTranspose ? cols : rows, m_isTranspose ? rows : cols);
+  bid = internal::UpperBidiagonalization<MatrixX>(m_useQrDecomp ? diagSize() : copyWorkspace.rows(),
+                                                  m_useQrDecomp ? diagSize() : copyWorkspace.cols());
+
+  if (m_compU) m_naiveU = MatrixXr::Zero(diagSize() + 1, diagSize() + 1 );
+  else         m_naiveU = MatrixXr::Zero(2, diagSize() + 1 );
+
+  if (m_compV) m_naiveV = MatrixXr::Zero(diagSize(), diagSize());
+
+  m_workspace.resize((diagSize()+1)*(diagSize()+1)*3);
+  m_workspaceI.resize(3*diagSize());
+}  // end allocate
+
+template <typename MatrixType, int Options>
+BDCSVD<MatrixType, Options>& BDCSVD<MatrixType, Options>::compute_impl(const MatrixType& matrix,
+                                                                       unsigned int computationOptions) {
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "\n\n\n======================================================================================================================\n\n\n";
+#endif
+  using std::abs;
+
+  allocate(matrix.rows(), matrix.cols(), computationOptions);
+
+  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
+
+  //**** step -1 - If the problem is too small, directly falls back to JacobiSVD and return
+  if(matrix.cols() < m_algoswap)
+  {
+    smallSvd.compute(matrix);
+    m_isInitialized = true;
+    m_info = smallSvd.info();
+    if (m_info == Success || m_info == NoConvergence) {
+      if (computeU()) m_matrixU = smallSvd.matrixU();
+      if (computeV()) m_matrixV = smallSvd.matrixV();
+      m_singularValues = smallSvd.singularValues();
+      m_nonzeroSingularValues = smallSvd.nonzeroSingularValues();
+    }
+    return *this;
+  }
+
+  //**** step 0 - Copy the input matrix and apply scaling to reduce over/under-flows
+  RealScalar scale = matrix.cwiseAbs().template maxCoeff<PropagateNaN>();
+  if (!(numext::isfinite)(scale)) {
+    m_isInitialized = true;
+    m_info = InvalidInput;
+    return *this;
+  }
+
+  if(numext::is_exactly_zero(scale)) scale = Literal(1);
+
+  if (m_isTranspose) copyWorkspace = matrix.adjoint() / scale;
+  else copyWorkspace = matrix / scale;
+
+  //**** step 1 - Bidiagonalization.
+  // If the problem is sufficiently rectangular, we perform R-Bidiagonalization: compute A = Q(R/0)
+  // and then bidiagonalize R. Otherwise, if the problem is relatively square, we
+  // bidiagonalize the input matrix directly.
+  if (m_useQrDecomp) {
+    qrDecomp.compute(copyWorkspace);
+    reducedTriangle = qrDecomp.matrixQR().topRows(diagSize());
+    reducedTriangle.template triangularView<StrictlyLower>().setZero();
+    bid.compute(reducedTriangle);
+  } else {
+    bid.compute(copyWorkspace);
+  }
+
+  //**** step 2 - Divide & Conquer
+  m_naiveU.setZero();
+  m_naiveV.setZero();
+  // FIXME this line involves a temporary matrix
+  m_computed.topRows(diagSize()) = bid.bidiagonal().toDenseMatrix().transpose();
+  m_computed.template bottomRows<1>().setZero();
+  divide(0, diagSize() - 1, 0, 0, 0);
+  if (m_info != Success && m_info != NoConvergence) {
+    m_isInitialized = true;
+    return *this;
+  }
+
+  //**** step 3 - Copy singular values and vectors
+  for (int i=0; i<diagSize(); i++)
+  {
+    RealScalar a = abs(m_computed.coeff(i, i));
+    m_singularValues.coeffRef(i) = a * scale;
+    if (a<considerZero)
+    {
+      m_nonzeroSingularValues = i;
+      m_singularValues.tail(diagSize() - i - 1).setZero();
+      break;
+    }
+    else if (i == diagSize() - 1)
+    {
+      m_nonzeroSingularValues = i + 1;
+      break;
+    }
+  }
+
+  //**** step 4 - Finalize unitaries U and V
+  if(m_isTranspose) copyUV(bid.householderV(), bid.householderU(), m_naiveV, m_naiveU);
+  else              copyUV(bid.householderU(), bid.householderV(), m_naiveU, m_naiveV);
+
+  if (m_useQrDecomp) {
+    if (m_isTranspose && computeV()) m_matrixV.applyOnTheLeft(qrDecomp.householderQ());
+    else if (!m_isTranspose && computeU()) m_matrixU.applyOnTheLeft(qrDecomp.householderQ());
+  }
+
+  m_isInitialized = true;
+  return *this;
+}  // end compute
+
+template <typename MatrixType, int Options>
+template <typename HouseholderU, typename HouseholderV, typename NaiveU, typename NaiveV>
+void BDCSVD<MatrixType, Options>::copyUV(const HouseholderU& householderU, const HouseholderV& householderV,
+                                         const NaiveU& naiveU, const NaiveV& naiveV) {
+  // Note exchange of U and V: m_matrixU is set from m_naiveV and vice versa
+  if (computeU())
+  {
+    Index Ucols = m_computeThinU ? diagSize() : rows();
+    m_matrixU = MatrixX::Identity(rows(), Ucols);
+    m_matrixU.topLeftCorner(diagSize(), diagSize()) = naiveV.template cast<Scalar>().topLeftCorner(diagSize(), diagSize());
+    // FIXME the following conditionals involve temporary buffers
+    if (m_useQrDecomp) m_matrixU.topLeftCorner(householderU.cols(), diagSize()).applyOnTheLeft(householderU);
+    else m_matrixU.applyOnTheLeft(householderU);
+  }
+  if (computeV())
+  {
+    Index Vcols = m_computeThinV ? diagSize() : cols();
+    m_matrixV = MatrixX::Identity(cols(), Vcols);
+    m_matrixV.topLeftCorner(diagSize(), diagSize()) = naiveU.template cast<Scalar>().topLeftCorner(diagSize(), diagSize());
+    // FIXME the following conditionals involve temporary buffers
+    if (m_useQrDecomp) m_matrixV.topLeftCorner(householderV.cols(), diagSize()).applyOnTheLeft(householderV);
+    else m_matrixV.applyOnTheLeft(householderV);
+  }
+}
+
+/** \internal
+  * Performs A = A * B exploiting the special structure of the matrix A. Splitting A as:
+  *  A = [A1]
+  *      [A2]
+  * such that A1.rows()==n1, then we assume that at least half of the columns of A1 and A2 are zeros.
+  * We can thus pack them prior to the the matrix product. However, this is only worth the effort if the matrix is large
+  * enough.
+  */
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::structured_update(Block<MatrixXr, Dynamic, Dynamic> A, const MatrixXr& B, Index n1) {
+  Index n = A.rows();
+  if(n>100)
+  {
+    // If the matrices are large enough, let's exploit the sparse structure of A by
+    // splitting it in half (wrt n1), and packing the non-zero columns.
+    Index n2 = n - n1;
+    Map<MatrixXr> A1(m_workspace.data()      , n1, n);
+    Map<MatrixXr> A2(m_workspace.data()+ n1*n, n2, n);
+    Map<MatrixXr> B1(m_workspace.data()+  n*n, n,  n);
+    Map<MatrixXr> B2(m_workspace.data()+2*n*n, n,  n);
+    Index k1=0, k2=0;
+    for(Index j=0; j<n; ++j)
+    {
+      if( (A.col(j).head(n1).array()!=Literal(0)).any() )
+      {
+        A1.col(k1) = A.col(j).head(n1);
+        B1.row(k1) = B.row(j);
+        ++k1;
+      }
+      if( (A.col(j).tail(n2).array()!=Literal(0)).any() )
+      {
+        A2.col(k2) = A.col(j).tail(n2);
+        B2.row(k2) = B.row(j);
+        ++k2;
+      }
+    }
+
+    A.topRows(n1).noalias()    = A1.leftCols(k1) * B1.topRows(k1);
+    A.bottomRows(n2).noalias() = A2.leftCols(k2) * B2.topRows(k2);
+  }
+  else
+  {
+    Map<MatrixXr,Aligned> tmp(m_workspace.data(),n,n);
+    tmp.noalias() = A*B;
+    A = tmp;
+  }
+}
+
+template <typename MatrixType, int Options>
+template <typename SVDType>
+void BDCSVD<MatrixType, Options>::computeBaseCase(SVDType& svd, Index n, Index firstCol, Index firstRowW,
+                                                  Index firstColW, Index shift) {
+  svd.compute(m_computed.block(firstCol, firstCol, n + 1, n));
+  m_info = svd.info();
+  if (m_info != Success && m_info != NoConvergence) return;
+  if (m_compU)
+    m_naiveU.block(firstCol, firstCol, n + 1, n + 1).real() = svd.matrixU();
+  else {
+    m_naiveU.row(0).segment(firstCol, n + 1).real() = svd.matrixU().row(0);
+    m_naiveU.row(1).segment(firstCol, n + 1).real() = svd.matrixU().row(n);
+  }
+  if (m_compV) m_naiveV.block(firstRowW, firstColW, n, n).real() = svd.matrixV();
+  m_computed.block(firstCol + shift, firstCol + shift, n + 1, n).setZero();
+  m_computed.diagonal().segment(firstCol + shift, n) = svd.singularValues().head(n);
+}
+
+// The divide algorithm is done "in place", we are always working on subsets of the same matrix. The divide methods
+// takes as argument the place of the submatrix we are currently working on.
+
+//@param firstCol : The Index of the first column of the submatrix of m_computed and for m_naiveU;
+//@param lastCol : The Index of the last column of the submatrix of m_computed and for m_naiveU;
+// lastCol + 1 - firstCol is the size of the submatrix.
+//@param firstRowW : The Index of the first row of the matrix W that we are to change. (see the reference paper section 1 for more information on W)
+//@param firstColW : Same as firstRowW with the column.
+//@param shift : Each time one takes the left submatrix, one must add 1 to the shift. Why? Because! We actually want the last column of the U submatrix
+// to become the first column (*coeff) and to shift all the other columns to the right. There are more details on the reference paper.
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::divide(Index firstCol, Index lastCol, Index firstRowW,
+                                         Index firstColW, Index shift) {
+  // requires rows = cols + 1;
+  using std::pow;
+  using std::sqrt;
+  using std::abs;
+  const Index n = lastCol - firstCol + 1;
+  const Index k = n/2;
+  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
+  RealScalar alphaK;
+  RealScalar betaK;
+  RealScalar r0;
+  RealScalar lambda, phi, c0, s0;
+  VectorType l, f;
+  // We use the other algorithm which is more efficient for small
+  // matrices.
+  if (n < m_algoswap)
+  {
+    // FIXME this block involves temporaries
+    if (m_compV) {
+      JacobiSVD<MatrixXr, ComputeFullU | ComputeFullV> baseSvd;
+      computeBaseCase(baseSvd, n, firstCol, firstRowW, firstColW, shift);
+    } else {
+      JacobiSVD<MatrixXr, ComputeFullU> baseSvd;
+      computeBaseCase(baseSvd, n, firstCol, firstRowW, firstColW, shift);
+    }
+    return;
+  }
+  // We use the divide and conquer algorithm
+  alphaK =  m_computed(firstCol + k, firstCol + k);
+  betaK = m_computed(firstCol + k + 1, firstCol + k);
+  // The divide must be done in that order in order to have good results. Divide change the data inside the submatrices
+  // and the divide of the right submatrice reads one column of the left submatrice. That's why we need to treat the
+  // right submatrix before the left one.
+  divide(k + 1 + firstCol, lastCol, k + 1 + firstRowW, k + 1 + firstColW, shift);
+  if (m_info != Success && m_info != NoConvergence) return;
+  divide(firstCol, k - 1 + firstCol, firstRowW, firstColW + 1, shift + 1);
+  if (m_info != Success && m_info != NoConvergence) return;
+
+  if (m_compU)
+  {
+    lambda = m_naiveU(firstCol + k, firstCol + k);
+    phi = m_naiveU(firstCol + k + 1, lastCol + 1);
+  }
+  else
+  {
+    lambda = m_naiveU(1, firstCol + k);
+    phi = m_naiveU(0, lastCol + 1);
+  }
+  r0 = sqrt((abs(alphaK * lambda) * abs(alphaK * lambda)) + abs(betaK * phi) * abs(betaK * phi));
+  if (m_compU)
+  {
+    l = m_naiveU.row(firstCol + k).segment(firstCol, k);
+    f = m_naiveU.row(firstCol + k + 1).segment(firstCol + k + 1, n - k - 1);
+  }
+  else
+  {
+    l = m_naiveU.row(1).segment(firstCol, k);
+    f = m_naiveU.row(0).segment(firstCol + k + 1, n - k - 1);
+  }
+  if (m_compV) m_naiveV(firstRowW+k, firstColW) = Literal(1);
+  if (r0<considerZero)
+  {
+    c0 = Literal(1);
+    s0 = Literal(0);
+  }
+  else
+  {
+    c0 = alphaK * lambda / r0;
+    s0 = betaK * phi / r0;
+  }
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(m_naiveU.allFinite());
+  eigen_internal_assert(m_naiveV.allFinite());
+  eigen_internal_assert(m_computed.allFinite());
+#endif
+
+  if (m_compU)
+  {
+    MatrixXr q1 (m_naiveU.col(firstCol + k).segment(firstCol, k + 1));
+    // we shiftW Q1 to the right
+    for (Index i = firstCol + k - 1; i >= firstCol; i--)
+      m_naiveU.col(i + 1).segment(firstCol, k + 1) = m_naiveU.col(i).segment(firstCol, k + 1);
+    // we shift q1 at the left with a factor c0
+    m_naiveU.col(firstCol).segment( firstCol, k + 1) = (q1 * c0);
+    // last column = q1 * - s0
+    m_naiveU.col(lastCol + 1).segment(firstCol, k + 1) = (q1 * ( - s0));
+    // first column = q2 * s0
+    m_naiveU.col(firstCol).segment(firstCol + k + 1, n - k) = m_naiveU.col(lastCol + 1).segment(firstCol + k + 1, n - k) * s0;
+    // q2 *= c0
+    m_naiveU.col(lastCol + 1).segment(firstCol + k + 1, n - k) *= c0;
+  }
+  else
+  {
+    RealScalar q1 = m_naiveU(0, firstCol + k);
+    // we shift Q1 to the right
+    for (Index i = firstCol + k - 1; i >= firstCol; i--)
+      m_naiveU(0, i + 1) = m_naiveU(0, i);
+    // we shift q1 at the left with a factor c0
+    m_naiveU(0, firstCol) = (q1 * c0);
+    // last column = q1 * - s0
+    m_naiveU(0, lastCol + 1) = (q1 * ( - s0));
+    // first column = q2 * s0
+    m_naiveU(1, firstCol) = m_naiveU(1, lastCol + 1) *s0;
+    // q2 *= c0
+    m_naiveU(1, lastCol + 1) *= c0;
+    m_naiveU.row(1).segment(firstCol + 1, k).setZero();
+    m_naiveU.row(0).segment(firstCol + k + 1, n - k - 1).setZero();
+  }
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(m_naiveU.allFinite());
+  eigen_internal_assert(m_naiveV.allFinite());
+  eigen_internal_assert(m_computed.allFinite());
+#endif
+
+  m_computed(firstCol + shift, firstCol + shift) = r0;
+  m_computed.col(firstCol + shift).segment(firstCol + shift + 1, k) = alphaK * l.transpose().real();
+  m_computed.col(firstCol + shift).segment(firstCol + shift + k + 1, n - k - 1) = betaK * f.transpose().real();
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  ArrayXr tmp1 = (m_computed.block(firstCol+shift, firstCol+shift, n, n)).jacobiSvd().singularValues();
+#endif
+  // Second part: try to deflate singular values in combined matrix
+  deflation(firstCol, lastCol, k, firstRowW, firstColW, shift);
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  ArrayXr tmp2 = (m_computed.block(firstCol+shift, firstCol+shift, n, n)).jacobiSvd().singularValues();
+  std::cout << "\n\nj1 = " << tmp1.transpose().format(bdcsvdfmt) << "\n";
+  std::cout << "j2 = " << tmp2.transpose().format(bdcsvdfmt) << "\n\n";
+  std::cout << "err:      " << ((tmp1-tmp2).abs()>1e-12*tmp2.abs()).transpose() << "\n";
+  static int count = 0;
+  std::cout << "# " << ++count << "\n\n";
+  eigen_internal_assert((tmp1-tmp2).matrix().norm() < 1e-14*tmp2.matrix().norm());
+//   eigen_internal_assert(count<681);
+//   eigen_internal_assert(((tmp1-tmp2).abs()<1e-13*tmp2.abs()).all());
+#endif
+
+  // Third part: compute SVD of combined matrix
+  MatrixXr UofSVD, VofSVD;
+  VectorType singVals;
+  computeSVDofM(firstCol + shift, n, UofSVD, singVals, VofSVD);
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(UofSVD.allFinite());
+  eigen_internal_assert(VofSVD.allFinite());
+#endif
+
+  if (m_compU)
+    structured_update(m_naiveU.block(firstCol, firstCol, n + 1, n + 1), UofSVD, (n+2)/2);
+  else
+  {
+    Map<Matrix<RealScalar,2,Dynamic>,Aligned> tmp(m_workspace.data(),2,n+1);
+    tmp.noalias() = m_naiveU.middleCols(firstCol, n+1) * UofSVD;
+    m_naiveU.middleCols(firstCol, n + 1) = tmp;
+  }
+
+  if (m_compV)  structured_update(m_naiveV.block(firstRowW, firstColW, n, n), VofSVD, (n+1)/2);
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(m_naiveU.allFinite());
+  eigen_internal_assert(m_naiveV.allFinite());
+  eigen_internal_assert(m_computed.allFinite());
+#endif
+
+  m_computed.block(firstCol + shift, firstCol + shift, n, n).setZero();
+  m_computed.block(firstCol + shift, firstCol + shift, n, n).diagonal() = singVals;
+}  // end divide
+
+// Compute SVD of m_computed.block(firstCol, firstCol, n + 1, n); this block only has non-zeros in
+// the first column and on the diagonal and has undergone deflation, so diagonal is in increasing
+// order except for possibly the (0,0) entry. The computed SVD is stored U, singVals and V, except
+// that if m_compV is false, then V is not computed. Singular values are sorted in decreasing order.
+//
+// TODO Opportunities for optimization: better root finding algo, better stopping criterion, better
+// handling of round-off errors, be consistent in ordering
+// For instance, to solve the secular equation using FMM, see http://www.stat.uchicago.edu/~lekheng/courses/302/classics/greengard-rokhlin.pdf
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::computeSVDofM(Index firstCol, Index n, MatrixXr& U,
+                                                VectorType& singVals, MatrixXr& V) {
+  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
+  using std::abs;
+  ArrayRef col0 = m_computed.col(firstCol).segment(firstCol, n);
+  m_workspace.head(n) =  m_computed.block(firstCol, firstCol, n, n).diagonal();
+  ArrayRef diag = m_workspace.head(n);
+  diag(0) = Literal(0);
+
+  // Allocate space for singular values and vectors
+  singVals.resize(n);
+  U.resize(n+1, n+1);
+  if (m_compV) V.resize(n, n);
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  if (col0.hasNaN() || diag.hasNaN())
+    std::cout << "\n\nHAS NAN\n\n";
+#endif
+
+  // Many singular values might have been deflated, the zero ones have been moved to the end,
+  // but others are interleaved and we must ignore them at this stage.
+  // To this end, let's compute a permutation skipping them:
+  Index actual_n = n;
+  while(actual_n>1 && numext::is_exactly_zero(diag(actual_n - 1))) {
+    --actual_n;
+    eigen_internal_assert(numext::is_exactly_zero(col0(actual_n)));
+  }
+  Index m = 0; // size of the deflated problem
+  for(Index k=0;k<actual_n;++k)
+    if(abs(col0(k))>considerZero)
+      m_workspaceI(m++) = k;
+  Map<ArrayXi> perm(m_workspaceI.data(),m);
+
+  Map<ArrayXr> shifts(m_workspace.data()+1*n, n);
+  Map<ArrayXr> mus(m_workspace.data()+2*n, n);
+  Map<ArrayXr> zhat(m_workspace.data()+3*n, n);
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "computeSVDofM using:\n";
+  std::cout << "  z: " << col0.transpose() << "\n";
+  std::cout << "  d: " << diag.transpose() << "\n";
+#endif
+
+  // Compute singVals, shifts, and mus
+  computeSingVals(col0, diag, perm, singVals, shifts, mus);
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "  j:        " << (m_computed.block(firstCol, firstCol, n, n)).jacobiSvd().singularValues().transpose().reverse() << "\n\n";
+  std::cout << "  sing-val: " << singVals.transpose() << "\n";
+  std::cout << "  mu:       " << mus.transpose() << "\n";
+  std::cout << "  shift:    " << shifts.transpose() << "\n";
+
+  {
+    std::cout << "\n\n    mus:    " << mus.head(actual_n).transpose() << "\n\n";
+    std::cout << "    check1 (expect0) : " << ((singVals.array()-(shifts+mus)) / singVals.array()).head(actual_n).transpose() << "\n\n";
+    eigen_internal_assert((((singVals.array()-(shifts+mus)) / singVals.array()).head(actual_n) >= 0).all());
+    std::cout << "    check2 (>0)      : " << ((singVals.array()-diag) / singVals.array()).head(actual_n).transpose() << "\n\n";
+    eigen_internal_assert((((singVals.array()-diag) / singVals.array()).head(actual_n) >= 0).all());
+  }
+#endif
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(singVals.allFinite());
+  eigen_internal_assert(mus.allFinite());
+  eigen_internal_assert(shifts.allFinite());
+#endif
+
+  // Compute zhat
+  perturbCol0(col0, diag, perm, singVals, shifts, mus, zhat);
+#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "  zhat: " << zhat.transpose() << "\n";
+#endif
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(zhat.allFinite());
+#endif
+
+  computeSingVecs(zhat, diag, perm, singVals, shifts, mus, U, V);
+
+#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "U^T U: " << (U.transpose() * U - MatrixXr(MatrixXr::Identity(U.cols(),U.cols()))).norm() << "\n";
+  std::cout << "V^T V: " << (V.transpose() * V - MatrixXr(MatrixXr::Identity(V.cols(),V.cols()))).norm() << "\n";
+#endif
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(m_naiveU.allFinite());
+  eigen_internal_assert(m_naiveV.allFinite());
+  eigen_internal_assert(m_computed.allFinite());
+  eigen_internal_assert(U.allFinite());
+  eigen_internal_assert(V.allFinite());
+//   eigen_internal_assert((U.transpose() * U - MatrixXr(MatrixXr::Identity(U.cols(),U.cols()))).norm() < 100*NumTraits<RealScalar>::epsilon() * n);
+//   eigen_internal_assert((V.transpose() * V - MatrixXr(MatrixXr::Identity(V.cols(),V.cols()))).norm() < 100*NumTraits<RealScalar>::epsilon() * n);
+#endif
+
+  // Because of deflation, the singular values might not be completely sorted.
+  // Fortunately, reordering them is a O(n) problem
+  for(Index i=0; i<actual_n-1; ++i)
+  {
+    if(singVals(i)>singVals(i+1))
+    {
+      using std::swap;
+      swap(singVals(i),singVals(i+1));
+      U.col(i).swap(U.col(i+1));
+      if(m_compV) V.col(i).swap(V.col(i+1));
+    }
+  }
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  {
+    bool singular_values_sorted = (((singVals.segment(1,actual_n-1)-singVals.head(actual_n-1))).array() >= 0).all();
+    if(!singular_values_sorted)
+      std::cout << "Singular values are not sorted: " << singVals.segment(1,actual_n).transpose() << "\n";
+    eigen_internal_assert(singular_values_sorted);
+  }
+#endif
+
+  // Reverse order so that singular values in increased order
+  // Because of deflation, the zeros singular-values are already at the end
+  singVals.head(actual_n).reverseInPlace();
+  U.leftCols(actual_n).rowwise().reverseInPlace();
+  if (m_compV) V.leftCols(actual_n).rowwise().reverseInPlace();
+
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  JacobiSVD<MatrixXr> jsvd(m_computed.block(firstCol, firstCol, n, n) );
+  std::cout << "  * j:        " << jsvd.singularValues().transpose() << "\n\n";
+  std::cout << "  * sing-val: " << singVals.transpose() << "\n";
+//   std::cout << "  * err:      " << ((jsvd.singularValues()-singVals)>1e-13*singVals.norm()).transpose() << "\n";
+#endif
+}
+
+template <typename MatrixType, int Options>
+typename BDCSVD<MatrixType, Options>::RealScalar BDCSVD<MatrixType, Options>::secularEq(
+    RealScalar mu, const ArrayRef& col0, const ArrayRef& diag, const IndicesRef& perm, const ArrayRef& diagShifted,
+    RealScalar shift) {
+  Index m = perm.size();
+  RealScalar res = Literal(1);
+  for(Index i=0; i<m; ++i)
+  {
+    Index j = perm(i);
+    // The following expression could be rewritten to involve only a single division,
+    // but this would make the expression more sensitive to overflow.
+    res += (col0(j) / (diagShifted(j) - mu)) * (col0(j) / (diag(j) + shift + mu));
+  }
+  return res;
+}
+
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::computeSingVals(const ArrayRef& col0, const ArrayRef& diag, const IndicesRef& perm,
+                                                  VectorType& singVals, ArrayRef shifts, ArrayRef mus) {
+  using std::abs;
+  using std::swap;
+  using std::sqrt;
+
+  Index n = col0.size();
+  Index actual_n = n;
+  // Note that here actual_n is computed based on col0(i)==0 instead of diag(i)==0 as above
+  // because 1) we have diag(i)==0 => col0(i)==0 and 2) if col0(i)==0, then diag(i) is already a singular value.
+  while(actual_n>1 && numext::is_exactly_zero(col0(actual_n - 1))) --actual_n;
+
+  for (Index k = 0; k < n; ++k)
+  {
+    if (numext::is_exactly_zero(col0(k)) || actual_n == 1)
+    {
+      // if col0(k) == 0, then entry is deflated, so singular value is on diagonal
+      // if actual_n==1, then the deflated problem is already diagonalized
+      singVals(k) = k==0 ? col0(0) : diag(k);
+      mus(k) = Literal(0);
+      shifts(k) = k==0 ? col0(0) : diag(k);
+      continue;
+    }
+
+    // otherwise, use secular equation to find singular value
+    RealScalar left = diag(k);
+    RealScalar right; // was: = (k != actual_n-1) ? diag(k+1) : (diag(actual_n-1) + col0.matrix().norm());
+    if(k==actual_n-1)
+      right = (diag(actual_n-1) + col0.matrix().norm());
+    else
+    {
+      // Skip deflated singular values,
+      // recall that at this stage we assume that z[j]!=0 and all entries for which z[j]==0 have been put aside.
+      // This should be equivalent to using perm[]
+      Index l = k+1;
+      while(numext::is_exactly_zero(col0(l))) { ++l; eigen_internal_assert(l < actual_n); }
+      right = diag(l);
+    }
+
+    // first decide whether it's closer to the left end or the right end
+    RealScalar mid = left + (right-left) / Literal(2);
+    RealScalar fMid = secularEq(mid, col0, diag, perm, diag, Literal(0));
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+    std::cout << "right-left = " << right-left << "\n";
+//     std::cout << "fMid = " << fMid << " " << secularEq(mid-left, col0, diag, perm, ArrayXr(diag-left), left)
+//                            << " " << secularEq(mid-right, col0, diag, perm, ArrayXr(diag-right), right)   << "\n";
+    std::cout << "     = " << secularEq(left+RealScalar(0.000001)*(right-left), col0, diag, perm, diag, 0)
+              << " "       << secularEq(left+RealScalar(0.1)     *(right-left), col0, diag, perm, diag, 0)
+              << " "       << secularEq(left+RealScalar(0.2)     *(right-left), col0, diag, perm, diag, 0)
+              << " "       << secularEq(left+RealScalar(0.3)     *(right-left), col0, diag, perm, diag, 0)
+              << " "       << secularEq(left+RealScalar(0.4)     *(right-left), col0, diag, perm, diag, 0)
+              << " "       << secularEq(left+RealScalar(0.49)    *(right-left), col0, diag, perm, diag, 0)
+              << " "       << secularEq(left+RealScalar(0.5)     *(right-left), col0, diag, perm, diag, 0)
+              << " "       << secularEq(left+RealScalar(0.51)    *(right-left), col0, diag, perm, diag, 0)
+              << " "       << secularEq(left+RealScalar(0.6)     *(right-left), col0, diag, perm, diag, 0)
+              << " "       << secularEq(left+RealScalar(0.7)     *(right-left), col0, diag, perm, diag, 0)
+              << " "       << secularEq(left+RealScalar(0.8)     *(right-left), col0, diag, perm, diag, 0)
+              << " "       << secularEq(left+RealScalar(0.9)     *(right-left), col0, diag, perm, diag, 0)
+              << " "       << secularEq(left+RealScalar(0.999999)*(right-left), col0, diag, perm, diag, 0) << "\n";
+#endif
+    RealScalar shift = (k == actual_n-1 || fMid > Literal(0)) ? left : right;
+
+    // measure everything relative to shift
+    Map<ArrayXr> diagShifted(m_workspace.data()+4*n, n);
+    diagShifted = diag - shift;
+
+    if(k!=actual_n-1)
+    {
+      // check that after the shift, f(mid) is still negative:
+      RealScalar midShifted = (right - left) / RealScalar(2);
+      // we can test exact equality here, because shift comes from `... ? left : right`
+      if(numext::equal_strict(shift, right))
+        midShifted = -midShifted;
+      RealScalar fMidShifted = secularEq(midShifted, col0, diag, perm, diagShifted, shift);
+      if(fMidShifted>0)
+      {
+        // fMid was erroneous, fix it:
+        shift =  fMidShifted > Literal(0) ? left : right;
+        diagShifted = diag - shift;
+      }
+    }
+
+    // initial guess
+    RealScalar muPrev, muCur;
+    // we can test exact equality here, because shift comes from `... ? left : right`
+    if (numext::equal_strict(shift, left))
+    {
+      muPrev = (right - left) * RealScalar(0.1);
+      if (k == actual_n-1) muCur = right - left;
+      else                 muCur = (right - left) * RealScalar(0.5);
+    }
+    else
+    {
+      muPrev = -(right - left) * RealScalar(0.1);
+      muCur = -(right - left) * RealScalar(0.5);
+    }
+
+    RealScalar fPrev = secularEq(muPrev, col0, diag, perm, diagShifted, shift);
+    RealScalar fCur = secularEq(muCur, col0, diag, perm, diagShifted, shift);
+    if (abs(fPrev) < abs(fCur))
+    {
+      swap(fPrev, fCur);
+      swap(muPrev, muCur);
+    }
+
+    // rational interpolation: fit a function of the form a / mu + b through the two previous
+    // iterates and use its zero to compute the next iterate
+    bool useBisection = fPrev*fCur>Literal(0);
+    while (!numext::is_exactly_zero(fCur) && abs(muCur - muPrev) > Literal(8) * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(abs(muCur), abs(muPrev)) && abs(fCur - fPrev) > NumTraits<RealScalar>::epsilon() && !useBisection)
+    {
+      ++m_numIters;
+
+      // Find a and b such that the function f(mu) = a / mu + b matches the current and previous samples.
+      RealScalar a = (fCur - fPrev) / (Literal(1)/muCur - Literal(1)/muPrev);
+      RealScalar b = fCur - a / muCur;
+      // And find mu such that f(mu)==0:
+      RealScalar muZero = -a/b;
+      RealScalar fZero = secularEq(muZero, col0, diag, perm, diagShifted, shift);
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+      eigen_internal_assert((numext::isfinite)(fZero));
+#endif
+
+      muPrev = muCur;
+      fPrev = fCur;
+      muCur = muZero;
+      fCur = fZero;
+
+      // we can test exact equality here, because shift comes from `... ? left : right`
+      if (numext::equal_strict(shift, left)  && (muCur < Literal(0) || muCur > right - left)) useBisection = true;
+      if (numext::equal_strict(shift, right) && (muCur < -(right - left) || muCur > Literal(0))) useBisection = true;
+      if (abs(fCur)>abs(fPrev)) useBisection = true;
+    }
+
+    // fall back on bisection method if rational interpolation did not work
+    if (useBisection)
+    {
+#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
+      std::cout << "useBisection for k = " << k << ", actual_n = " << actual_n << "\n";
+#endif
+      RealScalar leftShifted, rightShifted;
+      // we can test exact equality here, because shift comes from `... ? left : right`
+      if (numext::equal_strict(shift, left))
+      {
+        // to avoid overflow, we must have mu > max(real_min, |z(k)|/sqrt(real_max)),
+        // the factor 2 is to be more conservative
+        leftShifted = numext::maxi<RealScalar>( (std::numeric_limits<RealScalar>::min)(), Literal(2) * abs(col0(k)) / sqrt((std::numeric_limits<RealScalar>::max)()) );
+
+        // check that we did it right:
+        eigen_internal_assert( (numext::isfinite)( (col0(k)/leftShifted)*(col0(k)/(diag(k)+shift+leftShifted)) ) );
+        // I don't understand why the case k==0 would be special there:
+        // if (k == 0) rightShifted = right - left; else
+        rightShifted = (k==actual_n-1) ? right : ((right - left) * RealScalar(0.51)); // theoretically we can take 0.5, but let's be safe
+      }
+      else
+      {
+        leftShifted = -(right - left) * RealScalar(0.51);
+        if(k+1<n)
+          rightShifted = -numext::maxi<RealScalar>( (std::numeric_limits<RealScalar>::min)(), abs(col0(k+1)) / sqrt((std::numeric_limits<RealScalar>::max)()) );
+        else
+          rightShifted = -(std::numeric_limits<RealScalar>::min)();
+      }
+
+      RealScalar fLeft = secularEq(leftShifted, col0, diag, perm, diagShifted, shift);
+      eigen_internal_assert(fLeft<Literal(0));
+
+#if defined EIGEN_BDCSVD_DEBUG_VERBOSE || defined EIGEN_BDCSVD_SANITY_CHECKS || defined EIGEN_INTERNAL_DEBUGGING
+      RealScalar fRight = secularEq(rightShifted, col0, diag, perm, diagShifted, shift);
+#endif
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+      if(!(numext::isfinite)(fLeft))
+        std::cout << "f(" << leftShifted << ") =" << fLeft << " ; " << left << " " << shift << " " << right << "\n";
+      eigen_internal_assert((numext::isfinite)(fLeft));
+
+      if(!(numext::isfinite)(fRight))
+        std::cout << "f(" << rightShifted << ") =" << fRight << " ; " << left << " " << shift << " " << right << "\n";
+      // eigen_internal_assert((numext::isfinite)(fRight));
+#endif
+
+#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
+      if(!(fLeft * fRight<0))
+      {
+        std::cout << "f(leftShifted) using  leftShifted=" << leftShifted << " ;  diagShifted(1:10):" << diagShifted.head(10).transpose()  << "\n ; "
+                  << "left==shift=" << bool(left==shift) << " ; left-shift = " << (left-shift) << "\n";
+        std::cout << "k=" << k << ", " <<  fLeft << " * " << fRight << " == " << fLeft * fRight << "  ;  "
+                  << "[" << left << " .. " << right << "] -> [" << leftShifted << " " << rightShifted << "], shift=" << shift
+                  << " ,  f(right)=" << secularEq(0,     col0, diag, perm, diagShifted, shift)
+                           << " == " << secularEq(right, col0, diag, perm, diag, 0) << " == " << fRight << "\n";
+      }
+#endif
+      eigen_internal_assert(fLeft * fRight < Literal(0));
+
+      if(fLeft<Literal(0))
+      {
+        while (rightShifted - leftShifted > Literal(2) * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(abs(leftShifted), abs(rightShifted)))
+        {
+          RealScalar midShifted = (leftShifted + rightShifted) / Literal(2);
+          fMid = secularEq(midShifted, col0, diag, perm, diagShifted, shift);
+          eigen_internal_assert((numext::isfinite)(fMid));
+
+          if (fLeft * fMid < Literal(0))
+          {
+            rightShifted = midShifted;
+          }
+          else
+          {
+            leftShifted = midShifted;
+            fLeft = fMid;
+          }
+        }
+        muCur = (leftShifted + rightShifted) / Literal(2);
+      }
+      else
+      {
+        // We have a problem as shifting on the left or right give either a positive or negative value
+        // at the middle of [left,right]...
+        // Instead fo abbording or entering an infinite loop,
+        // let's just use the middle as the estimated zero-crossing:
+        muCur = (right - left) * RealScalar(0.5);
+        // we can test exact equality here, because shift comes from `... ? left : right`
+        if(numext::equal_strict(shift, right))
+          muCur = -muCur;
+      }
+    }
+
+    singVals[k] = shift + muCur;
+    shifts[k] = shift;
+    mus[k] = muCur;
+
+#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
+    if(k+1<n)
+      std::cout << "found " << singVals[k] << " == " << shift << " + " << muCur << " from " << diag(k) << " .. "  << diag(k+1) << "\n";
+#endif
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+    eigen_internal_assert(k==0 || singVals[k]>=singVals[k-1]);
+    eigen_internal_assert(singVals[k]>=diag(k));
+#endif
+
+    // perturb singular value slightly if it equals diagonal entry to avoid division by zero later
+    // (deflation is supposed to avoid this from happening)
+    // - this does no seem to be necessary anymore -
+    // if (singVals[k] == left) singVals[k] *= 1 + NumTraits<RealScalar>::epsilon();
+    // if (singVals[k] == right) singVals[k] *= 1 - NumTraits<RealScalar>::epsilon();
+  }
+}
+
+// zhat is perturbation of col0 for which singular vectors can be computed stably (see Section 3.1)
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::perturbCol0(const ArrayRef& col0, const ArrayRef& diag, const IndicesRef& perm,
+                                              const VectorType& singVals, const ArrayRef& shifts, const ArrayRef& mus,
+                                              ArrayRef zhat) {
+  using std::sqrt;
+  Index n = col0.size();
+  Index m = perm.size();
+  if(m==0)
+  {
+    zhat.setZero();
+    return;
+  }
+  Index lastIdx = perm(m-1);
+  // The offset permits to skip deflated entries while computing zhat
+  for (Index k = 0; k < n; ++k)
+  {
+    if (numext::is_exactly_zero(col0(k))) // deflated
+      zhat(k) = Literal(0);
+    else
+    {
+      // see equation (3.6)
+      RealScalar dk = diag(k);
+      RealScalar prod = (singVals(lastIdx) + dk) * (mus(lastIdx) + (shifts(lastIdx) - dk));
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+      if(prod<0) {
+        std::cout << "k = " << k << " ;  z(k)=" << col0(k) << ", diag(k)=" << dk << "\n";
+        std::cout << "prod = " << "(" << singVals(lastIdx) << " + " << dk << ") * (" << mus(lastIdx) << " + (" << shifts(lastIdx) << " - " << dk << "))" << "\n";
+        std::cout << "     = " << singVals(lastIdx) + dk << " * " << mus(lastIdx) + (shifts(lastIdx) - dk) <<  "\n";
+      }
+      eigen_internal_assert(prod>=0);
+#endif
+
+      for(Index l = 0; l<m; ++l)
+      {
+        Index i = perm(l);
+        if(i!=k)
+        {
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+          if(i>=k && (l==0 || l-1>=m))
+          {
+            std::cout << "Error in perturbCol0\n";
+            std::cout << "  " << k << "/" << n << " "  << l << "/" << m << " " << i << "/" << n << " ; " << col0(k) << " " << diag(k) << " "  <<  "\n";
+            std::cout << "  " <<diag(i) << "\n";
+            Index j = (i<k /*|| l==0*/) ? i : perm(l-1);
+            std::cout << "  " << "j=" << j << "\n";
+          }
+#endif
+          // Avoid index out of bounds.
+          // Will end up setting zhat(k) = 0.
+          if (i >= k && l == 0) {
+            m_info = NumericalIssue;
+            prod = 0;
+            break;
+          }
+          Index j = i<k ? i : l > 0 ? perm(l-1) : i;
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+          if(!(dk!=Literal(0) || diag(i)!=Literal(0)))
+          {
+            std::cout << "k=" << k << ", i=" << i << ", l=" << l << ", perm.size()=" << perm.size() << "\n";
+          }
+          eigen_internal_assert(dk!=Literal(0) || diag(i)!=Literal(0));
+#endif
+          prod *= ((singVals(j)+dk) / ((diag(i)+dk))) * ((mus(j)+(shifts(j)-dk)) / ((diag(i)-dk)));
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+          eigen_internal_assert(prod>=0);
+#endif
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+          if(i!=k && numext::abs(((singVals(j)+dk)*(mus(j)+(shifts(j)-dk)))/((diag(i)+dk)*(diag(i)-dk)) - 1) > 0.9 )
+            std::cout << "     " << ((singVals(j)+dk)*(mus(j)+(shifts(j)-dk)))/((diag(i)+dk)*(diag(i)-dk)) << " == (" << (singVals(j)+dk) << " * " << (mus(j)+(shifts(j)-dk))
+                       << ") / (" << (diag(i)+dk) << " * " << (diag(i)-dk) << ")\n";
+#endif
+        }
+      }
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+      std::cout << "zhat(" << k << ") =  sqrt( " << prod << ")  ;  " << (singVals(lastIdx) + dk) << " * " << mus(lastIdx) + shifts(lastIdx) << " - " << dk << "\n";
+#endif
+      RealScalar tmp = sqrt(prod);
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+      eigen_internal_assert((numext::isfinite)(tmp));
+#endif
+      zhat(k) = col0(k) > Literal(0) ? RealScalar(tmp) : RealScalar(-tmp);
+    }
+  }
+}
+
+// compute singular vectors
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::computeSingVecs(const ArrayRef& zhat, const ArrayRef& diag, const IndicesRef& perm,
+                                                  const VectorType& singVals, const ArrayRef& shifts,
+                                                  const ArrayRef& mus, MatrixXr& U, MatrixXr& V) {
+  Index n = zhat.size();
+  Index m = perm.size();
+
+  for (Index k = 0; k < n; ++k)
+  {
+    if (numext::is_exactly_zero(zhat(k)))
+    {
+      U.col(k) = VectorType::Unit(n+1, k);
+      if (m_compV) V.col(k) = VectorType::Unit(n, k);
+    }
+    else
+    {
+      U.col(k).setZero();
+      for(Index l=0;l<m;++l)
+      {
+        Index i = perm(l);
+        U(i,k) = zhat(i)/(((diag(i) - shifts(k)) - mus(k)) )/( (diag(i) + singVals[k]));
+      }
+      U(n,k) = Literal(0);
+      U.col(k).normalize();
+
+      if (m_compV)
+      {
+        V.col(k).setZero();
+        for(Index l=1;l<m;++l)
+        {
+          Index i = perm(l);
+          V(i,k) = diag(i) * zhat(i) / (((diag(i) - shifts(k)) - mus(k)) )/( (diag(i) + singVals[k]));
+        }
+        V(0,k) = Literal(-1);
+        V.col(k).normalize();
+      }
+    }
+  }
+  U.col(n) = VectorType::Unit(n+1, n);
+}
+
+// page 12_13
+// i >= 1, di almost null and zi non null.
+// We use a rotation to zero out zi applied to the left of M
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::deflation43(Index firstCol, Index shift, Index i,
+                                              Index size) {
+  using std::abs;
+  using std::sqrt;
+  using std::pow;
+  Index start = firstCol + shift;
+  RealScalar c = m_computed(start, start);
+  RealScalar s = m_computed(start+i, start);
+  RealScalar r = numext::hypot(c,s);
+  if (numext::is_exactly_zero(r))
+  {
+    m_computed(start+i, start+i) = Literal(0);
+    return;
+  }
+  m_computed(start,start) = r;
+  m_computed(start+i, start) = Literal(0);
+  m_computed(start+i, start+i) = Literal(0);
+
+  JacobiRotation<RealScalar> J(c/r,-s/r);
+  if (m_compU)  m_naiveU.middleRows(firstCol, size+1).applyOnTheRight(firstCol, firstCol+i, J);
+  else          m_naiveU.applyOnTheRight(firstCol, firstCol+i, J);
+}  // end deflation 43
+
+// page 13
+// i,j >= 1, i!=j and |di - dj| < epsilon * norm2(M)
+// We apply two rotations to have zj = 0;
+// TODO deflation44 is still broken and not properly tested
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::deflation44(Index firstColu, Index firstColm, Index firstRowW,
+                                              Index firstColW, Index i, Index j,
+                                              Index size) {
+  using std::abs;
+  using std::sqrt;
+  using std::conj;
+  using std::pow;
+  RealScalar c = m_computed(firstColm+i, firstColm);
+  RealScalar s = m_computed(firstColm+j, firstColm);
+  RealScalar r = sqrt(numext::abs2(c) + numext::abs2(s));
+#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "deflation 4.4: " << i << "," << j << " -> " << c << " " << s << " " << r << " ; "
+    << m_computed(firstColm + i-1, firstColm)  << " "
+    << m_computed(firstColm + i, firstColm)  << " "
+    << m_computed(firstColm + i+1, firstColm) << " "
+    << m_computed(firstColm + i+2, firstColm) << "\n";
+  std::cout << m_computed(firstColm + i-1, firstColm + i-1)  << " "
+    << m_computed(firstColm + i, firstColm+i)  << " "
+    << m_computed(firstColm + i+1, firstColm+i+1) << " "
+    << m_computed(firstColm + i+2, firstColm+i+2) << "\n";
+#endif
+  if (numext::is_exactly_zero(r))
+  {
+    m_computed(firstColm + i, firstColm + i) = m_computed(firstColm + j, firstColm + j);
+    return;
+  }
+  c/=r;
+  s/=r;
+  m_computed(firstColm + i, firstColm) = r;
+  m_computed(firstColm + j, firstColm + j) = m_computed(firstColm + i, firstColm + i);
+  m_computed(firstColm + j, firstColm) = Literal(0);
+
+  JacobiRotation<RealScalar> J(c,-s);
+  if (m_compU)  m_naiveU.middleRows(firstColu, size+1).applyOnTheRight(firstColu + i, firstColu + j, J);
+  else          m_naiveU.applyOnTheRight(firstColu+i, firstColu+j, J);
+  if (m_compV)  m_naiveV.middleRows(firstRowW, size).applyOnTheRight(firstColW + i, firstColW + j, J);
+}  // end deflation 44
+
+// acts on block from (firstCol+shift, firstCol+shift) to (lastCol+shift, lastCol+shift) [inclusive]
+template <typename MatrixType, int Options>
+void BDCSVD<MatrixType, Options>::deflation(Index firstCol, Index lastCol, Index k,
+                                            Index firstRowW, Index firstColW, Index shift) {
+  using std::sqrt;
+  using std::abs;
+  const Index length = lastCol + 1 - firstCol;
+
+  Block<MatrixXr,Dynamic,1> col0(m_computed, firstCol+shift, firstCol+shift, length, 1);
+  Diagonal<MatrixXr> fulldiag(m_computed);
+  VectorBlock<Diagonal<MatrixXr>,Dynamic> diag(fulldiag, firstCol+shift, length);
+
+  const RealScalar considerZero = (std::numeric_limits<RealScalar>::min)();
+  RealScalar maxDiag = diag.tail((std::max)(Index(1),length-1)).cwiseAbs().maxCoeff();
+  RealScalar epsilon_strict = numext::maxi<RealScalar>(considerZero,NumTraits<RealScalar>::epsilon() * maxDiag);
+  RealScalar epsilon_coarse = Literal(8) * NumTraits<RealScalar>::epsilon() * numext::maxi<RealScalar>(col0.cwiseAbs().maxCoeff(), maxDiag);
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(m_naiveU.allFinite());
+  eigen_internal_assert(m_naiveV.allFinite());
+  eigen_internal_assert(m_computed.allFinite());
+#endif
+
+#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "\ndeflate:" << diag.head(k+1).transpose() << "  |  " << diag.segment(k+1,length-k-1).transpose() << "\n";
+#endif
+
+  //condition 4.1
+  if (diag(0) < epsilon_coarse)
+  {
+#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
+    std::cout << "deflation 4.1, because " << diag(0) << " < " << epsilon_coarse << "\n";
+#endif
+    diag(0) = epsilon_coarse;
+  }
+
+  //condition 4.2
+  for (Index i=1;i<length;++i)
+    if (abs(col0(i)) < epsilon_strict)
+    {
+#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
+      std::cout << "deflation 4.2, set z(" << i << ") to zero because " << abs(col0(i)) << " < " << epsilon_strict << "  (diag(" << i << ")=" << diag(i) << ")\n";
+#endif
+      col0(i) = Literal(0);
+    }
+
+  //condition 4.3
+  for (Index i=1;i<length; i++)
+    if (diag(i) < epsilon_coarse)
+    {
+#ifdef  EIGEN_BDCSVD_DEBUG_VERBOSE
+      std::cout << "deflation 4.3, cancel z(" << i << ")=" << col0(i) << " because diag(" << i << ")=" << diag(i) << " < " << epsilon_coarse << "\n";
+#endif
+      deflation43(firstCol, shift, i, length);
+    }
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(m_naiveU.allFinite());
+  eigen_internal_assert(m_naiveV.allFinite());
+  eigen_internal_assert(m_computed.allFinite());
+#endif
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "to be sorted: " << diag.transpose() << "\n\n";
+  std::cout << "            : " << col0.transpose() << "\n\n";
+#endif
+  {
+    // Check for total deflation:
+    // If we have a total deflation, then we have to consider col0(0)==diag(0) as a singular value during sorting.
+    const bool total_deflation = (col0.tail(length-1).array().abs()<considerZero).all();
+
+    // Sort the diagonal entries, since diag(1:k-1) and diag(k:length) are already sorted, let's do a sorted merge.
+    // First, compute the respective permutation.
+    Index *permutation = m_workspaceI.data();
+    {
+      permutation[0] = 0;
+      Index p = 1;
+
+      // Move deflated diagonal entries at the end.
+      for(Index i=1; i<length; ++i)
+        if(abs(diag(i))<considerZero)
+          permutation[p++] = i;
+
+      Index i=1, j=k+1;
+      for( ; p < length; ++p)
+      {
+             if (i > k)             permutation[p] = j++;
+        else if (j >= length)       permutation[p] = i++;
+        else if (diag(i) < diag(j)) permutation[p] = j++;
+        else                        permutation[p] = i++;
+      }
+    }
+
+    // If we have a total deflation, then we have to insert diag(0) at the right place
+    if(total_deflation)
+    {
+      for(Index i=1; i<length; ++i)
+      {
+        Index pi = permutation[i];
+        if(abs(diag(pi))<considerZero || diag(0)<diag(pi))
+          permutation[i-1] = permutation[i];
+        else
+        {
+          permutation[i-1] = 0;
+          break;
+        }
+      }
+    }
+
+    // Current index of each col, and current column of each index
+    Index *realInd = m_workspaceI.data()+length;
+    Index *realCol = m_workspaceI.data()+2*length;
+
+    for(int pos = 0; pos< length; pos++)
+    {
+      realCol[pos] = pos;
+      realInd[pos] = pos;
+    }
+
+    for(Index i = total_deflation?0:1; i < length; i++)
+    {
+      const Index pi = permutation[length - (total_deflation ? i+1 : i)];
+      const Index J = realCol[pi];
+
+      using std::swap;
+      // swap diagonal and first column entries:
+      swap(diag(i), diag(J));
+      if(i!=0 && J!=0) swap(col0(i), col0(J));
+
+      // change columns
+      if (m_compU) m_naiveU.col(firstCol+i).segment(firstCol, length + 1).swap(m_naiveU.col(firstCol+J).segment(firstCol, length + 1));
+      else         m_naiveU.col(firstCol+i).segment(0, 2)                .swap(m_naiveU.col(firstCol+J).segment(0, 2));
+      if (m_compV) m_naiveV.col(firstColW + i).segment(firstRowW, length).swap(m_naiveV.col(firstColW + J).segment(firstRowW, length));
+
+      //update real pos
+      const Index realI = realInd[i];
+      realCol[realI] = J;
+      realCol[pi] = i;
+      realInd[J] = realI;
+      realInd[i] = pi;
+    }
+  }
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+  std::cout << "sorted: " << diag.transpose().format(bdcsvdfmt) << "\n";
+  std::cout << "      : " << col0.transpose() << "\n\n";
+#endif
+
+  //condition 4.4
+  {
+    Index i = length-1;
+    while(i>0 && (abs(diag(i))<considerZero || abs(col0(i))<considerZero)) --i;
+    for(; i>1;--i)
+       if( (diag(i) - diag(i-1)) < NumTraits<RealScalar>::epsilon()*maxDiag )
+      {
+#ifdef EIGEN_BDCSVD_DEBUG_VERBOSE
+        std::cout << "deflation 4.4 with i = " << i << " because " << diag(i) << " - " << diag(i-1) << " == " << (diag(i) - diag(i-1)) << " < " << NumTraits<RealScalar>::epsilon()*/*diag(i)*/maxDiag << "\n";
+#endif
+        eigen_internal_assert(abs(diag(i) - diag(i-1))<epsilon_coarse && " diagonal entries are not properly sorted");
+        deflation44(firstCol, firstCol + shift, firstRowW, firstColW, i-1, i, length);
+      }
+  }
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  for(Index j=2;j<length;++j)
+    eigen_internal_assert(diag(j-1)<=diag(j) || abs(diag(j))<considerZero);
+#endif
+
+#ifdef EIGEN_BDCSVD_SANITY_CHECKS
+  eigen_internal_assert(m_naiveU.allFinite());
+  eigen_internal_assert(m_naiveV.allFinite());
+  eigen_internal_assert(m_computed.allFinite());
+#endif
+}  // end deflation
+
+/** \svd_module
+  *
+  * \return the singular value decomposition of \c *this computed by Divide & Conquer algorithm
+  *
+  * \sa class BDCSVD
+  */
+template <typename Derived>
+template <int Options>
+BDCSVD<typename MatrixBase<Derived>::PlainObject, Options> MatrixBase<Derived>::bdcSvd() const {
+  return BDCSVD<PlainObject, Options>(*this);
+}
+
+/** \svd_module
+ *
+ * \return the singular value decomposition of \c *this computed by Divide & Conquer algorithm
+ *
+ * \sa class BDCSVD
+ */
+template <typename Derived>
+template <int Options>
+BDCSVD<typename MatrixBase<Derived>::PlainObject, Options> MatrixBase<Derived>::bdcSvd(
+    unsigned int computationOptions) const {
+  return BDCSVD<PlainObject, Options>(*this, computationOptions);
+}
+
+} // end namespace Eigen
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/BDCSVD_LAPACKE.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/BDCSVD_LAPACKE.h
new file mode 100644
index 0000000000000000000000000000000000000000..9a1e8437d68ad26e1ee407996b34fe52d2a69313
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/BDCSVD_LAPACKE.h
@@ -0,0 +1,163 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2022 Melven Roehrig-Zoellner <Melven.Roehrig-Zoellner@DLR.de>
+// Copyright (c) 2011, Intel Corporation. All rights reserved.
+//
+// This file is based on the JacobiSVD_LAPACKE.h originally from Intel -
+// see license notice below:
+/*
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *    Singular Value Decomposition - SVD (divide and conquer variant)
+ ********************************************************************************
+*/
+#ifndef EIGEN_BDCSVD_LAPACKE_H
+#define EIGEN_BDCSVD_LAPACKE_H
+
+namespace Eigen {
+
+namespace internal {
+
+namespace lapacke_helpers {
+
+/** \internal Specialization for the data types supported by LAPACKe */
+
+// defining a derived class to allow access to protected members
+template <typename MatrixType_, int Options>
+class BDCSVD_LAPACKE : public BDCSVD<MatrixType_, Options> {
+  typedef BDCSVD<MatrixType_, Options> SVD;
+  typedef typename SVD::MatrixType MatrixType;
+  typedef typename SVD::Scalar Scalar;
+  typedef typename SVD::RealScalar RealScalar;
+
+public:
+  // construct this by moving from a parent object
+  BDCSVD_LAPACKE(SVD&& svd) : SVD(std::move(svd)) {}
+
+  void compute_impl_lapacke(const MatrixType& matrix, unsigned int computationOptions) {
+
+    SVD::allocate(matrix.rows(), matrix.cols(), computationOptions);
+
+    SVD::m_nonzeroSingularValues = SVD::m_diagSize;
+
+    // prepare arguments to ?gesdd
+    const lapack_int matrix_order = lapack_storage_of(matrix);
+    const char jobz  = (SVD::m_computeFullU || SVD::m_computeFullV) ? 'A' : (SVD::m_computeThinU || SVD::m_computeThinV) ? 'S' : 'N';
+    const lapack_int u_cols = (jobz == 'A') ? to_lapack(SVD::rows()) : (jobz == 'S') ? to_lapack(SVD::diagSize()) : 1;
+    const lapack_int vt_rows = (jobz == 'A') ? to_lapack(SVD::cols()) : (jobz == 'S') ? to_lapack(SVD::diagSize()) : 1;
+    lapack_int ldu, ldvt;
+    Scalar *u, *vt, dummy;
+    MatrixType localU;
+    if (SVD::computeU() && !(SVD::m_computeThinU && SVD::m_computeFullV) ) {
+      ldu  = to_lapack(SVD::m_matrixU.outerStride());
+      u    = SVD::m_matrixU.data();
+    } else if (SVD::computeV()) {
+      localU.resize(SVD::rows(), u_cols);
+      ldu  = to_lapack(localU.outerStride());
+      u    = localU.data();
+    } else { ldu=1; u=&dummy; }
+    MatrixType localV;
+    if (SVD::computeU() || SVD::computeV()) {
+      localV.resize(vt_rows, SVD::cols());
+      ldvt  = to_lapack(localV.outerStride());
+      vt   = localV.data();
+    } else { ldvt=1; vt=&dummy; }
+    MatrixType temp; temp = matrix;
+
+    // actual call to ?gesdd
+    lapack_int info = gesdd( matrix_order, jobz, to_lapack(SVD::rows()), to_lapack(SVD::cols()),
+                             to_lapack(temp.data()), to_lapack(temp.outerStride()), (RealScalar*)SVD::m_singularValues.data(),
+                             to_lapack(u), ldu, to_lapack(vt), ldvt);
+
+    // Check the result of the LAPACK call
+    if (info < 0 || !SVD::m_singularValues.allFinite()) {
+      // this includes info == -4 => NaN entry in A
+      SVD::m_info = InvalidInput;
+    } else if (info > 0 ) {
+      SVD::m_info = NoConvergence;
+    } else {
+      SVD::m_info = Success;
+      if (SVD::m_computeThinU && SVD::m_computeFullV) {
+        SVD::m_matrixU = localU.leftCols(SVD::m_matrixU.cols());
+      }
+      if (SVD::computeV()) {
+        SVD::m_matrixV = localV.adjoint().leftCols(SVD::m_matrixV.cols());
+      }
+    }
+    SVD::m_isInitialized = true;
+  }
+};
+
+template<typename MatrixType_, int Options>
+BDCSVD<MatrixType_, Options>& BDCSVD_wrapper(BDCSVD<MatrixType_, Options>& svd, const MatrixType_& matrix, int computationOptions)
+{
+  // we need to move to the wrapper type and back
+  BDCSVD_LAPACKE<MatrixType_, Options> tmpSvd(std::move(svd));
+  tmpSvd.compute_impl_lapacke(matrix, computationOptions);
+  svd = std::move(tmpSvd);
+  return svd;
+}
+
+} // end namespace lapacke_helpers
+
+} // end namespace internal
+
+#define EIGEN_LAPACKE_SDD(EIGTYPE, EIGCOLROW, OPTIONS) \
+template<> inline \
+BDCSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>& \
+BDCSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>::compute_impl(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix, unsigned int computationOptions) {\
+  return internal::lapacke_helpers::BDCSVD_wrapper(*this, matrix, computationOptions); \
+}
+
+#define EIGEN_LAPACK_SDD_OPTIONS(OPTIONS) \
+  EIGEN_LAPACKE_SDD(double,   ColMajor, OPTIONS) \
+  EIGEN_LAPACKE_SDD(float,    ColMajor, OPTIONS) \
+  EIGEN_LAPACKE_SDD(dcomplex, ColMajor, OPTIONS) \
+  EIGEN_LAPACKE_SDD(scomplex, ColMajor, OPTIONS) \
+\
+  EIGEN_LAPACKE_SDD(double,   RowMajor, OPTIONS) \
+  EIGEN_LAPACKE_SDD(float,    RowMajor, OPTIONS) \
+  EIGEN_LAPACKE_SDD(dcomplex, RowMajor, OPTIONS) \
+  EIGEN_LAPACKE_SDD(scomplex, RowMajor, OPTIONS)
+
+EIGEN_LAPACK_SDD_OPTIONS(0)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeThinU)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeThinV)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeFullU)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeFullV)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeThinU | ComputeThinV)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeFullU | ComputeFullV)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeThinU | ComputeFullV)
+EIGEN_LAPACK_SDD_OPTIONS(ComputeFullU | ComputeThinV)
+
+#undef EIGEN_LAPACK_SDD_OPTIONS
+
+#undef EIGEN_LAPACKE_SDD
+
+} // end namespace Eigen
+
+#endif // EIGEN_BDCSVD_LAPACKE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..fa67b96acd4772639b6f900d08e018ca72055b9a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SVD_MODULE_H
+#error "Please include Eigen/SVD instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/JacobiSVD.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/JacobiSVD.h
new file mode 100644
index 0000000000000000000000000000000000000000..5fdb3dc543edf9c16fbe97f0e51a40cfc96293bd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/JacobiSVD.h
@@ -0,0 +1,841 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2013-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_JACOBISVD_H
+#define EIGEN_JACOBISVD_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// forward declaration (needed by ICC)
+// the empty body is required by MSVC
+template <typename MatrixType, int Options, bool IsComplex = NumTraits<typename MatrixType::Scalar>::IsComplex>
+struct svd_precondition_2x2_block_to_be_real {};
+
+/*** QR preconditioners (R-SVD)
+ ***
+ *** Their role is to reduce the problem of computing the SVD to the case of a square matrix.
+ *** This approach, known as R-SVD, is an optimization for rectangular-enough matrices, and is a requirement for
+ *** JacobiSVD which by itself is only able to work on square matrices.
+ ***/
+
+enum { PreconditionIfMoreColsThanRows, PreconditionIfMoreRowsThanCols };
+
+template<typename MatrixType, int QRPreconditioner, int Case>
+struct qr_preconditioner_should_do_anything
+{
+  enum { a = MatrixType::RowsAtCompileTime != Dynamic &&
+             MatrixType::ColsAtCompileTime != Dynamic &&
+             MatrixType::ColsAtCompileTime <= MatrixType::RowsAtCompileTime,
+         b = MatrixType::RowsAtCompileTime != Dynamic &&
+             MatrixType::ColsAtCompileTime != Dynamic &&
+             MatrixType::RowsAtCompileTime <= MatrixType::ColsAtCompileTime,
+         ret = !( (QRPreconditioner == NoQRPreconditioner) ||
+                  (Case == PreconditionIfMoreColsThanRows && bool(a)) ||
+                  (Case == PreconditionIfMoreRowsThanCols && bool(b)) )
+  };
+};
+
+template <typename MatrixType, int Options, int QRPreconditioner, int Case,
+          bool DoAnything = qr_preconditioner_should_do_anything<MatrixType, QRPreconditioner, Case>::ret>
+struct qr_preconditioner_impl {};
+
+template <typename MatrixType, int Options, int QRPreconditioner, int Case>
+class qr_preconditioner_impl<MatrixType, Options, QRPreconditioner, Case, false> {
+ public:
+  void allocate(const JacobiSVD<MatrixType, Options>&) {}
+  bool run(JacobiSVD<MatrixType, Options>&, const MatrixType&) { return false; }
+};
+
+/*** preconditioner using FullPivHouseholderQR ***/
+
+template <typename MatrixType, int Options>
+class qr_preconditioner_impl<MatrixType, Options, FullPivHouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols,
+                             true> {
+ public:
+  typedef typename MatrixType::Scalar Scalar;
+  typedef JacobiSVD<MatrixType, Options> SVDType;
+
+  enum { WorkspaceSize = MatrixType::RowsAtCompileTime, MaxWorkspaceSize = MatrixType::MaxRowsAtCompileTime };
+
+  typedef Matrix<Scalar, 1, WorkspaceSize, RowMajor, 1, MaxWorkspaceSize> WorkspaceType;
+
+  void allocate(const SVDType& svd) {
+    if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
+    {
+      internal::destroy_at(&m_qr);
+      internal::construct_at(&m_qr, svd.rows(), svd.cols());
+    }
+    if (svd.m_computeFullU) m_workspace.resize(svd.rows());
+  }
+
+  bool run(SVDType& svd, const MatrixType& matrix) {
+    if(matrix.rows() > matrix.cols())
+    {
+      m_qr.compute(matrix);
+      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>();
+      if(svd.m_computeFullU) m_qr.matrixQ().evalTo(svd.m_matrixU, m_workspace);
+      if(svd.computeV()) svd.m_matrixV = m_qr.colsPermutation();
+      return true;
+    }
+    return false;
+  }
+
+private:
+  typedef FullPivHouseholderQR<MatrixType> QRType;
+  QRType m_qr;
+  WorkspaceType m_workspace;
+};
+
+template <typename MatrixType, int Options>
+class qr_preconditioner_impl<MatrixType, Options, FullPivHouseholderQRPreconditioner, PreconditionIfMoreColsThanRows,
+                             true> {
+ public:
+  typedef typename MatrixType::Scalar Scalar;
+  typedef JacobiSVD<MatrixType, Options> SVDType;
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+    MatrixOptions = MatrixType::Options
+  };
+
+  typedef typename internal::make_proper_matrix_type<Scalar, ColsAtCompileTime, RowsAtCompileTime, MatrixOptions,
+                                                     MaxColsAtCompileTime, MaxRowsAtCompileTime>::type
+      TransposeTypeWithSameStorageOrder;
+
+  void allocate(const SVDType& svd) {
+    if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols())
+    {
+      internal::destroy_at(&m_qr);
+      internal::construct_at(&m_qr, svd.cols(), svd.rows());
+    }
+    m_adjoint.resize(svd.cols(), svd.rows());
+    if (svd.m_computeFullV) m_workspace.resize(svd.cols());
+  }
+
+  bool run(SVDType& svd, const MatrixType& matrix) {
+    if(matrix.cols() > matrix.rows())
+    {
+      m_adjoint = matrix.adjoint();
+      m_qr.compute(m_adjoint);
+      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint();
+      if(svd.m_computeFullV) m_qr.matrixQ().evalTo(svd.m_matrixV, m_workspace);
+      if(svd.computeU()) svd.m_matrixU = m_qr.colsPermutation();
+      return true;
+    }
+    else return false;
+  }
+
+private:
+  typedef FullPivHouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
+  QRType m_qr;
+  TransposeTypeWithSameStorageOrder m_adjoint;
+  typename plain_row_type<MatrixType>::type m_workspace;
+};
+
+/*** preconditioner using ColPivHouseholderQR ***/
+
+template <typename MatrixType, int Options>
+class qr_preconditioner_impl<MatrixType, Options, ColPivHouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols,
+                             true> {
+ public:
+  typedef typename MatrixType::Scalar Scalar;
+  typedef JacobiSVD<MatrixType, Options> SVDType;
+
+  enum {
+    WorkspaceSize = internal::traits<SVDType>::MatrixUColsAtCompileTime,
+    MaxWorkspaceSize = internal::traits<SVDType>::MatrixUMaxColsAtCompileTime
+  };
+
+  typedef Matrix<Scalar, 1, WorkspaceSize, RowMajor, 1, MaxWorkspaceSize> WorkspaceType;
+
+  void allocate(const SVDType& svd) {
+    if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
+    {
+      internal::destroy_at(&m_qr);
+      internal::construct_at(&m_qr, svd.rows(), svd.cols());
+    }
+    if (svd.m_computeFullU) m_workspace.resize(svd.rows());
+    else if (svd.m_computeThinU) m_workspace.resize(svd.cols());
+  }
+
+  bool run(SVDType& svd, const MatrixType& matrix) {
+    if(matrix.rows() > matrix.cols())
+    {
+      m_qr.compute(matrix);
+      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>();
+      if(svd.m_computeFullU) m_qr.householderQ().evalTo(svd.m_matrixU, m_workspace);
+      else if(svd.m_computeThinU)
+      {
+        svd.m_matrixU.setIdentity(matrix.rows(), matrix.cols());
+        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixU, m_workspace);
+      }
+      if(svd.computeV()) svd.m_matrixV = m_qr.colsPermutation();
+      return true;
+    }
+    return false;
+  }
+
+private:
+  typedef ColPivHouseholderQR<MatrixType> QRType;
+  QRType m_qr;
+  WorkspaceType m_workspace;
+};
+
+template <typename MatrixType, int Options>
+class qr_preconditioner_impl<MatrixType, Options, ColPivHouseholderQRPreconditioner, PreconditionIfMoreColsThanRows,
+                             true> {
+ public:
+  typedef typename MatrixType::Scalar Scalar;
+  typedef JacobiSVD<MatrixType, Options> SVDType;
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+    MatrixOptions = MatrixType::Options,
+    WorkspaceSize = internal::traits<SVDType>::MatrixVColsAtCompileTime,
+    MaxWorkspaceSize = internal::traits<SVDType>::MatrixVMaxColsAtCompileTime
+  };
+
+  typedef Matrix<Scalar, WorkspaceSize, 1, ColMajor, MaxWorkspaceSize, 1> WorkspaceType;
+
+  typedef typename internal::make_proper_matrix_type<Scalar, ColsAtCompileTime, RowsAtCompileTime, MatrixOptions,
+                                                     MaxColsAtCompileTime, MaxRowsAtCompileTime>::type
+      TransposeTypeWithSameStorageOrder;
+
+  void allocate(const SVDType& svd) {
+    if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols())
+    {
+      internal::destroy_at(&m_qr);
+      internal::construct_at(&m_qr, svd.cols(), svd.rows());
+    }
+    if (svd.m_computeFullV) m_workspace.resize(svd.cols());
+    else if (svd.m_computeThinV) m_workspace.resize(svd.rows());
+    m_adjoint.resize(svd.cols(), svd.rows());
+  }
+
+  bool run(SVDType& svd, const MatrixType& matrix) {
+    if(matrix.cols() > matrix.rows())
+    {
+      m_adjoint = matrix.adjoint();
+      m_qr.compute(m_adjoint);
+
+      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint();
+      if(svd.m_computeFullV) m_qr.householderQ().evalTo(svd.m_matrixV, m_workspace);
+      else if(svd.m_computeThinV)
+      {
+        svd.m_matrixV.setIdentity(matrix.cols(), matrix.rows());
+        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixV, m_workspace);
+      }
+      if(svd.computeU()) svd.m_matrixU = m_qr.colsPermutation();
+      return true;
+    }
+    else return false;
+  }
+
+private:
+  typedef ColPivHouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
+  QRType m_qr;
+  TransposeTypeWithSameStorageOrder m_adjoint;
+  WorkspaceType m_workspace;
+};
+
+/*** preconditioner using HouseholderQR ***/
+
+template <typename MatrixType, int Options>
+class qr_preconditioner_impl<MatrixType, Options, HouseholderQRPreconditioner, PreconditionIfMoreRowsThanCols, true> {
+ public:
+  typedef typename MatrixType::Scalar Scalar;
+  typedef JacobiSVD<MatrixType, Options> SVDType;
+
+  enum {
+    WorkspaceSize = internal::traits<SVDType>::MatrixUColsAtCompileTime,
+    MaxWorkspaceSize = internal::traits<SVDType>::MatrixUMaxColsAtCompileTime
+  };
+
+  typedef Matrix<Scalar, 1, WorkspaceSize, RowMajor, 1, MaxWorkspaceSize> WorkspaceType;
+
+  void allocate(const SVDType& svd) {
+    if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
+    {
+      internal::destroy_at(&m_qr);
+      internal::construct_at(&m_qr, svd.rows(), svd.cols());
+    }
+    if (svd.m_computeFullU) m_workspace.resize(svd.rows());
+    else if (svd.m_computeThinU) m_workspace.resize(svd.cols());
+  }
+
+  bool run(SVDType& svd, const MatrixType& matrix) {
+    if(matrix.rows() > matrix.cols())
+    {
+      m_qr.compute(matrix);
+      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.cols(),matrix.cols()).template triangularView<Upper>();
+      if(svd.m_computeFullU) m_qr.householderQ().evalTo(svd.m_matrixU, m_workspace);
+      else if(svd.m_computeThinU)
+      {
+        svd.m_matrixU.setIdentity(matrix.rows(), matrix.cols());
+        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixU, m_workspace);
+      }
+      if(svd.computeV()) svd.m_matrixV.setIdentity(matrix.cols(), matrix.cols());
+      return true;
+    }
+    return false;
+  }
+
+private:
+  typedef HouseholderQR<MatrixType> QRType;
+  QRType m_qr;
+  WorkspaceType m_workspace;
+};
+
+template <typename MatrixType, int Options>
+class qr_preconditioner_impl<MatrixType, Options, HouseholderQRPreconditioner, PreconditionIfMoreColsThanRows, true> {
+ public:
+  typedef typename MatrixType::Scalar Scalar;
+  typedef JacobiSVD<MatrixType, Options> SVDType;
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+    MatrixOptions = MatrixType::Options,
+    WorkspaceSize = internal::traits<SVDType>::MatrixVColsAtCompileTime,
+    MaxWorkspaceSize = internal::traits<SVDType>::MatrixVMaxColsAtCompileTime
+  };
+
+  typedef Matrix<Scalar, WorkspaceSize, 1, ColMajor, MaxWorkspaceSize, 1> WorkspaceType;
+
+  typedef typename internal::make_proper_matrix_type<Scalar, ColsAtCompileTime, RowsAtCompileTime, MatrixOptions,
+                                                     MaxColsAtCompileTime, MaxRowsAtCompileTime>::type
+      TransposeTypeWithSameStorageOrder;
+
+  void allocate(const SVDType& svd) {
+    if (svd.cols() != m_qr.rows() || svd.rows() != m_qr.cols())
+    {
+      internal::destroy_at(&m_qr);
+      internal::construct_at(&m_qr, svd.cols(), svd.rows());
+    }
+    if (svd.m_computeFullV) m_workspace.resize(svd.cols());
+    else if (svd.m_computeThinV) m_workspace.resize(svd.rows());
+    m_adjoint.resize(svd.cols(), svd.rows());
+  }
+
+  bool run(SVDType& svd, const MatrixType& matrix) {
+    if(matrix.cols() > matrix.rows())
+    {
+      m_adjoint = matrix.adjoint();
+      m_qr.compute(m_adjoint);
+
+      svd.m_workMatrix = m_qr.matrixQR().block(0,0,matrix.rows(),matrix.rows()).template triangularView<Upper>().adjoint();
+      if(svd.m_computeFullV) m_qr.householderQ().evalTo(svd.m_matrixV, m_workspace);
+      else if(svd.m_computeThinV)
+      {
+        svd.m_matrixV.setIdentity(matrix.cols(), matrix.rows());
+        m_qr.householderQ().applyThisOnTheLeft(svd.m_matrixV, m_workspace);
+      }
+      if(svd.computeU()) svd.m_matrixU.setIdentity(matrix.rows(), matrix.rows());
+      return true;
+    }
+    else return false;
+  }
+
+private:
+  typedef HouseholderQR<TransposeTypeWithSameStorageOrder> QRType;
+  QRType m_qr;
+  TransposeTypeWithSameStorageOrder m_adjoint;
+  WorkspaceType m_workspace;
+};
+
+/*** 2x2 SVD implementation
+ ***
+ *** JacobiSVD consists in performing a series of 2x2 SVD subproblems
+ ***/
+
+template <typename MatrixType, int Options>
+struct svd_precondition_2x2_block_to_be_real<MatrixType, Options, false> {
+  typedef JacobiSVD<MatrixType, Options> SVD;
+  typedef typename MatrixType::RealScalar RealScalar;
+  static bool run(typename SVD::WorkMatrixType&, SVD&, Index, Index, RealScalar&) { return true; }
+};
+
+template <typename MatrixType, int Options>
+struct svd_precondition_2x2_block_to_be_real<MatrixType, Options, true> {
+  typedef JacobiSVD<MatrixType, Options> SVD;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  static bool run(typename SVD::WorkMatrixType& work_matrix, SVD& svd, Index p, Index q, RealScalar& maxDiagEntry)
+  {
+    using std::sqrt;
+    using std::abs;
+    Scalar z;
+    JacobiRotation<Scalar> rot;
+    RealScalar n = sqrt(numext::abs2(work_matrix.coeff(p,p)) + numext::abs2(work_matrix.coeff(q,p)));
+
+    const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
+    const RealScalar precision = NumTraits<Scalar>::epsilon();
+
+    if(numext::is_exactly_zero(n))
+    {
+      // make sure first column is zero
+      work_matrix.coeffRef(p,p) = work_matrix.coeffRef(q,p) = Scalar(0);
+
+      if(abs(numext::imag(work_matrix.coeff(p,q)))>considerAsZero)
+      {
+        // work_matrix.coeff(p,q) can be zero if work_matrix.coeff(q,p) is not zero but small enough to underflow when computing n
+        z = abs(work_matrix.coeff(p,q)) / work_matrix.coeff(p,q);
+        work_matrix.row(p) *= z;
+        if(svd.computeU()) svd.m_matrixU.col(p) *= conj(z);
+      }
+      if(abs(numext::imag(work_matrix.coeff(q,q)))>considerAsZero)
+      {
+        z = abs(work_matrix.coeff(q,q)) / work_matrix.coeff(q,q);
+        work_matrix.row(q) *= z;
+        if(svd.computeU()) svd.m_matrixU.col(q) *= conj(z);
+      }
+      // otherwise the second row is already zero, so we have nothing to do.
+    }
+    else
+    {
+      rot.c() = conj(work_matrix.coeff(p,p)) / n;
+      rot.s() = work_matrix.coeff(q,p) / n;
+      work_matrix.applyOnTheLeft(p,q,rot);
+      if(svd.computeU()) svd.m_matrixU.applyOnTheRight(p,q,rot.adjoint());
+      if(abs(numext::imag(work_matrix.coeff(p,q)))>considerAsZero)
+      {
+        z = abs(work_matrix.coeff(p,q)) / work_matrix.coeff(p,q);
+        work_matrix.col(q) *= z;
+        if(svd.computeV()) svd.m_matrixV.col(q) *= z;
+      }
+      if(abs(numext::imag(work_matrix.coeff(q,q)))>considerAsZero)
+      {
+        z = abs(work_matrix.coeff(q,q)) / work_matrix.coeff(q,q);
+        work_matrix.row(q) *= z;
+        if(svd.computeU()) svd.m_matrixU.col(q) *= conj(z);
+      }
+    }
+
+    // update largest diagonal entry
+    maxDiagEntry = numext::maxi<RealScalar>(maxDiagEntry,numext::maxi<RealScalar>(abs(work_matrix.coeff(p,p)), abs(work_matrix.coeff(q,q))));
+    // and check whether the 2x2 block is already diagonal
+    RealScalar threshold = numext::maxi<RealScalar>(considerAsZero, precision * maxDiagEntry);
+    return abs(work_matrix.coeff(p,q))>threshold || abs(work_matrix.coeff(q,p)) > threshold;
+  }
+};
+
+template <typename MatrixType_, int Options>
+struct traits<JacobiSVD<MatrixType_, Options> > : svd_traits<MatrixType_, Options> {
+  typedef MatrixType_ MatrixType;
+};
+
+} // end namespace internal
+
+/** \ingroup SVD_Module
+ *
+ *
+ * \class JacobiSVD
+ *
+ * \brief Two-sided Jacobi SVD decomposition of a rectangular matrix
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the SVD decomposition
+ * \tparam Options this optional parameter allows one to specify the type of QR decomposition that will be used
+ * internally for the R-SVD step for non-square matrices. Additionally, it allows one to specify whether to compute thin
+ * or full unitaries \a U and \a V. See discussion of possible values below.
+ *
+ * SVD decomposition consists in decomposing any n-by-p matrix \a A as a product
+ *   \f[ A = U S V^* \f]
+ * where \a U is a n-by-n unitary, \a V is a p-by-p unitary, and \a S is a n-by-p real positive matrix which is zero
+ * outside of its main diagonal; the diagonal entries of S are known as the \em singular \em values of \a A and the
+ * columns of \a U and \a V are known as the left and right \em singular \em vectors of \a A respectively.
+ *
+ * Singular values are always sorted in decreasing order.
+ *
+ * This JacobiSVD decomposition computes only the singular values by default. If you want \a U or \a V, you need to ask
+ * for them explicitly.
+ *
+ * You can ask for only \em thin \a U or \a V to be computed, meaning the following. In case of a rectangular n-by-p
+ * matrix, letting \a m be the smaller value among \a n and \a p, there are only \a m singular vectors; the remaining
+ * columns of \a U and \a V do not correspond to actual singular vectors. Asking for \em thin \a U or \a V means asking
+ * for only their \a m first columns to be formed. So \a U is then a n-by-m matrix, and \a V is then a p-by-m matrix.
+ * Notice that thin \a U and \a V are all you need for (least squares) solving.
+ *
+ * Here's an example demonstrating basic usage:
+ * \include JacobiSVD_basic.cpp
+ * Output: \verbinclude JacobiSVD_basic.out
+ *
+ * This JacobiSVD class is a two-sided Jacobi R-SVD decomposition, ensuring optimal reliability and accuracy. The
+ * downside is that it's slower than bidiagonalizing SVD algorithms for large square matrices; however its complexity is
+ * still \f$ O(n^2p) \f$ where \a n is the smaller dimension and \a p is the greater dimension, meaning that it is still
+ * of the same order of complexity as the faster bidiagonalizing R-SVD algorithms. In particular, like any R-SVD, it
+ * takes advantage of non-squareness in that its complexity is only linear in the greater dimension.
+ *
+ * If the input matrix has inf or nan coefficients, the result of the computation is undefined, but the computation is
+ * guaranteed to terminate in finite (and reasonable) time.
+ *
+ * The possible QR preconditioners that can be set with Options template parameter are:
+ * \li ColPivHouseholderQRPreconditioner is the default. In practice it's very safe. It uses column-pivoting QR.
+ * \li FullPivHouseholderQRPreconditioner, is the safest and slowest. It uses full-pivoting QR.
+ *     Contrary to other QRs, it doesn't allow computing thin unitaries.
+ * \li HouseholderQRPreconditioner is the fastest, and less safe and accurate than the pivoting variants. It uses
+ * non-pivoting QR. This is very similar in safety and accuracy to the bidiagonalization process used by bidiagonalizing
+ * SVD algorithms (since bidiagonalization is inherently non-pivoting). However the resulting SVD is still more reliable
+ * than bidiagonalizing SVDs because the Jacobi-based iterarive process is more reliable than the optimized bidiagonal
+ * SVD iterations. \li NoQRPreconditioner allows not to use a QR preconditioner at all. This is useful if you know that
+ * you will only be computing JacobiSVD decompositions of square matrices. Non-square matrices require a QR
+ * preconditioner. Using this option will result in faster compilation and smaller executable code. It won't
+ * significantly speed up computation, since JacobiSVD is always checking if QR preconditioning is needed before
+ * applying it anyway.
+ *
+ * One may also use the Options template parameter to specify how the unitaries should be computed. The options are
+ * #ComputeThinU, #ComputeThinV, #ComputeFullU, #ComputeFullV. It is not possible to request both the thin and full
+ * versions of a unitary. By default, unitaries will not be computed.
+ *
+ * You can set the QRPreconditioner and unitary options together: JacobiSVD<MatrixType,
+ * ColPivHouseholderQRPreconditioner | ComputeThinU | ComputeFullV>
+ *
+ * \sa MatrixBase::jacobiSvd()
+ */
+template <typename MatrixType_, int Options_>
+class JacobiSVD : public SVDBase<JacobiSVD<MatrixType_, Options_> > {
+  typedef SVDBase<JacobiSVD> Base;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename Base::Scalar Scalar;
+  typedef typename Base::RealScalar RealScalar;
+  typedef typename Base::Index Index;
+  enum : int {
+    Options = Options_,
+    QRPreconditioner = internal::get_qr_preconditioner(Options),
+    RowsAtCompileTime = Base::RowsAtCompileTime,
+    ColsAtCompileTime = Base::ColsAtCompileTime,
+    DiagSizeAtCompileTime = Base::DiagSizeAtCompileTime,
+    MaxRowsAtCompileTime = Base::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = Base::MaxColsAtCompileTime,
+    MaxDiagSizeAtCompileTime = Base::MaxDiagSizeAtCompileTime,
+    MatrixOptions = Base::MatrixOptions
+  };
+
+  typedef typename Base::MatrixUType MatrixUType;
+  typedef typename Base::MatrixVType MatrixVType;
+  typedef typename Base::SingularValuesType SingularValuesType;
+  typedef Matrix<Scalar, DiagSizeAtCompileTime, DiagSizeAtCompileTime, MatrixOptions, MaxDiagSizeAtCompileTime,
+                 MaxDiagSizeAtCompileTime>
+      WorkMatrixType;
+
+  /** \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via JacobiSVD::compute(const MatrixType&).
+   */
+  JacobiSVD() {}
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem size and \a Options template parameter.
+   *
+   * \sa JacobiSVD()
+   */
+  JacobiSVD(Index rows, Index cols) { allocate(rows, cols, internal::get_computation_options(Options)); }
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem size.
+   *
+   * One \b cannot request unitaries using both the \a Options template parameter
+   * and the constructor. If possible, prefer using the \a Options template parameter.
+   *
+   * \param computationOptions specify whether to compute Thin/Full unitaries U/V
+   * \sa JacobiSVD()
+   *
+   * \deprecated Will be removed in the next major Eigen version. Options should
+   * be specified in the \a Options template parameter.
+   */
+  EIGEN_DEPRECATED
+  JacobiSVD(Index rows, Index cols, unsigned int computationOptions) {
+    internal::check_svd_options_assertions<MatrixType, Options>(computationOptions, rows, cols);
+    allocate(rows, cols, computationOptions);
+  }
+
+  /** \brief Constructor performing the decomposition of given matrix, using the custom options specified
+   *         with the \a Options template paramter.
+   *
+   * \param matrix the matrix to decompose
+   */
+  explicit JacobiSVD(const MatrixType& matrix) { compute_impl(matrix, internal::get_computation_options(Options)); }
+
+  /** \brief Constructor performing the decomposition of given matrix using specified options
+   *         for computing unitaries.
+   *
+   *  One \b cannot request unitiaries using both the \a Options template parameter
+   *  and the constructor. If possible, prefer using the \a Options template parameter.
+   *
+   * \param matrix the matrix to decompose
+   * \param computationOptions specify whether to compute Thin/Full unitaries U/V
+   *
+   * \deprecated Will be removed in the next major Eigen version. Options should
+   * be specified in the \a Options template parameter.
+   */
+  // EIGEN_DEPRECATED // TODO(cantonios): re-enable after fixing a few 3p libraries that error on deprecation warnings.
+  JacobiSVD(const MatrixType& matrix, unsigned int computationOptions) {
+    internal::check_svd_options_assertions<MatrixType, Options>(computationOptions, matrix.rows(), matrix.cols());
+    compute_impl(matrix, computationOptions);
+  }
+
+  /** \brief Method performing the decomposition of given matrix. Computes Thin/Full unitaries U/V if specified
+   *         using the \a Options template parameter or the class constructor.
+   *
+   * \param matrix the matrix to decompose
+   */
+  JacobiSVD& compute(const MatrixType& matrix) { return compute_impl(matrix, m_computationOptions); }
+
+  /** \brief Method performing the decomposition of given matrix, as specified by
+   *         the `computationOptions` parameter.
+   *
+   * \param matrix the matrix to decompose
+   * \param computationOptions specify whether to compute Thin/Full unitaries U/V
+   * 
+   * \deprecated Will be removed in the next major Eigen version. Options should
+   * be specified in the \a Options template parameter.
+   */
+  EIGEN_DEPRECATED
+  JacobiSVD& compute(const MatrixType& matrix, unsigned int computationOptions) {
+    internal::check_svd_options_assertions<MatrixType, Options>(m_computationOptions, matrix.rows(), matrix.cols());
+    return compute_impl(matrix, computationOptions);
+  }
+
+  using Base::computeU;
+  using Base::computeV;
+  using Base::rows;
+  using Base::cols;
+  using Base::diagSize;
+  using Base::rank;
+
+ private:
+  void allocate(Index rows, Index cols, unsigned int computationOptions);
+  JacobiSVD& compute_impl(const MatrixType& matrix, unsigned int computationOptions);
+
+ protected:
+  using Base::m_computationOptions;
+  using Base::m_computeFullU;
+  using Base::m_computeFullV;
+  using Base::m_computeThinU;
+  using Base::m_computeThinV;
+  using Base::m_info;
+  using Base::m_isAllocated;
+  using Base::m_isInitialized;
+  using Base::m_matrixU;
+  using Base::m_matrixV;
+  using Base::m_nonzeroSingularValues;
+  using Base::m_prescribedThreshold;
+  using Base::m_singularValues;
+  using Base::m_usePrescribedThreshold;
+  using Base::ShouldComputeThinU;
+  using Base::ShouldComputeThinV;
+
+  EIGEN_STATIC_ASSERT(!(ShouldComputeThinU && int(QRPreconditioner) == int(FullPivHouseholderQRPreconditioner)) &&
+                          !(ShouldComputeThinU && int(QRPreconditioner) == int(FullPivHouseholderQRPreconditioner)),
+                      "JacobiSVD: can't compute thin U or thin V with the FullPivHouseholderQR preconditioner. "
+                      "Use the ColPivHouseholderQR preconditioner instead.")
+
+  template <typename MatrixType__, int Options__, bool IsComplex_>
+  friend struct internal::svd_precondition_2x2_block_to_be_real;
+  template <typename MatrixType__, int Options__, int QRPreconditioner_, int Case_, bool DoAnything_>
+  friend struct internal::qr_preconditioner_impl;
+
+  internal::qr_preconditioner_impl<MatrixType, Options, QRPreconditioner, internal::PreconditionIfMoreColsThanRows>
+      m_qr_precond_morecols;
+  internal::qr_preconditioner_impl<MatrixType, Options, QRPreconditioner, internal::PreconditionIfMoreRowsThanCols>
+      m_qr_precond_morerows;
+  WorkMatrixType m_workMatrix;
+  MatrixType m_scaledMatrix;
+};
+
+template <typename MatrixType, int Options>
+void JacobiSVD<MatrixType, Options>::allocate(Index rows_, Index cols_, unsigned int computationOptions_) {
+  if (Base::allocate(rows_, cols_, computationOptions_)) return;
+
+  eigen_assert(!(ShouldComputeThinU && int(QRPreconditioner) == int(FullPivHouseholderQRPreconditioner)) &&
+               !(ShouldComputeThinU && int(QRPreconditioner) == int(FullPivHouseholderQRPreconditioner)) &&
+               "JacobiSVD: can't compute thin U or thin V with the FullPivHouseholderQR preconditioner. "
+               "Use the ColPivHouseholderQR preconditioner instead.");
+
+  m_workMatrix.resize(diagSize(), diagSize());
+  if(cols()>rows())   m_qr_precond_morecols.allocate(*this);
+  if(rows()>cols())   m_qr_precond_morerows.allocate(*this);
+  if(rows()!=cols())  m_scaledMatrix.resize(rows(),cols());
+}
+
+template <typename MatrixType, int Options>
+JacobiSVD<MatrixType, Options>& JacobiSVD<MatrixType, Options>::compute_impl(const MatrixType& matrix,
+                                                                             unsigned int computationOptions) {
+  using std::abs;
+
+  allocate(matrix.rows(), matrix.cols(), computationOptions);
+
+  // currently we stop when we reach precision 2*epsilon as the last bit of precision can require an unreasonable number of iterations,
+  // only worsening the precision of U and V as we accumulate more rotations
+  const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();
+
+  // limit for denormal numbers to be considered zero in order to avoid infinite loops (see bug 286)
+  const RealScalar considerAsZero = (std::numeric_limits<RealScalar>::min)();
+
+  // Scaling factor to reduce over/under-flows
+  RealScalar scale = matrix.cwiseAbs().template maxCoeff<PropagateNaN>();
+  if (!(numext::isfinite)(scale)) {
+    m_isInitialized = true;
+    m_info = InvalidInput;
+    m_nonzeroSingularValues = 0;
+    return *this;
+  }
+  if(numext::is_exactly_zero(scale)) scale = RealScalar(1);
+  
+  /*** step 1. The R-SVD step: we use a QR decomposition to reduce to the case of a square matrix */
+
+  if(rows() != cols())
+  {
+    m_scaledMatrix = matrix / scale;
+    m_qr_precond_morecols.run(*this, m_scaledMatrix);
+    m_qr_precond_morerows.run(*this, m_scaledMatrix);
+  }
+  else
+  {
+    m_workMatrix = matrix.template topLeftCorner<DiagSizeAtCompileTime,DiagSizeAtCompileTime>(diagSize(),diagSize()) / scale;
+    if(m_computeFullU) m_matrixU.setIdentity(rows(),rows());
+    if(m_computeThinU) m_matrixU.setIdentity(rows(),diagSize());
+    if(m_computeFullV) m_matrixV.setIdentity(cols(),cols());
+    if(m_computeThinV) m_matrixV.setIdentity(cols(),diagSize());
+  }
+
+  /*** step 2. The main Jacobi SVD iteration. ***/
+  RealScalar maxDiagEntry = m_workMatrix.cwiseAbs().diagonal().maxCoeff();
+
+  bool finished = false;
+  while(!finished)
+  {
+    finished = true;
+
+    // do a sweep: for all index pairs (p,q), perform SVD of the corresponding 2x2 sub-matrix
+
+    for(Index p = 1; p < diagSize(); ++p)
+    {
+      for(Index q = 0; q < p; ++q)
+      {
+        // if this 2x2 sub-matrix is not diagonal already...
+        // notice that this comparison will evaluate to false if any NaN is involved, ensuring that NaN's don't
+        // keep us iterating forever. Similarly, small denormal numbers are considered zero.
+        RealScalar threshold = numext::maxi<RealScalar>(considerAsZero, precision * maxDiagEntry);
+        if(abs(m_workMatrix.coeff(p,q))>threshold || abs(m_workMatrix.coeff(q,p)) > threshold)
+        {
+          finished = false;
+          // perform SVD decomposition of 2x2 sub-matrix corresponding to indices p,q to make it diagonal
+          // the complex to real operation returns true if the updated 2x2 block is not already diagonal
+          if (internal::svd_precondition_2x2_block_to_be_real<MatrixType, Options>::run(m_workMatrix, *this, p, q,
+                                                                                        maxDiagEntry)) {
+            JacobiRotation<RealScalar> j_left, j_right;
+            internal::real_2x2_jacobi_svd(m_workMatrix, p, q, &j_left, &j_right);
+
+            // accumulate resulting Jacobi rotations
+            m_workMatrix.applyOnTheLeft(p,q,j_left);
+            if(computeU()) m_matrixU.applyOnTheRight(p,q,j_left.transpose());
+
+            m_workMatrix.applyOnTheRight(p,q,j_right);
+            if(computeV()) m_matrixV.applyOnTheRight(p,q,j_right);
+
+            // keep track of the largest diagonal coefficient
+            maxDiagEntry = numext::maxi<RealScalar>(maxDiagEntry,numext::maxi<RealScalar>(abs(m_workMatrix.coeff(p,p)), abs(m_workMatrix.coeff(q,q))));
+          }
+        }
+      }
+    }
+  }
+
+  /*** step 3. The work matrix is now diagonal, so ensure it's positive so its diagonal entries are the singular values ***/
+
+  for(Index i = 0; i < diagSize(); ++i)
+  {
+    // For a complex matrix, some diagonal coefficients might note have been
+    // treated by svd_precondition_2x2_block_to_be_real, and the imaginary part
+    // of some diagonal entry might not be null.
+    if(NumTraits<Scalar>::IsComplex && abs(numext::imag(m_workMatrix.coeff(i,i)))>considerAsZero)
+    {
+      RealScalar a = abs(m_workMatrix.coeff(i,i));
+      m_singularValues.coeffRef(i) = abs(a);
+      if(computeU()) m_matrixU.col(i) *= m_workMatrix.coeff(i,i)/a;
+    }
+    else
+    {
+      // m_workMatrix.coeff(i,i) is already real, no difficulty:
+      RealScalar a = numext::real(m_workMatrix.coeff(i,i));
+      m_singularValues.coeffRef(i) = abs(a);
+      if(computeU() && (a<RealScalar(0))) m_matrixU.col(i) = -m_matrixU.col(i);
+    }
+  }
+  
+  m_singularValues *= scale;
+
+  /*** step 4. Sort singular values in descending order and compute the number of nonzero singular values ***/
+
+  m_nonzeroSingularValues = diagSize();
+  for(Index i = 0; i < diagSize(); i++)
+  {
+    Index pos;
+    RealScalar maxRemainingSingularValue = m_singularValues.tail(diagSize()-i).maxCoeff(&pos);
+    if(numext::is_exactly_zero(maxRemainingSingularValue))
+    {
+      m_nonzeroSingularValues = i;
+      break;
+    }
+    if(pos)
+    {
+      pos += i;
+      std::swap(m_singularValues.coeffRef(i), m_singularValues.coeffRef(pos));
+      if(computeU()) m_matrixU.col(pos).swap(m_matrixU.col(i));
+      if(computeV()) m_matrixV.col(pos).swap(m_matrixV.col(i));
+    }
+  }
+
+  m_isInitialized = true;
+  return *this;
+}
+
+/** \svd_module
+  *
+  * \return the singular value decomposition of \c *this computed by two-sided
+  * Jacobi transformations.
+  *
+  * \sa class JacobiSVD
+  */
+template <typename Derived>
+template <int Options>
+JacobiSVD<typename MatrixBase<Derived>::PlainObject, Options> MatrixBase<Derived>::jacobiSvd() const {
+  return JacobiSVD<PlainObject, Options>(*this);
+}
+
+template <typename Derived>
+template <int Options>
+JacobiSVD<typename MatrixBase<Derived>::PlainObject, Options> MatrixBase<Derived>::jacobiSvd(
+    unsigned int computationOptions) const {
+  return JacobiSVD<PlainObject, Options>(*this, computationOptions);
+}
+
+}  // end namespace Eigen
+
+#endif // EIGEN_JACOBISVD_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/JacobiSVD_LAPACKE.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/JacobiSVD_LAPACKE.h
new file mode 100644
index 0000000000000000000000000000000000000000..11247b199ebdac78c51dd0639be1d47e180f6d81
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/JacobiSVD_LAPACKE.h
@@ -0,0 +1,113 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *    Singular Value Decomposition - SVD.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_JACOBISVD_LAPACKE_H
+#define EIGEN_JACOBISVD_LAPACKE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \internal Specialization for the data types supported by LAPACKe */
+
+#define EIGEN_LAPACKE_SVD(EIGTYPE, LAPACKE_TYPE, LAPACKE_RTYPE, LAPACKE_PREFIX, EIGCOLROW, LAPACKE_COLROW, OPTIONS) \
+template<> inline \
+JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>& \
+JacobiSVD<Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>, OPTIONS>::compute_impl(const Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic>& matrix, \
+                                                                                                 unsigned int computationOptions) \
+{ \
+  typedef Matrix<EIGTYPE, Dynamic, Dynamic, EIGCOLROW, Dynamic, Dynamic> MatrixType; \
+  /*typedef MatrixType::Scalar Scalar;*/ \
+  /*typedef MatrixType::RealScalar RealScalar;*/ \
+  allocate(matrix.rows(), matrix.cols(), computationOptions); \
+\
+  /*const RealScalar precision = RealScalar(2) * NumTraits<Scalar>::epsilon();*/ \
+  m_nonzeroSingularValues = diagSize(); \
+\
+  lapack_int lda = internal::convert_index<lapack_int>(matrix.outerStride()), ldu, ldvt; \
+  lapack_int matrix_order = LAPACKE_COLROW; \
+  char jobu, jobvt; \
+  LAPACKE_TYPE *u, *vt, dummy; \
+  jobu  = (m_computeFullU) ? 'A' : (m_computeThinU) ? 'S' : 'N'; \
+  jobvt = (m_computeFullV) ? 'A' : (m_computeThinV) ? 'S' : 'N'; \
+  if (computeU()) { \
+    ldu  = internal::convert_index<lapack_int>(m_matrixU.outerStride()); \
+    u    = (LAPACKE_TYPE*)m_matrixU.data(); \
+  } else { ldu=1; u=&dummy; }\
+  MatrixType localV; \
+  lapack_int vt_rows = (m_computeFullV) ? internal::convert_index<lapack_int>(cols()) : (m_computeThinV) ? internal::convert_index<lapack_int>(diagSize()) : 1; \
+  if (computeV()) { \
+    localV.resize(vt_rows, cols()); \
+    ldvt  = internal::convert_index<lapack_int>(localV.outerStride()); \
+    vt   = (LAPACKE_TYPE*)localV.data(); \
+  } else { ldvt=1; vt=&dummy; }\
+  Matrix<LAPACKE_RTYPE, Dynamic, Dynamic> superb; superb.resize(diagSize(), 1); \
+  MatrixType m_temp; m_temp = matrix; \
+  lapack_int info = LAPACKE_##LAPACKE_PREFIX##gesvd( matrix_order, jobu, jobvt, internal::convert_index<lapack_int>(rows()), internal::convert_index<lapack_int>(cols()), (LAPACKE_TYPE*)m_temp.data(), lda, (LAPACKE_RTYPE*)m_singularValues.data(), u, ldu, vt, ldvt, superb.data()); \
+  /* Check the result of the LAPACK call */ \
+  if (info < 0 || !m_singularValues.allFinite()) { \
+    m_info = InvalidInput; \
+  } else if (info > 0 ) { \
+    m_info = NoConvergence; \
+  } else { \
+    m_info = Success; \
+    if (computeV()) m_matrixV = localV.adjoint(); \
+  } \
+ /* for(int i=0;i<diagSize();i++) if (m_singularValues.coeffRef(i) < precision) { m_nonzeroSingularValues--; m_singularValues.coeffRef(i)=RealScalar(0);}*/ \
+  m_isInitialized = true; \
+  return *this; \
+}
+
+#define EIGEN_LAPACK_SVD_OPTIONS(OPTIONS) \
+  EIGEN_LAPACKE_SVD(double,   double,                double, d, ColMajor, LAPACK_COL_MAJOR, OPTIONS) \
+  EIGEN_LAPACKE_SVD(float,    float,                 float , s, ColMajor, LAPACK_COL_MAJOR, OPTIONS) \
+  EIGEN_LAPACKE_SVD(dcomplex, lapack_complex_double, double, z, ColMajor, LAPACK_COL_MAJOR, OPTIONS) \
+  EIGEN_LAPACKE_SVD(scomplex, lapack_complex_float,  float , c, ColMajor, LAPACK_COL_MAJOR, OPTIONS) \
+\
+  EIGEN_LAPACKE_SVD(double,   double,                double, d, RowMajor, LAPACK_ROW_MAJOR, OPTIONS) \
+  EIGEN_LAPACKE_SVD(float,    float,                 float , s, RowMajor, LAPACK_ROW_MAJOR, OPTIONS) \
+  EIGEN_LAPACKE_SVD(dcomplex, lapack_complex_double, double, z, RowMajor, LAPACK_ROW_MAJOR, OPTIONS) \
+  EIGEN_LAPACKE_SVD(scomplex, lapack_complex_float,  float , c, RowMajor, LAPACK_ROW_MAJOR, OPTIONS)
+
+EIGEN_LAPACK_SVD_OPTIONS(0)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeThinU)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeThinV)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeFullU)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeFullV)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeThinU | ComputeThinV)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeFullU | ComputeFullV)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeThinU | ComputeFullV)
+EIGEN_LAPACK_SVD_OPTIONS(ComputeFullU | ComputeThinV)
+
+} // end namespace Eigen
+
+#endif // EIGEN_JACOBISVD_LAPACKE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/SVDBase.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/SVDBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..cb018ccffb208a4ad649c9a5078e79146460ae86
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/SVDBase.h
@@ -0,0 +1,451 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// Copyright (C) 2013 Gauthier Brun <brun.gauthier@gmail.com>
+// Copyright (C) 2013 Nicolas Carre <nicolas.carre@ensimag.fr>
+// Copyright (C) 2013 Jean Ceccato <jean.ceccato@ensimag.fr>
+// Copyright (C) 2013 Pierre Zoppitelli <pierre.zoppitelli@ensimag.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SVDBASE_H
+#define EIGEN_SVDBASE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+enum OptionsMasks {
+  QRPreconditionerBits = NoQRPreconditioner | HouseholderQRPreconditioner | ColPivHouseholderQRPreconditioner |
+                         FullPivHouseholderQRPreconditioner,
+  ComputationOptionsBits = ComputeThinU | ComputeFullU | ComputeThinV | ComputeFullV
+};
+
+constexpr int get_qr_preconditioner(int options) { return options & QRPreconditionerBits; }
+
+constexpr int get_computation_options(int options) { return options & ComputationOptionsBits; }
+
+constexpr bool should_svd_compute_thin_u(int options) { return (options & ComputeThinU) != 0; }
+constexpr bool should_svd_compute_full_u(int options) { return (options & ComputeFullU) != 0; }
+constexpr bool should_svd_compute_thin_v(int options) { return (options & ComputeThinV) != 0; }
+constexpr bool should_svd_compute_full_v(int options) { return (options & ComputeFullV) != 0; }
+
+template<typename MatrixType, int Options>
+void check_svd_options_assertions(unsigned int computationOptions, Index rows, Index cols) {
+  EIGEN_STATIC_ASSERT((Options & ComputationOptionsBits) == 0,
+                      "SVDBase: Cannot request U or V using both static and runtime options, even if they match. "
+                      "Requesting unitaries at runtime is DEPRECATED: "
+                      "Prefer requesting unitaries statically, using the Options template parameter.");
+  eigen_assert(!(should_svd_compute_thin_u(computationOptions) && cols < rows && MatrixType::RowsAtCompileTime != Dynamic) &&
+               !(should_svd_compute_thin_v(computationOptions) && rows < cols && MatrixType::ColsAtCompileTime != Dynamic) &&
+               "SVDBase: If thin U is requested at runtime, your matrix must have more rows than columns or a dynamic number of rows."
+               "Similarly, if thin V is requested at runtime, you matrix must have more columns than rows or a dynamic number of columns.");
+  (void)computationOptions;
+  (void)rows;
+  (void)cols;
+}
+
+template<typename Derived> struct traits<SVDBase<Derived> >
+ : traits<Derived>
+{
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef int StorageIndex;
+  enum { Flags = 0 };
+};
+
+template <typename MatrixType, int Options_>
+struct svd_traits : traits<MatrixType> {
+  static constexpr int Options = Options_;
+  static constexpr bool ShouldComputeFullU = internal::should_svd_compute_full_u(Options);
+  static constexpr bool ShouldComputeThinU = internal::should_svd_compute_thin_u(Options);
+  static constexpr bool ShouldComputeFullV = internal::should_svd_compute_full_v(Options);
+  static constexpr bool ShouldComputeThinV = internal::should_svd_compute_thin_v(Options);
+  enum {
+    DiagSizeAtCompileTime =
+        internal::min_size_prefer_dynamic(MatrixType::RowsAtCompileTime, MatrixType::ColsAtCompileTime),
+    MaxDiagSizeAtCompileTime =
+        internal::min_size_prefer_dynamic(MatrixType::MaxRowsAtCompileTime, MatrixType::MaxColsAtCompileTime),
+    MatrixUColsAtCompileTime = ShouldComputeThinU ? DiagSizeAtCompileTime
+                                                  : MatrixType::RowsAtCompileTime,
+    MatrixVColsAtCompileTime = ShouldComputeThinV ? DiagSizeAtCompileTime
+                                                  : MatrixType::ColsAtCompileTime,
+    MatrixUMaxColsAtCompileTime = ShouldComputeThinU ? MaxDiagSizeAtCompileTime
+                                                     : MatrixType::MaxRowsAtCompileTime,
+    MatrixVMaxColsAtCompileTime = ShouldComputeThinV ? MaxDiagSizeAtCompileTime
+                                                     : MatrixType::MaxColsAtCompileTime
+  };
+};
+}
+
+/** \ingroup SVD_Module
+ *
+ *
+ * \class SVDBase
+ *
+ * \brief Base class of SVD algorithms
+ *
+ * \tparam Derived the type of the actual SVD decomposition
+ *
+ * SVD decomposition consists in decomposing any n-by-p matrix \a A as a product
+ *   \f[ A = U S V^* \f]
+ * where \a U is a n-by-n unitary, \a V is a p-by-p unitary, and \a S is a n-by-p real positive matrix which is zero outside of its main diagonal;
+ * the diagonal entries of S are known as the \em singular \em values of \a A and the columns of \a U and \a V are known as the left
+ * and right \em singular \em vectors of \a A respectively.
+ *
+ * Singular values are always sorted in decreasing order.
+ *
+ * 
+ * You can ask for only \em thin \a U or \a V to be computed, meaning the following. In case of a rectangular n-by-p matrix, letting \a m be the
+ * smaller value among \a n and \a p, there are only \a m singular vectors; the remaining columns of \a U and \a V do not correspond to actual
+ * singular vectors. Asking for \em thin \a U or \a V means asking for only their \a m first columns to be formed. So \a U is then a n-by-m matrix,
+ * and \a V is then a p-by-m matrix. Notice that thin \a U and \a V are all you need for (least squares) solving.
+ * 
+ * The status of the computation can be retrieved using the \a info() method. Unless \a info() returns \a Success, the results should be not
+ * considered well defined.
+ *  
+ * If the input matrix has inf or nan coefficients, the result of the computation is undefined, and \a info() will return \a InvalidInput, but the computation is guaranteed to
+ * terminate in finite (and reasonable) time.
+ * \sa class BDCSVD, class JacobiSVD
+ */
+template<typename Derived> class SVDBase
+ : public SolverBase<SVDBase<Derived> >
+{
+public: 
+   
+  template<typename Derived_>
+  friend struct internal::solve_assertion;
+
+  typedef typename internal::traits<Derived>::MatrixType MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
+  typedef typename Eigen::internal::traits<SVDBase>::StorageIndex StorageIndex;
+  typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+
+  static constexpr bool ShouldComputeFullU = internal::traits<Derived>::ShouldComputeFullU;
+  static constexpr bool ShouldComputeThinU = internal::traits<Derived>::ShouldComputeThinU;
+  static constexpr bool ShouldComputeFullV = internal::traits<Derived>::ShouldComputeFullV;
+  static constexpr bool ShouldComputeThinV = internal::traits<Derived>::ShouldComputeThinV;
+
+  enum {
+    RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    DiagSizeAtCompileTime = internal::min_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime),
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+    MaxDiagSizeAtCompileTime = internal::min_size_prefer_fixed(MaxRowsAtCompileTime, MaxColsAtCompileTime),
+    MatrixOptions = MatrixType::Options,
+    MatrixUColsAtCompileTime = internal::traits<Derived>::MatrixUColsAtCompileTime,
+    MatrixVColsAtCompileTime = internal::traits<Derived>::MatrixVColsAtCompileTime,
+    MatrixUMaxColsAtCompileTime = internal::traits<Derived>::MatrixUMaxColsAtCompileTime,
+    MatrixVMaxColsAtCompileTime = internal::traits<Derived>::MatrixVMaxColsAtCompileTime
+  };
+
+  EIGEN_STATIC_ASSERT(!(ShouldComputeFullU && ShouldComputeThinU), "SVDBase: Cannot request both full and thin U")
+  EIGEN_STATIC_ASSERT(!(ShouldComputeFullV && ShouldComputeThinV), "SVDBase: Cannot request both full and thin V")
+
+  typedef
+      typename internal::make_proper_matrix_type<Scalar, RowsAtCompileTime, MatrixUColsAtCompileTime, MatrixOptions,
+                                                 MaxRowsAtCompileTime, MatrixUMaxColsAtCompileTime>::type MatrixUType;
+  typedef
+      typename internal::make_proper_matrix_type<Scalar, ColsAtCompileTime, MatrixVColsAtCompileTime, MatrixOptions,
+                                                 MaxColsAtCompileTime, MatrixVMaxColsAtCompileTime>::type MatrixVType;
+
+  typedef typename internal::plain_diag_type<MatrixType, RealScalar>::type SingularValuesType;
+
+  Derived& derived() { return *static_cast<Derived*>(this); }
+  const Derived& derived() const { return *static_cast<const Derived*>(this); }
+
+  /** \returns the \a U matrix.
+   *
+   * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
+   * the U matrix is n-by-n if you asked for \link Eigen::ComputeFullU ComputeFullU \endlink, and is n-by-m if you asked for \link Eigen::ComputeThinU ComputeThinU \endlink.
+   *
+   * The \a m first columns of \a U are the left singular vectors of the matrix being decomposed.
+   *
+   * This method asserts that you asked for \a U to be computed.
+   */
+  const MatrixUType& matrixU() const
+  {
+    _check_compute_assertions();
+    eigen_assert(computeU() && "This SVD decomposition didn't compute U. Did you ask for it?");
+    return m_matrixU;
+  }
+
+  /** \returns the \a V matrix.
+   *
+   * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p,
+   * the V matrix is p-by-p if you asked for \link Eigen::ComputeFullV ComputeFullV \endlink, and is p-by-m if you asked for \link Eigen::ComputeThinV ComputeThinV \endlink.
+   *
+   * The \a m first columns of \a V are the right singular vectors of the matrix being decomposed.
+   *
+   * This method asserts that you asked for \a V to be computed.
+   */
+  const MatrixVType& matrixV() const
+  {
+    _check_compute_assertions();
+    eigen_assert(computeV() && "This SVD decomposition didn't compute V. Did you ask for it?");
+    return m_matrixV;
+  }
+
+  /** \returns the vector of singular values.
+   *
+   * For the SVD decomposition of a n-by-p matrix, letting \a m be the minimum of \a n and \a p, the
+   * returned vector has size \a m.  Singular values are always sorted in decreasing order.
+   */
+  const SingularValuesType& singularValues() const
+  {
+    _check_compute_assertions();
+    return m_singularValues;
+  }
+
+  /** \returns the number of singular values that are not exactly 0 */
+  Index nonzeroSingularValues() const
+  {
+    _check_compute_assertions();
+    return m_nonzeroSingularValues;
+  }
+  
+  /** \returns the rank of the matrix of which \c *this is the SVD.
+    *
+    * \note This method has to determine which singular values should be considered nonzero.
+    *       For that, it uses the threshold value that you can control by calling
+    *       setThreshold(const RealScalar&).
+    */
+  inline Index rank() const
+  {
+    using std::abs;
+    _check_compute_assertions();
+    if(m_singularValues.size()==0) return 0;
+    RealScalar premultiplied_threshold = numext::maxi<RealScalar>(m_singularValues.coeff(0) * threshold(), (std::numeric_limits<RealScalar>::min)());
+    Index i = m_nonzeroSingularValues-1;
+    while(i>=0 && m_singularValues.coeff(i) < premultiplied_threshold) --i;
+    return i+1;
+  }
+  
+  /** Allows to prescribe a threshold to be used by certain methods, such as rank() and solve(),
+    * which need to determine when singular values are to be considered nonzero.
+    * This is not used for the SVD decomposition itself.
+    *
+    * When it needs to get the threshold value, Eigen calls threshold().
+    * The default is \c NumTraits<Scalar>::epsilon()
+    *
+    * \param threshold The new value to use as the threshold.
+    *
+    * A singular value will be considered nonzero if its value is strictly greater than
+    *  \f$ \vert singular value \vert \leqslant threshold \times \vert max singular value \vert \f$.
+    *
+    * If you want to come back to the default behavior, call setThreshold(Default_t)
+    */
+  Derived& setThreshold(const RealScalar& threshold)
+  {
+    m_usePrescribedThreshold = true;
+    m_prescribedThreshold = threshold;
+    return derived();
+  }
+
+  /** Allows to come back to the default behavior, letting Eigen use its default formula for
+    * determining the threshold.
+    *
+    * You should pass the special object Eigen::Default as parameter here.
+    * \code svd.setThreshold(Eigen::Default); \endcode
+    *
+    * See the documentation of setThreshold(const RealScalar&).
+    */
+  Derived& setThreshold(Default_t)
+  {
+    m_usePrescribedThreshold = false;
+    return derived();
+  }
+
+  /** Returns the threshold that will be used by certain methods such as rank().
+    *
+    * See the documentation of setThreshold(const RealScalar&).
+    */
+  RealScalar threshold() const
+  {
+    eigen_assert(m_isInitialized || m_usePrescribedThreshold);
+    // this temporary is needed to workaround a MSVC issue
+    Index diagSize = (std::max<Index>)(1,m_diagSize);
+    return m_usePrescribedThreshold ? m_prescribedThreshold
+                                    : RealScalar(diagSize)*NumTraits<Scalar>::epsilon();
+  }
+
+  /** \returns true if \a U (full or thin) is asked for in this SVD decomposition */
+  inline bool computeU() const { return m_computeFullU || m_computeThinU; }
+  /** \returns true if \a V (full or thin) is asked for in this SVD decomposition */
+  inline bool computeV() const { return m_computeFullV || m_computeThinV; }
+
+  inline Index rows() const { return m_rows.value(); }
+  inline Index cols() const { return m_cols.value(); }
+  inline Index diagSize() const { return m_diagSize.value(); }
+  
+  #ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** \returns a (least squares) solution of \f$ A x = b \f$ using the current SVD decomposition of A.
+    *
+    * \param b the right-hand-side of the equation to solve.
+    *
+    * \note Solving requires both U and V to be computed. Thin U and V are enough, there is no need for full U or V.
+    *
+    * \note SVD solving is implicitly least-squares. Thus, this method serves both purposes of exact solving and least-squares solving.
+    * In other words, the returned solution is guaranteed to minimize the Euclidean norm \f$ \Vert A x - b \Vert \f$.
+    */
+  template<typename Rhs>
+  inline const Solve<Derived, Rhs>
+  solve(const MatrixBase<Rhs>& b) const;
+  #endif
+
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful.
+   */
+  EIGEN_DEVICE_FUNC
+  ComputationInfo info() const
+  {
+    eigen_assert(m_isInitialized && "SVD is not initialized.");
+    return m_info;
+  }
+
+  #ifndef EIGEN_PARSED_BY_DOXYGEN
+  template<typename RhsType, typename DstType>
+  void _solve_impl(const RhsType &rhs, DstType &dst) const;
+
+  template<bool Conjugate, typename RhsType, typename DstType>
+  void _solve_impl_transposed(const RhsType &rhs, DstType &dst) const;
+  #endif
+
+protected:
+
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  void _check_compute_assertions() const {
+    eigen_assert(m_isInitialized && "SVD is not initialized.");
+  }
+
+  template<bool Transpose_, typename Rhs>
+  void _check_solve_assertion(const Rhs& b) const {
+      EIGEN_ONLY_USED_FOR_DEBUG(b);
+      _check_compute_assertions();
+      eigen_assert(computeU() && computeV() && "SVDBase::solve(): Both unitaries U and V are required to be computed (thin unitaries suffice).");
+      eigen_assert((Transpose_?cols():rows())==b.rows() && "SVDBase::solve(): invalid number of rows of the right hand side matrix b");
+  }
+
+  // return true if already allocated
+  bool allocate(Index rows, Index cols, unsigned int computationOptions);
+
+  MatrixUType m_matrixU;
+  MatrixVType m_matrixV;
+  SingularValuesType m_singularValues;
+  ComputationInfo m_info;
+  bool m_isInitialized, m_isAllocated, m_usePrescribedThreshold;
+  bool m_computeFullU, m_computeThinU;
+  bool m_computeFullV, m_computeThinV;
+  unsigned int m_computationOptions;
+  Index m_nonzeroSingularValues;
+  internal::variable_if_dynamic<Index, RowsAtCompileTime> m_rows;
+  internal::variable_if_dynamic<Index, ColsAtCompileTime> m_cols;
+  internal::variable_if_dynamic<Index, DiagSizeAtCompileTime> m_diagSize;
+  RealScalar m_prescribedThreshold;
+
+  /** \brief Default Constructor.
+   *
+   * Default constructor of SVDBase
+   */
+  SVDBase()
+      : m_matrixU(MatrixUType()),
+        m_matrixV(MatrixVType()),
+        m_singularValues(SingularValuesType()),
+        m_info(Success),
+        m_isInitialized(false),
+        m_isAllocated(false),
+        m_usePrescribedThreshold(false),
+        m_computeFullU(false),
+        m_computeThinU(false),
+        m_computeFullV(false),
+        m_computeThinV(false),
+        m_computationOptions(0),
+        m_nonzeroSingularValues(0),
+        m_rows(RowsAtCompileTime),
+        m_cols(ColsAtCompileTime),
+        m_diagSize(DiagSizeAtCompileTime),
+        m_prescribedThreshold(0) {}
+};
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename Derived>
+template<typename RhsType, typename DstType>
+void SVDBase<Derived>::_solve_impl(const RhsType &rhs, DstType &dst) const
+{
+  // A = U S V^*
+  // So A^{-1} = V S^{-1} U^*
+
+  Matrix<typename RhsType::Scalar, Dynamic, RhsType::ColsAtCompileTime, 0, MatrixType::MaxRowsAtCompileTime, RhsType::MaxColsAtCompileTime> tmp;
+  Index l_rank = rank();
+  tmp.noalias() =  m_matrixU.leftCols(l_rank).adjoint() * rhs;
+  tmp = m_singularValues.head(l_rank).asDiagonal().inverse() * tmp;
+  dst = m_matrixV.leftCols(l_rank) * tmp;
+}
+
+template<typename Derived>
+template<bool Conjugate, typename RhsType, typename DstType>
+void SVDBase<Derived>::_solve_impl_transposed(const RhsType &rhs, DstType &dst) const
+{
+  // A = U S V^*
+  // So  A^{-*} = U S^{-1} V^*
+  // And A^{-T} = U_conj S^{-1} V^T
+  Matrix<typename RhsType::Scalar, Dynamic, RhsType::ColsAtCompileTime, 0, MatrixType::MaxRowsAtCompileTime, RhsType::MaxColsAtCompileTime> tmp;
+  Index l_rank = rank();
+
+  tmp.noalias() =  m_matrixV.leftCols(l_rank).transpose().template conjugateIf<Conjugate>() * rhs;
+  tmp = m_singularValues.head(l_rank).asDiagonal().inverse() * tmp;
+  dst = m_matrixU.template conjugateIf<!Conjugate>().leftCols(l_rank) * tmp;
+}
+#endif
+
+template <typename Derived>
+bool SVDBase<Derived>::allocate(Index rows, Index cols, unsigned int computationOptions) {
+  eigen_assert(rows >= 0 && cols >= 0);
+
+  if (m_isAllocated &&
+      rows == m_rows.value() &&
+      cols == m_cols.value() &&
+      computationOptions == m_computationOptions)
+  {
+    return true;
+  }
+
+  m_rows.setValue(rows);
+  m_cols.setValue(cols);
+  m_info = Success;
+  m_isInitialized = false;
+  m_isAllocated = true;
+  m_computationOptions = computationOptions;
+  m_computeFullU = ShouldComputeFullU || internal::should_svd_compute_full_u(computationOptions);
+  m_computeThinU = ShouldComputeThinU || internal::should_svd_compute_thin_u(computationOptions);
+  m_computeFullV = ShouldComputeFullV || internal::should_svd_compute_full_v(computationOptions);
+  m_computeThinV = ShouldComputeThinV || internal::should_svd_compute_thin_v(computationOptions);
+
+  eigen_assert(!(m_computeFullU && m_computeThinU) && "SVDBase: you can't ask for both full and thin U");
+  eigen_assert(!(m_computeFullV && m_computeThinV) && "SVDBase: you can't ask for both full and thin V");
+
+  m_diagSize.setValue(numext::mini(m_rows.value(), m_cols.value()));
+  m_singularValues.resize(m_diagSize.value());
+  if(RowsAtCompileTime==Dynamic)
+    m_matrixU.resize(m_rows.value(), m_computeFullU ? m_rows.value() : m_computeThinU ? m_diagSize.value() : 0);
+  if(ColsAtCompileTime==Dynamic)
+    m_matrixV.resize(m_cols.value(), m_computeFullV ? m_cols.value() : m_computeThinV ? m_diagSize.value() : 0);
+
+  return false;
+}
+
+}// end namespace
+
+#endif // EIGEN_SVDBASE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/UpperBidiagonalization.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/UpperBidiagonalization.h
new file mode 100644
index 0000000000000000000000000000000000000000..b51bee30dde5bcb9d8907337b126803fd9d01789
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SVD/UpperBidiagonalization.h
@@ -0,0 +1,426 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2013-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BIDIAGONALIZATION_H
+#define EIGEN_BIDIAGONALIZATION_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+// UpperBidiagonalization will probably be replaced by a Bidiagonalization class, don't want to make it stable API.
+// At the same time, it's useful to keep for now as it's about the only thing that is testing the BandMatrix class.
+
+template<typename MatrixType_> class UpperBidiagonalization
+{
+  public:
+
+    typedef MatrixType_ MatrixType;
+    enum {
+      RowsAtCompileTime = MatrixType::RowsAtCompileTime,
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      ColsAtCompileTimeMinusOne = internal::decrement_size<ColsAtCompileTime>::ret
+    };
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef Eigen::Index Index; ///< \deprecated since Eigen 3.3
+    typedef Matrix<Scalar, 1, ColsAtCompileTime> RowVectorType;
+    typedef Matrix<Scalar, RowsAtCompileTime, 1> ColVectorType;
+    typedef BandMatrix<RealScalar, ColsAtCompileTime, ColsAtCompileTime, 1, 0, RowMajor> BidiagonalType;
+    typedef Matrix<Scalar, ColsAtCompileTime, 1> DiagVectorType;
+    typedef Matrix<Scalar, ColsAtCompileTimeMinusOne, 1> SuperDiagVectorType;
+    typedef HouseholderSequence<
+              const MatrixType,
+              const internal::remove_all_t<typename Diagonal<const MatrixType,0>::ConjugateReturnType>
+            > HouseholderUSequenceType;
+    typedef HouseholderSequence<
+              const internal::remove_all_t<typename MatrixType::ConjugateReturnType>,
+              Diagonal<const MatrixType,1>,
+              OnTheRight
+            > HouseholderVSequenceType;
+    
+    /**
+    * \brief Default Constructor.
+    *
+    * The default constructor is useful in cases in which the user intends to
+    * perform decompositions via Bidiagonalization::compute(const MatrixType&).
+    */
+    UpperBidiagonalization() : m_householder(), m_bidiagonal(0, 0), m_isInitialized(false) {}
+
+    explicit UpperBidiagonalization(const MatrixType& matrix)
+      : m_householder(matrix.rows(), matrix.cols()),
+        m_bidiagonal(matrix.cols(), matrix.cols()),
+        m_isInitialized(false)
+    {
+      compute(matrix);
+    }
+
+    UpperBidiagonalization(Index rows, Index cols)
+      : m_householder(rows, cols),
+        m_bidiagonal(cols, cols),
+        m_isInitialized(false)
+    {}
+
+    UpperBidiagonalization& compute(const MatrixType& matrix);
+    UpperBidiagonalization& computeUnblocked(const MatrixType& matrix);
+    
+    const MatrixType& householder() const { return m_householder; }
+    const BidiagonalType& bidiagonal() const { return m_bidiagonal; }
+    
+    const HouseholderUSequenceType householderU() const
+    {
+      eigen_assert(m_isInitialized && "UpperBidiagonalization is not initialized.");
+      return HouseholderUSequenceType(m_householder, m_householder.diagonal().conjugate());
+    }
+
+    const HouseholderVSequenceType householderV() // const here gives nasty errors and i'm lazy
+    {
+      eigen_assert(m_isInitialized && "UpperBidiagonalization is not initialized.");
+      return HouseholderVSequenceType(m_householder.conjugate(), m_householder.const_derived().template diagonal<1>())
+             .setLength(m_householder.cols()-1)
+             .setShift(1);
+    }
+    
+  protected:
+    MatrixType m_householder;
+    BidiagonalType m_bidiagonal;
+    bool m_isInitialized;
+};
+
+// Standard upper bidiagonalization without fancy optimizations
+// This version should be faster for small matrix size
+template<typename MatrixType>
+void upperbidiagonalization_inplace_unblocked(MatrixType& mat,
+                                              typename MatrixType::RealScalar *diagonal,
+                                              typename MatrixType::RealScalar *upper_diagonal,
+                                              typename MatrixType::Scalar* tempData = 0)
+{
+  typedef typename MatrixType::Scalar Scalar;
+
+  Index rows = mat.rows();
+  Index cols = mat.cols();
+
+  typedef Matrix<Scalar,Dynamic,1,ColMajor,MatrixType::MaxRowsAtCompileTime,1> TempType;
+  TempType tempVector;
+  if(tempData==0)
+  {
+    tempVector.resize(rows);
+    tempData = tempVector.data();
+  }
+
+  for (Index k = 0; /* breaks at k==cols-1 below */ ; ++k)
+  {
+    Index remainingRows = rows - k;
+    Index remainingCols = cols - k - 1;
+
+    // construct left householder transform in-place in A
+    mat.col(k).tail(remainingRows)
+       .makeHouseholderInPlace(mat.coeffRef(k,k), diagonal[k]);
+    // apply householder transform to remaining part of A on the left
+    mat.bottomRightCorner(remainingRows, remainingCols)
+       .applyHouseholderOnTheLeft(mat.col(k).tail(remainingRows-1), mat.coeff(k,k), tempData);
+
+    if(k == cols-1) break;
+
+    // construct right householder transform in-place in mat
+    mat.row(k).tail(remainingCols)
+       .makeHouseholderInPlace(mat.coeffRef(k,k+1), upper_diagonal[k]);
+    // apply householder transform to remaining part of mat on the left
+    mat.bottomRightCorner(remainingRows-1, remainingCols)
+       .applyHouseholderOnTheRight(mat.row(k).tail(remainingCols-1).adjoint(), mat.coeff(k,k+1), tempData);
+  }
+}
+
+/** \internal
+  * Helper routine for the block reduction to upper bidiagonal form.
+  *
+  * Let's partition the matrix A:
+  * 
+  *      | A00 A01 |
+  *  A = |         |
+  *      | A10 A11 |
+  *
+  * This function reduces to bidiagonal form the left \c rows x \a blockSize vertical panel [A00/A10]
+  * and the \a blockSize x \c cols horizontal panel [A00 A01] of the matrix \a A. The bottom-right block A11
+  * is updated using matrix-matrix products:
+  *   A22 -= V * Y^T - X * U^T
+  * where V and U contains the left and right Householder vectors. U and V are stored in A10, and A01
+  * respectively, and the update matrices X and Y are computed during the reduction.
+  * 
+  */
+template<typename MatrixType>
+void upperbidiagonalization_blocked_helper(MatrixType& A,
+                                           typename MatrixType::RealScalar *diagonal,
+                                           typename MatrixType::RealScalar *upper_diagonal,
+                                           Index bs,
+                                           Ref<Matrix<typename MatrixType::Scalar, Dynamic, Dynamic,
+                                                      traits<MatrixType>::Flags & RowMajorBit> > X,
+                                           Ref<Matrix<typename MatrixType::Scalar, Dynamic, Dynamic,
+                                                      traits<MatrixType>::Flags & RowMajorBit> > Y)
+{
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename NumTraits<RealScalar>::Literal Literal;
+  static constexpr int StorageOrder = (traits<MatrixType>::Flags & RowMajorBit) ? RowMajor : ColMajor;
+  typedef InnerStride<StorageOrder == ColMajor ? 1 : Dynamic> ColInnerStride;
+  typedef InnerStride<StorageOrder == ColMajor ? Dynamic : 1> RowInnerStride;
+  typedef Ref<Matrix<Scalar, Dynamic, 1>, 0, ColInnerStride>    SubColumnType;
+  typedef Ref<Matrix<Scalar, 1, Dynamic>, 0, RowInnerStride>    SubRowType;
+  typedef Ref<Matrix<Scalar, Dynamic, Dynamic, StorageOrder > > SubMatType;
+
+  Index brows = A.rows();
+  Index bcols = A.cols();
+
+  Scalar tau_u, tau_u_prev(0), tau_v;
+
+  for(Index k = 0; k < bs; ++k)
+  {
+    Index remainingRows = brows - k;
+    Index remainingCols = bcols - k - 1;
+
+    SubMatType X_k1( X.block(k,0, remainingRows,k) );
+    SubMatType V_k1( A.block(k,0, remainingRows,k) );
+
+    // 1 - update the k-th column of A
+    SubColumnType v_k = A.col(k).tail(remainingRows);
+          v_k -= V_k1 * Y.row(k).head(k).adjoint();
+    if(k) v_k -= X_k1 * A.col(k).head(k);
+    
+    // 2 - construct left Householder transform in-place
+    v_k.makeHouseholderInPlace(tau_v, diagonal[k]);
+       
+    if(k+1<bcols)
+    {
+      SubMatType Y_k  ( Y.block(k+1,0, remainingCols, k+1) );
+      SubMatType U_k1 ( A.block(0,k+1, k,remainingCols) );
+      
+      // this eases the application of Householder transforAions
+      // A(k,k) will store tau_v later
+      A(k,k) = Scalar(1);
+
+      // 3 - Compute y_k^T = tau_v * ( A^T*v_k - Y_k-1*V_k-1^T*v_k - U_k-1*X_k-1^T*v_k )
+      {
+        SubColumnType y_k( Y.col(k).tail(remainingCols) );
+        
+        // let's use the beginning of column k of Y as a temporary vector
+        SubColumnType tmp( Y.col(k).head(k) );
+        y_k.noalias()  = A.block(k,k+1, remainingRows,remainingCols).adjoint() * v_k; // bottleneck
+        tmp.noalias()  = V_k1.adjoint()  * v_k;
+        y_k.noalias() -= Y_k.leftCols(k) * tmp;
+        tmp.noalias()  = X_k1.adjoint()  * v_k;
+        y_k.noalias() -= U_k1.adjoint()  * tmp;
+        y_k *= numext::conj(tau_v);
+      }
+
+      // 4 - update k-th row of A (it will become u_k)
+      SubRowType u_k( A.row(k).tail(remainingCols) );
+      u_k = u_k.conjugate();
+      {
+        u_k -= Y_k * A.row(k).head(k+1).adjoint();
+        if(k) u_k -= U_k1.adjoint() * X.row(k).head(k).adjoint();
+      }
+
+      // 5 - construct right Householder transform in-place
+      u_k.makeHouseholderInPlace(tau_u, upper_diagonal[k]);
+
+      // this eases the application of Householder transformations
+      // A(k,k+1) will store tau_u later
+      A(k,k+1) = Scalar(1);
+
+      // 6 - Compute x_k = tau_u * ( A*u_k - X_k-1*U_k-1^T*u_k - V_k*Y_k^T*u_k )
+      {
+        SubColumnType x_k ( X.col(k).tail(remainingRows-1) );
+        
+        // let's use the beginning of column k of X as a temporary vectors
+        // note that tmp0 and tmp1 overlaps
+        SubColumnType tmp0 ( X.col(k).head(k) ),
+                      tmp1 ( X.col(k).head(k+1) );
+                    
+        x_k.noalias()   = A.block(k+1,k+1, remainingRows-1,remainingCols) * u_k.transpose(); // bottleneck
+        tmp0.noalias()  = U_k1 * u_k.transpose();
+        x_k.noalias()  -= X_k1.bottomRows(remainingRows-1) * tmp0;
+        tmp1.noalias()  = Y_k.adjoint() * u_k.transpose();
+        x_k.noalias()  -= A.block(k+1,0, remainingRows-1,k+1) * tmp1;
+        x_k *= numext::conj(tau_u);
+        tau_u = numext::conj(tau_u);
+        u_k = u_k.conjugate();
+      }
+
+      if(k>0) A.coeffRef(k-1,k) = tau_u_prev;
+      tau_u_prev = tau_u;
+    }
+    else
+      A.coeffRef(k-1,k) = tau_u_prev;
+
+    A.coeffRef(k,k) = tau_v;
+  }
+  
+  if(bs<bcols)
+    A.coeffRef(bs-1,bs) = tau_u_prev;
+
+  // update A22
+  if(bcols>bs && brows>bs)
+  {
+    SubMatType A11( A.bottomRightCorner(brows-bs,bcols-bs) );
+    SubMatType A10( A.block(bs,0, brows-bs,bs) );
+    SubMatType A01( A.block(0,bs, bs,bcols-bs) );
+    Scalar tmp = A01(bs-1,0);
+    A01(bs-1,0) = Literal(1);
+    A11.noalias() -= A10 * Y.topLeftCorner(bcols,bs).bottomRows(bcols-bs).adjoint();
+    A11.noalias() -= X.topLeftCorner(brows,bs).bottomRows(brows-bs) * A01;
+    A01(bs-1,0) = tmp;
+  }
+}
+
+/** \internal
+  *
+  * Implementation of a block-bidiagonal reduction.
+  * It is based on the following paper:
+  *   The Design of a Parallel Dense Linear Algebra Software Library: Reduction to Hessenberg, Tridiagonal, and Bidiagonal Form.
+  *   by Jaeyoung Choi, Jack J. Dongarra, David W. Walker. (1995)
+  *   section 3.3
+  */
+template<typename MatrixType, typename BidiagType>
+void upperbidiagonalization_inplace_blocked(MatrixType& A, BidiagType& bidiagonal,
+                                            Index maxBlockSize=32,
+                                            typename MatrixType::Scalar* /*tempData*/ = 0)
+{
+  typedef typename MatrixType::Scalar Scalar;
+  typedef Block<MatrixType,Dynamic,Dynamic> BlockType;
+
+  Index rows = A.rows();
+  Index cols = A.cols();
+  Index size = (std::min)(rows, cols);
+
+  // X and Y are work space
+  static constexpr int StorageOrder = (traits<MatrixType>::Flags & RowMajorBit) ? RowMajor : ColMajor;
+  Matrix<Scalar,
+         MatrixType::RowsAtCompileTime,
+         Dynamic,
+         StorageOrder,
+         MatrixType::MaxRowsAtCompileTime> X(rows,maxBlockSize);
+  Matrix<Scalar,
+         MatrixType::ColsAtCompileTime,
+         Dynamic,
+         StorageOrder,
+         MatrixType::MaxColsAtCompileTime> Y(cols,maxBlockSize);
+  Index blockSize = (std::min)(maxBlockSize,size);
+
+  Index k = 0;
+  for(k = 0; k < size; k += blockSize)
+  {
+    Index bs = (std::min)(size-k,blockSize);  // actual size of the block
+    Index brows = rows - k;                   // rows of the block
+    Index bcols = cols - k;                   // columns of the block
+
+    // partition the matrix A:
+    // 
+    //      | A00 A01 A02 |
+    //      |             |
+    // A  = | A10 A11 A12 |
+    //      |             |
+    //      | A20 A21 A22 |
+    //
+    // where A11 is a bs x bs diagonal block,
+    // and let:
+    //      | A11 A12 |
+    //  B = |         |
+    //      | A21 A22 |
+
+    BlockType B = A.block(k,k,brows,bcols);
+    
+    // This stage performs the bidiagonalization of A11, A21, A12, and updating of A22.
+    // Finally, the algorithm continue on the updated A22.
+    //
+    // However, if B is too small, or A22 empty, then let's use an unblocked strategy
+
+    auto upper_diagonal = bidiagonal.template diagonal<1>();
+    typename MatrixType::RealScalar* upper_diagonal_ptr = upper_diagonal.size() > 0 ? &upper_diagonal.coeffRef(k) : nullptr;
+
+    if(k+bs==cols || bcols<48) // somewhat arbitrary threshold
+    {
+      upperbidiagonalization_inplace_unblocked(B,
+                                               &(bidiagonal.template diagonal<0>().coeffRef(k)),
+                                               upper_diagonal_ptr,
+                                               X.data()
+                                              );
+      break; // We're done
+    }
+    else
+    {
+      upperbidiagonalization_blocked_helper<BlockType>( B,
+                                                        &(bidiagonal.template diagonal<0>().coeffRef(k)),
+                                                        upper_diagonal_ptr,
+                                                        bs,
+                                                        X.topLeftCorner(brows,bs),
+                                                        Y.topLeftCorner(bcols,bs)
+                                                      );
+    }
+  }
+}
+
+template<typename MatrixType_>
+UpperBidiagonalization<MatrixType_>& UpperBidiagonalization<MatrixType_>::computeUnblocked(const MatrixType_& matrix)
+{
+  Index rows = matrix.rows();
+  Index cols = matrix.cols();
+  EIGEN_ONLY_USED_FOR_DEBUG(cols);
+
+  eigen_assert(rows >= cols && "UpperBidiagonalization is only for Arices satisfying rows>=cols.");
+
+  m_householder = matrix;
+
+  ColVectorType temp(rows);
+
+  upperbidiagonalization_inplace_unblocked(m_householder,
+                                           &(m_bidiagonal.template diagonal<0>().coeffRef(0)),
+                                           &(m_bidiagonal.template diagonal<1>().coeffRef(0)),
+                                           temp.data());
+
+  m_isInitialized = true;
+  return *this;
+}
+
+template<typename MatrixType_>
+UpperBidiagonalization<MatrixType_>& UpperBidiagonalization<MatrixType_>::compute(const MatrixType_& matrix)
+{
+  Index rows = matrix.rows();
+  Index cols = matrix.cols();
+  EIGEN_ONLY_USED_FOR_DEBUG(rows);
+  EIGEN_ONLY_USED_FOR_DEBUG(cols);
+
+  eigen_assert(rows >= cols && "UpperBidiagonalization is only for Arices satisfying rows>=cols.");
+
+  m_householder = matrix;
+  upperbidiagonalization_inplace_blocked(m_householder, m_bidiagonal);
+            
+  m_isInitialized = true;
+  return *this;
+}
+
+#if 0
+/** \return the Householder QR decomposition of \c *this.
+  *
+  * \sa class Bidiagonalization
+  */
+template<typename Derived>
+const UpperBidiagonalization<typename MatrixBase<Derived>::PlainObject>
+MatrixBase<Derived>::bidiagonalization() const
+{
+  return UpperBidiagonalization<PlainObject>(eval());
+}
+#endif
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_BIDIAGONALIZATION_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCholesky/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCholesky/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..f8d876286fe88052629eeb38aef7cf5463c9e2f0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCholesky/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SPARSECHOLESKY_MODULE_H
+#error "Please include Eigen/SparseCholesky instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCholesky/SimplicialCholesky.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCholesky/SimplicialCholesky.h
new file mode 100644
index 0000000000000000000000000000000000000000..d90ca13746779bd512660f64f98fef01011968fc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCholesky/SimplicialCholesky.h
@@ -0,0 +1,699 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SIMPLICIAL_CHOLESKY_H
+#define EIGEN_SIMPLICIAL_CHOLESKY_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+enum SimplicialCholeskyMode {
+  SimplicialCholeskyLLT,
+  SimplicialCholeskyLDLT
+};
+
+namespace internal {
+  template<typename CholMatrixType, typename InputMatrixType>
+  struct simplicial_cholesky_grab_input {
+    typedef CholMatrixType const * ConstCholMatrixPtr;
+    static void run(const InputMatrixType& input, ConstCholMatrixPtr &pmat, CholMatrixType &tmp)
+    {
+      tmp = input;
+      pmat = &tmp;
+    }
+  };
+  
+  template<typename MatrixType>
+  struct simplicial_cholesky_grab_input<MatrixType,MatrixType> {
+    typedef MatrixType const * ConstMatrixPtr;
+    static void run(const MatrixType& input, ConstMatrixPtr &pmat, MatrixType &/*tmp*/)
+    {
+      pmat = &input;
+    }
+  };
+} // end namespace internal
+
+/** \ingroup SparseCholesky_Module
+  * \brief A base class for direct sparse Cholesky factorizations
+  *
+  * This is a base class for LL^T and LDL^T Cholesky factorizations of sparse matrices that are
+  * selfadjoint and positive definite. These factorizations allow for solving A.X = B where
+  * X and B can be either dense or sparse.
+  * 
+  * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization
+  * such that the factorized matrix is P A P^-1.
+  *
+  * \tparam Derived the type of the derived class, that is the actual factorization type.
+  *
+  */
+template<typename Derived>
+class SimplicialCholeskyBase : public SparseSolverBase<Derived>
+{
+    typedef SparseSolverBase<Derived> Base;
+    using Base::m_isInitialized;
+    
+  public:
+    typedef typename internal::traits<Derived>::MatrixType MatrixType;
+    typedef typename internal::traits<Derived>::OrderingType OrderingType;
+    enum { UpLo = internal::traits<Derived>::UpLo };
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> CholMatrixType;
+    typedef CholMatrixType const * ConstCholMatrixPtr;
+    typedef Matrix<Scalar,Dynamic,1> VectorType;
+    typedef Matrix<StorageIndex,Dynamic,1> VectorI;
+
+    enum {
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+
+  public:
+    
+    using Base::derived;
+
+    /** Default constructor */
+    SimplicialCholeskyBase()
+      : m_info(Success),
+        m_factorizationIsOk(false),
+        m_analysisIsOk(false),
+        m_shiftOffset(0),
+        m_shiftScale(1)
+    {}
+
+    explicit SimplicialCholeskyBase(const MatrixType& matrix)
+      : m_info(Success),
+        m_factorizationIsOk(false),
+        m_analysisIsOk(false),
+        m_shiftOffset(0),
+        m_shiftScale(1)
+    {
+      derived().compute(matrix);
+    }
+
+    ~SimplicialCholeskyBase()
+    {
+    }
+
+    Derived& derived() { return *static_cast<Derived*>(this); }
+    const Derived& derived() const { return *static_cast<const Derived*>(this); }
+    
+    inline Index cols() const { return m_matrix.cols(); }
+    inline Index rows() const { return m_matrix.rows(); }
+    
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful,
+      *          \c NumericalIssue if the matrix.appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+    
+    /** \returns the permutation P
+      * \sa permutationPinv() */
+    const PermutationMatrix<Dynamic,Dynamic,StorageIndex>& permutationP() const
+    { return m_P; }
+    
+    /** \returns the inverse P^-1 of the permutation P
+      * \sa permutationP() */
+    const PermutationMatrix<Dynamic,Dynamic,StorageIndex>& permutationPinv() const
+    { return m_Pinv; }
+
+    /** Sets the shift parameters that will be used to adjust the diagonal coefficients during the numerical factorization.
+      *
+      * During the numerical factorization, the diagonal coefficients are transformed by the following linear model:\n
+      * \c d_ii = \a offset + \a scale * \c d_ii
+      *
+      * The default is the identity transformation with \a offset=0, and \a scale=1.
+      *
+      * \returns a reference to \c *this.
+      */
+    Derived& setShift(const RealScalar& offset, const RealScalar& scale = 1)
+    {
+      m_shiftOffset = offset;
+      m_shiftScale = scale;
+      return derived();
+    }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** \internal */
+    template<typename Stream>
+    void dumpMemory(Stream& s)
+    {
+      int total = 0;
+      s << "  L:        " << ((total+=(m_matrix.cols()+1) * sizeof(int) + m_matrix.nonZeros()*(sizeof(int)+sizeof(Scalar))) >> 20) << "Mb" << "\n";
+      s << "  diag:     " << ((total+=m_diag.size() * sizeof(Scalar)) >> 20) << "Mb" << "\n";
+      s << "  tree:     " << ((total+=m_parent.size() * sizeof(int)) >> 20) << "Mb" << "\n";
+      s << "  nonzeros: " << ((total+=m_nonZerosPerCol.size() * sizeof(int)) >> 20) << "Mb" << "\n";
+      s << "  perm:     " << ((total+=m_P.size() * sizeof(int)) >> 20) << "Mb" << "\n";
+      s << "  perm^-1:  " << ((total+=m_Pinv.size() * sizeof(int)) >> 20) << "Mb" << "\n";
+      s << "  TOTAL:    " << (total>> 20) << "Mb" << "\n";
+    }
+
+    /** \internal */
+    template<typename Rhs,typename Dest>
+    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
+    {
+      eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
+      eigen_assert(m_matrix.rows()==b.rows());
+
+      if(m_info!=Success)
+        return;
+
+      if(m_P.size()>0)
+        dest = m_P * b;
+      else
+        dest = b;
+
+      if(m_matrix.nonZeros()>0) // otherwise L==I
+        derived().matrixL().solveInPlace(dest);
+
+      if(m_diag.size()>0)
+        dest = m_diag.asDiagonal().inverse() * dest;
+
+      if (m_matrix.nonZeros()>0) // otherwise U==I
+        derived().matrixU().solveInPlace(dest);
+
+      if(m_P.size()>0)
+        dest = m_Pinv * dest;
+    }
+    
+    template<typename Rhs,typename Dest>
+    void _solve_impl(const SparseMatrixBase<Rhs> &b, SparseMatrixBase<Dest> &dest) const
+    {
+      internal::solve_sparse_through_dense_panels(derived(), b, dest);
+    }
+
+#endif // EIGEN_PARSED_BY_DOXYGEN
+
+  protected:
+    
+    /** Computes the sparse Cholesky decomposition of \a matrix */
+    template<bool DoLDLT>
+    void compute(const MatrixType& matrix)
+    {
+      eigen_assert(matrix.rows()==matrix.cols());
+      Index size = matrix.cols();
+      CholMatrixType tmp(size,size);
+      ConstCholMatrixPtr pmat;
+      ordering(matrix, pmat, tmp);
+      analyzePattern_preordered(*pmat, DoLDLT);
+      factorize_preordered<DoLDLT>(*pmat);
+    }
+    
+    template<bool DoLDLT>
+    void factorize(const MatrixType& a)
+    {
+      eigen_assert(a.rows()==a.cols());
+      Index size = a.cols();
+      CholMatrixType tmp(size,size);
+      ConstCholMatrixPtr pmat;
+      
+      if(m_P.size() == 0 && (int(UpLo) & int(Upper)) == Upper)
+      {
+        // If there is no ordering, try to directly use the input matrix without any copy
+        internal::simplicial_cholesky_grab_input<CholMatrixType,MatrixType>::run(a, pmat, tmp);
+      }
+      else
+      {
+        tmp.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>().twistedBy(m_P);
+        pmat = &tmp;
+      }
+      
+      factorize_preordered<DoLDLT>(*pmat);
+    }
+
+    template<bool DoLDLT>
+    void factorize_preordered(const CholMatrixType& a);
+
+    void analyzePattern(const MatrixType& a, bool doLDLT)
+    {
+      eigen_assert(a.rows()==a.cols());
+      Index size = a.cols();
+      CholMatrixType tmp(size,size);
+      ConstCholMatrixPtr pmat;
+      ordering(a, pmat, tmp);
+      analyzePattern_preordered(*pmat,doLDLT);
+    }
+    void analyzePattern_preordered(const CholMatrixType& a, bool doLDLT);
+    
+    void ordering(const MatrixType& a, ConstCholMatrixPtr &pmat, CholMatrixType& ap);
+
+    /** keeps off-diagonal entries; drops diagonal entries */
+    struct keep_diag {
+      inline bool operator() (const Index& row, const Index& col, const Scalar&) const
+      {
+        return row!=col;
+      }
+    };
+
+    mutable ComputationInfo m_info;
+    bool m_factorizationIsOk;
+    bool m_analysisIsOk;
+    
+    CholMatrixType m_matrix;
+    VectorType m_diag;                                // the diagonal coefficients (LDLT mode)
+    VectorI m_parent;                                 // elimination tree
+    VectorI m_nonZerosPerCol;
+    PermutationMatrix<Dynamic,Dynamic,StorageIndex> m_P;     // the permutation
+    PermutationMatrix<Dynamic,Dynamic,StorageIndex> m_Pinv;  // the inverse permutation
+
+    RealScalar m_shiftOffset;
+    RealScalar m_shiftScale;
+};
+
+template<typename MatrixType_, int UpLo_ = Lower, typename Ordering_ = AMDOrdering<typename MatrixType_::StorageIndex> > class SimplicialLLT;
+template<typename MatrixType_, int UpLo_ = Lower, typename Ordering_ = AMDOrdering<typename MatrixType_::StorageIndex> > class SimplicialLDLT;
+template<typename MatrixType_, int UpLo_ = Lower, typename Ordering_ = AMDOrdering<typename MatrixType_::StorageIndex> > class SimplicialCholesky;
+
+namespace internal {
+
+template<typename MatrixType_, int UpLo_, typename Ordering_> struct traits<SimplicialLLT<MatrixType_,UpLo_,Ordering_> >
+{
+  typedef MatrixType_ MatrixType;
+  typedef Ordering_ OrderingType;
+  enum { UpLo = UpLo_ };
+  typedef typename MatrixType::Scalar                         Scalar;
+  typedef typename MatrixType::StorageIndex                   StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex>        CholMatrixType;
+  typedef TriangularView<const CholMatrixType, Eigen::Lower>  MatrixL;
+  typedef TriangularView<const typename CholMatrixType::AdjointReturnType, Eigen::Upper>   MatrixU;
+  static inline MatrixL getL(const CholMatrixType& m) { return MatrixL(m); }
+  static inline MatrixU getU(const CholMatrixType& m) { return MatrixU(m.adjoint()); }
+};
+
+template<typename MatrixType_,int UpLo_, typename Ordering_> struct traits<SimplicialLDLT<MatrixType_,UpLo_,Ordering_> >
+{
+  typedef MatrixType_ MatrixType;
+  typedef Ordering_ OrderingType;
+  enum { UpLo = UpLo_ };
+  typedef typename MatrixType::Scalar                             Scalar;
+  typedef typename MatrixType::StorageIndex                       StorageIndex;
+  typedef SparseMatrix<Scalar, ColMajor, StorageIndex>            CholMatrixType;
+  typedef TriangularView<const CholMatrixType, Eigen::UnitLower>  MatrixL;
+  typedef TriangularView<const typename CholMatrixType::AdjointReturnType, Eigen::UnitUpper> MatrixU;
+  static inline MatrixL getL(const CholMatrixType& m) { return MatrixL(m); }
+  static inline MatrixU getU(const CholMatrixType& m) { return MatrixU(m.adjoint()); }
+};
+
+template<typename MatrixType_, int UpLo_, typename Ordering_> struct traits<SimplicialCholesky<MatrixType_,UpLo_,Ordering_> >
+{
+  typedef MatrixType_ MatrixType;
+  typedef Ordering_ OrderingType;
+  enum { UpLo = UpLo_ };
+};
+
+}
+
+/** \ingroup SparseCholesky_Module
+  * \class SimplicialLLT
+  * \brief A direct sparse LLT Cholesky factorizations
+  *
+  * This class provides a LL^T Cholesky factorizations of sparse matrices that are
+  * selfadjoint and positive definite. The factorization allows for solving A.X = B where
+  * X and B can be either dense or sparse.
+  * 
+  * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization
+  * such that the factorized matrix is P A P^-1.
+  *
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+  *               or Upper. Default is Lower.
+  * \tparam Ordering_ The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is AMDOrdering<>
+  *
+  * \implsparsesolverconcept
+  *
+  * \sa class SimplicialLDLT, class AMDOrdering, class NaturalOrdering
+  */
+template<typename MatrixType_, int UpLo_, typename Ordering_>
+    class SimplicialLLT : public SimplicialCholeskyBase<SimplicialLLT<MatrixType_,UpLo_,Ordering_> >
+{
+public:
+    typedef MatrixType_ MatrixType;
+    enum { UpLo = UpLo_ };
+    typedef SimplicialCholeskyBase<SimplicialLLT> Base;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    typedef SparseMatrix<Scalar,ColMajor,Index> CholMatrixType;
+    typedef Matrix<Scalar,Dynamic,1> VectorType;
+    typedef internal::traits<SimplicialLLT> Traits;
+    typedef typename Traits::MatrixL  MatrixL;
+    typedef typename Traits::MatrixU  MatrixU;
+public:
+    /** Default constructor */
+    SimplicialLLT() : Base() {}
+    /** Constructs and performs the LLT factorization of \a matrix */
+    explicit SimplicialLLT(const MatrixType& matrix)
+        : Base(matrix) {}
+
+    /** \returns an expression of the factor L */
+    inline const MatrixL matrixL() const {
+        eigen_assert(Base::m_factorizationIsOk && "Simplicial LLT not factorized");
+        return Traits::getL(Base::m_matrix);
+    }
+
+    /** \returns an expression of the factor U (= L^*) */
+    inline const MatrixU matrixU() const {
+        eigen_assert(Base::m_factorizationIsOk && "Simplicial LLT not factorized");
+        return Traits::getU(Base::m_matrix);
+    }
+    
+    /** Computes the sparse Cholesky decomposition of \a matrix */
+    SimplicialLLT& compute(const MatrixType& matrix)
+    {
+      Base::template compute<false>(matrix);
+      return *this;
+    }
+
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      *
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& a)
+    {
+      Base::analyzePattern(a, false);
+    }
+
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
+      *
+      * \sa analyzePattern()
+      */
+    void factorize(const MatrixType& a)
+    {
+      Base::template factorize<false>(a);
+    }
+
+    /** \returns the determinant of the underlying matrix from the current factorization */
+    Scalar determinant() const
+    {
+      Scalar detL = Base::m_matrix.diagonal().prod();
+      return numext::abs2(detL);
+    }
+};
+
+/** \ingroup SparseCholesky_Module
+  * \class SimplicialLDLT
+  * \brief A direct sparse LDLT Cholesky factorizations without square root.
+  *
+  * This class provides a LDL^T Cholesky factorizations without square root of sparse matrices that are
+  * selfadjoint and positive definite. The factorization allows for solving A.X = B where
+  * X and B can be either dense or sparse.
+  * 
+  * In order to reduce the fill-in, a symmetric permutation P is applied prior to the factorization
+  * such that the factorized matrix is P A P^-1.
+  *
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+  *               or Upper. Default is Lower.
+  * \tparam Ordering_ The ordering method to use, either AMDOrdering<> or NaturalOrdering<>. Default is AMDOrdering<>
+  *
+  * \implsparsesolverconcept
+  *
+  * \sa class SimplicialLLT, class AMDOrdering, class NaturalOrdering
+  */
+template<typename MatrixType_, int UpLo_, typename Ordering_>
+    class SimplicialLDLT : public SimplicialCholeskyBase<SimplicialLDLT<MatrixType_,UpLo_,Ordering_> >
+{
+public:
+    typedef MatrixType_ MatrixType;
+    enum { UpLo = UpLo_ };
+    typedef SimplicialCholeskyBase<SimplicialLDLT> Base;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> CholMatrixType;
+    typedef Matrix<Scalar,Dynamic,1> VectorType;
+    typedef internal::traits<SimplicialLDLT> Traits;
+    typedef typename Traits::MatrixL  MatrixL;
+    typedef typename Traits::MatrixU  MatrixU;
+public:
+    /** Default constructor */
+    SimplicialLDLT() : Base() {}
+
+    /** Constructs and performs the LLT factorization of \a matrix */
+    explicit SimplicialLDLT(const MatrixType& matrix)
+        : Base(matrix) {}
+
+    /** \returns a vector expression of the diagonal D */
+    inline const VectorType vectorD() const {
+        eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
+        return Base::m_diag;
+    }
+    /** \returns an expression of the factor L */
+    inline const MatrixL matrixL() const {
+        eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
+        return Traits::getL(Base::m_matrix);
+    }
+
+    /** \returns an expression of the factor U (= L^*) */
+    inline const MatrixU matrixU() const {
+        eigen_assert(Base::m_factorizationIsOk && "Simplicial LDLT not factorized");
+        return Traits::getU(Base::m_matrix);
+    }
+
+    /** Computes the sparse Cholesky decomposition of \a matrix */
+    SimplicialLDLT& compute(const MatrixType& matrix)
+    {
+      Base::template compute<true>(matrix);
+      return *this;
+    }
+    
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      *
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& a)
+    {
+      Base::analyzePattern(a, true);
+    }
+
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
+      *
+      * \sa analyzePattern()
+      */
+    void factorize(const MatrixType& a)
+    {
+      Base::template factorize<true>(a);
+    }
+
+    /** \returns the determinant of the underlying matrix from the current factorization */
+    Scalar determinant() const
+    {
+      return Base::m_diag.prod();
+    }
+};
+
+/** \deprecated use SimplicialLDLT or class SimplicialLLT
+  * \ingroup SparseCholesky_Module
+  * \class SimplicialCholesky
+  *
+  * \sa class SimplicialLDLT, class SimplicialLLT
+  */
+template<typename MatrixType_, int UpLo_, typename Ordering_>
+    class SimplicialCholesky : public SimplicialCholeskyBase<SimplicialCholesky<MatrixType_,UpLo_,Ordering_> >
+{
+public:
+    typedef MatrixType_ MatrixType;
+    enum { UpLo = UpLo_ };
+    typedef SimplicialCholeskyBase<SimplicialCholesky> Base;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> CholMatrixType;
+    typedef Matrix<Scalar,Dynamic,1> VectorType;
+    typedef internal::traits<SimplicialCholesky> Traits;
+    typedef internal::traits<SimplicialLDLT<MatrixType,UpLo> > LDLTTraits;
+    typedef internal::traits<SimplicialLLT<MatrixType,UpLo>  > LLTTraits;
+  public:
+    SimplicialCholesky() : Base(), m_LDLT(true) {}
+
+    explicit SimplicialCholesky(const MatrixType& matrix)
+      : Base(), m_LDLT(true)
+    {
+      compute(matrix);
+    }
+
+    SimplicialCholesky& setMode(SimplicialCholeskyMode mode)
+    {
+      switch(mode)
+      {
+      case SimplicialCholeskyLLT:
+        m_LDLT = false;
+        break;
+      case SimplicialCholeskyLDLT:
+        m_LDLT = true;
+        break;
+      default:
+        break;
+      }
+
+      return *this;
+    }
+
+    inline const VectorType vectorD() const {
+        eigen_assert(Base::m_factorizationIsOk && "Simplicial Cholesky not factorized");
+        return Base::m_diag;
+    }
+    inline const CholMatrixType rawMatrix() const {
+        eigen_assert(Base::m_factorizationIsOk && "Simplicial Cholesky not factorized");
+        return Base::m_matrix;
+    }
+    
+    /** Computes the sparse Cholesky decomposition of \a matrix */
+    SimplicialCholesky& compute(const MatrixType& matrix)
+    {
+      if(m_LDLT)
+        Base::template compute<true>(matrix);
+      else
+        Base::template compute<false>(matrix);
+      return *this;
+    }
+
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      *
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& a)
+    {
+      Base::analyzePattern(a, m_LDLT);
+    }
+
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
+      *
+      * \sa analyzePattern()
+      */
+    void factorize(const MatrixType& a)
+    {
+      if(m_LDLT)
+        Base::template factorize<true>(a);
+      else
+        Base::template factorize<false>(a);
+    }
+
+    /** \internal */
+    template<typename Rhs,typename Dest>
+    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const
+    {
+      eigen_assert(Base::m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or symbolic()/numeric()");
+      eigen_assert(Base::m_matrix.rows()==b.rows());
+
+      if(Base::m_info!=Success)
+        return;
+
+      if(Base::m_P.size()>0)
+        dest = Base::m_P * b;
+      else
+        dest = b;
+
+      if(Base::m_matrix.nonZeros()>0) // otherwise L==I
+      {
+        if(m_LDLT)
+          LDLTTraits::getL(Base::m_matrix).solveInPlace(dest);
+        else
+          LLTTraits::getL(Base::m_matrix).solveInPlace(dest);
+      }
+
+      if(Base::m_diag.size()>0)
+        dest = Base::m_diag.real().asDiagonal().inverse() * dest;
+
+      if (Base::m_matrix.nonZeros()>0) // otherwise I==I
+      {
+        if(m_LDLT)
+          LDLTTraits::getU(Base::m_matrix).solveInPlace(dest);
+        else
+          LLTTraits::getU(Base::m_matrix).solveInPlace(dest);
+      }
+
+      if(Base::m_P.size()>0)
+        dest = Base::m_Pinv * dest;
+    }
+    
+    /** \internal */
+    template<typename Rhs,typename Dest>
+    void _solve_impl(const SparseMatrixBase<Rhs> &b, SparseMatrixBase<Dest> &dest) const
+    {
+      internal::solve_sparse_through_dense_panels(*this, b, dest);
+    }
+    
+    Scalar determinant() const
+    {
+      if(m_LDLT)
+      {
+        return Base::m_diag.prod();
+      }
+      else
+      {
+        Scalar detL = Diagonal<const CholMatrixType>(Base::m_matrix).prod();
+        return numext::abs2(detL);
+      }
+    }
+    
+  protected:
+    bool m_LDLT;
+};
+
+template<typename Derived>
+void SimplicialCholeskyBase<Derived>::ordering(const MatrixType& a, ConstCholMatrixPtr &pmat, CholMatrixType& ap)
+{
+  eigen_assert(a.rows()==a.cols());
+  const Index size = a.rows();
+  pmat = &ap;
+  // Note that ordering methods compute the inverse permutation
+  if(!internal::is_same<OrderingType,NaturalOrdering<Index> >::value)
+  {
+    {
+      CholMatrixType C;
+      C = a.template selfadjointView<UpLo>();
+      
+      OrderingType ordering;
+      ordering(C,m_Pinv);
+    }
+
+    if(m_Pinv.size()>0) m_P = m_Pinv.inverse();
+    else                m_P.resize(0);
+    
+    ap.resize(size,size);
+    ap.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>().twistedBy(m_P);
+  }
+  else
+  {
+    m_Pinv.resize(0);
+    m_P.resize(0);
+    if(int(UpLo)==int(Lower) || MatrixType::IsRowMajor)
+    {
+      // we have to transpose the lower part to to the upper one
+      ap.resize(size,size);
+      ap.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>();
+    }
+    else
+      internal::simplicial_cholesky_grab_input<CholMatrixType,MatrixType>::run(a, pmat, ap);
+  }  
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SIMPLICIAL_CHOLESKY_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
new file mode 100644
index 0000000000000000000000000000000000000000..3106c9bdd2e57513d38db7b2d7bcda7288d15679
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCholesky/SimplicialCholesky_impl.h
@@ -0,0 +1,176 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/*
+NOTE: these functions have been adapted from the LDL library:
+
+LDL Copyright (c) 2005 by Timothy A. Davis.  All Rights Reserved.
+
+The author of LDL, Timothy A. Davis., has executed a license with Google LLC
+to permit distribution of this code and derivative works as part of Eigen under
+the Mozilla Public License v. 2.0, as stated at the top of this file.
+ */
+
+#ifndef EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H
+#define EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template<typename Derived>
+void SimplicialCholeskyBase<Derived>::analyzePattern_preordered(const CholMatrixType& ap, bool doLDLT)
+{
+  const StorageIndex size = StorageIndex(ap.rows());
+  m_matrix.resize(size, size);
+  m_parent.resize(size);
+  m_nonZerosPerCol.resize(size);
+
+  ei_declare_aligned_stack_constructed_variable(StorageIndex, tags, size, 0);
+
+  for(StorageIndex k = 0; k < size; ++k)
+  {
+    /* L(k,:) pattern: all nodes reachable in etree from nz in A(0:k-1,k) */
+    m_parent[k] = -1;             /* parent of k is not yet known */
+    tags[k] = k;                  /* mark node k as visited */
+    m_nonZerosPerCol[k] = 0;      /* count of nonzeros in column k of L */
+    for(typename CholMatrixType::InnerIterator it(ap,k); it; ++it)
+    {
+      StorageIndex i = it.index();
+      if(i < k)
+      {
+        /* follow path from i to root of etree, stop at flagged node */
+        for(; tags[i] != k; i = m_parent[i])
+        {
+          /* find parent of i if not yet determined */
+          if (m_parent[i] == -1)
+            m_parent[i] = k;
+          m_nonZerosPerCol[i]++;        /* L (k,i) is nonzero */
+          tags[i] = k;                  /* mark i as visited */
+        }
+      }
+    }
+  }
+
+  /* construct Lp index array from m_nonZerosPerCol column counts */
+  StorageIndex* Lp = m_matrix.outerIndexPtr();
+  Lp[0] = 0;
+  for(StorageIndex k = 0; k < size; ++k)
+    Lp[k+1] = Lp[k] + m_nonZerosPerCol[k] + (doLDLT ? 0 : 1);
+
+  m_matrix.resizeNonZeros(Lp[size]);
+
+  m_isInitialized     = true;
+  m_info              = Success;
+  m_analysisIsOk      = true;
+  m_factorizationIsOk = false;
+}
+
+
+template<typename Derived>
+template<bool DoLDLT>
+void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType& ap)
+{
+  using std::sqrt;
+
+  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+  eigen_assert(ap.rows()==ap.cols());
+  eigen_assert(m_parent.size()==ap.rows());
+  eigen_assert(m_nonZerosPerCol.size()==ap.rows());
+
+  const StorageIndex size = StorageIndex(ap.rows());
+  const StorageIndex* Lp = m_matrix.outerIndexPtr();
+  StorageIndex* Li = m_matrix.innerIndexPtr();
+  Scalar* Lx = m_matrix.valuePtr();
+
+  ei_declare_aligned_stack_constructed_variable(Scalar, y, size, 0);
+  ei_declare_aligned_stack_constructed_variable(StorageIndex,  pattern, size, 0);
+  ei_declare_aligned_stack_constructed_variable(StorageIndex,  tags, size, 0);
+
+  bool ok = true;
+  m_diag.resize(DoLDLT ? size : 0);
+
+  for(StorageIndex k = 0; k < size; ++k)
+  {
+    // compute nonzero pattern of kth row of L, in topological order
+    y[k] = Scalar(0);                     // Y(0:k) is now all zero
+    StorageIndex top = size;               // stack for pattern is empty
+    tags[k] = k;                    // mark node k as visited
+    m_nonZerosPerCol[k] = 0;        // count of nonzeros in column k of L
+    for(typename CholMatrixType::InnerIterator it(ap,k); it; ++it)
+    {
+      StorageIndex i = it.index();
+      if(i <= k)
+      {
+        y[i] += numext::conj(it.value());            /* scatter A(i,k) into Y (sum duplicates) */
+        Index len;
+        for(len = 0; tags[i] != k; i = m_parent[i])
+        {
+          pattern[len++] = i;     /* L(k,i) is nonzero */
+          tags[i] = k;            /* mark i as visited */
+        }
+        while(len > 0)
+          pattern[--top] = pattern[--len];
+      }
+    }
+
+    /* compute numerical values kth row of L (a sparse triangular solve) */
+
+    RealScalar d = numext::real(y[k]) * m_shiftScale + m_shiftOffset;    // get D(k,k), apply the shift function, and clear Y(k)
+    y[k] = Scalar(0);
+    for(; top < size; ++top)
+    {
+      Index i = pattern[top];       /* pattern[top:n-1] is pattern of L(:,k) */
+      Scalar yi = y[i];             /* get and clear Y(i) */
+      y[i] = Scalar(0);
+
+      /* the nonzero entry L(k,i) */
+      Scalar l_ki;
+      if(DoLDLT)
+        l_ki = yi / numext::real(m_diag[i]);
+      else
+        yi = l_ki = yi / Lx[Lp[i]];
+
+      Index p2 = Lp[i] + m_nonZerosPerCol[i];
+      Index p;
+      for(p = Lp[i] + (DoLDLT ? 0 : 1); p < p2; ++p)
+        y[Li[p]] -= numext::conj(Lx[p]) * yi;
+      d -= numext::real(l_ki * numext::conj(yi));
+      Li[p] = k;                          /* store L(k,i) in column form of L */
+      Lx[p] = l_ki;
+      ++m_nonZerosPerCol[i];              /* increment count of nonzeros in col i */
+    }
+    if(DoLDLT)
+    {
+      m_diag[k] = d;
+      if(d == RealScalar(0))
+      {
+        ok = false;                         /* failure, D(k,k) is zero */
+        break;
+      }
+    }
+    else
+    {
+      Index p = Lp[k] + m_nonZerosPerCol[k]++;
+      Li[p] = k ;                /* store L(k,k) = sqrt (d) in column k */
+      if(d <= RealScalar(0)) {
+        ok = false;              /* failure, matrix is not positive definite */
+        break;
+      }
+      Lx[p] = sqrt(d) ;
+    }
+  }
+
+  m_info = ok ? Success : NumericalIssue;
+  m_factorizationIsOk = true;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SIMPLICIAL_CHOLESKY_IMPL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/AmbiVector.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/AmbiVector.h
new file mode 100644
index 0000000000000000000000000000000000000000..594e91db8e8eb8f28a303ff5c582458a8fd3e7c3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/AmbiVector.h
@@ -0,0 +1,380 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_AMBIVECTOR_H
+#define EIGEN_AMBIVECTOR_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \internal
+  * Hybrid sparse/dense vector class designed for intensive read-write operations.
+  *
+  * See BasicSparseLLT and SparseProduct for usage examples.
+  */
+template<typename Scalar_, typename StorageIndex_>
+class AmbiVector
+{
+  public:
+    typedef Scalar_ Scalar;
+    typedef StorageIndex_ StorageIndex;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    explicit AmbiVector(Index size)
+      : m_buffer(0), m_zero(0), m_size(0), m_end(0), m_allocatedSize(0), m_allocatedElements(0), m_mode(-1)
+    {
+      resize(size);
+    }
+
+    void init(double estimatedDensity);
+    void init(int mode);
+
+    Index nonZeros() const;
+
+    /** Specifies a sub-vector to work on */
+    void setBounds(Index start, Index end) { m_start = convert_index(start); m_end = convert_index(end); }
+
+    void setZero();
+
+    void restart();
+    Scalar& coeffRef(Index i);
+    Scalar& coeff(Index i);
+
+    class Iterator;
+
+    ~AmbiVector() { delete[] m_buffer; }
+
+    void resize(Index size)
+    {
+      if (m_allocatedSize < size)
+        reallocate(size);
+      m_size = convert_index(size);
+    }
+
+    StorageIndex size() const { return m_size; }
+
+  protected:
+    StorageIndex convert_index(Index idx)
+    {
+      return internal::convert_index<StorageIndex>(idx);
+    }
+
+    void reallocate(Index size)
+    {
+      // if the size of the matrix is not too large, let's allocate a bit more than needed such
+      // that we can handle dense vector even in sparse mode.
+      delete[] m_buffer;
+      if (size<1000)
+      {
+        Index allocSize = (size * sizeof(ListEl) + sizeof(Scalar) - 1)/sizeof(Scalar);
+        m_allocatedElements = convert_index((allocSize*sizeof(Scalar))/sizeof(ListEl));
+        m_buffer = new Scalar[allocSize];
+      }
+      else
+      {
+        m_allocatedElements = convert_index((size*sizeof(Scalar))/sizeof(ListEl));
+        m_buffer = new Scalar[size];
+      }
+      m_size = convert_index(size);
+      m_start = 0;
+      m_end = m_size;
+    }
+
+    void reallocateSparse()
+    {
+      Index copyElements = m_allocatedElements;
+      m_allocatedElements = (std::min)(StorageIndex(m_allocatedElements*1.5),m_size);
+      Index allocSize = m_allocatedElements * sizeof(ListEl);
+      allocSize = (allocSize + sizeof(Scalar) - 1)/sizeof(Scalar);
+      Scalar* newBuffer = new Scalar[allocSize];
+      std::memcpy(newBuffer,  m_buffer,  copyElements * sizeof(ListEl));
+      delete[] m_buffer;
+      m_buffer = newBuffer;
+    }
+
+  protected:
+    // element type of the linked list
+    struct ListEl
+    {
+      StorageIndex next;
+      StorageIndex index;
+      Scalar value;
+    };
+
+    // used to store data in both mode
+    Scalar* m_buffer;
+    Scalar m_zero;
+    StorageIndex m_size;
+    StorageIndex m_start;
+    StorageIndex m_end;
+    StorageIndex m_allocatedSize;
+    StorageIndex m_allocatedElements;
+    StorageIndex m_mode;
+
+    // linked list mode
+    StorageIndex m_llStart;
+    StorageIndex m_llCurrent;
+    StorageIndex m_llSize;
+};
+
+/** \returns the number of non zeros in the current sub vector */
+template<typename Scalar_,typename StorageIndex_>
+Index AmbiVector<Scalar_,StorageIndex_>::nonZeros() const
+{
+  if (m_mode==IsSparse)
+    return m_llSize;
+  else
+    return m_end - m_start;
+}
+
+template<typename Scalar_,typename StorageIndex_>
+void AmbiVector<Scalar_,StorageIndex_>::init(double estimatedDensity)
+{
+  if (estimatedDensity>0.1)
+    init(IsDense);
+  else
+    init(IsSparse);
+}
+
+template<typename Scalar_,typename StorageIndex_>
+void AmbiVector<Scalar_,StorageIndex_>::init(int mode)
+{
+  m_mode = mode;
+  // This is only necessary in sparse mode, but we set these unconditionally to avoid some maybe-uninitialized warnings
+  // if (m_mode==IsSparse)
+  {
+    m_llSize = 0;
+    m_llStart = -1;
+  }
+}
+
+/** Must be called whenever we might perform a write access
+  * with an index smaller than the previous one.
+  *
+  * Don't worry, this function is extremely cheap.
+  */
+template<typename Scalar_,typename StorageIndex_>
+void AmbiVector<Scalar_,StorageIndex_>::restart()
+{
+  m_llCurrent = m_llStart;
+}
+
+/** Set all coefficients of current subvector to zero */
+template<typename Scalar_,typename StorageIndex_>
+void AmbiVector<Scalar_,StorageIndex_>::setZero()
+{
+  if (m_mode==IsDense)
+  {
+    for (Index i=m_start; i<m_end; ++i)
+      m_buffer[i] = Scalar(0);
+  }
+  else
+  {
+    eigen_assert(m_mode==IsSparse);
+    m_llSize = 0;
+    m_llStart = -1;
+  }
+}
+
+template<typename Scalar_,typename StorageIndex_>
+Scalar_& AmbiVector<Scalar_,StorageIndex_>::coeffRef(Index i)
+{
+  if (m_mode==IsDense)
+    return m_buffer[i];
+  else
+  {
+    ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
+    // TODO factorize the following code to reduce code generation
+    eigen_assert(m_mode==IsSparse);
+    if (m_llSize==0)
+    {
+      // this is the first element
+      m_llStart = 0;
+      m_llCurrent = 0;
+      ++m_llSize;
+      llElements[0].value = Scalar(0);
+      llElements[0].index = convert_index(i);
+      llElements[0].next = -1;
+      return llElements[0].value;
+    }
+    else if (i<llElements[m_llStart].index)
+    {
+      // this is going to be the new first element of the list
+      ListEl& el = llElements[m_llSize];
+      el.value = Scalar(0);
+      el.index = convert_index(i);
+      el.next = m_llStart;
+      m_llStart = m_llSize;
+      ++m_llSize;
+      m_llCurrent = m_llStart;
+      return el.value;
+    }
+    else
+    {
+      StorageIndex nextel = llElements[m_llCurrent].next;
+      eigen_assert(i>=llElements[m_llCurrent].index && "you must call restart() before inserting an element with lower or equal index");
+      while (nextel >= 0 && llElements[nextel].index<=i)
+      {
+        m_llCurrent = nextel;
+        nextel = llElements[nextel].next;
+      }
+
+      if (llElements[m_llCurrent].index==i)
+      {
+        // the coefficient already exists and we found it !
+        return llElements[m_llCurrent].value;
+      }
+      else
+      {
+        if (m_llSize>=m_allocatedElements)
+        {
+          reallocateSparse();
+          llElements = reinterpret_cast<ListEl*>(m_buffer);
+        }
+        eigen_internal_assert(m_llSize<m_allocatedElements && "internal error: overflow in sparse mode");
+        // let's insert a new coefficient
+        ListEl& el = llElements[m_llSize];
+        el.value = Scalar(0);
+        el.index = convert_index(i);
+        el.next = llElements[m_llCurrent].next;
+        llElements[m_llCurrent].next = m_llSize;
+        ++m_llSize;
+        return el.value;
+      }
+    }
+  }
+}
+
+template<typename Scalar_,typename StorageIndex_>
+Scalar_& AmbiVector<Scalar_,StorageIndex_>::coeff(Index i)
+{
+  if (m_mode==IsDense)
+    return m_buffer[i];
+  else
+  {
+    ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
+    eigen_assert(m_mode==IsSparse);
+    if ((m_llSize==0) || (i<llElements[m_llStart].index))
+    {
+      return m_zero;
+    }
+    else
+    {
+      Index elid = m_llStart;
+      while (elid >= 0 && llElements[elid].index<i)
+        elid = llElements[elid].next;
+
+      if (llElements[elid].index==i)
+        return llElements[m_llCurrent].value;
+      else
+        return m_zero;
+    }
+  }
+}
+
+/** Iterator over the nonzero coefficients */
+template<typename Scalar_,typename StorageIndex_>
+class AmbiVector<Scalar_,StorageIndex_>::Iterator
+{
+  public:
+    typedef Scalar_ Scalar;
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    /** Default constructor
+      * \param vec the vector on which we iterate
+      * \param epsilon the minimal value used to prune zero coefficients.
+      * In practice, all coefficients having a magnitude smaller than \a epsilon
+      * are skipped.
+      */
+    explicit Iterator(const AmbiVector& vec, const RealScalar& epsilon = 0)
+      : m_vector(vec)
+    {
+      using std::abs;
+      m_epsilon = epsilon;
+      m_isDense = m_vector.m_mode==IsDense;
+      if (m_isDense)
+      {
+        m_currentEl = 0;   // this is to avoid a compilation warning
+        m_cachedValue = 0; // this is to avoid a compilation warning
+        m_cachedIndex = m_vector.m_start-1;
+        ++(*this);
+      }
+      else
+      {
+        ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
+        m_currentEl = m_vector.m_llStart;
+        while (m_currentEl>=0 && abs(llElements[m_currentEl].value)<=m_epsilon)
+          m_currentEl = llElements[m_currentEl].next;
+        if (m_currentEl<0)
+        {
+          m_cachedValue = 0; // this is to avoid a compilation warning
+          m_cachedIndex = -1;
+        }
+        else
+        {
+          m_cachedIndex = llElements[m_currentEl].index;
+          m_cachedValue = llElements[m_currentEl].value;
+        }
+      }
+    }
+
+    StorageIndex index() const { return m_cachedIndex; }
+    Scalar value() const { return m_cachedValue; }
+
+    operator bool() const { return m_cachedIndex>=0; }
+
+    Iterator& operator++()
+    {
+      using std::abs;
+      if (m_isDense)
+      {
+        do {
+          ++m_cachedIndex;
+        } while (m_cachedIndex<m_vector.m_end && abs(m_vector.m_buffer[m_cachedIndex])<=m_epsilon);
+        if (m_cachedIndex<m_vector.m_end)
+          m_cachedValue = m_vector.m_buffer[m_cachedIndex];
+        else
+          m_cachedIndex=-1;
+      }
+      else
+      {
+        ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
+        do {
+          m_currentEl = llElements[m_currentEl].next;
+        } while (m_currentEl>=0 && abs(llElements[m_currentEl].value)<=m_epsilon);
+        if (m_currentEl<0)
+        {
+          m_cachedIndex = -1;
+        }
+        else
+        {
+          m_cachedIndex = llElements[m_currentEl].index;
+          m_cachedValue = llElements[m_currentEl].value;
+        }
+      }
+      return *this;
+    }
+
+  protected:
+    const AmbiVector& m_vector; // the target vector
+    StorageIndex m_currentEl;   // the current element in sparse/linked-list mode
+    RealScalar m_epsilon;       // epsilon used to prune zero coefficients
+    StorageIndex m_cachedIndex; // current coordinate
+    Scalar m_cachedValue;       // current value
+    bool m_isDense;             // mode of the vector
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_AMBIVECTOR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/CompressedStorage.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/CompressedStorage.h
new file mode 100644
index 0000000000000000000000000000000000000000..6b0fb4bf1ffea6d503a13ed1febb6b6ab406e1e5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/CompressedStorage.h
@@ -0,0 +1,225 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMPRESSED_STORAGE_H
+#define EIGEN_COMPRESSED_STORAGE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \internal
+  * Stores a sparse set of values as a list of values and a list of indices.
+  *
+  */
+template<typename Scalar_,typename StorageIndex_>
+class CompressedStorage
+{
+  public:
+
+    typedef Scalar_ Scalar;
+    typedef StorageIndex_ StorageIndex;
+
+  protected:
+
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  public:
+
+    CompressedStorage()
+      : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0)
+    {}
+
+    explicit CompressedStorage(Index size)
+      : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0)
+    {
+      resize(size);
+    }
+
+    CompressedStorage(const CompressedStorage& other)
+      : m_values(0), m_indices(0), m_size(0), m_allocatedSize(0)
+    {
+      *this = other;
+    }
+
+    CompressedStorage& operator=(const CompressedStorage& other)
+    {
+      resize(other.size());
+      if(other.size()>0)
+      {
+        internal::smart_copy(other.m_values,  other.m_values  + m_size, m_values);
+        internal::smart_copy(other.m_indices, other.m_indices + m_size, m_indices);
+      }
+      return *this;
+    }
+
+    void swap(CompressedStorage& other)
+    {
+      std::swap(m_values, other.m_values);
+      std::swap(m_indices, other.m_indices);
+      std::swap(m_size, other.m_size);
+      std::swap(m_allocatedSize, other.m_allocatedSize);
+    }
+
+    ~CompressedStorage()
+    {
+      conditional_aligned_delete_auto<Scalar, true>(m_values, m_allocatedSize);
+      conditional_aligned_delete_auto<StorageIndex, true>(m_indices, m_allocatedSize);
+    }
+
+    void reserve(Index size)
+    {
+      Index newAllocatedSize = m_size + size;
+      if (newAllocatedSize > m_allocatedSize)
+        reallocate(newAllocatedSize);
+    }
+
+    void squeeze()
+    {
+      if (m_allocatedSize>m_size)
+        reallocate(m_size);
+    }
+
+    void resize(Index size, double reserveSizeFactor = 0)
+    {
+      if (m_allocatedSize<size)
+      {
+        Index realloc_size = (std::min<Index>)(NumTraits<StorageIndex>::highest(),  size + Index(reserveSizeFactor*double(size)));
+        if(realloc_size<size)
+          internal::throw_std_bad_alloc();
+        reallocate(realloc_size);
+      }
+      m_size = size;
+    }
+
+    void append(const Scalar& v, Index i)
+    {
+      Index id = m_size;
+      resize(m_size+1, 1);
+      m_values[id] = v;
+      m_indices[id] = internal::convert_index<StorageIndex>(i);
+    }
+
+    inline Index size() const { return m_size; }
+    inline Index allocatedSize() const { return m_allocatedSize; }
+    inline void clear() { m_size = 0; }
+
+    const Scalar* valuePtr() const { return m_values; }
+    Scalar* valuePtr() { return m_values; }
+    const StorageIndex* indexPtr() const { return m_indices; }
+    StorageIndex* indexPtr() { return m_indices; }
+
+    inline Scalar& value(Index i) { eigen_internal_assert(m_values!=0); return m_values[i]; }
+    inline const Scalar& value(Index i) const { eigen_internal_assert(m_values!=0); return m_values[i]; }
+
+    inline StorageIndex& index(Index i) { eigen_internal_assert(m_indices!=0); return m_indices[i]; }
+    inline const StorageIndex& index(Index i) const { eigen_internal_assert(m_indices!=0); return m_indices[i]; }
+
+    /** \returns the largest \c k such that for all \c j in [0,k) index[\c j]\<\a key */
+    inline Index searchLowerIndex(Index key) const
+    {
+      return searchLowerIndex(0, m_size, key);
+    }
+
+    /** \returns the largest \c k in [start,end) such that for all \c j in [start,k) index[\c j]\<\a key */
+    inline Index searchLowerIndex(Index start, Index end, Index key) const
+    {
+      return static_cast<Index>(std::distance(m_indices, std::lower_bound(m_indices + start, m_indices + end, key)));
+    }
+
+    /** \returns the stored value at index \a key
+      * If the value does not exist, then the value \a defaultValue is returned without any insertion. */
+    inline Scalar at(Index key, const Scalar& defaultValue = Scalar(0)) const
+    {
+      if (m_size==0)
+        return defaultValue;
+      else if (key==m_indices[m_size-1])
+        return m_values[m_size-1];
+      // ^^  optimization: let's first check if it is the last coefficient
+      // (very common in high level algorithms)
+      const Index id = searchLowerIndex(0,m_size-1,key);
+      return ((id<m_size) && (m_indices[id]==key)) ? m_values[id] : defaultValue;
+    }
+
+    /** Like at(), but the search is performed in the range [start,end) */
+    inline Scalar atInRange(Index start, Index end, Index key, const Scalar &defaultValue = Scalar(0)) const
+    {
+      if (start>=end)
+        return defaultValue;
+      else if (end>start && key==m_indices[end-1])
+        return m_values[end-1];
+      // ^^  optimization: let's first check if it is the last coefficient
+      // (very common in high level algorithms)
+      const Index id = searchLowerIndex(start,end-1,key);
+      return ((id<end) && (m_indices[id]==key)) ? m_values[id] : defaultValue;
+    }
+
+    /** \returns a reference to the value at index \a key
+      * If the value does not exist, then the value \a defaultValue is inserted
+      * such that the keys are sorted. */
+    inline Scalar& atWithInsertion(Index key, const Scalar& defaultValue = Scalar(0))
+    {
+      Index id = searchLowerIndex(0,m_size,key);
+      if (id>=m_size || m_indices[id]!=key)
+      {
+        if (m_allocatedSize<m_size+1)
+        {
+          Index newAllocatedSize = 2 * (m_size + 1);
+          m_values = conditional_aligned_realloc_new_auto<Scalar, true>(m_values, newAllocatedSize, m_allocatedSize);
+          m_indices =
+              conditional_aligned_realloc_new_auto<StorageIndex, true>(m_indices, newAllocatedSize, m_allocatedSize);
+          m_allocatedSize = newAllocatedSize;
+        }
+        if(m_size>id)
+        {
+          internal::smart_memmove(m_values +id, m_values +m_size, m_values +id+1);
+          internal::smart_memmove(m_indices+id, m_indices+m_size, m_indices+id+1);
+        }
+        m_size++;
+        m_indices[id] = internal::convert_index<StorageIndex>(key);
+        m_values[id] = defaultValue;
+      }
+      return m_values[id];
+    }
+
+    inline void moveChunk(Index from, Index to, Index chunkSize)
+    {
+      eigen_internal_assert(chunkSize >= 0 && to+chunkSize <= m_size);
+      internal::smart_memmove(m_values + from, m_values + from + chunkSize, m_values + to);
+      internal::smart_memmove(m_indices + from, m_indices + from + chunkSize, m_indices + to);
+    }
+
+  protected:
+
+    inline void reallocate(Index size)
+    {
+      #ifdef EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN
+        EIGEN_SPARSE_COMPRESSED_STORAGE_REALLOCATE_PLUGIN
+      #endif
+      eigen_internal_assert(size!=m_allocatedSize);
+      m_values = conditional_aligned_realloc_new_auto<Scalar, true>(m_values, size, m_allocatedSize);
+      m_indices = conditional_aligned_realloc_new_auto<StorageIndex, true>(m_indices, size, m_allocatedSize);
+      m_allocatedSize = size;
+    }
+
+  protected:
+    Scalar* m_values;
+    StorageIndex* m_indices;
+    Index m_size;
+    Index m_allocatedSize;
+
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_COMPRESSED_STORAGE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
new file mode 100644
index 0000000000000000000000000000000000000000..f8524931fb1fa281286782609caceeda29145315
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
@@ -0,0 +1,359 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
+#define EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, typename ResultType>
+static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res, bool sortedInsertion = false)
+{
+  typedef typename remove_all_t<Lhs>::Scalar LhsScalar;
+  typedef typename remove_all_t<Rhs>::Scalar RhsScalar;
+  typedef typename remove_all_t<ResultType>::Scalar ResScalar;
+
+  // make sure to call innerSize/outerSize since we fake the storage order.
+  Index rows = lhs.innerSize();
+  Index cols = rhs.outerSize();
+  eigen_assert(lhs.outerSize() == rhs.innerSize());
+
+  ei_declare_aligned_stack_constructed_variable(bool,   mask,     rows, 0);
+  ei_declare_aligned_stack_constructed_variable(ResScalar, values,   rows, 0);
+  ei_declare_aligned_stack_constructed_variable(Index,  indices,  rows, 0);
+
+  std::memset(mask,0,sizeof(bool)*rows);
+
+  evaluator<Lhs> lhsEval(lhs);
+  evaluator<Rhs> rhsEval(rhs);
+
+  // estimate the number of non zero entries
+  // given a rhs column containing Y non zeros, we assume that the respective Y columns
+  // of the lhs differs in average of one non zeros, thus the number of non zeros for
+  // the product of a rhs column with the lhs is X+Y where X is the average number of non zero
+  // per column of the lhs.
+  // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
+  Index estimated_nnz_prod = lhsEval.nonZerosEstimate() + rhsEval.nonZerosEstimate();
+
+  res.setZero();
+  res.reserve(Index(estimated_nnz_prod));
+  // we compute each column of the result, one after the other
+  for (Index j=0; j<cols; ++j)
+  {
+
+    res.startVec(j);
+    Index nnz = 0;
+    for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt)
+    {
+      RhsScalar y = rhsIt.value();
+      Index k = rhsIt.index();
+      for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, k); lhsIt; ++lhsIt)
+      {
+        Index i = lhsIt.index();
+        LhsScalar x = lhsIt.value();
+        if(!mask[i])
+        {
+          mask[i] = true;
+          values[i] = x * y;
+          indices[nnz] = i;
+          ++nnz;
+        }
+        else
+          values[i] += x * y;
+      }
+    }
+    if(!sortedInsertion)
+    {
+      // unordered insertion
+      for(Index k=0; k<nnz; ++k)
+      {
+        Index i = indices[k];
+        res.insertBackByOuterInnerUnordered(j,i) = values[i];
+        mask[i] = false;
+      }
+    }
+    else
+    {
+      // alternative ordered insertion code:
+      const Index t200 = rows/11; // 11 == (log2(200)*1.39)
+      const Index t = (rows*100)/139;
+
+      // FIXME reserve nnz non zeros
+      // FIXME implement faster sorting algorithms for very small nnz
+      // if the result is sparse enough => use a quick sort
+      // otherwise => loop through the entire vector
+      // In order to avoid to perform an expensive log2 when the
+      // result is clearly very sparse we use a linear bound up to 200.
+      if((nnz<200 && nnz<t200) || nnz * numext::log2(int(nnz)) < t)
+      {
+        if(nnz>1) std::sort(indices,indices+nnz);
+        for(Index k=0; k<nnz; ++k)
+        {
+          Index i = indices[k];
+          res.insertBackByOuterInner(j,i) = values[i];
+          mask[i] = false;
+        }
+      }
+      else
+      {
+        // dense path
+        for(Index i=0; i<rows; ++i)
+        {
+          if(mask[i])
+          {
+            mask[i] = false;
+            res.insertBackByOuterInner(j,i) = values[i];
+          }
+        }
+      }
+    }
+  }
+  res.finalize();
+}
+
+
+} // end namespace internal
+
+namespace internal {
+
+
+// Helper template to generate new sparse matrix types
+template<class Source, int Order>
+using WithStorageOrder = SparseMatrix<typename Source::Scalar, Order, typename Source::StorageIndex>;
+
+template<typename Lhs, typename Rhs, typename ResultType,
+  int LhsStorageOrder = (traits<Lhs>::Flags&RowMajorBit) ? RowMajor : ColMajor,
+  int RhsStorageOrder = (traits<Rhs>::Flags&RowMajorBit) ? RowMajor : ColMajor,
+  int ResStorageOrder = (traits<ResultType>::Flags&RowMajorBit) ? RowMajor : ColMajor>
+struct conservative_sparse_sparse_product_selector;
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
+{
+  typedef remove_all_t<Lhs> LhsCleaned;
+  typedef typename LhsCleaned::Scalar Scalar;
+
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    using RowMajorMatrix = WithStorageOrder<ResultType, RowMajor>;
+    using ColMajorMatrixAux = WithStorageOrder<ResultType, ColMajor>;
+
+    // If the result is tall and thin (in the extreme case a column vector)
+    // then it is faster to sort the coefficients inplace instead of transposing twice.
+    // FIXME, the following heuristic is probably not very good.
+    if(lhs.rows()>rhs.cols())
+    {
+      using ColMajorMatrix = typename sparse_eval<ColMajorMatrixAux,ResultType::RowsAtCompileTime,ResultType::ColsAtCompileTime,ColMajorMatrixAux::Flags>::type;
+      ColMajorMatrix resCol(lhs.rows(),rhs.cols());
+      // perform sorted insertion
+      internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol, true);
+      res = resCol.markAsRValue();
+    }
+    else
+    {
+      ColMajorMatrixAux resCol(lhs.rows(),rhs.cols());
+      // resort to transpose to sort the entries
+      internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorMatrixAux>(lhs, rhs, resCol, false);
+      RowMajorMatrix resRow(resCol);
+      res = resRow.markAsRValue();
+    }
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    using RowMajorRhs = WithStorageOrder<Rhs, RowMajor>;
+    using RowMajorRes = WithStorageOrder<ResultType, RowMajor>;
+    RowMajorRhs rhsRow = rhs;
+    RowMajorRes resRow(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<RowMajorRhs,Lhs,RowMajorRes>(rhsRow, lhs, resRow);
+    res = resRow;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    using RowMajorLhs = WithStorageOrder<Lhs, RowMajor>;
+    using RowMajorRes = WithStorageOrder<ResultType, RowMajor>;
+    RowMajorLhs lhsRow = lhs;
+    RowMajorRes resRow(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Rhs,RowMajorLhs,RowMajorRes>(rhs, lhsRow, resRow);
+    res = resRow;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    using RowMajorRes = WithStorageOrder<ResultType, RowMajor>;
+    RowMajorRes resRow(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorRes>(rhs, lhs, resRow);
+    res = resRow;
+  }
+};
+
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
+{
+  typedef typename traits<remove_all_t<Lhs>>::Scalar Scalar;
+
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    using ColMajorRes = WithStorageOrder<ResultType, ColMajor>;
+    ColMajorRes resCol(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Lhs,Rhs,ColMajorRes>(lhs, rhs, resCol);
+    res = resCol;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,RowMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    using ColMajorLhs = WithStorageOrder<Lhs, ColMajor>;
+    using ColMajorRes = WithStorageOrder<ResultType, ColMajor>;
+    ColMajorLhs lhsCol = lhs;
+    ColMajorRes resCol(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<ColMajorLhs,Rhs,ColMajorRes>(lhsCol, rhs, resCol);
+    res = resCol;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,RowMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    using ColMajorRhs = WithStorageOrder<Rhs, ColMajor>;
+    using ColMajorRes = WithStorageOrder<ResultType, ColMajor>;
+    ColMajorRhs rhsCol = rhs;
+    ColMajorRes resCol(lhs.rows(), rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Lhs,ColMajorRhs,ColMajorRes>(lhs, rhsCol, resCol);
+    res = resCol;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct conservative_sparse_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    using ColMajorRes = WithStorageOrder<ResultType, ColMajor>;
+    using RowMajorRes = WithStorageOrder<ResultType, RowMajor>;
+    RowMajorRes resRow(lhs.rows(),rhs.cols());
+    internal::conservative_sparse_sparse_product_impl<Rhs,Lhs,RowMajorRes>(rhs, lhs, resRow);
+    // sort the non zeros:
+    ColMajorRes resCol(resRow);
+    res = resCol;
+  }
+};
+
+} // end namespace internal
+
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, typename ResultType>
+static void sparse_sparse_to_dense_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+{
+  typedef typename remove_all_t<Lhs>::Scalar LhsScalar;
+  typedef typename remove_all_t<Rhs>::Scalar RhsScalar;
+  Index cols = rhs.outerSize();
+  eigen_assert(lhs.outerSize() == rhs.innerSize());
+
+  evaluator<Lhs> lhsEval(lhs);
+  evaluator<Rhs> rhsEval(rhs);
+
+  for (Index j=0; j<cols; ++j)
+  {
+    for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt)
+    {
+      RhsScalar y = rhsIt.value();
+      Index k = rhsIt.index();
+      for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, k); lhsIt; ++lhsIt)
+      {
+        Index i = lhsIt.index();
+        LhsScalar x = lhsIt.value();
+        res.coeffRef(i,j) += x * y;
+      }
+    }
+  }
+}
+
+
+} // end namespace internal
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, typename ResultType,
+  int LhsStorageOrder = (traits<Lhs>::Flags&RowMajorBit) ? RowMajor : ColMajor,
+  int RhsStorageOrder = (traits<Rhs>::Flags&RowMajorBit) ? RowMajor : ColMajor>
+struct sparse_sparse_to_dense_product_selector;
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    internal::sparse_sparse_to_dense_product_impl<Lhs,Rhs,ResultType>(lhs, rhs, res);
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    using ColMajorLhs = WithStorageOrder<Lhs, ColMajor>;
+    ColMajorLhs lhsCol(lhs);
+    internal::sparse_sparse_to_dense_product_impl<ColMajorLhs,Rhs,ResultType>(lhsCol, rhs, res);
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    using ColMajorRhs = WithStorageOrder<Rhs, ColMajor>;
+    ColMajorRhs rhsCol(rhs);
+    internal::sparse_sparse_to_dense_product_impl<Lhs,ColMajorRhs,ResultType>(lhs, rhsCol, res);
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_to_dense_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor>
+{
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+  {
+    Transpose<ResultType> trRes(res);
+    internal::sparse_sparse_to_dense_product_impl<Rhs,Lhs,Transpose<ResultType> >(rhs, lhs, trRes);
+  }
+};
+
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_CONSERVATIVESPARSESPARSEPRODUCT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..9de593657041cc26b99d987bae2d0e985d3d7538
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SPARSECORE_MODULE_H
+#error "Please include Eigen/SparseCore instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseAssign.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseAssign.h
new file mode 100644
index 0000000000000000000000000000000000000000..f8ab457729e6f0bbdbb37d3e8a45fa6e5c46c158
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseAssign.h
@@ -0,0 +1,278 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEASSIGN_H
+#define EIGEN_SPARSEASSIGN_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+template<typename Derived>    
+template<typename OtherDerived>
+Derived& SparseMatrixBase<Derived>::operator=(const EigenBase<OtherDerived> &other)
+{
+  internal::call_assignment_no_alias(derived(), other.derived());
+  return derived();
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+Derived& SparseMatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
+{
+  // TODO use the evaluator mechanism
+  other.evalTo(derived());
+  return derived();
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+inline Derived& SparseMatrixBase<Derived>::operator=(const SparseMatrixBase<OtherDerived>& other)
+{
+  // by default sparse evaluation do not alias, so we can safely bypass the generic call_assignment routine
+  internal::Assignment<Derived,OtherDerived,internal::assign_op<Scalar,typename OtherDerived::Scalar> >
+          ::run(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+
+template<typename Derived>
+inline Derived& SparseMatrixBase<Derived>::operator=(const Derived& other)
+{
+  internal::call_assignment_no_alias(derived(), other.derived());
+  return derived();
+}
+
+namespace internal {
+
+template<>
+struct storage_kind_to_evaluator_kind<Sparse> {
+  typedef IteratorBased Kind;
+};
+
+template<>
+struct storage_kind_to_shape<Sparse> {
+  typedef SparseShape Shape;
+};
+
+struct Sparse2Sparse {};
+struct Sparse2Dense  {};
+
+template<> struct AssignmentKind<SparseShape, SparseShape>           { typedef Sparse2Sparse Kind; };
+template<> struct AssignmentKind<SparseShape, SparseTriangularShape> { typedef Sparse2Sparse Kind; };
+template<> struct AssignmentKind<DenseShape,  SparseShape>           { typedef Sparse2Dense  Kind; };
+template<> struct AssignmentKind<DenseShape,  SparseTriangularShape> { typedef Sparse2Dense  Kind; };
+
+
+template<typename DstXprType, typename SrcXprType>
+void assign_sparse_to_sparse(DstXprType &dst, const SrcXprType &src)
+{
+  typedef typename DstXprType::Scalar Scalar;
+  typedef internal::evaluator<DstXprType> DstEvaluatorType;
+  typedef internal::evaluator<SrcXprType> SrcEvaluatorType;
+
+  SrcEvaluatorType srcEvaluator(src);
+
+  constexpr bool transpose = (DstEvaluatorType::Flags & RowMajorBit) != (SrcEvaluatorType::Flags & RowMajorBit);
+  const Index outerEvaluationSize = (SrcEvaluatorType::Flags&RowMajorBit) ? src.rows() : src.cols();
+
+  Index reserveSize = 0;
+  for (Index j = 0; j < outerEvaluationSize; ++j)
+    for (typename SrcEvaluatorType::InnerIterator it(srcEvaluator, j); it; ++it)
+      reserveSize++;
+
+  if ((!transpose) && src.isRValue())
+  {
+    // eval without temporary
+    dst.resize(src.rows(), src.cols());
+    dst.setZero();
+    dst.reserve(reserveSize);
+    for (Index j=0; j<outerEvaluationSize; ++j)
+    {
+      dst.startVec(j);
+      for (typename SrcEvaluatorType::InnerIterator it(srcEvaluator, j); it; ++it)
+      {
+        Scalar v = it.value();
+        dst.insertBackByOuterInner(j,it.index()) = v;
+      }
+    }
+    dst.finalize();
+  }
+  else
+  {
+    // eval through a temporary
+    eigen_assert(( ((internal::traits<DstXprType>::SupportedAccessPatterns & OuterRandomAccessPattern)==OuterRandomAccessPattern) ||
+              (!((DstEvaluatorType::Flags & RowMajorBit) != (SrcEvaluatorType::Flags & RowMajorBit)))) &&
+              "the transpose operation is supposed to be handled in SparseMatrix::operator=");
+
+    enum { Flip = (DstEvaluatorType::Flags & RowMajorBit) != (SrcEvaluatorType::Flags & RowMajorBit) };
+
+    
+    DstXprType temp(src.rows(), src.cols());
+
+    temp.reserve(reserveSize);
+    for (Index j=0; j<outerEvaluationSize; ++j)
+    {
+      temp.startVec(j);
+      for (typename SrcEvaluatorType::InnerIterator it(srcEvaluator, j); it; ++it)
+      {
+        Scalar v = it.value();
+        temp.insertBackByOuterInner(Flip?it.index():j,Flip?j:it.index()) = v;
+      }
+    }
+    temp.finalize();
+
+    dst = temp.markAsRValue();
+  }
+}
+
+// Generic Sparse to Sparse assignment
+template< typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, Sparse2Sparse>
+{
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
+  {
+    assign_sparse_to_sparse(dst.derived(), src.derived());
+  }
+};
+
+// Generic Sparse to Dense assignment
+template< typename DstXprType, typename SrcXprType, typename Functor, typename Weak>
+struct Assignment<DstXprType, SrcXprType, Functor, Sparse2Dense, Weak>
+{
+  static void run(DstXprType &dst, const SrcXprType &src, const Functor &func)
+  {
+    if(internal::is_same<Functor,internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> >::value)
+      dst.setZero();
+    
+    internal::evaluator<SrcXprType> srcEval(src);
+    resize_if_allowed(dst, src, func);
+    internal::evaluator<DstXprType> dstEval(dst);
+    
+    const Index outerEvaluationSize = (internal::evaluator<SrcXprType>::Flags&RowMajorBit) ? src.rows() : src.cols();
+    for (Index j=0; j<outerEvaluationSize; ++j)
+      for (typename internal::evaluator<SrcXprType>::InnerIterator i(srcEval,j); i; ++i)
+        func.assignCoeff(dstEval.coeffRef(i.row(),i.col()), i.value());
+  }
+};
+
+// Specialization for dense ?= dense +/- sparse and dense ?= sparse +/- dense
+template<typename DstXprType, typename Func1, typename Func2>
+struct assignment_from_dense_op_sparse
+{
+  template<typename SrcXprType, typename InitialFunc>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  void run(DstXprType &dst, const SrcXprType &src, const InitialFunc& /*func*/)
+  {
+    #ifdef EIGEN_SPARSE_ASSIGNMENT_FROM_DENSE_OP_SPARSE_PLUGIN
+    EIGEN_SPARSE_ASSIGNMENT_FROM_DENSE_OP_SPARSE_PLUGIN
+    #endif
+
+    call_assignment_no_alias(dst, src.lhs(), Func1());
+    call_assignment_no_alias(dst, src.rhs(), Func2());
+  }
+
+  // Specialization for dense1 = sparse + dense2; -> dense1 = dense2; dense1 += sparse;
+  template<typename Lhs, typename Rhs, typename Scalar>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  std::enable_if_t<internal::is_same<typename internal::evaluator_traits<Rhs>::Shape,DenseShape>::value>
+  run(DstXprType &dst, const CwiseBinaryOp<internal::scalar_sum_op<Scalar,Scalar>, const Lhs, const Rhs> &src,
+      const internal::assign_op<typename DstXprType::Scalar,Scalar>& /*func*/)
+  {
+    #ifdef EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_ADD_DENSE_PLUGIN
+    EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_ADD_DENSE_PLUGIN
+    #endif
+
+    // Apply the dense matrix first, then the sparse one.
+    call_assignment_no_alias(dst, src.rhs(), Func1());
+    call_assignment_no_alias(dst, src.lhs(), Func2());
+  }
+
+  // Specialization for dense1 = sparse - dense2; -> dense1 = -dense2; dense1 += sparse;
+  template<typename Lhs, typename Rhs, typename Scalar>
+  static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  std::enable_if_t<internal::is_same<typename internal::evaluator_traits<Rhs>::Shape,DenseShape>::value>
+  run(DstXprType &dst, const CwiseBinaryOp<internal::scalar_difference_op<Scalar,Scalar>, const Lhs, const Rhs> &src,
+      const internal::assign_op<typename DstXprType::Scalar,Scalar>& /*func*/)
+  {
+    #ifdef EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_SUB_DENSE_PLUGIN
+    EIGEN_SPARSE_ASSIGNMENT_FROM_SPARSE_SUB_DENSE_PLUGIN
+    #endif
+
+    // Apply the dense matrix first, then the sparse one.
+    call_assignment_no_alias(dst, -src.rhs(), Func1());
+    call_assignment_no_alias(dst,  src.lhs(), add_assign_op<typename DstXprType::Scalar,typename Lhs::Scalar>());
+  }
+};
+
+#define EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(ASSIGN_OP,BINOP,ASSIGN_OP2) \
+  template< typename DstXprType, typename Lhs, typename Rhs, typename Scalar> \
+  struct Assignment<DstXprType, CwiseBinaryOp<internal::BINOP<Scalar,Scalar>, const Lhs, const Rhs>, internal::ASSIGN_OP<typename DstXprType::Scalar,Scalar>, \
+                    Sparse2Dense, \
+                    std::enable_if_t<   internal::is_same<typename internal::evaluator_traits<Lhs>::Shape,DenseShape>::value \
+                                     || internal::is_same<typename internal::evaluator_traits<Rhs>::Shape,DenseShape>::value>> \
+    : assignment_from_dense_op_sparse<DstXprType, internal::ASSIGN_OP<typename DstXprType::Scalar,typename Lhs::Scalar>, internal::ASSIGN_OP2<typename DstXprType::Scalar,typename Rhs::Scalar> > \
+  {}
+
+EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(assign_op,    scalar_sum_op,add_assign_op);
+EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(add_assign_op,scalar_sum_op,add_assign_op);
+EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(sub_assign_op,scalar_sum_op,sub_assign_op);
+
+EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(assign_op,    scalar_difference_op,sub_assign_op);
+EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(add_assign_op,scalar_difference_op,sub_assign_op);
+EIGEN_CATCH_ASSIGN_DENSE_OP_SPARSE(sub_assign_op,scalar_difference_op,add_assign_op);
+
+
+// Specialization for "dst = dec.solve(rhs)"
+// NOTE we need to specialize it for Sparse2Sparse to avoid ambiguous specialization error
+template<typename DstXprType, typename DecType, typename RhsType, typename Scalar>
+struct Assignment<DstXprType, Solve<DecType,RhsType>, internal::assign_op<Scalar,Scalar>, Sparse2Sparse>
+{
+  typedef Solve<DecType,RhsType> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &)
+  {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+      dst.resize(dstRows, dstCols);
+
+    src.dec()._solve_impl(src.rhs(), dst);
+  }
+};
+
+struct Diagonal2Sparse {};
+
+template<> struct AssignmentKind<SparseShape,DiagonalShape> { typedef Diagonal2Sparse Kind; };
+
+template< typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, Diagonal2Sparse>
+{
+  typedef typename DstXprType::StorageIndex StorageIndex;
+  typedef typename DstXprType::Scalar Scalar;
+
+  template<int Options, typename AssignFunc>
+  static void run(SparseMatrix<Scalar,Options,StorageIndex> &dst, const SrcXprType &src, const AssignFunc &func)
+  { dst.assignDiagonal(src.diagonal(), func); }
+  
+  template<typename DstDerived>
+  static void run(SparseMatrixBase<DstDerived> &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
+  { dst.derived().diagonal() = src.diagonal(); }
+  
+  template<typename DstDerived>
+  static void run(SparseMatrixBase<DstDerived> &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
+  { dst.derived().diagonal() += src.diagonal(); }
+  
+  template<typename DstDerived>
+  static void run(SparseMatrixBase<DstDerived> &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &/*func*/)
+  { dst.derived().diagonal() -= src.diagonal(); }
+};
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEASSIGN_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseBlock.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseBlock.h
new file mode 100644
index 0000000000000000000000000000000000000000..b3fc859196075d4ec59993d7f70b365f74608a83
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseBlock.h
@@ -0,0 +1,568 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_BLOCK_H
+#define EIGEN_SPARSE_BLOCK_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// Subset of columns or rows
+template<typename XprType, int BlockRows, int BlockCols>
+class BlockImpl<XprType,BlockRows,BlockCols,true,Sparse>
+  : public SparseMatrixBase<Block<XprType,BlockRows,BlockCols,true> >
+{
+    typedef internal::remove_all_t<typename XprType::Nested> MatrixTypeNested_;
+    typedef Block<XprType, BlockRows, BlockCols, true> BlockType;
+public:
+    enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor };
+protected:
+    enum { OuterSize = IsRowMajor ? BlockRows : BlockCols };
+    typedef SparseMatrixBase<BlockType> Base;
+    using Base::convert_index;
+public:
+    EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType)
+
+    inline BlockImpl(XprType& xpr, Index i)
+      : m_matrix(xpr), m_outerStart(convert_index(i)), m_outerSize(OuterSize)
+    {}
+
+    inline BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
+      : m_matrix(xpr), m_outerStart(convert_index(IsRowMajor ? startRow : startCol)), m_outerSize(convert_index(IsRowMajor ? blockRows : blockCols))
+    {}
+
+    EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
+
+    Index nonZeros() const
+    {
+      typedef internal::evaluator<XprType> EvaluatorType;
+      EvaluatorType matEval(m_matrix);
+      Index nnz = 0;
+      Index end = m_outerStart + m_outerSize.value();
+      for(Index j=m_outerStart; j<end; ++j)
+        for(typename EvaluatorType::InnerIterator it(matEval, j); it; ++it)
+          ++nnz;
+      return nnz;
+    }
+
+    inline const Scalar coeff(Index row, Index col) const
+    {
+      return m_matrix.coeff(row + (IsRowMajor ? m_outerStart : 0), col + (IsRowMajor ? 0 :  m_outerStart));
+    }
+
+    inline const Scalar coeff(Index index) const
+    {
+      return m_matrix.coeff(IsRowMajor ? m_outerStart : index, IsRowMajor ? index :  m_outerStart);
+    }
+
+    inline const XprType& nestedExpression() const { return m_matrix; }
+    inline XprType& nestedExpression() { return m_matrix; }
+    Index startRow() const { return IsRowMajor ? m_outerStart : 0; }
+    Index startCol() const { return IsRowMajor ? 0 : m_outerStart; }
+    Index blockRows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
+    Index blockCols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
+
+  protected:
+
+    typename internal::ref_selector<XprType>::non_const_type m_matrix;
+    Index m_outerStart;
+    const internal::variable_if_dynamic<Index, OuterSize> m_outerSize;
+
+  protected:
+    // Disable assignment with clear error message.
+    // Note that simply removing operator= yields compilation errors with ICC+MSVC
+    template<typename T>
+    BlockImpl& operator=(const T&)
+    {
+      EIGEN_STATIC_ASSERT(sizeof(T)==0, THIS_SPARSE_BLOCK_SUBEXPRESSION_IS_READ_ONLY);
+      return *this;
+    }
+};
+
+
+/***************************************************************************
+* specialization for SparseMatrix
+***************************************************************************/
+
+namespace internal {
+
+template<typename SparseMatrixType, int BlockRows, int BlockCols>
+class sparse_matrix_block_impl
+  : public SparseCompressedBase<Block<SparseMatrixType,BlockRows,BlockCols,true> >
+{
+    typedef internal::remove_all_t<typename SparseMatrixType::Nested> MatrixTypeNested_;
+    typedef Block<SparseMatrixType, BlockRows, BlockCols, true> BlockType;
+    typedef SparseCompressedBase<Block<SparseMatrixType,BlockRows,BlockCols,true> > Base;
+    using Base::convert_index;
+public:
+    enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor };
+    EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType)
+protected:
+    typedef typename Base::IndexVector IndexVector;
+    enum { OuterSize = IsRowMajor ? BlockRows : BlockCols };
+public:
+
+    inline sparse_matrix_block_impl(SparseMatrixType& xpr, Index i)
+      : m_matrix(xpr), m_outerStart(convert_index(i)), m_outerSize(OuterSize)
+    {}
+
+    inline sparse_matrix_block_impl(SparseMatrixType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
+      : m_matrix(xpr), m_outerStart(convert_index(IsRowMajor ? startRow : startCol)), m_outerSize(convert_index(IsRowMajor ? blockRows : blockCols))
+    {}
+
+    template<typename OtherDerived>
+    inline BlockType& operator=(const SparseMatrixBase<OtherDerived>& other)
+    {
+      typedef internal::remove_all_t<typename SparseMatrixType::Nested> NestedMatrixType_;
+      NestedMatrixType_& matrix = m_matrix;
+      // This assignment is slow if this vector set is not empty
+      // and/or it is not at the end of the nonzeros of the underlying matrix.
+
+      // 1 - eval to a temporary to avoid transposition and/or aliasing issues
+      Ref<const SparseMatrix<Scalar, IsRowMajor ? RowMajor : ColMajor, StorageIndex> > tmp(other.derived());
+      eigen_internal_assert(tmp.outerSize()==m_outerSize.value());
+
+      // 2 - let's check whether there is enough allocated memory
+      Index nnz           = tmp.nonZeros();
+      Index start         = m_outerStart==0 ? 0 : m_matrix.outerIndexPtr()[m_outerStart]; // starting position of the current block
+      Index end           = m_matrix.outerIndexPtr()[m_outerStart+m_outerSize.value()]; // ending position of the current block
+      Index block_size    = end - start;                                                // available room in the current block
+      Index tail_size     = m_matrix.outerIndexPtr()[m_matrix.outerSize()] - end;
+
+      Index free_size     = m_matrix.isCompressed()
+                          ? Index(matrix.data().allocatedSize()) + block_size
+                          : block_size;
+
+      Index tmp_start = tmp.outerIndexPtr()[0];
+
+      bool update_trailing_pointers = false;
+      if(nnz>free_size)
+      {
+        // realloc manually to reduce copies
+        typename SparseMatrixType::Storage newdata(m_matrix.data().allocatedSize() - block_size + nnz);
+
+        internal::smart_copy(m_matrix.valuePtr(),       m_matrix.valuePtr() + start,      newdata.valuePtr());
+        internal::smart_copy(m_matrix.innerIndexPtr(),  m_matrix.innerIndexPtr() + start, newdata.indexPtr());
+
+        internal::smart_copy(tmp.valuePtr() + tmp_start,      tmp.valuePtr() + tmp_start + nnz,       newdata.valuePtr() + start);
+        internal::smart_copy(tmp.innerIndexPtr() + tmp_start, tmp.innerIndexPtr() + tmp_start + nnz,  newdata.indexPtr() + start);
+
+        internal::smart_copy(matrix.valuePtr()+end,       matrix.valuePtr()+end + tail_size,      newdata.valuePtr()+start+nnz);
+        internal::smart_copy(matrix.innerIndexPtr()+end,  matrix.innerIndexPtr()+end + tail_size, newdata.indexPtr()+start+nnz);
+
+        newdata.resize(m_matrix.outerIndexPtr()[m_matrix.outerSize()] - block_size + nnz);
+
+        matrix.data().swap(newdata);
+
+        update_trailing_pointers = true;
+      }
+      else
+      {
+        if(m_matrix.isCompressed() && nnz!=block_size)
+        {
+          // no need to realloc, simply copy the tail at its respective position and insert tmp
+          matrix.data().resize(start + nnz + tail_size);
+
+          internal::smart_memmove(matrix.valuePtr()+end,      matrix.valuePtr() + end+tail_size,      matrix.valuePtr() + start+nnz);
+          internal::smart_memmove(matrix.innerIndexPtr()+end, matrix.innerIndexPtr() + end+tail_size, matrix.innerIndexPtr() + start+nnz);
+
+          update_trailing_pointers = true;
+        }
+
+        internal::smart_copy(tmp.valuePtr() + tmp_start,      tmp.valuePtr() + tmp_start + nnz,       matrix.valuePtr() + start);
+        internal::smart_copy(tmp.innerIndexPtr() + tmp_start, tmp.innerIndexPtr() + tmp_start + nnz,  matrix.innerIndexPtr() + start);
+      }
+
+      // update outer index pointers and innerNonZeros
+      if(IsVectorAtCompileTime)
+      {
+        if(!m_matrix.isCompressed())
+          matrix.innerNonZeroPtr()[m_outerStart] = StorageIndex(nnz);
+        matrix.outerIndexPtr()[m_outerStart] = StorageIndex(start);
+      }
+      else
+      {
+        StorageIndex p = StorageIndex(start);
+        for(Index k=0; k<m_outerSize.value(); ++k)
+        {
+          StorageIndex nnz_k = internal::convert_index<StorageIndex>(tmp.innerVector(k).nonZeros());
+          if(!m_matrix.isCompressed())
+            matrix.innerNonZeroPtr()[m_outerStart+k] = nnz_k;
+          matrix.outerIndexPtr()[m_outerStart+k] = p;
+          p += nnz_k;
+        }
+      }
+
+      if(update_trailing_pointers)
+      {
+        StorageIndex offset = internal::convert_index<StorageIndex>(nnz - block_size);
+        for(Index k = m_outerStart + m_outerSize.value(); k<=matrix.outerSize(); ++k)
+        {
+          matrix.outerIndexPtr()[k] += offset;
+        }
+      }
+
+      return derived();
+    }
+
+    inline BlockType& operator=(const BlockType& other)
+    {
+      return operator=<BlockType>(other);
+    }
+
+    inline const Scalar* valuePtr() const
+    { return m_matrix.valuePtr(); }
+    inline Scalar* valuePtr()
+    { return m_matrix.valuePtr(); }
+
+    inline const StorageIndex* innerIndexPtr() const
+    { return m_matrix.innerIndexPtr(); }
+    inline StorageIndex* innerIndexPtr()
+    { return m_matrix.innerIndexPtr(); }
+
+    inline const StorageIndex* outerIndexPtr() const
+    { return m_matrix.outerIndexPtr() + m_outerStart; }
+    inline StorageIndex* outerIndexPtr()
+    { return m_matrix.outerIndexPtr() + m_outerStart; }
+
+    inline const StorageIndex* innerNonZeroPtr() const
+    { return isCompressed() ? 0 : (m_matrix.innerNonZeroPtr()+m_outerStart); }
+    inline StorageIndex* innerNonZeroPtr()
+    { return isCompressed() ? 0 : (m_matrix.innerNonZeroPtr()+m_outerStart); }
+
+    bool isCompressed() const { return m_matrix.innerNonZeroPtr()==0; }
+
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      return m_matrix.coeffRef(row + (IsRowMajor ? m_outerStart : 0), col + (IsRowMajor ? 0 :  m_outerStart));
+    }
+
+    inline const Scalar coeff(Index row, Index col) const
+    {
+      return m_matrix.coeff(row + (IsRowMajor ? m_outerStart : 0), col + (IsRowMajor ? 0 :  m_outerStart));
+    }
+
+    inline const Scalar coeff(Index index) const
+    {
+      return m_matrix.coeff(IsRowMajor ? m_outerStart : index, IsRowMajor ? index :  m_outerStart);
+    }
+
+    const Scalar& lastCoeff() const
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(sparse_matrix_block_impl);
+      eigen_assert(Base::nonZeros()>0);
+      if(m_matrix.isCompressed())
+        return m_matrix.valuePtr()[m_matrix.outerIndexPtr()[m_outerStart+1]-1];
+      else
+        return m_matrix.valuePtr()[m_matrix.outerIndexPtr()[m_outerStart]+m_matrix.innerNonZeroPtr()[m_outerStart]-1];
+    }
+
+    EIGEN_STRONG_INLINE Index rows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
+    EIGEN_STRONG_INLINE Index cols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
+
+    inline const SparseMatrixType& nestedExpression() const { return m_matrix; }
+    inline SparseMatrixType& nestedExpression() { return m_matrix; }
+    Index startRow() const { return IsRowMajor ? m_outerStart : 0; }
+    Index startCol() const { return IsRowMajor ? 0 : m_outerStart; }
+    Index blockRows() const { return IsRowMajor ? m_outerSize.value() : m_matrix.rows(); }
+    Index blockCols() const { return IsRowMajor ? m_matrix.cols() : m_outerSize.value(); }
+
+  protected:
+
+    typename internal::ref_selector<SparseMatrixType>::non_const_type m_matrix;
+    Index m_outerStart;
+    const internal::variable_if_dynamic<Index, OuterSize> m_outerSize;
+
+};
+
+} // namespace internal
+
+template<typename Scalar_, int Options_, typename StorageIndex_, int BlockRows, int BlockCols>
+class BlockImpl<SparseMatrix<Scalar_, Options_, StorageIndex_>,BlockRows,BlockCols,true,Sparse>
+  : public internal::sparse_matrix_block_impl<SparseMatrix<Scalar_, Options_, StorageIndex_>,BlockRows,BlockCols>
+{
+public:
+  typedef StorageIndex_ StorageIndex;
+  typedef SparseMatrix<Scalar_, Options_, StorageIndex_> SparseMatrixType;
+  typedef internal::sparse_matrix_block_impl<SparseMatrixType,BlockRows,BlockCols> Base;
+  inline BlockImpl(SparseMatrixType& xpr, Index i)
+    : Base(xpr, i)
+  {}
+
+  inline BlockImpl(SparseMatrixType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
+    : Base(xpr, startRow, startCol, blockRows, blockCols)
+  {}
+
+  using Base::operator=;
+};
+
+template<typename Scalar_, int Options_, typename StorageIndex_, int BlockRows, int BlockCols>
+class BlockImpl<const SparseMatrix<Scalar_, Options_, StorageIndex_>,BlockRows,BlockCols,true,Sparse>
+  : public internal::sparse_matrix_block_impl<const SparseMatrix<Scalar_, Options_, StorageIndex_>,BlockRows,BlockCols>
+{
+public:
+  typedef StorageIndex_ StorageIndex;
+  typedef const SparseMatrix<Scalar_, Options_, StorageIndex_> SparseMatrixType;
+  typedef internal::sparse_matrix_block_impl<SparseMatrixType,BlockRows,BlockCols> Base;
+  inline BlockImpl(SparseMatrixType& xpr, Index i)
+    : Base(xpr, i)
+  {}
+
+  inline BlockImpl(SparseMatrixType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
+    : Base(xpr, startRow, startCol, blockRows, blockCols)
+  {}
+
+  using Base::operator=;
+private:
+  template<typename Derived> BlockImpl(const SparseMatrixBase<Derived>& xpr, Index i);
+  template<typename Derived> BlockImpl(const SparseMatrixBase<Derived>& xpr);
+};
+
+//----------
+
+/** Generic implementation of sparse Block expression.
+  * Real-only.
+  */
+template<typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+class BlockImpl<XprType,BlockRows,BlockCols,InnerPanel,Sparse>
+  : public SparseMatrixBase<Block<XprType,BlockRows,BlockCols,InnerPanel> >, internal::no_assignment_operator
+{
+    typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
+    typedef SparseMatrixBase<BlockType> Base;
+    using Base::convert_index;
+public:
+    enum { IsRowMajor = internal::traits<BlockType>::IsRowMajor };
+    EIGEN_SPARSE_PUBLIC_INTERFACE(BlockType)
+
+    typedef internal::remove_all_t<typename XprType::Nested> MatrixTypeNested_;
+
+    /** Column or Row constructor
+      */
+    inline BlockImpl(XprType& xpr, Index i)
+      : m_matrix(xpr),
+        m_startRow( (BlockRows==1) && (BlockCols==XprType::ColsAtCompileTime) ? convert_index(i) : 0),
+        m_startCol( (BlockRows==XprType::RowsAtCompileTime) && (BlockCols==1) ? convert_index(i) : 0),
+        m_blockRows(BlockRows==1 ? 1 : xpr.rows()),
+        m_blockCols(BlockCols==1 ? 1 : xpr.cols())
+    {}
+
+    /** Dynamic-size constructor
+      */
+    inline BlockImpl(XprType& xpr, Index startRow, Index startCol, Index blockRows, Index blockCols)
+      : m_matrix(xpr), m_startRow(convert_index(startRow)), m_startCol(convert_index(startCol)), m_blockRows(convert_index(blockRows)), m_blockCols(convert_index(blockCols))
+    {}
+
+    inline Index rows() const { return m_blockRows.value(); }
+    inline Index cols() const { return m_blockCols.value(); }
+
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      return m_matrix.coeffRef(row + m_startRow.value(), col + m_startCol.value());
+    }
+
+    inline const Scalar coeff(Index row, Index col) const
+    {
+      return m_matrix.coeff(row + m_startRow.value(), col + m_startCol.value());
+    }
+
+    inline Scalar& coeffRef(Index index)
+    {
+      return m_matrix.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                               m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+    }
+
+    inline const Scalar coeff(Index index) const
+    {
+      return m_matrix.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                            m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+    }
+
+    inline const XprType& nestedExpression() const { return m_matrix; }
+    inline XprType& nestedExpression() { return m_matrix; }
+    Index startRow() const { return m_startRow.value(); }
+    Index startCol() const { return m_startCol.value(); }
+    Index blockRows() const { return m_blockRows.value(); }
+    Index blockCols() const { return m_blockCols.value(); }
+
+  protected:
+//     friend class internal::GenericSparseBlockInnerIteratorImpl<XprType,BlockRows,BlockCols,InnerPanel>;
+    friend struct internal::unary_evaluator<Block<XprType,BlockRows,BlockCols,InnerPanel>, internal::IteratorBased, Scalar >;
+
+    Index nonZeros() const { return Dynamic; }
+
+    typename internal::ref_selector<XprType>::non_const_type m_matrix;
+    const internal::variable_if_dynamic<Index, XprType::RowsAtCompileTime == 1 ? 0 : Dynamic> m_startRow;
+    const internal::variable_if_dynamic<Index, XprType::ColsAtCompileTime == 1 ? 0 : Dynamic> m_startCol;
+    const internal::variable_if_dynamic<Index, RowsAtCompileTime> m_blockRows;
+    const internal::variable_if_dynamic<Index, ColsAtCompileTime> m_blockCols;
+
+  protected:
+    // Disable assignment with clear error message.
+    // Note that simply removing operator= yields compilation errors with ICC+MSVC
+    template<typename T>
+    BlockImpl& operator=(const T&)
+    {
+      EIGEN_STATIC_ASSERT(sizeof(T)==0, THIS_SPARSE_BLOCK_SUBEXPRESSION_IS_READ_ONLY);
+      return *this;
+    }
+
+};
+
+namespace internal {
+
+template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
+struct unary_evaluator<Block<ArgType,BlockRows,BlockCols,InnerPanel>, IteratorBased >
+ : public evaluator_base<Block<ArgType,BlockRows,BlockCols,InnerPanel> >
+{
+    class InnerVectorInnerIterator;
+    class OuterVectorInnerIterator;
+  public:
+    typedef Block<ArgType,BlockRows,BlockCols,InnerPanel> XprType;
+    typedef typename XprType::StorageIndex StorageIndex;
+    typedef typename XprType::Scalar Scalar;
+
+    enum {
+      IsRowMajor = XprType::IsRowMajor,
+      OuterVector = (BlockCols == 1 && ArgType::IsRowMajor) || (BlockRows == 1 && !ArgType::IsRowMajor),
+      CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+      Flags = XprType::Flags
+    };
+
+    typedef std::conditional_t<OuterVector,OuterVectorInnerIterator,InnerVectorInnerIterator> InnerIterator;
+
+    explicit unary_evaluator(const XprType& op)
+      : m_argImpl(op.nestedExpression()), m_block(op)
+    {}
+
+    inline Index nonZerosEstimate() const {
+      const Index nnz = m_block.nonZeros();
+      if(nnz < 0) {
+        // Scale the non-zero estimate for the underlying expression linearly with block size.
+        // Return zero if the underlying block is empty.
+        const Index nested_sz = m_block.nestedExpression().size();        
+        return nested_sz == 0 ? 0 : m_argImpl.nonZerosEstimate() * m_block.size() / nested_sz;
+      }
+      return nnz;
+    }
+
+  protected:
+    typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
+
+    evaluator<ArgType> m_argImpl;
+    const XprType &m_block;
+};
+
+template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
+class unary_evaluator<Block<ArgType,BlockRows,BlockCols,InnerPanel>, IteratorBased>::InnerVectorInnerIterator
+ : public EvalIterator
+{
+  // NOTE MSVC fails to compile if we don't explicitly "import" IsRowMajor from unary_evaluator
+  //      because the base class EvalIterator has a private IsRowMajor enum too. (bug #1786)
+  // NOTE We cannot call it IsRowMajor because it would shadow unary_evaluator::IsRowMajor
+  enum { XprIsRowMajor = unary_evaluator::IsRowMajor };
+  const XprType& m_block;
+  Index m_end;
+public:
+
+  EIGEN_STRONG_INLINE InnerVectorInnerIterator(const unary_evaluator& aEval, Index outer)
+    : EvalIterator(aEval.m_argImpl, outer + (XprIsRowMajor ? aEval.m_block.startRow() : aEval.m_block.startCol())),
+      m_block(aEval.m_block),
+      m_end(XprIsRowMajor ? aEval.m_block.startCol()+aEval.m_block.blockCols() : aEval.m_block.startRow()+aEval.m_block.blockRows())
+  {
+    while( (EvalIterator::operator bool()) && (EvalIterator::index() < (XprIsRowMajor ? m_block.startCol() : m_block.startRow())) )
+      EvalIterator::operator++();
+  }
+
+  inline StorageIndex index() const { return EvalIterator::index() - convert_index<StorageIndex>(XprIsRowMajor ? m_block.startCol() : m_block.startRow()); }
+  inline Index outer()  const { return EvalIterator::outer() - (XprIsRowMajor ? m_block.startRow() : m_block.startCol()); }
+  inline Index row()    const { return EvalIterator::row()   - m_block.startRow(); }
+  inline Index col()    const { return EvalIterator::col()   - m_block.startCol(); }
+
+  inline operator bool() const { return EvalIterator::operator bool() && EvalIterator::index() < m_end; }
+};
+
+template<typename ArgType, int BlockRows, int BlockCols, bool InnerPanel>
+class unary_evaluator<Block<ArgType,BlockRows,BlockCols,InnerPanel>, IteratorBased>::OuterVectorInnerIterator
+{
+  // NOTE see above
+  enum { XprIsRowMajor = unary_evaluator::IsRowMajor };
+  const unary_evaluator& m_eval;
+  Index m_outerPos;
+  const Index m_innerIndex;
+  Index m_end;
+  EvalIterator m_it;
+public:
+
+  EIGEN_STRONG_INLINE OuterVectorInnerIterator(const unary_evaluator& aEval, Index outer)
+    : m_eval(aEval),
+      m_outerPos( (XprIsRowMajor ? aEval.m_block.startCol() : aEval.m_block.startRow()) ),
+      m_innerIndex(XprIsRowMajor ? aEval.m_block.startRow() : aEval.m_block.startCol()),
+      m_end(XprIsRowMajor ? aEval.m_block.startCol()+aEval.m_block.blockCols() : aEval.m_block.startRow()+aEval.m_block.blockRows()),
+      m_it(m_eval.m_argImpl, m_outerPos)
+  {
+    EIGEN_UNUSED_VARIABLE(outer);
+    eigen_assert(outer==0);
+
+    while(m_it && m_it.index() < m_innerIndex) ++m_it;
+    if((!m_it) || (m_it.index()!=m_innerIndex))
+      ++(*this);
+  }
+
+  inline StorageIndex index() const { return convert_index<StorageIndex>(m_outerPos - (XprIsRowMajor ? m_eval.m_block.startCol() : m_eval.m_block.startRow())); }
+  inline Index outer()  const { return 0; }
+  inline Index row()    const { return XprIsRowMajor ? 0 : index(); }
+  inline Index col()    const { return XprIsRowMajor ? index() : 0; }
+
+  inline Scalar value() const { return m_it.value(); }
+  inline Scalar& valueRef() { return m_it.valueRef(); }
+
+  inline OuterVectorInnerIterator& operator++()
+  {
+    // search next non-zero entry
+    while(++m_outerPos<m_end)
+    {
+      // Restart iterator at the next inner-vector:
+      internal::destroy_at(&m_it);
+      internal::construct_at(&m_it, m_eval.m_argImpl, m_outerPos);
+      // search for the key m_innerIndex in the current outer-vector
+      while(m_it && m_it.index() < m_innerIndex) ++m_it;
+      if(m_it && m_it.index()==m_innerIndex) break;
+    }
+    return *this;
+  }
+
+  inline operator bool() const { return m_outerPos < m_end; }
+};
+
+template<typename Scalar_, int Options_, typename StorageIndex_, int BlockRows, int BlockCols>
+struct unary_evaluator<Block<SparseMatrix<Scalar_, Options_, StorageIndex_>,BlockRows,BlockCols,true>, IteratorBased>
+  : evaluator<SparseCompressedBase<Block<SparseMatrix<Scalar_, Options_, StorageIndex_>,BlockRows,BlockCols,true> > >
+{
+  typedef Block<SparseMatrix<Scalar_, Options_, StorageIndex_>,BlockRows,BlockCols,true> XprType;
+  typedef evaluator<SparseCompressedBase<XprType> > Base;
+  explicit unary_evaluator(const XprType &xpr) : Base(xpr) {}
+};
+
+template<typename Scalar_, int Options_, typename StorageIndex_, int BlockRows, int BlockCols>
+struct unary_evaluator<Block<const SparseMatrix<Scalar_, Options_, StorageIndex_>,BlockRows,BlockCols,true>, IteratorBased>
+  : evaluator<SparseCompressedBase<Block<const SparseMatrix<Scalar_, Options_, StorageIndex_>,BlockRows,BlockCols,true> > >
+{
+  typedef Block<const SparseMatrix<Scalar_, Options_, StorageIndex_>,BlockRows,BlockCols,true> XprType;
+  typedef evaluator<SparseCompressedBase<XprType> > Base;
+  explicit unary_evaluator(const XprType &xpr) : Base(xpr) {}
+};
+
+} // end namespace internal
+
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_BLOCK_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseColEtree.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseColEtree.h
new file mode 100644
index 0000000000000000000000000000000000000000..ff32458279109cea03c0610244fa8d5614dc17c5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseColEtree.h
@@ -0,0 +1,208 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+
+/* 
+ 
+ * NOTE: This file is the modified version of sp_coletree.c file in SuperLU 
+ 
+ * -- SuperLU routine (version 3.1) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * August 1, 2008
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSE_COLETREE_H
+#define SPARSE_COLETREE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+/** Find the root of the tree/set containing the vertex i : Use Path halving */ 
+template<typename Index, typename IndexVector>
+Index etree_find (Index i, IndexVector& pp)
+{
+  Index p = pp(i); // Parent 
+  Index gp = pp(p); // Grand parent 
+  while (gp != p) 
+  {
+    pp(i) = gp; // Parent pointer on find path is changed to former grand parent
+    i = gp; 
+    p = pp(i);
+    gp = pp(p);
+  }
+  return p; 
+}
+
+/** Compute the column elimination tree of a sparse matrix
+  * \param mat The matrix in column-major format. 
+  * \param parent The elimination tree
+  * \param firstRowElt The column index of the first element in each row
+  * \param perm The permutation to apply to the column of \b mat
+  */
+template <typename MatrixType, typename IndexVector>
+int coletree(const MatrixType& mat, IndexVector& parent, IndexVector& firstRowElt, typename MatrixType::StorageIndex *perm=0)
+{
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  StorageIndex nc = convert_index<StorageIndex>(mat.cols()); // Number of columns
+  StorageIndex m = convert_index<StorageIndex>(mat.rows());
+  StorageIndex diagSize = (std::min)(nc,m);
+  IndexVector root(nc); // root of subtree of etree 
+  root.setZero();
+  IndexVector pp(nc); // disjoint sets 
+  pp.setZero(); // Initialize disjoint sets 
+  parent.resize(mat.cols());
+  //Compute first nonzero column in each row 
+  firstRowElt.resize(m);
+  firstRowElt.setConstant(nc);
+  firstRowElt.segment(0, diagSize).setLinSpaced(diagSize, 0, diagSize-1);
+  bool found_diag;
+  for (StorageIndex col = 0; col < nc; col++)
+  {
+    StorageIndex pcol = col;
+    if(perm) pcol  = perm[col];
+    for (typename MatrixType::InnerIterator it(mat, pcol); it; ++it)
+    { 
+      Index row = it.row();
+      firstRowElt(row) = (std::min)(firstRowElt(row), col);
+    }
+  }
+  /* Compute etree by Liu's algorithm for symmetric matrices,
+          except use (firstRowElt[r],c) in place of an edge (r,c) of A.
+    Thus each row clique in A'*A is replaced by a star
+    centered at its first vertex, which has the same fill. */
+  StorageIndex rset, cset, rroot;
+  for (StorageIndex col = 0; col < nc; col++) 
+  {
+    found_diag = col>=m;
+    pp(col) = col; 
+    cset = col; 
+    root(cset) = col; 
+    parent(col) = nc; 
+    /* The diagonal element is treated here even if it does not exist in the matrix
+     * hence the loop is executed once more */ 
+    StorageIndex pcol = col;
+    if(perm) pcol  = perm[col];
+    for (typename MatrixType::InnerIterator it(mat, pcol); it||!found_diag; ++it)
+    { //  A sequence of interleaved find and union is performed 
+      Index i = col;
+      if(it) i = it.index();
+      if (i == col) found_diag = true;
+      
+      StorageIndex row = firstRowElt(i);
+      if (row >= col) continue; 
+      rset = internal::etree_find(row, pp); // Find the name of the set containing row
+      rroot = root(rset);
+      if (rroot != col) 
+      {
+        parent(rroot) = col; 
+        pp(cset) = rset; 
+        cset = rset; 
+        root(cset) = col; 
+      }
+    }
+  }
+  return 0;  
+}
+
+/** 
+  * Depth-first search from vertex n.  No recursion.
+  * This routine was contributed by Cédric Doucet, CEDRAT Group, Meylan, France.
+*/
+template <typename IndexVector>
+void nr_etdfs (typename IndexVector::Scalar n, IndexVector& parent, IndexVector& first_kid, IndexVector& next_kid, IndexVector& post, typename IndexVector::Scalar postnum)
+{
+  typedef typename IndexVector::Scalar StorageIndex;
+  StorageIndex current = n, first, next;
+  while (postnum != n) 
+  {
+    // No kid for the current node
+    first = first_kid(current);
+    
+    // no kid for the current node
+    if (first == -1) 
+    {
+      // Numbering this node because it has no kid 
+      post(current) = postnum++;
+      
+      // looking for the next kid 
+      next = next_kid(current); 
+      while (next == -1) 
+      {
+        // No more kids : back to the parent node
+        current = parent(current); 
+        // numbering the parent node 
+        post(current) = postnum++;
+        
+        // Get the next kid 
+        next = next_kid(current); 
+      }
+      // stopping criterion 
+      if (postnum == n+1) return; 
+      
+      // Updating current node 
+      current = next; 
+    }
+    else 
+    {
+      current = first; 
+    }
+  }
+}
+
+
+/**
+  * \brief Post order a tree 
+  * \param n the number of nodes
+  * \param parent Input tree
+  * \param post postordered tree
+  */
+template <typename IndexVector>
+void treePostorder(typename IndexVector::Scalar n, IndexVector& parent, IndexVector& post)
+{
+  typedef typename IndexVector::Scalar StorageIndex;
+  IndexVector first_kid, next_kid; // Linked list of children 
+  StorageIndex postnum; 
+  // Allocate storage for working arrays and results 
+  first_kid.resize(n+1); 
+  next_kid.setZero(n+1);
+  post.setZero(n+1);
+  
+  // Set up structure describing children
+  first_kid.setConstant(-1); 
+  for (StorageIndex v = n-1; v >= 0; v--) 
+  {
+    StorageIndex dad = parent(v);
+    next_kid(v) = first_kid(dad); 
+    first_kid(dad) = v; 
+  }
+  
+  // Depth-first search from dummy root vertex #n
+  postnum = 0; 
+  internal::nr_etdfs(n, parent, first_kid, next_kid, post, postnum);
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // SPARSE_COLETREE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseCompressedBase.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseCompressedBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..c1aa42636be1f2968f13540fbddc6aeb0dc1076c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseCompressedBase.h
@@ -0,0 +1,582 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_COMPRESSED_BASE_H
+#define EIGEN_SPARSE_COMPRESSED_BASE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+template<typename Derived> class SparseCompressedBase;
+  
+namespace internal {
+
+template<typename Derived>
+struct traits<SparseCompressedBase<Derived> > : traits<Derived>
+{};
+
+template <typename Derived, class Comp, bool IsVector>
+struct inner_sort_impl;
+
+} // end namespace internal
+
+/** \ingroup SparseCore_Module
+  * \class SparseCompressedBase
+  * \brief Common base class for sparse [compressed]-{row|column}-storage format.
+  *
+  * This class defines the common interface for all derived classes implementing the compressed sparse storage format, such as:
+  *  - SparseMatrix
+  *  - Ref<SparseMatrixType,Options>
+  *  - Map<SparseMatrixType>
+  *
+  */
+template<typename Derived>
+class SparseCompressedBase
+  : public SparseMatrixBase<Derived>
+{
+  public:
+    typedef SparseMatrixBase<Derived> Base;
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseCompressedBase)
+    using Base::operator=;
+    using Base::IsRowMajor;
+    
+    class InnerIterator;
+    class ReverseInnerIterator;
+    
+  protected:
+    typedef typename Base::IndexVector IndexVector;
+    Eigen::Map<IndexVector> innerNonZeros() { return Eigen::Map<IndexVector>(innerNonZeroPtr(), isCompressed()?0:derived().outerSize()); }
+    const  Eigen::Map<const IndexVector> innerNonZeros() const { return Eigen::Map<const IndexVector>(innerNonZeroPtr(), isCompressed()?0:derived().outerSize()); }
+        
+  public:
+    
+    /** \returns the number of non zero coefficients */
+    inline Index nonZeros() const
+    {
+     if (Derived::IsVectorAtCompileTime && outerIndexPtr() == 0)
+       return derived().nonZeros();
+     else if (derived().outerSize() == 0)
+       return 0;
+     else if (isCompressed())
+       return outerIndexPtr()[derived().outerSize()] - outerIndexPtr()[0];
+     else
+       return innerNonZeros().sum();
+    }
+    
+    /** \returns a const pointer to the array of values.
+      * This function is aimed at interoperability with other libraries.
+      * \sa innerIndexPtr(), outerIndexPtr() */
+    inline const Scalar* valuePtr() const { return derived().valuePtr(); }
+    /** \returns a non-const pointer to the array of values.
+      * This function is aimed at interoperability with other libraries.
+      * \sa innerIndexPtr(), outerIndexPtr() */
+    inline Scalar* valuePtr() { return derived().valuePtr(); }
+
+    /** \returns a const pointer to the array of inner indices.
+      * This function is aimed at interoperability with other libraries.
+      * \sa valuePtr(), outerIndexPtr() */
+    inline const StorageIndex* innerIndexPtr() const { return derived().innerIndexPtr(); }
+    /** \returns a non-const pointer to the array of inner indices.
+      * This function is aimed at interoperability with other libraries.
+      * \sa valuePtr(), outerIndexPtr() */
+    inline StorageIndex* innerIndexPtr() { return derived().innerIndexPtr(); }
+
+    /** \returns a const pointer to the array of the starting positions of the inner vectors.
+      * This function is aimed at interoperability with other libraries.
+      * \warning it returns the null pointer 0 for SparseVector
+      * \sa valuePtr(), innerIndexPtr() */
+    inline const StorageIndex* outerIndexPtr() const { return derived().outerIndexPtr(); }
+    /** \returns a non-const pointer to the array of the starting positions of the inner vectors.
+      * This function is aimed at interoperability with other libraries.
+      * \warning it returns the null pointer 0 for SparseVector
+      * \sa valuePtr(), innerIndexPtr() */
+    inline StorageIndex* outerIndexPtr() { return derived().outerIndexPtr(); }
+
+    /** \returns a const pointer to the array of the number of non zeros of the inner vectors.
+      * This function is aimed at interoperability with other libraries.
+      * \warning it returns the null pointer 0 in compressed mode */
+    inline const StorageIndex* innerNonZeroPtr() const { return derived().innerNonZeroPtr(); }
+    /** \returns a non-const pointer to the array of the number of non zeros of the inner vectors.
+      * This function is aimed at interoperability with other libraries.
+      * \warning it returns the null pointer 0 in compressed mode */
+    inline StorageIndex* innerNonZeroPtr() { return derived().innerNonZeroPtr(); }
+    
+    /** \returns whether \c *this is in compressed form. */
+    inline bool isCompressed() const { return innerNonZeroPtr()==0; }
+
+    /** \returns a read-only view of the stored coefficients as a 1D array expression.
+      *
+      * \warning this method is for \b compressed \b storage \b only, and it will trigger an assertion otherwise.
+      *
+      * \sa valuePtr(), isCompressed() */
+    const Map<const Array<Scalar,Dynamic,1> > coeffs() const { eigen_assert(isCompressed()); return Array<Scalar,Dynamic,1>::Map(valuePtr(),nonZeros()); }
+
+    /** \returns a read-write view of the stored coefficients as a 1D array expression
+      *
+      * \warning this method is for \b compressed \b storage \b only, and it will trigger an assertion otherwise.
+      *
+      * Here is an example:
+      * \include SparseMatrix_coeffs.cpp
+      * and the output is:
+      * \include SparseMatrix_coeffs.out
+      *
+      * \sa valuePtr(), isCompressed() */
+    Map<Array<Scalar,Dynamic,1> > coeffs() { eigen_assert(isCompressed()); return Array<Scalar,Dynamic,1>::Map(valuePtr(),nonZeros()); }
+    
+    /** sorts the inner vectors in the range [begin,end) with respect to `Comp`  
+      * \sa innerIndicesAreSorted() */
+    template <class Comp = std::less<>>
+    inline void sortInnerIndices(Index begin, Index end) {
+      eigen_assert(begin >= 0 && end <= derived().outerSize() && end >= begin);
+      internal::inner_sort_impl<Derived, Comp, IsVectorAtCompileTime>::run(*this, begin, end);
+    }
+    
+    /** \returns the index of the first inner vector in the range [begin,end) that is not sorted with respect to `Comp`, or `end` if the range is fully sorted
+      * \sa sortInnerIndices() */
+    template <class Comp = std::less<>>
+    inline Index innerIndicesAreSorted(Index begin, Index end) const {
+      eigen_assert(begin >= 0 && end <= derived().outerSize() && end >= begin);
+      return internal::inner_sort_impl<Derived, Comp, IsVectorAtCompileTime>::check(*this, begin, end);
+    }
+
+    /** sorts the inner vectors in the range [0,outerSize) with respect to `Comp`
+      * \sa innerIndicesAreSorted() */
+    template <class Comp = std::less<>>
+    inline void sortInnerIndices() {
+      Index begin = 0;
+      Index end = derived().outerSize();
+      internal::inner_sort_impl<Derived, Comp, IsVectorAtCompileTime>::run(*this, begin, end);
+    }
+
+    /** \returns the index of the first inner vector in the range [0,outerSize) that is not sorted with respect to `Comp`, or `outerSize` if the range is fully sorted
+      * \sa sortInnerIndices() */
+    template<class Comp = std::less<>>
+    inline Index innerIndicesAreSorted() const {
+      Index begin = 0;
+      Index end = derived().outerSize();
+      return internal::inner_sort_impl<Derived, Comp, IsVectorAtCompileTime>::check(*this, begin, end);
+    }
+
+  protected:
+    /** Default constructor. Do nothing. */
+    SparseCompressedBase() {}
+
+    /** \internal return the index of the coeff at (row,col) or just before if it does not exist.
+      * This is an analogue of std::lower_bound.
+      */
+    internal::LowerBoundIndex lower_bound(Index row, Index col) const
+    {
+      eigen_internal_assert(row>=0 && row<this->rows() && col>=0 && col<this->cols());
+
+      const Index outer = Derived::IsRowMajor ? row : col;
+      const Index inner = Derived::IsRowMajor ? col : row;
+
+      Index start = this->outerIndexPtr()[outer];
+      Index end = this->isCompressed() ? this->outerIndexPtr()[outer+1] : this->outerIndexPtr()[outer] + this->innerNonZeroPtr()[outer];
+      eigen_assert(end>=start && "you are using a non finalized sparse matrix or written coefficient does not exist");
+      internal::LowerBoundIndex p;
+      p.value = std::lower_bound(this->innerIndexPtr()+start, this->innerIndexPtr()+end,inner) - this->innerIndexPtr();
+      p.found = (p.value<end) && (this->innerIndexPtr()[p.value]==inner);
+      return p;
+    }
+
+    friend struct internal::evaluator<SparseCompressedBase<Derived> >;
+
+  private:
+    template<typename OtherDerived> explicit SparseCompressedBase(const SparseCompressedBase<OtherDerived>&);
+};
+
+template<typename Derived>
+class SparseCompressedBase<Derived>::InnerIterator
+{
+  public:
+    InnerIterator()
+      : m_values(0), m_indices(0), m_outer(0), m_id(0), m_end(0)
+    {}
+
+    InnerIterator(const InnerIterator& other)
+      : m_values(other.m_values), m_indices(other.m_indices), m_outer(other.m_outer), m_id(other.m_id), m_end(other.m_end)
+    {}
+
+    InnerIterator& operator=(const InnerIterator& other)
+    {
+      m_values = other.m_values;
+      m_indices = other.m_indices;
+      const_cast<OuterType&>(m_outer).setValue(other.m_outer.value());
+      m_id = other.m_id;
+      m_end = other.m_end;
+      return *this;
+    }
+
+    InnerIterator(const SparseCompressedBase& mat, Index outer)
+      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(outer)
+    {
+      if(Derived::IsVectorAtCompileTime && mat.outerIndexPtr()==0)
+      {
+        m_id = 0;
+        m_end = mat.nonZeros();
+      }
+      else
+      {
+        m_id = mat.outerIndexPtr()[outer];
+        if(mat.isCompressed())
+          m_end = mat.outerIndexPtr()[outer+1];
+        else
+          m_end = m_id + mat.innerNonZeroPtr()[outer];
+      }
+    }
+
+    explicit InnerIterator(const SparseCompressedBase& mat) : InnerIterator(mat, Index(0))
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+    }
+
+    explicit InnerIterator(const internal::CompressedStorage<Scalar,StorageIndex>& data)
+      : m_values(data.valuePtr()), m_indices(data.indexPtr()), m_outer(0), m_id(0), m_end(data.size())
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+    }
+
+    inline InnerIterator& operator++() { m_id++; return *this; }
+    inline InnerIterator& operator+=(Index i) { m_id += i ; return *this; }
+
+    inline InnerIterator operator+(Index i) 
+    { 
+        InnerIterator result = *this;
+        result += i;
+        return result;
+    }
+
+    inline const Scalar& value() const { return m_values[m_id]; }
+    inline Scalar& valueRef() { return const_cast<Scalar&>(m_values[m_id]); }
+
+    inline StorageIndex index() const { return m_indices[m_id]; }
+    inline Index outer() const { return m_outer.value(); }
+    inline Index row() const { return IsRowMajor ? m_outer.value() : index(); }
+    inline Index col() const { return IsRowMajor ? index() : m_outer.value(); }
+
+    inline operator bool() const { return (m_id < m_end); }
+
+  protected:
+    const Scalar* m_values;
+    const StorageIndex* m_indices;
+    typedef internal::variable_if_dynamic<Index,Derived::IsVectorAtCompileTime?0:Dynamic> OuterType;
+    const OuterType m_outer;
+    Index m_id;
+    Index m_end;
+  private:
+    // If you get here, then you're not using the right InnerIterator type, e.g.:
+    //   SparseMatrix<double,RowMajor> A;
+    //   SparseMatrix<double>::InnerIterator it(A,0);
+    template<typename T> InnerIterator(const SparseMatrixBase<T>&, Index outer);
+};
+
+template<typename Derived>
+class SparseCompressedBase<Derived>::ReverseInnerIterator
+{
+  public:
+    ReverseInnerIterator(const SparseCompressedBase& mat, Index outer)
+      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(outer)
+    {
+      if(Derived::IsVectorAtCompileTime && mat.outerIndexPtr()==0)
+      {
+        m_start = 0;
+        m_id = mat.nonZeros();
+      }
+      else
+      {
+        m_start = mat.outerIndexPtr()[outer];
+        if(mat.isCompressed())
+          m_id = mat.outerIndexPtr()[outer+1];
+        else
+          m_id = m_start + mat.innerNonZeroPtr()[outer];
+      }
+    }
+
+    explicit ReverseInnerIterator(const SparseCompressedBase& mat)
+      : m_values(mat.valuePtr()), m_indices(mat.innerIndexPtr()), m_outer(0), m_start(0), m_id(mat.nonZeros())
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+    }
+
+    explicit ReverseInnerIterator(const internal::CompressedStorage<Scalar,StorageIndex>& data)
+      : m_values(data.valuePtr()), m_indices(data.indexPtr()), m_outer(0), m_start(0), m_id(data.size())
+    {
+      EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived);
+    }
+
+    inline ReverseInnerIterator& operator--() { --m_id; return *this; }
+    inline ReverseInnerIterator& operator-=(Index i) { m_id -= i; return *this; }
+
+    inline ReverseInnerIterator operator-(Index i) 
+    {
+        ReverseInnerIterator result = *this;
+        result -= i;
+        return result;
+    }
+
+    inline const Scalar& value() const { return m_values[m_id-1]; }
+    inline Scalar& valueRef() { return const_cast<Scalar&>(m_values[m_id-1]); }
+
+    inline StorageIndex index() const { return m_indices[m_id-1]; }
+    inline Index outer() const { return m_outer.value(); }
+    inline Index row() const { return IsRowMajor ? m_outer.value() : index(); }
+    inline Index col() const { return IsRowMajor ? index() : m_outer.value(); }
+
+    inline operator bool() const { return (m_id > m_start); }
+
+  protected:
+    const Scalar* m_values;
+    const StorageIndex* m_indices;
+    typedef internal::variable_if_dynamic<Index,Derived::IsVectorAtCompileTime?0:Dynamic> OuterType;
+    const OuterType m_outer;
+    Index m_start;
+    Index m_id;
+};
+
+namespace internal {
+
+// modified from https://artificial-mind.net/blog/2020/11/28/std-sort-multiple-ranges
+
+template <typename Scalar, typename StorageIndex>
+class StorageVal;
+template <typename Scalar, typename StorageIndex>
+class StorageRef;
+template <typename Scalar, typename StorageIndex>
+class CompressedStorageIterator;
+
+// class to hold an index/value pair
+template <typename Scalar, typename StorageIndex>
+class StorageVal
+{
+public:
+    
+  StorageVal(const StorageIndex& innerIndex, const Scalar& value) : m_innerIndex(innerIndex), m_value(value) {}
+  StorageVal(const StorageVal& other) : m_innerIndex(other.m_innerIndex), m_value(other.m_value) {}
+  StorageVal(StorageVal&& other) = default;
+
+  inline const StorageIndex& key() const { return m_innerIndex; }
+  inline StorageIndex& key() { return m_innerIndex; }
+  inline const Scalar& value() const { return m_value; }
+  inline Scalar& value() { return m_value; }
+
+  // enables StorageVal to be compared with respect to any type that is convertible to StorageIndex
+  inline operator StorageIndex() const { return m_innerIndex; }
+
+protected:
+  StorageIndex m_innerIndex;
+  Scalar m_value;
+private:
+  StorageVal() = delete;
+};
+// class to hold an index/value iterator pair
+// used to define assignment, swap, and comparison operators for CompressedStorageIterator
+template <typename Scalar, typename StorageIndex>
+class StorageRef 
+{
+public:
+  using value_type = StorageVal<Scalar, StorageIndex>;
+  
+  // StorageRef Needs to be move-able for sort on macos.
+  StorageRef(StorageRef&& other) = default;
+
+  inline StorageRef& operator=(const StorageRef& other) {
+    key() = other.key();
+    value() = other.value();
+    return *this;
+  }
+  inline StorageRef& operator=(const value_type& other) {
+    key() = other.key();
+    value() = other.value();
+    return *this;
+  }
+  inline operator value_type() const { return value_type(key(), value()); }
+  inline friend void swap(const StorageRef& a, const StorageRef& b) {
+    std::iter_swap(a.keyPtr(), b.keyPtr());
+    std::iter_swap(a.valuePtr(), b.valuePtr());
+  }
+
+  inline const StorageIndex& key() const { return *m_innerIndexIterator; }
+  inline StorageIndex& key() { return *m_innerIndexIterator; }
+  inline const Scalar& value() const { return *m_valueIterator; }
+  inline Scalar& value() { return *m_valueIterator; }
+  inline StorageIndex* keyPtr() const { return m_innerIndexIterator; }
+  inline Scalar* valuePtr() const { return m_valueIterator; }
+
+  // enables StorageRef to be compared with respect to any type that is convertible to StorageIndex
+  inline operator StorageIndex() const { return *m_innerIndexIterator; }
+
+protected:
+  StorageIndex* m_innerIndexIterator;
+  Scalar* m_valueIterator;
+private:
+  StorageRef() = delete;
+  // these constructors are called by the CompressedStorageIterator constructors for convenience only
+  StorageRef(StorageIndex* innerIndexIterator, Scalar* valueIterator) : m_innerIndexIterator(innerIndexIterator), m_valueIterator(valueIterator) {}
+  StorageRef(const StorageRef& other) : m_innerIndexIterator(other.m_innerIndexIterator), m_valueIterator(other.m_valueIterator) {}
+
+  friend class CompressedStorageIterator<Scalar, StorageIndex>;
+};
+
+// STL-compatible iterator class that operates on inner indices and values
+template<typename Scalar, typename StorageIndex>
+class CompressedStorageIterator
+{
+public:
+  using iterator_category = std::random_access_iterator_tag;
+  using reference = StorageRef<Scalar, StorageIndex>;
+  using difference_type = Index;
+  using value_type = typename reference::value_type;
+  using pointer = value_type*;
+
+  CompressedStorageIterator() = delete;
+  CompressedStorageIterator(difference_type index, StorageIndex* innerIndexPtr, Scalar* valuePtr) : m_index(index), m_data(innerIndexPtr, valuePtr) {}
+  CompressedStorageIterator(difference_type index, reference data) : m_index(index), m_data(data) {}
+  CompressedStorageIterator(const CompressedStorageIterator& other) : m_index(other.m_index), m_data(other.m_data) {}
+  CompressedStorageIterator(CompressedStorageIterator&& other) = default;
+  inline CompressedStorageIterator& operator=(const CompressedStorageIterator& other) {
+    m_index = other.m_index;
+    m_data = other.m_data;
+    return *this;
+  }
+
+  inline CompressedStorageIterator operator+(difference_type offset) const { return CompressedStorageIterator(m_index + offset, m_data); }
+  inline CompressedStorageIterator operator-(difference_type offset) const { return CompressedStorageIterator(m_index - offset, m_data); }
+  inline difference_type operator-(const CompressedStorageIterator& other) const { return m_index - other.m_index; }
+  inline CompressedStorageIterator& operator++() { ++m_index; return *this; }
+  inline CompressedStorageIterator& operator--() { --m_index; return *this; }
+  inline CompressedStorageIterator& operator+=(difference_type offset) { m_index += offset; return *this; }
+  inline CompressedStorageIterator& operator-=(difference_type offset) { m_index -= offset; return *this; }
+  inline reference operator*() const { return reference(m_data.keyPtr() + m_index, m_data.valuePtr() + m_index); }
+
+  #define MAKE_COMP(OP) inline bool operator OP(const CompressedStorageIterator& other) const { return m_index OP other.m_index; }
+  MAKE_COMP(<)
+  MAKE_COMP(>)
+  MAKE_COMP(>=)
+  MAKE_COMP(<=)
+  MAKE_COMP(!=)
+  MAKE_COMP(==)
+  #undef MAKE_COMP
+
+protected:
+  difference_type m_index;
+  reference m_data;
+};
+
+template <typename Derived, class Comp, bool IsVector>
+struct inner_sort_impl {
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::StorageIndex StorageIndex;
+  static inline void run(SparseCompressedBase<Derived>& obj, Index begin, Index end) {
+    const bool is_compressed = obj.isCompressed();
+    for (Index outer = begin; outer < end; outer++) {
+      Index begin_offset = obj.outerIndexPtr()[outer];
+      Index end_offset = is_compressed ? obj.outerIndexPtr()[outer + 1] : (begin_offset + obj.innerNonZeroPtr()[outer]);
+      CompressedStorageIterator<Scalar, StorageIndex> begin_it(begin_offset, obj.innerIndexPtr(), obj.valuePtr());
+      CompressedStorageIterator<Scalar, StorageIndex> end_it(end_offset, obj.innerIndexPtr(), obj.valuePtr());
+      std::sort(begin_it, end_it, Comp());
+    }
+  }
+  static inline Index check(const SparseCompressedBase<Derived>& obj, Index begin, Index end) {
+    const bool is_compressed = obj.isCompressed();
+    for (Index outer = begin; outer < end; outer++) {
+      Index begin_offset = obj.outerIndexPtr()[outer];
+      Index end_offset = is_compressed ? obj.outerIndexPtr()[outer + 1] : (begin_offset + obj.innerNonZeroPtr()[outer]);
+      const StorageIndex* begin_it = obj.innerIndexPtr() + begin_offset;
+      const StorageIndex* end_it = obj.innerIndexPtr() + end_offset;
+      bool is_sorted = std::is_sorted(begin_it, end_it, Comp());
+      if (!is_sorted) return outer;
+    }
+    return end;
+  }
+};
+template <typename Derived, class Comp>
+struct inner_sort_impl<Derived, Comp, true> {
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::StorageIndex StorageIndex;
+  static inline void run(SparseCompressedBase<Derived>& obj, Index, Index) {
+    Index begin_offset = 0;
+    Index end_offset = obj.nonZeros();
+    CompressedStorageIterator<Scalar, StorageIndex> begin_it(begin_offset, obj.innerIndexPtr(), obj.valuePtr());
+    CompressedStorageIterator<Scalar, StorageIndex> end_it(end_offset, obj.innerIndexPtr(), obj.valuePtr());
+    std::sort(begin_it, end_it, Comp());
+  }
+  static inline Index check(const SparseCompressedBase<Derived>& obj, Index, Index) {
+    Index begin_offset = 0;
+    Index end_offset = obj.nonZeros();
+    const StorageIndex* begin_it = obj.innerIndexPtr() + begin_offset;
+    const StorageIndex* end_it = obj.innerIndexPtr() + end_offset;
+    return std::is_sorted(begin_it, end_it, Comp()) ? 1 : 0;
+  }
+};
+
+template<typename Derived>
+struct evaluator<SparseCompressedBase<Derived> >
+  : evaluator_base<Derived>
+{
+  typedef typename Derived::Scalar Scalar;
+  typedef typename Derived::InnerIterator InnerIterator;
+  
+  enum {
+    CoeffReadCost = NumTraits<Scalar>::ReadCost,
+    Flags = Derived::Flags
+  };
+  
+  evaluator() : m_matrix(0), m_zero(0)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+  explicit evaluator(const Derived &mat) : m_matrix(&mat), m_zero(0)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+  
+  inline Index nonZerosEstimate() const {
+    return m_matrix->nonZeros();
+  }
+  
+  operator Derived&() { return m_matrix->const_cast_derived(); }
+  operator const Derived&() const { return *m_matrix; }
+  
+  typedef typename DenseCoeffsBase<Derived,ReadOnlyAccessors>::CoeffReturnType CoeffReturnType;
+  const Scalar& coeff(Index row, Index col) const
+  {
+    Index p = find(row,col);
+
+    if(p==Dynamic)
+      return m_zero;
+    else
+      return m_matrix->const_cast_derived().valuePtr()[p];
+  }
+
+  Scalar& coeffRef(Index row, Index col)
+  {
+    Index p = find(row,col);
+    eigen_assert(p!=Dynamic && "written coefficient does not exist");
+    return m_matrix->const_cast_derived().valuePtr()[p];
+  }
+
+protected:
+
+  Index find(Index row, Index col) const
+  {
+    internal::LowerBoundIndex p = m_matrix->lower_bound(row,col);
+    return p.found ? p.value : Dynamic;
+  }
+
+  const Derived *m_matrix;
+  const Scalar m_zero;
+};
+
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_COMPRESSED_BASE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseCwiseBinaryOp.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
new file mode 100644
index 0000000000000000000000000000000000000000..3aea5ecb540d107cbed8bb320cfbc24294cc25b2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseCwiseBinaryOp.h
@@ -0,0 +1,1000 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_CWISE_BINARY_OP_H
+#define EIGEN_SPARSE_CWISE_BINARY_OP_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+// Here we have to handle 3 cases:
+//  1 - sparse op dense
+//  2 - dense op sparse
+//  3 - sparse op sparse
+// We also need to implement a 4th iterator for:
+//  4 - dense op dense
+// Finally, we also need to distinguish between the product and other operations :
+//                configuration      returned mode
+//  1 - sparse op dense    product      sparse
+//                         generic      dense
+//  2 - dense op sparse    product      sparse
+//                         generic      dense
+//  3 - sparse op sparse   product      sparse
+//                         generic      sparse
+//  4 - dense op dense     product      dense
+//                         generic      dense
+//
+// TODO to ease compiler job, we could specialize product/quotient with a scalar
+//      and fallback to cwise-unary evaluator using bind1st_op and bind2nd_op.
+
+template<typename BinaryOp, typename Lhs, typename Rhs>
+class CwiseBinaryOpImpl<BinaryOp, Lhs, Rhs, Sparse>
+  : public SparseMatrixBase<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
+{
+  public:
+    typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> Derived;
+    typedef SparseMatrixBase<Derived> Base;
+    EIGEN_SPARSE_PUBLIC_INTERFACE(Derived)
+    EIGEN_STATIC_ASSERT((
+              (!internal::is_same<typename internal::traits<Lhs>::StorageKind,
+                                  typename internal::traits<Rhs>::StorageKind>::value)
+          ||  ((internal::evaluator<Lhs>::Flags&RowMajorBit) == (internal::evaluator<Rhs>::Flags&RowMajorBit))),
+          THE_STORAGE_ORDER_OF_BOTH_SIDES_MUST_MATCH)
+};
+
+namespace internal {
+
+// The default evaluator performs an "arithmetic" operation on two input arrays.
+// Given input arrays 'lhs' and 'rhs' and binary functor 'func', 
+// the sparse destination array 'dst' is evaluated as follows:
+//   if lhs(i,j) and rhs(i,j) are present, dst(i,j) = func(lhs(i,j), rhs(i,j))
+//   if lhs(i,j) is present and rhs(i,j) is null, dst(i,j) = func(lhs(i,j), 0)
+//   if lhs(i,j) is null and rhs(i,j) is present, dst(i,j) = func(0, rhs(i,j))
+  
+// Generic "sparse OP sparse"
+template<typename XprType> struct binary_sparse_evaluator;
+
+template<typename BinaryOp, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IteratorBased, IteratorBased>
+  : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
+{
+protected:
+  typedef typename evaluator<Lhs>::InnerIterator  LhsIterator;
+  typedef typename evaluator<Rhs>::InnerIterator  RhsIterator;
+  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
+  typedef typename traits<XprType>::Scalar Scalar;
+  typedef typename XprType::StorageIndex StorageIndex;
+public:
+
+  class InnerIterator
+  {
+  public:
+    
+    EIGEN_STRONG_INLINE InnerIterator(const binary_evaluator& aEval, Index outer)
+      : m_lhsIter(aEval.m_lhsImpl,outer), m_rhsIter(aEval.m_rhsImpl,outer), m_functor(aEval.m_functor), m_value(Scalar(0))
+    {
+      this->operator++();
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++()
+    {
+      if (m_lhsIter && m_rhsIter && (m_lhsIter.index() == m_rhsIter.index()))
+      {
+        m_id = m_lhsIter.index();
+        m_value = m_functor(m_lhsIter.value(), m_rhsIter.value());
+        ++m_lhsIter;
+        ++m_rhsIter;
+      }
+      else if (m_lhsIter && (!m_rhsIter || (m_lhsIter.index() < m_rhsIter.index())))
+      {
+        m_id = m_lhsIter.index();
+        m_value = m_functor(m_lhsIter.value(), Scalar(0));
+        ++m_lhsIter;
+      }
+      else if (m_rhsIter && (!m_lhsIter || (m_lhsIter.index() > m_rhsIter.index())))
+      {
+        m_id = m_rhsIter.index();
+        m_value = m_functor(Scalar(0), m_rhsIter.value());
+        ++m_rhsIter;
+      }
+      else
+      {
+        m_id = -1;
+      }
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const { return m_value; }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return Lhs::IsRowMajor ? m_lhsIter.row() : index(); }
+    EIGEN_STRONG_INLINE Index col() const { return Lhs::IsRowMajor ? index() : m_lhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_id>=0; }
+
+  protected:
+    LhsIterator m_lhsIter;
+    RhsIterator m_rhsIter;
+    const BinaryOp& m_functor;
+    Scalar m_value;
+    StorageIndex m_id;
+  };
+  
+  
+  enum {
+    CoeffReadCost = int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+  
+  explicit binary_evaluator(const XprType& xpr)
+    : m_functor(xpr.functor()),
+      m_lhsImpl(xpr.lhs()), 
+      m_rhsImpl(xpr.rhs())  
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+  
+  inline Index nonZerosEstimate() const {
+    return m_lhsImpl.nonZerosEstimate() + m_rhsImpl.nonZerosEstimate();
+  }
+
+protected:
+  const BinaryOp m_functor;
+  evaluator<Lhs> m_lhsImpl;
+  evaluator<Rhs> m_rhsImpl;
+};
+
+// dense op sparse
+template<typename BinaryOp, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IndexBased, IteratorBased>
+  : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
+{
+protected:
+  typedef typename evaluator<Rhs>::InnerIterator  RhsIterator;
+  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
+  typedef typename traits<XprType>::Scalar Scalar;
+  typedef typename XprType::StorageIndex StorageIndex;
+public:
+
+  class InnerIterator
+  {
+    enum { IsRowMajor = (int(Rhs::Flags)&RowMajorBit)==RowMajorBit };
+  public:
+
+    EIGEN_STRONG_INLINE InnerIterator(const binary_evaluator& aEval, Index outer)
+      : m_lhsEval(aEval.m_lhsImpl), m_rhsIter(aEval.m_rhsImpl,outer), m_functor(aEval.m_functor), m_value(0), m_id(-1), m_innerSize(aEval.m_expr.rhs().innerSize())
+    {
+      this->operator++();
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++()
+    {
+      ++m_id;
+      if(m_id<m_innerSize)
+      {
+        Scalar lhsVal = m_lhsEval.coeff(IsRowMajor?m_rhsIter.outer():m_id,
+                                        IsRowMajor?m_id:m_rhsIter.outer());
+        if(m_rhsIter && m_rhsIter.index()==m_id)
+        {
+          m_value = m_functor(lhsVal, m_rhsIter.value());
+          ++m_rhsIter;
+        }
+        else
+          m_value = m_functor(lhsVal, Scalar(0));
+      }
+
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const { eigen_internal_assert(m_id<m_innerSize); return m_value; }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index outer() const { return m_rhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_rhsIter.outer() : m_id; }
+    EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_rhsIter.outer(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_id<m_innerSize; }
+
+  protected:
+    const evaluator<Lhs> &m_lhsEval;
+    RhsIterator m_rhsIter;
+    const BinaryOp& m_functor;
+    Scalar m_value;
+    StorageIndex m_id;
+    StorageIndex m_innerSize;
+  };
+
+
+  enum {
+    CoeffReadCost = int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit binary_evaluator(const XprType& xpr)
+    : m_functor(xpr.functor()),
+      m_lhsImpl(xpr.lhs()),
+      m_rhsImpl(xpr.rhs()),
+      m_expr(xpr)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  inline Index nonZerosEstimate() const {
+    return m_expr.size();
+  }
+
+protected:
+  const BinaryOp m_functor;
+  evaluator<Lhs> m_lhsImpl;
+  evaluator<Rhs> m_rhsImpl;
+  const XprType &m_expr;
+};
+
+// sparse op dense
+template<typename BinaryOp, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<BinaryOp, Lhs, Rhs>, IteratorBased, IndexBased>
+  : evaluator_base<CwiseBinaryOp<BinaryOp, Lhs, Rhs> >
+{
+protected:
+  typedef typename evaluator<Lhs>::InnerIterator  LhsIterator;
+  typedef CwiseBinaryOp<BinaryOp, Lhs, Rhs> XprType;
+  typedef typename traits<XprType>::Scalar Scalar;
+  typedef typename XprType::StorageIndex StorageIndex;
+public:
+
+  class InnerIterator
+  {
+    enum { IsRowMajor = (int(Lhs::Flags)&RowMajorBit)==RowMajorBit };
+  public:
+
+    EIGEN_STRONG_INLINE InnerIterator(const binary_evaluator& aEval, Index outer)
+      : m_lhsIter(aEval.m_lhsImpl,outer), m_rhsEval(aEval.m_rhsImpl), m_functor(aEval.m_functor), m_value(0), m_id(-1), m_innerSize(aEval.m_expr.lhs().innerSize())
+    {
+      this->operator++();
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++()
+    {
+      ++m_id;
+      if(m_id<m_innerSize)
+      {
+        Scalar rhsVal = m_rhsEval.coeff(IsRowMajor?m_lhsIter.outer():m_id,
+                                        IsRowMajor?m_id:m_lhsIter.outer());
+        if(m_lhsIter && m_lhsIter.index()==m_id)
+        {
+          m_value = m_functor(m_lhsIter.value(), rhsVal);
+          ++m_lhsIter;
+        }
+        else
+          m_value = m_functor(Scalar(0),rhsVal);
+      }
+
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const { eigen_internal_assert(m_id<m_innerSize); return m_value; }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_lhsIter.outer() : m_id; }
+    EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_lhsIter.outer(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_id<m_innerSize; }
+
+  protected:
+    LhsIterator m_lhsIter;
+    const evaluator<Rhs> &m_rhsEval;
+    const BinaryOp& m_functor;
+    Scalar m_value;
+    StorageIndex m_id;
+    StorageIndex m_innerSize;
+  };
+
+
+  enum {
+    CoeffReadCost = int(evaluator<Lhs>::CoeffReadCost) + int(evaluator<Rhs>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit binary_evaluator(const XprType& xpr)
+    : m_functor(xpr.functor()),
+      m_lhsImpl(xpr.lhs()),
+      m_rhsImpl(xpr.rhs()),
+      m_expr(xpr)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  inline Index nonZerosEstimate() const {
+    return m_expr.size();
+  }
+
+protected:
+  const BinaryOp m_functor;
+  evaluator<Lhs> m_lhsImpl;
+  evaluator<Rhs> m_rhsImpl;
+  const XprType &m_expr;
+};
+
+template<typename T,
+         typename LhsKind   = typename evaluator_traits<typename T::Lhs>::Kind,
+         typename RhsKind   = typename evaluator_traits<typename T::Rhs>::Kind,
+         typename LhsScalar = typename traits<typename T::Lhs>::Scalar,
+         typename RhsScalar = typename traits<typename T::Rhs>::Scalar> struct sparse_conjunction_evaluator;
+
+// "sparse .* sparse"
+template<typename T1, typename T2, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs>, IteratorBased, IteratorBased>
+  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> >
+{
+  typedef CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> XprType;
+  typedef sparse_conjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+// "dense .* sparse"
+template<typename T1, typename T2, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs>, IndexBased, IteratorBased>
+  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> >
+{
+  typedef CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> XprType;
+  typedef sparse_conjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+// "sparse .* dense"
+template<typename T1, typename T2, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs>, IteratorBased, IndexBased>
+  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> >
+{
+  typedef CwiseBinaryOp<scalar_product_op<T1,T2>, Lhs, Rhs> XprType;
+  typedef sparse_conjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+
+// "sparse ./ dense"
+template<typename T1, typename T2, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_quotient_op<T1,T2>, Lhs, Rhs>, IteratorBased, IndexBased>
+  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_quotient_op<T1,T2>, Lhs, Rhs> >
+{
+  typedef CwiseBinaryOp<scalar_quotient_op<T1,T2>, Lhs, Rhs> XprType;
+  typedef sparse_conjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+
+// "sparse && sparse"
+template<typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs>, IteratorBased, IteratorBased>
+  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs> >
+{
+  typedef CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs> XprType;
+  typedef sparse_conjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+// "dense && sparse"
+template<typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs>, IndexBased, IteratorBased>
+  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs> >
+{
+  typedef CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs> XprType;
+  typedef sparse_conjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+// "sparse && dense"
+template<typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs>, IteratorBased, IndexBased>
+  : sparse_conjunction_evaluator<CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs> >
+{
+  typedef CwiseBinaryOp<scalar_boolean_and_op<bool>, Lhs, Rhs> XprType;
+  typedef sparse_conjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+
+// The conjunction "^" evaluator performs a logical "and" or set "intersection" operation on two input arrays.
+// Given input arrays 'lhs' and 'rhs' and binary functor 'func', 
+// the sparse destination array 'dst' is evaluated as follows:
+//   if lhs(i,j) and rhs(i,j) are present, dst(i,j) = func(lhs(i,j), rhs(i,j))
+//   if lhs(i,j) is present and rhs(i,j) is null, dst(i,j) is null
+//   if lhs(i,j) is null and rhs(i,j) is present, dst(i,j) is null
+
+// "sparse ^ sparse"
+template<typename XprType>
+struct sparse_conjunction_evaluator<XprType, IteratorBased, IteratorBased>
+  : evaluator_base<XprType>
+{
+protected:
+  typedef typename XprType::Functor BinaryOp;
+  typedef typename XprType::Lhs LhsArg;
+  typedef typename XprType::Rhs RhsArg;
+  typedef typename evaluator<LhsArg>::InnerIterator  LhsIterator;
+  typedef typename evaluator<RhsArg>::InnerIterator  RhsIterator;
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename traits<XprType>::Scalar Scalar;
+public:
+
+  class InnerIterator
+  {
+  public:
+    
+    EIGEN_STRONG_INLINE InnerIterator(const sparse_conjunction_evaluator& aEval, Index outer)
+      : m_lhsIter(aEval.m_lhsImpl,outer), m_rhsIter(aEval.m_rhsImpl,outer), m_functor(aEval.m_functor)
+    {
+      while (m_lhsIter && m_rhsIter && (m_lhsIter.index() != m_rhsIter.index()))
+      {
+        if (m_lhsIter.index() < m_rhsIter.index())
+          ++m_lhsIter;
+        else
+          ++m_rhsIter;
+      }
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++()
+    {
+      ++m_lhsIter;
+      ++m_rhsIter;
+      while (m_lhsIter && m_rhsIter && (m_lhsIter.index() != m_rhsIter.index()))
+      {
+        if (m_lhsIter.index() < m_rhsIter.index())
+          ++m_lhsIter;
+        else
+          ++m_rhsIter;
+      }
+      return *this;
+    }
+    
+    EIGEN_STRONG_INLINE Scalar value() const { return m_functor(m_lhsIter.value(), m_rhsIter.value()); }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_lhsIter.index(); }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); }
+    EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return (m_lhsIter && m_rhsIter); }
+
+  protected:
+    LhsIterator m_lhsIter;
+    RhsIterator m_rhsIter;
+    const BinaryOp& m_functor;
+  };
+  
+  
+  enum {
+    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+  
+  explicit sparse_conjunction_evaluator(const XprType& xpr)
+    : m_functor(xpr.functor()),
+      m_lhsImpl(xpr.lhs()), 
+      m_rhsImpl(xpr.rhs())  
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+  
+  inline Index nonZerosEstimate() const {
+    return (std::min)(m_lhsImpl.nonZerosEstimate(), m_rhsImpl.nonZerosEstimate());
+  }
+
+protected:
+  const BinaryOp m_functor;
+  evaluator<LhsArg> m_lhsImpl;
+  evaluator<RhsArg> m_rhsImpl;
+};
+
+// "dense ^ sparse"
+template<typename XprType>
+struct sparse_conjunction_evaluator<XprType, IndexBased, IteratorBased>
+  : evaluator_base<XprType>
+{
+protected:
+  typedef typename XprType::Functor BinaryOp;
+  typedef typename XprType::Lhs LhsArg;
+  typedef typename XprType::Rhs RhsArg;
+  typedef evaluator<LhsArg> LhsEvaluator;
+  typedef typename evaluator<RhsArg>::InnerIterator  RhsIterator;
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename traits<XprType>::Scalar Scalar;
+public:
+
+  class InnerIterator
+  {
+    enum { IsRowMajor = (int(RhsArg::Flags)&RowMajorBit)==RowMajorBit };
+
+  public:
+    
+    EIGEN_STRONG_INLINE InnerIterator(const sparse_conjunction_evaluator& aEval, Index outer)
+      : m_lhsEval(aEval.m_lhsImpl), m_rhsIter(aEval.m_rhsImpl,outer), m_functor(aEval.m_functor), m_outer(outer)
+    {}
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++()
+    {
+      ++m_rhsIter;
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const
+    { return m_functor(m_lhsEval.coeff(IsRowMajor?m_outer:m_rhsIter.index(),IsRowMajor?m_rhsIter.index():m_outer), m_rhsIter.value()); }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_rhsIter.index(); }
+    EIGEN_STRONG_INLINE Index outer() const { return m_rhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return m_rhsIter.row(); }
+    EIGEN_STRONG_INLINE Index col() const { return m_rhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_rhsIter; }
+    
+  protected:
+    const LhsEvaluator &m_lhsEval;
+    RhsIterator m_rhsIter;
+    const BinaryOp& m_functor;
+    const Index m_outer;
+  };
+  
+  
+  enum {
+    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+  
+  explicit sparse_conjunction_evaluator(const XprType& xpr)
+    : m_functor(xpr.functor()),
+      m_lhsImpl(xpr.lhs()), 
+      m_rhsImpl(xpr.rhs())  
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+  
+  inline Index nonZerosEstimate() const {
+    return m_rhsImpl.nonZerosEstimate();
+  }
+
+protected:
+  const BinaryOp m_functor;
+  evaluator<LhsArg> m_lhsImpl;
+  evaluator<RhsArg> m_rhsImpl;
+};
+
+// "sparse ^ dense"
+template<typename XprType>
+struct sparse_conjunction_evaluator<XprType, IteratorBased, IndexBased>
+  : evaluator_base<XprType>
+{
+protected:
+  typedef typename XprType::Functor BinaryOp;
+  typedef typename XprType::Lhs LhsArg;
+  typedef typename XprType::Rhs RhsArg;
+  typedef typename evaluator<LhsArg>::InnerIterator LhsIterator;
+  typedef evaluator<RhsArg> RhsEvaluator;
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename traits<XprType>::Scalar Scalar;
+public:
+
+  class InnerIterator
+  {
+    enum { IsRowMajor = (int(LhsArg::Flags)&RowMajorBit)==RowMajorBit };
+
+  public:
+    
+    EIGEN_STRONG_INLINE InnerIterator(const sparse_conjunction_evaluator& aEval, Index outer)
+      : m_lhsIter(aEval.m_lhsImpl,outer), m_rhsEval(aEval.m_rhsImpl), m_functor(aEval.m_functor), m_outer(outer)
+    {}
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++()
+    {
+      ++m_lhsIter;
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const
+    { return m_functor(m_lhsIter.value(),
+                       m_rhsEval.coeff(IsRowMajor?m_outer:m_lhsIter.index(),IsRowMajor?m_lhsIter.index():m_outer)); }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_lhsIter.index(); }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return m_lhsIter.row(); }
+    EIGEN_STRONG_INLINE Index col() const { return m_lhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_lhsIter; }
+    
+  protected:
+    LhsIterator m_lhsIter;
+    const evaluator<RhsArg> &m_rhsEval;
+    const BinaryOp& m_functor;
+    const Index m_outer;
+  };
+  
+  
+  enum {
+    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) + int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+  
+  explicit sparse_conjunction_evaluator(const XprType& xpr)
+    : m_functor(xpr.functor()),
+      m_lhsImpl(xpr.lhs()), 
+      m_rhsImpl(xpr.rhs())  
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+  
+  inline Index nonZerosEstimate() const {
+    return m_lhsImpl.nonZerosEstimate();
+  }
+
+protected:
+  const BinaryOp m_functor;
+  evaluator<LhsArg> m_lhsImpl;
+  evaluator<RhsArg> m_rhsImpl;
+};
+
+template<typename T,
+    typename LhsKind = typename evaluator_traits<typename T::Lhs>::Kind,
+    typename RhsKind = typename evaluator_traits<typename T::Rhs>::Kind,
+    typename LhsScalar = typename traits<typename T::Lhs>::Scalar,
+    typename RhsScalar = typename traits<typename T::Rhs>::Scalar> struct sparse_disjunction_evaluator;
+
+// The disjunction "v" evaluator performs a logical "or" or set "union" operation on two input arrays.
+// Given input arrays 'lhs' and 'rhs' and binary functor 'func', 
+// the sparse destination array 'dst' is evaluated as follows:
+//   if lhs(i,j) and rhs(i,j) are present, dst(i,j) = func(lhs(i,j), rhs(i,j))
+//   if lhs(i,j) is present and rhs(i,j) is null, dst(i,j) = lhs(i,j)
+//   if lhs(i,j) is null and rhs(i,j) is present, dst(i,j) = rhs(i,j)
+
+// "sparse v sparse"
+template <typename XprType>
+struct sparse_disjunction_evaluator<XprType, IteratorBased, IteratorBased> : evaluator_base<XprType> {
+ protected:
+  typedef typename XprType::Functor BinaryOp;
+  typedef typename XprType::Lhs LhsArg;
+  typedef typename XprType::Rhs RhsArg;
+  typedef typename evaluator<LhsArg>::InnerIterator LhsIterator;
+  typedef typename evaluator<RhsArg>::InnerIterator RhsIterator;
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename traits<XprType>::Scalar Scalar;
+
+ public:
+  class InnerIterator {
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const sparse_disjunction_evaluator& aEval, Index outer)
+        : m_lhsIter(aEval.m_lhsImpl, outer),
+          m_rhsIter(aEval.m_rhsImpl, outer),
+          m_functor(aEval.m_functor),
+          m_value(Scalar(0)) {
+      this->operator++();
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() {
+      if (m_lhsIter && m_rhsIter && (m_lhsIter.index() == m_rhsIter.index())) {
+        m_id = m_lhsIter.index();
+        m_value = m_functor(m_lhsIter.value(), m_rhsIter.value());
+        ++m_lhsIter;
+        ++m_rhsIter;
+      } else if (m_lhsIter && (!m_rhsIter || (m_lhsIter.index() < m_rhsIter.index()))) {
+        m_id = m_lhsIter.index();
+        m_value = m_lhsIter.value();
+        ++m_lhsIter;
+      } else if (m_rhsIter && (!m_lhsIter || (m_lhsIter.index() > m_rhsIter.index()))) {
+        m_id = m_rhsIter.index();
+        m_value = m_rhsIter.value();
+        ++m_rhsIter;
+      } else {
+        m_id = -1;
+      }
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const { return m_value; }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return LhsArg::IsRowMajor ? m_lhsIter.row() : index(); }
+    EIGEN_STRONG_INLINE Index col() const { return LhsArg::IsRowMajor ? index() : m_lhsIter.col(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_id >= 0; }
+
+   protected:
+    LhsIterator m_lhsIter;
+    RhsIterator m_rhsIter;
+    const BinaryOp& m_functor;
+    Scalar m_value;
+    StorageIndex m_id;
+  };
+
+  enum {
+    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) +
+                    int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit sparse_disjunction_evaluator(const XprType& xpr)
+      : m_functor(xpr.functor()), m_lhsImpl(xpr.lhs()), m_rhsImpl(xpr.rhs()) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  inline Index nonZerosEstimate() const { return m_lhsImpl.nonZerosEstimate() + m_rhsImpl.nonZerosEstimate(); }
+
+ protected:
+  const BinaryOp m_functor;
+  evaluator<LhsArg> m_lhsImpl;
+  evaluator<RhsArg> m_rhsImpl;
+};
+
+// "dense v sparse"
+template <typename XprType>
+struct sparse_disjunction_evaluator<XprType, IndexBased, IteratorBased> : evaluator_base<XprType> {
+ protected:
+  typedef typename XprType::Functor BinaryOp;
+  typedef typename XprType::Lhs LhsArg;
+  typedef typename XprType::Rhs RhsArg;
+  typedef evaluator<LhsArg> LhsEvaluator;
+  typedef typename evaluator<RhsArg>::InnerIterator RhsIterator;
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename traits<XprType>::Scalar Scalar;
+
+ public:
+  class InnerIterator {
+    enum { IsRowMajor = (int(RhsArg::Flags) & RowMajorBit) == RowMajorBit };
+
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const sparse_disjunction_evaluator& aEval, Index outer)
+        : m_lhsEval(aEval.m_lhsImpl),
+          m_rhsIter(aEval.m_rhsImpl, outer),
+          m_functor(aEval.m_functor),
+          m_value(0),
+          m_id(-1),
+          m_innerSize(aEval.m_expr.rhs().innerSize()) {
+      this->operator++();
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() {
+      ++m_id;
+      if (m_id < m_innerSize) {
+        Scalar lhsVal = m_lhsEval.coeff(IsRowMajor ? m_rhsIter.outer() : m_id, IsRowMajor ? m_id : m_rhsIter.outer());
+        if (m_rhsIter && m_rhsIter.index() == m_id) {
+          m_value = m_functor(lhsVal, m_rhsIter.value());
+          ++m_rhsIter;
+        } else
+          m_value = lhsVal;
+      }
+
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const {
+      eigen_internal_assert(m_id < m_innerSize);
+      return m_value;
+    }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index outer() const { return m_rhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_rhsIter.outer() : m_id; }
+    EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_rhsIter.outer(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_id < m_innerSize; }
+
+   protected:
+    const evaluator<LhsArg>& m_lhsEval;
+    RhsIterator m_rhsIter;
+    const BinaryOp& m_functor;
+    Scalar m_value;
+    StorageIndex m_id;
+    StorageIndex m_innerSize;
+  };
+
+  enum {
+    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) +
+                    int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit sparse_disjunction_evaluator(const XprType& xpr)
+      : m_functor(xpr.functor()), m_lhsImpl(xpr.lhs()), m_rhsImpl(xpr.rhs()), m_expr(xpr) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  inline Index nonZerosEstimate() const { return m_expr.size(); }
+
+ protected:
+  const BinaryOp m_functor;
+  evaluator<LhsArg> m_lhsImpl;
+  evaluator<RhsArg> m_rhsImpl;
+  const XprType& m_expr;
+};
+
+// "sparse v dense"
+template <typename XprType>
+struct sparse_disjunction_evaluator<XprType, IteratorBased, IndexBased> : evaluator_base<XprType> {
+ protected:
+  typedef typename XprType::Functor BinaryOp;
+  typedef typename XprType::Lhs LhsArg;
+  typedef typename XprType::Rhs RhsArg;
+  typedef typename evaluator<LhsArg>::InnerIterator LhsIterator;
+  typedef evaluator<RhsArg> RhsEvaluator;
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename traits<XprType>::Scalar Scalar;
+
+ public:
+  class InnerIterator {
+    enum { IsRowMajor = (int(LhsArg::Flags) & RowMajorBit) == RowMajorBit };
+
+   public:
+    EIGEN_STRONG_INLINE InnerIterator(const sparse_disjunction_evaluator& aEval, Index outer)
+        : m_lhsIter(aEval.m_lhsImpl, outer),
+          m_rhsEval(aEval.m_rhsImpl),
+          m_functor(aEval.m_functor),
+          m_value(0),
+          m_id(-1),
+          m_innerSize(aEval.m_expr.lhs().innerSize()) {
+      this->operator++();
+    }
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++() {
+      ++m_id;
+      if (m_id < m_innerSize) {
+        Scalar rhsVal = m_rhsEval.coeff(IsRowMajor ? m_lhsIter.outer() : m_id, IsRowMajor ? m_id : m_lhsIter.outer());
+        if (m_lhsIter && m_lhsIter.index() == m_id) {
+          m_value = m_functor(m_lhsIter.value(), rhsVal);
+          ++m_lhsIter;
+        } else
+          m_value = rhsVal;
+      }
+
+      return *this;
+    }
+
+    EIGEN_STRONG_INLINE Scalar value() const {
+      eigen_internal_assert(m_id < m_innerSize);
+      return m_value;
+    }
+
+    EIGEN_STRONG_INLINE StorageIndex index() const { return m_id; }
+    EIGEN_STRONG_INLINE Index outer() const { return m_lhsIter.outer(); }
+    EIGEN_STRONG_INLINE Index row() const { return IsRowMajor ? m_lhsIter.outer() : m_id; }
+    EIGEN_STRONG_INLINE Index col() const { return IsRowMajor ? m_id : m_lhsIter.outer(); }
+
+    EIGEN_STRONG_INLINE operator bool() const { return m_id < m_innerSize; }
+
+   protected:
+    LhsIterator m_lhsIter;
+    const evaluator<RhsArg>& m_rhsEval;
+    const BinaryOp& m_functor;
+    Scalar m_value;
+    StorageIndex m_id;
+    StorageIndex m_innerSize;
+  };
+
+  enum {
+    CoeffReadCost = int(evaluator<LhsArg>::CoeffReadCost) + int(evaluator<RhsArg>::CoeffReadCost) +
+                    int(functor_traits<BinaryOp>::Cost),
+    Flags = XprType::Flags
+  };
+
+  explicit sparse_disjunction_evaluator(const XprType& xpr)
+      : m_functor(xpr.functor()), m_lhsImpl(xpr.lhs()), m_rhsImpl(xpr.rhs()), m_expr(xpr) {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<BinaryOp>::Cost);
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+
+  inline Index nonZerosEstimate() const { return m_expr.size(); }
+
+ protected:
+  const BinaryOp m_functor;
+  evaluator<LhsArg> m_lhsImpl;
+  evaluator<RhsArg> m_rhsImpl;
+  const XprType& m_expr;
+};
+
+// when DupFunc is wrapped with scalar_dup_op, use disjunction evaulator
+template <typename T1, typename T2, typename DupFunc, typename Lhs, typename Rhs>
+struct binary_evaluator<CwiseBinaryOp<scalar_disjunction_op<DupFunc, T1, T2>, Lhs, Rhs>, IteratorBased, IteratorBased>
+    : sparse_disjunction_evaluator<CwiseBinaryOp<scalar_disjunction_op<DupFunc, T1, T2>, Lhs, Rhs> > {
+  typedef CwiseBinaryOp<scalar_disjunction_op<DupFunc, T1, T2>, Lhs, Rhs> XprType;
+  typedef sparse_disjunction_evaluator<XprType> Base;
+  explicit binary_evaluator(const XprType& xpr) : Base(xpr) {}
+};
+}
+
+/***************************************************************************
+* Implementation of SparseMatrixBase and SparseCwise functions/operators
+***************************************************************************/
+
+template<typename Derived>
+template<typename OtherDerived>
+Derived& SparseMatrixBase<Derived>::operator+=(const EigenBase<OtherDerived> &other)
+{
+  call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+Derived& SparseMatrixBase<Derived>::operator-=(const EigenBase<OtherDerived> &other)
+{
+  call_assignment(derived(), other.derived(), internal::assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE Derived &
+SparseMatrixBase<Derived>::operator-=(const SparseMatrixBase<OtherDerived> &other)
+{
+  return derived() = derived() - other.derived();
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE Derived &
+SparseMatrixBase<Derived>::operator+=(const SparseMatrixBase<OtherDerived>& other)
+{
+  return derived() = derived() + other.derived();
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+Derived& SparseMatrixBase<Derived>::operator+=(const DiagonalBase<OtherDerived>& other)
+{
+  call_assignment_no_alias(derived(), other.derived(), internal::add_assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+Derived& SparseMatrixBase<Derived>::operator-=(const DiagonalBase<OtherDerived>& other)
+{
+  call_assignment_no_alias(derived(), other.derived(), internal::sub_assign_op<Scalar,typename OtherDerived::Scalar>());
+  return derived();
+}
+    
+template<typename Derived>
+template<typename OtherDerived>
+EIGEN_STRONG_INLINE const typename SparseMatrixBase<Derived>::template CwiseProductDenseReturnType<OtherDerived>::Type
+SparseMatrixBase<Derived>::cwiseProduct(const MatrixBase<OtherDerived> &other) const
+{
+  return typename CwiseProductDenseReturnType<OtherDerived>::Type(derived(), other.derived());
+}
+
+template<typename DenseDerived, typename SparseDerived>
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>
+operator+(const MatrixBase<DenseDerived> &a, const SparseMatrixBase<SparseDerived> &b)
+{
+  return CwiseBinaryOp<internal::scalar_sum_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>(a.derived(), b.derived());
+}
+
+template<typename SparseDerived, typename DenseDerived>
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_sum_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>
+operator+(const SparseMatrixBase<SparseDerived> &a, const MatrixBase<DenseDerived> &b)
+{
+  return CwiseBinaryOp<internal::scalar_sum_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>(a.derived(), b.derived());
+}
+
+template<typename DenseDerived, typename SparseDerived>
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>
+operator-(const MatrixBase<DenseDerived> &a, const SparseMatrixBase<SparseDerived> &b)
+{
+  return CwiseBinaryOp<internal::scalar_difference_op<typename DenseDerived::Scalar,typename SparseDerived::Scalar>, const DenseDerived, const SparseDerived>(a.derived(), b.derived());
+}
+
+template<typename SparseDerived, typename DenseDerived>
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_difference_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>
+operator-(const SparseMatrixBase<SparseDerived> &a, const MatrixBase<DenseDerived> &b)
+{
+  return CwiseBinaryOp<internal::scalar_difference_op<typename SparseDerived::Scalar,typename DenseDerived::Scalar>, const SparseDerived, const DenseDerived>(a.derived(), b.derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_CWISE_BINARY_OP_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseCwiseUnaryOp.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
new file mode 100644
index 0000000000000000000000000000000000000000..6f48fa72a1b6fd09a59617d73ea6f01a98f737e8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseCwiseUnaryOp.h
@@ -0,0 +1,152 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_CWISE_UNARY_OP_H
+#define EIGEN_SPARSE_CWISE_UNARY_OP_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+  
+template<typename UnaryOp, typename ArgType>
+struct unary_evaluator<CwiseUnaryOp<UnaryOp,ArgType>, IteratorBased>
+  : public evaluator_base<CwiseUnaryOp<UnaryOp,ArgType> >
+{
+  public:
+    typedef CwiseUnaryOp<UnaryOp, ArgType> XprType;
+
+    class InnerIterator;
+    
+    enum {
+      CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<UnaryOp>::Cost),
+      Flags = XprType::Flags
+    };
+    
+    explicit unary_evaluator(const XprType& op) : m_functor(op.functor()), m_argImpl(op.nestedExpression())
+    {
+      EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<UnaryOp>::Cost);
+      EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+    }
+    
+    inline Index nonZerosEstimate() const {
+      return m_argImpl.nonZerosEstimate();
+    }
+
+  protected:
+    typedef typename evaluator<ArgType>::InnerIterator        EvalIterator;
+    
+    const UnaryOp m_functor;
+    evaluator<ArgType> m_argImpl;
+};
+
+template<typename UnaryOp, typename ArgType>
+class unary_evaluator<CwiseUnaryOp<UnaryOp,ArgType>, IteratorBased>::InnerIterator
+    : public unary_evaluator<CwiseUnaryOp<UnaryOp,ArgType>, IteratorBased>::EvalIterator
+{
+  protected:
+    typedef typename XprType::Scalar Scalar;
+    typedef typename unary_evaluator<CwiseUnaryOp<UnaryOp,ArgType>, IteratorBased>::EvalIterator Base;
+  public:
+
+    EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& unaryOp, Index outer)
+      : Base(unaryOp.m_argImpl,outer), m_functor(unaryOp.m_functor)
+    {}
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++()
+    { Base::operator++(); return *this; }
+
+    EIGEN_STRONG_INLINE Scalar value() const { return m_functor(Base::value()); }
+
+  protected:
+    const UnaryOp m_functor;
+  private:
+    Scalar& valueRef();
+};
+
+template<typename ViewOp, typename ArgType>
+struct unary_evaluator<CwiseUnaryView<ViewOp,ArgType>, IteratorBased>
+  : public evaluator_base<CwiseUnaryView<ViewOp,ArgType> >
+{
+  public:
+    typedef CwiseUnaryView<ViewOp, ArgType> XprType;
+
+    class InnerIterator;
+    
+    enum {
+      CoeffReadCost = int(evaluator<ArgType>::CoeffReadCost) + int(functor_traits<ViewOp>::Cost),
+      Flags = XprType::Flags
+    };
+    
+    explicit unary_evaluator(const XprType& op) : m_functor(op.functor()), m_argImpl(op.nestedExpression())
+    {
+      EIGEN_INTERNAL_CHECK_COST_VALUE(functor_traits<ViewOp>::Cost);
+      EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+    }
+
+  protected:
+    typedef typename evaluator<ArgType>::InnerIterator        EvalIterator;
+    
+    const ViewOp m_functor;
+    evaluator<ArgType> m_argImpl;
+};
+
+template<typename ViewOp, typename ArgType>
+class unary_evaluator<CwiseUnaryView<ViewOp,ArgType>, IteratorBased>::InnerIterator
+    : public unary_evaluator<CwiseUnaryView<ViewOp,ArgType>, IteratorBased>::EvalIterator
+{
+  protected:
+    typedef typename XprType::Scalar Scalar;
+    typedef typename unary_evaluator<CwiseUnaryView<ViewOp,ArgType>, IteratorBased>::EvalIterator Base;
+  public:
+
+    EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& unaryOp, Index outer)
+      : Base(unaryOp.m_argImpl,outer), m_functor(unaryOp.m_functor)
+    {}
+
+    EIGEN_STRONG_INLINE InnerIterator& operator++()
+    { Base::operator++(); return *this; }
+
+    EIGEN_STRONG_INLINE Scalar value() const { return m_functor(Base::value()); }
+    EIGEN_STRONG_INLINE Scalar& valueRef() { return m_functor(Base::valueRef()); }
+
+  protected:
+    const ViewOp m_functor;
+};
+
+} // end namespace internal
+
+template<typename Derived>
+EIGEN_STRONG_INLINE Derived&
+SparseMatrixBase<Derived>::operator*=(const Scalar& other)
+{
+  typedef typename internal::evaluator<Derived>::InnerIterator EvalIterator;
+  internal::evaluator<Derived> thisEval(derived());
+  for (Index j=0; j<outerSize(); ++j)
+    for (EvalIterator i(thisEval,j); i; ++i)
+      i.valueRef() *= other;
+  return derived();
+}
+
+template<typename Derived>
+EIGEN_STRONG_INLINE Derived&
+SparseMatrixBase<Derived>::operator/=(const Scalar& other)
+{
+  typedef typename internal::evaluator<Derived>::InnerIterator EvalIterator;
+  internal::evaluator<Derived> thisEval(derived());
+  for (Index j=0; j<outerSize(); ++j)
+    for (EvalIterator i(thisEval,j); i; ++i)
+      i.valueRef() /= other;
+  return derived();
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_CWISE_UNARY_OP_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseDenseProduct.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseDenseProduct.h
new file mode 100644
index 0000000000000000000000000000000000000000..9c0c531001acc6c39d7dc8c844aefa8746e8a344
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseDenseProduct.h
@@ -0,0 +1,352 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEDENSEPRODUCT_H
+#define EIGEN_SPARSEDENSEPRODUCT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+template <> struct product_promote_storage_type<Sparse,Dense, OuterProduct> { typedef Sparse ret; };
+template <> struct product_promote_storage_type<Dense,Sparse, OuterProduct> { typedef Sparse ret; };
+
+template<typename SparseLhsType, typename DenseRhsType, typename DenseResType,
+         typename AlphaType,
+         int LhsStorageOrder = ((SparseLhsType::Flags&RowMajorBit)==RowMajorBit) ? RowMajor : ColMajor,
+         bool ColPerCol = ((DenseRhsType::Flags&RowMajorBit)==0) || DenseRhsType::ColsAtCompileTime==1>
+struct sparse_time_dense_product_impl;
+
+template<typename SparseLhsType, typename DenseRhsType, typename DenseResType>
+struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, typename DenseResType::Scalar, RowMajor, true>
+{
+  typedef internal::remove_all_t<SparseLhsType> Lhs;
+  typedef internal::remove_all_t<DenseRhsType> Rhs;
+  typedef internal::remove_all_t<DenseResType> Res;
+  typedef typename evaluator<Lhs>::InnerIterator LhsInnerIterator;
+  typedef evaluator<Lhs> LhsEval;
+  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const typename Res::Scalar& alpha)
+  {
+    LhsEval lhsEval(lhs);
+    
+    Index n = lhs.outerSize();
+#ifdef EIGEN_HAS_OPENMP
+    Eigen::initParallel();
+    Index threads = Eigen::nbThreads();
+#endif
+    
+    for(Index c=0; c<rhs.cols(); ++c)
+    {
+#ifdef EIGEN_HAS_OPENMP
+      // This 20000 threshold has been found experimentally on 2D and 3D Poisson problems.
+      // It basically represents the minimal amount of work to be done to be worth it.
+      if(threads>1 && lhsEval.nonZerosEstimate() > 20000)
+      {
+        #pragma omp parallel for schedule(dynamic,(n+threads*4-1)/(threads*4)) num_threads(threads)
+        for(Index i=0; i<n; ++i)
+          processRow(lhsEval,rhs,res,alpha,i,c);
+      }
+      else
+#endif
+      {
+        for(Index i=0; i<n; ++i)
+          processRow(lhsEval,rhs,res,alpha,i,c);
+      }
+    }
+  }
+  
+  static void processRow(const LhsEval& lhsEval, const DenseRhsType& rhs, DenseResType& res, const typename Res::Scalar& alpha, Index i, Index col)
+  {
+    // Two accumulators, which breaks the dependency chain on the accumulator
+    // and allows more instruction-level parallelism in the following loop
+    typename Res::Scalar tmp_a(0);
+    typename Res::Scalar tmp_b(0);
+    for(LhsInnerIterator it(lhsEval,i); it ;++it) {
+      tmp_a += it.value() * rhs.coeff(it.index(), col);
+      ++it;
+      if(it) {
+        tmp_b += it.value() * rhs.coeff(it.index(), col);
+      }
+    }
+    res.coeffRef(i, col) += alpha * (tmp_a + tmp_b);
+  }
+  
+};
+
+// FIXME: what is the purpose of the following specialization? Is it for the BlockedSparse format?
+// -> let's disable it for now as it is conflicting with generic scalar*matrix and matrix*scalar operators
+// template<typename T1, typename T2/*, int Options_, typename StrideType_*/>
+// struct ScalarBinaryOpTraits<T1, Ref<T2/*, Options_, StrideType_*/> >
+// {
+//   enum {
+//     Defined = 1
+//   };
+//   typedef typename CwiseUnaryOp<scalar_multiple2_op<T1, typename T2::Scalar>, T2>::PlainObject ReturnType;
+// };
+
+template<typename SparseLhsType, typename DenseRhsType, typename DenseResType, typename AlphaType>
+struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, AlphaType, ColMajor, true>
+{
+  typedef internal::remove_all_t<SparseLhsType> Lhs;
+  typedef internal::remove_all_t<DenseRhsType> Rhs;
+  typedef internal::remove_all_t<DenseResType> Res;
+  typedef evaluator<Lhs> LhsEval;
+  typedef typename LhsEval::InnerIterator LhsInnerIterator;
+  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const AlphaType& alpha)
+  {
+    LhsEval lhsEval(lhs);
+    for(Index c=0; c<rhs.cols(); ++c)
+    {
+      for(Index j=0; j<lhs.outerSize(); ++j)
+      {
+//        typename Res::Scalar rhs_j = alpha * rhs.coeff(j,c);
+        typename ScalarBinaryOpTraits<AlphaType, typename Rhs::Scalar>::ReturnType rhs_j(alpha * rhs.coeff(j,c));
+        for(LhsInnerIterator it(lhsEval,j); it ;++it)
+          res.coeffRef(it.index(),c) += it.value() * rhs_j;
+      }
+    }
+  }
+};
+
+template<typename SparseLhsType, typename DenseRhsType, typename DenseResType>
+struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, typename DenseResType::Scalar, RowMajor, false>
+{
+  typedef internal::remove_all_t<SparseLhsType> Lhs;
+  typedef internal::remove_all_t<DenseRhsType> Rhs;
+  typedef internal::remove_all_t<DenseResType> Res;
+  typedef evaluator<Lhs> LhsEval;
+  typedef typename LhsEval::InnerIterator LhsInnerIterator;
+  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const typename Res::Scalar& alpha)
+  {
+    Index n = lhs.rows();
+    LhsEval lhsEval(lhs);
+
+#ifdef EIGEN_HAS_OPENMP
+    Eigen::initParallel();
+    Index threads = Eigen::nbThreads();
+    // This 20000 threshold has been found experimentally on 2D and 3D Poisson problems.
+    // It basically represents the minimal amount of work to be done to be worth it.
+    if(threads>1 && lhsEval.nonZerosEstimate()*rhs.cols() > 20000)
+    {
+      #pragma omp parallel for schedule(dynamic,(n+threads*4-1)/(threads*4)) num_threads(threads)
+      for(Index i=0; i<n; ++i)
+        processRow(lhsEval,rhs,res,alpha,i);
+    }
+    else
+#endif
+    {
+      for(Index i=0; i<n; ++i)
+        processRow(lhsEval, rhs, res, alpha, i);
+    }
+  }
+
+  static void processRow(const LhsEval& lhsEval, const DenseRhsType& rhs, Res& res, const typename Res::Scalar& alpha, Index i)
+  {
+    typename Res::RowXpr res_i(res.row(i));
+    for(LhsInnerIterator it(lhsEval,i); it ;++it)
+      res_i += (alpha*it.value()) * rhs.row(it.index());
+  }
+};
+
+template<typename SparseLhsType, typename DenseRhsType, typename DenseResType>
+struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, typename DenseResType::Scalar, ColMajor, false>
+{
+  typedef internal::remove_all_t<SparseLhsType> Lhs;
+  typedef internal::remove_all_t<DenseRhsType> Rhs;
+  typedef internal::remove_all_t<DenseResType> Res;
+  typedef typename evaluator<Lhs>::InnerIterator LhsInnerIterator;
+  static void run(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const typename Res::Scalar& alpha)
+  {
+    evaluator<Lhs> lhsEval(lhs);
+    for(Index j=0; j<lhs.outerSize(); ++j)
+    {
+      typename Rhs::ConstRowXpr rhs_j(rhs.row(j));
+      for(LhsInnerIterator it(lhsEval,j); it ;++it)
+        res.row(it.index()) += (alpha*it.value()) * rhs_j;
+    }
+  }
+};
+
+template<typename SparseLhsType, typename DenseRhsType, typename DenseResType,typename AlphaType>
+inline void sparse_time_dense_product(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const AlphaType& alpha)
+{
+  sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, AlphaType>::run(lhs, rhs, res, alpha);
+}
+
+} // end namespace internal
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, int ProductType>
+struct generic_product_impl<Lhs, Rhs, SparseShape, DenseShape, ProductType>
+ : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,SparseShape,DenseShape,ProductType> >
+{
+  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
+  
+  template<typename Dest>
+  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
+  {
+    typedef typename nested_eval<Lhs,((Rhs::Flags&RowMajorBit)==0) ? 1 : Rhs::ColsAtCompileTime>::type LhsNested;
+    typedef typename nested_eval<Rhs,((Lhs::Flags&RowMajorBit)==0) ? 1 : Dynamic>::type RhsNested;
+    LhsNested lhsNested(lhs);
+    RhsNested rhsNested(rhs);
+    internal::sparse_time_dense_product(lhsNested, rhsNested, dst, alpha);
+  }
+};
+
+template<typename Lhs, typename Rhs, int ProductType>
+struct generic_product_impl<Lhs, Rhs, SparseTriangularShape, DenseShape, ProductType>
+  : generic_product_impl<Lhs, Rhs, SparseShape, DenseShape, ProductType>
+{};
+
+template<typename Lhs, typename Rhs, int ProductType>
+struct generic_product_impl<Lhs, Rhs, DenseShape, SparseShape, ProductType>
+  : generic_product_impl_base<Lhs,Rhs,generic_product_impl<Lhs,Rhs,DenseShape,SparseShape,ProductType> >
+{
+  typedef typename Product<Lhs,Rhs>::Scalar Scalar;
+  
+  template<typename Dst>
+  static void scaleAndAddTo(Dst& dst, const Lhs& lhs, const Rhs& rhs, const Scalar& alpha)
+  {
+    typedef typename nested_eval<Lhs,((Rhs::Flags&RowMajorBit)==0) ? Dynamic : 1>::type LhsNested;
+    typedef typename nested_eval<Rhs,((Lhs::Flags&RowMajorBit)==RowMajorBit) ? 1 : Lhs::RowsAtCompileTime>::type RhsNested;
+    LhsNested lhsNested(lhs);
+    RhsNested rhsNested(rhs);
+    
+    // transpose everything
+    Transpose<Dst> dstT(dst);
+    internal::sparse_time_dense_product(rhsNested.transpose(), lhsNested.transpose(), dstT, alpha);
+  }
+};
+
+template<typename Lhs, typename Rhs, int ProductType>
+struct generic_product_impl<Lhs, Rhs, DenseShape, SparseTriangularShape, ProductType>
+  : generic_product_impl<Lhs, Rhs, DenseShape, SparseShape, ProductType>
+{};
+
+template<typename LhsT, typename RhsT, bool NeedToTranspose>
+struct sparse_dense_outer_product_evaluator
+{
+protected:
+  typedef std::conditional_t<NeedToTranspose,RhsT,LhsT> Lhs1;
+  typedef std::conditional_t<NeedToTranspose,LhsT,RhsT> ActualRhs;
+  typedef Product<LhsT,RhsT,DefaultProduct> ProdXprType;
+  
+  // if the actual left-hand side is a dense vector,
+  // then build a sparse-view so that we can seamlessly iterate over it.
+  typedef std::conditional_t<is_same<typename internal::traits<Lhs1>::StorageKind,Sparse>::value,
+            Lhs1, SparseView<Lhs1> > ActualLhs;
+  typedef std::conditional_t<is_same<typename internal::traits<Lhs1>::StorageKind,Sparse>::value,
+            Lhs1 const&, SparseView<Lhs1> > LhsArg;
+            
+  typedef evaluator<ActualLhs> LhsEval;
+  typedef evaluator<ActualRhs> RhsEval;
+  typedef typename evaluator<ActualLhs>::InnerIterator LhsIterator;
+  typedef typename ProdXprType::Scalar Scalar;
+  
+public:
+  enum {
+    Flags = NeedToTranspose ? RowMajorBit : 0,
+    CoeffReadCost = HugeCost
+  };
+  
+  class InnerIterator : public LhsIterator
+  {
+  public:
+    InnerIterator(const sparse_dense_outer_product_evaluator &xprEval, Index outer)
+      : LhsIterator(xprEval.m_lhsXprImpl, 0),
+        m_outer(outer),
+        m_empty(false),
+        m_factor(get(xprEval.m_rhsXprImpl, outer, typename internal::traits<ActualRhs>::StorageKind() ))
+    {}
+    
+    EIGEN_STRONG_INLINE Index outer() const { return m_outer; }
+    EIGEN_STRONG_INLINE Index row()   const { return NeedToTranspose ? m_outer : LhsIterator::index(); }
+    EIGEN_STRONG_INLINE Index col()   const { return NeedToTranspose ? LhsIterator::index() : m_outer; }
+
+    EIGEN_STRONG_INLINE Scalar value() const { return LhsIterator::value() * m_factor; }
+    EIGEN_STRONG_INLINE operator bool() const { return LhsIterator::operator bool() && (!m_empty); }
+    
+  protected:
+    Scalar get(const RhsEval &rhs, Index outer, Dense = Dense()) const
+    {
+      return rhs.coeff(outer);
+    }
+    
+    Scalar get(const RhsEval &rhs, Index outer, Sparse = Sparse())
+    {
+      typename RhsEval::InnerIterator it(rhs, outer);
+      if (it && it.index()==0 && it.value()!=Scalar(0))
+        return it.value();
+      m_empty = true;
+      return Scalar(0);
+    }
+    
+    Index m_outer;
+    bool m_empty;
+    Scalar m_factor;
+  };
+  
+  sparse_dense_outer_product_evaluator(const Lhs1 &lhs, const ActualRhs &rhs)
+     : m_lhs(lhs), m_lhsXprImpl(m_lhs), m_rhsXprImpl(rhs)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+  
+  // transpose case
+  sparse_dense_outer_product_evaluator(const ActualRhs &rhs, const Lhs1 &lhs)
+     : m_lhs(lhs), m_lhsXprImpl(m_lhs), m_rhsXprImpl(rhs)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+    
+protected:
+  const LhsArg m_lhs;
+  evaluator<ActualLhs> m_lhsXprImpl;
+  evaluator<ActualRhs> m_rhsXprImpl;
+};
+
+// sparse * dense outer product
+template<typename Lhs, typename Rhs>
+struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, OuterProduct, SparseShape, DenseShape>
+  : sparse_dense_outer_product_evaluator<Lhs,Rhs, Lhs::IsRowMajor>
+{
+  typedef sparse_dense_outer_product_evaluator<Lhs,Rhs, Lhs::IsRowMajor> Base;
+  
+  typedef Product<Lhs, Rhs> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+
+  explicit product_evaluator(const XprType& xpr)
+    : Base(xpr.lhs(), xpr.rhs())
+  {}
+  
+};
+
+template<typename Lhs, typename Rhs>
+struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, OuterProduct, DenseShape, SparseShape>
+  : sparse_dense_outer_product_evaluator<Lhs,Rhs, Rhs::IsRowMajor>
+{
+  typedef sparse_dense_outer_product_evaluator<Lhs,Rhs, Rhs::IsRowMajor> Base;
+  
+  typedef Product<Lhs, Rhs> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+
+  explicit product_evaluator(const XprType& xpr)
+    : Base(xpr.lhs(), xpr.rhs())
+  {}
+  
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEDENSEPRODUCT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseDiagonalProduct.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseDiagonalProduct.h
new file mode 100644
index 0000000000000000000000000000000000000000..4dc95028ecea02226278e4c3cdbd9b538f2f5f8e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseDiagonalProduct.h
@@ -0,0 +1,140 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_DIAGONAL_PRODUCT_H
+#define EIGEN_SPARSE_DIAGONAL_PRODUCT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+// The product of a diagonal matrix with a sparse matrix can be easily
+// implemented using expression template.
+// We have two consider very different cases:
+// 1 - diag * row-major sparse
+//     => each inner vector <=> scalar * sparse vector product
+//     => so we can reuse CwiseUnaryOp::InnerIterator
+// 2 - diag * col-major sparse
+//     => each inner vector <=> densevector * sparse vector cwise product
+//     => again, we can reuse specialization of CwiseBinaryOp::InnerIterator
+//        for that particular case
+// The two other cases are symmetric.
+
+namespace internal {
+
+enum {
+  SDP_AsScalarProduct,
+  SDP_AsCwiseProduct
+};
+  
+template<typename SparseXprType, typename DiagonalCoeffType, int SDP_Tag>
+struct sparse_diagonal_product_evaluator;
+
+template<typename Lhs, typename Rhs, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, ProductTag, DiagonalShape, SparseShape>
+  : public sparse_diagonal_product_evaluator<Rhs, typename Lhs::DiagonalVectorType, Rhs::Flags&RowMajorBit?SDP_AsScalarProduct:SDP_AsCwiseProduct>
+{
+  typedef Product<Lhs, Rhs, DefaultProduct> XprType;
+  enum { CoeffReadCost = HugeCost, Flags = Rhs::Flags&RowMajorBit, Alignment = 0 }; // FIXME CoeffReadCost & Flags
+  
+  typedef sparse_diagonal_product_evaluator<Rhs, typename Lhs::DiagonalVectorType, Rhs::Flags&RowMajorBit?SDP_AsScalarProduct:SDP_AsCwiseProduct> Base;
+  explicit product_evaluator(const XprType& xpr) : Base(xpr.rhs(), xpr.lhs().diagonal()) {}
+};
+
+template<typename Lhs, typename Rhs, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, ProductTag, SparseShape, DiagonalShape>
+  : public sparse_diagonal_product_evaluator<Lhs, Transpose<const typename Rhs::DiagonalVectorType>, Lhs::Flags&RowMajorBit?SDP_AsCwiseProduct:SDP_AsScalarProduct>
+{
+  typedef Product<Lhs, Rhs, DefaultProduct> XprType;
+  enum { CoeffReadCost = HugeCost, Flags = Lhs::Flags&RowMajorBit, Alignment = 0 }; // FIXME CoeffReadCost & Flags
+  
+  typedef sparse_diagonal_product_evaluator<Lhs, Transpose<const typename Rhs::DiagonalVectorType>, Lhs::Flags&RowMajorBit?SDP_AsCwiseProduct:SDP_AsScalarProduct> Base;
+  explicit product_evaluator(const XprType& xpr) : Base(xpr.lhs(), xpr.rhs().diagonal().transpose()) {}
+};
+
+template<typename SparseXprType, typename DiagonalCoeffType>
+struct sparse_diagonal_product_evaluator<SparseXprType, DiagonalCoeffType, SDP_AsScalarProduct>
+{
+protected:
+  typedef typename evaluator<SparseXprType>::InnerIterator SparseXprInnerIterator;
+  typedef typename SparseXprType::Scalar Scalar;
+  
+public:
+  class InnerIterator : public SparseXprInnerIterator
+  {
+  public:
+    InnerIterator(const sparse_diagonal_product_evaluator &xprEval, Index outer)
+      : SparseXprInnerIterator(xprEval.m_sparseXprImpl, outer),
+        m_coeff(xprEval.m_diagCoeffImpl.coeff(outer))
+    {}
+    
+    EIGEN_STRONG_INLINE Scalar value() const { return m_coeff * SparseXprInnerIterator::value(); }
+  protected:
+    typename DiagonalCoeffType::Scalar m_coeff;
+  };
+  
+  sparse_diagonal_product_evaluator(const SparseXprType &sparseXpr, const DiagonalCoeffType &diagCoeff)
+    : m_sparseXprImpl(sparseXpr), m_diagCoeffImpl(diagCoeff)
+  {}
+
+  Index nonZerosEstimate() const { return m_sparseXprImpl.nonZerosEstimate(); }
+    
+protected:
+  evaluator<SparseXprType> m_sparseXprImpl;
+  evaluator<DiagonalCoeffType> m_diagCoeffImpl;
+};
+
+
+template<typename SparseXprType, typename DiagCoeffType>
+struct sparse_diagonal_product_evaluator<SparseXprType, DiagCoeffType, SDP_AsCwiseProduct>
+{
+  typedef typename SparseXprType::Scalar Scalar;
+  typedef typename SparseXprType::StorageIndex StorageIndex;
+  
+  typedef typename nested_eval<DiagCoeffType,SparseXprType::IsRowMajor ? SparseXprType::RowsAtCompileTime
+                                                                       : SparseXprType::ColsAtCompileTime>::type DiagCoeffNested;
+  
+  class InnerIterator
+  {
+    typedef typename evaluator<SparseXprType>::InnerIterator SparseXprIter;
+  public:
+    InnerIterator(const sparse_diagonal_product_evaluator &xprEval, Index outer)
+      : m_sparseIter(xprEval.m_sparseXprEval, outer), m_diagCoeffNested(xprEval.m_diagCoeffNested)
+    {}
+    
+    inline Scalar value() const { return m_sparseIter.value() * m_diagCoeffNested.coeff(index()); }
+    inline StorageIndex index() const  { return m_sparseIter.index(); }
+    inline Index outer() const  { return m_sparseIter.outer(); }
+    inline Index col() const    { return SparseXprType::IsRowMajor ? m_sparseIter.index() : m_sparseIter.outer(); }
+    inline Index row() const    { return SparseXprType::IsRowMajor ? m_sparseIter.outer() : m_sparseIter.index(); }
+    
+    EIGEN_STRONG_INLINE InnerIterator& operator++() { ++m_sparseIter; return *this; }
+    inline operator bool() const  { return m_sparseIter; }
+    
+  protected:
+    SparseXprIter m_sparseIter;
+    DiagCoeffNested m_diagCoeffNested;
+  };
+  
+  sparse_diagonal_product_evaluator(const SparseXprType &sparseXpr, const DiagCoeffType &diagCoeff)
+    : m_sparseXprEval(sparseXpr), m_diagCoeffNested(diagCoeff)
+  {}
+
+  Index nonZerosEstimate() const { return m_sparseXprEval.nonZerosEstimate(); }
+    
+protected:
+  evaluator<SparseXprType> m_sparseXprEval;
+  DiagCoeffNested m_diagCoeffNested;
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_DIAGONAL_PRODUCT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseDot.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseDot.h
new file mode 100644
index 0000000000000000000000000000000000000000..a45ecfa0f684ce17b24b10578be3dd80e811c02f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseDot.h
@@ -0,0 +1,100 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_DOT_H
+#define EIGEN_SPARSE_DOT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+template<typename Derived>
+template<typename OtherDerived>
+typename internal::traits<Derived>::Scalar
+SparseMatrixBase<Derived>::dot(const MatrixBase<OtherDerived>& other) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  eigen_assert(size() == other.size());
+  eigen_assert(other.size()>0 && "you are using a non initialized vector");
+
+  internal::evaluator<Derived> thisEval(derived());
+  typename internal::evaluator<Derived>::InnerIterator i(thisEval, 0);
+  Scalar res(0);
+  while (i)
+  {
+    res += numext::conj(i.value()) * other.coeff(i.index());
+    ++i;
+  }
+  return res;
+}
+
+template<typename Derived>
+template<typename OtherDerived>
+typename internal::traits<Derived>::Scalar
+SparseMatrixBase<Derived>::dot(const SparseMatrixBase<OtherDerived>& other) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(OtherDerived)
+  EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived)
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+    YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  eigen_assert(size() == other.size());
+
+  internal::evaluator<Derived> thisEval(derived());
+  typename internal::evaluator<Derived>::InnerIterator i(thisEval, 0);
+  
+  internal::evaluator<OtherDerived>  otherEval(other.derived());
+  typename internal::evaluator<OtherDerived>::InnerIterator j(otherEval, 0);
+
+  Scalar res(0);
+  while (i && j)
+  {
+    if (i.index()==j.index())
+    {
+      res += numext::conj(i.value()) * j.value();
+      ++i; ++j;
+    }
+    else if (i.index()<j.index())
+      ++i;
+    else
+      ++j;
+  }
+  return res;
+}
+
+template<typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+SparseMatrixBase<Derived>::squaredNorm() const
+{
+  return numext::real((*this).cwiseAbs2().sum());
+}
+
+template<typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+SparseMatrixBase<Derived>::norm() const
+{
+  using std::sqrt;
+  return sqrt(squaredNorm());
+}
+
+template<typename Derived>
+inline typename NumTraits<typename internal::traits<Derived>::Scalar>::Real
+SparseMatrixBase<Derived>::blueNorm() const
+{
+  return internal::blueNorm_impl(*this);
+}
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_DOT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseFuzzy.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseFuzzy.h
new file mode 100644
index 0000000000000000000000000000000000000000..dcfdde99ef4e8e40607fc3a583808a140072401f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseFuzzy.h
@@ -0,0 +1,31 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_FUZZY_H
+#define EIGEN_SPARSE_FUZZY_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+  
+template<typename Derived>
+template<typename OtherDerived>
+bool SparseMatrixBase<Derived>::isApprox(const SparseMatrixBase<OtherDerived>& other, const RealScalar &prec) const
+{
+  const typename internal::nested_eval<Derived,2,PlainObject>::type actualA(derived());
+  std::conditional_t<bool(IsRowMajor)==bool(OtherDerived::IsRowMajor),
+    const typename internal::nested_eval<OtherDerived,2,PlainObject>::type,
+    const PlainObject> actualB(other.derived());
+
+  return (actualA - actualB).squaredNorm() <= prec * prec * numext::mini(actualA.squaredNorm(), actualB.squaredNorm());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_FUZZY_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseMap.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseMap.h
new file mode 100644
index 0000000000000000000000000000000000000000..0ee381354ec75f897b515cee1f910d46eb40dab9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseMap.h
@@ -0,0 +1,308 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_MAP_H
+#define EIGEN_SPARSE_MAP_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct traits<Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
+  : public traits<SparseMatrix<MatScalar,MatOptions,MatIndex> >
+{
+  typedef SparseMatrix<MatScalar,MatOptions,MatIndex> PlainObjectType;
+  typedef traits<PlainObjectType> TraitsBase;
+  enum {
+    Flags = TraitsBase::Flags & (~NestByRefBit)
+  };
+};
+
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct traits<Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
+  : public traits<SparseMatrix<MatScalar,MatOptions,MatIndex> >
+{
+  typedef SparseMatrix<MatScalar,MatOptions,MatIndex> PlainObjectType;
+  typedef traits<PlainObjectType> TraitsBase;
+  enum {
+    Flags = TraitsBase::Flags & (~ (NestByRefBit | LvalueBit))
+  };
+};
+
+} // end namespace internal
+
+template<typename Derived,
+         int Level = internal::accessors_level<Derived>::has_write_access ? WriteAccessors : ReadOnlyAccessors
+> class SparseMapBase;
+
+/** \ingroup SparseCore_Module
+  * class SparseMapBase
+  * \brief Common base class for Map and Ref instance of sparse matrix and vector.
+  */
+template<typename Derived>
+class SparseMapBase<Derived,ReadOnlyAccessors>
+  : public SparseCompressedBase<Derived>
+{
+  public:
+    typedef SparseCompressedBase<Derived> Base;
+    typedef typename Base::Scalar Scalar;
+    typedef typename Base::StorageIndex StorageIndex;
+    enum { IsRowMajor = Base::IsRowMajor };
+    using Base::operator=;
+  protected:
+    
+    typedef std::conditional_t<
+                 bool(internal::is_lvalue<Derived>::value),
+                 Scalar *, const Scalar *> ScalarPointer;
+    typedef std::conditional_t<
+                 bool(internal::is_lvalue<Derived>::value),
+                 StorageIndex *, const StorageIndex *> IndexPointer;
+
+    Index   m_outerSize;
+    Index   m_innerSize;
+    Array<StorageIndex,2,1>  m_zero_nnz;
+    IndexPointer  m_outerIndex;
+    IndexPointer  m_innerIndices;
+    ScalarPointer m_values;
+    IndexPointer  m_innerNonZeros;
+
+  public:
+
+    /** \copydoc SparseMatrixBase::rows() */
+    inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; }
+    /** \copydoc SparseMatrixBase::cols() */
+    inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; }
+    /** \copydoc SparseMatrixBase::innerSize() */
+    inline Index innerSize() const { return m_innerSize; }
+    /** \copydoc SparseMatrixBase::outerSize() */
+    inline Index outerSize() const { return m_outerSize; }
+    /** \copydoc SparseCompressedBase::nonZeros */
+    inline Index nonZeros() const { return m_zero_nnz[1]; }
+    
+    /** \copydoc SparseCompressedBase::isCompressed */
+    bool isCompressed() const { return m_innerNonZeros==0; }
+
+    //----------------------------------------
+    // direct access interface
+    /** \copydoc SparseMatrix::valuePtr */
+    inline const Scalar* valuePtr() const { return m_values; }
+    /** \copydoc SparseMatrix::innerIndexPtr */
+    inline const StorageIndex* innerIndexPtr() const { return m_innerIndices; }
+    /** \copydoc SparseMatrix::outerIndexPtr */
+    inline const StorageIndex* outerIndexPtr() const { return m_outerIndex; }
+    /** \copydoc SparseMatrix::innerNonZeroPtr */
+    inline const StorageIndex* innerNonZeroPtr() const { return m_innerNonZeros; }
+    //----------------------------------------
+
+    /** \copydoc SparseMatrix::coeff */
+    inline Scalar coeff(Index row, Index col) const
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      Index start = m_outerIndex[outer];
+      Index end = isCompressed() ? m_outerIndex[outer+1] : start + m_innerNonZeros[outer];
+      if (start==end)
+        return Scalar(0);
+      else if (end>0 && inner==m_innerIndices[end-1])
+        return m_values[end-1];
+      // ^^  optimization: let's first check if it is the last coefficient
+      // (very common in high level algorithms)
+
+      const StorageIndex* r = std::lower_bound(&m_innerIndices[start],&m_innerIndices[end-1],inner);
+      const Index id = r-&m_innerIndices[0];
+      return ((*r==inner) && (id<end)) ? m_values[id] : Scalar(0);
+    }
+
+    inline SparseMapBase(Index rows, Index cols, Index nnz, IndexPointer outerIndexPtr, IndexPointer innerIndexPtr,
+                              ScalarPointer valuePtr, IndexPointer innerNonZerosPtr = 0)
+      : m_outerSize(IsRowMajor?rows:cols), m_innerSize(IsRowMajor?cols:rows), m_zero_nnz(0,internal::convert_index<StorageIndex>(nnz)), m_outerIndex(outerIndexPtr),
+        m_innerIndices(innerIndexPtr), m_values(valuePtr), m_innerNonZeros(innerNonZerosPtr)
+    {}
+
+    // for vectors
+    inline SparseMapBase(Index size, Index nnz, IndexPointer innerIndexPtr, ScalarPointer valuePtr)
+      : m_outerSize(1), m_innerSize(size), m_zero_nnz(0,internal::convert_index<StorageIndex>(nnz)), m_outerIndex(m_zero_nnz.data()),
+        m_innerIndices(innerIndexPtr), m_values(valuePtr), m_innerNonZeros(0)
+    {}
+
+    /** Empty destructor */
+    inline ~SparseMapBase() {}
+
+  protected:
+    inline SparseMapBase() {}
+};
+
+/** \ingroup SparseCore_Module
+  * class SparseMapBase
+  * \brief Common base class for writable Map and Ref instance of sparse matrix and vector.
+  */
+template<typename Derived>
+class SparseMapBase<Derived,WriteAccessors>
+  : public SparseMapBase<Derived,ReadOnlyAccessors>
+{
+    typedef MapBase<Derived, ReadOnlyAccessors> ReadOnlyMapBase;
+    
+  public:
+    typedef SparseMapBase<Derived, ReadOnlyAccessors> Base;
+    typedef typename Base::Scalar Scalar;
+    typedef typename Base::StorageIndex StorageIndex;
+    enum { IsRowMajor = Base::IsRowMajor };
+    
+    using Base::operator=;
+
+  public:
+    
+    //----------------------------------------
+    // direct access interface
+    using Base::valuePtr;
+    using Base::innerIndexPtr;
+    using Base::outerIndexPtr;
+    using Base::innerNonZeroPtr;
+    /** \copydoc SparseMatrix::valuePtr */
+    inline Scalar* valuePtr()              { return Base::m_values; }
+    /** \copydoc SparseMatrix::innerIndexPtr */
+    inline StorageIndex* innerIndexPtr()   { return Base::m_innerIndices; }
+    /** \copydoc SparseMatrix::outerIndexPtr */
+    inline StorageIndex* outerIndexPtr()   { return Base::m_outerIndex; }
+    /** \copydoc SparseMatrix::innerNonZeroPtr */
+    inline StorageIndex* innerNonZeroPtr() { return Base::m_innerNonZeros; }
+    //----------------------------------------
+
+    /** \copydoc SparseMatrix::coeffRef */
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      Index start = Base::m_outerIndex[outer];
+      Index end = Base::isCompressed() ? Base::m_outerIndex[outer+1] : start + Base::m_innerNonZeros[outer];
+      eigen_assert(end>=start && "you probably called coeffRef on a non finalized matrix");
+      eigen_assert(end>start && "coeffRef cannot be called on a zero coefficient");
+      StorageIndex* r = std::lower_bound(&Base::m_innerIndices[start],&Base::m_innerIndices[end],inner);
+      const Index id = r - &Base::m_innerIndices[0];
+      eigen_assert((*r==inner) && (id<end) && "coeffRef cannot be called on a zero coefficient");
+      return const_cast<Scalar*>(Base::m_values)[id];
+    }
+    
+    inline SparseMapBase(Index rows, Index cols, Index nnz, StorageIndex* outerIndexPtr, StorageIndex* innerIndexPtr,
+                         Scalar* valuePtr, StorageIndex* innerNonZerosPtr = 0)
+      : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZerosPtr)
+    {}
+
+    // for vectors
+    inline SparseMapBase(Index size, Index nnz, StorageIndex* innerIndexPtr, Scalar* valuePtr)
+      : Base(size, nnz, innerIndexPtr, valuePtr)
+    {}
+
+    /** Empty destructor */
+    inline ~SparseMapBase() {}
+
+  protected:
+    inline SparseMapBase() {}
+};
+
+/** \ingroup SparseCore_Module
+  *
+  * \brief Specialization of class Map for SparseMatrix-like storage.
+  *
+  * \tparam SparseMatrixType the equivalent sparse matrix type of the referenced data, it must be a template instance of class SparseMatrix.
+  *
+  * \sa class Map, class SparseMatrix, class Ref<SparseMatrixType,Options>
+  */
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+class Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType>
+  : public SparseMapBase<Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
+#else
+template<typename SparseMatrixType>
+class Map<SparseMatrixType>
+  : public SparseMapBase<Derived,WriteAccessors>
+#endif
+{
+  public:
+    typedef SparseMapBase<Map> Base;
+    EIGEN_SPARSE_PUBLIC_INTERFACE(Map)
+    enum { IsRowMajor = Base::IsRowMajor };
+
+  public:
+
+    /** Constructs a read-write Map to a sparse matrix of size \a rows x \a cols, containing \a nnz non-zero coefficients,
+      * stored as a sparse format as defined by the pointers \a outerIndexPtr, \a innerIndexPtr, and \a valuePtr.
+      * If the optional parameter \a innerNonZerosPtr is the null pointer, then a standard compressed format is assumed.
+      * The inner indices must be sorted appropriately.
+      *
+      * This constructor is available only if \c SparseMatrixType is non-const.
+      *
+      * More details on the expected storage schemes are given in the \ref TutorialSparse "manual pages".
+      */
+    inline Map(Index rows, Index cols, Index nnz, StorageIndex* outerIndexPtr,
+               StorageIndex* innerIndexPtr, Scalar* valuePtr, StorageIndex* innerNonZerosPtr = 0)
+      : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZerosPtr)
+    {}
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** Empty destructor */
+    inline ~Map() {}
+};
+
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+class Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType>
+  : public SparseMapBase<Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
+{
+  public:
+    typedef SparseMapBase<Map> Base;
+    EIGEN_SPARSE_PUBLIC_INTERFACE(Map)
+    enum { IsRowMajor = Base::IsRowMajor };
+
+  public:
+#endif
+    /** This is the const version of the above constructor.
+      *
+      * This constructor is available only if \c SparseMatrixType is const, e.g.:
+      * \code Map<const SparseMatrix<double> >  \endcode
+      */
+    inline Map(Index rows, Index cols, Index nnz, const StorageIndex* outerIndexPtr,
+               const StorageIndex* innerIndexPtr, const Scalar* valuePtr, const StorageIndex* innerNonZerosPtr = 0)
+      : Base(rows, cols, nnz, outerIndexPtr, innerIndexPtr, valuePtr, innerNonZerosPtr)
+    {}
+
+    /** Empty destructor */
+    inline ~Map() {}
+};
+
+namespace internal {
+
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct evaluator<Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
+  : evaluator<SparseCompressedBase<Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
+{
+  typedef evaluator<SparseCompressedBase<Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
+  typedef Map<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;  
+  evaluator() : Base() {}
+  explicit evaluator(const XprType &mat) : Base(mat) {}
+};
+
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct evaluator<Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
+  : evaluator<SparseCompressedBase<Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
+{
+  typedef evaluator<SparseCompressedBase<Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
+  typedef Map<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;  
+  evaluator() : Base() {}
+  explicit evaluator(const XprType &mat) : Base(mat) {}
+};
+
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_MAP_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseMatrix.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseMatrix.h
new file mode 100644
index 0000000000000000000000000000000000000000..855a5a648db470d5434814d6c7bfd0dc0ec613f1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseMatrix.h
@@ -0,0 +1,1912 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEMATRIX_H
+#define EIGEN_SPARSEMATRIX_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \ingroup SparseCore_Module
+  *
+  * \class SparseMatrix
+  *
+  * \brief A versatible sparse matrix representation
+  *
+  * This class implements a more versatile variants of the common \em compressed row/column storage format.
+  * Each colmun's (resp. row) non zeros are stored as a pair of value with associated row (resp. colmiun) index.
+  * All the non zeros are stored in a single large buffer. Unlike the \em compressed format, there might be extra
+  * space in between the nonzeros of two successive colmuns (resp. rows) such that insertion of new non-zero
+  * can be done with limited memory reallocation and copies.
+  *
+  * A call to the function makeCompressed() turns the matrix into the standard \em compressed format
+  * compatible with many library.
+  *
+  * More details on this storage sceheme are given in the \ref TutorialSparse "manual pages".
+  *
+  * \tparam Scalar_ the scalar type, i.e. the type of the coefficients
+  * \tparam Options_ Union of bit flags controlling the storage scheme. Currently the only possibility
+  *                 is ColMajor or RowMajor. The default is 0 which means column-major.
+  * \tparam StorageIndex_ the type of the indices. It has to be a \b signed type (e.g., short, int, std::ptrdiff_t). Default is \c int.
+  *
+  * \warning In %Eigen 3.2, the undocumented type \c SparseMatrix::Index was improperly defined as the storage index type (e.g., int),
+  *          whereas it is now (starting from %Eigen 3.3) deprecated and always defined as Eigen::Index.
+  *          Codes making use of \c SparseMatrix::Index, might thus likely have to be changed to use \c SparseMatrix::StorageIndex instead.
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEMATRIX_PLUGIN.
+  */
+
+namespace internal {
+template<typename Scalar_, int Options_, typename StorageIndex_>
+struct traits<SparseMatrix<Scalar_, Options_, StorageIndex_> >
+{
+  typedef Scalar_ Scalar;
+  typedef StorageIndex_ StorageIndex;
+  typedef Sparse StorageKind;
+  typedef MatrixXpr XprKind;
+  enum {
+    RowsAtCompileTime = Dynamic,
+    ColsAtCompileTime = Dynamic,
+    MaxRowsAtCompileTime = Dynamic,
+    MaxColsAtCompileTime = Dynamic,
+    Flags = Options_ | NestByRefBit | LvalueBit | CompressedAccessBit,
+    SupportedAccessPatterns = InnerRandomAccessPattern
+  };
+};
+
+template<typename Scalar_, int Options_, typename StorageIndex_, int DiagIndex>
+struct traits<Diagonal<SparseMatrix<Scalar_, Options_, StorageIndex_>, DiagIndex> >
+{
+  typedef SparseMatrix<Scalar_, Options_, StorageIndex_> MatrixType;
+  typedef typename ref_selector<MatrixType>::type MatrixTypeNested;
+  typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNested_;
+
+  typedef Scalar_ Scalar;
+  typedef Dense StorageKind;
+  typedef StorageIndex_ StorageIndex;
+  typedef MatrixXpr XprKind;
+
+  enum {
+    RowsAtCompileTime = Dynamic,
+    ColsAtCompileTime = 1,
+    MaxRowsAtCompileTime = Dynamic,
+    MaxColsAtCompileTime = 1,
+    Flags = LvalueBit
+  };
+};
+
+template<typename Scalar_, int Options_, typename StorageIndex_, int DiagIndex>
+struct traits<Diagonal<const SparseMatrix<Scalar_, Options_, StorageIndex_>, DiagIndex> >
+ : public traits<Diagonal<SparseMatrix<Scalar_, Options_, StorageIndex_>, DiagIndex> >
+{
+  enum {
+    Flags = 0
+  };
+};
+
+template <typename StorageIndex>
+struct sparse_reserve_op {
+  EIGEN_DEVICE_FUNC sparse_reserve_op(Index begin, Index end, Index size) {
+    Index range = numext::mini(end - begin, size);
+    m_begin = begin;
+    m_end = begin + range;
+    m_val = StorageIndex(size / range);
+    m_remainder = StorageIndex(size % range);
+  }
+  template <typename IndexType>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE StorageIndex operator()(IndexType i) const {
+    if ((i >= m_begin) && (i < m_end))
+      return m_val + ((i - m_begin) < m_remainder ? 1 : 0);
+    else
+      return 0;
+  }
+  StorageIndex m_val, m_remainder;
+  Index m_begin, m_end;
+};
+
+template <typename Scalar>
+struct functor_traits<sparse_reserve_op<Scalar>> {
+  enum { Cost = 1, PacketAccess = false, IsRepeatable = true };
+};
+
+} // end namespace internal
+
+template<typename Scalar_, int Options_, typename StorageIndex_>
+class SparseMatrix
+  : public SparseCompressedBase<SparseMatrix<Scalar_, Options_, StorageIndex_> >
+{
+    typedef SparseCompressedBase<SparseMatrix> Base;
+    using Base::convert_index;
+    friend class SparseVector<Scalar_,0,StorageIndex_>;
+    template<typename, typename, typename, typename, typename>
+    friend struct internal::Assignment;
+  public:
+    using Base::isCompressed;
+    using Base::nonZeros;
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseMatrix)
+    using Base::operator+=;
+    using Base::operator-=;
+
+    typedef Eigen::Map<SparseMatrix<Scalar,Options_,StorageIndex>> Map;
+    typedef Diagonal<SparseMatrix> DiagonalReturnType;
+    typedef Diagonal<const SparseMatrix> ConstDiagonalReturnType;
+    typedef typename Base::InnerIterator InnerIterator;
+    typedef typename Base::ReverseInnerIterator ReverseInnerIterator;
+    
+
+    using Base::IsRowMajor;
+    typedef internal::CompressedStorage<Scalar,StorageIndex> Storage;
+    enum {
+      Options = Options_
+    };
+
+    typedef typename Base::IndexVector IndexVector;
+    typedef typename Base::ScalarVector ScalarVector;
+  protected:
+    typedef SparseMatrix<Scalar, IsRowMajor ? ColMajor : RowMajor, StorageIndex> TransposedSparseMatrix;
+
+    Index m_outerSize;
+    Index m_innerSize;
+    StorageIndex* m_outerIndex;
+    StorageIndex* m_innerNonZeros;     // optional, if null then the data is compressed
+    Storage m_data;
+
+  public:
+    
+    /** \returns the number of rows of the matrix */
+    inline Index rows() const { return IsRowMajor ? m_outerSize : m_innerSize; }
+    /** \returns the number of columns of the matrix */
+    inline Index cols() const { return IsRowMajor ? m_innerSize : m_outerSize; }
+
+    /** \returns the number of rows (resp. columns) of the matrix if the storage order column major (resp. row major) */
+    inline Index innerSize() const { return m_innerSize; }
+    /** \returns the number of columns (resp. rows) of the matrix if the storage order column major (resp. row major) */
+    inline Index outerSize() const { return m_outerSize; }
+    
+    /** \returns a const pointer to the array of values.
+      * This function is aimed at interoperability with other libraries.
+      * \sa innerIndexPtr(), outerIndexPtr() */
+    inline const Scalar* valuePtr() const { return m_data.valuePtr(); }
+    /** \returns a non-const pointer to the array of values.
+      * This function is aimed at interoperability with other libraries.
+      * \sa innerIndexPtr(), outerIndexPtr() */
+    inline Scalar* valuePtr() { return m_data.valuePtr(); }
+
+    /** \returns a const pointer to the array of inner indices.
+      * This function is aimed at interoperability with other libraries.
+      * \sa valuePtr(), outerIndexPtr() */
+    inline const StorageIndex* innerIndexPtr() const { return m_data.indexPtr(); }
+    /** \returns a non-const pointer to the array of inner indices.
+      * This function is aimed at interoperability with other libraries.
+      * \sa valuePtr(), outerIndexPtr() */
+    inline StorageIndex* innerIndexPtr() { return m_data.indexPtr(); }
+
+    /** \returns a const pointer to the array of the starting positions of the inner vectors.
+      * This function is aimed at interoperability with other libraries.
+      * \sa valuePtr(), innerIndexPtr() */
+    inline const StorageIndex* outerIndexPtr() const { return m_outerIndex; }
+    /** \returns a non-const pointer to the array of the starting positions of the inner vectors.
+      * This function is aimed at interoperability with other libraries.
+      * \sa valuePtr(), innerIndexPtr() */
+    inline StorageIndex* outerIndexPtr() { return m_outerIndex; }
+
+    /** \returns a const pointer to the array of the number of non zeros of the inner vectors.
+      * This function is aimed at interoperability with other libraries.
+      * \warning it returns the null pointer 0 in compressed mode */
+    inline const StorageIndex* innerNonZeroPtr() const { return m_innerNonZeros; }
+    /** \returns a non-const pointer to the array of the number of non zeros of the inner vectors.
+      * This function is aimed at interoperability with other libraries.
+      * \warning it returns the null pointer 0 in compressed mode */
+    inline StorageIndex* innerNonZeroPtr() { return m_innerNonZeros; }
+
+    /** \internal */
+    inline Storage& data() { return m_data; }
+    /** \internal */
+    inline const Storage& data() const { return m_data; }
+
+    /** \returns the value of the matrix at position \a i, \a j
+      * This function returns Scalar(0) if the element is an explicit \em zero */
+    inline Scalar coeff(Index row, Index col) const
+    {
+      eigen_assert(row>=0 && row<rows() && col>=0 && col<cols());
+      
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+      Index end = m_innerNonZeros ? m_outerIndex[outer] + m_innerNonZeros[outer] : m_outerIndex[outer+1];
+      return m_data.atInRange(m_outerIndex[outer], end, inner);
+    }
+
+    /** \returns a non-const reference to the value of the matrix at position \a i, \a j
+      *
+      * If the element does not exist then it is inserted via the insert(Index,Index) function
+      * which itself turns the matrix into a non compressed form if that was not the case.
+      *
+      * This is a O(log(nnz_j)) operation (binary search) plus the cost of insert(Index,Index)
+      * function if the element does not already exist.
+      */
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      eigen_assert(row>=0 && row<rows() && col>=0 && col<cols());
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+      Index start = m_outerIndex[outer];
+      Index end = isCompressed() ? m_outerIndex[outer + 1] : m_outerIndex[outer] + m_innerNonZeros[outer];
+      eigen_assert(end >= start && "you probably called coeffRef on a non finalized matrix");
+      Index dst = start == end ? end : m_data.searchLowerIndex(start, end, inner);
+      if (dst == end) {
+        Index capacity = m_outerIndex[outer + 1] - end;
+        if (capacity > 0) {
+          // implies uncompressed: push to back of vector
+          m_innerNonZeros[outer]++;
+          m_data.index(end) = StorageIndex(inner);
+          m_data.value(end) = Scalar(0);
+          return m_data.value(end);
+        }
+      }
+      if ((dst < end) && (m_data.index(dst) == inner))
+        // this coefficient exists, return a refernece to it
+        return m_data.value(dst);
+      else
+        // insertion will require reconfiguring the buffer
+        return insertAtByOuterInner(outer, inner, dst);
+    }
+
+    /** \returns a reference to a novel non zero coefficient with coordinates \a row x \a col.
+      * The non zero coefficient must \b not already exist.
+      *
+      * If the matrix \c *this is in compressed mode, then \c *this is turned into uncompressed
+      * mode while reserving room for 2 x this->innerSize() non zeros if reserve(Index) has not been called earlier.
+      * In this case, the insertion procedure is optimized for a \e sequential insertion mode where elements are assumed to be
+      * inserted by increasing outer-indices.
+      * 
+      * If that's not the case, then it is strongly recommended to either use a triplet-list to assemble the matrix, or to first
+      * call reserve(const SizesType &) to reserve the appropriate number of non-zero elements per inner vector.
+      *
+      * Assuming memory has been appropriately reserved, this function performs a sorted insertion in O(1)
+      * if the elements of each inner vector are inserted in increasing inner index order, and in O(nnz_j) for a random insertion.
+      *
+      */
+    inline Scalar& insert(Index row, Index col);
+
+  public:
+
+    /** Removes all non zeros but keep allocated memory
+      *
+      * This function does not free the currently allocated memory. To release as much as memory as possible,
+      * call \code mat.data().squeeze(); \endcode after resizing it.
+      * 
+      * \sa resize(Index,Index), data()
+      */
+    inline void setZero()
+    {
+      m_data.clear();
+      std::fill_n(m_outerIndex, m_outerSize + 1, StorageIndex(0));
+      if(m_innerNonZeros) {
+        std::fill_n(m_innerNonZeros, m_outerSize, StorageIndex(0));
+      }
+    }
+
+    /** Preallocates \a reserveSize non zeros.
+      *
+      * Precondition: the matrix must be in compressed mode. */
+    inline void reserve(Index reserveSize)
+    {
+      eigen_assert(isCompressed() && "This function does not make sense in non compressed mode.");
+      m_data.reserve(reserveSize);
+    }
+    
+    #ifdef EIGEN_PARSED_BY_DOXYGEN
+    /** Preallocates \a reserveSize[\c j] non zeros for each column (resp. row) \c j.
+      *
+      * This function turns the matrix in non-compressed mode.
+      * 
+      * The type \c SizesType must expose the following interface:
+        \code
+        typedef value_type;
+        const value_type& operator[](i) const;
+        \endcode
+      * for \c i in the [0,this->outerSize()[ range.
+      * Typical choices include std::vector<int>, Eigen::VectorXi, Eigen::VectorXi::Constant, etc.
+      */
+    template<class SizesType>
+    inline void reserve(const SizesType& reserveSizes);
+    #else
+    template<class SizesType>
+    inline void reserve(const SizesType& reserveSizes, const typename SizesType::value_type& enableif =
+        typename SizesType::value_type())
+    {
+      EIGEN_UNUSED_VARIABLE(enableif);
+      reserveInnerVectors(reserveSizes);
+    }
+    #endif // EIGEN_PARSED_BY_DOXYGEN
+  protected:
+    template<class SizesType>
+    inline void reserveInnerVectors(const SizesType& reserveSizes)
+    {
+      if(isCompressed())
+      {
+        Index totalReserveSize = 0;
+        for (Index j = 0; j < m_outerSize; ++j) totalReserveSize += internal::convert_index<Index>(reserveSizes[j]);
+
+        // if reserveSizes is empty, don't do anything!
+        if (totalReserveSize == 0) return;
+
+        // turn the matrix into non-compressed mode
+        m_innerNonZeros = internal::conditional_aligned_new_auto<StorageIndex, true>(m_outerSize);
+        
+        // temporarily use m_innerSizes to hold the new starting points.
+        StorageIndex* newOuterIndex = m_innerNonZeros;
+        
+        Index count = 0;
+        for(Index j=0; j<m_outerSize; ++j)
+        {
+          newOuterIndex[j] = internal::convert_index<StorageIndex>(count);
+          Index reserveSize = internal::convert_index<Index>(reserveSizes[j]);
+          count += reserveSize + internal::convert_index<Index>(m_outerIndex[j+1]-m_outerIndex[j]);
+        }
+
+        m_data.reserve(totalReserveSize);
+        StorageIndex previousOuterIndex = m_outerIndex[m_outerSize];
+        for(Index j=m_outerSize-1; j>=0; --j)
+        {
+          StorageIndex innerNNZ = previousOuterIndex - m_outerIndex[j];
+          StorageIndex begin = m_outerIndex[j];
+          StorageIndex end = begin + innerNNZ;
+          StorageIndex target = newOuterIndex[j];
+          internal::smart_memmove(innerIndexPtr() + begin, innerIndexPtr() + end, innerIndexPtr() + target);
+          internal::smart_memmove(valuePtr() + begin, valuePtr() + end, valuePtr() + target);
+          previousOuterIndex = m_outerIndex[j];
+          m_outerIndex[j] = newOuterIndex[j];
+          m_innerNonZeros[j] = innerNNZ;
+        }
+        if(m_outerSize>0)
+          m_outerIndex[m_outerSize] = m_outerIndex[m_outerSize-1] + m_innerNonZeros[m_outerSize-1] + internal::convert_index<StorageIndex>(reserveSizes[m_outerSize-1]);
+        
+        m_data.resize(m_outerIndex[m_outerSize]);
+      }
+      else
+      {
+        StorageIndex* newOuterIndex = internal::conditional_aligned_new_auto<StorageIndex, true>(m_outerSize + 1);
+        
+        Index count = 0;
+        for(Index j=0; j<m_outerSize; ++j)
+        {
+          newOuterIndex[j] = internal::convert_index<StorageIndex>(count);
+          Index alreadyReserved = internal::convert_index<Index>(m_outerIndex[j+1] - m_outerIndex[j] - m_innerNonZeros[j]);
+          Index reserveSize = internal::convert_index<Index>(reserveSizes[j]);
+          Index toReserve = numext::maxi(reserveSize, alreadyReserved);
+          count += toReserve + internal::convert_index<Index>(m_innerNonZeros[j]);
+        }
+        newOuterIndex[m_outerSize] = internal::convert_index<StorageIndex>(count);
+
+        m_data.resize(count);
+        for(Index j=m_outerSize-1; j>=0; --j)
+        {
+          StorageIndex innerNNZ = m_innerNonZeros[j];
+          StorageIndex begin = m_outerIndex[j];
+          StorageIndex target = newOuterIndex[j];
+          m_data.moveChunk(begin, target, innerNNZ);
+        }
+        
+        std::swap(m_outerIndex, newOuterIndex);
+        internal::conditional_aligned_delete_auto<StorageIndex, true>(newOuterIndex, m_outerSize + 1);
+      }
+      
+    }
+  public:
+
+    //--- low level purely coherent filling ---
+
+    /** \internal
+      * \returns a reference to the non zero coefficient at position \a row, \a col assuming that:
+      * - the nonzero does not already exist
+      * - the new coefficient is the last one according to the storage order
+      *
+      * Before filling a given inner vector you must call the statVec(Index) function.
+      *
+      * After an insertion session, you should call the finalize() function.
+      *
+      * \sa insert, insertBackByOuterInner, startVec */
+    inline Scalar& insertBack(Index row, Index col)
+    {
+      return insertBackByOuterInner(IsRowMajor?row:col, IsRowMajor?col:row);
+    }
+
+    /** \internal
+      * \sa insertBack, startVec */
+    inline Scalar& insertBackByOuterInner(Index outer, Index inner)
+    {
+      eigen_assert(Index(m_outerIndex[outer+1]) == m_data.size() && "Invalid ordered insertion (invalid outer index)");
+      eigen_assert( (m_outerIndex[outer+1]-m_outerIndex[outer]==0 || m_data.index(m_data.size()-1)<inner) && "Invalid ordered insertion (invalid inner index)");
+      StorageIndex p = m_outerIndex[outer+1];
+      ++m_outerIndex[outer+1];
+      m_data.append(Scalar(0), inner);
+      return m_data.value(p);
+    }
+
+    /** \internal
+      * \warning use it only if you know what you are doing */
+    inline Scalar& insertBackByOuterInnerUnordered(Index outer, Index inner)
+    {
+      StorageIndex p = m_outerIndex[outer+1];
+      ++m_outerIndex[outer+1];
+      m_data.append(Scalar(0), inner);
+      return m_data.value(p);
+    }
+
+    /** \internal
+      * \sa insertBack, insertBackByOuterInner */
+    inline void startVec(Index outer)
+    {
+      eigen_assert(m_outerIndex[outer]==Index(m_data.size()) && "You must call startVec for each inner vector sequentially");
+      eigen_assert(m_outerIndex[outer+1]==0 && "You must call startVec for each inner vector sequentially");
+      m_outerIndex[outer+1] = m_outerIndex[outer];
+    }
+
+    /** \internal
+      * Must be called after inserting a set of non zero entries using the low level compressed API.
+      */
+    inline void finalize()
+    {
+      if(isCompressed())
+      {
+        StorageIndex size = internal::convert_index<StorageIndex>(m_data.size());
+        Index i = m_outerSize;
+        // find the last filled column
+        while (i>=0 && m_outerIndex[i]==0)
+          --i;
+        ++i;
+        while (i<=m_outerSize)
+        {
+          m_outerIndex[i] = size;
+          ++i;
+        }
+      }
+    }
+
+    // remove outer vectors j, j+1 ... j+num-1 and resize the matrix
+    void removeOuterVectors(Index j, Index num = 1) {
+      eigen_assert(num >= 0 && j >= 0 && j + num <= m_outerSize && "Invalid parameters");
+
+      const Index newRows = IsRowMajor ? m_outerSize - num : rows();
+      const Index newCols = IsRowMajor ? cols() : m_outerSize - num;
+
+      const Index begin = j + num;
+      const Index end = m_outerSize;
+      const Index target = j;
+
+      // if the removed vectors are not empty, uncompress the matrix
+      if (m_outerIndex[j + num] > m_outerIndex[j]) uncompress();
+
+      // shift m_outerIndex and m_innerNonZeros [num] to the left
+      internal::smart_memmove(m_outerIndex + begin, m_outerIndex + end + 1, m_outerIndex + target);
+      if (!isCompressed())
+        internal::smart_memmove(m_innerNonZeros + begin, m_innerNonZeros + end, m_innerNonZeros + target);
+
+      // if m_outerIndex[0] > 0, shift the data within the first vector while it is easy to do so
+      if (m_outerIndex[0] > StorageIndex(0)) {
+        uncompress();
+        const Index from = internal::convert_index<Index>(m_outerIndex[0]);
+        const Index to = Index(0);
+        const Index chunkSize = internal::convert_index<Index>(m_innerNonZeros[0]);
+        m_data.moveChunk(from, to, chunkSize);
+        m_outerIndex[0] = StorageIndex(0);
+      }
+
+      // truncate the matrix to the smaller size
+      conservativeResize(newRows, newCols);
+    }
+
+    // insert empty outer vectors at indices j, j+1 ... j+num-1 and resize the matrix
+    void insertEmptyOuterVectors(Index j, Index num = 1) {
+      EIGEN_USING_STD(fill_n);
+      eigen_assert(num >= 0 && j >= 0 && j < m_outerSize && "Invalid parameters");
+
+      const Index newRows = IsRowMajor ? m_outerSize + num : rows();
+      const Index newCols = IsRowMajor ? cols() : m_outerSize + num;
+
+      const Index begin = j;
+      const Index end = m_outerSize;
+      const Index target = j + num;
+
+      // expand the matrix to the larger size
+      conservativeResize(newRows, newCols);
+
+      // shift m_outerIndex and m_innerNonZeros [num] to the right
+      internal::smart_memmove(m_outerIndex + begin, m_outerIndex + end + 1, m_outerIndex + target);
+      // m_outerIndex[begin] == m_outerIndex[target], set all indices in this range to same value
+      fill_n(m_outerIndex + begin, num, m_outerIndex[begin]);
+
+      if (!isCompressed()) {
+        internal::smart_memmove(m_innerNonZeros + begin, m_innerNonZeros + end, m_innerNonZeros + target);
+        // set the nonzeros of the newly inserted vectors to 0
+        fill_n(m_innerNonZeros + begin, num, StorageIndex(0));
+      }
+    }
+
+    template<typename InputIterators>
+    void setFromTriplets(const InputIterators& begin, const InputIterators& end);
+
+    template<typename InputIterators,typename DupFunctor>
+    void setFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func);
+
+    template<typename Derived, typename DupFunctor>
+    void collapseDuplicates(DenseBase<Derived>& wi, DupFunctor dup_func = DupFunctor());
+
+    template<typename InputIterators>
+    void setFromSortedTriplets(const InputIterators& begin, const InputIterators& end);
+
+    template<typename InputIterators, typename DupFunctor>
+    void setFromSortedTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func);
+
+    template<typename InputIterators>
+    void insertFromTriplets(const InputIterators& begin, const InputIterators& end);
+
+    template<typename InputIterators, typename DupFunctor>
+    void insertFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func);
+
+    template<typename InputIterators>
+    void insertFromSortedTriplets(const InputIterators& begin, const InputIterators& end);
+
+    template<typename InputIterators, typename DupFunctor>
+    void insertFromSortedTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func);
+
+    //---
+    
+    /** \internal
+      * same as insert(Index,Index) except that the indices are given relative to the storage order */
+    Scalar& insertByOuterInner(Index j, Index i)
+    {
+      Index start = m_outerIndex[j];
+      Index end = isCompressed() ? m_outerIndex[j + 1] : start + m_innerNonZeros[j];
+      Index dst = start == end ? end : m_data.searchLowerIndex(start, end, i);
+      if (dst == end) {
+        Index capacity = m_outerIndex[j + 1] - end;
+        if (capacity > 0) {
+          // implies uncompressed: push to back of vector
+          m_innerNonZeros[j]++;
+          m_data.index(end) = StorageIndex(i);
+          m_data.value(end) = Scalar(0);
+          return m_data.value(end);
+        }
+      }
+      eigen_assert((dst == end || m_data.index(dst) != i) &&
+          "you cannot insert an element that already exists, you must call coeffRef to this end");
+      return insertAtByOuterInner(j, i, dst);
+    }
+
+    /** Turns the matrix into the \em compressed format.
+      */
+    void makeCompressed()
+    {
+      if (isCompressed()) return;
+      
+      eigen_internal_assert(m_outerIndex!=0 && m_outerSize>0);
+
+      StorageIndex start = m_outerIndex[1];
+      m_outerIndex[1] = m_innerNonZeros[0];
+      // try to move fewer, larger contiguous chunks
+      Index copyStart = start;
+      Index copyTarget = m_innerNonZeros[0];
+      for (Index j = 1; j < m_outerSize; j++)
+      {
+        StorageIndex end = start + m_innerNonZeros[j];
+        StorageIndex nextStart = m_outerIndex[j + 1];
+        // dont forget to move the last chunk!
+        bool breakUpCopy = (end != nextStart) || (j == m_outerSize - 1);
+        if (breakUpCopy)
+        {
+          Index chunkSize = end - copyStart;
+          if(chunkSize > 0) m_data.moveChunk(copyStart, copyTarget, chunkSize);
+          copyStart = nextStart;
+          copyTarget += chunkSize;
+        }
+        start = nextStart;
+        m_outerIndex[j + 1] = m_outerIndex[j] + m_innerNonZeros[j];
+      }
+      m_data.resize(m_outerIndex[m_outerSize]);
+
+      // release as much memory as possible
+      internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
+      m_innerNonZeros = 0;
+      m_data.squeeze();
+    }
+
+    /** Turns the matrix into the uncompressed mode */
+    void uncompress()
+    {
+      if (!isCompressed()) return;
+      m_innerNonZeros = internal::conditional_aligned_new_auto<StorageIndex, true>(m_outerSize);
+      if (m_outerIndex[m_outerSize] == 0)
+        std::fill_n(m_innerNonZeros, m_outerSize, StorageIndex(0));
+      else
+        for (Index j = 0; j < m_outerSize; j++) m_innerNonZeros[j] = m_outerIndex[j + 1] - m_outerIndex[j];
+    }
+
+    /** Suppresses all nonzeros which are \b much \b smaller \b than \a reference under the tolerance \a epsilon */
+    void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision())
+    {
+      prune(default_prunning_func(reference,epsilon));
+    }
+    
+    /** Turns the matrix into compressed format, and suppresses all nonzeros which do not satisfy the predicate \a keep.
+      * The functor type \a KeepFunc must implement the following function:
+      * \code
+      * bool operator() (const Index& row, const Index& col, const Scalar& value) const;
+      * \endcode
+      * \sa prune(Scalar,RealScalar)
+      */
+    template<typename KeepFunc>
+    void prune(const KeepFunc& keep = KeepFunc())
+    {
+      StorageIndex k = 0;
+      for(Index j=0; j<m_outerSize; ++j)
+      {
+        StorageIndex previousStart = m_outerIndex[j];
+        if (isCompressed())
+          m_outerIndex[j] = k;
+        else
+          k = m_outerIndex[j];
+        StorageIndex end = isCompressed() ? m_outerIndex[j+1] : previousStart + m_innerNonZeros[j];
+        for(StorageIndex i=previousStart; i<end; ++i)
+        {
+          StorageIndex row = IsRowMajor ? StorageIndex(j) : m_data.index(i);
+          StorageIndex col = IsRowMajor ? m_data.index(i) : StorageIndex(j);
+          bool keepEntry = keep(row, col, m_data.value(i));
+          if (keepEntry) {
+            m_data.value(k) = m_data.value(i);
+            m_data.index(k) = m_data.index(i);
+            ++k;
+          } else if (!isCompressed())
+            m_innerNonZeros[j]--;
+        }
+      }
+      if (isCompressed()) {
+        m_outerIndex[m_outerSize] = k;
+        m_data.resize(k, 0);
+      }
+    }
+
+    /** Resizes the matrix to a \a rows x \a cols matrix leaving old values untouched.
+      *
+      * If the sizes of the matrix are decreased, then the matrix is turned to \b uncompressed-mode
+      * and the storage of the out of bounds coefficients is kept and reserved.
+      * Call makeCompressed() to pack the entries and squeeze extra memory.
+      *
+      * \sa reserve(), setZero(), makeCompressed()
+      */
+    void conservativeResize(Index rows, Index cols) {
+
+      // If one dimension is null, then there is nothing to be preserved
+      if (rows == 0 || cols == 0) return resize(rows, cols);
+
+      Index newOuterSize = IsRowMajor ? rows : cols;
+      Index newInnerSize = IsRowMajor ? cols : rows;
+
+      Index innerChange = newInnerSize - m_innerSize;
+      Index outerChange = newOuterSize - m_outerSize;
+
+      if (outerChange != 0) {
+        m_outerIndex = internal::conditional_aligned_realloc_new_auto<StorageIndex, true>(
+            m_outerIndex, newOuterSize + 1, m_outerSize + 1);
+
+        if (!isCompressed())
+          m_innerNonZeros = internal::conditional_aligned_realloc_new_auto<StorageIndex, true>(
+              m_innerNonZeros, newOuterSize, m_outerSize);
+
+        if (outerChange > 0) {
+          StorageIndex lastIdx = m_outerSize == 0 ? StorageIndex(0) : m_outerIndex[m_outerSize];
+          std::fill_n(m_outerIndex + m_outerSize, outerChange + 1, lastIdx);
+
+          if (!isCompressed()) std::fill_n(m_innerNonZeros + m_outerSize, outerChange, StorageIndex(0));
+        }
+      }
+      m_outerSize = newOuterSize;
+
+      if (innerChange < 0) {
+        for (Index j = 0; j < m_outerSize; j++) {
+          Index start = m_outerIndex[j];
+          Index end = isCompressed() ? m_outerIndex[j + 1] : start + m_innerNonZeros[j];
+          Index lb = m_data.searchLowerIndex(start, end, newInnerSize);
+          if (lb != end) {
+            uncompress();
+            m_innerNonZeros[j] = StorageIndex(lb - start);
+          }
+        }
+      }
+      m_innerSize = newInnerSize;
+
+      Index newSize = m_outerIndex[m_outerSize];
+      eigen_assert(newSize <= m_data.size());
+      m_data.resize(newSize);
+    }
+    
+    /** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero.
+      * 
+      * This function does not free the currently allocated memory. To release as much as memory as possible,
+      * call \code mat.data().squeeze(); \endcode after resizing it.
+      * 
+      * \sa reserve(), setZero()
+      */
+    void resize(Index rows, Index cols)
+    {
+      const Index outerSize = IsRowMajor ? rows : cols;
+      m_innerSize = IsRowMajor ? cols : rows;
+      m_data.clear();
+
+      if ((m_outerIndex == 0) || (m_outerSize != outerSize)) {
+        m_outerIndex = internal::conditional_aligned_realloc_new_auto<StorageIndex, true>(m_outerIndex, outerSize + 1, m_outerSize + 1);
+        m_outerSize = outerSize;
+      }
+
+      internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
+      m_innerNonZeros = 0;
+
+      std::fill_n(m_outerIndex, m_outerSize + 1, StorageIndex(0));
+    }
+
+    /** \internal
+      * Resize the nonzero vector to \a size */
+    void resizeNonZeros(Index size)
+    {
+      m_data.resize(size);
+    }
+
+    /** \returns a const expression of the diagonal coefficients. */
+    const ConstDiagonalReturnType diagonal() const { return ConstDiagonalReturnType(*this); }
+    
+    /** \returns a read-write expression of the diagonal coefficients.
+      * \warning If the diagonal entries are written, then all diagonal
+      * entries \b must already exist, otherwise an assertion will be raised.
+      */
+    DiagonalReturnType diagonal() { return DiagonalReturnType(*this); }
+
+    /** Default constructor yielding an empty \c 0 \c x \c 0 matrix */
+    inline SparseMatrix()
+      : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
+    {
+      resize(0, 0);
+    }
+
+    /** Constructs a \a rows \c x \a cols empty matrix */
+    inline SparseMatrix(Index rows, Index cols)
+      : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
+    {
+      resize(rows, cols);
+    }
+
+    /** Constructs a sparse matrix from the sparse expression \a other */
+    template<typename OtherDerived>
+    inline SparseMatrix(const SparseMatrixBase<OtherDerived>& other)
+      : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
+    {
+      EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+      const bool needToTranspose = (Flags & RowMajorBit) != (internal::evaluator<OtherDerived>::Flags & RowMajorBit);
+      if (needToTranspose)
+        *this = other.derived();
+      else
+      {
+        #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+          EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+        #endif
+        internal::call_assignment_no_alias(*this, other.derived());
+      }
+    }
+
+    /** Constructs a sparse matrix from the sparse selfadjoint view \a other */
+    template<typename OtherDerived, unsigned int UpLo>
+    inline SparseMatrix(const SparseSelfAdjointView<OtherDerived, UpLo>& other)
+      : m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
+    {
+      Base::operator=(other);
+    }
+
+    inline SparseMatrix(SparseMatrix&& other) : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
+    {
+      *this = other.derived().markAsRValue();
+    }
+
+    /** Copy constructor (it performs a deep copy) */
+    inline SparseMatrix(const SparseMatrix& other)
+      : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
+    {
+      *this = other.derived();
+    }
+
+    /** \brief Copy constructor with in-place evaluation */
+    template<typename OtherDerived>
+    SparseMatrix(const ReturnByValue<OtherDerived>& other)
+      : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
+    {
+      initAssignment(other);
+      other.evalTo(*this);
+    }
+
+    /** \brief Copy constructor with in-place evaluation */
+    template<typename OtherDerived>
+    explicit SparseMatrix(const DiagonalBase<OtherDerived>& other)
+      : Base(), m_outerSize(0), m_innerSize(0), m_outerIndex(0), m_innerNonZeros(0)
+    {
+      *this = other.derived();
+    }
+
+    /** Swaps the content of two sparse matrices of the same type.
+      * This is a fast operation that simply swaps the underlying pointers and parameters. */
+    inline void swap(SparseMatrix& other)
+    {
+      //EIGEN_DBG_SPARSE(std::cout << "SparseMatrix:: swap\n");
+      std::swap(m_outerIndex, other.m_outerIndex);
+      std::swap(m_innerSize, other.m_innerSize);
+      std::swap(m_outerSize, other.m_outerSize);
+      std::swap(m_innerNonZeros, other.m_innerNonZeros);
+      m_data.swap(other.m_data);
+    }
+
+    /** Sets *this to the identity matrix.
+      * This function also turns the matrix into compressed mode, and drop any reserved memory. */
+    inline void setIdentity()
+    {
+      eigen_assert(m_outerSize == m_innerSize && "ONLY FOR SQUARED MATRICES");
+      internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
+      m_innerNonZeros = 0;
+      m_data.resize(m_outerSize);
+      // is it necessary to squeeze?
+      m_data.squeeze();
+      std::iota(m_outerIndex, m_outerIndex + m_outerSize + 1, StorageIndex(0));
+      std::iota(innerIndexPtr(), innerIndexPtr() + m_outerSize, StorageIndex(0));
+      std::fill_n(valuePtr(), m_outerSize, Scalar(1));
+    }
+
+    inline SparseMatrix& operator=(const SparseMatrix& other)
+    {
+      if (other.isRValue())
+      {
+        swap(other.const_cast_derived());
+      }
+      else if(this!=&other)
+      {
+        #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+          EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+        #endif
+        initAssignment(other);
+        if(other.isCompressed())
+        {
+          internal::smart_copy(other.m_outerIndex, other.m_outerIndex + m_outerSize + 1, m_outerIndex);
+          m_data = other.m_data;
+        }
+        else
+        {
+          Base::operator=(other);
+        }
+      }
+      return *this;
+    }
+
+    inline SparseMatrix& operator=(SparseMatrix&& other) {
+      return *this = other.derived().markAsRValue();
+    }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename OtherDerived>
+    inline SparseMatrix& operator=(const EigenBase<OtherDerived>& other)
+    { return Base::operator=(other.derived()); }
+
+    template<typename Lhs, typename Rhs>
+    inline SparseMatrix& operator=(const Product<Lhs,Rhs,AliasFreeProduct>& other);
+#endif // EIGEN_PARSED_BY_DOXYGEN
+
+    template<typename OtherDerived>
+    EIGEN_DONT_INLINE SparseMatrix& operator=(const SparseMatrixBase<OtherDerived>& other);
+
+#ifndef EIGEN_NO_IO
+    friend std::ostream & operator << (std::ostream & s, const SparseMatrix& m)
+    {
+      EIGEN_DBG_SPARSE(
+        s << "Nonzero entries:\n";
+        if(m.isCompressed())
+        {
+          for (Index i=0; i<m.nonZeros(); ++i)
+            s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") ";
+        }
+        else
+        {
+          for (Index i=0; i<m.outerSize(); ++i)
+          {
+            Index p = m.m_outerIndex[i];
+            Index pe = m.m_outerIndex[i]+m.m_innerNonZeros[i];
+            Index k=p;
+            for (; k<pe; ++k) {
+              s << "(" << m.m_data.value(k) << "," << m.m_data.index(k) << ") ";
+            }
+            for (; k<m.m_outerIndex[i+1]; ++k) {
+              s << "(_,_) ";
+            }
+          }
+        }
+        s << std::endl;
+        s << std::endl;
+        s << "Outer pointers:\n";
+        for (Index i=0; i<m.outerSize(); ++i) {
+          s << m.m_outerIndex[i] << " ";
+        }
+        s << " $" << std::endl;
+        if(!m.isCompressed())
+        {
+          s << "Inner non zeros:\n";
+          for (Index i=0; i<m.outerSize(); ++i) {
+            s << m.m_innerNonZeros[i] << " ";
+          }
+          s << " $" << std::endl;
+        }
+        s << std::endl;
+      );
+      s << static_cast<const SparseMatrixBase<SparseMatrix>&>(m);
+      return s;
+    }
+#endif
+
+    /** Destructor */
+    inline ~SparseMatrix()
+    {
+      internal::conditional_aligned_delete_auto<StorageIndex, true>(m_outerIndex, m_outerSize + 1);
+      internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
+    }
+
+    /** Overloaded for performance */
+    Scalar sum() const;
+    
+#   ifdef EIGEN_SPARSEMATRIX_PLUGIN
+#     include EIGEN_SPARSEMATRIX_PLUGIN
+#   endif
+
+protected:
+
+    template<typename Other>
+    void initAssignment(const Other& other)
+    {
+      resize(other.rows(), other.cols());
+      internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
+      m_innerNonZeros = 0;
+    }
+
+    /** \internal
+      * \sa insert(Index,Index) */
+    EIGEN_DEPRECATED EIGEN_DONT_INLINE Scalar& insertCompressed(Index row, Index col);
+
+    /** \internal
+      * A vector object that is equal to 0 everywhere but v at the position i */
+    class SingletonVector
+    {
+        StorageIndex m_index;
+        StorageIndex m_value;
+      public:
+        typedef StorageIndex value_type;
+        SingletonVector(Index i, Index v)
+          : m_index(convert_index(i)), m_value(convert_index(v))
+        {}
+
+        StorageIndex operator[](Index i) const { return i==m_index ? m_value : 0; }
+    };
+
+    /** \internal
+      * \sa insert(Index,Index) */
+    EIGEN_DEPRECATED EIGEN_DONT_INLINE Scalar& insertUncompressed(Index row, Index col);
+
+public:
+    /** \internal
+      * \sa insert(Index,Index) */
+    EIGEN_STRONG_INLINE Scalar& insertBackUncompressed(Index row, Index col)
+    {
+      const Index outer = IsRowMajor ? row : col;
+      const Index inner = IsRowMajor ? col : row;
+
+      eigen_assert(!isCompressed());
+      eigen_assert(m_innerNonZeros[outer]<=(m_outerIndex[outer+1] - m_outerIndex[outer]));
+
+      Index p = m_outerIndex[outer] + m_innerNonZeros[outer]++;
+      m_data.index(p) = StorageIndex(inner);
+      m_data.value(p) = Scalar(0);
+      return m_data.value(p);
+    }
+protected:
+    struct IndexPosPair {
+      IndexPosPair(Index a_i, Index a_p) : i(a_i), p(a_p) {}
+      Index i;
+      Index p;
+    };
+
+    /** \internal assign \a diagXpr to the diagonal of \c *this
+      * There are different strategies:
+      *   1 - if *this is overwritten (Func==assign_op) or *this is empty, then we can work treat *this as a dense vector expression.
+      *   2 - otherwise, for each diagonal coeff,
+      *     2.a - if it already exists, then we update it,
+      *     2.b - if the correct position is at the end of the vector, and there is capacity, push to back
+      *     2.b - otherwise, the insertion requires a data move, record insertion locations and handle in a second pass
+      *   3 - at the end, if some entries failed to be updated in-place, then we alloc a new buffer, copy each chunk at the right position, and insert the new elements.
+      */
+    template<typename DiagXpr, typename Func>
+    void assignDiagonal(const DiagXpr diagXpr, const Func& assignFunc)
+    {
+      
+      constexpr StorageIndex kEmptyIndexVal(-1);
+      typedef typename ScalarVector::AlignedMapType ValueMap;
+
+      Index n = diagXpr.size();
+
+      const bool overwrite = internal::is_same<Func, internal::assign_op<Scalar,Scalar> >::value;
+      if(overwrite)
+      {
+        if((m_outerSize != n) || (m_innerSize != n))
+          resize(n, n);
+      }
+
+      if(m_data.size()==0 || overwrite)
+      {
+        internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
+        m_innerNonZeros = 0;
+        resizeNonZeros(n);
+        ValueMap valueMap(valuePtr(), n);
+        std::iota(m_outerIndex, m_outerIndex + n + 1, StorageIndex(0));
+        std::iota(innerIndexPtr(), innerIndexPtr() + n, StorageIndex(0));
+        valueMap.setZero();
+        internal::call_assignment_no_alias(valueMap, diagXpr, assignFunc);
+      }
+      else
+      {
+          internal::evaluator<DiagXpr> diaEval(diagXpr);
+
+          ei_declare_aligned_stack_constructed_variable(StorageIndex, tmp, n, 0);
+          typename IndexVector::AlignedMapType insertionLocations(tmp, n);
+          insertionLocations.setConstant(kEmptyIndexVal);
+
+          Index deferredInsertions = 0;
+          Index shift = 0;
+
+          for (Index j = 0; j < n; j++) {
+            Index begin = m_outerIndex[j];
+            Index end = isCompressed() ? m_outerIndex[j + 1] : begin + m_innerNonZeros[j];
+            Index capacity = m_outerIndex[j + 1] - end;
+            Index dst = m_data.searchLowerIndex(begin, end, j);
+            // the entry exists: update it now
+            if (dst != end && m_data.index(dst) == StorageIndex(j)) assignFunc.assignCoeff(m_data.value(dst), diaEval.coeff(j));
+            // the entry belongs at the back of the vector: push to back
+            else if (dst == end && capacity > 0)
+              assignFunc.assignCoeff(insertBackUncompressed(j, j), diaEval.coeff(j));
+            // the insertion requires a data move, record insertion location and handle in second pass
+            else {
+              insertionLocations.coeffRef(j) = StorageIndex(dst);
+              deferredInsertions++;
+              // if there is no capacity, all vectors to the right of this are shifted
+              if (capacity == 0) shift++;
+            }
+          }
+          
+          if (deferredInsertions > 0) {
+
+            m_data.resize(m_data.size() + shift);
+            Index copyEnd = isCompressed() ? m_outerIndex[m_outerSize]
+                                           : m_outerIndex[m_outerSize - 1] + m_innerNonZeros[m_outerSize - 1];
+            for (Index j = m_outerSize - 1; deferredInsertions > 0; j--) {
+              Index begin = m_outerIndex[j];
+              Index end = isCompressed() ? m_outerIndex[j + 1] : begin + m_innerNonZeros[j];
+              Index capacity = m_outerIndex[j + 1] - end;
+
+              bool doInsertion = insertionLocations(j) >= 0;
+              bool breakUpCopy = doInsertion && (capacity > 0);
+              // break up copy for sorted insertion into inactive nonzeros
+              // optionally, add another criterium, i.e. 'breakUpCopy || (capacity > threhsold)'
+              // where `threshold >= 0` to skip inactive nonzeros in each vector
+              // this reduces the total number of copied elements, but requires more moveChunk calls
+              if (breakUpCopy) {
+                Index copyBegin = m_outerIndex[j + 1];
+                Index to = copyBegin + shift;
+                Index chunkSize = copyEnd - copyBegin;
+                m_data.moveChunk(copyBegin, to, chunkSize);
+                copyEnd = end;
+              }
+
+              m_outerIndex[j + 1] += shift;
+              
+              if (doInsertion) {
+                // if there is capacity, shift into the inactive nonzeros
+                if (capacity > 0) shift++;
+                Index copyBegin = insertionLocations(j);
+                Index to = copyBegin + shift;
+                Index chunkSize = copyEnd - copyBegin;
+                m_data.moveChunk(copyBegin, to, chunkSize);
+                Index dst = to - 1;
+                m_data.index(dst) = StorageIndex(j);
+                m_data.value(dst) = Scalar(0);
+                assignFunc.assignCoeff(m_data.value(dst), diaEval.coeff(j));
+                if (!isCompressed()) m_innerNonZeros[j]++;
+                shift--;
+                deferredInsertions--;
+                copyEnd = copyBegin;
+              }
+            }
+          }     
+          eigen_assert((shift == 0) && (deferredInsertions == 0));
+      }
+    }
+
+    /* These functions are used to avoid a redundant binary search operation in functions such as coeffRef() and assume `dst` is the appropriate sorted insertion point */
+    EIGEN_STRONG_INLINE Scalar& insertAtByOuterInner(Index outer, Index inner, Index dst);
+    Scalar& insertCompressedAtByOuterInner(Index outer, Index inner, Index dst);
+    Scalar& insertUncompressedAtByOuterInner(Index outer, Index inner, Index dst);
+
+private:
+  EIGEN_STATIC_ASSERT(NumTraits<StorageIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE)
+  EIGEN_STATIC_ASSERT((Options&(ColMajor|RowMajor))==Options,INVALID_MATRIX_TEMPLATE_PARAMETERS)
+
+  struct default_prunning_func {
+    default_prunning_func(const Scalar& ref, const RealScalar& eps) : reference(ref), epsilon(eps) {}
+    inline bool operator() (const Index&, const Index&, const Scalar& value) const
+    {
+      return !internal::isMuchSmallerThan(value, reference, epsilon);
+    }
+    Scalar reference;
+    RealScalar epsilon;
+  };
+};
+
+namespace internal {
+
+// Creates a compressed sparse matrix from a range of unsorted triplets
+// Requires temporary storage to handle duplicate entries
+template <typename InputIterator, typename SparseMatrixType, typename DupFunctor>
+void set_from_triplets(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat,
+                       DupFunctor dup_func) {
+  constexpr bool IsRowMajor = SparseMatrixType::IsRowMajor;
+  using StorageIndex = typename SparseMatrixType::StorageIndex;
+  using IndexMap = typename VectorX<StorageIndex>::AlignedMapType;
+  using TransposedSparseMatrix = SparseMatrix<typename SparseMatrixType::Scalar, IsRowMajor ? ColMajor : RowMajor, StorageIndex>;
+
+  if (begin == end) return;
+
+  // There are two strategies to consider for constructing a matrix from unordered triplets:
+  // A) construct the 'mat' in its native storage order and sort in-place (less memory); or, 
+  // B) construct the transposed matrix and use an implicit sort upon assignment to `mat` (less time).
+  // This routine uses B) for faster execution time.
+  TransposedSparseMatrix trmat(mat.rows(), mat.cols());
+
+  // scan triplets to determine allocation size before constructing matrix
+  Index nonZeros = 0;
+  for (InputIterator it(begin); it != end; ++it) {
+    eigen_assert(it->row() >= 0 && it->row() < mat.rows() && it->col() >= 0 && it->col() < mat.cols());
+    StorageIndex j = convert_index<StorageIndex>(IsRowMajor ? it->col() : it->row());
+    if (nonZeros == NumTraits<StorageIndex>::highest()) internal::throw_std_bad_alloc();
+    trmat.outerIndexPtr()[j + 1]++;
+    nonZeros++;
+  }
+
+  std::partial_sum(trmat.outerIndexPtr(), trmat.outerIndexPtr() + trmat.outerSize() + 1, trmat.outerIndexPtr());
+  eigen_assert(nonZeros == trmat.outerIndexPtr()[trmat.outerSize()]);
+  trmat.resizeNonZeros(nonZeros);
+
+  // construct temporary array to track insertions (outersize) and collapse duplicates (innersize)
+  ei_declare_aligned_stack_constructed_variable(StorageIndex, tmp, numext::maxi(mat.innerSize(), mat.outerSize()), 0);
+  smart_copy(trmat.outerIndexPtr(), trmat.outerIndexPtr() + trmat.outerSize(), tmp);
+
+  // push triplets to back of each vector
+  for (InputIterator it(begin); it != end; ++it) {
+    StorageIndex j = convert_index<StorageIndex>(IsRowMajor ? it->col() : it->row());
+    StorageIndex i = convert_index<StorageIndex>(IsRowMajor ? it->row() : it->col());
+    StorageIndex k = tmp[j];
+    trmat.data().index(k) = i;
+    trmat.data().value(k) = it->value();
+    tmp[j]++;
+  }
+
+  IndexMap wi(tmp, trmat.innerSize());
+  trmat.collapseDuplicates(wi, dup_func);
+  // implicit sorting
+  mat = trmat;
+}
+
+// Creates a compressed sparse matrix from a sorted range of triplets
+template <typename InputIterator, typename SparseMatrixType, typename DupFunctor>
+void set_from_triplets_sorted(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat,
+                              DupFunctor dup_func) {
+  constexpr bool IsRowMajor = SparseMatrixType::IsRowMajor;
+  using StorageIndex = typename SparseMatrixType::StorageIndex;
+
+  if (begin == end) return;
+
+  constexpr StorageIndex kEmptyIndexValue(-1);
+  // deallocate inner nonzeros if present and zero outerIndexPtr
+  mat.resize(mat.rows(), mat.cols());
+  // use outer indices to count non zero entries (excluding duplicate entries)
+  StorageIndex previous_j = kEmptyIndexValue;
+  StorageIndex previous_i = kEmptyIndexValue;
+  // scan triplets to determine allocation size before constructing matrix
+  Index nonZeros = 0;
+  for (InputIterator it(begin); it != end; ++it) {
+    eigen_assert(it->row() >= 0 && it->row() < mat.rows() && it->col() >= 0 && it->col() < mat.cols());
+    StorageIndex j = convert_index<StorageIndex>(IsRowMajor ? it->row() : it->col());
+    StorageIndex i = convert_index<StorageIndex>(IsRowMajor ? it->col() : it->row());
+    eigen_assert(j > previous_j || (j == previous_j && i >= previous_i));
+    // identify duplicates by examining previous location
+    bool duplicate = (previous_j == j) && (previous_i == i);
+    if (!duplicate) {
+      if (nonZeros == NumTraits<StorageIndex>::highest()) internal::throw_std_bad_alloc();
+      nonZeros++;
+      mat.outerIndexPtr()[j + 1]++;
+      previous_j = j;
+      previous_i = i;
+    }
+  }
+
+  // finalize outer indices and allocate memory
+  std::partial_sum(mat.outerIndexPtr(), mat.outerIndexPtr() + mat.outerSize() + 1, mat.outerIndexPtr());
+  eigen_assert(nonZeros == mat.outerIndexPtr()[mat.outerSize()]);
+  mat.resizeNonZeros(nonZeros);
+
+  previous_i = kEmptyIndexValue;
+  previous_j = kEmptyIndexValue;
+  Index back = 0;
+  for (InputIterator it(begin); it != end; ++it) {
+    StorageIndex j = convert_index<StorageIndex>(IsRowMajor ? it->row() : it->col());
+    StorageIndex i = convert_index<StorageIndex>(IsRowMajor ? it->col() : it->row());
+    bool duplicate = (previous_j == j) && (previous_i == i);
+    if (duplicate) {
+      mat.data().value(back - 1) = dup_func(mat.data().value(back - 1), it->value());
+    } else {
+      // push triplets to back
+      mat.data().index(back) = i;
+      mat.data().value(back) = it->value();
+      previous_j = j;
+      previous_i = i;
+      back++;
+    }
+  }
+  eigen_assert(back == nonZeros);
+  // matrix is finalized
+}
+
+// thin wrapper around a generic binary functor to use the sparse disjunction evaulator instead of the default "arithmetic" evaulator
+template<typename DupFunctor, typename LhsScalar, typename RhsScalar = LhsScalar>
+struct scalar_disjunction_op
+{
+  using result_type = typename result_of<DupFunctor(LhsScalar, RhsScalar)>::type;
+  scalar_disjunction_op(const DupFunctor& op) : m_functor(op) {}
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE result_type operator() (const LhsScalar& a, const RhsScalar& b) const { return m_functor(a, b); }
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const DupFunctor& functor() const { return m_functor; }
+  const DupFunctor& m_functor;
+};
+
+template <typename DupFunctor, typename LhsScalar, typename RhsScalar>
+struct functor_traits<scalar_disjunction_op<DupFunctor, LhsScalar, RhsScalar>> : public functor_traits<DupFunctor> {};
+
+// Creates a compressed sparse matrix from its existing entries and those from an unsorted range of triplets
+template <typename InputIterator, typename SparseMatrixType, typename DupFunctor>
+void insert_from_triplets(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat,
+                          DupFunctor dup_func) {
+  using Scalar = typename SparseMatrixType::Scalar;
+  using SrcXprType = CwiseBinaryOp<scalar_disjunction_op<DupFunctor, Scalar>, const SparseMatrixType, const SparseMatrixType>;
+
+  // set_from_triplets is necessary to sort the inner indices and remove the duplicate entries
+  SparseMatrixType trips(mat.rows(), mat.cols());
+  set_from_triplets(begin, end, trips, dup_func);
+
+  SrcXprType src = mat.binaryExpr(trips, scalar_disjunction_op<DupFunctor, Scalar>(dup_func));
+  // the sparse assignment procedure creates a temporary matrix and swaps the final result
+  assign_sparse_to_sparse<SparseMatrixType, SrcXprType>(mat, src);
+}
+
+// Creates a compressed sparse matrix from its existing entries and those from an sorted range of triplets
+template <typename InputIterator, typename SparseMatrixType, typename DupFunctor>
+void insert_from_triplets_sorted(const InputIterator& begin, const InputIterator& end, SparseMatrixType& mat,
+                                 DupFunctor dup_func) {
+  using Scalar = typename SparseMatrixType::Scalar;
+  using SrcXprType = CwiseBinaryOp<scalar_disjunction_op<DupFunctor, Scalar>, const SparseMatrixType, const SparseMatrixType>;
+
+  // TODO: process triplets without making a copy
+  SparseMatrixType trips(mat.rows(), mat.cols());
+  set_from_triplets_sorted(begin, end, trips, dup_func);
+
+  SrcXprType src = mat.binaryExpr(trips, scalar_disjunction_op<DupFunctor, Scalar>(dup_func));
+  // the sparse assignment procedure creates a temporary matrix and swaps the final result
+  assign_sparse_to_sparse<SparseMatrixType, SrcXprType>(mat, src);
+}
+
+}  // namespace internal
+
+/** Fill the matrix \c *this with the list of \em triplets defined in the half-open range from \a begin to \a end.
+  *
+  * A \em triplet is a tuple (i,j,value) defining a non-zero element.
+  * The input list of triplets does not have to be sorted, and may contain duplicated elements.
+  * In any case, the result is a \b sorted and \b compressed sparse matrix where the duplicates have been summed up.
+  * This is a \em O(n) operation, with \em n the number of triplet elements.
+  * The initial contents of \c *this are destroyed.
+  * The matrix \c *this must be properly resized beforehand using the SparseMatrix(Index,Index) constructor,
+  * or the resize(Index,Index) method. The sizes are not extracted from the triplet list.
+  *
+  * The \a InputIterators value_type must provide the following interface:
+  * \code
+  * Scalar value() const; // the value
+  * IndexType row() const;   // the row index i
+  * IndexType col() const;   // the column index j
+  * \endcode
+  * See for instance the Eigen::Triplet template class.
+  *
+  * Here is a typical usage example:
+  * \code
+    typedef Triplet<double> T;
+    std::vector<T> tripletList;
+    tripletList.reserve(estimation_of_entries);
+    for(...)
+    {
+      // ...
+      tripletList.push_back(T(i,j,v_ij));
+    }
+    SparseMatrixType m(rows,cols);
+    m.setFromTriplets(tripletList.begin(), tripletList.end());
+    // m is ready to go!
+  * \endcode
+  *
+  * \warning The list of triplets is read multiple times (at least twice). Therefore, it is not recommended to define
+  * an abstract iterator over a complex data-structure that would be expensive to evaluate. The triplets should rather
+  * be explicitly stored into a std::vector for instance.
+  */
+template<typename Scalar, int Options_, typename StorageIndex_>
+template<typename InputIterators>
+void SparseMatrix<Scalar,Options_,StorageIndex_>::setFromTriplets(const InputIterators& begin, const InputIterators& end)
+{
+  internal::set_from_triplets<InputIterators, SparseMatrix<Scalar,Options_,StorageIndex_> >(begin, end, *this, internal::scalar_sum_op<Scalar,Scalar>());
+}
+
+/** The same as setFromTriplets but when duplicates are met the functor \a dup_func is applied:
+  * \code
+  * value = dup_func(OldValue, NewValue)
+  * \endcode
+  * Here is a C++11 example keeping the latest entry only:
+  * \code
+  * mat.setFromTriplets(triplets.begin(), triplets.end(), [] (const Scalar&,const Scalar &b) { return b; });
+  * \endcode
+  */
+template<typename Scalar, int Options_, typename StorageIndex_>
+template<typename InputIterators, typename DupFunctor>
+void SparseMatrix<Scalar, Options_, StorageIndex_>::setFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func)
+{
+  internal::set_from_triplets<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>, DupFunctor>(begin, end, *this, dup_func);
+}
+
+/** The same as setFromTriplets but triplets are assumed to be pre-sorted. This is faster and requires less temporary storage. 
+  * Two triplets `a` and `b` are appropriately ordered if:
+  * \code
+  * ColMajor: ((a.col() != b.col()) ? (a.col() < b.col()) : (a.row() < b.row())
+  * RowMajor: ((a.row() != b.row()) ? (a.row() < b.row()) : (a.col() < b.col())
+  * \endcode
+  */
+template<typename Scalar, int Options_, typename StorageIndex_>
+template<typename InputIterators>
+void SparseMatrix<Scalar, Options_, StorageIndex_>::setFromSortedTriplets(const InputIterators& begin, const InputIterators& end)
+{
+  internal::set_from_triplets_sorted<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_> >(begin, end, *this, internal::scalar_sum_op<Scalar, Scalar>());
+}
+
+/** The same as setFromSortedTriplets but when duplicates are met the functor \a dup_func is applied:
+  * \code
+  * value = dup_func(OldValue, NewValue)
+  * \endcode
+  * Here is a C++11 example keeping the latest entry only:
+  * \code
+  * mat.setFromSortedTriplets(triplets.begin(), triplets.end(), [] (const Scalar&,const Scalar &b) { return b; });
+  * \endcode
+  */
+template<typename Scalar, int Options_, typename StorageIndex_>
+template<typename InputIterators, typename DupFunctor>
+void SparseMatrix<Scalar, Options_, StorageIndex_>::setFromSortedTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func)
+{
+  internal::set_from_triplets_sorted<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>, DupFunctor>(begin, end, *this, dup_func);
+}
+
+/** Insert a batch of elements into the matrix \c *this with the list of \em triplets defined in the half-open range from \a begin to \a end.
+  *
+  * A \em triplet is a tuple (i,j,value) defining a non-zero element.
+  * The input list of triplets does not have to be sorted, and may contain duplicated elements.
+  * In any case, the result is a \b sorted and \b compressed sparse matrix where the duplicates have been summed up.
+  * This is a \em O(n) operation, with \em n the number of triplet elements.
+  * The initial contents of \c *this are preserved (except for the summation of duplicate elements).
+  * The matrix \c *this must be properly sized beforehand. The sizes are not extracted from the triplet list.
+  *
+  * The \a InputIterators value_type must provide the following interface:
+  * \code
+  * Scalar value() const; // the value
+  * IndexType row() const;   // the row index i
+  * IndexType col() const;   // the column index j
+  * \endcode
+  * See for instance the Eigen::Triplet template class.
+  *
+  * Here is a typical usage example:
+  * \code
+    SparseMatrixType m(rows,cols); // m contains nonzero entries
+    typedef Triplet<double> T;
+    std::vector<T> tripletList;
+    tripletList.reserve(estimation_of_entries);
+    for(...)
+    {
+      // ...
+      tripletList.push_back(T(i,j,v_ij));
+    }
+    
+    m.insertFromTriplets(tripletList.begin(), tripletList.end());
+    // m is ready to go!
+  * \endcode
+  *
+  * \warning The list of triplets is read multiple times (at least twice). Therefore, it is not recommended to define
+  * an abstract iterator over a complex data-structure that would be expensive to evaluate. The triplets should rather
+  * be explicitly stored into a std::vector for instance.
+  */
+template<typename Scalar, int Options_, typename StorageIndex_>
+template<typename InputIterators>
+void SparseMatrix<Scalar, Options_, StorageIndex_>::insertFromTriplets(const InputIterators& begin, const InputIterators& end)
+{
+  internal::insert_from_triplets<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_> >(begin, end, *this, internal::scalar_sum_op<Scalar, Scalar>());
+}
+
+/** The same as insertFromTriplets but when duplicates are met the functor \a dup_func is applied:
+  * \code
+  * value = dup_func(OldValue, NewValue)
+  * \endcode
+  * Here is a C++11 example keeping the latest entry only:
+  * \code
+  * mat.insertFromTriplets(triplets.begin(), triplets.end(), [] (const Scalar&,const Scalar &b) { return b; });
+  * \endcode
+  */
+template<typename Scalar, int Options_, typename StorageIndex_>
+template<typename InputIterators, typename DupFunctor>
+void SparseMatrix<Scalar, Options_, StorageIndex_>::insertFromTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func)
+{
+  internal::insert_from_triplets<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>, DupFunctor>(begin, end, *this, dup_func);
+}
+
+/** The same as insertFromTriplets but triplets are assumed to be pre-sorted. This is faster and requires less temporary storage.
+  * Two triplets `a` and `b` are appropriately ordered if:
+  * \code
+  * ColMajor: ((a.col() != b.col()) ? (a.col() < b.col()) : (a.row() < b.row())
+  * RowMajor: ((a.row() != b.row()) ? (a.row() < b.row()) : (a.col() < b.col())
+  * \endcode
+  */
+template<typename Scalar, int Options_, typename StorageIndex_>
+template<typename InputIterators>
+void SparseMatrix<Scalar, Options_, StorageIndex_>::insertFromSortedTriplets(const InputIterators& begin, const InputIterators& end)
+{
+  internal::insert_from_triplets_sorted<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_> >(begin, end, *this, internal::scalar_sum_op<Scalar, Scalar>());
+}
+
+/** The same as insertFromSortedTriplets but when duplicates are met the functor \a dup_func is applied:
+  * \code
+  * value = dup_func(OldValue, NewValue)
+  * \endcode
+  * Here is a C++11 example keeping the latest entry only:
+  * \code
+  * mat.insertFromSortedTriplets(triplets.begin(), triplets.end(), [] (const Scalar&,const Scalar &b) { return b; });
+  * \endcode
+  */
+template<typename Scalar, int Options_, typename StorageIndex_>
+template<typename InputIterators, typename DupFunctor>
+void SparseMatrix<Scalar, Options_, StorageIndex_>::insertFromSortedTriplets(const InputIterators& begin, const InputIterators& end, DupFunctor dup_func)
+{
+  internal::insert_from_triplets_sorted<InputIterators, SparseMatrix<Scalar, Options_, StorageIndex_>, DupFunctor>(begin, end, *this, dup_func);
+}
+
+/** \internal */
+template <typename Scalar_, int Options_, typename StorageIndex_>
+template <typename Derived, typename DupFunctor>
+void SparseMatrix<Scalar_, Options_, StorageIndex_>::collapseDuplicates(DenseBase<Derived>& wi, DupFunctor dup_func) {
+  // removes duplicate entries and compresses the matrix
+  // the excess allocated memory is not released
+  // the inner indices do not need to be sorted, nor is the matrix returned in a sorted state
+  eigen_assert(wi.size() == m_innerSize);
+  constexpr StorageIndex kEmptyIndexValue(-1);
+  wi.setConstant(kEmptyIndexValue);
+  StorageIndex count = 0;
+  const bool is_compressed = isCompressed();
+  // for each inner-vector, wi[inner_index] will hold the position of first element into the index/value buffers
+  for (Index j = 0; j < m_outerSize; ++j) {
+    const StorageIndex newBegin = count;
+    const StorageIndex end = is_compressed ? m_outerIndex[j + 1] : m_outerIndex[j] + m_innerNonZeros[j];
+    for (StorageIndex k = m_outerIndex[j]; k < end; ++k) {
+      StorageIndex i = m_data.index(k);
+      if (wi(i) >= newBegin) {
+        // entry at k is a duplicate
+        // accumulate it into the primary entry located at wi(i)
+        m_data.value(wi(i)) = dup_func(m_data.value(wi(i)), m_data.value(k));
+      } else {
+        // k is the primary entry in j with inner index i
+        // shift it to the left and record its location at wi(i)
+        m_data.index(count) = i;
+        m_data.value(count) = m_data.value(k);
+        wi(i) = count;
+        ++count;
+      }
+    }
+    m_outerIndex[j] = newBegin;
+  }
+  m_outerIndex[m_outerSize] = count;
+  m_data.resize(count);
+
+  // turn the matrix into compressed form (if it is not already)
+  internal::conditional_aligned_delete_auto<StorageIndex, true>(m_innerNonZeros, m_outerSize);
+  m_innerNonZeros = 0;
+}
+
+/** \internal */
+template<typename Scalar, int Options_, typename StorageIndex_>
+template<typename OtherDerived>
+EIGEN_DONT_INLINE SparseMatrix<Scalar,Options_,StorageIndex_>& SparseMatrix<Scalar,Options_,StorageIndex_>::operator=(const SparseMatrixBase<OtherDerived>& other)
+{
+  EIGEN_STATIC_ASSERT((internal::is_same<Scalar, typename OtherDerived::Scalar>::value),
+        YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+    EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+  #endif
+      
+  const bool needToTranspose = (Flags & RowMajorBit) != (internal::evaluator<OtherDerived>::Flags & RowMajorBit);
+  if (needToTranspose)
+  {
+    #ifdef EIGEN_SPARSE_TRANSPOSED_COPY_PLUGIN
+      EIGEN_SPARSE_TRANSPOSED_COPY_PLUGIN
+    #endif
+    // two passes algorithm:
+    //  1 - compute the number of coeffs per dest inner vector
+    //  2 - do the actual copy/eval
+    // Since each coeff of the rhs has to be evaluated twice, let's evaluate it if needed
+    typedef typename internal::nested_eval<OtherDerived,2,typename internal::plain_matrix_type<OtherDerived>::type >::type OtherCopy;
+    typedef internal::remove_all_t<OtherCopy> OtherCopy_;
+    typedef internal::evaluator<OtherCopy_> OtherCopyEval;
+    OtherCopy otherCopy(other.derived());
+    OtherCopyEval otherCopyEval(otherCopy);
+
+    SparseMatrix dest(other.rows(),other.cols());
+    Eigen::Map<IndexVector> (dest.m_outerIndex,dest.outerSize()).setZero();
+
+    // pass 1
+    // FIXME the above copy could be merged with that pass
+    for (Index j=0; j<otherCopy.outerSize(); ++j)
+      for (typename OtherCopyEval::InnerIterator it(otherCopyEval, j); it; ++it)
+        ++dest.m_outerIndex[it.index()];
+
+    // prefix sum
+    StorageIndex count = 0;
+    IndexVector positions(dest.outerSize());
+    for (Index j=0; j<dest.outerSize(); ++j)
+    {
+      StorageIndex tmp = dest.m_outerIndex[j];
+      dest.m_outerIndex[j] = count;
+      positions[j] = count;
+      count += tmp;
+    }
+    dest.m_outerIndex[dest.outerSize()] = count;
+    // alloc
+    dest.m_data.resize(count);
+    // pass 2
+    for (StorageIndex j=0; j<otherCopy.outerSize(); ++j)
+    {
+      for (typename OtherCopyEval::InnerIterator it(otherCopyEval, j); it; ++it)
+      {
+        Index pos = positions[it.index()]++;
+        dest.m_data.index(pos) = j;
+        dest.m_data.value(pos) = it.value();
+      }
+    }
+    this->swap(dest);
+    return *this;
+  }
+  else
+  {
+    if(other.isRValue())
+    {
+      initAssignment(other.derived());
+    }
+    // there is no special optimization
+    return Base::operator=(other.derived());
+  }
+}
+
+template <typename Scalar_, int Options_, typename StorageIndex_>
+inline typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar&
+SparseMatrix<Scalar_, Options_, StorageIndex_>::insert(Index row, Index col) {
+  return insertByOuterInner(IsRowMajor ? row : col, IsRowMajor ? col : row);
+}
+
+template <typename Scalar_, int Options_, typename StorageIndex_>
+EIGEN_STRONG_INLINE typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar&
+SparseMatrix<Scalar_, Options_, StorageIndex_>::insertAtByOuterInner(Index outer, Index inner, Index dst) {
+  // random insertion into compressed matrix is very slow
+  uncompress();
+  return insertUncompressedAtByOuterInner(outer, inner, dst);
+}
+
+template <typename Scalar_, int Options_, typename StorageIndex_>
+EIGEN_DEPRECATED EIGEN_DONT_INLINE typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar&
+SparseMatrix<Scalar_, Options_, StorageIndex_>::insertUncompressed(Index row, Index col) {
+  eigen_assert(!isCompressed());
+  Index outer = IsRowMajor ? row : col;
+  Index inner = IsRowMajor ? col : row;
+  Index start = m_outerIndex[outer];
+  Index end = start + m_innerNonZeros[outer];
+  Index dst = start == end ? end : m_data.searchLowerIndex(start, end, inner);
+  if (dst == end) {
+    Index capacity = m_outerIndex[outer + 1] - end;
+    if (capacity > 0) {
+      // implies uncompressed: push to back of vector
+      m_innerNonZeros[outer]++;
+      m_data.index(end) = StorageIndex(inner);
+      m_data.value(end) = Scalar(0);
+      return m_data.value(end);
+    }
+  }
+  eigen_assert((dst == end || m_data.index(dst) != inner) &&
+               "you cannot insert an element that already exists, you must call coeffRef to this end");
+  return insertUncompressedAtByOuterInner(outer, inner, dst);
+}
+
+template <typename Scalar_, int Options_, typename StorageIndex_>
+EIGEN_DEPRECATED EIGEN_DONT_INLINE typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar&
+SparseMatrix<Scalar_, Options_, StorageIndex_>::insertCompressed(Index row, Index col) {
+  eigen_assert(isCompressed());
+  Index outer = IsRowMajor ? row : col;
+  Index inner = IsRowMajor ? col : row;
+  Index start = m_outerIndex[outer];
+  Index end = m_outerIndex[outer + 1];
+  Index dst = start == end ? end : m_data.searchLowerIndex(start, end, inner);
+  eigen_assert((dst == end || m_data.index(dst) != inner) &&
+               "you cannot insert an element that already exists, you must call coeffRef to this end");
+  return insertCompressedAtByOuterInner(outer, inner, dst);
+}
+
+template <typename Scalar_, int Options_, typename StorageIndex_>
+typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar&
+SparseMatrix<Scalar_, Options_, StorageIndex_>::insertCompressedAtByOuterInner(Index outer, Index inner, Index dst) {
+  eigen_assert(isCompressed());
+  // compressed insertion always requires expanding the buffer
+  // first, check if there is adequate allocated memory
+  if (m_data.allocatedSize() <= m_data.size()) {
+    // if there is no capacity for a single insertion, double the capacity
+    // increase capacity by a mininum of 32
+    Index minReserve = 32;
+    Index reserveSize = numext::maxi(minReserve, m_data.allocatedSize());
+    m_data.reserve(reserveSize);
+  }
+  m_data.resize(m_data.size() + 1);
+  Index chunkSize = m_outerIndex[m_outerSize] - dst;
+  // shift the existing data to the right if necessary
+  m_data.moveChunk(dst, dst + 1, chunkSize);
+  // update nonzero counts
+  // potentially O(outerSize) bottleneck!
+  for (Index j = outer; j < m_outerSize; j++) m_outerIndex[j + 1]++;
+  // initialize the coefficient
+  m_data.index(dst) = StorageIndex(inner);
+  m_data.value(dst) = Scalar(0);
+  // return a reference to the coefficient
+  return m_data.value(dst);
+}
+
+template <typename Scalar_, int Options_, typename StorageIndex_>
+typename SparseMatrix<Scalar_, Options_, StorageIndex_>::Scalar&
+SparseMatrix<Scalar_, Options_, StorageIndex_>::insertUncompressedAtByOuterInner(Index outer, Index inner, Index dst) {
+  eigen_assert(!isCompressed());
+  // find a vector with capacity, starting at `outer` and searching to the left and right
+  for (Index leftTarget = outer - 1, rightTarget = outer; (leftTarget >= 0) || (rightTarget < m_outerSize);) {
+    if (rightTarget < m_outerSize) {
+      Index start = m_outerIndex[rightTarget];
+      Index end = start + m_innerNonZeros[rightTarget];
+      Index nextStart = m_outerIndex[rightTarget + 1];
+      Index capacity = nextStart - end;
+      if (capacity > 0) {
+        // move [dst, end) to dst+1 and insert at dst
+        Index chunkSize = end - dst;
+        if (chunkSize > 0) m_data.moveChunk(dst, dst + 1, chunkSize);
+        m_innerNonZeros[outer]++;
+        for (Index j = outer; j < rightTarget; j++) m_outerIndex[j + 1]++;
+        m_data.index(dst) = StorageIndex(inner);
+        m_data.value(dst) = Scalar(0);
+        return m_data.value(dst);
+      }
+      rightTarget++;
+    }
+    if (leftTarget >= 0) {
+      Index start = m_outerIndex[leftTarget];
+      Index end = start + m_innerNonZeros[leftTarget];
+      Index nextStart = m_outerIndex[leftTarget + 1];
+      Index capacity = nextStart - end;
+      if (capacity > 0) {
+        // tricky: dst is a lower bound, so we must insert at dst-1 when shifting left
+        // move [nextStart, dst) to nextStart-1 and insert at dst-1
+        Index chunkSize = dst - nextStart;
+        if (chunkSize > 0) m_data.moveChunk(nextStart, nextStart - 1, chunkSize);
+        m_innerNonZeros[outer]++;
+        for (Index j = leftTarget; j < outer; j++) m_outerIndex[j + 1]--;
+        m_data.index(dst - 1) = StorageIndex(inner);
+        m_data.value(dst - 1) = Scalar(0);
+        return m_data.value(dst - 1);
+      }
+      leftTarget--;
+    }
+  }
+
+  // no room for interior insertion
+  // nonZeros() == m_data.size()
+  // record offset as outerIndxPtr will change
+  Index dst_offset = dst - m_outerIndex[outer];
+  // allocate space for random insertion
+  if (m_data.allocatedSize() == 0) {
+    // fast method to allocate space for one element per vector in empty matrix
+    m_data.resize(m_outerSize);
+    std::iota(m_outerIndex, m_outerIndex + m_outerSize + 1, StorageIndex(0));
+  } else {
+    // check for integer overflow: if maxReserveSize == 0, insertion is not possible
+    Index maxReserveSize = static_cast<Index>(NumTraits<StorageIndex>::highest()) - m_data.allocatedSize();
+    eigen_assert(maxReserveSize > 0);
+    if (m_outerSize <= maxReserveSize) {
+      // allocate space for one additional element per vector
+      reserveInnerVectors(IndexVector::Constant(m_outerSize, 1));
+    } else {
+      // handle the edge case where StorageIndex is insufficient to reserve outerSize additional elements
+      // allocate space for one additional element in the interval [outer,maxReserveSize)
+      typedef internal::sparse_reserve_op<StorageIndex> ReserveSizesOp;
+      typedef CwiseNullaryOp<ReserveSizesOp, IndexVector> ReserveSizesXpr;
+      ReserveSizesXpr reserveSizesXpr(m_outerSize, 1, ReserveSizesOp(outer, m_outerSize, maxReserveSize));
+      reserveInnerVectors(reserveSizesXpr);
+    }
+  }
+  // insert element at `dst` with new outer indices
+  Index start = m_outerIndex[outer];
+  Index end = start + m_innerNonZeros[outer];
+  Index new_dst = start + dst_offset;
+  Index chunkSize = end - new_dst;
+  if (chunkSize > 0) m_data.moveChunk(new_dst, new_dst + 1, chunkSize);
+  m_innerNonZeros[outer]++;
+  m_data.index(new_dst) = StorageIndex(inner);
+  m_data.value(new_dst) = Scalar(0);
+  return m_data.value(new_dst);
+}
+
+namespace internal {
+
+    template <typename Scalar_, int Options_, typename StorageIndex_>
+    struct evaluator<SparseMatrix<Scalar_, Options_, StorageIndex_>>
+        : evaluator<SparseCompressedBase<SparseMatrix<Scalar_, Options_, StorageIndex_>>> {
+      typedef evaluator<SparseCompressedBase<SparseMatrix<Scalar_, Options_, StorageIndex_>>> Base;
+      typedef SparseMatrix<Scalar_, Options_, StorageIndex_> SparseMatrixType;
+      evaluator() : Base() {}
+      explicit evaluator(const SparseMatrixType& mat) : Base(mat) {}
+    };
+
+}
+
+// Specialization for SparseMatrix.
+// Serializes [rows, cols, isCompressed, outerSize, innerBufferSize,
+// innerNonZeros, outerIndices, innerIndices, values].
+template <typename Scalar, int Options, typename StorageIndex>
+class Serializer<SparseMatrix<Scalar, Options, StorageIndex>, void> {
+ public:
+  typedef SparseMatrix<Scalar, Options, StorageIndex> SparseMat;
+
+  struct Header {
+    typename SparseMat::Index rows;
+    typename SparseMat::Index cols;
+    bool compressed;
+    Index outer_size;
+    Index inner_buffer_size;
+  };
+
+  EIGEN_DEVICE_FUNC size_t size(const SparseMat& value) const {
+    // innerNonZeros.
+    std::size_t num_storage_indices = value.isCompressed() ? 0 : value.outerSize();
+    // Outer indices.
+    num_storage_indices += value.outerSize() + 1;
+    // Inner indices.
+    const StorageIndex inner_buffer_size = value.outerIndexPtr()[value.outerSize()];
+    num_storage_indices += inner_buffer_size;
+    // Values.
+    std::size_t num_values = inner_buffer_size;
+    return sizeof(Header) + sizeof(Scalar) * num_values +
+           sizeof(StorageIndex) * num_storage_indices;
+  }
+
+  EIGEN_DEVICE_FUNC uint8_t* serialize(uint8_t* dest, uint8_t* end,
+                                       const SparseMat& value) {
+    if (EIGEN_PREDICT_FALSE(dest == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(dest + size(value) > end)) return nullptr;
+
+    const size_t header_bytes = sizeof(Header);
+    Header header = {value.rows(), value.cols(), value.isCompressed(),
+                     value.outerSize(), value.outerIndexPtr()[value.outerSize()]};
+    EIGEN_USING_STD(memcpy)
+    memcpy(dest, &header, header_bytes);
+    dest += header_bytes;
+
+    // innerNonZeros.
+    if (!header.compressed) {
+      std::size_t data_bytes = sizeof(StorageIndex) * header.outer_size;
+      memcpy(dest, value.innerNonZeroPtr(), data_bytes);
+      dest += data_bytes;
+    }
+
+    // Outer indices.
+    std::size_t data_bytes = sizeof(StorageIndex) * (header.outer_size + 1);
+    memcpy(dest, value.outerIndexPtr(), data_bytes);
+    dest += data_bytes;
+
+    // Inner indices.
+    data_bytes = sizeof(StorageIndex) * header.inner_buffer_size;
+    memcpy(dest, value.innerIndexPtr(), data_bytes);
+    dest += data_bytes;
+
+    // Values.
+    data_bytes = sizeof(Scalar) * header.inner_buffer_size;
+    memcpy(dest, value.valuePtr(), data_bytes);
+    dest += data_bytes;
+
+    return dest;
+  }
+
+  EIGEN_DEVICE_FUNC const uint8_t* deserialize(const uint8_t* src,
+                                               const uint8_t* end,
+                                               SparseMat& value) const {
+    if (EIGEN_PREDICT_FALSE(src == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(src + sizeof(Header) > end)) return nullptr;
+
+    const size_t header_bytes = sizeof(Header);
+    Header header;
+    EIGEN_USING_STD(memcpy)
+    memcpy(&header, src, header_bytes);
+    src += header_bytes;
+
+    value.setZero();
+    value.resize(header.rows, header.cols);
+    if (header.compressed) {
+      value.makeCompressed();
+    } else {
+      value.uncompress();
+    }
+    
+    // Adjust value ptr size.
+    value.data().resize(header.inner_buffer_size);
+
+    // Initialize compressed state and inner non-zeros.
+    if (!header.compressed) {           
+      // Inner non-zero counts.
+      std::size_t data_bytes = sizeof(StorageIndex) * header.outer_size;
+      if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr;
+      memcpy(value.innerNonZeroPtr(), src, data_bytes);
+      src += data_bytes;
+    }
+
+    // Outer indices.
+    std::size_t data_bytes = sizeof(StorageIndex) * (header.outer_size + 1);
+    if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr;
+    memcpy(value.outerIndexPtr(), src, data_bytes);
+    src += data_bytes;
+
+    // Inner indices.
+    data_bytes = sizeof(StorageIndex) * header.inner_buffer_size;
+    if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr;
+    memcpy(value.innerIndexPtr(), src, data_bytes);
+    src += data_bytes;
+
+    // Values.
+    data_bytes = sizeof(Scalar) * header.inner_buffer_size;
+    if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr;
+    memcpy(value.valuePtr(), src, data_bytes);
+    src += data_bytes;
+    return src;
+  }
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEMATRIX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseMatrixBase.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseMatrixBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..dc78c2eeba7427564a302da4e73342399487519c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseMatrixBase.h
@@ -0,0 +1,399 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEMATRIXBASE_H
+#define EIGEN_SPARSEMATRIXBASE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \ingroup SparseCore_Module
+  *
+  * \class SparseMatrixBase
+  *
+  * \brief Base class of any sparse matrices or sparse expressions
+  *
+  * \tparam Derived is the derived type, e.g. a sparse matrix type, or an expression, etc.
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEMATRIXBASE_PLUGIN.
+  */
+template<typename Derived> class SparseMatrixBase
+  : public EigenBase<Derived>
+{
+  public:
+
+    typedef typename internal::traits<Derived>::Scalar Scalar;
+    
+    /** The numeric type of the expression' coefficients, e.g. float, double, int or std::complex<float>, etc.
+      *
+      * It is an alias for the Scalar type */
+    typedef Scalar value_type;
+    
+    typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+    typedef typename internal::traits<Derived>::StorageKind StorageKind;
+
+    /** The integer type used to \b store indices within a SparseMatrix.
+      * For a \c SparseMatrix<Scalar,Options,IndexType> it an alias of the third template parameter \c IndexType. */
+    typedef typename internal::traits<Derived>::StorageIndex StorageIndex;
+
+    typedef typename internal::add_const_on_value_type_if_arithmetic<
+                         typename internal::packet_traits<Scalar>::type
+                     >::type PacketReturnType;
+
+    typedef SparseMatrixBase StorageBaseType;
+
+    typedef Matrix<StorageIndex,Dynamic,1> IndexVector;
+    typedef Matrix<Scalar,Dynamic,1> ScalarVector;
+    
+    template<typename OtherDerived>
+    Derived& operator=(const EigenBase<OtherDerived> &other);
+
+    enum {
+
+      RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+        /**< The number of rows at compile-time. This is just a copy of the value provided
+          * by the \a Derived type. If a value is not known at compile-time,
+          * it is set to the \a Dynamic constant.
+          * \sa MatrixBase::rows(), MatrixBase::cols(), ColsAtCompileTime, SizeAtCompileTime */
+
+      ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+        /**< The number of columns at compile-time. This is just a copy of the value provided
+          * by the \a Derived type. If a value is not known at compile-time,
+          * it is set to the \a Dynamic constant.
+          * \sa MatrixBase::rows(), MatrixBase::cols(), RowsAtCompileTime, SizeAtCompileTime */
+
+
+      SizeAtCompileTime = (internal::size_of_xpr_at_compile_time<Derived>::ret),
+        /**< This is equal to the number of coefficients, i.e. the number of
+          * rows times the number of columns, or to \a Dynamic if this is not
+          * known at compile-time. \sa RowsAtCompileTime, ColsAtCompileTime */
+
+      MaxRowsAtCompileTime = RowsAtCompileTime,
+      MaxColsAtCompileTime = ColsAtCompileTime,
+
+      MaxSizeAtCompileTime = internal::size_at_compile_time(MaxRowsAtCompileTime, MaxColsAtCompileTime),
+
+      IsVectorAtCompileTime = RowsAtCompileTime == 1 || ColsAtCompileTime == 1,
+        /**< This is set to true if either the number of rows or the number of
+          * columns is known at compile-time to be equal to 1. Indeed, in that case,
+          * we are dealing with a column-vector (if there is only one column) or with
+          * a row-vector (if there is only one row). */
+
+      NumDimensions = int(MaxSizeAtCompileTime) == 1 ? 0 : bool(IsVectorAtCompileTime) ? 1 : 2,
+        /**< This value is equal to Tensor::NumDimensions, i.e. 0 for scalars, 1 for vectors,
+         * and 2 for matrices.
+         */
+
+      Flags = internal::traits<Derived>::Flags,
+        /**< This stores expression \ref flags flags which may or may not be inherited by new expressions
+          * constructed from this one. See the \ref flags "list of flags".
+          */
+
+      IsRowMajor = Flags&RowMajorBit ? 1 : 0,
+      
+      InnerSizeAtCompileTime = int(IsVectorAtCompileTime) ? int(SizeAtCompileTime)
+                             : int(IsRowMajor) ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
+
+      #ifndef EIGEN_PARSED_BY_DOXYGEN
+      HasDirectAccess_ = (int(Flags)&DirectAccessBit) ? 1 : 0 // workaround sunCC
+      #endif
+    };
+
+    /** \internal the return type of MatrixBase::adjoint() */
+    typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
+                        CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, Eigen::Transpose<const Derived> >,
+                        Transpose<const Derived>
+                     > AdjointReturnType;
+    typedef Transpose<Derived> TransposeReturnType;
+    typedef Transpose<const Derived> ConstTransposeReturnType;
+
+    // FIXME storage order do not match evaluator storage order
+    typedef SparseMatrix<Scalar, Flags&RowMajorBit ? RowMajor : ColMajor, StorageIndex> PlainObject;
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** This is the "real scalar" type; if the \a Scalar type is already real numbers
+      * (e.g. int, float or double) then \a RealScalar is just the same as \a Scalar. If
+      * \a Scalar is \a std::complex<T> then RealScalar is \a T.
+      *
+      * \sa class NumTraits
+      */
+    typedef typename NumTraits<Scalar>::Real RealScalar;
+
+    /** \internal the return type of coeff()
+      */
+    typedef std::conditional_t<HasDirectAccess_, const Scalar&, Scalar> CoeffReturnType;
+
+    /** \internal Represents a matrix with all coefficients equal to one another*/
+    typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>,Matrix<Scalar,Dynamic,Dynamic> > ConstantReturnType;
+
+    /** type of the equivalent dense matrix */
+    typedef Matrix<Scalar,RowsAtCompileTime,ColsAtCompileTime> DenseMatrixType;
+    /** type of the equivalent square matrix */
+    typedef Matrix<Scalar, internal::max_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime),
+                           internal::max_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime)> SquareMatrixType;
+
+    inline const Derived& derived() const { return *static_cast<const Derived*>(this); }
+    inline Derived& derived() { return *static_cast<Derived*>(this); }
+    inline Derived& const_cast_derived() const
+    { return *static_cast<Derived*>(const_cast<SparseMatrixBase*>(this)); }
+
+    typedef EigenBase<Derived> Base;
+
+#endif // not EIGEN_PARSED_BY_DOXYGEN
+
+#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::SparseMatrixBase
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+#define EIGEN_DOC_UNARY_ADDONS(METHOD,OP)           /** <p>This method does not change the sparsity of \c *this: the OP is applied to explicitly stored coefficients only. \sa SparseCompressedBase::coeffs() </p> */
+#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL      /** <p> \warning This method returns a read-only expression for any sparse matrices. \sa \ref TutorialSparse_SubMatrices "Sparse block operations" </p> */
+#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND) /** <p> \warning This method returns a read-write expression for COND sparse matrices only. Otherwise, the returned expression is read-only. \sa \ref TutorialSparse_SubMatrices "Sparse block operations" </p> */
+#else
+#define EIGEN_DOC_UNARY_ADDONS(X,Y)
+#define EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+#define EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(COND)
+#endif
+#   include "../plugins/CommonCwiseUnaryOps.h"
+#   include "../plugins/CommonCwiseBinaryOps.h"
+#   include "../plugins/MatrixCwiseUnaryOps.h"
+#   include "../plugins/MatrixCwiseBinaryOps.h"
+#   include "../plugins/BlockMethods.h"
+#   ifdef EIGEN_SPARSEMATRIXBASE_PLUGIN
+#     include EIGEN_SPARSEMATRIXBASE_PLUGIN
+#   endif
+#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
+#undef EIGEN_DOC_UNARY_ADDONS
+#undef EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+#undef EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF
+
+    /** \returns the number of rows. \sa cols() */
+    inline Index rows() const { return derived().rows(); }
+    /** \returns the number of columns. \sa rows() */
+    inline Index cols() const { return derived().cols(); }
+    /** \returns the number of coefficients, which is \a rows()*cols().
+      * \sa rows(), cols(). */
+    inline Index size() const { return rows() * cols(); }
+    /** \returns true if either the number of rows or the number of columns is equal to 1.
+      * In other words, this function returns
+      * \code rows()==1 || cols()==1 \endcode
+      * \sa rows(), cols(), IsVectorAtCompileTime. */
+    inline bool isVector() const { return rows()==1 || cols()==1; }
+    /** \returns the size of the storage major dimension,
+      * i.e., the number of columns for a columns major matrix, and the number of rows otherwise */
+    Index outerSize() const { return (int(Flags)&RowMajorBit) ? this->rows() : this->cols(); }
+    /** \returns the size of the inner dimension according to the storage order,
+      * i.e., the number of rows for a columns major matrix, and the number of cols otherwise */
+    Index innerSize() const { return (int(Flags)&RowMajorBit) ? this->cols() : this->rows(); }
+
+    bool isRValue() const { return m_isRValue; }
+    Derived& markAsRValue() { m_isRValue = true; return derived(); }
+
+    SparseMatrixBase() : m_isRValue(false) { /* TODO check flags */ }
+
+    
+    template<typename OtherDerived>
+    Derived& operator=(const ReturnByValue<OtherDerived>& other);
+
+    template<typename OtherDerived>
+    inline Derived& operator=(const SparseMatrixBase<OtherDerived>& other);
+
+    inline Derived& operator=(const Derived& other);
+
+  protected:
+
+    template<typename OtherDerived>
+    inline Derived& assign(const OtherDerived& other);
+
+    template<typename OtherDerived>
+    inline void assignGeneric(const OtherDerived& other);
+
+  public:
+#ifndef EIGEN_NO_IO
+    friend std::ostream & operator << (std::ostream & s, const SparseMatrixBase& m)
+    {
+      typedef typename Derived::Nested Nested;
+      typedef internal::remove_all_t<Nested> NestedCleaned;
+
+      if (Flags&RowMajorBit)
+      {
+        Nested nm(m.derived());
+        internal::evaluator<NestedCleaned> thisEval(nm);
+        for (Index row=0; row<nm.outerSize(); ++row)
+        {
+          Index col = 0;
+          for (typename internal::evaluator<NestedCleaned>::InnerIterator it(thisEval, row); it; ++it)
+          {
+            for ( ; col<it.index(); ++col)
+              s << "0 ";
+            s << it.value() << " ";
+            ++col;
+          }
+          for ( ; col<m.cols(); ++col)
+            s << "0 ";
+          s << std::endl;
+        }
+      }
+      else
+      {
+        Nested nm(m.derived());
+        internal::evaluator<NestedCleaned> thisEval(nm);
+        if (m.cols() == 1) {
+          Index row = 0;
+          for (typename internal::evaluator<NestedCleaned>::InnerIterator it(thisEval, 0); it; ++it)
+          {
+            for ( ; row<it.index(); ++row)
+              s << "0" << std::endl;
+            s << it.value() << std::endl;
+            ++row;
+          }
+          for ( ; row<m.rows(); ++row)
+            s << "0" << std::endl;
+        }
+        else
+        {
+          SparseMatrix<Scalar, RowMajorBit, StorageIndex> trans = m;
+          s << static_cast<const SparseMatrixBase<SparseMatrix<Scalar, RowMajorBit, StorageIndex> >&>(trans);
+        }
+      }
+      return s;
+    }
+#endif
+
+    template<typename OtherDerived>
+    Derived& operator+=(const SparseMatrixBase<OtherDerived>& other);
+    template<typename OtherDerived>
+    Derived& operator-=(const SparseMatrixBase<OtherDerived>& other);
+    
+    template<typename OtherDerived>
+    Derived& operator+=(const DiagonalBase<OtherDerived>& other);
+    template<typename OtherDerived>
+    Derived& operator-=(const DiagonalBase<OtherDerived>& other);
+
+    template<typename OtherDerived>
+    Derived& operator+=(const EigenBase<OtherDerived> &other);
+    template<typename OtherDerived>
+    Derived& operator-=(const EigenBase<OtherDerived> &other);
+
+    Derived& operator*=(const Scalar& other);
+    Derived& operator/=(const Scalar& other);
+
+    template<typename OtherDerived> struct CwiseProductDenseReturnType {
+      typedef CwiseBinaryOp<internal::scalar_product_op<typename ScalarBinaryOpTraits<
+                                                          typename internal::traits<Derived>::Scalar,
+                                                          typename internal::traits<OtherDerived>::Scalar
+                                                        >::ReturnType>,
+                            const Derived,
+                            const OtherDerived
+                          > Type;
+    };
+
+    template<typename OtherDerived>
+    EIGEN_STRONG_INLINE const typename CwiseProductDenseReturnType<OtherDerived>::Type
+    cwiseProduct(const MatrixBase<OtherDerived> &other) const;
+
+    // sparse * diagonal
+    template<typename OtherDerived>
+    const Product<Derived,OtherDerived>
+    operator*(const DiagonalBase<OtherDerived> &other) const
+    { return Product<Derived,OtherDerived>(derived(), other.derived()); }
+
+    // diagonal * sparse
+    template<typename OtherDerived> friend
+    const Product<OtherDerived,Derived>
+    operator*(const DiagonalBase<OtherDerived> &lhs, const SparseMatrixBase& rhs)
+    { return Product<OtherDerived,Derived>(lhs.derived(), rhs.derived()); }
+    
+    // sparse * sparse
+    template<typename OtherDerived>
+    const Product<Derived,OtherDerived,AliasFreeProduct>
+    operator*(const SparseMatrixBase<OtherDerived> &other) const;
+    
+    // sparse * dense
+    template<typename OtherDerived>
+    const Product<Derived,OtherDerived>
+    operator*(const MatrixBase<OtherDerived> &other) const
+    { return Product<Derived,OtherDerived>(derived(), other.derived()); }
+    
+    // dense * sparse
+    template<typename OtherDerived> friend
+    const Product<OtherDerived,Derived>
+    operator*(const MatrixBase<OtherDerived> &lhs, const SparseMatrixBase& rhs)
+    { return Product<OtherDerived,Derived>(lhs.derived(), rhs.derived()); }
+    
+     /** \returns an expression of P H P^-1 where H is the matrix represented by \c *this */
+    SparseSymmetricPermutationProduct<Derived,Upper|Lower> twistedBy(const PermutationMatrix<Dynamic,Dynamic,StorageIndex>& perm) const
+    {
+      return SparseSymmetricPermutationProduct<Derived,Upper|Lower>(derived(), perm);
+    }
+
+    template<typename OtherDerived>
+    Derived& operator*=(const SparseMatrixBase<OtherDerived>& other);
+
+    template<int Mode>
+    inline const TriangularView<const Derived, Mode> triangularView() const;
+    
+    template<unsigned int UpLo> struct SelfAdjointViewReturnType { typedef SparseSelfAdjointView<Derived, UpLo> Type; };
+    template<unsigned int UpLo> struct ConstSelfAdjointViewReturnType { typedef const SparseSelfAdjointView<const Derived, UpLo> Type; };
+
+    template<unsigned int UpLo> inline 
+    typename ConstSelfAdjointViewReturnType<UpLo>::Type selfadjointView() const;
+    template<unsigned int UpLo> inline
+    typename SelfAdjointViewReturnType<UpLo>::Type selfadjointView();
+
+    template<typename OtherDerived> Scalar dot(const MatrixBase<OtherDerived>& other) const;
+    template<typename OtherDerived> Scalar dot(const SparseMatrixBase<OtherDerived>& other) const;
+    RealScalar squaredNorm() const;
+    RealScalar norm()  const;
+    RealScalar blueNorm() const;
+
+    TransposeReturnType transpose() { return TransposeReturnType(derived()); }
+    const ConstTransposeReturnType transpose() const { return ConstTransposeReturnType(derived()); }
+    const AdjointReturnType adjoint() const { return AdjointReturnType(transpose()); }
+
+    DenseMatrixType toDense() const
+    {
+      return DenseMatrixType(derived());
+    }
+
+    template<typename OtherDerived>
+    bool isApprox(const SparseMatrixBase<OtherDerived>& other,
+                  const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const;
+
+    template<typename OtherDerived>
+    bool isApprox(const MatrixBase<OtherDerived>& other,
+                  const RealScalar& prec = NumTraits<Scalar>::dummy_precision()) const
+    { return toDense().isApprox(other,prec); }
+
+    /** \returns the matrix or vector obtained by evaluating this expression.
+      *
+      * Notice that in the case of a plain matrix or vector (not an expression) this function just returns
+      * a const reference, in order to avoid a useless copy.
+      */
+    inline const typename internal::eval<Derived>::type eval() const
+    { return typename internal::eval<Derived>::type(derived()); }
+
+    Scalar sum() const;
+    
+    inline const SparseView<Derived>
+    pruned(const Scalar& reference = Scalar(0), const RealScalar& epsilon = NumTraits<Scalar>::dummy_precision()) const;
+
+  protected:
+
+    bool m_isRValue;
+
+    static inline StorageIndex convert_index(const Index idx) {
+      return internal::convert_index<StorageIndex>(idx);
+    }
+  private:
+    template<typename Dest> void evalTo(Dest &) const;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEMATRIXBASE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparsePermutation.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparsePermutation.h
new file mode 100644
index 0000000000000000000000000000000000000000..7e402cced34e38cf43a2787043f620facb6cced3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparsePermutation.h
@@ -0,0 +1,252 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_PERMUTATION_H
+#define EIGEN_SPARSE_PERMUTATION_H
+
+// This file implements sparse * permutation products
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename ExpressionType, typename PlainObjectType, bool NeedEval = !is_same<ExpressionType, PlainObjectType>::value>
+struct XprHelper
+{
+    XprHelper(const ExpressionType& xpr) : m_xpr(xpr) {}
+    inline const PlainObjectType& xpr() const { return m_xpr; }
+    // this is a new PlainObjectType initialized by xpr
+    const PlainObjectType m_xpr;
+};
+template<typename ExpressionType, typename PlainObjectType>
+struct XprHelper<ExpressionType, PlainObjectType, false>
+{
+    XprHelper(const ExpressionType& xpr) : m_xpr(xpr) {}
+    inline const PlainObjectType& xpr() const { return m_xpr; }
+    // this is a reference to xpr
+    const PlainObjectType& m_xpr;
+};
+
+template<typename PermDerived, bool NeedInverseEval>
+struct PermHelper
+{
+    using IndicesType = typename PermDerived::IndicesType;
+    using PermutationIndex = typename IndicesType::Scalar;
+    using type = PermutationMatrix<IndicesType::SizeAtCompileTime, IndicesType::MaxSizeAtCompileTime, PermutationIndex>;
+    PermHelper(const PermDerived& perm) : m_perm(perm.inverse()) {}
+    inline const type& perm() const { return m_perm; }
+    // this is a new PermutationMatrix initialized by perm.inverse()
+    const type m_perm;
+};
+template<typename PermDerived>
+struct PermHelper<PermDerived, false>
+{
+    using type = PermDerived;
+    PermHelper(const PermDerived& perm) : m_perm(perm) {}
+    inline const type& perm() const { return m_perm; }
+    // this is a reference to perm
+    const type& m_perm;
+};
+
+template<typename ExpressionType, int Side, bool Transposed>
+struct permutation_matrix_product<ExpressionType, Side, Transposed, SparseShape>
+{
+    using MatrixType = typename nested_eval<ExpressionType, 1>::type;
+    using MatrixTypeCleaned = remove_all_t<MatrixType>;
+
+    using Scalar = typename MatrixTypeCleaned::Scalar;
+    using StorageIndex = typename MatrixTypeCleaned::StorageIndex;
+
+    // the actual "return type" is `Dest`. this is a temporary type
+    using ReturnType = SparseMatrix<Scalar, MatrixTypeCleaned::IsRowMajor ? RowMajor : ColMajor, StorageIndex>;
+    using TmpHelper = XprHelper<ExpressionType, ReturnType>;
+
+    static constexpr bool NeedOuterPermutation = ExpressionType::IsRowMajor ? Side == OnTheLeft : Side == OnTheRight;
+    static constexpr bool NeedInversePermutation = Transposed ? Side == OnTheLeft : Side == OnTheRight;
+
+    template <typename Dest, typename PermutationType>
+    static inline void permute_outer(Dest& dst, const PermutationType& perm, const ExpressionType& xpr) {
+
+        // if ExpressionType is not ReturnType, evaluate `xpr` (allocation)
+        // otherwise, just reference `xpr`
+        // TODO: handle trivial expressions such as CwiseBinaryOp without temporary
+        const TmpHelper tmpHelper(xpr);
+        const ReturnType& tmp = tmpHelper.xpr();
+
+        ReturnType result(tmp.rows(), tmp.cols());
+
+        for (Index j = 0; j < tmp.outerSize(); j++) {
+          Index jp = perm.indices().coeff(j);
+          Index jsrc = NeedInversePermutation ? jp : j;
+          Index jdst = NeedInversePermutation ? j : jp;
+          Index begin = tmp.outerIndexPtr()[jsrc];
+          Index end = tmp.isCompressed() ? tmp.outerIndexPtr()[jsrc + 1] : begin + tmp.innerNonZeroPtr()[jsrc];
+          result.outerIndexPtr()[jdst + 1] += end - begin;
+        }
+
+        std::partial_sum(result.outerIndexPtr(), result.outerIndexPtr() + result.outerSize() + 1,
+                         result.outerIndexPtr());
+        result.resizeNonZeros(result.nonZeros());
+
+        for (Index j = 0; j < tmp.outerSize(); j++) {
+          Index jp = perm.indices().coeff(j);
+          Index jsrc = NeedInversePermutation ? jp : j;
+          Index jdst = NeedInversePermutation ? j : jp;
+          Index begin = tmp.outerIndexPtr()[jsrc];
+          Index end = tmp.isCompressed() ? tmp.outerIndexPtr()[jsrc + 1] : begin + tmp.innerNonZeroPtr()[jsrc];
+          Index target = result.outerIndexPtr()[jdst];
+          smart_copy(tmp.innerIndexPtr() + begin, tmp.innerIndexPtr() + end, result.innerIndexPtr() + target);
+          smart_copy(tmp.valuePtr() + begin, tmp.valuePtr() + end, result.valuePtr() + target);
+        }
+        dst = std::move(result);
+    }
+
+    template <typename Dest, typename PermutationType>
+    static inline void permute_inner(Dest& dst, const PermutationType& perm, const ExpressionType& xpr) {
+        using InnerPermHelper = PermHelper<PermutationType, NeedInversePermutation>;
+        using InnerPermType = typename InnerPermHelper::type;
+
+        // if ExpressionType is not ReturnType, evaluate `xpr` (allocation)
+        // otherwise, just reference `xpr`
+        // TODO: handle trivial expressions such as CwiseBinaryOp without temporary
+        const TmpHelper tmpHelper(xpr);
+        const ReturnType& tmp = tmpHelper.xpr();
+
+        // if inverse permutation of inner indices is requested, calculate perm.inverse() (allocation)
+        // otherwise, just reference `perm`
+        const InnerPermHelper permHelper(perm);
+        const InnerPermType& innerPerm = permHelper.perm();
+
+        ReturnType result(tmp.rows(), tmp.cols());
+
+        for (Index j = 0; j < tmp.outerSize(); j++) {
+            Index begin = tmp.outerIndexPtr()[j];
+            Index end = tmp.isCompressed() ? tmp.outerIndexPtr()[j + 1] : begin + tmp.innerNonZeroPtr()[j];
+            result.outerIndexPtr()[j + 1] += end - begin;
+        }
+
+        std::partial_sum(result.outerIndexPtr(), result.outerIndexPtr() + result.outerSize() + 1, result.outerIndexPtr());
+        result.resizeNonZeros(result.nonZeros());
+
+        for (Index j = 0; j < tmp.outerSize(); j++) {
+            Index begin = tmp.outerIndexPtr()[j];
+            Index end = tmp.isCompressed() ? tmp.outerIndexPtr()[j + 1] : begin + tmp.innerNonZeroPtr()[j];
+            Index target = result.outerIndexPtr()[j];
+            std::transform(tmp.innerIndexPtr() + begin, tmp.innerIndexPtr() + end, result.innerIndexPtr() + target,
+                           [&innerPerm](StorageIndex i) { return innerPerm.indices().coeff(i); });
+            smart_copy(tmp.valuePtr() + begin, tmp.valuePtr() + end, result.valuePtr() + target);
+        }
+        // the inner indices were permuted, and must be sorted
+        result.sortInnerIndices();
+        dst = std::move(result);
+    }
+
+    template <typename Dest, typename PermutationType, bool DoOuter = NeedOuterPermutation, std::enable_if_t<DoOuter, int> = 0>
+    static inline void run(Dest& dst, const PermutationType& perm, const ExpressionType& xpr) { permute_outer(dst, perm, xpr); }
+
+    template <typename Dest, typename PermutationType, bool DoOuter = NeedOuterPermutation, std::enable_if_t<!DoOuter, int> = 0>
+    static inline void run(Dest& dst, const PermutationType& perm, const ExpressionType& xpr) { permute_inner(dst, perm, xpr); }
+};
+
+}
+
+namespace internal {
+
+template <int ProductTag> struct product_promote_storage_type<Sparse,             PermutationStorage, ProductTag> { typedef Sparse ret; };
+template <int ProductTag> struct product_promote_storage_type<PermutationStorage, Sparse,             ProductTag> { typedef Sparse ret; };
+
+// TODO, the following two overloads are only needed to define the right temporary type through 
+// typename traits<permutation_sparse_matrix_product<Rhs,Lhs,OnTheRight,false> >::ReturnType
+// whereas it should be correctly handled by traits<Product<> >::PlainObject
+
+template<typename Lhs, typename Rhs, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, AliasFreeProduct>, ProductTag, PermutationShape, SparseShape>
+  : public evaluator<typename permutation_matrix_product<Rhs,OnTheLeft,false,SparseShape>::ReturnType>
+{
+  typedef Product<Lhs, Rhs, AliasFreeProduct> XprType;
+  typedef typename permutation_matrix_product<Rhs,OnTheLeft,false,SparseShape>::ReturnType PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  enum {
+    Flags = Base::Flags | EvalBeforeNestingBit
+  };
+
+  explicit product_evaluator(const XprType& xpr)
+    : m_result(xpr.rows(), xpr.cols())
+  {
+    internal::construct_at<Base>(this, m_result);
+    generic_product_impl<Lhs, Rhs, PermutationShape, SparseShape, ProductTag>::evalTo(m_result, xpr.lhs(), xpr.rhs());
+  }
+
+protected:
+  PlainObject m_result;
+};
+
+template<typename Lhs, typename Rhs, int ProductTag>
+struct product_evaluator<Product<Lhs, Rhs, AliasFreeProduct>, ProductTag, SparseShape, PermutationShape >
+  : public evaluator<typename permutation_matrix_product<Lhs,OnTheRight,false,SparseShape>::ReturnType>
+{
+  typedef Product<Lhs, Rhs, AliasFreeProduct> XprType;
+  typedef typename permutation_matrix_product<Lhs,OnTheRight,false,SparseShape>::ReturnType PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  enum {
+    Flags = Base::Flags | EvalBeforeNestingBit
+  };
+
+  explicit product_evaluator(const XprType& xpr)
+    : m_result(xpr.rows(), xpr.cols())
+  {
+    ::new (static_cast<Base*>(this)) Base(m_result);
+    generic_product_impl<Lhs, Rhs, SparseShape, PermutationShape, ProductTag>::evalTo(m_result, xpr.lhs(), xpr.rhs());
+  }
+
+protected:
+  PlainObject m_result;
+};
+
+} // end namespace internal
+
+/** \returns the matrix with the permutation applied to the columns
+ */
+template <typename SparseDerived, typename PermDerived>
+inline const Product<SparseDerived, PermDerived, AliasFreeProduct> operator*(
+    const SparseMatrixBase<SparseDerived>& matrix, const PermutationBase<PermDerived>& perm) {
+  return Product<SparseDerived, PermDerived, AliasFreeProduct>(matrix.derived(), perm.derived());
+}
+
+/** \returns the matrix with the permutation applied to the rows
+ */
+template <typename SparseDerived, typename PermDerived>
+inline const Product<PermDerived, SparseDerived, AliasFreeProduct> operator*(
+    const PermutationBase<PermDerived>& perm, const SparseMatrixBase<SparseDerived>& matrix) {
+  return Product<PermDerived, SparseDerived, AliasFreeProduct>(perm.derived(), matrix.derived());
+}
+
+/** \returns the matrix with the inverse permutation applied to the columns.
+ */
+template <typename SparseDerived, typename PermutationType>
+inline const Product<SparseDerived, Inverse<PermutationType>, AliasFreeProduct> operator*(
+    const SparseMatrixBase<SparseDerived>& matrix, const InverseImpl<PermutationType, PermutationStorage>& tperm) {
+  return Product<SparseDerived, Inverse<PermutationType>, AliasFreeProduct>(matrix.derived(), tperm.derived());
+}
+
+/** \returns the matrix with the inverse permutation applied to the rows.
+ */
+template <typename SparseDerived, typename PermutationType>
+inline const Product<Inverse<PermutationType>, SparseDerived, AliasFreeProduct> operator*(
+    const InverseImpl<PermutationType, PermutationStorage>& tperm, const SparseMatrixBase<SparseDerived>& matrix) {
+  return Product<Inverse<PermutationType>, SparseDerived, AliasFreeProduct>(tperm.derived(), matrix.derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_SELFADJOINTVIEW_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseProduct.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseProduct.h
new file mode 100644
index 0000000000000000000000000000000000000000..85a8a1032b61b1ede3f4b6198203ffc08003a205
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseProduct.h
@@ -0,0 +1,183 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEPRODUCT_H
+#define EIGEN_SPARSEPRODUCT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \returns an expression of the product of two sparse matrices.
+  * By default a conservative product preserving the symbolic non zeros is performed.
+  * The automatic pruning of the small values can be achieved by calling the pruned() function
+  * in which case a totally different product algorithm is employed:
+  * \code
+  * C = (A*B).pruned();             // suppress numerical zeros (exact)
+  * C = (A*B).pruned(ref);
+  * C = (A*B).pruned(ref,epsilon);
+  * \endcode
+  * where \c ref is a meaningful non zero reference value.
+  * */
+template<typename Derived>
+template<typename OtherDerived>
+inline const Product<Derived,OtherDerived,AliasFreeProduct>
+SparseMatrixBase<Derived>::operator*(const SparseMatrixBase<OtherDerived> &other) const
+{
+  return Product<Derived,OtherDerived,AliasFreeProduct>(derived(), other.derived());
+}
+
+namespace internal {
+
+// sparse * sparse
+template<typename Lhs, typename Rhs, int ProductType>
+struct generic_product_impl<Lhs, Rhs, SparseShape, SparseShape, ProductType>
+{
+  template<typename Dest>
+  static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
+  {
+    evalTo(dst, lhs, rhs, typename evaluator_traits<Dest>::Shape());
+  }
+
+  // dense += sparse * sparse
+  template<typename Dest,typename ActualLhs>
+  static void addTo(Dest& dst, const ActualLhs& lhs, const Rhs& rhs, std::enable_if_t<is_same<typename evaluator_traits<Dest>::Shape,DenseShape>::value,int*>* = 0)
+  {
+    typedef typename nested_eval<ActualLhs,Dynamic>::type LhsNested;
+    typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
+    LhsNested lhsNested(lhs);
+    RhsNested rhsNested(rhs);
+    internal::sparse_sparse_to_dense_product_selector<remove_all_t<LhsNested>,
+                                                      remove_all_t<RhsNested>, Dest>::run(lhsNested,rhsNested,dst);
+  }
+
+  // dense -= sparse * sparse
+  template<typename Dest>
+  static void subTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, std::enable_if_t<is_same<typename evaluator_traits<Dest>::Shape,DenseShape>::value,int*>* = 0)
+  {
+    addTo(dst, -lhs, rhs);
+  }
+
+protected:
+
+  // sparse = sparse * sparse
+  template<typename Dest>
+  static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, SparseShape)
+  {
+    typedef typename nested_eval<Lhs,Dynamic>::type LhsNested;
+    typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
+    LhsNested lhsNested(lhs);
+    RhsNested rhsNested(rhs);
+    internal::conservative_sparse_sparse_product_selector<remove_all_t<LhsNested>,
+                                                          remove_all_t<RhsNested>, Dest>::run(lhsNested,rhsNested,dst);
+  }
+
+  // dense = sparse * sparse
+  template<typename Dest>
+  static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs, DenseShape)
+  {
+    dst.setZero();
+    addTo(dst, lhs, rhs);
+  }
+};
+
+// sparse * sparse-triangular
+template<typename Lhs, typename Rhs, int ProductType>
+struct generic_product_impl<Lhs, Rhs, SparseShape, SparseTriangularShape, ProductType>
+ : public generic_product_impl<Lhs, Rhs, SparseShape, SparseShape, ProductType>
+{};
+
+// sparse-triangular * sparse
+template<typename Lhs, typename Rhs, int ProductType>
+struct generic_product_impl<Lhs, Rhs, SparseTriangularShape, SparseShape, ProductType>
+ : public generic_product_impl<Lhs, Rhs, SparseShape, SparseShape, ProductType>
+{};
+
+// dense = sparse-product (can be sparse*sparse, sparse*perm, etc.)
+template< typename DstXprType, typename Lhs, typename Rhs>
+struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::assign_op<typename DstXprType::Scalar,typename Product<Lhs,Rhs,AliasFreeProduct>::Scalar>, Sparse2Dense>
+{
+  typedef Product<Lhs,Rhs,AliasFreeProduct> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &)
+  {
+    Index dstRows = src.rows();
+    Index dstCols = src.cols();
+    if((dst.rows()!=dstRows) || (dst.cols()!=dstCols))
+      dst.resize(dstRows, dstCols);
+    
+    generic_product_impl<Lhs, Rhs>::evalTo(dst,src.lhs(),src.rhs());
+  }
+};
+
+// dense += sparse-product (can be sparse*sparse, sparse*perm, etc.)
+template< typename DstXprType, typename Lhs, typename Rhs>
+struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::add_assign_op<typename DstXprType::Scalar,typename Product<Lhs,Rhs,AliasFreeProduct>::Scalar>, Sparse2Dense>
+{
+  typedef Product<Lhs,Rhs,AliasFreeProduct> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &)
+  {
+    generic_product_impl<Lhs, Rhs>::addTo(dst,src.lhs(),src.rhs());
+  }
+};
+
+// dense -= sparse-product (can be sparse*sparse, sparse*perm, etc.)
+template< typename DstXprType, typename Lhs, typename Rhs>
+struct Assignment<DstXprType, Product<Lhs,Rhs,AliasFreeProduct>, internal::sub_assign_op<typename DstXprType::Scalar,typename Product<Lhs,Rhs,AliasFreeProduct>::Scalar>, Sparse2Dense>
+{
+  typedef Product<Lhs,Rhs,AliasFreeProduct> SrcXprType;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> &)
+  {
+    generic_product_impl<Lhs, Rhs>::subTo(dst,src.lhs(),src.rhs());
+  }
+};
+
+template<typename Lhs, typename Rhs, int Options>
+struct unary_evaluator<SparseView<Product<Lhs, Rhs, Options> >, IteratorBased>
+ : public evaluator<typename Product<Lhs, Rhs, DefaultProduct>::PlainObject>
+{
+  typedef SparseView<Product<Lhs, Rhs, Options> > XprType;
+  typedef typename XprType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  explicit unary_evaluator(const XprType& xpr)
+    : m_result(xpr.rows(), xpr.cols())
+  {
+    using std::abs;
+    internal::construct_at<Base>(this, m_result);
+    typedef typename nested_eval<Lhs,Dynamic>::type LhsNested;
+    typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
+    LhsNested lhsNested(xpr.nestedExpression().lhs());
+    RhsNested rhsNested(xpr.nestedExpression().rhs());
+
+    internal::sparse_sparse_product_with_pruning_selector<remove_all_t<LhsNested>,
+                                                          remove_all_t<RhsNested>, PlainObject>::run(lhsNested,rhsNested,m_result,
+                                                                                                                  abs(xpr.reference())*xpr.epsilon());
+  }
+
+protected:
+  PlainObject m_result;
+};
+
+} // end namespace internal
+
+// sparse matrix = sparse-product (can be sparse*sparse, sparse*perm, etc.)
+template<typename Scalar, int Options_, typename StorageIndex_>
+template<typename Lhs, typename Rhs>
+SparseMatrix<Scalar,Options_,StorageIndex_>& SparseMatrix<Scalar,Options_,StorageIndex_>::operator=(const Product<Lhs,Rhs,AliasFreeProduct>& src)
+{
+  // std::cout << "in Assignment : " << DstOptions << "\n";
+  SparseMatrix dst(src.rows(),src.cols());
+  internal::generic_product_impl<Lhs, Rhs>::evalTo(dst,src.lhs(),src.rhs());
+  this->swap(dst);
+  return *this;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEPRODUCT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseRedux.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseRedux.h
new file mode 100644
index 0000000000000000000000000000000000000000..6b14c58336db78aad46c248701cbf93c0e0d13b3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseRedux.h
@@ -0,0 +1,51 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEREDUX_H
+#define EIGEN_SPARSEREDUX_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+template<typename Derived>
+typename internal::traits<Derived>::Scalar
+SparseMatrixBase<Derived>::sum() const
+{
+  eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix");
+  Scalar res(0);
+  internal::evaluator<Derived> thisEval(derived());
+  for (Index j=0; j<outerSize(); ++j)
+    for (typename internal::evaluator<Derived>::InnerIterator iter(thisEval,j); iter; ++iter)
+      res += iter.value();
+  return res;
+}
+
+template<typename Scalar_, int Options_, typename Index_>
+typename internal::traits<SparseMatrix<Scalar_,Options_,Index_> >::Scalar
+SparseMatrix<Scalar_,Options_,Index_>::sum() const
+{
+  eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix");
+  if(this->isCompressed())
+    return Matrix<Scalar,1,Dynamic>::Map(m_data.valuePtr(), m_data.size()).sum();
+  else
+    return Base::sum();
+}
+
+template<typename Scalar_, int Options_, typename Index_>
+typename internal::traits<SparseVector<Scalar_,Options_, Index_> >::Scalar
+SparseVector<Scalar_,Options_,Index_>::sum() const
+{
+  eigen_assert(rows()>0 && cols()>0 && "you are using a non initialized matrix");
+  return Matrix<Scalar,1,Dynamic>::Map(m_data.valuePtr(), m_data.size()).sum();
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEREDUX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseRef.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseRef.h
new file mode 100644
index 0000000000000000000000000000000000000000..9e69d93ef7cd8f23d2152dc7bb9ae815c18bed87
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseRef.h
@@ -0,0 +1,394 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_REF_H
+#define EIGEN_SPARSE_REF_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+enum {
+  StandardCompressedFormat = 2 /**< used by Ref<SparseMatrix> to specify whether the input storage must be in standard compressed form */
+};
+  
+namespace internal {
+
+template<typename Derived> class SparseRefBase;
+
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options_, typename StrideType_>
+struct traits<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options_, StrideType_> >
+  : public traits<SparseMatrix<MatScalar,MatOptions,MatIndex> >
+{
+  typedef SparseMatrix<MatScalar,MatOptions,MatIndex> PlainObjectType;
+  enum {
+    Options = Options_,
+    Flags = traits<PlainObjectType>::Flags | CompressedAccessBit | NestByRefBit
+  };
+
+  template<typename Derived> struct match {
+    enum {
+      StorageOrderMatch = PlainObjectType::IsVectorAtCompileTime || Derived::IsVectorAtCompileTime || ((PlainObjectType::Flags&RowMajorBit)==(Derived::Flags&RowMajorBit)),
+      MatchAtCompileTime = (Derived::Flags&CompressedAccessBit) && StorageOrderMatch
+    };
+    typedef std::conditional_t<MatchAtCompileTime,internal::true_type,internal::false_type> type;
+  };
+  
+};
+
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options_, typename StrideType_>
+struct traits<Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options_, StrideType_> >
+  : public traits<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options_, StrideType_> >
+{
+  enum {
+    Flags = (traits<SparseMatrix<MatScalar,MatOptions,MatIndex> >::Flags | CompressedAccessBit | NestByRefBit) & ~LvalueBit
+  };
+};
+
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options_, typename StrideType_>
+struct traits<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options_, StrideType_> >
+  : public traits<SparseVector<MatScalar,MatOptions,MatIndex> >
+{
+  typedef SparseVector<MatScalar,MatOptions,MatIndex> PlainObjectType;
+  enum {
+    Options = Options_,
+    Flags = traits<PlainObjectType>::Flags | CompressedAccessBit | NestByRefBit
+  };
+
+  template<typename Derived> struct match {
+    enum {
+      MatchAtCompileTime = (Derived::Flags&CompressedAccessBit) && Derived::IsVectorAtCompileTime
+    };
+    typedef std::conditional_t<MatchAtCompileTime,internal::true_type,internal::false_type> type;
+  };
+
+};
+
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options_, typename StrideType_>
+struct traits<Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options_, StrideType_> >
+  : public traits<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options_, StrideType_> >
+{
+  enum {
+    Flags = (traits<SparseVector<MatScalar,MatOptions,MatIndex> >::Flags | CompressedAccessBit | NestByRefBit) & ~LvalueBit
+  };
+};
+
+template<typename Derived>
+struct traits<SparseRefBase<Derived> > : public traits<Derived> {};
+
+template<typename Derived> class SparseRefBase
+  : public SparseMapBase<Derived>
+{
+public:
+
+  typedef SparseMapBase<Derived> Base;
+  EIGEN_SPARSE_PUBLIC_INTERFACE(SparseRefBase)
+
+  SparseRefBase()
+    : Base(RowsAtCompileTime==Dynamic?0:RowsAtCompileTime,ColsAtCompileTime==Dynamic?0:ColsAtCompileTime, 0, 0, 0, 0, 0)
+  {}
+  
+protected:
+
+  template<typename Expression>
+  void construct(Expression& expr)
+  {
+    if(expr.outerIndexPtr()==0)
+      internal::construct_at<Base>(this, expr.size(), expr.nonZeros(), expr.innerIndexPtr(), expr.valuePtr());
+    else
+      internal::construct_at<Base>(this, expr.rows(), expr.cols(), expr.nonZeros(), expr.outerIndexPtr(), expr.innerIndexPtr(), expr.valuePtr(), expr.innerNonZeroPtr());
+  }
+};
+
+} // namespace internal
+
+
+/** 
+  * \ingroup SparseCore_Module
+  *
+  * \brief A sparse matrix expression referencing an existing sparse expression
+  *
+  * \tparam SparseMatrixType the equivalent sparse matrix type of the referenced data, it must be a template instance of class SparseMatrix.
+  * \tparam Options specifies whether the a standard compressed format is required \c Options is  \c #StandardCompressedFormat, or \c 0.
+  *                The default is \c 0.
+  *
+  * \sa class Ref
+  */
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+class Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType >
+  : public internal::SparseRefBase<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType > >
+#else
+template<typename SparseMatrixType, int Options>
+class Ref<SparseMatrixType, Options>
+  : public SparseMapBase<Derived,WriteAccessors> // yes, that's weird to use Derived here, but that works!
+#endif
+{
+    typedef SparseMatrix<MatScalar,MatOptions,MatIndex> PlainObjectType;
+    typedef internal::traits<Ref> Traits;
+    template<int OtherOptions>
+    inline Ref(const SparseMatrix<MatScalar,OtherOptions,MatIndex>& expr);
+    template<int OtherOptions>
+    inline Ref(const Map<SparseMatrix<MatScalar,OtherOptions,MatIndex>>& expr);
+  public:
+
+    typedef internal::SparseRefBase<Ref> Base;
+    EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
+
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<int OtherOptions>
+    inline Ref(SparseMatrix<MatScalar,OtherOptions,MatIndex>& expr)
+    {
+      EIGEN_STATIC_ASSERT(bool(Traits::template match<SparseMatrix<MatScalar,OtherOptions,MatIndex> >::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
+      eigen_assert( ((Options & int(StandardCompressedFormat))==0) || (expr.isCompressed()) );
+      Base::construct(expr.derived());
+    }
+
+    template<int OtherOptions>
+    inline Ref(Map<SparseMatrix<MatScalar,OtherOptions,MatIndex> >& expr)
+    {
+      EIGEN_STATIC_ASSERT(bool(Traits::template match<SparseMatrix<MatScalar,OtherOptions,MatIndex> >::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
+      eigen_assert( ((Options & int(StandardCompressedFormat))==0) || (expr.isCompressed()) );
+      Base::construct(expr.derived());
+    }
+
+    template<typename Derived>
+    inline Ref(const SparseCompressedBase<Derived>& expr)
+    #else
+    /** Implicit constructor from any sparse expression (2D matrix or 1D vector) */
+    template<typename Derived>
+    inline Ref(SparseCompressedBase<Derived>& expr)
+    #endif
+    {
+      EIGEN_STATIC_ASSERT(bool(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
+      EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
+      eigen_assert( ((Options & int(StandardCompressedFormat))==0) || (expr.isCompressed()) );
+      Base::construct(expr.const_cast_derived());
+    }
+};
+
+// this is the const ref version
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+class Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType>
+  : public internal::SparseRefBase<Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
+{
+    typedef SparseMatrix<MatScalar,MatOptions,MatIndex> TPlainObjectType;
+    typedef internal::traits<Ref> Traits;
+  public:
+
+    typedef internal::SparseRefBase<Ref> Base;
+    EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
+
+    template<typename Derived>
+    inline Ref(const SparseMatrixBase<Derived>& expr) : m_hasCopy(false)
+    {
+      construct(expr.derived(), typename Traits::template match<Derived>::type());
+    }
+
+    inline Ref(const Ref& other) : Base(other), m_hasCopy(false) {
+      // copy constructor shall not copy the m_object, to avoid unnecessary malloc and copy
+    }
+
+    template<typename OtherRef>
+    inline Ref(const RefBase<OtherRef>& other) : m_hasCopy(false) {
+      construct(other.derived(), typename Traits::template match<OtherRef>::type());
+    }
+
+    ~Ref() {
+      if(m_hasCopy) {
+        internal::destroy_at(reinterpret_cast<TPlainObjectType*>(&m_storage));
+      }
+    }
+
+  protected:
+
+    template<typename Expression>
+    void construct(const Expression& expr,internal::true_type)
+    {
+      if((Options & int(StandardCompressedFormat)) && (!expr.isCompressed()))
+      {
+        TPlainObjectType* obj = internal::construct_at(reinterpret_cast<TPlainObjectType*>(&m_storage), expr);
+        m_hasCopy = true;
+        Base::construct(*obj);
+      }
+      else
+      {
+        Base::construct(expr);
+      }
+    }
+
+    template<typename Expression>
+    void construct(const Expression& expr, internal::false_type)
+    {
+      TPlainObjectType* obj = internal::construct_at(reinterpret_cast<TPlainObjectType*>(&m_storage), expr);
+      m_hasCopy = true;
+      Base::construct(*obj);
+    }
+
+  protected:
+    typename internal::aligned_storage<sizeof(TPlainObjectType), EIGEN_ALIGNOF(TPlainObjectType)>::type m_storage;
+    bool m_hasCopy;
+};
+
+
+
+/**
+  * \ingroup SparseCore_Module
+  *
+  * \brief A sparse vector expression referencing an existing sparse vector expression
+  *
+  * \tparam SparseVectorType the equivalent sparse vector type of the referenced data, it must be a template instance of class SparseVector.
+  *
+  * \sa class Ref
+  */
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+class Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType >
+  : public internal::SparseRefBase<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType > >
+#else
+template<typename SparseVectorType>
+class Ref<SparseVectorType>
+  : public SparseMapBase<Derived,WriteAccessors>
+#endif
+{
+    typedef SparseVector<MatScalar,MatOptions,MatIndex> PlainObjectType;
+    typedef internal::traits<Ref> Traits;
+    template<int OtherOptions>
+    inline Ref(const SparseVector<MatScalar,OtherOptions,MatIndex>& expr);
+  public:
+
+    typedef internal::SparseRefBase<Ref> Base;
+    EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<int OtherOptions>
+    inline Ref(SparseVector<MatScalar,OtherOptions,MatIndex>& expr)
+    {
+      EIGEN_STATIC_ASSERT(bool(Traits::template match<SparseVector<MatScalar,OtherOptions,MatIndex> >::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
+      Base::construct(expr.derived());
+    }
+
+    template<typename Derived>
+    inline Ref(const SparseCompressedBase<Derived>& expr)
+    #else
+    /** Implicit constructor from any 1D sparse vector expression */
+    template<typename Derived>
+    inline Ref(SparseCompressedBase<Derived>& expr)
+    #endif
+    {
+      EIGEN_STATIC_ASSERT(bool(internal::is_lvalue<Derived>::value), THIS_EXPRESSION_IS_NOT_A_LVALUE__IT_IS_READ_ONLY);
+      EIGEN_STATIC_ASSERT(bool(Traits::template match<Derived>::MatchAtCompileTime), STORAGE_LAYOUT_DOES_NOT_MATCH);
+      Base::construct(expr.const_cast_derived());
+    }
+};
+
+// this is the const ref version
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+class Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType>
+  : public internal::SparseRefBase<Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
+{
+    typedef SparseVector<MatScalar,MatOptions,MatIndex> TPlainObjectType;
+    typedef internal::traits<Ref> Traits;
+  public:
+
+    typedef internal::SparseRefBase<Ref> Base;
+    EIGEN_SPARSE_PUBLIC_INTERFACE(Ref)
+
+    template<typename Derived>
+    inline Ref(const SparseMatrixBase<Derived>& expr) : m_hasCopy(false)
+    {
+      construct(expr.derived(), typename Traits::template match<Derived>::type());
+    }
+
+    inline Ref(const Ref& other) : Base(other), m_hasCopy(false) {
+      // copy constructor shall not copy the m_object, to avoid unnecessary malloc and copy
+    }
+
+    template<typename OtherRef>
+    inline Ref(const RefBase<OtherRef>& other) : m_hasCopy(false) {
+      construct(other.derived(), typename Traits::template match<OtherRef>::type());
+    }
+
+    ~Ref() {
+      if(m_hasCopy) {
+        internal::destroy_at(reinterpret_cast<TPlainObjectType*>(&m_storage));
+      }
+    }
+
+  protected:
+
+    template<typename Expression>
+    void construct(const Expression& expr,internal::true_type)
+    {
+      Base::construct(expr);
+    }
+
+    template<typename Expression>
+    void construct(const Expression& expr, internal::false_type)
+    {
+      TPlainObjectType* obj = internal::construct_at(reinterpret_cast<TPlainObjectType*>(&m_storage), expr);
+      m_hasCopy = true;
+      Base::construct(*obj);
+    }
+
+  protected:
+    typename internal::aligned_storage<sizeof(TPlainObjectType), EIGEN_ALIGNOF(TPlainObjectType)>::type m_storage;
+    bool m_hasCopy;
+};
+
+namespace internal {
+
+// FIXME shall we introduce a general evaluatior_ref that we can specialize for any sparse object once, and thus remove this copy-pasta thing...
+
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct evaluator<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
+  : evaluator<SparseCompressedBase<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
+{
+  typedef evaluator<SparseCompressedBase<Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
+  typedef Ref<SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;
+  evaluator() : Base() {}
+  explicit evaluator(const XprType &mat) : Base(mat) {}
+};
+
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct evaluator<Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
+  : evaluator<SparseCompressedBase<Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
+{
+  typedef evaluator<SparseCompressedBase<Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
+  typedef Ref<const SparseMatrix<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;
+  evaluator() : Base() {}
+  explicit evaluator(const XprType &mat) : Base(mat) {}
+};
+
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct evaluator<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
+  : evaluator<SparseCompressedBase<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
+{
+  typedef evaluator<SparseCompressedBase<Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
+  typedef Ref<SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;
+  evaluator() : Base() {}
+  explicit evaluator(const XprType &mat) : Base(mat) {}
+};
+
+template<typename MatScalar, int MatOptions, typename MatIndex, int Options, typename StrideType>
+struct evaluator<Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> >
+  : evaluator<SparseCompressedBase<Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> > >
+{
+  typedef evaluator<SparseCompressedBase<Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> > > Base;
+  typedef Ref<const SparseVector<MatScalar,MatOptions,MatIndex>, Options, StrideType> XprType;
+  evaluator() : Base() {}
+  explicit evaluator(const XprType &mat) : Base(mat) {}
+};
+
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_REF_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseSelfAdjointView.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseSelfAdjointView.h
new file mode 100644
index 0000000000000000000000000000000000000000..211506e9c7b6c24a666989a8e75f2f4b6f86153f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseSelfAdjointView.h
@@ -0,0 +1,652 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_SELFADJOINTVIEW_H
+#define EIGEN_SPARSE_SELFADJOINTVIEW_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+  
+/** \ingroup SparseCore_Module
+  * \class SparseSelfAdjointView
+  *
+  * \brief Pseudo expression to manipulate a triangular sparse matrix as a selfadjoint matrix.
+  *
+  * \param MatrixType the type of the dense matrix storing the coefficients
+  * \param Mode can be either \c #Lower or \c #Upper
+  *
+  * This class is an expression of a sefladjoint matrix from a triangular part of a matrix
+  * with given dense storage of the coefficients. It is the return type of MatrixBase::selfadjointView()
+  * and most of the time this is the only way that it is used.
+  *
+  * \sa SparseMatrixBase::selfadjointView()
+  */
+namespace internal {
+  
+template<typename MatrixType, unsigned int Mode>
+struct traits<SparseSelfAdjointView<MatrixType,Mode> > : traits<MatrixType> {
+};
+
+template<int SrcMode,int DstMode,typename MatrixType,int DestOrder>
+void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::StorageIndex>& _dest, const typename MatrixType::StorageIndex* perm = 0);
+
+template<int Mode,typename MatrixType,int DestOrder>
+void permute_symm_to_fullsymm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::StorageIndex>& _dest, const typename MatrixType::StorageIndex* perm = 0);
+
+}
+
+template<typename MatrixType, unsigned int Mode_> class SparseSelfAdjointView
+  : public EigenBase<SparseSelfAdjointView<MatrixType,Mode_> >
+{
+  public:
+    
+    enum {
+      Mode = Mode_,
+      TransposeMode = ((Mode & Upper) ? Lower : 0) | ((Mode & Lower) ? Upper : 0),
+      RowsAtCompileTime = internal::traits<SparseSelfAdjointView>::RowsAtCompileTime,
+      ColsAtCompileTime = internal::traits<SparseSelfAdjointView>::ColsAtCompileTime
+    };
+
+    typedef EigenBase<SparseSelfAdjointView> Base;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    typedef Matrix<StorageIndex,Dynamic,1> VectorI;
+    typedef typename internal::ref_selector<MatrixType>::non_const_type MatrixTypeNested;
+    typedef internal::remove_all_t<MatrixTypeNested> MatrixTypeNested_;
+    
+    explicit inline SparseSelfAdjointView(MatrixType& matrix) : m_matrix(matrix)
+    {
+      eigen_assert(rows()==cols() && "SelfAdjointView is only for squared matrices");
+    }
+
+    inline Index rows() const { return m_matrix.rows(); }
+    inline Index cols() const { return m_matrix.cols(); }
+
+    /** \internal \returns a reference to the nested matrix */
+    const MatrixTypeNested_& matrix() const { return m_matrix; }
+    std::remove_reference_t<MatrixTypeNested>& matrix() { return m_matrix; }
+
+    /** \returns an expression of the matrix product between a sparse self-adjoint matrix \c *this and a sparse matrix \a rhs.
+      *
+      * Note that there is no algorithmic advantage of performing such a product compared to a general sparse-sparse matrix product.
+      * Indeed, the SparseSelfadjointView operand is first copied into a temporary SparseMatrix before computing the product.
+      */
+    template<typename OtherDerived>
+    Product<SparseSelfAdjointView, OtherDerived>
+    operator*(const SparseMatrixBase<OtherDerived>& rhs) const
+    {
+      return Product<SparseSelfAdjointView, OtherDerived>(*this, rhs.derived());
+    }
+
+    /** \returns an expression of the matrix product between a sparse matrix \a lhs and a sparse self-adjoint matrix \a rhs.
+      *
+      * Note that there is no algorithmic advantage of performing such a product compared to a general sparse-sparse matrix product.
+      * Indeed, the SparseSelfadjointView operand is first copied into a temporary SparseMatrix before computing the product.
+      */
+    template<typename OtherDerived> friend
+    Product<OtherDerived, SparseSelfAdjointView>
+    operator*(const SparseMatrixBase<OtherDerived>& lhs, const SparseSelfAdjointView& rhs)
+    {
+      return Product<OtherDerived, SparseSelfAdjointView>(lhs.derived(), rhs);
+    }
+    
+    /** Efficient sparse self-adjoint matrix times dense vector/matrix product */
+    template<typename OtherDerived>
+    Product<SparseSelfAdjointView,OtherDerived>
+    operator*(const MatrixBase<OtherDerived>& rhs) const
+    {
+      return Product<SparseSelfAdjointView,OtherDerived>(*this, rhs.derived());
+    }
+
+    /** Efficient dense vector/matrix times sparse self-adjoint matrix product */
+    template<typename OtherDerived> friend
+    Product<OtherDerived,SparseSelfAdjointView>
+    operator*(const MatrixBase<OtherDerived>& lhs, const SparseSelfAdjointView& rhs)
+    {
+      return Product<OtherDerived,SparseSelfAdjointView>(lhs.derived(), rhs);
+    }
+
+    /** Perform a symmetric rank K update of the selfadjoint matrix \c *this:
+      * \f$ this = this + \alpha ( u u^* ) \f$ where \a u is a vector or matrix.
+      *
+      * \returns a reference to \c *this
+      *
+      * To perform \f$ this = this + \alpha ( u^* u ) \f$ you can simply
+      * call this function with u.adjoint().
+      */
+    template<typename DerivedU>
+    SparseSelfAdjointView& rankUpdate(const SparseMatrixBase<DerivedU>& u, const Scalar& alpha = Scalar(1));
+    
+    /** \returns an expression of P H P^-1 */
+    // TODO implement twists in a more evaluator friendly fashion
+    SparseSymmetricPermutationProduct<MatrixTypeNested_,Mode> twistedBy(const PermutationMatrix<Dynamic,Dynamic,StorageIndex>& perm) const
+    {
+      return SparseSymmetricPermutationProduct<MatrixTypeNested_,Mode>(m_matrix, perm);
+    }
+
+    template<typename SrcMatrixType,int SrcMode>
+    SparseSelfAdjointView& operator=(const SparseSymmetricPermutationProduct<SrcMatrixType,SrcMode>& permutedMatrix)
+    {
+      internal::call_assignment_no_alias_no_transpose(*this, permutedMatrix);
+      return *this;
+    }
+
+    SparseSelfAdjointView& operator=(const SparseSelfAdjointView& src)
+    {
+      PermutationMatrix<Dynamic,Dynamic,StorageIndex> pnull;
+      return *this = src.twistedBy(pnull);
+    }
+
+    // Since we override the copy-assignment operator, we need to explicitly re-declare the copy-constructor
+    EIGEN_DEFAULT_COPY_CONSTRUCTOR(SparseSelfAdjointView)
+
+    template<typename SrcMatrixType,unsigned int SrcMode>
+    SparseSelfAdjointView& operator=(const SparseSelfAdjointView<SrcMatrixType,SrcMode>& src)
+    {
+      PermutationMatrix<Dynamic,Dynamic,StorageIndex> pnull;
+      return *this = src.twistedBy(pnull);
+    }
+    
+    void resize(Index rows, Index cols)
+    {
+      EIGEN_ONLY_USED_FOR_DEBUG(rows);
+      EIGEN_ONLY_USED_FOR_DEBUG(cols);
+      eigen_assert(rows == this->rows() && cols == this->cols()
+                && "SparseSelfadjointView::resize() does not actually allow to resize.");
+    }
+    
+  protected:
+
+    MatrixTypeNested m_matrix;
+    //mutable VectorI m_countPerRow;
+    //mutable VectorI m_countPerCol;
+  private:
+    template<typename Dest> void evalTo(Dest &) const;
+};
+
+/***************************************************************************
+* Implementation of SparseMatrixBase methods
+***************************************************************************/
+
+template<typename Derived>
+template<unsigned int UpLo>
+typename SparseMatrixBase<Derived>::template ConstSelfAdjointViewReturnType<UpLo>::Type SparseMatrixBase<Derived>::selfadjointView() const
+{
+  return SparseSelfAdjointView<const Derived, UpLo>(derived());
+}
+
+template<typename Derived>
+template<unsigned int UpLo>
+typename SparseMatrixBase<Derived>::template SelfAdjointViewReturnType<UpLo>::Type SparseMatrixBase<Derived>::selfadjointView()
+{
+  return SparseSelfAdjointView<Derived, UpLo>(derived());
+}
+
+/***************************************************************************
+* Implementation of SparseSelfAdjointView methods
+***************************************************************************/
+
+template<typename MatrixType, unsigned int Mode>
+template<typename DerivedU>
+SparseSelfAdjointView<MatrixType,Mode>&
+SparseSelfAdjointView<MatrixType,Mode>::rankUpdate(const SparseMatrixBase<DerivedU>& u, const Scalar& alpha)
+{
+  SparseMatrix<Scalar,(MatrixType::Flags&RowMajorBit)?RowMajor:ColMajor> tmp = u * u.adjoint();
+  if(alpha==Scalar(0))
+    m_matrix = tmp.template triangularView<Mode>();
+  else
+    m_matrix += alpha * tmp.template triangularView<Mode>();
+
+  return *this;
+}
+
+namespace internal {
+  
+// TODO currently a selfadjoint expression has the form SelfAdjointView<.,.>
+//      in the future selfadjoint-ness should be defined by the expression traits
+//      such that Transpose<SelfAdjointView<.,.> > is valid. (currently TriangularBase::transpose() is overloaded to make it work)
+template<typename MatrixType, unsigned int Mode>
+struct evaluator_traits<SparseSelfAdjointView<MatrixType,Mode> >
+{
+  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
+  typedef SparseSelfAdjointShape Shape;
+};
+
+struct SparseSelfAdjoint2Sparse {};
+
+template<> struct AssignmentKind<SparseShape,SparseSelfAdjointShape> { typedef SparseSelfAdjoint2Sparse Kind; };
+template<> struct AssignmentKind<SparseSelfAdjointShape,SparseShape> { typedef Sparse2Sparse Kind; };
+
+template< typename DstXprType, typename SrcXprType, typename Functor>
+struct Assignment<DstXprType, SrcXprType, Functor, SparseSelfAdjoint2Sparse>
+{
+  typedef typename DstXprType::StorageIndex StorageIndex;
+  typedef internal::assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar> AssignOpType;
+
+  template<typename DestScalar,int StorageOrder>
+  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src, const AssignOpType&/*func*/)
+  {
+    internal::permute_symm_to_fullsymm<SrcXprType::Mode>(src.matrix(), dst);
+  }
+
+  // FIXME: the handling of += and -= in sparse matrices should be cleanup so that next two overloads could be reduced to:
+  template<typename DestScalar,int StorageOrder,typename AssignFunc>
+  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src, const AssignFunc& func)
+  {
+    SparseMatrix<DestScalar,StorageOrder,StorageIndex> tmp(src.rows(),src.cols());
+    run(tmp, src, AssignOpType());
+    call_assignment_no_alias_no_transpose(dst, tmp, func);
+  }
+
+  template<typename DestScalar,int StorageOrder>
+  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src,
+                  const internal::add_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>& /* func */)
+  {
+    SparseMatrix<DestScalar,StorageOrder,StorageIndex> tmp(src.rows(),src.cols());
+    run(tmp, src, AssignOpType());
+    dst += tmp;
+  }
+
+  template<typename DestScalar,int StorageOrder>
+  static void run(SparseMatrix<DestScalar,StorageOrder,StorageIndex> &dst, const SrcXprType &src,
+                  const internal::sub_assign_op<typename DstXprType::Scalar,typename SrcXprType::Scalar>& /* func */)
+  {
+    SparseMatrix<DestScalar,StorageOrder,StorageIndex> tmp(src.rows(),src.cols());
+    run(tmp, src, AssignOpType());
+    dst -= tmp;
+  }
+};
+
+} // end namespace internal
+
+/***************************************************************************
+* Implementation of sparse self-adjoint time dense matrix
+***************************************************************************/
+
+namespace internal {
+
+template<int Mode, typename SparseLhsType, typename DenseRhsType, typename DenseResType, typename AlphaType>
+inline void sparse_selfadjoint_time_dense_product(const SparseLhsType& lhs, const DenseRhsType& rhs, DenseResType& res, const AlphaType& alpha)
+{
+  EIGEN_ONLY_USED_FOR_DEBUG(alpha);
+  
+  typedef typename internal::nested_eval<SparseLhsType,DenseRhsType::MaxColsAtCompileTime>::type SparseLhsTypeNested;
+  typedef internal::remove_all_t<SparseLhsTypeNested> SparseLhsTypeNestedCleaned;
+  typedef evaluator<SparseLhsTypeNestedCleaned> LhsEval;
+  typedef typename LhsEval::InnerIterator LhsIterator;
+  typedef typename SparseLhsType::Scalar LhsScalar;
+  
+  enum {
+    LhsIsRowMajor = (LhsEval::Flags&RowMajorBit)==RowMajorBit,
+    ProcessFirstHalf =
+              ((Mode&(Upper|Lower))==(Upper|Lower))
+          || ( (Mode&Upper) && !LhsIsRowMajor)
+          || ( (Mode&Lower) && LhsIsRowMajor),
+    ProcessSecondHalf = !ProcessFirstHalf
+  };
+  
+  SparseLhsTypeNested lhs_nested(lhs);
+  LhsEval lhsEval(lhs_nested);
+
+  // work on one column at once
+  for (Index k=0; k<rhs.cols(); ++k)
+  {
+    for (Index j=0; j<lhs.outerSize(); ++j)
+    {
+      LhsIterator i(lhsEval,j);
+      // handle diagonal coeff
+      if (ProcessSecondHalf)
+      {
+        while (i && i.index()<j) ++i;
+        if(i && i.index()==j)
+        {
+          res.coeffRef(j,k) += alpha * i.value() * rhs.coeff(j,k);
+          ++i;
+        }
+      }
+
+      // premultiplied rhs for scatters
+      typename ScalarBinaryOpTraits<AlphaType, typename DenseRhsType::Scalar>::ReturnType rhs_j(alpha*rhs(j,k));
+      // accumulator for partial scalar product
+      typename DenseResType::Scalar res_j(0);
+      for(; (ProcessFirstHalf ? i && i.index() < j : i) ; ++i)
+      {
+        LhsScalar lhs_ij = i.value();
+        if(!LhsIsRowMajor) lhs_ij = numext::conj(lhs_ij);
+        res_j += lhs_ij * rhs.coeff(i.index(),k);
+        res(i.index(),k) += numext::conj(lhs_ij) * rhs_j;
+      }
+      res.coeffRef(j,k) += alpha * res_j;
+
+      // handle diagonal coeff
+      if (ProcessFirstHalf && i && (i.index()==j))
+        res.coeffRef(j,k) += alpha * i.value() * rhs.coeff(j,k);
+    }
+  }
+}
+
+
+template<typename LhsView, typename Rhs, int ProductType>
+struct generic_product_impl<LhsView, Rhs, SparseSelfAdjointShape, DenseShape, ProductType>
+: generic_product_impl_base<LhsView, Rhs, generic_product_impl<LhsView, Rhs, SparseSelfAdjointShape, DenseShape, ProductType> >
+{
+  template<typename Dest>
+  static void scaleAndAddTo(Dest& dst, const LhsView& lhsView, const Rhs& rhs, const typename Dest::Scalar& alpha)
+  {
+    typedef typename LhsView::MatrixTypeNested_ Lhs;
+    typedef typename nested_eval<Lhs,Dynamic>::type LhsNested;
+    typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
+    LhsNested lhsNested(lhsView.matrix());
+    RhsNested rhsNested(rhs);
+    
+    internal::sparse_selfadjoint_time_dense_product<LhsView::Mode>(lhsNested, rhsNested, dst, alpha);
+  }
+};
+
+template<typename Lhs, typename RhsView, int ProductType>
+struct generic_product_impl<Lhs, RhsView, DenseShape, SparseSelfAdjointShape, ProductType>
+: generic_product_impl_base<Lhs, RhsView, generic_product_impl<Lhs, RhsView, DenseShape, SparseSelfAdjointShape, ProductType> >
+{
+  template<typename Dest>
+  static void scaleAndAddTo(Dest& dst, const Lhs& lhs, const RhsView& rhsView, const typename Dest::Scalar& alpha)
+  {
+    typedef typename RhsView::MatrixTypeNested_ Rhs;
+    typedef typename nested_eval<Lhs,Dynamic>::type LhsNested;
+    typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
+    LhsNested lhsNested(lhs);
+    RhsNested rhsNested(rhsView.matrix());
+    
+    // transpose everything
+    Transpose<Dest> dstT(dst);
+    internal::sparse_selfadjoint_time_dense_product<RhsView::TransposeMode>(rhsNested.transpose(), lhsNested.transpose(), dstT, alpha);
+  }
+};
+
+// NOTE: these two overloads are needed to evaluate the sparse selfadjoint view into a full sparse matrix
+// TODO: maybe the copy could be handled by generic_product_impl so that these overloads would not be needed anymore
+
+template<typename LhsView, typename Rhs, int ProductTag>
+struct product_evaluator<Product<LhsView, Rhs, DefaultProduct>, ProductTag, SparseSelfAdjointShape, SparseShape>
+  : public evaluator<typename Product<typename Rhs::PlainObject, Rhs, DefaultProduct>::PlainObject>
+{
+  typedef Product<LhsView, Rhs, DefaultProduct> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  product_evaluator(const XprType& xpr)
+    : m_lhs(xpr.lhs()), m_result(xpr.rows(), xpr.cols())
+  {
+    internal::construct_at<Base>(this, m_result);
+    generic_product_impl<typename Rhs::PlainObject, Rhs, SparseShape, SparseShape, ProductTag>::evalTo(m_result, m_lhs, xpr.rhs());
+  }
+  
+protected:
+  typename Rhs::PlainObject m_lhs;
+  PlainObject m_result;
+};
+
+template<typename Lhs, typename RhsView, int ProductTag>
+struct product_evaluator<Product<Lhs, RhsView, DefaultProduct>, ProductTag, SparseShape, SparseSelfAdjointShape>
+  : public evaluator<typename Product<Lhs, typename Lhs::PlainObject, DefaultProduct>::PlainObject>
+{
+  typedef Product<Lhs, RhsView, DefaultProduct> XprType;
+  typedef typename XprType::PlainObject PlainObject;
+  typedef evaluator<PlainObject> Base;
+
+  product_evaluator(const XprType& xpr)
+    : m_rhs(xpr.rhs()), m_result(xpr.rows(), xpr.cols())
+  {
+    ::new (static_cast<Base*>(this)) Base(m_result);
+    generic_product_impl<Lhs, typename Lhs::PlainObject, SparseShape, SparseShape, ProductTag>::evalTo(m_result, xpr.lhs(), m_rhs);
+  }
+  
+protected:
+  typename Lhs::PlainObject m_rhs;
+  PlainObject m_result;
+};
+
+} // namespace internal
+
+/***************************************************************************
+* Implementation of symmetric copies and permutations
+***************************************************************************/
+namespace internal {
+
+template<int Mode,typename MatrixType,int DestOrder>
+void permute_symm_to_fullsymm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DestOrder,typename MatrixType::StorageIndex>& _dest, const typename MatrixType::StorageIndex* perm)
+{
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef SparseMatrix<Scalar,DestOrder,StorageIndex> Dest;
+  typedef Matrix<StorageIndex,Dynamic,1> VectorI;
+  typedef evaluator<MatrixType> MatEval;
+  typedef typename evaluator<MatrixType>::InnerIterator MatIterator;
+  
+  MatEval matEval(mat);
+  Dest& dest(_dest.derived());
+  enum {
+    StorageOrderMatch = int(Dest::IsRowMajor) == int(MatrixType::IsRowMajor)
+  };
+  
+  Index size = mat.rows();
+  VectorI count;
+  count.resize(size);
+  count.setZero();
+  dest.resize(size,size);
+  for(Index j = 0; j<size; ++j)
+  {
+    Index jp = perm ? perm[j] : j;
+    for(MatIterator it(matEval,j); it; ++it)
+    {
+      Index i = it.index();
+      Index r = it.row();
+      Index c = it.col();
+      Index ip = perm ? perm[i] : i;
+      if(Mode==int(Upper|Lower))
+        count[StorageOrderMatch ? jp : ip]++;
+      else if(r==c)
+        count[ip]++;
+      else if(( Mode==Lower && r>c) || ( Mode==Upper && r<c))
+      {
+        count[ip]++;
+        count[jp]++;
+      }
+    }
+  }
+  Index nnz = count.sum();
+  
+  // reserve space
+  dest.resizeNonZeros(nnz);
+  dest.outerIndexPtr()[0] = 0;
+  for(Index j=0; j<size; ++j)
+    dest.outerIndexPtr()[j+1] = dest.outerIndexPtr()[j] + count[j];
+  for(Index j=0; j<size; ++j)
+    count[j] = dest.outerIndexPtr()[j];
+  
+  // copy data
+  for(StorageIndex j = 0; j<size; ++j)
+  {
+    for(MatIterator it(matEval,j); it; ++it)
+    {
+      StorageIndex i = internal::convert_index<StorageIndex>(it.index());
+      Index r = it.row();
+      Index c = it.col();
+      
+      StorageIndex jp = perm ? perm[j] : j;
+      StorageIndex ip = perm ? perm[i] : i;
+      
+      if(Mode==int(Upper|Lower))
+      {
+        Index k = count[StorageOrderMatch ? jp : ip]++;
+        dest.innerIndexPtr()[k] = StorageOrderMatch ? ip : jp;
+        dest.valuePtr()[k] = it.value();
+      }
+      else if(r==c)
+      {
+        Index k = count[ip]++;
+        dest.innerIndexPtr()[k] = ip;
+        dest.valuePtr()[k] = it.value();
+      }
+      else if(( (Mode&Lower)==Lower && r>c) || ( (Mode&Upper)==Upper && r<c))
+      {
+        if(!StorageOrderMatch)
+          std::swap(ip,jp);
+        Index k = count[jp]++;
+        dest.innerIndexPtr()[k] = ip;
+        dest.valuePtr()[k] = it.value();
+        k = count[ip]++;
+        dest.innerIndexPtr()[k] = jp;
+        dest.valuePtr()[k] = numext::conj(it.value());
+      }
+    }
+  }
+}
+
+template<int SrcMode_,int DstMode_,typename MatrixType,int DstOrder>
+void permute_symm_to_symm(const MatrixType& mat, SparseMatrix<typename MatrixType::Scalar,DstOrder,typename MatrixType::StorageIndex>& _dest, const typename MatrixType::StorageIndex* perm)
+{
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef typename MatrixType::Scalar Scalar;
+  SparseMatrix<Scalar,DstOrder,StorageIndex>& dest(_dest.derived());
+  typedef Matrix<StorageIndex,Dynamic,1> VectorI;
+  typedef evaluator<MatrixType> MatEval;
+  typedef typename evaluator<MatrixType>::InnerIterator MatIterator;
+
+  enum {
+    SrcOrder = MatrixType::IsRowMajor ? RowMajor : ColMajor,
+    StorageOrderMatch = int(SrcOrder) == int(DstOrder),
+    DstMode = DstOrder==RowMajor ? (DstMode_==Upper ? Lower : Upper) : DstMode_,
+    SrcMode = SrcOrder==RowMajor ? (SrcMode_==Upper ? Lower : Upper) : SrcMode_
+  };
+
+  MatEval matEval(mat);
+  
+  Index size = mat.rows();
+  VectorI count(size);
+  count.setZero();
+  dest.resize(size,size);
+  for(StorageIndex j = 0; j<size; ++j)
+  {
+    StorageIndex jp = perm ? perm[j] : j;
+    for(MatIterator it(matEval,j); it; ++it)
+    {
+      StorageIndex i = it.index();
+      if((int(SrcMode)==int(Lower) && i<j) || (int(SrcMode)==int(Upper) && i>j))
+        continue;
+                  
+      StorageIndex ip = perm ? perm[i] : i;
+      count[int(DstMode)==int(Lower) ? (std::min)(ip,jp) : (std::max)(ip,jp)]++;
+    }
+  }
+  dest.outerIndexPtr()[0] = 0;
+  for(Index j=0; j<size; ++j)
+    dest.outerIndexPtr()[j+1] = dest.outerIndexPtr()[j] + count[j];
+  dest.resizeNonZeros(dest.outerIndexPtr()[size]);
+  for(Index j=0; j<size; ++j)
+    count[j] = dest.outerIndexPtr()[j];
+  
+  for(StorageIndex j = 0; j<size; ++j)
+  {
+    
+    for(MatIterator it(matEval,j); it; ++it)
+    {
+      StorageIndex i = it.index();
+      if((int(SrcMode)==int(Lower) && i<j) || (int(SrcMode)==int(Upper) && i>j))
+        continue;
+                  
+      StorageIndex jp = perm ? perm[j] : j;
+      StorageIndex ip = perm? perm[i] : i;
+      
+      Index k = count[int(DstMode)==int(Lower) ? (std::min)(ip,jp) : (std::max)(ip,jp)]++;
+      dest.innerIndexPtr()[k] = int(DstMode)==int(Lower) ? (std::max)(ip,jp) : (std::min)(ip,jp);
+      
+      if(!StorageOrderMatch) std::swap(ip,jp);
+      if( ((int(DstMode)==int(Lower) && ip<jp) || (int(DstMode)==int(Upper) && ip>jp)))
+        dest.valuePtr()[k] = numext::conj(it.value());
+      else
+        dest.valuePtr()[k] = it.value();
+    }
+  }
+}
+
+}
+
+// TODO implement twists in a more evaluator friendly fashion
+
+namespace internal {
+
+template<typename MatrixType, int Mode>
+struct traits<SparseSymmetricPermutationProduct<MatrixType,Mode> > : traits<MatrixType> {
+};
+
+}
+
+template<typename MatrixType,int Mode>
+class SparseSymmetricPermutationProduct
+  : public EigenBase<SparseSymmetricPermutationProduct<MatrixType,Mode> >
+{
+  public:
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    enum {
+      RowsAtCompileTime = internal::traits<SparseSymmetricPermutationProduct>::RowsAtCompileTime,
+      ColsAtCompileTime = internal::traits<SparseSymmetricPermutationProduct>::ColsAtCompileTime
+    };
+  protected:
+    typedef PermutationMatrix<Dynamic,Dynamic,StorageIndex> Perm;
+  public:
+    typedef Matrix<StorageIndex,Dynamic,1> VectorI;
+    typedef typename MatrixType::Nested MatrixTypeNested;
+    typedef internal::remove_all_t<MatrixTypeNested> NestedExpression;
+    
+    SparseSymmetricPermutationProduct(const MatrixType& mat, const Perm& perm)
+      : m_matrix(mat), m_perm(perm)
+    {}
+    
+    inline Index rows() const { return m_matrix.rows(); }
+    inline Index cols() const { return m_matrix.cols(); }
+        
+    const NestedExpression& matrix() const { return m_matrix; }
+    const Perm& perm() const { return m_perm; }
+    
+  protected:
+    MatrixTypeNested m_matrix;
+    const Perm& m_perm;
+
+};
+
+namespace internal {
+  
+template<typename DstXprType, typename MatrixType, int Mode, typename Scalar>
+struct Assignment<DstXprType, SparseSymmetricPermutationProduct<MatrixType,Mode>, internal::assign_op<Scalar,typename MatrixType::Scalar>, Sparse2Sparse>
+{
+  typedef SparseSymmetricPermutationProduct<MatrixType,Mode> SrcXprType;
+  typedef typename DstXprType::StorageIndex DstIndex;
+  template<int Options>
+  static void run(SparseMatrix<Scalar,Options,DstIndex> &dst, const SrcXprType &src, const internal::assign_op<Scalar,typename MatrixType::Scalar> &)
+  {
+    // internal::permute_symm_to_fullsymm<Mode>(m_matrix,_dest,m_perm.indices().data());
+    SparseMatrix<Scalar,(Options&RowMajor)==RowMajor ? ColMajor : RowMajor, DstIndex> tmp;
+    internal::permute_symm_to_fullsymm<Mode>(src.matrix(),tmp,src.perm().indices().data());
+    dst = tmp;
+  }
+  
+  template<typename DestType,unsigned int DestMode>
+  static void run(SparseSelfAdjointView<DestType,DestMode>& dst, const SrcXprType &src, const internal::assign_op<Scalar,typename MatrixType::Scalar> &)
+  {
+    internal::permute_symm_to_symm<Mode,DestMode>(src.matrix(),dst.matrix(),src.perm().indices().data());
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_SELFADJOINTVIEW_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseSolverBase.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseSolverBase.h
new file mode 100644
index 0000000000000000000000000000000000000000..8261fb5c61f947fd5d11ffdd9c33caa3c50573a5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseSolverBase.h
@@ -0,0 +1,128 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSESOLVERBASE_H
+#define EIGEN_SPARSESOLVERBASE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+  /** \internal
+  * Helper functions to solve with a sparse right-hand-side and result.
+  * The rhs is decomposed into small vertical panels which are solved through dense temporaries.
+  */
+template<typename Decomposition, typename Rhs, typename Dest>
+std::enable_if_t<Rhs::ColsAtCompileTime!=1 && Dest::ColsAtCompileTime!=1>
+solve_sparse_through_dense_panels(const Decomposition &dec, const Rhs& rhs, Dest &dest)
+{
+  EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  typedef typename Dest::Scalar DestScalar;
+  // we process the sparse rhs per block of NbColsAtOnce columns temporarily stored into a dense matrix.
+  static const Index NbColsAtOnce = 4;
+  Index rhsCols = rhs.cols();
+  Index size = rhs.rows();
+  // the temporary matrices do not need more columns than NbColsAtOnce:
+  Index tmpCols = (std::min)(rhsCols, NbColsAtOnce); 
+  Eigen::Matrix<DestScalar,Dynamic,Dynamic> tmp(size,tmpCols);
+  Eigen::Matrix<DestScalar,Dynamic,Dynamic> tmpX(size,tmpCols);
+  for(Index k=0; k<rhsCols; k+=NbColsAtOnce)
+  {
+    Index actualCols = std::min<Index>(rhsCols-k, NbColsAtOnce);
+    tmp.leftCols(actualCols) = rhs.middleCols(k,actualCols);
+    tmpX.leftCols(actualCols) = dec.solve(tmp.leftCols(actualCols));
+    dest.middleCols(k,actualCols) = tmpX.leftCols(actualCols).sparseView();
+  }
+}
+
+// Overload for vector as rhs
+template<typename Decomposition, typename Rhs, typename Dest>
+std::enable_if_t<Rhs::ColsAtCompileTime==1 || Dest::ColsAtCompileTime==1>
+solve_sparse_through_dense_panels(const Decomposition &dec, const Rhs& rhs, Dest &dest)
+{
+  typedef typename Dest::Scalar DestScalar;
+  Index size = rhs.rows();
+  Eigen::Matrix<DestScalar,Dynamic,1> rhs_dense(rhs);
+  Eigen::Matrix<DestScalar,Dynamic,1> dest_dense(size);
+  dest_dense = dec.solve(rhs_dense);
+  dest = dest_dense.sparseView();
+}
+
+} // end namespace internal
+
+/** \class SparseSolverBase
+  * \ingroup SparseCore_Module
+  * \brief A base class for sparse solvers
+  *
+  * \tparam Derived the actual type of the solver.
+  *
+  */
+template<typename Derived>
+class SparseSolverBase : internal::noncopyable
+{
+  public:
+
+    /** Default constructor */
+    SparseSolverBase()
+      : m_isInitialized(false)
+    {}
+
+    SparseSolverBase(SparseSolverBase&&other ) : internal::noncopyable{}, m_isInitialized{other.m_isInitialized} {}
+
+    ~SparseSolverBase()
+    {}
+
+    Derived& derived() { return *static_cast<Derived*>(this); }
+    const Derived& derived() const { return *static_cast<const Derived*>(this); }
+    
+    /** \returns an expression of the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * \sa compute()
+      */
+    template<typename Rhs>
+    inline const Solve<Derived, Rhs>
+    solve(const MatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_isInitialized && "Solver is not initialized.");
+      eigen_assert(derived().rows()==b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
+      return Solve<Derived, Rhs>(derived(), b.derived());
+    }
+    
+    /** \returns an expression of the solution x of \f$ A x = b \f$ using the current decomposition of A.
+      *
+      * \sa compute()
+      */
+    template<typename Rhs>
+    inline const Solve<Derived, Rhs>
+    solve(const SparseMatrixBase<Rhs>& b) const
+    {
+      eigen_assert(m_isInitialized && "Solver is not initialized.");
+      eigen_assert(derived().rows()==b.rows() && "solve(): invalid number of rows of the right hand side matrix b");
+      return Solve<Derived, Rhs>(derived(), b.derived());
+    }
+    
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** \internal default implementation of solving with a sparse rhs */
+    template<typename Rhs,typename Dest>
+    void _solve_impl(const SparseMatrixBase<Rhs> &b, SparseMatrixBase<Dest> &dest) const
+    {
+      internal::solve_sparse_through_dense_panels(derived(), b.derived(), dest.derived());
+    }
+    #endif // EIGEN_PARSED_BY_DOXYGEN
+
+  protected:
+    
+    mutable bool m_isInitialized;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSESOLVERBASE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseSparseProductWithPruning.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
new file mode 100644
index 0000000000000000000000000000000000000000..ee0ec1bacb0e55d53f79057803960bebc0f468f0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseSparseProductWithPruning.h
@@ -0,0 +1,200 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
+#define EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+
+// perform a pseudo in-place sparse * sparse product assuming all matrices are col major
+template<typename Lhs, typename Rhs, typename ResultType>
+static void sparse_sparse_product_with_pruning_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res, const typename ResultType::RealScalar& tolerance)
+{
+  // return sparse_sparse_product_with_pruning_impl2(lhs,rhs,res);
+
+  typedef typename remove_all_t<Rhs>::Scalar RhsScalar;
+  typedef typename remove_all_t<ResultType>::Scalar ResScalar;
+  typedef typename remove_all_t<Lhs>::StorageIndex StorageIndex;
+
+  // make sure to call innerSize/outerSize since we fake the storage order.
+  Index rows = lhs.innerSize();
+  Index cols = rhs.outerSize();
+  //Index size = lhs.outerSize();
+  eigen_assert(lhs.outerSize() == rhs.innerSize());
+
+  // allocate a temporary buffer
+  AmbiVector<ResScalar,StorageIndex> tempVector(rows);
+
+  // mimics a resizeByInnerOuter:
+  if(ResultType::IsRowMajor)
+    res.resize(cols, rows);
+  else
+    res.resize(rows, cols);
+  
+  evaluator<Lhs> lhsEval(lhs);
+  evaluator<Rhs> rhsEval(rhs);
+  
+  // estimate the number of non zero entries
+  // given a rhs column containing Y non zeros, we assume that the respective Y columns
+  // of the lhs differs in average of one non zeros, thus the number of non zeros for
+  // the product of a rhs column with the lhs is X+Y where X is the average number of non zero
+  // per column of the lhs.
+  // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
+  Index estimated_nnz_prod = lhsEval.nonZerosEstimate() + rhsEval.nonZerosEstimate();
+
+  res.reserve(estimated_nnz_prod);
+  double ratioColRes = double(estimated_nnz_prod)/(double(lhs.rows())*double(rhs.cols()));
+  for (Index j=0; j<cols; ++j)
+  {
+    // FIXME:
+    //double ratioColRes = (double(rhs.innerVector(j).nonZeros()) + double(lhs.nonZeros())/double(lhs.cols()))/double(lhs.rows());
+    // let's do a more accurate determination of the nnz ratio for the current column j of res
+    tempVector.init(ratioColRes);
+    tempVector.setZero();
+    for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt)
+    {
+      // FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
+      tempVector.restart();
+      RhsScalar x = rhsIt.value();
+      for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, rhsIt.index()); lhsIt; ++lhsIt)
+      {
+        tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
+      }
+    }
+    res.startVec(j);
+    for (typename AmbiVector<ResScalar,StorageIndex>::Iterator it(tempVector,tolerance); it; ++it)
+      res.insertBackByOuterInner(j,it.index()) = it.value();
+  }
+  res.finalize();
+}
+
+template<typename Lhs, typename Rhs, typename ResultType,
+  int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit,
+  int RhsStorageOrder = traits<Rhs>::Flags&RowMajorBit,
+  int ResStorageOrder = traits<ResultType>::Flags&RowMajorBit>
+struct sparse_sparse_product_with_pruning_selector;
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
+{
+  typedef typename ResultType::RealScalar RealScalar;
+
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
+  {
+    remove_all_t<ResultType> _res(res.rows(), res.cols());
+    internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,ResultType>(lhs, rhs, _res, tolerance);
+    res.swap(_res);
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
+{
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
+  {
+    // we need a col-major matrix to hold the result
+    typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> SparseTemporaryType;
+    SparseTemporaryType _res(res.rows(), res.cols());
+    internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,SparseTemporaryType>(lhs, rhs, _res, tolerance);
+    res = _res;
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
+{
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
+  {
+    // let's transpose the product to get a column x column product
+    remove_all_t<ResultType> _res(res.rows(), res.cols());
+    internal::sparse_sparse_product_with_pruning_impl<Rhs,Lhs,ResultType>(rhs, lhs, _res, tolerance);
+    res.swap(_res);
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
+{
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
+  {
+    typedef SparseMatrix<typename Lhs::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixLhs;
+    typedef SparseMatrix<typename Rhs::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixRhs;
+    ColMajorMatrixLhs colLhs(lhs);
+    ColMajorMatrixRhs colRhs(rhs);
+    internal::sparse_sparse_product_with_pruning_impl<ColMajorMatrixLhs,ColMajorMatrixRhs,ResultType>(colLhs, colRhs, res, tolerance);
+
+    // let's transpose the product to get a column x column product
+//     typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
+//     SparseTemporaryType _res(res.cols(), res.rows());
+//     sparse_sparse_product_with_pruning_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, _res);
+//     res = _res.transpose();
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,RowMajor>
+{
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
+  {
+    typedef SparseMatrix<typename Lhs::Scalar,RowMajor,typename Lhs::StorageIndex> RowMajorMatrixLhs;
+    RowMajorMatrixLhs rowLhs(lhs);
+    sparse_sparse_product_with_pruning_selector<RowMajorMatrixLhs,Rhs,ResultType,RowMajor,RowMajor>(rowLhs,rhs,res,tolerance);
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,RowMajor>
+{
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
+  {
+    typedef SparseMatrix<typename Rhs::Scalar,RowMajor,typename Lhs::StorageIndex> RowMajorMatrixRhs;
+    RowMajorMatrixRhs rowRhs(rhs);
+    sparse_sparse_product_with_pruning_selector<Lhs,RowMajorMatrixRhs,ResultType,RowMajor,RowMajor,RowMajor>(lhs,rowRhs,res,tolerance);
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor>
+{
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
+  {
+    typedef SparseMatrix<typename Rhs::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixRhs;
+    ColMajorMatrixRhs colRhs(rhs);
+    internal::sparse_sparse_product_with_pruning_impl<Lhs,ColMajorMatrixRhs,ResultType>(lhs, colRhs, res, tolerance);
+  }
+};
+
+template<typename Lhs, typename Rhs, typename ResultType>
+struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor>
+{
+  typedef typename ResultType::RealScalar RealScalar;
+  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
+  {
+    typedef SparseMatrix<typename Lhs::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixLhs;
+    ColMajorMatrixLhs colLhs(lhs);
+    internal::sparse_sparse_product_with_pruning_impl<ColMajorMatrixLhs,Rhs,ResultType>(colLhs, rhs, res, tolerance);
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseTranspose.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseTranspose.h
new file mode 100644
index 0000000000000000000000000000000000000000..cce5903436197d1033a140b7997323d7b79e2c68
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseTranspose.h
@@ -0,0 +1,94 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSETRANSPOSE_H
+#define EIGEN_SPARSETRANSPOSE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+  template<typename MatrixType,int CompressedAccess=int(MatrixType::Flags&CompressedAccessBit)>
+  class SparseTransposeImpl
+    : public SparseMatrixBase<Transpose<MatrixType> >
+  {};
+  
+  template<typename MatrixType>
+  class SparseTransposeImpl<MatrixType,CompressedAccessBit>
+    : public SparseCompressedBase<Transpose<MatrixType> >
+  {
+    typedef SparseCompressedBase<Transpose<MatrixType> > Base;
+  public:
+    using Base::derived;
+    typedef typename Base::Scalar Scalar;
+    typedef typename Base::StorageIndex StorageIndex;
+
+    inline Index nonZeros() const { return derived().nestedExpression().nonZeros(); }
+    
+    inline const Scalar* valuePtr() const { return derived().nestedExpression().valuePtr(); }
+    inline const StorageIndex* innerIndexPtr() const { return derived().nestedExpression().innerIndexPtr(); }
+    inline const StorageIndex* outerIndexPtr() const { return derived().nestedExpression().outerIndexPtr(); }
+    inline const StorageIndex* innerNonZeroPtr() const { return derived().nestedExpression().innerNonZeroPtr(); }
+
+    inline Scalar* valuePtr() { return derived().nestedExpression().valuePtr(); }
+    inline StorageIndex* innerIndexPtr() { return derived().nestedExpression().innerIndexPtr(); }
+    inline StorageIndex* outerIndexPtr() { return derived().nestedExpression().outerIndexPtr(); }
+    inline StorageIndex* innerNonZeroPtr() { return derived().nestedExpression().innerNonZeroPtr(); }
+  };
+}
+  
+template<typename MatrixType> class TransposeImpl<MatrixType,Sparse>
+  : public internal::SparseTransposeImpl<MatrixType>
+{
+  protected:
+    typedef internal::SparseTransposeImpl<MatrixType> Base;
+};
+
+namespace internal {
+  
+template<typename ArgType>
+struct unary_evaluator<Transpose<ArgType>, IteratorBased>
+  : public evaluator_base<Transpose<ArgType> >
+{
+    typedef typename evaluator<ArgType>::InnerIterator        EvalIterator;
+  public:
+    typedef Transpose<ArgType> XprType;
+    
+    inline Index nonZerosEstimate() const {
+      return m_argImpl.nonZerosEstimate();
+    }
+
+    class InnerIterator : public EvalIterator
+    {
+    public:
+      EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& unaryOp, Index outer)
+        : EvalIterator(unaryOp.m_argImpl,outer)
+      {}
+      
+      Index row() const { return EvalIterator::col(); }
+      Index col() const { return EvalIterator::row(); }
+    };
+    
+    enum {
+      CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+      Flags = XprType::Flags
+    };
+    
+    explicit unary_evaluator(const XprType& op) :m_argImpl(op.nestedExpression()) {}
+
+  protected:
+    evaluator<ArgType> m_argImpl;
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSETRANSPOSE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseTriangularView.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseTriangularView.h
new file mode 100644
index 0000000000000000000000000000000000000000..5e7cea7bdda187162e9ef4324a7a6f161149bd92
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseTriangularView.h
@@ -0,0 +1,191 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_TRIANGULARVIEW_H
+#define EIGEN_SPARSE_TRIANGULARVIEW_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \ingroup SparseCore_Module
+  *
+  * \brief Base class for a triangular part in a \b sparse matrix
+  *
+  * This class is an abstract base class of class TriangularView, and objects of type TriangularViewImpl cannot be instantiated.
+  * It extends class TriangularView with additional methods which are available for sparse expressions only.
+  *
+  * \sa class TriangularView, SparseMatrixBase::triangularView()
+  */
+template<typename MatrixType, unsigned int Mode> class TriangularViewImpl<MatrixType,Mode,Sparse>
+  : public SparseMatrixBase<TriangularView<MatrixType,Mode> >
+{
+    enum { SkipFirst = ((Mode&Lower) && !(MatrixType::Flags&RowMajorBit))
+                    || ((Mode&Upper) &&  (MatrixType::Flags&RowMajorBit)),
+           SkipLast = !SkipFirst,
+           SkipDiag = (Mode&ZeroDiag) ? 1 : 0,
+           HasUnitDiag = (Mode&UnitDiag) ? 1 : 0
+    };
+    
+    typedef TriangularView<MatrixType,Mode> TriangularViewType;
+    
+  protected:
+    // dummy solve function to make TriangularView happy.
+    void solve() const;
+
+    typedef SparseMatrixBase<TriangularViewType> Base;
+  public:
+    
+    EIGEN_SPARSE_PUBLIC_INTERFACE(TriangularViewType)
+    
+    typedef typename MatrixType::Nested MatrixTypeNested;
+    typedef std::remove_reference_t<MatrixTypeNested> MatrixTypeNestedNonRef;
+    typedef internal::remove_all_t<MatrixTypeNested> MatrixTypeNestedCleaned;
+
+    template<typename RhsType, typename DstType>
+    EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE void _solve_impl(const RhsType &rhs, DstType &dst) const {
+      if(!(internal::is_same<RhsType,DstType>::value && internal::extract_data(dst) == internal::extract_data(rhs)))
+        dst = rhs;
+      this->solveInPlace(dst);
+    }
+
+    /** Applies the inverse of \c *this to the dense vector or matrix \a other, "in-place" */
+    template<typename OtherDerived> void solveInPlace(MatrixBase<OtherDerived>& other) const;
+
+    /** Applies the inverse of \c *this to the sparse vector or matrix \a other, "in-place" */
+    template<typename OtherDerived> void solveInPlace(SparseMatrixBase<OtherDerived>& other) const;
+  
+};
+
+namespace internal {
+
+template<typename ArgType, unsigned int Mode>
+struct unary_evaluator<TriangularView<ArgType,Mode>, IteratorBased>
+ : evaluator_base<TriangularView<ArgType,Mode> >
+{
+  typedef TriangularView<ArgType,Mode> XprType;
+  
+protected:
+  
+  typedef typename XprType::Scalar Scalar;
+  typedef typename XprType::StorageIndex StorageIndex;
+  typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
+  
+  enum { SkipFirst = ((Mode&Lower) && !(ArgType::Flags&RowMajorBit))
+                    || ((Mode&Upper) &&  (ArgType::Flags&RowMajorBit)),
+         SkipLast = !SkipFirst,
+         SkipDiag = (Mode&ZeroDiag) ? 1 : 0,
+         HasUnitDiag = (Mode&UnitDiag) ? 1 : 0
+  };
+  
+public:
+  
+  enum {
+    CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+    Flags = XprType::Flags
+  };
+    
+  explicit unary_evaluator(const XprType &xpr) : m_argImpl(xpr.nestedExpression()), m_arg(xpr.nestedExpression()) {}
+  
+  inline Index nonZerosEstimate() const {
+    return m_argImpl.nonZerosEstimate();
+  }
+  
+  class InnerIterator : public EvalIterator
+  {
+      typedef EvalIterator Base;
+    public:
+
+      EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& xprEval, Index outer)
+        : Base(xprEval.m_argImpl,outer), m_returnOne(false), m_containsDiag(Base::outer()<xprEval.m_arg.innerSize())
+      {
+        if(SkipFirst)
+        {
+          while((*this) && ((HasUnitDiag||SkipDiag)  ? this->index()<=outer : this->index()<outer))
+            Base::operator++();
+          if(HasUnitDiag)
+            m_returnOne = m_containsDiag;
+        }
+        else if(HasUnitDiag && ((!Base::operator bool()) || Base::index()>=Base::outer()))
+        {
+          if((!SkipFirst) && Base::operator bool())
+            Base::operator++();
+          m_returnOne = m_containsDiag;
+        }
+      }
+
+      EIGEN_STRONG_INLINE InnerIterator& operator++()
+      {
+        if(HasUnitDiag && m_returnOne)
+          m_returnOne = false;
+        else
+        {
+          Base::operator++();
+          if(HasUnitDiag && (!SkipFirst) && ((!Base::operator bool()) || Base::index()>=Base::outer()))
+          {
+            if((!SkipFirst) && Base::operator bool())
+              Base::operator++();
+            m_returnOne = m_containsDiag;
+          }
+        }
+        return *this;
+      }
+      
+      EIGEN_STRONG_INLINE operator bool() const
+      {
+        if(HasUnitDiag && m_returnOne)
+          return true;
+        if(SkipFirst) return  Base::operator bool();
+        else
+        {
+          if (SkipDiag) return (Base::operator bool() && this->index() < this->outer());
+          else return (Base::operator bool() && this->index() <= this->outer());
+        }
+      }
+
+//       inline Index row() const { return (ArgType::Flags&RowMajorBit ? Base::outer() : this->index()); }
+//       inline Index col() const { return (ArgType::Flags&RowMajorBit ? this->index() : Base::outer()); }
+      inline StorageIndex index() const
+      {
+        if(HasUnitDiag && m_returnOne)  return internal::convert_index<StorageIndex>(Base::outer());
+        else                            return Base::index();
+      }
+      inline Scalar value() const
+      {
+        if(HasUnitDiag && m_returnOne)  return Scalar(1);
+        else                            return Base::value();
+      }
+
+    protected:
+      bool m_returnOne;
+      bool m_containsDiag;
+    private:
+      Scalar& valueRef();
+  };
+  
+protected:
+  evaluator<ArgType> m_argImpl;
+  const ArgType& m_arg;
+};
+
+} // end namespace internal
+
+template<typename Derived>
+template<int Mode>
+inline const TriangularView<const Derived, Mode>
+SparseMatrixBase<Derived>::triangularView() const
+{
+  return TriangularView<const Derived, Mode>(derived());
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSE_TRIANGULARVIEW_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseUtil.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseUtil.h
new file mode 100644
index 0000000000000000000000000000000000000000..47f5ef6180ac8ab69de5463e08d0706293bb4838
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseUtil.h
@@ -0,0 +1,186 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEUTIL_H
+#define EIGEN_SPARSEUTIL_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+#ifdef NDEBUG
+#define EIGEN_DBG_SPARSE(X)
+#else
+#define EIGEN_DBG_SPARSE(X) X
+#endif
+
+#define EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, Op) \
+template<typename OtherDerived> \
+EIGEN_STRONG_INLINE Derived& operator Op(const Eigen::SparseMatrixBase<OtherDerived>& other) \
+{ \
+  return Base::operator Op(other.derived()); \
+} \
+EIGEN_STRONG_INLINE Derived& operator Op(const Derived& other) \
+{ \
+  return Base::operator Op(other); \
+}
+
+#define EIGEN_SPARSE_INHERIT_SCALAR_ASSIGNMENT_OPERATOR(Derived, Op) \
+template<typename Other> \
+EIGEN_STRONG_INLINE Derived& operator Op(const Other& scalar) \
+{ \
+  return Base::operator Op(scalar); \
+}
+
+#define EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATORS(Derived) \
+EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(Derived, =)
+
+
+#define EIGEN_SPARSE_PUBLIC_INTERFACE(Derived) \
+  EIGEN_GENERIC_PUBLIC_INTERFACE(Derived)
+
+  
+const int CoherentAccessPattern     = 0x1;
+const int InnerRandomAccessPattern  = 0x2 | CoherentAccessPattern;
+const int OuterRandomAccessPattern  = 0x4 | CoherentAccessPattern;
+const int RandomAccessPattern       = 0x8 | OuterRandomAccessPattern | InnerRandomAccessPattern;
+
+template<typename Scalar_, int Flags_ = 0, typename StorageIndex_ = int>  class SparseMatrix;
+template<typename Scalar_, int Flags_ = 0, typename StorageIndex_ = int>  class SparseVector;
+
+template<typename MatrixType, unsigned int UpLo>  class SparseSelfAdjointView;
+template<typename Lhs, typename Rhs>              class SparseDiagonalProduct;
+template<typename MatrixType> class SparseView;
+
+template<typename Lhs, typename Rhs>        class SparseSparseProduct;
+template<typename Lhs, typename Rhs>        class SparseTimeDenseProduct;
+template<typename Lhs, typename Rhs>        class DenseTimeSparseProduct;
+template<typename Lhs, typename Rhs, bool Transpose> class SparseDenseOuterProduct;
+
+template<typename Lhs, typename Rhs> struct SparseSparseProductReturnType;
+template<typename Lhs, typename Rhs,
+         int InnerSize = internal::min_size_prefer_fixed(internal::traits<Lhs>::ColsAtCompileTime, internal::traits<Rhs>::RowsAtCompileTime)> struct DenseSparseProductReturnType;
+         
+template<typename Lhs, typename Rhs,
+         int InnerSize = internal::min_size_prefer_fixed(internal::traits<Lhs>::ColsAtCompileTime, internal::traits<Rhs>::RowsAtCompileTime)> struct SparseDenseProductReturnType;
+template<typename MatrixType,int UpLo> class SparseSymmetricPermutationProduct;
+
+namespace internal {
+
+template<typename T,int Rows,int Cols,int Flags> struct sparse_eval;
+
+template<typename T> struct eval<T,Sparse>
+  : sparse_eval<T, traits<T>::RowsAtCompileTime,traits<T>::ColsAtCompileTime,traits<T>::Flags>
+{};
+
+template<typename T,int Cols,int Flags> struct sparse_eval<T,1,Cols,Flags> {
+    typedef typename traits<T>::Scalar Scalar_;
+    typedef typename traits<T>::StorageIndex StorageIndex_;
+  public:
+    typedef SparseVector<Scalar_, RowMajor, StorageIndex_> type;
+};
+
+template<typename T,int Rows,int Flags> struct sparse_eval<T,Rows,1,Flags> {
+    typedef typename traits<T>::Scalar Scalar_;
+    typedef typename traits<T>::StorageIndex StorageIndex_;
+  public:
+    typedef SparseVector<Scalar_, ColMajor, StorageIndex_> type;
+};
+
+// TODO this seems almost identical to plain_matrix_type<T, Sparse>
+template<typename T,int Rows,int Cols,int Flags> struct sparse_eval {
+    typedef typename traits<T>::Scalar Scalar_;
+    typedef typename traits<T>::StorageIndex StorageIndex_;
+    enum { Options_ = ((Flags&RowMajorBit)==RowMajorBit) ? RowMajor : ColMajor };
+  public:
+    typedef SparseMatrix<Scalar_, Options_, StorageIndex_> type;
+};
+
+template<typename T,int Flags> struct sparse_eval<T,1,1,Flags> {
+    typedef typename traits<T>::Scalar Scalar_;
+  public:
+    typedef Matrix<Scalar_, 1, 1> type;
+};
+
+template<typename T> struct plain_matrix_type<T,Sparse>
+{
+  typedef typename traits<T>::Scalar Scalar_;
+  typedef typename traits<T>::StorageIndex StorageIndex_;
+  enum { Options_ = ((evaluator<T>::Flags&RowMajorBit)==RowMajorBit) ? RowMajor : ColMajor };
+  public:
+    typedef SparseMatrix<Scalar_, Options_, StorageIndex_> type;
+};
+
+template<typename T>
+struct plain_object_eval<T,Sparse>
+  : sparse_eval<T, traits<T>::RowsAtCompileTime,traits<T>::ColsAtCompileTime, evaluator<T>::Flags>
+{};
+
+template<typename Decomposition, typename RhsType>
+struct solve_traits<Decomposition,RhsType,Sparse>
+{
+  typedef typename sparse_eval<RhsType, RhsType::RowsAtCompileTime, RhsType::ColsAtCompileTime,traits<RhsType>::Flags>::type PlainObject;
+};
+
+template<typename Derived>
+struct generic_xpr_base<Derived, MatrixXpr, Sparse>
+{
+  typedef SparseMatrixBase<Derived> type;
+};
+
+struct SparseTriangularShape  { static std::string debugName() { return "SparseTriangularShape"; } };
+struct SparseSelfAdjointShape { static std::string debugName() { return "SparseSelfAdjointShape"; } };
+
+template<> struct glue_shapes<SparseShape,SelfAdjointShape> { typedef SparseSelfAdjointShape type;  };
+template<> struct glue_shapes<SparseShape,TriangularShape > { typedef SparseTriangularShape  type;  };
+
+// return type of SparseCompressedBase::lower_bound;
+struct LowerBoundIndex {
+  LowerBoundIndex() : value(-1), found(false) {}
+  LowerBoundIndex(Index val, bool ok) : value(val), found(ok) {}
+  Index value;
+  bool found;
+};
+
+} // end namespace internal
+
+/** \ingroup SparseCore_Module
+  *
+  * \class Triplet
+  *
+  * \brief A small structure to hold a non zero as a triplet (i,j,value).
+  *
+  * \sa SparseMatrix::setFromTriplets()
+  */
+template<typename Scalar, typename StorageIndex=typename SparseMatrix<Scalar>::StorageIndex >
+class Triplet
+{
+public:
+  Triplet() : m_row(0), m_col(0), m_value(0) {}
+
+  Triplet(const StorageIndex& i, const StorageIndex& j, const Scalar& v = Scalar(0))
+    : m_row(i), m_col(j), m_value(v)
+  {}
+
+  /** \returns the row index of the element */
+  const StorageIndex& row() const { return m_row; }
+
+  /** \returns the column index of the element */
+  const StorageIndex& col() const { return m_col; }
+
+  /** \returns the value of the element */
+  const Scalar& value() const { return m_value; }
+protected:
+  StorageIndex m_row, m_col;
+  Scalar m_value;
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEUTIL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseVector.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseVector.h
new file mode 100644
index 0000000000000000000000000000000000000000..3b4d7b05e4e32fcefd8fb71b05ac375468a48dc1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseVector.h
@@ -0,0 +1,572 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEVECTOR_H
+#define EIGEN_SPARSEVECTOR_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+/** \ingroup SparseCore_Module
+  * \class SparseVector
+  *
+  * \brief a sparse vector class
+  *
+  * \tparam Scalar_ the scalar type, i.e. the type of the coefficients
+  *
+  * See http://www.netlib.org/linalg/html_templates/node91.html for details on the storage scheme.
+  *
+  * This class can be extended with the help of the plugin mechanism described on the page
+  * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEVECTOR_PLUGIN.
+  */
+
+namespace internal {
+template<typename Scalar_, int Options_, typename StorageIndex_>
+struct traits<SparseVector<Scalar_, Options_, StorageIndex_> >
+{
+  typedef Scalar_ Scalar;
+  typedef StorageIndex_ StorageIndex;
+  typedef Sparse StorageKind;
+  typedef MatrixXpr XprKind;
+  enum {
+    IsColVector = (Options_ & RowMajorBit) ? 0 : 1,
+
+    RowsAtCompileTime = IsColVector ? Dynamic : 1,
+    ColsAtCompileTime = IsColVector ? 1 : Dynamic,
+    MaxRowsAtCompileTime = RowsAtCompileTime,
+    MaxColsAtCompileTime = ColsAtCompileTime,
+    Flags = Options_ | NestByRefBit | LvalueBit | (IsColVector ? 0 : RowMajorBit) | CompressedAccessBit,
+    SupportedAccessPatterns = InnerRandomAccessPattern
+  };
+};
+
+// Sparse-Vector-Assignment kinds:
+enum {
+  SVA_RuntimeSwitch,
+  SVA_Inner,
+  SVA_Outer
+};
+
+template< typename Dest, typename Src,
+          int AssignmentKind = !bool(Src::IsVectorAtCompileTime) ? SVA_RuntimeSwitch
+                             : Src::InnerSizeAtCompileTime==1 ? SVA_Outer
+                             : SVA_Inner>
+struct sparse_vector_assign_selector;
+
+}
+
+template<typename Scalar_, int Options_, typename StorageIndex_>
+class SparseVector
+  : public SparseCompressedBase<SparseVector<Scalar_, Options_, StorageIndex_> >
+{
+    typedef SparseCompressedBase<SparseVector> Base;
+    using Base::convert_index;
+  public:
+    EIGEN_SPARSE_PUBLIC_INTERFACE(SparseVector)
+    EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, +=)
+    EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, -=)
+    
+    typedef internal::CompressedStorage<Scalar,StorageIndex> Storage;
+    enum { IsColVector = internal::traits<SparseVector>::IsColVector };
+    
+    enum {
+      Options = Options_
+    };
+    
+    EIGEN_STRONG_INLINE Index rows() const { return IsColVector ? m_size : 1; }
+    EIGEN_STRONG_INLINE Index cols() const { return IsColVector ? 1 : m_size; }
+    EIGEN_STRONG_INLINE Index innerSize() const { return m_size; }
+    EIGEN_STRONG_INLINE Index outerSize() const { return 1; }
+
+    EIGEN_STRONG_INLINE const Scalar* valuePtr() const { return m_data.valuePtr(); }
+    EIGEN_STRONG_INLINE Scalar* valuePtr() { return m_data.valuePtr(); }
+
+    EIGEN_STRONG_INLINE const StorageIndex* innerIndexPtr() const { return m_data.indexPtr(); }
+    EIGEN_STRONG_INLINE StorageIndex* innerIndexPtr() { return m_data.indexPtr(); }
+
+    inline const StorageIndex* outerIndexPtr() const { return 0; }
+    inline StorageIndex* outerIndexPtr() { return 0; }
+    inline const StorageIndex* innerNonZeroPtr() const { return 0; }
+    inline StorageIndex* innerNonZeroPtr() { return 0; }
+    
+    /** \internal */
+    inline Storage& data() { return m_data; }
+    /** \internal */
+    inline const Storage& data() const { return m_data; }
+
+    inline Scalar coeff(Index row, Index col) const
+    {
+      eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
+      return coeff(IsColVector ? row : col);
+    }
+    inline Scalar coeff(Index i) const
+    {
+      eigen_assert(i>=0 && i<m_size);
+      return m_data.at(StorageIndex(i));
+    }
+
+    inline Scalar& coeffRef(Index row, Index col)
+    {
+      eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
+      return coeffRef(IsColVector ? row : col);
+    }
+
+    /** \returns a reference to the coefficient value at given index \a i
+      * This operation involes a log(rho*size) binary search. If the coefficient does not
+      * exist yet, then a sorted insertion into a sequential buffer is performed.
+      *
+      * This insertion might be very costly if the number of nonzeros above \a i is large.
+      */
+    inline Scalar& coeffRef(Index i)
+    {
+      eigen_assert(i>=0 && i<m_size);
+
+      return m_data.atWithInsertion(StorageIndex(i));
+    }
+
+  public:
+
+    typedef typename Base::InnerIterator InnerIterator;
+    typedef typename Base::ReverseInnerIterator ReverseInnerIterator;
+
+    inline void setZero() { m_data.clear(); }
+
+    /** \returns the number of non zero coefficients */
+    inline Index nonZeros() const  { return m_data.size(); }
+
+    inline void startVec(Index outer)
+    {
+      EIGEN_UNUSED_VARIABLE(outer);
+      eigen_assert(outer==0);
+    }
+
+    inline Scalar& insertBackByOuterInner(Index outer, Index inner)
+    {
+      EIGEN_UNUSED_VARIABLE(outer);
+      eigen_assert(outer==0);
+      return insertBack(inner);
+    }
+    inline Scalar& insertBack(Index i)
+    {
+      m_data.append(0, i);
+      return m_data.value(m_data.size()-1);
+    }
+    
+    Scalar& insertBackByOuterInnerUnordered(Index outer, Index inner)
+    {
+      EIGEN_UNUSED_VARIABLE(outer);
+      eigen_assert(outer==0);
+      return insertBackUnordered(inner);
+    }
+    inline Scalar& insertBackUnordered(Index i)
+    {
+      m_data.append(0, i);
+      return m_data.value(m_data.size()-1);
+    }
+
+    inline Scalar& insert(Index row, Index col)
+    {
+      eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
+      
+      Index inner = IsColVector ? row : col;
+      Index outer = IsColVector ? col : row;
+      EIGEN_ONLY_USED_FOR_DEBUG(outer);
+      eigen_assert(outer==0);
+      return insert(inner);
+    }
+    Scalar& insert(Index i)
+    {
+      eigen_assert(i>=0 && i<m_size);
+      
+      Index startId = 0;
+      Index p = Index(m_data.size()) - 1;
+      // TODO smart realloc
+      m_data.resize(p+2,1);
+
+      while ( (p >= startId) && (m_data.index(p) > i) )
+      {
+        m_data.index(p+1) = m_data.index(p);
+        m_data.value(p+1) = m_data.value(p);
+        --p;
+      }
+      m_data.index(p+1) = convert_index(i);
+      m_data.value(p+1) = 0;
+      return m_data.value(p+1);
+    }
+
+    /**
+      */
+    inline void reserve(Index reserveSize) { m_data.reserve(reserveSize); }
+
+
+    inline void finalize() {}
+
+    /** \copydoc SparseMatrix::prune(const Scalar&,const RealScalar&) */
+    Index prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision()) {
+      return prune([&](const Scalar& val){ return !internal::isMuchSmallerThan(val, reference, epsilon); });
+    }
+
+    /**
+     * \brief Prunes the entries of the vector based on a `predicate`
+     * \tparam F Type of the predicate.
+     * \param keep_predicate The predicate that is used to test whether a value should be kept. A callable that
+     * gets passed om a `Scalar` value and returns a boolean. If the predicate returns true, the value is kept.
+     * \return The new number of structural non-zeros.
+     */
+    template<class F>
+    Index prune(F&& keep_predicate)
+    {
+      Index k = 0;
+      Index n = m_data.size();
+      for (Index i = 0; i < n; ++i)
+      {
+        if (keep_predicate(m_data.value(i)))
+        {
+          m_data.value(k) = std::move(m_data.value(i));
+          m_data.index(k) = m_data.index(i);
+          ++k;
+        }
+      }
+      m_data.resize(k);
+      return k;
+    }
+
+    /** Resizes the sparse vector to \a rows x \a cols
+      *
+      * This method is provided for compatibility with matrices.
+      * For a column vector, \a cols must be equal to 1.
+      * For a row vector, \a rows must be equal to 1.
+      *
+      * \sa resize(Index)
+      */
+    void resize(Index rows, Index cols)
+    {
+      eigen_assert((IsColVector ? cols : rows)==1 && "Outer dimension must equal 1");
+      resize(IsColVector ? rows : cols);
+    }
+
+    /** Resizes the sparse vector to \a newSize
+      * This method deletes all entries, thus leaving an empty sparse vector
+      *
+      * \sa  conservativeResize(), setZero() */
+    void resize(Index newSize)
+    {
+      m_size = newSize;
+      m_data.clear();
+    }
+
+    /** Resizes the sparse vector to \a newSize, while leaving old values untouched.
+      *
+      * If the size of the vector is decreased, then the storage of the out-of bounds coefficients is kept and reserved.
+      * Call .data().squeeze() to free extra memory.
+      *
+      * \sa reserve(), setZero()
+      */
+    void conservativeResize(Index newSize)
+    {
+      if (newSize < m_size)
+      {
+        Index i = 0;
+        while (i<m_data.size() && m_data.index(i)<newSize) ++i;
+        m_data.resize(i);
+      }
+      m_size = newSize;
+    }
+
+    void resizeNonZeros(Index size) { m_data.resize(size); }
+
+    inline SparseVector() : m_size(0) { resize(0); }
+
+    explicit inline SparseVector(Index size) : m_size(0) { resize(size); }
+
+    inline SparseVector(Index rows, Index cols) : m_size(0) { resize(rows,cols); }
+
+    template<typename OtherDerived>
+    inline SparseVector(const SparseMatrixBase<OtherDerived>& other)
+      : m_size(0)
+    {
+      #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+        EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
+      #endif
+      *this = other.derived();
+    }
+
+    inline SparseVector(const SparseVector& other)
+      : Base(other), m_size(0)
+    {
+      *this = other.derived();
+    }
+
+    /** Swaps the values of \c *this and \a other.
+      * Overloaded for performance: this version performs a \em shallow swap by swapping pointers and attributes only.
+      * \sa SparseMatrixBase::swap()
+      */
+    inline void swap(SparseVector& other)
+    {
+      std::swap(m_size, other.m_size);
+      m_data.swap(other.m_data);
+    }
+
+    template<int OtherOptions>
+    inline void swap(SparseMatrix<Scalar,OtherOptions,StorageIndex>& other)
+    {
+      eigen_assert(other.outerSize()==1);
+      std::swap(m_size, other.m_innerSize);
+      m_data.swap(other.m_data);
+    }
+
+    inline SparseVector& operator=(const SparseVector& other)
+    {
+      if (other.isRValue())
+      {
+        swap(other.const_cast_derived());
+      }
+      else
+      {
+        resize(other.size());
+        m_data = other.m_data;
+      }
+      return *this;
+    }
+
+    template<typename OtherDerived>
+    inline SparseVector& operator=(const SparseMatrixBase<OtherDerived>& other)
+    {
+      SparseVector tmp(other.size());
+      internal::sparse_vector_assign_selector<SparseVector,OtherDerived>::run(tmp,other.derived());
+      this->swap(tmp);
+      return *this;
+    }
+
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    template<typename Lhs, typename Rhs>
+    inline SparseVector& operator=(const SparseSparseProduct<Lhs,Rhs>& product)
+    {
+      return Base::operator=(product);
+    }
+    #endif
+
+#ifndef EIGEN_NO_IO
+    friend std::ostream & operator << (std::ostream & s, const SparseVector& m)
+    {
+      for (Index i=0; i<m.nonZeros(); ++i)
+        s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") ";
+      s << std::endl;
+      return s;
+    }
+#endif
+
+    /** Destructor */
+    inline ~SparseVector() {}
+
+    /** Overloaded for performance */
+    Scalar sum() const;
+
+  public:
+
+    /** \internal \deprecated use setZero() and reserve() */
+    EIGEN_DEPRECATED void startFill(Index reserve)
+    {
+      setZero();
+      m_data.reserve(reserve);
+    }
+
+    /** \internal \deprecated use insertBack(Index,Index) */
+    EIGEN_DEPRECATED Scalar& fill(Index r, Index c)
+    {
+      eigen_assert(r==0 || c==0);
+      return fill(IsColVector ? r : c);
+    }
+
+    /** \internal \deprecated use insertBack(Index) */
+    EIGEN_DEPRECATED Scalar& fill(Index i)
+    {
+      m_data.append(0, i);
+      return m_data.value(m_data.size()-1);
+    }
+
+    /** \internal \deprecated use insert(Index,Index) */
+    EIGEN_DEPRECATED Scalar& fillrand(Index r, Index c)
+    {
+      eigen_assert(r==0 || c==0);
+      return fillrand(IsColVector ? r : c);
+    }
+
+    /** \internal \deprecated use insert(Index) */
+    EIGEN_DEPRECATED Scalar& fillrand(Index i)
+    {
+      return insert(i);
+    }
+
+    /** \internal \deprecated use finalize() */
+    EIGEN_DEPRECATED void endFill() {}
+    
+    // These two functions were here in the 3.1 release, so let's keep them in case some code rely on them.
+    /** \internal \deprecated use data() */
+    EIGEN_DEPRECATED Storage& _data() { return m_data; }
+    /** \internal \deprecated use data() */
+    EIGEN_DEPRECATED const Storage& _data() const { return m_data; }
+    
+#   ifdef EIGEN_SPARSEVECTOR_PLUGIN
+#     include EIGEN_SPARSEVECTOR_PLUGIN
+#   endif
+
+protected:
+    EIGEN_STATIC_ASSERT(NumTraits<StorageIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE)
+    EIGEN_STATIC_ASSERT((Options_&(ColMajor|RowMajor))==Options,INVALID_MATRIX_TEMPLATE_PARAMETERS)
+
+    Storage m_data;
+    Index m_size;
+};
+
+namespace internal {
+
+template<typename Scalar_, int Options_, typename Index_>
+struct evaluator<SparseVector<Scalar_,Options_,Index_> >
+  : evaluator_base<SparseVector<Scalar_,Options_,Index_> >
+{
+  typedef SparseVector<Scalar_,Options_,Index_> SparseVectorType;
+  typedef evaluator_base<SparseVectorType> Base;
+  typedef typename SparseVectorType::InnerIterator InnerIterator;
+  typedef typename SparseVectorType::ReverseInnerIterator ReverseInnerIterator;
+  
+  enum {
+    CoeffReadCost = NumTraits<Scalar_>::ReadCost,
+    Flags = SparseVectorType::Flags
+  };
+
+  evaluator() : Base() {}
+  
+  explicit evaluator(const SparseVectorType &mat) : m_matrix(&mat)
+  {
+    EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
+  }
+  
+  inline Index nonZerosEstimate() const {
+    return m_matrix->nonZeros();
+  }
+  
+  operator SparseVectorType&() { return m_matrix->const_cast_derived(); }
+  operator const SparseVectorType&() const { return *m_matrix; }
+  
+  const SparseVectorType *m_matrix;
+};
+
+template< typename Dest, typename Src>
+struct sparse_vector_assign_selector<Dest,Src,SVA_Inner> {
+  static void run(Dest& dst, const Src& src) {
+    eigen_internal_assert(src.innerSize()==src.size());
+    typedef internal::evaluator<Src> SrcEvaluatorType;
+    SrcEvaluatorType srcEval(src);
+    for(typename SrcEvaluatorType::InnerIterator it(srcEval, 0); it; ++it)
+      dst.insert(it.index()) = it.value();
+  }
+};
+
+template< typename Dest, typename Src>
+struct sparse_vector_assign_selector<Dest,Src,SVA_Outer> {
+  static void run(Dest& dst, const Src& src) {
+    eigen_internal_assert(src.outerSize()==src.size());
+    typedef internal::evaluator<Src> SrcEvaluatorType;
+    SrcEvaluatorType srcEval(src);
+    for(Index i=0; i<src.size(); ++i)
+    {
+      typename SrcEvaluatorType::InnerIterator it(srcEval, i);
+      if(it)
+        dst.insert(i) = it.value();
+    }
+  }
+};
+
+template< typename Dest, typename Src>
+struct sparse_vector_assign_selector<Dest,Src,SVA_RuntimeSwitch> {
+  static void run(Dest& dst, const Src& src) {
+    if(src.outerSize()==1)  sparse_vector_assign_selector<Dest,Src,SVA_Inner>::run(dst, src);
+    else                    sparse_vector_assign_selector<Dest,Src,SVA_Outer>::run(dst, src);
+  }
+};
+
+}
+
+// Specialization for SparseVector.
+// Serializes [size, numNonZeros, innerIndices, values].
+template <typename Scalar, int Options, typename StorageIndex>
+class Serializer<SparseVector<Scalar, Options, StorageIndex>, void> {
+ public:
+  typedef SparseVector<Scalar, Options, StorageIndex> SparseMat;
+
+  struct Header {
+    typename SparseMat::Index size;
+    Index num_non_zeros;
+  };
+
+  EIGEN_DEVICE_FUNC size_t size(const SparseMat& value) const {
+    return sizeof(Header) +
+           (sizeof(Scalar) + sizeof(StorageIndex)) * value.nonZeros();
+  }
+
+  EIGEN_DEVICE_FUNC uint8_t* serialize(uint8_t* dest, uint8_t* end,
+                                       const SparseMat& value) {
+    if (EIGEN_PREDICT_FALSE(dest == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(dest + size(value) > end)) return nullptr;
+
+    const size_t header_bytes = sizeof(Header);
+    Header header = {value.innerSize(), value.nonZeros()};
+    EIGEN_USING_STD(memcpy)
+    memcpy(dest, &header, header_bytes);
+    dest += header_bytes;
+
+    // Inner indices.
+    std::size_t data_bytes = sizeof(StorageIndex) * header.num_non_zeros;
+    memcpy(dest, value.innerIndexPtr(), data_bytes);
+    dest += data_bytes;
+
+    // Values.
+    data_bytes = sizeof(Scalar) * header.num_non_zeros;
+    memcpy(dest, value.valuePtr(), data_bytes);
+    dest += data_bytes;
+
+    return dest;
+  }
+
+  EIGEN_DEVICE_FUNC const uint8_t* deserialize(const uint8_t* src,
+                                               const uint8_t* end,
+                                               SparseMat& value) const {
+    if (EIGEN_PREDICT_FALSE(src == nullptr)) return nullptr;
+    if (EIGEN_PREDICT_FALSE(src + sizeof(Header) > end)) return nullptr;
+
+    const size_t header_bytes = sizeof(Header);
+    Header header;
+    EIGEN_USING_STD(memcpy)
+    memcpy(&header, src, header_bytes);
+    src += header_bytes;
+
+    value.setZero();
+    value.resize(header.size);
+    value.resizeNonZeros(header.num_non_zeros);
+
+    // Inner indices.
+    std::size_t data_bytes = sizeof(StorageIndex) * header.num_non_zeros;
+    if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr;
+    memcpy(value.innerIndexPtr(), src, data_bytes);
+    src += data_bytes;
+
+    // Values.
+    data_bytes = sizeof(Scalar) * header.num_non_zeros;
+    if (EIGEN_PREDICT_FALSE(src + data_bytes > end)) return nullptr;
+    memcpy(value.valuePtr(), src, data_bytes);
+    src += data_bytes;
+    return src;
+  }
+};
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSEVECTOR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseView.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseView.h
new file mode 100644
index 0000000000000000000000000000000000000000..dbb4c434a420443b6b9798290c96a8a68e3899e3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/SparseView.h
@@ -0,0 +1,256 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2010 Daniel Lowengrub <lowdanie@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEVIEW_H
+#define EIGEN_SPARSEVIEW_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename MatrixType>
+struct traits<SparseView<MatrixType> > : traits<MatrixType>
+{
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef Sparse StorageKind;
+  enum {
+    Flags = int(traits<MatrixType>::Flags) & (RowMajorBit)
+  };
+};
+
+} // end namespace internal
+
+/** \ingroup SparseCore_Module
+  * \class SparseView
+  *
+  * \brief Expression of a dense or sparse matrix with zero or too small values removed
+  *
+  * \tparam MatrixType the type of the object of which we are removing the small entries
+  *
+  * This class represents an expression of a given dense or sparse matrix with
+  * entries smaller than \c reference * \c epsilon are removed.
+  * It is the return type of MatrixBase::sparseView() and SparseMatrixBase::pruned()
+  * and most of the time this is the only way it is used.
+  *
+  * \sa MatrixBase::sparseView(), SparseMatrixBase::pruned()
+  */
+template<typename MatrixType>
+class SparseView : public SparseMatrixBase<SparseView<MatrixType> >
+{
+  typedef typename MatrixType::Nested MatrixTypeNested;
+  typedef internal::remove_all_t<MatrixTypeNested> MatrixTypeNested_;
+  typedef SparseMatrixBase<SparseView > Base;
+public:
+  EIGEN_SPARSE_PUBLIC_INTERFACE(SparseView)
+  typedef internal::remove_all_t<MatrixType> NestedExpression;
+
+  explicit SparseView(const MatrixType& mat, const Scalar& reference = Scalar(0),
+                      const RealScalar &epsilon = NumTraits<Scalar>::dummy_precision())
+    : m_matrix(mat), m_reference(reference), m_epsilon(epsilon) {}
+
+  inline Index rows() const { return m_matrix.rows(); }
+  inline Index cols() const { return m_matrix.cols(); }
+
+  inline Index innerSize() const { return m_matrix.innerSize(); }
+  inline Index outerSize() const { return m_matrix.outerSize(); }
+  
+  /** \returns the nested expression */
+  const internal::remove_all_t<MatrixTypeNested>&
+  nestedExpression() const { return m_matrix; }
+  
+  Scalar reference() const { return m_reference; }
+  RealScalar epsilon() const { return m_epsilon; }
+  
+protected:
+  MatrixTypeNested m_matrix;
+  Scalar m_reference;
+  RealScalar m_epsilon;
+};
+
+namespace internal {
+
+// TODO find a way to unify the two following variants
+// This is tricky because implementing an inner iterator on top of an IndexBased evaluator is
+// not easy because the evaluators do not expose the sizes of the underlying expression.
+  
+template<typename ArgType>
+struct unary_evaluator<SparseView<ArgType>, IteratorBased>
+  : public evaluator_base<SparseView<ArgType> >
+{
+    typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
+  public:
+    typedef SparseView<ArgType> XprType;
+    
+    class InnerIterator : public EvalIterator
+    {
+      protected:
+        typedef typename XprType::Scalar Scalar;
+      public:
+
+        EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& sve, Index outer)
+          : EvalIterator(sve.m_argImpl,outer), m_view(sve.m_view)
+        {
+          incrementToNonZero();
+        }
+
+        EIGEN_STRONG_INLINE InnerIterator& operator++()
+        {
+          EvalIterator::operator++();
+          incrementToNonZero();
+          return *this;
+        }
+
+        using EvalIterator::value;
+
+      protected:
+        const XprType &m_view;
+
+      private:
+        void incrementToNonZero()
+        {
+          while((bool(*this)) && internal::isMuchSmallerThan(value(), m_view.reference(), m_view.epsilon()))
+          {
+            EvalIterator::operator++();
+          }
+        }
+    };
+    
+    enum {
+      CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+      Flags = XprType::Flags
+    };
+    
+    explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_view(xpr) {}
+
+  protected:
+    evaluator<ArgType> m_argImpl;
+    const XprType &m_view;
+};
+
+template<typename ArgType>
+struct unary_evaluator<SparseView<ArgType>, IndexBased>
+  : public evaluator_base<SparseView<ArgType> >
+{
+  public:
+    typedef SparseView<ArgType> XprType;
+  protected:
+    enum { IsRowMajor = (XprType::Flags&RowMajorBit)==RowMajorBit };
+    typedef typename XprType::Scalar Scalar;
+    typedef typename XprType::StorageIndex StorageIndex;
+  public:
+    
+    class InnerIterator
+    {
+      public:
+
+        EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& sve, Index outer)
+          : m_sve(sve), m_inner(0), m_outer(outer), m_end(sve.m_view.innerSize())
+        {
+          incrementToNonZero();
+        }
+
+        EIGEN_STRONG_INLINE InnerIterator& operator++()
+        {
+          m_inner++;
+          incrementToNonZero();
+          return *this;
+        }
+
+        EIGEN_STRONG_INLINE Scalar value() const
+        {
+          return (IsRowMajor) ? m_sve.m_argImpl.coeff(m_outer, m_inner)
+                              : m_sve.m_argImpl.coeff(m_inner, m_outer);
+        }
+
+        EIGEN_STRONG_INLINE StorageIndex index() const { return m_inner; }
+        inline Index row() const { return IsRowMajor ? m_outer : index(); }
+        inline Index col() const { return IsRowMajor ? index() : m_outer; }
+
+        EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; }
+
+      protected:
+        const unary_evaluator &m_sve;
+        Index m_inner;
+        const Index m_outer;
+        const Index m_end;
+
+      private:
+        void incrementToNonZero()
+        {
+          while((bool(*this)) && internal::isMuchSmallerThan(value(), m_sve.m_view.reference(), m_sve.m_view.epsilon()))
+          {
+            m_inner++;
+          }
+        }
+    };
+    
+    enum {
+      CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
+      Flags = XprType::Flags
+    };
+    
+    explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_view(xpr) {}
+
+  protected:
+    evaluator<ArgType> m_argImpl;
+    const XprType &m_view;
+};
+
+} // end namespace internal
+
+/** \ingroup SparseCore_Module
+  *
+  * \returns a sparse expression of the dense expression \c *this with values smaller than
+  * \a reference * \a epsilon removed.
+  *
+  * This method is typically used when prototyping to convert a quickly assembled dense Matrix \c D to a SparseMatrix \c S:
+  * \code
+  * MatrixXd D(n,m);
+  * SparseMatrix<double> S;
+  * S = D.sparseView();             // suppress numerical zeros (exact)
+  * S = D.sparseView(reference);
+  * S = D.sparseView(reference,epsilon);
+  * \endcode
+  * where \a reference is a meaningful non zero reference value,
+  * and \a epsilon is a tolerance factor defaulting to NumTraits<Scalar>::dummy_precision().
+  *
+  * \sa SparseMatrixBase::pruned(), class SparseView */
+template<typename Derived>
+const SparseView<Derived> MatrixBase<Derived>::sparseView(const Scalar& reference,
+                                                          const typename NumTraits<Scalar>::Real& epsilon) const
+{
+  return SparseView<Derived>(derived(), reference, epsilon);
+}
+
+/** \returns an expression of \c *this with values smaller than
+  * \a reference * \a epsilon removed.
+  *
+  * This method is typically used in conjunction with the product of two sparse matrices
+  * to automatically prune the smallest values as follows:
+  * \code
+  * C = (A*B).pruned();             // suppress numerical zeros (exact)
+  * C = (A*B).pruned(ref);
+  * C = (A*B).pruned(ref,epsilon);
+  * \endcode
+  * where \c ref is a meaningful non zero reference value.
+  * */
+template<typename Derived>
+const SparseView<Derived>
+SparseMatrixBase<Derived>::pruned(const Scalar& reference,
+                                  const RealScalar& epsilon) const
+{
+  return SparseView<Derived>(derived(), reference, epsilon);
+}
+
+} // end namespace Eigen
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/TriangularSolver.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/TriangularSolver.h
new file mode 100644
index 0000000000000000000000000000000000000000..a9fbeebc1031cf209a8a34a1502a28e4f078cc85
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseCore/TriangularSolver.h
@@ -0,0 +1,317 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSETRIANGULARSOLVER_H
+#define EIGEN_SPARSETRIANGULARSOLVER_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, int Mode,
+  int UpLo = (Mode & Lower)
+           ? Lower
+           : (Mode & Upper)
+           ? Upper
+           : -1,
+  int StorageOrder = int(traits<Lhs>::Flags) & RowMajorBit>
+struct sparse_solve_triangular_selector;
+
+// forward substitution, row-major
+template<typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Lower,RowMajor>
+{
+  typedef typename Rhs::Scalar Scalar;
+  typedef evaluator<Lhs> LhsEval;
+  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
+  static void run(const Lhs& lhs, Rhs& other)
+  {
+    LhsEval lhsEval(lhs);
+    for(Index col=0 ; col<other.cols() ; ++col)
+    {
+      for(Index i=0; i<lhs.rows(); ++i)
+      {
+        Scalar tmp = other.coeff(i,col);
+        Scalar lastVal(0);
+        Index lastIndex = 0;
+        for(LhsIterator it(lhsEval, i); it; ++it)
+        {
+          lastVal = it.value();
+          lastIndex = it.index();
+          if(lastIndex==i)
+            break;
+          tmp -= lastVal * other.coeff(lastIndex,col);
+        }
+        if (Mode & UnitDiag)
+          other.coeffRef(i,col) = tmp;
+        else
+        {
+          eigen_assert(lastIndex==i);
+          other.coeffRef(i,col) = tmp/lastVal;
+        }
+      }
+    }
+  }
+};
+
+// backward substitution, row-major
+template<typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Upper,RowMajor>
+{
+  typedef typename Rhs::Scalar Scalar;
+  typedef evaluator<Lhs> LhsEval;
+  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
+  static void run(const Lhs& lhs, Rhs& other)
+  {
+    LhsEval lhsEval(lhs);
+    for(Index col=0 ; col<other.cols() ; ++col)
+    {
+      for(Index i=lhs.rows()-1 ; i>=0 ; --i)
+      {
+        Scalar tmp = other.coeff(i,col);
+        Scalar l_ii(0);
+        LhsIterator it(lhsEval, i);
+        while(it && it.index()<i)
+          ++it;
+        if(!(Mode & UnitDiag))
+        {
+          eigen_assert(it && it.index()==i);
+          l_ii = it.value();
+          ++it;
+        }
+        else if (it && it.index() == i)
+          ++it;
+        for(; it; ++it)
+        {
+          tmp -= it.value() * other.coeff(it.index(),col);
+        }
+
+        if (Mode & UnitDiag)  other.coeffRef(i,col) = tmp;
+        else                  other.coeffRef(i,col) = tmp/l_ii;
+      }
+    }
+  }
+};
+
+// forward substitution, col-major
+template<typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Lower,ColMajor>
+{
+  typedef typename Rhs::Scalar Scalar;
+  typedef evaluator<Lhs> LhsEval;
+  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
+  static void run(const Lhs& lhs, Rhs& other)
+  {
+    LhsEval lhsEval(lhs);
+    for(Index col=0 ; col<other.cols() ; ++col)
+    {
+      for(Index i=0; i<lhs.cols(); ++i)
+      {
+        Scalar& tmp = other.coeffRef(i,col);
+        if (!numext::is_exactly_zero(tmp)) // optimization when other is actually sparse
+        {
+          LhsIterator it(lhsEval, i);
+          while(it && it.index()<i)
+            ++it;
+          if(!(Mode & UnitDiag))
+          {
+            eigen_assert(it && it.index()==i);
+            tmp /= it.value();
+          }
+          if (it && it.index()==i)
+            ++it;
+          for(; it; ++it)
+            other.coeffRef(it.index(), col) -= tmp * it.value();
+        }
+      }
+    }
+  }
+};
+
+// backward substitution, col-major
+template<typename Lhs, typename Rhs, int Mode>
+struct sparse_solve_triangular_selector<Lhs,Rhs,Mode,Upper,ColMajor>
+{
+  typedef typename Rhs::Scalar Scalar;
+  typedef evaluator<Lhs> LhsEval;
+  typedef typename evaluator<Lhs>::InnerIterator LhsIterator;
+  static void run(const Lhs& lhs, Rhs& other)
+  {
+    LhsEval lhsEval(lhs);
+    for(Index col=0 ; col<other.cols() ; ++col)
+    {
+      for(Index i=lhs.cols()-1; i>=0; --i)
+      {
+        Scalar& tmp = other.coeffRef(i,col);
+        if (!numext::is_exactly_zero(tmp)) // optimization when other is actually sparse
+        {
+          if(!(Mode & UnitDiag))
+          {
+            // TODO replace this by a binary search. make sure the binary search is safe for partially sorted elements
+            LhsIterator it(lhsEval, i);
+            while(it && it.index()!=i)
+              ++it;
+            eigen_assert(it && it.index()==i);
+            other.coeffRef(i,col) /= it.value();
+          }
+          LhsIterator it(lhsEval, i);
+          for(; it && it.index()<i; ++it)
+            other.coeffRef(it.index(), col) -= tmp * it.value();
+        }
+      }
+    }
+  }
+};
+
+} // end namespace internal
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+template<typename ExpressionType,unsigned int Mode>
+template<typename OtherDerived>
+void TriangularViewImpl<ExpressionType,Mode,Sparse>::solveInPlace(MatrixBase<OtherDerived>& other) const
+{
+  eigen_assert(derived().cols() == derived().rows() && derived().cols() == other.rows());
+  eigen_assert((!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower)));
+
+  enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit };
+
+  typedef std::conditional_t<copy,
+    typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&> OtherCopy;
+  OtherCopy otherCopy(other.derived());
+
+  internal::sparse_solve_triangular_selector<ExpressionType, std::remove_reference_t<OtherCopy>, Mode>::run(derived().nestedExpression(), otherCopy);
+
+  if (copy)
+    other = otherCopy;
+}
+#endif
+
+// pure sparse path
+
+namespace internal {
+
+template<typename Lhs, typename Rhs, int Mode,
+  int UpLo = (Mode & Lower)
+           ? Lower
+           : (Mode & Upper)
+           ? Upper
+           : -1,
+  int StorageOrder = int(Lhs::Flags) & (RowMajorBit)>
+struct sparse_solve_triangular_sparse_selector;
+
+// forward substitution, col-major
+template<typename Lhs, typename Rhs, int Mode, int UpLo>
+struct sparse_solve_triangular_sparse_selector<Lhs,Rhs,Mode,UpLo,ColMajor>
+{
+  typedef typename Rhs::Scalar Scalar;
+  typedef typename promote_index_type<typename traits<Lhs>::StorageIndex,
+                                      typename traits<Rhs>::StorageIndex>::type StorageIndex;
+  static void run(const Lhs& lhs, Rhs& other)
+  {
+    const bool IsLower = (UpLo==Lower);
+    AmbiVector<Scalar,StorageIndex> tempVector(other.rows()*2);
+    tempVector.setBounds(0,other.rows());
+
+    Rhs res(other.rows(), other.cols());
+    res.reserve(other.nonZeros());
+
+    for(Index col=0 ; col<other.cols() ; ++col)
+    {
+      // FIXME estimate number of non zeros
+      tempVector.init(.99/*float(other.col(col).nonZeros())/float(other.rows())*/);
+      tempVector.setZero();
+      tempVector.restart();
+      for (typename Rhs::InnerIterator rhsIt(other, col); rhsIt; ++rhsIt)
+      {
+        tempVector.coeffRef(rhsIt.index()) = rhsIt.value();
+      }
+
+      for(Index i=IsLower?0:lhs.cols()-1;
+          IsLower?i<lhs.cols():i>=0;
+          i+=IsLower?1:-1)
+      {
+        tempVector.restart();
+        Scalar& ci = tempVector.coeffRef(i);
+        if (!numext::is_exactly_zero(ci))
+        {
+          // find
+          typename Lhs::InnerIterator it(lhs, i);
+          if(!(Mode & UnitDiag))
+          {
+            if (IsLower)
+            {
+              eigen_assert(it.index()==i);
+              ci /= it.value();
+            }
+            else
+              ci /= lhs.coeff(i,i);
+          }
+          tempVector.restart();
+          if (IsLower)
+          {
+            if (it.index()==i)
+              ++it;
+            for(; it; ++it)
+              tempVector.coeffRef(it.index()) -= ci * it.value();
+          }
+          else
+          {
+            for(; it && it.index()<i; ++it)
+              tempVector.coeffRef(it.index()) -= ci * it.value();
+          }
+        }
+      }
+
+
+//       Index count = 0;
+      // FIXME compute a reference value to filter zeros
+      for (typename AmbiVector<Scalar,StorageIndex>::Iterator it(tempVector/*,1e-12*/); it; ++it)
+      {
+//         ++ count;
+//         std::cerr << "fill " << it.index() << ", " << col << "\n";
+//         std::cout << it.value() << "  ";
+        // FIXME use insertBack
+        res.insert(it.index(), col) = it.value();
+      }
+//       std::cout << "tempVector.nonZeros() == " << int(count) << " / " << (other.rows()) << "\n";
+    }
+    res.finalize();
+    other = res.markAsRValue();
+  }
+};
+
+} // end namespace internal
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename ExpressionType,unsigned int Mode>
+template<typename OtherDerived>
+void TriangularViewImpl<ExpressionType,Mode,Sparse>::solveInPlace(SparseMatrixBase<OtherDerived>& other) const
+{
+  eigen_assert(derived().cols() == derived().rows() && derived().cols() == other.rows());
+  eigen_assert( (!(Mode & ZeroDiag)) && bool(Mode & (Upper|Lower)));
+
+//   enum { copy = internal::traits<OtherDerived>::Flags & RowMajorBit };
+
+//   typedef std::conditional_t<copy,
+//     typename internal::plain_matrix_type_column_major<OtherDerived>::type, OtherDerived&> OtherCopy;
+//   OtherCopy otherCopy(other.derived());
+
+  internal::sparse_solve_triangular_sparse_selector<ExpressionType, OtherDerived, Mode>::run(derived().nestedExpression(), other.derived());
+
+//   if (copy)
+//     other = otherCopy;
+}
+#endif
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSETRIANGULARSOLVER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..78ebfcc045c4ba33fac6a28d147d4d2fb15dc774
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SPARSELU_MODULE_H
+#error "Please include Eigen/SparseLU instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU.h
new file mode 100644
index 0000000000000000000000000000000000000000..f70aab19736a700a8b1214ecc5035bfb46ff34aa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU.h
@@ -0,0 +1,978 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+
+#ifndef EIGEN_SPARSE_LU_H
+#define EIGEN_SPARSE_LU_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename MatrixType_, typename OrderingType_ = COLAMDOrdering<typename MatrixType_::StorageIndex> > class SparseLU;
+template <typename MappedSparseMatrixType> struct SparseLUMatrixLReturnType;
+template <typename MatrixLType, typename MatrixUType> struct SparseLUMatrixUReturnType;
+
+template <bool Conjugate,class SparseLUType>
+class SparseLUTransposeView : public SparseSolverBase<SparseLUTransposeView<Conjugate,SparseLUType> >
+{
+protected:
+  typedef SparseSolverBase<SparseLUTransposeView<Conjugate,SparseLUType> > APIBase;
+  using APIBase::m_isInitialized;
+public:
+  typedef typename SparseLUType::Scalar Scalar;
+  typedef typename SparseLUType::StorageIndex StorageIndex;
+  typedef typename SparseLUType::MatrixType MatrixType;
+  typedef typename SparseLUType::OrderingType OrderingType;
+
+  enum {
+    ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+  };
+
+  SparseLUTransposeView() : APIBase(), m_sparseLU(NULL) {}
+  SparseLUTransposeView(const SparseLUTransposeView& view) : APIBase() {
+    this->m_sparseLU = view.m_sparseLU;
+    this->m_isInitialized = view.m_isInitialized;
+  }
+  void setIsInitialized(const bool isInitialized) {this->m_isInitialized = isInitialized;}
+  void setSparseLU(SparseLUType* sparseLU) {m_sparseLU = sparseLU;}
+  using APIBase::_solve_impl;
+  template<typename Rhs, typename Dest>
+  bool _solve_impl(const MatrixBase<Rhs> &B, MatrixBase<Dest> &X_base) const
+  {
+    Dest& X(X_base.derived());
+    eigen_assert(m_sparseLU->info() == Success && "The matrix should be factorized first");
+    EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,
+                        THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+
+
+    // this ugly const_cast_derived() helps to detect aliasing when applying the permutations
+    for(Index j = 0; j < B.cols(); ++j){
+      X.col(j) = m_sparseLU->colsPermutation() * B.const_cast_derived().col(j);
+    }
+    //Forward substitution with transposed or adjoint of U
+    m_sparseLU->matrixU().template solveTransposedInPlace<Conjugate>(X);
+
+    //Backward substitution with transposed or adjoint of L
+    m_sparseLU->matrixL().template solveTransposedInPlace<Conjugate>(X);
+
+    // Permute back the solution
+    for (Index j = 0; j < B.cols(); ++j)
+      X.col(j) = m_sparseLU->rowsPermutation().transpose() * X.col(j);
+    return true;
+  }
+  inline Index rows() const { return m_sparseLU->rows(); }
+  inline Index cols() const { return m_sparseLU->cols(); }
+
+private:
+  SparseLUType *m_sparseLU;
+  SparseLUTransposeView& operator=(const SparseLUTransposeView&);
+};
+
+
+/** \ingroup SparseLU_Module
+  * \class SparseLU
+  * 
+  * \brief Sparse supernodal LU factorization for general matrices
+  * 
+  * This class implements the supernodal LU factorization for general matrices.
+  * It uses the main techniques from the sequential SuperLU package 
+  * (http://crd-legacy.lbl.gov/~xiaoye/SuperLU/). It handles transparently real 
+  * and complex arithmetic with single and double precision, depending on the 
+  * scalar type of your input matrix. 
+  * The code has been optimized to provide BLAS-3 operations during supernode-panel updates. 
+  * It benefits directly from the built-in high-performant Eigen BLAS routines. 
+  * Moreover, when the size of a supernode is very small, the BLAS calls are avoided to 
+  * enable a better optimization from the compiler. For best performance, 
+  * you should compile it with NDEBUG flag to avoid the numerous bounds checking on vectors. 
+  * 
+  * An important parameter of this class is the ordering method. It is used to reorder the columns 
+  * (and eventually the rows) of the matrix to reduce the number of new elements that are created during 
+  * numerical factorization. The cheapest method available is COLAMD. 
+  * See  \link OrderingMethods_Module the OrderingMethods module \endlink for the list of 
+  * built-in and external ordering methods. 
+  *
+  * Simple example with key steps 
+  * \code
+  * VectorXd x(n), b(n);
+  * SparseMatrix<double> A;
+  * SparseLU<SparseMatrix<double>, COLAMDOrdering<int> >   solver;
+  * // fill A and b;
+  * // Compute the ordering permutation vector from the structural pattern of A
+  * solver.analyzePattern(A); 
+  * // Compute the numerical factorization 
+  * solver.factorize(A); 
+  * //Use the factors to solve the linear system 
+  * x = solver.solve(b); 
+  * \endcode
+  * 
+  * \warning The input matrix A should be in a \b compressed and \b column-major form.
+  * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
+  * 
+  * \note Unlike the initial SuperLU implementation, there is no step to equilibrate the matrix. 
+  * For badly scaled matrices, this step can be useful to reduce the pivoting during factorization. 
+  * If this is the case for your matrices, you can try the basic scaling method at
+  *  "unsupported/Eigen/src/IterativeSolvers/Scaling.h"
+  * 
+  * \tparam MatrixType_ The type of the sparse matrix. It must be a column-major SparseMatrix<>
+  * \tparam OrderingType_ The ordering method to use, either AMD, COLAMD or METIS. Default is COLMAD
+  *
+  * \implsparsesolverconcept
+  * 
+  * \sa \ref TutorialSparseSolverConcept
+  * \sa \ref OrderingMethods_Module
+  */
+template <typename MatrixType_, typename OrderingType_>
+class SparseLU : public SparseSolverBase<SparseLU<MatrixType_,OrderingType_> >, public internal::SparseLUImpl<typename MatrixType_::Scalar, typename MatrixType_::StorageIndex>
+{
+  protected:
+    typedef SparseSolverBase<SparseLU<MatrixType_,OrderingType_> > APIBase;
+    using APIBase::m_isInitialized;
+  public:
+    using APIBase::_solve_impl;
+    
+    typedef MatrixType_ MatrixType;
+    typedef OrderingType_ OrderingType;
+    typedef typename MatrixType::Scalar Scalar; 
+    typedef typename MatrixType::RealScalar RealScalar; 
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> NCMatrix;
+    typedef internal::MappedSuperNodalMatrix<Scalar, StorageIndex> SCMatrix;
+    typedef Matrix<Scalar,Dynamic,1> ScalarVector;
+    typedef Matrix<StorageIndex,Dynamic,1> IndexVector;
+    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
+    typedef internal::SparseLUImpl<Scalar, StorageIndex> Base;
+
+    enum {
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+    
+  public:
+
+    SparseLU():m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1)
+    {
+      initperfvalues(); 
+    }
+    explicit SparseLU(const MatrixType& matrix)
+      : m_lastError(""),m_Ustore(0,0,0,0,0,0),m_symmetricmode(false),m_diagpivotthresh(1.0),m_detPermR(1)
+    {
+      initperfvalues(); 
+      compute(matrix);
+    }
+    
+    ~SparseLU()
+    {
+      // Free all explicit dynamic pointers 
+    }
+    
+    void analyzePattern (const MatrixType& matrix);
+    void factorize (const MatrixType& matrix);
+    void simplicialfactorize(const MatrixType& matrix);
+    
+    /**
+      * Compute the symbolic and numeric factorization of the input sparse matrix.
+      * The input matrix should be in column-major storage. 
+      */
+    void compute (const MatrixType& matrix)
+    {
+      // Analyze 
+      analyzePattern(matrix); 
+      //Factorize
+      factorize(matrix);
+    } 
+
+    /** \returns an expression of the transposed of the factored matrix.
+      *
+      * A typical usage is to solve for the transposed problem A^T x = b:
+      * \code
+      * solver.compute(A);
+      * x = solver.transpose().solve(b);
+      * \endcode
+      *
+      * \sa adjoint(), solve()
+      */
+    const SparseLUTransposeView<false,SparseLU<MatrixType_,OrderingType_> > transpose()
+    {
+      SparseLUTransposeView<false,  SparseLU<MatrixType_,OrderingType_> > transposeView;
+      transposeView.setSparseLU(this);
+      transposeView.setIsInitialized(this->m_isInitialized);
+      return transposeView;
+    }
+
+
+    /** \returns an expression of the adjoint of the factored matrix
+      *
+      * A typical usage is to solve for the adjoint problem A' x = b:
+      * \code
+      * solver.compute(A);
+      * x = solver.adjoint().solve(b);
+      * \endcode
+      *
+      * For real scalar types, this function is equivalent to transpose().
+      *
+      * \sa transpose(), solve()
+      */
+    const SparseLUTransposeView<true, SparseLU<MatrixType_,OrderingType_> > adjoint()
+    {
+      SparseLUTransposeView<true,  SparseLU<MatrixType_,OrderingType_> > adjointView;
+      adjointView.setSparseLU(this);
+      adjointView.setIsInitialized(this->m_isInitialized);
+      return adjointView;
+    }
+    
+    inline Index rows() const { return m_mat.rows(); }
+    inline Index cols() const { return m_mat.cols(); }
+    /** Indicate that the pattern of the input matrix is symmetric */
+    void isSymmetric(bool sym)
+    {
+      m_symmetricmode = sym;
+    }
+    
+    /** \returns an expression of the matrix L, internally stored as supernodes
+      * The only operation available with this expression is the triangular solve
+      * \code
+      * y = b; matrixL().solveInPlace(y);
+      * \endcode
+      */
+    SparseLUMatrixLReturnType<SCMatrix> matrixL() const
+    {
+      return SparseLUMatrixLReturnType<SCMatrix>(m_Lstore);
+    }
+    /** \returns an expression of the matrix U,
+      * The only operation available with this expression is the triangular solve
+      * \code
+      * y = b; matrixU().solveInPlace(y);
+      * \endcode
+      */
+    SparseLUMatrixUReturnType<SCMatrix,Map<SparseMatrix<Scalar,ColMajor,StorageIndex> > > matrixU() const
+    {
+      return SparseLUMatrixUReturnType<SCMatrix, Map<SparseMatrix<Scalar,ColMajor,StorageIndex> > >(m_Lstore, m_Ustore);
+    }
+
+    /**
+      * \returns a reference to the row matrix permutation \f$ P_r \f$ such that \f$P_r A P_c^T = L U\f$
+      * \sa colsPermutation()
+      */
+    inline const PermutationType& rowsPermutation() const
+    {
+      return m_perm_r;
+    }
+    /**
+      * \returns a reference to the column matrix permutation\f$ P_c^T \f$ such that \f$P_r A P_c^T = L U\f$
+      * \sa rowsPermutation()
+      */
+    inline const PermutationType& colsPermutation() const
+    {
+      return m_perm_c;
+    }
+    /** Set the threshold used for a diagonal entry to be an acceptable pivot. */
+    void setPivotThreshold(const RealScalar& thresh)
+    {
+      m_diagpivotthresh = thresh; 
+    }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+    /** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A.
+      *
+      * \warning the destination matrix X in X = this->solve(B) must be colmun-major.
+      *
+      * \sa compute()
+      */
+    template<typename Rhs>
+    inline const Solve<SparseLU, Rhs> solve(const MatrixBase<Rhs>& B) const;
+#endif // EIGEN_PARSED_BY_DOXYGEN
+    
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful,
+      *          \c NumericalIssue if the LU factorization reports a problem, zero diagonal for instance
+      *          \c InvalidInput if the input matrix is invalid
+      *
+      * \sa iparm()          
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+    
+    /**
+      * \returns A string describing the type of error
+      */
+    std::string lastErrorMessage() const
+    {
+      return m_lastError; 
+    }
+
+    template<typename Rhs, typename Dest>
+    bool _solve_impl(const MatrixBase<Rhs> &B, MatrixBase<Dest> &X_base) const
+    {
+      Dest& X(X_base.derived());
+      eigen_assert(m_factorizationIsOk && "The matrix should be factorized first");
+      EIGEN_STATIC_ASSERT((Dest::Flags&RowMajorBit)==0,
+                        THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+      
+      // Permute the right hand side to form X = Pr*B
+      // on return, X is overwritten by the computed solution
+      X.resize(B.rows(),B.cols());
+
+      // this ugly const_cast_derived() helps to detect aliasing when applying the permutations
+      for(Index j = 0; j < B.cols(); ++j)
+        X.col(j) = rowsPermutation() * B.const_cast_derived().col(j);
+      
+      //Forward substitution with L
+      this->matrixL().solveInPlace(X);
+      this->matrixU().solveInPlace(X);
+      
+      // Permute back the solution 
+      for (Index j = 0; j < B.cols(); ++j)
+        X.col(j) = colsPermutation().inverse() * X.col(j);
+      
+      return true; 
+    }
+    
+    /**
+      * \returns the absolute value of the determinant of the matrix of which
+      * *this is the QR decomposition.
+      *
+      * \warning a determinant can be very big or small, so for matrices
+      * of large enough dimension, there is a risk of overflow/underflow.
+      * One way to work around that is to use logAbsDeterminant() instead.
+      *
+      * \sa logAbsDeterminant(), signDeterminant()
+      */
+    Scalar absDeterminant()
+    {
+      using std::abs;
+      eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
+      // Initialize with the determinant of the row matrix
+      Scalar det = Scalar(1.);
+      // Note that the diagonal blocks of U are stored in supernodes,
+      // which are available in the  L part :)
+      for (Index j = 0; j < this->cols(); ++j)
+      {
+        for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it)
+        {
+          if(it.index() == j)
+          {
+            det *= abs(it.value());
+            break;
+          }
+        }
+      }
+      return det;
+    }
+
+    /** \returns the natural log of the absolute value of the determinant of the matrix
+      * of which **this is the QR decomposition
+      *
+      * \note This method is useful to work around the risk of overflow/underflow that's
+      * inherent to the determinant computation.
+      *
+      * \sa absDeterminant(), signDeterminant()
+      */
+    Scalar logAbsDeterminant() const
+    {
+      using std::log;
+      using std::abs;
+
+      eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
+      Scalar det = Scalar(0.);
+      for (Index j = 0; j < this->cols(); ++j)
+      {
+        for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it)
+        {
+          if(it.row() < j) continue;
+          if(it.row() == j)
+          {
+            det += log(abs(it.value()));
+            break;
+          }
+        }
+      }
+      return det;
+    }
+
+    /** \returns A number representing the sign of the determinant
+      *
+      * \sa absDeterminant(), logAbsDeterminant()
+      */
+    Scalar signDeterminant()
+    {
+      eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
+      // Initialize with the determinant of the row matrix
+      Index det = 1;
+      // Note that the diagonal blocks of U are stored in supernodes,
+      // which are available in the  L part :)
+      for (Index j = 0; j < this->cols(); ++j)
+      {
+        for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it)
+        {
+          if(it.index() == j)
+          {
+            if(it.value()<0)
+              det = -det;
+            else if(it.value()==0)
+              return 0;
+            break;
+          }
+        }
+      }
+      return det * m_detPermR * m_detPermC;
+    }
+    
+    /** \returns The determinant of the matrix.
+      *
+      * \sa absDeterminant(), logAbsDeterminant()
+      */
+    Scalar determinant()
+    {
+      eigen_assert(m_factorizationIsOk && "The matrix should be factorized first.");
+      // Initialize with the determinant of the row matrix
+      Scalar det = Scalar(1.);
+      // Note that the diagonal blocks of U are stored in supernodes,
+      // which are available in the  L part :)
+      for (Index j = 0; j < this->cols(); ++j)
+      {
+        for (typename SCMatrix::InnerIterator it(m_Lstore, j); it; ++it)
+        {
+          if(it.index() == j)
+          {
+            det *= it.value();
+            break;
+          }
+        }
+      }
+      return (m_detPermR * m_detPermC) > 0 ? det : -det;
+    }
+
+    Index nnzL() const { return m_nnzL; }
+    Index nnzU() const { return m_nnzU; }
+
+  protected:
+    // Functions 
+    void initperfvalues()
+    {
+      m_perfv.panel_size = 16;
+      m_perfv.relax = 1; 
+      m_perfv.maxsuper = 128; 
+      m_perfv.rowblk = 16; 
+      m_perfv.colblk = 8; 
+      m_perfv.fillfactor = 20;  
+    }
+      
+    // Variables 
+    mutable ComputationInfo m_info;
+    bool m_factorizationIsOk;
+    bool m_analysisIsOk;
+    std::string m_lastError;
+    NCMatrix m_mat; // The input (permuted ) matrix 
+    SCMatrix m_Lstore; // The lower triangular matrix (supernodal)
+    Map<SparseMatrix<Scalar,ColMajor,StorageIndex>> m_Ustore; // The upper triangular matrix
+    PermutationType m_perm_c; // Column permutation 
+    PermutationType m_perm_r ; // Row permutation
+    IndexVector m_etree; // Column elimination tree 
+    
+    typename Base::GlobalLU_t m_glu; 
+                               
+    // SparseLU options 
+    bool m_symmetricmode;
+    // values for performance 
+    internal::perfvalues m_perfv;
+    RealScalar m_diagpivotthresh; // Specifies the threshold used for a diagonal entry to be an acceptable pivot
+    Index m_nnzL, m_nnzU; // Nonzeros in L and U factors
+    Index m_detPermR, m_detPermC; // Determinants of the permutation matrices
+  private:
+    // Disable copy constructor 
+    SparseLU (const SparseLU& );
+}; // End class SparseLU
+
+
+
+// Functions needed by the anaysis phase
+/** 
+  * Compute the column permutation to minimize the fill-in
+  * 
+  *  - Apply this permutation to the input matrix - 
+  * 
+  *  - Compute the column elimination tree on the permuted matrix 
+  * 
+  *  - Postorder the elimination tree and the column permutation
+  * 
+  */
+template <typename MatrixType, typename OrderingType>
+void SparseLU<MatrixType, OrderingType>::analyzePattern(const MatrixType& mat)
+{
+  
+  //TODO  It is possible as in SuperLU to compute row and columns scaling vectors to equilibrate the matrix mat.
+  
+  // Firstly, copy the whole input matrix. 
+  m_mat = mat;
+  
+  // Compute fill-in ordering
+  OrderingType ord; 
+  ord(m_mat,m_perm_c);
+  
+  // Apply the permutation to the column of the input  matrix
+  if (m_perm_c.size())
+  {
+    m_mat.uncompress(); //NOTE: The effect of this command is only to create the InnerNonzeros pointers. FIXME : This vector is filled but not subsequently used.  
+    // Then, permute only the column pointers
+    ei_declare_aligned_stack_constructed_variable(StorageIndex,outerIndexPtr,mat.cols()+1,mat.isCompressed()?const_cast<StorageIndex*>(mat.outerIndexPtr()):0);
+    
+    // If the input matrix 'mat' is uncompressed, then the outer-indices do not match the ones of m_mat, and a copy is thus needed.
+    if(!mat.isCompressed()) 
+      IndexVector::Map(outerIndexPtr, mat.cols()+1) = IndexVector::Map(m_mat.outerIndexPtr(),mat.cols()+1);
+    
+    // Apply the permutation and compute the nnz per column.
+    for (Index i = 0; i < mat.cols(); i++)
+    {
+      m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i];
+      m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i+1] - outerIndexPtr[i];
+    }
+  }
+  
+  // Compute the column elimination tree of the permuted matrix 
+  IndexVector firstRowElt;
+  internal::coletree(m_mat, m_etree,firstRowElt); 
+     
+  // In symmetric mode, do not do postorder here
+  if (!m_symmetricmode) {
+    IndexVector post, iwork; 
+    // Post order etree
+    internal::treePostorder(StorageIndex(m_mat.cols()), m_etree, post); 
+      
+   
+    // Renumber etree in postorder 
+    Index m = m_mat.cols(); 
+    iwork.resize(m+1);
+    for (Index i = 0; i < m; ++i) iwork(post(i)) = post(m_etree(i));
+    m_etree = iwork;
+    
+    // Postmultiply A*Pc by post, i.e reorder the matrix according to the postorder of the etree
+    PermutationType post_perm(m); 
+    for (Index i = 0; i < m; i++) 
+      post_perm.indices()(i) = post(i); 
+        
+    // Combine the two permutations : postorder the permutation for future use
+    if(m_perm_c.size()) {
+      m_perm_c = post_perm * m_perm_c;
+    }
+    
+  } // end postordering 
+  
+  m_analysisIsOk = true; 
+}
+
+// Functions needed by the numerical factorization phase
+
+
+/** 
+  *  - Numerical factorization 
+  *  - Interleaved with the symbolic factorization 
+  * On exit,  info is 
+  * 
+  *    = 0: successful factorization
+  * 
+  *    > 0: if info = i, and i is
+  * 
+  *       <= A->ncol: U(i,i) is exactly zero. The factorization has
+  *          been completed, but the factor U is exactly singular,
+  *          and division by zero will occur if it is used to solve a
+  *          system of equations.
+  * 
+  *       > A->ncol: number of bytes allocated when memory allocation
+  *         failure occurred, plus A->ncol. If lwork = -1, it is
+  *         the estimated amount of space needed, plus A->ncol.  
+  */
+template <typename MatrixType, typename OrderingType>
+void SparseLU<MatrixType, OrderingType>::factorize(const MatrixType& matrix)
+{
+  using internal::emptyIdxLU;
+  eigen_assert(m_analysisIsOk && "analyzePattern() should be called first"); 
+  eigen_assert((matrix.rows() == matrix.cols()) && "Only for squared matrices");
+  
+  m_isInitialized = true;
+  
+  // Apply the column permutation computed in analyzepattern()
+  //   m_mat = matrix * m_perm_c.inverse(); 
+  m_mat = matrix;
+  if (m_perm_c.size()) 
+  {
+    m_mat.uncompress(); //NOTE: The effect of this command is only to create the InnerNonzeros pointers.
+    //Then, permute only the column pointers
+    const StorageIndex * outerIndexPtr;
+    if (matrix.isCompressed()) outerIndexPtr = matrix.outerIndexPtr();
+    else
+    {
+      StorageIndex* outerIndexPtr_t = new StorageIndex[matrix.cols()+1];
+      for(Index i = 0; i <= matrix.cols(); i++) outerIndexPtr_t[i] = m_mat.outerIndexPtr()[i];
+      outerIndexPtr = outerIndexPtr_t;
+    }
+    for (Index i = 0; i < matrix.cols(); i++)
+    {
+      m_mat.outerIndexPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i];
+      m_mat.innerNonZeroPtr()[m_perm_c.indices()(i)] = outerIndexPtr[i+1] - outerIndexPtr[i];
+    }
+    if(!matrix.isCompressed()) delete[] outerIndexPtr;
+  } 
+  else 
+  { //FIXME This should not be needed if the empty permutation is handled transparently
+    m_perm_c.resize(matrix.cols());
+    for(StorageIndex i = 0; i < matrix.cols(); ++i) m_perm_c.indices()(i) = i;
+  }
+  
+  Index m = m_mat.rows();
+  Index n = m_mat.cols();
+  Index nnz = m_mat.nonZeros();
+  Index maxpanel = m_perfv.panel_size * m;
+  // Allocate working storage common to the factor routines
+  Index lwork = 0;
+  Index info = Base::memInit(m, n, nnz, lwork, m_perfv.fillfactor, m_perfv.panel_size, m_glu); 
+  if (info) 
+  {
+    m_lastError = "UNABLE TO ALLOCATE WORKING MEMORY\n\n" ;
+    m_factorizationIsOk = false;
+    return ; 
+  }
+  
+  // Set up pointers for integer working arrays 
+  IndexVector segrep(m); segrep.setZero();
+  IndexVector parent(m); parent.setZero();
+  IndexVector xplore(m); xplore.setZero();
+  IndexVector repfnz(maxpanel);
+  IndexVector panel_lsub(maxpanel);
+  IndexVector xprune(n); xprune.setZero();
+  IndexVector marker(m*internal::LUNoMarker); marker.setZero();
+  
+  repfnz.setConstant(-1); 
+  panel_lsub.setConstant(-1);
+  
+  // Set up pointers for scalar working arrays 
+  ScalarVector dense; 
+  dense.setZero(maxpanel);
+  ScalarVector tempv; 
+  tempv.setZero(internal::LUnumTempV(m, m_perfv.panel_size, m_perfv.maxsuper, /*m_perfv.rowblk*/m) );
+  
+  // Compute the inverse of perm_c
+  PermutationType iperm_c(m_perm_c.inverse()); 
+  
+  // Identify initial relaxed snodes
+  IndexVector relax_end(n);
+  if ( m_symmetricmode == true ) 
+    Base::heap_relax_snode(n, m_etree, m_perfv.relax, marker, relax_end);
+  else
+    Base::relax_snode(n, m_etree, m_perfv.relax, marker, relax_end);
+  
+  
+  m_perm_r.resize(m); 
+  m_perm_r.indices().setConstant(-1);
+  marker.setConstant(-1);
+  m_detPermR = 1; // Record the determinant of the row permutation
+  
+  m_glu.supno(0) = emptyIdxLU; m_glu.xsup.setConstant(0);
+  m_glu.xsup(0) = m_glu.xlsub(0) = m_glu.xusub(0) = m_glu.xlusup(0) = Index(0);
+  
+  // Work on one 'panel' at a time. A panel is one of the following :
+  //  (a) a relaxed supernode at the bottom of the etree, or
+  //  (b) panel_size contiguous columns, <panel_size> defined by the user
+  Index jcol; 
+  Index pivrow; // Pivotal row number in the original row matrix
+  Index nseg1; // Number of segments in U-column above panel row jcol
+  Index nseg; // Number of segments in each U-column 
+  Index irep; 
+  Index i, k, jj; 
+  for (jcol = 0; jcol < n; )
+  {
+    // Adjust panel size so that a panel won't overlap with the next relaxed snode. 
+    Index panel_size = m_perfv.panel_size; // upper bound on panel width
+    for (k = jcol + 1; k < (std::min)(jcol+panel_size, n); k++)
+    {
+      if (relax_end(k) != emptyIdxLU) 
+      {
+        panel_size = k - jcol; 
+        break; 
+      }
+    }
+    if (k == n) 
+      panel_size = n - jcol; 
+      
+    // Symbolic outer factorization on a panel of columns 
+    Base::panel_dfs(m, panel_size, jcol, m_mat, m_perm_r.indices(), nseg1, dense, panel_lsub, segrep, repfnz, xprune, marker, parent, xplore, m_glu); 
+    
+    // Numeric sup-panel updates in topological order 
+    Base::panel_bmod(m, panel_size, jcol, nseg1, dense, tempv, segrep, repfnz, m_glu); 
+    
+    // Sparse LU within the panel, and below the panel diagonal 
+    for ( jj = jcol; jj< jcol + panel_size; jj++) 
+    {
+      k = (jj - jcol) * m; // Column index for w-wide arrays 
+      
+      nseg = nseg1; // begin after all the panel segments
+      //Depth-first-search for the current column
+      VectorBlock<IndexVector> panel_lsubk(panel_lsub, k, m);
+      VectorBlock<IndexVector> repfnz_k(repfnz, k, m); 
+      info = Base::column_dfs(m, jj, m_perm_r.indices(), m_perfv.maxsuper, nseg, panel_lsubk, segrep, repfnz_k, xprune, marker, parent, xplore, m_glu); 
+      if ( info ) 
+      {
+        m_lastError =  "UNABLE TO EXPAND MEMORY IN COLUMN_DFS() ";
+        m_info = NumericalIssue; 
+        m_factorizationIsOk = false; 
+        return; 
+      }
+      // Numeric updates to this column 
+      VectorBlock<ScalarVector> dense_k(dense, k, m); 
+      VectorBlock<IndexVector> segrep_k(segrep, nseg1, m-nseg1); 
+      info = Base::column_bmod(jj, (nseg - nseg1), dense_k, tempv, segrep_k, repfnz_k, jcol, m_glu); 
+      if ( info ) 
+      {
+        m_lastError = "UNABLE TO EXPAND MEMORY IN COLUMN_BMOD() ";
+        m_info = NumericalIssue; 
+        m_factorizationIsOk = false; 
+        return; 
+      }
+      
+      // Copy the U-segments to ucol(*)
+      info = Base::copy_to_ucol(jj, nseg, segrep, repfnz_k ,m_perm_r.indices(), dense_k, m_glu); 
+      if ( info ) 
+      {
+        m_lastError = "UNABLE TO EXPAND MEMORY IN COPY_TO_UCOL() ";
+        m_info = NumericalIssue; 
+        m_factorizationIsOk = false; 
+        return; 
+      }
+      
+      // Form the L-segment 
+      info = Base::pivotL(jj, m_diagpivotthresh, m_perm_r.indices(), iperm_c.indices(), pivrow, m_glu);
+      if ( info ) 
+      {
+        m_lastError = "THE MATRIX IS STRUCTURALLY SINGULAR";
+#ifndef EIGEN_NO_IO
+        std::ostringstream returnInfo;
+        returnInfo << " ... ZERO COLUMN AT ";
+        returnInfo << info;
+        m_lastError += returnInfo.str();
+#endif
+        m_info = NumericalIssue; 
+        m_factorizationIsOk = false; 
+        return; 
+      }
+      
+      // Update the determinant of the row permutation matrix
+      // FIXME: the following test is not correct, we should probably take iperm_c into account and pivrow is not directly the row pivot.
+      if (pivrow != jj) m_detPermR = -m_detPermR;
+
+      // Prune columns (0:jj-1) using column jj
+      Base::pruneL(jj, m_perm_r.indices(), pivrow, nseg, segrep, repfnz_k, xprune, m_glu); 
+      
+      // Reset repfnz for this column 
+      for (i = 0; i < nseg; i++)
+      {
+        irep = segrep(i); 
+        repfnz_k(irep) = emptyIdxLU; 
+      }
+    } // end SparseLU within the panel  
+    jcol += panel_size;  // Move to the next panel
+  } // end for -- end elimination 
+  
+  m_detPermR = m_perm_r.determinant();
+  m_detPermC = m_perm_c.determinant();
+  
+  // Count the number of nonzeros in factors 
+  Base::countnz(n, m_nnzL, m_nnzU, m_glu); 
+  // Apply permutation  to the L subscripts 
+  Base::fixupL(n, m_perm_r.indices(), m_glu);
+  
+  // Create supernode matrix L 
+  m_Lstore.setInfos(m, n, m_glu.lusup, m_glu.xlusup, m_glu.lsub, m_glu.xlsub, m_glu.supno, m_glu.xsup); 
+  // Create the column major upper sparse matrix  U; 
+  new (&m_Ustore) Map<SparseMatrix<Scalar, ColMajor, StorageIndex>> ( m, n, m_nnzU, m_glu.xusub.data(), m_glu.usub.data(), m_glu.ucol.data() );
+  
+  m_info = Success;
+  m_factorizationIsOk = true;
+}
+
+template<typename MappedSupernodalType>
+struct SparseLUMatrixLReturnType : internal::no_assignment_operator
+{
+  typedef typename MappedSupernodalType::Scalar Scalar;
+  explicit SparseLUMatrixLReturnType(const MappedSupernodalType& mapL) : m_mapL(mapL)
+  { }
+  Index rows() const { return m_mapL.rows(); }
+  Index cols() const { return m_mapL.cols(); }
+  template<typename Dest>
+  void solveInPlace( MatrixBase<Dest> &X) const
+  {
+    m_mapL.solveInPlace(X);
+  }
+  template<bool Conjugate, typename Dest>
+  void solveTransposedInPlace( MatrixBase<Dest> &X) const
+  {
+    m_mapL.template solveTransposedInPlace<Conjugate>(X);
+  }
+
+  SparseMatrix<Scalar, ColMajor, Index> toSparse() const {
+    ArrayXi colCount = ArrayXi::Ones(cols());
+    for (Index i = 0; i < cols(); i++) {
+      typename MappedSupernodalType::InnerIterator iter(m_mapL, i);
+      for (; iter; ++iter) {
+        if (iter.row() > iter.col()) {
+          colCount(iter.col())++;
+        }
+      }
+    }
+    SparseMatrix<Scalar, ColMajor, Index> sL(rows(), cols());
+    sL.reserve(colCount);
+    for (Index i = 0; i < cols(); i++) {
+      sL.insert(i, i) = 1.0;
+      typename MappedSupernodalType::InnerIterator iter(m_mapL, i);
+      for (; iter; ++iter) {
+        if (iter.row() > iter.col()) {
+          sL.insert(iter.row(), iter.col()) = iter.value();
+        }
+      }
+    }
+    sL.makeCompressed();
+    return sL;
+  }
+
+  const MappedSupernodalType& m_mapL;
+};
+
+template<typename MatrixLType, typename MatrixUType>
+struct SparseLUMatrixUReturnType : internal::no_assignment_operator
+{
+  typedef typename MatrixLType::Scalar Scalar;
+  SparseLUMatrixUReturnType(const MatrixLType& mapL, const MatrixUType& mapU)
+  : m_mapL(mapL),m_mapU(mapU)
+  { }
+  Index rows() const { return m_mapL.rows(); }
+  Index cols() const { return m_mapL.cols(); }
+
+  template<typename Dest>   void solveInPlace(MatrixBase<Dest> &X) const
+  {
+    Index nrhs = X.cols();
+    // Backward solve with U
+    for (Index k = m_mapL.nsuper(); k >= 0; k--)
+    {
+      Index fsupc = m_mapL.supToCol()[k];
+      Index lda = m_mapL.colIndexPtr()[fsupc+1] - m_mapL.colIndexPtr()[fsupc]; // leading dimension
+      Index nsupc = m_mapL.supToCol()[k+1] - fsupc;
+      Index luptr = m_mapL.colIndexPtr()[fsupc];
+
+      if (nsupc == 1)
+      {
+        for (Index j = 0; j < nrhs; j++)
+        {
+          X(fsupc, j) /= m_mapL.valuePtr()[luptr];
+        }
+      }
+      else
+      {
+        // FIXME: the following lines should use Block expressions and not Map!
+        Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(m_mapL.valuePtr()[luptr]), nsupc, nsupc, OuterStride<>(lda) );
+        typename Dest::RowsBlockXpr U = X.derived().middleRows(fsupc, nsupc);
+        U = A.template triangularView<Upper>().solve(U);
+      }
+
+      for (Index j = 0; j < nrhs; ++j)
+      {
+        for (Index jcol = fsupc; jcol < fsupc + nsupc; jcol++)
+        {
+          typename MatrixUType::InnerIterator it(m_mapU, jcol);
+          for ( ; it; ++it)
+          {
+            Index irow = it.index();
+            X(irow, j) -= X(jcol, j) * it.value();
+          }
+        }
+      }
+    } // End For U-solve
+  }
+
+  template<bool Conjugate, typename Dest>   void solveTransposedInPlace(MatrixBase<Dest> &X) const
+  {
+    using numext::conj;
+    Index nrhs = X.cols();
+    // Forward solve with U
+    for (Index k = 0; k <=  m_mapL.nsuper(); k++)
+    {
+      Index fsupc = m_mapL.supToCol()[k];
+      Index lda = m_mapL.colIndexPtr()[fsupc+1] - m_mapL.colIndexPtr()[fsupc]; // leading dimension
+      Index nsupc = m_mapL.supToCol()[k+1] - fsupc;
+      Index luptr = m_mapL.colIndexPtr()[fsupc];
+
+      for (Index j = 0; j < nrhs; ++j)
+      {
+        for (Index jcol = fsupc; jcol < fsupc + nsupc; jcol++)
+        {
+          typename MatrixUType::InnerIterator it(m_mapU, jcol);
+          for ( ; it; ++it)
+          {
+            Index irow = it.index();
+            X(jcol, j) -= X(irow, j) * (Conjugate? conj(it.value()): it.value());
+          }
+        }
+      }
+      if (nsupc == 1)
+      {
+        for (Index j = 0; j < nrhs; j++)
+        {
+          X(fsupc, j) /= (Conjugate? conj(m_mapL.valuePtr()[luptr]) : m_mapL.valuePtr()[luptr]);
+        }
+      }
+      else
+      {
+        Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(m_mapL.valuePtr()[luptr]), nsupc, nsupc, OuterStride<>(lda) );
+        typename Dest::RowsBlockXpr U = X.derived().middleRows(fsupc, nsupc);
+        if(Conjugate)
+          U = A.adjoint().template triangularView<Lower>().solve(U);
+        else
+          U = A.transpose().template triangularView<Lower>().solve(U);
+      }
+    }// End For U-solve
+  }
+
+  SparseMatrix<Scalar, RowMajor, Index> toSparse() {
+    ArrayXi rowCount = ArrayXi::Zero(rows());
+    for (Index i = 0; i < cols(); i++) {
+      typename MatrixLType::InnerIterator iter(m_mapL, i);
+      for (; iter; ++iter) {
+        if (iter.row() <= iter.col()) {
+          rowCount(iter.row())++;
+        }
+      }
+    }
+
+    SparseMatrix<Scalar, RowMajor, Index> sU(rows(), cols());
+    sU.reserve(rowCount);
+    for (Index i = 0; i < cols(); i++) {
+      typename MatrixLType::InnerIterator iter(m_mapL, i);
+      for (; iter; ++iter) {
+        if (iter.row() <= iter.col()) {
+          sU.insert(iter.row(), iter.col()) = iter.value();
+        }
+      }
+    }
+    sU.makeCompressed();
+    const SparseMatrix<Scalar, RowMajor, Index> u = m_mapU;  // convert to RowMajor
+    sU += u;
+    return sU;
+  }
+
+  const MatrixLType& m_mapL;
+  const MatrixUType& m_mapU;
+};
+
+} // End namespace Eigen 
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLUImpl.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLUImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..daec8376966290466025af15f19dbdb5b1acef01
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLUImpl.h
@@ -0,0 +1,68 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+#ifndef SPARSELU_IMPL_H
+#define SPARSELU_IMPL_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+  
+/** \ingroup SparseLU_Module
+  * \class SparseLUImpl
+  * Base class for sparseLU
+  */
+template <typename Scalar, typename StorageIndex>
+class SparseLUImpl
+{
+  public:
+    typedef Matrix<Scalar,Dynamic,1> ScalarVector;
+    typedef Matrix<StorageIndex,Dynamic,1> IndexVector; 
+    typedef Matrix<Scalar,Dynamic,Dynamic,ColMajor> ScalarMatrix;
+    typedef Map<ScalarMatrix, 0,  OuterStride<> > MappedMatrixBlock;
+    typedef typename ScalarVector::RealScalar RealScalar; 
+    typedef Ref<Matrix<Scalar,Dynamic,1> > BlockScalarVector;
+    typedef Ref<Matrix<StorageIndex,Dynamic,1> > BlockIndexVector;
+    typedef LU_GlobalLU_t<IndexVector, ScalarVector> GlobalLU_t; 
+    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> MatrixType; 
+    
+  protected:
+     template <typename VectorType>
+     Index expand(VectorType& vec, Index& length, Index nbElts, Index keep_prev, Index& num_expansions);
+     Index memInit(Index m, Index n, Index annz, Index lwork, Index fillratio, Index panel_size,  GlobalLU_t& glu); 
+     template <typename VectorType>
+     Index memXpand(VectorType& vec, Index& maxlen, Index nbElts, MemType memtype, Index& num_expansions);
+     void heap_relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end); 
+     void relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end); 
+     Index snode_dfs(const Index jcol, const Index kcol,const MatrixType& mat,  IndexVector& xprune, IndexVector& marker, GlobalLU_t& glu); 
+     Index snode_bmod (const Index jcol, const Index fsupc, ScalarVector& dense, GlobalLU_t& glu);
+     Index pivotL(const Index jcol, const RealScalar& diagpivotthresh, IndexVector& perm_r, IndexVector& iperm_c, Index& pivrow, GlobalLU_t& glu);
+     template <typename Traits>
+     void dfs_kernel(const StorageIndex jj, IndexVector& perm_r,
+                    Index& nseg, IndexVector& panel_lsub, IndexVector& segrep,
+                    Ref<IndexVector> repfnz_col, IndexVector& xprune, Ref<IndexVector> marker, IndexVector& parent,
+                    IndexVector& xplore, GlobalLU_t& glu, Index& nextl_col, Index krow, Traits& traits);
+     void panel_dfs(const Index m, const Index w, const Index jcol, MatrixType& A, IndexVector& perm_r, Index& nseg, ScalarVector& dense, IndexVector& panel_lsub, IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu);
+    
+     void panel_bmod(const Index m, const Index w, const Index jcol, const Index nseg, ScalarVector& dense, ScalarVector& tempv, IndexVector& segrep, IndexVector& repfnz, GlobalLU_t& glu);
+     Index column_dfs(const Index m, const Index jcol, IndexVector& perm_r, Index maxsuper, Index& nseg,  BlockIndexVector lsub_col, IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu);
+     Index column_bmod(const Index jcol, const Index nseg, BlockScalarVector dense, ScalarVector& tempv, BlockIndexVector segrep, BlockIndexVector repfnz, Index fpanelc, GlobalLU_t& glu); 
+     Index copy_to_ucol(const Index jcol, const Index nseg, IndexVector& segrep, BlockIndexVector repfnz ,IndexVector& perm_r, BlockScalarVector dense, GlobalLU_t& glu); 
+     void pruneL(const Index jcol, const IndexVector& perm_r, const Index pivrow, const Index nseg, const IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune, GlobalLU_t& glu);
+     void countnz(const Index n, Index& nnzL, Index& nnzU, GlobalLU_t& glu); 
+     void fixupL(const Index n, const IndexVector& perm_r, GlobalLU_t& glu); 
+     
+     template<typename , typename >
+     friend struct column_dfs_traits;
+}; 
+
+} // end namespace internal
+} // namespace Eigen
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_Memory.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_Memory.h
new file mode 100644
index 0000000000000000000000000000000000000000..798745f2015a7571bb5c5432a2353ae0e7d667dd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_Memory.h
@@ -0,0 +1,228 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* 
+ 
+ * NOTE: This file is the modified version of [s,d,c,z]memory.c files in SuperLU 
+ 
+ * -- SuperLU routine (version 3.1) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * August 1, 2008
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+
+#ifndef EIGEN_SPARSELU_MEMORY
+#define EIGEN_SPARSELU_MEMORY
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+  
+enum { LUNoMarker = 3 };
+enum {emptyIdxLU = -1};
+inline Index LUnumTempV(Index& m, Index& w, Index& t, Index& b)
+{
+  return (std::max)(m, (t+b)*w);
+}
+
+template< typename Scalar>
+inline Index LUTempSpace(Index&m, Index& w)
+{
+  return (2*w + 4 + LUNoMarker) * m * sizeof(Index) + (w + 1) * m * sizeof(Scalar);
+}
+
+
+
+
+/** 
+  * Expand the existing storage to accommodate more fill-ins
+  * \param vec Valid pointer to the vector to allocate or expand
+  * \param[in,out] length  At input, contain the current length of the vector that is to be increased. At output, length of the newly allocated vector
+  * \param[in] nbElts Current number of elements in the factors
+  * \param keep_prev  1: use length  and do not expand the vector; 0: compute new_len and expand
+  * \param[in,out] num_expansions Number of times the memory has been expanded
+  */
+template <typename Scalar, typename StorageIndex>
+template <typename VectorType>
+Index  SparseLUImpl<Scalar,StorageIndex>::expand(VectorType& vec, Index& length, Index nbElts, Index keep_prev, Index& num_expansions) 
+{
+  
+  float alpha = 1.5; // Ratio of the memory increase 
+  Index new_len; // New size of the allocated memory
+  
+  if(num_expansions == 0 || keep_prev) 
+    new_len = length ; // First time allocate requested
+  else 
+    new_len = (std::max)(length+1,Index(alpha * length));
+  
+  VectorType old_vec; // Temporary vector to hold the previous values   
+  if (nbElts > 0 )
+    old_vec = vec.segment(0,nbElts); 
+  
+  //Allocate or expand the current vector
+#ifdef EIGEN_EXCEPTIONS
+  try
+#endif
+  {
+    vec.resize(new_len); 
+  }
+#ifdef EIGEN_EXCEPTIONS
+  catch(std::bad_alloc& )
+#else
+  if(!vec.size())
+#endif
+  {
+    if (!num_expansions)
+    {
+      // First time to allocate from LUMemInit()
+      // Let LUMemInit() deals with it.
+      return -1;
+    }
+    if (keep_prev)
+    {
+      // In this case, the memory length should not not be reduced
+      return new_len;
+    }
+    else 
+    {
+      // Reduce the size and increase again 
+      Index tries = 0; // Number of attempts
+      do 
+      {
+        alpha = (alpha + 1)/2;
+        new_len = (std::max)(length+1,Index(alpha * length));
+#ifdef EIGEN_EXCEPTIONS
+        try
+#endif
+        {
+          vec.resize(new_len); 
+        }
+#ifdef EIGEN_EXCEPTIONS
+        catch(std::bad_alloc& )
+#else
+        if (!vec.size())
+#endif
+        {
+          tries += 1; 
+          if ( tries > 10) return new_len; 
+        }
+      } while (!vec.size());
+    }
+  }
+  //Copy the previous values to the newly allocated space 
+  if (nbElts > 0)
+    vec.segment(0, nbElts) = old_vec;   
+   
+  
+  length  = new_len;
+  if(num_expansions) ++num_expansions;
+  return 0; 
+}
+
+/**
+ * \brief  Allocate various working space for the numerical factorization phase.
+ * \param m number of rows of the input matrix 
+ * \param n number of columns 
+ * \param annz number of initial nonzeros in the matrix 
+ * \param lwork  if lwork=-1, this routine returns an estimated size of the required memory
+ * \param glu persistent data to facilitate multiple factors : will be deleted later ??
+ * \param fillratio estimated ratio of fill in the factors
+ * \param panel_size Size of a panel
+ * \return an estimated size of the required memory if lwork = -1; otherwise, return the size of actually allocated memory when allocation failed, and 0 on success
+ * \note Unlike SuperLU, this routine does not support successive factorization with the same pattern and the same row permutation
+ */
+template <typename Scalar, typename StorageIndex>
+Index SparseLUImpl<Scalar,StorageIndex>::memInit(Index m, Index n, Index annz, Index lwork, Index fillratio, Index panel_size,  GlobalLU_t& glu)
+{
+  Index& num_expansions = glu.num_expansions; //No memory expansions so far
+  num_expansions = 0;
+  glu.nzumax = glu.nzlumax = (std::min)(fillratio * (annz+1) / n, m) * n; // estimated number of nonzeros in U 
+  glu.nzlmax = (std::max)(Index(4), fillratio) * (annz+1) / 4; // estimated  nnz in L factor
+  // Return the estimated size to the user if necessary
+  Index tempSpace;
+  tempSpace = (2*panel_size + 4 + LUNoMarker) * m * sizeof(Index) + (panel_size + 1) * m * sizeof(Scalar);
+  if (lwork == emptyIdxLU) 
+  {
+    Index estimated_size;
+    estimated_size = (5 * n + 5) * sizeof(Index)  + tempSpace
+                    + (glu.nzlmax + glu.nzumax) * sizeof(Index) + (glu.nzlumax+glu.nzumax) *  sizeof(Scalar) + n; 
+    return estimated_size;
+  }
+  
+  // Setup the required space 
+  
+  // First allocate Integer pointers for L\U factors
+  glu.xsup.resize(n+1);
+  glu.supno.resize(n+1);
+  glu.xlsub.resize(n+1);
+  glu.xlusup.resize(n+1);
+  glu.xusub.resize(n+1);
+
+  // Reserve memory for L/U factors
+  do 
+  {
+    if(     (expand<ScalarVector>(glu.lusup, glu.nzlumax, 0, 0, num_expansions)<0)
+        ||  (expand<ScalarVector>(glu.ucol,  glu.nzumax,  0, 0, num_expansions)<0)
+        ||  (expand<IndexVector> (glu.lsub,  glu.nzlmax,  0, 0, num_expansions)<0)
+        ||  (expand<IndexVector> (glu.usub,  glu.nzumax,  0, 1, num_expansions)<0) )
+    {
+      //Reduce the estimated size and retry
+      glu.nzlumax /= 2;
+      glu.nzumax /= 2;
+      glu.nzlmax /= 2;
+      if (glu.nzlumax < annz ) return glu.nzlumax; 
+    }
+  } while (!glu.lusup.size() || !glu.ucol.size() || !glu.lsub.size() || !glu.usub.size());
+  
+  ++num_expansions;
+  return 0;
+  
+} // end LuMemInit
+
+/** 
+ * \brief Expand the existing storage 
+ * \param vec vector to expand 
+ * \param[in,out] maxlen On input, previous size of vec (Number of elements to copy ). on output, new size
+ * \param nbElts current number of elements in the vector.
+ * \param memtype Type of the element to expand
+ * \param num_expansions Number of expansions 
+ * \return 0 on success, > 0 size of the memory allocated so far
+ */
+template <typename Scalar, typename StorageIndex>
+template <typename VectorType>
+Index SparseLUImpl<Scalar,StorageIndex>::memXpand(VectorType& vec, Index& maxlen, Index nbElts, MemType memtype, Index& num_expansions)
+{
+  Index failed_size; 
+  if (memtype == USUB)
+     failed_size = this->expand<VectorType>(vec, maxlen, nbElts, 1, num_expansions);
+  else
+    failed_size = this->expand<VectorType>(vec, maxlen, nbElts, 0, num_expansions);
+
+  if (failed_size)
+    return failed_size; 
+  
+  return 0 ;  
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+#endif // EIGEN_SPARSELU_MEMORY
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_Structs.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_Structs.h
new file mode 100644
index 0000000000000000000000000000000000000000..3ab0c72bf3def49e1c1446cfee59689587959a76
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_Structs.h
@@ -0,0 +1,112 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* 
+ * NOTE: This file comes from a partly modified version of files slu_[s,d,c,z]defs.h
+ * -- SuperLU routine (version 4.1) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * November, 2010
+ * 
+ * Global data structures used in LU factorization -
+ * 
+ *   nsuper: #supernodes = nsuper + 1, numbered [0, nsuper].
+ *   (xsup,supno): supno[i] is the supernode no to which i belongs;
+ *  xsup(s) points to the beginning of the s-th supernode.
+ *  e.g.   supno 0 1 2 2 3 3 3 4 4 4 4 4   (n=12)
+ *          xsup 0 1 2 4 7 12
+ *  Note: dfs will be performed on supernode rep. relative to the new 
+ *        row pivoting ordering
+ *
+ *   (xlsub,lsub): lsub[*] contains the compressed subscript of
+ *  rectangular supernodes; xlsub[j] points to the starting
+ *  location of the j-th column in lsub[*]. Note that xlsub 
+ *  is indexed by column.
+ *  Storage: original row subscripts
+ *
+ *      During the course of sparse LU factorization, we also use
+ *  (xlsub,lsub) for the purpose of symmetric pruning. For each
+ *  supernode {s,s+1,...,t=s+r} with first column s and last
+ *  column t, the subscript set
+ *    lsub[j], j=xlsub[s], .., xlsub[s+1]-1
+ *  is the structure of column s (i.e. structure of this supernode).
+ *  It is used for the storage of numerical values.
+ *  Furthermore,
+ *    lsub[j], j=xlsub[t], .., xlsub[t+1]-1
+ *  is the structure of the last column t of this supernode.
+ *  It is for the purpose of symmetric pruning. Therefore, the
+ *  structural subscripts can be rearranged without making physical
+ *  interchanges among the numerical values.
+ *
+ *  However, if the supernode has only one column, then we
+ *  only keep one set of subscripts. For any subscript interchange
+ *  performed, similar interchange must be done on the numerical
+ *  values.
+ *
+ *  The last column structures (for pruning) will be removed
+ *  after the numercial LU factorization phase.
+ *
+ *   (xlusup,lusup): lusup[*] contains the numerical values of the
+ *  rectangular supernodes; xlusup[j] points to the starting
+ *  location of the j-th column in storage vector lusup[*]
+ *  Note: xlusup is indexed by column.
+ *  Each rectangular supernode is stored by column-major
+ *  scheme, consistent with Fortran 2-dim array storage.
+ *
+ *   (xusub,ucol,usub): ucol[*] stores the numerical values of
+ *  U-columns outside the rectangular supernodes. The row
+ *  subscript of nonzero ucol[k] is stored in usub[k].
+ *  xusub[i] points to the starting location of column i in ucol.
+ *  Storage: new row subscripts; that is subscripts of PA.
+ */
+
+#ifndef EIGEN_LU_STRUCTS
+#define EIGEN_LU_STRUCTS
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+enum MemType {LUSUP, UCOL, LSUB, USUB, LLVL, ULVL};
+
+template <typename IndexVector, typename ScalarVector>
+struct LU_GlobalLU_t {
+  typedef typename IndexVector::Scalar StorageIndex; 
+  IndexVector xsup; //First supernode column ... xsup(s) points to the beginning of the s-th supernode
+  IndexVector supno; // Supernode number corresponding to this column (column to supernode mapping)
+  ScalarVector  lusup; // nonzero values of L ordered by columns 
+  IndexVector lsub; // Compressed row indices of L rectangular supernodes. 
+  IndexVector xlusup; // pointers to the beginning of each column in lusup
+  IndexVector xlsub; // pointers to the beginning of each column in lsub
+  Index   nzlmax; // Current max size of lsub
+  Index   nzlumax; // Current max size of lusup
+  ScalarVector  ucol; // nonzero values of U ordered by columns 
+  IndexVector usub; // row indices of U columns in ucol
+  IndexVector xusub; // Pointers to the beginning of each column of U in ucol 
+  Index   nzumax; // Current max size of ucol
+  Index   n; // Number of columns in the matrix  
+  Index   num_expansions; 
+};
+
+// Values to set for performance
+struct perfvalues {
+  Index panel_size; // a panel consists of at most <panel_size> consecutive columns
+  Index relax; // To control degree of relaxing supernodes. If the number of nodes (columns) 
+                // in a subtree of the elimination tree is less than relax, this subtree is considered 
+                // as one supernode regardless of the row structures of those columns
+  Index maxsuper; // The maximum size for a supernode in complete LU
+  Index rowblk; // The minimum row dimension for 2-D blocking to be used;
+  Index colblk; // The minimum column dimension for 2-D blocking to be used;
+  Index fillfactor; // The estimated fills factors for L and U, compared with A
+}; 
+
+} // end namespace internal
+
+} // end namespace Eigen
+#endif // EIGEN_LU_STRUCTS
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
new file mode 100644
index 0000000000000000000000000000000000000000..adfc63a0e18d6e68a5f572e0e4cb1c828c9f6a30
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_SupernodalMatrix.h
@@ -0,0 +1,376 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSELU_SUPERNODAL_MATRIX_H
+#define EIGEN_SPARSELU_SUPERNODAL_MATRIX_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/** \ingroup SparseLU_Module
+ * \brief a class to manipulate the L supernodal factor from the SparseLU factorization
+ * 
+ * This class  contain the data to easily store 
+ * and manipulate the supernodes during the factorization and solution phase of Sparse LU. 
+ * Only the lower triangular matrix has supernodes.
+ * 
+ * NOTE : This class corresponds to the SCformat structure in SuperLU
+ * 
+ */
+/* TODO
+ * InnerIterator as for sparsematrix 
+ * SuperInnerIterator to iterate through all supernodes 
+ * Function for triangular solve
+ */
+template <typename Scalar_, typename StorageIndex_>
+class MappedSuperNodalMatrix
+{
+  public:
+    typedef Scalar_ Scalar;
+    typedef StorageIndex_ StorageIndex;
+    typedef Matrix<StorageIndex,Dynamic,1> IndexVector;
+    typedef Matrix<Scalar,Dynamic,1> ScalarVector;
+  public:
+    MappedSuperNodalMatrix()
+    {
+      
+    }
+    MappedSuperNodalMatrix(Index m, Index n,  ScalarVector& nzval, IndexVector& nzval_colptr, IndexVector& rowind,
+             IndexVector& rowind_colptr, IndexVector& col_to_sup, IndexVector& sup_to_col )
+    {
+      setInfos(m, n, nzval, nzval_colptr, rowind, rowind_colptr, col_to_sup, sup_to_col);
+    }
+    
+    ~MappedSuperNodalMatrix()
+    {
+      
+    }
+    /**
+     * Set appropriate pointers for the lower triangular supernodal matrix
+     * These infos are available at the end of the numerical factorization
+     * FIXME This class will be modified such that it can be use in the course 
+     * of the factorization.
+     */
+    void setInfos(Index m, Index n, ScalarVector& nzval, IndexVector& nzval_colptr, IndexVector& rowind,
+             IndexVector& rowind_colptr, IndexVector& col_to_sup, IndexVector& sup_to_col )
+    {
+      m_row = m;
+      m_col = n; 
+      m_nzval = nzval.data(); 
+      m_nzval_colptr = nzval_colptr.data(); 
+      m_rowind = rowind.data(); 
+      m_rowind_colptr = rowind_colptr.data(); 
+      m_nsuper = col_to_sup(n); 
+      m_col_to_sup = col_to_sup.data(); 
+      m_sup_to_col = sup_to_col.data(); 
+    }
+    
+    /**
+     * Number of rows
+     */
+    Index rows() const { return m_row; }
+    
+    /**
+     * Number of columns
+     */
+    Index cols() const { return m_col; }
+    
+    /**
+     * Return the array of nonzero values packed by column
+     * 
+     * The size is nnz
+     */
+    Scalar* valuePtr() {  return m_nzval; }
+    
+    const Scalar* valuePtr() const 
+    {
+      return m_nzval; 
+    }
+    /**
+     * Return the pointers to the beginning of each column in \ref valuePtr()
+     */
+    StorageIndex* colIndexPtr()
+    {
+      return m_nzval_colptr; 
+    }
+    
+    const StorageIndex* colIndexPtr() const
+    {
+      return m_nzval_colptr; 
+    }
+    
+    /**
+     * Return the array of compressed row indices of all supernodes
+     */
+    StorageIndex* rowIndex()  { return m_rowind; }
+    
+    const StorageIndex* rowIndex() const
+    {
+      return m_rowind; 
+    }
+    
+    /**
+     * Return the location in \em rowvaluePtr() which starts each column
+     */
+    StorageIndex* rowIndexPtr() { return m_rowind_colptr; }
+    
+    const StorageIndex* rowIndexPtr() const
+    {
+      return m_rowind_colptr; 
+    }
+    
+    /** 
+     * Return the array of column-to-supernode mapping 
+     */
+    StorageIndex* colToSup()  { return m_col_to_sup; }
+    
+    const StorageIndex* colToSup() const
+    {
+      return m_col_to_sup;       
+    }
+    /**
+     * Return the array of supernode-to-column mapping
+     */
+    StorageIndex* supToCol() { return m_sup_to_col; }
+    
+    const StorageIndex* supToCol() const
+    {
+      return m_sup_to_col;
+    }
+    
+    /**
+     * Return the number of supernodes
+     */
+    Index nsuper() const
+    {
+      return m_nsuper; 
+    }
+    
+    class InnerIterator; 
+    template<typename Dest>
+    void solveInPlace( MatrixBase<Dest>&X) const;
+    template<bool Conjugate, typename Dest>
+    void solveTransposedInPlace( MatrixBase<Dest>&X) const;
+
+    
+      
+      
+    
+  protected:
+    Index m_row; // Number of rows
+    Index m_col; // Number of columns
+    Index m_nsuper; // Number of supernodes
+    Scalar* m_nzval; //array of nonzero values packed by column
+    StorageIndex* m_nzval_colptr; //nzval_colptr[j] Stores the location in nzval[] which starts column j
+    StorageIndex* m_rowind; // Array of compressed row indices of rectangular supernodes
+    StorageIndex* m_rowind_colptr; //rowind_colptr[j] stores the location in rowind[] which starts column j
+    StorageIndex* m_col_to_sup; // col_to_sup[j] is the supernode number to which column j belongs
+    StorageIndex* m_sup_to_col; //sup_to_col[s] points to the starting column of the s-th supernode
+    
+  private :
+};
+
+/**
+  * \brief InnerIterator class to iterate over nonzero values of the current column in the supernodal matrix L
+  * 
+  */
+template<typename Scalar, typename StorageIndex>
+class MappedSuperNodalMatrix<Scalar,StorageIndex>::InnerIterator
+{
+  public:
+     InnerIterator(const MappedSuperNodalMatrix& mat, Index outer)
+      : m_matrix(mat),
+        m_outer(outer),
+        m_supno(mat.colToSup()[outer]),
+        m_idval(mat.colIndexPtr()[outer]),
+        m_startidval(m_idval),
+        m_endidval(mat.colIndexPtr()[outer+1]),
+        m_idrow(mat.rowIndexPtr()[mat.supToCol()[mat.colToSup()[outer]]]),
+        m_endidrow(mat.rowIndexPtr()[mat.supToCol()[mat.colToSup()[outer]]+1])
+    {}
+    inline InnerIterator& operator++()
+    { 
+      m_idval++; 
+      m_idrow++;
+      return *this;
+    }
+    inline Scalar value() const { return m_matrix.valuePtr()[m_idval]; }
+    
+    inline Scalar& valueRef() { return const_cast<Scalar&>(m_matrix.valuePtr()[m_idval]); }
+    
+    inline Index index() const { return m_matrix.rowIndex()[m_idrow]; }
+    inline Index row() const { return index(); }
+    inline Index col() const { return m_outer; }
+    
+    inline Index supIndex() const { return m_supno; }
+    
+    inline operator bool() const 
+    { 
+      return ( (m_idval < m_endidval) && (m_idval >= m_startidval)
+                && (m_idrow < m_endidrow) );
+    }
+    
+  protected:
+    const MappedSuperNodalMatrix& m_matrix; // Supernodal lower triangular matrix 
+    const Index m_outer;                    // Current column 
+    const Index m_supno;                    // Current SuperNode number
+    Index m_idval;                          // Index to browse the values in the current column
+    const Index m_startidval;               // Start of the column value
+    const Index m_endidval;                 // End of the column value
+    Index m_idrow;                          // Index to browse the row indices 
+    Index m_endidrow;                       // End index of row indices of the current column
+};
+
+/**
+ * \brief Solve with the supernode triangular matrix
+ * 
+ */
+template<typename Scalar, typename Index_>
+template<typename Dest>
+void MappedSuperNodalMatrix<Scalar,Index_>::solveInPlace( MatrixBase<Dest>&X) const
+{
+    /* Explicit type conversion as the Index type of MatrixBase<Dest> may be wider than Index */
+//    eigen_assert(X.rows() <= NumTraits<Index>::highest());
+//    eigen_assert(X.cols() <= NumTraits<Index>::highest());
+    Index n    = int(X.rows());
+    Index nrhs = Index(X.cols());
+    const Scalar * Lval = valuePtr();                 // Nonzero values 
+    Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor> work(n, nrhs);     // working vector
+    work.setZero();
+    for (Index k = 0; k <= nsuper(); k ++)
+    {
+      Index fsupc = supToCol()[k];                    // First column of the current supernode 
+      Index istart = rowIndexPtr()[fsupc];            // Pointer index to the subscript of the current column
+      Index nsupr = rowIndexPtr()[fsupc+1] - istart;  // Number of rows in the current supernode
+      Index nsupc = supToCol()[k+1] - fsupc;          // Number of columns in the current supernode
+      Index nrow = nsupr - nsupc;                     // Number of rows in the non-diagonal part of the supernode
+      Index irow;                                     //Current index row
+      
+      if (nsupc == 1 )
+      {
+        for (Index j = 0; j < nrhs; j++)
+        {
+          InnerIterator it(*this, fsupc);
+          ++it; // Skip the diagonal element
+          for (; it; ++it)
+          {
+            irow = it.row();
+            X(irow, j) -= X(fsupc, j) * it.value();
+          }
+        }
+      }
+      else
+      {
+        // The supernode has more than one column 
+        Index luptr = colIndexPtr()[fsupc]; 
+        Index lda = colIndexPtr()[fsupc+1] - luptr;
+        
+        // Triangular solve 
+        Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(Lval[luptr]), nsupc, nsupc, OuterStride<>(lda) );
+        typename Dest::RowsBlockXpr U = X.derived().middleRows(fsupc, nsupc);
+        U = A.template triangularView<UnitLower>().solve(U);        
+        // Matrix-vector product 
+        new (&A) Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > ( &(Lval[luptr+nsupc]), nrow, nsupc, OuterStride<>(lda) );
+        work.topRows(nrow).noalias() = A * U;
+        
+        //Begin Scatter 
+        for (Index j = 0; j < nrhs; j++)
+        {
+          Index iptr = istart + nsupc; 
+          for (Index i = 0; i < nrow; i++)
+          {
+            irow = rowIndex()[iptr]; 
+            X(irow, j) -= work(i, j); // Scatter operation
+            work(i, j) = Scalar(0); 
+            iptr++;
+          }
+        }
+      }
+    } 
+}
+
+template<typename Scalar, typename Index_>
+template<bool Conjugate, typename Dest>
+void MappedSuperNodalMatrix<Scalar,Index_>::solveTransposedInPlace( MatrixBase<Dest>&X) const
+{
+    using numext::conj;
+  Index n    = int(X.rows());
+  Index nrhs = Index(X.cols());
+  const Scalar * Lval = valuePtr();                 // Nonzero values
+  Matrix<Scalar,Dynamic,Dest::ColsAtCompileTime, ColMajor> work(n, nrhs);     // working vector
+  work.setZero();
+  for (Index k = nsuper(); k >= 0; k--)
+  {
+    Index fsupc = supToCol()[k];                    // First column of the current supernode
+    Index istart = rowIndexPtr()[fsupc];            // Pointer index to the subscript of the current column
+    Index nsupr = rowIndexPtr()[fsupc+1] - istart;  // Number of rows in the current supernode
+    Index nsupc = supToCol()[k+1] - fsupc;          // Number of columns in the current supernode
+    Index nrow = nsupr - nsupc;                     // Number of rows in the non-diagonal part of the supernode
+    Index irow;                                     //Current index row
+
+    if (nsupc == 1 )
+    {
+      for (Index j = 0; j < nrhs; j++)
+      {
+        InnerIterator it(*this, fsupc);
+        ++it; // Skip the diagonal element
+        for (; it; ++it)
+        {
+          irow = it.row();
+          X(fsupc,j) -= X(irow, j) * (Conjugate?conj(it.value()):it.value());
+        }
+      }
+    }
+    else
+    {
+      // The supernode has more than one column
+      Index luptr = colIndexPtr()[fsupc];
+      Index lda = colIndexPtr()[fsupc+1] - luptr;
+
+      //Begin Gather
+      for (Index j = 0; j < nrhs; j++)
+      {
+        Index iptr = istart + nsupc;
+        for (Index i = 0; i < nrow; i++)
+        {
+          irow = rowIndex()[iptr];
+          work.topRows(nrow)(i,j)= X(irow,j); // Gather operation
+          iptr++;
+        }
+      }
+
+      // Matrix-vector product with transposed submatrix
+      Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > A( &(Lval[luptr+nsupc]), nrow, nsupc, OuterStride<>(lda) );
+      typename Dest::RowsBlockXpr U = X.derived().middleRows(fsupc, nsupc);
+      if(Conjugate)
+        U = U - A.adjoint() * work.topRows(nrow);
+      else
+        U = U - A.transpose() * work.topRows(nrow);
+
+      // Triangular solve (of transposed diagonal block)
+      new (&A) Map<const Matrix<Scalar,Dynamic,Dynamic, ColMajor>, 0, OuterStride<> > ( &(Lval[luptr]), nsupc, nsupc, OuterStride<>(lda) );
+      if(Conjugate)
+        U = A.adjoint().template triangularView<UnitUpper>().solve(U);
+      else
+        U = A.transpose().template triangularView<UnitUpper>().solve(U);
+
+    }
+
+  }
+}
+
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_SPARSELU_MATRIX_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_Utils.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_Utils.h
new file mode 100644
index 0000000000000000000000000000000000000000..e399fed413222180c40376b8cc9cef5721ad7a02
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_Utils.h
@@ -0,0 +1,82 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+
+#ifndef EIGEN_SPARSELU_UTILS_H
+#define EIGEN_SPARSELU_UTILS_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/**
+ * \brief Count Nonzero elements in the factors
+ */
+template <typename Scalar, typename StorageIndex>
+void SparseLUImpl<Scalar,StorageIndex>::countnz(const Index n, Index& nnzL, Index& nnzU, GlobalLU_t& glu)
+{
+ nnzL = 0; 
+ nnzU = (glu.xusub)(n); 
+ Index nsuper = (glu.supno)(n); 
+ Index jlen; 
+ Index i, j, fsupc;
+ if (n <= 0 ) return; 
+ // For each supernode
+ for (i = 0; i <= nsuper; i++)
+ {
+   fsupc = glu.xsup(i); 
+   jlen = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); 
+   
+   for (j = fsupc; j < glu.xsup(i+1); j++)
+   {
+     nnzL += jlen; 
+     nnzU += j - fsupc + 1; 
+     jlen--; 
+   }
+ }
+}
+
+/**
+ * \brief Fix up the data storage lsub for L-subscripts. 
+ * 
+ * It removes the subscripts sets for structural pruning, 
+ * and applies permutation to the remaining subscripts
+ * 
+ */
+template <typename Scalar, typename StorageIndex>
+void SparseLUImpl<Scalar,StorageIndex>::fixupL(const Index n, const IndexVector& perm_r, GlobalLU_t& glu)
+{
+  Index fsupc, i, j, k, jstart; 
+  
+  StorageIndex nextl = 0; 
+  Index nsuper = (glu.supno)(n); 
+  
+  // For each supernode 
+  for (i = 0; i <= nsuper; i++)
+  {
+    fsupc = glu.xsup(i); 
+    jstart = glu.xlsub(fsupc); 
+    glu.xlsub(fsupc) = nextl; 
+    for (j = jstart; j < glu.xlsub(fsupc + 1); j++)
+    {
+      glu.lsub(nextl) = perm_r(glu.lsub(j)); // Now indexed into P*A
+      nextl++;
+    }
+    for (k = fsupc+1; k < glu.xsup(i+1); k++)
+      glu.xlsub(k) = nextl; // other columns in supernode i
+  }
+  
+  glu.xlsub(n) = nextl; 
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+#endif // EIGEN_SPARSELU_UTILS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_column_bmod.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_column_bmod.h
new file mode 100644
index 0000000000000000000000000000000000000000..d5c29b353b5d6fbf6ddcffe38fa06f076c6ba4fb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_column_bmod.h
@@ -0,0 +1,183 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* 
+ 
+ * NOTE: This file is the modified version of xcolumn_bmod.c file in SuperLU 
+ 
+ * -- SuperLU routine (version 3.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * October 15, 2003
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSELU_COLUMN_BMOD_H
+#define SPARSELU_COLUMN_BMOD_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+/**
+ * \brief Performs numeric block updates (sup-col) in topological order
+ * 
+ * \param jcol current column to update
+ * \param nseg Number of segments in the U part
+ * \param dense Store the full representation of the column
+ * \param tempv working array 
+ * \param segrep segment representative ...
+ * \param repfnz ??? First nonzero column in each row ???  ...
+ * \param fpanelc First column in the current panel
+ * \param glu Global LU data. 
+ * \return 0 - successful return 
+ *         > 0 - number of bytes allocated when run out of space
+ * 
+ */
+template <typename Scalar, typename StorageIndex>
+Index SparseLUImpl<Scalar,StorageIndex>::column_bmod(const Index jcol, const Index nseg, BlockScalarVector dense, ScalarVector& tempv,
+                                                     BlockIndexVector segrep, BlockIndexVector repfnz, Index fpanelc, GlobalLU_t& glu)
+{
+  Index  jsupno, k, ksub, krep, ksupno; 
+  Index lptr, nrow, isub, irow, nextlu, new_next, ufirst; 
+  Index fsupc, nsupc, nsupr, luptr, kfnz, no_zeros; 
+  /* krep = representative of current k-th supernode
+    * fsupc =  first supernodal column
+    * nsupc = number of columns in a supernode
+    * nsupr = number of rows in a supernode
+    * luptr = location of supernodal LU-block in storage
+    * kfnz = first nonz in the k-th supernodal segment
+    * no_zeros = no lf leading zeros in a supernodal U-segment
+    */
+  
+  jsupno = glu.supno(jcol);
+  // For each nonzero supernode segment of U[*,j] in topological order 
+  k = nseg - 1; 
+  Index d_fsupc; // distance between the first column of the current panel and the 
+               // first column of the current snode
+  Index fst_col; // First column within small LU update
+  Index segsize; 
+  for (ksub = 0; ksub < nseg; ksub++)
+  {
+    krep = segrep(k); k--; 
+    ksupno = glu.supno(krep); 
+    if (jsupno != ksupno )
+    {
+      // outside the rectangular supernode 
+      fsupc = glu.xsup(ksupno); 
+      fst_col = (std::max)(fsupc, fpanelc); 
+      
+      // Distance from the current supernode to the current panel; 
+      // d_fsupc = 0 if fsupc > fpanelc
+      d_fsupc = fst_col - fsupc; 
+      
+      luptr = glu.xlusup(fst_col) + d_fsupc; 
+      lptr = glu.xlsub(fsupc) + d_fsupc; 
+      
+      kfnz = repfnz(krep); 
+      kfnz = (std::max)(kfnz, fpanelc); 
+      
+      segsize = krep - kfnz + 1; 
+      nsupc = krep - fst_col + 1; 
+      nsupr = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); 
+      nrow = nsupr - d_fsupc - nsupc;
+      Index lda = glu.xlusup(fst_col+1) - glu.xlusup(fst_col);
+      
+      
+      // Perform a triangular solver and block update, 
+      // then scatter the result of sup-col update to dense
+      no_zeros = kfnz - fst_col; 
+      if(segsize==1)
+        LU_kernel_bmod<1>::run(segsize, dense, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
+      else
+        LU_kernel_bmod<Dynamic>::run(segsize, dense, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
+    } // end if jsupno 
+  } // end for each segment
+  
+  // Process the supernodal portion of  L\U[*,j]
+  nextlu = glu.xlusup(jcol); 
+  fsupc = glu.xsup(jsupno);
+  
+  // copy the SPA dense into L\U[*,j]
+  Index mem; 
+  new_next = nextlu + glu.xlsub(fsupc + 1) - glu.xlsub(fsupc); 
+  Index offset = internal::first_multiple<Index>(new_next, internal::packet_traits<Scalar>::size) - new_next;
+  if(offset)
+    new_next += offset;
+  while (new_next > glu.nzlumax )
+  {
+    mem = memXpand<ScalarVector>(glu.lusup, glu.nzlumax, nextlu, LUSUP, glu.num_expansions);  
+    if (mem) return mem; 
+  }
+  
+  for (isub = glu.xlsub(fsupc); isub < glu.xlsub(fsupc+1); isub++)
+  {
+    irow = glu.lsub(isub);
+    glu.lusup(nextlu) = dense(irow);
+    dense(irow) = Scalar(0.0); 
+    ++nextlu; 
+  }
+  
+  if(offset)
+  {
+    glu.lusup.segment(nextlu,offset).setZero();
+    nextlu += offset;
+  }
+  glu.xlusup(jcol + 1) = StorageIndex(nextlu);  // close L\U(*,jcol); 
+  
+  /* For more updates within the panel (also within the current supernode),
+   * should start from the first column of the panel, or the first column
+   * of the supernode, whichever is bigger. There are two cases:
+   *  1) fsupc < fpanelc, then fst_col <-- fpanelc
+   *  2) fsupc >= fpanelc, then fst_col <-- fsupc
+   */
+  fst_col = (std::max)(fsupc, fpanelc); 
+  
+  if (fst_col  < jcol)
+  {
+    // Distance between the current supernode and the current panel
+    // d_fsupc = 0 if fsupc >= fpanelc
+    d_fsupc = fst_col - fsupc; 
+    
+    lptr = glu.xlsub(fsupc) + d_fsupc; 
+    luptr = glu.xlusup(fst_col) + d_fsupc; 
+    nsupr = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); // leading dimension
+    nsupc = jcol - fst_col; // excluding jcol 
+    nrow = nsupr - d_fsupc - nsupc; 
+    
+    // points to the beginning of jcol in snode L\U(jsupno) 
+    ufirst = glu.xlusup(jcol) + d_fsupc; 
+    Index lda = glu.xlusup(jcol+1) - glu.xlusup(jcol);
+    MappedMatrixBlock A( &(glu.lusup.data()[luptr]), nsupc, nsupc, OuterStride<>(lda) );
+    VectorBlock<ScalarVector> u(glu.lusup, ufirst, nsupc); 
+    u = A.template triangularView<UnitLower>().solve(u); 
+    
+    new (&A) MappedMatrixBlock ( &(glu.lusup.data()[luptr+nsupc]), nrow, nsupc, OuterStride<>(lda) );
+    VectorBlock<ScalarVector> l(glu.lusup, ufirst+nsupc, nrow); 
+    l.noalias() -= A * u;
+    
+  } // End if fst_col
+  return 0; 
+}
+
+} // end namespace internal
+} // end namespace Eigen
+
+#endif // SPARSELU_COLUMN_BMOD_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_column_dfs.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_column_dfs.h
new file mode 100644
index 0000000000000000000000000000000000000000..be4cfd1a9e7558409cbbcaa6efe78f6053b7b2db
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_column_dfs.h
@@ -0,0 +1,181 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* 
+ 
+ * NOTE: This file is the modified version of [s,d,c,z]column_dfs.c file in SuperLU 
+ 
+ * -- SuperLU routine (version 2.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * November 15, 1997
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSELU_COLUMN_DFS_H
+#define SPARSELU_COLUMN_DFS_H
+
+template <typename Scalar, typename StorageIndex> class SparseLUImpl;
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename IndexVector, typename ScalarVector>
+struct column_dfs_traits : no_assignment_operator
+{
+  typedef typename ScalarVector::Scalar Scalar;
+  typedef typename IndexVector::Scalar StorageIndex;
+  column_dfs_traits(Index jcol, Index& jsuper, typename SparseLUImpl<Scalar, StorageIndex>::GlobalLU_t& glu, SparseLUImpl<Scalar, StorageIndex>& luImpl)
+   : m_jcol(jcol), m_jsuper_ref(jsuper), m_glu(glu), m_luImpl(luImpl)
+ {}
+  bool update_segrep(Index /*krep*/, Index /*jj*/)
+  {
+    return true;
+  }
+  void mem_expand(IndexVector& lsub, Index& nextl, Index chmark)
+  {
+    if (nextl >= m_glu.nzlmax)
+      m_luImpl.memXpand(lsub, m_glu.nzlmax, nextl, LSUB, m_glu.num_expansions); 
+    if (chmark != (m_jcol-1)) m_jsuper_ref = emptyIdxLU;
+  }
+  enum { ExpandMem = true };
+  
+  Index m_jcol;
+  Index& m_jsuper_ref;
+  typename SparseLUImpl<Scalar, StorageIndex>::GlobalLU_t& m_glu;
+  SparseLUImpl<Scalar, StorageIndex>& m_luImpl;
+};
+
+
+/**
+ * \brief Performs a symbolic factorization on column jcol and decide the supernode boundary
+ * 
+ * A supernode representative is the last column of a supernode.
+ * The nonzeros in U[*,j] are segments that end at supernodes representatives. 
+ * The routine returns a list of the supernodal representatives 
+ * in topological order of the dfs that generates them. 
+ * The location of the first nonzero in each supernodal segment 
+ * (supernodal entry location) is also returned. 
+ * 
+ * \param m number of rows in the matrix
+ * \param jcol Current column 
+ * \param perm_r Row permutation
+ * \param maxsuper  Maximum number of column allowed in a supernode
+ * \param [in,out] nseg Number of segments in current U[*,j] - new segments appended
+ * \param lsub_col defines the rhs vector to start the dfs
+ * \param [in,out] segrep Segment representatives - new segments appended 
+ * \param repfnz  First nonzero location in each row
+ * \param xprune 
+ * \param marker  marker[i] == jj, if i was visited during dfs of current column jj;
+ * \param parent
+ * \param xplore working array
+ * \param glu global LU data 
+ * \return 0 success
+ *         > 0 number of bytes allocated when run out of space
+ * 
+ */
+template <typename Scalar, typename StorageIndex>
+Index SparseLUImpl<Scalar,StorageIndex>::column_dfs(const Index m, const Index jcol, IndexVector& perm_r, Index maxsuper, Index& nseg,
+                                                    BlockIndexVector lsub_col, IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune,
+                                                    IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu)
+{
+  
+  Index jsuper = glu.supno(jcol); 
+  Index nextl = glu.xlsub(jcol); 
+  VectorBlock<IndexVector> marker2(marker, 2*m, m); 
+  
+  
+  column_dfs_traits<IndexVector, ScalarVector> traits(jcol, jsuper, glu, *this);
+  
+  // For each nonzero in A(*,jcol) do dfs 
+  for (Index k = 0; ((k < m) ? lsub_col[k] != emptyIdxLU : false) ; k++)
+  {
+    Index krow = lsub_col(k); 
+    lsub_col(k) = emptyIdxLU; 
+    Index kmark = marker2(krow); 
+    
+    // krow was visited before, go to the next nonz; 
+    if (kmark == jcol) continue;
+    
+    dfs_kernel(StorageIndex(jcol), perm_r, nseg, glu.lsub, segrep, repfnz, xprune, marker2, parent,
+                   xplore, glu, nextl, krow, traits);
+  } // for each nonzero ... 
+  
+  Index fsupc;
+  StorageIndex nsuper = glu.supno(jcol);
+  StorageIndex jcolp1 = StorageIndex(jcol) + 1;
+  Index jcolm1 = jcol - 1;
+  
+  // check to see if j belongs in the same supernode as j-1
+  if ( jcol == 0 )
+  { // Do nothing for column 0 
+    nsuper = glu.supno(0) = 0 ;
+  }
+  else 
+  {
+    fsupc = glu.xsup(nsuper); 
+    StorageIndex jptr = glu.xlsub(jcol); // Not yet compressed
+    StorageIndex jm1ptr = glu.xlsub(jcolm1); 
+    
+    // Use supernodes of type T2 : see SuperLU paper
+    if ( (nextl-jptr != jptr-jm1ptr-1) ) jsuper = emptyIdxLU;
+    
+    // Make sure the number of columns in a supernode doesn't
+    // exceed threshold
+    if ( (jcol - fsupc) >= maxsuper) jsuper = emptyIdxLU; 
+    
+    /* If jcol starts a new supernode, reclaim storage space in
+     * glu.lsub from previous supernode. Note we only store 
+     * the subscript set of the first and last columns of 
+     * a supernode. (first for num values, last for pruning)
+     */
+    if (jsuper == emptyIdxLU)
+    { // starts a new supernode 
+      if ( (fsupc < jcolm1-1) ) 
+      { // >= 3 columns in nsuper
+        StorageIndex ito = glu.xlsub(fsupc+1);
+        glu.xlsub(jcolm1) = ito; 
+        StorageIndex istop = ito + jptr - jm1ptr; 
+        xprune(jcolm1) = istop; // initialize xprune(jcol-1)
+        glu.xlsub(jcol) = istop; 
+        
+        for (StorageIndex ifrom = jm1ptr; ifrom < nextl; ++ifrom, ++ito)
+          glu.lsub(ito) = glu.lsub(ifrom); 
+        nextl = ito;  // = istop + length(jcol)
+      }
+      nsuper++; 
+      glu.supno(jcol) = nsuper; 
+    } // if a new supernode 
+  } // end else:  jcol > 0
+  
+  // Tidy up the pointers before exit
+  glu.xsup(nsuper+1) = jcolp1; 
+  glu.supno(jcolp1) = nsuper; 
+  xprune(jcol) = StorageIndex(nextl);  // Initialize upper bound for pruning
+  glu.xlsub(jcolp1) = StorageIndex(nextl); 
+  
+  return 0; 
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
new file mode 100644
index 0000000000000000000000000000000000000000..e06b2a03a6e6fdad4835be19a5eec9bbce55991f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_copy_to_ucol.h
@@ -0,0 +1,109 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+/* 
+ 
+ * NOTE: This file is the modified version of [s,d,c,z]copy_to_ucol.c file in SuperLU 
+ 
+ * -- SuperLU routine (version 2.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * November 15, 1997
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSELU_COPY_TO_UCOL_H
+#define SPARSELU_COPY_TO_UCOL_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/**
+ * \brief Performs numeric block updates (sup-col) in topological order
+ * 
+ * \param jcol current column to update
+ * \param nseg Number of segments in the U part
+ * \param segrep segment representative ...
+ * \param repfnz First nonzero column in each row  ...
+ * \param perm_r Row permutation 
+ * \param dense Store the full representation of the column
+ * \param glu Global LU data. 
+ * \return 0 - successful return 
+ *         > 0 - number of bytes allocated when run out of space
+ * 
+ */
+template <typename Scalar, typename StorageIndex>
+Index SparseLUImpl<Scalar,StorageIndex>::copy_to_ucol(const Index jcol, const Index nseg, IndexVector& segrep,
+                                                      BlockIndexVector repfnz ,IndexVector& perm_r, BlockScalarVector dense, GlobalLU_t& glu)
+{  
+  Index ksub, krep, ksupno; 
+    
+  Index jsupno = glu.supno(jcol);
+  
+  // For each nonzero supernode segment of U[*,j] in topological order 
+  Index k = nseg - 1, i; 
+  StorageIndex nextu = glu.xusub(jcol); 
+  Index kfnz, isub, segsize; 
+  Index new_next,irow; 
+  Index fsupc, mem; 
+  for (ksub = 0; ksub < nseg; ksub++)
+  {
+    krep = segrep(k); k--; 
+    ksupno = glu.supno(krep); 
+    if (jsupno != ksupno ) // should go into ucol(); 
+    {
+      kfnz = repfnz(krep); 
+      if (kfnz != emptyIdxLU)
+      { // Nonzero U-segment 
+        fsupc = glu.xsup(ksupno); 
+        isub = glu.xlsub(fsupc) + kfnz - fsupc; 
+        segsize = krep - kfnz + 1; 
+        new_next = nextu + segsize; 
+        while (new_next > glu.nzumax) 
+        {
+          mem = memXpand<ScalarVector>(glu.ucol, glu.nzumax, nextu, UCOL, glu.num_expansions); 
+          if (mem) return mem; 
+          mem = memXpand<IndexVector>(glu.usub, glu.nzumax, nextu, USUB, glu.num_expansions); 
+          if (mem) return mem; 
+          
+        }
+        
+        for (i = 0; i < segsize; i++)
+        {
+          irow = glu.lsub(isub); 
+          glu.usub(nextu) = perm_r(irow); // Unlike the L part, the U part is stored in its final order
+          glu.ucol(nextu) = dense(irow); 
+          dense(irow) = Scalar(0.0); 
+          nextu++;
+          isub++;
+        }
+        
+      } // end nonzero U-segment 
+      
+    } // end if jsupno 
+    
+  } // end for each segment
+  glu.xusub(jcol + 1) = nextu; // close U(*,jcol)
+  return 0; 
+}
+
+} // namespace internal
+} // end namespace Eigen
+
+#endif // SPARSELU_COPY_TO_UCOL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
new file mode 100644
index 0000000000000000000000000000000000000000..2a8d80b24a4ec0179948388f0902b9c7df316410
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_heap_relax_snode.h
@@ -0,0 +1,123 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* This file is a modified version of heap_relax_snode.c file in SuperLU
+ * -- SuperLU routine (version 3.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * October 15, 2003
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+
+#ifndef SPARSELU_HEAP_RELAX_SNODE_H
+#define SPARSELU_HEAP_RELAX_SNODE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/** 
+ * \brief Identify the initial relaxed supernodes
+ * 
+ * This routine applied to a symmetric elimination tree. 
+ * It assumes that the matrix has been reordered according to the postorder of the etree
+ * \param n The number of columns
+ * \param et elimination tree 
+ * \param relax_columns Maximum number of columns allowed in a relaxed snode 
+ * \param descendants Number of descendants of each node in the etree
+ * \param relax_end last column in a supernode
+ */
+template <typename Scalar, typename StorageIndex>
+void SparseLUImpl<Scalar,StorageIndex>::heap_relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end)
+{
+  
+  // The etree may not be postordered, but its heap ordered  
+  IndexVector post;
+  internal::treePostorder(StorageIndex(n), et, post); // Post order etree
+  IndexVector inv_post(n+1); 
+  for (StorageIndex i = 0; i < n+1; ++i) inv_post(post(i)) = i; // inv_post = post.inverse()???
+  
+  // Renumber etree in postorder 
+  IndexVector iwork(n);
+  IndexVector et_save(n+1);
+  for (Index i = 0; i < n; ++i)
+  {
+    iwork(post(i)) = post(et(i));
+  }
+  et_save = et; // Save the original etree
+  et = iwork; 
+  
+  // compute the number of descendants of each node in the etree
+  relax_end.setConstant(emptyIdxLU);
+  Index j, parent; 
+  descendants.setZero();
+  for (j = 0; j < n; j++) 
+  {
+    parent = et(j);
+    if (parent != n) // not the dummy root
+      descendants(parent) += descendants(j) + 1;
+  }
+  // Identify the relaxed supernodes by postorder traversal of the etree
+  Index snode_start; // beginning of a snode 
+  StorageIndex k;
+  StorageIndex l; 
+  for (j = 0; j < n; )
+  {
+    parent = et(j);
+    snode_start = j; 
+    while ( parent != n && descendants(parent) < relax_columns ) 
+    {
+      j = parent; 
+      parent = et(j);
+    }
+    // Found a supernode in postordered etree, j is the last column 
+    k = StorageIndex(n);
+    for (Index i = snode_start; i <= j; ++i)
+      k = (std::min)(k, inv_post(i));
+    l = inv_post(j);
+    if ( (l - k) == (j - snode_start) )  // Same number of columns in the snode
+    {
+      // This is also a supernode in the original etree
+      relax_end(k) = l; // Record last column 
+    }
+    else 
+    {
+      for (Index i = snode_start; i <= j; ++i) 
+      {
+        l = inv_post(i);
+        if (descendants(i) == 0) 
+        {
+          relax_end(l) = l;
+        }
+      }
+    }
+    j++;
+    // Search for a new leaf
+    while (descendants(j) != 0 && j < n) j++;
+  } // End postorder traversal of the etree
+  
+  // Recover the original etree
+  et = et_save; 
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+#endif // SPARSELU_HEAP_RELAX_SNODE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_kernel_bmod.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
new file mode 100644
index 0000000000000000000000000000000000000000..831b938905d40c873f1531c8e5ad3bdc99a17520
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_kernel_bmod.h
@@ -0,0 +1,131 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef SPARSELU_KERNEL_BMOD_H
+#define SPARSELU_KERNEL_BMOD_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+  
+template <int SegSizeAtCompileTime> struct LU_kernel_bmod
+{
+  /** \internal
+    * \brief Performs numeric block updates from a given supernode to a single column
+    *
+    * \param segsize Size of the segment (and blocks ) to use for updates
+    * \param[in,out] dense Packed values of the original matrix
+    * \param tempv temporary vector to use for updates
+    * \param lusup array containing the supernodes
+    * \param lda Leading dimension in the supernode
+    * \param nrow Number of rows in the rectangular part of the supernode
+    * \param lsub compressed row subscripts of supernodes
+    * \param lptr pointer to the first column of the current supernode in lsub
+    * \param no_zeros Number of nonzeros elements before the diagonal part of the supernode
+    */
+  template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
+  static EIGEN_DONT_INLINE void run(const Index segsize, BlockScalarVector& dense, ScalarVector& tempv, ScalarVector& lusup, Index& luptr, const Index lda,
+                                    const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros);
+};
+
+template <int SegSizeAtCompileTime>
+template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
+EIGEN_DONT_INLINE void LU_kernel_bmod<SegSizeAtCompileTime>::run(const Index segsize, BlockScalarVector& dense, ScalarVector& tempv, ScalarVector& lusup, Index& luptr, const Index lda,
+                                                                  const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros)
+{
+  typedef typename ScalarVector::Scalar Scalar;
+  // First, copy U[*,j] segment from dense(*) to tempv(*)
+  // The result of triangular solve is in tempv[*]; 
+    // The result of matric-vector update is in dense[*]
+  Index isub = lptr + no_zeros; 
+  Index i;
+  Index irow;
+  for (i = 0; i < ((SegSizeAtCompileTime==Dynamic)?segsize:SegSizeAtCompileTime); i++)
+  {
+    irow = lsub(isub); 
+    tempv(i) = dense(irow); 
+    ++isub; 
+  }
+  // Dense triangular solve -- start effective triangle
+  luptr += lda * no_zeros + no_zeros; 
+  // Form Eigen matrix and vector 
+  Map<Matrix<Scalar,SegSizeAtCompileTime,SegSizeAtCompileTime, ColMajor>, 0, OuterStride<> > A( &(lusup.data()[luptr]), segsize, segsize, OuterStride<>(lda) );
+  Map<Matrix<Scalar,SegSizeAtCompileTime,1> > u(tempv.data(), segsize);
+  
+  u = A.template triangularView<UnitLower>().solve(u); 
+  
+  // Dense matrix-vector product y <-- B*x 
+  luptr += segsize;
+  const Index PacketSize = internal::packet_traits<Scalar>::size;
+  Index ldl = internal::first_multiple(nrow, PacketSize);
+  Map<Matrix<Scalar,Dynamic,SegSizeAtCompileTime, ColMajor>, 0, OuterStride<> > B( &(lusup.data()[luptr]), nrow, segsize, OuterStride<>(lda) );
+  Index aligned_offset = internal::first_default_aligned(tempv.data()+segsize, PacketSize);
+  Index aligned_with_B_offset = (PacketSize-internal::first_default_aligned(B.data(), PacketSize))%PacketSize;
+  Map<Matrix<Scalar,Dynamic,1>, 0, OuterStride<> > l(tempv.data()+segsize+aligned_offset+aligned_with_B_offset, nrow, OuterStride<>(ldl) );
+  
+  l.noalias() = B * u;
+  
+  // Scatter tempv[] into SPA dense[] as a temporary storage 
+  isub = lptr + no_zeros;
+  for (i = 0; i < ((SegSizeAtCompileTime==Dynamic)?segsize:SegSizeAtCompileTime); i++)
+  {
+    irow = lsub(isub++); 
+    dense(irow) = tempv(i);
+  }
+  
+  // Scatter l into SPA dense[]
+  for (i = 0; i < nrow; i++)
+  {
+    irow = lsub(isub++); 
+    dense(irow) -= l(i);
+  } 
+}
+
+template <> struct LU_kernel_bmod<1>
+{
+  template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
+  static EIGEN_DONT_INLINE void run(const Index /*segsize*/, BlockScalarVector& dense, ScalarVector& /*tempv*/, ScalarVector& lusup, Index& luptr,
+                                    const Index lda, const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros);
+};
+
+
+template <typename BlockScalarVector, typename ScalarVector, typename IndexVector>
+EIGEN_DONT_INLINE void LU_kernel_bmod<1>::run(const Index /*segsize*/, BlockScalarVector& dense, ScalarVector& /*tempv*/, ScalarVector& lusup, Index& luptr,
+                                              const Index lda, const Index nrow, IndexVector& lsub, const Index lptr, const Index no_zeros)
+{
+  typedef typename ScalarVector::Scalar Scalar;
+  typedef typename IndexVector::Scalar StorageIndex;
+  Scalar f = dense(lsub(lptr + no_zeros));
+  luptr += lda * no_zeros + no_zeros + 1;
+  const Scalar* a(lusup.data() + luptr);
+  const StorageIndex*  irow(lsub.data()+lptr + no_zeros + 1);
+  Index i = 0;
+  for (; i+1 < nrow; i+=2)
+  {
+    Index i0 = *(irow++);
+    Index i1 = *(irow++);
+    Scalar a0 = *(a++);
+    Scalar a1 = *(a++);
+    Scalar d0 = dense.coeff(i0);
+    Scalar d1 = dense.coeff(i1);
+    d0 -= f*a0;
+    d1 -= f*a1;
+    dense.coeffRef(i0) = d0;
+    dense.coeffRef(i1) = d1;
+  }
+  if(i<nrow)
+    dense.coeffRef(*(irow++)) -= f * *(a++);
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+#endif // SPARSELU_KERNEL_BMOD_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_panel_bmod.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_panel_bmod.h
new file mode 100644
index 0000000000000000000000000000000000000000..fd1243d72273e8dd905fb4bea79e581e32839032
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_panel_bmod.h
@@ -0,0 +1,224 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* 
+ 
+ * NOTE: This file is the modified version of [s,d,c,z]panel_bmod.c file in SuperLU 
+ 
+ * -- SuperLU routine (version 3.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * October 15, 2003
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSELU_PANEL_BMOD_H
+#define SPARSELU_PANEL_BMOD_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/**
+ * \brief Performs numeric block updates (sup-panel) in topological order.
+ * 
+ * Before entering this routine, the original nonzeros in the panel
+ * were already copied into the spa[m,w]
+ * 
+ * \param m number of rows in the matrix
+ * \param w Panel size
+ * \param jcol Starting  column of the panel
+ * \param nseg Number of segments in the U part
+ * \param dense Store the full representation of the panel 
+ * \param tempv working array 
+ * \param segrep segment representative... first row in the segment
+ * \param repfnz First nonzero rows
+ * \param glu Global LU data. 
+ * 
+ * 
+ */
+template <typename Scalar, typename StorageIndex>
+void SparseLUImpl<Scalar,StorageIndex>::panel_bmod(const Index m, const Index w, const Index jcol, 
+                                            const Index nseg, ScalarVector& dense, ScalarVector& tempv,
+                                            IndexVector& segrep, IndexVector& repfnz, GlobalLU_t& glu)
+{
+  
+  Index ksub,jj,nextl_col; 
+  Index fsupc, nsupc, nsupr, nrow; 
+  Index krep, kfnz; 
+  Index lptr; // points to the row subscripts of a supernode 
+  Index luptr; // ...
+  Index segsize,no_zeros ; 
+  // For each nonz supernode segment of U[*,j] in topological order
+  Index k = nseg - 1; 
+  const Index PacketSize = internal::packet_traits<Scalar>::size;
+  
+  for (ksub = 0; ksub < nseg; ksub++)
+  { // For each updating supernode
+    /* krep = representative of current k-th supernode
+     * fsupc =  first supernodal column
+     * nsupc = number of columns in a supernode
+     * nsupr = number of rows in a supernode
+     */
+    krep = segrep(k); k--; 
+    fsupc = glu.xsup(glu.supno(krep)); 
+    nsupc = krep - fsupc + 1; 
+    nsupr = glu.xlsub(fsupc+1) - glu.xlsub(fsupc); 
+    nrow = nsupr - nsupc; 
+    lptr = glu.xlsub(fsupc); 
+    
+    // loop over the panel columns to detect the actual number of columns and rows
+    Index u_rows = 0;
+    Index u_cols = 0;
+    for (jj = jcol; jj < jcol + w; jj++)
+    {
+      nextl_col = (jj-jcol) * m; 
+      VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row
+      
+      kfnz = repfnz_col(krep); 
+      if ( kfnz == emptyIdxLU ) 
+        continue; // skip any zero segment
+      
+      segsize = krep - kfnz + 1;
+      u_cols++;
+      u_rows = (std::max)(segsize,u_rows);
+    }
+    
+    if(nsupc >= 2)
+    { 
+      Index ldu = internal::first_multiple<Index>(u_rows, PacketSize);
+      Map<ScalarMatrix, Aligned,  OuterStride<> > U(tempv.data(), u_rows, u_cols, OuterStride<>(ldu));
+      
+      // gather U
+      Index u_col = 0;
+      for (jj = jcol; jj < jcol + w; jj++)
+      {
+        nextl_col = (jj-jcol) * m; 
+        VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row
+        VectorBlock<ScalarVector> dense_col(dense, nextl_col, m); // Scatter/gather entire matrix column from/to here
+        
+        kfnz = repfnz_col(krep); 
+        if ( kfnz == emptyIdxLU ) 
+          continue; // skip any zero segment
+        
+        segsize = krep - kfnz + 1;
+        luptr = glu.xlusup(fsupc);    
+        no_zeros = kfnz - fsupc; 
+        
+        Index isub = lptr + no_zeros;
+        Index off = u_rows-segsize;
+        for (Index i = 0; i < off; i++) U(i,u_col) = 0;
+        for (Index i = 0; i < segsize; i++)
+        {
+          Index irow = glu.lsub(isub); 
+          U(i+off,u_col) = dense_col(irow); 
+          ++isub; 
+        }
+        u_col++;
+      }
+      // solve U = A^-1 U
+      luptr = glu.xlusup(fsupc);
+      Index lda = glu.xlusup(fsupc+1) - glu.xlusup(fsupc);
+      no_zeros = (krep - u_rows + 1) - fsupc;
+      luptr += lda * no_zeros + no_zeros;
+      MappedMatrixBlock A(glu.lusup.data()+luptr, u_rows, u_rows, OuterStride<>(lda) );
+      U = A.template triangularView<UnitLower>().solve(U);
+      
+      // update
+      luptr += u_rows;
+      MappedMatrixBlock B(glu.lusup.data()+luptr, nrow, u_rows, OuterStride<>(lda) );
+      eigen_assert(tempv.size()>w*ldu + nrow*w + 1);
+      
+      Index ldl = internal::first_multiple<Index>(nrow, PacketSize);
+      Index offset = (PacketSize-internal::first_default_aligned(B.data(), PacketSize)) % PacketSize;
+      MappedMatrixBlock L(tempv.data()+w*ldu+offset, nrow, u_cols, OuterStride<>(ldl));
+      
+      L.noalias() = B * U;
+      
+      // scatter U and L
+      u_col = 0;
+      for (jj = jcol; jj < jcol + w; jj++)
+      {
+        nextl_col = (jj-jcol) * m; 
+        VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row
+        VectorBlock<ScalarVector> dense_col(dense, nextl_col, m); // Scatter/gather entire matrix column from/to here
+        
+        kfnz = repfnz_col(krep); 
+        if ( kfnz == emptyIdxLU ) 
+          continue; // skip any zero segment
+        
+        segsize = krep - kfnz + 1;
+        no_zeros = kfnz - fsupc; 
+        Index isub = lptr + no_zeros;
+        
+        Index off = u_rows-segsize;
+        for (Index i = 0; i < segsize; i++)
+        {
+          Index irow = glu.lsub(isub++); 
+          dense_col(irow) = U.coeff(i+off,u_col);
+          U.coeffRef(i+off,u_col) = 0;
+        }
+        
+        // Scatter l into SPA dense[]
+        for (Index i = 0; i < nrow; i++)
+        {
+          Index irow = glu.lsub(isub++); 
+          dense_col(irow) -= L.coeff(i,u_col);
+          L.coeffRef(i,u_col) = 0;
+        }
+        u_col++;
+      }
+    }
+    else // level 2 only
+    {
+      // Sequence through each column in the panel
+      for (jj = jcol; jj < jcol + w; jj++)
+      {
+        nextl_col = (jj-jcol) * m; 
+        VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero column index for each row
+        VectorBlock<ScalarVector> dense_col(dense, nextl_col, m); // Scatter/gather entire matrix column from/to here
+        
+        kfnz = repfnz_col(krep); 
+        if ( kfnz == emptyIdxLU ) 
+          continue; // skip any zero segment
+        
+        segsize = krep - kfnz + 1;
+        luptr = glu.xlusup(fsupc);
+        
+        Index lda = glu.xlusup(fsupc+1)-glu.xlusup(fsupc);// nsupr
+        
+        // Perform a trianglar solve and block update, 
+        // then scatter the result of sup-col update to dense[]
+        no_zeros = kfnz - fsupc; 
+              if(segsize==1)  LU_kernel_bmod<1>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
+        else  if(segsize==2)  LU_kernel_bmod<2>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
+        else  if(segsize==3)  LU_kernel_bmod<3>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros);
+        else                  LU_kernel_bmod<Dynamic>::run(segsize, dense_col, tempv, glu.lusup, luptr, lda, nrow, glu.lsub, lptr, no_zeros); 
+      } // End for each column in the panel 
+    }
+    
+  } // End for each updating supernode
+} // end panel bmod
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // SPARSELU_PANEL_BMOD_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_panel_dfs.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_panel_dfs.h
new file mode 100644
index 0000000000000000000000000000000000000000..c3ff0135d3700097eee9f491e734ec9bfcccde99
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_panel_dfs.h
@@ -0,0 +1,260 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* 
+ 
+ * NOTE: This file is the modified version of [s,d,c,z]panel_dfs.c file in SuperLU 
+ 
+ * -- SuperLU routine (version 2.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * November 15, 1997
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSELU_PANEL_DFS_H
+#define SPARSELU_PANEL_DFS_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+  
+template<typename IndexVector>
+struct panel_dfs_traits
+{
+  typedef typename IndexVector::Scalar StorageIndex;
+  panel_dfs_traits(Index jcol, StorageIndex* marker)
+    : m_jcol(jcol), m_marker(marker)
+  {}
+  bool update_segrep(Index krep, StorageIndex jj)
+  {
+    if(m_marker[krep]<m_jcol)
+    {
+      m_marker[krep] = jj; 
+      return true;
+    }
+    return false;
+  }
+  void mem_expand(IndexVector& /*glu.lsub*/, Index /*nextl*/, Index /*chmark*/) {}
+  enum { ExpandMem = false };
+  Index m_jcol;
+  StorageIndex* m_marker;
+};
+
+
+template <typename Scalar, typename StorageIndex>
+template <typename Traits>
+void SparseLUImpl<Scalar,StorageIndex>::dfs_kernel(const StorageIndex jj, IndexVector& perm_r,
+                   Index& nseg, IndexVector& panel_lsub, IndexVector& segrep,
+                   Ref<IndexVector> repfnz_col, IndexVector& xprune, Ref<IndexVector> marker, IndexVector& parent,
+                   IndexVector& xplore, GlobalLU_t& glu,
+                   Index& nextl_col, Index krow, Traits& traits
+                  )
+{
+  
+  StorageIndex kmark = marker(krow);
+      
+  // For each unmarked krow of jj
+  marker(krow) = jj; 
+  StorageIndex kperm = perm_r(krow); 
+  if (kperm == emptyIdxLU ) {
+    // krow is in L : place it in structure of L(*, jj)
+    panel_lsub(nextl_col++) = StorageIndex(krow);  // krow is indexed into A
+    
+    traits.mem_expand(panel_lsub, nextl_col, kmark);
+  }
+  else 
+  {
+    // krow is in U : if its supernode-representative krep
+    // has been explored, update repfnz(*)
+    // krep = supernode representative of the current row
+    StorageIndex krep = glu.xsup(glu.supno(kperm)+1) - 1; 
+    // First nonzero element in the current column:
+    StorageIndex myfnz = repfnz_col(krep); 
+    
+    if (myfnz != emptyIdxLU )
+    {
+      // Representative visited before
+      if (myfnz > kperm ) repfnz_col(krep) = kperm; 
+      
+    }
+    else 
+    {
+      // Otherwise, perform dfs starting at krep
+      StorageIndex oldrep = emptyIdxLU; 
+      parent(krep) = oldrep; 
+      repfnz_col(krep) = kperm; 
+      StorageIndex xdfs =  glu.xlsub(krep); 
+      Index maxdfs = xprune(krep); 
+      
+      StorageIndex kpar;
+      do 
+      {
+        // For each unmarked kchild of krep
+        while (xdfs < maxdfs) 
+        {
+          StorageIndex kchild = glu.lsub(xdfs); 
+          xdfs++; 
+          StorageIndex chmark = marker(kchild); 
+          
+          if (chmark != jj ) 
+          {
+            marker(kchild) = jj; 
+            StorageIndex chperm = perm_r(kchild); 
+            
+            if (chperm == emptyIdxLU) 
+            {
+              // case kchild is in L: place it in L(*, j)
+              panel_lsub(nextl_col++) = kchild;
+              traits.mem_expand(panel_lsub, nextl_col, chmark);
+            }
+            else
+            {
+              // case kchild is in U :
+              // chrep = its supernode-rep. If its rep has been explored, 
+              // update its repfnz(*)
+              StorageIndex chrep = glu.xsup(glu.supno(chperm)+1) - 1; 
+              myfnz = repfnz_col(chrep); 
+              
+              if (myfnz != emptyIdxLU) 
+              { // Visited before 
+                if (myfnz > chperm) 
+                  repfnz_col(chrep) = chperm; 
+              }
+              else 
+              { // Cont. dfs at snode-rep of kchild
+                xplore(krep) = xdfs; 
+                oldrep = krep; 
+                krep = chrep; // Go deeper down G(L)
+                parent(krep) = oldrep; 
+                repfnz_col(krep) = chperm; 
+                xdfs = glu.xlsub(krep); 
+                maxdfs = xprune(krep); 
+                
+              } // end if myfnz != -1
+            } // end if chperm == -1 
+                
+          } // end if chmark !=jj
+        } // end while xdfs < maxdfs
+        
+        // krow has no more unexplored nbrs :
+        //    Place snode-rep krep in postorder DFS, if this 
+        //    segment is seen for the first time. (Note that 
+        //    "repfnz(krep)" may change later.)
+        //    Baktrack dfs to its parent
+        if(traits.update_segrep(krep,jj))
+        //if (marker1(krep) < jcol )
+        {
+          segrep(nseg) = krep; 
+          ++nseg; 
+          //marker1(krep) = jj; 
+        }
+        
+        kpar = parent(krep); // Pop recursion, mimic recursion 
+        if (kpar == emptyIdxLU) 
+          break; // dfs done 
+        krep = kpar; 
+        xdfs = xplore(krep); 
+        maxdfs = xprune(krep); 
+
+      } while (kpar != emptyIdxLU); // Do until empty stack 
+      
+    } // end if (myfnz = -1)
+
+  } // end if (kperm == -1)   
+}
+
+/**
+ * \brief Performs a symbolic factorization on a panel of columns [jcol, jcol+w)
+ * 
+ * A supernode representative is the last column of a supernode.
+ * The nonzeros in U[*,j] are segments that end at supernodes representatives
+ * 
+ * The routine returns a list of the supernodal representatives 
+ * in topological order of the dfs that generates them. This list is 
+ * a superset of the topological order of each individual column within 
+ * the panel.
+ * The location of the first nonzero in each supernodal segment 
+ * (supernodal entry location) is also returned. Each column has 
+ * a separate list for this purpose. 
+ * 
+ * Two markers arrays are used for dfs :
+ *    marker[i] == jj, if i was visited during dfs of current column jj;
+ *    marker1[i] >= jcol, if i was visited by earlier columns in this panel; 
+ * 
+ * \param[in] m number of rows in the matrix
+ * \param[in] w Panel size
+ * \param[in] jcol Starting  column of the panel
+ * \param[in] A Input matrix in column-major storage
+ * \param[in] perm_r Row permutation
+ * \param[out] nseg Number of U segments
+ * \param[out] dense Accumulate the column vectors of the panel
+ * \param[out] panel_lsub Subscripts of the row in the panel 
+ * \param[out] segrep Segment representative i.e first nonzero row of each segment
+ * \param[out] repfnz First nonzero location in each row
+ * \param[out] xprune The pruned elimination tree
+ * \param[out] marker work vector
+ * \param  parent The elimination tree
+ * \param xplore work vector
+ * \param glu The global data structure
+ * 
+ */
+
+template <typename Scalar, typename StorageIndex>
+void SparseLUImpl<Scalar,StorageIndex>::panel_dfs(const Index m, const Index w, const Index jcol, MatrixType& A, IndexVector& perm_r, Index& nseg, ScalarVector& dense, IndexVector& panel_lsub, IndexVector& segrep, IndexVector& repfnz, IndexVector& xprune, IndexVector& marker, IndexVector& parent, IndexVector& xplore, GlobalLU_t& glu)
+{
+  Index nextl_col; // Next available position in panel_lsub[*,jj] 
+  
+  // Initialize pointers 
+  VectorBlock<IndexVector> marker1(marker, m, m); 
+  nseg = 0; 
+  
+  panel_dfs_traits<IndexVector> traits(jcol, marker1.data());
+  
+  // For each column in the panel 
+  for (StorageIndex jj = StorageIndex(jcol); jj < jcol + w; jj++) 
+  {
+    nextl_col = (jj - jcol) * m; 
+    
+    VectorBlock<IndexVector> repfnz_col(repfnz, nextl_col, m); // First nonzero location in each row
+    VectorBlock<ScalarVector> dense_col(dense,nextl_col, m); // Accumulate a column vector here
+    
+    
+    // For each nnz in A[*, jj] do depth first search
+    for (typename MatrixType::InnerIterator it(A, jj); it; ++it)
+    {
+      Index krow = it.row(); 
+      dense_col(krow) = it.value();
+      
+      StorageIndex kmark = marker(krow); 
+      if (kmark == jj) 
+        continue; // krow visited before, go to the next nonzero
+      
+      dfs_kernel(jj, perm_r, nseg, panel_lsub, segrep, repfnz_col, xprune, marker, parent,
+                   xplore, glu, nextl_col, krow, traits);
+    }// end for nonzeros in column jj
+    
+  } // end for column jj
+}
+
+} // end namespace internal
+} // end namespace Eigen
+
+#endif // SPARSELU_PANEL_DFS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_pivotL.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_pivotL.h
new file mode 100644
index 0000000000000000000000000000000000000000..6daed91333f003ed78d5b367a6e3eded8ccba238
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_pivotL.h
@@ -0,0 +1,139 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* 
+ 
+ * NOTE: This file is the modified version of xpivotL.c file in SuperLU 
+ 
+ * -- SuperLU routine (version 3.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * October 15, 2003
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSELU_PIVOTL_H
+#define SPARSELU_PIVOTL_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+  
+/**
+ * \brief Performs the numerical pivotin on the current column of L, and the CDIV operation.
+ * 
+ * Pivot policy :
+ * (1) Compute thresh = u * max_(i>=j) abs(A_ij);
+ * (2) IF user specifies pivot row k and abs(A_kj) >= thresh THEN
+ *           pivot row = k;
+ *       ELSE IF abs(A_jj) >= thresh THEN
+ *           pivot row = j;
+ *       ELSE
+ *           pivot row = m;
+ * 
+ *   Note: If you absolutely want to use a given pivot order, then set u=0.0.
+ * 
+ * \param jcol The current column of L
+ * \param diagpivotthresh diagonal pivoting threshold
+ * \param[in,out] perm_r Row permutation (threshold pivoting)
+ * \param[in] iperm_c column permutation - used to finf diagonal of Pc*A*Pc'
+ * \param[out] pivrow  The pivot row
+ * \param glu Global LU data
+ * \return 0 if success, i > 0 if U(i,i) is exactly zero 
+ * 
+ */
+template <typename Scalar, typename StorageIndex>
+Index SparseLUImpl<Scalar,StorageIndex>::pivotL(const Index jcol, const RealScalar& diagpivotthresh, IndexVector& perm_r, IndexVector& iperm_c, Index& pivrow, GlobalLU_t& glu)
+{
+  
+  Index fsupc = (glu.xsup)((glu.supno)(jcol)); // First column in the supernode containing the column jcol
+  Index nsupc = jcol - fsupc; // Number of columns in the supernode portion, excluding jcol; nsupc >=0
+  Index lptr = glu.xlsub(fsupc); // pointer to the starting location of the row subscripts for this supernode portion
+  Index nsupr = glu.xlsub(fsupc+1) - lptr; // Number of rows in the supernode
+  Index lda = glu.xlusup(fsupc+1) - glu.xlusup(fsupc); // leading dimension
+  Scalar* lu_sup_ptr = &(glu.lusup.data()[glu.xlusup(fsupc)]); // Start of the current supernode
+  Scalar* lu_col_ptr = &(glu.lusup.data()[glu.xlusup(jcol)]); // Start of jcol in the supernode
+  StorageIndex* lsub_ptr = &(glu.lsub.data()[lptr]); // Start of row indices of the supernode
+  
+  // Determine the largest abs numerical value for partial pivoting 
+  Index diagind = iperm_c(jcol); // diagonal index 
+  RealScalar pivmax(-1.0);
+  Index pivptr = nsupc; 
+  Index diag = emptyIdxLU; 
+  RealScalar rtemp;
+  Index isub, icol, itemp, k; 
+  for (isub = nsupc; isub < nsupr; ++isub) {
+    using std::abs;
+    rtemp = abs(lu_col_ptr[isub]);
+    if (rtemp > pivmax) {
+      pivmax = rtemp; 
+      pivptr = isub;
+    } 
+    if (lsub_ptr[isub] == diagind) diag = isub;
+  }
+  
+  // Test for singularity
+  if ( pivmax <= RealScalar(0.0) ) {
+    // if pivmax == -1, the column is structurally empty, otherwise it is only numerically zero
+    pivrow = pivmax < RealScalar(0.0) ? diagind : lsub_ptr[pivptr];
+    perm_r(pivrow) = StorageIndex(jcol);
+    return (jcol+1);
+  }
+  
+  RealScalar thresh = diagpivotthresh * pivmax; 
+  
+  // Choose appropriate pivotal element 
+  
+  {
+    // Test if the diagonal element can be used as a pivot (given the threshold value)
+    if (diag >= 0 ) 
+    {
+      // Diagonal element exists
+      using std::abs;
+      rtemp = abs(lu_col_ptr[diag]);
+      if (rtemp != RealScalar(0.0) && rtemp >= thresh) pivptr = diag;
+    }
+    pivrow = lsub_ptr[pivptr];
+  }
+  
+  // Record pivot row
+  perm_r(pivrow) = StorageIndex(jcol);
+  // Interchange row subscripts
+  if (pivptr != nsupc )
+  {
+    std::swap( lsub_ptr[pivptr], lsub_ptr[nsupc] );
+    // Interchange numerical values as well, for the two rows in the whole snode
+    // such that L is indexed the same way as A
+    for (icol = 0; icol <= nsupc; icol++)
+    {
+      itemp = pivptr + icol * lda; 
+      std::swap(lu_sup_ptr[itemp], lu_sup_ptr[nsupc + icol * lda]);
+    }
+  }
+  // cdiv operations
+  Scalar temp = Scalar(1.0) / lu_col_ptr[nsupc];
+  for (k = nsupc+1; k < nsupr; k++)
+    lu_col_ptr[k] *= temp; 
+  return 0;
+}
+
+} // end namespace internal
+} // end namespace Eigen
+
+#endif // SPARSELU_PIVOTL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_pruneL.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_pruneL.h
new file mode 100644
index 0000000000000000000000000000000000000000..e5da73b934ca718989dc2325fa33f8492a6f6eb2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_pruneL.h
@@ -0,0 +1,138 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* 
+ 
+ * NOTE: This file is the modified version of [s,d,c,z]pruneL.c file in SuperLU 
+ 
+ * -- SuperLU routine (version 2.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * November 15, 1997
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+#ifndef SPARSELU_PRUNEL_H
+#define SPARSELU_PRUNEL_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+namespace internal {
+
+/**
+ * \brief Prunes the L-structure.
+ *
+ * It prunes the L-structure  of supernodes whose L-structure contains the current pivot row "pivrow"
+ * 
+ * 
+ * \param jcol The current column of L
+ * \param[in] perm_r Row permutation
+ * \param[out] pivrow  The pivot row
+ * \param nseg Number of segments
+ * \param segrep 
+ * \param repfnz
+ * \param[out] xprune 
+ * \param glu Global LU data
+ * 
+ */
+template <typename Scalar, typename StorageIndex>
+void SparseLUImpl<Scalar,StorageIndex>::pruneL(const Index jcol, const IndexVector& perm_r, const Index pivrow, const Index nseg,
+                                               const IndexVector& segrep, BlockIndexVector repfnz, IndexVector& xprune, GlobalLU_t& glu)
+{
+  // For each supernode-rep irep in U(*,j]
+  Index jsupno = glu.supno(jcol); 
+  Index i,irep,irep1; 
+  bool movnum, do_prune = false; 
+  Index kmin = 0, kmax = 0, minloc, maxloc,krow; 
+  for (i = 0; i < nseg; i++)
+  {
+    irep = segrep(i); 
+    irep1 = irep + 1; 
+    do_prune = false; 
+    
+    // Don't prune with a zero U-segment 
+    if (repfnz(irep) == emptyIdxLU) continue; 
+    
+    // If a snode overlaps with the next panel, then the U-segment
+    // is fragmented into two parts -- irep and irep1. We should let 
+    // pruning occur at the rep-column in irep1s snode. 
+    if (glu.supno(irep) == glu.supno(irep1) ) continue; // don't prune 
+    
+    // If it has not been pruned & it has a nonz in row L(pivrow,i)
+    if (glu.supno(irep) != jsupno )
+    {
+      if ( xprune (irep) >= glu.xlsub(irep1) )
+      {
+        kmin = glu.xlsub(irep);
+        kmax = glu.xlsub(irep1) - 1; 
+        for (krow = kmin; krow <= kmax; krow++)
+        {
+          if (glu.lsub(krow) == pivrow) 
+          {
+            do_prune = true; 
+            break; 
+          }
+        }
+      }
+      
+      if (do_prune) 
+      {
+        // do a quicksort-type partition
+        // movnum=true means that the num values have to be exchanged
+        movnum = false; 
+        if (irep == glu.xsup(glu.supno(irep)) ) // Snode of size 1 
+          movnum = true; 
+        
+        while (kmin <= kmax)
+        {
+          if (perm_r(glu.lsub(kmax)) == emptyIdxLU)
+            kmax--; 
+          else if ( perm_r(glu.lsub(kmin)) != emptyIdxLU)
+            kmin++;
+          else 
+          {
+            // kmin below pivrow (not yet pivoted), and kmax
+            // above pivrow: interchange the two suscripts
+            std::swap(glu.lsub(kmin), glu.lsub(kmax)); 
+            
+            // If the supernode has only one column, then we 
+            // only keep one set of subscripts. For any subscript
+            // intercnahge performed, similar interchange must be 
+            // done on the numerical values. 
+            if (movnum) 
+            {
+              minloc = glu.xlusup(irep) + ( kmin - glu.xlsub(irep) ); 
+              maxloc = glu.xlusup(irep) + ( kmax - glu.xlsub(irep) ); 
+              std::swap(glu.lusup(minloc), glu.lusup(maxloc)); 
+            }
+            kmin++;
+            kmax--;
+          }
+        } // end while 
+        
+        xprune(irep) = StorageIndex(kmin);  //Pruning 
+      } // end if do_prune 
+    } // end pruning 
+  } // End for each U-segment
+}
+
+} // end namespace internal
+} // end namespace Eigen
+
+#endif // SPARSELU_PRUNEL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_relax_snode.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_relax_snode.h
new file mode 100644
index 0000000000000000000000000000000000000000..ed795323ca48793f8ca398b6e3f67f89fa327234
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseLU/SparseLU_relax_snode.h
@@ -0,0 +1,85 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+/* This file is a modified version of heap_relax_snode.c file in SuperLU
+ * -- SuperLU routine (version 3.0) --
+ * Univ. of California Berkeley, Xerox Palo Alto Research Center,
+ * and Lawrence Berkeley National Lab.
+ * October 15, 2003
+ *
+ * Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+ *
+ * THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+ * EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+ *
+ * Permission is hereby granted to use or copy this program for any
+ * purpose, provided the above notices are retained on all copies.
+ * Permission to modify the code and to distribute modified code is
+ * granted, provided the above notices are retained, and a notice that
+ * the code was modified is included with the above copyright notice.
+ */
+
+#ifndef SPARSELU_RELAX_SNODE_H
+#define SPARSELU_RELAX_SNODE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+ 
+/** 
+ * \brief Identify the initial relaxed supernodes
+ * 
+ * This routine is applied to a column elimination tree. 
+ * It assumes that the matrix has been reordered according to the postorder of the etree
+ * \param n  the number of columns
+ * \param et elimination tree 
+ * \param relax_columns Maximum number of columns allowed in a relaxed snode 
+ * \param descendants Number of descendants of each node in the etree
+ * \param relax_end last column in a supernode
+ */
+template <typename Scalar, typename StorageIndex>
+void SparseLUImpl<Scalar,StorageIndex>::relax_snode (const Index n, IndexVector& et, const Index relax_columns, IndexVector& descendants, IndexVector& relax_end)
+{
+  
+  // compute the number of descendants of each node in the etree
+  Index parent; 
+  relax_end.setConstant(emptyIdxLU);
+  descendants.setZero();
+  for (Index j = 0; j < n; j++) 
+  {
+    parent = et(j);
+    if (parent != n) // not the dummy root
+      descendants(parent) += descendants(j) + 1;
+  }
+  // Identify the relaxed supernodes by postorder traversal of the etree
+  Index snode_start; // beginning of a snode 
+  for (Index j = 0; j < n; )
+  {
+    parent = et(j);
+    snode_start = j; 
+    while ( parent != n && descendants(parent) < relax_columns ) 
+    {
+      j = parent; 
+      parent = et(j);
+    }
+    // Found a supernode in postordered etree, j is the last column 
+    relax_end(snode_start) = StorageIndex(j); // Record last column
+    j++;
+    // Search for a new leaf
+    while (descendants(j) != 0 && j < n) j++;
+  } // End postorder traversal of the etree
+  
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseQR/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseQR/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..0564e932aeb837a8c0153a5315be1c84677cf0ff
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseQR/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SPARSEQR_MODULE_H
+#error "Please include Eigen/SparseQR instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseQR/SparseQR.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseQR/SparseQR.h
new file mode 100644
index 0000000000000000000000000000000000000000..f8250921b725bd150fef0d73024fdb6c08adec78
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SparseQR/SparseQR.h
@@ -0,0 +1,760 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012-2013 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_QR_H
+#define EIGEN_SPARSE_QR_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template<typename MatrixType, typename OrderingType> class SparseQR;
+template<typename SparseQRType> struct SparseQRMatrixQReturnType;
+template<typename SparseQRType> struct SparseQRMatrixQTransposeReturnType;
+template<typename SparseQRType, typename Derived> struct SparseQR_QProduct;
+namespace internal {
+  template <typename SparseQRType> struct traits<SparseQRMatrixQReturnType<SparseQRType> >
+  {
+    typedef typename SparseQRType::MatrixType ReturnType;
+    typedef typename ReturnType::StorageIndex StorageIndex;
+    typedef typename ReturnType::StorageKind StorageKind;
+    enum {
+      RowsAtCompileTime = Dynamic,
+      ColsAtCompileTime = Dynamic
+    };
+  };
+  template <typename SparseQRType> struct traits<SparseQRMatrixQTransposeReturnType<SparseQRType> >
+  {
+    typedef typename SparseQRType::MatrixType ReturnType;
+  };
+  template <typename SparseQRType, typename Derived> struct traits<SparseQR_QProduct<SparseQRType, Derived> >
+  {
+    typedef typename Derived::PlainObject ReturnType;
+  };
+} // End namespace internal
+
+/**
+  * \ingroup SparseQR_Module
+  * \class SparseQR
+  * \brief Sparse left-looking QR factorization with numerical column pivoting
+  * 
+  * This class implements a left-looking QR decomposition of sparse matrices
+  * with numerical column pivoting.
+  * When a column has a norm less than a given tolerance
+  * it is implicitly permuted to the end. The QR factorization thus obtained is 
+  * given by A*P = Q*R where R is upper triangular or trapezoidal. 
+  * 
+  * P is the column permutation which is the product of the fill-reducing and the
+  * numerical permutations. Use colsPermutation() to get it.
+  * 
+  * Q is the orthogonal matrix represented as products of Householder reflectors. 
+  * Use matrixQ() to get an expression and matrixQ().adjoint() to get the adjoint.
+  * You can then apply it to a vector.
+  * 
+  * R is the sparse triangular or trapezoidal matrix. The later occurs when A is rank-deficient.
+  * matrixR().topLeftCorner(rank(), rank()) always returns a triangular factor of full rank.
+  * 
+  * \tparam MatrixType_ The type of the sparse matrix A, must be a column-major SparseMatrix<>
+  * \tparam OrderingType_ The fill-reducing ordering method. See the \link OrderingMethods_Module
+  *  OrderingMethods \endlink module for the list of built-in and external ordering methods.
+  * 
+  * \implsparsesolverconcept
+  *
+  * The numerical pivoting strategy and default threshold are the same as in SuiteSparse QR, and
+  * detailed in the following paper:
+  * <i>
+  * Tim Davis, "Algorithm 915, SuiteSparseQR: Multifrontal Multithreaded Rank-Revealing
+  * Sparse QR Factorization, ACM Trans. on Math. Soft. 38(1), 2011.
+  * </i>
+  * Even though it is qualified as "rank-revealing", this strategy might fail for some 
+  * rank deficient problems. When this class is used to solve linear or least-square problems
+  * it is thus strongly recommended to check the accuracy of the computed solution. If it
+  * failed, it usually helps to increase the threshold with setPivotThreshold.
+  * 
+  * \warning The input sparse matrix A must be in compressed mode (see SparseMatrix::makeCompressed()).
+  * \warning For complex matrices matrixQ().transpose() will actually return the adjoint matrix.
+  * 
+  */
+template<typename MatrixType_, typename OrderingType_>
+class SparseQR : public SparseSolverBase<SparseQR<MatrixType_,OrderingType_> >
+{
+  protected:
+    typedef SparseSolverBase<SparseQR<MatrixType_,OrderingType_> > Base;
+    using Base::m_isInitialized;
+  public:
+    using Base::_solve_impl;
+    typedef MatrixType_ MatrixType;
+    typedef OrderingType_ OrderingType;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> QRMatrixType;
+    typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
+    typedef Matrix<Scalar, Dynamic, 1> ScalarVector;
+    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType;
+
+    enum {
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+    
+  public:
+    SparseQR () :  m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false),m_isEtreeOk(false)
+    { }
+    
+    /** Construct a QR factorization of the matrix \a mat.
+      * 
+      * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
+      * 
+      * \sa compute()
+      */
+    explicit SparseQR(const MatrixType& mat) : m_analysisIsok(false), m_lastError(""), m_useDefaultThreshold(true),m_isQSorted(false),m_isEtreeOk(false)
+    {
+      compute(mat);
+    }
+    
+    /** Computes the QR factorization of the sparse matrix \a mat.
+      * 
+      * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
+      * 
+      * \sa analyzePattern(), factorize()
+      */
+    void compute(const MatrixType& mat)
+    {
+      analyzePattern(mat);
+      factorize(mat);
+    }
+    void analyzePattern(const MatrixType& mat);
+    void factorize(const MatrixType& mat);
+    
+    /** \returns the number of rows of the represented matrix. 
+      */
+    inline Index rows() const { return m_pmat.rows(); }
+    
+    /** \returns the number of columns of the represented matrix. 
+      */
+    inline Index cols() const { return m_pmat.cols();}
+    
+    /** \returns a const reference to the \b sparse upper triangular matrix R of the QR factorization.
+      * \warning The entries of the returned matrix are not sorted. This means that using it in algorithms
+      *          expecting sorted entries will fail. This include random coefficient accesses (SpaseMatrix::coeff()),
+      *          and coefficient-wise operations. Matrix products and triangular solves are fine though.
+      *
+      * To sort the entries, you can assign it to a row-major matrix, and if a column-major matrix
+      * is required, you can copy it again:
+      * \code
+      * SparseMatrix<double>          R  = qr.matrixR();  // column-major, not sorted!
+      * SparseMatrix<double,RowMajor> Rr = qr.matrixR();  // row-major, sorted
+      * SparseMatrix<double>          Rc = Rr;            // column-major, sorted
+      * \endcode
+      */
+    const QRMatrixType& matrixR() const { return m_R; }
+    
+    /** \returns the number of non linearly dependent columns as determined by the pivoting threshold.
+      *
+      * \sa setPivotThreshold()
+      */
+    Index rank() const
+    {
+      eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
+      return m_nonzeropivots; 
+    }
+    
+    /** \returns an expression of the matrix Q as products of sparse Householder reflectors.
+    * The common usage of this function is to apply it to a dense matrix or vector
+    * \code
+    * VectorXd B1, B2;
+    * // Initialize B1
+    * B2 = matrixQ() * B1;
+    * \endcode
+    *
+    * To get a plain SparseMatrix representation of Q:
+    * \code
+    * SparseMatrix<double> Q;
+    * Q = SparseQR<SparseMatrix<double> >(A).matrixQ();
+    * \endcode
+    * Internally, this call simply performs a sparse product between the matrix Q
+    * and a sparse identity matrix. However, due to the fact that the sparse
+    * reflectors are stored unsorted, two transpositions are needed to sort
+    * them before performing the product.
+    */
+    SparseQRMatrixQReturnType<SparseQR> matrixQ() const 
+    { return SparseQRMatrixQReturnType<SparseQR>(*this); }
+    
+    /** \returns a const reference to the column permutation P that was applied to A such that A*P = Q*R
+      * It is the combination of the fill-in reducing permutation and numerical column pivoting.
+      */
+    const PermutationType& colsPermutation() const
+    { 
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_outputPerm_c;
+    }
+    
+    /** \returns A string describing the type of error.
+      * This method is provided to ease debugging, not to handle errors.
+      */
+    std::string lastErrorMessage() const { return m_lastError; }
+    
+    /** \internal */
+    template<typename Rhs, typename Dest>
+    bool _solve_impl(const MatrixBase<Rhs> &B, MatrixBase<Dest> &dest) const
+    {
+      eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
+      eigen_assert(this->rows() == B.rows() && "SparseQR::solve() : invalid number of rows in the right hand side matrix");
+
+      Index rank = this->rank();
+      
+      // Compute Q^* * b;
+      typename Dest::PlainObject y, b;
+      y = this->matrixQ().adjoint() * B;
+      b = y;
+      
+      // Solve with the triangular matrix R
+      y.resize((std::max<Index>)(cols(),y.rows()),y.cols());
+      y.topRows(rank) = this->matrixR().topLeftCorner(rank, rank).template triangularView<Upper>().solve(b.topRows(rank));
+      y.bottomRows(y.rows()-rank).setZero();
+      
+      // Apply the column permutation
+      if (m_perm_c.size())  dest = colsPermutation() * y.topRows(cols());
+      else                  dest = y.topRows(cols());
+      
+      m_info = Success;
+      return true;
+    }
+
+    /** Sets the threshold that is used to determine linearly dependent columns during the factorization.
+      *
+      * In practice, if during the factorization the norm of the column that has to be eliminated is below
+      * this threshold, then the entire column is treated as zero, and it is moved at the end.
+      */
+    void setPivotThreshold(const RealScalar& threshold)
+    {
+      m_useDefaultThreshold = false;
+      m_threshold = threshold;
+    }
+    
+    /** \returns the solution X of \f$ A X = B \f$ using the current decomposition of A.
+      *
+      * \sa compute()
+      */
+    template<typename Rhs>
+    inline const Solve<SparseQR, Rhs> solve(const MatrixBase<Rhs>& B) const 
+    {
+      eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
+      eigen_assert(this->rows() == B.rows() && "SparseQR::solve() : invalid number of rows in the right hand side matrix");
+      return Solve<SparseQR, Rhs>(*this, B.derived());
+    }
+    template<typename Rhs>
+    inline const Solve<SparseQR, Rhs> solve(const SparseMatrixBase<Rhs>& B) const
+    {
+          eigen_assert(m_isInitialized && "The factorization should be called first, use compute()");
+          eigen_assert(this->rows() == B.rows() && "SparseQR::solve() : invalid number of rows in the right hand side matrix");
+          return Solve<SparseQR, Rhs>(*this, B.derived());
+    }
+    
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful,
+      *          \c NumericalIssue if the QR factorization reports a numerical problem
+      *          \c InvalidInput if the input matrix is invalid
+      *
+      * \sa iparm()          
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+
+
+    /** \internal */
+    inline void _sort_matrix_Q()
+    {
+      if(this->m_isQSorted) return;
+      // The matrix Q is sorted during the transposition
+      SparseMatrix<Scalar, RowMajor, Index> mQrm(this->m_Q);
+      this->m_Q = mQrm;
+      this->m_isQSorted = true;
+    }
+
+    
+  protected:
+    bool m_analysisIsok;
+    bool m_factorizationIsok;
+    mutable ComputationInfo m_info;
+    std::string m_lastError;
+    QRMatrixType m_pmat;            // Temporary matrix
+    QRMatrixType m_R;               // The triangular factor matrix
+    QRMatrixType m_Q;               // The orthogonal reflectors
+    ScalarVector m_hcoeffs;         // The Householder coefficients
+    PermutationType m_perm_c;       // Fill-reducing  Column  permutation
+    PermutationType m_pivotperm;    // The permutation for rank revealing
+    PermutationType m_outputPerm_c; // The final column permutation
+    RealScalar m_threshold;         // Threshold to determine null Householder reflections
+    bool m_useDefaultThreshold;     // Use default threshold
+    Index m_nonzeropivots;          // Number of non zero pivots found
+    IndexVector m_etree;            // Column elimination tree
+    IndexVector m_firstRowElt;      // First element in each row
+    bool m_isQSorted;               // whether Q is sorted or not
+    bool m_isEtreeOk;               // whether the elimination tree match the initial input matrix
+    
+    template <typename, typename > friend struct SparseQR_QProduct;
+    
+};
+
+/** \brief Preprocessing step of a QR factorization 
+  * 
+  * \warning The matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
+  * 
+  * In this step, the fill-reducing permutation is computed and applied to the columns of A
+  * and the column elimination tree is computed as well. Only the sparsity pattern of \a mat is exploited.
+  * 
+  * \note In this step it is assumed that there is no empty row in the matrix \a mat.
+  */
+template <typename MatrixType, typename OrderingType>
+void SparseQR<MatrixType,OrderingType>::analyzePattern(const MatrixType& mat)
+{
+  eigen_assert(mat.isCompressed() && "SparseQR requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to SparseQR");
+  // Copy to a column major matrix if the input is rowmajor
+  std::conditional_t<MatrixType::IsRowMajor,QRMatrixType,const MatrixType&> matCpy(mat);
+  // Compute the column fill reducing ordering
+  OrderingType ord; 
+  ord(matCpy, m_perm_c); 
+  Index n = mat.cols();
+  Index m = mat.rows();
+  Index diagSize = (std::min)(m,n);
+  
+  if (!m_perm_c.size())
+  {
+    m_perm_c.resize(n);
+    m_perm_c.indices().setLinSpaced(n, 0,StorageIndex(n-1));
+  }
+  
+  // Compute the column elimination tree of the permuted matrix
+  m_outputPerm_c = m_perm_c.inverse();
+  internal::coletree(matCpy, m_etree, m_firstRowElt, m_outputPerm_c.indices().data());
+  m_isEtreeOk = true;
+  
+  m_R.resize(m, n);
+  m_Q.resize(m, diagSize);
+  
+  // Allocate space for nonzero elements: rough estimation
+  m_R.reserve(2*mat.nonZeros()); //FIXME Get a more accurate estimation through symbolic factorization with the etree
+  m_Q.reserve(2*mat.nonZeros());
+  m_hcoeffs.resize(diagSize);
+  m_analysisIsok = true;
+}
+
+/** \brief Performs the numerical QR factorization of the input matrix
+  * 
+  * The function SparseQR::analyzePattern(const MatrixType&) must have been called beforehand with
+  * a matrix having the same sparsity pattern than \a mat.
+  * 
+  * \param mat The sparse column-major matrix
+  */
+template <typename MatrixType, typename OrderingType>
+void SparseQR<MatrixType,OrderingType>::factorize(const MatrixType& mat)
+{
+  using std::abs;
+  
+  eigen_assert(m_analysisIsok && "analyzePattern() should be called before this step");
+  StorageIndex m = StorageIndex(mat.rows());
+  StorageIndex n = StorageIndex(mat.cols());
+  StorageIndex diagSize = (std::min)(m,n);
+  IndexVector mark((std::max)(m,n)); mark.setConstant(-1);  // Record the visited nodes
+  IndexVector Ridx(n), Qidx(m);                             // Store temporarily the row indexes for the current column of R and Q
+  Index nzcolR, nzcolQ;                                     // Number of nonzero for the current column of R and Q
+  ScalarVector tval(m);                                     // The dense vector used to compute the current column
+  RealScalar pivotThreshold = m_threshold;
+  
+  m_R.setZero();
+  m_Q.setZero();
+  m_pmat = mat;
+  if(!m_isEtreeOk)
+  {
+    m_outputPerm_c = m_perm_c.inverse();
+    internal::coletree(m_pmat, m_etree, m_firstRowElt, m_outputPerm_c.indices().data());
+    m_isEtreeOk = true;
+  }
+
+  m_pmat.uncompress(); // To have the innerNonZeroPtr allocated
+  
+  // Apply the fill-in reducing permutation lazily:
+  {
+    // If the input is row major, copy the original column indices,
+    // otherwise directly use the input matrix
+    // 
+    IndexVector originalOuterIndicesCpy;
+    const StorageIndex *originalOuterIndices = mat.outerIndexPtr();
+    if(MatrixType::IsRowMajor)
+    {
+      originalOuterIndicesCpy = IndexVector::Map(m_pmat.outerIndexPtr(),n+1);
+      originalOuterIndices = originalOuterIndicesCpy.data();
+    }
+    
+    for (int i = 0; i < n; i++)
+    {
+      Index p = m_perm_c.size() ? m_perm_c.indices()(i) : i;
+      m_pmat.outerIndexPtr()[p] = originalOuterIndices[i]; 
+      m_pmat.innerNonZeroPtr()[p] = originalOuterIndices[i+1] - originalOuterIndices[i]; 
+    }
+  }
+  
+  /* Compute the default threshold as in MatLab, see:
+   * Tim Davis, "Algorithm 915, SuiteSparseQR: Multifrontal Multithreaded Rank-Revealing
+   * Sparse QR Factorization, ACM Trans. on Math. Soft. 38(1), 2011, Page 8:3 
+   */
+  if(m_useDefaultThreshold) 
+  {
+    RealScalar max2Norm = 0.0;
+    for (int j = 0; j < n; j++) max2Norm = numext::maxi(max2Norm, m_pmat.col(j).norm());
+    if(max2Norm==RealScalar(0))
+      max2Norm = RealScalar(1);
+    pivotThreshold = 20 * (m + n) * max2Norm * NumTraits<RealScalar>::epsilon();
+  }
+  
+  // Initialize the numerical permutation
+  m_pivotperm.setIdentity(n);
+  
+  StorageIndex nonzeroCol = 0; // Record the number of valid pivots
+  m_Q.startVec(0);
+
+  // Left looking rank-revealing QR factorization: compute a column of R and Q at a time
+  for (StorageIndex col = 0; col < n; ++col)
+  {
+    mark.setConstant(-1);
+    m_R.startVec(col);
+    mark(nonzeroCol) = col;
+    Qidx(0) = nonzeroCol;
+    nzcolR = 0; nzcolQ = 1;
+    bool found_diag = nonzeroCol>=m;
+    tval.setZero(); 
+    
+    // Symbolic factorization: find the nonzero locations of the column k of the factors R and Q, i.e.,
+    // all the nodes (with indexes lower than rank) reachable through the column elimination tree (etree) rooted at node k.
+    // Note: if the diagonal entry does not exist, then its contribution must be explicitly added,
+    // thus the trick with found_diag that permits to do one more iteration on the diagonal element if this one has not been found.
+    for (typename QRMatrixType::InnerIterator itp(m_pmat, col); itp || !found_diag; ++itp)
+    {
+      StorageIndex curIdx = nonzeroCol;
+      if(itp) curIdx = StorageIndex(itp.row());
+      if(curIdx == nonzeroCol) found_diag = true;
+      
+      // Get the nonzeros indexes of the current column of R
+      StorageIndex st = m_firstRowElt(curIdx); // The traversal of the etree starts here
+      if (st < 0 )
+      {
+        m_lastError = "Empty row found during numerical factorization";
+        m_info = InvalidInput;
+        return;
+      }
+
+      // Traverse the etree 
+      Index bi = nzcolR;
+      for (; mark(st) != col; st = m_etree(st))
+      {
+        Ridx(nzcolR) = st;  // Add this row to the list,
+        mark(st) = col;     // and mark this row as visited
+        nzcolR++;
+      }
+
+      // Reverse the list to get the topological ordering
+      Index nt = nzcolR-bi;
+      for(Index i = 0; i < nt/2; i++) std::swap(Ridx(bi+i), Ridx(nzcolR-i-1));
+       
+      // Copy the current (curIdx,pcol) value of the input matrix
+      if(itp) tval(curIdx) = itp.value();
+      else    tval(curIdx) = Scalar(0);
+      
+      // Compute the pattern of Q(:,k)
+      if(curIdx > nonzeroCol && mark(curIdx) != col ) 
+      {
+        Qidx(nzcolQ) = curIdx;  // Add this row to the pattern of Q,
+        mark(curIdx) = col;     // and mark it as visited
+        nzcolQ++;
+      }
+    }
+
+    // Browse all the indexes of R(:,col) in reverse order
+    for (Index i = nzcolR-1; i >= 0; i--)
+    {
+      Index curIdx = Ridx(i);
+      
+      // Apply the curIdx-th householder vector to the current column (temporarily stored into tval)
+      Scalar tdot(0);
+      
+      // First compute q' * tval
+      tdot = m_Q.col(curIdx).dot(tval);
+
+      tdot *= m_hcoeffs(curIdx);
+      
+      // Then update tval = tval - q * tau
+      // FIXME: tval -= tdot * m_Q.col(curIdx) should amount to the same (need to check/add support for efficient "dense ?= sparse")
+      for (typename QRMatrixType::InnerIterator itq(m_Q, curIdx); itq; ++itq)
+        tval(itq.row()) -= itq.value() * tdot;
+
+      // Detect fill-in for the current column of Q
+      if(m_etree(Ridx(i)) == nonzeroCol)
+      {
+        for (typename QRMatrixType::InnerIterator itq(m_Q, curIdx); itq; ++itq)
+        {
+          StorageIndex iQ = StorageIndex(itq.row());
+          if (mark(iQ) != col)
+          {
+            Qidx(nzcolQ++) = iQ;  // Add this row to the pattern of Q,
+            mark(iQ) = col;       // and mark it as visited
+          }
+        }
+      }
+    } // End update current column
+    
+    Scalar tau = RealScalar(0);
+    RealScalar beta = 0;
+    
+    if(nonzeroCol < diagSize)
+    {
+      // Compute the Householder reflection that eliminate the current column
+      // FIXME this step should call the Householder module.
+      Scalar c0 = nzcolQ ? tval(Qidx(0)) : Scalar(0);
+      
+      // First, the squared norm of Q((col+1):m, col)
+      RealScalar sqrNorm = 0.;
+      for (Index itq = 1; itq < nzcolQ; ++itq) sqrNorm += numext::abs2(tval(Qidx(itq)));
+      if(sqrNorm == RealScalar(0) && numext::imag(c0) == RealScalar(0))
+      {
+        beta = numext::real(c0);
+        tval(Qidx(0)) = 1;
+      }
+      else
+      {
+        using std::sqrt;
+        beta = sqrt(numext::abs2(c0) + sqrNorm);
+        if(numext::real(c0) >= RealScalar(0))
+          beta = -beta;
+        tval(Qidx(0)) = 1;
+        for (Index itq = 1; itq < nzcolQ; ++itq)
+          tval(Qidx(itq)) /= (c0 - beta);
+        tau = numext::conj((beta-c0) / beta);
+          
+      }
+    }
+
+    // Insert values in R
+    for (Index  i = nzcolR-1; i >= 0; i--)
+    {
+      Index curIdx = Ridx(i);
+      if(curIdx < nonzeroCol) 
+      {
+        m_R.insertBackByOuterInnerUnordered(col, curIdx) = tval(curIdx);
+        tval(curIdx) = Scalar(0.);
+      }
+    }
+
+    if(nonzeroCol < diagSize && abs(beta) >= pivotThreshold)
+    {
+      m_R.insertBackByOuterInner(col, nonzeroCol) = beta;
+      // The householder coefficient
+      m_hcoeffs(nonzeroCol) = tau;
+      // Record the householder reflections
+      for (Index itq = 0; itq < nzcolQ; ++itq)
+      {
+        Index iQ = Qidx(itq);
+        m_Q.insertBackByOuterInnerUnordered(nonzeroCol,iQ) = tval(iQ);
+        tval(iQ) = Scalar(0.);
+      }
+      nonzeroCol++;
+      if(nonzeroCol<diagSize)
+        m_Q.startVec(nonzeroCol);
+    }
+    else
+    {
+      // Zero pivot found: move implicitly this column to the end
+      for (Index j = nonzeroCol; j < n-1; j++) 
+        std::swap(m_pivotperm.indices()(j), m_pivotperm.indices()[j+1]);
+      
+      // Recompute the column elimination tree
+      internal::coletree(m_pmat, m_etree, m_firstRowElt, m_pivotperm.indices().data());
+      m_isEtreeOk = false;
+    }
+  }
+  
+  m_hcoeffs.tail(diagSize-nonzeroCol).setZero();
+  
+  // Finalize the column pointers of the sparse matrices R and Q
+  m_Q.finalize();
+  m_Q.makeCompressed();
+  m_R.finalize();
+  m_R.makeCompressed();
+  m_isQSorted = false;
+
+  m_nonzeropivots = nonzeroCol;
+  
+  if(nonzeroCol<n)
+  {
+    // Permute the triangular factor to put the 'dead' columns to the end
+    QRMatrixType tempR(m_R);
+    m_R = tempR * m_pivotperm;
+    
+    // Update the column permutation
+    m_outputPerm_c = m_outputPerm_c * m_pivotperm;
+  }
+  
+  m_isInitialized = true; 
+  m_factorizationIsok = true;
+  m_info = Success;
+}
+
+template <typename SparseQRType, typename Derived>
+struct SparseQR_QProduct : ReturnByValue<SparseQR_QProduct<SparseQRType, Derived> >
+{
+  typedef typename SparseQRType::QRMatrixType MatrixType;
+  typedef typename SparseQRType::Scalar Scalar;
+  // Get the references 
+  SparseQR_QProduct(const SparseQRType& qr, const Derived& other, bool transpose) : 
+  m_qr(qr),m_other(other),m_transpose(transpose) {}
+  inline Index rows() const { return m_qr.matrixQ().rows(); }
+  inline Index cols() const { return m_other.cols(); }
+  
+  // Assign to a vector
+  template<typename DesType>
+  void evalTo(DesType& res) const
+  {
+    Index m = m_qr.rows();
+    Index n = m_qr.cols();
+    Index diagSize = (std::min)(m,n);
+    res = m_other;
+    if (m_transpose)
+    {
+      eigen_assert(m_qr.m_Q.rows() == m_other.rows() && "Non conforming object sizes");
+      //Compute res = Q' * other column by column
+      for(Index j = 0; j < res.cols(); j++){
+        for (Index k = 0; k < diagSize; k++)
+        {
+          Scalar tau = Scalar(0);
+          tau = m_qr.m_Q.col(k).dot(res.col(j));
+          if(tau==Scalar(0)) continue;
+          tau = tau * m_qr.m_hcoeffs(k);
+          res.col(j) -= tau * m_qr.m_Q.col(k);
+        }
+      }
+    }
+    else
+    {
+      eigen_assert(m_qr.matrixQ().cols() == m_other.rows() && "Non conforming object sizes");
+
+      res.conservativeResize(rows(), cols());
+
+      // Compute res = Q * other column by column
+      for(Index j = 0; j < res.cols(); j++)
+      {
+        Index start_k = internal::is_identity<Derived>::value ? numext::mini(j,diagSize-1) : diagSize-1;
+        for (Index k = start_k; k >=0; k--)
+        {
+          Scalar tau = Scalar(0);
+          tau = m_qr.m_Q.col(k).dot(res.col(j));
+          if(tau==Scalar(0)) continue;
+          tau = tau * numext::conj(m_qr.m_hcoeffs(k));
+          res.col(j) -= tau * m_qr.m_Q.col(k);
+        }
+      }
+    }
+  }
+  
+  const SparseQRType& m_qr;
+  const Derived& m_other;
+  bool m_transpose; // TODO this actually means adjoint
+};
+
+template<typename SparseQRType>
+struct SparseQRMatrixQReturnType : public EigenBase<SparseQRMatrixQReturnType<SparseQRType> >
+{  
+  typedef typename SparseQRType::Scalar Scalar;
+  typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
+  enum {
+    RowsAtCompileTime = Dynamic,
+    ColsAtCompileTime = Dynamic
+  };
+  explicit SparseQRMatrixQReturnType(const SparseQRType& qr) : m_qr(qr) {}
+  template<typename Derived>
+  SparseQR_QProduct<SparseQRType, Derived> operator*(const MatrixBase<Derived>& other)
+  {
+    return SparseQR_QProduct<SparseQRType,Derived>(m_qr,other.derived(),false);
+  }
+  // To use for operations with the adjoint of Q
+  SparseQRMatrixQTransposeReturnType<SparseQRType> adjoint() const
+  {
+    return SparseQRMatrixQTransposeReturnType<SparseQRType>(m_qr);
+  }
+  inline Index rows() const { return m_qr.rows(); }
+  inline Index cols() const { return m_qr.rows(); }
+  // To use for operations with the transpose of Q FIXME this is the same as adjoint at the moment
+  SparseQRMatrixQTransposeReturnType<SparseQRType> transpose() const
+  {
+    return SparseQRMatrixQTransposeReturnType<SparseQRType>(m_qr);
+  }
+  const SparseQRType& m_qr;
+};
+
+// TODO this actually represents the adjoint of Q
+template<typename SparseQRType>
+struct SparseQRMatrixQTransposeReturnType
+{
+  explicit SparseQRMatrixQTransposeReturnType(const SparseQRType& qr) : m_qr(qr) {}
+  template<typename Derived>
+  SparseQR_QProduct<SparseQRType,Derived> operator*(const MatrixBase<Derived>& other)
+  {
+    return SparseQR_QProduct<SparseQRType,Derived>(m_qr,other.derived(), true);
+  }
+  const SparseQRType& m_qr;
+};
+
+namespace internal {
+  
+template<typename SparseQRType>
+struct evaluator_traits<SparseQRMatrixQReturnType<SparseQRType> >
+{
+  typedef typename SparseQRType::MatrixType MatrixType;
+  typedef typename storage_kind_to_evaluator_kind<typename MatrixType::StorageKind>::Kind Kind;
+  typedef SparseShape Shape;
+};
+
+template< typename DstXprType, typename SparseQRType>
+struct Assignment<DstXprType, SparseQRMatrixQReturnType<SparseQRType>, internal::assign_op<typename DstXprType::Scalar,typename DstXprType::Scalar>, Sparse2Sparse>
+{
+  typedef SparseQRMatrixQReturnType<SparseQRType> SrcXprType;
+  typedef typename DstXprType::Scalar Scalar;
+  typedef typename DstXprType::StorageIndex StorageIndex;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &/*func*/)
+  {
+    typename DstXprType::PlainObject idMat(src.rows(), src.cols());
+    idMat.setIdentity();
+    // Sort the sparse householder reflectors if needed
+    const_cast<SparseQRType *>(&src.m_qr)->_sort_matrix_Q();
+    dst = SparseQR_QProduct<SparseQRType, DstXprType>(src.m_qr, idMat, false);
+  }
+};
+
+template< typename DstXprType, typename SparseQRType>
+struct Assignment<DstXprType, SparseQRMatrixQReturnType<SparseQRType>, internal::assign_op<typename DstXprType::Scalar,typename DstXprType::Scalar>, Sparse2Dense>
+{
+  typedef SparseQRMatrixQReturnType<SparseQRType> SrcXprType;
+  typedef typename DstXprType::Scalar Scalar;
+  typedef typename DstXprType::StorageIndex StorageIndex;
+  static void run(DstXprType &dst, const SrcXprType &src, const internal::assign_op<Scalar,Scalar> &/*func*/)
+  {
+    dst = src.m_qr.matrixQ() * DstXprType::Identity(src.m_qr.rows(), src.m_qr.rows());
+  }
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/StlSupport/StdDeque.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/StlSupport/StdDeque.h
new file mode 100644
index 0000000000000000000000000000000000000000..1e951821d64fb9e6be7d18bde1aa64b2a4868168
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/StlSupport/StdDeque.h
@@ -0,0 +1,51 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STDDEQUE_H
+#define EIGEN_STDDEQUE_H
+
+#ifndef EIGEN_STDDEQUE_MODULE_H
+#error "Please include Eigen/StdDeque instead of including this file directly."
+#endif
+
+#include "details.h"
+
+/**
+ * This section contains a convenience MACRO which allows an easy specialization of
+ * std::deque such that for data types with alignment issues the correct allocator
+ * is used automatically.
+ */
+#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...) \
+namespace std \
+{ \
+  template<> \
+  class deque<__VA_ARGS__, std::allocator<__VA_ARGS__> >           \
+    : public deque<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \
+  { \
+    typedef deque<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > deque_base; \
+  public: \
+    typedef __VA_ARGS__ value_type; \
+    typedef deque_base::allocator_type allocator_type; \
+    typedef deque_base::size_type size_type;  \
+    typedef deque_base::iterator iterator;  \
+    explicit deque(const allocator_type& a = allocator_type()) : deque_base(a) {}  \
+    template<typename InputIterator> \
+    deque(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : deque_base(first, last, a) {} \
+    deque(const deque& c) : deque_base(c) {}  \
+    explicit deque(size_type num, const value_type& val = value_type()) : deque_base(num, val) {} \
+    deque(iterator start_, iterator end_) : deque_base(start_, end_) {}  \
+    deque& operator=(const deque& x) {  \
+      deque_base::operator=(x);  \
+      return *this;  \
+    } \
+  }; \
+}
+
+#endif // EIGEN_STDDEQUE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/StlSupport/StdList.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/StlSupport/StdList.h
new file mode 100644
index 0000000000000000000000000000000000000000..da36677a225618b8acb9f854c657fc7341213867
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/StlSupport/StdList.h
@@ -0,0 +1,50 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STDLIST_H
+#define EIGEN_STDLIST_H
+
+#ifndef EIGEN_STDLIST_MODULE_H
+#error "Please include Eigen/StdList instead of including this file directly."
+#endif
+
+#include "details.h"
+
+/**
+ * This section contains a convenience MACRO which allows an easy specialization of
+ * std::list such that for data types with alignment issues the correct allocator
+ * is used automatically.
+ */
+#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...) \
+namespace std \
+{ \
+  template<> \
+  class list<__VA_ARGS__, std::allocator<__VA_ARGS__> >           \
+    : public list<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \
+  { \
+    typedef list<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > list_base; \
+  public: \
+    typedef __VA_ARGS__ value_type; \
+    typedef list_base::allocator_type allocator_type; \
+    typedef list_base::size_type size_type;  \
+    typedef list_base::iterator iterator;  \
+    explicit list(const allocator_type& a = allocator_type()) : list_base(a) {}  \
+    template<typename InputIterator> \
+    list(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : list_base(first, last, a) {} \
+    list(const list& c) : list_base(c) {}  \
+    explicit list(size_type num, const value_type& val = value_type()) : list_base(num, val) {} \
+    list(iterator start_, iterator end_) : list_base(start_, end_) {}  \
+    list& operator=(const list& x) {  \
+      list_base::operator=(x);  \
+      return *this;  \
+    } \
+  }; \
+}
+
+#endif // EIGEN_STDLIST_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/StlSupport/StdVector.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/StlSupport/StdVector.h
new file mode 100644
index 0000000000000000000000000000000000000000..02dfb3918654bdc31ab91ae0b24155b3706595c3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/StlSupport/StdVector.h
@@ -0,0 +1,51 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STDVECTOR_H
+#define EIGEN_STDVECTOR_H
+
+#ifndef EIGEN_STDVECTOR_MODULE_H
+#error "Please include Eigen/StdVector instead of including this file directly."
+#endif
+
+#include "details.h"
+
+/**
+ * This section contains a convenience MACRO which allows an easy specialization of
+ * std::vector such that for data types with alignment issues the correct allocator
+ * is used automatically.
+ */
+#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...) \
+namespace std \
+{ \
+  template<> \
+  class vector<__VA_ARGS__, std::allocator<__VA_ARGS__> >  \
+    : public vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > \
+  { \
+    typedef vector<__VA_ARGS__, EIGEN_ALIGNED_ALLOCATOR<__VA_ARGS__> > vector_base; \
+  public: \
+    typedef __VA_ARGS__ value_type; \
+    typedef vector_base::allocator_type allocator_type; \
+    typedef vector_base::size_type size_type;  \
+    typedef vector_base::iterator iterator;  \
+    explicit vector(const allocator_type& a = allocator_type()) : vector_base(a) {}  \
+    template<typename InputIterator> \
+    vector(InputIterator first, InputIterator last, const allocator_type& a = allocator_type()) : vector_base(first, last, a) {} \
+    vector(const vector& c) : vector_base(c) {}  \
+    explicit vector(size_type num, const value_type& val = value_type()) : vector_base(num, val) {} \
+    vector(iterator start_, iterator end_) : vector_base(start_, end_) {}  \
+    vector& operator=(const vector& x) {  \
+      vector_base::operator=(x);  \
+      return *this;  \
+    } \
+  }; \
+}
+
+#endif // EIGEN_STDVECTOR_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/StlSupport/details.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/StlSupport/details.h
new file mode 100644
index 0000000000000000000000000000000000000000..29fd8714d894e952d9b43bda7f164fe1c11fe54d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/StlSupport/details.h
@@ -0,0 +1,80 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STL_DETAILS_H
+#define EIGEN_STL_DETAILS_H
+
+#ifndef EIGEN_ALIGNED_ALLOCATOR
+  #define EIGEN_ALIGNED_ALLOCATOR Eigen::aligned_allocator
+#endif
+
+namespace Eigen {
+
+  // This one is needed to prevent reimplementing the whole std::vector.
+  template <class T>
+  class aligned_allocator_indirection : public EIGEN_ALIGNED_ALLOCATOR<T>
+  {
+  public:
+    typedef std::size_t     size_type;
+    typedef std::ptrdiff_t  difference_type;
+    typedef T*              pointer;
+    typedef const T*        const_pointer;
+    typedef T&              reference;
+    typedef const T&        const_reference;
+    typedef T               value_type;
+
+    template<class U>
+    struct rebind
+    {
+      typedef aligned_allocator_indirection<U> other;
+    };
+
+    aligned_allocator_indirection() {}
+    aligned_allocator_indirection(const aligned_allocator_indirection& ) : EIGEN_ALIGNED_ALLOCATOR<T>() {}
+    aligned_allocator_indirection(const EIGEN_ALIGNED_ALLOCATOR<T>& ) {}
+    template<class U>
+    aligned_allocator_indirection(const aligned_allocator_indirection<U>& ) {}
+    template<class U>
+    aligned_allocator_indirection(const EIGEN_ALIGNED_ALLOCATOR<U>& ) {}
+    ~aligned_allocator_indirection() {}
+  };
+
+#if EIGEN_COMP_MSVC
+
+  // sometimes, MSVC detects, at compile time, that the argument x
+  // in std::vector::resize(size_t s,T x) won't be aligned and generate an error
+  // even if this function is never called. Whence this little wrapper.
+#define EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T) \
+  std::conditional_t<Eigen::internal::is_arithmetic<T>::value, T, Eigen::internal::workaround_msvc_stl_support<T> >
+
+  namespace internal {
+  template<typename T> struct workaround_msvc_stl_support : public T
+  {
+    inline workaround_msvc_stl_support() : T() {}
+    inline workaround_msvc_stl_support(const T& other) : T(other) {}
+    inline operator T& () { return *static_cast<T*>(this); }
+    inline operator const T& () const { return *static_cast<const T*>(this); }
+    template<typename OtherT>
+    inline T& operator=(const OtherT& other)
+    { T::operator=(other); return *this; }
+    inline workaround_msvc_stl_support& operator=(const workaround_msvc_stl_support& other)
+    { T::operator=(other); return *this; }
+  };
+  }
+
+#else
+
+#define EIGEN_WORKAROUND_MSVC_STL_SUPPORT(T) T
+
+#endif
+
+}
+
+#endif // EIGEN_STL_DETAILS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SuperLUSupport/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SuperLUSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..94a62b55066b83097dd046a10fbd3943ce630c38
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SuperLUSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_SUPERLUSUPPORT_MODULE_H
+#error "Please include Eigen/SuperLUSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SuperLUSupport/SuperLUSupport.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SuperLUSupport/SuperLUSupport.h
new file mode 100644
index 0000000000000000000000000000000000000000..4bac22d4bfc9ff8384da26ddcb25e7b0397f5ecf
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/SuperLUSupport/SuperLUSupport.h
@@ -0,0 +1,1027 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SUPERLUSUPPORT_H
+#define EIGEN_SUPERLUSUPPORT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+#if defined(SUPERLU_MAJOR_VERSION) && (SUPERLU_MAJOR_VERSION >= 5)
+#define DECL_GSSVX(PREFIX,FLOATTYPE,KEYTYPE)		\
+    extern "C" {                                                                                          \
+      extern void PREFIX##gssvx(superlu_options_t *, SuperMatrix *, int *, int *, int *,                  \
+                                char *, FLOATTYPE *, FLOATTYPE *, SuperMatrix *, SuperMatrix *,           \
+                                void *, int, SuperMatrix *, SuperMatrix *,                                \
+                                FLOATTYPE *, FLOATTYPE *, FLOATTYPE *, FLOATTYPE *,                       \
+                                GlobalLU_t *, mem_usage_t *, SuperLUStat_t *, int *);                     \
+    }                                                                                                     \
+    inline float SuperLU_gssvx(superlu_options_t *options, SuperMatrix *A,                                \
+         int *perm_c, int *perm_r, int *etree, char *equed,                                               \
+         FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L,                                                      \
+         SuperMatrix *U, void *work, int lwork,                                                           \
+         SuperMatrix *B, SuperMatrix *X,                                                                  \
+         FLOATTYPE *recip_pivot_growth,                                                                   \
+         FLOATTYPE *rcond, FLOATTYPE *ferr, FLOATTYPE *berr,                                              \
+         SuperLUStat_t *stats, int *info, KEYTYPE) {                                                      \
+    mem_usage_t mem_usage;                                                                                \
+    GlobalLU_t gLU;                                                                                       \
+    PREFIX##gssvx(options, A, perm_c, perm_r, etree, equed, R, C, L,                                      \
+         U, work, lwork, B, X, recip_pivot_growth, rcond,                                                 \
+         ferr, berr, &gLU, &mem_usage, stats, info);                                                      \
+    return mem_usage.for_lu; /* bytes used by the factor storage */                                       \
+  }
+#else // version < 5.0
+#define DECL_GSSVX(PREFIX,FLOATTYPE,KEYTYPE)		\
+    extern "C" {                                                                                          \
+      extern void PREFIX##gssvx(superlu_options_t *, SuperMatrix *, int *, int *, int *,                  \
+                                char *, FLOATTYPE *, FLOATTYPE *, SuperMatrix *, SuperMatrix *,           \
+                                void *, int, SuperMatrix *, SuperMatrix *,                                \
+                                FLOATTYPE *, FLOATTYPE *, FLOATTYPE *, FLOATTYPE *,                       \
+                                mem_usage_t *, SuperLUStat_t *, int *);                                   \
+    }                                                                                                     \
+    inline float SuperLU_gssvx(superlu_options_t *options, SuperMatrix *A,                                \
+         int *perm_c, int *perm_r, int *etree, char *equed,                                               \
+         FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L,                                                      \
+         SuperMatrix *U, void *work, int lwork,                                                           \
+         SuperMatrix *B, SuperMatrix *X,                                                                  \
+         FLOATTYPE *recip_pivot_growth,                                                                   \
+         FLOATTYPE *rcond, FLOATTYPE *ferr, FLOATTYPE *berr,                                              \
+         SuperLUStat_t *stats, int *info, KEYTYPE) {                                                      \
+    mem_usage_t mem_usage;                                                                                \
+    PREFIX##gssvx(options, A, perm_c, perm_r, etree, equed, R, C, L,                                      \
+         U, work, lwork, B, X, recip_pivot_growth, rcond,                                                 \
+         ferr, berr, &mem_usage, stats, info);                                                            \
+    return mem_usage.for_lu; /* bytes used by the factor storage */                                       \
+  }
+#endif
+
+DECL_GSSVX(s,float,float)
+DECL_GSSVX(c,float,std::complex<float>)
+DECL_GSSVX(d,double,double)
+DECL_GSSVX(z,double,std::complex<double>)
+
+#ifdef MILU_ALPHA
+#define EIGEN_SUPERLU_HAS_ILU
+#endif
+
+#ifdef EIGEN_SUPERLU_HAS_ILU
+
+// similarly for the incomplete factorization using gsisx
+#define DECL_GSISX(PREFIX,FLOATTYPE,KEYTYPE)                                                    \
+    extern "C" {                                                                                \
+      extern void PREFIX##gsisx(superlu_options_t *, SuperMatrix *, int *, int *, int *,        \
+                         char *, FLOATTYPE *, FLOATTYPE *, SuperMatrix *, SuperMatrix *,        \
+                         void *, int, SuperMatrix *, SuperMatrix *, FLOATTYPE *, FLOATTYPE *,   \
+                         mem_usage_t *, SuperLUStat_t *, int *);                        \
+    }                                                                                           \
+    inline float SuperLU_gsisx(superlu_options_t *options, SuperMatrix *A,                      \
+         int *perm_c, int *perm_r, int *etree, char *equed,                                     \
+         FLOATTYPE *R, FLOATTYPE *C, SuperMatrix *L,                                            \
+         SuperMatrix *U, void *work, int lwork,                                                 \
+         SuperMatrix *B, SuperMatrix *X,                                                        \
+         FLOATTYPE *recip_pivot_growth,                                                         \
+         FLOATTYPE *rcond,                                                                      \
+         SuperLUStat_t *stats, int *info, KEYTYPE) {                                            \
+    mem_usage_t mem_usage;                                                              \
+    PREFIX##gsisx(options, A, perm_c, perm_r, etree, equed, R, C, L,                            \
+         U, work, lwork, B, X, recip_pivot_growth, rcond,                                       \
+         &mem_usage, stats, info);                                                              \
+    return mem_usage.for_lu; /* bytes used by the factor storage */                             \
+  }
+
+DECL_GSISX(s,float,float)
+DECL_GSISX(c,float,std::complex<float>)
+DECL_GSISX(d,double,double)
+DECL_GSISX(z,double,std::complex<double>)
+
+#endif
+
+template<typename MatrixType>
+struct SluMatrixMapHelper;
+
+/** \internal
+  *
+  * A wrapper class for SuperLU matrices. It supports only compressed sparse matrices
+  * and dense matrices. Supernodal and other fancy format are not supported by this wrapper.
+  *
+  * This wrapper class mainly aims to avoids the need of dynamic allocation of the storage structure.
+  */
+struct SluMatrix : SuperMatrix
+{
+  SluMatrix()
+  {
+    Store = &storage;
+  }
+
+  SluMatrix(const SluMatrix& other)
+    : SuperMatrix(other)
+  {
+    Store = &storage;
+    storage = other.storage;
+  }
+
+  SluMatrix& operator=(const SluMatrix& other)
+  {
+    SuperMatrix::operator=(static_cast<const SuperMatrix&>(other));
+    Store = &storage;
+    storage = other.storage;
+    return *this;
+  }
+
+  struct
+  {
+    union {int nnz;int lda;};
+    void *values;
+    int *innerInd;
+    int *outerInd;
+  } storage;
+
+  void setStorageType(Stype_t t)
+  {
+    Stype = t;
+    if (t==SLU_NC || t==SLU_NR || t==SLU_DN)
+      Store = &storage;
+    else
+    {
+      eigen_assert(false && "storage type not supported");
+      Store = 0;
+    }
+  }
+
+  template<typename Scalar>
+  void setScalarType()
+  {
+    if (internal::is_same<Scalar,float>::value)
+      Dtype = SLU_S;
+    else if (internal::is_same<Scalar,double>::value)
+      Dtype = SLU_D;
+    else if (internal::is_same<Scalar,std::complex<float> >::value)
+      Dtype = SLU_C;
+    else if (internal::is_same<Scalar,std::complex<double> >::value)
+      Dtype = SLU_Z;
+    else
+    {
+      eigen_assert(false && "Scalar type not supported by SuperLU");
+    }
+  }
+
+  template<typename MatrixType>
+  static SluMatrix Map(MatrixBase<MatrixType>& _mat)
+  {
+    MatrixType& mat(_mat.derived());
+    eigen_assert( ((MatrixType::Flags&RowMajorBit)!=RowMajorBit) && "row-major dense matrices are not supported by SuperLU");
+    SluMatrix res;
+    res.setStorageType(SLU_DN);
+    res.setScalarType<typename MatrixType::Scalar>();
+    res.Mtype     = SLU_GE;
+
+    res.nrow      = internal::convert_index<int>(mat.rows());
+    res.ncol      = internal::convert_index<int>(mat.cols());
+
+    res.storage.lda       = internal::convert_index<int>(MatrixType::IsVectorAtCompileTime ? mat.size() : mat.outerStride());
+    res.storage.values    = (void*)(mat.data());
+    return res;
+  }
+
+  template<typename MatrixType>
+  static SluMatrix Map(SparseMatrixBase<MatrixType>& a_mat)
+  {
+    MatrixType &mat(a_mat.derived());
+    SluMatrix res;
+    if ((MatrixType::Flags&RowMajorBit)==RowMajorBit)
+    {
+      res.setStorageType(SLU_NR);
+      res.nrow      = internal::convert_index<int>(mat.cols());
+      res.ncol      = internal::convert_index<int>(mat.rows());
+    }
+    else
+    {
+      res.setStorageType(SLU_NC);
+      res.nrow      = internal::convert_index<int>(mat.rows());
+      res.ncol      = internal::convert_index<int>(mat.cols());
+    }
+
+    res.Mtype       = SLU_GE;
+
+    res.storage.nnz       = internal::convert_index<int>(mat.nonZeros());
+    res.storage.values    = mat.valuePtr();
+    res.storage.innerInd  = mat.innerIndexPtr();
+    res.storage.outerInd  = mat.outerIndexPtr();
+
+    res.setScalarType<typename MatrixType::Scalar>();
+
+    // FIXME the following is not very accurate
+    if (int(MatrixType::Flags) & int(Upper))
+      res.Mtype = SLU_TRU;
+    if (int(MatrixType::Flags) & int(Lower))
+      res.Mtype = SLU_TRL;
+
+    eigen_assert(((int(MatrixType::Flags) & int(SelfAdjoint))==0) && "SelfAdjoint matrix shape not supported by SuperLU");
+
+    return res;
+  }
+};
+
+template<typename Scalar, int Rows, int Cols, int Options, int MRows, int MCols>
+struct SluMatrixMapHelper<Matrix<Scalar,Rows,Cols,Options,MRows,MCols> >
+{
+  typedef Matrix<Scalar,Rows,Cols,Options,MRows,MCols> MatrixType;
+  static void run(MatrixType& mat, SluMatrix& res)
+  {
+    eigen_assert( ((Options&RowMajor)!=RowMajor) && "row-major dense matrices is not supported by SuperLU");
+    res.setStorageType(SLU_DN);
+    res.setScalarType<Scalar>();
+    res.Mtype     = SLU_GE;
+
+    res.nrow      = mat.rows();
+    res.ncol      = mat.cols();
+
+    res.storage.lda       = mat.outerStride();
+    res.storage.values    = mat.data();
+  }
+};
+
+template<typename Derived>
+struct SluMatrixMapHelper<SparseMatrixBase<Derived> >
+{
+  typedef Derived MatrixType;
+  static void run(MatrixType& mat, SluMatrix& res)
+  {
+    if ((MatrixType::Flags&RowMajorBit)==RowMajorBit)
+    {
+      res.setStorageType(SLU_NR);
+      res.nrow      = mat.cols();
+      res.ncol      = mat.rows();
+    }
+    else
+    {
+      res.setStorageType(SLU_NC);
+      res.nrow      = mat.rows();
+      res.ncol      = mat.cols();
+    }
+
+    res.Mtype       = SLU_GE;
+
+    res.storage.nnz       = mat.nonZeros();
+    res.storage.values    = mat.valuePtr();
+    res.storage.innerInd  = mat.innerIndexPtr();
+    res.storage.outerInd  = mat.outerIndexPtr();
+
+    res.setScalarType<typename MatrixType::Scalar>();
+
+    // FIXME the following is not very accurate
+    if (MatrixType::Flags & Upper)
+      res.Mtype = SLU_TRU;
+    if (MatrixType::Flags & Lower)
+      res.Mtype = SLU_TRL;
+
+    eigen_assert(((MatrixType::Flags & SelfAdjoint)==0) && "SelfAdjoint matrix shape not supported by SuperLU");
+  }
+};
+
+namespace internal {
+
+template<typename MatrixType>
+SluMatrix asSluMatrix(MatrixType& mat)
+{
+  return SluMatrix::Map(mat);
+}
+
+/** View a Super LU matrix as an Eigen expression */
+template<typename Scalar, int Flags, typename Index>
+Map<SparseMatrix<Scalar,Flags,Index> > map_superlu(SluMatrix& sluMat)
+{
+  eigen_assert(((Flags&RowMajor)==RowMajor && sluMat.Stype == SLU_NR)
+         || ((Flags&ColMajor)==ColMajor && sluMat.Stype == SLU_NC));
+
+  Index outerSize = (Flags&RowMajor)==RowMajor ? sluMat.ncol : sluMat.nrow;
+
+  return Map<SparseMatrix<Scalar,Flags,Index> >(
+    sluMat.nrow, sluMat.ncol, sluMat.storage.outerInd[outerSize],
+    sluMat.storage.outerInd, sluMat.storage.innerInd, reinterpret_cast<Scalar*>(sluMat.storage.values) );
+}
+
+} // end namespace internal
+
+/** \ingroup SuperLUSupport_Module
+  * \class SuperLUBase
+  * \brief The base class for the direct and incomplete LU factorization of SuperLU
+  */
+template<typename MatrixType_, typename Derived>
+class SuperLUBase : public SparseSolverBase<Derived>
+{
+  protected:
+    typedef SparseSolverBase<Derived> Base;
+    using Base::derived;
+    using Base::m_isInitialized;
+  public:
+    typedef MatrixType_ MatrixType;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    typedef Matrix<Scalar,Dynamic,1> Vector;
+    typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
+    typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;    
+    typedef Map<PermutationMatrix<Dynamic,Dynamic,int> > PermutationMap;
+    typedef SparseMatrix<Scalar> LUMatrixType;
+    enum {
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+
+  public:
+
+    SuperLUBase() {}
+
+    ~SuperLUBase()
+    {
+      clearFactors();
+    }
+    
+    inline Index rows() const { return m_matrix.rows(); }
+    inline Index cols() const { return m_matrix.cols(); }
+    
+    /** \returns a reference to the Super LU option object to configure the  Super LU algorithms. */
+    inline superlu_options_t& options() { return m_sluOptions; }
+    
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful,
+      *          \c NumericalIssue if the matrix.appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+
+    /** Computes the sparse Cholesky decomposition of \a matrix */
+    void compute(const MatrixType& matrix)
+    {
+      derived().analyzePattern(matrix);
+      derived().factorize(matrix);
+    }
+
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      * 
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& /*matrix*/)
+    {
+      m_isInitialized = true;
+      m_info = Success;
+      m_analysisIsOk = true;
+      m_factorizationIsOk = false;
+    }
+    
+    template<typename Stream>
+    void dumpMemory(Stream& /*s*/)
+    {}
+    
+  protected:
+    
+    void initFactorization(const MatrixType& a)
+    {
+      set_default_options(&this->m_sluOptions);
+      
+      const Index size = a.rows();
+      m_matrix = a;
+
+      m_sluA = internal::asSluMatrix(m_matrix);
+      clearFactors();
+
+      m_p.resize(size);
+      m_q.resize(size);
+      m_sluRscale.resize(size);
+      m_sluCscale.resize(size);
+      m_sluEtree.resize(size);
+
+      // set empty B and X
+      m_sluB.setStorageType(SLU_DN);
+      m_sluB.setScalarType<Scalar>();
+      m_sluB.Mtype          = SLU_GE;
+      m_sluB.storage.values = 0;
+      m_sluB.nrow           = 0;
+      m_sluB.ncol           = 0;
+      m_sluB.storage.lda    = internal::convert_index<int>(size);
+      m_sluX                = m_sluB;
+      
+      m_extractedDataAreDirty = true;
+    }
+    
+    void init()
+    {
+      m_info = InvalidInput;
+      m_isInitialized = false;
+      m_sluL.Store = 0;
+      m_sluU.Store = 0;
+    }
+    
+    void extractData() const;
+
+    void clearFactors()
+    {
+      if(m_sluL.Store)
+        Destroy_SuperNode_Matrix(&m_sluL);
+      if(m_sluU.Store)
+        Destroy_CompCol_Matrix(&m_sluU);
+
+      m_sluL.Store = 0;
+      m_sluU.Store = 0;
+
+      memset(&m_sluL,0,sizeof m_sluL);
+      memset(&m_sluU,0,sizeof m_sluU);
+    }
+
+    // cached data to reduce reallocation, etc.
+    mutable LUMatrixType m_l;
+    mutable LUMatrixType m_u;
+    mutable IntColVectorType m_p;
+    mutable IntRowVectorType m_q;
+
+    mutable LUMatrixType m_matrix;  // copy of the factorized matrix
+    mutable SluMatrix m_sluA;
+    mutable SuperMatrix m_sluL, m_sluU;
+    mutable SluMatrix m_sluB, m_sluX;
+    mutable SuperLUStat_t m_sluStat;
+    mutable superlu_options_t m_sluOptions;
+    mutable std::vector<int> m_sluEtree;
+    mutable Matrix<RealScalar,Dynamic,1> m_sluRscale, m_sluCscale;
+    mutable Matrix<RealScalar,Dynamic,1> m_sluFerr, m_sluBerr;
+    mutable char m_sluEqued;
+
+    mutable ComputationInfo m_info;
+    int m_factorizationIsOk;
+    int m_analysisIsOk;
+    mutable bool m_extractedDataAreDirty;
+    
+  private:
+    SuperLUBase(SuperLUBase& ) { }
+};
+
+
+/** \ingroup SuperLUSupport_Module
+  * \class SuperLU
+  * \brief A sparse direct LU factorization and solver based on the SuperLU library
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a direct LU factorization
+  * using the SuperLU library. The sparse matrix A must be squared and invertible. The vectors or matrices
+  * X and B can be either dense or sparse.
+  *
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  *
+  * \warning This class is only for the 4.x versions of SuperLU. The 3.x and 5.x versions are not supported.
+  *
+  * \implsparsesolverconcept
+  *
+  * \sa \ref TutorialSparseSolverConcept, class SparseLU
+  */
+template<typename MatrixType_>
+class SuperLU : public SuperLUBase<MatrixType_,SuperLU<MatrixType_> >
+{
+  public:
+    typedef SuperLUBase<MatrixType_,SuperLU> Base;
+    typedef MatrixType_ MatrixType;
+    typedef typename Base::Scalar Scalar;
+    typedef typename Base::RealScalar RealScalar;
+    typedef typename Base::StorageIndex StorageIndex;
+    typedef typename Base::IntRowVectorType IntRowVectorType;
+    typedef typename Base::IntColVectorType IntColVectorType;   
+    typedef typename Base::PermutationMap PermutationMap;
+    typedef typename Base::LUMatrixType LUMatrixType;
+    typedef TriangularView<LUMatrixType, Lower|UnitDiag>  LMatrixType;
+    typedef TriangularView<LUMatrixType,  Upper>          UMatrixType;
+
+  public:
+    using Base::_solve_impl;
+
+    SuperLU() : Base() { init(); }
+
+    explicit SuperLU(const MatrixType& matrix) : Base()
+    {
+      init();
+      Base::compute(matrix);
+    }
+
+    ~SuperLU()
+    {
+    }
+    
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      * 
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& matrix)
+    {
+      m_info = InvalidInput;
+      m_isInitialized = false;
+      Base::analyzePattern(matrix);
+    }
+    
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
+      *
+      * \sa analyzePattern()
+      */
+    void factorize(const MatrixType& matrix);
+    
+    /** \internal */
+    template<typename Rhs,typename Dest>
+    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const;
+    
+    inline const LMatrixType& matrixL() const
+    {
+      if (m_extractedDataAreDirty) this->extractData();
+      return m_l;
+    }
+
+    inline const UMatrixType& matrixU() const
+    {
+      if (m_extractedDataAreDirty) this->extractData();
+      return m_u;
+    }
+
+    inline const IntColVectorType& permutationP() const
+    {
+      if (m_extractedDataAreDirty) this->extractData();
+      return m_p;
+    }
+
+    inline const IntRowVectorType& permutationQ() const
+    {
+      if (m_extractedDataAreDirty) this->extractData();
+      return m_q;
+    }
+    
+    Scalar determinant() const;
+    
+  protected:
+    
+    using Base::m_matrix;
+    using Base::m_sluOptions;
+    using Base::m_sluA;
+    using Base::m_sluB;
+    using Base::m_sluX;
+    using Base::m_p;
+    using Base::m_q;
+    using Base::m_sluEtree;
+    using Base::m_sluEqued;
+    using Base::m_sluRscale;
+    using Base::m_sluCscale;
+    using Base::m_sluL;
+    using Base::m_sluU;
+    using Base::m_sluStat;
+    using Base::m_sluFerr;
+    using Base::m_sluBerr;
+    using Base::m_l;
+    using Base::m_u;
+    
+    using Base::m_analysisIsOk;
+    using Base::m_factorizationIsOk;
+    using Base::m_extractedDataAreDirty;
+    using Base::m_isInitialized;
+    using Base::m_info;
+    
+    void init()
+    {
+      Base::init();
+      
+      set_default_options(&this->m_sluOptions);
+      m_sluOptions.PrintStat        = NO;
+      m_sluOptions.ConditionNumber  = NO;
+      m_sluOptions.Trans            = NOTRANS;
+      m_sluOptions.ColPerm          = COLAMD;
+    }
+    
+    
+  private:
+    SuperLU(SuperLU& ) { }
+};
+
+template<typename MatrixType>
+void SuperLU<MatrixType>::factorize(const MatrixType& a)
+{
+  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+  if(!m_analysisIsOk)
+  {
+    m_info = InvalidInput;
+    return;
+  }
+  
+  this->initFactorization(a);
+  
+  m_sluOptions.ColPerm = COLAMD;
+  int info = 0;
+  RealScalar recip_pivot_growth, rcond;
+  RealScalar ferr, berr;
+
+  StatInit(&m_sluStat);
+  SuperLU_gssvx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0],
+                &m_sluEqued, &m_sluRscale[0], &m_sluCscale[0],
+                &m_sluL, &m_sluU,
+                NULL, 0,
+                &m_sluB, &m_sluX,
+                &recip_pivot_growth, &rcond,
+                &ferr, &berr,
+                &m_sluStat, &info, Scalar());
+  StatFree(&m_sluStat);
+
+  m_extractedDataAreDirty = true;
+
+  // FIXME how to better check for errors ???
+  m_info = info == 0 ? Success : NumericalIssue;
+  m_factorizationIsOk = true;
+}
+
+template<typename MatrixType>
+template<typename Rhs,typename Dest>
+void SuperLU<MatrixType>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest>& x) const
+{
+  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()");
+
+  const Index rhsCols = b.cols();
+  eigen_assert(m_matrix.rows()==b.rows());
+
+  m_sluOptions.Trans = NOTRANS;
+  m_sluOptions.Fact = FACTORED;
+  m_sluOptions.IterRefine = NOREFINE;
+  
+
+  m_sluFerr.resize(rhsCols);
+  m_sluBerr.resize(rhsCols);
+  
+  Ref<const Matrix<typename Rhs::Scalar,Dynamic,Dynamic,ColMajor> > b_ref(b);
+  Ref<const Matrix<typename Dest::Scalar,Dynamic,Dynamic,ColMajor> > x_ref(x);
+  
+  m_sluB = SluMatrix::Map(b_ref.const_cast_derived());
+  m_sluX = SluMatrix::Map(x_ref.const_cast_derived());
+  
+  typename Rhs::PlainObject b_cpy;
+  if(m_sluEqued!='N')
+  {
+    b_cpy = b;
+    m_sluB = SluMatrix::Map(b_cpy.const_cast_derived());  
+  }
+
+  StatInit(&m_sluStat);
+  int info = 0;
+  RealScalar recip_pivot_growth, rcond;
+  SuperLU_gssvx(&m_sluOptions, &m_sluA,
+                m_q.data(), m_p.data(),
+                &m_sluEtree[0], &m_sluEqued,
+                &m_sluRscale[0], &m_sluCscale[0],
+                &m_sluL, &m_sluU,
+                NULL, 0,
+                &m_sluB, &m_sluX,
+                &recip_pivot_growth, &rcond,
+                &m_sluFerr[0], &m_sluBerr[0],
+                &m_sluStat, &info, Scalar());
+  StatFree(&m_sluStat);
+  
+  if(x.derived().data() != x_ref.data())
+    x = x_ref;
+  
+  m_info = info==0 ? Success : NumericalIssue;
+}
+
+// the code of this extractData() function has been adapted from the SuperLU's Matlab support code,
+//
+//  Copyright (c) 1994 by Xerox Corporation.  All rights reserved.
+//
+//  THIS MATERIAL IS PROVIDED AS IS, WITH ABSOLUTELY NO WARRANTY
+//  EXPRESSED OR IMPLIED.  ANY USE IS AT YOUR OWN RISK.
+//
+template<typename MatrixType, typename Derived>
+void SuperLUBase<MatrixType,Derived>::extractData() const
+{
+  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for extracting factors, you must first call either compute() or analyzePattern()/factorize()");
+  if (m_extractedDataAreDirty)
+  {
+    int         upper;
+    int         fsupc, istart, nsupr;
+    int         lastl = 0, lastu = 0;
+    SCformat    *Lstore = static_cast<SCformat*>(m_sluL.Store);
+    NCformat    *Ustore = static_cast<NCformat*>(m_sluU.Store);
+    Scalar      *SNptr;
+
+    const Index size = m_matrix.rows();
+    m_l.resize(size,size);
+    m_l.resizeNonZeros(Lstore->nnz);
+    m_u.resize(size,size);
+    m_u.resizeNonZeros(Ustore->nnz);
+
+    int* Lcol = m_l.outerIndexPtr();
+    int* Lrow = m_l.innerIndexPtr();
+    Scalar* Lval = m_l.valuePtr();
+
+    int* Ucol = m_u.outerIndexPtr();
+    int* Urow = m_u.innerIndexPtr();
+    Scalar* Uval = m_u.valuePtr();
+
+    Ucol[0] = 0;
+    Ucol[0] = 0;
+
+    /* for each supernode */
+    for (int k = 0; k <= Lstore->nsuper; ++k)
+    {
+      fsupc   = L_FST_SUPC(k);
+      istart  = L_SUB_START(fsupc);
+      nsupr   = L_SUB_START(fsupc+1) - istart;
+      upper   = 1;
+
+      /* for each column in the supernode */
+      for (int j = fsupc; j < L_FST_SUPC(k+1); ++j)
+      {
+        SNptr = &((Scalar*)Lstore->nzval)[L_NZ_START(j)];
+
+        /* Extract U */
+        for (int i = U_NZ_START(j); i < U_NZ_START(j+1); ++i)
+        {
+          Uval[lastu] = ((Scalar*)Ustore->nzval)[i];
+          /* Matlab doesn't like explicit zero. */
+          if (Uval[lastu] != 0.0)
+            Urow[lastu++] = U_SUB(i);
+        }
+        for (int i = 0; i < upper; ++i)
+        {
+          /* upper triangle in the supernode */
+          Uval[lastu] = SNptr[i];
+          /* Matlab doesn't like explicit zero. */
+          if (Uval[lastu] != 0.0)
+            Urow[lastu++] = L_SUB(istart+i);
+        }
+        Ucol[j+1] = lastu;
+
+        /* Extract L */
+        Lval[lastl] = 1.0; /* unit diagonal */
+        Lrow[lastl++] = L_SUB(istart + upper - 1);
+        for (int i = upper; i < nsupr; ++i)
+        {
+          Lval[lastl] = SNptr[i];
+          /* Matlab doesn't like explicit zero. */
+          if (Lval[lastl] != 0.0)
+            Lrow[lastl++] = L_SUB(istart+i);
+        }
+        Lcol[j+1] = lastl;
+
+        ++upper;
+      } /* for j ... */
+
+    } /* for k ... */
+
+    // squeeze the matrices :
+    m_l.resizeNonZeros(lastl);
+    m_u.resizeNonZeros(lastu);
+
+    m_extractedDataAreDirty = false;
+  }
+}
+
+template<typename MatrixType>
+typename SuperLU<MatrixType>::Scalar SuperLU<MatrixType>::determinant() const
+{
+  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for computing the determinant, you must first call either compute() or analyzePattern()/factorize()");
+  
+  if (m_extractedDataAreDirty)
+    this->extractData();
+
+  Scalar det = Scalar(1);
+  for (int j=0; j<m_u.cols(); ++j)
+  {
+    if (m_u.outerIndexPtr()[j+1]-m_u.outerIndexPtr()[j] > 0)
+    {
+      int lastId = m_u.outerIndexPtr()[j+1]-1;
+      eigen_assert(m_u.innerIndexPtr()[lastId]<=j);
+      if (m_u.innerIndexPtr()[lastId]==j)
+        det *= m_u.valuePtr()[lastId];
+    }
+  }
+  if(PermutationMap(m_p.data(),m_p.size()).determinant()*PermutationMap(m_q.data(),m_q.size()).determinant()<0)
+    det = -det;
+  if(m_sluEqued!='N')
+    return det/m_sluRscale.prod()/m_sluCscale.prod();
+  else
+    return det;
+}
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+#define EIGEN_SUPERLU_HAS_ILU
+#endif
+
+#ifdef EIGEN_SUPERLU_HAS_ILU
+
+/** \ingroup SuperLUSupport_Module
+  * \class SuperILU
+  * \brief A sparse direct \b incomplete LU factorization and solver based on the SuperLU library
+  *
+  * This class allows to solve for an approximate solution of A.X = B sparse linear problems via an incomplete LU factorization
+  * using the SuperLU library. This class is aimed to be used as a preconditioner of the iterative linear solvers.
+  *
+  * \warning This class is only for the 4.x versions of SuperLU. The 3.x and 5.x versions are not supported.
+  *
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  *
+  * \implsparsesolverconcept
+  *
+  * \sa \ref TutorialSparseSolverConcept, class IncompleteLUT, class ConjugateGradient, class BiCGSTAB
+  */
+
+template<typename MatrixType_>
+class SuperILU : public SuperLUBase<MatrixType_,SuperILU<MatrixType_> >
+{
+  public:
+    typedef SuperLUBase<MatrixType_,SuperILU> Base;
+    typedef MatrixType_ MatrixType;
+    typedef typename Base::Scalar Scalar;
+    typedef typename Base::RealScalar RealScalar;
+
+  public:
+    using Base::_solve_impl;
+
+    SuperILU() : Base() { init(); }
+
+    SuperILU(const MatrixType& matrix) : Base()
+    {
+      init();
+      Base::compute(matrix);
+    }
+
+    ~SuperILU()
+    {
+    }
+    
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      * 
+      * \sa factorize()
+      */
+    void analyzePattern(const MatrixType& matrix)
+    {
+      Base::analyzePattern(matrix);
+    }
+    
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the symbolic decomposition has been performed.
+      *
+      * \sa analyzePattern()
+      */
+    void factorize(const MatrixType& matrix);
+    
+    #ifndef EIGEN_PARSED_BY_DOXYGEN
+    /** \internal */
+    template<typename Rhs,typename Dest>
+    void _solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest> &dest) const;
+    #endif // EIGEN_PARSED_BY_DOXYGEN
+    
+  protected:
+    
+    using Base::m_matrix;
+    using Base::m_sluOptions;
+    using Base::m_sluA;
+    using Base::m_sluB;
+    using Base::m_sluX;
+    using Base::m_p;
+    using Base::m_q;
+    using Base::m_sluEtree;
+    using Base::m_sluEqued;
+    using Base::m_sluRscale;
+    using Base::m_sluCscale;
+    using Base::m_sluL;
+    using Base::m_sluU;
+    using Base::m_sluStat;
+    using Base::m_sluFerr;
+    using Base::m_sluBerr;
+    using Base::m_l;
+    using Base::m_u;
+    
+    using Base::m_analysisIsOk;
+    using Base::m_factorizationIsOk;
+    using Base::m_extractedDataAreDirty;
+    using Base::m_isInitialized;
+    using Base::m_info;
+
+    void init()
+    {
+      Base::init();
+      
+      ilu_set_default_options(&m_sluOptions);
+      m_sluOptions.PrintStat        = NO;
+      m_sluOptions.ConditionNumber  = NO;
+      m_sluOptions.Trans            = NOTRANS;
+      m_sluOptions.ColPerm          = MMD_AT_PLUS_A;
+      
+      // no attempt to preserve column sum
+      m_sluOptions.ILU_MILU = SILU;
+      // only basic ILU(k) support -- no direct control over memory consumption
+      // better to use ILU_DropRule = DROP_BASIC | DROP_AREA
+      // and set ILU_FillFactor to max memory growth
+      m_sluOptions.ILU_DropRule = DROP_BASIC;
+      m_sluOptions.ILU_DropTol = NumTraits<Scalar>::dummy_precision()*10;
+    }
+    
+  private:
+    SuperILU(SuperILU& ) { }
+};
+
+template<typename MatrixType>
+void SuperILU<MatrixType>::factorize(const MatrixType& a)
+{
+  eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+  if(!m_analysisIsOk)
+  {
+    m_info = InvalidInput;
+    return;
+  }
+  
+  this->initFactorization(a);
+
+  int info = 0;
+  RealScalar recip_pivot_growth, rcond;
+
+  StatInit(&m_sluStat);
+  SuperLU_gsisx(&m_sluOptions, &m_sluA, m_q.data(), m_p.data(), &m_sluEtree[0],
+                &m_sluEqued, &m_sluRscale[0], &m_sluCscale[0],
+                &m_sluL, &m_sluU,
+                NULL, 0,
+                &m_sluB, &m_sluX,
+                &recip_pivot_growth, &rcond,
+                &m_sluStat, &info, Scalar());
+  StatFree(&m_sluStat);
+
+  // FIXME how to better check for errors ???
+  m_info = info == 0 ? Success : NumericalIssue;
+  m_factorizationIsOk = true;
+}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template<typename MatrixType>
+template<typename Rhs,typename Dest>
+void SuperILU<MatrixType>::_solve_impl(const MatrixBase<Rhs> &b, MatrixBase<Dest>& x) const
+{
+  eigen_assert(m_factorizationIsOk && "The decomposition is not in a valid state for solving, you must first call either compute() or analyzePattern()/factorize()");
+
+  const int rhsCols = b.cols();
+  eigen_assert(m_matrix.rows()==b.rows());
+
+  m_sluOptions.Trans = NOTRANS;
+  m_sluOptions.Fact = FACTORED;
+  m_sluOptions.IterRefine = NOREFINE;
+
+  m_sluFerr.resize(rhsCols);
+  m_sluBerr.resize(rhsCols);
+  
+  Ref<const Matrix<typename Rhs::Scalar,Dynamic,Dynamic,ColMajor> > b_ref(b);
+  Ref<const Matrix<typename Dest::Scalar,Dynamic,Dynamic,ColMajor> > x_ref(x);
+  
+  m_sluB = SluMatrix::Map(b_ref.const_cast_derived());
+  m_sluX = SluMatrix::Map(x_ref.const_cast_derived());
+
+  typename Rhs::PlainObject b_cpy;
+  if(m_sluEqued!='N')
+  {
+    b_cpy = b;
+    m_sluB = SluMatrix::Map(b_cpy.const_cast_derived());  
+  }
+  
+  int info = 0;
+  RealScalar recip_pivot_growth, rcond;
+
+  StatInit(&m_sluStat);
+  SuperLU_gsisx(&m_sluOptions, &m_sluA,
+                m_q.data(), m_p.data(),
+                &m_sluEtree[0], &m_sluEqued,
+                &m_sluRscale[0], &m_sluCscale[0],
+                &m_sluL, &m_sluU,
+                NULL, 0,
+                &m_sluB, &m_sluX,
+                &recip_pivot_growth, &rcond,
+                &m_sluStat, &info, Scalar());
+  StatFree(&m_sluStat);
+  
+  if(x.derived().data() != x_ref.data())
+    x = x_ref;
+
+  m_info = info==0 ? Success : NumericalIssue;
+}
+#endif
+
+#endif
+
+} // end namespace Eigen
+
+#endif // EIGEN_SUPERLUSUPPORT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/Barrier.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/Barrier.h
new file mode 100644
index 0000000000000000000000000000000000000000..df58d87634afa92ca630e015d8f41cd2a464512d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/Barrier.h
@@ -0,0 +1,69 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2018 Rasmus Munk Larsen <rmlarsen@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// Barrier is an object that allows one or more threads to wait until
+// Notify has been called a specified number of times.
+
+#ifndef EIGEN_CXX11_THREADPOOL_BARRIER_H
+#define EIGEN_CXX11_THREADPOOL_BARRIER_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+class Barrier {
+ public:
+  Barrier(unsigned int count) : state_(count << 1), notified_(false) {
+    eigen_plain_assert(((count << 1) >> 1) == count);
+  }
+  ~Barrier() { eigen_plain_assert((state_ >> 1) == 0); }
+
+  void Notify() {
+    unsigned int v = state_.fetch_sub(2, std::memory_order_acq_rel) - 2;
+    if (v != 1) {
+      // Clear the lowest bit (waiter flag) and check that the original state
+      // value was not zero. If it was zero, it means that notify was called
+      // more times than the original count.
+      eigen_plain_assert(((v + 2) & ~1) != 0);
+      return;  // either count has not dropped to 0, or waiter is not waiting
+    }
+    EIGEN_MUTEX_LOCK l(mu_);
+    eigen_plain_assert(!notified_);
+    notified_ = true;
+    cv_.notify_all();
+  }
+
+  void Wait() {
+    unsigned int v = state_.fetch_or(1, std::memory_order_acq_rel);
+    if ((v >> 1) == 0) return;
+    EIGEN_MUTEX_LOCK l(mu_);
+    while (!notified_) {
+      cv_.wait(l);
+    }
+  }
+
+ private:
+  EIGEN_MUTEX mu_;
+  EIGEN_CONDVAR cv_;
+  std::atomic<unsigned int> state_;  // low bit is waiter flag
+  bool notified_;
+};
+
+// Notification is an object that allows a user to to wait for another
+// thread to signal a notification that an event has occurred.
+//
+// Multiple threads can wait on the same Notification object,
+// but only one caller must call Notify() on the object.
+struct Notification : Barrier {
+  Notification() : Barrier(1){};
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_BARRIER_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/EventCount.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/EventCount.h
new file mode 100644
index 0000000000000000000000000000000000000000..3b57d27e788c5a712dd44ab1f9cd88369dcda67c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/EventCount.h
@@ -0,0 +1,251 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_EVENTCOUNT_H
+#define EIGEN_CXX11_THREADPOOL_EVENTCOUNT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// EventCount allows to wait for arbitrary predicates in non-blocking
+// algorithms. Think of condition variable, but wait predicate does not need to
+// be protected by a mutex. Usage:
+// Waiting thread does:
+//
+//   if (predicate)
+//     return act();
+//   EventCount::Waiter& w = waiters[my_index];
+//   ec.Prewait(&w);
+//   if (predicate) {
+//     ec.CancelWait(&w);
+//     return act();
+//   }
+//   ec.CommitWait(&w);
+//
+// Notifying thread does:
+//
+//   predicate = true;
+//   ec.Notify(true);
+//
+// Notify is cheap if there are no waiting threads. Prewait/CommitWait are not
+// cheap, but they are executed only if the preceding predicate check has
+// failed.
+//
+// Algorithm outline:
+// There are two main variables: predicate (managed by user) and state_.
+// Operation closely resembles Dekker mutual algorithm:
+// https://en.wikipedia.org/wiki/Dekker%27s_algorithm
+// Waiting thread sets state_ then checks predicate, Notifying thread sets
+// predicate then checks state_. Due to seq_cst fences in between these
+// operations it is guaranteed than either waiter will see predicate change
+// and won't block, or notifying thread will see state_ change and will unblock
+// the waiter, or both. But it can't happen that both threads don't see each
+// other changes, which would lead to deadlock.
+class EventCount {
+ public:
+  class Waiter;
+
+  EventCount(MaxSizeVector<Waiter>& waiters)
+      : state_(kStackMask), waiters_(waiters) {
+    eigen_plain_assert(waiters.size() < (1 << kWaiterBits) - 1);
+  }
+
+  ~EventCount() {
+    // Ensure there are no waiters.
+    eigen_plain_assert(state_.load() == kStackMask);
+  }
+
+  // Prewait prepares for waiting.
+  // After calling Prewait, the thread must re-check the wait predicate
+  // and then call either CancelWait or CommitWait.
+  void Prewait() {
+    uint64_t state = state_.load(std::memory_order_relaxed);
+    for (;;) {
+      CheckState(state);
+      uint64_t newstate = state + kWaiterInc;
+      CheckState(newstate);
+      if (state_.compare_exchange_weak(state, newstate,
+                                       std::memory_order_seq_cst))
+        return;
+    }
+  }
+
+  // CommitWait commits waiting after Prewait.
+  void CommitWait(Waiter* w) {
+    eigen_plain_assert((w->epoch & ~kEpochMask) == 0);
+    w->state = Waiter::kNotSignaled;
+    const uint64_t me = (w - &waiters_[0]) | w->epoch;
+    uint64_t state = state_.load(std::memory_order_seq_cst);
+    for (;;) {
+      CheckState(state, true);
+      uint64_t newstate;
+      if ((state & kSignalMask) != 0) {
+        // Consume the signal and return immediately.
+        newstate = state - kWaiterInc - kSignalInc;
+      } else {
+        // Remove this thread from pre-wait counter and add to the waiter stack.
+        newstate = ((state & kWaiterMask) - kWaiterInc) | me;
+        w->next.store(state & (kStackMask | kEpochMask),
+                      std::memory_order_relaxed);
+      }
+      CheckState(newstate);
+      if (state_.compare_exchange_weak(state, newstate,
+                                       std::memory_order_acq_rel)) {
+        if ((state & kSignalMask) == 0) {
+          w->epoch += kEpochInc;
+          Park(w);
+        }
+        return;
+      }
+    }
+  }
+
+  // CancelWait cancels effects of the previous Prewait call.
+  void CancelWait() {
+    uint64_t state = state_.load(std::memory_order_relaxed);
+    for (;;) {
+      CheckState(state, true);
+      uint64_t newstate = state - kWaiterInc;
+      // We don't know if the thread was also notified or not,
+      // so we should not consume a signal unconditionally.
+      // Only if number of waiters is equal to number of signals,
+      // we know that the thread was notified and we must take away the signal.
+      if (((state & kWaiterMask) >> kWaiterShift) ==
+          ((state & kSignalMask) >> kSignalShift))
+        newstate -= kSignalInc;
+      CheckState(newstate);
+      if (state_.compare_exchange_weak(state, newstate,
+                                       std::memory_order_acq_rel))
+        return;
+    }
+  }
+
+  // Notify wakes one or all waiting threads.
+  // Must be called after changing the associated wait predicate.
+  void Notify(bool notifyAll) {
+    std::atomic_thread_fence(std::memory_order_seq_cst);
+    uint64_t state = state_.load(std::memory_order_acquire);
+    for (;;) {
+      CheckState(state);
+      const uint64_t waiters = (state & kWaiterMask) >> kWaiterShift;
+      const uint64_t signals = (state & kSignalMask) >> kSignalShift;
+      // Easy case: no waiters.
+      if ((state & kStackMask) == kStackMask && waiters == signals) return;
+      uint64_t newstate;
+      if (notifyAll) {
+        // Empty wait stack and set signal to number of pre-wait threads.
+        newstate =
+            (state & kWaiterMask) | (waiters << kSignalShift) | kStackMask;
+      } else if (signals < waiters) {
+        // There is a thread in pre-wait state, unblock it.
+        newstate = state + kSignalInc;
+      } else {
+        // Pop a waiter from list and unpark it.
+        Waiter* w = &waiters_[state & kStackMask];
+        uint64_t next = w->next.load(std::memory_order_relaxed);
+        newstate = (state & (kWaiterMask | kSignalMask)) | next;
+      }
+      CheckState(newstate);
+      if (state_.compare_exchange_weak(state, newstate,
+                                       std::memory_order_acq_rel)) {
+        if (!notifyAll && (signals < waiters))
+          return;  // unblocked pre-wait thread
+        if ((state & kStackMask) == kStackMask) return;
+        Waiter* w = &waiters_[state & kStackMask];
+        if (!notifyAll) w->next.store(kStackMask, std::memory_order_relaxed);
+        Unpark(w);
+        return;
+      }
+    }
+  }
+
+  class Waiter {
+    friend class EventCount;
+    // Align to 128 byte boundary to prevent false sharing with other Waiter
+    // objects in the same vector.
+    EIGEN_ALIGN_TO_BOUNDARY(128) std::atomic<uint64_t> next;
+    EIGEN_MUTEX mu;
+    EIGEN_CONDVAR cv;
+    uint64_t epoch = 0;
+    unsigned state = kNotSignaled;
+    enum {
+      kNotSignaled,
+      kWaiting,
+      kSignaled,
+    };
+  };
+
+ private:
+  // State_ layout:
+  // - low kWaiterBits is a stack of waiters committed wait
+  //   (indexes in waiters_ array are used as stack elements,
+  //   kStackMask means empty stack).
+  // - next kWaiterBits is count of waiters in prewait state.
+  // - next kWaiterBits is count of pending signals.
+  // - remaining bits are ABA counter for the stack.
+  //   (stored in Waiter node and incremented on push).
+  static const uint64_t kWaiterBits = 14;
+  static const uint64_t kStackMask = (1ull << kWaiterBits) - 1;
+  static const uint64_t kWaiterShift = kWaiterBits;
+  static const uint64_t kWaiterMask = ((1ull << kWaiterBits) - 1)
+                                      << kWaiterShift;
+  static const uint64_t kWaiterInc = 1ull << kWaiterShift;
+  static const uint64_t kSignalShift = 2 * kWaiterBits;
+  static const uint64_t kSignalMask = ((1ull << kWaiterBits) - 1)
+                                      << kSignalShift;
+  static const uint64_t kSignalInc = 1ull << kSignalShift;
+  static const uint64_t kEpochShift = 3 * kWaiterBits;
+  static const uint64_t kEpochBits = 64 - kEpochShift;
+  static const uint64_t kEpochMask = ((1ull << kEpochBits) - 1) << kEpochShift;
+  static const uint64_t kEpochInc = 1ull << kEpochShift;
+  std::atomic<uint64_t> state_;
+  MaxSizeVector<Waiter>& waiters_;
+
+  static void CheckState(uint64_t state, bool waiter = false) {
+    static_assert(kEpochBits >= 20, "not enough bits to prevent ABA problem");
+    const uint64_t waiters = (state & kWaiterMask) >> kWaiterShift;
+    const uint64_t signals = (state & kSignalMask) >> kSignalShift;
+    eigen_plain_assert(waiters >= signals);
+    eigen_plain_assert(waiters < (1 << kWaiterBits) - 1);
+    eigen_plain_assert(!waiter || waiters > 0);
+    (void)waiters;
+    (void)signals;
+  }
+
+  void Park(Waiter* w) {
+    EIGEN_MUTEX_LOCK lock(w->mu);
+    while (w->state != Waiter::kSignaled) {
+      w->state = Waiter::kWaiting;
+      w->cv.wait(lock);
+    }
+  }
+
+  void Unpark(Waiter* w) {
+    for (Waiter* next; w; w = next) {
+      uint64_t wnext = w->next.load(std::memory_order_relaxed) & kStackMask;
+      next = wnext == kStackMask ? nullptr : &waiters_[internal::convert_index<size_t>(wnext)];
+      unsigned state;
+      {
+        EIGEN_MUTEX_LOCK lock(w->mu);
+        state = w->state;
+        w->state = Waiter::kSignaled;
+      }
+      // Avoid notifying if it wasn't waiting.
+      if (state == Waiter::kWaiting) w->cv.notify_one();
+    }
+  }
+
+  EventCount(const EventCount&) = delete;
+  void operator=(const EventCount&) = delete;
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_EVENTCOUNT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..44c0fca205127fe40fc9a8acc65316a218273abe
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_THREADPOOL_MODULE_H
+#error "Please include unsupported/Eigen/CXX11/ThreadPool instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/NonBlockingThreadPool.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/NonBlockingThreadPool.h
new file mode 100644
index 0000000000000000000000000000000000000000..40f2f3fe65e422c70ac0e00f3bf9a5d3858f4b83
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/NonBlockingThreadPool.h
@@ -0,0 +1,488 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_NONBLOCKING_THREAD_POOL_H
+#define EIGEN_CXX11_THREADPOOL_NONBLOCKING_THREAD_POOL_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Environment>
+class ThreadPoolTempl : public Eigen::ThreadPoolInterface {
+ public:
+  typedef typename Environment::Task Task;
+  typedef RunQueue<Task, 1024> Queue;
+
+  ThreadPoolTempl(int num_threads, Environment env = Environment())
+      : ThreadPoolTempl(num_threads, true, env) {}
+
+  ThreadPoolTempl(int num_threads, bool allow_spinning,
+                  Environment env = Environment())
+      : env_(env),
+        num_threads_(num_threads),
+        allow_spinning_(allow_spinning),
+        thread_data_(num_threads),
+        all_coprimes_(num_threads),
+        waiters_(num_threads),
+        global_steal_partition_(EncodePartition(0, num_threads_)),
+        blocked_(0),
+        spinning_(0),
+        done_(false),
+        cancelled_(false),
+        ec_(waiters_) {
+    waiters_.resize(num_threads_);
+    // Calculate coprimes of all numbers [1, num_threads].
+    // Coprimes are used for random walks over all threads in Steal
+    // and NonEmptyQueueIndex. Iteration is based on the fact that if we take
+    // a random starting thread index t and calculate num_threads - 1 subsequent
+    // indices as (t + coprime) % num_threads, we will cover all threads without
+    // repetitions (effectively getting a presudo-random permutation of thread
+    // indices).
+    eigen_plain_assert(num_threads_ < kMaxThreads);
+    for (int i = 1; i <= num_threads_; ++i) {
+      all_coprimes_.emplace_back(i);
+      ComputeCoprimes(i, &all_coprimes_.back());
+    }
+#ifndef EIGEN_THREAD_LOCAL
+    init_barrier_.reset(new Barrier(num_threads_));
+#endif
+    thread_data_.resize(num_threads_);
+    for (int i = 0; i < num_threads_; i++) {
+      SetStealPartition(i, EncodePartition(0, num_threads_));
+      thread_data_[i].thread.reset(
+          env_.CreateThread([this, i]() { WorkerLoop(i); }));
+    }
+#ifndef EIGEN_THREAD_LOCAL
+    // Wait for workers to initialize per_thread_map_. Otherwise we might race
+    // with them in Schedule or CurrentThreadId.
+    init_barrier_->Wait();
+#endif
+  }
+
+  ~ThreadPoolTempl() {
+    done_ = true;
+
+    // Now if all threads block without work, they will start exiting.
+    // But note that threads can continue to work arbitrary long,
+    // block, submit new work, unblock and otherwise live full life.
+    if (!cancelled_) {
+      ec_.Notify(true);
+    } else {
+      // Since we were cancelled, there might be entries in the queues.
+      // Empty them to prevent their destructor from asserting.
+      for (size_t i = 0; i < thread_data_.size(); i++) {
+        thread_data_[i].queue.Flush();
+      }
+    }
+    // Join threads explicitly (by destroying) to avoid destruction order within
+    // this class.
+    for (size_t i = 0; i < thread_data_.size(); ++i)
+      thread_data_[i].thread.reset();
+  }
+
+  void SetStealPartitions(const std::vector<std::pair<unsigned, unsigned>>& partitions) {
+    eigen_plain_assert(partitions.size() == static_cast<std::size_t>(num_threads_));
+
+    // Pass this information to each thread queue.
+    for (int i = 0; i < num_threads_; i++) {
+      const auto& pair = partitions[i];
+      unsigned start = pair.first, end = pair.second;
+      AssertBounds(start, end);
+      unsigned val = EncodePartition(start, end);
+      SetStealPartition(i, val);
+    }
+  }
+
+  void Schedule(std::function<void()> fn) EIGEN_OVERRIDE {
+    ScheduleWithHint(std::move(fn), 0, num_threads_);
+  }
+
+  void ScheduleWithHint(std::function<void()> fn, int start,
+                        int limit) override {
+    Task t = env_.CreateTask(std::move(fn));
+    PerThread* pt = GetPerThread();
+    if (pt->pool == this) {
+      // Worker thread of this pool, push onto the thread's queue.
+      Queue& q = thread_data_[pt->thread_id].queue;
+      t = q.PushFront(std::move(t));
+    } else {
+      // A free-standing thread (or worker of another pool), push onto a random
+      // queue.
+      eigen_plain_assert(start < limit);
+      eigen_plain_assert(limit <= num_threads_);
+      int num_queues = limit - start;
+      int rnd = Rand(&pt->rand) % num_queues;
+      eigen_plain_assert(start + rnd < limit);
+      Queue& q = thread_data_[start + rnd].queue;
+      t = q.PushBack(std::move(t));
+    }
+    // Note: below we touch this after making w available to worker threads.
+    // Strictly speaking, this can lead to a racy-use-after-free. Consider that
+    // Schedule is called from a thread that is neither main thread nor a worker
+    // thread of this pool. Then, execution of w directly or indirectly
+    // completes overall computations, which in turn leads to destruction of
+    // this. We expect that such scenario is prevented by program, that is,
+    // this is kept alive while any threads can potentially be in Schedule.
+    if (!t.f) {
+      ec_.Notify(false);
+    } else {
+      env_.ExecuteTask(t);  // Push failed, execute directly.
+    }
+  }
+
+  void Cancel() EIGEN_OVERRIDE {
+    cancelled_ = true;
+    done_ = true;
+
+    // Let each thread know it's been cancelled.
+#ifdef EIGEN_THREAD_ENV_SUPPORTS_CANCELLATION
+    for (size_t i = 0; i < thread_data_.size(); i++) {
+      thread_data_[i].thread->OnCancel();
+    }
+#endif
+
+    // Wake up the threads without work to let them exit on their own.
+    ec_.Notify(true);
+  }
+
+  int NumThreads() const EIGEN_FINAL { return num_threads_; }
+
+  int CurrentThreadId() const EIGEN_FINAL {
+    const PerThread* pt = const_cast<ThreadPoolTempl*>(this)->GetPerThread();
+    if (pt->pool == this) {
+      return pt->thread_id;
+    } else {
+      return -1;
+    }
+  }
+
+ private:
+  // Create a single atomic<int> that encodes start and limit information for
+  // each thread.
+  // We expect num_threads_ < 65536, so we can store them in a single
+  // std::atomic<unsigned>.
+  // Exposed publicly as static functions so that external callers can reuse
+  // this encode/decode logic for maintaining their own thread-safe copies of
+  // scheduling and steal domain(s).
+  static const int kMaxPartitionBits = 16;
+  static const int kMaxThreads = 1 << kMaxPartitionBits;
+
+  inline unsigned EncodePartition(unsigned start, unsigned limit) {
+    return (start << kMaxPartitionBits) | limit;
+  }
+
+  inline void DecodePartition(unsigned val, unsigned* start, unsigned* limit) {
+    *limit = val & (kMaxThreads - 1);
+    val >>= kMaxPartitionBits;
+    *start = val;
+  }
+
+  void AssertBounds(int start, int end) {
+    eigen_plain_assert(start >= 0);
+    eigen_plain_assert(start < end);  // non-zero sized partition
+    eigen_plain_assert(end <= num_threads_);
+  }
+
+  inline void SetStealPartition(size_t i, unsigned val) {
+    thread_data_[i].steal_partition.store(val, std::memory_order_relaxed);
+  }
+
+  inline unsigned GetStealPartition(int i) {
+    return thread_data_[i].steal_partition.load(std::memory_order_relaxed);
+  }
+
+  void ComputeCoprimes(int N, MaxSizeVector<unsigned>* coprimes) {
+    for (int i = 1; i <= N; i++) {
+      unsigned a = i;
+      unsigned b = N;
+      // If GCD(a, b) == 1, then a and b are coprimes.
+      while (b != 0) {
+        unsigned tmp = a;
+        a = b;
+        b = tmp % b;
+      }
+      if (a == 1) {
+        coprimes->push_back(i);
+      }
+    }
+  }
+
+  typedef typename Environment::EnvThread Thread;
+
+  struct PerThread {
+    constexpr PerThread() : pool(NULL), rand(0), thread_id(-1) {}
+    ThreadPoolTempl* pool;  // Parent pool, or null for normal threads.
+    uint64_t rand;          // Random generator state.
+    int thread_id;          // Worker thread index in pool.
+#ifndef EIGEN_THREAD_LOCAL
+    // Prevent false sharing.
+    char pad_[128];
+#endif
+  };
+
+  struct ThreadData {
+    constexpr ThreadData() : thread(), steal_partition(0), queue() {}
+    std::unique_ptr<Thread> thread;
+    std::atomic<unsigned> steal_partition;
+    Queue queue;
+  };
+
+  Environment env_;
+  const int num_threads_;
+  const bool allow_spinning_;
+  MaxSizeVector<ThreadData> thread_data_;
+  MaxSizeVector<MaxSizeVector<unsigned>> all_coprimes_;
+  MaxSizeVector<EventCount::Waiter> waiters_;
+  unsigned global_steal_partition_;
+  std::atomic<unsigned> blocked_;
+  std::atomic<bool> spinning_;
+  std::atomic<bool> done_;
+  std::atomic<bool> cancelled_;
+  EventCount ec_;
+#ifndef EIGEN_THREAD_LOCAL
+  std::unique_ptr<Barrier> init_barrier_;
+  EIGEN_MUTEX per_thread_map_mutex_;  // Protects per_thread_map_.
+  std::unordered_map<uint64_t, std::unique_ptr<PerThread>> per_thread_map_;
+#endif
+
+  // Main worker thread loop.
+  void WorkerLoop(int thread_id) {
+#ifndef EIGEN_THREAD_LOCAL
+    std::unique_ptr<PerThread> new_pt(new PerThread());
+    per_thread_map_mutex_.lock();
+    bool insertOK = per_thread_map_.emplace(GlobalThreadIdHash(), std::move(new_pt)).second;
+    eigen_plain_assert(insertOK);
+    EIGEN_UNUSED_VARIABLE(insertOK);
+    per_thread_map_mutex_.unlock();
+    init_barrier_->Notify();
+    init_barrier_->Wait();
+#endif
+    PerThread* pt = GetPerThread();
+    pt->pool = this;
+    pt->rand = GlobalThreadIdHash();
+    pt->thread_id = thread_id;
+    Queue& q = thread_data_[thread_id].queue;
+    EventCount::Waiter* waiter = &waiters_[thread_id];
+    // TODO(dvyukov,rmlarsen): The time spent in NonEmptyQueueIndex() is
+    // proportional to num_threads_ and we assume that new work is scheduled at
+    // a constant rate, so we set spin_count to 5000 / num_threads_. The
+    // constant was picked based on a fair dice roll, tune it.
+    const int spin_count =
+        allow_spinning_ && num_threads_ > 0 ? 5000 / num_threads_ : 0;
+    if (num_threads_ == 1) {
+      // For num_threads_ == 1 there is no point in going through the expensive
+      // steal loop. Moreover, since NonEmptyQueueIndex() calls PopBack() on the
+      // victim queues it might reverse the order in which ops are executed
+      // compared to the order in which they are scheduled, which tends to be
+      // counter-productive for the types of I/O workloads the single thread
+      // pools tend to be used for.
+      while (!cancelled_) {
+        Task t = q.PopFront();
+        for (int i = 0; i < spin_count && !t.f; i++) {
+          if (!cancelled_.load(std::memory_order_relaxed)) {
+            t = q.PopFront();
+          }
+        }
+        if (!t.f) {
+          if (!WaitForWork(waiter, &t)) {
+            return;
+          }
+        }
+        if (t.f) {
+          env_.ExecuteTask(t);
+        }
+      }
+    } else {
+      while (!cancelled_) {
+        Task t = q.PopFront();
+        if (!t.f) {
+          t = LocalSteal();
+          if (!t.f) {
+            t = GlobalSteal();
+            if (!t.f) {
+              // Leave one thread spinning. This reduces latency.
+              if (allow_spinning_ && !spinning_ && !spinning_.exchange(true)) {
+                for (int i = 0; i < spin_count && !t.f; i++) {
+                  if (!cancelled_.load(std::memory_order_relaxed)) {
+                    t = GlobalSteal();
+                  } else {
+                    return;
+                  }
+                }
+                spinning_ = false;
+              }
+              if (!t.f) {
+                if (!WaitForWork(waiter, &t)) {
+                  return;
+                }
+              }
+            }
+          }
+        }
+        if (t.f) {
+          env_.ExecuteTask(t);
+        }
+      }
+    }
+  }
+
+  // Steal tries to steal work from other worker threads in the range [start,
+  // limit) in best-effort manner.
+  Task Steal(unsigned start, unsigned limit) {
+    PerThread* pt = GetPerThread();
+    const size_t size = limit - start;
+    unsigned r = Rand(&pt->rand);
+    // Reduce r into [0, size) range, this utilizes trick from
+    // https://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/
+    eigen_plain_assert(all_coprimes_[size - 1].size() < (1<<30));
+    unsigned victim = ((uint64_t)r * (uint64_t)size) >> 32;
+    unsigned index = ((uint64_t) all_coprimes_[size - 1].size() * (uint64_t)r) >> 32;
+    unsigned inc = all_coprimes_[size - 1][index];
+
+    for (unsigned i = 0; i < size; i++) {
+      eigen_plain_assert(start + victim < limit);
+      Task t = thread_data_[start + victim].queue.PopBack();
+      if (t.f) {
+        return t;
+      }
+      victim += inc;
+      if (victim >= size) {
+        victim -= size;
+      }
+    }
+    return Task();
+  }
+
+  // Steals work within threads belonging to the partition.
+  Task LocalSteal() {
+    PerThread* pt = GetPerThread();
+    unsigned partition = GetStealPartition(pt->thread_id);
+    // If thread steal partition is the same as global partition, there is no
+    // need to go through the steal loop twice.
+    if (global_steal_partition_ == partition) return Task();
+    unsigned start, limit;
+    DecodePartition(partition, &start, &limit);
+    AssertBounds(start, limit);
+
+    return Steal(start, limit);
+  }
+
+  // Steals work from any other thread in the pool.
+  Task GlobalSteal() {
+    return Steal(0, num_threads_);
+  }
+
+
+  // WaitForWork blocks until new work is available (returns true), or if it is
+  // time to exit (returns false). Can optionally return a task to execute in t
+  // (in such case t.f != nullptr on return).
+  bool WaitForWork(EventCount::Waiter* waiter, Task* t) {
+    eigen_plain_assert(!t->f);
+    // We already did best-effort emptiness check in Steal, so prepare for
+    // blocking.
+    ec_.Prewait();
+    // Now do a reliable emptiness check.
+    int victim = NonEmptyQueueIndex();
+    if (victim != -1) {
+      ec_.CancelWait();
+      if (cancelled_) {
+        return false;
+      } else {
+        *t = thread_data_[victim].queue.PopBack();
+        return true;
+      }
+    }
+    // Number of blocked threads is used as termination condition.
+    // If we are shutting down and all worker threads blocked without work,
+    // that's we are done.
+    blocked_++;
+    // TODO is blocked_ required to be unsigned?
+    if (done_ && blocked_ == static_cast<unsigned>(num_threads_)) {
+      ec_.CancelWait();
+      // Almost done, but need to re-check queues.
+      // Consider that all queues are empty and all worker threads are preempted
+      // right after incrementing blocked_ above. Now a free-standing thread
+      // submits work and calls destructor (which sets done_). If we don't
+      // re-check queues, we will exit leaving the work unexecuted.
+      if (NonEmptyQueueIndex() != -1) {
+        // Note: we must not pop from queues before we decrement blocked_,
+        // otherwise the following scenario is possible. Consider that instead
+        // of checking for emptiness we popped the only element from queues.
+        // Now other worker threads can start exiting, which is bad if the
+        // work item submits other work. So we just check emptiness here,
+        // which ensures that all worker threads exit at the same time.
+        blocked_--;
+        return true;
+      }
+      // Reached stable termination state.
+      ec_.Notify(true);
+      return false;
+    }
+    ec_.CommitWait(waiter);
+    blocked_--;
+    return true;
+  }
+
+  int NonEmptyQueueIndex() {
+    PerThread* pt = GetPerThread();
+    // We intentionally design NonEmptyQueueIndex to steal work from
+    // anywhere in the queue so threads don't block in WaitForWork() forever
+    // when all threads in their partition go to sleep. Steal is still local.
+    const size_t size = thread_data_.size();
+    unsigned r = Rand(&pt->rand);
+    unsigned inc = all_coprimes_[size - 1][r % all_coprimes_[size - 1].size()];
+    unsigned victim = r % size;
+    for (unsigned i = 0; i < size; i++) {
+      if (!thread_data_[victim].queue.Empty()) {
+        return victim;
+      }
+      victim += inc;
+      if (victim >= size) {
+        victim -= size;
+      }
+    }
+    return -1;
+  }
+
+  static EIGEN_STRONG_INLINE uint64_t GlobalThreadIdHash() {
+    return std::hash<std::thread::id>()(std::this_thread::get_id());
+  }
+
+  EIGEN_STRONG_INLINE PerThread* GetPerThread() {
+#ifndef EIGEN_THREAD_LOCAL
+    static PerThread dummy;
+    auto it = per_thread_map_.find(GlobalThreadIdHash());
+    if (it == per_thread_map_.end()) {
+      return &dummy;
+    } else {
+      return it->second.get();
+    }
+#else
+    EIGEN_THREAD_LOCAL PerThread per_thread_;
+    PerThread* pt = &per_thread_;
+    return pt;
+#endif
+  }
+
+  static EIGEN_STRONG_INLINE unsigned Rand(uint64_t* state) {
+    uint64_t current = *state;
+    // Update the internal state
+    *state = current * 6364136223846793005ULL + 0xda3e39cb94b95bdbULL;
+    // Generate the random output (using the PCG-XSH-RS scheme)
+    return static_cast<unsigned>((current ^ (current >> 22)) >>
+                                 (22 + (current >> 61)));
+  }
+};
+
+typedef ThreadPoolTempl<StlThreadEnvironment> ThreadPool;
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_NONBLOCKING_THREAD_POOL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/RunQueue.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/RunQueue.h
new file mode 100644
index 0000000000000000000000000000000000000000..2005fb25d3f3a6e680c27815e9f4ab9ded6b0157
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/RunQueue.h
@@ -0,0 +1,238 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Dmitry Vyukov <dvyukov@google.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_RUNQUEUE_H
+#define EIGEN_CXX11_THREADPOOL_RUNQUEUE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// RunQueue is a fixed-size, partially non-blocking deque or Work items.
+// Operations on front of the queue must be done by a single thread (owner),
+// operations on back of the queue can be done by multiple threads concurrently.
+//
+// Algorithm outline:
+// All remote threads operating on the queue back are serialized by a mutex.
+// This ensures that at most two threads access state: owner and one remote
+// thread (Size aside). The algorithm ensures that the occupied region of the
+// underlying array is logically continuous (can wraparound, but no stray
+// occupied elements). Owner operates on one end of this region, remote thread
+// operates on the other end. Synchronization between these threads
+// (potential consumption of the last element and take up of the last empty
+// element) happens by means of state variable in each element. States are:
+// empty, busy (in process of insertion of removal) and ready. Threads claim
+// elements (empty->busy and ready->busy transitions) by means of a CAS
+// operation. The finishing transition (busy->empty and busy->ready) are done
+// with plain store as the element is exclusively owned by the current thread.
+//
+// Note: we could permit only pointers as elements, then we would not need
+// separate state variable as null/non-null pointer value would serve as state,
+// but that would require malloc/free per operation for large, complex values
+// (and this is designed to store std::function<()>).
+template <typename Work, unsigned kSize>
+class RunQueue {
+ public:
+  RunQueue() : front_(0), back_(0) {
+    // require power-of-two for fast masking
+    eigen_plain_assert((kSize & (kSize - 1)) == 0);
+    eigen_plain_assert(kSize > 2);            // why would you do this?
+    eigen_plain_assert(kSize <= (64 << 10));  // leave enough space for counter
+    for (unsigned i = 0; i < kSize; i++)
+      array_[i].state.store(kEmpty, std::memory_order_relaxed);
+  }
+
+  ~RunQueue() { eigen_plain_assert(Size() == 0); }
+
+  // PushFront inserts w at the beginning of the queue.
+  // If queue is full returns w, otherwise returns default-constructed Work.
+  Work PushFront(Work w) {
+    unsigned front = front_.load(std::memory_order_relaxed);
+    Elem* e = &array_[front & kMask];
+    uint8_t s = e->state.load(std::memory_order_relaxed);
+    if (s != kEmpty ||
+        !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire))
+      return w;
+    front_.store(front + 1 + (kSize << 1), std::memory_order_relaxed);
+    e->w = std::move(w);
+    e->state.store(kReady, std::memory_order_release);
+    return Work();
+  }
+
+  // PopFront removes and returns the first element in the queue.
+  // If the queue was empty returns default-constructed Work.
+  Work PopFront() {
+    unsigned front = front_.load(std::memory_order_relaxed);
+    Elem* e = &array_[(front - 1) & kMask];
+    uint8_t s = e->state.load(std::memory_order_relaxed);
+    if (s != kReady ||
+        !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire))
+      return Work();
+    Work w = std::move(e->w);
+    e->state.store(kEmpty, std::memory_order_release);
+    front = ((front - 1) & kMask2) | (front & ~kMask2);
+    front_.store(front, std::memory_order_relaxed);
+    return w;
+  }
+
+  // PushBack adds w at the end of the queue.
+  // If queue is full returns w, otherwise returns default-constructed Work.
+  Work PushBack(Work w) {
+    EIGEN_MUTEX_LOCK lock(mutex_);
+    unsigned back = back_.load(std::memory_order_relaxed);
+    Elem* e = &array_[(back - 1) & kMask];
+    uint8_t s = e->state.load(std::memory_order_relaxed);
+    if (s != kEmpty ||
+        !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire))
+      return w;
+    back = ((back - 1) & kMask2) | (back & ~kMask2);
+    back_.store(back, std::memory_order_relaxed);
+    e->w = std::move(w);
+    e->state.store(kReady, std::memory_order_release);
+    return Work();
+  }
+
+  // PopBack removes and returns the last elements in the queue.
+  Work PopBack() {
+    if (Empty()) return Work();
+    EIGEN_MUTEX_LOCK lock(mutex_);
+    unsigned back = back_.load(std::memory_order_relaxed);
+    Elem* e = &array_[back & kMask];
+    uint8_t s = e->state.load(std::memory_order_relaxed);
+    if (s != kReady ||
+        !e->state.compare_exchange_strong(s, kBusy, std::memory_order_acquire))
+      return Work();
+    Work w = std::move(e->w);
+    e->state.store(kEmpty, std::memory_order_release);
+    back_.store(back + 1 + (kSize << 1), std::memory_order_relaxed);
+    return w;
+  }
+
+  // PopBackHalf removes and returns half last elements in the queue.
+  // Returns number of elements removed.
+  unsigned PopBackHalf(std::vector<Work>* result) {
+    if (Empty()) return 0;
+    EIGEN_MUTEX_LOCK lock(mutex_);
+    unsigned back = back_.load(std::memory_order_relaxed);
+    unsigned size = Size();
+    unsigned mid = back;
+    if (size > 1) mid = back + (size - 1) / 2;
+    unsigned n = 0;
+    unsigned start = 0;
+    for (; static_cast<int>(mid - back) >= 0; mid--) {
+      Elem* e = &array_[mid & kMask];
+      uint8_t s = e->state.load(std::memory_order_relaxed);
+      if (n == 0) {
+        if (s != kReady || !e->state.compare_exchange_strong(
+                               s, kBusy, std::memory_order_acquire))
+          continue;
+        start = mid;
+      } else {
+        // Note: no need to store temporal kBusy, we exclusively own these
+        // elements.
+        eigen_plain_assert(s == kReady);
+      }
+      result->push_back(std::move(e->w));
+      e->state.store(kEmpty, std::memory_order_release);
+      n++;
+    }
+    if (n != 0)
+      back_.store(start + 1 + (kSize << 1), std::memory_order_relaxed);
+    return n;
+  }
+
+  // Size returns current queue size.
+  // Can be called by any thread at any time.
+  unsigned Size() const { return SizeOrNotEmpty<true>(); }
+
+  // Empty tests whether container is empty.
+  // Can be called by any thread at any time.
+  bool Empty() const { return SizeOrNotEmpty<false>() == 0; }
+
+  // Delete all the elements from the queue.
+  void Flush() {
+    while (!Empty()) {
+      PopFront();
+    }
+  }
+
+ private:
+  static const unsigned kMask = kSize - 1;
+  static const unsigned kMask2 = (kSize << 1) - 1;
+  struct Elem {
+    std::atomic<uint8_t> state;
+    Work w;
+  };
+  enum {
+    kEmpty,
+    kBusy,
+    kReady,
+  };
+  EIGEN_MUTEX mutex_;
+  // Low log(kSize) + 1 bits in front_ and back_ contain rolling index of
+  // front/back, respectively. The remaining bits contain modification counters
+  // that are incremented on Push operations. This allows us to (1) distinguish
+  // between empty and full conditions (if we would use log(kSize) bits for
+  // position, these conditions would be indistinguishable); (2) obtain
+  // consistent snapshot of front_/back_ for Size operation using the
+  // modification counters.
+  std::atomic<unsigned> front_;
+  std::atomic<unsigned> back_;
+  Elem array_[kSize];
+
+  // SizeOrNotEmpty returns current queue size; if NeedSizeEstimate is false,
+  // only whether the size is 0 is guaranteed to be correct.
+  // Can be called by any thread at any time.
+  template<bool NeedSizeEstimate>
+  unsigned SizeOrNotEmpty() const {
+    // Emptiness plays critical role in thread pool blocking. So we go to great
+    // effort to not produce false positives (claim non-empty queue as empty).
+    unsigned front = front_.load(std::memory_order_acquire);
+    for (;;) {
+      // Capture a consistent snapshot of front/tail.
+      unsigned back = back_.load(std::memory_order_acquire);
+      unsigned front1 = front_.load(std::memory_order_relaxed);
+      if (front != front1) {
+        front = front1;
+        std::atomic_thread_fence(std::memory_order_acquire);
+        continue;
+      }
+      if (NeedSizeEstimate) {
+        return CalculateSize(front, back);
+      } else {
+        // This value will be 0 if the queue is empty, and undefined otherwise.
+        unsigned maybe_zero = ((front ^ back) & kMask2);
+        // Queue size estimate must agree with maybe zero check on the queue
+        // empty/non-empty state.
+        eigen_assert((CalculateSize(front, back) == 0) == (maybe_zero == 0));
+        return maybe_zero;
+      }
+    }
+  }
+
+  EIGEN_ALWAYS_INLINE
+  unsigned CalculateSize(unsigned front, unsigned back) const {
+    int size = (front & kMask2) - (back & kMask2);
+    // Fix overflow.
+    if (size < 0) size += 2 * kSize;
+    // Order of modification in push/pop is crafted to make the queue look
+    // larger than it is during concurrent modifications. E.g. push can
+    // increment size before the corresponding pop has decremented it.
+    // So the computed size can be up to kSize + 1, fix it.
+    if (size > static_cast<int>(kSize)) size = kSize;
+    return static_cast<unsigned>(size);
+  }
+
+  RunQueue(const RunQueue&) = delete;
+  void operator=(const RunQueue&) = delete;
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_RUNQUEUE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/ThreadCancel.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/ThreadCancel.h
new file mode 100644
index 0000000000000000000000000000000000000000..a05685f11d80cfe7033b9ff620f684380ab80a42
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/ThreadCancel.h
@@ -0,0 +1,23 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_THREAD_CANCEL_H
+#define EIGEN_CXX11_THREADPOOL_THREAD_CANCEL_H
+
+// Try to come up with a portable way to cancel a thread
+#if EIGEN_OS_GNULINUX
+  #define EIGEN_THREAD_CANCEL(t)                  \
+    pthread_cancel(t.native_handle());
+  #define EIGEN_SUPPORTS_THREAD_CANCELLATION 1
+#else
+#define EIGEN_THREAD_CANCEL(t)
+#endif
+
+
+#endif  // EIGEN_CXX11_THREADPOOL_THREAD_CANCEL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/ThreadEnvironment.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/ThreadEnvironment.h
new file mode 100644
index 0000000000000000000000000000000000000000..02ec36648b821131356cba74bce6e2d6dc266828
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/ThreadEnvironment.h
@@ -0,0 +1,42 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_THREAD_ENVIRONMENT_H
+#define EIGEN_CXX11_THREADPOOL_THREAD_ENVIRONMENT_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+struct StlThreadEnvironment {
+  struct Task {
+    std::function<void()> f;
+  };
+
+  // EnvThread constructor must start the thread,
+  // destructor must join the thread.
+  class EnvThread {
+   public:
+    EnvThread(std::function<void()> f) : thr_(std::move(f)) {}
+    ~EnvThread() { thr_.join(); }
+    // This function is called when the threadpool is cancelled.
+    void OnCancel() { }
+
+   private:
+    std::thread thr_;
+  };
+
+  EnvThread* CreateThread(std::function<void()> f) { return new EnvThread(std::move(f)); }
+  Task CreateTask(std::function<void()> f) { return Task{std::move(f)}; }
+  void ExecuteTask(const Task& t) { t.f(); }
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_THREAD_ENVIRONMENT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/ThreadLocal.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/ThreadLocal.h
new file mode 100644
index 0000000000000000000000000000000000000000..ff3f8346f6ac015545382691dfcdd765108c4e2f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/ThreadLocal.h
@@ -0,0 +1,299 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_THREAD_LOCAL_H
+#define EIGEN_CXX11_THREADPOOL_THREAD_LOCAL_H
+
+#ifdef EIGEN_AVOID_THREAD_LOCAL
+
+#ifdef EIGEN_THREAD_LOCAL
+#undef EIGEN_THREAD_LOCAL
+#endif
+
+#else
+
+#if ((EIGEN_COMP_GNUC) || __has_feature(cxx_thread_local) || EIGEN_COMP_MSVC )
+#define EIGEN_THREAD_LOCAL static thread_local
+#endif
+
+// Disable TLS for Apple and Android builds with older toolchains.
+#if defined(__APPLE__)
+// Included for TARGET_OS_IPHONE, __IPHONE_OS_VERSION_MIN_REQUIRED,
+// __IPHONE_8_0.
+#include <Availability.h>
+#include <TargetConditionals.h>
+#endif
+// Checks whether C++11's `thread_local` storage duration specifier is
+// supported.
+#if EIGEN_COMP_CLANGAPPLE && ((EIGEN_COMP_CLANGAPPLE < 8000042) || \
+     (TARGET_OS_IPHONE && __IPHONE_OS_VERSION_MIN_REQUIRED < __IPHONE_9_0))
+// Notes: Xcode's clang did not support `thread_local` until version
+// 8, and even then not for all iOS < 9.0.
+#undef EIGEN_THREAD_LOCAL
+
+#elif defined(__ANDROID__) && EIGEN_COMP_CLANG
+// There are platforms for which TLS should not be used even though the compiler
+// makes it seem like it's supported (Android NDK < r12b for example).
+// This is primarily because of linker problems and toolchain misconfiguration:
+// TLS isn't supported until NDK r12b per
+// https://developer.android.com/ndk/downloads/revision_history.html
+// Since NDK r16, `__NDK_MAJOR__` and `__NDK_MINOR__` are defined in
+// <android/ndk-version.h>. For NDK < r16, users should define these macros,
+// e.g. `-D__NDK_MAJOR__=11 -D__NKD_MINOR__=0` for NDK r11.
+#if __has_include(<android/ndk-version.h>)
+#include <android/ndk-version.h>
+#endif  // __has_include(<android/ndk-version.h>)
+#if defined(__ANDROID__) && defined(__clang__) && defined(__NDK_MAJOR__) && \
+    defined(__NDK_MINOR__) &&                                               \
+    ((__NDK_MAJOR__ < 12) || ((__NDK_MAJOR__ == 12) && (__NDK_MINOR__ < 1)))
+#undef EIGEN_THREAD_LOCAL
+#endif
+#endif  // defined(__ANDROID__) && defined(__clang__)
+
+#endif  // EIGEN_AVOID_THREAD_LOCAL
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename T>
+struct ThreadLocalNoOpInitialize {
+  void operator()(T&) const {}
+};
+
+template <typename T>
+struct ThreadLocalNoOpRelease {
+  void operator()(T&) const {}
+};
+
+}  // namespace internal
+
+// Thread local container for elements of type T, that does not use thread local
+// storage. As long as the number of unique threads accessing this storage
+// is smaller than `capacity_`, it is lock-free and wait-free. Otherwise it will
+// use a mutex for synchronization.
+//
+// Type `T` has to be default constructible, and by default each thread will get
+// a default constructed value. It is possible to specify custom `initialize`
+// callable, that will be called lazily from each thread accessing this object,
+// and will be passed a default initialized object of type `T`. Also it's
+// possible to pass a custom `release` callable, that will be invoked before
+// calling ~T().
+//
+// Example:
+//
+//   struct Counter {
+//     int value = 0;
+//   }
+//
+//   Eigen::ThreadLocal<Counter> counter(10);
+//
+//   // Each thread will have access to it's own counter object.
+//   Counter& cnt = counter.local();
+//   cnt++;
+//
+// WARNING: Eigen::ThreadLocal uses the OS-specific value returned by
+// std::this_thread::get_id() to identify threads. This value is not guaranteed
+// to be unique except for the life of the thread. A newly created thread may
+// get an OS-specific ID equal to that of an already destroyed thread.
+//
+// Somewhat similar to TBB thread local storage, with similar restrictions:
+// https://www.threadingbuildingblocks.org/docs/help/reference/thread_local_storage/enumerable_thread_specific_cls.html
+//
+template <typename T,
+          typename Initialize = internal::ThreadLocalNoOpInitialize<T>,
+          typename Release = internal::ThreadLocalNoOpRelease<T>>
+class ThreadLocal {
+  // We preallocate default constructed elements in MaxSizedVector.
+  static_assert(std::is_default_constructible<T>::value,
+                "ThreadLocal data type must be default constructible");
+
+ public:
+  explicit ThreadLocal(int capacity)
+      : ThreadLocal(capacity, internal::ThreadLocalNoOpInitialize<T>(),
+                    internal::ThreadLocalNoOpRelease<T>()) {}
+
+  ThreadLocal(int capacity, Initialize initialize)
+      : ThreadLocal(capacity, std::move(initialize),
+                    internal::ThreadLocalNoOpRelease<T>()) {}
+
+  ThreadLocal(int capacity, Initialize initialize, Release release)
+      : initialize_(std::move(initialize)),
+        release_(std::move(release)),
+        capacity_(capacity),
+        data_(capacity_),
+        ptr_(capacity_),
+        filled_records_(0) {
+    eigen_assert(capacity_ >= 0);
+    data_.resize(capacity_);
+    for (int i = 0; i < capacity_; ++i) {
+      ptr_.emplace_back(nullptr);
+    }
+  }
+
+  T& local() {
+    std::thread::id this_thread = std::this_thread::get_id();
+    if (capacity_ == 0) return SpilledLocal(this_thread);
+
+    std::size_t h = std::hash<std::thread::id>()(this_thread);
+    const int start_idx = h % capacity_;
+
+    // NOTE: From the definition of `std::this_thread::get_id()` it is
+    // guaranteed that we never can have concurrent insertions with the same key
+    // to our hash-map like data structure. If we didn't find an element during
+    // the initial traversal, it's guaranteed that no one else could have
+    // inserted it while we are in this function. This allows to massively
+    // simplify out lock-free insert-only hash map.
+
+    // Check if we already have an element for `this_thread`.
+    int idx = start_idx;
+    while (ptr_[idx].load() != nullptr) {
+      ThreadIdAndValue& record = *(ptr_[idx].load());
+      if (record.thread_id == this_thread) return record.value;
+
+      idx += 1;
+      if (idx >= capacity_) idx -= capacity_;
+      if (idx == start_idx) break;
+    }
+
+    // If we are here, it means that we found an insertion point in lookup
+    // table at `idx`, or we did a full traversal and table is full.
+
+    // If lock-free storage is full, fallback on mutex.
+    if (filled_records_.load() >= capacity_) return SpilledLocal(this_thread);
+
+    // We double check that we still have space to insert an element into a lock
+    // free storage. If old value in `filled_records_` is larger than the
+    // records capacity, it means that some other thread added an element while
+    // we were traversing lookup table.
+    int insertion_index =
+        filled_records_.fetch_add(1, std::memory_order_relaxed);
+    if (insertion_index >= capacity_) return SpilledLocal(this_thread);
+
+    // At this point it's guaranteed that we can access to
+    // data_[insertion_index_] without a data race.
+    data_[insertion_index].thread_id = this_thread;
+    initialize_(data_[insertion_index].value);
+
+    // That's the pointer we'll put into the lookup table.
+    ThreadIdAndValue* inserted = &data_[insertion_index];
+
+    // We'll use nullptr pointer to ThreadIdAndValue in a compare-and-swap loop.
+    ThreadIdAndValue* empty = nullptr;
+
+    // Now we have to find an insertion point into the lookup table. We start
+    // from the `idx` that was identified as an insertion point above, it's
+    // guaranteed that we will have an empty record somewhere in a lookup table
+    // (because we created a record in the `data_`).
+    const int insertion_idx = idx;
+
+    do {
+      // Always start search from the original insertion candidate.
+      idx = insertion_idx;
+      while (ptr_[idx].load() != nullptr) {
+        idx += 1;
+        if (idx >= capacity_) idx -= capacity_;
+        // If we did a full loop, it means that we don't have any free entries
+        // in the lookup table, and this means that something is terribly wrong.
+        eigen_assert(idx != insertion_idx);
+      }
+      // Atomic CAS of the pointer guarantees that any other thread, that will
+      // follow this pointer will see all the mutations in the `data_`.
+    } while (!ptr_[idx].compare_exchange_weak(empty, inserted));
+
+    return inserted->value;
+  }
+
+  // WARN: It's not thread safe to call it concurrently with `local()`.
+  void ForEach(std::function<void(std::thread::id, T&)> f) {
+    // Reading directly from `data_` is unsafe, because only CAS to the
+    // record in `ptr_` makes all changes visible to other threads.
+    for (auto& ptr : ptr_) {
+      ThreadIdAndValue* record = ptr.load();
+      if (record == nullptr) continue;
+      f(record->thread_id, record->value);
+    }
+
+    // We did not spill into the map based storage.
+    if (filled_records_.load(std::memory_order_relaxed) < capacity_) return;
+
+    // Adds a happens before edge from the last call to SpilledLocal().
+    EIGEN_MUTEX_LOCK lock(mu_);
+    for (auto& kv : per_thread_map_) {
+      f(kv.first, kv.second);
+    }
+  }
+
+  // WARN: It's not thread safe to call it concurrently with `local()`.
+  ~ThreadLocal() {
+    // Reading directly from `data_` is unsafe, because only CAS to the record
+    // in `ptr_` makes all changes visible to other threads.
+    for (auto& ptr : ptr_) {
+      ThreadIdAndValue* record = ptr.load();
+      if (record == nullptr) continue;
+      release_(record->value);
+    }
+
+    // We did not spill into the map based storage.
+    if (filled_records_.load(std::memory_order_relaxed) < capacity_) return;
+
+    // Adds a happens before edge from the last call to SpilledLocal().
+    EIGEN_MUTEX_LOCK lock(mu_);
+    for (auto& kv : per_thread_map_) {
+      release_(kv.second);
+    }
+  }
+
+ private:
+  struct ThreadIdAndValue {
+    std::thread::id thread_id;
+    T value;
+  };
+
+  // Use unordered map guarded by a mutex when lock free storage is full.
+  T& SpilledLocal(std::thread::id this_thread) {
+    EIGEN_MUTEX_LOCK lock(mu_);
+
+    auto it = per_thread_map_.find(this_thread);
+    if (it == per_thread_map_.end()) {
+      auto result = per_thread_map_.emplace(this_thread, T());
+      eigen_assert(result.second);
+      initialize_((*result.first).second);
+      return (*result.first).second;
+    } else {
+      return it->second;
+    }
+  }
+
+  Initialize initialize_;
+  Release release_;
+  const int capacity_;
+
+  // Storage that backs lock-free lookup table `ptr_`. Records stored in this
+  // storage contiguously starting from index 0.
+  MaxSizeVector<ThreadIdAndValue> data_;
+
+  // Atomic pointers to the data stored in `data_`. Used as a lookup table for
+  // linear probing hash map (https://en.wikipedia.org/wiki/Linear_probing).
+  MaxSizeVector<std::atomic<ThreadIdAndValue*>> ptr_;
+
+  // Number of records stored in the `data_`.
+  std::atomic<int> filled_records_;
+
+  // We fallback on per thread map if lock-free storage is full. In practice
+  // this should never happen, if `capacity_` is a reasonable estimate of the
+  // number of threads running in a system.
+  EIGEN_MUTEX mu_;  // Protects per_thread_map_.
+  std::unordered_map<std::thread::id, T> per_thread_map_;
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_THREAD_LOCAL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/ThreadPoolInterface.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/ThreadPoolInterface.h
new file mode 100644
index 0000000000000000000000000000000000000000..e6750a9cc3ee63eb84f3131f7fb86439c34fc66d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/ThreadPoolInterface.h
@@ -0,0 +1,50 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_THREAD_POOL_INTERFACE_H
+#define EIGEN_CXX11_THREADPOOL_THREAD_POOL_INTERFACE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+// This defines an interface that ThreadPoolDevice can take to use
+// custom thread pools underneath.
+class ThreadPoolInterface {
+ public:
+  // Submits a closure to be run by a thread in the pool.
+  virtual void Schedule(std::function<void()> fn) = 0;
+
+  // Submits a closure to be run by threads in the range [start, end) in the
+  // pool.
+  virtual void ScheduleWithHint(std::function<void()> fn, int /*start*/,
+                                int /*end*/) {
+    // Just defer to Schedule in case sub-classes aren't interested in
+    // overriding this functionality.
+    Schedule(fn);
+  }
+
+  // If implemented, stop processing the closures that have been enqueued.
+  // Currently running closures may still be processed.
+  // If not implemented, does nothing.
+  virtual void Cancel() {}
+
+  // Returns the number of threads in the pool.
+  virtual int NumThreads() const = 0;
+
+  // Returns a logical thread index between 0 and NumThreads() - 1 if called
+  // from one of the threads in the pool. Returns -1 otherwise.
+  virtual int CurrentThreadId() const = 0;
+
+  virtual ~ThreadPoolInterface() {}
+};
+
+}  // namespace Eigen
+
+#endif  // EIGEN_CXX11_THREADPOOL_THREAD_POOL_INTERFACE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/ThreadYield.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/ThreadYield.h
new file mode 100644
index 0000000000000000000000000000000000000000..f556ff632d527f7cccfbe237d6547100e9e291c0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/ThreadPool/ThreadYield.h
@@ -0,0 +1,16 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CXX11_THREADPOOL_THREAD_YIELD_H
+#define EIGEN_CXX11_THREADPOOL_THREAD_YIELD_H
+
+// Try to come up with a portable way to yield
+#define EIGEN_THREAD_YIELD() std::this_thread::yield()
+
+#endif  // EIGEN_CXX11_THREADPOOL_THREAD_YIELD_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/UmfPackSupport/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/UmfPackSupport/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..64112f1a4ca9a79cd087fa5b39897fce357e27dc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/UmfPackSupport/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_UMFPACKSUPPORT_MODULE_H
+#error "Please include Eigen/UmfPackSupport instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/UmfPackSupport/UmfPackSupport.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/UmfPackSupport/UmfPackSupport.h
new file mode 100644
index 0000000000000000000000000000000000000000..d9a8d38c447bd8f2b1df8c49793844b559f08a13
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/UmfPackSupport/UmfPackSupport.h
@@ -0,0 +1,644 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_UMFPACKSUPPORT_H
+#define EIGEN_UMFPACKSUPPORT_H
+
+// for compatibility with super old version of umfpack,
+// not sure this is really needed, but this is harmless.
+#ifndef SuiteSparse_long
+#ifdef UF_long
+#define SuiteSparse_long UF_long
+#else
+#error neither SuiteSparse_long nor UF_long are defined
+#endif
+#endif
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/* TODO extract L, extract U, compute det, etc... */
+
+// generic double/complex<double> wrapper functions:
+
+
+ // Defaults
+inline void umfpack_defaults(double control[UMFPACK_CONTROL], double, int)
+{ umfpack_di_defaults(control); }
+
+inline void umfpack_defaults(double control[UMFPACK_CONTROL], std::complex<double>, int)
+{ umfpack_zi_defaults(control); }
+
+inline void umfpack_defaults(double control[UMFPACK_CONTROL], double, SuiteSparse_long)
+{ umfpack_dl_defaults(control); }
+
+inline void umfpack_defaults(double control[UMFPACK_CONTROL], std::complex<double>, SuiteSparse_long)
+{ umfpack_zl_defaults(control); }
+
+// Report info
+inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], double, int)
+{ umfpack_di_report_info(control, info);}
+
+inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], std::complex<double>, int)
+{ umfpack_zi_report_info(control, info);}
+
+inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], double, SuiteSparse_long)
+{ umfpack_dl_report_info(control, info);}
+
+inline void umfpack_report_info(double control[UMFPACK_CONTROL], double info[UMFPACK_INFO], std::complex<double>, SuiteSparse_long)
+{ umfpack_zl_report_info(control, info);}
+
+// Report status
+inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, double, int)
+{ umfpack_di_report_status(control, status);}
+
+inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, std::complex<double>, int)
+{ umfpack_zi_report_status(control, status);}
+
+inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, double, SuiteSparse_long)
+{ umfpack_dl_report_status(control, status);}
+
+inline void umfpack_report_status(double control[UMFPACK_CONTROL], int status, std::complex<double>, SuiteSparse_long)
+{ umfpack_zl_report_status(control, status);}
+
+// report control
+inline void umfpack_report_control(double control[UMFPACK_CONTROL], double, int)
+{ umfpack_di_report_control(control);}
+
+inline void umfpack_report_control(double control[UMFPACK_CONTROL], std::complex<double>, int)
+{ umfpack_zi_report_control(control);}
+
+inline void umfpack_report_control(double control[UMFPACK_CONTROL], double, SuiteSparse_long)
+{ umfpack_dl_report_control(control);}
+
+inline void umfpack_report_control(double control[UMFPACK_CONTROL], std::complex<double>, SuiteSparse_long)
+{ umfpack_zl_report_control(control);}
+
+// Free numeric
+inline void umfpack_free_numeric(void **Numeric, double, int)
+{ umfpack_di_free_numeric(Numeric); *Numeric = 0; }
+
+inline void umfpack_free_numeric(void **Numeric, std::complex<double>, int)
+{ umfpack_zi_free_numeric(Numeric); *Numeric = 0; }
+
+inline void umfpack_free_numeric(void **Numeric, double, SuiteSparse_long)
+{ umfpack_dl_free_numeric(Numeric); *Numeric = 0; }
+
+inline void umfpack_free_numeric(void **Numeric, std::complex<double>, SuiteSparse_long)
+{ umfpack_zl_free_numeric(Numeric); *Numeric = 0; }
+
+// Free symbolic
+inline void umfpack_free_symbolic(void **Symbolic, double, int)
+{ umfpack_di_free_symbolic(Symbolic); *Symbolic = 0; }
+
+inline void umfpack_free_symbolic(void **Symbolic, std::complex<double>, int)
+{ umfpack_zi_free_symbolic(Symbolic); *Symbolic = 0; }
+
+inline void umfpack_free_symbolic(void **Symbolic, double, SuiteSparse_long)
+{ umfpack_dl_free_symbolic(Symbolic); *Symbolic = 0; }
+
+inline void umfpack_free_symbolic(void **Symbolic, std::complex<double>, SuiteSparse_long)
+{ umfpack_zl_free_symbolic(Symbolic); *Symbolic = 0; }
+
+// Symbolic
+inline int umfpack_symbolic(int n_row,int n_col,
+                            const int Ap[], const int Ai[], const double Ax[], void **Symbolic,
+                            const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
+{
+  return umfpack_di_symbolic(n_row,n_col,Ap,Ai,Ax,Symbolic,Control,Info);
+}
+
+inline int umfpack_symbolic(int n_row,int n_col,
+                            const int Ap[], const int Ai[], const std::complex<double> Ax[], void **Symbolic,
+                            const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
+{
+  return umfpack_zi_symbolic(n_row,n_col,Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Control,Info);
+}
+inline SuiteSparse_long umfpack_symbolic( SuiteSparse_long n_row,SuiteSparse_long n_col,
+                                          const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const double Ax[], void **Symbolic,
+                                          const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
+{
+  return umfpack_dl_symbolic(n_row,n_col,Ap,Ai,Ax,Symbolic,Control,Info);
+}
+
+inline SuiteSparse_long umfpack_symbolic( SuiteSparse_long n_row,SuiteSparse_long n_col,
+                                          const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const std::complex<double> Ax[], void **Symbolic,
+                                          const double Control [UMFPACK_CONTROL], double Info [UMFPACK_INFO])
+{
+  return umfpack_zl_symbolic(n_row,n_col,Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Control,Info);
+}
+
+// Numeric
+inline int umfpack_numeric( const int Ap[], const int Ai[], const double Ax[],
+                            void *Symbolic, void **Numeric,
+                            const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
+{
+  return umfpack_di_numeric(Ap,Ai,Ax,Symbolic,Numeric,Control,Info);
+}
+
+inline int umfpack_numeric( const int Ap[], const int Ai[], const std::complex<double> Ax[],
+                            void *Symbolic, void **Numeric,
+                            const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
+{
+  return umfpack_zi_numeric(Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Numeric,Control,Info);
+}
+inline SuiteSparse_long umfpack_numeric(const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const double Ax[],
+                                        void *Symbolic, void **Numeric,
+                                        const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
+{
+  return umfpack_dl_numeric(Ap,Ai,Ax,Symbolic,Numeric,Control,Info);
+}
+
+inline SuiteSparse_long umfpack_numeric(const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const std::complex<double> Ax[],
+                                        void *Symbolic, void **Numeric,
+                                        const double Control[UMFPACK_CONTROL],double Info [UMFPACK_INFO])
+{
+  return umfpack_zl_numeric(Ap,Ai,&numext::real_ref(Ax[0]),0,Symbolic,Numeric,Control,Info);
+}
+
+// solve
+inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const double Ax[],
+                          double X[], const double B[], void *Numeric,
+                          const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
+{
+  return umfpack_di_solve(sys,Ap,Ai,Ax,X,B,Numeric,Control,Info);
+}
+
+inline int umfpack_solve( int sys, const int Ap[], const int Ai[], const std::complex<double> Ax[],
+                          std::complex<double> X[], const std::complex<double> B[], void *Numeric,
+                          const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
+{
+  return umfpack_zi_solve(sys,Ap,Ai,&numext::real_ref(Ax[0]),0,&numext::real_ref(X[0]),0,&numext::real_ref(B[0]),0,Numeric,Control,Info);
+}
+
+inline SuiteSparse_long umfpack_solve(int sys, const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const double Ax[],
+                                      double X[], const double B[], void *Numeric,
+                                      const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
+{
+  return umfpack_dl_solve(sys,Ap,Ai,Ax,X,B,Numeric,Control,Info);
+}
+
+inline SuiteSparse_long umfpack_solve(int sys, const SuiteSparse_long Ap[], const SuiteSparse_long Ai[], const std::complex<double> Ax[],
+                                      std::complex<double> X[], const std::complex<double> B[], void *Numeric,
+                                      const double Control[UMFPACK_CONTROL], double Info[UMFPACK_INFO])
+{
+  return umfpack_zl_solve(sys,Ap,Ai,&numext::real_ref(Ax[0]),0,&numext::real_ref(X[0]),0,&numext::real_ref(B[0]),0,Numeric,Control,Info);
+}
+
+// Get Lunz
+inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, double)
+{
+  return umfpack_di_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
+}
+
+inline int umfpack_get_lunz(int *lnz, int *unz, int *n_row, int *n_col, int *nz_udiag, void *Numeric, std::complex<double>)
+{
+  return umfpack_zi_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
+}
+
+inline SuiteSparse_long umfpack_get_lunz( SuiteSparse_long *lnz, SuiteSparse_long *unz, SuiteSparse_long *n_row, SuiteSparse_long *n_col,
+                                          SuiteSparse_long *nz_udiag, void *Numeric, double)
+{
+  return umfpack_dl_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
+}
+
+inline SuiteSparse_long umfpack_get_lunz( SuiteSparse_long *lnz, SuiteSparse_long *unz, SuiteSparse_long *n_row, SuiteSparse_long *n_col,
+                                          SuiteSparse_long *nz_udiag, void *Numeric, std::complex<double>)
+{
+  return umfpack_zl_get_lunz(lnz,unz,n_row,n_col,nz_udiag,Numeric);
+}
+
+// Get Numeric
+inline int umfpack_get_numeric(int Lp[], int Lj[], double Lx[], int Up[], int Ui[], double Ux[],
+                               int P[], int Q[], double Dx[], int *do_recip, double Rs[], void *Numeric)
+{
+  return umfpack_di_get_numeric(Lp,Lj,Lx,Up,Ui,Ux,P,Q,Dx,do_recip,Rs,Numeric);
+}
+
+inline int umfpack_get_numeric(int Lp[], int Lj[], std::complex<double> Lx[], int Up[], int Ui[], std::complex<double> Ux[],
+                               int P[], int Q[], std::complex<double> Dx[], int *do_recip, double Rs[], void *Numeric)
+{
+  double& lx0_real = numext::real_ref(Lx[0]);
+  double& ux0_real = numext::real_ref(Ux[0]);
+  double& dx0_real = numext::real_ref(Dx[0]);
+  return umfpack_zi_get_numeric(Lp,Lj,Lx?&lx0_real:0,0,Up,Ui,Ux?&ux0_real:0,0,P,Q,
+                                Dx?&dx0_real:0,0,do_recip,Rs,Numeric);
+}
+inline SuiteSparse_long umfpack_get_numeric(SuiteSparse_long Lp[], SuiteSparse_long Lj[], double Lx[], SuiteSparse_long Up[], SuiteSparse_long Ui[], double Ux[],
+                                            SuiteSparse_long P[], SuiteSparse_long Q[], double Dx[], SuiteSparse_long *do_recip, double Rs[], void *Numeric)
+{
+  return umfpack_dl_get_numeric(Lp,Lj,Lx,Up,Ui,Ux,P,Q,Dx,do_recip,Rs,Numeric);
+}
+
+inline SuiteSparse_long umfpack_get_numeric(SuiteSparse_long Lp[], SuiteSparse_long Lj[], std::complex<double> Lx[], SuiteSparse_long Up[], SuiteSparse_long Ui[], std::complex<double> Ux[],
+                                            SuiteSparse_long P[], SuiteSparse_long Q[], std::complex<double> Dx[], SuiteSparse_long *do_recip, double Rs[], void *Numeric)
+{
+  double& lx0_real = numext::real_ref(Lx[0]);
+  double& ux0_real = numext::real_ref(Ux[0]);
+  double& dx0_real = numext::real_ref(Dx[0]);
+  return umfpack_zl_get_numeric(Lp,Lj,Lx?&lx0_real:0,0,Up,Ui,Ux?&ux0_real:0,0,P,Q,
+                                Dx?&dx0_real:0,0,do_recip,Rs,Numeric);
+}
+
+// Get Determinant
+inline int umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO], int)
+{
+  return umfpack_di_get_determinant(Mx,Ex,NumericHandle,User_Info);
+}
+
+inline int umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO], int)
+{
+  double& mx_real = numext::real_ref(*Mx);
+  return umfpack_zi_get_determinant(&mx_real,0,Ex,NumericHandle,User_Info);
+}
+
+inline SuiteSparse_long umfpack_get_determinant(double *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO], SuiteSparse_long)
+{
+  return umfpack_dl_get_determinant(Mx,Ex,NumericHandle,User_Info);
+}
+
+inline SuiteSparse_long umfpack_get_determinant(std::complex<double> *Mx, double *Ex, void *NumericHandle, double User_Info [UMFPACK_INFO], SuiteSparse_long)
+{
+  double& mx_real = numext::real_ref(*Mx);
+  return umfpack_zl_get_determinant(&mx_real,0,Ex,NumericHandle,User_Info);
+}
+
+
+/** \ingroup UmfPackSupport_Module
+  * \brief A sparse LU factorization and solver based on UmfPack
+  *
+  * This class allows to solve for A.X = B sparse linear problems via a LU factorization
+  * using the UmfPack library. The sparse matrix A must be squared and full rank.
+  * The vectors or matrices X and B can be either dense or sparse.
+  *
+  * \warning The input matrix A should be in a \b compressed and \b column-major form.
+  * Otherwise an expensive copy will be made. You can call the inexpensive makeCompressed() to get a compressed matrix.
+  * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+  *
+  * \implsparsesolverconcept
+  *
+  * \sa \ref TutorialSparseSolverConcept, class SparseLU
+  */
+template<typename MatrixType_>
+class UmfPackLU : public SparseSolverBase<UmfPackLU<MatrixType_> >
+{
+  protected:
+    typedef SparseSolverBase<UmfPackLU<MatrixType_> > Base;
+    using Base::m_isInitialized;
+  public:
+    using Base::_solve_impl;
+    typedef MatrixType_ MatrixType;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename MatrixType::StorageIndex StorageIndex;
+    typedef Matrix<Scalar,Dynamic,1> Vector;
+    typedef Matrix<int, 1, MatrixType::ColsAtCompileTime> IntRowVectorType;
+    typedef Matrix<int, MatrixType::RowsAtCompileTime, 1> IntColVectorType;
+    typedef SparseMatrix<Scalar> LUMatrixType;
+    typedef SparseMatrix<Scalar,ColMajor,StorageIndex> UmfpackMatrixType;
+    typedef Ref<const UmfpackMatrixType, StandardCompressedFormat> UmfpackMatrixRef;
+    enum {
+      ColsAtCompileTime = MatrixType::ColsAtCompileTime,
+      MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
+    };
+
+  public:
+
+    typedef Array<double, UMFPACK_CONTROL, 1> UmfpackControl;
+    typedef Array<double, UMFPACK_INFO, 1> UmfpackInfo;
+
+    UmfPackLU()
+      : m_dummy(0,0), mp_matrix(m_dummy)
+    {
+      init();
+    }
+
+    template<typename InputMatrixType>
+    explicit UmfPackLU(const InputMatrixType& matrix)
+      : mp_matrix(matrix)
+    {
+      init();
+      compute(matrix);
+    }
+
+    ~UmfPackLU()
+    {
+      if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar(), StorageIndex());
+      if(m_numeric)  umfpack_free_numeric(&m_numeric,Scalar(), StorageIndex());
+    }
+
+    inline Index rows() const { return mp_matrix.rows(); }
+    inline Index cols() const { return mp_matrix.cols(); }
+
+    /** \brief Reports whether previous computation was successful.
+      *
+      * \returns \c Success if computation was successful,
+      *          \c NumericalIssue if the matrix.appears to be negative.
+      */
+    ComputationInfo info() const
+    {
+      eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+      return m_info;
+    }
+
+    inline const LUMatrixType& matrixL() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_l;
+    }
+
+    inline const LUMatrixType& matrixU() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_u;
+    }
+
+    inline const IntColVectorType& permutationP() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_p;
+    }
+
+    inline const IntRowVectorType& permutationQ() const
+    {
+      if (m_extractedDataAreDirty) extractData();
+      return m_q;
+    }
+
+    /** Computes the sparse Cholesky decomposition of \a matrix
+     *  Note that the matrix should be column-major, and in compressed format for best performance.
+     *  \sa SparseMatrix::makeCompressed().
+     */
+    template<typename InputMatrixType>
+    void compute(const InputMatrixType& matrix)
+    {
+      if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar(),StorageIndex());
+      if(m_numeric)  umfpack_free_numeric(&m_numeric,Scalar(),StorageIndex());
+      grab(matrix.derived());
+      analyzePattern_impl();
+      factorize_impl();
+    }
+
+    /** Performs a symbolic decomposition on the sparcity of \a matrix.
+      *
+      * This function is particularly useful when solving for several problems having the same structure.
+      *
+      * \sa factorize(), compute()
+      */
+    template<typename InputMatrixType>
+    void analyzePattern(const InputMatrixType& matrix)
+    {
+      if(m_symbolic) umfpack_free_symbolic(&m_symbolic,Scalar(),StorageIndex());
+      if(m_numeric)  umfpack_free_numeric(&m_numeric,Scalar(),StorageIndex());
+
+      grab(matrix.derived());
+
+      analyzePattern_impl();
+    }
+
+    /** Provides the return status code returned by UmfPack during the numeric
+      * factorization.
+      *
+      * \sa factorize(), compute()
+      */
+    inline int umfpackFactorizeReturncode() const
+    {
+      eigen_assert(m_numeric && "UmfPackLU: you must first call factorize()");
+      return m_fact_errorCode;
+    }
+
+    /** Provides access to the control settings array used by UmfPack.
+      *
+      * If this array contains NaN's, the default values are used.
+      *
+      * See UMFPACK documentation for details.
+      */
+    inline const UmfpackControl& umfpackControl() const
+    {
+      return m_control;
+    }
+
+    /** Provides access to the control settings array used by UmfPack.
+      *
+      * If this array contains NaN's, the default values are used.
+      *
+      * See UMFPACK documentation for details.
+      */
+    inline UmfpackControl& umfpackControl()
+    {
+      return m_control;
+    }
+
+    /** Performs a numeric decomposition of \a matrix
+      *
+      * The given matrix must has the same sparcity than the matrix on which the pattern anylysis has been performed.
+      *
+      * \sa analyzePattern(), compute()
+      */
+    template<typename InputMatrixType>
+    void factorize(const InputMatrixType& matrix)
+    {
+      eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
+      if(m_numeric)
+        umfpack_free_numeric(&m_numeric,Scalar(),StorageIndex());
+
+      grab(matrix.derived());
+
+      factorize_impl();
+    }
+
+    /** Prints the current UmfPack control settings.
+      *
+      * \sa umfpackControl()
+      */
+    void printUmfpackControl()
+    {
+      umfpack_report_control(m_control.data(), Scalar(),StorageIndex());
+    }
+
+    /** Prints statistics collected by UmfPack.
+      *
+      * \sa analyzePattern(), compute()
+      */
+    void printUmfpackInfo()
+    {
+      eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
+      umfpack_report_info(m_control.data(), m_umfpackInfo.data(), Scalar(),StorageIndex());
+    }
+
+    /** Prints the status of the previous factorization operation performed by UmfPack (symbolic or numerical factorization).
+      *
+      * \sa analyzePattern(), compute()
+      */
+    void printUmfpackStatus() {
+      eigen_assert(m_analysisIsOk && "UmfPackLU: you must first call analyzePattern()");
+      umfpack_report_status(m_control.data(), m_fact_errorCode, Scalar(),StorageIndex());
+    }
+
+    /** \internal */
+    template<typename BDerived,typename XDerived>
+    bool _solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const;
+
+    Scalar determinant() const;
+
+    void extractData() const;
+
+  protected:
+
+    void init()
+    {
+      m_info                  = InvalidInput;
+      m_isInitialized         = false;
+      m_numeric               = 0;
+      m_symbolic              = 0;
+      m_extractedDataAreDirty = true;
+
+      umfpack_defaults(m_control.data(), Scalar(),StorageIndex());
+    }
+
+    void analyzePattern_impl()
+    {
+      m_fact_errorCode = umfpack_symbolic(internal::convert_index<StorageIndex>(mp_matrix.rows()),
+                                          internal::convert_index<StorageIndex>(mp_matrix.cols()),
+                                          mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
+                                          &m_symbolic, m_control.data(), m_umfpackInfo.data());
+
+      m_isInitialized = true;
+      m_info = m_fact_errorCode ? InvalidInput : Success;
+      m_analysisIsOk = true;
+      m_factorizationIsOk = false;
+      m_extractedDataAreDirty = true;
+    }
+
+    void factorize_impl()
+    {
+
+      m_fact_errorCode = umfpack_numeric(mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
+                                         m_symbolic, &m_numeric, m_control.data(), m_umfpackInfo.data());
+
+      m_info = m_fact_errorCode == UMFPACK_OK ? Success : NumericalIssue;
+      m_factorizationIsOk = true;
+      m_extractedDataAreDirty = true;
+    }
+
+    template<typename MatrixDerived>
+    void grab(const EigenBase<MatrixDerived> &A)
+    {
+      internal::destroy_at(&mp_matrix);
+      internal::construct_at(&mp_matrix, A.derived());
+    }
+
+    void grab(const UmfpackMatrixRef &A)
+    {
+      if(&(A.derived()) != &mp_matrix)
+      {
+        internal::destroy_at(&mp_matrix);
+        internal::construct_at(&mp_matrix, A);
+      }
+    }
+
+    // cached data to reduce reallocation, etc.
+    mutable LUMatrixType m_l;
+    StorageIndex m_fact_errorCode;
+    UmfpackControl m_control;
+    mutable UmfpackInfo m_umfpackInfo;
+
+    mutable LUMatrixType m_u;
+    mutable IntColVectorType m_p;
+    mutable IntRowVectorType m_q;
+
+    UmfpackMatrixType m_dummy;
+    UmfpackMatrixRef mp_matrix;
+
+    void* m_numeric;
+    void* m_symbolic;
+
+    mutable ComputationInfo m_info;
+    int m_factorizationIsOk;
+    int m_analysisIsOk;
+    mutable bool m_extractedDataAreDirty;
+
+  private:
+    UmfPackLU(const UmfPackLU& ) { }
+};
+
+
+template<typename MatrixType>
+void UmfPackLU<MatrixType>::extractData() const
+{
+  if (m_extractedDataAreDirty)
+  {
+    // get size of the data
+    StorageIndex lnz, unz, rows, cols, nz_udiag;
+    umfpack_get_lunz(&lnz, &unz, &rows, &cols, &nz_udiag, m_numeric, Scalar());
+
+    // allocate data
+    m_l.resize(rows,(std::min)(rows,cols));
+    m_l.resizeNonZeros(lnz);
+
+    m_u.resize((std::min)(rows,cols),cols);
+    m_u.resizeNonZeros(unz);
+
+    m_p.resize(rows);
+    m_q.resize(cols);
+
+    // extract
+    umfpack_get_numeric(m_l.outerIndexPtr(), m_l.innerIndexPtr(), m_l.valuePtr(),
+                        m_u.outerIndexPtr(), m_u.innerIndexPtr(), m_u.valuePtr(),
+                        m_p.data(), m_q.data(), 0, 0, 0, m_numeric);
+
+    m_extractedDataAreDirty = false;
+  }
+}
+
+template<typename MatrixType>
+typename UmfPackLU<MatrixType>::Scalar UmfPackLU<MatrixType>::determinant() const
+{
+  Scalar det;
+  umfpack_get_determinant(&det, 0, m_numeric, 0, StorageIndex());
+  return det;
+}
+
+template<typename MatrixType>
+template<typename BDerived,typename XDerived>
+bool UmfPackLU<MatrixType>::_solve_impl(const MatrixBase<BDerived> &b, MatrixBase<XDerived> &x) const
+{
+  Index rhsCols = b.cols();
+  eigen_assert((BDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major rhs yet");
+  eigen_assert((XDerived::Flags&RowMajorBit)==0 && "UmfPackLU backend does not support non col-major result yet");
+  eigen_assert(b.derived().data() != x.derived().data() && " Umfpack does not support inplace solve");
+
+  Scalar* x_ptr = 0;
+  Matrix<Scalar,Dynamic,1> x_tmp;
+  if(x.innerStride()!=1)
+  {
+    x_tmp.resize(x.rows());
+    x_ptr = x_tmp.data();
+  }
+  for (int j=0; j<rhsCols; ++j)
+  {
+    if(x.innerStride()==1)
+      x_ptr = &x.col(j).coeffRef(0);
+    StorageIndex errorCode = umfpack_solve(UMFPACK_A,
+                                mp_matrix.outerIndexPtr(), mp_matrix.innerIndexPtr(), mp_matrix.valuePtr(),
+                                x_ptr, &b.const_cast_derived().col(j).coeffRef(0),
+                                m_numeric, m_control.data(), m_umfpackInfo.data());
+    if(x.innerStride()!=1)
+      x.col(j) = x_tmp;
+    if (errorCode!=0)
+      return false;
+  }
+
+  return true;
+}
+
+} // end namespace Eigen
+
+#endif // EIGEN_UMFPACKSUPPORT_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/Image.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/Image.h
new file mode 100644
index 0000000000000000000000000000000000000000..b5000369a8a01f8152862e7644bbdb9cb27af267
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/Image.h
@@ -0,0 +1,84 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MISC_IMAGE_H
+#define EIGEN_MISC_IMAGE_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \class image_retval_base
+  *
+  */
+template<typename DecompositionType>
+struct traits<image_retval_base<DecompositionType> >
+{
+  typedef typename DecompositionType::MatrixType MatrixType;
+  typedef Matrix<
+    typename MatrixType::Scalar,
+    MatrixType::RowsAtCompileTime, // the image is a subspace of the destination space, whose
+                                   // dimension is the number of rows of the original matrix
+    Dynamic,                       // we don't know at compile time the dimension of the image (the rank)
+    MatrixType::Options,
+    MatrixType::MaxRowsAtCompileTime, // the image matrix will consist of columns from the original matrix,
+    MatrixType::MaxColsAtCompileTime  // so it has the same number of rows and at most as many columns.
+  > ReturnType;
+};
+
+template<typename DecompositionType_> struct image_retval_base
+ : public ReturnByValue<image_retval_base<DecompositionType_> >
+{
+  typedef DecompositionType_ DecompositionType;
+  typedef typename DecompositionType::MatrixType MatrixType;
+  typedef ReturnByValue<image_retval_base> Base;
+
+  image_retval_base(const DecompositionType& dec, const MatrixType& originalMatrix)
+    : m_dec(dec), m_rank(dec.rank()),
+      m_cols(m_rank == 0 ? 1 : m_rank),
+      m_originalMatrix(originalMatrix)
+  {}
+
+  inline Index rows() const { return m_dec.rows(); }
+  inline Index cols() const { return m_cols; }
+  inline Index rank() const { return m_rank; }
+  inline const DecompositionType& dec() const { return m_dec; }
+  inline const MatrixType& originalMatrix() const { return m_originalMatrix; }
+
+  template<typename Dest> inline void evalTo(Dest& dst) const
+  {
+    static_cast<const image_retval<DecompositionType>*>(this)->evalTo(dst);
+  }
+
+  protected:
+    const DecompositionType& m_dec;
+    Index m_rank, m_cols;
+    const MatrixType& m_originalMatrix;
+};
+
+} // end namespace internal
+
+#define EIGEN_MAKE_IMAGE_HELPERS(DecompositionType) \
+  typedef typename DecompositionType::MatrixType MatrixType; \
+  typedef typename MatrixType::Scalar Scalar; \
+  typedef typename MatrixType::RealScalar RealScalar; \
+  typedef Eigen::internal::image_retval_base<DecompositionType> Base; \
+  using Base::dec; \
+  using Base::originalMatrix; \
+  using Base::rank; \
+  using Base::rows; \
+  using Base::cols; \
+  image_retval(const DecompositionType& dec, const MatrixType& originalMatrix) \
+    : Base(dec, originalMatrix) {}
+
+} // end namespace Eigen
+
+#endif // EIGEN_MISC_IMAGE_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..1cea572dcf9875c3abf175513397c2a0be053c90
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_CORE_MODULE_H
+#error "Please include Eigen/Core instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/Kernel.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/Kernel.h
new file mode 100644
index 0000000000000000000000000000000000000000..7abfbb73ad258eb4345c9d31c54ee37e7bfacfac
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/Kernel.h
@@ -0,0 +1,81 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_MISC_KERNEL_H
+#define EIGEN_MISC_KERNEL_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen { 
+
+namespace internal {
+
+/** \class kernel_retval_base
+  *
+  */
+template<typename DecompositionType>
+struct traits<kernel_retval_base<DecompositionType> >
+{
+  typedef typename DecompositionType::MatrixType MatrixType;
+  typedef Matrix<
+    typename MatrixType::Scalar,
+    MatrixType::ColsAtCompileTime, // the number of rows in the "kernel matrix"
+                                   // is the number of cols of the original matrix
+                                   // so that the product "matrix * kernel = zero" makes sense
+    Dynamic,                       // we don't know at compile-time the dimension of the kernel
+    MatrixType::Options,
+    MatrixType::MaxColsAtCompileTime, // see explanation for 2nd template parameter
+    MatrixType::MaxColsAtCompileTime // the kernel is a subspace of the domain space,
+                                     // whose dimension is the number of columns of the original matrix
+  > ReturnType;
+};
+
+template<typename DecompositionType_> struct kernel_retval_base
+ : public ReturnByValue<kernel_retval_base<DecompositionType_> >
+{
+  typedef DecompositionType_ DecompositionType;
+  typedef ReturnByValue<kernel_retval_base> Base;
+
+  explicit kernel_retval_base(const DecompositionType& dec)
+    : m_dec(dec),
+      m_rank(dec.rank()),
+      m_cols(m_rank==dec.cols() ? 1 : dec.cols() - m_rank)
+  {}
+
+  inline Index rows() const { return m_dec.cols(); }
+  inline Index cols() const { return m_cols; }
+  inline Index rank() const { return m_rank; }
+  inline const DecompositionType& dec() const { return m_dec; }
+
+  template<typename Dest> inline void evalTo(Dest& dst) const
+  {
+    static_cast<const kernel_retval<DecompositionType>*>(this)->evalTo(dst);
+  }
+
+  protected:
+    const DecompositionType& m_dec;
+    Index m_rank, m_cols;
+};
+
+} // end namespace internal
+
+#define EIGEN_MAKE_KERNEL_HELPERS(DecompositionType) \
+  typedef typename DecompositionType::MatrixType MatrixType; \
+  typedef typename MatrixType::Scalar Scalar; \
+  typedef typename MatrixType::RealScalar RealScalar; \
+  typedef Eigen::internal::kernel_retval_base<DecompositionType> Base; \
+  using Base::dec; \
+  using Base::rank; \
+  using Base::rows; \
+  using Base::cols; \
+  kernel_retval(const DecompositionType& dec) : Base(dec) {}
+
+} // end namespace Eigen
+
+#endif // EIGEN_MISC_KERNEL_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/RealSvd2x2.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/RealSvd2x2.h
new file mode 100644
index 0000000000000000000000000000000000000000..5dd75f330359cbff77a134a373b1e0d897c86ed2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/RealSvd2x2.h
@@ -0,0 +1,57 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2013-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_REALSVD2X2_H
+#define EIGEN_REALSVD2X2_H
+
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template<typename MatrixType, typename RealScalar, typename Index>
+void real_2x2_jacobi_svd(const MatrixType& matrix, Index p, Index q,
+                         JacobiRotation<RealScalar> *j_left,
+                         JacobiRotation<RealScalar> *j_right)
+{
+  using std::sqrt;
+  using std::abs;
+  Matrix<RealScalar,2,2> m;
+  m << numext::real(matrix.coeff(p,p)), numext::real(matrix.coeff(p,q)),
+       numext::real(matrix.coeff(q,p)), numext::real(matrix.coeff(q,q));
+  JacobiRotation<RealScalar> rot1;
+  RealScalar t = m.coeff(0,0) + m.coeff(1,1);
+  RealScalar d = m.coeff(1,0) - m.coeff(0,1);
+
+  if(abs(d) < (std::numeric_limits<RealScalar>::min)())
+  {
+    rot1.s() = RealScalar(0);
+    rot1.c() = RealScalar(1);
+  }
+  else
+  {
+    // If d!=0, then t/d cannot overflow because the magnitude of the
+    // entries forming d are not too small compared to the ones forming t.
+    RealScalar u = t / d;
+    RealScalar tmp = sqrt(RealScalar(1) + numext::abs2(u));
+    rot1.s() = RealScalar(1) / tmp;
+    rot1.c() = u / tmp;
+  }
+  m.applyOnTheLeft(0,1,rot1);
+  j_right->makeJacobi(m,0,1);
+  *j_left = rot1 * j_right->transpose();
+}
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_REALSVD2X2_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/blas.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/blas.h
new file mode 100644
index 0000000000000000000000000000000000000000..0170eef4de20cc8b88b5b1a01b6fbc688e2c13f6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/blas.h
@@ -0,0 +1,66 @@
+#ifndef EIGEN_MISC_BLAS_H
+#define EIGEN_MISC_BLAS_H
+
+extern "C" {
+
+#define BLASFUNC(FUNC) FUNC##_
+
+/* Level 1 routines */
+
+int BLASFUNC(saxpy)(const int *, const float  *, const float  *, const int *, float  *, const int *);
+int BLASFUNC(daxpy)(const int *, const double *, const double *, const int *, double *, const int *);
+int BLASFUNC(caxpy)(const int *, const float  *, const float  *, const int *, float  *, const int *);
+int BLASFUNC(zaxpy)(const int *, const double *, const double *, const int *, double *, const int *);
+
+/* Level 2 routines */
+
+int BLASFUNC(sgemv)(const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
+int BLASFUNC(dgemv)(const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
+int BLASFUNC(cgemv)(const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
+int BLASFUNC(zgemv)(const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
+
+int BLASFUNC(strmv)(const char *, const char *, const char *, const int *, const float  *, const int *, float  *, const int *);
+int BLASFUNC(dtrmv)(const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
+int BLASFUNC(ctrmv)(const char *, const char *, const char *, const int *, const float  *, const int *, float  *, const int *);
+int BLASFUNC(ztrmv)(const char *, const char *, const char *, const int *, const double *, const int *, double *, const int *);
+
+int BLASFUNC(ssymv)(const char *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
+int BLASFUNC(dsymv)(const char *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
+
+int BLASFUNC(chemv)(const char *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
+int BLASFUNC(zhemv)(const char *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
+
+/* Level 3 routines */
+
+int BLASFUNC(sgemm)(const char *, const char *, const int *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
+int BLASFUNC(dgemm)(const char *, const char *, const int *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
+int BLASFUNC(cgemm)(const char *, const char *, const int *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
+int BLASFUNC(zgemm)(const char *, const char *, const int *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
+
+int BLASFUNC(strsm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,  const float *,  const int *, float *,  const int *);
+int BLASFUNC(dtrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
+int BLASFUNC(ctrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,  const float *,  const int *, float *,  const int *);
+int BLASFUNC(ztrsm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
+
+int BLASFUNC(strmm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,  const float *,  const int *, float *,  const int *);
+int BLASFUNC(dtrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
+int BLASFUNC(ctrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const float *,  const float *,  const int *, float *,  const int *);
+int BLASFUNC(ztrmm)(const char *, const char *, const char *, const char *, const int *, const int *, const double *, const double *, const int *, double *, const int *);
+
+int BLASFUNC(ssymm)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
+int BLASFUNC(dsymm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
+
+int BLASFUNC(ssyrk)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, float  *, const int *);
+int BLASFUNC(dsyrk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, double *, const int *);
+
+int BLASFUNC(chemm)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, const int *, const float  *, float  *, const int *);
+int BLASFUNC(zhemm)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, const int *, const double *, double *, const int *);
+
+int BLASFUNC(cherk)(const char *, const char *, const int *, const int *, const float  *, const float  *, const int *, const float  *, float  *, const int *);
+int BLASFUNC(zherk)(const char *, const char *, const int *, const int *, const double *, const double *, const int *, const double *, double *, const int *);
+
+#undef BLASFUNC
+
+}
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/lapacke.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/lapacke.h
new file mode 100644
index 0000000000000000000000000000000000000000..c20204cb655b228e11dc2eb74b89b449b6087ae3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/lapacke.h
@@ -0,0 +1,16294 @@
+/*****************************************************************************
+  Copyright (c) 2010, Intel Corp.
+  All rights reserved.
+
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+    * Redistributions of source code must retain the above copyright notice,
+      this list of conditions and the following disclaimer.
+    * Redistributions in binary form must reproduce the above copyright
+      notice, this list of conditions and the following disclaimer in the
+      documentation and/or other materials provided with the distribution.
+    * Neither the name of Intel Corporation nor the names of its contributors
+      may be used to endorse or promote products derived from this software
+      without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
+  THE POSSIBILITY OF SUCH DAMAGE.
+******************************************************************************
+* Contents: Native C interface to LAPACK
+* Author: Intel Corporation
+* Generated November, 2011
+*****************************************************************************/
+
+#ifndef _MKL_LAPACKE_H_
+
+#ifndef _LAPACKE_H_
+#define _LAPACKE_H_
+
+/*
+*  Turn on HAVE_LAPACK_CONFIG_H to redefine C-LAPACK datatypes
+*/
+#ifdef HAVE_LAPACK_CONFIG_H
+#include "lapacke_config.h"
+#endif
+
+#include <stdlib.h>
+
+#ifndef lapack_int
+  #ifdef LAPACK_ILP64
+    #define lapack_int int64_t
+  #else
+    #define lapack_int int
+  #endif
+#endif
+
+#ifndef lapack_logical
+#define lapack_logical lapack_int
+#endif
+
+/* Complex types are structures equivalent to the
+* Fortran complex types COMPLEX(4) and COMPLEX(8).
+*
+* One can also redefine the types with his own types
+* for example by including in the code definitions like
+*
+* #define lapack_complex_float std::complex<float>
+* #define lapack_complex_double std::complex<double>
+*
+* or define these types in the command line:
+*
+* -Dlapack_complex_float="std::complex<float>"
+* -Dlapack_complex_double="std::complex<double>"
+*/
+
+#ifndef LAPACK_COMPLEX_CUSTOM
+
+/* Complex type (single precision) */
+#ifndef lapack_complex_float
+#define lapack_complex_float std::complex<float>
+#endif
+
+#ifndef lapack_complex_float_real
+#define lapack_complex_float_real(z)       (creal(z))
+#endif
+
+#ifndef lapack_complex_float_imag
+#define lapack_complex_float_imag(z)       (cimag(z))
+#endif
+
+lapack_complex_float lapack_make_complex_float( float re, float im );
+
+/* Complex type (double precision) */
+#ifndef lapack_complex_double
+#define lapack_complex_double std::complex<double>
+#endif
+
+#ifndef lapack_complex_double_real
+#define lapack_complex_double_real(z)      (creal(z))
+#endif
+
+#ifndef lapack_complex_double_imag
+#define lapack_complex_double_imag(z)       (cimag(z))
+#endif
+
+lapack_complex_double lapack_make_complex_double( double re, double im );
+
+#endif
+
+
+#ifdef __cplusplus
+extern "C" {
+#endif /* __cplusplus */
+
+#ifndef LAPACKE_malloc
+#define LAPACKE_malloc( size ) malloc( size )
+#endif
+#ifndef LAPACKE_free
+#define LAPACKE_free( p )      free( p )
+#endif
+
+#define LAPACK_C2INT( x ) (lapack_int)(*((float*)&x ))
+#define LAPACK_Z2INT( x ) (lapack_int)(*((double*)&x ))
+
+#define LAPACK_ROW_MAJOR               101
+#define LAPACK_COL_MAJOR               102
+
+#define LAPACK_WORK_MEMORY_ERROR       -1010
+#define LAPACK_TRANSPOSE_MEMORY_ERROR  -1011
+
+/* Callback logical functions of one, two, or three arguments are used
+*  to select eigenvalues to sort to the top left of the Schur form.
+*  The value is selected if function returns TRUE (non-zero). */
+
+typedef lapack_logical (*LAPACK_S_SELECT2) ( const float*, const float* );
+typedef lapack_logical (*LAPACK_S_SELECT3)
+    ( const float*, const float*, const float* );
+typedef lapack_logical (*LAPACK_D_SELECT2) ( const double*, const double* );
+typedef lapack_logical (*LAPACK_D_SELECT3)
+    ( const double*, const double*, const double* );
+
+typedef lapack_logical (*LAPACK_C_SELECT1) ( const lapack_complex_float* );
+typedef lapack_logical (*LAPACK_C_SELECT2)
+    ( const lapack_complex_float*, const lapack_complex_float* );
+typedef lapack_logical (*LAPACK_Z_SELECT1) ( const lapack_complex_double* );
+typedef lapack_logical (*LAPACK_Z_SELECT2)
+    ( const lapack_complex_double*, const lapack_complex_double* );
+
+#include "lapacke_mangling.h"
+
+#define LAPACK_lsame LAPACK_GLOBAL(lsame,LSAME)
+lapack_logical LAPACK_lsame( char* ca,  char* cb,
+                              lapack_int lca, lapack_int lcb );
+
+/* C-LAPACK function prototypes */
+
+lapack_int LAPACKE_sbdsdc( int matrix_order, char uplo, char compq,
+                           lapack_int n, float* d, float* e, float* u,
+                           lapack_int ldu, float* vt, lapack_int ldvt, float* q,
+                           lapack_int* iq );
+lapack_int LAPACKE_dbdsdc( int matrix_order, char uplo, char compq,
+                           lapack_int n, double* d, double* e, double* u,
+                           lapack_int ldu, double* vt, lapack_int ldvt,
+                           double* q, lapack_int* iq );
+
+lapack_int LAPACKE_sbdsqr( int matrix_order, char uplo, lapack_int n,
+                           lapack_int ncvt, lapack_int nru, lapack_int ncc,
+                           float* d, float* e, float* vt, lapack_int ldvt,
+                           float* u, lapack_int ldu, float* c, lapack_int ldc );
+lapack_int LAPACKE_dbdsqr( int matrix_order, char uplo, lapack_int n,
+                           lapack_int ncvt, lapack_int nru, lapack_int ncc,
+                           double* d, double* e, double* vt, lapack_int ldvt,
+                           double* u, lapack_int ldu, double* c,
+                           lapack_int ldc );
+lapack_int LAPACKE_cbdsqr( int matrix_order, char uplo, lapack_int n,
+                           lapack_int ncvt, lapack_int nru, lapack_int ncc,
+                           float* d, float* e, lapack_complex_float* vt,
+                           lapack_int ldvt, lapack_complex_float* u,
+                           lapack_int ldu, lapack_complex_float* c,
+                           lapack_int ldc );
+lapack_int LAPACKE_zbdsqr( int matrix_order, char uplo, lapack_int n,
+                           lapack_int ncvt, lapack_int nru, lapack_int ncc,
+                           double* d, double* e, lapack_complex_double* vt,
+                           lapack_int ldvt, lapack_complex_double* u,
+                           lapack_int ldu, lapack_complex_double* c,
+                           lapack_int ldc );
+
+lapack_int LAPACKE_sdisna( char job, lapack_int m, lapack_int n, const float* d,
+                           float* sep );
+lapack_int LAPACKE_ddisna( char job, lapack_int m, lapack_int n,
+                           const double* d, double* sep );
+
+lapack_int LAPACKE_sgbbrd( int matrix_order, char vect, lapack_int m,
+                           lapack_int n, lapack_int ncc, lapack_int kl,
+                           lapack_int ku, float* ab, lapack_int ldab, float* d,
+                           float* e, float* q, lapack_int ldq, float* pt,
+                           lapack_int ldpt, float* c, lapack_int ldc );
+lapack_int LAPACKE_dgbbrd( int matrix_order, char vect, lapack_int m,
+                           lapack_int n, lapack_int ncc, lapack_int kl,
+                           lapack_int ku, double* ab, lapack_int ldab,
+                           double* d, double* e, double* q, lapack_int ldq,
+                           double* pt, lapack_int ldpt, double* c,
+                           lapack_int ldc );
+lapack_int LAPACKE_cgbbrd( int matrix_order, char vect, lapack_int m,
+                           lapack_int n, lapack_int ncc, lapack_int kl,
+                           lapack_int ku, lapack_complex_float* ab,
+                           lapack_int ldab, float* d, float* e,
+                           lapack_complex_float* q, lapack_int ldq,
+                           lapack_complex_float* pt, lapack_int ldpt,
+                           lapack_complex_float* c, lapack_int ldc );
+lapack_int LAPACKE_zgbbrd( int matrix_order, char vect, lapack_int m,
+                           lapack_int n, lapack_int ncc, lapack_int kl,
+                           lapack_int ku, lapack_complex_double* ab,
+                           lapack_int ldab, double* d, double* e,
+                           lapack_complex_double* q, lapack_int ldq,
+                           lapack_complex_double* pt, lapack_int ldpt,
+                           lapack_complex_double* c, lapack_int ldc );
+
+lapack_int LAPACKE_sgbcon( int matrix_order, char norm, lapack_int n,
+                           lapack_int kl, lapack_int ku, const float* ab,
+                           lapack_int ldab, const lapack_int* ipiv, float anorm,
+                           float* rcond );
+lapack_int LAPACKE_dgbcon( int matrix_order, char norm, lapack_int n,
+                           lapack_int kl, lapack_int ku, const double* ab,
+                           lapack_int ldab, const lapack_int* ipiv,
+                           double anorm, double* rcond );
+lapack_int LAPACKE_cgbcon( int matrix_order, char norm, lapack_int n,
+                           lapack_int kl, lapack_int ku,
+                           const lapack_complex_float* ab, lapack_int ldab,
+                           const lapack_int* ipiv, float anorm, float* rcond );
+lapack_int LAPACKE_zgbcon( int matrix_order, char norm, lapack_int n,
+                           lapack_int kl, lapack_int ku,
+                           const lapack_complex_double* ab, lapack_int ldab,
+                           const lapack_int* ipiv, double anorm,
+                           double* rcond );
+
+lapack_int LAPACKE_sgbequ( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int kl, lapack_int ku, const float* ab,
+                           lapack_int ldab, float* r, float* c, float* rowcnd,
+                           float* colcnd, float* amax );
+lapack_int LAPACKE_dgbequ( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int kl, lapack_int ku, const double* ab,
+                           lapack_int ldab, double* r, double* c,
+                           double* rowcnd, double* colcnd, double* amax );
+lapack_int LAPACKE_cgbequ( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int kl, lapack_int ku,
+                           const lapack_complex_float* ab, lapack_int ldab,
+                           float* r, float* c, float* rowcnd, float* colcnd,
+                           float* amax );
+lapack_int LAPACKE_zgbequ( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int kl, lapack_int ku,
+                           const lapack_complex_double* ab, lapack_int ldab,
+                           double* r, double* c, double* rowcnd, double* colcnd,
+                           double* amax );
+
+lapack_int LAPACKE_sgbequb( int matrix_order, lapack_int m, lapack_int n,
+                            lapack_int kl, lapack_int ku, const float* ab,
+                            lapack_int ldab, float* r, float* c, float* rowcnd,
+                            float* colcnd, float* amax );
+lapack_int LAPACKE_dgbequb( int matrix_order, lapack_int m, lapack_int n,
+                            lapack_int kl, lapack_int ku, const double* ab,
+                            lapack_int ldab, double* r, double* c,
+                            double* rowcnd, double* colcnd, double* amax );
+lapack_int LAPACKE_cgbequb( int matrix_order, lapack_int m, lapack_int n,
+                            lapack_int kl, lapack_int ku,
+                            const lapack_complex_float* ab, lapack_int ldab,
+                            float* r, float* c, float* rowcnd, float* colcnd,
+                            float* amax );
+lapack_int LAPACKE_zgbequb( int matrix_order, lapack_int m, lapack_int n,
+                            lapack_int kl, lapack_int ku,
+                            const lapack_complex_double* ab, lapack_int ldab,
+                            double* r, double* c, double* rowcnd,
+                            double* colcnd, double* amax );
+
+lapack_int LAPACKE_sgbrfs( int matrix_order, char trans, lapack_int n,
+                           lapack_int kl, lapack_int ku, lapack_int nrhs,
+                           const float* ab, lapack_int ldab, const float* afb,
+                           lapack_int ldafb, const lapack_int* ipiv,
+                           const float* b, lapack_int ldb, float* x,
+                           lapack_int ldx, float* ferr, float* berr );
+lapack_int LAPACKE_dgbrfs( int matrix_order, char trans, lapack_int n,
+                           lapack_int kl, lapack_int ku, lapack_int nrhs,
+                           const double* ab, lapack_int ldab, const double* afb,
+                           lapack_int ldafb, const lapack_int* ipiv,
+                           const double* b, lapack_int ldb, double* x,
+                           lapack_int ldx, double* ferr, double* berr );
+lapack_int LAPACKE_cgbrfs( int matrix_order, char trans, lapack_int n,
+                           lapack_int kl, lapack_int ku, lapack_int nrhs,
+                           const lapack_complex_float* ab, lapack_int ldab,
+                           const lapack_complex_float* afb, lapack_int ldafb,
+                           const lapack_int* ipiv,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_zgbrfs( int matrix_order, char trans, lapack_int n,
+                           lapack_int kl, lapack_int ku, lapack_int nrhs,
+                           const lapack_complex_double* ab, lapack_int ldab,
+                           const lapack_complex_double* afb, lapack_int ldafb,
+                           const lapack_int* ipiv,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+
+lapack_int LAPACKE_sgbrfsx( int matrix_order, char trans, char equed,
+                            lapack_int n, lapack_int kl, lapack_int ku,
+                            lapack_int nrhs, const float* ab, lapack_int ldab,
+                            const float* afb, lapack_int ldafb,
+                            const lapack_int* ipiv, const float* r,
+                            const float* c, const float* b, lapack_int ldb,
+                            float* x, lapack_int ldx, float* rcond, float* berr,
+                            lapack_int n_err_bnds, float* err_bnds_norm,
+                            float* err_bnds_comp, lapack_int nparams,
+                            float* params );
+lapack_int LAPACKE_dgbrfsx( int matrix_order, char trans, char equed,
+                            lapack_int n, lapack_int kl, lapack_int ku,
+                            lapack_int nrhs, const double* ab, lapack_int ldab,
+                            const double* afb, lapack_int ldafb,
+                            const lapack_int* ipiv, const double* r,
+                            const double* c, const double* b, lapack_int ldb,
+                            double* x, lapack_int ldx, double* rcond,
+                            double* berr, lapack_int n_err_bnds,
+                            double* err_bnds_norm, double* err_bnds_comp,
+                            lapack_int nparams, double* params );
+lapack_int LAPACKE_cgbrfsx( int matrix_order, char trans, char equed,
+                            lapack_int n, lapack_int kl, lapack_int ku,
+                            lapack_int nrhs, const lapack_complex_float* ab,
+                            lapack_int ldab, const lapack_complex_float* afb,
+                            lapack_int ldafb, const lapack_int* ipiv,
+                            const float* r, const float* c,
+                            const lapack_complex_float* b, lapack_int ldb,
+                            lapack_complex_float* x, lapack_int ldx,
+                            float* rcond, float* berr, lapack_int n_err_bnds,
+                            float* err_bnds_norm, float* err_bnds_comp,
+                            lapack_int nparams, float* params );
+lapack_int LAPACKE_zgbrfsx( int matrix_order, char trans, char equed,
+                            lapack_int n, lapack_int kl, lapack_int ku,
+                            lapack_int nrhs, const lapack_complex_double* ab,
+                            lapack_int ldab, const lapack_complex_double* afb,
+                            lapack_int ldafb, const lapack_int* ipiv,
+                            const double* r, const double* c,
+                            const lapack_complex_double* b, lapack_int ldb,
+                            lapack_complex_double* x, lapack_int ldx,
+                            double* rcond, double* berr, lapack_int n_err_bnds,
+                            double* err_bnds_norm, double* err_bnds_comp,
+                            lapack_int nparams, double* params );
+
+lapack_int LAPACKE_sgbsv( int matrix_order, lapack_int n, lapack_int kl,
+                          lapack_int ku, lapack_int nrhs, float* ab,
+                          lapack_int ldab, lapack_int* ipiv, float* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_dgbsv( int matrix_order, lapack_int n, lapack_int kl,
+                          lapack_int ku, lapack_int nrhs, double* ab,
+                          lapack_int ldab, lapack_int* ipiv, double* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_cgbsv( int matrix_order, lapack_int n, lapack_int kl,
+                          lapack_int ku, lapack_int nrhs,
+                          lapack_complex_float* ab, lapack_int ldab,
+                          lapack_int* ipiv, lapack_complex_float* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_zgbsv( int matrix_order, lapack_int n, lapack_int kl,
+                          lapack_int ku, lapack_int nrhs,
+                          lapack_complex_double* ab, lapack_int ldab,
+                          lapack_int* ipiv, lapack_complex_double* b,
+                          lapack_int ldb );
+
+lapack_int LAPACKE_sgbsvx( int matrix_order, char fact, char trans,
+                           lapack_int n, lapack_int kl, lapack_int ku,
+                           lapack_int nrhs, float* ab, lapack_int ldab,
+                           float* afb, lapack_int ldafb, lapack_int* ipiv,
+                           char* equed, float* r, float* c, float* b,
+                           lapack_int ldb, float* x, lapack_int ldx,
+                           float* rcond, float* ferr, float* berr,
+                           float* rpivot );
+lapack_int LAPACKE_dgbsvx( int matrix_order, char fact, char trans,
+                           lapack_int n, lapack_int kl, lapack_int ku,
+                           lapack_int nrhs, double* ab, lapack_int ldab,
+                           double* afb, lapack_int ldafb, lapack_int* ipiv,
+                           char* equed, double* r, double* c, double* b,
+                           lapack_int ldb, double* x, lapack_int ldx,
+                           double* rcond, double* ferr, double* berr,
+                           double* rpivot );
+lapack_int LAPACKE_cgbsvx( int matrix_order, char fact, char trans,
+                           lapack_int n, lapack_int kl, lapack_int ku,
+                           lapack_int nrhs, lapack_complex_float* ab,
+                           lapack_int ldab, lapack_complex_float* afb,
+                           lapack_int ldafb, lapack_int* ipiv, char* equed,
+                           float* r, float* c, lapack_complex_float* b,
+                           lapack_int ldb, lapack_complex_float* x,
+                           lapack_int ldx, float* rcond, float* ferr,
+                           float* berr, float* rpivot );
+lapack_int LAPACKE_zgbsvx( int matrix_order, char fact, char trans,
+                           lapack_int n, lapack_int kl, lapack_int ku,
+                           lapack_int nrhs, lapack_complex_double* ab,
+                           lapack_int ldab, lapack_complex_double* afb,
+                           lapack_int ldafb, lapack_int* ipiv, char* equed,
+                           double* r, double* c, lapack_complex_double* b,
+                           lapack_int ldb, lapack_complex_double* x,
+                           lapack_int ldx, double* rcond, double* ferr,
+                           double* berr, double* rpivot );
+
+lapack_int LAPACKE_sgbsvxx( int matrix_order, char fact, char trans,
+                            lapack_int n, lapack_int kl, lapack_int ku,
+                            lapack_int nrhs, float* ab, lapack_int ldab,
+                            float* afb, lapack_int ldafb, lapack_int* ipiv,
+                            char* equed, float* r, float* c, float* b,
+                            lapack_int ldb, float* x, lapack_int ldx,
+                            float* rcond, float* rpvgrw, float* berr,
+                            lapack_int n_err_bnds, float* err_bnds_norm,
+                            float* err_bnds_comp, lapack_int nparams,
+                            float* params );
+lapack_int LAPACKE_dgbsvxx( int matrix_order, char fact, char trans,
+                            lapack_int n, lapack_int kl, lapack_int ku,
+                            lapack_int nrhs, double* ab, lapack_int ldab,
+                            double* afb, lapack_int ldafb, lapack_int* ipiv,
+                            char* equed, double* r, double* c, double* b,
+                            lapack_int ldb, double* x, lapack_int ldx,
+                            double* rcond, double* rpvgrw, double* berr,
+                            lapack_int n_err_bnds, double* err_bnds_norm,
+                            double* err_bnds_comp, lapack_int nparams,
+                            double* params );
+lapack_int LAPACKE_cgbsvxx( int matrix_order, char fact, char trans,
+                            lapack_int n, lapack_int kl, lapack_int ku,
+                            lapack_int nrhs, lapack_complex_float* ab,
+                            lapack_int ldab, lapack_complex_float* afb,
+                            lapack_int ldafb, lapack_int* ipiv, char* equed,
+                            float* r, float* c, lapack_complex_float* b,
+                            lapack_int ldb, lapack_complex_float* x,
+                            lapack_int ldx, float* rcond, float* rpvgrw,
+                            float* berr, lapack_int n_err_bnds,
+                            float* err_bnds_norm, float* err_bnds_comp,
+                            lapack_int nparams, float* params );
+lapack_int LAPACKE_zgbsvxx( int matrix_order, char fact, char trans,
+                            lapack_int n, lapack_int kl, lapack_int ku,
+                            lapack_int nrhs, lapack_complex_double* ab,
+                            lapack_int ldab, lapack_complex_double* afb,
+                            lapack_int ldafb, lapack_int* ipiv, char* equed,
+                            double* r, double* c, lapack_complex_double* b,
+                            lapack_int ldb, lapack_complex_double* x,
+                            lapack_int ldx, double* rcond, double* rpvgrw,
+                            double* berr, lapack_int n_err_bnds,
+                            double* err_bnds_norm, double* err_bnds_comp,
+                            lapack_int nparams, double* params );
+
+lapack_int LAPACKE_sgbtrf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int kl, lapack_int ku, float* ab,
+                           lapack_int ldab, lapack_int* ipiv );
+lapack_int LAPACKE_dgbtrf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int kl, lapack_int ku, double* ab,
+                           lapack_int ldab, lapack_int* ipiv );
+lapack_int LAPACKE_cgbtrf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int kl, lapack_int ku,
+                           lapack_complex_float* ab, lapack_int ldab,
+                           lapack_int* ipiv );
+lapack_int LAPACKE_zgbtrf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int kl, lapack_int ku,
+                           lapack_complex_double* ab, lapack_int ldab,
+                           lapack_int* ipiv );
+
+lapack_int LAPACKE_sgbtrs( int matrix_order, char trans, lapack_int n,
+                           lapack_int kl, lapack_int ku, lapack_int nrhs,
+                           const float* ab, lapack_int ldab,
+                           const lapack_int* ipiv, float* b, lapack_int ldb );
+lapack_int LAPACKE_dgbtrs( int matrix_order, char trans, lapack_int n,
+                           lapack_int kl, lapack_int ku, lapack_int nrhs,
+                           const double* ab, lapack_int ldab,
+                           const lapack_int* ipiv, double* b, lapack_int ldb );
+lapack_int LAPACKE_cgbtrs( int matrix_order, char trans, lapack_int n,
+                           lapack_int kl, lapack_int ku, lapack_int nrhs,
+                           const lapack_complex_float* ab, lapack_int ldab,
+                           const lapack_int* ipiv, lapack_complex_float* b,
+                           lapack_int ldb );
+lapack_int LAPACKE_zgbtrs( int matrix_order, char trans, lapack_int n,
+                           lapack_int kl, lapack_int ku, lapack_int nrhs,
+                           const lapack_complex_double* ab, lapack_int ldab,
+                           const lapack_int* ipiv, lapack_complex_double* b,
+                           lapack_int ldb );
+
+lapack_int LAPACKE_sgebak( int matrix_order, char job, char side, lapack_int n,
+                           lapack_int ilo, lapack_int ihi, const float* scale,
+                           lapack_int m, float* v, lapack_int ldv );
+lapack_int LAPACKE_dgebak( int matrix_order, char job, char side, lapack_int n,
+                           lapack_int ilo, lapack_int ihi, const double* scale,
+                           lapack_int m, double* v, lapack_int ldv );
+lapack_int LAPACKE_cgebak( int matrix_order, char job, char side, lapack_int n,
+                           lapack_int ilo, lapack_int ihi, const float* scale,
+                           lapack_int m, lapack_complex_float* v,
+                           lapack_int ldv );
+lapack_int LAPACKE_zgebak( int matrix_order, char job, char side, lapack_int n,
+                           lapack_int ilo, lapack_int ihi, const double* scale,
+                           lapack_int m, lapack_complex_double* v,
+                           lapack_int ldv );
+
+lapack_int LAPACKE_sgebal( int matrix_order, char job, lapack_int n, float* a,
+                           lapack_int lda, lapack_int* ilo, lapack_int* ihi,
+                           float* scale );
+lapack_int LAPACKE_dgebal( int matrix_order, char job, lapack_int n, double* a,
+                           lapack_int lda, lapack_int* ilo, lapack_int* ihi,
+                           double* scale );
+lapack_int LAPACKE_cgebal( int matrix_order, char job, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_int* ilo, lapack_int* ihi, float* scale );
+lapack_int LAPACKE_zgebal( int matrix_order, char job, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_int* ilo, lapack_int* ihi, double* scale );
+
+lapack_int LAPACKE_sgebrd( int matrix_order, lapack_int m, lapack_int n,
+                           float* a, lapack_int lda, float* d, float* e,
+                           float* tauq, float* taup );
+lapack_int LAPACKE_dgebrd( int matrix_order, lapack_int m, lapack_int n,
+                           double* a, lapack_int lda, double* d, double* e,
+                           double* tauq, double* taup );
+lapack_int LAPACKE_cgebrd( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda, float* d,
+                           float* e, lapack_complex_float* tauq,
+                           lapack_complex_float* taup );
+lapack_int LAPACKE_zgebrd( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda, double* d,
+                           double* e, lapack_complex_double* tauq,
+                           lapack_complex_double* taup );
+
+lapack_int LAPACKE_sgecon( int matrix_order, char norm, lapack_int n,
+                           const float* a, lapack_int lda, float anorm,
+                           float* rcond );
+lapack_int LAPACKE_dgecon( int matrix_order, char norm, lapack_int n,
+                           const double* a, lapack_int lda, double anorm,
+                           double* rcond );
+lapack_int LAPACKE_cgecon( int matrix_order, char norm, lapack_int n,
+                           const lapack_complex_float* a, lapack_int lda,
+                           float anorm, float* rcond );
+lapack_int LAPACKE_zgecon( int matrix_order, char norm, lapack_int n,
+                           const lapack_complex_double* a, lapack_int lda,
+                           double anorm, double* rcond );
+
+lapack_int LAPACKE_sgeequ( int matrix_order, lapack_int m, lapack_int n,
+                           const float* a, lapack_int lda, float* r, float* c,
+                           float* rowcnd, float* colcnd, float* amax );
+lapack_int LAPACKE_dgeequ( int matrix_order, lapack_int m, lapack_int n,
+                           const double* a, lapack_int lda, double* r,
+                           double* c, double* rowcnd, double* colcnd,
+                           double* amax );
+lapack_int LAPACKE_cgeequ( int matrix_order, lapack_int m, lapack_int n,
+                           const lapack_complex_float* a, lapack_int lda,
+                           float* r, float* c, float* rowcnd, float* colcnd,
+                           float* amax );
+lapack_int LAPACKE_zgeequ( int matrix_order, lapack_int m, lapack_int n,
+                           const lapack_complex_double* a, lapack_int lda,
+                           double* r, double* c, double* rowcnd, double* colcnd,
+                           double* amax );
+
+lapack_int LAPACKE_sgeequb( int matrix_order, lapack_int m, lapack_int n,
+                            const float* a, lapack_int lda, float* r, float* c,
+                            float* rowcnd, float* colcnd, float* amax );
+lapack_int LAPACKE_dgeequb( int matrix_order, lapack_int m, lapack_int n,
+                            const double* a, lapack_int lda, double* r,
+                            double* c, double* rowcnd, double* colcnd,
+                            double* amax );
+lapack_int LAPACKE_cgeequb( int matrix_order, lapack_int m, lapack_int n,
+                            const lapack_complex_float* a, lapack_int lda,
+                            float* r, float* c, float* rowcnd, float* colcnd,
+                            float* amax );
+lapack_int LAPACKE_zgeequb( int matrix_order, lapack_int m, lapack_int n,
+                            const lapack_complex_double* a, lapack_int lda,
+                            double* r, double* c, double* rowcnd,
+                            double* colcnd, double* amax );
+
+lapack_int LAPACKE_sgees( int matrix_order, char jobvs, char sort,
+                          LAPACK_S_SELECT2 select, lapack_int n, float* a,
+                          lapack_int lda, lapack_int* sdim, float* wr,
+                          float* wi, float* vs, lapack_int ldvs );
+lapack_int LAPACKE_dgees( int matrix_order, char jobvs, char sort,
+                          LAPACK_D_SELECT2 select, lapack_int n, double* a,
+                          lapack_int lda, lapack_int* sdim, double* wr,
+                          double* wi, double* vs, lapack_int ldvs );
+lapack_int LAPACKE_cgees( int matrix_order, char jobvs, char sort,
+                          LAPACK_C_SELECT1 select, lapack_int n,
+                          lapack_complex_float* a, lapack_int lda,
+                          lapack_int* sdim, lapack_complex_float* w,
+                          lapack_complex_float* vs, lapack_int ldvs );
+lapack_int LAPACKE_zgees( int matrix_order, char jobvs, char sort,
+                          LAPACK_Z_SELECT1 select, lapack_int n,
+                          lapack_complex_double* a, lapack_int lda,
+                          lapack_int* sdim, lapack_complex_double* w,
+                          lapack_complex_double* vs, lapack_int ldvs );
+
+lapack_int LAPACKE_sgeesx( int matrix_order, char jobvs, char sort,
+                           LAPACK_S_SELECT2 select, char sense, lapack_int n,
+                           float* a, lapack_int lda, lapack_int* sdim,
+                           float* wr, float* wi, float* vs, lapack_int ldvs,
+                           float* rconde, float* rcondv );
+lapack_int LAPACKE_dgeesx( int matrix_order, char jobvs, char sort,
+                           LAPACK_D_SELECT2 select, char sense, lapack_int n,
+                           double* a, lapack_int lda, lapack_int* sdim,
+                           double* wr, double* wi, double* vs, lapack_int ldvs,
+                           double* rconde, double* rcondv );
+lapack_int LAPACKE_cgeesx( int matrix_order, char jobvs, char sort,
+                           LAPACK_C_SELECT1 select, char sense, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_int* sdim, lapack_complex_float* w,
+                           lapack_complex_float* vs, lapack_int ldvs,
+                           float* rconde, float* rcondv );
+lapack_int LAPACKE_zgeesx( int matrix_order, char jobvs, char sort,
+                           LAPACK_Z_SELECT1 select, char sense, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_int* sdim, lapack_complex_double* w,
+                           lapack_complex_double* vs, lapack_int ldvs,
+                           double* rconde, double* rcondv );
+
+lapack_int LAPACKE_sgeev( int matrix_order, char jobvl, char jobvr,
+                          lapack_int n, float* a, lapack_int lda, float* wr,
+                          float* wi, float* vl, lapack_int ldvl, float* vr,
+                          lapack_int ldvr );
+lapack_int LAPACKE_dgeev( int matrix_order, char jobvl, char jobvr,
+                          lapack_int n, double* a, lapack_int lda, double* wr,
+                          double* wi, double* vl, lapack_int ldvl, double* vr,
+                          lapack_int ldvr );
+lapack_int LAPACKE_cgeev( int matrix_order, char jobvl, char jobvr,
+                          lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* w, lapack_complex_float* vl,
+                          lapack_int ldvl, lapack_complex_float* vr,
+                          lapack_int ldvr );
+lapack_int LAPACKE_zgeev( int matrix_order, char jobvl, char jobvr,
+                          lapack_int n, lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* w,
+                          lapack_complex_double* vl, lapack_int ldvl,
+                          lapack_complex_double* vr, lapack_int ldvr );
+
+lapack_int LAPACKE_sgeevx( int matrix_order, char balanc, char jobvl,
+                           char jobvr, char sense, lapack_int n, float* a,
+                           lapack_int lda, float* wr, float* wi, float* vl,
+                           lapack_int ldvl, float* vr, lapack_int ldvr,
+                           lapack_int* ilo, lapack_int* ihi, float* scale,
+                           float* abnrm, float* rconde, float* rcondv );
+lapack_int LAPACKE_dgeevx( int matrix_order, char balanc, char jobvl,
+                           char jobvr, char sense, lapack_int n, double* a,
+                           lapack_int lda, double* wr, double* wi, double* vl,
+                           lapack_int ldvl, double* vr, lapack_int ldvr,
+                           lapack_int* ilo, lapack_int* ihi, double* scale,
+                           double* abnrm, double* rconde, double* rcondv );
+lapack_int LAPACKE_cgeevx( int matrix_order, char balanc, char jobvl,
+                           char jobvr, char sense, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* w, lapack_complex_float* vl,
+                           lapack_int ldvl, lapack_complex_float* vr,
+                           lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
+                           float* scale, float* abnrm, float* rconde,
+                           float* rcondv );
+lapack_int LAPACKE_zgeevx( int matrix_order, char balanc, char jobvl,
+                           char jobvr, char sense, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* w, lapack_complex_double* vl,
+                           lapack_int ldvl, lapack_complex_double* vr,
+                           lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
+                           double* scale, double* abnrm, double* rconde,
+                           double* rcondv );
+
+lapack_int LAPACKE_sgehrd( int matrix_order, lapack_int n, lapack_int ilo,
+                           lapack_int ihi, float* a, lapack_int lda,
+                           float* tau );
+lapack_int LAPACKE_dgehrd( int matrix_order, lapack_int n, lapack_int ilo,
+                           lapack_int ihi, double* a, lapack_int lda,
+                           double* tau );
+lapack_int LAPACKE_cgehrd( int matrix_order, lapack_int n, lapack_int ilo,
+                           lapack_int ihi, lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* tau );
+lapack_int LAPACKE_zgehrd( int matrix_order, lapack_int n, lapack_int ilo,
+                           lapack_int ihi, lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* tau );
+
+lapack_int LAPACKE_sgejsv( int matrix_order, char joba, char jobu, char jobv,
+                           char jobr, char jobt, char jobp, lapack_int m,
+                           lapack_int n, float* a, lapack_int lda, float* sva,
+                           float* u, lapack_int ldu, float* v, lapack_int ldv,
+                           float* stat, lapack_int* istat );
+lapack_int LAPACKE_dgejsv( int matrix_order, char joba, char jobu, char jobv,
+                           char jobr, char jobt, char jobp, lapack_int m,
+                           lapack_int n, double* a, lapack_int lda, double* sva,
+                           double* u, lapack_int ldu, double* v, lapack_int ldv,
+                           double* stat, lapack_int* istat );
+
+lapack_int LAPACKE_sgelq2( int matrix_order, lapack_int m, lapack_int n,
+                           float* a, lapack_int lda, float* tau );
+lapack_int LAPACKE_dgelq2( int matrix_order, lapack_int m, lapack_int n,
+                           double* a, lapack_int lda, double* tau );
+lapack_int LAPACKE_cgelq2( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* tau );
+lapack_int LAPACKE_zgelq2( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* tau );
+
+lapack_int LAPACKE_sgelqf( int matrix_order, lapack_int m, lapack_int n,
+                           float* a, lapack_int lda, float* tau );
+lapack_int LAPACKE_dgelqf( int matrix_order, lapack_int m, lapack_int n,
+                           double* a, lapack_int lda, double* tau );
+lapack_int LAPACKE_cgelqf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* tau );
+lapack_int LAPACKE_zgelqf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* tau );
+
+lapack_int LAPACKE_sgels( int matrix_order, char trans, lapack_int m,
+                          lapack_int n, lapack_int nrhs, float* a,
+                          lapack_int lda, float* b, lapack_int ldb );
+lapack_int LAPACKE_dgels( int matrix_order, char trans, lapack_int m,
+                          lapack_int n, lapack_int nrhs, double* a,
+                          lapack_int lda, double* b, lapack_int ldb );
+lapack_int LAPACKE_cgels( int matrix_order, char trans, lapack_int m,
+                          lapack_int n, lapack_int nrhs,
+                          lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zgels( int matrix_order, char trans, lapack_int m,
+                          lapack_int n, lapack_int nrhs,
+                          lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_sgelsd( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nrhs, float* a, lapack_int lda, float* b,
+                           lapack_int ldb, float* s, float rcond,
+                           lapack_int* rank );
+lapack_int LAPACKE_dgelsd( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nrhs, double* a, lapack_int lda,
+                           double* b, lapack_int ldb, double* s, double rcond,
+                           lapack_int* rank );
+lapack_int LAPACKE_cgelsd( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nrhs, lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* b,
+                           lapack_int ldb, float* s, float rcond,
+                           lapack_int* rank );
+lapack_int LAPACKE_zgelsd( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nrhs, lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* b,
+                           lapack_int ldb, double* s, double rcond,
+                           lapack_int* rank );
+
+lapack_int LAPACKE_sgelss( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nrhs, float* a, lapack_int lda, float* b,
+                           lapack_int ldb, float* s, float rcond,
+                           lapack_int* rank );
+lapack_int LAPACKE_dgelss( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nrhs, double* a, lapack_int lda,
+                           double* b, lapack_int ldb, double* s, double rcond,
+                           lapack_int* rank );
+lapack_int LAPACKE_cgelss( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nrhs, lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* b,
+                           lapack_int ldb, float* s, float rcond,
+                           lapack_int* rank );
+lapack_int LAPACKE_zgelss( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nrhs, lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* b,
+                           lapack_int ldb, double* s, double rcond,
+                           lapack_int* rank );
+
+lapack_int LAPACKE_sgelsy( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nrhs, float* a, lapack_int lda, float* b,
+                           lapack_int ldb, lapack_int* jpvt, float rcond,
+                           lapack_int* rank );
+lapack_int LAPACKE_dgelsy( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nrhs, double* a, lapack_int lda,
+                           double* b, lapack_int ldb, lapack_int* jpvt,
+                           double rcond, lapack_int* rank );
+lapack_int LAPACKE_cgelsy( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nrhs, lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* b,
+                           lapack_int ldb, lapack_int* jpvt, float rcond,
+                           lapack_int* rank );
+lapack_int LAPACKE_zgelsy( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nrhs, lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* b,
+                           lapack_int ldb, lapack_int* jpvt, double rcond,
+                           lapack_int* rank );
+
+lapack_int LAPACKE_sgeqlf( int matrix_order, lapack_int m, lapack_int n,
+                           float* a, lapack_int lda, float* tau );
+lapack_int LAPACKE_dgeqlf( int matrix_order, lapack_int m, lapack_int n,
+                           double* a, lapack_int lda, double* tau );
+lapack_int LAPACKE_cgeqlf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* tau );
+lapack_int LAPACKE_zgeqlf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* tau );
+
+lapack_int LAPACKE_sgeqp3( int matrix_order, lapack_int m, lapack_int n,
+                           float* a, lapack_int lda, lapack_int* jpvt,
+                           float* tau );
+lapack_int LAPACKE_dgeqp3( int matrix_order, lapack_int m, lapack_int n,
+                           double* a, lapack_int lda, lapack_int* jpvt,
+                           double* tau );
+lapack_int LAPACKE_cgeqp3( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_int* jpvt, lapack_complex_float* tau );
+lapack_int LAPACKE_zgeqp3( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_int* jpvt, lapack_complex_double* tau );
+
+lapack_int LAPACKE_sgeqpf( int matrix_order, lapack_int m, lapack_int n,
+                           float* a, lapack_int lda, lapack_int* jpvt,
+                           float* tau );
+lapack_int LAPACKE_dgeqpf( int matrix_order, lapack_int m, lapack_int n,
+                           double* a, lapack_int lda, lapack_int* jpvt,
+                           double* tau );
+lapack_int LAPACKE_cgeqpf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_int* jpvt, lapack_complex_float* tau );
+lapack_int LAPACKE_zgeqpf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_int* jpvt, lapack_complex_double* tau );
+
+lapack_int LAPACKE_sgeqr2( int matrix_order, lapack_int m, lapack_int n,
+                           float* a, lapack_int lda, float* tau );
+lapack_int LAPACKE_dgeqr2( int matrix_order, lapack_int m, lapack_int n,
+                           double* a, lapack_int lda, double* tau );
+lapack_int LAPACKE_cgeqr2( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* tau );
+lapack_int LAPACKE_zgeqr2( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* tau );
+
+lapack_int LAPACKE_sgeqrf( int matrix_order, lapack_int m, lapack_int n,
+                           float* a, lapack_int lda, float* tau );
+lapack_int LAPACKE_dgeqrf( int matrix_order, lapack_int m, lapack_int n,
+                           double* a, lapack_int lda, double* tau );
+lapack_int LAPACKE_cgeqrf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* tau );
+lapack_int LAPACKE_zgeqrf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* tau );
+
+lapack_int LAPACKE_sgeqrfp( int matrix_order, lapack_int m, lapack_int n,
+                            float* a, lapack_int lda, float* tau );
+lapack_int LAPACKE_dgeqrfp( int matrix_order, lapack_int m, lapack_int n,
+                            double* a, lapack_int lda, double* tau );
+lapack_int LAPACKE_cgeqrfp( int matrix_order, lapack_int m, lapack_int n,
+                            lapack_complex_float* a, lapack_int lda,
+                            lapack_complex_float* tau );
+lapack_int LAPACKE_zgeqrfp( int matrix_order, lapack_int m, lapack_int n,
+                            lapack_complex_double* a, lapack_int lda,
+                            lapack_complex_double* tau );
+
+lapack_int LAPACKE_sgerfs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const float* a, lapack_int lda,
+                           const float* af, lapack_int ldaf,
+                           const lapack_int* ipiv, const float* b,
+                           lapack_int ldb, float* x, lapack_int ldx,
+                           float* ferr, float* berr );
+lapack_int LAPACKE_dgerfs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const double* a, lapack_int lda,
+                           const double* af, lapack_int ldaf,
+                           const lapack_int* ipiv, const double* b,
+                           lapack_int ldb, double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+lapack_int LAPACKE_cgerfs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* a,
+                           lapack_int lda, const lapack_complex_float* af,
+                           lapack_int ldaf, const lapack_int* ipiv,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_zgerfs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* a,
+                           lapack_int lda, const lapack_complex_double* af,
+                           lapack_int ldaf, const lapack_int* ipiv,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+
+lapack_int LAPACKE_sgerfsx( int matrix_order, char trans, char equed,
+                            lapack_int n, lapack_int nrhs, const float* a,
+                            lapack_int lda, const float* af, lapack_int ldaf,
+                            const lapack_int* ipiv, const float* r,
+                            const float* c, const float* b, lapack_int ldb,
+                            float* x, lapack_int ldx, float* rcond, float* berr,
+                            lapack_int n_err_bnds, float* err_bnds_norm,
+                            float* err_bnds_comp, lapack_int nparams,
+                            float* params );
+lapack_int LAPACKE_dgerfsx( int matrix_order, char trans, char equed,
+                            lapack_int n, lapack_int nrhs, const double* a,
+                            lapack_int lda, const double* af, lapack_int ldaf,
+                            const lapack_int* ipiv, const double* r,
+                            const double* c, const double* b, lapack_int ldb,
+                            double* x, lapack_int ldx, double* rcond,
+                            double* berr, lapack_int n_err_bnds,
+                            double* err_bnds_norm, double* err_bnds_comp,
+                            lapack_int nparams, double* params );
+lapack_int LAPACKE_cgerfsx( int matrix_order, char trans, char equed,
+                            lapack_int n, lapack_int nrhs,
+                            const lapack_complex_float* a, lapack_int lda,
+                            const lapack_complex_float* af, lapack_int ldaf,
+                            const lapack_int* ipiv, const float* r,
+                            const float* c, const lapack_complex_float* b,
+                            lapack_int ldb, lapack_complex_float* x,
+                            lapack_int ldx, float* rcond, float* berr,
+                            lapack_int n_err_bnds, float* err_bnds_norm,
+                            float* err_bnds_comp, lapack_int nparams,
+                            float* params );
+lapack_int LAPACKE_zgerfsx( int matrix_order, char trans, char equed,
+                            lapack_int n, lapack_int nrhs,
+                            const lapack_complex_double* a, lapack_int lda,
+                            const lapack_complex_double* af, lapack_int ldaf,
+                            const lapack_int* ipiv, const double* r,
+                            const double* c, const lapack_complex_double* b,
+                            lapack_int ldb, lapack_complex_double* x,
+                            lapack_int ldx, double* rcond, double* berr,
+                            lapack_int n_err_bnds, double* err_bnds_norm,
+                            double* err_bnds_comp, lapack_int nparams,
+                            double* params );
+
+lapack_int LAPACKE_sgerqf( int matrix_order, lapack_int m, lapack_int n,
+                           float* a, lapack_int lda, float* tau );
+lapack_int LAPACKE_dgerqf( int matrix_order, lapack_int m, lapack_int n,
+                           double* a, lapack_int lda, double* tau );
+lapack_int LAPACKE_cgerqf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* tau );
+lapack_int LAPACKE_zgerqf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* tau );
+
+lapack_int LAPACKE_sgesdd( int matrix_order, char jobz, lapack_int m,
+                           lapack_int n, float* a, lapack_int lda, float* s,
+                           float* u, lapack_int ldu, float* vt,
+                           lapack_int ldvt );
+lapack_int LAPACKE_dgesdd( int matrix_order, char jobz, lapack_int m,
+                           lapack_int n, double* a, lapack_int lda, double* s,
+                           double* u, lapack_int ldu, double* vt,
+                           lapack_int ldvt );
+lapack_int LAPACKE_cgesdd( int matrix_order, char jobz, lapack_int m,
+                           lapack_int n, lapack_complex_float* a,
+                           lapack_int lda, float* s, lapack_complex_float* u,
+                           lapack_int ldu, lapack_complex_float* vt,
+                           lapack_int ldvt );
+lapack_int LAPACKE_zgesdd( int matrix_order, char jobz, lapack_int m,
+                           lapack_int n, lapack_complex_double* a,
+                           lapack_int lda, double* s, lapack_complex_double* u,
+                           lapack_int ldu, lapack_complex_double* vt,
+                           lapack_int ldvt );
+
+lapack_int LAPACKE_sgesv( int matrix_order, lapack_int n, lapack_int nrhs,
+                          float* a, lapack_int lda, lapack_int* ipiv, float* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_dgesv( int matrix_order, lapack_int n, lapack_int nrhs,
+                          double* a, lapack_int lda, lapack_int* ipiv,
+                          double* b, lapack_int ldb );
+lapack_int LAPACKE_cgesv( int matrix_order, lapack_int n, lapack_int nrhs,
+                          lapack_complex_float* a, lapack_int lda,
+                          lapack_int* ipiv, lapack_complex_float* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_zgesv( int matrix_order, lapack_int n, lapack_int nrhs,
+                          lapack_complex_double* a, lapack_int lda,
+                          lapack_int* ipiv, lapack_complex_double* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_dsgesv( int matrix_order, lapack_int n, lapack_int nrhs,
+                           double* a, lapack_int lda, lapack_int* ipiv,
+                           double* b, lapack_int ldb, double* x, lapack_int ldx,
+                           lapack_int* iter );
+lapack_int LAPACKE_zcgesv( int matrix_order, lapack_int n, lapack_int nrhs,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_int* ipiv, lapack_complex_double* b,
+                           lapack_int ldb, lapack_complex_double* x,
+                           lapack_int ldx, lapack_int* iter );
+
+lapack_int LAPACKE_sgesvd( int matrix_order, char jobu, char jobvt,
+                           lapack_int m, lapack_int n, float* a, lapack_int lda,
+                           float* s, float* u, lapack_int ldu, float* vt,
+                           lapack_int ldvt, float* superb );
+lapack_int LAPACKE_dgesvd( int matrix_order, char jobu, char jobvt,
+                           lapack_int m, lapack_int n, double* a,
+                           lapack_int lda, double* s, double* u, lapack_int ldu,
+                           double* vt, lapack_int ldvt, double* superb );
+lapack_int LAPACKE_cgesvd( int matrix_order, char jobu, char jobvt,
+                           lapack_int m, lapack_int n, lapack_complex_float* a,
+                           lapack_int lda, float* s, lapack_complex_float* u,
+                           lapack_int ldu, lapack_complex_float* vt,
+                           lapack_int ldvt, float* superb );
+lapack_int LAPACKE_zgesvd( int matrix_order, char jobu, char jobvt,
+                           lapack_int m, lapack_int n, lapack_complex_double* a,
+                           lapack_int lda, double* s, lapack_complex_double* u,
+                           lapack_int ldu, lapack_complex_double* vt,
+                           lapack_int ldvt, double* superb );
+
+lapack_int LAPACKE_sgesvj( int matrix_order, char joba, char jobu, char jobv,
+                           lapack_int m, lapack_int n, float* a, lapack_int lda,
+                           float* sva, lapack_int mv, float* v, lapack_int ldv,
+                           float* stat );
+lapack_int LAPACKE_dgesvj( int matrix_order, char joba, char jobu, char jobv,
+                           lapack_int m, lapack_int n, double* a,
+                           lapack_int lda, double* sva, lapack_int mv,
+                           double* v, lapack_int ldv, double* stat );
+
+lapack_int LAPACKE_sgesvx( int matrix_order, char fact, char trans,
+                           lapack_int n, lapack_int nrhs, float* a,
+                           lapack_int lda, float* af, lapack_int ldaf,
+                           lapack_int* ipiv, char* equed, float* r, float* c,
+                           float* b, lapack_int ldb, float* x, lapack_int ldx,
+                           float* rcond, float* ferr, float* berr,
+                           float* rpivot );
+lapack_int LAPACKE_dgesvx( int matrix_order, char fact, char trans,
+                           lapack_int n, lapack_int nrhs, double* a,
+                           lapack_int lda, double* af, lapack_int ldaf,
+                           lapack_int* ipiv, char* equed, double* r, double* c,
+                           double* b, lapack_int ldb, double* x, lapack_int ldx,
+                           double* rcond, double* ferr, double* berr,
+                           double* rpivot );
+lapack_int LAPACKE_cgesvx( int matrix_order, char fact, char trans,
+                           lapack_int n, lapack_int nrhs,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* af, lapack_int ldaf,
+                           lapack_int* ipiv, char* equed, float* r, float* c,
+                           lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx,
+                           float* rcond, float* ferr, float* berr,
+                           float* rpivot );
+lapack_int LAPACKE_zgesvx( int matrix_order, char fact, char trans,
+                           lapack_int n, lapack_int nrhs,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* af, lapack_int ldaf,
+                           lapack_int* ipiv, char* equed, double* r, double* c,
+                           lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* rcond, double* ferr, double* berr,
+                           double* rpivot );
+
+lapack_int LAPACKE_sgesvxx( int matrix_order, char fact, char trans,
+                            lapack_int n, lapack_int nrhs, float* a,
+                            lapack_int lda, float* af, lapack_int ldaf,
+                            lapack_int* ipiv, char* equed, float* r, float* c,
+                            float* b, lapack_int ldb, float* x, lapack_int ldx,
+                            float* rcond, float* rpvgrw, float* berr,
+                            lapack_int n_err_bnds, float* err_bnds_norm,
+                            float* err_bnds_comp, lapack_int nparams,
+                            float* params );
+lapack_int LAPACKE_dgesvxx( int matrix_order, char fact, char trans,
+                            lapack_int n, lapack_int nrhs, double* a,
+                            lapack_int lda, double* af, lapack_int ldaf,
+                            lapack_int* ipiv, char* equed, double* r, double* c,
+                            double* b, lapack_int ldb, double* x,
+                            lapack_int ldx, double* rcond, double* rpvgrw,
+                            double* berr, lapack_int n_err_bnds,
+                            double* err_bnds_norm, double* err_bnds_comp,
+                            lapack_int nparams, double* params );
+lapack_int LAPACKE_cgesvxx( int matrix_order, char fact, char trans,
+                            lapack_int n, lapack_int nrhs,
+                            lapack_complex_float* a, lapack_int lda,
+                            lapack_complex_float* af, lapack_int ldaf,
+                            lapack_int* ipiv, char* equed, float* r, float* c,
+                            lapack_complex_float* b, lapack_int ldb,
+                            lapack_complex_float* x, lapack_int ldx,
+                            float* rcond, float* rpvgrw, float* berr,
+                            lapack_int n_err_bnds, float* err_bnds_norm,
+                            float* err_bnds_comp, lapack_int nparams,
+                            float* params );
+lapack_int LAPACKE_zgesvxx( int matrix_order, char fact, char trans,
+                            lapack_int n, lapack_int nrhs,
+                            lapack_complex_double* a, lapack_int lda,
+                            lapack_complex_double* af, lapack_int ldaf,
+                            lapack_int* ipiv, char* equed, double* r, double* c,
+                            lapack_complex_double* b, lapack_int ldb,
+                            lapack_complex_double* x, lapack_int ldx,
+                            double* rcond, double* rpvgrw, double* berr,
+                            lapack_int n_err_bnds, double* err_bnds_norm,
+                            double* err_bnds_comp, lapack_int nparams,
+                            double* params );
+
+lapack_int LAPACKE_sgetf2( int matrix_order, lapack_int m, lapack_int n,
+                           float* a, lapack_int lda, lapack_int* ipiv );
+lapack_int LAPACKE_dgetf2( int matrix_order, lapack_int m, lapack_int n,
+                           double* a, lapack_int lda, lapack_int* ipiv );
+lapack_int LAPACKE_cgetf2( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_int* ipiv );
+lapack_int LAPACKE_zgetf2( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_int* ipiv );
+
+lapack_int LAPACKE_sgetrf( int matrix_order, lapack_int m, lapack_int n,
+                           float* a, lapack_int lda, lapack_int* ipiv );
+lapack_int LAPACKE_dgetrf( int matrix_order, lapack_int m, lapack_int n,
+                           double* a, lapack_int lda, lapack_int* ipiv );
+lapack_int LAPACKE_cgetrf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_int* ipiv );
+lapack_int LAPACKE_zgetrf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_int* ipiv );
+
+lapack_int LAPACKE_sgetri( int matrix_order, lapack_int n, float* a,
+                           lapack_int lda, const lapack_int* ipiv );
+lapack_int LAPACKE_dgetri( int matrix_order, lapack_int n, double* a,
+                           lapack_int lda, const lapack_int* ipiv );
+lapack_int LAPACKE_cgetri( int matrix_order, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           const lapack_int* ipiv );
+lapack_int LAPACKE_zgetri( int matrix_order, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           const lapack_int* ipiv );
+
+lapack_int LAPACKE_sgetrs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const float* a, lapack_int lda,
+                           const lapack_int* ipiv, float* b, lapack_int ldb );
+lapack_int LAPACKE_dgetrs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const double* a, lapack_int lda,
+                           const lapack_int* ipiv, double* b, lapack_int ldb );
+lapack_int LAPACKE_cgetrs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* a,
+                           lapack_int lda, const lapack_int* ipiv,
+                           lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zgetrs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* a,
+                           lapack_int lda, const lapack_int* ipiv,
+                           lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_sggbak( int matrix_order, char job, char side, lapack_int n,
+                           lapack_int ilo, lapack_int ihi, const float* lscale,
+                           const float* rscale, lapack_int m, float* v,
+                           lapack_int ldv );
+lapack_int LAPACKE_dggbak( int matrix_order, char job, char side, lapack_int n,
+                           lapack_int ilo, lapack_int ihi, const double* lscale,
+                           const double* rscale, lapack_int m, double* v,
+                           lapack_int ldv );
+lapack_int LAPACKE_cggbak( int matrix_order, char job, char side, lapack_int n,
+                           lapack_int ilo, lapack_int ihi, const float* lscale,
+                           const float* rscale, lapack_int m,
+                           lapack_complex_float* v, lapack_int ldv );
+lapack_int LAPACKE_zggbak( int matrix_order, char job, char side, lapack_int n,
+                           lapack_int ilo, lapack_int ihi, const double* lscale,
+                           const double* rscale, lapack_int m,
+                           lapack_complex_double* v, lapack_int ldv );
+
+lapack_int LAPACKE_sggbal( int matrix_order, char job, lapack_int n, float* a,
+                           lapack_int lda, float* b, lapack_int ldb,
+                           lapack_int* ilo, lapack_int* ihi, float* lscale,
+                           float* rscale );
+lapack_int LAPACKE_dggbal( int matrix_order, char job, lapack_int n, double* a,
+                           lapack_int lda, double* b, lapack_int ldb,
+                           lapack_int* ilo, lapack_int* ihi, double* lscale,
+                           double* rscale );
+lapack_int LAPACKE_cggbal( int matrix_order, char job, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* b, lapack_int ldb,
+                           lapack_int* ilo, lapack_int* ihi, float* lscale,
+                           float* rscale );
+lapack_int LAPACKE_zggbal( int matrix_order, char job, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* b, lapack_int ldb,
+                           lapack_int* ilo, lapack_int* ihi, double* lscale,
+                           double* rscale );
+
+lapack_int LAPACKE_sgges( int matrix_order, char jobvsl, char jobvsr, char sort,
+                          LAPACK_S_SELECT3 selctg, lapack_int n, float* a,
+                          lapack_int lda, float* b, lapack_int ldb,
+                          lapack_int* sdim, float* alphar, float* alphai,
+                          float* beta, float* vsl, lapack_int ldvsl, float* vsr,
+                          lapack_int ldvsr );
+lapack_int LAPACKE_dgges( int matrix_order, char jobvsl, char jobvsr, char sort,
+                          LAPACK_D_SELECT3 selctg, lapack_int n, double* a,
+                          lapack_int lda, double* b, lapack_int ldb,
+                          lapack_int* sdim, double* alphar, double* alphai,
+                          double* beta, double* vsl, lapack_int ldvsl,
+                          double* vsr, lapack_int ldvsr );
+lapack_int LAPACKE_cgges( int matrix_order, char jobvsl, char jobvsr, char sort,
+                          LAPACK_C_SELECT2 selctg, lapack_int n,
+                          lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* b, lapack_int ldb,
+                          lapack_int* sdim, lapack_complex_float* alpha,
+                          lapack_complex_float* beta, lapack_complex_float* vsl,
+                          lapack_int ldvsl, lapack_complex_float* vsr,
+                          lapack_int ldvsr );
+lapack_int LAPACKE_zgges( int matrix_order, char jobvsl, char jobvsr, char sort,
+                          LAPACK_Z_SELECT2 selctg, lapack_int n,
+                          lapack_complex_double* a, lapack_int lda,
+                          lapack_complex_double* b, lapack_int ldb,
+                          lapack_int* sdim, lapack_complex_double* alpha,
+                          lapack_complex_double* beta,
+                          lapack_complex_double* vsl, lapack_int ldvsl,
+                          lapack_complex_double* vsr, lapack_int ldvsr );
+
+lapack_int LAPACKE_sggesx( int matrix_order, char jobvsl, char jobvsr,
+                           char sort, LAPACK_S_SELECT3 selctg, char sense,
+                           lapack_int n, float* a, lapack_int lda, float* b,
+                           lapack_int ldb, lapack_int* sdim, float* alphar,
+                           float* alphai, float* beta, float* vsl,
+                           lapack_int ldvsl, float* vsr, lapack_int ldvsr,
+                           float* rconde, float* rcondv );
+lapack_int LAPACKE_dggesx( int matrix_order, char jobvsl, char jobvsr,
+                           char sort, LAPACK_D_SELECT3 selctg, char sense,
+                           lapack_int n, double* a, lapack_int lda, double* b,
+                           lapack_int ldb, lapack_int* sdim, double* alphar,
+                           double* alphai, double* beta, double* vsl,
+                           lapack_int ldvsl, double* vsr, lapack_int ldvsr,
+                           double* rconde, double* rcondv );
+lapack_int LAPACKE_cggesx( int matrix_order, char jobvsl, char jobvsr,
+                           char sort, LAPACK_C_SELECT2 selctg, char sense,
+                           lapack_int n, lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* b,
+                           lapack_int ldb, lapack_int* sdim,
+                           lapack_complex_float* alpha,
+                           lapack_complex_float* beta,
+                           lapack_complex_float* vsl, lapack_int ldvsl,
+                           lapack_complex_float* vsr, lapack_int ldvsr,
+                           float* rconde, float* rcondv );
+lapack_int LAPACKE_zggesx( int matrix_order, char jobvsl, char jobvsr,
+                           char sort, LAPACK_Z_SELECT2 selctg, char sense,
+                           lapack_int n, lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* b,
+                           lapack_int ldb, lapack_int* sdim,
+                           lapack_complex_double* alpha,
+                           lapack_complex_double* beta,
+                           lapack_complex_double* vsl, lapack_int ldvsl,
+                           lapack_complex_double* vsr, lapack_int ldvsr,
+                           double* rconde, double* rcondv );
+
+lapack_int LAPACKE_sggev( int matrix_order, char jobvl, char jobvr,
+                          lapack_int n, float* a, lapack_int lda, float* b,
+                          lapack_int ldb, float* alphar, float* alphai,
+                          float* beta, float* vl, lapack_int ldvl, float* vr,
+                          lapack_int ldvr );
+lapack_int LAPACKE_dggev( int matrix_order, char jobvl, char jobvr,
+                          lapack_int n, double* a, lapack_int lda, double* b,
+                          lapack_int ldb, double* alphar, double* alphai,
+                          double* beta, double* vl, lapack_int ldvl, double* vr,
+                          lapack_int ldvr );
+lapack_int LAPACKE_cggev( int matrix_order, char jobvl, char jobvr,
+                          lapack_int n, lapack_complex_float* a, lapack_int lda,
+                          lapack_complex_float* b, lapack_int ldb,
+                          lapack_complex_float* alpha,
+                          lapack_complex_float* beta, lapack_complex_float* vl,
+                          lapack_int ldvl, lapack_complex_float* vr,
+                          lapack_int ldvr );
+lapack_int LAPACKE_zggev( int matrix_order, char jobvl, char jobvr,
+                          lapack_int n, lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* b,
+                          lapack_int ldb, lapack_complex_double* alpha,
+                          lapack_complex_double* beta,
+                          lapack_complex_double* vl, lapack_int ldvl,
+                          lapack_complex_double* vr, lapack_int ldvr );
+
+lapack_int LAPACKE_sggevx( int matrix_order, char balanc, char jobvl,
+                           char jobvr, char sense, lapack_int n, float* a,
+                           lapack_int lda, float* b, lapack_int ldb,
+                           float* alphar, float* alphai, float* beta, float* vl,
+                           lapack_int ldvl, float* vr, lapack_int ldvr,
+                           lapack_int* ilo, lapack_int* ihi, float* lscale,
+                           float* rscale, float* abnrm, float* bbnrm,
+                           float* rconde, float* rcondv );
+lapack_int LAPACKE_dggevx( int matrix_order, char balanc, char jobvl,
+                           char jobvr, char sense, lapack_int n, double* a,
+                           lapack_int lda, double* b, lapack_int ldb,
+                           double* alphar, double* alphai, double* beta,
+                           double* vl, lapack_int ldvl, double* vr,
+                           lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
+                           double* lscale, double* rscale, double* abnrm,
+                           double* bbnrm, double* rconde, double* rcondv );
+lapack_int LAPACKE_cggevx( int matrix_order, char balanc, char jobvl,
+                           char jobvr, char sense, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* alpha,
+                           lapack_complex_float* beta, lapack_complex_float* vl,
+                           lapack_int ldvl, lapack_complex_float* vr,
+                           lapack_int ldvr, lapack_int* ilo, lapack_int* ihi,
+                           float* lscale, float* rscale, float* abnrm,
+                           float* bbnrm, float* rconde, float* rcondv );
+lapack_int LAPACKE_zggevx( int matrix_order, char balanc, char jobvl,
+                           char jobvr, char sense, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* alpha,
+                           lapack_complex_double* beta,
+                           lapack_complex_double* vl, lapack_int ldvl,
+                           lapack_complex_double* vr, lapack_int ldvr,
+                           lapack_int* ilo, lapack_int* ihi, double* lscale,
+                           double* rscale, double* abnrm, double* bbnrm,
+                           double* rconde, double* rcondv );
+
+lapack_int LAPACKE_sggglm( int matrix_order, lapack_int n, lapack_int m,
+                           lapack_int p, float* a, lapack_int lda, float* b,
+                           lapack_int ldb, float* d, float* x, float* y );
+lapack_int LAPACKE_dggglm( int matrix_order, lapack_int n, lapack_int m,
+                           lapack_int p, double* a, lapack_int lda, double* b,
+                           lapack_int ldb, double* d, double* x, double* y );
+lapack_int LAPACKE_cggglm( int matrix_order, lapack_int n, lapack_int m,
+                           lapack_int p, lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* b,
+                           lapack_int ldb, lapack_complex_float* d,
+                           lapack_complex_float* x, lapack_complex_float* y );
+lapack_int LAPACKE_zggglm( int matrix_order, lapack_int n, lapack_int m,
+                           lapack_int p, lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* b,
+                           lapack_int ldb, lapack_complex_double* d,
+                           lapack_complex_double* x, lapack_complex_double* y );
+
+lapack_int LAPACKE_sgghrd( int matrix_order, char compq, char compz,
+                           lapack_int n, lapack_int ilo, lapack_int ihi,
+                           float* a, lapack_int lda, float* b, lapack_int ldb,
+                           float* q, lapack_int ldq, float* z, lapack_int ldz );
+lapack_int LAPACKE_dgghrd( int matrix_order, char compq, char compz,
+                           lapack_int n, lapack_int ilo, lapack_int ihi,
+                           double* a, lapack_int lda, double* b, lapack_int ldb,
+                           double* q, lapack_int ldq, double* z,
+                           lapack_int ldz );
+lapack_int LAPACKE_cgghrd( int matrix_order, char compq, char compz,
+                           lapack_int n, lapack_int ilo, lapack_int ihi,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* q, lapack_int ldq,
+                           lapack_complex_float* z, lapack_int ldz );
+lapack_int LAPACKE_zgghrd( int matrix_order, char compq, char compz,
+                           lapack_int n, lapack_int ilo, lapack_int ihi,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* q, lapack_int ldq,
+                           lapack_complex_double* z, lapack_int ldz );
+
+lapack_int LAPACKE_sgglse( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int p, float* a, lapack_int lda, float* b,
+                           lapack_int ldb, float* c, float* d, float* x );
+lapack_int LAPACKE_dgglse( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int p, double* a, lapack_int lda, double* b,
+                           lapack_int ldb, double* c, double* d, double* x );
+lapack_int LAPACKE_cgglse( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int p, lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* b,
+                           lapack_int ldb, lapack_complex_float* c,
+                           lapack_complex_float* d, lapack_complex_float* x );
+lapack_int LAPACKE_zgglse( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int p, lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* b,
+                           lapack_int ldb, lapack_complex_double* c,
+                           lapack_complex_double* d, lapack_complex_double* x );
+
+lapack_int LAPACKE_sggqrf( int matrix_order, lapack_int n, lapack_int m,
+                           lapack_int p, float* a, lapack_int lda, float* taua,
+                           float* b, lapack_int ldb, float* taub );
+lapack_int LAPACKE_dggqrf( int matrix_order, lapack_int n, lapack_int m,
+                           lapack_int p, double* a, lapack_int lda,
+                           double* taua, double* b, lapack_int ldb,
+                           double* taub );
+lapack_int LAPACKE_cggqrf( int matrix_order, lapack_int n, lapack_int m,
+                           lapack_int p, lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* taua,
+                           lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* taub );
+lapack_int LAPACKE_zggqrf( int matrix_order, lapack_int n, lapack_int m,
+                           lapack_int p, lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* taua,
+                           lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* taub );
+
+lapack_int LAPACKE_sggrqf( int matrix_order, lapack_int m, lapack_int p,
+                           lapack_int n, float* a, lapack_int lda, float* taua,
+                           float* b, lapack_int ldb, float* taub );
+lapack_int LAPACKE_dggrqf( int matrix_order, lapack_int m, lapack_int p,
+                           lapack_int n, double* a, lapack_int lda,
+                           double* taua, double* b, lapack_int ldb,
+                           double* taub );
+lapack_int LAPACKE_cggrqf( int matrix_order, lapack_int m, lapack_int p,
+                           lapack_int n, lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* taua,
+                           lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* taub );
+lapack_int LAPACKE_zggrqf( int matrix_order, lapack_int m, lapack_int p,
+                           lapack_int n, lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* taua,
+                           lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* taub );
+
+lapack_int LAPACKE_sggsvd( int matrix_order, char jobu, char jobv, char jobq,
+                           lapack_int m, lapack_int n, lapack_int p,
+                           lapack_int* k, lapack_int* l, float* a,
+                           lapack_int lda, float* b, lapack_int ldb,
+                           float* alpha, float* beta, float* u, lapack_int ldu,
+                           float* v, lapack_int ldv, float* q, lapack_int ldq,
+                           lapack_int* iwork );
+lapack_int LAPACKE_dggsvd( int matrix_order, char jobu, char jobv, char jobq,
+                           lapack_int m, lapack_int n, lapack_int p,
+                           lapack_int* k, lapack_int* l, double* a,
+                           lapack_int lda, double* b, lapack_int ldb,
+                           double* alpha, double* beta, double* u,
+                           lapack_int ldu, double* v, lapack_int ldv, double* q,
+                           lapack_int ldq, lapack_int* iwork );
+lapack_int LAPACKE_cggsvd( int matrix_order, char jobu, char jobv, char jobq,
+                           lapack_int m, lapack_int n, lapack_int p,
+                           lapack_int* k, lapack_int* l,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* b, lapack_int ldb,
+                           float* alpha, float* beta, lapack_complex_float* u,
+                           lapack_int ldu, lapack_complex_float* v,
+                           lapack_int ldv, lapack_complex_float* q,
+                           lapack_int ldq, lapack_int* iwork );
+lapack_int LAPACKE_zggsvd( int matrix_order, char jobu, char jobv, char jobq,
+                           lapack_int m, lapack_int n, lapack_int p,
+                           lapack_int* k, lapack_int* l,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* b, lapack_int ldb,
+                           double* alpha, double* beta,
+                           lapack_complex_double* u, lapack_int ldu,
+                           lapack_complex_double* v, lapack_int ldv,
+                           lapack_complex_double* q, lapack_int ldq,
+                           lapack_int* iwork );
+
+lapack_int LAPACKE_sggsvp( int matrix_order, char jobu, char jobv, char jobq,
+                           lapack_int m, lapack_int p, lapack_int n, float* a,
+                           lapack_int lda, float* b, lapack_int ldb, float tola,
+                           float tolb, lapack_int* k, lapack_int* l, float* u,
+                           lapack_int ldu, float* v, lapack_int ldv, float* q,
+                           lapack_int ldq );
+lapack_int LAPACKE_dggsvp( int matrix_order, char jobu, char jobv, char jobq,
+                           lapack_int m, lapack_int p, lapack_int n, double* a,
+                           lapack_int lda, double* b, lapack_int ldb,
+                           double tola, double tolb, lapack_int* k,
+                           lapack_int* l, double* u, lapack_int ldu, double* v,
+                           lapack_int ldv, double* q, lapack_int ldq );
+lapack_int LAPACKE_cggsvp( int matrix_order, char jobu, char jobv, char jobq,
+                           lapack_int m, lapack_int p, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* b, lapack_int ldb, float tola,
+                           float tolb, lapack_int* k, lapack_int* l,
+                           lapack_complex_float* u, lapack_int ldu,
+                           lapack_complex_float* v, lapack_int ldv,
+                           lapack_complex_float* q, lapack_int ldq );
+lapack_int LAPACKE_zggsvp( int matrix_order, char jobu, char jobv, char jobq,
+                           lapack_int m, lapack_int p, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* b, lapack_int ldb,
+                           double tola, double tolb, lapack_int* k,
+                           lapack_int* l, lapack_complex_double* u,
+                           lapack_int ldu, lapack_complex_double* v,
+                           lapack_int ldv, lapack_complex_double* q,
+                           lapack_int ldq );
+
+lapack_int LAPACKE_sgtcon( char norm, lapack_int n, const float* dl,
+                           const float* d, const float* du, const float* du2,
+                           const lapack_int* ipiv, float anorm, float* rcond );
+lapack_int LAPACKE_dgtcon( char norm, lapack_int n, const double* dl,
+                           const double* d, const double* du, const double* du2,
+                           const lapack_int* ipiv, double anorm,
+                           double* rcond );
+lapack_int LAPACKE_cgtcon( char norm, lapack_int n,
+                           const lapack_complex_float* dl,
+                           const lapack_complex_float* d,
+                           const lapack_complex_float* du,
+                           const lapack_complex_float* du2,
+                           const lapack_int* ipiv, float anorm, float* rcond );
+lapack_int LAPACKE_zgtcon( char norm, lapack_int n,
+                           const lapack_complex_double* dl,
+                           const lapack_complex_double* d,
+                           const lapack_complex_double* du,
+                           const lapack_complex_double* du2,
+                           const lapack_int* ipiv, double anorm,
+                           double* rcond );
+
+lapack_int LAPACKE_sgtrfs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const float* dl, const float* d,
+                           const float* du, const float* dlf, const float* df,
+                           const float* duf, const float* du2,
+                           const lapack_int* ipiv, const float* b,
+                           lapack_int ldb, float* x, lapack_int ldx,
+                           float* ferr, float* berr );
+lapack_int LAPACKE_dgtrfs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const double* dl, const double* d,
+                           const double* du, const double* dlf,
+                           const double* df, const double* duf,
+                           const double* du2, const lapack_int* ipiv,
+                           const double* b, lapack_int ldb, double* x,
+                           lapack_int ldx, double* ferr, double* berr );
+lapack_int LAPACKE_cgtrfs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* dl,
+                           const lapack_complex_float* d,
+                           const lapack_complex_float* du,
+                           const lapack_complex_float* dlf,
+                           const lapack_complex_float* df,
+                           const lapack_complex_float* duf,
+                           const lapack_complex_float* du2,
+                           const lapack_int* ipiv,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_zgtrfs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* dl,
+                           const lapack_complex_double* d,
+                           const lapack_complex_double* du,
+                           const lapack_complex_double* dlf,
+                           const lapack_complex_double* df,
+                           const lapack_complex_double* duf,
+                           const lapack_complex_double* du2,
+                           const lapack_int* ipiv,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+
+lapack_int LAPACKE_sgtsv( int matrix_order, lapack_int n, lapack_int nrhs,
+                          float* dl, float* d, float* du, float* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_dgtsv( int matrix_order, lapack_int n, lapack_int nrhs,
+                          double* dl, double* d, double* du, double* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_cgtsv( int matrix_order, lapack_int n, lapack_int nrhs,
+                          lapack_complex_float* dl, lapack_complex_float* d,
+                          lapack_complex_float* du, lapack_complex_float* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_zgtsv( int matrix_order, lapack_int n, lapack_int nrhs,
+                          lapack_complex_double* dl, lapack_complex_double* d,
+                          lapack_complex_double* du, lapack_complex_double* b,
+                          lapack_int ldb );
+
+lapack_int LAPACKE_sgtsvx( int matrix_order, char fact, char trans,
+                           lapack_int n, lapack_int nrhs, const float* dl,
+                           const float* d, const float* du, float* dlf,
+                           float* df, float* duf, float* du2, lapack_int* ipiv,
+                           const float* b, lapack_int ldb, float* x,
+                           lapack_int ldx, float* rcond, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_dgtsvx( int matrix_order, char fact, char trans,
+                           lapack_int n, lapack_int nrhs, const double* dl,
+                           const double* d, const double* du, double* dlf,
+                           double* df, double* duf, double* du2,
+                           lapack_int* ipiv, const double* b, lapack_int ldb,
+                           double* x, lapack_int ldx, double* rcond,
+                           double* ferr, double* berr );
+lapack_int LAPACKE_cgtsvx( int matrix_order, char fact, char trans,
+                           lapack_int n, lapack_int nrhs,
+                           const lapack_complex_float* dl,
+                           const lapack_complex_float* d,
+                           const lapack_complex_float* du,
+                           lapack_complex_float* dlf, lapack_complex_float* df,
+                           lapack_complex_float* duf, lapack_complex_float* du2,
+                           lapack_int* ipiv, const lapack_complex_float* b,
+                           lapack_int ldb, lapack_complex_float* x,
+                           lapack_int ldx, float* rcond, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_zgtsvx( int matrix_order, char fact, char trans,
+                           lapack_int n, lapack_int nrhs,
+                           const lapack_complex_double* dl,
+                           const lapack_complex_double* d,
+                           const lapack_complex_double* du,
+                           lapack_complex_double* dlf,
+                           lapack_complex_double* df,
+                           lapack_complex_double* duf,
+                           lapack_complex_double* du2, lapack_int* ipiv,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* rcond, double* ferr, double* berr );
+
+lapack_int LAPACKE_sgttrf( lapack_int n, float* dl, float* d, float* du,
+                           float* du2, lapack_int* ipiv );
+lapack_int LAPACKE_dgttrf( lapack_int n, double* dl, double* d, double* du,
+                           double* du2, lapack_int* ipiv );
+lapack_int LAPACKE_cgttrf( lapack_int n, lapack_complex_float* dl,
+                           lapack_complex_float* d, lapack_complex_float* du,
+                           lapack_complex_float* du2, lapack_int* ipiv );
+lapack_int LAPACKE_zgttrf( lapack_int n, lapack_complex_double* dl,
+                           lapack_complex_double* d, lapack_complex_double* du,
+                           lapack_complex_double* du2, lapack_int* ipiv );
+
+lapack_int LAPACKE_sgttrs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const float* dl, const float* d,
+                           const float* du, const float* du2,
+                           const lapack_int* ipiv, float* b, lapack_int ldb );
+lapack_int LAPACKE_dgttrs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const double* dl, const double* d,
+                           const double* du, const double* du2,
+                           const lapack_int* ipiv, double* b, lapack_int ldb );
+lapack_int LAPACKE_cgttrs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* dl,
+                           const lapack_complex_float* d,
+                           const lapack_complex_float* du,
+                           const lapack_complex_float* du2,
+                           const lapack_int* ipiv, lapack_complex_float* b,
+                           lapack_int ldb );
+lapack_int LAPACKE_zgttrs( int matrix_order, char trans, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* dl,
+                           const lapack_complex_double* d,
+                           const lapack_complex_double* du,
+                           const lapack_complex_double* du2,
+                           const lapack_int* ipiv, lapack_complex_double* b,
+                           lapack_int ldb );
+
+lapack_int LAPACKE_chbev( int matrix_order, char jobz, char uplo, lapack_int n,
+                          lapack_int kd, lapack_complex_float* ab,
+                          lapack_int ldab, float* w, lapack_complex_float* z,
+                          lapack_int ldz );
+lapack_int LAPACKE_zhbev( int matrix_order, char jobz, char uplo, lapack_int n,
+                          lapack_int kd, lapack_complex_double* ab,
+                          lapack_int ldab, double* w, lapack_complex_double* z,
+                          lapack_int ldz );
+
+lapack_int LAPACKE_chbevd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           lapack_int kd, lapack_complex_float* ab,
+                           lapack_int ldab, float* w, lapack_complex_float* z,
+                           lapack_int ldz );
+lapack_int LAPACKE_zhbevd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           lapack_int kd, lapack_complex_double* ab,
+                           lapack_int ldab, double* w, lapack_complex_double* z,
+                           lapack_int ldz );
+
+lapack_int LAPACKE_chbevx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, lapack_int kd,
+                           lapack_complex_float* ab, lapack_int ldab,
+                           lapack_complex_float* q, lapack_int ldq, float vl,
+                           float vu, lapack_int il, lapack_int iu, float abstol,
+                           lapack_int* m, float* w, lapack_complex_float* z,
+                           lapack_int ldz, lapack_int* ifail );
+lapack_int LAPACKE_zhbevx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, lapack_int kd,
+                           lapack_complex_double* ab, lapack_int ldab,
+                           lapack_complex_double* q, lapack_int ldq, double vl,
+                           double vu, lapack_int il, lapack_int iu,
+                           double abstol, lapack_int* m, double* w,
+                           lapack_complex_double* z, lapack_int ldz,
+                           lapack_int* ifail );
+
+lapack_int LAPACKE_chbgst( int matrix_order, char vect, char uplo, lapack_int n,
+                           lapack_int ka, lapack_int kb,
+                           lapack_complex_float* ab, lapack_int ldab,
+                           const lapack_complex_float* bb, lapack_int ldbb,
+                           lapack_complex_float* x, lapack_int ldx );
+lapack_int LAPACKE_zhbgst( int matrix_order, char vect, char uplo, lapack_int n,
+                           lapack_int ka, lapack_int kb,
+                           lapack_complex_double* ab, lapack_int ldab,
+                           const lapack_complex_double* bb, lapack_int ldbb,
+                           lapack_complex_double* x, lapack_int ldx );
+
+lapack_int LAPACKE_chbgv( int matrix_order, char jobz, char uplo, lapack_int n,
+                          lapack_int ka, lapack_int kb,
+                          lapack_complex_float* ab, lapack_int ldab,
+                          lapack_complex_float* bb, lapack_int ldbb, float* w,
+                          lapack_complex_float* z, lapack_int ldz );
+lapack_int LAPACKE_zhbgv( int matrix_order, char jobz, char uplo, lapack_int n,
+                          lapack_int ka, lapack_int kb,
+                          lapack_complex_double* ab, lapack_int ldab,
+                          lapack_complex_double* bb, lapack_int ldbb, double* w,
+                          lapack_complex_double* z, lapack_int ldz );
+
+lapack_int LAPACKE_chbgvd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           lapack_int ka, lapack_int kb,
+                           lapack_complex_float* ab, lapack_int ldab,
+                           lapack_complex_float* bb, lapack_int ldbb, float* w,
+                           lapack_complex_float* z, lapack_int ldz );
+lapack_int LAPACKE_zhbgvd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           lapack_int ka, lapack_int kb,
+                           lapack_complex_double* ab, lapack_int ldab,
+                           lapack_complex_double* bb, lapack_int ldbb,
+                           double* w, lapack_complex_double* z,
+                           lapack_int ldz );
+
+lapack_int LAPACKE_chbgvx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, lapack_int ka, lapack_int kb,
+                           lapack_complex_float* ab, lapack_int ldab,
+                           lapack_complex_float* bb, lapack_int ldbb,
+                           lapack_complex_float* q, lapack_int ldq, float vl,
+                           float vu, lapack_int il, lapack_int iu, float abstol,
+                           lapack_int* m, float* w, lapack_complex_float* z,
+                           lapack_int ldz, lapack_int* ifail );
+lapack_int LAPACKE_zhbgvx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, lapack_int ka, lapack_int kb,
+                           lapack_complex_double* ab, lapack_int ldab,
+                           lapack_complex_double* bb, lapack_int ldbb,
+                           lapack_complex_double* q, lapack_int ldq, double vl,
+                           double vu, lapack_int il, lapack_int iu,
+                           double abstol, lapack_int* m, double* w,
+                           lapack_complex_double* z, lapack_int ldz,
+                           lapack_int* ifail );
+
+lapack_int LAPACKE_chbtrd( int matrix_order, char vect, char uplo, lapack_int n,
+                           lapack_int kd, lapack_complex_float* ab,
+                           lapack_int ldab, float* d, float* e,
+                           lapack_complex_float* q, lapack_int ldq );
+lapack_int LAPACKE_zhbtrd( int matrix_order, char vect, char uplo, lapack_int n,
+                           lapack_int kd, lapack_complex_double* ab,
+                           lapack_int ldab, double* d, double* e,
+                           lapack_complex_double* q, lapack_int ldq );
+
+lapack_int LAPACKE_checon( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_float* a, lapack_int lda,
+                           const lapack_int* ipiv, float anorm, float* rcond );
+lapack_int LAPACKE_zhecon( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_double* a, lapack_int lda,
+                           const lapack_int* ipiv, double anorm,
+                           double* rcond );
+
+lapack_int LAPACKE_cheequb( int matrix_order, char uplo, lapack_int n,
+                            const lapack_complex_float* a, lapack_int lda,
+                            float* s, float* scond, float* amax );
+lapack_int LAPACKE_zheequb( int matrix_order, char uplo, lapack_int n,
+                            const lapack_complex_double* a, lapack_int lda,
+                            double* s, double* scond, double* amax );
+
+lapack_int LAPACKE_cheev( int matrix_order, char jobz, char uplo, lapack_int n,
+                          lapack_complex_float* a, lapack_int lda, float* w );
+lapack_int LAPACKE_zheev( int matrix_order, char jobz, char uplo, lapack_int n,
+                          lapack_complex_double* a, lapack_int lda, double* w );
+
+lapack_int LAPACKE_cheevd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda, float* w );
+lapack_int LAPACKE_zheevd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           double* w );
+
+lapack_int LAPACKE_cheevr( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, lapack_complex_float* a,
+                           lapack_int lda, float vl, float vu, lapack_int il,
+                           lapack_int iu, float abstol, lapack_int* m, float* w,
+                           lapack_complex_float* z, lapack_int ldz,
+                           lapack_int* isuppz );
+lapack_int LAPACKE_zheevr( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, lapack_complex_double* a,
+                           lapack_int lda, double vl, double vu, lapack_int il,
+                           lapack_int iu, double abstol, lapack_int* m,
+                           double* w, lapack_complex_double* z, lapack_int ldz,
+                           lapack_int* isuppz );
+
+lapack_int LAPACKE_cheevx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, lapack_complex_float* a,
+                           lapack_int lda, float vl, float vu, lapack_int il,
+                           lapack_int iu, float abstol, lapack_int* m, float* w,
+                           lapack_complex_float* z, lapack_int ldz,
+                           lapack_int* ifail );
+lapack_int LAPACKE_zheevx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, lapack_complex_double* a,
+                           lapack_int lda, double vl, double vu, lapack_int il,
+                           lapack_int iu, double abstol, lapack_int* m,
+                           double* w, lapack_complex_double* z, lapack_int ldz,
+                           lapack_int* ifail );
+
+lapack_int LAPACKE_chegst( int matrix_order, lapack_int itype, char uplo,
+                           lapack_int n, lapack_complex_float* a,
+                           lapack_int lda, const lapack_complex_float* b,
+                           lapack_int ldb );
+lapack_int LAPACKE_zhegst( int matrix_order, lapack_int itype, char uplo,
+                           lapack_int n, lapack_complex_double* a,
+                           lapack_int lda, const lapack_complex_double* b,
+                           lapack_int ldb );
+
+lapack_int LAPACKE_chegv( int matrix_order, lapack_int itype, char jobz,
+                          char uplo, lapack_int n, lapack_complex_float* a,
+                          lapack_int lda, lapack_complex_float* b,
+                          lapack_int ldb, float* w );
+lapack_int LAPACKE_zhegv( int matrix_order, lapack_int itype, char jobz,
+                          char uplo, lapack_int n, lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* b,
+                          lapack_int ldb, double* w );
+
+lapack_int LAPACKE_chegvd( int matrix_order, lapack_int itype, char jobz,
+                           char uplo, lapack_int n, lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* b,
+                           lapack_int ldb, float* w );
+lapack_int LAPACKE_zhegvd( int matrix_order, lapack_int itype, char jobz,
+                           char uplo, lapack_int n, lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* b,
+                           lapack_int ldb, double* w );
+
+lapack_int LAPACKE_chegvx( int matrix_order, lapack_int itype, char jobz,
+                           char range, char uplo, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* b, lapack_int ldb, float vl,
+                           float vu, lapack_int il, lapack_int iu, float abstol,
+                           lapack_int* m, float* w, lapack_complex_float* z,
+                           lapack_int ldz, lapack_int* ifail );
+lapack_int LAPACKE_zhegvx( int matrix_order, lapack_int itype, char jobz,
+                           char range, char uplo, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* b, lapack_int ldb, double vl,
+                           double vu, lapack_int il, lapack_int iu,
+                           double abstol, lapack_int* m, double* w,
+                           lapack_complex_double* z, lapack_int ldz,
+                           lapack_int* ifail );
+
+lapack_int LAPACKE_cherfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* a,
+                           lapack_int lda, const lapack_complex_float* af,
+                           lapack_int ldaf, const lapack_int* ipiv,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_zherfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* a,
+                           lapack_int lda, const lapack_complex_double* af,
+                           lapack_int ldaf, const lapack_int* ipiv,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+
+lapack_int LAPACKE_cherfsx( int matrix_order, char uplo, char equed,
+                            lapack_int n, lapack_int nrhs,
+                            const lapack_complex_float* a, lapack_int lda,
+                            const lapack_complex_float* af, lapack_int ldaf,
+                            const lapack_int* ipiv, const float* s,
+                            const lapack_complex_float* b, lapack_int ldb,
+                            lapack_complex_float* x, lapack_int ldx,
+                            float* rcond, float* berr, lapack_int n_err_bnds,
+                            float* err_bnds_norm, float* err_bnds_comp,
+                            lapack_int nparams, float* params );
+lapack_int LAPACKE_zherfsx( int matrix_order, char uplo, char equed,
+                            lapack_int n, lapack_int nrhs,
+                            const lapack_complex_double* a, lapack_int lda,
+                            const lapack_complex_double* af, lapack_int ldaf,
+                            const lapack_int* ipiv, const double* s,
+                            const lapack_complex_double* b, lapack_int ldb,
+                            lapack_complex_double* x, lapack_int ldx,
+                            double* rcond, double* berr, lapack_int n_err_bnds,
+                            double* err_bnds_norm, double* err_bnds_comp,
+                            lapack_int nparams, double* params );
+
+lapack_int LAPACKE_chesv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, lapack_complex_float* a,
+                          lapack_int lda, lapack_int* ipiv,
+                          lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zhesv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, lapack_complex_double* a,
+                          lapack_int lda, lapack_int* ipiv,
+                          lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_chesvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* af,
+                           lapack_int ldaf, lapack_int* ipiv,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx,
+                           float* rcond, float* ferr, float* berr );
+lapack_int LAPACKE_zhesvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* af,
+                           lapack_int ldaf, lapack_int* ipiv,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* rcond, double* ferr, double* berr );
+
+lapack_int LAPACKE_chesvxx( int matrix_order, char fact, char uplo,
+                            lapack_int n, lapack_int nrhs,
+                            lapack_complex_float* a, lapack_int lda,
+                            lapack_complex_float* af, lapack_int ldaf,
+                            lapack_int* ipiv, char* equed, float* s,
+                            lapack_complex_float* b, lapack_int ldb,
+                            lapack_complex_float* x, lapack_int ldx,
+                            float* rcond, float* rpvgrw, float* berr,
+                            lapack_int n_err_bnds, float* err_bnds_norm,
+                            float* err_bnds_comp, lapack_int nparams,
+                            float* params );
+lapack_int LAPACKE_zhesvxx( int matrix_order, char fact, char uplo,
+                            lapack_int n, lapack_int nrhs,
+                            lapack_complex_double* a, lapack_int lda,
+                            lapack_complex_double* af, lapack_int ldaf,
+                            lapack_int* ipiv, char* equed, double* s,
+                            lapack_complex_double* b, lapack_int ldb,
+                            lapack_complex_double* x, lapack_int ldx,
+                            double* rcond, double* rpvgrw, double* berr,
+                            lapack_int n_err_bnds, double* err_bnds_norm,
+                            double* err_bnds_comp, lapack_int nparams,
+                            double* params );
+
+lapack_int LAPACKE_chetrd( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda, float* d,
+                           float* e, lapack_complex_float* tau );
+lapack_int LAPACKE_zhetrd( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda, double* d,
+                           double* e, lapack_complex_double* tau );
+
+lapack_int LAPACKE_chetrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_int* ipiv );
+lapack_int LAPACKE_zhetrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_int* ipiv );
+
+lapack_int LAPACKE_chetri( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           const lapack_int* ipiv );
+lapack_int LAPACKE_zhetri( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           const lapack_int* ipiv );
+
+lapack_int LAPACKE_chetrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* a,
+                           lapack_int lda, const lapack_int* ipiv,
+                           lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zhetrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* a,
+                           lapack_int lda, const lapack_int* ipiv,
+                           lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_chfrk( int matrix_order, char transr, char uplo, char trans,
+                          lapack_int n, lapack_int k, float alpha,
+                          const lapack_complex_float* a, lapack_int lda,
+                          float beta, lapack_complex_float* c );
+lapack_int LAPACKE_zhfrk( int matrix_order, char transr, char uplo, char trans,
+                          lapack_int n, lapack_int k, double alpha,
+                          const lapack_complex_double* a, lapack_int lda,
+                          double beta, lapack_complex_double* c );
+
+lapack_int LAPACKE_shgeqz( int matrix_order, char job, char compq, char compz,
+                           lapack_int n, lapack_int ilo, lapack_int ihi,
+                           float* h, lapack_int ldh, float* t, lapack_int ldt,
+                           float* alphar, float* alphai, float* beta, float* q,
+                           lapack_int ldq, float* z, lapack_int ldz );
+lapack_int LAPACKE_dhgeqz( int matrix_order, char job, char compq, char compz,
+                           lapack_int n, lapack_int ilo, lapack_int ihi,
+                           double* h, lapack_int ldh, double* t, lapack_int ldt,
+                           double* alphar, double* alphai, double* beta,
+                           double* q, lapack_int ldq, double* z,
+                           lapack_int ldz );
+lapack_int LAPACKE_chgeqz( int matrix_order, char job, char compq, char compz,
+                           lapack_int n, lapack_int ilo, lapack_int ihi,
+                           lapack_complex_float* h, lapack_int ldh,
+                           lapack_complex_float* t, lapack_int ldt,
+                           lapack_complex_float* alpha,
+                           lapack_complex_float* beta, lapack_complex_float* q,
+                           lapack_int ldq, lapack_complex_float* z,
+                           lapack_int ldz );
+lapack_int LAPACKE_zhgeqz( int matrix_order, char job, char compq, char compz,
+                           lapack_int n, lapack_int ilo, lapack_int ihi,
+                           lapack_complex_double* h, lapack_int ldh,
+                           lapack_complex_double* t, lapack_int ldt,
+                           lapack_complex_double* alpha,
+                           lapack_complex_double* beta,
+                           lapack_complex_double* q, lapack_int ldq,
+                           lapack_complex_double* z, lapack_int ldz );
+
+lapack_int LAPACKE_chpcon( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_float* ap,
+                           const lapack_int* ipiv, float anorm, float* rcond );
+lapack_int LAPACKE_zhpcon( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_double* ap,
+                           const lapack_int* ipiv, double anorm,
+                           double* rcond );
+
+lapack_int LAPACKE_chpev( int matrix_order, char jobz, char uplo, lapack_int n,
+                          lapack_complex_float* ap, float* w,
+                          lapack_complex_float* z, lapack_int ldz );
+lapack_int LAPACKE_zhpev( int matrix_order, char jobz, char uplo, lapack_int n,
+                          lapack_complex_double* ap, double* w,
+                          lapack_complex_double* z, lapack_int ldz );
+
+lapack_int LAPACKE_chpevd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           lapack_complex_float* ap, float* w,
+                           lapack_complex_float* z, lapack_int ldz );
+lapack_int LAPACKE_zhpevd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           lapack_complex_double* ap, double* w,
+                           lapack_complex_double* z, lapack_int ldz );
+
+lapack_int LAPACKE_chpevx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, lapack_complex_float* ap, float vl,
+                           float vu, lapack_int il, lapack_int iu, float abstol,
+                           lapack_int* m, float* w, lapack_complex_float* z,
+                           lapack_int ldz, lapack_int* ifail );
+lapack_int LAPACKE_zhpevx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, lapack_complex_double* ap, double vl,
+                           double vu, lapack_int il, lapack_int iu,
+                           double abstol, lapack_int* m, double* w,
+                           lapack_complex_double* z, lapack_int ldz,
+                           lapack_int* ifail );
+
+lapack_int LAPACKE_chpgst( int matrix_order, lapack_int itype, char uplo,
+                           lapack_int n, lapack_complex_float* ap,
+                           const lapack_complex_float* bp );
+lapack_int LAPACKE_zhpgst( int matrix_order, lapack_int itype, char uplo,
+                           lapack_int n, lapack_complex_double* ap,
+                           const lapack_complex_double* bp );
+
+lapack_int LAPACKE_chpgv( int matrix_order, lapack_int itype, char jobz,
+                          char uplo, lapack_int n, lapack_complex_float* ap,
+                          lapack_complex_float* bp, float* w,
+                          lapack_complex_float* z, lapack_int ldz );
+lapack_int LAPACKE_zhpgv( int matrix_order, lapack_int itype, char jobz,
+                          char uplo, lapack_int n, lapack_complex_double* ap,
+                          lapack_complex_double* bp, double* w,
+                          lapack_complex_double* z, lapack_int ldz );
+
+lapack_int LAPACKE_chpgvd( int matrix_order, lapack_int itype, char jobz,
+                           char uplo, lapack_int n, lapack_complex_float* ap,
+                           lapack_complex_float* bp, float* w,
+                           lapack_complex_float* z, lapack_int ldz );
+lapack_int LAPACKE_zhpgvd( int matrix_order, lapack_int itype, char jobz,
+                           char uplo, lapack_int n, lapack_complex_double* ap,
+                           lapack_complex_double* bp, double* w,
+                           lapack_complex_double* z, lapack_int ldz );
+
+lapack_int LAPACKE_chpgvx( int matrix_order, lapack_int itype, char jobz,
+                           char range, char uplo, lapack_int n,
+                           lapack_complex_float* ap, lapack_complex_float* bp,
+                           float vl, float vu, lapack_int il, lapack_int iu,
+                           float abstol, lapack_int* m, float* w,
+                           lapack_complex_float* z, lapack_int ldz,
+                           lapack_int* ifail );
+lapack_int LAPACKE_zhpgvx( int matrix_order, lapack_int itype, char jobz,
+                           char range, char uplo, lapack_int n,
+                           lapack_complex_double* ap, lapack_complex_double* bp,
+                           double vl, double vu, lapack_int il, lapack_int iu,
+                           double abstol, lapack_int* m, double* w,
+                           lapack_complex_double* z, lapack_int ldz,
+                           lapack_int* ifail );
+
+lapack_int LAPACKE_chprfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* ap,
+                           const lapack_complex_float* afp,
+                           const lapack_int* ipiv,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_zhprfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* ap,
+                           const lapack_complex_double* afp,
+                           const lapack_int* ipiv,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+
+lapack_int LAPACKE_chpsv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, lapack_complex_float* ap,
+                          lapack_int* ipiv, lapack_complex_float* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_zhpsv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, lapack_complex_double* ap,
+                          lapack_int* ipiv, lapack_complex_double* b,
+                          lapack_int ldb );
+
+lapack_int LAPACKE_chpsvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* ap,
+                           lapack_complex_float* afp, lapack_int* ipiv,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx,
+                           float* rcond, float* ferr, float* berr );
+lapack_int LAPACKE_zhpsvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* ap,
+                           lapack_complex_double* afp, lapack_int* ipiv,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* rcond, double* ferr, double* berr );
+
+lapack_int LAPACKE_chptrd( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* ap, float* d, float* e,
+                           lapack_complex_float* tau );
+lapack_int LAPACKE_zhptrd( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* ap, double* d, double* e,
+                           lapack_complex_double* tau );
+
+lapack_int LAPACKE_chptrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* ap, lapack_int* ipiv );
+lapack_int LAPACKE_zhptrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* ap, lapack_int* ipiv );
+
+lapack_int LAPACKE_chptri( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* ap, const lapack_int* ipiv );
+lapack_int LAPACKE_zhptri( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* ap, const lapack_int* ipiv );
+
+lapack_int LAPACKE_chptrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* ap,
+                           const lapack_int* ipiv, lapack_complex_float* b,
+                           lapack_int ldb );
+lapack_int LAPACKE_zhptrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* ap,
+                           const lapack_int* ipiv, lapack_complex_double* b,
+                           lapack_int ldb );
+
+lapack_int LAPACKE_shsein( int matrix_order, char job, char eigsrc, char initv,
+                           lapack_logical* select, lapack_int n, const float* h,
+                           lapack_int ldh, float* wr, const float* wi,
+                           float* vl, lapack_int ldvl, float* vr,
+                           lapack_int ldvr, lapack_int mm, lapack_int* m,
+                           lapack_int* ifaill, lapack_int* ifailr );
+lapack_int LAPACKE_dhsein( int matrix_order, char job, char eigsrc, char initv,
+                           lapack_logical* select, lapack_int n,
+                           const double* h, lapack_int ldh, double* wr,
+                           const double* wi, double* vl, lapack_int ldvl,
+                           double* vr, lapack_int ldvr, lapack_int mm,
+                           lapack_int* m, lapack_int* ifaill,
+                           lapack_int* ifailr );
+lapack_int LAPACKE_chsein( int matrix_order, char job, char eigsrc, char initv,
+                           const lapack_logical* select, lapack_int n,
+                           const lapack_complex_float* h, lapack_int ldh,
+                           lapack_complex_float* w, lapack_complex_float* vl,
+                           lapack_int ldvl, lapack_complex_float* vr,
+                           lapack_int ldvr, lapack_int mm, lapack_int* m,
+                           lapack_int* ifaill, lapack_int* ifailr );
+lapack_int LAPACKE_zhsein( int matrix_order, char job, char eigsrc, char initv,
+                           const lapack_logical* select, lapack_int n,
+                           const lapack_complex_double* h, lapack_int ldh,
+                           lapack_complex_double* w, lapack_complex_double* vl,
+                           lapack_int ldvl, lapack_complex_double* vr,
+                           lapack_int ldvr, lapack_int mm, lapack_int* m,
+                           lapack_int* ifaill, lapack_int* ifailr );
+
+lapack_int LAPACKE_shseqr( int matrix_order, char job, char compz, lapack_int n,
+                           lapack_int ilo, lapack_int ihi, float* h,
+                           lapack_int ldh, float* wr, float* wi, float* z,
+                           lapack_int ldz );
+lapack_int LAPACKE_dhseqr( int matrix_order, char job, char compz, lapack_int n,
+                           lapack_int ilo, lapack_int ihi, double* h,
+                           lapack_int ldh, double* wr, double* wi, double* z,
+                           lapack_int ldz );
+lapack_int LAPACKE_chseqr( int matrix_order, char job, char compz, lapack_int n,
+                           lapack_int ilo, lapack_int ihi,
+                           lapack_complex_float* h, lapack_int ldh,
+                           lapack_complex_float* w, lapack_complex_float* z,
+                           lapack_int ldz );
+lapack_int LAPACKE_zhseqr( int matrix_order, char job, char compz, lapack_int n,
+                           lapack_int ilo, lapack_int ihi,
+                           lapack_complex_double* h, lapack_int ldh,
+                           lapack_complex_double* w, lapack_complex_double* z,
+                           lapack_int ldz );
+
+lapack_int LAPACKE_clacgv( lapack_int n, lapack_complex_float* x,
+                           lapack_int incx );
+lapack_int LAPACKE_zlacgv( lapack_int n, lapack_complex_double* x,
+                           lapack_int incx );
+
+lapack_int LAPACKE_slacpy( int matrix_order, char uplo, lapack_int m,
+                           lapack_int n, const float* a, lapack_int lda, float* b,
+                           lapack_int ldb );
+lapack_int LAPACKE_dlacpy( int matrix_order, char uplo, lapack_int m,
+                           lapack_int n, const double* a, lapack_int lda, double* b,
+                           lapack_int ldb );
+lapack_int LAPACKE_clacpy( int matrix_order, char uplo, lapack_int m,
+                           lapack_int n, const lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* b,
+                           lapack_int ldb );
+lapack_int LAPACKE_zlacpy( int matrix_order, char uplo, lapack_int m,
+                           lapack_int n, const lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* b,
+                           lapack_int ldb );
+
+lapack_int LAPACKE_zlag2c( int matrix_order, lapack_int m, lapack_int n,
+                           const lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_float* sa, lapack_int ldsa );
+
+lapack_int LAPACKE_slag2d( int matrix_order, lapack_int m, lapack_int n,
+                           const float* sa, lapack_int ldsa, double* a,
+                           lapack_int lda );
+
+lapack_int LAPACKE_dlag2s( int matrix_order, lapack_int m, lapack_int n,
+                           const double* a, lapack_int lda, float* sa,
+                           lapack_int ldsa );
+
+lapack_int LAPACKE_clag2z( int matrix_order, lapack_int m, lapack_int n,
+                           const lapack_complex_float* sa, lapack_int ldsa,
+                           lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_slagge( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int kl, lapack_int ku, const float* d,
+                           float* a, lapack_int lda, lapack_int* iseed );
+lapack_int LAPACKE_dlagge( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int kl, lapack_int ku, const double* d,
+                           double* a, lapack_int lda, lapack_int* iseed );
+lapack_int LAPACKE_clagge( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int kl, lapack_int ku, const float* d,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_int* iseed );
+lapack_int LAPACKE_zlagge( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int kl, lapack_int ku, const double* d,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_int* iseed );
+
+float LAPACKE_slamch( char cmach );
+double LAPACKE_dlamch( char cmach );
+
+float LAPACKE_slange( int matrix_order, char norm, lapack_int m,
+                           lapack_int n, const float* a, lapack_int lda );
+double LAPACKE_dlange( int matrix_order, char norm, lapack_int m,
+                           lapack_int n, const double* a, lapack_int lda );
+float LAPACKE_clange( int matrix_order, char norm, lapack_int m,
+                           lapack_int n, const lapack_complex_float* a,
+                           lapack_int lda );
+double LAPACKE_zlange( int matrix_order, char norm, lapack_int m,
+                           lapack_int n, const lapack_complex_double* a,
+                           lapack_int lda );
+
+float LAPACKE_clanhe( int matrix_order, char norm, char uplo, lapack_int n,
+                           const lapack_complex_float* a, lapack_int lda );
+double LAPACKE_zlanhe( int matrix_order, char norm, char uplo, lapack_int n,
+                           const lapack_complex_double* a, lapack_int lda );
+
+float LAPACKE_slansy( int matrix_order, char norm, char uplo, lapack_int n,
+                           const float* a, lapack_int lda );
+double LAPACKE_dlansy( int matrix_order, char norm, char uplo, lapack_int n,
+                           const double* a, lapack_int lda );
+float LAPACKE_clansy( int matrix_order, char norm, char uplo, lapack_int n,
+                           const lapack_complex_float* a, lapack_int lda );
+double LAPACKE_zlansy( int matrix_order, char norm, char uplo, lapack_int n,
+                           const lapack_complex_double* a, lapack_int lda );
+
+float LAPACKE_slantr( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int m, lapack_int n, const float* a,
+                           lapack_int lda );
+double LAPACKE_dlantr( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int m, lapack_int n, const double* a,
+                           lapack_int lda );
+float LAPACKE_clantr( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int m, lapack_int n, const lapack_complex_float* a,
+                           lapack_int lda );
+double LAPACKE_zlantr( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int m, lapack_int n, const lapack_complex_double* a,
+                           lapack_int lda );
+
+
+lapack_int LAPACKE_slarfb( int matrix_order, char side, char trans, char direct,
+                           char storev, lapack_int m, lapack_int n,
+                           lapack_int k, const float* v, lapack_int ldv,
+                           const float* t, lapack_int ldt, float* c,
+                           lapack_int ldc );
+lapack_int LAPACKE_dlarfb( int matrix_order, char side, char trans, char direct,
+                           char storev, lapack_int m, lapack_int n,
+                           lapack_int k, const double* v, lapack_int ldv,
+                           const double* t, lapack_int ldt, double* c,
+                           lapack_int ldc );
+lapack_int LAPACKE_clarfb( int matrix_order, char side, char trans, char direct,
+                           char storev, lapack_int m, lapack_int n,
+                           lapack_int k, const lapack_complex_float* v,
+                           lapack_int ldv, const lapack_complex_float* t,
+                           lapack_int ldt, lapack_complex_float* c,
+                           lapack_int ldc );
+lapack_int LAPACKE_zlarfb( int matrix_order, char side, char trans, char direct,
+                           char storev, lapack_int m, lapack_int n,
+                           lapack_int k, const lapack_complex_double* v,
+                           lapack_int ldv, const lapack_complex_double* t,
+                           lapack_int ldt, lapack_complex_double* c,
+                           lapack_int ldc );
+
+lapack_int LAPACKE_slarfg( lapack_int n, float* alpha, float* x,
+                           lapack_int incx, float* tau );
+lapack_int LAPACKE_dlarfg( lapack_int n, double* alpha, double* x,
+                           lapack_int incx, double* tau );
+lapack_int LAPACKE_clarfg( lapack_int n, lapack_complex_float* alpha,
+                           lapack_complex_float* x, lapack_int incx,
+                           lapack_complex_float* tau );
+lapack_int LAPACKE_zlarfg( lapack_int n, lapack_complex_double* alpha,
+                           lapack_complex_double* x, lapack_int incx,
+                           lapack_complex_double* tau );
+
+lapack_int LAPACKE_slarft( int matrix_order, char direct, char storev,
+                           lapack_int n, lapack_int k, const float* v,
+                           lapack_int ldv, const float* tau, float* t,
+                           lapack_int ldt );
+lapack_int LAPACKE_dlarft( int matrix_order, char direct, char storev,
+                           lapack_int n, lapack_int k, const double* v,
+                           lapack_int ldv, const double* tau, double* t,
+                           lapack_int ldt );
+lapack_int LAPACKE_clarft( int matrix_order, char direct, char storev,
+                           lapack_int n, lapack_int k,
+                           const lapack_complex_float* v, lapack_int ldv,
+                           const lapack_complex_float* tau,
+                           lapack_complex_float* t, lapack_int ldt );
+lapack_int LAPACKE_zlarft( int matrix_order, char direct, char storev,
+                           lapack_int n, lapack_int k,
+                           const lapack_complex_double* v, lapack_int ldv,
+                           const lapack_complex_double* tau,
+                           lapack_complex_double* t, lapack_int ldt );
+
+lapack_int LAPACKE_slarfx( int matrix_order, char side, lapack_int m,
+                           lapack_int n, const float* v, float tau, float* c,
+                           lapack_int ldc, float* work );
+lapack_int LAPACKE_dlarfx( int matrix_order, char side, lapack_int m,
+                           lapack_int n, const double* v, double tau, double* c,
+                           lapack_int ldc, double* work );
+lapack_int LAPACKE_clarfx( int matrix_order, char side, lapack_int m,
+                           lapack_int n, const lapack_complex_float* v,
+                           lapack_complex_float tau, lapack_complex_float* c,
+                           lapack_int ldc, lapack_complex_float* work );
+lapack_int LAPACKE_zlarfx( int matrix_order, char side, lapack_int m,
+                           lapack_int n, const lapack_complex_double* v,
+                           lapack_complex_double tau, lapack_complex_double* c,
+                           lapack_int ldc, lapack_complex_double* work );
+
+lapack_int LAPACKE_slarnv( lapack_int idist, lapack_int* iseed, lapack_int n,
+                           float* x );
+lapack_int LAPACKE_dlarnv( lapack_int idist, lapack_int* iseed, lapack_int n,
+                           double* x );
+lapack_int LAPACKE_clarnv( lapack_int idist, lapack_int* iseed, lapack_int n,
+                           lapack_complex_float* x );
+lapack_int LAPACKE_zlarnv( lapack_int idist, lapack_int* iseed, lapack_int n,
+                           lapack_complex_double* x );
+
+lapack_int LAPACKE_slaset( int matrix_order, char uplo, lapack_int m,
+                           lapack_int n, float alpha, float beta, float* a,
+                           lapack_int lda );
+lapack_int LAPACKE_dlaset( int matrix_order, char uplo, lapack_int m,
+                           lapack_int n, double alpha, double beta, double* a,
+                           lapack_int lda );
+lapack_int LAPACKE_claset( int matrix_order, char uplo, lapack_int m,
+                           lapack_int n, lapack_complex_float alpha,
+                           lapack_complex_float beta, lapack_complex_float* a,
+                           lapack_int lda );
+lapack_int LAPACKE_zlaset( int matrix_order, char uplo, lapack_int m,
+                           lapack_int n, lapack_complex_double alpha,
+                           lapack_complex_double beta, lapack_complex_double* a,
+                           lapack_int lda );
+
+lapack_int LAPACKE_slasrt( char id, lapack_int n, float* d );
+lapack_int LAPACKE_dlasrt( char id, lapack_int n, double* d );
+
+lapack_int LAPACKE_slaswp( int matrix_order, lapack_int n, float* a,
+                           lapack_int lda, lapack_int k1, lapack_int k2,
+                           const lapack_int* ipiv, lapack_int incx );
+lapack_int LAPACKE_dlaswp( int matrix_order, lapack_int n, double* a,
+                           lapack_int lda, lapack_int k1, lapack_int k2,
+                           const lapack_int* ipiv, lapack_int incx );
+lapack_int LAPACKE_claswp( int matrix_order, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_int k1, lapack_int k2, const lapack_int* ipiv,
+                           lapack_int incx );
+lapack_int LAPACKE_zlaswp( int matrix_order, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_int k1, lapack_int k2, const lapack_int* ipiv,
+                           lapack_int incx );
+
+lapack_int LAPACKE_slatms( int matrix_order, lapack_int m, lapack_int n,
+                           char dist, lapack_int* iseed, char sym, float* d,
+                           lapack_int mode, float cond, float dmax,
+                           lapack_int kl, lapack_int ku, char pack, float* a,
+                           lapack_int lda );
+lapack_int LAPACKE_dlatms( int matrix_order, lapack_int m, lapack_int n,
+                           char dist, lapack_int* iseed, char sym, double* d,
+                           lapack_int mode, double cond, double dmax,
+                           lapack_int kl, lapack_int ku, char pack, double* a,
+                           lapack_int lda );
+lapack_int LAPACKE_clatms( int matrix_order, lapack_int m, lapack_int n,
+                           char dist, lapack_int* iseed, char sym, float* d,
+                           lapack_int mode, float cond, float dmax,
+                           lapack_int kl, lapack_int ku, char pack,
+                           lapack_complex_float* a, lapack_int lda );
+lapack_int LAPACKE_zlatms( int matrix_order, lapack_int m, lapack_int n,
+                           char dist, lapack_int* iseed, char sym, double* d,
+                           lapack_int mode, double cond, double dmax,
+                           lapack_int kl, lapack_int ku, char pack,
+                           lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_slauum( int matrix_order, char uplo, lapack_int n, float* a,
+                           lapack_int lda );
+lapack_int LAPACKE_dlauum( int matrix_order, char uplo, lapack_int n, double* a,
+                           lapack_int lda );
+lapack_int LAPACKE_clauum( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda );
+lapack_int LAPACKE_zlauum( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_sopgtr( int matrix_order, char uplo, lapack_int n,
+                           const float* ap, const float* tau, float* q,
+                           lapack_int ldq );
+lapack_int LAPACKE_dopgtr( int matrix_order, char uplo, lapack_int n,
+                           const double* ap, const double* tau, double* q,
+                           lapack_int ldq );
+
+lapack_int LAPACKE_sopmtr( int matrix_order, char side, char uplo, char trans,
+                           lapack_int m, lapack_int n, const float* ap,
+                           const float* tau, float* c, lapack_int ldc );
+lapack_int LAPACKE_dopmtr( int matrix_order, char side, char uplo, char trans,
+                           lapack_int m, lapack_int n, const double* ap,
+                           const double* tau, double* c, lapack_int ldc );
+
+lapack_int LAPACKE_sorgbr( int matrix_order, char vect, lapack_int m,
+                           lapack_int n, lapack_int k, float* a, lapack_int lda,
+                           const float* tau );
+lapack_int LAPACKE_dorgbr( int matrix_order, char vect, lapack_int m,
+                           lapack_int n, lapack_int k, double* a,
+                           lapack_int lda, const double* tau );
+
+lapack_int LAPACKE_sorghr( int matrix_order, lapack_int n, lapack_int ilo,
+                           lapack_int ihi, float* a, lapack_int lda,
+                           const float* tau );
+lapack_int LAPACKE_dorghr( int matrix_order, lapack_int n, lapack_int ilo,
+                           lapack_int ihi, double* a, lapack_int lda,
+                           const double* tau );
+
+lapack_int LAPACKE_sorglq( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, float* a, lapack_int lda,
+                           const float* tau );
+lapack_int LAPACKE_dorglq( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, double* a, lapack_int lda,
+                           const double* tau );
+
+lapack_int LAPACKE_sorgql( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, float* a, lapack_int lda,
+                           const float* tau );
+lapack_int LAPACKE_dorgql( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, double* a, lapack_int lda,
+                           const double* tau );
+
+lapack_int LAPACKE_sorgqr( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, float* a, lapack_int lda,
+                           const float* tau );
+lapack_int LAPACKE_dorgqr( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, double* a, lapack_int lda,
+                           const double* tau );
+
+lapack_int LAPACKE_sorgrq( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, float* a, lapack_int lda,
+                           const float* tau );
+lapack_int LAPACKE_dorgrq( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, double* a, lapack_int lda,
+                           const double* tau );
+
+lapack_int LAPACKE_sorgtr( int matrix_order, char uplo, lapack_int n, float* a,
+                           lapack_int lda, const float* tau );
+lapack_int LAPACKE_dorgtr( int matrix_order, char uplo, lapack_int n, double* a,
+                           lapack_int lda, const double* tau );
+
+lapack_int LAPACKE_sormbr( int matrix_order, char vect, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const float* a, lapack_int lda, const float* tau,
+                           float* c, lapack_int ldc );
+lapack_int LAPACKE_dormbr( int matrix_order, char vect, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const double* a, lapack_int lda, const double* tau,
+                           double* c, lapack_int ldc );
+
+lapack_int LAPACKE_sormhr( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int ilo,
+                           lapack_int ihi, const float* a, lapack_int lda,
+                           const float* tau, float* c, lapack_int ldc );
+lapack_int LAPACKE_dormhr( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int ilo,
+                           lapack_int ihi, const double* a, lapack_int lda,
+                           const double* tau, double* c, lapack_int ldc );
+
+lapack_int LAPACKE_sormlq( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const float* a, lapack_int lda, const float* tau,
+                           float* c, lapack_int ldc );
+lapack_int LAPACKE_dormlq( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const double* a, lapack_int lda, const double* tau,
+                           double* c, lapack_int ldc );
+
+lapack_int LAPACKE_sormql( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const float* a, lapack_int lda, const float* tau,
+                           float* c, lapack_int ldc );
+lapack_int LAPACKE_dormql( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const double* a, lapack_int lda, const double* tau,
+                           double* c, lapack_int ldc );
+
+lapack_int LAPACKE_sormqr( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const float* a, lapack_int lda, const float* tau,
+                           float* c, lapack_int ldc );
+lapack_int LAPACKE_dormqr( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const double* a, lapack_int lda, const double* tau,
+                           double* c, lapack_int ldc );
+
+lapack_int LAPACKE_sormrq( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const float* a, lapack_int lda, const float* tau,
+                           float* c, lapack_int ldc );
+lapack_int LAPACKE_dormrq( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const double* a, lapack_int lda, const double* tau,
+                           double* c, lapack_int ldc );
+
+lapack_int LAPACKE_sormrz( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           lapack_int l, const float* a, lapack_int lda,
+                           const float* tau, float* c, lapack_int ldc );
+lapack_int LAPACKE_dormrz( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           lapack_int l, const double* a, lapack_int lda,
+                           const double* tau, double* c, lapack_int ldc );
+
+lapack_int LAPACKE_sormtr( int matrix_order, char side, char uplo, char trans,
+                           lapack_int m, lapack_int n, const float* a,
+                           lapack_int lda, const float* tau, float* c,
+                           lapack_int ldc );
+lapack_int LAPACKE_dormtr( int matrix_order, char side, char uplo, char trans,
+                           lapack_int m, lapack_int n, const double* a,
+                           lapack_int lda, const double* tau, double* c,
+                           lapack_int ldc );
+
+lapack_int LAPACKE_spbcon( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, const float* ab, lapack_int ldab,
+                           float anorm, float* rcond );
+lapack_int LAPACKE_dpbcon( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, const double* ab, lapack_int ldab,
+                           double anorm, double* rcond );
+lapack_int LAPACKE_cpbcon( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, const lapack_complex_float* ab,
+                           lapack_int ldab, float anorm, float* rcond );
+lapack_int LAPACKE_zpbcon( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, const lapack_complex_double* ab,
+                           lapack_int ldab, double anorm, double* rcond );
+
+lapack_int LAPACKE_spbequ( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, const float* ab, lapack_int ldab,
+                           float* s, float* scond, float* amax );
+lapack_int LAPACKE_dpbequ( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, const double* ab, lapack_int ldab,
+                           double* s, double* scond, double* amax );
+lapack_int LAPACKE_cpbequ( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, const lapack_complex_float* ab,
+                           lapack_int ldab, float* s, float* scond,
+                           float* amax );
+lapack_int LAPACKE_zpbequ( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, const lapack_complex_double* ab,
+                           lapack_int ldab, double* s, double* scond,
+                           double* amax );
+
+lapack_int LAPACKE_spbrfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, lapack_int nrhs, const float* ab,
+                           lapack_int ldab, const float* afb, lapack_int ldafb,
+                           const float* b, lapack_int ldb, float* x,
+                           lapack_int ldx, float* ferr, float* berr );
+lapack_int LAPACKE_dpbrfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, lapack_int nrhs, const double* ab,
+                           lapack_int ldab, const double* afb, lapack_int ldafb,
+                           const double* b, lapack_int ldb, double* x,
+                           lapack_int ldx, double* ferr, double* berr );
+lapack_int LAPACKE_cpbrfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, lapack_int nrhs,
+                           const lapack_complex_float* ab, lapack_int ldab,
+                           const lapack_complex_float* afb, lapack_int ldafb,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_zpbrfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, lapack_int nrhs,
+                           const lapack_complex_double* ab, lapack_int ldab,
+                           const lapack_complex_double* afb, lapack_int ldafb,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+
+lapack_int LAPACKE_spbstf( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kb, float* bb, lapack_int ldbb );
+lapack_int LAPACKE_dpbstf( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kb, double* bb, lapack_int ldbb );
+lapack_int LAPACKE_cpbstf( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kb, lapack_complex_float* bb,
+                           lapack_int ldbb );
+lapack_int LAPACKE_zpbstf( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kb, lapack_complex_double* bb,
+                           lapack_int ldbb );
+
+lapack_int LAPACKE_spbsv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int kd, lapack_int nrhs, float* ab,
+                          lapack_int ldab, float* b, lapack_int ldb );
+lapack_int LAPACKE_dpbsv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int kd, lapack_int nrhs, double* ab,
+                          lapack_int ldab, double* b, lapack_int ldb );
+lapack_int LAPACKE_cpbsv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int kd, lapack_int nrhs,
+                          lapack_complex_float* ab, lapack_int ldab,
+                          lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zpbsv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int kd, lapack_int nrhs,
+                          lapack_complex_double* ab, lapack_int ldab,
+                          lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_spbsvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int kd, lapack_int nrhs, float* ab,
+                           lapack_int ldab, float* afb, lapack_int ldafb,
+                           char* equed, float* s, float* b, lapack_int ldb,
+                           float* x, lapack_int ldx, float* rcond, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_dpbsvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int kd, lapack_int nrhs, double* ab,
+                           lapack_int ldab, double* afb, lapack_int ldafb,
+                           char* equed, double* s, double* b, lapack_int ldb,
+                           double* x, lapack_int ldx, double* rcond,
+                           double* ferr, double* berr );
+lapack_int LAPACKE_cpbsvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int kd, lapack_int nrhs,
+                           lapack_complex_float* ab, lapack_int ldab,
+                           lapack_complex_float* afb, lapack_int ldafb,
+                           char* equed, float* s, lapack_complex_float* b,
+                           lapack_int ldb, lapack_complex_float* x,
+                           lapack_int ldx, float* rcond, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_zpbsvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int kd, lapack_int nrhs,
+                           lapack_complex_double* ab, lapack_int ldab,
+                           lapack_complex_double* afb, lapack_int ldafb,
+                           char* equed, double* s, lapack_complex_double* b,
+                           lapack_int ldb, lapack_complex_double* x,
+                           lapack_int ldx, double* rcond, double* ferr,
+                           double* berr );
+
+lapack_int LAPACKE_spbtrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, float* ab, lapack_int ldab );
+lapack_int LAPACKE_dpbtrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, double* ab, lapack_int ldab );
+lapack_int LAPACKE_cpbtrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, lapack_complex_float* ab,
+                           lapack_int ldab );
+lapack_int LAPACKE_zpbtrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, lapack_complex_double* ab,
+                           lapack_int ldab );
+
+lapack_int LAPACKE_spbtrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, lapack_int nrhs, const float* ab,
+                           lapack_int ldab, float* b, lapack_int ldb );
+lapack_int LAPACKE_dpbtrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, lapack_int nrhs, const double* ab,
+                           lapack_int ldab, double* b, lapack_int ldb );
+lapack_int LAPACKE_cpbtrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, lapack_int nrhs,
+                           const lapack_complex_float* ab, lapack_int ldab,
+                           lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zpbtrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int kd, lapack_int nrhs,
+                           const lapack_complex_double* ab, lapack_int ldab,
+                           lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_spftrf( int matrix_order, char transr, char uplo,
+                           lapack_int n, float* a );
+lapack_int LAPACKE_dpftrf( int matrix_order, char transr, char uplo,
+                           lapack_int n, double* a );
+lapack_int LAPACKE_cpftrf( int matrix_order, char transr, char uplo,
+                           lapack_int n, lapack_complex_float* a );
+lapack_int LAPACKE_zpftrf( int matrix_order, char transr, char uplo,
+                           lapack_int n, lapack_complex_double* a );
+
+lapack_int LAPACKE_spftri( int matrix_order, char transr, char uplo,
+                           lapack_int n, float* a );
+lapack_int LAPACKE_dpftri( int matrix_order, char transr, char uplo,
+                           lapack_int n, double* a );
+lapack_int LAPACKE_cpftri( int matrix_order, char transr, char uplo,
+                           lapack_int n, lapack_complex_float* a );
+lapack_int LAPACKE_zpftri( int matrix_order, char transr, char uplo,
+                           lapack_int n, lapack_complex_double* a );
+
+lapack_int LAPACKE_spftrs( int matrix_order, char transr, char uplo,
+                           lapack_int n, lapack_int nrhs, const float* a,
+                           float* b, lapack_int ldb );
+lapack_int LAPACKE_dpftrs( int matrix_order, char transr, char uplo,
+                           lapack_int n, lapack_int nrhs, const double* a,
+                           double* b, lapack_int ldb );
+lapack_int LAPACKE_cpftrs( int matrix_order, char transr, char uplo,
+                           lapack_int n, lapack_int nrhs,
+                           const lapack_complex_float* a,
+                           lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zpftrs( int matrix_order, char transr, char uplo,
+                           lapack_int n, lapack_int nrhs,
+                           const lapack_complex_double* a,
+                           lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_spocon( int matrix_order, char uplo, lapack_int n,
+                           const float* a, lapack_int lda, float anorm,
+                           float* rcond );
+lapack_int LAPACKE_dpocon( int matrix_order, char uplo, lapack_int n,
+                           const double* a, lapack_int lda, double anorm,
+                           double* rcond );
+lapack_int LAPACKE_cpocon( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_float* a, lapack_int lda,
+                           float anorm, float* rcond );
+lapack_int LAPACKE_zpocon( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_double* a, lapack_int lda,
+                           double anorm, double* rcond );
+
+lapack_int LAPACKE_spoequ( int matrix_order, lapack_int n, const float* a,
+                           lapack_int lda, float* s, float* scond,
+                           float* amax );
+lapack_int LAPACKE_dpoequ( int matrix_order, lapack_int n, const double* a,
+                           lapack_int lda, double* s, double* scond,
+                           double* amax );
+lapack_int LAPACKE_cpoequ( int matrix_order, lapack_int n,
+                           const lapack_complex_float* a, lapack_int lda,
+                           float* s, float* scond, float* amax );
+lapack_int LAPACKE_zpoequ( int matrix_order, lapack_int n,
+                           const lapack_complex_double* a, lapack_int lda,
+                           double* s, double* scond, double* amax );
+
+lapack_int LAPACKE_spoequb( int matrix_order, lapack_int n, const float* a,
+                            lapack_int lda, float* s, float* scond,
+                            float* amax );
+lapack_int LAPACKE_dpoequb( int matrix_order, lapack_int n, const double* a,
+                            lapack_int lda, double* s, double* scond,
+                            double* amax );
+lapack_int LAPACKE_cpoequb( int matrix_order, lapack_int n,
+                            const lapack_complex_float* a, lapack_int lda,
+                            float* s, float* scond, float* amax );
+lapack_int LAPACKE_zpoequb( int matrix_order, lapack_int n,
+                            const lapack_complex_double* a, lapack_int lda,
+                            double* s, double* scond, double* amax );
+
+lapack_int LAPACKE_sporfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const float* a, lapack_int lda,
+                           const float* af, lapack_int ldaf, const float* b,
+                           lapack_int ldb, float* x, lapack_int ldx,
+                           float* ferr, float* berr );
+lapack_int LAPACKE_dporfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const double* a, lapack_int lda,
+                           const double* af, lapack_int ldaf, const double* b,
+                           lapack_int ldb, double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+lapack_int LAPACKE_cporfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* a,
+                           lapack_int lda, const lapack_complex_float* af,
+                           lapack_int ldaf, const lapack_complex_float* b,
+                           lapack_int ldb, lapack_complex_float* x,
+                           lapack_int ldx, float* ferr, float* berr );
+lapack_int LAPACKE_zporfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* a,
+                           lapack_int lda, const lapack_complex_double* af,
+                           lapack_int ldaf, const lapack_complex_double* b,
+                           lapack_int ldb, lapack_complex_double* x,
+                           lapack_int ldx, double* ferr, double* berr );
+
+lapack_int LAPACKE_sporfsx( int matrix_order, char uplo, char equed,
+                            lapack_int n, lapack_int nrhs, const float* a,
+                            lapack_int lda, const float* af, lapack_int ldaf,
+                            const float* s, const float* b, lapack_int ldb,
+                            float* x, lapack_int ldx, float* rcond, float* berr,
+                            lapack_int n_err_bnds, float* err_bnds_norm,
+                            float* err_bnds_comp, lapack_int nparams,
+                            float* params );
+lapack_int LAPACKE_dporfsx( int matrix_order, char uplo, char equed,
+                            lapack_int n, lapack_int nrhs, const double* a,
+                            lapack_int lda, const double* af, lapack_int ldaf,
+                            const double* s, const double* b, lapack_int ldb,
+                            double* x, lapack_int ldx, double* rcond,
+                            double* berr, lapack_int n_err_bnds,
+                            double* err_bnds_norm, double* err_bnds_comp,
+                            lapack_int nparams, double* params );
+lapack_int LAPACKE_cporfsx( int matrix_order, char uplo, char equed,
+                            lapack_int n, lapack_int nrhs,
+                            const lapack_complex_float* a, lapack_int lda,
+                            const lapack_complex_float* af, lapack_int ldaf,
+                            const float* s, const lapack_complex_float* b,
+                            lapack_int ldb, lapack_complex_float* x,
+                            lapack_int ldx, float* rcond, float* berr,
+                            lapack_int n_err_bnds, float* err_bnds_norm,
+                            float* err_bnds_comp, lapack_int nparams,
+                            float* params );
+lapack_int LAPACKE_zporfsx( int matrix_order, char uplo, char equed,
+                            lapack_int n, lapack_int nrhs,
+                            const lapack_complex_double* a, lapack_int lda,
+                            const lapack_complex_double* af, lapack_int ldaf,
+                            const double* s, const lapack_complex_double* b,
+                            lapack_int ldb, lapack_complex_double* x,
+                            lapack_int ldx, double* rcond, double* berr,
+                            lapack_int n_err_bnds, double* err_bnds_norm,
+                            double* err_bnds_comp, lapack_int nparams,
+                            double* params );
+
+lapack_int LAPACKE_sposv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, float* a, lapack_int lda, float* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_dposv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, double* a, lapack_int lda, double* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_cposv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, lapack_complex_float* a,
+                          lapack_int lda, lapack_complex_float* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_zposv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, lapack_complex_double* a,
+                          lapack_int lda, lapack_complex_double* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_dsposv( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, double* a, lapack_int lda,
+                           double* b, lapack_int ldb, double* x, lapack_int ldx,
+                           lapack_int* iter );
+lapack_int LAPACKE_zcposv( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* b,
+                           lapack_int ldb, lapack_complex_double* x,
+                           lapack_int ldx, lapack_int* iter );
+
+lapack_int LAPACKE_sposvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, float* a, lapack_int lda, float* af,
+                           lapack_int ldaf, char* equed, float* s, float* b,
+                           lapack_int ldb, float* x, lapack_int ldx,
+                           float* rcond, float* ferr, float* berr );
+lapack_int LAPACKE_dposvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, double* a, lapack_int lda,
+                           double* af, lapack_int ldaf, char* equed, double* s,
+                           double* b, lapack_int ldb, double* x, lapack_int ldx,
+                           double* rcond, double* ferr, double* berr );
+lapack_int LAPACKE_cposvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* af,
+                           lapack_int ldaf, char* equed, float* s,
+                           lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx,
+                           float* rcond, float* ferr, float* berr );
+lapack_int LAPACKE_zposvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* af,
+                           lapack_int ldaf, char* equed, double* s,
+                           lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* rcond, double* ferr, double* berr );
+
+lapack_int LAPACKE_sposvxx( int matrix_order, char fact, char uplo,
+                            lapack_int n, lapack_int nrhs, float* a,
+                            lapack_int lda, float* af, lapack_int ldaf,
+                            char* equed, float* s, float* b, lapack_int ldb,
+                            float* x, lapack_int ldx, float* rcond,
+                            float* rpvgrw, float* berr, lapack_int n_err_bnds,
+                            float* err_bnds_norm, float* err_bnds_comp,
+                            lapack_int nparams, float* params );
+lapack_int LAPACKE_dposvxx( int matrix_order, char fact, char uplo,
+                            lapack_int n, lapack_int nrhs, double* a,
+                            lapack_int lda, double* af, lapack_int ldaf,
+                            char* equed, double* s, double* b, lapack_int ldb,
+                            double* x, lapack_int ldx, double* rcond,
+                            double* rpvgrw, double* berr, lapack_int n_err_bnds,
+                            double* err_bnds_norm, double* err_bnds_comp,
+                            lapack_int nparams, double* params );
+lapack_int LAPACKE_cposvxx( int matrix_order, char fact, char uplo,
+                            lapack_int n, lapack_int nrhs,
+                            lapack_complex_float* a, lapack_int lda,
+                            lapack_complex_float* af, lapack_int ldaf,
+                            char* equed, float* s, lapack_complex_float* b,
+                            lapack_int ldb, lapack_complex_float* x,
+                            lapack_int ldx, float* rcond, float* rpvgrw,
+                            float* berr, lapack_int n_err_bnds,
+                            float* err_bnds_norm, float* err_bnds_comp,
+                            lapack_int nparams, float* params );
+lapack_int LAPACKE_zposvxx( int matrix_order, char fact, char uplo,
+                            lapack_int n, lapack_int nrhs,
+                            lapack_complex_double* a, lapack_int lda,
+                            lapack_complex_double* af, lapack_int ldaf,
+                            char* equed, double* s, lapack_complex_double* b,
+                            lapack_int ldb, lapack_complex_double* x,
+                            lapack_int ldx, double* rcond, double* rpvgrw,
+                            double* berr, lapack_int n_err_bnds,
+                            double* err_bnds_norm, double* err_bnds_comp,
+                            lapack_int nparams, double* params );
+
+lapack_int LAPACKE_spotrf( int matrix_order, char uplo, lapack_int n, float* a,
+                           lapack_int lda );
+lapack_int LAPACKE_dpotrf( int matrix_order, char uplo, lapack_int n, double* a,
+                           lapack_int lda );
+lapack_int LAPACKE_cpotrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda );
+lapack_int LAPACKE_zpotrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_spotri( int matrix_order, char uplo, lapack_int n, float* a,
+                           lapack_int lda );
+lapack_int LAPACKE_dpotri( int matrix_order, char uplo, lapack_int n, double* a,
+                           lapack_int lda );
+lapack_int LAPACKE_cpotri( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda );
+lapack_int LAPACKE_zpotri( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_spotrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const float* a, lapack_int lda,
+                           float* b, lapack_int ldb );
+lapack_int LAPACKE_dpotrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const double* a, lapack_int lda,
+                           double* b, lapack_int ldb );
+lapack_int LAPACKE_cpotrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* b,
+                           lapack_int ldb );
+lapack_int LAPACKE_zpotrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* b,
+                           lapack_int ldb );
+
+lapack_int LAPACKE_sppcon( int matrix_order, char uplo, lapack_int n,
+                           const float* ap, float anorm, float* rcond );
+lapack_int LAPACKE_dppcon( int matrix_order, char uplo, lapack_int n,
+                           const double* ap, double anorm, double* rcond );
+lapack_int LAPACKE_cppcon( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_float* ap, float anorm,
+                           float* rcond );
+lapack_int LAPACKE_zppcon( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_double* ap, double anorm,
+                           double* rcond );
+
+lapack_int LAPACKE_sppequ( int matrix_order, char uplo, lapack_int n,
+                           const float* ap, float* s, float* scond,
+                           float* amax );
+lapack_int LAPACKE_dppequ( int matrix_order, char uplo, lapack_int n,
+                           const double* ap, double* s, double* scond,
+                           double* amax );
+lapack_int LAPACKE_cppequ( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_float* ap, float* s,
+                           float* scond, float* amax );
+lapack_int LAPACKE_zppequ( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_double* ap, double* s,
+                           double* scond, double* amax );
+
+lapack_int LAPACKE_spprfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const float* ap, const float* afp,
+                           const float* b, lapack_int ldb, float* x,
+                           lapack_int ldx, float* ferr, float* berr );
+lapack_int LAPACKE_dpprfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const double* ap, const double* afp,
+                           const double* b, lapack_int ldb, double* x,
+                           lapack_int ldx, double* ferr, double* berr );
+lapack_int LAPACKE_cpprfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* ap,
+                           const lapack_complex_float* afp,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_zpprfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* ap,
+                           const lapack_complex_double* afp,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+
+lapack_int LAPACKE_sppsv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, float* ap, float* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_dppsv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, double* ap, double* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_cppsv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, lapack_complex_float* ap,
+                          lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zppsv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, lapack_complex_double* ap,
+                          lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_sppsvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, float* ap, float* afp, char* equed,
+                           float* s, float* b, lapack_int ldb, float* x,
+                           lapack_int ldx, float* rcond, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_dppsvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, double* ap, double* afp,
+                           char* equed, double* s, double* b, lapack_int ldb,
+                           double* x, lapack_int ldx, double* rcond,
+                           double* ferr, double* berr );
+lapack_int LAPACKE_cppsvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, lapack_complex_float* ap,
+                           lapack_complex_float* afp, char* equed, float* s,
+                           lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx,
+                           float* rcond, float* ferr, float* berr );
+lapack_int LAPACKE_zppsvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, lapack_complex_double* ap,
+                           lapack_complex_double* afp, char* equed, double* s,
+                           lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* rcond, double* ferr, double* berr );
+
+lapack_int LAPACKE_spptrf( int matrix_order, char uplo, lapack_int n,
+                           float* ap );
+lapack_int LAPACKE_dpptrf( int matrix_order, char uplo, lapack_int n,
+                           double* ap );
+lapack_int LAPACKE_cpptrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* ap );
+lapack_int LAPACKE_zpptrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* ap );
+
+lapack_int LAPACKE_spptri( int matrix_order, char uplo, lapack_int n,
+                           float* ap );
+lapack_int LAPACKE_dpptri( int matrix_order, char uplo, lapack_int n,
+                           double* ap );
+lapack_int LAPACKE_cpptri( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* ap );
+lapack_int LAPACKE_zpptri( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* ap );
+
+lapack_int LAPACKE_spptrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const float* ap, float* b,
+                           lapack_int ldb );
+lapack_int LAPACKE_dpptrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const double* ap, double* b,
+                           lapack_int ldb );
+lapack_int LAPACKE_cpptrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* ap,
+                           lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zpptrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* ap,
+                           lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_spstrf( int matrix_order, char uplo, lapack_int n, float* a,
+                           lapack_int lda, lapack_int* piv, lapack_int* rank,
+                           float tol );
+lapack_int LAPACKE_dpstrf( int matrix_order, char uplo, lapack_int n, double* a,
+                           lapack_int lda, lapack_int* piv, lapack_int* rank,
+                           double tol );
+lapack_int LAPACKE_cpstrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_int* piv, lapack_int* rank, float tol );
+lapack_int LAPACKE_zpstrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_int* piv, lapack_int* rank, double tol );
+
+lapack_int LAPACKE_sptcon( lapack_int n, const float* d, const float* e,
+                           float anorm, float* rcond );
+lapack_int LAPACKE_dptcon( lapack_int n, const double* d, const double* e,
+                           double anorm, double* rcond );
+lapack_int LAPACKE_cptcon( lapack_int n, const float* d,
+                           const lapack_complex_float* e, float anorm,
+                           float* rcond );
+lapack_int LAPACKE_zptcon( lapack_int n, const double* d,
+                           const lapack_complex_double* e, double anorm,
+                           double* rcond );
+
+lapack_int LAPACKE_spteqr( int matrix_order, char compz, lapack_int n, float* d,
+                           float* e, float* z, lapack_int ldz );
+lapack_int LAPACKE_dpteqr( int matrix_order, char compz, lapack_int n,
+                           double* d, double* e, double* z, lapack_int ldz );
+lapack_int LAPACKE_cpteqr( int matrix_order, char compz, lapack_int n, float* d,
+                           float* e, lapack_complex_float* z, lapack_int ldz );
+lapack_int LAPACKE_zpteqr( int matrix_order, char compz, lapack_int n,
+                           double* d, double* e, lapack_complex_double* z,
+                           lapack_int ldz );
+
+lapack_int LAPACKE_sptrfs( int matrix_order, lapack_int n, lapack_int nrhs,
+                           const float* d, const float* e, const float* df,
+                           const float* ef, const float* b, lapack_int ldb,
+                           float* x, lapack_int ldx, float* ferr, float* berr );
+lapack_int LAPACKE_dptrfs( int matrix_order, lapack_int n, lapack_int nrhs,
+                           const double* d, const double* e, const double* df,
+                           const double* ef, const double* b, lapack_int ldb,
+                           double* x, lapack_int ldx, double* ferr,
+                           double* berr );
+lapack_int LAPACKE_cptrfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const float* d,
+                           const lapack_complex_float* e, const float* df,
+                           const lapack_complex_float* ef,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_zptrfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const double* d,
+                           const lapack_complex_double* e, const double* df,
+                           const lapack_complex_double* ef,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+
+lapack_int LAPACKE_sptsv( int matrix_order, lapack_int n, lapack_int nrhs,
+                          float* d, float* e, float* b, lapack_int ldb );
+lapack_int LAPACKE_dptsv( int matrix_order, lapack_int n, lapack_int nrhs,
+                          double* d, double* e, double* b, lapack_int ldb );
+lapack_int LAPACKE_cptsv( int matrix_order, lapack_int n, lapack_int nrhs,
+                          float* d, lapack_complex_float* e,
+                          lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zptsv( int matrix_order, lapack_int n, lapack_int nrhs,
+                          double* d, lapack_complex_double* e,
+                          lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_sptsvx( int matrix_order, char fact, lapack_int n,
+                           lapack_int nrhs, const float* d, const float* e,
+                           float* df, float* ef, const float* b, lapack_int ldb,
+                           float* x, lapack_int ldx, float* rcond, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_dptsvx( int matrix_order, char fact, lapack_int n,
+                           lapack_int nrhs, const double* d, const double* e,
+                           double* df, double* ef, const double* b,
+                           lapack_int ldb, double* x, lapack_int ldx,
+                           double* rcond, double* ferr, double* berr );
+lapack_int LAPACKE_cptsvx( int matrix_order, char fact, lapack_int n,
+                           lapack_int nrhs, const float* d,
+                           const lapack_complex_float* e, float* df,
+                           lapack_complex_float* ef,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx,
+                           float* rcond, float* ferr, float* berr );
+lapack_int LAPACKE_zptsvx( int matrix_order, char fact, lapack_int n,
+                           lapack_int nrhs, const double* d,
+                           const lapack_complex_double* e, double* df,
+                           lapack_complex_double* ef,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* rcond, double* ferr, double* berr );
+
+lapack_int LAPACKE_spttrf( lapack_int n, float* d, float* e );
+lapack_int LAPACKE_dpttrf( lapack_int n, double* d, double* e );
+lapack_int LAPACKE_cpttrf( lapack_int n, float* d, lapack_complex_float* e );
+lapack_int LAPACKE_zpttrf( lapack_int n, double* d, lapack_complex_double* e );
+
+lapack_int LAPACKE_spttrs( int matrix_order, lapack_int n, lapack_int nrhs,
+                           const float* d, const float* e, float* b,
+                           lapack_int ldb );
+lapack_int LAPACKE_dpttrs( int matrix_order, lapack_int n, lapack_int nrhs,
+                           const double* d, const double* e, double* b,
+                           lapack_int ldb );
+lapack_int LAPACKE_cpttrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const float* d,
+                           const lapack_complex_float* e,
+                           lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zpttrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const double* d,
+                           const lapack_complex_double* e,
+                           lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_ssbev( int matrix_order, char jobz, char uplo, lapack_int n,
+                          lapack_int kd, float* ab, lapack_int ldab, float* w,
+                          float* z, lapack_int ldz );
+lapack_int LAPACKE_dsbev( int matrix_order, char jobz, char uplo, lapack_int n,
+                          lapack_int kd, double* ab, lapack_int ldab, double* w,
+                          double* z, lapack_int ldz );
+
+lapack_int LAPACKE_ssbevd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           lapack_int kd, float* ab, lapack_int ldab, float* w,
+                           float* z, lapack_int ldz );
+lapack_int LAPACKE_dsbevd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           lapack_int kd, double* ab, lapack_int ldab,
+                           double* w, double* z, lapack_int ldz );
+
+lapack_int LAPACKE_ssbevx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, lapack_int kd, float* ab,
+                           lapack_int ldab, float* q, lapack_int ldq, float vl,
+                           float vu, lapack_int il, lapack_int iu, float abstol,
+                           lapack_int* m, float* w, float* z, lapack_int ldz,
+                           lapack_int* ifail );
+lapack_int LAPACKE_dsbevx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, lapack_int kd, double* ab,
+                           lapack_int ldab, double* q, lapack_int ldq,
+                           double vl, double vu, lapack_int il, lapack_int iu,
+                           double abstol, lapack_int* m, double* w, double* z,
+                           lapack_int ldz, lapack_int* ifail );
+
+lapack_int LAPACKE_ssbgst( int matrix_order, char vect, char uplo, lapack_int n,
+                           lapack_int ka, lapack_int kb, float* ab,
+                           lapack_int ldab, const float* bb, lapack_int ldbb,
+                           float* x, lapack_int ldx );
+lapack_int LAPACKE_dsbgst( int matrix_order, char vect, char uplo, lapack_int n,
+                           lapack_int ka, lapack_int kb, double* ab,
+                           lapack_int ldab, const double* bb, lapack_int ldbb,
+                           double* x, lapack_int ldx );
+
+lapack_int LAPACKE_ssbgv( int matrix_order, char jobz, char uplo, lapack_int n,
+                          lapack_int ka, lapack_int kb, float* ab,
+                          lapack_int ldab, float* bb, lapack_int ldbb, float* w,
+                          float* z, lapack_int ldz );
+lapack_int LAPACKE_dsbgv( int matrix_order, char jobz, char uplo, lapack_int n,
+                          lapack_int ka, lapack_int kb, double* ab,
+                          lapack_int ldab, double* bb, lapack_int ldbb,
+                          double* w, double* z, lapack_int ldz );
+
+lapack_int LAPACKE_ssbgvd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           lapack_int ka, lapack_int kb, float* ab,
+                           lapack_int ldab, float* bb, lapack_int ldbb,
+                           float* w, float* z, lapack_int ldz );
+lapack_int LAPACKE_dsbgvd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           lapack_int ka, lapack_int kb, double* ab,
+                           lapack_int ldab, double* bb, lapack_int ldbb,
+                           double* w, double* z, lapack_int ldz );
+
+lapack_int LAPACKE_ssbgvx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, lapack_int ka, lapack_int kb,
+                           float* ab, lapack_int ldab, float* bb,
+                           lapack_int ldbb, float* q, lapack_int ldq, float vl,
+                           float vu, lapack_int il, lapack_int iu, float abstol,
+                           lapack_int* m, float* w, float* z, lapack_int ldz,
+                           lapack_int* ifail );
+lapack_int LAPACKE_dsbgvx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, lapack_int ka, lapack_int kb,
+                           double* ab, lapack_int ldab, double* bb,
+                           lapack_int ldbb, double* q, lapack_int ldq,
+                           double vl, double vu, lapack_int il, lapack_int iu,
+                           double abstol, lapack_int* m, double* w, double* z,
+                           lapack_int ldz, lapack_int* ifail );
+
+lapack_int LAPACKE_ssbtrd( int matrix_order, char vect, char uplo, lapack_int n,
+                           lapack_int kd, float* ab, lapack_int ldab, float* d,
+                           float* e, float* q, lapack_int ldq );
+lapack_int LAPACKE_dsbtrd( int matrix_order, char vect, char uplo, lapack_int n,
+                           lapack_int kd, double* ab, lapack_int ldab,
+                           double* d, double* e, double* q, lapack_int ldq );
+
+lapack_int LAPACKE_ssfrk( int matrix_order, char transr, char uplo, char trans,
+                          lapack_int n, lapack_int k, float alpha,
+                          const float* a, lapack_int lda, float beta,
+                          float* c );
+lapack_int LAPACKE_dsfrk( int matrix_order, char transr, char uplo, char trans,
+                          lapack_int n, lapack_int k, double alpha,
+                          const double* a, lapack_int lda, double beta,
+                          double* c );
+
+lapack_int LAPACKE_sspcon( int matrix_order, char uplo, lapack_int n,
+                           const float* ap, const lapack_int* ipiv, float anorm,
+                           float* rcond );
+lapack_int LAPACKE_dspcon( int matrix_order, char uplo, lapack_int n,
+                           const double* ap, const lapack_int* ipiv,
+                           double anorm, double* rcond );
+lapack_int LAPACKE_cspcon( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_float* ap,
+                           const lapack_int* ipiv, float anorm, float* rcond );
+lapack_int LAPACKE_zspcon( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_double* ap,
+                           const lapack_int* ipiv, double anorm,
+                           double* rcond );
+
+lapack_int LAPACKE_sspev( int matrix_order, char jobz, char uplo, lapack_int n,
+                          float* ap, float* w, float* z, lapack_int ldz );
+lapack_int LAPACKE_dspev( int matrix_order, char jobz, char uplo, lapack_int n,
+                          double* ap, double* w, double* z, lapack_int ldz );
+
+lapack_int LAPACKE_sspevd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           float* ap, float* w, float* z, lapack_int ldz );
+lapack_int LAPACKE_dspevd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           double* ap, double* w, double* z, lapack_int ldz );
+
+lapack_int LAPACKE_sspevx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, float* ap, float vl, float vu,
+                           lapack_int il, lapack_int iu, float abstol,
+                           lapack_int* m, float* w, float* z, lapack_int ldz,
+                           lapack_int* ifail );
+lapack_int LAPACKE_dspevx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, double* ap, double vl, double vu,
+                           lapack_int il, lapack_int iu, double abstol,
+                           lapack_int* m, double* w, double* z, lapack_int ldz,
+                           lapack_int* ifail );
+
+lapack_int LAPACKE_sspgst( int matrix_order, lapack_int itype, char uplo,
+                           lapack_int n, float* ap, const float* bp );
+lapack_int LAPACKE_dspgst( int matrix_order, lapack_int itype, char uplo,
+                           lapack_int n, double* ap, const double* bp );
+
+lapack_int LAPACKE_sspgv( int matrix_order, lapack_int itype, char jobz,
+                          char uplo, lapack_int n, float* ap, float* bp,
+                          float* w, float* z, lapack_int ldz );
+lapack_int LAPACKE_dspgv( int matrix_order, lapack_int itype, char jobz,
+                          char uplo, lapack_int n, double* ap, double* bp,
+                          double* w, double* z, lapack_int ldz );
+
+lapack_int LAPACKE_sspgvd( int matrix_order, lapack_int itype, char jobz,
+                           char uplo, lapack_int n, float* ap, float* bp,
+                           float* w, float* z, lapack_int ldz );
+lapack_int LAPACKE_dspgvd( int matrix_order, lapack_int itype, char jobz,
+                           char uplo, lapack_int n, double* ap, double* bp,
+                           double* w, double* z, lapack_int ldz );
+
+lapack_int LAPACKE_sspgvx( int matrix_order, lapack_int itype, char jobz,
+                           char range, char uplo, lapack_int n, float* ap,
+                           float* bp, float vl, float vu, lapack_int il,
+                           lapack_int iu, float abstol, lapack_int* m, float* w,
+                           float* z, lapack_int ldz, lapack_int* ifail );
+lapack_int LAPACKE_dspgvx( int matrix_order, lapack_int itype, char jobz,
+                           char range, char uplo, lapack_int n, double* ap,
+                           double* bp, double vl, double vu, lapack_int il,
+                           lapack_int iu, double abstol, lapack_int* m,
+                           double* w, double* z, lapack_int ldz,
+                           lapack_int* ifail );
+
+lapack_int LAPACKE_ssprfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const float* ap, const float* afp,
+                           const lapack_int* ipiv, const float* b,
+                           lapack_int ldb, float* x, lapack_int ldx,
+                           float* ferr, float* berr );
+lapack_int LAPACKE_dsprfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const double* ap, const double* afp,
+                           const lapack_int* ipiv, const double* b,
+                           lapack_int ldb, double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+lapack_int LAPACKE_csprfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* ap,
+                           const lapack_complex_float* afp,
+                           const lapack_int* ipiv,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_zsprfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* ap,
+                           const lapack_complex_double* afp,
+                           const lapack_int* ipiv,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+
+lapack_int LAPACKE_sspsv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, float* ap, lapack_int* ipiv,
+                          float* b, lapack_int ldb );
+lapack_int LAPACKE_dspsv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, double* ap, lapack_int* ipiv,
+                          double* b, lapack_int ldb );
+lapack_int LAPACKE_cspsv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, lapack_complex_float* ap,
+                          lapack_int* ipiv, lapack_complex_float* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_zspsv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, lapack_complex_double* ap,
+                          lapack_int* ipiv, lapack_complex_double* b,
+                          lapack_int ldb );
+
+lapack_int LAPACKE_sspsvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, const float* ap, float* afp,
+                           lapack_int* ipiv, const float* b, lapack_int ldb,
+                           float* x, lapack_int ldx, float* rcond, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_dspsvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, const double* ap, double* afp,
+                           lapack_int* ipiv, const double* b, lapack_int ldb,
+                           double* x, lapack_int ldx, double* rcond,
+                           double* ferr, double* berr );
+lapack_int LAPACKE_cspsvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* ap,
+                           lapack_complex_float* afp, lapack_int* ipiv,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx,
+                           float* rcond, float* ferr, float* berr );
+lapack_int LAPACKE_zspsvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* ap,
+                           lapack_complex_double* afp, lapack_int* ipiv,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* rcond, double* ferr, double* berr );
+
+lapack_int LAPACKE_ssptrd( int matrix_order, char uplo, lapack_int n, float* ap,
+                           float* d, float* e, float* tau );
+lapack_int LAPACKE_dsptrd( int matrix_order, char uplo, lapack_int n,
+                           double* ap, double* d, double* e, double* tau );
+
+lapack_int LAPACKE_ssptrf( int matrix_order, char uplo, lapack_int n, float* ap,
+                           lapack_int* ipiv );
+lapack_int LAPACKE_dsptrf( int matrix_order, char uplo, lapack_int n,
+                           double* ap, lapack_int* ipiv );
+lapack_int LAPACKE_csptrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* ap, lapack_int* ipiv );
+lapack_int LAPACKE_zsptrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* ap, lapack_int* ipiv );
+
+lapack_int LAPACKE_ssptri( int matrix_order, char uplo, lapack_int n, float* ap,
+                           const lapack_int* ipiv );
+lapack_int LAPACKE_dsptri( int matrix_order, char uplo, lapack_int n,
+                           double* ap, const lapack_int* ipiv );
+lapack_int LAPACKE_csptri( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* ap, const lapack_int* ipiv );
+lapack_int LAPACKE_zsptri( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* ap, const lapack_int* ipiv );
+
+lapack_int LAPACKE_ssptrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const float* ap,
+                           const lapack_int* ipiv, float* b, lapack_int ldb );
+lapack_int LAPACKE_dsptrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const double* ap,
+                           const lapack_int* ipiv, double* b, lapack_int ldb );
+lapack_int LAPACKE_csptrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* ap,
+                           const lapack_int* ipiv, lapack_complex_float* b,
+                           lapack_int ldb );
+lapack_int LAPACKE_zsptrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* ap,
+                           const lapack_int* ipiv, lapack_complex_double* b,
+                           lapack_int ldb );
+
+lapack_int LAPACKE_sstebz( char range, char order, lapack_int n, float vl,
+                           float vu, lapack_int il, lapack_int iu, float abstol,
+                           const float* d, const float* e, lapack_int* m,
+                           lapack_int* nsplit, float* w, lapack_int* iblock,
+                           lapack_int* isplit );
+lapack_int LAPACKE_dstebz( char range, char order, lapack_int n, double vl,
+                           double vu, lapack_int il, lapack_int iu,
+                           double abstol, const double* d, const double* e,
+                           lapack_int* m, lapack_int* nsplit, double* w,
+                           lapack_int* iblock, lapack_int* isplit );
+
+lapack_int LAPACKE_sstedc( int matrix_order, char compz, lapack_int n, float* d,
+                           float* e, float* z, lapack_int ldz );
+lapack_int LAPACKE_dstedc( int matrix_order, char compz, lapack_int n,
+                           double* d, double* e, double* z, lapack_int ldz );
+lapack_int LAPACKE_cstedc( int matrix_order, char compz, lapack_int n, float* d,
+                           float* e, lapack_complex_float* z, lapack_int ldz );
+lapack_int LAPACKE_zstedc( int matrix_order, char compz, lapack_int n,
+                           double* d, double* e, lapack_complex_double* z,
+                           lapack_int ldz );
+
+lapack_int LAPACKE_sstegr( int matrix_order, char jobz, char range,
+                           lapack_int n, float* d, float* e, float vl, float vu,
+                           lapack_int il, lapack_int iu, float abstol,
+                           lapack_int* m, float* w, float* z, lapack_int ldz,
+                           lapack_int* isuppz );
+lapack_int LAPACKE_dstegr( int matrix_order, char jobz, char range,
+                           lapack_int n, double* d, double* e, double vl,
+                           double vu, lapack_int il, lapack_int iu,
+                           double abstol, lapack_int* m, double* w, double* z,
+                           lapack_int ldz, lapack_int* isuppz );
+lapack_int LAPACKE_cstegr( int matrix_order, char jobz, char range,
+                           lapack_int n, float* d, float* e, float vl, float vu,
+                           lapack_int il, lapack_int iu, float abstol,
+                           lapack_int* m, float* w, lapack_complex_float* z,
+                           lapack_int ldz, lapack_int* isuppz );
+lapack_int LAPACKE_zstegr( int matrix_order, char jobz, char range,
+                           lapack_int n, double* d, double* e, double vl,
+                           double vu, lapack_int il, lapack_int iu,
+                           double abstol, lapack_int* m, double* w,
+                           lapack_complex_double* z, lapack_int ldz,
+                           lapack_int* isuppz );
+
+lapack_int LAPACKE_sstein( int matrix_order, lapack_int n, const float* d,
+                           const float* e, lapack_int m, const float* w,
+                           const lapack_int* iblock, const lapack_int* isplit,
+                           float* z, lapack_int ldz, lapack_int* ifailv );
+lapack_int LAPACKE_dstein( int matrix_order, lapack_int n, const double* d,
+                           const double* e, lapack_int m, const double* w,
+                           const lapack_int* iblock, const lapack_int* isplit,
+                           double* z, lapack_int ldz, lapack_int* ifailv );
+lapack_int LAPACKE_cstein( int matrix_order, lapack_int n, const float* d,
+                           const float* e, lapack_int m, const float* w,
+                           const lapack_int* iblock, const lapack_int* isplit,
+                           lapack_complex_float* z, lapack_int ldz,
+                           lapack_int* ifailv );
+lapack_int LAPACKE_zstein( int matrix_order, lapack_int n, const double* d,
+                           const double* e, lapack_int m, const double* w,
+                           const lapack_int* iblock, const lapack_int* isplit,
+                           lapack_complex_double* z, lapack_int ldz,
+                           lapack_int* ifailv );
+
+lapack_int LAPACKE_sstemr( int matrix_order, char jobz, char range,
+                           lapack_int n, float* d, float* e, float vl, float vu,
+                           lapack_int il, lapack_int iu, lapack_int* m,
+                           float* w, float* z, lapack_int ldz, lapack_int nzc,
+                           lapack_int* isuppz, lapack_logical* tryrac );
+lapack_int LAPACKE_dstemr( int matrix_order, char jobz, char range,
+                           lapack_int n, double* d, double* e, double vl,
+                           double vu, lapack_int il, lapack_int iu,
+                           lapack_int* m, double* w, double* z, lapack_int ldz,
+                           lapack_int nzc, lapack_int* isuppz,
+                           lapack_logical* tryrac );
+lapack_int LAPACKE_cstemr( int matrix_order, char jobz, char range,
+                           lapack_int n, float* d, float* e, float vl, float vu,
+                           lapack_int il, lapack_int iu, lapack_int* m,
+                           float* w, lapack_complex_float* z, lapack_int ldz,
+                           lapack_int nzc, lapack_int* isuppz,
+                           lapack_logical* tryrac );
+lapack_int LAPACKE_zstemr( int matrix_order, char jobz, char range,
+                           lapack_int n, double* d, double* e, double vl,
+                           double vu, lapack_int il, lapack_int iu,
+                           lapack_int* m, double* w, lapack_complex_double* z,
+                           lapack_int ldz, lapack_int nzc, lapack_int* isuppz,
+                           lapack_logical* tryrac );
+
+lapack_int LAPACKE_ssteqr( int matrix_order, char compz, lapack_int n, float* d,
+                           float* e, float* z, lapack_int ldz );
+lapack_int LAPACKE_dsteqr( int matrix_order, char compz, lapack_int n,
+                           double* d, double* e, double* z, lapack_int ldz );
+lapack_int LAPACKE_csteqr( int matrix_order, char compz, lapack_int n, float* d,
+                           float* e, lapack_complex_float* z, lapack_int ldz );
+lapack_int LAPACKE_zsteqr( int matrix_order, char compz, lapack_int n,
+                           double* d, double* e, lapack_complex_double* z,
+                           lapack_int ldz );
+
+lapack_int LAPACKE_ssterf( lapack_int n, float* d, float* e );
+lapack_int LAPACKE_dsterf( lapack_int n, double* d, double* e );
+
+lapack_int LAPACKE_sstev( int matrix_order, char jobz, lapack_int n, float* d,
+                          float* e, float* z, lapack_int ldz );
+lapack_int LAPACKE_dstev( int matrix_order, char jobz, lapack_int n, double* d,
+                          double* e, double* z, lapack_int ldz );
+
+lapack_int LAPACKE_sstevd( int matrix_order, char jobz, lapack_int n, float* d,
+                           float* e, float* z, lapack_int ldz );
+lapack_int LAPACKE_dstevd( int matrix_order, char jobz, lapack_int n, double* d,
+                           double* e, double* z, lapack_int ldz );
+
+lapack_int LAPACKE_sstevr( int matrix_order, char jobz, char range,
+                           lapack_int n, float* d, float* e, float vl, float vu,
+                           lapack_int il, lapack_int iu, float abstol,
+                           lapack_int* m, float* w, float* z, lapack_int ldz,
+                           lapack_int* isuppz );
+lapack_int LAPACKE_dstevr( int matrix_order, char jobz, char range,
+                           lapack_int n, double* d, double* e, double vl,
+                           double vu, lapack_int il, lapack_int iu,
+                           double abstol, lapack_int* m, double* w, double* z,
+                           lapack_int ldz, lapack_int* isuppz );
+
+lapack_int LAPACKE_sstevx( int matrix_order, char jobz, char range,
+                           lapack_int n, float* d, float* e, float vl, float vu,
+                           lapack_int il, lapack_int iu, float abstol,
+                           lapack_int* m, float* w, float* z, lapack_int ldz,
+                           lapack_int* ifail );
+lapack_int LAPACKE_dstevx( int matrix_order, char jobz, char range,
+                           lapack_int n, double* d, double* e, double vl,
+                           double vu, lapack_int il, lapack_int iu,
+                           double abstol, lapack_int* m, double* w, double* z,
+                           lapack_int ldz, lapack_int* ifail );
+
+lapack_int LAPACKE_ssycon( int matrix_order, char uplo, lapack_int n,
+                           const float* a, lapack_int lda,
+                           const lapack_int* ipiv, float anorm, float* rcond );
+lapack_int LAPACKE_dsycon( int matrix_order, char uplo, lapack_int n,
+                           const double* a, lapack_int lda,
+                           const lapack_int* ipiv, double anorm,
+                           double* rcond );
+lapack_int LAPACKE_csycon( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_float* a, lapack_int lda,
+                           const lapack_int* ipiv, float anorm, float* rcond );
+lapack_int LAPACKE_zsycon( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_double* a, lapack_int lda,
+                           const lapack_int* ipiv, double anorm,
+                           double* rcond );
+
+lapack_int LAPACKE_ssyequb( int matrix_order, char uplo, lapack_int n,
+                            const float* a, lapack_int lda, float* s,
+                            float* scond, float* amax );
+lapack_int LAPACKE_dsyequb( int matrix_order, char uplo, lapack_int n,
+                            const double* a, lapack_int lda, double* s,
+                            double* scond, double* amax );
+lapack_int LAPACKE_csyequb( int matrix_order, char uplo, lapack_int n,
+                            const lapack_complex_float* a, lapack_int lda,
+                            float* s, float* scond, float* amax );
+lapack_int LAPACKE_zsyequb( int matrix_order, char uplo, lapack_int n,
+                            const lapack_complex_double* a, lapack_int lda,
+                            double* s, double* scond, double* amax );
+
+lapack_int LAPACKE_ssyev( int matrix_order, char jobz, char uplo, lapack_int n,
+                          float* a, lapack_int lda, float* w );
+lapack_int LAPACKE_dsyev( int matrix_order, char jobz, char uplo, lapack_int n,
+                          double* a, lapack_int lda, double* w );
+
+lapack_int LAPACKE_ssyevd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           float* a, lapack_int lda, float* w );
+lapack_int LAPACKE_dsyevd( int matrix_order, char jobz, char uplo, lapack_int n,
+                           double* a, lapack_int lda, double* w );
+
+lapack_int LAPACKE_ssyevr( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, float* a, lapack_int lda, float vl,
+                           float vu, lapack_int il, lapack_int iu, float abstol,
+                           lapack_int* m, float* w, float* z, lapack_int ldz,
+                           lapack_int* isuppz );
+lapack_int LAPACKE_dsyevr( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, double* a, lapack_int lda, double vl,
+                           double vu, lapack_int il, lapack_int iu,
+                           double abstol, lapack_int* m, double* w, double* z,
+                           lapack_int ldz, lapack_int* isuppz );
+
+lapack_int LAPACKE_ssyevx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, float* a, lapack_int lda, float vl,
+                           float vu, lapack_int il, lapack_int iu, float abstol,
+                           lapack_int* m, float* w, float* z, lapack_int ldz,
+                           lapack_int* ifail );
+lapack_int LAPACKE_dsyevx( int matrix_order, char jobz, char range, char uplo,
+                           lapack_int n, double* a, lapack_int lda, double vl,
+                           double vu, lapack_int il, lapack_int iu,
+                           double abstol, lapack_int* m, double* w, double* z,
+                           lapack_int ldz, lapack_int* ifail );
+
+lapack_int LAPACKE_ssygst( int matrix_order, lapack_int itype, char uplo,
+                           lapack_int n, float* a, lapack_int lda,
+                           const float* b, lapack_int ldb );
+lapack_int LAPACKE_dsygst( int matrix_order, lapack_int itype, char uplo,
+                           lapack_int n, double* a, lapack_int lda,
+                           const double* b, lapack_int ldb );
+
+lapack_int LAPACKE_ssygv( int matrix_order, lapack_int itype, char jobz,
+                          char uplo, lapack_int n, float* a, lapack_int lda,
+                          float* b, lapack_int ldb, float* w );
+lapack_int LAPACKE_dsygv( int matrix_order, lapack_int itype, char jobz,
+                          char uplo, lapack_int n, double* a, lapack_int lda,
+                          double* b, lapack_int ldb, double* w );
+
+lapack_int LAPACKE_ssygvd( int matrix_order, lapack_int itype, char jobz,
+                           char uplo, lapack_int n, float* a, lapack_int lda,
+                           float* b, lapack_int ldb, float* w );
+lapack_int LAPACKE_dsygvd( int matrix_order, lapack_int itype, char jobz,
+                           char uplo, lapack_int n, double* a, lapack_int lda,
+                           double* b, lapack_int ldb, double* w );
+
+lapack_int LAPACKE_ssygvx( int matrix_order, lapack_int itype, char jobz,
+                           char range, char uplo, lapack_int n, float* a,
+                           lapack_int lda, float* b, lapack_int ldb, float vl,
+                           float vu, lapack_int il, lapack_int iu, float abstol,
+                           lapack_int* m, float* w, float* z, lapack_int ldz,
+                           lapack_int* ifail );
+lapack_int LAPACKE_dsygvx( int matrix_order, lapack_int itype, char jobz,
+                           char range, char uplo, lapack_int n, double* a,
+                           lapack_int lda, double* b, lapack_int ldb, double vl,
+                           double vu, lapack_int il, lapack_int iu,
+                           double abstol, lapack_int* m, double* w, double* z,
+                           lapack_int ldz, lapack_int* ifail );
+
+lapack_int LAPACKE_ssyrfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const float* a, lapack_int lda,
+                           const float* af, lapack_int ldaf,
+                           const lapack_int* ipiv, const float* b,
+                           lapack_int ldb, float* x, lapack_int ldx,
+                           float* ferr, float* berr );
+lapack_int LAPACKE_dsyrfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const double* a, lapack_int lda,
+                           const double* af, lapack_int ldaf,
+                           const lapack_int* ipiv, const double* b,
+                           lapack_int ldb, double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+lapack_int LAPACKE_csyrfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* a,
+                           lapack_int lda, const lapack_complex_float* af,
+                           lapack_int ldaf, const lapack_int* ipiv,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_zsyrfs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* a,
+                           lapack_int lda, const lapack_complex_double* af,
+                           lapack_int ldaf, const lapack_int* ipiv,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+
+lapack_int LAPACKE_ssyrfsx( int matrix_order, char uplo, char equed,
+                            lapack_int n, lapack_int nrhs, const float* a,
+                            lapack_int lda, const float* af, lapack_int ldaf,
+                            const lapack_int* ipiv, const float* s,
+                            const float* b, lapack_int ldb, float* x,
+                            lapack_int ldx, float* rcond, float* berr,
+                            lapack_int n_err_bnds, float* err_bnds_norm,
+                            float* err_bnds_comp, lapack_int nparams,
+                            float* params );
+lapack_int LAPACKE_dsyrfsx( int matrix_order, char uplo, char equed,
+                            lapack_int n, lapack_int nrhs, const double* a,
+                            lapack_int lda, const double* af, lapack_int ldaf,
+                            const lapack_int* ipiv, const double* s,
+                            const double* b, lapack_int ldb, double* x,
+                            lapack_int ldx, double* rcond, double* berr,
+                            lapack_int n_err_bnds, double* err_bnds_norm,
+                            double* err_bnds_comp, lapack_int nparams,
+                            double* params );
+lapack_int LAPACKE_csyrfsx( int matrix_order, char uplo, char equed,
+                            lapack_int n, lapack_int nrhs,
+                            const lapack_complex_float* a, lapack_int lda,
+                            const lapack_complex_float* af, lapack_int ldaf,
+                            const lapack_int* ipiv, const float* s,
+                            const lapack_complex_float* b, lapack_int ldb,
+                            lapack_complex_float* x, lapack_int ldx,
+                            float* rcond, float* berr, lapack_int n_err_bnds,
+                            float* err_bnds_norm, float* err_bnds_comp,
+                            lapack_int nparams, float* params );
+lapack_int LAPACKE_zsyrfsx( int matrix_order, char uplo, char equed,
+                            lapack_int n, lapack_int nrhs,
+                            const lapack_complex_double* a, lapack_int lda,
+                            const lapack_complex_double* af, lapack_int ldaf,
+                            const lapack_int* ipiv, const double* s,
+                            const lapack_complex_double* b, lapack_int ldb,
+                            lapack_complex_double* x, lapack_int ldx,
+                            double* rcond, double* berr, lapack_int n_err_bnds,
+                            double* err_bnds_norm, double* err_bnds_comp,
+                            lapack_int nparams, double* params );
+
+lapack_int LAPACKE_ssysv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, float* a, lapack_int lda,
+                          lapack_int* ipiv, float* b, lapack_int ldb );
+lapack_int LAPACKE_dsysv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, double* a, lapack_int lda,
+                          lapack_int* ipiv, double* b, lapack_int ldb );
+lapack_int LAPACKE_csysv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, lapack_complex_float* a,
+                          lapack_int lda, lapack_int* ipiv,
+                          lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zsysv( int matrix_order, char uplo, lapack_int n,
+                          lapack_int nrhs, lapack_complex_double* a,
+                          lapack_int lda, lapack_int* ipiv,
+                          lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_ssysvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, const float* a, lapack_int lda,
+                           float* af, lapack_int ldaf, lapack_int* ipiv,
+                           const float* b, lapack_int ldb, float* x,
+                           lapack_int ldx, float* rcond, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_dsysvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, const double* a, lapack_int lda,
+                           double* af, lapack_int ldaf, lapack_int* ipiv,
+                           const double* b, lapack_int ldb, double* x,
+                           lapack_int ldx, double* rcond, double* ferr,
+                           double* berr );
+lapack_int LAPACKE_csysvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* af,
+                           lapack_int ldaf, lapack_int* ipiv,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* x, lapack_int ldx,
+                           float* rcond, float* ferr, float* berr );
+lapack_int LAPACKE_zsysvx( int matrix_order, char fact, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* af,
+                           lapack_int ldaf, lapack_int* ipiv,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* x, lapack_int ldx,
+                           double* rcond, double* ferr, double* berr );
+
+lapack_int LAPACKE_ssysvxx( int matrix_order, char fact, char uplo,
+                            lapack_int n, lapack_int nrhs, float* a,
+                            lapack_int lda, float* af, lapack_int ldaf,
+                            lapack_int* ipiv, char* equed, float* s, float* b,
+                            lapack_int ldb, float* x, lapack_int ldx,
+                            float* rcond, float* rpvgrw, float* berr,
+                            lapack_int n_err_bnds, float* err_bnds_norm,
+                            float* err_bnds_comp, lapack_int nparams,
+                            float* params );
+lapack_int LAPACKE_dsysvxx( int matrix_order, char fact, char uplo,
+                            lapack_int n, lapack_int nrhs, double* a,
+                            lapack_int lda, double* af, lapack_int ldaf,
+                            lapack_int* ipiv, char* equed, double* s, double* b,
+                            lapack_int ldb, double* x, lapack_int ldx,
+                            double* rcond, double* rpvgrw, double* berr,
+                            lapack_int n_err_bnds, double* err_bnds_norm,
+                            double* err_bnds_comp, lapack_int nparams,
+                            double* params );
+lapack_int LAPACKE_csysvxx( int matrix_order, char fact, char uplo,
+                            lapack_int n, lapack_int nrhs,
+                            lapack_complex_float* a, lapack_int lda,
+                            lapack_complex_float* af, lapack_int ldaf,
+                            lapack_int* ipiv, char* equed, float* s,
+                            lapack_complex_float* b, lapack_int ldb,
+                            lapack_complex_float* x, lapack_int ldx,
+                            float* rcond, float* rpvgrw, float* berr,
+                            lapack_int n_err_bnds, float* err_bnds_norm,
+                            float* err_bnds_comp, lapack_int nparams,
+                            float* params );
+lapack_int LAPACKE_zsysvxx( int matrix_order, char fact, char uplo,
+                            lapack_int n, lapack_int nrhs,
+                            lapack_complex_double* a, lapack_int lda,
+                            lapack_complex_double* af, lapack_int ldaf,
+                            lapack_int* ipiv, char* equed, double* s,
+                            lapack_complex_double* b, lapack_int ldb,
+                            lapack_complex_double* x, lapack_int ldx,
+                            double* rcond, double* rpvgrw, double* berr,
+                            lapack_int n_err_bnds, double* err_bnds_norm,
+                            double* err_bnds_comp, lapack_int nparams,
+                            double* params );
+
+lapack_int LAPACKE_ssytrd( int matrix_order, char uplo, lapack_int n, float* a,
+                           lapack_int lda, float* d, float* e, float* tau );
+lapack_int LAPACKE_dsytrd( int matrix_order, char uplo, lapack_int n, double* a,
+                           lapack_int lda, double* d, double* e, double* tau );
+
+lapack_int LAPACKE_ssytrf( int matrix_order, char uplo, lapack_int n, float* a,
+                           lapack_int lda, lapack_int* ipiv );
+lapack_int LAPACKE_dsytrf( int matrix_order, char uplo, lapack_int n, double* a,
+                           lapack_int lda, lapack_int* ipiv );
+lapack_int LAPACKE_csytrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_int* ipiv );
+lapack_int LAPACKE_zsytrf( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_int* ipiv );
+
+lapack_int LAPACKE_ssytri( int matrix_order, char uplo, lapack_int n, float* a,
+                           lapack_int lda, const lapack_int* ipiv );
+lapack_int LAPACKE_dsytri( int matrix_order, char uplo, lapack_int n, double* a,
+                           lapack_int lda, const lapack_int* ipiv );
+lapack_int LAPACKE_csytri( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           const lapack_int* ipiv );
+lapack_int LAPACKE_zsytri( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           const lapack_int* ipiv );
+
+lapack_int LAPACKE_ssytrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const float* a, lapack_int lda,
+                           const lapack_int* ipiv, float* b, lapack_int ldb );
+lapack_int LAPACKE_dsytrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const double* a, lapack_int lda,
+                           const lapack_int* ipiv, double* b, lapack_int ldb );
+lapack_int LAPACKE_csytrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_float* a,
+                           lapack_int lda, const lapack_int* ipiv,
+                           lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zsytrs( int matrix_order, char uplo, lapack_int n,
+                           lapack_int nrhs, const lapack_complex_double* a,
+                           lapack_int lda, const lapack_int* ipiv,
+                           lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_stbcon( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int n, lapack_int kd, const float* ab,
+                           lapack_int ldab, float* rcond );
+lapack_int LAPACKE_dtbcon( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int n, lapack_int kd, const double* ab,
+                           lapack_int ldab, double* rcond );
+lapack_int LAPACKE_ctbcon( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int n, lapack_int kd,
+                           const lapack_complex_float* ab, lapack_int ldab,
+                           float* rcond );
+lapack_int LAPACKE_ztbcon( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int n, lapack_int kd,
+                           const lapack_complex_double* ab, lapack_int ldab,
+                           double* rcond );
+
+lapack_int LAPACKE_stbrfs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int kd, lapack_int nrhs,
+                           const float* ab, lapack_int ldab, const float* b,
+                           lapack_int ldb, const float* x, lapack_int ldx,
+                           float* ferr, float* berr );
+lapack_int LAPACKE_dtbrfs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int kd, lapack_int nrhs,
+                           const double* ab, lapack_int ldab, const double* b,
+                           lapack_int ldb, const double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+lapack_int LAPACKE_ctbrfs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int kd, lapack_int nrhs,
+                           const lapack_complex_float* ab, lapack_int ldab,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           const lapack_complex_float* x, lapack_int ldx,
+                           float* ferr, float* berr );
+lapack_int LAPACKE_ztbrfs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int kd, lapack_int nrhs,
+                           const lapack_complex_double* ab, lapack_int ldab,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           const lapack_complex_double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+
+lapack_int LAPACKE_stbtrs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int kd, lapack_int nrhs,
+                           const float* ab, lapack_int ldab, float* b,
+                           lapack_int ldb );
+lapack_int LAPACKE_dtbtrs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int kd, lapack_int nrhs,
+                           const double* ab, lapack_int ldab, double* b,
+                           lapack_int ldb );
+lapack_int LAPACKE_ctbtrs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int kd, lapack_int nrhs,
+                           const lapack_complex_float* ab, lapack_int ldab,
+                           lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_ztbtrs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int kd, lapack_int nrhs,
+                           const lapack_complex_double* ab, lapack_int ldab,
+                           lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_stfsm( int matrix_order, char transr, char side, char uplo,
+                          char trans, char diag, lapack_int m, lapack_int n,
+                          float alpha, const float* a, float* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_dtfsm( int matrix_order, char transr, char side, char uplo,
+                          char trans, char diag, lapack_int m, lapack_int n,
+                          double alpha, const double* a, double* b,
+                          lapack_int ldb );
+lapack_int LAPACKE_ctfsm( int matrix_order, char transr, char side, char uplo,
+                          char trans, char diag, lapack_int m, lapack_int n,
+                          lapack_complex_float alpha,
+                          const lapack_complex_float* a,
+                          lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_ztfsm( int matrix_order, char transr, char side, char uplo,
+                          char trans, char diag, lapack_int m, lapack_int n,
+                          lapack_complex_double alpha,
+                          const lapack_complex_double* a,
+                          lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_stftri( int matrix_order, char transr, char uplo, char diag,
+                           lapack_int n, float* a );
+lapack_int LAPACKE_dtftri( int matrix_order, char transr, char uplo, char diag,
+                           lapack_int n, double* a );
+lapack_int LAPACKE_ctftri( int matrix_order, char transr, char uplo, char diag,
+                           lapack_int n, lapack_complex_float* a );
+lapack_int LAPACKE_ztftri( int matrix_order, char transr, char uplo, char diag,
+                           lapack_int n, lapack_complex_double* a );
+
+lapack_int LAPACKE_stfttp( int matrix_order, char transr, char uplo,
+                           lapack_int n, const float* arf, float* ap );
+lapack_int LAPACKE_dtfttp( int matrix_order, char transr, char uplo,
+                           lapack_int n, const double* arf, double* ap );
+lapack_int LAPACKE_ctfttp( int matrix_order, char transr, char uplo,
+                           lapack_int n, const lapack_complex_float* arf,
+                           lapack_complex_float* ap );
+lapack_int LAPACKE_ztfttp( int matrix_order, char transr, char uplo,
+                           lapack_int n, const lapack_complex_double* arf,
+                           lapack_complex_double* ap );
+
+lapack_int LAPACKE_stfttr( int matrix_order, char transr, char uplo,
+                           lapack_int n, const float* arf, float* a,
+                           lapack_int lda );
+lapack_int LAPACKE_dtfttr( int matrix_order, char transr, char uplo,
+                           lapack_int n, const double* arf, double* a,
+                           lapack_int lda );
+lapack_int LAPACKE_ctfttr( int matrix_order, char transr, char uplo,
+                           lapack_int n, const lapack_complex_float* arf,
+                           lapack_complex_float* a, lapack_int lda );
+lapack_int LAPACKE_ztfttr( int matrix_order, char transr, char uplo,
+                           lapack_int n, const lapack_complex_double* arf,
+                           lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_stgevc( int matrix_order, char side, char howmny,
+                           const lapack_logical* select, lapack_int n,
+                           const float* s, lapack_int lds, const float* p,
+                           lapack_int ldp, float* vl, lapack_int ldvl,
+                           float* vr, lapack_int ldvr, lapack_int mm,
+                           lapack_int* m );
+lapack_int LAPACKE_dtgevc( int matrix_order, char side, char howmny,
+                           const lapack_logical* select, lapack_int n,
+                           const double* s, lapack_int lds, const double* p,
+                           lapack_int ldp, double* vl, lapack_int ldvl,
+                           double* vr, lapack_int ldvr, lapack_int mm,
+                           lapack_int* m );
+lapack_int LAPACKE_ctgevc( int matrix_order, char side, char howmny,
+                           const lapack_logical* select, lapack_int n,
+                           const lapack_complex_float* s, lapack_int lds,
+                           const lapack_complex_float* p, lapack_int ldp,
+                           lapack_complex_float* vl, lapack_int ldvl,
+                           lapack_complex_float* vr, lapack_int ldvr,
+                           lapack_int mm, lapack_int* m );
+lapack_int LAPACKE_ztgevc( int matrix_order, char side, char howmny,
+                           const lapack_logical* select, lapack_int n,
+                           const lapack_complex_double* s, lapack_int lds,
+                           const lapack_complex_double* p, lapack_int ldp,
+                           lapack_complex_double* vl, lapack_int ldvl,
+                           lapack_complex_double* vr, lapack_int ldvr,
+                           lapack_int mm, lapack_int* m );
+
+lapack_int LAPACKE_stgexc( int matrix_order, lapack_logical wantq,
+                           lapack_logical wantz, lapack_int n, float* a,
+                           lapack_int lda, float* b, lapack_int ldb, float* q,
+                           lapack_int ldq, float* z, lapack_int ldz,
+                           lapack_int* ifst, lapack_int* ilst );
+lapack_int LAPACKE_dtgexc( int matrix_order, lapack_logical wantq,
+                           lapack_logical wantz, lapack_int n, double* a,
+                           lapack_int lda, double* b, lapack_int ldb, double* q,
+                           lapack_int ldq, double* z, lapack_int ldz,
+                           lapack_int* ifst, lapack_int* ilst );
+lapack_int LAPACKE_ctgexc( int matrix_order, lapack_logical wantq,
+                           lapack_logical wantz, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* q, lapack_int ldq,
+                           lapack_complex_float* z, lapack_int ldz,
+                           lapack_int ifst, lapack_int ilst );
+lapack_int LAPACKE_ztgexc( int matrix_order, lapack_logical wantq,
+                           lapack_logical wantz, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* q, lapack_int ldq,
+                           lapack_complex_double* z, lapack_int ldz,
+                           lapack_int ifst, lapack_int ilst );
+
+lapack_int LAPACKE_stgsen( int matrix_order, lapack_int ijob,
+                           lapack_logical wantq, lapack_logical wantz,
+                           const lapack_logical* select, lapack_int n, float* a,
+                           lapack_int lda, float* b, lapack_int ldb,
+                           float* alphar, float* alphai, float* beta, float* q,
+                           lapack_int ldq, float* z, lapack_int ldz,
+                           lapack_int* m, float* pl, float* pr, float* dif );
+lapack_int LAPACKE_dtgsen( int matrix_order, lapack_int ijob,
+                           lapack_logical wantq, lapack_logical wantz,
+                           const lapack_logical* select, lapack_int n,
+                           double* a, lapack_int lda, double* b, lapack_int ldb,
+                           double* alphar, double* alphai, double* beta,
+                           double* q, lapack_int ldq, double* z, lapack_int ldz,
+                           lapack_int* m, double* pl, double* pr, double* dif );
+lapack_int LAPACKE_ctgsen( int matrix_order, lapack_int ijob,
+                           lapack_logical wantq, lapack_logical wantz,
+                           const lapack_logical* select, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* alpha,
+                           lapack_complex_float* beta, lapack_complex_float* q,
+                           lapack_int ldq, lapack_complex_float* z,
+                           lapack_int ldz, lapack_int* m, float* pl, float* pr,
+                           float* dif );
+lapack_int LAPACKE_ztgsen( int matrix_order, lapack_int ijob,
+                           lapack_logical wantq, lapack_logical wantz,
+                           const lapack_logical* select, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* alpha,
+                           lapack_complex_double* beta,
+                           lapack_complex_double* q, lapack_int ldq,
+                           lapack_complex_double* z, lapack_int ldz,
+                           lapack_int* m, double* pl, double* pr, double* dif );
+
+lapack_int LAPACKE_stgsja( int matrix_order, char jobu, char jobv, char jobq,
+                           lapack_int m, lapack_int p, lapack_int n,
+                           lapack_int k, lapack_int l, float* a, lapack_int lda,
+                           float* b, lapack_int ldb, float tola, float tolb,
+                           float* alpha, float* beta, float* u, lapack_int ldu,
+                           float* v, lapack_int ldv, float* q, lapack_int ldq,
+                           lapack_int* ncycle );
+lapack_int LAPACKE_dtgsja( int matrix_order, char jobu, char jobv, char jobq,
+                           lapack_int m, lapack_int p, lapack_int n,
+                           lapack_int k, lapack_int l, double* a,
+                           lapack_int lda, double* b, lapack_int ldb,
+                           double tola, double tolb, double* alpha,
+                           double* beta, double* u, lapack_int ldu, double* v,
+                           lapack_int ldv, double* q, lapack_int ldq,
+                           lapack_int* ncycle );
+lapack_int LAPACKE_ctgsja( int matrix_order, char jobu, char jobv, char jobq,
+                           lapack_int m, lapack_int p, lapack_int n,
+                           lapack_int k, lapack_int l, lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* b,
+                           lapack_int ldb, float tola, float tolb, float* alpha,
+                           float* beta, lapack_complex_float* u, lapack_int ldu,
+                           lapack_complex_float* v, lapack_int ldv,
+                           lapack_complex_float* q, lapack_int ldq,
+                           lapack_int* ncycle );
+lapack_int LAPACKE_ztgsja( int matrix_order, char jobu, char jobv, char jobq,
+                           lapack_int m, lapack_int p, lapack_int n,
+                           lapack_int k, lapack_int l, lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* b,
+                           lapack_int ldb, double tola, double tolb,
+                           double* alpha, double* beta,
+                           lapack_complex_double* u, lapack_int ldu,
+                           lapack_complex_double* v, lapack_int ldv,
+                           lapack_complex_double* q, lapack_int ldq,
+                           lapack_int* ncycle );
+
+lapack_int LAPACKE_stgsna( int matrix_order, char job, char howmny,
+                           const lapack_logical* select, lapack_int n,
+                           const float* a, lapack_int lda, const float* b,
+                           lapack_int ldb, const float* vl, lapack_int ldvl,
+                           const float* vr, lapack_int ldvr, float* s,
+                           float* dif, lapack_int mm, lapack_int* m );
+lapack_int LAPACKE_dtgsna( int matrix_order, char job, char howmny,
+                           const lapack_logical* select, lapack_int n,
+                           const double* a, lapack_int lda, const double* b,
+                           lapack_int ldb, const double* vl, lapack_int ldvl,
+                           const double* vr, lapack_int ldvr, double* s,
+                           double* dif, lapack_int mm, lapack_int* m );
+lapack_int LAPACKE_ctgsna( int matrix_order, char job, char howmny,
+                           const lapack_logical* select, lapack_int n,
+                           const lapack_complex_float* a, lapack_int lda,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           const lapack_complex_float* vl, lapack_int ldvl,
+                           const lapack_complex_float* vr, lapack_int ldvr,
+                           float* s, float* dif, lapack_int mm, lapack_int* m );
+lapack_int LAPACKE_ztgsna( int matrix_order, char job, char howmny,
+                           const lapack_logical* select, lapack_int n,
+                           const lapack_complex_double* a, lapack_int lda,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           const lapack_complex_double* vl, lapack_int ldvl,
+                           const lapack_complex_double* vr, lapack_int ldvr,
+                           double* s, double* dif, lapack_int mm,
+                           lapack_int* m );
+
+lapack_int LAPACKE_stgsyl( int matrix_order, char trans, lapack_int ijob,
+                           lapack_int m, lapack_int n, const float* a,
+                           lapack_int lda, const float* b, lapack_int ldb,
+                           float* c, lapack_int ldc, const float* d,
+                           lapack_int ldd, const float* e, lapack_int lde,
+                           float* f, lapack_int ldf, float* scale, float* dif );
+lapack_int LAPACKE_dtgsyl( int matrix_order, char trans, lapack_int ijob,
+                           lapack_int m, lapack_int n, const double* a,
+                           lapack_int lda, const double* b, lapack_int ldb,
+                           double* c, lapack_int ldc, const double* d,
+                           lapack_int ldd, const double* e, lapack_int lde,
+                           double* f, lapack_int ldf, double* scale,
+                           double* dif );
+lapack_int LAPACKE_ctgsyl( int matrix_order, char trans, lapack_int ijob,
+                           lapack_int m, lapack_int n,
+                           const lapack_complex_float* a, lapack_int lda,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* c, lapack_int ldc,
+                           const lapack_complex_float* d, lapack_int ldd,
+                           const lapack_complex_float* e, lapack_int lde,
+                           lapack_complex_float* f, lapack_int ldf,
+                           float* scale, float* dif );
+lapack_int LAPACKE_ztgsyl( int matrix_order, char trans, lapack_int ijob,
+                           lapack_int m, lapack_int n,
+                           const lapack_complex_double* a, lapack_int lda,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* c, lapack_int ldc,
+                           const lapack_complex_double* d, lapack_int ldd,
+                           const lapack_complex_double* e, lapack_int lde,
+                           lapack_complex_double* f, lapack_int ldf,
+                           double* scale, double* dif );
+
+lapack_int LAPACKE_stpcon( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int n, const float* ap, float* rcond );
+lapack_int LAPACKE_dtpcon( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int n, const double* ap, double* rcond );
+lapack_int LAPACKE_ctpcon( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int n, const lapack_complex_float* ap,
+                           float* rcond );
+lapack_int LAPACKE_ztpcon( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int n, const lapack_complex_double* ap,
+                           double* rcond );
+
+lapack_int LAPACKE_stprfs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs, const float* ap,
+                           const float* b, lapack_int ldb, const float* x,
+                           lapack_int ldx, float* ferr, float* berr );
+lapack_int LAPACKE_dtprfs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs, const double* ap,
+                           const double* b, lapack_int ldb, const double* x,
+                           lapack_int ldx, double* ferr, double* berr );
+lapack_int LAPACKE_ctprfs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs,
+                           const lapack_complex_float* ap,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           const lapack_complex_float* x, lapack_int ldx,
+                           float* ferr, float* berr );
+lapack_int LAPACKE_ztprfs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs,
+                           const lapack_complex_double* ap,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           const lapack_complex_double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+
+lapack_int LAPACKE_stptri( int matrix_order, char uplo, char diag, lapack_int n,
+                           float* ap );
+lapack_int LAPACKE_dtptri( int matrix_order, char uplo, char diag, lapack_int n,
+                           double* ap );
+lapack_int LAPACKE_ctptri( int matrix_order, char uplo, char diag, lapack_int n,
+                           lapack_complex_float* ap );
+lapack_int LAPACKE_ztptri( int matrix_order, char uplo, char diag, lapack_int n,
+                           lapack_complex_double* ap );
+
+lapack_int LAPACKE_stptrs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs, const float* ap,
+                           float* b, lapack_int ldb );
+lapack_int LAPACKE_dtptrs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs, const double* ap,
+                           double* b, lapack_int ldb );
+lapack_int LAPACKE_ctptrs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs,
+                           const lapack_complex_float* ap,
+                           lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_ztptrs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs,
+                           const lapack_complex_double* ap,
+                           lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_stpttf( int matrix_order, char transr, char uplo,
+                           lapack_int n, const float* ap, float* arf );
+lapack_int LAPACKE_dtpttf( int matrix_order, char transr, char uplo,
+                           lapack_int n, const double* ap, double* arf );
+lapack_int LAPACKE_ctpttf( int matrix_order, char transr, char uplo,
+                           lapack_int n, const lapack_complex_float* ap,
+                           lapack_complex_float* arf );
+lapack_int LAPACKE_ztpttf( int matrix_order, char transr, char uplo,
+                           lapack_int n, const lapack_complex_double* ap,
+                           lapack_complex_double* arf );
+
+lapack_int LAPACKE_stpttr( int matrix_order, char uplo, lapack_int n,
+                           const float* ap, float* a, lapack_int lda );
+lapack_int LAPACKE_dtpttr( int matrix_order, char uplo, lapack_int n,
+                           const double* ap, double* a, lapack_int lda );
+lapack_int LAPACKE_ctpttr( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_float* ap,
+                           lapack_complex_float* a, lapack_int lda );
+lapack_int LAPACKE_ztpttr( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_double* ap,
+                           lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_strcon( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int n, const float* a, lapack_int lda,
+                           float* rcond );
+lapack_int LAPACKE_dtrcon( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int n, const double* a, lapack_int lda,
+                           double* rcond );
+lapack_int LAPACKE_ctrcon( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int n, const lapack_complex_float* a,
+                           lapack_int lda, float* rcond );
+lapack_int LAPACKE_ztrcon( int matrix_order, char norm, char uplo, char diag,
+                           lapack_int n, const lapack_complex_double* a,
+                           lapack_int lda, double* rcond );
+
+lapack_int LAPACKE_strevc( int matrix_order, char side, char howmny,
+                           lapack_logical* select, lapack_int n, const float* t,
+                           lapack_int ldt, float* vl, lapack_int ldvl,
+                           float* vr, lapack_int ldvr, lapack_int mm,
+                           lapack_int* m );
+lapack_int LAPACKE_dtrevc( int matrix_order, char side, char howmny,
+                           lapack_logical* select, lapack_int n,
+                           const double* t, lapack_int ldt, double* vl,
+                           lapack_int ldvl, double* vr, lapack_int ldvr,
+                           lapack_int mm, lapack_int* m );
+lapack_int LAPACKE_ctrevc( int matrix_order, char side, char howmny,
+                           const lapack_logical* select, lapack_int n,
+                           lapack_complex_float* t, lapack_int ldt,
+                           lapack_complex_float* vl, lapack_int ldvl,
+                           lapack_complex_float* vr, lapack_int ldvr,
+                           lapack_int mm, lapack_int* m );
+lapack_int LAPACKE_ztrevc( int matrix_order, char side, char howmny,
+                           const lapack_logical* select, lapack_int n,
+                           lapack_complex_double* t, lapack_int ldt,
+                           lapack_complex_double* vl, lapack_int ldvl,
+                           lapack_complex_double* vr, lapack_int ldvr,
+                           lapack_int mm, lapack_int* m );
+
+lapack_int LAPACKE_strexc( int matrix_order, char compq, lapack_int n, float* t,
+                           lapack_int ldt, float* q, lapack_int ldq,
+                           lapack_int* ifst, lapack_int* ilst );
+lapack_int LAPACKE_dtrexc( int matrix_order, char compq, lapack_int n,
+                           double* t, lapack_int ldt, double* q, lapack_int ldq,
+                           lapack_int* ifst, lapack_int* ilst );
+lapack_int LAPACKE_ctrexc( int matrix_order, char compq, lapack_int n,
+                           lapack_complex_float* t, lapack_int ldt,
+                           lapack_complex_float* q, lapack_int ldq,
+                           lapack_int ifst, lapack_int ilst );
+lapack_int LAPACKE_ztrexc( int matrix_order, char compq, lapack_int n,
+                           lapack_complex_double* t, lapack_int ldt,
+                           lapack_complex_double* q, lapack_int ldq,
+                           lapack_int ifst, lapack_int ilst );
+
+lapack_int LAPACKE_strrfs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs, const float* a,
+                           lapack_int lda, const float* b, lapack_int ldb,
+                           const float* x, lapack_int ldx, float* ferr,
+                           float* berr );
+lapack_int LAPACKE_dtrrfs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs, const double* a,
+                           lapack_int lda, const double* b, lapack_int ldb,
+                           const double* x, lapack_int ldx, double* ferr,
+                           double* berr );
+lapack_int LAPACKE_ctrrfs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs,
+                           const lapack_complex_float* a, lapack_int lda,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           const lapack_complex_float* x, lapack_int ldx,
+                           float* ferr, float* berr );
+lapack_int LAPACKE_ztrrfs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs,
+                           const lapack_complex_double* a, lapack_int lda,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           const lapack_complex_double* x, lapack_int ldx,
+                           double* ferr, double* berr );
+
+lapack_int LAPACKE_strsen( int matrix_order, char job, char compq,
+                           const lapack_logical* select, lapack_int n, float* t,
+                           lapack_int ldt, float* q, lapack_int ldq, float* wr,
+                           float* wi, lapack_int* m, float* s, float* sep );
+lapack_int LAPACKE_dtrsen( int matrix_order, char job, char compq,
+                           const lapack_logical* select, lapack_int n,
+                           double* t, lapack_int ldt, double* q, lapack_int ldq,
+                           double* wr, double* wi, lapack_int* m, double* s,
+                           double* sep );
+lapack_int LAPACKE_ctrsen( int matrix_order, char job, char compq,
+                           const lapack_logical* select, lapack_int n,
+                           lapack_complex_float* t, lapack_int ldt,
+                           lapack_complex_float* q, lapack_int ldq,
+                           lapack_complex_float* w, lapack_int* m, float* s,
+                           float* sep );
+lapack_int LAPACKE_ztrsen( int matrix_order, char job, char compq,
+                           const lapack_logical* select, lapack_int n,
+                           lapack_complex_double* t, lapack_int ldt,
+                           lapack_complex_double* q, lapack_int ldq,
+                           lapack_complex_double* w, lapack_int* m, double* s,
+                           double* sep );
+
+lapack_int LAPACKE_strsna( int matrix_order, char job, char howmny,
+                           const lapack_logical* select, lapack_int n,
+                           const float* t, lapack_int ldt, const float* vl,
+                           lapack_int ldvl, const float* vr, lapack_int ldvr,
+                           float* s, float* sep, lapack_int mm, lapack_int* m );
+lapack_int LAPACKE_dtrsna( int matrix_order, char job, char howmny,
+                           const lapack_logical* select, lapack_int n,
+                           const double* t, lapack_int ldt, const double* vl,
+                           lapack_int ldvl, const double* vr, lapack_int ldvr,
+                           double* s, double* sep, lapack_int mm,
+                           lapack_int* m );
+lapack_int LAPACKE_ctrsna( int matrix_order, char job, char howmny,
+                           const lapack_logical* select, lapack_int n,
+                           const lapack_complex_float* t, lapack_int ldt,
+                           const lapack_complex_float* vl, lapack_int ldvl,
+                           const lapack_complex_float* vr, lapack_int ldvr,
+                           float* s, float* sep, lapack_int mm, lapack_int* m );
+lapack_int LAPACKE_ztrsna( int matrix_order, char job, char howmny,
+                           const lapack_logical* select, lapack_int n,
+                           const lapack_complex_double* t, lapack_int ldt,
+                           const lapack_complex_double* vl, lapack_int ldvl,
+                           const lapack_complex_double* vr, lapack_int ldvr,
+                           double* s, double* sep, lapack_int mm,
+                           lapack_int* m );
+
+lapack_int LAPACKE_strsyl( int matrix_order, char trana, char tranb,
+                           lapack_int isgn, lapack_int m, lapack_int n,
+                           const float* a, lapack_int lda, const float* b,
+                           lapack_int ldb, float* c, lapack_int ldc,
+                           float* scale );
+lapack_int LAPACKE_dtrsyl( int matrix_order, char trana, char tranb,
+                           lapack_int isgn, lapack_int m, lapack_int n,
+                           const double* a, lapack_int lda, const double* b,
+                           lapack_int ldb, double* c, lapack_int ldc,
+                           double* scale );
+lapack_int LAPACKE_ctrsyl( int matrix_order, char trana, char tranb,
+                           lapack_int isgn, lapack_int m, lapack_int n,
+                           const lapack_complex_float* a, lapack_int lda,
+                           const lapack_complex_float* b, lapack_int ldb,
+                           lapack_complex_float* c, lapack_int ldc,
+                           float* scale );
+lapack_int LAPACKE_ztrsyl( int matrix_order, char trana, char tranb,
+                           lapack_int isgn, lapack_int m, lapack_int n,
+                           const lapack_complex_double* a, lapack_int lda,
+                           const lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* c, lapack_int ldc,
+                           double* scale );
+
+lapack_int LAPACKE_strtri( int matrix_order, char uplo, char diag, lapack_int n,
+                           float* a, lapack_int lda );
+lapack_int LAPACKE_dtrtri( int matrix_order, char uplo, char diag, lapack_int n,
+                           double* a, lapack_int lda );
+lapack_int LAPACKE_ctrtri( int matrix_order, char uplo, char diag, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda );
+lapack_int LAPACKE_ztrtri( int matrix_order, char uplo, char diag, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_strtrs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs, const float* a,
+                           lapack_int lda, float* b, lapack_int ldb );
+lapack_int LAPACKE_dtrtrs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs, const double* a,
+                           lapack_int lda, double* b, lapack_int ldb );
+lapack_int LAPACKE_ctrtrs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs,
+                           const lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_ztrtrs( int matrix_order, char uplo, char trans, char diag,
+                           lapack_int n, lapack_int nrhs,
+                           const lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_strttf( int matrix_order, char transr, char uplo,
+                           lapack_int n, const float* a, lapack_int lda,
+                           float* arf );
+lapack_int LAPACKE_dtrttf( int matrix_order, char transr, char uplo,
+                           lapack_int n, const double* a, lapack_int lda,
+                           double* arf );
+lapack_int LAPACKE_ctrttf( int matrix_order, char transr, char uplo,
+                           lapack_int n, const lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* arf );
+lapack_int LAPACKE_ztrttf( int matrix_order, char transr, char uplo,
+                           lapack_int n, const lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* arf );
+
+lapack_int LAPACKE_strttp( int matrix_order, char uplo, lapack_int n,
+                           const float* a, lapack_int lda, float* ap );
+lapack_int LAPACKE_dtrttp( int matrix_order, char uplo, lapack_int n,
+                           const double* a, lapack_int lda, double* ap );
+lapack_int LAPACKE_ctrttp( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* ap );
+lapack_int LAPACKE_ztrttp( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* ap );
+
+lapack_int LAPACKE_stzrzf( int matrix_order, lapack_int m, lapack_int n,
+                           float* a, lapack_int lda, float* tau );
+lapack_int LAPACKE_dtzrzf( int matrix_order, lapack_int m, lapack_int n,
+                           double* a, lapack_int lda, double* tau );
+lapack_int LAPACKE_ctzrzf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* tau );
+lapack_int LAPACKE_ztzrzf( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* tau );
+
+lapack_int LAPACKE_cungbr( int matrix_order, char vect, lapack_int m,
+                           lapack_int n, lapack_int k, lapack_complex_float* a,
+                           lapack_int lda, const lapack_complex_float* tau );
+lapack_int LAPACKE_zungbr( int matrix_order, char vect, lapack_int m,
+                           lapack_int n, lapack_int k, lapack_complex_double* a,
+                           lapack_int lda, const lapack_complex_double* tau );
+
+lapack_int LAPACKE_cunghr( int matrix_order, lapack_int n, lapack_int ilo,
+                           lapack_int ihi, lapack_complex_float* a,
+                           lapack_int lda, const lapack_complex_float* tau );
+lapack_int LAPACKE_zunghr( int matrix_order, lapack_int n, lapack_int ilo,
+                           lapack_int ihi, lapack_complex_double* a,
+                           lapack_int lda, const lapack_complex_double* tau );
+
+lapack_int LAPACKE_cunglq( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, lapack_complex_float* a,
+                           lapack_int lda, const lapack_complex_float* tau );
+lapack_int LAPACKE_zunglq( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, lapack_complex_double* a,
+                           lapack_int lda, const lapack_complex_double* tau );
+
+lapack_int LAPACKE_cungql( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, lapack_complex_float* a,
+                           lapack_int lda, const lapack_complex_float* tau );
+lapack_int LAPACKE_zungql( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, lapack_complex_double* a,
+                           lapack_int lda, const lapack_complex_double* tau );
+
+lapack_int LAPACKE_cungqr( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, lapack_complex_float* a,
+                           lapack_int lda, const lapack_complex_float* tau );
+lapack_int LAPACKE_zungqr( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, lapack_complex_double* a,
+                           lapack_int lda, const lapack_complex_double* tau );
+
+lapack_int LAPACKE_cungrq( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, lapack_complex_float* a,
+                           lapack_int lda, const lapack_complex_float* tau );
+lapack_int LAPACKE_zungrq( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int k, lapack_complex_double* a,
+                           lapack_int lda, const lapack_complex_double* tau );
+
+lapack_int LAPACKE_cungtr( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_float* a, lapack_int lda,
+                           const lapack_complex_float* tau );
+lapack_int LAPACKE_zungtr( int matrix_order, char uplo, lapack_int n,
+                           lapack_complex_double* a, lapack_int lda,
+                           const lapack_complex_double* tau );
+
+lapack_int LAPACKE_cunmbr( int matrix_order, char vect, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const lapack_complex_float* a, lapack_int lda,
+                           const lapack_complex_float* tau,
+                           lapack_complex_float* c, lapack_int ldc );
+lapack_int LAPACKE_zunmbr( int matrix_order, char vect, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const lapack_complex_double* a, lapack_int lda,
+                           const lapack_complex_double* tau,
+                           lapack_complex_double* c, lapack_int ldc );
+
+lapack_int LAPACKE_cunmhr( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int ilo,
+                           lapack_int ihi, const lapack_complex_float* a,
+                           lapack_int lda, const lapack_complex_float* tau,
+                           lapack_complex_float* c, lapack_int ldc );
+lapack_int LAPACKE_zunmhr( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int ilo,
+                           lapack_int ihi, const lapack_complex_double* a,
+                           lapack_int lda, const lapack_complex_double* tau,
+                           lapack_complex_double* c, lapack_int ldc );
+
+lapack_int LAPACKE_cunmlq( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const lapack_complex_float* a, lapack_int lda,
+                           const lapack_complex_float* tau,
+                           lapack_complex_float* c, lapack_int ldc );
+lapack_int LAPACKE_zunmlq( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const lapack_complex_double* a, lapack_int lda,
+                           const lapack_complex_double* tau,
+                           lapack_complex_double* c, lapack_int ldc );
+
+lapack_int LAPACKE_cunmql( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const lapack_complex_float* a, lapack_int lda,
+                           const lapack_complex_float* tau,
+                           lapack_complex_float* c, lapack_int ldc );
+lapack_int LAPACKE_zunmql( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const lapack_complex_double* a, lapack_int lda,
+                           const lapack_complex_double* tau,
+                           lapack_complex_double* c, lapack_int ldc );
+
+lapack_int LAPACKE_cunmqr( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const lapack_complex_float* a, lapack_int lda,
+                           const lapack_complex_float* tau,
+                           lapack_complex_float* c, lapack_int ldc );
+lapack_int LAPACKE_zunmqr( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const lapack_complex_double* a, lapack_int lda,
+                           const lapack_complex_double* tau,
+                           lapack_complex_double* c, lapack_int ldc );
+
+lapack_int LAPACKE_cunmrq( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const lapack_complex_float* a, lapack_int lda,
+                           const lapack_complex_float* tau,
+                           lapack_complex_float* c, lapack_int ldc );
+lapack_int LAPACKE_zunmrq( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           const lapack_complex_double* a, lapack_int lda,
+                           const lapack_complex_double* tau,
+                           lapack_complex_double* c, lapack_int ldc );
+
+lapack_int LAPACKE_cunmrz( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           lapack_int l, const lapack_complex_float* a,
+                           lapack_int lda, const lapack_complex_float* tau,
+                           lapack_complex_float* c, lapack_int ldc );
+lapack_int LAPACKE_zunmrz( int matrix_order, char side, char trans,
+                           lapack_int m, lapack_int n, lapack_int k,
+                           lapack_int l, const lapack_complex_double* a,
+                           lapack_int lda, const lapack_complex_double* tau,
+                           lapack_complex_double* c, lapack_int ldc );
+
+lapack_int LAPACKE_cunmtr( int matrix_order, char side, char uplo, char trans,
+                           lapack_int m, lapack_int n,
+                           const lapack_complex_float* a, lapack_int lda,
+                           const lapack_complex_float* tau,
+                           lapack_complex_float* c, lapack_int ldc );
+lapack_int LAPACKE_zunmtr( int matrix_order, char side, char uplo, char trans,
+                           lapack_int m, lapack_int n,
+                           const lapack_complex_double* a, lapack_int lda,
+                           const lapack_complex_double* tau,
+                           lapack_complex_double* c, lapack_int ldc );
+
+lapack_int LAPACKE_cupgtr( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_float* ap,
+                           const lapack_complex_float* tau,
+                           lapack_complex_float* q, lapack_int ldq );
+lapack_int LAPACKE_zupgtr( int matrix_order, char uplo, lapack_int n,
+                           const lapack_complex_double* ap,
+                           const lapack_complex_double* tau,
+                           lapack_complex_double* q, lapack_int ldq );
+
+lapack_int LAPACKE_cupmtr( int matrix_order, char side, char uplo, char trans,
+                           lapack_int m, lapack_int n,
+                           const lapack_complex_float* ap,
+                           const lapack_complex_float* tau,
+                           lapack_complex_float* c, lapack_int ldc );
+lapack_int LAPACKE_zupmtr( int matrix_order, char side, char uplo, char trans,
+                           lapack_int m, lapack_int n,
+                           const lapack_complex_double* ap,
+                           const lapack_complex_double* tau,
+                           lapack_complex_double* c, lapack_int ldc );
+
+lapack_int LAPACKE_sbdsdc_work( int matrix_order, char uplo, char compq,
+                                lapack_int n, float* d, float* e, float* u,
+                                lapack_int ldu, float* vt, lapack_int ldvt,
+                                float* q, lapack_int* iq, float* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dbdsdc_work( int matrix_order, char uplo, char compq,
+                                lapack_int n, double* d, double* e, double* u,
+                                lapack_int ldu, double* vt, lapack_int ldvt,
+                                double* q, lapack_int* iq, double* work,
+                                lapack_int* iwork );
+
+lapack_int LAPACKE_sbdsqr_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int ncvt, lapack_int nru, lapack_int ncc,
+                                float* d, float* e, float* vt, lapack_int ldvt,
+                                float* u, lapack_int ldu, float* c,
+                                lapack_int ldc, float* work );
+lapack_int LAPACKE_dbdsqr_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int ncvt, lapack_int nru, lapack_int ncc,
+                                double* d, double* e, double* vt,
+                                lapack_int ldvt, double* u, lapack_int ldu,
+                                double* c, lapack_int ldc, double* work );
+lapack_int LAPACKE_cbdsqr_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int ncvt, lapack_int nru, lapack_int ncc,
+                                float* d, float* e, lapack_complex_float* vt,
+                                lapack_int ldvt, lapack_complex_float* u,
+                                lapack_int ldu, lapack_complex_float* c,
+                                lapack_int ldc, float* work );
+lapack_int LAPACKE_zbdsqr_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int ncvt, lapack_int nru, lapack_int ncc,
+                                double* d, double* e, lapack_complex_double* vt,
+                                lapack_int ldvt, lapack_complex_double* u,
+                                lapack_int ldu, lapack_complex_double* c,
+                                lapack_int ldc, double* work );
+
+lapack_int LAPACKE_sdisna_work( char job, lapack_int m, lapack_int n,
+                                const float* d, float* sep );
+lapack_int LAPACKE_ddisna_work( char job, lapack_int m, lapack_int n,
+                                const double* d, double* sep );
+
+lapack_int LAPACKE_sgbbrd_work( int matrix_order, char vect, lapack_int m,
+                                lapack_int n, lapack_int ncc, lapack_int kl,
+                                lapack_int ku, float* ab, lapack_int ldab,
+                                float* d, float* e, float* q, lapack_int ldq,
+                                float* pt, lapack_int ldpt, float* c,
+                                lapack_int ldc, float* work );
+lapack_int LAPACKE_dgbbrd_work( int matrix_order, char vect, lapack_int m,
+                                lapack_int n, lapack_int ncc, lapack_int kl,
+                                lapack_int ku, double* ab, lapack_int ldab,
+                                double* d, double* e, double* q, lapack_int ldq,
+                                double* pt, lapack_int ldpt, double* c,
+                                lapack_int ldc, double* work );
+lapack_int LAPACKE_cgbbrd_work( int matrix_order, char vect, lapack_int m,
+                                lapack_int n, lapack_int ncc, lapack_int kl,
+                                lapack_int ku, lapack_complex_float* ab,
+                                lapack_int ldab, float* d, float* e,
+                                lapack_complex_float* q, lapack_int ldq,
+                                lapack_complex_float* pt, lapack_int ldpt,
+                                lapack_complex_float* c, lapack_int ldc,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zgbbrd_work( int matrix_order, char vect, lapack_int m,
+                                lapack_int n, lapack_int ncc, lapack_int kl,
+                                lapack_int ku, lapack_complex_double* ab,
+                                lapack_int ldab, double* d, double* e,
+                                lapack_complex_double* q, lapack_int ldq,
+                                lapack_complex_double* pt, lapack_int ldpt,
+                                lapack_complex_double* c, lapack_int ldc,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_sgbcon_work( int matrix_order, char norm, lapack_int n,
+                                lapack_int kl, lapack_int ku, const float* ab,
+                                lapack_int ldab, const lapack_int* ipiv,
+                                float anorm, float* rcond, float* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dgbcon_work( int matrix_order, char norm, lapack_int n,
+                                lapack_int kl, lapack_int ku, const double* ab,
+                                lapack_int ldab, const lapack_int* ipiv,
+                                double anorm, double* rcond, double* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_cgbcon_work( int matrix_order, char norm, lapack_int n,
+                                lapack_int kl, lapack_int ku,
+                                const lapack_complex_float* ab, lapack_int ldab,
+                                const lapack_int* ipiv, float anorm,
+                                float* rcond, lapack_complex_float* work,
+                                float* rwork );
+lapack_int LAPACKE_zgbcon_work( int matrix_order, char norm, lapack_int n,
+                                lapack_int kl, lapack_int ku,
+                                const lapack_complex_double* ab,
+                                lapack_int ldab, const lapack_int* ipiv,
+                                double anorm, double* rcond,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_sgbequ_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int kl, lapack_int ku, const float* ab,
+                                lapack_int ldab, float* r, float* c,
+                                float* rowcnd, float* colcnd, float* amax );
+lapack_int LAPACKE_dgbequ_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int kl, lapack_int ku, const double* ab,
+                                lapack_int ldab, double* r, double* c,
+                                double* rowcnd, double* colcnd, double* amax );
+lapack_int LAPACKE_cgbequ_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int kl, lapack_int ku,
+                                const lapack_complex_float* ab, lapack_int ldab,
+                                float* r, float* c, float* rowcnd,
+                                float* colcnd, float* amax );
+lapack_int LAPACKE_zgbequ_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int kl, lapack_int ku,
+                                const lapack_complex_double* ab,
+                                lapack_int ldab, double* r, double* c,
+                                double* rowcnd, double* colcnd, double* amax );
+
+lapack_int LAPACKE_sgbequb_work( int matrix_order, lapack_int m, lapack_int n,
+                                 lapack_int kl, lapack_int ku, const float* ab,
+                                 lapack_int ldab, float* r, float* c,
+                                 float* rowcnd, float* colcnd, float* amax );
+lapack_int LAPACKE_dgbequb_work( int matrix_order, lapack_int m, lapack_int n,
+                                 lapack_int kl, lapack_int ku, const double* ab,
+                                 lapack_int ldab, double* r, double* c,
+                                 double* rowcnd, double* colcnd, double* amax );
+lapack_int LAPACKE_cgbequb_work( int matrix_order, lapack_int m, lapack_int n,
+                                 lapack_int kl, lapack_int ku,
+                                 const lapack_complex_float* ab,
+                                 lapack_int ldab, float* r, float* c,
+                                 float* rowcnd, float* colcnd, float* amax );
+lapack_int LAPACKE_zgbequb_work( int matrix_order, lapack_int m, lapack_int n,
+                                 lapack_int kl, lapack_int ku,
+                                 const lapack_complex_double* ab,
+                                 lapack_int ldab, double* r, double* c,
+                                 double* rowcnd, double* colcnd, double* amax );
+
+lapack_int LAPACKE_sgbrfs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int kl, lapack_int ku, lapack_int nrhs,
+                                const float* ab, lapack_int ldab,
+                                const float* afb, lapack_int ldafb,
+                                const lapack_int* ipiv, const float* b,
+                                lapack_int ldb, float* x, lapack_int ldx,
+                                float* ferr, float* berr, float* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dgbrfs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int kl, lapack_int ku, lapack_int nrhs,
+                                const double* ab, lapack_int ldab,
+                                const double* afb, lapack_int ldafb,
+                                const lapack_int* ipiv, const double* b,
+                                lapack_int ldb, double* x, lapack_int ldx,
+                                double* ferr, double* berr, double* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_cgbrfs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int kl, lapack_int ku, lapack_int nrhs,
+                                const lapack_complex_float* ab, lapack_int ldab,
+                                const lapack_complex_float* afb,
+                                lapack_int ldafb, const lapack_int* ipiv,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx,
+                                float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zgbrfs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int kl, lapack_int ku, lapack_int nrhs,
+                                const lapack_complex_double* ab,
+                                lapack_int ldab,
+                                const lapack_complex_double* afb,
+                                lapack_int ldafb, const lapack_int* ipiv,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_sgbrfsx_work( int matrix_order, char trans, char equed,
+                                 lapack_int n, lapack_int kl, lapack_int ku,
+                                 lapack_int nrhs, const float* ab,
+                                 lapack_int ldab, const float* afb,
+                                 lapack_int ldafb, const lapack_int* ipiv,
+                                 const float* r, const float* c, const float* b,
+                                 lapack_int ldb, float* x, lapack_int ldx,
+                                 float* rcond, float* berr,
+                                 lapack_int n_err_bnds, float* err_bnds_norm,
+                                 float* err_bnds_comp, lapack_int nparams,
+                                 float* params, float* work,
+                                 lapack_int* iwork );
+lapack_int LAPACKE_dgbrfsx_work( int matrix_order, char trans, char equed,
+                                 lapack_int n, lapack_int kl, lapack_int ku,
+                                 lapack_int nrhs, const double* ab,
+                                 lapack_int ldab, const double* afb,
+                                 lapack_int ldafb, const lapack_int* ipiv,
+                                 const double* r, const double* c,
+                                 const double* b, lapack_int ldb, double* x,
+                                 lapack_int ldx, double* rcond, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, double* work,
+                                 lapack_int* iwork );
+lapack_int LAPACKE_cgbrfsx_work( int matrix_order, char trans, char equed,
+                                 lapack_int n, lapack_int kl, lapack_int ku,
+                                 lapack_int nrhs,
+                                 const lapack_complex_float* ab,
+                                 lapack_int ldab,
+                                 const lapack_complex_float* afb,
+                                 lapack_int ldafb, const lapack_int* ipiv,
+                                 const float* r, const float* c,
+                                 const lapack_complex_float* b, lapack_int ldb,
+                                 lapack_complex_float* x, lapack_int ldx,
+                                 float* rcond, float* berr,
+                                 lapack_int n_err_bnds, float* err_bnds_norm,
+                                 float* err_bnds_comp, lapack_int nparams,
+                                 float* params, lapack_complex_float* work,
+                                 float* rwork );
+lapack_int LAPACKE_zgbrfsx_work( int matrix_order, char trans, char equed,
+                                 lapack_int n, lapack_int kl, lapack_int ku,
+                                 lapack_int nrhs,
+                                 const lapack_complex_double* ab,
+                                 lapack_int ldab,
+                                 const lapack_complex_double* afb,
+                                 lapack_int ldafb, const lapack_int* ipiv,
+                                 const double* r, const double* c,
+                                 const lapack_complex_double* b, lapack_int ldb,
+                                 lapack_complex_double* x, lapack_int ldx,
+                                 double* rcond, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, lapack_complex_double* work,
+                                 double* rwork );
+
+lapack_int LAPACKE_sgbsv_work( int matrix_order, lapack_int n, lapack_int kl,
+                               lapack_int ku, lapack_int nrhs, float* ab,
+                               lapack_int ldab, lapack_int* ipiv, float* b,
+                               lapack_int ldb );
+lapack_int LAPACKE_dgbsv_work( int matrix_order, lapack_int n, lapack_int kl,
+                               lapack_int ku, lapack_int nrhs, double* ab,
+                               lapack_int ldab, lapack_int* ipiv, double* b,
+                               lapack_int ldb );
+lapack_int LAPACKE_cgbsv_work( int matrix_order, lapack_int n, lapack_int kl,
+                               lapack_int ku, lapack_int nrhs,
+                               lapack_complex_float* ab, lapack_int ldab,
+                               lapack_int* ipiv, lapack_complex_float* b,
+                               lapack_int ldb );
+lapack_int LAPACKE_zgbsv_work( int matrix_order, lapack_int n, lapack_int kl,
+                               lapack_int ku, lapack_int nrhs,
+                               lapack_complex_double* ab, lapack_int ldab,
+                               lapack_int* ipiv, lapack_complex_double* b,
+                               lapack_int ldb );
+
+lapack_int LAPACKE_sgbsvx_work( int matrix_order, char fact, char trans,
+                                lapack_int n, lapack_int kl, lapack_int ku,
+                                lapack_int nrhs, float* ab, lapack_int ldab,
+                                float* afb, lapack_int ldafb, lapack_int* ipiv,
+                                char* equed, float* r, float* c, float* b,
+                                lapack_int ldb, float* x, lapack_int ldx,
+                                float* rcond, float* ferr, float* berr,
+                                float* work, lapack_int* iwork );
+lapack_int LAPACKE_dgbsvx_work( int matrix_order, char fact, char trans,
+                                lapack_int n, lapack_int kl, lapack_int ku,
+                                lapack_int nrhs, double* ab, lapack_int ldab,
+                                double* afb, lapack_int ldafb, lapack_int* ipiv,
+                                char* equed, double* r, double* c, double* b,
+                                lapack_int ldb, double* x, lapack_int ldx,
+                                double* rcond, double* ferr, double* berr,
+                                double* work, lapack_int* iwork );
+lapack_int LAPACKE_cgbsvx_work( int matrix_order, char fact, char trans,
+                                lapack_int n, lapack_int kl, lapack_int ku,
+                                lapack_int nrhs, lapack_complex_float* ab,
+                                lapack_int ldab, lapack_complex_float* afb,
+                                lapack_int ldafb, lapack_int* ipiv, char* equed,
+                                float* r, float* c, lapack_complex_float* b,
+                                lapack_int ldb, lapack_complex_float* x,
+                                lapack_int ldx, float* rcond, float* ferr,
+                                float* berr, lapack_complex_float* work,
+                                float* rwork );
+lapack_int LAPACKE_zgbsvx_work( int matrix_order, char fact, char trans,
+                                lapack_int n, lapack_int kl, lapack_int ku,
+                                lapack_int nrhs, lapack_complex_double* ab,
+                                lapack_int ldab, lapack_complex_double* afb,
+                                lapack_int ldafb, lapack_int* ipiv, char* equed,
+                                double* r, double* c, lapack_complex_double* b,
+                                lapack_int ldb, lapack_complex_double* x,
+                                lapack_int ldx, double* rcond, double* ferr,
+                                double* berr, lapack_complex_double* work,
+                                double* rwork );
+
+lapack_int LAPACKE_sgbsvxx_work( int matrix_order, char fact, char trans,
+                                 lapack_int n, lapack_int kl, lapack_int ku,
+                                 lapack_int nrhs, float* ab, lapack_int ldab,
+                                 float* afb, lapack_int ldafb, lapack_int* ipiv,
+                                 char* equed, float* r, float* c, float* b,
+                                 lapack_int ldb, float* x, lapack_int ldx,
+                                 float* rcond, float* rpvgrw, float* berr,
+                                 lapack_int n_err_bnds, float* err_bnds_norm,
+                                 float* err_bnds_comp, lapack_int nparams,
+                                 float* params, float* work,
+                                 lapack_int* iwork );
+lapack_int LAPACKE_dgbsvxx_work( int matrix_order, char fact, char trans,
+                                 lapack_int n, lapack_int kl, lapack_int ku,
+                                 lapack_int nrhs, double* ab, lapack_int ldab,
+                                 double* afb, lapack_int ldafb,
+                                 lapack_int* ipiv, char* equed, double* r,
+                                 double* c, double* b, lapack_int ldb,
+                                 double* x, lapack_int ldx, double* rcond,
+                                 double* rpvgrw, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, double* work,
+                                 lapack_int* iwork );
+lapack_int LAPACKE_cgbsvxx_work( int matrix_order, char fact, char trans,
+                                 lapack_int n, lapack_int kl, lapack_int ku,
+                                 lapack_int nrhs, lapack_complex_float* ab,
+                                 lapack_int ldab, lapack_complex_float* afb,
+                                 lapack_int ldafb, lapack_int* ipiv,
+                                 char* equed, float* r, float* c,
+                                 lapack_complex_float* b, lapack_int ldb,
+                                 lapack_complex_float* x, lapack_int ldx,
+                                 float* rcond, float* rpvgrw, float* berr,
+                                 lapack_int n_err_bnds, float* err_bnds_norm,
+                                 float* err_bnds_comp, lapack_int nparams,
+                                 float* params, lapack_complex_float* work,
+                                 float* rwork );
+lapack_int LAPACKE_zgbsvxx_work( int matrix_order, char fact, char trans,
+                                 lapack_int n, lapack_int kl, lapack_int ku,
+                                 lapack_int nrhs, lapack_complex_double* ab,
+                                 lapack_int ldab, lapack_complex_double* afb,
+                                 lapack_int ldafb, lapack_int* ipiv,
+                                 char* equed, double* r, double* c,
+                                 lapack_complex_double* b, lapack_int ldb,
+                                 lapack_complex_double* x, lapack_int ldx,
+                                 double* rcond, double* rpvgrw, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, lapack_complex_double* work,
+                                 double* rwork );
+
+lapack_int LAPACKE_sgbtrf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int kl, lapack_int ku, float* ab,
+                                lapack_int ldab, lapack_int* ipiv );
+lapack_int LAPACKE_dgbtrf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int kl, lapack_int ku, double* ab,
+                                lapack_int ldab, lapack_int* ipiv );
+lapack_int LAPACKE_cgbtrf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int kl, lapack_int ku,
+                                lapack_complex_float* ab, lapack_int ldab,
+                                lapack_int* ipiv );
+lapack_int LAPACKE_zgbtrf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int kl, lapack_int ku,
+                                lapack_complex_double* ab, lapack_int ldab,
+                                lapack_int* ipiv );
+
+lapack_int LAPACKE_sgbtrs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int kl, lapack_int ku, lapack_int nrhs,
+                                const float* ab, lapack_int ldab,
+                                const lapack_int* ipiv, float* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_dgbtrs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int kl, lapack_int ku, lapack_int nrhs,
+                                const double* ab, lapack_int ldab,
+                                const lapack_int* ipiv, double* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_cgbtrs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int kl, lapack_int ku, lapack_int nrhs,
+                                const lapack_complex_float* ab, lapack_int ldab,
+                                const lapack_int* ipiv, lapack_complex_float* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_zgbtrs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int kl, lapack_int ku, lapack_int nrhs,
+                                const lapack_complex_double* ab,
+                                lapack_int ldab, const lapack_int* ipiv,
+                                lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_sgebak_work( int matrix_order, char job, char side,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                const float* scale, lapack_int m, float* v,
+                                lapack_int ldv );
+lapack_int LAPACKE_dgebak_work( int matrix_order, char job, char side,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                const double* scale, lapack_int m, double* v,
+                                lapack_int ldv );
+lapack_int LAPACKE_cgebak_work( int matrix_order, char job, char side,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                const float* scale, lapack_int m,
+                                lapack_complex_float* v, lapack_int ldv );
+lapack_int LAPACKE_zgebak_work( int matrix_order, char job, char side,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                const double* scale, lapack_int m,
+                                lapack_complex_double* v, lapack_int ldv );
+
+lapack_int LAPACKE_sgebal_work( int matrix_order, char job, lapack_int n,
+                                float* a, lapack_int lda, lapack_int* ilo,
+                                lapack_int* ihi, float* scale );
+lapack_int LAPACKE_dgebal_work( int matrix_order, char job, lapack_int n,
+                                double* a, lapack_int lda, lapack_int* ilo,
+                                lapack_int* ihi, double* scale );
+lapack_int LAPACKE_cgebal_work( int matrix_order, char job, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_int* ilo, lapack_int* ihi,
+                                float* scale );
+lapack_int LAPACKE_zgebal_work( int matrix_order, char job, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_int* ilo, lapack_int* ihi,
+                                double* scale );
+
+lapack_int LAPACKE_sgebrd_work( int matrix_order, lapack_int m, lapack_int n,
+                                float* a, lapack_int lda, float* d, float* e,
+                                float* tauq, float* taup, float* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_dgebrd_work( int matrix_order, lapack_int m, lapack_int n,
+                                double* a, lapack_int lda, double* d, double* e,
+                                double* tauq, double* taup, double* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_cgebrd_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                float* d, float* e, lapack_complex_float* tauq,
+                                lapack_complex_float* taup,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zgebrd_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                double* d, double* e,
+                                lapack_complex_double* tauq,
+                                lapack_complex_double* taup,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sgecon_work( int matrix_order, char norm, lapack_int n,
+                                const float* a, lapack_int lda, float anorm,
+                                float* rcond, float* work, lapack_int* iwork );
+lapack_int LAPACKE_dgecon_work( int matrix_order, char norm, lapack_int n,
+                                const double* a, lapack_int lda, double anorm,
+                                double* rcond, double* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_cgecon_work( int matrix_order, char norm, lapack_int n,
+                                const lapack_complex_float* a, lapack_int lda,
+                                float anorm, float* rcond,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zgecon_work( int matrix_order, char norm, lapack_int n,
+                                const lapack_complex_double* a, lapack_int lda,
+                                double anorm, double* rcond,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_sgeequ_work( int matrix_order, lapack_int m, lapack_int n,
+                                const float* a, lapack_int lda, float* r,
+                                float* c, float* rowcnd, float* colcnd,
+                                float* amax );
+lapack_int LAPACKE_dgeequ_work( int matrix_order, lapack_int m, lapack_int n,
+                                const double* a, lapack_int lda, double* r,
+                                double* c, double* rowcnd, double* colcnd,
+                                double* amax );
+lapack_int LAPACKE_cgeequ_work( int matrix_order, lapack_int m, lapack_int n,
+                                const lapack_complex_float* a, lapack_int lda,
+                                float* r, float* c, float* rowcnd,
+                                float* colcnd, float* amax );
+lapack_int LAPACKE_zgeequ_work( int matrix_order, lapack_int m, lapack_int n,
+                                const lapack_complex_double* a, lapack_int lda,
+                                double* r, double* c, double* rowcnd,
+                                double* colcnd, double* amax );
+
+lapack_int LAPACKE_sgeequb_work( int matrix_order, lapack_int m, lapack_int n,
+                                 const float* a, lapack_int lda, float* r,
+                                 float* c, float* rowcnd, float* colcnd,
+                                 float* amax );
+lapack_int LAPACKE_dgeequb_work( int matrix_order, lapack_int m, lapack_int n,
+                                 const double* a, lapack_int lda, double* r,
+                                 double* c, double* rowcnd, double* colcnd,
+                                 double* amax );
+lapack_int LAPACKE_cgeequb_work( int matrix_order, lapack_int m, lapack_int n,
+                                 const lapack_complex_float* a, lapack_int lda,
+                                 float* r, float* c, float* rowcnd,
+                                 float* colcnd, float* amax );
+lapack_int LAPACKE_zgeequb_work( int matrix_order, lapack_int m, lapack_int n,
+                                 const lapack_complex_double* a, lapack_int lda,
+                                 double* r, double* c, double* rowcnd,
+                                 double* colcnd, double* amax );
+
+lapack_int LAPACKE_sgees_work( int matrix_order, char jobvs, char sort,
+                               LAPACK_S_SELECT2 select, lapack_int n, float* a,
+                               lapack_int lda, lapack_int* sdim, float* wr,
+                               float* wi, float* vs, lapack_int ldvs,
+                               float* work, lapack_int lwork,
+                               lapack_logical* bwork );
+lapack_int LAPACKE_dgees_work( int matrix_order, char jobvs, char sort,
+                               LAPACK_D_SELECT2 select, lapack_int n, double* a,
+                               lapack_int lda, lapack_int* sdim, double* wr,
+                               double* wi, double* vs, lapack_int ldvs,
+                               double* work, lapack_int lwork,
+                               lapack_logical* bwork );
+lapack_int LAPACKE_cgees_work( int matrix_order, char jobvs, char sort,
+                               LAPACK_C_SELECT1 select, lapack_int n,
+                               lapack_complex_float* a, lapack_int lda,
+                               lapack_int* sdim, lapack_complex_float* w,
+                               lapack_complex_float* vs, lapack_int ldvs,
+                               lapack_complex_float* work, lapack_int lwork,
+                               float* rwork, lapack_logical* bwork );
+lapack_int LAPACKE_zgees_work( int matrix_order, char jobvs, char sort,
+                               LAPACK_Z_SELECT1 select, lapack_int n,
+                               lapack_complex_double* a, lapack_int lda,
+                               lapack_int* sdim, lapack_complex_double* w,
+                               lapack_complex_double* vs, lapack_int ldvs,
+                               lapack_complex_double* work, lapack_int lwork,
+                               double* rwork, lapack_logical* bwork );
+
+lapack_int LAPACKE_sgeesx_work( int matrix_order, char jobvs, char sort,
+                                LAPACK_S_SELECT2 select, char sense,
+                                lapack_int n, float* a, lapack_int lda,
+                                lapack_int* sdim, float* wr, float* wi,
+                                float* vs, lapack_int ldvs, float* rconde,
+                                float* rcondv, float* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork,
+                                lapack_logical* bwork );
+lapack_int LAPACKE_dgeesx_work( int matrix_order, char jobvs, char sort,
+                                LAPACK_D_SELECT2 select, char sense,
+                                lapack_int n, double* a, lapack_int lda,
+                                lapack_int* sdim, double* wr, double* wi,
+                                double* vs, lapack_int ldvs, double* rconde,
+                                double* rcondv, double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork,
+                                lapack_logical* bwork );
+lapack_int LAPACKE_cgeesx_work( int matrix_order, char jobvs, char sort,
+                                LAPACK_C_SELECT1 select, char sense,
+                                lapack_int n, lapack_complex_float* a,
+                                lapack_int lda, lapack_int* sdim,
+                                lapack_complex_float* w,
+                                lapack_complex_float* vs, lapack_int ldvs,
+                                float* rconde, float* rcondv,
+                                lapack_complex_float* work, lapack_int lwork,
+                                float* rwork, lapack_logical* bwork );
+lapack_int LAPACKE_zgeesx_work( int matrix_order, char jobvs, char sort,
+                                LAPACK_Z_SELECT1 select, char sense,
+                                lapack_int n, lapack_complex_double* a,
+                                lapack_int lda, lapack_int* sdim,
+                                lapack_complex_double* w,
+                                lapack_complex_double* vs, lapack_int ldvs,
+                                double* rconde, double* rcondv,
+                                lapack_complex_double* work, lapack_int lwork,
+                                double* rwork, lapack_logical* bwork );
+
+lapack_int LAPACKE_sgeev_work( int matrix_order, char jobvl, char jobvr,
+                               lapack_int n, float* a, lapack_int lda,
+                               float* wr, float* wi, float* vl, lapack_int ldvl,
+                               float* vr, lapack_int ldvr, float* work,
+                               lapack_int lwork );
+lapack_int LAPACKE_dgeev_work( int matrix_order, char jobvl, char jobvr,
+                               lapack_int n, double* a, lapack_int lda,
+                               double* wr, double* wi, double* vl,
+                               lapack_int ldvl, double* vr, lapack_int ldvr,
+                               double* work, lapack_int lwork );
+lapack_int LAPACKE_cgeev_work( int matrix_order, char jobvl, char jobvr,
+                               lapack_int n, lapack_complex_float* a,
+                               lapack_int lda, lapack_complex_float* w,
+                               lapack_complex_float* vl, lapack_int ldvl,
+                               lapack_complex_float* vr, lapack_int ldvr,
+                               lapack_complex_float* work, lapack_int lwork,
+                               float* rwork );
+lapack_int LAPACKE_zgeev_work( int matrix_order, char jobvl, char jobvr,
+                               lapack_int n, lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* w,
+                               lapack_complex_double* vl, lapack_int ldvl,
+                               lapack_complex_double* vr, lapack_int ldvr,
+                               lapack_complex_double* work, lapack_int lwork,
+                               double* rwork );
+
+lapack_int LAPACKE_sgeevx_work( int matrix_order, char balanc, char jobvl,
+                                char jobvr, char sense, lapack_int n, float* a,
+                                lapack_int lda, float* wr, float* wi, float* vl,
+                                lapack_int ldvl, float* vr, lapack_int ldvr,
+                                lapack_int* ilo, lapack_int* ihi, float* scale,
+                                float* abnrm, float* rconde, float* rcondv,
+                                float* work, lapack_int lwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dgeevx_work( int matrix_order, char balanc, char jobvl,
+                                char jobvr, char sense, lapack_int n, double* a,
+                                lapack_int lda, double* wr, double* wi,
+                                double* vl, lapack_int ldvl, double* vr,
+                                lapack_int ldvr, lapack_int* ilo,
+                                lapack_int* ihi, double* scale, double* abnrm,
+                                double* rconde, double* rcondv, double* work,
+                                lapack_int lwork, lapack_int* iwork );
+lapack_int LAPACKE_cgeevx_work( int matrix_order, char balanc, char jobvl,
+                                char jobvr, char sense, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* w,
+                                lapack_complex_float* vl, lapack_int ldvl,
+                                lapack_complex_float* vr, lapack_int ldvr,
+                                lapack_int* ilo, lapack_int* ihi, float* scale,
+                                float* abnrm, float* rconde, float* rcondv,
+                                lapack_complex_float* work, lapack_int lwork,
+                                float* rwork );
+lapack_int LAPACKE_zgeevx_work( int matrix_order, char balanc, char jobvl,
+                                char jobvr, char sense, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* w,
+                                lapack_complex_double* vl, lapack_int ldvl,
+                                lapack_complex_double* vr, lapack_int ldvr,
+                                lapack_int* ilo, lapack_int* ihi, double* scale,
+                                double* abnrm, double* rconde, double* rcondv,
+                                lapack_complex_double* work, lapack_int lwork,
+                                double* rwork );
+
+lapack_int LAPACKE_sgehrd_work( int matrix_order, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, float* a, lapack_int lda,
+                                float* tau, float* work, lapack_int lwork );
+lapack_int LAPACKE_dgehrd_work( int matrix_order, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, double* a, lapack_int lda,
+                                double* tau, double* work, lapack_int lwork );
+lapack_int LAPACKE_cgehrd_work( int matrix_order, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, lapack_complex_float* a,
+                                lapack_int lda, lapack_complex_float* tau,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zgehrd_work( int matrix_order, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* tau,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sgejsv_work( int matrix_order, char joba, char jobu,
+                                char jobv, char jobr, char jobt, char jobp,
+                                lapack_int m, lapack_int n, float* a,
+                                lapack_int lda, float* sva, float* u,
+                                lapack_int ldu, float* v, lapack_int ldv,
+                                float* work, lapack_int lwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dgejsv_work( int matrix_order, char joba, char jobu,
+                                char jobv, char jobr, char jobt, char jobp,
+                                lapack_int m, lapack_int n, double* a,
+                                lapack_int lda, double* sva, double* u,
+                                lapack_int ldu, double* v, lapack_int ldv,
+                                double* work, lapack_int lwork,
+                                lapack_int* iwork );
+
+lapack_int LAPACKE_sgelq2_work( int matrix_order, lapack_int m, lapack_int n,
+                                float* a, lapack_int lda, float* tau,
+                                float* work );
+lapack_int LAPACKE_dgelq2_work( int matrix_order, lapack_int m, lapack_int n,
+                                double* a, lapack_int lda, double* tau,
+                                double* work );
+lapack_int LAPACKE_cgelq2_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* tau,
+                                lapack_complex_float* work );
+lapack_int LAPACKE_zgelq2_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* tau,
+                                lapack_complex_double* work );
+
+lapack_int LAPACKE_sgelqf_work( int matrix_order, lapack_int m, lapack_int n,
+                                float* a, lapack_int lda, float* tau,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dgelqf_work( int matrix_order, lapack_int m, lapack_int n,
+                                double* a, lapack_int lda, double* tau,
+                                double* work, lapack_int lwork );
+lapack_int LAPACKE_cgelqf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* tau,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zgelqf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* tau,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sgels_work( int matrix_order, char trans, lapack_int m,
+                               lapack_int n, lapack_int nrhs, float* a,
+                               lapack_int lda, float* b, lapack_int ldb,
+                               float* work, lapack_int lwork );
+lapack_int LAPACKE_dgels_work( int matrix_order, char trans, lapack_int m,
+                               lapack_int n, lapack_int nrhs, double* a,
+                               lapack_int lda, double* b, lapack_int ldb,
+                               double* work, lapack_int lwork );
+lapack_int LAPACKE_cgels_work( int matrix_order, char trans, lapack_int m,
+                               lapack_int n, lapack_int nrhs,
+                               lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* b, lapack_int ldb,
+                               lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zgels_work( int matrix_order, char trans, lapack_int m,
+                               lapack_int n, lapack_int nrhs,
+                               lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sgelsd_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nrhs, float* a, lapack_int lda,
+                                float* b, lapack_int ldb, float* s, float rcond,
+                                lapack_int* rank, float* work, lapack_int lwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dgelsd_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nrhs, double* a, lapack_int lda,
+                                double* b, lapack_int ldb, double* s,
+                                double rcond, lapack_int* rank, double* work,
+                                lapack_int lwork, lapack_int* iwork );
+lapack_int LAPACKE_cgelsd_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nrhs, lapack_complex_float* a,
+                                lapack_int lda, lapack_complex_float* b,
+                                lapack_int ldb, float* s, float rcond,
+                                lapack_int* rank, lapack_complex_float* work,
+                                lapack_int lwork, float* rwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_zgelsd_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nrhs, lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* b,
+                                lapack_int ldb, double* s, double rcond,
+                                lapack_int* rank, lapack_complex_double* work,
+                                lapack_int lwork, double* rwork,
+                                lapack_int* iwork );
+
+lapack_int LAPACKE_sgelss_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nrhs, float* a, lapack_int lda,
+                                float* b, lapack_int ldb, float* s, float rcond,
+                                lapack_int* rank, float* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_dgelss_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nrhs, double* a, lapack_int lda,
+                                double* b, lapack_int ldb, double* s,
+                                double rcond, lapack_int* rank, double* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_cgelss_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nrhs, lapack_complex_float* a,
+                                lapack_int lda, lapack_complex_float* b,
+                                lapack_int ldb, float* s, float rcond,
+                                lapack_int* rank, lapack_complex_float* work,
+                                lapack_int lwork, float* rwork );
+lapack_int LAPACKE_zgelss_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nrhs, lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* b,
+                                lapack_int ldb, double* s, double rcond,
+                                lapack_int* rank, lapack_complex_double* work,
+                                lapack_int lwork, double* rwork );
+
+lapack_int LAPACKE_sgelsy_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nrhs, float* a, lapack_int lda,
+                                float* b, lapack_int ldb, lapack_int* jpvt,
+                                float rcond, lapack_int* rank, float* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_dgelsy_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nrhs, double* a, lapack_int lda,
+                                double* b, lapack_int ldb, lapack_int* jpvt,
+                                double rcond, lapack_int* rank, double* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_cgelsy_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nrhs, lapack_complex_float* a,
+                                lapack_int lda, lapack_complex_float* b,
+                                lapack_int ldb, lapack_int* jpvt, float rcond,
+                                lapack_int* rank, lapack_complex_float* work,
+                                lapack_int lwork, float* rwork );
+lapack_int LAPACKE_zgelsy_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nrhs, lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* b,
+                                lapack_int ldb, lapack_int* jpvt, double rcond,
+                                lapack_int* rank, lapack_complex_double* work,
+                                lapack_int lwork, double* rwork );
+
+lapack_int LAPACKE_sgeqlf_work( int matrix_order, lapack_int m, lapack_int n,
+                                float* a, lapack_int lda, float* tau,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dgeqlf_work( int matrix_order, lapack_int m, lapack_int n,
+                                double* a, lapack_int lda, double* tau,
+                                double* work, lapack_int lwork );
+lapack_int LAPACKE_cgeqlf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* tau,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zgeqlf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* tau,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sgeqp3_work( int matrix_order, lapack_int m, lapack_int n,
+                                float* a, lapack_int lda, lapack_int* jpvt,
+                                float* tau, float* work, lapack_int lwork );
+lapack_int LAPACKE_dgeqp3_work( int matrix_order, lapack_int m, lapack_int n,
+                                double* a, lapack_int lda, lapack_int* jpvt,
+                                double* tau, double* work, lapack_int lwork );
+lapack_int LAPACKE_cgeqp3_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_int* jpvt, lapack_complex_float* tau,
+                                lapack_complex_float* work, lapack_int lwork,
+                                float* rwork );
+lapack_int LAPACKE_zgeqp3_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_int* jpvt, lapack_complex_double* tau,
+                                lapack_complex_double* work, lapack_int lwork,
+                                double* rwork );
+
+lapack_int LAPACKE_sgeqpf_work( int matrix_order, lapack_int m, lapack_int n,
+                                float* a, lapack_int lda, lapack_int* jpvt,
+                                float* tau, float* work );
+lapack_int LAPACKE_dgeqpf_work( int matrix_order, lapack_int m, lapack_int n,
+                                double* a, lapack_int lda, lapack_int* jpvt,
+                                double* tau, double* work );
+lapack_int LAPACKE_cgeqpf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_int* jpvt, lapack_complex_float* tau,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zgeqpf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_int* jpvt, lapack_complex_double* tau,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_sgeqr2_work( int matrix_order, lapack_int m, lapack_int n,
+                                float* a, lapack_int lda, float* tau,
+                                float* work );
+lapack_int LAPACKE_dgeqr2_work( int matrix_order, lapack_int m, lapack_int n,
+                                double* a, lapack_int lda, double* tau,
+                                double* work );
+lapack_int LAPACKE_cgeqr2_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* tau,
+                                lapack_complex_float* work );
+lapack_int LAPACKE_zgeqr2_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* tau,
+                                lapack_complex_double* work );
+
+lapack_int LAPACKE_sgeqrf_work( int matrix_order, lapack_int m, lapack_int n,
+                                float* a, lapack_int lda, float* tau,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dgeqrf_work( int matrix_order, lapack_int m, lapack_int n,
+                                double* a, lapack_int lda, double* tau,
+                                double* work, lapack_int lwork );
+lapack_int LAPACKE_cgeqrf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* tau,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zgeqrf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* tau,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sgeqrfp_work( int matrix_order, lapack_int m, lapack_int n,
+                                 float* a, lapack_int lda, float* tau,
+                                 float* work, lapack_int lwork );
+lapack_int LAPACKE_dgeqrfp_work( int matrix_order, lapack_int m, lapack_int n,
+                                 double* a, lapack_int lda, double* tau,
+                                 double* work, lapack_int lwork );
+lapack_int LAPACKE_cgeqrfp_work( int matrix_order, lapack_int m, lapack_int n,
+                                 lapack_complex_float* a, lapack_int lda,
+                                 lapack_complex_float* tau,
+                                 lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zgeqrfp_work( int matrix_order, lapack_int m, lapack_int n,
+                                 lapack_complex_double* a, lapack_int lda,
+                                 lapack_complex_double* tau,
+                                 lapack_complex_double* work,
+                                 lapack_int lwork );
+
+lapack_int LAPACKE_sgerfs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs, const float* a, lapack_int lda,
+                                const float* af, lapack_int ldaf,
+                                const lapack_int* ipiv, const float* b,
+                                lapack_int ldb, float* x, lapack_int ldx,
+                                float* ferr, float* berr, float* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dgerfs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs, const double* a,
+                                lapack_int lda, const double* af,
+                                lapack_int ldaf, const lapack_int* ipiv,
+                                const double* b, lapack_int ldb, double* x,
+                                lapack_int ldx, double* ferr, double* berr,
+                                double* work, lapack_int* iwork );
+lapack_int LAPACKE_cgerfs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* a,
+                                lapack_int lda, const lapack_complex_float* af,
+                                lapack_int ldaf, const lapack_int* ipiv,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx,
+                                float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zgerfs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_double* a,
+                                lapack_int lda, const lapack_complex_double* af,
+                                lapack_int ldaf, const lapack_int* ipiv,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_sgerfsx_work( int matrix_order, char trans, char equed,
+                                 lapack_int n, lapack_int nrhs, const float* a,
+                                 lapack_int lda, const float* af,
+                                 lapack_int ldaf, const lapack_int* ipiv,
+                                 const float* r, const float* c, const float* b,
+                                 lapack_int ldb, float* x, lapack_int ldx,
+                                 float* rcond, float* berr,
+                                 lapack_int n_err_bnds, float* err_bnds_norm,
+                                 float* err_bnds_comp, lapack_int nparams,
+                                 float* params, float* work,
+                                 lapack_int* iwork );
+lapack_int LAPACKE_dgerfsx_work( int matrix_order, char trans, char equed,
+                                 lapack_int n, lapack_int nrhs, const double* a,
+                                 lapack_int lda, const double* af,
+                                 lapack_int ldaf, const lapack_int* ipiv,
+                                 const double* r, const double* c,
+                                 const double* b, lapack_int ldb, double* x,
+                                 lapack_int ldx, double* rcond, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, double* work,
+                                 lapack_int* iwork );
+lapack_int LAPACKE_cgerfsx_work( int matrix_order, char trans, char equed,
+                                 lapack_int n, lapack_int nrhs,
+                                 const lapack_complex_float* a, lapack_int lda,
+                                 const lapack_complex_float* af,
+                                 lapack_int ldaf, const lapack_int* ipiv,
+                                 const float* r, const float* c,
+                                 const lapack_complex_float* b, lapack_int ldb,
+                                 lapack_complex_float* x, lapack_int ldx,
+                                 float* rcond, float* berr,
+                                 lapack_int n_err_bnds, float* err_bnds_norm,
+                                 float* err_bnds_comp, lapack_int nparams,
+                                 float* params, lapack_complex_float* work,
+                                 float* rwork );
+lapack_int LAPACKE_zgerfsx_work( int matrix_order, char trans, char equed,
+                                 lapack_int n, lapack_int nrhs,
+                                 const lapack_complex_double* a, lapack_int lda,
+                                 const lapack_complex_double* af,
+                                 lapack_int ldaf, const lapack_int* ipiv,
+                                 const double* r, const double* c,
+                                 const lapack_complex_double* b, lapack_int ldb,
+                                 lapack_complex_double* x, lapack_int ldx,
+                                 double* rcond, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, lapack_complex_double* work,
+                                 double* rwork );
+
+lapack_int LAPACKE_sgerqf_work( int matrix_order, lapack_int m, lapack_int n,
+                                float* a, lapack_int lda, float* tau,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dgerqf_work( int matrix_order, lapack_int m, lapack_int n,
+                                double* a, lapack_int lda, double* tau,
+                                double* work, lapack_int lwork );
+lapack_int LAPACKE_cgerqf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* tau,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zgerqf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* tau,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sgesdd_work( int matrix_order, char jobz, lapack_int m,
+                                lapack_int n, float* a, lapack_int lda,
+                                float* s, float* u, lapack_int ldu, float* vt,
+                                lapack_int ldvt, float* work, lapack_int lwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dgesdd_work( int matrix_order, char jobz, lapack_int m,
+                                lapack_int n, double* a, lapack_int lda,
+                                double* s, double* u, lapack_int ldu,
+                                double* vt, lapack_int ldvt, double* work,
+                                lapack_int lwork, lapack_int* iwork );
+lapack_int LAPACKE_cgesdd_work( int matrix_order, char jobz, lapack_int m,
+                                lapack_int n, lapack_complex_float* a,
+                                lapack_int lda, float* s,
+                                lapack_complex_float* u, lapack_int ldu,
+                                lapack_complex_float* vt, lapack_int ldvt,
+                                lapack_complex_float* work, lapack_int lwork,
+                                float* rwork, lapack_int* iwork );
+lapack_int LAPACKE_zgesdd_work( int matrix_order, char jobz, lapack_int m,
+                                lapack_int n, lapack_complex_double* a,
+                                lapack_int lda, double* s,
+                                lapack_complex_double* u, lapack_int ldu,
+                                lapack_complex_double* vt, lapack_int ldvt,
+                                lapack_complex_double* work, lapack_int lwork,
+                                double* rwork, lapack_int* iwork );
+
+lapack_int LAPACKE_sgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                               float* a, lapack_int lda, lapack_int* ipiv,
+                               float* b, lapack_int ldb );
+lapack_int LAPACKE_dgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                               double* a, lapack_int lda, lapack_int* ipiv,
+                               double* b, lapack_int ldb );
+lapack_int LAPACKE_cgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                               lapack_complex_float* a, lapack_int lda,
+                               lapack_int* ipiv, lapack_complex_float* b,
+                               lapack_int ldb );
+lapack_int LAPACKE_zgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                               lapack_complex_double* a, lapack_int lda,
+                               lapack_int* ipiv, lapack_complex_double* b,
+                               lapack_int ldb );
+lapack_int LAPACKE_dsgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                                double* a, lapack_int lda, lapack_int* ipiv,
+                                double* b, lapack_int ldb, double* x,
+                                lapack_int ldx, double* work, float* swork,
+                                lapack_int* iter );
+lapack_int LAPACKE_zcgesv_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_int* ipiv, lapack_complex_double* b,
+                                lapack_int ldb, lapack_complex_double* x,
+                                lapack_int ldx, lapack_complex_double* work,
+                                lapack_complex_float* swork, double* rwork,
+                                lapack_int* iter );
+
+lapack_int LAPACKE_sgesvd_work( int matrix_order, char jobu, char jobvt,
+                                lapack_int m, lapack_int n, float* a,
+                                lapack_int lda, float* s, float* u,
+                                lapack_int ldu, float* vt, lapack_int ldvt,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dgesvd_work( int matrix_order, char jobu, char jobvt,
+                                lapack_int m, lapack_int n, double* a,
+                                lapack_int lda, double* s, double* u,
+                                lapack_int ldu, double* vt, lapack_int ldvt,
+                                double* work, lapack_int lwork );
+lapack_int LAPACKE_cgesvd_work( int matrix_order, char jobu, char jobvt,
+                                lapack_int m, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                float* s, lapack_complex_float* u,
+                                lapack_int ldu, lapack_complex_float* vt,
+                                lapack_int ldvt, lapack_complex_float* work,
+                                lapack_int lwork, float* rwork );
+lapack_int LAPACKE_zgesvd_work( int matrix_order, char jobu, char jobvt,
+                                lapack_int m, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                double* s, lapack_complex_double* u,
+                                lapack_int ldu, lapack_complex_double* vt,
+                                lapack_int ldvt, lapack_complex_double* work,
+                                lapack_int lwork, double* rwork );
+
+lapack_int LAPACKE_sgesvj_work( int matrix_order, char joba, char jobu,
+                                char jobv, lapack_int m, lapack_int n, float* a,
+                                lapack_int lda, float* sva, lapack_int mv,
+                                float* v, lapack_int ldv, float* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_dgesvj_work( int matrix_order, char joba, char jobu,
+                                char jobv, lapack_int m, lapack_int n,
+                                double* a, lapack_int lda, double* sva,
+                                lapack_int mv, double* v, lapack_int ldv,
+                                double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sgesvx_work( int matrix_order, char fact, char trans,
+                                lapack_int n, lapack_int nrhs, float* a,
+                                lapack_int lda, float* af, lapack_int ldaf,
+                                lapack_int* ipiv, char* equed, float* r,
+                                float* c, float* b, lapack_int ldb, float* x,
+                                lapack_int ldx, float* rcond, float* ferr,
+                                float* berr, float* work, lapack_int* iwork );
+lapack_int LAPACKE_dgesvx_work( int matrix_order, char fact, char trans,
+                                lapack_int n, lapack_int nrhs, double* a,
+                                lapack_int lda, double* af, lapack_int ldaf,
+                                lapack_int* ipiv, char* equed, double* r,
+                                double* c, double* b, lapack_int ldb, double* x,
+                                lapack_int ldx, double* rcond, double* ferr,
+                                double* berr, double* work, lapack_int* iwork );
+lapack_int LAPACKE_cgesvx_work( int matrix_order, char fact, char trans,
+                                lapack_int n, lapack_int nrhs,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* af, lapack_int ldaf,
+                                lapack_int* ipiv, char* equed, float* r,
+                                float* c, lapack_complex_float* b,
+                                lapack_int ldb, lapack_complex_float* x,
+                                lapack_int ldx, float* rcond, float* ferr,
+                                float* berr, lapack_complex_float* work,
+                                float* rwork );
+lapack_int LAPACKE_zgesvx_work( int matrix_order, char fact, char trans,
+                                lapack_int n, lapack_int nrhs,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* af, lapack_int ldaf,
+                                lapack_int* ipiv, char* equed, double* r,
+                                double* c, lapack_complex_double* b,
+                                lapack_int ldb, lapack_complex_double* x,
+                                lapack_int ldx, double* rcond, double* ferr,
+                                double* berr, lapack_complex_double* work,
+                                double* rwork );
+
+lapack_int LAPACKE_sgesvxx_work( int matrix_order, char fact, char trans,
+                                 lapack_int n, lapack_int nrhs, float* a,
+                                 lapack_int lda, float* af, lapack_int ldaf,
+                                 lapack_int* ipiv, char* equed, float* r,
+                                 float* c, float* b, lapack_int ldb, float* x,
+                                 lapack_int ldx, float* rcond, float* rpvgrw,
+                                 float* berr, lapack_int n_err_bnds,
+                                 float* err_bnds_norm, float* err_bnds_comp,
+                                 lapack_int nparams, float* params, float* work,
+                                 lapack_int* iwork );
+lapack_int LAPACKE_dgesvxx_work( int matrix_order, char fact, char trans,
+                                 lapack_int n, lapack_int nrhs, double* a,
+                                 lapack_int lda, double* af, lapack_int ldaf,
+                                 lapack_int* ipiv, char* equed, double* r,
+                                 double* c, double* b, lapack_int ldb,
+                                 double* x, lapack_int ldx, double* rcond,
+                                 double* rpvgrw, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, double* work,
+                                 lapack_int* iwork );
+lapack_int LAPACKE_cgesvxx_work( int matrix_order, char fact, char trans,
+                                 lapack_int n, lapack_int nrhs,
+                                 lapack_complex_float* a, lapack_int lda,
+                                 lapack_complex_float* af, lapack_int ldaf,
+                                 lapack_int* ipiv, char* equed, float* r,
+                                 float* c, lapack_complex_float* b,
+                                 lapack_int ldb, lapack_complex_float* x,
+                                 lapack_int ldx, float* rcond, float* rpvgrw,
+                                 float* berr, lapack_int n_err_bnds,
+                                 float* err_bnds_norm, float* err_bnds_comp,
+                                 lapack_int nparams, float* params,
+                                 lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zgesvxx_work( int matrix_order, char fact, char trans,
+                                 lapack_int n, lapack_int nrhs,
+                                 lapack_complex_double* a, lapack_int lda,
+                                 lapack_complex_double* af, lapack_int ldaf,
+                                 lapack_int* ipiv, char* equed, double* r,
+                                 double* c, lapack_complex_double* b,
+                                 lapack_int ldb, lapack_complex_double* x,
+                                 lapack_int ldx, double* rcond, double* rpvgrw,
+                                 double* berr, lapack_int n_err_bnds,
+                                 double* err_bnds_norm, double* err_bnds_comp,
+                                 lapack_int nparams, double* params,
+                                 lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_sgetf2_work( int matrix_order, lapack_int m, lapack_int n,
+                                float* a, lapack_int lda, lapack_int* ipiv );
+lapack_int LAPACKE_dgetf2_work( int matrix_order, lapack_int m, lapack_int n,
+                                double* a, lapack_int lda, lapack_int* ipiv );
+lapack_int LAPACKE_cgetf2_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_int* ipiv );
+lapack_int LAPACKE_zgetf2_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_int* ipiv );
+
+lapack_int LAPACKE_sgetrf_work( int matrix_order, lapack_int m, lapack_int n,
+                                float* a, lapack_int lda, lapack_int* ipiv );
+lapack_int LAPACKE_dgetrf_work( int matrix_order, lapack_int m, lapack_int n,
+                                double* a, lapack_int lda, lapack_int* ipiv );
+lapack_int LAPACKE_cgetrf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_int* ipiv );
+lapack_int LAPACKE_zgetrf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_int* ipiv );
+
+lapack_int LAPACKE_sgetri_work( int matrix_order, lapack_int n, float* a,
+                                lapack_int lda, const lapack_int* ipiv,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dgetri_work( int matrix_order, lapack_int n, double* a,
+                                lapack_int lda, const lapack_int* ipiv,
+                                double* work, lapack_int lwork );
+lapack_int LAPACKE_cgetri_work( int matrix_order, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                const lapack_int* ipiv,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zgetri_work( int matrix_order, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                const lapack_int* ipiv,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sgetrs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs, const float* a, lapack_int lda,
+                                const lapack_int* ipiv, float* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_dgetrs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs, const double* a,
+                                lapack_int lda, const lapack_int* ipiv,
+                                double* b, lapack_int ldb );
+lapack_int LAPACKE_cgetrs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* a,
+                                lapack_int lda, const lapack_int* ipiv,
+                                lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zgetrs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_double* a,
+                                lapack_int lda, const lapack_int* ipiv,
+                                lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_sggbak_work( int matrix_order, char job, char side,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                const float* lscale, const float* rscale,
+                                lapack_int m, float* v, lapack_int ldv );
+lapack_int LAPACKE_dggbak_work( int matrix_order, char job, char side,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                const double* lscale, const double* rscale,
+                                lapack_int m, double* v, lapack_int ldv );
+lapack_int LAPACKE_cggbak_work( int matrix_order, char job, char side,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                const float* lscale, const float* rscale,
+                                lapack_int m, lapack_complex_float* v,
+                                lapack_int ldv );
+lapack_int LAPACKE_zggbak_work( int matrix_order, char job, char side,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                const double* lscale, const double* rscale,
+                                lapack_int m, lapack_complex_double* v,
+                                lapack_int ldv );
+
+lapack_int LAPACKE_sggbal_work( int matrix_order, char job, lapack_int n,
+                                float* a, lapack_int lda, float* b,
+                                lapack_int ldb, lapack_int* ilo,
+                                lapack_int* ihi, float* lscale, float* rscale,
+                                float* work );
+lapack_int LAPACKE_dggbal_work( int matrix_order, char job, lapack_int n,
+                                double* a, lapack_int lda, double* b,
+                                lapack_int ldb, lapack_int* ilo,
+                                lapack_int* ihi, double* lscale, double* rscale,
+                                double* work );
+lapack_int LAPACKE_cggbal_work( int matrix_order, char job, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* b, lapack_int ldb,
+                                lapack_int* ilo, lapack_int* ihi, float* lscale,
+                                float* rscale, float* work );
+lapack_int LAPACKE_zggbal_work( int matrix_order, char job, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* b, lapack_int ldb,
+                                lapack_int* ilo, lapack_int* ihi,
+                                double* lscale, double* rscale, double* work );
+
+lapack_int LAPACKE_sgges_work( int matrix_order, char jobvsl, char jobvsr,
+                               char sort, LAPACK_S_SELECT3 selctg, lapack_int n,
+                               float* a, lapack_int lda, float* b,
+                               lapack_int ldb, lapack_int* sdim, float* alphar,
+                               float* alphai, float* beta, float* vsl,
+                               lapack_int ldvsl, float* vsr, lapack_int ldvsr,
+                               float* work, lapack_int lwork,
+                               lapack_logical* bwork );
+lapack_int LAPACKE_dgges_work( int matrix_order, char jobvsl, char jobvsr,
+                               char sort, LAPACK_D_SELECT3 selctg, lapack_int n,
+                               double* a, lapack_int lda, double* b,
+                               lapack_int ldb, lapack_int* sdim, double* alphar,
+                               double* alphai, double* beta, double* vsl,
+                               lapack_int ldvsl, double* vsr, lapack_int ldvsr,
+                               double* work, lapack_int lwork,
+                               lapack_logical* bwork );
+lapack_int LAPACKE_cgges_work( int matrix_order, char jobvsl, char jobvsr,
+                               char sort, LAPACK_C_SELECT2 selctg, lapack_int n,
+                               lapack_complex_float* a, lapack_int lda,
+                               lapack_complex_float* b, lapack_int ldb,
+                               lapack_int* sdim, lapack_complex_float* alpha,
+                               lapack_complex_float* beta,
+                               lapack_complex_float* vsl, lapack_int ldvsl,
+                               lapack_complex_float* vsr, lapack_int ldvsr,
+                               lapack_complex_float* work, lapack_int lwork,
+                               float* rwork, lapack_logical* bwork );
+lapack_int LAPACKE_zgges_work( int matrix_order, char jobvsl, char jobvsr,
+                               char sort, LAPACK_Z_SELECT2 selctg, lapack_int n,
+                               lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* b, lapack_int ldb,
+                               lapack_int* sdim, lapack_complex_double* alpha,
+                               lapack_complex_double* beta,
+                               lapack_complex_double* vsl, lapack_int ldvsl,
+                               lapack_complex_double* vsr, lapack_int ldvsr,
+                               lapack_complex_double* work, lapack_int lwork,
+                               double* rwork, lapack_logical* bwork );
+
+lapack_int LAPACKE_sggesx_work( int matrix_order, char jobvsl, char jobvsr,
+                                char sort, LAPACK_S_SELECT3 selctg, char sense,
+                                lapack_int n, float* a, lapack_int lda,
+                                float* b, lapack_int ldb, lapack_int* sdim,
+                                float* alphar, float* alphai, float* beta,
+                                float* vsl, lapack_int ldvsl, float* vsr,
+                                lapack_int ldvsr, float* rconde, float* rcondv,
+                                float* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork,
+                                lapack_logical* bwork );
+lapack_int LAPACKE_dggesx_work( int matrix_order, char jobvsl, char jobvsr,
+                                char sort, LAPACK_D_SELECT3 selctg, char sense,
+                                lapack_int n, double* a, lapack_int lda,
+                                double* b, lapack_int ldb, lapack_int* sdim,
+                                double* alphar, double* alphai, double* beta,
+                                double* vsl, lapack_int ldvsl, double* vsr,
+                                lapack_int ldvsr, double* rconde,
+                                double* rcondv, double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork,
+                                lapack_logical* bwork );
+lapack_int LAPACKE_cggesx_work( int matrix_order, char jobvsl, char jobvsr,
+                                char sort, LAPACK_C_SELECT2 selctg, char sense,
+                                lapack_int n, lapack_complex_float* a,
+                                lapack_int lda, lapack_complex_float* b,
+                                lapack_int ldb, lapack_int* sdim,
+                                lapack_complex_float* alpha,
+                                lapack_complex_float* beta,
+                                lapack_complex_float* vsl, lapack_int ldvsl,
+                                lapack_complex_float* vsr, lapack_int ldvsr,
+                                float* rconde, float* rcondv,
+                                lapack_complex_float* work, lapack_int lwork,
+                                float* rwork, lapack_int* iwork,
+                                lapack_int liwork, lapack_logical* bwork );
+lapack_int LAPACKE_zggesx_work( int matrix_order, char jobvsl, char jobvsr,
+                                char sort, LAPACK_Z_SELECT2 selctg, char sense,
+                                lapack_int n, lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* b,
+                                lapack_int ldb, lapack_int* sdim,
+                                lapack_complex_double* alpha,
+                                lapack_complex_double* beta,
+                                lapack_complex_double* vsl, lapack_int ldvsl,
+                                lapack_complex_double* vsr, lapack_int ldvsr,
+                                double* rconde, double* rcondv,
+                                lapack_complex_double* work, lapack_int lwork,
+                                double* rwork, lapack_int* iwork,
+                                lapack_int liwork, lapack_logical* bwork );
+
+lapack_int LAPACKE_sggev_work( int matrix_order, char jobvl, char jobvr,
+                               lapack_int n, float* a, lapack_int lda, float* b,
+                               lapack_int ldb, float* alphar, float* alphai,
+                               float* beta, float* vl, lapack_int ldvl,
+                               float* vr, lapack_int ldvr, float* work,
+                               lapack_int lwork );
+lapack_int LAPACKE_dggev_work( int matrix_order, char jobvl, char jobvr,
+                               lapack_int n, double* a, lapack_int lda,
+                               double* b, lapack_int ldb, double* alphar,
+                               double* alphai, double* beta, double* vl,
+                               lapack_int ldvl, double* vr, lapack_int ldvr,
+                               double* work, lapack_int lwork );
+lapack_int LAPACKE_cggev_work( int matrix_order, char jobvl, char jobvr,
+                               lapack_int n, lapack_complex_float* a,
+                               lapack_int lda, lapack_complex_float* b,
+                               lapack_int ldb, lapack_complex_float* alpha,
+                               lapack_complex_float* beta,
+                               lapack_complex_float* vl, lapack_int ldvl,
+                               lapack_complex_float* vr, lapack_int ldvr,
+                               lapack_complex_float* work, lapack_int lwork,
+                               float* rwork );
+lapack_int LAPACKE_zggev_work( int matrix_order, char jobvl, char jobvr,
+                               lapack_int n, lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* b,
+                               lapack_int ldb, lapack_complex_double* alpha,
+                               lapack_complex_double* beta,
+                               lapack_complex_double* vl, lapack_int ldvl,
+                               lapack_complex_double* vr, lapack_int ldvr,
+                               lapack_complex_double* work, lapack_int lwork,
+                               double* rwork );
+
+lapack_int LAPACKE_sggevx_work( int matrix_order, char balanc, char jobvl,
+                                char jobvr, char sense, lapack_int n, float* a,
+                                lapack_int lda, float* b, lapack_int ldb,
+                                float* alphar, float* alphai, float* beta,
+                                float* vl, lapack_int ldvl, float* vr,
+                                lapack_int ldvr, lapack_int* ilo,
+                                lapack_int* ihi, float* lscale, float* rscale,
+                                float* abnrm, float* bbnrm, float* rconde,
+                                float* rcondv, float* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_logical* bwork );
+lapack_int LAPACKE_dggevx_work( int matrix_order, char balanc, char jobvl,
+                                char jobvr, char sense, lapack_int n, double* a,
+                                lapack_int lda, double* b, lapack_int ldb,
+                                double* alphar, double* alphai, double* beta,
+                                double* vl, lapack_int ldvl, double* vr,
+                                lapack_int ldvr, lapack_int* ilo,
+                                lapack_int* ihi, double* lscale, double* rscale,
+                                double* abnrm, double* bbnrm, double* rconde,
+                                double* rcondv, double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_logical* bwork );
+lapack_int LAPACKE_cggevx_work( int matrix_order, char balanc, char jobvl,
+                                char jobvr, char sense, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* alpha,
+                                lapack_complex_float* beta,
+                                lapack_complex_float* vl, lapack_int ldvl,
+                                lapack_complex_float* vr, lapack_int ldvr,
+                                lapack_int* ilo, lapack_int* ihi, float* lscale,
+                                float* rscale, float* abnrm, float* bbnrm,
+                                float* rconde, float* rcondv,
+                                lapack_complex_float* work, lapack_int lwork,
+                                float* rwork, lapack_int* iwork,
+                                lapack_logical* bwork );
+lapack_int LAPACKE_zggevx_work( int matrix_order, char balanc, char jobvl,
+                                char jobvr, char sense, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* alpha,
+                                lapack_complex_double* beta,
+                                lapack_complex_double* vl, lapack_int ldvl,
+                                lapack_complex_double* vr, lapack_int ldvr,
+                                lapack_int* ilo, lapack_int* ihi,
+                                double* lscale, double* rscale, double* abnrm,
+                                double* bbnrm, double* rconde, double* rcondv,
+                                lapack_complex_double* work, lapack_int lwork,
+                                double* rwork, lapack_int* iwork,
+                                lapack_logical* bwork );
+
+lapack_int LAPACKE_sggglm_work( int matrix_order, lapack_int n, lapack_int m,
+                                lapack_int p, float* a, lapack_int lda,
+                                float* b, lapack_int ldb, float* d, float* x,
+                                float* y, float* work, lapack_int lwork );
+lapack_int LAPACKE_dggglm_work( int matrix_order, lapack_int n, lapack_int m,
+                                lapack_int p, double* a, lapack_int lda,
+                                double* b, lapack_int ldb, double* d, double* x,
+                                double* y, double* work, lapack_int lwork );
+lapack_int LAPACKE_cggglm_work( int matrix_order, lapack_int n, lapack_int m,
+                                lapack_int p, lapack_complex_float* a,
+                                lapack_int lda, lapack_complex_float* b,
+                                lapack_int ldb, lapack_complex_float* d,
+                                lapack_complex_float* x,
+                                lapack_complex_float* y,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zggglm_work( int matrix_order, lapack_int n, lapack_int m,
+                                lapack_int p, lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* b,
+                                lapack_int ldb, lapack_complex_double* d,
+                                lapack_complex_double* x,
+                                lapack_complex_double* y,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sgghrd_work( int matrix_order, char compq, char compz,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                float* a, lapack_int lda, float* b,
+                                lapack_int ldb, float* q, lapack_int ldq,
+                                float* z, lapack_int ldz );
+lapack_int LAPACKE_dgghrd_work( int matrix_order, char compq, char compz,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                double* a, lapack_int lda, double* b,
+                                lapack_int ldb, double* q, lapack_int ldq,
+                                double* z, lapack_int ldz );
+lapack_int LAPACKE_cgghrd_work( int matrix_order, char compq, char compz,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* q, lapack_int ldq,
+                                lapack_complex_float* z, lapack_int ldz );
+lapack_int LAPACKE_zgghrd_work( int matrix_order, char compq, char compz,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* q, lapack_int ldq,
+                                lapack_complex_double* z, lapack_int ldz );
+
+lapack_int LAPACKE_sgglse_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int p, float* a, lapack_int lda,
+                                float* b, lapack_int ldb, float* c, float* d,
+                                float* x, float* work, lapack_int lwork );
+lapack_int LAPACKE_dgglse_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int p, double* a, lapack_int lda,
+                                double* b, lapack_int ldb, double* c, double* d,
+                                double* x, double* work, lapack_int lwork );
+lapack_int LAPACKE_cgglse_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int p, lapack_complex_float* a,
+                                lapack_int lda, lapack_complex_float* b,
+                                lapack_int ldb, lapack_complex_float* c,
+                                lapack_complex_float* d,
+                                lapack_complex_float* x,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zgglse_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int p, lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* b,
+                                lapack_int ldb, lapack_complex_double* c,
+                                lapack_complex_double* d,
+                                lapack_complex_double* x,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sggqrf_work( int matrix_order, lapack_int n, lapack_int m,
+                                lapack_int p, float* a, lapack_int lda,
+                                float* taua, float* b, lapack_int ldb,
+                                float* taub, float* work, lapack_int lwork );
+lapack_int LAPACKE_dggqrf_work( int matrix_order, lapack_int n, lapack_int m,
+                                lapack_int p, double* a, lapack_int lda,
+                                double* taua, double* b, lapack_int ldb,
+                                double* taub, double* work, lapack_int lwork );
+lapack_int LAPACKE_cggqrf_work( int matrix_order, lapack_int n, lapack_int m,
+                                lapack_int p, lapack_complex_float* a,
+                                lapack_int lda, lapack_complex_float* taua,
+                                lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* taub,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zggqrf_work( int matrix_order, lapack_int n, lapack_int m,
+                                lapack_int p, lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* taua,
+                                lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* taub,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sggrqf_work( int matrix_order, lapack_int m, lapack_int p,
+                                lapack_int n, float* a, lapack_int lda,
+                                float* taua, float* b, lapack_int ldb,
+                                float* taub, float* work, lapack_int lwork );
+lapack_int LAPACKE_dggrqf_work( int matrix_order, lapack_int m, lapack_int p,
+                                lapack_int n, double* a, lapack_int lda,
+                                double* taua, double* b, lapack_int ldb,
+                                double* taub, double* work, lapack_int lwork );
+lapack_int LAPACKE_cggrqf_work( int matrix_order, lapack_int m, lapack_int p,
+                                lapack_int n, lapack_complex_float* a,
+                                lapack_int lda, lapack_complex_float* taua,
+                                lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* taub,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zggrqf_work( int matrix_order, lapack_int m, lapack_int p,
+                                lapack_int n, lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* taua,
+                                lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* taub,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sggsvd_work( int matrix_order, char jobu, char jobv,
+                                char jobq, lapack_int m, lapack_int n,
+                                lapack_int p, lapack_int* k, lapack_int* l,
+                                float* a, lapack_int lda, float* b,
+                                lapack_int ldb, float* alpha, float* beta,
+                                float* u, lapack_int ldu, float* v,
+                                lapack_int ldv, float* q, lapack_int ldq,
+                                float* work, lapack_int* iwork );
+lapack_int LAPACKE_dggsvd_work( int matrix_order, char jobu, char jobv,
+                                char jobq, lapack_int m, lapack_int n,
+                                lapack_int p, lapack_int* k, lapack_int* l,
+                                double* a, lapack_int lda, double* b,
+                                lapack_int ldb, double* alpha, double* beta,
+                                double* u, lapack_int ldu, double* v,
+                                lapack_int ldv, double* q, lapack_int ldq,
+                                double* work, lapack_int* iwork );
+lapack_int LAPACKE_cggsvd_work( int matrix_order, char jobu, char jobv,
+                                char jobq, lapack_int m, lapack_int n,
+                                lapack_int p, lapack_int* k, lapack_int* l,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* b, lapack_int ldb,
+                                float* alpha, float* beta,
+                                lapack_complex_float* u, lapack_int ldu,
+                                lapack_complex_float* v, lapack_int ldv,
+                                lapack_complex_float* q, lapack_int ldq,
+                                lapack_complex_float* work, float* rwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_zggsvd_work( int matrix_order, char jobu, char jobv,
+                                char jobq, lapack_int m, lapack_int n,
+                                lapack_int p, lapack_int* k, lapack_int* l,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* b, lapack_int ldb,
+                                double* alpha, double* beta,
+                                lapack_complex_double* u, lapack_int ldu,
+                                lapack_complex_double* v, lapack_int ldv,
+                                lapack_complex_double* q, lapack_int ldq,
+                                lapack_complex_double* work, double* rwork,
+                                lapack_int* iwork );
+
+lapack_int LAPACKE_sggsvp_work( int matrix_order, char jobu, char jobv,
+                                char jobq, lapack_int m, lapack_int p,
+                                lapack_int n, float* a, lapack_int lda,
+                                float* b, lapack_int ldb, float tola,
+                                float tolb, lapack_int* k, lapack_int* l,
+                                float* u, lapack_int ldu, float* v,
+                                lapack_int ldv, float* q, lapack_int ldq,
+                                lapack_int* iwork, float* tau, float* work );
+lapack_int LAPACKE_dggsvp_work( int matrix_order, char jobu, char jobv,
+                                char jobq, lapack_int m, lapack_int p,
+                                lapack_int n, double* a, lapack_int lda,
+                                double* b, lapack_int ldb, double tola,
+                                double tolb, lapack_int* k, lapack_int* l,
+                                double* u, lapack_int ldu, double* v,
+                                lapack_int ldv, double* q, lapack_int ldq,
+                                lapack_int* iwork, double* tau, double* work );
+lapack_int LAPACKE_cggsvp_work( int matrix_order, char jobu, char jobv,
+                                char jobq, lapack_int m, lapack_int p,
+                                lapack_int n, lapack_complex_float* a,
+                                lapack_int lda, lapack_complex_float* b,
+                                lapack_int ldb, float tola, float tolb,
+                                lapack_int* k, lapack_int* l,
+                                lapack_complex_float* u, lapack_int ldu,
+                                lapack_complex_float* v, lapack_int ldv,
+                                lapack_complex_float* q, lapack_int ldq,
+                                lapack_int* iwork, float* rwork,
+                                lapack_complex_float* tau,
+                                lapack_complex_float* work );
+lapack_int LAPACKE_zggsvp_work( int matrix_order, char jobu, char jobv,
+                                char jobq, lapack_int m, lapack_int p,
+                                lapack_int n, lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* b,
+                                lapack_int ldb, double tola, double tolb,
+                                lapack_int* k, lapack_int* l,
+                                lapack_complex_double* u, lapack_int ldu,
+                                lapack_complex_double* v, lapack_int ldv,
+                                lapack_complex_double* q, lapack_int ldq,
+                                lapack_int* iwork, double* rwork,
+                                lapack_complex_double* tau,
+                                lapack_complex_double* work );
+
+lapack_int LAPACKE_sgtcon_work( char norm, lapack_int n, const float* dl,
+                                const float* d, const float* du,
+                                const float* du2, const lapack_int* ipiv,
+                                float anorm, float* rcond, float* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dgtcon_work( char norm, lapack_int n, const double* dl,
+                                const double* d, const double* du,
+                                const double* du2, const lapack_int* ipiv,
+                                double anorm, double* rcond, double* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_cgtcon_work( char norm, lapack_int n,
+                                const lapack_complex_float* dl,
+                                const lapack_complex_float* d,
+                                const lapack_complex_float* du,
+                                const lapack_complex_float* du2,
+                                const lapack_int* ipiv, float anorm,
+                                float* rcond, lapack_complex_float* work );
+lapack_int LAPACKE_zgtcon_work( char norm, lapack_int n,
+                                const lapack_complex_double* dl,
+                                const lapack_complex_double* d,
+                                const lapack_complex_double* du,
+                                const lapack_complex_double* du2,
+                                const lapack_int* ipiv, double anorm,
+                                double* rcond, lapack_complex_double* work );
+
+lapack_int LAPACKE_sgtrfs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs, const float* dl,
+                                const float* d, const float* du,
+                                const float* dlf, const float* df,
+                                const float* duf, const float* du2,
+                                const lapack_int* ipiv, const float* b,
+                                lapack_int ldb, float* x, lapack_int ldx,
+                                float* ferr, float* berr, float* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dgtrfs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs, const double* dl,
+                                const double* d, const double* du,
+                                const double* dlf, const double* df,
+                                const double* duf, const double* du2,
+                                const lapack_int* ipiv, const double* b,
+                                lapack_int ldb, double* x, lapack_int ldx,
+                                double* ferr, double* berr, double* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_cgtrfs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* dl,
+                                const lapack_complex_float* d,
+                                const lapack_complex_float* du,
+                                const lapack_complex_float* dlf,
+                                const lapack_complex_float* df,
+                                const lapack_complex_float* duf,
+                                const lapack_complex_float* du2,
+                                const lapack_int* ipiv,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx,
+                                float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zgtrfs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs,
+                                const lapack_complex_double* dl,
+                                const lapack_complex_double* d,
+                                const lapack_complex_double* du,
+                                const lapack_complex_double* dlf,
+                                const lapack_complex_double* df,
+                                const lapack_complex_double* duf,
+                                const lapack_complex_double* du2,
+                                const lapack_int* ipiv,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_sgtsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                               float* dl, float* d, float* du, float* b,
+                               lapack_int ldb );
+lapack_int LAPACKE_dgtsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                               double* dl, double* d, double* du, double* b,
+                               lapack_int ldb );
+lapack_int LAPACKE_cgtsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                               lapack_complex_float* dl,
+                               lapack_complex_float* d,
+                               lapack_complex_float* du,
+                               lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zgtsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                               lapack_complex_double* dl,
+                               lapack_complex_double* d,
+                               lapack_complex_double* du,
+                               lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_sgtsvx_work( int matrix_order, char fact, char trans,
+                                lapack_int n, lapack_int nrhs, const float* dl,
+                                const float* d, const float* du, float* dlf,
+                                float* df, float* duf, float* du2,
+                                lapack_int* ipiv, const float* b,
+                                lapack_int ldb, float* x, lapack_int ldx,
+                                float* rcond, float* ferr, float* berr,
+                                float* work, lapack_int* iwork );
+lapack_int LAPACKE_dgtsvx_work( int matrix_order, char fact, char trans,
+                                lapack_int n, lapack_int nrhs, const double* dl,
+                                const double* d, const double* du, double* dlf,
+                                double* df, double* duf, double* du2,
+                                lapack_int* ipiv, const double* b,
+                                lapack_int ldb, double* x, lapack_int ldx,
+                                double* rcond, double* ferr, double* berr,
+                                double* work, lapack_int* iwork );
+lapack_int LAPACKE_cgtsvx_work( int matrix_order, char fact, char trans,
+                                lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* dl,
+                                const lapack_complex_float* d,
+                                const lapack_complex_float* du,
+                                lapack_complex_float* dlf,
+                                lapack_complex_float* df,
+                                lapack_complex_float* duf,
+                                lapack_complex_float* du2, lapack_int* ipiv,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx,
+                                float* rcond, float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zgtsvx_work( int matrix_order, char fact, char trans,
+                                lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* dl,
+                                const lapack_complex_double* d,
+                                const lapack_complex_double* du,
+                                lapack_complex_double* dlf,
+                                lapack_complex_double* df,
+                                lapack_complex_double* duf,
+                                lapack_complex_double* du2, lapack_int* ipiv,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* rcond, double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_sgttrf_work( lapack_int n, float* dl, float* d, float* du,
+                                float* du2, lapack_int* ipiv );
+lapack_int LAPACKE_dgttrf_work( lapack_int n, double* dl, double* d, double* du,
+                                double* du2, lapack_int* ipiv );
+lapack_int LAPACKE_cgttrf_work( lapack_int n, lapack_complex_float* dl,
+                                lapack_complex_float* d,
+                                lapack_complex_float* du,
+                                lapack_complex_float* du2, lapack_int* ipiv );
+lapack_int LAPACKE_zgttrf_work( lapack_int n, lapack_complex_double* dl,
+                                lapack_complex_double* d,
+                                lapack_complex_double* du,
+                                lapack_complex_double* du2, lapack_int* ipiv );
+
+lapack_int LAPACKE_sgttrs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs, const float* dl,
+                                const float* d, const float* du,
+                                const float* du2, const lapack_int* ipiv,
+                                float* b, lapack_int ldb );
+lapack_int LAPACKE_dgttrs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs, const double* dl,
+                                const double* d, const double* du,
+                                const double* du2, const lapack_int* ipiv,
+                                double* b, lapack_int ldb );
+lapack_int LAPACKE_cgttrs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* dl,
+                                const lapack_complex_float* d,
+                                const lapack_complex_float* du,
+                                const lapack_complex_float* du2,
+                                const lapack_int* ipiv, lapack_complex_float* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_zgttrs_work( int matrix_order, char trans, lapack_int n,
+                                lapack_int nrhs,
+                                const lapack_complex_double* dl,
+                                const lapack_complex_double* d,
+                                const lapack_complex_double* du,
+                                const lapack_complex_double* du2,
+                                const lapack_int* ipiv,
+                                lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_chbev_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, lapack_int kd,
+                               lapack_complex_float* ab, lapack_int ldab,
+                               float* w, lapack_complex_float* z,
+                               lapack_int ldz, lapack_complex_float* work,
+                               float* rwork );
+lapack_int LAPACKE_zhbev_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, lapack_int kd,
+                               lapack_complex_double* ab, lapack_int ldab,
+                               double* w, lapack_complex_double* z,
+                               lapack_int ldz, lapack_complex_double* work,
+                               double* rwork );
+
+lapack_int LAPACKE_chbevd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, lapack_int kd,
+                                lapack_complex_float* ab, lapack_int ldab,
+                                float* w, lapack_complex_float* z,
+                                lapack_int ldz, lapack_complex_float* work,
+                                lapack_int lwork, float* rwork,
+                                lapack_int lrwork, lapack_int* iwork,
+                                lapack_int liwork );
+lapack_int LAPACKE_zhbevd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, lapack_int kd,
+                                lapack_complex_double* ab, lapack_int ldab,
+                                double* w, lapack_complex_double* z,
+                                lapack_int ldz, lapack_complex_double* work,
+                                lapack_int lwork, double* rwork,
+                                lapack_int lrwork, lapack_int* iwork,
+                                lapack_int liwork );
+
+lapack_int LAPACKE_chbevx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n, lapack_int kd,
+                                lapack_complex_float* ab, lapack_int ldab,
+                                lapack_complex_float* q, lapack_int ldq,
+                                float vl, float vu, lapack_int il,
+                                lapack_int iu, float abstol, lapack_int* m,
+                                float* w, lapack_complex_float* z,
+                                lapack_int ldz, lapack_complex_float* work,
+                                float* rwork, lapack_int* iwork,
+                                lapack_int* ifail );
+lapack_int LAPACKE_zhbevx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n, lapack_int kd,
+                                lapack_complex_double* ab, lapack_int ldab,
+                                lapack_complex_double* q, lapack_int ldq,
+                                double vl, double vu, lapack_int il,
+                                lapack_int iu, double abstol, lapack_int* m,
+                                double* w, lapack_complex_double* z,
+                                lapack_int ldz, lapack_complex_double* work,
+                                double* rwork, lapack_int* iwork,
+                                lapack_int* ifail );
+
+lapack_int LAPACKE_chbgst_work( int matrix_order, char vect, char uplo,
+                                lapack_int n, lapack_int ka, lapack_int kb,
+                                lapack_complex_float* ab, lapack_int ldab,
+                                const lapack_complex_float* bb, lapack_int ldbb,
+                                lapack_complex_float* x, lapack_int ldx,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zhbgst_work( int matrix_order, char vect, char uplo,
+                                lapack_int n, lapack_int ka, lapack_int kb,
+                                lapack_complex_double* ab, lapack_int ldab,
+                                const lapack_complex_double* bb,
+                                lapack_int ldbb, lapack_complex_double* x,
+                                lapack_int ldx, lapack_complex_double* work,
+                                double* rwork );
+
+lapack_int LAPACKE_chbgv_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, lapack_int ka, lapack_int kb,
+                               lapack_complex_float* ab, lapack_int ldab,
+                               lapack_complex_float* bb, lapack_int ldbb,
+                               float* w, lapack_complex_float* z,
+                               lapack_int ldz, lapack_complex_float* work,
+                               float* rwork );
+lapack_int LAPACKE_zhbgv_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, lapack_int ka, lapack_int kb,
+                               lapack_complex_double* ab, lapack_int ldab,
+                               lapack_complex_double* bb, lapack_int ldbb,
+                               double* w, lapack_complex_double* z,
+                               lapack_int ldz, lapack_complex_double* work,
+                               double* rwork );
+
+lapack_int LAPACKE_chbgvd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, lapack_int ka, lapack_int kb,
+                                lapack_complex_float* ab, lapack_int ldab,
+                                lapack_complex_float* bb, lapack_int ldbb,
+                                float* w, lapack_complex_float* z,
+                                lapack_int ldz, lapack_complex_float* work,
+                                lapack_int lwork, float* rwork,
+                                lapack_int lrwork, lapack_int* iwork,
+                                lapack_int liwork );
+lapack_int LAPACKE_zhbgvd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, lapack_int ka, lapack_int kb,
+                                lapack_complex_double* ab, lapack_int ldab,
+                                lapack_complex_double* bb, lapack_int ldbb,
+                                double* w, lapack_complex_double* z,
+                                lapack_int ldz, lapack_complex_double* work,
+                                lapack_int lwork, double* rwork,
+                                lapack_int lrwork, lapack_int* iwork,
+                                lapack_int liwork );
+
+lapack_int LAPACKE_chbgvx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n, lapack_int ka,
+                                lapack_int kb, lapack_complex_float* ab,
+                                lapack_int ldab, lapack_complex_float* bb,
+                                lapack_int ldbb, lapack_complex_float* q,
+                                lapack_int ldq, float vl, float vu,
+                                lapack_int il, lapack_int iu, float abstol,
+                                lapack_int* m, float* w,
+                                lapack_complex_float* z, lapack_int ldz,
+                                lapack_complex_float* work, float* rwork,
+                                lapack_int* iwork, lapack_int* ifail );
+lapack_int LAPACKE_zhbgvx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n, lapack_int ka,
+                                lapack_int kb, lapack_complex_double* ab,
+                                lapack_int ldab, lapack_complex_double* bb,
+                                lapack_int ldbb, lapack_complex_double* q,
+                                lapack_int ldq, double vl, double vu,
+                                lapack_int il, lapack_int iu, double abstol,
+                                lapack_int* m, double* w,
+                                lapack_complex_double* z, lapack_int ldz,
+                                lapack_complex_double* work, double* rwork,
+                                lapack_int* iwork, lapack_int* ifail );
+
+lapack_int LAPACKE_chbtrd_work( int matrix_order, char vect, char uplo,
+                                lapack_int n, lapack_int kd,
+                                lapack_complex_float* ab, lapack_int ldab,
+                                float* d, float* e, lapack_complex_float* q,
+                                lapack_int ldq, lapack_complex_float* work );
+lapack_int LAPACKE_zhbtrd_work( int matrix_order, char vect, char uplo,
+                                lapack_int n, lapack_int kd,
+                                lapack_complex_double* ab, lapack_int ldab,
+                                double* d, double* e, lapack_complex_double* q,
+                                lapack_int ldq, lapack_complex_double* work );
+
+lapack_int LAPACKE_checon_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_float* a, lapack_int lda,
+                                const lapack_int* ipiv, float anorm,
+                                float* rcond, lapack_complex_float* work );
+lapack_int LAPACKE_zhecon_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_double* a, lapack_int lda,
+                                const lapack_int* ipiv, double anorm,
+                                double* rcond, lapack_complex_double* work );
+
+lapack_int LAPACKE_cheequb_work( int matrix_order, char uplo, lapack_int n,
+                                 const lapack_complex_float* a, lapack_int lda,
+                                 float* s, float* scond, float* amax,
+                                 lapack_complex_float* work );
+lapack_int LAPACKE_zheequb_work( int matrix_order, char uplo, lapack_int n,
+                                 const lapack_complex_double* a, lapack_int lda,
+                                 double* s, double* scond, double* amax,
+                                 lapack_complex_double* work );
+
+lapack_int LAPACKE_cheev_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, lapack_complex_float* a,
+                               lapack_int lda, float* w,
+                               lapack_complex_float* work, lapack_int lwork,
+                               float* rwork );
+lapack_int LAPACKE_zheev_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, lapack_complex_double* a,
+                               lapack_int lda, double* w,
+                               lapack_complex_double* work, lapack_int lwork,
+                               double* rwork );
+
+lapack_int LAPACKE_cheevd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, lapack_complex_float* a,
+                                lapack_int lda, float* w,
+                                lapack_complex_float* work, lapack_int lwork,
+                                float* rwork, lapack_int lrwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_zheevd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, lapack_complex_double* a,
+                                lapack_int lda, double* w,
+                                lapack_complex_double* work, lapack_int lwork,
+                                double* rwork, lapack_int lrwork,
+                                lapack_int* iwork, lapack_int liwork );
+
+lapack_int LAPACKE_cheevr_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                float vl, float vu, lapack_int il,
+                                lapack_int iu, float abstol, lapack_int* m,
+                                float* w, lapack_complex_float* z,
+                                lapack_int ldz, lapack_int* isuppz,
+                                lapack_complex_float* work, lapack_int lwork,
+                                float* rwork, lapack_int lrwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_zheevr_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                double vl, double vu, lapack_int il,
+                                lapack_int iu, double abstol, lapack_int* m,
+                                double* w, lapack_complex_double* z,
+                                lapack_int ldz, lapack_int* isuppz,
+                                lapack_complex_double* work, lapack_int lwork,
+                                double* rwork, lapack_int lrwork,
+                                lapack_int* iwork, lapack_int liwork );
+
+lapack_int LAPACKE_cheevx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                float vl, float vu, lapack_int il,
+                                lapack_int iu, float abstol, lapack_int* m,
+                                float* w, lapack_complex_float* z,
+                                lapack_int ldz, lapack_complex_float* work,
+                                lapack_int lwork, float* rwork,
+                                lapack_int* iwork, lapack_int* ifail );
+lapack_int LAPACKE_zheevx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                double vl, double vu, lapack_int il,
+                                lapack_int iu, double abstol, lapack_int* m,
+                                double* w, lapack_complex_double* z,
+                                lapack_int ldz, lapack_complex_double* work,
+                                lapack_int lwork, double* rwork,
+                                lapack_int* iwork, lapack_int* ifail );
+
+lapack_int LAPACKE_chegst_work( int matrix_order, lapack_int itype, char uplo,
+                                lapack_int n, lapack_complex_float* a,
+                                lapack_int lda, const lapack_complex_float* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_zhegst_work( int matrix_order, lapack_int itype, char uplo,
+                                lapack_int n, lapack_complex_double* a,
+                                lapack_int lda, const lapack_complex_double* b,
+                                lapack_int ldb );
+
+lapack_int LAPACKE_chegv_work( int matrix_order, lapack_int itype, char jobz,
+                               char uplo, lapack_int n, lapack_complex_float* a,
+                               lapack_int lda, lapack_complex_float* b,
+                               lapack_int ldb, float* w,
+                               lapack_complex_float* work, lapack_int lwork,
+                               float* rwork );
+lapack_int LAPACKE_zhegv_work( int matrix_order, lapack_int itype, char jobz,
+                               char uplo, lapack_int n,
+                               lapack_complex_double* a, lapack_int lda,
+                               lapack_complex_double* b, lapack_int ldb,
+                               double* w, lapack_complex_double* work,
+                               lapack_int lwork, double* rwork );
+
+lapack_int LAPACKE_chegvd_work( int matrix_order, lapack_int itype, char jobz,
+                                char uplo, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* b, lapack_int ldb,
+                                float* w, lapack_complex_float* work,
+                                lapack_int lwork, float* rwork,
+                                lapack_int lrwork, lapack_int* iwork,
+                                lapack_int liwork );
+lapack_int LAPACKE_zhegvd_work( int matrix_order, lapack_int itype, char jobz,
+                                char uplo, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* b, lapack_int ldb,
+                                double* w, lapack_complex_double* work,
+                                lapack_int lwork, double* rwork,
+                                lapack_int lrwork, lapack_int* iwork,
+                                lapack_int liwork );
+
+lapack_int LAPACKE_chegvx_work( int matrix_order, lapack_int itype, char jobz,
+                                char range, char uplo, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* b, lapack_int ldb,
+                                float vl, float vu, lapack_int il,
+                                lapack_int iu, float abstol, lapack_int* m,
+                                float* w, lapack_complex_float* z,
+                                lapack_int ldz, lapack_complex_float* work,
+                                lapack_int lwork, float* rwork,
+                                lapack_int* iwork, lapack_int* ifail );
+lapack_int LAPACKE_zhegvx_work( int matrix_order, lapack_int itype, char jobz,
+                                char range, char uplo, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* b, lapack_int ldb,
+                                double vl, double vu, lapack_int il,
+                                lapack_int iu, double abstol, lapack_int* m,
+                                double* w, lapack_complex_double* z,
+                                lapack_int ldz, lapack_complex_double* work,
+                                lapack_int lwork, double* rwork,
+                                lapack_int* iwork, lapack_int* ifail );
+
+lapack_int LAPACKE_cherfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* a,
+                                lapack_int lda, const lapack_complex_float* af,
+                                lapack_int ldaf, const lapack_int* ipiv,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx,
+                                float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zherfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_double* a,
+                                lapack_int lda, const lapack_complex_double* af,
+                                lapack_int ldaf, const lapack_int* ipiv,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_cherfsx_work( int matrix_order, char uplo, char equed,
+                                 lapack_int n, lapack_int nrhs,
+                                 const lapack_complex_float* a, lapack_int lda,
+                                 const lapack_complex_float* af,
+                                 lapack_int ldaf, const lapack_int* ipiv,
+                                 const float* s, const lapack_complex_float* b,
+                                 lapack_int ldb, lapack_complex_float* x,
+                                 lapack_int ldx, float* rcond, float* berr,
+                                 lapack_int n_err_bnds, float* err_bnds_norm,
+                                 float* err_bnds_comp, lapack_int nparams,
+                                 float* params, lapack_complex_float* work,
+                                 float* rwork );
+lapack_int LAPACKE_zherfsx_work( int matrix_order, char uplo, char equed,
+                                 lapack_int n, lapack_int nrhs,
+                                 const lapack_complex_double* a, lapack_int lda,
+                                 const lapack_complex_double* af,
+                                 lapack_int ldaf, const lapack_int* ipiv,
+                                 const double* s,
+                                 const lapack_complex_double* b, lapack_int ldb,
+                                 lapack_complex_double* x, lapack_int ldx,
+                                 double* rcond, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, lapack_complex_double* work,
+                                 double* rwork );
+
+lapack_int LAPACKE_chesv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, lapack_complex_float* a,
+                               lapack_int lda, lapack_int* ipiv,
+                               lapack_complex_float* b, lapack_int ldb,
+                               lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zhesv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, lapack_complex_double* a,
+                               lapack_int lda, lapack_int* ipiv,
+                               lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_chesvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* af, lapack_int ldaf,
+                                lapack_int* ipiv, const lapack_complex_float* b,
+                                lapack_int ldb, lapack_complex_float* x,
+                                lapack_int ldx, float* rcond, float* ferr,
+                                float* berr, lapack_complex_float* work,
+                                lapack_int lwork, float* rwork );
+lapack_int LAPACKE_zhesvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* af, lapack_int ldaf,
+                                lapack_int* ipiv,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* rcond, double* ferr, double* berr,
+                                lapack_complex_double* work, lapack_int lwork,
+                                double* rwork );
+
+lapack_int LAPACKE_chesvxx_work( int matrix_order, char fact, char uplo,
+                                 lapack_int n, lapack_int nrhs,
+                                 lapack_complex_float* a, lapack_int lda,
+                                 lapack_complex_float* af, lapack_int ldaf,
+                                 lapack_int* ipiv, char* equed, float* s,
+                                 lapack_complex_float* b, lapack_int ldb,
+                                 lapack_complex_float* x, lapack_int ldx,
+                                 float* rcond, float* rpvgrw, float* berr,
+                                 lapack_int n_err_bnds, float* err_bnds_norm,
+                                 float* err_bnds_comp, lapack_int nparams,
+                                 float* params, lapack_complex_float* work,
+                                 float* rwork );
+lapack_int LAPACKE_zhesvxx_work( int matrix_order, char fact, char uplo,
+                                 lapack_int n, lapack_int nrhs,
+                                 lapack_complex_double* a, lapack_int lda,
+                                 lapack_complex_double* af, lapack_int ldaf,
+                                 lapack_int* ipiv, char* equed, double* s,
+                                 lapack_complex_double* b, lapack_int ldb,
+                                 lapack_complex_double* x, lapack_int ldx,
+                                 double* rcond, double* rpvgrw, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, lapack_complex_double* work,
+                                 double* rwork );
+
+lapack_int LAPACKE_chetrd_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                float* d, float* e, lapack_complex_float* tau,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zhetrd_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                double* d, double* e,
+                                lapack_complex_double* tau,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_chetrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_int* ipiv, lapack_complex_float* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_zhetrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_int* ipiv, lapack_complex_double* work,
+                                lapack_int lwork );
+
+lapack_int LAPACKE_chetri_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                const lapack_int* ipiv,
+                                lapack_complex_float* work );
+lapack_int LAPACKE_zhetri_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                const lapack_int* ipiv,
+                                lapack_complex_double* work );
+
+lapack_int LAPACKE_chetrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* a,
+                                lapack_int lda, const lapack_int* ipiv,
+                                lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zhetrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_double* a,
+                                lapack_int lda, const lapack_int* ipiv,
+                                lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_chfrk_work( int matrix_order, char transr, char uplo,
+                               char trans, lapack_int n, lapack_int k,
+                               float alpha, const lapack_complex_float* a,
+                               lapack_int lda, float beta,
+                               lapack_complex_float* c );
+lapack_int LAPACKE_zhfrk_work( int matrix_order, char transr, char uplo,
+                               char trans, lapack_int n, lapack_int k,
+                               double alpha, const lapack_complex_double* a,
+                               lapack_int lda, double beta,
+                               lapack_complex_double* c );
+
+lapack_int LAPACKE_shgeqz_work( int matrix_order, char job, char compq,
+                                char compz, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, float* h, lapack_int ldh,
+                                float* t, lapack_int ldt, float* alphar,
+                                float* alphai, float* beta, float* q,
+                                lapack_int ldq, float* z, lapack_int ldz,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dhgeqz_work( int matrix_order, char job, char compq,
+                                char compz, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, double* h, lapack_int ldh,
+                                double* t, lapack_int ldt, double* alphar,
+                                double* alphai, double* beta, double* q,
+                                lapack_int ldq, double* z, lapack_int ldz,
+                                double* work, lapack_int lwork );
+lapack_int LAPACKE_chgeqz_work( int matrix_order, char job, char compq,
+                                char compz, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, lapack_complex_float* h,
+                                lapack_int ldh, lapack_complex_float* t,
+                                lapack_int ldt, lapack_complex_float* alpha,
+                                lapack_complex_float* beta,
+                                lapack_complex_float* q, lapack_int ldq,
+                                lapack_complex_float* z, lapack_int ldz,
+                                lapack_complex_float* work, lapack_int lwork,
+                                float* rwork );
+lapack_int LAPACKE_zhgeqz_work( int matrix_order, char job, char compq,
+                                char compz, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, lapack_complex_double* h,
+                                lapack_int ldh, lapack_complex_double* t,
+                                lapack_int ldt, lapack_complex_double* alpha,
+                                lapack_complex_double* beta,
+                                lapack_complex_double* q, lapack_int ldq,
+                                lapack_complex_double* z, lapack_int ldz,
+                                lapack_complex_double* work, lapack_int lwork,
+                                double* rwork );
+
+lapack_int LAPACKE_chpcon_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_float* ap,
+                                const lapack_int* ipiv, float anorm,
+                                float* rcond, lapack_complex_float* work );
+lapack_int LAPACKE_zhpcon_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_double* ap,
+                                const lapack_int* ipiv, double anorm,
+                                double* rcond, lapack_complex_double* work );
+
+lapack_int LAPACKE_chpev_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, lapack_complex_float* ap, float* w,
+                               lapack_complex_float* z, lapack_int ldz,
+                               lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zhpev_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, lapack_complex_double* ap,
+                               double* w, lapack_complex_double* z,
+                               lapack_int ldz, lapack_complex_double* work,
+                               double* rwork );
+
+lapack_int LAPACKE_chpevd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, lapack_complex_float* ap,
+                                float* w, lapack_complex_float* z,
+                                lapack_int ldz, lapack_complex_float* work,
+                                lapack_int lwork, float* rwork,
+                                lapack_int lrwork, lapack_int* iwork,
+                                lapack_int liwork );
+lapack_int LAPACKE_zhpevd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, lapack_complex_double* ap,
+                                double* w, lapack_complex_double* z,
+                                lapack_int ldz, lapack_complex_double* work,
+                                lapack_int lwork, double* rwork,
+                                lapack_int lrwork, lapack_int* iwork,
+                                lapack_int liwork );
+
+lapack_int LAPACKE_chpevx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n,
+                                lapack_complex_float* ap, float vl, float vu,
+                                lapack_int il, lapack_int iu, float abstol,
+                                lapack_int* m, float* w,
+                                lapack_complex_float* z, lapack_int ldz,
+                                lapack_complex_float* work, float* rwork,
+                                lapack_int* iwork, lapack_int* ifail );
+lapack_int LAPACKE_zhpevx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n,
+                                lapack_complex_double* ap, double vl, double vu,
+                                lapack_int il, lapack_int iu, double abstol,
+                                lapack_int* m, double* w,
+                                lapack_complex_double* z, lapack_int ldz,
+                                lapack_complex_double* work, double* rwork,
+                                lapack_int* iwork, lapack_int* ifail );
+
+lapack_int LAPACKE_chpgst_work( int matrix_order, lapack_int itype, char uplo,
+                                lapack_int n, lapack_complex_float* ap,
+                                const lapack_complex_float* bp );
+lapack_int LAPACKE_zhpgst_work( int matrix_order, lapack_int itype, char uplo,
+                                lapack_int n, lapack_complex_double* ap,
+                                const lapack_complex_double* bp );
+
+lapack_int LAPACKE_chpgv_work( int matrix_order, lapack_int itype, char jobz,
+                               char uplo, lapack_int n,
+                               lapack_complex_float* ap,
+                               lapack_complex_float* bp, float* w,
+                               lapack_complex_float* z, lapack_int ldz,
+                               lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zhpgv_work( int matrix_order, lapack_int itype, char jobz,
+                               char uplo, lapack_int n,
+                               lapack_complex_double* ap,
+                               lapack_complex_double* bp, double* w,
+                               lapack_complex_double* z, lapack_int ldz,
+                               lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_chpgvd_work( int matrix_order, lapack_int itype, char jobz,
+                                char uplo, lapack_int n,
+                                lapack_complex_float* ap,
+                                lapack_complex_float* bp, float* w,
+                                lapack_complex_float* z, lapack_int ldz,
+                                lapack_complex_float* work, lapack_int lwork,
+                                float* rwork, lapack_int lrwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_zhpgvd_work( int matrix_order, lapack_int itype, char jobz,
+                                char uplo, lapack_int n,
+                                lapack_complex_double* ap,
+                                lapack_complex_double* bp, double* w,
+                                lapack_complex_double* z, lapack_int ldz,
+                                lapack_complex_double* work, lapack_int lwork,
+                                double* rwork, lapack_int lrwork,
+                                lapack_int* iwork, lapack_int liwork );
+
+lapack_int LAPACKE_chpgvx_work( int matrix_order, lapack_int itype, char jobz,
+                                char range, char uplo, lapack_int n,
+                                lapack_complex_float* ap,
+                                lapack_complex_float* bp, float vl, float vu,
+                                lapack_int il, lapack_int iu, float abstol,
+                                lapack_int* m, float* w,
+                                lapack_complex_float* z, lapack_int ldz,
+                                lapack_complex_float* work, float* rwork,
+                                lapack_int* iwork, lapack_int* ifail );
+lapack_int LAPACKE_zhpgvx_work( int matrix_order, lapack_int itype, char jobz,
+                                char range, char uplo, lapack_int n,
+                                lapack_complex_double* ap,
+                                lapack_complex_double* bp, double vl, double vu,
+                                lapack_int il, lapack_int iu, double abstol,
+                                lapack_int* m, double* w,
+                                lapack_complex_double* z, lapack_int ldz,
+                                lapack_complex_double* work, double* rwork,
+                                lapack_int* iwork, lapack_int* ifail );
+
+lapack_int LAPACKE_chprfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* ap,
+                                const lapack_complex_float* afp,
+                                const lapack_int* ipiv,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx,
+                                float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zhprfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs,
+                                const lapack_complex_double* ap,
+                                const lapack_complex_double* afp,
+                                const lapack_int* ipiv,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_chpsv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, lapack_complex_float* ap,
+                               lapack_int* ipiv, lapack_complex_float* b,
+                               lapack_int ldb );
+lapack_int LAPACKE_zhpsv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, lapack_complex_double* ap,
+                               lapack_int* ipiv, lapack_complex_double* b,
+                               lapack_int ldb );
+
+lapack_int LAPACKE_chpsvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* ap,
+                                lapack_complex_float* afp, lapack_int* ipiv,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx,
+                                float* rcond, float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zhpsvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* ap,
+                                lapack_complex_double* afp, lapack_int* ipiv,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* rcond, double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_chptrd_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* ap, float* d, float* e,
+                                lapack_complex_float* tau );
+lapack_int LAPACKE_zhptrd_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* ap, double* d, double* e,
+                                lapack_complex_double* tau );
+
+lapack_int LAPACKE_chptrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* ap, lapack_int* ipiv );
+lapack_int LAPACKE_zhptrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* ap, lapack_int* ipiv );
+
+lapack_int LAPACKE_chptri_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* ap,
+                                const lapack_int* ipiv,
+                                lapack_complex_float* work );
+lapack_int LAPACKE_zhptri_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* ap,
+                                const lapack_int* ipiv,
+                                lapack_complex_double* work );
+
+lapack_int LAPACKE_chptrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* ap,
+                                const lapack_int* ipiv, lapack_complex_float* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_zhptrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs,
+                                const lapack_complex_double* ap,
+                                const lapack_int* ipiv,
+                                lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_shsein_work( int matrix_order, char job, char eigsrc,
+                                char initv, lapack_logical* select,
+                                lapack_int n, const float* h, lapack_int ldh,
+                                float* wr, const float* wi, float* vl,
+                                lapack_int ldvl, float* vr, lapack_int ldvr,
+                                lapack_int mm, lapack_int* m, float* work,
+                                lapack_int* ifaill, lapack_int* ifailr );
+lapack_int LAPACKE_dhsein_work( int matrix_order, char job, char eigsrc,
+                                char initv, lapack_logical* select,
+                                lapack_int n, const double* h, lapack_int ldh,
+                                double* wr, const double* wi, double* vl,
+                                lapack_int ldvl, double* vr, lapack_int ldvr,
+                                lapack_int mm, lapack_int* m, double* work,
+                                lapack_int* ifaill, lapack_int* ifailr );
+lapack_int LAPACKE_chsein_work( int matrix_order, char job, char eigsrc,
+                                char initv, const lapack_logical* select,
+                                lapack_int n, const lapack_complex_float* h,
+                                lapack_int ldh, lapack_complex_float* w,
+                                lapack_complex_float* vl, lapack_int ldvl,
+                                lapack_complex_float* vr, lapack_int ldvr,
+                                lapack_int mm, lapack_int* m,
+                                lapack_complex_float* work, float* rwork,
+                                lapack_int* ifaill, lapack_int* ifailr );
+lapack_int LAPACKE_zhsein_work( int matrix_order, char job, char eigsrc,
+                                char initv, const lapack_logical* select,
+                                lapack_int n, const lapack_complex_double* h,
+                                lapack_int ldh, lapack_complex_double* w,
+                                lapack_complex_double* vl, lapack_int ldvl,
+                                lapack_complex_double* vr, lapack_int ldvr,
+                                lapack_int mm, lapack_int* m,
+                                lapack_complex_double* work, double* rwork,
+                                lapack_int* ifaill, lapack_int* ifailr );
+
+lapack_int LAPACKE_shseqr_work( int matrix_order, char job, char compz,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                float* h, lapack_int ldh, float* wr, float* wi,
+                                float* z, lapack_int ldz, float* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_dhseqr_work( int matrix_order, char job, char compz,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                double* h, lapack_int ldh, double* wr,
+                                double* wi, double* z, lapack_int ldz,
+                                double* work, lapack_int lwork );
+lapack_int LAPACKE_chseqr_work( int matrix_order, char job, char compz,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                lapack_complex_float* h, lapack_int ldh,
+                                lapack_complex_float* w,
+                                lapack_complex_float* z, lapack_int ldz,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zhseqr_work( int matrix_order, char job, char compz,
+                                lapack_int n, lapack_int ilo, lapack_int ihi,
+                                lapack_complex_double* h, lapack_int ldh,
+                                lapack_complex_double* w,
+                                lapack_complex_double* z, lapack_int ldz,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_clacgv_work( lapack_int n, lapack_complex_float* x,
+                                lapack_int incx );
+lapack_int LAPACKE_zlacgv_work( lapack_int n, lapack_complex_double* x,
+                                lapack_int incx );
+
+lapack_int LAPACKE_slacpy_work( int matrix_order, char uplo, lapack_int m,
+                                lapack_int n, const float* a, lapack_int lda,
+                                float* b, lapack_int ldb );
+lapack_int LAPACKE_dlacpy_work( int matrix_order, char uplo, lapack_int m,
+                                lapack_int n, const double* a, lapack_int lda,
+                                double* b, lapack_int ldb );
+lapack_int LAPACKE_clacpy_work( int matrix_order, char uplo, lapack_int m,
+                                lapack_int n, const lapack_complex_float* a,
+                                lapack_int lda, lapack_complex_float* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_zlacpy_work( int matrix_order, char uplo, lapack_int m,
+                                lapack_int n, const lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* b,
+                                lapack_int ldb );
+
+lapack_int LAPACKE_zlag2c_work( int matrix_order, lapack_int m, lapack_int n,
+                                const lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_float* sa, lapack_int ldsa );
+
+lapack_int LAPACKE_slag2d_work( int matrix_order, lapack_int m, lapack_int n,
+                                const float* sa, lapack_int ldsa, double* a,
+                                lapack_int lda );
+
+lapack_int LAPACKE_dlag2s_work( int matrix_order, lapack_int m, lapack_int n,
+                                const double* a, lapack_int lda, float* sa,
+                                lapack_int ldsa );
+
+lapack_int LAPACKE_clag2z_work( int matrix_order, lapack_int m, lapack_int n,
+                                const lapack_complex_float* sa, lapack_int ldsa,
+                                lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_slagge_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int kl, lapack_int ku, const float* d,
+                                float* a, lapack_int lda, lapack_int* iseed,
+                                float* work );
+lapack_int LAPACKE_dlagge_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int kl, lapack_int ku, const double* d,
+                                double* a, lapack_int lda, lapack_int* iseed,
+                                double* work );
+lapack_int LAPACKE_clagge_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int kl, lapack_int ku, const float* d,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_int* iseed, lapack_complex_float* work );
+lapack_int LAPACKE_zlagge_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int kl, lapack_int ku, const double* d,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_int* iseed,
+                                lapack_complex_double* work );
+                                
+lapack_int LAPACKE_claghe_work( int matrix_order, lapack_int n, lapack_int k,
+                                const float* d, lapack_complex_float* a,
+                                lapack_int lda, lapack_int* iseed,
+                                lapack_complex_float* work );
+lapack_int LAPACKE_zlaghe_work( int matrix_order, lapack_int n, lapack_int k,
+                                const double* d, lapack_complex_double* a,
+                                lapack_int lda, lapack_int* iseed,
+                                lapack_complex_double* work );
+
+lapack_int LAPACKE_slagsy_work( int matrix_order, lapack_int n, lapack_int k,
+                                const float* d, float* a, lapack_int lda,
+                                lapack_int* iseed, float* work );
+lapack_int LAPACKE_dlagsy_work( int matrix_order, lapack_int n, lapack_int k,
+                                const double* d, double* a, lapack_int lda,
+                                lapack_int* iseed, double* work );
+lapack_int LAPACKE_clagsy_work( int matrix_order, lapack_int n, lapack_int k,
+                                const float* d, lapack_complex_float* a,
+                                lapack_int lda, lapack_int* iseed,
+                                lapack_complex_float* work );
+lapack_int LAPACKE_zlagsy_work( int matrix_order, lapack_int n, lapack_int k,
+                                const double* d, lapack_complex_double* a,
+                                lapack_int lda, lapack_int* iseed,
+                                lapack_complex_double* work );
+
+lapack_int LAPACKE_slapmr_work( int matrix_order, lapack_logical forwrd,
+                                lapack_int m, lapack_int n, float* x,
+                                lapack_int ldx, lapack_int* k );
+lapack_int LAPACKE_dlapmr_work( int matrix_order, lapack_logical forwrd,
+                                lapack_int m, lapack_int n, double* x,
+                                lapack_int ldx, lapack_int* k );
+lapack_int LAPACKE_clapmr_work( int matrix_order, lapack_logical forwrd,
+                                lapack_int m, lapack_int n,
+                                lapack_complex_float* x, lapack_int ldx,
+                                lapack_int* k );
+lapack_int LAPACKE_zlapmr_work( int matrix_order, lapack_logical forwrd,
+                                lapack_int m, lapack_int n,
+                                lapack_complex_double* x, lapack_int ldx,
+                                lapack_int* k );
+
+lapack_int LAPACKE_slartgp_work( float f, float g, float* cs, float* sn,
+                                 float* r );
+lapack_int LAPACKE_dlartgp_work( double f, double g, double* cs, double* sn,
+                                 double* r );
+
+lapack_int LAPACKE_slartgs_work( float x, float y, float sigma, float* cs,
+                                 float* sn );
+lapack_int LAPACKE_dlartgs_work( double x, double y, double sigma, double* cs,
+                                 double* sn );
+                                
+float LAPACKE_slapy2_work( float x, float y );
+double LAPACKE_dlapy2_work( double x, double y );
+
+float LAPACKE_slapy3_work( float x, float y, float z );
+double LAPACKE_dlapy3_work( double x, double y, double z );
+
+float LAPACKE_slamch_work( char cmach );
+double LAPACKE_dlamch_work( char cmach );
+
+float LAPACKE_slange_work( int matrix_order, char norm, lapack_int m,
+                                lapack_int n, const float* a, lapack_int lda,
+                                float* work );
+double LAPACKE_dlange_work( int matrix_order, char norm, lapack_int m,
+                                lapack_int n, const double* a, lapack_int lda,
+                                double* work );
+float LAPACKE_clange_work( int matrix_order, char norm, lapack_int m,
+                                lapack_int n, const lapack_complex_float* a,
+                                lapack_int lda, float* work );
+double LAPACKE_zlange_work( int matrix_order, char norm, lapack_int m,
+                                lapack_int n, const lapack_complex_double* a,
+                                lapack_int lda, double* work );
+
+float LAPACKE_clanhe_work( int matrix_order, char norm, char uplo,
+                                lapack_int n, const lapack_complex_float* a,
+                                lapack_int lda, float* work );
+double LAPACKE_zlanhe_work( int matrix_order, char norm, char uplo,
+                                lapack_int n, const lapack_complex_double* a,
+                                lapack_int lda, double* work );
+
+float LAPACKE_slansy_work( int matrix_order, char norm, char uplo,
+                                lapack_int n, const float* a, lapack_int lda,
+                                float* work );
+double LAPACKE_dlansy_work( int matrix_order, char norm, char uplo,
+                                lapack_int n, const double* a, lapack_int lda,
+                                double* work );
+float LAPACKE_clansy_work( int matrix_order, char norm, char uplo,
+                                lapack_int n, const lapack_complex_float* a,
+                                lapack_int lda, float* work );
+double LAPACKE_zlansy_work( int matrix_order, char norm, char uplo,
+                                lapack_int n, const lapack_complex_double* a,
+                                lapack_int lda, double* work );
+
+float LAPACKE_slantr_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int m, lapack_int n, const float* a,
+                                lapack_int lda, float* work );
+double LAPACKE_dlantr_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int m, lapack_int n,
+                                const double* a, lapack_int lda, double* work );
+float LAPACKE_clantr_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int m, lapack_int n,
+                                const lapack_complex_float* a, lapack_int lda,
+                                float* work );
+double LAPACKE_zlantr_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int m, lapack_int n,
+                                const lapack_complex_double* a, lapack_int lda,
+                                double* work );
+
+lapack_int LAPACKE_slarfb_work( int matrix_order, char side, char trans,
+                                char direct, char storev, lapack_int m,
+                                lapack_int n, lapack_int k, const float* v,
+                                lapack_int ldv, const float* t, lapack_int ldt,
+                                float* c, lapack_int ldc, float* work,
+                                lapack_int ldwork );
+lapack_int LAPACKE_dlarfb_work( int matrix_order, char side, char trans,
+                                char direct, char storev, lapack_int m,
+                                lapack_int n, lapack_int k, const double* v,
+                                lapack_int ldv, const double* t, lapack_int ldt,
+                                double* c, lapack_int ldc, double* work,
+                                lapack_int ldwork );
+lapack_int LAPACKE_clarfb_work( int matrix_order, char side, char trans,
+                                char direct, char storev, lapack_int m,
+                                lapack_int n, lapack_int k,
+                                const lapack_complex_float* v, lapack_int ldv,
+                                const lapack_complex_float* t, lapack_int ldt,
+                                lapack_complex_float* c, lapack_int ldc,
+                                lapack_complex_float* work, lapack_int ldwork );
+lapack_int LAPACKE_zlarfb_work( int matrix_order, char side, char trans,
+                                char direct, char storev, lapack_int m,
+                                lapack_int n, lapack_int k,
+                                const lapack_complex_double* v, lapack_int ldv,
+                                const lapack_complex_double* t, lapack_int ldt,
+                                lapack_complex_double* c, lapack_int ldc,
+                                lapack_complex_double* work,
+                                lapack_int ldwork );
+
+lapack_int LAPACKE_slarfg_work( lapack_int n, float* alpha, float* x,
+                                lapack_int incx, float* tau );
+lapack_int LAPACKE_dlarfg_work( lapack_int n, double* alpha, double* x,
+                                lapack_int incx, double* tau );
+lapack_int LAPACKE_clarfg_work( lapack_int n, lapack_complex_float* alpha,
+                                lapack_complex_float* x, lapack_int incx,
+                                lapack_complex_float* tau );
+lapack_int LAPACKE_zlarfg_work( lapack_int n, lapack_complex_double* alpha,
+                                lapack_complex_double* x, lapack_int incx,
+                                lapack_complex_double* tau );
+
+lapack_int LAPACKE_slarft_work( int matrix_order, char direct, char storev,
+                                lapack_int n, lapack_int k, const float* v,
+                                lapack_int ldv, const float* tau, float* t,
+                                lapack_int ldt );
+lapack_int LAPACKE_dlarft_work( int matrix_order, char direct, char storev,
+                                lapack_int n, lapack_int k, const double* v,
+                                lapack_int ldv, const double* tau, double* t,
+                                lapack_int ldt );
+lapack_int LAPACKE_clarft_work( int matrix_order, char direct, char storev,
+                                lapack_int n, lapack_int k,
+                                const lapack_complex_float* v, lapack_int ldv,
+                                const lapack_complex_float* tau,
+                                lapack_complex_float* t, lapack_int ldt );
+lapack_int LAPACKE_zlarft_work( int matrix_order, char direct, char storev,
+                                lapack_int n, lapack_int k,
+                                const lapack_complex_double* v, lapack_int ldv,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* t, lapack_int ldt );
+
+lapack_int LAPACKE_slarfx_work( int matrix_order, char side, lapack_int m,
+                                lapack_int n, const float* v, float tau,
+                                float* c, lapack_int ldc, float* work );
+lapack_int LAPACKE_dlarfx_work( int matrix_order, char side, lapack_int m,
+                                lapack_int n, const double* v, double tau,
+                                double* c, lapack_int ldc, double* work );
+lapack_int LAPACKE_clarfx_work( int matrix_order, char side, lapack_int m,
+                                lapack_int n, const lapack_complex_float* v,
+                                lapack_complex_float tau,
+                                lapack_complex_float* c, lapack_int ldc,
+                                lapack_complex_float* work );
+lapack_int LAPACKE_zlarfx_work( int matrix_order, char side, lapack_int m,
+                                lapack_int n, const lapack_complex_double* v,
+                                lapack_complex_double tau,
+                                lapack_complex_double* c, lapack_int ldc,
+                                lapack_complex_double* work );
+
+lapack_int LAPACKE_slarnv_work( lapack_int idist, lapack_int* iseed,
+                                lapack_int n, float* x );
+lapack_int LAPACKE_dlarnv_work( lapack_int idist, lapack_int* iseed,
+                                lapack_int n, double* x );
+lapack_int LAPACKE_clarnv_work( lapack_int idist, lapack_int* iseed,
+                                lapack_int n, lapack_complex_float* x );
+lapack_int LAPACKE_zlarnv_work( lapack_int idist, lapack_int* iseed,
+                                lapack_int n, lapack_complex_double* x );
+
+lapack_int LAPACKE_slaset_work( int matrix_order, char uplo, lapack_int m,
+                                lapack_int n, float alpha, float beta, float* a,
+                                lapack_int lda );
+lapack_int LAPACKE_dlaset_work( int matrix_order, char uplo, lapack_int m,
+                                lapack_int n, double alpha, double beta,
+                                double* a, lapack_int lda );
+lapack_int LAPACKE_claset_work( int matrix_order, char uplo, lapack_int m,
+                                lapack_int n, lapack_complex_float alpha,
+                                lapack_complex_float beta,
+                                lapack_complex_float* a, lapack_int lda );
+lapack_int LAPACKE_zlaset_work( int matrix_order, char uplo, lapack_int m,
+                                lapack_int n, lapack_complex_double alpha,
+                                lapack_complex_double beta,
+                                lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_slasrt_work( char id, lapack_int n, float* d );
+lapack_int LAPACKE_dlasrt_work( char id, lapack_int n, double* d );
+
+lapack_int LAPACKE_slaswp_work( int matrix_order, lapack_int n, float* a,
+                                lapack_int lda, lapack_int k1, lapack_int k2,
+                                const lapack_int* ipiv, lapack_int incx );
+lapack_int LAPACKE_dlaswp_work( int matrix_order, lapack_int n, double* a,
+                                lapack_int lda, lapack_int k1, lapack_int k2,
+                                const lapack_int* ipiv, lapack_int incx );
+lapack_int LAPACKE_claswp_work( int matrix_order, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_int k1, lapack_int k2,
+                                const lapack_int* ipiv, lapack_int incx );
+lapack_int LAPACKE_zlaswp_work( int matrix_order, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_int k1, lapack_int k2,
+                                const lapack_int* ipiv, lapack_int incx );
+
+lapack_int LAPACKE_slatms_work( int matrix_order, lapack_int m, lapack_int n,
+                                char dist, lapack_int* iseed, char sym,
+                                float* d, lapack_int mode, float cond,
+                                float dmax, lapack_int kl, lapack_int ku,
+                                char pack, float* a, lapack_int lda,
+                                float* work );
+lapack_int LAPACKE_dlatms_work( int matrix_order, lapack_int m, lapack_int n,
+                                char dist, lapack_int* iseed, char sym,
+                                double* d, lapack_int mode, double cond,
+                                double dmax, lapack_int kl, lapack_int ku,
+                                char pack, double* a, lapack_int lda,
+                                double* work );
+lapack_int LAPACKE_clatms_work( int matrix_order, lapack_int m, lapack_int n,
+                                char dist, lapack_int* iseed, char sym,
+                                float* d, lapack_int mode, float cond,
+                                float dmax, lapack_int kl, lapack_int ku,
+                                char pack, lapack_complex_float* a,
+                                lapack_int lda, lapack_complex_float* work );
+lapack_int LAPACKE_zlatms_work( int matrix_order, lapack_int m, lapack_int n,
+                                char dist, lapack_int* iseed, char sym,
+                                double* d, lapack_int mode, double cond,
+                                double dmax, lapack_int kl, lapack_int ku,
+                                char pack, lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* work );
+
+lapack_int LAPACKE_slauum_work( int matrix_order, char uplo, lapack_int n,
+                                float* a, lapack_int lda );
+lapack_int LAPACKE_dlauum_work( int matrix_order, char uplo, lapack_int n,
+                                double* a, lapack_int lda );
+lapack_int LAPACKE_clauum_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda );
+lapack_int LAPACKE_zlauum_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_sopgtr_work( int matrix_order, char uplo, lapack_int n,
+                                const float* ap, const float* tau, float* q,
+                                lapack_int ldq, float* work );
+lapack_int LAPACKE_dopgtr_work( int matrix_order, char uplo, lapack_int n,
+                                const double* ap, const double* tau, double* q,
+                                lapack_int ldq, double* work );
+
+lapack_int LAPACKE_sopmtr_work( int matrix_order, char side, char uplo,
+                                char trans, lapack_int m, lapack_int n,
+                                const float* ap, const float* tau, float* c,
+                                lapack_int ldc, float* work );
+lapack_int LAPACKE_dopmtr_work( int matrix_order, char side, char uplo,
+                                char trans, lapack_int m, lapack_int n,
+                                const double* ap, const double* tau, double* c,
+                                lapack_int ldc, double* work );
+
+lapack_int LAPACKE_sorgbr_work( int matrix_order, char vect, lapack_int m,
+                                lapack_int n, lapack_int k, float* a,
+                                lapack_int lda, const float* tau, float* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_dorgbr_work( int matrix_order, char vect, lapack_int m,
+                                lapack_int n, lapack_int k, double* a,
+                                lapack_int lda, const double* tau, double* work,
+                                lapack_int lwork );
+
+lapack_int LAPACKE_sorghr_work( int matrix_order, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, float* a, lapack_int lda,
+                                const float* tau, float* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_dorghr_work( int matrix_order, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, double* a, lapack_int lda,
+                                const double* tau, double* work,
+                                lapack_int lwork );
+
+lapack_int LAPACKE_sorglq_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, float* a, lapack_int lda,
+                                const float* tau, float* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_dorglq_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, double* a, lapack_int lda,
+                                const double* tau, double* work,
+                                lapack_int lwork );
+
+lapack_int LAPACKE_sorgql_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, float* a, lapack_int lda,
+                                const float* tau, float* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_dorgql_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, double* a, lapack_int lda,
+                                const double* tau, double* work,
+                                lapack_int lwork );
+
+lapack_int LAPACKE_sorgqr_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, float* a, lapack_int lda,
+                                const float* tau, float* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_dorgqr_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, double* a, lapack_int lda,
+                                const double* tau, double* work,
+                                lapack_int lwork );
+
+lapack_int LAPACKE_sorgrq_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, float* a, lapack_int lda,
+                                const float* tau, float* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_dorgrq_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, double* a, lapack_int lda,
+                                const double* tau, double* work,
+                                lapack_int lwork );
+
+lapack_int LAPACKE_sorgtr_work( int matrix_order, char uplo, lapack_int n,
+                                float* a, lapack_int lda, const float* tau,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dorgtr_work( int matrix_order, char uplo, lapack_int n,
+                                double* a, lapack_int lda, const double* tau,
+                                double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sormbr_work( int matrix_order, char vect, char side,
+                                char trans, lapack_int m, lapack_int n,
+                                lapack_int k, const float* a, lapack_int lda,
+                                const float* tau, float* c, lapack_int ldc,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dormbr_work( int matrix_order, char vect, char side,
+                                char trans, lapack_int m, lapack_int n,
+                                lapack_int k, const double* a, lapack_int lda,
+                                const double* tau, double* c, lapack_int ldc,
+                                double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sormhr_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, const float* a, lapack_int lda,
+                                const float* tau, float* c, lapack_int ldc,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dormhr_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, const double* a, lapack_int lda,
+                                const double* tau, double* c, lapack_int ldc,
+                                double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sormlq_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const float* a, lapack_int lda,
+                                const float* tau, float* c, lapack_int ldc,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dormlq_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const double* a, lapack_int lda,
+                                const double* tau, double* c, lapack_int ldc,
+                                double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sormql_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const float* a, lapack_int lda,
+                                const float* tau, float* c, lapack_int ldc,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dormql_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const double* a, lapack_int lda,
+                                const double* tau, double* c, lapack_int ldc,
+                                double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sormqr_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const float* a, lapack_int lda,
+                                const float* tau, float* c, lapack_int ldc,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dormqr_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const double* a, lapack_int lda,
+                                const double* tau, double* c, lapack_int ldc,
+                                double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sormrq_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const float* a, lapack_int lda,
+                                const float* tau, float* c, lapack_int ldc,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dormrq_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const double* a, lapack_int lda,
+                                const double* tau, double* c, lapack_int ldc,
+                                double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sormrz_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                lapack_int l, const float* a, lapack_int lda,
+                                const float* tau, float* c, lapack_int ldc,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dormrz_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                lapack_int l, const double* a, lapack_int lda,
+                                const double* tau, double* c, lapack_int ldc,
+                                double* work, lapack_int lwork );
+
+lapack_int LAPACKE_sormtr_work( int matrix_order, char side, char uplo,
+                                char trans, lapack_int m, lapack_int n,
+                                const float* a, lapack_int lda,
+                                const float* tau, float* c, lapack_int ldc,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dormtr_work( int matrix_order, char side, char uplo,
+                                char trans, lapack_int m, lapack_int n,
+                                const double* a, lapack_int lda,
+                                const double* tau, double* c, lapack_int ldc,
+                                double* work, lapack_int lwork );
+
+lapack_int LAPACKE_spbcon_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, const float* ab, lapack_int ldab,
+                                float anorm, float* rcond, float* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dpbcon_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, const double* ab,
+                                lapack_int ldab, double anorm, double* rcond,
+                                double* work, lapack_int* iwork );
+lapack_int LAPACKE_cpbcon_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, const lapack_complex_float* ab,
+                                lapack_int ldab, float anorm, float* rcond,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zpbcon_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, const lapack_complex_double* ab,
+                                lapack_int ldab, double anorm, double* rcond,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_spbequ_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, const float* ab, lapack_int ldab,
+                                float* s, float* scond, float* amax );
+lapack_int LAPACKE_dpbequ_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, const double* ab,
+                                lapack_int ldab, double* s, double* scond,
+                                double* amax );
+lapack_int LAPACKE_cpbequ_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, const lapack_complex_float* ab,
+                                lapack_int ldab, float* s, float* scond,
+                                float* amax );
+lapack_int LAPACKE_zpbequ_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, const lapack_complex_double* ab,
+                                lapack_int ldab, double* s, double* scond,
+                                double* amax );
+
+lapack_int LAPACKE_spbrfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, lapack_int nrhs, const float* ab,
+                                lapack_int ldab, const float* afb,
+                                lapack_int ldafb, const float* b,
+                                lapack_int ldb, float* x, lapack_int ldx,
+                                float* ferr, float* berr, float* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dpbrfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, lapack_int nrhs,
+                                const double* ab, lapack_int ldab,
+                                const double* afb, lapack_int ldafb,
+                                const double* b, lapack_int ldb, double* x,
+                                lapack_int ldx, double* ferr, double* berr,
+                                double* work, lapack_int* iwork );
+lapack_int LAPACKE_cpbrfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, lapack_int nrhs,
+                                const lapack_complex_float* ab, lapack_int ldab,
+                                const lapack_complex_float* afb,
+                                lapack_int ldafb, const lapack_complex_float* b,
+                                lapack_int ldb, lapack_complex_float* x,
+                                lapack_int ldx, float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zpbrfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, lapack_int nrhs,
+                                const lapack_complex_double* ab,
+                                lapack_int ldab,
+                                const lapack_complex_double* afb,
+                                lapack_int ldafb,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_spbstf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kb, float* bb, lapack_int ldbb );
+lapack_int LAPACKE_dpbstf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kb, double* bb, lapack_int ldbb );
+lapack_int LAPACKE_cpbstf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kb, lapack_complex_float* bb,
+                                lapack_int ldbb );
+lapack_int LAPACKE_zpbstf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kb, lapack_complex_double* bb,
+                                lapack_int ldbb );
+
+lapack_int LAPACKE_spbsv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int kd, lapack_int nrhs, float* ab,
+                               lapack_int ldab, float* b, lapack_int ldb );
+lapack_int LAPACKE_dpbsv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int kd, lapack_int nrhs, double* ab,
+                               lapack_int ldab, double* b, lapack_int ldb );
+lapack_int LAPACKE_cpbsv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int kd, lapack_int nrhs,
+                               lapack_complex_float* ab, lapack_int ldab,
+                               lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zpbsv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int kd, lapack_int nrhs,
+                               lapack_complex_double* ab, lapack_int ldab,
+                               lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_spbsvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int kd, lapack_int nrhs,
+                                float* ab, lapack_int ldab, float* afb,
+                                lapack_int ldafb, char* equed, float* s,
+                                float* b, lapack_int ldb, float* x,
+                                lapack_int ldx, float* rcond, float* ferr,
+                                float* berr, float* work, lapack_int* iwork );
+lapack_int LAPACKE_dpbsvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int kd, lapack_int nrhs,
+                                double* ab, lapack_int ldab, double* afb,
+                                lapack_int ldafb, char* equed, double* s,
+                                double* b, lapack_int ldb, double* x,
+                                lapack_int ldx, double* rcond, double* ferr,
+                                double* berr, double* work, lapack_int* iwork );
+lapack_int LAPACKE_cpbsvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int kd, lapack_int nrhs,
+                                lapack_complex_float* ab, lapack_int ldab,
+                                lapack_complex_float* afb, lapack_int ldafb,
+                                char* equed, float* s, lapack_complex_float* b,
+                                lapack_int ldb, lapack_complex_float* x,
+                                lapack_int ldx, float* rcond, float* ferr,
+                                float* berr, lapack_complex_float* work,
+                                float* rwork );
+lapack_int LAPACKE_zpbsvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int kd, lapack_int nrhs,
+                                lapack_complex_double* ab, lapack_int ldab,
+                                lapack_complex_double* afb, lapack_int ldafb,
+                                char* equed, double* s,
+                                lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* rcond, double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_spbtrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, float* ab, lapack_int ldab );
+lapack_int LAPACKE_dpbtrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, double* ab, lapack_int ldab );
+lapack_int LAPACKE_cpbtrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, lapack_complex_float* ab,
+                                lapack_int ldab );
+lapack_int LAPACKE_zpbtrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, lapack_complex_double* ab,
+                                lapack_int ldab );
+
+lapack_int LAPACKE_spbtrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, lapack_int nrhs, const float* ab,
+                                lapack_int ldab, float* b, lapack_int ldb );
+lapack_int LAPACKE_dpbtrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, lapack_int nrhs,
+                                const double* ab, lapack_int ldab, double* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_cpbtrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, lapack_int nrhs,
+                                const lapack_complex_float* ab, lapack_int ldab,
+                                lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zpbtrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int kd, lapack_int nrhs,
+                                const lapack_complex_double* ab,
+                                lapack_int ldab, lapack_complex_double* b,
+                                lapack_int ldb );
+
+lapack_int LAPACKE_spftrf_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, float* a );
+lapack_int LAPACKE_dpftrf_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, double* a );
+lapack_int LAPACKE_cpftrf_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, lapack_complex_float* a );
+lapack_int LAPACKE_zpftrf_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, lapack_complex_double* a );
+
+lapack_int LAPACKE_spftri_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, float* a );
+lapack_int LAPACKE_dpftri_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, double* a );
+lapack_int LAPACKE_cpftri_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, lapack_complex_float* a );
+lapack_int LAPACKE_zpftri_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, lapack_complex_double* a );
+
+lapack_int LAPACKE_spftrs_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, lapack_int nrhs, const float* a,
+                                float* b, lapack_int ldb );
+lapack_int LAPACKE_dpftrs_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, lapack_int nrhs, const double* a,
+                                double* b, lapack_int ldb );
+lapack_int LAPACKE_cpftrs_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* a,
+                                lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zpftrs_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* a,
+                                lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_spocon_work( int matrix_order, char uplo, lapack_int n,
+                                const float* a, lapack_int lda, float anorm,
+                                float* rcond, float* work, lapack_int* iwork );
+lapack_int LAPACKE_dpocon_work( int matrix_order, char uplo, lapack_int n,
+                                const double* a, lapack_int lda, double anorm,
+                                double* rcond, double* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_cpocon_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_float* a, lapack_int lda,
+                                float anorm, float* rcond,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zpocon_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_double* a, lapack_int lda,
+                                double anorm, double* rcond,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_spoequ_work( int matrix_order, lapack_int n, const float* a,
+                                lapack_int lda, float* s, float* scond,
+                                float* amax );
+lapack_int LAPACKE_dpoequ_work( int matrix_order, lapack_int n, const double* a,
+                                lapack_int lda, double* s, double* scond,
+                                double* amax );
+lapack_int LAPACKE_cpoequ_work( int matrix_order, lapack_int n,
+                                const lapack_complex_float* a, lapack_int lda,
+                                float* s, float* scond, float* amax );
+lapack_int LAPACKE_zpoequ_work( int matrix_order, lapack_int n,
+                                const lapack_complex_double* a, lapack_int lda,
+                                double* s, double* scond, double* amax );
+
+lapack_int LAPACKE_spoequb_work( int matrix_order, lapack_int n, const float* a,
+                                 lapack_int lda, float* s, float* scond,
+                                 float* amax );
+lapack_int LAPACKE_dpoequb_work( int matrix_order, lapack_int n,
+                                 const double* a, lapack_int lda, double* s,
+                                 double* scond, double* amax );
+lapack_int LAPACKE_cpoequb_work( int matrix_order, lapack_int n,
+                                 const lapack_complex_float* a, lapack_int lda,
+                                 float* s, float* scond, float* amax );
+lapack_int LAPACKE_zpoequb_work( int matrix_order, lapack_int n,
+                                 const lapack_complex_double* a, lapack_int lda,
+                                 double* s, double* scond, double* amax );
+
+lapack_int LAPACKE_sporfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const float* a, lapack_int lda,
+                                const float* af, lapack_int ldaf,
+                                const float* b, lapack_int ldb, float* x,
+                                lapack_int ldx, float* ferr, float* berr,
+                                float* work, lapack_int* iwork );
+lapack_int LAPACKE_dporfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const double* a,
+                                lapack_int lda, const double* af,
+                                lapack_int ldaf, const double* b,
+                                lapack_int ldb, double* x, lapack_int ldx,
+                                double* ferr, double* berr, double* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_cporfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* a,
+                                lapack_int lda, const lapack_complex_float* af,
+                                lapack_int ldaf, const lapack_complex_float* b,
+                                lapack_int ldb, lapack_complex_float* x,
+                                lapack_int ldx, float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zporfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_double* a,
+                                lapack_int lda, const lapack_complex_double* af,
+                                lapack_int ldaf, const lapack_complex_double* b,
+                                lapack_int ldb, lapack_complex_double* x,
+                                lapack_int ldx, double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_sporfsx_work( int matrix_order, char uplo, char equed,
+                                 lapack_int n, lapack_int nrhs, const float* a,
+                                 lapack_int lda, const float* af,
+                                 lapack_int ldaf, const float* s,
+                                 const float* b, lapack_int ldb, float* x,
+                                 lapack_int ldx, float* rcond, float* berr,
+                                 lapack_int n_err_bnds, float* err_bnds_norm,
+                                 float* err_bnds_comp, lapack_int nparams,
+                                 float* params, float* work,
+                                 lapack_int* iwork );
+lapack_int LAPACKE_dporfsx_work( int matrix_order, char uplo, char equed,
+                                 lapack_int n, lapack_int nrhs, const double* a,
+                                 lapack_int lda, const double* af,
+                                 lapack_int ldaf, const double* s,
+                                 const double* b, lapack_int ldb, double* x,
+                                 lapack_int ldx, double* rcond, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, double* work,
+                                 lapack_int* iwork );
+lapack_int LAPACKE_cporfsx_work( int matrix_order, char uplo, char equed,
+                                 lapack_int n, lapack_int nrhs,
+                                 const lapack_complex_float* a, lapack_int lda,
+                                 const lapack_complex_float* af,
+                                 lapack_int ldaf, const float* s,
+                                 const lapack_complex_float* b, lapack_int ldb,
+                                 lapack_complex_float* x, lapack_int ldx,
+                                 float* rcond, float* berr,
+                                 lapack_int n_err_bnds, float* err_bnds_norm,
+                                 float* err_bnds_comp, lapack_int nparams,
+                                 float* params, lapack_complex_float* work,
+                                 float* rwork );
+lapack_int LAPACKE_zporfsx_work( int matrix_order, char uplo, char equed,
+                                 lapack_int n, lapack_int nrhs,
+                                 const lapack_complex_double* a, lapack_int lda,
+                                 const lapack_complex_double* af,
+                                 lapack_int ldaf, const double* s,
+                                 const lapack_complex_double* b, lapack_int ldb,
+                                 lapack_complex_double* x, lapack_int ldx,
+                                 double* rcond, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, lapack_complex_double* work,
+                                 double* rwork );
+
+lapack_int LAPACKE_sposv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, float* a, lapack_int lda,
+                               float* b, lapack_int ldb );
+lapack_int LAPACKE_dposv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, double* a, lapack_int lda,
+                               double* b, lapack_int ldb );
+lapack_int LAPACKE_cposv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, lapack_complex_float* a,
+                               lapack_int lda, lapack_complex_float* b,
+                               lapack_int ldb );
+lapack_int LAPACKE_zposv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, lapack_complex_double* a,
+                               lapack_int lda, lapack_complex_double* b,
+                               lapack_int ldb );
+lapack_int LAPACKE_dsposv_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, double* a, lapack_int lda,
+                                double* b, lapack_int ldb, double* x,
+                                lapack_int ldx, double* work, float* swork,
+                                lapack_int* iter );
+lapack_int LAPACKE_zcposv_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* b,
+                                lapack_int ldb, lapack_complex_double* x,
+                                lapack_int ldx, lapack_complex_double* work,
+                                lapack_complex_float* swork, double* rwork,
+                                lapack_int* iter );
+
+lapack_int LAPACKE_sposvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs, float* a,
+                                lapack_int lda, float* af, lapack_int ldaf,
+                                char* equed, float* s, float* b, lapack_int ldb,
+                                float* x, lapack_int ldx, float* rcond,
+                                float* ferr, float* berr, float* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dposvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs, double* a,
+                                lapack_int lda, double* af, lapack_int ldaf,
+                                char* equed, double* s, double* b,
+                                lapack_int ldb, double* x, lapack_int ldx,
+                                double* rcond, double* ferr, double* berr,
+                                double* work, lapack_int* iwork );
+lapack_int LAPACKE_cposvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* af, lapack_int ldaf,
+                                char* equed, float* s, lapack_complex_float* b,
+                                lapack_int ldb, lapack_complex_float* x,
+                                lapack_int ldx, float* rcond, float* ferr,
+                                float* berr, lapack_complex_float* work,
+                                float* rwork );
+lapack_int LAPACKE_zposvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* af, lapack_int ldaf,
+                                char* equed, double* s,
+                                lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* rcond, double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_sposvxx_work( int matrix_order, char fact, char uplo,
+                                 lapack_int n, lapack_int nrhs, float* a,
+                                 lapack_int lda, float* af, lapack_int ldaf,
+                                 char* equed, float* s, float* b,
+                                 lapack_int ldb, float* x, lapack_int ldx,
+                                 float* rcond, float* rpvgrw, float* berr,
+                                 lapack_int n_err_bnds, float* err_bnds_norm,
+                                 float* err_bnds_comp, lapack_int nparams,
+                                 float* params, float* work,
+                                 lapack_int* iwork );
+lapack_int LAPACKE_dposvxx_work( int matrix_order, char fact, char uplo,
+                                 lapack_int n, lapack_int nrhs, double* a,
+                                 lapack_int lda, double* af, lapack_int ldaf,
+                                 char* equed, double* s, double* b,
+                                 lapack_int ldb, double* x, lapack_int ldx,
+                                 double* rcond, double* rpvgrw, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, double* work,
+                                 lapack_int* iwork );
+lapack_int LAPACKE_cposvxx_work( int matrix_order, char fact, char uplo,
+                                 lapack_int n, lapack_int nrhs,
+                                 lapack_complex_float* a, lapack_int lda,
+                                 lapack_complex_float* af, lapack_int ldaf,
+                                 char* equed, float* s, lapack_complex_float* b,
+                                 lapack_int ldb, lapack_complex_float* x,
+                                 lapack_int ldx, float* rcond, float* rpvgrw,
+                                 float* berr, lapack_int n_err_bnds,
+                                 float* err_bnds_norm, float* err_bnds_comp,
+                                 lapack_int nparams, float* params,
+                                 lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zposvxx_work( int matrix_order, char fact, char uplo,
+                                 lapack_int n, lapack_int nrhs,
+                                 lapack_complex_double* a, lapack_int lda,
+                                 lapack_complex_double* af, lapack_int ldaf,
+                                 char* equed, double* s,
+                                 lapack_complex_double* b, lapack_int ldb,
+                                 lapack_complex_double* x, lapack_int ldx,
+                                 double* rcond, double* rpvgrw, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, lapack_complex_double* work,
+                                 double* rwork );
+
+lapack_int LAPACKE_spotrf_work( int matrix_order, char uplo, lapack_int n,
+                                float* a, lapack_int lda );
+lapack_int LAPACKE_dpotrf_work( int matrix_order, char uplo, lapack_int n,
+                                double* a, lapack_int lda );
+lapack_int LAPACKE_cpotrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda );
+lapack_int LAPACKE_zpotrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_spotri_work( int matrix_order, char uplo, lapack_int n,
+                                float* a, lapack_int lda );
+lapack_int LAPACKE_dpotri_work( int matrix_order, char uplo, lapack_int n,
+                                double* a, lapack_int lda );
+lapack_int LAPACKE_cpotri_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda );
+lapack_int LAPACKE_zpotri_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_spotrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const float* a, lapack_int lda,
+                                float* b, lapack_int ldb );
+lapack_int LAPACKE_dpotrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const double* a,
+                                lapack_int lda, double* b, lapack_int ldb );
+lapack_int LAPACKE_cpotrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* a,
+                                lapack_int lda, lapack_complex_float* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_zpotrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* b,
+                                lapack_int ldb );
+
+lapack_int LAPACKE_sppcon_work( int matrix_order, char uplo, lapack_int n,
+                                const float* ap, float anorm, float* rcond,
+                                float* work, lapack_int* iwork );
+lapack_int LAPACKE_dppcon_work( int matrix_order, char uplo, lapack_int n,
+                                const double* ap, double anorm, double* rcond,
+                                double* work, lapack_int* iwork );
+lapack_int LAPACKE_cppcon_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_float* ap, float anorm,
+                                float* rcond, lapack_complex_float* work,
+                                float* rwork );
+lapack_int LAPACKE_zppcon_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_double* ap, double anorm,
+                                double* rcond, lapack_complex_double* work,
+                                double* rwork );
+
+lapack_int LAPACKE_sppequ_work( int matrix_order, char uplo, lapack_int n,
+                                const float* ap, float* s, float* scond,
+                                float* amax );
+lapack_int LAPACKE_dppequ_work( int matrix_order, char uplo, lapack_int n,
+                                const double* ap, double* s, double* scond,
+                                double* amax );
+lapack_int LAPACKE_cppequ_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_float* ap, float* s,
+                                float* scond, float* amax );
+lapack_int LAPACKE_zppequ_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_double* ap, double* s,
+                                double* scond, double* amax );
+
+lapack_int LAPACKE_spprfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const float* ap,
+                                const float* afp, const float* b,
+                                lapack_int ldb, float* x, lapack_int ldx,
+                                float* ferr, float* berr, float* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dpprfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const double* ap,
+                                const double* afp, const double* b,
+                                lapack_int ldb, double* x, lapack_int ldx,
+                                double* ferr, double* berr, double* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_cpprfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* ap,
+                                const lapack_complex_float* afp,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx,
+                                float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zpprfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs,
+                                const lapack_complex_double* ap,
+                                const lapack_complex_double* afp,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_sppsv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, float* ap, float* b,
+                               lapack_int ldb );
+lapack_int LAPACKE_dppsv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, double* ap, double* b,
+                               lapack_int ldb );
+lapack_int LAPACKE_cppsv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, lapack_complex_float* ap,
+                               lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zppsv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, lapack_complex_double* ap,
+                               lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_sppsvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs, float* ap,
+                                float* afp, char* equed, float* s, float* b,
+                                lapack_int ldb, float* x, lapack_int ldx,
+                                float* rcond, float* ferr, float* berr,
+                                float* work, lapack_int* iwork );
+lapack_int LAPACKE_dppsvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs, double* ap,
+                                double* afp, char* equed, double* s, double* b,
+                                lapack_int ldb, double* x, lapack_int ldx,
+                                double* rcond, double* ferr, double* berr,
+                                double* work, lapack_int* iwork );
+lapack_int LAPACKE_cppsvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs,
+                                lapack_complex_float* ap,
+                                lapack_complex_float* afp, char* equed,
+                                float* s, lapack_complex_float* b,
+                                lapack_int ldb, lapack_complex_float* x,
+                                lapack_int ldx, float* rcond, float* ferr,
+                                float* berr, lapack_complex_float* work,
+                                float* rwork );
+lapack_int LAPACKE_zppsvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs,
+                                lapack_complex_double* ap,
+                                lapack_complex_double* afp, char* equed,
+                                double* s, lapack_complex_double* b,
+                                lapack_int ldb, lapack_complex_double* x,
+                                lapack_int ldx, double* rcond, double* ferr,
+                                double* berr, lapack_complex_double* work,
+                                double* rwork );
+
+lapack_int LAPACKE_spptrf_work( int matrix_order, char uplo, lapack_int n,
+                                float* ap );
+lapack_int LAPACKE_dpptrf_work( int matrix_order, char uplo, lapack_int n,
+                                double* ap );
+lapack_int LAPACKE_cpptrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* ap );
+lapack_int LAPACKE_zpptrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* ap );
+
+lapack_int LAPACKE_spptri_work( int matrix_order, char uplo, lapack_int n,
+                                float* ap );
+lapack_int LAPACKE_dpptri_work( int matrix_order, char uplo, lapack_int n,
+                                double* ap );
+lapack_int LAPACKE_cpptri_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* ap );
+lapack_int LAPACKE_zpptri_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* ap );
+
+lapack_int LAPACKE_spptrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const float* ap, float* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_dpptrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const double* ap, double* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_cpptrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* ap,
+                                lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zpptrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs,
+                                const lapack_complex_double* ap,
+                                lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_spstrf_work( int matrix_order, char uplo, lapack_int n,
+                                float* a, lapack_int lda, lapack_int* piv,
+                                lapack_int* rank, float tol, float* work );
+lapack_int LAPACKE_dpstrf_work( int matrix_order, char uplo, lapack_int n,
+                                double* a, lapack_int lda, lapack_int* piv,
+                                lapack_int* rank, double tol, double* work );
+lapack_int LAPACKE_cpstrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_int* piv, lapack_int* rank, float tol,
+                                float* work );
+lapack_int LAPACKE_zpstrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_int* piv, lapack_int* rank, double tol,
+                                double* work );
+
+lapack_int LAPACKE_sptcon_work( lapack_int n, const float* d, const float* e,
+                                float anorm, float* rcond, float* work );
+lapack_int LAPACKE_dptcon_work( lapack_int n, const double* d, const double* e,
+                                double anorm, double* rcond, double* work );
+lapack_int LAPACKE_cptcon_work( lapack_int n, const float* d,
+                                const lapack_complex_float* e, float anorm,
+                                float* rcond, float* work );
+lapack_int LAPACKE_zptcon_work( lapack_int n, const double* d,
+                                const lapack_complex_double* e, double anorm,
+                                double* rcond, double* work );
+
+lapack_int LAPACKE_spteqr_work( int matrix_order, char compz, lapack_int n,
+                                float* d, float* e, float* z, lapack_int ldz,
+                                float* work );
+lapack_int LAPACKE_dpteqr_work( int matrix_order, char compz, lapack_int n,
+                                double* d, double* e, double* z, lapack_int ldz,
+                                double* work );
+lapack_int LAPACKE_cpteqr_work( int matrix_order, char compz, lapack_int n,
+                                float* d, float* e, lapack_complex_float* z,
+                                lapack_int ldz, float* work );
+lapack_int LAPACKE_zpteqr_work( int matrix_order, char compz, lapack_int n,
+                                double* d, double* e, lapack_complex_double* z,
+                                lapack_int ldz, double* work );
+
+lapack_int LAPACKE_sptrfs_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                                const float* d, const float* e, const float* df,
+                                const float* ef, const float* b, lapack_int ldb,
+                                float* x, lapack_int ldx, float* ferr,
+                                float* berr, float* work );
+lapack_int LAPACKE_dptrfs_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                                const double* d, const double* e,
+                                const double* df, const double* ef,
+                                const double* b, lapack_int ldb, double* x,
+                                lapack_int ldx, double* ferr, double* berr,
+                                double* work );
+lapack_int LAPACKE_cptrfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const float* d,
+                                const lapack_complex_float* e, const float* df,
+                                const lapack_complex_float* ef,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx,
+                                float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zptrfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const double* d,
+                                const lapack_complex_double* e,
+                                const double* df,
+                                const lapack_complex_double* ef,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_sptsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                               float* d, float* e, float* b, lapack_int ldb );
+lapack_int LAPACKE_dptsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                               double* d, double* e, double* b,
+                               lapack_int ldb );
+lapack_int LAPACKE_cptsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                               float* d, lapack_complex_float* e,
+                               lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zptsv_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                               double* d, lapack_complex_double* e,
+                               lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_sptsvx_work( int matrix_order, char fact, lapack_int n,
+                                lapack_int nrhs, const float* d, const float* e,
+                                float* df, float* ef, const float* b,
+                                lapack_int ldb, float* x, lapack_int ldx,
+                                float* rcond, float* ferr, float* berr,
+                                float* work );
+lapack_int LAPACKE_dptsvx_work( int matrix_order, char fact, lapack_int n,
+                                lapack_int nrhs, const double* d,
+                                const double* e, double* df, double* ef,
+                                const double* b, lapack_int ldb, double* x,
+                                lapack_int ldx, double* rcond, double* ferr,
+                                double* berr, double* work );
+lapack_int LAPACKE_cptsvx_work( int matrix_order, char fact, lapack_int n,
+                                lapack_int nrhs, const float* d,
+                                const lapack_complex_float* e, float* df,
+                                lapack_complex_float* ef,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx,
+                                float* rcond, float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zptsvx_work( int matrix_order, char fact, lapack_int n,
+                                lapack_int nrhs, const double* d,
+                                const lapack_complex_double* e, double* df,
+                                lapack_complex_double* ef,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* rcond, double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_spttrf_work( lapack_int n, float* d, float* e );
+lapack_int LAPACKE_dpttrf_work( lapack_int n, double* d, double* e );
+lapack_int LAPACKE_cpttrf_work( lapack_int n, float* d,
+                                lapack_complex_float* e );
+lapack_int LAPACKE_zpttrf_work( lapack_int n, double* d,
+                                lapack_complex_double* e );
+
+lapack_int LAPACKE_spttrs_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                                const float* d, const float* e, float* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_dpttrs_work( int matrix_order, lapack_int n, lapack_int nrhs,
+                                const double* d, const double* e, double* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_cpttrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const float* d,
+                                const lapack_complex_float* e,
+                                lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zpttrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const double* d,
+                                const lapack_complex_double* e,
+                                lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_ssbev_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, lapack_int kd, float* ab,
+                               lapack_int ldab, float* w, float* z,
+                               lapack_int ldz, float* work );
+lapack_int LAPACKE_dsbev_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, lapack_int kd, double* ab,
+                               lapack_int ldab, double* w, double* z,
+                               lapack_int ldz, double* work );
+
+lapack_int LAPACKE_ssbevd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, lapack_int kd, float* ab,
+                                lapack_int ldab, float* w, float* z,
+                                lapack_int ldz, float* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_dsbevd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, lapack_int kd, double* ab,
+                                lapack_int ldab, double* w, double* z,
+                                lapack_int ldz, double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+
+lapack_int LAPACKE_ssbevx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n, lapack_int kd,
+                                float* ab, lapack_int ldab, float* q,
+                                lapack_int ldq, float vl, float vu,
+                                lapack_int il, lapack_int iu, float abstol,
+                                lapack_int* m, float* w, float* z,
+                                lapack_int ldz, float* work, lapack_int* iwork,
+                                lapack_int* ifail );
+lapack_int LAPACKE_dsbevx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n, lapack_int kd,
+                                double* ab, lapack_int ldab, double* q,
+                                lapack_int ldq, double vl, double vu,
+                                lapack_int il, lapack_int iu, double abstol,
+                                lapack_int* m, double* w, double* z,
+                                lapack_int ldz, double* work, lapack_int* iwork,
+                                lapack_int* ifail );
+
+lapack_int LAPACKE_ssbgst_work( int matrix_order, char vect, char uplo,
+                                lapack_int n, lapack_int ka, lapack_int kb,
+                                float* ab, lapack_int ldab, const float* bb,
+                                lapack_int ldbb, float* x, lapack_int ldx,
+                                float* work );
+lapack_int LAPACKE_dsbgst_work( int matrix_order, char vect, char uplo,
+                                lapack_int n, lapack_int ka, lapack_int kb,
+                                double* ab, lapack_int ldab, const double* bb,
+                                lapack_int ldbb, double* x, lapack_int ldx,
+                                double* work );
+
+lapack_int LAPACKE_ssbgv_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, lapack_int ka, lapack_int kb,
+                               float* ab, lapack_int ldab, float* bb,
+                               lapack_int ldbb, float* w, float* z,
+                               lapack_int ldz, float* work );
+lapack_int LAPACKE_dsbgv_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, lapack_int ka, lapack_int kb,
+                               double* ab, lapack_int ldab, double* bb,
+                               lapack_int ldbb, double* w, double* z,
+                               lapack_int ldz, double* work );
+
+lapack_int LAPACKE_ssbgvd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, lapack_int ka, lapack_int kb,
+                                float* ab, lapack_int ldab, float* bb,
+                                lapack_int ldbb, float* w, float* z,
+                                lapack_int ldz, float* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_dsbgvd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, lapack_int ka, lapack_int kb,
+                                double* ab, lapack_int ldab, double* bb,
+                                lapack_int ldbb, double* w, double* z,
+                                lapack_int ldz, double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+
+lapack_int LAPACKE_ssbgvx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n, lapack_int ka,
+                                lapack_int kb, float* ab, lapack_int ldab,
+                                float* bb, lapack_int ldbb, float* q,
+                                lapack_int ldq, float vl, float vu,
+                                lapack_int il, lapack_int iu, float abstol,
+                                lapack_int* m, float* w, float* z,
+                                lapack_int ldz, float* work, lapack_int* iwork,
+                                lapack_int* ifail );
+lapack_int LAPACKE_dsbgvx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n, lapack_int ka,
+                                lapack_int kb, double* ab, lapack_int ldab,
+                                double* bb, lapack_int ldbb, double* q,
+                                lapack_int ldq, double vl, double vu,
+                                lapack_int il, lapack_int iu, double abstol,
+                                lapack_int* m, double* w, double* z,
+                                lapack_int ldz, double* work, lapack_int* iwork,
+                                lapack_int* ifail );
+
+lapack_int LAPACKE_ssbtrd_work( int matrix_order, char vect, char uplo,
+                                lapack_int n, lapack_int kd, float* ab,
+                                lapack_int ldab, float* d, float* e, float* q,
+                                lapack_int ldq, float* work );
+lapack_int LAPACKE_dsbtrd_work( int matrix_order, char vect, char uplo,
+                                lapack_int n, lapack_int kd, double* ab,
+                                lapack_int ldab, double* d, double* e,
+                                double* q, lapack_int ldq, double* work );
+
+lapack_int LAPACKE_ssfrk_work( int matrix_order, char transr, char uplo,
+                               char trans, lapack_int n, lapack_int k,
+                               float alpha, const float* a, lapack_int lda,
+                               float beta, float* c );
+lapack_int LAPACKE_dsfrk_work( int matrix_order, char transr, char uplo,
+                               char trans, lapack_int n, lapack_int k,
+                               double alpha, const double* a, lapack_int lda,
+                               double beta, double* c );
+
+lapack_int LAPACKE_sspcon_work( int matrix_order, char uplo, lapack_int n,
+                                const float* ap, const lapack_int* ipiv,
+                                float anorm, float* rcond, float* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dspcon_work( int matrix_order, char uplo, lapack_int n,
+                                const double* ap, const lapack_int* ipiv,
+                                double anorm, double* rcond, double* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_cspcon_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_float* ap,
+                                const lapack_int* ipiv, float anorm,
+                                float* rcond, lapack_complex_float* work );
+lapack_int LAPACKE_zspcon_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_double* ap,
+                                const lapack_int* ipiv, double anorm,
+                                double* rcond, lapack_complex_double* work );
+
+lapack_int LAPACKE_sspev_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, float* ap, float* w, float* z,
+                               lapack_int ldz, float* work );
+lapack_int LAPACKE_dspev_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, double* ap, double* w, double* z,
+                               lapack_int ldz, double* work );
+
+lapack_int LAPACKE_sspevd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, float* ap, float* w, float* z,
+                                lapack_int ldz, float* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_dspevd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, double* ap, double* w, double* z,
+                                lapack_int ldz, double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+
+lapack_int LAPACKE_sspevx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n, float* ap, float vl,
+                                float vu, lapack_int il, lapack_int iu,
+                                float abstol, lapack_int* m, float* w, float* z,
+                                lapack_int ldz, float* work, lapack_int* iwork,
+                                lapack_int* ifail );
+lapack_int LAPACKE_dspevx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n, double* ap, double vl,
+                                double vu, lapack_int il, lapack_int iu,
+                                double abstol, lapack_int* m, double* w,
+                                double* z, lapack_int ldz, double* work,
+                                lapack_int* iwork, lapack_int* ifail );
+
+lapack_int LAPACKE_sspgst_work( int matrix_order, lapack_int itype, char uplo,
+                                lapack_int n, float* ap, const float* bp );
+lapack_int LAPACKE_dspgst_work( int matrix_order, lapack_int itype, char uplo,
+                                lapack_int n, double* ap, const double* bp );
+
+lapack_int LAPACKE_sspgv_work( int matrix_order, lapack_int itype, char jobz,
+                               char uplo, lapack_int n, float* ap, float* bp,
+                               float* w, float* z, lapack_int ldz,
+                               float* work );
+lapack_int LAPACKE_dspgv_work( int matrix_order, lapack_int itype, char jobz,
+                               char uplo, lapack_int n, double* ap, double* bp,
+                               double* w, double* z, lapack_int ldz,
+                               double* work );
+
+lapack_int LAPACKE_sspgvd_work( int matrix_order, lapack_int itype, char jobz,
+                                char uplo, lapack_int n, float* ap, float* bp,
+                                float* w, float* z, lapack_int ldz, float* work,
+                                lapack_int lwork, lapack_int* iwork,
+                                lapack_int liwork );
+lapack_int LAPACKE_dspgvd_work( int matrix_order, lapack_int itype, char jobz,
+                                char uplo, lapack_int n, double* ap, double* bp,
+                                double* w, double* z, lapack_int ldz,
+                                double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+
+lapack_int LAPACKE_sspgvx_work( int matrix_order, lapack_int itype, char jobz,
+                                char range, char uplo, lapack_int n, float* ap,
+                                float* bp, float vl, float vu, lapack_int il,
+                                lapack_int iu, float abstol, lapack_int* m,
+                                float* w, float* z, lapack_int ldz, float* work,
+                                lapack_int* iwork, lapack_int* ifail );
+lapack_int LAPACKE_dspgvx_work( int matrix_order, lapack_int itype, char jobz,
+                                char range, char uplo, lapack_int n, double* ap,
+                                double* bp, double vl, double vu, lapack_int il,
+                                lapack_int iu, double abstol, lapack_int* m,
+                                double* w, double* z, lapack_int ldz,
+                                double* work, lapack_int* iwork,
+                                lapack_int* ifail );
+
+lapack_int LAPACKE_ssprfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const float* ap,
+                                const float* afp, const lapack_int* ipiv,
+                                const float* b, lapack_int ldb, float* x,
+                                lapack_int ldx, float* ferr, float* berr,
+                                float* work, lapack_int* iwork );
+lapack_int LAPACKE_dsprfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const double* ap,
+                                const double* afp, const lapack_int* ipiv,
+                                const double* b, lapack_int ldb, double* x,
+                                lapack_int ldx, double* ferr, double* berr,
+                                double* work, lapack_int* iwork );
+lapack_int LAPACKE_csprfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* ap,
+                                const lapack_complex_float* afp,
+                                const lapack_int* ipiv,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx,
+                                float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zsprfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs,
+                                const lapack_complex_double* ap,
+                                const lapack_complex_double* afp,
+                                const lapack_int* ipiv,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_sspsv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, float* ap, lapack_int* ipiv,
+                               float* b, lapack_int ldb );
+lapack_int LAPACKE_dspsv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, double* ap, lapack_int* ipiv,
+                               double* b, lapack_int ldb );
+lapack_int LAPACKE_cspsv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, lapack_complex_float* ap,
+                               lapack_int* ipiv, lapack_complex_float* b,
+                               lapack_int ldb );
+lapack_int LAPACKE_zspsv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, lapack_complex_double* ap,
+                               lapack_int* ipiv, lapack_complex_double* b,
+                               lapack_int ldb );
+
+lapack_int LAPACKE_sspsvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs, const float* ap,
+                                float* afp, lapack_int* ipiv, const float* b,
+                                lapack_int ldb, float* x, lapack_int ldx,
+                                float* rcond, float* ferr, float* berr,
+                                float* work, lapack_int* iwork );
+lapack_int LAPACKE_dspsvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs, const double* ap,
+                                double* afp, lapack_int* ipiv, const double* b,
+                                lapack_int ldb, double* x, lapack_int ldx,
+                                double* rcond, double* ferr, double* berr,
+                                double* work, lapack_int* iwork );
+lapack_int LAPACKE_cspsvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* ap,
+                                lapack_complex_float* afp, lapack_int* ipiv,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx,
+                                float* rcond, float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zspsvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* ap,
+                                lapack_complex_double* afp, lapack_int* ipiv,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* rcond, double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_ssptrd_work( int matrix_order, char uplo, lapack_int n,
+                                float* ap, float* d, float* e, float* tau );
+lapack_int LAPACKE_dsptrd_work( int matrix_order, char uplo, lapack_int n,
+                                double* ap, double* d, double* e, double* tau );
+
+lapack_int LAPACKE_ssptrf_work( int matrix_order, char uplo, lapack_int n,
+                                float* ap, lapack_int* ipiv );
+lapack_int LAPACKE_dsptrf_work( int matrix_order, char uplo, lapack_int n,
+                                double* ap, lapack_int* ipiv );
+lapack_int LAPACKE_csptrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* ap, lapack_int* ipiv );
+lapack_int LAPACKE_zsptrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* ap, lapack_int* ipiv );
+
+lapack_int LAPACKE_ssptri_work( int matrix_order, char uplo, lapack_int n,
+                                float* ap, const lapack_int* ipiv,
+                                float* work );
+lapack_int LAPACKE_dsptri_work( int matrix_order, char uplo, lapack_int n,
+                                double* ap, const lapack_int* ipiv,
+                                double* work );
+lapack_int LAPACKE_csptri_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* ap,
+                                const lapack_int* ipiv,
+                                lapack_complex_float* work );
+lapack_int LAPACKE_zsptri_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* ap,
+                                const lapack_int* ipiv,
+                                lapack_complex_double* work );
+
+lapack_int LAPACKE_ssptrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const float* ap,
+                                const lapack_int* ipiv, float* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_dsptrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const double* ap,
+                                const lapack_int* ipiv, double* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_csptrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* ap,
+                                const lapack_int* ipiv, lapack_complex_float* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_zsptrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs,
+                                const lapack_complex_double* ap,
+                                const lapack_int* ipiv,
+                                lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_sstebz_work( char range, char order, lapack_int n, float vl,
+                                float vu, lapack_int il, lapack_int iu,
+                                float abstol, const float* d, const float* e,
+                                lapack_int* m, lapack_int* nsplit, float* w,
+                                lapack_int* iblock, lapack_int* isplit,
+                                float* work, lapack_int* iwork );
+lapack_int LAPACKE_dstebz_work( char range, char order, lapack_int n, double vl,
+                                double vu, lapack_int il, lapack_int iu,
+                                double abstol, const double* d, const double* e,
+                                lapack_int* m, lapack_int* nsplit, double* w,
+                                lapack_int* iblock, lapack_int* isplit,
+                                double* work, lapack_int* iwork );
+
+lapack_int LAPACKE_sstedc_work( int matrix_order, char compz, lapack_int n,
+                                float* d, float* e, float* z, lapack_int ldz,
+                                float* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_dstedc_work( int matrix_order, char compz, lapack_int n,
+                                double* d, double* e, double* z, lapack_int ldz,
+                                double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_cstedc_work( int matrix_order, char compz, lapack_int n,
+                                float* d, float* e, lapack_complex_float* z,
+                                lapack_int ldz, lapack_complex_float* work,
+                                lapack_int lwork, float* rwork,
+                                lapack_int lrwork, lapack_int* iwork,
+                                lapack_int liwork );
+lapack_int LAPACKE_zstedc_work( int matrix_order, char compz, lapack_int n,
+                                double* d, double* e, lapack_complex_double* z,
+                                lapack_int ldz, lapack_complex_double* work,
+                                lapack_int lwork, double* rwork,
+                                lapack_int lrwork, lapack_int* iwork,
+                                lapack_int liwork );
+
+lapack_int LAPACKE_sstegr_work( int matrix_order, char jobz, char range,
+                                lapack_int n, float* d, float* e, float vl,
+                                float vu, lapack_int il, lapack_int iu,
+                                float abstol, lapack_int* m, float* w, float* z,
+                                lapack_int ldz, lapack_int* isuppz, float* work,
+                                lapack_int lwork, lapack_int* iwork,
+                                lapack_int liwork );
+lapack_int LAPACKE_dstegr_work( int matrix_order, char jobz, char range,
+                                lapack_int n, double* d, double* e, double vl,
+                                double vu, lapack_int il, lapack_int iu,
+                                double abstol, lapack_int* m, double* w,
+                                double* z, lapack_int ldz, lapack_int* isuppz,
+                                double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_cstegr_work( int matrix_order, char jobz, char range,
+                                lapack_int n, float* d, float* e, float vl,
+                                float vu, lapack_int il, lapack_int iu,
+                                float abstol, lapack_int* m, float* w,
+                                lapack_complex_float* z, lapack_int ldz,
+                                lapack_int* isuppz, float* work,
+                                lapack_int lwork, lapack_int* iwork,
+                                lapack_int liwork );
+lapack_int LAPACKE_zstegr_work( int matrix_order, char jobz, char range,
+                                lapack_int n, double* d, double* e, double vl,
+                                double vu, lapack_int il, lapack_int iu,
+                                double abstol, lapack_int* m, double* w,
+                                lapack_complex_double* z, lapack_int ldz,
+                                lapack_int* isuppz, double* work,
+                                lapack_int lwork, lapack_int* iwork,
+                                lapack_int liwork );
+
+lapack_int LAPACKE_sstein_work( int matrix_order, lapack_int n, const float* d,
+                                const float* e, lapack_int m, const float* w,
+                                const lapack_int* iblock,
+                                const lapack_int* isplit, float* z,
+                                lapack_int ldz, float* work, lapack_int* iwork,
+                                lapack_int* ifailv );
+lapack_int LAPACKE_dstein_work( int matrix_order, lapack_int n, const double* d,
+                                const double* e, lapack_int m, const double* w,
+                                const lapack_int* iblock,
+                                const lapack_int* isplit, double* z,
+                                lapack_int ldz, double* work, lapack_int* iwork,
+                                lapack_int* ifailv );
+lapack_int LAPACKE_cstein_work( int matrix_order, lapack_int n, const float* d,
+                                const float* e, lapack_int m, const float* w,
+                                const lapack_int* iblock,
+                                const lapack_int* isplit,
+                                lapack_complex_float* z, lapack_int ldz,
+                                float* work, lapack_int* iwork,
+                                lapack_int* ifailv );
+lapack_int LAPACKE_zstein_work( int matrix_order, lapack_int n, const double* d,
+                                const double* e, lapack_int m, const double* w,
+                                const lapack_int* iblock,
+                                const lapack_int* isplit,
+                                lapack_complex_double* z, lapack_int ldz,
+                                double* work, lapack_int* iwork,
+                                lapack_int* ifailv );
+
+lapack_int LAPACKE_sstemr_work( int matrix_order, char jobz, char range,
+                                lapack_int n, float* d, float* e, float vl,
+                                float vu, lapack_int il, lapack_int iu,
+                                lapack_int* m, float* w, float* z,
+                                lapack_int ldz, lapack_int nzc,
+                                lapack_int* isuppz, lapack_logical* tryrac,
+                                float* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_dstemr_work( int matrix_order, char jobz, char range,
+                                lapack_int n, double* d, double* e, double vl,
+                                double vu, lapack_int il, lapack_int iu,
+                                lapack_int* m, double* w, double* z,
+                                lapack_int ldz, lapack_int nzc,
+                                lapack_int* isuppz, lapack_logical* tryrac,
+                                double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_cstemr_work( int matrix_order, char jobz, char range,
+                                lapack_int n, float* d, float* e, float vl,
+                                float vu, lapack_int il, lapack_int iu,
+                                lapack_int* m, float* w,
+                                lapack_complex_float* z, lapack_int ldz,
+                                lapack_int nzc, lapack_int* isuppz,
+                                lapack_logical* tryrac, float* work,
+                                lapack_int lwork, lapack_int* iwork,
+                                lapack_int liwork );
+lapack_int LAPACKE_zstemr_work( int matrix_order, char jobz, char range,
+                                lapack_int n, double* d, double* e, double vl,
+                                double vu, lapack_int il, lapack_int iu,
+                                lapack_int* m, double* w,
+                                lapack_complex_double* z, lapack_int ldz,
+                                lapack_int nzc, lapack_int* isuppz,
+                                lapack_logical* tryrac, double* work,
+                                lapack_int lwork, lapack_int* iwork,
+                                lapack_int liwork );
+
+lapack_int LAPACKE_ssteqr_work( int matrix_order, char compz, lapack_int n,
+                                float* d, float* e, float* z, lapack_int ldz,
+                                float* work );
+lapack_int LAPACKE_dsteqr_work( int matrix_order, char compz, lapack_int n,
+                                double* d, double* e, double* z, lapack_int ldz,
+                                double* work );
+lapack_int LAPACKE_csteqr_work( int matrix_order, char compz, lapack_int n,
+                                float* d, float* e, lapack_complex_float* z,
+                                lapack_int ldz, float* work );
+lapack_int LAPACKE_zsteqr_work( int matrix_order, char compz, lapack_int n,
+                                double* d, double* e, lapack_complex_double* z,
+                                lapack_int ldz, double* work );
+
+lapack_int LAPACKE_ssterf_work( lapack_int n, float* d, float* e );
+lapack_int LAPACKE_dsterf_work( lapack_int n, double* d, double* e );
+
+lapack_int LAPACKE_sstev_work( int matrix_order, char jobz, lapack_int n,
+                               float* d, float* e, float* z, lapack_int ldz,
+                               float* work );
+lapack_int LAPACKE_dstev_work( int matrix_order, char jobz, lapack_int n,
+                               double* d, double* e, double* z, lapack_int ldz,
+                               double* work );
+
+lapack_int LAPACKE_sstevd_work( int matrix_order, char jobz, lapack_int n,
+                                float* d, float* e, float* z, lapack_int ldz,
+                                float* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_dstevd_work( int matrix_order, char jobz, lapack_int n,
+                                double* d, double* e, double* z, lapack_int ldz,
+                                double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+
+lapack_int LAPACKE_sstevr_work( int matrix_order, char jobz, char range,
+                                lapack_int n, float* d, float* e, float vl,
+                                float vu, lapack_int il, lapack_int iu,
+                                float abstol, lapack_int* m, float* w, float* z,
+                                lapack_int ldz, lapack_int* isuppz, float* work,
+                                lapack_int lwork, lapack_int* iwork,
+                                lapack_int liwork );
+lapack_int LAPACKE_dstevr_work( int matrix_order, char jobz, char range,
+                                lapack_int n, double* d, double* e, double vl,
+                                double vu, lapack_int il, lapack_int iu,
+                                double abstol, lapack_int* m, double* w,
+                                double* z, lapack_int ldz, lapack_int* isuppz,
+                                double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+
+lapack_int LAPACKE_sstevx_work( int matrix_order, char jobz, char range,
+                                lapack_int n, float* d, float* e, float vl,
+                                float vu, lapack_int il, lapack_int iu,
+                                float abstol, lapack_int* m, float* w, float* z,
+                                lapack_int ldz, float* work, lapack_int* iwork,
+                                lapack_int* ifail );
+lapack_int LAPACKE_dstevx_work( int matrix_order, char jobz, char range,
+                                lapack_int n, double* d, double* e, double vl,
+                                double vu, lapack_int il, lapack_int iu,
+                                double abstol, lapack_int* m, double* w,
+                                double* z, lapack_int ldz, double* work,
+                                lapack_int* iwork, lapack_int* ifail );
+
+lapack_int LAPACKE_ssycon_work( int matrix_order, char uplo, lapack_int n,
+                                const float* a, lapack_int lda,
+                                const lapack_int* ipiv, float anorm,
+                                float* rcond, float* work, lapack_int* iwork );
+lapack_int LAPACKE_dsycon_work( int matrix_order, char uplo, lapack_int n,
+                                const double* a, lapack_int lda,
+                                const lapack_int* ipiv, double anorm,
+                                double* rcond, double* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_csycon_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_float* a, lapack_int lda,
+                                const lapack_int* ipiv, float anorm,
+                                float* rcond, lapack_complex_float* work );
+lapack_int LAPACKE_zsycon_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_double* a, lapack_int lda,
+                                const lapack_int* ipiv, double anorm,
+                                double* rcond, lapack_complex_double* work );
+
+lapack_int LAPACKE_ssyequb_work( int matrix_order, char uplo, lapack_int n,
+                                 const float* a, lapack_int lda, float* s,
+                                 float* scond, float* amax, float* work );
+lapack_int LAPACKE_dsyequb_work( int matrix_order, char uplo, lapack_int n,
+                                 const double* a, lapack_int lda, double* s,
+                                 double* scond, double* amax, double* work );
+lapack_int LAPACKE_csyequb_work( int matrix_order, char uplo, lapack_int n,
+                                 const lapack_complex_float* a, lapack_int lda,
+                                 float* s, float* scond, float* amax,
+                                 lapack_complex_float* work );
+lapack_int LAPACKE_zsyequb_work( int matrix_order, char uplo, lapack_int n,
+                                 const lapack_complex_double* a, lapack_int lda,
+                                 double* s, double* scond, double* amax,
+                                 lapack_complex_double* work );
+
+lapack_int LAPACKE_ssyev_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, float* a, lapack_int lda, float* w,
+                               float* work, lapack_int lwork );
+lapack_int LAPACKE_dsyev_work( int matrix_order, char jobz, char uplo,
+                               lapack_int n, double* a, lapack_int lda,
+                               double* w, double* work, lapack_int lwork );
+
+lapack_int LAPACKE_ssyevd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, float* a, lapack_int lda,
+                                float* w, float* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_dsyevd_work( int matrix_order, char jobz, char uplo,
+                                lapack_int n, double* a, lapack_int lda,
+                                double* w, double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+
+lapack_int LAPACKE_ssyevr_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n, float* a,
+                                lapack_int lda, float vl, float vu,
+                                lapack_int il, lapack_int iu, float abstol,
+                                lapack_int* m, float* w, float* z,
+                                lapack_int ldz, lapack_int* isuppz, float* work,
+                                lapack_int lwork, lapack_int* iwork,
+                                lapack_int liwork );
+lapack_int LAPACKE_dsyevr_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n, double* a,
+                                lapack_int lda, double vl, double vu,
+                                lapack_int il, lapack_int iu, double abstol,
+                                lapack_int* m, double* w, double* z,
+                                lapack_int ldz, lapack_int* isuppz,
+                                double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+
+lapack_int LAPACKE_ssyevx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n, float* a,
+                                lapack_int lda, float vl, float vu,
+                                lapack_int il, lapack_int iu, float abstol,
+                                lapack_int* m, float* w, float* z,
+                                lapack_int ldz, float* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int* ifail );
+lapack_int LAPACKE_dsyevx_work( int matrix_order, char jobz, char range,
+                                char uplo, lapack_int n, double* a,
+                                lapack_int lda, double vl, double vu,
+                                lapack_int il, lapack_int iu, double abstol,
+                                lapack_int* m, double* w, double* z,
+                                lapack_int ldz, double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int* ifail );
+
+lapack_int LAPACKE_ssygst_work( int matrix_order, lapack_int itype, char uplo,
+                                lapack_int n, float* a, lapack_int lda,
+                                const float* b, lapack_int ldb );
+lapack_int LAPACKE_dsygst_work( int matrix_order, lapack_int itype, char uplo,
+                                lapack_int n, double* a, lapack_int lda,
+                                const double* b, lapack_int ldb );
+
+lapack_int LAPACKE_ssygv_work( int matrix_order, lapack_int itype, char jobz,
+                               char uplo, lapack_int n, float* a,
+                               lapack_int lda, float* b, lapack_int ldb,
+                               float* w, float* work, lapack_int lwork );
+lapack_int LAPACKE_dsygv_work( int matrix_order, lapack_int itype, char jobz,
+                               char uplo, lapack_int n, double* a,
+                               lapack_int lda, double* b, lapack_int ldb,
+                               double* w, double* work, lapack_int lwork );
+
+lapack_int LAPACKE_ssygvd_work( int matrix_order, lapack_int itype, char jobz,
+                                char uplo, lapack_int n, float* a,
+                                lapack_int lda, float* b, lapack_int ldb,
+                                float* w, float* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_dsygvd_work( int matrix_order, lapack_int itype, char jobz,
+                                char uplo, lapack_int n, double* a,
+                                lapack_int lda, double* b, lapack_int ldb,
+                                double* w, double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+
+lapack_int LAPACKE_ssygvx_work( int matrix_order, lapack_int itype, char jobz,
+                                char range, char uplo, lapack_int n, float* a,
+                                lapack_int lda, float* b, lapack_int ldb,
+                                float vl, float vu, lapack_int il,
+                                lapack_int iu, float abstol, lapack_int* m,
+                                float* w, float* z, lapack_int ldz, float* work,
+                                lapack_int lwork, lapack_int* iwork,
+                                lapack_int* ifail );
+lapack_int LAPACKE_dsygvx_work( int matrix_order, lapack_int itype, char jobz,
+                                char range, char uplo, lapack_int n, double* a,
+                                lapack_int lda, double* b, lapack_int ldb,
+                                double vl, double vu, lapack_int il,
+                                lapack_int iu, double abstol, lapack_int* m,
+                                double* w, double* z, lapack_int ldz,
+                                double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int* ifail );
+
+lapack_int LAPACKE_ssyrfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const float* a, lapack_int lda,
+                                const float* af, lapack_int ldaf,
+                                const lapack_int* ipiv, const float* b,
+                                lapack_int ldb, float* x, lapack_int ldx,
+                                float* ferr, float* berr, float* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dsyrfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const double* a,
+                                lapack_int lda, const double* af,
+                                lapack_int ldaf, const lapack_int* ipiv,
+                                const double* b, lapack_int ldb, double* x,
+                                lapack_int ldx, double* ferr, double* berr,
+                                double* work, lapack_int* iwork );
+lapack_int LAPACKE_csyrfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* a,
+                                lapack_int lda, const lapack_complex_float* af,
+                                lapack_int ldaf, const lapack_int* ipiv,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* x, lapack_int ldx,
+                                float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_zsyrfs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_double* a,
+                                lapack_int lda, const lapack_complex_double* af,
+                                lapack_int ldaf, const lapack_int* ipiv,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_ssyrfsx_work( int matrix_order, char uplo, char equed,
+                                 lapack_int n, lapack_int nrhs, const float* a,
+                                 lapack_int lda, const float* af,
+                                 lapack_int ldaf, const lapack_int* ipiv,
+                                 const float* s, const float* b, lapack_int ldb,
+                                 float* x, lapack_int ldx, float* rcond,
+                                 float* berr, lapack_int n_err_bnds,
+                                 float* err_bnds_norm, float* err_bnds_comp,
+                                 lapack_int nparams, float* params, float* work,
+                                 lapack_int* iwork );
+lapack_int LAPACKE_dsyrfsx_work( int matrix_order, char uplo, char equed,
+                                 lapack_int n, lapack_int nrhs, const double* a,
+                                 lapack_int lda, const double* af,
+                                 lapack_int ldaf, const lapack_int* ipiv,
+                                 const double* s, const double* b,
+                                 lapack_int ldb, double* x, lapack_int ldx,
+                                 double* rcond, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, double* work,
+                                 lapack_int* iwork );
+lapack_int LAPACKE_csyrfsx_work( int matrix_order, char uplo, char equed,
+                                 lapack_int n, lapack_int nrhs,
+                                 const lapack_complex_float* a, lapack_int lda,
+                                 const lapack_complex_float* af,
+                                 lapack_int ldaf, const lapack_int* ipiv,
+                                 const float* s, const lapack_complex_float* b,
+                                 lapack_int ldb, lapack_complex_float* x,
+                                 lapack_int ldx, float* rcond, float* berr,
+                                 lapack_int n_err_bnds, float* err_bnds_norm,
+                                 float* err_bnds_comp, lapack_int nparams,
+                                 float* params, lapack_complex_float* work,
+                                 float* rwork );
+lapack_int LAPACKE_zsyrfsx_work( int matrix_order, char uplo, char equed,
+                                 lapack_int n, lapack_int nrhs,
+                                 const lapack_complex_double* a, lapack_int lda,
+                                 const lapack_complex_double* af,
+                                 lapack_int ldaf, const lapack_int* ipiv,
+                                 const double* s,
+                                 const lapack_complex_double* b, lapack_int ldb,
+                                 lapack_complex_double* x, lapack_int ldx,
+                                 double* rcond, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, lapack_complex_double* work,
+                                 double* rwork );
+
+lapack_int LAPACKE_ssysv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, float* a, lapack_int lda,
+                               lapack_int* ipiv, float* b, lapack_int ldb,
+                               float* work, lapack_int lwork );
+lapack_int LAPACKE_dsysv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, double* a, lapack_int lda,
+                               lapack_int* ipiv, double* b, lapack_int ldb,
+                               double* work, lapack_int lwork );
+lapack_int LAPACKE_csysv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, lapack_complex_float* a,
+                               lapack_int lda, lapack_int* ipiv,
+                               lapack_complex_float* b, lapack_int ldb,
+                               lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zsysv_work( int matrix_order, char uplo, lapack_int n,
+                               lapack_int nrhs, lapack_complex_double* a,
+                               lapack_int lda, lapack_int* ipiv,
+                               lapack_complex_double* b, lapack_int ldb,
+                               lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_ssysvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs, const float* a,
+                                lapack_int lda, float* af, lapack_int ldaf,
+                                lapack_int* ipiv, const float* b,
+                                lapack_int ldb, float* x, lapack_int ldx,
+                                float* rcond, float* ferr, float* berr,
+                                float* work, lapack_int lwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dsysvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs, const double* a,
+                                lapack_int lda, double* af, lapack_int ldaf,
+                                lapack_int* ipiv, const double* b,
+                                lapack_int ldb, double* x, lapack_int ldx,
+                                double* rcond, double* ferr, double* berr,
+                                double* work, lapack_int lwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_csysvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* af, lapack_int ldaf,
+                                lapack_int* ipiv, const lapack_complex_float* b,
+                                lapack_int ldb, lapack_complex_float* x,
+                                lapack_int ldx, float* rcond, float* ferr,
+                                float* berr, lapack_complex_float* work,
+                                lapack_int lwork, float* rwork );
+lapack_int LAPACKE_zsysvx_work( int matrix_order, char fact, char uplo,
+                                lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* af, lapack_int ldaf,
+                                lapack_int* ipiv,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* x, lapack_int ldx,
+                                double* rcond, double* ferr, double* berr,
+                                lapack_complex_double* work, lapack_int lwork,
+                                double* rwork );
+
+lapack_int LAPACKE_ssysvxx_work( int matrix_order, char fact, char uplo,
+                                 lapack_int n, lapack_int nrhs, float* a,
+                                 lapack_int lda, float* af, lapack_int ldaf,
+                                 lapack_int* ipiv, char* equed, float* s,
+                                 float* b, lapack_int ldb, float* x,
+                                 lapack_int ldx, float* rcond, float* rpvgrw,
+                                 float* berr, lapack_int n_err_bnds,
+                                 float* err_bnds_norm, float* err_bnds_comp,
+                                 lapack_int nparams, float* params, float* work,
+                                 lapack_int* iwork );
+lapack_int LAPACKE_dsysvxx_work( int matrix_order, char fact, char uplo,
+                                 lapack_int n, lapack_int nrhs, double* a,
+                                 lapack_int lda, double* af, lapack_int ldaf,
+                                 lapack_int* ipiv, char* equed, double* s,
+                                 double* b, lapack_int ldb, double* x,
+                                 lapack_int ldx, double* rcond, double* rpvgrw,
+                                 double* berr, lapack_int n_err_bnds,
+                                 double* err_bnds_norm, double* err_bnds_comp,
+                                 lapack_int nparams, double* params,
+                                 double* work, lapack_int* iwork );
+lapack_int LAPACKE_csysvxx_work( int matrix_order, char fact, char uplo,
+                                 lapack_int n, lapack_int nrhs,
+                                 lapack_complex_float* a, lapack_int lda,
+                                 lapack_complex_float* af, lapack_int ldaf,
+                                 lapack_int* ipiv, char* equed, float* s,
+                                 lapack_complex_float* b, lapack_int ldb,
+                                 lapack_complex_float* x, lapack_int ldx,
+                                 float* rcond, float* rpvgrw, float* berr,
+                                 lapack_int n_err_bnds, float* err_bnds_norm,
+                                 float* err_bnds_comp, lapack_int nparams,
+                                 float* params, lapack_complex_float* work,
+                                 float* rwork );
+lapack_int LAPACKE_zsysvxx_work( int matrix_order, char fact, char uplo,
+                                 lapack_int n, lapack_int nrhs,
+                                 lapack_complex_double* a, lapack_int lda,
+                                 lapack_complex_double* af, lapack_int ldaf,
+                                 lapack_int* ipiv, char* equed, double* s,
+                                 lapack_complex_double* b, lapack_int ldb,
+                                 lapack_complex_double* x, lapack_int ldx,
+                                 double* rcond, double* rpvgrw, double* berr,
+                                 lapack_int n_err_bnds, double* err_bnds_norm,
+                                 double* err_bnds_comp, lapack_int nparams,
+                                 double* params, lapack_complex_double* work,
+                                 double* rwork );
+
+lapack_int LAPACKE_ssytrd_work( int matrix_order, char uplo, lapack_int n,
+                                float* a, lapack_int lda, float* d, float* e,
+                                float* tau, float* work, lapack_int lwork );
+lapack_int LAPACKE_dsytrd_work( int matrix_order, char uplo, lapack_int n,
+                                double* a, lapack_int lda, double* d, double* e,
+                                double* tau, double* work, lapack_int lwork );
+
+lapack_int LAPACKE_ssytrf_work( int matrix_order, char uplo, lapack_int n,
+                                float* a, lapack_int lda, lapack_int* ipiv,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dsytrf_work( int matrix_order, char uplo, lapack_int n,
+                                double* a, lapack_int lda, lapack_int* ipiv,
+                                double* work, lapack_int lwork );
+lapack_int LAPACKE_csytrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_int* ipiv, lapack_complex_float* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_zsytrf_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_int* ipiv, lapack_complex_double* work,
+                                lapack_int lwork );
+
+lapack_int LAPACKE_ssytri_work( int matrix_order, char uplo, lapack_int n,
+                                float* a, lapack_int lda,
+                                const lapack_int* ipiv, float* work );
+lapack_int LAPACKE_dsytri_work( int matrix_order, char uplo, lapack_int n,
+                                double* a, lapack_int lda,
+                                const lapack_int* ipiv, double* work );
+lapack_int LAPACKE_csytri_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                const lapack_int* ipiv,
+                                lapack_complex_float* work );
+lapack_int LAPACKE_zsytri_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                const lapack_int* ipiv,
+                                lapack_complex_double* work );
+
+lapack_int LAPACKE_ssytrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const float* a, lapack_int lda,
+                                const lapack_int* ipiv, float* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_dsytrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const double* a,
+                                lapack_int lda, const lapack_int* ipiv,
+                                double* b, lapack_int ldb );
+lapack_int LAPACKE_csytrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_float* a,
+                                lapack_int lda, const lapack_int* ipiv,
+                                lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_zsytrs_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_int nrhs, const lapack_complex_double* a,
+                                lapack_int lda, const lapack_int* ipiv,
+                                lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_stbcon_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int n, lapack_int kd,
+                                const float* ab, lapack_int ldab, float* rcond,
+                                float* work, lapack_int* iwork );
+lapack_int LAPACKE_dtbcon_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int n, lapack_int kd,
+                                const double* ab, lapack_int ldab,
+                                double* rcond, double* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_ctbcon_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int n, lapack_int kd,
+                                const lapack_complex_float* ab, lapack_int ldab,
+                                float* rcond, lapack_complex_float* work,
+                                float* rwork );
+lapack_int LAPACKE_ztbcon_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int n, lapack_int kd,
+                                const lapack_complex_double* ab,
+                                lapack_int ldab, double* rcond,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_stbrfs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int kd,
+                                lapack_int nrhs, const float* ab,
+                                lapack_int ldab, const float* b, lapack_int ldb,
+                                const float* x, lapack_int ldx, float* ferr,
+                                float* berr, float* work, lapack_int* iwork );
+lapack_int LAPACKE_dtbrfs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int kd,
+                                lapack_int nrhs, const double* ab,
+                                lapack_int ldab, const double* b,
+                                lapack_int ldb, const double* x, lapack_int ldx,
+                                double* ferr, double* berr, double* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_ctbrfs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int kd,
+                                lapack_int nrhs, const lapack_complex_float* ab,
+                                lapack_int ldab, const lapack_complex_float* b,
+                                lapack_int ldb, const lapack_complex_float* x,
+                                lapack_int ldx, float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_ztbrfs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int kd,
+                                lapack_int nrhs,
+                                const lapack_complex_double* ab,
+                                lapack_int ldab, const lapack_complex_double* b,
+                                lapack_int ldb, const lapack_complex_double* x,
+                                lapack_int ldx, double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_stbtrs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int kd,
+                                lapack_int nrhs, const float* ab,
+                                lapack_int ldab, float* b, lapack_int ldb );
+lapack_int LAPACKE_dtbtrs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int kd,
+                                lapack_int nrhs, const double* ab,
+                                lapack_int ldab, double* b, lapack_int ldb );
+lapack_int LAPACKE_ctbtrs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int kd,
+                                lapack_int nrhs, const lapack_complex_float* ab,
+                                lapack_int ldab, lapack_complex_float* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_ztbtrs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int kd,
+                                lapack_int nrhs,
+                                const lapack_complex_double* ab,
+                                lapack_int ldab, lapack_complex_double* b,
+                                lapack_int ldb );
+
+lapack_int LAPACKE_stfsm_work( int matrix_order, char transr, char side,
+                               char uplo, char trans, char diag, lapack_int m,
+                               lapack_int n, float alpha, const float* a,
+                               float* b, lapack_int ldb );
+lapack_int LAPACKE_dtfsm_work( int matrix_order, char transr, char side,
+                               char uplo, char trans, char diag, lapack_int m,
+                               lapack_int n, double alpha, const double* a,
+                               double* b, lapack_int ldb );
+lapack_int LAPACKE_ctfsm_work( int matrix_order, char transr, char side,
+                               char uplo, char trans, char diag, lapack_int m,
+                               lapack_int n, lapack_complex_float alpha,
+                               const lapack_complex_float* a,
+                               lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_ztfsm_work( int matrix_order, char transr, char side,
+                               char uplo, char trans, char diag, lapack_int m,
+                               lapack_int n, lapack_complex_double alpha,
+                               const lapack_complex_double* a,
+                               lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_stftri_work( int matrix_order, char transr, char uplo,
+                                char diag, lapack_int n, float* a );
+lapack_int LAPACKE_dtftri_work( int matrix_order, char transr, char uplo,
+                                char diag, lapack_int n, double* a );
+lapack_int LAPACKE_ctftri_work( int matrix_order, char transr, char uplo,
+                                char diag, lapack_int n,
+                                lapack_complex_float* a );
+lapack_int LAPACKE_ztftri_work( int matrix_order, char transr, char uplo,
+                                char diag, lapack_int n,
+                                lapack_complex_double* a );
+
+lapack_int LAPACKE_stfttp_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const float* arf, float* ap );
+lapack_int LAPACKE_dtfttp_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const double* arf, double* ap );
+lapack_int LAPACKE_ctfttp_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const lapack_complex_float* arf,
+                                lapack_complex_float* ap );
+lapack_int LAPACKE_ztfttp_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const lapack_complex_double* arf,
+                                lapack_complex_double* ap );
+
+lapack_int LAPACKE_stfttr_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const float* arf, float* a,
+                                lapack_int lda );
+lapack_int LAPACKE_dtfttr_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const double* arf, double* a,
+                                lapack_int lda );
+lapack_int LAPACKE_ctfttr_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const lapack_complex_float* arf,
+                                lapack_complex_float* a, lapack_int lda );
+lapack_int LAPACKE_ztfttr_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const lapack_complex_double* arf,
+                                lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_stgevc_work( int matrix_order, char side, char howmny,
+                                const lapack_logical* select, lapack_int n,
+                                const float* s, lapack_int lds, const float* p,
+                                lapack_int ldp, float* vl, lapack_int ldvl,
+                                float* vr, lapack_int ldvr, lapack_int mm,
+                                lapack_int* m, float* work );
+lapack_int LAPACKE_dtgevc_work( int matrix_order, char side, char howmny,
+                                const lapack_logical* select, lapack_int n,
+                                const double* s, lapack_int lds,
+                                const double* p, lapack_int ldp, double* vl,
+                                lapack_int ldvl, double* vr, lapack_int ldvr,
+                                lapack_int mm, lapack_int* m, double* work );
+lapack_int LAPACKE_ctgevc_work( int matrix_order, char side, char howmny,
+                                const lapack_logical* select, lapack_int n,
+                                const lapack_complex_float* s, lapack_int lds,
+                                const lapack_complex_float* p, lapack_int ldp,
+                                lapack_complex_float* vl, lapack_int ldvl,
+                                lapack_complex_float* vr, lapack_int ldvr,
+                                lapack_int mm, lapack_int* m,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_ztgevc_work( int matrix_order, char side, char howmny,
+                                const lapack_logical* select, lapack_int n,
+                                const lapack_complex_double* s, lapack_int lds,
+                                const lapack_complex_double* p, lapack_int ldp,
+                                lapack_complex_double* vl, lapack_int ldvl,
+                                lapack_complex_double* vr, lapack_int ldvr,
+                                lapack_int mm, lapack_int* m,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_stgexc_work( int matrix_order, lapack_logical wantq,
+                                lapack_logical wantz, lapack_int n, float* a,
+                                lapack_int lda, float* b, lapack_int ldb,
+                                float* q, lapack_int ldq, float* z,
+                                lapack_int ldz, lapack_int* ifst,
+                                lapack_int* ilst, float* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_dtgexc_work( int matrix_order, lapack_logical wantq,
+                                lapack_logical wantz, lapack_int n, double* a,
+                                lapack_int lda, double* b, lapack_int ldb,
+                                double* q, lapack_int ldq, double* z,
+                                lapack_int ldz, lapack_int* ifst,
+                                lapack_int* ilst, double* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_ctgexc_work( int matrix_order, lapack_logical wantq,
+                                lapack_logical wantz, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* q, lapack_int ldq,
+                                lapack_complex_float* z, lapack_int ldz,
+                                lapack_int ifst, lapack_int ilst );
+lapack_int LAPACKE_ztgexc_work( int matrix_order, lapack_logical wantq,
+                                lapack_logical wantz, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* q, lapack_int ldq,
+                                lapack_complex_double* z, lapack_int ldz,
+                                lapack_int ifst, lapack_int ilst );
+
+lapack_int LAPACKE_stgsen_work( int matrix_order, lapack_int ijob,
+                                lapack_logical wantq, lapack_logical wantz,
+                                const lapack_logical* select, lapack_int n,
+                                float* a, lapack_int lda, float* b,
+                                lapack_int ldb, float* alphar, float* alphai,
+                                float* beta, float* q, lapack_int ldq, float* z,
+                                lapack_int ldz, lapack_int* m, float* pl,
+                                float* pr, float* dif, float* work,
+                                lapack_int lwork, lapack_int* iwork,
+                                lapack_int liwork );
+lapack_int LAPACKE_dtgsen_work( int matrix_order, lapack_int ijob,
+                                lapack_logical wantq, lapack_logical wantz,
+                                const lapack_logical* select, lapack_int n,
+                                double* a, lapack_int lda, double* b,
+                                lapack_int ldb, double* alphar, double* alphai,
+                                double* beta, double* q, lapack_int ldq,
+                                double* z, lapack_int ldz, lapack_int* m,
+                                double* pl, double* pr, double* dif,
+                                double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_ctgsen_work( int matrix_order, lapack_int ijob,
+                                lapack_logical wantq, lapack_logical wantz,
+                                const lapack_logical* select, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* alpha,
+                                lapack_complex_float* beta,
+                                lapack_complex_float* q, lapack_int ldq,
+                                lapack_complex_float* z, lapack_int ldz,
+                                lapack_int* m, float* pl, float* pr, float* dif,
+                                lapack_complex_float* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_ztgsen_work( int matrix_order, lapack_int ijob,
+                                lapack_logical wantq, lapack_logical wantz,
+                                const lapack_logical* select, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* alpha,
+                                lapack_complex_double* beta,
+                                lapack_complex_double* q, lapack_int ldq,
+                                lapack_complex_double* z, lapack_int ldz,
+                                lapack_int* m, double* pl, double* pr,
+                                double* dif, lapack_complex_double* work,
+                                lapack_int lwork, lapack_int* iwork,
+                                lapack_int liwork );
+
+lapack_int LAPACKE_stgsja_work( int matrix_order, char jobu, char jobv,
+                                char jobq, lapack_int m, lapack_int p,
+                                lapack_int n, lapack_int k, lapack_int l,
+                                float* a, lapack_int lda, float* b,
+                                lapack_int ldb, float tola, float tolb,
+                                float* alpha, float* beta, float* u,
+                                lapack_int ldu, float* v, lapack_int ldv,
+                                float* q, lapack_int ldq, float* work,
+                                lapack_int* ncycle );
+lapack_int LAPACKE_dtgsja_work( int matrix_order, char jobu, char jobv,
+                                char jobq, lapack_int m, lapack_int p,
+                                lapack_int n, lapack_int k, lapack_int l,
+                                double* a, lapack_int lda, double* b,
+                                lapack_int ldb, double tola, double tolb,
+                                double* alpha, double* beta, double* u,
+                                lapack_int ldu, double* v, lapack_int ldv,
+                                double* q, lapack_int ldq, double* work,
+                                lapack_int* ncycle );
+lapack_int LAPACKE_ctgsja_work( int matrix_order, char jobu, char jobv,
+                                char jobq, lapack_int m, lapack_int p,
+                                lapack_int n, lapack_int k, lapack_int l,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* b, lapack_int ldb,
+                                float tola, float tolb, float* alpha,
+                                float* beta, lapack_complex_float* u,
+                                lapack_int ldu, lapack_complex_float* v,
+                                lapack_int ldv, lapack_complex_float* q,
+                                lapack_int ldq, lapack_complex_float* work,
+                                lapack_int* ncycle );
+lapack_int LAPACKE_ztgsja_work( int matrix_order, char jobu, char jobv,
+                                char jobq, lapack_int m, lapack_int p,
+                                lapack_int n, lapack_int k, lapack_int l,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* b, lapack_int ldb,
+                                double tola, double tolb, double* alpha,
+                                double* beta, lapack_complex_double* u,
+                                lapack_int ldu, lapack_complex_double* v,
+                                lapack_int ldv, lapack_complex_double* q,
+                                lapack_int ldq, lapack_complex_double* work,
+                                lapack_int* ncycle );
+
+lapack_int LAPACKE_stgsna_work( int matrix_order, char job, char howmny,
+                                const lapack_logical* select, lapack_int n,
+                                const float* a, lapack_int lda, const float* b,
+                                lapack_int ldb, const float* vl,
+                                lapack_int ldvl, const float* vr,
+                                lapack_int ldvr, float* s, float* dif,
+                                lapack_int mm, lapack_int* m, float* work,
+                                lapack_int lwork, lapack_int* iwork );
+lapack_int LAPACKE_dtgsna_work( int matrix_order, char job, char howmny,
+                                const lapack_logical* select, lapack_int n,
+                                const double* a, lapack_int lda,
+                                const double* b, lapack_int ldb,
+                                const double* vl, lapack_int ldvl,
+                                const double* vr, lapack_int ldvr, double* s,
+                                double* dif, lapack_int mm, lapack_int* m,
+                                double* work, lapack_int lwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_ctgsna_work( int matrix_order, char job, char howmny,
+                                const lapack_logical* select, lapack_int n,
+                                const lapack_complex_float* a, lapack_int lda,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                const lapack_complex_float* vl, lapack_int ldvl,
+                                const lapack_complex_float* vr, lapack_int ldvr,
+                                float* s, float* dif, lapack_int mm,
+                                lapack_int* m, lapack_complex_float* work,
+                                lapack_int lwork, lapack_int* iwork );
+lapack_int LAPACKE_ztgsna_work( int matrix_order, char job, char howmny,
+                                const lapack_logical* select, lapack_int n,
+                                const lapack_complex_double* a, lapack_int lda,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                const lapack_complex_double* vl,
+                                lapack_int ldvl,
+                                const lapack_complex_double* vr,
+                                lapack_int ldvr, double* s, double* dif,
+                                lapack_int mm, lapack_int* m,
+                                lapack_complex_double* work, lapack_int lwork,
+                                lapack_int* iwork );
+
+lapack_int LAPACKE_stgsyl_work( int matrix_order, char trans, lapack_int ijob,
+                                lapack_int m, lapack_int n, const float* a,
+                                lapack_int lda, const float* b, lapack_int ldb,
+                                float* c, lapack_int ldc, const float* d,
+                                lapack_int ldd, const float* e, lapack_int lde,
+                                float* f, lapack_int ldf, float* scale,
+                                float* dif, float* work, lapack_int lwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dtgsyl_work( int matrix_order, char trans, lapack_int ijob,
+                                lapack_int m, lapack_int n, const double* a,
+                                lapack_int lda, const double* b, lapack_int ldb,
+                                double* c, lapack_int ldc, const double* d,
+                                lapack_int ldd, const double* e, lapack_int lde,
+                                double* f, lapack_int ldf, double* scale,
+                                double* dif, double* work, lapack_int lwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_ctgsyl_work( int matrix_order, char trans, lapack_int ijob,
+                                lapack_int m, lapack_int n,
+                                const lapack_complex_float* a, lapack_int lda,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* c, lapack_int ldc,
+                                const lapack_complex_float* d, lapack_int ldd,
+                                const lapack_complex_float* e, lapack_int lde,
+                                lapack_complex_float* f, lapack_int ldf,
+                                float* scale, float* dif,
+                                lapack_complex_float* work, lapack_int lwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_ztgsyl_work( int matrix_order, char trans, lapack_int ijob,
+                                lapack_int m, lapack_int n,
+                                const lapack_complex_double* a, lapack_int lda,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* c, lapack_int ldc,
+                                const lapack_complex_double* d, lapack_int ldd,
+                                const lapack_complex_double* e, lapack_int lde,
+                                lapack_complex_double* f, lapack_int ldf,
+                                double* scale, double* dif,
+                                lapack_complex_double* work, lapack_int lwork,
+                                lapack_int* iwork );
+
+lapack_int LAPACKE_stpcon_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int n, const float* ap,
+                                float* rcond, float* work, lapack_int* iwork );
+lapack_int LAPACKE_dtpcon_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int n, const double* ap,
+                                double* rcond, double* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_ctpcon_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int n,
+                                const lapack_complex_float* ap, float* rcond,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_ztpcon_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int n,
+                                const lapack_complex_double* ap, double* rcond,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_stprfs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const float* ap, const float* b, lapack_int ldb,
+                                const float* x, lapack_int ldx, float* ferr,
+                                float* berr, float* work, lapack_int* iwork );
+lapack_int LAPACKE_dtprfs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const double* ap, const double* b,
+                                lapack_int ldb, const double* x, lapack_int ldx,
+                                double* ferr, double* berr, double* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_ctprfs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* ap,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                const lapack_complex_float* x, lapack_int ldx,
+                                float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_ztprfs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* ap,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                const lapack_complex_double* x, lapack_int ldx,
+                                double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_stptri_work( int matrix_order, char uplo, char diag,
+                                lapack_int n, float* ap );
+lapack_int LAPACKE_dtptri_work( int matrix_order, char uplo, char diag,
+                                lapack_int n, double* ap );
+lapack_int LAPACKE_ctptri_work( int matrix_order, char uplo, char diag,
+                                lapack_int n, lapack_complex_float* ap );
+lapack_int LAPACKE_ztptri_work( int matrix_order, char uplo, char diag,
+                                lapack_int n, lapack_complex_double* ap );
+
+lapack_int LAPACKE_stptrs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const float* ap, float* b, lapack_int ldb );
+lapack_int LAPACKE_dtptrs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const double* ap, double* b, lapack_int ldb );
+lapack_int LAPACKE_ctptrs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* ap,
+                                lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_ztptrs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* ap,
+                                lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_stpttf_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const float* ap, float* arf );
+lapack_int LAPACKE_dtpttf_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const double* ap, double* arf );
+lapack_int LAPACKE_ctpttf_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const lapack_complex_float* ap,
+                                lapack_complex_float* arf );
+lapack_int LAPACKE_ztpttf_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const lapack_complex_double* ap,
+                                lapack_complex_double* arf );
+
+lapack_int LAPACKE_stpttr_work( int matrix_order, char uplo, lapack_int n,
+                                const float* ap, float* a, lapack_int lda );
+lapack_int LAPACKE_dtpttr_work( int matrix_order, char uplo, lapack_int n,
+                                const double* ap, double* a, lapack_int lda );
+lapack_int LAPACKE_ctpttr_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_float* ap,
+                                lapack_complex_float* a, lapack_int lda );
+lapack_int LAPACKE_ztpttr_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_double* ap,
+                                lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_strcon_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int n, const float* a,
+                                lapack_int lda, float* rcond, float* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dtrcon_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int n, const double* a,
+                                lapack_int lda, double* rcond, double* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_ctrcon_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int n,
+                                const lapack_complex_float* a, lapack_int lda,
+                                float* rcond, lapack_complex_float* work,
+                                float* rwork );
+lapack_int LAPACKE_ztrcon_work( int matrix_order, char norm, char uplo,
+                                char diag, lapack_int n,
+                                const lapack_complex_double* a, lapack_int lda,
+                                double* rcond, lapack_complex_double* work,
+                                double* rwork );
+
+lapack_int LAPACKE_strevc_work( int matrix_order, char side, char howmny,
+                                lapack_logical* select, lapack_int n,
+                                const float* t, lapack_int ldt, float* vl,
+                                lapack_int ldvl, float* vr, lapack_int ldvr,
+                                lapack_int mm, lapack_int* m, float* work );
+lapack_int LAPACKE_dtrevc_work( int matrix_order, char side, char howmny,
+                                lapack_logical* select, lapack_int n,
+                                const double* t, lapack_int ldt, double* vl,
+                                lapack_int ldvl, double* vr, lapack_int ldvr,
+                                lapack_int mm, lapack_int* m, double* work );
+lapack_int LAPACKE_ctrevc_work( int matrix_order, char side, char howmny,
+                                const lapack_logical* select, lapack_int n,
+                                lapack_complex_float* t, lapack_int ldt,
+                                lapack_complex_float* vl, lapack_int ldvl,
+                                lapack_complex_float* vr, lapack_int ldvr,
+                                lapack_int mm, lapack_int* m,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_ztrevc_work( int matrix_order, char side, char howmny,
+                                const lapack_logical* select, lapack_int n,
+                                lapack_complex_double* t, lapack_int ldt,
+                                lapack_complex_double* vl, lapack_int ldvl,
+                                lapack_complex_double* vr, lapack_int ldvr,
+                                lapack_int mm, lapack_int* m,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_strexc_work( int matrix_order, char compq, lapack_int n,
+                                float* t, lapack_int ldt, float* q,
+                                lapack_int ldq, lapack_int* ifst,
+                                lapack_int* ilst, float* work );
+lapack_int LAPACKE_dtrexc_work( int matrix_order, char compq, lapack_int n,
+                                double* t, lapack_int ldt, double* q,
+                                lapack_int ldq, lapack_int* ifst,
+                                lapack_int* ilst, double* work );
+lapack_int LAPACKE_ctrexc_work( int matrix_order, char compq, lapack_int n,
+                                lapack_complex_float* t, lapack_int ldt,
+                                lapack_complex_float* q, lapack_int ldq,
+                                lapack_int ifst, lapack_int ilst );
+lapack_int LAPACKE_ztrexc_work( int matrix_order, char compq, lapack_int n,
+                                lapack_complex_double* t, lapack_int ldt,
+                                lapack_complex_double* q, lapack_int ldq,
+                                lapack_int ifst, lapack_int ilst );
+
+lapack_int LAPACKE_strrfs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const float* a, lapack_int lda, const float* b,
+                                lapack_int ldb, const float* x, lapack_int ldx,
+                                float* ferr, float* berr, float* work,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dtrrfs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const double* a, lapack_int lda,
+                                const double* b, lapack_int ldb,
+                                const double* x, lapack_int ldx, double* ferr,
+                                double* berr, double* work, lapack_int* iwork );
+lapack_int LAPACKE_ctrrfs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* a, lapack_int lda,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                const lapack_complex_float* x, lapack_int ldx,
+                                float* ferr, float* berr,
+                                lapack_complex_float* work, float* rwork );
+lapack_int LAPACKE_ztrrfs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* a, lapack_int lda,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                const lapack_complex_double* x, lapack_int ldx,
+                                double* ferr, double* berr,
+                                lapack_complex_double* work, double* rwork );
+
+lapack_int LAPACKE_strsen_work( int matrix_order, char job, char compq,
+                                const lapack_logical* select, lapack_int n,
+                                float* t, lapack_int ldt, float* q,
+                                lapack_int ldq, float* wr, float* wi,
+                                lapack_int* m, float* s, float* sep,
+                                float* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_dtrsen_work( int matrix_order, char job, char compq,
+                                const lapack_logical* select, lapack_int n,
+                                double* t, lapack_int ldt, double* q,
+                                lapack_int ldq, double* wr, double* wi,
+                                lapack_int* m, double* s, double* sep,
+                                double* work, lapack_int lwork,
+                                lapack_int* iwork, lapack_int liwork );
+lapack_int LAPACKE_ctrsen_work( int matrix_order, char job, char compq,
+                                const lapack_logical* select, lapack_int n,
+                                lapack_complex_float* t, lapack_int ldt,
+                                lapack_complex_float* q, lapack_int ldq,
+                                lapack_complex_float* w, lapack_int* m,
+                                float* s, float* sep,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_ztrsen_work( int matrix_order, char job, char compq,
+                                const lapack_logical* select, lapack_int n,
+                                lapack_complex_double* t, lapack_int ldt,
+                                lapack_complex_double* q, lapack_int ldq,
+                                lapack_complex_double* w, lapack_int* m,
+                                double* s, double* sep,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_strsna_work( int matrix_order, char job, char howmny,
+                                const lapack_logical* select, lapack_int n,
+                                const float* t, lapack_int ldt, const float* vl,
+                                lapack_int ldvl, const float* vr,
+                                lapack_int ldvr, float* s, float* sep,
+                                lapack_int mm, lapack_int* m, float* work,
+                                lapack_int ldwork, lapack_int* iwork );
+lapack_int LAPACKE_dtrsna_work( int matrix_order, char job, char howmny,
+                                const lapack_logical* select, lapack_int n,
+                                const double* t, lapack_int ldt,
+                                const double* vl, lapack_int ldvl,
+                                const double* vr, lapack_int ldvr, double* s,
+                                double* sep, lapack_int mm, lapack_int* m,
+                                double* work, lapack_int ldwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_ctrsna_work( int matrix_order, char job, char howmny,
+                                const lapack_logical* select, lapack_int n,
+                                const lapack_complex_float* t, lapack_int ldt,
+                                const lapack_complex_float* vl, lapack_int ldvl,
+                                const lapack_complex_float* vr, lapack_int ldvr,
+                                float* s, float* sep, lapack_int mm,
+                                lapack_int* m, lapack_complex_float* work,
+                                lapack_int ldwork, float* rwork );
+lapack_int LAPACKE_ztrsna_work( int matrix_order, char job, char howmny,
+                                const lapack_logical* select, lapack_int n,
+                                const lapack_complex_double* t, lapack_int ldt,
+                                const lapack_complex_double* vl,
+                                lapack_int ldvl,
+                                const lapack_complex_double* vr,
+                                lapack_int ldvr, double* s, double* sep,
+                                lapack_int mm, lapack_int* m,
+                                lapack_complex_double* work, lapack_int ldwork,
+                                double* rwork );
+
+lapack_int LAPACKE_strsyl_work( int matrix_order, char trana, char tranb,
+                                lapack_int isgn, lapack_int m, lapack_int n,
+                                const float* a, lapack_int lda, const float* b,
+                                lapack_int ldb, float* c, lapack_int ldc,
+                                float* scale );
+lapack_int LAPACKE_dtrsyl_work( int matrix_order, char trana, char tranb,
+                                lapack_int isgn, lapack_int m, lapack_int n,
+                                const double* a, lapack_int lda,
+                                const double* b, lapack_int ldb, double* c,
+                                lapack_int ldc, double* scale );
+lapack_int LAPACKE_ctrsyl_work( int matrix_order, char trana, char tranb,
+                                lapack_int isgn, lapack_int m, lapack_int n,
+                                const lapack_complex_float* a, lapack_int lda,
+                                const lapack_complex_float* b, lapack_int ldb,
+                                lapack_complex_float* c, lapack_int ldc,
+                                float* scale );
+lapack_int LAPACKE_ztrsyl_work( int matrix_order, char trana, char tranb,
+                                lapack_int isgn, lapack_int m, lapack_int n,
+                                const lapack_complex_double* a, lapack_int lda,
+                                const lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* c, lapack_int ldc,
+                                double* scale );
+
+lapack_int LAPACKE_strtri_work( int matrix_order, char uplo, char diag,
+                                lapack_int n, float* a, lapack_int lda );
+lapack_int LAPACKE_dtrtri_work( int matrix_order, char uplo, char diag,
+                                lapack_int n, double* a, lapack_int lda );
+lapack_int LAPACKE_ctrtri_work( int matrix_order, char uplo, char diag,
+                                lapack_int n, lapack_complex_float* a,
+                                lapack_int lda );
+lapack_int LAPACKE_ztrtri_work( int matrix_order, char uplo, char diag,
+                                lapack_int n, lapack_complex_double* a,
+                                lapack_int lda );
+
+lapack_int LAPACKE_strtrs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const float* a, lapack_int lda, float* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_dtrtrs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const double* a, lapack_int lda, double* b,
+                                lapack_int ldb );
+lapack_int LAPACKE_ctrtrs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_ztrtrs_work( int matrix_order, char uplo, char trans,
+                                char diag, lapack_int n, lapack_int nrhs,
+                                const lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_strttf_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const float* a, lapack_int lda,
+                                float* arf );
+lapack_int LAPACKE_dtrttf_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const double* a, lapack_int lda,
+                                double* arf );
+lapack_int LAPACKE_ctrttf_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const lapack_complex_float* a,
+                                lapack_int lda, lapack_complex_float* arf );
+lapack_int LAPACKE_ztrttf_work( int matrix_order, char transr, char uplo,
+                                lapack_int n, const lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* arf );
+
+lapack_int LAPACKE_strttp_work( int matrix_order, char uplo, lapack_int n,
+                                const float* a, lapack_int lda, float* ap );
+lapack_int LAPACKE_dtrttp_work( int matrix_order, char uplo, lapack_int n,
+                                const double* a, lapack_int lda, double* ap );
+lapack_int LAPACKE_ctrttp_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* ap );
+lapack_int LAPACKE_ztrttp_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* ap );
+
+lapack_int LAPACKE_stzrzf_work( int matrix_order, lapack_int m, lapack_int n,
+                                float* a, lapack_int lda, float* tau,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_dtzrzf_work( int matrix_order, lapack_int m, lapack_int n,
+                                double* a, lapack_int lda, double* tau,
+                                double* work, lapack_int lwork );
+lapack_int LAPACKE_ctzrzf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* tau,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_ztzrzf_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* tau,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cungbr_work( int matrix_order, char vect, lapack_int m,
+                                lapack_int n, lapack_int k,
+                                lapack_complex_float* a, lapack_int lda,
+                                const lapack_complex_float* tau,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zungbr_work( int matrix_order, char vect, lapack_int m,
+                                lapack_int n, lapack_int k,
+                                lapack_complex_double* a, lapack_int lda,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cunghr_work( int matrix_order, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, lapack_complex_float* a,
+                                lapack_int lda, const lapack_complex_float* tau,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zunghr_work( int matrix_order, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, lapack_complex_double* a,
+                                lapack_int lda,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cunglq_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, lapack_complex_float* a,
+                                lapack_int lda, const lapack_complex_float* tau,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zunglq_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, lapack_complex_double* a,
+                                lapack_int lda,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cungql_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, lapack_complex_float* a,
+                                lapack_int lda, const lapack_complex_float* tau,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zungql_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, lapack_complex_double* a,
+                                lapack_int lda,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cungqr_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, lapack_complex_float* a,
+                                lapack_int lda, const lapack_complex_float* tau,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zungqr_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, lapack_complex_double* a,
+                                lapack_int lda,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cungrq_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, lapack_complex_float* a,
+                                lapack_int lda, const lapack_complex_float* tau,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zungrq_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int k, lapack_complex_double* a,
+                                lapack_int lda,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cungtr_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_float* a, lapack_int lda,
+                                const lapack_complex_float* tau,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zungtr_work( int matrix_order, char uplo, lapack_int n,
+                                lapack_complex_double* a, lapack_int lda,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cunmbr_work( int matrix_order, char vect, char side,
+                                char trans, lapack_int m, lapack_int n,
+                                lapack_int k, const lapack_complex_float* a,
+                                lapack_int lda, const lapack_complex_float* tau,
+                                lapack_complex_float* c, lapack_int ldc,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zunmbr_work( int matrix_order, char vect, char side,
+                                char trans, lapack_int m, lapack_int n,
+                                lapack_int k, const lapack_complex_double* a,
+                                lapack_int lda,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* c, lapack_int ldc,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cunmhr_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, const lapack_complex_float* a,
+                                lapack_int lda, const lapack_complex_float* tau,
+                                lapack_complex_float* c, lapack_int ldc,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zunmhr_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int ilo,
+                                lapack_int ihi, const lapack_complex_double* a,
+                                lapack_int lda,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* c, lapack_int ldc,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cunmlq_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const lapack_complex_float* a, lapack_int lda,
+                                const lapack_complex_float* tau,
+                                lapack_complex_float* c, lapack_int ldc,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zunmlq_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const lapack_complex_double* a, lapack_int lda,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* c, lapack_int ldc,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cunmql_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const lapack_complex_float* a, lapack_int lda,
+                                const lapack_complex_float* tau,
+                                lapack_complex_float* c, lapack_int ldc,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zunmql_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const lapack_complex_double* a, lapack_int lda,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* c, lapack_int ldc,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cunmqr_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const lapack_complex_float* a, lapack_int lda,
+                                const lapack_complex_float* tau,
+                                lapack_complex_float* c, lapack_int ldc,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zunmqr_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const lapack_complex_double* a, lapack_int lda,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* c, lapack_int ldc,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cunmrq_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const lapack_complex_float* a, lapack_int lda,
+                                const lapack_complex_float* tau,
+                                lapack_complex_float* c, lapack_int ldc,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zunmrq_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                const lapack_complex_double* a, lapack_int lda,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* c, lapack_int ldc,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cunmrz_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                lapack_int l, const lapack_complex_float* a,
+                                lapack_int lda, const lapack_complex_float* tau,
+                                lapack_complex_float* c, lapack_int ldc,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zunmrz_work( int matrix_order, char side, char trans,
+                                lapack_int m, lapack_int n, lapack_int k,
+                                lapack_int l, const lapack_complex_double* a,
+                                lapack_int lda,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* c, lapack_int ldc,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cunmtr_work( int matrix_order, char side, char uplo,
+                                char trans, lapack_int m, lapack_int n,
+                                const lapack_complex_float* a, lapack_int lda,
+                                const lapack_complex_float* tau,
+                                lapack_complex_float* c, lapack_int ldc,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_zunmtr_work( int matrix_order, char side, char uplo,
+                                char trans, lapack_int m, lapack_int n,
+                                const lapack_complex_double* a, lapack_int lda,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* c, lapack_int ldc,
+                                lapack_complex_double* work, lapack_int lwork );
+
+lapack_int LAPACKE_cupgtr_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_float* ap,
+                                const lapack_complex_float* tau,
+                                lapack_complex_float* q, lapack_int ldq,
+                                lapack_complex_float* work );
+lapack_int LAPACKE_zupgtr_work( int matrix_order, char uplo, lapack_int n,
+                                const lapack_complex_double* ap,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* q, lapack_int ldq,
+                                lapack_complex_double* work );
+
+lapack_int LAPACKE_cupmtr_work( int matrix_order, char side, char uplo,
+                                char trans, lapack_int m, lapack_int n,
+                                const lapack_complex_float* ap,
+                                const lapack_complex_float* tau,
+                                lapack_complex_float* c, lapack_int ldc,
+                                lapack_complex_float* work );
+lapack_int LAPACKE_zupmtr_work( int matrix_order, char side, char uplo,
+                                char trans, lapack_int m, lapack_int n,
+                                const lapack_complex_double* ap,
+                                const lapack_complex_double* tau,
+                                lapack_complex_double* c, lapack_int ldc,
+                                lapack_complex_double* work );
+
+lapack_int LAPACKE_claghe( int matrix_order, lapack_int n, lapack_int k,
+                           const float* d, lapack_complex_float* a,
+                           lapack_int lda, lapack_int* iseed );
+lapack_int LAPACKE_zlaghe( int matrix_order, lapack_int n, lapack_int k,
+                           const double* d, lapack_complex_double* a,
+                           lapack_int lda, lapack_int* iseed );
+
+lapack_int LAPACKE_slagsy( int matrix_order, lapack_int n, lapack_int k,
+                           const float* d, float* a, lapack_int lda,
+                           lapack_int* iseed );
+lapack_int LAPACKE_dlagsy( int matrix_order, lapack_int n, lapack_int k,
+                           const double* d, double* a, lapack_int lda,
+                           lapack_int* iseed );
+lapack_int LAPACKE_clagsy( int matrix_order, lapack_int n, lapack_int k,
+                           const float* d, lapack_complex_float* a,
+                           lapack_int lda, lapack_int* iseed );
+lapack_int LAPACKE_zlagsy( int matrix_order, lapack_int n, lapack_int k,
+                           const double* d, lapack_complex_double* a,
+                           lapack_int lda, lapack_int* iseed );
+
+lapack_int LAPACKE_slapmr( int matrix_order, lapack_logical forwrd,
+                           lapack_int m, lapack_int n, float* x, lapack_int ldx,
+                           lapack_int* k );
+lapack_int LAPACKE_dlapmr( int matrix_order, lapack_logical forwrd,
+                           lapack_int m, lapack_int n, double* x,
+                           lapack_int ldx, lapack_int* k );
+lapack_int LAPACKE_clapmr( int matrix_order, lapack_logical forwrd,
+                           lapack_int m, lapack_int n, lapack_complex_float* x,
+                           lapack_int ldx, lapack_int* k );
+lapack_int LAPACKE_zlapmr( int matrix_order, lapack_logical forwrd,
+                           lapack_int m, lapack_int n, lapack_complex_double* x,
+                           lapack_int ldx, lapack_int* k );
+
+
+float LAPACKE_slapy2( float x, float y );
+double LAPACKE_dlapy2( double x, double y );
+
+float LAPACKE_slapy3( float x, float y, float z );
+double LAPACKE_dlapy3( double x, double y, double z );
+
+lapack_int LAPACKE_slartgp( float f, float g, float* cs, float* sn, float* r );
+lapack_int LAPACKE_dlartgp( double f, double g, double* cs, double* sn,
+                            double* r );
+
+lapack_int LAPACKE_slartgs( float x, float y, float sigma, float* cs,
+                            float* sn );
+lapack_int LAPACKE_dlartgs( double x, double y, double sigma, double* cs,
+                            double* sn );
+
+
+//LAPACK 3.3.0
+lapack_int LAPACKE_cbbcsd( int matrix_order, char jobu1, char jobu2,
+                           char jobv1t, char jobv2t, char trans, lapack_int m,
+                           lapack_int p, lapack_int q, float* theta, float* phi,
+                           lapack_complex_float* u1, lapack_int ldu1,
+                           lapack_complex_float* u2, lapack_int ldu2,
+                           lapack_complex_float* v1t, lapack_int ldv1t,
+                           lapack_complex_float* v2t, lapack_int ldv2t,
+                           float* b11d, float* b11e, float* b12d, float* b12e,
+                           float* b21d, float* b21e, float* b22d, float* b22e );
+lapack_int LAPACKE_cbbcsd_work( int matrix_order, char jobu1, char jobu2,
+                                char jobv1t, char jobv2t, char trans,
+                                lapack_int m, lapack_int p, lapack_int q,
+                                float* theta, float* phi,
+                                lapack_complex_float* u1, lapack_int ldu1,
+                                lapack_complex_float* u2, lapack_int ldu2,
+                                lapack_complex_float* v1t, lapack_int ldv1t,
+                                lapack_complex_float* v2t, lapack_int ldv2t,
+                                float* b11d, float* b11e, float* b12d,
+                                float* b12e, float* b21d, float* b21e,
+                                float* b22d, float* b22e, float* rwork,
+                                lapack_int lrwork );
+lapack_int LAPACKE_cheswapr( int matrix_order, char uplo, lapack_int n,
+                             lapack_complex_float* a, lapack_int i1,
+                             lapack_int i2 );
+lapack_int LAPACKE_cheswapr_work( int matrix_order, char uplo, lapack_int n,
+                                  lapack_complex_float* a, lapack_int i1,
+                                  lapack_int i2 );
+lapack_int LAPACKE_chetri2( int matrix_order, char uplo, lapack_int n,
+                            lapack_complex_float* a, lapack_int lda,
+                            const lapack_int* ipiv );
+lapack_int LAPACKE_chetri2_work( int matrix_order, char uplo, lapack_int n,
+                                 lapack_complex_float* a, lapack_int lda,
+                                 const lapack_int* ipiv,
+                                 lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_chetri2x( int matrix_order, char uplo, lapack_int n,
+                             lapack_complex_float* a, lapack_int lda,
+                             const lapack_int* ipiv, lapack_int nb );
+lapack_int LAPACKE_chetri2x_work( int matrix_order, char uplo, lapack_int n,
+                                  lapack_complex_float* a, lapack_int lda,
+                                  const lapack_int* ipiv,
+                                  lapack_complex_float* work, lapack_int nb );
+lapack_int LAPACKE_chetrs2( int matrix_order, char uplo, lapack_int n,
+                            lapack_int nrhs, const lapack_complex_float* a,
+                            lapack_int lda, const lapack_int* ipiv,
+                            lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_chetrs2_work( int matrix_order, char uplo, lapack_int n,
+                                 lapack_int nrhs, const lapack_complex_float* a,
+                                 lapack_int lda, const lapack_int* ipiv,
+                                 lapack_complex_float* b, lapack_int ldb,
+                                 lapack_complex_float* work );
+lapack_int LAPACKE_csyconv( int matrix_order, char uplo, char way, lapack_int n,
+                            lapack_complex_float* a, lapack_int lda,
+                            const lapack_int* ipiv );
+lapack_int LAPACKE_csyconv_work( int matrix_order, char uplo, char way,
+                                 lapack_int n, lapack_complex_float* a,
+                                 lapack_int lda, const lapack_int* ipiv,
+                                 lapack_complex_float* work );
+lapack_int LAPACKE_csyswapr( int matrix_order, char uplo, lapack_int n,
+                             lapack_complex_float* a, lapack_int i1,
+                             lapack_int i2 );
+lapack_int LAPACKE_csyswapr_work( int matrix_order, char uplo, lapack_int n,
+                                  lapack_complex_float* a, lapack_int i1,
+                                  lapack_int i2 );
+lapack_int LAPACKE_csytri2( int matrix_order, char uplo, lapack_int n,
+                            lapack_complex_float* a, lapack_int lda,
+                            const lapack_int* ipiv );
+lapack_int LAPACKE_csytri2_work( int matrix_order, char uplo, lapack_int n,
+                                 lapack_complex_float* a, lapack_int lda,
+                                 const lapack_int* ipiv,
+                                 lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_csytri2x( int matrix_order, char uplo, lapack_int n,
+                             lapack_complex_float* a, lapack_int lda,
+                             const lapack_int* ipiv, lapack_int nb );
+lapack_int LAPACKE_csytri2x_work( int matrix_order, char uplo, lapack_int n,
+                                  lapack_complex_float* a, lapack_int lda,
+                                  const lapack_int* ipiv,
+                                  lapack_complex_float* work, lapack_int nb );
+lapack_int LAPACKE_csytrs2( int matrix_order, char uplo, lapack_int n,
+                            lapack_int nrhs, const lapack_complex_float* a,
+                            lapack_int lda, const lapack_int* ipiv,
+                            lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_csytrs2_work( int matrix_order, char uplo, lapack_int n,
+                                 lapack_int nrhs, const lapack_complex_float* a,
+                                 lapack_int lda, const lapack_int* ipiv,
+                                 lapack_complex_float* b, lapack_int ldb,
+                                 lapack_complex_float* work );
+lapack_int LAPACKE_cunbdb( int matrix_order, char trans, char signs,
+                           lapack_int m, lapack_int p, lapack_int q,
+                           lapack_complex_float* x11, lapack_int ldx11,
+                           lapack_complex_float* x12, lapack_int ldx12,
+                           lapack_complex_float* x21, lapack_int ldx21,
+                           lapack_complex_float* x22, lapack_int ldx22,
+                           float* theta, float* phi,
+                           lapack_complex_float* taup1,
+                           lapack_complex_float* taup2,
+                           lapack_complex_float* tauq1,
+                           lapack_complex_float* tauq2 );
+lapack_int LAPACKE_cunbdb_work( int matrix_order, char trans, char signs,
+                                lapack_int m, lapack_int p, lapack_int q,
+                                lapack_complex_float* x11, lapack_int ldx11,
+                                lapack_complex_float* x12, lapack_int ldx12,
+                                lapack_complex_float* x21, lapack_int ldx21,
+                                lapack_complex_float* x22, lapack_int ldx22,
+                                float* theta, float* phi,
+                                lapack_complex_float* taup1,
+                                lapack_complex_float* taup2,
+                                lapack_complex_float* tauq1,
+                                lapack_complex_float* tauq2,
+                                lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_cuncsd( int matrix_order, char jobu1, char jobu2,
+                           char jobv1t, char jobv2t, char trans, char signs,
+                           lapack_int m, lapack_int p, lapack_int q,
+                           lapack_complex_float* x11, lapack_int ldx11,
+                           lapack_complex_float* x12, lapack_int ldx12,
+                           lapack_complex_float* x21, lapack_int ldx21,
+                           lapack_complex_float* x22, lapack_int ldx22,
+                           float* theta, lapack_complex_float* u1,
+                           lapack_int ldu1, lapack_complex_float* u2,
+                           lapack_int ldu2, lapack_complex_float* v1t,
+                           lapack_int ldv1t, lapack_complex_float* v2t,
+                           lapack_int ldv2t );
+lapack_int LAPACKE_cuncsd_work( int matrix_order, char jobu1, char jobu2,
+                                char jobv1t, char jobv2t, char trans,
+                                char signs, lapack_int m, lapack_int p,
+                                lapack_int q, lapack_complex_float* x11,
+                                lapack_int ldx11, lapack_complex_float* x12,
+                                lapack_int ldx12, lapack_complex_float* x21,
+                                lapack_int ldx21, lapack_complex_float* x22,
+                                lapack_int ldx22, float* theta,
+                                lapack_complex_float* u1, lapack_int ldu1,
+                                lapack_complex_float* u2, lapack_int ldu2,
+                                lapack_complex_float* v1t, lapack_int ldv1t,
+                                lapack_complex_float* v2t, lapack_int ldv2t,
+                                lapack_complex_float* work, lapack_int lwork,
+                                float* rwork, lapack_int lrwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dbbcsd( int matrix_order, char jobu1, char jobu2,
+                           char jobv1t, char jobv2t, char trans, lapack_int m,
+                           lapack_int p, lapack_int q, double* theta,
+                           double* phi, double* u1, lapack_int ldu1, double* u2,
+                           lapack_int ldu2, double* v1t, lapack_int ldv1t,
+                           double* v2t, lapack_int ldv2t, double* b11d,
+                           double* b11e, double* b12d, double* b12e,
+                           double* b21d, double* b21e, double* b22d,
+                           double* b22e );
+lapack_int LAPACKE_dbbcsd_work( int matrix_order, char jobu1, char jobu2,
+                                char jobv1t, char jobv2t, char trans,
+                                lapack_int m, lapack_int p, lapack_int q,
+                                double* theta, double* phi, double* u1,
+                                lapack_int ldu1, double* u2, lapack_int ldu2,
+                                double* v1t, lapack_int ldv1t, double* v2t,
+                                lapack_int ldv2t, double* b11d, double* b11e,
+                                double* b12d, double* b12e, double* b21d,
+                                double* b21e, double* b22d, double* b22e,
+                                double* work, lapack_int lwork );
+lapack_int LAPACKE_dorbdb( int matrix_order, char trans, char signs,
+                           lapack_int m, lapack_int p, lapack_int q,
+                           double* x11, lapack_int ldx11, double* x12,
+                           lapack_int ldx12, double* x21, lapack_int ldx21,
+                           double* x22, lapack_int ldx22, double* theta,
+                           double* phi, double* taup1, double* taup2,
+                           double* tauq1, double* tauq2 );
+lapack_int LAPACKE_dorbdb_work( int matrix_order, char trans, char signs,
+                                lapack_int m, lapack_int p, lapack_int q,
+                                double* x11, lapack_int ldx11, double* x12,
+                                lapack_int ldx12, double* x21, lapack_int ldx21,
+                                double* x22, lapack_int ldx22, double* theta,
+                                double* phi, double* taup1, double* taup2,
+                                double* tauq1, double* tauq2, double* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_dorcsd( int matrix_order, char jobu1, char jobu2,
+                           char jobv1t, char jobv2t, char trans, char signs,
+                           lapack_int m, lapack_int p, lapack_int q,
+                           double* x11, lapack_int ldx11, double* x12,
+                           lapack_int ldx12, double* x21, lapack_int ldx21,
+                           double* x22, lapack_int ldx22, double* theta,
+                           double* u1, lapack_int ldu1, double* u2,
+                           lapack_int ldu2, double* v1t, lapack_int ldv1t,
+                           double* v2t, lapack_int ldv2t );
+lapack_int LAPACKE_dorcsd_work( int matrix_order, char jobu1, char jobu2,
+                                char jobv1t, char jobv2t, char trans,
+                                char signs, lapack_int m, lapack_int p,
+                                lapack_int q, double* x11, lapack_int ldx11,
+                                double* x12, lapack_int ldx12, double* x21,
+                                lapack_int ldx21, double* x22, lapack_int ldx22,
+                                double* theta, double* u1, lapack_int ldu1,
+                                double* u2, lapack_int ldu2, double* v1t,
+                                lapack_int ldv1t, double* v2t, lapack_int ldv2t,
+                                double* work, lapack_int lwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_dsyconv( int matrix_order, char uplo, char way, lapack_int n,
+                            double* a, lapack_int lda, const lapack_int* ipiv );
+lapack_int LAPACKE_dsyconv_work( int matrix_order, char uplo, char way,
+                                 lapack_int n, double* a, lapack_int lda,
+                                 const lapack_int* ipiv, double* work );
+lapack_int LAPACKE_dsyswapr( int matrix_order, char uplo, lapack_int n,
+                             double* a, lapack_int i1, lapack_int i2 );
+lapack_int LAPACKE_dsyswapr_work( int matrix_order, char uplo, lapack_int n,
+                                  double* a, lapack_int i1, lapack_int i2 );
+lapack_int LAPACKE_dsytri2( int matrix_order, char uplo, lapack_int n,
+                            double* a, lapack_int lda, const lapack_int* ipiv );
+lapack_int LAPACKE_dsytri2_work( int matrix_order, char uplo, lapack_int n,
+                                 double* a, lapack_int lda,
+                                 const lapack_int* ipiv,
+                                 lapack_complex_double* work, lapack_int lwork );
+lapack_int LAPACKE_dsytri2x( int matrix_order, char uplo, lapack_int n,
+                             double* a, lapack_int lda, const lapack_int* ipiv,
+                             lapack_int nb );
+lapack_int LAPACKE_dsytri2x_work( int matrix_order, char uplo, lapack_int n,
+                                  double* a, lapack_int lda,
+                                  const lapack_int* ipiv, double* work,
+                                  lapack_int nb );
+lapack_int LAPACKE_dsytrs2( int matrix_order, char uplo, lapack_int n,
+                            lapack_int nrhs, const double* a, lapack_int lda,
+                            const lapack_int* ipiv, double* b, lapack_int ldb );
+lapack_int LAPACKE_dsytrs2_work( int matrix_order, char uplo, lapack_int n,
+                                 lapack_int nrhs, const double* a,
+                                 lapack_int lda, const lapack_int* ipiv,
+                                 double* b, lapack_int ldb, double* work );
+lapack_int LAPACKE_sbbcsd( int matrix_order, char jobu1, char jobu2,
+                           char jobv1t, char jobv2t, char trans, lapack_int m,
+                           lapack_int p, lapack_int q, float* theta, float* phi,
+                           float* u1, lapack_int ldu1, float* u2,
+                           lapack_int ldu2, float* v1t, lapack_int ldv1t,
+                           float* v2t, lapack_int ldv2t, float* b11d,
+                           float* b11e, float* b12d, float* b12e, float* b21d,
+                           float* b21e, float* b22d, float* b22e );
+lapack_int LAPACKE_sbbcsd_work( int matrix_order, char jobu1, char jobu2,
+                                char jobv1t, char jobv2t, char trans,
+                                lapack_int m, lapack_int p, lapack_int q,
+                                float* theta, float* phi, float* u1,
+                                lapack_int ldu1, float* u2, lapack_int ldu2,
+                                float* v1t, lapack_int ldv1t, float* v2t,
+                                lapack_int ldv2t, float* b11d, float* b11e,
+                                float* b12d, float* b12e, float* b21d,
+                                float* b21e, float* b22d, float* b22e,
+                                float* work, lapack_int lwork );
+lapack_int LAPACKE_sorbdb( int matrix_order, char trans, char signs,
+                           lapack_int m, lapack_int p, lapack_int q, float* x11,
+                           lapack_int ldx11, float* x12, lapack_int ldx12,
+                           float* x21, lapack_int ldx21, float* x22,
+                           lapack_int ldx22, float* theta, float* phi,
+                           float* taup1, float* taup2, float* tauq1,
+                           float* tauq2 );
+lapack_int LAPACKE_sorbdb_work( int matrix_order, char trans, char signs,
+                                lapack_int m, lapack_int p, lapack_int q,
+                                float* x11, lapack_int ldx11, float* x12,
+                                lapack_int ldx12, float* x21, lapack_int ldx21,
+                                float* x22, lapack_int ldx22, float* theta,
+                                float* phi, float* taup1, float* taup2,
+                                float* tauq1, float* tauq2, float* work,
+                                lapack_int lwork );
+lapack_int LAPACKE_sorcsd( int matrix_order, char jobu1, char jobu2,
+                           char jobv1t, char jobv2t, char trans, char signs,
+                           lapack_int m, lapack_int p, lapack_int q, float* x11,
+                           lapack_int ldx11, float* x12, lapack_int ldx12,
+                           float* x21, lapack_int ldx21, float* x22,
+                           lapack_int ldx22, float* theta, float* u1,
+                           lapack_int ldu1, float* u2, lapack_int ldu2,
+                           float* v1t, lapack_int ldv1t, float* v2t,
+                           lapack_int ldv2t );
+lapack_int LAPACKE_sorcsd_work( int matrix_order, char jobu1, char jobu2,
+                                char jobv1t, char jobv2t, char trans,
+                                char signs, lapack_int m, lapack_int p,
+                                lapack_int q, float* x11, lapack_int ldx11,
+                                float* x12, lapack_int ldx12, float* x21,
+                                lapack_int ldx21, float* x22, lapack_int ldx22,
+                                float* theta, float* u1, lapack_int ldu1,
+                                float* u2, lapack_int ldu2, float* v1t,
+                                lapack_int ldv1t, float* v2t, lapack_int ldv2t,
+                                float* work, lapack_int lwork,
+                                lapack_int* iwork );
+lapack_int LAPACKE_ssyconv( int matrix_order, char uplo, char way, lapack_int n,
+                            float* a, lapack_int lda, const lapack_int* ipiv );
+lapack_int LAPACKE_ssyconv_work( int matrix_order, char uplo, char way,
+                                 lapack_int n, float* a, lapack_int lda,
+                                 const lapack_int* ipiv, float* work );
+lapack_int LAPACKE_ssyswapr( int matrix_order, char uplo, lapack_int n,
+                             float* a, lapack_int i1, lapack_int i2 );
+lapack_int LAPACKE_ssyswapr_work( int matrix_order, char uplo, lapack_int n,
+                                  float* a, lapack_int i1, lapack_int i2 );
+lapack_int LAPACKE_ssytri2( int matrix_order, char uplo, lapack_int n, float* a,
+                            lapack_int lda, const lapack_int* ipiv );
+lapack_int LAPACKE_ssytri2_work( int matrix_order, char uplo, lapack_int n,
+                                 float* a, lapack_int lda,
+                                 const lapack_int* ipiv,
+                                 lapack_complex_float* work, lapack_int lwork );
+lapack_int LAPACKE_ssytri2x( int matrix_order, char uplo, lapack_int n,
+                             float* a, lapack_int lda, const lapack_int* ipiv,
+                             lapack_int nb );
+lapack_int LAPACKE_ssytri2x_work( int matrix_order, char uplo, lapack_int n,
+                                  float* a, lapack_int lda,
+                                  const lapack_int* ipiv, float* work,
+                                  lapack_int nb );
+lapack_int LAPACKE_ssytrs2( int matrix_order, char uplo, lapack_int n,
+                            lapack_int nrhs, const float* a, lapack_int lda,
+                            const lapack_int* ipiv, float* b, lapack_int ldb );
+lapack_int LAPACKE_ssytrs2_work( int matrix_order, char uplo, lapack_int n,
+                                 lapack_int nrhs, const float* a,
+                                 lapack_int lda, const lapack_int* ipiv,
+                                 float* b, lapack_int ldb, float* work );
+lapack_int LAPACKE_zbbcsd( int matrix_order, char jobu1, char jobu2,
+                           char jobv1t, char jobv2t, char trans, lapack_int m,
+                           lapack_int p, lapack_int q, double* theta,
+                           double* phi, lapack_complex_double* u1,
+                           lapack_int ldu1, lapack_complex_double* u2,
+                           lapack_int ldu2, lapack_complex_double* v1t,
+                           lapack_int ldv1t, lapack_complex_double* v2t,
+                           lapack_int ldv2t, double* b11d, double* b11e,
+                           double* b12d, double* b12e, double* b21d,
+                           double* b21e, double* b22d, double* b22e );
+lapack_int LAPACKE_zbbcsd_work( int matrix_order, char jobu1, char jobu2,
+                                char jobv1t, char jobv2t, char trans,
+                                lapack_int m, lapack_int p, lapack_int q,
+                                double* theta, double* phi,
+                                lapack_complex_double* u1, lapack_int ldu1,
+                                lapack_complex_double* u2, lapack_int ldu2,
+                                lapack_complex_double* v1t, lapack_int ldv1t,
+                                lapack_complex_double* v2t, lapack_int ldv2t,
+                                double* b11d, double* b11e, double* b12d,
+                                double* b12e, double* b21d, double* b21e,
+                                double* b22d, double* b22e, double* rwork,
+                                lapack_int lrwork );
+lapack_int LAPACKE_zheswapr( int matrix_order, char uplo, lapack_int n,
+                             lapack_complex_double* a, lapack_int i1,
+                             lapack_int i2 );
+lapack_int LAPACKE_zheswapr_work( int matrix_order, char uplo, lapack_int n,
+                                  lapack_complex_double* a, lapack_int i1,
+                                  lapack_int i2 );
+lapack_int LAPACKE_zhetri2( int matrix_order, char uplo, lapack_int n,
+                            lapack_complex_double* a, lapack_int lda,
+                            const lapack_int* ipiv );
+lapack_int LAPACKE_zhetri2_work( int matrix_order, char uplo, lapack_int n,
+                                 lapack_complex_double* a, lapack_int lda,
+                                 const lapack_int* ipiv,
+                                 lapack_complex_double* work, lapack_int lwork );
+lapack_int LAPACKE_zhetri2x( int matrix_order, char uplo, lapack_int n,
+                             lapack_complex_double* a, lapack_int lda,
+                             const lapack_int* ipiv, lapack_int nb );
+lapack_int LAPACKE_zhetri2x_work( int matrix_order, char uplo, lapack_int n,
+                                  lapack_complex_double* a, lapack_int lda,
+                                  const lapack_int* ipiv,
+                                  lapack_complex_double* work, lapack_int nb );
+lapack_int LAPACKE_zhetrs2( int matrix_order, char uplo, lapack_int n,
+                            lapack_int nrhs, const lapack_complex_double* a,
+                            lapack_int lda, const lapack_int* ipiv,
+                            lapack_complex_double* b, lapack_int ldb );
+lapack_int LAPACKE_zhetrs2_work( int matrix_order, char uplo, lapack_int n,
+                                 lapack_int nrhs, const lapack_complex_double* a,
+                                 lapack_int lda, const lapack_int* ipiv,
+                                 lapack_complex_double* b, lapack_int ldb,
+                                 lapack_complex_double* work );
+lapack_int LAPACKE_zsyconv( int matrix_order, char uplo, char way, lapack_int n,
+                            lapack_complex_double* a, lapack_int lda,
+                            const lapack_int* ipiv );
+lapack_int LAPACKE_zsyconv_work( int matrix_order, char uplo, char way,
+                                 lapack_int n, lapack_complex_double* a,
+                                 lapack_int lda, const lapack_int* ipiv,
+                                 lapack_complex_double* work );
+lapack_int LAPACKE_zsyswapr( int matrix_order, char uplo, lapack_int n,
+                             lapack_complex_double* a, lapack_int i1,
+                             lapack_int i2 );
+lapack_int LAPACKE_zsyswapr_work( int matrix_order, char uplo, lapack_int n,
+                                  lapack_complex_double* a, lapack_int i1,
+                                  lapack_int i2 );
+lapack_int LAPACKE_zsytri2( int matrix_order, char uplo, lapack_int n,
+                            lapack_complex_double* a, lapack_int lda,
+                            const lapack_int* ipiv );
+lapack_int LAPACKE_zsytri2_work( int matrix_order, char uplo, lapack_int n,
+                                 lapack_complex_double* a, lapack_int lda,
+                                 const lapack_int* ipiv,
+                                 lapack_complex_double* work, lapack_int lwork );
+lapack_int LAPACKE_zsytri2x( int matrix_order, char uplo, lapack_int n,
+                             lapack_complex_double* a, lapack_int lda,
+                             const lapack_int* ipiv, lapack_int nb );
+lapack_int LAPACKE_zsytri2x_work( int matrix_order, char uplo, lapack_int n,
+                                  lapack_complex_double* a, lapack_int lda,
+                                  const lapack_int* ipiv,
+                                  lapack_complex_double* work, lapack_int nb );
+lapack_int LAPACKE_zsytrs2( int matrix_order, char uplo, lapack_int n,
+                            lapack_int nrhs, const lapack_complex_double* a,
+                            lapack_int lda, const lapack_int* ipiv,
+                            lapack_complex_double* b, lapack_int ldb );
+lapack_int LAPACKE_zsytrs2_work( int matrix_order, char uplo, lapack_int n,
+                                 lapack_int nrhs, const lapack_complex_double* a,
+                                 lapack_int lda, const lapack_int* ipiv,
+                                 lapack_complex_double* b, lapack_int ldb,
+                                 lapack_complex_double* work );
+lapack_int LAPACKE_zunbdb( int matrix_order, char trans, char signs,
+                           lapack_int m, lapack_int p, lapack_int q,
+                           lapack_complex_double* x11, lapack_int ldx11,
+                           lapack_complex_double* x12, lapack_int ldx12,
+                           lapack_complex_double* x21, lapack_int ldx21,
+                           lapack_complex_double* x22, lapack_int ldx22,
+                           double* theta, double* phi,
+                           lapack_complex_double* taup1,
+                           lapack_complex_double* taup2,
+                           lapack_complex_double* tauq1,
+                           lapack_complex_double* tauq2 );
+lapack_int LAPACKE_zunbdb_work( int matrix_order, char trans, char signs,
+                                lapack_int m, lapack_int p, lapack_int q,
+                                lapack_complex_double* x11, lapack_int ldx11,
+                                lapack_complex_double* x12, lapack_int ldx12,
+                                lapack_complex_double* x21, lapack_int ldx21,
+                                lapack_complex_double* x22, lapack_int ldx22,
+                                double* theta, double* phi,
+                                lapack_complex_double* taup1,
+                                lapack_complex_double* taup2,
+                                lapack_complex_double* tauq1,
+                                lapack_complex_double* tauq2,
+                                lapack_complex_double* work, lapack_int lwork );
+lapack_int LAPACKE_zuncsd( int matrix_order, char jobu1, char jobu2,
+                           char jobv1t, char jobv2t, char trans, char signs,
+                           lapack_int m, lapack_int p, lapack_int q,
+                           lapack_complex_double* x11, lapack_int ldx11,
+                           lapack_complex_double* x12, lapack_int ldx12,
+                           lapack_complex_double* x21, lapack_int ldx21,
+                           lapack_complex_double* x22, lapack_int ldx22,
+                           double* theta, lapack_complex_double* u1,
+                           lapack_int ldu1, lapack_complex_double* u2,
+                           lapack_int ldu2, lapack_complex_double* v1t,
+                           lapack_int ldv1t, lapack_complex_double* v2t,
+                           lapack_int ldv2t );
+lapack_int LAPACKE_zuncsd_work( int matrix_order, char jobu1, char jobu2,
+                                char jobv1t, char jobv2t, char trans,
+                                char signs, lapack_int m, lapack_int p,
+                                lapack_int q, lapack_complex_double* x11,
+                                lapack_int ldx11, lapack_complex_double* x12,
+                                lapack_int ldx12, lapack_complex_double* x21,
+                                lapack_int ldx21, lapack_complex_double* x22,
+                                lapack_int ldx22, double* theta,
+                                lapack_complex_double* u1, lapack_int ldu1,
+                                lapack_complex_double* u2, lapack_int ldu2,
+                                lapack_complex_double* v1t, lapack_int ldv1t,
+                                lapack_complex_double* v2t, lapack_int ldv2t,
+                                lapack_complex_double* work, lapack_int lwork,
+                                double* rwork, lapack_int lrwork,
+                                lapack_int* iwork );
+//LAPACK 3.4.0
+lapack_int LAPACKE_sgemqrt( int matrix_order, char side, char trans,
+                            lapack_int m, lapack_int n, lapack_int k,
+                            lapack_int nb, const float* v, lapack_int ldv,
+                            const float* t, lapack_int ldt, float* c,
+                            lapack_int ldc );
+lapack_int LAPACKE_dgemqrt( int matrix_order, char side, char trans,
+                            lapack_int m, lapack_int n, lapack_int k,
+                            lapack_int nb, const double* v, lapack_int ldv,
+                            const double* t, lapack_int ldt, double* c,
+                            lapack_int ldc );
+lapack_int LAPACKE_cgemqrt( int matrix_order, char side, char trans,
+                            lapack_int m, lapack_int n, lapack_int k,
+                            lapack_int nb, const lapack_complex_float* v,
+                            lapack_int ldv, const lapack_complex_float* t,
+                            lapack_int ldt, lapack_complex_float* c,
+                            lapack_int ldc );
+lapack_int LAPACKE_zgemqrt( int matrix_order, char side, char trans,
+                            lapack_int m, lapack_int n, lapack_int k,
+                            lapack_int nb, const lapack_complex_double* v,
+                            lapack_int ldv, const lapack_complex_double* t,
+                            lapack_int ldt, lapack_complex_double* c,
+                            lapack_int ldc );
+
+lapack_int LAPACKE_sgeqrt( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nb, float* a, lapack_int lda, float* t,
+                           lapack_int ldt );
+lapack_int LAPACKE_dgeqrt( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nb, double* a, lapack_int lda, double* t,
+                           lapack_int ldt );
+lapack_int LAPACKE_cgeqrt( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nb, lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* t,
+                           lapack_int ldt );
+lapack_int LAPACKE_zgeqrt( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int nb, lapack_complex_double* a,
+                           lapack_int lda, lapack_complex_double* t,
+                           lapack_int ldt );
+
+lapack_int LAPACKE_sgeqrt2( int matrix_order, lapack_int m, lapack_int n,
+                            float* a, lapack_int lda, float* t,
+                            lapack_int ldt );
+lapack_int LAPACKE_dgeqrt2( int matrix_order, lapack_int m, lapack_int n,
+                            double* a, lapack_int lda, double* t,
+                            lapack_int ldt );
+lapack_int LAPACKE_cgeqrt2( int matrix_order, lapack_int m, lapack_int n,
+                            lapack_complex_float* a, lapack_int lda,
+                            lapack_complex_float* t, lapack_int ldt );
+lapack_int LAPACKE_zgeqrt2( int matrix_order, lapack_int m, lapack_int n,
+                            lapack_complex_double* a, lapack_int lda,
+                            lapack_complex_double* t, lapack_int ldt );
+
+lapack_int LAPACKE_sgeqrt3( int matrix_order, lapack_int m, lapack_int n,
+                            float* a, lapack_int lda, float* t,
+                            lapack_int ldt );
+lapack_int LAPACKE_dgeqrt3( int matrix_order, lapack_int m, lapack_int n,
+                            double* a, lapack_int lda, double* t,
+                            lapack_int ldt );
+lapack_int LAPACKE_cgeqrt3( int matrix_order, lapack_int m, lapack_int n,
+                            lapack_complex_float* a, lapack_int lda,
+                            lapack_complex_float* t, lapack_int ldt );
+lapack_int LAPACKE_zgeqrt3( int matrix_order, lapack_int m, lapack_int n,
+                            lapack_complex_double* a, lapack_int lda,
+                            lapack_complex_double* t, lapack_int ldt );
+
+lapack_int LAPACKE_stpmqrt( int matrix_order, char side, char trans,
+                            lapack_int m, lapack_int n, lapack_int k,
+                            lapack_int l, lapack_int nb, const float* v,
+                            lapack_int ldv, const float* t, lapack_int ldt,
+                            float* a, lapack_int lda, float* b,
+                            lapack_int ldb );
+lapack_int LAPACKE_dtpmqrt( int matrix_order, char side, char trans,
+                            lapack_int m, lapack_int n, lapack_int k,
+                            lapack_int l, lapack_int nb, const double* v,
+                            lapack_int ldv, const double* t, lapack_int ldt,
+                            double* a, lapack_int lda, double* b,
+                            lapack_int ldb );
+lapack_int LAPACKE_ctpmqrt( int matrix_order, char side, char trans,
+                            lapack_int m, lapack_int n, lapack_int k,
+                            lapack_int l, lapack_int nb,
+                            const lapack_complex_float* v, lapack_int ldv,
+                            const lapack_complex_float* t, lapack_int ldt,
+                            lapack_complex_float* a, lapack_int lda,
+                            lapack_complex_float* b, lapack_int ldb );
+lapack_int LAPACKE_ztpmqrt( int matrix_order, char side, char trans,
+                            lapack_int m, lapack_int n, lapack_int k,
+                            lapack_int l, lapack_int nb,
+                            const lapack_complex_double* v, lapack_int ldv,
+                            const lapack_complex_double* t, lapack_int ldt,
+                            lapack_complex_double* a, lapack_int lda,
+                            lapack_complex_double* b, lapack_int ldb );
+
+lapack_int LAPACKE_dtpqrt( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int l, lapack_int nb, double* a,
+                           lapack_int lda, double* b, lapack_int ldb, double* t,
+                           lapack_int ldt );
+lapack_int LAPACKE_ctpqrt( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int l, lapack_int nb, lapack_complex_float* a,
+                           lapack_int lda, lapack_complex_float* t,
+                           lapack_complex_float* b, lapack_int ldb,
+                           lapack_int ldt );
+lapack_int LAPACKE_ztpqrt( int matrix_order, lapack_int m, lapack_int n,
+                           lapack_int l, lapack_int nb,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* b, lapack_int ldb,
+                           lapack_complex_double* t, lapack_int ldt );
+
+lapack_int LAPACKE_stpqrt2( int matrix_order, lapack_int m, lapack_int n,
+                            float* a, lapack_int lda, float* b, lapack_int ldb,
+                            float* t, lapack_int ldt );
+lapack_int LAPACKE_dtpqrt2( int matrix_order, lapack_int m, lapack_int n,
+                            double* a, lapack_int lda, double* b,
+                            lapack_int ldb, double* t, lapack_int ldt );
+lapack_int LAPACKE_ctpqrt2( int matrix_order, lapack_int m, lapack_int n,
+                            lapack_complex_float* a, lapack_int lda,
+                            lapack_complex_float* b, lapack_int ldb,
+                            lapack_complex_float* t, lapack_int ldt );
+lapack_int LAPACKE_ztpqrt2( int matrix_order, lapack_int m, lapack_int n,
+                            lapack_complex_double* a, lapack_int lda,
+                            lapack_complex_double* b, lapack_int ldb,
+                            lapack_complex_double* t, lapack_int ldt );
+
+lapack_int LAPACKE_stprfb( int matrix_order, char side, char trans, char direct,
+                           char storev, lapack_int m, lapack_int n,
+                           lapack_int k, lapack_int l, const float* v,
+                           lapack_int ldv, const float* t, lapack_int ldt,
+                           float* a, lapack_int lda, float* b, lapack_int ldb,
+                           lapack_int myldwork );
+lapack_int LAPACKE_dtprfb( int matrix_order, char side, char trans, char direct,
+                           char storev, lapack_int m, lapack_int n,
+                           lapack_int k, lapack_int l, const double* v,
+                           lapack_int ldv, const double* t, lapack_int ldt,
+                           double* a, lapack_int lda, double* b, lapack_int ldb,
+                           lapack_int myldwork );
+lapack_int LAPACKE_ctprfb( int matrix_order, char side, char trans, char direct,
+                           char storev, lapack_int m, lapack_int n,
+                           lapack_int k, lapack_int l,
+                           const lapack_complex_float* v, lapack_int ldv,
+                           const lapack_complex_float* t, lapack_int ldt,
+                           lapack_complex_float* a, lapack_int lda,
+                           lapack_complex_float* b, lapack_int ldb,
+                           lapack_int myldwork );
+lapack_int LAPACKE_ztprfb( int matrix_order, char side, char trans, char direct,
+                           char storev, lapack_int m, lapack_int n,
+                           lapack_int k, lapack_int l,
+                           const lapack_complex_double* v, lapack_int ldv,
+                           const lapack_complex_double* t, lapack_int ldt,
+                           lapack_complex_double* a, lapack_int lda,
+                           lapack_complex_double* b, lapack_int ldb,
+                           lapack_int myldwork );
+
+lapack_int LAPACKE_sgemqrt_work( int matrix_order, char side, char trans,
+                                 lapack_int m, lapack_int n, lapack_int k,
+                                 lapack_int nb, const float* v, lapack_int ldv,
+                                 const float* t, lapack_int ldt, float* c,
+                                 lapack_int ldc, float* work );
+lapack_int LAPACKE_dgemqrt_work( int matrix_order, char side, char trans,
+                                 lapack_int m, lapack_int n, lapack_int k,
+                                 lapack_int nb, const double* v, lapack_int ldv,
+                                 const double* t, lapack_int ldt, double* c,
+                                 lapack_int ldc, double* work );
+lapack_int LAPACKE_cgemqrt_work( int matrix_order, char side, char trans,
+                                 lapack_int m, lapack_int n, lapack_int k,
+                                 lapack_int nb, const lapack_complex_float* v,
+                                 lapack_int ldv, const lapack_complex_float* t,
+                                 lapack_int ldt, lapack_complex_float* c,
+                                 lapack_int ldc, lapack_complex_float* work );
+lapack_int LAPACKE_zgemqrt_work( int matrix_order, char side, char trans,
+                                 lapack_int m, lapack_int n, lapack_int k,
+                                 lapack_int nb, const lapack_complex_double* v,
+                                 lapack_int ldv, const lapack_complex_double* t,
+                                 lapack_int ldt, lapack_complex_double* c,
+                                 lapack_int ldc, lapack_complex_double* work );
+
+lapack_int LAPACKE_sgeqrt_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nb, float* a, lapack_int lda,
+                                float* t, lapack_int ldt, float* work );
+lapack_int LAPACKE_dgeqrt_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nb, double* a, lapack_int lda,
+                                double* t, lapack_int ldt, double* work );
+lapack_int LAPACKE_cgeqrt_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nb, lapack_complex_float* a,
+                                lapack_int lda, lapack_complex_float* t,
+                                lapack_int ldt, lapack_complex_float* work );
+lapack_int LAPACKE_zgeqrt_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int nb, lapack_complex_double* a,
+                                lapack_int lda, lapack_complex_double* t,
+                                lapack_int ldt, lapack_complex_double* work );
+
+lapack_int LAPACKE_sgeqrt2_work( int matrix_order, lapack_int m, lapack_int n,
+                                 float* a, lapack_int lda, float* t,
+                                 lapack_int ldt );
+lapack_int LAPACKE_dgeqrt2_work( int matrix_order, lapack_int m, lapack_int n,
+                                 double* a, lapack_int lda, double* t,
+                                 lapack_int ldt );
+lapack_int LAPACKE_cgeqrt2_work( int matrix_order, lapack_int m, lapack_int n,
+                                 lapack_complex_float* a, lapack_int lda,
+                                 lapack_complex_float* t, lapack_int ldt );
+lapack_int LAPACKE_zgeqrt2_work( int matrix_order, lapack_int m, lapack_int n,
+                                 lapack_complex_double* a, lapack_int lda,
+                                 lapack_complex_double* t, lapack_int ldt );
+
+lapack_int LAPACKE_sgeqrt3_work( int matrix_order, lapack_int m, lapack_int n,
+                                 float* a, lapack_int lda, float* t,
+                                 lapack_int ldt );
+lapack_int LAPACKE_dgeqrt3_work( int matrix_order, lapack_int m, lapack_int n,
+                                 double* a, lapack_int lda, double* t,
+                                 lapack_int ldt );
+lapack_int LAPACKE_cgeqrt3_work( int matrix_order, lapack_int m, lapack_int n,
+                                 lapack_complex_float* a, lapack_int lda,
+                                 lapack_complex_float* t, lapack_int ldt );
+lapack_int LAPACKE_zgeqrt3_work( int matrix_order, lapack_int m, lapack_int n,
+                                 lapack_complex_double* a, lapack_int lda,
+                                 lapack_complex_double* t, lapack_int ldt );
+
+lapack_int LAPACKE_stpmqrt_work( int matrix_order, char side, char trans,
+                                 lapack_int m, lapack_int n, lapack_int k,
+                                 lapack_int l, lapack_int nb, const float* v,
+                                 lapack_int ldv, const float* t, lapack_int ldt,
+                                 float* a, lapack_int lda, float* b,
+                                 lapack_int ldb, float* work );
+lapack_int LAPACKE_dtpmqrt_work( int matrix_order, char side, char trans,
+                                 lapack_int m, lapack_int n, lapack_int k,
+                                 lapack_int l, lapack_int nb, const double* v,
+                                 lapack_int ldv, const double* t,
+                                 lapack_int ldt, double* a, lapack_int lda,
+                                 double* b, lapack_int ldb, double* work );
+lapack_int LAPACKE_ctpmqrt_work( int matrix_order, char side, char trans,
+                                 lapack_int m, lapack_int n, lapack_int k,
+                                 lapack_int l, lapack_int nb,
+                                 const lapack_complex_float* v, lapack_int ldv,
+                                 const lapack_complex_float* t, lapack_int ldt,
+                                 lapack_complex_float* a, lapack_int lda,
+                                 lapack_complex_float* b, lapack_int ldb,
+                                 lapack_complex_float* work );
+lapack_int LAPACKE_ztpmqrt_work( int matrix_order, char side, char trans,
+                                 lapack_int m, lapack_int n, lapack_int k,
+                                 lapack_int l, lapack_int nb,
+                                 const lapack_complex_double* v, lapack_int ldv,
+                                 const lapack_complex_double* t, lapack_int ldt,
+                                 lapack_complex_double* a, lapack_int lda,
+                                 lapack_complex_double* b, lapack_int ldb,
+                                 lapack_complex_double* work );
+
+lapack_int LAPACKE_dtpqrt_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int l, lapack_int nb, double* a,
+                                lapack_int lda, double* b, lapack_int ldb,
+                                double* t, lapack_int ldt, double* work );
+lapack_int LAPACKE_ctpqrt_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int l, lapack_int nb,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* t,
+                                lapack_complex_float* b, lapack_int ldb,
+                                lapack_int ldt, lapack_complex_float* work );
+lapack_int LAPACKE_ztpqrt_work( int matrix_order, lapack_int m, lapack_int n,
+                                lapack_int l, lapack_int nb,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* b, lapack_int ldb,
+                                lapack_complex_double* t, lapack_int ldt,
+                                lapack_complex_double* work );
+
+lapack_int LAPACKE_stpqrt2_work( int matrix_order, lapack_int m, lapack_int n,
+                                 float* a, lapack_int lda, float* b,
+                                 lapack_int ldb, float* t, lapack_int ldt );
+lapack_int LAPACKE_dtpqrt2_work( int matrix_order, lapack_int m, lapack_int n,
+                                 double* a, lapack_int lda, double* b,
+                                 lapack_int ldb, double* t, lapack_int ldt );
+lapack_int LAPACKE_ctpqrt2_work( int matrix_order, lapack_int m, lapack_int n,
+                                 lapack_complex_float* a, lapack_int lda,
+                                 lapack_complex_float* b, lapack_int ldb,
+                                 lapack_complex_float* t, lapack_int ldt );
+lapack_int LAPACKE_ztpqrt2_work( int matrix_order, lapack_int m, lapack_int n,
+                                 lapack_complex_double* a, lapack_int lda,
+                                 lapack_complex_double* b, lapack_int ldb,
+                                 lapack_complex_double* t, lapack_int ldt );
+
+lapack_int LAPACKE_stprfb_work( int matrix_order, char side, char trans,
+                                char direct, char storev, lapack_int m,
+                                lapack_int n, lapack_int k, lapack_int l,
+                                const float* v, lapack_int ldv, const float* t,
+                                lapack_int ldt, float* a, lapack_int lda,
+                                float* b, lapack_int ldb, const float* mywork,
+                                lapack_int myldwork );
+lapack_int LAPACKE_dtprfb_work( int matrix_order, char side, char trans,
+                                char direct, char storev, lapack_int m,
+                                lapack_int n, lapack_int k, lapack_int l,
+                                const double* v, lapack_int ldv,
+                                const double* t, lapack_int ldt, double* a,
+                                lapack_int lda, double* b, lapack_int ldb,
+                                const double* mywork, lapack_int myldwork );
+lapack_int LAPACKE_ctprfb_work( int matrix_order, char side, char trans,
+                                char direct, char storev, lapack_int m,
+                                lapack_int n, lapack_int k, lapack_int l,
+                                const lapack_complex_float* v, lapack_int ldv,
+                                const lapack_complex_float* t, lapack_int ldt,
+                                lapack_complex_float* a, lapack_int lda,
+                                lapack_complex_float* b, lapack_int ldb,
+                                const float* mywork, lapack_int myldwork );
+lapack_int LAPACKE_ztprfb_work( int matrix_order, char side, char trans,
+                                char direct, char storev, lapack_int m,
+                                lapack_int n, lapack_int k, lapack_int l,
+                                const lapack_complex_double* v, lapack_int ldv,
+                                const lapack_complex_double* t, lapack_int ldt,
+                                lapack_complex_double* a, lapack_int lda,
+                                lapack_complex_double* b, lapack_int ldb,
+                                const double* mywork, lapack_int myldwork );
+//LAPACK 3.X.X
+lapack_int LAPACKE_csyr( int matrix_order, char uplo, lapack_int n,
+                             lapack_complex_float alpha,
+                             const lapack_complex_float* x, lapack_int incx,
+                             lapack_complex_float* a, lapack_int lda );
+lapack_int LAPACKE_zsyr( int matrix_order, char uplo, lapack_int n,
+                             lapack_complex_double alpha,
+                             const lapack_complex_double* x, lapack_int incx,
+                             lapack_complex_double* a, lapack_int lda );
+
+lapack_int LAPACKE_csyr_work( int matrix_order, char uplo, lapack_int n,
+                                  lapack_complex_float alpha,
+                                  const lapack_complex_float* x,
+                                  lapack_int incx, lapack_complex_float* a,
+                                  lapack_int lda );
+lapack_int LAPACKE_zsyr_work( int matrix_order, char uplo, lapack_int n,
+                                  lapack_complex_double alpha,
+                                  const lapack_complex_double* x,
+                                  lapack_int incx, lapack_complex_double* a,
+                                  lapack_int lda );
+
+
+
+#define LAPACK_sgetrf LAPACK_GLOBAL(sgetrf,SGETRF)
+#define LAPACK_dgetrf LAPACK_GLOBAL(dgetrf,DGETRF)
+#define LAPACK_cgetrf LAPACK_GLOBAL(cgetrf,CGETRF)
+#define LAPACK_zgetrf LAPACK_GLOBAL(zgetrf,ZGETRF)
+#define LAPACK_sgbtrf LAPACK_GLOBAL(sgbtrf,SGBTRF)
+#define LAPACK_dgbtrf LAPACK_GLOBAL(dgbtrf,DGBTRF)
+#define LAPACK_cgbtrf LAPACK_GLOBAL(cgbtrf,CGBTRF)
+#define LAPACK_zgbtrf LAPACK_GLOBAL(zgbtrf,ZGBTRF)
+#define LAPACK_sgttrf LAPACK_GLOBAL(sgttrf,SGTTRF)
+#define LAPACK_dgttrf LAPACK_GLOBAL(dgttrf,DGTTRF)
+#define LAPACK_cgttrf LAPACK_GLOBAL(cgttrf,CGTTRF)
+#define LAPACK_zgttrf LAPACK_GLOBAL(zgttrf,ZGTTRF)
+#define LAPACK_spotrf LAPACK_GLOBAL(spotrf,SPOTRF)
+#define LAPACK_dpotrf LAPACK_GLOBAL(dpotrf,DPOTRF)
+#define LAPACK_cpotrf LAPACK_GLOBAL(cpotrf,CPOTRF)
+#define LAPACK_zpotrf LAPACK_GLOBAL(zpotrf,ZPOTRF)
+#define LAPACK_dpstrf LAPACK_GLOBAL(dpstrf,DPSTRF)
+#define LAPACK_spstrf LAPACK_GLOBAL(spstrf,SPSTRF)
+#define LAPACK_zpstrf LAPACK_GLOBAL(zpstrf,ZPSTRF)
+#define LAPACK_cpstrf LAPACK_GLOBAL(cpstrf,CPSTRF)
+#define LAPACK_dpftrf LAPACK_GLOBAL(dpftrf,DPFTRF)
+#define LAPACK_spftrf LAPACK_GLOBAL(spftrf,SPFTRF)
+#define LAPACK_zpftrf LAPACK_GLOBAL(zpftrf,ZPFTRF)
+#define LAPACK_cpftrf LAPACK_GLOBAL(cpftrf,CPFTRF)
+#define LAPACK_spptrf LAPACK_GLOBAL(spptrf,SPPTRF)
+#define LAPACK_dpptrf LAPACK_GLOBAL(dpptrf,DPPTRF)
+#define LAPACK_cpptrf LAPACK_GLOBAL(cpptrf,CPPTRF)
+#define LAPACK_zpptrf LAPACK_GLOBAL(zpptrf,ZPPTRF)
+#define LAPACK_spbtrf LAPACK_GLOBAL(spbtrf,SPBTRF)
+#define LAPACK_dpbtrf LAPACK_GLOBAL(dpbtrf,DPBTRF)
+#define LAPACK_cpbtrf LAPACK_GLOBAL(cpbtrf,CPBTRF)
+#define LAPACK_zpbtrf LAPACK_GLOBAL(zpbtrf,ZPBTRF)
+#define LAPACK_spttrf LAPACK_GLOBAL(spttrf,SPTTRF)
+#define LAPACK_dpttrf LAPACK_GLOBAL(dpttrf,DPTTRF)
+#define LAPACK_cpttrf LAPACK_GLOBAL(cpttrf,CPTTRF)
+#define LAPACK_zpttrf LAPACK_GLOBAL(zpttrf,ZPTTRF)
+#define LAPACK_ssytrf LAPACK_GLOBAL(ssytrf,SSYTRF)
+#define LAPACK_dsytrf LAPACK_GLOBAL(dsytrf,DSYTRF)
+#define LAPACK_csytrf LAPACK_GLOBAL(csytrf,CSYTRF)
+#define LAPACK_zsytrf LAPACK_GLOBAL(zsytrf,ZSYTRF)
+#define LAPACK_chetrf LAPACK_GLOBAL(chetrf,CHETRF)
+#define LAPACK_zhetrf LAPACK_GLOBAL(zhetrf,ZHETRF)
+#define LAPACK_ssptrf LAPACK_GLOBAL(ssptrf,SSPTRF)
+#define LAPACK_dsptrf LAPACK_GLOBAL(dsptrf,DSPTRF)
+#define LAPACK_csptrf LAPACK_GLOBAL(csptrf,CSPTRF)
+#define LAPACK_zsptrf LAPACK_GLOBAL(zsptrf,ZSPTRF)
+#define LAPACK_chptrf LAPACK_GLOBAL(chptrf,CHPTRF)
+#define LAPACK_zhptrf LAPACK_GLOBAL(zhptrf,ZHPTRF)
+#define LAPACK_sgetrs LAPACK_GLOBAL(sgetrs,SGETRS)
+#define LAPACK_dgetrs LAPACK_GLOBAL(dgetrs,DGETRS)
+#define LAPACK_cgetrs LAPACK_GLOBAL(cgetrs,CGETRS)
+#define LAPACK_zgetrs LAPACK_GLOBAL(zgetrs,ZGETRS)
+#define LAPACK_sgbtrs LAPACK_GLOBAL(sgbtrs,SGBTRS)
+#define LAPACK_dgbtrs LAPACK_GLOBAL(dgbtrs,DGBTRS)
+#define LAPACK_cgbtrs LAPACK_GLOBAL(cgbtrs,CGBTRS)
+#define LAPACK_zgbtrs LAPACK_GLOBAL(zgbtrs,ZGBTRS)
+#define LAPACK_sgttrs LAPACK_GLOBAL(sgttrs,SGTTRS)
+#define LAPACK_dgttrs LAPACK_GLOBAL(dgttrs,DGTTRS)
+#define LAPACK_cgttrs LAPACK_GLOBAL(cgttrs,CGTTRS)
+#define LAPACK_zgttrs LAPACK_GLOBAL(zgttrs,ZGTTRS)
+#define LAPACK_spotrs LAPACK_GLOBAL(spotrs,SPOTRS)
+#define LAPACK_dpotrs LAPACK_GLOBAL(dpotrs,DPOTRS)
+#define LAPACK_cpotrs LAPACK_GLOBAL(cpotrs,CPOTRS)
+#define LAPACK_zpotrs LAPACK_GLOBAL(zpotrs,ZPOTRS)
+#define LAPACK_dpftrs LAPACK_GLOBAL(dpftrs,DPFTRS)
+#define LAPACK_spftrs LAPACK_GLOBAL(spftrs,SPFTRS)
+#define LAPACK_zpftrs LAPACK_GLOBAL(zpftrs,ZPFTRS)
+#define LAPACK_cpftrs LAPACK_GLOBAL(cpftrs,CPFTRS)
+#define LAPACK_spptrs LAPACK_GLOBAL(spptrs,SPPTRS)
+#define LAPACK_dpptrs LAPACK_GLOBAL(dpptrs,DPPTRS)
+#define LAPACK_cpptrs LAPACK_GLOBAL(cpptrs,CPPTRS)
+#define LAPACK_zpptrs LAPACK_GLOBAL(zpptrs,ZPPTRS)
+#define LAPACK_spbtrs LAPACK_GLOBAL(spbtrs,SPBTRS)
+#define LAPACK_dpbtrs LAPACK_GLOBAL(dpbtrs,DPBTRS)
+#define LAPACK_cpbtrs LAPACK_GLOBAL(cpbtrs,CPBTRS)
+#define LAPACK_zpbtrs LAPACK_GLOBAL(zpbtrs,ZPBTRS)
+#define LAPACK_spttrs LAPACK_GLOBAL(spttrs,SPTTRS)
+#define LAPACK_dpttrs LAPACK_GLOBAL(dpttrs,DPTTRS)
+#define LAPACK_cpttrs LAPACK_GLOBAL(cpttrs,CPTTRS)
+#define LAPACK_zpttrs LAPACK_GLOBAL(zpttrs,ZPTTRS)
+#define LAPACK_ssytrs LAPACK_GLOBAL(ssytrs,SSYTRS)
+#define LAPACK_dsytrs LAPACK_GLOBAL(dsytrs,DSYTRS)
+#define LAPACK_csytrs LAPACK_GLOBAL(csytrs,CSYTRS)
+#define LAPACK_zsytrs LAPACK_GLOBAL(zsytrs,ZSYTRS)
+#define LAPACK_chetrs LAPACK_GLOBAL(chetrs,CHETRS)
+#define LAPACK_zhetrs LAPACK_GLOBAL(zhetrs,ZHETRS)
+#define LAPACK_ssptrs LAPACK_GLOBAL(ssptrs,SSPTRS)
+#define LAPACK_dsptrs LAPACK_GLOBAL(dsptrs,DSPTRS)
+#define LAPACK_csptrs LAPACK_GLOBAL(csptrs,CSPTRS)
+#define LAPACK_zsptrs LAPACK_GLOBAL(zsptrs,ZSPTRS)
+#define LAPACK_chptrs LAPACK_GLOBAL(chptrs,CHPTRS)
+#define LAPACK_zhptrs LAPACK_GLOBAL(zhptrs,ZHPTRS)
+#define LAPACK_strtrs LAPACK_GLOBAL(strtrs,STRTRS)
+#define LAPACK_dtrtrs LAPACK_GLOBAL(dtrtrs,DTRTRS)
+#define LAPACK_ctrtrs LAPACK_GLOBAL(ctrtrs,CTRTRS)
+#define LAPACK_ztrtrs LAPACK_GLOBAL(ztrtrs,ZTRTRS)
+#define LAPACK_stptrs LAPACK_GLOBAL(stptrs,STPTRS)
+#define LAPACK_dtptrs LAPACK_GLOBAL(dtptrs,DTPTRS)
+#define LAPACK_ctptrs LAPACK_GLOBAL(ctptrs,CTPTRS)
+#define LAPACK_ztptrs LAPACK_GLOBAL(ztptrs,ZTPTRS)
+#define LAPACK_stbtrs LAPACK_GLOBAL(stbtrs,STBTRS)
+#define LAPACK_dtbtrs LAPACK_GLOBAL(dtbtrs,DTBTRS)
+#define LAPACK_ctbtrs LAPACK_GLOBAL(ctbtrs,CTBTRS)
+#define LAPACK_ztbtrs LAPACK_GLOBAL(ztbtrs,ZTBTRS)
+#define LAPACK_sgecon LAPACK_GLOBAL(sgecon,SGECON)
+#define LAPACK_dgecon LAPACK_GLOBAL(dgecon,DGECON)
+#define LAPACK_cgecon LAPACK_GLOBAL(cgecon,CGECON)
+#define LAPACK_zgecon LAPACK_GLOBAL(zgecon,ZGECON)
+#define LAPACK_sgbcon LAPACK_GLOBAL(sgbcon,SGBCON)
+#define LAPACK_dgbcon LAPACK_GLOBAL(dgbcon,DGBCON)
+#define LAPACK_cgbcon LAPACK_GLOBAL(cgbcon,CGBCON)
+#define LAPACK_zgbcon LAPACK_GLOBAL(zgbcon,ZGBCON)
+#define LAPACK_sgtcon LAPACK_GLOBAL(sgtcon,SGTCON)
+#define LAPACK_dgtcon LAPACK_GLOBAL(dgtcon,DGTCON)
+#define LAPACK_cgtcon LAPACK_GLOBAL(cgtcon,CGTCON)
+#define LAPACK_zgtcon LAPACK_GLOBAL(zgtcon,ZGTCON)
+#define LAPACK_spocon LAPACK_GLOBAL(spocon,SPOCON)
+#define LAPACK_dpocon LAPACK_GLOBAL(dpocon,DPOCON)
+#define LAPACK_cpocon LAPACK_GLOBAL(cpocon,CPOCON)
+#define LAPACK_zpocon LAPACK_GLOBAL(zpocon,ZPOCON)
+#define LAPACK_sppcon LAPACK_GLOBAL(sppcon,SPPCON)
+#define LAPACK_dppcon LAPACK_GLOBAL(dppcon,DPPCON)
+#define LAPACK_cppcon LAPACK_GLOBAL(cppcon,CPPCON)
+#define LAPACK_zppcon LAPACK_GLOBAL(zppcon,ZPPCON)
+#define LAPACK_spbcon LAPACK_GLOBAL(spbcon,SPBCON)
+#define LAPACK_dpbcon LAPACK_GLOBAL(dpbcon,DPBCON)
+#define LAPACK_cpbcon LAPACK_GLOBAL(cpbcon,CPBCON)
+#define LAPACK_zpbcon LAPACK_GLOBAL(zpbcon,ZPBCON)
+#define LAPACK_sptcon LAPACK_GLOBAL(sptcon,SPTCON)
+#define LAPACK_dptcon LAPACK_GLOBAL(dptcon,DPTCON)
+#define LAPACK_cptcon LAPACK_GLOBAL(cptcon,CPTCON)
+#define LAPACK_zptcon LAPACK_GLOBAL(zptcon,ZPTCON)
+#define LAPACK_ssycon LAPACK_GLOBAL(ssycon,SSYCON)
+#define LAPACK_dsycon LAPACK_GLOBAL(dsycon,DSYCON)
+#define LAPACK_csycon LAPACK_GLOBAL(csycon,CSYCON)
+#define LAPACK_zsycon LAPACK_GLOBAL(zsycon,ZSYCON)
+#define LAPACK_checon LAPACK_GLOBAL(checon,CHECON)
+#define LAPACK_zhecon LAPACK_GLOBAL(zhecon,ZHECON)
+#define LAPACK_sspcon LAPACK_GLOBAL(sspcon,SSPCON)
+#define LAPACK_dspcon LAPACK_GLOBAL(dspcon,DSPCON)
+#define LAPACK_cspcon LAPACK_GLOBAL(cspcon,CSPCON)
+#define LAPACK_zspcon LAPACK_GLOBAL(zspcon,ZSPCON)
+#define LAPACK_chpcon LAPACK_GLOBAL(chpcon,CHPCON)
+#define LAPACK_zhpcon LAPACK_GLOBAL(zhpcon,ZHPCON)
+#define LAPACK_strcon LAPACK_GLOBAL(strcon,STRCON)
+#define LAPACK_dtrcon LAPACK_GLOBAL(dtrcon,DTRCON)
+#define LAPACK_ctrcon LAPACK_GLOBAL(ctrcon,CTRCON)
+#define LAPACK_ztrcon LAPACK_GLOBAL(ztrcon,ZTRCON)
+#define LAPACK_stpcon LAPACK_GLOBAL(stpcon,STPCON)
+#define LAPACK_dtpcon LAPACK_GLOBAL(dtpcon,DTPCON)
+#define LAPACK_ctpcon LAPACK_GLOBAL(ctpcon,CTPCON)
+#define LAPACK_ztpcon LAPACK_GLOBAL(ztpcon,ZTPCON)
+#define LAPACK_stbcon LAPACK_GLOBAL(stbcon,STBCON)
+#define LAPACK_dtbcon LAPACK_GLOBAL(dtbcon,DTBCON)
+#define LAPACK_ctbcon LAPACK_GLOBAL(ctbcon,CTBCON)
+#define LAPACK_ztbcon LAPACK_GLOBAL(ztbcon,ZTBCON)
+#define LAPACK_sgerfs LAPACK_GLOBAL(sgerfs,SGERFS)
+#define LAPACK_dgerfs LAPACK_GLOBAL(dgerfs,DGERFS)
+#define LAPACK_cgerfs LAPACK_GLOBAL(cgerfs,CGERFS)
+#define LAPACK_zgerfs LAPACK_GLOBAL(zgerfs,ZGERFS)
+#define LAPACK_dgerfsx LAPACK_GLOBAL(dgerfsx,DGERFSX)
+#define LAPACK_sgerfsx LAPACK_GLOBAL(sgerfsx,SGERFSX)
+#define LAPACK_zgerfsx LAPACK_GLOBAL(zgerfsx,ZGERFSX)
+#define LAPACK_cgerfsx LAPACK_GLOBAL(cgerfsx,CGERFSX)
+#define LAPACK_sgbrfs LAPACK_GLOBAL(sgbrfs,SGBRFS)
+#define LAPACK_dgbrfs LAPACK_GLOBAL(dgbrfs,DGBRFS)
+#define LAPACK_cgbrfs LAPACK_GLOBAL(cgbrfs,CGBRFS)
+#define LAPACK_zgbrfs LAPACK_GLOBAL(zgbrfs,ZGBRFS)
+#define LAPACK_dgbrfsx LAPACK_GLOBAL(dgbrfsx,DGBRFSX)
+#define LAPACK_sgbrfsx LAPACK_GLOBAL(sgbrfsx,SGBRFSX)
+#define LAPACK_zgbrfsx LAPACK_GLOBAL(zgbrfsx,ZGBRFSX)
+#define LAPACK_cgbrfsx LAPACK_GLOBAL(cgbrfsx,CGBRFSX)
+#define LAPACK_sgtrfs LAPACK_GLOBAL(sgtrfs,SGTRFS)
+#define LAPACK_dgtrfs LAPACK_GLOBAL(dgtrfs,DGTRFS)
+#define LAPACK_cgtrfs LAPACK_GLOBAL(cgtrfs,CGTRFS)
+#define LAPACK_zgtrfs LAPACK_GLOBAL(zgtrfs,ZGTRFS)
+#define LAPACK_sporfs LAPACK_GLOBAL(sporfs,SPORFS)
+#define LAPACK_dporfs LAPACK_GLOBAL(dporfs,DPORFS)
+#define LAPACK_cporfs LAPACK_GLOBAL(cporfs,CPORFS)
+#define LAPACK_zporfs LAPACK_GLOBAL(zporfs,ZPORFS)
+#define LAPACK_dporfsx LAPACK_GLOBAL(dporfsx,DPORFSX)
+#define LAPACK_sporfsx LAPACK_GLOBAL(sporfsx,SPORFSX)
+#define LAPACK_zporfsx LAPACK_GLOBAL(zporfsx,ZPORFSX)
+#define LAPACK_cporfsx LAPACK_GLOBAL(cporfsx,CPORFSX)
+#define LAPACK_spprfs LAPACK_GLOBAL(spprfs,SPPRFS)
+#define LAPACK_dpprfs LAPACK_GLOBAL(dpprfs,DPPRFS)
+#define LAPACK_cpprfs LAPACK_GLOBAL(cpprfs,CPPRFS)
+#define LAPACK_zpprfs LAPACK_GLOBAL(zpprfs,ZPPRFS)
+#define LAPACK_spbrfs LAPACK_GLOBAL(spbrfs,SPBRFS)
+#define LAPACK_dpbrfs LAPACK_GLOBAL(dpbrfs,DPBRFS)
+#define LAPACK_cpbrfs LAPACK_GLOBAL(cpbrfs,CPBRFS)
+#define LAPACK_zpbrfs LAPACK_GLOBAL(zpbrfs,ZPBRFS)
+#define LAPACK_sptrfs LAPACK_GLOBAL(sptrfs,SPTRFS)
+#define LAPACK_dptrfs LAPACK_GLOBAL(dptrfs,DPTRFS)
+#define LAPACK_cptrfs LAPACK_GLOBAL(cptrfs,CPTRFS)
+#define LAPACK_zptrfs LAPACK_GLOBAL(zptrfs,ZPTRFS)
+#define LAPACK_ssyrfs LAPACK_GLOBAL(ssyrfs,SSYRFS)
+#define LAPACK_dsyrfs LAPACK_GLOBAL(dsyrfs,DSYRFS)
+#define LAPACK_csyrfs LAPACK_GLOBAL(csyrfs,CSYRFS)
+#define LAPACK_zsyrfs LAPACK_GLOBAL(zsyrfs,ZSYRFS)
+#define LAPACK_dsyrfsx LAPACK_GLOBAL(dsyrfsx,DSYRFSX)
+#define LAPACK_ssyrfsx LAPACK_GLOBAL(ssyrfsx,SSYRFSX)
+#define LAPACK_zsyrfsx LAPACK_GLOBAL(zsyrfsx,ZSYRFSX)
+#define LAPACK_csyrfsx LAPACK_GLOBAL(csyrfsx,CSYRFSX)
+#define LAPACK_cherfs LAPACK_GLOBAL(cherfs,CHERFS)
+#define LAPACK_zherfs LAPACK_GLOBAL(zherfs,ZHERFS)
+#define LAPACK_zherfsx LAPACK_GLOBAL(zherfsx,ZHERFSX)
+#define LAPACK_cherfsx LAPACK_GLOBAL(cherfsx,CHERFSX)
+#define LAPACK_ssprfs LAPACK_GLOBAL(ssprfs,SSPRFS)
+#define LAPACK_dsprfs LAPACK_GLOBAL(dsprfs,DSPRFS)
+#define LAPACK_csprfs LAPACK_GLOBAL(csprfs,CSPRFS)
+#define LAPACK_zsprfs LAPACK_GLOBAL(zsprfs,ZSPRFS)
+#define LAPACK_chprfs LAPACK_GLOBAL(chprfs,CHPRFS)
+#define LAPACK_zhprfs LAPACK_GLOBAL(zhprfs,ZHPRFS)
+#define LAPACK_strrfs LAPACK_GLOBAL(strrfs,STRRFS)
+#define LAPACK_dtrrfs LAPACK_GLOBAL(dtrrfs,DTRRFS)
+#define LAPACK_ctrrfs LAPACK_GLOBAL(ctrrfs,CTRRFS)
+#define LAPACK_ztrrfs LAPACK_GLOBAL(ztrrfs,ZTRRFS)
+#define LAPACK_stprfs LAPACK_GLOBAL(stprfs,STPRFS)
+#define LAPACK_dtprfs LAPACK_GLOBAL(dtprfs,DTPRFS)
+#define LAPACK_ctprfs LAPACK_GLOBAL(ctprfs,CTPRFS)
+#define LAPACK_ztprfs LAPACK_GLOBAL(ztprfs,ZTPRFS)
+#define LAPACK_stbrfs LAPACK_GLOBAL(stbrfs,STBRFS)
+#define LAPACK_dtbrfs LAPACK_GLOBAL(dtbrfs,DTBRFS)
+#define LAPACK_ctbrfs LAPACK_GLOBAL(ctbrfs,CTBRFS)
+#define LAPACK_ztbrfs LAPACK_GLOBAL(ztbrfs,ZTBRFS)
+#define LAPACK_sgetri LAPACK_GLOBAL(sgetri,SGETRI)
+#define LAPACK_dgetri LAPACK_GLOBAL(dgetri,DGETRI)
+#define LAPACK_cgetri LAPACK_GLOBAL(cgetri,CGETRI)
+#define LAPACK_zgetri LAPACK_GLOBAL(zgetri,ZGETRI)
+#define LAPACK_spotri LAPACK_GLOBAL(spotri,SPOTRI)
+#define LAPACK_dpotri LAPACK_GLOBAL(dpotri,DPOTRI)
+#define LAPACK_cpotri LAPACK_GLOBAL(cpotri,CPOTRI)
+#define LAPACK_zpotri LAPACK_GLOBAL(zpotri,ZPOTRI)
+#define LAPACK_dpftri LAPACK_GLOBAL(dpftri,DPFTRI)
+#define LAPACK_spftri LAPACK_GLOBAL(spftri,SPFTRI)
+#define LAPACK_zpftri LAPACK_GLOBAL(zpftri,ZPFTRI)
+#define LAPACK_cpftri LAPACK_GLOBAL(cpftri,CPFTRI)
+#define LAPACK_spptri LAPACK_GLOBAL(spptri,SPPTRI)
+#define LAPACK_dpptri LAPACK_GLOBAL(dpptri,DPPTRI)
+#define LAPACK_cpptri LAPACK_GLOBAL(cpptri,CPPTRI)
+#define LAPACK_zpptri LAPACK_GLOBAL(zpptri,ZPPTRI)
+#define LAPACK_ssytri LAPACK_GLOBAL(ssytri,SSYTRI)
+#define LAPACK_dsytri LAPACK_GLOBAL(dsytri,DSYTRI)
+#define LAPACK_csytri LAPACK_GLOBAL(csytri,CSYTRI)
+#define LAPACK_zsytri LAPACK_GLOBAL(zsytri,ZSYTRI)
+#define LAPACK_chetri LAPACK_GLOBAL(chetri,CHETRI)
+#define LAPACK_zhetri LAPACK_GLOBAL(zhetri,ZHETRI)
+#define LAPACK_ssptri LAPACK_GLOBAL(ssptri,SSPTRI)
+#define LAPACK_dsptri LAPACK_GLOBAL(dsptri,DSPTRI)
+#define LAPACK_csptri LAPACK_GLOBAL(csptri,CSPTRI)
+#define LAPACK_zsptri LAPACK_GLOBAL(zsptri,ZSPTRI)
+#define LAPACK_chptri LAPACK_GLOBAL(chptri,CHPTRI)
+#define LAPACK_zhptri LAPACK_GLOBAL(zhptri,ZHPTRI)
+#define LAPACK_strtri LAPACK_GLOBAL(strtri,STRTRI)
+#define LAPACK_dtrtri LAPACK_GLOBAL(dtrtri,DTRTRI)
+#define LAPACK_ctrtri LAPACK_GLOBAL(ctrtri,CTRTRI)
+#define LAPACK_ztrtri LAPACK_GLOBAL(ztrtri,ZTRTRI)
+#define LAPACK_dtftri LAPACK_GLOBAL(dtftri,DTFTRI)
+#define LAPACK_stftri LAPACK_GLOBAL(stftri,STFTRI)
+#define LAPACK_ztftri LAPACK_GLOBAL(ztftri,ZTFTRI)
+#define LAPACK_ctftri LAPACK_GLOBAL(ctftri,CTFTRI)
+#define LAPACK_stptri LAPACK_GLOBAL(stptri,STPTRI)
+#define LAPACK_dtptri LAPACK_GLOBAL(dtptri,DTPTRI)
+#define LAPACK_ctptri LAPACK_GLOBAL(ctptri,CTPTRI)
+#define LAPACK_ztptri LAPACK_GLOBAL(ztptri,ZTPTRI)
+#define LAPACK_sgeequ LAPACK_GLOBAL(sgeequ,SGEEQU)
+#define LAPACK_dgeequ LAPACK_GLOBAL(dgeequ,DGEEQU)
+#define LAPACK_cgeequ LAPACK_GLOBAL(cgeequ,CGEEQU)
+#define LAPACK_zgeequ LAPACK_GLOBAL(zgeequ,ZGEEQU)
+#define LAPACK_dgeequb LAPACK_GLOBAL(dgeequb,DGEEQUB)
+#define LAPACK_sgeequb LAPACK_GLOBAL(sgeequb,SGEEQUB)
+#define LAPACK_zgeequb LAPACK_GLOBAL(zgeequb,ZGEEQUB)
+#define LAPACK_cgeequb LAPACK_GLOBAL(cgeequb,CGEEQUB)
+#define LAPACK_sgbequ LAPACK_GLOBAL(sgbequ,SGBEQU)
+#define LAPACK_dgbequ LAPACK_GLOBAL(dgbequ,DGBEQU)
+#define LAPACK_cgbequ LAPACK_GLOBAL(cgbequ,CGBEQU)
+#define LAPACK_zgbequ LAPACK_GLOBAL(zgbequ,ZGBEQU)
+#define LAPACK_dgbequb LAPACK_GLOBAL(dgbequb,DGBEQUB)
+#define LAPACK_sgbequb LAPACK_GLOBAL(sgbequb,SGBEQUB)
+#define LAPACK_zgbequb LAPACK_GLOBAL(zgbequb,ZGBEQUB)
+#define LAPACK_cgbequb LAPACK_GLOBAL(cgbequb,CGBEQUB)
+#define LAPACK_spoequ LAPACK_GLOBAL(spoequ,SPOEQU)
+#define LAPACK_dpoequ LAPACK_GLOBAL(dpoequ,DPOEQU)
+#define LAPACK_cpoequ LAPACK_GLOBAL(cpoequ,CPOEQU)
+#define LAPACK_zpoequ LAPACK_GLOBAL(zpoequ,ZPOEQU)
+#define LAPACK_dpoequb LAPACK_GLOBAL(dpoequb,DPOEQUB)
+#define LAPACK_spoequb LAPACK_GLOBAL(spoequb,SPOEQUB)
+#define LAPACK_zpoequb LAPACK_GLOBAL(zpoequb,ZPOEQUB)
+#define LAPACK_cpoequb LAPACK_GLOBAL(cpoequb,CPOEQUB)
+#define LAPACK_sppequ LAPACK_GLOBAL(sppequ,SPPEQU)
+#define LAPACK_dppequ LAPACK_GLOBAL(dppequ,DPPEQU)
+#define LAPACK_cppequ LAPACK_GLOBAL(cppequ,CPPEQU)
+#define LAPACK_zppequ LAPACK_GLOBAL(zppequ,ZPPEQU)
+#define LAPACK_spbequ LAPACK_GLOBAL(spbequ,SPBEQU)
+#define LAPACK_dpbequ LAPACK_GLOBAL(dpbequ,DPBEQU)
+#define LAPACK_cpbequ LAPACK_GLOBAL(cpbequ,CPBEQU)
+#define LAPACK_zpbequ LAPACK_GLOBAL(zpbequ,ZPBEQU)
+#define LAPACK_dsyequb LAPACK_GLOBAL(dsyequb,DSYEQUB)
+#define LAPACK_ssyequb LAPACK_GLOBAL(ssyequb,SSYEQUB)
+#define LAPACK_zsyequb LAPACK_GLOBAL(zsyequb,ZSYEQUB)
+#define LAPACK_csyequb LAPACK_GLOBAL(csyequb,CSYEQUB)
+#define LAPACK_zheequb LAPACK_GLOBAL(zheequb,ZHEEQUB)
+#define LAPACK_cheequb LAPACK_GLOBAL(cheequb,CHEEQUB)
+#define LAPACK_sgesv LAPACK_GLOBAL(sgesv,SGESV)
+#define LAPACK_dgesv LAPACK_GLOBAL(dgesv,DGESV)
+#define LAPACK_cgesv LAPACK_GLOBAL(cgesv,CGESV)
+#define LAPACK_zgesv LAPACK_GLOBAL(zgesv,ZGESV)
+#define LAPACK_dsgesv LAPACK_GLOBAL(dsgesv,DSGESV)
+#define LAPACK_zcgesv LAPACK_GLOBAL(zcgesv,ZCGESV)
+#define LAPACK_sgesvx LAPACK_GLOBAL(sgesvx,SGESVX)
+#define LAPACK_dgesvx LAPACK_GLOBAL(dgesvx,DGESVX)
+#define LAPACK_cgesvx LAPACK_GLOBAL(cgesvx,CGESVX)
+#define LAPACK_zgesvx LAPACK_GLOBAL(zgesvx,ZGESVX)
+#define LAPACK_dgesvxx LAPACK_GLOBAL(dgesvxx,DGESVXX)
+#define LAPACK_sgesvxx LAPACK_GLOBAL(sgesvxx,SGESVXX)
+#define LAPACK_zgesvxx LAPACK_GLOBAL(zgesvxx,ZGESVXX)
+#define LAPACK_cgesvxx LAPACK_GLOBAL(cgesvxx,CGESVXX)
+#define LAPACK_sgbsv LAPACK_GLOBAL(sgbsv,SGBSV)
+#define LAPACK_dgbsv LAPACK_GLOBAL(dgbsv,DGBSV)
+#define LAPACK_cgbsv LAPACK_GLOBAL(cgbsv,CGBSV)
+#define LAPACK_zgbsv LAPACK_GLOBAL(zgbsv,ZGBSV)
+#define LAPACK_sgbsvx LAPACK_GLOBAL(sgbsvx,SGBSVX)
+#define LAPACK_dgbsvx LAPACK_GLOBAL(dgbsvx,DGBSVX)
+#define LAPACK_cgbsvx LAPACK_GLOBAL(cgbsvx,CGBSVX)
+#define LAPACK_zgbsvx LAPACK_GLOBAL(zgbsvx,ZGBSVX)
+#define LAPACK_dgbsvxx LAPACK_GLOBAL(dgbsvxx,DGBSVXX)
+#define LAPACK_sgbsvxx LAPACK_GLOBAL(sgbsvxx,SGBSVXX)
+#define LAPACK_zgbsvxx LAPACK_GLOBAL(zgbsvxx,ZGBSVXX)
+#define LAPACK_cgbsvxx LAPACK_GLOBAL(cgbsvxx,CGBSVXX)
+#define LAPACK_sgtsv LAPACK_GLOBAL(sgtsv,SGTSV)
+#define LAPACK_dgtsv LAPACK_GLOBAL(dgtsv,DGTSV)
+#define LAPACK_cgtsv LAPACK_GLOBAL(cgtsv,CGTSV)
+#define LAPACK_zgtsv LAPACK_GLOBAL(zgtsv,ZGTSV)
+#define LAPACK_sgtsvx LAPACK_GLOBAL(sgtsvx,SGTSVX)
+#define LAPACK_dgtsvx LAPACK_GLOBAL(dgtsvx,DGTSVX)
+#define LAPACK_cgtsvx LAPACK_GLOBAL(cgtsvx,CGTSVX)
+#define LAPACK_zgtsvx LAPACK_GLOBAL(zgtsvx,ZGTSVX)
+#define LAPACK_sposv LAPACK_GLOBAL(sposv,SPOSV)
+#define LAPACK_dposv LAPACK_GLOBAL(dposv,DPOSV)
+#define LAPACK_cposv LAPACK_GLOBAL(cposv,CPOSV)
+#define LAPACK_zposv LAPACK_GLOBAL(zposv,ZPOSV)
+#define LAPACK_dsposv LAPACK_GLOBAL(dsposv,DSPOSV)
+#define LAPACK_zcposv LAPACK_GLOBAL(zcposv,ZCPOSV)
+#define LAPACK_sposvx LAPACK_GLOBAL(sposvx,SPOSVX)
+#define LAPACK_dposvx LAPACK_GLOBAL(dposvx,DPOSVX)
+#define LAPACK_cposvx LAPACK_GLOBAL(cposvx,CPOSVX)
+#define LAPACK_zposvx LAPACK_GLOBAL(zposvx,ZPOSVX)
+#define LAPACK_dposvxx LAPACK_GLOBAL(dposvxx,DPOSVXX)
+#define LAPACK_sposvxx LAPACK_GLOBAL(sposvxx,SPOSVXX)
+#define LAPACK_zposvxx LAPACK_GLOBAL(zposvxx,ZPOSVXX)
+#define LAPACK_cposvxx LAPACK_GLOBAL(cposvxx,CPOSVXX)
+#define LAPACK_sppsv LAPACK_GLOBAL(sppsv,SPPSV)
+#define LAPACK_dppsv LAPACK_GLOBAL(dppsv,DPPSV)
+#define LAPACK_cppsv LAPACK_GLOBAL(cppsv,CPPSV)
+#define LAPACK_zppsv LAPACK_GLOBAL(zppsv,ZPPSV)
+#define LAPACK_sppsvx LAPACK_GLOBAL(sppsvx,SPPSVX)
+#define LAPACK_dppsvx LAPACK_GLOBAL(dppsvx,DPPSVX)
+#define LAPACK_cppsvx LAPACK_GLOBAL(cppsvx,CPPSVX)
+#define LAPACK_zppsvx LAPACK_GLOBAL(zppsvx,ZPPSVX)
+#define LAPACK_spbsv LAPACK_GLOBAL(spbsv,SPBSV)
+#define LAPACK_dpbsv LAPACK_GLOBAL(dpbsv,DPBSV)
+#define LAPACK_cpbsv LAPACK_GLOBAL(cpbsv,CPBSV)
+#define LAPACK_zpbsv LAPACK_GLOBAL(zpbsv,ZPBSV)
+#define LAPACK_spbsvx LAPACK_GLOBAL(spbsvx,SPBSVX)
+#define LAPACK_dpbsvx LAPACK_GLOBAL(dpbsvx,DPBSVX)
+#define LAPACK_cpbsvx LAPACK_GLOBAL(cpbsvx,CPBSVX)
+#define LAPACK_zpbsvx LAPACK_GLOBAL(zpbsvx,ZPBSVX)
+#define LAPACK_sptsv LAPACK_GLOBAL(sptsv,SPTSV)
+#define LAPACK_dptsv LAPACK_GLOBAL(dptsv,DPTSV)
+#define LAPACK_cptsv LAPACK_GLOBAL(cptsv,CPTSV)
+#define LAPACK_zptsv LAPACK_GLOBAL(zptsv,ZPTSV)
+#define LAPACK_sptsvx LAPACK_GLOBAL(sptsvx,SPTSVX)
+#define LAPACK_dptsvx LAPACK_GLOBAL(dptsvx,DPTSVX)
+#define LAPACK_cptsvx LAPACK_GLOBAL(cptsvx,CPTSVX)
+#define LAPACK_zptsvx LAPACK_GLOBAL(zptsvx,ZPTSVX)
+#define LAPACK_ssysv LAPACK_GLOBAL(ssysv,SSYSV)
+#define LAPACK_dsysv LAPACK_GLOBAL(dsysv,DSYSV)
+#define LAPACK_csysv LAPACK_GLOBAL(csysv,CSYSV)
+#define LAPACK_zsysv LAPACK_GLOBAL(zsysv,ZSYSV)
+#define LAPACK_ssysvx LAPACK_GLOBAL(ssysvx,SSYSVX)
+#define LAPACK_dsysvx LAPACK_GLOBAL(dsysvx,DSYSVX)
+#define LAPACK_csysvx LAPACK_GLOBAL(csysvx,CSYSVX)
+#define LAPACK_zsysvx LAPACK_GLOBAL(zsysvx,ZSYSVX)
+#define LAPACK_dsysvxx LAPACK_GLOBAL(dsysvxx,DSYSVXX)
+#define LAPACK_ssysvxx LAPACK_GLOBAL(ssysvxx,SSYSVXX)
+#define LAPACK_zsysvxx LAPACK_GLOBAL(zsysvxx,ZSYSVXX)
+#define LAPACK_csysvxx LAPACK_GLOBAL(csysvxx,CSYSVXX)
+#define LAPACK_chesv LAPACK_GLOBAL(chesv,CHESV)
+#define LAPACK_zhesv LAPACK_GLOBAL(zhesv,ZHESV)
+#define LAPACK_chesvx LAPACK_GLOBAL(chesvx,CHESVX)
+#define LAPACK_zhesvx LAPACK_GLOBAL(zhesvx,ZHESVX)
+#define LAPACK_zhesvxx LAPACK_GLOBAL(zhesvxx,ZHESVXX)
+#define LAPACK_chesvxx LAPACK_GLOBAL(chesvxx,CHESVXX)
+#define LAPACK_sspsv LAPACK_GLOBAL(sspsv,SSPSV)
+#define LAPACK_dspsv LAPACK_GLOBAL(dspsv,DSPSV)
+#define LAPACK_cspsv LAPACK_GLOBAL(cspsv,CSPSV)
+#define LAPACK_zspsv LAPACK_GLOBAL(zspsv,ZSPSV)
+#define LAPACK_sspsvx LAPACK_GLOBAL(sspsvx,SSPSVX)
+#define LAPACK_dspsvx LAPACK_GLOBAL(dspsvx,DSPSVX)
+#define LAPACK_cspsvx LAPACK_GLOBAL(cspsvx,CSPSVX)
+#define LAPACK_zspsvx LAPACK_GLOBAL(zspsvx,ZSPSVX)
+#define LAPACK_chpsv LAPACK_GLOBAL(chpsv,CHPSV)
+#define LAPACK_zhpsv LAPACK_GLOBAL(zhpsv,ZHPSV)
+#define LAPACK_chpsvx LAPACK_GLOBAL(chpsvx,CHPSVX)
+#define LAPACK_zhpsvx LAPACK_GLOBAL(zhpsvx,ZHPSVX)
+#define LAPACK_sgeqrf LAPACK_GLOBAL(sgeqrf,SGEQRF)
+#define LAPACK_dgeqrf LAPACK_GLOBAL(dgeqrf,DGEQRF)
+#define LAPACK_cgeqrf LAPACK_GLOBAL(cgeqrf,CGEQRF)
+#define LAPACK_zgeqrf LAPACK_GLOBAL(zgeqrf,ZGEQRF)
+#define LAPACK_sgeqpf LAPACK_GLOBAL(sgeqpf,SGEQPF)
+#define LAPACK_dgeqpf LAPACK_GLOBAL(dgeqpf,DGEQPF)
+#define LAPACK_cgeqpf LAPACK_GLOBAL(cgeqpf,CGEQPF)
+#define LAPACK_zgeqpf LAPACK_GLOBAL(zgeqpf,ZGEQPF)
+#define LAPACK_sgeqp3 LAPACK_GLOBAL(sgeqp3,SGEQP3)
+#define LAPACK_dgeqp3 LAPACK_GLOBAL(dgeqp3,DGEQP3)
+#define LAPACK_cgeqp3 LAPACK_GLOBAL(cgeqp3,CGEQP3)
+#define LAPACK_zgeqp3 LAPACK_GLOBAL(zgeqp3,ZGEQP3)
+#define LAPACK_sorgqr LAPACK_GLOBAL(sorgqr,SORGQR)
+#define LAPACK_dorgqr LAPACK_GLOBAL(dorgqr,DORGQR)
+#define LAPACK_sormqr LAPACK_GLOBAL(sormqr,SORMQR)
+#define LAPACK_dormqr LAPACK_GLOBAL(dormqr,DORMQR)
+#define LAPACK_cungqr LAPACK_GLOBAL(cungqr,CUNGQR)
+#define LAPACK_zungqr LAPACK_GLOBAL(zungqr,ZUNGQR)
+#define LAPACK_cunmqr LAPACK_GLOBAL(cunmqr,CUNMQR)
+#define LAPACK_zunmqr LAPACK_GLOBAL(zunmqr,ZUNMQR)
+#define LAPACK_sgelqf LAPACK_GLOBAL(sgelqf,SGELQF)
+#define LAPACK_dgelqf LAPACK_GLOBAL(dgelqf,DGELQF)
+#define LAPACK_cgelqf LAPACK_GLOBAL(cgelqf,CGELQF)
+#define LAPACK_zgelqf LAPACK_GLOBAL(zgelqf,ZGELQF)
+#define LAPACK_sorglq LAPACK_GLOBAL(sorglq,SORGLQ)
+#define LAPACK_dorglq LAPACK_GLOBAL(dorglq,DORGLQ)
+#define LAPACK_sormlq LAPACK_GLOBAL(sormlq,SORMLQ)
+#define LAPACK_dormlq LAPACK_GLOBAL(dormlq,DORMLQ)
+#define LAPACK_cunglq LAPACK_GLOBAL(cunglq,CUNGLQ)
+#define LAPACK_zunglq LAPACK_GLOBAL(zunglq,ZUNGLQ)
+#define LAPACK_cunmlq LAPACK_GLOBAL(cunmlq,CUNMLQ)
+#define LAPACK_zunmlq LAPACK_GLOBAL(zunmlq,ZUNMLQ)
+#define LAPACK_sgeqlf LAPACK_GLOBAL(sgeqlf,SGEQLF)
+#define LAPACK_dgeqlf LAPACK_GLOBAL(dgeqlf,DGEQLF)
+#define LAPACK_cgeqlf LAPACK_GLOBAL(cgeqlf,CGEQLF)
+#define LAPACK_zgeqlf LAPACK_GLOBAL(zgeqlf,ZGEQLF)
+#define LAPACK_sorgql LAPACK_GLOBAL(sorgql,SORGQL)
+#define LAPACK_dorgql LAPACK_GLOBAL(dorgql,DORGQL)
+#define LAPACK_cungql LAPACK_GLOBAL(cungql,CUNGQL)
+#define LAPACK_zungql LAPACK_GLOBAL(zungql,ZUNGQL)
+#define LAPACK_sormql LAPACK_GLOBAL(sormql,SORMQL)
+#define LAPACK_dormql LAPACK_GLOBAL(dormql,DORMQL)
+#define LAPACK_cunmql LAPACK_GLOBAL(cunmql,CUNMQL)
+#define LAPACK_zunmql LAPACK_GLOBAL(zunmql,ZUNMQL)
+#define LAPACK_sgerqf LAPACK_GLOBAL(sgerqf,SGERQF)
+#define LAPACK_dgerqf LAPACK_GLOBAL(dgerqf,DGERQF)
+#define LAPACK_cgerqf LAPACK_GLOBAL(cgerqf,CGERQF)
+#define LAPACK_zgerqf LAPACK_GLOBAL(zgerqf,ZGERQF)
+#define LAPACK_sorgrq LAPACK_GLOBAL(sorgrq,SORGRQ)
+#define LAPACK_dorgrq LAPACK_GLOBAL(dorgrq,DORGRQ)
+#define LAPACK_cungrq LAPACK_GLOBAL(cungrq,CUNGRQ)
+#define LAPACK_zungrq LAPACK_GLOBAL(zungrq,ZUNGRQ)
+#define LAPACK_sormrq LAPACK_GLOBAL(sormrq,SORMRQ)
+#define LAPACK_dormrq LAPACK_GLOBAL(dormrq,DORMRQ)
+#define LAPACK_cunmrq LAPACK_GLOBAL(cunmrq,CUNMRQ)
+#define LAPACK_zunmrq LAPACK_GLOBAL(zunmrq,ZUNMRQ)
+#define LAPACK_stzrzf LAPACK_GLOBAL(stzrzf,STZRZF)
+#define LAPACK_dtzrzf LAPACK_GLOBAL(dtzrzf,DTZRZF)
+#define LAPACK_ctzrzf LAPACK_GLOBAL(ctzrzf,CTZRZF)
+#define LAPACK_ztzrzf LAPACK_GLOBAL(ztzrzf,ZTZRZF)
+#define LAPACK_sormrz LAPACK_GLOBAL(sormrz,SORMRZ)
+#define LAPACK_dormrz LAPACK_GLOBAL(dormrz,DORMRZ)
+#define LAPACK_cunmrz LAPACK_GLOBAL(cunmrz,CUNMRZ)
+#define LAPACK_zunmrz LAPACK_GLOBAL(zunmrz,ZUNMRZ)
+#define LAPACK_sggqrf LAPACK_GLOBAL(sggqrf,SGGQRF)
+#define LAPACK_dggqrf LAPACK_GLOBAL(dggqrf,DGGQRF)
+#define LAPACK_cggqrf LAPACK_GLOBAL(cggqrf,CGGQRF)
+#define LAPACK_zggqrf LAPACK_GLOBAL(zggqrf,ZGGQRF)
+#define LAPACK_sggrqf LAPACK_GLOBAL(sggrqf,SGGRQF)
+#define LAPACK_dggrqf LAPACK_GLOBAL(dggrqf,DGGRQF)
+#define LAPACK_cggrqf LAPACK_GLOBAL(cggrqf,CGGRQF)
+#define LAPACK_zggrqf LAPACK_GLOBAL(zggrqf,ZGGRQF)
+#define LAPACK_sgebrd LAPACK_GLOBAL(sgebrd,SGEBRD)
+#define LAPACK_dgebrd LAPACK_GLOBAL(dgebrd,DGEBRD)
+#define LAPACK_cgebrd LAPACK_GLOBAL(cgebrd,CGEBRD)
+#define LAPACK_zgebrd LAPACK_GLOBAL(zgebrd,ZGEBRD)
+#define LAPACK_sgbbrd LAPACK_GLOBAL(sgbbrd,SGBBRD)
+#define LAPACK_dgbbrd LAPACK_GLOBAL(dgbbrd,DGBBRD)
+#define LAPACK_cgbbrd LAPACK_GLOBAL(cgbbrd,CGBBRD)
+#define LAPACK_zgbbrd LAPACK_GLOBAL(zgbbrd,ZGBBRD)
+#define LAPACK_sorgbr LAPACK_GLOBAL(sorgbr,SORGBR)
+#define LAPACK_dorgbr LAPACK_GLOBAL(dorgbr,DORGBR)
+#define LAPACK_sormbr LAPACK_GLOBAL(sormbr,SORMBR)
+#define LAPACK_dormbr LAPACK_GLOBAL(dormbr,DORMBR)
+#define LAPACK_cungbr LAPACK_GLOBAL(cungbr,CUNGBR)
+#define LAPACK_zungbr LAPACK_GLOBAL(zungbr,ZUNGBR)
+#define LAPACK_cunmbr LAPACK_GLOBAL(cunmbr,CUNMBR)
+#define LAPACK_zunmbr LAPACK_GLOBAL(zunmbr,ZUNMBR)
+#define LAPACK_sbdsqr LAPACK_GLOBAL(sbdsqr,SBDSQR)
+#define LAPACK_dbdsqr LAPACK_GLOBAL(dbdsqr,DBDSQR)
+#define LAPACK_cbdsqr LAPACK_GLOBAL(cbdsqr,CBDSQR)
+#define LAPACK_zbdsqr LAPACK_GLOBAL(zbdsqr,ZBDSQR)
+#define LAPACK_sbdsdc LAPACK_GLOBAL(sbdsdc,SBDSDC)
+#define LAPACK_dbdsdc LAPACK_GLOBAL(dbdsdc,DBDSDC)
+#define LAPACK_ssytrd LAPACK_GLOBAL(ssytrd,SSYTRD)
+#define LAPACK_dsytrd LAPACK_GLOBAL(dsytrd,DSYTRD)
+#define LAPACK_sorgtr LAPACK_GLOBAL(sorgtr,SORGTR)
+#define LAPACK_dorgtr LAPACK_GLOBAL(dorgtr,DORGTR)
+#define LAPACK_sormtr LAPACK_GLOBAL(sormtr,SORMTR)
+#define LAPACK_dormtr LAPACK_GLOBAL(dormtr,DORMTR)
+#define LAPACK_chetrd LAPACK_GLOBAL(chetrd,CHETRD)
+#define LAPACK_zhetrd LAPACK_GLOBAL(zhetrd,ZHETRD)
+#define LAPACK_cungtr LAPACK_GLOBAL(cungtr,CUNGTR)
+#define LAPACK_zungtr LAPACK_GLOBAL(zungtr,ZUNGTR)
+#define LAPACK_cunmtr LAPACK_GLOBAL(cunmtr,CUNMTR)
+#define LAPACK_zunmtr LAPACK_GLOBAL(zunmtr,ZUNMTR)
+#define LAPACK_ssptrd LAPACK_GLOBAL(ssptrd,SSPTRD)
+#define LAPACK_dsptrd LAPACK_GLOBAL(dsptrd,DSPTRD)
+#define LAPACK_sopgtr LAPACK_GLOBAL(sopgtr,SOPGTR)
+#define LAPACK_dopgtr LAPACK_GLOBAL(dopgtr,DOPGTR)
+#define LAPACK_sopmtr LAPACK_GLOBAL(sopmtr,SOPMTR)
+#define LAPACK_dopmtr LAPACK_GLOBAL(dopmtr,DOPMTR)
+#define LAPACK_chptrd LAPACK_GLOBAL(chptrd,CHPTRD)
+#define LAPACK_zhptrd LAPACK_GLOBAL(zhptrd,ZHPTRD)
+#define LAPACK_cupgtr LAPACK_GLOBAL(cupgtr,CUPGTR)
+#define LAPACK_zupgtr LAPACK_GLOBAL(zupgtr,ZUPGTR)
+#define LAPACK_cupmtr LAPACK_GLOBAL(cupmtr,CUPMTR)
+#define LAPACK_zupmtr LAPACK_GLOBAL(zupmtr,ZUPMTR)
+#define LAPACK_ssbtrd LAPACK_GLOBAL(ssbtrd,SSBTRD)
+#define LAPACK_dsbtrd LAPACK_GLOBAL(dsbtrd,DSBTRD)
+#define LAPACK_chbtrd LAPACK_GLOBAL(chbtrd,CHBTRD)
+#define LAPACK_zhbtrd LAPACK_GLOBAL(zhbtrd,ZHBTRD)
+#define LAPACK_ssterf LAPACK_GLOBAL(ssterf,SSTERF)
+#define LAPACK_dsterf LAPACK_GLOBAL(dsterf,DSTERF)
+#define LAPACK_ssteqr LAPACK_GLOBAL(ssteqr,SSTEQR)
+#define LAPACK_dsteqr LAPACK_GLOBAL(dsteqr,DSTEQR)
+#define LAPACK_csteqr LAPACK_GLOBAL(csteqr,CSTEQR)
+#define LAPACK_zsteqr LAPACK_GLOBAL(zsteqr,ZSTEQR)
+#define LAPACK_sstemr LAPACK_GLOBAL(sstemr,SSTEMR)
+#define LAPACK_dstemr LAPACK_GLOBAL(dstemr,DSTEMR)
+#define LAPACK_cstemr LAPACK_GLOBAL(cstemr,CSTEMR)
+#define LAPACK_zstemr LAPACK_GLOBAL(zstemr,ZSTEMR)
+#define LAPACK_sstedc LAPACK_GLOBAL(sstedc,SSTEDC)
+#define LAPACK_dstedc LAPACK_GLOBAL(dstedc,DSTEDC)
+#define LAPACK_cstedc LAPACK_GLOBAL(cstedc,CSTEDC)
+#define LAPACK_zstedc LAPACK_GLOBAL(zstedc,ZSTEDC)
+#define LAPACK_sstegr LAPACK_GLOBAL(sstegr,SSTEGR)
+#define LAPACK_dstegr LAPACK_GLOBAL(dstegr,DSTEGR)
+#define LAPACK_cstegr LAPACK_GLOBAL(cstegr,CSTEGR)
+#define LAPACK_zstegr LAPACK_GLOBAL(zstegr,ZSTEGR)
+#define LAPACK_spteqr LAPACK_GLOBAL(spteqr,SPTEQR)
+#define LAPACK_dpteqr LAPACK_GLOBAL(dpteqr,DPTEQR)
+#define LAPACK_cpteqr LAPACK_GLOBAL(cpteqr,CPTEQR)
+#define LAPACK_zpteqr LAPACK_GLOBAL(zpteqr,ZPTEQR)
+#define LAPACK_sstebz LAPACK_GLOBAL(sstebz,SSTEBZ)
+#define LAPACK_dstebz LAPACK_GLOBAL(dstebz,DSTEBZ)
+#define LAPACK_sstein LAPACK_GLOBAL(sstein,SSTEIN)
+#define LAPACK_dstein LAPACK_GLOBAL(dstein,DSTEIN)
+#define LAPACK_cstein LAPACK_GLOBAL(cstein,CSTEIN)
+#define LAPACK_zstein LAPACK_GLOBAL(zstein,ZSTEIN)
+#define LAPACK_sdisna LAPACK_GLOBAL(sdisna,SDISNA)
+#define LAPACK_ddisna LAPACK_GLOBAL(ddisna,DDISNA)
+#define LAPACK_ssygst LAPACK_GLOBAL(ssygst,SSYGST)
+#define LAPACK_dsygst LAPACK_GLOBAL(dsygst,DSYGST)
+#define LAPACK_chegst LAPACK_GLOBAL(chegst,CHEGST)
+#define LAPACK_zhegst LAPACK_GLOBAL(zhegst,ZHEGST)
+#define LAPACK_sspgst LAPACK_GLOBAL(sspgst,SSPGST)
+#define LAPACK_dspgst LAPACK_GLOBAL(dspgst,DSPGST)
+#define LAPACK_chpgst LAPACK_GLOBAL(chpgst,CHPGST)
+#define LAPACK_zhpgst LAPACK_GLOBAL(zhpgst,ZHPGST)
+#define LAPACK_ssbgst LAPACK_GLOBAL(ssbgst,SSBGST)
+#define LAPACK_dsbgst LAPACK_GLOBAL(dsbgst,DSBGST)
+#define LAPACK_chbgst LAPACK_GLOBAL(chbgst,CHBGST)
+#define LAPACK_zhbgst LAPACK_GLOBAL(zhbgst,ZHBGST)
+#define LAPACK_spbstf LAPACK_GLOBAL(spbstf,SPBSTF)
+#define LAPACK_dpbstf LAPACK_GLOBAL(dpbstf,DPBSTF)
+#define LAPACK_cpbstf LAPACK_GLOBAL(cpbstf,CPBSTF)
+#define LAPACK_zpbstf LAPACK_GLOBAL(zpbstf,ZPBSTF)
+#define LAPACK_sgehrd LAPACK_GLOBAL(sgehrd,SGEHRD)
+#define LAPACK_dgehrd LAPACK_GLOBAL(dgehrd,DGEHRD)
+#define LAPACK_cgehrd LAPACK_GLOBAL(cgehrd,CGEHRD)
+#define LAPACK_zgehrd LAPACK_GLOBAL(zgehrd,ZGEHRD)
+#define LAPACK_sorghr LAPACK_GLOBAL(sorghr,SORGHR)
+#define LAPACK_dorghr LAPACK_GLOBAL(dorghr,DORGHR)
+#define LAPACK_sormhr LAPACK_GLOBAL(sormhr,SORMHR)
+#define LAPACK_dormhr LAPACK_GLOBAL(dormhr,DORMHR)
+#define LAPACK_cunghr LAPACK_GLOBAL(cunghr,CUNGHR)
+#define LAPACK_zunghr LAPACK_GLOBAL(zunghr,ZUNGHR)
+#define LAPACK_cunmhr LAPACK_GLOBAL(cunmhr,CUNMHR)
+#define LAPACK_zunmhr LAPACK_GLOBAL(zunmhr,ZUNMHR)
+#define LAPACK_sgebal LAPACK_GLOBAL(sgebal,SGEBAL)
+#define LAPACK_dgebal LAPACK_GLOBAL(dgebal,DGEBAL)
+#define LAPACK_cgebal LAPACK_GLOBAL(cgebal,CGEBAL)
+#define LAPACK_zgebal LAPACK_GLOBAL(zgebal,ZGEBAL)
+#define LAPACK_sgebak LAPACK_GLOBAL(sgebak,SGEBAK)
+#define LAPACK_dgebak LAPACK_GLOBAL(dgebak,DGEBAK)
+#define LAPACK_cgebak LAPACK_GLOBAL(cgebak,CGEBAK)
+#define LAPACK_zgebak LAPACK_GLOBAL(zgebak,ZGEBAK)
+#define LAPACK_shseqr LAPACK_GLOBAL(shseqr,SHSEQR)
+#define LAPACK_dhseqr LAPACK_GLOBAL(dhseqr,DHSEQR)
+#define LAPACK_chseqr LAPACK_GLOBAL(chseqr,CHSEQR)
+#define LAPACK_zhseqr LAPACK_GLOBAL(zhseqr,ZHSEQR)
+#define LAPACK_shsein LAPACK_GLOBAL(shsein,SHSEIN)
+#define LAPACK_dhsein LAPACK_GLOBAL(dhsein,DHSEIN)
+#define LAPACK_chsein LAPACK_GLOBAL(chsein,CHSEIN)
+#define LAPACK_zhsein LAPACK_GLOBAL(zhsein,ZHSEIN)
+#define LAPACK_strevc LAPACK_GLOBAL(strevc,STREVC)
+#define LAPACK_dtrevc LAPACK_GLOBAL(dtrevc,DTREVC)
+#define LAPACK_ctrevc LAPACK_GLOBAL(ctrevc,CTREVC)
+#define LAPACK_ztrevc LAPACK_GLOBAL(ztrevc,ZTREVC)
+#define LAPACK_strsna LAPACK_GLOBAL(strsna,STRSNA)
+#define LAPACK_dtrsna LAPACK_GLOBAL(dtrsna,DTRSNA)
+#define LAPACK_ctrsna LAPACK_GLOBAL(ctrsna,CTRSNA)
+#define LAPACK_ztrsna LAPACK_GLOBAL(ztrsna,ZTRSNA)
+#define LAPACK_strexc LAPACK_GLOBAL(strexc,STREXC)
+#define LAPACK_dtrexc LAPACK_GLOBAL(dtrexc,DTREXC)
+#define LAPACK_ctrexc LAPACK_GLOBAL(ctrexc,CTREXC)
+#define LAPACK_ztrexc LAPACK_GLOBAL(ztrexc,ZTREXC)
+#define LAPACK_strsen LAPACK_GLOBAL(strsen,STRSEN)
+#define LAPACK_dtrsen LAPACK_GLOBAL(dtrsen,DTRSEN)
+#define LAPACK_ctrsen LAPACK_GLOBAL(ctrsen,CTRSEN)
+#define LAPACK_ztrsen LAPACK_GLOBAL(ztrsen,ZTRSEN)
+#define LAPACK_strsyl LAPACK_GLOBAL(strsyl,STRSYL)
+#define LAPACK_dtrsyl LAPACK_GLOBAL(dtrsyl,DTRSYL)
+#define LAPACK_ctrsyl LAPACK_GLOBAL(ctrsyl,CTRSYL)
+#define LAPACK_ztrsyl LAPACK_GLOBAL(ztrsyl,ZTRSYL)
+#define LAPACK_sgghrd LAPACK_GLOBAL(sgghrd,SGGHRD)
+#define LAPACK_dgghrd LAPACK_GLOBAL(dgghrd,DGGHRD)
+#define LAPACK_cgghrd LAPACK_GLOBAL(cgghrd,CGGHRD)
+#define LAPACK_zgghrd LAPACK_GLOBAL(zgghrd,ZGGHRD)
+#define LAPACK_sggbal LAPACK_GLOBAL(sggbal,SGGBAL)
+#define LAPACK_dggbal LAPACK_GLOBAL(dggbal,DGGBAL)
+#define LAPACK_cggbal LAPACK_GLOBAL(cggbal,CGGBAL)
+#define LAPACK_zggbal LAPACK_GLOBAL(zggbal,ZGGBAL)
+#define LAPACK_sggbak LAPACK_GLOBAL(sggbak,SGGBAK)
+#define LAPACK_dggbak LAPACK_GLOBAL(dggbak,DGGBAK)
+#define LAPACK_cggbak LAPACK_GLOBAL(cggbak,CGGBAK)
+#define LAPACK_zggbak LAPACK_GLOBAL(zggbak,ZGGBAK)
+#define LAPACK_shgeqz LAPACK_GLOBAL(shgeqz,SHGEQZ)
+#define LAPACK_dhgeqz LAPACK_GLOBAL(dhgeqz,DHGEQZ)
+#define LAPACK_chgeqz LAPACK_GLOBAL(chgeqz,CHGEQZ)
+#define LAPACK_zhgeqz LAPACK_GLOBAL(zhgeqz,ZHGEQZ)
+#define LAPACK_stgevc LAPACK_GLOBAL(stgevc,STGEVC)
+#define LAPACK_dtgevc LAPACK_GLOBAL(dtgevc,DTGEVC)
+#define LAPACK_ctgevc LAPACK_GLOBAL(ctgevc,CTGEVC)
+#define LAPACK_ztgevc LAPACK_GLOBAL(ztgevc,ZTGEVC)
+#define LAPACK_stgexc LAPACK_GLOBAL(stgexc,STGEXC)
+#define LAPACK_dtgexc LAPACK_GLOBAL(dtgexc,DTGEXC)
+#define LAPACK_ctgexc LAPACK_GLOBAL(ctgexc,CTGEXC)
+#define LAPACK_ztgexc LAPACK_GLOBAL(ztgexc,ZTGEXC)
+#define LAPACK_stgsen LAPACK_GLOBAL(stgsen,STGSEN)
+#define LAPACK_dtgsen LAPACK_GLOBAL(dtgsen,DTGSEN)
+#define LAPACK_ctgsen LAPACK_GLOBAL(ctgsen,CTGSEN)
+#define LAPACK_ztgsen LAPACK_GLOBAL(ztgsen,ZTGSEN)
+#define LAPACK_stgsyl LAPACK_GLOBAL(stgsyl,STGSYL)
+#define LAPACK_dtgsyl LAPACK_GLOBAL(dtgsyl,DTGSYL)
+#define LAPACK_ctgsyl LAPACK_GLOBAL(ctgsyl,CTGSYL)
+#define LAPACK_ztgsyl LAPACK_GLOBAL(ztgsyl,ZTGSYL)
+#define LAPACK_stgsna LAPACK_GLOBAL(stgsna,STGSNA)
+#define LAPACK_dtgsna LAPACK_GLOBAL(dtgsna,DTGSNA)
+#define LAPACK_ctgsna LAPACK_GLOBAL(ctgsna,CTGSNA)
+#define LAPACK_ztgsna LAPACK_GLOBAL(ztgsna,ZTGSNA)
+#define LAPACK_sggsvp LAPACK_GLOBAL(sggsvp,SGGSVP)
+#define LAPACK_dggsvp LAPACK_GLOBAL(dggsvp,DGGSVP)
+#define LAPACK_cggsvp LAPACK_GLOBAL(cggsvp,CGGSVP)
+#define LAPACK_zggsvp LAPACK_GLOBAL(zggsvp,ZGGSVP)
+#define LAPACK_stgsja LAPACK_GLOBAL(stgsja,STGSJA)
+#define LAPACK_dtgsja LAPACK_GLOBAL(dtgsja,DTGSJA)
+#define LAPACK_ctgsja LAPACK_GLOBAL(ctgsja,CTGSJA)
+#define LAPACK_ztgsja LAPACK_GLOBAL(ztgsja,ZTGSJA)
+#define LAPACK_sgels LAPACK_GLOBAL(sgels,SGELS)
+#define LAPACK_dgels LAPACK_GLOBAL(dgels,DGELS)
+#define LAPACK_cgels LAPACK_GLOBAL(cgels,CGELS)
+#define LAPACK_zgels LAPACK_GLOBAL(zgels,ZGELS)
+#define LAPACK_sgelsy LAPACK_GLOBAL(sgelsy,SGELSY)
+#define LAPACK_dgelsy LAPACK_GLOBAL(dgelsy,DGELSY)
+#define LAPACK_cgelsy LAPACK_GLOBAL(cgelsy,CGELSY)
+#define LAPACK_zgelsy LAPACK_GLOBAL(zgelsy,ZGELSY)
+#define LAPACK_sgelss LAPACK_GLOBAL(sgelss,SGELSS)
+#define LAPACK_dgelss LAPACK_GLOBAL(dgelss,DGELSS)
+#define LAPACK_cgelss LAPACK_GLOBAL(cgelss,CGELSS)
+#define LAPACK_zgelss LAPACK_GLOBAL(zgelss,ZGELSS)
+#define LAPACK_sgelsd LAPACK_GLOBAL(sgelsd,SGELSD)
+#define LAPACK_dgelsd LAPACK_GLOBAL(dgelsd,DGELSD)
+#define LAPACK_cgelsd LAPACK_GLOBAL(cgelsd,CGELSD)
+#define LAPACK_zgelsd LAPACK_GLOBAL(zgelsd,ZGELSD)
+#define LAPACK_sgglse LAPACK_GLOBAL(sgglse,SGGLSE)
+#define LAPACK_dgglse LAPACK_GLOBAL(dgglse,DGGLSE)
+#define LAPACK_cgglse LAPACK_GLOBAL(cgglse,CGGLSE)
+#define LAPACK_zgglse LAPACK_GLOBAL(zgglse,ZGGLSE)
+#define LAPACK_sggglm LAPACK_GLOBAL(sggglm,SGGGLM)
+#define LAPACK_dggglm LAPACK_GLOBAL(dggglm,DGGGLM)
+#define LAPACK_cggglm LAPACK_GLOBAL(cggglm,CGGGLM)
+#define LAPACK_zggglm LAPACK_GLOBAL(zggglm,ZGGGLM)
+#define LAPACK_ssyev LAPACK_GLOBAL(ssyev,SSYEV)
+#define LAPACK_dsyev LAPACK_GLOBAL(dsyev,DSYEV)
+#define LAPACK_cheev LAPACK_GLOBAL(cheev,CHEEV)
+#define LAPACK_zheev LAPACK_GLOBAL(zheev,ZHEEV)
+#define LAPACK_ssyevd LAPACK_GLOBAL(ssyevd,SSYEVD)
+#define LAPACK_dsyevd LAPACK_GLOBAL(dsyevd,DSYEVD)
+#define LAPACK_cheevd LAPACK_GLOBAL(cheevd,CHEEVD)
+#define LAPACK_zheevd LAPACK_GLOBAL(zheevd,ZHEEVD)
+#define LAPACK_ssyevx LAPACK_GLOBAL(ssyevx,SSYEVX)
+#define LAPACK_dsyevx LAPACK_GLOBAL(dsyevx,DSYEVX)
+#define LAPACK_cheevx LAPACK_GLOBAL(cheevx,CHEEVX)
+#define LAPACK_zheevx LAPACK_GLOBAL(zheevx,ZHEEVX)
+#define LAPACK_ssyevr LAPACK_GLOBAL(ssyevr,SSYEVR)
+#define LAPACK_dsyevr LAPACK_GLOBAL(dsyevr,DSYEVR)
+#define LAPACK_cheevr LAPACK_GLOBAL(cheevr,CHEEVR)
+#define LAPACK_zheevr LAPACK_GLOBAL(zheevr,ZHEEVR)
+#define LAPACK_sspev LAPACK_GLOBAL(sspev,SSPEV)
+#define LAPACK_dspev LAPACK_GLOBAL(dspev,DSPEV)
+#define LAPACK_chpev LAPACK_GLOBAL(chpev,CHPEV)
+#define LAPACK_zhpev LAPACK_GLOBAL(zhpev,ZHPEV)
+#define LAPACK_sspevd LAPACK_GLOBAL(sspevd,SSPEVD)
+#define LAPACK_dspevd LAPACK_GLOBAL(dspevd,DSPEVD)
+#define LAPACK_chpevd LAPACK_GLOBAL(chpevd,CHPEVD)
+#define LAPACK_zhpevd LAPACK_GLOBAL(zhpevd,ZHPEVD)
+#define LAPACK_sspevx LAPACK_GLOBAL(sspevx,SSPEVX)
+#define LAPACK_dspevx LAPACK_GLOBAL(dspevx,DSPEVX)
+#define LAPACK_chpevx LAPACK_GLOBAL(chpevx,CHPEVX)
+#define LAPACK_zhpevx LAPACK_GLOBAL(zhpevx,ZHPEVX)
+#define LAPACK_ssbev LAPACK_GLOBAL(ssbev,SSBEV)
+#define LAPACK_dsbev LAPACK_GLOBAL(dsbev,DSBEV)
+#define LAPACK_chbev LAPACK_GLOBAL(chbev,CHBEV)
+#define LAPACK_zhbev LAPACK_GLOBAL(zhbev,ZHBEV)
+#define LAPACK_ssbevd LAPACK_GLOBAL(ssbevd,SSBEVD)
+#define LAPACK_dsbevd LAPACK_GLOBAL(dsbevd,DSBEVD)
+#define LAPACK_chbevd LAPACK_GLOBAL(chbevd,CHBEVD)
+#define LAPACK_zhbevd LAPACK_GLOBAL(zhbevd,ZHBEVD)
+#define LAPACK_ssbevx LAPACK_GLOBAL(ssbevx,SSBEVX)
+#define LAPACK_dsbevx LAPACK_GLOBAL(dsbevx,DSBEVX)
+#define LAPACK_chbevx LAPACK_GLOBAL(chbevx,CHBEVX)
+#define LAPACK_zhbevx LAPACK_GLOBAL(zhbevx,ZHBEVX)
+#define LAPACK_sstev LAPACK_GLOBAL(sstev,SSTEV)
+#define LAPACK_dstev LAPACK_GLOBAL(dstev,DSTEV)
+#define LAPACK_sstevd LAPACK_GLOBAL(sstevd,SSTEVD)
+#define LAPACK_dstevd LAPACK_GLOBAL(dstevd,DSTEVD)
+#define LAPACK_sstevx LAPACK_GLOBAL(sstevx,SSTEVX)
+#define LAPACK_dstevx LAPACK_GLOBAL(dstevx,DSTEVX)
+#define LAPACK_sstevr LAPACK_GLOBAL(sstevr,SSTEVR)
+#define LAPACK_dstevr LAPACK_GLOBAL(dstevr,DSTEVR)
+#define LAPACK_sgees LAPACK_GLOBAL(sgees,SGEES)
+#define LAPACK_dgees LAPACK_GLOBAL(dgees,DGEES)
+#define LAPACK_cgees LAPACK_GLOBAL(cgees,CGEES)
+#define LAPACK_zgees LAPACK_GLOBAL(zgees,ZGEES)
+#define LAPACK_sgeesx LAPACK_GLOBAL(sgeesx,SGEESX)
+#define LAPACK_dgeesx LAPACK_GLOBAL(dgeesx,DGEESX)
+#define LAPACK_cgeesx LAPACK_GLOBAL(cgeesx,CGEESX)
+#define LAPACK_zgeesx LAPACK_GLOBAL(zgeesx,ZGEESX)
+#define LAPACK_sgeev LAPACK_GLOBAL(sgeev,SGEEV)
+#define LAPACK_dgeev LAPACK_GLOBAL(dgeev,DGEEV)
+#define LAPACK_cgeev LAPACK_GLOBAL(cgeev,CGEEV)
+#define LAPACK_zgeev LAPACK_GLOBAL(zgeev,ZGEEV)
+#define LAPACK_sgeevx LAPACK_GLOBAL(sgeevx,SGEEVX)
+#define LAPACK_dgeevx LAPACK_GLOBAL(dgeevx,DGEEVX)
+#define LAPACK_cgeevx LAPACK_GLOBAL(cgeevx,CGEEVX)
+#define LAPACK_zgeevx LAPACK_GLOBAL(zgeevx,ZGEEVX)
+#define LAPACK_sgesvd LAPACK_GLOBAL(sgesvd,SGESVD)
+#define LAPACK_dgesvd LAPACK_GLOBAL(dgesvd,DGESVD)
+#define LAPACK_cgesvd LAPACK_GLOBAL(cgesvd,CGESVD)
+#define LAPACK_zgesvd LAPACK_GLOBAL(zgesvd,ZGESVD)
+#define LAPACK_sgesdd LAPACK_GLOBAL(sgesdd,SGESDD)
+#define LAPACK_dgesdd LAPACK_GLOBAL(dgesdd,DGESDD)
+#define LAPACK_cgesdd LAPACK_GLOBAL(cgesdd,CGESDD)
+#define LAPACK_zgesdd LAPACK_GLOBAL(zgesdd,ZGESDD)
+#define LAPACK_dgejsv LAPACK_GLOBAL(dgejsv,DGEJSV)
+#define LAPACK_sgejsv LAPACK_GLOBAL(sgejsv,SGEJSV)
+#define LAPACK_dgesvj LAPACK_GLOBAL(dgesvj,DGESVJ)
+#define LAPACK_sgesvj LAPACK_GLOBAL(sgesvj,SGESVJ)
+#define LAPACK_sggsvd LAPACK_GLOBAL(sggsvd,SGGSVD)
+#define LAPACK_dggsvd LAPACK_GLOBAL(dggsvd,DGGSVD)
+#define LAPACK_cggsvd LAPACK_GLOBAL(cggsvd,CGGSVD)
+#define LAPACK_zggsvd LAPACK_GLOBAL(zggsvd,ZGGSVD)
+#define LAPACK_ssygv LAPACK_GLOBAL(ssygv,SSYGV)
+#define LAPACK_dsygv LAPACK_GLOBAL(dsygv,DSYGV)
+#define LAPACK_chegv LAPACK_GLOBAL(chegv,CHEGV)
+#define LAPACK_zhegv LAPACK_GLOBAL(zhegv,ZHEGV)
+#define LAPACK_ssygvd LAPACK_GLOBAL(ssygvd,SSYGVD)
+#define LAPACK_dsygvd LAPACK_GLOBAL(dsygvd,DSYGVD)
+#define LAPACK_chegvd LAPACK_GLOBAL(chegvd,CHEGVD)
+#define LAPACK_zhegvd LAPACK_GLOBAL(zhegvd,ZHEGVD)
+#define LAPACK_ssygvx LAPACK_GLOBAL(ssygvx,SSYGVX)
+#define LAPACK_dsygvx LAPACK_GLOBAL(dsygvx,DSYGVX)
+#define LAPACK_chegvx LAPACK_GLOBAL(chegvx,CHEGVX)
+#define LAPACK_zhegvx LAPACK_GLOBAL(zhegvx,ZHEGVX)
+#define LAPACK_sspgv LAPACK_GLOBAL(sspgv,SSPGV)
+#define LAPACK_dspgv LAPACK_GLOBAL(dspgv,DSPGV)
+#define LAPACK_chpgv LAPACK_GLOBAL(chpgv,CHPGV)
+#define LAPACK_zhpgv LAPACK_GLOBAL(zhpgv,ZHPGV)
+#define LAPACK_sspgvd LAPACK_GLOBAL(sspgvd,SSPGVD)
+#define LAPACK_dspgvd LAPACK_GLOBAL(dspgvd,DSPGVD)
+#define LAPACK_chpgvd LAPACK_GLOBAL(chpgvd,CHPGVD)
+#define LAPACK_zhpgvd LAPACK_GLOBAL(zhpgvd,ZHPGVD)
+#define LAPACK_sspgvx LAPACK_GLOBAL(sspgvx,SSPGVX)
+#define LAPACK_dspgvx LAPACK_GLOBAL(dspgvx,DSPGVX)
+#define LAPACK_chpgvx LAPACK_GLOBAL(chpgvx,CHPGVX)
+#define LAPACK_zhpgvx LAPACK_GLOBAL(zhpgvx,ZHPGVX)
+#define LAPACK_ssbgv LAPACK_GLOBAL(ssbgv,SSBGV)
+#define LAPACK_dsbgv LAPACK_GLOBAL(dsbgv,DSBGV)
+#define LAPACK_chbgv LAPACK_GLOBAL(chbgv,CHBGV)
+#define LAPACK_zhbgv LAPACK_GLOBAL(zhbgv,ZHBGV)
+#define LAPACK_ssbgvd LAPACK_GLOBAL(ssbgvd,SSBGVD)
+#define LAPACK_dsbgvd LAPACK_GLOBAL(dsbgvd,DSBGVD)
+#define LAPACK_chbgvd LAPACK_GLOBAL(chbgvd,CHBGVD)
+#define LAPACK_zhbgvd LAPACK_GLOBAL(zhbgvd,ZHBGVD)
+#define LAPACK_ssbgvx LAPACK_GLOBAL(ssbgvx,SSBGVX)
+#define LAPACK_dsbgvx LAPACK_GLOBAL(dsbgvx,DSBGVX)
+#define LAPACK_chbgvx LAPACK_GLOBAL(chbgvx,CHBGVX)
+#define LAPACK_zhbgvx LAPACK_GLOBAL(zhbgvx,ZHBGVX)
+#define LAPACK_sgges LAPACK_GLOBAL(sgges,SGGES)
+#define LAPACK_dgges LAPACK_GLOBAL(dgges,DGGES)
+#define LAPACK_cgges LAPACK_GLOBAL(cgges,CGGES)
+#define LAPACK_zgges LAPACK_GLOBAL(zgges,ZGGES)
+#define LAPACK_sggesx LAPACK_GLOBAL(sggesx,SGGESX)
+#define LAPACK_dggesx LAPACK_GLOBAL(dggesx,DGGESX)
+#define LAPACK_cggesx LAPACK_GLOBAL(cggesx,CGGESX)
+#define LAPACK_zggesx LAPACK_GLOBAL(zggesx,ZGGESX)
+#define LAPACK_sggev LAPACK_GLOBAL(sggev,SGGEV)
+#define LAPACK_dggev LAPACK_GLOBAL(dggev,DGGEV)
+#define LAPACK_cggev LAPACK_GLOBAL(cggev,CGGEV)
+#define LAPACK_zggev LAPACK_GLOBAL(zggev,ZGGEV)
+#define LAPACK_sggevx LAPACK_GLOBAL(sggevx,SGGEVX)
+#define LAPACK_dggevx LAPACK_GLOBAL(dggevx,DGGEVX)
+#define LAPACK_cggevx LAPACK_GLOBAL(cggevx,CGGEVX)
+#define LAPACK_zggevx LAPACK_GLOBAL(zggevx,ZGGEVX)
+#define LAPACK_dsfrk LAPACK_GLOBAL(dsfrk,DSFRK)
+#define LAPACK_ssfrk LAPACK_GLOBAL(ssfrk,SSFRK)
+#define LAPACK_zhfrk LAPACK_GLOBAL(zhfrk,ZHFRK)
+#define LAPACK_chfrk LAPACK_GLOBAL(chfrk,CHFRK)
+#define LAPACK_dtfsm LAPACK_GLOBAL(dtfsm,DTFSM)
+#define LAPACK_stfsm LAPACK_GLOBAL(stfsm,STFSM)
+#define LAPACK_ztfsm LAPACK_GLOBAL(ztfsm,ZTFSM)
+#define LAPACK_ctfsm LAPACK_GLOBAL(ctfsm,CTFSM)
+#define LAPACK_dtfttp LAPACK_GLOBAL(dtfttp,DTFTTP)
+#define LAPACK_stfttp LAPACK_GLOBAL(stfttp,STFTTP)
+#define LAPACK_ztfttp LAPACK_GLOBAL(ztfttp,ZTFTTP)
+#define LAPACK_ctfttp LAPACK_GLOBAL(ctfttp,CTFTTP)
+#define LAPACK_dtfttr LAPACK_GLOBAL(dtfttr,DTFTTR)
+#define LAPACK_stfttr LAPACK_GLOBAL(stfttr,STFTTR)
+#define LAPACK_ztfttr LAPACK_GLOBAL(ztfttr,ZTFTTR)
+#define LAPACK_ctfttr LAPACK_GLOBAL(ctfttr,CTFTTR)
+#define LAPACK_dtpttf LAPACK_GLOBAL(dtpttf,DTPTTF)
+#define LAPACK_stpttf LAPACK_GLOBAL(stpttf,STPTTF)
+#define LAPACK_ztpttf LAPACK_GLOBAL(ztpttf,ZTPTTF)
+#define LAPACK_ctpttf LAPACK_GLOBAL(ctpttf,CTPTTF)
+#define LAPACK_dtpttr LAPACK_GLOBAL(dtpttr,DTPTTR)
+#define LAPACK_stpttr LAPACK_GLOBAL(stpttr,STPTTR)
+#define LAPACK_ztpttr LAPACK_GLOBAL(ztpttr,ZTPTTR)
+#define LAPACK_ctpttr LAPACK_GLOBAL(ctpttr,CTPTTR)
+#define LAPACK_dtrttf LAPACK_GLOBAL(dtrttf,DTRTTF)
+#define LAPACK_strttf LAPACK_GLOBAL(strttf,STRTTF)
+#define LAPACK_ztrttf LAPACK_GLOBAL(ztrttf,ZTRTTF)
+#define LAPACK_ctrttf LAPACK_GLOBAL(ctrttf,CTRTTF)
+#define LAPACK_dtrttp LAPACK_GLOBAL(dtrttp,DTRTTP)
+#define LAPACK_strttp LAPACK_GLOBAL(strttp,STRTTP)
+#define LAPACK_ztrttp LAPACK_GLOBAL(ztrttp,ZTRTTP)
+#define LAPACK_ctrttp LAPACK_GLOBAL(ctrttp,CTRTTP)
+#define LAPACK_sgeqrfp LAPACK_GLOBAL(sgeqrfp,SGEQRFP)
+#define LAPACK_dgeqrfp LAPACK_GLOBAL(dgeqrfp,DGEQRFP)
+#define LAPACK_cgeqrfp LAPACK_GLOBAL(cgeqrfp,CGEQRFP)
+#define LAPACK_zgeqrfp LAPACK_GLOBAL(zgeqrfp,ZGEQRFP)
+#define LAPACK_clacgv LAPACK_GLOBAL(clacgv,CLACGV)
+#define LAPACK_zlacgv LAPACK_GLOBAL(zlacgv,ZLACGV)
+#define LAPACK_slarnv LAPACK_GLOBAL(slarnv,SLARNV)
+#define LAPACK_dlarnv LAPACK_GLOBAL(dlarnv,DLARNV)
+#define LAPACK_clarnv LAPACK_GLOBAL(clarnv,CLARNV)
+#define LAPACK_zlarnv LAPACK_GLOBAL(zlarnv,ZLARNV)
+#define LAPACK_sgeqr2 LAPACK_GLOBAL(sgeqr2,SGEQR2)
+#define LAPACK_dgeqr2 LAPACK_GLOBAL(dgeqr2,DGEQR2)
+#define LAPACK_cgeqr2 LAPACK_GLOBAL(cgeqr2,CGEQR2)
+#define LAPACK_zgeqr2 LAPACK_GLOBAL(zgeqr2,ZGEQR2)
+#define LAPACK_slacpy LAPACK_GLOBAL(slacpy,SLACPY)
+#define LAPACK_dlacpy LAPACK_GLOBAL(dlacpy,DLACPY)
+#define LAPACK_clacpy LAPACK_GLOBAL(clacpy,CLACPY)
+#define LAPACK_zlacpy LAPACK_GLOBAL(zlacpy,ZLACPY)
+#define LAPACK_sgetf2 LAPACK_GLOBAL(sgetf2,SGETF2)
+#define LAPACK_dgetf2 LAPACK_GLOBAL(dgetf2,DGETF2)
+#define LAPACK_cgetf2 LAPACK_GLOBAL(cgetf2,CGETF2)
+#define LAPACK_zgetf2 LAPACK_GLOBAL(zgetf2,ZGETF2)
+#define LAPACK_slaswp LAPACK_GLOBAL(slaswp,SLASWP)
+#define LAPACK_dlaswp LAPACK_GLOBAL(dlaswp,DLASWP)
+#define LAPACK_claswp LAPACK_GLOBAL(claswp,CLASWP)
+#define LAPACK_zlaswp LAPACK_GLOBAL(zlaswp,ZLASWP)
+#define LAPACK_slange LAPACK_GLOBAL(slange,SLANGE)
+#define LAPACK_dlange LAPACK_GLOBAL(dlange,DLANGE)
+#define LAPACK_clange LAPACK_GLOBAL(clange,CLANGE)
+#define LAPACK_zlange LAPACK_GLOBAL(zlange,ZLANGE)
+#define LAPACK_clanhe LAPACK_GLOBAL(clanhe,CLANHE)
+#define LAPACK_zlanhe LAPACK_GLOBAL(zlanhe,ZLANHE)
+#define LAPACK_slansy LAPACK_GLOBAL(slansy,SLANSY)
+#define LAPACK_dlansy LAPACK_GLOBAL(dlansy,DLANSY)
+#define LAPACK_clansy LAPACK_GLOBAL(clansy,CLANSY)
+#define LAPACK_zlansy LAPACK_GLOBAL(zlansy,ZLANSY)
+#define LAPACK_slantr LAPACK_GLOBAL(slantr,SLANTR)
+#define LAPACK_dlantr LAPACK_GLOBAL(dlantr,DLANTR)
+#define LAPACK_clantr LAPACK_GLOBAL(clantr,CLANTR)
+#define LAPACK_zlantr LAPACK_GLOBAL(zlantr,ZLANTR)
+#define LAPACK_slamch LAPACK_GLOBAL(slamch,SLAMCH)
+#define LAPACK_dlamch LAPACK_GLOBAL(dlamch,DLAMCH)
+#define LAPACK_sgelq2 LAPACK_GLOBAL(sgelq2,SGELQ2)
+#define LAPACK_dgelq2 LAPACK_GLOBAL(dgelq2,DGELQ2)
+#define LAPACK_cgelq2 LAPACK_GLOBAL(cgelq2,CGELQ2)
+#define LAPACK_zgelq2 LAPACK_GLOBAL(zgelq2,ZGELQ2)
+#define LAPACK_slarfb LAPACK_GLOBAL(slarfb,SLARFB)
+#define LAPACK_dlarfb LAPACK_GLOBAL(dlarfb,DLARFB)
+#define LAPACK_clarfb LAPACK_GLOBAL(clarfb,CLARFB)
+#define LAPACK_zlarfb LAPACK_GLOBAL(zlarfb,ZLARFB)
+#define LAPACK_slarfg LAPACK_GLOBAL(slarfg,SLARFG)
+#define LAPACK_dlarfg LAPACK_GLOBAL(dlarfg,DLARFG)
+#define LAPACK_clarfg LAPACK_GLOBAL(clarfg,CLARFG)
+#define LAPACK_zlarfg LAPACK_GLOBAL(zlarfg,ZLARFG)
+#define LAPACK_slarft LAPACK_GLOBAL(slarft,SLARFT)
+#define LAPACK_dlarft LAPACK_GLOBAL(dlarft,DLARFT)
+#define LAPACK_clarft LAPACK_GLOBAL(clarft,CLARFT)
+#define LAPACK_zlarft LAPACK_GLOBAL(zlarft,ZLARFT)
+#define LAPACK_slarfx LAPACK_GLOBAL(slarfx,SLARFX)
+#define LAPACK_dlarfx LAPACK_GLOBAL(dlarfx,DLARFX)
+#define LAPACK_clarfx LAPACK_GLOBAL(clarfx,CLARFX)
+#define LAPACK_zlarfx LAPACK_GLOBAL(zlarfx,ZLARFX)
+#define LAPACK_slatms LAPACK_GLOBAL(slatms,SLATMS)
+#define LAPACK_dlatms LAPACK_GLOBAL(dlatms,DLATMS)
+#define LAPACK_clatms LAPACK_GLOBAL(clatms,CLATMS)
+#define LAPACK_zlatms LAPACK_GLOBAL(zlatms,ZLATMS)
+#define LAPACK_slag2d LAPACK_GLOBAL(slag2d,SLAG2D)
+#define LAPACK_dlag2s LAPACK_GLOBAL(dlag2s,DLAG2S)
+#define LAPACK_clag2z LAPACK_GLOBAL(clag2z,CLAG2Z)
+#define LAPACK_zlag2c LAPACK_GLOBAL(zlag2c,ZLAG2C)
+#define LAPACK_slauum LAPACK_GLOBAL(slauum,SLAUUM)
+#define LAPACK_dlauum LAPACK_GLOBAL(dlauum,DLAUUM)
+#define LAPACK_clauum LAPACK_GLOBAL(clauum,CLAUUM)
+#define LAPACK_zlauum LAPACK_GLOBAL(zlauum,ZLAUUM)
+#define LAPACK_slagge LAPACK_GLOBAL(slagge,SLAGGE)
+#define LAPACK_dlagge LAPACK_GLOBAL(dlagge,DLAGGE)
+#define LAPACK_clagge LAPACK_GLOBAL(clagge,CLAGGE)
+#define LAPACK_zlagge LAPACK_GLOBAL(zlagge,ZLAGGE)
+#define LAPACK_slaset LAPACK_GLOBAL(slaset,SLASET)
+#define LAPACK_dlaset LAPACK_GLOBAL(dlaset,DLASET)
+#define LAPACK_claset LAPACK_GLOBAL(claset,CLASET)
+#define LAPACK_zlaset LAPACK_GLOBAL(zlaset,ZLASET)
+#define LAPACK_slasrt LAPACK_GLOBAL(slasrt,SLASRT)
+#define LAPACK_dlasrt LAPACK_GLOBAL(dlasrt,DLASRT)
+#define LAPACK_slagsy LAPACK_GLOBAL(slagsy,SLAGSY)
+#define LAPACK_dlagsy LAPACK_GLOBAL(dlagsy,DLAGSY)
+#define LAPACK_clagsy LAPACK_GLOBAL(clagsy,CLAGSY)
+#define LAPACK_zlagsy LAPACK_GLOBAL(zlagsy,ZLAGSY)
+#define LAPACK_claghe LAPACK_GLOBAL(claghe,CLAGHE)
+#define LAPACK_zlaghe LAPACK_GLOBAL(zlaghe,ZLAGHE)
+#define LAPACK_slapmr LAPACK_GLOBAL(slapmr,SLAPMR)
+#define LAPACK_dlapmr LAPACK_GLOBAL(dlapmr,DLAPMR)
+#define LAPACK_clapmr LAPACK_GLOBAL(clapmr,CLAPMR)
+#define LAPACK_zlapmr LAPACK_GLOBAL(zlapmr,ZLAPMR)
+#define LAPACK_slapy2 LAPACK_GLOBAL(slapy2,SLAPY2)
+#define LAPACK_dlapy2 LAPACK_GLOBAL(dlapy2,DLAPY2)
+#define LAPACK_slapy3 LAPACK_GLOBAL(slapy3,SLAPY3)
+#define LAPACK_dlapy3 LAPACK_GLOBAL(dlapy3,DLAPY3)
+#define LAPACK_slartgp LAPACK_GLOBAL(slartgp,SLARTGP)
+#define LAPACK_dlartgp LAPACK_GLOBAL(dlartgp,DLARTGP)
+#define LAPACK_slartgs LAPACK_GLOBAL(slartgs,SLARTGS)
+#define LAPACK_dlartgs LAPACK_GLOBAL(dlartgs,DLARTGS)
+// LAPACK 3.3.0
+#define LAPACK_cbbcsd LAPACK_GLOBAL(cbbcsd,CBBCSD)
+#define LAPACK_cheswapr LAPACK_GLOBAL(cheswapr,CHESWAPR)
+#define LAPACK_chetri2 LAPACK_GLOBAL(chetri2,CHETRI2)
+#define LAPACK_chetri2x LAPACK_GLOBAL(chetri2x,CHETRI2X)
+#define LAPACK_chetrs2 LAPACK_GLOBAL(chetrs2,CHETRS2)
+#define LAPACK_csyconv LAPACK_GLOBAL(csyconv,CSYCONV)
+#define LAPACK_csyswapr LAPACK_GLOBAL(csyswapr,CSYSWAPR)
+#define LAPACK_csytri2 LAPACK_GLOBAL(csytri2,CSYTRI2)
+#define LAPACK_csytri2x LAPACK_GLOBAL(csytri2x,CSYTRI2X)
+#define LAPACK_csytrs2 LAPACK_GLOBAL(csytrs2,CSYTRS2)
+#define LAPACK_cunbdb LAPACK_GLOBAL(cunbdb,CUNBDB)
+#define LAPACK_cuncsd LAPACK_GLOBAL(cuncsd,CUNCSD)
+#define LAPACK_dbbcsd LAPACK_GLOBAL(dbbcsd,DBBCSD)
+#define LAPACK_dorbdb LAPACK_GLOBAL(dorbdb,DORBDB)
+#define LAPACK_dorcsd LAPACK_GLOBAL(dorcsd,DORCSD)
+#define LAPACK_dsyconv LAPACK_GLOBAL(dsyconv,DSYCONV)
+#define LAPACK_dsyswapr LAPACK_GLOBAL(dsyswapr,DSYSWAPR)
+#define LAPACK_dsytri2 LAPACK_GLOBAL(dsytri2,DSYTRI2)
+#define LAPACK_dsytri2x LAPACK_GLOBAL(dsytri2x,DSYTRI2X)
+#define LAPACK_dsytrs2 LAPACK_GLOBAL(dsytrs2,DSYTRS2)
+#define LAPACK_sbbcsd LAPACK_GLOBAL(sbbcsd,SBBCSD)
+#define LAPACK_sorbdb LAPACK_GLOBAL(sorbdb,SORBDB)
+#define LAPACK_sorcsd LAPACK_GLOBAL(sorcsd,SORCSD)
+#define LAPACK_ssyconv LAPACK_GLOBAL(ssyconv,SSYCONV)
+#define LAPACK_ssyswapr LAPACK_GLOBAL(ssyswapr,SSYSWAPR)
+#define LAPACK_ssytri2 LAPACK_GLOBAL(ssytri2,SSYTRI2)
+#define LAPACK_ssytri2x LAPACK_GLOBAL(ssytri2x,SSYTRI2X)
+#define LAPACK_ssytrs2 LAPACK_GLOBAL(ssytrs2,SSYTRS2)
+#define LAPACK_zbbcsd LAPACK_GLOBAL(zbbcsd,ZBBCSD)
+#define LAPACK_zheswapr LAPACK_GLOBAL(zheswapr,ZHESWAPR)
+#define LAPACK_zhetri2 LAPACK_GLOBAL(zhetri2,ZHETRI2)
+#define LAPACK_zhetri2x LAPACK_GLOBAL(zhetri2x,ZHETRI2X)
+#define LAPACK_zhetrs2 LAPACK_GLOBAL(zhetrs2,ZHETRS2)
+#define LAPACK_zsyconv LAPACK_GLOBAL(zsyconv,ZSYCONV)
+#define LAPACK_zsyswapr LAPACK_GLOBAL(zsyswapr,ZSYSWAPR)
+#define LAPACK_zsytri2 LAPACK_GLOBAL(zsytri2,ZSYTRI2)
+#define LAPACK_zsytri2x LAPACK_GLOBAL(zsytri2x,ZSYTRI2X)
+#define LAPACK_zsytrs2 LAPACK_GLOBAL(zsytrs2,ZSYTRS2)
+#define LAPACK_zunbdb LAPACK_GLOBAL(zunbdb,ZUNBDB)
+#define LAPACK_zuncsd LAPACK_GLOBAL(zuncsd,ZUNCSD)
+// LAPACK 3.4.0
+#define LAPACK_sgemqrt LAPACK_GLOBAL(sgemqrt,SGEMQRT)
+#define LAPACK_dgemqrt LAPACK_GLOBAL(dgemqrt,DGEMQRT)
+#define LAPACK_cgemqrt LAPACK_GLOBAL(cgemqrt,CGEMQRT)
+#define LAPACK_zgemqrt LAPACK_GLOBAL(zgemqrt,ZGEMQRT)
+#define LAPACK_sgeqrt LAPACK_GLOBAL(sgeqrt,SGEQRT)
+#define LAPACK_dgeqrt LAPACK_GLOBAL(dgeqrt,DGEQRT)
+#define LAPACK_cgeqrt LAPACK_GLOBAL(cgeqrt,CGEQRT)
+#define LAPACK_zgeqrt LAPACK_GLOBAL(zgeqrt,ZGEQRT)
+#define LAPACK_sgeqrt2 LAPACK_GLOBAL(sgeqrt2,SGEQRT2)
+#define LAPACK_dgeqrt2 LAPACK_GLOBAL(dgeqrt2,DGEQRT2)
+#define LAPACK_cgeqrt2 LAPACK_GLOBAL(cgeqrt2,CGEQRT2)
+#define LAPACK_zgeqrt2 LAPACK_GLOBAL(zgeqrt2,ZGEQRT2)
+#define LAPACK_sgeqrt3 LAPACK_GLOBAL(sgeqrt3,SGEQRT3)
+#define LAPACK_dgeqrt3 LAPACK_GLOBAL(dgeqrt3,DGEQRT3)
+#define LAPACK_cgeqrt3 LAPACK_GLOBAL(cgeqrt3,CGEQRT3)
+#define LAPACK_zgeqrt3 LAPACK_GLOBAL(zgeqrt3,ZGEQRT3)
+#define LAPACK_stpmqrt LAPACK_GLOBAL(stpmqrt,STPMQRT)
+#define LAPACK_dtpmqrt LAPACK_GLOBAL(dtpmqrt,DTPMQRT)
+#define LAPACK_ctpmqrt LAPACK_GLOBAL(ctpmqrt,CTPMQRT)
+#define LAPACK_ztpmqrt LAPACK_GLOBAL(ztpmqrt,ZTPMQRT)
+#define LAPACK_dtpqrt LAPACK_GLOBAL(dtpqrt,DTPQRT)
+#define LAPACK_ctpqrt LAPACK_GLOBAL(ctpqrt,CTPQRT)
+#define LAPACK_ztpqrt LAPACK_GLOBAL(ztpqrt,ZTPQRT)
+#define LAPACK_stpqrt2 LAPACK_GLOBAL(stpqrt2,STPQRT2)
+#define LAPACK_dtpqrt2 LAPACK_GLOBAL(dtpqrt2,DTPQRT2)
+#define LAPACK_ctpqrt2 LAPACK_GLOBAL(ctpqrt2,CTPQRT2)
+#define LAPACK_ztpqrt2 LAPACK_GLOBAL(ztpqrt2,ZTPQRT2)
+#define LAPACK_stprfb LAPACK_GLOBAL(stprfb,STPRFB)
+#define LAPACK_dtprfb LAPACK_GLOBAL(dtprfb,DTPRFB)
+#define LAPACK_ctprfb LAPACK_GLOBAL(ctprfb,CTPRFB)
+#define LAPACK_ztprfb LAPACK_GLOBAL(ztprfb,ZTPRFB)
+// LAPACK 3.X.X
+#define LAPACK_csyr LAPACK_GLOBAL(csyr,CSYR)
+#define LAPACK_zsyr LAPACK_GLOBAL(zsyr,ZSYR)
+
+
+void LAPACK_sgetrf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                    lapack_int* ipiv, lapack_int *info );
+void LAPACK_dgetrf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                    lapack_int* ipiv, lapack_int *info );
+void LAPACK_cgetrf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_int* ipiv, lapack_int *info );
+void LAPACK_zgetrf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_int* ipiv, lapack_int *info );
+void LAPACK_sgbtrf( lapack_int* m, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, float* ab, lapack_int* ldab,
+                    lapack_int* ipiv, lapack_int *info );
+void LAPACK_dgbtrf( lapack_int* m, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, double* ab, lapack_int* ldab,
+                    lapack_int* ipiv, lapack_int *info );
+void LAPACK_cgbtrf( lapack_int* m, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, lapack_complex_float* ab, lapack_int* ldab,
+                    lapack_int* ipiv, lapack_int *info );
+void LAPACK_zgbtrf( lapack_int* m, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, lapack_complex_double* ab, lapack_int* ldab,
+                    lapack_int* ipiv, lapack_int *info );
+void LAPACK_sgttrf( lapack_int* n, float* dl, float* d, float* du, float* du2,
+                    lapack_int* ipiv, lapack_int *info );
+void LAPACK_dgttrf( lapack_int* n, double* dl, double* d, double* du,
+                    double* du2, lapack_int* ipiv, lapack_int *info );
+void LAPACK_cgttrf( lapack_int* n, lapack_complex_float* dl,
+                    lapack_complex_float* d, lapack_complex_float* du,
+                    lapack_complex_float* du2, lapack_int* ipiv,
+                    lapack_int *info );
+void LAPACK_zgttrf( lapack_int* n, lapack_complex_double* dl,
+                    lapack_complex_double* d, lapack_complex_double* du,
+                    lapack_complex_double* du2, lapack_int* ipiv,
+                    lapack_int *info );
+void LAPACK_spotrf( char* uplo, lapack_int* n, float* a, lapack_int* lda,
+                    lapack_int *info );
+void LAPACK_dpotrf( char* uplo, lapack_int* n, double* a, lapack_int* lda,
+                    lapack_int *info );
+void LAPACK_cpotrf( char* uplo, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_int *info );
+void LAPACK_zpotrf( char* uplo, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_int *info );
+void LAPACK_dpstrf( char* uplo, lapack_int* n, double* a, lapack_int* lda,
+                    lapack_int* piv, lapack_int* rank, double* tol,
+                    double* work, lapack_int *info );
+void LAPACK_spstrf( char* uplo, lapack_int* n, float* a, lapack_int* lda,
+                    lapack_int* piv, lapack_int* rank, float* tol, float* work,
+                    lapack_int *info );
+void LAPACK_zpstrf( char* uplo, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_int* piv, lapack_int* rank,
+                    double* tol, double* work, lapack_int *info );
+void LAPACK_cpstrf( char* uplo, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_int* piv, lapack_int* rank,
+                    float* tol, float* work, lapack_int *info );
+void LAPACK_dpftrf( char* transr, char* uplo, lapack_int* n, double* a,
+                    lapack_int *info );
+void LAPACK_spftrf( char* transr, char* uplo, lapack_int* n, float* a,
+                    lapack_int *info );
+void LAPACK_zpftrf( char* transr, char* uplo, lapack_int* n,
+                    lapack_complex_double* a, lapack_int *info );
+void LAPACK_cpftrf( char* transr, char* uplo, lapack_int* n,
+                    lapack_complex_float* a, lapack_int *info );
+void LAPACK_spptrf( char* uplo, lapack_int* n, float* ap, lapack_int *info );
+void LAPACK_dpptrf( char* uplo, lapack_int* n, double* ap, lapack_int *info );
+void LAPACK_cpptrf( char* uplo, lapack_int* n, lapack_complex_float* ap,
+                    lapack_int *info );
+void LAPACK_zpptrf( char* uplo, lapack_int* n, lapack_complex_double* ap,
+                    lapack_int *info );
+void LAPACK_spbtrf( char* uplo, lapack_int* n, lapack_int* kd, float* ab,
+                    lapack_int* ldab, lapack_int *info );
+void LAPACK_dpbtrf( char* uplo, lapack_int* n, lapack_int* kd, double* ab,
+                    lapack_int* ldab, lapack_int *info );
+void LAPACK_cpbtrf( char* uplo, lapack_int* n, lapack_int* kd,
+                    lapack_complex_float* ab, lapack_int* ldab,
+                    lapack_int *info );
+void LAPACK_zpbtrf( char* uplo, lapack_int* n, lapack_int* kd,
+                    lapack_complex_double* ab, lapack_int* ldab,
+                    lapack_int *info );
+void LAPACK_spttrf( lapack_int* n, float* d, float* e, lapack_int *info );
+void LAPACK_dpttrf( lapack_int* n, double* d, double* e, lapack_int *info );
+void LAPACK_cpttrf( lapack_int* n, float* d, lapack_complex_float* e,
+                    lapack_int *info );
+void LAPACK_zpttrf( lapack_int* n, double* d, lapack_complex_double* e,
+                    lapack_int *info );
+void LAPACK_ssytrf( char* uplo, lapack_int* n, float* a, lapack_int* lda,
+                    lapack_int* ipiv, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dsytrf( char* uplo, lapack_int* n, double* a, lapack_int* lda,
+                    lapack_int* ipiv, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_csytrf( char* uplo, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_int* ipiv,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zsytrf( char* uplo, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_int* ipiv,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_chetrf( char* uplo, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_int* ipiv,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zhetrf( char* uplo, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_int* ipiv,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_ssptrf( char* uplo, lapack_int* n, float* ap, lapack_int* ipiv,
+                    lapack_int *info );
+void LAPACK_dsptrf( char* uplo, lapack_int* n, double* ap, lapack_int* ipiv,
+                    lapack_int *info );
+void LAPACK_csptrf( char* uplo, lapack_int* n, lapack_complex_float* ap,
+                    lapack_int* ipiv, lapack_int *info );
+void LAPACK_zsptrf( char* uplo, lapack_int* n, lapack_complex_double* ap,
+                    lapack_int* ipiv, lapack_int *info );
+void LAPACK_chptrf( char* uplo, lapack_int* n, lapack_complex_float* ap,
+                    lapack_int* ipiv, lapack_int *info );
+void LAPACK_zhptrf( char* uplo, lapack_int* n, lapack_complex_double* ap,
+                    lapack_int* ipiv, lapack_int *info );
+void LAPACK_sgetrs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const float* a, lapack_int* lda, const lapack_int* ipiv,
+                    float* b, lapack_int* ldb, lapack_int *info );
+void LAPACK_dgetrs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const double* a, lapack_int* lda, const lapack_int* ipiv,
+                    double* b, lapack_int* ldb, lapack_int *info );
+void LAPACK_cgetrs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* a, lapack_int* lda,
+                    const lapack_int* ipiv, lapack_complex_float* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_zgetrs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    const lapack_int* ipiv, lapack_complex_double* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_sgbtrs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
+                    lapack_int* nrhs, const float* ab, lapack_int* ldab,
+                    const lapack_int* ipiv, float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_dgbtrs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
+                    lapack_int* nrhs, const double* ab, lapack_int* ldab,
+                    const lapack_int* ipiv, double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_cgbtrs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
+                    lapack_int* nrhs, const lapack_complex_float* ab,
+                    lapack_int* ldab, const lapack_int* ipiv,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_zgbtrs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
+                    lapack_int* nrhs, const lapack_complex_double* ab,
+                    lapack_int* ldab, const lapack_int* ipiv,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_sgttrs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const float* dl, const float* d, const float* du,
+                    const float* du2, const lapack_int* ipiv, float* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_dgttrs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const double* dl, const double* d, const double* du,
+                    const double* du2, const lapack_int* ipiv, double* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_cgttrs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* dl,
+                    const lapack_complex_float* d,
+                    const lapack_complex_float* du,
+                    const lapack_complex_float* du2, const lapack_int* ipiv,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_zgttrs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* dl,
+                    const lapack_complex_double* d,
+                    const lapack_complex_double* du,
+                    const lapack_complex_double* du2, const lapack_int* ipiv,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_spotrs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* a,
+                    lapack_int* lda, float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_dpotrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const double* a, lapack_int* lda, double* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_cpotrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_zpotrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_dpftrs( char* transr, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const double* a, double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_spftrs( char* transr, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const float* a, float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_zpftrs( char* transr, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* a, lapack_complex_double* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_cpftrs( char* transr, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* a, lapack_complex_float* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_spptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const float* ap, float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_dpptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const double* ap, double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_cpptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* ap, lapack_complex_float* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_zpptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* ap, lapack_complex_double* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_spbtrs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                    const float* ab, lapack_int* ldab, float* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_dpbtrs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                    const double* ab, lapack_int* ldab, double* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_cpbtrs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                    const lapack_complex_float* ab, lapack_int* ldab,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_zpbtrs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                    const lapack_complex_double* ab, lapack_int* ldab,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_spttrs( lapack_int* n, lapack_int* nrhs, const float* d,
+                    const float* e, float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_dpttrs( lapack_int* n, lapack_int* nrhs, const double* d,
+                    const double* e, double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_cpttrs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* d,
+                    const lapack_complex_float* e, lapack_complex_float* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_zpttrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const double* d, const lapack_complex_double* e,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_ssytrs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* a,
+                    lapack_int* lda, const lapack_int* ipiv, float* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_dsytrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const double* a, lapack_int* lda, const lapack_int* ipiv,
+                    double* b, lapack_int* ldb, lapack_int *info );
+void LAPACK_csytrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* a, lapack_int* lda,
+                    const lapack_int* ipiv, lapack_complex_float* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_zsytrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    const lapack_int* ipiv, lapack_complex_double* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_chetrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* a, lapack_int* lda,
+                    const lapack_int* ipiv, lapack_complex_float* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_zhetrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    const lapack_int* ipiv, lapack_complex_double* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_ssptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const float* ap, const lapack_int* ipiv, float* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_dsptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const double* ap, const lapack_int* ipiv, double* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_csptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* ap, const lapack_int* ipiv,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_zsptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* ap, const lapack_int* ipiv,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_chptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* ap, const lapack_int* ipiv,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_zhptrs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* ap, const lapack_int* ipiv,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_strtrs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const float* a, lapack_int* lda, float* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_dtrtrs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const double* a, lapack_int* lda,
+                    double* b, lapack_int* ldb, lapack_int *info );
+void LAPACK_ctrtrs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const lapack_complex_float* a,
+                    lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_ztrtrs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const lapack_complex_double* a,
+                    lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_stptrs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const float* ap, float* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_dtptrs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const double* ap, double* b,
+                    lapack_int* ldb, lapack_int *info );
+void LAPACK_ctptrs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const lapack_complex_float* ap,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_ztptrs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const lapack_complex_double* ap,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_stbtrs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* kd, lapack_int* nrhs, const float* ab,
+                    lapack_int* ldab, float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_dtbtrs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* kd, lapack_int* nrhs, const double* ab,
+                    lapack_int* ldab, double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_ctbtrs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* kd, lapack_int* nrhs,
+                    const lapack_complex_float* ab, lapack_int* ldab,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_ztbtrs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* kd, lapack_int* nrhs,
+                    const lapack_complex_double* ab, lapack_int* ldab,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_sgecon( char* norm, lapack_int* n, const float* a, lapack_int* lda,
+                    float* anorm, float* rcond, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dgecon( char* norm, lapack_int* n, const double* a, lapack_int* lda,
+                    double* anorm, double* rcond, double* work,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_cgecon( char* norm, lapack_int* n, const lapack_complex_float* a,
+                    lapack_int* lda, float* anorm, float* rcond,
+                    lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zgecon( char* norm, lapack_int* n, const lapack_complex_double* a,
+                    lapack_int* lda, double* anorm, double* rcond,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_sgbcon( char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku,
+                    const float* ab, lapack_int* ldab, const lapack_int* ipiv,
+                    float* anorm, float* rcond, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dgbcon( char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku,
+                    const double* ab, lapack_int* ldab, const lapack_int* ipiv,
+                    double* anorm, double* rcond, double* work,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_cgbcon( char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku,
+                    const lapack_complex_float* ab, lapack_int* ldab,
+                    const lapack_int* ipiv, float* anorm, float* rcond,
+                    lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zgbcon( char* norm, lapack_int* n, lapack_int* kl, lapack_int* ku,
+                    const lapack_complex_double* ab, lapack_int* ldab,
+                    const lapack_int* ipiv, double* anorm, double* rcond,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_sgtcon( char* norm, lapack_int* n, const float* dl, const float* d,
+                    const float* du, const float* du2, const lapack_int* ipiv,
+                    float* anorm, float* rcond, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dgtcon( char* norm, lapack_int* n, const double* dl,
+                    const double* d, const double* du, const double* du2,
+                    const lapack_int* ipiv, double* anorm, double* rcond,
+                    double* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_cgtcon( char* norm, lapack_int* n, const lapack_complex_float* dl,
+                    const lapack_complex_float* d,
+                    const lapack_complex_float* du,
+                    const lapack_complex_float* du2, const lapack_int* ipiv,
+                    float* anorm, float* rcond, lapack_complex_float* work,
+                    lapack_int *info );
+void LAPACK_zgtcon( char* norm, lapack_int* n, const lapack_complex_double* dl,
+                    const lapack_complex_double* d,
+                    const lapack_complex_double* du,
+                    const lapack_complex_double* du2, const lapack_int* ipiv,
+                    double* anorm, double* rcond, lapack_complex_double* work,
+                    lapack_int *info );
+void LAPACK_spocon( char* uplo, lapack_int* n, const float* a, lapack_int* lda,
+                    float* anorm, float* rcond, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dpocon( char* uplo, lapack_int* n, const double* a, lapack_int* lda,
+                    double* anorm, double* rcond, double* work,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_cpocon( char* uplo, lapack_int* n, const lapack_complex_float* a,
+                    lapack_int* lda, float* anorm, float* rcond,
+                    lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zpocon( char* uplo, lapack_int* n, const lapack_complex_double* a,
+                    lapack_int* lda, double* anorm, double* rcond,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_sppcon( char* uplo, lapack_int* n, const float* ap, float* anorm,
+                    float* rcond, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dppcon( char* uplo, lapack_int* n, const double* ap, double* anorm,
+                    double* rcond, double* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_cppcon( char* uplo, lapack_int* n, const lapack_complex_float* ap,
+                    float* anorm, float* rcond, lapack_complex_float* work,
+                    float* rwork, lapack_int *info );
+void LAPACK_zppcon( char* uplo, lapack_int* n, const lapack_complex_double* ap,
+                    double* anorm, double* rcond, lapack_complex_double* work,
+                    double* rwork, lapack_int *info );
+void LAPACK_spbcon( char* uplo, lapack_int* n, lapack_int* kd, const float* ab,
+                    lapack_int* ldab, float* anorm, float* rcond, float* work,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_dpbcon( char* uplo, lapack_int* n, lapack_int* kd, const double* ab,
+                    lapack_int* ldab, double* anorm, double* rcond,
+                    double* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_cpbcon( char* uplo, lapack_int* n, lapack_int* kd,
+                    const lapack_complex_float* ab, lapack_int* ldab,
+                    float* anorm, float* rcond, lapack_complex_float* work,
+                    float* rwork, lapack_int *info );
+void LAPACK_zpbcon( char* uplo, lapack_int* n, lapack_int* kd,
+                    const lapack_complex_double* ab, lapack_int* ldab,
+                    double* anorm, double* rcond, lapack_complex_double* work,
+                    double* rwork, lapack_int *info );
+void LAPACK_sptcon( lapack_int* n, const float* d, const float* e, float* anorm,
+                    float* rcond, float* work, lapack_int *info );
+void LAPACK_dptcon( lapack_int* n, const double* d, const double* e,
+                    double* anorm, double* rcond, double* work,
+                    lapack_int *info );
+void LAPACK_cptcon( lapack_int* n, const float* d,
+                    const lapack_complex_float* e, float* anorm, float* rcond,
+                    float* work, lapack_int *info );
+void LAPACK_zptcon( lapack_int* n, const double* d,
+                    const lapack_complex_double* e, double* anorm,
+                    double* rcond, double* work, lapack_int *info );
+void LAPACK_ssycon( char* uplo, lapack_int* n, const float* a, lapack_int* lda,
+                    const lapack_int* ipiv, float* anorm, float* rcond,
+                    float* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_dsycon( char* uplo, lapack_int* n, const double* a, lapack_int* lda,
+                    const lapack_int* ipiv, double* anorm, double* rcond,
+                    double* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_csycon( char* uplo, lapack_int* n, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_int* ipiv, float* anorm,
+                    float* rcond, lapack_complex_float* work,
+                    lapack_int *info );
+void LAPACK_zsycon( char* uplo, lapack_int* n, const lapack_complex_double* a,
+                    lapack_int* lda, const lapack_int* ipiv, double* anorm,
+                    double* rcond, lapack_complex_double* work,
+                    lapack_int *info );
+void LAPACK_checon( char* uplo, lapack_int* n, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_int* ipiv, float* anorm,
+                    float* rcond, lapack_complex_float* work,
+                    lapack_int *info );
+void LAPACK_zhecon( char* uplo, lapack_int* n, const lapack_complex_double* a,
+                    lapack_int* lda, const lapack_int* ipiv, double* anorm,
+                    double* rcond, lapack_complex_double* work,
+                    lapack_int *info );
+void LAPACK_sspcon( char* uplo, lapack_int* n, const float* ap,
+                    const lapack_int* ipiv, float* anorm, float* rcond,
+                    float* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_dspcon( char* uplo, lapack_int* n, const double* ap,
+                    const lapack_int* ipiv, double* anorm, double* rcond,
+                    double* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_cspcon( char* uplo, lapack_int* n, const lapack_complex_float* ap,
+                    const lapack_int* ipiv, float* anorm, float* rcond,
+                    lapack_complex_float* work, lapack_int *info );
+void LAPACK_zspcon( char* uplo, lapack_int* n, const lapack_complex_double* ap,
+                    const lapack_int* ipiv, double* anorm, double* rcond,
+                    lapack_complex_double* work, lapack_int *info );
+void LAPACK_chpcon( char* uplo, lapack_int* n, const lapack_complex_float* ap,
+                    const lapack_int* ipiv, float* anorm, float* rcond,
+                    lapack_complex_float* work, lapack_int *info );
+void LAPACK_zhpcon( char* uplo, lapack_int* n, const lapack_complex_double* ap,
+                    const lapack_int* ipiv, double* anorm, double* rcond,
+                    lapack_complex_double* work, lapack_int *info );
+void LAPACK_strcon( char* norm, char* uplo, char* diag, lapack_int* n,
+                    const float* a, lapack_int* lda, float* rcond, float* work,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_dtrcon( char* norm, char* uplo, char* diag, lapack_int* n,
+                    const double* a, lapack_int* lda, double* rcond,
+                    double* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_ctrcon( char* norm, char* uplo, char* diag, lapack_int* n,
+                    const lapack_complex_float* a, lapack_int* lda,
+                    float* rcond, lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_ztrcon( char* norm, char* uplo, char* diag, lapack_int* n,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    double* rcond, lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_stpcon( char* norm, char* uplo, char* diag, lapack_int* n,
+                    const float* ap, float* rcond, float* work,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_dtpcon( char* norm, char* uplo, char* diag, lapack_int* n,
+                    const double* ap, double* rcond, double* work,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_ctpcon( char* norm, char* uplo, char* diag, lapack_int* n,
+                    const lapack_complex_float* ap, float* rcond,
+                    lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_ztpcon( char* norm, char* uplo, char* diag, lapack_int* n,
+                    const lapack_complex_double* ap, double* rcond,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_stbcon( char* norm, char* uplo, char* diag, lapack_int* n,
+                    lapack_int* kd, const float* ab, lapack_int* ldab,
+                    float* rcond, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dtbcon( char* norm, char* uplo, char* diag, lapack_int* n,
+                    lapack_int* kd, const double* ab, lapack_int* ldab,
+                    double* rcond, double* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_ctbcon( char* norm, char* uplo, char* diag, lapack_int* n,
+                    lapack_int* kd, const lapack_complex_float* ab,
+                    lapack_int* ldab, float* rcond, lapack_complex_float* work,
+                    float* rwork, lapack_int *info );
+void LAPACK_ztbcon( char* norm, char* uplo, char* diag, lapack_int* n,
+                    lapack_int* kd, const lapack_complex_double* ab,
+                    lapack_int* ldab, double* rcond,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_sgerfs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const float* a, lapack_int* lda, const float* af,
+                    lapack_int* ldaf, const lapack_int* ipiv, const float* b,
+                    lapack_int* ldb, float* x, lapack_int* ldx, float* ferr,
+                    float* berr, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dgerfs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const double* a, lapack_int* lda, const double* af,
+                    lapack_int* ldaf, const lapack_int* ipiv, const double* b,
+                    lapack_int* ldb, double* x, lapack_int* ldx, double* ferr,
+                    double* berr, double* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_cgerfs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* a, lapack_int* lda,
+                    const lapack_complex_float* af, lapack_int* ldaf,
+                    const lapack_int* ipiv, const lapack_complex_float* b,
+                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                    float* ferr, float* berr, lapack_complex_float* work,
+                    float* rwork, lapack_int *info );
+void LAPACK_zgerfs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    const lapack_complex_double* af, lapack_int* ldaf,
+                    const lapack_int* ipiv, const lapack_complex_double* b,
+                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                    double* ferr, double* berr, lapack_complex_double* work,
+                    double* rwork, lapack_int *info );
+void LAPACK_dgerfsx( char* trans, char* equed, lapack_int* n, lapack_int* nrhs,
+                     const double* a, lapack_int* lda, const double* af,
+                     lapack_int* ldaf, const lapack_int* ipiv, const double* r,
+                     const double* c, const double* b, lapack_int* ldb,
+                     double* x, lapack_int* ldx, double* rcond, double* berr,
+                     lapack_int* n_err_bnds, double* err_bnds_norm,
+                     double* err_bnds_comp, lapack_int* nparams, double* params,
+                     double* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_sgerfsx( char* trans, char* equed, lapack_int* n, lapack_int* nrhs,
+                     const float* a, lapack_int* lda, const float* af,
+                     lapack_int* ldaf, const lapack_int* ipiv, const float* r,
+                     const float* c, const float* b, lapack_int* ldb, float* x,
+                     lapack_int* ldx, float* rcond, float* berr,
+                     lapack_int* n_err_bnds, float* err_bnds_norm,
+                     float* err_bnds_comp, lapack_int* nparams, float* params,
+                     float* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_zgerfsx( char* trans, char* equed, lapack_int* n, lapack_int* nrhs,
+                     const lapack_complex_double* a, lapack_int* lda,
+                     const lapack_complex_double* af, lapack_int* ldaf,
+                     const lapack_int* ipiv, const double* r, const double* c,
+                     const lapack_complex_double* b, lapack_int* ldb,
+                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                     double* berr, lapack_int* n_err_bnds,
+                     double* err_bnds_norm, double* err_bnds_comp,
+                     lapack_int* nparams, double* params,
+                     lapack_complex_double* work, double* rwork,
+                     lapack_int *info );
+void LAPACK_cgerfsx( char* trans, char* equed, lapack_int* n, lapack_int* nrhs,
+                     const lapack_complex_float* a, lapack_int* lda,
+                     const lapack_complex_float* af, lapack_int* ldaf,
+                     const lapack_int* ipiv, const float* r, const float* c,
+                     const lapack_complex_float* b, lapack_int* ldb,
+                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                     float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
+                     float* err_bnds_comp, lapack_int* nparams, float* params,
+                     lapack_complex_float* work, float* rwork,
+                     lapack_int *info );
+void LAPACK_sgbrfs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
+                    lapack_int* nrhs, const float* ab, lapack_int* ldab,
+                    const float* afb, lapack_int* ldafb, const lapack_int* ipiv,
+                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                    float* ferr, float* berr, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dgbrfs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
+                    lapack_int* nrhs, const double* ab, lapack_int* ldab,
+                    const double* afb, lapack_int* ldafb,
+                    const lapack_int* ipiv, const double* b, lapack_int* ldb,
+                    double* x, lapack_int* ldx, double* ferr, double* berr,
+                    double* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_cgbrfs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
+                    lapack_int* nrhs, const lapack_complex_float* ab,
+                    lapack_int* ldab, const lapack_complex_float* afb,
+                    lapack_int* ldafb, const lapack_int* ipiv,
+                    const lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
+                    float* berr, lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zgbrfs( char* trans, lapack_int* n, lapack_int* kl, lapack_int* ku,
+                    lapack_int* nrhs, const lapack_complex_double* ab,
+                    lapack_int* ldab, const lapack_complex_double* afb,
+                    lapack_int* ldafb, const lapack_int* ipiv,
+                    const lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
+                    double* berr, lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_dgbrfsx( char* trans, char* equed, lapack_int* n, lapack_int* kl,
+                     lapack_int* ku, lapack_int* nrhs, const double* ab,
+                     lapack_int* ldab, const double* afb, lapack_int* ldafb,
+                     const lapack_int* ipiv, const double* r, const double* c,
+                     const double* b, lapack_int* ldb, double* x,
+                     lapack_int* ldx, double* rcond, double* berr,
+                     lapack_int* n_err_bnds, double* err_bnds_norm,
+                     double* err_bnds_comp, lapack_int* nparams, double* params,
+                     double* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_sgbrfsx( char* trans, char* equed, lapack_int* n, lapack_int* kl,
+                     lapack_int* ku, lapack_int* nrhs, const float* ab,
+                     lapack_int* ldab, const float* afb, lapack_int* ldafb,
+                     const lapack_int* ipiv, const float* r, const float* c,
+                     const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                     float* rcond, float* berr, lapack_int* n_err_bnds,
+                     float* err_bnds_norm, float* err_bnds_comp,
+                     lapack_int* nparams, float* params, float* work,
+                     lapack_int* iwork, lapack_int *info );
+void LAPACK_zgbrfsx( char* trans, char* equed, lapack_int* n, lapack_int* kl,
+                     lapack_int* ku, lapack_int* nrhs,
+                     const lapack_complex_double* ab, lapack_int* ldab,
+                     const lapack_complex_double* afb, lapack_int* ldafb,
+                     const lapack_int* ipiv, const double* r, const double* c,
+                     const lapack_complex_double* b, lapack_int* ldb,
+                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                     double* berr, lapack_int* n_err_bnds,
+                     double* err_bnds_norm, double* err_bnds_comp,
+                     lapack_int* nparams, double* params,
+                     lapack_complex_double* work, double* rwork,
+                     lapack_int *info );
+void LAPACK_cgbrfsx( char* trans, char* equed, lapack_int* n, lapack_int* kl,
+                     lapack_int* ku, lapack_int* nrhs,
+                     const lapack_complex_float* ab, lapack_int* ldab,
+                     const lapack_complex_float* afb, lapack_int* ldafb,
+                     const lapack_int* ipiv, const float* r, const float* c,
+                     const lapack_complex_float* b, lapack_int* ldb,
+                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                     float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
+                     float* err_bnds_comp, lapack_int* nparams, float* params,
+                     lapack_complex_float* work, float* rwork,
+                     lapack_int *info );
+void LAPACK_sgtrfs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const float* dl, const float* d, const float* du,
+                    const float* dlf, const float* df, const float* duf,
+                    const float* du2, const lapack_int* ipiv, const float* b,
+                    lapack_int* ldb, float* x, lapack_int* ldx, float* ferr,
+                    float* berr, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dgtrfs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const double* dl, const double* d, const double* du,
+                    const double* dlf, const double* df, const double* duf,
+                    const double* du2, const lapack_int* ipiv, const double* b,
+                    lapack_int* ldb, double* x, lapack_int* ldx, double* ferr,
+                    double* berr, double* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_cgtrfs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* dl,
+                    const lapack_complex_float* d,
+                    const lapack_complex_float* du,
+                    const lapack_complex_float* dlf,
+                    const lapack_complex_float* df,
+                    const lapack_complex_float* duf,
+                    const lapack_complex_float* du2, const lapack_int* ipiv,
+                    const lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
+                    float* berr, lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zgtrfs( char* trans, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* dl,
+                    const lapack_complex_double* d,
+                    const lapack_complex_double* du,
+                    const lapack_complex_double* dlf,
+                    const lapack_complex_double* df,
+                    const lapack_complex_double* duf,
+                    const lapack_complex_double* du2, const lapack_int* ipiv,
+                    const lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
+                    double* berr, lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_sporfs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* a,
+                    lapack_int* lda, const float* af, lapack_int* ldaf,
+                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                    float* ferr, float* berr, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dporfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const double* a, lapack_int* lda, const double* af,
+                    lapack_int* ldaf, const double* b, lapack_int* ldb,
+                    double* x, lapack_int* ldx, double* ferr, double* berr,
+                    double* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_cporfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* a, lapack_int* lda,
+                    const lapack_complex_float* af, lapack_int* ldaf,
+                    const lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
+                    float* berr, lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zporfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    const lapack_complex_double* af, lapack_int* ldaf,
+                    const lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
+                    double* berr, lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_dporfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
+                     const double* a, lapack_int* lda, const double* af,
+                     lapack_int* ldaf, const double* s, const double* b,
+                     lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
+                     double* berr, lapack_int* n_err_bnds,
+                     double* err_bnds_norm, double* err_bnds_comp,
+                     lapack_int* nparams, double* params, double* work,
+                     lapack_int* iwork, lapack_int *info );
+void LAPACK_sporfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
+                     const float* a, lapack_int* lda, const float* af,
+                     lapack_int* ldaf, const float* s, const float* b,
+                     lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
+                     float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
+                     float* err_bnds_comp, lapack_int* nparams, float* params,
+                     float* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_zporfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
+                     const lapack_complex_double* a, lapack_int* lda,
+                     const lapack_complex_double* af, lapack_int* ldaf,
+                     const double* s, const lapack_complex_double* b,
+                     lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                     double* rcond, double* berr, lapack_int* n_err_bnds,
+                     double* err_bnds_norm, double* err_bnds_comp,
+                     lapack_int* nparams, double* params,
+                     lapack_complex_double* work, double* rwork,
+                     lapack_int *info );
+void LAPACK_cporfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
+                     const lapack_complex_float* a, lapack_int* lda,
+                     const lapack_complex_float* af, lapack_int* ldaf,
+                     const float* s, const lapack_complex_float* b,
+                     lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                     float* rcond, float* berr, lapack_int* n_err_bnds,
+                     float* err_bnds_norm, float* err_bnds_comp,
+                     lapack_int* nparams, float* params,
+                     lapack_complex_float* work, float* rwork,
+                     lapack_int *info );
+void LAPACK_spprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const float* ap, const float* afp, const float* b,
+                    lapack_int* ldb, float* x, lapack_int* ldx, float* ferr,
+                    float* berr, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dpprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const double* ap, const double* afp, const double* b,
+                    lapack_int* ldb, double* x, lapack_int* ldx, double* ferr,
+                    double* berr, double* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_cpprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* ap,
+                    const lapack_complex_float* afp,
+                    const lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
+                    float* berr, lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zpprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* ap,
+                    const lapack_complex_double* afp,
+                    const lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
+                    double* berr, lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_spbrfs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                    const float* ab, lapack_int* ldab, const float* afb,
+                    lapack_int* ldafb, const float* b, lapack_int* ldb,
+                    float* x, lapack_int* ldx, float* ferr, float* berr,
+                    float* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_dpbrfs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                    const double* ab, lapack_int* ldab, const double* afb,
+                    lapack_int* ldafb, const double* b, lapack_int* ldb,
+                    double* x, lapack_int* ldx, double* ferr, double* berr,
+                    double* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_cpbrfs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                    const lapack_complex_float* ab, lapack_int* ldab,
+                    const lapack_complex_float* afb, lapack_int* ldafb,
+                    const lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
+                    float* berr, lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zpbrfs( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                    const lapack_complex_double* ab, lapack_int* ldab,
+                    const lapack_complex_double* afb, lapack_int* ldafb,
+                    const lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
+                    double* berr, lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_sptrfs( lapack_int* n, lapack_int* nrhs, const float* d,
+                    const float* e, const float* df, const float* ef,
+                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                    float* ferr, float* berr, float* work, lapack_int *info );
+void LAPACK_dptrfs( lapack_int* n, lapack_int* nrhs, const double* d,
+                    const double* e, const double* df, const double* ef,
+                    const double* b, lapack_int* ldb, double* x,
+                    lapack_int* ldx, double* ferr, double* berr, double* work,
+                    lapack_int *info );
+void LAPACK_cptrfs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* d,
+                    const lapack_complex_float* e, const float* df,
+                    const lapack_complex_float* ef,
+                    const lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
+                    float* berr, lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zptrfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const double* d, const lapack_complex_double* e,
+                    const double* df, const lapack_complex_double* ef,
+                    const lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
+                    double* berr, lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_ssyrfs( char* uplo, lapack_int* n, lapack_int* nrhs, const float* a,
+                    lapack_int* lda, const float* af, lapack_int* ldaf,
+                    const lapack_int* ipiv, const float* b, lapack_int* ldb,
+                    float* x, lapack_int* ldx, float* ferr, float* berr,
+                    float* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_dsyrfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const double* a, lapack_int* lda, const double* af,
+                    lapack_int* ldaf, const lapack_int* ipiv, const double* b,
+                    lapack_int* ldb, double* x, lapack_int* ldx, double* ferr,
+                    double* berr, double* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_csyrfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* a, lapack_int* lda,
+                    const lapack_complex_float* af, lapack_int* ldaf,
+                    const lapack_int* ipiv, const lapack_complex_float* b,
+                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                    float* ferr, float* berr, lapack_complex_float* work,
+                    float* rwork, lapack_int *info );
+void LAPACK_zsyrfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    const lapack_complex_double* af, lapack_int* ldaf,
+                    const lapack_int* ipiv, const lapack_complex_double* b,
+                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                    double* ferr, double* berr, lapack_complex_double* work,
+                    double* rwork, lapack_int *info );
+void LAPACK_dsyrfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
+                     const double* a, lapack_int* lda, const double* af,
+                     lapack_int* ldaf, const lapack_int* ipiv, const double* s,
+                     const double* b, lapack_int* ldb, double* x,
+                     lapack_int* ldx, double* rcond, double* berr,
+                     lapack_int* n_err_bnds, double* err_bnds_norm,
+                     double* err_bnds_comp, lapack_int* nparams, double* params,
+                     double* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_ssyrfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
+                     const float* a, lapack_int* lda, const float* af,
+                     lapack_int* ldaf, const lapack_int* ipiv, const float* s,
+                     const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                     float* rcond, float* berr, lapack_int* n_err_bnds,
+                     float* err_bnds_norm, float* err_bnds_comp,
+                     lapack_int* nparams, float* params, float* work,
+                     lapack_int* iwork, lapack_int *info );
+void LAPACK_zsyrfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
+                     const lapack_complex_double* a, lapack_int* lda,
+                     const lapack_complex_double* af, lapack_int* ldaf,
+                     const lapack_int* ipiv, const double* s,
+                     const lapack_complex_double* b, lapack_int* ldb,
+                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                     double* berr, lapack_int* n_err_bnds,
+                     double* err_bnds_norm, double* err_bnds_comp,
+                     lapack_int* nparams, double* params,
+                     lapack_complex_double* work, double* rwork,
+                     lapack_int *info );
+void LAPACK_csyrfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
+                     const lapack_complex_float* a, lapack_int* lda,
+                     const lapack_complex_float* af, lapack_int* ldaf,
+                     const lapack_int* ipiv, const float* s,
+                     const lapack_complex_float* b, lapack_int* ldb,
+                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                     float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
+                     float* err_bnds_comp, lapack_int* nparams, float* params,
+                     lapack_complex_float* work, float* rwork,
+                     lapack_int *info );
+void LAPACK_cherfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* a, lapack_int* lda,
+                    const lapack_complex_float* af, lapack_int* ldaf,
+                    const lapack_int* ipiv, const lapack_complex_float* b,
+                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                    float* ferr, float* berr, lapack_complex_float* work,
+                    float* rwork, lapack_int *info );
+void LAPACK_zherfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    const lapack_complex_double* af, lapack_int* ldaf,
+                    const lapack_int* ipiv, const lapack_complex_double* b,
+                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                    double* ferr, double* berr, lapack_complex_double* work,
+                    double* rwork, lapack_int *info );
+void LAPACK_zherfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
+                     const lapack_complex_double* a, lapack_int* lda,
+                     const lapack_complex_double* af, lapack_int* ldaf,
+                     const lapack_int* ipiv, const double* s,
+                     const lapack_complex_double* b, lapack_int* ldb,
+                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                     double* berr, lapack_int* n_err_bnds,
+                     double* err_bnds_norm, double* err_bnds_comp,
+                     lapack_int* nparams, double* params,
+                     lapack_complex_double* work, double* rwork,
+                     lapack_int *info );
+void LAPACK_cherfsx( char* uplo, char* equed, lapack_int* n, lapack_int* nrhs,
+                     const lapack_complex_float* a, lapack_int* lda,
+                     const lapack_complex_float* af, lapack_int* ldaf,
+                     const lapack_int* ipiv, const float* s,
+                     const lapack_complex_float* b, lapack_int* ldb,
+                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                     float* berr, lapack_int* n_err_bnds, float* err_bnds_norm,
+                     float* err_bnds_comp, lapack_int* nparams, float* params,
+                     lapack_complex_float* work, float* rwork,
+                     lapack_int *info );
+void LAPACK_ssprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const float* ap, const float* afp, const lapack_int* ipiv,
+                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                    float* ferr, float* berr, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dsprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const double* ap, const double* afp, const lapack_int* ipiv,
+                    const double* b, lapack_int* ldb, double* x,
+                    lapack_int* ldx, double* ferr, double* berr, double* work,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_csprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* ap,
+                    const lapack_complex_float* afp, const lapack_int* ipiv,
+                    const lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
+                    float* berr, lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zsprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* ap,
+                    const lapack_complex_double* afp, const lapack_int* ipiv,
+                    const lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
+                    double* berr, lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_chprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* ap,
+                    const lapack_complex_float* afp, const lapack_int* ipiv,
+                    const lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* x, lapack_int* ldx, float* ferr,
+                    float* berr, lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zhprfs( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* ap,
+                    const lapack_complex_double* afp, const lapack_int* ipiv,
+                    const lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx, double* ferr,
+                    double* berr, lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_strrfs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const float* a, lapack_int* lda,
+                    const float* b, lapack_int* ldb, const float* x,
+                    lapack_int* ldx, float* ferr, float* berr, float* work,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_dtrrfs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const double* a, lapack_int* lda,
+                    const double* b, lapack_int* ldb, const double* x,
+                    lapack_int* ldx, double* ferr, double* berr, double* work,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_ctrrfs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_complex_float* b,
+                    lapack_int* ldb, const lapack_complex_float* x,
+                    lapack_int* ldx, float* ferr, float* berr,
+                    lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_ztrrfs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const lapack_complex_double* a,
+                    lapack_int* lda, const lapack_complex_double* b,
+                    lapack_int* ldb, const lapack_complex_double* x,
+                    lapack_int* ldx, double* ferr, double* berr,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_stprfs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const float* ap, const float* b,
+                    lapack_int* ldb, const float* x, lapack_int* ldx,
+                    float* ferr, float* berr, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dtprfs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const double* ap, const double* b,
+                    lapack_int* ldb, const double* x, lapack_int* ldx,
+                    double* ferr, double* berr, double* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_ctprfs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const lapack_complex_float* ap,
+                    const lapack_complex_float* b, lapack_int* ldb,
+                    const lapack_complex_float* x, lapack_int* ldx, float* ferr,
+                    float* berr, lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_ztprfs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* nrhs, const lapack_complex_double* ap,
+                    const lapack_complex_double* b, lapack_int* ldb,
+                    const lapack_complex_double* x, lapack_int* ldx,
+                    double* ferr, double* berr, lapack_complex_double* work,
+                    double* rwork, lapack_int *info );
+void LAPACK_stbrfs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* kd, lapack_int* nrhs, const float* ab,
+                    lapack_int* ldab, const float* b, lapack_int* ldb,
+                    const float* x, lapack_int* ldx, float* ferr, float* berr,
+                    float* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_dtbrfs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* kd, lapack_int* nrhs, const double* ab,
+                    lapack_int* ldab, const double* b, lapack_int* ldb,
+                    const double* x, lapack_int* ldx, double* ferr,
+                    double* berr, double* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_ctbrfs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* kd, lapack_int* nrhs,
+                    const lapack_complex_float* ab, lapack_int* ldab,
+                    const lapack_complex_float* b, lapack_int* ldb,
+                    const lapack_complex_float* x, lapack_int* ldx, float* ferr,
+                    float* berr, lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_ztbrfs( char* uplo, char* trans, char* diag, lapack_int* n,
+                    lapack_int* kd, lapack_int* nrhs,
+                    const lapack_complex_double* ab, lapack_int* ldab,
+                    const lapack_complex_double* b, lapack_int* ldb,
+                    const lapack_complex_double* x, lapack_int* ldx,
+                    double* ferr, double* berr, lapack_complex_double* work,
+                    double* rwork, lapack_int *info );
+void LAPACK_sgetri( lapack_int* n, float* a, lapack_int* lda,
+                    const lapack_int* ipiv, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dgetri( lapack_int* n, double* a, lapack_int* lda,
+                    const lapack_int* ipiv, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cgetri( lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                    const lapack_int* ipiv, lapack_complex_float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_zgetri( lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                    const lapack_int* ipiv, lapack_complex_double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_spotri( char* uplo, lapack_int* n, float* a, lapack_int* lda,
+                    lapack_int *info );
+void LAPACK_dpotri( char* uplo, lapack_int* n, double* a, lapack_int* lda,
+                    lapack_int *info );
+void LAPACK_cpotri( char* uplo, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_int *info );
+void LAPACK_zpotri( char* uplo, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_int *info );
+void LAPACK_dpftri( char* transr, char* uplo, lapack_int* n, double* a,
+                    lapack_int *info );
+void LAPACK_spftri( char* transr, char* uplo, lapack_int* n, float* a,
+                    lapack_int *info );
+void LAPACK_zpftri( char* transr, char* uplo, lapack_int* n,
+                    lapack_complex_double* a, lapack_int *info );
+void LAPACK_cpftri( char* transr, char* uplo, lapack_int* n,
+                    lapack_complex_float* a, lapack_int *info );
+void LAPACK_spptri( char* uplo, lapack_int* n, float* ap, lapack_int *info );
+void LAPACK_dpptri( char* uplo, lapack_int* n, double* ap, lapack_int *info );
+void LAPACK_cpptri( char* uplo, lapack_int* n, lapack_complex_float* ap,
+                    lapack_int *info );
+void LAPACK_zpptri( char* uplo, lapack_int* n, lapack_complex_double* ap,
+                    lapack_int *info );
+void LAPACK_ssytri( char* uplo, lapack_int* n, float* a, lapack_int* lda,
+                    const lapack_int* ipiv, float* work, lapack_int *info );
+void LAPACK_dsytri( char* uplo, lapack_int* n, double* a, lapack_int* lda,
+                    const lapack_int* ipiv, double* work, lapack_int *info );
+void LAPACK_csytri( char* uplo, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, const lapack_int* ipiv,
+                    lapack_complex_float* work, lapack_int *info );
+void LAPACK_zsytri( char* uplo, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, const lapack_int* ipiv,
+                    lapack_complex_double* work, lapack_int *info );
+void LAPACK_chetri( char* uplo, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, const lapack_int* ipiv,
+                    lapack_complex_float* work, lapack_int *info );
+void LAPACK_zhetri( char* uplo, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, const lapack_int* ipiv,
+                    lapack_complex_double* work, lapack_int *info );
+void LAPACK_ssptri( char* uplo, lapack_int* n, float* ap,
+                    const lapack_int* ipiv, float* work, lapack_int *info );
+void LAPACK_dsptri( char* uplo, lapack_int* n, double* ap,
+                    const lapack_int* ipiv, double* work, lapack_int *info );
+void LAPACK_csptri( char* uplo, lapack_int* n, lapack_complex_float* ap,
+                    const lapack_int* ipiv, lapack_complex_float* work,
+                    lapack_int *info );
+void LAPACK_zsptri( char* uplo, lapack_int* n, lapack_complex_double* ap,
+                    const lapack_int* ipiv, lapack_complex_double* work,
+                    lapack_int *info );
+void LAPACK_chptri( char* uplo, lapack_int* n, lapack_complex_float* ap,
+                    const lapack_int* ipiv, lapack_complex_float* work,
+                    lapack_int *info );
+void LAPACK_zhptri( char* uplo, lapack_int* n, lapack_complex_double* ap,
+                    const lapack_int* ipiv, lapack_complex_double* work,
+                    lapack_int *info );
+void LAPACK_strtri( char* uplo, char* diag, lapack_int* n, float* a,
+                    lapack_int* lda, lapack_int *info );
+void LAPACK_dtrtri( char* uplo, char* diag, lapack_int* n, double* a,
+                    lapack_int* lda, lapack_int *info );
+void LAPACK_ctrtri( char* uplo, char* diag, lapack_int* n,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_int *info );
+void LAPACK_ztrtri( char* uplo, char* diag, lapack_int* n,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_int *info );
+void LAPACK_dtftri( char* transr, char* uplo, char* diag, lapack_int* n,
+                    double* a, lapack_int *info );
+void LAPACK_stftri( char* transr, char* uplo, char* diag, lapack_int* n,
+                    float* a, lapack_int *info );
+void LAPACK_ztftri( char* transr, char* uplo, char* diag, lapack_int* n,
+                    lapack_complex_double* a, lapack_int *info );
+void LAPACK_ctftri( char* transr, char* uplo, char* diag, lapack_int* n,
+                    lapack_complex_float* a, lapack_int *info );
+void LAPACK_stptri( char* uplo, char* diag, lapack_int* n, float* ap,
+                    lapack_int *info );
+void LAPACK_dtptri( char* uplo, char* diag, lapack_int* n, double* ap,
+                    lapack_int *info );
+void LAPACK_ctptri( char* uplo, char* diag, lapack_int* n,
+                    lapack_complex_float* ap, lapack_int *info );
+void LAPACK_ztptri( char* uplo, char* diag, lapack_int* n,
+                    lapack_complex_double* ap, lapack_int *info );
+void LAPACK_sgeequ( lapack_int* m, lapack_int* n, const float* a,
+                    lapack_int* lda, float* r, float* c, float* rowcnd,
+                    float* colcnd, float* amax, lapack_int *info );
+void LAPACK_dgeequ( lapack_int* m, lapack_int* n, const double* a,
+                    lapack_int* lda, double* r, double* c, double* rowcnd,
+                    double* colcnd, double* amax, lapack_int *info );
+void LAPACK_cgeequ( lapack_int* m, lapack_int* n, const lapack_complex_float* a,
+                    lapack_int* lda, float* r, float* c, float* rowcnd,
+                    float* colcnd, float* amax, lapack_int *info );
+void LAPACK_zgeequ( lapack_int* m, lapack_int* n,
+                    const lapack_complex_double* a, lapack_int* lda, double* r,
+                    double* c, double* rowcnd, double* colcnd, double* amax,
+                    lapack_int *info );
+void LAPACK_dgeequb( lapack_int* m, lapack_int* n, const double* a,
+                     lapack_int* lda, double* r, double* c, double* rowcnd,
+                     double* colcnd, double* amax, lapack_int *info );
+void LAPACK_sgeequb( lapack_int* m, lapack_int* n, const float* a,
+                     lapack_int* lda, float* r, float* c, float* rowcnd,
+                     float* colcnd, float* amax, lapack_int *info );
+void LAPACK_zgeequb( lapack_int* m, lapack_int* n,
+                     const lapack_complex_double* a, lapack_int* lda, double* r,
+                     double* c, double* rowcnd, double* colcnd, double* amax,
+                     lapack_int *info );
+void LAPACK_cgeequb( lapack_int* m, lapack_int* n,
+                     const lapack_complex_float* a, lapack_int* lda, float* r,
+                     float* c, float* rowcnd, float* colcnd, float* amax,
+                     lapack_int *info );
+void LAPACK_sgbequ( lapack_int* m, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, const float* ab, lapack_int* ldab, float* r,
+                    float* c, float* rowcnd, float* colcnd, float* amax,
+                    lapack_int *info );
+void LAPACK_dgbequ( lapack_int* m, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, const double* ab, lapack_int* ldab,
+                    double* r, double* c, double* rowcnd, double* colcnd,
+                    double* amax, lapack_int *info );
+void LAPACK_cgbequ( lapack_int* m, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, const lapack_complex_float* ab,
+                    lapack_int* ldab, float* r, float* c, float* rowcnd,
+                    float* colcnd, float* amax, lapack_int *info );
+void LAPACK_zgbequ( lapack_int* m, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, const lapack_complex_double* ab,
+                    lapack_int* ldab, double* r, double* c, double* rowcnd,
+                    double* colcnd, double* amax, lapack_int *info );
+void LAPACK_dgbequb( lapack_int* m, lapack_int* n, lapack_int* kl,
+                     lapack_int* ku, const double* ab, lapack_int* ldab,
+                     double* r, double* c, double* rowcnd, double* colcnd,
+                     double* amax, lapack_int *info );
+void LAPACK_sgbequb( lapack_int* m, lapack_int* n, lapack_int* kl,
+                     lapack_int* ku, const float* ab, lapack_int* ldab,
+                     float* r, float* c, float* rowcnd, float* colcnd,
+                     float* amax, lapack_int *info );
+void LAPACK_zgbequb( lapack_int* m, lapack_int* n, lapack_int* kl,
+                     lapack_int* ku, const lapack_complex_double* ab,
+                     lapack_int* ldab, double* r, double* c, double* rowcnd,
+                     double* colcnd, double* amax, lapack_int *info );
+void LAPACK_cgbequb( lapack_int* m, lapack_int* n, lapack_int* kl,
+                     lapack_int* ku, const lapack_complex_float* ab,
+                     lapack_int* ldab, float* r, float* c, float* rowcnd,
+                     float* colcnd, float* amax, lapack_int *info );
+void LAPACK_spoequ( lapack_int* n, const float* a, lapack_int* lda, float* s,
+                    float* scond, float* amax, lapack_int *info );
+void LAPACK_dpoequ( lapack_int* n, const double* a, lapack_int* lda, double* s,
+                    double* scond, double* amax, lapack_int *info );
+void LAPACK_cpoequ( lapack_int* n, const lapack_complex_float* a,
+                    lapack_int* lda, float* s, float* scond, float* amax,
+                    lapack_int *info );
+void LAPACK_zpoequ( lapack_int* n, const lapack_complex_double* a,
+                    lapack_int* lda, double* s, double* scond, double* amax,
+                    lapack_int *info );
+void LAPACK_dpoequb( lapack_int* n, const double* a, lapack_int* lda, double* s,
+                     double* scond, double* amax, lapack_int *info );
+void LAPACK_spoequb( lapack_int* n, const float* a, lapack_int* lda, float* s,
+                     float* scond, float* amax, lapack_int *info );
+void LAPACK_zpoequb( lapack_int* n, const lapack_complex_double* a,
+                     lapack_int* lda, double* s, double* scond, double* amax,
+                     lapack_int *info );
+void LAPACK_cpoequb( lapack_int* n, const lapack_complex_float* a,
+                     lapack_int* lda, float* s, float* scond, float* amax,
+                     lapack_int *info );
+void LAPACK_sppequ( char* uplo, lapack_int* n, const float* ap, float* s,
+                    float* scond, float* amax, lapack_int *info );
+void LAPACK_dppequ( char* uplo, lapack_int* n, const double* ap, double* s,
+                    double* scond, double* amax, lapack_int *info );
+void LAPACK_cppequ( char* uplo, lapack_int* n, const lapack_complex_float* ap,
+                    float* s, float* scond, float* amax, lapack_int *info );
+void LAPACK_zppequ( char* uplo, lapack_int* n, const lapack_complex_double* ap,
+                    double* s, double* scond, double* amax, lapack_int *info );
+void LAPACK_spbequ( char* uplo, lapack_int* n, lapack_int* kd, const float* ab,
+                    lapack_int* ldab, float* s, float* scond, float* amax,
+                    lapack_int *info );
+void LAPACK_dpbequ( char* uplo, lapack_int* n, lapack_int* kd, const double* ab,
+                    lapack_int* ldab, double* s, double* scond, double* amax,
+                    lapack_int *info );
+void LAPACK_cpbequ( char* uplo, lapack_int* n, lapack_int* kd,
+                    const lapack_complex_float* ab, lapack_int* ldab, float* s,
+                    float* scond, float* amax, lapack_int *info );
+void LAPACK_zpbequ( char* uplo, lapack_int* n, lapack_int* kd,
+                    const lapack_complex_double* ab, lapack_int* ldab,
+                    double* s, double* scond, double* amax, lapack_int *info );
+void LAPACK_dsyequb( char* uplo, lapack_int* n, const double* a,
+                     lapack_int* lda, double* s, double* scond, double* amax,
+                     double* work, lapack_int *info );
+void LAPACK_ssyequb( char* uplo, lapack_int* n, const float* a, lapack_int* lda,
+                     float* s, float* scond, float* amax, float* work,
+                     lapack_int *info );
+void LAPACK_zsyequb( char* uplo, lapack_int* n, const lapack_complex_double* a,
+                     lapack_int* lda, double* s, double* scond, double* amax,
+                     lapack_complex_double* work, lapack_int *info );
+void LAPACK_csyequb( char* uplo, lapack_int* n, const lapack_complex_float* a,
+                     lapack_int* lda, float* s, float* scond, float* amax,
+                     lapack_complex_float* work, lapack_int *info );
+void LAPACK_zheequb( char* uplo, lapack_int* n, const lapack_complex_double* a,
+                     lapack_int* lda, double* s, double* scond, double* amax,
+                     lapack_complex_double* work, lapack_int *info );
+void LAPACK_cheequb( char* uplo, lapack_int* n, const lapack_complex_float* a,
+                     lapack_int* lda, float* s, float* scond, float* amax,
+                     lapack_complex_float* work, lapack_int *info );
+void LAPACK_sgesv( lapack_int* n, lapack_int* nrhs, float* a, lapack_int* lda,
+                   lapack_int* ipiv, float* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_dgesv( lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda,
+                   lapack_int* ipiv, double* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_cgesv( lapack_int* n, lapack_int* nrhs, lapack_complex_float* a,
+                   lapack_int* lda, lapack_int* ipiv, lapack_complex_float* b,
+                   lapack_int* ldb, lapack_int *info );
+void LAPACK_zgesv( lapack_int* n, lapack_int* nrhs, lapack_complex_double* a,
+                   lapack_int* lda, lapack_int* ipiv, lapack_complex_double* b,
+                   lapack_int* ldb, lapack_int *info );
+void LAPACK_dsgesv( lapack_int* n, lapack_int* nrhs, double* a, lapack_int* lda,
+                    lapack_int* ipiv, double* b, lapack_int* ldb, double* x,
+                    lapack_int* ldx, double* work, float* swork,
+                    lapack_int* iter, lapack_int *info );
+void LAPACK_zcgesv( lapack_int* n, lapack_int* nrhs, lapack_complex_double* a,
+                    lapack_int* lda, lapack_int* ipiv, lapack_complex_double* b,
+                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                    lapack_complex_double* work, lapack_complex_float* swork,
+                    double* rwork, lapack_int* iter, lapack_int *info );
+void LAPACK_sgesvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
+                    float* a, lapack_int* lda, float* af, lapack_int* ldaf,
+                    lapack_int* ipiv, char* equed, float* r, float* c, float* b,
+                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
+                    float* ferr, float* berr, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dgesvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
+                    double* a, lapack_int* lda, double* af, lapack_int* ldaf,
+                    lapack_int* ipiv, char* equed, double* r, double* c,
+                    double* b, lapack_int* ldb, double* x, lapack_int* ldx,
+                    double* rcond, double* ferr, double* berr, double* work,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_cgesvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* af, lapack_int* ldaf,
+                    lapack_int* ipiv, char* equed, float* r, float* c,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                    float* ferr, float* berr, lapack_complex_float* work,
+                    float* rwork, lapack_int *info );
+void LAPACK_zgesvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* af, lapack_int* ldaf,
+                    lapack_int* ipiv, char* equed, double* r, double* c,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                    double* ferr, double* berr, lapack_complex_double* work,
+                    double* rwork, lapack_int *info );
+void LAPACK_dgesvxx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
+                     double* a, lapack_int* lda, double* af, lapack_int* ldaf,
+                     lapack_int* ipiv, char* equed, double* r, double* c,
+                     double* b, lapack_int* ldb, double* x, lapack_int* ldx,
+                     double* rcond, double* rpvgrw, double* berr,
+                     lapack_int* n_err_bnds, double* err_bnds_norm,
+                     double* err_bnds_comp, lapack_int* nparams, double* params,
+                     double* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_sgesvxx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
+                     float* a, lapack_int* lda, float* af, lapack_int* ldaf,
+                     lapack_int* ipiv, char* equed, float* r, float* c,
+                     float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                     float* rcond, float* rpvgrw, float* berr,
+                     lapack_int* n_err_bnds, float* err_bnds_norm,
+                     float* err_bnds_comp, lapack_int* nparams, float* params,
+                     float* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_zgesvxx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
+                     lapack_complex_double* a, lapack_int* lda,
+                     lapack_complex_double* af, lapack_int* ldaf,
+                     lapack_int* ipiv, char* equed, double* r, double* c,
+                     lapack_complex_double* b, lapack_int* ldb,
+                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
+                     double* err_bnds_norm, double* err_bnds_comp,
+                     lapack_int* nparams, double* params,
+                     lapack_complex_double* work, double* rwork,
+                     lapack_int *info );
+void LAPACK_cgesvxx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
+                     lapack_complex_float* a, lapack_int* lda,
+                     lapack_complex_float* af, lapack_int* ldaf,
+                     lapack_int* ipiv, char* equed, float* r, float* c,
+                     lapack_complex_float* b, lapack_int* ldb,
+                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
+                     float* err_bnds_norm, float* err_bnds_comp,
+                     lapack_int* nparams, float* params,
+                     lapack_complex_float* work, float* rwork,
+                     lapack_int *info );
+void LAPACK_sgbsv( lapack_int* n, lapack_int* kl, lapack_int* ku,
+                   lapack_int* nrhs, float* ab, lapack_int* ldab,
+                   lapack_int* ipiv, float* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_dgbsv( lapack_int* n, lapack_int* kl, lapack_int* ku,
+                   lapack_int* nrhs, double* ab, lapack_int* ldab,
+                   lapack_int* ipiv, double* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_cgbsv( lapack_int* n, lapack_int* kl, lapack_int* ku,
+                   lapack_int* nrhs, lapack_complex_float* ab, lapack_int* ldab,
+                   lapack_int* ipiv, lapack_complex_float* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_zgbsv( lapack_int* n, lapack_int* kl, lapack_int* ku,
+                   lapack_int* nrhs, lapack_complex_double* ab,
+                   lapack_int* ldab, lapack_int* ipiv, lapack_complex_double* b,
+                   lapack_int* ldb, lapack_int *info );
+void LAPACK_sgbsvx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, lapack_int* nrhs, float* ab,
+                    lapack_int* ldab, float* afb, lapack_int* ldafb,
+                    lapack_int* ipiv, char* equed, float* r, float* c, float* b,
+                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
+                    float* ferr, float* berr, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dgbsvx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, lapack_int* nrhs, double* ab,
+                    lapack_int* ldab, double* afb, lapack_int* ldafb,
+                    lapack_int* ipiv, char* equed, double* r, double* c,
+                    double* b, lapack_int* ldb, double* x, lapack_int* ldx,
+                    double* rcond, double* ferr, double* berr, double* work,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_cgbsvx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, lapack_int* nrhs, lapack_complex_float* ab,
+                    lapack_int* ldab, lapack_complex_float* afb,
+                    lapack_int* ldafb, lapack_int* ipiv, char* equed, float* r,
+                    float* c, lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                    float* ferr, float* berr, lapack_complex_float* work,
+                    float* rwork, lapack_int *info );
+void LAPACK_zgbsvx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, lapack_int* nrhs, lapack_complex_double* ab,
+                    lapack_int* ldab, lapack_complex_double* afb,
+                    lapack_int* ldafb, lapack_int* ipiv, char* equed, double* r,
+                    double* c, lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                    double* ferr, double* berr, lapack_complex_double* work,
+                    double* rwork, lapack_int *info );
+void LAPACK_dgbsvxx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
+                     lapack_int* ku, lapack_int* nrhs, double* ab,
+                     lapack_int* ldab, double* afb, lapack_int* ldafb,
+                     lapack_int* ipiv, char* equed, double* r, double* c,
+                     double* b, lapack_int* ldb, double* x, lapack_int* ldx,
+                     double* rcond, double* rpvgrw, double* berr,
+                     lapack_int* n_err_bnds, double* err_bnds_norm,
+                     double* err_bnds_comp, lapack_int* nparams, double* params,
+                     double* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_sgbsvxx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
+                     lapack_int* ku, lapack_int* nrhs, float* ab,
+                     lapack_int* ldab, float* afb, lapack_int* ldafb,
+                     lapack_int* ipiv, char* equed, float* r, float* c,
+                     float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                     float* rcond, float* rpvgrw, float* berr,
+                     lapack_int* n_err_bnds, float* err_bnds_norm,
+                     float* err_bnds_comp, lapack_int* nparams, float* params,
+                     float* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_zgbsvxx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
+                     lapack_int* ku, lapack_int* nrhs,
+                     lapack_complex_double* ab, lapack_int* ldab,
+                     lapack_complex_double* afb, lapack_int* ldafb,
+                     lapack_int* ipiv, char* equed, double* r, double* c,
+                     lapack_complex_double* b, lapack_int* ldb,
+                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
+                     double* err_bnds_norm, double* err_bnds_comp,
+                     lapack_int* nparams, double* params,
+                     lapack_complex_double* work, double* rwork,
+                     lapack_int *info );
+void LAPACK_cgbsvxx( char* fact, char* trans, lapack_int* n, lapack_int* kl,
+                     lapack_int* ku, lapack_int* nrhs, lapack_complex_float* ab,
+                     lapack_int* ldab, lapack_complex_float* afb,
+                     lapack_int* ldafb, lapack_int* ipiv, char* equed, float* r,
+                     float* c, lapack_complex_float* b, lapack_int* ldb,
+                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
+                     float* err_bnds_norm, float* err_bnds_comp,
+                     lapack_int* nparams, float* params,
+                     lapack_complex_float* work, float* rwork,
+                     lapack_int *info );
+void LAPACK_sgtsv( lapack_int* n, lapack_int* nrhs, float* dl, float* d,
+                   float* du, float* b, lapack_int* ldb, lapack_int *info );
+void LAPACK_dgtsv( lapack_int* n, lapack_int* nrhs, double* dl, double* d,
+                   double* du, double* b, lapack_int* ldb, lapack_int *info );
+void LAPACK_cgtsv( lapack_int* n, lapack_int* nrhs, lapack_complex_float* dl,
+                   lapack_complex_float* d, lapack_complex_float* du,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_int *info );
+void LAPACK_zgtsv( lapack_int* n, lapack_int* nrhs, lapack_complex_double* dl,
+                   lapack_complex_double* d, lapack_complex_double* du,
+                   lapack_complex_double* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_sgtsvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
+                    const float* dl, const float* d, const float* du,
+                    float* dlf, float* df, float* duf, float* du2,
+                    lapack_int* ipiv, const float* b, lapack_int* ldb, float* x,
+                    lapack_int* ldx, float* rcond, float* ferr, float* berr,
+                    float* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_dgtsvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
+                    const double* dl, const double* d, const double* du,
+                    double* dlf, double* df, double* duf, double* du2,
+                    lapack_int* ipiv, const double* b, lapack_int* ldb,
+                    double* x, lapack_int* ldx, double* rcond, double* ferr,
+                    double* berr, double* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_cgtsvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* dl,
+                    const lapack_complex_float* d,
+                    const lapack_complex_float* du, lapack_complex_float* dlf,
+                    lapack_complex_float* df, lapack_complex_float* duf,
+                    lapack_complex_float* du2, lapack_int* ipiv,
+                    const lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                    float* ferr, float* berr, lapack_complex_float* work,
+                    float* rwork, lapack_int *info );
+void LAPACK_zgtsvx( char* fact, char* trans, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* dl,
+                    const lapack_complex_double* d,
+                    const lapack_complex_double* du, lapack_complex_double* dlf,
+                    lapack_complex_double* df, lapack_complex_double* duf,
+                    lapack_complex_double* du2, lapack_int* ipiv,
+                    const lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                    double* ferr, double* berr, lapack_complex_double* work,
+                    double* rwork, lapack_int *info );
+void LAPACK_sposv( char* uplo, lapack_int* n, lapack_int* nrhs, float* a,
+                   lapack_int* lda, float* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_dposv( char* uplo, lapack_int* n, lapack_int* nrhs, double* a,
+                   lapack_int* lda, double* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_cposv( char* uplo, lapack_int* n, lapack_int* nrhs,
+                   lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_int *info );
+void LAPACK_zposv( char* uplo, lapack_int* n, lapack_int* nrhs,
+                   lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_dsposv( char* uplo, lapack_int* n, lapack_int* nrhs, double* a,
+                    lapack_int* lda, double* b, lapack_int* ldb, double* x,
+                    lapack_int* ldx, double* work, float* swork,
+                    lapack_int* iter, lapack_int *info );
+void LAPACK_zcposv( char* uplo, lapack_int* n, lapack_int* nrhs,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx,
+                    lapack_complex_double* work, lapack_complex_float* swork,
+                    double* rwork, lapack_int* iter, lapack_int *info );
+void LAPACK_sposvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    float* a, lapack_int* lda, float* af, lapack_int* ldaf,
+                    char* equed, float* s, float* b, lapack_int* ldb, float* x,
+                    lapack_int* ldx, float* rcond, float* ferr, float* berr,
+                    float* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_dposvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    double* a, lapack_int* lda, double* af, lapack_int* ldaf,
+                    char* equed, double* s, double* b, lapack_int* ldb,
+                    double* x, lapack_int* ldx, double* rcond, double* ferr,
+                    double* berr, double* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_cposvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* af, lapack_int* ldaf, char* equed,
+                    float* s, lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                    float* ferr, float* berr, lapack_complex_float* work,
+                    float* rwork, lapack_int *info );
+void LAPACK_zposvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* af, lapack_int* ldaf, char* equed,
+                    double* s, lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                    double* ferr, double* berr, lapack_complex_double* work,
+                    double* rwork, lapack_int *info );
+void LAPACK_dposvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                     double* a, lapack_int* lda, double* af, lapack_int* ldaf,
+                     char* equed, double* s, double* b, lapack_int* ldb,
+                     double* x, lapack_int* ldx, double* rcond, double* rpvgrw,
+                     double* berr, lapack_int* n_err_bnds,
+                     double* err_bnds_norm, double* err_bnds_comp,
+                     lapack_int* nparams, double* params, double* work,
+                     lapack_int* iwork, lapack_int *info );
+void LAPACK_sposvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                     float* a, lapack_int* lda, float* af, lapack_int* ldaf,
+                     char* equed, float* s, float* b, lapack_int* ldb, float* x,
+                     lapack_int* ldx, float* rcond, float* rpvgrw, float* berr,
+                     lapack_int* n_err_bnds, float* err_bnds_norm,
+                     float* err_bnds_comp, lapack_int* nparams, float* params,
+                     float* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_zposvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                     lapack_complex_double* a, lapack_int* lda,
+                     lapack_complex_double* af, lapack_int* ldaf, char* equed,
+                     double* s, lapack_complex_double* b, lapack_int* ldb,
+                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
+                     double* err_bnds_norm, double* err_bnds_comp,
+                     lapack_int* nparams, double* params,
+                     lapack_complex_double* work, double* rwork,
+                     lapack_int *info );
+void LAPACK_cposvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                     lapack_complex_float* a, lapack_int* lda,
+                     lapack_complex_float* af, lapack_int* ldaf, char* equed,
+                     float* s, lapack_complex_float* b, lapack_int* ldb,
+                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
+                     float* err_bnds_norm, float* err_bnds_comp,
+                     lapack_int* nparams, float* params,
+                     lapack_complex_float* work, float* rwork,
+                     lapack_int *info );
+void LAPACK_sppsv( char* uplo, lapack_int* n, lapack_int* nrhs, float* ap,
+                   float* b, lapack_int* ldb, lapack_int *info );
+void LAPACK_dppsv( char* uplo, lapack_int* n, lapack_int* nrhs, double* ap,
+                   double* b, lapack_int* ldb, lapack_int *info );
+void LAPACK_cppsv( char* uplo, lapack_int* n, lapack_int* nrhs,
+                   lapack_complex_float* ap, lapack_complex_float* b,
+                   lapack_int* ldb, lapack_int *info );
+void LAPACK_zppsv( char* uplo, lapack_int* n, lapack_int* nrhs,
+                   lapack_complex_double* ap, lapack_complex_double* b,
+                   lapack_int* ldb, lapack_int *info );
+void LAPACK_sppsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    float* ap, float* afp, char* equed, float* s, float* b,
+                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
+                    float* ferr, float* berr, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dppsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    double* ap, double* afp, char* equed, double* s, double* b,
+                    lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
+                    double* ferr, double* berr, double* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_cppsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    lapack_complex_float* ap, lapack_complex_float* afp,
+                    char* equed, float* s, lapack_complex_float* b,
+                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                    float* rcond, float* ferr, float* berr,
+                    lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zppsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    lapack_complex_double* ap, lapack_complex_double* afp,
+                    char* equed, double* s, lapack_complex_double* b,
+                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                    double* rcond, double* ferr, double* berr,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_spbsv( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                   float* ab, lapack_int* ldab, float* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_dpbsv( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                   double* ab, lapack_int* ldab, double* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_cpbsv( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                   lapack_complex_float* ab, lapack_int* ldab,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_int *info );
+void LAPACK_zpbsv( char* uplo, lapack_int* n, lapack_int* kd, lapack_int* nrhs,
+                   lapack_complex_double* ab, lapack_int* ldab,
+                   lapack_complex_double* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_spbsvx( char* fact, char* uplo, lapack_int* n, lapack_int* kd,
+                    lapack_int* nrhs, float* ab, lapack_int* ldab, float* afb,
+                    lapack_int* ldafb, char* equed, float* s, float* b,
+                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
+                    float* ferr, float* berr, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dpbsvx( char* fact, char* uplo, lapack_int* n, lapack_int* kd,
+                    lapack_int* nrhs, double* ab, lapack_int* ldab, double* afb,
+                    lapack_int* ldafb, char* equed, double* s, double* b,
+                    lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
+                    double* ferr, double* berr, double* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_cpbsvx( char* fact, char* uplo, lapack_int* n, lapack_int* kd,
+                    lapack_int* nrhs, lapack_complex_float* ab,
+                    lapack_int* ldab, lapack_complex_float* afb,
+                    lapack_int* ldafb, char* equed, float* s,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                    float* ferr, float* berr, lapack_complex_float* work,
+                    float* rwork, lapack_int *info );
+void LAPACK_zpbsvx( char* fact, char* uplo, lapack_int* n, lapack_int* kd,
+                    lapack_int* nrhs, lapack_complex_double* ab,
+                    lapack_int* ldab, lapack_complex_double* afb,
+                    lapack_int* ldafb, char* equed, double* s,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                    double* ferr, double* berr, lapack_complex_double* work,
+                    double* rwork, lapack_int *info );
+void LAPACK_sptsv( lapack_int* n, lapack_int* nrhs, float* d, float* e,
+                   float* b, lapack_int* ldb, lapack_int *info );
+void LAPACK_dptsv( lapack_int* n, lapack_int* nrhs, double* d, double* e,
+                   double* b, lapack_int* ldb, lapack_int *info );
+void LAPACK_cptsv( lapack_int* n, lapack_int* nrhs, float* d,
+                   lapack_complex_float* e, lapack_complex_float* b,
+                   lapack_int* ldb, lapack_int *info );
+void LAPACK_zptsv( lapack_int* n, lapack_int* nrhs, double* d,
+                   lapack_complex_double* e, lapack_complex_double* b,
+                   lapack_int* ldb, lapack_int *info );
+void LAPACK_sptsvx( char* fact, lapack_int* n, lapack_int* nrhs, const float* d,
+                    const float* e, float* df, float* ef, const float* b,
+                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
+                    float* ferr, float* berr, float* work, lapack_int *info );
+void LAPACK_dptsvx( char* fact, lapack_int* n, lapack_int* nrhs,
+                    const double* d, const double* e, double* df, double* ef,
+                    const double* b, lapack_int* ldb, double* x,
+                    lapack_int* ldx, double* rcond, double* ferr, double* berr,
+                    double* work, lapack_int *info );
+void LAPACK_cptsvx( char* fact, lapack_int* n, lapack_int* nrhs, const float* d,
+                    const lapack_complex_float* e, float* df,
+                    lapack_complex_float* ef, const lapack_complex_float* b,
+                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                    float* rcond, float* ferr, float* berr,
+                    lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zptsvx( char* fact, lapack_int* n, lapack_int* nrhs,
+                    const double* d, const lapack_complex_double* e, double* df,
+                    lapack_complex_double* ef, const lapack_complex_double* b,
+                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                    double* rcond, double* ferr, double* berr,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_ssysv( char* uplo, lapack_int* n, lapack_int* nrhs, float* a,
+                   lapack_int* lda, lapack_int* ipiv, float* b, lapack_int* ldb,
+                   float* work, lapack_int* lwork, lapack_int *info );
+void LAPACK_dsysv( char* uplo, lapack_int* n, lapack_int* nrhs, double* a,
+                   lapack_int* lda, lapack_int* ipiv, double* b,
+                   lapack_int* ldb, double* work, lapack_int* lwork,
+                   lapack_int *info );
+void LAPACK_csysv( char* uplo, lapack_int* n, lapack_int* nrhs,
+                   lapack_complex_float* a, lapack_int* lda, lapack_int* ipiv,
+                   lapack_complex_float* b, lapack_int* ldb,
+                   lapack_complex_float* work, lapack_int* lwork,
+                   lapack_int *info );
+void LAPACK_zsysv( char* uplo, lapack_int* n, lapack_int* nrhs,
+                   lapack_complex_double* a, lapack_int* lda, lapack_int* ipiv,
+                   lapack_complex_double* b, lapack_int* ldb,
+                   lapack_complex_double* work, lapack_int* lwork,
+                   lapack_int *info );
+void LAPACK_ssysvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const float* a, lapack_int* lda, float* af,
+                    lapack_int* ldaf, lapack_int* ipiv, const float* b,
+                    lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
+                    float* ferr, float* berr, float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_dsysvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const double* a, lapack_int* lda, double* af,
+                    lapack_int* ldaf, lapack_int* ipiv, const double* b,
+                    lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
+                    double* ferr, double* berr, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_csysvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* af, lapack_int* ldaf,
+                    lapack_int* ipiv, const lapack_complex_float* b,
+                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                    float* rcond, float* ferr, float* berr,
+                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                    lapack_int *info );
+void LAPACK_zsysvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* af, lapack_int* ldaf,
+                    lapack_int* ipiv, const lapack_complex_double* b,
+                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                    double* rcond, double* ferr, double* berr,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int *info );
+void LAPACK_dsysvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                     double* a, lapack_int* lda, double* af, lapack_int* ldaf,
+                     lapack_int* ipiv, char* equed, double* s, double* b,
+                     lapack_int* ldb, double* x, lapack_int* ldx, double* rcond,
+                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
+                     double* err_bnds_norm, double* err_bnds_comp,
+                     lapack_int* nparams, double* params, double* work,
+                     lapack_int* iwork, lapack_int *info );
+void LAPACK_ssysvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                     float* a, lapack_int* lda, float* af, lapack_int* ldaf,
+                     lapack_int* ipiv, char* equed, float* s, float* b,
+                     lapack_int* ldb, float* x, lapack_int* ldx, float* rcond,
+                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
+                     float* err_bnds_norm, float* err_bnds_comp,
+                     lapack_int* nparams, float* params, float* work,
+                     lapack_int* iwork, lapack_int *info );
+void LAPACK_zsysvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                     lapack_complex_double* a, lapack_int* lda,
+                     lapack_complex_double* af, lapack_int* ldaf,
+                     lapack_int* ipiv, char* equed, double* s,
+                     lapack_complex_double* b, lapack_int* ldb,
+                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
+                     double* err_bnds_norm, double* err_bnds_comp,
+                     lapack_int* nparams, double* params,
+                     lapack_complex_double* work, double* rwork,
+                     lapack_int *info );
+void LAPACK_csysvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                     lapack_complex_float* a, lapack_int* lda,
+                     lapack_complex_float* af, lapack_int* ldaf,
+                     lapack_int* ipiv, char* equed, float* s,
+                     lapack_complex_float* b, lapack_int* ldb,
+                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
+                     float* err_bnds_norm, float* err_bnds_comp,
+                     lapack_int* nparams, float* params,
+                     lapack_complex_float* work, float* rwork,
+                     lapack_int *info );
+void LAPACK_chesv( char* uplo, lapack_int* n, lapack_int* nrhs,
+                   lapack_complex_float* a, lapack_int* lda, lapack_int* ipiv,
+                   lapack_complex_float* b, lapack_int* ldb,
+                   lapack_complex_float* work, lapack_int* lwork,
+                   lapack_int *info );
+void LAPACK_zhesv( char* uplo, lapack_int* n, lapack_int* nrhs,
+                   lapack_complex_double* a, lapack_int* lda, lapack_int* ipiv,
+                   lapack_complex_double* b, lapack_int* ldb,
+                   lapack_complex_double* work, lapack_int* lwork,
+                   lapack_int *info );
+void LAPACK_chesvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* af, lapack_int* ldaf,
+                    lapack_int* ipiv, const lapack_complex_float* b,
+                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                    float* rcond, float* ferr, float* berr,
+                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                    lapack_int *info );
+void LAPACK_zhesvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* af, lapack_int* ldaf,
+                    lapack_int* ipiv, const lapack_complex_double* b,
+                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                    double* rcond, double* ferr, double* berr,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int *info );
+void LAPACK_zhesvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                     lapack_complex_double* a, lapack_int* lda,
+                     lapack_complex_double* af, lapack_int* ldaf,
+                     lapack_int* ipiv, char* equed, double* s,
+                     lapack_complex_double* b, lapack_int* ldb,
+                     lapack_complex_double* x, lapack_int* ldx, double* rcond,
+                     double* rpvgrw, double* berr, lapack_int* n_err_bnds,
+                     double* err_bnds_norm, double* err_bnds_comp,
+                     lapack_int* nparams, double* params,
+                     lapack_complex_double* work, double* rwork,
+                     lapack_int *info );
+void LAPACK_chesvxx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                     lapack_complex_float* a, lapack_int* lda,
+                     lapack_complex_float* af, lapack_int* ldaf,
+                     lapack_int* ipiv, char* equed, float* s,
+                     lapack_complex_float* b, lapack_int* ldb,
+                     lapack_complex_float* x, lapack_int* ldx, float* rcond,
+                     float* rpvgrw, float* berr, lapack_int* n_err_bnds,
+                     float* err_bnds_norm, float* err_bnds_comp,
+                     lapack_int* nparams, float* params,
+                     lapack_complex_float* work, float* rwork,
+                     lapack_int *info );
+void LAPACK_sspsv( char* uplo, lapack_int* n, lapack_int* nrhs, float* ap,
+                   lapack_int* ipiv, float* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_dspsv( char* uplo, lapack_int* n, lapack_int* nrhs, double* ap,
+                   lapack_int* ipiv, double* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_cspsv( char* uplo, lapack_int* n, lapack_int* nrhs,
+                   lapack_complex_float* ap, lapack_int* ipiv,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_int *info );
+void LAPACK_zspsv( char* uplo, lapack_int* n, lapack_int* nrhs,
+                   lapack_complex_double* ap, lapack_int* ipiv,
+                   lapack_complex_double* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_sspsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const float* ap, float* afp, lapack_int* ipiv,
+                    const float* b, lapack_int* ldb, float* x, lapack_int* ldx,
+                    float* rcond, float* ferr, float* berr, float* work,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_dspsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const double* ap, double* afp, lapack_int* ipiv,
+                    const double* b, lapack_int* ldb, double* x,
+                    lapack_int* ldx, double* rcond, double* ferr, double* berr,
+                    double* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_cspsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* ap, lapack_complex_float* afp,
+                    lapack_int* ipiv, const lapack_complex_float* b,
+                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                    float* rcond, float* ferr, float* berr,
+                    lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zspsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* ap, lapack_complex_double* afp,
+                    lapack_int* ipiv, const lapack_complex_double* b,
+                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                    double* rcond, double* ferr, double* berr,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_chpsv( char* uplo, lapack_int* n, lapack_int* nrhs,
+                   lapack_complex_float* ap, lapack_int* ipiv,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_int *info );
+void LAPACK_zhpsv( char* uplo, lapack_int* n, lapack_int* nrhs,
+                   lapack_complex_double* ap, lapack_int* ipiv,
+                   lapack_complex_double* b, lapack_int* ldb,
+                   lapack_int *info );
+void LAPACK_chpsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_float* ap, lapack_complex_float* afp,
+                    lapack_int* ipiv, const lapack_complex_float* b,
+                    lapack_int* ldb, lapack_complex_float* x, lapack_int* ldx,
+                    float* rcond, float* ferr, float* berr,
+                    lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zhpsvx( char* fact, char* uplo, lapack_int* n, lapack_int* nrhs,
+                    const lapack_complex_double* ap, lapack_complex_double* afp,
+                    lapack_int* ipiv, const lapack_complex_double* b,
+                    lapack_int* ldb, lapack_complex_double* x, lapack_int* ldx,
+                    double* rcond, double* ferr, double* berr,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_sgeqrf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                    float* tau, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dgeqrf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                    double* tau, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cgeqrf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_complex_float* tau,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zgeqrf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_complex_double* tau,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_sgeqpf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                    lapack_int* jpvt, float* tau, float* work,
+                    lapack_int *info );
+void LAPACK_dgeqpf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                    lapack_int* jpvt, double* tau, double* work,
+                    lapack_int *info );
+void LAPACK_cgeqpf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_int* jpvt,
+                    lapack_complex_float* tau, lapack_complex_float* work,
+                    float* rwork, lapack_int *info );
+void LAPACK_zgeqpf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_int* jpvt,
+                    lapack_complex_double* tau, lapack_complex_double* work,
+                    double* rwork, lapack_int *info );
+void LAPACK_sgeqp3( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                    lapack_int* jpvt, float* tau, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dgeqp3( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                    lapack_int* jpvt, double* tau, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_cgeqp3( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_int* jpvt,
+                    lapack_complex_float* tau, lapack_complex_float* work,
+                    lapack_int* lwork, float* rwork, lapack_int *info );
+void LAPACK_zgeqp3( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_int* jpvt,
+                    lapack_complex_double* tau, lapack_complex_double* work,
+                    lapack_int* lwork, double* rwork, lapack_int *info );
+void LAPACK_sorgqr( lapack_int* m, lapack_int* n, lapack_int* k, float* a,
+                    lapack_int* lda, const float* tau, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dorgqr( lapack_int* m, lapack_int* n, lapack_int* k, double* a,
+                    lapack_int* lda, const double* tau, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_sormqr( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const float* a, lapack_int* lda,
+                    const float* tau, float* c, lapack_int* ldc, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dormqr( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const double* a, lapack_int* lda,
+                    const double* tau, double* c, lapack_int* ldc, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_cungqr( lapack_int* m, lapack_int* n, lapack_int* k,
+                    lapack_complex_float* a, lapack_int* lda,
+                    const lapack_complex_float* tau, lapack_complex_float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_zungqr( lapack_int* m, lapack_int* n, lapack_int* k,
+                    lapack_complex_double* a, lapack_int* lda,
+                    const lapack_complex_double* tau,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cunmqr( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_complex_float* tau,
+                    lapack_complex_float* c, lapack_int* ldc,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zunmqr( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const lapack_complex_double* a,
+                    lapack_int* lda, const lapack_complex_double* tau,
+                    lapack_complex_double* c, lapack_int* ldc,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_sgelqf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                    float* tau, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dgelqf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                    double* tau, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cgelqf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_complex_float* tau,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zgelqf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_complex_double* tau,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_sorglq( lapack_int* m, lapack_int* n, lapack_int* k, float* a,
+                    lapack_int* lda, const float* tau, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dorglq( lapack_int* m, lapack_int* n, lapack_int* k, double* a,
+                    lapack_int* lda, const double* tau, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_sormlq( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const float* a, lapack_int* lda,
+                    const float* tau, float* c, lapack_int* ldc, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dormlq( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const double* a, lapack_int* lda,
+                    const double* tau, double* c, lapack_int* ldc, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_cunglq( lapack_int* m, lapack_int* n, lapack_int* k,
+                    lapack_complex_float* a, lapack_int* lda,
+                    const lapack_complex_float* tau, lapack_complex_float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_zunglq( lapack_int* m, lapack_int* n, lapack_int* k,
+                    lapack_complex_double* a, lapack_int* lda,
+                    const lapack_complex_double* tau,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cunmlq( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_complex_float* tau,
+                    lapack_complex_float* c, lapack_int* ldc,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zunmlq( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const lapack_complex_double* a,
+                    lapack_int* lda, const lapack_complex_double* tau,
+                    lapack_complex_double* c, lapack_int* ldc,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_sgeqlf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                    float* tau, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dgeqlf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                    double* tau, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cgeqlf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_complex_float* tau,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zgeqlf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_complex_double* tau,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_sorgql( lapack_int* m, lapack_int* n, lapack_int* k, float* a,
+                    lapack_int* lda, const float* tau, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dorgql( lapack_int* m, lapack_int* n, lapack_int* k, double* a,
+                    lapack_int* lda, const double* tau, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_cungql( lapack_int* m, lapack_int* n, lapack_int* k,
+                    lapack_complex_float* a, lapack_int* lda,
+                    const lapack_complex_float* tau, lapack_complex_float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_zungql( lapack_int* m, lapack_int* n, lapack_int* k,
+                    lapack_complex_double* a, lapack_int* lda,
+                    const lapack_complex_double* tau,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_sormql( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const float* a, lapack_int* lda,
+                    const float* tau, float* c, lapack_int* ldc, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dormql( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const double* a, lapack_int* lda,
+                    const double* tau, double* c, lapack_int* ldc, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_cunmql( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_complex_float* tau,
+                    lapack_complex_float* c, lapack_int* ldc,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zunmql( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const lapack_complex_double* a,
+                    lapack_int* lda, const lapack_complex_double* tau,
+                    lapack_complex_double* c, lapack_int* ldc,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_sgerqf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                    float* tau, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dgerqf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                    double* tau, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cgerqf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_complex_float* tau,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zgerqf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_complex_double* tau,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_sorgrq( lapack_int* m, lapack_int* n, lapack_int* k, float* a,
+                    lapack_int* lda, const float* tau, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dorgrq( lapack_int* m, lapack_int* n, lapack_int* k, double* a,
+                    lapack_int* lda, const double* tau, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_cungrq( lapack_int* m, lapack_int* n, lapack_int* k,
+                    lapack_complex_float* a, lapack_int* lda,
+                    const lapack_complex_float* tau, lapack_complex_float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_zungrq( lapack_int* m, lapack_int* n, lapack_int* k,
+                    lapack_complex_double* a, lapack_int* lda,
+                    const lapack_complex_double* tau,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_sormrq( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const float* a, lapack_int* lda,
+                    const float* tau, float* c, lapack_int* ldc, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dormrq( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const double* a, lapack_int* lda,
+                    const double* tau, double* c, lapack_int* ldc, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_cunmrq( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_complex_float* tau,
+                    lapack_complex_float* c, lapack_int* ldc,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zunmrq( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, const lapack_complex_double* a,
+                    lapack_int* lda, const lapack_complex_double* tau,
+                    lapack_complex_double* c, lapack_int* ldc,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_stzrzf( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                    float* tau, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dtzrzf( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                    double* tau, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_ctzrzf( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_complex_float* tau,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_ztzrzf( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_complex_double* tau,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_sormrz( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, lapack_int* l, const float* a,
+                    lapack_int* lda, const float* tau, float* c,
+                    lapack_int* ldc, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dormrz( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, lapack_int* l, const double* a,
+                    lapack_int* lda, const double* tau, double* c,
+                    lapack_int* ldc, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cunmrz( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, lapack_int* l, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_complex_float* tau,
+                    lapack_complex_float* c, lapack_int* ldc,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zunmrz( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* k, lapack_int* l,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    const lapack_complex_double* tau, lapack_complex_double* c,
+                    lapack_int* ldc, lapack_complex_double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_sggqrf( lapack_int* n, lapack_int* m, lapack_int* p, float* a,
+                    lapack_int* lda, float* taua, float* b, lapack_int* ldb,
+                    float* taub, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dggqrf( lapack_int* n, lapack_int* m, lapack_int* p, double* a,
+                    lapack_int* lda, double* taua, double* b, lapack_int* ldb,
+                    double* taub, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cggqrf( lapack_int* n, lapack_int* m, lapack_int* p,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* taua, lapack_complex_float* b,
+                    lapack_int* ldb, lapack_complex_float* taub,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zggqrf( lapack_int* n, lapack_int* m, lapack_int* p,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* taua, lapack_complex_double* b,
+                    lapack_int* ldb, lapack_complex_double* taub,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_sggrqf( lapack_int* m, lapack_int* p, lapack_int* n, float* a,
+                    lapack_int* lda, float* taua, float* b, lapack_int* ldb,
+                    float* taub, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dggrqf( lapack_int* m, lapack_int* p, lapack_int* n, double* a,
+                    lapack_int* lda, double* taua, double* b, lapack_int* ldb,
+                    double* taub, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cggrqf( lapack_int* m, lapack_int* p, lapack_int* n,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* taua, lapack_complex_float* b,
+                    lapack_int* ldb, lapack_complex_float* taub,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zggrqf( lapack_int* m, lapack_int* p, lapack_int* n,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* taua, lapack_complex_double* b,
+                    lapack_int* ldb, lapack_complex_double* taub,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_sgebrd( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                    float* d, float* e, float* tauq, float* taup, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dgebrd( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                    double* d, double* e, double* tauq, double* taup,
+                    double* work, lapack_int* lwork, lapack_int *info );
+void LAPACK_cgebrd( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, float* d, float* e,
+                    lapack_complex_float* tauq, lapack_complex_float* taup,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zgebrd( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, double* d, double* e,
+                    lapack_complex_double* tauq, lapack_complex_double* taup,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_sgbbrd( char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc,
+                    lapack_int* kl, lapack_int* ku, float* ab, lapack_int* ldab,
+                    float* d, float* e, float* q, lapack_int* ldq, float* pt,
+                    lapack_int* ldpt, float* c, lapack_int* ldc, float* work,
+                    lapack_int *info );
+void LAPACK_dgbbrd( char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc,
+                    lapack_int* kl, lapack_int* ku, double* ab,
+                    lapack_int* ldab, double* d, double* e, double* q,
+                    lapack_int* ldq, double* pt, lapack_int* ldpt, double* c,
+                    lapack_int* ldc, double* work, lapack_int *info );
+void LAPACK_cgbbrd( char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc,
+                    lapack_int* kl, lapack_int* ku, lapack_complex_float* ab,
+                    lapack_int* ldab, float* d, float* e,
+                    lapack_complex_float* q, lapack_int* ldq,
+                    lapack_complex_float* pt, lapack_int* ldpt,
+                    lapack_complex_float* c, lapack_int* ldc,
+                    lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zgbbrd( char* vect, lapack_int* m, lapack_int* n, lapack_int* ncc,
+                    lapack_int* kl, lapack_int* ku, lapack_complex_double* ab,
+                    lapack_int* ldab, double* d, double* e,
+                    lapack_complex_double* q, lapack_int* ldq,
+                    lapack_complex_double* pt, lapack_int* ldpt,
+                    lapack_complex_double* c, lapack_int* ldc,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_sorgbr( char* vect, lapack_int* m, lapack_int* n, lapack_int* k,
+                    float* a, lapack_int* lda, const float* tau, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dorgbr( char* vect, lapack_int* m, lapack_int* n, lapack_int* k,
+                    double* a, lapack_int* lda, const double* tau, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_sormbr( char* vect, char* side, char* trans, lapack_int* m,
+                    lapack_int* n, lapack_int* k, const float* a,
+                    lapack_int* lda, const float* tau, float* c,
+                    lapack_int* ldc, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dormbr( char* vect, char* side, char* trans, lapack_int* m,
+                    lapack_int* n, lapack_int* k, const double* a,
+                    lapack_int* lda, const double* tau, double* c,
+                    lapack_int* ldc, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cungbr( char* vect, lapack_int* m, lapack_int* n, lapack_int* k,
+                    lapack_complex_float* a, lapack_int* lda,
+                    const lapack_complex_float* tau, lapack_complex_float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_zungbr( char* vect, lapack_int* m, lapack_int* n, lapack_int* k,
+                    lapack_complex_double* a, lapack_int* lda,
+                    const lapack_complex_double* tau,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cunmbr( char* vect, char* side, char* trans, lapack_int* m,
+                    lapack_int* n, lapack_int* k, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_complex_float* tau,
+                    lapack_complex_float* c, lapack_int* ldc,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zunmbr( char* vect, char* side, char* trans, lapack_int* m,
+                    lapack_int* n, lapack_int* k,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    const lapack_complex_double* tau, lapack_complex_double* c,
+                    lapack_int* ldc, lapack_complex_double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_sbdsqr( char* uplo, lapack_int* n, lapack_int* ncvt,
+                    lapack_int* nru, lapack_int* ncc, float* d, float* e,
+                    float* vt, lapack_int* ldvt, float* u, lapack_int* ldu,
+                    float* c, lapack_int* ldc, float* work, lapack_int *info );
+void LAPACK_dbdsqr( char* uplo, lapack_int* n, lapack_int* ncvt,
+                    lapack_int* nru, lapack_int* ncc, double* d, double* e,
+                    double* vt, lapack_int* ldvt, double* u, lapack_int* ldu,
+                    double* c, lapack_int* ldc, double* work,
+                    lapack_int *info );
+void LAPACK_cbdsqr( char* uplo, lapack_int* n, lapack_int* ncvt,
+                    lapack_int* nru, lapack_int* ncc, float* d, float* e,
+                    lapack_complex_float* vt, lapack_int* ldvt,
+                    lapack_complex_float* u, lapack_int* ldu,
+                    lapack_complex_float* c, lapack_int* ldc, float* work,
+                    lapack_int *info );
+void LAPACK_zbdsqr( char* uplo, lapack_int* n, lapack_int* ncvt,
+                    lapack_int* nru, lapack_int* ncc, double* d, double* e,
+                    lapack_complex_double* vt, lapack_int* ldvt,
+                    lapack_complex_double* u, lapack_int* ldu,
+                    lapack_complex_double* c, lapack_int* ldc, double* work,
+                    lapack_int *info );
+void LAPACK_sbdsdc( char* uplo, char* compq, lapack_int* n, float* d, float* e,
+                    float* u, lapack_int* ldu, float* vt, lapack_int* ldvt,
+                    float* q, lapack_int* iq, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dbdsdc( char* uplo, char* compq, lapack_int* n, double* d,
+                    double* e, double* u, lapack_int* ldu, double* vt,
+                    lapack_int* ldvt, double* q, lapack_int* iq, double* work,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_ssytrd( char* uplo, lapack_int* n, float* a, lapack_int* lda,
+                    float* d, float* e, float* tau, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dsytrd( char* uplo, lapack_int* n, double* a, lapack_int* lda,
+                    double* d, double* e, double* tau, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_sorgtr( char* uplo, lapack_int* n, float* a, lapack_int* lda,
+                    const float* tau, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dorgtr( char* uplo, lapack_int* n, double* a, lapack_int* lda,
+                    const double* tau, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_sormtr( char* side, char* uplo, char* trans, lapack_int* m,
+                    lapack_int* n, const float* a, lapack_int* lda,
+                    const float* tau, float* c, lapack_int* ldc, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dormtr( char* side, char* uplo, char* trans, lapack_int* m,
+                    lapack_int* n, const double* a, lapack_int* lda,
+                    const double* tau, double* c, lapack_int* ldc, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_chetrd( char* uplo, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, float* d, float* e,
+                    lapack_complex_float* tau, lapack_complex_float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_zhetrd( char* uplo, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, double* d, double* e,
+                    lapack_complex_double* tau, lapack_complex_double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_cungtr( char* uplo, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, const lapack_complex_float* tau,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zungtr( char* uplo, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, const lapack_complex_double* tau,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cunmtr( char* side, char* uplo, char* trans, lapack_int* m,
+                    lapack_int* n, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_complex_float* tau,
+                    lapack_complex_float* c, lapack_int* ldc,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_zunmtr( char* side, char* uplo, char* trans, lapack_int* m,
+                    lapack_int* n, const lapack_complex_double* a,
+                    lapack_int* lda, const lapack_complex_double* tau,
+                    lapack_complex_double* c, lapack_int* ldc,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_ssptrd( char* uplo, lapack_int* n, float* ap, float* d, float* e,
+                    float* tau, lapack_int *info );
+void LAPACK_dsptrd( char* uplo, lapack_int* n, double* ap, double* d, double* e,
+                    double* tau, lapack_int *info );
+void LAPACK_sopgtr( char* uplo, lapack_int* n, const float* ap,
+                    const float* tau, float* q, lapack_int* ldq, float* work,
+                    lapack_int *info );
+void LAPACK_dopgtr( char* uplo, lapack_int* n, const double* ap,
+                    const double* tau, double* q, lapack_int* ldq, double* work,
+                    lapack_int *info );
+void LAPACK_sopmtr( char* side, char* uplo, char* trans, lapack_int* m,
+                    lapack_int* n, const float* ap, const float* tau, float* c,
+                    lapack_int* ldc, float* work, lapack_int *info );
+void LAPACK_dopmtr( char* side, char* uplo, char* trans, lapack_int* m,
+                    lapack_int* n, const double* ap, const double* tau,
+                    double* c, lapack_int* ldc, double* work,
+                    lapack_int *info );
+void LAPACK_chptrd( char* uplo, lapack_int* n, lapack_complex_float* ap,
+                    float* d, float* e, lapack_complex_float* tau,
+                    lapack_int *info );
+void LAPACK_zhptrd( char* uplo, lapack_int* n, lapack_complex_double* ap,
+                    double* d, double* e, lapack_complex_double* tau,
+                    lapack_int *info );
+void LAPACK_cupgtr( char* uplo, lapack_int* n, const lapack_complex_float* ap,
+                    const lapack_complex_float* tau, lapack_complex_float* q,
+                    lapack_int* ldq, lapack_complex_float* work,
+                    lapack_int *info );
+void LAPACK_zupgtr( char* uplo, lapack_int* n, const lapack_complex_double* ap,
+                    const lapack_complex_double* tau, lapack_complex_double* q,
+                    lapack_int* ldq, lapack_complex_double* work,
+                    lapack_int *info );
+void LAPACK_cupmtr( char* side, char* uplo, char* trans, lapack_int* m,
+                    lapack_int* n, const lapack_complex_float* ap,
+                    const lapack_complex_float* tau, lapack_complex_float* c,
+                    lapack_int* ldc, lapack_complex_float* work,
+                    lapack_int *info );
+void LAPACK_zupmtr( char* side, char* uplo, char* trans, lapack_int* m,
+                    lapack_int* n, const lapack_complex_double* ap,
+                    const lapack_complex_double* tau, lapack_complex_double* c,
+                    lapack_int* ldc, lapack_complex_double* work,
+                    lapack_int *info );
+void LAPACK_ssbtrd( char* vect, char* uplo, lapack_int* n, lapack_int* kd,
+                    float* ab, lapack_int* ldab, float* d, float* e, float* q,
+                    lapack_int* ldq, float* work, lapack_int *info );
+void LAPACK_dsbtrd( char* vect, char* uplo, lapack_int* n, lapack_int* kd,
+                    double* ab, lapack_int* ldab, double* d, double* e,
+                    double* q, lapack_int* ldq, double* work,
+                    lapack_int *info );
+void LAPACK_chbtrd( char* vect, char* uplo, lapack_int* n, lapack_int* kd,
+                    lapack_complex_float* ab, lapack_int* ldab, float* d,
+                    float* e, lapack_complex_float* q, lapack_int* ldq,
+                    lapack_complex_float* work, lapack_int *info );
+void LAPACK_zhbtrd( char* vect, char* uplo, lapack_int* n, lapack_int* kd,
+                    lapack_complex_double* ab, lapack_int* ldab, double* d,
+                    double* e, lapack_complex_double* q, lapack_int* ldq,
+                    lapack_complex_double* work, lapack_int *info );
+void LAPACK_ssterf( lapack_int* n, float* d, float* e, lapack_int *info );
+void LAPACK_dsterf( lapack_int* n, double* d, double* e, lapack_int *info );
+void LAPACK_ssteqr( char* compz, lapack_int* n, float* d, float* e, float* z,
+                    lapack_int* ldz, float* work, lapack_int *info );
+void LAPACK_dsteqr( char* compz, lapack_int* n, double* d, double* e, double* z,
+                    lapack_int* ldz, double* work, lapack_int *info );
+void LAPACK_csteqr( char* compz, lapack_int* n, float* d, float* e,
+                    lapack_complex_float* z, lapack_int* ldz, float* work,
+                    lapack_int *info );
+void LAPACK_zsteqr( char* compz, lapack_int* n, double* d, double* e,
+                    lapack_complex_double* z, lapack_int* ldz, double* work,
+                    lapack_int *info );
+void LAPACK_sstemr( char* jobz, char* range, lapack_int* n, float* d, float* e,
+                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
+                    lapack_int* m, float* w, float* z, lapack_int* ldz,
+                    lapack_int* nzc, lapack_int* isuppz, lapack_logical* tryrac,
+                    float* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_dstemr( char* jobz, char* range, lapack_int* n, double* d,
+                    double* e, double* vl, double* vu, lapack_int* il,
+                    lapack_int* iu, lapack_int* m, double* w, double* z,
+                    lapack_int* ldz, lapack_int* nzc, lapack_int* isuppz,
+                    lapack_logical* tryrac, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_cstemr( char* jobz, char* range, lapack_int* n, float* d, float* e,
+                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
+                    lapack_int* m, float* w, lapack_complex_float* z,
+                    lapack_int* ldz, lapack_int* nzc, lapack_int* isuppz,
+                    lapack_logical* tryrac, float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_zstemr( char* jobz, char* range, lapack_int* n, double* d,
+                    double* e, double* vl, double* vu, lapack_int* il,
+                    lapack_int* iu, lapack_int* m, double* w,
+                    lapack_complex_double* z, lapack_int* ldz, lapack_int* nzc,
+                    lapack_int* isuppz, lapack_logical* tryrac, double* work,
+                    lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                    lapack_int *info );
+void LAPACK_sstedc( char* compz, lapack_int* n, float* d, float* e, float* z,
+                    lapack_int* ldz, float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_dstedc( char* compz, lapack_int* n, double* d, double* e, double* z,
+                    lapack_int* ldz, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_cstedc( char* compz, lapack_int* n, float* d, float* e,
+                    lapack_complex_float* z, lapack_int* ldz,
+                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
+                    lapack_int *info );
+void LAPACK_zstedc( char* compz, lapack_int* n, double* d, double* e,
+                    lapack_complex_double* z, lapack_int* ldz,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_sstegr( char* jobz, char* range, lapack_int* n, float* d, float* e,
+                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
+                    float* abstol, lapack_int* m, float* w, float* z,
+                    lapack_int* ldz, lapack_int* isuppz, float* work,
+                    lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                    lapack_int *info );
+void LAPACK_dstegr( char* jobz, char* range, lapack_int* n, double* d,
+                    double* e, double* vl, double* vu, lapack_int* il,
+                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
+                    double* z, lapack_int* ldz, lapack_int* isuppz,
+                    double* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_cstegr( char* jobz, char* range, lapack_int* n, float* d, float* e,
+                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
+                    float* abstol, lapack_int* m, float* w,
+                    lapack_complex_float* z, lapack_int* ldz,
+                    lapack_int* isuppz, float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_zstegr( char* jobz, char* range, lapack_int* n, double* d,
+                    double* e, double* vl, double* vu, lapack_int* il,
+                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
+                    lapack_complex_double* z, lapack_int* ldz,
+                    lapack_int* isuppz, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_spteqr( char* compz, lapack_int* n, float* d, float* e, float* z,
+                    lapack_int* ldz, float* work, lapack_int *info );
+void LAPACK_dpteqr( char* compz, lapack_int* n, double* d, double* e, double* z,
+                    lapack_int* ldz, double* work, lapack_int *info );
+void LAPACK_cpteqr( char* compz, lapack_int* n, float* d, float* e,
+                    lapack_complex_float* z, lapack_int* ldz, float* work,
+                    lapack_int *info );
+void LAPACK_zpteqr( char* compz, lapack_int* n, double* d, double* e,
+                    lapack_complex_double* z, lapack_int* ldz, double* work,
+                    lapack_int *info );
+void LAPACK_sstebz( char* range, char* order, lapack_int* n, float* vl,
+                    float* vu, lapack_int* il, lapack_int* iu, float* abstol,
+                    const float* d, const float* e, lapack_int* m,
+                    lapack_int* nsplit, float* w, lapack_int* iblock,
+                    lapack_int* isplit, float* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dstebz( char* range, char* order, lapack_int* n, double* vl,
+                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
+                    const double* d, const double* e, lapack_int* m,
+                    lapack_int* nsplit, double* w, lapack_int* iblock,
+                    lapack_int* isplit, double* work, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_sstein( lapack_int* n, const float* d, const float* e,
+                    lapack_int* m, const float* w, const lapack_int* iblock,
+                    const lapack_int* isplit, float* z, lapack_int* ldz,
+                    float* work, lapack_int* iwork, lapack_int* ifailv,
+                    lapack_int *info );
+void LAPACK_dstein( lapack_int* n, const double* d, const double* e,
+                    lapack_int* m, const double* w, const lapack_int* iblock,
+                    const lapack_int* isplit, double* z, lapack_int* ldz,
+                    double* work, lapack_int* iwork, lapack_int* ifailv,
+                    lapack_int *info );
+void LAPACK_cstein( lapack_int* n, const float* d, const float* e,
+                    lapack_int* m, const float* w, const lapack_int* iblock,
+                    const lapack_int* isplit, lapack_complex_float* z,
+                    lapack_int* ldz, float* work, lapack_int* iwork,
+                    lapack_int* ifailv, lapack_int *info );
+void LAPACK_zstein( lapack_int* n, const double* d, const double* e,
+                    lapack_int* m, const double* w, const lapack_int* iblock,
+                    const lapack_int* isplit, lapack_complex_double* z,
+                    lapack_int* ldz, double* work, lapack_int* iwork,
+                    lapack_int* ifailv, lapack_int *info );
+void LAPACK_sdisna( char* job, lapack_int* m, lapack_int* n, const float* d,
+                    float* sep, lapack_int *info );
+void LAPACK_ddisna( char* job, lapack_int* m, lapack_int* n, const double* d,
+                    double* sep, lapack_int *info );
+void LAPACK_ssygst( lapack_int* itype, char* uplo, lapack_int* n, float* a,
+                    lapack_int* lda, const float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_dsygst( lapack_int* itype, char* uplo, lapack_int* n, double* a,
+                    lapack_int* lda, const double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_chegst( lapack_int* itype, char* uplo, lapack_int* n,
+                    lapack_complex_float* a, lapack_int* lda,
+                    const lapack_complex_float* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_zhegst( lapack_int* itype, char* uplo, lapack_int* n,
+                    lapack_complex_double* a, lapack_int* lda,
+                    const lapack_complex_double* b, lapack_int* ldb,
+                    lapack_int *info );
+void LAPACK_sspgst( lapack_int* itype, char* uplo, lapack_int* n, float* ap,
+                    const float* bp, lapack_int *info );
+void LAPACK_dspgst( lapack_int* itype, char* uplo, lapack_int* n, double* ap,
+                    const double* bp, lapack_int *info );
+void LAPACK_chpgst( lapack_int* itype, char* uplo, lapack_int* n,
+                    lapack_complex_float* ap, const lapack_complex_float* bp,
+                    lapack_int *info );
+void LAPACK_zhpgst( lapack_int* itype, char* uplo, lapack_int* n,
+                    lapack_complex_double* ap, const lapack_complex_double* bp,
+                    lapack_int *info );
+void LAPACK_ssbgst( char* vect, char* uplo, lapack_int* n, lapack_int* ka,
+                    lapack_int* kb, float* ab, lapack_int* ldab,
+                    const float* bb, lapack_int* ldbb, float* x,
+                    lapack_int* ldx, float* work, lapack_int *info );
+void LAPACK_dsbgst( char* vect, char* uplo, lapack_int* n, lapack_int* ka,
+                    lapack_int* kb, double* ab, lapack_int* ldab,
+                    const double* bb, lapack_int* ldbb, double* x,
+                    lapack_int* ldx, double* work, lapack_int *info );
+void LAPACK_chbgst( char* vect, char* uplo, lapack_int* n, lapack_int* ka,
+                    lapack_int* kb, lapack_complex_float* ab, lapack_int* ldab,
+                    const lapack_complex_float* bb, lapack_int* ldbb,
+                    lapack_complex_float* x, lapack_int* ldx,
+                    lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_zhbgst( char* vect, char* uplo, lapack_int* n, lapack_int* ka,
+                    lapack_int* kb, lapack_complex_double* ab, lapack_int* ldab,
+                    const lapack_complex_double* bb, lapack_int* ldbb,
+                    lapack_complex_double* x, lapack_int* ldx,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_spbstf( char* uplo, lapack_int* n, lapack_int* kb, float* bb,
+                    lapack_int* ldbb, lapack_int *info );
+void LAPACK_dpbstf( char* uplo, lapack_int* n, lapack_int* kb, double* bb,
+                    lapack_int* ldbb, lapack_int *info );
+void LAPACK_cpbstf( char* uplo, lapack_int* n, lapack_int* kb,
+                    lapack_complex_float* bb, lapack_int* ldbb,
+                    lapack_int *info );
+void LAPACK_zpbstf( char* uplo, lapack_int* n, lapack_int* kb,
+                    lapack_complex_double* bb, lapack_int* ldbb,
+                    lapack_int *info );
+void LAPACK_sgehrd( lapack_int* n, lapack_int* ilo, lapack_int* ihi, float* a,
+                    lapack_int* lda, float* tau, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dgehrd( lapack_int* n, lapack_int* ilo, lapack_int* ihi, double* a,
+                    lapack_int* lda, double* tau, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_cgehrd( lapack_int* n, lapack_int* ilo, lapack_int* ihi,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* tau, lapack_complex_float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_zgehrd( lapack_int* n, lapack_int* ilo, lapack_int* ihi,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* tau, lapack_complex_double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_sorghr( lapack_int* n, lapack_int* ilo, lapack_int* ihi, float* a,
+                    lapack_int* lda, const float* tau, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dorghr( lapack_int* n, lapack_int* ilo, lapack_int* ihi, double* a,
+                    lapack_int* lda, const double* tau, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_sormhr( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* ilo, lapack_int* ihi, const float* a,
+                    lapack_int* lda, const float* tau, float* c,
+                    lapack_int* ldc, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dormhr( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* ilo, lapack_int* ihi, const double* a,
+                    lapack_int* lda, const double* tau, double* c,
+                    lapack_int* ldc, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cunghr( lapack_int* n, lapack_int* ilo, lapack_int* ihi,
+                    lapack_complex_float* a, lapack_int* lda,
+                    const lapack_complex_float* tau, lapack_complex_float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_zunghr( lapack_int* n, lapack_int* ilo, lapack_int* ihi,
+                    lapack_complex_double* a, lapack_int* lda,
+                    const lapack_complex_double* tau,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cunmhr( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* ilo, lapack_int* ihi,
+                    const lapack_complex_float* a, lapack_int* lda,
+                    const lapack_complex_float* tau, lapack_complex_float* c,
+                    lapack_int* ldc, lapack_complex_float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_zunmhr( char* side, char* trans, lapack_int* m, lapack_int* n,
+                    lapack_int* ilo, lapack_int* ihi,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    const lapack_complex_double* tau, lapack_complex_double* c,
+                    lapack_int* ldc, lapack_complex_double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_sgebal( char* job, lapack_int* n, float* a, lapack_int* lda,
+                    lapack_int* ilo, lapack_int* ihi, float* scale,
+                    lapack_int *info );
+void LAPACK_dgebal( char* job, lapack_int* n, double* a, lapack_int* lda,
+                    lapack_int* ilo, lapack_int* ihi, double* scale,
+                    lapack_int *info );
+void LAPACK_cgebal( char* job, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_int* ilo, lapack_int* ihi,
+                    float* scale, lapack_int *info );
+void LAPACK_zgebal( char* job, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_int* ilo, lapack_int* ihi,
+                    double* scale, lapack_int *info );
+void LAPACK_sgebak( char* job, char* side, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, const float* scale, lapack_int* m,
+                    float* v, lapack_int* ldv, lapack_int *info );
+void LAPACK_dgebak( char* job, char* side, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, const double* scale, lapack_int* m,
+                    double* v, lapack_int* ldv, lapack_int *info );
+void LAPACK_cgebak( char* job, char* side, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, const float* scale, lapack_int* m,
+                    lapack_complex_float* v, lapack_int* ldv,
+                    lapack_int *info );
+void LAPACK_zgebak( char* job, char* side, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, const double* scale, lapack_int* m,
+                    lapack_complex_double* v, lapack_int* ldv,
+                    lapack_int *info );
+void LAPACK_shseqr( char* job, char* compz, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, float* h, lapack_int* ldh, float* wr,
+                    float* wi, float* z, lapack_int* ldz, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dhseqr( char* job, char* compz, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, double* h, lapack_int* ldh, double* wr,
+                    double* wi, double* z, lapack_int* ldz, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_chseqr( char* job, char* compz, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, lapack_complex_float* h, lapack_int* ldh,
+                    lapack_complex_float* w, lapack_complex_float* z,
+                    lapack_int* ldz, lapack_complex_float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_zhseqr( char* job, char* compz, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, lapack_complex_double* h, lapack_int* ldh,
+                    lapack_complex_double* w, lapack_complex_double* z,
+                    lapack_int* ldz, lapack_complex_double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_shsein( char* job, char* eigsrc, char* initv,
+                    lapack_logical* select, lapack_int* n, const float* h,
+                    lapack_int* ldh, float* wr, const float* wi, float* vl,
+                    lapack_int* ldvl, float* vr, lapack_int* ldvr,
+                    lapack_int* mm, lapack_int* m, float* work,
+                    lapack_int* ifaill, lapack_int* ifailr, lapack_int *info );
+void LAPACK_dhsein( char* job, char* eigsrc, char* initv,
+                    lapack_logical* select, lapack_int* n, const double* h,
+                    lapack_int* ldh, double* wr, const double* wi, double* vl,
+                    lapack_int* ldvl, double* vr, lapack_int* ldvr,
+                    lapack_int* mm, lapack_int* m, double* work,
+                    lapack_int* ifaill, lapack_int* ifailr, lapack_int *info );
+void LAPACK_chsein( char* job, char* eigsrc, char* initv,
+                    const lapack_logical* select, lapack_int* n,
+                    const lapack_complex_float* h, lapack_int* ldh,
+                    lapack_complex_float* w, lapack_complex_float* vl,
+                    lapack_int* ldvl, lapack_complex_float* vr,
+                    lapack_int* ldvr, lapack_int* mm, lapack_int* m,
+                    lapack_complex_float* work, float* rwork,
+                    lapack_int* ifaill, lapack_int* ifailr, lapack_int *info );
+void LAPACK_zhsein( char* job, char* eigsrc, char* initv,
+                    const lapack_logical* select, lapack_int* n,
+                    const lapack_complex_double* h, lapack_int* ldh,
+                    lapack_complex_double* w, lapack_complex_double* vl,
+                    lapack_int* ldvl, lapack_complex_double* vr,
+                    lapack_int* ldvr, lapack_int* mm, lapack_int* m,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int* ifaill, lapack_int* ifailr, lapack_int *info );
+void LAPACK_strevc( char* side, char* howmny, lapack_logical* select,
+                    lapack_int* n, const float* t, lapack_int* ldt, float* vl,
+                    lapack_int* ldvl, float* vr, lapack_int* ldvr,
+                    lapack_int* mm, lapack_int* m, float* work,
+                    lapack_int *info );
+void LAPACK_dtrevc( char* side, char* howmny, lapack_logical* select,
+                    lapack_int* n, const double* t, lapack_int* ldt, double* vl,
+                    lapack_int* ldvl, double* vr, lapack_int* ldvr,
+                    lapack_int* mm, lapack_int* m, double* work,
+                    lapack_int *info );
+void LAPACK_ctrevc( char* side, char* howmny, const lapack_logical* select,
+                    lapack_int* n, lapack_complex_float* t, lapack_int* ldt,
+                    lapack_complex_float* vl, lapack_int* ldvl,
+                    lapack_complex_float* vr, lapack_int* ldvr, lapack_int* mm,
+                    lapack_int* m, lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_ztrevc( char* side, char* howmny, const lapack_logical* select,
+                    lapack_int* n, lapack_complex_double* t, lapack_int* ldt,
+                    lapack_complex_double* vl, lapack_int* ldvl,
+                    lapack_complex_double* vr, lapack_int* ldvr, lapack_int* mm,
+                    lapack_int* m, lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_strsna( char* job, char* howmny, const lapack_logical* select,
+                    lapack_int* n, const float* t, lapack_int* ldt,
+                    const float* vl, lapack_int* ldvl, const float* vr,
+                    lapack_int* ldvr, float* s, float* sep, lapack_int* mm,
+                    lapack_int* m, float* work, lapack_int* ldwork,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_dtrsna( char* job, char* howmny, const lapack_logical* select,
+                    lapack_int* n, const double* t, lapack_int* ldt,
+                    const double* vl, lapack_int* ldvl, const double* vr,
+                    lapack_int* ldvr, double* s, double* sep, lapack_int* mm,
+                    lapack_int* m, double* work, lapack_int* ldwork,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_ctrsna( char* job, char* howmny, const lapack_logical* select,
+                    lapack_int* n, const lapack_complex_float* t,
+                    lapack_int* ldt, const lapack_complex_float* vl,
+                    lapack_int* ldvl, const lapack_complex_float* vr,
+                    lapack_int* ldvr, float* s, float* sep, lapack_int* mm,
+                    lapack_int* m, lapack_complex_float* work,
+                    lapack_int* ldwork, float* rwork, lapack_int *info );
+void LAPACK_ztrsna( char* job, char* howmny, const lapack_logical* select,
+                    lapack_int* n, const lapack_complex_double* t,
+                    lapack_int* ldt, const lapack_complex_double* vl,
+                    lapack_int* ldvl, const lapack_complex_double* vr,
+                    lapack_int* ldvr, double* s, double* sep, lapack_int* mm,
+                    lapack_int* m, lapack_complex_double* work,
+                    lapack_int* ldwork, double* rwork, lapack_int *info );
+void LAPACK_strexc( char* compq, lapack_int* n, float* t, lapack_int* ldt,
+                    float* q, lapack_int* ldq, lapack_int* ifst,
+                    lapack_int* ilst, float* work, lapack_int *info );
+void LAPACK_dtrexc( char* compq, lapack_int* n, double* t, lapack_int* ldt,
+                    double* q, lapack_int* ldq, lapack_int* ifst,
+                    lapack_int* ilst, double* work, lapack_int *info );
+void LAPACK_ctrexc( char* compq, lapack_int* n, lapack_complex_float* t,
+                    lapack_int* ldt, lapack_complex_float* q, lapack_int* ldq,
+                    lapack_int* ifst, lapack_int* ilst, lapack_int *info );
+void LAPACK_ztrexc( char* compq, lapack_int* n, lapack_complex_double* t,
+                    lapack_int* ldt, lapack_complex_double* q, lapack_int* ldq,
+                    lapack_int* ifst, lapack_int* ilst, lapack_int *info );
+void LAPACK_strsen( char* job, char* compq, const lapack_logical* select,
+                    lapack_int* n, float* t, lapack_int* ldt, float* q,
+                    lapack_int* ldq, float* wr, float* wi, lapack_int* m,
+                    float* s, float* sep, float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_dtrsen( char* job, char* compq, const lapack_logical* select,
+                    lapack_int* n, double* t, lapack_int* ldt, double* q,
+                    lapack_int* ldq, double* wr, double* wi, lapack_int* m,
+                    double* s, double* sep, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_ctrsen( char* job, char* compq, const lapack_logical* select,
+                    lapack_int* n, lapack_complex_float* t, lapack_int* ldt,
+                    lapack_complex_float* q, lapack_int* ldq,
+                    lapack_complex_float* w, lapack_int* m, float* s,
+                    float* sep, lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_ztrsen( char* job, char* compq, const lapack_logical* select,
+                    lapack_int* n, lapack_complex_double* t, lapack_int* ldt,
+                    lapack_complex_double* q, lapack_int* ldq,
+                    lapack_complex_double* w, lapack_int* m, double* s,
+                    double* sep, lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_strsyl( char* trana, char* tranb, lapack_int* isgn, lapack_int* m,
+                    lapack_int* n, const float* a, lapack_int* lda,
+                    const float* b, lapack_int* ldb, float* c, lapack_int* ldc,
+                    float* scale, lapack_int *info );
+void LAPACK_dtrsyl( char* trana, char* tranb, lapack_int* isgn, lapack_int* m,
+                    lapack_int* n, const double* a, lapack_int* lda,
+                    const double* b, lapack_int* ldb, double* c,
+                    lapack_int* ldc, double* scale, lapack_int *info );
+void LAPACK_ctrsyl( char* trana, char* tranb, lapack_int* isgn, lapack_int* m,
+                    lapack_int* n, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_complex_float* b,
+                    lapack_int* ldb, lapack_complex_float* c, lapack_int* ldc,
+                    float* scale, lapack_int *info );
+void LAPACK_ztrsyl( char* trana, char* tranb, lapack_int* isgn, lapack_int* m,
+                    lapack_int* n, const lapack_complex_double* a,
+                    lapack_int* lda, const lapack_complex_double* b,
+                    lapack_int* ldb, lapack_complex_double* c, lapack_int* ldc,
+                    double* scale, lapack_int *info );
+void LAPACK_sgghrd( char* compq, char* compz, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, float* a, lapack_int* lda, float* b,
+                    lapack_int* ldb, float* q, lapack_int* ldq, float* z,
+                    lapack_int* ldz, lapack_int *info );
+void LAPACK_dgghrd( char* compq, char* compz, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, double* a, lapack_int* lda, double* b,
+                    lapack_int* ldb, double* q, lapack_int* ldq, double* z,
+                    lapack_int* ldz, lapack_int *info );
+void LAPACK_cgghrd( char* compq, char* compz, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* q, lapack_int* ldq,
+                    lapack_complex_float* z, lapack_int* ldz,
+                    lapack_int *info );
+void LAPACK_zgghrd( char* compq, char* compz, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* q, lapack_int* ldq,
+                    lapack_complex_double* z, lapack_int* ldz,
+                    lapack_int *info );
+void LAPACK_sggbal( char* job, lapack_int* n, float* a, lapack_int* lda,
+                    float* b, lapack_int* ldb, lapack_int* ilo, lapack_int* ihi,
+                    float* lscale, float* rscale, float* work,
+                    lapack_int *info );
+void LAPACK_dggbal( char* job, lapack_int* n, double* a, lapack_int* lda,
+                    double* b, lapack_int* ldb, lapack_int* ilo,
+                    lapack_int* ihi, double* lscale, double* rscale,
+                    double* work, lapack_int *info );
+void LAPACK_cggbal( char* job, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
+                    lapack_int* ilo, lapack_int* ihi, float* lscale,
+                    float* rscale, float* work, lapack_int *info );
+void LAPACK_zggbal( char* job, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
+                    lapack_int* ilo, lapack_int* ihi, double* lscale,
+                    double* rscale, double* work, lapack_int *info );
+void LAPACK_sggbak( char* job, char* side, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, const float* lscale, const float* rscale,
+                    lapack_int* m, float* v, lapack_int* ldv,
+                    lapack_int *info );
+void LAPACK_dggbak( char* job, char* side, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, const double* lscale, const double* rscale,
+                    lapack_int* m, double* v, lapack_int* ldv,
+                    lapack_int *info );
+void LAPACK_cggbak( char* job, char* side, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, const float* lscale, const float* rscale,
+                    lapack_int* m, lapack_complex_float* v, lapack_int* ldv,
+                    lapack_int *info );
+void LAPACK_zggbak( char* job, char* side, lapack_int* n, lapack_int* ilo,
+                    lapack_int* ihi, const double* lscale, const double* rscale,
+                    lapack_int* m, lapack_complex_double* v, lapack_int* ldv,
+                    lapack_int *info );
+void LAPACK_shgeqz( char* job, char* compq, char* compz, lapack_int* n,
+                    lapack_int* ilo, lapack_int* ihi, float* h, lapack_int* ldh,
+                    float* t, lapack_int* ldt, float* alphar, float* alphai,
+                    float* beta, float* q, lapack_int* ldq, float* z,
+                    lapack_int* ldz, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dhgeqz( char* job, char* compq, char* compz, lapack_int* n,
+                    lapack_int* ilo, lapack_int* ihi, double* h,
+                    lapack_int* ldh, double* t, lapack_int* ldt, double* alphar,
+                    double* alphai, double* beta, double* q, lapack_int* ldq,
+                    double* z, lapack_int* ldz, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_chgeqz( char* job, char* compq, char* compz, lapack_int* n,
+                    lapack_int* ilo, lapack_int* ihi, lapack_complex_float* h,
+                    lapack_int* ldh, lapack_complex_float* t, lapack_int* ldt,
+                    lapack_complex_float* alpha, lapack_complex_float* beta,
+                    lapack_complex_float* q, lapack_int* ldq,
+                    lapack_complex_float* z, lapack_int* ldz,
+                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                    lapack_int *info );
+void LAPACK_zhgeqz( char* job, char* compq, char* compz, lapack_int* n,
+                    lapack_int* ilo, lapack_int* ihi, lapack_complex_double* h,
+                    lapack_int* ldh, lapack_complex_double* t, lapack_int* ldt,
+                    lapack_complex_double* alpha, lapack_complex_double* beta,
+                    lapack_complex_double* q, lapack_int* ldq,
+                    lapack_complex_double* z, lapack_int* ldz,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int *info );
+void LAPACK_stgevc( char* side, char* howmny, const lapack_logical* select,
+                    lapack_int* n, const float* s, lapack_int* lds,
+                    const float* p, lapack_int* ldp, float* vl,
+                    lapack_int* ldvl, float* vr, lapack_int* ldvr,
+                    lapack_int* mm, lapack_int* m, float* work,
+                    lapack_int *info );
+void LAPACK_dtgevc( char* side, char* howmny, const lapack_logical* select,
+                    lapack_int* n, const double* s, lapack_int* lds,
+                    const double* p, lapack_int* ldp, double* vl,
+                    lapack_int* ldvl, double* vr, lapack_int* ldvr,
+                    lapack_int* mm, lapack_int* m, double* work,
+                    lapack_int *info );
+void LAPACK_ctgevc( char* side, char* howmny, const lapack_logical* select,
+                    lapack_int* n, const lapack_complex_float* s,
+                    lapack_int* lds, const lapack_complex_float* p,
+                    lapack_int* ldp, lapack_complex_float* vl, lapack_int* ldvl,
+                    lapack_complex_float* vr, lapack_int* ldvr, lapack_int* mm,
+                    lapack_int* m, lapack_complex_float* work, float* rwork,
+                    lapack_int *info );
+void LAPACK_ztgevc( char* side, char* howmny, const lapack_logical* select,
+                    lapack_int* n, const lapack_complex_double* s,
+                    lapack_int* lds, const lapack_complex_double* p,
+                    lapack_int* ldp, lapack_complex_double* vl,
+                    lapack_int* ldvl, lapack_complex_double* vr,
+                    lapack_int* ldvr, lapack_int* mm, lapack_int* m,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int *info );
+void LAPACK_stgexc( lapack_logical* wantq, lapack_logical* wantz, lapack_int* n,
+                    float* a, lapack_int* lda, float* b, lapack_int* ldb,
+                    float* q, lapack_int* ldq, float* z, lapack_int* ldz,
+                    lapack_int* ifst, lapack_int* ilst, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dtgexc( lapack_logical* wantq, lapack_logical* wantz, lapack_int* n,
+                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
+                    double* q, lapack_int* ldq, double* z, lapack_int* ldz,
+                    lapack_int* ifst, lapack_int* ilst, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_ctgexc( lapack_logical* wantq, lapack_logical* wantz, lapack_int* n,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* q, lapack_int* ldq,
+                    lapack_complex_float* z, lapack_int* ldz, lapack_int* ifst,
+                    lapack_int* ilst, lapack_int *info );
+void LAPACK_ztgexc( lapack_logical* wantq, lapack_logical* wantz, lapack_int* n,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* q, lapack_int* ldq,
+                    lapack_complex_double* z, lapack_int* ldz, lapack_int* ifst,
+                    lapack_int* ilst, lapack_int *info );
+void LAPACK_stgsen( lapack_int* ijob, lapack_logical* wantq,
+                    lapack_logical* wantz, const lapack_logical* select,
+                    lapack_int* n, float* a, lapack_int* lda, float* b,
+                    lapack_int* ldb, float* alphar, float* alphai, float* beta,
+                    float* q, lapack_int* ldq, float* z, lapack_int* ldz,
+                    lapack_int* m, float* pl, float* pr, float* dif,
+                    float* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_dtgsen( lapack_int* ijob, lapack_logical* wantq,
+                    lapack_logical* wantz, const lapack_logical* select,
+                    lapack_int* n, double* a, lapack_int* lda, double* b,
+                    lapack_int* ldb, double* alphar, double* alphai,
+                    double* beta, double* q, lapack_int* ldq, double* z,
+                    lapack_int* ldz, lapack_int* m, double* pl, double* pr,
+                    double* dif, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_ctgsen( lapack_int* ijob, lapack_logical* wantq,
+                    lapack_logical* wantz, const lapack_logical* select,
+                    lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* alpha, lapack_complex_float* beta,
+                    lapack_complex_float* q, lapack_int* ldq,
+                    lapack_complex_float* z, lapack_int* ldz, lapack_int* m,
+                    float* pl, float* pr, float* dif,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_ztgsen( lapack_int* ijob, lapack_logical* wantq,
+                    lapack_logical* wantz, const lapack_logical* select,
+                    lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* alpha, lapack_complex_double* beta,
+                    lapack_complex_double* q, lapack_int* ldq,
+                    lapack_complex_double* z, lapack_int* ldz, lapack_int* m,
+                    double* pl, double* pr, double* dif,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_stgsyl( char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n,
+                    const float* a, lapack_int* lda, const float* b,
+                    lapack_int* ldb, float* c, lapack_int* ldc, const float* d,
+                    lapack_int* ldd, const float* e, lapack_int* lde, float* f,
+                    lapack_int* ldf, float* scale, float* dif, float* work,
+                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
+void LAPACK_dtgsyl( char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n,
+                    const double* a, lapack_int* lda, const double* b,
+                    lapack_int* ldb, double* c, lapack_int* ldc,
+                    const double* d, lapack_int* ldd, const double* e,
+                    lapack_int* lde, double* f, lapack_int* ldf, double* scale,
+                    double* dif, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_ctgsyl( char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n,
+                    const lapack_complex_float* a, lapack_int* lda,
+                    const lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* c, lapack_int* ldc,
+                    const lapack_complex_float* d, lapack_int* ldd,
+                    const lapack_complex_float* e, lapack_int* lde,
+                    lapack_complex_float* f, lapack_int* ldf, float* scale,
+                    float* dif, lapack_complex_float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_ztgsyl( char* trans, lapack_int* ijob, lapack_int* m, lapack_int* n,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    const lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* c, lapack_int* ldc,
+                    const lapack_complex_double* d, lapack_int* ldd,
+                    const lapack_complex_double* e, lapack_int* lde,
+                    lapack_complex_double* f, lapack_int* ldf, double* scale,
+                    double* dif, lapack_complex_double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_stgsna( char* job, char* howmny, const lapack_logical* select,
+                    lapack_int* n, const float* a, lapack_int* lda,
+                    const float* b, lapack_int* ldb, const float* vl,
+                    lapack_int* ldvl, const float* vr, lapack_int* ldvr,
+                    float* s, float* dif, lapack_int* mm, lapack_int* m,
+                    float* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dtgsna( char* job, char* howmny, const lapack_logical* select,
+                    lapack_int* n, const double* a, lapack_int* lda,
+                    const double* b, lapack_int* ldb, const double* vl,
+                    lapack_int* ldvl, const double* vr, lapack_int* ldvr,
+                    double* s, double* dif, lapack_int* mm, lapack_int* m,
+                    double* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_ctgsna( char* job, char* howmny, const lapack_logical* select,
+                    lapack_int* n, const lapack_complex_float* a,
+                    lapack_int* lda, const lapack_complex_float* b,
+                    lapack_int* ldb, const lapack_complex_float* vl,
+                    lapack_int* ldvl, const lapack_complex_float* vr,
+                    lapack_int* ldvr, float* s, float* dif, lapack_int* mm,
+                    lapack_int* m, lapack_complex_float* work,
+                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
+void LAPACK_ztgsna( char* job, char* howmny, const lapack_logical* select,
+                    lapack_int* n, const lapack_complex_double* a,
+                    lapack_int* lda, const lapack_complex_double* b,
+                    lapack_int* ldb, const lapack_complex_double* vl,
+                    lapack_int* ldvl, const lapack_complex_double* vr,
+                    lapack_int* ldvr, double* s, double* dif, lapack_int* mm,
+                    lapack_int* m, lapack_complex_double* work,
+                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
+void LAPACK_sggsvp( char* jobu, char* jobv, char* jobq, lapack_int* m,
+                    lapack_int* p, lapack_int* n, float* a, lapack_int* lda,
+                    float* b, lapack_int* ldb, float* tola, float* tolb,
+                    lapack_int* k, lapack_int* l, float* u, lapack_int* ldu,
+                    float* v, lapack_int* ldv, float* q, lapack_int* ldq,
+                    lapack_int* iwork, float* tau, float* work,
+                    lapack_int *info );
+void LAPACK_dggsvp( char* jobu, char* jobv, char* jobq, lapack_int* m,
+                    lapack_int* p, lapack_int* n, double* a, lapack_int* lda,
+                    double* b, lapack_int* ldb, double* tola, double* tolb,
+                    lapack_int* k, lapack_int* l, double* u, lapack_int* ldu,
+                    double* v, lapack_int* ldv, double* q, lapack_int* ldq,
+                    lapack_int* iwork, double* tau, double* work,
+                    lapack_int *info );
+void LAPACK_cggsvp( char* jobu, char* jobv, char* jobq, lapack_int* m,
+                    lapack_int* p, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
+                    float* tola, float* tolb, lapack_int* k, lapack_int* l,
+                    lapack_complex_float* u, lapack_int* ldu,
+                    lapack_complex_float* v, lapack_int* ldv,
+                    lapack_complex_float* q, lapack_int* ldq, lapack_int* iwork,
+                    float* rwork, lapack_complex_float* tau,
+                    lapack_complex_float* work, lapack_int *info );
+void LAPACK_zggsvp( char* jobu, char* jobv, char* jobq, lapack_int* m,
+                    lapack_int* p, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
+                    double* tola, double* tolb, lapack_int* k, lapack_int* l,
+                    lapack_complex_double* u, lapack_int* ldu,
+                    lapack_complex_double* v, lapack_int* ldv,
+                    lapack_complex_double* q, lapack_int* ldq,
+                    lapack_int* iwork, double* rwork,
+                    lapack_complex_double* tau, lapack_complex_double* work,
+                    lapack_int *info );
+void LAPACK_stgsja( char* jobu, char* jobv, char* jobq, lapack_int* m,
+                    lapack_int* p, lapack_int* n, lapack_int* k, lapack_int* l,
+                    float* a, lapack_int* lda, float* b, lapack_int* ldb,
+                    float* tola, float* tolb, float* alpha, float* beta,
+                    float* u, lapack_int* ldu, float* v, lapack_int* ldv,
+                    float* q, lapack_int* ldq, float* work, lapack_int* ncycle,
+                    lapack_int *info );
+void LAPACK_dtgsja( char* jobu, char* jobv, char* jobq, lapack_int* m,
+                    lapack_int* p, lapack_int* n, lapack_int* k, lapack_int* l,
+                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
+                    double* tola, double* tolb, double* alpha, double* beta,
+                    double* u, lapack_int* ldu, double* v, lapack_int* ldv,
+                    double* q, lapack_int* ldq, double* work,
+                    lapack_int* ncycle, lapack_int *info );
+void LAPACK_ctgsja( char* jobu, char* jobv, char* jobq, lapack_int* m,
+                    lapack_int* p, lapack_int* n, lapack_int* k, lapack_int* l,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb, float* tola,
+                    float* tolb, float* alpha, float* beta,
+                    lapack_complex_float* u, lapack_int* ldu,
+                    lapack_complex_float* v, lapack_int* ldv,
+                    lapack_complex_float* q, lapack_int* ldq,
+                    lapack_complex_float* work, lapack_int* ncycle,
+                    lapack_int *info );
+void LAPACK_ztgsja( char* jobu, char* jobv, char* jobq, lapack_int* m,
+                    lapack_int* p, lapack_int* n, lapack_int* k, lapack_int* l,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb, double* tola,
+                    double* tolb, double* alpha, double* beta,
+                    lapack_complex_double* u, lapack_int* ldu,
+                    lapack_complex_double* v, lapack_int* ldv,
+                    lapack_complex_double* q, lapack_int* ldq,
+                    lapack_complex_double* work, lapack_int* ncycle,
+                    lapack_int *info );
+void LAPACK_sgels( char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs,
+                   float* a, lapack_int* lda, float* b, lapack_int* ldb,
+                   float* work, lapack_int* lwork, lapack_int *info );
+void LAPACK_dgels( char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs,
+                   double* a, lapack_int* lda, double* b, lapack_int* ldb,
+                   double* work, lapack_int* lwork, lapack_int *info );
+void LAPACK_cgels( char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs,
+                   lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* b, lapack_int* ldb,
+                   lapack_complex_float* work, lapack_int* lwork,
+                   lapack_int *info );
+void LAPACK_zgels( char* trans, lapack_int* m, lapack_int* n, lapack_int* nrhs,
+                   lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* b, lapack_int* ldb,
+                   lapack_complex_double* work, lapack_int* lwork,
+                   lapack_int *info );
+void LAPACK_sgelsy( lapack_int* m, lapack_int* n, lapack_int* nrhs, float* a,
+                    lapack_int* lda, float* b, lapack_int* ldb,
+                    lapack_int* jpvt, float* rcond, lapack_int* rank,
+                    float* work, lapack_int* lwork, lapack_int *info );
+void LAPACK_dgelsy( lapack_int* m, lapack_int* n, lapack_int* nrhs, double* a,
+                    lapack_int* lda, double* b, lapack_int* ldb,
+                    lapack_int* jpvt, double* rcond, lapack_int* rank,
+                    double* work, lapack_int* lwork, lapack_int *info );
+void LAPACK_cgelsy( lapack_int* m, lapack_int* n, lapack_int* nrhs,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb, lapack_int* jpvt,
+                    float* rcond, lapack_int* rank, lapack_complex_float* work,
+                    lapack_int* lwork, float* rwork, lapack_int *info );
+void LAPACK_zgelsy( lapack_int* m, lapack_int* n, lapack_int* nrhs,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb, lapack_int* jpvt,
+                    double* rcond, lapack_int* rank,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int *info );
+void LAPACK_sgelss( lapack_int* m, lapack_int* n, lapack_int* nrhs, float* a,
+                    lapack_int* lda, float* b, lapack_int* ldb, float* s,
+                    float* rcond, lapack_int* rank, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dgelss( lapack_int* m, lapack_int* n, lapack_int* nrhs, double* a,
+                    lapack_int* lda, double* b, lapack_int* ldb, double* s,
+                    double* rcond, lapack_int* rank, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_cgelss( lapack_int* m, lapack_int* n, lapack_int* nrhs,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb, float* s,
+                    float* rcond, lapack_int* rank, lapack_complex_float* work,
+                    lapack_int* lwork, float* rwork, lapack_int *info );
+void LAPACK_zgelss( lapack_int* m, lapack_int* n, lapack_int* nrhs,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb, double* s,
+                    double* rcond, lapack_int* rank,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int *info );
+void LAPACK_sgelsd( lapack_int* m, lapack_int* n, lapack_int* nrhs, float* a,
+                    lapack_int* lda, float* b, lapack_int* ldb, float* s,
+                    float* rcond, lapack_int* rank, float* work,
+                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
+void LAPACK_dgelsd( lapack_int* m, lapack_int* n, lapack_int* nrhs, double* a,
+                    lapack_int* lda, double* b, lapack_int* ldb, double* s,
+                    double* rcond, lapack_int* rank, double* work,
+                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
+void LAPACK_cgelsd( lapack_int* m, lapack_int* n, lapack_int* nrhs,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb, float* s,
+                    float* rcond, lapack_int* rank, lapack_complex_float* work,
+                    lapack_int* lwork, float* rwork, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_zgelsd( lapack_int* m, lapack_int* n, lapack_int* nrhs,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb, double* s,
+                    double* rcond, lapack_int* rank,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int* iwork, lapack_int *info );
+void LAPACK_sgglse( lapack_int* m, lapack_int* n, lapack_int* p, float* a,
+                    lapack_int* lda, float* b, lapack_int* ldb, float* c,
+                    float* d, float* x, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dgglse( lapack_int* m, lapack_int* n, lapack_int* p, double* a,
+                    lapack_int* lda, double* b, lapack_int* ldb, double* c,
+                    double* d, double* x, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cgglse( lapack_int* m, lapack_int* n, lapack_int* p,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* c, lapack_complex_float* d,
+                    lapack_complex_float* x, lapack_complex_float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_zgglse( lapack_int* m, lapack_int* n, lapack_int* p,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* c, lapack_complex_double* d,
+                    lapack_complex_double* x, lapack_complex_double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_sggglm( lapack_int* n, lapack_int* m, lapack_int* p, float* a,
+                    lapack_int* lda, float* b, lapack_int* ldb, float* d,
+                    float* x, float* y, float* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_dggglm( lapack_int* n, lapack_int* m, lapack_int* p, double* a,
+                    lapack_int* lda, double* b, lapack_int* ldb, double* d,
+                    double* x, double* y, double* work, lapack_int* lwork,
+                    lapack_int *info );
+void LAPACK_cggglm( lapack_int* n, lapack_int* m, lapack_int* p,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* d, lapack_complex_float* x,
+                    lapack_complex_float* y, lapack_complex_float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_zggglm( lapack_int* n, lapack_int* m, lapack_int* p,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* d, lapack_complex_double* x,
+                    lapack_complex_double* y, lapack_complex_double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_ssyev( char* jobz, char* uplo, lapack_int* n, float* a,
+                   lapack_int* lda, float* w, float* work, lapack_int* lwork,
+                   lapack_int *info );
+void LAPACK_dsyev( char* jobz, char* uplo, lapack_int* n, double* a,
+                   lapack_int* lda, double* w, double* work, lapack_int* lwork,
+                   lapack_int *info );
+void LAPACK_cheev( char* jobz, char* uplo, lapack_int* n,
+                   lapack_complex_float* a, lapack_int* lda, float* w,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                   lapack_int *info );
+void LAPACK_zheev( char* jobz, char* uplo, lapack_int* n,
+                   lapack_complex_double* a, lapack_int* lda, double* w,
+                   lapack_complex_double* work, lapack_int* lwork,
+                   double* rwork, lapack_int *info );
+void LAPACK_ssyevd( char* jobz, char* uplo, lapack_int* n, float* a,
+                    lapack_int* lda, float* w, float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_dsyevd( char* jobz, char* uplo, lapack_int* n, double* a,
+                    lapack_int* lda, double* w, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_cheevd( char* jobz, char* uplo, lapack_int* n,
+                    lapack_complex_float* a, lapack_int* lda, float* w,
+                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
+                    lapack_int *info );
+void LAPACK_zheevd( char* jobz, char* uplo, lapack_int* n,
+                    lapack_complex_double* a, lapack_int* lda, double* w,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_ssyevx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    float* a, lapack_int* lda, float* vl, float* vu,
+                    lapack_int* il, lapack_int* iu, float* abstol,
+                    lapack_int* m, float* w, float* z, lapack_int* ldz,
+                    float* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int* ifail, lapack_int *info );
+void LAPACK_dsyevx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    double* a, lapack_int* lda, double* vl, double* vu,
+                    lapack_int* il, lapack_int* iu, double* abstol,
+                    lapack_int* m, double* w, double* z, lapack_int* ldz,
+                    double* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int* ifail, lapack_int *info );
+void LAPACK_cheevx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    lapack_complex_float* a, lapack_int* lda, float* vl,
+                    float* vu, lapack_int* il, lapack_int* iu, float* abstol,
+                    lapack_int* m, float* w, lapack_complex_float* z,
+                    lapack_int* ldz, lapack_complex_float* work,
+                    lapack_int* lwork, float* rwork, lapack_int* iwork,
+                    lapack_int* ifail, lapack_int *info );
+void LAPACK_zheevx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    lapack_complex_double* a, lapack_int* lda, double* vl,
+                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
+                    lapack_int* m, double* w, lapack_complex_double* z,
+                    lapack_int* ldz, lapack_complex_double* work,
+                    lapack_int* lwork, double* rwork, lapack_int* iwork,
+                    lapack_int* ifail, lapack_int *info );
+void LAPACK_ssyevr( char* jobz, char* range, char* uplo, lapack_int* n,
+                    float* a, lapack_int* lda, float* vl, float* vu,
+                    lapack_int* il, lapack_int* iu, float* abstol,
+                    lapack_int* m, float* w, float* z, lapack_int* ldz,
+                    lapack_int* isuppz, float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_dsyevr( char* jobz, char* range, char* uplo, lapack_int* n,
+                    double* a, lapack_int* lda, double* vl, double* vu,
+                    lapack_int* il, lapack_int* iu, double* abstol,
+                    lapack_int* m, double* w, double* z, lapack_int* ldz,
+                    lapack_int* isuppz, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_cheevr( char* jobz, char* range, char* uplo, lapack_int* n,
+                    lapack_complex_float* a, lapack_int* lda, float* vl,
+                    float* vu, lapack_int* il, lapack_int* iu, float* abstol,
+                    lapack_int* m, float* w, lapack_complex_float* z,
+                    lapack_int* ldz, lapack_int* isuppz,
+                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
+                    lapack_int *info );
+void LAPACK_zheevr( char* jobz, char* range, char* uplo, lapack_int* n,
+                    lapack_complex_double* a, lapack_int* lda, double* vl,
+                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
+                    lapack_int* m, double* w, lapack_complex_double* z,
+                    lapack_int* ldz, lapack_int* isuppz,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_sspev( char* jobz, char* uplo, lapack_int* n, float* ap, float* w,
+                   float* z, lapack_int* ldz, float* work, lapack_int *info );
+void LAPACK_dspev( char* jobz, char* uplo, lapack_int* n, double* ap, double* w,
+                   double* z, lapack_int* ldz, double* work, lapack_int *info );
+void LAPACK_chpev( char* jobz, char* uplo, lapack_int* n,
+                   lapack_complex_float* ap, float* w, lapack_complex_float* z,
+                   lapack_int* ldz, lapack_complex_float* work, float* rwork,
+                   lapack_int *info );
+void LAPACK_zhpev( char* jobz, char* uplo, lapack_int* n,
+                   lapack_complex_double* ap, double* w,
+                   lapack_complex_double* z, lapack_int* ldz,
+                   lapack_complex_double* work, double* rwork,
+                   lapack_int *info );
+void LAPACK_sspevd( char* jobz, char* uplo, lapack_int* n, float* ap, float* w,
+                    float* z, lapack_int* ldz, float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_dspevd( char* jobz, char* uplo, lapack_int* n, double* ap,
+                    double* w, double* z, lapack_int* ldz, double* work,
+                    lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                    lapack_int *info );
+void LAPACK_chpevd( char* jobz, char* uplo, lapack_int* n,
+                    lapack_complex_float* ap, float* w, lapack_complex_float* z,
+                    lapack_int* ldz, lapack_complex_float* work,
+                    lapack_int* lwork, float* rwork, lapack_int* lrwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_zhpevd( char* jobz, char* uplo, lapack_int* n,
+                    lapack_complex_double* ap, double* w,
+                    lapack_complex_double* z, lapack_int* ldz,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_sspevx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    float* ap, float* vl, float* vu, lapack_int* il,
+                    lapack_int* iu, float* abstol, lapack_int* m, float* w,
+                    float* z, lapack_int* ldz, float* work, lapack_int* iwork,
+                    lapack_int* ifail, lapack_int *info );
+void LAPACK_dspevx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    double* ap, double* vl, double* vu, lapack_int* il,
+                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
+                    double* z, lapack_int* ldz, double* work, lapack_int* iwork,
+                    lapack_int* ifail, lapack_int *info );
+void LAPACK_chpevx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    lapack_complex_float* ap, float* vl, float* vu,
+                    lapack_int* il, lapack_int* iu, float* abstol,
+                    lapack_int* m, float* w, lapack_complex_float* z,
+                    lapack_int* ldz, lapack_complex_float* work, float* rwork,
+                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
+void LAPACK_zhpevx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    lapack_complex_double* ap, double* vl, double* vu,
+                    lapack_int* il, lapack_int* iu, double* abstol,
+                    lapack_int* m, double* w, lapack_complex_double* z,
+                    lapack_int* ldz, lapack_complex_double* work, double* rwork,
+                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
+void LAPACK_ssbev( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
+                   float* ab, lapack_int* ldab, float* w, float* z,
+                   lapack_int* ldz, float* work, lapack_int *info );
+void LAPACK_dsbev( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
+                   double* ab, lapack_int* ldab, double* w, double* z,
+                   lapack_int* ldz, double* work, lapack_int *info );
+void LAPACK_chbev( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
+                   lapack_complex_float* ab, lapack_int* ldab, float* w,
+                   lapack_complex_float* z, lapack_int* ldz,
+                   lapack_complex_float* work, float* rwork, lapack_int *info );
+void LAPACK_zhbev( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
+                   lapack_complex_double* ab, lapack_int* ldab, double* w,
+                   lapack_complex_double* z, lapack_int* ldz,
+                   lapack_complex_double* work, double* rwork,
+                   lapack_int *info );
+void LAPACK_ssbevd( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
+                    float* ab, lapack_int* ldab, float* w, float* z,
+                    lapack_int* ldz, float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_dsbevd( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
+                    double* ab, lapack_int* ldab, double* w, double* z,
+                    lapack_int* ldz, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_chbevd( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
+                    lapack_complex_float* ab, lapack_int* ldab, float* w,
+                    lapack_complex_float* z, lapack_int* ldz,
+                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
+                    lapack_int *info );
+void LAPACK_zhbevd( char* jobz, char* uplo, lapack_int* n, lapack_int* kd,
+                    lapack_complex_double* ab, lapack_int* ldab, double* w,
+                    lapack_complex_double* z, lapack_int* ldz,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_ssbevx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    lapack_int* kd, float* ab, lapack_int* ldab, float* q,
+                    lapack_int* ldq, float* vl, float* vu, lapack_int* il,
+                    lapack_int* iu, float* abstol, lapack_int* m, float* w,
+                    float* z, lapack_int* ldz, float* work, lapack_int* iwork,
+                    lapack_int* ifail, lapack_int *info );
+void LAPACK_dsbevx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    lapack_int* kd, double* ab, lapack_int* ldab, double* q,
+                    lapack_int* ldq, double* vl, double* vu, lapack_int* il,
+                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
+                    double* z, lapack_int* ldz, double* work, lapack_int* iwork,
+                    lapack_int* ifail, lapack_int *info );
+void LAPACK_chbevx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    lapack_int* kd, lapack_complex_float* ab, lapack_int* ldab,
+                    lapack_complex_float* q, lapack_int* ldq, float* vl,
+                    float* vu, lapack_int* il, lapack_int* iu, float* abstol,
+                    lapack_int* m, float* w, lapack_complex_float* z,
+                    lapack_int* ldz, lapack_complex_float* work, float* rwork,
+                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
+void LAPACK_zhbevx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    lapack_int* kd, lapack_complex_double* ab, lapack_int* ldab,
+                    lapack_complex_double* q, lapack_int* ldq, double* vl,
+                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
+                    lapack_int* m, double* w, lapack_complex_double* z,
+                    lapack_int* ldz, lapack_complex_double* work, double* rwork,
+                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
+void LAPACK_sstev( char* jobz, lapack_int* n, float* d, float* e, float* z,
+                   lapack_int* ldz, float* work, lapack_int *info );
+void LAPACK_dstev( char* jobz, lapack_int* n, double* d, double* e, double* z,
+                   lapack_int* ldz, double* work, lapack_int *info );
+void LAPACK_sstevd( char* jobz, lapack_int* n, float* d, float* e, float* z,
+                    lapack_int* ldz, float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_dstevd( char* jobz, lapack_int* n, double* d, double* e, double* z,
+                    lapack_int* ldz, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_sstevx( char* jobz, char* range, lapack_int* n, float* d, float* e,
+                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
+                    float* abstol, lapack_int* m, float* w, float* z,
+                    lapack_int* ldz, float* work, lapack_int* iwork,
+                    lapack_int* ifail, lapack_int *info );
+void LAPACK_dstevx( char* jobz, char* range, lapack_int* n, double* d,
+                    double* e, double* vl, double* vu, lapack_int* il,
+                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
+                    double* z, lapack_int* ldz, double* work, lapack_int* iwork,
+                    lapack_int* ifail, lapack_int *info );
+void LAPACK_sstevr( char* jobz, char* range, lapack_int* n, float* d, float* e,
+                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
+                    float* abstol, lapack_int* m, float* w, float* z,
+                    lapack_int* ldz, lapack_int* isuppz, float* work,
+                    lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                    lapack_int *info );
+void LAPACK_dstevr( char* jobz, char* range, lapack_int* n, double* d,
+                    double* e, double* vl, double* vu, lapack_int* il,
+                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
+                    double* z, lapack_int* ldz, lapack_int* isuppz,
+                    double* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_sgees( char* jobvs, char* sort, LAPACK_S_SELECT2 select,
+                   lapack_int* n, float* a, lapack_int* lda, lapack_int* sdim,
+                   float* wr, float* wi, float* vs, lapack_int* ldvs,
+                   float* work, lapack_int* lwork, lapack_logical* bwork,
+                   lapack_int *info );
+void LAPACK_dgees( char* jobvs, char* sort, LAPACK_D_SELECT2 select,
+                   lapack_int* n, double* a, lapack_int* lda, lapack_int* sdim,
+                   double* wr, double* wi, double* vs, lapack_int* ldvs,
+                   double* work, lapack_int* lwork, lapack_logical* bwork,
+                   lapack_int *info );
+void LAPACK_cgees( char* jobvs, char* sort, LAPACK_C_SELECT1 select,
+                   lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                   lapack_int* sdim, lapack_complex_float* w,
+                   lapack_complex_float* vs, lapack_int* ldvs,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                   lapack_logical* bwork, lapack_int *info );
+void LAPACK_zgees( char* jobvs, char* sort, LAPACK_Z_SELECT1 select,
+                   lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                   lapack_int* sdim, lapack_complex_double* w,
+                   lapack_complex_double* vs, lapack_int* ldvs,
+                   lapack_complex_double* work, lapack_int* lwork,
+                   double* rwork, lapack_logical* bwork, lapack_int *info );
+void LAPACK_sgeesx( char* jobvs, char* sort, LAPACK_S_SELECT2 select,
+                    char* sense, lapack_int* n, float* a, lapack_int* lda,
+                    lapack_int* sdim, float* wr, float* wi, float* vs,
+                    lapack_int* ldvs, float* rconde, float* rcondv, float* work,
+                    lapack_int* lwork, lapack_int* iwork, lapack_int* liwork,
+                    lapack_logical* bwork, lapack_int *info );
+void LAPACK_dgeesx( char* jobvs, char* sort, LAPACK_D_SELECT2 select,
+                    char* sense, lapack_int* n, double* a, lapack_int* lda,
+                    lapack_int* sdim, double* wr, double* wi, double* vs,
+                    lapack_int* ldvs, double* rconde, double* rcondv,
+                    double* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_logical* bwork,
+                    lapack_int *info );
+void LAPACK_cgeesx( char* jobvs, char* sort, LAPACK_C_SELECT1 select,
+                    char* sense, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_int* sdim, lapack_complex_float* w,
+                    lapack_complex_float* vs, lapack_int* ldvs, float* rconde,
+                    float* rcondv, lapack_complex_float* work,
+                    lapack_int* lwork, float* rwork, lapack_logical* bwork,
+                    lapack_int *info );
+void LAPACK_zgeesx( char* jobvs, char* sort, LAPACK_Z_SELECT1 select,
+                    char* sense, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_int* sdim, lapack_complex_double* w,
+                    lapack_complex_double* vs, lapack_int* ldvs, double* rconde,
+                    double* rcondv, lapack_complex_double* work,
+                    lapack_int* lwork, double* rwork, lapack_logical* bwork,
+                    lapack_int *info );
+void LAPACK_sgeev( char* jobvl, char* jobvr, lapack_int* n, float* a,
+                   lapack_int* lda, float* wr, float* wi, float* vl,
+                   lapack_int* ldvl, float* vr, lapack_int* ldvr, float* work,
+                   lapack_int* lwork, lapack_int *info );
+void LAPACK_dgeev( char* jobvl, char* jobvr, lapack_int* n, double* a,
+                   lapack_int* lda, double* wr, double* wi, double* vl,
+                   lapack_int* ldvl, double* vr, lapack_int* ldvr, double* work,
+                   lapack_int* lwork, lapack_int *info );
+void LAPACK_cgeev( char* jobvl, char* jobvr, lapack_int* n,
+                   lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* w, lapack_complex_float* vl,
+                   lapack_int* ldvl, lapack_complex_float* vr, lapack_int* ldvr,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                   lapack_int *info );
+void LAPACK_zgeev( char* jobvl, char* jobvr, lapack_int* n,
+                   lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* w, lapack_complex_double* vl,
+                   lapack_int* ldvl, lapack_complex_double* vr,
+                   lapack_int* ldvr, lapack_complex_double* work,
+                   lapack_int* lwork, double* rwork, lapack_int *info );
+void LAPACK_sgeevx( char* balanc, char* jobvl, char* jobvr, char* sense,
+                    lapack_int* n, float* a, lapack_int* lda, float* wr,
+                    float* wi, float* vl, lapack_int* ldvl, float* vr,
+                    lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi,
+                    float* scale, float* abnrm, float* rconde, float* rcondv,
+                    float* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_dgeevx( char* balanc, char* jobvl, char* jobvr, char* sense,
+                    lapack_int* n, double* a, lapack_int* lda, double* wr,
+                    double* wi, double* vl, lapack_int* ldvl, double* vr,
+                    lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi,
+                    double* scale, double* abnrm, double* rconde,
+                    double* rcondv, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_cgeevx( char* balanc, char* jobvl, char* jobvr, char* sense,
+                    lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* w, lapack_complex_float* vl,
+                    lapack_int* ldvl, lapack_complex_float* vr,
+                    lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi,
+                    float* scale, float* abnrm, float* rconde, float* rcondv,
+                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                    lapack_int *info );
+void LAPACK_zgeevx( char* balanc, char* jobvl, char* jobvr, char* sense,
+                    lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* w, lapack_complex_double* vl,
+                    lapack_int* ldvl, lapack_complex_double* vr,
+                    lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi,
+                    double* scale, double* abnrm, double* rconde,
+                    double* rcondv, lapack_complex_double* work,
+                    lapack_int* lwork, double* rwork, lapack_int *info );
+void LAPACK_sgesvd( char* jobu, char* jobvt, lapack_int* m, lapack_int* n,
+                    float* a, lapack_int* lda, float* s, float* u,
+                    lapack_int* ldu, float* vt, lapack_int* ldvt, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_dgesvd( char* jobu, char* jobvt, lapack_int* m, lapack_int* n,
+                    double* a, lapack_int* lda, double* s, double* u,
+                    lapack_int* ldu, double* vt, lapack_int* ldvt, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_cgesvd( char* jobu, char* jobvt, lapack_int* m, lapack_int* n,
+                    lapack_complex_float* a, lapack_int* lda, float* s,
+                    lapack_complex_float* u, lapack_int* ldu,
+                    lapack_complex_float* vt, lapack_int* ldvt,
+                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                    lapack_int *info );
+void LAPACK_zgesvd( char* jobu, char* jobvt, lapack_int* m, lapack_int* n,
+                    lapack_complex_double* a, lapack_int* lda, double* s,
+                    lapack_complex_double* u, lapack_int* ldu,
+                    lapack_complex_double* vt, lapack_int* ldvt,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int *info );
+void LAPACK_sgesdd( char* jobz, lapack_int* m, lapack_int* n, float* a,
+                    lapack_int* lda, float* s, float* u, lapack_int* ldu,
+                    float* vt, lapack_int* ldvt, float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_dgesdd( char* jobz, lapack_int* m, lapack_int* n, double* a,
+                    lapack_int* lda, double* s, double* u, lapack_int* ldu,
+                    double* vt, lapack_int* ldvt, double* work,
+                    lapack_int* lwork, lapack_int* iwork, lapack_int *info );
+void LAPACK_cgesdd( char* jobz, lapack_int* m, lapack_int* n,
+                    lapack_complex_float* a, lapack_int* lda, float* s,
+                    lapack_complex_float* u, lapack_int* ldu,
+                    lapack_complex_float* vt, lapack_int* ldvt,
+                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_zgesdd( char* jobz, lapack_int* m, lapack_int* n,
+                    lapack_complex_double* a, lapack_int* lda, double* s,
+                    lapack_complex_double* u, lapack_int* ldu,
+                    lapack_complex_double* vt, lapack_int* ldvt,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int* iwork, lapack_int *info );
+void LAPACK_dgejsv( char* joba, char* jobu, char* jobv, char* jobr, char* jobt,
+                    char* jobp, lapack_int* m, lapack_int* n, double* a,
+                    lapack_int* lda, double* sva, double* u, lapack_int* ldu,
+                    double* v, lapack_int* ldv, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_sgejsv( char* joba, char* jobu, char* jobv, char* jobr, char* jobt,
+                    char* jobp, lapack_int* m, lapack_int* n, float* a,
+                    lapack_int* lda, float* sva, float* u, lapack_int* ldu,
+                    float* v, lapack_int* ldv, float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_dgesvj( char* joba, char* jobu, char* jobv, lapack_int* m,
+                    lapack_int* n, double* a, lapack_int* lda, double* sva,
+                    lapack_int* mv, double* v, lapack_int* ldv, double* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_sgesvj( char* joba, char* jobu, char* jobv, lapack_int* m,
+                    lapack_int* n, float* a, lapack_int* lda, float* sva,
+                    lapack_int* mv, float* v, lapack_int* ldv, float* work,
+                    lapack_int* lwork, lapack_int *info );
+void LAPACK_sggsvd( char* jobu, char* jobv, char* jobq, lapack_int* m,
+                    lapack_int* n, lapack_int* p, lapack_int* k, lapack_int* l,
+                    float* a, lapack_int* lda, float* b, lapack_int* ldb,
+                    float* alpha, float* beta, float* u, lapack_int* ldu,
+                    float* v, lapack_int* ldv, float* q, lapack_int* ldq,
+                    float* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_dggsvd( char* jobu, char* jobv, char* jobq, lapack_int* m,
+                    lapack_int* n, lapack_int* p, lapack_int* k, lapack_int* l,
+                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
+                    double* alpha, double* beta, double* u, lapack_int* ldu,
+                    double* v, lapack_int* ldv, double* q, lapack_int* ldq,
+                    double* work, lapack_int* iwork, lapack_int *info );
+void LAPACK_cggsvd( char* jobu, char* jobv, char* jobq, lapack_int* m,
+                    lapack_int* n, lapack_int* p, lapack_int* k, lapack_int* l,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb, float* alpha,
+                    float* beta, lapack_complex_float* u, lapack_int* ldu,
+                    lapack_complex_float* v, lapack_int* ldv,
+                    lapack_complex_float* q, lapack_int* ldq,
+                    lapack_complex_float* work, float* rwork, lapack_int* iwork,
+                    lapack_int *info );
+void LAPACK_zggsvd( char* jobu, char* jobv, char* jobq, lapack_int* m,
+                    lapack_int* n, lapack_int* p, lapack_int* k, lapack_int* l,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb, double* alpha,
+                    double* beta, lapack_complex_double* u, lapack_int* ldu,
+                    lapack_complex_double* v, lapack_int* ldv,
+                    lapack_complex_double* q, lapack_int* ldq,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int* iwork, lapack_int *info );
+void LAPACK_ssygv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                   float* a, lapack_int* lda, float* b, lapack_int* ldb,
+                   float* w, float* work, lapack_int* lwork, lapack_int *info );
+void LAPACK_dsygv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                   double* a, lapack_int* lda, double* b, lapack_int* ldb,
+                   double* w, double* work, lapack_int* lwork,
+                   lapack_int *info );
+void LAPACK_chegv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                   lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* b, lapack_int* ldb, float* w,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                   lapack_int *info );
+void LAPACK_zhegv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                   lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* b, lapack_int* ldb, double* w,
+                   lapack_complex_double* work, lapack_int* lwork,
+                   double* rwork, lapack_int *info );
+void LAPACK_ssygvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                    float* a, lapack_int* lda, float* b, lapack_int* ldb,
+                    float* w, float* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_dsygvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
+                    double* w, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_chegvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb, float* w,
+                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
+                    lapack_int *info );
+void LAPACK_zhegvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb, double* w,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_ssygvx( lapack_int* itype, char* jobz, char* range, char* uplo,
+                    lapack_int* n, float* a, lapack_int* lda, float* b,
+                    lapack_int* ldb, float* vl, float* vu, lapack_int* il,
+                    lapack_int* iu, float* abstol, lapack_int* m, float* w,
+                    float* z, lapack_int* ldz, float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
+void LAPACK_dsygvx( lapack_int* itype, char* jobz, char* range, char* uplo,
+                    lapack_int* n, double* a, lapack_int* lda, double* b,
+                    lapack_int* ldb, double* vl, double* vu, lapack_int* il,
+                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
+                    double* z, lapack_int* ldz, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
+void LAPACK_chegvx( lapack_int* itype, char* jobz, char* range, char* uplo,
+                    lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb, float* vl,
+                    float* vu, lapack_int* il, lapack_int* iu, float* abstol,
+                    lapack_int* m, float* w, lapack_complex_float* z,
+                    lapack_int* ldz, lapack_complex_float* work,
+                    lapack_int* lwork, float* rwork, lapack_int* iwork,
+                    lapack_int* ifail, lapack_int *info );
+void LAPACK_zhegvx( lapack_int* itype, char* jobz, char* range, char* uplo,
+                    lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb, double* vl,
+                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
+                    lapack_int* m, double* w, lapack_complex_double* z,
+                    lapack_int* ldz, lapack_complex_double* work,
+                    lapack_int* lwork, double* rwork, lapack_int* iwork,
+                    lapack_int* ifail, lapack_int *info );
+void LAPACK_sspgv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                   float* ap, float* bp, float* w, float* z, lapack_int* ldz,
+                   float* work, lapack_int *info );
+void LAPACK_dspgv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                   double* ap, double* bp, double* w, double* z,
+                   lapack_int* ldz, double* work, lapack_int *info );
+void LAPACK_chpgv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                   lapack_complex_float* ap, lapack_complex_float* bp, float* w,
+                   lapack_complex_float* z, lapack_int* ldz,
+                   lapack_complex_float* work, float* rwork, lapack_int *info );
+void LAPACK_zhpgv( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                   lapack_complex_double* ap, lapack_complex_double* bp,
+                   double* w, lapack_complex_double* z, lapack_int* ldz,
+                   lapack_complex_double* work, double* rwork,
+                   lapack_int *info );
+void LAPACK_sspgvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                    float* ap, float* bp, float* w, float* z, lapack_int* ldz,
+                    float* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_dspgvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                    double* ap, double* bp, double* w, double* z,
+                    lapack_int* ldz, double* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_int* liwork, lapack_int *info );
+void LAPACK_chpgvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                    lapack_complex_float* ap, lapack_complex_float* bp,
+                    float* w, lapack_complex_float* z, lapack_int* ldz,
+                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
+                    lapack_int *info );
+void LAPACK_zhpgvd( lapack_int* itype, char* jobz, char* uplo, lapack_int* n,
+                    lapack_complex_double* ap, lapack_complex_double* bp,
+                    double* w, lapack_complex_double* z, lapack_int* ldz,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_sspgvx( lapack_int* itype, char* jobz, char* range, char* uplo,
+                    lapack_int* n, float* ap, float* bp, float* vl, float* vu,
+                    lapack_int* il, lapack_int* iu, float* abstol,
+                    lapack_int* m, float* w, float* z, lapack_int* ldz,
+                    float* work, lapack_int* iwork, lapack_int* ifail,
+                    lapack_int *info );
+void LAPACK_dspgvx( lapack_int* itype, char* jobz, char* range, char* uplo,
+                    lapack_int* n, double* ap, double* bp, double* vl,
+                    double* vu, lapack_int* il, lapack_int* iu, double* abstol,
+                    lapack_int* m, double* w, double* z, lapack_int* ldz,
+                    double* work, lapack_int* iwork, lapack_int* ifail,
+                    lapack_int *info );
+void LAPACK_chpgvx( lapack_int* itype, char* jobz, char* range, char* uplo,
+                    lapack_int* n, lapack_complex_float* ap,
+                    lapack_complex_float* bp, float* vl, float* vu,
+                    lapack_int* il, lapack_int* iu, float* abstol,
+                    lapack_int* m, float* w, lapack_complex_float* z,
+                    lapack_int* ldz, lapack_complex_float* work, float* rwork,
+                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
+void LAPACK_zhpgvx( lapack_int* itype, char* jobz, char* range, char* uplo,
+                    lapack_int* n, lapack_complex_double* ap,
+                    lapack_complex_double* bp, double* vl, double* vu,
+                    lapack_int* il, lapack_int* iu, double* abstol,
+                    lapack_int* m, double* w, lapack_complex_double* z,
+                    lapack_int* ldz, lapack_complex_double* work, double* rwork,
+                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
+void LAPACK_ssbgv( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
+                   lapack_int* kb, float* ab, lapack_int* ldab, float* bb,
+                   lapack_int* ldbb, float* w, float* z, lapack_int* ldz,
+                   float* work, lapack_int *info );
+void LAPACK_dsbgv( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
+                   lapack_int* kb, double* ab, lapack_int* ldab, double* bb,
+                   lapack_int* ldbb, double* w, double* z, lapack_int* ldz,
+                   double* work, lapack_int *info );
+void LAPACK_chbgv( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
+                   lapack_int* kb, lapack_complex_float* ab, lapack_int* ldab,
+                   lapack_complex_float* bb, lapack_int* ldbb, float* w,
+                   lapack_complex_float* z, lapack_int* ldz,
+                   lapack_complex_float* work, float* rwork, lapack_int *info );
+void LAPACK_zhbgv( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
+                   lapack_int* kb, lapack_complex_double* ab, lapack_int* ldab,
+                   lapack_complex_double* bb, lapack_int* ldbb, double* w,
+                   lapack_complex_double* z, lapack_int* ldz,
+                   lapack_complex_double* work, double* rwork,
+                   lapack_int *info );
+void LAPACK_ssbgvd( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
+                    lapack_int* kb, float* ab, lapack_int* ldab, float* bb,
+                    lapack_int* ldbb, float* w, float* z, lapack_int* ldz,
+                    float* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_dsbgvd( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
+                    lapack_int* kb, double* ab, lapack_int* ldab, double* bb,
+                    lapack_int* ldbb, double* w, double* z, lapack_int* ldz,
+                    double* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_chbgvd( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
+                    lapack_int* kb, lapack_complex_float* ab, lapack_int* ldab,
+                    lapack_complex_float* bb, lapack_int* ldbb, float* w,
+                    lapack_complex_float* z, lapack_int* ldz,
+                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                    lapack_int* lrwork, lapack_int* iwork, lapack_int* liwork,
+                    lapack_int *info );
+void LAPACK_zhbgvd( char* jobz, char* uplo, lapack_int* n, lapack_int* ka,
+                    lapack_int* kb, lapack_complex_double* ab, lapack_int* ldab,
+                    lapack_complex_double* bb, lapack_int* ldbb, double* w,
+                    lapack_complex_double* z, lapack_int* ldz,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int* lrwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_int *info );
+void LAPACK_ssbgvx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    lapack_int* ka, lapack_int* kb, float* ab, lapack_int* ldab,
+                    float* bb, lapack_int* ldbb, float* q, lapack_int* ldq,
+                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
+                    float* abstol, lapack_int* m, float* w, float* z,
+                    lapack_int* ldz, float* work, lapack_int* iwork,
+                    lapack_int* ifail, lapack_int *info );
+void LAPACK_dsbgvx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    lapack_int* ka, lapack_int* kb, double* ab,
+                    lapack_int* ldab, double* bb, lapack_int* ldbb, double* q,
+                    lapack_int* ldq, double* vl, double* vu, lapack_int* il,
+                    lapack_int* iu, double* abstol, lapack_int* m, double* w,
+                    double* z, lapack_int* ldz, double* work, lapack_int* iwork,
+                    lapack_int* ifail, lapack_int *info );
+void LAPACK_chbgvx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    lapack_int* ka, lapack_int* kb, lapack_complex_float* ab,
+                    lapack_int* ldab, lapack_complex_float* bb,
+                    lapack_int* ldbb, lapack_complex_float* q, lapack_int* ldq,
+                    float* vl, float* vu, lapack_int* il, lapack_int* iu,
+                    float* abstol, lapack_int* m, float* w,
+                    lapack_complex_float* z, lapack_int* ldz,
+                    lapack_complex_float* work, float* rwork, lapack_int* iwork,
+                    lapack_int* ifail, lapack_int *info );
+void LAPACK_zhbgvx( char* jobz, char* range, char* uplo, lapack_int* n,
+                    lapack_int* ka, lapack_int* kb, lapack_complex_double* ab,
+                    lapack_int* ldab, lapack_complex_double* bb,
+                    lapack_int* ldbb, lapack_complex_double* q, lapack_int* ldq,
+                    double* vl, double* vu, lapack_int* il, lapack_int* iu,
+                    double* abstol, lapack_int* m, double* w,
+                    lapack_complex_double* z, lapack_int* ldz,
+                    lapack_complex_double* work, double* rwork,
+                    lapack_int* iwork, lapack_int* ifail, lapack_int *info );
+void LAPACK_sgges( char* jobvsl, char* jobvsr, char* sort,
+                   LAPACK_S_SELECT3 selctg, lapack_int* n, float* a,
+                   lapack_int* lda, float* b, lapack_int* ldb, lapack_int* sdim,
+                   float* alphar, float* alphai, float* beta, float* vsl,
+                   lapack_int* ldvsl, float* vsr, lapack_int* ldvsr,
+                   float* work, lapack_int* lwork, lapack_logical* bwork,
+                   lapack_int *info );
+void LAPACK_dgges( char* jobvsl, char* jobvsr, char* sort,
+                   LAPACK_D_SELECT3 selctg, lapack_int* n, double* a,
+                   lapack_int* lda, double* b, lapack_int* ldb,
+                   lapack_int* sdim, double* alphar, double* alphai,
+                   double* beta, double* vsl, lapack_int* ldvsl, double* vsr,
+                   lapack_int* ldvsr, double* work, lapack_int* lwork,
+                   lapack_logical* bwork, lapack_int *info );
+void LAPACK_cgges( char* jobvsl, char* jobvsr, char* sort,
+                   LAPACK_C_SELECT2 selctg, lapack_int* n,
+                   lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* b, lapack_int* ldb, lapack_int* sdim,
+                   lapack_complex_float* alpha, lapack_complex_float* beta,
+                   lapack_complex_float* vsl, lapack_int* ldvsl,
+                   lapack_complex_float* vsr, lapack_int* ldvsr,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                   lapack_logical* bwork, lapack_int *info );
+void LAPACK_zgges( char* jobvsl, char* jobvsr, char* sort,
+                   LAPACK_Z_SELECT2 selctg, lapack_int* n,
+                   lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* b, lapack_int* ldb, lapack_int* sdim,
+                   lapack_complex_double* alpha, lapack_complex_double* beta,
+                   lapack_complex_double* vsl, lapack_int* ldvsl,
+                   lapack_complex_double* vsr, lapack_int* ldvsr,
+                   lapack_complex_double* work, lapack_int* lwork,
+                   double* rwork, lapack_logical* bwork, lapack_int *info );
+void LAPACK_sggesx( char* jobvsl, char* jobvsr, char* sort,
+                    LAPACK_S_SELECT3 selctg, char* sense, lapack_int* n,
+                    float* a, lapack_int* lda, float* b, lapack_int* ldb,
+                    lapack_int* sdim, float* alphar, float* alphai, float* beta,
+                    float* vsl, lapack_int* ldvsl, float* vsr,
+                    lapack_int* ldvsr, float* rconde, float* rcondv,
+                    float* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_logical* bwork,
+                    lapack_int *info );
+void LAPACK_dggesx( char* jobvsl, char* jobvsr, char* sort,
+                    LAPACK_D_SELECT3 selctg, char* sense, lapack_int* n,
+                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
+                    lapack_int* sdim, double* alphar, double* alphai,
+                    double* beta, double* vsl, lapack_int* ldvsl, double* vsr,
+                    lapack_int* ldvsr, double* rconde, double* rcondv,
+                    double* work, lapack_int* lwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_logical* bwork,
+                    lapack_int *info );
+void LAPACK_cggesx( char* jobvsl, char* jobvsr, char* sort,
+                    LAPACK_C_SELECT2 selctg, char* sense, lapack_int* n,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb, lapack_int* sdim,
+                    lapack_complex_float* alpha, lapack_complex_float* beta,
+                    lapack_complex_float* vsl, lapack_int* ldvsl,
+                    lapack_complex_float* vsr, lapack_int* ldvsr, float* rconde,
+                    float* rcondv, lapack_complex_float* work,
+                    lapack_int* lwork, float* rwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_logical* bwork,
+                    lapack_int *info );
+void LAPACK_zggesx( char* jobvsl, char* jobvsr, char* sort,
+                    LAPACK_Z_SELECT2 selctg, char* sense, lapack_int* n,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb, lapack_int* sdim,
+                    lapack_complex_double* alpha, lapack_complex_double* beta,
+                    lapack_complex_double* vsl, lapack_int* ldvsl,
+                    lapack_complex_double* vsr, lapack_int* ldvsr,
+                    double* rconde, double* rcondv, lapack_complex_double* work,
+                    lapack_int* lwork, double* rwork, lapack_int* iwork,
+                    lapack_int* liwork, lapack_logical* bwork,
+                    lapack_int *info );
+void LAPACK_sggev( char* jobvl, char* jobvr, lapack_int* n, float* a,
+                   lapack_int* lda, float* b, lapack_int* ldb, float* alphar,
+                   float* alphai, float* beta, float* vl, lapack_int* ldvl,
+                   float* vr, lapack_int* ldvr, float* work, lapack_int* lwork,
+                   lapack_int *info );
+void LAPACK_dggev( char* jobvl, char* jobvr, lapack_int* n, double* a,
+                   lapack_int* lda, double* b, lapack_int* ldb, double* alphar,
+                   double* alphai, double* beta, double* vl, lapack_int* ldvl,
+                   double* vr, lapack_int* ldvr, double* work,
+                   lapack_int* lwork, lapack_int *info );
+void LAPACK_cggev( char* jobvl, char* jobvr, lapack_int* n,
+                   lapack_complex_float* a, lapack_int* lda,
+                   lapack_complex_float* b, lapack_int* ldb,
+                   lapack_complex_float* alpha, lapack_complex_float* beta,
+                   lapack_complex_float* vl, lapack_int* ldvl,
+                   lapack_complex_float* vr, lapack_int* ldvr,
+                   lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                   lapack_int *info );
+void LAPACK_zggev( char* jobvl, char* jobvr, lapack_int* n,
+                   lapack_complex_double* a, lapack_int* lda,
+                   lapack_complex_double* b, lapack_int* ldb,
+                   lapack_complex_double* alpha, lapack_complex_double* beta,
+                   lapack_complex_double* vl, lapack_int* ldvl,
+                   lapack_complex_double* vr, lapack_int* ldvr,
+                   lapack_complex_double* work, lapack_int* lwork,
+                   double* rwork, lapack_int *info );
+void LAPACK_sggevx( char* balanc, char* jobvl, char* jobvr, char* sense,
+                    lapack_int* n, float* a, lapack_int* lda, float* b,
+                    lapack_int* ldb, float* alphar, float* alphai, float* beta,
+                    float* vl, lapack_int* ldvl, float* vr, lapack_int* ldvr,
+                    lapack_int* ilo, lapack_int* ihi, float* lscale,
+                    float* rscale, float* abnrm, float* bbnrm, float* rconde,
+                    float* rcondv, float* work, lapack_int* lwork,
+                    lapack_int* iwork, lapack_logical* bwork,
+                    lapack_int *info );
+void LAPACK_dggevx( char* balanc, char* jobvl, char* jobvr, char* sense,
+                    lapack_int* n, double* a, lapack_int* lda, double* b,
+                    lapack_int* ldb, double* alphar, double* alphai,
+                    double* beta, double* vl, lapack_int* ldvl, double* vr,
+                    lapack_int* ldvr, lapack_int* ilo, lapack_int* ihi,
+                    double* lscale, double* rscale, double* abnrm,
+                    double* bbnrm, double* rconde, double* rcondv, double* work,
+                    lapack_int* lwork, lapack_int* iwork, lapack_logical* bwork,
+                    lapack_int *info );
+void LAPACK_cggevx( char* balanc, char* jobvl, char* jobvr, char* sense,
+                    lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    lapack_complex_float* alpha, lapack_complex_float* beta,
+                    lapack_complex_float* vl, lapack_int* ldvl,
+                    lapack_complex_float* vr, lapack_int* ldvr, lapack_int* ilo,
+                    lapack_int* ihi, float* lscale, float* rscale, float* abnrm,
+                    float* bbnrm, float* rconde, float* rcondv,
+                    lapack_complex_float* work, lapack_int* lwork, float* rwork,
+                    lapack_int* iwork, lapack_logical* bwork,
+                    lapack_int *info );
+void LAPACK_zggevx( char* balanc, char* jobvl, char* jobvr, char* sense,
+                    lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* alpha, lapack_complex_double* beta,
+                    lapack_complex_double* vl, lapack_int* ldvl,
+                    lapack_complex_double* vr, lapack_int* ldvr,
+                    lapack_int* ilo, lapack_int* ihi, double* lscale,
+                    double* rscale, double* abnrm, double* bbnrm,
+                    double* rconde, double* rcondv, lapack_complex_double* work,
+                    lapack_int* lwork, double* rwork, lapack_int* iwork,
+                    lapack_logical* bwork, lapack_int *info );
+void LAPACK_dsfrk( char* transr, char* uplo, char* trans, lapack_int* n,
+                   lapack_int* k, double* alpha, const double* a,
+                   lapack_int* lda, double* beta, double* c );
+void LAPACK_ssfrk( char* transr, char* uplo, char* trans, lapack_int* n,
+                   lapack_int* k, float* alpha, const float* a, lapack_int* lda,
+                   float* beta, float* c );
+void LAPACK_zhfrk( char* transr, char* uplo, char* trans, lapack_int* n,
+                   lapack_int* k, double* alpha, const lapack_complex_double* a,
+                   lapack_int* lda, double* beta, lapack_complex_double* c );
+void LAPACK_chfrk( char* transr, char* uplo, char* trans, lapack_int* n,
+                   lapack_int* k, float* alpha, const lapack_complex_float* a,
+                   lapack_int* lda, float* beta, lapack_complex_float* c );
+void LAPACK_dtfsm( char* transr, char* side, char* uplo, char* trans,
+                   char* diag, lapack_int* m, lapack_int* n, double* alpha,
+                   const double* a, double* b, lapack_int* ldb );
+void LAPACK_stfsm( char* transr, char* side, char* uplo, char* trans,
+                   char* diag, lapack_int* m, lapack_int* n, float* alpha,
+                   const float* a, float* b, lapack_int* ldb );
+void LAPACK_ztfsm( char* transr, char* side, char* uplo, char* trans,
+                   char* diag, lapack_int* m, lapack_int* n,
+                   lapack_complex_double* alpha, const lapack_complex_double* a,
+                   lapack_complex_double* b, lapack_int* ldb );
+void LAPACK_ctfsm( char* transr, char* side, char* uplo, char* trans,
+                   char* diag, lapack_int* m, lapack_int* n,
+                   lapack_complex_float* alpha, const lapack_complex_float* a,
+                   lapack_complex_float* b, lapack_int* ldb );
+void LAPACK_dtfttp( char* transr, char* uplo, lapack_int* n, const double* arf,
+                    double* ap, lapack_int *info );
+void LAPACK_stfttp( char* transr, char* uplo, lapack_int* n, const float* arf,
+                    float* ap, lapack_int *info );
+void LAPACK_ztfttp( char* transr, char* uplo, lapack_int* n,
+                    const lapack_complex_double* arf, lapack_complex_double* ap,
+                    lapack_int *info );
+void LAPACK_ctfttp( char* transr, char* uplo, lapack_int* n,
+                    const lapack_complex_float* arf, lapack_complex_float* ap,
+                    lapack_int *info );
+void LAPACK_dtfttr( char* transr, char* uplo, lapack_int* n, const double* arf,
+                    double* a, lapack_int* lda, lapack_int *info );
+void LAPACK_stfttr( char* transr, char* uplo, lapack_int* n, const float* arf,
+                    float* a, lapack_int* lda, lapack_int *info );
+void LAPACK_ztfttr( char* transr, char* uplo, lapack_int* n,
+                    const lapack_complex_double* arf, lapack_complex_double* a,
+                    lapack_int* lda, lapack_int *info );
+void LAPACK_ctfttr( char* transr, char* uplo, lapack_int* n,
+                    const lapack_complex_float* arf, lapack_complex_float* a,
+                    lapack_int* lda, lapack_int *info );
+void LAPACK_dtpttf( char* transr, char* uplo, lapack_int* n, const double* ap,
+                    double* arf, lapack_int *info );
+void LAPACK_stpttf( char* transr, char* uplo, lapack_int* n, const float* ap,
+                    float* arf, lapack_int *info );
+void LAPACK_ztpttf( char* transr, char* uplo, lapack_int* n,
+                    const lapack_complex_double* ap, lapack_complex_double* arf,
+                    lapack_int *info );
+void LAPACK_ctpttf( char* transr, char* uplo, lapack_int* n,
+                    const lapack_complex_float* ap, lapack_complex_float* arf,
+                    lapack_int *info );
+void LAPACK_dtpttr( char* uplo, lapack_int* n, const double* ap, double* a,
+                    lapack_int* lda, lapack_int *info );
+void LAPACK_stpttr( char* uplo, lapack_int* n, const float* ap, float* a,
+                    lapack_int* lda, lapack_int *info );
+void LAPACK_ztpttr( char* uplo, lapack_int* n, const lapack_complex_double* ap,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_int *info );
+void LAPACK_ctpttr( char* uplo, lapack_int* n, const lapack_complex_float* ap,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_int *info );
+void LAPACK_dtrttf( char* transr, char* uplo, lapack_int* n, const double* a,
+                    lapack_int* lda, double* arf, lapack_int *info );
+void LAPACK_strttf( char* transr, char* uplo, lapack_int* n, const float* a,
+                    lapack_int* lda, float* arf, lapack_int *info );
+void LAPACK_ztrttf( char* transr, char* uplo, lapack_int* n,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* arf, lapack_int *info );
+void LAPACK_ctrttf( char* transr, char* uplo, lapack_int* n,
+                    const lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* arf, lapack_int *info );
+void LAPACK_dtrttp( char* uplo, lapack_int* n, const double* a, lapack_int* lda,
+                    double* ap, lapack_int *info );
+void LAPACK_strttp( char* uplo, lapack_int* n, const float* a, lapack_int* lda,
+                    float* ap, lapack_int *info );
+void LAPACK_ztrttp( char* uplo, lapack_int* n, const lapack_complex_double* a,
+                    lapack_int* lda, lapack_complex_double* ap,
+                    lapack_int *info );
+void LAPACK_ctrttp( char* uplo, lapack_int* n, const lapack_complex_float* a,
+                    lapack_int* lda, lapack_complex_float* ap,
+                    lapack_int *info );
+void LAPACK_sgeqrfp( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                     float* tau, float* work, lapack_int* lwork,
+                     lapack_int *info );
+void LAPACK_dgeqrfp( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                     double* tau, double* work, lapack_int* lwork,
+                     lapack_int *info );
+void LAPACK_cgeqrfp( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                     lapack_int* lda, lapack_complex_float* tau,
+                     lapack_complex_float* work, lapack_int* lwork,
+                     lapack_int *info );
+void LAPACK_zgeqrfp( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                     lapack_int* lda, lapack_complex_double* tau,
+                     lapack_complex_double* work, lapack_int* lwork,
+                     lapack_int *info );
+void LAPACK_clacgv( lapack_int* n, lapack_complex_float* x, lapack_int* incx );
+void LAPACK_zlacgv( lapack_int* n, lapack_complex_double* x, lapack_int* incx );
+void LAPACK_slarnv( lapack_int* idist, lapack_int* iseed, lapack_int* n,
+                    float* x );
+void LAPACK_dlarnv( lapack_int* idist, lapack_int* iseed, lapack_int* n,
+                    double* x );
+void LAPACK_clarnv( lapack_int* idist, lapack_int* iseed, lapack_int* n,
+                    lapack_complex_float* x );
+void LAPACK_zlarnv( lapack_int* idist, lapack_int* iseed, lapack_int* n,
+                    lapack_complex_double* x );
+void LAPACK_sgeqr2( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                    float* tau, float* work, lapack_int *info );
+void LAPACK_dgeqr2( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                    double* tau, double* work, lapack_int *info );
+void LAPACK_cgeqr2( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_complex_float* tau,
+                    lapack_complex_float* work, lapack_int *info );
+void LAPACK_zgeqr2( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_complex_double* tau,
+                    lapack_complex_double* work, lapack_int *info );
+void LAPACK_slacpy( char* uplo, lapack_int* m, lapack_int* n, const float* a,
+                    lapack_int* lda, float* b, lapack_int* ldb );
+void LAPACK_dlacpy( char* uplo, lapack_int* m, lapack_int* n, const double* a,
+                    lapack_int* lda, double* b, lapack_int* ldb );
+void LAPACK_clacpy( char* uplo, lapack_int* m, lapack_int* n,
+                    const lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb );
+void LAPACK_zlacpy( char* uplo, lapack_int* m, lapack_int* n,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb );
+void LAPACK_sgetf2( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                    lapack_int* ipiv, lapack_int *info );
+void LAPACK_dgetf2( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                    lapack_int* ipiv, lapack_int *info );
+void LAPACK_cgetf2( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_int* ipiv, lapack_int *info );
+void LAPACK_zgetf2( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_int* ipiv, lapack_int *info );
+void LAPACK_slaswp( lapack_int* n, float* a, lapack_int* lda, lapack_int* k1,
+                    lapack_int* k2, const lapack_int* ipiv, lapack_int* incx );
+void LAPACK_dlaswp( lapack_int* n, double* a, lapack_int* lda, lapack_int* k1,
+                    lapack_int* k2, const lapack_int* ipiv, lapack_int* incx );
+void LAPACK_claswp( lapack_int* n, lapack_complex_float* a, lapack_int* lda,
+                    lapack_int* k1, lapack_int* k2, const lapack_int* ipiv,
+                    lapack_int* incx );
+void LAPACK_zlaswp( lapack_int* n, lapack_complex_double* a, lapack_int* lda,
+                    lapack_int* k1, lapack_int* k2, const lapack_int* ipiv,
+                    lapack_int* incx );
+float LAPACK_slange( char* norm, lapack_int* m, lapack_int* n, const float* a,
+                    lapack_int* lda, float* work );
+double LAPACK_dlange( char* norm, lapack_int* m, lapack_int* n, const double* a,
+                    lapack_int* lda, double* work );
+float LAPACK_clange( char* norm, lapack_int* m, lapack_int* n,
+                    const lapack_complex_float* a, lapack_int* lda, float* work );
+double LAPACK_zlange( char* norm, lapack_int* m, lapack_int* n,
+                    const lapack_complex_double* a, lapack_int* lda, double* work );
+float LAPACK_clanhe( char* norm, char* uplo, lapack_int* n,
+                    const lapack_complex_float* a, lapack_int* lda, float* work );
+double LAPACK_zlanhe( char* norm, char* uplo, lapack_int* n,
+                    const lapack_complex_double* a, lapack_int* lda, double* work );
+float LAPACK_slansy( char* norm, char* uplo, lapack_int* n, const float* a,
+                    lapack_int* lda, float* work );
+double LAPACK_dlansy( char* norm, char* uplo, lapack_int* n, const double* a,
+                    lapack_int* lda, double* work );
+float LAPACK_clansy( char* norm, char* uplo, lapack_int* n,
+                    const lapack_complex_float* a, lapack_int* lda, float* work );
+double LAPACK_zlansy( char* norm, char* uplo, lapack_int* n,
+                    const lapack_complex_double* a, lapack_int* lda, double* work );
+float LAPACK_slantr( char* norm, char* uplo, char* diag, lapack_int* m,
+                    lapack_int* n, const float* a, lapack_int* lda, float* work );
+double LAPACK_dlantr( char* norm, char* uplo, char* diag, lapack_int* m,
+                    lapack_int* n, const double* a, lapack_int* lda, double* work );
+float LAPACK_clantr( char* norm, char* uplo, char* diag, lapack_int* m,
+                    lapack_int* n, const lapack_complex_float* a, lapack_int* lda,
+                    float* work );
+double LAPACK_zlantr( char* norm, char* uplo, char* diag, lapack_int* m,
+                    lapack_int* n, const lapack_complex_double* a, lapack_int* lda,
+                    double* work );
+float LAPACK_slamch( char* cmach );
+double LAPACK_dlamch( char* cmach );
+void LAPACK_sgelq2( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                    float* tau, float* work, lapack_int *info );
+void LAPACK_dgelq2( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                    double* tau, double* work, lapack_int *info );
+void LAPACK_cgelq2( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_complex_float* tau,
+                    lapack_complex_float* work, lapack_int *info );
+void LAPACK_zgelq2( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_complex_double* tau,
+                    lapack_complex_double* work, lapack_int *info );
+void LAPACK_slarfb( char* side, char* trans, char* direct, char* storev,
+                    lapack_int* m, lapack_int* n, lapack_int* k, const float* v,
+                    lapack_int* ldv, const float* t, lapack_int* ldt, float* c,
+                    lapack_int* ldc, float* work, lapack_int* ldwork );
+void LAPACK_dlarfb( char* side, char* trans, char* direct, char* storev,
+                    lapack_int* m, lapack_int* n, lapack_int* k,
+                    const double* v, lapack_int* ldv, const double* t,
+                    lapack_int* ldt, double* c, lapack_int* ldc, double* work,
+                    lapack_int* ldwork );
+void LAPACK_clarfb( char* side, char* trans, char* direct, char* storev,
+                    lapack_int* m, lapack_int* n, lapack_int* k,
+                    const lapack_complex_float* v, lapack_int* ldv,
+                    const lapack_complex_float* t, lapack_int* ldt,
+                    lapack_complex_float* c, lapack_int* ldc,
+                    lapack_complex_float* work, lapack_int* ldwork );
+void LAPACK_zlarfb( char* side, char* trans, char* direct, char* storev,
+                    lapack_int* m, lapack_int* n, lapack_int* k,
+                    const lapack_complex_double* v, lapack_int* ldv,
+                    const lapack_complex_double* t, lapack_int* ldt,
+                    lapack_complex_double* c, lapack_int* ldc,
+                    lapack_complex_double* work, lapack_int* ldwork );
+void LAPACK_slarfg( lapack_int* n, float* alpha, float* x, lapack_int* incx,
+                    float* tau );
+void LAPACK_dlarfg( lapack_int* n, double* alpha, double* x, lapack_int* incx,
+                    double* tau );
+void LAPACK_clarfg( lapack_int* n, lapack_complex_float* alpha,
+                    lapack_complex_float* x, lapack_int* incx,
+                    lapack_complex_float* tau );
+void LAPACK_zlarfg( lapack_int* n, lapack_complex_double* alpha,
+                    lapack_complex_double* x, lapack_int* incx,
+                    lapack_complex_double* tau );
+void LAPACK_slarft( char* direct, char* storev, lapack_int* n, lapack_int* k,
+                    const float* v, lapack_int* ldv, const float* tau, float* t,
+                    lapack_int* ldt );
+void LAPACK_dlarft( char* direct, char* storev, lapack_int* n, lapack_int* k,
+                    const double* v, lapack_int* ldv, const double* tau,
+                    double* t, lapack_int* ldt );
+void LAPACK_clarft( char* direct, char* storev, lapack_int* n, lapack_int* k,
+                    const lapack_complex_float* v, lapack_int* ldv,
+                    const lapack_complex_float* tau, lapack_complex_float* t,
+                    lapack_int* ldt );
+void LAPACK_zlarft( char* direct, char* storev, lapack_int* n, lapack_int* k,
+                    const lapack_complex_double* v, lapack_int* ldv,
+                    const lapack_complex_double* tau, lapack_complex_double* t,
+                    lapack_int* ldt );
+void LAPACK_slarfx( char* side, lapack_int* m, lapack_int* n, const float* v,
+                    float* tau, float* c, lapack_int* ldc, float* work );
+void LAPACK_dlarfx( char* side, lapack_int* m, lapack_int* n, const double* v,
+                    double* tau, double* c, lapack_int* ldc, double* work );
+void LAPACK_clarfx( char* side, lapack_int* m, lapack_int* n,
+                    const lapack_complex_float* v, lapack_complex_float* tau,
+                    lapack_complex_float* c, lapack_int* ldc,
+                    lapack_complex_float* work );
+void LAPACK_zlarfx( char* side, lapack_int* m, lapack_int* n,
+                    const lapack_complex_double* v, lapack_complex_double* tau,
+                    lapack_complex_double* c, lapack_int* ldc,
+                    lapack_complex_double* work );
+void LAPACK_slatms( lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed,
+                    char* sym, float* d, lapack_int* mode, float* cond,
+                    float* dmax, lapack_int* kl, lapack_int* ku, char* pack,
+                    float* a, lapack_int* lda, float* work, lapack_int *info );
+void LAPACK_dlatms( lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed,
+                    char* sym, double* d, lapack_int* mode, double* cond,
+                    double* dmax, lapack_int* kl, lapack_int* ku, char* pack,
+                    double* a, lapack_int* lda, double* work,
+                    lapack_int *info );
+void LAPACK_clatms( lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed,
+                    char* sym, float* d, lapack_int* mode, float* cond,
+                    float* dmax, lapack_int* kl, lapack_int* ku, char* pack,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* work, lapack_int *info );
+void LAPACK_zlatms( lapack_int* m, lapack_int* n, char* dist, lapack_int* iseed,
+                    char* sym, double* d, lapack_int* mode, double* cond,
+                    double* dmax, lapack_int* kl, lapack_int* ku, char* pack,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* work, lapack_int *info );
+void LAPACK_slag2d( lapack_int* m, lapack_int* n, const float* sa,
+                    lapack_int* ldsa, double* a, lapack_int* lda,
+                    lapack_int *info );
+void LAPACK_dlag2s( lapack_int* m, lapack_int* n, const double* a,
+                    lapack_int* lda, float* sa, lapack_int* ldsa,
+                    lapack_int *info );
+void LAPACK_clag2z( lapack_int* m, lapack_int* n,
+                    const lapack_complex_float* sa, lapack_int* ldsa,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_int *info );
+void LAPACK_zlag2c( lapack_int* m, lapack_int* n,
+                    const lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_float* sa, lapack_int* ldsa,
+                    lapack_int *info );
+void LAPACK_slauum( char* uplo, lapack_int* n, float* a, lapack_int* lda,
+                    lapack_int *info );
+void LAPACK_dlauum( char* uplo, lapack_int* n, double* a, lapack_int* lda,
+                    lapack_int *info );
+void LAPACK_clauum( char* uplo, lapack_int* n, lapack_complex_float* a,
+                    lapack_int* lda, lapack_int *info );
+void LAPACK_zlauum( char* uplo, lapack_int* n, lapack_complex_double* a,
+                    lapack_int* lda, lapack_int *info );
+void LAPACK_slagge( lapack_int* m, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, const float* d, float* a, lapack_int* lda,
+                    lapack_int* iseed, float* work, lapack_int *info );
+void LAPACK_dlagge( lapack_int* m, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, const double* d, double* a, lapack_int* lda,
+                    lapack_int* iseed, double* work, lapack_int *info );
+void LAPACK_clagge( lapack_int* m, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, const float* d, lapack_complex_float* a,
+                    lapack_int* lda, lapack_int* iseed,
+                    lapack_complex_float* work, lapack_int *info );
+void LAPACK_zlagge( lapack_int* m, lapack_int* n, lapack_int* kl,
+                    lapack_int* ku, const double* d, lapack_complex_double* a,
+                    lapack_int* lda, lapack_int* iseed,
+                    lapack_complex_double* work, lapack_int *info );
+void LAPACK_slaset( char* uplo, lapack_int* m, lapack_int* n, float* alpha,
+                    float* beta, float* a, lapack_int* lda );
+void LAPACK_dlaset( char* uplo, lapack_int* m, lapack_int* n, double* alpha,
+                    double* beta, double* a, lapack_int* lda );
+void LAPACK_claset( char* uplo, lapack_int* m, lapack_int* n,
+                    lapack_complex_float* alpha, lapack_complex_float* beta,
+                    lapack_complex_float* a, lapack_int* lda );
+void LAPACK_zlaset( char* uplo, lapack_int* m, lapack_int* n,
+                    lapack_complex_double* alpha, lapack_complex_double* beta,
+                    lapack_complex_double* a, lapack_int* lda );
+void LAPACK_slasrt( char* id, lapack_int* n, float* d, lapack_int *info );
+void LAPACK_dlasrt( char* id, lapack_int* n, double* d, lapack_int *info );
+void LAPACK_claghe( lapack_int* n, lapack_int* k, const float* d,
+                    lapack_complex_float* a, lapack_int* lda, lapack_int* iseed,
+                    lapack_complex_float* work, lapack_int *info );
+void LAPACK_zlaghe( lapack_int* n, lapack_int* k, const double* d,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_int* iseed, lapack_complex_double* work,
+                    lapack_int *info );
+void LAPACK_slagsy( lapack_int* n, lapack_int* k, const float* d, float* a,
+                    lapack_int* lda, lapack_int* iseed, float* work,
+                    lapack_int *info );
+void LAPACK_dlagsy( lapack_int* n, lapack_int* k, const double* d, double* a,
+                    lapack_int* lda, lapack_int* iseed, double* work,
+                    lapack_int *info );
+void LAPACK_clagsy( lapack_int* n, lapack_int* k, const float* d,
+                    lapack_complex_float* a, lapack_int* lda, lapack_int* iseed,
+                    lapack_complex_float* work, lapack_int *info );
+void LAPACK_zlagsy( lapack_int* n, lapack_int* k, const double* d,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_int* iseed, lapack_complex_double* work,
+                    lapack_int *info );
+void LAPACK_slapmr( lapack_logical* forwrd, lapack_int* m, lapack_int* n,
+                    float* x, lapack_int* ldx, lapack_int* k );
+void LAPACK_dlapmr( lapack_logical* forwrd, lapack_int* m, lapack_int* n,
+                    double* x, lapack_int* ldx, lapack_int* k );
+void LAPACK_clapmr( lapack_logical* forwrd, lapack_int* m, lapack_int* n,
+                    lapack_complex_float* x, lapack_int* ldx, lapack_int* k );
+void LAPACK_zlapmr( lapack_logical* forwrd, lapack_int* m, lapack_int* n,
+                    lapack_complex_double* x, lapack_int* ldx, lapack_int* k );
+float LAPACK_slapy2( float* x, float* y );
+double LAPACK_dlapy2( double* x, double* y );
+float LAPACK_slapy3( float* x, float* y, float* z );
+double LAPACK_dlapy3( double* x, double* y, double* z );
+void LAPACK_slartgp( float* f, float* g, float* cs, float* sn, float* r );
+void LAPACK_dlartgp( double* f, double* g, double* cs, double* sn, double* r );
+void LAPACK_slartgs( float* x, float* y, float* sigma, float* cs, float* sn );
+void LAPACK_dlartgs( double* x, double* y, double* sigma, double* cs,
+                     double* sn );
+// LAPACK 3.3.0
+void LAPACK_cbbcsd( char* jobu1, char* jobu2,
+                    char* jobv1t, char* jobv2t, char* trans,
+                    lapack_int* m, lapack_int* p, lapack_int* q,
+                    float* theta, float* phi,
+                    lapack_complex_float* u1, lapack_int* ldu1,
+                    lapack_complex_float* u2, lapack_int* ldu2,
+                    lapack_complex_float* v1t, lapack_int* ldv1t,
+                    lapack_complex_float* v2t, lapack_int* ldv2t,
+                    float* b11d, float* b11e, float* b12d,
+                    float* b12e, float* b21d, float* b21e,
+                    float* b22d, float* b22e, float* rwork,
+                    lapack_int* lrwork , lapack_int *info );
+void LAPACK_cheswapr( char* uplo, lapack_int* n,
+                      lapack_complex_float* a, lapack_int* i1,
+                      lapack_int* i2 );
+void LAPACK_chetri2( char* uplo, lapack_int* n,
+                     lapack_complex_float* a, lapack_int* lda,
+                     const lapack_int* ipiv,
+                     lapack_complex_float* work, lapack_int* lwork , lapack_int *info );
+void LAPACK_chetri2x( char* uplo, lapack_int* n,
+                      lapack_complex_float* a, lapack_int* lda,
+                      const lapack_int* ipiv,
+                      lapack_complex_float* work, lapack_int* nb , lapack_int *info );
+void LAPACK_chetrs2( char* uplo, lapack_int* n,
+                     lapack_int* nrhs, const lapack_complex_float* a,
+                     lapack_int* lda, const lapack_int* ipiv,
+                     lapack_complex_float* b, lapack_int* ldb,
+                     lapack_complex_float* work , lapack_int *info );
+void LAPACK_csyconv( char* uplo, char* way,
+                     lapack_int* n, lapack_complex_float* a,
+                     lapack_int* lda, const lapack_int* ipiv,
+                     lapack_complex_float* work , lapack_int *info );
+void LAPACK_csyswapr( char* uplo, lapack_int* n,
+                      lapack_complex_float* a, lapack_int* i1,
+                      lapack_int* i2 );
+void LAPACK_csytri2( char* uplo, lapack_int* n,
+                     lapack_complex_float* a, lapack_int* lda,
+                     const lapack_int* ipiv,
+                     lapack_complex_float* work, lapack_int* lwork , lapack_int *info );
+void LAPACK_csytri2x( char* uplo, lapack_int* n,
+                      lapack_complex_float* a, lapack_int* lda,
+                      const lapack_int* ipiv,
+                      lapack_complex_float* work, lapack_int* nb , lapack_int *info );
+void LAPACK_csytrs2( char* uplo, lapack_int* n,
+                     lapack_int* nrhs, const lapack_complex_float* a,
+                     lapack_int* lda, const lapack_int* ipiv,
+                     lapack_complex_float* b, lapack_int* ldb,
+                     lapack_complex_float* work , lapack_int *info );
+void LAPACK_cunbdb( char* trans, char* signs,
+                    lapack_int* m, lapack_int* p, lapack_int* q,
+                    lapack_complex_float* x11, lapack_int* ldx11,
+                    lapack_complex_float* x12, lapack_int* ldx12,
+                    lapack_complex_float* x21, lapack_int* ldx21,
+                    lapack_complex_float* x22, lapack_int* ldx22,
+                    float* theta, float* phi,
+                    lapack_complex_float* taup1,
+                    lapack_complex_float* taup2,
+                    lapack_complex_float* tauq1,
+                    lapack_complex_float* tauq2,
+                    lapack_complex_float* work, lapack_int* lwork , lapack_int *info );
+void LAPACK_cuncsd( char* jobu1, char* jobu2,
+                    char* jobv1t, char* jobv2t, char* trans,
+                    char* signs, lapack_int* m, lapack_int* p,
+                    lapack_int* q, lapack_complex_float* x11,
+                    lapack_int* ldx11, lapack_complex_float* x12,
+                    lapack_int* ldx12, lapack_complex_float* x21,
+                    lapack_int* ldx21, lapack_complex_float* x22,
+                    lapack_int* ldx22, float* theta,
+                    lapack_complex_float* u1, lapack_int* ldu1,
+                    lapack_complex_float* u2, lapack_int* ldu2,
+                    lapack_complex_float* v1t, lapack_int* ldv1t,
+                    lapack_complex_float* v2t, lapack_int* ldv2t,
+                    lapack_complex_float* work, lapack_int* lwork,
+                    float* rwork, lapack_int* lrwork,
+                    lapack_int* iwork , lapack_int *info );
+void LAPACK_dbbcsd( char* jobu1, char* jobu2,
+                    char* jobv1t, char* jobv2t, char* trans,
+                    lapack_int* m, lapack_int* p, lapack_int* q,
+                    double* theta, double* phi, double* u1,
+                    lapack_int* ldu1, double* u2, lapack_int* ldu2,
+                    double* v1t, lapack_int* ldv1t, double* v2t,
+                    lapack_int* ldv2t, double* b11d, double* b11e,
+                    double* b12d, double* b12e, double* b21d,
+                    double* b21e, double* b22d, double* b22e,
+                    double* work, lapack_int* lwork , lapack_int *info );
+void LAPACK_dorbdb( char* trans, char* signs,
+                    lapack_int* m, lapack_int* p, lapack_int* q,
+                    double* x11, lapack_int* ldx11, double* x12,
+                    lapack_int* ldx12, double* x21, lapack_int* ldx21,
+                    double* x22, lapack_int* ldx22, double* theta,
+                    double* phi, double* taup1, double* taup2,
+                    double* tauq1, double* tauq2, double* work,
+                    lapack_int* lwork , lapack_int *info );
+void LAPACK_dorcsd( char* jobu1, char* jobu2,
+                    char* jobv1t, char* jobv2t, char* trans,
+                    char* signs, lapack_int* m, lapack_int* p,
+                    lapack_int* q, double* x11, lapack_int* ldx11,
+                    double* x12, lapack_int* ldx12, double* x21,
+                    lapack_int* ldx21, double* x22, lapack_int* ldx22,
+                    double* theta, double* u1, lapack_int* ldu1,
+                    double* u2, lapack_int* ldu2, double* v1t,
+                    lapack_int* ldv1t, double* v2t, lapack_int* ldv2t,
+                    double* work, lapack_int* lwork,
+                    lapack_int* iwork , lapack_int *info );
+void LAPACK_dsyconv( char* uplo, char* way,
+                     lapack_int* n, double* a, lapack_int* lda,
+                     const lapack_int* ipiv, double* work , lapack_int *info );
+void LAPACK_dsyswapr( char* uplo, lapack_int* n,
+                      double* a, lapack_int* i1, lapack_int* i2 );
+void LAPACK_dsytri2( char* uplo, lapack_int* n,
+                     double* a, lapack_int* lda,
+                     const lapack_int* ipiv,
+                     lapack_complex_double* work, lapack_int* lwork , lapack_int *info );
+void LAPACK_dsytri2x( char* uplo, lapack_int* n,
+                      double* a, lapack_int* lda,
+                      const lapack_int* ipiv, double* work,
+                      lapack_int* nb , lapack_int *info );
+void LAPACK_dsytrs2( char* uplo, lapack_int* n,
+                     lapack_int* nrhs, const double* a,
+                     lapack_int* lda, const lapack_int* ipiv,
+                     double* b, lapack_int* ldb, double* work , lapack_int *info );
+void LAPACK_sbbcsd( char* jobu1, char* jobu2,
+                    char* jobv1t, char* jobv2t, char* trans,
+                    lapack_int* m, lapack_int* p, lapack_int* q,
+                    float* theta, float* phi, float* u1,
+                    lapack_int* ldu1, float* u2, lapack_int* ldu2,
+                    float* v1t, lapack_int* ldv1t, float* v2t,
+                    lapack_int* ldv2t, float* b11d, float* b11e,
+                    float* b12d, float* b12e, float* b21d,
+                    float* b21e, float* b22d, float* b22e,
+                    float* work, lapack_int* lwork , lapack_int *info );
+void LAPACK_sorbdb( char* trans, char* signs,
+                    lapack_int* m, lapack_int* p, lapack_int* q,
+                    float* x11, lapack_int* ldx11, float* x12,
+                    lapack_int* ldx12, float* x21, lapack_int* ldx21,
+                    float* x22, lapack_int* ldx22, float* theta,
+                    float* phi, float* taup1, float* taup2,
+                    float* tauq1, float* tauq2, float* work,
+                    lapack_int* lwork , lapack_int *info );
+void LAPACK_sorcsd( char* jobu1, char* jobu2,
+                    char* jobv1t, char* jobv2t, char* trans,
+                    char* signs, lapack_int* m, lapack_int* p,
+                    lapack_int* q, float* x11, lapack_int* ldx11,
+                    float* x12, lapack_int* ldx12, float* x21,
+                    lapack_int* ldx21, float* x22, lapack_int* ldx22,
+                    float* theta, float* u1, lapack_int* ldu1,
+                    float* u2, lapack_int* ldu2, float* v1t,
+                    lapack_int* ldv1t, float* v2t, lapack_int* ldv2t,
+                    float* work, lapack_int* lwork,
+                    lapack_int* iwork , lapack_int *info );
+void LAPACK_ssyconv( char* uplo, char* way,
+                     lapack_int* n, float* a, lapack_int* lda,
+                     const lapack_int* ipiv, float* work , lapack_int *info );
+void LAPACK_ssyswapr( char* uplo, lapack_int* n,
+                      float* a, lapack_int* i1, lapack_int* i2 );
+void LAPACK_ssytri2( char* uplo, lapack_int* n,
+                     float* a, lapack_int* lda,
+                     const lapack_int* ipiv,
+                     lapack_complex_float* work, lapack_int* lwork , lapack_int *info );
+void LAPACK_ssytri2x( char* uplo, lapack_int* n,
+                      float* a, lapack_int* lda,
+                      const lapack_int* ipiv, float* work,
+                      lapack_int* nb , lapack_int *info );
+void LAPACK_ssytrs2( char* uplo, lapack_int* n,
+                     lapack_int* nrhs, const float* a,
+                     lapack_int* lda, const lapack_int* ipiv,
+                     float* b, lapack_int* ldb, float* work , lapack_int *info );
+void LAPACK_zbbcsd( char* jobu1, char* jobu2,
+                    char* jobv1t, char* jobv2t, char* trans,
+                    lapack_int* m, lapack_int* p, lapack_int* q,
+                    double* theta, double* phi,
+                    lapack_complex_double* u1, lapack_int* ldu1,
+                    lapack_complex_double* u2, lapack_int* ldu2,
+                    lapack_complex_double* v1t, lapack_int* ldv1t,
+                    lapack_complex_double* v2t, lapack_int* ldv2t,
+                    double* b11d, double* b11e, double* b12d,
+                    double* b12e, double* b21d, double* b21e,
+                    double* b22d, double* b22e, double* rwork,
+                    lapack_int* lrwork , lapack_int *info );
+void LAPACK_zheswapr( char* uplo, lapack_int* n,
+                      lapack_complex_double* a, lapack_int* i1,
+                      lapack_int* i2 );
+void LAPACK_zhetri2( char* uplo, lapack_int* n,
+                     lapack_complex_double* a, lapack_int* lda,
+                     const lapack_int* ipiv,
+                     lapack_complex_double* work, lapack_int* lwork , lapack_int *info );
+void LAPACK_zhetri2x( char* uplo, lapack_int* n,
+                      lapack_complex_double* a, lapack_int* lda,
+                      const lapack_int* ipiv,
+                      lapack_complex_double* work, lapack_int* nb , lapack_int *info );
+void LAPACK_zhetrs2( char* uplo, lapack_int* n,
+                     lapack_int* nrhs,
+                     const lapack_complex_double* a, lapack_int* lda,
+                     const lapack_int* ipiv,
+                     lapack_complex_double* b, lapack_int* ldb,
+                     lapack_complex_double* work , lapack_int *info );
+void LAPACK_zsyconv( char* uplo, char* way,
+                     lapack_int* n, lapack_complex_double* a,
+                     lapack_int* lda, const lapack_int* ipiv,
+                     lapack_complex_double* work , lapack_int *info );
+void LAPACK_zsyswapr( char* uplo, lapack_int* n,
+                      lapack_complex_double* a, lapack_int* i1,
+                      lapack_int* i2 );
+void LAPACK_zsytri2( char* uplo, lapack_int* n,
+                     lapack_complex_double* a, lapack_int* lda,
+                     const lapack_int* ipiv,
+                     lapack_complex_double* work, lapack_int* lwork , lapack_int *info );
+void LAPACK_zsytri2x( char* uplo, lapack_int* n,
+                      lapack_complex_double* a, lapack_int* lda,
+                      const lapack_int* ipiv,
+                      lapack_complex_double* work, lapack_int* nb , lapack_int *info );
+void LAPACK_zsytrs2( char* uplo, lapack_int* n,
+                     lapack_int* nrhs,
+                     const lapack_complex_double* a, lapack_int* lda,
+                     const lapack_int* ipiv,
+                     lapack_complex_double* b, lapack_int* ldb,
+                     lapack_complex_double* work , lapack_int *info );
+void LAPACK_zunbdb( char* trans, char* signs,
+                    lapack_int* m, lapack_int* p, lapack_int* q,
+                    lapack_complex_double* x11, lapack_int* ldx11,
+                    lapack_complex_double* x12, lapack_int* ldx12,
+                    lapack_complex_double* x21, lapack_int* ldx21,
+                    lapack_complex_double* x22, lapack_int* ldx22,
+                    double* theta, double* phi,
+                    lapack_complex_double* taup1,
+                    lapack_complex_double* taup2,
+                    lapack_complex_double* tauq1,
+                    lapack_complex_double* tauq2,
+                    lapack_complex_double* work, lapack_int* lwork , lapack_int *info );
+void LAPACK_zuncsd( char* jobu1, char* jobu2,
+                    char* jobv1t, char* jobv2t, char* trans,
+                    char* signs, lapack_int* m, lapack_int* p,
+                    lapack_int* q, lapack_complex_double* x11,
+                    lapack_int* ldx11, lapack_complex_double* x12,
+                    lapack_int* ldx12, lapack_complex_double* x21,
+                    lapack_int* ldx21, lapack_complex_double* x22,
+                    lapack_int* ldx22, double* theta,
+                    lapack_complex_double* u1, lapack_int* ldu1,
+                    lapack_complex_double* u2, lapack_int* ldu2,
+                    lapack_complex_double* v1t, lapack_int* ldv1t,
+                    lapack_complex_double* v2t, lapack_int* ldv2t,
+                    lapack_complex_double* work, lapack_int* lwork,
+                    double* rwork, lapack_int* lrwork,
+                    lapack_int* iwork , lapack_int *info );
+// LAPACK 3.4.0
+void LAPACK_sgemqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
+                     lapack_int* k, lapack_int* nb, const float* v,
+                     lapack_int* ldv, const float* t, lapack_int* ldt, float* c,
+                     lapack_int* ldc, float* work, lapack_int *info );
+void LAPACK_dgemqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
+                     lapack_int* k, lapack_int* nb, const double* v,
+                     lapack_int* ldv, const double* t, lapack_int* ldt,
+                     double* c, lapack_int* ldc, double* work,
+                     lapack_int *info );
+void LAPACK_cgemqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
+                     lapack_int* k, lapack_int* nb,
+                     const lapack_complex_float* v, lapack_int* ldv,
+                     const lapack_complex_float* t, lapack_int* ldt,
+                     lapack_complex_float* c, lapack_int* ldc,
+                     lapack_complex_float* work, lapack_int *info );
+void LAPACK_zgemqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
+                     lapack_int* k, lapack_int* nb,
+                     const lapack_complex_double* v, lapack_int* ldv,
+                     const lapack_complex_double* t, lapack_int* ldt,
+                     lapack_complex_double* c, lapack_int* ldc,
+                     lapack_complex_double* work, lapack_int *info );
+void LAPACK_sgeqrt( lapack_int* m, lapack_int* n, lapack_int* nb, float* a,
+                    lapack_int* lda, float* t, lapack_int* ldt, float* work,
+                    lapack_int *info );
+void LAPACK_dgeqrt( lapack_int* m, lapack_int* n, lapack_int* nb, double* a,
+                    lapack_int* lda, double* t, lapack_int* ldt, double* work,
+                    lapack_int *info );
+void LAPACK_cgeqrt( lapack_int* m, lapack_int* n, lapack_int* nb,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* t, lapack_int* ldt,
+                    lapack_complex_float* work, lapack_int *info );
+void LAPACK_zgeqrt( lapack_int* m, lapack_int* n, lapack_int* nb,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* t, lapack_int* ldt,
+                    lapack_complex_double* work, lapack_int *info );
+void LAPACK_sgeqrt2( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                     float* t, lapack_int* ldt, lapack_int *info );
+void LAPACK_dgeqrt2( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                     double* t, lapack_int* ldt, lapack_int *info );
+void LAPACK_cgeqrt2( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                     lapack_int* lda, lapack_complex_float* t, lapack_int* ldt,
+                     lapack_int *info );
+void LAPACK_zgeqrt2( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                     lapack_int* lda, lapack_complex_double* t, lapack_int* ldt,
+                     lapack_int *info );
+void LAPACK_sgeqrt3( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                     float* t, lapack_int* ldt, lapack_int *info );
+void LAPACK_dgeqrt3( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                     double* t, lapack_int* ldt, lapack_int *info );
+void LAPACK_cgeqrt3( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                     lapack_int* lda, lapack_complex_float* t, lapack_int* ldt,
+                     lapack_int *info );
+void LAPACK_zgeqrt3( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                     lapack_int* lda, lapack_complex_double* t, lapack_int* ldt,
+                     lapack_int *info );
+void LAPACK_stpmqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
+                     lapack_int* k, lapack_int* l, lapack_int* nb,
+                     const float* v, lapack_int* ldv, const float* t,
+                     lapack_int* ldt, float* a, lapack_int* lda, float* b,
+                     lapack_int* ldb, float* work, lapack_int *info );
+void LAPACK_dtpmqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
+                     lapack_int* k, lapack_int* l, lapack_int* nb,
+                     const double* v, lapack_int* ldv, const double* t,
+                     lapack_int* ldt, double* a, lapack_int* lda, double* b,
+                     lapack_int* ldb, double* work, lapack_int *info );
+void LAPACK_ctpmqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
+                     lapack_int* k, lapack_int* l, lapack_int* nb,
+                     const lapack_complex_float* v, lapack_int* ldv,
+                     const lapack_complex_float* t, lapack_int* ldt,
+                     lapack_complex_float* a, lapack_int* lda,
+                     lapack_complex_float* b, lapack_int* ldb,
+                     lapack_complex_float* work, lapack_int *info );
+void LAPACK_ztpmqrt( char* side, char* trans, lapack_int* m, lapack_int* n,
+                     lapack_int* k, lapack_int* l, lapack_int* nb,
+                     const lapack_complex_double* v, lapack_int* ldv,
+                     const lapack_complex_double* t, lapack_int* ldt,
+                     lapack_complex_double* a, lapack_int* lda,
+                     lapack_complex_double* b, lapack_int* ldb,
+                     lapack_complex_double* work, lapack_int *info );
+void LAPACK_dtpqrt( lapack_int* m, lapack_int* n, lapack_int* l, lapack_int* nb,
+                    double* a, lapack_int* lda, double* b, lapack_int* ldb,
+                    double* t, lapack_int* ldt, double* work,
+                    lapack_int *info );
+void LAPACK_ctpqrt( lapack_int* m, lapack_int* n, lapack_int* l, lapack_int* nb,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* t, lapack_complex_float* b,
+                    lapack_int* ldb, lapack_int* ldt,
+                    lapack_complex_float* work, lapack_int *info );
+void LAPACK_ztpqrt( lapack_int* m, lapack_int* n, lapack_int* l, lapack_int* nb,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    lapack_complex_double* t, lapack_int* ldt,
+                    lapack_complex_double* work, lapack_int *info );
+void LAPACK_stpqrt2( lapack_int* m, lapack_int* n, float* a, lapack_int* lda,
+                     float* b, lapack_int* ldb, float* t, lapack_int* ldt,
+                     lapack_int *info );
+void LAPACK_dtpqrt2( lapack_int* m, lapack_int* n, double* a, lapack_int* lda,
+                     double* b, lapack_int* ldb, double* t, lapack_int* ldt,
+                     lapack_int *info );
+void LAPACK_ctpqrt2( lapack_int* m, lapack_int* n, lapack_complex_float* a,
+                     lapack_int* lda, lapack_complex_float* b, lapack_int* ldb,
+                     lapack_complex_float* t, lapack_int* ldt,
+                     lapack_int *info );
+void LAPACK_ztpqrt2( lapack_int* m, lapack_int* n, lapack_complex_double* a,
+                     lapack_int* lda, lapack_complex_double* b, lapack_int* ldb,
+                     lapack_complex_double* t, lapack_int* ldt,
+                     lapack_int *info );
+void LAPACK_stprfb( char* side, char* trans, char* direct, char* storev,
+                    lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l,
+                    const float* v, lapack_int* ldv, const float* t,
+                    lapack_int* ldt, float* a, lapack_int* lda, float* b,
+                    lapack_int* ldb, const float* mywork,
+                    lapack_int* myldwork );
+void LAPACK_dtprfb( char* side, char* trans, char* direct, char* storev,
+                    lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l,
+                    const double* v, lapack_int* ldv, const double* t,
+                    lapack_int* ldt, double* a, lapack_int* lda, double* b,
+                    lapack_int* ldb, const double* mywork,
+                    lapack_int* myldwork );
+void LAPACK_ctprfb( char* side, char* trans, char* direct, char* storev,
+                    lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l,
+                    const lapack_complex_float* v, lapack_int* ldv,
+                    const lapack_complex_float* t, lapack_int* ldt,
+                    lapack_complex_float* a, lapack_int* lda,
+                    lapack_complex_float* b, lapack_int* ldb,
+                    const float* mywork, lapack_int* myldwork );
+void LAPACK_ztprfb( char* side, char* trans, char* direct, char* storev,
+                    lapack_int* m, lapack_int* n, lapack_int* k, lapack_int* l,
+                    const lapack_complex_double* v, lapack_int* ldv,
+                    const lapack_complex_double* t, lapack_int* ldt,
+                    lapack_complex_double* a, lapack_int* lda,
+                    lapack_complex_double* b, lapack_int* ldb,
+                    const double* mywork, lapack_int* myldwork );
+// LAPACK 3.X.X
+void LAPACK_csyr( char* uplo, lapack_int* n, lapack_complex_float* alpha,
+                      const lapack_complex_float* x, lapack_int* incx,
+                      lapack_complex_float* a, lapack_int* lda );
+void LAPACK_zsyr( char* uplo, lapack_int* n, lapack_complex_double* alpha,
+                      const lapack_complex_double* x, lapack_int* incx,
+                      lapack_complex_double* a, lapack_int* lda );
+
+#ifdef __cplusplus
+}
+#endif /* __cplusplus */
+
+#endif /* _LAPACKE_H_ */
+
+#endif /* _MKL_LAPACKE_H_ */
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/lapacke_helpers.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/lapacke_helpers.h
new file mode 100644
index 0000000000000000000000000000000000000000..0703c2b9b51d635ed6bbd183d528cc0261299cfb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/lapacke_helpers.h
@@ -0,0 +1,154 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2021 Erik Schultheis <erik.schultheis@aalto.fi>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LAPACKE_HELPERS_H
+#define EIGEN_LAPACKE_HELPERS_H
+
+#include "./InternalHeaderCheck.h"
+
+#ifdef EIGEN_USE_MKL
+#include "mkl_lapacke.h"
+#else
+#include "lapacke.h"
+#endif
+
+namespace Eigen {
+namespace internal {
+/**
+ * \internal
+ * \brief Implementation details and helper functions for the lapacke glue code.
+ */
+namespace lapacke_helpers {
+
+// ---------------------------------------------------------------------------------------------------------------------
+//                  Translation from Eigen to Lapacke for types and constants
+// ---------------------------------------------------------------------------------------------------------------------
+
+// For complex numbers, the types in Eigen and Lapacke are different, but layout compatible.
+template<typename Scalar>
+struct translate_type_imp;
+template<>
+struct translate_type_imp<float> {
+    using type = float;
+};
+template<>
+struct translate_type_imp<double> {
+    using type = double;
+};
+template<>
+struct translate_type_imp<std::complex<double>> {
+    using type = lapack_complex_double;
+};
+template<>
+struct translate_type_imp<std::complex<float>> {
+    using type = lapack_complex_float;
+};
+
+/// Given an Eigen types, this is defined to be the corresponding, layout-compatible lapack type
+template<typename Scalar>
+using translated_type = typename translate_type_imp<Scalar>::type;
+
+/// These functions convert their arguments from Eigen to Lapack types
+/// This function performs conversion for any of the translations defined above.
+template<typename Source, typename Target=translated_type<Source>>
+EIGEN_ALWAYS_INLINE auto to_lapack(Source value) { return static_cast<Target>(value); }
+
+/// This function performs conversions for pointer types corresponding to the translations abovce.
+/// This is valid because the translations are between layout-compatible types.
+template<typename Source, typename Target=translated_type<Source>>
+EIGEN_ALWAYS_INLINE auto to_lapack(Source *value) { return reinterpret_cast<Target*>(value); }
+
+/// This function converts the Eigen Index to a lapack index, with possible range checks
+/// \sa internal::convert_index
+EIGEN_ALWAYS_INLINE lapack_int to_lapack(Index index) {
+  return convert_index<lapack_int>(index);
+}
+
+/// translates storage order of the given Eigen object to the corresponding lapack constant
+template<typename Derived>
+EIGEN_ALWAYS_INLINE EIGEN_CONSTEXPR lapack_int lapack_storage_of(const EigenBase<Derived> &) {
+  return Derived::IsRowMajor ? LAPACK_ROW_MAJOR : LAPACK_COL_MAJOR;
+}
+
+// ---------------------------------------------------------------------------------------------------------------------
+//              Automatic generation of low-level wrappers
+// ---------------------------------------------------------------------------------------------------------------------
+
+/*!
+ * \internal
+ * \brief Helper type to facilitate the wrapping of raw LAPACKE functions for different types into a single, overloaded C++ function.
+ * This is achieved in combination with \r EIGEN_MAKE_LAPACKE_WRAPPER
+ * \details This implementation works by providing an overloaded call function that just forwards its arguments to the
+ * underlying lapack function. Each of these overloads is enabled only if the call is actually well formed.
+ * Because these lapack functions take pointers to the underlying scalar type as arguments, even though the actual Scalars
+ * would be implicitly convertible, the pointers are not and therefore only a single overload can be valid at the same time.
+ * Thus, despite all functions taking fully generic `Args&&... args` as arguments, there is never any ambiguity.
+ */
+template<typename DoubleFn, typename SingleFn, typename DoubleCpxFn, typename SingleCpxFn>
+struct WrappingHelper {
+  // The naming of double, single, double complex and single complex is purely for readability
+  // and doesn't actually affect the workings of this class. In principle, the arguments can
+  // be supplied in any permuted order.
+  DoubleFn double_; SingleFn single_; DoubleCpxFn double_cpx_; SingleCpxFn single_cpx_;
+
+  template<typename... Args>
+  auto call(Args&&... args) -> decltype(double_(std::forward<Args>(args)...)) {
+    return double_(std::forward<Args>(args)...);
+  }
+
+  template<typename... Args>
+  auto call(Args&&... args) -> decltype(single_(std::forward<Args>(args)...)){
+    return single_(std::forward<Args>(args)...);
+  }
+
+  template<typename... Args>
+  auto call(Args&&... args) -> decltype(double_cpx_(std::forward<Args>(args)...)){
+    return double_cpx_(std::forward<Args>(args)...);
+  }
+
+  template<typename... Args>
+  auto call(Args&&... args) -> decltype(single_cpx_(std::forward<Args>(args)...)){
+    return single_cpx_(std::forward<Args>(args)...);
+  }
+};
+
+/** \internal Helper function that generates a `WrappingHelper` object with the given function pointers and
+ * invokes its `call` method, thus selecting one of the overloads.
+ * \sa EIGEN_MAKE_LAPACKE_WRAPPER
+ */
+template<typename DoubleFn, typename SingleFn, typename DoubleCpxFn, typename SingleCpxFn, typename... Args>
+EIGEN_ALWAYS_INLINE auto call_wrapper(DoubleFn df, SingleFn sf, DoubleCpxFn dcf, SingleCpxFn scf, Args&&... args) {
+  WrappingHelper<DoubleFn, SingleFn, DoubleCpxFn, SingleCpxFn> helper{df, sf, dcf, scf};
+  return helper.call(std::forward<Args>(args)...);
+}
+
+/**
+ * \internal
+ * Generates a new function `Function` that dispatches to the corresponding LAPACKE_? prefixed functions.
+ * \sa WrappingHelper
+ */
+#define EIGEN_MAKE_LAPACKE_WRAPPER(FUNCTION) \
+template<typename... Args> \
+EIGEN_ALWAYS_INLINE auto FUNCTION(Args&&... args) { return call_wrapper(LAPACKE_d##FUNCTION, LAPACKE_s##FUNCTION, LAPACKE_z##FUNCTION, LAPACKE_c##FUNCTION, std::forward<Args>(args)...); }
+
+// Now with this macro and the helper wrappers, we can generate the dispatch for all the lapacke functions that are
+// used in Eigen.
+// We define these here instead of in the files where they are used because this allows us to #undef the macro again
+// right here
+EIGEN_MAKE_LAPACKE_WRAPPER(potrf)
+EIGEN_MAKE_LAPACKE_WRAPPER(getrf)
+EIGEN_MAKE_LAPACKE_WRAPPER(geqrf)
+EIGEN_MAKE_LAPACKE_WRAPPER(gesdd)
+
+#undef EIGEN_MAKE_LAPACKE_WRAPPER
+}
+}
+}
+
+#endif // EIGEN_LAPACKE_HELPERS_H
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/lapacke_mangling.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/lapacke_mangling.h
new file mode 100644
index 0000000000000000000000000000000000000000..6211fd144d361701309e3d39ace6b5b9e14d188e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/misc/lapacke_mangling.h
@@ -0,0 +1,17 @@
+#ifndef LAPACK_HEADER_INCLUDED
+#define LAPACK_HEADER_INCLUDED
+
+#ifndef LAPACK_GLOBAL
+#if defined(LAPACK_GLOBAL_PATTERN_LC) || defined(ADD_)
+#define LAPACK_GLOBAL(lcname,UCNAME)  lcname##_
+#elif defined(LAPACK_GLOBAL_PATTERN_UC) || defined(UPPER)
+#define LAPACK_GLOBAL(lcname,UCNAME)  UCNAME
+#elif defined(LAPACK_GLOBAL_PATTERN_MC) || defined(NOCHANGE)
+#define LAPACK_GLOBAL(lcname,UCNAME)  lcname
+#else
+#define LAPACK_GLOBAL(lcname,UCNAME)  lcname##_
+#endif
+#endif
+
+#endif
+
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/ArrayCwiseBinaryOps.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/ArrayCwiseBinaryOps.h
new file mode 100644
index 0000000000000000000000000000000000000000..35461da8bf8a9f364bc6a4ce1a762b33a035c854
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/ArrayCwiseBinaryOps.h
@@ -0,0 +1,351 @@
+
+/** \returns an expression of the coefficient wise product of \c *this and \a other
+  *
+  * \sa MatrixBase::cwiseProduct
+  */
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)
+operator*(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
+{
+  return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient wise quotient of \c *this and \a other
+  *
+  * \sa MatrixBase::cwiseQuotient
+  */
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_quotient_op<Scalar,typename OtherDerived::Scalar>, const Derived, const OtherDerived>
+operator/(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
+{
+  return CwiseBinaryOp<internal::scalar_quotient_op<Scalar,typename OtherDerived::Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise min of \c *this and \a other
+  *
+  * Example: \include Cwise_min.cpp
+  * Output: \verbinclude Cwise_min.out
+  *
+  * \sa max()
+  */
+template <int NaNPropagation=PropagateFast, typename OtherDerived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+min
+#else
+(min)
+#endif
+(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
+{
+  return CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise min of \c *this and scalar \a other
+  *
+  * \sa max()
+  */
+template <int NaNPropagation=PropagateFast>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,NaNPropagation>, const Derived,
+    const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+min
+#else
+(min)
+#endif
+(const Scalar &other) const
+{
+  return (min<NaNPropagation>)(Derived::PlainObject::Constant(rows(), cols(), other));
+}
+
+/** \returns an expression of the coefficient-wise max of \c *this and \a other
+  *
+  * Example: \include Cwise_max.cpp
+  * Output: \verbinclude Cwise_max.out
+  *
+  * \sa min()
+  */
+template <int NaNPropagation=PropagateFast, typename OtherDerived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+max
+#else
+(max)
+#endif
+(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
+{
+  return CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise max of \c *this and scalar \a other
+  *
+  * \sa min()
+  */
+template <int NaNPropagation=PropagateFast>
+EIGEN_DEVICE_FUNC
+    EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,NaNPropagation>, const Derived,
+                                        const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+max
+#else
+(max)
+#endif
+(const Scalar &other) const
+{
+  return (max<NaNPropagation>)(Derived::PlainObject::Constant(rows(), cols(), other));
+}
+
+/** \returns an expression of the coefficient-wise absdiff of \c *this and \a other
+  *
+  * Example: \include Cwise_absolute_difference.cpp
+  * Output: \verbinclude Cwise_absolute_difference.out
+  *
+  * \sa absolute_difference()
+  */
+EIGEN_MAKE_CWISE_BINARY_OP(absolute_difference,absolute_difference)
+
+/** \returns an expression of the coefficient-wise absolute_difference of \c *this and scalar \a other
+  *
+  * \sa absolute_difference()
+  */
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_absolute_difference_op<Scalar,Scalar>, const Derived,
+                                        const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> >
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+absolute_difference
+#else
+(absolute_difference)
+#endif
+(const Scalar &other) const
+{
+  return (absolute_difference)(Derived::PlainObject::Constant(rows(), cols(), other));
+}
+
+/** \returns an expression of the coefficient-wise power of \c *this to the given array of \a exponents.
+  *
+  * This function computes the coefficient-wise power.
+  *
+  * Example: \include Cwise_array_power_array.cpp
+  * Output: \verbinclude Cwise_array_power_array.out
+  */
+EIGEN_MAKE_CWISE_BINARY_OP(pow,pow)
+
+/** \returns an expression of the coefficient-wise atan2(\c *this, \a y), where \a y is the given array argument.
+  *
+  * This function computes the coefficient-wise atan2.
+  *
+  */
+EIGEN_MAKE_CWISE_BINARY_OP(atan2,atan2)
+
+
+// TODO code generating macros could be moved to Macros.h and could include generation of documentation
+#define EIGEN_MAKE_CWISE_COMP_OP(OP, COMPARATOR) \
+template<typename OtherDerived> \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_cmp_op<Scalar, typename OtherDerived::Scalar, internal::cmp_ ## COMPARATOR>, const Derived, const OtherDerived> \
+OP(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
+{ \
+  return CwiseBinaryOp<internal::scalar_cmp_op<Scalar, typename OtherDerived::Scalar, internal::cmp_ ## COMPARATOR>, const Derived, const OtherDerived>(derived(), other.derived()); \
+}\
+typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar,Scalar, internal::cmp_ ## COMPARATOR>, const Derived, const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> > Cmp ## COMPARATOR ## ReturnType; \
+typedef CwiseBinaryOp<internal::scalar_cmp_op<Scalar,Scalar, internal::cmp_ ## COMPARATOR>, const CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject>, const Derived > RCmp ## COMPARATOR ## ReturnType; \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Cmp ## COMPARATOR ## ReturnType \
+OP(const Scalar& s) const { \
+  return this->OP(Derived::PlainObject::Constant(rows(), cols(), s)); \
+} \
+EIGEN_DEVICE_FUNC friend EIGEN_STRONG_INLINE const RCmp ## COMPARATOR ## ReturnType \
+OP(const Scalar& s, const EIGEN_CURRENT_STORAGE_BASE_CLASS<Derived>& d) { \
+  return Derived::PlainObject::Constant(d.rows(), d.cols(), s).OP(d); \
+}
+
+#define EIGEN_MAKE_CWISE_COMP_R_OP(OP, R_OP, RCOMPARATOR) \
+template<typename OtherDerived> \
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_cmp_op<typename OtherDerived::Scalar, Scalar, internal::cmp_##RCOMPARATOR>, const OtherDerived, const Derived> \
+OP(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const \
+{ \
+  return CwiseBinaryOp<internal::scalar_cmp_op<typename OtherDerived::Scalar, Scalar, internal::cmp_##RCOMPARATOR>, const OtherDerived, const Derived>(other.derived(), derived()); \
+} \
+EIGEN_DEVICE_FUNC \
+inline const RCmp ## RCOMPARATOR ## ReturnType \
+OP(const Scalar& s) const { \
+  return Derived::PlainObject::Constant(rows(), cols(), s).R_OP(*this); \
+} \
+friend inline const Cmp ## RCOMPARATOR ## ReturnType \
+OP(const Scalar& s, const Derived& d) { \
+  return d.R_OP(Derived::PlainObject::Constant(d.rows(), d.cols(), s)); \
+}
+
+
+
+/** \returns an expression of the coefficient-wise \< operator of *this and \a other
+  *
+  * Example: \include Cwise_less.cpp
+  * Output: \verbinclude Cwise_less.out
+  *
+  * \sa all(), any(), operator>(), operator<=()
+  */
+EIGEN_MAKE_CWISE_COMP_OP(operator<, LT)
+
+/** \returns an expression of the coefficient-wise \<= operator of *this and \a other
+  *
+  * Example: \include Cwise_less_equal.cpp
+  * Output: \verbinclude Cwise_less_equal.out
+  *
+  * \sa all(), any(), operator>=(), operator<()
+  */
+EIGEN_MAKE_CWISE_COMP_OP(operator<=, LE)
+
+/** \returns an expression of the coefficient-wise \> operator of *this and \a other
+  *
+  * Example: \include Cwise_greater.cpp
+  * Output: \verbinclude Cwise_greater.out
+  *
+  * \sa all(), any(), operator>=(), operator<()
+  */
+EIGEN_MAKE_CWISE_COMP_R_OP(operator>, operator<, LT)
+
+/** \returns an expression of the coefficient-wise \>= operator of *this and \a other
+  *
+  * Example: \include Cwise_greater_equal.cpp
+  * Output: \verbinclude Cwise_greater_equal.out
+  *
+  * \sa all(), any(), operator>(), operator<=()
+  */
+EIGEN_MAKE_CWISE_COMP_R_OP(operator>=, operator<=, LE)
+
+/** \returns an expression of the coefficient-wise == operator of *this and \a other
+  *
+  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
+  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
+  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
+  * isMuchSmallerThan().
+  *
+  * Example: \include Cwise_equal_equal.cpp
+  * Output: \verbinclude Cwise_equal_equal.out
+  *
+  * \sa all(), any(), isApprox(), isMuchSmallerThan()
+  */
+EIGEN_MAKE_CWISE_COMP_OP(operator==, EQ)
+
+/** \returns an expression of the coefficient-wise != operator of *this and \a other
+  *
+  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
+  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
+  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
+  * isMuchSmallerThan().
+  *
+  * Example: \include Cwise_not_equal.cpp
+  * Output: \verbinclude Cwise_not_equal.out
+  *
+  * \sa all(), any(), isApprox(), isMuchSmallerThan()
+  */
+EIGEN_MAKE_CWISE_COMP_OP(operator!=, NEQ)
+
+
+#undef EIGEN_MAKE_CWISE_COMP_OP
+#undef EIGEN_MAKE_CWISE_COMP_R_OP
+
+// scalar addition
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+EIGEN_MAKE_SCALAR_BINARY_OP(operator+,sum)
+#else
+/** \returns an expression of \c *this with each coeff incremented by the constant \a scalar
+  *
+  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
+  *
+  * Example: \include Cwise_plus.cpp
+  * Output: \verbinclude Cwise_plus.out
+  *
+  * \sa operator+=(), operator-()
+  */
+template<typename T>
+const CwiseBinaryOp<internal::scalar_sum_op<Scalar,T>,Derived,Constant<T> > operator+(const T& scalar) const;
+/** \returns an expression of \a expr with each coeff incremented by the constant \a scalar
+  *
+  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
+  */
+template<typename T> friend
+const CwiseBinaryOp<internal::scalar_sum_op<T,Scalar>,Constant<T>,Derived> operator+(const T& scalar, const StorageBaseType& expr);
+#endif
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+EIGEN_MAKE_SCALAR_BINARY_OP(operator-,difference)
+#else
+/** \returns an expression of \c *this with each coeff decremented by the constant \a scalar
+  *
+  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
+  *
+  * Example: \include Cwise_minus.cpp
+  * Output: \verbinclude Cwise_minus.out
+  *
+  * \sa operator+=(), operator-()
+  */
+template<typename T>
+const CwiseBinaryOp<internal::scalar_difference_op<Scalar,T>,Derived,Constant<T> > operator-(const T& scalar) const;
+/** \returns an expression of the constant matrix of value \a scalar decremented by the coefficients of \a expr
+  *
+  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
+  */
+template<typename T> friend
+const CwiseBinaryOp<internal::scalar_difference_op<T,Scalar>,Constant<T>,Derived> operator-(const T& scalar, const StorageBaseType& expr);
+#endif
+
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  EIGEN_MAKE_SCALAR_BINARY_OP_ONTHELEFT(operator/,quotient)
+#else
+  /**
+    * \brief Component-wise division of the scalar \a s by array elements of \a a.
+    *
+    * \tparam Scalar is the scalar type of \a x. It must be compatible with the scalar type of the given array expression (\c Derived::Scalar).
+    */
+  template<typename T> friend
+  inline const CwiseBinaryOp<internal::scalar_quotient_op<T,Scalar>,Constant<T>,Derived>
+  operator/(const T& s,const StorageBaseType& a);
+#endif
+
+// NOTE disabled until we agree on argument order
+#if 0
+/** \cpp11 \returns an expression of the coefficient-wise polygamma function.
+  *
+  * \specialfunctions_module
+  *
+  * It returns the \a n -th derivative of the digamma(psi) evaluated at \c *this.
+  *
+  * \warning Be careful with the order of the parameters: x.polygamma(n) is equivalent to polygamma(n,x)
+  *
+  * \sa Eigen::polygamma()
+  */
+template<typename DerivedN>
+inline const CwiseBinaryOp<internal::scalar_polygamma_op<Scalar>, const DerivedN, const Derived>
+polygamma(const EIGEN_CURRENT_STORAGE_BASE_CLASS<DerivedN> &n) const
+{
+  return CwiseBinaryOp<internal::scalar_polygamma_op<Scalar>, const DerivedN, const Derived>(n.derived(), this->derived());
+}
+#endif
+
+/** \returns an expression of the coefficient-wise zeta function.
+  *
+  * \specialfunctions_module
+  *
+  * It returns the Riemann zeta function of two arguments \c *this and \a q:
+  *
+  * \param q is the shift, it must be > 0
+  *
+  * \note *this is the exponent, it must be > 1.
+  * \note This function supports only float and double scalar types. To support other scalar types, the user has
+  * to provide implementations of zeta(T,T) for any scalar type T to be supported.
+  *
+  * This method is an alias for zeta(*this,q);
+  *
+  * \sa Eigen::zeta()
+  */
+template<typename DerivedQ>
+inline const CwiseBinaryOp<internal::scalar_zeta_op<Scalar>, const Derived, const DerivedQ>
+zeta(const EIGEN_CURRENT_STORAGE_BASE_CLASS<DerivedQ> &q) const
+{
+  return CwiseBinaryOp<internal::scalar_zeta_op<Scalar>, const Derived, const DerivedQ>(this->derived(), q.derived());
+}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/ArrayCwiseUnaryOps.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/ArrayCwiseUnaryOps.h
new file mode 100644
index 0000000000000000000000000000000000000000..301a900a10e603464960a162ecf27fcce5485e44
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/ArrayCwiseUnaryOps.h
@@ -0,0 +1,732 @@
+
+
+typedef CwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived> AbsReturnType;
+typedef CwiseUnaryOp<internal::scalar_arg_op<Scalar>, const Derived> ArgReturnType;
+typedef CwiseUnaryOp<internal::scalar_carg_op<Scalar>, const Derived> CArgReturnType;
+typedef CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const Derived> Abs2ReturnType;
+typedef CwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived> SqrtReturnType;
+typedef CwiseUnaryOp<internal::scalar_rsqrt_op<Scalar>, const Derived> RsqrtReturnType;
+typedef CwiseUnaryOp<internal::scalar_sign_op<Scalar>, const Derived> SignReturnType;
+typedef CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived> InverseReturnType;
+typedef CwiseUnaryOp<internal::scalar_boolean_not_op<Scalar>, const Derived> BooleanNotReturnType;
+typedef CwiseUnaryOp<internal::scalar_bitwise_not_op<Scalar>, const Derived> BitwiseNotReturnType;
+
+typedef CwiseUnaryOp<internal::scalar_exp_op<Scalar>, const Derived> ExpReturnType;
+typedef CwiseUnaryOp<internal::scalar_expm1_op<Scalar>, const Derived> Expm1ReturnType;
+typedef CwiseUnaryOp<internal::scalar_log_op<Scalar>, const Derived> LogReturnType;
+typedef CwiseUnaryOp<internal::scalar_log1p_op<Scalar>, const Derived> Log1pReturnType;
+typedef CwiseUnaryOp<internal::scalar_log10_op<Scalar>, const Derived> Log10ReturnType;
+typedef CwiseUnaryOp<internal::scalar_log2_op<Scalar>, const Derived> Log2ReturnType;
+typedef CwiseUnaryOp<internal::scalar_cos_op<Scalar>, const Derived> CosReturnType;
+typedef CwiseUnaryOp<internal::scalar_sin_op<Scalar>, const Derived> SinReturnType;
+typedef CwiseUnaryOp<internal::scalar_tan_op<Scalar>, const Derived> TanReturnType;
+typedef CwiseUnaryOp<internal::scalar_acos_op<Scalar>, const Derived> AcosReturnType;
+typedef CwiseUnaryOp<internal::scalar_asin_op<Scalar>, const Derived> AsinReturnType;
+typedef CwiseUnaryOp<internal::scalar_atan_op<Scalar>, const Derived> AtanReturnType;
+typedef CwiseUnaryOp<internal::scalar_tanh_op<Scalar>, const Derived> TanhReturnType;
+typedef CwiseUnaryOp<internal::scalar_logistic_op<Scalar>, const Derived> LogisticReturnType;
+typedef CwiseUnaryOp<internal::scalar_sinh_op<Scalar>, const Derived> SinhReturnType;
+typedef CwiseUnaryOp<internal::scalar_atanh_op<Scalar>, const Derived> AtanhReturnType;
+typedef CwiseUnaryOp<internal::scalar_asinh_op<Scalar>, const Derived> AsinhReturnType;
+typedef CwiseUnaryOp<internal::scalar_acosh_op<Scalar>, const Derived> AcoshReturnType;
+typedef CwiseUnaryOp<internal::scalar_cosh_op<Scalar>, const Derived> CoshReturnType;
+typedef CwiseUnaryOp<internal::scalar_square_op<Scalar>, const Derived> SquareReturnType;
+typedef CwiseUnaryOp<internal::scalar_cube_op<Scalar>, const Derived> CubeReturnType;
+typedef CwiseUnaryOp<internal::scalar_round_op<Scalar>, const Derived> RoundReturnType;
+typedef CwiseUnaryOp<internal::scalar_rint_op<Scalar>, const Derived> RintReturnType;
+typedef CwiseUnaryOp<internal::scalar_floor_op<Scalar>, const Derived> FloorReturnType;
+typedef CwiseUnaryOp<internal::scalar_ceil_op<Scalar>, const Derived> CeilReturnType;
+typedef CwiseUnaryOp<internal::scalar_isnan_op<Scalar>, const Derived> IsNaNReturnType;
+typedef CwiseUnaryOp<internal::scalar_isinf_op<Scalar>, const Derived> IsInfReturnType;
+typedef CwiseUnaryOp<internal::scalar_isfinite_op<Scalar>, const Derived> IsFiniteReturnType;
+
+/** \returns an expression of the coefficient-wise absolute value of \c *this
+  *
+  * Example: \include Cwise_abs.cpp
+  * Output: \verbinclude Cwise_abs.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_abs">Math functions</a>, abs2()
+  */
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const AbsReturnType
+abs() const
+{
+  return AbsReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise phase angle of \c *this
+  *
+  * Example: \include Cwise_arg.cpp
+  * Output: \verbinclude Cwise_arg.out
+  *
+  * \sa abs()
+  */
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const ArgReturnType
+arg() const
+{
+  return ArgReturnType(derived());
+}
+
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const CArgReturnType
+carg() const { return CArgReturnType(derived()); }
+
+/** \returns an expression of the coefficient-wise squared absolute value of \c *this
+  *
+  * Example: \include Cwise_abs2.cpp
+  * Output: \verbinclude Cwise_abs2.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_abs2">Math functions</a>, abs(), square()
+  */
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const Abs2ReturnType
+abs2() const
+{
+  return Abs2ReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise exponential of *this.
+  *
+  * This function computes the coefficient-wise exponential. The function MatrixBase::exp() in the
+  * unsupported module MatrixFunctions computes the matrix exponential.
+  *
+  * Example: \include Cwise_exp.cpp
+  * Output: \verbinclude Cwise_exp.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_exp">Math functions</a>, pow(), log(), sin(), cos()
+  */
+EIGEN_DEVICE_FUNC
+inline const ExpReturnType
+exp() const
+{
+  return ExpReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise exponential of *this minus 1.
+  *
+  * In exact arithmetic, \c x.expm1() is equivalent to \c x.exp() - 1,
+  * however, with finite precision, this function is much more accurate when \c x is close to zero.
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_expm1">Math functions</a>, exp()
+  */
+EIGEN_DEVICE_FUNC
+inline const Expm1ReturnType
+expm1() const
+{
+  return Expm1ReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise logarithm of *this.
+  *
+  * This function computes the coefficient-wise logarithm. The function MatrixBase::log() in the
+  * unsupported module MatrixFunctions computes the matrix logarithm.
+  *
+  * Example: \include Cwise_log.cpp
+  * Output: \verbinclude Cwise_log.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_log">Math functions</a>, log()
+  */
+EIGEN_DEVICE_FUNC
+inline const LogReturnType
+log() const
+{
+  return LogReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise logarithm of 1 plus \c *this.
+  *
+  * In exact arithmetic, \c x.log() is equivalent to \c (x+1).log(),
+  * however, with finite precision, this function is much more accurate when \c x is close to zero.
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_log1p">Math functions</a>, log()
+  */
+EIGEN_DEVICE_FUNC
+inline const Log1pReturnType
+log1p() const
+{
+  return Log1pReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise base-10 logarithm of *this.
+  *
+  * This function computes the coefficient-wise base-10 logarithm.
+  *
+  * Example: \include Cwise_log10.cpp
+  * Output: \verbinclude Cwise_log10.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_log10">Math functions</a>, log()
+  */
+EIGEN_DEVICE_FUNC
+inline const Log10ReturnType
+log10() const
+{
+  return Log10ReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise base-2 logarithm of *this.
+  *
+  * This function computes the coefficient-wise base-2 logarithm.
+  *
+  */
+EIGEN_DEVICE_FUNC
+inline const Log2ReturnType
+log2() const
+{
+  return Log2ReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise square root of *this.
+  *
+  * This function computes the coefficient-wise square root. The function MatrixBase::sqrt() in the
+  * unsupported module MatrixFunctions computes the matrix square root.
+  *
+  * Example: \include Cwise_sqrt.cpp
+  * Output: \verbinclude Cwise_sqrt.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_sqrt">Math functions</a>, pow(), square()
+  */
+EIGEN_DEVICE_FUNC
+inline const SqrtReturnType
+sqrt() const
+{
+  return SqrtReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise inverse square root of *this.
+  *
+  * This function computes the coefficient-wise inverse square root.
+  *
+  * Example: \include Cwise_sqrt.cpp
+  * Output: \verbinclude Cwise_sqrt.out
+  *
+  * \sa pow(), square()
+  */
+EIGEN_DEVICE_FUNC
+inline const RsqrtReturnType
+rsqrt() const
+{
+  return RsqrtReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise signum of *this.
+  *
+  * This function computes the coefficient-wise signum.
+  *
+  * Example: \include Cwise_sign.cpp
+  * Output: \verbinclude Cwise_sign.out
+  *
+  * \sa pow(), square()
+  */
+EIGEN_DEVICE_FUNC
+inline const SignReturnType
+sign() const
+{
+  return SignReturnType(derived());
+}
+
+
+/** \returns an expression of the coefficient-wise cosine of *this.
+  *
+  * This function computes the coefficient-wise cosine. The function MatrixBase::cos() in the
+  * unsupported module MatrixFunctions computes the matrix cosine.
+  *
+  * Example: \include Cwise_cos.cpp
+  * Output: \verbinclude Cwise_cos.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_cos">Math functions</a>, sin(), acos()
+  */
+EIGEN_DEVICE_FUNC
+inline const CosReturnType
+cos() const
+{
+  return CosReturnType(derived());
+}
+
+
+/** \returns an expression of the coefficient-wise sine of *this.
+  *
+  * This function computes the coefficient-wise sine. The function MatrixBase::sin() in the
+  * unsupported module MatrixFunctions computes the matrix sine.
+  *
+  * Example: \include Cwise_sin.cpp
+  * Output: \verbinclude Cwise_sin.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_sin">Math functions</a>, cos(), asin()
+  */
+EIGEN_DEVICE_FUNC
+inline const SinReturnType
+sin() const
+{
+  return SinReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise tan of *this.
+  *
+  * Example: \include Cwise_tan.cpp
+  * Output: \verbinclude Cwise_tan.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_tan">Math functions</a>, cos(), sin()
+  */
+EIGEN_DEVICE_FUNC
+inline const TanReturnType
+tan() const
+{
+  return TanReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise arc tan of *this.
+  *
+  * Example: \include Cwise_atan.cpp
+  * Output: \verbinclude Cwise_atan.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_atan">Math functions</a>, tan(), asin(), acos()
+  */
+EIGEN_DEVICE_FUNC
+inline const AtanReturnType
+atan() const
+{
+  return AtanReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise arc cosine of *this.
+  *
+  * Example: \include Cwise_acos.cpp
+  * Output: \verbinclude Cwise_acos.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_acos">Math functions</a>, cos(), asin()
+  */
+EIGEN_DEVICE_FUNC
+inline const AcosReturnType
+acos() const
+{
+  return AcosReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise arc sine of *this.
+  *
+  * Example: \include Cwise_asin.cpp
+  * Output: \verbinclude Cwise_asin.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_asin">Math functions</a>, sin(), acos()
+  */
+EIGEN_DEVICE_FUNC
+inline const AsinReturnType
+asin() const
+{
+  return AsinReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise hyperbolic tan of *this.
+  *
+  * Example: \include Cwise_tanh.cpp
+  * Output: \verbinclude Cwise_tanh.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_tanh">Math functions</a>, tan(), sinh(), cosh()
+  */
+EIGEN_DEVICE_FUNC
+inline const TanhReturnType
+tanh() const
+{
+  return TanhReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise hyperbolic sin of *this.
+  *
+  * Example: \include Cwise_sinh.cpp
+  * Output: \verbinclude Cwise_sinh.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_sinh">Math functions</a>, sin(), tanh(), cosh()
+  */
+EIGEN_DEVICE_FUNC
+inline const SinhReturnType
+sinh() const
+{
+  return SinhReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise hyperbolic cos of *this.
+  *
+  * Example: \include Cwise_cosh.cpp
+  * Output: \verbinclude Cwise_cosh.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_cosh">Math functions</a>, tanh(), sinh(), cosh()
+  */
+EIGEN_DEVICE_FUNC
+inline const CoshReturnType
+cosh() const
+{
+  return CoshReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise inverse hyperbolic tan of *this.
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_atanh">Math functions</a>, atanh(), asinh(), acosh()
+  */
+EIGEN_DEVICE_FUNC
+inline const AtanhReturnType
+atanh() const
+{
+  return AtanhReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise inverse hyperbolic sin of *this.
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_asinh">Math functions</a>, atanh(), asinh(), acosh()
+  */
+EIGEN_DEVICE_FUNC
+inline const AsinhReturnType
+asinh() const
+{
+  return AsinhReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise inverse hyperbolic cos of *this.
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_acosh">Math functions</a>, atanh(), asinh(), acosh()
+  */
+EIGEN_DEVICE_FUNC
+inline const AcoshReturnType
+acosh() const
+{
+  return AcoshReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise logistic of *this.
+  */
+EIGEN_DEVICE_FUNC
+inline const LogisticReturnType
+logistic() const
+{
+  return LogisticReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise inverse of *this.
+  *
+  * Example: \include Cwise_inverse.cpp
+  * Output: \verbinclude Cwise_inverse.out
+  *
+  * \sa operator/(), operator*()
+  */
+EIGEN_DEVICE_FUNC
+inline const InverseReturnType
+inverse() const
+{
+  return InverseReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise square of *this.
+  *
+  * Example: \include Cwise_square.cpp
+  * Output: \verbinclude Cwise_square.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_squareE">Math functions</a>, abs2(), cube(), pow()
+  */
+EIGEN_DEVICE_FUNC
+inline const SquareReturnType
+square() const
+{
+  return SquareReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise cube of *this.
+  *
+  * Example: \include Cwise_cube.cpp
+  * Output: \verbinclude Cwise_cube.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_cube">Math functions</a>, square(), pow()
+  */
+EIGEN_DEVICE_FUNC
+inline const CubeReturnType
+cube() const
+{
+  return CubeReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise rint of *this.
+  *
+  * Example: \include Cwise_rint.cpp
+  * Output: \verbinclude Cwise_rint.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_rint">Math functions</a>, ceil(), floor()
+  */
+EIGEN_DEVICE_FUNC
+inline const RintReturnType
+rint() const
+{
+  return RintReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise round of *this.
+  *
+  * Example: \include Cwise_round.cpp
+  * Output: \verbinclude Cwise_round.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_round">Math functions</a>, ceil(), floor()
+  */
+EIGEN_DEVICE_FUNC
+inline const RoundReturnType
+round() const
+{
+  return RoundReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise floor of *this.
+  *
+  * Example: \include Cwise_floor.cpp
+  * Output: \verbinclude Cwise_floor.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_floor">Math functions</a>, ceil(), round()
+  */
+EIGEN_DEVICE_FUNC
+inline const FloorReturnType
+floor() const
+{
+  return FloorReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise ceil of *this.
+  *
+  * Example: \include Cwise_ceil.cpp
+  * Output: \verbinclude Cwise_ceil.out
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_ceil">Math functions</a>, floor(), round()
+  */
+EIGEN_DEVICE_FUNC
+inline const CeilReturnType
+ceil() const
+{
+  return CeilReturnType(derived());
+}
+
+template<int N> struct ShiftRightXpr {
+  typedef CwiseUnaryOp<internal::scalar_shift_right_op<Scalar, N>, const Derived> Type;
+};
+
+/** \returns an expression of \c *this with the \a Scalar type arithmetically
+  * shifted right by \a N bit positions.
+  *
+  * The template parameter \a N specifies the number of bit positions to shift.
+  * 
+  * \sa shiftLeft()
+  */
+template<int N>
+EIGEN_DEVICE_FUNC
+typename ShiftRightXpr<N>::Type
+shiftRight() const
+{
+  return typename ShiftRightXpr<N>::Type(derived());
+}
+
+
+template<int N> struct ShiftLeftXpr {
+  typedef CwiseUnaryOp<internal::scalar_shift_left_op<Scalar, N>, const Derived> Type;
+};
+
+/** \returns an expression of \c *this with the \a Scalar type logically
+  * shifted left by \a N bit positions.
+  *
+  * The template parameter \a N specifies the number of bit positions to shift.
+  *
+  * \sa shiftRight()
+  */
+template<int N>
+EIGEN_DEVICE_FUNC
+typename ShiftLeftXpr<N>::Type
+shiftLeft() const
+{
+  return typename ShiftLeftXpr<N>::Type(derived());
+}
+
+/** \returns an expression of the coefficient-wise isnan of *this.
+  *
+  * Example: \include Cwise_isNaN.cpp
+  * Output: \verbinclude Cwise_isNaN.out
+  *
+  * \sa isfinite(), isinf()
+  */
+EIGEN_DEVICE_FUNC
+inline const IsNaNReturnType
+isNaN() const
+{
+  return IsNaNReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise isinf of *this.
+  *
+  * Example: \include Cwise_isInf.cpp
+  * Output: \verbinclude Cwise_isInf.out
+  *
+  * \sa isnan(), isfinite()
+  */
+EIGEN_DEVICE_FUNC
+inline const IsInfReturnType
+isInf() const
+{
+  return IsInfReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise isfinite of *this.
+  *
+  * Example: \include Cwise_isFinite.cpp
+  * Output: \verbinclude Cwise_isFinite.out
+  *
+  * \sa isnan(), isinf()
+  */
+EIGEN_DEVICE_FUNC
+inline const IsFiniteReturnType
+isFinite() const
+{
+  return IsFiniteReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise ! operator of *this
+  *
+  * Example: \include Cwise_boolean_not.cpp
+  * Output: \verbinclude Cwise_boolean_not.out
+  *
+  * \sa operator!=()
+  */
+EIGEN_DEVICE_FUNC
+inline const BooleanNotReturnType
+operator!() const
+{
+  return BooleanNotReturnType(derived());
+}
+
+/** \returns an expression of the bitwise ~ operator of *this
+  */
+EIGEN_DEVICE_FUNC
+inline const BitwiseNotReturnType
+operator~() const
+{
+  return BitwiseNotReturnType(derived());
+}
+
+
+// --- SpecialFunctions module ---
+
+typedef CwiseUnaryOp<internal::scalar_lgamma_op<Scalar>, const Derived> LgammaReturnType;
+typedef CwiseUnaryOp<internal::scalar_digamma_op<Scalar>, const Derived> DigammaReturnType;
+typedef CwiseUnaryOp<internal::scalar_erf_op<Scalar>, const Derived> ErfReturnType;
+typedef CwiseUnaryOp<internal::scalar_erfc_op<Scalar>, const Derived> ErfcReturnType;
+typedef CwiseUnaryOp<internal::scalar_ndtri_op<Scalar>, const Derived> NdtriReturnType;
+
+/** \cpp11 \returns an expression of the coefficient-wise ln(|gamma(*this)|).
+  *
+  * \specialfunctions_module
+  *
+  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
+  * or float/double in non c++11 mode, the user has to provide implementations of lgamma(T) for any scalar
+  * type T to be supported.
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_lgamma">Math functions</a>, digamma()
+  */
+EIGEN_DEVICE_FUNC
+inline const LgammaReturnType
+lgamma() const
+{
+  return LgammaReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise digamma (psi, derivative of lgamma).
+  *
+  * \specialfunctions_module
+  *
+  * \note This function supports only float and double scalar types. To support other scalar types,
+  * the user has to provide implementations of digamma(T) for any scalar
+  * type T to be supported.
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_digamma">Math functions</a>, Eigen::digamma(), Eigen::polygamma(), lgamma()
+  */
+EIGEN_DEVICE_FUNC
+inline const DigammaReturnType
+digamma() const
+{
+  return DigammaReturnType(derived());
+}
+
+/** \cpp11 \returns an expression of the coefficient-wise Gauss error
+  * function of *this.
+  *
+  * \specialfunctions_module
+  *
+  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
+  * or float/double in non c++11 mode, the user has to provide implementations of erf(T) for any scalar
+  * type T to be supported.
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_erf">Math functions</a>, erfc()
+  */
+EIGEN_DEVICE_FUNC
+inline const ErfReturnType
+erf() const
+{
+  return ErfReturnType(derived());
+}
+
+/** \cpp11 \returns an expression of the coefficient-wise Complementary error
+  * function of *this.
+  *
+  * \specialfunctions_module
+  *
+  * \note This function supports only float and double scalar types in c++11 mode. To support other scalar types,
+  * or float/double in non c++11 mode, the user has to provide implementations of erfc(T) for any scalar
+  * type T to be supported.
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_erfc">Math functions</a>, erf()
+  */
+EIGEN_DEVICE_FUNC
+inline const ErfcReturnType
+erfc() const
+{
+  return ErfcReturnType(derived());
+}
+
+/** \returns an expression of the coefficient-wise inverse of the CDF of the Normal distribution function
+  * function of *this.
+  *
+  * \specialfunctions_module
+  * 
+  * In other words, considering `x = ndtri(y)`, it returns the argument, x, for which the area under the
+  * Gaussian probability density function (integrated from minus infinity to x) is equal to y.
+  *
+  * \note This function supports only float and double scalar types. To support other scalar types,
+  * the user has to provide implementations of ndtri(T) for any scalar type T to be supported.
+  *
+  * \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_ndtri">Math functions</a>
+  */
+EIGEN_DEVICE_FUNC
+inline const NdtriReturnType
+ndtri() const
+{
+  return NdtriReturnType(derived());
+}
+
+template <typename ScalarExponent>
+using UnaryPowReturnType =
+    std::enable_if_t<internal::is_arithmetic<typename NumTraits<ScalarExponent>::Real>::value,
+                     CwiseUnaryOp<internal::scalar_unary_pow_op<Scalar, ScalarExponent>, const Derived>>;
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename ScalarExponent>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const UnaryPowReturnType<ScalarExponent> pow(
+    const ScalarExponent& exponent) const {
+  return UnaryPowReturnType<ScalarExponent>(derived(), internal::scalar_unary_pow_op<Scalar, ScalarExponent>(exponent));
+#else
+/** \returns an expression of the coefficients of \c *this rasied to the constant power \a exponent
+ *
+ * \tparam T is the scalar type of \a exponent. It must be compatible with the scalar type of the given expression.
+ *
+ * This function computes the coefficient-wise power. The function MatrixBase::pow() in the
+ * unsupported module MatrixFunctions computes the matrix power.
+ *
+ * Example: \include Cwise_pow.cpp
+ * Output: \verbinclude Cwise_pow.out
+ *
+ * \sa ArrayBase::pow(ArrayBase), square(), cube(), exp(), log()
+ */
+template <typename ScalarExponent>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const UnaryPowReturnType<ScalarExponent> pow(
+    const ScalarExponent& exponent) const;
+#endif
+}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/BlockMethods.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/BlockMethods.h
new file mode 100644
index 0000000000000000000000000000000000000000..68b94130b59de62aab07911fdedff75d5b5a99d4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/BlockMethods.h
@@ -0,0 +1,1442 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+/// \internal expression type of a column */
+typedef Block<Derived, internal::traits<Derived>::RowsAtCompileTime, 1, !IsRowMajor> ColXpr;
+typedef const Block<const Derived, internal::traits<Derived>::RowsAtCompileTime, 1, !IsRowMajor> ConstColXpr;
+/// \internal expression type of a row */
+typedef Block<Derived, 1, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> RowXpr;
+typedef const Block<const Derived, 1, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> ConstRowXpr;
+/// \internal expression type of a block of whole columns */
+typedef Block<Derived, internal::traits<Derived>::RowsAtCompileTime, Dynamic, !IsRowMajor> ColsBlockXpr;
+typedef const Block<const Derived, internal::traits<Derived>::RowsAtCompileTime, Dynamic, !IsRowMajor> ConstColsBlockXpr;
+/// \internal expression type of a block of whole rows */
+typedef Block<Derived, Dynamic, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> RowsBlockXpr;
+typedef const Block<const Derived, Dynamic, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> ConstRowsBlockXpr;
+/// \internal expression type of a block of whole columns */
+template<int N> struct NColsBlockXpr { typedef Block<Derived, internal::traits<Derived>::RowsAtCompileTime, N, !IsRowMajor> Type; };
+template<int N> struct ConstNColsBlockXpr { typedef const Block<const Derived, internal::traits<Derived>::RowsAtCompileTime, N, !IsRowMajor> Type; };
+/// \internal expression type of a block of whole rows */
+template<int N> struct NRowsBlockXpr { typedef Block<Derived, N, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> Type; };
+template<int N> struct ConstNRowsBlockXpr { typedef const Block<const Derived, N, internal::traits<Derived>::ColsAtCompileTime, IsRowMajor> Type; };
+/// \internal expression of a block */
+typedef Block<Derived> BlockXpr;
+typedef const Block<const Derived> ConstBlockXpr;
+/// \internal expression of a block of fixed sizes */
+template<int Rows, int Cols> struct FixedBlockXpr { typedef Block<Derived,Rows,Cols> Type; };
+template<int Rows, int Cols> struct ConstFixedBlockXpr { typedef Block<const Derived,Rows,Cols> Type; };
+
+typedef VectorBlock<Derived> SegmentReturnType;
+typedef const VectorBlock<const Derived> ConstSegmentReturnType;
+template<int Size> struct FixedSegmentReturnType { typedef VectorBlock<Derived, Size> Type; };
+template<int Size> struct ConstFixedSegmentReturnType { typedef const VectorBlock<const Derived, Size> Type; };
+
+/// \internal inner-vector
+typedef Block<Derived,IsRowMajor?1:Dynamic,IsRowMajor?Dynamic:1,true>       InnerVectorReturnType;
+typedef Block<const Derived,IsRowMajor?1:Dynamic,IsRowMajor?Dynamic:1,true> ConstInnerVectorReturnType;
+
+/// \internal set of inner-vectors
+typedef Block<Derived,Dynamic,Dynamic,true> InnerVectorsReturnType;
+typedef Block<const Derived,Dynamic,Dynamic,true> ConstInnerVectorsReturnType;
+
+#endif // not EIGEN_PARSED_BY_DOXYGEN
+
+/// \returns an expression of a block in \c *this with either dynamic or fixed sizes.
+///
+/// \param  startRow  the first row in the block
+/// \param  startCol  the first column in the block
+/// \param  blockRows number of rows in the block, specified at either run-time or compile-time
+/// \param  blockCols number of columns in the block, specified at either run-time or compile-time
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example using runtime (aka dynamic) sizes: \include MatrixBase_block_int_int_int_int.cpp
+/// Output: \verbinclude MatrixBase_block_int_int_int_int.out
+///
+/// \newin{3.4}:
+///
+/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments. In the later case, \c n plays the role of a runtime fallback value in case \c N equals Eigen::Dynamic.
+/// Here is an example with a fixed number of rows \c NRows and dynamic number of columns \c cols:
+/// \code
+/// mat.block(i,j,fix<NRows>,cols)
+/// \endcode
+///
+/// This function thus fully covers the features offered by the following overloads block<NRows,NCols>(Index, Index),
+/// and block<NRows,NCols>(Index, Index, Index, Index) that are thus obsolete. Indeed, this generic version avoids
+/// redundancy, it preserves the argument order, and prevents the need to rely on the template keyword in templated code.
+///
+/// but with less redundancy and more consistency as it does not modify the argument order
+/// and seamlessly enable hybrid fixed/dynamic sizes.
+///
+/// \note Even in the case that the returned expression has dynamic size, in the case
+/// when it is applied to a fixed-size matrix, it inherits a fixed maximal size,
+/// which means that evaluating it does not cause a dynamic memory allocation.
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa class Block, fix, fix<N>(int)
+///
+template<typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+#else
+typename FixedBlockXpr<...,...>::Type
+#endif
+block(Index startRow, Index startCol, NRowsType blockRows, NColsType blockCols)
+{
+  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type(
+            derived(), startRow, startCol, internal::get_runtime_value(blockRows), internal::get_runtime_value(blockCols));
+}
+
+/// This is the const version of block(Index,Index,NRowsType,NColsType)
+template<typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+#else
+const typename ConstFixedBlockXpr<...,...>::Type
+#endif
+block(Index startRow, Index startCol, NRowsType blockRows, NColsType blockCols) const
+{
+  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type(
+            derived(), startRow, startCol, internal::get_runtime_value(blockRows), internal::get_runtime_value(blockCols));
+}
+
+
+
+/// \returns a expression of a top-right corner of \c *this with either dynamic or fixed sizes.
+///
+/// \param cRows the number of rows in the corner
+/// \param cCols the number of columns in the corner
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example with dynamic sizes: \include MatrixBase_topRightCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_topRightCorner_int_int.out
+///
+/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+#else
+typename FixedBlockXpr<...,...>::Type
+#endif
+topRightCorner(NRowsType cRows, NColsType cCols)
+{
+  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+            (derived(), 0, cols() - internal::get_runtime_value(cCols), internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
+}
+
+/// This is the const version of topRightCorner(NRowsType, NColsType).
+template<typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+#else
+const typename ConstFixedBlockXpr<...,...>::Type
+#endif
+topRightCorner(NRowsType cRows, NColsType cCols) const
+{
+  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+            (derived(), 0, cols() - internal::get_runtime_value(cCols), internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
+}
+
+/// \returns an expression of a fixed-size top-right corner of \c *this.
+///
+/// \tparam CRows the number of rows in the corner
+/// \tparam CCols the number of columns in the corner
+///
+/// Example: \include MatrixBase_template_int_int_topRightCorner.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_topRightCorner.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa class Block, block<int,int>(Index,Index)
+///
+template<int CRows, int CCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename FixedBlockXpr<CRows,CCols>::Type topRightCorner()
+{
+  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), 0, cols() - CCols);
+}
+
+/// This is the const version of topRightCorner<int, int>().
+template<int CRows, int CCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const typename ConstFixedBlockXpr<CRows,CCols>::Type topRightCorner() const
+{
+  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), 0, cols() - CCols);
+}
+
+/// \returns an expression of a top-right corner of \c *this.
+///
+/// \tparam CRows number of rows in corner as specified at compile-time
+/// \tparam CCols number of columns in corner as specified at compile-time
+/// \param  cRows number of rows in corner as specified at run-time
+/// \param  cCols number of columns in corner as specified at run-time
+///
+/// This function is mainly useful for corners where the number of rows is specified at compile-time
+/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
+/// information should not contradict. In other words, \a cRows should equal \a CRows unless
+/// \a CRows is \a Dynamic, and the same for the number of columns.
+///
+/// Example: \include MatrixBase_template_int_int_topRightCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_topRightCorner_int_int.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa class Block
+///
+template<int CRows, int CCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename FixedBlockXpr<CRows,CCols>::Type topRightCorner(Index cRows, Index cCols)
+{
+  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), 0, cols() - cCols, cRows, cCols);
+}
+
+/// This is the const version of topRightCorner<int, int>(Index, Index).
+template<int CRows, int CCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const typename ConstFixedBlockXpr<CRows,CCols>::Type topRightCorner(Index cRows, Index cCols) const
+{
+  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), 0, cols() - cCols, cRows, cCols);
+}
+
+
+
+/// \returns an expression of a top-left corner of \c *this  with either dynamic or fixed sizes.
+///
+/// \param cRows the number of rows in the corner
+/// \param cCols the number of columns in the corner
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example: \include MatrixBase_topLeftCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_topLeftCorner_int_int.out
+///
+/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+#else
+typename FixedBlockXpr<...,...>::Type
+#endif
+topLeftCorner(NRowsType cRows, NColsType cCols)
+{
+  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+            (derived(), 0, 0, internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
+}
+
+/// This is the const version of topLeftCorner(Index, Index).
+template<typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+#else
+const typename ConstFixedBlockXpr<...,...>::Type
+#endif
+topLeftCorner(NRowsType cRows, NColsType cCols) const
+{
+  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+            (derived(), 0, 0, internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
+}
+
+/// \returns an expression of a fixed-size top-left corner of \c *this.
+///
+/// The template parameters CRows and CCols are the number of rows and columns in the corner.
+///
+/// Example: \include MatrixBase_template_int_int_topLeftCorner.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_topLeftCorner.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<int CRows, int CCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename FixedBlockXpr<CRows,CCols>::Type topLeftCorner()
+{
+  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), 0, 0);
+}
+
+/// This is the const version of topLeftCorner<int, int>().
+template<int CRows, int CCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const typename ConstFixedBlockXpr<CRows,CCols>::Type topLeftCorner() const
+{
+  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), 0, 0);
+}
+
+/// \returns an expression of a top-left corner of \c *this.
+///
+/// \tparam CRows number of rows in corner as specified at compile-time
+/// \tparam CCols number of columns in corner as specified at compile-time
+/// \param  cRows number of rows in corner as specified at run-time
+/// \param  cCols number of columns in corner as specified at run-time
+///
+/// This function is mainly useful for corners where the number of rows is specified at compile-time
+/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
+/// information should not contradict. In other words, \a cRows should equal \a CRows unless
+/// \a CRows is \a Dynamic, and the same for the number of columns.
+///
+/// Example: \include MatrixBase_template_int_int_topLeftCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_topLeftCorner_int_int.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa class Block
+///
+template<int CRows, int CCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename FixedBlockXpr<CRows,CCols>::Type topLeftCorner(Index cRows, Index cCols)
+{
+  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), 0, 0, cRows, cCols);
+}
+
+/// This is the const version of topLeftCorner<int, int>(Index, Index).
+template<int CRows, int CCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const typename ConstFixedBlockXpr<CRows,CCols>::Type topLeftCorner(Index cRows, Index cCols) const
+{
+  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), 0, 0, cRows, cCols);
+}
+
+
+
+/// \returns an expression of a bottom-right corner of \c *this  with either dynamic or fixed sizes.
+///
+/// \param cRows the number of rows in the corner
+/// \param cCols the number of columns in the corner
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example: \include MatrixBase_bottomRightCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_bottomRightCorner_int_int.out
+///
+/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+#else
+typename FixedBlockXpr<...,...>::Type
+#endif
+bottomRightCorner(NRowsType cRows, NColsType cCols)
+{
+  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+            (derived(), rows() - internal::get_runtime_value(cRows), cols() - internal::get_runtime_value(cCols),
+                        internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
+}
+
+/// This is the const version of bottomRightCorner(NRowsType, NColsType).
+template<typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+#else
+const typename ConstFixedBlockXpr<...,...>::Type
+#endif
+bottomRightCorner(NRowsType cRows, NColsType cCols) const
+{
+  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+            (derived(), rows() - internal::get_runtime_value(cRows), cols() - internal::get_runtime_value(cCols),
+                        internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
+}
+
+/// \returns an expression of a fixed-size bottom-right corner of \c *this.
+///
+/// The template parameters CRows and CCols are the number of rows and columns in the corner.
+///
+/// Example: \include MatrixBase_template_int_int_bottomRightCorner.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_bottomRightCorner.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<int CRows, int CCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename FixedBlockXpr<CRows,CCols>::Type bottomRightCorner()
+{
+  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), rows() - CRows, cols() - CCols);
+}
+
+/// This is the const version of bottomRightCorner<int, int>().
+template<int CRows, int CCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const typename ConstFixedBlockXpr<CRows,CCols>::Type bottomRightCorner() const
+{
+  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), rows() - CRows, cols() - CCols);
+}
+
+/// \returns an expression of a bottom-right corner of \c *this.
+///
+/// \tparam CRows number of rows in corner as specified at compile-time
+/// \tparam CCols number of columns in corner as specified at compile-time
+/// \param  cRows number of rows in corner as specified at run-time
+/// \param  cCols number of columns in corner as specified at run-time
+///
+/// This function is mainly useful for corners where the number of rows is specified at compile-time
+/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
+/// information should not contradict. In other words, \a cRows should equal \a CRows unless
+/// \a CRows is \a Dynamic, and the same for the number of columns.
+///
+/// Example: \include MatrixBase_template_int_int_bottomRightCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_bottomRightCorner_int_int.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa class Block
+///
+template<int CRows, int CCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename FixedBlockXpr<CRows,CCols>::Type bottomRightCorner(Index cRows, Index cCols)
+{
+  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), rows() - cRows, cols() - cCols, cRows, cCols);
+}
+
+/// This is the const version of bottomRightCorner<int, int>(Index, Index).
+template<int CRows, int CCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const typename ConstFixedBlockXpr<CRows,CCols>::Type bottomRightCorner(Index cRows, Index cCols) const
+{
+  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), rows() - cRows, cols() - cCols, cRows, cCols);
+}
+
+
+
+/// \returns an expression of a bottom-left corner of \c *this  with either dynamic or fixed sizes.
+///
+/// \param cRows the number of rows in the corner
+/// \param cCols the number of columns in the corner
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example: \include MatrixBase_bottomLeftCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_bottomLeftCorner_int_int.out
+///
+/// The number of rows \a blockRows and columns \a blockCols can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments. See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+#else
+typename FixedBlockXpr<...,...>::Type
+#endif
+bottomLeftCorner(NRowsType cRows, NColsType cCols)
+{
+  return typename FixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+            (derived(), rows() - internal::get_runtime_value(cRows), 0,
+                        internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
+}
+
+/// This is the const version of bottomLeftCorner(NRowsType, NColsType).
+template<typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+#else
+typename ConstFixedBlockXpr<...,...>::Type
+#endif
+bottomLeftCorner(NRowsType cRows, NColsType cCols) const
+{
+  return typename ConstFixedBlockXpr<internal::get_fixed_value<NRowsType>::value,internal::get_fixed_value<NColsType>::value>::Type
+            (derived(), rows() - internal::get_runtime_value(cRows), 0,
+                        internal::get_runtime_value(cRows), internal::get_runtime_value(cCols));
+}
+
+/// \returns an expression of a fixed-size bottom-left corner of \c *this.
+///
+/// The template parameters CRows and CCols are the number of rows and columns in the corner.
+///
+/// Example: \include MatrixBase_template_int_int_bottomLeftCorner.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_bottomLeftCorner.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<int CRows, int CCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename FixedBlockXpr<CRows,CCols>::Type bottomLeftCorner()
+{
+  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), rows() - CRows, 0);
+}
+
+/// This is the const version of bottomLeftCorner<int, int>().
+template<int CRows, int CCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const typename ConstFixedBlockXpr<CRows,CCols>::Type bottomLeftCorner() const
+{
+  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), rows() - CRows, 0);
+}
+
+/// \returns an expression of a bottom-left corner of \c *this.
+///
+/// \tparam CRows number of rows in corner as specified at compile-time
+/// \tparam CCols number of columns in corner as specified at compile-time
+/// \param  cRows number of rows in corner as specified at run-time
+/// \param  cCols number of columns in corner as specified at run-time
+///
+/// This function is mainly useful for corners where the number of rows is specified at compile-time
+/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
+/// information should not contradict. In other words, \a cRows should equal \a CRows unless
+/// \a CRows is \a Dynamic, and the same for the number of columns.
+///
+/// Example: \include MatrixBase_template_int_int_bottomLeftCorner_int_int.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_bottomLeftCorner_int_int.out
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa class Block
+///
+template<int CRows, int CCols>
+EIGEN_STRONG_INLINE
+typename FixedBlockXpr<CRows,CCols>::Type bottomLeftCorner(Index cRows, Index cCols)
+{
+  return typename FixedBlockXpr<CRows,CCols>::Type(derived(), rows() - cRows, 0, cRows, cCols);
+}
+
+/// This is the const version of bottomLeftCorner<int, int>(Index, Index).
+template<int CRows, int CCols>
+EIGEN_STRONG_INLINE
+const typename ConstFixedBlockXpr<CRows,CCols>::Type bottomLeftCorner(Index cRows, Index cCols) const
+{
+  return typename ConstFixedBlockXpr<CRows,CCols>::Type(derived(), rows() - cRows, 0, cRows, cCols);
+}
+
+
+
+/// \returns a block consisting of the top rows of \c *this.
+///
+/// \param n the number of rows in the block
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+///
+/// Example: \include MatrixBase_topRows_int.cpp
+/// Output: \verbinclude MatrixBase_topRows_int.out
+///
+/// The number of rows \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<typename NRowsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+#else
+typename NRowsBlockXpr<...>::Type
+#endif
+topRows(NRowsType n)
+{
+  return typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+            (derived(), 0, 0, internal::get_runtime_value(n), cols());
+}
+
+/// This is the const version of topRows(NRowsType).
+template<typename NRowsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+#else
+const typename ConstNRowsBlockXpr<...>::Type
+#endif
+topRows(NRowsType n) const
+{
+  return typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+            (derived(), 0, 0, internal::get_runtime_value(n), cols());
+}
+
+/// \returns a block consisting of the top rows of \c *this.
+///
+/// \tparam N the number of rows in the block as specified at compile-time
+/// \param n the number of rows in the block as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include MatrixBase_template_int_topRows.cpp
+/// Output: \verbinclude MatrixBase_template_int_topRows.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename NRowsBlockXpr<N>::Type topRows(Index n = N)
+{
+  return typename NRowsBlockXpr<N>::Type(derived(), 0, 0, n, cols());
+}
+
+/// This is the const version of topRows<int>().
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename ConstNRowsBlockXpr<N>::Type topRows(Index n = N) const
+{
+  return typename ConstNRowsBlockXpr<N>::Type(derived(), 0, 0, n, cols());
+}
+
+
+
+/// \returns a block consisting of the bottom rows of \c *this.
+///
+/// \param n the number of rows in the block
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+///
+/// Example: \include MatrixBase_bottomRows_int.cpp
+/// Output: \verbinclude MatrixBase_bottomRows_int.out
+///
+/// The number of rows \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<typename NRowsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+#else
+typename NRowsBlockXpr<...>::Type
+#endif
+bottomRows(NRowsType n)
+{
+  return typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+            (derived(), rows() - internal::get_runtime_value(n), 0, internal::get_runtime_value(n), cols());
+}
+
+/// This is the const version of bottomRows(NRowsType).
+template<typename NRowsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+#else
+const typename ConstNRowsBlockXpr<...>::Type
+#endif
+bottomRows(NRowsType n) const
+{
+  return typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+            (derived(), rows() - internal::get_runtime_value(n), 0, internal::get_runtime_value(n), cols());
+}
+
+/// \returns a block consisting of the bottom rows of \c *this.
+///
+/// \tparam N the number of rows in the block as specified at compile-time
+/// \param n the number of rows in the block as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include MatrixBase_template_int_bottomRows.cpp
+/// Output: \verbinclude MatrixBase_template_int_bottomRows.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename NRowsBlockXpr<N>::Type bottomRows(Index n = N)
+{
+  return typename NRowsBlockXpr<N>::Type(derived(), rows() - n, 0, n, cols());
+}
+
+/// This is the const version of bottomRows<int>().
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename ConstNRowsBlockXpr<N>::Type bottomRows(Index n = N) const
+{
+  return typename ConstNRowsBlockXpr<N>::Type(derived(), rows() - n, 0, n, cols());
+}
+
+
+
+/// \returns a block consisting of a range of rows of \c *this.
+///
+/// \param startRow the index of the first row in the block
+/// \param n the number of rows in the block
+/// \tparam NRowsType the type of the value handling the number of rows in the block, typically Index.
+///
+/// Example: \include DenseBase_middleRows_int.cpp
+/// Output: \verbinclude DenseBase_middleRows_int.out
+///
+/// The number of rows \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<typename NRowsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+#else
+typename NRowsBlockXpr<...>::Type
+#endif
+middleRows(Index startRow, NRowsType n)
+{
+  return typename NRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+            (derived(), startRow, 0, internal::get_runtime_value(n), cols());
+}
+
+/// This is the const version of middleRows(Index,NRowsType).
+template<typename NRowsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+#else
+const typename ConstNRowsBlockXpr<...>::Type
+#endif
+middleRows(Index startRow, NRowsType n) const
+{
+  return typename ConstNRowsBlockXpr<internal::get_fixed_value<NRowsType>::value>::Type
+            (derived(), startRow, 0, internal::get_runtime_value(n), cols());
+}
+
+/// \returns a block consisting of a range of rows of \c *this.
+///
+/// \tparam N the number of rows in the block as specified at compile-time
+/// \param startRow the index of the first row in the block
+/// \param n the number of rows in the block as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include DenseBase_template_int_middleRows.cpp
+/// Output: \verbinclude DenseBase_template_int_middleRows.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename NRowsBlockXpr<N>::Type middleRows(Index startRow, Index n = N)
+{
+  return typename NRowsBlockXpr<N>::Type(derived(), startRow, 0, n, cols());
+}
+
+/// This is the const version of middleRows<int>().
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename ConstNRowsBlockXpr<N>::Type middleRows(Index startRow, Index n = N) const
+{
+  return typename ConstNRowsBlockXpr<N>::Type(derived(), startRow, 0, n, cols());
+}
+
+
+
+/// \returns a block consisting of the left columns of \c *this.
+///
+/// \param n the number of columns in the block
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example: \include MatrixBase_leftCols_int.cpp
+/// Output: \verbinclude MatrixBase_leftCols_int.out
+///
+/// The number of columns \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+#else
+typename NColsBlockXpr<...>::Type
+#endif
+leftCols(NColsType n)
+{
+  return typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+            (derived(), 0, 0, rows(), internal::get_runtime_value(n));
+}
+
+/// This is the const version of leftCols(NColsType).
+template<typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+#else
+const typename ConstNColsBlockXpr<...>::Type
+#endif
+leftCols(NColsType n) const
+{
+  return typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+            (derived(), 0, 0, rows(), internal::get_runtime_value(n));
+}
+
+/// \returns a block consisting of the left columns of \c *this.
+///
+/// \tparam N the number of columns in the block as specified at compile-time
+/// \param n the number of columns in the block as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include MatrixBase_template_int_leftCols.cpp
+/// Output: \verbinclude MatrixBase_template_int_leftCols.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename NColsBlockXpr<N>::Type leftCols(Index n = N)
+{
+  return typename NColsBlockXpr<N>::Type(derived(), 0, 0, rows(), n);
+}
+
+/// This is the const version of leftCols<int>().
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename ConstNColsBlockXpr<N>::Type leftCols(Index n = N) const
+{
+  return typename ConstNColsBlockXpr<N>::Type(derived(), 0, 0, rows(), n);
+}
+
+
+
+/// \returns a block consisting of the right columns of \c *this.
+///
+/// \param n the number of columns in the block
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example: \include MatrixBase_rightCols_int.cpp
+/// Output: \verbinclude MatrixBase_rightCols_int.out
+///
+/// The number of columns \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+#else
+typename NColsBlockXpr<...>::Type
+#endif
+rightCols(NColsType n)
+{
+  return typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+            (derived(), 0, cols() - internal::get_runtime_value(n), rows(), internal::get_runtime_value(n));
+}
+
+/// This is the const version of rightCols(NColsType).
+template<typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+#else
+const typename ConstNColsBlockXpr<...>::Type
+#endif
+rightCols(NColsType n) const
+{
+  return typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+            (derived(), 0, cols() - internal::get_runtime_value(n), rows(), internal::get_runtime_value(n));
+}
+
+/// \returns a block consisting of the right columns of \c *this.
+///
+/// \tparam N the number of columns in the block as specified at compile-time
+/// \param n the number of columns in the block as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include MatrixBase_template_int_rightCols.cpp
+/// Output: \verbinclude MatrixBase_template_int_rightCols.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename NColsBlockXpr<N>::Type rightCols(Index n = N)
+{
+  return typename NColsBlockXpr<N>::Type(derived(), 0, cols() - n, rows(), n);
+}
+
+/// This is the const version of rightCols<int>().
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename ConstNColsBlockXpr<N>::Type rightCols(Index n = N) const
+{
+  return typename ConstNColsBlockXpr<N>::Type(derived(), 0, cols() - n, rows(), n);
+}
+
+
+
+/// \returns a block consisting of a range of columns of \c *this.
+///
+/// \param startCol the index of the first column in the block
+/// \param numCols the number of columns in the block
+/// \tparam NColsType the type of the value handling the number of columns in the block, typically Index.
+///
+/// Example: \include DenseBase_middleCols_int.cpp
+/// Output: \verbinclude DenseBase_middleCols_int.out
+///
+/// The number of columns \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+#else
+typename NColsBlockXpr<...>::Type
+#endif
+middleCols(Index startCol, NColsType numCols)
+{
+  return typename NColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+            (derived(), 0, startCol, rows(), internal::get_runtime_value(numCols));
+}
+
+/// This is the const version of middleCols(Index,NColsType).
+template<typename NColsType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+#else
+const typename ConstNColsBlockXpr<...>::Type
+#endif
+middleCols(Index startCol, NColsType numCols) const
+{
+  return typename ConstNColsBlockXpr<internal::get_fixed_value<NColsType>::value>::Type
+            (derived(), 0, startCol, rows(), internal::get_runtime_value(numCols));
+}
+
+/// \returns a block consisting of a range of columns of \c *this.
+///
+/// \tparam N the number of columns in the block as specified at compile-time
+/// \param startCol the index of the first column in the block
+/// \param n the number of columns in the block as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include DenseBase_template_int_middleCols.cpp
+/// Output: \verbinclude DenseBase_template_int_middleCols.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename NColsBlockXpr<N>::Type middleCols(Index startCol, Index n = N)
+{
+  return typename NColsBlockXpr<N>::Type(derived(), 0, startCol, rows(), n);
+}
+
+/// This is the const version of middleCols<int>().
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename ConstNColsBlockXpr<N>::Type middleCols(Index startCol, Index n = N) const
+{
+  return typename ConstNColsBlockXpr<N>::Type(derived(), 0, startCol, rows(), n);
+}
+
+
+
+/// \returns a fixed-size expression of a block of \c *this.
+///
+/// The template parameters \a NRows and \a NCols are the number of
+/// rows and columns in the block.
+///
+/// \param startRow the first row in the block
+/// \param startCol the first column in the block
+///
+/// Example: \include MatrixBase_block_int_int.cpp
+/// Output: \verbinclude MatrixBase_block_int_int.out
+///
+/// \note The usage of of this overload is discouraged from %Eigen 3.4, better used the generic
+/// block(Index,Index,NRowsType,NColsType), here is the one-to-one equivalence:
+/// \code
+/// mat.template block<NRows,NCols>(i,j)  <-->  mat.block(i,j,fix<NRows>,fix<NCols>)
+/// \endcode
+///
+/// \note since block is a templated member, the keyword template has to be used
+/// if the matrix type is also a template parameter: \code m.template block<3,3>(1,1); \endcode
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<int NRows, int NCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename FixedBlockXpr<NRows,NCols>::Type block(Index startRow, Index startCol)
+{
+  return typename FixedBlockXpr<NRows,NCols>::Type(derived(), startRow, startCol);
+}
+
+/// This is the const version of block<>(Index, Index). */
+template<int NRows, int NCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const typename ConstFixedBlockXpr<NRows,NCols>::Type block(Index startRow, Index startCol) const
+{
+  return typename ConstFixedBlockXpr<NRows,NCols>::Type(derived(), startRow, startCol);
+}
+
+/// \returns an expression of a block of \c *this.
+///
+/// \tparam NRows number of rows in block as specified at compile-time
+/// \tparam NCols number of columns in block as specified at compile-time
+/// \param  startRow  the first row in the block
+/// \param  startCol  the first column in the block
+/// \param  blockRows number of rows in block as specified at run-time
+/// \param  blockCols number of columns in block as specified at run-time
+///
+/// This function is mainly useful for blocks where the number of rows is specified at compile-time
+/// and the number of columns is specified at run-time, or vice versa. The compile-time and run-time
+/// information should not contradict. In other words, \a blockRows should equal \a NRows unless
+/// \a NRows is \a Dynamic, and the same for the number of columns.
+///
+/// Example: \include MatrixBase_template_int_int_block_int_int_int_int.cpp
+/// Output: \verbinclude MatrixBase_template_int_int_block_int_int_int_int.out
+///
+/// \note The usage of of this overload is discouraged from %Eigen 3.4, better used the generic
+/// block(Index,Index,NRowsType,NColsType), here is the one-to-one complete equivalence:
+/// \code
+/// mat.template block<NRows,NCols>(i,j,rows,cols)     <-->  mat.block(i,j,fix<NRows>(rows),fix<NCols>(cols))
+/// \endcode
+/// If we known that, e.g., NRows==Dynamic and NCols!=Dynamic, then the equivalence becomes:
+/// \code
+/// mat.template block<Dynamic,NCols>(i,j,rows,NCols)  <-->  mat.block(i,j,rows,fix<NCols>)
+/// \endcode
+///
+EIGEN_DOC_BLOCK_ADDONS_NOT_INNER_PANEL
+///
+/// \sa block(Index,Index,NRowsType,NColsType), class Block
+///
+template<int NRows, int NCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename FixedBlockXpr<NRows,NCols>::Type block(Index startRow, Index startCol,
+                                                  Index blockRows, Index blockCols)
+{
+  return typename FixedBlockXpr<NRows,NCols>::Type(derived(), startRow, startCol, blockRows, blockCols);
+}
+
+/// This is the const version of block<>(Index, Index, Index, Index).
+template<int NRows, int NCols>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const typename ConstFixedBlockXpr<NRows,NCols>::Type block(Index startRow, Index startCol,
+                                                              Index blockRows, Index blockCols) const
+{
+  return typename ConstFixedBlockXpr<NRows,NCols>::Type(derived(), startRow, startCol, blockRows, blockCols);
+}
+
+/// \returns an expression of the \a i-th column of \c *this. Note that the numbering starts at 0.
+///
+/// Example: \include MatrixBase_col.cpp
+/// Output: \verbinclude MatrixBase_col.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(column-major)
+/**
+  * \sa row(), class Block */
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+ColXpr col(Index i)
+{
+  return ColXpr(derived(), i);
+}
+
+/// This is the const version of col().
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+ConstColXpr col(Index i) const
+{
+  return ConstColXpr(derived(), i);
+}
+
+/// \returns an expression of the \a i-th row of \c *this. Note that the numbering starts at 0.
+///
+/// Example: \include MatrixBase_row.cpp
+/// Output: \verbinclude MatrixBase_row.out
+///
+EIGEN_DOC_BLOCK_ADDONS_INNER_PANEL_IF(row-major)
+/**
+  * \sa col(), class Block */
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+RowXpr row(Index i)
+{
+  return RowXpr(derived(), i);
+}
+
+/// This is the const version of row(). */
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+ConstRowXpr row(Index i) const
+{
+  return ConstRowXpr(derived(), i);
+}
+
+/// \returns an expression of a segment (i.e. a vector block) in \c *this with either dynamic or fixed sizes.
+///
+/// \only_for_vectors
+///
+/// \param start the first coefficient in the segment
+/// \param n the number of coefficients in the segment
+/// \tparam NType the type of the value handling the number of coefficients in the segment, typically Index.
+///
+/// Example: \include MatrixBase_segment_int_int.cpp
+/// Output: \verbinclude MatrixBase_segment_int_int.out
+///
+/// The number of coefficients \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+/// \note Even in the case that the returned expression has dynamic size, in the case
+/// when it is applied to a fixed-size vector, it inherits a fixed maximal size,
+/// which means that evaluating it does not cause a dynamic memory allocation.
+///
+/// \sa block(Index,Index,NRowsType,NColsType), fix<N>, fix<N>(int), class Block
+///
+template<typename NType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+#else
+typename FixedSegmentReturnType<...>::Type
+#endif
+segment(Index start, NType n)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+            (derived(), start, internal::get_runtime_value(n));
+}
+
+
+/// This is the const version of segment(Index,NType).
+template<typename NType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+#else
+const typename ConstFixedSegmentReturnType<...>::Type
+#endif
+segment(Index start, NType n) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+            (derived(), start, internal::get_runtime_value(n));
+}
+
+/// \returns an expression of the first coefficients of \c *this with either dynamic or fixed sizes.
+///
+/// \only_for_vectors
+///
+/// \param n the number of coefficients in the segment
+/// \tparam NType the type of the value handling the number of coefficients in the segment, typically Index.
+///
+/// Example: \include MatrixBase_start_int.cpp
+/// Output: \verbinclude MatrixBase_start_int.out
+///
+/// The number of coefficients \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+/// \note Even in the case that the returned expression has dynamic size, in the case
+/// when it is applied to a fixed-size vector, it inherits a fixed maximal size,
+/// which means that evaluating it does not cause a dynamic memory allocation.
+///
+/// \sa class Block, block(Index,Index)
+///
+template<typename NType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+#else
+typename FixedSegmentReturnType<...>::Type
+#endif
+head(NType n)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+              (derived(), 0, internal::get_runtime_value(n));
+}
+
+/// This is the const version of head(NType).
+template<typename NType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+#else
+const typename ConstFixedSegmentReturnType<...>::Type
+#endif
+head(NType n) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+            (derived(), 0, internal::get_runtime_value(n));
+}
+
+/// \returns an expression of a last coefficients of \c *this with either dynamic or fixed sizes.
+///
+/// \only_for_vectors
+///
+/// \param n the number of coefficients in the segment
+/// \tparam NType the type of the value handling the number of coefficients in the segment, typically Index.
+///
+/// Example: \include MatrixBase_end_int.cpp
+/// Output: \verbinclude MatrixBase_end_int.out
+///
+/// The number of coefficients \a n can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments.
+/// See \link block(Index,Index,NRowsType,NColsType) block() \endlink for the details.
+///
+/// \note Even in the case that the returned expression has dynamic size, in the case
+/// when it is applied to a fixed-size vector, it inherits a fixed maximal size,
+/// which means that evaluating it does not cause a dynamic memory allocation.
+///
+/// \sa class Block, block(Index,Index)
+///
+template<typename NType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+#else
+typename FixedSegmentReturnType<...>::Type
+#endif
+tail(NType n)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename FixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+            (derived(), this->size() - internal::get_runtime_value(n), internal::get_runtime_value(n));
+}
+
+/// This is the const version of tail(Index).
+template<typename NType>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+const typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+#else
+const typename ConstFixedSegmentReturnType<...>::Type
+#endif
+tail(NType n) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename ConstFixedSegmentReturnType<internal::get_fixed_value<NType>::value>::Type
+            (derived(), this->size() - internal::get_runtime_value(n), internal::get_runtime_value(n));
+}
+
+/// \returns a fixed-size expression of a segment (i.e. a vector block) in \c *this
+///
+/// \only_for_vectors
+///
+/// \tparam N the number of coefficients in the segment as specified at compile-time
+/// \param start the index of the first element in the segment
+/// \param n the number of coefficients in the segment as specified at compile-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include MatrixBase_template_int_segment.cpp
+/// Output: \verbinclude MatrixBase_template_int_segment.out
+///
+/// \sa segment(Index,NType), class Block
+///
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename FixedSegmentReturnType<N>::Type segment(Index start, Index n = N)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename FixedSegmentReturnType<N>::Type(derived(), start, n);
+}
+
+/// This is the const version of segment<int>(Index).
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename ConstFixedSegmentReturnType<N>::Type segment(Index start, Index n = N) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename ConstFixedSegmentReturnType<N>::Type(derived(), start, n);
+}
+
+/// \returns a fixed-size expression of the first coefficients of \c *this.
+///
+/// \only_for_vectors
+///
+/// \tparam N the number of coefficients in the segment as specified at compile-time
+/// \param  n the number of coefficients in the segment as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include MatrixBase_template_int_start.cpp
+/// Output: \verbinclude MatrixBase_template_int_start.out
+///
+/// \sa head(NType), class Block
+///
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename FixedSegmentReturnType<N>::Type head(Index n = N)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename FixedSegmentReturnType<N>::Type(derived(), 0, n);
+}
+
+/// This is the const version of head<int>().
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename ConstFixedSegmentReturnType<N>::Type head(Index n = N) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename ConstFixedSegmentReturnType<N>::Type(derived(), 0, n);
+}
+
+/// \returns a fixed-size expression of the last coefficients of \c *this.
+///
+/// \only_for_vectors
+///
+/// \tparam N the number of coefficients in the segment as specified at compile-time
+/// \param  n the number of coefficients in the segment as specified at run-time
+///
+/// The compile-time and run-time information should not contradict. In other words,
+/// \a n should equal \a N unless \a N is \a Dynamic.
+///
+/// Example: \include MatrixBase_template_int_end.cpp
+/// Output: \verbinclude MatrixBase_template_int_end.out
+///
+/// \sa tail(NType), class Block
+///
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename FixedSegmentReturnType<N>::Type tail(Index n = N)
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename FixedSegmentReturnType<N>::Type(derived(), size() - n);
+}
+
+/// This is the const version of tail<int>.
+template<int N>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+typename ConstFixedSegmentReturnType<N>::Type tail(Index n = N) const
+{
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return typename ConstFixedSegmentReturnType<N>::Type(derived(), size() - n);
+}
+
+/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+/// is col-major (resp. row-major).
+///
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+InnerVectorReturnType innerVector(Index outer)
+{ return InnerVectorReturnType(derived(), outer); }
+
+/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+/// is col-major (resp. row-major). Read-only.
+///
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const ConstInnerVectorReturnType innerVector(Index outer) const
+{ return ConstInnerVectorReturnType(derived(), outer); }
+
+/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+/// is col-major (resp. row-major).
+///
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+InnerVectorsReturnType
+innerVectors(Index outerStart, Index outerSize)
+{
+  return Block<Derived,Dynamic,Dynamic,true>(derived(),
+                                             IsRowMajor ? outerStart : 0, IsRowMajor ? 0 : outerStart,
+                                             IsRowMajor ? outerSize : rows(), IsRowMajor ? cols() : outerSize);
+
+}
+
+/// \returns the \a outer -th column (resp. row) of the matrix \c *this if \c *this
+/// is col-major (resp. row-major). Read-only.
+///
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+const ConstInnerVectorsReturnType
+innerVectors(Index outerStart, Index outerSize) const
+{
+  return Block<const Derived,Dynamic,Dynamic,true>(derived(),
+                                                  IsRowMajor ? outerStart : 0, IsRowMajor ? 0 : outerStart,
+                                                  IsRowMajor ? outerSize : rows(), IsRowMajor ? cols() : outerSize);
+
+}
+
+/** \returns the i-th subvector (column or vector) according to the \c Direction
+  * \sa subVectors()
+  */
+template<DirectionType Direction>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+std::conditional_t<Direction==Vertical,ColXpr,RowXpr>
+subVector(Index i)
+{
+  return std::conditional_t<Direction==Vertical,ColXpr,RowXpr>(derived(),i);
+}
+
+/** This is the const version of subVector(Index) */
+template<DirectionType Direction>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+std::conditional_t<Direction==Vertical,ConstColXpr,ConstRowXpr>
+subVector(Index i) const
+{
+  return std::conditional_t<Direction==Vertical,ConstColXpr,ConstRowXpr>(derived(),i);
+}
+
+/** \returns the number of subvectors (rows or columns) in the direction \c Direction
+  * \sa subVector(Index)
+  */
+template<DirectionType Direction>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EIGEN_CONSTEXPR
+Index subVectors() const
+{ return (Direction==Vertical)?cols():rows(); }
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/CommonCwiseBinaryOps.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/CommonCwiseBinaryOps.h
new file mode 100644
index 0000000000000000000000000000000000000000..964913be7c804b608f75f52b38213fddcbf28ec2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/CommonCwiseBinaryOps.h
@@ -0,0 +1,137 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2016 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// This file is a base class plugin containing common coefficient wise functions.
+
+/** \returns an expression of the difference of \c *this and \a other
+  *
+  * \note If you want to subtract a given scalar from all coefficients, see Cwise::operator-().
+  *
+  * \sa class CwiseBinaryOp, operator-=()
+  */
+EIGEN_MAKE_CWISE_BINARY_OP(operator-,difference)
+
+/** \returns an expression of the sum of \c *this and \a other
+  *
+  * \note If you want to add a given scalar to all coefficients, see Cwise::operator+().
+  *
+  * \sa class CwiseBinaryOp, operator+=()
+  */
+EIGEN_MAKE_CWISE_BINARY_OP(operator+,sum)
+
+/** \returns an expression of a custom coefficient-wise operator \a func of *this and \a other
+  *
+  * The template parameter \a CustomBinaryOp is the type of the functor
+  * of the custom operator (see class CwiseBinaryOp for an example)
+  *
+  * Here is an example illustrating the use of custom functors:
+  * \include class_CwiseBinaryOp.cpp
+  * Output: \verbinclude class_CwiseBinaryOp.out
+  *
+  * \sa class CwiseBinaryOp, operator+(), operator-(), cwiseProduct()
+  */
+template<typename CustomBinaryOp, typename OtherDerived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const CwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>
+binaryExpr(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other, const CustomBinaryOp& func = CustomBinaryOp()) const
+{
+  return CwiseBinaryOp<CustomBinaryOp, const Derived, const OtherDerived>(derived(), other.derived(), func);
+}
+
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+EIGEN_MAKE_SCALAR_BINARY_OP(operator*,product)
+#else
+/** \returns an expression of \c *this scaled by the scalar factor \a scalar
+  *
+  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
+  */
+template<typename T>
+const CwiseBinaryOp<internal::scalar_product_op<Scalar,T>,Derived,Constant<T> > operator*(const T& scalar) const;
+/** \returns an expression of \a expr scaled by the scalar factor \a scalar
+  *
+  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
+  */
+template<typename T> friend
+const CwiseBinaryOp<internal::scalar_product_op<T,Scalar>,Constant<T>,Derived> operator*(const T& scalar, const StorageBaseType& expr);
+#endif
+
+
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+EIGEN_MAKE_SCALAR_BINARY_OP_ONTHERIGHT(operator/,quotient)
+#else
+/** \returns an expression of \c *this divided by the scalar value \a scalar
+  *
+  * \tparam T is the scalar type of \a scalar. It must be compatible with the scalar type of the given expression.
+  */
+template<typename T>
+const CwiseBinaryOp<internal::scalar_quotient_op<Scalar,T>,Derived,Constant<T> > operator/(const T& scalar) const;
+#endif
+
+/** \returns an expression of the coefficient-wise boolean \b and operator of \c *this and \a other
+ *
+ * Example: \include Cwise_boolean_and.cpp
+ * Output: \verbinclude Cwise_boolean_and.out
+ *
+ * \sa operator||(), select()
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC inline const CwiseBinaryOp<internal::scalar_boolean_and_op<Scalar>, const Derived, const OtherDerived>
+operator&&(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryOp<internal::scalar_boolean_and_op<Scalar>, const Derived, const OtherDerived>(derived(),
+                                                                                                   other.derived());
+}
+
+/** \returns an expression of the coefficient-wise boolean \b or operator of \c *this and \a other
+ *
+ * Example: \include Cwise_boolean_or.cpp
+ * Output: \verbinclude Cwise_boolean_or.out
+ *
+ * \sa operator&&(), select()
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC inline const CwiseBinaryOp<internal::scalar_boolean_or_op<Scalar>, const Derived, const OtherDerived>
+operator||(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryOp<internal::scalar_boolean_or_op<Scalar>, const Derived, const OtherDerived>(derived(),
+                                                                                                  other.derived());
+}
+
+/** \returns an expression of the bitwise \b and operator of \c *this and \a other
+ *
+ * \sa operator|(), operator^()
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC inline const CwiseBinaryOp<internal::scalar_bitwise_and_op<Scalar>, const Derived, const OtherDerived>
+operator&(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryOp<internal::scalar_bitwise_and_op<Scalar>, const Derived, const OtherDerived>(derived(),
+                                                                                                   other.derived());
+}
+
+/** \returns an expression of the bitwise boolean \b or operator of \c *this and \a other
+ *
+ * \sa operator&(), operator^()
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC inline const CwiseBinaryOp<internal::scalar_bitwise_or_op<Scalar>, const Derived, const OtherDerived>
+operator|(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryOp<internal::scalar_bitwise_or_op<Scalar>, const Derived, const OtherDerived>(derived(),
+                                                                                                  other.derived());
+}
+
+/** \returns an expression of the bitwise xor operator of *this and \a other
+ * \sa operator&(), operator|()
+ */
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC inline const CwiseBinaryOp<internal::scalar_bitwise_xor_op<Scalar>, const Derived, const OtherDerived>
+operator^(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const {
+  return CwiseBinaryOp<internal::scalar_bitwise_xor_op<Scalar>, const Derived, const OtherDerived>(derived(),
+                                                                                                   other.derived());
+}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/CommonCwiseUnaryOps.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/CommonCwiseUnaryOps.h
new file mode 100644
index 0000000000000000000000000000000000000000..1c6b2845104d9ece5e713bc22938382757c638e1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/CommonCwiseUnaryOps.h
@@ -0,0 +1,177 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// This file is a base class plugin containing common coefficient wise functions.
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+
+/** \internal the return type of conjugate() */
+typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
+            const CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, const Derived>,
+            const Derived&
+          > ConjugateReturnType;
+/** \internal the return type of real() const */
+typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
+            const CwiseUnaryOp<internal::scalar_real_op<Scalar>, const Derived>,
+            const Derived&
+          > RealReturnType;
+/** \internal the return type of real() */
+typedef std::conditional_t<NumTraits<Scalar>::IsComplex,
+            CwiseUnaryView<internal::scalar_real_ref_op<Scalar>, Derived>,
+            Derived&
+          > NonConstRealReturnType;
+/** \internal the return type of imag() const */
+typedef CwiseUnaryOp<internal::scalar_imag_op<Scalar>, const Derived> ImagReturnType;
+/** \internal the return type of imag() */
+typedef CwiseUnaryView<internal::scalar_imag_ref_op<Scalar>, Derived> NonConstImagReturnType;
+
+typedef CwiseUnaryOp<internal::scalar_opposite_op<Scalar>, const Derived> NegativeReturnType;
+
+#endif // not EIGEN_PARSED_BY_DOXYGEN
+
+/// \returns an expression of the opposite of \c *this
+///
+EIGEN_DOC_UNARY_ADDONS(operator-,opposite)
+///
+EIGEN_DEVICE_FUNC
+inline const NegativeReturnType
+operator-() const { return NegativeReturnType(derived()); }
+
+
+template<class NewType> struct CastXpr { typedef typename internal::cast_return_type<Derived,const CwiseUnaryOp<internal::core_cast_op<Scalar, NewType>, const Derived> >::type Type; };
+
+/// \returns an expression of \c *this with the \a Scalar type casted to
+/// \a NewScalar.
+///
+/// The template parameter \a NewScalar is the type we are casting the scalars to.
+///
+EIGEN_DOC_UNARY_ADDONS(cast,conversion function)
+///
+/// \sa class CwiseUnaryOp
+///
+template<typename NewType>
+EIGEN_DEVICE_FUNC
+typename CastXpr<NewType>::Type
+cast() const
+{
+  return typename CastXpr<NewType>::Type(derived());
+}
+
+/// \returns an expression of the complex conjugate of \c *this.
+///
+EIGEN_DOC_UNARY_ADDONS(conjugate,complex conjugate)
+///
+/// \sa <a href="group__CoeffwiseMathFunctions.html#cwisetable_conj">Math functions</a>, MatrixBase::adjoint()
+EIGEN_DEVICE_FUNC
+inline ConjugateReturnType
+conjugate() const
+{
+  return ConjugateReturnType(derived());
+}
+
+/// \returns an expression of the complex conjugate of \c *this if Cond==true, returns derived() otherwise.
+///
+EIGEN_DOC_UNARY_ADDONS(conjugate,complex conjugate)
+///
+/// \sa conjugate()
+template<bool Cond>
+EIGEN_DEVICE_FUNC
+inline std::conditional_t<Cond,ConjugateReturnType,const Derived&>
+conjugateIf() const
+{
+  typedef std::conditional_t<Cond,ConjugateReturnType,const Derived&> ReturnType;
+  return ReturnType(derived());
+}
+
+/// \returns a read-only expression of the real part of \c *this.
+///
+EIGEN_DOC_UNARY_ADDONS(real,real part function)
+///
+/// \sa imag()
+EIGEN_DEVICE_FUNC
+inline RealReturnType
+real() const { return RealReturnType(derived()); }
+
+/// \returns an read-only expression of the imaginary part of \c *this.
+///
+EIGEN_DOC_UNARY_ADDONS(imag,imaginary part function)
+///
+/// \sa real()
+EIGEN_DEVICE_FUNC
+inline const ImagReturnType
+imag() const { return ImagReturnType(derived()); }
+
+/// \brief Apply a unary operator coefficient-wise
+/// \param[in]  func  Functor implementing the unary operator
+/// \tparam  CustomUnaryOp Type of \a func
+/// \returns An expression of a custom coefficient-wise unary operator \a func of *this
+///
+/// The function \c ptr_fun() from the C++ standard library can be used to make functors out of normal functions.
+///
+/// Example:
+/// \include class_CwiseUnaryOp_ptrfun.cpp
+/// Output: \verbinclude class_CwiseUnaryOp_ptrfun.out
+///
+/// Genuine functors allow for more possibilities, for instance it may contain a state.
+///
+/// Example:
+/// \include class_CwiseUnaryOp.cpp
+/// Output: \verbinclude class_CwiseUnaryOp.out
+///
+EIGEN_DOC_UNARY_ADDONS(unaryExpr,unary function)
+///
+/// \sa unaryViewExpr, binaryExpr, class CwiseUnaryOp
+///
+template<typename CustomUnaryOp>
+EIGEN_DEVICE_FUNC
+inline const CwiseUnaryOp<CustomUnaryOp, const Derived>
+unaryExpr(const CustomUnaryOp& func = CustomUnaryOp()) const
+{
+  return CwiseUnaryOp<CustomUnaryOp, const Derived>(derived(), func);
+}
+
+/// \returns an expression of a custom coefficient-wise unary operator \a func of *this
+///
+/// The template parameter \a CustomUnaryOp is the type of the functor
+/// of the custom unary operator.
+///
+/// Example:
+/// \include class_CwiseUnaryOp.cpp
+/// Output: \verbinclude class_CwiseUnaryOp.out
+///
+EIGEN_DOC_UNARY_ADDONS(unaryViewExpr,unary function)
+///
+/// \sa unaryExpr, binaryExpr class CwiseUnaryOp
+///
+template<typename CustomViewOp>
+EIGEN_DEVICE_FUNC
+inline const CwiseUnaryView<CustomViewOp, const Derived>
+unaryViewExpr(const CustomViewOp& func = CustomViewOp()) const
+{
+  return CwiseUnaryView<CustomViewOp, const Derived>(derived(), func);
+}
+
+/// \returns a non const expression of the real part of \c *this.
+///
+EIGEN_DOC_UNARY_ADDONS(real,real part function)
+///
+/// \sa imag()
+EIGEN_DEVICE_FUNC
+inline NonConstRealReturnType
+real() { return NonConstRealReturnType(derived()); }
+
+/// \returns a non const expression of the imaginary part of \c *this.
+///
+EIGEN_DOC_UNARY_ADDONS(imag,imaginary part function)
+///
+/// \sa real()
+EIGEN_DEVICE_FUNC
+inline NonConstImagReturnType
+imag() { return NonConstImagReturnType(derived()); }
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/IndexedViewMethods.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/IndexedViewMethods.h
new file mode 100644
index 0000000000000000000000000000000000000000..e5432eacce7435fde2b251abab17858489a8ba6f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/IndexedViewMethods.h
@@ -0,0 +1,350 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+
+#if !defined(EIGEN_PARSED_BY_DOXYGEN)
+
+protected:
+// define some aliases to ease readability
+
+template <typename Indices>
+using IvcRowType = typename internal::IndexedViewCompatibleType<Indices, RowsAtCompileTime>::type;
+
+template <typename Indices>
+using IvcColType = typename internal::IndexedViewCompatibleType<Indices, ColsAtCompileTime>::type;
+
+template <typename Indices>
+using IvcType = typename internal::IndexedViewCompatibleType<Indices, SizeAtCompileTime>::type;
+
+typedef typename internal::IndexedViewCompatibleType<Index, 1>::type IvcIndex;
+
+template <typename Indices>
+inline IvcRowType<Indices> ivcRow(const Indices& indices) const {
+  return internal::makeIndexedViewCompatible(
+      indices, internal::variable_if_dynamic<Index, RowsAtCompileTime>(derived().rows()), Specialized);
+}
+
+template <typename Indices>
+inline IvcColType<Indices> ivcCol(const Indices& indices) const {
+  return internal::makeIndexedViewCompatible(
+      indices, internal::variable_if_dynamic<Index, ColsAtCompileTime>(derived().cols()), Specialized);
+}
+
+template <typename Indices>
+inline IvcType<Indices> ivcSize(const Indices& indices) const {
+  return internal::makeIndexedViewCompatible(
+      indices, internal::variable_if_dynamic<Index, SizeAtCompileTime>(derived().size()), Specialized);
+}
+
+// this helper class assumes internal::valid_indexed_view_overload<RowIndices, ColIndices>::value == true
+template <typename RowIndices, typename ColIndices,
+          bool UseSymbolic = internal::traits<IndexedView<Derived, IvcRowType<RowIndices>, IvcColType<ColIndices>>>::ReturnAsScalar,
+          bool UseBlock = internal::traits<IndexedView<Derived, IvcRowType<RowIndices>, IvcColType<ColIndices>>>::ReturnAsBlock,
+          bool UseGeneric = internal::traits<IndexedView<Derived, IvcRowType<RowIndices>, IvcColType<ColIndices>>>::ReturnAsIndexedView>
+struct IndexedViewSelector;
+
+// Generic
+template <typename RowIndices, typename ColIndices>
+struct IndexedViewSelector<RowIndices, ColIndices, false, false, true> {
+  using ReturnType = IndexedView<Derived, IvcRowType<RowIndices>, IvcColType<ColIndices>>;
+  using ConstReturnType = IndexedView<const Derived, IvcRowType<RowIndices>, IvcColType<ColIndices>>;
+
+  static inline ReturnType run(Derived& derived, const RowIndices& rowIndices, const ColIndices& colIndices) {
+    return ReturnType(derived, derived.ivcRow(rowIndices), derived.ivcCol(colIndices));
+  }
+  static inline ConstReturnType run(const Derived& derived, const RowIndices& rowIndices,
+                                    const ColIndices& colIndices) {
+    return ConstReturnType(derived, derived.ivcRow(rowIndices), derived.ivcCol(colIndices));
+  }
+};
+
+// Block
+template <typename RowIndices, typename ColIndices>
+struct IndexedViewSelector<RowIndices, ColIndices, false, true, false> {
+  using IndexedViewType = IndexedView<Derived, IvcRowType<RowIndices>, IvcColType<ColIndices>>;
+  using ConstIndexedViewType = IndexedView<const Derived, IvcRowType<RowIndices>, IvcColType<ColIndices>>;
+  using ReturnType = typename internal::traits<IndexedViewType>::BlockType;
+  using ConstReturnType = typename internal::traits<ConstIndexedViewType>::BlockType;
+
+  static inline ReturnType run(Derived& derived, const RowIndices& rowIndices, const ColIndices& colIndices) {
+    IvcRowType<RowIndices> actualRowIndices = derived.ivcRow(rowIndices);
+    IvcColType<ColIndices> actualColIndices = derived.ivcCol(colIndices);
+    return ReturnType(derived, internal::first(actualRowIndices), internal::first(actualColIndices),
+                      internal::index_list_size(actualRowIndices), internal::index_list_size(actualColIndices));
+  }
+  static inline ConstReturnType run(const Derived& derived, const RowIndices& rowIndices,
+                                    const ColIndices& colIndices) {
+    IvcRowType<RowIndices> actualRowIndices = derived.ivcRow(rowIndices);
+    IvcColType<ColIndices> actualColIndices = derived.ivcCol(colIndices);
+    return ConstReturnType(derived, internal::first(actualRowIndices), internal::first(actualColIndices),
+                           internal::index_list_size(actualRowIndices), internal::index_list_size(actualColIndices));
+  }
+};
+
+// Symbolic
+template <typename RowIndices, typename ColIndices>
+struct IndexedViewSelector<RowIndices, ColIndices, true, false, false> {
+  using ReturnType = typename DenseBase<Derived>::Scalar&;
+  using ConstReturnType = typename DenseBase<Derived>::CoeffReturnType;
+
+  static inline ReturnType run(Derived& derived, const RowIndices& rowIndices, const ColIndices& colIndices) {
+    return derived(internal::eval_expr_given_size(rowIndices, derived.rows()),
+                   internal::eval_expr_given_size(colIndices, derived.cols()));
+  }
+  static inline ConstReturnType run(const Derived& derived, const RowIndices& rowIndices,
+                                    const ColIndices& colIndices) {
+    return derived(internal::eval_expr_given_size(rowIndices, derived.rows()),
+                   internal::eval_expr_given_size(colIndices, derived.cols()));
+  }
+};
+
+// this helper class assumes internal::is_valid_index_type<Indices>::value == false
+template <typename Indices, 
+          bool UseSymbolic = symbolic::is_symbolic<Indices>::value,
+          bool UseBlock = !UseSymbolic && internal::get_compile_time_incr<IvcType<Indices>>::value == 1,
+          bool UseGeneric = !UseSymbolic && !UseBlock>
+struct VectorIndexedViewSelector;
+
+// Generic
+template <typename Indices>
+struct VectorIndexedViewSelector<Indices, false, false, true> {
+
+  static constexpr bool IsRowMajor = DenseBase<Derived>::IsRowMajor;
+
+  using RowMajorReturnType = IndexedView<Derived, IvcIndex, IvcType<Indices>>;
+  using ConstRowMajorReturnType = IndexedView<const Derived, IvcIndex, IvcType<Indices>>;
+
+  using ColMajorReturnType = IndexedView<Derived, IvcType<Indices>, IvcIndex>;
+  using ConstColMajorReturnType = IndexedView<const Derived, IvcType<Indices>, IvcIndex>;
+
+  using ReturnType = typename internal::conditional<IsRowMajor, RowMajorReturnType, ColMajorReturnType>::type;
+  using ConstReturnType =
+      typename internal::conditional<IsRowMajor, ConstRowMajorReturnType, ConstColMajorReturnType>::type;
+
+  template <bool UseRowMajor = IsRowMajor, std::enable_if_t<UseRowMajor, bool> = true>
+  static inline RowMajorReturnType run(Derived& derived, const Indices& indices) {
+    return RowMajorReturnType(derived, IvcIndex(0), derived.ivcCol(indices));
+  }
+  template <bool UseRowMajor = IsRowMajor, std::enable_if_t<UseRowMajor, bool> = true>
+  static inline ConstRowMajorReturnType run(const Derived& derived, const Indices& indices) {
+    return ConstRowMajorReturnType(derived, IvcIndex(0), derived.ivcCol(indices));
+  }
+  template <bool UseRowMajor = IsRowMajor, std::enable_if_t<!UseRowMajor, bool> = true>
+  static inline ColMajorReturnType run(Derived& derived, const Indices& indices) {
+    return ColMajorReturnType(derived, derived.ivcRow(indices), IvcIndex(0));
+  }
+  template <bool UseRowMajor = IsRowMajor, std::enable_if_t<!UseRowMajor, bool> = true>
+  static inline ConstColMajorReturnType run(const Derived& derived, const Indices& indices) {
+    return ConstColMajorReturnType(derived, derived.ivcRow(indices), IvcIndex(0));
+  }
+};
+
+// Block
+template <typename Indices>
+struct VectorIndexedViewSelector<Indices, false, true, false> {
+
+  using ReturnType = VectorBlock<Derived, internal::array_size<Indices>::value>;
+  using ConstReturnType = VectorBlock<const Derived, internal::array_size<Indices>::value>;
+
+  static inline ReturnType run(Derived& derived, const Indices& indices) {
+    IvcType<Indices> actualIndices = derived.ivcSize(indices);
+    return ReturnType(derived, internal::first(actualIndices), internal::index_list_size(actualIndices));
+  }
+  static inline ConstReturnType run(const Derived& derived, const Indices& indices) {
+    IvcType<Indices> actualIndices = derived.ivcSize(indices);
+    return ConstReturnType(derived, internal::first(actualIndices), internal::index_list_size(actualIndices));
+  }
+};
+
+// Symbolic
+template <typename Indices>
+struct VectorIndexedViewSelector<Indices, true, false, false> {
+
+  using ReturnType = typename DenseBase<Derived>::Scalar&;
+  using ConstReturnType = typename DenseBase<Derived>::CoeffReturnType;
+
+  static inline ReturnType run(Derived& derived, const Indices& id) {
+    return derived(internal::eval_expr_given_size(id, derived.size()));
+  }
+  static inline ConstReturnType run(const Derived& derived, const Indices& id) {
+    return derived(internal::eval_expr_given_size(id, derived.size()));
+  }
+};
+
+// SFINAE dummy types
+
+template <typename RowIndices, typename ColIndices>
+using EnableOverload = std::enable_if_t<
+    internal::valid_indexed_view_overload<RowIndices, ColIndices>::value && internal::is_lvalue<Derived>::value, bool>;
+
+template <typename RowIndices, typename ColIndices>
+using EnableConstOverload =
+    std::enable_if_t<internal::valid_indexed_view_overload<RowIndices, ColIndices>::value, bool>;
+
+template <typename Indices>
+using EnableVectorOverload =
+    std::enable_if_t<!internal::is_valid_index_type<Indices>::value && internal::is_lvalue<Derived>::value, bool>;
+
+template <typename Indices>
+using EnableConstVectorOverload = std::enable_if_t<!internal::is_valid_index_type<Indices>::value, bool>;
+
+public:
+
+// Public API for 2D matrices/arrays
+
+// non-const versions
+
+template <typename RowIndices, typename ColIndices>
+using IndexedViewType = typename IndexedViewSelector<RowIndices, ColIndices>::ReturnType;
+
+template <typename RowIndices, typename ColIndices, EnableOverload<RowIndices, ColIndices> = true>
+IndexedViewType<RowIndices, ColIndices> operator()(const RowIndices& rowIndices, const ColIndices& colIndices) {
+  return IndexedViewSelector<RowIndices, ColIndices>::run(derived(), rowIndices, colIndices);
+}
+
+template <typename RowType, size_t RowSize, typename ColIndices, typename RowIndices = Array<RowType, RowSize, 1>,
+          EnableOverload<RowIndices, ColIndices> = true>
+IndexedViewType<RowIndices, ColIndices> operator()(const RowType (&rowIndices)[RowSize], const ColIndices& colIndices) {
+  return IndexedViewSelector<RowIndices, ColIndices>::run(derived(), RowIndices{rowIndices}, colIndices);
+}
+
+template <typename RowIndices, typename ColType, size_t ColSize, typename ColIndices = Array<ColType, ColSize, 1>,
+          EnableOverload<RowIndices, ColIndices> = true>
+IndexedViewType<RowIndices, ColIndices> operator()(const RowIndices& rowIndices, const ColType (&colIndices)[ColSize]) {
+  return IndexedViewSelector<RowIndices, ColIndices>::run(derived(), rowIndices, ColIndices{colIndices});
+}
+
+template <typename RowType, size_t RowSize, typename ColType, size_t ColSize,
+          typename RowIndices = Array<RowType, RowSize, 1>, typename ColIndices = Array<ColType, ColSize, 1>,
+          EnableOverload<RowIndices, ColIndices> = true>
+IndexedViewType<RowIndices, ColIndices> operator()(const RowType (&rowIndices)[RowSize],
+                                                   const ColType (&colIndices)[ColSize]) {
+  return IndexedViewSelector<RowIndices, ColIndices>::run(derived(), RowIndices{rowIndices}, ColIndices{colIndices});
+}
+
+// const versions
+
+template <typename RowIndices, typename ColIndices>
+using ConstIndexedViewType = typename IndexedViewSelector<RowIndices, ColIndices>::ConstReturnType;
+
+template <typename RowIndices, typename ColIndices, EnableConstOverload<RowIndices, ColIndices> = true>
+ConstIndexedViewType<RowIndices, ColIndices> operator()(const RowIndices& rowIndices,
+                                                        const ColIndices& colIndices) const {
+  return IndexedViewSelector<RowIndices, ColIndices>::run(derived(), rowIndices, colIndices);
+}
+
+template <typename RowType, size_t RowSize, typename ColIndices, typename RowIndices = Array<RowType, RowSize, 1>,
+          EnableConstOverload<RowIndices, ColIndices> = true>
+ConstIndexedViewType<RowIndices, ColIndices> operator()(const RowType (&rowIndices)[RowSize],
+                                                        const ColIndices& colIndices) const {
+  return IndexedViewSelector<RowIndices, ColIndices>::run(derived(), RowIndices{rowIndices}, colIndices);
+}
+
+template <typename RowIndices, typename ColType, size_t ColSize, typename ColIndices = Array<ColType, ColSize, 1>,
+          EnableConstOverload<RowIndices, ColIndices> = true>
+ConstIndexedViewType<RowIndices, ColIndices> operator()(const RowIndices& rowIndices,
+                                                        const ColType (&colIndices)[ColSize]) const {
+  return IndexedViewSelector<RowIndices, ColIndices>::run(derived(), rowIndices, ColIndices{colIndices});
+}
+
+template <typename RowType, size_t RowSize, typename ColType, size_t ColSize,
+          typename RowIndices = Array<RowType, RowSize, 1>, typename ColIndices = Array<ColType, ColSize, 1>,
+          EnableConstOverload<RowIndices, ColIndices> = true>
+ConstIndexedViewType<RowIndices, ColIndices> operator()(const RowType (&rowIndices)[RowSize],
+                                                        const ColType (&colIndices)[ColSize]) const {
+  return IndexedViewSelector<RowIndices, ColIndices>::run(derived(), RowIndices{rowIndices}, ColIndices{colIndices});
+}
+
+// Public API for 1D vectors/arrays
+
+// non-const versions
+
+template <typename Indices>
+using VectorIndexedViewType = typename VectorIndexedViewSelector<Indices>::ReturnType;
+
+template <typename Indices, EnableVectorOverload<Indices> = true>
+VectorIndexedViewType<Indices> operator()(const Indices& indices) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return VectorIndexedViewSelector<Indices>::run(derived(), indices);
+}
+
+template <typename IndexType, size_t Size, typename Indices = Array<IndexType, Size, 1>,
+          EnableVectorOverload<Indices> = true>
+VectorIndexedViewType<Indices> operator()(const IndexType (&indices)[Size]) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return VectorIndexedViewSelector<Indices>::run(derived(), Indices{indices});
+}
+
+// const versions
+
+template <typename Indices>
+using ConstVectorIndexedViewType = typename VectorIndexedViewSelector<Indices>::ConstReturnType;
+
+template <typename Indices, EnableConstVectorOverload<Indices> = true>
+ConstVectorIndexedViewType<Indices> operator()(const Indices& indices) const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return VectorIndexedViewSelector<Indices>::run(derived(), indices);
+}
+
+template <typename IndexType, size_t Size, typename Indices = Array<IndexType, Size, 1>,
+          EnableConstVectorOverload<Indices> = true>
+ConstVectorIndexedViewType<Indices> operator()(const IndexType (&indices)[Size]) const {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(Derived)
+  return VectorIndexedViewSelector<Indices>::run(derived(), Indices{indices});
+}
+
+#else // EIGEN_PARSED_BY_DOXYGEN
+
+/**
+  * \returns a generic submatrix view defined by the rows and columns indexed \a rowIndices and \a colIndices respectively.
+  *
+  * Each parameter must either be:
+  *  - An integer indexing a single row or column
+  *  - Eigen::placeholders::all indexing the full set of respective rows or columns in increasing order
+  *  - An ArithmeticSequence as returned by the Eigen::seq and Eigen::seqN functions
+  *  - Any %Eigen's vector/array of integers or expressions
+  *  - Plain C arrays: \c int[N]
+  *  - And more generally any type exposing the following two member functions:
+  * \code
+  * <integral type> operator[](<integral type>) const;
+  * <integral type> size() const;
+  * \endcode
+  * where \c <integral \c type>  stands for any integer type compatible with Eigen::Index (i.e. \c std::ptrdiff_t).
+  *
+  * The last statement implies compatibility with \c std::vector, \c std::valarray, \c std::array, many of the Range-v3's ranges, etc.
+  *
+  * If the submatrix can be represented using a starting position \c (i,j) and positive sizes \c (rows,columns), then this
+  * method will returns a Block object after extraction of the relevant information from the passed arguments. This is the case
+  * when all arguments are either:
+  *  - An integer
+  *  - Eigen::placeholders::all
+  *  - An ArithmeticSequence with compile-time increment strictly equal to 1, as returned by Eigen::seq(a,b), and Eigen::seqN(a,N).
+  *
+  * Otherwise a more general IndexedView<Derived,RowIndices',ColIndices'> object will be returned, after conversion of the inputs
+  * to more suitable types \c RowIndices' and \c ColIndices'.
+  *
+  * For 1D vectors and arrays, you better use the operator()(const Indices&) overload, which behave the same way but taking a single parameter.
+  *
+  * See also this <a href="https://stackoverflow.com/questions/46110917/eigen-replicate-items-along-one-dimension-without-useless-allocations">question</a> and its answer for an example of how to duplicate coefficients.
+  *
+  * \sa operator()(const Indices&), class Block, class IndexedView, DenseBase::block(Index,Index,Index,Index)
+  */
+template<typename RowIndices, typename ColIndices>
+IndexedView_or_Block
+operator()(const RowIndices& rowIndices, const ColIndices& colIndices);
+
+/** This is an overload of operator()(const RowIndices&, const ColIndices&) for 1D vectors or arrays
+  *
+  * \only_for_vectors
+  */
+template<typename Indices>
+IndexedView_or_VectorBlock
+operator()(const Indices& indices);
+
+#endif  // EIGEN_PARSED_BY_DOXYGEN
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/InternalHeaderCheck.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/InternalHeaderCheck.h
new file mode 100644
index 0000000000000000000000000000000000000000..ac6821d18644993b7157999bd452ddb88d7c7d7f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/InternalHeaderCheck.h
@@ -0,0 +1,3 @@
+#ifndef EIGEN_CORE_MODULE_H
+#error "Please include Eigen/plugins instead of including headers inside the src directory directly."
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/MatrixCwiseBinaryOps.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/MatrixCwiseBinaryOps.h
new file mode 100644
index 0000000000000000000000000000000000000000..fd5941452d8a3eeb4eec015e83415a79311127a4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/MatrixCwiseBinaryOps.h
@@ -0,0 +1,307 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// This file is a base class plugin containing matrix specifics coefficient wise functions.
+
+/** \returns an expression of the Schur product (coefficient wise product) of *this and \a other
+  *
+  * Example: \include MatrixBase_cwiseProduct.cpp
+  * Output: \verbinclude MatrixBase_cwiseProduct.out
+  *
+  * \sa class CwiseBinaryOp, cwiseAbs2
+  */
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)
+cwiseProduct(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
+{
+  return EIGEN_CWISE_BINARY_RETURN_TYPE(Derived,OtherDerived,product)(derived(), other.derived());
+}
+
+template<typename OtherDerived> using CwiseBinaryEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ>, const Derived, const OtherDerived>;
+template<typename OtherDerived> using CwiseBinaryNotEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>, const Derived, const OtherDerived>;
+template<typename OtherDerived> using CwiseBinaryLessReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>, const Derived, const OtherDerived>;
+template<typename OtherDerived> using CwiseBinaryGreaterReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>, const Derived, const OtherDerived>;
+template<typename OtherDerived> using CwiseBinaryLessOrEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>, const Derived, const OtherDerived>;
+template<typename OtherDerived> using CwiseBinaryGreaterOrEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>, const Derived, const OtherDerived>;
+
+/** \returns an expression of the coefficient-wise == operator of *this and \a other
+  *
+  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
+  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
+  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
+  * isMuchSmallerThan().
+  *
+  * Example: \include MatrixBase_cwiseEqual.cpp
+  * Output: \verbinclude MatrixBase_cwiseEqual.out
+  *
+  * \sa cwiseNotEqual(), isApprox(), isMuchSmallerThan()
+  */
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+inline const CwiseBinaryEqualReturnType<OtherDerived>
+cwiseEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
+{
+  return CwiseBinaryEqualReturnType<OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise != operator of *this and \a other
+  *
+  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
+  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
+  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
+  * isMuchSmallerThan().
+  *
+  * Example: \include MatrixBase_cwiseNotEqual.cpp
+  * Output: \verbinclude MatrixBase_cwiseNotEqual.out
+  *
+  * \sa cwiseEqual(), isApprox(), isMuchSmallerThan()
+  */
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+inline const CwiseBinaryNotEqualReturnType<OtherDerived>
+cwiseNotEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
+{
+  return CwiseBinaryNotEqualReturnType<OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise < operator of *this and \a other */
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+inline const CwiseBinaryLessReturnType<OtherDerived>
+cwiseLess(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const
+{
+  return CwiseBinaryLessReturnType<OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise > operator of *this and \a other */
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+inline const CwiseBinaryGreaterReturnType<OtherDerived>
+cwiseGreater(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const
+{
+  return CwiseBinaryGreaterReturnType<OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise <= operator of *this and \a other */
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+inline const CwiseBinaryLessOrEqualReturnType<OtherDerived>
+cwiseLessOrEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const
+{
+  return CwiseBinaryLessOrEqualReturnType<OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise >= operator of *this and \a other */
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+inline const CwiseBinaryGreaterOrEqualReturnType<OtherDerived>
+cwiseGreaterOrEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const
+{
+  return CwiseBinaryGreaterOrEqualReturnType<OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise min of *this and \a other
+  *
+  * Example: \include MatrixBase_cwiseMin.cpp
+  * Output: \verbinclude MatrixBase_cwiseMin.out
+  *
+  * \sa class CwiseBinaryOp, max()
+  */
+template<int NaNPropagation=PropagateFast, typename OtherDerived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>
+cwiseMin(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
+{
+  return CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise min of *this and scalar \a other
+  *
+  * \sa class CwiseBinaryOp, min()
+  */
+template<int NaNPropagation=PropagateFast>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_min_op<Scalar,Scalar,NaNPropagation>, const Derived, const ConstantReturnType>
+cwiseMin(const Scalar &other) const
+{
+  return cwiseMin<NaNPropagation>(Derived::Constant(rows(), cols(), other));
+}
+
+/** \returns an expression of the coefficient-wise max of *this and \a other
+  *
+  * Example: \include MatrixBase_cwiseMax.cpp
+  * Output: \verbinclude MatrixBase_cwiseMax.out
+  *
+  * \sa class CwiseBinaryOp, min()
+  */
+template<int NaNPropagation=PropagateFast, typename OtherDerived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>
+cwiseMax(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
+{
+  return CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,NaNPropagation>, const Derived, const OtherDerived>(derived(), other.derived());
+}
+
+/** \returns an expression of the coefficient-wise max of *this and scalar \a other
+  *
+  * \sa class CwiseBinaryOp, min()
+  */
+template<int NaNPropagation=PropagateFast>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_max_op<Scalar,Scalar,NaNPropagation>, const Derived, const ConstantReturnType>
+cwiseMax(const Scalar &other) const
+{
+  return cwiseMax<NaNPropagation>(Derived::Constant(rows(), cols(), other));
+}
+
+
+/** \returns an expression of the coefficient-wise quotient of *this and \a other
+  *
+  * Example: \include MatrixBase_cwiseQuotient.cpp
+  * Output: \verbinclude MatrixBase_cwiseQuotient.out
+  *
+  * \sa class CwiseBinaryOp, cwiseProduct(), cwiseInverse()
+  */
+template<typename OtherDerived>
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>
+cwiseQuotient(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived> &other) const
+{
+  return CwiseBinaryOp<internal::scalar_quotient_op<Scalar>, const Derived, const OtherDerived>(derived(), other.derived());
+}
+
+using CwiseScalarEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar,Scalar,internal::cmp_EQ>, const Derived, const ConstantReturnType>;
+using CwiseScalarNotEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ>, const Derived, const ConstantReturnType>;
+using CwiseScalarLessReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT>, const Derived, const ConstantReturnType>;
+using CwiseScalarGreaterReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT>, const Derived, const ConstantReturnType>;
+using CwiseScalarLessOrEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE>, const Derived, const ConstantReturnType>;
+using CwiseScalarGreaterOrEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE>, const Derived, const ConstantReturnType>;
+
+/** \returns an expression of the coefficient-wise == operator of \c *this and a scalar \a s
+  *
+  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
+  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
+  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
+  * isMuchSmallerThan().
+  *
+  * \sa cwiseEqual(const MatrixBase<OtherDerived> &) const
+  */
+EIGEN_DEVICE_FUNC
+inline const CwiseScalarEqualReturnType
+cwiseEqual(const Scalar& s) const
+{
+  return CwiseScalarEqualReturnType(derived(), Derived::Constant(rows(), cols(), s));
+}
+
+
+/** \returns an expression of the coefficient-wise == operator of \c *this and a scalar \a s
+  *
+  * \warning this performs an exact comparison, which is generally a bad idea with floating-point types.
+  * In order to check for equality between two vectors or matrices with floating-point coefficients, it is
+  * generally a far better idea to use a fuzzy comparison as provided by isApprox() and
+  * isMuchSmallerThan().
+  *
+  * \sa cwiseEqual(const MatrixBase<OtherDerived> &) const
+  */
+EIGEN_DEVICE_FUNC
+inline const CwiseScalarNotEqualReturnType
+cwiseNotEqual(const Scalar& s) const
+{
+  return CwiseScalarNotEqualReturnType(derived(), Derived::Constant(rows(), cols(), s));
+}
+
+/** \returns an expression of the coefficient-wise < operator of \c *this and a scalar \a s */
+EIGEN_DEVICE_FUNC
+inline const CwiseScalarLessReturnType
+cwiseLess(const Scalar& s) const
+{
+  return CwiseScalarLessReturnType(derived(), Derived::Constant(rows(), cols(), s));
+}
+
+/** \returns an expression of the coefficient-wise > operator of \c *this and a scalar \a s */
+EIGEN_DEVICE_FUNC
+inline const CwiseScalarGreaterReturnType
+cwiseGreater(const Scalar& s) const
+{
+  return CwiseScalarGreaterReturnType(derived(), Derived::Constant(rows(), cols(), s));
+}
+
+/** \returns an expression of the coefficient-wise <= operator of \c *this and a scalar \a s */
+EIGEN_DEVICE_FUNC
+inline const CwiseScalarLessOrEqualReturnType
+cwiseLessOrEqual(const Scalar& s) const
+{
+  return CwiseScalarLessOrEqualReturnType(derived(), Derived::Constant(rows(), cols(), s));
+}
+
+/** \returns an expression of the coefficient-wise >= operator of \c *this and a scalar \a s */
+EIGEN_DEVICE_FUNC
+inline const CwiseScalarGreaterOrEqualReturnType
+cwiseGreaterOrEqual(const Scalar& s) const
+{
+  return CwiseScalarGreaterOrEqualReturnType(derived(), Derived::Constant(rows(), cols(), s));
+}
+
+template<typename OtherDerived> using CwiseBinaryTypedEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ, true>, const Derived, const OtherDerived>;
+template<typename OtherDerived> using CwiseBinaryTypedNotEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ, true>, const Derived, const OtherDerived>;
+template<typename OtherDerived> using CwiseBinaryTypedLessReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT, true>, const Derived, const OtherDerived>;
+template<typename OtherDerived> using CwiseBinaryTypedGreaterReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT, true>, const Derived, const OtherDerived>;
+template<typename OtherDerived> using CwiseBinaryTypedLessOrEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE, true>, const Derived, const OtherDerived>;
+template<typename OtherDerived> using CwiseBinaryTypedGreaterOrEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE, true>, const Derived, const OtherDerived>;
+
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryTypedEqualReturnType<OtherDerived>
+cwiseTypedEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const { return CwiseBinaryTypedEqualReturnType<OtherDerived>(derived(), other.derived()); }
+
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryTypedNotEqualReturnType<OtherDerived>
+cwiseTypedNotEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const { return CwiseBinaryTypedNotEqualReturnType<OtherDerived>(derived(), other.derived()); }
+
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryTypedLessReturnType<OtherDerived>
+cwiseTypedLess(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const { return CwiseBinaryTypedLessReturnType<OtherDerived>(derived(), other.derived()); }
+
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryTypedGreaterReturnType<OtherDerived>
+cwiseTypedGreater(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const { return CwiseBinaryTypedGreaterReturnType<OtherDerived>(derived(), other.derived()); }
+
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryTypedLessOrEqualReturnType<OtherDerived>
+cwiseTypedLessOrEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const { return CwiseBinaryTypedLessOrEqualReturnType<OtherDerived>(derived(), other.derived()); }
+
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseBinaryTypedGreaterOrEqualReturnType<OtherDerived>
+cwiseTypedGreaterOrEqual(const EIGEN_CURRENT_STORAGE_BASE_CLASS<OtherDerived>& other) const { return CwiseBinaryTypedGreaterOrEqualReturnType<OtherDerived>(derived(), other.derived()); }
+
+using CwiseScalarTypedEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_EQ, true>, const Derived, const ConstantReturnType>;
+using CwiseScalarTypedNotEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_NEQ, true>, const Derived, const ConstantReturnType>;
+using CwiseScalarTypedLessReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LT, true>, const Derived, const ConstantReturnType>;
+using CwiseScalarTypedGreaterReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GT, true>, const Derived, const ConstantReturnType>;
+using CwiseScalarTypedLessOrEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_LE, true>, const Derived, const ConstantReturnType>;
+using CwiseScalarTypedGreaterOrEqualReturnType = CwiseBinaryOp<internal::scalar_cmp_op<Scalar, Scalar, internal::cmp_GE, true>, const Derived, const ConstantReturnType>;
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseScalarTypedEqualReturnType
+cwiseTypedEqual(const Scalar& s) const { return CwiseScalarTypedEqualReturnType(derived(), ConstantReturnType(rows(), cols(), s)); }
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseScalarTypedNotEqualReturnType
+cwiseTypedNotEqual(const Scalar& s) const { return CwiseScalarTypedNotEqualReturnType(derived(), ConstantReturnType(rows(), cols(), s)); }
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseScalarTypedLessReturnType
+cwiseTypedLess(const Scalar& s) const { return CwiseScalarTypedLessReturnType(derived(), ConstantReturnType(rows(), cols(), s)); }
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseScalarTypedGreaterReturnType
+cwiseTypedGreater(const Scalar& s) const { return CwiseScalarTypedGreaterReturnType(derived(), ConstantReturnType(rows(), cols(), s)); }
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseScalarTypedLessOrEqualReturnType
+cwiseTypedLessOrEqual(const Scalar& s) const { return CwiseScalarTypedLessOrEqualReturnType(derived(), ConstantReturnType(rows(), cols(), s)); }
+
+EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CwiseScalarTypedGreaterOrEqualReturnType
+cwiseTypedGreaterOrEqual(const Scalar& s) const { return CwiseScalarTypedGreaterOrEqualReturnType(derived(), ConstantReturnType(rows(), cols(), s)); }
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/MatrixCwiseUnaryOps.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/MatrixCwiseUnaryOps.h
new file mode 100644
index 0000000000000000000000000000000000000000..0222137ae2d3820f5d6f105620e27c8d203f22c9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/MatrixCwiseUnaryOps.h
@@ -0,0 +1,110 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+// This file is included into the body of the base classes supporting matrix specific coefficient-wise functions.
+// This include MatrixBase and SparseMatrixBase.
+
+
+typedef CwiseUnaryOp<internal::scalar_abs_op<Scalar>, const Derived> CwiseAbsReturnType;
+typedef CwiseUnaryOp<internal::scalar_abs2_op<Scalar>, const Derived> CwiseAbs2ReturnType;
+typedef CwiseUnaryOp<internal::scalar_arg_op<Scalar>, const Derived> CwiseArgReturnType;
+typedef CwiseUnaryOp<internal::scalar_carg_op<Scalar>, const Derived> CwiseCArgReturnType;
+typedef CwiseUnaryOp<internal::scalar_sqrt_op<Scalar>, const Derived> CwiseSqrtReturnType;
+typedef CwiseUnaryOp<internal::scalar_sign_op<Scalar>, const Derived> CwiseSignReturnType;
+typedef CwiseUnaryOp<internal::scalar_inverse_op<Scalar>, const Derived> CwiseInverseReturnType;
+
+/// \returns an expression of the coefficient-wise absolute value of \c *this
+///
+/// Example: \include MatrixBase_cwiseAbs.cpp
+/// Output: \verbinclude MatrixBase_cwiseAbs.out
+///
+EIGEN_DOC_UNARY_ADDONS(cwiseAbs,absolute value)
+///
+/// \sa cwiseAbs2()
+///
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const CwiseAbsReturnType
+cwiseAbs() const { return CwiseAbsReturnType(derived()); }
+
+/// \returns an expression of the coefficient-wise squared absolute value of \c *this
+///
+/// Example: \include MatrixBase_cwiseAbs2.cpp
+/// Output: \verbinclude MatrixBase_cwiseAbs2.out
+///
+EIGEN_DOC_UNARY_ADDONS(cwiseAbs2,squared absolute value)
+///
+/// \sa cwiseAbs()
+///
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const CwiseAbs2ReturnType
+cwiseAbs2() const { return CwiseAbs2ReturnType(derived()); }
+
+/// \returns an expression of the coefficient-wise square root of *this.
+///
+/// Example: \include MatrixBase_cwiseSqrt.cpp
+/// Output: \verbinclude MatrixBase_cwiseSqrt.out
+///
+EIGEN_DOC_UNARY_ADDONS(cwiseSqrt,square-root)
+///
+/// \sa cwisePow(), cwiseSquare()
+///
+EIGEN_DEVICE_FUNC
+inline const CwiseSqrtReturnType
+cwiseSqrt() const { return CwiseSqrtReturnType(derived()); }
+
+/// \returns an expression of the coefficient-wise signum of *this.
+///
+/// Example: \include MatrixBase_cwiseSign.cpp
+/// Output: \verbinclude MatrixBase_cwiseSign.out
+///
+EIGEN_DOC_UNARY_ADDONS(cwiseSign,sign function)
+///
+EIGEN_DEVICE_FUNC
+inline const CwiseSignReturnType
+cwiseSign() const { return CwiseSignReturnType(derived()); }
+
+
+/// \returns an expression of the coefficient-wise inverse of *this.
+///
+/// Example: \include MatrixBase_cwiseInverse.cpp
+/// Output: \verbinclude MatrixBase_cwiseInverse.out
+///
+EIGEN_DOC_UNARY_ADDONS(cwiseInverse,inverse)
+///
+/// \sa cwiseProduct()
+///
+EIGEN_DEVICE_FUNC
+inline const CwiseInverseReturnType
+cwiseInverse() const { return CwiseInverseReturnType(derived()); }
+
+/// \returns an expression of the coefficient-wise phase angle of \c *this
+///
+/// Example: \include MatrixBase_cwiseArg.cpp
+/// Output: \verbinclude MatrixBase_cwiseArg.out
+///
+EIGEN_DOC_UNARY_ADDONS(cwiseArg,arg)
+
+EIGEN_DEVICE_FUNC
+inline const CwiseArgReturnType
+cwiseArg() const { return CwiseArgReturnType(derived()); }
+
+EIGEN_DEVICE_FUNC
+EIGEN_STRONG_INLINE const CwiseCArgReturnType
+cwiseCArg() const { return CwiseCArgReturnType(derived()); }
+
+template <typename ScalarExponent>
+using CwisePowReturnType =
+    std::enable_if_t<internal::is_arithmetic<typename NumTraits<ScalarExponent>::Real>::value,
+                     CwiseUnaryOp<internal::scalar_unary_pow_op<Scalar, ScalarExponent>, const Derived>>;
+
+template <typename ScalarExponent>
+EIGEN_DEVICE_FUNC inline const CwisePowReturnType<ScalarExponent> cwisePow(const ScalarExponent& exponent) const {
+  return CwisePowReturnType<ScalarExponent>(derived(), internal::scalar_unary_pow_op<Scalar, ScalarExponent>(exponent));
+}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/ReshapedMethods.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/ReshapedMethods.h
new file mode 100644
index 0000000000000000000000000000000000000000..2cb1cf682b135dd340f3a855b21a6b621972daea
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/Eigen/src/plugins/ReshapedMethods.h
@@ -0,0 +1,149 @@
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+
+/// \returns an expression of \c *this with reshaped sizes.
+///
+/// \param nRows the number of rows in the reshaped expression, specified at either run-time or compile-time, or AutoSize
+/// \param nCols the number of columns in the reshaped expression, specified at either run-time or compile-time, or AutoSize
+/// \tparam Order specifies whether the coefficients should be processed in column-major-order (ColMajor), in row-major-order (RowMajor),
+///               or follows the \em natural order of the nested expression (AutoOrder). The default is ColMajor.
+/// \tparam NRowsType the type of the value handling the number of rows, typically Index.
+/// \tparam NColsType the type of the value handling the number of columns, typically Index.
+///
+/// Dynamic size example: \include MatrixBase_reshaped_int_int.cpp
+/// Output: \verbinclude MatrixBase_reshaped_int_int.out
+///
+/// The number of rows \a nRows and columns \a nCols can also be specified at compile-time by passing Eigen::fix<N>,
+/// or Eigen::fix<N>(n) as arguments. In the later case, \c n plays the role of a runtime fallback value in case \c N equals Eigen::Dynamic.
+/// Here is an example with a fixed number of rows and columns:
+/// \include MatrixBase_reshaped_fixed.cpp
+/// Output: \verbinclude MatrixBase_reshaped_fixed.out
+///
+/// Finally, one of the sizes parameter can be automatically deduced from the other one by passing AutoSize as in the following example:
+/// \include MatrixBase_reshaped_auto.cpp
+/// Output: \verbinclude MatrixBase_reshaped_auto.out
+/// AutoSize does preserve compile-time sizes when possible, i.e., when the sizes of the input are known at compile time \b and
+/// that the other size is passed at compile-time using Eigen::fix<N> as above.
+///
+/// \sa class Reshaped, fix, fix<N>(int)
+///
+template<int Order = ColMajor, typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC
+inline Reshaped<Derived,...>
+reshaped(NRowsType nRows, NColsType nCols);
+
+/// This is the const version of reshaped(NRowsType,NColsType).
+template<int Order = ColMajor, typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC
+inline const Reshaped<const Derived,...>
+reshaped(NRowsType nRows, NColsType nCols) const;
+
+/// \returns an expression of \c *this with columns (or rows) stacked to a linear column vector
+///
+/// \tparam Order specifies whether the coefficients should be processed in column-major-order (ColMajor), in row-major-order (RowMajor),
+///               or follows the \em natural order of the nested expression (AutoOrder). The default is ColMajor.
+///
+/// This overloads is essentially a shortcut for `A.reshaped<Order>(AutoSize,fix<1>)`.
+///
+/// - If `Order==ColMajor` (the default), then it returns a column-vector from the stacked columns of \c *this.
+/// - If `Order==RowMajor`, then it returns a column-vector from the stacked rows of \c *this.
+/// - If `Order==AutoOrder`, then it returns a column-vector with elements stacked following the storage order of \c *this.
+///   This mode is the recommended one when the particular ordering of the element is not relevant.
+///
+/// Example:
+/// \include MatrixBase_reshaped_to_vector.cpp
+/// Output: \verbinclude MatrixBase_reshaped_to_vector.out
+///
+/// If you want more control, you can still fall back to reshaped(NRowsType,NColsType).
+///
+/// \sa reshaped(NRowsType,NColsType), class Reshaped
+///
+template<int Order = ColMajor>
+EIGEN_DEVICE_FUNC
+inline Reshaped<Derived,...>
+reshaped();
+
+/// This is the const version of reshaped().
+template<int Order = ColMajor>
+EIGEN_DEVICE_FUNC
+inline const Reshaped<const Derived,...>
+reshaped() const;
+
+#else
+
+// This file is automatically included twice to generate const and non-const versions
+
+#ifndef EIGEN_RESHAPED_METHOD_2ND_PASS
+#define EIGEN_RESHAPED_METHOD_CONST const
+#else
+#define EIGEN_RESHAPED_METHOD_CONST
+#endif
+
+#ifndef EIGEN_RESHAPED_METHOD_2ND_PASS
+
+// This part is included once
+
+#endif
+
+template<typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC
+inline Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,
+                internal::get_compiletime_reshape_size<NRowsType,NColsType,SizeAtCompileTime>::value,
+                internal::get_compiletime_reshape_size<NColsType,NRowsType,SizeAtCompileTime>::value>
+reshaped(NRowsType nRows, NColsType nCols) EIGEN_RESHAPED_METHOD_CONST
+{
+  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,
+                  internal::get_compiletime_reshape_size<NRowsType,NColsType,SizeAtCompileTime>::value,
+                  internal::get_compiletime_reshape_size<NColsType,NRowsType,SizeAtCompileTime>::value>
+                (derived(),
+                 internal::get_runtime_reshape_size(nRows,internal::get_runtime_value(nCols),size()),
+                 internal::get_runtime_reshape_size(nCols,internal::get_runtime_value(nRows),size()));
+}
+
+template<int Order, typename NRowsType, typename NColsType>
+EIGEN_DEVICE_FUNC
+inline Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,
+                internal::get_compiletime_reshape_size<NRowsType,NColsType,SizeAtCompileTime>::value,
+                internal::get_compiletime_reshape_size<NColsType,NRowsType,SizeAtCompileTime>::value,
+                internal::get_compiletime_reshape_order(Flags, Order)>
+reshaped(NRowsType nRows, NColsType nCols) EIGEN_RESHAPED_METHOD_CONST
+{
+  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,
+                  internal::get_compiletime_reshape_size<NRowsType,NColsType,SizeAtCompileTime>::value,
+                  internal::get_compiletime_reshape_size<NColsType,NRowsType,SizeAtCompileTime>::value,
+                  internal::get_compiletime_reshape_order(Flags, Order)>
+                (derived(),
+                 internal::get_runtime_reshape_size(nRows,internal::get_runtime_value(nCols),size()),
+                 internal::get_runtime_reshape_size(nCols,internal::get_runtime_value(nRows),size()));
+}
+
+// Views as linear vectors
+
+EIGEN_DEVICE_FUNC
+inline Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,SizeAtCompileTime,1>
+reshaped() EIGEN_RESHAPED_METHOD_CONST
+{
+  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived,SizeAtCompileTime,1>(derived(),size(),1);
+}
+
+template<int Order>
+EIGEN_DEVICE_FUNC
+inline Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived, SizeAtCompileTime, 1,
+                internal::get_compiletime_reshape_order(Flags, Order)>
+reshaped() EIGEN_RESHAPED_METHOD_CONST
+{
+  EIGEN_STATIC_ASSERT(Order==RowMajor || Order==ColMajor || Order==AutoOrder, INVALID_TEMPLATE_PARAMETER);
+  return Reshaped<EIGEN_RESHAPED_METHOD_CONST Derived, SizeAtCompileTime, 1,
+                  internal::get_compiletime_reshape_order(Flags, Order)>
+                (derived(), size(), 1);
+}
+
+#undef EIGEN_RESHAPED_METHOD_CONST
+
+#ifndef EIGEN_RESHAPED_METHOD_2ND_PASS
+#define EIGEN_RESHAPED_METHOD_2ND_PASS
+#include "ReshapedMethods.h"
+#undef EIGEN_RESHAPED_METHOD_2ND_PASS
+#endif
+
+#endif // EIGEN_PARSED_BY_DOXYGEN
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/algorithm/algorithm.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/algorithm/algorithm.h
new file mode 100644
index 0000000000000000000000000000000000000000..e9b4733872788dc25ceaace921625209983c40a1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/algorithm/algorithm.h
@@ -0,0 +1,159 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: algorithm.h
+// -----------------------------------------------------------------------------
+//
+// This header file contains Google extensions to the standard <algorithm> C++
+// header.
+
+#ifndef ABSL_ALGORITHM_ALGORITHM_H_
+#define ABSL_ALGORITHM_ALGORITHM_H_
+
+#include <algorithm>
+#include <iterator>
+#include <type_traits>
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+namespace algorithm_internal {
+
+// Performs comparisons with operator==, similar to C++14's `std::equal_to<>`.
+struct EqualTo {
+  template <typename T, typename U>
+  bool operator()(const T& a, const U& b) const {
+    return a == b;
+  }
+};
+
+template <typename InputIter1, typename InputIter2, typename Pred>
+bool EqualImpl(InputIter1 first1, InputIter1 last1, InputIter2 first2,
+               InputIter2 last2, Pred pred, std::input_iterator_tag,
+               std::input_iterator_tag) {
+  while (true) {
+    if (first1 == last1) return first2 == last2;
+    if (first2 == last2) return false;
+    if (!pred(*first1, *first2)) return false;
+    ++first1;
+    ++first2;
+  }
+}
+
+template <typename InputIter1, typename InputIter2, typename Pred>
+bool EqualImpl(InputIter1 first1, InputIter1 last1, InputIter2 first2,
+               InputIter2 last2, Pred&& pred, std::random_access_iterator_tag,
+               std::random_access_iterator_tag) {
+  return (last1 - first1 == last2 - first2) &&
+         std::equal(first1, last1, first2, std::forward<Pred>(pred));
+}
+
+// When we are using our own internal predicate that just applies operator==, we
+// forward to the non-predicate form of std::equal. This enables an optimization
+// in libstdc++ that can result in std::memcmp being used for integer types.
+template <typename InputIter1, typename InputIter2>
+bool EqualImpl(InputIter1 first1, InputIter1 last1, InputIter2 first2,
+               InputIter2 last2, algorithm_internal::EqualTo /* unused */,
+               std::random_access_iterator_tag,
+               std::random_access_iterator_tag) {
+  return (last1 - first1 == last2 - first2) &&
+         std::equal(first1, last1, first2);
+}
+
+template <typename It>
+It RotateImpl(It first, It middle, It last, std::true_type) {
+  return std::rotate(first, middle, last);
+}
+
+template <typename It>
+It RotateImpl(It first, It middle, It last, std::false_type) {
+  std::rotate(first, middle, last);
+  return std::next(first, std::distance(middle, last));
+}
+
+}  // namespace algorithm_internal
+
+// equal()
+//
+// Compares the equality of two ranges specified by pairs of iterators, using
+// the given predicate, returning true iff for each corresponding iterator i1
+// and i2 in the first and second range respectively, pred(*i1, *i2) == true
+//
+// This comparison takes at most min(`last1` - `first1`, `last2` - `first2`)
+// invocations of the predicate. Additionally, if InputIter1 and InputIter2 are
+// both random-access iterators, and `last1` - `first1` != `last2` - `first2`,
+// then the predicate is never invoked and the function returns false.
+//
+// This is a C++11-compatible implementation of C++14 `std::equal`.  See
+// https://en.cppreference.com/w/cpp/algorithm/equal for more information.
+template <typename InputIter1, typename InputIter2, typename Pred>
+bool equal(InputIter1 first1, InputIter1 last1, InputIter2 first2,
+           InputIter2 last2, Pred&& pred) {
+  return algorithm_internal::EqualImpl(
+      first1, last1, first2, last2, std::forward<Pred>(pred),
+      typename std::iterator_traits<InputIter1>::iterator_category{},
+      typename std::iterator_traits<InputIter2>::iterator_category{});
+}
+
+// Overload of equal() that performs comparison of two ranges specified by pairs
+// of iterators using operator==.
+template <typename InputIter1, typename InputIter2>
+bool equal(InputIter1 first1, InputIter1 last1, InputIter2 first2,
+           InputIter2 last2) {
+  return absl::equal(first1, last1, first2, last2,
+                     algorithm_internal::EqualTo{});
+}
+
+// linear_search()
+//
+// Performs a linear search for `value` using the iterator `first` up to
+// but not including `last`, returning true if [`first`, `last`) contains an
+// element equal to `value`.
+//
+// A linear search is of O(n) complexity which is guaranteed to make at most
+// n = (`last` - `first`) comparisons. A linear search over short containers
+// may be faster than a binary search, even when the container is sorted.
+template <typename InputIterator, typename EqualityComparable>
+bool linear_search(InputIterator first, InputIterator last,
+                   const EqualityComparable& value) {
+  return std::find(first, last, value) != last;
+}
+
+// rotate()
+//
+// Performs a left rotation on a range of elements (`first`, `last`) such that
+// `middle` is now the first element. `rotate()` returns an iterator pointing to
+// the first element before rotation. This function is exactly the same as
+// `std::rotate`, but fixes a bug in gcc
+// <= 4.9 where `std::rotate` returns `void` instead of an iterator.
+//
+// The complexity of this algorithm is the same as that of `std::rotate`, but if
+// `ForwardIterator` is not a random-access iterator, then `absl::rotate`
+// performs an additional pass over the range to construct the return value.
+template <typename ForwardIterator>
+ForwardIterator rotate(ForwardIterator first, ForwardIterator middle,
+                       ForwardIterator last) {
+  return algorithm_internal::RotateImpl(
+      first, middle, last,
+      std::is_same<decltype(std::rotate(first, middle, last)),
+                   ForwardIterator>());
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_ALGORITHM_ALGORITHM_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/algorithm/container.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/algorithm/container.h
new file mode 100644
index 0000000000000000000000000000000000000000..c7782d4f038e51e9ee250bf607b1237be57388f3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/algorithm/container.h
@@ -0,0 +1,1773 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: container.h
+// -----------------------------------------------------------------------------
+//
+// This header file provides Container-based versions of algorithmic functions
+// within the C++ standard library. The following standard library sets of
+// functions are covered within this file:
+//
+//   * Algorithmic <iterator> functions
+//   * Algorithmic <numeric> functions
+//   * <algorithm> functions
+//
+// The standard library functions operate on iterator ranges; the functions
+// within this API operate on containers, though many return iterator ranges.
+//
+// All functions within this API are named with a `c_` prefix. Calls such as
+// `absl::c_xx(container, ...) are equivalent to std:: functions such as
+// `std::xx(std::begin(cont), std::end(cont), ...)`. Functions that act on
+// iterators but not conceptually on iterator ranges (e.g. `std::iter_swap`)
+// have no equivalent here.
+//
+// For template parameter and variable naming, `C` indicates the container type
+// to which the function is applied, `Pred` indicates the predicate object type
+// to be used by the function and `T` indicates the applicable element type.
+
+#ifndef ABSL_ALGORITHM_CONTAINER_H_
+#define ABSL_ALGORITHM_CONTAINER_H_
+
+#include <algorithm>
+#include <cassert>
+#include <iterator>
+#include <numeric>
+#include <type_traits>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+#include <vector>
+
+#include "absl/algorithm/algorithm.h"
+#include "absl/base/macros.h"
+#include "absl/meta/type_traits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_algorithm_internal {
+
+// NOTE: it is important to defer to ADL lookup for building with C++ modules,
+// especially for headers like <valarray> which are not visible from this file
+// but specialize std::begin and std::end.
+using std::begin;
+using std::end;
+
+// The type of the iterator given by begin(c) (possibly std::begin(c)).
+// ContainerIter<const vector<T>> gives vector<T>::const_iterator,
+// while ContainerIter<vector<T>> gives vector<T>::iterator.
+template <typename C>
+using ContainerIter = decltype(begin(std::declval<C&>()));
+
+// An MSVC bug involving template parameter substitution requires us to use
+// decltype() here instead of just std::pair.
+template <typename C1, typename C2>
+using ContainerIterPairType =
+    decltype(std::make_pair(ContainerIter<C1>(), ContainerIter<C2>()));
+
+template <typename C>
+using ContainerDifferenceType = decltype(std::distance(
+    std::declval<ContainerIter<C>>(), std::declval<ContainerIter<C>>()));
+
+template <typename C>
+using ContainerPointerType =
+    typename std::iterator_traits<ContainerIter<C>>::pointer;
+
+// container_algorithm_internal::c_begin and
+// container_algorithm_internal::c_end are abbreviations for proper ADL
+// lookup of std::begin and std::end, i.e.
+//   using std::begin;
+//   using std::end;
+//   std::foo(begin(c), end(c));
+// becomes
+//   std::foo(container_algorithm_internal::begin(c),
+//            container_algorithm_internal::end(c));
+// These are meant for internal use only.
+
+template <typename C>
+ContainerIter<C> c_begin(C& c) {
+  return begin(c);
+}
+
+template <typename C>
+ContainerIter<C> c_end(C& c) {
+  return end(c);
+}
+
+template <typename T>
+struct IsUnorderedContainer : std::false_type {};
+
+template <class Key, class T, class Hash, class KeyEqual, class Allocator>
+struct IsUnorderedContainer<
+    std::unordered_map<Key, T, Hash, KeyEqual, Allocator>> : std::true_type {};
+
+template <class Key, class Hash, class KeyEqual, class Allocator>
+struct IsUnorderedContainer<std::unordered_set<Key, Hash, KeyEqual, Allocator>>
+    : std::true_type {};
+
+// container_algorithm_internal::c_size. It is meant for internal use only.
+
+template <class C>
+auto c_size(C& c) -> decltype(c.size()) {
+  return c.size();
+}
+
+template <class T, std::size_t N>
+constexpr std::size_t c_size(T (&)[N]) {
+  return N;
+}
+
+}  // namespace container_algorithm_internal
+
+// PUBLIC API
+
+//------------------------------------------------------------------------------
+// Abseil algorithm.h functions
+//------------------------------------------------------------------------------
+
+// c_linear_search()
+//
+// Container-based version of absl::linear_search() for performing a linear
+// search within a container.
+template <typename C, typename EqualityComparable>
+bool c_linear_search(const C& c, EqualityComparable&& value) {
+  return linear_search(container_algorithm_internal::c_begin(c),
+                       container_algorithm_internal::c_end(c),
+                       std::forward<EqualityComparable>(value));
+}
+
+//------------------------------------------------------------------------------
+// <iterator> algorithms
+//------------------------------------------------------------------------------
+
+// c_distance()
+//
+// Container-based version of the <iterator> `std::distance()` function to
+// return the number of elements within a container.
+template <typename C>
+container_algorithm_internal::ContainerDifferenceType<const C> c_distance(
+    const C& c) {
+  return std::distance(container_algorithm_internal::c_begin(c),
+                       container_algorithm_internal::c_end(c));
+}
+
+//------------------------------------------------------------------------------
+// <algorithm> Non-modifying sequence operations
+//------------------------------------------------------------------------------
+
+// c_all_of()
+//
+// Container-based version of the <algorithm> `std::all_of()` function to
+// test if all elements within a container satisfy a condition.
+template <typename C, typename Pred>
+bool c_all_of(const C& c, Pred&& pred) {
+  return std::all_of(container_algorithm_internal::c_begin(c),
+                     container_algorithm_internal::c_end(c),
+                     std::forward<Pred>(pred));
+}
+
+// c_any_of()
+//
+// Container-based version of the <algorithm> `std::any_of()` function to
+// test if any element in a container fulfills a condition.
+template <typename C, typename Pred>
+bool c_any_of(const C& c, Pred&& pred) {
+  return std::any_of(container_algorithm_internal::c_begin(c),
+                     container_algorithm_internal::c_end(c),
+                     std::forward<Pred>(pred));
+}
+
+// c_none_of()
+//
+// Container-based version of the <algorithm> `std::none_of()` function to
+// test if no elements in a container fulfill a condition.
+template <typename C, typename Pred>
+bool c_none_of(const C& c, Pred&& pred) {
+  return std::none_of(container_algorithm_internal::c_begin(c),
+                      container_algorithm_internal::c_end(c),
+                      std::forward<Pred>(pred));
+}
+
+// c_for_each()
+//
+// Container-based version of the <algorithm> `std::for_each()` function to
+// apply a function to a container's elements.
+template <typename C, typename Function>
+decay_t<Function> c_for_each(C&& c, Function&& f) {
+  return std::for_each(container_algorithm_internal::c_begin(c),
+                       container_algorithm_internal::c_end(c),
+                       std::forward<Function>(f));
+}
+
+// c_find()
+//
+// Container-based version of the <algorithm> `std::find()` function to find
+// the first element containing the passed value within a container value.
+template <typename C, typename T>
+container_algorithm_internal::ContainerIter<C> c_find(C& c, T&& value) {
+  return std::find(container_algorithm_internal::c_begin(c),
+                   container_algorithm_internal::c_end(c),
+                   std::forward<T>(value));
+}
+
+// c_find_if()
+//
+// Container-based version of the <algorithm> `std::find_if()` function to find
+// the first element in a container matching the given condition.
+template <typename C, typename Pred>
+container_algorithm_internal::ContainerIter<C> c_find_if(C& c, Pred&& pred) {
+  return std::find_if(container_algorithm_internal::c_begin(c),
+                      container_algorithm_internal::c_end(c),
+                      std::forward<Pred>(pred));
+}
+
+// c_find_if_not()
+//
+// Container-based version of the <algorithm> `std::find_if_not()` function to
+// find the first element in a container not matching the given condition.
+template <typename C, typename Pred>
+container_algorithm_internal::ContainerIter<C> c_find_if_not(C& c,
+                                                             Pred&& pred) {
+  return std::find_if_not(container_algorithm_internal::c_begin(c),
+                          container_algorithm_internal::c_end(c),
+                          std::forward<Pred>(pred));
+}
+
+// c_find_end()
+//
+// Container-based version of the <algorithm> `std::find_end()` function to
+// find the last subsequence within a container.
+template <typename Sequence1, typename Sequence2>
+container_algorithm_internal::ContainerIter<Sequence1> c_find_end(
+    Sequence1& sequence, Sequence2& subsequence) {
+  return std::find_end(container_algorithm_internal::c_begin(sequence),
+                       container_algorithm_internal::c_end(sequence),
+                       container_algorithm_internal::c_begin(subsequence),
+                       container_algorithm_internal::c_end(subsequence));
+}
+
+// Overload of c_find_end() for using a predicate evaluation other than `==` as
+// the function's test condition.
+template <typename Sequence1, typename Sequence2, typename BinaryPredicate>
+container_algorithm_internal::ContainerIter<Sequence1> c_find_end(
+    Sequence1& sequence, Sequence2& subsequence, BinaryPredicate&& pred) {
+  return std::find_end(container_algorithm_internal::c_begin(sequence),
+                       container_algorithm_internal::c_end(sequence),
+                       container_algorithm_internal::c_begin(subsequence),
+                       container_algorithm_internal::c_end(subsequence),
+                       std::forward<BinaryPredicate>(pred));
+}
+
+// c_find_first_of()
+//
+// Container-based version of the <algorithm> `std::find_first_of()` function to
+// find the first element within the container that is also within the options
+// container.
+template <typename C1, typename C2>
+container_algorithm_internal::ContainerIter<C1> c_find_first_of(C1& container,
+                                                                C2& options) {
+  return std::find_first_of(container_algorithm_internal::c_begin(container),
+                            container_algorithm_internal::c_end(container),
+                            container_algorithm_internal::c_begin(options),
+                            container_algorithm_internal::c_end(options));
+}
+
+// Overload of c_find_first_of() for using a predicate evaluation other than
+// `==` as the function's test condition.
+template <typename C1, typename C2, typename BinaryPredicate>
+container_algorithm_internal::ContainerIter<C1> c_find_first_of(
+    C1& container, C2& options, BinaryPredicate&& pred) {
+  return std::find_first_of(container_algorithm_internal::c_begin(container),
+                            container_algorithm_internal::c_end(container),
+                            container_algorithm_internal::c_begin(options),
+                            container_algorithm_internal::c_end(options),
+                            std::forward<BinaryPredicate>(pred));
+}
+
+// c_adjacent_find()
+//
+// Container-based version of the <algorithm> `std::adjacent_find()` function to
+// find equal adjacent elements within a container.
+template <typename Sequence>
+container_algorithm_internal::ContainerIter<Sequence> c_adjacent_find(
+    Sequence& sequence) {
+  return std::adjacent_find(container_algorithm_internal::c_begin(sequence),
+                            container_algorithm_internal::c_end(sequence));
+}
+
+// Overload of c_adjacent_find() for using a predicate evaluation other than
+// `==` as the function's test condition.
+template <typename Sequence, typename BinaryPredicate>
+container_algorithm_internal::ContainerIter<Sequence> c_adjacent_find(
+    Sequence& sequence, BinaryPredicate&& pred) {
+  return std::adjacent_find(container_algorithm_internal::c_begin(sequence),
+                            container_algorithm_internal::c_end(sequence),
+                            std::forward<BinaryPredicate>(pred));
+}
+
+// c_count()
+//
+// Container-based version of the <algorithm> `std::count()` function to count
+// values that match within a container.
+template <typename C, typename T>
+container_algorithm_internal::ContainerDifferenceType<const C> c_count(
+    const C& c, T&& value) {
+  return std::count(container_algorithm_internal::c_begin(c),
+                    container_algorithm_internal::c_end(c),
+                    std::forward<T>(value));
+}
+
+// c_count_if()
+//
+// Container-based version of the <algorithm> `std::count_if()` function to
+// count values matching a condition within a container.
+template <typename C, typename Pred>
+container_algorithm_internal::ContainerDifferenceType<const C> c_count_if(
+    const C& c, Pred&& pred) {
+  return std::count_if(container_algorithm_internal::c_begin(c),
+                       container_algorithm_internal::c_end(c),
+                       std::forward<Pred>(pred));
+}
+
+// c_mismatch()
+//
+// Container-based version of the <algorithm> `std::mismatch()` function to
+// return the first element where two ordered containers differ. Applies `==` to
+// the first N elements of `c1` and `c2`, where N = min(size(c1), size(c2)).
+template <typename C1, typename C2>
+container_algorithm_internal::ContainerIterPairType<C1, C2> c_mismatch(C1& c1,
+                                                                       C2& c2) {
+  auto first1 = container_algorithm_internal::c_begin(c1);
+  auto last1 = container_algorithm_internal::c_end(c1);
+  auto first2 = container_algorithm_internal::c_begin(c2);
+  auto last2 = container_algorithm_internal::c_end(c2);
+
+  for (; first1 != last1 && first2 != last2; ++first1, (void)++first2) {
+    // Negates equality because Cpp17EqualityComparable doesn't require clients
+    // to overload both `operator==` and `operator!=`.
+    if (!(*first1 == *first2)) {
+      break;
+    }
+  }
+
+  return std::make_pair(first1, first2);
+}
+
+// Overload of c_mismatch() for using a predicate evaluation other than `==` as
+// the function's test condition. Applies `pred`to the first N elements of `c1`
+// and `c2`, where N = min(size(c1), size(c2)).
+template <typename C1, typename C2, typename BinaryPredicate>
+container_algorithm_internal::ContainerIterPairType<C1, C2> c_mismatch(
+    C1& c1, C2& c2, BinaryPredicate pred) {
+  auto first1 = container_algorithm_internal::c_begin(c1);
+  auto last1 = container_algorithm_internal::c_end(c1);
+  auto first2 = container_algorithm_internal::c_begin(c2);
+  auto last2 = container_algorithm_internal::c_end(c2);
+
+  for (; first1 != last1 && first2 != last2; ++first1, (void)++first2) {
+    if (!pred(*first1, *first2)) {
+      break;
+    }
+  }
+
+  return std::make_pair(first1, first2);
+}
+
+// c_equal()
+//
+// Container-based version of the <algorithm> `std::equal()` function to
+// test whether two containers are equal.
+//
+// NOTE: the semantics of c_equal() are slightly different than those of
+// equal(): while the latter iterates over the second container only up to the
+// size of the first container, c_equal() also checks whether the container
+// sizes are equal.  This better matches expectations about c_equal() based on
+// its signature.
+//
+// Example:
+//   vector v1 = <1, 2, 3>;
+//   vector v2 = <1, 2, 3, 4>;
+//   equal(std::begin(v1), std::end(v1), std::begin(v2)) returns true
+//   c_equal(v1, v2) returns false
+
+template <typename C1, typename C2>
+bool c_equal(const C1& c1, const C2& c2) {
+  return ((container_algorithm_internal::c_size(c1) ==
+           container_algorithm_internal::c_size(c2)) &&
+          std::equal(container_algorithm_internal::c_begin(c1),
+                     container_algorithm_internal::c_end(c1),
+                     container_algorithm_internal::c_begin(c2)));
+}
+
+// Overload of c_equal() for using a predicate evaluation other than `==` as
+// the function's test condition.
+template <typename C1, typename C2, typename BinaryPredicate>
+bool c_equal(const C1& c1, const C2& c2, BinaryPredicate&& pred) {
+  return ((container_algorithm_internal::c_size(c1) ==
+           container_algorithm_internal::c_size(c2)) &&
+          std::equal(container_algorithm_internal::c_begin(c1),
+                     container_algorithm_internal::c_end(c1),
+                     container_algorithm_internal::c_begin(c2),
+                     std::forward<BinaryPredicate>(pred)));
+}
+
+// c_is_permutation()
+//
+// Container-based version of the <algorithm> `std::is_permutation()` function
+// to test whether a container is a permutation of another.
+template <typename C1, typename C2>
+bool c_is_permutation(const C1& c1, const C2& c2) {
+  using std::begin;
+  using std::end;
+  return c1.size() == c2.size() &&
+         std::is_permutation(begin(c1), end(c1), begin(c2));
+}
+
+// Overload of c_is_permutation() for using a predicate evaluation other than
+// `==` as the function's test condition.
+template <typename C1, typename C2, typename BinaryPredicate>
+bool c_is_permutation(const C1& c1, const C2& c2, BinaryPredicate&& pred) {
+  using std::begin;
+  using std::end;
+  return c1.size() == c2.size() &&
+         std::is_permutation(begin(c1), end(c1), begin(c2),
+                             std::forward<BinaryPredicate>(pred));
+}
+
+// c_search()
+//
+// Container-based version of the <algorithm> `std::search()` function to search
+// a container for a subsequence.
+template <typename Sequence1, typename Sequence2>
+container_algorithm_internal::ContainerIter<Sequence1> c_search(
+    Sequence1& sequence, Sequence2& subsequence) {
+  return std::search(container_algorithm_internal::c_begin(sequence),
+                     container_algorithm_internal::c_end(sequence),
+                     container_algorithm_internal::c_begin(subsequence),
+                     container_algorithm_internal::c_end(subsequence));
+}
+
+// Overload of c_search() for using a predicate evaluation other than
+// `==` as the function's test condition.
+template <typename Sequence1, typename Sequence2, typename BinaryPredicate>
+container_algorithm_internal::ContainerIter<Sequence1> c_search(
+    Sequence1& sequence, Sequence2& subsequence, BinaryPredicate&& pred) {
+  return std::search(container_algorithm_internal::c_begin(sequence),
+                     container_algorithm_internal::c_end(sequence),
+                     container_algorithm_internal::c_begin(subsequence),
+                     container_algorithm_internal::c_end(subsequence),
+                     std::forward<BinaryPredicate>(pred));
+}
+
+// c_search_n()
+//
+// Container-based version of the <algorithm> `std::search_n()` function to
+// search a container for the first sequence of N elements.
+template <typename Sequence, typename Size, typename T>
+container_algorithm_internal::ContainerIter<Sequence> c_search_n(
+    Sequence& sequence, Size count, T&& value) {
+  return std::search_n(container_algorithm_internal::c_begin(sequence),
+                       container_algorithm_internal::c_end(sequence), count,
+                       std::forward<T>(value));
+}
+
+// Overload of c_search_n() for using a predicate evaluation other than
+// `==` as the function's test condition.
+template <typename Sequence, typename Size, typename T,
+          typename BinaryPredicate>
+container_algorithm_internal::ContainerIter<Sequence> c_search_n(
+    Sequence& sequence, Size count, T&& value, BinaryPredicate&& pred) {
+  return std::search_n(container_algorithm_internal::c_begin(sequence),
+                       container_algorithm_internal::c_end(sequence), count,
+                       std::forward<T>(value),
+                       std::forward<BinaryPredicate>(pred));
+}
+
+//------------------------------------------------------------------------------
+// <algorithm> Modifying sequence operations
+//------------------------------------------------------------------------------
+
+// c_copy()
+//
+// Container-based version of the <algorithm> `std::copy()` function to copy a
+// container's elements into an iterator.
+template <typename InputSequence, typename OutputIterator>
+OutputIterator c_copy(const InputSequence& input, OutputIterator output) {
+  return std::copy(container_algorithm_internal::c_begin(input),
+                   container_algorithm_internal::c_end(input), output);
+}
+
+// c_copy_n()
+//
+// Container-based version of the <algorithm> `std::copy_n()` function to copy a
+// container's first N elements into an iterator.
+template <typename C, typename Size, typename OutputIterator>
+OutputIterator c_copy_n(const C& input, Size n, OutputIterator output) {
+  return std::copy_n(container_algorithm_internal::c_begin(input), n, output);
+}
+
+// c_copy_if()
+//
+// Container-based version of the <algorithm> `std::copy_if()` function to copy
+// a container's elements satisfying some condition into an iterator.
+template <typename InputSequence, typename OutputIterator, typename Pred>
+OutputIterator c_copy_if(const InputSequence& input, OutputIterator output,
+                         Pred&& pred) {
+  return std::copy_if(container_algorithm_internal::c_begin(input),
+                      container_algorithm_internal::c_end(input), output,
+                      std::forward<Pred>(pred));
+}
+
+// c_copy_backward()
+//
+// Container-based version of the <algorithm> `std::copy_backward()` function to
+// copy a container's elements in reverse order into an iterator.
+template <typename C, typename BidirectionalIterator>
+BidirectionalIterator c_copy_backward(const C& src,
+                                      BidirectionalIterator dest) {
+  return std::copy_backward(container_algorithm_internal::c_begin(src),
+                            container_algorithm_internal::c_end(src), dest);
+}
+
+// c_move()
+//
+// Container-based version of the <algorithm> `std::move()` function to move
+// a container's elements into an iterator.
+template <typename C, typename OutputIterator>
+OutputIterator c_move(C&& src, OutputIterator dest) {
+  return std::move(container_algorithm_internal::c_begin(src),
+                   container_algorithm_internal::c_end(src), dest);
+}
+
+// c_move_backward()
+//
+// Container-based version of the <algorithm> `std::move_backward()` function to
+// move a container's elements into an iterator in reverse order.
+template <typename C, typename BidirectionalIterator>
+BidirectionalIterator c_move_backward(C&& src, BidirectionalIterator dest) {
+  return std::move_backward(container_algorithm_internal::c_begin(src),
+                            container_algorithm_internal::c_end(src), dest);
+}
+
+// c_swap_ranges()
+//
+// Container-based version of the <algorithm> `std::swap_ranges()` function to
+// swap a container's elements with another container's elements. Swaps the
+// first N elements of `c1` and `c2`, where N = min(size(c1), size(c2)).
+template <typename C1, typename C2>
+container_algorithm_internal::ContainerIter<C2> c_swap_ranges(C1& c1, C2& c2) {
+  auto first1 = container_algorithm_internal::c_begin(c1);
+  auto last1 = container_algorithm_internal::c_end(c1);
+  auto first2 = container_algorithm_internal::c_begin(c2);
+  auto last2 = container_algorithm_internal::c_end(c2);
+
+  using std::swap;
+  for (; first1 != last1 && first2 != last2; ++first1, (void)++first2) {
+    swap(*first1, *first2);
+  }
+  return first2;
+}
+
+// c_transform()
+//
+// Container-based version of the <algorithm> `std::transform()` function to
+// transform a container's elements using the unary operation, storing the
+// result in an iterator pointing to the last transformed element in the output
+// range.
+template <typename InputSequence, typename OutputIterator, typename UnaryOp>
+OutputIterator c_transform(const InputSequence& input, OutputIterator output,
+                           UnaryOp&& unary_op) {
+  return std::transform(container_algorithm_internal::c_begin(input),
+                        container_algorithm_internal::c_end(input), output,
+                        std::forward<UnaryOp>(unary_op));
+}
+
+// Overload of c_transform() for performing a transformation using a binary
+// predicate. Applies `binary_op` to the first N elements of `c1` and `c2`,
+// where N = min(size(c1), size(c2)).
+template <typename InputSequence1, typename InputSequence2,
+          typename OutputIterator, typename BinaryOp>
+OutputIterator c_transform(const InputSequence1& input1,
+                           const InputSequence2& input2, OutputIterator output,
+                           BinaryOp&& binary_op) {
+  auto first1 = container_algorithm_internal::c_begin(input1);
+  auto last1 = container_algorithm_internal::c_end(input1);
+  auto first2 = container_algorithm_internal::c_begin(input2);
+  auto last2 = container_algorithm_internal::c_end(input2);
+  for (; first1 != last1 && first2 != last2;
+       ++first1, (void)++first2, ++output) {
+    *output = binary_op(*first1, *first2);
+  }
+
+  return output;
+}
+
+// c_replace()
+//
+// Container-based version of the <algorithm> `std::replace()` function to
+// replace a container's elements of some value with a new value. The container
+// is modified in place.
+template <typename Sequence, typename T>
+void c_replace(Sequence& sequence, const T& old_value, const T& new_value) {
+  std::replace(container_algorithm_internal::c_begin(sequence),
+               container_algorithm_internal::c_end(sequence), old_value,
+               new_value);
+}
+
+// c_replace_if()
+//
+// Container-based version of the <algorithm> `std::replace_if()` function to
+// replace a container's elements of some value with a new value based on some
+// condition. The container is modified in place.
+template <typename C, typename Pred, typename T>
+void c_replace_if(C& c, Pred&& pred, T&& new_value) {
+  std::replace_if(container_algorithm_internal::c_begin(c),
+                  container_algorithm_internal::c_end(c),
+                  std::forward<Pred>(pred), std::forward<T>(new_value));
+}
+
+// c_replace_copy()
+//
+// Container-based version of the <algorithm> `std::replace_copy()` function to
+// replace a container's elements of some value with a new value  and return the
+// results within an iterator.
+template <typename C, typename OutputIterator, typename T>
+OutputIterator c_replace_copy(const C& c, OutputIterator result, T&& old_value,
+                              T&& new_value) {
+  return std::replace_copy(container_algorithm_internal::c_begin(c),
+                           container_algorithm_internal::c_end(c), result,
+                           std::forward<T>(old_value),
+                           std::forward<T>(new_value));
+}
+
+// c_replace_copy_if()
+//
+// Container-based version of the <algorithm> `std::replace_copy_if()` function
+// to replace a container's elements of some value with a new value based on
+// some condition, and return the results within an iterator.
+template <typename C, typename OutputIterator, typename Pred, typename T>
+OutputIterator c_replace_copy_if(const C& c, OutputIterator result, Pred&& pred,
+                                 const T& new_value) {
+  return std::replace_copy_if(container_algorithm_internal::c_begin(c),
+                              container_algorithm_internal::c_end(c), result,
+                              std::forward<Pred>(pred), new_value);
+}
+
+// c_fill()
+//
+// Container-based version of the <algorithm> `std::fill()` function to fill a
+// container with some value.
+template <typename C, typename T>
+void c_fill(C& c, const T& value) {
+  std::fill(container_algorithm_internal::c_begin(c),
+            container_algorithm_internal::c_end(c), value);
+}
+
+// c_fill_n()
+//
+// Container-based version of the <algorithm> `std::fill_n()` function to fill
+// the first N elements in a container with some value.
+template <typename C, typename Size, typename T>
+void c_fill_n(C& c, Size n, const T& value) {
+  std::fill_n(container_algorithm_internal::c_begin(c), n, value);
+}
+
+// c_generate()
+//
+// Container-based version of the <algorithm> `std::generate()` function to
+// assign a container's elements to the values provided by the given generator.
+template <typename C, typename Generator>
+void c_generate(C& c, Generator&& gen) {
+  std::generate(container_algorithm_internal::c_begin(c),
+                container_algorithm_internal::c_end(c),
+                std::forward<Generator>(gen));
+}
+
+// c_generate_n()
+//
+// Container-based version of the <algorithm> `std::generate_n()` function to
+// assign a container's first N elements to the values provided by the given
+// generator.
+template <typename C, typename Size, typename Generator>
+container_algorithm_internal::ContainerIter<C> c_generate_n(C& c, Size n,
+                                                            Generator&& gen) {
+  return std::generate_n(container_algorithm_internal::c_begin(c), n,
+                         std::forward<Generator>(gen));
+}
+
+// Note: `c_xx()` <algorithm> container versions for `remove()`, `remove_if()`,
+// and `unique()` are omitted, because it's not clear whether or not such
+// functions should call erase on their supplied sequences afterwards. Either
+// behavior would be surprising for a different set of users.
+
+// c_remove_copy()
+//
+// Container-based version of the <algorithm> `std::remove_copy()` function to
+// copy a container's elements while removing any elements matching the given
+// `value`.
+template <typename C, typename OutputIterator, typename T>
+OutputIterator c_remove_copy(const C& c, OutputIterator result,
+                             const T& value) {
+  return std::remove_copy(container_algorithm_internal::c_begin(c),
+                          container_algorithm_internal::c_end(c), result,
+                          value);
+}
+
+// c_remove_copy_if()
+//
+// Container-based version of the <algorithm> `std::remove_copy_if()` function
+// to copy a container's elements while removing any elements matching the given
+// condition.
+template <typename C, typename OutputIterator, typename Pred>
+OutputIterator c_remove_copy_if(const C& c, OutputIterator result,
+                                Pred&& pred) {
+  return std::remove_copy_if(container_algorithm_internal::c_begin(c),
+                             container_algorithm_internal::c_end(c), result,
+                             std::forward<Pred>(pred));
+}
+
+// c_unique_copy()
+//
+// Container-based version of the <algorithm> `std::unique_copy()` function to
+// copy a container's elements while removing any elements containing duplicate
+// values.
+template <typename C, typename OutputIterator>
+OutputIterator c_unique_copy(const C& c, OutputIterator result) {
+  return std::unique_copy(container_algorithm_internal::c_begin(c),
+                          container_algorithm_internal::c_end(c), result);
+}
+
+// Overload of c_unique_copy() for using a predicate evaluation other than
+// `==` for comparing uniqueness of the element values.
+template <typename C, typename OutputIterator, typename BinaryPredicate>
+OutputIterator c_unique_copy(const C& c, OutputIterator result,
+                             BinaryPredicate&& pred) {
+  return std::unique_copy(container_algorithm_internal::c_begin(c),
+                          container_algorithm_internal::c_end(c), result,
+                          std::forward<BinaryPredicate>(pred));
+}
+
+// c_reverse()
+//
+// Container-based version of the <algorithm> `std::reverse()` function to
+// reverse a container's elements.
+template <typename Sequence>
+void c_reverse(Sequence& sequence) {
+  std::reverse(container_algorithm_internal::c_begin(sequence),
+               container_algorithm_internal::c_end(sequence));
+}
+
+// c_reverse_copy()
+//
+// Container-based version of the <algorithm> `std::reverse()` function to
+// reverse a container's elements and write them to an iterator range.
+template <typename C, typename OutputIterator>
+OutputIterator c_reverse_copy(const C& sequence, OutputIterator result) {
+  return std::reverse_copy(container_algorithm_internal::c_begin(sequence),
+                           container_algorithm_internal::c_end(sequence),
+                           result);
+}
+
+// c_rotate()
+//
+// Container-based version of the <algorithm> `std::rotate()` function to
+// shift a container's elements leftward such that the `middle` element becomes
+// the first element in the container.
+template <typename C,
+          typename Iterator = container_algorithm_internal::ContainerIter<C>>
+Iterator c_rotate(C& sequence, Iterator middle) {
+  return absl::rotate(container_algorithm_internal::c_begin(sequence), middle,
+                      container_algorithm_internal::c_end(sequence));
+}
+
+// c_rotate_copy()
+//
+// Container-based version of the <algorithm> `std::rotate_copy()` function to
+// shift a container's elements leftward such that the `middle` element becomes
+// the first element in a new iterator range.
+template <typename C, typename OutputIterator>
+OutputIterator c_rotate_copy(
+    const C& sequence,
+    container_algorithm_internal::ContainerIter<const C> middle,
+    OutputIterator result) {
+  return std::rotate_copy(container_algorithm_internal::c_begin(sequence),
+                          middle, container_algorithm_internal::c_end(sequence),
+                          result);
+}
+
+// c_shuffle()
+//
+// Container-based version of the <algorithm> `std::shuffle()` function to
+// randomly shuffle elements within the container using a `gen()` uniform random
+// number generator.
+template <typename RandomAccessContainer, typename UniformRandomBitGenerator>
+void c_shuffle(RandomAccessContainer& c, UniformRandomBitGenerator&& gen) {
+  std::shuffle(container_algorithm_internal::c_begin(c),
+               container_algorithm_internal::c_end(c),
+               std::forward<UniformRandomBitGenerator>(gen));
+}
+
+//------------------------------------------------------------------------------
+// <algorithm> Partition functions
+//------------------------------------------------------------------------------
+
+// c_is_partitioned()
+//
+// Container-based version of the <algorithm> `std::is_partitioned()` function
+// to test whether all elements in the container for which `pred` returns `true`
+// precede those for which `pred` is `false`.
+template <typename C, typename Pred>
+bool c_is_partitioned(const C& c, Pred&& pred) {
+  return std::is_partitioned(container_algorithm_internal::c_begin(c),
+                             container_algorithm_internal::c_end(c),
+                             std::forward<Pred>(pred));
+}
+
+// c_partition()
+//
+// Container-based version of the <algorithm> `std::partition()` function
+// to rearrange all elements in a container in such a way that all elements for
+// which `pred` returns `true` precede all those for which it returns `false`,
+// returning an iterator to the first element of the second group.
+template <typename C, typename Pred>
+container_algorithm_internal::ContainerIter<C> c_partition(C& c, Pred&& pred) {
+  return std::partition(container_algorithm_internal::c_begin(c),
+                        container_algorithm_internal::c_end(c),
+                        std::forward<Pred>(pred));
+}
+
+// c_stable_partition()
+//
+// Container-based version of the <algorithm> `std::stable_partition()` function
+// to rearrange all elements in a container in such a way that all elements for
+// which `pred` returns `true` precede all those for which it returns `false`,
+// preserving the relative ordering between the two groups. The function returns
+// an iterator to the first element of the second group.
+template <typename C, typename Pred>
+container_algorithm_internal::ContainerIter<C> c_stable_partition(C& c,
+                                                                  Pred&& pred) {
+  return std::stable_partition(container_algorithm_internal::c_begin(c),
+                               container_algorithm_internal::c_end(c),
+                               std::forward<Pred>(pred));
+}
+
+// c_partition_copy()
+//
+// Container-based version of the <algorithm> `std::partition_copy()` function
+// to partition a container's elements and return them into two iterators: one
+// for which `pred` returns `true`, and one for which `pred` returns `false.`
+
+template <typename C, typename OutputIterator1, typename OutputIterator2,
+          typename Pred>
+std::pair<OutputIterator1, OutputIterator2> c_partition_copy(
+    const C& c, OutputIterator1 out_true, OutputIterator2 out_false,
+    Pred&& pred) {
+  return std::partition_copy(container_algorithm_internal::c_begin(c),
+                             container_algorithm_internal::c_end(c), out_true,
+                             out_false, std::forward<Pred>(pred));
+}
+
+// c_partition_point()
+//
+// Container-based version of the <algorithm> `std::partition_point()` function
+// to return the first element of an already partitioned container for which
+// the given `pred` is not `true`.
+template <typename C, typename Pred>
+container_algorithm_internal::ContainerIter<C> c_partition_point(C& c,
+                                                                 Pred&& pred) {
+  return std::partition_point(container_algorithm_internal::c_begin(c),
+                              container_algorithm_internal::c_end(c),
+                              std::forward<Pred>(pred));
+}
+
+//------------------------------------------------------------------------------
+// <algorithm> Sorting functions
+//------------------------------------------------------------------------------
+
+// c_sort()
+//
+// Container-based version of the <algorithm> `std::sort()` function
+// to sort elements in ascending order of their values.
+template <typename C>
+void c_sort(C& c) {
+  std::sort(container_algorithm_internal::c_begin(c),
+            container_algorithm_internal::c_end(c));
+}
+
+// Overload of c_sort() for performing a `comp` comparison other than the
+// default `operator<`.
+template <typename C, typename LessThan>
+void c_sort(C& c, LessThan&& comp) {
+  std::sort(container_algorithm_internal::c_begin(c),
+            container_algorithm_internal::c_end(c),
+            std::forward<LessThan>(comp));
+}
+
+// c_stable_sort()
+//
+// Container-based version of the <algorithm> `std::stable_sort()` function
+// to sort elements in ascending order of their values, preserving the order
+// of equivalents.
+template <typename C>
+void c_stable_sort(C& c) {
+  std::stable_sort(container_algorithm_internal::c_begin(c),
+                   container_algorithm_internal::c_end(c));
+}
+
+// Overload of c_stable_sort() for performing a `comp` comparison other than the
+// default `operator<`.
+template <typename C, typename LessThan>
+void c_stable_sort(C& c, LessThan&& comp) {
+  std::stable_sort(container_algorithm_internal::c_begin(c),
+                   container_algorithm_internal::c_end(c),
+                   std::forward<LessThan>(comp));
+}
+
+// c_is_sorted()
+//
+// Container-based version of the <algorithm> `std::is_sorted()` function
+// to evaluate whether the given container is sorted in ascending order.
+template <typename C>
+bool c_is_sorted(const C& c) {
+  return std::is_sorted(container_algorithm_internal::c_begin(c),
+                        container_algorithm_internal::c_end(c));
+}
+
+// c_is_sorted() overload for performing a `comp` comparison other than the
+// default `operator<`.
+template <typename C, typename LessThan>
+bool c_is_sorted(const C& c, LessThan&& comp) {
+  return std::is_sorted(container_algorithm_internal::c_begin(c),
+                        container_algorithm_internal::c_end(c),
+                        std::forward<LessThan>(comp));
+}
+
+// c_partial_sort()
+//
+// Container-based version of the <algorithm> `std::partial_sort()` function
+// to rearrange elements within a container such that elements before `middle`
+// are sorted in ascending order.
+template <typename RandomAccessContainer>
+void c_partial_sort(
+    RandomAccessContainer& sequence,
+    container_algorithm_internal::ContainerIter<RandomAccessContainer> middle) {
+  std::partial_sort(container_algorithm_internal::c_begin(sequence), middle,
+                    container_algorithm_internal::c_end(sequence));
+}
+
+// Overload of c_partial_sort() for performing a `comp` comparison other than
+// the default `operator<`.
+template <typename RandomAccessContainer, typename LessThan>
+void c_partial_sort(
+    RandomAccessContainer& sequence,
+    container_algorithm_internal::ContainerIter<RandomAccessContainer> middle,
+    LessThan&& comp) {
+  std::partial_sort(container_algorithm_internal::c_begin(sequence), middle,
+                    container_algorithm_internal::c_end(sequence),
+                    std::forward<LessThan>(comp));
+}
+
+// c_partial_sort_copy()
+//
+// Container-based version of the <algorithm> `std::partial_sort_copy()`
+// function to sort the elements in the given range `result` within the larger
+// `sequence` in ascending order (and using `result` as the output parameter).
+// At most min(result.last - result.first, sequence.last - sequence.first)
+// elements from the sequence will be stored in the result.
+template <typename C, typename RandomAccessContainer>
+container_algorithm_internal::ContainerIter<RandomAccessContainer>
+c_partial_sort_copy(const C& sequence, RandomAccessContainer& result) {
+  return std::partial_sort_copy(container_algorithm_internal::c_begin(sequence),
+                                container_algorithm_internal::c_end(sequence),
+                                container_algorithm_internal::c_begin(result),
+                                container_algorithm_internal::c_end(result));
+}
+
+// Overload of c_partial_sort_copy() for performing a `comp` comparison other
+// than the default `operator<`.
+template <typename C, typename RandomAccessContainer, typename LessThan>
+container_algorithm_internal::ContainerIter<RandomAccessContainer>
+c_partial_sort_copy(const C& sequence, RandomAccessContainer& result,
+                    LessThan&& comp) {
+  return std::partial_sort_copy(container_algorithm_internal::c_begin(sequence),
+                                container_algorithm_internal::c_end(sequence),
+                                container_algorithm_internal::c_begin(result),
+                                container_algorithm_internal::c_end(result),
+                                std::forward<LessThan>(comp));
+}
+
+// c_is_sorted_until()
+//
+// Container-based version of the <algorithm> `std::is_sorted_until()` function
+// to return the first element within a container that is not sorted in
+// ascending order as an iterator.
+template <typename C>
+container_algorithm_internal::ContainerIter<C> c_is_sorted_until(C& c) {
+  return std::is_sorted_until(container_algorithm_internal::c_begin(c),
+                              container_algorithm_internal::c_end(c));
+}
+
+// Overload of c_is_sorted_until() for performing a `comp` comparison other than
+// the default `operator<`.
+template <typename C, typename LessThan>
+container_algorithm_internal::ContainerIter<C> c_is_sorted_until(
+    C& c, LessThan&& comp) {
+  return std::is_sorted_until(container_algorithm_internal::c_begin(c),
+                              container_algorithm_internal::c_end(c),
+                              std::forward<LessThan>(comp));
+}
+
+// c_nth_element()
+//
+// Container-based version of the <algorithm> `std::nth_element()` function
+// to rearrange the elements within a container such that the `nth` element
+// would be in that position in an ordered sequence; other elements may be in
+// any order, except that all preceding `nth` will be less than that element,
+// and all following `nth` will be greater than that element.
+template <typename RandomAccessContainer>
+void c_nth_element(
+    RandomAccessContainer& sequence,
+    container_algorithm_internal::ContainerIter<RandomAccessContainer> nth) {
+  std::nth_element(container_algorithm_internal::c_begin(sequence), nth,
+                   container_algorithm_internal::c_end(sequence));
+}
+
+// Overload of c_nth_element() for performing a `comp` comparison other than
+// the default `operator<`.
+template <typename RandomAccessContainer, typename LessThan>
+void c_nth_element(
+    RandomAccessContainer& sequence,
+    container_algorithm_internal::ContainerIter<RandomAccessContainer> nth,
+    LessThan&& comp) {
+  std::nth_element(container_algorithm_internal::c_begin(sequence), nth,
+                   container_algorithm_internal::c_end(sequence),
+                   std::forward<LessThan>(comp));
+}
+
+//------------------------------------------------------------------------------
+// <algorithm> Binary Search
+//------------------------------------------------------------------------------
+
+// c_lower_bound()
+//
+// Container-based version of the <algorithm> `std::lower_bound()` function
+// to return an iterator pointing to the first element in a sorted container
+// which does not compare less than `value`.
+template <typename Sequence, typename T>
+container_algorithm_internal::ContainerIter<Sequence> c_lower_bound(
+    Sequence& sequence, const T& value) {
+  return std::lower_bound(container_algorithm_internal::c_begin(sequence),
+                          container_algorithm_internal::c_end(sequence), value);
+}
+
+// Overload of c_lower_bound() for performing a `comp` comparison other than
+// the default `operator<`.
+template <typename Sequence, typename T, typename LessThan>
+container_algorithm_internal::ContainerIter<Sequence> c_lower_bound(
+    Sequence& sequence, const T& value, LessThan&& comp) {
+  return std::lower_bound(container_algorithm_internal::c_begin(sequence),
+                          container_algorithm_internal::c_end(sequence), value,
+                          std::forward<LessThan>(comp));
+}
+
+// c_upper_bound()
+//
+// Container-based version of the <algorithm> `std::upper_bound()` function
+// to return an iterator pointing to the first element in a sorted container
+// which is greater than `value`.
+template <typename Sequence, typename T>
+container_algorithm_internal::ContainerIter<Sequence> c_upper_bound(
+    Sequence& sequence, const T& value) {
+  return std::upper_bound(container_algorithm_internal::c_begin(sequence),
+                          container_algorithm_internal::c_end(sequence), value);
+}
+
+// Overload of c_upper_bound() for performing a `comp` comparison other than
+// the default `operator<`.
+template <typename Sequence, typename T, typename LessThan>
+container_algorithm_internal::ContainerIter<Sequence> c_upper_bound(
+    Sequence& sequence, const T& value, LessThan&& comp) {
+  return std::upper_bound(container_algorithm_internal::c_begin(sequence),
+                          container_algorithm_internal::c_end(sequence), value,
+                          std::forward<LessThan>(comp));
+}
+
+// c_equal_range()
+//
+// Container-based version of the <algorithm> `std::equal_range()` function
+// to return an iterator pair pointing to the first and last elements in a
+// sorted container which compare equal to `value`.
+template <typename Sequence, typename T>
+container_algorithm_internal::ContainerIterPairType<Sequence, Sequence>
+c_equal_range(Sequence& sequence, const T& value) {
+  return std::equal_range(container_algorithm_internal::c_begin(sequence),
+                          container_algorithm_internal::c_end(sequence), value);
+}
+
+// Overload of c_equal_range() for performing a `comp` comparison other than
+// the default `operator<`.
+template <typename Sequence, typename T, typename LessThan>
+container_algorithm_internal::ContainerIterPairType<Sequence, Sequence>
+c_equal_range(Sequence& sequence, const T& value, LessThan&& comp) {
+  return std::equal_range(container_algorithm_internal::c_begin(sequence),
+                          container_algorithm_internal::c_end(sequence), value,
+                          std::forward<LessThan>(comp));
+}
+
+// c_binary_search()
+//
+// Container-based version of the <algorithm> `std::binary_search()` function
+// to test if any element in the sorted container contains a value equivalent to
+// 'value'.
+template <typename Sequence, typename T>
+bool c_binary_search(Sequence&& sequence, const T& value) {
+  return std::binary_search(container_algorithm_internal::c_begin(sequence),
+                            container_algorithm_internal::c_end(sequence),
+                            value);
+}
+
+// Overload of c_binary_search() for performing a `comp` comparison other than
+// the default `operator<`.
+template <typename Sequence, typename T, typename LessThan>
+bool c_binary_search(Sequence&& sequence, const T& value, LessThan&& comp) {
+  return std::binary_search(container_algorithm_internal::c_begin(sequence),
+                            container_algorithm_internal::c_end(sequence),
+                            value, std::forward<LessThan>(comp));
+}
+
+//------------------------------------------------------------------------------
+// <algorithm> Merge functions
+//------------------------------------------------------------------------------
+
+// c_merge()
+//
+// Container-based version of the <algorithm> `std::merge()` function
+// to merge two sorted containers into a single sorted iterator.
+template <typename C1, typename C2, typename OutputIterator>
+OutputIterator c_merge(const C1& c1, const C2& c2, OutputIterator result) {
+  return std::merge(container_algorithm_internal::c_begin(c1),
+                    container_algorithm_internal::c_end(c1),
+                    container_algorithm_internal::c_begin(c2),
+                    container_algorithm_internal::c_end(c2), result);
+}
+
+// Overload of c_merge() for performing a `comp` comparison other than
+// the default `operator<`.
+template <typename C1, typename C2, typename OutputIterator, typename LessThan>
+OutputIterator c_merge(const C1& c1, const C2& c2, OutputIterator result,
+                       LessThan&& comp) {
+  return std::merge(container_algorithm_internal::c_begin(c1),
+                    container_algorithm_internal::c_end(c1),
+                    container_algorithm_internal::c_begin(c2),
+                    container_algorithm_internal::c_end(c2), result,
+                    std::forward<LessThan>(comp));
+}
+
+// c_inplace_merge()
+//
+// Container-based version of the <algorithm> `std::inplace_merge()` function
+// to merge a supplied iterator `middle` into a container.
+template <typename C>
+void c_inplace_merge(C& c,
+                     container_algorithm_internal::ContainerIter<C> middle) {
+  std::inplace_merge(container_algorithm_internal::c_begin(c), middle,
+                     container_algorithm_internal::c_end(c));
+}
+
+// Overload of c_inplace_merge() for performing a merge using a `comp` other
+// than `operator<`.
+template <typename C, typename LessThan>
+void c_inplace_merge(C& c,
+                     container_algorithm_internal::ContainerIter<C> middle,
+                     LessThan&& comp) {
+  std::inplace_merge(container_algorithm_internal::c_begin(c), middle,
+                     container_algorithm_internal::c_end(c),
+                     std::forward<LessThan>(comp));
+}
+
+// c_includes()
+//
+// Container-based version of the <algorithm> `std::includes()` function
+// to test whether a sorted container `c1` entirely contains another sorted
+// container `c2`.
+template <typename C1, typename C2>
+bool c_includes(const C1& c1, const C2& c2) {
+  return std::includes(container_algorithm_internal::c_begin(c1),
+                       container_algorithm_internal::c_end(c1),
+                       container_algorithm_internal::c_begin(c2),
+                       container_algorithm_internal::c_end(c2));
+}
+
+// Overload of c_includes() for performing a merge using a `comp` other than
+// `operator<`.
+template <typename C1, typename C2, typename LessThan>
+bool c_includes(const C1& c1, const C2& c2, LessThan&& comp) {
+  return std::includes(container_algorithm_internal::c_begin(c1),
+                       container_algorithm_internal::c_end(c1),
+                       container_algorithm_internal::c_begin(c2),
+                       container_algorithm_internal::c_end(c2),
+                       std::forward<LessThan>(comp));
+}
+
+// c_set_union()
+//
+// Container-based version of the <algorithm> `std::set_union()` function
+// to return an iterator containing the union of two containers; duplicate
+// values are not copied into the output.
+template <typename C1, typename C2, typename OutputIterator,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C1>::value,
+              void>::type,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C2>::value,
+              void>::type>
+OutputIterator c_set_union(const C1& c1, const C2& c2, OutputIterator output) {
+  return std::set_union(container_algorithm_internal::c_begin(c1),
+                        container_algorithm_internal::c_end(c1),
+                        container_algorithm_internal::c_begin(c2),
+                        container_algorithm_internal::c_end(c2), output);
+}
+
+// Overload of c_set_union() for performing a merge using a `comp` other than
+// `operator<`.
+template <typename C1, typename C2, typename OutputIterator, typename LessThan,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C1>::value,
+              void>::type,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C2>::value,
+              void>::type>
+OutputIterator c_set_union(const C1& c1, const C2& c2, OutputIterator output,
+                           LessThan&& comp) {
+  return std::set_union(container_algorithm_internal::c_begin(c1),
+                        container_algorithm_internal::c_end(c1),
+                        container_algorithm_internal::c_begin(c2),
+                        container_algorithm_internal::c_end(c2), output,
+                        std::forward<LessThan>(comp));
+}
+
+// c_set_intersection()
+//
+// Container-based version of the <algorithm> `std::set_intersection()` function
+// to return an iterator containing the intersection of two sorted containers.
+template <typename C1, typename C2, typename OutputIterator,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C1>::value,
+              void>::type,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C2>::value,
+              void>::type>
+OutputIterator c_set_intersection(const C1& c1, const C2& c2,
+                                  OutputIterator output) {
+  // In debug builds, ensure that both containers are sorted with respect to the
+  // default comparator. std::set_intersection requires the containers be sorted
+  // using operator<.
+  assert(absl::c_is_sorted(c1));
+  assert(absl::c_is_sorted(c2));
+  return std::set_intersection(container_algorithm_internal::c_begin(c1),
+                               container_algorithm_internal::c_end(c1),
+                               container_algorithm_internal::c_begin(c2),
+                               container_algorithm_internal::c_end(c2), output);
+}
+
+// Overload of c_set_intersection() for performing a merge using a `comp` other
+// than `operator<`.
+template <typename C1, typename C2, typename OutputIterator, typename LessThan,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C1>::value,
+              void>::type,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C2>::value,
+              void>::type>
+OutputIterator c_set_intersection(const C1& c1, const C2& c2,
+                                  OutputIterator output, LessThan&& comp) {
+  // In debug builds, ensure that both containers are sorted with respect to the
+  // default comparator. std::set_intersection requires the containers be sorted
+  // using the same comparator.
+  assert(absl::c_is_sorted(c1, comp));
+  assert(absl::c_is_sorted(c2, comp));
+  return std::set_intersection(container_algorithm_internal::c_begin(c1),
+                               container_algorithm_internal::c_end(c1),
+                               container_algorithm_internal::c_begin(c2),
+                               container_algorithm_internal::c_end(c2), output,
+                               std::forward<LessThan>(comp));
+}
+
+// c_set_difference()
+//
+// Container-based version of the <algorithm> `std::set_difference()` function
+// to return an iterator containing elements present in the first container but
+// not in the second.
+template <typename C1, typename C2, typename OutputIterator,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C1>::value,
+              void>::type,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C2>::value,
+              void>::type>
+OutputIterator c_set_difference(const C1& c1, const C2& c2,
+                                OutputIterator output) {
+  return std::set_difference(container_algorithm_internal::c_begin(c1),
+                             container_algorithm_internal::c_end(c1),
+                             container_algorithm_internal::c_begin(c2),
+                             container_algorithm_internal::c_end(c2), output);
+}
+
+// Overload of c_set_difference() for performing a merge using a `comp` other
+// than `operator<`.
+template <typename C1, typename C2, typename OutputIterator, typename LessThan,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C1>::value,
+              void>::type,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C2>::value,
+              void>::type>
+OutputIterator c_set_difference(const C1& c1, const C2& c2,
+                                OutputIterator output, LessThan&& comp) {
+  return std::set_difference(container_algorithm_internal::c_begin(c1),
+                             container_algorithm_internal::c_end(c1),
+                             container_algorithm_internal::c_begin(c2),
+                             container_algorithm_internal::c_end(c2), output,
+                             std::forward<LessThan>(comp));
+}
+
+// c_set_symmetric_difference()
+//
+// Container-based version of the <algorithm> `std::set_symmetric_difference()`
+// function to return an iterator containing elements present in either one
+// container or the other, but not both.
+template <typename C1, typename C2, typename OutputIterator,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C1>::value,
+              void>::type,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C2>::value,
+              void>::type>
+OutputIterator c_set_symmetric_difference(const C1& c1, const C2& c2,
+                                          OutputIterator output) {
+  return std::set_symmetric_difference(
+      container_algorithm_internal::c_begin(c1),
+      container_algorithm_internal::c_end(c1),
+      container_algorithm_internal::c_begin(c2),
+      container_algorithm_internal::c_end(c2), output);
+}
+
+// Overload of c_set_symmetric_difference() for performing a merge using a
+// `comp` other than `operator<`.
+template <typename C1, typename C2, typename OutputIterator, typename LessThan,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C1>::value,
+              void>::type,
+          typename = typename std::enable_if<
+              !container_algorithm_internal::IsUnorderedContainer<C2>::value,
+              void>::type>
+OutputIterator c_set_symmetric_difference(const C1& c1, const C2& c2,
+                                          OutputIterator output,
+                                          LessThan&& comp) {
+  return std::set_symmetric_difference(
+      container_algorithm_internal::c_begin(c1),
+      container_algorithm_internal::c_end(c1),
+      container_algorithm_internal::c_begin(c2),
+      container_algorithm_internal::c_end(c2), output,
+      std::forward<LessThan>(comp));
+}
+
+//------------------------------------------------------------------------------
+// <algorithm> Heap functions
+//------------------------------------------------------------------------------
+
+// c_push_heap()
+//
+// Container-based version of the <algorithm> `std::push_heap()` function
+// to push a value onto a container heap.
+template <typename RandomAccessContainer>
+void c_push_heap(RandomAccessContainer& sequence) {
+  std::push_heap(container_algorithm_internal::c_begin(sequence),
+                 container_algorithm_internal::c_end(sequence));
+}
+
+// Overload of c_push_heap() for performing a push operation on a heap using a
+// `comp` other than `operator<`.
+template <typename RandomAccessContainer, typename LessThan>
+void c_push_heap(RandomAccessContainer& sequence, LessThan&& comp) {
+  std::push_heap(container_algorithm_internal::c_begin(sequence),
+                 container_algorithm_internal::c_end(sequence),
+                 std::forward<LessThan>(comp));
+}
+
+// c_pop_heap()
+//
+// Container-based version of the <algorithm> `std::pop_heap()` function
+// to pop a value from a heap container.
+template <typename RandomAccessContainer>
+void c_pop_heap(RandomAccessContainer& sequence) {
+  std::pop_heap(container_algorithm_internal::c_begin(sequence),
+                container_algorithm_internal::c_end(sequence));
+}
+
+// Overload of c_pop_heap() for performing a pop operation on a heap using a
+// `comp` other than `operator<`.
+template <typename RandomAccessContainer, typename LessThan>
+void c_pop_heap(RandomAccessContainer& sequence, LessThan&& comp) {
+  std::pop_heap(container_algorithm_internal::c_begin(sequence),
+                container_algorithm_internal::c_end(sequence),
+                std::forward<LessThan>(comp));
+}
+
+// c_make_heap()
+//
+// Container-based version of the <algorithm> `std::make_heap()` function
+// to make a container a heap.
+template <typename RandomAccessContainer>
+void c_make_heap(RandomAccessContainer& sequence) {
+  std::make_heap(container_algorithm_internal::c_begin(sequence),
+                 container_algorithm_internal::c_end(sequence));
+}
+
+// Overload of c_make_heap() for performing heap comparisons using a
+// `comp` other than `operator<`
+template <typename RandomAccessContainer, typename LessThan>
+void c_make_heap(RandomAccessContainer& sequence, LessThan&& comp) {
+  std::make_heap(container_algorithm_internal::c_begin(sequence),
+                 container_algorithm_internal::c_end(sequence),
+                 std::forward<LessThan>(comp));
+}
+
+// c_sort_heap()
+//
+// Container-based version of the <algorithm> `std::sort_heap()` function
+// to sort a heap into ascending order (after which it is no longer a heap).
+template <typename RandomAccessContainer>
+void c_sort_heap(RandomAccessContainer& sequence) {
+  std::sort_heap(container_algorithm_internal::c_begin(sequence),
+                 container_algorithm_internal::c_end(sequence));
+}
+
+// Overload of c_sort_heap() for performing heap comparisons using a
+// `comp` other than `operator<`
+template <typename RandomAccessContainer, typename LessThan>
+void c_sort_heap(RandomAccessContainer& sequence, LessThan&& comp) {
+  std::sort_heap(container_algorithm_internal::c_begin(sequence),
+                 container_algorithm_internal::c_end(sequence),
+                 std::forward<LessThan>(comp));
+}
+
+// c_is_heap()
+//
+// Container-based version of the <algorithm> `std::is_heap()` function
+// to check whether the given container is a heap.
+template <typename RandomAccessContainer>
+bool c_is_heap(const RandomAccessContainer& sequence) {
+  return std::is_heap(container_algorithm_internal::c_begin(sequence),
+                      container_algorithm_internal::c_end(sequence));
+}
+
+// Overload of c_is_heap() for performing heap comparisons using a
+// `comp` other than `operator<`
+template <typename RandomAccessContainer, typename LessThan>
+bool c_is_heap(const RandomAccessContainer& sequence, LessThan&& comp) {
+  return std::is_heap(container_algorithm_internal::c_begin(sequence),
+                      container_algorithm_internal::c_end(sequence),
+                      std::forward<LessThan>(comp));
+}
+
+// c_is_heap_until()
+//
+// Container-based version of the <algorithm> `std::is_heap_until()` function
+// to find the first element in a given container which is not in heap order.
+template <typename RandomAccessContainer>
+container_algorithm_internal::ContainerIter<RandomAccessContainer>
+c_is_heap_until(RandomAccessContainer& sequence) {
+  return std::is_heap_until(container_algorithm_internal::c_begin(sequence),
+                            container_algorithm_internal::c_end(sequence));
+}
+
+// Overload of c_is_heap_until() for performing heap comparisons using a
+// `comp` other than `operator<`
+template <typename RandomAccessContainer, typename LessThan>
+container_algorithm_internal::ContainerIter<RandomAccessContainer>
+c_is_heap_until(RandomAccessContainer& sequence, LessThan&& comp) {
+  return std::is_heap_until(container_algorithm_internal::c_begin(sequence),
+                            container_algorithm_internal::c_end(sequence),
+                            std::forward<LessThan>(comp));
+}
+
+//------------------------------------------------------------------------------
+//  <algorithm> Min/max
+//------------------------------------------------------------------------------
+
+// c_min_element()
+//
+// Container-based version of the <algorithm> `std::min_element()` function
+// to return an iterator pointing to the element with the smallest value, using
+// `operator<` to make the comparisons.
+template <typename Sequence>
+container_algorithm_internal::ContainerIter<Sequence> c_min_element(
+    Sequence& sequence) {
+  return std::min_element(container_algorithm_internal::c_begin(sequence),
+                          container_algorithm_internal::c_end(sequence));
+}
+
+// Overload of c_min_element() for performing a `comp` comparison other than
+// `operator<`.
+template <typename Sequence, typename LessThan>
+container_algorithm_internal::ContainerIter<Sequence> c_min_element(
+    Sequence& sequence, LessThan&& comp) {
+  return std::min_element(container_algorithm_internal::c_begin(sequence),
+                          container_algorithm_internal::c_end(sequence),
+                          std::forward<LessThan>(comp));
+}
+
+// c_max_element()
+//
+// Container-based version of the <algorithm> `std::max_element()` function
+// to return an iterator pointing to the element with the largest value, using
+// `operator<` to make the comparisons.
+template <typename Sequence>
+container_algorithm_internal::ContainerIter<Sequence> c_max_element(
+    Sequence& sequence) {
+  return std::max_element(container_algorithm_internal::c_begin(sequence),
+                          container_algorithm_internal::c_end(sequence));
+}
+
+// Overload of c_max_element() for performing a `comp` comparison other than
+// `operator<`.
+template <typename Sequence, typename LessThan>
+container_algorithm_internal::ContainerIter<Sequence> c_max_element(
+    Sequence& sequence, LessThan&& comp) {
+  return std::max_element(container_algorithm_internal::c_begin(sequence),
+                          container_algorithm_internal::c_end(sequence),
+                          std::forward<LessThan>(comp));
+}
+
+// c_minmax_element()
+//
+// Container-based version of the <algorithm> `std::minmax_element()` function
+// to return a pair of iterators pointing to the elements containing the
+// smallest and largest values, respectively, using `operator<` to make the
+// comparisons.
+template <typename C>
+container_algorithm_internal::ContainerIterPairType<C, C> c_minmax_element(
+    C& c) {
+  return std::minmax_element(container_algorithm_internal::c_begin(c),
+                             container_algorithm_internal::c_end(c));
+}
+
+// Overload of c_minmax_element() for performing `comp` comparisons other than
+// `operator<`.
+template <typename C, typename LessThan>
+container_algorithm_internal::ContainerIterPairType<C, C> c_minmax_element(
+    C& c, LessThan&& comp) {
+  return std::minmax_element(container_algorithm_internal::c_begin(c),
+                             container_algorithm_internal::c_end(c),
+                             std::forward<LessThan>(comp));
+}
+
+//------------------------------------------------------------------------------
+//  <algorithm> Lexicographical Comparisons
+//------------------------------------------------------------------------------
+
+// c_lexicographical_compare()
+//
+// Container-based version of the <algorithm> `std::lexicographical_compare()`
+// function to lexicographically compare (e.g. sort words alphabetically) two
+// container sequences. The comparison is performed using `operator<`. Note
+// that capital letters ("A-Z") have ASCII values less than lowercase letters
+// ("a-z").
+template <typename Sequence1, typename Sequence2>
+bool c_lexicographical_compare(const Sequence1& sequence1,
+                               const Sequence2& sequence2) {
+  return std::lexicographical_compare(
+      container_algorithm_internal::c_begin(sequence1),
+      container_algorithm_internal::c_end(sequence1),
+      container_algorithm_internal::c_begin(sequence2),
+      container_algorithm_internal::c_end(sequence2));
+}
+
+// Overload of c_lexicographical_compare() for performing a lexicographical
+// comparison using a `comp` operator instead of `operator<`.
+template <typename Sequence1, typename Sequence2, typename LessThan>
+bool c_lexicographical_compare(const Sequence1& sequence1,
+                               const Sequence2& sequence2, LessThan&& comp) {
+  return std::lexicographical_compare(
+      container_algorithm_internal::c_begin(sequence1),
+      container_algorithm_internal::c_end(sequence1),
+      container_algorithm_internal::c_begin(sequence2),
+      container_algorithm_internal::c_end(sequence2),
+      std::forward<LessThan>(comp));
+}
+
+// c_next_permutation()
+//
+// Container-based version of the <algorithm> `std::next_permutation()` function
+// to rearrange a container's elements into the next lexicographically greater
+// permutation.
+template <typename C>
+bool c_next_permutation(C& c) {
+  return std::next_permutation(container_algorithm_internal::c_begin(c),
+                               container_algorithm_internal::c_end(c));
+}
+
+// Overload of c_next_permutation() for performing a lexicographical
+// comparison using a `comp` operator instead of `operator<`.
+template <typename C, typename LessThan>
+bool c_next_permutation(C& c, LessThan&& comp) {
+  return std::next_permutation(container_algorithm_internal::c_begin(c),
+                               container_algorithm_internal::c_end(c),
+                               std::forward<LessThan>(comp));
+}
+
+// c_prev_permutation()
+//
+// Container-based version of the <algorithm> `std::prev_permutation()` function
+// to rearrange a container's elements into the next lexicographically lesser
+// permutation.
+template <typename C>
+bool c_prev_permutation(C& c) {
+  return std::prev_permutation(container_algorithm_internal::c_begin(c),
+                               container_algorithm_internal::c_end(c));
+}
+
+// Overload of c_prev_permutation() for performing a lexicographical
+// comparison using a `comp` operator instead of `operator<`.
+template <typename C, typename LessThan>
+bool c_prev_permutation(C& c, LessThan&& comp) {
+  return std::prev_permutation(container_algorithm_internal::c_begin(c),
+                               container_algorithm_internal::c_end(c),
+                               std::forward<LessThan>(comp));
+}
+
+//------------------------------------------------------------------------------
+// <numeric> algorithms
+//------------------------------------------------------------------------------
+
+// c_iota()
+//
+// Container-based version of the <numeric> `std::iota()` function
+// to compute successive values of `value`, as if incremented with `++value`
+// after each element is written. and write them to the container.
+template <typename Sequence, typename T>
+void c_iota(Sequence& sequence, const T& value) {
+  std::iota(container_algorithm_internal::c_begin(sequence),
+            container_algorithm_internal::c_end(sequence), value);
+}
+
+// c_accumulate()
+//
+// Container-based version of the <numeric> `std::accumulate()` function
+// to accumulate the element values of a container to `init` and return that
+// accumulation by value.
+//
+// Note: Due to a language technicality this function has return type
+// absl::decay_t<T>. As a user of this function you can casually read
+// this as "returns T by value" and assume it does the right thing.
+template <typename Sequence, typename T>
+decay_t<T> c_accumulate(const Sequence& sequence, T&& init) {
+  return std::accumulate(container_algorithm_internal::c_begin(sequence),
+                         container_algorithm_internal::c_end(sequence),
+                         std::forward<T>(init));
+}
+
+// Overload of c_accumulate() for using a binary operations other than
+// addition for computing the accumulation.
+template <typename Sequence, typename T, typename BinaryOp>
+decay_t<T> c_accumulate(const Sequence& sequence, T&& init,
+                        BinaryOp&& binary_op) {
+  return std::accumulate(container_algorithm_internal::c_begin(sequence),
+                         container_algorithm_internal::c_end(sequence),
+                         std::forward<T>(init),
+                         std::forward<BinaryOp>(binary_op));
+}
+
+// c_inner_product()
+//
+// Container-based version of the <numeric> `std::inner_product()` function
+// to compute the cumulative inner product of container element pairs.
+//
+// Note: Due to a language technicality this function has return type
+// absl::decay_t<T>. As a user of this function you can casually read
+// this as "returns T by value" and assume it does the right thing.
+template <typename Sequence1, typename Sequence2, typename T>
+decay_t<T> c_inner_product(const Sequence1& factors1, const Sequence2& factors2,
+                           T&& sum) {
+  return std::inner_product(container_algorithm_internal::c_begin(factors1),
+                            container_algorithm_internal::c_end(factors1),
+                            container_algorithm_internal::c_begin(factors2),
+                            std::forward<T>(sum));
+}
+
+// Overload of c_inner_product() for using binary operations other than
+// `operator+` (for computing the accumulation) and `operator*` (for computing
+// the product between the two container's element pair).
+template <typename Sequence1, typename Sequence2, typename T,
+          typename BinaryOp1, typename BinaryOp2>
+decay_t<T> c_inner_product(const Sequence1& factors1, const Sequence2& factors2,
+                           T&& sum, BinaryOp1&& op1, BinaryOp2&& op2) {
+  return std::inner_product(container_algorithm_internal::c_begin(factors1),
+                            container_algorithm_internal::c_end(factors1),
+                            container_algorithm_internal::c_begin(factors2),
+                            std::forward<T>(sum), std::forward<BinaryOp1>(op1),
+                            std::forward<BinaryOp2>(op2));
+}
+
+// c_adjacent_difference()
+//
+// Container-based version of the <numeric> `std::adjacent_difference()`
+// function to compute the difference between each element and the one preceding
+// it and write it to an iterator.
+template <typename InputSequence, typename OutputIt>
+OutputIt c_adjacent_difference(const InputSequence& input,
+                               OutputIt output_first) {
+  return std::adjacent_difference(container_algorithm_internal::c_begin(input),
+                                  container_algorithm_internal::c_end(input),
+                                  output_first);
+}
+
+// Overload of c_adjacent_difference() for using a binary operation other than
+// subtraction to compute the adjacent difference.
+template <typename InputSequence, typename OutputIt, typename BinaryOp>
+OutputIt c_adjacent_difference(const InputSequence& input,
+                               OutputIt output_first, BinaryOp&& op) {
+  return std::adjacent_difference(container_algorithm_internal::c_begin(input),
+                                  container_algorithm_internal::c_end(input),
+                                  output_first, std::forward<BinaryOp>(op));
+}
+
+// c_partial_sum()
+//
+// Container-based version of the <numeric> `std::partial_sum()` function
+// to compute the partial sum of the elements in a sequence and write them
+// to an iterator. The partial sum is the sum of all element values so far in
+// the sequence.
+template <typename InputSequence, typename OutputIt>
+OutputIt c_partial_sum(const InputSequence& input, OutputIt output_first) {
+  return std::partial_sum(container_algorithm_internal::c_begin(input),
+                          container_algorithm_internal::c_end(input),
+                          output_first);
+}
+
+// Overload of c_partial_sum() for using a binary operation other than addition
+// to compute the "partial sum".
+template <typename InputSequence, typename OutputIt, typename BinaryOp>
+OutputIt c_partial_sum(const InputSequence& input, OutputIt output_first,
+                       BinaryOp&& op) {
+  return std::partial_sum(container_algorithm_internal::c_begin(input),
+                          container_algorithm_internal::c_end(input),
+                          output_first, std::forward<BinaryOp>(op));
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_ALGORITHM_CONTAINER_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/attributes.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/attributes.h
new file mode 100644
index 0000000000000000000000000000000000000000..b7826e7766bcd673fb08c307abc29ba0d6a13b00
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/attributes.h
@@ -0,0 +1,782 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// This header file defines macros for declaring attributes for functions,
+// types, and variables.
+//
+// These macros are used within Abseil and allow the compiler to optimize, where
+// applicable, certain function calls.
+//
+// Most macros here are exposing GCC or Clang features, and are stubbed out for
+// other compilers.
+//
+// GCC attributes documentation:
+//   https://gcc.gnu.org/onlinedocs/gcc-4.7.0/gcc/Function-Attributes.html
+//   https://gcc.gnu.org/onlinedocs/gcc-4.7.0/gcc/Variable-Attributes.html
+//   https://gcc.gnu.org/onlinedocs/gcc-4.7.0/gcc/Type-Attributes.html
+//
+// Most attributes in this file are already supported by GCC 4.7. However, some
+// of them are not supported in older version of Clang. Thus, we check
+// `__has_attribute()` first. If the check fails, we check if we are on GCC and
+// assume the attribute exists on GCC (which is verified on GCC 4.7).
+
+#ifndef ABSL_BASE_ATTRIBUTES_H_
+#define ABSL_BASE_ATTRIBUTES_H_
+
+#include "absl/base/config.h"
+
+// ABSL_HAVE_ATTRIBUTE
+//
+// A function-like feature checking macro that is a wrapper around
+// `__has_attribute`, which is defined by GCC 5+ and Clang and evaluates to a
+// nonzero constant integer if the attribute is supported or 0 if not.
+//
+// It evaluates to zero if `__has_attribute` is not defined by the compiler.
+//
+// GCC: https://gcc.gnu.org/gcc-5/changes.html
+// Clang: https://clang.llvm.org/docs/LanguageExtensions.html
+#ifdef __has_attribute
+#define ABSL_HAVE_ATTRIBUTE(x) __has_attribute(x)
+#else
+#define ABSL_HAVE_ATTRIBUTE(x) 0
+#endif
+
+// ABSL_HAVE_CPP_ATTRIBUTE
+//
+// A function-like feature checking macro that accepts C++11 style attributes.
+// It's a wrapper around `__has_cpp_attribute`, defined by ISO C++ SD-6
+// (https://en.cppreference.com/w/cpp/experimental/feature_test). If we don't
+// find `__has_cpp_attribute`, will evaluate to 0.
+#if defined(__cplusplus) && defined(__has_cpp_attribute)
+// NOTE: requiring __cplusplus above should not be necessary, but
+// works around https://bugs.llvm.org/show_bug.cgi?id=23435.
+#define ABSL_HAVE_CPP_ATTRIBUTE(x) __has_cpp_attribute(x)
+#else
+#define ABSL_HAVE_CPP_ATTRIBUTE(x) 0
+#endif
+
+// -----------------------------------------------------------------------------
+// Function Attributes
+// -----------------------------------------------------------------------------
+//
+// GCC: https://gcc.gnu.org/onlinedocs/gcc/Function-Attributes.html
+// Clang: https://clang.llvm.org/docs/AttributeReference.html
+
+// ABSL_PRINTF_ATTRIBUTE
+// ABSL_SCANF_ATTRIBUTE
+//
+// Tells the compiler to perform `printf` format string checking if the
+// compiler supports it; see the 'format' attribute in
+// <https://gcc.gnu.org/onlinedocs/gcc-4.7.0/gcc/Function-Attributes.html>.
+//
+// Note: As the GCC manual states, "[s]ince non-static C++ methods
+// have an implicit 'this' argument, the arguments of such methods
+// should be counted from two, not one."
+#if ABSL_HAVE_ATTRIBUTE(format) || (defined(__GNUC__) && !defined(__clang__))
+#define ABSL_PRINTF_ATTRIBUTE(string_index, first_to_check) \
+  __attribute__((__format__(__printf__, string_index, first_to_check)))
+#define ABSL_SCANF_ATTRIBUTE(string_index, first_to_check) \
+  __attribute__((__format__(__scanf__, string_index, first_to_check)))
+#else
+#define ABSL_PRINTF_ATTRIBUTE(string_index, first_to_check)
+#define ABSL_SCANF_ATTRIBUTE(string_index, first_to_check)
+#endif
+
+// ABSL_ATTRIBUTE_ALWAYS_INLINE
+// ABSL_ATTRIBUTE_NOINLINE
+//
+// Forces functions to either inline or not inline. Introduced in gcc 3.1.
+#if ABSL_HAVE_ATTRIBUTE(always_inline) || \
+    (defined(__GNUC__) && !defined(__clang__))
+#define ABSL_ATTRIBUTE_ALWAYS_INLINE __attribute__((always_inline))
+#define ABSL_HAVE_ATTRIBUTE_ALWAYS_INLINE 1
+#else
+#define ABSL_ATTRIBUTE_ALWAYS_INLINE
+#endif
+
+#if ABSL_HAVE_ATTRIBUTE(noinline) || (defined(__GNUC__) && !defined(__clang__))
+#define ABSL_ATTRIBUTE_NOINLINE __attribute__((noinline))
+#define ABSL_HAVE_ATTRIBUTE_NOINLINE 1
+#else
+#define ABSL_ATTRIBUTE_NOINLINE
+#endif
+
+// ABSL_ATTRIBUTE_NO_TAIL_CALL
+//
+// Prevents the compiler from optimizing away stack frames for functions which
+// end in a call to another function.
+#if ABSL_HAVE_ATTRIBUTE(disable_tail_calls)
+#define ABSL_HAVE_ATTRIBUTE_NO_TAIL_CALL 1
+#define ABSL_ATTRIBUTE_NO_TAIL_CALL __attribute__((disable_tail_calls))
+#elif defined(__GNUC__) && !defined(__clang__) && !defined(__e2k__)
+#define ABSL_HAVE_ATTRIBUTE_NO_TAIL_CALL 1
+#define ABSL_ATTRIBUTE_NO_TAIL_CALL \
+  __attribute__((optimize("no-optimize-sibling-calls")))
+#else
+#define ABSL_ATTRIBUTE_NO_TAIL_CALL
+#define ABSL_HAVE_ATTRIBUTE_NO_TAIL_CALL 0
+#endif
+
+// ABSL_ATTRIBUTE_WEAK
+//
+// Tags a function as weak for the purposes of compilation and linking.
+// Weak attributes did not work properly in LLVM's Windows backend before
+// 9.0.0, so disable them there. See https://bugs.llvm.org/show_bug.cgi?id=37598
+// for further information.
+// The MinGW compiler doesn't complain about the weak attribute until the link
+// step, presumably because Windows doesn't use ELF binaries.
+#if (ABSL_HAVE_ATTRIBUTE(weak) ||                                         \
+     (defined(__GNUC__) && !defined(__clang__))) &&                       \
+    (!defined(_WIN32) || (defined(__clang__) && __clang_major__ >= 9)) && \
+    !defined(__MINGW32__)
+#undef ABSL_ATTRIBUTE_WEAK
+#define ABSL_ATTRIBUTE_WEAK __attribute__((weak))
+#define ABSL_HAVE_ATTRIBUTE_WEAK 1
+#else
+#define ABSL_ATTRIBUTE_WEAK
+#define ABSL_HAVE_ATTRIBUTE_WEAK 0
+#endif
+
+// ABSL_ATTRIBUTE_NONNULL
+//
+// Tells the compiler either (a) that a particular function parameter
+// should be a non-null pointer, or (b) that all pointer arguments should
+// be non-null.
+//
+// Note: As the GCC manual states, "[s]ince non-static C++ methods
+// have an implicit 'this' argument, the arguments of such methods
+// should be counted from two, not one."
+//
+// Args are indexed starting at 1.
+//
+// For non-static class member functions, the implicit `this` argument
+// is arg 1, and the first explicit argument is arg 2. For static class member
+// functions, there is no implicit `this`, and the first explicit argument is
+// arg 1.
+//
+// Example:
+//
+//   /* arg_a cannot be null, but arg_b can */
+//   void Function(void* arg_a, void* arg_b) ABSL_ATTRIBUTE_NONNULL(1);
+//
+//   class C {
+//     /* arg_a cannot be null, but arg_b can */
+//     void Method(void* arg_a, void* arg_b) ABSL_ATTRIBUTE_NONNULL(2);
+//
+//     /* arg_a cannot be null, but arg_b can */
+//     static void StaticMethod(void* arg_a, void* arg_b)
+//     ABSL_ATTRIBUTE_NONNULL(1);
+//   };
+//
+// If no arguments are provided, then all pointer arguments should be non-null.
+//
+//  /* No pointer arguments may be null. */
+//  void Function(void* arg_a, void* arg_b, int arg_c) ABSL_ATTRIBUTE_NONNULL();
+//
+// NOTE: The GCC nonnull attribute actually accepts a list of arguments, but
+// ABSL_ATTRIBUTE_NONNULL does not.
+#if ABSL_HAVE_ATTRIBUTE(nonnull) || (defined(__GNUC__) && !defined(__clang__))
+#define ABSL_ATTRIBUTE_NONNULL(arg_index) __attribute__((nonnull(arg_index)))
+#else
+#define ABSL_ATTRIBUTE_NONNULL(...)
+#endif
+
+// ABSL_ATTRIBUTE_NORETURN
+//
+// Tells the compiler that a given function never returns.
+#if ABSL_HAVE_ATTRIBUTE(noreturn) || (defined(__GNUC__) && !defined(__clang__))
+#define ABSL_ATTRIBUTE_NORETURN __attribute__((noreturn))
+#elif defined(_MSC_VER)
+#define ABSL_ATTRIBUTE_NORETURN __declspec(noreturn)
+#else
+#define ABSL_ATTRIBUTE_NORETURN
+#endif
+
+// ABSL_ATTRIBUTE_NO_SANITIZE_ADDRESS
+//
+// Tells the AddressSanitizer (or other memory testing tools) to ignore a given
+// function. Useful for cases when a function reads random locations on stack,
+// calls _exit from a cloned subprocess, deliberately accesses buffer
+// out of bounds or does other scary things with memory.
+// NOTE: GCC supports AddressSanitizer(asan) since 4.8.
+// https://gcc.gnu.org/gcc-4.8/changes.html
+#if ABSL_HAVE_ATTRIBUTE(no_sanitize_address)
+#define ABSL_ATTRIBUTE_NO_SANITIZE_ADDRESS __attribute__((no_sanitize_address))
+#elif defined(_MSC_VER) && _MSC_VER >= 1928
+// https://docs.microsoft.com/en-us/cpp/cpp/no-sanitize-address
+#define ABSL_ATTRIBUTE_NO_SANITIZE_ADDRESS __declspec(no_sanitize_address)
+#else
+#define ABSL_ATTRIBUTE_NO_SANITIZE_ADDRESS
+#endif
+
+// ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY
+//
+// Tells the MemorySanitizer to relax the handling of a given function. All "Use
+// of uninitialized value" warnings from such functions will be suppressed, and
+// all values loaded from memory will be considered fully initialized.  This
+// attribute is similar to the ABSL_ATTRIBUTE_NO_SANITIZE_ADDRESS attribute
+// above, but deals with initialized-ness rather than addressability issues.
+// NOTE: MemorySanitizer(msan) is supported by Clang but not GCC.
+#if ABSL_HAVE_ATTRIBUTE(no_sanitize_memory)
+#define ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY __attribute__((no_sanitize_memory))
+#else
+#define ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY
+#endif
+
+// ABSL_ATTRIBUTE_NO_SANITIZE_THREAD
+//
+// Tells the ThreadSanitizer to not instrument a given function.
+// NOTE: GCC supports ThreadSanitizer(tsan) since 4.8.
+// https://gcc.gnu.org/gcc-4.8/changes.html
+#if ABSL_HAVE_ATTRIBUTE(no_sanitize_thread)
+#define ABSL_ATTRIBUTE_NO_SANITIZE_THREAD __attribute__((no_sanitize_thread))
+#else
+#define ABSL_ATTRIBUTE_NO_SANITIZE_THREAD
+#endif
+
+// ABSL_ATTRIBUTE_NO_SANITIZE_UNDEFINED
+//
+// Tells the UndefinedSanitizer to ignore a given function. Useful for cases
+// where certain behavior (eg. division by zero) is being used intentionally.
+// NOTE: GCC supports UndefinedBehaviorSanitizer(ubsan) since 4.9.
+// https://gcc.gnu.org/gcc-4.9/changes.html
+#if ABSL_HAVE_ATTRIBUTE(no_sanitize_undefined)
+#define ABSL_ATTRIBUTE_NO_SANITIZE_UNDEFINED \
+  __attribute__((no_sanitize_undefined))
+#elif ABSL_HAVE_ATTRIBUTE(no_sanitize)
+#define ABSL_ATTRIBUTE_NO_SANITIZE_UNDEFINED \
+  __attribute__((no_sanitize("undefined")))
+#else
+#define ABSL_ATTRIBUTE_NO_SANITIZE_UNDEFINED
+#endif
+
+// ABSL_ATTRIBUTE_NO_SANITIZE_CFI
+//
+// Tells the ControlFlowIntegrity sanitizer to not instrument a given function.
+// See https://clang.llvm.org/docs/ControlFlowIntegrity.html for details.
+#if ABSL_HAVE_ATTRIBUTE(no_sanitize)
+#define ABSL_ATTRIBUTE_NO_SANITIZE_CFI __attribute__((no_sanitize("cfi")))
+#else
+#define ABSL_ATTRIBUTE_NO_SANITIZE_CFI
+#endif
+
+// ABSL_ATTRIBUTE_NO_SANITIZE_SAFESTACK
+//
+// Tells the SafeStack to not instrument a given function.
+// See https://clang.llvm.org/docs/SafeStack.html for details.
+#if ABSL_HAVE_ATTRIBUTE(no_sanitize)
+#define ABSL_ATTRIBUTE_NO_SANITIZE_SAFESTACK \
+  __attribute__((no_sanitize("safe-stack")))
+#else
+#define ABSL_ATTRIBUTE_NO_SANITIZE_SAFESTACK
+#endif
+
+// ABSL_ATTRIBUTE_RETURNS_NONNULL
+//
+// Tells the compiler that a particular function never returns a null pointer.
+#if ABSL_HAVE_ATTRIBUTE(returns_nonnull)
+#define ABSL_ATTRIBUTE_RETURNS_NONNULL __attribute__((returns_nonnull))
+#else
+#define ABSL_ATTRIBUTE_RETURNS_NONNULL
+#endif
+
+// ABSL_HAVE_ATTRIBUTE_SECTION
+//
+// Indicates whether labeled sections are supported. Weak symbol support is
+// a prerequisite. Labeled sections are not supported on Darwin/iOS.
+#ifdef ABSL_HAVE_ATTRIBUTE_SECTION
+#error ABSL_HAVE_ATTRIBUTE_SECTION cannot be directly set
+#elif (ABSL_HAVE_ATTRIBUTE(section) ||                \
+       (defined(__GNUC__) && !defined(__clang__))) && \
+    !defined(__APPLE__) && ABSL_HAVE_ATTRIBUTE_WEAK
+#define ABSL_HAVE_ATTRIBUTE_SECTION 1
+
+// ABSL_ATTRIBUTE_SECTION
+//
+// Tells the compiler/linker to put a given function into a section and define
+// `__start_ ## name` and `__stop_ ## name` symbols to bracket the section.
+// This functionality is supported by GNU linker.  Any function annotated with
+// `ABSL_ATTRIBUTE_SECTION` must not be inlined, or it will be placed into
+// whatever section its caller is placed into.
+//
+#ifndef ABSL_ATTRIBUTE_SECTION
+#define ABSL_ATTRIBUTE_SECTION(name) \
+  __attribute__((section(#name))) __attribute__((noinline))
+#endif
+
+// ABSL_ATTRIBUTE_SECTION_VARIABLE
+//
+// Tells the compiler/linker to put a given variable into a section and define
+// `__start_ ## name` and `__stop_ ## name` symbols to bracket the section.
+// This functionality is supported by GNU linker.
+#ifndef ABSL_ATTRIBUTE_SECTION_VARIABLE
+#ifdef _AIX
+// __attribute__((section(#name))) on AIX is achived by using the `.csect` psudo
+// op which includes an additional integer as part of its syntax indcating
+// alignment. If data fall under different alignments then you might get a
+// compilation error indicating a `Section type conflict`.
+#define ABSL_ATTRIBUTE_SECTION_VARIABLE(name)
+#else
+#define ABSL_ATTRIBUTE_SECTION_VARIABLE(name) __attribute__((section(#name)))
+#endif
+#endif
+
+// ABSL_DECLARE_ATTRIBUTE_SECTION_VARS
+//
+// A weak section declaration to be used as a global declaration
+// for ABSL_ATTRIBUTE_SECTION_START|STOP(name) to compile and link
+// even without functions with ABSL_ATTRIBUTE_SECTION(name).
+// ABSL_DEFINE_ATTRIBUTE_SECTION should be in the exactly one file; it's
+// a no-op on ELF but not on Mach-O.
+//
+#ifndef ABSL_DECLARE_ATTRIBUTE_SECTION_VARS
+#define ABSL_DECLARE_ATTRIBUTE_SECTION_VARS(name)   \
+  extern char __start_##name[] ABSL_ATTRIBUTE_WEAK; \
+  extern char __stop_##name[] ABSL_ATTRIBUTE_WEAK
+#endif
+#ifndef ABSL_DEFINE_ATTRIBUTE_SECTION_VARS
+#define ABSL_INIT_ATTRIBUTE_SECTION_VARS(name)
+#define ABSL_DEFINE_ATTRIBUTE_SECTION_VARS(name)
+#endif
+
+// ABSL_ATTRIBUTE_SECTION_START
+//
+// Returns `void*` pointers to start/end of a section of code with
+// functions having ABSL_ATTRIBUTE_SECTION(name).
+// Returns 0 if no such functions exist.
+// One must ABSL_DECLARE_ATTRIBUTE_SECTION_VARS(name) for this to compile and
+// link.
+//
+#define ABSL_ATTRIBUTE_SECTION_START(name) \
+  (reinterpret_cast<void *>(__start_##name))
+#define ABSL_ATTRIBUTE_SECTION_STOP(name) \
+  (reinterpret_cast<void *>(__stop_##name))
+
+#else  // !ABSL_HAVE_ATTRIBUTE_SECTION
+
+#define ABSL_HAVE_ATTRIBUTE_SECTION 0
+
+// provide dummy definitions
+#define ABSL_ATTRIBUTE_SECTION(name)
+#define ABSL_ATTRIBUTE_SECTION_VARIABLE(name)
+#define ABSL_INIT_ATTRIBUTE_SECTION_VARS(name)
+#define ABSL_DEFINE_ATTRIBUTE_SECTION_VARS(name)
+#define ABSL_DECLARE_ATTRIBUTE_SECTION_VARS(name)
+#define ABSL_ATTRIBUTE_SECTION_START(name) (reinterpret_cast<void *>(0))
+#define ABSL_ATTRIBUTE_SECTION_STOP(name) (reinterpret_cast<void *>(0))
+
+#endif  // ABSL_ATTRIBUTE_SECTION
+
+// ABSL_ATTRIBUTE_STACK_ALIGN_FOR_OLD_LIBC
+//
+// Support for aligning the stack on 32-bit x86.
+#if ABSL_HAVE_ATTRIBUTE(force_align_arg_pointer) || \
+    (defined(__GNUC__) && !defined(__clang__))
+#if defined(__i386__)
+#define ABSL_ATTRIBUTE_STACK_ALIGN_FOR_OLD_LIBC \
+  __attribute__((force_align_arg_pointer))
+#define ABSL_REQUIRE_STACK_ALIGN_TRAMPOLINE (0)
+#elif defined(__x86_64__)
+#define ABSL_REQUIRE_STACK_ALIGN_TRAMPOLINE (1)
+#define ABSL_ATTRIBUTE_STACK_ALIGN_FOR_OLD_LIBC
+#else  // !__i386__ && !__x86_64
+#define ABSL_REQUIRE_STACK_ALIGN_TRAMPOLINE (0)
+#define ABSL_ATTRIBUTE_STACK_ALIGN_FOR_OLD_LIBC
+#endif  // __i386__
+#else
+#define ABSL_ATTRIBUTE_STACK_ALIGN_FOR_OLD_LIBC
+#define ABSL_REQUIRE_STACK_ALIGN_TRAMPOLINE (0)
+#endif
+
+// ABSL_MUST_USE_RESULT
+//
+// Tells the compiler to warn about unused results.
+//
+// For code or headers that are assured to only build with C++17 and up, prefer
+// just using the standard `[[nodiscard]]` directly over this macro.
+//
+// When annotating a function, it must appear as the first part of the
+// declaration or definition. The compiler will warn if the return value from
+// such a function is unused:
+//
+//   ABSL_MUST_USE_RESULT Sprocket* AllocateSprocket();
+//   AllocateSprocket();  // Triggers a warning.
+//
+// When annotating a class, it is equivalent to annotating every function which
+// returns an instance.
+//
+//   class ABSL_MUST_USE_RESULT Sprocket {};
+//   Sprocket();  // Triggers a warning.
+//
+//   Sprocket MakeSprocket();
+//   MakeSprocket();  // Triggers a warning.
+//
+// Note that references and pointers are not instances:
+//
+//   Sprocket* SprocketPointer();
+//   SprocketPointer();  // Does *not* trigger a warning.
+//
+// ABSL_MUST_USE_RESULT allows using cast-to-void to suppress the unused result
+// warning. For that, warn_unused_result is used only for clang but not for gcc.
+// https://gcc.gnu.org/bugzilla/show_bug.cgi?id=66425
+//
+// Note: past advice was to place the macro after the argument list.
+//
+// TODO(b/176172494): Use ABSL_HAVE_CPP_ATTRIBUTE(nodiscard) when all code is
+// compliant with the stricter [[nodiscard]].
+#if defined(__clang__) && ABSL_HAVE_ATTRIBUTE(warn_unused_result)
+#define ABSL_MUST_USE_RESULT __attribute__((warn_unused_result))
+#else
+#define ABSL_MUST_USE_RESULT
+#endif
+
+// ABSL_ATTRIBUTE_HOT, ABSL_ATTRIBUTE_COLD
+//
+// Tells GCC that a function is hot or cold. GCC can use this information to
+// improve static analysis, i.e. a conditional branch to a cold function
+// is likely to be not-taken.
+// This annotation is used for function declarations.
+//
+// Example:
+//
+//   int foo() ABSL_ATTRIBUTE_HOT;
+#if ABSL_HAVE_ATTRIBUTE(hot) || (defined(__GNUC__) && !defined(__clang__))
+#define ABSL_ATTRIBUTE_HOT __attribute__((hot))
+#else
+#define ABSL_ATTRIBUTE_HOT
+#endif
+
+#if ABSL_HAVE_ATTRIBUTE(cold) || (defined(__GNUC__) && !defined(__clang__))
+#define ABSL_ATTRIBUTE_COLD __attribute__((cold))
+#else
+#define ABSL_ATTRIBUTE_COLD
+#endif
+
+// ABSL_XRAY_ALWAYS_INSTRUMENT, ABSL_XRAY_NEVER_INSTRUMENT, ABSL_XRAY_LOG_ARGS
+//
+// We define the ABSL_XRAY_ALWAYS_INSTRUMENT and ABSL_XRAY_NEVER_INSTRUMENT
+// macro used as an attribute to mark functions that must always or never be
+// instrumented by XRay. Currently, this is only supported in Clang/LLVM.
+//
+// For reference on the LLVM XRay instrumentation, see
+// http://llvm.org/docs/XRay.html.
+//
+// A function with the XRAY_ALWAYS_INSTRUMENT macro attribute in its declaration
+// will always get the XRay instrumentation sleds. These sleds may introduce
+// some binary size and runtime overhead and must be used sparingly.
+//
+// These attributes only take effect when the following conditions are met:
+//
+//   * The file/target is built in at least C++11 mode, with a Clang compiler
+//     that supports XRay attributes.
+//   * The file/target is built with the -fxray-instrument flag set for the
+//     Clang/LLVM compiler.
+//   * The function is defined in the translation unit (the compiler honors the
+//     attribute in either the definition or the declaration, and must match).
+//
+// There are cases when, even when building with XRay instrumentation, users
+// might want to control specifically which functions are instrumented for a
+// particular build using special-case lists provided to the compiler. These
+// special case lists are provided to Clang via the
+// -fxray-always-instrument=... and -fxray-never-instrument=... flags. The
+// attributes in source take precedence over these special-case lists.
+//
+// To disable the XRay attributes at build-time, users may define
+// ABSL_NO_XRAY_ATTRIBUTES. Do NOT define ABSL_NO_XRAY_ATTRIBUTES on specific
+// packages/targets, as this may lead to conflicting definitions of functions at
+// link-time.
+//
+// XRay isn't currently supported on Android:
+// https://github.com/android/ndk/issues/368
+#if ABSL_HAVE_CPP_ATTRIBUTE(clang::xray_always_instrument) && \
+    !defined(ABSL_NO_XRAY_ATTRIBUTES) && !defined(__ANDROID__)
+#define ABSL_XRAY_ALWAYS_INSTRUMENT [[clang::xray_always_instrument]]
+#define ABSL_XRAY_NEVER_INSTRUMENT [[clang::xray_never_instrument]]
+#if ABSL_HAVE_CPP_ATTRIBUTE(clang::xray_log_args)
+#define ABSL_XRAY_LOG_ARGS(N) \
+  [[clang::xray_always_instrument, clang::xray_log_args(N)]]
+#else
+#define ABSL_XRAY_LOG_ARGS(N) [[clang::xray_always_instrument]]
+#endif
+#else
+#define ABSL_XRAY_ALWAYS_INSTRUMENT
+#define ABSL_XRAY_NEVER_INSTRUMENT
+#define ABSL_XRAY_LOG_ARGS(N)
+#endif
+
+// ABSL_ATTRIBUTE_REINITIALIZES
+//
+// Indicates that a member function reinitializes the entire object to a known
+// state, independent of the previous state of the object.
+//
+// The clang-tidy check bugprone-use-after-move allows member functions marked
+// with this attribute to be called on objects that have been moved from;
+// without the attribute, this would result in a use-after-move warning.
+#if ABSL_HAVE_CPP_ATTRIBUTE(clang::reinitializes)
+#define ABSL_ATTRIBUTE_REINITIALIZES [[clang::reinitializes]]
+#else
+#define ABSL_ATTRIBUTE_REINITIALIZES
+#endif
+
+// -----------------------------------------------------------------------------
+// Variable Attributes
+// -----------------------------------------------------------------------------
+
+// ABSL_ATTRIBUTE_UNUSED
+//
+// Prevents the compiler from complaining about variables that appear unused.
+//
+// For code or headers that are assured to only build with C++17 and up, prefer
+// just using the standard '[[maybe_unused]]' directly over this macro.
+//
+// Due to differences in positioning requirements between the old, compiler
+// specific __attribute__ syntax and the now standard [[maybe_unused]], this
+// macro does not attempt to take advantage of '[[maybe_unused]]'.
+#if ABSL_HAVE_ATTRIBUTE(unused) || (defined(__GNUC__) && !defined(__clang__))
+#undef ABSL_ATTRIBUTE_UNUSED
+#define ABSL_ATTRIBUTE_UNUSED __attribute__((__unused__))
+#else
+#define ABSL_ATTRIBUTE_UNUSED
+#endif
+
+// ABSL_ATTRIBUTE_INITIAL_EXEC
+//
+// Tells the compiler to use "initial-exec" mode for a thread-local variable.
+// See http://people.redhat.com/drepper/tls.pdf for the gory details.
+#if ABSL_HAVE_ATTRIBUTE(tls_model) || (defined(__GNUC__) && !defined(__clang__))
+#define ABSL_ATTRIBUTE_INITIAL_EXEC __attribute__((tls_model("initial-exec")))
+#else
+#define ABSL_ATTRIBUTE_INITIAL_EXEC
+#endif
+
+// ABSL_ATTRIBUTE_PACKED
+//
+// Instructs the compiler not to use natural alignment for a tagged data
+// structure, but instead to reduce its alignment to 1.
+//
+// Therefore, DO NOT APPLY THIS ATTRIBUTE TO STRUCTS CONTAINING ATOMICS. Doing
+// so can cause atomic variables to be mis-aligned and silently violate
+// atomicity on x86.
+//
+// This attribute can either be applied to members of a structure or to a
+// structure in its entirety. Applying this attribute (judiciously) to a
+// structure in its entirety to optimize the memory footprint of very
+// commonly-used structs is fine. Do not apply this attribute to a structure in
+// its entirety if the purpose is to control the offsets of the members in the
+// structure. Instead, apply this attribute only to structure members that need
+// it.
+//
+// When applying ABSL_ATTRIBUTE_PACKED only to specific structure members the
+// natural alignment of structure members not annotated is preserved. Aligned
+// member accesses are faster than non-aligned member accesses even if the
+// targeted microprocessor supports non-aligned accesses.
+#if ABSL_HAVE_ATTRIBUTE(packed) || (defined(__GNUC__) && !defined(__clang__))
+#define ABSL_ATTRIBUTE_PACKED __attribute__((__packed__))
+#else
+#define ABSL_ATTRIBUTE_PACKED
+#endif
+
+// ABSL_ATTRIBUTE_FUNC_ALIGN
+//
+// Tells the compiler to align the function start at least to certain
+// alignment boundary
+#if ABSL_HAVE_ATTRIBUTE(aligned) || (defined(__GNUC__) && !defined(__clang__))
+#define ABSL_ATTRIBUTE_FUNC_ALIGN(bytes) __attribute__((aligned(bytes)))
+#else
+#define ABSL_ATTRIBUTE_FUNC_ALIGN(bytes)
+#endif
+
+// ABSL_FALLTHROUGH_INTENDED
+//
+// Annotates implicit fall-through between switch labels, allowing a case to
+// indicate intentional fallthrough and turn off warnings about any lack of a
+// `break` statement. The ABSL_FALLTHROUGH_INTENDED macro should be followed by
+// a semicolon and can be used in most places where `break` can, provided that
+// no statements exist between it and the next switch label.
+//
+// Example:
+//
+//  switch (x) {
+//    case 40:
+//    case 41:
+//      if (truth_is_out_there) {
+//        ++x;
+//        ABSL_FALLTHROUGH_INTENDED;  // Use instead of/along with annotations
+//                                    // in comments
+//      } else {
+//        return x;
+//      }
+//    case 42:
+//      ...
+//
+// Notes: When supported, GCC and Clang can issue a warning on switch labels
+// with unannotated fallthrough using the warning `-Wimplicit-fallthrough`. See
+// clang documentation on language extensions for details:
+// https://clang.llvm.org/docs/AttributeReference.html#fallthrough-clang-fallthrough
+//
+// When used with unsupported compilers, the ABSL_FALLTHROUGH_INTENDED macro has
+// no effect on diagnostics. In any case this macro has no effect on runtime
+// behavior and performance of code.
+
+#ifdef ABSL_FALLTHROUGH_INTENDED
+#error "ABSL_FALLTHROUGH_INTENDED should not be defined."
+#elif ABSL_HAVE_CPP_ATTRIBUTE(fallthrough)
+#define ABSL_FALLTHROUGH_INTENDED [[fallthrough]]
+#elif ABSL_HAVE_CPP_ATTRIBUTE(clang::fallthrough)
+#define ABSL_FALLTHROUGH_INTENDED [[clang::fallthrough]]
+#elif ABSL_HAVE_CPP_ATTRIBUTE(gnu::fallthrough)
+#define ABSL_FALLTHROUGH_INTENDED [[gnu::fallthrough]]
+#else
+#define ABSL_FALLTHROUGH_INTENDED \
+  do {                            \
+  } while (0)
+#endif
+
+// ABSL_DEPRECATED()
+//
+// Marks a deprecated class, struct, enum, function, method and variable
+// declarations. The macro argument is used as a custom diagnostic message (e.g.
+// suggestion of a better alternative).
+//
+// For code or headers that are assured to only build with C++14 and up, prefer
+// just using the standard `[[deprecated("message")]]` directly over this macro.
+//
+// Examples:
+//
+//   class ABSL_DEPRECATED("Use Bar instead") Foo {...};
+//
+//   ABSL_DEPRECATED("Use Baz() instead") void Bar() {...}
+//
+//   template <typename T>
+//   ABSL_DEPRECATED("Use DoThat() instead")
+//   void DoThis();
+//
+//   enum FooEnum {
+//     kBar ABSL_DEPRECATED("Use kBaz instead"),
+//   };
+//
+// Every usage of a deprecated entity will trigger a warning when compiled with
+// GCC/Clang's `-Wdeprecated-declarations` option. Google's production toolchain
+// turns this warning off by default, instead relying on clang-tidy to report
+// new uses of deprecated code.
+#if ABSL_HAVE_ATTRIBUTE(deprecated)
+#define ABSL_DEPRECATED(message) __attribute__((deprecated(message)))
+#else
+#define ABSL_DEPRECATED(message)
+#endif
+
+// ABSL_CONST_INIT
+//
+// A variable declaration annotated with the `ABSL_CONST_INIT` attribute will
+// not compile (on supported platforms) unless the variable has a constant
+// initializer. This is useful for variables with static and thread storage
+// duration, because it guarantees that they will not suffer from the so-called
+// "static init order fiasco".
+//
+// This attribute must be placed on the initializing declaration of the
+// variable. Some compilers will give a -Wmissing-constinit warning when this
+// attribute is placed on some other declaration but missing from the
+// initializing declaration.
+//
+// In some cases (notably with thread_local variables), `ABSL_CONST_INIT` can
+// also be used in a non-initializing declaration to tell the compiler that a
+// variable is already initialized, reducing overhead that would otherwise be
+// incurred by a hidden guard variable. Thus annotating all declarations with
+// this attribute is recommended to potentially enhance optimization.
+//
+// Example:
+//
+//   class MyClass {
+//    public:
+//     ABSL_CONST_INIT static MyType my_var;
+//   };
+//
+//   ABSL_CONST_INIT MyType MyClass::my_var = MakeMyType(...);
+//
+// For code or headers that are assured to only build with C++20 and up, prefer
+// just using the standard `constinit` keyword directly over this macro.
+//
+// Note that this attribute is redundant if the variable is declared constexpr.
+#if defined(__cpp_constinit) && __cpp_constinit >= 201907L
+#define ABSL_CONST_INIT constinit
+#elif ABSL_HAVE_CPP_ATTRIBUTE(clang::require_constant_initialization)
+#define ABSL_CONST_INIT [[clang::require_constant_initialization]]
+#else
+#define ABSL_CONST_INIT
+#endif
+
+// These annotations are not available yet due to fear of breaking code.
+#define ABSL_ATTRIBUTE_PURE_FUNCTION
+#define ABSL_ATTRIBUTE_CONST_FUNCTION
+
+// ABSL_ATTRIBUTE_LIFETIME_BOUND indicates that a resource owned by a function
+// parameter or implicit object parameter is retained by the return value of the
+// annotated function (or, for a parameter of a constructor, in the value of the
+// constructed object). This attribute causes warnings to be produced if a
+// temporary object does not live long enough.
+//
+// When applied to a reference parameter, the referenced object is assumed to be
+// retained by the return value of the function. When applied to a non-reference
+// parameter (for example, a pointer or a class type), all temporaries
+// referenced by the parameter are assumed to be retained by the return value of
+// the function.
+//
+// See also the upstream documentation:
+// https://clang.llvm.org/docs/AttributeReference.html#lifetimebound
+#if ABSL_HAVE_CPP_ATTRIBUTE(clang::lifetimebound)
+#define ABSL_ATTRIBUTE_LIFETIME_BOUND [[clang::lifetimebound]]
+#elif ABSL_HAVE_ATTRIBUTE(lifetimebound)
+#define ABSL_ATTRIBUTE_LIFETIME_BOUND __attribute__((lifetimebound))
+#else
+#define ABSL_ATTRIBUTE_LIFETIME_BOUND
+#endif
+
+// ABSL_ATTRIBUTE_TRIVIAL_ABI
+// Indicates that a type is "trivially relocatable" -- meaning it can be
+// relocated without invoking the constructor/destructor, using a form of move
+// elision.
+//
+// From a memory safety point of view, putting aside destructor ordering, it's
+// safe to apply ABSL_ATTRIBUTE_TRIVIAL_ABI if an object's location
+// can change over the course of its lifetime: if a constructor can be run one
+// place, and then the object magically teleports to another place where some
+// methods are run, and then the object teleports to yet another place where it
+// is destroyed. This is notably not true for self-referential types, where the
+// move-constructor must keep the self-reference up to date. If the type changed
+// location without invoking the move constructor, it would have a dangling
+// self-reference.
+//
+// The use of this teleporting machinery means that the number of paired
+// move/destroy operations can change, and so it is a bad idea to apply this to
+// a type meant to count the number of moves.
+//
+// Warning: applying this can, rarely, break callers. Objects passed by value
+// will be destroyed at the end of the call, instead of the end of the
+// full-expression containing the call. In addition, it changes the ABI
+// of functions accepting this type by value (e.g. to pass in registers).
+//
+// See also the upstream documentation:
+// https://clang.llvm.org/docs/AttributeReference.html#trivial-abi
+//
+#if ABSL_HAVE_CPP_ATTRIBUTE(clang::trivial_abi)
+#define ABSL_ATTRIBUTE_TRIVIAL_ABI [[clang::trivial_abi]]
+#define ABSL_HAVE_ATTRIBUTE_TRIVIAL_ABI 1
+#elif ABSL_HAVE_ATTRIBUTE(trivial_abi)
+#define ABSL_ATTRIBUTE_TRIVIAL_ABI __attribute__((trivial_abi))
+#define ABSL_HAVE_ATTRIBUTE_TRIVIAL_ABI 1
+#else
+#define ABSL_ATTRIBUTE_TRIVIAL_ABI
+#endif
+
+#endif  // ABSL_BASE_ATTRIBUTES_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/call_once.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/call_once.h
new file mode 100644
index 0000000000000000000000000000000000000000..96109f537cb9a0358ddeafaa532e6ce6fb2bcf40
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/call_once.h
@@ -0,0 +1,219 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: call_once.h
+// -----------------------------------------------------------------------------
+//
+// This header file provides an Abseil version of `std::call_once` for invoking
+// a given function at most once, across all threads. This Abseil version is
+// faster than the C++11 version and incorporates the C++17 argument-passing
+// fix, so that (for example) non-const references may be passed to the invoked
+// function.
+
+#ifndef ABSL_BASE_CALL_ONCE_H_
+#define ABSL_BASE_CALL_ONCE_H_
+
+#include <algorithm>
+#include <atomic>
+#include <cstdint>
+#include <type_traits>
+#include <utility>
+
+#include "absl/base/internal/invoke.h"
+#include "absl/base/internal/low_level_scheduling.h"
+#include "absl/base/internal/raw_logging.h"
+#include "absl/base/internal/scheduling_mode.h"
+#include "absl/base/internal/spinlock_wait.h"
+#include "absl/base/macros.h"
+#include "absl/base/optimization.h"
+#include "absl/base/port.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+class once_flag;
+
+namespace base_internal {
+std::atomic<uint32_t>* ControlWord(absl::once_flag* flag);
+}  // namespace base_internal
+
+// call_once()
+//
+// For all invocations using a given `once_flag`, invokes a given `fn` exactly
+// once across all threads. The first call to `call_once()` with a particular
+// `once_flag` argument (that does not throw an exception) will run the
+// specified function with the provided `args`; other calls with the same
+// `once_flag` argument will not run the function, but will wait
+// for the provided function to finish running (if it is still running).
+//
+// This mechanism provides a safe, simple, and fast mechanism for one-time
+// initialization in a multi-threaded process.
+//
+// Example:
+//
+// class MyInitClass {
+//  public:
+//  ...
+//  mutable absl::once_flag once_;
+//
+//  MyInitClass* init() const {
+//    absl::call_once(once_, &MyInitClass::Init, this);
+//    return ptr_;
+//  }
+//
+template <typename Callable, typename... Args>
+void call_once(absl::once_flag& flag, Callable&& fn, Args&&... args);
+
+// once_flag
+//
+// Objects of this type are used to distinguish calls to `call_once()` and
+// ensure the provided function is only invoked once across all threads. This
+// type is not copyable or movable. However, it has a `constexpr`
+// constructor, and is safe to use as a namespace-scoped global variable.
+class once_flag {
+ public:
+  constexpr once_flag() : control_(0) {}
+  once_flag(const once_flag&) = delete;
+  once_flag& operator=(const once_flag&) = delete;
+
+ private:
+  friend std::atomic<uint32_t>* base_internal::ControlWord(once_flag* flag);
+  std::atomic<uint32_t> control_;
+};
+
+//------------------------------------------------------------------------------
+// End of public interfaces.
+// Implementation details follow.
+//------------------------------------------------------------------------------
+
+namespace base_internal {
+
+// Like call_once, but uses KERNEL_ONLY scheduling. Intended to be used to
+// initialize entities used by the scheduler implementation.
+template <typename Callable, typename... Args>
+void LowLevelCallOnce(absl::once_flag* flag, Callable&& fn, Args&&... args);
+
+// Disables scheduling while on stack when scheduling mode is non-cooperative.
+// No effect for cooperative scheduling modes.
+class SchedulingHelper {
+ public:
+  explicit SchedulingHelper(base_internal::SchedulingMode mode) : mode_(mode) {
+    if (mode_ == base_internal::SCHEDULE_KERNEL_ONLY) {
+      guard_result_ = base_internal::SchedulingGuard::DisableRescheduling();
+    }
+  }
+
+  ~SchedulingHelper() {
+    if (mode_ == base_internal::SCHEDULE_KERNEL_ONLY) {
+      base_internal::SchedulingGuard::EnableRescheduling(guard_result_);
+    }
+  }
+
+ private:
+  base_internal::SchedulingMode mode_;
+  bool guard_result_;
+};
+
+// Bit patterns for call_once state machine values.  Internal implementation
+// detail, not for use by clients.
+//
+// The bit patterns are arbitrarily chosen from unlikely values, to aid in
+// debugging.  However, kOnceInit must be 0, so that a zero-initialized
+// once_flag will be valid for immediate use.
+enum {
+  kOnceInit = 0,
+  kOnceRunning = 0x65C2937B,
+  kOnceWaiter = 0x05A308D2,
+  // A very small constant is chosen for kOnceDone so that it fit in a single
+  // compare with immediate instruction for most common ISAs.  This is verified
+  // for x86, POWER and ARM.
+  kOnceDone = 221,    // Random Number
+};
+
+template <typename Callable, typename... Args>
+ABSL_ATTRIBUTE_NOINLINE
+void CallOnceImpl(std::atomic<uint32_t>* control,
+                  base_internal::SchedulingMode scheduling_mode, Callable&& fn,
+                  Args&&... args) {
+#ifndef NDEBUG
+  {
+    uint32_t old_control = control->load(std::memory_order_relaxed);
+    if (old_control != kOnceInit &&
+        old_control != kOnceRunning &&
+        old_control != kOnceWaiter &&
+        old_control != kOnceDone) {
+      ABSL_RAW_LOG(FATAL, "Unexpected value for control word: 0x%lx",
+                   static_cast<unsigned long>(old_control));  // NOLINT
+    }
+  }
+#endif  // NDEBUG
+  static const base_internal::SpinLockWaitTransition trans[] = {
+      {kOnceInit, kOnceRunning, true},
+      {kOnceRunning, kOnceWaiter, false},
+      {kOnceDone, kOnceDone, true}};
+
+  // Must do this before potentially modifying control word's state.
+  base_internal::SchedulingHelper maybe_disable_scheduling(scheduling_mode);
+  // Short circuit the simplest case to avoid procedure call overhead.
+  // The base_internal::SpinLockWait() call returns either kOnceInit or
+  // kOnceDone. If it returns kOnceDone, it must have loaded the control word
+  // with std::memory_order_acquire and seen a value of kOnceDone.
+  uint32_t old_control = kOnceInit;
+  if (control->compare_exchange_strong(old_control, kOnceRunning,
+                                       std::memory_order_relaxed) ||
+      base_internal::SpinLockWait(control, ABSL_ARRAYSIZE(trans), trans,
+                                  scheduling_mode) == kOnceInit) {
+    base_internal::invoke(std::forward<Callable>(fn),
+                          std::forward<Args>(args)...);
+    old_control =
+        control->exchange(base_internal::kOnceDone, std::memory_order_release);
+    if (old_control == base_internal::kOnceWaiter) {
+      base_internal::SpinLockWake(control, true);
+    }
+  }  // else *control is already kOnceDone
+}
+
+inline std::atomic<uint32_t>* ControlWord(once_flag* flag) {
+  return &flag->control_;
+}
+
+template <typename Callable, typename... Args>
+void LowLevelCallOnce(absl::once_flag* flag, Callable&& fn, Args&&... args) {
+  std::atomic<uint32_t>* once = base_internal::ControlWord(flag);
+  uint32_t s = once->load(std::memory_order_acquire);
+  if (ABSL_PREDICT_FALSE(s != base_internal::kOnceDone)) {
+    base_internal::CallOnceImpl(once, base_internal::SCHEDULE_KERNEL_ONLY,
+                                std::forward<Callable>(fn),
+                                std::forward<Args>(args)...);
+  }
+}
+
+}  // namespace base_internal
+
+template <typename Callable, typename... Args>
+void call_once(absl::once_flag& flag, Callable&& fn, Args&&... args) {
+  std::atomic<uint32_t>* once = base_internal::ControlWord(&flag);
+  uint32_t s = once->load(std::memory_order_acquire);
+  if (ABSL_PREDICT_FALSE(s != base_internal::kOnceDone)) {
+    base_internal::CallOnceImpl(
+        once, base_internal::SCHEDULE_COOPERATIVE_AND_KERNEL,
+        std::forward<Callable>(fn), std::forward<Args>(args)...);
+  }
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_CALL_ONCE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/casts.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/casts.h
new file mode 100644
index 0000000000000000000000000000000000000000..b99adb0699e4f0b42b0c51ab8342314d2e1a5e7b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/casts.h
@@ -0,0 +1,180 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: casts.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines casting templates to fit use cases not covered by
+// the standard casts provided in the C++ standard. As with all cast operations,
+// use these with caution and only if alternatives do not exist.
+
+#ifndef ABSL_BASE_CASTS_H_
+#define ABSL_BASE_CASTS_H_
+
+#include <cstring>
+#include <memory>
+#include <type_traits>
+#include <utility>
+
+#if defined(__cpp_lib_bit_cast) && __cpp_lib_bit_cast >= 201806L
+#include <bit>  // For std::bit_cast.
+#endif  // defined(__cpp_lib_bit_cast) && __cpp_lib_bit_cast >= 201806L
+
+#include "absl/base/internal/identity.h"
+#include "absl/base/macros.h"
+#include "absl/meta/type_traits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// implicit_cast()
+//
+// Performs an implicit conversion between types following the language
+// rules for implicit conversion; if an implicit conversion is otherwise
+// allowed by the language in the given context, this function performs such an
+// implicit conversion.
+//
+// Example:
+//
+//   // If the context allows implicit conversion:
+//   From from;
+//   To to = from;
+//
+//   // Such code can be replaced by:
+//   implicit_cast<To>(from);
+//
+// An `implicit_cast()` may also be used to annotate numeric type conversions
+// that, although safe, may produce compiler warnings (such as `long` to `int`).
+// Additionally, an `implicit_cast()` is also useful within return statements to
+// indicate a specific implicit conversion is being undertaken.
+//
+// Example:
+//
+//   return implicit_cast<double>(size_in_bytes) / capacity_;
+//
+// Annotating code with `implicit_cast()` allows you to explicitly select
+// particular overloads and template instantiations, while providing a safer
+// cast than `reinterpret_cast()` or `static_cast()`.
+//
+// Additionally, an `implicit_cast()` can be used to allow upcasting within a
+// type hierarchy where incorrect use of `static_cast()` could accidentally
+// allow downcasting.
+//
+// Finally, an `implicit_cast()` can be used to perform implicit conversions
+// from unrelated types that otherwise couldn't be implicitly cast directly;
+// C++ will normally only implicitly cast "one step" in such conversions.
+//
+// That is, if C is a type which can be implicitly converted to B, with B being
+// a type that can be implicitly converted to A, an `implicit_cast()` can be
+// used to convert C to B (which the compiler can then implicitly convert to A
+// using language rules).
+//
+// Example:
+//
+//   // Assume an object C is convertible to B, which is implicitly convertible
+//   // to A
+//   A a = implicit_cast<B>(C);
+//
+// Such implicit cast chaining may be useful within template logic.
+template <typename To>
+constexpr To implicit_cast(typename absl::internal::identity_t<To> to) {
+  return to;
+}
+
+// bit_cast()
+//
+// Creates a value of the new type `Dest` whose representation is the same as
+// that of the argument, which is of (deduced) type `Source` (a "bitwise cast";
+// every bit in the value representation of the result is equal to the
+// corresponding bit in the object representation of the source). Source and
+// destination types must be of the same size, and both types must be trivially
+// copyable.
+//
+// As with most casts, use with caution. A `bit_cast()` might be needed when you
+// need to treat a value as the value of some other type, for example, to access
+// the individual bits of an object which are not normally accessible through
+// the object's type, such as for working with the binary representation of a
+// floating point value:
+//
+//   float f = 3.14159265358979;
+//   int i = bit_cast<int>(f);
+//   // i = 0x40490fdb
+//
+// Reinterpreting and accessing a value directly as a different type (as shown
+// below) usually results in undefined behavior.
+//
+// Example:
+//
+//   // WRONG
+//   float f = 3.14159265358979;
+//   int i = reinterpret_cast<int&>(f);    // Wrong
+//   int j = *reinterpret_cast<int*>(&f);  // Equally wrong
+//   int k = *bit_cast<int*>(&f);          // Equally wrong
+//
+// Reinterpret-casting results in undefined behavior according to the ISO C++
+// specification, section [basic.lval]. Roughly, this section says: if an object
+// in memory has one type, and a program accesses it with a different type, the
+// result is undefined behavior for most "different type".
+//
+// Using bit_cast on a pointer and then dereferencing it is no better than using
+// reinterpret_cast. You should only use bit_cast on the value itself.
+//
+// Such casting results in type punning: holding an object in memory of one type
+// and reading its bits back using a different type. A `bit_cast()` avoids this
+// issue by copying the object representation to a new value, which avoids
+// introducing this undefined behavior (since the original value is never
+// accessed in the wrong way).
+//
+// The requirements of `absl::bit_cast` are more strict than that of
+// `std::bit_cast` unless compiler support is available. Specifically, without
+// compiler support, this implementation also requires `Dest` to be
+// default-constructible. In C++20, `absl::bit_cast` is replaced by
+// `std::bit_cast`.
+#if defined(__cpp_lib_bit_cast) && __cpp_lib_bit_cast >= 201806L
+
+using std::bit_cast;
+
+#else  // defined(__cpp_lib_bit_cast) && __cpp_lib_bit_cast >= 201806L
+
+template <typename Dest, typename Source,
+          typename std::enable_if<
+              sizeof(Dest) == sizeof(Source) &&
+                  type_traits_internal::is_trivially_copyable<Source>::value &&
+                  type_traits_internal::is_trivially_copyable<Dest>::value
+#if !ABSL_HAVE_BUILTIN(__builtin_bit_cast)
+                  && std::is_default_constructible<Dest>::value
+#endif  // !ABSL_HAVE_BUILTIN(__builtin_bit_cast)
+              ,
+              int>::type = 0>
+#if ABSL_HAVE_BUILTIN(__builtin_bit_cast)
+inline constexpr Dest bit_cast(const Source& source) {
+  return __builtin_bit_cast(Dest, source);
+}
+#else  // ABSL_HAVE_BUILTIN(__builtin_bit_cast)
+inline Dest bit_cast(const Source& source) {
+  Dest dest;
+  memcpy(static_cast<void*>(std::addressof(dest)),
+         static_cast<const void*>(std::addressof(source)), sizeof(dest));
+  return dest;
+}
+#endif  // ABSL_HAVE_BUILTIN(__builtin_bit_cast)
+
+#endif  // defined(__cpp_lib_bit_cast) && __cpp_lib_bit_cast >= 201806L
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_CASTS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/config.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/config.h
new file mode 100644
index 0000000000000000000000000000000000000000..0631ab60996f782e30dd6e833f4420b5e81a66e6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/config.h
@@ -0,0 +1,954 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: config.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines a set of macros for checking the presence of
+// important compiler and platform features. Such macros can be used to
+// produce portable code by parameterizing compilation based on the presence or
+// lack of a given feature.
+//
+// We define a "feature" as some interface we wish to program to: for example,
+// a library function or system call. A value of `1` indicates support for
+// that feature; any other value indicates the feature support is undefined.
+//
+// Example:
+//
+// Suppose a programmer wants to write a program that uses the 'mmap()' system
+// call. The Abseil macro for that feature (`ABSL_HAVE_MMAP`) allows you to
+// selectively include the `mmap.h` header and bracket code using that feature
+// in the macro:
+//
+//   #include "absl/base/config.h"
+//
+//   #ifdef ABSL_HAVE_MMAP
+//   #include "sys/mman.h"
+//   #endif  //ABSL_HAVE_MMAP
+//
+//   ...
+//   #ifdef ABSL_HAVE_MMAP
+//   void *ptr = mmap(...);
+//   ...
+//   #endif  // ABSL_HAVE_MMAP
+
+#ifndef ABSL_BASE_CONFIG_H_
+#define ABSL_BASE_CONFIG_H_
+
+// Included for the __GLIBC__ macro (or similar macros on other systems).
+#include <limits.h>
+
+#ifdef __cplusplus
+// Included for __GLIBCXX__, _LIBCPP_VERSION
+#include <cstddef>
+#endif  // __cplusplus
+
+// ABSL_INTERNAL_CPLUSPLUS_LANG
+//
+// MSVC does not set the value of __cplusplus correctly, but instead uses
+// _MSVC_LANG as a stand-in.
+// https://docs.microsoft.com/en-us/cpp/preprocessor/predefined-macros
+//
+// However, there are reports that MSVC even sets _MSVC_LANG incorrectly at
+// times, for example:
+// https://github.com/microsoft/vscode-cpptools/issues/1770
+// https://reviews.llvm.org/D70996
+//
+// For this reason, this symbol is considered INTERNAL and code outside of
+// Abseil must not use it.
+#if defined(_MSVC_LANG)
+#define ABSL_INTERNAL_CPLUSPLUS_LANG _MSVC_LANG
+#elif defined(__cplusplus)
+#define ABSL_INTERNAL_CPLUSPLUS_LANG __cplusplus
+#endif
+
+#if defined(__APPLE__)
+// Included for TARGET_OS_IPHONE, __IPHONE_OS_VERSION_MIN_REQUIRED,
+// __IPHONE_8_0.
+#include <Availability.h>
+#include <TargetConditionals.h>
+#endif
+
+#include "absl/base/options.h"
+#include "absl/base/policy_checks.h"
+
+// Abseil long-term support (LTS) releases will define
+// `ABSL_LTS_RELEASE_VERSION` to the integer representing the date string of the
+// LTS release version, and will define `ABSL_LTS_RELEASE_PATCH_LEVEL` to the
+// integer representing the patch-level for that release.
+//
+// For example, for LTS release version "20300401.2", this would give us
+// ABSL_LTS_RELEASE_VERSION == 20300401 && ABSL_LTS_RELEASE_PATCH_LEVEL == 2
+//
+// These symbols will not be defined in non-LTS code.
+//
+// Abseil recommends that clients live-at-head. Therefore, if you are using
+// these symbols to assert a minimum version requirement, we recommend you do it
+// as
+//
+// #if defined(ABSL_LTS_RELEASE_VERSION) && ABSL_LTS_RELEASE_VERSION < 20300401
+// #error Project foo requires Abseil LTS version >= 20300401
+// #endif
+//
+// The `defined(ABSL_LTS_RELEASE_VERSION)` part of the check excludes
+// live-at-head clients from the minimum version assertion.
+//
+// See https://abseil.io/about/releases for more information on Abseil release
+// management.
+//
+// LTS releases can be obtained from
+// https://github.com/abseil/abseil-cpp/releases.
+#define ABSL_LTS_RELEASE_VERSION 20230125
+#define ABSL_LTS_RELEASE_PATCH_LEVEL 2
+
+// Helper macro to convert a CPP variable to a string literal.
+#define ABSL_INTERNAL_DO_TOKEN_STR(x) #x
+#define ABSL_INTERNAL_TOKEN_STR(x) ABSL_INTERNAL_DO_TOKEN_STR(x)
+
+// -----------------------------------------------------------------------------
+// Abseil namespace annotations
+// -----------------------------------------------------------------------------
+
+// ABSL_NAMESPACE_BEGIN/ABSL_NAMESPACE_END
+//
+// An annotation placed at the beginning/end of each `namespace absl` scope.
+// This is used to inject an inline namespace.
+//
+// The proper way to write Abseil code in the `absl` namespace is:
+//
+// namespace absl {
+// ABSL_NAMESPACE_BEGIN
+//
+// void Foo();  // absl::Foo().
+//
+// ABSL_NAMESPACE_END
+// }  // namespace absl
+//
+// Users of Abseil should not use these macros, because users of Abseil should
+// not write `namespace absl {` in their own code for any reason.  (Abseil does
+// not support forward declarations of its own types, nor does it support
+// user-provided specialization of Abseil templates.  Code that violates these
+// rules may be broken without warning.)
+#if !defined(ABSL_OPTION_USE_INLINE_NAMESPACE) || \
+    !defined(ABSL_OPTION_INLINE_NAMESPACE_NAME)
+#error options.h is misconfigured.
+#endif
+
+// Check that ABSL_OPTION_INLINE_NAMESPACE_NAME is neither "head" nor ""
+#if defined(__cplusplus) && ABSL_OPTION_USE_INLINE_NAMESPACE == 1
+
+#define ABSL_INTERNAL_INLINE_NAMESPACE_STR \
+  ABSL_INTERNAL_TOKEN_STR(ABSL_OPTION_INLINE_NAMESPACE_NAME)
+
+static_assert(ABSL_INTERNAL_INLINE_NAMESPACE_STR[0] != '\0',
+              "options.h misconfigured: ABSL_OPTION_INLINE_NAMESPACE_NAME must "
+              "not be empty.");
+static_assert(ABSL_INTERNAL_INLINE_NAMESPACE_STR[0] != 'h' ||
+                  ABSL_INTERNAL_INLINE_NAMESPACE_STR[1] != 'e' ||
+                  ABSL_INTERNAL_INLINE_NAMESPACE_STR[2] != 'a' ||
+                  ABSL_INTERNAL_INLINE_NAMESPACE_STR[3] != 'd' ||
+                  ABSL_INTERNAL_INLINE_NAMESPACE_STR[4] != '\0',
+              "options.h misconfigured: ABSL_OPTION_INLINE_NAMESPACE_NAME must "
+              "be changed to a new, unique identifier name.");
+
+#endif
+
+#if ABSL_OPTION_USE_INLINE_NAMESPACE == 0
+#define ABSL_NAMESPACE_BEGIN
+#define ABSL_NAMESPACE_END
+#define ABSL_INTERNAL_C_SYMBOL(x) x
+#elif ABSL_OPTION_USE_INLINE_NAMESPACE == 1
+#define ABSL_NAMESPACE_BEGIN \
+  inline namespace ABSL_OPTION_INLINE_NAMESPACE_NAME {
+#define ABSL_NAMESPACE_END }
+#define ABSL_INTERNAL_C_SYMBOL_HELPER_2(x, v) x##_##v
+#define ABSL_INTERNAL_C_SYMBOL_HELPER_1(x, v) \
+  ABSL_INTERNAL_C_SYMBOL_HELPER_2(x, v)
+#define ABSL_INTERNAL_C_SYMBOL(x) \
+  ABSL_INTERNAL_C_SYMBOL_HELPER_1(x, ABSL_OPTION_INLINE_NAMESPACE_NAME)
+#else
+#error options.h is misconfigured.
+#endif
+
+// -----------------------------------------------------------------------------
+// Compiler Feature Checks
+// -----------------------------------------------------------------------------
+
+// ABSL_HAVE_BUILTIN()
+//
+// Checks whether the compiler supports a Clang Feature Checking Macro, and if
+// so, checks whether it supports the provided builtin function "x" where x
+// is one of the functions noted in
+// https://clang.llvm.org/docs/LanguageExtensions.html
+//
+// Note: Use this macro to avoid an extra level of #ifdef __has_builtin check.
+// http://releases.llvm.org/3.3/tools/clang/docs/LanguageExtensions.html
+#ifdef __has_builtin
+#define ABSL_HAVE_BUILTIN(x) __has_builtin(x)
+#else
+#define ABSL_HAVE_BUILTIN(x) 0
+#endif
+
+#ifdef __has_feature
+#define ABSL_HAVE_FEATURE(f) __has_feature(f)
+#else
+#define ABSL_HAVE_FEATURE(f) 0
+#endif
+
+// Portable check for GCC minimum version:
+// https://gcc.gnu.org/onlinedocs/cpp/Common-Predefined-Macros.html
+#if defined(__GNUC__) && defined(__GNUC_MINOR__)
+#define ABSL_INTERNAL_HAVE_MIN_GNUC_VERSION(x, y) \
+  (__GNUC__ > (x) || __GNUC__ == (x) && __GNUC_MINOR__ >= (y))
+#else
+#define ABSL_INTERNAL_HAVE_MIN_GNUC_VERSION(x, y) 0
+#endif
+
+#if defined(__clang__) && defined(__clang_major__) && defined(__clang_minor__)
+#define ABSL_INTERNAL_HAVE_MIN_CLANG_VERSION(x, y) \
+  (__clang_major__ > (x) || __clang_major__ == (x) && __clang_minor__ >= (y))
+#else
+#define ABSL_INTERNAL_HAVE_MIN_CLANG_VERSION(x, y) 0
+#endif
+
+// ABSL_HAVE_TLS is defined to 1 when __thread should be supported.
+// We assume __thread is supported on Linux or Asylo when compiled with Clang or
+// compiled against libstdc++ with _GLIBCXX_HAVE_TLS defined.
+#ifdef ABSL_HAVE_TLS
+#error ABSL_HAVE_TLS cannot be directly set
+#elif (defined(__linux__) || defined(__ASYLO__)) && \
+    (defined(__clang__) || defined(_GLIBCXX_HAVE_TLS))
+#define ABSL_HAVE_TLS 1
+#endif
+
+// ABSL_HAVE_STD_IS_TRIVIALLY_DESTRUCTIBLE
+//
+// Checks whether `std::is_trivially_destructible<T>` is supported.
+//
+// Notes: All supported compilers using libc++ support this feature, as does
+// gcc >= 4.8.1 using libstdc++, and Visual Studio.
+#ifdef ABSL_HAVE_STD_IS_TRIVIALLY_DESTRUCTIBLE
+#error ABSL_HAVE_STD_IS_TRIVIALLY_DESTRUCTIBLE cannot be directly set
+#elif defined(_LIBCPP_VERSION) || defined(_MSC_VER) || \
+    (defined(__clang__) && __clang_major__ >= 15) ||   \
+    (!defined(__clang__) && defined(__GLIBCXX__) &&    \
+     ABSL_INTERNAL_HAVE_MIN_GNUC_VERSION(4, 8))
+#define ABSL_HAVE_STD_IS_TRIVIALLY_DESTRUCTIBLE 1
+#endif
+
+// ABSL_HAVE_STD_IS_TRIVIALLY_CONSTRUCTIBLE
+//
+// Checks whether `std::is_trivially_default_constructible<T>` and
+// `std::is_trivially_copy_constructible<T>` are supported.
+
+// ABSL_HAVE_STD_IS_TRIVIALLY_ASSIGNABLE
+//
+// Checks whether `std::is_trivially_copy_assignable<T>` is supported.
+
+// Notes: Clang with libc++ supports these features, as does gcc >= 7.4 with
+// libstdc++, or gcc >= 8.2 with libc++, and Visual Studio (but not NVCC).
+#if defined(ABSL_HAVE_STD_IS_TRIVIALLY_CONSTRUCTIBLE)
+#error ABSL_HAVE_STD_IS_TRIVIALLY_CONSTRUCTIBLE cannot be directly set
+#elif defined(ABSL_HAVE_STD_IS_TRIVIALLY_ASSIGNABLE)
+#error ABSL_HAVE_STD_IS_TRIVIALLY_ASSIGNABLE cannot directly set
+#elif (defined(__clang__) && defined(_LIBCPP_VERSION)) ||                    \
+    (defined(__clang__) && __clang_major__ >= 15) ||                         \
+    (!defined(__clang__) &&                                                  \
+     ((ABSL_INTERNAL_HAVE_MIN_GNUC_VERSION(7, 4) && defined(__GLIBCXX__)) || \
+      (ABSL_INTERNAL_HAVE_MIN_GNUC_VERSION(8, 2) &&                          \
+       defined(_LIBCPP_VERSION)))) ||                                        \
+    (defined(_MSC_VER) && !defined(__NVCC__) && !defined(__clang__))
+#define ABSL_HAVE_STD_IS_TRIVIALLY_CONSTRUCTIBLE 1
+#define ABSL_HAVE_STD_IS_TRIVIALLY_ASSIGNABLE 1
+#endif
+
+// ABSL_HAVE_STD_IS_TRIVIALLY_COPYABLE
+//
+// Checks whether `std::is_trivially_copyable<T>` is supported.
+//
+// Notes: Clang 15+ with libc++ supports these features, GCC hasn't been tested.
+#if defined(ABSL_HAVE_STD_IS_TRIVIALLY_COPYABLE)
+#error ABSL_HAVE_STD_IS_TRIVIALLY_COPYABLE cannot be directly set
+#elif defined(__clang__) && (__clang_major__ >= 15)
+#define ABSL_HAVE_STD_IS_TRIVIALLY_COPYABLE 1
+#endif
+
+// ABSL_HAVE_THREAD_LOCAL
+//
+// Checks whether C++11's `thread_local` storage duration specifier is
+// supported.
+#ifdef ABSL_HAVE_THREAD_LOCAL
+#error ABSL_HAVE_THREAD_LOCAL cannot be directly set
+#elif defined(__APPLE__)
+// Notes:
+// * Xcode's clang did not support `thread_local` until version 8, and
+//   even then not for all iOS < 9.0.
+// * Xcode 9.3 started disallowing `thread_local` for 32-bit iOS simulator
+//   targeting iOS 9.x.
+// * Xcode 10 moves the deployment target check for iOS < 9.0 to link time
+//   making ABSL_HAVE_FEATURE unreliable there.
+//
+#if ABSL_HAVE_FEATURE(cxx_thread_local) && \
+    !(TARGET_OS_IPHONE && __IPHONE_OS_VERSION_MIN_REQUIRED < __IPHONE_9_0)
+#define ABSL_HAVE_THREAD_LOCAL 1
+#endif
+#else  // !defined(__APPLE__)
+#define ABSL_HAVE_THREAD_LOCAL 1
+#endif
+
+// There are platforms for which TLS should not be used even though the compiler
+// makes it seem like it's supported (Android NDK < r12b for example).
+// This is primarily because of linker problems and toolchain misconfiguration:
+// Abseil does not intend to support this indefinitely. Currently, the newest
+// toolchain that we intend to support that requires this behavior is the
+// r11 NDK - allowing for a 5 year support window on that means this option
+// is likely to be removed around June of 2021.
+// TLS isn't supported until NDK r12b per
+// https://developer.android.com/ndk/downloads/revision_history.html
+// Since NDK r16, `__NDK_MAJOR__` and `__NDK_MINOR__` are defined in
+// <android/ndk-version.h>. For NDK < r16, users should define these macros,
+// e.g. `-D__NDK_MAJOR__=11 -D__NKD_MINOR__=0` for NDK r11.
+#if defined(__ANDROID__) && defined(__clang__)
+#if __has_include(<android/ndk-version.h>)
+#include <android/ndk-version.h>
+#endif  // __has_include(<android/ndk-version.h>)
+#if defined(__ANDROID__) && defined(__clang__) && defined(__NDK_MAJOR__) && \
+    defined(__NDK_MINOR__) &&                                               \
+    ((__NDK_MAJOR__ < 12) || ((__NDK_MAJOR__ == 12) && (__NDK_MINOR__ < 1)))
+#undef ABSL_HAVE_TLS
+#undef ABSL_HAVE_THREAD_LOCAL
+#endif
+#endif  // defined(__ANDROID__) && defined(__clang__)
+
+// ABSL_HAVE_INTRINSIC_INT128
+//
+// Checks whether the __int128 compiler extension for a 128-bit integral type is
+// supported.
+//
+// Note: __SIZEOF_INT128__ is defined by Clang and GCC when __int128 is
+// supported, but we avoid using it in certain cases:
+// * On Clang:
+//   * Building using Clang for Windows, where the Clang runtime library has
+//     128-bit support only on LP64 architectures, but Windows is LLP64.
+// * On Nvidia's nvcc:
+//   * nvcc also defines __GNUC__ and __SIZEOF_INT128__, but not all versions
+//     actually support __int128.
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+#error ABSL_HAVE_INTRINSIC_INT128 cannot be directly set
+#elif defined(__SIZEOF_INT128__)
+#if (defined(__clang__) && !defined(_WIN32)) || \
+    (defined(__CUDACC__) && __CUDACC_VER_MAJOR__ >= 9) ||                \
+    (defined(__GNUC__) && !defined(__clang__) && !defined(__CUDACC__))
+#define ABSL_HAVE_INTRINSIC_INT128 1
+#elif defined(__CUDACC__)
+// __CUDACC_VER__ is a full version number before CUDA 9, and is defined to a
+// string explaining that it has been removed starting with CUDA 9. We use
+// nested #ifs because there is no short-circuiting in the preprocessor.
+// NOTE: `__CUDACC__` could be undefined while `__CUDACC_VER__` is defined.
+#if __CUDACC_VER__ >= 70000
+#define ABSL_HAVE_INTRINSIC_INT128 1
+#endif  // __CUDACC_VER__ >= 70000
+#endif  // defined(__CUDACC__)
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+
+// ABSL_HAVE_EXCEPTIONS
+//
+// Checks whether the compiler both supports and enables exceptions. Many
+// compilers support a "no exceptions" mode that disables exceptions.
+//
+// Generally, when ABSL_HAVE_EXCEPTIONS is not defined:
+//
+// * Code using `throw` and `try` may not compile.
+// * The `noexcept` specifier will still compile and behave as normal.
+// * The `noexcept` operator may still return `false`.
+//
+// For further details, consult the compiler's documentation.
+#ifdef ABSL_HAVE_EXCEPTIONS
+#error ABSL_HAVE_EXCEPTIONS cannot be directly set.
+#elif ABSL_INTERNAL_HAVE_MIN_CLANG_VERSION(3, 6)
+// Clang >= 3.6
+#if ABSL_HAVE_FEATURE(cxx_exceptions)
+#define ABSL_HAVE_EXCEPTIONS 1
+#endif  // ABSL_HAVE_FEATURE(cxx_exceptions)
+#elif defined(__clang__)
+// Clang < 3.6
+// http://releases.llvm.org/3.6.0/tools/clang/docs/ReleaseNotes.html#the-exceptions-macro
+#if defined(__EXCEPTIONS) && ABSL_HAVE_FEATURE(cxx_exceptions)
+#define ABSL_HAVE_EXCEPTIONS 1
+#endif  // defined(__EXCEPTIONS) && ABSL_HAVE_FEATURE(cxx_exceptions)
+// Handle remaining special cases and default to exceptions being supported.
+#elif !(defined(__GNUC__) && (__GNUC__ < 5) && !defined(__EXCEPTIONS)) && \
+    !(ABSL_INTERNAL_HAVE_MIN_GNUC_VERSION(5, 0) &&                        \
+      !defined(__cpp_exceptions)) &&                                      \
+    !(defined(_MSC_VER) && !defined(_CPPUNWIND))
+#define ABSL_HAVE_EXCEPTIONS 1
+#endif
+
+// -----------------------------------------------------------------------------
+// Platform Feature Checks
+// -----------------------------------------------------------------------------
+
+// Currently supported operating systems and associated preprocessor
+// symbols:
+//
+//   Linux and Linux-derived           __linux__
+//   Android                           __ANDROID__ (implies __linux__)
+//   Linux (non-Android)               __linux__ && !__ANDROID__
+//   Darwin (macOS and iOS)            __APPLE__
+//   Akaros (http://akaros.org)        __ros__
+//   Windows                           _WIN32
+//   NaCL                              __native_client__
+//   AsmJS                             __asmjs__
+//   WebAssembly                       __wasm__
+//   Fuchsia                           __Fuchsia__
+//
+// Note that since Android defines both __ANDROID__ and __linux__, one
+// may probe for either Linux or Android by simply testing for __linux__.
+
+// ABSL_HAVE_MMAP
+//
+// Checks whether the platform has an mmap(2) implementation as defined in
+// POSIX.1-2001.
+#ifdef ABSL_HAVE_MMAP
+#error ABSL_HAVE_MMAP cannot be directly set
+#elif defined(__linux__) || defined(__APPLE__) || defined(__FreeBSD__) || \
+    defined(_AIX) || defined(__ros__) || defined(__native_client__) ||    \
+    defined(__asmjs__) || defined(__wasm__) || defined(__Fuchsia__) ||    \
+    defined(__sun) || defined(__ASYLO__) || defined(__myriad2__) ||       \
+    defined(__HAIKU__) || defined(__OpenBSD__) || defined(__NetBSD__) ||  \
+    defined(__QNX__)
+#define ABSL_HAVE_MMAP 1
+#endif
+
+// ABSL_HAVE_PTHREAD_GETSCHEDPARAM
+//
+// Checks whether the platform implements the pthread_(get|set)schedparam(3)
+// functions as defined in POSIX.1-2001.
+#ifdef ABSL_HAVE_PTHREAD_GETSCHEDPARAM
+#error ABSL_HAVE_PTHREAD_GETSCHEDPARAM cannot be directly set
+#elif defined(__linux__) || defined(__APPLE__) || defined(__FreeBSD__) || \
+    defined(_AIX) || defined(__ros__) || defined(__OpenBSD__) ||          \
+    defined(__NetBSD__)
+#define ABSL_HAVE_PTHREAD_GETSCHEDPARAM 1
+#endif
+
+// ABSL_HAVE_SCHED_GETCPU
+//
+// Checks whether sched_getcpu is available.
+#ifdef ABSL_HAVE_SCHED_GETCPU
+#error ABSL_HAVE_SCHED_GETCPU cannot be directly set
+#elif defined(__linux__)
+#define ABSL_HAVE_SCHED_GETCPU 1
+#endif
+
+// ABSL_HAVE_SCHED_YIELD
+//
+// Checks whether the platform implements sched_yield(2) as defined in
+// POSIX.1-2001.
+#ifdef ABSL_HAVE_SCHED_YIELD
+#error ABSL_HAVE_SCHED_YIELD cannot be directly set
+#elif defined(__linux__) || defined(__ros__) || defined(__native_client__)
+#define ABSL_HAVE_SCHED_YIELD 1
+#endif
+
+// ABSL_HAVE_SEMAPHORE_H
+//
+// Checks whether the platform supports the <semaphore.h> header and sem_init(3)
+// family of functions as standardized in POSIX.1-2001.
+//
+// Note: While Apple provides <semaphore.h> for both iOS and macOS, it is
+// explicitly deprecated and will cause build failures if enabled for those
+// platforms.  We side-step the issue by not defining it here for Apple
+// platforms.
+#ifdef ABSL_HAVE_SEMAPHORE_H
+#error ABSL_HAVE_SEMAPHORE_H cannot be directly set
+#elif defined(__linux__) || defined(__ros__)
+#define ABSL_HAVE_SEMAPHORE_H 1
+#endif
+
+// ABSL_HAVE_ALARM
+//
+// Checks whether the platform supports the <signal.h> header and alarm(2)
+// function as standardized in POSIX.1-2001.
+#ifdef ABSL_HAVE_ALARM
+#error ABSL_HAVE_ALARM cannot be directly set
+#elif defined(__GOOGLE_GRTE_VERSION__)
+// feature tests for Google's GRTE
+#define ABSL_HAVE_ALARM 1
+#elif defined(__GLIBC__)
+// feature test for glibc
+#define ABSL_HAVE_ALARM 1
+#elif defined(_MSC_VER)
+// feature tests for Microsoft's library
+#elif defined(__MINGW32__)
+// mingw32 doesn't provide alarm(2):
+// https://osdn.net/projects/mingw/scm/git/mingw-org-wsl/blobs/5.2-trunk/mingwrt/include/unistd.h
+// mingw-w64 provides a no-op implementation:
+// https://sourceforge.net/p/mingw-w64/mingw-w64/ci/master/tree/mingw-w64-crt/misc/alarm.c
+#elif defined(__EMSCRIPTEN__)
+// emscripten doesn't support signals
+#elif defined(__Fuchsia__)
+// Signals don't exist on fuchsia.
+#elif defined(__native_client__)
+#else
+// other standard libraries
+#define ABSL_HAVE_ALARM 1
+#endif
+
+// ABSL_IS_LITTLE_ENDIAN
+// ABSL_IS_BIG_ENDIAN
+//
+// Checks the endianness of the platform.
+//
+// Notes: uses the built in endian macros provided by GCC (since 4.6) and
+// Clang (since 3.2); see
+// https://gcc.gnu.org/onlinedocs/cpp/Common-Predefined-Macros.html.
+// Otherwise, if _WIN32, assume little endian. Otherwise, bail with an error.
+#if defined(ABSL_IS_BIG_ENDIAN)
+#error "ABSL_IS_BIG_ENDIAN cannot be directly set."
+#endif
+#if defined(ABSL_IS_LITTLE_ENDIAN)
+#error "ABSL_IS_LITTLE_ENDIAN cannot be directly set."
+#endif
+
+#if (defined(__BYTE_ORDER__) && defined(__ORDER_LITTLE_ENDIAN__) && \
+     __BYTE_ORDER__ == __ORDER_LITTLE_ENDIAN__)
+#define ABSL_IS_LITTLE_ENDIAN 1
+#elif defined(__BYTE_ORDER__) && defined(__ORDER_BIG_ENDIAN__) && \
+    __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__
+#define ABSL_IS_BIG_ENDIAN 1
+#elif defined(_WIN32)
+#define ABSL_IS_LITTLE_ENDIAN 1
+#else
+#error "absl endian detection needs to be set up for your compiler"
+#endif
+
+// macOS < 10.13 and iOS < 11 don't let you use <any>, <optional>, or <variant>
+// even though the headers exist and are publicly noted to work, because the
+// libc++ shared library shipped on the system doesn't have the requisite
+// exported symbols.  See https://github.com/abseil/abseil-cpp/issues/207 and
+// https://developer.apple.com/documentation/xcode_release_notes/xcode_10_release_notes
+//
+// libc++ spells out the availability requirements in the file
+// llvm-project/libcxx/include/__config via the #define
+// _LIBCPP_AVAILABILITY_BAD_OPTIONAL_ACCESS.
+//
+// Unfortunately, Apple initially mis-stated the requirements as macOS < 10.14
+// and iOS < 12 in the libc++ headers. This was corrected by
+// https://github.com/llvm/llvm-project/commit/7fb40e1569dd66292b647f4501b85517e9247953
+// which subsequently made it into the XCode 12.5 release. We need to match the
+// old (incorrect) conditions when built with old XCode, but can use the
+// corrected earlier versions with new XCode.
+#if defined(__APPLE__) && defined(_LIBCPP_VERSION) &&               \
+    ((_LIBCPP_VERSION >= 11000 && /* XCode 12.5 or later: */        \
+      ((defined(__ENVIRONMENT_MAC_OS_X_VERSION_MIN_REQUIRED__) &&   \
+        __ENVIRONMENT_MAC_OS_X_VERSION_MIN_REQUIRED__ < 101300) ||  \
+       (defined(__ENVIRONMENT_IPHONE_OS_VERSION_MIN_REQUIRED__) &&  \
+        __ENVIRONMENT_IPHONE_OS_VERSION_MIN_REQUIRED__ < 110000) || \
+       (defined(__ENVIRONMENT_WATCH_OS_VERSION_MIN_REQUIRED__) &&   \
+        __ENVIRONMENT_WATCH_OS_VERSION_MIN_REQUIRED__ < 40000) ||   \
+       (defined(__ENVIRONMENT_TV_OS_VERSION_MIN_REQUIRED__) &&      \
+        __ENVIRONMENT_TV_OS_VERSION_MIN_REQUIRED__ < 110000))) ||   \
+     (_LIBCPP_VERSION < 11000 && /* Pre-XCode 12.5: */              \
+      ((defined(__ENVIRONMENT_MAC_OS_X_VERSION_MIN_REQUIRED__) &&   \
+        __ENVIRONMENT_MAC_OS_X_VERSION_MIN_REQUIRED__ < 101400) ||  \
+       (defined(__ENVIRONMENT_IPHONE_OS_VERSION_MIN_REQUIRED__) &&  \
+        __ENVIRONMENT_IPHONE_OS_VERSION_MIN_REQUIRED__ < 120000) || \
+       (defined(__ENVIRONMENT_WATCH_OS_VERSION_MIN_REQUIRED__) &&   \
+        __ENVIRONMENT_WATCH_OS_VERSION_MIN_REQUIRED__ < 50000) ||   \
+       (defined(__ENVIRONMENT_TV_OS_VERSION_MIN_REQUIRED__) &&      \
+        __ENVIRONMENT_TV_OS_VERSION_MIN_REQUIRED__ < 120000))))
+#define ABSL_INTERNAL_APPLE_CXX17_TYPES_UNAVAILABLE 1
+#else
+#define ABSL_INTERNAL_APPLE_CXX17_TYPES_UNAVAILABLE 0
+#endif
+
+// ABSL_HAVE_STD_ANY
+//
+// Checks whether C++17 std::any is available by checking whether <any> exists.
+#ifdef ABSL_HAVE_STD_ANY
+#error "ABSL_HAVE_STD_ANY cannot be directly set."
+#endif
+
+#ifdef __has_include
+#if __has_include(<any>) && defined(__cplusplus) && __cplusplus >= 201703L && \
+    !ABSL_INTERNAL_APPLE_CXX17_TYPES_UNAVAILABLE
+#define ABSL_HAVE_STD_ANY 1
+#endif
+#endif
+
+// ABSL_HAVE_STD_OPTIONAL
+//
+// Checks whether C++17 std::optional is available.
+#ifdef ABSL_HAVE_STD_OPTIONAL
+#error "ABSL_HAVE_STD_OPTIONAL cannot be directly set."
+#endif
+
+#ifdef __has_include
+#if __has_include(<optional>) && defined(__cplusplus) && \
+    __cplusplus >= 201703L && !ABSL_INTERNAL_APPLE_CXX17_TYPES_UNAVAILABLE
+#define ABSL_HAVE_STD_OPTIONAL 1
+#endif
+#endif
+
+// ABSL_HAVE_STD_VARIANT
+//
+// Checks whether C++17 std::variant is available.
+#ifdef ABSL_HAVE_STD_VARIANT
+#error "ABSL_HAVE_STD_VARIANT cannot be directly set."
+#endif
+
+#ifdef __has_include
+#if __has_include(<variant>) && defined(__cplusplus) && \
+    __cplusplus >= 201703L && !ABSL_INTERNAL_APPLE_CXX17_TYPES_UNAVAILABLE
+#define ABSL_HAVE_STD_VARIANT 1
+#endif
+#endif
+
+// ABSL_HAVE_STD_STRING_VIEW
+//
+// Checks whether C++17 std::string_view is available.
+#ifdef ABSL_HAVE_STD_STRING_VIEW
+#error "ABSL_HAVE_STD_STRING_VIEW cannot be directly set."
+#endif
+
+#ifdef __has_include
+#if __has_include(<string_view>) && defined(__cplusplus) && \
+    __cplusplus >= 201703L
+#define ABSL_HAVE_STD_STRING_VIEW 1
+#endif
+#endif
+
+// For MSVC, `__has_include` is supported in VS 2017 15.3, which is later than
+// the support for <optional>, <any>, <string_view>, <variant>. So we use
+// _MSC_VER to check whether we have VS 2017 RTM (when <optional>, <any>,
+// <string_view>, <variant> is implemented) or higher. Also, `__cplusplus` is
+// not correctly set by MSVC, so we use `_MSVC_LANG` to check the language
+// version.
+// TODO(zhangxy): fix tests before enabling aliasing for `std::any`.
+#if defined(_MSC_VER) && _MSC_VER >= 1910 &&         \
+    ((defined(_MSVC_LANG) && _MSVC_LANG > 201402) || \
+     (defined(__cplusplus) && __cplusplus > 201402))
+// #define ABSL_HAVE_STD_ANY 1
+#define ABSL_HAVE_STD_OPTIONAL 1
+#define ABSL_HAVE_STD_VARIANT 1
+#define ABSL_HAVE_STD_STRING_VIEW 1
+#endif
+
+// ABSL_USES_STD_ANY
+//
+// Indicates whether absl::any is an alias for std::any.
+#if !defined(ABSL_OPTION_USE_STD_ANY)
+#error options.h is misconfigured.
+#elif ABSL_OPTION_USE_STD_ANY == 0 || \
+    (ABSL_OPTION_USE_STD_ANY == 2 && !defined(ABSL_HAVE_STD_ANY))
+#undef ABSL_USES_STD_ANY
+#elif ABSL_OPTION_USE_STD_ANY == 1 || \
+    (ABSL_OPTION_USE_STD_ANY == 2 && defined(ABSL_HAVE_STD_ANY))
+#define ABSL_USES_STD_ANY 1
+#else
+#error options.h is misconfigured.
+#endif
+
+// ABSL_USES_STD_OPTIONAL
+//
+// Indicates whether absl::optional is an alias for std::optional.
+#if !defined(ABSL_OPTION_USE_STD_OPTIONAL)
+#error options.h is misconfigured.
+#elif ABSL_OPTION_USE_STD_OPTIONAL == 0 || \
+    (ABSL_OPTION_USE_STD_OPTIONAL == 2 && !defined(ABSL_HAVE_STD_OPTIONAL))
+#undef ABSL_USES_STD_OPTIONAL
+#elif ABSL_OPTION_USE_STD_OPTIONAL == 1 || \
+    (ABSL_OPTION_USE_STD_OPTIONAL == 2 && defined(ABSL_HAVE_STD_OPTIONAL))
+#define ABSL_USES_STD_OPTIONAL 1
+#else
+#error options.h is misconfigured.
+#endif
+
+// ABSL_USES_STD_VARIANT
+//
+// Indicates whether absl::variant is an alias for std::variant.
+#if !defined(ABSL_OPTION_USE_STD_VARIANT)
+#error options.h is misconfigured.
+#elif ABSL_OPTION_USE_STD_VARIANT == 0 || \
+    (ABSL_OPTION_USE_STD_VARIANT == 2 && !defined(ABSL_HAVE_STD_VARIANT))
+#undef ABSL_USES_STD_VARIANT
+#elif ABSL_OPTION_USE_STD_VARIANT == 1 || \
+    (ABSL_OPTION_USE_STD_VARIANT == 2 && defined(ABSL_HAVE_STD_VARIANT))
+#define ABSL_USES_STD_VARIANT 1
+#else
+#error options.h is misconfigured.
+#endif
+
+// ABSL_USES_STD_STRING_VIEW
+//
+// Indicates whether absl::string_view is an alias for std::string_view.
+#if !defined(ABSL_OPTION_USE_STD_STRING_VIEW)
+#error options.h is misconfigured.
+#elif ABSL_OPTION_USE_STD_STRING_VIEW == 0 || \
+    (ABSL_OPTION_USE_STD_STRING_VIEW == 2 &&  \
+     !defined(ABSL_HAVE_STD_STRING_VIEW))
+#undef ABSL_USES_STD_STRING_VIEW
+#elif ABSL_OPTION_USE_STD_STRING_VIEW == 1 || \
+    (ABSL_OPTION_USE_STD_STRING_VIEW == 2 &&  \
+     defined(ABSL_HAVE_STD_STRING_VIEW))
+#define ABSL_USES_STD_STRING_VIEW 1
+#else
+#error options.h is misconfigured.
+#endif
+
+// In debug mode, MSVC 2017's std::variant throws a EXCEPTION_ACCESS_VIOLATION
+// SEH exception from emplace for variant<SomeStruct> when constructing the
+// struct can throw. This defeats some of variant_test and
+// variant_exception_safety_test.
+#if defined(_MSC_VER) && _MSC_VER >= 1700 && defined(_DEBUG)
+#define ABSL_INTERNAL_MSVC_2017_DBG_MODE
+#endif
+
+// ABSL_INTERNAL_MANGLED_NS
+// ABSL_INTERNAL_MANGLED_BACKREFERENCE
+//
+// Internal macros for building up mangled names in our internal fork of CCTZ.
+// This implementation detail is only needed and provided for the MSVC build.
+//
+// These macros both expand to string literals.  ABSL_INTERNAL_MANGLED_NS is
+// the mangled spelling of the `absl` namespace, and
+// ABSL_INTERNAL_MANGLED_BACKREFERENCE is a back-reference integer representing
+// the proper count to skip past the CCTZ fork namespace names.  (This number
+// is one larger when there is an inline namespace name to skip.)
+#if defined(_MSC_VER)
+#if ABSL_OPTION_USE_INLINE_NAMESPACE == 0
+#define ABSL_INTERNAL_MANGLED_NS "absl"
+#define ABSL_INTERNAL_MANGLED_BACKREFERENCE "5"
+#else
+#define ABSL_INTERNAL_MANGLED_NS \
+  ABSL_INTERNAL_TOKEN_STR(ABSL_OPTION_INLINE_NAMESPACE_NAME) "@absl"
+#define ABSL_INTERNAL_MANGLED_BACKREFERENCE "6"
+#endif
+#endif
+
+// ABSL_DLL
+//
+// When building Abseil as a DLL, this macro expands to `__declspec(dllexport)`
+// so we can annotate symbols appropriately as being exported. When used in
+// headers consuming a DLL, this macro expands to `__declspec(dllimport)` so
+// that consumers know the symbol is defined inside the DLL. In all other cases,
+// the macro expands to nothing.
+#if defined(_MSC_VER)
+#if defined(ABSL_BUILD_DLL)
+#define ABSL_DLL __declspec(dllexport)
+#elif defined(ABSL_CONSUME_DLL)
+#define ABSL_DLL __declspec(dllimport)
+#else
+#define ABSL_DLL
+#endif
+#else
+#define ABSL_DLL
+#endif  // defined(_MSC_VER)
+
+#if defined(_MSC_VER)
+#if defined(ABSL_BUILD_TEST_DLL)
+#define ABSL_TEST_DLL __declspec(dllexport)
+#elif defined(ABSL_CONSUME_TEST_DLL)
+#define ABSL_TEST_DLL __declspec(dllimport)
+#else
+#define ABSL_TEST_DLL
+#endif
+#else
+#define ABSL_TEST_DLL
+#endif  // defined(_MSC_VER)
+
+// ABSL_HAVE_MEMORY_SANITIZER
+//
+// MemorySanitizer (MSan) is a detector of uninitialized reads. It consists of
+// a compiler instrumentation module and a run-time library.
+#ifdef ABSL_HAVE_MEMORY_SANITIZER
+#error "ABSL_HAVE_MEMORY_SANITIZER cannot be directly set."
+#elif !defined(__native_client__) && ABSL_HAVE_FEATURE(memory_sanitizer)
+#define ABSL_HAVE_MEMORY_SANITIZER 1
+#endif
+
+// ABSL_HAVE_THREAD_SANITIZER
+//
+// ThreadSanitizer (TSan) is a fast data race detector.
+#ifdef ABSL_HAVE_THREAD_SANITIZER
+#error "ABSL_HAVE_THREAD_SANITIZER cannot be directly set."
+#elif defined(__SANITIZE_THREAD__)
+#define ABSL_HAVE_THREAD_SANITIZER 1
+#elif ABSL_HAVE_FEATURE(thread_sanitizer)
+#define ABSL_HAVE_THREAD_SANITIZER 1
+#endif
+
+// ABSL_HAVE_ADDRESS_SANITIZER
+//
+// AddressSanitizer (ASan) is a fast memory error detector.
+#ifdef ABSL_HAVE_ADDRESS_SANITIZER
+#error "ABSL_HAVE_ADDRESS_SANITIZER cannot be directly set."
+#elif defined(__SANITIZE_ADDRESS__)
+#define ABSL_HAVE_ADDRESS_SANITIZER 1
+#elif ABSL_HAVE_FEATURE(address_sanitizer)
+#define ABSL_HAVE_ADDRESS_SANITIZER 1
+#endif
+
+// ABSL_HAVE_HWADDRESS_SANITIZER
+//
+// Hardware-Assisted AddressSanitizer (or HWASAN) is even faster than asan
+// memory error detector which can use CPU features like ARM TBI, Intel LAM or
+// AMD UAI.
+#ifdef ABSL_HAVE_HWADDRESS_SANITIZER
+#error "ABSL_HAVE_HWADDRESS_SANITIZER cannot be directly set."
+#elif defined(__SANITIZE_HWADDRESS__)
+#define ABSL_HAVE_HWADDRESS_SANITIZER 1
+#elif ABSL_HAVE_FEATURE(hwaddress_sanitizer)
+#define ABSL_HAVE_HWADDRESS_SANITIZER 1
+#endif
+
+// ABSL_HAVE_LEAK_SANITIZER
+//
+// LeakSanitizer (or lsan) is a detector of memory leaks.
+// https://clang.llvm.org/docs/LeakSanitizer.html
+// https://github.com/google/sanitizers/wiki/AddressSanitizerLeakSanitizer
+//
+// The macro ABSL_HAVE_LEAK_SANITIZER can be used to detect at compile-time
+// whether the LeakSanitizer is potentially available. However, just because the
+// LeakSanitizer is available does not mean it is active. Use the
+// always-available run-time interface in //absl/debugging/leak_check.h for
+// interacting with LeakSanitizer.
+#ifdef ABSL_HAVE_LEAK_SANITIZER
+#error "ABSL_HAVE_LEAK_SANITIZER cannot be directly set."
+#elif defined(LEAK_SANITIZER)
+// GCC provides no method for detecting the presense of the standalone
+// LeakSanitizer (-fsanitize=leak), so GCC users of -fsanitize=leak should also
+// use -DLEAK_SANITIZER.
+#define ABSL_HAVE_LEAK_SANITIZER 1
+// Clang standalone LeakSanitizer (-fsanitize=leak)
+#elif ABSL_HAVE_FEATURE(leak_sanitizer)
+#define ABSL_HAVE_LEAK_SANITIZER 1
+#elif defined(ABSL_HAVE_ADDRESS_SANITIZER)
+// GCC or Clang using the LeakSanitizer integrated into AddressSanitizer.
+#define ABSL_HAVE_LEAK_SANITIZER 1
+#endif
+
+// ABSL_HAVE_CLASS_TEMPLATE_ARGUMENT_DEDUCTION
+//
+// Class template argument deduction is a language feature added in C++17.
+#ifdef ABSL_HAVE_CLASS_TEMPLATE_ARGUMENT_DEDUCTION
+#error "ABSL_HAVE_CLASS_TEMPLATE_ARGUMENT_DEDUCTION cannot be directly set."
+#elif defined(__cpp_deduction_guides)
+#define ABSL_HAVE_CLASS_TEMPLATE_ARGUMENT_DEDUCTION 1
+#endif
+
+// ABSL_INTERNAL_NEED_REDUNDANT_CONSTEXPR_DECL
+//
+// Prior to C++17, static constexpr variables defined in classes required a
+// separate definition outside of the class body, for example:
+//
+// class Foo {
+//   static constexpr int kBar = 0;
+// };
+// constexpr int Foo::kBar;
+//
+// In C++17, these variables defined in classes are considered inline variables,
+// and the extra declaration is redundant. Since some compilers warn on the
+// extra declarations, ABSL_INTERNAL_NEED_REDUNDANT_CONSTEXPR_DECL can be used
+// conditionally ignore them:
+//
+// #ifdef ABSL_INTERNAL_NEED_REDUNDANT_CONSTEXPR_DECL
+// constexpr int Foo::kBar;
+// #endif
+#if defined(ABSL_INTERNAL_CPLUSPLUS_LANG) && \
+    ABSL_INTERNAL_CPLUSPLUS_LANG < 201703L
+#define ABSL_INTERNAL_NEED_REDUNDANT_CONSTEXPR_DECL 1
+#endif
+
+// `ABSL_INTERNAL_HAS_RTTI` determines whether abseil is being compiled with
+// RTTI support.
+#ifdef ABSL_INTERNAL_HAS_RTTI
+#error ABSL_INTERNAL_HAS_RTTI cannot be directly set
+#elif !defined(__GNUC__) || defined(__GXX_RTTI)
+#define ABSL_INTERNAL_HAS_RTTI 1
+#endif  // !defined(__GNUC__) || defined(__GXX_RTTI)
+
+// ABSL_INTERNAL_HAVE_SSE is used for compile-time detection of SSE support.
+// See https://gcc.gnu.org/onlinedocs/gcc/x86-Options.html for an overview of
+// which architectures support the various x86 instruction sets.
+#ifdef ABSL_INTERNAL_HAVE_SSE
+#error ABSL_INTERNAL_HAVE_SSE cannot be directly set
+#elif defined(__SSE__)
+#define ABSL_INTERNAL_HAVE_SSE 1
+#elif defined(_M_X64) || (defined(_M_IX86_FP) && _M_IX86_FP >= 1)
+// MSVC only defines _M_IX86_FP for x86 32-bit code, and _M_IX86_FP >= 1
+// indicates that at least SSE was targeted with the /arch:SSE option.
+// All x86-64 processors support SSE, so support can be assumed.
+// https://docs.microsoft.com/en-us/cpp/preprocessor/predefined-macros
+#define ABSL_INTERNAL_HAVE_SSE 1
+#endif
+
+// ABSL_INTERNAL_HAVE_SSE2 is used for compile-time detection of SSE2 support.
+// See https://gcc.gnu.org/onlinedocs/gcc/x86-Options.html for an overview of
+// which architectures support the various x86 instruction sets.
+#ifdef ABSL_INTERNAL_HAVE_SSE2
+#error ABSL_INTERNAL_HAVE_SSE2 cannot be directly set
+#elif defined(__SSE2__)
+#define ABSL_INTERNAL_HAVE_SSE2 1
+#elif defined(_M_X64) || (defined(_M_IX86_FP) && _M_IX86_FP >= 2)
+// MSVC only defines _M_IX86_FP for x86 32-bit code, and _M_IX86_FP >= 2
+// indicates that at least SSE2 was targeted with the /arch:SSE2 option.
+// All x86-64 processors support SSE2, so support can be assumed.
+// https://docs.microsoft.com/en-us/cpp/preprocessor/predefined-macros
+#define ABSL_INTERNAL_HAVE_SSE2 1
+#endif
+
+// ABSL_INTERNAL_HAVE_SSSE3 is used for compile-time detection of SSSE3 support.
+// See https://gcc.gnu.org/onlinedocs/gcc/x86-Options.html for an overview of
+// which architectures support the various x86 instruction sets.
+//
+// MSVC does not have a mode that targets SSSE3 at compile-time. To use SSSE3
+// with MSVC requires either assuming that the code will only every run on CPUs
+// that support SSSE3, otherwise __cpuid() can be used to detect support at
+// runtime and fallback to a non-SSSE3 implementation when SSSE3 is unsupported
+// by the CPU.
+#ifdef ABSL_INTERNAL_HAVE_SSSE3
+#error ABSL_INTERNAL_HAVE_SSSE3 cannot be directly set
+#elif defined(__SSSE3__)
+#define ABSL_INTERNAL_HAVE_SSSE3 1
+#endif
+
+// ABSL_INTERNAL_HAVE_ARM_NEON is used for compile-time detection of NEON (ARM
+// SIMD).
+//
+// If __CUDA_ARCH__ is defined, then we are compiling CUDA code in device mode.
+// In device mode, NEON intrinsics are not available, regardless of host
+// platform.
+// https://llvm.org/docs/CompileCudaWithLLVM.html#detecting-clang-vs-nvcc-from-code
+#ifdef ABSL_INTERNAL_HAVE_ARM_NEON
+#error ABSL_INTERNAL_HAVE_ARM_NEON cannot be directly set
+#elif defined(__ARM_NEON) && !defined(__CUDA_ARCH__)
+#define ABSL_INTERNAL_HAVE_ARM_NEON 1
+#endif
+
+// ABSL_HAVE_CONSTANT_EVALUATED is used for compile-time detection of
+// constant evaluation support through `absl::is_constant_evaluated`.
+#ifdef ABSL_HAVE_CONSTANT_EVALUATED
+#error ABSL_HAVE_CONSTANT_EVALUATED cannot be directly set
+#endif
+#ifdef __cpp_lib_is_constant_evaluated
+#define ABSL_HAVE_CONSTANT_EVALUATED 1
+#elif ABSL_HAVE_BUILTIN(__builtin_is_constant_evaluated)
+#define ABSL_HAVE_CONSTANT_EVALUATED 1
+#endif
+
+#endif  // ABSL_BASE_CONFIG_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/const_init.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/const_init.h
new file mode 100644
index 0000000000000000000000000000000000000000..16520b61d95b6ee0cb60e66ae7c7be4aeb40aa3d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/const_init.h
@@ -0,0 +1,76 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// kConstInit
+// -----------------------------------------------------------------------------
+//
+// A constructor tag used to mark an object as safe for use as a global
+// variable, avoiding the usual lifetime issues that can affect globals.
+
+#ifndef ABSL_BASE_CONST_INIT_H_
+#define ABSL_BASE_CONST_INIT_H_
+
+#include "absl/base/config.h"
+
+// In general, objects with static storage duration (such as global variables)
+// can trigger tricky object lifetime situations.  Attempting to access them
+// from the constructors or destructors of other global objects can result in
+// undefined behavior, unless their constructors and destructors are designed
+// with this issue in mind.
+//
+// The normal way to deal with this issue in C++11 is to use constant
+// initialization and trivial destructors.
+//
+// Constant initialization is guaranteed to occur before any other code
+// executes.  Constructors that are declared 'constexpr' are eligible for
+// constant initialization.  You can annotate a variable declaration with the
+// ABSL_CONST_INIT macro to express this intent.  For compilers that support
+// it, this annotation will cause a compilation error for declarations that
+// aren't subject to constant initialization (perhaps because a runtime value
+// was passed as a constructor argument).
+//
+// On program shutdown, lifetime issues can be avoided on global objects by
+// ensuring that they contain  trivial destructors.  A class has a trivial
+// destructor unless it has a user-defined destructor, a virtual method or base
+// class, or a data member or base class with a non-trivial destructor of its
+// own.  Objects with static storage duration and a trivial destructor are not
+// cleaned up on program shutdown, and are thus safe to access from other code
+// running during shutdown.
+//
+// For a few core Abseil classes, we make a best effort to allow for safe global
+// instances, even though these classes have non-trivial destructors.  These
+// objects can be created with the absl::kConstInit tag.  For example:
+//   ABSL_CONST_INIT absl::Mutex global_mutex(absl::kConstInit);
+//
+// The line above declares a global variable of type absl::Mutex which can be
+// accessed at any point during startup or shutdown.  global_mutex's destructor
+// will still run, but will not invalidate the object.  Note that C++ specifies
+// that accessing an object after its destructor has run results in undefined
+// behavior, but this pattern works on the toolchains we support.
+//
+// The absl::kConstInit tag should only be used to define objects with static
+// or thread_local storage duration.
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+enum ConstInitType {
+  kConstInit,
+};
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_CONST_INIT_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/dynamic_annotations.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/dynamic_annotations.h
new file mode 100644
index 0000000000000000000000000000000000000000..3ea7c1568ccd8362db4e3917bd8e89157dbe55be
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/dynamic_annotations.h
@@ -0,0 +1,471 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// This file defines dynamic annotations for use with dynamic analysis tool
+// such as valgrind, PIN, etc.
+//
+// Dynamic annotation is a source code annotation that affects the generated
+// code (that is, the annotation is not a comment). Each such annotation is
+// attached to a particular instruction and/or to a particular object (address)
+// in the program.
+//
+// The annotations that should be used by users are macros in all upper-case
+// (e.g., ABSL_ANNOTATE_THREAD_NAME).
+//
+// Actual implementation of these macros may differ depending on the dynamic
+// analysis tool being used.
+//
+// This file supports the following configurations:
+// - Dynamic Annotations enabled (with static thread-safety warnings disabled).
+//   In this case, macros expand to functions implemented by Thread Sanitizer,
+//   when building with TSan. When not provided an external implementation,
+//   dynamic_annotations.cc provides no-op implementations.
+//
+// - Static Clang thread-safety warnings enabled.
+//   When building with a Clang compiler that supports thread-safety warnings,
+//   a subset of annotations can be statically-checked at compile-time. We
+//   expand these macros to static-inline functions that can be analyzed for
+//   thread-safety, but afterwards elided when building the final binary.
+//
+// - All annotations are disabled.
+//   If neither Dynamic Annotations nor Clang thread-safety warnings are
+//   enabled, then all annotation-macros expand to empty.
+
+#ifndef ABSL_BASE_DYNAMIC_ANNOTATIONS_H_
+#define ABSL_BASE_DYNAMIC_ANNOTATIONS_H_
+
+#include <stddef.h>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#ifdef __cplusplus
+#include "absl/base/macros.h"
+#endif
+
+// TODO(rogeeff): Remove after the backward compatibility period.
+#include "absl/base/internal/dynamic_annotations.h"  // IWYU pragma: export
+
+// -------------------------------------------------------------------------
+// Decide which features are enabled.
+
+#ifdef ABSL_HAVE_THREAD_SANITIZER
+
+#define ABSL_INTERNAL_RACE_ANNOTATIONS_ENABLED 1
+#define ABSL_INTERNAL_READS_ANNOTATIONS_ENABLED 1
+#define ABSL_INTERNAL_WRITES_ANNOTATIONS_ENABLED 1
+#define ABSL_INTERNAL_ANNOTALYSIS_ENABLED 0
+#define ABSL_INTERNAL_READS_WRITES_ANNOTATIONS_ENABLED 1
+
+#else
+
+#define ABSL_INTERNAL_RACE_ANNOTATIONS_ENABLED 0
+#define ABSL_INTERNAL_READS_ANNOTATIONS_ENABLED 0
+#define ABSL_INTERNAL_WRITES_ANNOTATIONS_ENABLED 0
+
+// Clang provides limited support for static thread-safety analysis through a
+// feature called Annotalysis. We configure macro-definitions according to
+// whether Annotalysis support is available. When running in opt-mode, GCC
+// will issue a warning, if these attributes are compiled. Only include them
+// when compiling using Clang.
+
+#if defined(__clang__)
+#define ABSL_INTERNAL_ANNOTALYSIS_ENABLED 1
+#if !defined(SWIG)
+#define ABSL_INTERNAL_IGNORE_READS_ATTRIBUTE_ENABLED 1
+#endif
+#else
+#define ABSL_INTERNAL_ANNOTALYSIS_ENABLED 0
+#endif
+
+// Read/write annotations are enabled in Annotalysis mode; disabled otherwise.
+#define ABSL_INTERNAL_READS_WRITES_ANNOTATIONS_ENABLED \
+  ABSL_INTERNAL_ANNOTALYSIS_ENABLED
+
+#endif  // ABSL_HAVE_THREAD_SANITIZER
+
+#ifdef __cplusplus
+#define ABSL_INTERNAL_BEGIN_EXTERN_C extern "C" {
+#define ABSL_INTERNAL_END_EXTERN_C }  // extern "C"
+#define ABSL_INTERNAL_GLOBAL_SCOPED(F) ::F
+#define ABSL_INTERNAL_STATIC_INLINE inline
+#else
+#define ABSL_INTERNAL_BEGIN_EXTERN_C  // empty
+#define ABSL_INTERNAL_END_EXTERN_C    // empty
+#define ABSL_INTERNAL_GLOBAL_SCOPED(F) F
+#define ABSL_INTERNAL_STATIC_INLINE static inline
+#endif
+
+// -------------------------------------------------------------------------
+// Define race annotations.
+
+#if ABSL_INTERNAL_RACE_ANNOTATIONS_ENABLED == 1
+// Some of the symbols used in this section (e.g. AnnotateBenignRaceSized) are
+// defined by the compiler-based santizer implementation, not by the Abseil
+// library. Therefore they do not use ABSL_INTERNAL_C_SYMBOL.
+
+// -------------------------------------------------------------
+// Annotations that suppress errors. It is usually better to express the
+// program's synchronization using the other annotations, but these can be used
+// when all else fails.
+
+// Report that we may have a benign race at `pointer`, with size
+// "sizeof(*(pointer))". `pointer` must be a non-void* pointer. Insert at the
+// point where `pointer` has been allocated, preferably close to the point
+// where the race happens. See also ABSL_ANNOTATE_BENIGN_RACE_STATIC.
+#define ABSL_ANNOTATE_BENIGN_RACE(pointer, description) \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateBenignRaceSized)  \
+  (__FILE__, __LINE__, pointer, sizeof(*(pointer)), description)
+
+// Same as ABSL_ANNOTATE_BENIGN_RACE(`address`, `description`), but applies to
+// the memory range [`address`, `address`+`size`).
+#define ABSL_ANNOTATE_BENIGN_RACE_SIZED(address, size, description) \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateBenignRaceSized)              \
+  (__FILE__, __LINE__, address, size, description)
+
+// Enable (`enable`!=0) or disable (`enable`==0) race detection for all threads.
+// This annotation could be useful if you want to skip expensive race analysis
+// during some period of program execution, e.g. during initialization.
+#define ABSL_ANNOTATE_ENABLE_RACE_DETECTION(enable)        \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateEnableRaceDetection) \
+  (__FILE__, __LINE__, enable)
+
+// -------------------------------------------------------------
+// Annotations useful for debugging.
+
+// Report the current thread `name` to a race detector.
+#define ABSL_ANNOTATE_THREAD_NAME(name) \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateThreadName)(__FILE__, __LINE__, name)
+
+// -------------------------------------------------------------
+// Annotations useful when implementing locks. They are not normally needed by
+// modules that merely use locks. The `lock` argument is a pointer to the lock
+// object.
+
+// Report that a lock has been created at address `lock`.
+#define ABSL_ANNOTATE_RWLOCK_CREATE(lock) \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateRWLockCreate)(__FILE__, __LINE__, lock)
+
+// Report that a linker initialized lock has been created at address `lock`.
+#ifdef ABSL_HAVE_THREAD_SANITIZER
+#define ABSL_ANNOTATE_RWLOCK_CREATE_STATIC(lock)          \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateRWLockCreateStatic) \
+  (__FILE__, __LINE__, lock)
+#else
+#define ABSL_ANNOTATE_RWLOCK_CREATE_STATIC(lock) \
+  ABSL_ANNOTATE_RWLOCK_CREATE(lock)
+#endif
+
+// Report that the lock at address `lock` is about to be destroyed.
+#define ABSL_ANNOTATE_RWLOCK_DESTROY(lock) \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateRWLockDestroy)(__FILE__, __LINE__, lock)
+
+// Report that the lock at address `lock` has been acquired.
+// `is_w`=1 for writer lock, `is_w`=0 for reader lock.
+#define ABSL_ANNOTATE_RWLOCK_ACQUIRED(lock, is_w)     \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateRWLockAcquired) \
+  (__FILE__, __LINE__, lock, is_w)
+
+// Report that the lock at address `lock` is about to be released.
+// `is_w`=1 for writer lock, `is_w`=0 for reader lock.
+#define ABSL_ANNOTATE_RWLOCK_RELEASED(lock, is_w)     \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateRWLockReleased) \
+  (__FILE__, __LINE__, lock, is_w)
+
+// Apply ABSL_ANNOTATE_BENIGN_RACE_SIZED to a static variable `static_var`.
+#define ABSL_ANNOTATE_BENIGN_RACE_STATIC(static_var, description)      \
+  namespace {                                                          \
+  class static_var##_annotator {                                       \
+   public:                                                             \
+    static_var##_annotator() {                                         \
+      ABSL_ANNOTATE_BENIGN_RACE_SIZED(&static_var, sizeof(static_var), \
+                                      #static_var ": " description);   \
+    }                                                                  \
+  };                                                                   \
+  static static_var##_annotator the##static_var##_annotator;           \
+  }  // namespace
+
+// Function prototypes of annotations provided by the compiler-based sanitizer
+// implementation.
+ABSL_INTERNAL_BEGIN_EXTERN_C
+void AnnotateRWLockCreate(const char* file, int line,
+                          const volatile void* lock);
+void AnnotateRWLockCreateStatic(const char* file, int line,
+                                const volatile void* lock);
+void AnnotateRWLockDestroy(const char* file, int line,
+                           const volatile void* lock);
+void AnnotateRWLockAcquired(const char* file, int line,
+                            const volatile void* lock, long is_w);  // NOLINT
+void AnnotateRWLockReleased(const char* file, int line,
+                            const volatile void* lock, long is_w);  // NOLINT
+void AnnotateBenignRace(const char* file, int line,
+                        const volatile void* address, const char* description);
+void AnnotateBenignRaceSized(const char* file, int line,
+                             const volatile void* address, size_t size,
+                             const char* description);
+void AnnotateThreadName(const char* file, int line, const char* name);
+void AnnotateEnableRaceDetection(const char* file, int line, int enable);
+ABSL_INTERNAL_END_EXTERN_C
+
+#else  // ABSL_INTERNAL_RACE_ANNOTATIONS_ENABLED == 0
+
+#define ABSL_ANNOTATE_RWLOCK_CREATE(lock)                            // empty
+#define ABSL_ANNOTATE_RWLOCK_CREATE_STATIC(lock)                     // empty
+#define ABSL_ANNOTATE_RWLOCK_DESTROY(lock)                           // empty
+#define ABSL_ANNOTATE_RWLOCK_ACQUIRED(lock, is_w)                    // empty
+#define ABSL_ANNOTATE_RWLOCK_RELEASED(lock, is_w)                    // empty
+#define ABSL_ANNOTATE_BENIGN_RACE(address, description)              // empty
+#define ABSL_ANNOTATE_BENIGN_RACE_SIZED(address, size, description)  // empty
+#define ABSL_ANNOTATE_THREAD_NAME(name)                              // empty
+#define ABSL_ANNOTATE_ENABLE_RACE_DETECTION(enable)                  // empty
+#define ABSL_ANNOTATE_BENIGN_RACE_STATIC(static_var, description)    // empty
+
+#endif  // ABSL_INTERNAL_RACE_ANNOTATIONS_ENABLED
+
+// -------------------------------------------------------------------------
+// Define memory annotations.
+
+#ifdef ABSL_HAVE_MEMORY_SANITIZER
+
+#include <sanitizer/msan_interface.h>
+
+#define ABSL_ANNOTATE_MEMORY_IS_INITIALIZED(address, size) \
+  __msan_unpoison(address, size)
+
+#define ABSL_ANNOTATE_MEMORY_IS_UNINITIALIZED(address, size) \
+  __msan_allocated_memory(address, size)
+
+#else  // !defined(ABSL_HAVE_MEMORY_SANITIZER)
+
+// TODO(rogeeff): remove this branch
+#ifdef ABSL_HAVE_THREAD_SANITIZER
+#define ABSL_ANNOTATE_MEMORY_IS_INITIALIZED(address, size) \
+  do {                                                     \
+    (void)(address);                                       \
+    (void)(size);                                          \
+  } while (0)
+#define ABSL_ANNOTATE_MEMORY_IS_UNINITIALIZED(address, size) \
+  do {                                                       \
+    (void)(address);                                         \
+    (void)(size);                                            \
+  } while (0)
+#else
+
+#define ABSL_ANNOTATE_MEMORY_IS_INITIALIZED(address, size)    // empty
+#define ABSL_ANNOTATE_MEMORY_IS_UNINITIALIZED(address, size)  // empty
+
+#endif
+
+#endif  // ABSL_HAVE_MEMORY_SANITIZER
+
+// -------------------------------------------------------------------------
+// Define IGNORE_READS_BEGIN/_END attributes.
+
+#if defined(ABSL_INTERNAL_IGNORE_READS_ATTRIBUTE_ENABLED)
+
+#define ABSL_INTERNAL_IGNORE_READS_BEGIN_ATTRIBUTE \
+  __attribute((exclusive_lock_function("*")))
+#define ABSL_INTERNAL_IGNORE_READS_END_ATTRIBUTE \
+  __attribute((unlock_function("*")))
+
+#else  // !defined(ABSL_INTERNAL_IGNORE_READS_ATTRIBUTE_ENABLED)
+
+#define ABSL_INTERNAL_IGNORE_READS_BEGIN_ATTRIBUTE  // empty
+#define ABSL_INTERNAL_IGNORE_READS_END_ATTRIBUTE    // empty
+
+#endif  // defined(ABSL_INTERNAL_IGNORE_READS_ATTRIBUTE_ENABLED)
+
+// -------------------------------------------------------------------------
+// Define IGNORE_READS_BEGIN/_END annotations.
+
+#if ABSL_INTERNAL_READS_ANNOTATIONS_ENABLED == 1
+// Some of the symbols used in this section (e.g. AnnotateIgnoreReadsBegin) are
+// defined by the compiler-based implementation, not by the Abseil
+// library. Therefore they do not use ABSL_INTERNAL_C_SYMBOL.
+
+// Request the analysis tool to ignore all reads in the current thread until
+// ABSL_ANNOTATE_IGNORE_READS_END is called. Useful to ignore intentional racey
+// reads, while still checking other reads and all writes.
+// See also ABSL_ANNOTATE_UNPROTECTED_READ.
+#define ABSL_ANNOTATE_IGNORE_READS_BEGIN()              \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateIgnoreReadsBegin) \
+  (__FILE__, __LINE__)
+
+// Stop ignoring reads.
+#define ABSL_ANNOTATE_IGNORE_READS_END()              \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateIgnoreReadsEnd) \
+  (__FILE__, __LINE__)
+
+// Function prototypes of annotations provided by the compiler-based sanitizer
+// implementation.
+ABSL_INTERNAL_BEGIN_EXTERN_C
+void AnnotateIgnoreReadsBegin(const char* file, int line)
+    ABSL_INTERNAL_IGNORE_READS_BEGIN_ATTRIBUTE;
+void AnnotateIgnoreReadsEnd(const char* file,
+                            int line) ABSL_INTERNAL_IGNORE_READS_END_ATTRIBUTE;
+ABSL_INTERNAL_END_EXTERN_C
+
+#elif defined(ABSL_INTERNAL_ANNOTALYSIS_ENABLED)
+
+// When Annotalysis is enabled without Dynamic Annotations, the use of
+// static-inline functions allows the annotations to be read at compile-time,
+// while still letting the compiler elide the functions from the final build.
+//
+// TODO(delesley) -- The exclusive lock here ignores writes as well, but
+// allows IGNORE_READS_AND_WRITES to work properly.
+
+#define ABSL_ANNOTATE_IGNORE_READS_BEGIN()                          \
+  ABSL_INTERNAL_GLOBAL_SCOPED(                                      \
+      ABSL_INTERNAL_C_SYMBOL(AbslInternalAnnotateIgnoreReadsBegin)) \
+  ()
+
+#define ABSL_ANNOTATE_IGNORE_READS_END()                          \
+  ABSL_INTERNAL_GLOBAL_SCOPED(                                    \
+      ABSL_INTERNAL_C_SYMBOL(AbslInternalAnnotateIgnoreReadsEnd)) \
+  ()
+
+ABSL_INTERNAL_STATIC_INLINE void ABSL_INTERNAL_C_SYMBOL(
+    AbslInternalAnnotateIgnoreReadsBegin)()
+    ABSL_INTERNAL_IGNORE_READS_BEGIN_ATTRIBUTE {}
+
+ABSL_INTERNAL_STATIC_INLINE void ABSL_INTERNAL_C_SYMBOL(
+    AbslInternalAnnotateIgnoreReadsEnd)()
+    ABSL_INTERNAL_IGNORE_READS_END_ATTRIBUTE {}
+
+#else
+
+#define ABSL_ANNOTATE_IGNORE_READS_BEGIN()  // empty
+#define ABSL_ANNOTATE_IGNORE_READS_END()    // empty
+
+#endif
+
+// -------------------------------------------------------------------------
+// Define IGNORE_WRITES_BEGIN/_END annotations.
+
+#if ABSL_INTERNAL_WRITES_ANNOTATIONS_ENABLED == 1
+
+// Similar to ABSL_ANNOTATE_IGNORE_READS_BEGIN, but ignore writes instead.
+#define ABSL_ANNOTATE_IGNORE_WRITES_BEGIN() \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateIgnoreWritesBegin)(__FILE__, __LINE__)
+
+// Stop ignoring writes.
+#define ABSL_ANNOTATE_IGNORE_WRITES_END() \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateIgnoreWritesEnd)(__FILE__, __LINE__)
+
+// Function prototypes of annotations provided by the compiler-based sanitizer
+// implementation.
+ABSL_INTERNAL_BEGIN_EXTERN_C
+void AnnotateIgnoreWritesBegin(const char* file, int line);
+void AnnotateIgnoreWritesEnd(const char* file, int line);
+ABSL_INTERNAL_END_EXTERN_C
+
+#else
+
+#define ABSL_ANNOTATE_IGNORE_WRITES_BEGIN()  // empty
+#define ABSL_ANNOTATE_IGNORE_WRITES_END()    // empty
+
+#endif
+
+// -------------------------------------------------------------------------
+// Define the ABSL_ANNOTATE_IGNORE_READS_AND_WRITES_* annotations using the more
+// primitive annotations defined above.
+//
+//     Instead of doing
+//        ABSL_ANNOTATE_IGNORE_READS_BEGIN();
+//        ... = x;
+//        ABSL_ANNOTATE_IGNORE_READS_END();
+//     one can use
+//        ... = ABSL_ANNOTATE_UNPROTECTED_READ(x);
+
+#if defined(ABSL_INTERNAL_READS_WRITES_ANNOTATIONS_ENABLED)
+
+// Start ignoring all memory accesses (both reads and writes).
+#define ABSL_ANNOTATE_IGNORE_READS_AND_WRITES_BEGIN() \
+  do {                                                \
+    ABSL_ANNOTATE_IGNORE_READS_BEGIN();               \
+    ABSL_ANNOTATE_IGNORE_WRITES_BEGIN();              \
+  } while (0)
+
+// Stop ignoring both reads and writes.
+#define ABSL_ANNOTATE_IGNORE_READS_AND_WRITES_END() \
+  do {                                              \
+    ABSL_ANNOTATE_IGNORE_WRITES_END();              \
+    ABSL_ANNOTATE_IGNORE_READS_END();               \
+  } while (0)
+
+#ifdef __cplusplus
+// ABSL_ANNOTATE_UNPROTECTED_READ is the preferred way to annotate racey reads.
+#define ABSL_ANNOTATE_UNPROTECTED_READ(x) \
+  absl::base_internal::AnnotateUnprotectedRead(x)
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+template <typename T>
+inline T AnnotateUnprotectedRead(const volatile T& x) {  // NOLINT
+  ABSL_ANNOTATE_IGNORE_READS_BEGIN();
+  T res = x;
+  ABSL_ANNOTATE_IGNORE_READS_END();
+  return res;
+}
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+#endif
+
+#else
+
+#define ABSL_ANNOTATE_IGNORE_READS_AND_WRITES_BEGIN()  // empty
+#define ABSL_ANNOTATE_IGNORE_READS_AND_WRITES_END()    // empty
+#define ABSL_ANNOTATE_UNPROTECTED_READ(x) (x)
+
+#endif
+
+// -------------------------------------------------------------------------
+// Address sanitizer annotations
+
+#ifdef ABSL_HAVE_ADDRESS_SANITIZER
+// Describe the current state of a contiguous container such as e.g.
+// std::vector or std::string. For more details see
+// sanitizer/common_interface_defs.h, which is provided by the compiler.
+#include <sanitizer/common_interface_defs.h>
+
+#define ABSL_ANNOTATE_CONTIGUOUS_CONTAINER(beg, end, old_mid, new_mid) \
+  __sanitizer_annotate_contiguous_container(beg, end, old_mid, new_mid)
+#define ABSL_ADDRESS_SANITIZER_REDZONE(name) \
+  struct {                                   \
+    alignas(8) char x[8];                    \
+  } name
+
+#else
+
+#define ABSL_ANNOTATE_CONTIGUOUS_CONTAINER(beg, end, old_mid, new_mid)  // empty
+#define ABSL_ADDRESS_SANITIZER_REDZONE(name) static_assert(true, "")
+
+#endif  // ABSL_HAVE_ADDRESS_SANITIZER
+
+// -------------------------------------------------------------------------
+// Undefine the macros intended only for this file.
+
+#undef ABSL_INTERNAL_RACE_ANNOTATIONS_ENABLED
+#undef ABSL_INTERNAL_READS_ANNOTATIONS_ENABLED
+#undef ABSL_INTERNAL_WRITES_ANNOTATIONS_ENABLED
+#undef ABSL_INTERNAL_ANNOTALYSIS_ENABLED
+#undef ABSL_INTERNAL_READS_WRITES_ANNOTATIONS_ENABLED
+#undef ABSL_INTERNAL_BEGIN_EXTERN_C
+#undef ABSL_INTERNAL_END_EXTERN_C
+#undef ABSL_INTERNAL_STATIC_INLINE
+
+#endif  // ABSL_BASE_DYNAMIC_ANNOTATIONS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/atomic_hook.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/atomic_hook.h
new file mode 100644
index 0000000000000000000000000000000000000000..ae21cd7fe50af011c94d73a0e848badd77d43ed5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/atomic_hook.h
@@ -0,0 +1,200 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_BASE_INTERNAL_ATOMIC_HOOK_H_
+#define ABSL_BASE_INTERNAL_ATOMIC_HOOK_H_
+
+#include <atomic>
+#include <cassert>
+#include <cstdint>
+#include <utility>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+
+#if defined(_MSC_VER) && !defined(__clang__)
+#define ABSL_HAVE_WORKING_CONSTEXPR_STATIC_INIT 0
+#else
+#define ABSL_HAVE_WORKING_CONSTEXPR_STATIC_INIT 1
+#endif
+
+#if defined(_MSC_VER)
+#define ABSL_HAVE_WORKING_ATOMIC_POINTER 0
+#else
+#define ABSL_HAVE_WORKING_ATOMIC_POINTER 1
+#endif
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+template <typename T>
+class AtomicHook;
+
+// To workaround AtomicHook not being constant-initializable on some platforms,
+// prefer to annotate instances with `ABSL_INTERNAL_ATOMIC_HOOK_ATTRIBUTES`
+// instead of `ABSL_CONST_INIT`.
+#if ABSL_HAVE_WORKING_CONSTEXPR_STATIC_INIT
+#define ABSL_INTERNAL_ATOMIC_HOOK_ATTRIBUTES ABSL_CONST_INIT
+#else
+#define ABSL_INTERNAL_ATOMIC_HOOK_ATTRIBUTES
+#endif
+
+// `AtomicHook` is a helper class, templatized on a raw function pointer type,
+// for implementing Abseil customization hooks.  It is a callable object that
+// dispatches to the registered hook.  Objects of type `AtomicHook` must have
+// static or thread storage duration.
+//
+// A default constructed object performs a no-op (and returns a default
+// constructed object) if no hook has been registered.
+//
+// Hooks can be pre-registered via constant initialization, for example:
+//
+// ABSL_INTERNAL_ATOMIC_HOOK_ATTRIBUTES static AtomicHook<void(*)()>
+//     my_hook(DefaultAction);
+//
+// and then changed at runtime via a call to `Store()`.
+//
+// Reads and writes guarantee memory_order_acquire/memory_order_release
+// semantics.
+template <typename ReturnType, typename... Args>
+class AtomicHook<ReturnType (*)(Args...)> {
+ public:
+  using FnPtr = ReturnType (*)(Args...);
+
+  // Constructs an object that by default performs a no-op (and
+  // returns a default constructed object) when no hook as been registered.
+  constexpr AtomicHook() : AtomicHook(DummyFunction) {}
+
+  // Constructs an object that by default dispatches to/returns the
+  // pre-registered default_fn when no hook has been registered at runtime.
+#if ABSL_HAVE_WORKING_ATOMIC_POINTER && ABSL_HAVE_WORKING_CONSTEXPR_STATIC_INIT
+  explicit constexpr AtomicHook(FnPtr default_fn)
+      : hook_(default_fn), default_fn_(default_fn) {}
+#elif ABSL_HAVE_WORKING_CONSTEXPR_STATIC_INIT
+  explicit constexpr AtomicHook(FnPtr default_fn)
+      : hook_(kUninitialized), default_fn_(default_fn) {}
+#else
+  // As of January 2020, on all known versions of MSVC this constructor runs in
+  // the global constructor sequence.  If `Store()` is called by a dynamic
+  // initializer, we want to preserve the value, even if this constructor runs
+  // after the call to `Store()`.  If not, `hook_` will be
+  // zero-initialized by the linker and we have no need to set it.
+  // https://developercommunity.visualstudio.com/content/problem/336946/class-with-constexpr-constructor-not-using-static.html
+  explicit constexpr AtomicHook(FnPtr default_fn)
+      : /* hook_(deliberately omitted), */ default_fn_(default_fn) {
+    static_assert(kUninitialized == 0, "here we rely on zero-initialization");
+  }
+#endif
+
+  // Stores the provided function pointer as the value for this hook.
+  //
+  // This is intended to be called once.  Multiple calls are legal only if the
+  // same function pointer is provided for each call.  The store is implemented
+  // as a memory_order_release operation, and read accesses are implemented as
+  // memory_order_acquire.
+  void Store(FnPtr fn) {
+    bool success = DoStore(fn);
+    static_cast<void>(success);
+    assert(success);
+  }
+
+  // Invokes the registered callback.  If no callback has yet been registered, a
+  // default-constructed object of the appropriate type is returned instead.
+  template <typename... CallArgs>
+  ReturnType operator()(CallArgs&&... args) const {
+    return DoLoad()(std::forward<CallArgs>(args)...);
+  }
+
+  // Returns the registered callback, or nullptr if none has been registered.
+  // Useful if client code needs to conditionalize behavior based on whether a
+  // callback was registered.
+  //
+  // Note that atomic_hook.Load()() and atomic_hook() have different semantics:
+  // operator()() will perform a no-op if no callback was registered, while
+  // Load()() will dereference a null function pointer.  Prefer operator()() to
+  // Load()() unless you must conditionalize behavior on whether a hook was
+  // registered.
+  FnPtr Load() const {
+    FnPtr ptr = DoLoad();
+    return (ptr == DummyFunction) ? nullptr : ptr;
+  }
+
+ private:
+  static ReturnType DummyFunction(Args...) {
+    return ReturnType();
+  }
+
+  // Current versions of MSVC (as of September 2017) have a broken
+  // implementation of std::atomic<T*>:  Its constructor attempts to do the
+  // equivalent of a reinterpret_cast in a constexpr context, which is not
+  // allowed.
+  //
+  // This causes an issue when building with LLVM under Windows.  To avoid this,
+  // we use a less-efficient, intptr_t-based implementation on Windows.
+#if ABSL_HAVE_WORKING_ATOMIC_POINTER
+  // Return the stored value, or DummyFunction if no value has been stored.
+  FnPtr DoLoad() const { return hook_.load(std::memory_order_acquire); }
+
+  // Store the given value.  Returns false if a different value was already
+  // stored to this object.
+  bool DoStore(FnPtr fn) {
+    assert(fn);
+    FnPtr expected = default_fn_;
+    const bool store_succeeded = hook_.compare_exchange_strong(
+        expected, fn, std::memory_order_acq_rel, std::memory_order_acquire);
+    const bool same_value_already_stored = (expected == fn);
+    return store_succeeded || same_value_already_stored;
+  }
+
+  std::atomic<FnPtr> hook_;
+#else  // !ABSL_HAVE_WORKING_ATOMIC_POINTER
+  // Use a sentinel value unlikely to be the address of an actual function.
+  static constexpr intptr_t kUninitialized = 0;
+
+  static_assert(sizeof(intptr_t) >= sizeof(FnPtr),
+                "intptr_t can't contain a function pointer");
+
+  FnPtr DoLoad() const {
+    const intptr_t value = hook_.load(std::memory_order_acquire);
+    if (value == kUninitialized) {
+      return default_fn_;
+    }
+    return reinterpret_cast<FnPtr>(value);
+  }
+
+  bool DoStore(FnPtr fn) {
+    assert(fn);
+    const auto value = reinterpret_cast<intptr_t>(fn);
+    intptr_t expected = kUninitialized;
+    const bool store_succeeded = hook_.compare_exchange_strong(
+        expected, value, std::memory_order_acq_rel, std::memory_order_acquire);
+    const bool same_value_already_stored = (expected == value);
+    return store_succeeded || same_value_already_stored;
+  }
+
+  std::atomic<intptr_t> hook_;
+#endif
+
+  const FnPtr default_fn_;
+};
+
+#undef ABSL_HAVE_WORKING_ATOMIC_POINTER
+#undef ABSL_HAVE_WORKING_CONSTEXPR_STATIC_INIT
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_ATOMIC_HOOK_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/cycleclock.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/cycleclock.h
new file mode 100644
index 0000000000000000000000000000000000000000..cbfdf579987431866d9302cd3d0e495f92b852cd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/cycleclock.h
@@ -0,0 +1,144 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+// -----------------------------------------------------------------------------
+// File: cycleclock.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines a `CycleClock`, which yields the value and frequency
+// of a cycle counter that increments at a rate that is approximately constant.
+//
+// NOTE:
+//
+// The cycle counter frequency is not necessarily related to the core clock
+// frequency and should not be treated as such. That is, `CycleClock` cycles are
+// not necessarily "CPU cycles" and code should not rely on that behavior, even
+// if experimentally observed.
+//
+// An arbitrary offset may have been added to the counter at power on.
+//
+// On some platforms, the rate and offset of the counter may differ
+// slightly when read from different CPUs of a multiprocessor. Usually,
+// we try to ensure that the operating system adjusts values periodically
+// so that values agree approximately.   If you need stronger guarantees,
+// consider using alternate interfaces.
+//
+// The CPU is not required to maintain the ordering of a cycle counter read
+// with respect to surrounding instructions.
+
+#ifndef ABSL_BASE_INTERNAL_CYCLECLOCK_H_
+#define ABSL_BASE_INTERNAL_CYCLECLOCK_H_
+
+#include <atomic>
+#include <cstdint>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/base/internal/cycleclock_config.h"
+#include "absl/base/internal/unscaledcycleclock.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+using CycleClockSourceFunc = int64_t (*)();
+
+// -----------------------------------------------------------------------------
+// CycleClock
+// -----------------------------------------------------------------------------
+class CycleClock {
+ public:
+  // CycleClock::Now()
+  //
+  // Returns the value of a cycle counter that counts at a rate that is
+  // approximately constant.
+  static int64_t Now();
+
+  // CycleClock::Frequency()
+  //
+  // Returns the amount by which `CycleClock::Now()` increases per second. Note
+  // that this value may not necessarily match the core CPU clock frequency.
+  static double Frequency();
+
+ private:
+#if ABSL_USE_UNSCALED_CYCLECLOCK
+  static CycleClockSourceFunc LoadCycleClockSource();
+
+  static constexpr int32_t kShift = kCycleClockShift;
+  static constexpr double kFrequencyScale = kCycleClockFrequencyScale;
+
+  ABSL_CONST_INIT static std::atomic<CycleClockSourceFunc> cycle_clock_source_;
+#endif  //  ABSL_USE_UNSCALED_CYCLECLOC
+
+  CycleClock() = delete;  // no instances
+  CycleClock(const CycleClock&) = delete;
+  CycleClock& operator=(const CycleClock&) = delete;
+
+  friend class CycleClockSource;
+};
+
+class CycleClockSource {
+ private:
+  // CycleClockSource::Register()
+  //
+  // Register a function that provides an alternate source for the unscaled CPU
+  // cycle count value. The source function must be async signal safe, must not
+  // call CycleClock::Now(), and must have a frequency that matches that of the
+  // unscaled clock used by CycleClock. A nullptr value resets CycleClock to use
+  // the default source.
+  static void Register(CycleClockSourceFunc source);
+};
+
+#if ABSL_USE_UNSCALED_CYCLECLOCK
+
+inline CycleClockSourceFunc CycleClock::LoadCycleClockSource() {
+#if !defined(__x86_64__)
+  // Optimize for the common case (no callback) by first doing a relaxed load;
+  // this is significantly faster on non-x86 platforms.
+  if (cycle_clock_source_.load(std::memory_order_relaxed) == nullptr) {
+    return nullptr;
+  }
+#endif  // !defined(__x86_64__)
+
+  // This corresponds to the store(std::memory_order_release) in
+  // CycleClockSource::Register, and makes sure that any updates made prior to
+  // registering the callback are visible to this thread before the callback
+  // is invoked.
+  return cycle_clock_source_.load(std::memory_order_acquire);
+}
+
+// Accessing globals in inlined code in Window DLLs is problematic.
+#ifndef _WIN32
+inline int64_t CycleClock::Now() {
+  auto fn = LoadCycleClockSource();
+  if (fn == nullptr) {
+    return base_internal::UnscaledCycleClock::Now() >> kShift;
+  }
+  return fn() >> kShift;
+}
+#endif
+
+inline double CycleClock::Frequency() {
+  return kFrequencyScale * base_internal::UnscaledCycleClock::Frequency();
+}
+
+#endif  // ABSL_USE_UNSCALED_CYCLECLOCK
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_CYCLECLOCK_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/cycleclock_config.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/cycleclock_config.h
new file mode 100644
index 0000000000000000000000000000000000000000..191112b58efddef348e825ffa7d85ccc1d3fbf17
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/cycleclock_config.h
@@ -0,0 +1,55 @@
+// Copyright 2022 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_BASE_INTERNAL_CYCLECLOCK_CONFIG_H_
+#define ABSL_BASE_INTERNAL_CYCLECLOCK_CONFIG_H_
+
+#include <cstdint>
+
+#include "absl/base/config.h"
+#include "absl/base/internal/inline_variable.h"
+#include "absl/base/internal/unscaledcycleclock_config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+#if ABSL_USE_UNSCALED_CYCLECLOCK
+#ifdef NDEBUG
+#ifdef ABSL_INTERNAL_UNSCALED_CYCLECLOCK_FREQUENCY_IS_CPU_FREQUENCY
+// Not debug mode and the UnscaledCycleClock frequency is the CPU
+// frequency.  Scale the CycleClock to prevent overflow if someone
+// tries to represent the time as cycles since the Unix epoch.
+ABSL_INTERNAL_INLINE_CONSTEXPR(int32_t, kCycleClockShift, 1);
+#else
+// Not debug mode and the UnscaledCycleClock isn't operating at the
+// raw CPU frequency. There is no need to do any scaling, so don't
+// needlessly sacrifice precision.
+ABSL_INTERNAL_INLINE_CONSTEXPR(int32_t, kCycleClockShift, 0);
+#endif
+#else   // NDEBUG
+// In debug mode use a different shift to discourage depending on a
+// particular shift value.
+ABSL_INTERNAL_INLINE_CONSTEXPR(int32_t, kCycleClockShift, 2);
+#endif  // NDEBUG
+
+ABSL_INTERNAL_INLINE_CONSTEXPR(double, kCycleClockFrequencyScale,
+                               1.0 / (1 << kCycleClockShift));
+#endif  //  ABSL_USE_UNSCALED_CYCLECLOC
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_CYCLECLOCK_CONFIG_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/direct_mmap.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/direct_mmap.h
new file mode 100644
index 0000000000000000000000000000000000000000..815b8d23ba35f399a4e511767c0e9bb569b4a626
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/direct_mmap.h
@@ -0,0 +1,170 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Functions for directly invoking mmap() via syscall, avoiding the case where
+// mmap() has been locally overridden.
+
+#ifndef ABSL_BASE_INTERNAL_DIRECT_MMAP_H_
+#define ABSL_BASE_INTERNAL_DIRECT_MMAP_H_
+
+#include "absl/base/config.h"
+
+#ifdef ABSL_HAVE_MMAP
+
+#include <sys/mman.h>
+
+#ifdef __linux__
+
+#include <sys/types.h>
+#ifdef __BIONIC__
+#include <sys/syscall.h>
+#else
+#include <syscall.h>
+#endif
+
+#include <linux/unistd.h>
+#include <unistd.h>
+#include <cerrno>
+#include <cstdarg>
+#include <cstdint>
+
+#ifdef __mips__
+// Include definitions of the ABI currently in use.
+#if defined(__BIONIC__) || !defined(__GLIBC__)
+// Android doesn't have sgidefs.h, but does have asm/sgidefs.h, which has the
+// definitions we need.
+#include <asm/sgidefs.h>
+#else
+#include <sgidefs.h>
+#endif  // __BIONIC__ || !__GLIBC__
+#endif  // __mips__
+
+// SYS_mmap and SYS_munmap are not defined in Android.
+#ifdef __BIONIC__
+extern "C" void* __mmap2(void*, size_t, int, int, int, size_t);
+#if defined(__NR_mmap) && !defined(SYS_mmap)
+#define SYS_mmap __NR_mmap
+#endif
+#ifndef SYS_munmap
+#define SYS_munmap __NR_munmap
+#endif
+#endif  // __BIONIC__
+
+#if defined(__NR_mmap2) && !defined(SYS_mmap2)
+#define SYS_mmap2 __NR_mmap2
+#endif
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+// Platform specific logic extracted from
+// https://chromium.googlesource.com/linux-syscall-support/+/master/linux_syscall_support.h
+inline void* DirectMmap(void* start, size_t length, int prot, int flags, int fd,
+                        off64_t offset) noexcept {
+#if defined(__i386__) || defined(__ARM_ARCH_3__) || defined(__ARM_EABI__) || \
+    defined(__m68k__) || defined(__sh__) ||                                  \
+    (defined(__hppa__) && !defined(__LP64__)) ||                             \
+    (defined(__mips__) && _MIPS_SIM == _MIPS_SIM_ABI32) ||                   \
+    (defined(__PPC__) && !defined(__PPC64__)) ||                             \
+    (defined(__riscv) && __riscv_xlen == 32) ||                              \
+    (defined(__s390__) && !defined(__s390x__)) ||                            \
+    (defined(__sparc__) && !defined(__arch64__))
+  // On these architectures, implement mmap with mmap2.
+  static int pagesize = 0;
+  if (pagesize == 0) {
+#if defined(__wasm__) || defined(__asmjs__)
+    pagesize = getpagesize();
+#else
+    pagesize = sysconf(_SC_PAGESIZE);
+#endif
+  }
+  if (offset < 0 || offset % pagesize != 0) {
+    errno = EINVAL;
+    return MAP_FAILED;
+  }
+#ifdef __BIONIC__
+  // SYS_mmap2 has problems on Android API level <= 16.
+  // Workaround by invoking __mmap2() instead.
+  return __mmap2(start, length, prot, flags, fd,
+                 static_cast<size_t>(offset / pagesize));
+#else
+  return reinterpret_cast<void*>(
+      syscall(SYS_mmap2, start, length, prot, flags, fd,
+              static_cast<off_t>(offset / pagesize)));
+#endif
+#elif defined(__s390x__)
+  // On s390x, mmap() arguments are passed in memory.
+  unsigned long buf[6] = {reinterpret_cast<unsigned long>(start),  // NOLINT
+                          static_cast<unsigned long>(length),      // NOLINT
+                          static_cast<unsigned long>(prot),        // NOLINT
+                          static_cast<unsigned long>(flags),       // NOLINT
+                          static_cast<unsigned long>(fd),          // NOLINT
+                          static_cast<unsigned long>(offset)};     // NOLINT
+  return reinterpret_cast<void*>(syscall(SYS_mmap, buf));
+#elif defined(__x86_64__)
+// The x32 ABI has 32 bit longs, but the syscall interface is 64 bit.
+// We need to explicitly cast to an unsigned 64 bit type to avoid implicit
+// sign extension.  We can't cast pointers directly because those are
+// 32 bits, and gcc will dump ugly warnings about casting from a pointer
+// to an integer of a different size. We also need to make sure __off64_t
+// isn't truncated to 32-bits under x32.
+#define MMAP_SYSCALL_ARG(x) ((uint64_t)(uintptr_t)(x))
+  return reinterpret_cast<void*>(
+      syscall(SYS_mmap, MMAP_SYSCALL_ARG(start), MMAP_SYSCALL_ARG(length),
+              MMAP_SYSCALL_ARG(prot), MMAP_SYSCALL_ARG(flags),
+              MMAP_SYSCALL_ARG(fd), static_cast<uint64_t>(offset)));
+#undef MMAP_SYSCALL_ARG
+#else  // Remaining 64-bit aritectures.
+  static_assert(sizeof(unsigned long) == 8, "Platform is not 64-bit");
+  return reinterpret_cast<void*>(
+      syscall(SYS_mmap, start, length, prot, flags, fd, offset));
+#endif
+}
+
+inline int DirectMunmap(void* start, size_t length) {
+  return static_cast<int>(syscall(SYS_munmap, start, length));
+}
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#else  // !__linux__
+
+// For non-linux platforms where we have mmap, just dispatch directly to the
+// actual mmap()/munmap() methods.
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+inline void* DirectMmap(void* start, size_t length, int prot, int flags, int fd,
+                        off_t offset) {
+  return mmap(start, length, prot, flags, fd, offset);
+}
+
+inline int DirectMunmap(void* start, size_t length) {
+  return munmap(start, length);
+}
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // __linux__
+
+#endif  // ABSL_HAVE_MMAP
+
+#endif  // ABSL_BASE_INTERNAL_DIRECT_MMAP_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/dynamic_annotations.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/dynamic_annotations.h
new file mode 100644
index 0000000000000000000000000000000000000000..b23c5ec1c41ea140bfc7df65a4a3b67fd8ca0f7c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/dynamic_annotations.h
@@ -0,0 +1,398 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// This file defines dynamic annotations for use with dynamic analysis tool
+// such as valgrind, PIN, etc.
+//
+// Dynamic annotation is a source code annotation that affects the generated
+// code (that is, the annotation is not a comment). Each such annotation is
+// attached to a particular instruction and/or to a particular object (address)
+// in the program.
+//
+// The annotations that should be used by users are macros in all upper-case
+// (e.g., ANNOTATE_THREAD_NAME).
+//
+// Actual implementation of these macros may differ depending on the dynamic
+// analysis tool being used.
+//
+// This file supports the following configurations:
+// - Dynamic Annotations enabled (with static thread-safety warnings disabled).
+//   In this case, macros expand to functions implemented by Thread Sanitizer,
+//   when building with TSan. When not provided an external implementation,
+//   dynamic_annotations.cc provides no-op implementations.
+//
+// - Static Clang thread-safety warnings enabled.
+//   When building with a Clang compiler that supports thread-safety warnings,
+//   a subset of annotations can be statically-checked at compile-time. We
+//   expand these macros to static-inline functions that can be analyzed for
+//   thread-safety, but afterwards elided when building the final binary.
+//
+// - All annotations are disabled.
+//   If neither Dynamic Annotations nor Clang thread-safety warnings are
+//   enabled, then all annotation-macros expand to empty.
+
+#ifndef ABSL_BASE_INTERNAL_DYNAMIC_ANNOTATIONS_H_
+#define ABSL_BASE_INTERNAL_DYNAMIC_ANNOTATIONS_H_
+
+#include <stddef.h>
+
+#include "absl/base/config.h"
+
+// -------------------------------------------------------------------------
+// Decide which features are enabled
+
+#ifndef DYNAMIC_ANNOTATIONS_ENABLED
+#define DYNAMIC_ANNOTATIONS_ENABLED 0
+#endif
+
+#if defined(__clang__) && !defined(SWIG)
+#define ABSL_INTERNAL_IGNORE_READS_ATTRIBUTE_ENABLED 1
+#endif
+
+#if DYNAMIC_ANNOTATIONS_ENABLED != 0
+
+#define ABSL_INTERNAL_RACE_ANNOTATIONS_ENABLED 1
+#define ABSL_INTERNAL_READS_ANNOTATIONS_ENABLED 1
+#define ABSL_INTERNAL_WRITES_ANNOTATIONS_ENABLED 1
+#define ABSL_INTERNAL_ANNOTALYSIS_ENABLED 0
+#define ABSL_INTERNAL_READS_WRITES_ANNOTATIONS_ENABLED 1
+
+#else
+
+#define ABSL_INTERNAL_RACE_ANNOTATIONS_ENABLED 0
+#define ABSL_INTERNAL_READS_ANNOTATIONS_ENABLED 0
+#define ABSL_INTERNAL_WRITES_ANNOTATIONS_ENABLED 0
+
+// Clang provides limited support for static thread-safety analysis through a
+// feature called Annotalysis. We configure macro-definitions according to
+// whether Annotalysis support is available. When running in opt-mode, GCC
+// will issue a warning, if these attributes are compiled. Only include them
+// when compiling using Clang.
+
+// ANNOTALYSIS_ENABLED == 1 when IGNORE_READ_ATTRIBUTE_ENABLED == 1
+#define ABSL_INTERNAL_ANNOTALYSIS_ENABLED \
+  defined(ABSL_INTERNAL_IGNORE_READS_ATTRIBUTE_ENABLED)
+// Read/write annotations are enabled in Annotalysis mode; disabled otherwise.
+#define ABSL_INTERNAL_READS_WRITES_ANNOTATIONS_ENABLED \
+  ABSL_INTERNAL_ANNOTALYSIS_ENABLED
+#endif
+
+// Memory annotations are also made available to LLVM's Memory Sanitizer
+#if defined(ABSL_HAVE_MEMORY_SANITIZER) && !defined(__native_client__)
+#define ABSL_INTERNAL_MEMORY_ANNOTATIONS_ENABLED 1
+#endif
+
+#ifndef ABSL_INTERNAL_MEMORY_ANNOTATIONS_ENABLED
+#define ABSL_INTERNAL_MEMORY_ANNOTATIONS_ENABLED 0
+#endif
+
+#ifdef __cplusplus
+#define ABSL_INTERNAL_BEGIN_EXTERN_C extern "C" {
+#define ABSL_INTERNAL_END_EXTERN_C }  // extern "C"
+#define ABSL_INTERNAL_GLOBAL_SCOPED(F) ::F
+#define ABSL_INTERNAL_STATIC_INLINE inline
+#else
+#define ABSL_INTERNAL_BEGIN_EXTERN_C  // empty
+#define ABSL_INTERNAL_END_EXTERN_C    // empty
+#define ABSL_INTERNAL_GLOBAL_SCOPED(F) F
+#define ABSL_INTERNAL_STATIC_INLINE static inline
+#endif
+
+// -------------------------------------------------------------------------
+// Define race annotations.
+
+#if ABSL_INTERNAL_RACE_ANNOTATIONS_ENABLED == 1
+
+// -------------------------------------------------------------
+// Annotations that suppress errors. It is usually better to express the
+// program's synchronization using the other annotations, but these can be used
+// when all else fails.
+
+// Report that we may have a benign race at `pointer`, with size
+// "sizeof(*(pointer))". `pointer` must be a non-void* pointer. Insert at the
+// point where `pointer` has been allocated, preferably close to the point
+// where the race happens. See also ANNOTATE_BENIGN_RACE_STATIC.
+#define ANNOTATE_BENIGN_RACE(pointer, description)     \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateBenignRaceSized) \
+  (__FILE__, __LINE__, pointer, sizeof(*(pointer)), description)
+
+// Same as ANNOTATE_BENIGN_RACE(`address`, `description`), but applies to
+// the memory range [`address`, `address`+`size`).
+#define ANNOTATE_BENIGN_RACE_SIZED(address, size, description) \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateBenignRaceSized)         \
+  (__FILE__, __LINE__, address, size, description)
+
+// Enable (`enable`!=0) or disable (`enable`==0) race detection for all threads.
+// This annotation could be useful if you want to skip expensive race analysis
+// during some period of program execution, e.g. during initialization.
+#define ANNOTATE_ENABLE_RACE_DETECTION(enable)             \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateEnableRaceDetection) \
+  (__FILE__, __LINE__, enable)
+
+// -------------------------------------------------------------
+// Annotations useful for debugging.
+
+// Report the current thread `name` to a race detector.
+#define ANNOTATE_THREAD_NAME(name) \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateThreadName)(__FILE__, __LINE__, name)
+
+// -------------------------------------------------------------
+// Annotations useful when implementing locks. They are not normally needed by
+// modules that merely use locks. The `lock` argument is a pointer to the lock
+// object.
+
+// Report that a lock has been created at address `lock`.
+#define ANNOTATE_RWLOCK_CREATE(lock) \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateRWLockCreate)(__FILE__, __LINE__, lock)
+
+// Report that a linker initialized lock has been created at address `lock`.
+#ifdef ABSL_HAVE_THREAD_SANITIZER
+#define ANNOTATE_RWLOCK_CREATE_STATIC(lock)               \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateRWLockCreateStatic) \
+  (__FILE__, __LINE__, lock)
+#else
+#define ANNOTATE_RWLOCK_CREATE_STATIC(lock) ANNOTATE_RWLOCK_CREATE(lock)
+#endif
+
+// Report that the lock at address `lock` is about to be destroyed.
+#define ANNOTATE_RWLOCK_DESTROY(lock) \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateRWLockDestroy)(__FILE__, __LINE__, lock)
+
+// Report that the lock at address `lock` has been acquired.
+// `is_w`=1 for writer lock, `is_w`=0 for reader lock.
+#define ANNOTATE_RWLOCK_ACQUIRED(lock, is_w)          \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateRWLockAcquired) \
+  (__FILE__, __LINE__, lock, is_w)
+
+// Report that the lock at address `lock` is about to be released.
+// `is_w`=1 for writer lock, `is_w`=0 for reader lock.
+#define ANNOTATE_RWLOCK_RELEASED(lock, is_w)          \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateRWLockReleased) \
+  (__FILE__, __LINE__, lock, is_w)
+
+// Apply ANNOTATE_BENIGN_RACE_SIZED to a static variable `static_var`.
+#define ANNOTATE_BENIGN_RACE_STATIC(static_var, description)      \
+  namespace {                                                     \
+  class static_var##_annotator {                                  \
+   public:                                                        \
+    static_var##_annotator() {                                    \
+      ANNOTATE_BENIGN_RACE_SIZED(&static_var, sizeof(static_var), \
+                                 #static_var ": " description);   \
+    }                                                             \
+  };                                                              \
+  static static_var##_annotator the##static_var##_annotator;      \
+  }  // namespace
+
+#else  // ABSL_INTERNAL_RACE_ANNOTATIONS_ENABLED == 0
+
+#define ANNOTATE_RWLOCK_CREATE(lock)                            // empty
+#define ANNOTATE_RWLOCK_CREATE_STATIC(lock)                     // empty
+#define ANNOTATE_RWLOCK_DESTROY(lock)                           // empty
+#define ANNOTATE_RWLOCK_ACQUIRED(lock, is_w)                    // empty
+#define ANNOTATE_RWLOCK_RELEASED(lock, is_w)                    // empty
+#define ANNOTATE_BENIGN_RACE(address, description)              // empty
+#define ANNOTATE_BENIGN_RACE_SIZED(address, size, description)  // empty
+#define ANNOTATE_THREAD_NAME(name)                              // empty
+#define ANNOTATE_ENABLE_RACE_DETECTION(enable)                  // empty
+#define ANNOTATE_BENIGN_RACE_STATIC(static_var, description)    // empty
+
+#endif  // ABSL_INTERNAL_RACE_ANNOTATIONS_ENABLED
+
+// -------------------------------------------------------------------------
+// Define memory annotations.
+
+#if ABSL_INTERNAL_MEMORY_ANNOTATIONS_ENABLED == 1
+
+#include <sanitizer/msan_interface.h>
+
+#define ANNOTATE_MEMORY_IS_INITIALIZED(address, size) \
+  __msan_unpoison(address, size)
+
+#define ANNOTATE_MEMORY_IS_UNINITIALIZED(address, size) \
+  __msan_allocated_memory(address, size)
+
+#else  // ABSL_INTERNAL_MEMORY_ANNOTATIONS_ENABLED == 0
+
+#if DYNAMIC_ANNOTATIONS_ENABLED == 1
+#define ANNOTATE_MEMORY_IS_INITIALIZED(address, size) \
+  do {                                                \
+    (void)(address);                                  \
+    (void)(size);                                     \
+  } while (0)
+#define ANNOTATE_MEMORY_IS_UNINITIALIZED(address, size) \
+  do {                                                  \
+    (void)(address);                                    \
+    (void)(size);                                       \
+  } while (0)
+#else
+#define ANNOTATE_MEMORY_IS_INITIALIZED(address, size)    // empty
+#define ANNOTATE_MEMORY_IS_UNINITIALIZED(address, size)  // empty
+#endif
+
+#endif  // ABSL_INTERNAL_MEMORY_ANNOTATIONS_ENABLED
+
+// -------------------------------------------------------------------------
+// Define IGNORE_READS_BEGIN/_END attributes.
+
+#if defined(ABSL_INTERNAL_IGNORE_READS_ATTRIBUTE_ENABLED)
+
+#define ABSL_INTERNAL_IGNORE_READS_BEGIN_ATTRIBUTE \
+  __attribute((exclusive_lock_function("*")))
+#define ABSL_INTERNAL_IGNORE_READS_END_ATTRIBUTE \
+  __attribute((unlock_function("*")))
+
+#else  // !defined(ABSL_INTERNAL_IGNORE_READS_ATTRIBUTE_ENABLED)
+
+#define ABSL_INTERNAL_IGNORE_READS_BEGIN_ATTRIBUTE  // empty
+#define ABSL_INTERNAL_IGNORE_READS_END_ATTRIBUTE    // empty
+
+#endif  // defined(ABSL_INTERNAL_IGNORE_READS_ATTRIBUTE_ENABLED)
+
+// -------------------------------------------------------------------------
+// Define IGNORE_READS_BEGIN/_END annotations.
+
+#if ABSL_INTERNAL_READS_ANNOTATIONS_ENABLED == 1
+
+// Request the analysis tool to ignore all reads in the current thread until
+// ANNOTATE_IGNORE_READS_END is called. Useful to ignore intentional racey
+// reads, while still checking other reads and all writes.
+// See also ANNOTATE_UNPROTECTED_READ.
+#define ANNOTATE_IGNORE_READS_BEGIN() \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateIgnoreReadsBegin)(__FILE__, __LINE__)
+
+// Stop ignoring reads.
+#define ANNOTATE_IGNORE_READS_END() \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateIgnoreReadsEnd)(__FILE__, __LINE__)
+
+#elif defined(ABSL_INTERNAL_ANNOTALYSIS_ENABLED)
+
+// When Annotalysis is enabled without Dynamic Annotations, the use of
+// static-inline functions allows the annotations to be read at compile-time,
+// while still letting the compiler elide the functions from the final build.
+//
+// TODO(delesley) -- The exclusive lock here ignores writes as well, but
+// allows IGNORE_READS_AND_WRITES to work properly.
+
+#define ANNOTATE_IGNORE_READS_BEGIN() \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AbslInternalAnnotateIgnoreReadsBegin)()
+
+#define ANNOTATE_IGNORE_READS_END() \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AbslInternalAnnotateIgnoreReadsEnd)()
+
+#else
+
+#define ANNOTATE_IGNORE_READS_BEGIN()  // empty
+#define ANNOTATE_IGNORE_READS_END()    // empty
+
+#endif
+
+// -------------------------------------------------------------------------
+// Define IGNORE_WRITES_BEGIN/_END annotations.
+
+#if ABSL_INTERNAL_WRITES_ANNOTATIONS_ENABLED == 1
+
+// Similar to ANNOTATE_IGNORE_READS_BEGIN, but ignore writes instead.
+#define ANNOTATE_IGNORE_WRITES_BEGIN() \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateIgnoreWritesBegin)(__FILE__, __LINE__)
+
+// Stop ignoring writes.
+#define ANNOTATE_IGNORE_WRITES_END() \
+  ABSL_INTERNAL_GLOBAL_SCOPED(AnnotateIgnoreWritesEnd)(__FILE__, __LINE__)
+
+#else
+
+#define ANNOTATE_IGNORE_WRITES_BEGIN()  // empty
+#define ANNOTATE_IGNORE_WRITES_END()    // empty
+
+#endif
+
+// -------------------------------------------------------------------------
+// Define the ANNOTATE_IGNORE_READS_AND_WRITES_* annotations using the more
+// primitive annotations defined above.
+//
+//     Instead of doing
+//        ANNOTATE_IGNORE_READS_BEGIN();
+//        ... = x;
+//        ANNOTATE_IGNORE_READS_END();
+//     one can use
+//        ... = ANNOTATE_UNPROTECTED_READ(x);
+
+#if defined(ABSL_INTERNAL_READS_WRITES_ANNOTATIONS_ENABLED)
+
+// Start ignoring all memory accesses (both reads and writes).
+#define ANNOTATE_IGNORE_READS_AND_WRITES_BEGIN() \
+  do {                                           \
+    ANNOTATE_IGNORE_READS_BEGIN();               \
+    ANNOTATE_IGNORE_WRITES_BEGIN();              \
+  } while (0)
+
+// Stop ignoring both reads and writes.
+#define ANNOTATE_IGNORE_READS_AND_WRITES_END() \
+  do {                                         \
+    ANNOTATE_IGNORE_WRITES_END();              \
+    ANNOTATE_IGNORE_READS_END();               \
+  } while (0)
+
+#ifdef __cplusplus
+// ANNOTATE_UNPROTECTED_READ is the preferred way to annotate racey reads.
+#define ANNOTATE_UNPROTECTED_READ(x) \
+  absl::base_internal::AnnotateUnprotectedRead(x)
+
+#endif
+
+#else
+
+#define ANNOTATE_IGNORE_READS_AND_WRITES_BEGIN()  // empty
+#define ANNOTATE_IGNORE_READS_AND_WRITES_END()    // empty
+#define ANNOTATE_UNPROTECTED_READ(x) (x)
+
+#endif
+
+// -------------------------------------------------------------------------
+// Address sanitizer annotations
+
+#ifdef ABSL_HAVE_ADDRESS_SANITIZER
+// Describe the current state of a contiguous container such as e.g.
+// std::vector or std::string. For more details see
+// sanitizer/common_interface_defs.h, which is provided by the compiler.
+#include <sanitizer/common_interface_defs.h>
+
+#define ANNOTATE_CONTIGUOUS_CONTAINER(beg, end, old_mid, new_mid) \
+  __sanitizer_annotate_contiguous_container(beg, end, old_mid, new_mid)
+#define ADDRESS_SANITIZER_REDZONE(name)    \
+  struct {                                 \
+    char x[8] __attribute__((aligned(8))); \
+  } name
+
+#else
+
+#define ANNOTATE_CONTIGUOUS_CONTAINER(beg, end, old_mid, new_mid)
+#define ADDRESS_SANITIZER_REDZONE(name) static_assert(true, "")
+
+#endif  // ABSL_HAVE_ADDRESS_SANITIZER
+
+// -------------------------------------------------------------------------
+// Undefine the macros intended only for this file.
+
+#undef ABSL_INTERNAL_RACE_ANNOTATIONS_ENABLED
+#undef ABSL_INTERNAL_MEMORY_ANNOTATIONS_ENABLED
+#undef ABSL_INTERNAL_READS_ANNOTATIONS_ENABLED
+#undef ABSL_INTERNAL_WRITES_ANNOTATIONS_ENABLED
+#undef ABSL_INTERNAL_ANNOTALYSIS_ENABLED
+#undef ABSL_INTERNAL_READS_WRITES_ANNOTATIONS_ENABLED
+#undef ABSL_INTERNAL_BEGIN_EXTERN_C
+#undef ABSL_INTERNAL_END_EXTERN_C
+#undef ABSL_INTERNAL_STATIC_INLINE
+
+#endif  // ABSL_BASE_INTERNAL_DYNAMIC_ANNOTATIONS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/endian.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/endian.h
new file mode 100644
index 0000000000000000000000000000000000000000..50747d75ec0eef1f717906c2e81021900d87ced3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/endian.h
@@ -0,0 +1,282 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+#ifndef ABSL_BASE_INTERNAL_ENDIAN_H_
+#define ABSL_BASE_INTERNAL_ENDIAN_H_
+
+#include <cstdint>
+#include <cstdlib>
+
+#include "absl/base/casts.h"
+#include "absl/base/config.h"
+#include "absl/base/internal/unaligned_access.h"
+#include "absl/base/port.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+inline uint64_t gbswap_64(uint64_t host_int) {
+#if ABSL_HAVE_BUILTIN(__builtin_bswap64) || defined(__GNUC__)
+  return __builtin_bswap64(host_int);
+#elif defined(_MSC_VER)
+  return _byteswap_uint64(host_int);
+#else
+  return (((host_int & uint64_t{0xFF}) << 56) |
+          ((host_int & uint64_t{0xFF00}) << 40) |
+          ((host_int & uint64_t{0xFF0000}) << 24) |
+          ((host_int & uint64_t{0xFF000000}) << 8) |
+          ((host_int & uint64_t{0xFF00000000}) >> 8) |
+          ((host_int & uint64_t{0xFF0000000000}) >> 24) |
+          ((host_int & uint64_t{0xFF000000000000}) >> 40) |
+          ((host_int & uint64_t{0xFF00000000000000}) >> 56));
+#endif
+}
+
+inline uint32_t gbswap_32(uint32_t host_int) {
+#if ABSL_HAVE_BUILTIN(__builtin_bswap32) || defined(__GNUC__)
+  return __builtin_bswap32(host_int);
+#elif defined(_MSC_VER)
+  return _byteswap_ulong(host_int);
+#else
+  return (((host_int & uint32_t{0xFF}) << 24) |
+          ((host_int & uint32_t{0xFF00}) << 8) |
+          ((host_int & uint32_t{0xFF0000}) >> 8) |
+          ((host_int & uint32_t{0xFF000000}) >> 24));
+#endif
+}
+
+inline uint16_t gbswap_16(uint16_t host_int) {
+#if ABSL_HAVE_BUILTIN(__builtin_bswap16) || defined(__GNUC__)
+  return __builtin_bswap16(host_int);
+#elif defined(_MSC_VER)
+  return _byteswap_ushort(host_int);
+#else
+  return (((host_int & uint16_t{0xFF}) << 8) |
+          ((host_int & uint16_t{0xFF00}) >> 8));
+#endif
+}
+
+#ifdef ABSL_IS_LITTLE_ENDIAN
+
+// Portable definitions for htonl (host-to-network) and friends on little-endian
+// architectures.
+inline uint16_t ghtons(uint16_t x) { return gbswap_16(x); }
+inline uint32_t ghtonl(uint32_t x) { return gbswap_32(x); }
+inline uint64_t ghtonll(uint64_t x) { return gbswap_64(x); }
+
+#elif defined ABSL_IS_BIG_ENDIAN
+
+// Portable definitions for htonl (host-to-network) etc on big-endian
+// architectures. These definitions are simpler since the host byte order is the
+// same as network byte order.
+inline uint16_t ghtons(uint16_t x) { return x; }
+inline uint32_t ghtonl(uint32_t x) { return x; }
+inline uint64_t ghtonll(uint64_t x) { return x; }
+
+#else
+#error \
+    "Unsupported byte order: Either ABSL_IS_BIG_ENDIAN or " \
+       "ABSL_IS_LITTLE_ENDIAN must be defined"
+#endif  // byte order
+
+inline uint16_t gntohs(uint16_t x) { return ghtons(x); }
+inline uint32_t gntohl(uint32_t x) { return ghtonl(x); }
+inline uint64_t gntohll(uint64_t x) { return ghtonll(x); }
+
+// Utilities to convert numbers between the current hosts's native byte
+// order and little-endian byte order
+//
+// Load/Store methods are alignment safe
+namespace little_endian {
+// Conversion functions.
+#ifdef ABSL_IS_LITTLE_ENDIAN
+
+inline uint16_t FromHost16(uint16_t x) { return x; }
+inline uint16_t ToHost16(uint16_t x) { return x; }
+
+inline uint32_t FromHost32(uint32_t x) { return x; }
+inline uint32_t ToHost32(uint32_t x) { return x; }
+
+inline uint64_t FromHost64(uint64_t x) { return x; }
+inline uint64_t ToHost64(uint64_t x) { return x; }
+
+inline constexpr bool IsLittleEndian() { return true; }
+
+#elif defined ABSL_IS_BIG_ENDIAN
+
+inline uint16_t FromHost16(uint16_t x) { return gbswap_16(x); }
+inline uint16_t ToHost16(uint16_t x) { return gbswap_16(x); }
+
+inline uint32_t FromHost32(uint32_t x) { return gbswap_32(x); }
+inline uint32_t ToHost32(uint32_t x) { return gbswap_32(x); }
+
+inline uint64_t FromHost64(uint64_t x) { return gbswap_64(x); }
+inline uint64_t ToHost64(uint64_t x) { return gbswap_64(x); }
+
+inline constexpr bool IsLittleEndian() { return false; }
+
+#endif /* ENDIAN */
+
+inline uint8_t FromHost(uint8_t x) { return x; }
+inline uint16_t FromHost(uint16_t x) { return FromHost16(x); }
+inline uint32_t FromHost(uint32_t x) { return FromHost32(x); }
+inline uint64_t FromHost(uint64_t x) { return FromHost64(x); }
+inline uint8_t ToHost(uint8_t x) { return x; }
+inline uint16_t ToHost(uint16_t x) { return ToHost16(x); }
+inline uint32_t ToHost(uint32_t x) { return ToHost32(x); }
+inline uint64_t ToHost(uint64_t x) { return ToHost64(x); }
+
+inline int8_t FromHost(int8_t x) { return x; }
+inline int16_t FromHost(int16_t x) {
+  return bit_cast<int16_t>(FromHost16(bit_cast<uint16_t>(x)));
+}
+inline int32_t FromHost(int32_t x) {
+  return bit_cast<int32_t>(FromHost32(bit_cast<uint32_t>(x)));
+}
+inline int64_t FromHost(int64_t x) {
+  return bit_cast<int64_t>(FromHost64(bit_cast<uint64_t>(x)));
+}
+inline int8_t ToHost(int8_t x) { return x; }
+inline int16_t ToHost(int16_t x) {
+  return bit_cast<int16_t>(ToHost16(bit_cast<uint16_t>(x)));
+}
+inline int32_t ToHost(int32_t x) {
+  return bit_cast<int32_t>(ToHost32(bit_cast<uint32_t>(x)));
+}
+inline int64_t ToHost(int64_t x) {
+  return bit_cast<int64_t>(ToHost64(bit_cast<uint64_t>(x)));
+}
+
+// Functions to do unaligned loads and stores in little-endian order.
+inline uint16_t Load16(const void *p) {
+  return ToHost16(ABSL_INTERNAL_UNALIGNED_LOAD16(p));
+}
+
+inline void Store16(void *p, uint16_t v) {
+  ABSL_INTERNAL_UNALIGNED_STORE16(p, FromHost16(v));
+}
+
+inline uint32_t Load32(const void *p) {
+  return ToHost32(ABSL_INTERNAL_UNALIGNED_LOAD32(p));
+}
+
+inline void Store32(void *p, uint32_t v) {
+  ABSL_INTERNAL_UNALIGNED_STORE32(p, FromHost32(v));
+}
+
+inline uint64_t Load64(const void *p) {
+  return ToHost64(ABSL_INTERNAL_UNALIGNED_LOAD64(p));
+}
+
+inline void Store64(void *p, uint64_t v) {
+  ABSL_INTERNAL_UNALIGNED_STORE64(p, FromHost64(v));
+}
+
+}  // namespace little_endian
+
+// Utilities to convert numbers between the current hosts's native byte
+// order and big-endian byte order (same as network byte order)
+//
+// Load/Store methods are alignment safe
+namespace big_endian {
+#ifdef ABSL_IS_LITTLE_ENDIAN
+
+inline uint16_t FromHost16(uint16_t x) { return gbswap_16(x); }
+inline uint16_t ToHost16(uint16_t x) { return gbswap_16(x); }
+
+inline uint32_t FromHost32(uint32_t x) { return gbswap_32(x); }
+inline uint32_t ToHost32(uint32_t x) { return gbswap_32(x); }
+
+inline uint64_t FromHost64(uint64_t x) { return gbswap_64(x); }
+inline uint64_t ToHost64(uint64_t x) { return gbswap_64(x); }
+
+inline constexpr bool IsLittleEndian() { return true; }
+
+#elif defined ABSL_IS_BIG_ENDIAN
+
+inline uint16_t FromHost16(uint16_t x) { return x; }
+inline uint16_t ToHost16(uint16_t x) { return x; }
+
+inline uint32_t FromHost32(uint32_t x) { return x; }
+inline uint32_t ToHost32(uint32_t x) { return x; }
+
+inline uint64_t FromHost64(uint64_t x) { return x; }
+inline uint64_t ToHost64(uint64_t x) { return x; }
+
+inline constexpr bool IsLittleEndian() { return false; }
+
+#endif /* ENDIAN */
+
+inline uint8_t FromHost(uint8_t x) { return x; }
+inline uint16_t FromHost(uint16_t x) { return FromHost16(x); }
+inline uint32_t FromHost(uint32_t x) { return FromHost32(x); }
+inline uint64_t FromHost(uint64_t x) { return FromHost64(x); }
+inline uint8_t ToHost(uint8_t x) { return x; }
+inline uint16_t ToHost(uint16_t x) { return ToHost16(x); }
+inline uint32_t ToHost(uint32_t x) { return ToHost32(x); }
+inline uint64_t ToHost(uint64_t x) { return ToHost64(x); }
+
+inline int8_t FromHost(int8_t x) { return x; }
+inline int16_t FromHost(int16_t x) {
+  return bit_cast<int16_t>(FromHost16(bit_cast<uint16_t>(x)));
+}
+inline int32_t FromHost(int32_t x) {
+  return bit_cast<int32_t>(FromHost32(bit_cast<uint32_t>(x)));
+}
+inline int64_t FromHost(int64_t x) {
+  return bit_cast<int64_t>(FromHost64(bit_cast<uint64_t>(x)));
+}
+inline int8_t ToHost(int8_t x) { return x; }
+inline int16_t ToHost(int16_t x) {
+  return bit_cast<int16_t>(ToHost16(bit_cast<uint16_t>(x)));
+}
+inline int32_t ToHost(int32_t x) {
+  return bit_cast<int32_t>(ToHost32(bit_cast<uint32_t>(x)));
+}
+inline int64_t ToHost(int64_t x) {
+  return bit_cast<int64_t>(ToHost64(bit_cast<uint64_t>(x)));
+}
+
+// Functions to do unaligned loads and stores in big-endian order.
+inline uint16_t Load16(const void *p) {
+  return ToHost16(ABSL_INTERNAL_UNALIGNED_LOAD16(p));
+}
+
+inline void Store16(void *p, uint16_t v) {
+  ABSL_INTERNAL_UNALIGNED_STORE16(p, FromHost16(v));
+}
+
+inline uint32_t Load32(const void *p) {
+  return ToHost32(ABSL_INTERNAL_UNALIGNED_LOAD32(p));
+}
+
+inline void Store32(void *p, uint32_t v) {
+  ABSL_INTERNAL_UNALIGNED_STORE32(p, FromHost32(v));
+}
+
+inline uint64_t Load64(const void *p) {
+  return ToHost64(ABSL_INTERNAL_UNALIGNED_LOAD64(p));
+}
+
+inline void Store64(void *p, uint64_t v) {
+  ABSL_INTERNAL_UNALIGNED_STORE64(p, FromHost64(v));
+}
+
+}  // namespace big_endian
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_ENDIAN_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/errno_saver.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/errno_saver.h
new file mode 100644
index 0000000000000000000000000000000000000000..251de510fc939d42fd10a9d2d148554f7998a17e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/errno_saver.h
@@ -0,0 +1,43 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_BASE_INTERNAL_ERRNO_SAVER_H_
+#define ABSL_BASE_INTERNAL_ERRNO_SAVER_H_
+
+#include <cerrno>
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+// `ErrnoSaver` captures the value of `errno` upon construction and restores it
+// upon deletion.  It is used in low-level code and must be super fast.  Do not
+// add instrumentation, even in debug modes.
+class ErrnoSaver {
+ public:
+  ErrnoSaver() : saved_errno_(errno) {}
+  ~ErrnoSaver() { errno = saved_errno_; }
+  int operator()() const { return saved_errno_; }
+
+ private:
+  const int saved_errno_;
+};
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_ERRNO_SAVER_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/fast_type_id.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/fast_type_id.h
new file mode 100644
index 0000000000000000000000000000000000000000..a547b3a8bc234efc3e0dd1402d612e6dc5977f43
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/fast_type_id.h
@@ -0,0 +1,50 @@
+//
+// Copyright 2020 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+#ifndef ABSL_BASE_INTERNAL_FAST_TYPE_ID_H_
+#define ABSL_BASE_INTERNAL_FAST_TYPE_ID_H_
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+template <typename Type>
+struct FastTypeTag {
+  constexpr static char dummy_var = 0;
+};
+
+#ifdef ABSL_INTERNAL_NEED_REDUNDANT_CONSTEXPR_DECL
+template <typename Type>
+constexpr char FastTypeTag<Type>::dummy_var;
+#endif
+
+// FastTypeId<Type>() evaluates at compile/link-time to a unique pointer for the
+// passed-in type. These are meant to be good match for keys into maps or
+// straight up comparisons.
+using FastTypeIdType = const void*;
+
+template <typename Type>
+constexpr inline FastTypeIdType FastTypeId() {
+  return &FastTypeTag<Type>::dummy_var;
+}
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_FAST_TYPE_ID_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/hide_ptr.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/hide_ptr.h
new file mode 100644
index 0000000000000000000000000000000000000000..1dba80909a9b512b07543f80fe73433069b31e99
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/hide_ptr.h
@@ -0,0 +1,51 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_BASE_INTERNAL_HIDE_PTR_H_
+#define ABSL_BASE_INTERNAL_HIDE_PTR_H_
+
+#include <cstdint>
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+// Arbitrary value with high bits set. Xor'ing with it is unlikely
+// to map one valid pointer to another valid pointer.
+constexpr uintptr_t HideMask() {
+  return (uintptr_t{0xF03A5F7BU} << (sizeof(uintptr_t) - 4) * 8) | 0xF03A5F7BU;
+}
+
+// Hide a pointer from the leak checker. For internal use only.
+// Differs from absl::IgnoreLeak(ptr) in that absl::IgnoreLeak(ptr) causes ptr
+// and all objects reachable from ptr to be ignored by the leak checker.
+template <class T>
+inline uintptr_t HidePtr(T* ptr) {
+  return reinterpret_cast<uintptr_t>(ptr) ^ HideMask();
+}
+
+// Return a pointer that has been hidden from the leak checker.
+// For internal use only.
+template <class T>
+inline T* UnhidePtr(uintptr_t hidden) {
+  return reinterpret_cast<T*>(hidden ^ HideMask());
+}
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_HIDE_PTR_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/identity.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/identity.h
new file mode 100644
index 0000000000000000000000000000000000000000..a3154ed7bc592ebce69d733c7ef0aae3b5403e53
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/identity.h
@@ -0,0 +1,37 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+#ifndef ABSL_BASE_INTERNAL_IDENTITY_H_
+#define ABSL_BASE_INTERNAL_IDENTITY_H_
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace internal {
+
+template <typename T>
+struct identity {
+  typedef T type;
+};
+
+template <typename T>
+using identity_t = typename identity<T>::type;
+
+}  // namespace internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_IDENTITY_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/inline_variable.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/inline_variable.h
new file mode 100644
index 0000000000000000000000000000000000000000..df933faff50b96f3cf528feb0771a2b97d4e0df6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/inline_variable.h
@@ -0,0 +1,107 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_BASE_INTERNAL_INLINE_VARIABLE_H_
+#define ABSL_BASE_INTERNAL_INLINE_VARIABLE_H_
+
+#include <type_traits>
+
+#include "absl/base/internal/identity.h"
+
+// File:
+//   This file define a macro that allows the creation of or emulation of C++17
+//   inline variables based on whether or not the feature is supported.
+
+////////////////////////////////////////////////////////////////////////////////
+// Macro: ABSL_INTERNAL_INLINE_CONSTEXPR(type, name, init)
+//
+// Description:
+//   Expands to the equivalent of an inline constexpr instance of the specified
+//   `type` and `name`, initialized to the value `init`. If the compiler being
+//   used is detected as supporting actual inline variables as a language
+//   feature, then the macro expands to an actual inline variable definition.
+//
+// Requires:
+//   `type` is a type that is usable in an extern variable declaration.
+//
+// Requires: `name` is a valid identifier
+//
+// Requires:
+//   `init` is an expression that can be used in the following definition:
+//     constexpr type name = init;
+//
+// Usage:
+//
+//   // Equivalent to: `inline constexpr size_t variant_npos = -1;`
+//   ABSL_INTERNAL_INLINE_CONSTEXPR(size_t, variant_npos, -1);
+//
+// Differences in implementation:
+//   For a direct, language-level inline variable, decltype(name) will be the
+//   type that was specified along with const qualification, whereas for
+//   emulated inline variables, decltype(name) may be different (in practice
+//   it will likely be a reference type).
+////////////////////////////////////////////////////////////////////////////////
+
+#ifdef __cpp_inline_variables
+
+// Clang's -Wmissing-variable-declarations option erroneously warned that
+// inline constexpr objects need to be pre-declared. This has now been fixed,
+// but we will need to support this workaround for people building with older
+// versions of clang.
+//
+// Bug: https://bugs.llvm.org/show_bug.cgi?id=35862
+//
+// Note:
+//   identity_t is used here so that the const and name are in the
+//   appropriate place for pointer types, reference types, function pointer
+//   types, etc..
+#if defined(__clang__)
+#define ABSL_INTERNAL_EXTERN_DECL(type, name) \
+  extern const ::absl::internal::identity_t<type> name;
+#else  // Otherwise, just define the macro to do nothing.
+#define ABSL_INTERNAL_EXTERN_DECL(type, name)
+#endif  // defined(__clang__)
+
+// See above comment at top of file for details.
+#define ABSL_INTERNAL_INLINE_CONSTEXPR(type, name, init) \
+  ABSL_INTERNAL_EXTERN_DECL(type, name)                  \
+  inline constexpr ::absl::internal::identity_t<type> name = init
+
+#else
+
+// See above comment at top of file for details.
+//
+// Note:
+//   identity_t is used here so that the const and name are in the
+//   appropriate place for pointer types, reference types, function pointer
+//   types, etc..
+#define ABSL_INTERNAL_INLINE_CONSTEXPR(var_type, name, init)                  \
+  template <class /*AbslInternalDummy*/ = void>                               \
+  struct AbslInternalInlineVariableHolder##name {                             \
+    static constexpr ::absl::internal::identity_t<var_type> kInstance = init; \
+  };                                                                          \
+                                                                              \
+  template <class AbslInternalDummy>                                          \
+  constexpr ::absl::internal::identity_t<var_type>                            \
+      AbslInternalInlineVariableHolder##name<AbslInternalDummy>::kInstance;   \
+                                                                              \
+  static constexpr const ::absl::internal::identity_t<var_type>&              \
+      name = /* NOLINT */                                                     \
+      AbslInternalInlineVariableHolder##name<>::kInstance;                    \
+  static_assert(sizeof(void (*)(decltype(name))) != 0,                        \
+                "Silence unused variable warnings.")
+
+#endif  // __cpp_inline_variables
+
+#endif  // ABSL_BASE_INTERNAL_INLINE_VARIABLE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/invoke.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/invoke.h
new file mode 100644
index 0000000000000000000000000000000000000000..643c2a42f084662114ba3408029b1c426a94170b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/invoke.h
@@ -0,0 +1,241 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// absl::base_internal::invoke(f, args...) is an implementation of
+// INVOKE(f, args...) from section [func.require] of the C++ standard.
+// When compiled as C++17 and later versions, it is implemented as an alias of
+// std::invoke.
+//
+// [func.require]
+// Define INVOKE (f, t1, t2, ..., tN) as follows:
+// 1. (t1.*f)(t2, ..., tN) when f is a pointer to a member function of a class T
+//    and t1 is an object of type T or a reference to an object of type T or a
+//    reference to an object of a type derived from T;
+// 2. ((*t1).*f)(t2, ..., tN) when f is a pointer to a member function of a
+//    class T and t1 is not one of the types described in the previous item;
+// 3. t1.*f when N == 1 and f is a pointer to member data of a class T and t1 is
+//    an object of type T or a reference to an object of type T or a reference
+//    to an object of a type derived from T;
+// 4. (*t1).*f when N == 1 and f is a pointer to member data of a class T and t1
+//    is not one of the types described in the previous item;
+// 5. f(t1, t2, ..., tN) in all other cases.
+//
+// The implementation is SFINAE-friendly: substitution failure within invoke()
+// isn't an error.
+
+#ifndef ABSL_BASE_INTERNAL_INVOKE_H_
+#define ABSL_BASE_INTERNAL_INVOKE_H_
+
+#include "absl/base/config.h"
+
+#if ABSL_INTERNAL_CPLUSPLUS_LANG >= 201703L
+
+#include <functional>
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+using std::invoke;
+using std::invoke_result_t;
+using std::is_invocable_r;
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#else  // ABSL_INTERNAL_CPLUSPLUS_LANG >= 201703L
+
+#include <algorithm>
+#include <type_traits>
+#include <utility>
+
+#include "absl/meta/type_traits.h"
+
+// The following code is internal implementation detail.  See the comment at the
+// top of this file for the API documentation.
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+// The five classes below each implement one of the clauses from the definition
+// of INVOKE. The inner class template Accept<F, Args...> checks whether the
+// clause is applicable; static function template Invoke(f, args...) does the
+// invocation.
+//
+// By separating the clause selection logic from invocation we make sure that
+// Invoke() does exactly what the standard says.
+
+template <typename Derived>
+struct StrippedAccept {
+  template <typename... Args>
+  struct Accept : Derived::template AcceptImpl<typename std::remove_cv<
+                      typename std::remove_reference<Args>::type>::type...> {};
+};
+
+// (t1.*f)(t2, ..., tN) when f is a pointer to a member function of a class T
+// and t1 is an object of type T or a reference to an object of type T or a
+// reference to an object of a type derived from T.
+struct MemFunAndRef : StrippedAccept<MemFunAndRef> {
+  template <typename... Args>
+  struct AcceptImpl : std::false_type {};
+
+  template <typename MemFunType, typename C, typename Obj, typename... Args>
+  struct AcceptImpl<MemFunType C::*, Obj, Args...>
+      : std::integral_constant<bool, std::is_base_of<C, Obj>::value &&
+                                         absl::is_function<MemFunType>::value> {
+  };
+
+  template <typename MemFun, typename Obj, typename... Args>
+  static decltype((std::declval<Obj>().*
+                   std::declval<MemFun>())(std::declval<Args>()...))
+  Invoke(MemFun&& mem_fun, Obj&& obj, Args&&... args) {
+// Ignore bogus GCC warnings on this line.
+// See https://gcc.gnu.org/bugzilla/show_bug.cgi?id=101436 for similar example.
+#if ABSL_INTERNAL_HAVE_MIN_GNUC_VERSION(11, 0)
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Warray-bounds"
+#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
+#endif
+    return (std::forward<Obj>(obj).*
+            std::forward<MemFun>(mem_fun))(std::forward<Args>(args)...);
+#if ABSL_INTERNAL_HAVE_MIN_GNUC_VERSION(11, 0)
+#pragma GCC diagnostic pop
+#endif
+  }
+};
+
+// ((*t1).*f)(t2, ..., tN) when f is a pointer to a member function of a
+// class T and t1 is not one of the types described in the previous item.
+struct MemFunAndPtr : StrippedAccept<MemFunAndPtr> {
+  template <typename... Args>
+  struct AcceptImpl : std::false_type {};
+
+  template <typename MemFunType, typename C, typename Ptr, typename... Args>
+  struct AcceptImpl<MemFunType C::*, Ptr, Args...>
+      : std::integral_constant<bool, !std::is_base_of<C, Ptr>::value &&
+                                         absl::is_function<MemFunType>::value> {
+  };
+
+  template <typename MemFun, typename Ptr, typename... Args>
+  static decltype(((*std::declval<Ptr>()).*
+                   std::declval<MemFun>())(std::declval<Args>()...))
+  Invoke(MemFun&& mem_fun, Ptr&& ptr, Args&&... args) {
+    return ((*std::forward<Ptr>(ptr)).*
+            std::forward<MemFun>(mem_fun))(std::forward<Args>(args)...);
+  }
+};
+
+// t1.*f when N == 1 and f is a pointer to member data of a class T and t1 is
+// an object of type T or a reference to an object of type T or a reference
+// to an object of a type derived from T.
+struct DataMemAndRef : StrippedAccept<DataMemAndRef> {
+  template <typename... Args>
+  struct AcceptImpl : std::false_type {};
+
+  template <typename R, typename C, typename Obj>
+  struct AcceptImpl<R C::*, Obj>
+      : std::integral_constant<bool, std::is_base_of<C, Obj>::value &&
+                                         !absl::is_function<R>::value> {};
+
+  template <typename DataMem, typename Ref>
+  static decltype(std::declval<Ref>().*std::declval<DataMem>()) Invoke(
+      DataMem&& data_mem, Ref&& ref) {
+    return std::forward<Ref>(ref).*std::forward<DataMem>(data_mem);
+  }
+};
+
+// (*t1).*f when N == 1 and f is a pointer to member data of a class T and t1
+// is not one of the types described in the previous item.
+struct DataMemAndPtr : StrippedAccept<DataMemAndPtr> {
+  template <typename... Args>
+  struct AcceptImpl : std::false_type {};
+
+  template <typename R, typename C, typename Ptr>
+  struct AcceptImpl<R C::*, Ptr>
+      : std::integral_constant<bool, !std::is_base_of<C, Ptr>::value &&
+                                         !absl::is_function<R>::value> {};
+
+  template <typename DataMem, typename Ptr>
+  static decltype((*std::declval<Ptr>()).*std::declval<DataMem>()) Invoke(
+      DataMem&& data_mem, Ptr&& ptr) {
+    return (*std::forward<Ptr>(ptr)).*std::forward<DataMem>(data_mem);
+  }
+};
+
+// f(t1, t2, ..., tN) in all other cases.
+struct Callable {
+  // Callable doesn't have Accept because it's the last clause that gets picked
+  // when none of the previous clauses are applicable.
+  template <typename F, typename... Args>
+  static decltype(std::declval<F>()(std::declval<Args>()...)) Invoke(
+      F&& f, Args&&... args) {
+    return std::forward<F>(f)(std::forward<Args>(args)...);
+  }
+};
+
+// Resolves to the first matching clause.
+template <typename... Args>
+struct Invoker {
+  typedef typename std::conditional<
+      MemFunAndRef::Accept<Args...>::value, MemFunAndRef,
+      typename std::conditional<
+          MemFunAndPtr::Accept<Args...>::value, MemFunAndPtr,
+          typename std::conditional<
+              DataMemAndRef::Accept<Args...>::value, DataMemAndRef,
+              typename std::conditional<DataMemAndPtr::Accept<Args...>::value,
+                                        DataMemAndPtr, Callable>::type>::type>::
+          type>::type type;
+};
+
+// The result type of Invoke<F, Args...>.
+template <typename F, typename... Args>
+using invoke_result_t = decltype(Invoker<F, Args...>::type::Invoke(
+    std::declval<F>(), std::declval<Args>()...));
+
+// Invoke(f, args...) is an implementation of INVOKE(f, args...) from section
+// [func.require] of the C++ standard.
+template <typename F, typename... Args>
+invoke_result_t<F, Args...> invoke(F&& f, Args&&... args) {
+  return Invoker<F, Args...>::type::Invoke(std::forward<F>(f),
+                                           std::forward<Args>(args)...);
+}
+
+template <typename AlwaysVoid, typename, typename, typename...>
+struct IsInvocableRImpl : std::false_type {};
+
+template <typename R, typename F, typename... Args>
+struct IsInvocableRImpl<
+    absl::void_t<absl::base_internal::invoke_result_t<F, Args...> >, R, F,
+    Args...>
+    : std::integral_constant<
+          bool,
+          std::is_convertible<absl::base_internal::invoke_result_t<F, Args...>,
+                              R>::value ||
+              std::is_void<R>::value> {};
+
+// Type trait whose member `value` is true if invoking `F` with `Args` is valid,
+// and either the return type is convertible to `R`, or `R` is void.
+// C++11-compatible version of `std::is_invocable_r`.
+template <typename R, typename F, typename... Args>
+using is_invocable_r = IsInvocableRImpl<void, R, F, Args...>;
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_INTERNAL_CPLUSPLUS_LANG >= 201703L
+
+#endif  // ABSL_BASE_INTERNAL_INVOKE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/low_level_alloc.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/low_level_alloc.h
new file mode 100644
index 0000000000000000000000000000000000000000..eabb14a9b49fd3a83e306bc4a2c0019033c5b1b7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/low_level_alloc.h
@@ -0,0 +1,126 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+#ifndef ABSL_BASE_INTERNAL_LOW_LEVEL_ALLOC_H_
+#define ABSL_BASE_INTERNAL_LOW_LEVEL_ALLOC_H_
+
+// A simple thread-safe memory allocator that does not depend on
+// mutexes or thread-specific data.  It is intended to be used
+// sparingly, and only when malloc() would introduce an unwanted
+// dependency, such as inside the heap-checker, or the Mutex
+// implementation.
+
+// IWYU pragma: private, include "base/low_level_alloc.h"
+
+#include <sys/types.h>
+
+#include <cstdint>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+
+// LowLevelAlloc requires that the platform support low-level
+// allocation of virtual memory. Platforms lacking this cannot use
+// LowLevelAlloc.
+#ifdef ABSL_LOW_LEVEL_ALLOC_MISSING
+#error ABSL_LOW_LEVEL_ALLOC_MISSING cannot be directly set
+#elif !defined(ABSL_HAVE_MMAP) && !defined(_WIN32)
+#define ABSL_LOW_LEVEL_ALLOC_MISSING 1
+#endif
+
+// Using LowLevelAlloc with kAsyncSignalSafe isn't supported on Windows or
+// asm.js / WebAssembly.
+// See https://kripken.github.io/emscripten-site/docs/porting/pthreads.html
+// for more information.
+#ifdef ABSL_LOW_LEVEL_ALLOC_ASYNC_SIGNAL_SAFE_MISSING
+#error ABSL_LOW_LEVEL_ALLOC_ASYNC_SIGNAL_SAFE_MISSING cannot be directly set
+#elif defined(_WIN32) || defined(__asmjs__) || defined(__wasm__)
+#define ABSL_LOW_LEVEL_ALLOC_ASYNC_SIGNAL_SAFE_MISSING 1
+#endif
+
+#include <cstddef>
+
+#include "absl/base/port.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+class LowLevelAlloc {
+ public:
+  struct Arena;       // an arena from which memory may be allocated
+
+  // Returns a pointer to a block of at least "request" bytes
+  // that have been newly allocated from the specific arena.
+  // for Alloc() call the DefaultArena() is used.
+  // Returns 0 if passed request==0.
+  // Does not return 0 under other circumstances; it crashes if memory
+  // is not available.
+  static void *Alloc(size_t request) ABSL_ATTRIBUTE_SECTION(malloc_hook);
+  static void *AllocWithArena(size_t request, Arena *arena)
+      ABSL_ATTRIBUTE_SECTION(malloc_hook);
+
+  // Deallocates a region of memory that was previously allocated with
+  // Alloc().   Does nothing if passed 0.   "s" must be either 0,
+  // or must have been returned from a call to Alloc() and not yet passed to
+  // Free() since that call to Alloc().  The space is returned to the arena
+  // from which it was allocated.
+  static void Free(void *s) ABSL_ATTRIBUTE_SECTION(malloc_hook);
+
+  // ABSL_ATTRIBUTE_SECTION(malloc_hook) for Alloc* and Free
+  // are to put all callers of MallocHook::Invoke* in this module
+  // into special section,
+  // so that MallocHook::GetCallerStackTrace can function accurately.
+
+  // Create a new arena.
+  // The root metadata for the new arena is allocated in the
+  // meta_data_arena; the DefaultArena() can be passed for meta_data_arena.
+  // These values may be ored into flags:
+  enum {
+    // Report calls to Alloc() and Free() via the MallocHook interface.
+    // Set in the DefaultArena.
+    kCallMallocHook = 0x0001,
+
+#ifndef ABSL_LOW_LEVEL_ALLOC_ASYNC_SIGNAL_SAFE_MISSING
+    // Make calls to Alloc(), Free() be async-signal-safe. Not set in
+    // DefaultArena(). Not supported on all platforms.
+    kAsyncSignalSafe = 0x0002,
+#endif
+  };
+  // Construct a new arena.  The allocation of the underlying metadata honors
+  // the provided flags.  For example, the call NewArena(kAsyncSignalSafe)
+  // is itself async-signal-safe, as well as generatating an arena that provides
+  // async-signal-safe Alloc/Free.
+  static Arena *NewArena(uint32_t flags);
+
+  // Destroys an arena allocated by NewArena and returns true,
+  // provided no allocated blocks remain in the arena.
+  // If allocated blocks remain in the arena, does nothing and
+  // returns false.
+  // It is illegal to attempt to destroy the DefaultArena().
+  static bool DeleteArena(Arena *arena);
+
+  // The default arena that always exists.
+  static Arena *DefaultArena();
+
+ private:
+  LowLevelAlloc();      // no instances
+};
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_LOW_LEVEL_ALLOC_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/low_level_scheduling.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/low_level_scheduling.h
new file mode 100644
index 0000000000000000000000000000000000000000..9baccc0659beb4da0e9bd10ceb5282a522ce1aac
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/low_level_scheduling.h
@@ -0,0 +1,134 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Core interfaces and definitions used by by low-level interfaces such as
+// SpinLock.
+
+#ifndef ABSL_BASE_INTERNAL_LOW_LEVEL_SCHEDULING_H_
+#define ABSL_BASE_INTERNAL_LOW_LEVEL_SCHEDULING_H_
+
+#include "absl/base/internal/raw_logging.h"
+#include "absl/base/internal/scheduling_mode.h"
+#include "absl/base/macros.h"
+
+// The following two declarations exist so SchedulingGuard may friend them with
+// the appropriate language linkage.  These callbacks allow libc internals, such
+// as function level statics, to schedule cooperatively when locking.
+extern "C" bool __google_disable_rescheduling(void);
+extern "C" void __google_enable_rescheduling(bool disable_result);
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+class CondVar;
+class Mutex;
+
+namespace synchronization_internal {
+int MutexDelay(int32_t c, int mode);
+}  // namespace synchronization_internal
+
+namespace base_internal {
+
+class SchedulingHelper;  // To allow use of SchedulingGuard.
+class SpinLock;          // To allow use of SchedulingGuard.
+
+// SchedulingGuard
+// Provides guard semantics that may be used to disable cooperative rescheduling
+// of the calling thread within specific program blocks.  This is used to
+// protect resources (e.g. low-level SpinLocks or Domain code) that cooperative
+// scheduling depends on.
+//
+// Domain implementations capable of rescheduling in reaction to involuntary
+// kernel thread actions (e.g blocking due to a pagefault or syscall) must
+// guarantee that an annotated thread is not allowed to (cooperatively)
+// reschedule until the annotated region is complete.
+//
+// It is an error to attempt to use a cooperatively scheduled resource (e.g.
+// Mutex) within a rescheduling-disabled region.
+//
+// All methods are async-signal safe.
+class SchedulingGuard {
+ public:
+  // Returns true iff the calling thread may be cooperatively rescheduled.
+  static bool ReschedulingIsAllowed();
+  SchedulingGuard(const SchedulingGuard&) = delete;
+  SchedulingGuard& operator=(const SchedulingGuard&) = delete;
+
+ private:
+  // Disable cooperative rescheduling of the calling thread.  It may still
+  // initiate scheduling operations (e.g. wake-ups), however, it may not itself
+  // reschedule.  Nestable.  The returned result is opaque, clients should not
+  // attempt to interpret it.
+  // REQUIRES: Result must be passed to a pairing EnableScheduling().
+  static bool DisableRescheduling();
+
+  // Marks the end of a rescheduling disabled region, previously started by
+  // DisableRescheduling().
+  // REQUIRES: Pairs with innermost call (and result) of DisableRescheduling().
+  static void EnableRescheduling(bool disable_result);
+
+  // A scoped helper for {Disable, Enable}Rescheduling().
+  // REQUIRES: destructor must run in same thread as constructor.
+  struct ScopedDisable {
+    ScopedDisable() { disabled = SchedulingGuard::DisableRescheduling(); }
+    ~ScopedDisable() { SchedulingGuard::EnableRescheduling(disabled); }
+
+    bool disabled;
+  };
+
+  // A scoped helper to enable rescheduling temporarily.
+  // REQUIRES: destructor must run in same thread as constructor.
+  class ScopedEnable {
+   public:
+    ScopedEnable();
+    ~ScopedEnable();
+
+   private:
+    int scheduling_disabled_depth_;
+  };
+
+  // Access to SchedulingGuard is explicitly permitted.
+  friend class absl::CondVar;
+  friend class absl::Mutex;
+  friend class SchedulingHelper;
+  friend class SpinLock;
+  friend int absl::synchronization_internal::MutexDelay(int32_t c, int mode);
+};
+
+//------------------------------------------------------------------------------
+// End of public interfaces.
+//------------------------------------------------------------------------------
+
+inline bool SchedulingGuard::ReschedulingIsAllowed() {
+  return false;
+}
+
+inline bool SchedulingGuard::DisableRescheduling() {
+  return false;
+}
+
+inline void SchedulingGuard::EnableRescheduling(bool /* disable_result */) {
+  return;
+}
+
+inline SchedulingGuard::ScopedEnable::ScopedEnable()
+    : scheduling_disabled_depth_(0) {}
+inline SchedulingGuard::ScopedEnable::~ScopedEnable() {
+  ABSL_RAW_CHECK(scheduling_disabled_depth_ == 0, "disable unused warning");
+}
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_LOW_LEVEL_SCHEDULING_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/per_thread_tls.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/per_thread_tls.h
new file mode 100644
index 0000000000000000000000000000000000000000..cf5e97a0470eb57c15862e0c24195ebbf783ac12
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/per_thread_tls.h
@@ -0,0 +1,52 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_BASE_INTERNAL_PER_THREAD_TLS_H_
+#define ABSL_BASE_INTERNAL_PER_THREAD_TLS_H_
+
+// This header defines two macros:
+//
+// If the platform supports thread-local storage:
+//
+// * ABSL_PER_THREAD_TLS_KEYWORD is the C keyword needed to declare a
+//   thread-local variable
+// * ABSL_PER_THREAD_TLS is 1
+//
+// Otherwise:
+//
+// * ABSL_PER_THREAD_TLS_KEYWORD is empty
+// * ABSL_PER_THREAD_TLS is 0
+//
+// Microsoft C supports thread-local storage.
+// GCC supports it if the appropriate version of glibc is available,
+// which the programmer can indicate by defining ABSL_HAVE_TLS
+
+#include "absl/base/port.h"  // For ABSL_HAVE_TLS
+
+#if defined(ABSL_PER_THREAD_TLS)
+#error ABSL_PER_THREAD_TLS cannot be directly set
+#elif defined(ABSL_PER_THREAD_TLS_KEYWORD)
+#error ABSL_PER_THREAD_TLS_KEYWORD cannot be directly set
+#elif defined(ABSL_HAVE_TLS)
+#define ABSL_PER_THREAD_TLS_KEYWORD __thread
+#define ABSL_PER_THREAD_TLS 1
+#elif defined(_MSC_VER)
+#define ABSL_PER_THREAD_TLS_KEYWORD __declspec(thread)
+#define ABSL_PER_THREAD_TLS 1
+#else
+#define ABSL_PER_THREAD_TLS_KEYWORD
+#define ABSL_PER_THREAD_TLS 0
+#endif
+
+#endif  // ABSL_BASE_INTERNAL_PER_THREAD_TLS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/prefetch.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/prefetch.h
new file mode 100644
index 0000000000000000000000000000000000000000..06419283bad2545cc679fd9ccebb8b51dbc612b0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/prefetch.h
@@ -0,0 +1,138 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_BASE_INTERNAL_PREFETCH_H_
+#define ABSL_BASE_INTERNAL_PREFETCH_H_
+
+#include "absl/base/config.h"
+
+#ifdef __SSE__
+#include <xmmintrin.h>
+#endif
+
+#if defined(_MSC_VER) && defined(ABSL_INTERNAL_HAVE_SSE)
+#include <intrin.h>
+#pragma intrinsic(_mm_prefetch)
+#endif
+
+// Compatibility wrappers around __builtin_prefetch, to prefetch data
+// for read if supported by the toolchain.
+
+// Move data into the cache before it is read, or "prefetch" it.
+//
+// The value of `addr` is the address of the memory to prefetch. If
+// the target and compiler support it, data prefetch instructions are
+// generated. If the prefetch is done some time before the memory is
+// read, it may be in the cache by the time the read occurs.
+//
+// The function names specify the temporal locality heuristic applied,
+// using the names of Intel prefetch instructions:
+//
+//   T0 - high degree of temporal locality; data should be left in as
+//        many levels of the cache possible
+//   T1 - moderate degree of temporal locality
+//   T2 - low degree of temporal locality
+//   Nta - no temporal locality, data need not be left in the cache
+//         after the read
+//
+// Incorrect or gratuitous use of these functions can degrade
+// performance, so use them only when representative benchmarks show
+// an improvement.
+//
+// Example usage:
+//
+//   absl::base_internal::PrefetchT0(addr);
+//
+// Currently, the different prefetch calls behave on some Intel
+// architectures as follows:
+//
+//                 SNB..SKL   SKX
+// PrefetchT0()   L1/L2/L3  L1/L2
+// PrefetchT1()      L2/L3     L2
+// PrefetchT2()      L2/L3     L2
+// PrefetchNta()  L1/--/L3  L1*
+//
+// * On SKX PrefetchNta() will bring the line into L1 but will evict
+//   from L3 cache. This might result in surprising behavior.
+//
+// SNB = Sandy Bridge, SKL = Skylake, SKX = Skylake Xeon.
+//
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+void PrefetchT0(const void* addr);
+void PrefetchT1(const void* addr);
+void PrefetchT2(const void* addr);
+void PrefetchNta(const void* addr);
+
+// Implementation details follow.
+
+#if ABSL_HAVE_BUILTIN(__builtin_prefetch) || defined(__GNUC__)
+
+#define ABSL_INTERNAL_HAVE_PREFETCH 1
+
+// See __builtin_prefetch:
+// https://gcc.gnu.org/onlinedocs/gcc/Other-Builtins.html.
+//
+// These functions speculatively load for read only. This is
+// safe for all currently supported platforms. However, prefetch for
+// store may have problems depending on the target platform.
+//
+inline void PrefetchT0(const void* addr) {
+  // Note: this uses prefetcht0 on Intel.
+  __builtin_prefetch(addr, 0, 3);
+}
+inline void PrefetchT1(const void* addr) {
+  // Note: this uses prefetcht1 on Intel.
+  __builtin_prefetch(addr, 0, 2);
+}
+inline void PrefetchT2(const void* addr) {
+  // Note: this uses prefetcht2 on Intel.
+  __builtin_prefetch(addr, 0, 1);
+}
+inline void PrefetchNta(const void* addr) {
+  // Note: this uses prefetchtnta on Intel.
+  __builtin_prefetch(addr, 0, 0);
+}
+
+#elif defined(ABSL_INTERNAL_HAVE_SSE)
+
+#define ABSL_INTERNAL_HAVE_PREFETCH 1
+
+inline void PrefetchT0(const void* addr) {
+  _mm_prefetch(reinterpret_cast<const char*>(addr), _MM_HINT_T0);
+}
+inline void PrefetchT1(const void* addr) {
+  _mm_prefetch(reinterpret_cast<const char*>(addr), _MM_HINT_T1);
+}
+inline void PrefetchT2(const void* addr) {
+  _mm_prefetch(reinterpret_cast<const char*>(addr), _MM_HINT_T2);
+}
+inline void PrefetchNta(const void* addr) {
+  _mm_prefetch(reinterpret_cast<const char*>(addr), _MM_HINT_NTA);
+}
+
+#else
+inline void PrefetchT0(const void*) {}
+inline void PrefetchT1(const void*) {}
+inline void PrefetchT2(const void*) {}
+inline void PrefetchNta(const void*) {}
+#endif
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_PREFETCH_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/raw_logging.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/raw_logging.h
new file mode 100644
index 0000000000000000000000000000000000000000..c7b889cd8a6b92c05f75c07acb4a4e62c7b4023a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/raw_logging.h
@@ -0,0 +1,195 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Thread-safe logging routines that do not allocate any memory or
+// acquire any locks, and can therefore be used by low-level memory
+// allocation, synchronization, and signal-handling code.
+
+#ifndef ABSL_BASE_INTERNAL_RAW_LOGGING_H_
+#define ABSL_BASE_INTERNAL_RAW_LOGGING_H_
+
+#include <string>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/base/internal/atomic_hook.h"
+#include "absl/base/log_severity.h"
+#include "absl/base/macros.h"
+#include "absl/base/optimization.h"
+#include "absl/base/port.h"
+
+// This is similar to LOG(severity) << format..., but
+// * it is to be used ONLY by low-level modules that can't use normal LOG()
+// * it is designed to be a low-level logger that does not allocate any
+//   memory and does not need any locks, hence:
+// * it logs straight and ONLY to STDERR w/o buffering
+// * it uses an explicit printf-format and arguments list
+// * it will silently chop off really long message strings
+// Usage example:
+//   ABSL_RAW_LOG(ERROR, "Failed foo with %i: %s", status, error);
+// This will print an almost standard log line like this to stderr only:
+//   E0821 211317 file.cc:123] RAW: Failed foo with 22: bad_file
+
+#define ABSL_RAW_LOG(severity, ...)                                            \
+  do {                                                                         \
+    constexpr const char* absl_raw_log_internal_basename =                     \
+        ::absl::raw_log_internal::Basename(__FILE__, sizeof(__FILE__) - 1);    \
+    ::absl::raw_log_internal::RawLog(ABSL_RAW_LOG_INTERNAL_##severity,         \
+                                     absl_raw_log_internal_basename, __LINE__, \
+                                     __VA_ARGS__);                             \
+  } while (0)
+
+// Similar to CHECK(condition) << message, but for low-level modules:
+// we use only ABSL_RAW_LOG that does not allocate memory.
+// We do not want to provide args list here to encourage this usage:
+//   if (!cond)  ABSL_RAW_LOG(FATAL, "foo ...", hard_to_compute_args);
+// so that the args are not computed when not needed.
+#define ABSL_RAW_CHECK(condition, message)                             \
+  do {                                                                 \
+    if (ABSL_PREDICT_FALSE(!(condition))) {                            \
+      ABSL_RAW_LOG(FATAL, "Check %s failed: %s", #condition, message); \
+    }                                                                  \
+  } while (0)
+
+// ABSL_INTERNAL_LOG and ABSL_INTERNAL_CHECK work like the RAW variants above,
+// except that if the richer log library is linked into the binary, we dispatch
+// to that instead.  This is potentially useful for internal logging and
+// assertions, where we are using RAW_LOG neither for its async-signal-safety
+// nor for its non-allocating nature, but rather because raw logging has very
+// few other dependencies.
+//
+// The API is a subset of the above: each macro only takes two arguments.  Use
+// StrCat if you need to build a richer message.
+#define ABSL_INTERNAL_LOG(severity, message)                              \
+  do {                                                                    \
+    constexpr const char* absl_raw_log_internal_filename = __FILE__;      \
+    ::absl::raw_log_internal::internal_log_function(                      \
+        ABSL_RAW_LOG_INTERNAL_##severity, absl_raw_log_internal_filename, \
+        __LINE__, message);                                               \
+    if (ABSL_RAW_LOG_INTERNAL_##severity == ::absl::LogSeverity::kFatal)  \
+      ABSL_UNREACHABLE();                                                 \
+  } while (0)
+
+#define ABSL_INTERNAL_CHECK(condition, message)                    \
+  do {                                                             \
+    if (ABSL_PREDICT_FALSE(!(condition))) {                        \
+      std::string death_message = "Check " #condition " failed: "; \
+      death_message += std::string(message);                       \
+      ABSL_INTERNAL_LOG(FATAL, death_message);                     \
+    }                                                              \
+  } while (0)
+
+#define ABSL_RAW_LOG_INTERNAL_INFO ::absl::LogSeverity::kInfo
+#define ABSL_RAW_LOG_INTERNAL_WARNING ::absl::LogSeverity::kWarning
+#define ABSL_RAW_LOG_INTERNAL_ERROR ::absl::LogSeverity::kError
+#define ABSL_RAW_LOG_INTERNAL_FATAL ::absl::LogSeverity::kFatal
+#define ABSL_RAW_LOG_INTERNAL_LEVEL(severity) \
+  ::absl::NormalizeLogSeverity(severity)
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace raw_log_internal {
+
+// Helper function to implement ABSL_RAW_LOG
+// Logs format... at "severity" level, reporting it
+// as called from file:line.
+// This does not allocate memory or acquire locks.
+void RawLog(absl::LogSeverity severity, const char* file, int line,
+            const char* format, ...) ABSL_PRINTF_ATTRIBUTE(4, 5);
+
+// Writes the provided buffer directly to stderr, in a signal-safe, low-level
+// manner.
+void AsyncSignalSafeWriteToStderr(const char* s, size_t len);
+
+// compile-time function to get the "base" filename, that is, the part of
+// a filename after the last "/" or "\" path separator.  The search starts at
+// the end of the string; the second parameter is the length of the string.
+constexpr const char* Basename(const char* fname, int offset) {
+  return offset == 0 || fname[offset - 1] == '/' || fname[offset - 1] == '\\'
+             ? fname + offset
+             : Basename(fname, offset - 1);
+}
+
+// For testing only.
+// Returns true if raw logging is fully supported. When it is not
+// fully supported, no messages will be emitted, but a log at FATAL
+// severity will cause an abort.
+//
+// TODO(gfalcon): Come up with a better name for this method.
+bool RawLoggingFullySupported();
+
+// Function type for a raw_log customization hook for suppressing messages
+// by severity, and for writing custom prefixes on non-suppressed messages.
+//
+// The installed hook is called for every raw log invocation.  The message will
+// be logged to stderr only if the hook returns true.  FATAL errors will cause
+// the process to abort, even if writing to stderr is suppressed.  The hook is
+// also provided with an output buffer, where it can write a custom log message
+// prefix.
+//
+// The raw_log system does not allocate memory or grab locks.  User-provided
+// hooks must avoid these operations, and must not throw exceptions.
+//
+// 'severity' is the severity level of the message being written.
+// 'file' and 'line' are the file and line number where the ABSL_RAW_LOG macro
+// was located.
+// 'buf' and 'buf_size' are pointers to the buffer and buffer size.  If the
+// hook writes a prefix, it must increment *buf and decrement *buf_size
+// accordingly.
+using LogFilterAndPrefixHook = bool (*)(absl::LogSeverity severity,
+                                        const char* file, int line, char** buf,
+                                        int* buf_size);
+
+// Function type for a raw_log customization hook called to abort a process
+// when a FATAL message is logged.  If the provided AbortHook() returns, the
+// logging system will call abort().
+//
+// 'file' and 'line' are the file and line number where the ABSL_RAW_LOG macro
+// was located.
+// The NUL-terminated logged message lives in the buffer between 'buf_start'
+// and 'buf_end'.  'prefix_end' points to the first non-prefix character of the
+// buffer (as written by the LogFilterAndPrefixHook.)
+//
+// The lifetime of the filename and message buffers will not end while the
+// process remains alive.
+using AbortHook = void (*)(const char* file, int line, const char* buf_start,
+                           const char* prefix_end, const char* buf_end);
+
+// Internal logging function for ABSL_INTERNAL_LOG to dispatch to.
+//
+// TODO(gfalcon): When string_view no longer depends on base, change this
+// interface to take its message as a string_view instead.
+using InternalLogFunction = void (*)(absl::LogSeverity severity,
+                                     const char* file, int line,
+                                     const std::string& message);
+
+ABSL_INTERNAL_ATOMIC_HOOK_ATTRIBUTES ABSL_DLL extern base_internal::AtomicHook<
+    InternalLogFunction>
+    internal_log_function;
+
+// Registers hooks of the above types.  Only a single hook of each type may be
+// registered.  It is an error to call these functions multiple times with
+// different input arguments.
+//
+// These functions are safe to call at any point during initialization; they do
+// not block or malloc, and are async-signal safe.
+void RegisterLogFilterAndPrefixHook(LogFilterAndPrefixHook func);
+void RegisterAbortHook(AbortHook func);
+void RegisterInternalLogFunction(InternalLogFunction func);
+
+}  // namespace raw_log_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_RAW_LOGGING_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/scheduling_mode.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/scheduling_mode.h
new file mode 100644
index 0000000000000000000000000000000000000000..8be5ab6dd3c00bd61ce847892e8434e467478fe8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/scheduling_mode.h
@@ -0,0 +1,58 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Core interfaces and definitions used by by low-level interfaces such as
+// SpinLock.
+
+#ifndef ABSL_BASE_INTERNAL_SCHEDULING_MODE_H_
+#define ABSL_BASE_INTERNAL_SCHEDULING_MODE_H_
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+// Used to describe how a thread may be scheduled.  Typically associated with
+// the declaration of a resource supporting synchronized access.
+//
+// SCHEDULE_COOPERATIVE_AND_KERNEL:
+// Specifies that when waiting, a cooperative thread (e.g. a Fiber) may
+// reschedule (using base::scheduling semantics); allowing other cooperative
+// threads to proceed.
+//
+// SCHEDULE_KERNEL_ONLY: (Also described as "non-cooperative")
+// Specifies that no cooperative scheduling semantics may be used, even if the
+// current thread is itself cooperatively scheduled.  This means that
+// cooperative threads will NOT allow other cooperative threads to execute in
+// their place while waiting for a resource of this type.  Host operating system
+// semantics (e.g. a futex) may still be used.
+//
+// When optional, clients should strongly prefer SCHEDULE_COOPERATIVE_AND_KERNEL
+// by default.  SCHEDULE_KERNEL_ONLY should only be used for resources on which
+// base::scheduling (e.g. the implementation of a Scheduler) may depend.
+//
+// NOTE: Cooperative resources may not be nested below non-cooperative ones.
+// This means that it is invalid to to acquire a SCHEDULE_COOPERATIVE_AND_KERNEL
+// resource if a SCHEDULE_KERNEL_ONLY resource is already held.
+enum SchedulingMode {
+  SCHEDULE_KERNEL_ONLY = 0,         // Allow scheduling only the host OS.
+  SCHEDULE_COOPERATIVE_AND_KERNEL,  // Also allow cooperative scheduling.
+};
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_SCHEDULING_MODE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock.h
new file mode 100644
index 0000000000000000000000000000000000000000..09ba5824b1c62719b4efbfd73881517703027997
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock.h
@@ -0,0 +1,252 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+//  Most users requiring mutual exclusion should use Mutex.
+//  SpinLock is provided for use in two situations:
+//   - for use by Abseil internal code that Mutex itself depends on
+//   - for async signal safety (see below)
+
+// SpinLock is async signal safe.  If a spinlock is used within a signal
+// handler, all code that acquires the lock must ensure that the signal cannot
+// arrive while they are holding the lock.  Typically, this is done by blocking
+// the signal.
+//
+// Threads waiting on a SpinLock may be woken in an arbitrary order.
+
+#ifndef ABSL_BASE_INTERNAL_SPINLOCK_H_
+#define ABSL_BASE_INTERNAL_SPINLOCK_H_
+
+#include <atomic>
+#include <cstdint>
+
+#include "absl/base/attributes.h"
+#include "absl/base/const_init.h"
+#include "absl/base/dynamic_annotations.h"
+#include "absl/base/internal/low_level_scheduling.h"
+#include "absl/base/internal/raw_logging.h"
+#include "absl/base/internal/scheduling_mode.h"
+#include "absl/base/internal/tsan_mutex_interface.h"
+#include "absl/base/thread_annotations.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+class ABSL_LOCKABLE SpinLock {
+ public:
+  SpinLock() : lockword_(kSpinLockCooperative) {
+    ABSL_TSAN_MUTEX_CREATE(this, __tsan_mutex_not_static);
+  }
+
+  // Constructors that allow non-cooperative spinlocks to be created for use
+  // inside thread schedulers.  Normal clients should not use these.
+  explicit SpinLock(base_internal::SchedulingMode mode);
+
+  // Constructor for global SpinLock instances.  See absl/base/const_init.h.
+  constexpr SpinLock(absl::ConstInitType, base_internal::SchedulingMode mode)
+      : lockword_(IsCooperative(mode) ? kSpinLockCooperative : 0) {}
+
+  // For global SpinLock instances prefer trivial destructor when possible.
+  // Default but non-trivial destructor in some build configurations causes an
+  // extra static initializer.
+#ifdef ABSL_INTERNAL_HAVE_TSAN_INTERFACE
+  ~SpinLock() { ABSL_TSAN_MUTEX_DESTROY(this, __tsan_mutex_not_static); }
+#else
+  ~SpinLock() = default;
+#endif
+
+  // Acquire this SpinLock.
+  inline void Lock() ABSL_EXCLUSIVE_LOCK_FUNCTION() {
+    ABSL_TSAN_MUTEX_PRE_LOCK(this, 0);
+    if (!TryLockImpl()) {
+      SlowLock();
+    }
+    ABSL_TSAN_MUTEX_POST_LOCK(this, 0, 0);
+  }
+
+  // Try to acquire this SpinLock without blocking and return true if the
+  // acquisition was successful.  If the lock was not acquired, false is
+  // returned.  If this SpinLock is free at the time of the call, TryLock
+  // will return true with high probability.
+  inline bool TryLock() ABSL_EXCLUSIVE_TRYLOCK_FUNCTION(true) {
+    ABSL_TSAN_MUTEX_PRE_LOCK(this, __tsan_mutex_try_lock);
+    bool res = TryLockImpl();
+    ABSL_TSAN_MUTEX_POST_LOCK(
+        this, __tsan_mutex_try_lock | (res ? 0 : __tsan_mutex_try_lock_failed),
+        0);
+    return res;
+  }
+
+  // Release this SpinLock, which must be held by the calling thread.
+  inline void Unlock() ABSL_UNLOCK_FUNCTION() {
+    ABSL_TSAN_MUTEX_PRE_UNLOCK(this, 0);
+    uint32_t lock_value = lockword_.load(std::memory_order_relaxed);
+    lock_value = lockword_.exchange(lock_value & kSpinLockCooperative,
+                                    std::memory_order_release);
+
+    if ((lock_value & kSpinLockDisabledScheduling) != 0) {
+      base_internal::SchedulingGuard::EnableRescheduling(true);
+    }
+    if ((lock_value & kWaitTimeMask) != 0) {
+      // Collect contentionz profile info, and speed the wakeup of any waiter.
+      // The wait_cycles value indicates how long this thread spent waiting
+      // for the lock.
+      SlowUnlock(lock_value);
+    }
+    ABSL_TSAN_MUTEX_POST_UNLOCK(this, 0);
+  }
+
+  // Determine if the lock is held.  When the lock is held by the invoking
+  // thread, true will always be returned. Intended to be used as
+  // CHECK(lock.IsHeld()).
+  inline bool IsHeld() const {
+    return (lockword_.load(std::memory_order_relaxed) & kSpinLockHeld) != 0;
+  }
+
+  // Return immediately if this thread holds the SpinLock exclusively.
+  // Otherwise, report an error by crashing with a diagnostic.
+  inline void AssertHeld() const ABSL_ASSERT_EXCLUSIVE_LOCK() {
+    if (!IsHeld()) {
+      ABSL_RAW_LOG(FATAL, "thread should hold the lock on SpinLock");
+    }
+  }
+
+ protected:
+  // These should not be exported except for testing.
+
+  // Store number of cycles between wait_start_time and wait_end_time in a
+  // lock value.
+  static uint32_t EncodeWaitCycles(int64_t wait_start_time,
+                                   int64_t wait_end_time);
+
+  // Extract number of wait cycles in a lock value.
+  static int64_t DecodeWaitCycles(uint32_t lock_value);
+
+  // Provide access to protected method above.  Use for testing only.
+  friend struct SpinLockTest;
+
+ private:
+  // lockword_ is used to store the following:
+  //
+  // bit[0] encodes whether a lock is being held.
+  // bit[1] encodes whether a lock uses cooperative scheduling.
+  // bit[2] encodes whether the current lock holder disabled scheduling when
+  //        acquiring the lock. Only set when kSpinLockHeld is also set.
+  // bit[3:31] encodes time a lock spent on waiting as a 29-bit unsigned int.
+  //        This is set by the lock holder to indicate how long it waited on
+  //        the lock before eventually acquiring it. The number of cycles is
+  //        encoded as a 29-bit unsigned int, or in the case that the current
+  //        holder did not wait but another waiter is queued, the LSB
+  //        (kSpinLockSleeper) is set. The implementation does not explicitly
+  //        track the number of queued waiters beyond this. It must always be
+  //        assumed that waiters may exist if the current holder was required to
+  //        queue.
+  //
+  // Invariant: if the lock is not held, the value is either 0 or
+  // kSpinLockCooperative.
+  static constexpr uint32_t kSpinLockHeld = 1;
+  static constexpr uint32_t kSpinLockCooperative = 2;
+  static constexpr uint32_t kSpinLockDisabledScheduling = 4;
+  static constexpr uint32_t kSpinLockSleeper = 8;
+  // Includes kSpinLockSleeper.
+  static constexpr uint32_t kWaitTimeMask =
+      ~(kSpinLockHeld | kSpinLockCooperative | kSpinLockDisabledScheduling);
+
+  // Returns true if the provided scheduling mode is cooperative.
+  static constexpr bool IsCooperative(
+      base_internal::SchedulingMode scheduling_mode) {
+    return scheduling_mode == base_internal::SCHEDULE_COOPERATIVE_AND_KERNEL;
+  }
+
+  uint32_t TryLockInternal(uint32_t lock_value, uint32_t wait_cycles);
+  void SlowLock() ABSL_ATTRIBUTE_COLD;
+  void SlowUnlock(uint32_t lock_value) ABSL_ATTRIBUTE_COLD;
+  uint32_t SpinLoop();
+
+  inline bool TryLockImpl() {
+    uint32_t lock_value = lockword_.load(std::memory_order_relaxed);
+    return (TryLockInternal(lock_value, 0) & kSpinLockHeld) == 0;
+  }
+
+  std::atomic<uint32_t> lockword_;
+
+  SpinLock(const SpinLock&) = delete;
+  SpinLock& operator=(const SpinLock&) = delete;
+};
+
+// Corresponding locker object that arranges to acquire a spinlock for
+// the duration of a C++ scope.
+class ABSL_SCOPED_LOCKABLE SpinLockHolder {
+ public:
+  inline explicit SpinLockHolder(SpinLock* l) ABSL_EXCLUSIVE_LOCK_FUNCTION(l)
+      : lock_(l) {
+    l->Lock();
+  }
+  inline ~SpinLockHolder() ABSL_UNLOCK_FUNCTION() { lock_->Unlock(); }
+
+  SpinLockHolder(const SpinLockHolder&) = delete;
+  SpinLockHolder& operator=(const SpinLockHolder&) = delete;
+
+ private:
+  SpinLock* lock_;
+};
+
+// Register a hook for profiling support.
+//
+// The function pointer registered here will be called whenever a spinlock is
+// contended.  The callback is given an opaque handle to the contended spinlock
+// and the number of wait cycles.  This is thread-safe, but only a single
+// profiler can be registered.  It is an error to call this function multiple
+// times with different arguments.
+void RegisterSpinLockProfiler(void (*fn)(const void* lock,
+                                         int64_t wait_cycles));
+
+//------------------------------------------------------------------------------
+// Public interface ends here.
+//------------------------------------------------------------------------------
+
+// If (result & kSpinLockHeld) == 0, then *this was successfully locked.
+// Otherwise, returns last observed value for lockword_.
+inline uint32_t SpinLock::TryLockInternal(uint32_t lock_value,
+                                          uint32_t wait_cycles) {
+  if ((lock_value & kSpinLockHeld) != 0) {
+    return lock_value;
+  }
+
+  uint32_t sched_disabled_bit = 0;
+  if ((lock_value & kSpinLockCooperative) == 0) {
+    // For non-cooperative locks we must make sure we mark ourselves as
+    // non-reschedulable before we attempt to CompareAndSwap.
+    if (base_internal::SchedulingGuard::DisableRescheduling()) {
+      sched_disabled_bit = kSpinLockDisabledScheduling;
+    }
+  }
+
+  if (!lockword_.compare_exchange_strong(
+          lock_value,
+          kSpinLockHeld | lock_value | wait_cycles | sched_disabled_bit,
+          std::memory_order_acquire, std::memory_order_relaxed)) {
+    base_internal::SchedulingGuard::EnableRescheduling(sched_disabled_bit != 0);
+  }
+
+  return lock_value;
+}
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_SPINLOCK_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock_akaros.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock_akaros.inc
new file mode 100644
index 0000000000000000000000000000000000000000..7b0cada4f131b30c6d952b92c999b972df80654b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock_akaros.inc
@@ -0,0 +1,35 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// This file is an Akaros-specific part of spinlock_wait.cc
+
+#include <atomic>
+
+#include "absl/base/internal/scheduling_mode.h"
+
+extern "C" {
+
+ABSL_ATTRIBUTE_WEAK void ABSL_INTERNAL_C_SYMBOL(AbslInternalSpinLockDelay)(
+    std::atomic<uint32_t>* /* lock_word */, uint32_t /* value */,
+    int /* loop */, absl::base_internal::SchedulingMode /* mode */) {
+  // In Akaros, one must take care not to call anything that could cause a
+  // malloc(), a blocking system call, or a uthread_yield() while holding a
+  // spinlock. Our callers assume will not call into libraries or other
+  // arbitrary code.
+}
+
+ABSL_ATTRIBUTE_WEAK void ABSL_INTERNAL_C_SYMBOL(AbslInternalSpinLockWake)(
+    std::atomic<uint32_t>* /* lock_word */, bool /* all */) {}
+
+}  // extern "C"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock_linux.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock_linux.inc
new file mode 100644
index 0000000000000000000000000000000000000000..fe8ba674f50ca65978c4b1d327662da5888563db
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock_linux.inc
@@ -0,0 +1,71 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// This file is a Linux-specific part of spinlock_wait.cc
+
+#include <linux/futex.h>
+#include <sys/syscall.h>
+#include <unistd.h>
+
+#include <atomic>
+#include <climits>
+#include <cstdint>
+#include <ctime>
+
+#include "absl/base/attributes.h"
+#include "absl/base/internal/errno_saver.h"
+
+// The SpinLock lockword is `std::atomic<uint32_t>`. Here we assert that
+// `std::atomic<uint32_t>` is bitwise equivalent of the `int` expected
+// by SYS_futex. We also assume that reads/writes done to the lockword
+// by SYS_futex have rational semantics with regard to the
+// std::atomic<> API. C++ provides no guarantees of these assumptions,
+// but they are believed to hold in practice.
+static_assert(sizeof(std::atomic<uint32_t>) == sizeof(int),
+              "SpinLock lockword has the wrong size for a futex");
+
+// Some Android headers are missing these definitions even though they
+// support these futex operations.
+#ifdef __BIONIC__
+#ifndef SYS_futex
+#define SYS_futex __NR_futex
+#endif
+#ifndef FUTEX_PRIVATE_FLAG
+#define FUTEX_PRIVATE_FLAG 128
+#endif
+#endif
+
+#if defined(__NR_futex_time64) && !defined(SYS_futex_time64)
+#define SYS_futex_time64 __NR_futex_time64
+#endif
+
+#if defined(SYS_futex_time64) && !defined(SYS_futex)
+#define SYS_futex SYS_futex_time64
+#endif
+
+extern "C" {
+
+ABSL_ATTRIBUTE_WEAK void ABSL_INTERNAL_C_SYMBOL(AbslInternalSpinLockDelay)(
+    std::atomic<uint32_t> *w, uint32_t value, int,
+    absl::base_internal::SchedulingMode) {
+  absl::base_internal::ErrnoSaver errno_saver;
+  syscall(SYS_futex, w, FUTEX_WAIT | FUTEX_PRIVATE_FLAG, value, nullptr);
+}
+
+ABSL_ATTRIBUTE_WEAK void ABSL_INTERNAL_C_SYMBOL(AbslInternalSpinLockWake)(
+    std::atomic<uint32_t> *w, bool all) {
+  syscall(SYS_futex, w, FUTEX_WAKE | FUTEX_PRIVATE_FLAG, all ? INT_MAX : 1, 0);
+}
+
+}  // extern "C"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock_posix.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock_posix.inc
new file mode 100644
index 0000000000000000000000000000000000000000..4f6f887d993c56d8e1e830b4e9b461c09ba8c496
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock_posix.inc
@@ -0,0 +1,46 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// This file is a Posix-specific part of spinlock_wait.cc
+
+#include <sched.h>
+
+#include <atomic>
+#include <ctime>
+
+#include "absl/base/internal/errno_saver.h"
+#include "absl/base/internal/scheduling_mode.h"
+#include "absl/base/port.h"
+
+extern "C" {
+
+ABSL_ATTRIBUTE_WEAK void ABSL_INTERNAL_C_SYMBOL(AbslInternalSpinLockDelay)(
+    std::atomic<uint32_t>* /* lock_word */, uint32_t /* value */, int loop,
+    absl::base_internal::SchedulingMode /* mode */) {
+  absl::base_internal::ErrnoSaver errno_saver;
+  if (loop == 0) {
+  } else if (loop == 1) {
+    sched_yield();
+  } else {
+    struct timespec tm;
+    tm.tv_sec = 0;
+    tm.tv_nsec = absl::base_internal::SpinLockSuggestedDelayNS(loop);
+    nanosleep(&tm, nullptr);
+  }
+}
+
+ABSL_ATTRIBUTE_WEAK void ABSL_INTERNAL_C_SYMBOL(AbslInternalSpinLockWake)(
+    std::atomic<uint32_t>* /* lock_word */, bool /* all */) {}
+
+}  // extern "C"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock_wait.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock_wait.h
new file mode 100644
index 0000000000000000000000000000000000000000..9a1adcda5eaaa8bdebea1f0997880c87e0f92161
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock_wait.h
@@ -0,0 +1,95 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_BASE_INTERNAL_SPINLOCK_WAIT_H_
+#define ABSL_BASE_INTERNAL_SPINLOCK_WAIT_H_
+
+// Operations to make atomic transitions on a word, and to allow
+// waiting for those transitions to become possible.
+
+#include <stdint.h>
+#include <atomic>
+
+#include "absl/base/internal/scheduling_mode.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+// SpinLockWait() waits until it can perform one of several transitions from
+// "from" to "to".  It returns when it performs a transition where done==true.
+struct SpinLockWaitTransition {
+  uint32_t from;
+  uint32_t to;
+  bool done;
+};
+
+// Wait until *w can transition from trans[i].from to trans[i].to for some i
+// satisfying 0<=i<n && trans[i].done, atomically make the transition,
+// then return the old value of *w.   Make any other atomic transitions
+// where !trans[i].done, but continue waiting.
+//
+// Wakeups for threads blocked on SpinLockWait do not respect priorities.
+uint32_t SpinLockWait(std::atomic<uint32_t> *w, int n,
+                      const SpinLockWaitTransition trans[],
+                      SchedulingMode scheduling_mode);
+
+// If possible, wake some thread that has called SpinLockDelay(w, ...). If `all`
+// is true, wake all such threads. On some systems, this may be a no-op; on
+// those systems, threads calling SpinLockDelay() will always wake eventually
+// even if SpinLockWake() is never called.
+void SpinLockWake(std::atomic<uint32_t> *w, bool all);
+
+// Wait for an appropriate spin delay on iteration "loop" of a
+// spin loop on location *w, whose previously observed value was "value".
+// SpinLockDelay() may do nothing, may yield the CPU, may sleep a clock tick,
+// or may wait for a call to SpinLockWake(w).
+void SpinLockDelay(std::atomic<uint32_t> *w, uint32_t value, int loop,
+                   base_internal::SchedulingMode scheduling_mode);
+
+// Helper used by AbslInternalSpinLockDelay.
+// Returns a suggested delay in nanoseconds for iteration number "loop".
+int SpinLockSuggestedDelayNS(int loop);
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+// In some build configurations we pass --detect-odr-violations to the
+// gold linker.  This causes it to flag weak symbol overrides as ODR
+// violations.  Because ODR only applies to C++ and not C,
+// --detect-odr-violations ignores symbols not mangled with C++ names.
+// By changing our extension points to be extern "C", we dodge this
+// check.
+extern "C" {
+void ABSL_INTERNAL_C_SYMBOL(AbslInternalSpinLockWake)(std::atomic<uint32_t> *w,
+                                                      bool all);
+void ABSL_INTERNAL_C_SYMBOL(AbslInternalSpinLockDelay)(
+    std::atomic<uint32_t> *w, uint32_t value, int loop,
+    absl::base_internal::SchedulingMode scheduling_mode);
+}
+
+inline void absl::base_internal::SpinLockWake(std::atomic<uint32_t> *w,
+                                              bool all) {
+  ABSL_INTERNAL_C_SYMBOL(AbslInternalSpinLockWake)(w, all);
+}
+
+inline void absl::base_internal::SpinLockDelay(
+    std::atomic<uint32_t> *w, uint32_t value, int loop,
+    absl::base_internal::SchedulingMode scheduling_mode) {
+  ABSL_INTERNAL_C_SYMBOL(AbslInternalSpinLockDelay)
+  (w, value, loop, scheduling_mode);
+}
+
+#endif  // ABSL_BASE_INTERNAL_SPINLOCK_WAIT_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock_win32.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock_win32.inc
new file mode 100644
index 0000000000000000000000000000000000000000..934c2016fb6c3d2a064916d55b5fd7c9e8697882
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/spinlock_win32.inc
@@ -0,0 +1,40 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// This file is a Win32-specific part of spinlock_wait.cc
+
+#include <windows.h>
+#include <atomic>
+#include "absl/base/internal/scheduling_mode.h"
+
+extern "C" {
+
+void ABSL_INTERNAL_C_SYMBOL(AbslInternalSpinLockDelay)(
+    std::atomic<uint32_t>* /* lock_word */, uint32_t /* value */, int loop,
+    absl::base_internal::SchedulingMode /* mode */) {
+  if (loop == 0) {
+  } else if (loop == 1) {
+    Sleep(0);
+  } else {
+    // SpinLockSuggestedDelayNS() always returns a positive integer, so this
+    // static_cast is safe.
+    Sleep(static_cast<DWORD>(
+        absl::base_internal::SpinLockSuggestedDelayNS(loop) / 1000000));
+  }
+}
+
+void ABSL_INTERNAL_C_SYMBOL(AbslInternalSpinLockWake)(
+    std::atomic<uint32_t>* /* lock_word */, bool /* all */) {}
+
+}  // extern "C"
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/strerror.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/strerror.h
new file mode 100644
index 0000000000000000000000000000000000000000..350097366eedd935cb322c430ec39fbe3b8608a9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/strerror.h
@@ -0,0 +1,39 @@
+// Copyright 2020 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_BASE_INTERNAL_STRERROR_H_
+#define ABSL_BASE_INTERNAL_STRERROR_H_
+
+#include <string>
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+// A portable and thread-safe alternative to C89's `strerror`.
+//
+// The C89 specification of `strerror` is not suitable for use in a
+// multi-threaded application as the returned string may be changed by calls to
+// `strerror` from another thread.  The many non-stdlib alternatives differ
+// enough in their names, availability, and semantics to justify this wrapper
+// around them.  `errno` will not be modified by a call to `absl::StrError`.
+std::string StrError(int errnum);
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_STRERROR_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/sysinfo.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/sysinfo.h
new file mode 100644
index 0000000000000000000000000000000000000000..119cf1f0e85b7482df3fe3e01da0f6c023e52831
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/sysinfo.h
@@ -0,0 +1,74 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// This file includes routines to find out characteristics
+// of the machine a program is running on.  It is undoubtedly
+// system-dependent.
+
+// Functions listed here that accept a pid_t as an argument act on the
+// current process if the pid_t argument is 0
+// All functions here are thread-hostile due to file caching unless
+// commented otherwise.
+
+#ifndef ABSL_BASE_INTERNAL_SYSINFO_H_
+#define ABSL_BASE_INTERNAL_SYSINFO_H_
+
+#ifndef _WIN32
+#include <sys/types.h>
+#endif
+
+#include <cstdint>
+
+#include "absl/base/config.h"
+#include "absl/base/port.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+// Nominal core processor cycles per second of each processor.   This is _not_
+// necessarily the frequency of the CycleClock counter (see cycleclock.h)
+// Thread-safe.
+double NominalCPUFrequency();
+
+// Number of logical processors (hyperthreads) in system. Thread-safe.
+int NumCPUs();
+
+// Return the thread id of the current thread, as told by the system.
+// No two currently-live threads implemented by the OS shall have the same ID.
+// Thread ids of exited threads may be reused.   Multiple user-level threads
+// may have the same thread ID if multiplexed on the same OS thread.
+//
+// On Linux, you may send a signal to the resulting ID with kill().  However,
+// it is recommended for portability that you use pthread_kill() instead.
+#ifdef _WIN32
+// On Windows, process id and thread id are of the same type according to the
+// return types of GetProcessId() and GetThreadId() are both DWORD, an unsigned
+// 32-bit type.
+using pid_t = uint32_t;
+#endif
+pid_t GetTID();
+
+// Like GetTID(), but caches the result in thread-local storage in order
+// to avoid unnecessary system calls. Note that there are some cases where
+// one must call through to GetTID directly, which is why this exists as a
+// separate function. For example, GetCachedTID() is not safe to call in
+// an asynchronous signal-handling context nor right after a call to fork().
+pid_t GetCachedTID();
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_SYSINFO_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/thread_annotations.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/thread_annotations.h
new file mode 100644
index 0000000000000000000000000000000000000000..8c5c67e0df2a9d45f877a7060f2846b16705b649
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/thread_annotations.h
@@ -0,0 +1,280 @@
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: thread_annotations.h
+// -----------------------------------------------------------------------------
+//
+// WARNING: This is a backwards compatible header and it will be removed after
+// the migration to prefixed thread annotations is finished; please include
+// "absl/base/thread_annotations.h".
+//
+// This header file contains macro definitions for thread safety annotations
+// that allow developers to document the locking policies of multi-threaded
+// code. The annotations can also help program analysis tools to identify
+// potential thread safety issues.
+//
+// These annotations are implemented using compiler attributes. Using the macros
+// defined here instead of raw attributes allow for portability and future
+// compatibility.
+//
+// When referring to mutexes in the arguments of the attributes, you should
+// use variable names or more complex expressions (e.g. my_object->mutex_)
+// that evaluate to a concrete mutex object whenever possible. If the mutex
+// you want to refer to is not in scope, you may use a member pointer
+// (e.g. &MyClass::mutex_) to refer to a mutex in some (unknown) object.
+
+#ifndef ABSL_BASE_INTERNAL_THREAD_ANNOTATIONS_H_
+#define ABSL_BASE_INTERNAL_THREAD_ANNOTATIONS_H_
+
+// ABSL_LEGACY_THREAD_ANNOTATIONS is a *temporary* compatibility macro that can
+// be defined on the compile command-line to restore the legacy spellings of the
+// thread annotations macros/functions. The macros in this file are available
+// under ABSL_ prefixed spellings in absl/base/thread_annotations.h. This macro
+// and the legacy spellings will be removed in the future.
+#ifdef ABSL_LEGACY_THREAD_ANNOTATIONS
+
+#if defined(__clang__)
+#define THREAD_ANNOTATION_ATTRIBUTE__(x)   __attribute__((x))
+#else
+#define THREAD_ANNOTATION_ATTRIBUTE__(x)   // no-op
+#endif
+
+// GUARDED_BY()
+//
+// Documents if a shared field or global variable needs to be protected by a
+// mutex. GUARDED_BY() allows the user to specify a particular mutex that
+// should be held when accessing the annotated variable.
+//
+// Although this annotation (and PT_GUARDED_BY, below) cannot be applied to
+// local variables, a local variable and its associated mutex can often be
+// combined into a small class or struct, thereby allowing the annotation.
+//
+// Example:
+//
+//   class Foo {
+//     Mutex mu_;
+//     int p1_ GUARDED_BY(mu_);
+//     ...
+//   };
+#define GUARDED_BY(x) THREAD_ANNOTATION_ATTRIBUTE__(guarded_by(x))
+
+// PT_GUARDED_BY()
+//
+// Documents if the memory location pointed to by a pointer should be guarded
+// by a mutex when dereferencing the pointer.
+//
+// Example:
+//   class Foo {
+//     Mutex mu_;
+//     int *p1_ PT_GUARDED_BY(mu_);
+//     ...
+//   };
+//
+// Note that a pointer variable to a shared memory location could itself be a
+// shared variable.
+//
+// Example:
+//
+//   // `q_`, guarded by `mu1_`, points to a shared memory location that is
+//   // guarded by `mu2_`:
+//   int *q_ GUARDED_BY(mu1_) PT_GUARDED_BY(mu2_);
+#define PT_GUARDED_BY(x) THREAD_ANNOTATION_ATTRIBUTE__(pt_guarded_by(x))
+
+// ACQUIRED_AFTER() / ACQUIRED_BEFORE()
+//
+// Documents the acquisition order between locks that can be held
+// simultaneously by a thread. For any two locks that need to be annotated
+// to establish an acquisition order, only one of them needs the annotation.
+// (i.e. You don't have to annotate both locks with both ACQUIRED_AFTER
+// and ACQUIRED_BEFORE.)
+//
+// As with GUARDED_BY, this is only applicable to mutexes that are shared
+// fields or global variables.
+//
+// Example:
+//
+//   Mutex m1_;
+//   Mutex m2_ ACQUIRED_AFTER(m1_);
+#define ACQUIRED_AFTER(...) \
+  THREAD_ANNOTATION_ATTRIBUTE__(acquired_after(__VA_ARGS__))
+
+#define ACQUIRED_BEFORE(...) \
+  THREAD_ANNOTATION_ATTRIBUTE__(acquired_before(__VA_ARGS__))
+
+// EXCLUSIVE_LOCKS_REQUIRED() / SHARED_LOCKS_REQUIRED()
+//
+// Documents a function that expects a mutex to be held prior to entry.
+// The mutex is expected to be held both on entry to, and exit from, the
+// function.
+//
+// An exclusive lock allows read-write access to the guarded data member(s), and
+// only one thread can acquire a lock exclusively at any one time. A shared lock
+// allows read-only access, and any number of threads can acquire a shared lock
+// concurrently.
+//
+// Generally, non-const methods should be annotated with
+// EXCLUSIVE_LOCKS_REQUIRED, while const methods should be annotated with
+// SHARED_LOCKS_REQUIRED.
+//
+// Example:
+//
+//   Mutex mu1, mu2;
+//   int a GUARDED_BY(mu1);
+//   int b GUARDED_BY(mu2);
+//
+//   void foo() EXCLUSIVE_LOCKS_REQUIRED(mu1, mu2) { ... }
+//   void bar() const SHARED_LOCKS_REQUIRED(mu1, mu2) { ... }
+#define EXCLUSIVE_LOCKS_REQUIRED(...) \
+  THREAD_ANNOTATION_ATTRIBUTE__(exclusive_locks_required(__VA_ARGS__))
+
+#define SHARED_LOCKS_REQUIRED(...) \
+  THREAD_ANNOTATION_ATTRIBUTE__(shared_locks_required(__VA_ARGS__))
+
+// LOCKS_EXCLUDED()
+//
+// Documents the locks acquired in the body of the function. These locks
+// cannot be held when calling this function (as Abseil's `Mutex` locks are
+// non-reentrant).
+#define LOCKS_EXCLUDED(...) \
+  THREAD_ANNOTATION_ATTRIBUTE__(locks_excluded(__VA_ARGS__))
+
+// LOCK_RETURNED()
+//
+// Documents a function that returns a mutex without acquiring it.  For example,
+// a public getter method that returns a pointer to a private mutex should
+// be annotated with LOCK_RETURNED.
+#define LOCK_RETURNED(x) \
+  THREAD_ANNOTATION_ATTRIBUTE__(lock_returned(x))
+
+// LOCKABLE
+//
+// Documents if a class/type is a lockable type (such as the `Mutex` class).
+#define LOCKABLE \
+  THREAD_ANNOTATION_ATTRIBUTE__(lockable)
+
+// SCOPED_LOCKABLE
+//
+// Documents if a class does RAII locking (such as the `MutexLock` class).
+// The constructor should use `LOCK_FUNCTION()` to specify the mutex that is
+// acquired, and the destructor should use `UNLOCK_FUNCTION()` with no
+// arguments; the analysis will assume that the destructor unlocks whatever the
+// constructor locked.
+#define SCOPED_LOCKABLE \
+  THREAD_ANNOTATION_ATTRIBUTE__(scoped_lockable)
+
+// EXCLUSIVE_LOCK_FUNCTION()
+//
+// Documents functions that acquire a lock in the body of a function, and do
+// not release it.
+#define EXCLUSIVE_LOCK_FUNCTION(...) \
+  THREAD_ANNOTATION_ATTRIBUTE__(exclusive_lock_function(__VA_ARGS__))
+
+// SHARED_LOCK_FUNCTION()
+//
+// Documents functions that acquire a shared (reader) lock in the body of a
+// function, and do not release it.
+#define SHARED_LOCK_FUNCTION(...) \
+  THREAD_ANNOTATION_ATTRIBUTE__(shared_lock_function(__VA_ARGS__))
+
+// UNLOCK_FUNCTION()
+//
+// Documents functions that expect a lock to be held on entry to the function,
+// and release it in the body of the function.
+#define UNLOCK_FUNCTION(...) \
+  THREAD_ANNOTATION_ATTRIBUTE__(unlock_function(__VA_ARGS__))
+
+// EXCLUSIVE_TRYLOCK_FUNCTION() / SHARED_TRYLOCK_FUNCTION()
+//
+// Documents functions that try to acquire a lock, and return success or failure
+// (or a non-boolean value that can be interpreted as a boolean).
+// The first argument should be `true` for functions that return `true` on
+// success, or `false` for functions that return `false` on success. The second
+// argument specifies the mutex that is locked on success. If unspecified, this
+// mutex is assumed to be `this`.
+#define EXCLUSIVE_TRYLOCK_FUNCTION(...) \
+  THREAD_ANNOTATION_ATTRIBUTE__(exclusive_trylock_function(__VA_ARGS__))
+
+#define SHARED_TRYLOCK_FUNCTION(...) \
+  THREAD_ANNOTATION_ATTRIBUTE__(shared_trylock_function(__VA_ARGS__))
+
+// ASSERT_EXCLUSIVE_LOCK() / ASSERT_SHARED_LOCK()
+//
+// Documents functions that dynamically check to see if a lock is held, and fail
+// if it is not held.
+#define ASSERT_EXCLUSIVE_LOCK(...) \
+  THREAD_ANNOTATION_ATTRIBUTE__(assert_exclusive_lock(__VA_ARGS__))
+
+#define ASSERT_SHARED_LOCK(...) \
+  THREAD_ANNOTATION_ATTRIBUTE__(assert_shared_lock(__VA_ARGS__))
+
+// NO_THREAD_SAFETY_ANALYSIS
+//
+// Turns off thread safety checking within the body of a particular function.
+// This annotation is used to mark functions that are known to be correct, but
+// the locking behavior is more complicated than the analyzer can handle.
+#define NO_THREAD_SAFETY_ANALYSIS \
+  THREAD_ANNOTATION_ATTRIBUTE__(no_thread_safety_analysis)
+
+//------------------------------------------------------------------------------
+// Tool-Supplied Annotations
+//------------------------------------------------------------------------------
+
+// TS_UNCHECKED should be placed around lock expressions that are not valid
+// C++ syntax, but which are present for documentation purposes.  These
+// annotations will be ignored by the analysis.
+#define TS_UNCHECKED(x) ""
+
+// TS_FIXME is used to mark lock expressions that are not valid C++ syntax.
+// It is used by automated tools to mark and disable invalid expressions.
+// The annotation should either be fixed, or changed to TS_UNCHECKED.
+#define TS_FIXME(x) ""
+
+// Like NO_THREAD_SAFETY_ANALYSIS, this turns off checking within the body of
+// a particular function.  However, this attribute is used to mark functions
+// that are incorrect and need to be fixed.  It is used by automated tools to
+// avoid breaking the build when the analysis is updated.
+// Code owners are expected to eventually fix the routine.
+#define NO_THREAD_SAFETY_ANALYSIS_FIXME  NO_THREAD_SAFETY_ANALYSIS
+
+// Similar to NO_THREAD_SAFETY_ANALYSIS_FIXME, this macro marks a GUARDED_BY
+// annotation that needs to be fixed, because it is producing thread safety
+// warning.  It disables the GUARDED_BY.
+#define GUARDED_BY_FIXME(x)
+
+// Disables warnings for a single read operation.  This can be used to avoid
+// warnings when it is known that the read is not actually involved in a race,
+// but the compiler cannot confirm that.
+#define TS_UNCHECKED_READ(x) thread_safety_analysis::ts_unchecked_read(x)
+
+
+namespace thread_safety_analysis {
+
+// Takes a reference to a guarded data member, and returns an unguarded
+// reference.
+template <typename T>
+inline const T& ts_unchecked_read(const T& v) NO_THREAD_SAFETY_ANALYSIS {
+  return v;
+}
+
+template <typename T>
+inline T& ts_unchecked_read(T& v) NO_THREAD_SAFETY_ANALYSIS {
+  return v;
+}
+
+}  // namespace thread_safety_analysis
+
+#endif  // defined(ABSL_LEGACY_THREAD_ANNOTATIONS)
+
+#endif  // ABSL_BASE_INTERNAL_THREAD_ANNOTATIONS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/thread_identity.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/thread_identity.h
new file mode 100644
index 0000000000000000000000000000000000000000..463acbc74ee8d7b141c8970846d059152418fc4e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/thread_identity.h
@@ -0,0 +1,269 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Each active thread has an ThreadIdentity that may represent the thread in
+// various level interfaces.  ThreadIdentity objects are never deallocated.
+// When a thread terminates, its ThreadIdentity object may be reused for a
+// thread created later.
+
+#ifndef ABSL_BASE_INTERNAL_THREAD_IDENTITY_H_
+#define ABSL_BASE_INTERNAL_THREAD_IDENTITY_H_
+
+#ifndef _WIN32
+#include <pthread.h>
+// Defines __GOOGLE_GRTE_VERSION__ (via glibc-specific features.h) when
+// supported.
+#include <unistd.h>
+#endif
+
+#include <atomic>
+#include <cstdint>
+
+#include "absl/base/config.h"
+#include "absl/base/internal/per_thread_tls.h"
+#include "absl/base/optimization.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+struct SynchLocksHeld;
+struct SynchWaitParams;
+
+namespace base_internal {
+
+class SpinLock;
+struct ThreadIdentity;
+
+// Used by the implementation of absl::Mutex and absl::CondVar.
+struct PerThreadSynch {
+  // The internal representation of absl::Mutex and absl::CondVar rely
+  // on the alignment of PerThreadSynch. Both store the address of the
+  // PerThreadSynch in the high-order bits of their internal state,
+  // which means the low kLowZeroBits of the address of PerThreadSynch
+  // must be zero.
+  static constexpr int kLowZeroBits = 8;
+  static constexpr int kAlignment = 1 << kLowZeroBits;
+
+  // Returns the associated ThreadIdentity.
+  // This can be implemented as a cast because we guarantee
+  // PerThreadSynch is the first element of ThreadIdentity.
+  ThreadIdentity* thread_identity() {
+    return reinterpret_cast<ThreadIdentity*>(this);
+  }
+
+  PerThreadSynch *next;  // Circular waiter queue; initialized to 0.
+  PerThreadSynch *skip;  // If non-zero, all entries in Mutex queue
+                         // up to and including "skip" have same
+                         // condition as this, and will be woken later
+  bool may_skip;         // if false while on mutex queue, a mutex unlocker
+                         // is using this PerThreadSynch as a terminator.  Its
+                         // skip field must not be filled in because the loop
+                         // might then skip over the terminator.
+  bool wake;             // This thread is to be woken from a Mutex.
+  // If "x" is on a waiter list for a mutex, "x->cond_waiter" is true iff the
+  // waiter is waiting on the mutex as part of a CV Wait or Mutex Await.
+  //
+  // The value of "x->cond_waiter" is meaningless if "x" is not on a
+  // Mutex waiter list.
+  bool cond_waiter;
+  bool maybe_unlocking;  // Valid at head of Mutex waiter queue;
+                         // true if UnlockSlow could be searching
+                         // for a waiter to wake.  Used for an optimization
+                         // in Enqueue().  true is always a valid value.
+                         // Can be reset to false when the unlocker or any
+                         // writer releases the lock, or a reader fully
+                         // releases the lock.  It may not be set to false
+                         // by a reader that decrements the count to
+                         // non-zero. protected by mutex spinlock
+  bool suppress_fatal_errors;  // If true, try to proceed even in the face
+                               // of broken invariants.  This is used within
+                               // fatal signal handlers to improve the
+                               // chances of debug logging information being
+                               // output successfully.
+  int priority;                // Priority of thread (updated every so often).
+
+  // State values:
+  //   kAvailable: This PerThreadSynch is available.
+  //   kQueued: This PerThreadSynch is unavailable, it's currently queued on a
+  //            Mutex or CondVar waistlist.
+  //
+  // Transitions from kQueued to kAvailable require a release
+  // barrier. This is needed as a waiter may use "state" to
+  // independently observe that it's no longer queued.
+  //
+  // Transitions from kAvailable to kQueued require no barrier, they
+  // are externally ordered by the Mutex.
+  enum State {
+    kAvailable,
+    kQueued
+  };
+  std::atomic<State> state;
+
+  // The wait parameters of the current wait.  waitp is null if the
+  // thread is not waiting. Transitions from null to non-null must
+  // occur before the enqueue commit point (state = kQueued in
+  // Enqueue() and CondVarEnqueue()). Transitions from non-null to
+  // null must occur after the wait is finished (state = kAvailable in
+  // Mutex::Block() and CondVar::WaitCommon()). This field may be
+  // changed only by the thread that describes this PerThreadSynch.  A
+  // special case is Fer(), which calls Enqueue() on another thread,
+  // but with an identical SynchWaitParams pointer, thus leaving the
+  // pointer unchanged.
+  SynchWaitParams* waitp;
+
+  intptr_t readers;     // Number of readers in mutex.
+
+  // When priority will next be read (cycles).
+  int64_t next_priority_read_cycles;
+
+  // Locks held; used during deadlock detection.
+  // Allocated in Synch_GetAllLocks() and freed in ReclaimThreadIdentity().
+  SynchLocksHeld *all_locks;
+};
+
+// The instances of this class are allocated in NewThreadIdentity() with an
+// alignment of PerThreadSynch::kAlignment.
+//
+// NOTE: The layout of fields in this structure is critical, please do not
+//       add, remove, or modify the field placements without fully auditing the
+//       layout.
+struct ThreadIdentity {
+  // Must be the first member.  The Mutex implementation requires that
+  // the PerThreadSynch object associated with each thread is
+  // PerThreadSynch::kAlignment aligned.  We provide this alignment on
+  // ThreadIdentity itself.
+  PerThreadSynch per_thread_synch;
+
+  // Private: Reserved for absl::synchronization_internal::Waiter.
+  struct WaiterState {
+    alignas(void*) char data[128];
+  } waiter_state;
+
+  // Used by PerThreadSem::{Get,Set}ThreadBlockedCounter().
+  std::atomic<int>* blocked_count_ptr;
+
+  // The following variables are mostly read/written just by the
+  // thread itself.  The only exception is that these are read by
+  // a ticker thread as a hint.
+  std::atomic<int> ticker;      // Tick counter, incremented once per second.
+  std::atomic<int> wait_start;  // Ticker value when thread started waiting.
+  std::atomic<bool> is_idle;    // Has thread become idle yet?
+
+  ThreadIdentity* next;
+};
+
+// Returns the ThreadIdentity object representing the calling thread; guaranteed
+// to be unique for its lifetime.  The returned object will remain valid for the
+// program's lifetime; although it may be re-assigned to a subsequent thread.
+// If one does not exist, return nullptr instead.
+//
+// Does not malloc(*), and is async-signal safe.
+// [*] Technically pthread_setspecific() does malloc on first use; however this
+// is handled internally within tcmalloc's initialization already.
+//
+// New ThreadIdentity objects can be constructed and associated with a thread
+// by calling GetOrCreateCurrentThreadIdentity() in per-thread-sem.h.
+ThreadIdentity* CurrentThreadIdentityIfPresent();
+
+using ThreadIdentityReclaimerFunction = void (*)(void*);
+
+// Sets the current thread identity to the given value.  'reclaimer' is a
+// pointer to the global function for cleaning up instances on thread
+// destruction.
+void SetCurrentThreadIdentity(ThreadIdentity* identity,
+                              ThreadIdentityReclaimerFunction reclaimer);
+
+// Removes the currently associated ThreadIdentity from the running thread.
+// This must be called from inside the ThreadIdentityReclaimerFunction, and only
+// from that function.
+void ClearCurrentThreadIdentity();
+
+// May be chosen at compile time via: -DABSL_FORCE_THREAD_IDENTITY_MODE=<mode
+// index>
+#ifdef ABSL_THREAD_IDENTITY_MODE_USE_POSIX_SETSPECIFIC
+#error ABSL_THREAD_IDENTITY_MODE_USE_POSIX_SETSPECIFIC cannot be directly set
+#else
+#define ABSL_THREAD_IDENTITY_MODE_USE_POSIX_SETSPECIFIC 0
+#endif
+
+#ifdef ABSL_THREAD_IDENTITY_MODE_USE_TLS
+#error ABSL_THREAD_IDENTITY_MODE_USE_TLS cannot be directly set
+#else
+#define ABSL_THREAD_IDENTITY_MODE_USE_TLS 1
+#endif
+
+#ifdef ABSL_THREAD_IDENTITY_MODE_USE_CPP11
+#error ABSL_THREAD_IDENTITY_MODE_USE_CPP11 cannot be directly set
+#else
+#define ABSL_THREAD_IDENTITY_MODE_USE_CPP11 2
+#endif
+
+#ifdef ABSL_THREAD_IDENTITY_MODE
+#error ABSL_THREAD_IDENTITY_MODE cannot be directly set
+#elif defined(ABSL_FORCE_THREAD_IDENTITY_MODE)
+#define ABSL_THREAD_IDENTITY_MODE ABSL_FORCE_THREAD_IDENTITY_MODE
+#elif defined(_WIN32) && !defined(__MINGW32__)
+#define ABSL_THREAD_IDENTITY_MODE ABSL_THREAD_IDENTITY_MODE_USE_CPP11
+#elif defined(__APPLE__) && defined(ABSL_HAVE_THREAD_LOCAL)
+#define ABSL_THREAD_IDENTITY_MODE ABSL_THREAD_IDENTITY_MODE_USE_CPP11
+#elif ABSL_PER_THREAD_TLS && defined(__GOOGLE_GRTE_VERSION__) &&        \
+    (__GOOGLE_GRTE_VERSION__ >= 20140228L)
+// Support for async-safe TLS was specifically added in GRTEv4.  It's not
+// present in the upstream eglibc.
+// Note:  Current default for production systems.
+#define ABSL_THREAD_IDENTITY_MODE ABSL_THREAD_IDENTITY_MODE_USE_TLS
+#else
+#define ABSL_THREAD_IDENTITY_MODE \
+  ABSL_THREAD_IDENTITY_MODE_USE_POSIX_SETSPECIFIC
+#endif
+
+#if ABSL_THREAD_IDENTITY_MODE == ABSL_THREAD_IDENTITY_MODE_USE_TLS || \
+    ABSL_THREAD_IDENTITY_MODE == ABSL_THREAD_IDENTITY_MODE_USE_CPP11
+
+#if ABSL_PER_THREAD_TLS
+ABSL_CONST_INIT extern ABSL_PER_THREAD_TLS_KEYWORD ThreadIdentity*
+    thread_identity_ptr;
+#elif defined(ABSL_HAVE_THREAD_LOCAL)
+ABSL_CONST_INIT extern thread_local ThreadIdentity* thread_identity_ptr;
+#else
+#error Thread-local storage not detected on this platform
+#endif
+
+// thread_local variables cannot be in headers exposed by DLLs or in certain
+// build configurations on Apple platforms. However, it is important for
+// performance reasons in general that `CurrentThreadIdentityIfPresent` be
+// inlined. In the other cases we opt to have the function not be inlined. Note
+// that `CurrentThreadIdentityIfPresent` is declared above so we can exclude
+// this entire inline definition.
+#if !defined(__APPLE__) && !defined(ABSL_BUILD_DLL) && \
+    !defined(ABSL_CONSUME_DLL)
+#define ABSL_INTERNAL_INLINE_CURRENT_THREAD_IDENTITY_IF_PRESENT 1
+#endif
+
+#ifdef ABSL_INTERNAL_INLINE_CURRENT_THREAD_IDENTITY_IF_PRESENT
+inline ThreadIdentity* CurrentThreadIdentityIfPresent() {
+  return thread_identity_ptr;
+}
+#endif
+
+#elif ABSL_THREAD_IDENTITY_MODE != \
+    ABSL_THREAD_IDENTITY_MODE_USE_POSIX_SETSPECIFIC
+#error Unknown ABSL_THREAD_IDENTITY_MODE
+#endif
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_THREAD_IDENTITY_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/throw_delegate.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/throw_delegate.h
new file mode 100644
index 0000000000000000000000000000000000000000..075f5272543ab64d8c2e9a5ef7047c60f807f504
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/throw_delegate.h
@@ -0,0 +1,75 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+#ifndef ABSL_BASE_INTERNAL_THROW_DELEGATE_H_
+#define ABSL_BASE_INTERNAL_THROW_DELEGATE_H_
+
+#include <string>
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+// Helper functions that allow throwing exceptions consistently from anywhere.
+// The main use case is for header-based libraries (eg templates), as they will
+// be built by many different targets with their own compiler options.
+// In particular, this will allow a safe way to throw exceptions even if the
+// caller is compiled with -fno-exceptions.  This is intended for implementing
+// things like map<>::at(), which the standard documents as throwing an
+// exception on error.
+//
+// Using other techniques like #if tricks could lead to ODR violations.
+//
+// You shouldn't use it unless you're writing code that you know will be built
+// both with and without exceptions and you need to conform to an interface
+// that uses exceptions.
+
+[[noreturn]] void ThrowStdLogicError(const std::string& what_arg);
+[[noreturn]] void ThrowStdLogicError(const char* what_arg);
+[[noreturn]] void ThrowStdInvalidArgument(const std::string& what_arg);
+[[noreturn]] void ThrowStdInvalidArgument(const char* what_arg);
+[[noreturn]] void ThrowStdDomainError(const std::string& what_arg);
+[[noreturn]] void ThrowStdDomainError(const char* what_arg);
+[[noreturn]] void ThrowStdLengthError(const std::string& what_arg);
+[[noreturn]] void ThrowStdLengthError(const char* what_arg);
+[[noreturn]] void ThrowStdOutOfRange(const std::string& what_arg);
+[[noreturn]] void ThrowStdOutOfRange(const char* what_arg);
+[[noreturn]] void ThrowStdRuntimeError(const std::string& what_arg);
+[[noreturn]] void ThrowStdRuntimeError(const char* what_arg);
+[[noreturn]] void ThrowStdRangeError(const std::string& what_arg);
+[[noreturn]] void ThrowStdRangeError(const char* what_arg);
+[[noreturn]] void ThrowStdOverflowError(const std::string& what_arg);
+[[noreturn]] void ThrowStdOverflowError(const char* what_arg);
+[[noreturn]] void ThrowStdUnderflowError(const std::string& what_arg);
+[[noreturn]] void ThrowStdUnderflowError(const char* what_arg);
+
+[[noreturn]] void ThrowStdBadFunctionCall();
+[[noreturn]] void ThrowStdBadAlloc();
+
+// ThrowStdBadArrayNewLength() cannot be consistently supported because
+// std::bad_array_new_length is missing in libstdc++ until 4.9.0.
+// https://gcc.gnu.org/onlinedocs/gcc-4.8.3/libstdc++/api/a01379_source.html
+// https://gcc.gnu.org/onlinedocs/gcc-4.9.0/libstdc++/api/a01327_source.html
+// libcxx (as of 3.2) and msvc (as of 2015) both have it.
+// [[noreturn]] void ThrowStdBadArrayNewLength();
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_INTERNAL_THROW_DELEGATE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/tsan_mutex_interface.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/tsan_mutex_interface.h
new file mode 100644
index 0000000000000000000000000000000000000000..39207d8a5c42890f4167bef6e98c4e164622f893
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/tsan_mutex_interface.h
@@ -0,0 +1,68 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// This file is intended solely for spinlock.h.
+// It provides ThreadSanitizer annotations for custom mutexes.
+// See <sanitizer/tsan_interface.h> for meaning of these annotations.
+
+#ifndef ABSL_BASE_INTERNAL_TSAN_MUTEX_INTERFACE_H_
+#define ABSL_BASE_INTERNAL_TSAN_MUTEX_INTERFACE_H_
+
+#include "absl/base/config.h"
+
+// ABSL_INTERNAL_HAVE_TSAN_INTERFACE
+// Macro intended only for internal use.
+//
+// Checks whether LLVM Thread Sanitizer interfaces are available.
+// First made available in LLVM 5.0 (Sep 2017).
+#ifdef ABSL_INTERNAL_HAVE_TSAN_INTERFACE
+#error "ABSL_INTERNAL_HAVE_TSAN_INTERFACE cannot be directly set."
+#endif
+
+#if defined(ABSL_HAVE_THREAD_SANITIZER) && defined(__has_include)
+#if __has_include(<sanitizer/tsan_interface.h>)
+#define ABSL_INTERNAL_HAVE_TSAN_INTERFACE 1
+#endif
+#endif
+
+#ifdef ABSL_INTERNAL_HAVE_TSAN_INTERFACE
+#include <sanitizer/tsan_interface.h>
+
+#define ABSL_TSAN_MUTEX_CREATE __tsan_mutex_create
+#define ABSL_TSAN_MUTEX_DESTROY __tsan_mutex_destroy
+#define ABSL_TSAN_MUTEX_PRE_LOCK __tsan_mutex_pre_lock
+#define ABSL_TSAN_MUTEX_POST_LOCK __tsan_mutex_post_lock
+#define ABSL_TSAN_MUTEX_PRE_UNLOCK __tsan_mutex_pre_unlock
+#define ABSL_TSAN_MUTEX_POST_UNLOCK __tsan_mutex_post_unlock
+#define ABSL_TSAN_MUTEX_PRE_SIGNAL __tsan_mutex_pre_signal
+#define ABSL_TSAN_MUTEX_POST_SIGNAL __tsan_mutex_post_signal
+#define ABSL_TSAN_MUTEX_PRE_DIVERT __tsan_mutex_pre_divert
+#define ABSL_TSAN_MUTEX_POST_DIVERT __tsan_mutex_post_divert
+
+#else
+
+#define ABSL_TSAN_MUTEX_CREATE(...)
+#define ABSL_TSAN_MUTEX_DESTROY(...)
+#define ABSL_TSAN_MUTEX_PRE_LOCK(...)
+#define ABSL_TSAN_MUTEX_POST_LOCK(...)
+#define ABSL_TSAN_MUTEX_PRE_UNLOCK(...)
+#define ABSL_TSAN_MUTEX_POST_UNLOCK(...)
+#define ABSL_TSAN_MUTEX_PRE_SIGNAL(...)
+#define ABSL_TSAN_MUTEX_POST_SIGNAL(...)
+#define ABSL_TSAN_MUTEX_PRE_DIVERT(...)
+#define ABSL_TSAN_MUTEX_POST_DIVERT(...)
+
+#endif
+
+#endif  // ABSL_BASE_INTERNAL_TSAN_MUTEX_INTERFACE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/unaligned_access.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/unaligned_access.h
new file mode 100644
index 0000000000000000000000000000000000000000..093dd9b499f099e51407e8189bd7039a84158550
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/unaligned_access.h
@@ -0,0 +1,82 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+#ifndef ABSL_BASE_INTERNAL_UNALIGNED_ACCESS_H_
+#define ABSL_BASE_INTERNAL_UNALIGNED_ACCESS_H_
+
+#include <string.h>
+
+#include <cstdint>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+
+// unaligned APIs
+
+// Portable handling of unaligned loads, stores, and copies.
+
+// The unaligned API is C++ only.  The declarations use C++ features
+// (namespaces, inline) which are absent or incompatible in C.
+#if defined(__cplusplus)
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+inline uint16_t UnalignedLoad16(const void *p) {
+  uint16_t t;
+  memcpy(&t, p, sizeof t);
+  return t;
+}
+
+inline uint32_t UnalignedLoad32(const void *p) {
+  uint32_t t;
+  memcpy(&t, p, sizeof t);
+  return t;
+}
+
+inline uint64_t UnalignedLoad64(const void *p) {
+  uint64_t t;
+  memcpy(&t, p, sizeof t);
+  return t;
+}
+
+inline void UnalignedStore16(void *p, uint16_t v) { memcpy(p, &v, sizeof v); }
+
+inline void UnalignedStore32(void *p, uint32_t v) { memcpy(p, &v, sizeof v); }
+
+inline void UnalignedStore64(void *p, uint64_t v) { memcpy(p, &v, sizeof v); }
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#define ABSL_INTERNAL_UNALIGNED_LOAD16(_p) \
+  (absl::base_internal::UnalignedLoad16(_p))
+#define ABSL_INTERNAL_UNALIGNED_LOAD32(_p) \
+  (absl::base_internal::UnalignedLoad32(_p))
+#define ABSL_INTERNAL_UNALIGNED_LOAD64(_p) \
+  (absl::base_internal::UnalignedLoad64(_p))
+
+#define ABSL_INTERNAL_UNALIGNED_STORE16(_p, _val) \
+  (absl::base_internal::UnalignedStore16(_p, _val))
+#define ABSL_INTERNAL_UNALIGNED_STORE32(_p, _val) \
+  (absl::base_internal::UnalignedStore32(_p, _val))
+#define ABSL_INTERNAL_UNALIGNED_STORE64(_p, _val) \
+  (absl::base_internal::UnalignedStore64(_p, _val))
+
+#endif  // defined(__cplusplus), end of unaligned API
+
+#endif  // ABSL_BASE_INTERNAL_UNALIGNED_ACCESS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/unscaledcycleclock.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/unscaledcycleclock.h
new file mode 100644
index 0000000000000000000000000000000000000000..cc1276ba0891863ef1b1dc1feaadb8750e89f087
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/unscaledcycleclock.h
@@ -0,0 +1,96 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// UnscaledCycleClock
+//    An UnscaledCycleClock yields the value and frequency of a cycle counter
+//    that increments at a rate that is approximately constant.
+//    This class is for internal use only, you should consider using CycleClock
+//    instead.
+//
+// Notes:
+// The cycle counter frequency is not necessarily the core clock frequency.
+// That is, CycleCounter cycles are not necessarily "CPU cycles".
+//
+// An arbitrary offset may have been added to the counter at power on.
+//
+// On some platforms, the rate and offset of the counter may differ
+// slightly when read from different CPUs of a multiprocessor.  Usually,
+// we try to ensure that the operating system adjusts values periodically
+// so that values agree approximately.   If you need stronger guarantees,
+// consider using alternate interfaces.
+//
+// The CPU is not required to maintain the ordering of a cycle counter read
+// with respect to surrounding instructions.
+
+#ifndef ABSL_BASE_INTERNAL_UNSCALEDCYCLECLOCK_H_
+#define ABSL_BASE_INTERNAL_UNSCALEDCYCLECLOCK_H_
+
+#include <cstdint>
+
+#if defined(__APPLE__)
+#include <TargetConditionals.h>
+#endif
+
+#include "absl/base/config.h"
+#include "absl/base/internal/unscaledcycleclock_config.h"
+
+#if ABSL_USE_UNSCALED_CYCLECLOCK
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace time_internal {
+class UnscaledCycleClockWrapperForGetCurrentTime;
+}  // namespace time_internal
+
+namespace base_internal {
+class CycleClock;
+class UnscaledCycleClockWrapperForInitializeFrequency;
+
+class UnscaledCycleClock {
+ private:
+  UnscaledCycleClock() = delete;
+
+  // Return the value of a cycle counter that counts at a rate that is
+  // approximately constant.
+  static int64_t Now();
+
+  // Return the how much UnscaledCycleClock::Now() increases per second.
+  // This is not necessarily the core CPU clock frequency.
+  // It may be the nominal value report by the kernel, rather than a measured
+  // value.
+  static double Frequency();
+
+  // Allowed users
+  friend class base_internal::CycleClock;
+  friend class time_internal::UnscaledCycleClockWrapperForGetCurrentTime;
+  friend class base_internal::UnscaledCycleClockWrapperForInitializeFrequency;
+};
+
+#if defined(__x86_64__)
+
+inline int64_t UnscaledCycleClock::Now() {
+  uint64_t low, high;
+  __asm__ volatile("rdtsc" : "=a"(low), "=d"(high));
+  return static_cast<int64_t>((high << 32) | low);
+}
+
+#endif
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_USE_UNSCALED_CYCLECLOCK
+
+#endif  // ABSL_BASE_INTERNAL_UNSCALEDCYCLECLOCK_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/unscaledcycleclock_config.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/unscaledcycleclock_config.h
new file mode 100644
index 0000000000000000000000000000000000000000..24b324ac99f814ac9d3aee4f24cbc8089c1fea57
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/internal/unscaledcycleclock_config.h
@@ -0,0 +1,62 @@
+// Copyright 2022 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_BASE_INTERNAL_UNSCALEDCYCLECLOCK_CONFIG_H_
+#define ABSL_BASE_INTERNAL_UNSCALEDCYCLECLOCK_CONFIG_H_
+
+#if defined(__APPLE__)
+#include <TargetConditionals.h>
+#endif
+
+// The following platforms have an implementation of a hardware counter.
+#if defined(__i386__) || defined(__x86_64__) || defined(__aarch64__) || \
+    defined(__powerpc__) || defined(__ppc__) || defined(__riscv) ||     \
+    defined(_M_IX86) || (defined(_M_X64) && !defined(_M_ARM64EC))
+#define ABSL_HAVE_UNSCALED_CYCLECLOCK_IMPLEMENTATION 1
+#else
+#define ABSL_HAVE_UNSCALED_CYCLECLOCK_IMPLEMENTATION 0
+#endif
+
+// The following platforms often disable access to the hardware
+// counter (through a sandbox) even if the underlying hardware has a
+// usable counter. The CycleTimer interface also requires a *scaled*
+// CycleClock that runs at atleast 1 MHz. We've found some Android
+// ARM64 devices where this is not the case, so we disable it by
+// default on Android ARM64.
+#if defined(__native_client__) || (defined(__APPLE__)) || \
+    (defined(__ANDROID__) && defined(__aarch64__))
+#define ABSL_USE_UNSCALED_CYCLECLOCK_DEFAULT 0
+#else
+#define ABSL_USE_UNSCALED_CYCLECLOCK_DEFAULT 1
+#endif
+
+// UnscaledCycleClock is an optional internal feature.
+// Use "#if ABSL_USE_UNSCALED_CYCLECLOCK" to test for its presence.
+// Can be overridden at compile-time via -DABSL_USE_UNSCALED_CYCLECLOCK=0|1
+#if !defined(ABSL_USE_UNSCALED_CYCLECLOCK)
+#define ABSL_USE_UNSCALED_CYCLECLOCK               \
+  (ABSL_HAVE_UNSCALED_CYCLECLOCK_IMPLEMENTATION && \
+   ABSL_USE_UNSCALED_CYCLECLOCK_DEFAULT)
+#endif
+
+#if ABSL_USE_UNSCALED_CYCLECLOCK
+// This macro can be used to test if UnscaledCycleClock::Frequency()
+// is NominalCPUFrequency() on a particular platform.
+#if (defined(__i386__) || defined(__x86_64__) || defined(__riscv) || \
+     defined(_M_IX86) || defined(_M_X64))
+#define ABSL_INTERNAL_UNSCALED_CYCLECLOCK_FREQUENCY_IS_CPU_FREQUENCY
+#endif
+#endif
+
+#endif  // ABSL_BASE_INTERNAL_UNSCALEDCYCLECLOCK_CONFIG_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/log_severity.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/log_severity.h
new file mode 100644
index 0000000000000000000000000000000000000000..8bdca38b5fa0b348ba7acfb5c2f9f64a576cbc46
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/log_severity.h
@@ -0,0 +1,172 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_BASE_LOG_SEVERITY_H_
+#define ABSL_BASE_LOG_SEVERITY_H_
+
+#include <array>
+#include <ostream>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// absl::LogSeverity
+//
+// Four severity levels are defined. Logging APIs should terminate the program
+// when a message is logged at severity `kFatal`; the other levels have no
+// special semantics.
+//
+// Values other than the four defined levels (e.g. produced by `static_cast`)
+// are valid, but their semantics when passed to a function, macro, or flag
+// depend on the function, macro, or flag. The usual behavior is to normalize
+// such values to a defined severity level, however in some cases values other
+// than the defined levels are useful for comparison.
+//
+// Example:
+//
+//   // Effectively disables all logging:
+//   SetMinLogLevel(static_cast<absl::LogSeverity>(100));
+//
+// Abseil flags may be defined with type `LogSeverity`. Dependency layering
+// constraints require that the `AbslParseFlag()` overload be declared and
+// defined in the flags library itself rather than here. The `AbslUnparseFlag()`
+// overload is defined there as well for consistency.
+//
+// absl::LogSeverity Flag String Representation
+//
+// An `absl::LogSeverity` has a string representation used for parsing
+// command-line flags based on the enumerator name (e.g. `kFatal`) or
+// its unprefixed name (without the `k`) in any case-insensitive form. (E.g.
+// "FATAL", "fatal" or "Fatal" are all valid.) Unparsing such flags produces an
+// unprefixed string representation in all caps (e.g. "FATAL") or an integer.
+//
+// Additionally, the parser accepts arbitrary integers (as if the type were
+// `int`).
+//
+// Examples:
+//
+//   --my_log_level=kInfo
+//   --my_log_level=INFO
+//   --my_log_level=info
+//   --my_log_level=0
+//
+// Unparsing a flag produces the same result as `absl::LogSeverityName()` for
+// the standard levels and a base-ten integer otherwise.
+enum class LogSeverity : int {
+  kInfo = 0,
+  kWarning = 1,
+  kError = 2,
+  kFatal = 3,
+};
+
+// LogSeverities()
+//
+// Returns an iterable of all standard `absl::LogSeverity` values, ordered from
+// least to most severe.
+constexpr std::array<absl::LogSeverity, 4> LogSeverities() {
+  return {{absl::LogSeverity::kInfo, absl::LogSeverity::kWarning,
+           absl::LogSeverity::kError, absl::LogSeverity::kFatal}};
+}
+
+// LogSeverityName()
+//
+// Returns the all-caps string representation (e.g. "INFO") of the specified
+// severity level if it is one of the standard levels and "UNKNOWN" otherwise.
+constexpr const char* LogSeverityName(absl::LogSeverity s) {
+  return s == absl::LogSeverity::kInfo
+             ? "INFO"
+             : s == absl::LogSeverity::kWarning
+                   ? "WARNING"
+                   : s == absl::LogSeverity::kError
+                         ? "ERROR"
+                         : s == absl::LogSeverity::kFatal ? "FATAL" : "UNKNOWN";
+}
+
+// NormalizeLogSeverity()
+//
+// Values less than `kInfo` normalize to `kInfo`; values greater than `kFatal`
+// normalize to `kError` (**NOT** `kFatal`).
+constexpr absl::LogSeverity NormalizeLogSeverity(absl::LogSeverity s) {
+  return s < absl::LogSeverity::kInfo
+             ? absl::LogSeverity::kInfo
+             : s > absl::LogSeverity::kFatal ? absl::LogSeverity::kError : s;
+}
+constexpr absl::LogSeverity NormalizeLogSeverity(int s) {
+  return absl::NormalizeLogSeverity(static_cast<absl::LogSeverity>(s));
+}
+
+// operator<<
+//
+// The exact representation of a streamed `absl::LogSeverity` is deliberately
+// unspecified; do not rely on it.
+std::ostream& operator<<(std::ostream& os, absl::LogSeverity s);
+
+// Enums representing a lower bound for LogSeverity. APIs that only operate on
+// messages of at least a certain level (for example, `SetMinLogLevel()`) use
+// this type to specify that level. absl::LogSeverityAtLeast::kInfinity is
+// a level above all threshold levels and therefore no log message will
+// ever meet this threshold.
+enum class LogSeverityAtLeast : int {
+  kInfo = static_cast<int>(absl::LogSeverity::kInfo),
+  kWarning = static_cast<int>(absl::LogSeverity::kWarning),
+  kError = static_cast<int>(absl::LogSeverity::kError),
+  kFatal = static_cast<int>(absl::LogSeverity::kFatal),
+  kInfinity = 1000,
+};
+
+std::ostream& operator<<(std::ostream& os, absl::LogSeverityAtLeast s);
+
+// Enums representing an upper bound for LogSeverity. APIs that only operate on
+// messages of at most a certain level (for example, buffer all messages at or
+// below a certain level) use this type to specify that level.
+// absl::LogSeverityAtMost::kNegativeInfinity is a level below all threshold
+// levels and therefore will exclude all log messages.
+enum class LogSeverityAtMost : int {
+  kNegativeInfinity = -1000,
+  kInfo = static_cast<int>(absl::LogSeverity::kInfo),
+  kWarning = static_cast<int>(absl::LogSeverity::kWarning),
+  kError = static_cast<int>(absl::LogSeverity::kError),
+  kFatal = static_cast<int>(absl::LogSeverity::kFatal),
+};
+
+std::ostream& operator<<(std::ostream& os, absl::LogSeverityAtMost s);
+
+#define COMPOP(op1, op2, T)                                         \
+  constexpr bool operator op1(absl::T lhs, absl::LogSeverity rhs) { \
+    return static_cast<absl::LogSeverity>(lhs) op1 rhs;             \
+  }                                                                 \
+  constexpr bool operator op2(absl::LogSeverity lhs, absl::T rhs) { \
+    return lhs op2 static_cast<absl::LogSeverity>(rhs);             \
+  }
+
+// Comparisons between `LogSeverity` and `LogSeverityAtLeast`/
+// `LogSeverityAtMost` are only supported in one direction.
+// Valid checks are:
+//   LogSeverity >= LogSeverityAtLeast
+//   LogSeverity < LogSeverityAtLeast
+//   LogSeverity <= LogSeverityAtMost
+//   LogSeverity > LogSeverityAtMost
+COMPOP(>, <, LogSeverityAtLeast)
+COMPOP(<=, >=, LogSeverityAtLeast)
+COMPOP(<, >, LogSeverityAtMost)
+COMPOP(>=, <=, LogSeverityAtMost)
+#undef COMPOP
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_LOG_SEVERITY_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/macros.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/macros.h
new file mode 100644
index 0000000000000000000000000000000000000000..f33cd1927bfda7058e11e414821c43d9c23a8b4a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/macros.h
@@ -0,0 +1,141 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: macros.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines the set of language macros used within Abseil code.
+// For the set of macros used to determine supported compilers and platforms,
+// see absl/base/config.h instead.
+//
+// This code is compiled directly on many platforms, including client
+// platforms like Windows, Mac, and embedded systems.  Before making
+// any changes here, make sure that you're not breaking any platforms.
+
+#ifndef ABSL_BASE_MACROS_H_
+#define ABSL_BASE_MACROS_H_
+
+#include <cassert>
+#include <cstddef>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/base/optimization.h"
+#include "absl/base/port.h"
+
+// ABSL_ARRAYSIZE()
+//
+// Returns the number of elements in an array as a compile-time constant, which
+// can be used in defining new arrays. If you use this macro on a pointer by
+// mistake, you will get a compile-time error.
+#define ABSL_ARRAYSIZE(array) \
+  (sizeof(::absl::macros_internal::ArraySizeHelper(array)))
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace macros_internal {
+// Note: this internal template function declaration is used by ABSL_ARRAYSIZE.
+// The function doesn't need a definition, as we only use its type.
+template <typename T, size_t N>
+auto ArraySizeHelper(const T (&array)[N]) -> char (&)[N];
+}  // namespace macros_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+// ABSL_BAD_CALL_IF()
+//
+// Used on a function overload to trap bad calls: any call that matches the
+// overload will cause a compile-time error. This macro uses a clang-specific
+// "enable_if" attribute, as described at
+// https://clang.llvm.org/docs/AttributeReference.html#enable-if
+//
+// Overloads which use this macro should be bracketed by
+// `#ifdef ABSL_BAD_CALL_IF`.
+//
+// Example:
+//
+//   int isdigit(int c);
+//   #ifdef ABSL_BAD_CALL_IF
+//   int isdigit(int c)
+//     ABSL_BAD_CALL_IF(c <= -1 || c > 255,
+//                       "'c' must have the value of an unsigned char or EOF");
+//   #endif // ABSL_BAD_CALL_IF
+#if ABSL_HAVE_ATTRIBUTE(enable_if)
+#define ABSL_BAD_CALL_IF(expr, msg) \
+  __attribute__((enable_if(expr, "Bad call trap"), unavailable(msg)))
+#endif
+
+// ABSL_ASSERT()
+//
+// In C++11, `assert` can't be used portably within constexpr functions.
+// ABSL_ASSERT functions as a runtime assert but works in C++11 constexpr
+// functions.  Example:
+//
+// constexpr double Divide(double a, double b) {
+//   return ABSL_ASSERT(b != 0), a / b;
+// }
+//
+// This macro is inspired by
+// https://akrzemi1.wordpress.com/2017/05/18/asserts-in-constexpr-functions/
+#if defined(NDEBUG)
+#define ABSL_ASSERT(expr) \
+  (false ? static_cast<void>(expr) : static_cast<void>(0))
+#else
+#define ABSL_ASSERT(expr)                           \
+  (ABSL_PREDICT_TRUE((expr)) ? static_cast<void>(0) \
+                             : [] { assert(false && #expr); }())  // NOLINT
+#endif
+
+// `ABSL_INTERNAL_HARDENING_ABORT()` controls how `ABSL_HARDENING_ASSERT()`
+// aborts the program in release mode (when NDEBUG is defined). The
+// implementation should abort the program as quickly as possible and ideally it
+// should not be possible to ignore the abort request.
+#define ABSL_INTERNAL_HARDENING_ABORT()   \
+  do {                                    \
+    ABSL_INTERNAL_IMMEDIATE_ABORT_IMPL(); \
+    ABSL_INTERNAL_UNREACHABLE_IMPL();     \
+  } while (false)
+
+// ABSL_HARDENING_ASSERT()
+//
+// `ABSL_HARDENING_ASSERT()` is like `ABSL_ASSERT()`, but used to implement
+// runtime assertions that should be enabled in hardened builds even when
+// `NDEBUG` is defined.
+//
+// When `NDEBUG` is not defined, `ABSL_HARDENING_ASSERT()` is identical to
+// `ABSL_ASSERT()`.
+//
+// See `ABSL_OPTION_HARDENED` in `absl/base/options.h` for more information on
+// hardened mode.
+#if ABSL_OPTION_HARDENED == 1 && defined(NDEBUG)
+#define ABSL_HARDENING_ASSERT(expr)                 \
+  (ABSL_PREDICT_TRUE((expr)) ? static_cast<void>(0) \
+                             : [] { ABSL_INTERNAL_HARDENING_ABORT(); }())
+#else
+#define ABSL_HARDENING_ASSERT(expr) ABSL_ASSERT(expr)
+#endif
+
+#ifdef ABSL_HAVE_EXCEPTIONS
+#define ABSL_INTERNAL_TRY try
+#define ABSL_INTERNAL_CATCH_ANY catch (...)
+#define ABSL_INTERNAL_RETHROW do { throw; } while (false)
+#else  // ABSL_HAVE_EXCEPTIONS
+#define ABSL_INTERNAL_TRY if (true)
+#define ABSL_INTERNAL_CATCH_ANY else if (false)
+#define ABSL_INTERNAL_RETHROW do {} while (false)
+#endif  // ABSL_HAVE_EXCEPTIONS
+
+#endif  // ABSL_BASE_MACROS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/optimization.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/optimization.h
new file mode 100644
index 0000000000000000000000000000000000000000..ad0121ad3be5c22687749bdac397100275fd2edd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/optimization.h
@@ -0,0 +1,304 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: optimization.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines portable macros for performance optimization.
+
+#ifndef ABSL_BASE_OPTIMIZATION_H_
+#define ABSL_BASE_OPTIMIZATION_H_
+
+#include <assert.h>
+
+#include "absl/base/config.h"
+
+// ABSL_BLOCK_TAIL_CALL_OPTIMIZATION
+//
+// Instructs the compiler to avoid optimizing tail-call recursion. This macro is
+// useful when you wish to preserve the existing function order within a stack
+// trace for logging, debugging, or profiling purposes.
+//
+// Example:
+//
+//   int f() {
+//     int result = g();
+//     ABSL_BLOCK_TAIL_CALL_OPTIMIZATION();
+//     return result;
+//   }
+#if defined(__pnacl__)
+#define ABSL_BLOCK_TAIL_CALL_OPTIMIZATION() if (volatile int x = 0) { (void)x; }
+#elif defined(__clang__)
+// Clang will not tail call given inline volatile assembly.
+#define ABSL_BLOCK_TAIL_CALL_OPTIMIZATION() __asm__ __volatile__("")
+#elif defined(__GNUC__)
+// GCC will not tail call given inline volatile assembly.
+#define ABSL_BLOCK_TAIL_CALL_OPTIMIZATION() __asm__ __volatile__("")
+#elif defined(_MSC_VER)
+#include <intrin.h>
+// The __nop() intrinsic blocks the optimisation.
+#define ABSL_BLOCK_TAIL_CALL_OPTIMIZATION() __nop()
+#else
+#define ABSL_BLOCK_TAIL_CALL_OPTIMIZATION() if (volatile int x = 0) { (void)x; }
+#endif
+
+// ABSL_CACHELINE_SIZE
+//
+// Explicitly defines the size of the L1 cache for purposes of alignment.
+// Setting the cacheline size allows you to specify that certain objects be
+// aligned on a cacheline boundary with `ABSL_CACHELINE_ALIGNED` declarations.
+// (See below.)
+//
+// NOTE: this macro should be replaced with the following C++17 features, when
+// those are generally available:
+//
+//   * `std::hardware_constructive_interference_size`
+//   * `std::hardware_destructive_interference_size`
+//
+// See http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2016/p0154r1.html
+// for more information.
+#if defined(__GNUC__)
+// Cache line alignment
+#if defined(__i386__) || defined(__x86_64__)
+#define ABSL_CACHELINE_SIZE 64
+#elif defined(__powerpc64__)
+#define ABSL_CACHELINE_SIZE 128
+#elif defined(__aarch64__)
+// We would need to read special register ctr_el0 to find out L1 dcache size.
+// This value is a good estimate based on a real aarch64 machine.
+#define ABSL_CACHELINE_SIZE 64
+#elif defined(__arm__)
+// Cache line sizes for ARM: These values are not strictly correct since
+// cache line sizes depend on implementations, not architectures.  There
+// are even implementations with cache line sizes configurable at boot
+// time.
+#if defined(__ARM_ARCH_5T__)
+#define ABSL_CACHELINE_SIZE 32
+#elif defined(__ARM_ARCH_7A__)
+#define ABSL_CACHELINE_SIZE 64
+#endif
+#endif
+#endif
+
+#ifndef ABSL_CACHELINE_SIZE
+// A reasonable default guess.  Note that overestimates tend to waste more
+// space, while underestimates tend to waste more time.
+#define ABSL_CACHELINE_SIZE 64
+#endif
+
+// ABSL_CACHELINE_ALIGNED
+//
+// Indicates that the declared object be cache aligned using
+// `ABSL_CACHELINE_SIZE` (see above). Cacheline aligning objects allows you to
+// load a set of related objects in the L1 cache for performance improvements.
+// Cacheline aligning objects properly allows constructive memory sharing and
+// prevents destructive (or "false") memory sharing.
+//
+// NOTE: callers should replace uses of this macro with `alignas()` using
+// `std::hardware_constructive_interference_size` and/or
+// `std::hardware_destructive_interference_size` when C++17 becomes available to
+// them.
+//
+// See http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2016/p0154r1.html
+// for more information.
+//
+// On some compilers, `ABSL_CACHELINE_ALIGNED` expands to an `__attribute__`
+// or `__declspec` attribute. For compilers where this is not known to work,
+// the macro expands to nothing.
+//
+// No further guarantees are made here. The result of applying the macro
+// to variables and types is always implementation-defined.
+//
+// WARNING: It is easy to use this attribute incorrectly, even to the point
+// of causing bugs that are difficult to diagnose, crash, etc. It does not
+// of itself guarantee that objects are aligned to a cache line.
+//
+// NOTE: Some compilers are picky about the locations of annotations such as
+// this attribute, so prefer to put it at the beginning of your declaration.
+// For example,
+//
+//   ABSL_CACHELINE_ALIGNED static Foo* foo = ...
+//
+//   class ABSL_CACHELINE_ALIGNED Bar { ...
+//
+// Recommendations:
+//
+// 1) Consult compiler documentation; this comment is not kept in sync as
+//    toolchains evolve.
+// 2) Verify your use has the intended effect. This often requires inspecting
+//    the generated machine code.
+// 3) Prefer applying this attribute to individual variables. Avoid
+//    applying it to types. This tends to localize the effect.
+#if defined(__clang__) || defined(__GNUC__)
+#define ABSL_CACHELINE_ALIGNED __attribute__((aligned(ABSL_CACHELINE_SIZE)))
+#elif defined(_MSC_VER)
+#define ABSL_CACHELINE_ALIGNED __declspec(align(ABSL_CACHELINE_SIZE))
+#else
+#define ABSL_CACHELINE_ALIGNED
+#endif
+
+// ABSL_PREDICT_TRUE, ABSL_PREDICT_FALSE
+//
+// Enables the compiler to prioritize compilation using static analysis for
+// likely paths within a boolean branch.
+//
+// Example:
+//
+//   if (ABSL_PREDICT_TRUE(expression)) {
+//     return result;                        // Faster if more likely
+//   } else {
+//     return 0;
+//   }
+//
+// Compilers can use the information that a certain branch is not likely to be
+// taken (for instance, a CHECK failure) to optimize for the common case in
+// the absence of better information (ie. compiling gcc with `-fprofile-arcs`).
+//
+// Recommendation: Modern CPUs dynamically predict branch execution paths,
+// typically with accuracy greater than 97%. As a result, annotating every
+// branch in a codebase is likely counterproductive; however, annotating
+// specific branches that are both hot and consistently mispredicted is likely
+// to yield performance improvements.
+#if ABSL_HAVE_BUILTIN(__builtin_expect) || \
+    (defined(__GNUC__) && !defined(__clang__))
+#define ABSL_PREDICT_FALSE(x) (__builtin_expect(false || (x), false))
+#define ABSL_PREDICT_TRUE(x) (__builtin_expect(false || (x), true))
+#else
+#define ABSL_PREDICT_FALSE(x) (x)
+#define ABSL_PREDICT_TRUE(x) (x)
+#endif
+
+// `ABSL_INTERNAL_IMMEDIATE_ABORT_IMPL()` aborts the program in the fastest
+// possible way, with no attempt at logging. One use is to implement hardening
+// aborts with ABSL_OPTION_HARDENED.  Since this is an internal symbol, it
+// should not be used directly outside of Abseil.
+#if ABSL_HAVE_BUILTIN(__builtin_trap) || \
+    (defined(__GNUC__) && !defined(__clang__))
+#define ABSL_INTERNAL_IMMEDIATE_ABORT_IMPL() __builtin_trap()
+#else
+#define ABSL_INTERNAL_IMMEDIATE_ABORT_IMPL() abort()
+#endif
+
+// `ABSL_INTERNAL_UNREACHABLE_IMPL()` is the platform specific directive to
+// indicate that a statement is unreachable, and to allow the compiler to
+// optimize accordingly. Clients should use `ABSL_UNREACHABLE()`, which is
+// defined below.
+#if defined(__cpp_lib_unreachable) && __cpp_lib_unreachable >= 202202L
+#define ABSL_INTERNAL_UNREACHABLE_IMPL() std::unreachable()
+#elif defined(__GNUC__) || ABSL_HAVE_BUILTIN(__builtin_unreachable)
+#define ABSL_INTERNAL_UNREACHABLE_IMPL() __builtin_unreachable()
+#elif ABSL_HAVE_BUILTIN(__builtin_assume)
+#define ABSL_INTERNAL_UNREACHABLE_IMPL() __builtin_assume(false)
+#elif defined(_MSC_VER)
+#define ABSL_INTERNAL_UNREACHABLE_IMPL() __assume(false)
+#else
+#define ABSL_INTERNAL_UNREACHABLE_IMPL()
+#endif
+
+// `ABSL_UNREACHABLE()` is an unreachable statement.  A program which reaches
+// one has undefined behavior, and the compiler may optimize accordingly.
+#if ABSL_OPTION_HARDENED == 1 && defined(NDEBUG)
+// Abort in hardened mode to avoid dangerous undefined behavior.
+#define ABSL_UNREACHABLE()                \
+  do {                                    \
+    ABSL_INTERNAL_IMMEDIATE_ABORT_IMPL(); \
+    ABSL_INTERNAL_UNREACHABLE_IMPL();     \
+  } while (false)
+#else
+// The assert only fires in debug mode to aid in debugging.
+// When NDEBUG is defined, reaching ABSL_UNREACHABLE() is undefined behavior.
+#define ABSL_UNREACHABLE()                       \
+  do {                                           \
+    /* NOLINTNEXTLINE: misc-static-assert */     \
+    assert(false && "ABSL_UNREACHABLE reached"); \
+    ABSL_INTERNAL_UNREACHABLE_IMPL();            \
+  } while (false)
+#endif
+
+// ABSL_ASSUME(cond)
+//
+// Informs the compiler that a condition is always true and that it can assume
+// it to be true for optimization purposes.
+//
+// WARNING: If the condition is false, the program can produce undefined and
+// potentially dangerous behavior.
+//
+// In !NDEBUG mode, the condition is checked with an assert().
+//
+// NOTE: The expression must not have side effects, as it may only be evaluated
+// in some compilation modes and not others. Some compilers may issue a warning
+// if the compiler cannot prove the expression has no side effects. For example,
+// the expression should not use a function call since the compiler cannot prove
+// that a function call does not have side effects.
+//
+// Example:
+//
+//   int x = ...;
+//   ABSL_ASSUME(x >= 0);
+//   // The compiler can optimize the division to a simple right shift using the
+//   // assumption specified above.
+//   int y = x / 16;
+//
+#if !defined(NDEBUG)
+#define ABSL_ASSUME(cond) assert(cond)
+#elif ABSL_HAVE_BUILTIN(__builtin_assume)
+#define ABSL_ASSUME(cond) __builtin_assume(cond)
+#elif defined(_MSC_VER)
+#define ABSL_ASSUME(cond) __assume(cond)
+#elif defined(__cpp_lib_unreachable) && __cpp_lib_unreachable >= 202202L
+#define ABSL_ASSUME(cond)            \
+  do {                               \
+    if (!(cond)) std::unreachable(); \
+  } while (false)
+#elif defined(__GNUC__) || ABSL_HAVE_BUILTIN(__builtin_unreachable)
+#define ABSL_ASSUME(cond)                 \
+  do {                                    \
+    if (!(cond)) __builtin_unreachable(); \
+  } while (false)
+#else
+#define ABSL_ASSUME(cond)               \
+  do {                                  \
+    static_cast<void>(false && (cond)); \
+  } while (false)
+#endif
+
+// ABSL_INTERNAL_UNIQUE_SMALL_NAME(cond)
+// This macro forces small unique name on a static file level symbols like
+// static local variables or static functions. This is intended to be used in
+// macro definitions to optimize the cost of generated code. Do NOT use it on
+// symbols exported from translation unit since it may cause a link time
+// conflict.
+//
+// Example:
+//
+// #define MY_MACRO(txt)
+// namespace {
+//  char VeryVeryLongVarName[] ABSL_INTERNAL_UNIQUE_SMALL_NAME() = txt;
+//  const char* VeryVeryLongFuncName() ABSL_INTERNAL_UNIQUE_SMALL_NAME();
+//  const char* VeryVeryLongFuncName() { return txt; }
+// }
+//
+
+#if defined(__GNUC__)
+#define ABSL_INTERNAL_UNIQUE_SMALL_NAME2(x) #x
+#define ABSL_INTERNAL_UNIQUE_SMALL_NAME1(x) ABSL_INTERNAL_UNIQUE_SMALL_NAME2(x)
+#define ABSL_INTERNAL_UNIQUE_SMALL_NAME() \
+  asm(ABSL_INTERNAL_UNIQUE_SMALL_NAME1(.absl.__COUNTER__))
+#else
+#define ABSL_INTERNAL_UNIQUE_SMALL_NAME()
+#endif
+
+#endif  // ABSL_BASE_OPTIMIZATION_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/options.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/options.h
new file mode 100644
index 0000000000000000000000000000000000000000..b6de636d9b18af5ccbd1f2cdced1fb9fa3d38e17
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/options.h
@@ -0,0 +1,232 @@
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: options.h
+// -----------------------------------------------------------------------------
+//
+// This file contains Abseil configuration options for setting specific
+// implementations instead of letting Abseil determine which implementation to
+// use at compile-time. Setting these options may be useful for package or build
+// managers who wish to guarantee ABI stability within binary builds (which are
+// otherwise difficult to enforce).
+//
+// *** IMPORTANT NOTICE FOR PACKAGE MANAGERS:  It is important that
+// maintainers of package managers who wish to package Abseil read and
+// understand this file! ***
+//
+// Abseil contains a number of possible configuration endpoints, based on
+// parameters such as the detected platform, language version, or command-line
+// flags used to invoke the underlying binary. As is the case with all
+// libraries, binaries which contain Abseil code must ensure that separate
+// packages use the same compiled copy of Abseil to avoid a diamond dependency
+// problem, which can occur if two packages built with different Abseil
+// configuration settings are linked together. Diamond dependency problems in
+// C++ may manifest as violations to the One Definition Rule (ODR) (resulting in
+// linker errors), or undefined behavior (resulting in crashes).
+//
+// Diamond dependency problems can be avoided if all packages utilize the same
+// exact version of Abseil. Building from source code with the same compilation
+// parameters is the easiest way to avoid such dependency problems. However, for
+// package managers who cannot control such compilation parameters, we are
+// providing the file to allow you to inject ABI (Application Binary Interface)
+// stability across builds. Settings options in this file will neither change
+// API nor ABI, providing a stable copy of Abseil between packages.
+//
+// Care must be taken to keep options within these configurations isolated
+// from any other dynamic settings, such as command-line flags which could alter
+// these options. This file is provided specifically to help build and package
+// managers provide a stable copy of Abseil within their libraries and binaries;
+// other developers should not have need to alter the contents of this file.
+//
+// -----------------------------------------------------------------------------
+// Usage
+// -----------------------------------------------------------------------------
+//
+// For any particular package release, set the appropriate definitions within
+// this file to whatever value makes the most sense for your package(s). Note
+// that, by default, most of these options, at the moment, affect the
+// implementation of types; future options may affect other implementation
+// details.
+//
+// NOTE: the defaults within this file all assume that Abseil can select the
+// proper Abseil implementation at compile-time, which will not be sufficient
+// to guarantee ABI stability to package managers.
+
+#ifndef ABSL_BASE_OPTIONS_H_
+#define ABSL_BASE_OPTIONS_H_
+
+// -----------------------------------------------------------------------------
+// Type Compatibility Options
+// -----------------------------------------------------------------------------
+//
+// ABSL_OPTION_USE_STD_ANY
+//
+// This option controls whether absl::any is implemented as an alias to
+// std::any, or as an independent implementation.
+//
+// A value of 0 means to use Abseil's implementation.  This requires only C++11
+// support, and is expected to work on every toolchain we support.
+//
+// A value of 1 means to use an alias to std::any.  This requires that all code
+// using Abseil is built in C++17 mode or later.
+//
+// A value of 2 means to detect the C++ version being used to compile Abseil,
+// and use an alias only if a working std::any is available.  This option is
+// useful when you are building your entire program, including all of its
+// dependencies, from source.  It should not be used otherwise -- for example,
+// if you are distributing Abseil in a binary package manager -- since in
+// mode 2, absl::any will name a different type, with a different mangled name
+// and binary layout, depending on the compiler flags passed by the end user.
+// For more info, see https://abseil.io/about/design/dropin-types.
+//
+// User code should not inspect this macro.  To check in the preprocessor if
+// absl::any is a typedef of std::any, use the feature macro ABSL_USES_STD_ANY.
+
+#define ABSL_OPTION_USE_STD_ANY 2
+
+
+// ABSL_OPTION_USE_STD_OPTIONAL
+//
+// This option controls whether absl::optional is implemented as an alias to
+// std::optional, or as an independent implementation.
+//
+// A value of 0 means to use Abseil's implementation.  This requires only C++11
+// support, and is expected to work on every toolchain we support.
+//
+// A value of 1 means to use an alias to std::optional.  This requires that all
+// code using Abseil is built in C++17 mode or later.
+//
+// A value of 2 means to detect the C++ version being used to compile Abseil,
+// and use an alias only if a working std::optional is available.  This option
+// is useful when you are building your program from source.  It should not be
+// used otherwise -- for example, if you are distributing Abseil in a binary
+// package manager -- since in mode 2, absl::optional will name a different
+// type, with a different mangled name and binary layout, depending on the
+// compiler flags passed by the end user.  For more info, see
+// https://abseil.io/about/design/dropin-types.
+
+// User code should not inspect this macro.  To check in the preprocessor if
+// absl::optional is a typedef of std::optional, use the feature macro
+// ABSL_USES_STD_OPTIONAL.
+
+#define ABSL_OPTION_USE_STD_OPTIONAL 2
+
+
+// ABSL_OPTION_USE_STD_STRING_VIEW
+//
+// This option controls whether absl::string_view is implemented as an alias to
+// std::string_view, or as an independent implementation.
+//
+// A value of 0 means to use Abseil's implementation.  This requires only C++11
+// support, and is expected to work on every toolchain we support.
+//
+// A value of 1 means to use an alias to std::string_view.  This requires that
+// all code using Abseil is built in C++17 mode or later.
+//
+// A value of 2 means to detect the C++ version being used to compile Abseil,
+// and use an alias only if a working std::string_view is available.  This
+// option is useful when you are building your program from source.  It should
+// not be used otherwise -- for example, if you are distributing Abseil in a
+// binary package manager -- since in mode 2, absl::string_view will name a
+// different type, with a different mangled name and binary layout, depending on
+// the compiler flags passed by the end user.  For more info, see
+// https://abseil.io/about/design/dropin-types.
+//
+// User code should not inspect this macro.  To check in the preprocessor if
+// absl::string_view is a typedef of std::string_view, use the feature macro
+// ABSL_USES_STD_STRING_VIEW.
+
+#define ABSL_OPTION_USE_STD_STRING_VIEW 2
+
+// ABSL_OPTION_USE_STD_VARIANT
+//
+// This option controls whether absl::variant is implemented as an alias to
+// std::variant, or as an independent implementation.
+//
+// A value of 0 means to use Abseil's implementation.  This requires only C++11
+// support, and is expected to work on every toolchain we support.
+//
+// A value of 1 means to use an alias to std::variant.  This requires that all
+// code using Abseil is built in C++17 mode or later.
+//
+// A value of 2 means to detect the C++ version being used to compile Abseil,
+// and use an alias only if a working std::variant is available.  This option
+// is useful when you are building your program from source.  It should not be
+// used otherwise -- for example, if you are distributing Abseil in a binary
+// package manager -- since in mode 2, absl::variant will name a different
+// type, with a different mangled name and binary layout, depending on the
+// compiler flags passed by the end user.  For more info, see
+// https://abseil.io/about/design/dropin-types.
+//
+// User code should not inspect this macro.  To check in the preprocessor if
+// absl::variant is a typedef of std::variant, use the feature macro
+// ABSL_USES_STD_VARIANT.
+
+#define ABSL_OPTION_USE_STD_VARIANT 2
+
+
+// ABSL_OPTION_USE_INLINE_NAMESPACE
+// ABSL_OPTION_INLINE_NAMESPACE_NAME
+//
+// These options controls whether all entities in the absl namespace are
+// contained within an inner inline namespace.  This does not affect the
+// user-visible API of Abseil, but it changes the mangled names of all symbols.
+//
+// This can be useful as a version tag if you are distributing Abseil in
+// precompiled form.  This will prevent a binary library build of Abseil with
+// one inline namespace being used with headers configured with a different
+// inline namespace name.  Binary packagers are reminded that Abseil does not
+// guarantee any ABI stability in Abseil, so any update of Abseil or
+// configuration change in such a binary package should be combined with a
+// new, unique value for the inline namespace name.
+//
+// A value of 0 means not to use inline namespaces.
+//
+// A value of 1 means to use an inline namespace with the given name inside
+// namespace absl.  If this is set, ABSL_OPTION_INLINE_NAMESPACE_NAME must also
+// be changed to a new, unique identifier name.  In particular "head" is not
+// allowed.
+
+#define ABSL_OPTION_USE_INLINE_NAMESPACE 1
+#define ABSL_OPTION_INLINE_NAMESPACE_NAME lts_20230125
+
+// ABSL_OPTION_HARDENED
+//
+// This option enables a "hardened" build in release mode (in this context,
+// release mode is defined as a build where the `NDEBUG` macro is defined).
+//
+// A value of 0 means that "hardened" mode is not enabled.
+//
+// A value of 1 means that "hardened" mode is enabled.
+//
+// Hardened builds have additional security checks enabled when `NDEBUG` is
+// defined. Defining `NDEBUG` is normally used to turn `assert()` macro into a
+// no-op, as well as disabling other bespoke program consistency checks. By
+// defining ABSL_OPTION_HARDENED to 1, a select set of checks remain enabled in
+// release mode. These checks guard against programming errors that may lead to
+// security vulnerabilities. In release mode, when one of these programming
+// errors is encountered, the program will immediately abort, possibly without
+// any attempt at logging.
+//
+// The checks enabled by this option are not free; they do incur runtime cost.
+//
+// The checks enabled by this option are always active when `NDEBUG` is not
+// defined, even in the case when ABSL_OPTION_HARDENED is defined to 0. The
+// checks enabled by this option may abort the program in a different way and
+// log additional information when `NDEBUG` is not defined.
+
+#define ABSL_OPTION_HARDENED 0
+
+#endif  // ABSL_BASE_OPTIONS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/policy_checks.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/policy_checks.h
new file mode 100644
index 0000000000000000000000000000000000000000..b8cd4c94405101142d1ef779c5f6f692367328f8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/policy_checks.h
@@ -0,0 +1,113 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: policy_checks.h
+// -----------------------------------------------------------------------------
+//
+// This header enforces a minimum set of policies at build time, such as the
+// supported compiler and library versions. Unsupported configurations are
+// reported with `#error`. This enforcement is best effort, so successfully
+// compiling this header does not guarantee a supported configuration.
+
+#ifndef ABSL_BASE_POLICY_CHECKS_H_
+#define ABSL_BASE_POLICY_CHECKS_H_
+
+// Included for the __GLIBC_PREREQ macro used below.
+#include <limits.h>
+
+// Included for the _STLPORT_VERSION macro used below.
+#if defined(__cplusplus)
+#include <cstddef>
+#endif
+
+// -----------------------------------------------------------------------------
+// Operating System Check
+// -----------------------------------------------------------------------------
+
+#if defined(__CYGWIN__)
+#error "Cygwin is not supported."
+#endif
+
+// -----------------------------------------------------------------------------
+// Toolchain Check
+// -----------------------------------------------------------------------------
+
+// We support Visual Studio 2017 (MSVC++ 15.0) and later.
+// This minimum will go up.
+#if defined(_MSC_VER) && _MSC_VER < 1910 && !defined(__clang__)
+#error "This package requires Visual Studio 2017 (MSVC++ 15.0) or higher."
+#endif
+
+// We support GCC 7 and later.
+// This minimum will go up.
+#if defined(__GNUC__) && !defined(__clang__)
+#if __GNUC__ < 7
+#error "This package requires GCC 7 or higher."
+#endif
+#endif
+
+// We support Apple Xcode clang 4.2.1 (version 421.11.65) and later.
+// This corresponds to Apple Xcode version 4.5.
+// This minimum will go up.
+#if defined(__apple_build_version__) && __apple_build_version__ < 4211165
+#error "This package requires __apple_build_version__ of 4211165 or higher."
+#endif
+
+// -----------------------------------------------------------------------------
+// C++ Version Check
+// -----------------------------------------------------------------------------
+
+// Enforce C++14 as the minimum.
+#if defined(_MSVC_LANG)
+#if _MSVC_LANG < 201402L
+#error "C++ versions less than C++14 are not supported."
+#endif  // _MSVC_LANG < 201402L
+#elif defined(__cplusplus)
+#if __cplusplus < 201402L
+#error "C++ versions less than C++14 are not supported."
+#endif  // __cplusplus < 201402L
+#endif
+
+// -----------------------------------------------------------------------------
+// Standard Library Check
+// -----------------------------------------------------------------------------
+
+#if defined(_STLPORT_VERSION)
+#error "STLPort is not supported."
+#endif
+
+// -----------------------------------------------------------------------------
+// `char` Size Check
+// -----------------------------------------------------------------------------
+
+// Abseil currently assumes CHAR_BIT == 8. If you would like to use Abseil on a
+// platform where this is not the case, please provide us with the details about
+// your platform so we can consider relaxing this requirement.
+#if CHAR_BIT != 8
+#error "Abseil assumes CHAR_BIT == 8."
+#endif
+
+// -----------------------------------------------------------------------------
+// `int` Size Check
+// -----------------------------------------------------------------------------
+
+// Abseil currently assumes that an int is 4 bytes. If you would like to use
+// Abseil on a platform where this is not the case, please provide us with the
+// details about your platform so we can consider relaxing this requirement.
+#if INT_MAX < 2147483647
+#error "Abseil assumes that int is at least 4 bytes. "
+#endif
+
+#endif  // ABSL_BASE_POLICY_CHECKS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/port.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/port.h
new file mode 100644
index 0000000000000000000000000000000000000000..5bc4d6cd95d4954a4a3e09947499a9abdcda386c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/port.h
@@ -0,0 +1,25 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// This files is a forwarding header for other headers containing various
+// portability macros and functions.
+
+#ifndef ABSL_BASE_PORT_H_
+#define ABSL_BASE_PORT_H_
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/base/optimization.h"
+
+#endif  // ABSL_BASE_PORT_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/thread_annotations.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/thread_annotations.h
new file mode 100644
index 0000000000000000000000000000000000000000..bc8a6203478bef0f609df5e5b18b36b003e33512
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/base/thread_annotations.h
@@ -0,0 +1,335 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: thread_annotations.h
+// -----------------------------------------------------------------------------
+//
+// This header file contains macro definitions for thread safety annotations
+// that allow developers to document the locking policies of multi-threaded
+// code. The annotations can also help program analysis tools to identify
+// potential thread safety issues.
+//
+// These annotations are implemented using compiler attributes. Using the macros
+// defined here instead of raw attributes allow for portability and future
+// compatibility.
+//
+// When referring to mutexes in the arguments of the attributes, you should
+// use variable names or more complex expressions (e.g. my_object->mutex_)
+// that evaluate to a concrete mutex object whenever possible. If the mutex
+// you want to refer to is not in scope, you may use a member pointer
+// (e.g. &MyClass::mutex_) to refer to a mutex in some (unknown) object.
+
+#ifndef ABSL_BASE_THREAD_ANNOTATIONS_H_
+#define ABSL_BASE_THREAD_ANNOTATIONS_H_
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+// TODO(mbonadei): Remove after the backward compatibility period.
+#include "absl/base/internal/thread_annotations.h"  // IWYU pragma: export
+
+// ABSL_GUARDED_BY()
+//
+// Documents if a shared field or global variable needs to be protected by a
+// mutex. ABSL_GUARDED_BY() allows the user to specify a particular mutex that
+// should be held when accessing the annotated variable.
+//
+// Although this annotation (and ABSL_PT_GUARDED_BY, below) cannot be applied to
+// local variables, a local variable and its associated mutex can often be
+// combined into a small class or struct, thereby allowing the annotation.
+//
+// Example:
+//
+//   class Foo {
+//     Mutex mu_;
+//     int p1_ ABSL_GUARDED_BY(mu_);
+//     ...
+//   };
+#if ABSL_HAVE_ATTRIBUTE(guarded_by)
+#define ABSL_GUARDED_BY(x) __attribute__((guarded_by(x)))
+#else
+#define ABSL_GUARDED_BY(x)
+#endif
+
+// ABSL_PT_GUARDED_BY()
+//
+// Documents if the memory location pointed to by a pointer should be guarded
+// by a mutex when dereferencing the pointer.
+//
+// Example:
+//   class Foo {
+//     Mutex mu_;
+//     int *p1_ ABSL_PT_GUARDED_BY(mu_);
+//     ...
+//   };
+//
+// Note that a pointer variable to a shared memory location could itself be a
+// shared variable.
+//
+// Example:
+//
+//   // `q_`, guarded by `mu1_`, points to a shared memory location that is
+//   // guarded by `mu2_`:
+//   int *q_ ABSL_GUARDED_BY(mu1_) ABSL_PT_GUARDED_BY(mu2_);
+#if ABSL_HAVE_ATTRIBUTE(pt_guarded_by)
+#define ABSL_PT_GUARDED_BY(x) __attribute__((pt_guarded_by(x)))
+#else
+#define ABSL_PT_GUARDED_BY(x)
+#endif
+
+// ABSL_ACQUIRED_AFTER() / ABSL_ACQUIRED_BEFORE()
+//
+// Documents the acquisition order between locks that can be held
+// simultaneously by a thread. For any two locks that need to be annotated
+// to establish an acquisition order, only one of them needs the annotation.
+// (i.e. You don't have to annotate both locks with both ABSL_ACQUIRED_AFTER
+// and ABSL_ACQUIRED_BEFORE.)
+//
+// As with ABSL_GUARDED_BY, this is only applicable to mutexes that are shared
+// fields or global variables.
+//
+// Example:
+//
+//   Mutex m1_;
+//   Mutex m2_ ABSL_ACQUIRED_AFTER(m1_);
+#if ABSL_HAVE_ATTRIBUTE(acquired_after)
+#define ABSL_ACQUIRED_AFTER(...) __attribute__((acquired_after(__VA_ARGS__)))
+#else
+#define ABSL_ACQUIRED_AFTER(...)
+#endif
+
+#if ABSL_HAVE_ATTRIBUTE(acquired_before)
+#define ABSL_ACQUIRED_BEFORE(...) __attribute__((acquired_before(__VA_ARGS__)))
+#else
+#define ABSL_ACQUIRED_BEFORE(...)
+#endif
+
+// ABSL_EXCLUSIVE_LOCKS_REQUIRED() / ABSL_SHARED_LOCKS_REQUIRED()
+//
+// Documents a function that expects a mutex to be held prior to entry.
+// The mutex is expected to be held both on entry to, and exit from, the
+// function.
+//
+// An exclusive lock allows read-write access to the guarded data member(s), and
+// only one thread can acquire a lock exclusively at any one time. A shared lock
+// allows read-only access, and any number of threads can acquire a shared lock
+// concurrently.
+//
+// Generally, non-const methods should be annotated with
+// ABSL_EXCLUSIVE_LOCKS_REQUIRED, while const methods should be annotated with
+// ABSL_SHARED_LOCKS_REQUIRED.
+//
+// Example:
+//
+//   Mutex mu1, mu2;
+//   int a ABSL_GUARDED_BY(mu1);
+//   int b ABSL_GUARDED_BY(mu2);
+//
+//   void foo() ABSL_EXCLUSIVE_LOCKS_REQUIRED(mu1, mu2) { ... }
+//   void bar() const ABSL_SHARED_LOCKS_REQUIRED(mu1, mu2) { ... }
+#if ABSL_HAVE_ATTRIBUTE(exclusive_locks_required)
+#define ABSL_EXCLUSIVE_LOCKS_REQUIRED(...) \
+  __attribute__((exclusive_locks_required(__VA_ARGS__)))
+#else
+#define ABSL_EXCLUSIVE_LOCKS_REQUIRED(...)
+#endif
+
+#if ABSL_HAVE_ATTRIBUTE(shared_locks_required)
+#define ABSL_SHARED_LOCKS_REQUIRED(...) \
+  __attribute__((shared_locks_required(__VA_ARGS__)))
+#else
+#define ABSL_SHARED_LOCKS_REQUIRED(...)
+#endif
+
+// ABSL_LOCKS_EXCLUDED()
+//
+// Documents the locks that cannot be held by callers of this function, as they
+// might be acquired by this function (Abseil's `Mutex` locks are
+// non-reentrant).
+#if ABSL_HAVE_ATTRIBUTE(locks_excluded)
+#define ABSL_LOCKS_EXCLUDED(...) __attribute__((locks_excluded(__VA_ARGS__)))
+#else
+#define ABSL_LOCKS_EXCLUDED(...)
+#endif
+
+// ABSL_LOCK_RETURNED()
+//
+// Documents a function that returns a mutex without acquiring it.  For example,
+// a public getter method that returns a pointer to a private mutex should
+// be annotated with ABSL_LOCK_RETURNED.
+#if ABSL_HAVE_ATTRIBUTE(lock_returned)
+#define ABSL_LOCK_RETURNED(x) __attribute__((lock_returned(x)))
+#else
+#define ABSL_LOCK_RETURNED(x)
+#endif
+
+// ABSL_LOCKABLE
+//
+// Documents if a class/type is a lockable type (such as the `Mutex` class).
+#if ABSL_HAVE_ATTRIBUTE(lockable)
+#define ABSL_LOCKABLE __attribute__((lockable))
+#else
+#define ABSL_LOCKABLE
+#endif
+
+// ABSL_SCOPED_LOCKABLE
+//
+// Documents if a class does RAII locking (such as the `MutexLock` class).
+// The constructor should use `LOCK_FUNCTION()` to specify the mutex that is
+// acquired, and the destructor should use `UNLOCK_FUNCTION()` with no
+// arguments; the analysis will assume that the destructor unlocks whatever the
+// constructor locked.
+#if ABSL_HAVE_ATTRIBUTE(scoped_lockable)
+#define ABSL_SCOPED_LOCKABLE __attribute__((scoped_lockable))
+#else
+#define ABSL_SCOPED_LOCKABLE
+#endif
+
+// ABSL_EXCLUSIVE_LOCK_FUNCTION()
+//
+// Documents functions that acquire a lock in the body of a function, and do
+// not release it.
+#if ABSL_HAVE_ATTRIBUTE(exclusive_lock_function)
+#define ABSL_EXCLUSIVE_LOCK_FUNCTION(...) \
+  __attribute__((exclusive_lock_function(__VA_ARGS__)))
+#else
+#define ABSL_EXCLUSIVE_LOCK_FUNCTION(...)
+#endif
+
+// ABSL_SHARED_LOCK_FUNCTION()
+//
+// Documents functions that acquire a shared (reader) lock in the body of a
+// function, and do not release it.
+#if ABSL_HAVE_ATTRIBUTE(shared_lock_function)
+#define ABSL_SHARED_LOCK_FUNCTION(...) \
+  __attribute__((shared_lock_function(__VA_ARGS__)))
+#else
+#define ABSL_SHARED_LOCK_FUNCTION(...)
+#endif
+
+// ABSL_UNLOCK_FUNCTION()
+//
+// Documents functions that expect a lock to be held on entry to the function,
+// and release it in the body of the function.
+#if ABSL_HAVE_ATTRIBUTE(unlock_function)
+#define ABSL_UNLOCK_FUNCTION(...) __attribute__((unlock_function(__VA_ARGS__)))
+#else
+#define ABSL_UNLOCK_FUNCTION(...)
+#endif
+
+// ABSL_EXCLUSIVE_TRYLOCK_FUNCTION() / ABSL_SHARED_TRYLOCK_FUNCTION()
+//
+// Documents functions that try to acquire a lock, and return success or failure
+// (or a non-boolean value that can be interpreted as a boolean).
+// The first argument should be `true` for functions that return `true` on
+// success, or `false` for functions that return `false` on success. The second
+// argument specifies the mutex that is locked on success. If unspecified, this
+// mutex is assumed to be `this`.
+#if ABSL_HAVE_ATTRIBUTE(exclusive_trylock_function)
+#define ABSL_EXCLUSIVE_TRYLOCK_FUNCTION(...) \
+  __attribute__((exclusive_trylock_function(__VA_ARGS__)))
+#else
+#define ABSL_EXCLUSIVE_TRYLOCK_FUNCTION(...)
+#endif
+
+#if ABSL_HAVE_ATTRIBUTE(shared_trylock_function)
+#define ABSL_SHARED_TRYLOCK_FUNCTION(...) \
+  __attribute__((shared_trylock_function(__VA_ARGS__)))
+#else
+#define ABSL_SHARED_TRYLOCK_FUNCTION(...)
+#endif
+
+// ABSL_ASSERT_EXCLUSIVE_LOCK() / ABSL_ASSERT_SHARED_LOCK()
+//
+// Documents functions that dynamically check to see if a lock is held, and fail
+// if it is not held.
+#if ABSL_HAVE_ATTRIBUTE(assert_exclusive_lock)
+#define ABSL_ASSERT_EXCLUSIVE_LOCK(...) \
+  __attribute__((assert_exclusive_lock(__VA_ARGS__)))
+#else
+#define ABSL_ASSERT_EXCLUSIVE_LOCK(...)
+#endif
+
+#if ABSL_HAVE_ATTRIBUTE(assert_shared_lock)
+#define ABSL_ASSERT_SHARED_LOCK(...) \
+  __attribute__((assert_shared_lock(__VA_ARGS__)))
+#else
+#define ABSL_ASSERT_SHARED_LOCK(...)
+#endif
+
+// ABSL_NO_THREAD_SAFETY_ANALYSIS
+//
+// Turns off thread safety checking within the body of a particular function.
+// This annotation is used to mark functions that are known to be correct, but
+// the locking behavior is more complicated than the analyzer can handle.
+#if ABSL_HAVE_ATTRIBUTE(no_thread_safety_analysis)
+#define ABSL_NO_THREAD_SAFETY_ANALYSIS \
+  __attribute__((no_thread_safety_analysis))
+#else
+#define ABSL_NO_THREAD_SAFETY_ANALYSIS
+#endif
+
+//------------------------------------------------------------------------------
+// Tool-Supplied Annotations
+//------------------------------------------------------------------------------
+
+// ABSL_TS_UNCHECKED should be placed around lock expressions that are not valid
+// C++ syntax, but which are present for documentation purposes.  These
+// annotations will be ignored by the analysis.
+#define ABSL_TS_UNCHECKED(x) ""
+
+// ABSL_TS_FIXME is used to mark lock expressions that are not valid C++ syntax.
+// It is used by automated tools to mark and disable invalid expressions.
+// The annotation should either be fixed, or changed to ABSL_TS_UNCHECKED.
+#define ABSL_TS_FIXME(x) ""
+
+// Like ABSL_NO_THREAD_SAFETY_ANALYSIS, this turns off checking within the body
+// of a particular function.  However, this attribute is used to mark functions
+// that are incorrect and need to be fixed.  It is used by automated tools to
+// avoid breaking the build when the analysis is updated.
+// Code owners are expected to eventually fix the routine.
+#define ABSL_NO_THREAD_SAFETY_ANALYSIS_FIXME ABSL_NO_THREAD_SAFETY_ANALYSIS
+
+// Similar to ABSL_NO_THREAD_SAFETY_ANALYSIS_FIXME, this macro marks a
+// ABSL_GUARDED_BY annotation that needs to be fixed, because it is producing
+// thread safety warning. It disables the ABSL_GUARDED_BY.
+#define ABSL_GUARDED_BY_FIXME(x)
+
+// Disables warnings for a single read operation.  This can be used to avoid
+// warnings when it is known that the read is not actually involved in a race,
+// but the compiler cannot confirm that.
+#define ABSL_TS_UNCHECKED_READ(x) absl::base_internal::ts_unchecked_read(x)
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace base_internal {
+
+// Takes a reference to a guarded data member, and returns an unguarded
+// reference.
+// Do not use this function directly, use ABSL_TS_UNCHECKED_READ instead.
+template <typename T>
+inline const T& ts_unchecked_read(const T& v) ABSL_NO_THREAD_SAFETY_ANALYSIS {
+  return v;
+}
+
+template <typename T>
+inline T& ts_unchecked_read(T& v) ABSL_NO_THREAD_SAFETY_ANALYSIS {
+  return v;
+}
+
+}  // namespace base_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_BASE_THREAD_ANNOTATIONS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/cleanup/cleanup.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/cleanup/cleanup.h
new file mode 100644
index 0000000000000000000000000000000000000000..960ccd080e0db4213c512ab1cf206d1ce448563e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/cleanup/cleanup.h
@@ -0,0 +1,140 @@
+// Copyright 2021 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: cleanup.h
+// -----------------------------------------------------------------------------
+//
+// `absl::Cleanup` implements the scope guard idiom, invoking the contained
+// callback's `operator()() &&` on scope exit.
+//
+// Example:
+//
+// ```
+//   absl::Status CopyGoodData(const char* source_path, const char* sink_path) {
+//     FILE* source_file = fopen(source_path, "r");
+//     if (source_file == nullptr) {
+//       return absl::NotFoundError("No source file");  // No cleanups execute
+//     }
+//
+//     // C++17 style cleanup using class template argument deduction
+//     absl::Cleanup source_closer = [source_file] { fclose(source_file); };
+//
+//     FILE* sink_file = fopen(sink_path, "w");
+//     if (sink_file == nullptr) {
+//       return absl::NotFoundError("No sink file");  // First cleanup executes
+//     }
+//
+//     // C++11 style cleanup using the factory function
+//     auto sink_closer = absl::MakeCleanup([sink_file] { fclose(sink_file); });
+//
+//     Data data;
+//     while (ReadData(source_file, &data)) {
+//       if (!data.IsGood()) {
+//         absl::Status result = absl::FailedPreconditionError("Read bad data");
+//         return result;  // Both cleanups execute
+//       }
+//       SaveData(sink_file, &data);
+//     }
+//
+//     return absl::OkStatus();  // Both cleanups execute
+//   }
+// ```
+//
+// Methods:
+//
+// `std::move(cleanup).Cancel()` will prevent the callback from executing.
+//
+// `std::move(cleanup).Invoke()` will execute the callback early, before
+// destruction, and prevent the callback from executing in the destructor.
+//
+// Usage:
+//
+// `absl::Cleanup` is not an interface type. It is only intended to be used
+// within the body of a function. It is not a value type and instead models a
+// control flow construct. Check out `defer` in Golang for something similar.
+
+#ifndef ABSL_CLEANUP_CLEANUP_H_
+#define ABSL_CLEANUP_CLEANUP_H_
+
+#include <utility>
+
+#include "absl/base/config.h"
+#include "absl/base/macros.h"
+#include "absl/cleanup/internal/cleanup.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+template <typename Arg, typename Callback = void()>
+class ABSL_MUST_USE_RESULT Cleanup final {
+  static_assert(cleanup_internal::WasDeduced<Arg>(),
+                "Explicit template parameters are not supported.");
+
+  static_assert(cleanup_internal::ReturnsVoid<Callback>(),
+                "Callbacks that return values are not supported.");
+
+ public:
+  Cleanup(Callback callback) : storage_(std::move(callback)) {}  // NOLINT
+
+  Cleanup(Cleanup&& other) = default;
+
+  void Cancel() && {
+    ABSL_HARDENING_ASSERT(storage_.IsCallbackEngaged());
+    storage_.DestroyCallback();
+  }
+
+  void Invoke() && {
+    ABSL_HARDENING_ASSERT(storage_.IsCallbackEngaged());
+    storage_.InvokeCallback();
+    storage_.DestroyCallback();
+  }
+
+  ~Cleanup() {
+    if (storage_.IsCallbackEngaged()) {
+      storage_.InvokeCallback();
+      storage_.DestroyCallback();
+    }
+  }
+
+ private:
+  cleanup_internal::Storage<Callback> storage_;
+};
+
+// `absl::Cleanup c = /* callback */;`
+//
+// C++17 type deduction API for creating an instance of `absl::Cleanup`
+#if defined(ABSL_HAVE_CLASS_TEMPLATE_ARGUMENT_DEDUCTION)
+template <typename Callback>
+Cleanup(Callback callback) -> Cleanup<cleanup_internal::Tag, Callback>;
+#endif  // defined(ABSL_HAVE_CLASS_TEMPLATE_ARGUMENT_DEDUCTION)
+
+// `auto c = absl::MakeCleanup(/* callback */);`
+//
+// C++11 type deduction API for creating an instance of `absl::Cleanup`
+template <typename... Args, typename Callback>
+absl::Cleanup<cleanup_internal::Tag, Callback> MakeCleanup(Callback callback) {
+  static_assert(cleanup_internal::WasDeduced<cleanup_internal::Tag, Args...>(),
+                "Explicit template parameters are not supported.");
+
+  static_assert(cleanup_internal::ReturnsVoid<Callback>(),
+                "Callbacks that return values are not supported.");
+
+  return {std::move(callback)};
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CLEANUP_CLEANUP_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/cleanup/internal/cleanup.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/cleanup/internal/cleanup.h
new file mode 100644
index 0000000000000000000000000000000000000000..2783fcb7c16f2ef2445204e2bbd4698b5b7ca4b3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/cleanup/internal/cleanup.h
@@ -0,0 +1,100 @@
+// Copyright 2021 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CLEANUP_INTERNAL_CLEANUP_H_
+#define ABSL_CLEANUP_INTERNAL_CLEANUP_H_
+
+#include <new>
+#include <type_traits>
+#include <utility>
+
+#include "absl/base/internal/invoke.h"
+#include "absl/base/macros.h"
+#include "absl/base/thread_annotations.h"
+#include "absl/utility/utility.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+namespace cleanup_internal {
+
+struct Tag {};
+
+template <typename Arg, typename... Args>
+constexpr bool WasDeduced() {
+  return (std::is_same<cleanup_internal::Tag, Arg>::value) &&
+         (sizeof...(Args) == 0);
+}
+
+template <typename Callback>
+constexpr bool ReturnsVoid() {
+  return (std::is_same<base_internal::invoke_result_t<Callback>, void>::value);
+}
+
+template <typename Callback>
+class Storage {
+ public:
+  Storage() = delete;
+
+  explicit Storage(Callback callback) {
+    // Placement-new into a character buffer is used for eager destruction when
+    // the cleanup is invoked or cancelled. To ensure this optimizes well, the
+    // behavior is implemented locally instead of using an absl::optional.
+    ::new (GetCallbackBuffer()) Callback(std::move(callback));
+    is_callback_engaged_ = true;
+  }
+
+  Storage(Storage&& other) {
+    ABSL_HARDENING_ASSERT(other.IsCallbackEngaged());
+
+    ::new (GetCallbackBuffer()) Callback(std::move(other.GetCallback()));
+    is_callback_engaged_ = true;
+
+    other.DestroyCallback();
+  }
+
+  Storage(const Storage& other) = delete;
+
+  Storage& operator=(Storage&& other) = delete;
+
+  Storage& operator=(const Storage& other) = delete;
+
+  void* GetCallbackBuffer() { return static_cast<void*>(+callback_buffer_); }
+
+  Callback& GetCallback() {
+    return *reinterpret_cast<Callback*>(GetCallbackBuffer());
+  }
+
+  bool IsCallbackEngaged() const { return is_callback_engaged_; }
+
+  void DestroyCallback() {
+    is_callback_engaged_ = false;
+    GetCallback().~Callback();
+  }
+
+  void InvokeCallback() ABSL_NO_THREAD_SAFETY_ANALYSIS {
+    std::move(GetCallback())();
+  }
+
+ private:
+  bool is_callback_engaged_;
+  alignas(Callback) char callback_buffer_[sizeof(Callback)];
+};
+
+}  // namespace cleanup_internal
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CLEANUP_INTERNAL_CLEANUP_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/btree_map.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/btree_map.h
new file mode 100644
index 0000000000000000000000000000000000000000..cd3ee2b422950c51612e6b8610f41e02eeb96702
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/btree_map.h
@@ -0,0 +1,885 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: btree_map.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines B-tree maps: sorted associative containers mapping
+// keys to values.
+//
+//     * `absl::btree_map<>`
+//     * `absl::btree_multimap<>`
+//
+// These B-tree types are similar to the corresponding types in the STL
+// (`std::map` and `std::multimap`) and generally conform to the STL interfaces
+// of those types. However, because they are implemented using B-trees, they
+// are more efficient in most situations.
+//
+// Unlike `std::map` and `std::multimap`, which are commonly implemented using
+// red-black tree nodes, B-tree maps use more generic B-tree nodes able to hold
+// multiple values per node. Holding multiple values per node often makes
+// B-tree maps perform better than their `std::map` counterparts, because
+// multiple entries can be checked within the same cache hit.
+//
+// However, these types should not be considered drop-in replacements for
+// `std::map` and `std::multimap` as there are some API differences, which are
+// noted in this header file. The most consequential differences with respect to
+// migrating to b-tree from the STL types are listed in the next paragraph.
+// Other API differences are minor.
+//
+// Importantly, insertions and deletions may invalidate outstanding iterators,
+// pointers, and references to elements. Such invalidations are typically only
+// an issue if insertion and deletion operations are interleaved with the use of
+// more than one iterator, pointer, or reference simultaneously.  For this
+// reason, `insert()`, `erase()`, and `extract_and_get_next()` return a valid
+// iterator at the current position. Another important difference is that
+// key-types must be copy-constructible.
+//
+// Another API difference is that btree iterators can be subtracted, and this
+// is faster than using std::distance.
+
+#ifndef ABSL_CONTAINER_BTREE_MAP_H_
+#define ABSL_CONTAINER_BTREE_MAP_H_
+
+#include "absl/container/internal/btree.h"  // IWYU pragma: export
+#include "absl/container/internal/btree_container.h"  // IWYU pragma: export
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+namespace container_internal {
+
+template <typename Key, typename Data, typename Compare, typename Alloc,
+          int TargetNodeSize, bool IsMulti>
+struct map_params;
+
+}  // namespace container_internal
+
+// absl::btree_map<>
+//
+// An `absl::btree_map<K, V>` is an ordered associative container of
+// unique keys and associated values designed to be a more efficient replacement
+// for `std::map` (in most cases).
+//
+// Keys are sorted using an (optional) comparison function, which defaults to
+// `std::less<K>`.
+//
+// An `absl::btree_map<K, V>` uses a default allocator of
+// `std::allocator<std::pair<const K, V>>` to allocate (and deallocate)
+// nodes, and construct and destruct values within those nodes. You may
+// instead specify a custom allocator `A` (which in turn requires specifying a
+// custom comparator `C`) as in `absl::btree_map<K, V, C, A>`.
+//
+template <typename Key, typename Value, typename Compare = std::less<Key>,
+          typename Alloc = std::allocator<std::pair<const Key, Value>>>
+class btree_map
+    : public container_internal::btree_map_container<
+          container_internal::btree<container_internal::map_params<
+              Key, Value, Compare, Alloc, /*TargetNodeSize=*/256,
+              /*IsMulti=*/false>>> {
+  using Base = typename btree_map::btree_map_container;
+
+ public:
+  // Constructors and Assignment Operators
+  //
+  // A `btree_map` supports the same overload set as `std::map`
+  // for construction and assignment:
+  //
+  // * Default constructor
+  //
+  //   absl::btree_map<int, std::string> map1;
+  //
+  // * Initializer List constructor
+  //
+  //   absl::btree_map<int, std::string> map2 =
+  //       {{1, "huey"}, {2, "dewey"}, {3, "louie"},};
+  //
+  // * Copy constructor
+  //
+  //   absl::btree_map<int, std::string> map3(map2);
+  //
+  // * Copy assignment operator
+  //
+  //  absl::btree_map<int, std::string> map4;
+  //  map4 = map3;
+  //
+  // * Move constructor
+  //
+  //   // Move is guaranteed efficient
+  //   absl::btree_map<int, std::string> map5(std::move(map4));
+  //
+  // * Move assignment operator
+  //
+  //   // May be efficient if allocators are compatible
+  //   absl::btree_map<int, std::string> map6;
+  //   map6 = std::move(map5);
+  //
+  // * Range constructor
+  //
+  //   std::vector<std::pair<int, std::string>> v = {{1, "a"}, {2, "b"}};
+  //   absl::btree_map<int, std::string> map7(v.begin(), v.end());
+  btree_map() {}
+  using Base::Base;
+
+  // btree_map::begin()
+  //
+  // Returns an iterator to the beginning of the `btree_map`.
+  using Base::begin;
+
+  // btree_map::cbegin()
+  //
+  // Returns a const iterator to the beginning of the `btree_map`.
+  using Base::cbegin;
+
+  // btree_map::end()
+  //
+  // Returns an iterator to the end of the `btree_map`.
+  using Base::end;
+
+  // btree_map::cend()
+  //
+  // Returns a const iterator to the end of the `btree_map`.
+  using Base::cend;
+
+  // btree_map::empty()
+  //
+  // Returns whether or not the `btree_map` is empty.
+  using Base::empty;
+
+  // btree_map::max_size()
+  //
+  // Returns the largest theoretical possible number of elements within a
+  // `btree_map` under current memory constraints. This value can be thought
+  // of as the largest value of `std::distance(begin(), end())` for a
+  // `btree_map<Key, T>`.
+  using Base::max_size;
+
+  // btree_map::size()
+  //
+  // Returns the number of elements currently within the `btree_map`.
+  using Base::size;
+
+  // btree_map::clear()
+  //
+  // Removes all elements from the `btree_map`. Invalidates any references,
+  // pointers, or iterators referring to contained elements.
+  using Base::clear;
+
+  // btree_map::erase()
+  //
+  // Erases elements within the `btree_map`. If an erase occurs, any references,
+  // pointers, or iterators are invalidated.
+  // Overloads are listed below.
+  //
+  // iterator erase(iterator position):
+  // iterator erase(const_iterator position):
+  //
+  //   Erases the element at `position` of the `btree_map`, returning
+  //   the iterator pointing to the element after the one that was erased
+  //   (or end() if none exists).
+  //
+  // iterator erase(const_iterator first, const_iterator last):
+  //
+  //   Erases the elements in the open interval [`first`, `last`), returning
+  //   the iterator pointing to the element after the interval that was erased
+  //   (or end() if none exists).
+  //
+  // template <typename K> size_type erase(const K& key):
+  //
+  //   Erases the element with the matching key, if it exists, returning the
+  //   number of elements erased (0 or 1).
+  using Base::erase;
+
+  // btree_map::insert()
+  //
+  // Inserts an element of the specified value into the `btree_map`,
+  // returning an iterator pointing to the newly inserted element, provided that
+  // an element with the given key does not already exist. If an insertion
+  // occurs, any references, pointers, or iterators are invalidated.
+  // Overloads are listed below.
+  //
+  // std::pair<iterator,bool> insert(const value_type& value):
+  //
+  //   Inserts a value into the `btree_map`. Returns a pair consisting of an
+  //   iterator to the inserted element (or to the element that prevented the
+  //   insertion) and a bool denoting whether the insertion took place.
+  //
+  // std::pair<iterator,bool> insert(value_type&& value):
+  //
+  //   Inserts a moveable value into the `btree_map`. Returns a pair
+  //   consisting of an iterator to the inserted element (or to the element that
+  //   prevented the insertion) and a bool denoting whether the insertion took
+  //   place.
+  //
+  // iterator insert(const_iterator hint, const value_type& value):
+  // iterator insert(const_iterator hint, value_type&& value):
+  //
+  //   Inserts a value, using the position of `hint` as a non-binding suggestion
+  //   for where to begin the insertion search. Returns an iterator to the
+  //   inserted element, or to the existing element that prevented the
+  //   insertion.
+  //
+  // void insert(InputIterator first, InputIterator last):
+  //
+  //   Inserts a range of values [`first`, `last`).
+  //
+  // void insert(std::initializer_list<init_type> ilist):
+  //
+  //   Inserts the elements within the initializer list `ilist`.
+  using Base::insert;
+
+  // btree_map::insert_or_assign()
+  //
+  // Inserts an element of the specified value into the `btree_map` provided
+  // that a value with the given key does not already exist, or replaces the
+  // corresponding mapped type with the forwarded `obj` argument if a key for
+  // that value already exists, returning an iterator pointing to the newly
+  // inserted element. Overloads are listed below.
+  //
+  // pair<iterator, bool> insert_or_assign(const key_type& k, M&& obj):
+  // pair<iterator, bool> insert_or_assign(key_type&& k, M&& obj):
+  //
+  //   Inserts/Assigns (or moves) the element of the specified key into the
+  //   `btree_map`. If the returned bool is true, insertion took place, and if
+  //   it's false, assignment took place.
+  //
+  // iterator insert_or_assign(const_iterator hint,
+  //                           const key_type& k, M&& obj):
+  // iterator insert_or_assign(const_iterator hint, key_type&& k, M&& obj):
+  //
+  //   Inserts/Assigns (or moves) the element of the specified key into the
+  //   `btree_map` using the position of `hint` as a non-binding suggestion
+  //   for where to begin the insertion search.
+  using Base::insert_or_assign;
+
+  // btree_map::emplace()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `btree_map`, provided that no element with the given key
+  // already exists.
+  //
+  // The element may be constructed even if there already is an element with the
+  // key in the container, in which case the newly constructed element will be
+  // destroyed immediately. Prefer `try_emplace()` unless your key is not
+  // copyable or moveable.
+  //
+  // If an insertion occurs, any references, pointers, or iterators are
+  // invalidated.
+  using Base::emplace;
+
+  // btree_map::emplace_hint()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `btree_map`, using the position of `hint` as a non-binding
+  // suggestion for where to begin the insertion search, and only inserts
+  // provided that no element with the given key already exists.
+  //
+  // The element may be constructed even if there already is an element with the
+  // key in the container, in which case the newly constructed element will be
+  // destroyed immediately. Prefer `try_emplace()` unless your key is not
+  // copyable or moveable.
+  //
+  // If an insertion occurs, any references, pointers, or iterators are
+  // invalidated.
+  using Base::emplace_hint;
+
+  // btree_map::try_emplace()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `btree_map`, provided that no element with the given key
+  // already exists. Unlike `emplace()`, if an element with the given key
+  // already exists, we guarantee that no element is constructed.
+  //
+  // If an insertion occurs, any references, pointers, or iterators are
+  // invalidated.
+  //
+  // Overloads are listed below.
+  //
+  //   std::pair<iterator, bool> try_emplace(const key_type& k, Args&&... args):
+  //   std::pair<iterator, bool> try_emplace(key_type&& k, Args&&... args):
+  //
+  // Inserts (via copy or move) the element of the specified key into the
+  // `btree_map`.
+  //
+  //   iterator try_emplace(const_iterator hint,
+  //                        const key_type& k, Args&&... args):
+  //   iterator try_emplace(const_iterator hint, key_type&& k, Args&&... args):
+  //
+  // Inserts (via copy or move) the element of the specified key into the
+  // `btree_map` using the position of `hint` as a non-binding suggestion
+  // for where to begin the insertion search.
+  using Base::try_emplace;
+
+  // btree_map::extract()
+  //
+  // Extracts the indicated element, erasing it in the process, and returns it
+  // as a C++17-compatible node handle. Any references, pointers, or iterators
+  // are invalidated. Overloads are listed below.
+  //
+  // node_type extract(const_iterator position):
+  //
+  //   Extracts the element at the indicated position and returns a node handle
+  //   owning that extracted data.
+  //
+  // template <typename K> node_type extract(const K& k):
+  //
+  //   Extracts the element with the key matching the passed key value and
+  //   returns a node handle owning that extracted data. If the `btree_map`
+  //   does not contain an element with a matching key, this function returns an
+  //   empty node handle.
+  //
+  // NOTE: when compiled in an earlier version of C++ than C++17,
+  // `node_type::key()` returns a const reference to the key instead of a
+  // mutable reference. We cannot safely return a mutable reference without
+  // std::launder (which is not available before C++17).
+  //
+  // NOTE: In this context, `node_type` refers to the C++17 concept of a
+  // move-only type that owns and provides access to the elements in associative
+  // containers (https://en.cppreference.com/w/cpp/container/node_handle).
+  // It does NOT refer to the data layout of the underlying btree.
+  using Base::extract;
+
+  // btree_map::extract_and_get_next()
+  //
+  // Extracts the indicated element, erasing it in the process, and returns it
+  // as a C++17-compatible node handle along with an iterator to the next
+  // element.
+  //
+  // extract_and_get_next_return_type extract_and_get_next(
+  //     const_iterator position):
+  //
+  //   Extracts the element at the indicated position, returns a struct
+  //   containing a member named `node`: a node handle owning that extracted
+  //   data and a member named `next`: an iterator pointing to the next element
+  //   in the btree.
+  using Base::extract_and_get_next;
+
+  // btree_map::merge()
+  //
+  // Extracts elements from a given `source` btree_map into this
+  // `btree_map`. If the destination `btree_map` already contains an
+  // element with an equivalent key, that element is not extracted.
+  using Base::merge;
+
+  // btree_map::swap(btree_map& other)
+  //
+  // Exchanges the contents of this `btree_map` with those of the `other`
+  // btree_map, avoiding invocation of any move, copy, or swap operations on
+  // individual elements.
+  //
+  // All iterators and references on the `btree_map` remain valid, excepting
+  // for the past-the-end iterator, which is invalidated.
+  using Base::swap;
+
+  // btree_map::at()
+  //
+  // Returns a reference to the mapped value of the element with key equivalent
+  // to the passed key.
+  using Base::at;
+
+  // btree_map::contains()
+  //
+  // template <typename K> bool contains(const K& key) const:
+  //
+  // Determines whether an element comparing equal to the given `key` exists
+  // within the `btree_map`, returning `true` if so or `false` otherwise.
+  //
+  // Supports heterogeneous lookup, provided that the map has a compatible
+  // heterogeneous comparator.
+  using Base::contains;
+
+  // btree_map::count()
+  //
+  // template <typename K> size_type count(const K& key) const:
+  //
+  // Returns the number of elements comparing equal to the given `key` within
+  // the `btree_map`. Note that this function will return either `1` or `0`
+  // since duplicate elements are not allowed within a `btree_map`.
+  //
+  // Supports heterogeneous lookup, provided that the map has a compatible
+  // heterogeneous comparator.
+  using Base::count;
+
+  // btree_map::equal_range()
+  //
+  // Returns a half-open range [first, last), defined by a `std::pair` of two
+  // iterators, containing all elements with the passed key in the `btree_map`.
+  using Base::equal_range;
+
+  // btree_map::find()
+  //
+  // template <typename K> iterator find(const K& key):
+  // template <typename K> const_iterator find(const K& key) const:
+  //
+  // Finds an element with the passed `key` within the `btree_map`.
+  //
+  // Supports heterogeneous lookup, provided that the map has a compatible
+  // heterogeneous comparator.
+  using Base::find;
+
+  // btree_map::lower_bound()
+  //
+  // template <typename K> iterator lower_bound(const K& key):
+  // template <typename K> const_iterator lower_bound(const K& key) const:
+  //
+  // Finds the first element with a key that is not less than `key` within the
+  // `btree_map`.
+  //
+  // Supports heterogeneous lookup, provided that the map has a compatible
+  // heterogeneous comparator.
+  using Base::lower_bound;
+
+  // btree_map::upper_bound()
+  //
+  // template <typename K> iterator upper_bound(const K& key):
+  // template <typename K> const_iterator upper_bound(const K& key) const:
+  //
+  // Finds the first element with a key that is greater than `key` within the
+  // `btree_map`.
+  //
+  // Supports heterogeneous lookup, provided that the map has a compatible
+  // heterogeneous comparator.
+  using Base::upper_bound;
+
+  // btree_map::operator[]()
+  //
+  // Returns a reference to the value mapped to the passed key within the
+  // `btree_map`, performing an `insert()` if the key does not already
+  // exist.
+  //
+  // If an insertion occurs, any references, pointers, or iterators are
+  // invalidated. Otherwise iterators are not affected and references are not
+  // invalidated. Overloads are listed below.
+  //
+  // T& operator[](key_type&& key):
+  // T& operator[](const key_type& key):
+  //
+  //   Inserts a value_type object constructed in-place if the element with the
+  //   given key does not exist.
+  using Base::operator[];
+
+  // btree_map::get_allocator()
+  //
+  // Returns the allocator function associated with this `btree_map`.
+  using Base::get_allocator;
+
+  // btree_map::key_comp();
+  //
+  // Returns the key comparator associated with this `btree_map`.
+  using Base::key_comp;
+
+  // btree_map::value_comp();
+  //
+  // Returns the value comparator associated with this `btree_map`.
+  using Base::value_comp;
+};
+
+// absl::swap(absl::btree_map<>, absl::btree_map<>)
+//
+// Swaps the contents of two `absl::btree_map` containers.
+template <typename K, typename V, typename C, typename A>
+void swap(btree_map<K, V, C, A> &x, btree_map<K, V, C, A> &y) {
+  return x.swap(y);
+}
+
+// absl::erase_if(absl::btree_map<>, Pred)
+//
+// Erases all elements that satisfy the predicate pred from the container.
+// Returns the number of erased elements.
+template <typename K, typename V, typename C, typename A, typename Pred>
+typename btree_map<K, V, C, A>::size_type erase_if(
+    btree_map<K, V, C, A> &map, Pred pred) {
+  return container_internal::btree_access::erase_if(map, std::move(pred));
+}
+
+// absl::btree_multimap
+//
+// An `absl::btree_multimap<K, V>` is an ordered associative container of
+// keys and associated values designed to be a more efficient replacement for
+// `std::multimap` (in most cases). Unlike `absl::btree_map`, a B-tree multimap
+// allows multiple elements with equivalent keys.
+//
+// Keys are sorted using an (optional) comparison function, which defaults to
+// `std::less<K>`.
+//
+// An `absl::btree_multimap<K, V>` uses a default allocator of
+// `std::allocator<std::pair<const K, V>>` to allocate (and deallocate)
+// nodes, and construct and destruct values within those nodes. You may
+// instead specify a custom allocator `A` (which in turn requires specifying a
+// custom comparator `C`) as in `absl::btree_multimap<K, V, C, A>`.
+//
+template <typename Key, typename Value, typename Compare = std::less<Key>,
+          typename Alloc = std::allocator<std::pair<const Key, Value>>>
+class btree_multimap
+    : public container_internal::btree_multimap_container<
+          container_internal::btree<container_internal::map_params<
+              Key, Value, Compare, Alloc, /*TargetNodeSize=*/256,
+              /*IsMulti=*/true>>> {
+  using Base = typename btree_multimap::btree_multimap_container;
+
+ public:
+  // Constructors and Assignment Operators
+  //
+  // A `btree_multimap` supports the same overload set as `std::multimap`
+  // for construction and assignment:
+  //
+  // * Default constructor
+  //
+  //   absl::btree_multimap<int, std::string> map1;
+  //
+  // * Initializer List constructor
+  //
+  //   absl::btree_multimap<int, std::string> map2 =
+  //       {{1, "huey"}, {2, "dewey"}, {3, "louie"},};
+  //
+  // * Copy constructor
+  //
+  //   absl::btree_multimap<int, std::string> map3(map2);
+  //
+  // * Copy assignment operator
+  //
+  //  absl::btree_multimap<int, std::string> map4;
+  //  map4 = map3;
+  //
+  // * Move constructor
+  //
+  //   // Move is guaranteed efficient
+  //   absl::btree_multimap<int, std::string> map5(std::move(map4));
+  //
+  // * Move assignment operator
+  //
+  //   // May be efficient if allocators are compatible
+  //   absl::btree_multimap<int, std::string> map6;
+  //   map6 = std::move(map5);
+  //
+  // * Range constructor
+  //
+  //   std::vector<std::pair<int, std::string>> v = {{1, "a"}, {2, "b"}};
+  //   absl::btree_multimap<int, std::string> map7(v.begin(), v.end());
+  btree_multimap() {}
+  using Base::Base;
+
+  // btree_multimap::begin()
+  //
+  // Returns an iterator to the beginning of the `btree_multimap`.
+  using Base::begin;
+
+  // btree_multimap::cbegin()
+  //
+  // Returns a const iterator to the beginning of the `btree_multimap`.
+  using Base::cbegin;
+
+  // btree_multimap::end()
+  //
+  // Returns an iterator to the end of the `btree_multimap`.
+  using Base::end;
+
+  // btree_multimap::cend()
+  //
+  // Returns a const iterator to the end of the `btree_multimap`.
+  using Base::cend;
+
+  // btree_multimap::empty()
+  //
+  // Returns whether or not the `btree_multimap` is empty.
+  using Base::empty;
+
+  // btree_multimap::max_size()
+  //
+  // Returns the largest theoretical possible number of elements within a
+  // `btree_multimap` under current memory constraints. This value can be
+  // thought of as the largest value of `std::distance(begin(), end())` for a
+  // `btree_multimap<Key, T>`.
+  using Base::max_size;
+
+  // btree_multimap::size()
+  //
+  // Returns the number of elements currently within the `btree_multimap`.
+  using Base::size;
+
+  // btree_multimap::clear()
+  //
+  // Removes all elements from the `btree_multimap`. Invalidates any references,
+  // pointers, or iterators referring to contained elements.
+  using Base::clear;
+
+  // btree_multimap::erase()
+  //
+  // Erases elements within the `btree_multimap`. If an erase occurs, any
+  // references, pointers, or iterators are invalidated.
+  // Overloads are listed below.
+  //
+  // iterator erase(iterator position):
+  // iterator erase(const_iterator position):
+  //
+  //   Erases the element at `position` of the `btree_multimap`, returning
+  //   the iterator pointing to the element after the one that was erased
+  //   (or end() if none exists).
+  //
+  // iterator erase(const_iterator first, const_iterator last):
+  //
+  //   Erases the elements in the open interval [`first`, `last`), returning
+  //   the iterator pointing to the element after the interval that was erased
+  //   (or end() if none exists).
+  //
+  // template <typename K> size_type erase(const K& key):
+  //
+  //   Erases the elements matching the key, if any exist, returning the
+  //   number of elements erased.
+  using Base::erase;
+
+  // btree_multimap::insert()
+  //
+  // Inserts an element of the specified value into the `btree_multimap`,
+  // returning an iterator pointing to the newly inserted element.
+  // Any references, pointers, or iterators are invalidated.  Overloads are
+  // listed below.
+  //
+  // iterator insert(const value_type& value):
+  //
+  //   Inserts a value into the `btree_multimap`, returning an iterator to the
+  //   inserted element.
+  //
+  // iterator insert(value_type&& value):
+  //
+  //   Inserts a moveable value into the `btree_multimap`, returning an iterator
+  //   to the inserted element.
+  //
+  // iterator insert(const_iterator hint, const value_type& value):
+  // iterator insert(const_iterator hint, value_type&& value):
+  //
+  //   Inserts a value, using the position of `hint` as a non-binding suggestion
+  //   for where to begin the insertion search. Returns an iterator to the
+  //   inserted element.
+  //
+  // void insert(InputIterator first, InputIterator last):
+  //
+  //   Inserts a range of values [`first`, `last`).
+  //
+  // void insert(std::initializer_list<init_type> ilist):
+  //
+  //   Inserts the elements within the initializer list `ilist`.
+  using Base::insert;
+
+  // btree_multimap::emplace()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `btree_multimap`. Any references, pointers, or iterators are
+  // invalidated.
+  using Base::emplace;
+
+  // btree_multimap::emplace_hint()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `btree_multimap`, using the position of `hint` as a non-binding
+  // suggestion for where to begin the insertion search.
+  //
+  // Any references, pointers, or iterators are invalidated.
+  using Base::emplace_hint;
+
+  // btree_multimap::extract()
+  //
+  // Extracts the indicated element, erasing it in the process, and returns it
+  // as a C++17-compatible node handle. Overloads are listed below.
+  //
+  // node_type extract(const_iterator position):
+  //
+  //   Extracts the element at the indicated position and returns a node handle
+  //   owning that extracted data.
+  //
+  // template <typename K> node_type extract(const K& k):
+  //
+  //   Extracts the element with the key matching the passed key value and
+  //   returns a node handle owning that extracted data. If the `btree_multimap`
+  //   does not contain an element with a matching key, this function returns an
+  //   empty node handle.
+  //
+  // NOTE: when compiled in an earlier version of C++ than C++17,
+  // `node_type::key()` returns a const reference to the key instead of a
+  // mutable reference. We cannot safely return a mutable reference without
+  // std::launder (which is not available before C++17).
+  //
+  // NOTE: In this context, `node_type` refers to the C++17 concept of a
+  // move-only type that owns and provides access to the elements in associative
+  // containers (https://en.cppreference.com/w/cpp/container/node_handle).
+  // It does NOT refer to the data layout of the underlying btree.
+  using Base::extract;
+
+  // btree_multimap::extract_and_get_next()
+  //
+  // Extracts the indicated element, erasing it in the process, and returns it
+  // as a C++17-compatible node handle along with an iterator to the next
+  // element.
+  //
+  // extract_and_get_next_return_type extract_and_get_next(
+  //     const_iterator position):
+  //
+  //   Extracts the element at the indicated position, returns a struct
+  //   containing a member named `node`: a node handle owning that extracted
+  //   data and a member named `next`: an iterator pointing to the next element
+  //   in the btree.
+  using Base::extract_and_get_next;
+
+  // btree_multimap::merge()
+  //
+  // Extracts all elements from a given `source` btree_multimap into this
+  // `btree_multimap`.
+  using Base::merge;
+
+  // btree_multimap::swap(btree_multimap& other)
+  //
+  // Exchanges the contents of this `btree_multimap` with those of the `other`
+  // btree_multimap, avoiding invocation of any move, copy, or swap operations
+  // on individual elements.
+  //
+  // All iterators and references on the `btree_multimap` remain valid,
+  // excepting for the past-the-end iterator, which is invalidated.
+  using Base::swap;
+
+  // btree_multimap::contains()
+  //
+  // template <typename K> bool contains(const K& key) const:
+  //
+  // Determines whether an element comparing equal to the given `key` exists
+  // within the `btree_multimap`, returning `true` if so or `false` otherwise.
+  //
+  // Supports heterogeneous lookup, provided that the map has a compatible
+  // heterogeneous comparator.
+  using Base::contains;
+
+  // btree_multimap::count()
+  //
+  // template <typename K> size_type count(const K& key) const:
+  //
+  // Returns the number of elements comparing equal to the given `key` within
+  // the `btree_multimap`.
+  //
+  // Supports heterogeneous lookup, provided that the map has a compatible
+  // heterogeneous comparator.
+  using Base::count;
+
+  // btree_multimap::equal_range()
+  //
+  // Returns a half-open range [first, last), defined by a `std::pair` of two
+  // iterators, containing all elements with the passed key in the
+  // `btree_multimap`.
+  using Base::equal_range;
+
+  // btree_multimap::find()
+  //
+  // template <typename K> iterator find(const K& key):
+  // template <typename K> const_iterator find(const K& key) const:
+  //
+  // Finds an element with the passed `key` within the `btree_multimap`.
+  //
+  // Supports heterogeneous lookup, provided that the map has a compatible
+  // heterogeneous comparator.
+  using Base::find;
+
+  // btree_multimap::lower_bound()
+  //
+  // template <typename K> iterator lower_bound(const K& key):
+  // template <typename K> const_iterator lower_bound(const K& key) const:
+  //
+  // Finds the first element with a key that is not less than `key` within the
+  // `btree_multimap`.
+  //
+  // Supports heterogeneous lookup, provided that the map has a compatible
+  // heterogeneous comparator.
+  using Base::lower_bound;
+
+  // btree_multimap::upper_bound()
+  //
+  // template <typename K> iterator upper_bound(const K& key):
+  // template <typename K> const_iterator upper_bound(const K& key) const:
+  //
+  // Finds the first element with a key that is greater than `key` within the
+  // `btree_multimap`.
+  //
+  // Supports heterogeneous lookup, provided that the map has a compatible
+  // heterogeneous comparator.
+  using Base::upper_bound;
+
+  // btree_multimap::get_allocator()
+  //
+  // Returns the allocator function associated with this `btree_multimap`.
+  using Base::get_allocator;
+
+  // btree_multimap::key_comp();
+  //
+  // Returns the key comparator associated with this `btree_multimap`.
+  using Base::key_comp;
+
+  // btree_multimap::value_comp();
+  //
+  // Returns the value comparator associated with this `btree_multimap`.
+  using Base::value_comp;
+};
+
+// absl::swap(absl::btree_multimap<>, absl::btree_multimap<>)
+//
+// Swaps the contents of two `absl::btree_multimap` containers.
+template <typename K, typename V, typename C, typename A>
+void swap(btree_multimap<K, V, C, A> &x, btree_multimap<K, V, C, A> &y) {
+  return x.swap(y);
+}
+
+// absl::erase_if(absl::btree_multimap<>, Pred)
+//
+// Erases all elements that satisfy the predicate pred from the container.
+// Returns the number of erased elements.
+template <typename K, typename V, typename C, typename A, typename Pred>
+typename btree_multimap<K, V, C, A>::size_type erase_if(
+    btree_multimap<K, V, C, A> &map, Pred pred) {
+  return container_internal::btree_access::erase_if(map, std::move(pred));
+}
+
+namespace container_internal {
+
+// A parameters structure for holding the type parameters for a btree_map.
+// Compare and Alloc should be nothrow copy-constructible.
+template <typename Key, typename Data, typename Compare, typename Alloc,
+          int TargetNodeSize, bool IsMulti>
+struct map_params : common_params<Key, Compare, Alloc, TargetNodeSize, IsMulti,
+                                  /*IsMap=*/true, map_slot_policy<Key, Data>> {
+  using super_type = typename map_params::common_params;
+  using mapped_type = Data;
+  // This type allows us to move keys when it is safe to do so. It is safe
+  // for maps in which value_type and mutable_value_type are layout compatible.
+  using slot_policy = typename super_type::slot_policy;
+  using slot_type = typename super_type::slot_type;
+  using value_type = typename super_type::value_type;
+  using init_type = typename super_type::init_type;
+
+  template <typename V>
+  static auto key(const V &value) -> decltype(value.first) {
+    return value.first;
+  }
+  static const Key &key(const slot_type *s) { return slot_policy::key(s); }
+  static const Key &key(slot_type *s) { return slot_policy::key(s); }
+  // For use in node handle.
+  static auto mutable_key(slot_type *s)
+      -> decltype(slot_policy::mutable_key(s)) {
+    return slot_policy::mutable_key(s);
+  }
+  static mapped_type &value(value_type *value) { return value->second; }
+};
+
+}  // namespace container_internal
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_BTREE_MAP_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/btree_set.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/btree_set.h
new file mode 100644
index 0000000000000000000000000000000000000000..51dc42b797d316da9275aeeb38aac95d0364af6e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/btree_set.h
@@ -0,0 +1,821 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: btree_set.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines B-tree sets: sorted associative containers of
+// values.
+//
+//     * `absl::btree_set<>`
+//     * `absl::btree_multiset<>`
+//
+// These B-tree types are similar to the corresponding types in the STL
+// (`std::set` and `std::multiset`) and generally conform to the STL interfaces
+// of those types. However, because they are implemented using B-trees, they
+// are more efficient in most situations.
+//
+// Unlike `std::set` and `std::multiset`, which are commonly implemented using
+// red-black tree nodes, B-tree sets use more generic B-tree nodes able to hold
+// multiple values per node. Holding multiple values per node often makes
+// B-tree sets perform better than their `std::set` counterparts, because
+// multiple entries can be checked within the same cache hit.
+//
+// However, these types should not be considered drop-in replacements for
+// `std::set` and `std::multiset` as there are some API differences, which are
+// noted in this header file. The most consequential differences with respect to
+// migrating to b-tree from the STL types are listed in the next paragraph.
+// Other API differences are minor.
+//
+// Importantly, insertions and deletions may invalidate outstanding iterators,
+// pointers, and references to elements. Such invalidations are typically only
+// an issue if insertion and deletion operations are interleaved with the use of
+// more than one iterator, pointer, or reference simultaneously. For this
+// reason, `insert()`, `erase()`, and `extract_and_get_next()` return a valid
+// iterator at the current position.
+//
+// Another API difference is that btree iterators can be subtracted, and this
+// is faster than using std::distance.
+
+#ifndef ABSL_CONTAINER_BTREE_SET_H_
+#define ABSL_CONTAINER_BTREE_SET_H_
+
+#include "absl/container/internal/btree.h"  // IWYU pragma: export
+#include "absl/container/internal/btree_container.h"  // IWYU pragma: export
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+namespace container_internal {
+
+template <typename Key>
+struct set_slot_policy;
+
+template <typename Key, typename Compare, typename Alloc, int TargetNodeSize,
+          bool IsMulti>
+struct set_params;
+
+}  // namespace container_internal
+
+// absl::btree_set<>
+//
+// An `absl::btree_set<K>` is an ordered associative container of unique key
+// values designed to be a more efficient replacement for `std::set` (in most
+// cases).
+//
+// Keys are sorted using an (optional) comparison function, which defaults to
+// `std::less<K>`.
+//
+// An `absl::btree_set<K>` uses a default allocator of `std::allocator<K>` to
+// allocate (and deallocate) nodes, and construct and destruct values within
+// those nodes. You may instead specify a custom allocator `A` (which in turn
+// requires specifying a custom comparator `C`) as in
+// `absl::btree_set<K, C, A>`.
+//
+template <typename Key, typename Compare = std::less<Key>,
+          typename Alloc = std::allocator<Key>>
+class btree_set
+    : public container_internal::btree_set_container<
+          container_internal::btree<container_internal::set_params<
+              Key, Compare, Alloc, /*TargetNodeSize=*/256,
+              /*IsMulti=*/false>>> {
+  using Base = typename btree_set::btree_set_container;
+
+ public:
+  // Constructors and Assignment Operators
+  //
+  // A `btree_set` supports the same overload set as `std::set`
+  // for construction and assignment:
+  //
+  // * Default constructor
+  //
+  //   absl::btree_set<std::string> set1;
+  //
+  // * Initializer List constructor
+  //
+  //   absl::btree_set<std::string> set2 =
+  //       {{"huey"}, {"dewey"}, {"louie"},};
+  //
+  // * Copy constructor
+  //
+  //   absl::btree_set<std::string> set3(set2);
+  //
+  // * Copy assignment operator
+  //
+  //  absl::btree_set<std::string> set4;
+  //  set4 = set3;
+  //
+  // * Move constructor
+  //
+  //   // Move is guaranteed efficient
+  //   absl::btree_set<std::string> set5(std::move(set4));
+  //
+  // * Move assignment operator
+  //
+  //   // May be efficient if allocators are compatible
+  //   absl::btree_set<std::string> set6;
+  //   set6 = std::move(set5);
+  //
+  // * Range constructor
+  //
+  //   std::vector<std::string> v = {"a", "b"};
+  //   absl::btree_set<std::string> set7(v.begin(), v.end());
+  btree_set() {}
+  using Base::Base;
+
+  // btree_set::begin()
+  //
+  // Returns an iterator to the beginning of the `btree_set`.
+  using Base::begin;
+
+  // btree_set::cbegin()
+  //
+  // Returns a const iterator to the beginning of the `btree_set`.
+  using Base::cbegin;
+
+  // btree_set::end()
+  //
+  // Returns an iterator to the end of the `btree_set`.
+  using Base::end;
+
+  // btree_set::cend()
+  //
+  // Returns a const iterator to the end of the `btree_set`.
+  using Base::cend;
+
+  // btree_set::empty()
+  //
+  // Returns whether or not the `btree_set` is empty.
+  using Base::empty;
+
+  // btree_set::max_size()
+  //
+  // Returns the largest theoretical possible number of elements within a
+  // `btree_set` under current memory constraints. This value can be thought
+  // of as the largest value of `std::distance(begin(), end())` for a
+  // `btree_set<Key>`.
+  using Base::max_size;
+
+  // btree_set::size()
+  //
+  // Returns the number of elements currently within the `btree_set`.
+  using Base::size;
+
+  // btree_set::clear()
+  //
+  // Removes all elements from the `btree_set`. Invalidates any references,
+  // pointers, or iterators referring to contained elements.
+  using Base::clear;
+
+  // btree_set::erase()
+  //
+  // Erases elements within the `btree_set`. Overloads are listed below.
+  //
+  // iterator erase(iterator position):
+  // iterator erase(const_iterator position):
+  //
+  //   Erases the element at `position` of the `btree_set`, returning
+  //   the iterator pointing to the element after the one that was erased
+  //   (or end() if none exists).
+  //
+  // iterator erase(const_iterator first, const_iterator last):
+  //
+  //   Erases the elements in the open interval [`first`, `last`), returning
+  //   the iterator pointing to the element after the interval that was erased
+  //   (or end() if none exists).
+  //
+  // template <typename K> size_type erase(const K& key):
+  //
+  //   Erases the element with the matching key, if it exists, returning the
+  //   number of elements erased (0 or 1).
+  using Base::erase;
+
+  // btree_set::insert()
+  //
+  // Inserts an element of the specified value into the `btree_set`,
+  // returning an iterator pointing to the newly inserted element, provided that
+  // an element with the given key does not already exist. If an insertion
+  // occurs, any references, pointers, or iterators are invalidated.
+  // Overloads are listed below.
+  //
+  // std::pair<iterator,bool> insert(const value_type& value):
+  //
+  //   Inserts a value into the `btree_set`. Returns a pair consisting of an
+  //   iterator to the inserted element (or to the element that prevented the
+  //   insertion) and a bool denoting whether the insertion took place.
+  //
+  // std::pair<iterator,bool> insert(value_type&& value):
+  //
+  //   Inserts a moveable value into the `btree_set`. Returns a pair
+  //   consisting of an iterator to the inserted element (or to the element that
+  //   prevented the insertion) and a bool denoting whether the insertion took
+  //   place.
+  //
+  // iterator insert(const_iterator hint, const value_type& value):
+  // iterator insert(const_iterator hint, value_type&& value):
+  //
+  //   Inserts a value, using the position of `hint` as a non-binding suggestion
+  //   for where to begin the insertion search. Returns an iterator to the
+  //   inserted element, or to the existing element that prevented the
+  //   insertion.
+  //
+  // void insert(InputIterator first, InputIterator last):
+  //
+  //   Inserts a range of values [`first`, `last`).
+  //
+  // void insert(std::initializer_list<init_type> ilist):
+  //
+  //   Inserts the elements within the initializer list `ilist`.
+  using Base::insert;
+
+  // btree_set::emplace()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `btree_set`, provided that no element with the given key
+  // already exists.
+  //
+  // The element may be constructed even if there already is an element with the
+  // key in the container, in which case the newly constructed element will be
+  // destroyed immediately.
+  //
+  // If an insertion occurs, any references, pointers, or iterators are
+  // invalidated.
+  using Base::emplace;
+
+  // btree_set::emplace_hint()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `btree_set`, using the position of `hint` as a non-binding
+  // suggestion for where to begin the insertion search, and only inserts
+  // provided that no element with the given key already exists.
+  //
+  // The element may be constructed even if there already is an element with the
+  // key in the container, in which case the newly constructed element will be
+  // destroyed immediately.
+  //
+  // If an insertion occurs, any references, pointers, or iterators are
+  // invalidated.
+  using Base::emplace_hint;
+
+  // btree_set::extract()
+  //
+  // Extracts the indicated element, erasing it in the process, and returns it
+  // as a C++17-compatible node handle. Any references, pointers, or iterators
+  // are invalidated. Overloads are listed below.
+  //
+  // node_type extract(const_iterator position):
+  //
+  //   Extracts the element at the indicated position and returns a node handle
+  //   owning that extracted data.
+  //
+  // template <typename K> node_type extract(const K& k):
+  //
+  //   Extracts the element with the key matching the passed key value and
+  //   returns a node handle owning that extracted data. If the `btree_set`
+  //   does not contain an element with a matching key, this function returns an
+  //   empty node handle.
+  //
+  // NOTE: In this context, `node_type` refers to the C++17 concept of a
+  // move-only type that owns and provides access to the elements in associative
+  // containers (https://en.cppreference.com/w/cpp/container/node_handle).
+  // It does NOT refer to the data layout of the underlying btree.
+  using Base::extract;
+
+  // btree_set::extract_and_get_next()
+  //
+  // Extracts the indicated element, erasing it in the process, and returns it
+  // as a C++17-compatible node handle along with an iterator to the next
+  // element.
+  //
+  // extract_and_get_next_return_type extract_and_get_next(
+  //     const_iterator position):
+  //
+  //   Extracts the element at the indicated position, returns a struct
+  //   containing a member named `node`: a node handle owning that extracted
+  //   data and a member named `next`: an iterator pointing to the next element
+  //   in the btree.
+  using Base::extract_and_get_next;
+
+  // btree_set::merge()
+  //
+  // Extracts elements from a given `source` btree_set into this
+  // `btree_set`. If the destination `btree_set` already contains an
+  // element with an equivalent key, that element is not extracted.
+  using Base::merge;
+
+  // btree_set::swap(btree_set& other)
+  //
+  // Exchanges the contents of this `btree_set` with those of the `other`
+  // btree_set, avoiding invocation of any move, copy, or swap operations on
+  // individual elements.
+  //
+  // All iterators and references on the `btree_set` remain valid, excepting
+  // for the past-the-end iterator, which is invalidated.
+  using Base::swap;
+
+  // btree_set::contains()
+  //
+  // template <typename K> bool contains(const K& key) const:
+  //
+  // Determines whether an element comparing equal to the given `key` exists
+  // within the `btree_set`, returning `true` if so or `false` otherwise.
+  //
+  // Supports heterogeneous lookup, provided that the set has a compatible
+  // heterogeneous comparator.
+  using Base::contains;
+
+  // btree_set::count()
+  //
+  // template <typename K> size_type count(const K& key) const:
+  //
+  // Returns the number of elements comparing equal to the given `key` within
+  // the `btree_set`. Note that this function will return either `1` or `0`
+  // since duplicate elements are not allowed within a `btree_set`.
+  //
+  // Supports heterogeneous lookup, provided that the set has a compatible
+  // heterogeneous comparator.
+  using Base::count;
+
+  // btree_set::equal_range()
+  //
+  // Returns a closed range [first, last], defined by a `std::pair` of two
+  // iterators, containing all elements with the passed key in the
+  // `btree_set`.
+  using Base::equal_range;
+
+  // btree_set::find()
+  //
+  // template <typename K> iterator find(const K& key):
+  // template <typename K> const_iterator find(const K& key) const:
+  //
+  // Finds an element with the passed `key` within the `btree_set`.
+  //
+  // Supports heterogeneous lookup, provided that the set has a compatible
+  // heterogeneous comparator.
+  using Base::find;
+
+  // btree_set::lower_bound()
+  //
+  // template <typename K> iterator lower_bound(const K& key):
+  // template <typename K> const_iterator lower_bound(const K& key) const:
+  //
+  // Finds the first element that is not less than `key` within the `btree_set`.
+  //
+  // Supports heterogeneous lookup, provided that the set has a compatible
+  // heterogeneous comparator.
+  using Base::lower_bound;
+
+  // btree_set::upper_bound()
+  //
+  // template <typename K> iterator upper_bound(const K& key):
+  // template <typename K> const_iterator upper_bound(const K& key) const:
+  //
+  // Finds the first element that is greater than `key` within the `btree_set`.
+  //
+  // Supports heterogeneous lookup, provided that the set has a compatible
+  // heterogeneous comparator.
+  using Base::upper_bound;
+
+  // btree_set::get_allocator()
+  //
+  // Returns the allocator function associated with this `btree_set`.
+  using Base::get_allocator;
+
+  // btree_set::key_comp();
+  //
+  // Returns the key comparator associated with this `btree_set`.
+  using Base::key_comp;
+
+  // btree_set::value_comp();
+  //
+  // Returns the value comparator associated with this `btree_set`. The keys to
+  // sort the elements are the values themselves, therefore `value_comp` and its
+  // sibling member function `key_comp` are equivalent.
+  using Base::value_comp;
+};
+
+// absl::swap(absl::btree_set<>, absl::btree_set<>)
+//
+// Swaps the contents of two `absl::btree_set` containers.
+template <typename K, typename C, typename A>
+void swap(btree_set<K, C, A> &x, btree_set<K, C, A> &y) {
+  return x.swap(y);
+}
+
+// absl::erase_if(absl::btree_set<>, Pred)
+//
+// Erases all elements that satisfy the predicate pred from the container.
+// Returns the number of erased elements.
+template <typename K, typename C, typename A, typename Pred>
+typename btree_set<K, C, A>::size_type erase_if(btree_set<K, C, A> &set,
+                                                Pred pred) {
+  return container_internal::btree_access::erase_if(set, std::move(pred));
+}
+
+// absl::btree_multiset<>
+//
+// An `absl::btree_multiset<K>` is an ordered associative container of
+// keys and associated values designed to be a more efficient replacement
+// for `std::multiset` (in most cases). Unlike `absl::btree_set`, a B-tree
+// multiset allows equivalent elements.
+//
+// Keys are sorted using an (optional) comparison function, which defaults to
+// `std::less<K>`.
+//
+// An `absl::btree_multiset<K>` uses a default allocator of `std::allocator<K>`
+// to allocate (and deallocate) nodes, and construct and destruct values within
+// those nodes. You may instead specify a custom allocator `A` (which in turn
+// requires specifying a custom comparator `C`) as in
+// `absl::btree_multiset<K, C, A>`.
+//
+template <typename Key, typename Compare = std::less<Key>,
+          typename Alloc = std::allocator<Key>>
+class btree_multiset
+    : public container_internal::btree_multiset_container<
+          container_internal::btree<container_internal::set_params<
+              Key, Compare, Alloc, /*TargetNodeSize=*/256,
+              /*IsMulti=*/true>>> {
+  using Base = typename btree_multiset::btree_multiset_container;
+
+ public:
+  // Constructors and Assignment Operators
+  //
+  // A `btree_multiset` supports the same overload set as `std::set`
+  // for construction and assignment:
+  //
+  // * Default constructor
+  //
+  //   absl::btree_multiset<std::string> set1;
+  //
+  // * Initializer List constructor
+  //
+  //   absl::btree_multiset<std::string> set2 =
+  //       {{"huey"}, {"dewey"}, {"louie"},};
+  //
+  // * Copy constructor
+  //
+  //   absl::btree_multiset<std::string> set3(set2);
+  //
+  // * Copy assignment operator
+  //
+  //  absl::btree_multiset<std::string> set4;
+  //  set4 = set3;
+  //
+  // * Move constructor
+  //
+  //   // Move is guaranteed efficient
+  //   absl::btree_multiset<std::string> set5(std::move(set4));
+  //
+  // * Move assignment operator
+  //
+  //   // May be efficient if allocators are compatible
+  //   absl::btree_multiset<std::string> set6;
+  //   set6 = std::move(set5);
+  //
+  // * Range constructor
+  //
+  //   std::vector<std::string> v = {"a", "b"};
+  //   absl::btree_multiset<std::string> set7(v.begin(), v.end());
+  btree_multiset() {}
+  using Base::Base;
+
+  // btree_multiset::begin()
+  //
+  // Returns an iterator to the beginning of the `btree_multiset`.
+  using Base::begin;
+
+  // btree_multiset::cbegin()
+  //
+  // Returns a const iterator to the beginning of the `btree_multiset`.
+  using Base::cbegin;
+
+  // btree_multiset::end()
+  //
+  // Returns an iterator to the end of the `btree_multiset`.
+  using Base::end;
+
+  // btree_multiset::cend()
+  //
+  // Returns a const iterator to the end of the `btree_multiset`.
+  using Base::cend;
+
+  // btree_multiset::empty()
+  //
+  // Returns whether or not the `btree_multiset` is empty.
+  using Base::empty;
+
+  // btree_multiset::max_size()
+  //
+  // Returns the largest theoretical possible number of elements within a
+  // `btree_multiset` under current memory constraints. This value can be
+  // thought of as the largest value of `std::distance(begin(), end())` for a
+  // `btree_multiset<Key>`.
+  using Base::max_size;
+
+  // btree_multiset::size()
+  //
+  // Returns the number of elements currently within the `btree_multiset`.
+  using Base::size;
+
+  // btree_multiset::clear()
+  //
+  // Removes all elements from the `btree_multiset`. Invalidates any references,
+  // pointers, or iterators referring to contained elements.
+  using Base::clear;
+
+  // btree_multiset::erase()
+  //
+  // Erases elements within the `btree_multiset`. Overloads are listed below.
+  //
+  // iterator erase(iterator position):
+  // iterator erase(const_iterator position):
+  //
+  //   Erases the element at `position` of the `btree_multiset`, returning
+  //   the iterator pointing to the element after the one that was erased
+  //   (or end() if none exists).
+  //
+  // iterator erase(const_iterator first, const_iterator last):
+  //
+  //   Erases the elements in the open interval [`first`, `last`), returning
+  //   the iterator pointing to the element after the interval that was erased
+  //   (or end() if none exists).
+  //
+  // template <typename K> size_type erase(const K& key):
+  //
+  //   Erases the elements matching the key, if any exist, returning the
+  //   number of elements erased.
+  using Base::erase;
+
+  // btree_multiset::insert()
+  //
+  // Inserts an element of the specified value into the `btree_multiset`,
+  // returning an iterator pointing to the newly inserted element.
+  // Any references, pointers, or iterators are invalidated.  Overloads are
+  // listed below.
+  //
+  // iterator insert(const value_type& value):
+  //
+  //   Inserts a value into the `btree_multiset`, returning an iterator to the
+  //   inserted element.
+  //
+  // iterator insert(value_type&& value):
+  //
+  //   Inserts a moveable value into the `btree_multiset`, returning an iterator
+  //   to the inserted element.
+  //
+  // iterator insert(const_iterator hint, const value_type& value):
+  // iterator insert(const_iterator hint, value_type&& value):
+  //
+  //   Inserts a value, using the position of `hint` as a non-binding suggestion
+  //   for where to begin the insertion search. Returns an iterator to the
+  //   inserted element.
+  //
+  // void insert(InputIterator first, InputIterator last):
+  //
+  //   Inserts a range of values [`first`, `last`).
+  //
+  // void insert(std::initializer_list<init_type> ilist):
+  //
+  //   Inserts the elements within the initializer list `ilist`.
+  using Base::insert;
+
+  // btree_multiset::emplace()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `btree_multiset`. Any references, pointers, or iterators are
+  // invalidated.
+  using Base::emplace;
+
+  // btree_multiset::emplace_hint()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `btree_multiset`, using the position of `hint` as a non-binding
+  // suggestion for where to begin the insertion search.
+  //
+  // Any references, pointers, or iterators are invalidated.
+  using Base::emplace_hint;
+
+  // btree_multiset::extract()
+  //
+  // Extracts the indicated element, erasing it in the process, and returns it
+  // as a C++17-compatible node handle. Overloads are listed below.
+  //
+  // node_type extract(const_iterator position):
+  //
+  //   Extracts the element at the indicated position and returns a node handle
+  //   owning that extracted data.
+  //
+  // template <typename K> node_type extract(const K& k):
+  //
+  //   Extracts the element with the key matching the passed key value and
+  //   returns a node handle owning that extracted data. If the `btree_multiset`
+  //   does not contain an element with a matching key, this function returns an
+  //   empty node handle.
+  //
+  // NOTE: In this context, `node_type` refers to the C++17 concept of a
+  // move-only type that owns and provides access to the elements in associative
+  // containers (https://en.cppreference.com/w/cpp/container/node_handle).
+  // It does NOT refer to the data layout of the underlying btree.
+  using Base::extract;
+
+  // btree_multiset::extract_and_get_next()
+  //
+  // Extracts the indicated element, erasing it in the process, and returns it
+  // as a C++17-compatible node handle along with an iterator to the next
+  // element.
+  //
+  // extract_and_get_next_return_type extract_and_get_next(
+  //     const_iterator position):
+  //
+  //   Extracts the element at the indicated position, returns a struct
+  //   containing a member named `node`: a node handle owning that extracted
+  //   data and a member named `next`: an iterator pointing to the next element
+  //   in the btree.
+  using Base::extract_and_get_next;
+
+  // btree_multiset::merge()
+  //
+  // Extracts all elements from a given `source` btree_multiset into this
+  // `btree_multiset`.
+  using Base::merge;
+
+  // btree_multiset::swap(btree_multiset& other)
+  //
+  // Exchanges the contents of this `btree_multiset` with those of the `other`
+  // btree_multiset, avoiding invocation of any move, copy, or swap operations
+  // on individual elements.
+  //
+  // All iterators and references on the `btree_multiset` remain valid,
+  // excepting for the past-the-end iterator, which is invalidated.
+  using Base::swap;
+
+  // btree_multiset::contains()
+  //
+  // template <typename K> bool contains(const K& key) const:
+  //
+  // Determines whether an element comparing equal to the given `key` exists
+  // within the `btree_multiset`, returning `true` if so or `false` otherwise.
+  //
+  // Supports heterogeneous lookup, provided that the set has a compatible
+  // heterogeneous comparator.
+  using Base::contains;
+
+  // btree_multiset::count()
+  //
+  // template <typename K> size_type count(const K& key) const:
+  //
+  // Returns the number of elements comparing equal to the given `key` within
+  // the `btree_multiset`.
+  //
+  // Supports heterogeneous lookup, provided that the set has a compatible
+  // heterogeneous comparator.
+  using Base::count;
+
+  // btree_multiset::equal_range()
+  //
+  // Returns a closed range [first, last], defined by a `std::pair` of two
+  // iterators, containing all elements with the passed key in the
+  // `btree_multiset`.
+  using Base::equal_range;
+
+  // btree_multiset::find()
+  //
+  // template <typename K> iterator find(const K& key):
+  // template <typename K> const_iterator find(const K& key) const:
+  //
+  // Finds an element with the passed `key` within the `btree_multiset`.
+  //
+  // Supports heterogeneous lookup, provided that the set has a compatible
+  // heterogeneous comparator.
+  using Base::find;
+
+  // btree_multiset::lower_bound()
+  //
+  // template <typename K> iterator lower_bound(const K& key):
+  // template <typename K> const_iterator lower_bound(const K& key) const:
+  //
+  // Finds the first element that is not less than `key` within the
+  // `btree_multiset`.
+  //
+  // Supports heterogeneous lookup, provided that the set has a compatible
+  // heterogeneous comparator.
+  using Base::lower_bound;
+
+  // btree_multiset::upper_bound()
+  //
+  // template <typename K> iterator upper_bound(const K& key):
+  // template <typename K> const_iterator upper_bound(const K& key) const:
+  //
+  // Finds the first element that is greater than `key` within the
+  // `btree_multiset`.
+  //
+  // Supports heterogeneous lookup, provided that the set has a compatible
+  // heterogeneous comparator.
+  using Base::upper_bound;
+
+  // btree_multiset::get_allocator()
+  //
+  // Returns the allocator function associated with this `btree_multiset`.
+  using Base::get_allocator;
+
+  // btree_multiset::key_comp();
+  //
+  // Returns the key comparator associated with this `btree_multiset`.
+  using Base::key_comp;
+
+  // btree_multiset::value_comp();
+  //
+  // Returns the value comparator associated with this `btree_multiset`. The
+  // keys to sort the elements are the values themselves, therefore `value_comp`
+  // and its sibling member function `key_comp` are equivalent.
+  using Base::value_comp;
+};
+
+// absl::swap(absl::btree_multiset<>, absl::btree_multiset<>)
+//
+// Swaps the contents of two `absl::btree_multiset` containers.
+template <typename K, typename C, typename A>
+void swap(btree_multiset<K, C, A> &x, btree_multiset<K, C, A> &y) {
+  return x.swap(y);
+}
+
+// absl::erase_if(absl::btree_multiset<>, Pred)
+//
+// Erases all elements that satisfy the predicate pred from the container.
+// Returns the number of erased elements.
+template <typename K, typename C, typename A, typename Pred>
+typename btree_multiset<K, C, A>::size_type erase_if(
+   btree_multiset<K, C, A> & set, Pred pred) {
+  return container_internal::btree_access::erase_if(set, std::move(pred));
+}
+
+namespace container_internal {
+
+// This type implements the necessary functions from the
+// absl::container_internal::slot_type interface for btree_(multi)set.
+template <typename Key>
+struct set_slot_policy {
+  using slot_type = Key;
+  using value_type = Key;
+  using mutable_value_type = Key;
+
+  static value_type &element(slot_type *slot) { return *slot; }
+  static const value_type &element(const slot_type *slot) { return *slot; }
+
+  template <typename Alloc, class... Args>
+  static void construct(Alloc *alloc, slot_type *slot, Args &&...args) {
+    absl::allocator_traits<Alloc>::construct(*alloc, slot,
+                                             std::forward<Args>(args)...);
+  }
+
+  template <typename Alloc>
+  static void construct(Alloc *alloc, slot_type *slot, slot_type *other) {
+    absl::allocator_traits<Alloc>::construct(*alloc, slot, std::move(*other));
+  }
+
+  template <typename Alloc>
+  static void construct(Alloc *alloc, slot_type *slot, const slot_type *other) {
+    absl::allocator_traits<Alloc>::construct(*alloc, slot, *other);
+  }
+
+  template <typename Alloc>
+  static void destroy(Alloc *alloc, slot_type *slot) {
+    absl::allocator_traits<Alloc>::destroy(*alloc, slot);
+  }
+};
+
+// A parameters structure for holding the type parameters for a btree_set.
+// Compare and Alloc should be nothrow copy-constructible.
+template <typename Key, typename Compare, typename Alloc, int TargetNodeSize,
+          bool IsMulti>
+struct set_params : common_params<Key, Compare, Alloc, TargetNodeSize, IsMulti,
+                                  /*IsMap=*/false, set_slot_policy<Key>> {
+  using value_type = Key;
+  using slot_type = typename set_params::common_params::slot_type;
+
+  template <typename V>
+  static const V &key(const V &value) {
+    return value;
+  }
+  static const Key &key(const slot_type *slot) { return *slot; }
+  static const Key &key(slot_type *slot) { return *slot; }
+};
+
+}  // namespace container_internal
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_BTREE_SET_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/fixed_array.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/fixed_array.h
new file mode 100644
index 0000000000000000000000000000000000000000..b67379cfe7a5e8033a508850667d76955b6bdee3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/fixed_array.h
@@ -0,0 +1,531 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: fixed_array.h
+// -----------------------------------------------------------------------------
+//
+// A `FixedArray<T>` represents a non-resizable array of `T` where the length of
+// the array can be determined at run-time. It is a good replacement for
+// non-standard and deprecated uses of `alloca()` and variable length arrays
+// within the GCC extension. (See
+// https://gcc.gnu.org/onlinedocs/gcc/Variable-Length.html).
+//
+// `FixedArray` allocates small arrays inline, keeping performance fast by
+// avoiding heap operations. It also helps reduce the chances of
+// accidentally overflowing your stack if large input is passed to
+// your function.
+
+#ifndef ABSL_CONTAINER_FIXED_ARRAY_H_
+#define ABSL_CONTAINER_FIXED_ARRAY_H_
+
+#include <algorithm>
+#include <cassert>
+#include <cstddef>
+#include <initializer_list>
+#include <iterator>
+#include <limits>
+#include <memory>
+#include <new>
+#include <type_traits>
+
+#include "absl/algorithm/algorithm.h"
+#include "absl/base/config.h"
+#include "absl/base/dynamic_annotations.h"
+#include "absl/base/internal/throw_delegate.h"
+#include "absl/base/macros.h"
+#include "absl/base/optimization.h"
+#include "absl/base/port.h"
+#include "absl/container/internal/compressed_tuple.h"
+#include "absl/memory/memory.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+constexpr static auto kFixedArrayUseDefault = static_cast<size_t>(-1);
+
+// -----------------------------------------------------------------------------
+// FixedArray
+// -----------------------------------------------------------------------------
+//
+// A `FixedArray` provides a run-time fixed-size array, allocating a small array
+// inline for efficiency.
+//
+// Most users should not specify the `N` template parameter and let `FixedArray`
+// automatically determine the number of elements to store inline based on
+// `sizeof(T)`. If `N` is specified, the `FixedArray` implementation will use
+// inline storage for arrays with a length <= `N`.
+//
+// Note that a `FixedArray` constructed with a `size_type` argument will
+// default-initialize its values by leaving trivially constructible types
+// uninitialized (e.g. int, int[4], double), and others default-constructed.
+// This matches the behavior of c-style arrays and `std::array`, but not
+// `std::vector`.
+template <typename T, size_t N = kFixedArrayUseDefault,
+          typename A = std::allocator<T>>
+class FixedArray {
+  static_assert(!std::is_array<T>::value || std::extent<T>::value > 0,
+                "Arrays with unknown bounds cannot be used with FixedArray.");
+
+  static constexpr size_t kInlineBytesDefault = 256;
+
+  using AllocatorTraits = std::allocator_traits<A>;
+  // std::iterator_traits isn't guaranteed to be SFINAE-friendly until C++17,
+  // but this seems to be mostly pedantic.
+  template <typename Iterator>
+  using EnableIfForwardIterator = absl::enable_if_t<std::is_convertible<
+      typename std::iterator_traits<Iterator>::iterator_category,
+      std::forward_iterator_tag>::value>;
+  static constexpr bool NoexceptCopyable() {
+    return std::is_nothrow_copy_constructible<StorageElement>::value &&
+           absl::allocator_is_nothrow<allocator_type>::value;
+  }
+  static constexpr bool NoexceptMovable() {
+    return std::is_nothrow_move_constructible<StorageElement>::value &&
+           absl::allocator_is_nothrow<allocator_type>::value;
+  }
+  static constexpr bool DefaultConstructorIsNonTrivial() {
+    return !absl::is_trivially_default_constructible<StorageElement>::value;
+  }
+
+ public:
+  using allocator_type = typename AllocatorTraits::allocator_type;
+  using value_type = typename AllocatorTraits::value_type;
+  using pointer = typename AllocatorTraits::pointer;
+  using const_pointer = typename AllocatorTraits::const_pointer;
+  using reference = value_type&;
+  using const_reference = const value_type&;
+  using size_type = typename AllocatorTraits::size_type;
+  using difference_type = typename AllocatorTraits::difference_type;
+  using iterator = pointer;
+  using const_iterator = const_pointer;
+  using reverse_iterator = std::reverse_iterator<iterator>;
+  using const_reverse_iterator = std::reverse_iterator<const_iterator>;
+
+  static constexpr size_type inline_elements =
+      (N == kFixedArrayUseDefault ? kInlineBytesDefault / sizeof(value_type)
+                                  : static_cast<size_type>(N));
+
+  FixedArray(
+      const FixedArray& other,
+      const allocator_type& a = allocator_type()) noexcept(NoexceptCopyable())
+      : FixedArray(other.begin(), other.end(), a) {}
+
+  FixedArray(
+      FixedArray&& other,
+      const allocator_type& a = allocator_type()) noexcept(NoexceptMovable())
+      : FixedArray(std::make_move_iterator(other.begin()),
+                   std::make_move_iterator(other.end()), a) {}
+
+  // Creates an array object that can store `n` elements.
+  // Note that trivially constructible elements will be uninitialized.
+  explicit FixedArray(size_type n, const allocator_type& a = allocator_type())
+      : storage_(n, a) {
+    if (DefaultConstructorIsNonTrivial()) {
+      memory_internal::ConstructRange(storage_.alloc(), storage_.begin(),
+                                      storage_.end());
+    }
+  }
+
+  // Creates an array initialized with `n` copies of `val`.
+  FixedArray(size_type n, const value_type& val,
+             const allocator_type& a = allocator_type())
+      : storage_(n, a) {
+    memory_internal::ConstructRange(storage_.alloc(), storage_.begin(),
+                                    storage_.end(), val);
+  }
+
+  // Creates an array initialized with the size and contents of `init_list`.
+  FixedArray(std::initializer_list<value_type> init_list,
+             const allocator_type& a = allocator_type())
+      : FixedArray(init_list.begin(), init_list.end(), a) {}
+
+  // Creates an array initialized with the elements from the input
+  // range. The array's size will always be `std::distance(first, last)`.
+  // REQUIRES: Iterator must be a forward_iterator or better.
+  template <typename Iterator, EnableIfForwardIterator<Iterator>* = nullptr>
+  FixedArray(Iterator first, Iterator last,
+             const allocator_type& a = allocator_type())
+      : storage_(std::distance(first, last), a) {
+    memory_internal::CopyRange(storage_.alloc(), storage_.begin(), first, last);
+  }
+
+  ~FixedArray() noexcept {
+    for (auto* cur = storage_.begin(); cur != storage_.end(); ++cur) {
+      AllocatorTraits::destroy(storage_.alloc(), cur);
+    }
+  }
+
+  // Assignments are deleted because they break the invariant that the size of a
+  // `FixedArray` never changes.
+  void operator=(FixedArray&&) = delete;
+  void operator=(const FixedArray&) = delete;
+
+  // FixedArray::size()
+  //
+  // Returns the length of the fixed array.
+  size_type size() const { return storage_.size(); }
+
+  // FixedArray::max_size()
+  //
+  // Returns the largest possible value of `std::distance(begin(), end())` for a
+  // `FixedArray<T>`. This is equivalent to the most possible addressable bytes
+  // over the number of bytes taken by T.
+  constexpr size_type max_size() const {
+    return (std::numeric_limits<difference_type>::max)() / sizeof(value_type);
+  }
+
+  // FixedArray::empty()
+  //
+  // Returns whether or not the fixed array is empty.
+  bool empty() const { return size() == 0; }
+
+  // FixedArray::memsize()
+  //
+  // Returns the memory size of the fixed array in bytes.
+  size_t memsize() const { return size() * sizeof(value_type); }
+
+  // FixedArray::data()
+  //
+  // Returns a const T* pointer to elements of the `FixedArray`. This pointer
+  // can be used to access (but not modify) the contained elements.
+  const_pointer data() const { return AsValueType(storage_.begin()); }
+
+  // Overload of FixedArray::data() to return a T* pointer to elements of the
+  // fixed array. This pointer can be used to access and modify the contained
+  // elements.
+  pointer data() { return AsValueType(storage_.begin()); }
+
+  // FixedArray::operator[]
+  //
+  // Returns a reference the ith element of the fixed array.
+  // REQUIRES: 0 <= i < size()
+  reference operator[](size_type i) {
+    ABSL_HARDENING_ASSERT(i < size());
+    return data()[i];
+  }
+
+  // Overload of FixedArray::operator()[] to return a const reference to the
+  // ith element of the fixed array.
+  // REQUIRES: 0 <= i < size()
+  const_reference operator[](size_type i) const {
+    ABSL_HARDENING_ASSERT(i < size());
+    return data()[i];
+  }
+
+  // FixedArray::at
+  //
+  // Bounds-checked access.  Returns a reference to the ith element of the fixed
+  // array, or throws std::out_of_range
+  reference at(size_type i) {
+    if (ABSL_PREDICT_FALSE(i >= size())) {
+      base_internal::ThrowStdOutOfRange("FixedArray::at failed bounds check");
+    }
+    return data()[i];
+  }
+
+  // Overload of FixedArray::at() to return a const reference to the ith element
+  // of the fixed array.
+  const_reference at(size_type i) const {
+    if (ABSL_PREDICT_FALSE(i >= size())) {
+      base_internal::ThrowStdOutOfRange("FixedArray::at failed bounds check");
+    }
+    return data()[i];
+  }
+
+  // FixedArray::front()
+  //
+  // Returns a reference to the first element of the fixed array.
+  reference front() {
+    ABSL_HARDENING_ASSERT(!empty());
+    return data()[0];
+  }
+
+  // Overload of FixedArray::front() to return a reference to the first element
+  // of a fixed array of const values.
+  const_reference front() const {
+    ABSL_HARDENING_ASSERT(!empty());
+    return data()[0];
+  }
+
+  // FixedArray::back()
+  //
+  // Returns a reference to the last element of the fixed array.
+  reference back() {
+    ABSL_HARDENING_ASSERT(!empty());
+    return data()[size() - 1];
+  }
+
+  // Overload of FixedArray::back() to return a reference to the last element
+  // of a fixed array of const values.
+  const_reference back() const {
+    ABSL_HARDENING_ASSERT(!empty());
+    return data()[size() - 1];
+  }
+
+  // FixedArray::begin()
+  //
+  // Returns an iterator to the beginning of the fixed array.
+  iterator begin() { return data(); }
+
+  // Overload of FixedArray::begin() to return a const iterator to the
+  // beginning of the fixed array.
+  const_iterator begin() const { return data(); }
+
+  // FixedArray::cbegin()
+  //
+  // Returns a const iterator to the beginning of the fixed array.
+  const_iterator cbegin() const { return begin(); }
+
+  // FixedArray::end()
+  //
+  // Returns an iterator to the end of the fixed array.
+  iterator end() { return data() + size(); }
+
+  // Overload of FixedArray::end() to return a const iterator to the end of the
+  // fixed array.
+  const_iterator end() const { return data() + size(); }
+
+  // FixedArray::cend()
+  //
+  // Returns a const iterator to the end of the fixed array.
+  const_iterator cend() const { return end(); }
+
+  // FixedArray::rbegin()
+  //
+  // Returns a reverse iterator from the end of the fixed array.
+  reverse_iterator rbegin() { return reverse_iterator(end()); }
+
+  // Overload of FixedArray::rbegin() to return a const reverse iterator from
+  // the end of the fixed array.
+  const_reverse_iterator rbegin() const {
+    return const_reverse_iterator(end());
+  }
+
+  // FixedArray::crbegin()
+  //
+  // Returns a const reverse iterator from the end of the fixed array.
+  const_reverse_iterator crbegin() const { return rbegin(); }
+
+  // FixedArray::rend()
+  //
+  // Returns a reverse iterator from the beginning of the fixed array.
+  reverse_iterator rend() { return reverse_iterator(begin()); }
+
+  // Overload of FixedArray::rend() for returning a const reverse iterator
+  // from the beginning of the fixed array.
+  const_reverse_iterator rend() const {
+    return const_reverse_iterator(begin());
+  }
+
+  // FixedArray::crend()
+  //
+  // Returns a reverse iterator from the beginning of the fixed array.
+  const_reverse_iterator crend() const { return rend(); }
+
+  // FixedArray::fill()
+  //
+  // Assigns the given `value` to all elements in the fixed array.
+  void fill(const value_type& val) { std::fill(begin(), end(), val); }
+
+  // Relational operators. Equality operators are elementwise using
+  // `operator==`, while order operators order FixedArrays lexicographically.
+  friend bool operator==(const FixedArray& lhs, const FixedArray& rhs) {
+    return absl::equal(lhs.begin(), lhs.end(), rhs.begin(), rhs.end());
+  }
+
+  friend bool operator!=(const FixedArray& lhs, const FixedArray& rhs) {
+    return !(lhs == rhs);
+  }
+
+  friend bool operator<(const FixedArray& lhs, const FixedArray& rhs) {
+    return std::lexicographical_compare(lhs.begin(), lhs.end(), rhs.begin(),
+                                        rhs.end());
+  }
+
+  friend bool operator>(const FixedArray& lhs, const FixedArray& rhs) {
+    return rhs < lhs;
+  }
+
+  friend bool operator<=(const FixedArray& lhs, const FixedArray& rhs) {
+    return !(rhs < lhs);
+  }
+
+  friend bool operator>=(const FixedArray& lhs, const FixedArray& rhs) {
+    return !(lhs < rhs);
+  }
+
+  template <typename H>
+  friend H AbslHashValue(H h, const FixedArray& v) {
+    return H::combine(H::combine_contiguous(std::move(h), v.data(), v.size()),
+                      v.size());
+  }
+
+ private:
+  // StorageElement
+  //
+  // For FixedArrays with a C-style-array value_type, StorageElement is a POD
+  // wrapper struct called StorageElementWrapper that holds the value_type
+  // instance inside. This is needed for construction and destruction of the
+  // entire array regardless of how many dimensions it has. For all other cases,
+  // StorageElement is just an alias of value_type.
+  //
+  // Maintainer's Note: The simpler solution would be to simply wrap value_type
+  // in a struct whether it's an array or not. That causes some paranoid
+  // diagnostics to misfire, believing that 'data()' returns a pointer to a
+  // single element, rather than the packed array that it really is.
+  // e.g.:
+  //
+  //     FixedArray<char> buf(1);
+  //     sprintf(buf.data(), "foo");
+  //
+  //     error: call to int __builtin___sprintf_chk(etc...)
+  //     will always overflow destination buffer [-Werror]
+  //
+  template <typename OuterT, typename InnerT = absl::remove_extent_t<OuterT>,
+            size_t InnerN = std::extent<OuterT>::value>
+  struct StorageElementWrapper {
+    InnerT array[InnerN];
+  };
+
+  using StorageElement =
+      absl::conditional_t<std::is_array<value_type>::value,
+                          StorageElementWrapper<value_type>, value_type>;
+
+  static pointer AsValueType(pointer ptr) { return ptr; }
+  static pointer AsValueType(StorageElementWrapper<value_type>* ptr) {
+    return std::addressof(ptr->array);
+  }
+
+  static_assert(sizeof(StorageElement) == sizeof(value_type), "");
+  static_assert(alignof(StorageElement) == alignof(value_type), "");
+
+  class NonEmptyInlinedStorage {
+   public:
+    StorageElement* data() { return reinterpret_cast<StorageElement*>(buff_); }
+    void AnnotateConstruct(size_type n);
+    void AnnotateDestruct(size_type n);
+
+#ifdef ABSL_HAVE_ADDRESS_SANITIZER
+    void* RedzoneBegin() { return &redzone_begin_; }
+    void* RedzoneEnd() { return &redzone_end_ + 1; }
+#endif  // ABSL_HAVE_ADDRESS_SANITIZER
+
+   private:
+    ABSL_ADDRESS_SANITIZER_REDZONE(redzone_begin_);
+    alignas(StorageElement) char buff_[sizeof(StorageElement[inline_elements])];
+    ABSL_ADDRESS_SANITIZER_REDZONE(redzone_end_);
+  };
+
+  class EmptyInlinedStorage {
+   public:
+    StorageElement* data() { return nullptr; }
+    void AnnotateConstruct(size_type) {}
+    void AnnotateDestruct(size_type) {}
+  };
+
+  using InlinedStorage =
+      absl::conditional_t<inline_elements == 0, EmptyInlinedStorage,
+                          NonEmptyInlinedStorage>;
+
+  // Storage
+  //
+  // An instance of Storage manages the inline and out-of-line memory for
+  // instances of FixedArray. This guarantees that even when construction of
+  // individual elements fails in the FixedArray constructor body, the
+  // destructor for Storage will still be called and out-of-line memory will be
+  // properly deallocated.
+  //
+  class Storage : public InlinedStorage {
+   public:
+    Storage(size_type n, const allocator_type& a)
+        : size_alloc_(n, a), data_(InitializeData()) {}
+
+    ~Storage() noexcept {
+      if (UsingInlinedStorage(size())) {
+        InlinedStorage::AnnotateDestruct(size());
+      } else {
+        AllocatorTraits::deallocate(alloc(), AsValueType(begin()), size());
+      }
+    }
+
+    size_type size() const { return size_alloc_.template get<0>(); }
+    StorageElement* begin() const { return data_; }
+    StorageElement* end() const { return begin() + size(); }
+    allocator_type& alloc() { return size_alloc_.template get<1>(); }
+
+   private:
+    static bool UsingInlinedStorage(size_type n) {
+      return n <= inline_elements;
+    }
+
+#ifdef ABSL_HAVE_ADDRESS_SANITIZER
+    ABSL_ATTRIBUTE_NOINLINE
+#endif  // ABSL_HAVE_ADDRESS_SANITIZER
+    StorageElement* InitializeData() {
+      if (UsingInlinedStorage(size())) {
+        InlinedStorage::AnnotateConstruct(size());
+        return InlinedStorage::data();
+      } else {
+        return reinterpret_cast<StorageElement*>(
+            AllocatorTraits::allocate(alloc(), size()));
+      }
+    }
+
+    // `CompressedTuple` takes advantage of EBCO for stateless `allocator_type`s
+    container_internal::CompressedTuple<size_type, allocator_type> size_alloc_;
+    StorageElement* data_;
+  };
+
+  Storage storage_;
+};
+
+#ifdef ABSL_INTERNAL_NEED_REDUNDANT_CONSTEXPR_DECL
+template <typename T, size_t N, typename A>
+constexpr size_t FixedArray<T, N, A>::kInlineBytesDefault;
+
+template <typename T, size_t N, typename A>
+constexpr typename FixedArray<T, N, A>::size_type
+    FixedArray<T, N, A>::inline_elements;
+#endif
+
+template <typename T, size_t N, typename A>
+void FixedArray<T, N, A>::NonEmptyInlinedStorage::AnnotateConstruct(
+    typename FixedArray<T, N, A>::size_type n) {
+#ifdef ABSL_HAVE_ADDRESS_SANITIZER
+  if (!n) return;
+  ABSL_ANNOTATE_CONTIGUOUS_CONTAINER(data(), RedzoneEnd(), RedzoneEnd(),
+                                     data() + n);
+  ABSL_ANNOTATE_CONTIGUOUS_CONTAINER(RedzoneBegin(), data(), data(),
+                                     RedzoneBegin());
+#endif  // ABSL_HAVE_ADDRESS_SANITIZER
+  static_cast<void>(n);  // Mark used when not in asan mode
+}
+
+template <typename T, size_t N, typename A>
+void FixedArray<T, N, A>::NonEmptyInlinedStorage::AnnotateDestruct(
+    typename FixedArray<T, N, A>::size_type n) {
+#ifdef ABSL_HAVE_ADDRESS_SANITIZER
+  if (!n) return;
+  ABSL_ANNOTATE_CONTIGUOUS_CONTAINER(data(), RedzoneEnd(), data() + n,
+                                     RedzoneEnd());
+  ABSL_ANNOTATE_CONTIGUOUS_CONTAINER(RedzoneBegin(), data(), RedzoneBegin(),
+                                     data());
+#endif  // ABSL_HAVE_ADDRESS_SANITIZER
+  static_cast<void>(n);  // Mark used when not in asan mode
+}
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_FIXED_ARRAY_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/flat_hash_map.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/flat_hash_map.h
new file mode 100644
index 0000000000000000000000000000000000000000..e6bdbd9e4f78c8c1d215dbb6f90151d65d42ba70
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/flat_hash_map.h
@@ -0,0 +1,613 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: flat_hash_map.h
+// -----------------------------------------------------------------------------
+//
+// An `absl::flat_hash_map<K, V>` is an unordered associative container of
+// unique keys and associated values designed to be a more efficient replacement
+// for `std::unordered_map`. Like `unordered_map`, search, insertion, and
+// deletion of map elements can be done as an `O(1)` operation. However,
+// `flat_hash_map` (and other unordered associative containers known as the
+// collection of Abseil "Swiss tables") contain other optimizations that result
+// in both memory and computation advantages.
+//
+// In most cases, your default choice for a hash map should be a map of type
+// `flat_hash_map`.
+
+#ifndef ABSL_CONTAINER_FLAT_HASH_MAP_H_
+#define ABSL_CONTAINER_FLAT_HASH_MAP_H_
+
+#include <cstddef>
+#include <new>
+#include <type_traits>
+#include <utility>
+
+#include "absl/algorithm/container.h"
+#include "absl/base/macros.h"
+#include "absl/container/internal/container_memory.h"
+#include "absl/container/internal/hash_function_defaults.h"  // IWYU pragma: export
+#include "absl/container/internal/raw_hash_map.h"  // IWYU pragma: export
+#include "absl/memory/memory.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+template <class K, class V>
+struct FlatHashMapPolicy;
+}  // namespace container_internal
+
+// -----------------------------------------------------------------------------
+// absl::flat_hash_map
+// -----------------------------------------------------------------------------
+//
+// An `absl::flat_hash_map<K, V>` is an unordered associative container which
+// has been optimized for both speed and memory footprint in most common use
+// cases. Its interface is similar to that of `std::unordered_map<K, V>` with
+// the following notable differences:
+//
+// * Requires keys that are CopyConstructible
+// * Requires values that are MoveConstructible
+// * Supports heterogeneous lookup, through `find()`, `operator[]()` and
+//   `insert()`, provided that the map is provided a compatible heterogeneous
+//   hashing function and equality operator.
+// * Invalidates any references and pointers to elements within the table after
+//   `rehash()`.
+// * Contains a `capacity()` member function indicating the number of element
+//   slots (open, deleted, and empty) within the hash map.
+// * Returns `void` from the `erase(iterator)` overload.
+//
+// By default, `flat_hash_map` uses the `absl::Hash` hashing framework.
+// All fundamental and Abseil types that support the `absl::Hash` framework have
+// a compatible equality operator for comparing insertions into `flat_hash_map`.
+// If your type is not yet supported by the `absl::Hash` framework, see
+// absl/hash/hash.h for information on extending Abseil hashing to user-defined
+// types.
+//
+// Using `absl::flat_hash_map` at interface boundaries in dynamically loaded
+// libraries (e.g. .dll, .so) is unsupported due to way `absl::Hash` values may
+// be randomized across dynamically loaded libraries.
+//
+// NOTE: A `flat_hash_map` stores its value types directly inside its
+// implementation array to avoid memory indirection. Because a `flat_hash_map`
+// is designed to move data when rehashed, map values will not retain pointer
+// stability. If you require pointer stability, or if your values are large,
+// consider using `absl::flat_hash_map<Key, std::unique_ptr<Value>>` instead.
+// If your types are not moveable or you require pointer stability for keys,
+// consider `absl::node_hash_map`.
+//
+// Example:
+//
+//   // Create a flat hash map of three strings (that map to strings)
+//   absl::flat_hash_map<std::string, std::string> ducks =
+//     {{"a", "huey"}, {"b", "dewey"}, {"c", "louie"}};
+//
+//  // Insert a new element into the flat hash map
+//  ducks.insert({"d", "donald"});
+//
+//  // Force a rehash of the flat hash map
+//  ducks.rehash(0);
+//
+//  // Find the element with the key "b"
+//  std::string search_key = "b";
+//  auto result = ducks.find(search_key);
+//  if (result != ducks.end()) {
+//    std::cout << "Result: " << result->second << std::endl;
+//  }
+template <class K, class V,
+          class Hash = absl::container_internal::hash_default_hash<K>,
+          class Eq = absl::container_internal::hash_default_eq<K>,
+          class Allocator = std::allocator<std::pair<const K, V>>>
+class flat_hash_map : public absl::container_internal::raw_hash_map<
+                          absl::container_internal::FlatHashMapPolicy<K, V>,
+                          Hash, Eq, Allocator> {
+  using Base = typename flat_hash_map::raw_hash_map;
+
+ public:
+  // Constructors and Assignment Operators
+  //
+  // A flat_hash_map supports the same overload set as `std::unordered_map`
+  // for construction and assignment:
+  //
+  // *  Default constructor
+  //
+  //    // No allocation for the table's elements is made.
+  //    absl::flat_hash_map<int, std::string> map1;
+  //
+  // * Initializer List constructor
+  //
+  //   absl::flat_hash_map<int, std::string> map2 =
+  //       {{1, "huey"}, {2, "dewey"}, {3, "louie"},};
+  //
+  // * Copy constructor
+  //
+  //   absl::flat_hash_map<int, std::string> map3(map2);
+  //
+  // * Copy assignment operator
+  //
+  //  // Hash functor and Comparator are copied as well
+  //  absl::flat_hash_map<int, std::string> map4;
+  //  map4 = map3;
+  //
+  // * Move constructor
+  //
+  //   // Move is guaranteed efficient
+  //   absl::flat_hash_map<int, std::string> map5(std::move(map4));
+  //
+  // * Move assignment operator
+  //
+  //   // May be efficient if allocators are compatible
+  //   absl::flat_hash_map<int, std::string> map6;
+  //   map6 = std::move(map5);
+  //
+  // * Range constructor
+  //
+  //   std::vector<std::pair<int, std::string>> v = {{1, "a"}, {2, "b"}};
+  //   absl::flat_hash_map<int, std::string> map7(v.begin(), v.end());
+  flat_hash_map() {}
+  using Base::Base;
+
+  // flat_hash_map::begin()
+  //
+  // Returns an iterator to the beginning of the `flat_hash_map`.
+  using Base::begin;
+
+  // flat_hash_map::cbegin()
+  //
+  // Returns a const iterator to the beginning of the `flat_hash_map`.
+  using Base::cbegin;
+
+  // flat_hash_map::cend()
+  //
+  // Returns a const iterator to the end of the `flat_hash_map`.
+  using Base::cend;
+
+  // flat_hash_map::end()
+  //
+  // Returns an iterator to the end of the `flat_hash_map`.
+  using Base::end;
+
+  // flat_hash_map::capacity()
+  //
+  // Returns the number of element slots (assigned, deleted, and empty)
+  // available within the `flat_hash_map`.
+  //
+  // NOTE: this member function is particular to `absl::flat_hash_map` and is
+  // not provided in the `std::unordered_map` API.
+  using Base::capacity;
+
+  // flat_hash_map::empty()
+  //
+  // Returns whether or not the `flat_hash_map` is empty.
+  using Base::empty;
+
+  // flat_hash_map::max_size()
+  //
+  // Returns the largest theoretical possible number of elements within a
+  // `flat_hash_map` under current memory constraints. This value can be thought
+  // of the largest value of `std::distance(begin(), end())` for a
+  // `flat_hash_map<K, V>`.
+  using Base::max_size;
+
+  // flat_hash_map::size()
+  //
+  // Returns the number of elements currently within the `flat_hash_map`.
+  using Base::size;
+
+  // flat_hash_map::clear()
+  //
+  // Removes all elements from the `flat_hash_map`. Invalidates any references,
+  // pointers, or iterators referring to contained elements.
+  //
+  // NOTE: this operation may shrink the underlying buffer. To avoid shrinking
+  // the underlying buffer call `erase(begin(), end())`.
+  using Base::clear;
+
+  // flat_hash_map::erase()
+  //
+  // Erases elements within the `flat_hash_map`. Erasing does not trigger a
+  // rehash. Overloads are listed below.
+  //
+  // void erase(const_iterator pos):
+  //
+  //   Erases the element at `position` of the `flat_hash_map`, returning
+  //   `void`.
+  //
+  //   NOTE: returning `void` in this case is different than that of STL
+  //   containers in general and `std::unordered_map` in particular (which
+  //   return an iterator to the element following the erased element). If that
+  //   iterator is needed, simply post increment the iterator:
+  //
+  //     map.erase(it++);
+  //
+  // iterator erase(const_iterator first, const_iterator last):
+  //
+  //   Erases the elements in the open interval [`first`, `last`), returning an
+  //   iterator pointing to `last`.
+  //
+  // size_type erase(const key_type& key):
+  //
+  //   Erases the element with the matching key, if it exists, returning the
+  //   number of elements erased (0 or 1).
+  using Base::erase;
+
+  // flat_hash_map::insert()
+  //
+  // Inserts an element of the specified value into the `flat_hash_map`,
+  // returning an iterator pointing to the newly inserted element, provided that
+  // an element with the given key does not already exist. If rehashing occurs
+  // due to the insertion, all iterators are invalidated. Overloads are listed
+  // below.
+  //
+  // std::pair<iterator,bool> insert(const init_type& value):
+  //
+  //   Inserts a value into the `flat_hash_map`. Returns a pair consisting of an
+  //   iterator to the inserted element (or to the element that prevented the
+  //   insertion) and a bool denoting whether the insertion took place.
+  //
+  // std::pair<iterator,bool> insert(T&& value):
+  // std::pair<iterator,bool> insert(init_type&& value):
+  //
+  //   Inserts a moveable value into the `flat_hash_map`. Returns a pair
+  //   consisting of an iterator to the inserted element (or to the element that
+  //   prevented the insertion) and a bool denoting whether the insertion took
+  //   place.
+  //
+  // iterator insert(const_iterator hint, const init_type& value):
+  // iterator insert(const_iterator hint, T&& value):
+  // iterator insert(const_iterator hint, init_type&& value);
+  //
+  //   Inserts a value, using the position of `hint` as a non-binding suggestion
+  //   for where to begin the insertion search. Returns an iterator to the
+  //   inserted element, or to the existing element that prevented the
+  //   insertion.
+  //
+  // void insert(InputIterator first, InputIterator last):
+  //
+  //   Inserts a range of values [`first`, `last`).
+  //
+  //   NOTE: Although the STL does not specify which element may be inserted if
+  //   multiple keys compare equivalently, for `flat_hash_map` we guarantee the
+  //   first match is inserted.
+  //
+  // void insert(std::initializer_list<init_type> ilist):
+  //
+  //   Inserts the elements within the initializer list `ilist`.
+  //
+  //   NOTE: Although the STL does not specify which element may be inserted if
+  //   multiple keys compare equivalently within the initializer list, for
+  //   `flat_hash_map` we guarantee the first match is inserted.
+  using Base::insert;
+
+  // flat_hash_map::insert_or_assign()
+  //
+  // Inserts an element of the specified value into the `flat_hash_map` provided
+  // that a value with the given key does not already exist, or replaces it with
+  // the element value if a key for that value already exists, returning an
+  // iterator pointing to the newly inserted element.  If rehashing occurs due
+  // to the insertion, all existing iterators are invalidated. Overloads are
+  // listed below.
+  //
+  // pair<iterator, bool> insert_or_assign(const init_type& k, T&& obj):
+  // pair<iterator, bool> insert_or_assign(init_type&& k, T&& obj):
+  //
+  //   Inserts/Assigns (or moves) the element of the specified key into the
+  //   `flat_hash_map`.
+  //
+  // iterator insert_or_assign(const_iterator hint,
+  //                           const init_type& k, T&& obj):
+  // iterator insert_or_assign(const_iterator hint, init_type&& k, T&& obj):
+  //
+  //   Inserts/Assigns (or moves) the element of the specified key into the
+  //   `flat_hash_map` using the position of `hint` as a non-binding suggestion
+  //   for where to begin the insertion search.
+  using Base::insert_or_assign;
+
+  // flat_hash_map::emplace()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `flat_hash_map`, provided that no element with the given key
+  // already exists.
+  //
+  // The element may be constructed even if there already is an element with the
+  // key in the container, in which case the newly constructed element will be
+  // destroyed immediately. Prefer `try_emplace()` unless your key is not
+  // copyable or moveable.
+  //
+  // If rehashing occurs due to the insertion, all iterators are invalidated.
+  using Base::emplace;
+
+  // flat_hash_map::emplace_hint()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `flat_hash_map`, using the position of `hint` as a non-binding
+  // suggestion for where to begin the insertion search, and only inserts
+  // provided that no element with the given key already exists.
+  //
+  // The element may be constructed even if there already is an element with the
+  // key in the container, in which case the newly constructed element will be
+  // destroyed immediately. Prefer `try_emplace()` unless your key is not
+  // copyable or moveable.
+  //
+  // If rehashing occurs due to the insertion, all iterators are invalidated.
+  using Base::emplace_hint;
+
+  // flat_hash_map::try_emplace()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `flat_hash_map`, provided that no element with the given key
+  // already exists. Unlike `emplace()`, if an element with the given key
+  // already exists, we guarantee that no element is constructed.
+  //
+  // If rehashing occurs due to the insertion, all iterators are invalidated.
+  // Overloads are listed below.
+  //
+  //   pair<iterator, bool> try_emplace(const key_type& k, Args&&... args):
+  //   pair<iterator, bool> try_emplace(key_type&& k, Args&&... args):
+  //
+  // Inserts (via copy or move) the element of the specified key into the
+  // `flat_hash_map`.
+  //
+  //   iterator try_emplace(const_iterator hint,
+  //                        const key_type& k, Args&&... args):
+  //   iterator try_emplace(const_iterator hint, key_type&& k, Args&&... args):
+  //
+  // Inserts (via copy or move) the element of the specified key into the
+  // `flat_hash_map` using the position of `hint` as a non-binding suggestion
+  // for where to begin the insertion search.
+  //
+  // All `try_emplace()` overloads make the same guarantees regarding rvalue
+  // arguments as `std::unordered_map::try_emplace()`, namely that these
+  // functions will not move from rvalue arguments if insertions do not happen.
+  using Base::try_emplace;
+
+  // flat_hash_map::extract()
+  //
+  // Extracts the indicated element, erasing it in the process, and returns it
+  // as a C++17-compatible node handle. Overloads are listed below.
+  //
+  // node_type extract(const_iterator position):
+  //
+  //   Extracts the key,value pair of the element at the indicated position and
+  //   returns a node handle owning that extracted data.
+  //
+  // node_type extract(const key_type& x):
+  //
+  //   Extracts the key,value pair of the element with a key matching the passed
+  //   key value and returns a node handle owning that extracted data. If the
+  //   `flat_hash_map` does not contain an element with a matching key, this
+  //   function returns an empty node handle.
+  //
+  // NOTE: when compiled in an earlier version of C++ than C++17,
+  // `node_type::key()` returns a const reference to the key instead of a
+  // mutable reference. We cannot safely return a mutable reference without
+  // std::launder (which is not available before C++17).
+  using Base::extract;
+
+  // flat_hash_map::merge()
+  //
+  // Extracts elements from a given `source` flat hash map into this
+  // `flat_hash_map`. If the destination `flat_hash_map` already contains an
+  // element with an equivalent key, that element is not extracted.
+  using Base::merge;
+
+  // flat_hash_map::swap(flat_hash_map& other)
+  //
+  // Exchanges the contents of this `flat_hash_map` with those of the `other`
+  // flat hash map, avoiding invocation of any move, copy, or swap operations on
+  // individual elements.
+  //
+  // All iterators and references on the `flat_hash_map` remain valid, excepting
+  // for the past-the-end iterator, which is invalidated.
+  //
+  // `swap()` requires that the flat hash map's hashing and key equivalence
+  // functions be Swappable, and are exchanged using unqualified calls to
+  // non-member `swap()`. If the map's allocator has
+  // `std::allocator_traits<allocator_type>::propagate_on_container_swap::value`
+  // set to `true`, the allocators are also exchanged using an unqualified call
+  // to non-member `swap()`; otherwise, the allocators are not swapped.
+  using Base::swap;
+
+  // flat_hash_map::rehash(count)
+  //
+  // Rehashes the `flat_hash_map`, setting the number of slots to be at least
+  // the passed value. If the new number of slots increases the load factor more
+  // than the current maximum load factor
+  // (`count` < `size()` / `max_load_factor()`), then the new number of slots
+  // will be at least `size()` / `max_load_factor()`.
+  //
+  // To force a rehash, pass rehash(0).
+  //
+  // NOTE: unlike behavior in `std::unordered_map`, references are also
+  // invalidated upon a `rehash()`.
+  using Base::rehash;
+
+  // flat_hash_map::reserve(count)
+  //
+  // Sets the number of slots in the `flat_hash_map` to the number needed to
+  // accommodate at least `count` total elements without exceeding the current
+  // maximum load factor, and may rehash the container if needed.
+  using Base::reserve;
+
+  // flat_hash_map::at()
+  //
+  // Returns a reference to the mapped value of the element with key equivalent
+  // to the passed key.
+  using Base::at;
+
+  // flat_hash_map::contains()
+  //
+  // Determines whether an element with a key comparing equal to the given `key`
+  // exists within the `flat_hash_map`, returning `true` if so or `false`
+  // otherwise.
+  using Base::contains;
+
+  // flat_hash_map::count(const Key& key) const
+  //
+  // Returns the number of elements with a key comparing equal to the given
+  // `key` within the `flat_hash_map`. note that this function will return
+  // either `1` or `0` since duplicate keys are not allowed within a
+  // `flat_hash_map`.
+  using Base::count;
+
+  // flat_hash_map::equal_range()
+  //
+  // Returns a closed range [first, last], defined by a `std::pair` of two
+  // iterators, containing all elements with the passed key in the
+  // `flat_hash_map`.
+  using Base::equal_range;
+
+  // flat_hash_map::find()
+  //
+  // Finds an element with the passed `key` within the `flat_hash_map`.
+  using Base::find;
+
+  // flat_hash_map::operator[]()
+  //
+  // Returns a reference to the value mapped to the passed key within the
+  // `flat_hash_map`, performing an `insert()` if the key does not already
+  // exist.
+  //
+  // If an insertion occurs and results in a rehashing of the container, all
+  // iterators are invalidated. Otherwise iterators are not affected and
+  // references are not invalidated. Overloads are listed below.
+  //
+  // T& operator[](const Key& key):
+  //
+  //   Inserts an init_type object constructed in-place if the element with the
+  //   given key does not exist.
+  //
+  // T& operator[](Key&& key):
+  //
+  //   Inserts an init_type object constructed in-place provided that an element
+  //   with the given key does not exist.
+  using Base::operator[];
+
+  // flat_hash_map::bucket_count()
+  //
+  // Returns the number of "buckets" within the `flat_hash_map`. Note that
+  // because a flat hash map contains all elements within its internal storage,
+  // this value simply equals the current capacity of the `flat_hash_map`.
+  using Base::bucket_count;
+
+  // flat_hash_map::load_factor()
+  //
+  // Returns the current load factor of the `flat_hash_map` (the average number
+  // of slots occupied with a value within the hash map).
+  using Base::load_factor;
+
+  // flat_hash_map::max_load_factor()
+  //
+  // Manages the maximum load factor of the `flat_hash_map`. Overloads are
+  // listed below.
+  //
+  // float flat_hash_map::max_load_factor()
+  //
+  //   Returns the current maximum load factor of the `flat_hash_map`.
+  //
+  // void flat_hash_map::max_load_factor(float ml)
+  //
+  //   Sets the maximum load factor of the `flat_hash_map` to the passed value.
+  //
+  //   NOTE: This overload is provided only for API compatibility with the STL;
+  //   `flat_hash_map` will ignore any set load factor and manage its rehashing
+  //   internally as an implementation detail.
+  using Base::max_load_factor;
+
+  // flat_hash_map::get_allocator()
+  //
+  // Returns the allocator function associated with this `flat_hash_map`.
+  using Base::get_allocator;
+
+  // flat_hash_map::hash_function()
+  //
+  // Returns the hashing function used to hash the keys within this
+  // `flat_hash_map`.
+  using Base::hash_function;
+
+  // flat_hash_map::key_eq()
+  //
+  // Returns the function used for comparing keys equality.
+  using Base::key_eq;
+};
+
+// erase_if(flat_hash_map<>, Pred)
+//
+// Erases all elements that satisfy the predicate `pred` from the container `c`.
+// Returns the number of erased elements.
+template <typename K, typename V, typename H, typename E, typename A,
+          typename Predicate>
+typename flat_hash_map<K, V, H, E, A>::size_type erase_if(
+    flat_hash_map<K, V, H, E, A>& c, Predicate pred) {
+  return container_internal::EraseIf(pred, &c);
+}
+
+namespace container_internal {
+
+template <class K, class V>
+struct FlatHashMapPolicy {
+  using slot_policy = container_internal::map_slot_policy<K, V>;
+  using slot_type = typename slot_policy::slot_type;
+  using key_type = K;
+  using mapped_type = V;
+  using init_type = std::pair</*non const*/ key_type, mapped_type>;
+
+  template <class Allocator, class... Args>
+  static void construct(Allocator* alloc, slot_type* slot, Args&&... args) {
+    slot_policy::construct(alloc, slot, std::forward<Args>(args)...);
+  }
+
+  template <class Allocator>
+  static void destroy(Allocator* alloc, slot_type* slot) {
+    slot_policy::destroy(alloc, slot);
+  }
+
+  template <class Allocator>
+  static void transfer(Allocator* alloc, slot_type* new_slot,
+                       slot_type* old_slot) {
+    slot_policy::transfer(alloc, new_slot, old_slot);
+  }
+
+  template <class F, class... Args>
+  static decltype(absl::container_internal::DecomposePair(
+      std::declval<F>(), std::declval<Args>()...))
+  apply(F&& f, Args&&... args) {
+    return absl::container_internal::DecomposePair(std::forward<F>(f),
+                                                   std::forward<Args>(args)...);
+  }
+
+  static size_t space_used(const slot_type*) { return 0; }
+
+  static std::pair<const K, V>& element(slot_type* slot) { return slot->value; }
+
+  static V& value(std::pair<const K, V>* kv) { return kv->second; }
+  static const V& value(const std::pair<const K, V>* kv) { return kv->second; }
+};
+
+}  // namespace container_internal
+
+namespace container_algorithm_internal {
+
+// Specialization of trait in absl/algorithm/container.h
+template <class Key, class T, class Hash, class KeyEqual, class Allocator>
+struct IsUnorderedContainer<
+    absl::flat_hash_map<Key, T, Hash, KeyEqual, Allocator>> : std::true_type {};
+
+}  // namespace container_algorithm_internal
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_FLAT_HASH_MAP_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/flat_hash_set.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/flat_hash_set.h
new file mode 100644
index 0000000000000000000000000000000000000000..f5376f991a8b1e20e878e7fb2aed3f7312fcddd3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/flat_hash_set.h
@@ -0,0 +1,503 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: flat_hash_set.h
+// -----------------------------------------------------------------------------
+//
+// An `absl::flat_hash_set<T>` is an unordered associative container designed to
+// be a more efficient replacement for `std::unordered_set`. Like
+// `unordered_set`, search, insertion, and deletion of set elements can be done
+// as an `O(1)` operation. However, `flat_hash_set` (and other unordered
+// associative containers known as the collection of Abseil "Swiss tables")
+// contain other optimizations that result in both memory and computation
+// advantages.
+//
+// In most cases, your default choice for a hash set should be a set of type
+// `flat_hash_set`.
+#ifndef ABSL_CONTAINER_FLAT_HASH_SET_H_
+#define ABSL_CONTAINER_FLAT_HASH_SET_H_
+
+#include <type_traits>
+#include <utility>
+
+#include "absl/algorithm/container.h"
+#include "absl/base/macros.h"
+#include "absl/container/internal/container_memory.h"
+#include "absl/container/internal/hash_function_defaults.h"  // IWYU pragma: export
+#include "absl/container/internal/raw_hash_set.h"  // IWYU pragma: export
+#include "absl/memory/memory.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+template <typename T>
+struct FlatHashSetPolicy;
+}  // namespace container_internal
+
+// -----------------------------------------------------------------------------
+// absl::flat_hash_set
+// -----------------------------------------------------------------------------
+//
+// An `absl::flat_hash_set<T>` is an unordered associative container which has
+// been optimized for both speed and memory footprint in most common use cases.
+// Its interface is similar to that of `std::unordered_set<T>` with the
+// following notable differences:
+//
+// * Requires keys that are CopyConstructible
+// * Supports heterogeneous lookup, through `find()` and `insert()`, provided
+//   that the set is provided a compatible heterogeneous hashing function and
+//   equality operator.
+// * Invalidates any references and pointers to elements within the table after
+//   `rehash()`.
+// * Contains a `capacity()` member function indicating the number of element
+//   slots (open, deleted, and empty) within the hash set.
+// * Returns `void` from the `erase(iterator)` overload.
+//
+// By default, `flat_hash_set` uses the `absl::Hash` hashing framework. All
+// fundamental and Abseil types that support the `absl::Hash` framework have a
+// compatible equality operator for comparing insertions into `flat_hash_set`.
+// If your type is not yet supported by the `absl::Hash` framework, see
+// absl/hash/hash.h for information on extending Abseil hashing to user-defined
+// types.
+//
+// Using `absl::flat_hash_set` at interface boundaries in dynamically loaded
+// libraries (e.g. .dll, .so) is unsupported due to way `absl::Hash` values may
+// be randomized across dynamically loaded libraries.
+//
+// NOTE: A `flat_hash_set` stores its keys directly inside its implementation
+// array to avoid memory indirection. Because a `flat_hash_set` is designed to
+// move data when rehashed, set keys will not retain pointer stability. If you
+// require pointer stability, consider using
+// `absl::flat_hash_set<std::unique_ptr<T>>`. If your type is not moveable and
+// you require pointer stability, consider `absl::node_hash_set` instead.
+//
+// Example:
+//
+//   // Create a flat hash set of three strings
+//   absl::flat_hash_set<std::string> ducks =
+//     {"huey", "dewey", "louie"};
+//
+//  // Insert a new element into the flat hash set
+//  ducks.insert("donald");
+//
+//  // Force a rehash of the flat hash set
+//  ducks.rehash(0);
+//
+//  // See if "dewey" is present
+//  if (ducks.contains("dewey")) {
+//    std::cout << "We found dewey!" << std::endl;
+//  }
+template <class T, class Hash = absl::container_internal::hash_default_hash<T>,
+          class Eq = absl::container_internal::hash_default_eq<T>,
+          class Allocator = std::allocator<T>>
+class flat_hash_set
+    : public absl::container_internal::raw_hash_set<
+          absl::container_internal::FlatHashSetPolicy<T>, Hash, Eq, Allocator> {
+  using Base = typename flat_hash_set::raw_hash_set;
+
+ public:
+  // Constructors and Assignment Operators
+  //
+  // A flat_hash_set supports the same overload set as `std::unordered_set`
+  // for construction and assignment:
+  //
+  // *  Default constructor
+  //
+  //    // No allocation for the table's elements is made.
+  //    absl::flat_hash_set<std::string> set1;
+  //
+  // * Initializer List constructor
+  //
+  //   absl::flat_hash_set<std::string> set2 =
+  //       {{"huey"}, {"dewey"}, {"louie"},};
+  //
+  // * Copy constructor
+  //
+  //   absl::flat_hash_set<std::string> set3(set2);
+  //
+  // * Copy assignment operator
+  //
+  //  // Hash functor and Comparator are copied as well
+  //  absl::flat_hash_set<std::string> set4;
+  //  set4 = set3;
+  //
+  // * Move constructor
+  //
+  //   // Move is guaranteed efficient
+  //   absl::flat_hash_set<std::string> set5(std::move(set4));
+  //
+  // * Move assignment operator
+  //
+  //   // May be efficient if allocators are compatible
+  //   absl::flat_hash_set<std::string> set6;
+  //   set6 = std::move(set5);
+  //
+  // * Range constructor
+  //
+  //   std::vector<std::string> v = {"a", "b"};
+  //   absl::flat_hash_set<std::string> set7(v.begin(), v.end());
+  flat_hash_set() {}
+  using Base::Base;
+
+  // flat_hash_set::begin()
+  //
+  // Returns an iterator to the beginning of the `flat_hash_set`.
+  using Base::begin;
+
+  // flat_hash_set::cbegin()
+  //
+  // Returns a const iterator to the beginning of the `flat_hash_set`.
+  using Base::cbegin;
+
+  // flat_hash_set::cend()
+  //
+  // Returns a const iterator to the end of the `flat_hash_set`.
+  using Base::cend;
+
+  // flat_hash_set::end()
+  //
+  // Returns an iterator to the end of the `flat_hash_set`.
+  using Base::end;
+
+  // flat_hash_set::capacity()
+  //
+  // Returns the number of element slots (assigned, deleted, and empty)
+  // available within the `flat_hash_set`.
+  //
+  // NOTE: this member function is particular to `absl::flat_hash_set` and is
+  // not provided in the `std::unordered_set` API.
+  using Base::capacity;
+
+  // flat_hash_set::empty()
+  //
+  // Returns whether or not the `flat_hash_set` is empty.
+  using Base::empty;
+
+  // flat_hash_set::max_size()
+  //
+  // Returns the largest theoretical possible number of elements within a
+  // `flat_hash_set` under current memory constraints. This value can be thought
+  // of the largest value of `std::distance(begin(), end())` for a
+  // `flat_hash_set<T>`.
+  using Base::max_size;
+
+  // flat_hash_set::size()
+  //
+  // Returns the number of elements currently within the `flat_hash_set`.
+  using Base::size;
+
+  // flat_hash_set::clear()
+  //
+  // Removes all elements from the `flat_hash_set`. Invalidates any references,
+  // pointers, or iterators referring to contained elements.
+  //
+  // NOTE: this operation may shrink the underlying buffer. To avoid shrinking
+  // the underlying buffer call `erase(begin(), end())`.
+  using Base::clear;
+
+  // flat_hash_set::erase()
+  //
+  // Erases elements within the `flat_hash_set`. Erasing does not trigger a
+  // rehash. Overloads are listed below.
+  //
+  // void erase(const_iterator pos):
+  //
+  //   Erases the element at `position` of the `flat_hash_set`, returning
+  //   `void`.
+  //
+  //   NOTE: returning `void` in this case is different than that of STL
+  //   containers in general and `std::unordered_set` in particular (which
+  //   return an iterator to the element following the erased element). If that
+  //   iterator is needed, simply post increment the iterator:
+  //
+  //     set.erase(it++);
+  //
+  // iterator erase(const_iterator first, const_iterator last):
+  //
+  //   Erases the elements in the open interval [`first`, `last`), returning an
+  //   iterator pointing to `last`.
+  //
+  // size_type erase(const key_type& key):
+  //
+  //   Erases the element with the matching key, if it exists, returning the
+  //   number of elements erased (0 or 1).
+  using Base::erase;
+
+  // flat_hash_set::insert()
+  //
+  // Inserts an element of the specified value into the `flat_hash_set`,
+  // returning an iterator pointing to the newly inserted element, provided that
+  // an element with the given key does not already exist. If rehashing occurs
+  // due to the insertion, all iterators are invalidated. Overloads are listed
+  // below.
+  //
+  // std::pair<iterator,bool> insert(const T& value):
+  //
+  //   Inserts a value into the `flat_hash_set`. Returns a pair consisting of an
+  //   iterator to the inserted element (or to the element that prevented the
+  //   insertion) and a bool denoting whether the insertion took place.
+  //
+  // std::pair<iterator,bool> insert(T&& value):
+  //
+  //   Inserts a moveable value into the `flat_hash_set`. Returns a pair
+  //   consisting of an iterator to the inserted element (or to the element that
+  //   prevented the insertion) and a bool denoting whether the insertion took
+  //   place.
+  //
+  // iterator insert(const_iterator hint, const T& value):
+  // iterator insert(const_iterator hint, T&& value):
+  //
+  //   Inserts a value, using the position of `hint` as a non-binding suggestion
+  //   for where to begin the insertion search. Returns an iterator to the
+  //   inserted element, or to the existing element that prevented the
+  //   insertion.
+  //
+  // void insert(InputIterator first, InputIterator last):
+  //
+  //   Inserts a range of values [`first`, `last`).
+  //
+  //   NOTE: Although the STL does not specify which element may be inserted if
+  //   multiple keys compare equivalently, for `flat_hash_set` we guarantee the
+  //   first match is inserted.
+  //
+  // void insert(std::initializer_list<T> ilist):
+  //
+  //   Inserts the elements within the initializer list `ilist`.
+  //
+  //   NOTE: Although the STL does not specify which element may be inserted if
+  //   multiple keys compare equivalently within the initializer list, for
+  //   `flat_hash_set` we guarantee the first match is inserted.
+  using Base::insert;
+
+  // flat_hash_set::emplace()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `flat_hash_set`, provided that no element with the given key
+  // already exists.
+  //
+  // The element may be constructed even if there already is an element with the
+  // key in the container, in which case the newly constructed element will be
+  // destroyed immediately.
+  //
+  // If rehashing occurs due to the insertion, all iterators are invalidated.
+  using Base::emplace;
+
+  // flat_hash_set::emplace_hint()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `flat_hash_set`, using the position of `hint` as a non-binding
+  // suggestion for where to begin the insertion search, and only inserts
+  // provided that no element with the given key already exists.
+  //
+  // The element may be constructed even if there already is an element with the
+  // key in the container, in which case the newly constructed element will be
+  // destroyed immediately.
+  //
+  // If rehashing occurs due to the insertion, all iterators are invalidated.
+  using Base::emplace_hint;
+
+  // flat_hash_set::extract()
+  //
+  // Extracts the indicated element, erasing it in the process, and returns it
+  // as a C++17-compatible node handle. Overloads are listed below.
+  //
+  // node_type extract(const_iterator position):
+  //
+  //   Extracts the element at the indicated position and returns a node handle
+  //   owning that extracted data.
+  //
+  // node_type extract(const key_type& x):
+  //
+  //   Extracts the element with the key matching the passed key value and
+  //   returns a node handle owning that extracted data. If the `flat_hash_set`
+  //   does not contain an element with a matching key, this function returns an
+  //   empty node handle.
+  using Base::extract;
+
+  // flat_hash_set::merge()
+  //
+  // Extracts elements from a given `source` flat hash set into this
+  // `flat_hash_set`. If the destination `flat_hash_set` already contains an
+  // element with an equivalent key, that element is not extracted.
+  using Base::merge;
+
+  // flat_hash_set::swap(flat_hash_set& other)
+  //
+  // Exchanges the contents of this `flat_hash_set` with those of the `other`
+  // flat hash set, avoiding invocation of any move, copy, or swap operations on
+  // individual elements.
+  //
+  // All iterators and references on the `flat_hash_set` remain valid, excepting
+  // for the past-the-end iterator, which is invalidated.
+  //
+  // `swap()` requires that the flat hash set's hashing and key equivalence
+  // functions be Swappable, and are exchaged using unqualified calls to
+  // non-member `swap()`. If the set's allocator has
+  // `std::allocator_traits<allocator_type>::propagate_on_container_swap::value`
+  // set to `true`, the allocators are also exchanged using an unqualified call
+  // to non-member `swap()`; otherwise, the allocators are not swapped.
+  using Base::swap;
+
+  // flat_hash_set::rehash(count)
+  //
+  // Rehashes the `flat_hash_set`, setting the number of slots to be at least
+  // the passed value. If the new number of slots increases the load factor more
+  // than the current maximum load factor
+  // (`count` < `size()` / `max_load_factor()`), then the new number of slots
+  // will be at least `size()` / `max_load_factor()`.
+  //
+  // To force a rehash, pass rehash(0).
+  //
+  // NOTE: unlike behavior in `std::unordered_set`, references are also
+  // invalidated upon a `rehash()`.
+  using Base::rehash;
+
+  // flat_hash_set::reserve(count)
+  //
+  // Sets the number of slots in the `flat_hash_set` to the number needed to
+  // accommodate at least `count` total elements without exceeding the current
+  // maximum load factor, and may rehash the container if needed.
+  using Base::reserve;
+
+  // flat_hash_set::contains()
+  //
+  // Determines whether an element comparing equal to the given `key` exists
+  // within the `flat_hash_set`, returning `true` if so or `false` otherwise.
+  using Base::contains;
+
+  // flat_hash_set::count(const Key& key) const
+  //
+  // Returns the number of elements comparing equal to the given `key` within
+  // the `flat_hash_set`. note that this function will return either `1` or `0`
+  // since duplicate elements are not allowed within a `flat_hash_set`.
+  using Base::count;
+
+  // flat_hash_set::equal_range()
+  //
+  // Returns a closed range [first, last], defined by a `std::pair` of two
+  // iterators, containing all elements with the passed key in the
+  // `flat_hash_set`.
+  using Base::equal_range;
+
+  // flat_hash_set::find()
+  //
+  // Finds an element with the passed `key` within the `flat_hash_set`.
+  using Base::find;
+
+  // flat_hash_set::bucket_count()
+  //
+  // Returns the number of "buckets" within the `flat_hash_set`. Note that
+  // because a flat hash set contains all elements within its internal storage,
+  // this value simply equals the current capacity of the `flat_hash_set`.
+  using Base::bucket_count;
+
+  // flat_hash_set::load_factor()
+  //
+  // Returns the current load factor of the `flat_hash_set` (the average number
+  // of slots occupied with a value within the hash set).
+  using Base::load_factor;
+
+  // flat_hash_set::max_load_factor()
+  //
+  // Manages the maximum load factor of the `flat_hash_set`. Overloads are
+  // listed below.
+  //
+  // float flat_hash_set::max_load_factor()
+  //
+  //   Returns the current maximum load factor of the `flat_hash_set`.
+  //
+  // void flat_hash_set::max_load_factor(float ml)
+  //
+  //   Sets the maximum load factor of the `flat_hash_set` to the passed value.
+  //
+  //   NOTE: This overload is provided only for API compatibility with the STL;
+  //   `flat_hash_set` will ignore any set load factor and manage its rehashing
+  //   internally as an implementation detail.
+  using Base::max_load_factor;
+
+  // flat_hash_set::get_allocator()
+  //
+  // Returns the allocator function associated with this `flat_hash_set`.
+  using Base::get_allocator;
+
+  // flat_hash_set::hash_function()
+  //
+  // Returns the hashing function used to hash the keys within this
+  // `flat_hash_set`.
+  using Base::hash_function;
+
+  // flat_hash_set::key_eq()
+  //
+  // Returns the function used for comparing keys equality.
+  using Base::key_eq;
+};
+
+// erase_if(flat_hash_set<>, Pred)
+//
+// Erases all elements that satisfy the predicate `pred` from the container `c`.
+// Returns the number of erased elements.
+template <typename T, typename H, typename E, typename A, typename Predicate>
+typename flat_hash_set<T, H, E, A>::size_type erase_if(
+    flat_hash_set<T, H, E, A>& c, Predicate pred) {
+  return container_internal::EraseIf(pred, &c);
+}
+
+namespace container_internal {
+
+template <class T>
+struct FlatHashSetPolicy {
+  using slot_type = T;
+  using key_type = T;
+  using init_type = T;
+  using constant_iterators = std::true_type;
+
+  template <class Allocator, class... Args>
+  static void construct(Allocator* alloc, slot_type* slot, Args&&... args) {
+    absl::allocator_traits<Allocator>::construct(*alloc, slot,
+                                                 std::forward<Args>(args)...);
+  }
+
+  template <class Allocator>
+  static void destroy(Allocator* alloc, slot_type* slot) {
+    absl::allocator_traits<Allocator>::destroy(*alloc, slot);
+  }
+
+  static T& element(slot_type* slot) { return *slot; }
+
+  template <class F, class... Args>
+  static decltype(absl::container_internal::DecomposeValue(
+      std::declval<F>(), std::declval<Args>()...))
+  apply(F&& f, Args&&... args) {
+    return absl::container_internal::DecomposeValue(
+        std::forward<F>(f), std::forward<Args>(args)...);
+  }
+
+  static size_t space_used(const T*) { return 0; }
+};
+}  // namespace container_internal
+
+namespace container_algorithm_internal {
+
+// Specialization of trait in absl/algorithm/container.h
+template <class Key, class Hash, class KeyEqual, class Allocator>
+struct IsUnorderedContainer<absl::flat_hash_set<Key, Hash, KeyEqual, Allocator>>
+    : std::true_type {};
+
+}  // namespace container_algorithm_internal
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_FLAT_HASH_SET_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/inlined_vector.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/inlined_vector.h
new file mode 100644
index 0000000000000000000000000000000000000000..7058f375e7954cf87afd3b4b7788c9928da10c71
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/inlined_vector.h
@@ -0,0 +1,914 @@
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: inlined_vector.h
+// -----------------------------------------------------------------------------
+//
+// This header file contains the declaration and definition of an "inlined
+// vector" which behaves in an equivalent fashion to a `std::vector`, except
+// that storage for small sequences of the vector are provided inline without
+// requiring any heap allocation.
+//
+// An `absl::InlinedVector<T, N>` specifies the default capacity `N` as one of
+// its template parameters. Instances where `size() <= N` hold contained
+// elements in inline space. Typically `N` is very small so that sequences that
+// are expected to be short do not require allocations.
+//
+// An `absl::InlinedVector` does not usually require a specific allocator. If
+// the inlined vector grows beyond its initial constraints, it will need to
+// allocate (as any normal `std::vector` would). This is usually performed with
+// the default allocator (defined as `std::allocator<T>`). Optionally, a custom
+// allocator type may be specified as `A` in `absl::InlinedVector<T, N, A>`.
+
+#ifndef ABSL_CONTAINER_INLINED_VECTOR_H_
+#define ABSL_CONTAINER_INLINED_VECTOR_H_
+
+#include <algorithm>
+#include <cstddef>
+#include <cstdlib>
+#include <cstring>
+#include <initializer_list>
+#include <iterator>
+#include <memory>
+#include <type_traits>
+#include <utility>
+
+#include "absl/algorithm/algorithm.h"
+#include "absl/base/internal/throw_delegate.h"
+#include "absl/base/macros.h"
+#include "absl/base/optimization.h"
+#include "absl/base/port.h"
+#include "absl/container/internal/inlined_vector.h"
+#include "absl/memory/memory.h"
+#include "absl/meta/type_traits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+// -----------------------------------------------------------------------------
+// InlinedVector
+// -----------------------------------------------------------------------------
+//
+// An `absl::InlinedVector` is designed to be a drop-in replacement for
+// `std::vector` for use cases where the vector's size is sufficiently small
+// that it can be inlined. If the inlined vector does grow beyond its estimated
+// capacity, it will trigger an initial allocation on the heap, and will behave
+// as a `std::vector`. The API of the `absl::InlinedVector` within this file is
+// designed to cover the same API footprint as covered by `std::vector`.
+template <typename T, size_t N, typename A = std::allocator<T>>
+class InlinedVector {
+  static_assert(N > 0, "`absl::InlinedVector` requires an inlined capacity.");
+
+  using Storage = inlined_vector_internal::Storage<T, N, A>;
+
+  template <typename TheA>
+  using AllocatorTraits = inlined_vector_internal::AllocatorTraits<TheA>;
+  template <typename TheA>
+  using MoveIterator = inlined_vector_internal::MoveIterator<TheA>;
+  template <typename TheA>
+  using IsMemcpyOk = inlined_vector_internal::IsMemcpyOk<TheA>;
+  template <typename TheA>
+  using IsMoveAssignOk = inlined_vector_internal::IsMoveAssignOk<TheA>;
+
+  template <typename TheA, typename Iterator>
+  using IteratorValueAdapter =
+      inlined_vector_internal::IteratorValueAdapter<TheA, Iterator>;
+  template <typename TheA>
+  using CopyValueAdapter = inlined_vector_internal::CopyValueAdapter<TheA>;
+  template <typename TheA>
+  using DefaultValueAdapter =
+      inlined_vector_internal::DefaultValueAdapter<TheA>;
+
+  template <typename Iterator>
+  using EnableIfAtLeastForwardIterator = absl::enable_if_t<
+      inlined_vector_internal::IsAtLeastForwardIterator<Iterator>::value, int>;
+  template <typename Iterator>
+  using DisableIfAtLeastForwardIterator = absl::enable_if_t<
+      !inlined_vector_internal::IsAtLeastForwardIterator<Iterator>::value, int>;
+
+  using MemcpyPolicy = typename Storage::MemcpyPolicy;
+  using ElementwiseAssignPolicy = typename Storage::ElementwiseAssignPolicy;
+  using ElementwiseConstructPolicy =
+      typename Storage::ElementwiseConstructPolicy;
+  using MoveAssignmentPolicy = typename Storage::MoveAssignmentPolicy;
+
+ public:
+  using allocator_type = A;
+  using value_type = inlined_vector_internal::ValueType<A>;
+  using pointer = inlined_vector_internal::Pointer<A>;
+  using const_pointer = inlined_vector_internal::ConstPointer<A>;
+  using size_type = inlined_vector_internal::SizeType<A>;
+  using difference_type = inlined_vector_internal::DifferenceType<A>;
+  using reference = inlined_vector_internal::Reference<A>;
+  using const_reference = inlined_vector_internal::ConstReference<A>;
+  using iterator = inlined_vector_internal::Iterator<A>;
+  using const_iterator = inlined_vector_internal::ConstIterator<A>;
+  using reverse_iterator = inlined_vector_internal::ReverseIterator<A>;
+  using const_reverse_iterator =
+      inlined_vector_internal::ConstReverseIterator<A>;
+
+  // ---------------------------------------------------------------------------
+  // InlinedVector Constructors and Destructor
+  // ---------------------------------------------------------------------------
+
+  // Creates an empty inlined vector with a value-initialized allocator.
+  InlinedVector() noexcept(noexcept(allocator_type())) : storage_() {}
+
+  // Creates an empty inlined vector with a copy of `allocator`.
+  explicit InlinedVector(const allocator_type& allocator) noexcept
+      : storage_(allocator) {}
+
+  // Creates an inlined vector with `n` copies of `value_type()`.
+  explicit InlinedVector(size_type n,
+                         const allocator_type& allocator = allocator_type())
+      : storage_(allocator) {
+    storage_.Initialize(DefaultValueAdapter<A>(), n);
+  }
+
+  // Creates an inlined vector with `n` copies of `v`.
+  InlinedVector(size_type n, const_reference v,
+                const allocator_type& allocator = allocator_type())
+      : storage_(allocator) {
+    storage_.Initialize(CopyValueAdapter<A>(std::addressof(v)), n);
+  }
+
+  // Creates an inlined vector with copies of the elements of `list`.
+  InlinedVector(std::initializer_list<value_type> list,
+                const allocator_type& allocator = allocator_type())
+      : InlinedVector(list.begin(), list.end(), allocator) {}
+
+  // Creates an inlined vector with elements constructed from the provided
+  // forward iterator range [`first`, `last`).
+  //
+  // NOTE: the `enable_if` prevents ambiguous interpretation between a call to
+  // this constructor with two integral arguments and a call to the above
+  // `InlinedVector(size_type, const_reference)` constructor.
+  template <typename ForwardIterator,
+            EnableIfAtLeastForwardIterator<ForwardIterator> = 0>
+  InlinedVector(ForwardIterator first, ForwardIterator last,
+                const allocator_type& allocator = allocator_type())
+      : storage_(allocator) {
+    storage_.Initialize(IteratorValueAdapter<A, ForwardIterator>(first),
+                        static_cast<size_t>(std::distance(first, last)));
+  }
+
+  // Creates an inlined vector with elements constructed from the provided input
+  // iterator range [`first`, `last`).
+  template <typename InputIterator,
+            DisableIfAtLeastForwardIterator<InputIterator> = 0>
+  InlinedVector(InputIterator first, InputIterator last,
+                const allocator_type& allocator = allocator_type())
+      : storage_(allocator) {
+    std::copy(first, last, std::back_inserter(*this));
+  }
+
+  // Creates an inlined vector by copying the contents of `other` using
+  // `other`'s allocator.
+  InlinedVector(const InlinedVector& other)
+      : InlinedVector(other, other.storage_.GetAllocator()) {}
+
+  // Creates an inlined vector by copying the contents of `other` using the
+  // provided `allocator`.
+  InlinedVector(const InlinedVector& other, const allocator_type& allocator)
+      : storage_(allocator) {
+    if (other.empty()) {
+      // Empty; nothing to do.
+    } else if (IsMemcpyOk<A>::value && !other.storage_.GetIsAllocated()) {
+      // Memcpy-able and do not need allocation.
+      storage_.MemcpyFrom(other.storage_);
+    } else {
+      storage_.InitFrom(other.storage_);
+    }
+  }
+
+  // Creates an inlined vector by moving in the contents of `other` without
+  // allocating. If `other` contains allocated memory, the newly-created inlined
+  // vector will take ownership of that memory. However, if `other` does not
+  // contain allocated memory, the newly-created inlined vector will perform
+  // element-wise move construction of the contents of `other`.
+  //
+  // NOTE: since no allocation is performed for the inlined vector in either
+  // case, the `noexcept(...)` specification depends on whether moving the
+  // underlying objects can throw. It is assumed assumed that...
+  //  a) move constructors should only throw due to allocation failure.
+  //  b) if `value_type`'s move constructor allocates, it uses the same
+  //     allocation function as the inlined vector's allocator.
+  // Thus, the move constructor is non-throwing if the allocator is non-throwing
+  // or `value_type`'s move constructor is specified as `noexcept`.
+  InlinedVector(InlinedVector&& other) noexcept(
+      absl::allocator_is_nothrow<allocator_type>::value ||
+      std::is_nothrow_move_constructible<value_type>::value)
+      : storage_(other.storage_.GetAllocator()) {
+    if (IsMemcpyOk<A>::value) {
+      storage_.MemcpyFrom(other.storage_);
+
+      other.storage_.SetInlinedSize(0);
+    } else if (other.storage_.GetIsAllocated()) {
+      storage_.SetAllocation({other.storage_.GetAllocatedData(),
+                              other.storage_.GetAllocatedCapacity()});
+      storage_.SetAllocatedSize(other.storage_.GetSize());
+
+      other.storage_.SetInlinedSize(0);
+    } else {
+      IteratorValueAdapter<A, MoveIterator<A>> other_values(
+          MoveIterator<A>(other.storage_.GetInlinedData()));
+
+      inlined_vector_internal::ConstructElements<A>(
+          storage_.GetAllocator(), storage_.GetInlinedData(), other_values,
+          other.storage_.GetSize());
+
+      storage_.SetInlinedSize(other.storage_.GetSize());
+    }
+  }
+
+  // Creates an inlined vector by moving in the contents of `other` with a copy
+  // of `allocator`.
+  //
+  // NOTE: if `other`'s allocator is not equal to `allocator`, even if `other`
+  // contains allocated memory, this move constructor will still allocate. Since
+  // allocation is performed, this constructor can only be `noexcept` if the
+  // specified allocator is also `noexcept`.
+  InlinedVector(
+      InlinedVector&& other,
+      const allocator_type&
+          allocator) noexcept(absl::allocator_is_nothrow<allocator_type>::value)
+      : storage_(allocator) {
+    if (IsMemcpyOk<A>::value) {
+      storage_.MemcpyFrom(other.storage_);
+
+      other.storage_.SetInlinedSize(0);
+    } else if ((storage_.GetAllocator() == other.storage_.GetAllocator()) &&
+               other.storage_.GetIsAllocated()) {
+      storage_.SetAllocation({other.storage_.GetAllocatedData(),
+                              other.storage_.GetAllocatedCapacity()});
+      storage_.SetAllocatedSize(other.storage_.GetSize());
+
+      other.storage_.SetInlinedSize(0);
+    } else {
+      storage_.Initialize(IteratorValueAdapter<A, MoveIterator<A>>(
+                              MoveIterator<A>(other.data())),
+                          other.size());
+    }
+  }
+
+  ~InlinedVector() {}
+
+  // ---------------------------------------------------------------------------
+  // InlinedVector Member Accessors
+  // ---------------------------------------------------------------------------
+
+  // `InlinedVector::empty()`
+  //
+  // Returns whether the inlined vector contains no elements.
+  bool empty() const noexcept { return !size(); }
+
+  // `InlinedVector::size()`
+  //
+  // Returns the number of elements in the inlined vector.
+  size_type size() const noexcept { return storage_.GetSize(); }
+
+  // `InlinedVector::max_size()`
+  //
+  // Returns the maximum number of elements the inlined vector can hold.
+  size_type max_size() const noexcept {
+    // One bit of the size storage is used to indicate whether the inlined
+    // vector contains allocated memory. As a result, the maximum size that the
+    // inlined vector can express is the minimum of the limit of how many
+    // objects we can allocate and std::numeric_limits<size_type>::max() / 2.
+    return (std::min)(AllocatorTraits<A>::max_size(storage_.GetAllocator()),
+                      (std::numeric_limits<size_type>::max)() / 2);
+  }
+
+  // `InlinedVector::capacity()`
+  //
+  // Returns the number of elements that could be stored in the inlined vector
+  // without requiring a reallocation.
+  //
+  // NOTE: for most inlined vectors, `capacity()` should be equal to the
+  // template parameter `N`. For inlined vectors which exceed this capacity,
+  // they will no longer be inlined and `capacity()` will equal the capactity of
+  // the allocated memory.
+  size_type capacity() const noexcept {
+    return storage_.GetIsAllocated() ? storage_.GetAllocatedCapacity()
+                                     : storage_.GetInlinedCapacity();
+  }
+
+  // `InlinedVector::data()`
+  //
+  // Returns a `pointer` to the elements of the inlined vector. This pointer
+  // can be used to access and modify the contained elements.
+  //
+  // NOTE: only elements within [`data()`, `data() + size()`) are valid.
+  pointer data() noexcept {
+    return storage_.GetIsAllocated() ? storage_.GetAllocatedData()
+                                     : storage_.GetInlinedData();
+  }
+
+  // Overload of `InlinedVector::data()` that returns a `const_pointer` to the
+  // elements of the inlined vector. This pointer can be used to access but not
+  // modify the contained elements.
+  //
+  // NOTE: only elements within [`data()`, `data() + size()`) are valid.
+  const_pointer data() const noexcept {
+    return storage_.GetIsAllocated() ? storage_.GetAllocatedData()
+                                     : storage_.GetInlinedData();
+  }
+
+  // `InlinedVector::operator[](...)`
+  //
+  // Returns a `reference` to the `i`th element of the inlined vector.
+  reference operator[](size_type i) {
+    ABSL_HARDENING_ASSERT(i < size());
+    return data()[i];
+  }
+
+  // Overload of `InlinedVector::operator[](...)` that returns a
+  // `const_reference` to the `i`th element of the inlined vector.
+  const_reference operator[](size_type i) const {
+    ABSL_HARDENING_ASSERT(i < size());
+    return data()[i];
+  }
+
+  // `InlinedVector::at(...)`
+  //
+  // Returns a `reference` to the `i`th element of the inlined vector.
+  //
+  // NOTE: if `i` is not within the required range of `InlinedVector::at(...)`,
+  // in both debug and non-debug builds, `std::out_of_range` will be thrown.
+  reference at(size_type i) {
+    if (ABSL_PREDICT_FALSE(i >= size())) {
+      base_internal::ThrowStdOutOfRange(
+          "`InlinedVector::at(size_type)` failed bounds check");
+    }
+    return data()[i];
+  }
+
+  // Overload of `InlinedVector::at(...)` that returns a `const_reference` to
+  // the `i`th element of the inlined vector.
+  //
+  // NOTE: if `i` is not within the required range of `InlinedVector::at(...)`,
+  // in both debug and non-debug builds, `std::out_of_range` will be thrown.
+  const_reference at(size_type i) const {
+    if (ABSL_PREDICT_FALSE(i >= size())) {
+      base_internal::ThrowStdOutOfRange(
+          "`InlinedVector::at(size_type) const` failed bounds check");
+    }
+    return data()[i];
+  }
+
+  // `InlinedVector::front()`
+  //
+  // Returns a `reference` to the first element of the inlined vector.
+  reference front() {
+    ABSL_HARDENING_ASSERT(!empty());
+    return data()[0];
+  }
+
+  // Overload of `InlinedVector::front()` that returns a `const_reference` to
+  // the first element of the inlined vector.
+  const_reference front() const {
+    ABSL_HARDENING_ASSERT(!empty());
+    return data()[0];
+  }
+
+  // `InlinedVector::back()`
+  //
+  // Returns a `reference` to the last element of the inlined vector.
+  reference back() {
+    ABSL_HARDENING_ASSERT(!empty());
+    return data()[size() - 1];
+  }
+
+  // Overload of `InlinedVector::back()` that returns a `const_reference` to the
+  // last element of the inlined vector.
+  const_reference back() const {
+    ABSL_HARDENING_ASSERT(!empty());
+    return data()[size() - 1];
+  }
+
+  // `InlinedVector::begin()`
+  //
+  // Returns an `iterator` to the beginning of the inlined vector.
+  iterator begin() noexcept { return data(); }
+
+  // Overload of `InlinedVector::begin()` that returns a `const_iterator` to
+  // the beginning of the inlined vector.
+  const_iterator begin() const noexcept { return data(); }
+
+  // `InlinedVector::end()`
+  //
+  // Returns an `iterator` to the end of the inlined vector.
+  iterator end() noexcept { return data() + size(); }
+
+  // Overload of `InlinedVector::end()` that returns a `const_iterator` to the
+  // end of the inlined vector.
+  const_iterator end() const noexcept { return data() + size(); }
+
+  // `InlinedVector::cbegin()`
+  //
+  // Returns a `const_iterator` to the beginning of the inlined vector.
+  const_iterator cbegin() const noexcept { return begin(); }
+
+  // `InlinedVector::cend()`
+  //
+  // Returns a `const_iterator` to the end of the inlined vector.
+  const_iterator cend() const noexcept { return end(); }
+
+  // `InlinedVector::rbegin()`
+  //
+  // Returns a `reverse_iterator` from the end of the inlined vector.
+  reverse_iterator rbegin() noexcept { return reverse_iterator(end()); }
+
+  // Overload of `InlinedVector::rbegin()` that returns a
+  // `const_reverse_iterator` from the end of the inlined vector.
+  const_reverse_iterator rbegin() const noexcept {
+    return const_reverse_iterator(end());
+  }
+
+  // `InlinedVector::rend()`
+  //
+  // Returns a `reverse_iterator` from the beginning of the inlined vector.
+  reverse_iterator rend() noexcept { return reverse_iterator(begin()); }
+
+  // Overload of `InlinedVector::rend()` that returns a `const_reverse_iterator`
+  // from the beginning of the inlined vector.
+  const_reverse_iterator rend() const noexcept {
+    return const_reverse_iterator(begin());
+  }
+
+  // `InlinedVector::crbegin()`
+  //
+  // Returns a `const_reverse_iterator` from the end of the inlined vector.
+  const_reverse_iterator crbegin() const noexcept { return rbegin(); }
+
+  // `InlinedVector::crend()`
+  //
+  // Returns a `const_reverse_iterator` from the beginning of the inlined
+  // vector.
+  const_reverse_iterator crend() const noexcept { return rend(); }
+
+  // `InlinedVector::get_allocator()`
+  //
+  // Returns a copy of the inlined vector's allocator.
+  allocator_type get_allocator() const { return storage_.GetAllocator(); }
+
+  // ---------------------------------------------------------------------------
+  // InlinedVector Member Mutators
+  // ---------------------------------------------------------------------------
+
+  // `InlinedVector::operator=(...)`
+  //
+  // Replaces the elements of the inlined vector with copies of the elements of
+  // `list`.
+  InlinedVector& operator=(std::initializer_list<value_type> list) {
+    assign(list.begin(), list.end());
+
+    return *this;
+  }
+
+  // Overload of `InlinedVector::operator=(...)` that replaces the elements of
+  // the inlined vector with copies of the elements of `other`.
+  InlinedVector& operator=(const InlinedVector& other) {
+    if (ABSL_PREDICT_TRUE(this != std::addressof(other))) {
+      const_pointer other_data = other.data();
+      assign(other_data, other_data + other.size());
+    }
+
+    return *this;
+  }
+
+  // Overload of `InlinedVector::operator=(...)` that moves the elements of
+  // `other` into the inlined vector.
+  //
+  // NOTE: as a result of calling this overload, `other` is left in a valid but
+  // unspecified state.
+  InlinedVector& operator=(InlinedVector&& other) {
+    if (ABSL_PREDICT_TRUE(this != std::addressof(other))) {
+      MoveAssignment(MoveAssignmentPolicy{}, std::move(other));
+    }
+
+    return *this;
+  }
+
+  // `InlinedVector::assign(...)`
+  //
+  // Replaces the contents of the inlined vector with `n` copies of `v`.
+  void assign(size_type n, const_reference v) {
+    storage_.Assign(CopyValueAdapter<A>(std::addressof(v)), n);
+  }
+
+  // Overload of `InlinedVector::assign(...)` that replaces the contents of the
+  // inlined vector with copies of the elements of `list`.
+  void assign(std::initializer_list<value_type> list) {
+    assign(list.begin(), list.end());
+  }
+
+  // Overload of `InlinedVector::assign(...)` to replace the contents of the
+  // inlined vector with the range [`first`, `last`).
+  //
+  // NOTE: this overload is for iterators that are "forward" category or better.
+  template <typename ForwardIterator,
+            EnableIfAtLeastForwardIterator<ForwardIterator> = 0>
+  void assign(ForwardIterator first, ForwardIterator last) {
+    storage_.Assign(IteratorValueAdapter<A, ForwardIterator>(first),
+                    static_cast<size_t>(std::distance(first, last)));
+  }
+
+  // Overload of `InlinedVector::assign(...)` to replace the contents of the
+  // inlined vector with the range [`first`, `last`).
+  //
+  // NOTE: this overload is for iterators that are "input" category.
+  template <typename InputIterator,
+            DisableIfAtLeastForwardIterator<InputIterator> = 0>
+  void assign(InputIterator first, InputIterator last) {
+    size_type i = 0;
+    for (; i < size() && first != last; ++i, static_cast<void>(++first)) {
+      data()[i] = *first;
+    }
+
+    erase(data() + i, data() + size());
+    std::copy(first, last, std::back_inserter(*this));
+  }
+
+  // `InlinedVector::resize(...)`
+  //
+  // Resizes the inlined vector to contain `n` elements.
+  //
+  // NOTE: If `n` is smaller than `size()`, extra elements are destroyed. If `n`
+  // is larger than `size()`, new elements are value-initialized.
+  void resize(size_type n) {
+    ABSL_HARDENING_ASSERT(n <= max_size());
+    storage_.Resize(DefaultValueAdapter<A>(), n);
+  }
+
+  // Overload of `InlinedVector::resize(...)` that resizes the inlined vector to
+  // contain `n` elements.
+  //
+  // NOTE: if `n` is smaller than `size()`, extra elements are destroyed. If `n`
+  // is larger than `size()`, new elements are copied-constructed from `v`.
+  void resize(size_type n, const_reference v) {
+    ABSL_HARDENING_ASSERT(n <= max_size());
+    storage_.Resize(CopyValueAdapter<A>(std::addressof(v)), n);
+  }
+
+  // `InlinedVector::insert(...)`
+  //
+  // Inserts a copy of `v` at `pos`, returning an `iterator` to the newly
+  // inserted element.
+  iterator insert(const_iterator pos, const_reference v) {
+    return emplace(pos, v);
+  }
+
+  // Overload of `InlinedVector::insert(...)` that inserts `v` at `pos` using
+  // move semantics, returning an `iterator` to the newly inserted element.
+  iterator insert(const_iterator pos, value_type&& v) {
+    return emplace(pos, std::move(v));
+  }
+
+  // Overload of `InlinedVector::insert(...)` that inserts `n` contiguous copies
+  // of `v` starting at `pos`, returning an `iterator` pointing to the first of
+  // the newly inserted elements.
+  iterator insert(const_iterator pos, size_type n, const_reference v) {
+    ABSL_HARDENING_ASSERT(pos >= begin());
+    ABSL_HARDENING_ASSERT(pos <= end());
+
+    if (ABSL_PREDICT_TRUE(n != 0)) {
+      value_type dealias = v;
+      // https://gcc.gnu.org/bugzilla/show_bug.cgi?id=102329#c2
+      // It appears that GCC thinks that since `pos` is a const pointer and may
+      // point to uninitialized memory at this point, a warning should be
+      // issued. But `pos` is actually only used to compute an array index to
+      // write to.
+#if !defined(__clang__) && defined(__GNUC__)
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
+#endif
+      return storage_.Insert(pos, CopyValueAdapter<A>(std::addressof(dealias)),
+                             n);
+#if !defined(__clang__) && defined(__GNUC__)
+#pragma GCC diagnostic pop
+#endif
+    } else {
+      return const_cast<iterator>(pos);
+    }
+  }
+
+  // Overload of `InlinedVector::insert(...)` that inserts copies of the
+  // elements of `list` starting at `pos`, returning an `iterator` pointing to
+  // the first of the newly inserted elements.
+  iterator insert(const_iterator pos, std::initializer_list<value_type> list) {
+    return insert(pos, list.begin(), list.end());
+  }
+
+  // Overload of `InlinedVector::insert(...)` that inserts the range [`first`,
+  // `last`) starting at `pos`, returning an `iterator` pointing to the first
+  // of the newly inserted elements.
+  //
+  // NOTE: this overload is for iterators that are "forward" category or better.
+  template <typename ForwardIterator,
+            EnableIfAtLeastForwardIterator<ForwardIterator> = 0>
+  iterator insert(const_iterator pos, ForwardIterator first,
+                  ForwardIterator last) {
+    ABSL_HARDENING_ASSERT(pos >= begin());
+    ABSL_HARDENING_ASSERT(pos <= end());
+
+    if (ABSL_PREDICT_TRUE(first != last)) {
+      return storage_.Insert(
+          pos, IteratorValueAdapter<A, ForwardIterator>(first),
+          static_cast<size_type>(std::distance(first, last)));
+    } else {
+      return const_cast<iterator>(pos);
+    }
+  }
+
+  // Overload of `InlinedVector::insert(...)` that inserts the range [`first`,
+  // `last`) starting at `pos`, returning an `iterator` pointing to the first
+  // of the newly inserted elements.
+  //
+  // NOTE: this overload is for iterators that are "input" category.
+  template <typename InputIterator,
+            DisableIfAtLeastForwardIterator<InputIterator> = 0>
+  iterator insert(const_iterator pos, InputIterator first, InputIterator last) {
+    ABSL_HARDENING_ASSERT(pos >= begin());
+    ABSL_HARDENING_ASSERT(pos <= end());
+
+    size_type index = static_cast<size_type>(std::distance(cbegin(), pos));
+    for (size_type i = index; first != last; ++i, static_cast<void>(++first)) {
+      insert(data() + i, *first);
+    }
+
+    return iterator(data() + index);
+  }
+
+  // `InlinedVector::emplace(...)`
+  //
+  // Constructs and inserts an element using `args...` in the inlined vector at
+  // `pos`, returning an `iterator` pointing to the newly emplaced element.
+  template <typename... Args>
+  iterator emplace(const_iterator pos, Args&&... args) {
+    ABSL_HARDENING_ASSERT(pos >= begin());
+    ABSL_HARDENING_ASSERT(pos <= end());
+
+    value_type dealias(std::forward<Args>(args)...);
+    // https://gcc.gnu.org/bugzilla/show_bug.cgi?id=102329#c2
+    // It appears that GCC thinks that since `pos` is a const pointer and may
+    // point to uninitialized memory at this point, a warning should be
+    // issued. But `pos` is actually only used to compute an array index to
+    // write to.
+#if !defined(__clang__) && defined(__GNUC__)
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
+#endif
+    return storage_.Insert(pos,
+                           IteratorValueAdapter<A, MoveIterator<A>>(
+                               MoveIterator<A>(std::addressof(dealias))),
+                           1);
+#if !defined(__clang__) && defined(__GNUC__)
+#pragma GCC diagnostic pop
+#endif
+  }
+
+  // `InlinedVector::emplace_back(...)`
+  //
+  // Constructs and inserts an element using `args...` in the inlined vector at
+  // `end()`, returning a `reference` to the newly emplaced element.
+  template <typename... Args>
+  reference emplace_back(Args&&... args) {
+    return storage_.EmplaceBack(std::forward<Args>(args)...);
+  }
+
+  // `InlinedVector::push_back(...)`
+  //
+  // Inserts a copy of `v` in the inlined vector at `end()`.
+  void push_back(const_reference v) { static_cast<void>(emplace_back(v)); }
+
+  // Overload of `InlinedVector::push_back(...)` for inserting `v` at `end()`
+  // using move semantics.
+  void push_back(value_type&& v) {
+    static_cast<void>(emplace_back(std::move(v)));
+  }
+
+  // `InlinedVector::pop_back()`
+  //
+  // Destroys the element at `back()`, reducing the size by `1`.
+  void pop_back() noexcept {
+    ABSL_HARDENING_ASSERT(!empty());
+
+    AllocatorTraits<A>::destroy(storage_.GetAllocator(), data() + (size() - 1));
+    storage_.SubtractSize(1);
+  }
+
+  // `InlinedVector::erase(...)`
+  //
+  // Erases the element at `pos`, returning an `iterator` pointing to where the
+  // erased element was located.
+  //
+  // NOTE: may return `end()`, which is not dereferencable.
+  iterator erase(const_iterator pos) {
+    ABSL_HARDENING_ASSERT(pos >= begin());
+    ABSL_HARDENING_ASSERT(pos < end());
+
+    return storage_.Erase(pos, pos + 1);
+  }
+
+  // Overload of `InlinedVector::erase(...)` that erases every element in the
+  // range [`from`, `to`), returning an `iterator` pointing to where the first
+  // erased element was located.
+  //
+  // NOTE: may return `end()`, which is not dereferencable.
+  iterator erase(const_iterator from, const_iterator to) {
+    ABSL_HARDENING_ASSERT(from >= begin());
+    ABSL_HARDENING_ASSERT(from <= to);
+    ABSL_HARDENING_ASSERT(to <= end());
+
+    if (ABSL_PREDICT_TRUE(from != to)) {
+      return storage_.Erase(from, to);
+    } else {
+      return const_cast<iterator>(from);
+    }
+  }
+
+  // `InlinedVector::clear()`
+  //
+  // Destroys all elements in the inlined vector, setting the size to `0` and
+  // deallocating any held memory.
+  void clear() noexcept {
+    inlined_vector_internal::DestroyAdapter<A>::DestroyElements(
+        storage_.GetAllocator(), data(), size());
+    storage_.DeallocateIfAllocated();
+
+    storage_.SetInlinedSize(0);
+  }
+
+  // `InlinedVector::reserve(...)`
+  //
+  // Ensures that there is enough room for at least `n` elements.
+  void reserve(size_type n) { storage_.Reserve(n); }
+
+  // `InlinedVector::shrink_to_fit()`
+  //
+  // Attempts to reduce memory usage by moving elements to (or keeping elements
+  // in) the smallest available buffer sufficient for containing `size()`
+  // elements.
+  //
+  // If `size()` is sufficiently small, the elements will be moved into (or kept
+  // in) the inlined space.
+  void shrink_to_fit() {
+    if (storage_.GetIsAllocated()) {
+      storage_.ShrinkToFit();
+    }
+  }
+
+  // `InlinedVector::swap(...)`
+  //
+  // Swaps the contents of the inlined vector with `other`.
+  void swap(InlinedVector& other) {
+    if (ABSL_PREDICT_TRUE(this != std::addressof(other))) {
+      storage_.Swap(std::addressof(other.storage_));
+    }
+  }
+
+ private:
+  template <typename H, typename TheT, size_t TheN, typename TheA>
+  friend H AbslHashValue(H h, const absl::InlinedVector<TheT, TheN, TheA>& a);
+
+  void MoveAssignment(MemcpyPolicy, InlinedVector&& other) {
+    inlined_vector_internal::DestroyAdapter<A>::DestroyElements(
+        storage_.GetAllocator(), data(), size());
+    storage_.DeallocateIfAllocated();
+    storage_.MemcpyFrom(other.storage_);
+
+    other.storage_.SetInlinedSize(0);
+  }
+
+  void MoveAssignment(ElementwiseAssignPolicy, InlinedVector&& other) {
+    if (other.storage_.GetIsAllocated()) {
+      MoveAssignment(MemcpyPolicy{}, std::move(other));
+    } else {
+      storage_.Assign(IteratorValueAdapter<A, MoveIterator<A>>(
+                          MoveIterator<A>(other.storage_.GetInlinedData())),
+                      other.size());
+    }
+  }
+
+  void MoveAssignment(ElementwiseConstructPolicy, InlinedVector&& other) {
+    if (other.storage_.GetIsAllocated()) {
+      MoveAssignment(MemcpyPolicy{}, std::move(other));
+    } else {
+      inlined_vector_internal::DestroyAdapter<A>::DestroyElements(
+          storage_.GetAllocator(), data(), size());
+      storage_.DeallocateIfAllocated();
+
+      IteratorValueAdapter<A, MoveIterator<A>> other_values(
+          MoveIterator<A>(other.storage_.GetInlinedData()));
+      inlined_vector_internal::ConstructElements<A>(
+          storage_.GetAllocator(), storage_.GetInlinedData(), other_values,
+          other.storage_.GetSize());
+      storage_.SetInlinedSize(other.storage_.GetSize());
+    }
+  }
+
+  Storage storage_;
+};
+
+// -----------------------------------------------------------------------------
+// InlinedVector Non-Member Functions
+// -----------------------------------------------------------------------------
+
+// `swap(...)`
+//
+// Swaps the contents of two inlined vectors.
+template <typename T, size_t N, typename A>
+void swap(absl::InlinedVector<T, N, A>& a,
+          absl::InlinedVector<T, N, A>& b) noexcept(noexcept(a.swap(b))) {
+  a.swap(b);
+}
+
+// `operator==(...)`
+//
+// Tests for value-equality of two inlined vectors.
+template <typename T, size_t N, typename A>
+bool operator==(const absl::InlinedVector<T, N, A>& a,
+                const absl::InlinedVector<T, N, A>& b) {
+  auto a_data = a.data();
+  auto b_data = b.data();
+  return absl::equal(a_data, a_data + a.size(), b_data, b_data + b.size());
+}
+
+// `operator!=(...)`
+//
+// Tests for value-inequality of two inlined vectors.
+template <typename T, size_t N, typename A>
+bool operator!=(const absl::InlinedVector<T, N, A>& a,
+                const absl::InlinedVector<T, N, A>& b) {
+  return !(a == b);
+}
+
+// `operator<(...)`
+//
+// Tests whether the value of an inlined vector is less than the value of
+// another inlined vector using a lexicographical comparison algorithm.
+template <typename T, size_t N, typename A>
+bool operator<(const absl::InlinedVector<T, N, A>& a,
+               const absl::InlinedVector<T, N, A>& b) {
+  auto a_data = a.data();
+  auto b_data = b.data();
+  return std::lexicographical_compare(a_data, a_data + a.size(), b_data,
+                                      b_data + b.size());
+}
+
+// `operator>(...)`
+//
+// Tests whether the value of an inlined vector is greater than the value of
+// another inlined vector using a lexicographical comparison algorithm.
+template <typename T, size_t N, typename A>
+bool operator>(const absl::InlinedVector<T, N, A>& a,
+               const absl::InlinedVector<T, N, A>& b) {
+  return b < a;
+}
+
+// `operator<=(...)`
+//
+// Tests whether the value of an inlined vector is less than or equal to the
+// value of another inlined vector using a lexicographical comparison algorithm.
+template <typename T, size_t N, typename A>
+bool operator<=(const absl::InlinedVector<T, N, A>& a,
+                const absl::InlinedVector<T, N, A>& b) {
+  return !(b < a);
+}
+
+// `operator>=(...)`
+//
+// Tests whether the value of an inlined vector is greater than or equal to the
+// value of another inlined vector using a lexicographical comparison algorithm.
+template <typename T, size_t N, typename A>
+bool operator>=(const absl::InlinedVector<T, N, A>& a,
+                const absl::InlinedVector<T, N, A>& b) {
+  return !(a < b);
+}
+
+// `AbslHashValue(...)`
+//
+// Provides `absl::Hash` support for `absl::InlinedVector`. It is uncommon to
+// call this directly.
+template <typename H, typename T, size_t N, typename A>
+H AbslHashValue(H h, const absl::InlinedVector<T, N, A>& a) {
+  auto size = a.size();
+  return H::combine(H::combine_contiguous(std::move(h), a.data(), size), size);
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_INLINED_VECTOR_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/btree.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/btree.h
new file mode 100644
index 0000000000000000000000000000000000000000..d734676a2146fc80c4734964adc9cffce5e2e9e3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/btree.h
@@ -0,0 +1,2982 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// A btree implementation of the STL set and map interfaces. A btree is smaller
+// and generally also faster than STL set/map (refer to the benchmarks below).
+// The red-black tree implementation of STL set/map has an overhead of 3
+// pointers (left, right and parent) plus the node color information for each
+// stored value. So a set<int32_t> consumes 40 bytes for each value stored in
+// 64-bit mode. This btree implementation stores multiple values on fixed
+// size nodes (usually 256 bytes) and doesn't store child pointers for leaf
+// nodes. The result is that a btree_set<int32_t> may use much less memory per
+// stored value. For the random insertion benchmark in btree_bench.cc, a
+// btree_set<int32_t> with node-size of 256 uses 5.1 bytes per stored value.
+//
+// The packing of multiple values on to each node of a btree has another effect
+// besides better space utilization: better cache locality due to fewer cache
+// lines being accessed. Better cache locality translates into faster
+// operations.
+//
+// CAVEATS
+//
+// Insertions and deletions on a btree can cause splitting, merging or
+// rebalancing of btree nodes. And even without these operations, insertions
+// and deletions on a btree will move values around within a node. In both
+// cases, the result is that insertions and deletions can invalidate iterators
+// pointing to values other than the one being inserted/deleted. Therefore, this
+// container does not provide pointer stability. This is notably different from
+// STL set/map which takes care to not invalidate iterators on insert/erase
+// except, of course, for iterators pointing to the value being erased.  A
+// partial workaround when erasing is available: erase() returns an iterator
+// pointing to the item just after the one that was erased (or end() if none
+// exists).
+
+#ifndef ABSL_CONTAINER_INTERNAL_BTREE_H_
+#define ABSL_CONTAINER_INTERNAL_BTREE_H_
+
+#include <algorithm>
+#include <cassert>
+#include <cstddef>
+#include <cstdint>
+#include <cstring>
+#include <functional>
+#include <iterator>
+#include <limits>
+#include <new>
+#include <string>
+#include <type_traits>
+#include <utility>
+
+#include "absl/base/internal/raw_logging.h"
+#include "absl/base/macros.h"
+#include "absl/container/internal/common.h"
+#include "absl/container/internal/common_policy_traits.h"
+#include "absl/container/internal/compressed_tuple.h"
+#include "absl/container/internal/container_memory.h"
+#include "absl/container/internal/layout.h"
+#include "absl/memory/memory.h"
+#include "absl/meta/type_traits.h"
+#include "absl/strings/cord.h"
+#include "absl/strings/string_view.h"
+#include "absl/types/compare.h"
+#include "absl/utility/utility.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+
+#ifdef ABSL_BTREE_ENABLE_GENERATIONS
+#error ABSL_BTREE_ENABLE_GENERATIONS cannot be directly set
+#elif defined(ABSL_HAVE_ADDRESS_SANITIZER) || \
+    defined(ABSL_HAVE_MEMORY_SANITIZER)
+// When compiled in sanitizer mode, we add generation integers to the nodes and
+// iterators. When iterators are used, we validate that the container has not
+// been mutated since the iterator was constructed.
+#define ABSL_BTREE_ENABLE_GENERATIONS
+#endif
+
+template <typename Compare, typename T, typename U>
+using compare_result_t = absl::result_of_t<const Compare(const T &, const U &)>;
+
+// A helper class that indicates if the Compare parameter is a key-compare-to
+// comparator.
+template <typename Compare, typename T>
+using btree_is_key_compare_to =
+    std::is_convertible<compare_result_t<Compare, T, T>, absl::weak_ordering>;
+
+struct StringBtreeDefaultLess {
+  using is_transparent = void;
+
+  StringBtreeDefaultLess() = default;
+
+  // Compatibility constructor.
+  StringBtreeDefaultLess(std::less<std::string>) {}        // NOLINT
+  StringBtreeDefaultLess(std::less<absl::string_view>) {}  // NOLINT
+
+  // Allow converting to std::less for use in key_comp()/value_comp().
+  explicit operator std::less<std::string>() const { return {}; }
+  explicit operator std::less<absl::string_view>() const { return {}; }
+  explicit operator std::less<absl::Cord>() const { return {}; }
+
+  absl::weak_ordering operator()(absl::string_view lhs,
+                                 absl::string_view rhs) const {
+    return compare_internal::compare_result_as_ordering(lhs.compare(rhs));
+  }
+  StringBtreeDefaultLess(std::less<absl::Cord>) {}  // NOLINT
+  absl::weak_ordering operator()(const absl::Cord &lhs,
+                                 const absl::Cord &rhs) const {
+    return compare_internal::compare_result_as_ordering(lhs.Compare(rhs));
+  }
+  absl::weak_ordering operator()(const absl::Cord &lhs,
+                                 absl::string_view rhs) const {
+    return compare_internal::compare_result_as_ordering(lhs.Compare(rhs));
+  }
+  absl::weak_ordering operator()(absl::string_view lhs,
+                                 const absl::Cord &rhs) const {
+    return compare_internal::compare_result_as_ordering(-rhs.Compare(lhs));
+  }
+};
+
+struct StringBtreeDefaultGreater {
+  using is_transparent = void;
+
+  StringBtreeDefaultGreater() = default;
+
+  StringBtreeDefaultGreater(std::greater<std::string>) {}        // NOLINT
+  StringBtreeDefaultGreater(std::greater<absl::string_view>) {}  // NOLINT
+
+  // Allow converting to std::greater for use in key_comp()/value_comp().
+  explicit operator std::greater<std::string>() const { return {}; }
+  explicit operator std::greater<absl::string_view>() const { return {}; }
+  explicit operator std::greater<absl::Cord>() const { return {}; }
+
+  absl::weak_ordering operator()(absl::string_view lhs,
+                                 absl::string_view rhs) const {
+    return compare_internal::compare_result_as_ordering(rhs.compare(lhs));
+  }
+  StringBtreeDefaultGreater(std::greater<absl::Cord>) {}  // NOLINT
+  absl::weak_ordering operator()(const absl::Cord &lhs,
+                                 const absl::Cord &rhs) const {
+    return compare_internal::compare_result_as_ordering(rhs.Compare(lhs));
+  }
+  absl::weak_ordering operator()(const absl::Cord &lhs,
+                                 absl::string_view rhs) const {
+    return compare_internal::compare_result_as_ordering(-lhs.Compare(rhs));
+  }
+  absl::weak_ordering operator()(absl::string_view lhs,
+                                 const absl::Cord &rhs) const {
+    return compare_internal::compare_result_as_ordering(rhs.Compare(lhs));
+  }
+};
+
+// See below comments for checked_compare.
+template <typename Compare, bool is_class = std::is_class<Compare>::value>
+struct checked_compare_base : Compare {
+  using Compare::Compare;
+  explicit checked_compare_base(Compare c) : Compare(std::move(c)) {}
+  const Compare &comp() const { return *this; }
+};
+template <typename Compare>
+struct checked_compare_base<Compare, false> {
+  explicit checked_compare_base(Compare c) : compare(std::move(c)) {}
+  const Compare &comp() const { return compare; }
+  Compare compare;
+};
+
+// A mechanism for opting out of checked_compare for use only in btree_test.cc.
+struct BtreeTestOnlyCheckedCompareOptOutBase {};
+
+// A helper class to adapt the specified comparator for two use cases:
+// (1) When using common Abseil string types with common comparison functors,
+// convert a boolean comparison into a three-way comparison that returns an
+// `absl::weak_ordering`. This helper class is specialized for
+// less<std::string>, greater<std::string>, less<string_view>,
+// greater<string_view>, less<absl::Cord>, and greater<absl::Cord>.
+// (2) Adapt the comparator to diagnose cases of non-strict-weak-ordering (see
+// https://en.cppreference.com/w/cpp/named_req/Compare) in debug mode. Whenever
+// a comparison is made, we will make assertions to verify that the comparator
+// is valid.
+template <typename Compare, typename Key>
+struct key_compare_adapter {
+  // Inherit from checked_compare_base to support function pointers and also
+  // keep empty-base-optimization (EBO) support for classes.
+  // Note: we can't use CompressedTuple here because that would interfere
+  // with the EBO for `btree::rightmost_`. `btree::rightmost_` is itself a
+  // CompressedTuple and nested `CompressedTuple`s don't support EBO.
+  // TODO(b/214288561): use CompressedTuple instead once it supports EBO for
+  // nested `CompressedTuple`s.
+  struct checked_compare : checked_compare_base<Compare> {
+   private:
+    using Base = typename checked_compare::checked_compare_base;
+    using Base::comp;
+
+    // If possible, returns whether `t` is equivalent to itself. We can only do
+    // this for `Key`s because we can't be sure that it's safe to call
+    // `comp()(k, k)` otherwise. Even if SFINAE allows it, there could be a
+    // compilation failure inside the implementation of the comparison operator.
+    bool is_self_equivalent(const Key &k) const {
+      // Note: this works for both boolean and three-way comparators.
+      return comp()(k, k) == 0;
+    }
+    // If we can't compare `t` with itself, returns true unconditionally.
+    template <typename T>
+    bool is_self_equivalent(const T &) const {
+      return true;
+    }
+
+   public:
+    using Base::Base;
+    checked_compare(Compare comp) : Base(std::move(comp)) {}  // NOLINT
+
+    // Allow converting to Compare for use in key_comp()/value_comp().
+    explicit operator Compare() const { return comp(); }
+
+    template <typename T, typename U,
+              absl::enable_if_t<
+                  std::is_same<bool, compare_result_t<Compare, T, U>>::value,
+                  int> = 0>
+    bool operator()(const T &lhs, const U &rhs) const {
+      // NOTE: if any of these assertions fail, then the comparator does not
+      // establish a strict-weak-ordering (see
+      // https://en.cppreference.com/w/cpp/named_req/Compare).
+      assert(is_self_equivalent(lhs));
+      assert(is_self_equivalent(rhs));
+      const bool lhs_comp_rhs = comp()(lhs, rhs);
+      assert(!lhs_comp_rhs || !comp()(rhs, lhs));
+      return lhs_comp_rhs;
+    }
+
+    template <
+        typename T, typename U,
+        absl::enable_if_t<std::is_convertible<compare_result_t<Compare, T, U>,
+                                              absl::weak_ordering>::value,
+                          int> = 0>
+    absl::weak_ordering operator()(const T &lhs, const U &rhs) const {
+      // NOTE: if any of these assertions fail, then the comparator does not
+      // establish a strict-weak-ordering (see
+      // https://en.cppreference.com/w/cpp/named_req/Compare).
+      assert(is_self_equivalent(lhs));
+      assert(is_self_equivalent(rhs));
+      const absl::weak_ordering lhs_comp_rhs = comp()(lhs, rhs);
+#ifndef NDEBUG
+      const absl::weak_ordering rhs_comp_lhs = comp()(rhs, lhs);
+      if (lhs_comp_rhs > 0) {
+        assert(rhs_comp_lhs < 0 && "lhs_comp_rhs > 0 -> rhs_comp_lhs < 0");
+      } else if (lhs_comp_rhs == 0) {
+        assert(rhs_comp_lhs == 0 && "lhs_comp_rhs == 0 -> rhs_comp_lhs == 0");
+      } else {
+        assert(rhs_comp_lhs > 0 && "lhs_comp_rhs < 0 -> rhs_comp_lhs > 0");
+      }
+#endif
+      return lhs_comp_rhs;
+    }
+  };
+  using type = absl::conditional_t<
+      std::is_base_of<BtreeTestOnlyCheckedCompareOptOutBase, Compare>::value,
+      Compare, checked_compare>;
+};
+
+template <>
+struct key_compare_adapter<std::less<std::string>, std::string> {
+  using type = StringBtreeDefaultLess;
+};
+
+template <>
+struct key_compare_adapter<std::greater<std::string>, std::string> {
+  using type = StringBtreeDefaultGreater;
+};
+
+template <>
+struct key_compare_adapter<std::less<absl::string_view>, absl::string_view> {
+  using type = StringBtreeDefaultLess;
+};
+
+template <>
+struct key_compare_adapter<std::greater<absl::string_view>, absl::string_view> {
+  using type = StringBtreeDefaultGreater;
+};
+
+template <>
+struct key_compare_adapter<std::less<absl::Cord>, absl::Cord> {
+  using type = StringBtreeDefaultLess;
+};
+
+template <>
+struct key_compare_adapter<std::greater<absl::Cord>, absl::Cord> {
+  using type = StringBtreeDefaultGreater;
+};
+
+// Detects an 'absl_btree_prefer_linear_node_search' member. This is
+// a protocol used as an opt-in or opt-out of linear search.
+//
+//  For example, this would be useful for key types that wrap an integer
+//  and define their own cheap operator<(). For example:
+//
+//   class K {
+//    public:
+//     using absl_btree_prefer_linear_node_search = std::true_type;
+//     ...
+//    private:
+//     friend bool operator<(K a, K b) { return a.k_ < b.k_; }
+//     int k_;
+//   };
+//
+//   btree_map<K, V> m;  // Uses linear search
+//
+// If T has the preference tag, then it has a preference.
+// Btree will use the tag's truth value.
+template <typename T, typename = void>
+struct has_linear_node_search_preference : std::false_type {};
+template <typename T, typename = void>
+struct prefers_linear_node_search : std::false_type {};
+template <typename T>
+struct has_linear_node_search_preference<
+    T, absl::void_t<typename T::absl_btree_prefer_linear_node_search>>
+    : std::true_type {};
+template <typename T>
+struct prefers_linear_node_search<
+    T, absl::void_t<typename T::absl_btree_prefer_linear_node_search>>
+    : T::absl_btree_prefer_linear_node_search {};
+
+template <typename Compare, typename Key>
+constexpr bool compare_has_valid_result_type() {
+  using compare_result_type = compare_result_t<Compare, Key, Key>;
+  return std::is_same<compare_result_type, bool>::value ||
+         std::is_convertible<compare_result_type, absl::weak_ordering>::value;
+}
+
+template <typename original_key_compare, typename value_type>
+class map_value_compare {
+  template <typename Params>
+  friend class btree;
+
+  // Note: this `protected` is part of the API of std::map::value_compare. See
+  // https://en.cppreference.com/w/cpp/container/map/value_compare.
+ protected:
+  explicit map_value_compare(original_key_compare c) : comp(std::move(c)) {}
+
+  original_key_compare comp;  // NOLINT
+
+ public:
+  auto operator()(const value_type &lhs, const value_type &rhs) const
+      -> decltype(comp(lhs.first, rhs.first)) {
+    return comp(lhs.first, rhs.first);
+  }
+};
+
+template <typename Key, typename Compare, typename Alloc, int TargetNodeSize,
+          bool IsMulti, bool IsMap, typename SlotPolicy>
+struct common_params : common_policy_traits<SlotPolicy> {
+  using original_key_compare = Compare;
+
+  // If Compare is a common comparator for a string-like type, then we adapt it
+  // to use heterogeneous lookup and to be a key-compare-to comparator.
+  // We also adapt the comparator to diagnose invalid comparators in debug mode.
+  // We disable this when `Compare` is invalid in a way that will cause
+  // adaptation to fail (having invalid return type) so that we can give a
+  // better compilation failure in static_assert_validation. If we don't do
+  // this, then there will be cascading compilation failures that are confusing
+  // for users.
+  using key_compare =
+      absl::conditional_t<!compare_has_valid_result_type<Compare, Key>(),
+                          Compare,
+                          typename key_compare_adapter<Compare, Key>::type>;
+
+  static constexpr bool kIsKeyCompareStringAdapted =
+      std::is_same<key_compare, StringBtreeDefaultLess>::value ||
+      std::is_same<key_compare, StringBtreeDefaultGreater>::value;
+  static constexpr bool kIsKeyCompareTransparent =
+      IsTransparent<original_key_compare>::value || kIsKeyCompareStringAdapted;
+  static constexpr bool kEnableGenerations =
+#ifdef ABSL_BTREE_ENABLE_GENERATIONS
+      true;
+#else
+      false;
+#endif
+
+  // A type which indicates if we have a key-compare-to functor or a plain old
+  // key-compare functor.
+  using is_key_compare_to = btree_is_key_compare_to<key_compare, Key>;
+
+  using allocator_type = Alloc;
+  using key_type = Key;
+  using size_type = size_t;
+  using difference_type = ptrdiff_t;
+
+  using slot_policy = SlotPolicy;
+  using slot_type = typename slot_policy::slot_type;
+  using value_type = typename slot_policy::value_type;
+  using init_type = typename slot_policy::mutable_value_type;
+  using pointer = value_type *;
+  using const_pointer = const value_type *;
+  using reference = value_type &;
+  using const_reference = const value_type &;
+
+  using value_compare =
+      absl::conditional_t<IsMap,
+                          map_value_compare<original_key_compare, value_type>,
+                          original_key_compare>;
+  using is_map_container = std::integral_constant<bool, IsMap>;
+
+  // For the given lookup key type, returns whether we can have multiple
+  // equivalent keys in the btree. If this is a multi-container, then we can.
+  // Otherwise, we can have multiple equivalent keys only if all of the
+  // following conditions are met:
+  // - The comparator is transparent.
+  // - The lookup key type is not the same as key_type.
+  // - The comparator is not a StringBtreeDefault{Less,Greater} comparator
+  //   that we know has the same equivalence classes for all lookup types.
+  template <typename LookupKey>
+  constexpr static bool can_have_multiple_equivalent_keys() {
+    return IsMulti || (IsTransparent<key_compare>::value &&
+                       !std::is_same<LookupKey, Key>::value &&
+                       !kIsKeyCompareStringAdapted);
+  }
+
+  enum {
+    kTargetNodeSize = TargetNodeSize,
+
+    // Upper bound for the available space for slots. This is largest for leaf
+    // nodes, which have overhead of at least a pointer + 4 bytes (for storing
+    // 3 field_types and an enum).
+    kNodeSlotSpace = TargetNodeSize - /*minimum overhead=*/(sizeof(void *) + 4),
+  };
+
+  // This is an integral type large enough to hold as many slots as will fit a
+  // node of TargetNodeSize bytes.
+  using node_count_type =
+      absl::conditional_t<(kNodeSlotSpace / sizeof(slot_type) >
+                           (std::numeric_limits<uint8_t>::max)()),
+                          uint16_t, uint8_t>;  // NOLINT
+};
+
+// An adapter class that converts a lower-bound compare into an upper-bound
+// compare. Note: there is no need to make a version of this adapter specialized
+// for key-compare-to functors because the upper-bound (the first value greater
+// than the input) is never an exact match.
+template <typename Compare>
+struct upper_bound_adapter {
+  explicit upper_bound_adapter(const Compare &c) : comp(c) {}
+  template <typename K1, typename K2>
+  bool operator()(const K1 &a, const K2 &b) const {
+    // Returns true when a is not greater than b.
+    return !compare_internal::compare_result_as_less_than(comp(b, a));
+  }
+
+ private:
+  Compare comp;
+};
+
+enum class MatchKind : uint8_t { kEq, kNe };
+
+template <typename V, bool IsCompareTo>
+struct SearchResult {
+  V value;
+  MatchKind match;
+
+  static constexpr bool HasMatch() { return true; }
+  bool IsEq() const { return match == MatchKind::kEq; }
+};
+
+// When we don't use CompareTo, `match` is not present.
+// This ensures that callers can't use it accidentally when it provides no
+// useful information.
+template <typename V>
+struct SearchResult<V, false> {
+  SearchResult() {}
+  explicit SearchResult(V v) : value(v) {}
+  SearchResult(V v, MatchKind /*match*/) : value(v) {}
+
+  V value;
+
+  static constexpr bool HasMatch() { return false; }
+  static constexpr bool IsEq() { return false; }
+};
+
+// A node in the btree holding. The same node type is used for both internal
+// and leaf nodes in the btree, though the nodes are allocated in such a way
+// that the children array is only valid in internal nodes.
+template <typename Params>
+class btree_node {
+  using is_key_compare_to = typename Params::is_key_compare_to;
+  using field_type = typename Params::node_count_type;
+  using allocator_type = typename Params::allocator_type;
+  using slot_type = typename Params::slot_type;
+  using original_key_compare = typename Params::original_key_compare;
+
+ public:
+  using params_type = Params;
+  using key_type = typename Params::key_type;
+  using value_type = typename Params::value_type;
+  using pointer = typename Params::pointer;
+  using const_pointer = typename Params::const_pointer;
+  using reference = typename Params::reference;
+  using const_reference = typename Params::const_reference;
+  using key_compare = typename Params::key_compare;
+  using size_type = typename Params::size_type;
+  using difference_type = typename Params::difference_type;
+
+  // Btree decides whether to use linear node search as follows:
+  //   - If the comparator expresses a preference, use that.
+  //   - If the key expresses a preference, use that.
+  //   - If the key is arithmetic and the comparator is std::less or
+  //     std::greater, choose linear.
+  //   - Otherwise, choose binary.
+  // TODO(ezb): Might make sense to add condition(s) based on node-size.
+  using use_linear_search = std::integral_constant<
+      bool, has_linear_node_search_preference<original_key_compare>::value
+                ? prefers_linear_node_search<original_key_compare>::value
+            : has_linear_node_search_preference<key_type>::value
+                ? prefers_linear_node_search<key_type>::value
+                : std::is_arithmetic<key_type>::value &&
+                      (std::is_same<std::less<key_type>,
+                                    original_key_compare>::value ||
+                       std::is_same<std::greater<key_type>,
+                                    original_key_compare>::value)>;
+
+  // This class is organized by absl::container_internal::Layout as if it had
+  // the following structure:
+  //   // A pointer to the node's parent.
+  //   btree_node *parent;
+  //
+  //   // When ABSL_BTREE_ENABLE_GENERATIONS is defined, we also have a
+  //   // generation integer in order to check that when iterators are
+  //   // used, they haven't been invalidated already. Only the generation on
+  //   // the root is used, but we have one on each node because whether a node
+  //   // is root or not can change.
+  //   uint32_t generation;
+  //
+  //   // The position of the node in the node's parent.
+  //   field_type position;
+  //   // The index of the first populated value in `values`.
+  //   // TODO(ezb): right now, `start` is always 0. Update insertion/merge
+  //   // logic to allow for floating storage within nodes.
+  //   field_type start;
+  //   // The index after the last populated value in `values`. Currently, this
+  //   // is the same as the count of values.
+  //   field_type finish;
+  //   // The maximum number of values the node can hold. This is an integer in
+  //   // [1, kNodeSlots] for root leaf nodes, kNodeSlots for non-root leaf
+  //   // nodes, and kInternalNodeMaxCount (as a sentinel value) for internal
+  //   // nodes (even though there are still kNodeSlots values in the node).
+  //   // TODO(ezb): make max_count use only 4 bits and record log2(capacity)
+  //   // to free extra bits for is_root, etc.
+  //   field_type max_count;
+  //
+  //   // The array of values. The capacity is `max_count` for leaf nodes and
+  //   // kNodeSlots for internal nodes. Only the values in
+  //   // [start, finish) have been initialized and are valid.
+  //   slot_type values[max_count];
+  //
+  //   // The array of child pointers. The keys in children[i] are all less
+  //   // than key(i). The keys in children[i + 1] are all greater than key(i).
+  //   // There are 0 children for leaf nodes and kNodeSlots + 1 children for
+  //   // internal nodes.
+  //   btree_node *children[kNodeSlots + 1];
+  //
+  // This class is only constructed by EmptyNodeType. Normally, pointers to the
+  // layout above are allocated, cast to btree_node*, and de-allocated within
+  // the btree implementation.
+  ~btree_node() = default;
+  btree_node(btree_node const &) = delete;
+  btree_node &operator=(btree_node const &) = delete;
+
+  // Public for EmptyNodeType.
+  constexpr static size_type Alignment() {
+    static_assert(LeafLayout(1).Alignment() == InternalLayout().Alignment(),
+                  "Alignment of all nodes must be equal.");
+    return InternalLayout().Alignment();
+  }
+
+ protected:
+  btree_node() = default;
+
+ private:
+  using layout_type =
+      absl::container_internal::Layout<btree_node *, uint32_t, field_type,
+                                       slot_type, btree_node *>;
+  constexpr static size_type SizeWithNSlots(size_type n) {
+    return layout_type(
+               /*parent*/ 1,
+               /*generation*/ params_type::kEnableGenerations ? 1 : 0,
+               /*position, start, finish, max_count*/ 4,
+               /*slots*/ n,
+               /*children*/ 0)
+        .AllocSize();
+  }
+  // A lower bound for the overhead of fields other than slots in a leaf node.
+  constexpr static size_type MinimumOverhead() {
+    return SizeWithNSlots(1) - sizeof(slot_type);
+  }
+
+  // Compute how many values we can fit onto a leaf node taking into account
+  // padding.
+  constexpr static size_type NodeTargetSlots(const size_type begin,
+                                             const size_type end) {
+    return begin == end ? begin
+           : SizeWithNSlots((begin + end) / 2 + 1) >
+                   params_type::kTargetNodeSize
+               ? NodeTargetSlots(begin, (begin + end) / 2)
+               : NodeTargetSlots((begin + end) / 2 + 1, end);
+  }
+
+  constexpr static size_type kTargetNodeSize = params_type::kTargetNodeSize;
+  constexpr static size_type kNodeTargetSlots =
+      NodeTargetSlots(0, kTargetNodeSize);
+
+  // We need a minimum of 3 slots per internal node in order to perform
+  // splitting (1 value for the two nodes involved in the split and 1 value
+  // propagated to the parent as the delimiter for the split). For performance
+  // reasons, we don't allow 3 slots-per-node due to bad worst case occupancy of
+  // 1/3 (for a node, not a b-tree).
+  constexpr static size_type kMinNodeSlots = 4;
+
+  constexpr static size_type kNodeSlots =
+      kNodeTargetSlots >= kMinNodeSlots ? kNodeTargetSlots : kMinNodeSlots;
+
+  // The node is internal (i.e. is not a leaf node) if and only if `max_count`
+  // has this value.
+  constexpr static field_type kInternalNodeMaxCount = 0;
+
+  // Leaves can have less than kNodeSlots values.
+  constexpr static layout_type LeafLayout(
+      const size_type slot_count = kNodeSlots) {
+    return layout_type(
+        /*parent*/ 1,
+        /*generation*/ params_type::kEnableGenerations ? 1 : 0,
+        /*position, start, finish, max_count*/ 4,
+        /*slots*/ slot_count,
+        /*children*/ 0);
+  }
+  constexpr static layout_type InternalLayout() {
+    return layout_type(
+        /*parent*/ 1,
+        /*generation*/ params_type::kEnableGenerations ? 1 : 0,
+        /*position, start, finish, max_count*/ 4,
+        /*slots*/ kNodeSlots,
+        /*children*/ kNodeSlots + 1);
+  }
+  constexpr static size_type LeafSize(const size_type slot_count = kNodeSlots) {
+    return LeafLayout(slot_count).AllocSize();
+  }
+  constexpr static size_type InternalSize() {
+    return InternalLayout().AllocSize();
+  }
+
+  // N is the index of the type in the Layout definition.
+  // ElementType<N> is the Nth type in the Layout definition.
+  template <size_type N>
+  inline typename layout_type::template ElementType<N> *GetField() {
+    // We assert that we don't read from values that aren't there.
+    assert(N < 4 || is_internal());
+    return InternalLayout().template Pointer<N>(reinterpret_cast<char *>(this));
+  }
+  template <size_type N>
+  inline const typename layout_type::template ElementType<N> *GetField() const {
+    assert(N < 4 || is_internal());
+    return InternalLayout().template Pointer<N>(
+        reinterpret_cast<const char *>(this));
+  }
+  void set_parent(btree_node *p) { *GetField<0>() = p; }
+  field_type &mutable_finish() { return GetField<2>()[2]; }
+  slot_type *slot(size_type i) { return &GetField<3>()[i]; }
+  slot_type *start_slot() { return slot(start()); }
+  slot_type *finish_slot() { return slot(finish()); }
+  const slot_type *slot(size_type i) const { return &GetField<3>()[i]; }
+  void set_position(field_type v) { GetField<2>()[0] = v; }
+  void set_start(field_type v) { GetField<2>()[1] = v; }
+  void set_finish(field_type v) { GetField<2>()[2] = v; }
+  // This method is only called by the node init methods.
+  void set_max_count(field_type v) { GetField<2>()[3] = v; }
+
+ public:
+  // Whether this is a leaf node or not. This value doesn't change after the
+  // node is created.
+  bool is_leaf() const { return GetField<2>()[3] != kInternalNodeMaxCount; }
+  // Whether this is an internal node or not. This value doesn't change after
+  // the node is created.
+  bool is_internal() const { return !is_leaf(); }
+
+  // Getter for the position of this node in its parent.
+  field_type position() const { return GetField<2>()[0]; }
+
+  // Getter for the offset of the first value in the `values` array.
+  field_type start() const {
+    // TODO(ezb): when floating storage is implemented, return GetField<2>()[1];
+    assert(GetField<2>()[1] == 0);
+    return 0;
+  }
+
+  // Getter for the offset after the last value in the `values` array.
+  field_type finish() const { return GetField<2>()[2]; }
+
+  // Getters for the number of values stored in this node.
+  field_type count() const {
+    assert(finish() >= start());
+    return finish() - start();
+  }
+  field_type max_count() const {
+    // Internal nodes have max_count==kInternalNodeMaxCount.
+    // Leaf nodes have max_count in [1, kNodeSlots].
+    const field_type max_count = GetField<2>()[3];
+    return max_count == field_type{kInternalNodeMaxCount}
+               ? field_type{kNodeSlots}
+               : max_count;
+  }
+
+  // Getter for the parent of this node.
+  btree_node *parent() const { return *GetField<0>(); }
+  // Getter for whether the node is the root of the tree. The parent of the
+  // root of the tree is the leftmost node in the tree which is guaranteed to
+  // be a leaf.
+  bool is_root() const { return parent()->is_leaf(); }
+  void make_root() {
+    assert(parent()->is_root());
+    set_generation(parent()->generation());
+    set_parent(parent()->parent());
+  }
+
+  // Gets the root node's generation integer, which is the one used by the tree.
+  uint32_t *get_root_generation() const {
+    assert(params_type::kEnableGenerations);
+    const btree_node *curr = this;
+    for (; !curr->is_root(); curr = curr->parent()) continue;
+    return const_cast<uint32_t *>(&curr->GetField<1>()[0]);
+  }
+
+  // Returns the generation for iterator validation.
+  uint32_t generation() const {
+    return params_type::kEnableGenerations ? *get_root_generation() : 0;
+  }
+  // Updates generation. Should only be called on a root node or during node
+  // initialization.
+  void set_generation(uint32_t generation) {
+    if (params_type::kEnableGenerations) GetField<1>()[0] = generation;
+  }
+  // Updates the generation. We do this whenever the node is mutated.
+  void next_generation() {
+    if (params_type::kEnableGenerations) ++*get_root_generation();
+  }
+
+  // Getters for the key/value at position i in the node.
+  const key_type &key(size_type i) const { return params_type::key(slot(i)); }
+  reference value(size_type i) { return params_type::element(slot(i)); }
+  const_reference value(size_type i) const {
+    return params_type::element(slot(i));
+  }
+
+  // Getters/setter for the child at position i in the node.
+  btree_node *child(field_type i) const { return GetField<4>()[i]; }
+  btree_node *start_child() const { return child(start()); }
+  btree_node *&mutable_child(field_type i) { return GetField<4>()[i]; }
+  void clear_child(field_type i) {
+    absl::container_internal::SanitizerPoisonObject(&mutable_child(i));
+  }
+  void set_child(field_type i, btree_node *c) {
+    absl::container_internal::SanitizerUnpoisonObject(&mutable_child(i));
+    mutable_child(i) = c;
+    c->set_position(i);
+  }
+  void init_child(field_type i, btree_node *c) {
+    set_child(i, c);
+    c->set_parent(this);
+  }
+
+  // Returns the position of the first value whose key is not less than k.
+  template <typename K>
+  SearchResult<size_type, is_key_compare_to::value> lower_bound(
+      const K &k, const key_compare &comp) const {
+    return use_linear_search::value ? linear_search(k, comp)
+                                    : binary_search(k, comp);
+  }
+  // Returns the position of the first value whose key is greater than k.
+  template <typename K>
+  size_type upper_bound(const K &k, const key_compare &comp) const {
+    auto upper_compare = upper_bound_adapter<key_compare>(comp);
+    return use_linear_search::value ? linear_search(k, upper_compare).value
+                                    : binary_search(k, upper_compare).value;
+  }
+
+  template <typename K, typename Compare>
+  SearchResult<size_type, btree_is_key_compare_to<Compare, key_type>::value>
+  linear_search(const K &k, const Compare &comp) const {
+    return linear_search_impl(k, start(), finish(), comp,
+                              btree_is_key_compare_to<Compare, key_type>());
+  }
+
+  template <typename K, typename Compare>
+  SearchResult<size_type, btree_is_key_compare_to<Compare, key_type>::value>
+  binary_search(const K &k, const Compare &comp) const {
+    return binary_search_impl(k, start(), finish(), comp,
+                              btree_is_key_compare_to<Compare, key_type>());
+  }
+
+  // Returns the position of the first value whose key is not less than k using
+  // linear search performed using plain compare.
+  template <typename K, typename Compare>
+  SearchResult<size_type, false> linear_search_impl(
+      const K &k, size_type s, const size_type e, const Compare &comp,
+      std::false_type /* IsCompareTo */) const {
+    while (s < e) {
+      if (!comp(key(s), k)) {
+        break;
+      }
+      ++s;
+    }
+    return SearchResult<size_type, false>{s};
+  }
+
+  // Returns the position of the first value whose key is not less than k using
+  // linear search performed using compare-to.
+  template <typename K, typename Compare>
+  SearchResult<size_type, true> linear_search_impl(
+      const K &k, size_type s, const size_type e, const Compare &comp,
+      std::true_type /* IsCompareTo */) const {
+    while (s < e) {
+      const absl::weak_ordering c = comp(key(s), k);
+      if (c == 0) {
+        return {s, MatchKind::kEq};
+      } else if (c > 0) {
+        break;
+      }
+      ++s;
+    }
+    return {s, MatchKind::kNe};
+  }
+
+  // Returns the position of the first value whose key is not less than k using
+  // binary search performed using plain compare.
+  template <typename K, typename Compare>
+  SearchResult<size_type, false> binary_search_impl(
+      const K &k, size_type s, size_type e, const Compare &comp,
+      std::false_type /* IsCompareTo */) const {
+    while (s != e) {
+      const size_type mid = (s + e) >> 1;
+      if (comp(key(mid), k)) {
+        s = mid + 1;
+      } else {
+        e = mid;
+      }
+    }
+    return SearchResult<size_type, false>{s};
+  }
+
+  // Returns the position of the first value whose key is not less than k using
+  // binary search performed using compare-to.
+  template <typename K, typename CompareTo>
+  SearchResult<size_type, true> binary_search_impl(
+      const K &k, size_type s, size_type e, const CompareTo &comp,
+      std::true_type /* IsCompareTo */) const {
+    if (params_type::template can_have_multiple_equivalent_keys<K>()) {
+      MatchKind exact_match = MatchKind::kNe;
+      while (s != e) {
+        const size_type mid = (s + e) >> 1;
+        const absl::weak_ordering c = comp(key(mid), k);
+        if (c < 0) {
+          s = mid + 1;
+        } else {
+          e = mid;
+          if (c == 0) {
+            // Need to return the first value whose key is not less than k,
+            // which requires continuing the binary search if there could be
+            // multiple equivalent keys.
+            exact_match = MatchKind::kEq;
+          }
+        }
+      }
+      return {s, exact_match};
+    } else {  // Can't have multiple equivalent keys.
+      while (s != e) {
+        const size_type mid = (s + e) >> 1;
+        const absl::weak_ordering c = comp(key(mid), k);
+        if (c < 0) {
+          s = mid + 1;
+        } else if (c > 0) {
+          e = mid;
+        } else {
+          return {mid, MatchKind::kEq};
+        }
+      }
+      return {s, MatchKind::kNe};
+    }
+  }
+
+  // Emplaces a value at position i, shifting all existing values and
+  // children at positions >= i to the right by 1.
+  template <typename... Args>
+  void emplace_value(field_type i, allocator_type *alloc, Args &&...args);
+
+  // Removes the values at positions [i, i + to_erase), shifting all existing
+  // values and children after that range to the left by to_erase. Clears all
+  // children between [i, i + to_erase).
+  void remove_values(field_type i, field_type to_erase, allocator_type *alloc);
+
+  // Rebalances a node with its right sibling.
+  void rebalance_right_to_left(field_type to_move, btree_node *right,
+                               allocator_type *alloc);
+  void rebalance_left_to_right(field_type to_move, btree_node *right,
+                               allocator_type *alloc);
+
+  // Splits a node, moving a portion of the node's values to its right sibling.
+  void split(int insert_position, btree_node *dest, allocator_type *alloc);
+
+  // Merges a node with its right sibling, moving all of the values and the
+  // delimiting key in the parent node onto itself, and deleting the src node.
+  void merge(btree_node *src, allocator_type *alloc);
+
+  // Node allocation/deletion routines.
+  void init_leaf(field_type max_count, btree_node *parent) {
+    set_generation(0);
+    set_parent(parent);
+    set_position(0);
+    set_start(0);
+    set_finish(0);
+    set_max_count(max_count);
+    absl::container_internal::SanitizerPoisonMemoryRegion(
+        start_slot(), max_count * sizeof(slot_type));
+  }
+  void init_internal(btree_node *parent) {
+    init_leaf(kNodeSlots, parent);
+    // Set `max_count` to a sentinel value to indicate that this node is
+    // internal.
+    set_max_count(kInternalNodeMaxCount);
+    absl::container_internal::SanitizerPoisonMemoryRegion(
+        &mutable_child(start()), (kNodeSlots + 1) * sizeof(btree_node *));
+  }
+
+  static void deallocate(const size_type size, btree_node *node,
+                         allocator_type *alloc) {
+    absl::container_internal::SanitizerUnpoisonMemoryRegion(node, size);
+    absl::container_internal::Deallocate<Alignment()>(alloc, node, size);
+  }
+
+  // Deletes a node and all of its children.
+  static void clear_and_delete(btree_node *node, allocator_type *alloc);
+
+ private:
+  template <typename... Args>
+  void value_init(const field_type i, allocator_type *alloc, Args &&...args) {
+    next_generation();
+    absl::container_internal::SanitizerUnpoisonObject(slot(i));
+    params_type::construct(alloc, slot(i), std::forward<Args>(args)...);
+  }
+  void value_destroy(const field_type i, allocator_type *alloc) {
+    next_generation();
+    params_type::destroy(alloc, slot(i));
+    absl::container_internal::SanitizerPoisonObject(slot(i));
+  }
+  void value_destroy_n(const field_type i, const field_type n,
+                       allocator_type *alloc) {
+    next_generation();
+    for (slot_type *s = slot(i), *end = slot(i + n); s != end; ++s) {
+      params_type::destroy(alloc, s);
+      absl::container_internal::SanitizerPoisonObject(s);
+    }
+  }
+
+  static void transfer(slot_type *dest, slot_type *src, allocator_type *alloc) {
+    absl::container_internal::SanitizerUnpoisonObject(dest);
+    params_type::transfer(alloc, dest, src);
+    absl::container_internal::SanitizerPoisonObject(src);
+  }
+
+  // Transfers value from slot `src_i` in `src_node` to slot `dest_i` in `this`.
+  void transfer(const size_type dest_i, const size_type src_i,
+                btree_node *src_node, allocator_type *alloc) {
+    next_generation();
+    transfer(slot(dest_i), src_node->slot(src_i), alloc);
+  }
+
+  // Transfers `n` values starting at value `src_i` in `src_node` into the
+  // values starting at value `dest_i` in `this`.
+  void transfer_n(const size_type n, const size_type dest_i,
+                  const size_type src_i, btree_node *src_node,
+                  allocator_type *alloc) {
+    next_generation();
+    for (slot_type *src = src_node->slot(src_i), *end = src + n,
+                   *dest = slot(dest_i);
+         src != end; ++src, ++dest) {
+      transfer(dest, src, alloc);
+    }
+  }
+
+  // Same as above, except that we start at the end and work our way to the
+  // beginning.
+  void transfer_n_backward(const size_type n, const size_type dest_i,
+                           const size_type src_i, btree_node *src_node,
+                           allocator_type *alloc) {
+    next_generation();
+    for (slot_type *src = src_node->slot(src_i + n), *end = src - n,
+                   *dest = slot(dest_i + n);
+         src != end; --src, --dest) {
+      // If we modified the loop index calculations above to avoid the -1s here,
+      // it would result in UB in the computation of `end` (and possibly `src`
+      // as well, if n == 0), since slot() is effectively an array index and it
+      // is UB to compute the address of any out-of-bounds array element except
+      // for one-past-the-end.
+      transfer(dest - 1, src - 1, alloc);
+    }
+  }
+
+  template <typename P>
+  friend class btree;
+  template <typename N, typename R, typename P>
+  friend class btree_iterator;
+  friend class BtreeNodePeer;
+  friend struct btree_access;
+};
+
+template <typename Node>
+bool AreNodesFromSameContainer(const Node *node_a, const Node *node_b) {
+  // If either node is null, then give up on checking whether they're from the
+  // same container. (If exactly one is null, then we'll trigger the
+  // default-constructed assert in Equals.)
+  if (node_a == nullptr || node_b == nullptr) return true;
+  while (!node_a->is_root()) node_a = node_a->parent();
+  while (!node_b->is_root()) node_b = node_b->parent();
+  return node_a == node_b;
+}
+
+class btree_iterator_generation_info_enabled {
+ public:
+  explicit btree_iterator_generation_info_enabled(uint32_t g)
+      : generation_(g) {}
+
+  // Updates the generation. For use internally right before we return an
+  // iterator to the user.
+  template <typename Node>
+  void update_generation(const Node *node) {
+    if (node != nullptr) generation_ = node->generation();
+  }
+  uint32_t generation() const { return generation_; }
+
+  template <typename Node>
+  void assert_valid_generation(const Node *node) const {
+    if (node != nullptr && node->generation() != generation_) {
+      ABSL_INTERNAL_LOG(
+          FATAL,
+          "Attempting to use an invalidated iterator. The corresponding b-tree "
+          "container has been mutated since this iterator was constructed.");
+    }
+  }
+
+ private:
+  // Used to check that the iterator hasn't been invalidated.
+  uint32_t generation_;
+};
+
+class btree_iterator_generation_info_disabled {
+ public:
+  explicit btree_iterator_generation_info_disabled(uint32_t) {}
+  void update_generation(const void *) {}
+  uint32_t generation() const { return 0; }
+  void assert_valid_generation(const void *) const {}
+};
+
+#ifdef ABSL_BTREE_ENABLE_GENERATIONS
+using btree_iterator_generation_info = btree_iterator_generation_info_enabled;
+#else
+using btree_iterator_generation_info = btree_iterator_generation_info_disabled;
+#endif
+
+template <typename Node, typename Reference, typename Pointer>
+class btree_iterator : private btree_iterator_generation_info {
+  using field_type = typename Node::field_type;
+  using key_type = typename Node::key_type;
+  using size_type = typename Node::size_type;
+  using params_type = typename Node::params_type;
+  using is_map_container = typename params_type::is_map_container;
+
+  using node_type = Node;
+  using normal_node = typename std::remove_const<Node>::type;
+  using const_node = const Node;
+  using normal_pointer = typename params_type::pointer;
+  using normal_reference = typename params_type::reference;
+  using const_pointer = typename params_type::const_pointer;
+  using const_reference = typename params_type::const_reference;
+  using slot_type = typename params_type::slot_type;
+
+  using iterator =
+     btree_iterator<normal_node, normal_reference, normal_pointer>;
+  using const_iterator =
+      btree_iterator<const_node, const_reference, const_pointer>;
+
+ public:
+  // These aliases are public for std::iterator_traits.
+  using difference_type = typename Node::difference_type;
+  using value_type = typename params_type::value_type;
+  using pointer = Pointer;
+  using reference = Reference;
+  using iterator_category = std::bidirectional_iterator_tag;
+
+  btree_iterator() : btree_iterator(nullptr, -1) {}
+  explicit btree_iterator(Node *n) : btree_iterator(n, n->start()) {}
+  btree_iterator(Node *n, int p)
+      : btree_iterator_generation_info(n != nullptr ? n->generation()
+                                                    : ~uint32_t{}),
+        node_(n),
+        position_(p) {}
+
+  // NOTE: this SFINAE allows for implicit conversions from iterator to
+  // const_iterator, but it specifically avoids hiding the copy constructor so
+  // that the trivial one will be used when possible.
+  template <typename N, typename R, typename P,
+            absl::enable_if_t<
+                std::is_same<btree_iterator<N, R, P>, iterator>::value &&
+                    std::is_same<btree_iterator, const_iterator>::value,
+                int> = 0>
+  btree_iterator(const btree_iterator<N, R, P> other)  // NOLINT
+      : btree_iterator_generation_info(other),
+        node_(other.node_),
+        position_(other.position_) {}
+
+  bool operator==(const iterator &other) const {
+    return Equals(other);
+  }
+  bool operator==(const const_iterator &other) const {
+    return Equals(other);
+  }
+  bool operator!=(const iterator &other) const {
+    return !Equals(other);
+  }
+  bool operator!=(const const_iterator &other) const {
+    return !Equals(other);
+  }
+
+  // Returns n such that n calls to ++other yields *this.
+  // Precondition: n exists.
+  difference_type operator-(const_iterator other) const {
+    if (node_ == other.node_) {
+      if (node_->is_leaf()) return position_ - other.position_;
+      if (position_ == other.position_) return 0;
+    }
+    return distance_slow(other);
+  }
+
+  // Accessors for the key/value the iterator is pointing at.
+  reference operator*() const {
+    ABSL_HARDENING_ASSERT(node_ != nullptr);
+    assert_valid_generation(node_);
+    ABSL_HARDENING_ASSERT(position_ >= node_->start());
+    if (position_ >= node_->finish()) {
+      ABSL_HARDENING_ASSERT(!IsEndIterator() && "Dereferencing end() iterator");
+      ABSL_HARDENING_ASSERT(position_ < node_->finish());
+    }
+    return node_->value(static_cast<field_type>(position_));
+  }
+  pointer operator->() const { return &operator*(); }
+
+  btree_iterator &operator++() {
+    increment();
+    return *this;
+  }
+  btree_iterator &operator--() {
+    decrement();
+    return *this;
+  }
+  btree_iterator operator++(int) {
+    btree_iterator tmp = *this;
+    ++*this;
+    return tmp;
+  }
+  btree_iterator operator--(int) {
+    btree_iterator tmp = *this;
+    --*this;
+    return tmp;
+  }
+
+ private:
+  friend iterator;
+  friend const_iterator;
+  template <typename Params>
+  friend class btree;
+  template <typename Tree>
+  friend class btree_container;
+  template <typename Tree>
+  friend class btree_set_container;
+  template <typename Tree>
+  friend class btree_map_container;
+  template <typename Tree>
+  friend class btree_multiset_container;
+  template <typename TreeType, typename CheckerType>
+  friend class base_checker;
+  friend struct btree_access;
+
+  // This SFINAE allows explicit conversions from const_iterator to
+  // iterator, but also avoids hiding the copy constructor.
+  // NOTE: the const_cast is safe because this constructor is only called by
+  // non-const methods and the container owns the nodes.
+  template <typename N, typename R, typename P,
+            absl::enable_if_t<
+                std::is_same<btree_iterator<N, R, P>, const_iterator>::value &&
+                    std::is_same<btree_iterator, iterator>::value,
+                int> = 0>
+  explicit btree_iterator(const btree_iterator<N, R, P> other)
+      : btree_iterator_generation_info(other.generation()),
+        node_(const_cast<node_type *>(other.node_)),
+        position_(other.position_) {}
+
+  bool Equals(const const_iterator other) const {
+    ABSL_HARDENING_ASSERT(((node_ == nullptr && other.node_ == nullptr) ||
+                           (node_ != nullptr && other.node_ != nullptr)) &&
+                          "Comparing default-constructed iterator with "
+                          "non-default-constructed iterator.");
+    // Note: we use assert instead of ABSL_HARDENING_ASSERT here because this
+    // changes the complexity of Equals from O(1) to O(log(N) + log(M)) where
+    // N/M are sizes of the containers containing node_/other.node_.
+    assert(AreNodesFromSameContainer(node_, other.node_) &&
+           "Comparing iterators from different containers.");
+    assert_valid_generation(node_);
+    other.assert_valid_generation(other.node_);
+    return node_ == other.node_ && position_ == other.position_;
+  }
+
+  bool IsEndIterator() const {
+    if (position_ != node_->finish()) return false;
+    node_type *node = node_;
+    while (!node->is_root()) {
+      if (node->position() != node->parent()->finish()) return false;
+      node = node->parent();
+    }
+    return true;
+  }
+
+  // Returns n such that n calls to ++other yields *this.
+  // Precondition: n exists && (this->node_ != other.node_ ||
+  // !this->node_->is_leaf() || this->position_ != other.position_).
+  difference_type distance_slow(const_iterator other) const;
+
+  // Increment/decrement the iterator.
+  void increment() {
+    assert_valid_generation(node_);
+    if (node_->is_leaf() && ++position_ < node_->finish()) {
+      return;
+    }
+    increment_slow();
+  }
+  void increment_slow();
+
+  void decrement() {
+    assert_valid_generation(node_);
+    if (node_->is_leaf() && --position_ >= node_->start()) {
+      return;
+    }
+    decrement_slow();
+  }
+  void decrement_slow();
+
+  const key_type &key() const {
+    return node_->key(static_cast<size_type>(position_));
+  }
+  decltype(std::declval<Node *>()->slot(0)) slot() {
+    return node_->slot(static_cast<size_type>(position_));
+  }
+
+  void update_generation() {
+    btree_iterator_generation_info::update_generation(node_);
+  }
+
+  // The node in the tree the iterator is pointing at.
+  Node *node_;
+  // The position within the node of the tree the iterator is pointing at.
+  // NOTE: this is an int rather than a field_type because iterators can point
+  // to invalid positions (such as -1) in certain circumstances.
+  int position_;
+};
+
+template <typename Params>
+class btree {
+  using node_type = btree_node<Params>;
+  using is_key_compare_to = typename Params::is_key_compare_to;
+  using field_type = typename node_type::field_type;
+
+  // We use a static empty node for the root/leftmost/rightmost of empty btrees
+  // in order to avoid branching in begin()/end().
+  struct alignas(node_type::Alignment()) EmptyNodeType : node_type {
+    using field_type = typename node_type::field_type;
+    node_type *parent;
+#ifdef ABSL_BTREE_ENABLE_GENERATIONS
+    uint32_t generation = 0;
+#endif
+    field_type position = 0;
+    field_type start = 0;
+    field_type finish = 0;
+    // max_count must be != kInternalNodeMaxCount (so that this node is regarded
+    // as a leaf node). max_count() is never called when the tree is empty.
+    field_type max_count = node_type::kInternalNodeMaxCount + 1;
+
+#ifdef _MSC_VER
+    // MSVC has constexpr code generations bugs here.
+    EmptyNodeType() : parent(this) {}
+#else
+    explicit constexpr EmptyNodeType(node_type *p) : parent(p) {}
+#endif
+  };
+
+  static node_type *EmptyNode() {
+#ifdef _MSC_VER
+    static EmptyNodeType *empty_node = new EmptyNodeType;
+    // This assert fails on some other construction methods.
+    assert(empty_node->parent == empty_node);
+    return empty_node;
+#else
+    static constexpr EmptyNodeType empty_node(
+        const_cast<EmptyNodeType *>(&empty_node));
+    return const_cast<EmptyNodeType *>(&empty_node);
+#endif
+  }
+
+  enum : uint32_t {
+    kNodeSlots = node_type::kNodeSlots,
+    kMinNodeValues = kNodeSlots / 2,
+  };
+
+  struct node_stats {
+    using size_type = typename Params::size_type;
+
+    node_stats(size_type l, size_type i) : leaf_nodes(l), internal_nodes(i) {}
+
+    node_stats &operator+=(const node_stats &other) {
+      leaf_nodes += other.leaf_nodes;
+      internal_nodes += other.internal_nodes;
+      return *this;
+    }
+
+    size_type leaf_nodes;
+    size_type internal_nodes;
+  };
+
+ public:
+  using key_type = typename Params::key_type;
+  using value_type = typename Params::value_type;
+  using size_type = typename Params::size_type;
+  using difference_type = typename Params::difference_type;
+  using key_compare = typename Params::key_compare;
+  using original_key_compare = typename Params::original_key_compare;
+  using value_compare = typename Params::value_compare;
+  using allocator_type = typename Params::allocator_type;
+  using reference = typename Params::reference;
+  using const_reference = typename Params::const_reference;
+  using pointer = typename Params::pointer;
+  using const_pointer = typename Params::const_pointer;
+  using iterator =
+      typename btree_iterator<node_type, reference, pointer>::iterator;
+  using const_iterator = typename iterator::const_iterator;
+  using reverse_iterator = std::reverse_iterator<iterator>;
+  using const_reverse_iterator = std::reverse_iterator<const_iterator>;
+  using node_handle_type = node_handle<Params, Params, allocator_type>;
+
+  // Internal types made public for use by btree_container types.
+  using params_type = Params;
+  using slot_type = typename Params::slot_type;
+
+ private:
+  // Copies or moves (depending on the template parameter) the values in
+  // other into this btree in their order in other. This btree must be empty
+  // before this method is called. This method is used in copy construction,
+  // copy assignment, and move assignment.
+  template <typename Btree>
+  void copy_or_move_values_in_order(Btree &other);
+
+  // Validates that various assumptions/requirements are true at compile time.
+  constexpr static bool static_assert_validation();
+
+ public:
+  btree(const key_compare &comp, const allocator_type &alloc)
+      : root_(EmptyNode()), rightmost_(comp, alloc, EmptyNode()), size_(0) {}
+
+  btree(const btree &other) : btree(other, other.allocator()) {}
+  btree(const btree &other, const allocator_type &alloc)
+      : btree(other.key_comp(), alloc) {
+    copy_or_move_values_in_order(other);
+  }
+  btree(btree &&other) noexcept
+      : root_(absl::exchange(other.root_, EmptyNode())),
+        rightmost_(std::move(other.rightmost_)),
+        size_(absl::exchange(other.size_, 0u)) {
+    other.mutable_rightmost() = EmptyNode();
+  }
+  btree(btree &&other, const allocator_type &alloc)
+      : btree(other.key_comp(), alloc) {
+    if (alloc == other.allocator()) {
+      swap(other);
+    } else {
+      // Move values from `other` one at a time when allocators are different.
+      copy_or_move_values_in_order(other);
+    }
+  }
+
+  ~btree() {
+    // Put static_asserts in destructor to avoid triggering them before the type
+    // is complete.
+    static_assert(static_assert_validation(), "This call must be elided.");
+    clear();
+  }
+
+  // Assign the contents of other to *this.
+  btree &operator=(const btree &other);
+  btree &operator=(btree &&other) noexcept;
+
+  iterator begin() { return iterator(leftmost()); }
+  const_iterator begin() const { return const_iterator(leftmost()); }
+  iterator end() { return iterator(rightmost(), rightmost()->finish()); }
+  const_iterator end() const {
+    return const_iterator(rightmost(), rightmost()->finish());
+  }
+  reverse_iterator rbegin() { return reverse_iterator(end()); }
+  const_reverse_iterator rbegin() const {
+    return const_reverse_iterator(end());
+  }
+  reverse_iterator rend() { return reverse_iterator(begin()); }
+  const_reverse_iterator rend() const {
+    return const_reverse_iterator(begin());
+  }
+
+  // Finds the first element whose key is not less than `key`.
+  template <typename K>
+  iterator lower_bound(const K &key) {
+    return internal_end(internal_lower_bound(key).value);
+  }
+  template <typename K>
+  const_iterator lower_bound(const K &key) const {
+    return internal_end(internal_lower_bound(key).value);
+  }
+
+  // Finds the first element whose key is not less than `key` and also returns
+  // whether that element is equal to `key`.
+  template <typename K>
+  std::pair<iterator, bool> lower_bound_equal(const K &key) const;
+
+  // Finds the first element whose key is greater than `key`.
+  template <typename K>
+  iterator upper_bound(const K &key) {
+    return internal_end(internal_upper_bound(key));
+  }
+  template <typename K>
+  const_iterator upper_bound(const K &key) const {
+    return internal_end(internal_upper_bound(key));
+  }
+
+  // Finds the range of values which compare equal to key. The first member of
+  // the returned pair is equal to lower_bound(key). The second member of the
+  // pair is equal to upper_bound(key).
+  template <typename K>
+  std::pair<iterator, iterator> equal_range(const K &key);
+  template <typename K>
+  std::pair<const_iterator, const_iterator> equal_range(const K &key) const {
+    return const_cast<btree *>(this)->equal_range(key);
+  }
+
+  // Inserts a value into the btree only if it does not already exist. The
+  // boolean return value indicates whether insertion succeeded or failed.
+  // Requirement: if `key` already exists in the btree, does not consume `args`.
+  // Requirement: `key` is never referenced after consuming `args`.
+  template <typename K, typename... Args>
+  std::pair<iterator, bool> insert_unique(const K &key, Args &&...args);
+
+  // Inserts with hint. Checks to see if the value should be placed immediately
+  // before `position` in the tree. If so, then the insertion will take
+  // amortized constant time. If not, the insertion will take amortized
+  // logarithmic time as if a call to insert_unique() were made.
+  // Requirement: if `key` already exists in the btree, does not consume `args`.
+  // Requirement: `key` is never referenced after consuming `args`.
+  template <typename K, typename... Args>
+  std::pair<iterator, bool> insert_hint_unique(iterator position, const K &key,
+                                               Args &&...args);
+
+  // Insert a range of values into the btree.
+  // Note: the first overload avoids constructing a value_type if the key
+  // already exists in the btree.
+  template <typename InputIterator,
+            typename = decltype(std::declval<const key_compare &>()(
+                params_type::key(*std::declval<InputIterator>()),
+                std::declval<const key_type &>()))>
+  void insert_iterator_unique(InputIterator b, InputIterator e, int);
+  // We need the second overload for cases in which we need to construct a
+  // value_type in order to compare it with the keys already in the btree.
+  template <typename InputIterator>
+  void insert_iterator_unique(InputIterator b, InputIterator e, char);
+
+  // Inserts a value into the btree.
+  template <typename ValueType>
+  iterator insert_multi(const key_type &key, ValueType &&v);
+
+  // Inserts a value into the btree.
+  template <typename ValueType>
+  iterator insert_multi(ValueType &&v) {
+    return insert_multi(params_type::key(v), std::forward<ValueType>(v));
+  }
+
+  // Insert with hint. Check to see if the value should be placed immediately
+  // before position in the tree. If it does, then the insertion will take
+  // amortized constant time. If not, the insertion will take amortized
+  // logarithmic time as if a call to insert_multi(v) were made.
+  template <typename ValueType>
+  iterator insert_hint_multi(iterator position, ValueType &&v);
+
+  // Insert a range of values into the btree.
+  template <typename InputIterator>
+  void insert_iterator_multi(InputIterator b, InputIterator e);
+
+  // Erase the specified iterator from the btree. The iterator must be valid
+  // (i.e. not equal to end()).  Return an iterator pointing to the node after
+  // the one that was erased (or end() if none exists).
+  // Requirement: does not read the value at `*iter`.
+  iterator erase(iterator iter);
+
+  // Erases range. Returns the number of keys erased and an iterator pointing
+  // to the element after the last erased element.
+  std::pair<size_type, iterator> erase_range(iterator begin, iterator end);
+
+  // Finds an element with key equivalent to `key` or returns `end()` if `key`
+  // is not present.
+  template <typename K>
+  iterator find(const K &key) {
+    return internal_end(internal_find(key));
+  }
+  template <typename K>
+  const_iterator find(const K &key) const {
+    return internal_end(internal_find(key));
+  }
+
+  // Clear the btree, deleting all of the values it contains.
+  void clear();
+
+  // Swaps the contents of `this` and `other`.
+  void swap(btree &other);
+
+  const key_compare &key_comp() const noexcept {
+    return rightmost_.template get<0>();
+  }
+  template <typename K1, typename K2>
+  bool compare_keys(const K1 &a, const K2 &b) const {
+    return compare_internal::compare_result_as_less_than(key_comp()(a, b));
+  }
+
+  value_compare value_comp() const {
+    return value_compare(original_key_compare(key_comp()));
+  }
+
+  // Verifies the structure of the btree.
+  void verify() const;
+
+  // Size routines.
+  size_type size() const { return size_; }
+  size_type max_size() const { return (std::numeric_limits<size_type>::max)(); }
+  bool empty() const { return size_ == 0; }
+
+  // The height of the btree. An empty tree will have height 0.
+  size_type height() const {
+    size_type h = 0;
+    if (!empty()) {
+      // Count the length of the chain from the leftmost node up to the
+      // root. We actually count from the root back around to the level below
+      // the root, but the calculation is the same because of the circularity
+      // of that traversal.
+      const node_type *n = root();
+      do {
+        ++h;
+        n = n->parent();
+      } while (n != root());
+    }
+    return h;
+  }
+
+  // The number of internal, leaf and total nodes used by the btree.
+  size_type leaf_nodes() const { return internal_stats(root()).leaf_nodes; }
+  size_type internal_nodes() const {
+    return internal_stats(root()).internal_nodes;
+  }
+  size_type nodes() const {
+    node_stats stats = internal_stats(root());
+    return stats.leaf_nodes + stats.internal_nodes;
+  }
+
+  // The total number of bytes used by the btree.
+  // TODO(b/169338300): update to support node_btree_*.
+  size_type bytes_used() const {
+    node_stats stats = internal_stats(root());
+    if (stats.leaf_nodes == 1 && stats.internal_nodes == 0) {
+      return sizeof(*this) + node_type::LeafSize(root()->max_count());
+    } else {
+      return sizeof(*this) + stats.leaf_nodes * node_type::LeafSize() +
+             stats.internal_nodes * node_type::InternalSize();
+    }
+  }
+
+  // The average number of bytes used per value stored in the btree assuming
+  // random insertion order.
+  static double average_bytes_per_value() {
+    // The expected number of values per node with random insertion order is the
+    // average of the maximum and minimum numbers of values per node.
+    const double expected_values_per_node = (kNodeSlots + kMinNodeValues) / 2.0;
+    return node_type::LeafSize() / expected_values_per_node;
+  }
+
+  // The fullness of the btree. Computed as the number of elements in the btree
+  // divided by the maximum number of elements a tree with the current number
+  // of nodes could hold. A value of 1 indicates perfect space
+  // utilization. Smaller values indicate space wastage.
+  // Returns 0 for empty trees.
+  double fullness() const {
+    if (empty()) return 0.0;
+    return static_cast<double>(size()) / (nodes() * kNodeSlots);
+  }
+  // The overhead of the btree structure in bytes per node. Computed as the
+  // total number of bytes used by the btree minus the number of bytes used for
+  // storing elements divided by the number of elements.
+  // Returns 0 for empty trees.
+  double overhead() const {
+    if (empty()) return 0.0;
+    return (bytes_used() - size() * sizeof(value_type)) /
+           static_cast<double>(size());
+  }
+
+  // The allocator used by the btree.
+  allocator_type get_allocator() const { return allocator(); }
+
+ private:
+  friend struct btree_access;
+
+  // Internal accessor routines.
+  node_type *root() { return root_; }
+  const node_type *root() const { return root_; }
+  node_type *&mutable_root() noexcept { return root_; }
+  node_type *rightmost() { return rightmost_.template get<2>(); }
+  const node_type *rightmost() const { return rightmost_.template get<2>(); }
+  node_type *&mutable_rightmost() noexcept {
+    return rightmost_.template get<2>();
+  }
+  key_compare *mutable_key_comp() noexcept {
+    return &rightmost_.template get<0>();
+  }
+
+  // The leftmost node is stored as the parent of the root node.
+  node_type *leftmost() { return root()->parent(); }
+  const node_type *leftmost() const { return root()->parent(); }
+
+  // Allocator routines.
+  allocator_type *mutable_allocator() noexcept {
+    return &rightmost_.template get<1>();
+  }
+  const allocator_type &allocator() const noexcept {
+    return rightmost_.template get<1>();
+  }
+
+  // Allocates a correctly aligned node of at least size bytes using the
+  // allocator.
+  node_type *allocate(size_type size) {
+    return reinterpret_cast<node_type *>(
+        absl::container_internal::Allocate<node_type::Alignment()>(
+            mutable_allocator(), size));
+  }
+
+  // Node creation/deletion routines.
+  node_type *new_internal_node(node_type *parent) {
+    node_type *n = allocate(node_type::InternalSize());
+    n->init_internal(parent);
+    return n;
+  }
+  node_type *new_leaf_node(node_type *parent) {
+    node_type *n = allocate(node_type::LeafSize());
+    n->init_leaf(kNodeSlots, parent);
+    return n;
+  }
+  node_type *new_leaf_root_node(field_type max_count) {
+    node_type *n = allocate(node_type::LeafSize(max_count));
+    n->init_leaf(max_count, /*parent=*/n);
+    return n;
+  }
+
+  // Deletion helper routines.
+  iterator rebalance_after_delete(iterator iter);
+
+  // Rebalances or splits the node iter points to.
+  void rebalance_or_split(iterator *iter);
+
+  // Merges the values of left, right and the delimiting key on their parent
+  // onto left, removing the delimiting key and deleting right.
+  void merge_nodes(node_type *left, node_type *right);
+
+  // Tries to merge node with its left or right sibling, and failing that,
+  // rebalance with its left or right sibling. Returns true if a merge
+  // occurred, at which point it is no longer valid to access node. Returns
+  // false if no merging took place.
+  bool try_merge_or_rebalance(iterator *iter);
+
+  // Tries to shrink the height of the tree by 1.
+  void try_shrink();
+
+  iterator internal_end(iterator iter) {
+    return iter.node_ != nullptr ? iter : end();
+  }
+  const_iterator internal_end(const_iterator iter) const {
+    return iter.node_ != nullptr ? iter : end();
+  }
+
+  // Emplaces a value into the btree immediately before iter. Requires that
+  // key(v) <= iter.key() and (--iter).key() <= key(v).
+  template <typename... Args>
+  iterator internal_emplace(iterator iter, Args &&...args);
+
+  // Returns an iterator pointing to the first value >= the value "iter" is
+  // pointing at. Note that "iter" might be pointing to an invalid location such
+  // as iter.position_ == iter.node_->finish(). This routine simply moves iter
+  // up in the tree to a valid location. Requires: iter.node_ is non-null.
+  template <typename IterType>
+  static IterType internal_last(IterType iter);
+
+  // Returns an iterator pointing to the leaf position at which key would
+  // reside in the tree, unless there is an exact match - in which case, the
+  // result may not be on a leaf. When there's a three-way comparator, we can
+  // return whether there was an exact match. This allows the caller to avoid a
+  // subsequent comparison to determine if an exact match was made, which is
+  // important for keys with expensive comparison, such as strings.
+  template <typename K>
+  SearchResult<iterator, is_key_compare_to::value> internal_locate(
+      const K &key) const;
+
+  // Internal routine which implements lower_bound().
+  template <typename K>
+  SearchResult<iterator, is_key_compare_to::value> internal_lower_bound(
+      const K &key) const;
+
+  // Internal routine which implements upper_bound().
+  template <typename K>
+  iterator internal_upper_bound(const K &key) const;
+
+  // Internal routine which implements find().
+  template <typename K>
+  iterator internal_find(const K &key) const;
+
+  // Verifies the tree structure of node.
+  size_type internal_verify(const node_type *node, const key_type *lo,
+                            const key_type *hi) const;
+
+  node_stats internal_stats(const node_type *node) const {
+    // The root can be a static empty node.
+    if (node == nullptr || (node == root() && empty())) {
+      return node_stats(0, 0);
+    }
+    if (node->is_leaf()) {
+      return node_stats(1, 0);
+    }
+    node_stats res(0, 1);
+    for (int i = node->start(); i <= node->finish(); ++i) {
+      res += internal_stats(node->child(i));
+    }
+    return res;
+  }
+
+  node_type *root_;
+
+  // A pointer to the rightmost node. Note that the leftmost node is stored as
+  // the root's parent. We use compressed tuple in order to save space because
+  // key_compare and allocator_type are usually empty.
+  absl::container_internal::CompressedTuple<key_compare, allocator_type,
+                                            node_type *>
+      rightmost_;
+
+  // Number of values.
+  size_type size_;
+};
+
+////
+// btree_node methods
+template <typename P>
+template <typename... Args>
+inline void btree_node<P>::emplace_value(const field_type i,
+                                         allocator_type *alloc,
+                                         Args &&...args) {
+  assert(i >= start());
+  assert(i <= finish());
+  // Shift old values to create space for new value and then construct it in
+  // place.
+  if (i < finish()) {
+    transfer_n_backward(finish() - i, /*dest_i=*/i + 1, /*src_i=*/i, this,
+                        alloc);
+  }
+  value_init(static_cast<field_type>(i), alloc, std::forward<Args>(args)...);
+  set_finish(finish() + 1);
+
+  if (is_internal() && finish() > i + 1) {
+    for (field_type j = finish(); j > i + 1; --j) {
+      set_child(j, child(j - 1));
+    }
+    clear_child(i + 1);
+  }
+}
+
+template <typename P>
+inline void btree_node<P>::remove_values(const field_type i,
+                                         const field_type to_erase,
+                                         allocator_type *alloc) {
+  // Transfer values after the removed range into their new places.
+  value_destroy_n(i, to_erase, alloc);
+  const field_type orig_finish = finish();
+  const field_type src_i = i + to_erase;
+  transfer_n(orig_finish - src_i, i, src_i, this, alloc);
+
+  if (is_internal()) {
+    // Delete all children between begin and end.
+    for (field_type j = 0; j < to_erase; ++j) {
+      clear_and_delete(child(i + j + 1), alloc);
+    }
+    // Rotate children after end into new positions.
+    for (field_type j = i + to_erase + 1; j <= orig_finish; ++j) {
+      set_child(j - to_erase, child(j));
+      clear_child(j);
+    }
+  }
+  set_finish(orig_finish - to_erase);
+}
+
+template <typename P>
+void btree_node<P>::rebalance_right_to_left(field_type to_move,
+                                            btree_node *right,
+                                            allocator_type *alloc) {
+  assert(parent() == right->parent());
+  assert(position() + 1 == right->position());
+  assert(right->count() >= count());
+  assert(to_move >= 1);
+  assert(to_move <= right->count());
+
+  // 1) Move the delimiting value in the parent to the left node.
+  transfer(finish(), position(), parent(), alloc);
+
+  // 2) Move the (to_move - 1) values from the right node to the left node.
+  transfer_n(to_move - 1, finish() + 1, right->start(), right, alloc);
+
+  // 3) Move the new delimiting value to the parent from the right node.
+  parent()->transfer(position(), right->start() + to_move - 1, right, alloc);
+
+  // 4) Shift the values in the right node to their correct positions.
+  right->transfer_n(right->count() - to_move, right->start(),
+                    right->start() + to_move, right, alloc);
+
+  if (is_internal()) {
+    // Move the child pointers from the right to the left node.
+    for (field_type i = 0; i < to_move; ++i) {
+      init_child(finish() + i + 1, right->child(i));
+    }
+    for (field_type i = right->start(); i <= right->finish() - to_move; ++i) {
+      assert(i + to_move <= right->max_count());
+      right->init_child(i, right->child(i + to_move));
+      right->clear_child(i + to_move);
+    }
+  }
+
+  // Fixup `finish` on the left and right nodes.
+  set_finish(finish() + to_move);
+  right->set_finish(right->finish() - to_move);
+}
+
+template <typename P>
+void btree_node<P>::rebalance_left_to_right(field_type to_move,
+                                            btree_node *right,
+                                            allocator_type *alloc) {
+  assert(parent() == right->parent());
+  assert(position() + 1 == right->position());
+  assert(count() >= right->count());
+  assert(to_move >= 1);
+  assert(to_move <= count());
+
+  // Values in the right node are shifted to the right to make room for the
+  // new to_move values. Then, the delimiting value in the parent and the
+  // other (to_move - 1) values in the left node are moved into the right node.
+  // Lastly, a new delimiting value is moved from the left node into the
+  // parent, and the remaining empty left node entries are destroyed.
+
+  // 1) Shift existing values in the right node to their correct positions.
+  right->transfer_n_backward(right->count(), right->start() + to_move,
+                             right->start(), right, alloc);
+
+  // 2) Move the delimiting value in the parent to the right node.
+  right->transfer(right->start() + to_move - 1, position(), parent(), alloc);
+
+  // 3) Move the (to_move - 1) values from the left node to the right node.
+  right->transfer_n(to_move - 1, right->start(), finish() - (to_move - 1), this,
+                    alloc);
+
+  // 4) Move the new delimiting value to the parent from the left node.
+  parent()->transfer(position(), finish() - to_move, this, alloc);
+
+  if (is_internal()) {
+    // Move the child pointers from the left to the right node.
+    for (field_type i = right->finish() + 1; i > right->start(); --i) {
+      right->init_child(i - 1 + to_move, right->child(i - 1));
+      right->clear_child(i - 1);
+    }
+    for (field_type i = 1; i <= to_move; ++i) {
+      right->init_child(i - 1, child(finish() - to_move + i));
+      clear_child(finish() - to_move + i);
+    }
+  }
+
+  // Fixup the counts on the left and right nodes.
+  set_finish(finish() - to_move);
+  right->set_finish(right->finish() + to_move);
+}
+
+template <typename P>
+void btree_node<P>::split(const int insert_position, btree_node *dest,
+                          allocator_type *alloc) {
+  assert(dest->count() == 0);
+  assert(max_count() == kNodeSlots);
+
+  // We bias the split based on the position being inserted. If we're
+  // inserting at the beginning of the left node then bias the split to put
+  // more values on the right node. If we're inserting at the end of the
+  // right node then bias the split to put more values on the left node.
+  if (insert_position == start()) {
+    dest->set_finish(dest->start() + finish() - 1);
+  } else if (insert_position == kNodeSlots) {
+    dest->set_finish(dest->start());
+  } else {
+    dest->set_finish(dest->start() + count() / 2);
+  }
+  set_finish(finish() - dest->count());
+  assert(count() >= 1);
+
+  // Move values from the left sibling to the right sibling.
+  dest->transfer_n(dest->count(), dest->start(), finish(), this, alloc);
+
+  // The split key is the largest value in the left sibling.
+  --mutable_finish();
+  parent()->emplace_value(position(), alloc, finish_slot());
+  value_destroy(finish(), alloc);
+  parent()->init_child(position() + 1, dest);
+
+  if (is_internal()) {
+    for (field_type i = dest->start(), j = finish() + 1; i <= dest->finish();
+         ++i, ++j) {
+      assert(child(j) != nullptr);
+      dest->init_child(i, child(j));
+      clear_child(j);
+    }
+  }
+}
+
+template <typename P>
+void btree_node<P>::merge(btree_node *src, allocator_type *alloc) {
+  assert(parent() == src->parent());
+  assert(position() + 1 == src->position());
+
+  // Move the delimiting value to the left node.
+  value_init(finish(), alloc, parent()->slot(position()));
+
+  // Move the values from the right to the left node.
+  transfer_n(src->count(), finish() + 1, src->start(), src, alloc);
+
+  if (is_internal()) {
+    // Move the child pointers from the right to the left node.
+    for (field_type i = src->start(), j = finish() + 1; i <= src->finish();
+         ++i, ++j) {
+      init_child(j, src->child(i));
+      src->clear_child(i);
+    }
+  }
+
+  // Fixup `finish` on the src and dest nodes.
+  set_finish(start() + 1 + count() + src->count());
+  src->set_finish(src->start());
+
+  // Remove the value on the parent node and delete the src node.
+  parent()->remove_values(position(), /*to_erase=*/1, alloc);
+}
+
+template <typename P>
+void btree_node<P>::clear_and_delete(btree_node *node, allocator_type *alloc) {
+  if (node->is_leaf()) {
+    node->value_destroy_n(node->start(), node->count(), alloc);
+    deallocate(LeafSize(node->max_count()), node, alloc);
+    return;
+  }
+  if (node->count() == 0) {
+    deallocate(InternalSize(), node, alloc);
+    return;
+  }
+
+  // The parent of the root of the subtree we are deleting.
+  btree_node *delete_root_parent = node->parent();
+
+  // Navigate to the leftmost leaf under node, and then delete upwards.
+  while (node->is_internal()) node = node->start_child();
+#ifdef ABSL_BTREE_ENABLE_GENERATIONS
+  // When generations are enabled, we delete the leftmost leaf last in case it's
+  // the parent of the root and we need to check whether it's a leaf before we
+  // can update the root's generation.
+  // TODO(ezb): if we change btree_node::is_root to check a bool inside the node
+  // instead of checking whether the parent is a leaf, we can remove this logic.
+  btree_node *leftmost_leaf = node;
+#endif
+  // Use `size_type` because `pos` needs to be able to hold `kNodeSlots+1`,
+  // which isn't guaranteed to be a valid `field_type`.
+  size_type pos = node->position();
+  btree_node *parent = node->parent();
+  for (;;) {
+    // In each iteration of the next loop, we delete one leaf node and go right.
+    assert(pos <= parent->finish());
+    do {
+      node = parent->child(static_cast<field_type>(pos));
+      if (node->is_internal()) {
+        // Navigate to the leftmost leaf under node.
+        while (node->is_internal()) node = node->start_child();
+        pos = node->position();
+        parent = node->parent();
+      }
+      node->value_destroy_n(node->start(), node->count(), alloc);
+#ifdef ABSL_BTREE_ENABLE_GENERATIONS
+      if (leftmost_leaf != node)
+#endif
+        deallocate(LeafSize(node->max_count()), node, alloc);
+      ++pos;
+    } while (pos <= parent->finish());
+
+    // Once we've deleted all children of parent, delete parent and go up/right.
+    assert(pos > parent->finish());
+    do {
+      node = parent;
+      pos = node->position();
+      parent = node->parent();
+      node->value_destroy_n(node->start(), node->count(), alloc);
+      deallocate(InternalSize(), node, alloc);
+      if (parent == delete_root_parent) {
+#ifdef ABSL_BTREE_ENABLE_GENERATIONS
+        deallocate(LeafSize(leftmost_leaf->max_count()), leftmost_leaf, alloc);
+#endif
+        return;
+      }
+      ++pos;
+    } while (pos > parent->finish());
+  }
+}
+
+////
+// btree_iterator methods
+
+// Note: the implementation here is based on btree_node::clear_and_delete.
+template <typename N, typename R, typename P>
+auto btree_iterator<N, R, P>::distance_slow(const_iterator other) const
+    -> difference_type {
+  const_iterator begin = other;
+  const_iterator end = *this;
+  assert(begin.node_ != end.node_ || !begin.node_->is_leaf() ||
+         begin.position_ != end.position_);
+
+  const node_type *node = begin.node_;
+  // We need to compensate for double counting if begin.node_ is a leaf node.
+  difference_type count = node->is_leaf() ? -begin.position_ : 0;
+
+  // First navigate to the leftmost leaf node past begin.
+  if (node->is_internal()) {
+    ++count;
+    node = node->child(begin.position_ + 1);
+  }
+  while (node->is_internal()) node = node->start_child();
+
+  // Use `size_type` because `pos` needs to be able to hold `kNodeSlots+1`,
+  // which isn't guaranteed to be a valid `field_type`.
+  size_type pos = node->position();
+  const node_type *parent = node->parent();
+  for (;;) {
+    // In each iteration of the next loop, we count one leaf node and go right.
+    assert(pos <= parent->finish());
+    do {
+      node = parent->child(static_cast<field_type>(pos));
+      if (node->is_internal()) {
+        // Navigate to the leftmost leaf under node.
+        while (node->is_internal()) node = node->start_child();
+        pos = node->position();
+        parent = node->parent();
+      }
+      if (node == end.node_) return count + end.position_;
+      if (parent == end.node_ && pos == static_cast<size_type>(end.position_))
+        return count + node->count();
+      // +1 is for the next internal node value.
+      count += node->count() + 1;
+      ++pos;
+    } while (pos <= parent->finish());
+
+    // Once we've counted all children of parent, go up/right.
+    assert(pos > parent->finish());
+    do {
+      node = parent;
+      pos = node->position();
+      parent = node->parent();
+      // -1 because we counted the value at end and shouldn't.
+      if (parent == end.node_ && pos == static_cast<size_type>(end.position_))
+        return count - 1;
+      ++pos;
+    } while (pos > parent->finish());
+  }
+}
+
+template <typename N, typename R, typename P>
+void btree_iterator<N, R, P>::increment_slow() {
+  if (node_->is_leaf()) {
+    assert(position_ >= node_->finish());
+    btree_iterator save(*this);
+    while (position_ == node_->finish() && !node_->is_root()) {
+      assert(node_->parent()->child(node_->position()) == node_);
+      position_ = node_->position();
+      node_ = node_->parent();
+    }
+    // TODO(ezb): assert we aren't incrementing end() instead of handling.
+    if (position_ == node_->finish()) {
+      *this = save;
+    }
+  } else {
+    assert(position_ < node_->finish());
+    node_ = node_->child(static_cast<field_type>(position_ + 1));
+    while (node_->is_internal()) {
+      node_ = node_->start_child();
+    }
+    position_ = node_->start();
+  }
+}
+
+template <typename N, typename R, typename P>
+void btree_iterator<N, R, P>::decrement_slow() {
+  if (node_->is_leaf()) {
+    assert(position_ <= -1);
+    btree_iterator save(*this);
+    while (position_ < node_->start() && !node_->is_root()) {
+      assert(node_->parent()->child(node_->position()) == node_);
+      position_ = node_->position() - 1;
+      node_ = node_->parent();
+    }
+    // TODO(ezb): assert we aren't decrementing begin() instead of handling.
+    if (position_ < node_->start()) {
+      *this = save;
+    }
+  } else {
+    assert(position_ >= node_->start());
+    node_ = node_->child(static_cast<field_type>(position_));
+    while (node_->is_internal()) {
+      node_ = node_->child(node_->finish());
+    }
+    position_ = node_->finish() - 1;
+  }
+}
+
+////
+// btree methods
+template <typename P>
+template <typename Btree>
+void btree<P>::copy_or_move_values_in_order(Btree &other) {
+  static_assert(std::is_same<btree, Btree>::value ||
+                    std::is_same<const btree, Btree>::value,
+                "Btree type must be same or const.");
+  assert(empty());
+
+  // We can avoid key comparisons because we know the order of the
+  // values is the same order we'll store them in.
+  auto iter = other.begin();
+  if (iter == other.end()) return;
+  insert_multi(iter.slot());
+  ++iter;
+  for (; iter != other.end(); ++iter) {
+    // If the btree is not empty, we can just insert the new value at the end
+    // of the tree.
+    internal_emplace(end(), iter.slot());
+  }
+}
+
+template <typename P>
+constexpr bool btree<P>::static_assert_validation() {
+  static_assert(std::is_nothrow_copy_constructible<key_compare>::value,
+                "Key comparison must be nothrow copy constructible");
+  static_assert(std::is_nothrow_copy_constructible<allocator_type>::value,
+                "Allocator must be nothrow copy constructible");
+  static_assert(type_traits_internal::is_trivially_copyable<iterator>::value,
+                "iterator not trivially copyable.");
+
+  // Note: We assert that kTargetValues, which is computed from
+  // Params::kTargetNodeSize, must fit the node_type::field_type.
+  static_assert(
+      kNodeSlots < (1 << (8 * sizeof(typename node_type::field_type))),
+      "target node size too large");
+
+  // Verify that key_compare returns an absl::{weak,strong}_ordering or bool.
+  static_assert(
+      compare_has_valid_result_type<key_compare, key_type>(),
+      "key comparison function must return absl::{weak,strong}_ordering or "
+      "bool.");
+
+  // Test the assumption made in setting kNodeSlotSpace.
+  static_assert(node_type::MinimumOverhead() >= sizeof(void *) + 4,
+                "node space assumption incorrect");
+
+  return true;
+}
+
+template <typename P>
+template <typename K>
+auto btree<P>::lower_bound_equal(const K &key) const
+    -> std::pair<iterator, bool> {
+  const SearchResult<iterator, is_key_compare_to::value> res =
+      internal_lower_bound(key);
+  const iterator lower = iterator(internal_end(res.value));
+  const bool equal = res.HasMatch()
+                         ? res.IsEq()
+                         : lower != end() && !compare_keys(key, lower.key());
+  return {lower, equal};
+}
+
+template <typename P>
+template <typename K>
+auto btree<P>::equal_range(const K &key) -> std::pair<iterator, iterator> {
+  const std::pair<iterator, bool> lower_and_equal = lower_bound_equal(key);
+  const iterator lower = lower_and_equal.first;
+  if (!lower_and_equal.second) {
+    return {lower, lower};
+  }
+
+  const iterator next = std::next(lower);
+  if (!params_type::template can_have_multiple_equivalent_keys<K>()) {
+    // The next iterator after lower must point to a key greater than `key`.
+    // Note: if this assert fails, then it may indicate that the comparator does
+    // not meet the equivalence requirements for Compare
+    // (see https://en.cppreference.com/w/cpp/named_req/Compare).
+    assert(next == end() || compare_keys(key, next.key()));
+    return {lower, next};
+  }
+  // Try once more to avoid the call to upper_bound() if there's only one
+  // equivalent key. This should prevent all calls to upper_bound() in cases of
+  // unique-containers with heterogeneous comparators in which all comparison
+  // operators have the same equivalence classes.
+  if (next == end() || compare_keys(key, next.key())) return {lower, next};
+
+  // In this case, we need to call upper_bound() to avoid worst case O(N)
+  // behavior if we were to iterate over equal keys.
+  return {lower, upper_bound(key)};
+}
+
+template <typename P>
+template <typename K, typename... Args>
+auto btree<P>::insert_unique(const K &key, Args &&...args)
+    -> std::pair<iterator, bool> {
+  if (empty()) {
+    mutable_root() = mutable_rightmost() = new_leaf_root_node(1);
+  }
+
+  SearchResult<iterator, is_key_compare_to::value> res = internal_locate(key);
+  iterator iter = res.value;
+
+  if (res.HasMatch()) {
+    if (res.IsEq()) {
+      // The key already exists in the tree, do nothing.
+      return {iter, false};
+    }
+  } else {
+    iterator last = internal_last(iter);
+    if (last.node_ && !compare_keys(key, last.key())) {
+      // The key already exists in the tree, do nothing.
+      return {last, false};
+    }
+  }
+  return {internal_emplace(iter, std::forward<Args>(args)...), true};
+}
+
+template <typename P>
+template <typename K, typename... Args>
+inline auto btree<P>::insert_hint_unique(iterator position, const K &key,
+                                         Args &&...args)
+    -> std::pair<iterator, bool> {
+  if (!empty()) {
+    if (position == end() || compare_keys(key, position.key())) {
+      if (position == begin() || compare_keys(std::prev(position).key(), key)) {
+        // prev.key() < key < position.key()
+        return {internal_emplace(position, std::forward<Args>(args)...), true};
+      }
+    } else if (compare_keys(position.key(), key)) {
+      ++position;
+      if (position == end() || compare_keys(key, position.key())) {
+        // {original `position`}.key() < key < {current `position`}.key()
+        return {internal_emplace(position, std::forward<Args>(args)...), true};
+      }
+    } else {
+      // position.key() == key
+      return {position, false};
+    }
+  }
+  return insert_unique(key, std::forward<Args>(args)...);
+}
+
+template <typename P>
+template <typename InputIterator, typename>
+void btree<P>::insert_iterator_unique(InputIterator b, InputIterator e, int) {
+  for (; b != e; ++b) {
+    insert_hint_unique(end(), params_type::key(*b), *b);
+  }
+}
+
+template <typename P>
+template <typename InputIterator>
+void btree<P>::insert_iterator_unique(InputIterator b, InputIterator e, char) {
+  for (; b != e; ++b) {
+    // Use a node handle to manage a temp slot.
+    auto node_handle =
+        CommonAccess::Construct<node_handle_type>(get_allocator(), *b);
+    slot_type *slot = CommonAccess::GetSlot(node_handle);
+    insert_hint_unique(end(), params_type::key(slot), slot);
+  }
+}
+
+template <typename P>
+template <typename ValueType>
+auto btree<P>::insert_multi(const key_type &key, ValueType &&v) -> iterator {
+  if (empty()) {
+    mutable_root() = mutable_rightmost() = new_leaf_root_node(1);
+  }
+
+  iterator iter = internal_upper_bound(key);
+  if (iter.node_ == nullptr) {
+    iter = end();
+  }
+  return internal_emplace(iter, std::forward<ValueType>(v));
+}
+
+template <typename P>
+template <typename ValueType>
+auto btree<P>::insert_hint_multi(iterator position, ValueType &&v) -> iterator {
+  if (!empty()) {
+    const key_type &key = params_type::key(v);
+    if (position == end() || !compare_keys(position.key(), key)) {
+      if (position == begin() ||
+          !compare_keys(key, std::prev(position).key())) {
+        // prev.key() <= key <= position.key()
+        return internal_emplace(position, std::forward<ValueType>(v));
+      }
+    } else {
+      ++position;
+      if (position == end() || !compare_keys(position.key(), key)) {
+        // {original `position`}.key() < key < {current `position`}.key()
+        return internal_emplace(position, std::forward<ValueType>(v));
+      }
+    }
+  }
+  return insert_multi(std::forward<ValueType>(v));
+}
+
+template <typename P>
+template <typename InputIterator>
+void btree<P>::insert_iterator_multi(InputIterator b, InputIterator e) {
+  for (; b != e; ++b) {
+    insert_hint_multi(end(), *b);
+  }
+}
+
+template <typename P>
+auto btree<P>::operator=(const btree &other) -> btree & {
+  if (this != &other) {
+    clear();
+
+    *mutable_key_comp() = other.key_comp();
+    if (absl::allocator_traits<
+            allocator_type>::propagate_on_container_copy_assignment::value) {
+      *mutable_allocator() = other.allocator();
+    }
+
+    copy_or_move_values_in_order(other);
+  }
+  return *this;
+}
+
+template <typename P>
+auto btree<P>::operator=(btree &&other) noexcept -> btree & {
+  if (this != &other) {
+    clear();
+
+    using std::swap;
+    if (absl::allocator_traits<
+            allocator_type>::propagate_on_container_copy_assignment::value) {
+      swap(root_, other.root_);
+      // Note: `rightmost_` also contains the allocator and the key comparator.
+      swap(rightmost_, other.rightmost_);
+      swap(size_, other.size_);
+    } else {
+      if (allocator() == other.allocator()) {
+        swap(mutable_root(), other.mutable_root());
+        swap(*mutable_key_comp(), *other.mutable_key_comp());
+        swap(mutable_rightmost(), other.mutable_rightmost());
+        swap(size_, other.size_);
+      } else {
+        // We aren't allowed to propagate the allocator and the allocator is
+        // different so we can't take over its memory. We must move each element
+        // individually. We need both `other` and `this` to have `other`s key
+        // comparator while moving the values so we can't swap the key
+        // comparators.
+        *mutable_key_comp() = other.key_comp();
+        copy_or_move_values_in_order(other);
+      }
+    }
+  }
+  return *this;
+}
+
+template <typename P>
+auto btree<P>::erase(iterator iter) -> iterator {
+  iter.node_->value_destroy(static_cast<field_type>(iter.position_),
+                            mutable_allocator());
+  iter.update_generation();
+
+  const bool internal_delete = iter.node_->is_internal();
+  if (internal_delete) {
+    // Deletion of a value on an internal node. First, transfer the largest
+    // value from our left child here, then erase/rebalance from that position.
+    // We can get to the largest value from our left child by decrementing iter.
+    iterator internal_iter(iter);
+    --iter;
+    assert(iter.node_->is_leaf());
+    internal_iter.node_->transfer(
+        static_cast<size_type>(internal_iter.position_),
+        static_cast<size_type>(iter.position_), iter.node_,
+        mutable_allocator());
+  } else {
+    // Shift values after erased position in leaf. In the internal case, we
+    // don't need to do this because the leaf position is the end of the node.
+    const field_type transfer_from =
+        static_cast<field_type>(iter.position_ + 1);
+    const field_type num_to_transfer = iter.node_->finish() - transfer_from;
+    iter.node_->transfer_n(num_to_transfer,
+                           static_cast<size_type>(iter.position_),
+                           transfer_from, iter.node_, mutable_allocator());
+  }
+  // Update node finish and container size.
+  iter.node_->set_finish(iter.node_->finish() - 1);
+  --size_;
+
+  // We want to return the next value after the one we just erased. If we
+  // erased from an internal node (internal_delete == true), then the next
+  // value is ++(++iter). If we erased from a leaf node (internal_delete ==
+  // false) then the next value is ++iter. Note that ++iter may point to an
+  // internal node and the value in the internal node may move to a leaf node
+  // (iter.node_) when rebalancing is performed at the leaf level.
+
+  iterator res = rebalance_after_delete(iter);
+
+  // If we erased from an internal node, advance the iterator.
+  if (internal_delete) {
+    ++res;
+  }
+  return res;
+}
+
+template <typename P>
+auto btree<P>::rebalance_after_delete(iterator iter) -> iterator {
+  // Merge/rebalance as we walk back up the tree.
+  iterator res(iter);
+  bool first_iteration = true;
+  for (;;) {
+    if (iter.node_ == root()) {
+      try_shrink();
+      if (empty()) {
+        return end();
+      }
+      break;
+    }
+    if (iter.node_->count() >= kMinNodeValues) {
+      break;
+    }
+    bool merged = try_merge_or_rebalance(&iter);
+    // On the first iteration, we should update `res` with `iter` because `res`
+    // may have been invalidated.
+    if (first_iteration) {
+      res = iter;
+      first_iteration = false;
+    }
+    if (!merged) {
+      break;
+    }
+    iter.position_ = iter.node_->position();
+    iter.node_ = iter.node_->parent();
+  }
+  res.update_generation();
+
+  // Adjust our return value. If we're pointing at the end of a node, advance
+  // the iterator.
+  if (res.position_ == res.node_->finish()) {
+    res.position_ = res.node_->finish() - 1;
+    ++res;
+  }
+
+  return res;
+}
+
+template <typename P>
+auto btree<P>::erase_range(iterator begin, iterator end)
+    -> std::pair<size_type, iterator> {
+  size_type count = static_cast<size_type>(end - begin);
+  assert(count >= 0);
+
+  if (count == 0) {
+    return {0, begin};
+  }
+
+  if (static_cast<size_type>(count) == size_) {
+    clear();
+    return {count, this->end()};
+  }
+
+  if (begin.node_ == end.node_) {
+    assert(end.position_ > begin.position_);
+    begin.node_->remove_values(
+        static_cast<field_type>(begin.position_),
+        static_cast<field_type>(end.position_ - begin.position_),
+        mutable_allocator());
+    size_ -= count;
+    return {count, rebalance_after_delete(begin)};
+  }
+
+  const size_type target_size = size_ - count;
+  while (size_ > target_size) {
+    if (begin.node_->is_leaf()) {
+      const size_type remaining_to_erase = size_ - target_size;
+      const size_type remaining_in_node =
+          static_cast<size_type>(begin.node_->finish() - begin.position_);
+      const field_type to_erase = static_cast<field_type>(
+          (std::min)(remaining_to_erase, remaining_in_node));
+      begin.node_->remove_values(static_cast<field_type>(begin.position_),
+                                 to_erase, mutable_allocator());
+      size_ -= to_erase;
+      begin = rebalance_after_delete(begin);
+    } else {
+      begin = erase(begin);
+    }
+  }
+  begin.update_generation();
+  return {count, begin};
+}
+
+template <typename P>
+void btree<P>::clear() {
+  if (!empty()) {
+    node_type::clear_and_delete(root(), mutable_allocator());
+  }
+  mutable_root() = mutable_rightmost() = EmptyNode();
+  size_ = 0;
+}
+
+template <typename P>
+void btree<P>::swap(btree &other) {
+  using std::swap;
+  if (absl::allocator_traits<
+          allocator_type>::propagate_on_container_swap::value) {
+    // Note: `rightmost_` also contains the allocator and the key comparator.
+    swap(rightmost_, other.rightmost_);
+  } else {
+    // It's undefined behavior if the allocators are unequal here.
+    assert(allocator() == other.allocator());
+    swap(mutable_rightmost(), other.mutable_rightmost());
+    swap(*mutable_key_comp(), *other.mutable_key_comp());
+  }
+  swap(mutable_root(), other.mutable_root());
+  swap(size_, other.size_);
+}
+
+template <typename P>
+void btree<P>::verify() const {
+  assert(root() != nullptr);
+  assert(leftmost() != nullptr);
+  assert(rightmost() != nullptr);
+  assert(empty() || size() == internal_verify(root(), nullptr, nullptr));
+  assert(leftmost() == (++const_iterator(root(), -1)).node_);
+  assert(rightmost() == (--const_iterator(root(), root()->finish())).node_);
+  assert(leftmost()->is_leaf());
+  assert(rightmost()->is_leaf());
+}
+
+template <typename P>
+void btree<P>::rebalance_or_split(iterator *iter) {
+  node_type *&node = iter->node_;
+  int &insert_position = iter->position_;
+  assert(node->count() == node->max_count());
+  assert(kNodeSlots == node->max_count());
+
+  // First try to make room on the node by rebalancing.
+  node_type *parent = node->parent();
+  if (node != root()) {
+    if (node->position() > parent->start()) {
+      // Try rebalancing with our left sibling.
+      node_type *left = parent->child(node->position() - 1);
+      assert(left->max_count() == kNodeSlots);
+      if (left->count() < kNodeSlots) {
+        // We bias rebalancing based on the position being inserted. If we're
+        // inserting at the end of the right node then we bias rebalancing to
+        // fill up the left node.
+        field_type to_move =
+            (kNodeSlots - left->count()) /
+            (1 + (static_cast<field_type>(insert_position) < kNodeSlots));
+        to_move = (std::max)(field_type{1}, to_move);
+
+        if (static_cast<field_type>(insert_position) - to_move >=
+                node->start() ||
+            left->count() + to_move < kNodeSlots) {
+          left->rebalance_right_to_left(to_move, node, mutable_allocator());
+
+          assert(node->max_count() - node->count() == to_move);
+          insert_position = static_cast<int>(
+              static_cast<field_type>(insert_position) - to_move);
+          if (insert_position < node->start()) {
+            insert_position = insert_position + left->count() + 1;
+            node = left;
+          }
+
+          assert(node->count() < node->max_count());
+          return;
+        }
+      }
+    }
+
+    if (node->position() < parent->finish()) {
+      // Try rebalancing with our right sibling.
+      node_type *right = parent->child(node->position() + 1);
+      assert(right->max_count() == kNodeSlots);
+      if (right->count() < kNodeSlots) {
+        // We bias rebalancing based on the position being inserted. If we're
+        // inserting at the beginning of the left node then we bias rebalancing
+        // to fill up the right node.
+        field_type to_move = (kNodeSlots - right->count()) /
+                             (1 + (insert_position > node->start()));
+        to_move = (std::max)(field_type{1}, to_move);
+
+        if (static_cast<field_type>(insert_position) <=
+                node->finish() - to_move ||
+            right->count() + to_move < kNodeSlots) {
+          node->rebalance_left_to_right(to_move, right, mutable_allocator());
+
+          if (insert_position > node->finish()) {
+            insert_position = insert_position - node->count() - 1;
+            node = right;
+          }
+
+          assert(node->count() < node->max_count());
+          return;
+        }
+      }
+    }
+
+    // Rebalancing failed, make sure there is room on the parent node for a new
+    // value.
+    assert(parent->max_count() == kNodeSlots);
+    if (parent->count() == kNodeSlots) {
+      iterator parent_iter(node->parent(), node->position());
+      rebalance_or_split(&parent_iter);
+    }
+  } else {
+    // Rebalancing not possible because this is the root node.
+    // Create a new root node and set the current root node as the child of the
+    // new root.
+    parent = new_internal_node(parent);
+    parent->set_generation(root()->generation());
+    parent->init_child(parent->start(), root());
+    mutable_root() = parent;
+    // If the former root was a leaf node, then it's now the rightmost node.
+    assert(parent->start_child()->is_internal() ||
+           parent->start_child() == rightmost());
+  }
+
+  // Split the node.
+  node_type *split_node;
+  if (node->is_leaf()) {
+    split_node = new_leaf_node(parent);
+    node->split(insert_position, split_node, mutable_allocator());
+    if (rightmost() == node) mutable_rightmost() = split_node;
+  } else {
+    split_node = new_internal_node(parent);
+    node->split(insert_position, split_node, mutable_allocator());
+  }
+
+  if (insert_position > node->finish()) {
+    insert_position = insert_position - node->count() - 1;
+    node = split_node;
+  }
+}
+
+template <typename P>
+void btree<P>::merge_nodes(node_type *left, node_type *right) {
+  left->merge(right, mutable_allocator());
+  if (rightmost() == right) mutable_rightmost() = left;
+}
+
+template <typename P>
+bool btree<P>::try_merge_or_rebalance(iterator *iter) {
+  node_type *parent = iter->node_->parent();
+  if (iter->node_->position() > parent->start()) {
+    // Try merging with our left sibling.
+    node_type *left = parent->child(iter->node_->position() - 1);
+    assert(left->max_count() == kNodeSlots);
+    if (1U + left->count() + iter->node_->count() <= kNodeSlots) {
+      iter->position_ += 1 + left->count();
+      merge_nodes(left, iter->node_);
+      iter->node_ = left;
+      return true;
+    }
+  }
+  if (iter->node_->position() < parent->finish()) {
+    // Try merging with our right sibling.
+    node_type *right = parent->child(iter->node_->position() + 1);
+    assert(right->max_count() == kNodeSlots);
+    if (1U + iter->node_->count() + right->count() <= kNodeSlots) {
+      merge_nodes(iter->node_, right);
+      return true;
+    }
+    // Try rebalancing with our right sibling. We don't perform rebalancing if
+    // we deleted the first element from iter->node_ and the node is not
+    // empty. This is a small optimization for the common pattern of deleting
+    // from the front of the tree.
+    if (right->count() > kMinNodeValues &&
+        (iter->node_->count() == 0 || iter->position_ > iter->node_->start())) {
+      field_type to_move = (right->count() - iter->node_->count()) / 2;
+      to_move =
+          (std::min)(to_move, static_cast<field_type>(right->count() - 1));
+      iter->node_->rebalance_right_to_left(to_move, right, mutable_allocator());
+      return false;
+    }
+  }
+  if (iter->node_->position() > parent->start()) {
+    // Try rebalancing with our left sibling. We don't perform rebalancing if
+    // we deleted the last element from iter->node_ and the node is not
+    // empty. This is a small optimization for the common pattern of deleting
+    // from the back of the tree.
+    node_type *left = parent->child(iter->node_->position() - 1);
+    if (left->count() > kMinNodeValues &&
+        (iter->node_->count() == 0 ||
+         iter->position_ < iter->node_->finish())) {
+      field_type to_move = (left->count() - iter->node_->count()) / 2;
+      to_move = (std::min)(to_move, static_cast<field_type>(left->count() - 1));
+      left->rebalance_left_to_right(to_move, iter->node_, mutable_allocator());
+      iter->position_ += to_move;
+      return false;
+    }
+  }
+  return false;
+}
+
+template <typename P>
+void btree<P>::try_shrink() {
+  node_type *orig_root = root();
+  if (orig_root->count() > 0) {
+    return;
+  }
+  // Deleted the last item on the root node, shrink the height of the tree.
+  if (orig_root->is_leaf()) {
+    assert(size() == 0);
+    mutable_root() = mutable_rightmost() = EmptyNode();
+  } else {
+    node_type *child = orig_root->start_child();
+    child->make_root();
+    mutable_root() = child;
+  }
+  node_type::clear_and_delete(orig_root, mutable_allocator());
+}
+
+template <typename P>
+template <typename IterType>
+inline IterType btree<P>::internal_last(IterType iter) {
+  assert(iter.node_ != nullptr);
+  while (iter.position_ == iter.node_->finish()) {
+    iter.position_ = iter.node_->position();
+    iter.node_ = iter.node_->parent();
+    if (iter.node_->is_leaf()) {
+      iter.node_ = nullptr;
+      break;
+    }
+  }
+  iter.update_generation();
+  return iter;
+}
+
+template <typename P>
+template <typename... Args>
+inline auto btree<P>::internal_emplace(iterator iter, Args &&...args)
+    -> iterator {
+  if (iter.node_->is_internal()) {
+    // We can't insert on an internal node. Instead, we'll insert after the
+    // previous value which is guaranteed to be on a leaf node.
+    --iter;
+    ++iter.position_;
+  }
+  const field_type max_count = iter.node_->max_count();
+  allocator_type *alloc = mutable_allocator();
+  if (iter.node_->count() == max_count) {
+    // Make room in the leaf for the new item.
+    if (max_count < kNodeSlots) {
+      // Insertion into the root where the root is smaller than the full node
+      // size. Simply grow the size of the root node.
+      assert(iter.node_ == root());
+      iter.node_ = new_leaf_root_node(static_cast<field_type>(
+          (std::min)(static_cast<int>(kNodeSlots), 2 * max_count)));
+      // Transfer the values from the old root to the new root.
+      node_type *old_root = root();
+      node_type *new_root = iter.node_;
+      new_root->transfer_n(old_root->count(), new_root->start(),
+                           old_root->start(), old_root, alloc);
+      new_root->set_finish(old_root->finish());
+      old_root->set_finish(old_root->start());
+      new_root->set_generation(old_root->generation());
+      node_type::clear_and_delete(old_root, alloc);
+      mutable_root() = mutable_rightmost() = new_root;
+    } else {
+      rebalance_or_split(&iter);
+    }
+  }
+  iter.node_->emplace_value(static_cast<field_type>(iter.position_), alloc,
+                            std::forward<Args>(args)...);
+  ++size_;
+  iter.update_generation();
+  return iter;
+}
+
+template <typename P>
+template <typename K>
+inline auto btree<P>::internal_locate(const K &key) const
+    -> SearchResult<iterator, is_key_compare_to::value> {
+  iterator iter(const_cast<node_type *>(root()));
+  for (;;) {
+    SearchResult<size_type, is_key_compare_to::value> res =
+        iter.node_->lower_bound(key, key_comp());
+    iter.position_ = static_cast<int>(res.value);
+    if (res.IsEq()) {
+      return {iter, MatchKind::kEq};
+    }
+    // Note: in the non-key-compare-to case, we don't need to walk all the way
+    // down the tree if the keys are equal, but determining equality would
+    // require doing an extra comparison on each node on the way down, and we
+    // will need to go all the way to the leaf node in the expected case.
+    if (iter.node_->is_leaf()) {
+      break;
+    }
+    iter.node_ = iter.node_->child(static_cast<field_type>(iter.position_));
+  }
+  // Note: in the non-key-compare-to case, the key may actually be equivalent
+  // here (and the MatchKind::kNe is ignored).
+  return {iter, MatchKind::kNe};
+}
+
+template <typename P>
+template <typename K>
+auto btree<P>::internal_lower_bound(const K &key) const
+    -> SearchResult<iterator, is_key_compare_to::value> {
+  if (!params_type::template can_have_multiple_equivalent_keys<K>()) {
+    SearchResult<iterator, is_key_compare_to::value> ret = internal_locate(key);
+    ret.value = internal_last(ret.value);
+    return ret;
+  }
+  iterator iter(const_cast<node_type *>(root()));
+  SearchResult<size_type, is_key_compare_to::value> res;
+  bool seen_eq = false;
+  for (;;) {
+    res = iter.node_->lower_bound(key, key_comp());
+    iter.position_ = static_cast<int>(res.value);
+    if (iter.node_->is_leaf()) {
+      break;
+    }
+    seen_eq = seen_eq || res.IsEq();
+    iter.node_ = iter.node_->child(static_cast<field_type>(iter.position_));
+  }
+  if (res.IsEq()) return {iter, MatchKind::kEq};
+  return {internal_last(iter), seen_eq ? MatchKind::kEq : MatchKind::kNe};
+}
+
+template <typename P>
+template <typename K>
+auto btree<P>::internal_upper_bound(const K &key) const -> iterator {
+  iterator iter(const_cast<node_type *>(root()));
+  for (;;) {
+    iter.position_ = static_cast<int>(iter.node_->upper_bound(key, key_comp()));
+    if (iter.node_->is_leaf()) {
+      break;
+    }
+    iter.node_ = iter.node_->child(static_cast<field_type>(iter.position_));
+  }
+  return internal_last(iter);
+}
+
+template <typename P>
+template <typename K>
+auto btree<P>::internal_find(const K &key) const -> iterator {
+  SearchResult<iterator, is_key_compare_to::value> res = internal_locate(key);
+  if (res.HasMatch()) {
+    if (res.IsEq()) {
+      return res.value;
+    }
+  } else {
+    const iterator iter = internal_last(res.value);
+    if (iter.node_ != nullptr && !compare_keys(key, iter.key())) {
+      return iter;
+    }
+  }
+  return {nullptr, 0};
+}
+
+template <typename P>
+typename btree<P>::size_type btree<P>::internal_verify(
+    const node_type *node, const key_type *lo, const key_type *hi) const {
+  assert(node->count() > 0);
+  assert(node->count() <= node->max_count());
+  if (lo) {
+    assert(!compare_keys(node->key(node->start()), *lo));
+  }
+  if (hi) {
+    assert(!compare_keys(*hi, node->key(node->finish() - 1)));
+  }
+  for (int i = node->start() + 1; i < node->finish(); ++i) {
+    assert(!compare_keys(node->key(i), node->key(i - 1)));
+  }
+  size_type count = node->count();
+  if (node->is_internal()) {
+    for (field_type i = node->start(); i <= node->finish(); ++i) {
+      assert(node->child(i) != nullptr);
+      assert(node->child(i)->parent() == node);
+      assert(node->child(i)->position() == i);
+      count += internal_verify(node->child(i),
+                               i == node->start() ? lo : &node->key(i - 1),
+                               i == node->finish() ? hi : &node->key(i));
+    }
+  }
+  return count;
+}
+
+struct btree_access {
+  template <typename BtreeContainer, typename Pred>
+  static auto erase_if(BtreeContainer &container, Pred pred) ->
+      typename BtreeContainer::size_type {
+    const auto initial_size = container.size();
+    auto &tree = container.tree_;
+    auto *alloc = tree.mutable_allocator();
+    for (auto it = container.begin(); it != container.end();) {
+      if (!pred(*it)) {
+        ++it;
+        continue;
+      }
+      auto *node = it.node_;
+      if (node->is_internal()) {
+        // Handle internal nodes normally.
+        it = container.erase(it);
+        continue;
+      }
+      // If this is a leaf node, then we do all the erases from this node
+      // at once before doing rebalancing.
+
+      // The current position to transfer slots to.
+      int to_pos = it.position_;
+      node->value_destroy(it.position_, alloc);
+      while (++it.position_ < node->finish()) {
+        it.update_generation();
+        if (pred(*it)) {
+          node->value_destroy(it.position_, alloc);
+        } else {
+          node->transfer(node->slot(to_pos++), node->slot(it.position_), alloc);
+        }
+      }
+      const int num_deleted = node->finish() - to_pos;
+      tree.size_ -= num_deleted;
+      node->set_finish(to_pos);
+      it.position_ = to_pos;
+      it = tree.rebalance_after_delete(it);
+    }
+    return initial_size - container.size();
+  }
+};
+
+#undef ABSL_BTREE_ENABLE_GENERATIONS
+
+}  // namespace container_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_INTERNAL_BTREE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/btree_container.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/btree_container.h
new file mode 100644
index 0000000000000000000000000000000000000000..2bff11db04295bfa083c9397b338326888438e95
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/btree_container.h
@@ -0,0 +1,713 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CONTAINER_INTERNAL_BTREE_CONTAINER_H_
+#define ABSL_CONTAINER_INTERNAL_BTREE_CONTAINER_H_
+
+#include <algorithm>
+#include <initializer_list>
+#include <iterator>
+#include <utility>
+
+#include "absl/base/attributes.h"
+#include "absl/base/internal/throw_delegate.h"
+#include "absl/container/internal/btree.h"  // IWYU pragma: export
+#include "absl/container/internal/common.h"
+#include "absl/memory/memory.h"
+#include "absl/meta/type_traits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+
+// A common base class for btree_set, btree_map, btree_multiset, and
+// btree_multimap.
+template <typename Tree>
+class btree_container {
+  using params_type = typename Tree::params_type;
+
+ protected:
+  // Alias used for heterogeneous lookup functions.
+  // `key_arg<K>` evaluates to `K` when the functors are transparent and to
+  // `key_type` otherwise. It permits template argument deduction on `K` for the
+  // transparent case.
+  template <class K>
+  using key_arg =
+      typename KeyArg<params_type::kIsKeyCompareTransparent>::template type<
+          K, typename Tree::key_type>;
+
+ public:
+  using key_type = typename Tree::key_type;
+  using value_type = typename Tree::value_type;
+  using size_type = typename Tree::size_type;
+  using difference_type = typename Tree::difference_type;
+  using key_compare = typename Tree::original_key_compare;
+  using value_compare = typename Tree::value_compare;
+  using allocator_type = typename Tree::allocator_type;
+  using reference = typename Tree::reference;
+  using const_reference = typename Tree::const_reference;
+  using pointer = typename Tree::pointer;
+  using const_pointer = typename Tree::const_pointer;
+  using iterator = typename Tree::iterator;
+  using const_iterator = typename Tree::const_iterator;
+  using reverse_iterator = typename Tree::reverse_iterator;
+  using const_reverse_iterator = typename Tree::const_reverse_iterator;
+  using node_type = typename Tree::node_handle_type;
+
+  struct extract_and_get_next_return_type {
+    node_type node;
+    iterator next;
+  };
+
+  // Constructors/assignments.
+  btree_container() : tree_(key_compare(), allocator_type()) {}
+  explicit btree_container(const key_compare &comp,
+                           const allocator_type &alloc = allocator_type())
+      : tree_(comp, alloc) {}
+  explicit btree_container(const allocator_type &alloc)
+      : tree_(key_compare(), alloc) {}
+
+  btree_container(const btree_container &other)
+      : btree_container(other, absl::allocator_traits<allocator_type>::
+                                   select_on_container_copy_construction(
+                                       other.get_allocator())) {}
+  btree_container(const btree_container &other, const allocator_type &alloc)
+      : tree_(other.tree_, alloc) {}
+
+  btree_container(btree_container &&other) noexcept(
+      std::is_nothrow_move_constructible<Tree>::value) = default;
+  btree_container(btree_container &&other, const allocator_type &alloc)
+      : tree_(std::move(other.tree_), alloc) {}
+
+  btree_container &operator=(const btree_container &other) = default;
+  btree_container &operator=(btree_container &&other) noexcept(
+      std::is_nothrow_move_assignable<Tree>::value) = default;
+
+  // Iterator routines.
+  iterator begin() { return tree_.begin(); }
+  const_iterator begin() const { return tree_.begin(); }
+  const_iterator cbegin() const { return tree_.begin(); }
+  iterator end() { return tree_.end(); }
+  const_iterator end() const { return tree_.end(); }
+  const_iterator cend() const { return tree_.end(); }
+  reverse_iterator rbegin() { return tree_.rbegin(); }
+  const_reverse_iterator rbegin() const { return tree_.rbegin(); }
+  const_reverse_iterator crbegin() const { return tree_.rbegin(); }
+  reverse_iterator rend() { return tree_.rend(); }
+  const_reverse_iterator rend() const { return tree_.rend(); }
+  const_reverse_iterator crend() const { return tree_.rend(); }
+
+  // Lookup routines.
+  template <typename K = key_type>
+  size_type count(const key_arg<K> &key) const {
+    auto equal_range = this->equal_range(key);
+    return equal_range.second - equal_range.first;
+  }
+  template <typename K = key_type>
+  iterator find(const key_arg<K> &key) {
+    return tree_.find(key);
+  }
+  template <typename K = key_type>
+  const_iterator find(const key_arg<K> &key) const {
+    return tree_.find(key);
+  }
+  template <typename K = key_type>
+  bool contains(const key_arg<K> &key) const {
+    return find(key) != end();
+  }
+  template <typename K = key_type>
+  iterator lower_bound(const key_arg<K> &key) {
+    return tree_.lower_bound(key);
+  }
+  template <typename K = key_type>
+  const_iterator lower_bound(const key_arg<K> &key) const {
+    return tree_.lower_bound(key);
+  }
+  template <typename K = key_type>
+  iterator upper_bound(const key_arg<K> &key) {
+    return tree_.upper_bound(key);
+  }
+  template <typename K = key_type>
+  const_iterator upper_bound(const key_arg<K> &key) const {
+    return tree_.upper_bound(key);
+  }
+  template <typename K = key_type>
+  std::pair<iterator, iterator> equal_range(const key_arg<K> &key) {
+    return tree_.equal_range(key);
+  }
+  template <typename K = key_type>
+  std::pair<const_iterator, const_iterator> equal_range(
+      const key_arg<K> &key) const {
+    return tree_.equal_range(key);
+  }
+
+  // Deletion routines. Note that there is also a deletion routine that is
+  // specific to btree_set_container/btree_multiset_container.
+
+  // Erase the specified iterator from the btree. The iterator must be valid
+  // (i.e. not equal to end()).  Return an iterator pointing to the node after
+  // the one that was erased (or end() if none exists).
+  iterator erase(const_iterator iter) { return tree_.erase(iterator(iter)); }
+  iterator erase(iterator iter) { return tree_.erase(iter); }
+  iterator erase(const_iterator first, const_iterator last) {
+    return tree_.erase_range(iterator(first), iterator(last)).second;
+  }
+  template <typename K = key_type>
+  size_type erase(const key_arg<K> &key) {
+    auto equal_range = this->equal_range(key);
+    return tree_.erase_range(equal_range.first, equal_range.second).first;
+  }
+
+  // Extract routines.
+  extract_and_get_next_return_type extract_and_get_next(
+      const_iterator position) {
+    // Use Construct instead of Transfer because the rebalancing code will
+    // destroy the slot later.
+    // Note: we rely on erase() taking place after Construct().
+    return {CommonAccess::Construct<node_type>(get_allocator(),
+                                               iterator(position).slot()),
+            erase(position)};
+  }
+  node_type extract(iterator position) {
+    // Use Construct instead of Transfer because the rebalancing code will
+    // destroy the slot later.
+    auto node =
+        CommonAccess::Construct<node_type>(get_allocator(), position.slot());
+    erase(position);
+    return node;
+  }
+  node_type extract(const_iterator position) {
+    return extract(iterator(position));
+  }
+
+  // Utility routines.
+  ABSL_ATTRIBUTE_REINITIALIZES void clear() { tree_.clear(); }
+  void swap(btree_container &other) { tree_.swap(other.tree_); }
+  void verify() const { tree_.verify(); }
+
+  // Size routines.
+  size_type size() const { return tree_.size(); }
+  size_type max_size() const { return tree_.max_size(); }
+  bool empty() const { return tree_.empty(); }
+
+  friend bool operator==(const btree_container &x, const btree_container &y) {
+    if (x.size() != y.size()) return false;
+    return std::equal(x.begin(), x.end(), y.begin());
+  }
+
+  friend bool operator!=(const btree_container &x, const btree_container &y) {
+    return !(x == y);
+  }
+
+  friend bool operator<(const btree_container &x, const btree_container &y) {
+    return std::lexicographical_compare(x.begin(), x.end(), y.begin(), y.end());
+  }
+
+  friend bool operator>(const btree_container &x, const btree_container &y) {
+    return y < x;
+  }
+
+  friend bool operator<=(const btree_container &x, const btree_container &y) {
+    return !(y < x);
+  }
+
+  friend bool operator>=(const btree_container &x, const btree_container &y) {
+    return !(x < y);
+  }
+
+  // The allocator used by the btree.
+  allocator_type get_allocator() const { return tree_.get_allocator(); }
+
+  // The key comparator used by the btree.
+  key_compare key_comp() const { return key_compare(tree_.key_comp()); }
+  value_compare value_comp() const { return tree_.value_comp(); }
+
+  // Support absl::Hash.
+  template <typename State>
+  friend State AbslHashValue(State h, const btree_container &b) {
+    for (const auto &v : b) {
+      h = State::combine(std::move(h), v);
+    }
+    return State::combine(std::move(h), b.size());
+  }
+
+ protected:
+  friend struct btree_access;
+  Tree tree_;
+};
+
+// A common base class for btree_set and btree_map.
+template <typename Tree>
+class btree_set_container : public btree_container<Tree> {
+  using super_type = btree_container<Tree>;
+  using params_type = typename Tree::params_type;
+  using init_type = typename params_type::init_type;
+  using is_key_compare_to = typename params_type::is_key_compare_to;
+  friend class BtreeNodePeer;
+
+ protected:
+  template <class K>
+  using key_arg = typename super_type::template key_arg<K>;
+
+ public:
+  using key_type = typename Tree::key_type;
+  using value_type = typename Tree::value_type;
+  using size_type = typename Tree::size_type;
+  using key_compare = typename Tree::original_key_compare;
+  using allocator_type = typename Tree::allocator_type;
+  using iterator = typename Tree::iterator;
+  using const_iterator = typename Tree::const_iterator;
+  using node_type = typename super_type::node_type;
+  using insert_return_type = InsertReturnType<iterator, node_type>;
+
+  // Inherit constructors.
+  using super_type::super_type;
+  btree_set_container() {}
+
+  // Range constructors.
+  template <class InputIterator>
+  btree_set_container(InputIterator b, InputIterator e,
+                      const key_compare &comp = key_compare(),
+                      const allocator_type &alloc = allocator_type())
+      : super_type(comp, alloc) {
+    insert(b, e);
+  }
+  template <class InputIterator>
+  btree_set_container(InputIterator b, InputIterator e,
+                      const allocator_type &alloc)
+      : btree_set_container(b, e, key_compare(), alloc) {}
+
+  // Initializer list constructors.
+  btree_set_container(std::initializer_list<init_type> init,
+                      const key_compare &comp = key_compare(),
+                      const allocator_type &alloc = allocator_type())
+      : btree_set_container(init.begin(), init.end(), comp, alloc) {}
+  btree_set_container(std::initializer_list<init_type> init,
+                      const allocator_type &alloc)
+      : btree_set_container(init.begin(), init.end(), alloc) {}
+
+  // Insertion routines.
+  std::pair<iterator, bool> insert(const value_type &v) {
+    return this->tree_.insert_unique(params_type::key(v), v);
+  }
+  std::pair<iterator, bool> insert(value_type &&v) {
+    return this->tree_.insert_unique(params_type::key(v), std::move(v));
+  }
+  template <typename... Args>
+  std::pair<iterator, bool> emplace(Args &&... args) {
+    // Use a node handle to manage a temp slot.
+    auto node = CommonAccess::Construct<node_type>(this->get_allocator(),
+                                                   std::forward<Args>(args)...);
+    auto *slot = CommonAccess::GetSlot(node);
+    return this->tree_.insert_unique(params_type::key(slot), slot);
+  }
+  iterator insert(const_iterator hint, const value_type &v) {
+    return this->tree_
+        .insert_hint_unique(iterator(hint), params_type::key(v), v)
+        .first;
+  }
+  iterator insert(const_iterator hint, value_type &&v) {
+    return this->tree_
+        .insert_hint_unique(iterator(hint), params_type::key(v), std::move(v))
+        .first;
+  }
+  template <typename... Args>
+  iterator emplace_hint(const_iterator hint, Args &&... args) {
+    // Use a node handle to manage a temp slot.
+    auto node = CommonAccess::Construct<node_type>(this->get_allocator(),
+                                                   std::forward<Args>(args)...);
+    auto *slot = CommonAccess::GetSlot(node);
+    return this->tree_
+        .insert_hint_unique(iterator(hint), params_type::key(slot), slot)
+        .first;
+  }
+  template <typename InputIterator>
+  void insert(InputIterator b, InputIterator e) {
+    this->tree_.insert_iterator_unique(b, e, 0);
+  }
+  void insert(std::initializer_list<init_type> init) {
+    this->tree_.insert_iterator_unique(init.begin(), init.end(), 0);
+  }
+  insert_return_type insert(node_type &&node) {
+    if (!node) return {this->end(), false, node_type()};
+    std::pair<iterator, bool> res =
+        this->tree_.insert_unique(params_type::key(CommonAccess::GetSlot(node)),
+                                  CommonAccess::GetSlot(node));
+    if (res.second) {
+      CommonAccess::Destroy(&node);
+      return {res.first, true, node_type()};
+    } else {
+      return {res.first, false, std::move(node)};
+    }
+  }
+  iterator insert(const_iterator hint, node_type &&node) {
+    if (!node) return this->end();
+    std::pair<iterator, bool> res = this->tree_.insert_hint_unique(
+        iterator(hint), params_type::key(CommonAccess::GetSlot(node)),
+        CommonAccess::GetSlot(node));
+    if (res.second) CommonAccess::Destroy(&node);
+    return res.first;
+  }
+
+  // Node extraction routines.
+  template <typename K = key_type>
+  node_type extract(const key_arg<K> &key) {
+    const std::pair<iterator, bool> lower_and_equal =
+        this->tree_.lower_bound_equal(key);
+    return lower_and_equal.second ? extract(lower_and_equal.first)
+                                  : node_type();
+  }
+  using super_type::extract;
+
+  // Merge routines.
+  // Moves elements from `src` into `this`. If the element already exists in
+  // `this`, it is left unmodified in `src`.
+  template <
+      typename T,
+      typename absl::enable_if_t<
+          absl::conjunction<
+              std::is_same<value_type, typename T::value_type>,
+              std::is_same<allocator_type, typename T::allocator_type>,
+              std::is_same<typename params_type::is_map_container,
+                           typename T::params_type::is_map_container>>::value,
+          int> = 0>
+  void merge(btree_container<T> &src) {  // NOLINT
+    for (auto src_it = src.begin(); src_it != src.end();) {
+      if (insert(std::move(params_type::element(src_it.slot()))).second) {
+        src_it = src.erase(src_it);
+      } else {
+        ++src_it;
+      }
+    }
+  }
+
+  template <
+      typename T,
+      typename absl::enable_if_t<
+          absl::conjunction<
+              std::is_same<value_type, typename T::value_type>,
+              std::is_same<allocator_type, typename T::allocator_type>,
+              std::is_same<typename params_type::is_map_container,
+                           typename T::params_type::is_map_container>>::value,
+          int> = 0>
+  void merge(btree_container<T> &&src) {
+    merge(src);
+  }
+};
+
+// Base class for btree_map.
+template <typename Tree>
+class btree_map_container : public btree_set_container<Tree> {
+  using super_type = btree_set_container<Tree>;
+  using params_type = typename Tree::params_type;
+  friend class BtreeNodePeer;
+
+ private:
+  template <class K>
+  using key_arg = typename super_type::template key_arg<K>;
+
+ public:
+  using key_type = typename Tree::key_type;
+  using mapped_type = typename params_type::mapped_type;
+  using value_type = typename Tree::value_type;
+  using key_compare = typename Tree::original_key_compare;
+  using allocator_type = typename Tree::allocator_type;
+  using iterator = typename Tree::iterator;
+  using const_iterator = typename Tree::const_iterator;
+
+  // Inherit constructors.
+  using super_type::super_type;
+  btree_map_container() {}
+
+  // Insertion routines.
+  // Note: the nullptr template arguments and extra `const M&` overloads allow
+  // for supporting bitfield arguments.
+  template <typename K = key_type, class M>
+  std::pair<iterator, bool> insert_or_assign(const key_arg<K> &k,
+                                             const M &obj) {
+    return insert_or_assign_impl(k, obj);
+  }
+  template <typename K = key_type, class M, K * = nullptr>
+  std::pair<iterator, bool> insert_or_assign(key_arg<K> &&k, const M &obj) {
+    return insert_or_assign_impl(std::forward<K>(k), obj);
+  }
+  template <typename K = key_type, class M, M * = nullptr>
+  std::pair<iterator, bool> insert_or_assign(const key_arg<K> &k, M &&obj) {
+    return insert_or_assign_impl(k, std::forward<M>(obj));
+  }
+  template <typename K = key_type, class M, K * = nullptr, M * = nullptr>
+  std::pair<iterator, bool> insert_or_assign(key_arg<K> &&k, M &&obj) {
+    return insert_or_assign_impl(std::forward<K>(k), std::forward<M>(obj));
+  }
+  template <typename K = key_type, class M>
+  iterator insert_or_assign(const_iterator hint, const key_arg<K> &k,
+                            const M &obj) {
+    return insert_or_assign_hint_impl(hint, k, obj);
+  }
+  template <typename K = key_type, class M, K * = nullptr>
+  iterator insert_or_assign(const_iterator hint, key_arg<K> &&k, const M &obj) {
+    return insert_or_assign_hint_impl(hint, std::forward<K>(k), obj);
+  }
+  template <typename K = key_type, class M, M * = nullptr>
+  iterator insert_or_assign(const_iterator hint, const key_arg<K> &k, M &&obj) {
+    return insert_or_assign_hint_impl(hint, k, std::forward<M>(obj));
+  }
+  template <typename K = key_type, class M, K * = nullptr, M * = nullptr>
+  iterator insert_or_assign(const_iterator hint, key_arg<K> &&k, M &&obj) {
+    return insert_or_assign_hint_impl(hint, std::forward<K>(k),
+                                      std::forward<M>(obj));
+  }
+
+  template <typename K = key_type, typename... Args,
+            typename absl::enable_if_t<
+                !std::is_convertible<K, const_iterator>::value, int> = 0>
+  std::pair<iterator, bool> try_emplace(const key_arg<K> &k, Args &&... args) {
+    return try_emplace_impl(k, std::forward<Args>(args)...);
+  }
+  template <typename K = key_type, typename... Args,
+            typename absl::enable_if_t<
+                !std::is_convertible<K, const_iterator>::value, int> = 0>
+  std::pair<iterator, bool> try_emplace(key_arg<K> &&k, Args &&... args) {
+    return try_emplace_impl(std::forward<K>(k), std::forward<Args>(args)...);
+  }
+  template <typename K = key_type, typename... Args>
+  iterator try_emplace(const_iterator hint, const key_arg<K> &k,
+                       Args &&... args) {
+    return try_emplace_hint_impl(hint, k, std::forward<Args>(args)...);
+  }
+  template <typename K = key_type, typename... Args>
+  iterator try_emplace(const_iterator hint, key_arg<K> &&k, Args &&... args) {
+    return try_emplace_hint_impl(hint, std::forward<K>(k),
+                                 std::forward<Args>(args)...);
+  }
+
+  template <typename K = key_type>
+  mapped_type &operator[](const key_arg<K> &k) {
+    return try_emplace(k).first->second;
+  }
+  template <typename K = key_type>
+  mapped_type &operator[](key_arg<K> &&k) {
+    return try_emplace(std::forward<K>(k)).first->second;
+  }
+
+  template <typename K = key_type>
+  mapped_type &at(const key_arg<K> &key) {
+    auto it = this->find(key);
+    if (it == this->end())
+      base_internal::ThrowStdOutOfRange("absl::btree_map::at");
+    return it->second;
+  }
+  template <typename K = key_type>
+  const mapped_type &at(const key_arg<K> &key) const {
+    auto it = this->find(key);
+    if (it == this->end())
+      base_internal::ThrowStdOutOfRange("absl::btree_map::at");
+    return it->second;
+  }
+
+ private:
+  // Note: when we call `std::forward<M>(obj)` twice, it's safe because
+  // insert_unique/insert_hint_unique are guaranteed to not consume `obj` when
+  // `ret.second` is false.
+  template <class K, class M>
+  std::pair<iterator, bool> insert_or_assign_impl(K &&k, M &&obj) {
+    const std::pair<iterator, bool> ret =
+        this->tree_.insert_unique(k, std::forward<K>(k), std::forward<M>(obj));
+    if (!ret.second) ret.first->second = std::forward<M>(obj);
+    return ret;
+  }
+  template <class K, class M>
+  iterator insert_or_assign_hint_impl(const_iterator hint, K &&k, M &&obj) {
+    const std::pair<iterator, bool> ret = this->tree_.insert_hint_unique(
+        iterator(hint), k, std::forward<K>(k), std::forward<M>(obj));
+    if (!ret.second) ret.first->second = std::forward<M>(obj);
+    return ret.first;
+  }
+
+  template <class K, class... Args>
+  std::pair<iterator, bool> try_emplace_impl(K &&k, Args &&... args) {
+    return this->tree_.insert_unique(
+        k, std::piecewise_construct, std::forward_as_tuple(std::forward<K>(k)),
+        std::forward_as_tuple(std::forward<Args>(args)...));
+  }
+  template <class K, class... Args>
+  iterator try_emplace_hint_impl(const_iterator hint, K &&k, Args &&... args) {
+    return this->tree_
+        .insert_hint_unique(iterator(hint), k, std::piecewise_construct,
+                            std::forward_as_tuple(std::forward<K>(k)),
+                            std::forward_as_tuple(std::forward<Args>(args)...))
+        .first;
+  }
+};
+
+// A common base class for btree_multiset and btree_multimap.
+template <typename Tree>
+class btree_multiset_container : public btree_container<Tree> {
+  using super_type = btree_container<Tree>;
+  using params_type = typename Tree::params_type;
+  using init_type = typename params_type::init_type;
+  using is_key_compare_to = typename params_type::is_key_compare_to;
+  friend class BtreeNodePeer;
+
+  template <class K>
+  using key_arg = typename super_type::template key_arg<K>;
+
+ public:
+  using key_type = typename Tree::key_type;
+  using value_type = typename Tree::value_type;
+  using size_type = typename Tree::size_type;
+  using key_compare = typename Tree::original_key_compare;
+  using allocator_type = typename Tree::allocator_type;
+  using iterator = typename Tree::iterator;
+  using const_iterator = typename Tree::const_iterator;
+  using node_type = typename super_type::node_type;
+
+  // Inherit constructors.
+  using super_type::super_type;
+  btree_multiset_container() {}
+
+  // Range constructors.
+  template <class InputIterator>
+  btree_multiset_container(InputIterator b, InputIterator e,
+                           const key_compare &comp = key_compare(),
+                           const allocator_type &alloc = allocator_type())
+      : super_type(comp, alloc) {
+    insert(b, e);
+  }
+  template <class InputIterator>
+  btree_multiset_container(InputIterator b, InputIterator e,
+                           const allocator_type &alloc)
+      : btree_multiset_container(b, e, key_compare(), alloc) {}
+
+  // Initializer list constructors.
+  btree_multiset_container(std::initializer_list<init_type> init,
+                           const key_compare &comp = key_compare(),
+                           const allocator_type &alloc = allocator_type())
+      : btree_multiset_container(init.begin(), init.end(), comp, alloc) {}
+  btree_multiset_container(std::initializer_list<init_type> init,
+                           const allocator_type &alloc)
+      : btree_multiset_container(init.begin(), init.end(), alloc) {}
+
+  // Insertion routines.
+  iterator insert(const value_type &v) { return this->tree_.insert_multi(v); }
+  iterator insert(value_type &&v) {
+    return this->tree_.insert_multi(std::move(v));
+  }
+  iterator insert(const_iterator hint, const value_type &v) {
+    return this->tree_.insert_hint_multi(iterator(hint), v);
+  }
+  iterator insert(const_iterator hint, value_type &&v) {
+    return this->tree_.insert_hint_multi(iterator(hint), std::move(v));
+  }
+  template <typename InputIterator>
+  void insert(InputIterator b, InputIterator e) {
+    this->tree_.insert_iterator_multi(b, e);
+  }
+  void insert(std::initializer_list<init_type> init) {
+    this->tree_.insert_iterator_multi(init.begin(), init.end());
+  }
+  template <typename... Args>
+  iterator emplace(Args &&... args) {
+    // Use a node handle to manage a temp slot.
+    auto node = CommonAccess::Construct<node_type>(this->get_allocator(),
+                                                   std::forward<Args>(args)...);
+    return this->tree_.insert_multi(CommonAccess::GetSlot(node));
+  }
+  template <typename... Args>
+  iterator emplace_hint(const_iterator hint, Args &&... args) {
+    // Use a node handle to manage a temp slot.
+    auto node = CommonAccess::Construct<node_type>(this->get_allocator(),
+                                                   std::forward<Args>(args)...);
+    return this->tree_.insert_hint_multi(iterator(hint),
+                                         CommonAccess::GetSlot(node));
+  }
+  iterator insert(node_type &&node) {
+    if (!node) return this->end();
+    iterator res =
+        this->tree_.insert_multi(params_type::key(CommonAccess::GetSlot(node)),
+                                 CommonAccess::GetSlot(node));
+    CommonAccess::Destroy(&node);
+    return res;
+  }
+  iterator insert(const_iterator hint, node_type &&node) {
+    if (!node) return this->end();
+    iterator res = this->tree_.insert_hint_multi(
+        iterator(hint),
+        std::move(params_type::element(CommonAccess::GetSlot(node))));
+    CommonAccess::Destroy(&node);
+    return res;
+  }
+
+  // Node extraction routines.
+  template <typename K = key_type>
+  node_type extract(const key_arg<K> &key) {
+    const std::pair<iterator, bool> lower_and_equal =
+        this->tree_.lower_bound_equal(key);
+    return lower_and_equal.second ? extract(lower_and_equal.first)
+                                  : node_type();
+  }
+  using super_type::extract;
+
+  // Merge routines.
+  // Moves all elements from `src` into `this`.
+  template <
+      typename T,
+      typename absl::enable_if_t<
+          absl::conjunction<
+              std::is_same<value_type, typename T::value_type>,
+              std::is_same<allocator_type, typename T::allocator_type>,
+              std::is_same<typename params_type::is_map_container,
+                           typename T::params_type::is_map_container>>::value,
+          int> = 0>
+  void merge(btree_container<T> &src) {  // NOLINT
+    for (auto src_it = src.begin(), end = src.end(); src_it != end; ++src_it) {
+      insert(std::move(params_type::element(src_it.slot())));
+    }
+    src.clear();
+  }
+
+  template <
+      typename T,
+      typename absl::enable_if_t<
+          absl::conjunction<
+              std::is_same<value_type, typename T::value_type>,
+              std::is_same<allocator_type, typename T::allocator_type>,
+              std::is_same<typename params_type::is_map_container,
+                           typename T::params_type::is_map_container>>::value,
+          int> = 0>
+  void merge(btree_container<T> &&src) {
+    merge(src);
+  }
+};
+
+// A base class for btree_multimap.
+template <typename Tree>
+class btree_multimap_container : public btree_multiset_container<Tree> {
+  using super_type = btree_multiset_container<Tree>;
+  using params_type = typename Tree::params_type;
+  friend class BtreeNodePeer;
+
+ public:
+  using mapped_type = typename params_type::mapped_type;
+
+  // Inherit constructors.
+  using super_type::super_type;
+  btree_multimap_container() {}
+};
+
+}  // namespace container_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_INTERNAL_BTREE_CONTAINER_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/common.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/common.h
new file mode 100644
index 0000000000000000000000000000000000000000..9239bb4d0938ce2e7c6c1b16f3e536addf38dac8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/common.h
@@ -0,0 +1,207 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CONTAINER_INTERNAL_COMMON_H_
+#define ABSL_CONTAINER_INTERNAL_COMMON_H_
+
+#include <cassert>
+#include <type_traits>
+
+#include "absl/meta/type_traits.h"
+#include "absl/types/optional.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+
+template <class, class = void>
+struct IsTransparent : std::false_type {};
+template <class T>
+struct IsTransparent<T, absl::void_t<typename T::is_transparent>>
+    : std::true_type {};
+
+template <bool is_transparent>
+struct KeyArg {
+  // Transparent. Forward `K`.
+  template <typename K, typename key_type>
+  using type = K;
+};
+
+template <>
+struct KeyArg<false> {
+  // Not transparent. Always use `key_type`.
+  template <typename K, typename key_type>
+  using type = key_type;
+};
+
+// The node_handle concept from C++17.
+// We specialize node_handle for sets and maps. node_handle_base holds the
+// common API of both.
+template <typename PolicyTraits, typename Alloc>
+class node_handle_base {
+ protected:
+  using slot_type = typename PolicyTraits::slot_type;
+
+ public:
+  using allocator_type = Alloc;
+
+  constexpr node_handle_base() = default;
+  node_handle_base(node_handle_base&& other) noexcept {
+    *this = std::move(other);
+  }
+  ~node_handle_base() { destroy(); }
+  node_handle_base& operator=(node_handle_base&& other) noexcept {
+    destroy();
+    if (!other.empty()) {
+      alloc_ = other.alloc_;
+      PolicyTraits::transfer(alloc(), slot(), other.slot());
+      other.reset();
+    }
+    return *this;
+  }
+
+  bool empty() const noexcept { return !alloc_; }
+  explicit operator bool() const noexcept { return !empty(); }
+  allocator_type get_allocator() const { return *alloc_; }
+
+ protected:
+  friend struct CommonAccess;
+
+  struct transfer_tag_t {};
+  node_handle_base(transfer_tag_t, const allocator_type& a, slot_type* s)
+      : alloc_(a) {
+    PolicyTraits::transfer(alloc(), slot(), s);
+  }
+
+  struct construct_tag_t {};
+  template <typename... Args>
+  node_handle_base(construct_tag_t, const allocator_type& a, Args&&... args)
+      : alloc_(a) {
+    PolicyTraits::construct(alloc(), slot(), std::forward<Args>(args)...);
+  }
+
+  void destroy() {
+    if (!empty()) {
+      PolicyTraits::destroy(alloc(), slot());
+      reset();
+    }
+  }
+
+  void reset() {
+    assert(alloc_.has_value());
+    alloc_ = absl::nullopt;
+  }
+
+  slot_type* slot() const {
+    assert(!empty());
+    return reinterpret_cast<slot_type*>(std::addressof(slot_space_));
+  }
+  allocator_type* alloc() { return std::addressof(*alloc_); }
+
+ private:
+  absl::optional<allocator_type> alloc_ = {};
+  alignas(slot_type) mutable unsigned char slot_space_[sizeof(slot_type)] = {};
+};
+
+// For sets.
+template <typename Policy, typename PolicyTraits, typename Alloc,
+          typename = void>
+class node_handle : public node_handle_base<PolicyTraits, Alloc> {
+  using Base = node_handle_base<PolicyTraits, Alloc>;
+
+ public:
+  using value_type = typename PolicyTraits::value_type;
+
+  constexpr node_handle() {}
+
+  value_type& value() const { return PolicyTraits::element(this->slot()); }
+
+ private:
+  friend struct CommonAccess;
+
+  using Base::Base;
+};
+
+// For maps.
+template <typename Policy, typename PolicyTraits, typename Alloc>
+class node_handle<Policy, PolicyTraits, Alloc,
+                  absl::void_t<typename Policy::mapped_type>>
+    : public node_handle_base<PolicyTraits, Alloc> {
+  using Base = node_handle_base<PolicyTraits, Alloc>;
+  using slot_type = typename PolicyTraits::slot_type;
+
+ public:
+  using key_type = typename Policy::key_type;
+  using mapped_type = typename Policy::mapped_type;
+
+  constexpr node_handle() {}
+
+  // When C++17 is available, we can use std::launder to provide mutable
+  // access to the key. Otherwise, we provide const access.
+  auto key() const
+      -> decltype(PolicyTraits::mutable_key(std::declval<slot_type*>())) {
+    return PolicyTraits::mutable_key(this->slot());
+  }
+
+  mapped_type& mapped() const {
+    return PolicyTraits::value(&PolicyTraits::element(this->slot()));
+  }
+
+ private:
+  friend struct CommonAccess;
+
+  using Base::Base;
+};
+
+// Provide access to non-public node-handle functions.
+struct CommonAccess {
+  template <typename Node>
+  static auto GetSlot(const Node& node) -> decltype(node.slot()) {
+    return node.slot();
+  }
+
+  template <typename Node>
+  static void Destroy(Node* node) {
+    node->destroy();
+  }
+
+  template <typename Node>
+  static void Reset(Node* node) {
+    node->reset();
+  }
+
+  template <typename T, typename... Args>
+  static T Transfer(Args&&... args) {
+    return T(typename T::transfer_tag_t{}, std::forward<Args>(args)...);
+  }
+
+  template <typename T, typename... Args>
+  static T Construct(Args&&... args) {
+    return T(typename T::construct_tag_t{}, std::forward<Args>(args)...);
+  }
+};
+
+// Implement the insert_return_type<> concept of C++17.
+template <class Iterator, class NodeType>
+struct InsertReturnType {
+  Iterator position;
+  bool inserted;
+  NodeType node;
+};
+
+}  // namespace container_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_INTERNAL_COMMON_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/common_policy_traits.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/common_policy_traits.h
new file mode 100644
index 0000000000000000000000000000000000000000..b42c9a482ea4a2cb6edc2d75e39df0780d9aea9e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/common_policy_traits.h
@@ -0,0 +1,132 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CONTAINER_INTERNAL_COMMON_POLICY_TRAITS_H_
+#define ABSL_CONTAINER_INTERNAL_COMMON_POLICY_TRAITS_H_
+
+#include <cstddef>
+#include <cstring>
+#include <memory>
+#include <new>
+#include <type_traits>
+#include <utility>
+
+#include "absl/meta/type_traits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+
+// Defines how slots are initialized/destroyed/moved.
+template <class Policy, class = void>
+struct common_policy_traits {
+  // The actual object stored in the container.
+  using slot_type = typename Policy::slot_type;
+  using reference = decltype(Policy::element(std::declval<slot_type*>()));
+  using value_type = typename std::remove_reference<reference>::type;
+
+  // PRECONDITION: `slot` is UNINITIALIZED
+  // POSTCONDITION: `slot` is INITIALIZED
+  template <class Alloc, class... Args>
+  static void construct(Alloc* alloc, slot_type* slot, Args&&... args) {
+    Policy::construct(alloc, slot, std::forward<Args>(args)...);
+  }
+
+  // PRECONDITION: `slot` is INITIALIZED
+  // POSTCONDITION: `slot` is UNINITIALIZED
+  template <class Alloc>
+  static void destroy(Alloc* alloc, slot_type* slot) {
+    Policy::destroy(alloc, slot);
+  }
+
+  // Transfers the `old_slot` to `new_slot`. Any memory allocated by the
+  // allocator inside `old_slot` to `new_slot` can be transferred.
+  //
+  // OPTIONAL: defaults to:
+  //
+  //     clone(new_slot, std::move(*old_slot));
+  //     destroy(old_slot);
+  //
+  // PRECONDITION: `new_slot` is UNINITIALIZED and `old_slot` is INITIALIZED
+  // POSTCONDITION: `new_slot` is INITIALIZED and `old_slot` is
+  //                UNINITIALIZED
+  template <class Alloc>
+  static void transfer(Alloc* alloc, slot_type* new_slot, slot_type* old_slot) {
+    transfer_impl(alloc, new_slot, old_slot, Rank0{});
+  }
+
+  // PRECONDITION: `slot` is INITIALIZED
+  // POSTCONDITION: `slot` is INITIALIZED
+  // Note: we use remove_const_t so that the two overloads have different args
+  // in the case of sets with explicitly const value_types.
+  template <class P = Policy>
+  static auto element(absl::remove_const_t<slot_type>* slot)
+      -> decltype(P::element(slot)) {
+    return P::element(slot);
+  }
+  template <class P = Policy>
+  static auto element(const slot_type* slot) -> decltype(P::element(slot)) {
+    return P::element(slot);
+  }
+
+  static constexpr bool transfer_uses_memcpy() {
+    return std::is_same<decltype(transfer_impl<std::allocator<char>>(
+                            nullptr, nullptr, nullptr, Rank0{})),
+                        std::true_type>::value;
+  }
+
+ private:
+  // To rank the overloads below for overload resoltion. Rank0 is preferred.
+  struct Rank2 {};
+  struct Rank1 : Rank2 {};
+  struct Rank0 : Rank1 {};
+
+  // Use auto -> decltype as an enabler.
+  template <class Alloc, class P = Policy>
+  static auto transfer_impl(Alloc* alloc, slot_type* new_slot,
+                            slot_type* old_slot, Rank0)
+      -> decltype((void)P::transfer(alloc, new_slot, old_slot)) {
+    P::transfer(alloc, new_slot, old_slot);
+  }
+#if defined(__cpp_lib_launder) && __cpp_lib_launder >= 201606
+  // This overload returns true_type for the trait below.
+  // The conditional_t is to make the enabler type dependent.
+  template <class Alloc,
+            typename = std::enable_if_t<absl::is_trivially_relocatable<
+                std::conditional_t<false, Alloc, value_type>>::value>>
+  static std::true_type transfer_impl(Alloc*, slot_type* new_slot,
+                                      slot_type* old_slot, Rank1) {
+    // TODO(b/247130232): remove casts after fixing warnings.
+    // TODO(b/251814870): remove casts after fixing warnings.
+    std::memcpy(
+        static_cast<void*>(std::launder(
+            const_cast<std::remove_const_t<value_type>*>(&element(new_slot)))),
+        static_cast<const void*>(&element(old_slot)), sizeof(value_type));
+    return {};
+  }
+#endif
+
+  template <class Alloc>
+  static void transfer_impl(Alloc* alloc, slot_type* new_slot,
+                            slot_type* old_slot, Rank2) {
+    construct(alloc, new_slot, std::move(element(old_slot)));
+    destroy(alloc, old_slot);
+  }
+};
+
+}  // namespace container_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_INTERNAL_COMMON_POLICY_TRAITS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/compressed_tuple.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/compressed_tuple.h
new file mode 100644
index 0000000000000000000000000000000000000000..5ebe164942490273ec032d51bfacd3ac4a8be5c5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/compressed_tuple.h
@@ -0,0 +1,290 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Helper class to perform the Empty Base Optimization.
+// Ts can contain classes and non-classes, empty or not. For the ones that
+// are empty classes, we perform the optimization. If all types in Ts are empty
+// classes, then CompressedTuple<Ts...> is itself an empty class.
+//
+// To access the members, use member get<N>() function.
+//
+// Eg:
+//   absl::container_internal::CompressedTuple<int, T1, T2, T3> value(7, t1, t2,
+//                                                                    t3);
+//   assert(value.get<0>() == 7);
+//   T1& t1 = value.get<1>();
+//   const T2& t2 = value.get<2>();
+//   ...
+//
+// https://en.cppreference.com/w/cpp/language/ebo
+
+#ifndef ABSL_CONTAINER_INTERNAL_COMPRESSED_TUPLE_H_
+#define ABSL_CONTAINER_INTERNAL_COMPRESSED_TUPLE_H_
+
+#include <initializer_list>
+#include <tuple>
+#include <type_traits>
+#include <utility>
+
+#include "absl/utility/utility.h"
+
+#if defined(_MSC_VER) && !defined(__NVCC__)
+// We need to mark these classes with this declspec to ensure that
+// CompressedTuple happens.
+#define ABSL_INTERNAL_COMPRESSED_TUPLE_DECLSPEC __declspec(empty_bases)
+#else
+#define ABSL_INTERNAL_COMPRESSED_TUPLE_DECLSPEC
+#endif
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+
+template <typename... Ts>
+class CompressedTuple;
+
+namespace internal_compressed_tuple {
+
+template <typename D, size_t I>
+struct Elem;
+template <typename... B, size_t I>
+struct Elem<CompressedTuple<B...>, I>
+    : std::tuple_element<I, std::tuple<B...>> {};
+template <typename D, size_t I>
+using ElemT = typename Elem<D, I>::type;
+
+// Use the __is_final intrinsic if available. Where it's not available, classes
+// declared with the 'final' specifier cannot be used as CompressedTuple
+// elements.
+// TODO(sbenza): Replace this with std::is_final in C++14.
+template <typename T>
+constexpr bool IsFinal() {
+#if defined(__clang__) || defined(__GNUC__)
+  return __is_final(T);
+#else
+  return false;
+#endif
+}
+
+// We can't use EBCO on other CompressedTuples because that would mean that we
+// derive from multiple Storage<> instantiations with the same I parameter,
+// and potentially from multiple identical Storage<> instantiations.  So anytime
+// we use type inheritance rather than encapsulation, we mark
+// CompressedTupleImpl, to make this easy to detect.
+struct uses_inheritance {};
+
+template <typename T>
+constexpr bool ShouldUseBase() {
+  return std::is_class<T>::value && std::is_empty<T>::value && !IsFinal<T>() &&
+         !std::is_base_of<uses_inheritance, T>::value;
+}
+
+// The storage class provides two specializations:
+//  - For empty classes, it stores T as a base class.
+//  - For everything else, it stores T as a member.
+template <typename T, size_t I,
+#if defined(_MSC_VER)
+          bool UseBase =
+              ShouldUseBase<typename std::enable_if<true, T>::type>()>
+#else
+          bool UseBase = ShouldUseBase<T>()>
+#endif
+struct Storage {
+  T value;
+  constexpr Storage() = default;
+  template <typename V>
+  explicit constexpr Storage(absl::in_place_t, V&& v)
+      : value(absl::forward<V>(v)) {}
+  constexpr const T& get() const& { return value; }
+  T& get() & { return value; }
+  constexpr const T&& get() const&& { return absl::move(*this).value; }
+  T&& get() && { return std::move(*this).value; }
+};
+
+template <typename T, size_t I>
+struct ABSL_INTERNAL_COMPRESSED_TUPLE_DECLSPEC Storage<T, I, true> : T {
+  constexpr Storage() = default;
+
+  template <typename V>
+  explicit constexpr Storage(absl::in_place_t, V&& v)
+      : T(absl::forward<V>(v)) {}
+
+  constexpr const T& get() const& { return *this; }
+  T& get() & { return *this; }
+  constexpr const T&& get() const&& { return absl::move(*this); }
+  T&& get() && { return std::move(*this); }
+};
+
+template <typename D, typename I, bool ShouldAnyUseBase>
+struct ABSL_INTERNAL_COMPRESSED_TUPLE_DECLSPEC CompressedTupleImpl;
+
+template <typename... Ts, size_t... I, bool ShouldAnyUseBase>
+struct ABSL_INTERNAL_COMPRESSED_TUPLE_DECLSPEC CompressedTupleImpl<
+    CompressedTuple<Ts...>, absl::index_sequence<I...>, ShouldAnyUseBase>
+    // We use the dummy identity function through std::integral_constant to
+    // convince MSVC of accepting and expanding I in that context. Without it
+    // you would get:
+    //   error C3548: 'I': parameter pack cannot be used in this context
+    : uses_inheritance,
+      Storage<Ts, std::integral_constant<size_t, I>::value>... {
+  constexpr CompressedTupleImpl() = default;
+  template <typename... Vs>
+  explicit constexpr CompressedTupleImpl(absl::in_place_t, Vs&&... args)
+      : Storage<Ts, I>(absl::in_place, absl::forward<Vs>(args))... {}
+  friend CompressedTuple<Ts...>;
+};
+
+template <typename... Ts, size_t... I>
+struct ABSL_INTERNAL_COMPRESSED_TUPLE_DECLSPEC CompressedTupleImpl<
+    CompressedTuple<Ts...>, absl::index_sequence<I...>, false>
+    // We use the dummy identity function as above...
+    : Storage<Ts, std::integral_constant<size_t, I>::value, false>... {
+  constexpr CompressedTupleImpl() = default;
+  template <typename... Vs>
+  explicit constexpr CompressedTupleImpl(absl::in_place_t, Vs&&... args)
+      : Storage<Ts, I, false>(absl::in_place, absl::forward<Vs>(args))... {}
+  friend CompressedTuple<Ts...>;
+};
+
+std::false_type Or(std::initializer_list<std::false_type>);
+std::true_type Or(std::initializer_list<bool>);
+
+// MSVC requires this to be done separately rather than within the declaration
+// of CompressedTuple below.
+template <typename... Ts>
+constexpr bool ShouldAnyUseBase() {
+  return decltype(
+      Or({std::integral_constant<bool, ShouldUseBase<Ts>()>()...})){};
+}
+
+template <typename T, typename V>
+using TupleElementMoveConstructible =
+    typename std::conditional<std::is_reference<T>::value,
+                              std::is_convertible<V, T>,
+                              std::is_constructible<T, V&&>>::type;
+
+template <bool SizeMatches, class T, class... Vs>
+struct TupleMoveConstructible : std::false_type {};
+
+template <class... Ts, class... Vs>
+struct TupleMoveConstructible<true, CompressedTuple<Ts...>, Vs...>
+    : std::integral_constant<
+          bool, absl::conjunction<
+                    TupleElementMoveConstructible<Ts, Vs&&>...>::value> {};
+
+template <typename T>
+struct compressed_tuple_size;
+
+template <typename... Es>
+struct compressed_tuple_size<CompressedTuple<Es...>>
+    : public std::integral_constant<std::size_t, sizeof...(Es)> {};
+
+template <class T, class... Vs>
+struct TupleItemsMoveConstructible
+    : std::integral_constant<
+          bool, TupleMoveConstructible<compressed_tuple_size<T>::value ==
+                                           sizeof...(Vs),
+                                       T, Vs...>::value> {};
+
+}  // namespace internal_compressed_tuple
+
+// Helper class to perform the Empty Base Class Optimization.
+// Ts can contain classes and non-classes, empty or not. For the ones that
+// are empty classes, we perform the CompressedTuple. If all types in Ts are
+// empty classes, then CompressedTuple<Ts...> is itself an empty class.  (This
+// does not apply when one or more of those empty classes is itself an empty
+// CompressedTuple.)
+//
+// To access the members, use member .get<N>() function.
+//
+// Eg:
+//   absl::container_internal::CompressedTuple<int, T1, T2, T3> value(7, t1, t2,
+//                                                                    t3);
+//   assert(value.get<0>() == 7);
+//   T1& t1 = value.get<1>();
+//   const T2& t2 = value.get<2>();
+//   ...
+//
+// https://en.cppreference.com/w/cpp/language/ebo
+template <typename... Ts>
+class ABSL_INTERNAL_COMPRESSED_TUPLE_DECLSPEC CompressedTuple
+    : private internal_compressed_tuple::CompressedTupleImpl<
+          CompressedTuple<Ts...>, absl::index_sequence_for<Ts...>,
+          internal_compressed_tuple::ShouldAnyUseBase<Ts...>()> {
+ private:
+  template <int I>
+  using ElemT = internal_compressed_tuple::ElemT<CompressedTuple, I>;
+
+  template <int I>
+  using StorageT = internal_compressed_tuple::Storage<ElemT<I>, I>;
+
+ public:
+  // There seems to be a bug in MSVC dealing in which using '=default' here will
+  // cause the compiler to ignore the body of other constructors. The work-
+  // around is to explicitly implement the default constructor.
+#if defined(_MSC_VER)
+  constexpr CompressedTuple() : CompressedTuple::CompressedTupleImpl() {}
+#else
+  constexpr CompressedTuple() = default;
+#endif
+  explicit constexpr CompressedTuple(const Ts&... base)
+      : CompressedTuple::CompressedTupleImpl(absl::in_place, base...) {}
+
+  template <typename First, typename... Vs,
+            absl::enable_if_t<
+                absl::conjunction<
+                    // Ensure we are not hiding default copy/move constructors.
+                    absl::negation<std::is_same<void(CompressedTuple),
+                                                void(absl::decay_t<First>)>>,
+                    internal_compressed_tuple::TupleItemsMoveConstructible<
+                        CompressedTuple<Ts...>, First, Vs...>>::value,
+                bool> = true>
+  explicit constexpr CompressedTuple(First&& first, Vs&&... base)
+      : CompressedTuple::CompressedTupleImpl(absl::in_place,
+                                             absl::forward<First>(first),
+                                             absl::forward<Vs>(base)...) {}
+
+  template <int I>
+  ElemT<I>& get() & {
+    return StorageT<I>::get();
+  }
+
+  template <int I>
+  constexpr const ElemT<I>& get() const& {
+    return StorageT<I>::get();
+  }
+
+  template <int I>
+  ElemT<I>&& get() && {
+    return std::move(*this).StorageT<I>::get();
+  }
+
+  template <int I>
+  constexpr const ElemT<I>&& get() const&& {
+    return absl::move(*this).StorageT<I>::get();
+  }
+};
+
+// Explicit specialization for a zero-element tuple
+// (needed to avoid ambiguous overloads for the default constructor).
+template <>
+class ABSL_INTERNAL_COMPRESSED_TUPLE_DECLSPEC CompressedTuple<> {};
+
+}  // namespace container_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#undef ABSL_INTERNAL_COMPRESSED_TUPLE_DECLSPEC
+
+#endif  // ABSL_CONTAINER_INTERNAL_COMPRESSED_TUPLE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/container_memory.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/container_memory.h
new file mode 100644
index 0000000000000000000000000000000000000000..bfa4ff93d74c9fca1b33080735de93c2305f9d27
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/container_memory.h
@@ -0,0 +1,454 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CONTAINER_INTERNAL_CONTAINER_MEMORY_H_
+#define ABSL_CONTAINER_INTERNAL_CONTAINER_MEMORY_H_
+
+#include <cassert>
+#include <cstddef>
+#include <cstring>
+#include <memory>
+#include <new>
+#include <tuple>
+#include <type_traits>
+#include <utility>
+
+#include "absl/base/config.h"
+#include "absl/memory/memory.h"
+#include "absl/meta/type_traits.h"
+#include "absl/utility/utility.h"
+
+#ifdef ABSL_HAVE_ADDRESS_SANITIZER
+#include <sanitizer/asan_interface.h>
+#endif
+
+#ifdef ABSL_HAVE_MEMORY_SANITIZER
+#include <sanitizer/msan_interface.h>
+#endif
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+
+template <size_t Alignment>
+struct alignas(Alignment) AlignedType {};
+
+// Allocates at least n bytes aligned to the specified alignment.
+// Alignment must be a power of 2. It must be positive.
+//
+// Note that many allocators don't honor alignment requirements above certain
+// threshold (usually either alignof(std::max_align_t) or alignof(void*)).
+// Allocate() doesn't apply alignment corrections. If the underlying allocator
+// returns insufficiently alignment pointer, that's what you are going to get.
+template <size_t Alignment, class Alloc>
+void* Allocate(Alloc* alloc, size_t n) {
+  static_assert(Alignment > 0, "");
+  assert(n && "n must be positive");
+  using M = AlignedType<Alignment>;
+  using A = typename absl::allocator_traits<Alloc>::template rebind_alloc<M>;
+  using AT = typename absl::allocator_traits<Alloc>::template rebind_traits<M>;
+  // On macOS, "mem_alloc" is a #define with one argument defined in
+  // rpc/types.h, so we can't name the variable "mem_alloc" and initialize it
+  // with the "foo(bar)" syntax.
+  A my_mem_alloc(*alloc);
+  void* p = AT::allocate(my_mem_alloc, (n + sizeof(M) - 1) / sizeof(M));
+  assert(reinterpret_cast<uintptr_t>(p) % Alignment == 0 &&
+         "allocator does not respect alignment");
+  return p;
+}
+
+// The pointer must have been previously obtained by calling
+// Allocate<Alignment>(alloc, n).
+template <size_t Alignment, class Alloc>
+void Deallocate(Alloc* alloc, void* p, size_t n) {
+  static_assert(Alignment > 0, "");
+  assert(n && "n must be positive");
+  using M = AlignedType<Alignment>;
+  using A = typename absl::allocator_traits<Alloc>::template rebind_alloc<M>;
+  using AT = typename absl::allocator_traits<Alloc>::template rebind_traits<M>;
+  // On macOS, "mem_alloc" is a #define with one argument defined in
+  // rpc/types.h, so we can't name the variable "mem_alloc" and initialize it
+  // with the "foo(bar)" syntax.
+  A my_mem_alloc(*alloc);
+  AT::deallocate(my_mem_alloc, static_cast<M*>(p),
+                 (n + sizeof(M) - 1) / sizeof(M));
+}
+
+namespace memory_internal {
+
+// Constructs T into uninitialized storage pointed by `ptr` using the args
+// specified in the tuple.
+template <class Alloc, class T, class Tuple, size_t... I>
+void ConstructFromTupleImpl(Alloc* alloc, T* ptr, Tuple&& t,
+                            absl::index_sequence<I...>) {
+  absl::allocator_traits<Alloc>::construct(
+      *alloc, ptr, std::get<I>(std::forward<Tuple>(t))...);
+}
+
+template <class T, class F>
+struct WithConstructedImplF {
+  template <class... Args>
+  decltype(std::declval<F>()(std::declval<T>())) operator()(
+      Args&&... args) const {
+    return std::forward<F>(f)(T(std::forward<Args>(args)...));
+  }
+  F&& f;
+};
+
+template <class T, class Tuple, size_t... Is, class F>
+decltype(std::declval<F>()(std::declval<T>())) WithConstructedImpl(
+    Tuple&& t, absl::index_sequence<Is...>, F&& f) {
+  return WithConstructedImplF<T, F>{std::forward<F>(f)}(
+      std::get<Is>(std::forward<Tuple>(t))...);
+}
+
+template <class T, size_t... Is>
+auto TupleRefImpl(T&& t, absl::index_sequence<Is...>)
+    -> decltype(std::forward_as_tuple(std::get<Is>(std::forward<T>(t))...)) {
+  return std::forward_as_tuple(std::get<Is>(std::forward<T>(t))...);
+}
+
+// Returns a tuple of references to the elements of the input tuple. T must be a
+// tuple.
+template <class T>
+auto TupleRef(T&& t) -> decltype(
+    TupleRefImpl(std::forward<T>(t),
+                 absl::make_index_sequence<
+                     std::tuple_size<typename std::decay<T>::type>::value>())) {
+  return TupleRefImpl(
+      std::forward<T>(t),
+      absl::make_index_sequence<
+          std::tuple_size<typename std::decay<T>::type>::value>());
+}
+
+template <class F, class K, class V>
+decltype(std::declval<F>()(std::declval<const K&>(), std::piecewise_construct,
+                           std::declval<std::tuple<K>>(), std::declval<V>()))
+DecomposePairImpl(F&& f, std::pair<std::tuple<K>, V> p) {
+  const auto& key = std::get<0>(p.first);
+  return std::forward<F>(f)(key, std::piecewise_construct, std::move(p.first),
+                            std::move(p.second));
+}
+
+}  // namespace memory_internal
+
+// Constructs T into uninitialized storage pointed by `ptr` using the args
+// specified in the tuple.
+template <class Alloc, class T, class Tuple>
+void ConstructFromTuple(Alloc* alloc, T* ptr, Tuple&& t) {
+  memory_internal::ConstructFromTupleImpl(
+      alloc, ptr, std::forward<Tuple>(t),
+      absl::make_index_sequence<
+          std::tuple_size<typename std::decay<Tuple>::type>::value>());
+}
+
+// Constructs T using the args specified in the tuple and calls F with the
+// constructed value.
+template <class T, class Tuple, class F>
+decltype(std::declval<F>()(std::declval<T>())) WithConstructed(
+    Tuple&& t, F&& f) {
+  return memory_internal::WithConstructedImpl<T>(
+      std::forward<Tuple>(t),
+      absl::make_index_sequence<
+          std::tuple_size<typename std::decay<Tuple>::type>::value>(),
+      std::forward<F>(f));
+}
+
+// Given arguments of an std::pair's consructor, PairArgs() returns a pair of
+// tuples with references to the passed arguments. The tuples contain
+// constructor arguments for the first and the second elements of the pair.
+//
+// The following two snippets are equivalent.
+//
+// 1. std::pair<F, S> p(args...);
+//
+// 2. auto a = PairArgs(args...);
+//    std::pair<F, S> p(std::piecewise_construct,
+//                      std::move(a.first), std::move(a.second));
+inline std::pair<std::tuple<>, std::tuple<>> PairArgs() { return {}; }
+template <class F, class S>
+std::pair<std::tuple<F&&>, std::tuple<S&&>> PairArgs(F&& f, S&& s) {
+  return {std::piecewise_construct, std::forward_as_tuple(std::forward<F>(f)),
+          std::forward_as_tuple(std::forward<S>(s))};
+}
+template <class F, class S>
+std::pair<std::tuple<const F&>, std::tuple<const S&>> PairArgs(
+    const std::pair<F, S>& p) {
+  return PairArgs(p.first, p.second);
+}
+template <class F, class S>
+std::pair<std::tuple<F&&>, std::tuple<S&&>> PairArgs(std::pair<F, S>&& p) {
+  return PairArgs(std::forward<F>(p.first), std::forward<S>(p.second));
+}
+template <class F, class S>
+auto PairArgs(std::piecewise_construct_t, F&& f, S&& s)
+    -> decltype(std::make_pair(memory_internal::TupleRef(std::forward<F>(f)),
+                               memory_internal::TupleRef(std::forward<S>(s)))) {
+  return std::make_pair(memory_internal::TupleRef(std::forward<F>(f)),
+                        memory_internal::TupleRef(std::forward<S>(s)));
+}
+
+// A helper function for implementing apply() in map policies.
+template <class F, class... Args>
+auto DecomposePair(F&& f, Args&&... args)
+    -> decltype(memory_internal::DecomposePairImpl(
+        std::forward<F>(f), PairArgs(std::forward<Args>(args)...))) {
+  return memory_internal::DecomposePairImpl(
+      std::forward<F>(f), PairArgs(std::forward<Args>(args)...));
+}
+
+// A helper function for implementing apply() in set policies.
+template <class F, class Arg>
+decltype(std::declval<F>()(std::declval<const Arg&>(), std::declval<Arg>()))
+DecomposeValue(F&& f, Arg&& arg) {
+  const auto& key = arg;
+  return std::forward<F>(f)(key, std::forward<Arg>(arg));
+}
+
+// Helper functions for asan and msan.
+inline void SanitizerPoisonMemoryRegion(const void* m, size_t s) {
+#ifdef ABSL_HAVE_ADDRESS_SANITIZER
+  ASAN_POISON_MEMORY_REGION(m, s);
+#endif
+#ifdef ABSL_HAVE_MEMORY_SANITIZER
+  __msan_poison(m, s);
+#endif
+  (void)m;
+  (void)s;
+}
+
+inline void SanitizerUnpoisonMemoryRegion(const void* m, size_t s) {
+#ifdef ABSL_HAVE_ADDRESS_SANITIZER
+  ASAN_UNPOISON_MEMORY_REGION(m, s);
+#endif
+#ifdef ABSL_HAVE_MEMORY_SANITIZER
+  __msan_unpoison(m, s);
+#endif
+  (void)m;
+  (void)s;
+}
+
+template <typename T>
+inline void SanitizerPoisonObject(const T* object) {
+  SanitizerPoisonMemoryRegion(object, sizeof(T));
+}
+
+template <typename T>
+inline void SanitizerUnpoisonObject(const T* object) {
+  SanitizerUnpoisonMemoryRegion(object, sizeof(T));
+}
+
+namespace memory_internal {
+
+// If Pair is a standard-layout type, OffsetOf<Pair>::kFirst and
+// OffsetOf<Pair>::kSecond are equivalent to offsetof(Pair, first) and
+// offsetof(Pair, second) respectively. Otherwise they are -1.
+//
+// The purpose of OffsetOf is to avoid calling offsetof() on non-standard-layout
+// type, which is non-portable.
+template <class Pair, class = std::true_type>
+struct OffsetOf {
+  static constexpr size_t kFirst = static_cast<size_t>(-1);
+  static constexpr size_t kSecond = static_cast<size_t>(-1);
+};
+
+template <class Pair>
+struct OffsetOf<Pair, typename std::is_standard_layout<Pair>::type> {
+  static constexpr size_t kFirst = offsetof(Pair, first);
+  static constexpr size_t kSecond = offsetof(Pair, second);
+};
+
+template <class K, class V>
+struct IsLayoutCompatible {
+ private:
+  struct Pair {
+    K first;
+    V second;
+  };
+
+  // Is P layout-compatible with Pair?
+  template <class P>
+  static constexpr bool LayoutCompatible() {
+    return std::is_standard_layout<P>() && sizeof(P) == sizeof(Pair) &&
+           alignof(P) == alignof(Pair) &&
+           memory_internal::OffsetOf<P>::kFirst ==
+               memory_internal::OffsetOf<Pair>::kFirst &&
+           memory_internal::OffsetOf<P>::kSecond ==
+               memory_internal::OffsetOf<Pair>::kSecond;
+  }
+
+ public:
+  // Whether pair<const K, V> and pair<K, V> are layout-compatible. If they are,
+  // then it is safe to store them in a union and read from either.
+  static constexpr bool value = std::is_standard_layout<K>() &&
+                                std::is_standard_layout<Pair>() &&
+                                memory_internal::OffsetOf<Pair>::kFirst == 0 &&
+                                LayoutCompatible<std::pair<K, V>>() &&
+                                LayoutCompatible<std::pair<const K, V>>();
+};
+
+}  // namespace memory_internal
+
+// The internal storage type for key-value containers like flat_hash_map.
+//
+// It is convenient for the value_type of a flat_hash_map<K, V> to be
+// pair<const K, V>; the "const K" prevents accidental modification of the key
+// when dealing with the reference returned from find() and similar methods.
+// However, this creates other problems; we want to be able to emplace(K, V)
+// efficiently with move operations, and similarly be able to move a
+// pair<K, V> in insert().
+//
+// The solution is this union, which aliases the const and non-const versions
+// of the pair. This also allows flat_hash_map<const K, V> to work, even though
+// that has the same efficiency issues with move in emplace() and insert() -
+// but people do it anyway.
+//
+// If kMutableKeys is false, only the value member can be accessed.
+//
+// If kMutableKeys is true, key can be accessed through all slots while value
+// and mutable_value must be accessed only via INITIALIZED slots. Slots are
+// created and destroyed via mutable_value so that the key can be moved later.
+//
+// Accessing one of the union fields while the other is active is safe as
+// long as they are layout-compatible, which is guaranteed by the definition of
+// kMutableKeys. For C++11, the relevant section of the standard is
+// https://timsong-cpp.github.io/cppwp/n3337/class.mem#19 (9.2.19)
+template <class K, class V>
+union map_slot_type {
+  map_slot_type() {}
+  ~map_slot_type() = delete;
+  using value_type = std::pair<const K, V>;
+  using mutable_value_type =
+      std::pair<absl::remove_const_t<K>, absl::remove_const_t<V>>;
+
+  value_type value;
+  mutable_value_type mutable_value;
+  absl::remove_const_t<K> key;
+};
+
+template <class K, class V>
+struct map_slot_policy {
+  using slot_type = map_slot_type<K, V>;
+  using value_type = std::pair<const K, V>;
+  using mutable_value_type =
+      std::pair<absl::remove_const_t<K>, absl::remove_const_t<V>>;
+
+ private:
+  static void emplace(slot_type* slot) {
+    // The construction of union doesn't do anything at runtime but it allows us
+    // to access its members without violating aliasing rules.
+    new (slot) slot_type;
+  }
+  // If pair<const K, V> and pair<K, V> are layout-compatible, we can accept one
+  // or the other via slot_type. We are also free to access the key via
+  // slot_type::key in this case.
+  using kMutableKeys = memory_internal::IsLayoutCompatible<K, V>;
+
+ public:
+  static value_type& element(slot_type* slot) { return slot->value; }
+  static const value_type& element(const slot_type* slot) {
+    return slot->value;
+  }
+
+  // When C++17 is available, we can use std::launder to provide mutable
+  // access to the key for use in node handle.
+#if defined(__cpp_lib_launder) && __cpp_lib_launder >= 201606
+  static K& mutable_key(slot_type* slot) {
+    // Still check for kMutableKeys so that we can avoid calling std::launder
+    // unless necessary because it can interfere with optimizations.
+    return kMutableKeys::value ? slot->key
+                               : *std::launder(const_cast<K*>(
+                                     std::addressof(slot->value.first)));
+  }
+#else  // !(defined(__cpp_lib_launder) && __cpp_lib_launder >= 201606)
+  static const K& mutable_key(slot_type* slot) { return key(slot); }
+#endif
+
+  static const K& key(const slot_type* slot) {
+    return kMutableKeys::value ? slot->key : slot->value.first;
+  }
+
+  template <class Allocator, class... Args>
+  static void construct(Allocator* alloc, slot_type* slot, Args&&... args) {
+    emplace(slot);
+    if (kMutableKeys::value) {
+      absl::allocator_traits<Allocator>::construct(*alloc, &slot->mutable_value,
+                                                   std::forward<Args>(args)...);
+    } else {
+      absl::allocator_traits<Allocator>::construct(*alloc, &slot->value,
+                                                   std::forward<Args>(args)...);
+    }
+  }
+
+  // Construct this slot by moving from another slot.
+  template <class Allocator>
+  static void construct(Allocator* alloc, slot_type* slot, slot_type* other) {
+    emplace(slot);
+    if (kMutableKeys::value) {
+      absl::allocator_traits<Allocator>::construct(
+          *alloc, &slot->mutable_value, std::move(other->mutable_value));
+    } else {
+      absl::allocator_traits<Allocator>::construct(*alloc, &slot->value,
+                                                   std::move(other->value));
+    }
+  }
+
+  // Construct this slot by copying from another slot.
+  template <class Allocator>
+  static void construct(Allocator* alloc, slot_type* slot,
+                        const slot_type* other) {
+    emplace(slot);
+    absl::allocator_traits<Allocator>::construct(*alloc, &slot->value,
+                                                 other->value);
+  }
+
+  template <class Allocator>
+  static void destroy(Allocator* alloc, slot_type* slot) {
+    if (kMutableKeys::value) {
+      absl::allocator_traits<Allocator>::destroy(*alloc, &slot->mutable_value);
+    } else {
+      absl::allocator_traits<Allocator>::destroy(*alloc, &slot->value);
+    }
+  }
+
+  template <class Allocator>
+  static void transfer(Allocator* alloc, slot_type* new_slot,
+                       slot_type* old_slot) {
+    emplace(new_slot);
+#if defined(__cpp_lib_launder) && __cpp_lib_launder >= 201606
+    if (absl::is_trivially_relocatable<value_type>()) {
+      // TODO(b/247130232,b/251814870): remove casts after fixing warnings.
+      std::memcpy(static_cast<void*>(std::launder(&new_slot->value)),
+                  static_cast<const void*>(&old_slot->value),
+                  sizeof(value_type));
+      return;
+    }
+#endif
+
+    if (kMutableKeys::value) {
+      absl::allocator_traits<Allocator>::construct(
+          *alloc, &new_slot->mutable_value, std::move(old_slot->mutable_value));
+    } else {
+      absl::allocator_traits<Allocator>::construct(*alloc, &new_slot->value,
+                                                   std::move(old_slot->value));
+    }
+    destroy(alloc, old_slot);
+  }
+};
+
+}  // namespace container_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_INTERNAL_CONTAINER_MEMORY_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/hash_function_defaults.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/hash_function_defaults.h
new file mode 100644
index 0000000000000000000000000000000000000000..250e662c9dd1636c81bfa30414602b36a5151b2f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/hash_function_defaults.h
@@ -0,0 +1,163 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Define the default Hash and Eq functions for SwissTable containers.
+//
+// std::hash<T> and std::equal_to<T> are not appropriate hash and equal
+// functions for SwissTable containers. There are two reasons for this.
+//
+// SwissTable containers are power of 2 sized containers:
+//
+// This means they use the lower bits of the hash value to find the slot for
+// each entry. The typical hash function for integral types is the identity.
+// This is a very weak hash function for SwissTable and any power of 2 sized
+// hashtable implementation which will lead to excessive collisions. For
+// SwissTable we use murmur3 style mixing to reduce collisions to a minimum.
+//
+// SwissTable containers support heterogeneous lookup:
+//
+// In order to make heterogeneous lookup work, hash and equal functions must be
+// polymorphic. At the same time they have to satisfy the same requirements the
+// C++ standard imposes on hash functions and equality operators. That is:
+//
+//   if hash_default_eq<T>(a, b) returns true for any a and b of type T, then
+//   hash_default_hash<T>(a) must equal hash_default_hash<T>(b)
+//
+// For SwissTable containers this requirement is relaxed to allow a and b of
+// any and possibly different types. Note that like the standard the hash and
+// equal functions are still bound to T. This is important because some type U
+// can be hashed by/tested for equality differently depending on T. A notable
+// example is `const char*`. `const char*` is treated as a c-style string when
+// the hash function is hash<std::string> but as a pointer when the hash
+// function is hash<void*>.
+//
+#ifndef ABSL_CONTAINER_INTERNAL_HASH_FUNCTION_DEFAULTS_H_
+#define ABSL_CONTAINER_INTERNAL_HASH_FUNCTION_DEFAULTS_H_
+
+#include <stdint.h>
+#include <cstddef>
+#include <memory>
+#include <string>
+#include <type_traits>
+
+#include "absl/base/config.h"
+#include "absl/hash/hash.h"
+#include "absl/strings/cord.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+
+// The hash of an object of type T is computed by using absl::Hash.
+template <class T, class E = void>
+struct HashEq {
+  using Hash = absl::Hash<T>;
+  using Eq = std::equal_to<T>;
+};
+
+struct StringHash {
+  using is_transparent = void;
+
+  size_t operator()(absl::string_view v) const {
+    return absl::Hash<absl::string_view>{}(v);
+  }
+  size_t operator()(const absl::Cord& v) const {
+    return absl::Hash<absl::Cord>{}(v);
+  }
+};
+
+struct StringEq {
+  using is_transparent = void;
+  bool operator()(absl::string_view lhs, absl::string_view rhs) const {
+    return lhs == rhs;
+  }
+  bool operator()(const absl::Cord& lhs, const absl::Cord& rhs) const {
+    return lhs == rhs;
+  }
+  bool operator()(const absl::Cord& lhs, absl::string_view rhs) const {
+    return lhs == rhs;
+  }
+  bool operator()(absl::string_view lhs, const absl::Cord& rhs) const {
+    return lhs == rhs;
+  }
+};
+
+// Supports heterogeneous lookup for string-like elements.
+struct StringHashEq {
+  using Hash = StringHash;
+  using Eq = StringEq;
+};
+
+template <>
+struct HashEq<std::string> : StringHashEq {};
+template <>
+struct HashEq<absl::string_view> : StringHashEq {};
+template <>
+struct HashEq<absl::Cord> : StringHashEq {};
+
+// Supports heterogeneous lookup for pointers and smart pointers.
+template <class T>
+struct HashEq<T*> {
+  struct Hash {
+    using is_transparent = void;
+    template <class U>
+    size_t operator()(const U& ptr) const {
+      return absl::Hash<const T*>{}(HashEq::ToPtr(ptr));
+    }
+  };
+  struct Eq {
+    using is_transparent = void;
+    template <class A, class B>
+    bool operator()(const A& a, const B& b) const {
+      return HashEq::ToPtr(a) == HashEq::ToPtr(b);
+    }
+  };
+
+ private:
+  static const T* ToPtr(const T* ptr) { return ptr; }
+  template <class U, class D>
+  static const T* ToPtr(const std::unique_ptr<U, D>& ptr) {
+    return ptr.get();
+  }
+  template <class U>
+  static const T* ToPtr(const std::shared_ptr<U>& ptr) {
+    return ptr.get();
+  }
+};
+
+template <class T, class D>
+struct HashEq<std::unique_ptr<T, D>> : HashEq<T*> {};
+template <class T>
+struct HashEq<std::shared_ptr<T>> : HashEq<T*> {};
+
+// This header's visibility is restricted.  If you need to access the default
+// hasher please use the container's ::hasher alias instead.
+//
+// Example: typename Hash = typename absl::flat_hash_map<K, V>::hasher
+template <class T>
+using hash_default_hash = typename container_internal::HashEq<T>::Hash;
+
+// This header's visibility is restricted.  If you need to access the default
+// key equal please use the container's ::key_equal alias instead.
+//
+// Example: typename Eq = typename absl::flat_hash_map<K, V, Hash>::key_equal
+template <class T>
+using hash_default_eq = typename container_internal::HashEq<T>::Eq;
+
+}  // namespace container_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_INTERNAL_HASH_FUNCTION_DEFAULTS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/hash_policy_traits.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/hash_policy_traits.h
new file mode 100644
index 0000000000000000000000000000000000000000..164ec123166d4152d97c0d1c105c8e3ef2a646c7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/hash_policy_traits.h
@@ -0,0 +1,157 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CONTAINER_INTERNAL_HASH_POLICY_TRAITS_H_
+#define ABSL_CONTAINER_INTERNAL_HASH_POLICY_TRAITS_H_
+
+#include <cstddef>
+#include <memory>
+#include <new>
+#include <type_traits>
+#include <utility>
+
+#include "absl/container/internal/common_policy_traits.h"
+#include "absl/meta/type_traits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+
+// Defines how slots are initialized/destroyed/moved.
+template <class Policy, class = void>
+struct hash_policy_traits : common_policy_traits<Policy> {
+  // The type of the keys stored in the hashtable.
+  using key_type = typename Policy::key_type;
+
+ private:
+  struct ReturnKey {
+    // When C++17 is available, we can use std::launder to provide mutable
+    // access to the key for use in node handle.
+#if defined(__cpp_lib_launder) && __cpp_lib_launder >= 201606
+    template <class Key,
+              absl::enable_if_t<std::is_lvalue_reference<Key>::value, int> = 0>
+    static key_type& Impl(Key&& k, int) {
+      return *std::launder(
+          const_cast<key_type*>(std::addressof(std::forward<Key>(k))));
+    }
+#endif
+
+    template <class Key>
+    static Key Impl(Key&& k, char) {
+      return std::forward<Key>(k);
+    }
+
+    // When Key=T&, we forward the lvalue reference.
+    // When Key=T, we return by value to avoid a dangling reference.
+    // eg, for string_hash_map.
+    template <class Key, class... Args>
+    auto operator()(Key&& k, const Args&...) const
+        -> decltype(Impl(std::forward<Key>(k), 0)) {
+      return Impl(std::forward<Key>(k), 0);
+    }
+  };
+
+  template <class P = Policy, class = void>
+  struct ConstantIteratorsImpl : std::false_type {};
+
+  template <class P>
+  struct ConstantIteratorsImpl<P, absl::void_t<typename P::constant_iterators>>
+      : P::constant_iterators {};
+
+ public:
+  // The actual object stored in the hash table.
+  using slot_type = typename Policy::slot_type;
+
+  // The argument type for insertions into the hashtable. This is different
+  // from value_type for increased performance. See initializer_list constructor
+  // and insert() member functions for more details.
+  using init_type = typename Policy::init_type;
+
+  using reference = decltype(Policy::element(std::declval<slot_type*>()));
+  using pointer = typename std::remove_reference<reference>::type*;
+  using value_type = typename std::remove_reference<reference>::type;
+
+  // Policies can set this variable to tell raw_hash_set that all iterators
+  // should be constant, even `iterator`. This is useful for set-like
+  // containers.
+  // Defaults to false if not provided by the policy.
+  using constant_iterators = ConstantIteratorsImpl<>;
+
+  // Returns the amount of memory owned by `slot`, exclusive of `sizeof(*slot)`.
+  //
+  // If `slot` is nullptr, returns the constant amount of memory owned by any
+  // full slot or -1 if slots own variable amounts of memory.
+  //
+  // PRECONDITION: `slot` is INITIALIZED or nullptr
+  template <class P = Policy>
+  static size_t space_used(const slot_type* slot) {
+    return P::space_used(slot);
+  }
+
+  // Provides generalized access to the key for elements, both for elements in
+  // the table and for elements that have not yet been inserted (or even
+  // constructed).  We would like an API that allows us to say: `key(args...)`
+  // but we cannot do that for all cases, so we use this more general API that
+  // can be used for many things, including the following:
+  //
+  //   - Given an element in a table, get its key.
+  //   - Given an element initializer, get its key.
+  //   - Given `emplace()` arguments, get the element key.
+  //
+  // Implementations of this must adhere to a very strict technical
+  // specification around aliasing and consuming arguments:
+  //
+  // Let `value_type` be the result type of `element()` without ref- and
+  // cv-qualifiers. The first argument is a functor, the rest are constructor
+  // arguments for `value_type`. Returns `std::forward<F>(f)(k, xs...)`, where
+  // `k` is the element key, and `xs...` are the new constructor arguments for
+  // `value_type`. It's allowed for `k` to alias `xs...`, and for both to alias
+  // `ts...`. The key won't be touched once `xs...` are used to construct an
+  // element; `ts...` won't be touched at all, which allows `apply()` to consume
+  // any rvalues among them.
+  //
+  // If `value_type` is constructible from `Ts&&...`, `Policy::apply()` must not
+  // trigger a hard compile error unless it originates from `f`. In other words,
+  // `Policy::apply()` must be SFINAE-friendly. If `value_type` is not
+  // constructible from `Ts&&...`, either SFINAE or a hard compile error is OK.
+  //
+  // If `Ts...` is `[cv] value_type[&]` or `[cv] init_type[&]`,
+  // `Policy::apply()` must work. A compile error is not allowed, SFINAE or not.
+  template <class F, class... Ts, class P = Policy>
+  static auto apply(F&& f, Ts&&... ts)
+      -> decltype(P::apply(std::forward<F>(f), std::forward<Ts>(ts)...)) {
+    return P::apply(std::forward<F>(f), std::forward<Ts>(ts)...);
+  }
+
+  // Returns the "key" portion of the slot.
+  // Used for node handle manipulation.
+  template <class P = Policy>
+  static auto mutable_key(slot_type* slot)
+      -> decltype(P::apply(ReturnKey(), hash_policy_traits::element(slot))) {
+    return P::apply(ReturnKey(), hash_policy_traits::element(slot));
+  }
+
+  // Returns the "value" (as opposed to the "key") portion of the element. Used
+  // by maps to implement `operator[]`, `at()` and `insert_or_assign()`.
+  template <class T, class P = Policy>
+  static auto value(T* elem) -> decltype(P::value(elem)) {
+    return P::value(elem);
+  }
+};
+
+}  // namespace container_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_INTERNAL_HASH_POLICY_TRAITS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/hashtable_debug_hooks.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/hashtable_debug_hooks.h
new file mode 100644
index 0000000000000000000000000000000000000000..3e9ea5954e0a19ba625d5af7398b6ac8117a7c23
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/hashtable_debug_hooks.h
@@ -0,0 +1,85 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Provides the internal API for hashtable_debug.h.
+
+#ifndef ABSL_CONTAINER_INTERNAL_HASHTABLE_DEBUG_HOOKS_H_
+#define ABSL_CONTAINER_INTERNAL_HASHTABLE_DEBUG_HOOKS_H_
+
+#include <cstddef>
+
+#include <algorithm>
+#include <type_traits>
+#include <vector>
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+namespace hashtable_debug_internal {
+
+// If it is a map, call get<0>().
+using std::get;
+template <typename T, typename = typename T::mapped_type>
+auto GetKey(const typename T::value_type& pair, int) -> decltype(get<0>(pair)) {
+  return get<0>(pair);
+}
+
+// If it is not a map, return the value directly.
+template <typename T>
+const typename T::key_type& GetKey(const typename T::key_type& key, char) {
+  return key;
+}
+
+// Containers should specialize this to provide debug information for that
+// container.
+template <class Container, typename Enabler = void>
+struct HashtableDebugAccess {
+  // Returns the number of probes required to find `key` in `c`.  The "number of
+  // probes" is a concept that can vary by container.  Implementations should
+  // return 0 when `key` was found in the minimum number of operations and
+  // should increment the result for each non-trivial operation required to find
+  // `key`.
+  //
+  // The default implementation uses the bucket api from the standard and thus
+  // works for `std::unordered_*` containers.
+  static size_t GetNumProbes(const Container& c,
+                             const typename Container::key_type& key) {
+    if (!c.bucket_count()) return {};
+    size_t num_probes = 0;
+    size_t bucket = c.bucket(key);
+    for (auto it = c.begin(bucket), e = c.end(bucket);; ++it, ++num_probes) {
+      if (it == e) return num_probes;
+      if (c.key_eq()(key, GetKey<Container>(*it, 0))) return num_probes;
+    }
+  }
+
+  // Returns the number of bytes requested from the allocator by the container
+  // and not freed.
+  //
+  // static size_t AllocatedByteSize(const Container& c);
+
+  // Returns a tight lower bound for AllocatedByteSize(c) where `c` is of type
+  // `Container` and `c.size()` is equal to `num_elements`.
+  //
+  // static size_t LowerBoundAllocatedByteSize(size_t num_elements);
+};
+
+}  // namespace hashtable_debug_internal
+}  // namespace container_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_INTERNAL_HASHTABLE_DEBUG_HOOKS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/hashtablez_sampler.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/hashtablez_sampler.h
new file mode 100644
index 0000000000000000000000000000000000000000..d8fd8f34f97074a9bcb0c6f8a42d70624035c4d4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/hashtablez_sampler.h
@@ -0,0 +1,267 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: hashtablez_sampler.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines the API for a low level library to sample hashtables
+// and collect runtime statistics about them.
+//
+// `HashtablezSampler` controls the lifecycle of `HashtablezInfo` objects which
+// store information about a single sample.
+//
+// `Record*` methods store information into samples.
+// `Sample()` and `Unsample()` make use of a single global sampler with
+// properties controlled by the flags hashtablez_enabled,
+// hashtablez_sample_rate, and hashtablez_max_samples.
+//
+// WARNING
+//
+// Using this sampling API may cause sampled Swiss tables to use the global
+// allocator (operator `new`) in addition to any custom allocator.  If you
+// are using a table in an unusual circumstance where allocation or calling a
+// linux syscall is unacceptable, this could interfere.
+//
+// This utility is internal-only. Use at your own risk.
+
+#ifndef ABSL_CONTAINER_INTERNAL_HASHTABLEZ_SAMPLER_H_
+#define ABSL_CONTAINER_INTERNAL_HASHTABLEZ_SAMPLER_H_
+
+#include <atomic>
+#include <functional>
+#include <memory>
+#include <vector>
+
+#include "absl/base/config.h"
+#include "absl/base/internal/per_thread_tls.h"
+#include "absl/base/optimization.h"
+#include "absl/profiling/internal/sample_recorder.h"
+#include "absl/synchronization/mutex.h"
+#include "absl/utility/utility.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+
+// Stores information about a sampled hashtable.  All mutations to this *must*
+// be made through `Record*` functions below.  All reads from this *must* only
+// occur in the callback to `HashtablezSampler::Iterate`.
+struct HashtablezInfo : public profiling_internal::Sample<HashtablezInfo> {
+  // Constructs the object but does not fill in any fields.
+  HashtablezInfo();
+  ~HashtablezInfo();
+  HashtablezInfo(const HashtablezInfo&) = delete;
+  HashtablezInfo& operator=(const HashtablezInfo&) = delete;
+
+  // Puts the object into a clean state, fills in the logically `const` members,
+  // blocking for any readers that are currently sampling the object.
+  void PrepareForSampling(int64_t stride, size_t inline_element_size_value)
+      ABSL_EXCLUSIVE_LOCKS_REQUIRED(init_mu);
+
+  // These fields are mutated by the various Record* APIs and need to be
+  // thread-safe.
+  std::atomic<size_t> capacity;
+  std::atomic<size_t> size;
+  std::atomic<size_t> num_erases;
+  std::atomic<size_t> num_rehashes;
+  std::atomic<size_t> max_probe_length;
+  std::atomic<size_t> total_probe_length;
+  std::atomic<size_t> hashes_bitwise_or;
+  std::atomic<size_t> hashes_bitwise_and;
+  std::atomic<size_t> hashes_bitwise_xor;
+  std::atomic<size_t> max_reserve;
+
+  // All of the fields below are set by `PrepareForSampling`, they must not be
+  // mutated in `Record*` functions.  They are logically `const` in that sense.
+  // These are guarded by init_mu, but that is not externalized to clients,
+  // which can read them only during `SampleRecorder::Iterate` which will hold
+  // the lock.
+  static constexpr int kMaxStackDepth = 64;
+  absl::Time create_time;
+  int32_t depth;
+  void* stack[kMaxStackDepth];
+  size_t inline_element_size;  // How big is the slot?
+};
+
+void RecordRehashSlow(HashtablezInfo* info, size_t total_probe_length);
+
+void RecordReservationSlow(HashtablezInfo* info, size_t target_capacity);
+
+void RecordClearedReservationSlow(HashtablezInfo* info);
+
+void RecordStorageChangedSlow(HashtablezInfo* info, size_t size,
+                              size_t capacity);
+
+void RecordInsertSlow(HashtablezInfo* info, size_t hash,
+                      size_t distance_from_desired);
+
+void RecordEraseSlow(HashtablezInfo* info);
+
+struct SamplingState {
+  int64_t next_sample;
+  // When we make a sampling decision, we record that distance so we can weight
+  // each sample.
+  int64_t sample_stride;
+};
+
+HashtablezInfo* SampleSlow(SamplingState& next_sample,
+                           size_t inline_element_size);
+void UnsampleSlow(HashtablezInfo* info);
+
+#if defined(ABSL_INTERNAL_HASHTABLEZ_SAMPLE)
+#error ABSL_INTERNAL_HASHTABLEZ_SAMPLE cannot be directly set
+#endif  // defined(ABSL_INTERNAL_HASHTABLEZ_SAMPLE)
+
+#if defined(ABSL_INTERNAL_HASHTABLEZ_SAMPLE)
+class HashtablezInfoHandle {
+ public:
+  explicit HashtablezInfoHandle() : info_(nullptr) {}
+  explicit HashtablezInfoHandle(HashtablezInfo* info) : info_(info) {}
+
+  // We do not have a destructor. Caller is responsible for calling Unregister
+  // before destroying the handle.
+  void Unregister() {
+    if (ABSL_PREDICT_TRUE(info_ == nullptr)) return;
+    UnsampleSlow(info_);
+  }
+
+  HashtablezInfoHandle(const HashtablezInfoHandle&) = delete;
+  HashtablezInfoHandle& operator=(const HashtablezInfoHandle&) = delete;
+
+  HashtablezInfoHandle(HashtablezInfoHandle&& o) noexcept
+      : info_(absl::exchange(o.info_, nullptr)) {}
+  HashtablezInfoHandle& operator=(HashtablezInfoHandle&& o) noexcept {
+    if (ABSL_PREDICT_FALSE(info_ != nullptr)) {
+      UnsampleSlow(info_);
+    }
+    info_ = absl::exchange(o.info_, nullptr);
+    return *this;
+  }
+
+  inline void RecordStorageChanged(size_t size, size_t capacity) {
+    if (ABSL_PREDICT_TRUE(info_ == nullptr)) return;
+    RecordStorageChangedSlow(info_, size, capacity);
+  }
+
+  inline void RecordRehash(size_t total_probe_length) {
+    if (ABSL_PREDICT_TRUE(info_ == nullptr)) return;
+    RecordRehashSlow(info_, total_probe_length);
+  }
+
+  inline void RecordReservation(size_t target_capacity) {
+    if (ABSL_PREDICT_TRUE(info_ == nullptr)) return;
+    RecordReservationSlow(info_, target_capacity);
+  }
+
+  inline void RecordClearedReservation() {
+    if (ABSL_PREDICT_TRUE(info_ == nullptr)) return;
+    RecordClearedReservationSlow(info_);
+  }
+
+  inline void RecordInsert(size_t hash, size_t distance_from_desired) {
+    if (ABSL_PREDICT_TRUE(info_ == nullptr)) return;
+    RecordInsertSlow(info_, hash, distance_from_desired);
+  }
+
+  inline void RecordErase() {
+    if (ABSL_PREDICT_TRUE(info_ == nullptr)) return;
+    RecordEraseSlow(info_);
+  }
+
+  friend inline void swap(HashtablezInfoHandle& lhs,
+                          HashtablezInfoHandle& rhs) {
+    std::swap(lhs.info_, rhs.info_);
+  }
+
+ private:
+  friend class HashtablezInfoHandlePeer;
+  HashtablezInfo* info_;
+};
+#else
+// Ensure that when Hashtablez is turned off at compile time, HashtablezInfo can
+// be removed by the linker, in order to reduce the binary size.
+class HashtablezInfoHandle {
+ public:
+  explicit HashtablezInfoHandle() = default;
+  explicit HashtablezInfoHandle(std::nullptr_t) {}
+
+  inline void Unregister() {}
+  inline void RecordStorageChanged(size_t /*size*/, size_t /*capacity*/) {}
+  inline void RecordRehash(size_t /*total_probe_length*/) {}
+  inline void RecordReservation(size_t /*target_capacity*/) {}
+  inline void RecordClearedReservation() {}
+  inline void RecordInsert(size_t /*hash*/, size_t /*distance_from_desired*/) {}
+  inline void RecordErase() {}
+
+  friend inline void swap(HashtablezInfoHandle& /*lhs*/,
+                          HashtablezInfoHandle& /*rhs*/) {}
+};
+#endif  // defined(ABSL_INTERNAL_HASHTABLEZ_SAMPLE)
+
+#if defined(ABSL_INTERNAL_HASHTABLEZ_SAMPLE)
+extern ABSL_PER_THREAD_TLS_KEYWORD SamplingState global_next_sample;
+#endif  // defined(ABSL_INTERNAL_HASHTABLEZ_SAMPLE)
+
+// Returns an RAII sampling handle that manages registration and unregistation
+// with the global sampler.
+inline HashtablezInfoHandle Sample(
+    size_t inline_element_size ABSL_ATTRIBUTE_UNUSED) {
+#if defined(ABSL_INTERNAL_HASHTABLEZ_SAMPLE)
+  if (ABSL_PREDICT_TRUE(--global_next_sample.next_sample > 0)) {
+    return HashtablezInfoHandle(nullptr);
+  }
+  return HashtablezInfoHandle(
+      SampleSlow(global_next_sample, inline_element_size));
+#else
+  return HashtablezInfoHandle(nullptr);
+#endif  // !ABSL_PER_THREAD_TLS
+}
+
+using HashtablezSampler =
+    ::absl::profiling_internal::SampleRecorder<HashtablezInfo>;
+
+// Returns a global Sampler.
+HashtablezSampler& GlobalHashtablezSampler();
+
+using HashtablezConfigListener = void (*)();
+void SetHashtablezConfigListener(HashtablezConfigListener l);
+
+// Enables or disables sampling for Swiss tables.
+bool IsHashtablezEnabled();
+void SetHashtablezEnabled(bool enabled);
+void SetHashtablezEnabledInternal(bool enabled);
+
+// Sets the rate at which Swiss tables will be sampled.
+int32_t GetHashtablezSampleParameter();
+void SetHashtablezSampleParameter(int32_t rate);
+void SetHashtablezSampleParameterInternal(int32_t rate);
+
+// Sets a soft max for the number of samples that will be kept.
+size_t GetHashtablezMaxSamples();
+void SetHashtablezMaxSamples(size_t max);
+void SetHashtablezMaxSamplesInternal(size_t max);
+
+// Configuration override.
+// This allows process-wide sampling without depending on order of
+// initialization of static storage duration objects.
+// The definition of this constant is weak, which allows us to inject a
+// different value for it at link time.
+extern "C" bool ABSL_INTERNAL_C_SYMBOL(AbslContainerInternalSampleEverything)();
+
+}  // namespace container_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_INTERNAL_HASHTABLEZ_SAMPLER_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/inlined_vector.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/inlined_vector.h
new file mode 100644
index 0000000000000000000000000000000000000000..0398f53008bcf49e1d7dce9bd3c46bdd22508cea
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/inlined_vector.h
@@ -0,0 +1,1031 @@
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CONTAINER_INTERNAL_INLINED_VECTOR_H_
+#define ABSL_CONTAINER_INTERNAL_INLINED_VECTOR_H_
+
+#include <algorithm>
+#include <cstddef>
+#include <cstring>
+#include <iterator>
+#include <limits>
+#include <memory>
+#include <new>
+#include <type_traits>
+#include <utility>
+
+#include "absl/base/attributes.h"
+#include "absl/base/macros.h"
+#include "absl/container/internal/compressed_tuple.h"
+#include "absl/memory/memory.h"
+#include "absl/meta/type_traits.h"
+#include "absl/types/span.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace inlined_vector_internal {
+
+// GCC does not deal very well with the below code
+#if !defined(__clang__) && defined(__GNUC__)
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Warray-bounds"
+#endif
+
+template <typename A>
+using AllocatorTraits = std::allocator_traits<A>;
+template <typename A>
+using ValueType = typename AllocatorTraits<A>::value_type;
+template <typename A>
+using SizeType = typename AllocatorTraits<A>::size_type;
+template <typename A>
+using Pointer = typename AllocatorTraits<A>::pointer;
+template <typename A>
+using ConstPointer = typename AllocatorTraits<A>::const_pointer;
+template <typename A>
+using SizeType = typename AllocatorTraits<A>::size_type;
+template <typename A>
+using DifferenceType = typename AllocatorTraits<A>::difference_type;
+template <typename A>
+using Reference = ValueType<A>&;
+template <typename A>
+using ConstReference = const ValueType<A>&;
+template <typename A>
+using Iterator = Pointer<A>;
+template <typename A>
+using ConstIterator = ConstPointer<A>;
+template <typename A>
+using ReverseIterator = typename std::reverse_iterator<Iterator<A>>;
+template <typename A>
+using ConstReverseIterator = typename std::reverse_iterator<ConstIterator<A>>;
+template <typename A>
+using MoveIterator = typename std::move_iterator<Iterator<A>>;
+
+template <typename Iterator>
+using IsAtLeastForwardIterator = std::is_convertible<
+    typename std::iterator_traits<Iterator>::iterator_category,
+    std::forward_iterator_tag>;
+
+template <typename A>
+using IsMemcpyOk =
+    absl::conjunction<std::is_same<A, std::allocator<ValueType<A>>>,
+                      absl::is_trivially_copy_constructible<ValueType<A>>,
+                      absl::is_trivially_copy_assignable<ValueType<A>>,
+                      absl::is_trivially_destructible<ValueType<A>>>;
+
+template <typename A>
+using IsMoveAssignOk = std::is_move_assignable<ValueType<A>>;
+template <typename A>
+using IsSwapOk = absl::type_traits_internal::IsSwappable<ValueType<A>>;
+
+template <typename T>
+struct TypeIdentity {
+  using type = T;
+};
+
+// Used for function arguments in template functions to prevent ADL by forcing
+// callers to explicitly specify the template parameter.
+template <typename T>
+using NoTypeDeduction = typename TypeIdentity<T>::type;
+
+template <typename A, bool IsTriviallyDestructible =
+                          absl::is_trivially_destructible<ValueType<A>>::value>
+struct DestroyAdapter;
+
+template <typename A>
+struct DestroyAdapter<A, /* IsTriviallyDestructible */ false> {
+  static void DestroyElements(A& allocator, Pointer<A> destroy_first,
+                              SizeType<A> destroy_size) {
+    for (SizeType<A> i = destroy_size; i != 0;) {
+      --i;
+      AllocatorTraits<A>::destroy(allocator, destroy_first + i);
+    }
+  }
+};
+
+template <typename A>
+struct DestroyAdapter<A, /* IsTriviallyDestructible */ true> {
+  static void DestroyElements(A& allocator, Pointer<A> destroy_first,
+                              SizeType<A> destroy_size) {
+    static_cast<void>(allocator);
+    static_cast<void>(destroy_first);
+    static_cast<void>(destroy_size);
+  }
+};
+
+template <typename A>
+struct Allocation {
+  Pointer<A> data = nullptr;
+  SizeType<A> capacity = 0;
+};
+
+template <typename A,
+          bool IsOverAligned =
+              (alignof(ValueType<A>) > ABSL_INTERNAL_DEFAULT_NEW_ALIGNMENT)>
+struct MallocAdapter {
+  static Allocation<A> Allocate(A& allocator, SizeType<A> requested_capacity) {
+    return {AllocatorTraits<A>::allocate(allocator, requested_capacity),
+            requested_capacity};
+  }
+
+  static void Deallocate(A& allocator, Pointer<A> pointer,
+                         SizeType<A> capacity) {
+    AllocatorTraits<A>::deallocate(allocator, pointer, capacity);
+  }
+};
+
+template <typename A, typename ValueAdapter>
+void ConstructElements(NoTypeDeduction<A>& allocator,
+                       Pointer<A> construct_first, ValueAdapter& values,
+                       SizeType<A> construct_size) {
+  for (SizeType<A> i = 0; i < construct_size; ++i) {
+    ABSL_INTERNAL_TRY { values.ConstructNext(allocator, construct_first + i); }
+    ABSL_INTERNAL_CATCH_ANY {
+      DestroyAdapter<A>::DestroyElements(allocator, construct_first, i);
+      ABSL_INTERNAL_RETHROW;
+    }
+  }
+}
+
+template <typename A, typename ValueAdapter>
+void AssignElements(Pointer<A> assign_first, ValueAdapter& values,
+                    SizeType<A> assign_size) {
+  for (SizeType<A> i = 0; i < assign_size; ++i) {
+    values.AssignNext(assign_first + i);
+  }
+}
+
+template <typename A>
+struct StorageView {
+  Pointer<A> data;
+  SizeType<A> size;
+  SizeType<A> capacity;
+};
+
+template <typename A, typename Iterator>
+class IteratorValueAdapter {
+ public:
+  explicit IteratorValueAdapter(const Iterator& it) : it_(it) {}
+
+  void ConstructNext(A& allocator, Pointer<A> construct_at) {
+    AllocatorTraits<A>::construct(allocator, construct_at, *it_);
+    ++it_;
+  }
+
+  void AssignNext(Pointer<A> assign_at) {
+    *assign_at = *it_;
+    ++it_;
+  }
+
+ private:
+  Iterator it_;
+};
+
+template <typename A>
+class CopyValueAdapter {
+ public:
+  explicit CopyValueAdapter(ConstPointer<A> p) : ptr_(p) {}
+
+  void ConstructNext(A& allocator, Pointer<A> construct_at) {
+    AllocatorTraits<A>::construct(allocator, construct_at, *ptr_);
+  }
+
+  void AssignNext(Pointer<A> assign_at) { *assign_at = *ptr_; }
+
+ private:
+  ConstPointer<A> ptr_;
+};
+
+template <typename A>
+class DefaultValueAdapter {
+ public:
+  explicit DefaultValueAdapter() {}
+
+  void ConstructNext(A& allocator, Pointer<A> construct_at) {
+    AllocatorTraits<A>::construct(allocator, construct_at);
+  }
+
+  void AssignNext(Pointer<A> assign_at) { *assign_at = ValueType<A>(); }
+};
+
+template <typename A>
+class AllocationTransaction {
+ public:
+  explicit AllocationTransaction(A& allocator)
+      : allocator_data_(allocator, nullptr), capacity_(0) {}
+
+  ~AllocationTransaction() {
+    if (DidAllocate()) {
+      MallocAdapter<A>::Deallocate(GetAllocator(), GetData(), GetCapacity());
+    }
+  }
+
+  AllocationTransaction(const AllocationTransaction&) = delete;
+  void operator=(const AllocationTransaction&) = delete;
+
+  A& GetAllocator() { return allocator_data_.template get<0>(); }
+  Pointer<A>& GetData() { return allocator_data_.template get<1>(); }
+  SizeType<A>& GetCapacity() { return capacity_; }
+
+  bool DidAllocate() { return GetData() != nullptr; }
+
+  Pointer<A> Allocate(SizeType<A> requested_capacity) {
+    Allocation<A> result =
+        MallocAdapter<A>::Allocate(GetAllocator(), requested_capacity);
+    GetData() = result.data;
+    GetCapacity() = result.capacity;
+    return result.data;
+  }
+
+  ABSL_MUST_USE_RESULT Allocation<A> Release() && {
+    Allocation<A> result = {GetData(), GetCapacity()};
+    Reset();
+    return result;
+  }
+
+ private:
+  void Reset() {
+    GetData() = nullptr;
+    GetCapacity() = 0;
+  }
+
+  container_internal::CompressedTuple<A, Pointer<A>> allocator_data_;
+  SizeType<A> capacity_;
+};
+
+template <typename A>
+class ConstructionTransaction {
+ public:
+  explicit ConstructionTransaction(A& allocator)
+      : allocator_data_(allocator, nullptr), size_(0) {}
+
+  ~ConstructionTransaction() {
+    if (DidConstruct()) {
+      DestroyAdapter<A>::DestroyElements(GetAllocator(), GetData(), GetSize());
+    }
+  }
+
+  ConstructionTransaction(const ConstructionTransaction&) = delete;
+  void operator=(const ConstructionTransaction&) = delete;
+
+  A& GetAllocator() { return allocator_data_.template get<0>(); }
+  Pointer<A>& GetData() { return allocator_data_.template get<1>(); }
+  SizeType<A>& GetSize() { return size_; }
+
+  bool DidConstruct() { return GetData() != nullptr; }
+  template <typename ValueAdapter>
+  void Construct(Pointer<A> data, ValueAdapter& values, SizeType<A> size) {
+    ConstructElements<A>(GetAllocator(), data, values, size);
+    GetData() = data;
+    GetSize() = size;
+  }
+  void Commit() && {
+    GetData() = nullptr;
+    GetSize() = 0;
+  }
+
+ private:
+  container_internal::CompressedTuple<A, Pointer<A>> allocator_data_;
+  SizeType<A> size_;
+};
+
+template <typename T, size_t N, typename A>
+class Storage {
+ public:
+  struct MemcpyPolicy {};
+  struct ElementwiseAssignPolicy {};
+  struct ElementwiseSwapPolicy {};
+  struct ElementwiseConstructPolicy {};
+
+  using MoveAssignmentPolicy = absl::conditional_t<
+      IsMemcpyOk<A>::value, MemcpyPolicy,
+      absl::conditional_t<IsMoveAssignOk<A>::value, ElementwiseAssignPolicy,
+                          ElementwiseConstructPolicy>>;
+  using SwapPolicy = absl::conditional_t<
+      IsMemcpyOk<A>::value, MemcpyPolicy,
+      absl::conditional_t<IsSwapOk<A>::value, ElementwiseSwapPolicy,
+                          ElementwiseConstructPolicy>>;
+
+  static SizeType<A> NextCapacity(SizeType<A> current_capacity) {
+    return current_capacity * 2;
+  }
+
+  static SizeType<A> ComputeCapacity(SizeType<A> current_capacity,
+                                     SizeType<A> requested_capacity) {
+    return (std::max)(NextCapacity(current_capacity), requested_capacity);
+  }
+
+  // ---------------------------------------------------------------------------
+  // Storage Constructors and Destructor
+  // ---------------------------------------------------------------------------
+
+  Storage() : metadata_(A(), /* size and is_allocated */ 0u) {}
+
+  explicit Storage(const A& allocator)
+      : metadata_(allocator, /* size and is_allocated */ 0u) {}
+
+  ~Storage() {
+    if (GetSizeAndIsAllocated() == 0) {
+      // Empty and not allocated; nothing to do.
+    } else if (IsMemcpyOk<A>::value) {
+      // No destructors need to be run; just deallocate if necessary.
+      DeallocateIfAllocated();
+    } else {
+      DestroyContents();
+    }
+  }
+
+  // ---------------------------------------------------------------------------
+  // Storage Member Accessors
+  // ---------------------------------------------------------------------------
+
+  SizeType<A>& GetSizeAndIsAllocated() { return metadata_.template get<1>(); }
+
+  const SizeType<A>& GetSizeAndIsAllocated() const {
+    return metadata_.template get<1>();
+  }
+
+  SizeType<A> GetSize() const { return GetSizeAndIsAllocated() >> 1; }
+
+  bool GetIsAllocated() const { return GetSizeAndIsAllocated() & 1; }
+
+  Pointer<A> GetAllocatedData() { return data_.allocated.allocated_data; }
+
+  ConstPointer<A> GetAllocatedData() const {
+    return data_.allocated.allocated_data;
+  }
+
+  Pointer<A> GetInlinedData() {
+    return reinterpret_cast<Pointer<A>>(
+        std::addressof(data_.inlined.inlined_data[0]));
+  }
+
+  ConstPointer<A> GetInlinedData() const {
+    return reinterpret_cast<ConstPointer<A>>(
+        std::addressof(data_.inlined.inlined_data[0]));
+  }
+
+  SizeType<A> GetAllocatedCapacity() const {
+    return data_.allocated.allocated_capacity;
+  }
+
+  SizeType<A> GetInlinedCapacity() const {
+    return static_cast<SizeType<A>>(kOptimalInlinedSize);
+  }
+
+  StorageView<A> MakeStorageView() {
+    return GetIsAllocated() ? StorageView<A>{GetAllocatedData(), GetSize(),
+                                             GetAllocatedCapacity()}
+                            : StorageView<A>{GetInlinedData(), GetSize(),
+                                             GetInlinedCapacity()};
+  }
+
+  A& GetAllocator() { return metadata_.template get<0>(); }
+
+  const A& GetAllocator() const { return metadata_.template get<0>(); }
+
+  // ---------------------------------------------------------------------------
+  // Storage Member Mutators
+  // ---------------------------------------------------------------------------
+
+  ABSL_ATTRIBUTE_NOINLINE void InitFrom(const Storage& other);
+
+  template <typename ValueAdapter>
+  void Initialize(ValueAdapter values, SizeType<A> new_size);
+
+  template <typename ValueAdapter>
+  void Assign(ValueAdapter values, SizeType<A> new_size);
+
+  template <typename ValueAdapter>
+  void Resize(ValueAdapter values, SizeType<A> new_size);
+
+  template <typename ValueAdapter>
+  Iterator<A> Insert(ConstIterator<A> pos, ValueAdapter values,
+                     SizeType<A> insert_count);
+
+  template <typename... Args>
+  Reference<A> EmplaceBack(Args&&... args);
+
+  Iterator<A> Erase(ConstIterator<A> from, ConstIterator<A> to);
+
+  void Reserve(SizeType<A> requested_capacity);
+
+  void ShrinkToFit();
+
+  void Swap(Storage* other_storage_ptr);
+
+  void SetIsAllocated() {
+    GetSizeAndIsAllocated() |= static_cast<SizeType<A>>(1);
+  }
+
+  void UnsetIsAllocated() {
+    GetSizeAndIsAllocated() &= ((std::numeric_limits<SizeType<A>>::max)() - 1);
+  }
+
+  void SetSize(SizeType<A> size) {
+    GetSizeAndIsAllocated() =
+        (size << 1) | static_cast<SizeType<A>>(GetIsAllocated());
+  }
+
+  void SetAllocatedSize(SizeType<A> size) {
+    GetSizeAndIsAllocated() = (size << 1) | static_cast<SizeType<A>>(1);
+  }
+
+  void SetInlinedSize(SizeType<A> size) {
+    GetSizeAndIsAllocated() = size << static_cast<SizeType<A>>(1);
+  }
+
+  void AddSize(SizeType<A> count) {
+    GetSizeAndIsAllocated() += count << static_cast<SizeType<A>>(1);
+  }
+
+  void SubtractSize(SizeType<A> count) {
+    ABSL_HARDENING_ASSERT(count <= GetSize());
+
+    GetSizeAndIsAllocated() -= count << static_cast<SizeType<A>>(1);
+  }
+
+  void SetAllocation(Allocation<A> allocation) {
+    data_.allocated.allocated_data = allocation.data;
+    data_.allocated.allocated_capacity = allocation.capacity;
+  }
+
+  void MemcpyFrom(const Storage& other_storage) {
+    ABSL_HARDENING_ASSERT(IsMemcpyOk<A>::value ||
+                          other_storage.GetIsAllocated());
+
+    GetSizeAndIsAllocated() = other_storage.GetSizeAndIsAllocated();
+    data_ = other_storage.data_;
+  }
+
+  void DeallocateIfAllocated() {
+    if (GetIsAllocated()) {
+      MallocAdapter<A>::Deallocate(GetAllocator(), GetAllocatedData(),
+                                   GetAllocatedCapacity());
+    }
+  }
+
+ private:
+  ABSL_ATTRIBUTE_NOINLINE void DestroyContents();
+
+  using Metadata = container_internal::CompressedTuple<A, SizeType<A>>;
+
+  struct Allocated {
+    Pointer<A> allocated_data;
+    SizeType<A> allocated_capacity;
+  };
+
+  // `kOptimalInlinedSize` is an automatically adjusted inlined capacity of the
+  // `InlinedVector`. Sometimes, it is possible to increase the capacity (from
+  // the user requested `N`) without increasing the size of the `InlinedVector`.
+  static constexpr size_t kOptimalInlinedSize =
+      (std::max)(N, sizeof(Allocated) / sizeof(ValueType<A>));
+
+  struct Inlined {
+    alignas(ValueType<A>) char inlined_data[sizeof(
+        ValueType<A>[kOptimalInlinedSize])];
+  };
+
+  union Data {
+    Allocated allocated;
+    Inlined inlined;
+  };
+
+  void SwapN(ElementwiseSwapPolicy, Storage* other, SizeType<A> n);
+  void SwapN(ElementwiseConstructPolicy, Storage* other, SizeType<A> n);
+
+  void SwapInlinedElements(MemcpyPolicy, Storage* other);
+  template <typename NotMemcpyPolicy>
+  void SwapInlinedElements(NotMemcpyPolicy, Storage* other);
+
+  template <typename... Args>
+  ABSL_ATTRIBUTE_NOINLINE Reference<A> EmplaceBackSlow(Args&&... args);
+
+  Metadata metadata_;
+  Data data_;
+};
+
+template <typename T, size_t N, typename A>
+void Storage<T, N, A>::DestroyContents() {
+  Pointer<A> data = GetIsAllocated() ? GetAllocatedData() : GetInlinedData();
+  DestroyAdapter<A>::DestroyElements(GetAllocator(), data, GetSize());
+  DeallocateIfAllocated();
+}
+
+template <typename T, size_t N, typename A>
+void Storage<T, N, A>::InitFrom(const Storage& other) {
+  const SizeType<A> n = other.GetSize();
+  ABSL_HARDENING_ASSERT(n > 0);  // Empty sources handled handled in caller.
+  ConstPointer<A> src;
+  Pointer<A> dst;
+  if (!other.GetIsAllocated()) {
+    dst = GetInlinedData();
+    src = other.GetInlinedData();
+  } else {
+    // Because this is only called from the `InlinedVector` constructors, it's
+    // safe to take on the allocation with size `0`. If `ConstructElements(...)`
+    // throws, deallocation will be automatically handled by `~Storage()`.
+    SizeType<A> requested_capacity = ComputeCapacity(GetInlinedCapacity(), n);
+    Allocation<A> allocation =
+        MallocAdapter<A>::Allocate(GetAllocator(), requested_capacity);
+    SetAllocation(allocation);
+    dst = allocation.data;
+    src = other.GetAllocatedData();
+  }
+  if (IsMemcpyOk<A>::value) {
+    std::memcpy(reinterpret_cast<char*>(dst),
+                reinterpret_cast<const char*>(src), n * sizeof(ValueType<A>));
+  } else {
+    auto values = IteratorValueAdapter<A, ConstPointer<A>>(src);
+    ConstructElements<A>(GetAllocator(), dst, values, n);
+  }
+  GetSizeAndIsAllocated() = other.GetSizeAndIsAllocated();
+}
+
+template <typename T, size_t N, typename A>
+template <typename ValueAdapter>
+auto Storage<T, N, A>::Initialize(ValueAdapter values, SizeType<A> new_size)
+    -> void {
+  // Only callable from constructors!
+  ABSL_HARDENING_ASSERT(!GetIsAllocated());
+  ABSL_HARDENING_ASSERT(GetSize() == 0);
+
+  Pointer<A> construct_data;
+  if (new_size > GetInlinedCapacity()) {
+    // Because this is only called from the `InlinedVector` constructors, it's
+    // safe to take on the allocation with size `0`. If `ConstructElements(...)`
+    // throws, deallocation will be automatically handled by `~Storage()`.
+    SizeType<A> requested_capacity =
+        ComputeCapacity(GetInlinedCapacity(), new_size);
+    Allocation<A> allocation =
+        MallocAdapter<A>::Allocate(GetAllocator(), requested_capacity);
+    construct_data = allocation.data;
+    SetAllocation(allocation);
+    SetIsAllocated();
+  } else {
+    construct_data = GetInlinedData();
+  }
+
+  ConstructElements<A>(GetAllocator(), construct_data, values, new_size);
+
+  // Since the initial size was guaranteed to be `0` and the allocated bit is
+  // already correct for either case, *adding* `new_size` gives us the correct
+  // result faster than setting it directly.
+  AddSize(new_size);
+}
+
+template <typename T, size_t N, typename A>
+template <typename ValueAdapter>
+auto Storage<T, N, A>::Assign(ValueAdapter values, SizeType<A> new_size)
+    -> void {
+  StorageView<A> storage_view = MakeStorageView();
+
+  AllocationTransaction<A> allocation_tx(GetAllocator());
+
+  absl::Span<ValueType<A>> assign_loop;
+  absl::Span<ValueType<A>> construct_loop;
+  absl::Span<ValueType<A>> destroy_loop;
+
+  if (new_size > storage_view.capacity) {
+    SizeType<A> requested_capacity =
+        ComputeCapacity(storage_view.capacity, new_size);
+    construct_loop = {allocation_tx.Allocate(requested_capacity), new_size};
+    destroy_loop = {storage_view.data, storage_view.size};
+  } else if (new_size > storage_view.size) {
+    assign_loop = {storage_view.data, storage_view.size};
+    construct_loop = {storage_view.data + storage_view.size,
+                      new_size - storage_view.size};
+  } else {
+    assign_loop = {storage_view.data, new_size};
+    destroy_loop = {storage_view.data + new_size, storage_view.size - new_size};
+  }
+
+  AssignElements<A>(assign_loop.data(), values, assign_loop.size());
+
+  ConstructElements<A>(GetAllocator(), construct_loop.data(), values,
+                       construct_loop.size());
+
+  DestroyAdapter<A>::DestroyElements(GetAllocator(), destroy_loop.data(),
+                                     destroy_loop.size());
+
+  if (allocation_tx.DidAllocate()) {
+    DeallocateIfAllocated();
+    SetAllocation(std::move(allocation_tx).Release());
+    SetIsAllocated();
+  }
+
+  SetSize(new_size);
+}
+
+template <typename T, size_t N, typename A>
+template <typename ValueAdapter>
+auto Storage<T, N, A>::Resize(ValueAdapter values, SizeType<A> new_size)
+    -> void {
+  StorageView<A> storage_view = MakeStorageView();
+  Pointer<A> const base = storage_view.data;
+  const SizeType<A> size = storage_view.size;
+  A& alloc = GetAllocator();
+  if (new_size <= size) {
+    // Destroy extra old elements.
+    DestroyAdapter<A>::DestroyElements(alloc, base + new_size, size - new_size);
+  } else if (new_size <= storage_view.capacity) {
+    // Construct new elements in place.
+    ConstructElements<A>(alloc, base + size, values, new_size - size);
+  } else {
+    // Steps:
+    //  a. Allocate new backing store.
+    //  b. Construct new elements in new backing store.
+    //  c. Move existing elements from old backing store to new backing store.
+    //  d. Destroy all elements in old backing store.
+    // Use transactional wrappers for the first two steps so we can roll
+    // back if necessary due to exceptions.
+    AllocationTransaction<A> allocation_tx(alloc);
+    SizeType<A> requested_capacity =
+        ComputeCapacity(storage_view.capacity, new_size);
+    Pointer<A> new_data = allocation_tx.Allocate(requested_capacity);
+
+    ConstructionTransaction<A> construction_tx(alloc);
+    construction_tx.Construct(new_data + size, values, new_size - size);
+
+    IteratorValueAdapter<A, MoveIterator<A>> move_values(
+        (MoveIterator<A>(base)));
+    ConstructElements<A>(alloc, new_data, move_values, size);
+
+    DestroyAdapter<A>::DestroyElements(alloc, base, size);
+    std::move(construction_tx).Commit();
+    DeallocateIfAllocated();
+    SetAllocation(std::move(allocation_tx).Release());
+    SetIsAllocated();
+  }
+  SetSize(new_size);
+}
+
+template <typename T, size_t N, typename A>
+template <typename ValueAdapter>
+auto Storage<T, N, A>::Insert(ConstIterator<A> pos, ValueAdapter values,
+                              SizeType<A> insert_count) -> Iterator<A> {
+  StorageView<A> storage_view = MakeStorageView();
+
+  auto insert_index = static_cast<SizeType<A>>(
+      std::distance(ConstIterator<A>(storage_view.data), pos));
+  SizeType<A> insert_end_index = insert_index + insert_count;
+  SizeType<A> new_size = storage_view.size + insert_count;
+
+  if (new_size > storage_view.capacity) {
+    AllocationTransaction<A> allocation_tx(GetAllocator());
+    ConstructionTransaction<A> construction_tx(GetAllocator());
+    ConstructionTransaction<A> move_construction_tx(GetAllocator());
+
+    IteratorValueAdapter<A, MoveIterator<A>> move_values(
+        MoveIterator<A>(storage_view.data));
+
+    SizeType<A> requested_capacity =
+        ComputeCapacity(storage_view.capacity, new_size);
+    Pointer<A> new_data = allocation_tx.Allocate(requested_capacity);
+
+    construction_tx.Construct(new_data + insert_index, values, insert_count);
+
+    move_construction_tx.Construct(new_data, move_values, insert_index);
+
+    ConstructElements<A>(GetAllocator(), new_data + insert_end_index,
+                         move_values, storage_view.size - insert_index);
+
+    DestroyAdapter<A>::DestroyElements(GetAllocator(), storage_view.data,
+                                       storage_view.size);
+
+    std::move(construction_tx).Commit();
+    std::move(move_construction_tx).Commit();
+    DeallocateIfAllocated();
+    SetAllocation(std::move(allocation_tx).Release());
+
+    SetAllocatedSize(new_size);
+    return Iterator<A>(new_data + insert_index);
+  } else {
+    SizeType<A> move_construction_destination_index =
+        (std::max)(insert_end_index, storage_view.size);
+
+    ConstructionTransaction<A> move_construction_tx(GetAllocator());
+
+    IteratorValueAdapter<A, MoveIterator<A>> move_construction_values(
+        MoveIterator<A>(storage_view.data +
+                        (move_construction_destination_index - insert_count)));
+    absl::Span<ValueType<A>> move_construction = {
+        storage_view.data + move_construction_destination_index,
+        new_size - move_construction_destination_index};
+
+    Pointer<A> move_assignment_values = storage_view.data + insert_index;
+    absl::Span<ValueType<A>> move_assignment = {
+        storage_view.data + insert_end_index,
+        move_construction_destination_index - insert_end_index};
+
+    absl::Span<ValueType<A>> insert_assignment = {move_assignment_values,
+                                                  move_construction.size()};
+
+    absl::Span<ValueType<A>> insert_construction = {
+        insert_assignment.data() + insert_assignment.size(),
+        insert_count - insert_assignment.size()};
+
+    move_construction_tx.Construct(move_construction.data(),
+                                   move_construction_values,
+                                   move_construction.size());
+
+    for (Pointer<A>
+             destination = move_assignment.data() + move_assignment.size(),
+             last_destination = move_assignment.data(),
+             source = move_assignment_values + move_assignment.size();
+         ;) {
+      --destination;
+      --source;
+      if (destination < last_destination) break;
+      *destination = std::move(*source);
+    }
+
+    AssignElements<A>(insert_assignment.data(), values,
+                      insert_assignment.size());
+
+    ConstructElements<A>(GetAllocator(), insert_construction.data(), values,
+                         insert_construction.size());
+
+    std::move(move_construction_tx).Commit();
+
+    AddSize(insert_count);
+    return Iterator<A>(storage_view.data + insert_index);
+  }
+}
+
+template <typename T, size_t N, typename A>
+template <typename... Args>
+auto Storage<T, N, A>::EmplaceBack(Args&&... args) -> Reference<A> {
+  StorageView<A> storage_view = MakeStorageView();
+  const SizeType<A> n = storage_view.size;
+  if (ABSL_PREDICT_TRUE(n != storage_view.capacity)) {
+    // Fast path; new element fits.
+    Pointer<A> last_ptr = storage_view.data + n;
+    AllocatorTraits<A>::construct(GetAllocator(), last_ptr,
+                                  std::forward<Args>(args)...);
+    AddSize(1);
+    return *last_ptr;
+  }
+  // TODO(b/173712035): Annotate with musttail attribute to prevent regression.
+  return EmplaceBackSlow(std::forward<Args>(args)...);
+}
+
+template <typename T, size_t N, typename A>
+template <typename... Args>
+auto Storage<T, N, A>::EmplaceBackSlow(Args&&... args) -> Reference<A> {
+  StorageView<A> storage_view = MakeStorageView();
+  AllocationTransaction<A> allocation_tx(GetAllocator());
+  IteratorValueAdapter<A, MoveIterator<A>> move_values(
+      MoveIterator<A>(storage_view.data));
+  SizeType<A> requested_capacity = NextCapacity(storage_view.capacity);
+  Pointer<A> construct_data = allocation_tx.Allocate(requested_capacity);
+  Pointer<A> last_ptr = construct_data + storage_view.size;
+
+  // Construct new element.
+  AllocatorTraits<A>::construct(GetAllocator(), last_ptr,
+                                std::forward<Args>(args)...);
+  // Move elements from old backing store to new backing store.
+  ABSL_INTERNAL_TRY {
+    ConstructElements<A>(GetAllocator(), allocation_tx.GetData(), move_values,
+                         storage_view.size);
+  }
+  ABSL_INTERNAL_CATCH_ANY {
+    AllocatorTraits<A>::destroy(GetAllocator(), last_ptr);
+    ABSL_INTERNAL_RETHROW;
+  }
+  // Destroy elements in old backing store.
+  DestroyAdapter<A>::DestroyElements(GetAllocator(), storage_view.data,
+                                     storage_view.size);
+
+  DeallocateIfAllocated();
+  SetAllocation(std::move(allocation_tx).Release());
+  SetIsAllocated();
+  AddSize(1);
+  return *last_ptr;
+}
+
+template <typename T, size_t N, typename A>
+auto Storage<T, N, A>::Erase(ConstIterator<A> from, ConstIterator<A> to)
+    -> Iterator<A> {
+  StorageView<A> storage_view = MakeStorageView();
+
+  auto erase_size = static_cast<SizeType<A>>(std::distance(from, to));
+  auto erase_index = static_cast<SizeType<A>>(
+      std::distance(ConstIterator<A>(storage_view.data), from));
+  SizeType<A> erase_end_index = erase_index + erase_size;
+
+  IteratorValueAdapter<A, MoveIterator<A>> move_values(
+      MoveIterator<A>(storage_view.data + erase_end_index));
+
+  AssignElements<A>(storage_view.data + erase_index, move_values,
+                    storage_view.size - erase_end_index);
+
+  DestroyAdapter<A>::DestroyElements(
+      GetAllocator(), storage_view.data + (storage_view.size - erase_size),
+      erase_size);
+
+  SubtractSize(erase_size);
+  return Iterator<A>(storage_view.data + erase_index);
+}
+
+template <typename T, size_t N, typename A>
+auto Storage<T, N, A>::Reserve(SizeType<A> requested_capacity) -> void {
+  StorageView<A> storage_view = MakeStorageView();
+
+  if (ABSL_PREDICT_FALSE(requested_capacity <= storage_view.capacity)) return;
+
+  AllocationTransaction<A> allocation_tx(GetAllocator());
+
+  IteratorValueAdapter<A, MoveIterator<A>> move_values(
+      MoveIterator<A>(storage_view.data));
+
+  SizeType<A> new_requested_capacity =
+      ComputeCapacity(storage_view.capacity, requested_capacity);
+  Pointer<A> new_data = allocation_tx.Allocate(new_requested_capacity);
+
+  ConstructElements<A>(GetAllocator(), new_data, move_values,
+                       storage_view.size);
+
+  DestroyAdapter<A>::DestroyElements(GetAllocator(), storage_view.data,
+                                     storage_view.size);
+
+  DeallocateIfAllocated();
+  SetAllocation(std::move(allocation_tx).Release());
+  SetIsAllocated();
+}
+
+template <typename T, size_t N, typename A>
+auto Storage<T, N, A>::ShrinkToFit() -> void {
+  // May only be called on allocated instances!
+  ABSL_HARDENING_ASSERT(GetIsAllocated());
+
+  StorageView<A> storage_view{GetAllocatedData(), GetSize(),
+                              GetAllocatedCapacity()};
+
+  if (ABSL_PREDICT_FALSE(storage_view.size == storage_view.capacity)) return;
+
+  AllocationTransaction<A> allocation_tx(GetAllocator());
+
+  IteratorValueAdapter<A, MoveIterator<A>> move_values(
+      MoveIterator<A>(storage_view.data));
+
+  Pointer<A> construct_data;
+  if (storage_view.size > GetInlinedCapacity()) {
+    SizeType<A> requested_capacity = storage_view.size;
+    construct_data = allocation_tx.Allocate(requested_capacity);
+    if (allocation_tx.GetCapacity() >= storage_view.capacity) {
+      // Already using the smallest available heap allocation.
+      return;
+    }
+  } else {
+    construct_data = GetInlinedData();
+  }
+
+  ABSL_INTERNAL_TRY {
+    ConstructElements<A>(GetAllocator(), construct_data, move_values,
+                         storage_view.size);
+  }
+  ABSL_INTERNAL_CATCH_ANY {
+    SetAllocation({storage_view.data, storage_view.capacity});
+    ABSL_INTERNAL_RETHROW;
+  }
+
+  DestroyAdapter<A>::DestroyElements(GetAllocator(), storage_view.data,
+                                     storage_view.size);
+
+  MallocAdapter<A>::Deallocate(GetAllocator(), storage_view.data,
+                               storage_view.capacity);
+
+  if (allocation_tx.DidAllocate()) {
+    SetAllocation(std::move(allocation_tx).Release());
+  } else {
+    UnsetIsAllocated();
+  }
+}
+
+template <typename T, size_t N, typename A>
+auto Storage<T, N, A>::Swap(Storage* other_storage_ptr) -> void {
+  using std::swap;
+  ABSL_HARDENING_ASSERT(this != other_storage_ptr);
+
+  if (GetIsAllocated() && other_storage_ptr->GetIsAllocated()) {
+    swap(data_.allocated, other_storage_ptr->data_.allocated);
+  } else if (!GetIsAllocated() && !other_storage_ptr->GetIsAllocated()) {
+    SwapInlinedElements(SwapPolicy{}, other_storage_ptr);
+  } else {
+    Storage* allocated_ptr = this;
+    Storage* inlined_ptr = other_storage_ptr;
+    if (!allocated_ptr->GetIsAllocated()) swap(allocated_ptr, inlined_ptr);
+
+    StorageView<A> allocated_storage_view{
+        allocated_ptr->GetAllocatedData(), allocated_ptr->GetSize(),
+        allocated_ptr->GetAllocatedCapacity()};
+
+    IteratorValueAdapter<A, MoveIterator<A>> move_values(
+        MoveIterator<A>(inlined_ptr->GetInlinedData()));
+
+    ABSL_INTERNAL_TRY {
+      ConstructElements<A>(inlined_ptr->GetAllocator(),
+                           allocated_ptr->GetInlinedData(), move_values,
+                           inlined_ptr->GetSize());
+    }
+    ABSL_INTERNAL_CATCH_ANY {
+      allocated_ptr->SetAllocation(Allocation<A>{
+          allocated_storage_view.data, allocated_storage_view.capacity});
+      ABSL_INTERNAL_RETHROW;
+    }
+
+    DestroyAdapter<A>::DestroyElements(inlined_ptr->GetAllocator(),
+                                       inlined_ptr->GetInlinedData(),
+                                       inlined_ptr->GetSize());
+
+    inlined_ptr->SetAllocation(Allocation<A>{allocated_storage_view.data,
+                                             allocated_storage_view.capacity});
+  }
+
+  swap(GetSizeAndIsAllocated(), other_storage_ptr->GetSizeAndIsAllocated());
+  swap(GetAllocator(), other_storage_ptr->GetAllocator());
+}
+
+template <typename T, size_t N, typename A>
+void Storage<T, N, A>::SwapN(ElementwiseSwapPolicy, Storage* other,
+                             SizeType<A> n) {
+  std::swap_ranges(GetInlinedData(), GetInlinedData() + n,
+                   other->GetInlinedData());
+}
+
+template <typename T, size_t N, typename A>
+void Storage<T, N, A>::SwapN(ElementwiseConstructPolicy, Storage* other,
+                             SizeType<A> n) {
+  Pointer<A> a = GetInlinedData();
+  Pointer<A> b = other->GetInlinedData();
+  // see note on allocators in `SwapInlinedElements`.
+  A& allocator_a = GetAllocator();
+  A& allocator_b = other->GetAllocator();
+  for (SizeType<A> i = 0; i < n; ++i, ++a, ++b) {
+    ValueType<A> tmp(std::move(*a));
+
+    AllocatorTraits<A>::destroy(allocator_a, a);
+    AllocatorTraits<A>::construct(allocator_b, a, std::move(*b));
+
+    AllocatorTraits<A>::destroy(allocator_b, b);
+    AllocatorTraits<A>::construct(allocator_a, b, std::move(tmp));
+  }
+}
+
+template <typename T, size_t N, typename A>
+void Storage<T, N, A>::SwapInlinedElements(MemcpyPolicy, Storage* other) {
+  Data tmp = data_;
+  data_ = other->data_;
+  other->data_ = tmp;
+}
+
+template <typename T, size_t N, typename A>
+template <typename NotMemcpyPolicy>
+void Storage<T, N, A>::SwapInlinedElements(NotMemcpyPolicy policy,
+                                           Storage* other) {
+  // Note: `destroy` needs to use pre-swap allocator while `construct` -
+  // post-swap allocator. Allocators will be swaped later on outside of
+  // `SwapInlinedElements`.
+  Storage* small_ptr = this;
+  Storage* large_ptr = other;
+  if (small_ptr->GetSize() > large_ptr->GetSize()) {
+    std::swap(small_ptr, large_ptr);
+  }
+
+  auto small_size = small_ptr->GetSize();
+  auto diff = large_ptr->GetSize() - small_size;
+  SwapN(policy, other, small_size);
+
+  IteratorValueAdapter<A, MoveIterator<A>> move_values(
+      MoveIterator<A>(large_ptr->GetInlinedData() + small_size));
+
+  ConstructElements<A>(large_ptr->GetAllocator(),
+                       small_ptr->GetInlinedData() + small_size, move_values,
+                       diff);
+
+  DestroyAdapter<A>::DestroyElements(large_ptr->GetAllocator(),
+                                     large_ptr->GetInlinedData() + small_size,
+                                     diff);
+}
+
+// End ignore "array-bounds"
+#if !defined(__clang__) && defined(__GNUC__)
+#pragma GCC diagnostic pop
+#endif
+
+}  // namespace inlined_vector_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_INTERNAL_INLINED_VECTOR_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/layout.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/layout.h
new file mode 100644
index 0000000000000000000000000000000000000000..a59a2430591f8c1c1c0411e4b762aae54cfb1482
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/layout.h
@@ -0,0 +1,743 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+//                           MOTIVATION AND TUTORIAL
+//
+// If you want to put in a single heap allocation N doubles followed by M ints,
+// it's easy if N and M are known at compile time.
+//
+//   struct S {
+//     double a[N];
+//     int b[M];
+//   };
+//
+//   S* p = new S;
+//
+// But what if N and M are known only in run time? Class template Layout to the
+// rescue! It's a portable generalization of the technique known as struct hack.
+//
+//   // This object will tell us everything we need to know about the memory
+//   // layout of double[N] followed by int[M]. It's structurally identical to
+//   // size_t[2] that stores N and M. It's very cheap to create.
+//   const Layout<double, int> layout(N, M);
+//
+//   // Allocate enough memory for both arrays. `AllocSize()` tells us how much
+//   // memory is needed. We are free to use any allocation function we want as
+//   // long as it returns aligned memory.
+//   std::unique_ptr<unsigned char[]> p(new unsigned char[layout.AllocSize()]);
+//
+//   // Obtain the pointer to the array of doubles.
+//   // Equivalent to `reinterpret_cast<double*>(p.get())`.
+//   //
+//   // We could have written layout.Pointer<0>(p) instead. If all the types are
+//   // unique you can use either form, but if some types are repeated you must
+//   // use the index form.
+//   double* a = layout.Pointer<double>(p.get());
+//
+//   // Obtain the pointer to the array of ints.
+//   // Equivalent to `reinterpret_cast<int*>(p.get() + N * 8)`.
+//   int* b = layout.Pointer<int>(p);
+//
+// If we are unable to specify sizes of all fields, we can pass as many sizes as
+// we can to `Partial()`. In return, it'll allow us to access the fields whose
+// locations and sizes can be computed from the provided information.
+// `Partial()` comes in handy when the array sizes are embedded into the
+// allocation.
+//
+//   // size_t[1] containing N, size_t[1] containing M, double[N], int[M].
+//   using L = Layout<size_t, size_t, double, int>;
+//
+//   unsigned char* Allocate(size_t n, size_t m) {
+//     const L layout(1, 1, n, m);
+//     unsigned char* p = new unsigned char[layout.AllocSize()];
+//     *layout.Pointer<0>(p) = n;
+//     *layout.Pointer<1>(p) = m;
+//     return p;
+//   }
+//
+//   void Use(unsigned char* p) {
+//     // First, extract N and M.
+//     // Specify that the first array has only one element. Using `prefix` we
+//     // can access the first two arrays but not more.
+//     constexpr auto prefix = L::Partial(1);
+//     size_t n = *prefix.Pointer<0>(p);
+//     size_t m = *prefix.Pointer<1>(p);
+//
+//     // Now we can get pointers to the payload.
+//     const L layout(1, 1, n, m);
+//     double* a = layout.Pointer<double>(p);
+//     int* b = layout.Pointer<int>(p);
+//   }
+//
+// The layout we used above combines fixed-size with dynamically-sized fields.
+// This is quite common. Layout is optimized for this use case and generates
+// optimal code. All computations that can be performed at compile time are
+// indeed performed at compile time.
+//
+// Efficiency tip: The order of fields matters. In `Layout<T1, ..., TN>` try to
+// ensure that `alignof(T1) >= ... >= alignof(TN)`. This way you'll have no
+// padding in between arrays.
+//
+// You can manually override the alignment of an array by wrapping the type in
+// `Aligned<T, N>`. `Layout<..., Aligned<T, N>, ...>` has exactly the same API
+// and behavior as `Layout<..., T, ...>` except that the first element of the
+// array of `T` is aligned to `N` (the rest of the elements follow without
+// padding). `N` cannot be less than `alignof(T)`.
+//
+// `AllocSize()` and `Pointer()` are the most basic methods for dealing with
+// memory layouts. Check out the reference or code below to discover more.
+//
+//                            EXAMPLE
+//
+//   // Immutable move-only string with sizeof equal to sizeof(void*). The
+//   // string size and the characters are kept in the same heap allocation.
+//   class CompactString {
+//    public:
+//     CompactString(const char* s = "") {
+//       const size_t size = strlen(s);
+//       // size_t[1] followed by char[size + 1].
+//       const L layout(1, size + 1);
+//       p_.reset(new unsigned char[layout.AllocSize()]);
+//       // If running under ASAN, mark the padding bytes, if any, to catch
+//       // memory errors.
+//       layout.PoisonPadding(p_.get());
+//       // Store the size in the allocation.
+//       *layout.Pointer<size_t>(p_.get()) = size;
+//       // Store the characters in the allocation.
+//       memcpy(layout.Pointer<char>(p_.get()), s, size + 1);
+//     }
+//
+//     size_t size() const {
+//       // Equivalent to reinterpret_cast<size_t&>(*p).
+//       return *L::Partial().Pointer<size_t>(p_.get());
+//     }
+//
+//     const char* c_str() const {
+//       // Equivalent to reinterpret_cast<char*>(p.get() + sizeof(size_t)).
+//       // The argument in Partial(1) specifies that we have size_t[1] in front
+//       // of the characters.
+//       return L::Partial(1).Pointer<char>(p_.get());
+//     }
+//
+//    private:
+//     // Our heap allocation contains a size_t followed by an array of chars.
+//     using L = Layout<size_t, char>;
+//     std::unique_ptr<unsigned char[]> p_;
+//   };
+//
+//   int main() {
+//     CompactString s = "hello";
+//     assert(s.size() == 5);
+//     assert(strcmp(s.c_str(), "hello") == 0);
+//   }
+//
+//                               DOCUMENTATION
+//
+// The interface exported by this file consists of:
+// - class `Layout<>` and its public members.
+// - The public members of class `internal_layout::LayoutImpl<>`. That class
+//   isn't intended to be used directly, and its name and template parameter
+//   list are internal implementation details, but the class itself provides
+//   most of the functionality in this file. See comments on its members for
+//   detailed documentation.
+//
+// `Layout<T1,... Tn>::Partial(count1,..., countm)` (where `m` <= `n`) returns a
+// `LayoutImpl<>` object. `Layout<T1,..., Tn> layout(count1,..., countn)`
+// creates a `Layout` object, which exposes the same functionality by inheriting
+// from `LayoutImpl<>`.
+
+#ifndef ABSL_CONTAINER_INTERNAL_LAYOUT_H_
+#define ABSL_CONTAINER_INTERNAL_LAYOUT_H_
+
+#include <assert.h>
+#include <stddef.h>
+#include <stdint.h>
+
+#include <ostream>
+#include <string>
+#include <tuple>
+#include <type_traits>
+#include <typeinfo>
+#include <utility>
+
+#include "absl/base/config.h"
+#include "absl/meta/type_traits.h"
+#include "absl/strings/str_cat.h"
+#include "absl/types/span.h"
+#include "absl/utility/utility.h"
+
+#ifdef ABSL_HAVE_ADDRESS_SANITIZER
+#include <sanitizer/asan_interface.h>
+#endif
+
+#if defined(__GXX_RTTI)
+#define ABSL_INTERNAL_HAS_CXA_DEMANGLE
+#endif
+
+#ifdef ABSL_INTERNAL_HAS_CXA_DEMANGLE
+#include <cxxabi.h>
+#endif
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+
+// A type wrapper that instructs `Layout` to use the specific alignment for the
+// array. `Layout<..., Aligned<T, N>, ...>` has exactly the same API
+// and behavior as `Layout<..., T, ...>` except that the first element of the
+// array of `T` is aligned to `N` (the rest of the elements follow without
+// padding).
+//
+// Requires: `N >= alignof(T)` and `N` is a power of 2.
+template <class T, size_t N>
+struct Aligned;
+
+namespace internal_layout {
+
+template <class T>
+struct NotAligned {};
+
+template <class T, size_t N>
+struct NotAligned<const Aligned<T, N>> {
+  static_assert(sizeof(T) == 0, "Aligned<T, N> cannot be const-qualified");
+};
+
+template <size_t>
+using IntToSize = size_t;
+
+template <class>
+using TypeToSize = size_t;
+
+template <class T>
+struct Type : NotAligned<T> {
+  using type = T;
+};
+
+template <class T, size_t N>
+struct Type<Aligned<T, N>> {
+  using type = T;
+};
+
+template <class T>
+struct SizeOf : NotAligned<T>, std::integral_constant<size_t, sizeof(T)> {};
+
+template <class T, size_t N>
+struct SizeOf<Aligned<T, N>> : std::integral_constant<size_t, sizeof(T)> {};
+
+// Note: workaround for https://gcc.gnu.org/PR88115
+template <class T>
+struct AlignOf : NotAligned<T> {
+  static constexpr size_t value = alignof(T);
+};
+
+template <class T, size_t N>
+struct AlignOf<Aligned<T, N>> {
+  static_assert(N % alignof(T) == 0,
+                "Custom alignment can't be lower than the type's alignment");
+  static constexpr size_t value = N;
+};
+
+// Does `Ts...` contain `T`?
+template <class T, class... Ts>
+using Contains = absl::disjunction<std::is_same<T, Ts>...>;
+
+template <class From, class To>
+using CopyConst =
+    typename std::conditional<std::is_const<From>::value, const To, To>::type;
+
+// Note: We're not qualifying this with absl:: because it doesn't compile under
+// MSVC.
+template <class T>
+using SliceType = Span<T>;
+
+// This namespace contains no types. It prevents functions defined in it from
+// being found by ADL.
+namespace adl_barrier {
+
+template <class Needle, class... Ts>
+constexpr size_t Find(Needle, Needle, Ts...) {
+  static_assert(!Contains<Needle, Ts...>(), "Duplicate element type");
+  return 0;
+}
+
+template <class Needle, class T, class... Ts>
+constexpr size_t Find(Needle, T, Ts...) {
+  return adl_barrier::Find(Needle(), Ts()...) + 1;
+}
+
+constexpr bool IsPow2(size_t n) { return !(n & (n - 1)); }
+
+// Returns `q * m` for the smallest `q` such that `q * m >= n`.
+// Requires: `m` is a power of two. It's enforced by IsLegalElementType below.
+constexpr size_t Align(size_t n, size_t m) { return (n + m - 1) & ~(m - 1); }
+
+constexpr size_t Min(size_t a, size_t b) { return b < a ? b : a; }
+
+constexpr size_t Max(size_t a) { return a; }
+
+template <class... Ts>
+constexpr size_t Max(size_t a, size_t b, Ts... rest) {
+  return adl_barrier::Max(b < a ? a : b, rest...);
+}
+
+template <class T>
+std::string TypeName() {
+  std::string out;
+  int status = 0;
+  char* demangled = nullptr;
+#ifdef ABSL_INTERNAL_HAS_CXA_DEMANGLE
+  demangled = abi::__cxa_demangle(typeid(T).name(), nullptr, nullptr, &status);
+#endif
+  if (status == 0 && demangled != nullptr) {  // Demangling succeeded.
+    absl::StrAppend(&out, "<", demangled, ">");
+    free(demangled);
+  } else {
+#if defined(__GXX_RTTI) || defined(_CPPRTTI)
+    absl::StrAppend(&out, "<", typeid(T).name(), ">");
+#endif
+  }
+  return out;
+}
+
+}  // namespace adl_barrier
+
+template <bool C>
+using EnableIf = typename std::enable_if<C, int>::type;
+
+// Can `T` be a template argument of `Layout`?
+template <class T>
+using IsLegalElementType = std::integral_constant<
+    bool, !std::is_reference<T>::value && !std::is_volatile<T>::value &&
+              !std::is_reference<typename Type<T>::type>::value &&
+              !std::is_volatile<typename Type<T>::type>::value &&
+              adl_barrier::IsPow2(AlignOf<T>::value)>;
+
+template <class Elements, class SizeSeq, class OffsetSeq>
+class LayoutImpl;
+
+// Public base class of `Layout` and the result type of `Layout::Partial()`.
+//
+// `Elements...` contains all template arguments of `Layout` that created this
+// instance.
+//
+// `SizeSeq...` is `[0, NumSizes)` where `NumSizes` is the number of arguments
+// passed to `Layout::Partial()` or `Layout::Layout()`.
+//
+// `OffsetSeq...` is `[0, NumOffsets)` where `NumOffsets` is
+// `Min(sizeof...(Elements), NumSizes + 1)` (the number of arrays for which we
+// can compute offsets).
+template <class... Elements, size_t... SizeSeq, size_t... OffsetSeq>
+class LayoutImpl<std::tuple<Elements...>, absl::index_sequence<SizeSeq...>,
+                 absl::index_sequence<OffsetSeq...>> {
+ private:
+  static_assert(sizeof...(Elements) > 0, "At least one field is required");
+  static_assert(absl::conjunction<IsLegalElementType<Elements>...>::value,
+                "Invalid element type (see IsLegalElementType)");
+
+  enum {
+    NumTypes = sizeof...(Elements),
+    NumSizes = sizeof...(SizeSeq),
+    NumOffsets = sizeof...(OffsetSeq),
+  };
+
+  // These are guaranteed by `Layout`.
+  static_assert(NumOffsets == adl_barrier::Min(NumTypes, NumSizes + 1),
+                "Internal error");
+  static_assert(NumTypes > 0, "Internal error");
+
+  // Returns the index of `T` in `Elements...`. Results in a compilation error
+  // if `Elements...` doesn't contain exactly one instance of `T`.
+  template <class T>
+  static constexpr size_t ElementIndex() {
+    static_assert(Contains<Type<T>, Type<typename Type<Elements>::type>...>(),
+                  "Type not found");
+    return adl_barrier::Find(Type<T>(),
+                             Type<typename Type<Elements>::type>()...);
+  }
+
+  template <size_t N>
+  using ElementAlignment =
+      AlignOf<typename std::tuple_element<N, std::tuple<Elements...>>::type>;
+
+ public:
+  // Element types of all arrays packed in a tuple.
+  using ElementTypes = std::tuple<typename Type<Elements>::type...>;
+
+  // Element type of the Nth array.
+  template <size_t N>
+  using ElementType = typename std::tuple_element<N, ElementTypes>::type;
+
+  constexpr explicit LayoutImpl(IntToSize<SizeSeq>... sizes)
+      : size_{sizes...} {}
+
+  // Alignment of the layout, equal to the strictest alignment of all elements.
+  // All pointers passed to the methods of layout must be aligned to this value.
+  static constexpr size_t Alignment() {
+    return adl_barrier::Max(AlignOf<Elements>::value...);
+  }
+
+  // Offset in bytes of the Nth array.
+  //
+  //   // int[3], 4 bytes of padding, double[4].
+  //   Layout<int, double> x(3, 4);
+  //   assert(x.Offset<0>() == 0);   // The ints starts from 0.
+  //   assert(x.Offset<1>() == 16);  // The doubles starts from 16.
+  //
+  // Requires: `N <= NumSizes && N < sizeof...(Ts)`.
+  template <size_t N, EnableIf<N == 0> = 0>
+  constexpr size_t Offset() const {
+    return 0;
+  }
+
+  template <size_t N, EnableIf<N != 0> = 0>
+  constexpr size_t Offset() const {
+    static_assert(N < NumOffsets, "Index out of bounds");
+    return adl_barrier::Align(
+        Offset<N - 1>() + SizeOf<ElementType<N - 1>>::value * size_[N - 1],
+        ElementAlignment<N>::value);
+  }
+
+  // Offset in bytes of the array with the specified element type. There must
+  // be exactly one such array and its zero-based index must be at most
+  // `NumSizes`.
+  //
+  //   // int[3], 4 bytes of padding, double[4].
+  //   Layout<int, double> x(3, 4);
+  //   assert(x.Offset<int>() == 0);      // The ints starts from 0.
+  //   assert(x.Offset<double>() == 16);  // The doubles starts from 16.
+  template <class T>
+  constexpr size_t Offset() const {
+    return Offset<ElementIndex<T>()>();
+  }
+
+  // Offsets in bytes of all arrays for which the offsets are known.
+  constexpr std::array<size_t, NumOffsets> Offsets() const {
+    return {{Offset<OffsetSeq>()...}};
+  }
+
+  // The number of elements in the Nth array. This is the Nth argument of
+  // `Layout::Partial()` or `Layout::Layout()` (zero-based).
+  //
+  //   // int[3], 4 bytes of padding, double[4].
+  //   Layout<int, double> x(3, 4);
+  //   assert(x.Size<0>() == 3);
+  //   assert(x.Size<1>() == 4);
+  //
+  // Requires: `N < NumSizes`.
+  template <size_t N>
+  constexpr size_t Size() const {
+    static_assert(N < NumSizes, "Index out of bounds");
+    return size_[N];
+  }
+
+  // The number of elements in the array with the specified element type.
+  // There must be exactly one such array and its zero-based index must be
+  // at most `NumSizes`.
+  //
+  //   // int[3], 4 bytes of padding, double[4].
+  //   Layout<int, double> x(3, 4);
+  //   assert(x.Size<int>() == 3);
+  //   assert(x.Size<double>() == 4);
+  template <class T>
+  constexpr size_t Size() const {
+    return Size<ElementIndex<T>()>();
+  }
+
+  // The number of elements of all arrays for which they are known.
+  constexpr std::array<size_t, NumSizes> Sizes() const {
+    return {{Size<SizeSeq>()...}};
+  }
+
+  // Pointer to the beginning of the Nth array.
+  //
+  // `Char` must be `[const] [signed|unsigned] char`.
+  //
+  //   // int[3], 4 bytes of padding, double[4].
+  //   Layout<int, double> x(3, 4);
+  //   unsigned char* p = new unsigned char[x.AllocSize()];
+  //   int* ints = x.Pointer<0>(p);
+  //   double* doubles = x.Pointer<1>(p);
+  //
+  // Requires: `N <= NumSizes && N < sizeof...(Ts)`.
+  // Requires: `p` is aligned to `Alignment()`.
+  template <size_t N, class Char>
+  CopyConst<Char, ElementType<N>>* Pointer(Char* p) const {
+    using C = typename std::remove_const<Char>::type;
+    static_assert(
+        std::is_same<C, char>() || std::is_same<C, unsigned char>() ||
+            std::is_same<C, signed char>(),
+        "The argument must be a pointer to [const] [signed|unsigned] char");
+    constexpr size_t alignment = Alignment();
+    (void)alignment;
+    assert(reinterpret_cast<uintptr_t>(p) % alignment == 0);
+    return reinterpret_cast<CopyConst<Char, ElementType<N>>*>(p + Offset<N>());
+  }
+
+  // Pointer to the beginning of the array with the specified element type.
+  // There must be exactly one such array and its zero-based index must be at
+  // most `NumSizes`.
+  //
+  // `Char` must be `[const] [signed|unsigned] char`.
+  //
+  //   // int[3], 4 bytes of padding, double[4].
+  //   Layout<int, double> x(3, 4);
+  //   unsigned char* p = new unsigned char[x.AllocSize()];
+  //   int* ints = x.Pointer<int>(p);
+  //   double* doubles = x.Pointer<double>(p);
+  //
+  // Requires: `p` is aligned to `Alignment()`.
+  template <class T, class Char>
+  CopyConst<Char, T>* Pointer(Char* p) const {
+    return Pointer<ElementIndex<T>()>(p);
+  }
+
+  // Pointers to all arrays for which pointers are known.
+  //
+  // `Char` must be `[const] [signed|unsigned] char`.
+  //
+  //   // int[3], 4 bytes of padding, double[4].
+  //   Layout<int, double> x(3, 4);
+  //   unsigned char* p = new unsigned char[x.AllocSize()];
+  //
+  //   int* ints;
+  //   double* doubles;
+  //   std::tie(ints, doubles) = x.Pointers(p);
+  //
+  // Requires: `p` is aligned to `Alignment()`.
+  //
+  // Note: We're not using ElementType alias here because it does not compile
+  // under MSVC.
+  template <class Char>
+  std::tuple<CopyConst<
+      Char, typename std::tuple_element<OffsetSeq, ElementTypes>::type>*...>
+  Pointers(Char* p) const {
+    return std::tuple<CopyConst<Char, ElementType<OffsetSeq>>*...>(
+        Pointer<OffsetSeq>(p)...);
+  }
+
+  // The Nth array.
+  //
+  // `Char` must be `[const] [signed|unsigned] char`.
+  //
+  //   // int[3], 4 bytes of padding, double[4].
+  //   Layout<int, double> x(3, 4);
+  //   unsigned char* p = new unsigned char[x.AllocSize()];
+  //   Span<int> ints = x.Slice<0>(p);
+  //   Span<double> doubles = x.Slice<1>(p);
+  //
+  // Requires: `N < NumSizes`.
+  // Requires: `p` is aligned to `Alignment()`.
+  template <size_t N, class Char>
+  SliceType<CopyConst<Char, ElementType<N>>> Slice(Char* p) const {
+    return SliceType<CopyConst<Char, ElementType<N>>>(Pointer<N>(p), Size<N>());
+  }
+
+  // The array with the specified element type. There must be exactly one
+  // such array and its zero-based index must be less than `NumSizes`.
+  //
+  // `Char` must be `[const] [signed|unsigned] char`.
+  //
+  //   // int[3], 4 bytes of padding, double[4].
+  //   Layout<int, double> x(3, 4);
+  //   unsigned char* p = new unsigned char[x.AllocSize()];
+  //   Span<int> ints = x.Slice<int>(p);
+  //   Span<double> doubles = x.Slice<double>(p);
+  //
+  // Requires: `p` is aligned to `Alignment()`.
+  template <class T, class Char>
+  SliceType<CopyConst<Char, T>> Slice(Char* p) const {
+    return Slice<ElementIndex<T>()>(p);
+  }
+
+  // All arrays with known sizes.
+  //
+  // `Char` must be `[const] [signed|unsigned] char`.
+  //
+  //   // int[3], 4 bytes of padding, double[4].
+  //   Layout<int, double> x(3, 4);
+  //   unsigned char* p = new unsigned char[x.AllocSize()];
+  //
+  //   Span<int> ints;
+  //   Span<double> doubles;
+  //   std::tie(ints, doubles) = x.Slices(p);
+  //
+  // Requires: `p` is aligned to `Alignment()`.
+  //
+  // Note: We're not using ElementType alias here because it does not compile
+  // under MSVC.
+  template <class Char>
+  std::tuple<SliceType<CopyConst<
+      Char, typename std::tuple_element<SizeSeq, ElementTypes>::type>>...>
+  Slices(Char* p) const {
+    // Workaround for https://gcc.gnu.org/bugzilla/show_bug.cgi?id=63875 (fixed
+    // in 6.1).
+    (void)p;
+    return std::tuple<SliceType<CopyConst<Char, ElementType<SizeSeq>>>...>(
+        Slice<SizeSeq>(p)...);
+  }
+
+  // The size of the allocation that fits all arrays.
+  //
+  //   // int[3], 4 bytes of padding, double[4].
+  //   Layout<int, double> x(3, 4);
+  //   unsigned char* p = new unsigned char[x.AllocSize()];  // 48 bytes
+  //
+  // Requires: `NumSizes == sizeof...(Ts)`.
+  constexpr size_t AllocSize() const {
+    static_assert(NumTypes == NumSizes, "You must specify sizes of all fields");
+    return Offset<NumTypes - 1>() +
+        SizeOf<ElementType<NumTypes - 1>>::value * size_[NumTypes - 1];
+  }
+
+  // If built with --config=asan, poisons padding bytes (if any) in the
+  // allocation. The pointer must point to a memory block at least
+  // `AllocSize()` bytes in length.
+  //
+  // `Char` must be `[const] [signed|unsigned] char`.
+  //
+  // Requires: `p` is aligned to `Alignment()`.
+  template <class Char, size_t N = NumOffsets - 1, EnableIf<N == 0> = 0>
+  void PoisonPadding(const Char* p) const {
+    Pointer<0>(p);  // verify the requirements on `Char` and `p`
+  }
+
+  template <class Char, size_t N = NumOffsets - 1, EnableIf<N != 0> = 0>
+  void PoisonPadding(const Char* p) const {
+    static_assert(N < NumOffsets, "Index out of bounds");
+    (void)p;
+#ifdef ABSL_HAVE_ADDRESS_SANITIZER
+    PoisonPadding<Char, N - 1>(p);
+    // The `if` is an optimization. It doesn't affect the observable behaviour.
+    if (ElementAlignment<N - 1>::value % ElementAlignment<N>::value) {
+      size_t start =
+          Offset<N - 1>() + SizeOf<ElementType<N - 1>>::value * size_[N - 1];
+      ASAN_POISON_MEMORY_REGION(p + start, Offset<N>() - start);
+    }
+#endif
+  }
+
+  // Human-readable description of the memory layout. Useful for debugging.
+  // Slow.
+  //
+  //   // char[5], 3 bytes of padding, int[3], 4 bytes of padding, followed
+  //   // by an unknown number of doubles.
+  //   auto x = Layout<char, int, double>::Partial(5, 3);
+  //   assert(x.DebugString() ==
+  //          "@0<char>(1)[5]; @8<int>(4)[3]; @24<double>(8)");
+  //
+  // Each field is in the following format: @offset<type>(sizeof)[size] (<type>
+  // may be missing depending on the target platform). For example,
+  // @8<int>(4)[3] means that at offset 8 we have an array of ints, where each
+  // int is 4 bytes, and we have 3 of those ints. The size of the last field may
+  // be missing (as in the example above). Only fields with known offsets are
+  // described. Type names may differ across platforms: one compiler might
+  // produce "unsigned*" where another produces "unsigned int *".
+  std::string DebugString() const {
+    const auto offsets = Offsets();
+    const size_t sizes[] = {SizeOf<ElementType<OffsetSeq>>::value...};
+    const std::string types[] = {
+        adl_barrier::TypeName<ElementType<OffsetSeq>>()...};
+    std::string res = absl::StrCat("@0", types[0], "(", sizes[0], ")");
+    for (size_t i = 0; i != NumOffsets - 1; ++i) {
+      absl::StrAppend(&res, "[", size_[i], "]; @", offsets[i + 1], types[i + 1],
+                      "(", sizes[i + 1], ")");
+    }
+    // NumSizes is a constant that may be zero. Some compilers cannot see that
+    // inside the if statement "size_[NumSizes - 1]" must be valid.
+    int last = static_cast<int>(NumSizes) - 1;
+    if (NumTypes == NumSizes && last >= 0) {
+      absl::StrAppend(&res, "[", size_[last], "]");
+    }
+    return res;
+  }
+
+ private:
+  // Arguments of `Layout::Partial()` or `Layout::Layout()`.
+  size_t size_[NumSizes > 0 ? NumSizes : 1];
+};
+
+template <size_t NumSizes, class... Ts>
+using LayoutType = LayoutImpl<
+    std::tuple<Ts...>, absl::make_index_sequence<NumSizes>,
+    absl::make_index_sequence<adl_barrier::Min(sizeof...(Ts), NumSizes + 1)>>;
+
+}  // namespace internal_layout
+
+// Descriptor of arrays of various types and sizes laid out in memory one after
+// another. See the top of the file for documentation.
+//
+// Check out the public API of internal_layout::LayoutImpl above. The type is
+// internal to the library but its methods are public, and they are inherited
+// by `Layout`.
+template <class... Ts>
+class Layout : public internal_layout::LayoutType<sizeof...(Ts), Ts...> {
+ public:
+  static_assert(sizeof...(Ts) > 0, "At least one field is required");
+  static_assert(
+      absl::conjunction<internal_layout::IsLegalElementType<Ts>...>::value,
+      "Invalid element type (see IsLegalElementType)");
+
+  // The result type of `Partial()` with `NumSizes` arguments.
+  template <size_t NumSizes>
+  using PartialType = internal_layout::LayoutType<NumSizes, Ts...>;
+
+  // `Layout` knows the element types of the arrays we want to lay out in
+  // memory but not the number of elements in each array.
+  // `Partial(size1, ..., sizeN)` allows us to specify the latter. The
+  // resulting immutable object can be used to obtain pointers to the
+  // individual arrays.
+  //
+  // It's allowed to pass fewer array sizes than the number of arrays. E.g.,
+  // if all you need is to the offset of the second array, you only need to
+  // pass one argument -- the number of elements in the first array.
+  //
+  //   // int[3] followed by 4 bytes of padding and an unknown number of
+  //   // doubles.
+  //   auto x = Layout<int, double>::Partial(3);
+  //   // doubles start at byte 16.
+  //   assert(x.Offset<1>() == 16);
+  //
+  // If you know the number of elements in all arrays, you can still call
+  // `Partial()` but it's more convenient to use the constructor of `Layout`.
+  //
+  //   Layout<int, double> x(3, 5);
+  //
+  // Note: The sizes of the arrays must be specified in number of elements,
+  // not in bytes.
+  //
+  // Requires: `sizeof...(Sizes) <= sizeof...(Ts)`.
+  // Requires: all arguments are convertible to `size_t`.
+  template <class... Sizes>
+  static constexpr PartialType<sizeof...(Sizes)> Partial(Sizes&&... sizes) {
+    static_assert(sizeof...(Sizes) <= sizeof...(Ts), "");
+    return PartialType<sizeof...(Sizes)>(absl::forward<Sizes>(sizes)...);
+  }
+
+  // Creates a layout with the sizes of all arrays specified. If you know
+  // only the sizes of the first N arrays (where N can be zero), you can use
+  // `Partial()` defined above. The constructor is essentially equivalent to
+  // calling `Partial()` and passing in all array sizes; the constructor is
+  // provided as a convenient abbreviation.
+  //
+  // Note: The sizes of the arrays must be specified in number of elements,
+  // not in bytes.
+  constexpr explicit Layout(internal_layout::TypeToSize<Ts>... sizes)
+      : internal_layout::LayoutType<sizeof...(Ts), Ts...>(sizes...) {}
+};
+
+}  // namespace container_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_INTERNAL_LAYOUT_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/node_slot_policy.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/node_slot_policy.h
new file mode 100644
index 0000000000000000000000000000000000000000..baba5743c800453b105ede3d25f526745df76131
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/node_slot_policy.h
@@ -0,0 +1,92 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Adapts a policy for nodes.
+//
+// The node policy should model:
+//
+// struct Policy {
+//   // Returns a new node allocated and constructed using the allocator, using
+//   // the specified arguments.
+//   template <class Alloc, class... Args>
+//   value_type* new_element(Alloc* alloc, Args&&... args) const;
+//
+//   // Destroys and deallocates node using the allocator.
+//   template <class Alloc>
+//   void delete_element(Alloc* alloc, value_type* node) const;
+// };
+//
+// It may also optionally define `value()` and `apply()`. For documentation on
+// these, see hash_policy_traits.h.
+
+#ifndef ABSL_CONTAINER_INTERNAL_NODE_SLOT_POLICY_H_
+#define ABSL_CONTAINER_INTERNAL_NODE_SLOT_POLICY_H_
+
+#include <cassert>
+#include <cstddef>
+#include <memory>
+#include <type_traits>
+#include <utility>
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+
+template <class Reference, class Policy>
+struct node_slot_policy {
+  static_assert(std::is_lvalue_reference<Reference>::value, "");
+
+  using slot_type = typename std::remove_cv<
+      typename std::remove_reference<Reference>::type>::type*;
+
+  template <class Alloc, class... Args>
+  static void construct(Alloc* alloc, slot_type* slot, Args&&... args) {
+    *slot = Policy::new_element(alloc, std::forward<Args>(args)...);
+  }
+
+  template <class Alloc>
+  static void destroy(Alloc* alloc, slot_type* slot) {
+    Policy::delete_element(alloc, *slot);
+  }
+
+  template <class Alloc>
+  static void transfer(Alloc*, slot_type* new_slot, slot_type* old_slot) {
+    *new_slot = *old_slot;
+  }
+
+  static size_t space_used(const slot_type* slot) {
+    if (slot == nullptr) return Policy::element_space_used(nullptr);
+    return Policy::element_space_used(*slot);
+  }
+
+  static Reference element(slot_type* slot) { return **slot; }
+
+  template <class T, class P = Policy>
+  static auto value(T* elem) -> decltype(P::value(elem)) {
+    return P::value(elem);
+  }
+
+  template <class... Ts, class P = Policy>
+  static auto apply(Ts&&... ts) -> decltype(P::apply(std::forward<Ts>(ts)...)) {
+    return P::apply(std::forward<Ts>(ts)...);
+  }
+};
+
+}  // namespace container_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_INTERNAL_NODE_SLOT_POLICY_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/raw_hash_map.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/raw_hash_map.h
new file mode 100644
index 0000000000000000000000000000000000000000..c7df2efc62a539652716640cf97fc7456ea5eead
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/raw_hash_map.h
@@ -0,0 +1,198 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CONTAINER_INTERNAL_RAW_HASH_MAP_H_
+#define ABSL_CONTAINER_INTERNAL_RAW_HASH_MAP_H_
+
+#include <tuple>
+#include <type_traits>
+#include <utility>
+
+#include "absl/base/internal/throw_delegate.h"
+#include "absl/container/internal/container_memory.h"
+#include "absl/container/internal/raw_hash_set.h"  // IWYU pragma: export
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+
+template <class Policy, class Hash, class Eq, class Alloc>
+class raw_hash_map : public raw_hash_set<Policy, Hash, Eq, Alloc> {
+  // P is Policy. It's passed as a template argument to support maps that have
+  // incomplete types as values, as in unordered_map<K, IncompleteType>.
+  // MappedReference<> may be a non-reference type.
+  template <class P>
+  using MappedReference = decltype(P::value(
+      std::addressof(std::declval<typename raw_hash_map::reference>())));
+
+  // MappedConstReference<> may be a non-reference type.
+  template <class P>
+  using MappedConstReference = decltype(P::value(
+      std::addressof(std::declval<typename raw_hash_map::const_reference>())));
+
+  using KeyArgImpl =
+      KeyArg<IsTransparent<Eq>::value && IsTransparent<Hash>::value>;
+
+ public:
+  using key_type = typename Policy::key_type;
+  using mapped_type = typename Policy::mapped_type;
+  template <class K>
+  using key_arg = typename KeyArgImpl::template type<K, key_type>;
+
+  static_assert(!std::is_reference<key_type>::value, "");
+
+  // TODO(b/187807849): Evaluate whether to support reference mapped_type and
+  // remove this assertion if/when it is supported.
+  static_assert(!std::is_reference<mapped_type>::value, "");
+
+  using iterator = typename raw_hash_map::raw_hash_set::iterator;
+  using const_iterator = typename raw_hash_map::raw_hash_set::const_iterator;
+
+  raw_hash_map() {}
+  using raw_hash_map::raw_hash_set::raw_hash_set;
+
+  // The last two template parameters ensure that both arguments are rvalues
+  // (lvalue arguments are handled by the overloads below). This is necessary
+  // for supporting bitfield arguments.
+  //
+  //   union { int n : 1; };
+  //   flat_hash_map<int, int> m;
+  //   m.insert_or_assign(n, n);
+  template <class K = key_type, class V = mapped_type, K* = nullptr,
+            V* = nullptr>
+  std::pair<iterator, bool> insert_or_assign(key_arg<K>&& k, V&& v) {
+    return insert_or_assign_impl(std::forward<K>(k), std::forward<V>(v));
+  }
+
+  template <class K = key_type, class V = mapped_type, K* = nullptr>
+  std::pair<iterator, bool> insert_or_assign(key_arg<K>&& k, const V& v) {
+    return insert_or_assign_impl(std::forward<K>(k), v);
+  }
+
+  template <class K = key_type, class V = mapped_type, V* = nullptr>
+  std::pair<iterator, bool> insert_or_assign(const key_arg<K>& k, V&& v) {
+    return insert_or_assign_impl(k, std::forward<V>(v));
+  }
+
+  template <class K = key_type, class V = mapped_type>
+  std::pair<iterator, bool> insert_or_assign(const key_arg<K>& k, const V& v) {
+    return insert_or_assign_impl(k, v);
+  }
+
+  template <class K = key_type, class V = mapped_type, K* = nullptr,
+            V* = nullptr>
+  iterator insert_or_assign(const_iterator, key_arg<K>&& k, V&& v) {
+    return insert_or_assign(std::forward<K>(k), std::forward<V>(v)).first;
+  }
+
+  template <class K = key_type, class V = mapped_type, K* = nullptr>
+  iterator insert_or_assign(const_iterator, key_arg<K>&& k, const V& v) {
+    return insert_or_assign(std::forward<K>(k), v).first;
+  }
+
+  template <class K = key_type, class V = mapped_type, V* = nullptr>
+  iterator insert_or_assign(const_iterator, const key_arg<K>& k, V&& v) {
+    return insert_or_assign(k, std::forward<V>(v)).first;
+  }
+
+  template <class K = key_type, class V = mapped_type>
+  iterator insert_or_assign(const_iterator, const key_arg<K>& k, const V& v) {
+    return insert_or_assign(k, v).first;
+  }
+
+  // All `try_emplace()` overloads make the same guarantees regarding rvalue
+  // arguments as `std::unordered_map::try_emplace()`, namely that these
+  // functions will not move from rvalue arguments if insertions do not happen.
+  template <class K = key_type, class... Args,
+            typename std::enable_if<
+                !std::is_convertible<K, const_iterator>::value, int>::type = 0,
+            K* = nullptr>
+  std::pair<iterator, bool> try_emplace(key_arg<K>&& k, Args&&... args) {
+    return try_emplace_impl(std::forward<K>(k), std::forward<Args>(args)...);
+  }
+
+  template <class K = key_type, class... Args,
+            typename std::enable_if<
+                !std::is_convertible<K, const_iterator>::value, int>::type = 0>
+  std::pair<iterator, bool> try_emplace(const key_arg<K>& k, Args&&... args) {
+    return try_emplace_impl(k, std::forward<Args>(args)...);
+  }
+
+  template <class K = key_type, class... Args, K* = nullptr>
+  iterator try_emplace(const_iterator, key_arg<K>&& k, Args&&... args) {
+    return try_emplace(std::forward<K>(k), std::forward<Args>(args)...).first;
+  }
+
+  template <class K = key_type, class... Args>
+  iterator try_emplace(const_iterator, const key_arg<K>& k, Args&&... args) {
+    return try_emplace(k, std::forward<Args>(args)...).first;
+  }
+
+  template <class K = key_type, class P = Policy>
+  MappedReference<P> at(const key_arg<K>& key) {
+    auto it = this->find(key);
+    if (it == this->end()) {
+      base_internal::ThrowStdOutOfRange(
+          "absl::container_internal::raw_hash_map<>::at");
+    }
+    return Policy::value(&*it);
+  }
+
+  template <class K = key_type, class P = Policy>
+  MappedConstReference<P> at(const key_arg<K>& key) const {
+    auto it = this->find(key);
+    if (it == this->end()) {
+      base_internal::ThrowStdOutOfRange(
+          "absl::container_internal::raw_hash_map<>::at");
+    }
+    return Policy::value(&*it);
+  }
+
+  template <class K = key_type, class P = Policy, K* = nullptr>
+  MappedReference<P> operator[](key_arg<K>&& key) {
+    return Policy::value(&*try_emplace(std::forward<K>(key)).first);
+  }
+
+  template <class K = key_type, class P = Policy>
+  MappedReference<P> operator[](const key_arg<K>& key) {
+    return Policy::value(&*try_emplace(key).first);
+  }
+
+ private:
+  template <class K, class V>
+  std::pair<iterator, bool> insert_or_assign_impl(K&& k, V&& v) {
+    auto res = this->find_or_prepare_insert(k);
+    if (res.second)
+      this->emplace_at(res.first, std::forward<K>(k), std::forward<V>(v));
+    else
+      Policy::value(&*this->iterator_at(res.first)) = std::forward<V>(v);
+    return {this->iterator_at(res.first), res.second};
+  }
+
+  template <class K = key_type, class... Args>
+  std::pair<iterator, bool> try_emplace_impl(K&& k, Args&&... args) {
+    auto res = this->find_or_prepare_insert(k);
+    if (res.second)
+      this->emplace_at(res.first, std::piecewise_construct,
+                       std::forward_as_tuple(std::forward<K>(k)),
+                       std::forward_as_tuple(std::forward<Args>(args)...));
+    return {this->iterator_at(res.first), res.second};
+  }
+};
+
+}  // namespace container_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_INTERNAL_RAW_HASH_MAP_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/raw_hash_set.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/raw_hash_set.h
new file mode 100644
index 0000000000000000000000000000000000000000..61ef196d47260868084d3594e5bab7caf3f37334
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/internal/raw_hash_set.h
@@ -0,0 +1,2685 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// An open-addressing
+// hashtable with quadratic probing.
+//
+// This is a low level hashtable on top of which different interfaces can be
+// implemented, like flat_hash_set, node_hash_set, string_hash_set, etc.
+//
+// The table interface is similar to that of std::unordered_set. Notable
+// differences are that most member functions support heterogeneous keys when
+// BOTH the hash and eq functions are marked as transparent. They do so by
+// providing a typedef called `is_transparent`.
+//
+// When heterogeneous lookup is enabled, functions that take key_type act as if
+// they have an overload set like:
+//
+//   iterator find(const key_type& key);
+//   template <class K>
+//   iterator find(const K& key);
+//
+//   size_type erase(const key_type& key);
+//   template <class K>
+//   size_type erase(const K& key);
+//
+//   std::pair<iterator, iterator> equal_range(const key_type& key);
+//   template <class K>
+//   std::pair<iterator, iterator> equal_range(const K& key);
+//
+// When heterogeneous lookup is disabled, only the explicit `key_type` overloads
+// exist.
+//
+// find() also supports passing the hash explicitly:
+//
+//   iterator find(const key_type& key, size_t hash);
+//   template <class U>
+//   iterator find(const U& key, size_t hash);
+//
+// In addition the pointer to element and iterator stability guarantees are
+// weaker: all iterators and pointers are invalidated after a new element is
+// inserted.
+//
+// IMPLEMENTATION DETAILS
+//
+// # Table Layout
+//
+// A raw_hash_set's backing array consists of control bytes followed by slots
+// that may or may not contain objects.
+//
+// The layout of the backing array, for `capacity` slots, is thus, as a
+// pseudo-struct:
+//
+//   struct BackingArray {
+//     // Control bytes for the "real" slots.
+//     ctrl_t ctrl[capacity];
+//     // Always `ctrl_t::kSentinel`. This is used by iterators to find when to
+//     // stop and serves no other purpose.
+//     ctrl_t sentinel;
+//     // A copy of the first `kWidth - 1` elements of `ctrl`. This is used so
+//     // that if a probe sequence picks a value near the end of `ctrl`,
+//     // `Group` will have valid control bytes to look at.
+//     ctrl_t clones[kWidth - 1];
+//     // The actual slot data.
+//     slot_type slots[capacity];
+//   };
+//
+// The length of this array is computed by `AllocSize()` below.
+//
+// Control bytes (`ctrl_t`) are bytes (collected into groups of a
+// platform-specific size) that define the state of the corresponding slot in
+// the slot array. Group manipulation is tightly optimized to be as efficient
+// as possible: SSE and friends on x86, clever bit operations on other arches.
+//
+//      Group 1         Group 2        Group 3
+// +---------------+---------------+---------------+
+// | | | | | | | | | | | | | | | | | | | | | | | | |
+// +---------------+---------------+---------------+
+//
+// Each control byte is either a special value for empty slots, deleted slots
+// (sometimes called *tombstones*), and a special end-of-table marker used by
+// iterators, or, if occupied, seven bits (H2) from the hash of the value in the
+// corresponding slot.
+//
+// Storing control bytes in a separate array also has beneficial cache effects,
+// since more logical slots will fit into a cache line.
+//
+// # Hashing
+//
+// We compute two separate hashes, `H1` and `H2`, from the hash of an object.
+// `H1(hash(x))` is an index into `slots`, and essentially the starting point
+// for the probe sequence. `H2(hash(x))` is a 7-bit value used to filter out
+// objects that cannot possibly be the one we are looking for.
+//
+// # Table operations.
+//
+// The key operations are `insert`, `find`, and `erase`.
+//
+// Since `insert` and `erase` are implemented in terms of `find`, we describe
+// `find` first. To `find` a value `x`, we compute `hash(x)`. From
+// `H1(hash(x))` and the capacity, we construct a `probe_seq` that visits every
+// group of slots in some interesting order.
+//
+// We now walk through these indices. At each index, we select the entire group
+// starting with that index and extract potential candidates: occupied slots
+// with a control byte equal to `H2(hash(x))`. If we find an empty slot in the
+// group, we stop and return an error. Each candidate slot `y` is compared with
+// `x`; if `x == y`, we are done and return `&y`; otherwise we contine to the
+// next probe index. Tombstones effectively behave like full slots that never
+// match the value we're looking for.
+//
+// The `H2` bits ensure when we compare a slot to an object with `==`, we are
+// likely to have actually found the object.  That is, the chance is low that
+// `==` is called and returns `false`.  Thus, when we search for an object, we
+// are unlikely to call `==` many times.  This likelyhood can be analyzed as
+// follows (assuming that H2 is a random enough hash function).
+//
+// Let's assume that there are `k` "wrong" objects that must be examined in a
+// probe sequence.  For example, when doing a `find` on an object that is in the
+// table, `k` is the number of objects between the start of the probe sequence
+// and the final found object (not including the final found object).  The
+// expected number of objects with an H2 match is then `k/128`.  Measurements
+// and analysis indicate that even at high load factors, `k` is less than 32,
+// meaning that the number of "false positive" comparisons we must perform is
+// less than 1/8 per `find`.
+
+// `insert` is implemented in terms of `unchecked_insert`, which inserts a
+// value presumed to not be in the table (violating this requirement will cause
+// the table to behave erratically). Given `x` and its hash `hash(x)`, to insert
+// it, we construct a `probe_seq` once again, and use it to find the first
+// group with an unoccupied (empty *or* deleted) slot. We place `x` into the
+// first such slot in the group and mark it as full with `x`'s H2.
+//
+// To `insert`, we compose `unchecked_insert` with `find`. We compute `h(x)` and
+// perform a `find` to see if it's already present; if it is, we're done. If
+// it's not, we may decide the table is getting overcrowded (i.e. the load
+// factor is greater than 7/8 for big tables; `is_small()` tables use a max load
+// factor of 1); in this case, we allocate a bigger array, `unchecked_insert`
+// each element of the table into the new array (we know that no insertion here
+// will insert an already-present value), and discard the old backing array. At
+// this point, we may `unchecked_insert` the value `x`.
+//
+// Below, `unchecked_insert` is partly implemented by `prepare_insert`, which
+// presents a viable, initialized slot pointee to the caller.
+//
+// `erase` is implemented in terms of `erase_at`, which takes an index to a
+// slot. Given an offset, we simply create a tombstone and destroy its contents.
+// If we can prove that the slot would not appear in a probe sequence, we can
+// make the slot as empty, instead. We can prove this by observing that if a
+// group has any empty slots, it has never been full (assuming we never create
+// an empty slot in a group with no empties, which this heuristic guarantees we
+// never do) and find would stop at this group anyways (since it does not probe
+// beyond groups with empties).
+//
+// `erase` is `erase_at` composed with `find`: if we
+// have a value `x`, we can perform a `find`, and then `erase_at` the resulting
+// slot.
+//
+// To iterate, we simply traverse the array, skipping empty and deleted slots
+// and stopping when we hit a `kSentinel`.
+
+#ifndef ABSL_CONTAINER_INTERNAL_RAW_HASH_SET_H_
+#define ABSL_CONTAINER_INTERNAL_RAW_HASH_SET_H_
+
+#include <algorithm>
+#include <cmath>
+#include <cstdint>
+#include <cstring>
+#include <iterator>
+#include <limits>
+#include <memory>
+#include <tuple>
+#include <type_traits>
+#include <utility>
+
+#include "absl/base/config.h"
+#include "absl/base/internal/endian.h"
+#include "absl/base/internal/prefetch.h"
+#include "absl/base/internal/raw_logging.h"
+#include "absl/base/optimization.h"
+#include "absl/base/port.h"
+#include "absl/container/internal/common.h"
+#include "absl/container/internal/compressed_tuple.h"
+#include "absl/container/internal/container_memory.h"
+#include "absl/container/internal/hash_policy_traits.h"
+#include "absl/container/internal/hashtable_debug_hooks.h"
+#include "absl/container/internal/hashtablez_sampler.h"
+#include "absl/memory/memory.h"
+#include "absl/meta/type_traits.h"
+#include "absl/numeric/bits.h"
+#include "absl/utility/utility.h"
+
+#ifdef ABSL_INTERNAL_HAVE_SSE2
+#include <emmintrin.h>
+#endif
+
+#ifdef ABSL_INTERNAL_HAVE_SSSE3
+#include <tmmintrin.h>
+#endif
+
+#ifdef _MSC_VER
+#include <intrin.h>
+#endif
+
+#ifdef ABSL_INTERNAL_HAVE_ARM_NEON
+#include <arm_neon.h>
+#endif
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+
+#ifdef ABSL_SWISSTABLE_ENABLE_GENERATIONS
+#error ABSL_SWISSTABLE_ENABLE_GENERATIONS cannot be directly set
+#elif defined(ABSL_HAVE_ADDRESS_SANITIZER) || \
+    defined(ABSL_HAVE_MEMORY_SANITIZER)
+// When compiled in sanitizer mode, we add generation integers to the backing
+// array and iterators. In the backing array, we store the generation between
+// the control bytes and the slots. When iterators are dereferenced, we assert
+// that the container has not been mutated in a way that could cause iterator
+// invalidation since the iterator was initialized.
+#define ABSL_SWISSTABLE_ENABLE_GENERATIONS
+#endif
+
+// We use uint8_t so we don't need to worry about padding.
+using GenerationType = uint8_t;
+
+#ifdef ABSL_SWISSTABLE_ENABLE_GENERATIONS
+constexpr bool SwisstableGenerationsEnabled() { return true; }
+constexpr size_t NumGenerationBytes() { return sizeof(GenerationType); }
+#else
+constexpr bool SwisstableGenerationsEnabled() { return false; }
+constexpr size_t NumGenerationBytes() { return 0; }
+#endif
+
+template <typename AllocType>
+void SwapAlloc(AllocType& lhs, AllocType& rhs,
+               std::true_type /* propagate_on_container_swap */) {
+  using std::swap;
+  swap(lhs, rhs);
+}
+template <typename AllocType>
+void SwapAlloc(AllocType& /*lhs*/, AllocType& /*rhs*/,
+               std::false_type /* propagate_on_container_swap */) {}
+
+// The state for a probe sequence.
+//
+// Currently, the sequence is a triangular progression of the form
+//
+//   p(i) := Width * (i^2 + i)/2 + hash (mod mask + 1)
+//
+// The use of `Width` ensures that each probe step does not overlap groups;
+// the sequence effectively outputs the addresses of *groups* (although not
+// necessarily aligned to any boundary). The `Group` machinery allows us
+// to check an entire group with minimal branching.
+//
+// Wrapping around at `mask + 1` is important, but not for the obvious reason.
+// As described above, the first few entries of the control byte array
+// are mirrored at the end of the array, which `Group` will find and use
+// for selecting candidates. However, when those candidates' slots are
+// actually inspected, there are no corresponding slots for the cloned bytes,
+// so we need to make sure we've treated those offsets as "wrapping around".
+//
+// It turns out that this probe sequence visits every group exactly once if the
+// number of groups is a power of two, since (i^2+i)/2 is a bijection in
+// Z/(2^m). See https://en.wikipedia.org/wiki/Quadratic_probing
+template <size_t Width>
+class probe_seq {
+ public:
+  // Creates a new probe sequence using `hash` as the initial value of the
+  // sequence and `mask` (usually the capacity of the table) as the mask to
+  // apply to each value in the progression.
+  probe_seq(size_t hash, size_t mask) {
+    assert(((mask + 1) & mask) == 0 && "not a mask");
+    mask_ = mask;
+    offset_ = hash & mask_;
+  }
+
+  // The offset within the table, i.e., the value `p(i)` above.
+  size_t offset() const { return offset_; }
+  size_t offset(size_t i) const { return (offset_ + i) & mask_; }
+
+  void next() {
+    index_ += Width;
+    offset_ += index_;
+    offset_ &= mask_;
+  }
+  // 0-based probe index, a multiple of `Width`.
+  size_t index() const { return index_; }
+
+ private:
+  size_t mask_;
+  size_t offset_;
+  size_t index_ = 0;
+};
+
+template <class ContainerKey, class Hash, class Eq>
+struct RequireUsableKey {
+  template <class PassedKey, class... Args>
+  std::pair<
+      decltype(std::declval<const Hash&>()(std::declval<const PassedKey&>())),
+      decltype(std::declval<const Eq&>()(std::declval<const ContainerKey&>(),
+                                         std::declval<const PassedKey&>()))>*
+  operator()(const PassedKey&, const Args&...) const;
+};
+
+template <class E, class Policy, class Hash, class Eq, class... Ts>
+struct IsDecomposable : std::false_type {};
+
+template <class Policy, class Hash, class Eq, class... Ts>
+struct IsDecomposable<
+    absl::void_t<decltype(Policy::apply(
+        RequireUsableKey<typename Policy::key_type, Hash, Eq>(),
+        std::declval<Ts>()...))>,
+    Policy, Hash, Eq, Ts...> : std::true_type {};
+
+// TODO(alkis): Switch to std::is_nothrow_swappable when gcc/clang supports it.
+template <class T>
+constexpr bool IsNoThrowSwappable(std::true_type = {} /* is_swappable */) {
+  using std::swap;
+  return noexcept(swap(std::declval<T&>(), std::declval<T&>()));
+}
+template <class T>
+constexpr bool IsNoThrowSwappable(std::false_type /* is_swappable */) {
+  return false;
+}
+
+template <typename T>
+uint32_t TrailingZeros(T x) {
+  ABSL_ASSUME(x != 0);
+  return static_cast<uint32_t>(countr_zero(x));
+}
+
+// An abstract bitmask, such as that emitted by a SIMD instruction.
+//
+// Specifically, this type implements a simple bitset whose representation is
+// controlled by `SignificantBits` and `Shift`. `SignificantBits` is the number
+// of abstract bits in the bitset, while `Shift` is the log-base-two of the
+// width of an abstract bit in the representation.
+// This mask provides operations for any number of real bits set in an abstract
+// bit. To add iteration on top of that, implementation must guarantee no more
+// than one real bit is set in an abstract bit.
+template <class T, int SignificantBits, int Shift = 0>
+class NonIterableBitMask {
+ public:
+  explicit NonIterableBitMask(T mask) : mask_(mask) {}
+
+  explicit operator bool() const { return this->mask_ != 0; }
+
+  // Returns the index of the lowest *abstract* bit set in `self`.
+  uint32_t LowestBitSet() const {
+    return container_internal::TrailingZeros(mask_) >> Shift;
+  }
+
+  // Returns the index of the highest *abstract* bit set in `self`.
+  uint32_t HighestBitSet() const {
+    return static_cast<uint32_t>((bit_width(mask_) - 1) >> Shift);
+  }
+
+  // Return the number of trailing zero *abstract* bits.
+  uint32_t TrailingZeros() const {
+    return container_internal::TrailingZeros(mask_) >> Shift;
+  }
+
+  // Return the number of leading zero *abstract* bits.
+  uint32_t LeadingZeros() const {
+    constexpr int total_significant_bits = SignificantBits << Shift;
+    constexpr int extra_bits = sizeof(T) * 8 - total_significant_bits;
+    return static_cast<uint32_t>(countl_zero(mask_ << extra_bits)) >> Shift;
+  }
+
+  T mask_;
+};
+
+// Mask that can be iterable
+//
+// For example, when `SignificantBits` is 16 and `Shift` is zero, this is just
+// an ordinary 16-bit bitset occupying the low 16 bits of `mask`. When
+// `SignificantBits` is 8 and `Shift` is 3, abstract bits are represented as
+// the bytes `0x00` and `0x80`, and it occupies all 64 bits of the bitmask.
+//
+// For example:
+//   for (int i : BitMask<uint32_t, 16>(0b101)) -> yields 0, 2
+//   for (int i : BitMask<uint64_t, 8, 3>(0x0000000080800000)) -> yields 2, 3
+template <class T, int SignificantBits, int Shift = 0>
+class BitMask : public NonIterableBitMask<T, SignificantBits, Shift> {
+  using Base = NonIterableBitMask<T, SignificantBits, Shift>;
+  static_assert(std::is_unsigned<T>::value, "");
+  static_assert(Shift == 0 || Shift == 3, "");
+
+ public:
+  explicit BitMask(T mask) : Base(mask) {}
+  // BitMask is an iterator over the indices of its abstract bits.
+  using value_type = int;
+  using iterator = BitMask;
+  using const_iterator = BitMask;
+
+  BitMask& operator++() {
+    this->mask_ &= (this->mask_ - 1);
+    return *this;
+  }
+
+  uint32_t operator*() const { return Base::LowestBitSet(); }
+
+  BitMask begin() const { return *this; }
+  BitMask end() const { return BitMask(0); }
+
+ private:
+  friend bool operator==(const BitMask& a, const BitMask& b) {
+    return a.mask_ == b.mask_;
+  }
+  friend bool operator!=(const BitMask& a, const BitMask& b) {
+    return a.mask_ != b.mask_;
+  }
+};
+
+using h2_t = uint8_t;
+
+// The values here are selected for maximum performance. See the static asserts
+// below for details.
+
+// A `ctrl_t` is a single control byte, which can have one of four
+// states: empty, deleted, full (which has an associated seven-bit h2_t value)
+// and the sentinel. They have the following bit patterns:
+//
+//      empty: 1 0 0 0 0 0 0 0
+//    deleted: 1 1 1 1 1 1 1 0
+//       full: 0 h h h h h h h  // h represents the hash bits.
+//   sentinel: 1 1 1 1 1 1 1 1
+//
+// These values are specifically tuned for SSE-flavored SIMD.
+// The static_asserts below detail the source of these choices.
+//
+// We use an enum class so that when strict aliasing is enabled, the compiler
+// knows ctrl_t doesn't alias other types.
+enum class ctrl_t : int8_t {
+  kEmpty = -128,   // 0b10000000
+  kDeleted = -2,   // 0b11111110
+  kSentinel = -1,  // 0b11111111
+};
+static_assert(
+    (static_cast<int8_t>(ctrl_t::kEmpty) &
+     static_cast<int8_t>(ctrl_t::kDeleted) &
+     static_cast<int8_t>(ctrl_t::kSentinel) & 0x80) != 0,
+    "Special markers need to have the MSB to make checking for them efficient");
+static_assert(
+    ctrl_t::kEmpty < ctrl_t::kSentinel && ctrl_t::kDeleted < ctrl_t::kSentinel,
+    "ctrl_t::kEmpty and ctrl_t::kDeleted must be smaller than "
+    "ctrl_t::kSentinel to make the SIMD test of IsEmptyOrDeleted() efficient");
+static_assert(
+    ctrl_t::kSentinel == static_cast<ctrl_t>(-1),
+    "ctrl_t::kSentinel must be -1 to elide loading it from memory into SIMD "
+    "registers (pcmpeqd xmm, xmm)");
+static_assert(ctrl_t::kEmpty == static_cast<ctrl_t>(-128),
+              "ctrl_t::kEmpty must be -128 to make the SIMD check for its "
+              "existence efficient (psignb xmm, xmm)");
+static_assert(
+    (~static_cast<int8_t>(ctrl_t::kEmpty) &
+     ~static_cast<int8_t>(ctrl_t::kDeleted) &
+     static_cast<int8_t>(ctrl_t::kSentinel) & 0x7F) != 0,
+    "ctrl_t::kEmpty and ctrl_t::kDeleted must share an unset bit that is not "
+    "shared by ctrl_t::kSentinel to make the scalar test for "
+    "MaskEmptyOrDeleted() efficient");
+static_assert(ctrl_t::kDeleted == static_cast<ctrl_t>(-2),
+              "ctrl_t::kDeleted must be -2 to make the implementation of "
+              "ConvertSpecialToEmptyAndFullToDeleted efficient");
+
+ABSL_DLL extern const ctrl_t kEmptyGroup[17];
+
+// Returns a pointer to a control byte group that can be used by empty tables.
+inline ctrl_t* EmptyGroup() {
+  // Const must be cast away here; no uses of this function will actually write
+  // to it, because it is only used for empty tables.
+  return const_cast<ctrl_t*>(kEmptyGroup);
+}
+
+// Returns a pointer to the generation byte at the end of the empty group, if it
+// exists.
+inline GenerationType* EmptyGeneration() {
+  return reinterpret_cast<GenerationType*>(EmptyGroup() + 16);
+}
+
+// Mixes a randomly generated per-process seed with `hash` and `ctrl` to
+// randomize insertion order within groups.
+bool ShouldInsertBackwards(size_t hash, const ctrl_t* ctrl);
+
+// Returns a per-table, hash salt, which changes on resize. This gets mixed into
+// H1 to randomize iteration order per-table.
+//
+// The seed consists of the ctrl_ pointer, which adds enough entropy to ensure
+// non-determinism of iteration order in most cases.
+inline size_t PerTableSalt(const ctrl_t* ctrl) {
+  // The low bits of the pointer have little or no entropy because of
+  // alignment. We shift the pointer to try to use higher entropy bits. A
+  // good number seems to be 12 bits, because that aligns with page size.
+  return reinterpret_cast<uintptr_t>(ctrl) >> 12;
+}
+// Extracts the H1 portion of a hash: 57 bits mixed with a per-table salt.
+inline size_t H1(size_t hash, const ctrl_t* ctrl) {
+  return (hash >> 7) ^ PerTableSalt(ctrl);
+}
+
+// Extracts the H2 portion of a hash: the 7 bits not used for H1.
+//
+// These are used as an occupied control byte.
+inline h2_t H2(size_t hash) { return hash & 0x7F; }
+
+// Helpers for checking the state of a control byte.
+inline bool IsEmpty(ctrl_t c) { return c == ctrl_t::kEmpty; }
+inline bool IsFull(ctrl_t c) { return c >= static_cast<ctrl_t>(0); }
+inline bool IsDeleted(ctrl_t c) { return c == ctrl_t::kDeleted; }
+inline bool IsEmptyOrDeleted(ctrl_t c) { return c < ctrl_t::kSentinel; }
+
+#ifdef ABSL_INTERNAL_HAVE_SSE2
+// Quick reference guide for intrinsics used below:
+//
+// * __m128i: An XMM (128-bit) word.
+//
+// * _mm_setzero_si128: Returns a zero vector.
+// * _mm_set1_epi8:     Returns a vector with the same i8 in each lane.
+//
+// * _mm_subs_epi8:    Saturating-subtracts two i8 vectors.
+// * _mm_and_si128:    Ands two i128s together.
+// * _mm_or_si128:     Ors two i128s together.
+// * _mm_andnot_si128: And-nots two i128s together.
+//
+// * _mm_cmpeq_epi8: Component-wise compares two i8 vectors for equality,
+//                   filling each lane with 0x00 or 0xff.
+// * _mm_cmpgt_epi8: Same as above, but using > rather than ==.
+//
+// * _mm_loadu_si128:  Performs an unaligned load of an i128.
+// * _mm_storeu_si128: Performs an unaligned store of an i128.
+//
+// * _mm_sign_epi8:     Retains, negates, or zeroes each i8 lane of the first
+//                      argument if the corresponding lane of the second
+//                      argument is positive, negative, or zero, respectively.
+// * _mm_movemask_epi8: Selects the sign bit out of each i8 lane and produces a
+//                      bitmask consisting of those bits.
+// * _mm_shuffle_epi8:  Selects i8s from the first argument, using the low
+//                      four bits of each i8 lane in the second argument as
+//                      indices.
+
+// https://github.com/abseil/abseil-cpp/issues/209
+// https://gcc.gnu.org/bugzilla/show_bug.cgi?id=87853
+// _mm_cmpgt_epi8 is broken under GCC with -funsigned-char
+// Work around this by using the portable implementation of Group
+// when using -funsigned-char under GCC.
+inline __m128i _mm_cmpgt_epi8_fixed(__m128i a, __m128i b) {
+#if defined(__GNUC__) && !defined(__clang__)
+  if (std::is_unsigned<char>::value) {
+    const __m128i mask = _mm_set1_epi8(0x80);
+    const __m128i diff = _mm_subs_epi8(b, a);
+    return _mm_cmpeq_epi8(_mm_and_si128(diff, mask), mask);
+  }
+#endif
+  return _mm_cmpgt_epi8(a, b);
+}
+
+struct GroupSse2Impl {
+  static constexpr size_t kWidth = 16;  // the number of slots per group
+
+  explicit GroupSse2Impl(const ctrl_t* pos) {
+    ctrl = _mm_loadu_si128(reinterpret_cast<const __m128i*>(pos));
+  }
+
+  // Returns a bitmask representing the positions of slots that match hash.
+  BitMask<uint32_t, kWidth> Match(h2_t hash) const {
+    auto match = _mm_set1_epi8(static_cast<char>(hash));
+    return BitMask<uint32_t, kWidth>(
+        static_cast<uint32_t>(_mm_movemask_epi8(_mm_cmpeq_epi8(match, ctrl))));
+  }
+
+  // Returns a bitmask representing the positions of empty slots.
+  NonIterableBitMask<uint32_t, kWidth> MaskEmpty() const {
+#ifdef ABSL_INTERNAL_HAVE_SSSE3
+    // This only works because ctrl_t::kEmpty is -128.
+    return NonIterableBitMask<uint32_t, kWidth>(
+        static_cast<uint32_t>(_mm_movemask_epi8(_mm_sign_epi8(ctrl, ctrl))));
+#else
+    auto match = _mm_set1_epi8(static_cast<char>(ctrl_t::kEmpty));
+    return NonIterableBitMask<uint32_t, kWidth>(
+        static_cast<uint32_t>(_mm_movemask_epi8(_mm_cmpeq_epi8(match, ctrl))));
+#endif
+  }
+
+  // Returns a bitmask representing the positions of empty or deleted slots.
+  NonIterableBitMask<uint32_t, kWidth> MaskEmptyOrDeleted() const {
+    auto special = _mm_set1_epi8(static_cast<char>(ctrl_t::kSentinel));
+    return NonIterableBitMask<uint32_t, kWidth>(static_cast<uint32_t>(
+        _mm_movemask_epi8(_mm_cmpgt_epi8_fixed(special, ctrl))));
+  }
+
+  // Returns the number of trailing empty or deleted elements in the group.
+  uint32_t CountLeadingEmptyOrDeleted() const {
+    auto special = _mm_set1_epi8(static_cast<char>(ctrl_t::kSentinel));
+    return TrailingZeros(static_cast<uint32_t>(
+        _mm_movemask_epi8(_mm_cmpgt_epi8_fixed(special, ctrl)) + 1));
+  }
+
+  void ConvertSpecialToEmptyAndFullToDeleted(ctrl_t* dst) const {
+    auto msbs = _mm_set1_epi8(static_cast<char>(-128));
+    auto x126 = _mm_set1_epi8(126);
+#ifdef ABSL_INTERNAL_HAVE_SSSE3
+    auto res = _mm_or_si128(_mm_shuffle_epi8(x126, ctrl), msbs);
+#else
+    auto zero = _mm_setzero_si128();
+    auto special_mask = _mm_cmpgt_epi8_fixed(zero, ctrl);
+    auto res = _mm_or_si128(msbs, _mm_andnot_si128(special_mask, x126));
+#endif
+    _mm_storeu_si128(reinterpret_cast<__m128i*>(dst), res);
+  }
+
+  __m128i ctrl;
+};
+#endif  // ABSL_INTERNAL_RAW_HASH_SET_HAVE_SSE2
+
+#if defined(ABSL_INTERNAL_HAVE_ARM_NEON) && defined(ABSL_IS_LITTLE_ENDIAN)
+struct GroupAArch64Impl {
+  static constexpr size_t kWidth = 8;
+
+  explicit GroupAArch64Impl(const ctrl_t* pos) {
+    ctrl = vld1_u8(reinterpret_cast<const uint8_t*>(pos));
+  }
+
+  BitMask<uint64_t, kWidth, 3> Match(h2_t hash) const {
+    uint8x8_t dup = vdup_n_u8(hash);
+    auto mask = vceq_u8(ctrl, dup);
+    constexpr uint64_t msbs = 0x8080808080808080ULL;
+    return BitMask<uint64_t, kWidth, 3>(
+        vget_lane_u64(vreinterpret_u64_u8(mask), 0) & msbs);
+  }
+
+  NonIterableBitMask<uint64_t, kWidth, 3> MaskEmpty() const {
+    uint64_t mask =
+        vget_lane_u64(vreinterpret_u64_u8(vceq_s8(
+                          vdup_n_s8(static_cast<int8_t>(ctrl_t::kEmpty)),
+                          vreinterpret_s8_u8(ctrl))),
+                      0);
+    return NonIterableBitMask<uint64_t, kWidth, 3>(mask);
+  }
+
+  NonIterableBitMask<uint64_t, kWidth, 3> MaskEmptyOrDeleted() const {
+    uint64_t mask =
+        vget_lane_u64(vreinterpret_u64_u8(vcgt_s8(
+                          vdup_n_s8(static_cast<int8_t>(ctrl_t::kSentinel)),
+                          vreinterpret_s8_u8(ctrl))),
+                      0);
+    return NonIterableBitMask<uint64_t, kWidth, 3>(mask);
+  }
+
+  uint32_t CountLeadingEmptyOrDeleted() const {
+    uint64_t mask =
+        vget_lane_u64(vreinterpret_u64_u8(vcle_s8(
+                          vdup_n_s8(static_cast<int8_t>(ctrl_t::kSentinel)),
+                          vreinterpret_s8_u8(ctrl))),
+                      0);
+    // Similar to MaskEmptyorDeleted() but we invert the logic to invert the
+    // produced bitfield. We then count number of trailing zeros.
+    // Clang and GCC optimize countr_zero to rbit+clz without any check for 0,
+    // so we should be fine.
+    return static_cast<uint32_t>(countr_zero(mask)) >> 3;
+  }
+
+  void ConvertSpecialToEmptyAndFullToDeleted(ctrl_t* dst) const {
+    uint64_t mask = vget_lane_u64(vreinterpret_u64_u8(ctrl), 0);
+    constexpr uint64_t msbs = 0x8080808080808080ULL;
+    constexpr uint64_t lsbs = 0x0101010101010101ULL;
+    auto x = mask & msbs;
+    auto res = (~x + (x >> 7)) & ~lsbs;
+    little_endian::Store64(dst, res);
+  }
+
+  uint8x8_t ctrl;
+};
+#endif  // ABSL_INTERNAL_HAVE_ARM_NEON && ABSL_IS_LITTLE_ENDIAN
+
+struct GroupPortableImpl {
+  static constexpr size_t kWidth = 8;
+
+  explicit GroupPortableImpl(const ctrl_t* pos)
+      : ctrl(little_endian::Load64(pos)) {}
+
+  BitMask<uint64_t, kWidth, 3> Match(h2_t hash) const {
+    // For the technique, see:
+    // http://graphics.stanford.edu/~seander/bithacks.html##ValueInWord
+    // (Determine if a word has a byte equal to n).
+    //
+    // Caveat: there are false positives but:
+    // - they only occur if there is a real match
+    // - they never occur on ctrl_t::kEmpty, ctrl_t::kDeleted, ctrl_t::kSentinel
+    // - they will be handled gracefully by subsequent checks in code
+    //
+    // Example:
+    //   v = 0x1716151413121110
+    //   hash = 0x12
+    //   retval = (v - lsbs) & ~v & msbs = 0x0000000080800000
+    constexpr uint64_t msbs = 0x8080808080808080ULL;
+    constexpr uint64_t lsbs = 0x0101010101010101ULL;
+    auto x = ctrl ^ (lsbs * hash);
+    return BitMask<uint64_t, kWidth, 3>((x - lsbs) & ~x & msbs);
+  }
+
+  NonIterableBitMask<uint64_t, kWidth, 3> MaskEmpty() const {
+    constexpr uint64_t msbs = 0x8080808080808080ULL;
+    return NonIterableBitMask<uint64_t, kWidth, 3>((ctrl & (~ctrl << 6)) &
+                                                   msbs);
+  }
+
+  NonIterableBitMask<uint64_t, kWidth, 3> MaskEmptyOrDeleted() const {
+    constexpr uint64_t msbs = 0x8080808080808080ULL;
+    return NonIterableBitMask<uint64_t, kWidth, 3>((ctrl & (~ctrl << 7)) &
+                                                   msbs);
+  }
+
+  uint32_t CountLeadingEmptyOrDeleted() const {
+    // ctrl | ~(ctrl >> 7) will have the lowest bit set to zero for kEmpty and
+    // kDeleted. We lower all other bits and count number of trailing zeros.
+    constexpr uint64_t bits = 0x0101010101010101ULL;
+    return static_cast<uint32_t>(countr_zero((ctrl | ~(ctrl >> 7)) & bits) >>
+                                 3);
+  }
+
+  void ConvertSpecialToEmptyAndFullToDeleted(ctrl_t* dst) const {
+    constexpr uint64_t msbs = 0x8080808080808080ULL;
+    constexpr uint64_t lsbs = 0x0101010101010101ULL;
+    auto x = ctrl & msbs;
+    auto res = (~x + (x >> 7)) & ~lsbs;
+    little_endian::Store64(dst, res);
+  }
+
+  uint64_t ctrl;
+};
+
+#ifdef ABSL_INTERNAL_HAVE_SSE2
+using Group = GroupSse2Impl;
+#elif defined(ABSL_INTERNAL_HAVE_ARM_NEON) && defined(ABSL_IS_LITTLE_ENDIAN)
+using Group = GroupAArch64Impl;
+#else
+using Group = GroupPortableImpl;
+#endif
+
+class CommonFieldsGenerationInfoEnabled {
+  // A sentinel value for reserved_growth_ indicating that we just ran out of
+  // reserved growth on the last insertion. When reserve is called and then
+  // insertions take place, reserved_growth_'s state machine is N, ..., 1,
+  // kReservedGrowthJustRanOut, 0.
+  static constexpr size_t kReservedGrowthJustRanOut =
+      (std::numeric_limits<size_t>::max)();
+
+ public:
+  CommonFieldsGenerationInfoEnabled() = default;
+  CommonFieldsGenerationInfoEnabled(CommonFieldsGenerationInfoEnabled&& that)
+      : reserved_growth_(that.reserved_growth_), generation_(that.generation_) {
+    that.reserved_growth_ = 0;
+    that.generation_ = EmptyGeneration();
+  }
+  CommonFieldsGenerationInfoEnabled& operator=(
+      CommonFieldsGenerationInfoEnabled&&) = default;
+
+  // Whether we should rehash on insert in order to detect bugs of using invalid
+  // references. We rehash on the first insertion after reserved_growth_ reaches
+  // 0 after a call to reserve.
+  // TODO(b/254649633): we could potentially do a rehash with low probability
+  // whenever reserved_growth_ is zero.
+  bool should_rehash_for_bug_detection_on_insert() const {
+    return reserved_growth_ == kReservedGrowthJustRanOut;
+  }
+  void maybe_increment_generation_on_insert() {
+    if (reserved_growth_ == kReservedGrowthJustRanOut) reserved_growth_ = 0;
+
+    if (reserved_growth_ > 0) {
+      if (--reserved_growth_ == 0) reserved_growth_ = kReservedGrowthJustRanOut;
+    } else {
+      ++*generation_;
+    }
+  }
+  void reset_reserved_growth(size_t reservation, size_t size) {
+    reserved_growth_ = reservation - size;
+  }
+  size_t reserved_growth() const { return reserved_growth_; }
+  void set_reserved_growth(size_t r) { reserved_growth_ = r; }
+  GenerationType generation() const { return *generation_; }
+  void set_generation(GenerationType g) { *generation_ = g; }
+  GenerationType* generation_ptr() const { return generation_; }
+  void set_generation_ptr(GenerationType* g) { generation_ = g; }
+
+ private:
+  // The number of insertions remaining that are guaranteed to not rehash due to
+  // a prior call to reserve. Note: we store reserved growth rather than
+  // reservation size because calls to erase() decrease size_ but don't decrease
+  // reserved growth.
+  size_t reserved_growth_ = 0;
+  // Pointer to the generation counter, which is used to validate iterators and
+  // is stored in the backing array between the control bytes and the slots.
+  // Note that we can't store the generation inside the container itself and
+  // keep a pointer to the container in the iterators because iterators must
+  // remain valid when the container is moved.
+  // Note: we could derive this pointer from the control pointer, but it makes
+  // the code more complicated, and there's a benefit in having the sizes of
+  // raw_hash_set in sanitizer mode and non-sanitizer mode a bit more different,
+  // which is that tests are less likely to rely on the size remaining the same.
+  GenerationType* generation_ = EmptyGeneration();
+};
+
+class CommonFieldsGenerationInfoDisabled {
+ public:
+  CommonFieldsGenerationInfoDisabled() = default;
+  CommonFieldsGenerationInfoDisabled(CommonFieldsGenerationInfoDisabled&&) =
+      default;
+  CommonFieldsGenerationInfoDisabled& operator=(
+      CommonFieldsGenerationInfoDisabled&&) = default;
+
+  bool should_rehash_for_bug_detection_on_insert() const { return false; }
+  void maybe_increment_generation_on_insert() {}
+  void reset_reserved_growth(size_t, size_t) {}
+  size_t reserved_growth() const { return 0; }
+  void set_reserved_growth(size_t) {}
+  GenerationType generation() const { return 0; }
+  void set_generation(GenerationType) {}
+  GenerationType* generation_ptr() const { return nullptr; }
+  void set_generation_ptr(GenerationType*) {}
+};
+
+class HashSetIteratorGenerationInfoEnabled {
+ public:
+  HashSetIteratorGenerationInfoEnabled() = default;
+  explicit HashSetIteratorGenerationInfoEnabled(
+      const GenerationType* generation_ptr)
+      : generation_ptr_(generation_ptr), generation_(*generation_ptr) {}
+
+  GenerationType generation() const { return generation_; }
+  void reset_generation() { generation_ = *generation_ptr_; }
+  const GenerationType* generation_ptr() const { return generation_ptr_; }
+  void set_generation_ptr(const GenerationType* ptr) { generation_ptr_ = ptr; }
+
+ private:
+  const GenerationType* generation_ptr_ = EmptyGeneration();
+  GenerationType generation_ = *generation_ptr_;
+};
+
+class HashSetIteratorGenerationInfoDisabled {
+ public:
+  HashSetIteratorGenerationInfoDisabled() = default;
+  explicit HashSetIteratorGenerationInfoDisabled(const GenerationType*) {}
+
+  GenerationType generation() const { return 0; }
+  void reset_generation() {}
+  const GenerationType* generation_ptr() const { return nullptr; }
+  void set_generation_ptr(const GenerationType*) {}
+};
+
+#ifdef ABSL_SWISSTABLE_ENABLE_GENERATIONS
+using CommonFieldsGenerationInfo = CommonFieldsGenerationInfoEnabled;
+using HashSetIteratorGenerationInfo = HashSetIteratorGenerationInfoEnabled;
+#else
+using CommonFieldsGenerationInfo = CommonFieldsGenerationInfoDisabled;
+using HashSetIteratorGenerationInfo = HashSetIteratorGenerationInfoDisabled;
+#endif
+
+// CommonFields hold the fields in raw_hash_set that do not depend
+// on template parameters. This allows us to conveniently pass all
+// of this state to helper functions as a single argument.
+class CommonFields : public CommonFieldsGenerationInfo {
+ public:
+  CommonFields() = default;
+
+  // Not copyable
+  CommonFields(const CommonFields&) = delete;
+  CommonFields& operator=(const CommonFields&) = delete;
+
+  // Movable
+  CommonFields(CommonFields&& that)
+      : CommonFieldsGenerationInfo(
+            std::move(static_cast<CommonFieldsGenerationInfo&&>(that))),
+        // Explicitly copying fields into "this" and then resetting "that"
+        // fields generates less code then calling absl::exchange per field.
+        control_(that.control_),
+        slots_(that.slots_),
+        size_(that.size_),
+        capacity_(that.capacity_),
+        compressed_tuple_(that.growth_left(), std::move(that.infoz())) {
+    that.control_ = EmptyGroup();
+    that.slots_ = nullptr;
+    that.size_ = 0;
+    that.capacity_ = 0;
+    that.growth_left() = 0;
+  }
+  CommonFields& operator=(CommonFields&&) = default;
+
+  // The number of slots we can still fill without needing to rehash.
+  size_t& growth_left() { return compressed_tuple_.template get<0>(); }
+
+  HashtablezInfoHandle& infoz() { return compressed_tuple_.template get<1>(); }
+  const HashtablezInfoHandle& infoz() const {
+    return compressed_tuple_.template get<1>();
+  }
+
+  void reset_reserved_growth(size_t reservation) {
+    CommonFieldsGenerationInfo::reset_reserved_growth(reservation, size_);
+  }
+
+  // TODO(b/259599413): Investigate removing some of these fields:
+  // - control/slots can be derived from each other
+  // - size can be moved into the slot array
+
+  // The control bytes (and, also, a pointer to the base of the backing array).
+  //
+  // This contains `capacity + 1 + NumClonedBytes()` entries, even
+  // when the table is empty (hence EmptyGroup).
+  ctrl_t* control_ = EmptyGroup();
+
+  // The beginning of the slots, located at `SlotOffset()` bytes after
+  // `control`. May be null for empty tables.
+  void* slots_ = nullptr;
+
+  // The number of filled slots.
+  size_t size_ = 0;
+
+  // The total number of available slots.
+  size_t capacity_ = 0;
+
+  // Bundle together growth_left and HashtablezInfoHandle to ensure EBO for
+  // HashtablezInfoHandle when sampling is turned off.
+  absl::container_internal::CompressedTuple<size_t, HashtablezInfoHandle>
+      compressed_tuple_{0u, HashtablezInfoHandle{}};
+};
+
+// Returns he number of "cloned control bytes".
+//
+// This is the number of control bytes that are present both at the beginning
+// of the control byte array and at the end, such that we can create a
+// `Group::kWidth`-width probe window starting from any control byte.
+constexpr size_t NumClonedBytes() { return Group::kWidth - 1; }
+
+template <class Policy, class Hash, class Eq, class Alloc>
+class raw_hash_set;
+
+// Returns whether `n` is a valid capacity (i.e., number of slots).
+//
+// A valid capacity is a non-zero integer `2^m - 1`.
+inline bool IsValidCapacity(size_t n) { return ((n + 1) & n) == 0 && n > 0; }
+
+// Returns the next valid capacity after `n`.
+inline size_t NextCapacity(size_t n) {
+  assert(IsValidCapacity(n) || n == 0);
+  return n * 2 + 1;
+}
+
+// Applies the following mapping to every byte in the control array:
+//   * kDeleted -> kEmpty
+//   * kEmpty -> kEmpty
+//   * _ -> kDeleted
+// PRECONDITION:
+//   IsValidCapacity(capacity)
+//   ctrl[capacity] == ctrl_t::kSentinel
+//   ctrl[i] != ctrl_t::kSentinel for all i < capacity
+void ConvertDeletedToEmptyAndFullToDeleted(ctrl_t* ctrl, size_t capacity);
+
+// Converts `n` into the next valid capacity, per `IsValidCapacity`.
+inline size_t NormalizeCapacity(size_t n) {
+  return n ? ~size_t{} >> countl_zero(n) : 1;
+}
+
+// General notes on capacity/growth methods below:
+// - We use 7/8th as maximum load factor. For 16-wide groups, that gives an
+//   average of two empty slots per group.
+// - For (capacity+1) >= Group::kWidth, growth is 7/8*capacity.
+// - For (capacity+1) < Group::kWidth, growth == capacity. In this case, we
+//   never need to probe (the whole table fits in one group) so we don't need a
+//   load factor less than 1.
+
+// Given `capacity`, applies the load factor; i.e., it returns the maximum
+// number of values we should put into the table before a resizing rehash.
+inline size_t CapacityToGrowth(size_t capacity) {
+  assert(IsValidCapacity(capacity));
+  // `capacity*7/8`
+  if (Group::kWidth == 8 && capacity == 7) {
+    // x-x/8 does not work when x==7.
+    return 6;
+  }
+  return capacity - capacity / 8;
+}
+
+// Given `growth`, "unapplies" the load factor to find how large the capacity
+// should be to stay within the load factor.
+//
+// This might not be a valid capacity and `NormalizeCapacity()` should be
+// called on this.
+inline size_t GrowthToLowerboundCapacity(size_t growth) {
+  // `growth*8/7`
+  if (Group::kWidth == 8 && growth == 7) {
+    // x+(x-1)/7 does not work when x==7.
+    return 8;
+  }
+  return growth + static_cast<size_t>((static_cast<int64_t>(growth) - 1) / 7);
+}
+
+template <class InputIter>
+size_t SelectBucketCountForIterRange(InputIter first, InputIter last,
+                                     size_t bucket_count) {
+  if (bucket_count != 0) {
+    return bucket_count;
+  }
+  using InputIterCategory =
+      typename std::iterator_traits<InputIter>::iterator_category;
+  if (std::is_base_of<std::random_access_iterator_tag,
+                      InputIterCategory>::value) {
+    return GrowthToLowerboundCapacity(
+        static_cast<size_t>(std::distance(first, last)));
+  }
+  return 0;
+}
+
+#define ABSL_INTERNAL_ASSERT_IS_FULL(ctrl, generation, generation_ptr,         \
+                                     operation)                                \
+  do {                                                                         \
+    ABSL_HARDENING_ASSERT(                                                     \
+        (ctrl != nullptr) && operation                                         \
+        " called on invalid iterator. The iterator might be an end() "         \
+        "iterator or may have been default constructed.");                     \
+    if (SwisstableGenerationsEnabled() && generation != *generation_ptr)       \
+      ABSL_INTERNAL_LOG(FATAL, operation                                       \
+                        " called on invalidated iterator. The table could "    \
+                        "have rehashed since this iterator was initialized."); \
+    ABSL_HARDENING_ASSERT(                                                     \
+        (IsFull(*ctrl)) && operation                                           \
+        " called on invalid iterator. The element might have been erased or "  \
+        "the table might have rehashed.");                                     \
+  } while (0)
+
+// Note that for comparisons, null/end iterators are valid.
+inline void AssertIsValidForComparison(const ctrl_t* ctrl,
+                                       GenerationType generation,
+                                       const GenerationType* generation_ptr) {
+  ABSL_HARDENING_ASSERT((ctrl == nullptr || IsFull(*ctrl)) &&
+                        "Invalid iterator comparison. The element might have "
+                        "been erased or the table might have rehashed.");
+  if (SwisstableGenerationsEnabled() && generation != *generation_ptr) {
+    ABSL_INTERNAL_LOG(FATAL,
+                      "Invalid iterator comparison. The table could have "
+                      "rehashed since this iterator was initialized.");
+  }
+}
+
+// If the two iterators come from the same container, then their pointers will
+// interleave such that ctrl_a <= ctrl_b < slot_a <= slot_b or vice/versa.
+// Note: we take slots by reference so that it's not UB if they're uninitialized
+// as long as we don't read them (when ctrl is null).
+inline bool AreItersFromSameContainer(const ctrl_t* ctrl_a,
+                                      const ctrl_t* ctrl_b,
+                                      const void* const& slot_a,
+                                      const void* const& slot_b) {
+  // If either control byte is null, then we can't tell.
+  if (ctrl_a == nullptr || ctrl_b == nullptr) return true;
+  const void* low_slot = slot_a;
+  const void* hi_slot = slot_b;
+  if (ctrl_a > ctrl_b) {
+    std::swap(ctrl_a, ctrl_b);
+    std::swap(low_slot, hi_slot);
+  }
+  return ctrl_b < low_slot && low_slot <= hi_slot;
+}
+
+// Asserts that two iterators come from the same container.
+// Note: we take slots by reference so that it's not UB if they're uninitialized
+// as long as we don't read them (when ctrl is null).
+// TODO(b/254649633): when generations are enabled, we can detect more cases of
+// different containers by comparing the pointers to the generations - this
+// can cover cases of end iterators that we would otherwise miss.
+inline void AssertSameContainer(const ctrl_t* ctrl_a, const ctrl_t* ctrl_b,
+                                const void* const& slot_a,
+                                const void* const& slot_b) {
+  ABSL_HARDENING_ASSERT(
+      AreItersFromSameContainer(ctrl_a, ctrl_b, slot_a, slot_b) &&
+      "Invalid iterator comparison. The iterators may be from different "
+      "containers or the container might have rehashed.");
+}
+
+struct FindInfo {
+  size_t offset;
+  size_t probe_length;
+};
+
+// Whether a table is "small". A small table fits entirely into a probing
+// group, i.e., has a capacity < `Group::kWidth`.
+//
+// In small mode we are able to use the whole capacity. The extra control
+// bytes give us at least one "empty" control byte to stop the iteration.
+// This is important to make 1 a valid capacity.
+//
+// In small mode only the first `capacity` control bytes after the sentinel
+// are valid. The rest contain dummy ctrl_t::kEmpty values that do not
+// represent a real slot. This is important to take into account on
+// `find_first_non_full()`, where we never try
+// `ShouldInsertBackwards()` for small tables.
+inline bool is_small(size_t capacity) { return capacity < Group::kWidth - 1; }
+
+// Begins a probing operation on `common.control`, using `hash`.
+inline probe_seq<Group::kWidth> probe(const CommonFields& common, size_t hash) {
+  const ctrl_t* ctrl = common.control_;
+  const size_t capacity = common.capacity_;
+  return probe_seq<Group::kWidth>(H1(hash, ctrl), capacity);
+}
+
+// Probes an array of control bits using a probe sequence derived from `hash`,
+// and returns the offset corresponding to the first deleted or empty slot.
+//
+// Behavior when the entire table is full is undefined.
+//
+// NOTE: this function must work with tables having both empty and deleted
+// slots in the same group. Such tables appear during `erase()`.
+template <typename = void>
+inline FindInfo find_first_non_full(const CommonFields& common, size_t hash) {
+  auto seq = probe(common, hash);
+  const ctrl_t* ctrl = common.control_;
+  while (true) {
+    Group g{ctrl + seq.offset()};
+    auto mask = g.MaskEmptyOrDeleted();
+    if (mask) {
+#if !defined(NDEBUG)
+      // We want to add entropy even when ASLR is not enabled.
+      // In debug build we will randomly insert in either the front or back of
+      // the group.
+      // TODO(kfm,sbenza): revisit after we do unconditional mixing
+      if (!is_small(common.capacity_) && ShouldInsertBackwards(hash, ctrl)) {
+        return {seq.offset(mask.HighestBitSet()), seq.index()};
+      }
+#endif
+      return {seq.offset(mask.LowestBitSet()), seq.index()};
+    }
+    seq.next();
+    assert(seq.index() <= common.capacity_ && "full table!");
+  }
+}
+
+// Extern template for inline function keep possibility of inlining.
+// When compiler decided to not inline, no symbols will be added to the
+// corresponding translation unit.
+extern template FindInfo find_first_non_full(const CommonFields&, size_t);
+
+// Non-inlined version of find_first_non_full for use in less
+// performance critical routines.
+FindInfo find_first_non_full_outofline(const CommonFields&, size_t);
+
+inline void ResetGrowthLeft(CommonFields& common) {
+  common.growth_left() = CapacityToGrowth(common.capacity_) - common.size_;
+}
+
+// Sets `ctrl` to `{kEmpty, kSentinel, ..., kEmpty}`, marking the entire
+// array as marked as empty.
+inline void ResetCtrl(CommonFields& common, size_t slot_size) {
+  const size_t capacity = common.capacity_;
+  ctrl_t* ctrl = common.control_;
+  std::memset(ctrl, static_cast<int8_t>(ctrl_t::kEmpty),
+              capacity + 1 + NumClonedBytes());
+  ctrl[capacity] = ctrl_t::kSentinel;
+  SanitizerPoisonMemoryRegion(common.slots_, slot_size * capacity);
+  ResetGrowthLeft(common);
+}
+
+// Sets `ctrl[i]` to `h`.
+//
+// Unlike setting it directly, this function will perform bounds checks and
+// mirror the value to the cloned tail if necessary.
+inline void SetCtrl(const CommonFields& common, size_t i, ctrl_t h,
+                    size_t slot_size) {
+  const size_t capacity = common.capacity_;
+  assert(i < capacity);
+
+  auto* slot_i = static_cast<const char*>(common.slots_) + i * slot_size;
+  if (IsFull(h)) {
+    SanitizerUnpoisonMemoryRegion(slot_i, slot_size);
+  } else {
+    SanitizerPoisonMemoryRegion(slot_i, slot_size);
+  }
+
+  ctrl_t* ctrl = common.control_;
+  ctrl[i] = h;
+  ctrl[((i - NumClonedBytes()) & capacity) + (NumClonedBytes() & capacity)] = h;
+}
+
+// Overload for setting to an occupied `h2_t` rather than a special `ctrl_t`.
+inline void SetCtrl(const CommonFields& common, size_t i, h2_t h,
+                    size_t slot_size) {
+  SetCtrl(common, i, static_cast<ctrl_t>(h), slot_size);
+}
+
+// Given the capacity of a table, computes the offset (from the start of the
+// backing allocation) of the generation counter (if it exists).
+inline size_t GenerationOffset(size_t capacity) {
+  assert(IsValidCapacity(capacity));
+  const size_t num_control_bytes = capacity + 1 + NumClonedBytes();
+  return num_control_bytes;
+}
+
+// Given the capacity of a table, computes the offset (from the start of the
+// backing allocation) at which the slots begin.
+inline size_t SlotOffset(size_t capacity, size_t slot_align) {
+  assert(IsValidCapacity(capacity));
+  const size_t num_control_bytes = capacity + 1 + NumClonedBytes();
+  return (num_control_bytes + NumGenerationBytes() + slot_align - 1) &
+         (~slot_align + 1);
+}
+
+// Given the capacity of a table, computes the total size of the backing
+// array.
+inline size_t AllocSize(size_t capacity, size_t slot_size, size_t slot_align) {
+  return SlotOffset(capacity, slot_align) + capacity * slot_size;
+}
+
+template <typename Alloc, size_t SizeOfSlot, size_t AlignOfSlot>
+ABSL_ATTRIBUTE_NOINLINE void InitializeSlots(CommonFields& c, Alloc alloc) {
+  assert(c.capacity_);
+  // Folks with custom allocators often make unwarranted assumptions about the
+  // behavior of their classes vis-a-vis trivial destructability and what
+  // calls they will or won't make.  Avoid sampling for people with custom
+  // allocators to get us out of this mess.  This is not a hard guarantee but
+  // a workaround while we plan the exact guarantee we want to provide.
+  const size_t sample_size =
+      (std::is_same<Alloc, std::allocator<char>>::value && c.slots_ == nullptr)
+          ? SizeOfSlot
+          : 0;
+
+  const size_t cap = c.capacity_;
+  char* mem = static_cast<char*>(
+      Allocate<AlignOfSlot>(&alloc, AllocSize(cap, SizeOfSlot, AlignOfSlot)));
+  const GenerationType old_generation = c.generation();
+  c.set_generation_ptr(
+      reinterpret_cast<GenerationType*>(mem + GenerationOffset(cap)));
+  c.set_generation(old_generation + 1);
+  c.control_ = reinterpret_cast<ctrl_t*>(mem);
+  c.slots_ = mem + SlotOffset(cap, AlignOfSlot);
+  ResetCtrl(c, SizeOfSlot);
+  if (sample_size) {
+    c.infoz() = Sample(sample_size);
+  }
+  c.infoz().RecordStorageChanged(c.size_, cap);
+}
+
+// PolicyFunctions bundles together some information for a particular
+// raw_hash_set<T, ...> instantiation. This information is passed to
+// type-erased functions that want to do small amounts of type-specific
+// work.
+struct PolicyFunctions {
+  size_t slot_size;
+
+  // Return the hash of the pointed-to slot.
+  size_t (*hash_slot)(void* set, void* slot);
+
+  // Transfer the contents of src_slot to dst_slot.
+  void (*transfer)(void* set, void* dst_slot, void* src_slot);
+
+  // Deallocate the specified backing store which is sized for n slots.
+  void (*dealloc)(void* set, const PolicyFunctions& policy, ctrl_t* ctrl,
+                  void* slot_array, size_t n);
+};
+
+// ClearBackingArray clears the backing array, either modifying it in place,
+// or creating a new one based on the value of "reuse".
+// REQUIRES: c.capacity > 0
+void ClearBackingArray(CommonFields& c, const PolicyFunctions& policy,
+                       bool reuse);
+
+// Type-erased version of raw_hash_set::erase_meta_only.
+void EraseMetaOnly(CommonFields& c, ctrl_t* it, size_t slot_size);
+
+// Function to place in PolicyFunctions::dealloc for raw_hash_sets
+// that are using std::allocator. This allows us to share the same
+// function body for raw_hash_set instantiations that have the
+// same slot alignment.
+template <size_t AlignOfSlot>
+ABSL_ATTRIBUTE_NOINLINE void DeallocateStandard(void*,
+                                                const PolicyFunctions& policy,
+                                                ctrl_t* ctrl, void* slot_array,
+                                                size_t n) {
+  // Unpoison before returning the memory to the allocator.
+  SanitizerUnpoisonMemoryRegion(slot_array, policy.slot_size * n);
+
+  std::allocator<char> alloc;
+  Deallocate<AlignOfSlot>(&alloc, ctrl,
+                          AllocSize(n, policy.slot_size, AlignOfSlot));
+}
+
+// For trivially relocatable types we use memcpy directly. This allows us to
+// share the same function body for raw_hash_set instantiations that have the
+// same slot size as long as they are relocatable.
+template <size_t SizeOfSlot>
+ABSL_ATTRIBUTE_NOINLINE void TransferRelocatable(void*, void* dst, void* src) {
+  memcpy(dst, src, SizeOfSlot);
+}
+
+// Type-erased version of raw_hash_set::drop_deletes_without_resize.
+void DropDeletesWithoutResize(CommonFields& common,
+                              const PolicyFunctions& policy, void* tmp_space);
+
+// A SwissTable.
+//
+// Policy: a policy defines how to perform different operations on
+// the slots of the hashtable (see hash_policy_traits.h for the full interface
+// of policy).
+//
+// Hash: a (possibly polymorphic) functor that hashes keys of the hashtable. The
+// functor should accept a key and return size_t as hash. For best performance
+// it is important that the hash function provides high entropy across all bits
+// of the hash.
+//
+// Eq: a (possibly polymorphic) functor that compares two keys for equality. It
+// should accept two (of possibly different type) keys and return a bool: true
+// if they are equal, false if they are not. If two keys compare equal, then
+// their hash values as defined by Hash MUST be equal.
+//
+// Allocator: an Allocator
+// [https://en.cppreference.com/w/cpp/named_req/Allocator] with which
+// the storage of the hashtable will be allocated and the elements will be
+// constructed and destroyed.
+template <class Policy, class Hash, class Eq, class Alloc>
+class raw_hash_set {
+  using PolicyTraits = hash_policy_traits<Policy>;
+  using KeyArgImpl =
+      KeyArg<IsTransparent<Eq>::value && IsTransparent<Hash>::value>;
+
+ public:
+  using init_type = typename PolicyTraits::init_type;
+  using key_type = typename PolicyTraits::key_type;
+  // TODO(sbenza): Hide slot_type as it is an implementation detail. Needs user
+  // code fixes!
+  using slot_type = typename PolicyTraits::slot_type;
+  using allocator_type = Alloc;
+  using size_type = size_t;
+  using difference_type = ptrdiff_t;
+  using hasher = Hash;
+  using key_equal = Eq;
+  using policy_type = Policy;
+  using value_type = typename PolicyTraits::value_type;
+  using reference = value_type&;
+  using const_reference = const value_type&;
+  using pointer = typename absl::allocator_traits<
+      allocator_type>::template rebind_traits<value_type>::pointer;
+  using const_pointer = typename absl::allocator_traits<
+      allocator_type>::template rebind_traits<value_type>::const_pointer;
+
+  // Alias used for heterogeneous lookup functions.
+  // `key_arg<K>` evaluates to `K` when the functors are transparent and to
+  // `key_type` otherwise. It permits template argument deduction on `K` for the
+  // transparent case.
+  template <class K>
+  using key_arg = typename KeyArgImpl::template type<K, key_type>;
+
+ private:
+  // Give an early error when key_type is not hashable/eq.
+  auto KeyTypeCanBeHashed(const Hash& h, const key_type& k) -> decltype(h(k));
+  auto KeyTypeCanBeEq(const Eq& eq, const key_type& k) -> decltype(eq(k, k));
+
+  using AllocTraits = absl::allocator_traits<allocator_type>;
+  using SlotAlloc = typename absl::allocator_traits<
+      allocator_type>::template rebind_alloc<slot_type>;
+  using SlotAllocTraits = typename absl::allocator_traits<
+      allocator_type>::template rebind_traits<slot_type>;
+
+  static_assert(std::is_lvalue_reference<reference>::value,
+                "Policy::element() must return a reference");
+
+  template <typename T>
+  struct SameAsElementReference
+      : std::is_same<typename std::remove_cv<
+                         typename std::remove_reference<reference>::type>::type,
+                     typename std::remove_cv<
+                         typename std::remove_reference<T>::type>::type> {};
+
+  // An enabler for insert(T&&): T must be convertible to init_type or be the
+  // same as [cv] value_type [ref].
+  // Note: we separate SameAsElementReference into its own type to avoid using
+  // reference unless we need to. MSVC doesn't seem to like it in some
+  // cases.
+  template <class T>
+  using RequiresInsertable = typename std::enable_if<
+      absl::disjunction<std::is_convertible<T, init_type>,
+                        SameAsElementReference<T>>::value,
+      int>::type;
+
+  // RequiresNotInit is a workaround for gcc prior to 7.1.
+  // See https://godbolt.org/g/Y4xsUh.
+  template <class T>
+  using RequiresNotInit =
+      typename std::enable_if<!std::is_same<T, init_type>::value, int>::type;
+
+  template <class... Ts>
+  using IsDecomposable = IsDecomposable<void, PolicyTraits, Hash, Eq, Ts...>;
+
+ public:
+  static_assert(std::is_same<pointer, value_type*>::value,
+                "Allocators with custom pointer types are not supported");
+  static_assert(std::is_same<const_pointer, const value_type*>::value,
+                "Allocators with custom pointer types are not supported");
+
+  class iterator : private HashSetIteratorGenerationInfo {
+    friend class raw_hash_set;
+
+   public:
+    using iterator_category = std::forward_iterator_tag;
+    using value_type = typename raw_hash_set::value_type;
+    using reference =
+        absl::conditional_t<PolicyTraits::constant_iterators::value,
+                            const value_type&, value_type&>;
+    using pointer = absl::remove_reference_t<reference>*;
+    using difference_type = typename raw_hash_set::difference_type;
+
+    iterator() {}
+
+    // PRECONDITION: not an end() iterator.
+    reference operator*() const {
+      ABSL_INTERNAL_ASSERT_IS_FULL(ctrl_, generation(), generation_ptr(),
+                                   "operator*()");
+      return PolicyTraits::element(slot_);
+    }
+
+    // PRECONDITION: not an end() iterator.
+    pointer operator->() const {
+      ABSL_INTERNAL_ASSERT_IS_FULL(ctrl_, generation(), generation_ptr(),
+                                   "operator->");
+      return &operator*();
+    }
+
+    // PRECONDITION: not an end() iterator.
+    iterator& operator++() {
+      ABSL_INTERNAL_ASSERT_IS_FULL(ctrl_, generation(), generation_ptr(),
+                                   "operator++");
+      ++ctrl_;
+      ++slot_;
+      skip_empty_or_deleted();
+      return *this;
+    }
+    // PRECONDITION: not an end() iterator.
+    iterator operator++(int) {
+      auto tmp = *this;
+      ++*this;
+      return tmp;
+    }
+
+    friend bool operator==(const iterator& a, const iterator& b) {
+      AssertSameContainer(a.ctrl_, b.ctrl_, a.slot_, b.slot_);
+      AssertIsValidForComparison(a.ctrl_, a.generation(), a.generation_ptr());
+      AssertIsValidForComparison(b.ctrl_, b.generation(), b.generation_ptr());
+      return a.ctrl_ == b.ctrl_;
+    }
+    friend bool operator!=(const iterator& a, const iterator& b) {
+      return !(a == b);
+    }
+
+   private:
+    iterator(ctrl_t* ctrl, slot_type* slot,
+             const GenerationType* generation_ptr)
+        : HashSetIteratorGenerationInfo(generation_ptr),
+          ctrl_(ctrl),
+          slot_(slot) {
+      // This assumption helps the compiler know that any non-end iterator is
+      // not equal to any end iterator.
+      ABSL_ASSUME(ctrl != nullptr);
+    }
+    // For end() iterators.
+    explicit iterator(const GenerationType* generation_ptr)
+        : HashSetIteratorGenerationInfo(generation_ptr) {}
+
+    // Fixes up `ctrl_` to point to a full by advancing it and `slot_` until
+    // they reach one.
+    //
+    // If a sentinel is reached, we null `ctrl_` out instead.
+    void skip_empty_or_deleted() {
+      while (IsEmptyOrDeleted(*ctrl_)) {
+        uint32_t shift = Group{ctrl_}.CountLeadingEmptyOrDeleted();
+        ctrl_ += shift;
+        slot_ += shift;
+      }
+      if (ABSL_PREDICT_FALSE(*ctrl_ == ctrl_t::kSentinel)) ctrl_ = nullptr;
+    }
+
+    ctrl_t* ctrl_ = nullptr;
+    // To avoid uninitialized member warnings, put slot_ in an anonymous union.
+    // The member is not initialized on singleton and end iterators.
+    union {
+      slot_type* slot_;
+    };
+  };
+
+  class const_iterator {
+    friend class raw_hash_set;
+
+   public:
+    using iterator_category = typename iterator::iterator_category;
+    using value_type = typename raw_hash_set::value_type;
+    using reference = typename raw_hash_set::const_reference;
+    using pointer = typename raw_hash_set::const_pointer;
+    using difference_type = typename raw_hash_set::difference_type;
+
+    const_iterator() = default;
+    // Implicit construction from iterator.
+    const_iterator(iterator i) : inner_(std::move(i)) {}  // NOLINT
+
+    reference operator*() const { return *inner_; }
+    pointer operator->() const { return inner_.operator->(); }
+
+    const_iterator& operator++() {
+      ++inner_;
+      return *this;
+    }
+    const_iterator operator++(int) { return inner_++; }
+
+    friend bool operator==(const const_iterator& a, const const_iterator& b) {
+      return a.inner_ == b.inner_;
+    }
+    friend bool operator!=(const const_iterator& a, const const_iterator& b) {
+      return !(a == b);
+    }
+
+   private:
+    const_iterator(const ctrl_t* ctrl, const slot_type* slot,
+                   const GenerationType* gen)
+        : inner_(const_cast<ctrl_t*>(ctrl), const_cast<slot_type*>(slot), gen) {
+    }
+
+    iterator inner_;
+  };
+
+  using node_type = node_handle<Policy, hash_policy_traits<Policy>, Alloc>;
+  using insert_return_type = InsertReturnType<iterator, node_type>;
+
+  // Note: can't use `= default` due to non-default noexcept (causes
+  // problems for some compilers). NOLINTNEXTLINE
+  raw_hash_set() noexcept(
+      std::is_nothrow_default_constructible<hasher>::value&&
+          std::is_nothrow_default_constructible<key_equal>::value&&
+              std::is_nothrow_default_constructible<allocator_type>::value) {}
+
+  ABSL_ATTRIBUTE_NOINLINE explicit raw_hash_set(
+      size_t bucket_count, const hasher& hash = hasher(),
+      const key_equal& eq = key_equal(),
+      const allocator_type& alloc = allocator_type())
+      : settings_(CommonFields{}, hash, eq, alloc) {
+    if (bucket_count) {
+      common().capacity_ = NormalizeCapacity(bucket_count);
+      initialize_slots();
+    }
+  }
+
+  raw_hash_set(size_t bucket_count, const hasher& hash,
+               const allocator_type& alloc)
+      : raw_hash_set(bucket_count, hash, key_equal(), alloc) {}
+
+  raw_hash_set(size_t bucket_count, const allocator_type& alloc)
+      : raw_hash_set(bucket_count, hasher(), key_equal(), alloc) {}
+
+  explicit raw_hash_set(const allocator_type& alloc)
+      : raw_hash_set(0, hasher(), key_equal(), alloc) {}
+
+  template <class InputIter>
+  raw_hash_set(InputIter first, InputIter last, size_t bucket_count = 0,
+               const hasher& hash = hasher(), const key_equal& eq = key_equal(),
+               const allocator_type& alloc = allocator_type())
+      : raw_hash_set(SelectBucketCountForIterRange(first, last, bucket_count),
+                     hash, eq, alloc) {
+    insert(first, last);
+  }
+
+  template <class InputIter>
+  raw_hash_set(InputIter first, InputIter last, size_t bucket_count,
+               const hasher& hash, const allocator_type& alloc)
+      : raw_hash_set(first, last, bucket_count, hash, key_equal(), alloc) {}
+
+  template <class InputIter>
+  raw_hash_set(InputIter first, InputIter last, size_t bucket_count,
+               const allocator_type& alloc)
+      : raw_hash_set(first, last, bucket_count, hasher(), key_equal(), alloc) {}
+
+  template <class InputIter>
+  raw_hash_set(InputIter first, InputIter last, const allocator_type& alloc)
+      : raw_hash_set(first, last, 0, hasher(), key_equal(), alloc) {}
+
+  // Instead of accepting std::initializer_list<value_type> as the first
+  // argument like std::unordered_set<value_type> does, we have two overloads
+  // that accept std::initializer_list<T> and std::initializer_list<init_type>.
+  // This is advantageous for performance.
+  //
+  //   // Turns {"abc", "def"} into std::initializer_list<std::string>, then
+  //   // copies the strings into the set.
+  //   std::unordered_set<std::string> s = {"abc", "def"};
+  //
+  //   // Turns {"abc", "def"} into std::initializer_list<const char*>, then
+  //   // copies the strings into the set.
+  //   absl::flat_hash_set<std::string> s = {"abc", "def"};
+  //
+  // The same trick is used in insert().
+  //
+  // The enabler is necessary to prevent this constructor from triggering where
+  // the copy constructor is meant to be called.
+  //
+  //   absl::flat_hash_set<int> a, b{a};
+  //
+  // RequiresNotInit<T> is a workaround for gcc prior to 7.1.
+  template <class T, RequiresNotInit<T> = 0, RequiresInsertable<T> = 0>
+  raw_hash_set(std::initializer_list<T> init, size_t bucket_count = 0,
+               const hasher& hash = hasher(), const key_equal& eq = key_equal(),
+               const allocator_type& alloc = allocator_type())
+      : raw_hash_set(init.begin(), init.end(), bucket_count, hash, eq, alloc) {}
+
+  raw_hash_set(std::initializer_list<init_type> init, size_t bucket_count = 0,
+               const hasher& hash = hasher(), const key_equal& eq = key_equal(),
+               const allocator_type& alloc = allocator_type())
+      : raw_hash_set(init.begin(), init.end(), bucket_count, hash, eq, alloc) {}
+
+  template <class T, RequiresNotInit<T> = 0, RequiresInsertable<T> = 0>
+  raw_hash_set(std::initializer_list<T> init, size_t bucket_count,
+               const hasher& hash, const allocator_type& alloc)
+      : raw_hash_set(init, bucket_count, hash, key_equal(), alloc) {}
+
+  raw_hash_set(std::initializer_list<init_type> init, size_t bucket_count,
+               const hasher& hash, const allocator_type& alloc)
+      : raw_hash_set(init, bucket_count, hash, key_equal(), alloc) {}
+
+  template <class T, RequiresNotInit<T> = 0, RequiresInsertable<T> = 0>
+  raw_hash_set(std::initializer_list<T> init, size_t bucket_count,
+               const allocator_type& alloc)
+      : raw_hash_set(init, bucket_count, hasher(), key_equal(), alloc) {}
+
+  raw_hash_set(std::initializer_list<init_type> init, size_t bucket_count,
+               const allocator_type& alloc)
+      : raw_hash_set(init, bucket_count, hasher(), key_equal(), alloc) {}
+
+  template <class T, RequiresNotInit<T> = 0, RequiresInsertable<T> = 0>
+  raw_hash_set(std::initializer_list<T> init, const allocator_type& alloc)
+      : raw_hash_set(init, 0, hasher(), key_equal(), alloc) {}
+
+  raw_hash_set(std::initializer_list<init_type> init,
+               const allocator_type& alloc)
+      : raw_hash_set(init, 0, hasher(), key_equal(), alloc) {}
+
+  raw_hash_set(const raw_hash_set& that)
+      : raw_hash_set(that, AllocTraits::select_on_container_copy_construction(
+                               that.alloc_ref())) {}
+
+  raw_hash_set(const raw_hash_set& that, const allocator_type& a)
+      : raw_hash_set(0, that.hash_ref(), that.eq_ref(), a) {
+    reserve(that.size());
+    // Because the table is guaranteed to be empty, we can do something faster
+    // than a full `insert`.
+    for (const auto& v : that) {
+      const size_t hash = PolicyTraits::apply(HashElement{hash_ref()}, v);
+      auto target = find_first_non_full_outofline(common(), hash);
+      SetCtrl(common(), target.offset, H2(hash), sizeof(slot_type));
+      emplace_at(target.offset, v);
+      common().maybe_increment_generation_on_insert();
+      infoz().RecordInsert(hash, target.probe_length);
+    }
+    common().size_ = that.size();
+    growth_left() -= that.size();
+  }
+
+  ABSL_ATTRIBUTE_NOINLINE raw_hash_set(raw_hash_set&& that) noexcept(
+      std::is_nothrow_copy_constructible<hasher>::value&&
+          std::is_nothrow_copy_constructible<key_equal>::value&&
+              std::is_nothrow_copy_constructible<allocator_type>::value)
+      :  // Hash, equality and allocator are copied instead of moved because
+         // `that` must be left valid. If Hash is std::function<Key>, moving it
+         // would create a nullptr functor that cannot be called.
+        settings_(absl::exchange(that.common(), CommonFields{}),
+                  that.hash_ref(), that.eq_ref(), that.alloc_ref()) {}
+
+  raw_hash_set(raw_hash_set&& that, const allocator_type& a)
+      : settings_(CommonFields{}, that.hash_ref(), that.eq_ref(), a) {
+    if (a == that.alloc_ref()) {
+      std::swap(common(), that.common());
+    } else {
+      reserve(that.size());
+      // Note: this will copy elements of dense_set and unordered_set instead of
+      // moving them. This can be fixed if it ever becomes an issue.
+      for (auto& elem : that) insert(std::move(elem));
+    }
+  }
+
+  raw_hash_set& operator=(const raw_hash_set& that) {
+    raw_hash_set tmp(that,
+                     AllocTraits::propagate_on_container_copy_assignment::value
+                         ? that.alloc_ref()
+                         : alloc_ref());
+    swap(tmp);
+    return *this;
+  }
+
+  raw_hash_set& operator=(raw_hash_set&& that) noexcept(
+      absl::allocator_traits<allocator_type>::is_always_equal::value&&
+          std::is_nothrow_move_assignable<hasher>::value&&
+              std::is_nothrow_move_assignable<key_equal>::value) {
+    // TODO(sbenza): We should only use the operations from the noexcept clause
+    // to make sure we actually adhere to that contract.
+    // NOLINTNEXTLINE: not returning *this for performance.
+    return move_assign(
+        std::move(that),
+        typename AllocTraits::propagate_on_container_move_assignment());
+  }
+
+  ~raw_hash_set() {
+    const size_t cap = capacity();
+    if (!cap) return;
+    destroy_slots();
+
+    // Unpoison before returning the memory to the allocator.
+    SanitizerUnpoisonMemoryRegion(slot_array(), sizeof(slot_type) * cap);
+    Deallocate<alignof(slot_type)>(
+        &alloc_ref(), control(),
+        AllocSize(cap, sizeof(slot_type), alignof(slot_type)));
+
+    infoz().Unregister();
+  }
+
+  iterator begin() {
+    auto it = iterator_at(0);
+    it.skip_empty_or_deleted();
+    return it;
+  }
+  iterator end() { return iterator(common().generation_ptr()); }
+
+  const_iterator begin() const {
+    return const_cast<raw_hash_set*>(this)->begin();
+  }
+  const_iterator end() const { return iterator(common().generation_ptr()); }
+  const_iterator cbegin() const { return begin(); }
+  const_iterator cend() const { return end(); }
+
+  bool empty() const { return !size(); }
+  size_t size() const { return common().size_; }
+  size_t capacity() const { return common().capacity_; }
+  size_t max_size() const { return (std::numeric_limits<size_t>::max)(); }
+
+  ABSL_ATTRIBUTE_REINITIALIZES void clear() {
+    // Iterating over this container is O(bucket_count()). When bucket_count()
+    // is much greater than size(), iteration becomes prohibitively expensive.
+    // For clear() it is more important to reuse the allocated array when the
+    // container is small because allocation takes comparatively long time
+    // compared to destruction of the elements of the container. So we pick the
+    // largest bucket_count() threshold for which iteration is still fast and
+    // past that we simply deallocate the array.
+    const size_t cap = capacity();
+    if (cap == 0) {
+      // Already guaranteed to be empty; so nothing to do.
+    } else {
+      destroy_slots();
+      ClearBackingArray(common(), GetPolicyFunctions(),
+                        /*reuse=*/cap < 128);
+    }
+    common().set_reserved_growth(0);
+  }
+
+  inline void destroy_slots() {
+    const size_t cap = capacity();
+    const ctrl_t* ctrl = control();
+    slot_type* slot = slot_array();
+    for (size_t i = 0; i != cap; ++i) {
+      if (IsFull(ctrl[i])) {
+        PolicyTraits::destroy(&alloc_ref(), slot + i);
+      }
+    }
+  }
+
+  // This overload kicks in when the argument is an rvalue of insertable and
+  // decomposable type other than init_type.
+  //
+  //   flat_hash_map<std::string, int> m;
+  //   m.insert(std::make_pair("abc", 42));
+  // TODO(cheshire): A type alias T2 is introduced as a workaround for the nvcc
+  // bug.
+  template <class T, RequiresInsertable<T> = 0, class T2 = T,
+            typename std::enable_if<IsDecomposable<T2>::value, int>::type = 0,
+            T* = nullptr>
+  std::pair<iterator, bool> insert(T&& value) {
+    return emplace(std::forward<T>(value));
+  }
+
+  // This overload kicks in when the argument is a bitfield or an lvalue of
+  // insertable and decomposable type.
+  //
+  //   union { int n : 1; };
+  //   flat_hash_set<int> s;
+  //   s.insert(n);
+  //
+  //   flat_hash_set<std::string> s;
+  //   const char* p = "hello";
+  //   s.insert(p);
+  //
+  // TODO(romanp): Once we stop supporting gcc 5.1 and below, replace
+  // RequiresInsertable<T> with RequiresInsertable<const T&>.
+  // We are hitting this bug: https://godbolt.org/g/1Vht4f.
+  template <
+      class T, RequiresInsertable<T> = 0,
+      typename std::enable_if<IsDecomposable<const T&>::value, int>::type = 0>
+  std::pair<iterator, bool> insert(const T& value) {
+    return emplace(value);
+  }
+
+  // This overload kicks in when the argument is an rvalue of init_type. Its
+  // purpose is to handle brace-init-list arguments.
+  //
+  //   flat_hash_map<std::string, int> s;
+  //   s.insert({"abc", 42});
+  std::pair<iterator, bool> insert(init_type&& value) {
+    return emplace(std::move(value));
+  }
+
+  // TODO(cheshire): A type alias T2 is introduced as a workaround for the nvcc
+  // bug.
+  template <class T, RequiresInsertable<T> = 0, class T2 = T,
+            typename std::enable_if<IsDecomposable<T2>::value, int>::type = 0,
+            T* = nullptr>
+  iterator insert(const_iterator, T&& value) {
+    return insert(std::forward<T>(value)).first;
+  }
+
+  // TODO(romanp): Once we stop supporting gcc 5.1 and below, replace
+  // RequiresInsertable<T> with RequiresInsertable<const T&>.
+  // We are hitting this bug: https://godbolt.org/g/1Vht4f.
+  template <
+      class T, RequiresInsertable<T> = 0,
+      typename std::enable_if<IsDecomposable<const T&>::value, int>::type = 0>
+  iterator insert(const_iterator, const T& value) {
+    return insert(value).first;
+  }
+
+  iterator insert(const_iterator, init_type&& value) {
+    return insert(std::move(value)).first;
+  }
+
+  template <class InputIt>
+  void insert(InputIt first, InputIt last) {
+    for (; first != last; ++first) emplace(*first);
+  }
+
+  template <class T, RequiresNotInit<T> = 0, RequiresInsertable<const T&> = 0>
+  void insert(std::initializer_list<T> ilist) {
+    insert(ilist.begin(), ilist.end());
+  }
+
+  void insert(std::initializer_list<init_type> ilist) {
+    insert(ilist.begin(), ilist.end());
+  }
+
+  insert_return_type insert(node_type&& node) {
+    if (!node) return {end(), false, node_type()};
+    const auto& elem = PolicyTraits::element(CommonAccess::GetSlot(node));
+    auto res = PolicyTraits::apply(
+        InsertSlot<false>{*this, std::move(*CommonAccess::GetSlot(node))},
+        elem);
+    if (res.second) {
+      CommonAccess::Reset(&node);
+      return {res.first, true, node_type()};
+    } else {
+      return {res.first, false, std::move(node)};
+    }
+  }
+
+  iterator insert(const_iterator, node_type&& node) {
+    auto res = insert(std::move(node));
+    node = std::move(res.node);
+    return res.position;
+  }
+
+  // This overload kicks in if we can deduce the key from args. This enables us
+  // to avoid constructing value_type if an entry with the same key already
+  // exists.
+  //
+  // For example:
+  //
+  //   flat_hash_map<std::string, std::string> m = {{"abc", "def"}};
+  //   // Creates no std::string copies and makes no heap allocations.
+  //   m.emplace("abc", "xyz");
+  template <class... Args, typename std::enable_if<
+                               IsDecomposable<Args...>::value, int>::type = 0>
+  std::pair<iterator, bool> emplace(Args&&... args) {
+    return PolicyTraits::apply(EmplaceDecomposable{*this},
+                               std::forward<Args>(args)...);
+  }
+
+  // This overload kicks in if we cannot deduce the key from args. It constructs
+  // value_type unconditionally and then either moves it into the table or
+  // destroys.
+  template <class... Args, typename std::enable_if<
+                               !IsDecomposable<Args...>::value, int>::type = 0>
+  std::pair<iterator, bool> emplace(Args&&... args) {
+    alignas(slot_type) unsigned char raw[sizeof(slot_type)];
+    slot_type* slot = reinterpret_cast<slot_type*>(&raw);
+
+    PolicyTraits::construct(&alloc_ref(), slot, std::forward<Args>(args)...);
+    const auto& elem = PolicyTraits::element(slot);
+    return PolicyTraits::apply(InsertSlot<true>{*this, std::move(*slot)}, elem);
+  }
+
+  template <class... Args>
+  iterator emplace_hint(const_iterator, Args&&... args) {
+    return emplace(std::forward<Args>(args)...).first;
+  }
+
+  // Extension API: support for lazy emplace.
+  //
+  // Looks up key in the table. If found, returns the iterator to the element.
+  // Otherwise calls `f` with one argument of type `raw_hash_set::constructor`.
+  //
+  // `f` must abide by several restrictions:
+  //  - it MUST call `raw_hash_set::constructor` with arguments as if a
+  //    `raw_hash_set::value_type` is constructed,
+  //  - it MUST NOT access the container before the call to
+  //    `raw_hash_set::constructor`, and
+  //  - it MUST NOT erase the lazily emplaced element.
+  // Doing any of these is undefined behavior.
+  //
+  // For example:
+  //
+  //   std::unordered_set<ArenaString> s;
+  //   // Makes ArenaStr even if "abc" is in the map.
+  //   s.insert(ArenaString(&arena, "abc"));
+  //
+  //   flat_hash_set<ArenaStr> s;
+  //   // Makes ArenaStr only if "abc" is not in the map.
+  //   s.lazy_emplace("abc", [&](const constructor& ctor) {
+  //     ctor(&arena, "abc");
+  //   });
+  //
+  // WARNING: This API is currently experimental. If there is a way to implement
+  // the same thing with the rest of the API, prefer that.
+  class constructor {
+    friend class raw_hash_set;
+
+   public:
+    template <class... Args>
+    void operator()(Args&&... args) const {
+      assert(*slot_);
+      PolicyTraits::construct(alloc_, *slot_, std::forward<Args>(args)...);
+      *slot_ = nullptr;
+    }
+
+   private:
+    constructor(allocator_type* a, slot_type** slot) : alloc_(a), slot_(slot) {}
+
+    allocator_type* alloc_;
+    slot_type** slot_;
+  };
+
+  template <class K = key_type, class F>
+  iterator lazy_emplace(const key_arg<K>& key, F&& f) {
+    auto res = find_or_prepare_insert(key);
+    if (res.second) {
+      slot_type* slot = slot_array() + res.first;
+      std::forward<F>(f)(constructor(&alloc_ref(), &slot));
+      assert(!slot);
+    }
+    return iterator_at(res.first);
+  }
+
+  // Extension API: support for heterogeneous keys.
+  //
+  //   std::unordered_set<std::string> s;
+  //   // Turns "abc" into std::string.
+  //   s.erase("abc");
+  //
+  //   flat_hash_set<std::string> s;
+  //   // Uses "abc" directly without copying it into std::string.
+  //   s.erase("abc");
+  template <class K = key_type>
+  size_type erase(const key_arg<K>& key) {
+    auto it = find(key);
+    if (it == end()) return 0;
+    erase(it);
+    return 1;
+  }
+
+  // Erases the element pointed to by `it`.  Unlike `std::unordered_set::erase`,
+  // this method returns void to reduce algorithmic complexity to O(1).  The
+  // iterator is invalidated, so any increment should be done before calling
+  // erase.  In order to erase while iterating across a map, use the following
+  // idiom (which also works for standard containers):
+  //
+  // for (auto it = m.begin(), end = m.end(); it != end;) {
+  //   // `erase()` will invalidate `it`, so advance `it` first.
+  //   auto copy_it = it++;
+  //   if (<pred>) {
+  //     m.erase(copy_it);
+  //   }
+  // }
+  void erase(const_iterator cit) { erase(cit.inner_); }
+
+  // This overload is necessary because otherwise erase<K>(const K&) would be
+  // a better match if non-const iterator is passed as an argument.
+  void erase(iterator it) {
+    ABSL_INTERNAL_ASSERT_IS_FULL(it.ctrl_, it.generation(), it.generation_ptr(),
+                                 "erase()");
+    PolicyTraits::destroy(&alloc_ref(), it.slot_);
+    erase_meta_only(it);
+  }
+
+  iterator erase(const_iterator first, const_iterator last) {
+    while (first != last) {
+      erase(first++);
+    }
+    return last.inner_;
+  }
+
+  // Moves elements from `src` into `this`.
+  // If the element already exists in `this`, it is left unmodified in `src`.
+  template <typename H, typename E>
+  void merge(raw_hash_set<Policy, H, E, Alloc>& src) {  // NOLINT
+    assert(this != &src);
+    for (auto it = src.begin(), e = src.end(); it != e;) {
+      auto next = std::next(it);
+      if (PolicyTraits::apply(InsertSlot<false>{*this, std::move(*it.slot_)},
+                              PolicyTraits::element(it.slot_))
+              .second) {
+        src.erase_meta_only(it);
+      }
+      it = next;
+    }
+  }
+
+  template <typename H, typename E>
+  void merge(raw_hash_set<Policy, H, E, Alloc>&& src) {
+    merge(src);
+  }
+
+  node_type extract(const_iterator position) {
+    ABSL_INTERNAL_ASSERT_IS_FULL(position.inner_.ctrl_,
+                                 position.inner_.generation(),
+                                 position.inner_.generation_ptr(), "extract()");
+    auto node =
+        CommonAccess::Transfer<node_type>(alloc_ref(), position.inner_.slot_);
+    erase_meta_only(position);
+    return node;
+  }
+
+  template <
+      class K = key_type,
+      typename std::enable_if<!std::is_same<K, iterator>::value, int>::type = 0>
+  node_type extract(const key_arg<K>& key) {
+    auto it = find(key);
+    return it == end() ? node_type() : extract(const_iterator{it});
+  }
+
+  void swap(raw_hash_set& that) noexcept(
+      IsNoThrowSwappable<hasher>() && IsNoThrowSwappable<key_equal>() &&
+      IsNoThrowSwappable<allocator_type>(
+          typename AllocTraits::propagate_on_container_swap{})) {
+    using std::swap;
+    swap(common(), that.common());
+    swap(hash_ref(), that.hash_ref());
+    swap(eq_ref(), that.eq_ref());
+    SwapAlloc(alloc_ref(), that.alloc_ref(),
+              typename AllocTraits::propagate_on_container_swap{});
+  }
+
+  void rehash(size_t n) {
+    if (n == 0 && capacity() == 0) return;
+    if (n == 0 && size() == 0) {
+      ClearBackingArray(common(), GetPolicyFunctions(),
+                        /*reuse=*/false);
+      return;
+    }
+
+    // bitor is a faster way of doing `max` here. We will round up to the next
+    // power-of-2-minus-1, so bitor is good enough.
+    auto m = NormalizeCapacity(n | GrowthToLowerboundCapacity(size()));
+    // n == 0 unconditionally rehashes as per the standard.
+    if (n == 0 || m > capacity()) {
+      resize(m);
+
+      // This is after resize, to ensure that we have completed the allocation
+      // and have potentially sampled the hashtable.
+      infoz().RecordReservation(n);
+    }
+  }
+
+  void reserve(size_t n) {
+    if (n > size() + growth_left()) {
+      size_t m = GrowthToLowerboundCapacity(n);
+      resize(NormalizeCapacity(m));
+
+      // This is after resize, to ensure that we have completed the allocation
+      // and have potentially sampled the hashtable.
+      infoz().RecordReservation(n);
+    }
+    common().reset_reserved_growth(n);
+  }
+
+  // Extension API: support for heterogeneous keys.
+  //
+  //   std::unordered_set<std::string> s;
+  //   // Turns "abc" into std::string.
+  //   s.count("abc");
+  //
+  //   ch_set<std::string> s;
+  //   // Uses "abc" directly without copying it into std::string.
+  //   s.count("abc");
+  template <class K = key_type>
+  size_t count(const key_arg<K>& key) const {
+    return find(key) == end() ? 0 : 1;
+  }
+
+  // Issues CPU prefetch instructions for the memory needed to find or insert
+  // a key.  Like all lookup functions, this support heterogeneous keys.
+  //
+  // NOTE: This is a very low level operation and should not be used without
+  // specific benchmarks indicating its importance.
+  template <class K = key_type>
+  void prefetch(const key_arg<K>& key) const {
+    (void)key;
+    // Avoid probing if we won't be able to prefetch the addresses received.
+#ifdef ABSL_INTERNAL_HAVE_PREFETCH
+    prefetch_heap_block();
+    auto seq = probe(common(), hash_ref()(key));
+    base_internal::PrefetchT0(control() + seq.offset());
+    base_internal::PrefetchT0(slot_array() + seq.offset());
+#endif  // ABSL_INTERNAL_HAVE_PREFETCH
+  }
+
+  // The API of find() has two extensions.
+  //
+  // 1. The hash can be passed by the user. It must be equal to the hash of the
+  // key.
+  //
+  // 2. The type of the key argument doesn't have to be key_type. This is so
+  // called heterogeneous key support.
+  template <class K = key_type>
+  iterator find(const key_arg<K>& key, size_t hash) {
+    auto seq = probe(common(), hash);
+    slot_type* slot_ptr = slot_array();
+    const ctrl_t* ctrl = control();
+    while (true) {
+      Group g{ctrl + seq.offset()};
+      for (uint32_t i : g.Match(H2(hash))) {
+        if (ABSL_PREDICT_TRUE(PolicyTraits::apply(
+                EqualElement<K>{key, eq_ref()},
+                PolicyTraits::element(slot_ptr + seq.offset(i)))))
+          return iterator_at(seq.offset(i));
+      }
+      if (ABSL_PREDICT_TRUE(g.MaskEmpty())) return end();
+      seq.next();
+      assert(seq.index() <= capacity() && "full table!");
+    }
+  }
+  template <class K = key_type>
+  iterator find(const key_arg<K>& key) {
+    prefetch_heap_block();
+    return find(key, hash_ref()(key));
+  }
+
+  template <class K = key_type>
+  const_iterator find(const key_arg<K>& key, size_t hash) const {
+    return const_cast<raw_hash_set*>(this)->find(key, hash);
+  }
+  template <class K = key_type>
+  const_iterator find(const key_arg<K>& key) const {
+    prefetch_heap_block();
+    return find(key, hash_ref()(key));
+  }
+
+  template <class K = key_type>
+  bool contains(const key_arg<K>& key) const {
+    return find(key) != end();
+  }
+
+  template <class K = key_type>
+  std::pair<iterator, iterator> equal_range(const key_arg<K>& key) {
+    auto it = find(key);
+    if (it != end()) return {it, std::next(it)};
+    return {it, it};
+  }
+  template <class K = key_type>
+  std::pair<const_iterator, const_iterator> equal_range(
+      const key_arg<K>& key) const {
+    auto it = find(key);
+    if (it != end()) return {it, std::next(it)};
+    return {it, it};
+  }
+
+  size_t bucket_count() const { return capacity(); }
+  float load_factor() const {
+    return capacity() ? static_cast<double>(size()) / capacity() : 0.0;
+  }
+  float max_load_factor() const { return 1.0f; }
+  void max_load_factor(float) {
+    // Does nothing.
+  }
+
+  hasher hash_function() const { return hash_ref(); }
+  key_equal key_eq() const { return eq_ref(); }
+  allocator_type get_allocator() const { return alloc_ref(); }
+
+  friend bool operator==(const raw_hash_set& a, const raw_hash_set& b) {
+    if (a.size() != b.size()) return false;
+    const raw_hash_set* outer = &a;
+    const raw_hash_set* inner = &b;
+    if (outer->capacity() > inner->capacity()) std::swap(outer, inner);
+    for (const value_type& elem : *outer)
+      if (!inner->has_element(elem)) return false;
+    return true;
+  }
+
+  friend bool operator!=(const raw_hash_set& a, const raw_hash_set& b) {
+    return !(a == b);
+  }
+
+  template <typename H>
+  friend typename std::enable_if<H::template is_hashable<value_type>::value,
+                                 H>::type
+  AbslHashValue(H h, const raw_hash_set& s) {
+    return H::combine(H::combine_unordered(std::move(h), s.begin(), s.end()),
+                      s.size());
+  }
+
+  friend void swap(raw_hash_set& a,
+                   raw_hash_set& b) noexcept(noexcept(a.swap(b))) {
+    a.swap(b);
+  }
+
+ private:
+  template <class Container, typename Enabler>
+  friend struct absl::container_internal::hashtable_debug_internal::
+      HashtableDebugAccess;
+
+  struct FindElement {
+    template <class K, class... Args>
+    const_iterator operator()(const K& key, Args&&...) const {
+      return s.find(key);
+    }
+    const raw_hash_set& s;
+  };
+
+  struct HashElement {
+    template <class K, class... Args>
+    size_t operator()(const K& key, Args&&...) const {
+      return h(key);
+    }
+    const hasher& h;
+  };
+
+  template <class K1>
+  struct EqualElement {
+    template <class K2, class... Args>
+    bool operator()(const K2& lhs, Args&&...) const {
+      return eq(lhs, rhs);
+    }
+    const K1& rhs;
+    const key_equal& eq;
+  };
+
+  struct EmplaceDecomposable {
+    template <class K, class... Args>
+    std::pair<iterator, bool> operator()(const K& key, Args&&... args) const {
+      auto res = s.find_or_prepare_insert(key);
+      if (res.second) {
+        s.emplace_at(res.first, std::forward<Args>(args)...);
+      }
+      return {s.iterator_at(res.first), res.second};
+    }
+    raw_hash_set& s;
+  };
+
+  template <bool do_destroy>
+  struct InsertSlot {
+    template <class K, class... Args>
+    std::pair<iterator, bool> operator()(const K& key, Args&&...) && {
+      auto res = s.find_or_prepare_insert(key);
+      if (res.second) {
+        PolicyTraits::transfer(&s.alloc_ref(), s.slot_array() + res.first,
+                               &slot);
+      } else if (do_destroy) {
+        PolicyTraits::destroy(&s.alloc_ref(), &slot);
+      }
+      return {s.iterator_at(res.first), res.second};
+    }
+    raw_hash_set& s;
+    // Constructed slot. Either moved into place or destroyed.
+    slot_type&& slot;
+  };
+
+  // Erases, but does not destroy, the value pointed to by `it`.
+  //
+  // This merely updates the pertinent control byte. This can be used in
+  // conjunction with Policy::transfer to move the object to another place.
+  void erase_meta_only(const_iterator it) {
+    EraseMetaOnly(common(), it.inner_.ctrl_, sizeof(slot_type));
+  }
+
+  // Allocates a backing array for `self` and initializes its control bytes.
+  // This reads `capacity` and updates all other fields based on the result of
+  // the allocation.
+  //
+  // This does not free the currently held array; `capacity` must be nonzero.
+  inline void initialize_slots() {
+    // People are often sloppy with the exact type of their allocator (sometimes
+    // it has an extra const or is missing the pair, but rebinds made it work
+    // anyway).
+    using CharAlloc =
+        typename absl::allocator_traits<Alloc>::template rebind_alloc<char>;
+    InitializeSlots<CharAlloc, sizeof(slot_type), alignof(slot_type)>(
+        common(), CharAlloc(alloc_ref()));
+  }
+
+  ABSL_ATTRIBUTE_NOINLINE void resize(size_t new_capacity) {
+    assert(IsValidCapacity(new_capacity));
+    auto* old_ctrl = control();
+    auto* old_slots = slot_array();
+    const size_t old_capacity = common().capacity_;
+    common().capacity_ = new_capacity;
+    initialize_slots();
+
+    auto* new_slots = slot_array();
+    size_t total_probe_length = 0;
+    for (size_t i = 0; i != old_capacity; ++i) {
+      if (IsFull(old_ctrl[i])) {
+        size_t hash = PolicyTraits::apply(HashElement{hash_ref()},
+                                          PolicyTraits::element(old_slots + i));
+        auto target = find_first_non_full(common(), hash);
+        size_t new_i = target.offset;
+        total_probe_length += target.probe_length;
+        SetCtrl(common(), new_i, H2(hash), sizeof(slot_type));
+        PolicyTraits::transfer(&alloc_ref(), new_slots + new_i, old_slots + i);
+      }
+    }
+    if (old_capacity) {
+      SanitizerUnpoisonMemoryRegion(old_slots,
+                                    sizeof(slot_type) * old_capacity);
+      Deallocate<alignof(slot_type)>(
+          &alloc_ref(), old_ctrl,
+          AllocSize(old_capacity, sizeof(slot_type), alignof(slot_type)));
+    }
+    infoz().RecordRehash(total_probe_length);
+  }
+
+  // Prunes control bytes to remove as many tombstones as possible.
+  //
+  // See the comment on `rehash_and_grow_if_necessary()`.
+  inline void drop_deletes_without_resize() {
+    // Stack-allocate space for swapping elements.
+    alignas(slot_type) unsigned char tmp[sizeof(slot_type)];
+    DropDeletesWithoutResize(common(), GetPolicyFunctions(), tmp);
+  }
+
+  // Called whenever the table *might* need to conditionally grow.
+  //
+  // This function is an optimization opportunity to perform a rehash even when
+  // growth is unnecessary, because vacating tombstones is beneficial for
+  // performance in the long-run.
+  void rehash_and_grow_if_necessary() {
+    const size_t cap = capacity();
+    if (cap > Group::kWidth &&
+        // Do these calcuations in 64-bit to avoid overflow.
+        size() * uint64_t{32} <= cap* uint64_t{25}) {
+      // Squash DELETED without growing if there is enough capacity.
+      //
+      // Rehash in place if the current size is <= 25/32 of capacity.
+      // Rationale for such a high factor: 1) drop_deletes_without_resize() is
+      // faster than resize, and 2) it takes quite a bit of work to add
+      // tombstones.  In the worst case, seems to take approximately 4
+      // insert/erase pairs to create a single tombstone and so if we are
+      // rehashing because of tombstones, we can afford to rehash-in-place as
+      // long as we are reclaiming at least 1/8 the capacity without doing more
+      // than 2X the work.  (Where "work" is defined to be size() for rehashing
+      // or rehashing in place, and 1 for an insert or erase.)  But rehashing in
+      // place is faster per operation than inserting or even doubling the size
+      // of the table, so we actually afford to reclaim even less space from a
+      // resize-in-place.  The decision is to rehash in place if we can reclaim
+      // at about 1/8th of the usable capacity (specifically 3/28 of the
+      // capacity) which means that the total cost of rehashing will be a small
+      // fraction of the total work.
+      //
+      // Here is output of an experiment using the BM_CacheInSteadyState
+      // benchmark running the old case (where we rehash-in-place only if we can
+      // reclaim at least 7/16*capacity) vs. this code (which rehashes in place
+      // if we can recover 3/32*capacity).
+      //
+      // Note that although in the worst-case number of rehashes jumped up from
+      // 15 to 190, but the number of operations per second is almost the same.
+      //
+      // Abridged output of running BM_CacheInSteadyState benchmark from
+      // raw_hash_set_benchmark.   N is the number of insert/erase operations.
+      //
+      //      | OLD (recover >= 7/16        | NEW (recover >= 3/32)
+      // size |    N/s LoadFactor NRehashes |    N/s LoadFactor NRehashes
+      //  448 | 145284       0.44        18 | 140118       0.44        19
+      //  493 | 152546       0.24        11 | 151417       0.48        28
+      //  538 | 151439       0.26        11 | 151152       0.53        38
+      //  583 | 151765       0.28        11 | 150572       0.57        50
+      //  628 | 150241       0.31        11 | 150853       0.61        66
+      //  672 | 149602       0.33        12 | 150110       0.66        90
+      //  717 | 149998       0.35        12 | 149531       0.70       129
+      //  762 | 149836       0.37        13 | 148559       0.74       190
+      //  807 | 149736       0.39        14 | 151107       0.39        14
+      //  852 | 150204       0.42        15 | 151019       0.42        15
+      drop_deletes_without_resize();
+    } else {
+      // Otherwise grow the container.
+      resize(NextCapacity(cap));
+    }
+  }
+
+  bool has_element(const value_type& elem) const {
+    size_t hash = PolicyTraits::apply(HashElement{hash_ref()}, elem);
+    auto seq = probe(common(), hash);
+    const ctrl_t* ctrl = control();
+    while (true) {
+      Group g{ctrl + seq.offset()};
+      for (uint32_t i : g.Match(H2(hash))) {
+        if (ABSL_PREDICT_TRUE(
+                PolicyTraits::element(slot_array() + seq.offset(i)) == elem))
+          return true;
+      }
+      if (ABSL_PREDICT_TRUE(g.MaskEmpty())) return false;
+      seq.next();
+      assert(seq.index() <= capacity() && "full table!");
+    }
+    return false;
+  }
+
+  // TODO(alkis): Optimize this assuming *this and that don't overlap.
+  raw_hash_set& move_assign(raw_hash_set&& that, std::true_type) {
+    raw_hash_set tmp(std::move(that));
+    swap(tmp);
+    return *this;
+  }
+  raw_hash_set& move_assign(raw_hash_set&& that, std::false_type) {
+    raw_hash_set tmp(std::move(that), alloc_ref());
+    swap(tmp);
+    return *this;
+  }
+
+ protected:
+  // Attempts to find `key` in the table; if it isn't found, returns a slot that
+  // the value can be inserted into, with the control byte already set to
+  // `key`'s H2.
+  template <class K>
+  std::pair<size_t, bool> find_or_prepare_insert(const K& key) {
+    prefetch_heap_block();
+    auto hash = hash_ref()(key);
+    auto seq = probe(common(), hash);
+    const ctrl_t* ctrl = control();
+    while (true) {
+      Group g{ctrl + seq.offset()};
+      for (uint32_t i : g.Match(H2(hash))) {
+        if (ABSL_PREDICT_TRUE(PolicyTraits::apply(
+                EqualElement<K>{key, eq_ref()},
+                PolicyTraits::element(slot_array() + seq.offset(i)))))
+          return {seq.offset(i), false};
+      }
+      if (ABSL_PREDICT_TRUE(g.MaskEmpty())) break;
+      seq.next();
+      assert(seq.index() <= capacity() && "full table!");
+    }
+    return {prepare_insert(hash), true};
+  }
+
+  // Given the hash of a value not currently in the table, finds the next
+  // viable slot index to insert it at.
+  //
+  // REQUIRES: At least one non-full slot available.
+  size_t prepare_insert(size_t hash) ABSL_ATTRIBUTE_NOINLINE {
+    const bool rehash_for_bug_detection =
+        common().should_rehash_for_bug_detection_on_insert();
+    if (rehash_for_bug_detection) {
+      // Move to a different heap allocation in order to detect bugs.
+      const size_t cap = capacity();
+      resize(growth_left() > 0 ? cap : NextCapacity(cap));
+    }
+    auto target = find_first_non_full(common(), hash);
+    if (!rehash_for_bug_detection &&
+        ABSL_PREDICT_FALSE(growth_left() == 0 &&
+                           !IsDeleted(control()[target.offset]))) {
+      rehash_and_grow_if_necessary();
+      target = find_first_non_full(common(), hash);
+    }
+    ++common().size_;
+    growth_left() -= IsEmpty(control()[target.offset]);
+    SetCtrl(common(), target.offset, H2(hash), sizeof(slot_type));
+    common().maybe_increment_generation_on_insert();
+    infoz().RecordInsert(hash, target.probe_length);
+    return target.offset;
+  }
+
+  // Constructs the value in the space pointed by the iterator. This only works
+  // after an unsuccessful find_or_prepare_insert() and before any other
+  // modifications happen in the raw_hash_set.
+  //
+  // PRECONDITION: i is an index returned from find_or_prepare_insert(k), where
+  // k is the key decomposed from `forward<Args>(args)...`, and the bool
+  // returned by find_or_prepare_insert(k) was true.
+  // POSTCONDITION: *m.iterator_at(i) == value_type(forward<Args>(args)...).
+  template <class... Args>
+  void emplace_at(size_t i, Args&&... args) {
+    PolicyTraits::construct(&alloc_ref(), slot_array() + i,
+                            std::forward<Args>(args)...);
+
+    assert(PolicyTraits::apply(FindElement{*this}, *iterator_at(i)) ==
+               iterator_at(i) &&
+           "constructed value does not match the lookup key");
+  }
+
+  iterator iterator_at(size_t i) {
+    return {control() + i, slot_array() + i, common().generation_ptr()};
+  }
+  const_iterator iterator_at(size_t i) const {
+    return {control() + i, slot_array() + i, common().generation_ptr()};
+  }
+
+ private:
+  friend struct RawHashSetTestOnlyAccess;
+
+  // The number of slots we can still fill without needing to rehash.
+  //
+  // This is stored separately due to tombstones: we do not include tombstones
+  // in the growth capacity, because we'd like to rehash when the table is
+  // otherwise filled with tombstones: otherwise, probe sequences might get
+  // unacceptably long without triggering a rehash. Callers can also force a
+  // rehash via the standard `rehash(0)`, which will recompute this value as a
+  // side-effect.
+  //
+  // See `CapacityToGrowth()`.
+  size_t& growth_left() { return common().growth_left(); }
+
+  // Prefetch the heap-allocated memory region to resolve potential TLB misses.
+  // This is intended to overlap with execution of calculating the hash for a
+  // key.
+  void prefetch_heap_block() const { base_internal::PrefetchT2(control()); }
+
+  CommonFields& common() { return settings_.template get<0>(); }
+  const CommonFields& common() const { return settings_.template get<0>(); }
+
+  ctrl_t* control() const { return common().control_; }
+  slot_type* slot_array() const {
+    return static_cast<slot_type*>(common().slots_);
+  }
+  HashtablezInfoHandle& infoz() { return common().infoz(); }
+
+  hasher& hash_ref() { return settings_.template get<1>(); }
+  const hasher& hash_ref() const { return settings_.template get<1>(); }
+  key_equal& eq_ref() { return settings_.template get<2>(); }
+  const key_equal& eq_ref() const { return settings_.template get<2>(); }
+  allocator_type& alloc_ref() { return settings_.template get<3>(); }
+  const allocator_type& alloc_ref() const {
+    return settings_.template get<3>();
+  }
+
+  // Make type-specific functions for this type's PolicyFunctions struct.
+  static size_t hash_slot_fn(void* set, void* slot) {
+    auto* h = static_cast<raw_hash_set*>(set);
+    return PolicyTraits::apply(
+        HashElement{h->hash_ref()},
+        PolicyTraits::element(static_cast<slot_type*>(slot)));
+  }
+  static void transfer_slot_fn(void* set, void* dst, void* src) {
+    auto* h = static_cast<raw_hash_set*>(set);
+    PolicyTraits::transfer(&h->alloc_ref(), static_cast<slot_type*>(dst),
+                           static_cast<slot_type*>(src));
+  }
+  // Note: dealloc_fn will only be used if we have a non-standard allocator.
+  static void dealloc_fn(void* set, const PolicyFunctions&, ctrl_t* ctrl,
+                         void* slot_mem, size_t n) {
+    auto* h = static_cast<raw_hash_set*>(set);
+
+    // Unpoison before returning the memory to the allocator.
+    SanitizerUnpoisonMemoryRegion(slot_mem, sizeof(slot_type) * n);
+
+    Deallocate<alignof(slot_type)>(
+        &h->alloc_ref(), ctrl,
+        AllocSize(n, sizeof(slot_type), alignof(slot_type)));
+  }
+
+  static const PolicyFunctions& GetPolicyFunctions() {
+    static constexpr PolicyFunctions value = {
+        sizeof(slot_type),
+        &raw_hash_set::hash_slot_fn,
+        PolicyTraits::transfer_uses_memcpy()
+            ? TransferRelocatable<sizeof(slot_type)>
+            : &raw_hash_set::transfer_slot_fn,
+        (std::is_same<SlotAlloc, std::allocator<slot_type>>::value
+             ? &DeallocateStandard<alignof(slot_type)>
+             : &raw_hash_set::dealloc_fn),
+    };
+    return value;
+  }
+
+  // Bundle together CommonFields plus other objects which might be empty.
+  // CompressedTuple will ensure that sizeof is not affected by any of the empty
+  // fields that occur after CommonFields.
+  absl::container_internal::CompressedTuple<CommonFields, hasher, key_equal,
+                                            allocator_type>
+      settings_{CommonFields{}, hasher{}, key_equal{}, allocator_type{}};
+};
+
+// Erases all elements that satisfy the predicate `pred` from the container `c`.
+template <typename P, typename H, typename E, typename A, typename Predicate>
+typename raw_hash_set<P, H, E, A>::size_type EraseIf(
+    Predicate& pred, raw_hash_set<P, H, E, A>* c) {
+  const auto initial_size = c->size();
+  for (auto it = c->begin(), last = c->end(); it != last;) {
+    if (pred(*it)) {
+      c->erase(it++);
+    } else {
+      ++it;
+    }
+  }
+  return initial_size - c->size();
+}
+
+namespace hashtable_debug_internal {
+template <typename Set>
+struct HashtableDebugAccess<Set, absl::void_t<typename Set::raw_hash_set>> {
+  using Traits = typename Set::PolicyTraits;
+  using Slot = typename Traits::slot_type;
+
+  static size_t GetNumProbes(const Set& set,
+                             const typename Set::key_type& key) {
+    size_t num_probes = 0;
+    size_t hash = set.hash_ref()(key);
+    auto seq = probe(set.common(), hash);
+    const ctrl_t* ctrl = set.control();
+    while (true) {
+      container_internal::Group g{ctrl + seq.offset()};
+      for (uint32_t i : g.Match(container_internal::H2(hash))) {
+        if (Traits::apply(
+                typename Set::template EqualElement<typename Set::key_type>{
+                    key, set.eq_ref()},
+                Traits::element(set.slot_array() + seq.offset(i))))
+          return num_probes;
+        ++num_probes;
+      }
+      if (g.MaskEmpty()) return num_probes;
+      seq.next();
+      ++num_probes;
+    }
+  }
+
+  static size_t AllocatedByteSize(const Set& c) {
+    size_t capacity = c.capacity();
+    if (capacity == 0) return 0;
+    size_t m = AllocSize(capacity, sizeof(Slot), alignof(Slot));
+
+    size_t per_slot = Traits::space_used(static_cast<const Slot*>(nullptr));
+    if (per_slot != ~size_t{}) {
+      m += per_slot * c.size();
+    } else {
+      const ctrl_t* ctrl = c.control();
+      for (size_t i = 0; i != capacity; ++i) {
+        if (container_internal::IsFull(ctrl[i])) {
+          m += Traits::space_used(c.slot_array() + i);
+        }
+      }
+    }
+    return m;
+  }
+
+  static size_t LowerBoundAllocatedByteSize(size_t size) {
+    size_t capacity = GrowthToLowerboundCapacity(size);
+    if (capacity == 0) return 0;
+    size_t m =
+        AllocSize(NormalizeCapacity(capacity), sizeof(Slot), alignof(Slot));
+    size_t per_slot = Traits::space_used(static_cast<const Slot*>(nullptr));
+    if (per_slot != ~size_t{}) {
+      m += per_slot * size;
+    }
+    return m;
+  }
+};
+
+}  // namespace hashtable_debug_internal
+}  // namespace container_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#undef ABSL_SWISSTABLE_ENABLE_GENERATIONS
+#undef ABSL_INTERNAL_ASSERT_IS_FULL
+
+#endif  // ABSL_CONTAINER_INTERNAL_RAW_HASH_SET_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/node_hash_map.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/node_hash_map.h
new file mode 100644
index 0000000000000000000000000000000000000000..6868e63a427524bbdcd3298dae236c9c3559b742
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/node_hash_map.h
@@ -0,0 +1,604 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: node_hash_map.h
+// -----------------------------------------------------------------------------
+//
+// An `absl::node_hash_map<K, V>` is an unordered associative container of
+// unique keys and associated values designed to be a more efficient replacement
+// for `std::unordered_map`. Like `unordered_map`, search, insertion, and
+// deletion of map elements can be done as an `O(1)` operation. However,
+// `node_hash_map` (and other unordered associative containers known as the
+// collection of Abseil "Swiss tables") contain other optimizations that result
+// in both memory and computation advantages.
+//
+// In most cases, your default choice for a hash map should be a map of type
+// `flat_hash_map`. However, if you need pointer stability and cannot store
+// a `flat_hash_map` with `unique_ptr` elements, a `node_hash_map` may be a
+// valid alternative. As well, if you are migrating your code from using
+// `std::unordered_map`, a `node_hash_map` provides a more straightforward
+// migration, because it guarantees pointer stability. Consider migrating to
+// `node_hash_map` and perhaps converting to a more efficient `flat_hash_map`
+// upon further review.
+
+#ifndef ABSL_CONTAINER_NODE_HASH_MAP_H_
+#define ABSL_CONTAINER_NODE_HASH_MAP_H_
+
+#include <tuple>
+#include <type_traits>
+#include <utility>
+
+#include "absl/algorithm/container.h"
+#include "absl/base/macros.h"
+#include "absl/container/internal/container_memory.h"
+#include "absl/container/internal/hash_function_defaults.h"  // IWYU pragma: export
+#include "absl/container/internal/node_slot_policy.h"
+#include "absl/container/internal/raw_hash_map.h"  // IWYU pragma: export
+#include "absl/memory/memory.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+template <class Key, class Value>
+class NodeHashMapPolicy;
+}  // namespace container_internal
+
+// -----------------------------------------------------------------------------
+// absl::node_hash_map
+// -----------------------------------------------------------------------------
+//
+// An `absl::node_hash_map<K, V>` is an unordered associative container which
+// has been optimized for both speed and memory footprint in most common use
+// cases. Its interface is similar to that of `std::unordered_map<K, V>` with
+// the following notable differences:
+//
+// * Supports heterogeneous lookup, through `find()`, `operator[]()` and
+//   `insert()`, provided that the map is provided a compatible heterogeneous
+//   hashing function and equality operator.
+// * Contains a `capacity()` member function indicating the number of element
+//   slots (open, deleted, and empty) within the hash map.
+// * Returns `void` from the `erase(iterator)` overload.
+//
+// By default, `node_hash_map` uses the `absl::Hash` hashing framework.
+// All fundamental and Abseil types that support the `absl::Hash` framework have
+// a compatible equality operator for comparing insertions into `node_hash_map`.
+// If your type is not yet supported by the `absl::Hash` framework, see
+// absl/hash/hash.h for information on extending Abseil hashing to user-defined
+// types.
+//
+// Using `absl::node_hash_map` at interface boundaries in dynamically loaded
+// libraries (e.g. .dll, .so) is unsupported due to way `absl::Hash` values may
+// be randomized across dynamically loaded libraries.
+//
+// Example:
+//
+//   // Create a node hash map of three strings (that map to strings)
+//   absl::node_hash_map<std::string, std::string> ducks =
+//     {{"a", "huey"}, {"b", "dewey"}, {"c", "louie"}};
+//
+//  // Insert a new element into the node hash map
+//  ducks.insert({"d", "donald"}};
+//
+//  // Force a rehash of the node hash map
+//  ducks.rehash(0);
+//
+//  // Find the element with the key "b"
+//  std::string search_key = "b";
+//  auto result = ducks.find(search_key);
+//  if (result != ducks.end()) {
+//    std::cout << "Result: " << result->second << std::endl;
+//  }
+template <class Key, class Value,
+          class Hash = absl::container_internal::hash_default_hash<Key>,
+          class Eq = absl::container_internal::hash_default_eq<Key>,
+          class Alloc = std::allocator<std::pair<const Key, Value>>>
+class node_hash_map
+    : public absl::container_internal::raw_hash_map<
+          absl::container_internal::NodeHashMapPolicy<Key, Value>, Hash, Eq,
+          Alloc> {
+  using Base = typename node_hash_map::raw_hash_map;
+
+ public:
+  // Constructors and Assignment Operators
+  //
+  // A node_hash_map supports the same overload set as `std::unordered_map`
+  // for construction and assignment:
+  //
+  // *  Default constructor
+  //
+  //    // No allocation for the table's elements is made.
+  //    absl::node_hash_map<int, std::string> map1;
+  //
+  // * Initializer List constructor
+  //
+  //   absl::node_hash_map<int, std::string> map2 =
+  //       {{1, "huey"}, {2, "dewey"}, {3, "louie"},};
+  //
+  // * Copy constructor
+  //
+  //   absl::node_hash_map<int, std::string> map3(map2);
+  //
+  // * Copy assignment operator
+  //
+  //  // Hash functor and Comparator are copied as well
+  //  absl::node_hash_map<int, std::string> map4;
+  //  map4 = map3;
+  //
+  // * Move constructor
+  //
+  //   // Move is guaranteed efficient
+  //   absl::node_hash_map<int, std::string> map5(std::move(map4));
+  //
+  // * Move assignment operator
+  //
+  //   // May be efficient if allocators are compatible
+  //   absl::node_hash_map<int, std::string> map6;
+  //   map6 = std::move(map5);
+  //
+  // * Range constructor
+  //
+  //   std::vector<std::pair<int, std::string>> v = {{1, "a"}, {2, "b"}};
+  //   absl::node_hash_map<int, std::string> map7(v.begin(), v.end());
+  node_hash_map() {}
+  using Base::Base;
+
+  // node_hash_map::begin()
+  //
+  // Returns an iterator to the beginning of the `node_hash_map`.
+  using Base::begin;
+
+  // node_hash_map::cbegin()
+  //
+  // Returns a const iterator to the beginning of the `node_hash_map`.
+  using Base::cbegin;
+
+  // node_hash_map::cend()
+  //
+  // Returns a const iterator to the end of the `node_hash_map`.
+  using Base::cend;
+
+  // node_hash_map::end()
+  //
+  // Returns an iterator to the end of the `node_hash_map`.
+  using Base::end;
+
+  // node_hash_map::capacity()
+  //
+  // Returns the number of element slots (assigned, deleted, and empty)
+  // available within the `node_hash_map`.
+  //
+  // NOTE: this member function is particular to `absl::node_hash_map` and is
+  // not provided in the `std::unordered_map` API.
+  using Base::capacity;
+
+  // node_hash_map::empty()
+  //
+  // Returns whether or not the `node_hash_map` is empty.
+  using Base::empty;
+
+  // node_hash_map::max_size()
+  //
+  // Returns the largest theoretical possible number of elements within a
+  // `node_hash_map` under current memory constraints. This value can be thought
+  // of as the largest value of `std::distance(begin(), end())` for a
+  // `node_hash_map<K, V>`.
+  using Base::max_size;
+
+  // node_hash_map::size()
+  //
+  // Returns the number of elements currently within the `node_hash_map`.
+  using Base::size;
+
+  // node_hash_map::clear()
+  //
+  // Removes all elements from the `node_hash_map`. Invalidates any references,
+  // pointers, or iterators referring to contained elements.
+  //
+  // NOTE: this operation may shrink the underlying buffer. To avoid shrinking
+  // the underlying buffer call `erase(begin(), end())`.
+  using Base::clear;
+
+  // node_hash_map::erase()
+  //
+  // Erases elements within the `node_hash_map`. Erasing does not trigger a
+  // rehash. Overloads are listed below.
+  //
+  // void erase(const_iterator pos):
+  //
+  //   Erases the element at `position` of the `node_hash_map`, returning
+  //   `void`.
+  //
+  //   NOTE: this return behavior is different than that of STL containers in
+  //   general and `std::unordered_map` in particular.
+  //
+  // iterator erase(const_iterator first, const_iterator last):
+  //
+  //   Erases the elements in the open interval [`first`, `last`), returning an
+  //   iterator pointing to `last`.
+  //
+  // size_type erase(const key_type& key):
+  //
+  //   Erases the element with the matching key, if it exists, returning the
+  //   number of elements erased (0 or 1).
+  using Base::erase;
+
+  // node_hash_map::insert()
+  //
+  // Inserts an element of the specified value into the `node_hash_map`,
+  // returning an iterator pointing to the newly inserted element, provided that
+  // an element with the given key does not already exist. If rehashing occurs
+  // due to the insertion, all iterators are invalidated. Overloads are listed
+  // below.
+  //
+  // std::pair<iterator,bool> insert(const init_type& value):
+  //
+  //   Inserts a value into the `node_hash_map`. Returns a pair consisting of an
+  //   iterator to the inserted element (or to the element that prevented the
+  //   insertion) and a `bool` denoting whether the insertion took place.
+  //
+  // std::pair<iterator,bool> insert(T&& value):
+  // std::pair<iterator,bool> insert(init_type&& value):
+  //
+  //   Inserts a moveable value into the `node_hash_map`. Returns a `std::pair`
+  //   consisting of an iterator to the inserted element (or to the element that
+  //   prevented the insertion) and a `bool` denoting whether the insertion took
+  //   place.
+  //
+  // iterator insert(const_iterator hint, const init_type& value):
+  // iterator insert(const_iterator hint, T&& value):
+  // iterator insert(const_iterator hint, init_type&& value);
+  //
+  //   Inserts a value, using the position of `hint` as a non-binding suggestion
+  //   for where to begin the insertion search. Returns an iterator to the
+  //   inserted element, or to the existing element that prevented the
+  //   insertion.
+  //
+  // void insert(InputIterator first, InputIterator last):
+  //
+  //   Inserts a range of values [`first`, `last`).
+  //
+  //   NOTE: Although the STL does not specify which element may be inserted if
+  //   multiple keys compare equivalently, for `node_hash_map` we guarantee the
+  //   first match is inserted.
+  //
+  // void insert(std::initializer_list<init_type> ilist):
+  //
+  //   Inserts the elements within the initializer list `ilist`.
+  //
+  //   NOTE: Although the STL does not specify which element may be inserted if
+  //   multiple keys compare equivalently within the initializer list, for
+  //   `node_hash_map` we guarantee the first match is inserted.
+  using Base::insert;
+
+  // node_hash_map::insert_or_assign()
+  //
+  // Inserts an element of the specified value into the `node_hash_map` provided
+  // that a value with the given key does not already exist, or replaces it with
+  // the element value if a key for that value already exists, returning an
+  // iterator pointing to the newly inserted element. If rehashing occurs due to
+  // the insertion, all iterators are invalidated. Overloads are listed
+  // below.
+  //
+  // std::pair<iterator, bool> insert_or_assign(const init_type& k, T&& obj):
+  // std::pair<iterator, bool> insert_or_assign(init_type&& k, T&& obj):
+  //
+  //   Inserts/Assigns (or moves) the element of the specified key into the
+  //   `node_hash_map`.
+  //
+  // iterator insert_or_assign(const_iterator hint,
+  //                           const init_type& k, T&& obj):
+  // iterator insert_or_assign(const_iterator hint, init_type&& k, T&& obj):
+  //
+  //   Inserts/Assigns (or moves) the element of the specified key into the
+  //   `node_hash_map` using the position of `hint` as a non-binding suggestion
+  //   for where to begin the insertion search.
+  using Base::insert_or_assign;
+
+  // node_hash_map::emplace()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `node_hash_map`, provided that no element with the given key
+  // already exists.
+  //
+  // The element may be constructed even if there already is an element with the
+  // key in the container, in which case the newly constructed element will be
+  // destroyed immediately. Prefer `try_emplace()` unless your key is not
+  // copyable or moveable.
+  //
+  // If rehashing occurs due to the insertion, all iterators are invalidated.
+  using Base::emplace;
+
+  // node_hash_map::emplace_hint()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `node_hash_map`, using the position of `hint` as a non-binding
+  // suggestion for where to begin the insertion search, and only inserts
+  // provided that no element with the given key already exists.
+  //
+  // The element may be constructed even if there already is an element with the
+  // key in the container, in which case the newly constructed element will be
+  // destroyed immediately. Prefer `try_emplace()` unless your key is not
+  // copyable or moveable.
+  //
+  // If rehashing occurs due to the insertion, all iterators are invalidated.
+  using Base::emplace_hint;
+
+  // node_hash_map::try_emplace()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `node_hash_map`, provided that no element with the given key
+  // already exists. Unlike `emplace()`, if an element with the given key
+  // already exists, we guarantee that no element is constructed.
+  //
+  // If rehashing occurs due to the insertion, all iterators are invalidated.
+  // Overloads are listed below.
+  //
+  //   std::pair<iterator, bool> try_emplace(const key_type& k, Args&&... args):
+  //   std::pair<iterator, bool> try_emplace(key_type&& k, Args&&... args):
+  //
+  // Inserts (via copy or move) the element of the specified key into the
+  // `node_hash_map`.
+  //
+  //   iterator try_emplace(const_iterator hint,
+  //                        const key_type& k, Args&&... args):
+  //   iterator try_emplace(const_iterator hint, key_type&& k, Args&&... args):
+  //
+  // Inserts (via copy or move) the element of the specified key into the
+  // `node_hash_map` using the position of `hint` as a non-binding suggestion
+  // for where to begin the insertion search.
+  //
+  // All `try_emplace()` overloads make the same guarantees regarding rvalue
+  // arguments as `std::unordered_map::try_emplace()`, namely that these
+  // functions will not move from rvalue arguments if insertions do not happen.
+  using Base::try_emplace;
+
+  // node_hash_map::extract()
+  //
+  // Extracts the indicated element, erasing it in the process, and returns it
+  // as a C++17-compatible node handle. Overloads are listed below.
+  //
+  // node_type extract(const_iterator position):
+  //
+  //   Extracts the key,value pair of the element at the indicated position and
+  //   returns a node handle owning that extracted data.
+  //
+  // node_type extract(const key_type& x):
+  //
+  //   Extracts the key,value pair of the element with a key matching the passed
+  //   key value and returns a node handle owning that extracted data. If the
+  //   `node_hash_map` does not contain an element with a matching key, this
+  //   function returns an empty node handle.
+  //
+  // NOTE: when compiled in an earlier version of C++ than C++17,
+  // `node_type::key()` returns a const reference to the key instead of a
+  // mutable reference. We cannot safely return a mutable reference without
+  // std::launder (which is not available before C++17).
+  using Base::extract;
+
+  // node_hash_map::merge()
+  //
+  // Extracts elements from a given `source` node hash map into this
+  // `node_hash_map`. If the destination `node_hash_map` already contains an
+  // element with an equivalent key, that element is not extracted.
+  using Base::merge;
+
+  // node_hash_map::swap(node_hash_map& other)
+  //
+  // Exchanges the contents of this `node_hash_map` with those of the `other`
+  // node hash map, avoiding invocation of any move, copy, or swap operations on
+  // individual elements.
+  //
+  // All iterators and references on the `node_hash_map` remain valid, excepting
+  // for the past-the-end iterator, which is invalidated.
+  //
+  // `swap()` requires that the node hash map's hashing and key equivalence
+  // functions be Swappable, and are exchaged using unqualified calls to
+  // non-member `swap()`. If the map's allocator has
+  // `std::allocator_traits<allocator_type>::propagate_on_container_swap::value`
+  // set to `true`, the allocators are also exchanged using an unqualified call
+  // to non-member `swap()`; otherwise, the allocators are not swapped.
+  using Base::swap;
+
+  // node_hash_map::rehash(count)
+  //
+  // Rehashes the `node_hash_map`, setting the number of slots to be at least
+  // the passed value. If the new number of slots increases the load factor more
+  // than the current maximum load factor
+  // (`count` < `size()` / `max_load_factor()`), then the new number of slots
+  // will be at least `size()` / `max_load_factor()`.
+  //
+  // To force a rehash, pass rehash(0).
+  using Base::rehash;
+
+  // node_hash_map::reserve(count)
+  //
+  // Sets the number of slots in the `node_hash_map` to the number needed to
+  // accommodate at least `count` total elements without exceeding the current
+  // maximum load factor, and may rehash the container if needed.
+  using Base::reserve;
+
+  // node_hash_map::at()
+  //
+  // Returns a reference to the mapped value of the element with key equivalent
+  // to the passed key.
+  using Base::at;
+
+  // node_hash_map::contains()
+  //
+  // Determines whether an element with a key comparing equal to the given `key`
+  // exists within the `node_hash_map`, returning `true` if so or `false`
+  // otherwise.
+  using Base::contains;
+
+  // node_hash_map::count(const Key& key) const
+  //
+  // Returns the number of elements with a key comparing equal to the given
+  // `key` within the `node_hash_map`. note that this function will return
+  // either `1` or `0` since duplicate keys are not allowed within a
+  // `node_hash_map`.
+  using Base::count;
+
+  // node_hash_map::equal_range()
+  //
+  // Returns a closed range [first, last], defined by a `std::pair` of two
+  // iterators, containing all elements with the passed key in the
+  // `node_hash_map`.
+  using Base::equal_range;
+
+  // node_hash_map::find()
+  //
+  // Finds an element with the passed `key` within the `node_hash_map`.
+  using Base::find;
+
+  // node_hash_map::operator[]()
+  //
+  // Returns a reference to the value mapped to the passed key within the
+  // `node_hash_map`, performing an `insert()` if the key does not already
+  // exist. If an insertion occurs and results in a rehashing of the container,
+  // all iterators are invalidated. Otherwise iterators are not affected and
+  // references are not invalidated. Overloads are listed below.
+  //
+  // T& operator[](const Key& key):
+  //
+  //   Inserts an init_type object constructed in-place if the element with the
+  //   given key does not exist.
+  //
+  // T& operator[](Key&& key):
+  //
+  //   Inserts an init_type object constructed in-place provided that an element
+  //   with the given key does not exist.
+  using Base::operator[];
+
+  // node_hash_map::bucket_count()
+  //
+  // Returns the number of "buckets" within the `node_hash_map`.
+  using Base::bucket_count;
+
+  // node_hash_map::load_factor()
+  //
+  // Returns the current load factor of the `node_hash_map` (the average number
+  // of slots occupied with a value within the hash map).
+  using Base::load_factor;
+
+  // node_hash_map::max_load_factor()
+  //
+  // Manages the maximum load factor of the `node_hash_map`. Overloads are
+  // listed below.
+  //
+  // float node_hash_map::max_load_factor()
+  //
+  //   Returns the current maximum load factor of the `node_hash_map`.
+  //
+  // void node_hash_map::max_load_factor(float ml)
+  //
+  //   Sets the maximum load factor of the `node_hash_map` to the passed value.
+  //
+  //   NOTE: This overload is provided only for API compatibility with the STL;
+  //   `node_hash_map` will ignore any set load factor and manage its rehashing
+  //   internally as an implementation detail.
+  using Base::max_load_factor;
+
+  // node_hash_map::get_allocator()
+  //
+  // Returns the allocator function associated with this `node_hash_map`.
+  using Base::get_allocator;
+
+  // node_hash_map::hash_function()
+  //
+  // Returns the hashing function used to hash the keys within this
+  // `node_hash_map`.
+  using Base::hash_function;
+
+  // node_hash_map::key_eq()
+  //
+  // Returns the function used for comparing keys equality.
+  using Base::key_eq;
+};
+
+// erase_if(node_hash_map<>, Pred)
+//
+// Erases all elements that satisfy the predicate `pred` from the container `c`.
+// Returns the number of erased elements.
+template <typename K, typename V, typename H, typename E, typename A,
+          typename Predicate>
+typename node_hash_map<K, V, H, E, A>::size_type erase_if(
+    node_hash_map<K, V, H, E, A>& c, Predicate pred) {
+  return container_internal::EraseIf(pred, &c);
+}
+
+namespace container_internal {
+
+template <class Key, class Value>
+class NodeHashMapPolicy
+    : public absl::container_internal::node_slot_policy<
+          std::pair<const Key, Value>&, NodeHashMapPolicy<Key, Value>> {
+  using value_type = std::pair<const Key, Value>;
+
+ public:
+  using key_type = Key;
+  using mapped_type = Value;
+  using init_type = std::pair</*non const*/ key_type, mapped_type>;
+
+  template <class Allocator, class... Args>
+  static value_type* new_element(Allocator* alloc, Args&&... args) {
+    using PairAlloc = typename absl::allocator_traits<
+        Allocator>::template rebind_alloc<value_type>;
+    PairAlloc pair_alloc(*alloc);
+    value_type* res =
+        absl::allocator_traits<PairAlloc>::allocate(pair_alloc, 1);
+    absl::allocator_traits<PairAlloc>::construct(pair_alloc, res,
+                                                 std::forward<Args>(args)...);
+    return res;
+  }
+
+  template <class Allocator>
+  static void delete_element(Allocator* alloc, value_type* pair) {
+    using PairAlloc = typename absl::allocator_traits<
+        Allocator>::template rebind_alloc<value_type>;
+    PairAlloc pair_alloc(*alloc);
+    absl::allocator_traits<PairAlloc>::destroy(pair_alloc, pair);
+    absl::allocator_traits<PairAlloc>::deallocate(pair_alloc, pair, 1);
+  }
+
+  template <class F, class... Args>
+  static decltype(absl::container_internal::DecomposePair(
+      std::declval<F>(), std::declval<Args>()...))
+  apply(F&& f, Args&&... args) {
+    return absl::container_internal::DecomposePair(std::forward<F>(f),
+                                                   std::forward<Args>(args)...);
+  }
+
+  static size_t element_space_used(const value_type*) {
+    return sizeof(value_type);
+  }
+
+  static Value& value(value_type* elem) { return elem->second; }
+  static const Value& value(const value_type* elem) { return elem->second; }
+};
+}  // namespace container_internal
+
+namespace container_algorithm_internal {
+
+// Specialization of trait in absl/algorithm/container.h
+template <class Key, class T, class Hash, class KeyEqual, class Allocator>
+struct IsUnorderedContainer<
+    absl::node_hash_map<Key, T, Hash, KeyEqual, Allocator>> : std::true_type {};
+
+}  // namespace container_algorithm_internal
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_NODE_HASH_MAP_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/node_hash_set.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/node_hash_set.h
new file mode 100644
index 0000000000000000000000000000000000000000..f2cc70c3f6cc9b6e38e56c9c3b44a26f2f9bf097
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/container/node_hash_set.h
@@ -0,0 +1,500 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: node_hash_set.h
+// -----------------------------------------------------------------------------
+//
+// An `absl::node_hash_set<T>` is an unordered associative container designed to
+// be a more efficient replacement for `std::unordered_set`. Like
+// `unordered_set`, search, insertion, and deletion of set elements can be done
+// as an `O(1)` operation. However, `node_hash_set` (and other unordered
+// associative containers known as the collection of Abseil "Swiss tables")
+// contain other optimizations that result in both memory and computation
+// advantages.
+//
+// In most cases, your default choice for a hash table should be a map of type
+// `flat_hash_map` or a set of type `flat_hash_set`. However, if you need
+// pointer stability, a `node_hash_set` should be your preferred choice. As
+// well, if you are migrating your code from using `std::unordered_set`, a
+// `node_hash_set` should be an easy migration. Consider migrating to
+// `node_hash_set` and perhaps converting to a more efficient `flat_hash_set`
+// upon further review.
+
+#ifndef ABSL_CONTAINER_NODE_HASH_SET_H_
+#define ABSL_CONTAINER_NODE_HASH_SET_H_
+
+#include <type_traits>
+
+#include "absl/algorithm/container.h"
+#include "absl/base/macros.h"
+#include "absl/container/internal/hash_function_defaults.h"  // IWYU pragma: export
+#include "absl/container/internal/node_slot_policy.h"
+#include "absl/container/internal/raw_hash_set.h"  // IWYU pragma: export
+#include "absl/memory/memory.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace container_internal {
+template <typename T>
+struct NodeHashSetPolicy;
+}  // namespace container_internal
+
+// -----------------------------------------------------------------------------
+// absl::node_hash_set
+// -----------------------------------------------------------------------------
+//
+// An `absl::node_hash_set<T>` is an unordered associative container which
+// has been optimized for both speed and memory footprint in most common use
+// cases. Its interface is similar to that of `std::unordered_set<T>` with the
+// following notable differences:
+//
+// * Supports heterogeneous lookup, through `find()`, `operator[]()` and
+//   `insert()`, provided that the set is provided a compatible heterogeneous
+//   hashing function and equality operator.
+// * Contains a `capacity()` member function indicating the number of element
+//   slots (open, deleted, and empty) within the hash set.
+// * Returns `void` from the `erase(iterator)` overload.
+//
+// By default, `node_hash_set` uses the `absl::Hash` hashing framework.
+// All fundamental and Abseil types that support the `absl::Hash` framework have
+// a compatible equality operator for comparing insertions into `node_hash_set`.
+// If your type is not yet supported by the `absl::Hash` framework, see
+// absl/hash/hash.h for information on extending Abseil hashing to user-defined
+// types.
+//
+// Using `absl::node_hash_set` at interface boundaries in dynamically loaded
+// libraries (e.g. .dll, .so) is unsupported due to way `absl::Hash` values may
+// be randomized across dynamically loaded libraries.
+//
+// Example:
+//
+//   // Create a node hash set of three strings
+//   absl::node_hash_set<std::string> ducks =
+//     {"huey", "dewey", "louie"};
+//
+//  // Insert a new element into the node hash set
+//  ducks.insert("donald");
+//
+//  // Force a rehash of the node hash set
+//  ducks.rehash(0);
+//
+//  // See if "dewey" is present
+//  if (ducks.contains("dewey")) {
+//    std::cout << "We found dewey!" << std::endl;
+//  }
+template <class T, class Hash = absl::container_internal::hash_default_hash<T>,
+          class Eq = absl::container_internal::hash_default_eq<T>,
+          class Alloc = std::allocator<T>>
+class node_hash_set
+    : public absl::container_internal::raw_hash_set<
+          absl::container_internal::NodeHashSetPolicy<T>, Hash, Eq, Alloc> {
+  using Base = typename node_hash_set::raw_hash_set;
+
+ public:
+  // Constructors and Assignment Operators
+  //
+  // A node_hash_set supports the same overload set as `std::unordered_set`
+  // for construction and assignment:
+  //
+  // *  Default constructor
+  //
+  //    // No allocation for the table's elements is made.
+  //    absl::node_hash_set<std::string> set1;
+  //
+  // * Initializer List constructor
+  //
+  //   absl::node_hash_set<std::string> set2 =
+  //       {{"huey"}, {"dewey"}, {"louie"}};
+  //
+  // * Copy constructor
+  //
+  //   absl::node_hash_set<std::string> set3(set2);
+  //
+  // * Copy assignment operator
+  //
+  //  // Hash functor and Comparator are copied as well
+  //  absl::node_hash_set<std::string> set4;
+  //  set4 = set3;
+  //
+  // * Move constructor
+  //
+  //   // Move is guaranteed efficient
+  //   absl::node_hash_set<std::string> set5(std::move(set4));
+  //
+  // * Move assignment operator
+  //
+  //   // May be efficient if allocators are compatible
+  //   absl::node_hash_set<std::string> set6;
+  //   set6 = std::move(set5);
+  //
+  // * Range constructor
+  //
+  //   std::vector<std::string> v = {"a", "b"};
+  //   absl::node_hash_set<std::string> set7(v.begin(), v.end());
+  node_hash_set() {}
+  using Base::Base;
+
+  // node_hash_set::begin()
+  //
+  // Returns an iterator to the beginning of the `node_hash_set`.
+  using Base::begin;
+
+  // node_hash_set::cbegin()
+  //
+  // Returns a const iterator to the beginning of the `node_hash_set`.
+  using Base::cbegin;
+
+  // node_hash_set::cend()
+  //
+  // Returns a const iterator to the end of the `node_hash_set`.
+  using Base::cend;
+
+  // node_hash_set::end()
+  //
+  // Returns an iterator to the end of the `node_hash_set`.
+  using Base::end;
+
+  // node_hash_set::capacity()
+  //
+  // Returns the number of element slots (assigned, deleted, and empty)
+  // available within the `node_hash_set`.
+  //
+  // NOTE: this member function is particular to `absl::node_hash_set` and is
+  // not provided in the `std::unordered_set` API.
+  using Base::capacity;
+
+  // node_hash_set::empty()
+  //
+  // Returns whether or not the `node_hash_set` is empty.
+  using Base::empty;
+
+  // node_hash_set::max_size()
+  //
+  // Returns the largest theoretical possible number of elements within a
+  // `node_hash_set` under current memory constraints. This value can be thought
+  // of the largest value of `std::distance(begin(), end())` for a
+  // `node_hash_set<T>`.
+  using Base::max_size;
+
+  // node_hash_set::size()
+  //
+  // Returns the number of elements currently within the `node_hash_set`.
+  using Base::size;
+
+  // node_hash_set::clear()
+  //
+  // Removes all elements from the `node_hash_set`. Invalidates any references,
+  // pointers, or iterators referring to contained elements.
+  //
+  // NOTE: this operation may shrink the underlying buffer. To avoid shrinking
+  // the underlying buffer call `erase(begin(), end())`.
+  using Base::clear;
+
+  // node_hash_set::erase()
+  //
+  // Erases elements within the `node_hash_set`. Erasing does not trigger a
+  // rehash. Overloads are listed below.
+  //
+  // void erase(const_iterator pos):
+  //
+  //   Erases the element at `position` of the `node_hash_set`, returning
+  //   `void`.
+  //
+  //   NOTE: this return behavior is different than that of STL containers in
+  //   general and `std::unordered_set` in particular.
+  //
+  // iterator erase(const_iterator first, const_iterator last):
+  //
+  //   Erases the elements in the open interval [`first`, `last`), returning an
+  //   iterator pointing to `last`.
+  //
+  // size_type erase(const key_type& key):
+  //
+  //   Erases the element with the matching key, if it exists, returning the
+  //   number of elements erased (0 or 1).
+  using Base::erase;
+
+  // node_hash_set::insert()
+  //
+  // Inserts an element of the specified value into the `node_hash_set`,
+  // returning an iterator pointing to the newly inserted element, provided that
+  // an element with the given key does not already exist. If rehashing occurs
+  // due to the insertion, all iterators are invalidated. Overloads are listed
+  // below.
+  //
+  // std::pair<iterator,bool> insert(const T& value):
+  //
+  //   Inserts a value into the `node_hash_set`. Returns a pair consisting of an
+  //   iterator to the inserted element (or to the element that prevented the
+  //   insertion) and a bool denoting whether the insertion took place.
+  //
+  // std::pair<iterator,bool> insert(T&& value):
+  //
+  //   Inserts a moveable value into the `node_hash_set`. Returns a pair
+  //   consisting of an iterator to the inserted element (or to the element that
+  //   prevented the insertion) and a bool denoting whether the insertion took
+  //   place.
+  //
+  // iterator insert(const_iterator hint, const T& value):
+  // iterator insert(const_iterator hint, T&& value):
+  //
+  //   Inserts a value, using the position of `hint` as a non-binding suggestion
+  //   for where to begin the insertion search. Returns an iterator to the
+  //   inserted element, or to the existing element that prevented the
+  //   insertion.
+  //
+  // void insert(InputIterator first, InputIterator last):
+  //
+  //   Inserts a range of values [`first`, `last`).
+  //
+  //   NOTE: Although the STL does not specify which element may be inserted if
+  //   multiple keys compare equivalently, for `node_hash_set` we guarantee the
+  //   first match is inserted.
+  //
+  // void insert(std::initializer_list<T> ilist):
+  //
+  //   Inserts the elements within the initializer list `ilist`.
+  //
+  //   NOTE: Although the STL does not specify which element may be inserted if
+  //   multiple keys compare equivalently within the initializer list, for
+  //   `node_hash_set` we guarantee the first match is inserted.
+  using Base::insert;
+
+  // node_hash_set::emplace()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `node_hash_set`, provided that no element with the given key
+  // already exists.
+  //
+  // The element may be constructed even if there already is an element with the
+  // key in the container, in which case the newly constructed element will be
+  // destroyed immediately.
+  //
+  // If rehashing occurs due to the insertion, all iterators are invalidated.
+  using Base::emplace;
+
+  // node_hash_set::emplace_hint()
+  //
+  // Inserts an element of the specified value by constructing it in-place
+  // within the `node_hash_set`, using the position of `hint` as a non-binding
+  // suggestion for where to begin the insertion search, and only inserts
+  // provided that no element with the given key already exists.
+  //
+  // The element may be constructed even if there already is an element with the
+  // key in the container, in which case the newly constructed element will be
+  // destroyed immediately.
+  //
+  // If rehashing occurs due to the insertion, all iterators are invalidated.
+  using Base::emplace_hint;
+
+  // node_hash_set::extract()
+  //
+  // Extracts the indicated element, erasing it in the process, and returns it
+  // as a C++17-compatible node handle. Overloads are listed below.
+  //
+  // node_type extract(const_iterator position):
+  //
+  //   Extracts the element at the indicated position and returns a node handle
+  //   owning that extracted data.
+  //
+  // node_type extract(const key_type& x):
+  //
+  //   Extracts the element with the key matching the passed key value and
+  //   returns a node handle owning that extracted data. If the `node_hash_set`
+  //   does not contain an element with a matching key, this function returns an
+  // empty node handle.
+  using Base::extract;
+
+  // node_hash_set::merge()
+  //
+  // Extracts elements from a given `source` node hash set into this
+  // `node_hash_set`. If the destination `node_hash_set` already contains an
+  // element with an equivalent key, that element is not extracted.
+  using Base::merge;
+
+  // node_hash_set::swap(node_hash_set& other)
+  //
+  // Exchanges the contents of this `node_hash_set` with those of the `other`
+  // node hash set, avoiding invocation of any move, copy, or swap operations on
+  // individual elements.
+  //
+  // All iterators and references on the `node_hash_set` remain valid, excepting
+  // for the past-the-end iterator, which is invalidated.
+  //
+  // `swap()` requires that the node hash set's hashing and key equivalence
+  // functions be Swappable, and are exchaged using unqualified calls to
+  // non-member `swap()`. If the set's allocator has
+  // `std::allocator_traits<allocator_type>::propagate_on_container_swap::value`
+  // set to `true`, the allocators are also exchanged using an unqualified call
+  // to non-member `swap()`; otherwise, the allocators are not swapped.
+  using Base::swap;
+
+  // node_hash_set::rehash(count)
+  //
+  // Rehashes the `node_hash_set`, setting the number of slots to be at least
+  // the passed value. If the new number of slots increases the load factor more
+  // than the current maximum load factor
+  // (`count` < `size()` / `max_load_factor()`), then the new number of slots
+  // will be at least `size()` / `max_load_factor()`.
+  //
+  // To force a rehash, pass rehash(0).
+  //
+  // NOTE: unlike behavior in `std::unordered_set`, references are also
+  // invalidated upon a `rehash()`.
+  using Base::rehash;
+
+  // node_hash_set::reserve(count)
+  //
+  // Sets the number of slots in the `node_hash_set` to the number needed to
+  // accommodate at least `count` total elements without exceeding the current
+  // maximum load factor, and may rehash the container if needed.
+  using Base::reserve;
+
+  // node_hash_set::contains()
+  //
+  // Determines whether an element comparing equal to the given `key` exists
+  // within the `node_hash_set`, returning `true` if so or `false` otherwise.
+  using Base::contains;
+
+  // node_hash_set::count(const Key& key) const
+  //
+  // Returns the number of elements comparing equal to the given `key` within
+  // the `node_hash_set`. note that this function will return either `1` or `0`
+  // since duplicate elements are not allowed within a `node_hash_set`.
+  using Base::count;
+
+  // node_hash_set::equal_range()
+  //
+  // Returns a closed range [first, last], defined by a `std::pair` of two
+  // iterators, containing all elements with the passed key in the
+  // `node_hash_set`.
+  using Base::equal_range;
+
+  // node_hash_set::find()
+  //
+  // Finds an element with the passed `key` within the `node_hash_set`.
+  using Base::find;
+
+  // node_hash_set::bucket_count()
+  //
+  // Returns the number of "buckets" within the `node_hash_set`. Note that
+  // because a node hash set contains all elements within its internal storage,
+  // this value simply equals the current capacity of the `node_hash_set`.
+  using Base::bucket_count;
+
+  // node_hash_set::load_factor()
+  //
+  // Returns the current load factor of the `node_hash_set` (the average number
+  // of slots occupied with a value within the hash set).
+  using Base::load_factor;
+
+  // node_hash_set::max_load_factor()
+  //
+  // Manages the maximum load factor of the `node_hash_set`. Overloads are
+  // listed below.
+  //
+  // float node_hash_set::max_load_factor()
+  //
+  //   Returns the current maximum load factor of the `node_hash_set`.
+  //
+  // void node_hash_set::max_load_factor(float ml)
+  //
+  //   Sets the maximum load factor of the `node_hash_set` to the passed value.
+  //
+  //   NOTE: This overload is provided only for API compatibility with the STL;
+  //   `node_hash_set` will ignore any set load factor and manage its rehashing
+  //   internally as an implementation detail.
+  using Base::max_load_factor;
+
+  // node_hash_set::get_allocator()
+  //
+  // Returns the allocator function associated with this `node_hash_set`.
+  using Base::get_allocator;
+
+  // node_hash_set::hash_function()
+  //
+  // Returns the hashing function used to hash the keys within this
+  // `node_hash_set`.
+  using Base::hash_function;
+
+  // node_hash_set::key_eq()
+  //
+  // Returns the function used for comparing keys equality.
+  using Base::key_eq;
+};
+
+// erase_if(node_hash_set<>, Pred)
+//
+// Erases all elements that satisfy the predicate `pred` from the container `c`.
+// Returns the number of erased elements.
+template <typename T, typename H, typename E, typename A, typename Predicate>
+typename node_hash_set<T, H, E, A>::size_type erase_if(
+    node_hash_set<T, H, E, A>& c, Predicate pred) {
+  return container_internal::EraseIf(pred, &c);
+}
+
+namespace container_internal {
+
+template <class T>
+struct NodeHashSetPolicy
+    : absl::container_internal::node_slot_policy<T&, NodeHashSetPolicy<T>> {
+  using key_type = T;
+  using init_type = T;
+  using constant_iterators = std::true_type;
+
+  template <class Allocator, class... Args>
+  static T* new_element(Allocator* alloc, Args&&... args) {
+    using ValueAlloc =
+        typename absl::allocator_traits<Allocator>::template rebind_alloc<T>;
+    ValueAlloc value_alloc(*alloc);
+    T* res = absl::allocator_traits<ValueAlloc>::allocate(value_alloc, 1);
+    absl::allocator_traits<ValueAlloc>::construct(value_alloc, res,
+                                                  std::forward<Args>(args)...);
+    return res;
+  }
+
+  template <class Allocator>
+  static void delete_element(Allocator* alloc, T* elem) {
+    using ValueAlloc =
+        typename absl::allocator_traits<Allocator>::template rebind_alloc<T>;
+    ValueAlloc value_alloc(*alloc);
+    absl::allocator_traits<ValueAlloc>::destroy(value_alloc, elem);
+    absl::allocator_traits<ValueAlloc>::deallocate(value_alloc, elem, 1);
+  }
+
+  template <class F, class... Args>
+  static decltype(absl::container_internal::DecomposeValue(
+      std::declval<F>(), std::declval<Args>()...))
+  apply(F&& f, Args&&... args) {
+    return absl::container_internal::DecomposeValue(
+        std::forward<F>(f), std::forward<Args>(args)...);
+  }
+
+  static size_t element_space_used(const T*) { return sizeof(T); }
+};
+}  // namespace container_internal
+
+namespace container_algorithm_internal {
+
+// Specialization of trait in absl/algorithm/container.h
+template <class Key, class Hash, class KeyEqual, class Allocator>
+struct IsUnorderedContainer<absl::node_hash_set<Key, Hash, KeyEqual, Allocator>>
+    : std::true_type {};
+
+}  // namespace container_algorithm_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CONTAINER_NODE_HASH_SET_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/crc32c.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/crc32c.h
new file mode 100644
index 0000000000000000000000000000000000000000..ba09e52a01837aaf2f65d2f95f122157c0bcf8ee
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/crc32c.h
@@ -0,0 +1,183 @@
+// Copyright 2022 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: crc32c.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines the API for computing CRC32C values as checksums
+// for arbitrary sequences of bytes provided as a string buffer.
+//
+// The API includes the basic functions for computing such CRC32C values and
+// some utility functions for performing more efficient mathematical
+// computations using an existing checksum.
+#ifndef ABSL_CRC_CRC32C_H_
+#define ABSL_CRC_CRC32C_H_
+
+#include <cstdint>
+#include <ostream>
+
+#include "absl/crc/internal/crc32c_inline.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+//-----------------------------------------------------------------------------
+// crc32c_t
+//-----------------------------------------------------------------------------
+
+// `crc32c_t` defines a strongly-typed integer for holding a CRC32C value.
+//
+// Some operators are intentionally omitted. Only equality operators are defined
+// so that `crc32c_t` can be directly compared. Methods for putting `crc32c_t`
+// directly into a set are omitted because this is bug-prone due to checksum
+// collisions. Use an explicit conversion to the `uint32_t` space for operations
+// that treat `crc32c_t` as an integer.
+class crc32c_t final {
+ public:
+  crc32c_t() = default;
+  constexpr explicit crc32c_t(uint32_t crc) : crc_(crc) {}
+
+  crc32c_t(const crc32c_t&) = default;
+  crc32c_t& operator=(const crc32c_t&) = default;
+
+  explicit operator uint32_t() const { return crc_; }
+
+  friend bool operator==(crc32c_t lhs, crc32c_t rhs) {
+    return static_cast<uint32_t>(lhs) == static_cast<uint32_t>(rhs);
+  }
+
+  friend bool operator!=(crc32c_t lhs, crc32c_t rhs) { return !(lhs == rhs); }
+
+ private:
+  uint32_t crc_;
+};
+
+namespace crc_internal {
+// Non-inline code path for `absl::ExtendCrc32c()`. Do not call directly.
+// Call `absl::ExtendCrc32c()` (defined below) instead.
+crc32c_t ExtendCrc32cInternal(crc32c_t initial_crc,
+                              absl::string_view buf_to_add);
+}  // namespace crc_internal
+
+// -----------------------------------------------------------------------------
+// CRC32C Computation Functions
+// -----------------------------------------------------------------------------
+
+// ComputeCrc32c()
+//
+// Returns the CRC32C value of the provided string.
+crc32c_t ComputeCrc32c(absl::string_view buf);
+
+// ExtendCrc32c()
+//
+// Computes a CRC32C value from an `initial_crc` CRC32C value including the
+// `buf_to_add` bytes of an additional buffer. Using this function is more
+// efficient than computing a CRC32C value for the combined buffer from
+// scratch.
+//
+// Note: `ExtendCrc32c` with an initial_crc of 0 is equivalent to
+// `ComputeCrc32c`.
+//
+// This operation has a runtime cost of O(`buf_to_add.size()`)
+inline crc32c_t ExtendCrc32c(crc32c_t initial_crc,
+                             absl::string_view buf_to_add) {
+  // Approximately 75% of calls have size <= 64.
+  if (buf_to_add.size() <= 64) {
+    uint32_t crc = static_cast<uint32_t>(initial_crc);
+    if (crc_internal::ExtendCrc32cInline(&crc, buf_to_add.data(),
+                                         buf_to_add.size())) {
+      return crc32c_t{crc};
+    }
+  }
+  return crc_internal::ExtendCrc32cInternal(initial_crc, buf_to_add);
+}
+
+// ExtendCrc32cByZeroes()
+//
+// Computes a CRC32C value for a buffer with an `initial_crc` CRC32C value,
+// where `length` bytes with a value of 0 are appended to the buffer. Using this
+// function is more efficient than computing a CRC32C value for the combined
+// buffer from scratch.
+//
+// This operation has a runtime cost of O(log(`length`))
+crc32c_t ExtendCrc32cByZeroes(crc32c_t initial_crc, size_t length);
+
+// MemcpyCrc32c()
+//
+// Copies `src` to `dest` using `memcpy()` semantics, returning the CRC32C
+// value of the copied buffer.
+//
+// Using `MemcpyCrc32c()` is potentially faster than performing the `memcpy()`
+// and `ComputeCrc32c()` operations separately.
+crc32c_t MemcpyCrc32c(void* dest, const void* src, size_t count,
+                      crc32c_t initial_crc = crc32c_t{0});
+
+// -----------------------------------------------------------------------------
+// CRC32C Arithmetic Functions
+// -----------------------------------------------------------------------------
+
+// The following functions perform arithmetic on CRC32C values, which are
+// generally more efficient than recalculating any given result's CRC32C value.
+
+// ConcatCrc32c()
+//
+// Calculates the CRC32C value of two buffers with known CRC32C values
+// concatenated together.
+//
+// Given a buffer with CRC32C value `crc1` and a buffer with
+// CRC32C value `crc2` and length, `crc2_length`, returns the CRC32C value of
+// the concatenation of these two buffers.
+//
+// This operation has a runtime cost of O(log(`crc2_length`)).
+crc32c_t ConcatCrc32c(crc32c_t crc1, crc32c_t crc2, size_t crc2_length);
+
+// RemoveCrc32cPrefix()
+//
+// Calculates the CRC32C value of an existing buffer with a series of bytes
+// (the prefix) removed from the beginning of that buffer.
+//
+// Given the CRC32C value of an existing buffer, `full_string_crc`; The CRC32C
+// value of a prefix of that buffer, `prefix_crc`; and the length of the buffer
+// with the prefix removed, `remaining_string_length` , return the CRC32C
+// value of the buffer with the prefix removed.
+//
+// This operation has a runtime cost of O(log(`remaining_string_length`)).
+crc32c_t RemoveCrc32cPrefix(crc32c_t prefix_crc, crc32c_t full_string_crc,
+                            size_t remaining_string_length);
+// RemoveCrc32cSuffix()
+//
+// Calculates the CRC32C value of an existing buffer with a series of bytes
+// (the suffix) removed from the end of that buffer.
+//
+// Given a CRC32C value of an existing buffer `full_string_crc`, the CRC32C
+// value of the suffix to remove `suffix_crc`, and the length of that suffix
+// `suffix_len`, returns the CRC32C value of the buffer with suffix removed.
+//
+// This operation has a runtime cost of O(log(`suffix_len`))
+crc32c_t RemoveCrc32cSuffix(crc32c_t full_string_crc, crc32c_t suffix_crc,
+                            size_t suffix_length);
+
+// operator<<
+//
+// Streams the CRC32C value `crc` to the stream `os`.
+inline std::ostream& operator<<(std::ostream& os, crc32c_t crc) {
+  return os << static_cast<uint32_t>(crc);
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CRC_CRC32C_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/cpu_detect.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/cpu_detect.h
new file mode 100644
index 0000000000000000000000000000000000000000..6054f6960d1817e982e48543e56eaaff7045a282
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/cpu_detect.h
@@ -0,0 +1,57 @@
+// Copyright 2022 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CRC_INTERNAL_CPU_DETECT_H_
+#define ABSL_CRC_INTERNAL_CPU_DETECT_H_
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace crc_internal {
+
+// Enumeration of architectures that we have special-case tuning parameters for.
+// This set may change over time.
+enum class CpuType {
+  kUnknown,
+  kIntelHaswell,
+  kAmdRome,
+  kAmdNaples,
+  kAmdMilan,
+  kIntelCascadelakeXeon,
+  kIntelSkylakeXeon,
+  kIntelBroadwell,
+  kIntelSkylake,
+  kIntelIvybridge,
+  kIntelSandybridge,
+  kIntelWestmere,
+  kArmNeoverseN1,
+};
+
+// Returns the type of host CPU this code is running on.  Returns kUnknown if
+// the host CPU is of unknown type, or if detection otherwise fails.
+CpuType GetCpuType();
+
+// Returns whether the host CPU supports the CPU features needed for our
+// accelerated implementations. The CpuTypes enumerated above apart from
+// kUnknown support the required features. On unknown CPUs, we can use
+// this to see if it's safe to use hardware acceleration, though without any
+// tuning.
+bool SupportsArmCRC32PMULL();
+
+}  // namespace crc_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CRC_INTERNAL_CPU_DETECT_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc.h
new file mode 100644
index 0000000000000000000000000000000000000000..72515b061da1abaec21d331fde51a2e32698f98d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc.h
@@ -0,0 +1,91 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CRC_INTERNAL_CRC_H_
+#define ABSL_CRC_INTERNAL_CRC_H_
+
+#include <cstdint>
+
+#include "absl/base/config.h"
+
+// This class implements CRCs (aka Rabin Fingerprints).
+// Treats the input as a polynomial with coefficients in Z(2),
+// and finds the remainder when divided by an primitive polynomial
+// of the appropriate length.
+
+// A polynomial is represented by the bit pattern formed by its coefficients,
+// but with the highest order bit not stored.
+// The highest degree coefficient is stored in the lowest numbered bit
+// in the lowest addressed byte.   Thus, in what follows, the highest degree
+// coefficient that is stored is in the low order bit of "lo" or "*lo".
+
+// Hardware acceleration is used when available.
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace crc_internal {
+
+class CRC {
+ public:
+  virtual ~CRC();
+
+  // Place the CRC of the empty string in "*crc"
+  virtual void Empty(uint32_t* crc) const = 0;
+
+  // If "*crc" is the CRC of bytestring A, place the CRC of
+  // the bytestring formed from the concatenation of A and the "length"
+  // bytes at "bytes" into "*crc".
+  virtual void Extend(uint32_t* crc, const void* bytes,
+                      size_t length) const = 0;
+
+  // Equivalent to Extend(crc, bytes, length) where "bytes"
+  // points to an array of "length" zero bytes.
+  virtual void ExtendByZeroes(uint32_t* crc, size_t length) const = 0;
+
+  // Inverse opration of ExtendByZeroes.  If `crc` is the CRC value of a string
+  // ending in `length` zero bytes, this returns a CRC value of that string
+  // with those zero bytes removed.
+  virtual void UnextendByZeroes(uint32_t* crc, size_t length) const = 0;
+
+  // If *px is the CRC (as defined by *crc) of some string X,
+  // and y is the CRC of some string Y that is ylen bytes long, set
+  // *px to the CRC of the concatenation of X followed by Y.
+  virtual void Concat(uint32_t* px, uint32_t y, size_t ylen);
+
+  // Apply a non-linear transformation to "*crc" so that
+  // it is safe to CRC the result with the same polynomial without
+  // any reduction of error-detection ability in the outer CRC.
+  // Unscramble() performs the inverse transformation.
+  // It is strongly recommended that CRCs be scrambled before storage or
+  // transmission, and unscrambled at the other end before futher manipulation.
+  virtual void Scramble(uint32_t* crc) const = 0;
+  virtual void Unscramble(uint32_t* crc) const = 0;
+
+  // Crc32c() returns the singleton implementation of CRC for the CRC32C
+  // polynomial.  Returns a handle that MUST NOT be destroyed with delete.
+  static CRC* Crc32c();
+
+ protected:
+  CRC();  // Clients may not call constructor; use Crc32c() instead.
+
+ private:
+  CRC(const CRC&) = delete;
+  CRC& operator=(const CRC&) = delete;
+};
+
+}  // namespace crc_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CRC_INTERNAL_CRC_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc32_x86_arm_combined_simd.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc32_x86_arm_combined_simd.h
new file mode 100644
index 0000000000000000000000000000000000000000..fb643986312aa811eb6df4cba2c591a23e7da06a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc32_x86_arm_combined_simd.h
@@ -0,0 +1,269 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CRC_INTERNAL_CRC32_X86_ARM_COMBINED_SIMD_H_
+#define ABSL_CRC_INTERNAL_CRC32_X86_ARM_COMBINED_SIMD_H_
+
+#include <cstdint>
+
+#include "absl/base/config.h"
+
+// -------------------------------------------------------------------------
+// Many x86 and ARM machines have CRC acceleration hardware.
+// We can do a faster version of Extend() on such machines.
+// We define a translation layer for both x86 and ARM for the ease of use and
+// most performance gains.
+
+// This implementation requires 64-bit CRC instructions (part of SSE 4.2) and
+// PCLMULQDQ instructions. 32-bit builds with SSE 4.2 do exist, so the
+// __x86_64__ condition is necessary.
+#if defined(__x86_64__) && defined(__SSE4_2__) && defined(__PCLMUL__)
+
+#include <x86intrin.h>
+#define ABSL_CRC_INTERNAL_HAVE_X86_SIMD
+
+#elif defined(_MSC_VER) && defined(__AVX__)
+
+// MSVC AVX (/arch:AVX) implies SSE 4.2 and PCLMULQDQ.
+#include <intrin.h>
+#define ABSL_CRC_INTERNAL_HAVE_X86_SIMD
+
+#elif defined(__aarch64__) && defined(__LITTLE_ENDIAN__) && \
+    defined(__ARM_FEATURE_CRC32) && defined(ABSL_INTERNAL_HAVE_ARM_NEON) &&  \
+    defined(__ARM_FEATURE_CRYPTO)
+
+#include <arm_acle.h>
+#include <arm_neon.h>
+#define ABSL_CRC_INTERNAL_HAVE_ARM_SIMD
+
+#endif
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace crc_internal {
+
+#if defined(ABSL_CRC_INTERNAL_HAVE_ARM_SIMD) || \
+    defined(ABSL_CRC_INTERNAL_HAVE_X86_SIMD)
+
+#if defined(ABSL_CRC_INTERNAL_HAVE_ARM_SIMD)
+using V128 = uint64x2_t;
+#else
+using V128 = __m128i;
+#endif
+
+// Starting with the initial value in |crc|, accumulates a CRC32 value for
+// unsigned integers of different sizes.
+uint32_t CRC32_u8(uint32_t crc, uint8_t v);
+
+uint32_t CRC32_u16(uint32_t crc, uint16_t v);
+
+uint32_t CRC32_u32(uint32_t crc, uint32_t v);
+
+uint32_t CRC32_u64(uint32_t crc, uint64_t v);
+
+// Loads 128 bits of integer data. |src| must be 16-byte aligned.
+V128 V128_Load(const V128* src);
+
+// Load 128 bits of integer data. |src| does not need to be aligned.
+V128 V128_LoadU(const V128* src);
+
+// Polynomially multiplies the high 64 bits of |l| and |r|.
+V128 V128_PMulHi(const V128 l, const V128 r);
+
+// Polynomially multiplies the low 64 bits of |l| and |r|.
+V128 V128_PMulLow(const V128 l, const V128 r);
+
+// Polynomially multiplies the low 64 bits of |r| and high 64 bits of |l|.
+V128 V128_PMul01(const V128 l, const V128 r);
+
+// Polynomially multiplies the low 64 bits of |l| and high 64 bits of |r|.
+V128 V128_PMul10(const V128 l, const V128 r);
+
+// Produces a XOR operation of |l| and |r|.
+V128 V128_Xor(const V128 l, const V128 r);
+
+// Produces an AND operation of |l| and |r|.
+V128 V128_And(const V128 l, const V128 r);
+
+// Sets two 64 bit integers to one 128 bit vector. The order is reverse.
+// dst[63:0] := |r|
+// dst[127:64] := |l|
+V128 V128_From2x64(const uint64_t l, const uint64_t r);
+
+// Shift |l| right by |imm| bytes while shifting in zeros.
+template <int imm>
+V128 V128_ShiftRight(const V128 l);
+
+// Extracts a 32-bit integer from |l|, selected with |imm|.
+template <int imm>
+int V128_Extract32(const V128 l);
+
+// Extracts the low 64 bits from V128.
+int64_t V128_Low64(const V128 l);
+
+// Left-shifts packed 64-bit integers in l by r.
+V128 V128_ShiftLeft64(const V128 l, const V128 r);
+
+#endif
+
+#if defined(ABSL_CRC_INTERNAL_HAVE_X86_SIMD)
+
+inline uint32_t CRC32_u8(uint32_t crc, uint8_t v) {
+  return _mm_crc32_u8(crc, v);
+}
+
+inline uint32_t CRC32_u16(uint32_t crc, uint16_t v) {
+  return _mm_crc32_u16(crc, v);
+}
+
+inline uint32_t CRC32_u32(uint32_t crc, uint32_t v) {
+  return _mm_crc32_u32(crc, v);
+}
+
+inline uint32_t CRC32_u64(uint32_t crc, uint64_t v) {
+  return static_cast<uint32_t>(_mm_crc32_u64(crc, v));
+}
+
+inline V128 V128_Load(const V128* src) { return _mm_load_si128(src); }
+
+inline V128 V128_LoadU(const V128* src) { return _mm_loadu_si128(src); }
+
+inline V128 V128_PMulHi(const V128 l, const V128 r) {
+  return _mm_clmulepi64_si128(l, r, 0x11);
+}
+
+inline V128 V128_PMulLow(const V128 l, const V128 r) {
+  return _mm_clmulepi64_si128(l, r, 0x00);
+}
+
+inline V128 V128_PMul01(const V128 l, const V128 r) {
+  return _mm_clmulepi64_si128(l, r, 0x01);
+}
+
+inline V128 V128_PMul10(const V128 l, const V128 r) {
+  return _mm_clmulepi64_si128(l, r, 0x10);
+}
+
+inline V128 V128_Xor(const V128 l, const V128 r) { return _mm_xor_si128(l, r); }
+
+inline V128 V128_And(const V128 l, const V128 r) { return _mm_and_si128(l, r); }
+
+inline V128 V128_From2x64(const uint64_t l, const uint64_t r) {
+  return _mm_set_epi64x(static_cast<int64_t>(l), static_cast<int64_t>(r));
+}
+
+template <int imm>
+inline V128 V128_ShiftRight(const V128 l) {
+  return _mm_srli_si128(l, imm);
+}
+
+template <int imm>
+inline int V128_Extract32(const V128 l) {
+  return _mm_extract_epi32(l, imm);
+}
+
+inline int64_t V128_Low64(const V128 l) { return _mm_cvtsi128_si64(l); }
+
+inline V128 V128_ShiftLeft64(const V128 l, const V128 r) {
+  return _mm_sll_epi64(l, r);
+}
+
+#elif defined(ABSL_CRC_INTERNAL_HAVE_ARM_SIMD)
+
+inline uint32_t CRC32_u8(uint32_t crc, uint8_t v) { return __crc32cb(crc, v); }
+
+inline uint32_t CRC32_u16(uint32_t crc, uint16_t v) {
+  return __crc32ch(crc, v);
+}
+
+inline uint32_t CRC32_u32(uint32_t crc, uint32_t v) {
+  return __crc32cw(crc, v);
+}
+
+inline uint32_t CRC32_u64(uint32_t crc, uint64_t v) {
+  return __crc32cd(crc, v);
+}
+
+inline V128 V128_Load(const V128* src) {
+  return vld1q_u64(reinterpret_cast<const uint64_t*>(src));
+}
+
+inline V128 V128_LoadU(const V128* src) {
+  return vld1q_u64(reinterpret_cast<const uint64_t*>(src));
+}
+
+// Using inline assembly as clang does not generate the pmull2 instruction and
+// performance drops by 15-20%.
+// TODO(b/193678732): Investigate why the compiler decides not to generate
+// such instructions and why it becomes so much worse.
+inline V128 V128_PMulHi(const V128 l, const V128 r) {
+  uint64x2_t res;
+  __asm__ __volatile__("pmull2 %0.1q, %1.2d, %2.2d \n\t"
+                       : "=w"(res)
+                       : "w"(l), "w"(r));
+  return res;
+}
+
+inline V128 V128_PMulLow(const V128 l, const V128 r) {
+  return reinterpret_cast<V128>(vmull_p64(
+      reinterpret_cast<poly64_t>(vget_low_p64(vreinterpretq_p64_u64(l))),
+      reinterpret_cast<poly64_t>(vget_low_p64(vreinterpretq_p64_u64(r)))));
+}
+
+inline V128 V128_PMul01(const V128 l, const V128 r) {
+  return reinterpret_cast<V128>(vmull_p64(
+      reinterpret_cast<poly64_t>(vget_high_p64(vreinterpretq_p64_u64(l))),
+      reinterpret_cast<poly64_t>(vget_low_p64(vreinterpretq_p64_u64(r)))));
+}
+
+inline V128 V128_PMul10(const V128 l, const V128 r) {
+  return reinterpret_cast<V128>(vmull_p64(
+      reinterpret_cast<poly64_t>(vget_low_p64(vreinterpretq_p64_u64(l))),
+      reinterpret_cast<poly64_t>(vget_high_p64(vreinterpretq_p64_u64(r)))));
+}
+
+inline V128 V128_Xor(const V128 l, const V128 r) { return veorq_u64(l, r); }
+
+inline V128 V128_And(const V128 l, const V128 r) { return vandq_u64(l, r); }
+
+inline V128 V128_From2x64(const uint64_t l, const uint64_t r) {
+  return vcombine_u64(vcreate_u64(r), vcreate_u64(l));
+}
+
+template <int imm>
+inline V128 V128_ShiftRight(const V128 l) {
+  return vreinterpretq_u64_s8(
+      vextq_s8(vreinterpretq_s8_u64(l), vdupq_n_s8(0), imm));
+}
+
+template <int imm>
+inline int V128_Extract32(const V128 l) {
+  return vgetq_lane_s32(vreinterpretq_s32_u64(l), imm);
+}
+
+inline int64_t V128_Low64(const V128 l) {
+  return vgetq_lane_s64(vreinterpretq_s64_u64(l), 0);
+}
+
+inline V128 V128_ShiftLeft64(const V128 l, const V128 r) {
+  return vshlq_u64(l, vreinterpretq_s64_u64(r));
+}
+
+#endif
+
+}  // namespace crc_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CRC_INTERNAL_CRC32_X86_ARM_COMBINED_SIMD_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc32c.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc32c.h
new file mode 100644
index 0000000000000000000000000000000000000000..34027c55e42bfbdf7b619fae8ed6bde44ff7c7c1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc32c.h
@@ -0,0 +1,39 @@
+// Copyright 2022 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CRC_INTERNAL_CRC32C_H_
+#define ABSL_CRC_INTERNAL_CRC32C_H_
+
+#include "absl/base/config.h"
+#include "absl/crc/crc32c.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace crc_internal {
+
+// Modifies a CRC32 value by removing `length` bytes with a value of 0 from
+// the end of the string.
+//
+// This is the inverse operation of ExtendCrc32cByZeroes().
+//
+// This operation has a runtime cost of O(log(`length`))
+//
+// Internal implementation detail, exposed for testing only.
+crc32c_t UnextendCrc32cByZeroes(crc32c_t initial_crc, size_t length);
+
+}  // namespace crc_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CRC_INTERNAL_CRC32C_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc32c_inline.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc32c_inline.h
new file mode 100644
index 0000000000000000000000000000000000000000..6236c10b3b5e46439398ee389ad9c37e0cf33c6a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc32c_inline.h
@@ -0,0 +1,72 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CRC_INTERNAL_CRC32C_INLINE_H_
+#define ABSL_CRC_INTERNAL_CRC32C_INLINE_H_
+
+#include <cstdint>
+
+#include "absl/base/config.h"
+#include "absl/base/internal/endian.h"
+#include "absl/crc/internal/crc32_x86_arm_combined_simd.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace crc_internal {
+
+// CRC32C implementation optimized for small inputs.
+// Either computes crc and return true, or if there is
+// no hardware support does nothing and returns false.
+inline bool ExtendCrc32cInline(uint32_t* crc, const char* p, size_t n) {
+#if defined(ABSL_CRC_INTERNAL_HAVE_ARM_SIMD) || \
+    defined(ABSL_CRC_INTERNAL_HAVE_X86_SIMD)
+  constexpr uint32_t kCrc32Xor = 0xffffffffU;
+  *crc ^= kCrc32Xor;
+  if (n & 1) {
+    *crc = CRC32_u8(*crc, static_cast<uint8_t>(*p));
+    n--;
+    p++;
+  }
+  if (n & 2) {
+    *crc = CRC32_u16(*crc, absl::little_endian::Load16(p));
+    n -= 2;
+    p += 2;
+  }
+  if (n & 4) {
+    *crc = CRC32_u32(*crc, absl::little_endian::Load32(p));
+    n -= 4;
+    p += 4;
+  }
+  while (n) {
+    *crc = CRC32_u64(*crc, absl::little_endian::Load64(p));
+    n -= 8;
+    p += 8;
+  }
+  *crc ^= kCrc32Xor;
+  return true;
+#else
+  // No hardware support, signal the need to fallback.
+  static_cast<void>(crc);
+  static_cast<void>(p);
+  static_cast<void>(n);
+  return false;
+#endif  // defined(ABSL_CRC_INTERNAL_HAVE_ARM_SIMD) ||
+        // defined(ABSL_CRC_INTERNAL_HAVE_X86_SIMD)
+}
+
+}  // namespace crc_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CRC_INTERNAL_CRC32C_INLINE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc_cord_state.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc_cord_state.h
new file mode 100644
index 0000000000000000000000000000000000000000..d305424ce6e2b4cce7e0389d988f607bc5dda3fa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc_cord_state.h
@@ -0,0 +1,159 @@
+// Copyright 2022 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CRC_INTERNAL_CRC_CORD_STATE_H_
+#define ABSL_CRC_INTERNAL_CRC_CORD_STATE_H_
+
+#include <atomic>
+#include <cstddef>
+#include <deque>
+
+#include "absl/base/config.h"
+#include "absl/crc/crc32c.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace crc_internal {
+
+// CrcCordState is a copy-on-write class that holds the chunked CRC32C data
+// that allows CrcCord to perform efficient substring operations. CrcCordState
+// is used as a member variable in CrcCord. When a CrcCord is converted to a
+// Cord, the CrcCordState is shallow-copied into the root node of the Cord. If
+// the converted Cord is modified outside of CrcCord, the CrcCordState is
+// discarded from the Cord. If the Cord is converted back to a CrcCord, and the
+// Cord is still carrying the CrcCordState in its root node, the CrcCord can
+// re-use the CrcCordState, making the construction of the CrcCord cheap.
+//
+// CrcCordState does not try to encapsulate the CRC32C state (CrcCord requires
+// knowledge of how CrcCordState represents the CRC32C state). It does
+// encapsulate the copy-on-write nature of the state.
+class CrcCordState {
+ public:
+  // Constructors.
+  CrcCordState();
+  CrcCordState(const CrcCordState&);
+  CrcCordState(CrcCordState&&);
+
+  // Destructor. Atomically unreferences the data.
+  ~CrcCordState();
+
+  // Copy and move operators.
+  CrcCordState& operator=(const CrcCordState&);
+  CrcCordState& operator=(CrcCordState&&);
+
+  // A (length, crc) pair.
+  struct PrefixCrc {
+    PrefixCrc() = default;
+    PrefixCrc(size_t length_arg, absl::crc32c_t crc_arg)
+        : length(length_arg), crc(crc_arg) {}
+
+    size_t length = 0;
+
+    // TODO(absl-team): Memory stomping often zeros out memory. If this struct
+    // gets overwritten, we could end up with {0, 0}, which is the correct CRC
+    // for a string of length 0. Consider storing a scrambled value and
+    // unscrambling it before verifying it.
+    absl::crc32c_t crc = absl::crc32c_t{0};
+  };
+
+  // The representation of the chunked CRC32C data.
+  struct Rep {
+    // `removed_prefix` is the crc and length of any prefix that has been
+    // removed from the Cord (for example, by calling
+    // `CrcCord::RemovePrefix()`). To get the checkum of any prefix of the cord,
+    // this value must be subtracted from `prefix_crc`. See `Checksum()` for an
+    // example.
+    //
+    // CrcCordState is said to be "normalized" if removed_prefix.length == 0.
+    PrefixCrc removed_prefix;
+
+    // A deque of (length, crc) pairs, representing length and crc of a prefix
+    // of the Cord, before removed_prefix has been subtracted. The lengths of
+    // the prefixes are stored in increasing order. If the Cord is not empty,
+    // the last value in deque is the contains the CRC32C of the entire Cord
+    // when removed_prefix is subtracted from it.
+    std::deque<PrefixCrc> prefix_crc;
+  };
+
+  // Returns a reference to the representation of the chunked CRC32C data.
+  const Rep& rep() const { return refcounted_rep_->rep; }
+
+  // Returns a mutable reference to the representation of the chunked CRC32C
+  // data. Calling this function will copy the data if another instance also
+  // holds a reference to the data, so it is important to call rep() instead if
+  // the data may not be mutated.
+  Rep* mutable_rep() {
+    if (refcounted_rep_->count.load(std::memory_order_acquire) != 1) {
+      RefcountedRep* copy = new RefcountedRep;
+      copy->rep = refcounted_rep_->rep;
+      Unref(refcounted_rep_);
+      refcounted_rep_ = copy;
+    }
+    return &refcounted_rep_->rep;
+  }
+
+  // Returns the CRC32C of the entire Cord.
+  absl::crc32c_t Checksum() const;
+
+  // Returns true if the chunked CRC32C cached is normalized.
+  bool IsNormalized() const { return rep().removed_prefix.length == 0; }
+
+  // Normalizes the chunked CRC32C checksum cache by substracting any removed
+  // prefix from the chunks.
+  void Normalize();
+
+  // Returns the number of cached chunks.
+  size_t NumChunks() const { return rep().prefix_crc.size(); }
+
+  // Helper that returns the (length, crc) of the `n`-th cached chunked.
+  PrefixCrc NormalizedPrefixCrcAtNthChunk(size_t n) const;
+
+  // Poisons all chunks to so that Checksum() will likely be incorrect with high
+  // probability.
+  void Poison();
+
+ private:
+  struct RefcountedRep {
+    std::atomic<int32_t> count{1};
+    Rep rep;
+  };
+
+  // Adds a reference to the shared global empty `RefcountedRep`, and returns a
+  // pointer to the `RefcountedRep`. This is an optimization to avoid unneeded
+  // allocations when the allocation is unlikely to ever be used. The returned
+  // pointer can be `Unref()`ed when it is no longer needed.  Since the returned
+  // instance will always have a reference counter greater than 1, attempts to
+  // modify it (by calling `mutable_rep()`) will create a new unshared copy.
+  static RefcountedRep* RefSharedEmptyRep();
+
+  static void Ref(RefcountedRep* r) {
+    assert(r != nullptr);
+    r->count.fetch_add(1, std::memory_order_relaxed);
+  }
+
+  static void Unref(RefcountedRep* r) {
+    assert(r != nullptr);
+    if (r->count.fetch_sub(1, std::memory_order_acq_rel) == 1) {
+      delete r;
+    }
+  }
+
+  RefcountedRep* refcounted_rep_;
+};
+
+}  // namespace crc_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CRC_INTERNAL_CRC_CORD_STATE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc_internal.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc_internal.h
new file mode 100644
index 0000000000000000000000000000000000000000..0611b383e432a837aab05eb10cdbee167fb73fb1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc_internal.h
@@ -0,0 +1,179 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CRC_INTERNAL_CRC_INTERNAL_H_
+#define ABSL_CRC_INTERNAL_CRC_INTERNAL_H_
+
+#include <cstdint>
+#include <memory>
+#include <vector>
+
+#include "absl/base/internal/raw_logging.h"
+#include "absl/crc/internal/crc.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+namespace crc_internal {
+
+// Prefetch constants used in some Extend() implementations
+constexpr int kPrefetchHorizon = ABSL_CACHELINE_SIZE * 4;  // Prefetch this far
+// Shorter prefetch distance for smaller buffers
+constexpr int kPrefetchHorizonMedium = ABSL_CACHELINE_SIZE * 1;
+static_assert(kPrefetchHorizon >= 64, "CRCPrefetchHorizon less than loop len");
+
+// We require the Scramble() function:
+//  - to be reversible (Unscramble() must exist)
+//  - to be non-linear in the polynomial's Galois field (so the CRC of a
+//    scrambled CRC is not linearly affected by the scrambled CRC, even if
+//    using the same polynomial)
+//  - not to be its own inverse.  Preferably, if X=Scramble^N(X) and N!=0, then
+//    N is large.
+//  - to be fast.
+//  - not to change once defined.
+// We introduce non-linearity in two ways:
+//     Addition of a constant.
+//         - The carries introduce non-linearity; we use bits of an irrational
+//           (phi) to make it unlikely that we introduce no carries.
+//     Rotate by a constant number of bits.
+//         - We use floor(degree/2)+1, which does not divide the degree, and
+//           splits the bits nearly evenly, which makes it less likely the
+//           halves will be the same or one will be all zeroes.
+// We do both things to improve the chances of non-linearity in the face of
+// bit patterns with low numbers of bits set, while still being fast.
+// Below is the constant that we add.  The bits are the first 128 bits of the
+// fractional part of phi, with a 1 ored into the bottom bit to maximize the
+// cycle length of repeated adds.
+constexpr uint64_t kScrambleHi = (static_cast<uint64_t>(0x4f1bbcdcU) << 32) |
+                                 static_cast<uint64_t>(0xbfa53e0aU);
+constexpr uint64_t kScrambleLo = (static_cast<uint64_t>(0xf9ce6030U) << 32) |
+                                 static_cast<uint64_t>(0x2e76e41bU);
+
+class CRCImpl : public CRC {  // Implemention of the abstract class CRC
+ public:
+  using Uint32By256 = uint32_t[256];
+
+  CRCImpl() {}
+  ~CRCImpl() override = default;
+
+  // The internal version of CRC::New().
+  static CRCImpl* NewInternal();
+
+  void Empty(uint32_t* crc) const override;
+
+  // Fill in a table for updating a CRC by one word of 'word_size' bytes
+  // [last_lo, last_hi] contains the answer if the last bit in the word
+  // is set.
+  static void FillWordTable(uint32_t poly, uint32_t last, int word_size,
+                            Uint32By256* t);
+
+  // Build the table for extending by zeroes, returning the number of entries.
+  // For a in {1, 2, ..., ZEROES_BASE-1}, b in {0, 1, 2, 3, ...},
+  // entry j=a-1+(ZEROES_BASE-1)*b
+  // contains a polynomial Pi such that multiplying
+  // a CRC by Pi mod P, where P is the CRC polynomial, is equivalent to
+  // appending a*2**(ZEROES_BASE_LG*b) zero bytes to the original string.
+  static int FillZeroesTable(uint32_t poly, Uint32By256* t);
+
+  virtual void InitTables() = 0;
+
+ private:
+  CRCImpl(const CRCImpl&) = delete;
+  CRCImpl& operator=(const CRCImpl&) = delete;
+};
+
+// This is the 32-bit implementation.  It handles all sizes from 8 to 32.
+class CRC32 : public CRCImpl {
+ public:
+  CRC32() {}
+  ~CRC32() override {}
+
+  void Extend(uint32_t* crc, const void* bytes, size_t length) const override;
+  void ExtendByZeroes(uint32_t* crc, size_t length) const override;
+  void Scramble(uint32_t* crc) const override;
+  void Unscramble(uint32_t* crc) const override;
+  void UnextendByZeroes(uint32_t* crc, size_t length) const override;
+
+  void InitTables() override;
+
+ private:
+  // Common implementation guts for ExtendByZeroes and UnextendByZeroes().
+  //
+  // zeroes_table is a table as returned by FillZeroesTable(), containing
+  // polynomials representing CRCs of strings-of-zeros of various lenghts,
+  // and which can be combined by polynomial multiplication.  poly_table is
+  // a table of CRC byte extension values.  These tables are determined by
+  // the generator polynomial.
+  //
+  // These will be set to reverse_zeroes_ and reverse_table0_ for Unextend, and
+  // CRC32::zeroes_ and CRC32::table0_ for Extend.
+  void ExtendByZeroesImpl(uint32_t* crc, size_t length,
+                          const uint32_t zeroes_table[256],
+                          const uint32_t poly_table[256]) const;
+
+  uint32_t table0_[256];  // table of byte extensions
+  uint32_t zeroes_[256];  // table of zero extensions
+
+  // table of 4-byte extensions shifted by 12 bytes of zeroes
+  uint32_t table_[4][256];
+
+  // Reverse lookup tables, using the alternate polynomial used by
+  // UnextendByZeroes().
+  uint32_t reverse_table0_[256];  // table of reverse byte extensions
+  uint32_t reverse_zeroes_[256];  // table of reverse zero extensions
+
+  CRC32(const CRC32&) = delete;
+  CRC32& operator=(const CRC32&) = delete;
+};
+
+// Helpers
+
+// Return a bit mask containing len 1-bits.
+// Requires 0 < len <= sizeof(T)
+template <typename T>
+T MaskOfLength(int len) {
+  // shift 2 by len-1 rather than 1 by len because shifts of wordsize
+  // are undefined.
+  return (T(2) << (len - 1)) - 1;
+}
+
+// Rotate low-order "width" bits of "in" right by "r" bits,
+// setting other bits in word to arbitrary values.
+template <typename T>
+T RotateRight(T in, int width, int r) {
+  return (in << (width - r)) | ((in >> r) & MaskOfLength<T>(width - r));
+}
+
+// RoundUp<N>(p) returns the lowest address >= p aligned to an N-byte
+// boundary.  Requires that N is a power of 2.
+template <int alignment>
+const uint8_t* RoundUp(const uint8_t* p) {
+  static_assert((alignment & (alignment - 1)) == 0, "alignment is not 2^n");
+  constexpr uintptr_t mask = alignment - 1;
+  const uintptr_t as_uintptr = reinterpret_cast<uintptr_t>(p);
+  return reinterpret_cast<const uint8_t*>((as_uintptr + mask) & ~mask);
+}
+
+// Return a newly created CRC32AcceleratedX86ARMCombined if we can use Intel's
+// or ARM's CRC acceleration for a given polynomial.  Return nullptr otherwise.
+CRCImpl* TryNewCRC32AcceleratedX86ARMCombined();
+
+// Return all possible hardware accelerated implementations. For testing only.
+std::vector<std::unique_ptr<CRCImpl>> NewCRC32AcceleratedX86ARMCombinedAll();
+
+}  // namespace crc_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CRC_INTERNAL_CRC_INTERNAL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc_memcpy.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc_memcpy.h
new file mode 100644
index 0000000000000000000000000000000000000000..4909d433662432ae239d5c409677622a43f210db
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/crc_memcpy.h
@@ -0,0 +1,119 @@
+// Copyright 2022 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CRC_INTERNAL_CRC_MEMCPY_H_
+#define ABSL_CRC_INTERNAL_CRC_MEMCPY_H_
+
+#include <cstddef>
+#include <memory>
+
+#include "absl/base/config.h"
+#include "absl/crc/crc32c.h"
+
+// Defined if the class AcceleratedCrcMemcpyEngine exists.
+#if defined(__x86_64__) && defined(__SSE4_2__)
+#define ABSL_INTERNAL_HAVE_X86_64_ACCELERATED_CRC_MEMCPY_ENGINE 1
+#elif defined(_MSC_VER) && defined(__AVX__)
+#define ABSL_INTERNAL_HAVE_X86_64_ACCELERATED_CRC_MEMCPY_ENGINE 1
+#endif
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace crc_internal {
+
+class CrcMemcpyEngine {
+ public:
+  virtual ~CrcMemcpyEngine() = default;
+
+  virtual crc32c_t Compute(void* __restrict dst, const void* __restrict src,
+                           std::size_t length, crc32c_t initial_crc) const = 0;
+
+ protected:
+  CrcMemcpyEngine() = default;
+};
+
+class CrcMemcpy {
+ public:
+  static crc32c_t CrcAndCopy(void* __restrict dst, const void* __restrict src,
+                             std::size_t length,
+                             crc32c_t initial_crc = crc32c_t{0},
+                             bool non_temporal = false) {
+    static const ArchSpecificEngines engines = GetArchSpecificEngines();
+    auto* engine = non_temporal ? engines.non_temporal : engines.temporal;
+    return engine->Compute(dst, src, length, initial_crc);
+  }
+
+  // For testing only: get an architecture-specific engine for tests.
+  static std::unique_ptr<CrcMemcpyEngine> GetTestEngine(int vector,
+                                                        int integer);
+
+ private:
+  struct ArchSpecificEngines {
+    CrcMemcpyEngine* temporal;
+    CrcMemcpyEngine* non_temporal;
+  };
+
+  static ArchSpecificEngines GetArchSpecificEngines();
+};
+
+// Fallback CRC-memcpy engine.
+class FallbackCrcMemcpyEngine : public CrcMemcpyEngine {
+ public:
+  FallbackCrcMemcpyEngine() = default;
+  FallbackCrcMemcpyEngine(const FallbackCrcMemcpyEngine&) = delete;
+  FallbackCrcMemcpyEngine operator=(const FallbackCrcMemcpyEngine&) = delete;
+
+  crc32c_t Compute(void* __restrict dst, const void* __restrict src,
+                   std::size_t length, crc32c_t initial_crc) const override;
+};
+
+// CRC Non-Temporal-Memcpy engine.
+class CrcNonTemporalMemcpyEngine : public CrcMemcpyEngine {
+ public:
+  CrcNonTemporalMemcpyEngine() = default;
+  CrcNonTemporalMemcpyEngine(const CrcNonTemporalMemcpyEngine&) = delete;
+  CrcNonTemporalMemcpyEngine operator=(const CrcNonTemporalMemcpyEngine&) =
+      delete;
+
+  crc32c_t Compute(void* __restrict dst, const void* __restrict src,
+                   std::size_t length, crc32c_t initial_crc) const override;
+};
+
+// CRC Non-Temporal-Memcpy AVX engine.
+class CrcNonTemporalMemcpyAVXEngine : public CrcMemcpyEngine {
+ public:
+  CrcNonTemporalMemcpyAVXEngine() = default;
+  CrcNonTemporalMemcpyAVXEngine(const CrcNonTemporalMemcpyAVXEngine&) = delete;
+  CrcNonTemporalMemcpyAVXEngine operator=(
+      const CrcNonTemporalMemcpyAVXEngine&) = delete;
+
+  crc32c_t Compute(void* __restrict dst, const void* __restrict src,
+                   std::size_t length, crc32c_t initial_crc) const override;
+};
+
+// Copy source to destination and return the CRC32C of the data copied.  If an
+// accelerated version is available, use the accelerated version, otherwise use
+// the generic fallback version.
+inline crc32c_t Crc32CAndCopy(void* __restrict dst, const void* __restrict src,
+                              std::size_t length,
+                              crc32c_t initial_crc = crc32c_t{0},
+                              bool non_temporal = false) {
+  return CrcMemcpy::CrcAndCopy(dst, src, length, initial_crc, non_temporal);
+}
+
+}  // namespace crc_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CRC_INTERNAL_CRC_MEMCPY_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/non_temporal_arm_intrinsics.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/non_temporal_arm_intrinsics.h
new file mode 100644
index 0000000000000000000000000000000000000000..9e5ccfc49d8a3e4130192602731b970f3f917d96
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/non_temporal_arm_intrinsics.h
@@ -0,0 +1,79 @@
+// Copyright 2022 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CRC_INTERNAL_NON_TEMPORAL_ARM_INTRINSICS_H_
+#define ABSL_CRC_INTERNAL_NON_TEMPORAL_ARM_INTRINSICS_H_
+
+#include "absl/base/config.h"
+
+#ifdef __aarch64__
+#include <arm_neon.h>
+
+typedef int64x2_t __m128i; /* 128-bit vector containing integers */
+#define vreinterpretq_m128i_s32(x) vreinterpretq_s64_s32(x)
+#define vreinterpretq_s64_m128i(x) (x)
+
+// Guarantees that every preceding store is globally visible before any
+// subsequent store.
+// https://msdn.microsoft.com/en-us/library/5h2w73d1%28v=vs.90%29.aspx
+static inline __attribute__((always_inline)) void _mm_sfence(void) {
+  __sync_synchronize();
+}
+
+// Load 128-bits of integer data from unaligned memory into dst. This intrinsic
+// may perform better than _mm_loadu_si128 when the data crosses a cache line
+// boundary.
+//
+//   dst[127:0] := MEM[mem_addr+127:mem_addr]
+//
+// https://software.intel.com/sites/landingpage/IntrinsicsGuide/#text=_mm_lddqu_si128
+#define _mm_lddqu_si128 _mm_loadu_si128
+
+// Loads 128-bit value. :
+// https://msdn.microsoft.com/zh-cn/library/f4k12ae8(v=vs.90).aspx
+static inline __attribute__((always_inline)) __m128i _mm_loadu_si128(
+    const __m128i *p) {
+  return vreinterpretq_m128i_s32(vld1q_s32((const int32_t *)p));
+}
+
+// Stores the data in a to the address p without polluting the caches.  If the
+// cache line containing address p is already in the cache, the cache will be
+// updated.
+// https://msdn.microsoft.com/en-us/library/ba08y07y%28v=vs.90%29.aspx
+static inline __attribute__((always_inline)) void _mm_stream_si128(__m128i *p,
+                                                                   __m128i a) {
+#if ABSL_HAVE_BUILTIN(__builtin_nontemporal_store)
+  __builtin_nontemporal_store(a, p);
+#else
+  vst1q_s64((int64_t *)p, vreinterpretq_s64_m128i(a));
+#endif
+}
+
+// Sets the 16 signed 8-bit integer values.
+// https://msdn.microsoft.com/en-us/library/x0cx8zd3(v=vs.90).aspx
+static inline __attribute__((always_inline)) __m128i _mm_set_epi8(
+    signed char b15, signed char b14, signed char b13, signed char b12,
+    signed char b11, signed char b10, signed char b9, signed char b8,
+    signed char b7, signed char b6, signed char b5, signed char b4,
+    signed char b3, signed char b2, signed char b1, signed char b0) {
+  int8_t __attribute__((aligned(16)))
+  data[16] = {(int8_t)b0,  (int8_t)b1,  (int8_t)b2,  (int8_t)b3,
+              (int8_t)b4,  (int8_t)b5,  (int8_t)b6,  (int8_t)b7,
+              (int8_t)b8,  (int8_t)b9,  (int8_t)b10, (int8_t)b11,
+              (int8_t)b12, (int8_t)b13, (int8_t)b14, (int8_t)b15};
+  return (__m128i)vld1q_s8(data);
+}
+#endif  // __aarch64__
+
+#endif  // ABSL_CRC_INTERNAL_NON_TEMPORAL_ARM_INTRINSICS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/non_temporal_memcpy.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/non_temporal_memcpy.h
new file mode 100644
index 0000000000000000000000000000000000000000..b3d94badfc161ece52dc6f81be2ee50f9e7e76be
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/crc/internal/non_temporal_memcpy.h
@@ -0,0 +1,180 @@
+// Copyright 2022 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_CRC_INTERNAL_NON_TEMPORAL_MEMCPY_H_
+#define ABSL_CRC_INTERNAL_NON_TEMPORAL_MEMCPY_H_
+
+#ifdef _MSC_VER
+#include <intrin.h>
+#endif
+
+#ifdef __SSE__
+#include <xmmintrin.h>
+#endif
+
+#ifdef __SSE2__
+#include <emmintrin.h>
+#endif
+
+#ifdef __SSE3__
+#include <pmmintrin.h>
+#endif
+
+#ifdef __AVX__
+#include <immintrin.h>
+#endif
+
+#ifdef __aarch64__
+#include "absl/crc/internal/non_temporal_arm_intrinsics.h"
+#endif
+
+#include <algorithm>
+#include <cassert>
+#include <cstdint>
+#include <cstring>
+
+#include "absl/base/config.h"
+#include "absl/base/optimization.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace crc_internal {
+
+// This non-temporal memcpy does regular load and non-temporal store memory
+// copy. It is compatible to both 16-byte aligned and unaligned addresses. If
+// data at the destination is not immediately accessed, using non-temporal
+// memcpy can save 1 DRAM load of the destination cacheline.
+constexpr size_t kCacheLineSize = ABSL_CACHELINE_SIZE;
+
+// If the objects overlap, the behavior is undefined.
+inline void *non_temporal_store_memcpy(void *__restrict dst,
+                                       const void *__restrict src, size_t len) {
+#if defined(__SSE3__) || defined(__aarch64__) || \
+    (defined(_MSC_VER) && defined(__AVX__))
+  // This implementation requires SSE3.
+  // MSVC cannot target SSE3 directly, but when MSVC targets AVX,
+  // SSE3 support is implied.
+  uint8_t *d = reinterpret_cast<uint8_t *>(dst);
+  const uint8_t *s = reinterpret_cast<const uint8_t *>(src);
+
+  // memcpy() the misaligned header. At the end of this if block, <d> is
+  // aligned to a 64-byte cacheline boundary or <len> == 0.
+  if (reinterpret_cast<uintptr_t>(d) & (kCacheLineSize - 1)) {
+    uintptr_t bytes_before_alignment_boundary =
+        kCacheLineSize -
+        (reinterpret_cast<uintptr_t>(d) & (kCacheLineSize - 1));
+    size_t header_len = (std::min)(bytes_before_alignment_boundary, len);
+    assert(bytes_before_alignment_boundary < kCacheLineSize);
+    memcpy(d, s, header_len);
+    d += header_len;
+    s += header_len;
+    len -= header_len;
+  }
+
+  if (len >= kCacheLineSize) {
+    _mm_sfence();
+    __m128i *dst_cacheline = reinterpret_cast<__m128i *>(d);
+    const __m128i *src_cacheline = reinterpret_cast<const __m128i *>(s);
+    constexpr int kOpsPerCacheLine = kCacheLineSize / sizeof(__m128i);
+    size_t loops = len / kCacheLineSize;
+
+    while (len >= kCacheLineSize) {
+      __m128i temp1, temp2, temp3, temp4;
+      temp1 = _mm_lddqu_si128(src_cacheline + 0);
+      temp2 = _mm_lddqu_si128(src_cacheline + 1);
+      temp3 = _mm_lddqu_si128(src_cacheline + 2);
+      temp4 = _mm_lddqu_si128(src_cacheline + 3);
+      _mm_stream_si128(dst_cacheline + 0, temp1);
+      _mm_stream_si128(dst_cacheline + 1, temp2);
+      _mm_stream_si128(dst_cacheline + 2, temp3);
+      _mm_stream_si128(dst_cacheline + 3, temp4);
+      src_cacheline += kOpsPerCacheLine;
+      dst_cacheline += kOpsPerCacheLine;
+      len -= kCacheLineSize;
+    }
+    d += loops * kCacheLineSize;
+    s += loops * kCacheLineSize;
+    _mm_sfence();
+  }
+
+  // memcpy the tail.
+  if (len) {
+    memcpy(d, s, len);
+  }
+  return dst;
+#else
+  // Fallback to regular memcpy.
+  return memcpy(dst, src, len);
+#endif  // __SSE3__ || __aarch64__ || (_MSC_VER && __AVX__)
+}
+
+inline void *non_temporal_store_memcpy_avx(void *__restrict dst,
+                                           const void *__restrict src,
+                                           size_t len) {
+#ifdef __AVX__
+  uint8_t *d = reinterpret_cast<uint8_t *>(dst);
+  const uint8_t *s = reinterpret_cast<const uint8_t *>(src);
+
+  // memcpy() the misaligned header. At the end of this if block, <d> is
+  // aligned to a 64-byte cacheline boundary or <len> == 0.
+  if (reinterpret_cast<uintptr_t>(d) & (kCacheLineSize - 1)) {
+    uintptr_t bytes_before_alignment_boundary =
+        kCacheLineSize -
+        (reinterpret_cast<uintptr_t>(d) & (kCacheLineSize - 1));
+    size_t header_len = (std::min)(bytes_before_alignment_boundary, len);
+    assert(bytes_before_alignment_boundary < kCacheLineSize);
+    memcpy(d, s, header_len);
+    d += header_len;
+    s += header_len;
+    len -= header_len;
+  }
+
+  if (len >= kCacheLineSize) {
+    _mm_sfence();
+    __m256i *dst_cacheline = reinterpret_cast<__m256i *>(d);
+    const __m256i *src_cacheline = reinterpret_cast<const __m256i *>(s);
+    constexpr int kOpsPerCacheLine = kCacheLineSize / sizeof(__m256i);
+    size_t loops = len / kCacheLineSize;
+
+    while (len >= kCacheLineSize) {
+      __m256i temp1, temp2;
+      temp1 = _mm256_lddqu_si256(src_cacheline + 0);
+      temp2 = _mm256_lddqu_si256(src_cacheline + 1);
+      _mm256_stream_si256(dst_cacheline + 0, temp1);
+      _mm256_stream_si256(dst_cacheline + 1, temp2);
+      src_cacheline += kOpsPerCacheLine;
+      dst_cacheline += kOpsPerCacheLine;
+      len -= kCacheLineSize;
+    }
+    d += loops * kCacheLineSize;
+    s += loops * kCacheLineSize;
+    _mm_sfence();
+  }
+
+  // memcpy the tail.
+  if (len) {
+    memcpy(d, s, len);
+  }
+  return dst;
+#else
+  // Fallback to regular memcpy when AVX is not available.
+  return memcpy(dst, src, len);
+#endif  // __AVX__
+}
+
+}  // namespace crc_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_CRC_INTERNAL_NON_TEMPORAL_MEMCPY_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/address_is_readable.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/address_is_readable.h
new file mode 100644
index 0000000000000000000000000000000000000000..4bbaf4d69bd8e91242c8cd0584a3c91477be7791
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/address_is_readable.h
@@ -0,0 +1,32 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_DEBUGGING_INTERNAL_ADDRESS_IS_READABLE_H_
+#define ABSL_DEBUGGING_INTERNAL_ADDRESS_IS_READABLE_H_
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+
+// Return whether the byte at *addr is readable, without faulting.
+// Save and restores errno.
+bool AddressIsReadable(const void *addr);
+
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_DEBUGGING_INTERNAL_ADDRESS_IS_READABLE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/demangle.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/demangle.h
new file mode 100644
index 0000000000000000000000000000000000000000..e1f156989f61ddd5909d10681ba4fd16cfa62189
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/demangle.h
@@ -0,0 +1,71 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// An async-signal-safe and thread-safe demangler for Itanium C++ ABI
+// (aka G++ V3 ABI).
+//
+// The demangler is implemented to be used in async signal handlers to
+// symbolize stack traces.  We cannot use libstdc++'s
+// abi::__cxa_demangle() in such signal handlers since it's not async
+// signal safe (it uses malloc() internally).
+//
+// Note that this demangler doesn't support full demangling.  More
+// specifically, it doesn't print types of function parameters and
+// types of template arguments.  It just skips them.  However, it's
+// still very useful to extract basic information such as class,
+// function, constructor, destructor, and operator names.
+//
+// See the implementation note in demangle.cc if you are interested.
+//
+// Example:
+//
+// | Mangled Name  | The Demangler | abi::__cxa_demangle()
+// |---------------|---------------|-----------------------
+// | _Z1fv         | f()           | f()
+// | _Z1fi         | f()           | f(int)
+// | _Z3foo3bar    | foo()         | foo(bar)
+// | _Z1fIiEvi     | f<>()         | void f<int>(int)
+// | _ZN1N1fE      | N::f          | N::f
+// | _ZN3Foo3BarEv | Foo::Bar()    | Foo::Bar()
+// | _Zrm1XS_"     | operator%()   | operator%(X, X)
+// | _ZN3FooC1Ev   | Foo::Foo()    | Foo::Foo()
+// | _Z1fSs        | f()           | f(std::basic_string<char,
+// |               |               |   std::char_traits<char>,
+// |               |               |   std::allocator<char> >)
+//
+// See the unit test for more examples.
+//
+// Note: we might want to write demanglers for ABIs other than Itanium
+// C++ ABI in the future.
+//
+
+#ifndef ABSL_DEBUGGING_INTERNAL_DEMANGLE_H_
+#define ABSL_DEBUGGING_INTERNAL_DEMANGLE_H_
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+
+// Demangle `mangled`.  On success, return true and write the
+// demangled symbol name to `out`.  Otherwise, return false.
+// `out` is modified even if demangling is unsuccessful.
+bool Demangle(const char* mangled, char* out, size_t out_size);
+
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_DEBUGGING_INTERNAL_DEMANGLE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/elf_mem_image.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/elf_mem_image.h
new file mode 100644
index 0000000000000000000000000000000000000000..113071a9d19a9843c94428f9b3b15523fda6fb50
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/elf_mem_image.h
@@ -0,0 +1,139 @@
+/*
+ * Copyright 2017 The Abseil Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// Allow dynamic symbol lookup for in-memory Elf images.
+
+#ifndef ABSL_DEBUGGING_INTERNAL_ELF_MEM_IMAGE_H_
+#define ABSL_DEBUGGING_INTERNAL_ELF_MEM_IMAGE_H_
+
+// Including this will define the __GLIBC__ macro if glibc is being
+// used.
+#include <climits>
+
+#include "absl/base/config.h"
+
+// Maybe one day we can rewrite this file not to require the elf
+// symbol extensions in glibc, but for right now we need them.
+#ifdef ABSL_HAVE_ELF_MEM_IMAGE
+#error ABSL_HAVE_ELF_MEM_IMAGE cannot be directly set
+#endif
+
+#if defined(__ELF__) && !defined(__OpenBSD__) && !defined(__QNX__) && \
+    !defined(__native_client__) && !defined(__asmjs__) &&             \
+    !defined(__wasm__) && !defined(__HAIKU__)
+#define ABSL_HAVE_ELF_MEM_IMAGE 1
+#endif
+
+#ifdef ABSL_HAVE_ELF_MEM_IMAGE
+
+#include <link.h>  // for ElfW
+
+#if defined(__FreeBSD__) && !defined(ElfW)
+#define ElfW(x) __ElfN(x)
+#endif
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+
+// An in-memory ELF image (may not exist on disk).
+class ElfMemImage {
+ private:
+  // Sentinel: there could never be an elf image at &kInvalidBaseSentinel.
+  static const int kInvalidBaseSentinel;
+
+ public:
+  // Sentinel: there could never be an elf image at this address.
+  static constexpr const void *const kInvalidBase =
+    static_cast<const void*>(&kInvalidBaseSentinel);
+
+  // Information about a single vdso symbol.
+  // All pointers are into .dynsym, .dynstr, or .text of the VDSO.
+  // Do not free() them or modify through them.
+  struct SymbolInfo {
+    const char      *name;      // E.g. "__vdso_getcpu"
+    const char      *version;   // E.g. "LINUX_2.6", could be ""
+                                // for unversioned symbol.
+    const void      *address;   // Relocated symbol address.
+    const ElfW(Sym) *symbol;    // Symbol in the dynamic symbol table.
+  };
+
+  // Supports iteration over all dynamic symbols.
+  class SymbolIterator {
+   public:
+    friend class ElfMemImage;
+    const SymbolInfo *operator->() const;
+    const SymbolInfo &operator*() const;
+    SymbolIterator& operator++();
+    bool operator!=(const SymbolIterator &rhs) const;
+    bool operator==(const SymbolIterator &rhs) const;
+   private:
+    SymbolIterator(const void *const image, int index);
+    void Update(int incr);
+    SymbolInfo info_;
+    int index_;
+    const void *const image_;
+  };
+
+
+  explicit ElfMemImage(const void *base);
+  void                 Init(const void *base);
+  bool                 IsPresent() const { return ehdr_ != nullptr; }
+  const ElfW(Phdr)*    GetPhdr(int index) const;
+  const ElfW(Sym)*     GetDynsym(int index) const;
+  const ElfW(Versym)*  GetVersym(int index) const;
+  const ElfW(Verdef)*  GetVerdef(int index) const;
+  const ElfW(Verdaux)* GetVerdefAux(const ElfW(Verdef) *verdef) const;
+  const char*          GetDynstr(ElfW(Word) offset) const;
+  const void*          GetSymAddr(const ElfW(Sym) *sym) const;
+  const char*          GetVerstr(ElfW(Word) offset) const;
+  int                  GetNumSymbols() const;
+
+  SymbolIterator begin() const;
+  SymbolIterator end() const;
+
+  // Look up versioned dynamic symbol in the image.
+  // Returns false if image is not present, or doesn't contain given
+  // symbol/version/type combination.
+  // If info_out is non-null, additional details are filled in.
+  bool LookupSymbol(const char *name, const char *version,
+                    int symbol_type, SymbolInfo *info_out) const;
+
+  // Find info about symbol (if any) which overlaps given address.
+  // Returns true if symbol was found; false if image isn't present
+  // or doesn't have a symbol overlapping given address.
+  // If info_out is non-null, additional details are filled in.
+  bool LookupSymbolByAddress(const void *address, SymbolInfo *info_out) const;
+
+ private:
+  const ElfW(Ehdr) *ehdr_;
+  const ElfW(Sym) *dynsym_;
+  const ElfW(Versym) *versym_;
+  const ElfW(Verdef) *verdef_;
+  const ElfW(Word) *hash_;
+  const char *dynstr_;
+  size_t strsize_;
+  size_t verdefnum_;
+  ElfW(Addr) link_base_;     // Link-time base (p_vaddr of first PT_LOAD).
+};
+
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_HAVE_ELF_MEM_IMAGE
+
+#endif  // ABSL_DEBUGGING_INTERNAL_ELF_MEM_IMAGE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/examine_stack.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/examine_stack.h
new file mode 100644
index 0000000000000000000000000000000000000000..190af87f1c348a1e85810dfd1e25af099cd2da39
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/examine_stack.h
@@ -0,0 +1,64 @@
+//
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+#ifndef ABSL_DEBUGGING_INTERNAL_EXAMINE_STACK_H_
+#define ABSL_DEBUGGING_INTERNAL_EXAMINE_STACK_H_
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+
+// Type of function used for printing in stack trace dumping, etc.
+// We avoid closures to keep things simple.
+typedef void OutputWriter(const char*, void*);
+
+// RegisterDebugStackTraceHook() allows to register a single routine
+// `hook` that is called each time DumpStackTrace() is called.
+// `hook` may be called from a signal handler.
+typedef void (*SymbolizeUrlEmitter)(void* const stack[], int depth,
+                                    OutputWriter* writer, void* writer_arg);
+
+// Registration of SymbolizeUrlEmitter for use inside of a signal handler.
+// This is inherently unsafe and must be signal safe code.
+void RegisterDebugStackTraceHook(SymbolizeUrlEmitter hook);
+SymbolizeUrlEmitter GetDebugStackTraceHook();
+
+// Returns the program counter from signal context, or nullptr if
+// unknown. `vuc` is a ucontext_t*. We use void* to avoid the use of
+// ucontext_t on non-POSIX systems.
+void* GetProgramCounter(void* const vuc);
+
+// Uses `writer` to dump the program counter, stack trace, and stack
+// frame sizes.
+void DumpPCAndFrameSizesAndStackTrace(void* const pc, void* const stack[],
+                                      int frame_sizes[], int depth,
+                                      int min_dropped_frames,
+                                      bool symbolize_stacktrace,
+                                      OutputWriter* writer, void* writer_arg);
+
+// Dump current stack trace omitting the topmost `min_dropped_frames` stack
+// frames.
+void DumpStackTrace(int min_dropped_frames, int max_num_frames,
+                    bool symbolize_stacktrace, OutputWriter* writer,
+                    void* writer_arg);
+
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_DEBUGGING_INTERNAL_EXAMINE_STACK_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_aarch64-inl.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_aarch64-inl.inc
new file mode 100644
index 0000000000000000000000000000000000000000..71cdaf0940671348c2918d8d8fe18e8d04e5f949
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_aarch64-inl.inc
@@ -0,0 +1,206 @@
+#ifndef ABSL_DEBUGGING_INTERNAL_STACKTRACE_AARCH64_INL_H_
+#define ABSL_DEBUGGING_INTERNAL_STACKTRACE_AARCH64_INL_H_
+
+// Generate stack tracer for aarch64
+
+#if defined(__linux__)
+#include <sys/mman.h>
+#include <ucontext.h>
+#include <unistd.h>
+#endif
+
+#include <atomic>
+#include <cassert>
+#include <cstdint>
+#include <iostream>
+
+#include "absl/base/attributes.h"
+#include "absl/debugging/internal/address_is_readable.h"
+#include "absl/debugging/internal/vdso_support.h"  // a no-op on non-elf or non-glibc systems
+#include "absl/debugging/stacktrace.h"
+
+static const size_t kUnknownFrameSize = 0;
+
+#if defined(__linux__)
+// Returns the address of the VDSO __kernel_rt_sigreturn function, if present.
+static const unsigned char* GetKernelRtSigreturnAddress() {
+  constexpr uintptr_t kImpossibleAddress = 1;
+  ABSL_CONST_INIT static std::atomic<uintptr_t> memoized{kImpossibleAddress};
+  uintptr_t address = memoized.load(std::memory_order_relaxed);
+  if (address != kImpossibleAddress) {
+    return reinterpret_cast<const unsigned char*>(address);
+  }
+
+  address = reinterpret_cast<uintptr_t>(nullptr);
+
+#ifdef ABSL_HAVE_VDSO_SUPPORT
+  absl::debugging_internal::VDSOSupport vdso;
+  if (vdso.IsPresent()) {
+    absl::debugging_internal::VDSOSupport::SymbolInfo symbol_info;
+    auto lookup = [&](int type) {
+      return vdso.LookupSymbol("__kernel_rt_sigreturn", "LINUX_2.6.39", type,
+                               &symbol_info);
+    };
+    if ((!lookup(STT_FUNC) && !lookup(STT_NOTYPE)) ||
+        symbol_info.address == nullptr) {
+      // Unexpected: VDSO is present, yet the expected symbol is missing
+      // or null.
+      assert(false && "VDSO is present, but doesn't have expected symbol");
+    } else {
+      if (reinterpret_cast<uintptr_t>(symbol_info.address) !=
+          kImpossibleAddress) {
+        address = reinterpret_cast<uintptr_t>(symbol_info.address);
+      } else {
+        assert(false && "VDSO returned invalid address");
+      }
+    }
+  }
+#endif
+
+  memoized.store(address, std::memory_order_relaxed);
+  return reinterpret_cast<const unsigned char*>(address);
+}
+#endif  // __linux__
+
+// Compute the size of a stack frame in [low..high).  We assume that
+// low < high.  Return size of kUnknownFrameSize.
+template<typename T>
+static inline size_t ComputeStackFrameSize(const T* low,
+                                           const T* high) {
+  const char* low_char_ptr = reinterpret_cast<const char *>(low);
+  const char* high_char_ptr = reinterpret_cast<const char *>(high);
+  return low < high ? static_cast<size_t>(high_char_ptr - low_char_ptr)
+                    : kUnknownFrameSize;
+}
+
+// Given a pointer to a stack frame, locate and return the calling
+// stackframe, or return null if no stackframe can be found. Perform sanity
+// checks (the strictness of which is controlled by the boolean parameter
+// "STRICT_UNWINDING") to reduce the chance that a bad pointer is returned.
+template<bool STRICT_UNWINDING, bool WITH_CONTEXT>
+ABSL_ATTRIBUTE_NO_SANITIZE_ADDRESS  // May read random elements from stack.
+ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY   // May read random elements from stack.
+static void **NextStackFrame(void **old_frame_pointer, const void *uc) {
+  void **new_frame_pointer = reinterpret_cast<void**>(*old_frame_pointer);
+  bool check_frame_size = true;
+
+#if defined(__linux__)
+  if (WITH_CONTEXT && uc != nullptr) {
+    // Check to see if next frame's return address is __kernel_rt_sigreturn.
+    if (old_frame_pointer[1] == GetKernelRtSigreturnAddress()) {
+      const ucontext_t *ucv = static_cast<const ucontext_t *>(uc);
+      // old_frame_pointer[0] is not suitable for unwinding, look at
+      // ucontext to discover frame pointer before signal.
+      void **const pre_signal_frame_pointer =
+          reinterpret_cast<void **>(ucv->uc_mcontext.regs[29]);
+
+      // Check that alleged frame pointer is actually readable. This is to
+      // prevent "double fault" in case we hit the first fault due to e.g.
+      // stack corruption.
+      if (!absl::debugging_internal::AddressIsReadable(
+              pre_signal_frame_pointer))
+        return nullptr;
+
+      // Alleged frame pointer is readable, use it for further unwinding.
+      new_frame_pointer = pre_signal_frame_pointer;
+
+      // Skip frame size check if we return from a signal. We may be using a
+      // an alternate stack for signals.
+      check_frame_size = false;
+    }
+  }
+#endif
+
+  // The frame pointer should be 8-byte aligned.
+  if ((reinterpret_cast<uintptr_t>(new_frame_pointer) & 7) != 0)
+    return nullptr;
+
+  // Check frame size.  In strict mode, we assume frames to be under
+  // 100,000 bytes.  In non-strict mode, we relax the limit to 1MB.
+  if (check_frame_size) {
+    const size_t max_size = STRICT_UNWINDING ? 100000 : 1000000;
+    const size_t frame_size =
+        ComputeStackFrameSize(old_frame_pointer, new_frame_pointer);
+    if (frame_size == kUnknownFrameSize || frame_size > max_size)
+      return nullptr;
+  }
+
+  return new_frame_pointer;
+}
+
+template <bool IS_STACK_FRAMES, bool IS_WITH_CONTEXT>
+ABSL_ATTRIBUTE_NO_SANITIZE_ADDRESS  // May read random elements from stack.
+ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY   // May read random elements from stack.
+static int UnwindImpl(void** result, int* sizes, int max_depth, int skip_count,
+                      const void *ucp, int *min_dropped_frames) {
+#ifdef __GNUC__
+  void **frame_pointer = reinterpret_cast<void**>(__builtin_frame_address(0));
+#else
+# error reading stack point not yet supported on this platform.
+#endif
+
+  skip_count++;    // Skip the frame for this function.
+  int n = 0;
+
+  // The frame pointer points to low address of a frame.  The first 64-bit
+  // word of a frame points to the next frame up the call chain, which normally
+  // is just after the high address of the current frame.  The second word of
+  // a frame contains return adress of to the caller.   To find a pc value
+  // associated with the current frame, we need to go down a level in the call
+  // chain.  So we remember return the address of the last frame seen.  This
+  // does not work for the first stack frame, which belongs to UnwindImp() but
+  // we skip the frame for UnwindImp() anyway.
+  void* prev_return_address = nullptr;
+
+  while (frame_pointer && n < max_depth) {
+    // The absl::GetStackFrames routine is called when we are in some
+    // informational context (the failure signal handler for example).
+    // Use the non-strict unwinding rules to produce a stack trace
+    // that is as complete as possible (even if it contains a few bogus
+    // entries in some rare cases).
+    void **next_frame_pointer =
+        NextStackFrame<!IS_STACK_FRAMES, IS_WITH_CONTEXT>(frame_pointer, ucp);
+
+    if (skip_count > 0) {
+      skip_count--;
+    } else {
+      result[n] = prev_return_address;
+      if (IS_STACK_FRAMES) {
+        sizes[n] = static_cast<int>(
+            ComputeStackFrameSize(frame_pointer, next_frame_pointer));
+      }
+      n++;
+    }
+    prev_return_address = frame_pointer[1];
+    frame_pointer = next_frame_pointer;
+  }
+  if (min_dropped_frames != nullptr) {
+    // Implementation detail: we clamp the max of frames we are willing to
+    // count, so as not to spend too much time in the loop below.
+    const int kMaxUnwind = 200;
+    int num_dropped_frames = 0;
+    for (int j = 0; frame_pointer != nullptr && j < kMaxUnwind; j++) {
+      if (skip_count > 0) {
+        skip_count--;
+      } else {
+        num_dropped_frames++;
+      }
+      frame_pointer =
+          NextStackFrame<!IS_STACK_FRAMES, IS_WITH_CONTEXT>(frame_pointer, ucp);
+    }
+    *min_dropped_frames = num_dropped_frames;
+  }
+  return n;
+}
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+bool StackTraceWorksForTest() {
+  return true;
+}
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_DEBUGGING_INTERNAL_STACKTRACE_AARCH64_INL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_arm-inl.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_arm-inl.inc
new file mode 100644
index 0000000000000000000000000000000000000000..102a2a1251aa980049b99ca553ad31f11e6a6f8d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_arm-inl.inc
@@ -0,0 +1,139 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// This is inspired by Craig Silverstein's PowerPC stacktrace code.
+
+#ifndef ABSL_DEBUGGING_INTERNAL_STACKTRACE_ARM_INL_H_
+#define ABSL_DEBUGGING_INTERNAL_STACKTRACE_ARM_INL_H_
+
+#include <cstdint>
+
+#include "absl/debugging/stacktrace.h"
+
+// WARNING:
+// This only works if all your code is in either ARM or THUMB mode.  With
+// interworking, the frame pointer of the caller can either be in r11 (ARM
+// mode) or r7 (THUMB mode).  A callee only saves the frame pointer of its
+// mode in a fixed location on its stack frame.  If the caller is a different
+// mode, there is no easy way to find the frame pointer.  It can either be
+// still in the designated register or saved on stack along with other callee
+// saved registers.
+
+// Given a pointer to a stack frame, locate and return the calling
+// stackframe, or return nullptr if no stackframe can be found. Perform sanity
+// checks (the strictness of which is controlled by the boolean parameter
+// "STRICT_UNWINDING") to reduce the chance that a bad pointer is returned.
+template<bool STRICT_UNWINDING>
+static void **NextStackFrame(void **old_sp) {
+  void **new_sp = (void**) old_sp[-1];
+
+  // Check that the transition from frame pointer old_sp to frame
+  // pointer new_sp isn't clearly bogus
+  if (STRICT_UNWINDING) {
+    // With the stack growing downwards, older stack frame must be
+    // at a greater address that the current one.
+    if (new_sp <= old_sp) return nullptr;
+    // Assume stack frames larger than 100,000 bytes are bogus.
+    if ((uintptr_t)new_sp - (uintptr_t)old_sp > 100000) return nullptr;
+  } else {
+    // In the non-strict mode, allow discontiguous stack frames.
+    // (alternate-signal-stacks for example).
+    if (new_sp == old_sp) return nullptr;
+    // And allow frames upto about 1MB.
+    if ((new_sp > old_sp)
+        && ((uintptr_t)new_sp - (uintptr_t)old_sp > 1000000)) return nullptr;
+  }
+  if ((uintptr_t)new_sp & (sizeof(void *) - 1)) return nullptr;
+  return new_sp;
+}
+
+// This ensures that absl::GetStackTrace sets up the Link Register properly.
+#ifdef __GNUC__
+void StacktraceArmDummyFunction() __attribute__((noinline));
+void StacktraceArmDummyFunction() { __asm__ volatile(""); }
+#else
+# error StacktraceArmDummyFunction() needs to be ported to this platform.
+#endif
+
+template <bool IS_STACK_FRAMES, bool IS_WITH_CONTEXT>
+static int UnwindImpl(void** result, int* sizes, int max_depth, int skip_count,
+                      const void * /* ucp */, int *min_dropped_frames) {
+#ifdef __GNUC__
+  void **sp = reinterpret_cast<void**>(__builtin_frame_address(0));
+#else
+# error reading stack point not yet supported on this platform.
+#endif
+
+  // On ARM, the return address is stored in the link register (r14).
+  // This is not saved on the stack frame of a leaf function.  To
+  // simplify code that reads return addresses, we call a dummy
+  // function so that the return address of this function is also
+  // stored in the stack frame.  This works at least for gcc.
+  StacktraceArmDummyFunction();
+
+  int n = 0;
+  while (sp && n < max_depth) {
+    // The absl::GetStackFrames routine is called when we are in some
+    // informational context (the failure signal handler for example).
+    // Use the non-strict unwinding rules to produce a stack trace
+    // that is as complete as possible (even if it contains a few bogus
+    // entries in some rare cases).
+    void **next_sp = NextStackFrame<!IS_STACK_FRAMES>(sp);
+
+    if (skip_count > 0) {
+      skip_count--;
+    } else {
+      result[n] = *sp;
+
+      if (IS_STACK_FRAMES) {
+        if (next_sp > sp) {
+          sizes[n] = (uintptr_t)next_sp - (uintptr_t)sp;
+        } else {
+          // A frame-size of 0 is used to indicate unknown frame size.
+          sizes[n] = 0;
+        }
+      }
+      n++;
+    }
+    sp = next_sp;
+  }
+  if (min_dropped_frames != nullptr) {
+    // Implementation detail: we clamp the max of frames we are willing to
+    // count, so as not to spend too much time in the loop below.
+    const int kMaxUnwind = 200;
+    int num_dropped_frames = 0;
+    for (int j = 0; sp != nullptr && j < kMaxUnwind; j++) {
+      if (skip_count > 0) {
+        skip_count--;
+      } else {
+        num_dropped_frames++;
+      }
+      sp = NextStackFrame<!IS_STACK_FRAMES>(sp);
+    }
+    *min_dropped_frames = num_dropped_frames;
+  }
+  return n;
+}
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+bool StackTraceWorksForTest() {
+  return false;
+}
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_DEBUGGING_INTERNAL_STACKTRACE_ARM_INL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_config.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_config.h
new file mode 100644
index 0000000000000000000000000000000000000000..3929b1b734992a65eee9a727c94f826fc3c8eb79
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_config.h
@@ -0,0 +1,88 @@
+/*
+ * Copyright 2017 The Abseil Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+
+ * Defines ABSL_STACKTRACE_INL_HEADER to the *-inl.h containing
+ * actual unwinder implementation.
+ * This header is "private" to stacktrace.cc.
+ * DO NOT include it into any other files.
+*/
+#ifndef ABSL_DEBUGGING_INTERNAL_STACKTRACE_CONFIG_H_
+#define ABSL_DEBUGGING_INTERNAL_STACKTRACE_CONFIG_H_
+
+#include "absl/base/config.h"
+
+#if defined(ABSL_STACKTRACE_INL_HEADER)
+#error ABSL_STACKTRACE_INL_HEADER cannot be directly set
+
+#elif defined(_WIN32)
+#define ABSL_STACKTRACE_INL_HEADER \
+    "absl/debugging/internal/stacktrace_win32-inl.inc"
+
+#elif defined(__APPLE__)
+#ifdef ABSL_HAVE_THREAD_LOCAL
+// Thread local support required for UnwindImpl.
+#define ABSL_STACKTRACE_INL_HEADER \
+  "absl/debugging/internal/stacktrace_generic-inl.inc"
+#endif  // defined(ABSL_HAVE_THREAD_LOCAL)
+
+// Emscripten stacktraces rely on JS. Do not use them in standalone mode.
+#elif defined(__EMSCRIPTEN__) && !defined(STANDALONE_WASM)
+#define ABSL_STACKTRACE_INL_HEADER \
+  "absl/debugging/internal/stacktrace_emscripten-inl.inc"
+
+#elif defined(__linux__) && !defined(__ANDROID__)
+
+#if defined(NO_FRAME_POINTER) && \
+    (defined(__i386__) || defined(__x86_64__) || defined(__aarch64__))
+// Note: The libunwind-based implementation is not available to open-source
+// users.
+#define ABSL_STACKTRACE_INL_HEADER \
+  "absl/debugging/internal/stacktrace_libunwind-inl.inc"
+#define STACKTRACE_USES_LIBUNWIND 1
+#elif defined(NO_FRAME_POINTER) && defined(__has_include)
+#if __has_include(<execinfo.h>)
+// Note: When using glibc this may require -funwind-tables to function properly.
+#define ABSL_STACKTRACE_INL_HEADER \
+  "absl/debugging/internal/stacktrace_generic-inl.inc"
+#endif  // __has_include(<execinfo.h>)
+#elif defined(__i386__) || defined(__x86_64__)
+#define ABSL_STACKTRACE_INL_HEADER \
+  "absl/debugging/internal/stacktrace_x86-inl.inc"
+#elif defined(__ppc__) || defined(__PPC__)
+#define ABSL_STACKTRACE_INL_HEADER \
+  "absl/debugging/internal/stacktrace_powerpc-inl.inc"
+#elif defined(__aarch64__)
+#define ABSL_STACKTRACE_INL_HEADER \
+  "absl/debugging/internal/stacktrace_aarch64-inl.inc"
+#elif defined(__riscv)
+#define ABSL_STACKTRACE_INL_HEADER \
+  "absl/debugging/internal/stacktrace_riscv-inl.inc"
+#elif defined(__has_include)
+#if __has_include(<execinfo.h>)
+// Note: When using glibc this may require -funwind-tables to function properly.
+#define ABSL_STACKTRACE_INL_HEADER \
+  "absl/debugging/internal/stacktrace_generic-inl.inc"
+#endif  // __has_include(<execinfo.h>)
+#endif  // defined(__has_include)
+
+#endif  // defined(__linux__) && !defined(__ANDROID__)
+
+// Fallback to the empty implementation.
+#if !defined(ABSL_STACKTRACE_INL_HEADER)
+#define ABSL_STACKTRACE_INL_HEADER \
+  "absl/debugging/internal/stacktrace_unimplemented-inl.inc"
+#endif
+
+#endif  // ABSL_DEBUGGING_INTERNAL_STACKTRACE_CONFIG_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_emscripten-inl.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_emscripten-inl.inc
new file mode 100644
index 0000000000000000000000000000000000000000..0f44451438624e6db2176aaecce42514aa09617d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_emscripten-inl.inc
@@ -0,0 +1,110 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Portable implementation - just use glibc
+//
+// Note:  The glibc implementation may cause a call to malloc.
+// This can cause a deadlock in HeapProfiler.
+
+#ifndef ABSL_DEBUGGING_INTERNAL_STACKTRACE_EMSCRIPTEN_INL_H_
+#define ABSL_DEBUGGING_INTERNAL_STACKTRACE_EMSCRIPTEN_INL_H_
+
+#include <emscripten.h>
+
+#include <atomic>
+#include <cstring>
+
+#include "absl/base/attributes.h"
+#include "absl/debugging/stacktrace.h"
+
+extern "C" {
+uintptr_t emscripten_stack_snapshot();
+uint32_t emscripten_stack_unwind_buffer(uintptr_t pc, void *buffer,
+                                        uint32_t depth);
+}
+
+// Sometimes, we can try to get a stack trace from within a stack
+// trace, which can cause a self-deadlock.
+// Protect against such reentrant call by failing to get a stack trace.
+//
+// We use __thread here because the code here is extremely low level -- it is
+// called while collecting stack traces from within malloc and mmap, and thus
+// can not call anything which might call malloc or mmap itself.
+static __thread int recursive = 0;
+
+// The stack trace function might be invoked very early in the program's
+// execution (e.g. from the very first malloc).
+// As such, we suppress usage of backtrace during this early stage of execution.
+static std::atomic<bool> disable_stacktraces(true);  // Disabled until healthy.
+// Waiting until static initializers run seems to be late enough.
+// This file is included into stacktrace.cc so this will only run once.
+ABSL_ATTRIBUTE_UNUSED static int stacktraces_enabler = []() {
+  // Check if we can even create stacktraces. If not, bail early and leave
+  // disable_stacktraces set as-is.
+  // clang-format off
+  if (!EM_ASM_INT({ return (typeof wasmOffsetConverter !== 'undefined'); })) {
+    return 0;
+  }
+  // clang-format on
+  disable_stacktraces.store(false, std::memory_order_relaxed);
+  return 0;
+}();
+
+template <bool IS_STACK_FRAMES, bool IS_WITH_CONTEXT>
+static int UnwindImpl(void **result, int *sizes, int max_depth, int skip_count,
+                      const void *ucp, int *min_dropped_frames) {
+  if (recursive || disable_stacktraces.load(std::memory_order_relaxed)) {
+    return 0;
+  }
+  ++recursive;
+
+  static_cast<void>(ucp);  // Unused.
+  constexpr int kStackLength = 64;
+  void *stack[kStackLength];
+
+  int size;
+  uintptr_t pc = emscripten_stack_snapshot();
+  size = emscripten_stack_unwind_buffer(pc, stack, kStackLength);
+
+  int result_count = size - skip_count;
+  if (result_count < 0) result_count = 0;
+  if (result_count > max_depth) result_count = max_depth;
+  for (int i = 0; i < result_count; i++) result[i] = stack[i + skip_count];
+
+  if (IS_STACK_FRAMES) {
+    // No implementation for finding out the stack frame sizes yet.
+    memset(sizes, 0, sizeof(*sizes) * result_count);
+  }
+  if (min_dropped_frames != nullptr) {
+    if (size - skip_count - max_depth > 0) {
+      *min_dropped_frames = size - skip_count - max_depth;
+    } else {
+      *min_dropped_frames = 0;
+    }
+  }
+
+  --recursive;
+
+  return result_count;
+}
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+bool StackTraceWorksForTest() { return true; }
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_DEBUGGING_INTERNAL_STACKTRACE_EMSCRIPTEN_INL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_generic-inl.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_generic-inl.inc
new file mode 100644
index 0000000000000000000000000000000000000000..5fa169a7ec88dfdd820c4646867b37b6fbafd33f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_generic-inl.inc
@@ -0,0 +1,108 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Portable implementation - just use glibc
+//
+// Note:  The glibc implementation may cause a call to malloc.
+// This can cause a deadlock in HeapProfiler.
+
+#ifndef ABSL_DEBUGGING_INTERNAL_STACKTRACE_GENERIC_INL_H_
+#define ABSL_DEBUGGING_INTERNAL_STACKTRACE_GENERIC_INL_H_
+
+#include <execinfo.h>
+#include <atomic>
+#include <cstring>
+
+#include "absl/debugging/stacktrace.h"
+#include "absl/base/attributes.h"
+
+// Sometimes, we can try to get a stack trace from within a stack
+// trace, because we don't block signals inside this code (which would be too
+// expensive: the two extra system calls per stack trace do matter here).
+// That can cause a self-deadlock.
+// Protect against such reentrant call by failing to get a stack trace.
+//
+// We use __thread here because the code here is extremely low level -- it is
+// called while collecting stack traces from within malloc and mmap, and thus
+// can not call anything which might call malloc or mmap itself.
+static __thread int recursive = 0;
+
+// The stack trace function might be invoked very early in the program's
+// execution (e.g. from the very first malloc if using tcmalloc). Also, the
+// glibc implementation itself will trigger malloc the first time it is called.
+// As such, we suppress usage of backtrace during this early stage of execution.
+static std::atomic<bool> disable_stacktraces(true);  // Disabled until healthy.
+// Waiting until static initializers run seems to be late enough.
+// This file is included into stacktrace.cc so this will only run once.
+ABSL_ATTRIBUTE_UNUSED static int stacktraces_enabler = []() {
+  void* unused_stack[1];
+  // Force the first backtrace to happen early to get the one-time shared lib
+  // loading (allocation) out of the way. After the first call it is much safer
+  // to use backtrace from a signal handler if we crash somewhere later.
+  backtrace(unused_stack, 1);
+  disable_stacktraces.store(false, std::memory_order_relaxed);
+  return 0;
+}();
+
+template <bool IS_STACK_FRAMES, bool IS_WITH_CONTEXT>
+static int UnwindImpl(void** result, int* sizes, int max_depth, int skip_count,
+                      const void *ucp, int *min_dropped_frames) {
+  if (recursive || disable_stacktraces.load(std::memory_order_relaxed)) {
+    return 0;
+  }
+  ++recursive;
+
+  static_cast<void>(ucp);  // Unused.
+  static const int kStackLength = 64;
+  void * stack[kStackLength];
+  int size;
+
+  size = backtrace(stack, kStackLength);
+  skip_count++;  // we want to skip the current frame as well
+  int result_count = size - skip_count;
+  if (result_count < 0)
+    result_count = 0;
+  if (result_count > max_depth)
+    result_count = max_depth;
+  for (int i = 0; i < result_count; i++)
+    result[i] = stack[i + skip_count];
+
+  if (IS_STACK_FRAMES) {
+    // No implementation for finding out the stack frame sizes yet.
+    memset(sizes, 0, sizeof(*sizes) * static_cast<size_t>(result_count));
+  }
+  if (min_dropped_frames != nullptr) {
+    if (size - skip_count - max_depth > 0) {
+      *min_dropped_frames = size - skip_count - max_depth;
+    } else {
+      *min_dropped_frames = 0;
+    }
+  }
+
+  --recursive;
+
+  return result_count;
+}
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+bool StackTraceWorksForTest() {
+  return true;
+}
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_DEBUGGING_INTERNAL_STACKTRACE_GENERIC_INL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_powerpc-inl.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_powerpc-inl.inc
new file mode 100644
index 0000000000000000000000000000000000000000..085cef67026f0a5bcbf6135889b9d2d611fced19
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_powerpc-inl.inc
@@ -0,0 +1,258 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Produce stack trace.  I'm guessing (hoping!) the code is much like
+// for x86.  For apple machines, at least, it seems to be; see
+//    https://developer.apple.com/documentation/mac/runtimehtml/RTArch-59.html
+//    https://www.linux-foundation.org/spec/ELF/ppc64/PPC-elf64abi-1.9.html#STACK
+// Linux has similar code: http://patchwork.ozlabs.org/linuxppc/patch?id=8882
+
+#ifndef ABSL_DEBUGGING_INTERNAL_STACKTRACE_POWERPC_INL_H_
+#define ABSL_DEBUGGING_INTERNAL_STACKTRACE_POWERPC_INL_H_
+
+#if defined(__linux__)
+#include <asm/ptrace.h>   // for PT_NIP.
+#include <ucontext.h>     // for ucontext_t
+#endif
+
+#include <unistd.h>
+#include <cassert>
+#include <cstdint>
+#include <cstdio>
+
+#include "absl/base/attributes.h"
+#include "absl/base/optimization.h"
+#include "absl/base/port.h"
+#include "absl/debugging/stacktrace.h"
+#include "absl/debugging/internal/address_is_readable.h"
+#include "absl/debugging/internal/vdso_support.h"  // a no-op on non-elf or non-glibc systems
+
+// Given a stack pointer, return the saved link register value.
+// Note that this is the link register for a callee.
+static inline void *StacktracePowerPCGetLR(void **sp) {
+  // PowerPC has 3 main ABIs, which say where in the stack the
+  // Link Register is.  For DARWIN and AIX (used by apple and
+  // linux ppc64), it's in sp[2].  For SYSV (used by linux ppc),
+  // it's in sp[1].
+#if defined(_CALL_AIX) || defined(_CALL_DARWIN)
+  return *(sp+2);
+#elif defined(_CALL_SYSV)
+  return *(sp+1);
+#elif defined(__APPLE__) || defined(__FreeBSD__) || \
+    (defined(__linux__) && defined(__PPC64__))
+  // This check is in case the compiler doesn't define _CALL_AIX/etc.
+  return *(sp+2);
+#elif defined(__linux)
+  // This check is in case the compiler doesn't define _CALL_SYSV.
+  return *(sp+1);
+#else
+#error Need to specify the PPC ABI for your archiecture.
+#endif
+}
+
+// Given a pointer to a stack frame, locate and return the calling
+// stackframe, or return null if no stackframe can be found. Perform sanity
+// checks (the strictness of which is controlled by the boolean parameter
+// "STRICT_UNWINDING") to reduce the chance that a bad pointer is returned.
+template<bool STRICT_UNWINDING, bool IS_WITH_CONTEXT>
+ABSL_ATTRIBUTE_NO_SANITIZE_ADDRESS  // May read random elements from stack.
+ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY   // May read random elements from stack.
+static void **NextStackFrame(void **old_sp, const void *uc) {
+  void **new_sp = (void **) *old_sp;
+  enum { kStackAlignment = 16 };
+
+  // Check that the transition from frame pointer old_sp to frame
+  // pointer new_sp isn't clearly bogus
+  if (STRICT_UNWINDING) {
+    // With the stack growing downwards, older stack frame must be
+    // at a greater address that the current one.
+    if (new_sp <= old_sp) return nullptr;
+    // Assume stack frames larger than 100,000 bytes are bogus.
+    if ((uintptr_t)new_sp - (uintptr_t)old_sp > 100000) return nullptr;
+  } else {
+    // In the non-strict mode, allow discontiguous stack frames.
+    // (alternate-signal-stacks for example).
+    if (new_sp == old_sp) return nullptr;
+    // And allow frames upto about 1MB.
+    if ((new_sp > old_sp)
+        && ((uintptr_t)new_sp - (uintptr_t)old_sp > 1000000)) return nullptr;
+  }
+  if ((uintptr_t)new_sp % kStackAlignment != 0) return nullptr;
+
+#if defined(__linux__)
+  enum StackTraceKernelSymbolStatus {
+      kNotInitialized = 0, kAddressValid, kAddressInvalid };
+
+  if (IS_WITH_CONTEXT && uc != nullptr) {
+    static StackTraceKernelSymbolStatus kernel_symbol_status =
+        kNotInitialized;  // Sentinel: not computed yet.
+    // Initialize with sentinel value: __kernel_rt_sigtramp_rt64 can not
+    // possibly be there.
+    static const unsigned char *kernel_sigtramp_rt64_address = nullptr;
+    if (kernel_symbol_status == kNotInitialized) {
+      absl::debugging_internal::VDSOSupport vdso;
+      if (vdso.IsPresent()) {
+        absl::debugging_internal::VDSOSupport::SymbolInfo
+            sigtramp_rt64_symbol_info;
+        if (!vdso.LookupSymbol(
+                "__kernel_sigtramp_rt64", "LINUX_2.6.15",
+                absl::debugging_internal::VDSOSupport::kVDSOSymbolType,
+                &sigtramp_rt64_symbol_info) ||
+            sigtramp_rt64_symbol_info.address == nullptr) {
+          // Unexpected: VDSO is present, yet the expected symbol is missing
+          // or null.
+          assert(false && "VDSO is present, but doesn't have expected symbol");
+          kernel_symbol_status = kAddressInvalid;
+        } else {
+          kernel_sigtramp_rt64_address =
+              reinterpret_cast<const unsigned char *>(
+                  sigtramp_rt64_symbol_info.address);
+          kernel_symbol_status = kAddressValid;
+        }
+      } else {
+        kernel_symbol_status = kAddressInvalid;
+      }
+    }
+
+    if (new_sp != nullptr &&
+        kernel_symbol_status == kAddressValid &&
+        StacktracePowerPCGetLR(new_sp) == kernel_sigtramp_rt64_address) {
+      const ucontext_t* signal_context =
+          reinterpret_cast<const ucontext_t*>(uc);
+      void **const sp_before_signal =
+#if defined(__PPC64__)
+          reinterpret_cast<void **>(signal_context->uc_mcontext.gp_regs[PT_R1]);
+#else
+          reinterpret_cast<void **>(
+              signal_context->uc_mcontext.uc_regs->gregs[PT_R1]);
+#endif
+      // Check that alleged sp before signal is nonnull and is reasonably
+      // aligned.
+      if (sp_before_signal != nullptr &&
+          ((uintptr_t)sp_before_signal % kStackAlignment) == 0) {
+        // Check that alleged stack pointer is actually readable. This is to
+        // prevent a "double fault" in case we hit the first fault due to e.g.
+        // a stack corruption.
+        if (absl::debugging_internal::AddressIsReadable(sp_before_signal)) {
+          // Alleged stack pointer is readable, use it for further unwinding.
+          new_sp = sp_before_signal;
+        }
+      }
+    }
+  }
+#endif
+
+  return new_sp;
+}
+
+// This ensures that absl::GetStackTrace sets up the Link Register properly.
+ABSL_ATTRIBUTE_NOINLINE static void AbslStacktracePowerPCDummyFunction() {
+  ABSL_BLOCK_TAIL_CALL_OPTIMIZATION();
+}
+
+template <bool IS_STACK_FRAMES, bool IS_WITH_CONTEXT>
+ABSL_ATTRIBUTE_NO_SANITIZE_ADDRESS  // May read random elements from stack.
+ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY   // May read random elements from stack.
+static int UnwindImpl(void** result, int* sizes, int max_depth, int skip_count,
+                      const void *ucp, int *min_dropped_frames) {
+  void **sp;
+  // Apple macOS uses an old version of gnu as -- both Darwin 7.9.0 (Panther)
+  // and Darwin 8.8.1 (Tiger) use as 1.38.  This means we have to use a
+  // different asm syntax.  I don't know quite the best way to discriminate
+  // systems using the old as from the new one; I've gone with __APPLE__.
+#ifdef __APPLE__
+  __asm__ volatile ("mr %0,r1" : "=r" (sp));
+#else
+  __asm__ volatile ("mr %0,1" : "=r" (sp));
+#endif
+
+  // On PowerPC, the "Link Register" or "Link Record" (LR), is a stack
+  // entry that holds the return address of the subroutine call (what
+  // instruction we run after our function finishes).  This is the
+  // same as the stack-pointer of our parent routine, which is what we
+  // want here.  While the compiler will always(?) set up LR for
+  // subroutine calls, it may not for leaf functions (such as this one).
+  // This routine forces the compiler (at least gcc) to push it anyway.
+  AbslStacktracePowerPCDummyFunction();
+
+  // The LR save area is used by the callee, so the top entry is bogus.
+  skip_count++;
+
+  int n = 0;
+
+  // Unlike ABIs of X86 and ARM, PowerPC ABIs say that return address (in
+  // the link register) of a function call is stored in the caller's stack
+  // frame instead of the callee's.  When we look for the return address
+  // associated with a stack frame, we need to make sure that there is a
+  // caller frame before it.  So we call NextStackFrame before entering the
+  // loop below and check next_sp instead of sp for loop termination.
+  // The outermost frame is set up by runtimes and it does not have a
+  // caller frame, so it is skipped.
+
+  // The absl::GetStackFrames routine is called when we are in some
+  // informational context (the failure signal handler for example).
+  // Use the non-strict unwinding rules to produce a stack trace
+  // that is as complete as possible (even if it contains a few
+  // bogus entries in some rare cases).
+  void **next_sp = NextStackFrame<!IS_STACK_FRAMES, IS_WITH_CONTEXT>(sp, ucp);
+
+  while (next_sp && n < max_depth) {
+    if (skip_count > 0) {
+      skip_count--;
+    } else {
+      result[n] = StacktracePowerPCGetLR(sp);
+      if (IS_STACK_FRAMES) {
+        if (next_sp > sp) {
+          sizes[n] = (uintptr_t)next_sp - (uintptr_t)sp;
+        } else {
+          // A frame-size of 0 is used to indicate unknown frame size.
+          sizes[n] = 0;
+        }
+      }
+      n++;
+    }
+
+    sp = next_sp;
+    next_sp = NextStackFrame<!IS_STACK_FRAMES, IS_WITH_CONTEXT>(sp, ucp);
+  }
+
+  if (min_dropped_frames != nullptr) {
+    // Implementation detail: we clamp the max of frames we are willing to
+    // count, so as not to spend too much time in the loop below.
+    const int kMaxUnwind = 1000;
+    int num_dropped_frames = 0;
+    for (int j = 0; next_sp != nullptr && j < kMaxUnwind; j++) {
+      if (skip_count > 0) {
+        skip_count--;
+      } else {
+        num_dropped_frames++;
+      }
+      next_sp = NextStackFrame<!IS_STACK_FRAMES, IS_WITH_CONTEXT>(next_sp, ucp);
+    }
+    *min_dropped_frames = num_dropped_frames;
+  }
+  return n;
+}
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+bool StackTraceWorksForTest() {
+  return true;
+}
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_DEBUGGING_INTERNAL_STACKTRACE_POWERPC_INL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_riscv-inl.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_riscv-inl.inc
new file mode 100644
index 0000000000000000000000000000000000000000..20183fa3213b01c9c9d8242d92fe74846c7c5ef6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_riscv-inl.inc
@@ -0,0 +1,191 @@
+// Copyright 2021 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_DEBUGGING_INTERNAL_STACKTRACE_RISCV_INL_H_
+#define ABSL_DEBUGGING_INTERNAL_STACKTRACE_RISCV_INL_H_
+
+// Generate stack trace for riscv
+
+#include <sys/ucontext.h>
+
+#include "absl/base/config.h"
+#if defined(__linux__)
+#include <sys/mman.h>
+#include <ucontext.h>
+#include <unistd.h>
+#endif
+
+#include <atomic>
+#include <cassert>
+#include <cstdint>
+#include <iostream>
+#include <limits>
+#include <utility>
+
+#include "absl/base/attributes.h"
+#include "absl/debugging/stacktrace.h"
+
+static const uintptr_t kUnknownFrameSize = 0;
+
+// Compute the size of a stack frame in [low..high).  We assume that low < high.
+// Return size of kUnknownFrameSize.
+template <typename T>
+static inline uintptr_t ComputeStackFrameSize(const T *low, const T *high) {
+  const char *low_char_ptr = reinterpret_cast<const char *>(low);
+  const char *high_char_ptr = reinterpret_cast<const char *>(high);
+  return low < high ? high_char_ptr - low_char_ptr : kUnknownFrameSize;
+}
+
+// Given a pointer to a stack frame, locate and return the calling stackframe,
+// or return null if no stackframe can be found. Perform sanity checks (the
+// strictness of which is controlled by the boolean parameter
+// "STRICT_UNWINDING") to reduce the chance that a bad pointer is returned.
+template <bool STRICT_UNWINDING, bool WITH_CONTEXT>
+ABSL_ATTRIBUTE_NO_SANITIZE_ADDRESS  // May read random elements from stack.
+ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY   // May read random elements from stack.
+static void ** NextStackFrame(void **old_frame_pointer, const void *uc,
+                              const std::pair<size_t, size_t> range) {
+  //               .
+  //               .
+  //               .
+  //   +-> +----------------+
+  //   |   | return address |
+  //   |   |   previous fp  |
+  //   |   |      ...       |
+  //   |   +----------------+ <-+
+  //   |   | return address |   |
+  //   +---|-  previous fp  |   |
+  //       |      ...       |   |
+  // $fp ->|----------------+   |
+  //       | return address |   |
+  //       |   previous fp -|---+
+  // $sp ->|      ...       |
+  //       +----------------+
+  void **new_frame_pointer = reinterpret_cast<void **>(old_frame_pointer[-2]);
+  uintptr_t frame_pointer = reinterpret_cast<uintptr_t>(new_frame_pointer);
+
+  // The RISCV ELF psABI mandates that the stack pointer is always 16-byte
+  // aligned.
+  // TODO(#1236) this doesn't hold for ILP32E which only mandates a 4-byte
+  // alignment.
+  if (frame_pointer & 15)
+    return nullptr;
+
+  // If the new frame pointer matches the signal context, avoid terminating
+  // early to deal with alternate signal stacks.
+  if (WITH_CONTEXT)
+    if (const ucontext_t *ucv = static_cast<const ucontext_t *>(uc))
+      // RISCV ELF psABI has the frame pointer at x8/fp/s0.
+      // -- RISCV psABI Table 18.2
+      if (ucv->uc_mcontext.__gregs[8] == frame_pointer)
+        return new_frame_pointer;
+
+  // Check frame size.  In strict mode, we assume frames to be under 100,000
+  // bytes.  In non-strict mode, we relax the limit to 1MB.
+  const uintptr_t max_size = STRICT_UNWINDING ? 100000 : 1000000;
+  const uintptr_t frame_size =
+      ComputeStackFrameSize(old_frame_pointer, new_frame_pointer);
+  if (frame_size == kUnknownFrameSize) {
+    if (STRICT_UNWINDING)
+      return nullptr;
+
+    // In non-strict mode permit non-contiguous stacks (e.g. alternate signal
+    // frame handling).
+    if (reinterpret_cast<uintptr_t>(new_frame_pointer) < range.first ||
+        reinterpret_cast<uintptr_t>(new_frame_pointer) > range.second)
+      return nullptr;
+  }
+
+  if (frame_size > max_size)
+    return nullptr;
+
+  return new_frame_pointer;
+}
+
+template <bool IS_STACK_FRAMES, bool IS_WITH_CONTEXT>
+ABSL_ATTRIBUTE_NO_SANITIZE_ADDRESS  // May read random elements from stack.
+ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY   // May read random elements from stack.
+static int UnwindImpl(void **result, int *sizes, int max_depth, int skip_count,
+                      const void *ucp, int *min_dropped_frames) {
+  // The `frame_pointer` that is computed here points to the top of the frame.
+  // The two words preceding the address are the return address and the previous
+  // frame pointer.
+#if defined(__GNUC__)
+  void **frame_pointer = reinterpret_cast<void **>(__builtin_frame_address(0));
+#else
+#error reading stack pointer not yet supported on this platform
+#endif
+
+  std::pair<size_t, size_t> stack = {
+      // assume that the first page is not the stack.
+      static_cast<size_t>(sysconf(_SC_PAGESIZE)),
+      std::numeric_limits<size_t>::max() - sizeof(void *)
+  };
+
+  int n = 0;
+  void *return_address = nullptr;
+  while (frame_pointer && n < max_depth) {
+    return_address = frame_pointer[-1];
+
+    // The absl::GetStackFrames routine is called when we are in some
+    // informational context (the failure signal handler for example).  Use the
+    // non-strict unwinding rules to produce a stack trace that is as complete
+    // as possible (even if it contains a few bogus entries in some rare cases).
+    void **next_frame_pointer =
+        NextStackFrame<!IS_STACK_FRAMES, IS_WITH_CONTEXT>(frame_pointer, ucp,
+                                                          stack);
+
+    if (skip_count > 0) {
+      skip_count--;
+    } else {
+      result[n] = return_address;
+      if (IS_STACK_FRAMES) {
+        sizes[n] = ComputeStackFrameSize(frame_pointer, next_frame_pointer);
+      }
+      n++;
+    }
+
+    frame_pointer = next_frame_pointer;
+  }
+
+  if (min_dropped_frames != nullptr) {
+    // Implementation detail: we clamp the max of frames we are willing to
+    // count, so as not to spend too much time in the loop below.
+    const int kMaxUnwind = 200;
+    int num_dropped_frames = 0;
+    for (int j = 0; frame_pointer != nullptr && j < kMaxUnwind; j++) {
+      if (skip_count > 0) {
+        skip_count--;
+      } else {
+        num_dropped_frames++;
+      }
+      frame_pointer =
+          NextStackFrame<!IS_STACK_FRAMES, IS_WITH_CONTEXT>(frame_pointer, ucp,
+                                                            stack);
+    }
+    *min_dropped_frames = num_dropped_frames;
+  }
+
+  return n;
+}
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+bool StackTraceWorksForTest() { return true; }
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_unimplemented-inl.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_unimplemented-inl.inc
new file mode 100644
index 0000000000000000000000000000000000000000..5b8fb191b65a33000b1c1d2ce53907b4d6e2f2b1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_unimplemented-inl.inc
@@ -0,0 +1,24 @@
+#ifndef ABSL_DEBUGGING_INTERNAL_STACKTRACE_UNIMPLEMENTED_INL_H_
+#define ABSL_DEBUGGING_INTERNAL_STACKTRACE_UNIMPLEMENTED_INL_H_
+
+template <bool IS_STACK_FRAMES, bool IS_WITH_CONTEXT>
+static int UnwindImpl(void** /* result */, int* /* sizes */,
+                      int /* max_depth */, int /* skip_count */,
+                      const void* /* ucp */, int *min_dropped_frames) {
+  if (min_dropped_frames != nullptr) {
+    *min_dropped_frames = 0;
+  }
+  return 0;
+}
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+bool StackTraceWorksForTest() {
+  return false;
+}
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_DEBUGGING_INTERNAL_STACKTRACE_UNIMPLEMENTED_INL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_win32-inl.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_win32-inl.inc
new file mode 100644
index 0000000000000000000000000000000000000000..ef2b973ec3e3dad0cce7b893e98867b5dabd023d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_win32-inl.inc
@@ -0,0 +1,94 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Produces a stack trace for Windows.  Normally, one could use
+// stacktrace_x86-inl.h or stacktrace_x86_64-inl.h -- and indeed, that
+// should work for binaries compiled using MSVC in "debug" mode.
+// However, in "release" mode, Windows uses frame-pointer
+// optimization, which makes getting a stack trace very difficult.
+//
+// There are several approaches one can take.  One is to use Windows
+// intrinsics like StackWalk64.  These can work, but have restrictions
+// on how successful they can be.  Another attempt is to write a
+// version of stacktrace_x86-inl.h that has heuristic support for
+// dealing with FPO, similar to what WinDbg does (see
+// http://www.nynaeve.net/?p=97).  There are (non-working) examples of
+// these approaches, complete with TODOs, in stacktrace_win32-inl.h#1
+//
+// The solution we've ended up doing is to call the undocumented
+// windows function RtlCaptureStackBackTrace, which probably doesn't
+// work with FPO but at least is fast, and doesn't require a symbol
+// server.
+//
+// This code is inspired by a patch from David Vitek:
+//   https://code.google.com/p/google-perftools/issues/detail?id=83
+
+#ifndef ABSL_DEBUGGING_INTERNAL_STACKTRACE_WIN32_INL_H_
+#define ABSL_DEBUGGING_INTERNAL_STACKTRACE_WIN32_INL_H_
+
+#include <windows.h>    // for GetProcAddress and GetModuleHandle
+#include <cassert>
+
+typedef USHORT NTAPI RtlCaptureStackBackTrace_Function(
+    IN ULONG frames_to_skip,
+    IN ULONG frames_to_capture,
+    OUT PVOID *backtrace,
+    OUT PULONG backtrace_hash);
+
+// It is not possible to load RtlCaptureStackBackTrace at static init time in
+// UWP. CaptureStackBackTrace is the public version of RtlCaptureStackBackTrace
+#if WINAPI_FAMILY_PARTITION(WINAPI_PARTITION_APP) && \
+    !WINAPI_FAMILY_PARTITION(WINAPI_PARTITION_DESKTOP)
+static RtlCaptureStackBackTrace_Function* const RtlCaptureStackBackTrace_fn =
+    &::CaptureStackBackTrace;
+#else
+// Load the function we need at static init time, where we don't have
+// to worry about someone else holding the loader's lock.
+static RtlCaptureStackBackTrace_Function* const RtlCaptureStackBackTrace_fn =
+    (RtlCaptureStackBackTrace_Function*)GetProcAddress(
+        GetModuleHandleA("ntdll.dll"), "RtlCaptureStackBackTrace");
+#endif  // WINAPI_PARTITION_APP && !WINAPI_PARTITION_DESKTOP
+
+template <bool IS_STACK_FRAMES, bool IS_WITH_CONTEXT>
+static int UnwindImpl(void** result, int* sizes, int max_depth, int skip_count,
+                      const void*, int* min_dropped_frames) {
+  USHORT n = 0;
+  if (!RtlCaptureStackBackTrace_fn || skip_count < 0 || max_depth < 0) {
+    // can't get a stacktrace with no function/invalid args
+  } else {
+    n = RtlCaptureStackBackTrace_fn(static_cast<ULONG>(skip_count) + 2,
+                                    static_cast<ULONG>(max_depth), result, 0);
+  }
+  if (IS_STACK_FRAMES) {
+    // No implementation for finding out the stack frame sizes yet.
+    memset(sizes, 0, sizeof(*sizes) * n);
+  }
+  if (min_dropped_frames != nullptr) {
+    // Not implemented.
+    *min_dropped_frames = 0;
+  }
+  return n;
+}
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+bool StackTraceWorksForTest() {
+  return false;
+}
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_DEBUGGING_INTERNAL_STACKTRACE_WIN32_INL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_x86-inl.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_x86-inl.inc
new file mode 100644
index 0000000000000000000000000000000000000000..7b26464eb48ccfb25b9e8f2e1d41078f0e2d8a6d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/stacktrace_x86-inl.inc
@@ -0,0 +1,394 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Produce stack trace
+
+#ifndef ABSL_DEBUGGING_INTERNAL_STACKTRACE_X86_INL_INC_
+#define ABSL_DEBUGGING_INTERNAL_STACKTRACE_X86_INL_INC_
+
+#if defined(__linux__) && (defined(__i386__) || defined(__x86_64__))
+#include <ucontext.h>  // for ucontext_t
+#endif
+
+#if !defined(_WIN32)
+#include <unistd.h>
+#endif
+
+#include <cassert>
+#include <cstdint>
+#include <limits>
+
+#include "absl/base/attributes.h"
+#include "absl/base/macros.h"
+#include "absl/base/port.h"
+#include "absl/debugging/internal/address_is_readable.h"
+#include "absl/debugging/internal/vdso_support.h"  // a no-op on non-elf or non-glibc systems
+#include "absl/debugging/stacktrace.h"
+
+using absl::debugging_internal::AddressIsReadable;
+
+#if defined(__linux__) && defined(__i386__)
+// Count "push %reg" instructions in VDSO __kernel_vsyscall(),
+// preceeding "syscall" or "sysenter".
+// If __kernel_vsyscall uses frame pointer, answer 0.
+//
+// kMaxBytes tells how many instruction bytes of __kernel_vsyscall
+// to analyze before giving up. Up to kMaxBytes+1 bytes of
+// instructions could be accessed.
+//
+// Here are known __kernel_vsyscall instruction sequences:
+//
+// SYSENTER (linux-2.6.26/arch/x86/vdso/vdso32/sysenter.S).
+// Used on Intel.
+//  0xffffe400 <__kernel_vsyscall+0>:       push   %ecx
+//  0xffffe401 <__kernel_vsyscall+1>:       push   %edx
+//  0xffffe402 <__kernel_vsyscall+2>:       push   %ebp
+//  0xffffe403 <__kernel_vsyscall+3>:       mov    %esp,%ebp
+//  0xffffe405 <__kernel_vsyscall+5>:       sysenter
+//
+// SYSCALL (see linux-2.6.26/arch/x86/vdso/vdso32/syscall.S).
+// Used on AMD.
+//  0xffffe400 <__kernel_vsyscall+0>:       push   %ebp
+//  0xffffe401 <__kernel_vsyscall+1>:       mov    %ecx,%ebp
+//  0xffffe403 <__kernel_vsyscall+3>:       syscall
+//
+
+// The sequence below isn't actually expected in Google fleet,
+// here only for completeness. Remove this comment from OSS release.
+
+// i386 (see linux-2.6.26/arch/x86/vdso/vdso32/int80.S)
+//  0xffffe400 <__kernel_vsyscall+0>:       int $0x80
+//  0xffffe401 <__kernel_vsyscall+1>:       ret
+//
+static const int kMaxBytes = 10;
+
+// We use assert()s instead of DCHECK()s -- this is too low level
+// for DCHECK().
+
+static int CountPushInstructions(const unsigned char *const addr) {
+  int result = 0;
+  for (int i = 0; i < kMaxBytes; ++i) {
+    if (addr[i] == 0x89) {
+      // "mov reg,reg"
+      if (addr[i + 1] == 0xE5) {
+        // Found "mov %esp,%ebp".
+        return 0;
+      }
+      ++i;  // Skip register encoding byte.
+    } else if (addr[i] == 0x0F &&
+               (addr[i + 1] == 0x34 || addr[i + 1] == 0x05)) {
+      // Found "sysenter" or "syscall".
+      return result;
+    } else if ((addr[i] & 0xF0) == 0x50) {
+      // Found "push %reg".
+      ++result;
+    } else if (addr[i] == 0xCD && addr[i + 1] == 0x80) {
+      // Found "int $0x80"
+      assert(result == 0);
+      return 0;
+    } else {
+      // Unexpected instruction.
+      assert(false && "unexpected instruction in __kernel_vsyscall");
+      return 0;
+    }
+  }
+  // Unexpected: didn't find SYSENTER or SYSCALL in
+  // [__kernel_vsyscall, __kernel_vsyscall + kMaxBytes) interval.
+  assert(false && "did not find SYSENTER or SYSCALL in __kernel_vsyscall");
+  return 0;
+}
+#endif
+
+// Assume stack frames larger than 100,000 bytes are bogus.
+static const int kMaxFrameBytes = 100000;
+// Stack end to use when we don't know the actual stack end
+// (effectively just the end of address space).
+constexpr uintptr_t kUnknownStackEnd =
+    std::numeric_limits<size_t>::max() - sizeof(void *);
+
+// Returns the stack frame pointer from signal context, 0 if unknown.
+// vuc is a ucontext_t *.  We use void* to avoid the use
+// of ucontext_t on non-POSIX systems.
+static uintptr_t GetFP(const void *vuc) {
+#if !defined(__linux__)
+  static_cast<void>(vuc);  // Avoid an unused argument compiler warning.
+#else
+  if (vuc != nullptr) {
+    auto *uc = reinterpret_cast<const ucontext_t *>(vuc);
+#if defined(__i386__)
+    const auto bp = uc->uc_mcontext.gregs[REG_EBP];
+    const auto sp = uc->uc_mcontext.gregs[REG_ESP];
+#elif defined(__x86_64__)
+    const auto bp = uc->uc_mcontext.gregs[REG_RBP];
+    const auto sp = uc->uc_mcontext.gregs[REG_RSP];
+#else
+    const uintptr_t bp = 0;
+    const uintptr_t sp = 0;
+#endif
+    // Sanity-check that the base pointer is valid. It's possible that some
+    // code in the process is compiled with --copt=-fomit-frame-pointer or
+    // --copt=-momit-leaf-frame-pointer.
+    //
+    // TODO(bcmills): -momit-leaf-frame-pointer is currently the default
+    // behavior when building with clang.  Talk to the C++ toolchain team about
+    // fixing that.
+    if (bp >= sp && bp - sp <= kMaxFrameBytes)
+      return static_cast<uintptr_t>(bp);
+
+    // If bp isn't a plausible frame pointer, return the stack pointer instead.
+    // If we're lucky, it points to the start of a stack frame; otherwise, we'll
+    // get one frame of garbage in the stack trace and fail the sanity check on
+    // the next iteration.
+    return static_cast<uintptr_t>(sp);
+  }
+#endif
+  return 0;
+}
+
+// Given a pointer to a stack frame, locate and return the calling
+// stackframe, or return null if no stackframe can be found. Perform sanity
+// checks (the strictness of which is controlled by the boolean parameter
+// "STRICT_UNWINDING") to reduce the chance that a bad pointer is returned.
+template <bool STRICT_UNWINDING, bool WITH_CONTEXT>
+ABSL_ATTRIBUTE_NO_SANITIZE_ADDRESS  // May read random elements from stack.
+ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY   // May read random elements from stack.
+static void **NextStackFrame(void **old_fp, const void *uc,
+                             size_t stack_low, size_t stack_high) {
+  void **new_fp = (void **)*old_fp;
+
+#if defined(__linux__) && defined(__i386__)
+  if (WITH_CONTEXT && uc != nullptr) {
+    // How many "push %reg" instructions are there at __kernel_vsyscall?
+    // This is constant for a given kernel and processor, so compute
+    // it only once.
+    static int num_push_instructions = -1;  // Sentinel: not computed yet.
+    // Initialize with sentinel value: __kernel_rt_sigreturn can not possibly
+    // be there.
+    static const unsigned char *kernel_rt_sigreturn_address = nullptr;
+    static const unsigned char *kernel_vsyscall_address = nullptr;
+    if (num_push_instructions == -1) {
+#ifdef ABSL_HAVE_VDSO_SUPPORT
+      absl::debugging_internal::VDSOSupport vdso;
+      if (vdso.IsPresent()) {
+        absl::debugging_internal::VDSOSupport::SymbolInfo
+            rt_sigreturn_symbol_info;
+        absl::debugging_internal::VDSOSupport::SymbolInfo vsyscall_symbol_info;
+        if (!vdso.LookupSymbol("__kernel_rt_sigreturn", "LINUX_2.5", STT_FUNC,
+                               &rt_sigreturn_symbol_info) ||
+            !vdso.LookupSymbol("__kernel_vsyscall", "LINUX_2.5", STT_FUNC,
+                               &vsyscall_symbol_info) ||
+            rt_sigreturn_symbol_info.address == nullptr ||
+            vsyscall_symbol_info.address == nullptr) {
+          // Unexpected: 32-bit VDSO is present, yet one of the expected
+          // symbols is missing or null.
+          assert(false && "VDSO is present, but doesn't have expected symbols");
+          num_push_instructions = 0;
+        } else {
+          kernel_rt_sigreturn_address =
+              reinterpret_cast<const unsigned char *>(
+                  rt_sigreturn_symbol_info.address);
+          kernel_vsyscall_address =
+              reinterpret_cast<const unsigned char *>(
+                  vsyscall_symbol_info.address);
+          num_push_instructions =
+              CountPushInstructions(kernel_vsyscall_address);
+        }
+      } else {
+        num_push_instructions = 0;
+      }
+#else  // ABSL_HAVE_VDSO_SUPPORT
+      num_push_instructions = 0;
+#endif  // ABSL_HAVE_VDSO_SUPPORT
+    }
+    if (num_push_instructions != 0 && kernel_rt_sigreturn_address != nullptr &&
+        old_fp[1] == kernel_rt_sigreturn_address) {
+      const ucontext_t *ucv = static_cast<const ucontext_t *>(uc);
+      // This kernel does not use frame pointer in its VDSO code,
+      // and so %ebp is not suitable for unwinding.
+      void **const reg_ebp =
+          reinterpret_cast<void **>(ucv->uc_mcontext.gregs[REG_EBP]);
+      const unsigned char *const reg_eip =
+          reinterpret_cast<unsigned char *>(ucv->uc_mcontext.gregs[REG_EIP]);
+      if (new_fp == reg_ebp && kernel_vsyscall_address <= reg_eip &&
+          reg_eip - kernel_vsyscall_address < kMaxBytes) {
+        // We "stepped up" to __kernel_vsyscall, but %ebp is not usable.
+        // Restore from 'ucv' instead.
+        void **const reg_esp =
+            reinterpret_cast<void **>(ucv->uc_mcontext.gregs[REG_ESP]);
+        // Check that alleged %esp is not null and is reasonably aligned.
+        if (reg_esp &&
+            ((uintptr_t)reg_esp & (sizeof(reg_esp) - 1)) == 0) {
+          // Check that alleged %esp is actually readable. This is to prevent
+          // "double fault" in case we hit the first fault due to e.g. stack
+          // corruption.
+          void *const reg_esp2 = reg_esp[num_push_instructions - 1];
+          if (AddressIsReadable(reg_esp2)) {
+            // Alleged %esp is readable, use it for further unwinding.
+            new_fp = reinterpret_cast<void **>(reg_esp2);
+          }
+        }
+      }
+    }
+  }
+#endif
+
+  const uintptr_t old_fp_u = reinterpret_cast<uintptr_t>(old_fp);
+  const uintptr_t new_fp_u = reinterpret_cast<uintptr_t>(new_fp);
+
+  // Check that the transition from frame pointer old_fp to frame
+  // pointer new_fp isn't clearly bogus.  Skip the checks if new_fp
+  // matches the signal context, so that we don't skip out early when
+  // using an alternate signal stack.
+  //
+  // TODO(bcmills): The GetFP call should be completely unnecessary when
+  // ENABLE_COMBINED_UNWINDER is set (because we should be back in the thread's
+  // stack by this point), but it is empirically still needed (e.g. when the
+  // stack includes a call to abort).  unw_get_reg returns UNW_EBADREG for some
+  // frames.  Figure out why GetValidFrameAddr and/or libunwind isn't doing what
+  // it's supposed to.
+  if (STRICT_UNWINDING &&
+      (!WITH_CONTEXT || uc == nullptr || new_fp_u != GetFP(uc))) {
+    // With the stack growing downwards, older stack frame must be
+    // at a greater address that the current one.
+    if (new_fp_u <= old_fp_u) return nullptr;
+
+    // If we get a very large frame size, it may be an indication that we
+    // guessed frame pointers incorrectly and now risk a paging fault
+    // dereferencing a wrong frame pointer. Or maybe not because large frames
+    // are possible as well. The main stack is assumed to be readable,
+    // so we assume the large frame is legit if we know the real stack bounds
+    // and are within the stack.
+    if (new_fp_u - old_fp_u > kMaxFrameBytes) {
+      if (stack_high < kUnknownStackEnd &&
+          static_cast<size_t>(getpagesize()) < stack_low) {
+        // Stack bounds are known.
+        if (!(stack_low < new_fp_u && new_fp_u <= stack_high)) {
+          // new_fp_u is not within the known stack.
+          return nullptr;
+        }
+      } else {
+        // Stack bounds are unknown, prefer truncated stack to possible crash.
+        return nullptr;
+      }
+    }
+    if (stack_low < old_fp_u && old_fp_u <= stack_high) {
+      // Old BP was in the expected stack region...
+      if (!(stack_low < new_fp_u && new_fp_u <= stack_high)) {
+        // ... but new BP is outside of expected stack region.
+        // It is most likely bogus.
+        return nullptr;
+      }
+    } else {
+      // We may be here if we are executing in a co-routine with a
+      // separate stack. We can't do safety checks in this case.
+    }
+  } else {
+    if (new_fp == nullptr) return nullptr;  // skip AddressIsReadable() below
+    // In the non-strict mode, allow discontiguous stack frames.
+    // (alternate-signal-stacks for example).
+    if (new_fp == old_fp) return nullptr;
+  }
+
+  if (new_fp_u & (sizeof(void *) - 1)) return nullptr;
+#ifdef __i386__
+  // On 32-bit machines, the stack pointer can be very close to
+  // 0xffffffff, so we explicitly check for a pointer into the
+  // last two pages in the address space
+  if (new_fp_u >= 0xffffe000) return nullptr;
+#endif
+#if !defined(_WIN32)
+  if (!STRICT_UNWINDING) {
+    // Lax sanity checks cause a crash in 32-bit tcmalloc/crash_reason_test
+    // on AMD-based machines with VDSO-enabled kernels.
+    // Make an extra sanity check to insure new_fp is readable.
+    // Note: NextStackFrame<false>() is only called while the program
+    //       is already on its last leg, so it's ok to be slow here.
+
+    if (!AddressIsReadable(new_fp)) {
+      return nullptr;
+    }
+  }
+#endif
+  return new_fp;
+}
+
+template <bool IS_STACK_FRAMES, bool IS_WITH_CONTEXT>
+ABSL_ATTRIBUTE_NO_SANITIZE_ADDRESS  // May read random elements from stack.
+ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY   // May read random elements from stack.
+ABSL_ATTRIBUTE_NOINLINE
+static int UnwindImpl(void **result, int *sizes, int max_depth, int skip_count,
+                      const void *ucp, int *min_dropped_frames) {
+  int n = 0;
+  void **fp = reinterpret_cast<void **>(__builtin_frame_address(0));
+
+  // Assume that the first page is not stack.
+  size_t stack_low = static_cast<size_t>(getpagesize());
+  size_t stack_high = kUnknownStackEnd;
+
+  while (fp && n < max_depth) {
+    if (*(fp + 1) == reinterpret_cast<void *>(0)) {
+      // In 64-bit code, we often see a frame that
+      // points to itself and has a return address of 0.
+      break;
+    }
+    void **next_fp = NextStackFrame<!IS_STACK_FRAMES, IS_WITH_CONTEXT>(
+        fp, ucp, stack_low, stack_high);
+    if (skip_count > 0) {
+      skip_count--;
+    } else {
+      result[n] = *(fp + 1);
+      if (IS_STACK_FRAMES) {
+        if (next_fp > fp) {
+          sizes[n] = static_cast<int>(
+              reinterpret_cast<uintptr_t>(next_fp) -
+              reinterpret_cast<uintptr_t>(fp));
+        } else {
+          // A frame-size of 0 is used to indicate unknown frame size.
+          sizes[n] = 0;
+        }
+      }
+      n++;
+    }
+    fp = next_fp;
+  }
+  if (min_dropped_frames != nullptr) {
+    // Implementation detail: we clamp the max of frames we are willing to
+    // count, so as not to spend too much time in the loop below.
+    const int kMaxUnwind = 1000;
+    int num_dropped_frames = 0;
+    for (int j = 0; fp != nullptr && j < kMaxUnwind; j++) {
+      if (skip_count > 0) {
+        skip_count--;
+      } else {
+        num_dropped_frames++;
+      }
+      fp = NextStackFrame<!IS_STACK_FRAMES, IS_WITH_CONTEXT>(fp, ucp, stack_low,
+                                                             stack_high);
+    }
+    *min_dropped_frames = num_dropped_frames;
+  }
+  return n;
+}
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+bool StackTraceWorksForTest() {
+  return true;
+}
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_DEBUGGING_INTERNAL_STACKTRACE_X86_INL_INC_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/symbolize.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/symbolize.h
new file mode 100644
index 0000000000000000000000000000000000000000..27d5e6521ec5ddbb4cb871247e6adab612056c43
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/symbolize.h
@@ -0,0 +1,153 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// This file contains internal parts of the Abseil symbolizer.
+// Do not depend on the anything in this file, it may change at anytime.
+
+#ifndef ABSL_DEBUGGING_INTERNAL_SYMBOLIZE_H_
+#define ABSL_DEBUGGING_INTERNAL_SYMBOLIZE_H_
+
+#ifdef __cplusplus
+
+#include <cstddef>
+#include <cstdint>
+
+#include "absl/base/config.h"
+#include "absl/strings/string_view.h"
+
+#ifdef ABSL_INTERNAL_HAVE_ELF_SYMBOLIZE
+#error ABSL_INTERNAL_HAVE_ELF_SYMBOLIZE cannot be directly set
+#elif defined(__ELF__) && defined(__GLIBC__) && !defined(__native_client__) \
+      && !defined(__asmjs__) && !defined(__wasm__)
+#define ABSL_INTERNAL_HAVE_ELF_SYMBOLIZE 1
+
+#include <elf.h>
+#include <link.h>  // For ElfW() macro.
+#include <functional>
+#include <string>
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+
+// Iterates over all sections, invoking callback on each with the section name
+// and the section header.
+//
+// Returns true on success; otherwise returns false in case of errors.
+//
+// This is not async-signal-safe.
+bool ForEachSection(int fd,
+                    const std::function<bool(absl::string_view name,
+                                             const ElfW(Shdr) &)>& callback);
+
+// Gets the section header for the given name, if it exists. Returns true on
+// success. Otherwise, returns false.
+bool GetSectionHeaderByName(int fd, const char *name, size_t name_len,
+                            ElfW(Shdr) *out);
+
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_INTERNAL_HAVE_ELF_SYMBOLIZE
+
+#ifdef ABSL_INTERNAL_HAVE_DARWIN_SYMBOLIZE
+#error ABSL_INTERNAL_HAVE_DARWIN_SYMBOLIZE cannot be directly set
+#elif defined(__APPLE__)
+#define ABSL_INTERNAL_HAVE_DARWIN_SYMBOLIZE 1
+#endif
+
+#ifdef ABSL_INTERNAL_HAVE_EMSCRIPTEN_SYMBOLIZE
+#error ABSL_INTERNAL_HAVE_EMSCRIPTEN_SYMBOLIZE cannot be directly set
+#elif defined(__EMSCRIPTEN__)
+#define ABSL_INTERNAL_HAVE_EMSCRIPTEN_SYMBOLIZE 1
+#endif
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+
+struct SymbolDecoratorArgs {
+  // The program counter we are getting symbolic name for.
+  const void *pc;
+  // 0 for main executable, load address for shared libraries.
+  ptrdiff_t relocation;
+  // Read-only file descriptor for ELF image covering "pc",
+  // or -1 if no such ELF image exists in /proc/self/maps.
+  int fd;
+  // Output buffer, size.
+  // Note: the buffer may not be empty -- default symbolizer may have already
+  // produced some output, and earlier decorators may have adorned it in
+  // some way. You are free to replace or augment the contents (within the
+  // symbol_buf_size limit).
+  char *const symbol_buf;
+  size_t symbol_buf_size;
+  // Temporary scratch space, size.
+  // Use that space in preference to allocating your own stack buffer to
+  // conserve stack.
+  char *const tmp_buf;
+  size_t tmp_buf_size;
+  // User-provided argument
+  void* arg;
+};
+using SymbolDecorator = void (*)(const SymbolDecoratorArgs *);
+
+// Installs a function-pointer as a decorator. Returns a value less than zero
+// if the system cannot install the decorator. Otherwise, returns a unique
+// identifier corresponding to the decorator. This identifier can be used to
+// uninstall the decorator - See RemoveSymbolDecorator() below.
+int InstallSymbolDecorator(SymbolDecorator decorator, void* arg);
+
+// Removes a previously installed function-pointer decorator. Parameter "ticket"
+// is the return-value from calling InstallSymbolDecorator().
+bool RemoveSymbolDecorator(int ticket);
+
+// Remove all installed decorators.  Returns true if successful, false if
+// symbolization is currently in progress.
+bool RemoveAllSymbolDecorators(void);
+
+// Registers an address range to a file mapping.
+//
+// Preconditions:
+//   start <= end
+//   filename != nullptr
+//
+// Returns true if the file was successfully registered.
+bool RegisterFileMappingHint(const void* start, const void* end,
+                             uint64_t offset, const char* filename);
+
+// Looks up the file mapping registered by RegisterFileMappingHint for an
+// address range. If there is one, the file name is stored in *filename and
+// *start and *end are modified to reflect the registered mapping. Returns
+// whether any hint was found.
+bool GetFileMappingHint(const void** start, const void** end, uint64_t* offset,
+                        const char** filename);
+
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // __cplusplus
+
+#include <stdbool.h>
+
+#ifdef __cplusplus
+extern "C"
+#endif  // __cplusplus
+
+    bool
+    AbslInternalGetFileMappingHint(const void** start, const void** end,
+                                   uint64_t* offset, const char** filename);
+
+#endif  // ABSL_DEBUGGING_INTERNAL_SYMBOLIZE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/vdso_support.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/vdso_support.h
new file mode 100644
index 0000000000000000000000000000000000000000..6562c6c2350aaa2d40c5341737c9a6a63589ba47
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/internal/vdso_support.h
@@ -0,0 +1,158 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+// Allow dynamic symbol lookup in the kernel VDSO page.
+//
+// VDSO stands for "Virtual Dynamic Shared Object" -- a page of
+// executable code, which looks like a shared library, but doesn't
+// necessarily exist anywhere on disk, and which gets mmap()ed into
+// every process by kernels which support VDSO, such as 2.6.x for 32-bit
+// executables, and 2.6.24 and above for 64-bit executables.
+//
+// More details could be found here:
+// http://www.trilithium.com/johan/2005/08/linux-gate/
+//
+// VDSOSupport -- a class representing kernel VDSO (if present).
+//
+// Example usage:
+//  VDSOSupport vdso;
+//  VDSOSupport::SymbolInfo info;
+//  typedef (*FN)(unsigned *, void *, void *);
+//  FN fn = nullptr;
+//  if (vdso.LookupSymbol("__vdso_getcpu", "LINUX_2.6", STT_FUNC, &info)) {
+//     fn = reinterpret_cast<FN>(info.address);
+//  }
+
+#ifndef ABSL_DEBUGGING_INTERNAL_VDSO_SUPPORT_H_
+#define ABSL_DEBUGGING_INTERNAL_VDSO_SUPPORT_H_
+
+#include <atomic>
+
+#include "absl/base/attributes.h"
+#include "absl/debugging/internal/elf_mem_image.h"
+
+#ifdef ABSL_HAVE_ELF_MEM_IMAGE
+
+#ifdef ABSL_HAVE_VDSO_SUPPORT
+#error ABSL_HAVE_VDSO_SUPPORT cannot be directly set
+#else
+#define ABSL_HAVE_VDSO_SUPPORT 1
+#endif
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace debugging_internal {
+
+// NOTE: this class may be used from within tcmalloc, and can not
+// use any memory allocation routines.
+class VDSOSupport {
+ public:
+  VDSOSupport();
+
+  typedef ElfMemImage::SymbolInfo SymbolInfo;
+  typedef ElfMemImage::SymbolIterator SymbolIterator;
+
+  // On PowerPC64 VDSO symbols can either be of type STT_FUNC or STT_NOTYPE
+  // depending on how the kernel is built.  The kernel is normally built with
+  // STT_NOTYPE type VDSO symbols.  Let's make things simpler first by using a
+  // compile-time constant.
+#ifdef __powerpc64__
+  enum { kVDSOSymbolType = STT_NOTYPE };
+#else
+  enum { kVDSOSymbolType = STT_FUNC };
+#endif
+
+  // Answers whether we have a vdso at all.
+  bool IsPresent() const { return image_.IsPresent(); }
+
+  // Allow to iterate over all VDSO symbols.
+  SymbolIterator begin() const { return image_.begin(); }
+  SymbolIterator end() const { return image_.end(); }
+
+  // Look up versioned dynamic symbol in the kernel VDSO.
+  // Returns false if VDSO is not present, or doesn't contain given
+  // symbol/version/type combination.
+  // If info_out != nullptr, additional details are filled in.
+  bool LookupSymbol(const char *name, const char *version,
+                    int symbol_type, SymbolInfo *info_out) const;
+
+  // Find info about symbol (if any) which overlaps given address.
+  // Returns true if symbol was found; false if VDSO isn't present
+  // or doesn't have a symbol overlapping given address.
+  // If info_out != nullptr, additional details are filled in.
+  bool LookupSymbolByAddress(const void *address, SymbolInfo *info_out) const;
+
+  // Used only for testing. Replace real VDSO base with a mock.
+  // Returns previous value of vdso_base_. After you are done testing,
+  // you are expected to call SetBase() with previous value, in order to
+  // reset state to the way it was.
+  const void *SetBase(const void *s);
+
+  // Computes vdso_base_ and returns it. Should be called as early as
+  // possible; before any thread creation, chroot or setuid.
+  static const void *Init();
+
+ private:
+  // image_ represents VDSO ELF image in memory.
+  // image_.ehdr_ == nullptr implies there is no VDSO.
+  ElfMemImage image_;
+
+  // Cached value of auxv AT_SYSINFO_EHDR, computed once.
+  // This is a tri-state:
+  //   kInvalidBase   => value hasn't been determined yet.
+  //              0   => there is no VDSO.
+  //           else   => vma of VDSO Elf{32,64}_Ehdr.
+  //
+  // When testing with mock VDSO, low bit is set.
+  // The low bit is always available because vdso_base_ is
+  // page-aligned.
+  static std::atomic<const void *> vdso_base_;
+
+  // NOLINT on 'long' because these routines mimic kernel api.
+  // The 'cache' parameter may be used by some versions of the kernel,
+  // and should be nullptr or point to a static buffer containing at
+  // least two 'long's.
+  static long InitAndGetCPU(unsigned *cpu, void *cache,     // NOLINT 'long'.
+                            void *unused);
+  static long GetCPUViaSyscall(unsigned *cpu, void *cache,  // NOLINT 'long'.
+                               void *unused);
+  typedef long (*GetCpuFn)(unsigned *cpu, void *cache,      // NOLINT 'long'.
+                           void *unused);
+
+  // This function pointer may point to InitAndGetCPU,
+  // GetCPUViaSyscall, or __vdso_getcpu at different stages of initialization.
+  ABSL_CONST_INIT static std::atomic<GetCpuFn> getcpu_fn_;
+
+  friend int GetCPU(void);  // Needs access to getcpu_fn_.
+
+  VDSOSupport(const VDSOSupport&) = delete;
+  VDSOSupport& operator=(const VDSOSupport&) = delete;
+};
+
+// Same as sched_getcpu() on later glibc versions.
+// Return current CPU, using (fast) __vdso_getcpu@LINUX_2.6 if present,
+// otherwise use syscall(SYS_getcpu,...).
+// May return -1 with errno == ENOSYS if the kernel doesn't
+// support SYS_getcpu.
+int GetCPU();
+
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_HAVE_ELF_MEM_IMAGE
+
+#endif  // ABSL_DEBUGGING_INTERNAL_VDSO_SUPPORT_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/leak_check.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/leak_check.h
new file mode 100644
index 0000000000000000000000000000000000000000..6bd794062369a6279573f84511ac9d7e31192f9d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/leak_check.h
@@ -0,0 +1,150 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: leak_check.h
+// -----------------------------------------------------------------------------
+//
+// This file contains functions that affect leak checking behavior within
+// targets built with the LeakSanitizer (LSan), a memory leak detector that is
+// integrated within the AddressSanitizer (ASan) as an additional component, or
+// which can be used standalone. LSan and ASan are included (or can be provided)
+// as additional components for most compilers such as Clang, gcc and MSVC.
+// Note: this leak checking API is not yet supported in MSVC.
+// Leak checking is enabled by default in all ASan builds.
+//
+// https://clang.llvm.org/docs/LeakSanitizer.html
+// https://github.com/google/sanitizers/wiki/AddressSanitizerLeakSanitizer
+//
+// GCC and Clang both automatically enable LeakSanitizer when AddressSanitizer
+// is enabled. To use the mode, simply pass `-fsanitize=address` to both the
+// compiler and linker. An example Bazel command could be
+//
+//   $ bazel test --copt=-fsanitize=address --linkopt=-fsanitize=address ...
+//
+// GCC and Clang auto support a standalone LeakSanitizer mode (a mode which does
+// not also use AddressSanitizer). To use the mode, simply pass
+// `-fsanitize=leak` to both the compiler and linker. Since GCC does not
+// currently provide a way of detecting this mode at compile-time, GCC users
+// must also pass -DLEAK_SANITIZER to the compiler. An example Bazel command
+// could be
+//
+//   $ bazel test --copt=-DLEAK_SANITIZER --copt=-fsanitize=leak
+//     --linkopt=-fsanitize=leak ...
+//
+// -----------------------------------------------------------------------------
+#ifndef ABSL_DEBUGGING_LEAK_CHECK_H_
+#define ABSL_DEBUGGING_LEAK_CHECK_H_
+
+#include <cstddef>
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// HaveLeakSanitizer()
+//
+// Returns true if a leak-checking sanitizer (either ASan or standalone LSan) is
+// currently built into this target.
+bool HaveLeakSanitizer();
+
+// LeakCheckerIsActive()
+//
+// Returns true if a leak-checking sanitizer (either ASan or standalone LSan) is
+// currently built into this target and is turned on.
+bool LeakCheckerIsActive();
+
+// DoIgnoreLeak()
+//
+// Implements `IgnoreLeak()` below. This function should usually
+// not be called directly; calling `IgnoreLeak()` is preferred.
+void DoIgnoreLeak(const void* ptr);
+
+// IgnoreLeak()
+//
+// Instruct the leak sanitizer to ignore leak warnings on the object referenced
+// by the passed pointer, as well as all heap objects transitively referenced
+// by it. The passed object pointer can point to either the beginning of the
+// object or anywhere within it.
+//
+// Example:
+//
+//   static T* obj = IgnoreLeak(new T(...));
+//
+// If the passed `ptr` does not point to an actively allocated object at the
+// time `IgnoreLeak()` is called, the call is a no-op; if it is actively
+// allocated, leak sanitizer will assume this object is referenced even if
+// there is no actual reference in user memory.
+//
+template <typename T>
+T* IgnoreLeak(T* ptr) {
+  DoIgnoreLeak(ptr);
+  return ptr;
+}
+
+// FindAndReportLeaks()
+//
+// If any leaks are detected, prints a leak report and returns true.  This
+// function may be called repeatedly, and does not affect end-of-process leak
+// checking.
+//
+// Example:
+// if (FindAndReportLeaks()) {
+//   ... diagnostic already printed. Exit with failure code.
+//   exit(1)
+// }
+bool FindAndReportLeaks();
+
+// LeakCheckDisabler
+//
+// This helper class indicates that any heap allocations done in the code block
+// covered by the scoped object, which should be allocated on the stack, will
+// not be reported as leaks. Leak check disabling will occur within the code
+// block and any nested function calls within the code block.
+//
+// Example:
+//
+//   void Foo() {
+//     LeakCheckDisabler disabler;
+//     ... code that allocates objects whose leaks should be ignored ...
+//   }
+//
+// REQUIRES: Destructor runs in same thread as constructor
+class LeakCheckDisabler {
+ public:
+  LeakCheckDisabler();
+  LeakCheckDisabler(const LeakCheckDisabler&) = delete;
+  LeakCheckDisabler& operator=(const LeakCheckDisabler&) = delete;
+  ~LeakCheckDisabler();
+};
+
+// RegisterLivePointers()
+//
+// Registers `ptr[0,size-1]` as pointers to memory that is still actively being
+// referenced and for which leak checking should be ignored. This function is
+// useful if you store pointers in mapped memory, for memory ranges that we know
+// are correct but for which normal analysis would flag as leaked code.
+void RegisterLivePointers(const void* ptr, size_t size);
+
+// UnRegisterLivePointers()
+//
+// Deregisters the pointers previously marked as active in
+// `RegisterLivePointers()`, enabling leak checking of those pointers.
+void UnRegisterLivePointers(const void* ptr, size_t size);
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_DEBUGGING_LEAK_CHECK_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/stacktrace.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/stacktrace.h
new file mode 100644
index 0000000000000000000000000000000000000000..0ec0ffdabd422e07be93660860202c61c4c60406
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/stacktrace.h
@@ -0,0 +1,231 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: stacktrace.h
+// -----------------------------------------------------------------------------
+//
+// This file contains routines to extract the current stack trace and associated
+// stack frames. These functions are thread-safe and async-signal-safe.
+//
+// Note that stack trace functionality is platform dependent and requires
+// additional support from the compiler/build system in most cases. (That is,
+// this functionality generally only works on platforms/builds that have been
+// specifically configured to support it.)
+//
+// Note: stack traces in Abseil that do not utilize a symbolizer will result in
+// frames consisting of function addresses rather than human-readable function
+// names. (See symbolize.h for information on symbolizing these values.)
+
+#ifndef ABSL_DEBUGGING_STACKTRACE_H_
+#define ABSL_DEBUGGING_STACKTRACE_H_
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// GetStackFrames()
+//
+// Records program counter values for up to `max_depth` frames, skipping the
+// most recent `skip_count` stack frames, stores their corresponding values
+// and sizes in `results` and `sizes` buffers, and returns the number of frames
+// stored. (Note that the frame generated for the `absl::GetStackFrames()`
+// routine itself is also skipped.)
+//
+// Example:
+//
+//      main() { foo(); }
+//      foo() { bar(); }
+//      bar() {
+//        void* result[10];
+//        int sizes[10];
+//        int depth = absl::GetStackFrames(result, sizes, 10, 1);
+//      }
+//
+// The current stack frame would consist of three function calls: `bar()`,
+// `foo()`, and then `main()`; however, since the `GetStackFrames()` call sets
+// `skip_count` to `1`, it will skip the frame for `bar()`, the most recently
+// invoked function call. It will therefore return 2 and fill `result` with
+// program counters within the following functions:
+//
+//      result[0]       foo()
+//      result[1]       main()
+//
+// (Note: in practice, a few more entries after `main()` may be added to account
+// for startup processes.)
+//
+// Corresponding stack frame sizes will also be recorded:
+//
+//    sizes[0]       16
+//    sizes[1]       16
+//
+// (Stack frame sizes of `16` above are just for illustration purposes.)
+//
+// Stack frame sizes of 0 or less indicate that those frame sizes couldn't
+// be identified.
+//
+// This routine may return fewer stack frame entries than are
+// available. Also note that `result` and `sizes` must both be non-null.
+extern int GetStackFrames(void** result, int* sizes, int max_depth,
+                          int skip_count);
+
+// GetStackFramesWithContext()
+//
+// Records program counter values obtained from a signal handler. Records
+// program counter values for up to `max_depth` frames, skipping the most recent
+// `skip_count` stack frames, stores their corresponding values and sizes in
+// `results` and `sizes` buffers, and returns the number of frames stored. (Note
+// that the frame generated for the `absl::GetStackFramesWithContext()` routine
+// itself is also skipped.)
+//
+// The `uc` parameter, if non-null, should be a pointer to a `ucontext_t` value
+// passed to a signal handler registered via the `sa_sigaction` field of a
+// `sigaction` struct. (See
+// http://man7.org/linux/man-pages/man2/sigaction.2.html.) The `uc` value may
+// help a stack unwinder to provide a better stack trace under certain
+// conditions. `uc` may safely be null.
+//
+// The `min_dropped_frames` output parameter, if non-null, points to the
+// location to note any dropped stack frames, if any, due to buffer limitations
+// or other reasons. (This value will be set to `0` if no frames were dropped.)
+// The number of total stack frames is guaranteed to be >= skip_count +
+// max_depth + *min_dropped_frames.
+extern int GetStackFramesWithContext(void** result, int* sizes, int max_depth,
+                                     int skip_count, const void* uc,
+                                     int* min_dropped_frames);
+
+// GetStackTrace()
+//
+// Records program counter values for up to `max_depth` frames, skipping the
+// most recent `skip_count` stack frames, stores their corresponding values
+// in `results`, and returns the number of frames
+// stored. Note that this function is similar to `absl::GetStackFrames()`
+// except that it returns the stack trace only, and not stack frame sizes.
+//
+// Example:
+//
+//      main() { foo(); }
+//      foo() { bar(); }
+//      bar() {
+//        void* result[10];
+//        int depth = absl::GetStackTrace(result, 10, 1);
+//      }
+//
+// This produces:
+//
+//      result[0]       foo
+//      result[1]       main
+//           ....       ...
+//
+// `result` must not be null.
+extern int GetStackTrace(void** result, int max_depth, int skip_count);
+
+// GetStackTraceWithContext()
+//
+// Records program counter values obtained from a signal handler. Records
+// program counter values for up to `max_depth` frames, skipping the most recent
+// `skip_count` stack frames, stores their corresponding values in `results`,
+// and returns the number of frames stored. (Note that the frame generated for
+// the `absl::GetStackFramesWithContext()` routine itself is also skipped.)
+//
+// The `uc` parameter, if non-null, should be a pointer to a `ucontext_t` value
+// passed to a signal handler registered via the `sa_sigaction` field of a
+// `sigaction` struct. (See
+// http://man7.org/linux/man-pages/man2/sigaction.2.html.) The `uc` value may
+// help a stack unwinder to provide a better stack trace under certain
+// conditions. `uc` may safely be null.
+//
+// The `min_dropped_frames` output parameter, if non-null, points to the
+// location to note any dropped stack frames, if any, due to buffer limitations
+// or other reasons. (This value will be set to `0` if no frames were dropped.)
+// The number of total stack frames is guaranteed to be >= skip_count +
+// max_depth + *min_dropped_frames.
+extern int GetStackTraceWithContext(void** result, int max_depth,
+                                    int skip_count, const void* uc,
+                                    int* min_dropped_frames);
+
+// SetStackUnwinder()
+//
+// Provides a custom function for unwinding stack frames that will be used in
+// place of the default stack unwinder when invoking the static
+// GetStack{Frames,Trace}{,WithContext}() functions above.
+//
+// The arguments passed to the unwinder function will match the
+// arguments passed to `absl::GetStackFramesWithContext()` except that sizes
+// will be non-null iff the caller is interested in frame sizes.
+//
+// If unwinder is set to null, we revert to the default stack-tracing behavior.
+//
+// *****************************************************************************
+// WARNING
+// *****************************************************************************
+//
+// absl::SetStackUnwinder is not suitable for general purpose use.  It is
+// provided for custom runtimes.
+// Some things to watch out for when calling `absl::SetStackUnwinder()`:
+//
+// (a) The unwinder may be called from within signal handlers and
+// therefore must be async-signal-safe.
+//
+// (b) Even after a custom stack unwinder has been unregistered, other
+// threads may still be in the process of using that unwinder.
+// Therefore do not clean up any state that may be needed by an old
+// unwinder.
+// *****************************************************************************
+extern void SetStackUnwinder(int (*unwinder)(void** pcs, int* sizes,
+                                             int max_depth, int skip_count,
+                                             const void* uc,
+                                             int* min_dropped_frames));
+
+// DefaultStackUnwinder()
+//
+// Records program counter values of up to `max_depth` frames, skipping the most
+// recent `skip_count` stack frames, and stores their corresponding values in
+// `pcs`. (Note that the frame generated for this call itself is also skipped.)
+// This function acts as a generic stack-unwinder; prefer usage of the more
+// specific `GetStack{Trace,Frames}{,WithContext}()` functions above.
+//
+// If you have set your own stack unwinder (with the `SetStackUnwinder()`
+// function above, you can still get the default stack unwinder by calling
+// `DefaultStackUnwinder()`, which will ignore any previously set stack unwinder
+// and use the default one instead.
+//
+// Because this function is generic, only `pcs` is guaranteed to be non-null
+// upon return. It is legal for `sizes`, `uc`, and `min_dropped_frames` to all
+// be null when called.
+//
+// The semantics are the same as the corresponding `GetStack*()` function in the
+// case where `absl::SetStackUnwinder()` was never called. Equivalents are:
+//
+//                       null sizes         |        non-nullptr sizes
+//             |==========================================================|
+//     null uc | GetStackTrace()            | GetStackFrames()            |
+// non-null uc | GetStackTraceWithContext() | GetStackFramesWithContext() |
+//             |==========================================================|
+extern int DefaultStackUnwinder(void** pcs, int* sizes, int max_depth,
+                                int skip_count, const void* uc,
+                                int* min_dropped_frames);
+
+namespace debugging_internal {
+// Returns true for platforms which are expected to have functioning stack trace
+// implementations. Intended to be used for tests which want to exclude
+// verification of logic known to be broken because stack traces are not
+// working.
+extern bool StackTraceWorksForTest();
+}  // namespace debugging_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_DEBUGGING_STACKTRACE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize.h
new file mode 100644
index 0000000000000000000000000000000000000000..43d93a86822eb5129a9c995bbda54370a973528e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize.h
@@ -0,0 +1,99 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: symbolize.h
+// -----------------------------------------------------------------------------
+//
+// This file configures the Abseil symbolizer for use in converting instruction
+// pointer addresses (program counters) into human-readable names (function
+// calls, etc.) within Abseil code.
+//
+// The symbolizer may be invoked from several sources:
+//
+//   * Implicitly, through the installation of an Abseil failure signal handler.
+//     (See failure_signal_handler.h for more information.)
+//   * By calling `Symbolize()` directly on a program counter you obtain through
+//     `absl::GetStackTrace()` or `absl::GetStackFrames()`. (See stacktrace.h
+//     for more information.
+//   * By calling `Symbolize()` directly on a program counter you obtain through
+//     other means (which would be platform-dependent).
+//
+// In all of the above cases, the symbolizer must first be initialized before
+// any program counter values can be symbolized. If you are installing a failure
+// signal handler, initialize the symbolizer before you do so.
+//
+// Example:
+//
+//   int main(int argc, char** argv) {
+//     // Initialize the Symbolizer before installing the failure signal handler
+//     absl::InitializeSymbolizer(argv[0]);
+//
+//     // Now you may install the failure signal handler
+//     absl::FailureSignalHandlerOptions options;
+//     absl::InstallFailureSignalHandler(options);
+//
+//     // Start running your main program
+//     ...
+//     return 0;
+//  }
+//
+#ifndef ABSL_DEBUGGING_SYMBOLIZE_H_
+#define ABSL_DEBUGGING_SYMBOLIZE_H_
+
+#include "absl/debugging/internal/symbolize.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// InitializeSymbolizer()
+//
+// Initializes the program counter symbolizer, given the path of the program
+// (typically obtained through `main()`s `argv[0]`). The Abseil symbolizer
+// allows you to read program counters (instruction pointer values) using their
+// human-readable names within output such as stack traces.
+//
+// Example:
+//
+// int main(int argc, char *argv[]) {
+//   absl::InitializeSymbolizer(argv[0]);
+//   // Now you can use the symbolizer
+// }
+void InitializeSymbolizer(const char* argv0);
+//
+// Symbolize()
+//
+// Symbolizes a program counter (instruction pointer value) `pc` and, on
+// success, writes the name to `out`. The symbol name is demangled, if possible.
+// Note that the symbolized name may be truncated and will be NUL-terminated.
+// Demangling is supported for symbols generated by GCC 3.x or newer). Returns
+// `false` on failure.
+//
+// Example:
+//
+//   // Print a program counter and its symbol name.
+//   static void DumpPCAndSymbol(void *pc) {
+//     char tmp[1024];
+//     const char *symbol = "(unknown)";
+//     if (absl::Symbolize(pc, tmp, sizeof(tmp))) {
+//       symbol = tmp;
+//     }
+//     absl::PrintF("%p  %s\n", pc, symbol);
+//  }
+bool Symbolize(const void *pc, char *out, int out_size);
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_DEBUGGING_SYMBOLIZE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize_darwin.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize_darwin.inc
new file mode 100644
index 0000000000000000000000000000000000000000..cf63d1919b8fe3fa1111c68c5d35b6b89e18eb1b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize_darwin.inc
@@ -0,0 +1,102 @@
+// Copyright 2020 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <cxxabi.h>
+#include <execinfo.h>
+
+#include <algorithm>
+#include <cstring>
+
+#include "absl/base/internal/raw_logging.h"
+#include "absl/debugging/internal/demangle.h"
+#include "absl/strings/numbers.h"
+#include "absl/strings/str_cat.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+void InitializeSymbolizer(const char*) {}
+
+namespace debugging_internal {
+namespace {
+
+static std::string GetSymbolString(absl::string_view backtrace_line) {
+  // Example Backtrace lines:
+  // 0   libimaging_shared.dylib             0x018c152a
+  // _ZNSt11_Deque_baseIN3nik7mediadb4PageESaIS2_EE17_M_initialize_mapEm + 3478
+  //
+  // or
+  // 0   libimaging_shared.dylib             0x0000000001895c39
+  // _ZN3nik4util19register_shared_ptrINS_3gpu7TextureEEEvPKvS5_ + 39
+  //
+  // or
+  // 0   mysterious_app                      0x0124000120120009 main + 17
+  auto address_pos = backtrace_line.find(" 0x");
+  if (address_pos == absl::string_view::npos) return std::string();
+  absl::string_view symbol_view = backtrace_line.substr(address_pos + 1);
+
+  auto space_pos = symbol_view.find(" ");
+  if (space_pos == absl::string_view::npos) return std::string();
+  symbol_view = symbol_view.substr(space_pos + 1);  // to mangled symbol
+
+  auto plus_pos = symbol_view.find(" + ");
+  if (plus_pos == absl::string_view::npos) return std::string();
+  symbol_view = symbol_view.substr(0, plus_pos);  // strip remainng
+
+  return std::string(symbol_view);
+}
+
+}  // namespace
+}  // namespace debugging_internal
+
+bool Symbolize(const void* pc, char* out, int out_size) {
+  if (out_size <= 0 || pc == nullptr) {
+    out = nullptr;
+    return false;
+  }
+
+  // This allocates a char* array.
+  char** frame_strings = backtrace_symbols(const_cast<void**>(&pc), 1);
+
+  if (frame_strings == nullptr) return false;
+
+  std::string symbol = debugging_internal::GetSymbolString(frame_strings[0]);
+  free(frame_strings);
+
+  char tmp_buf[1024];
+  if (debugging_internal::Demangle(symbol.c_str(), tmp_buf, sizeof(tmp_buf))) {
+    size_t len = strlen(tmp_buf);
+    if (len + 1 <= static_cast<size_t>(out_size)) {  // +1 for '\0'
+      assert(len < sizeof(tmp_buf));
+      memmove(out, tmp_buf, len + 1);
+    }
+  } else {
+    strncpy(out, symbol.c_str(), static_cast<size_t>(out_size));
+  }
+
+  if (out[out_size - 1] != '\0') {
+    // strncpy() does not '\0' terminate when it truncates.
+    static constexpr char kEllipsis[] = "...";
+    size_t ellipsis_size =
+        std::min(sizeof(kEllipsis) - 1, static_cast<size_t>(out_size) - 1);
+    memcpy(out + out_size - ellipsis_size - 1, kEllipsis, ellipsis_size);
+    out[out_size - 1] = '\0';
+  }
+
+  return true;
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize_elf.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize_elf.inc
new file mode 100644
index 0000000000000000000000000000000000000000..ffb4eecfb914d4946ab42fa5052ef90c1ed2966d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize_elf.inc
@@ -0,0 +1,1637 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// This library provides Symbolize() function that symbolizes program
+// counters to their corresponding symbol names on linux platforms.
+// This library has a minimal implementation of an ELF symbol table
+// reader (i.e. it doesn't depend on libelf, etc.).
+//
+// The algorithm used in Symbolize() is as follows.
+//
+//   1. Go through a list of maps in /proc/self/maps and find the map
+//   containing the program counter.
+//
+//   2. Open the mapped file and find a regular symbol table inside.
+//   Iterate over symbols in the symbol table and look for the symbol
+//   containing the program counter.  If such a symbol is found,
+//   obtain the symbol name, and demangle the symbol if possible.
+//   If the symbol isn't found in the regular symbol table (binary is
+//   stripped), try the same thing with a dynamic symbol table.
+//
+// Note that Symbolize() is originally implemented to be used in
+// signal handlers, hence it doesn't use malloc() and other unsafe
+// operations.  It should be both thread-safe and async-signal-safe.
+//
+// Implementation note:
+//
+// We don't use heaps but only use stacks.  We want to reduce the
+// stack consumption so that the symbolizer can run on small stacks.
+//
+// Here are some numbers collected with GCC 4.1.0 on x86:
+// - sizeof(Elf32_Sym)  = 16
+// - sizeof(Elf32_Shdr) = 40
+// - sizeof(Elf64_Sym)  = 24
+// - sizeof(Elf64_Shdr) = 64
+//
+// This implementation is intended to be async-signal-safe but uses some
+// functions which are not guaranteed to be so, such as memchr() and
+// memmove().  We assume they are async-signal-safe.
+
+#include <dlfcn.h>
+#include <elf.h>
+#include <fcntl.h>
+#include <link.h>  // For ElfW() macro.
+#include <sys/stat.h>
+#include <sys/types.h>
+#include <unistd.h>
+
+#include <algorithm>
+#include <array>
+#include <atomic>
+#include <cerrno>
+#include <cinttypes>
+#include <climits>
+#include <cstdint>
+#include <cstdio>
+#include <cstdlib>
+#include <cstring>
+
+#include "absl/base/casts.h"
+#include "absl/base/dynamic_annotations.h"
+#include "absl/base/internal/low_level_alloc.h"
+#include "absl/base/internal/raw_logging.h"
+#include "absl/base/internal/spinlock.h"
+#include "absl/base/port.h"
+#include "absl/debugging/internal/demangle.h"
+#include "absl/debugging/internal/vdso_support.h"
+#include "absl/strings/string_view.h"
+
+#if defined(__FreeBSD__) && !defined(ElfW)
+#define ElfW(x) __ElfN(x)
+#endif
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// Value of argv[0]. Used by MaybeInitializeObjFile().
+static char *argv0_value = nullptr;
+
+void InitializeSymbolizer(const char *argv0) {
+#ifdef ABSL_HAVE_VDSO_SUPPORT
+  // We need to make sure VDSOSupport::Init() is called before any setuid or
+  // chroot calls, so InitializeSymbolizer() should be called very early in the
+  // life of a program.
+  absl::debugging_internal::VDSOSupport::Init();
+#endif
+  if (argv0_value != nullptr) {
+    free(argv0_value);
+    argv0_value = nullptr;
+  }
+  if (argv0 != nullptr && argv0[0] != '\0') {
+    argv0_value = strdup(argv0);
+  }
+}
+
+namespace debugging_internal {
+namespace {
+
+// Re-runs fn until it doesn't cause EINTR.
+#define NO_INTR(fn) \
+  do {              \
+  } while ((fn) < 0 && errno == EINTR)
+
+// On Linux, ELF_ST_* are defined in <linux/elf.h>.  To make this portable
+// we define our own ELF_ST_BIND and ELF_ST_TYPE if not available.
+#ifndef ELF_ST_BIND
+#define ELF_ST_BIND(info) (((unsigned char)(info)) >> 4)
+#endif
+
+#ifndef ELF_ST_TYPE
+#define ELF_ST_TYPE(info) (((unsigned char)(info)) & 0xF)
+#endif
+
+// Some platforms use a special .opd section to store function pointers.
+const char kOpdSectionName[] = ".opd";
+
+#if (defined(__powerpc__) && !(_CALL_ELF > 1)) || defined(__ia64)
+// Use opd section for function descriptors on these platforms, the function
+// address is the first word of the descriptor.
+enum { kPlatformUsesOPDSections = 1 };
+#else  // not PPC or IA64
+enum { kPlatformUsesOPDSections = 0 };
+#endif
+
+// This works for PowerPC & IA64 only.  A function descriptor consist of two
+// pointers and the first one is the function's entry.
+const size_t kFunctionDescriptorSize = sizeof(void *) * 2;
+
+const int kMaxDecorators = 10;  // Seems like a reasonable upper limit.
+
+struct InstalledSymbolDecorator {
+  SymbolDecorator fn;
+  void *arg;
+  int ticket;
+};
+
+int g_num_decorators;
+InstalledSymbolDecorator g_decorators[kMaxDecorators];
+
+struct FileMappingHint {
+  const void *start;
+  const void *end;
+  uint64_t offset;
+  const char *filename;
+};
+
+// Protects g_decorators.
+// We are using SpinLock and not a Mutex here, because we may be called
+// from inside Mutex::Lock itself, and it prohibits recursive calls.
+// This happens in e.g. base/stacktrace_syscall_unittest.
+// Moreover, we are using only TryLock(), if the decorator list
+// is being modified (is busy), we skip all decorators, and possibly
+// loose some info. Sorry, that's the best we could do.
+ABSL_CONST_INIT absl::base_internal::SpinLock g_decorators_mu(
+    absl::kConstInit, absl::base_internal::SCHEDULE_KERNEL_ONLY);
+
+const int kMaxFileMappingHints = 8;
+int g_num_file_mapping_hints;
+FileMappingHint g_file_mapping_hints[kMaxFileMappingHints];
+// Protects g_file_mapping_hints.
+ABSL_CONST_INIT absl::base_internal::SpinLock g_file_mapping_mu(
+    absl::kConstInit, absl::base_internal::SCHEDULE_KERNEL_ONLY);
+
+// Async-signal-safe function to zero a buffer.
+// memset() is not guaranteed to be async-signal-safe.
+static void SafeMemZero(void* p, size_t size) {
+  unsigned char *c = static_cast<unsigned char *>(p);
+  while (size--) {
+    *c++ = 0;
+  }
+}
+
+struct ObjFile {
+  ObjFile()
+      : filename(nullptr),
+        start_addr(nullptr),
+        end_addr(nullptr),
+        offset(0),
+        fd(-1),
+        elf_type(-1) {
+    SafeMemZero(&elf_header, sizeof(elf_header));
+    SafeMemZero(&phdr[0], sizeof(phdr));
+  }
+
+  char *filename;
+  const void *start_addr;
+  const void *end_addr;
+  uint64_t offset;
+
+  // The following fields are initialized on the first access to the
+  // object file.
+  int fd;
+  int elf_type;
+  ElfW(Ehdr) elf_header;
+
+  // PT_LOAD program header describing executable code.
+  // Normally we expect just one, but SWIFT binaries have two.
+  // CUDA binaries have 3 (see cr/473913254 description).
+  std::array<ElfW(Phdr), 4> phdr;
+};
+
+// Build 4-way associative cache for symbols. Within each cache line, symbols
+// are replaced in LRU order.
+enum {
+  ASSOCIATIVITY = 4,
+};
+struct SymbolCacheLine {
+  const void *pc[ASSOCIATIVITY];
+  char *name[ASSOCIATIVITY];
+
+  // age[i] is incremented when a line is accessed. it's reset to zero if the
+  // i'th entry is read.
+  uint32_t age[ASSOCIATIVITY];
+};
+
+// ---------------------------------------------------------------
+// An async-signal-safe arena for LowLevelAlloc
+static std::atomic<base_internal::LowLevelAlloc::Arena *> g_sig_safe_arena;
+
+static base_internal::LowLevelAlloc::Arena *SigSafeArena() {
+  return g_sig_safe_arena.load(std::memory_order_acquire);
+}
+
+static void InitSigSafeArena() {
+  if (SigSafeArena() == nullptr) {
+    base_internal::LowLevelAlloc::Arena *new_arena =
+        base_internal::LowLevelAlloc::NewArena(
+            base_internal::LowLevelAlloc::kAsyncSignalSafe);
+    base_internal::LowLevelAlloc::Arena *old_value = nullptr;
+    if (!g_sig_safe_arena.compare_exchange_strong(old_value, new_arena,
+                                                  std::memory_order_release,
+                                                  std::memory_order_relaxed)) {
+      // We lost a race to allocate an arena; deallocate.
+      base_internal::LowLevelAlloc::DeleteArena(new_arena);
+    }
+  }
+}
+
+// ---------------------------------------------------------------
+// An AddrMap is a vector of ObjFile, using SigSafeArena() for allocation.
+
+class AddrMap {
+ public:
+  AddrMap() : size_(0), allocated_(0), obj_(nullptr) {}
+  ~AddrMap() { base_internal::LowLevelAlloc::Free(obj_); }
+  size_t Size() const { return size_; }
+  ObjFile *At(size_t i) { return &obj_[i]; }
+  ObjFile *Add();
+  void Clear();
+
+ private:
+  size_t size_;       // count of valid elements (<= allocated_)
+  size_t allocated_;  // count of allocated elements
+  ObjFile *obj_;      // array of allocated_ elements
+  AddrMap(const AddrMap &) = delete;
+  AddrMap &operator=(const AddrMap &) = delete;
+};
+
+void AddrMap::Clear() {
+  for (size_t i = 0; i != size_; i++) {
+    At(i)->~ObjFile();
+  }
+  size_ = 0;
+}
+
+ObjFile *AddrMap::Add() {
+  if (size_ == allocated_) {
+    size_t new_allocated = allocated_ * 2 + 50;
+    ObjFile *new_obj_ =
+        static_cast<ObjFile *>(base_internal::LowLevelAlloc::AllocWithArena(
+            new_allocated * sizeof(*new_obj_), SigSafeArena()));
+    if (obj_) {
+      memcpy(new_obj_, obj_, allocated_ * sizeof(*new_obj_));
+      base_internal::LowLevelAlloc::Free(obj_);
+    }
+    obj_ = new_obj_;
+    allocated_ = new_allocated;
+  }
+  return new (&obj_[size_++]) ObjFile;
+}
+
+// ---------------------------------------------------------------
+
+enum FindSymbolResult { SYMBOL_NOT_FOUND = 1, SYMBOL_TRUNCATED, SYMBOL_FOUND };
+
+class Symbolizer {
+ public:
+  Symbolizer();
+  ~Symbolizer();
+  const char *GetSymbol(const void *const pc);
+
+ private:
+  char *CopyString(const char *s) {
+    size_t len = strlen(s);
+    char *dst = static_cast<char *>(
+        base_internal::LowLevelAlloc::AllocWithArena(len + 1, SigSafeArena()));
+    ABSL_RAW_CHECK(dst != nullptr, "out of memory");
+    memcpy(dst, s, len + 1);
+    return dst;
+  }
+  ObjFile *FindObjFile(const void *const start,
+                       size_t size) ABSL_ATTRIBUTE_NOINLINE;
+  static bool RegisterObjFile(const char *filename,
+                              const void *const start_addr,
+                              const void *const end_addr, uint64_t offset,
+                              void *arg);
+  SymbolCacheLine *GetCacheLine(const void *const pc);
+  const char *FindSymbolInCache(const void *const pc);
+  const char *InsertSymbolInCache(const void *const pc, const char *name);
+  void AgeSymbols(SymbolCacheLine *line);
+  void ClearAddrMap();
+  FindSymbolResult GetSymbolFromObjectFile(const ObjFile &obj,
+                                           const void *const pc,
+                                           const ptrdiff_t relocation,
+                                           char *out, size_t out_size,
+                                           char *tmp_buf, size_t tmp_buf_size);
+  const char *GetUncachedSymbol(const void *pc);
+
+  enum {
+    SYMBOL_BUF_SIZE = 3072,
+    TMP_BUF_SIZE = 1024,
+    SYMBOL_CACHE_LINES = 128,
+  };
+
+  AddrMap addr_map_;
+
+  bool ok_;
+  bool addr_map_read_;
+
+  char symbol_buf_[SYMBOL_BUF_SIZE];
+
+  // tmp_buf_ will be used to store arrays of ElfW(Shdr) and ElfW(Sym)
+  // so we ensure that tmp_buf_ is properly aligned to store either.
+  alignas(16) char tmp_buf_[TMP_BUF_SIZE];
+  static_assert(alignof(ElfW(Shdr)) <= 16,
+                "alignment of tmp buf too small for Shdr");
+  static_assert(alignof(ElfW(Sym)) <= 16,
+                "alignment of tmp buf too small for Sym");
+
+  SymbolCacheLine symbol_cache_[SYMBOL_CACHE_LINES];
+};
+
+static std::atomic<Symbolizer *> g_cached_symbolizer;
+
+}  // namespace
+
+static size_t SymbolizerSize() {
+#if defined(__wasm__) || defined(__asmjs__)
+  auto pagesize = static_cast<size_t>(getpagesize());
+#else
+  auto pagesize = static_cast<size_t>(sysconf(_SC_PAGESIZE));
+#endif
+  return ((sizeof(Symbolizer) - 1) / pagesize + 1) * pagesize;
+}
+
+// Return (and set null) g_cached_symbolized_state if it is not null.
+// Otherwise return a new symbolizer.
+static Symbolizer *AllocateSymbolizer() {
+  InitSigSafeArena();
+  Symbolizer *symbolizer =
+      g_cached_symbolizer.exchange(nullptr, std::memory_order_acquire);
+  if (symbolizer != nullptr) {
+    return symbolizer;
+  }
+  return new (base_internal::LowLevelAlloc::AllocWithArena(
+      SymbolizerSize(), SigSafeArena())) Symbolizer();
+}
+
+// Set g_cached_symbolize_state to s if it is null, otherwise
+// delete s.
+static void FreeSymbolizer(Symbolizer *s) {
+  Symbolizer *old_cached_symbolizer = nullptr;
+  if (!g_cached_symbolizer.compare_exchange_strong(old_cached_symbolizer, s,
+                                                   std::memory_order_release,
+                                                   std::memory_order_relaxed)) {
+    s->~Symbolizer();
+    base_internal::LowLevelAlloc::Free(s);
+  }
+}
+
+Symbolizer::Symbolizer() : ok_(true), addr_map_read_(false) {
+  for (SymbolCacheLine &symbol_cache_line : symbol_cache_) {
+    for (size_t j = 0; j < ABSL_ARRAYSIZE(symbol_cache_line.name); ++j) {
+      symbol_cache_line.pc[j] = nullptr;
+      symbol_cache_line.name[j] = nullptr;
+      symbol_cache_line.age[j] = 0;
+    }
+  }
+}
+
+Symbolizer::~Symbolizer() {
+  for (SymbolCacheLine &symbol_cache_line : symbol_cache_) {
+    for (char *s : symbol_cache_line.name) {
+      base_internal::LowLevelAlloc::Free(s);
+    }
+  }
+  ClearAddrMap();
+}
+
+// We don't use assert() since it's not guaranteed to be
+// async-signal-safe.  Instead we define a minimal assertion
+// macro. So far, we don't need pretty printing for __FILE__, etc.
+#define SAFE_ASSERT(expr) ((expr) ? static_cast<void>(0) : abort())
+
+// Read up to "count" bytes from file descriptor "fd" into the buffer
+// starting at "buf" while handling short reads and EINTR.  On
+// success, return the number of bytes read.  Otherwise, return -1.
+static ssize_t ReadPersistent(int fd, void *buf, size_t count) {
+  SAFE_ASSERT(fd >= 0);
+  SAFE_ASSERT(count <= SSIZE_MAX);
+  char *buf0 = reinterpret_cast<char *>(buf);
+  size_t num_bytes = 0;
+  while (num_bytes < count) {
+    ssize_t len;
+    NO_INTR(len = read(fd, buf0 + num_bytes, count - num_bytes));
+    if (len < 0) {  // There was an error other than EINTR.
+      ABSL_RAW_LOG(WARNING, "read failed: errno=%d", errno);
+      return -1;
+    }
+    if (len == 0) {  // Reached EOF.
+      break;
+    }
+    num_bytes += static_cast<size_t>(len);
+  }
+  SAFE_ASSERT(num_bytes <= count);
+  return static_cast<ssize_t>(num_bytes);
+}
+
+// Read up to "count" bytes from "offset" in the file pointed by file
+// descriptor "fd" into the buffer starting at "buf".  On success,
+// return the number of bytes read.  Otherwise, return -1.
+static ssize_t ReadFromOffset(const int fd, void *buf, const size_t count,
+                              const off_t offset) {
+  off_t off = lseek(fd, offset, SEEK_SET);
+  if (off == (off_t)-1) {
+    ABSL_RAW_LOG(WARNING, "lseek(%d, %jd, SEEK_SET) failed: errno=%d", fd,
+                 static_cast<intmax_t>(offset), errno);
+    return -1;
+  }
+  return ReadPersistent(fd, buf, count);
+}
+
+// Try reading exactly "count" bytes from "offset" bytes in a file
+// pointed by "fd" into the buffer starting at "buf" while handling
+// short reads and EINTR.  On success, return true. Otherwise, return
+// false.
+static bool ReadFromOffsetExact(const int fd, void *buf, const size_t count,
+                                const off_t offset) {
+  ssize_t len = ReadFromOffset(fd, buf, count, offset);
+  return len >= 0 && static_cast<size_t>(len) == count;
+}
+
+// Returns elf_header.e_type if the file pointed by fd is an ELF binary.
+static int FileGetElfType(const int fd) {
+  ElfW(Ehdr) elf_header;
+  if (!ReadFromOffsetExact(fd, &elf_header, sizeof(elf_header), 0)) {
+    return -1;
+  }
+  if (memcmp(elf_header.e_ident, ELFMAG, SELFMAG) != 0) {
+    return -1;
+  }
+  return elf_header.e_type;
+}
+
+// Read the section headers in the given ELF binary, and if a section
+// of the specified type is found, set the output to this section header
+// and return true.  Otherwise, return false.
+// To keep stack consumption low, we would like this function to not get
+// inlined.
+static ABSL_ATTRIBUTE_NOINLINE bool GetSectionHeaderByType(
+    const int fd, ElfW(Half) sh_num, const off_t sh_offset, ElfW(Word) type,
+    ElfW(Shdr) * out, char *tmp_buf, size_t tmp_buf_size) {
+  ElfW(Shdr) *buf = reinterpret_cast<ElfW(Shdr) *>(tmp_buf);
+  const size_t buf_entries = tmp_buf_size / sizeof(buf[0]);
+  const size_t buf_bytes = buf_entries * sizeof(buf[0]);
+
+  for (size_t i = 0; static_cast<int>(i) < sh_num;) {
+    const size_t num_bytes_left =
+        (static_cast<size_t>(sh_num) - i) * sizeof(buf[0]);
+    const size_t num_bytes_to_read =
+        (buf_bytes > num_bytes_left) ? num_bytes_left : buf_bytes;
+    const off_t offset = sh_offset + static_cast<off_t>(i * sizeof(buf[0]));
+    const ssize_t len = ReadFromOffset(fd, buf, num_bytes_to_read, offset);
+    if (len < 0) {
+      ABSL_RAW_LOG(
+          WARNING,
+          "Reading %zu bytes from offset %ju returned %zd which is negative.",
+          num_bytes_to_read, static_cast<intmax_t>(offset), len);
+      return false;
+    }
+    if (static_cast<size_t>(len) % sizeof(buf[0]) != 0) {
+      ABSL_RAW_LOG(
+          WARNING,
+          "Reading %zu bytes from offset %jd returned %zd which is not a "
+          "multiple of %zu.",
+          num_bytes_to_read, static_cast<intmax_t>(offset), len,
+          sizeof(buf[0]));
+      return false;
+    }
+    const size_t num_headers_in_buf = static_cast<size_t>(len) / sizeof(buf[0]);
+    SAFE_ASSERT(num_headers_in_buf <= buf_entries);
+    for (size_t j = 0; j < num_headers_in_buf; ++j) {
+      if (buf[j].sh_type == type) {
+        *out = buf[j];
+        return true;
+      }
+    }
+    i += num_headers_in_buf;
+  }
+  return false;
+}
+
+// There is no particular reason to limit section name to 63 characters,
+// but there has (as yet) been no need for anything longer either.
+const int kMaxSectionNameLen = 64;
+
+bool ForEachSection(int fd,
+                    const std::function<bool(absl::string_view name,
+                                             const ElfW(Shdr) &)> &callback) {
+  ElfW(Ehdr) elf_header;
+  if (!ReadFromOffsetExact(fd, &elf_header, sizeof(elf_header), 0)) {
+    return false;
+  }
+
+  ElfW(Shdr) shstrtab;
+  off_t shstrtab_offset = static_cast<off_t>(elf_header.e_shoff) +
+                          elf_header.e_shentsize * elf_header.e_shstrndx;
+  if (!ReadFromOffsetExact(fd, &shstrtab, sizeof(shstrtab), shstrtab_offset)) {
+    return false;
+  }
+
+  for (int i = 0; i < elf_header.e_shnum; ++i) {
+    ElfW(Shdr) out;
+    off_t section_header_offset =
+        static_cast<off_t>(elf_header.e_shoff) + elf_header.e_shentsize * i;
+    if (!ReadFromOffsetExact(fd, &out, sizeof(out), section_header_offset)) {
+      return false;
+    }
+    off_t name_offset = static_cast<off_t>(shstrtab.sh_offset) + out.sh_name;
+    char header_name[kMaxSectionNameLen];
+    ssize_t n_read =
+        ReadFromOffset(fd, &header_name, kMaxSectionNameLen, name_offset);
+    if (n_read < 0) {
+      return false;
+    } else if (n_read > kMaxSectionNameLen) {
+      // Long read?
+      return false;
+    }
+
+    absl::string_view name(header_name,
+                           strnlen(header_name, static_cast<size_t>(n_read)));
+    if (!callback(name, out)) {
+      break;
+    }
+  }
+  return true;
+}
+
+// name_len should include terminating '\0'.
+bool GetSectionHeaderByName(int fd, const char *name, size_t name_len,
+                            ElfW(Shdr) * out) {
+  char header_name[kMaxSectionNameLen];
+  if (sizeof(header_name) < name_len) {
+    ABSL_RAW_LOG(WARNING,
+                 "Section name '%s' is too long (%zu); "
+                 "section will not be found (even if present).",
+                 name, name_len);
+    // No point in even trying.
+    return false;
+  }
+
+  ElfW(Ehdr) elf_header;
+  if (!ReadFromOffsetExact(fd, &elf_header, sizeof(elf_header), 0)) {
+    return false;
+  }
+
+  ElfW(Shdr) shstrtab;
+  off_t shstrtab_offset = static_cast<off_t>(elf_header.e_shoff) +
+                          elf_header.e_shentsize * elf_header.e_shstrndx;
+  if (!ReadFromOffsetExact(fd, &shstrtab, sizeof(shstrtab), shstrtab_offset)) {
+    return false;
+  }
+
+  for (int i = 0; i < elf_header.e_shnum; ++i) {
+    off_t section_header_offset =
+        static_cast<off_t>(elf_header.e_shoff) + elf_header.e_shentsize * i;
+    if (!ReadFromOffsetExact(fd, out, sizeof(*out), section_header_offset)) {
+      return false;
+    }
+    off_t name_offset = static_cast<off_t>(shstrtab.sh_offset) + out->sh_name;
+    ssize_t n_read = ReadFromOffset(fd, &header_name, name_len, name_offset);
+    if (n_read < 0) {
+      return false;
+    } else if (static_cast<size_t>(n_read) != name_len) {
+      // Short read -- name could be at end of file.
+      continue;
+    }
+    if (memcmp(header_name, name, name_len) == 0) {
+      return true;
+    }
+  }
+  return false;
+}
+
+// Compare symbols at in the same address.
+// Return true if we should pick symbol1.
+static bool ShouldPickFirstSymbol(const ElfW(Sym) & symbol1,
+                                  const ElfW(Sym) & symbol2) {
+  // If one of the symbols is weak and the other is not, pick the one
+  // this is not a weak symbol.
+  char bind1 = ELF_ST_BIND(symbol1.st_info);
+  char bind2 = ELF_ST_BIND(symbol1.st_info);
+  if (bind1 == STB_WEAK && bind2 != STB_WEAK) return false;
+  if (bind2 == STB_WEAK && bind1 != STB_WEAK) return true;
+
+  // If one of the symbols has zero size and the other is not, pick the
+  // one that has non-zero size.
+  if (symbol1.st_size != 0 && symbol2.st_size == 0) {
+    return true;
+  }
+  if (symbol1.st_size == 0 && symbol2.st_size != 0) {
+    return false;
+  }
+
+  // If one of the symbols has no type and the other is not, pick the
+  // one that has a type.
+  char type1 = ELF_ST_TYPE(symbol1.st_info);
+  char type2 = ELF_ST_TYPE(symbol1.st_info);
+  if (type1 != STT_NOTYPE && type2 == STT_NOTYPE) {
+    return true;
+  }
+  if (type1 == STT_NOTYPE && type2 != STT_NOTYPE) {
+    return false;
+  }
+
+  // Pick the first one, if we still cannot decide.
+  return true;
+}
+
+// Return true if an address is inside a section.
+static bool InSection(const void *address, const ElfW(Shdr) * section) {
+  const char *start = reinterpret_cast<const char *>(section->sh_addr);
+  size_t size = static_cast<size_t>(section->sh_size);
+  return start <= address && address < (start + size);
+}
+
+static const char *ComputeOffset(const char *base, ptrdiff_t offset) {
+  // Note: cast to intptr_t to avoid undefined behavior when base evaluates to
+  // zero and offset is non-zero.
+  return reinterpret_cast<const char *>(reinterpret_cast<intptr_t>(base) +
+                                        offset);
+}
+
+// Read a symbol table and look for the symbol containing the
+// pc. Iterate over symbols in a symbol table and look for the symbol
+// containing "pc".  If the symbol is found, and its name fits in
+// out_size, the name is written into out and SYMBOL_FOUND is returned.
+// If the name does not fit, truncated name is written into out,
+// and SYMBOL_TRUNCATED is returned. Out is NUL-terminated.
+// If the symbol is not found, SYMBOL_NOT_FOUND is returned;
+// To keep stack consumption low, we would like this function to not get
+// inlined.
+static ABSL_ATTRIBUTE_NOINLINE FindSymbolResult FindSymbol(
+    const void *const pc, const int fd, char *out, size_t out_size,
+    ptrdiff_t relocation, const ElfW(Shdr) * strtab, const ElfW(Shdr) * symtab,
+    const ElfW(Shdr) * opd, char *tmp_buf, size_t tmp_buf_size) {
+  if (symtab == nullptr) {
+    return SYMBOL_NOT_FOUND;
+  }
+
+  // Read multiple symbols at once to save read() calls.
+  ElfW(Sym) *buf = reinterpret_cast<ElfW(Sym) *>(tmp_buf);
+  const size_t buf_entries = tmp_buf_size / sizeof(buf[0]);
+
+  const size_t num_symbols = symtab->sh_size / symtab->sh_entsize;
+
+  // On platforms using an .opd section (PowerPC & IA64), a function symbol
+  // has the address of a function descriptor, which contains the real
+  // starting address.  However, we do not always want to use the real
+  // starting address because we sometimes want to symbolize a function
+  // pointer into the .opd section, e.g. FindSymbol(&foo,...).
+  const bool pc_in_opd =
+      kPlatformUsesOPDSections && opd != nullptr && InSection(pc, opd);
+  const bool deref_function_descriptor_pointer =
+      kPlatformUsesOPDSections && opd != nullptr && !pc_in_opd;
+
+  ElfW(Sym) best_match;
+  SafeMemZero(&best_match, sizeof(best_match));
+  bool found_match = false;
+  for (size_t i = 0; i < num_symbols;) {
+    off_t offset =
+        static_cast<off_t>(symtab->sh_offset + i * symtab->sh_entsize);
+    const size_t num_remaining_symbols = num_symbols - i;
+    const size_t entries_in_chunk =
+        std::min(num_remaining_symbols, buf_entries);
+    const size_t bytes_in_chunk = entries_in_chunk * sizeof(buf[0]);
+    const ssize_t len = ReadFromOffset(fd, buf, bytes_in_chunk, offset);
+    SAFE_ASSERT(len >= 0);
+    SAFE_ASSERT(static_cast<size_t>(len) % sizeof(buf[0]) == 0);
+    const size_t num_symbols_in_buf = static_cast<size_t>(len) / sizeof(buf[0]);
+    SAFE_ASSERT(num_symbols_in_buf <= entries_in_chunk);
+    for (size_t j = 0; j < num_symbols_in_buf; ++j) {
+      const ElfW(Sym) &symbol = buf[j];
+
+      // For a DSO, a symbol address is relocated by the loading address.
+      // We keep the original address for opd redirection below.
+      const char *const original_start_address =
+          reinterpret_cast<const char *>(symbol.st_value);
+      const char *start_address =
+          ComputeOffset(original_start_address, relocation);
+
+#ifdef __arm__
+      // ARM functions are always aligned to multiples of two bytes; the
+      // lowest-order bit in start_address is ignored by the CPU and indicates
+      // whether the function contains ARM (0) or Thumb (1) code. We don't care
+      // about what encoding is being used; we just want the real start address
+      // of the function.
+      start_address = reinterpret_cast<const char *>(
+          reinterpret_cast<uintptr_t>(start_address) & ~1u);
+#endif
+
+      if (deref_function_descriptor_pointer &&
+          InSection(original_start_address, opd)) {
+        // The opd section is mapped into memory.  Just dereference
+        // start_address to get the first double word, which points to the
+        // function entry.
+        start_address = *reinterpret_cast<const char *const *>(start_address);
+      }
+
+      // If pc is inside the .opd section, it points to a function descriptor.
+      const size_t size = pc_in_opd ? kFunctionDescriptorSize : symbol.st_size;
+      const void *const end_address =
+          ComputeOffset(start_address, static_cast<ptrdiff_t>(size));
+      if (symbol.st_value != 0 &&  // Skip null value symbols.
+          symbol.st_shndx != 0 &&  // Skip undefined symbols.
+#ifdef STT_TLS
+          ELF_ST_TYPE(symbol.st_info) != STT_TLS &&  // Skip thread-local data.
+#endif                                               // STT_TLS
+          ((start_address <= pc && pc < end_address) ||
+           (start_address == pc && pc == end_address))) {
+        if (!found_match || ShouldPickFirstSymbol(symbol, best_match)) {
+          found_match = true;
+          best_match = symbol;
+        }
+      }
+    }
+    i += num_symbols_in_buf;
+  }
+
+  if (found_match) {
+    const off_t off =
+        static_cast<off_t>(strtab->sh_offset) + best_match.st_name;
+    const ssize_t n_read = ReadFromOffset(fd, out, out_size, off);
+    if (n_read <= 0) {
+      // This should never happen.
+      ABSL_RAW_LOG(WARNING,
+                   "Unable to read from fd %d at offset %lld: n_read = %zd", fd,
+                   static_cast<long long>(off), n_read);
+      return SYMBOL_NOT_FOUND;
+    }
+    ABSL_RAW_CHECK(static_cast<size_t>(n_read) <= out_size,
+                   "ReadFromOffset read too much data.");
+
+    // strtab->sh_offset points into .strtab-like section that contains
+    // NUL-terminated strings: '\0foo\0barbaz\0...".
+    //
+    // sh_offset+st_name points to the start of symbol name, but we don't know
+    // how long the symbol is, so we try to read as much as we have space for,
+    // and usually over-read (i.e. there is a NUL somewhere before n_read).
+    if (memchr(out, '\0', static_cast<size_t>(n_read)) == nullptr) {
+      // Either out_size was too small (n_read == out_size and no NUL), or
+      // we tried to read past the EOF (n_read < out_size) and .strtab is
+      // corrupt (missing terminating NUL; should never happen for valid ELF).
+      out[n_read - 1] = '\0';
+      return SYMBOL_TRUNCATED;
+    }
+    return SYMBOL_FOUND;
+  }
+
+  return SYMBOL_NOT_FOUND;
+}
+
+// Get the symbol name of "pc" from the file pointed by "fd".  Process
+// both regular and dynamic symbol tables if necessary.
+// See FindSymbol() comment for description of return value.
+FindSymbolResult Symbolizer::GetSymbolFromObjectFile(
+    const ObjFile &obj, const void *const pc, const ptrdiff_t relocation,
+    char *out, size_t out_size, char *tmp_buf, size_t tmp_buf_size) {
+  ElfW(Shdr) symtab;
+  ElfW(Shdr) strtab;
+  ElfW(Shdr) opd;
+  ElfW(Shdr) *opd_ptr = nullptr;
+
+  // On platforms using an .opd sections for function descriptor, read
+  // the section header.  The .opd section is in data segment and should be
+  // loaded but we check that it is mapped just to be extra careful.
+  if (kPlatformUsesOPDSections) {
+    if (GetSectionHeaderByName(obj.fd, kOpdSectionName,
+                               sizeof(kOpdSectionName) - 1, &opd) &&
+        FindObjFile(reinterpret_cast<const char *>(opd.sh_addr) + relocation,
+                    opd.sh_size) != nullptr) {
+      opd_ptr = &opd;
+    } else {
+      return SYMBOL_NOT_FOUND;
+    }
+  }
+
+  // Consult a regular symbol table, then fall back to the dynamic symbol table.
+  for (const auto symbol_table_type : {SHT_SYMTAB, SHT_DYNSYM}) {
+    if (!GetSectionHeaderByType(obj.fd, obj.elf_header.e_shnum,
+                                static_cast<off_t>(obj.elf_header.e_shoff),
+                                static_cast<ElfW(Word)>(symbol_table_type),
+                                &symtab, tmp_buf, tmp_buf_size)) {
+      continue;
+    }
+    if (!ReadFromOffsetExact(
+            obj.fd, &strtab, sizeof(strtab),
+            static_cast<off_t>(obj.elf_header.e_shoff +
+                               symtab.sh_link * sizeof(symtab)))) {
+      continue;
+    }
+    const FindSymbolResult rc =
+        FindSymbol(pc, obj.fd, out, out_size, relocation, &strtab, &symtab,
+                   opd_ptr, tmp_buf, tmp_buf_size);
+    if (rc != SYMBOL_NOT_FOUND) {
+      return rc;
+    }
+  }
+
+  return SYMBOL_NOT_FOUND;
+}
+
+namespace {
+// Thin wrapper around a file descriptor so that the file descriptor
+// gets closed for sure.
+class FileDescriptor {
+ public:
+  explicit FileDescriptor(int fd) : fd_(fd) {}
+  FileDescriptor(const FileDescriptor &) = delete;
+  FileDescriptor &operator=(const FileDescriptor &) = delete;
+
+  ~FileDescriptor() {
+    if (fd_ >= 0) {
+      close(fd_);
+    }
+  }
+
+  int get() const { return fd_; }
+
+ private:
+  const int fd_;
+};
+
+// Helper class for reading lines from file.
+//
+// Note: we don't use ProcMapsIterator since the object is big (it has
+// a 5k array member) and uses async-unsafe functions such as sscanf()
+// and snprintf().
+class LineReader {
+ public:
+  explicit LineReader(int fd, char *buf, size_t buf_len)
+      : fd_(fd),
+        buf_len_(buf_len),
+        buf_(buf),
+        bol_(buf),
+        eol_(buf),
+        eod_(buf) {}
+
+  LineReader(const LineReader &) = delete;
+  LineReader &operator=(const LineReader &) = delete;
+
+  // Read '\n'-terminated line from file.  On success, modify "bol"
+  // and "eol", then return true.  Otherwise, return false.
+  //
+  // Note: if the last line doesn't end with '\n', the line will be
+  // dropped.  It's an intentional behavior to make the code simple.
+  bool ReadLine(const char **bol, const char **eol) {
+    if (BufferIsEmpty()) {  // First time.
+      const ssize_t num_bytes = ReadPersistent(fd_, buf_, buf_len_);
+      if (num_bytes <= 0) {  // EOF or error.
+        return false;
+      }
+      eod_ = buf_ + num_bytes;
+      bol_ = buf_;
+    } else {
+      bol_ = eol_ + 1;            // Advance to the next line in the buffer.
+      SAFE_ASSERT(bol_ <= eod_);  // "bol_" can point to "eod_".
+      if (!HasCompleteLine()) {
+        const auto incomplete_line_length = static_cast<size_t>(eod_ - bol_);
+        // Move the trailing incomplete line to the beginning.
+        memmove(buf_, bol_, incomplete_line_length);
+        // Read text from file and append it.
+        char *const append_pos = buf_ + incomplete_line_length;
+        const size_t capacity_left = buf_len_ - incomplete_line_length;
+        const ssize_t num_bytes =
+            ReadPersistent(fd_, append_pos, capacity_left);
+        if (num_bytes <= 0) {  // EOF or error.
+          return false;
+        }
+        eod_ = append_pos + num_bytes;
+        bol_ = buf_;
+      }
+    }
+    eol_ = FindLineFeed();
+    if (eol_ == nullptr) {  // '\n' not found.  Malformed line.
+      return false;
+    }
+    *eol_ = '\0';  // Replace '\n' with '\0'.
+
+    *bol = bol_;
+    *eol = eol_;
+    return true;
+  }
+
+ private:
+  char *FindLineFeed() const {
+    return reinterpret_cast<char *>(
+        memchr(bol_, '\n', static_cast<size_t>(eod_ - bol_)));
+  }
+
+  bool BufferIsEmpty() const { return buf_ == eod_; }
+
+  bool HasCompleteLine() const {
+    return !BufferIsEmpty() && FindLineFeed() != nullptr;
+  }
+
+  const int fd_;
+  const size_t buf_len_;
+  char *const buf_;
+  char *bol_;
+  char *eol_;
+  const char *eod_;  // End of data in "buf_".
+};
+}  // namespace
+
+// Place the hex number read from "start" into "*hex".  The pointer to
+// the first non-hex character or "end" is returned.
+static const char *GetHex(const char *start, const char *end,
+                          uint64_t *const value) {
+  uint64_t hex = 0;
+  const char *p;
+  for (p = start; p < end; ++p) {
+    int ch = *p;
+    if ((ch >= '0' && ch <= '9') || (ch >= 'A' && ch <= 'F') ||
+        (ch >= 'a' && ch <= 'f')) {
+      hex = (hex << 4) |
+            static_cast<uint64_t>(ch < 'A' ? ch - '0' : (ch & 0xF) + 9);
+    } else {  // Encountered the first non-hex character.
+      break;
+    }
+  }
+  SAFE_ASSERT(p <= end);
+  *value = hex;
+  return p;
+}
+
+static const char *GetHex(const char *start, const char *end,
+                          const void **const addr) {
+  uint64_t hex = 0;
+  const char *p = GetHex(start, end, &hex);
+  *addr = reinterpret_cast<void *>(hex);
+  return p;
+}
+
+// Normally we are only interested in "r?x" maps.
+// On the PowerPC, function pointers point to descriptors in the .opd
+// section.  The descriptors themselves are not executable code, so
+// we need to relax the check below to "r??".
+static bool ShouldUseMapping(const char *const flags) {
+  return flags[0] == 'r' && (kPlatformUsesOPDSections || flags[2] == 'x');
+}
+
+// Read /proc/self/maps and run "callback" for each mmapped file found.  If
+// "callback" returns false, stop scanning and return true. Else continue
+// scanning /proc/self/maps. Return true if no parse error is found.
+static ABSL_ATTRIBUTE_NOINLINE bool ReadAddrMap(
+    bool (*callback)(const char *filename, const void *const start_addr,
+                     const void *const end_addr, uint64_t offset, void *arg),
+    void *arg, void *tmp_buf, size_t tmp_buf_size) {
+  // Use /proc/self/task/<pid>/maps instead of /proc/self/maps. The latter
+  // requires kernel to stop all threads, and is significantly slower when there
+  // are 1000s of threads.
+  char maps_path[80];
+  snprintf(maps_path, sizeof(maps_path), "/proc/self/task/%d/maps", getpid());
+
+  int maps_fd;
+  NO_INTR(maps_fd = open(maps_path, O_RDONLY));
+  FileDescriptor wrapped_maps_fd(maps_fd);
+  if (wrapped_maps_fd.get() < 0) {
+    ABSL_RAW_LOG(WARNING, "%s: errno=%d", maps_path, errno);
+    return false;
+  }
+
+  // Iterate over maps and look for the map containing the pc.  Then
+  // look into the symbol tables inside.
+  LineReader reader(wrapped_maps_fd.get(), static_cast<char *>(tmp_buf),
+                    tmp_buf_size);
+  while (true) {
+    const char *cursor;
+    const char *eol;
+    if (!reader.ReadLine(&cursor, &eol)) {  // EOF or malformed line.
+      break;
+    }
+
+    const char *line = cursor;
+    const void *start_address;
+    // Start parsing line in /proc/self/maps.  Here is an example:
+    //
+    // 08048000-0804c000 r-xp 00000000 08:01 2142121    /bin/cat
+    //
+    // We want start address (08048000), end address (0804c000), flags
+    // (r-xp) and file name (/bin/cat).
+
+    // Read start address.
+    cursor = GetHex(cursor, eol, &start_address);
+    if (cursor == eol || *cursor != '-') {
+      ABSL_RAW_LOG(WARNING, "Corrupt /proc/self/maps line: %s", line);
+      return false;
+    }
+    ++cursor;  // Skip '-'.
+
+    // Read end address.
+    const void *end_address;
+    cursor = GetHex(cursor, eol, &end_address);
+    if (cursor == eol || *cursor != ' ') {
+      ABSL_RAW_LOG(WARNING, "Corrupt /proc/self/maps line: %s", line);
+      return false;
+    }
+    ++cursor;  // Skip ' '.
+
+    // Read flags.  Skip flags until we encounter a space or eol.
+    const char *const flags_start = cursor;
+    while (cursor < eol && *cursor != ' ') {
+      ++cursor;
+    }
+    // We expect at least four letters for flags (ex. "r-xp").
+    if (cursor == eol || cursor < flags_start + 4) {
+      ABSL_RAW_LOG(WARNING, "Corrupt /proc/self/maps: %s", line);
+      return false;
+    }
+
+    // Check flags.
+    if (!ShouldUseMapping(flags_start)) {
+      continue;  // We skip this map.
+    }
+    ++cursor;  // Skip ' '.
+
+    // Read file offset.
+    uint64_t offset;
+    cursor = GetHex(cursor, eol, &offset);
+    ++cursor;  // Skip ' '.
+
+    // Skip to file name.  "cursor" now points to dev.  We need to skip at least
+    // two spaces for dev and inode.
+    int num_spaces = 0;
+    while (cursor < eol) {
+      if (*cursor == ' ') {
+        ++num_spaces;
+      } else if (num_spaces >= 2) {
+        // The first non-space character after  skipping two spaces
+        // is the beginning of the file name.
+        break;
+      }
+      ++cursor;
+    }
+
+    // Check whether this entry corresponds to our hint table for the true
+    // filename.
+    bool hinted =
+        GetFileMappingHint(&start_address, &end_address, &offset, &cursor);
+    if (!hinted && (cursor == eol || cursor[0] == '[')) {
+      // not an object file, typically [vdso] or [vsyscall]
+      continue;
+    }
+    if (!callback(cursor, start_address, end_address, offset, arg)) break;
+  }
+  return true;
+}
+
+// Find the objfile mapped in address region containing [addr, addr + len).
+ObjFile *Symbolizer::FindObjFile(const void *const addr, size_t len) {
+  for (int i = 0; i < 2; ++i) {
+    if (!ok_) return nullptr;
+
+    // Read /proc/self/maps if necessary
+    if (!addr_map_read_) {
+      addr_map_read_ = true;
+      if (!ReadAddrMap(RegisterObjFile, this, tmp_buf_, TMP_BUF_SIZE)) {
+        ok_ = false;
+        return nullptr;
+      }
+    }
+
+    size_t lo = 0;
+    size_t hi = addr_map_.Size();
+    while (lo < hi) {
+      size_t mid = (lo + hi) / 2;
+      if (addr < addr_map_.At(mid)->end_addr) {
+        hi = mid;
+      } else {
+        lo = mid + 1;
+      }
+    }
+    if (lo != addr_map_.Size()) {
+      ObjFile *obj = addr_map_.At(lo);
+      SAFE_ASSERT(obj->end_addr > addr);
+      if (addr >= obj->start_addr &&
+          reinterpret_cast<const char *>(addr) + len <= obj->end_addr)
+        return obj;
+    }
+
+    // The address mapping may have changed since it was last read.  Retry.
+    ClearAddrMap();
+  }
+  return nullptr;
+}
+
+void Symbolizer::ClearAddrMap() {
+  for (size_t i = 0; i != addr_map_.Size(); i++) {
+    ObjFile *o = addr_map_.At(i);
+    base_internal::LowLevelAlloc::Free(o->filename);
+    if (o->fd >= 0) {
+      close(o->fd);
+    }
+  }
+  addr_map_.Clear();
+  addr_map_read_ = false;
+}
+
+// Callback for ReadAddrMap to register objfiles in an in-memory table.
+bool Symbolizer::RegisterObjFile(const char *filename,
+                                 const void *const start_addr,
+                                 const void *const end_addr, uint64_t offset,
+                                 void *arg) {
+  Symbolizer *impl = static_cast<Symbolizer *>(arg);
+
+  // Files are supposed to be added in the increasing address order.  Make
+  // sure that's the case.
+  size_t addr_map_size = impl->addr_map_.Size();
+  if (addr_map_size != 0) {
+    ObjFile *old = impl->addr_map_.At(addr_map_size - 1);
+    if (old->end_addr > end_addr) {
+      ABSL_RAW_LOG(ERROR,
+                   "Unsorted addr map entry: 0x%" PRIxPTR ": %s <-> 0x%" PRIxPTR
+                   ": %s",
+                   reinterpret_cast<uintptr_t>(end_addr), filename,
+                   reinterpret_cast<uintptr_t>(old->end_addr), old->filename);
+      return true;
+    } else if (old->end_addr == end_addr) {
+      // The same entry appears twice. This sometimes happens for [vdso].
+      if (old->start_addr != start_addr ||
+          strcmp(old->filename, filename) != 0) {
+        ABSL_RAW_LOG(ERROR,
+                     "Duplicate addr 0x%" PRIxPTR ": %s <-> 0x%" PRIxPTR ": %s",
+                     reinterpret_cast<uintptr_t>(end_addr), filename,
+                     reinterpret_cast<uintptr_t>(old->end_addr), old->filename);
+      }
+      return true;
+    } else if (old->end_addr == start_addr &&
+               reinterpret_cast<uintptr_t>(old->start_addr) - old->offset ==
+                   reinterpret_cast<uintptr_t>(start_addr) - offset &&
+               strcmp(old->filename, filename) == 0) {
+      // Two contiguous map entries that span a contiguous region of the file,
+      // perhaps because some part of the file was mlock()ed. Combine them.
+      old->end_addr = end_addr;
+      return true;
+    }
+  }
+  ObjFile *obj = impl->addr_map_.Add();
+  obj->filename = impl->CopyString(filename);
+  obj->start_addr = start_addr;
+  obj->end_addr = end_addr;
+  obj->offset = offset;
+  obj->elf_type = -1;  // filled on demand
+  obj->fd = -1;        // opened on demand
+  return true;
+}
+
+// This function wraps the Demangle function to provide an interface
+// where the input symbol is demangled in-place.
+// To keep stack consumption low, we would like this function to not
+// get inlined.
+static ABSL_ATTRIBUTE_NOINLINE void DemangleInplace(char *out, size_t out_size,
+                                                    char *tmp_buf,
+                                                    size_t tmp_buf_size) {
+  if (Demangle(out, tmp_buf, tmp_buf_size)) {
+    // Demangling succeeded. Copy to out if the space allows.
+    size_t len = strlen(tmp_buf);
+    if (len + 1 <= out_size) {  // +1 for '\0'.
+      SAFE_ASSERT(len < tmp_buf_size);
+      memmove(out, tmp_buf, len + 1);
+    }
+  }
+}
+
+SymbolCacheLine *Symbolizer::GetCacheLine(const void *const pc) {
+  uintptr_t pc0 = reinterpret_cast<uintptr_t>(pc);
+  pc0 >>= 3;  // drop the low 3 bits
+
+  // Shuffle bits.
+  pc0 ^= (pc0 >> 6) ^ (pc0 >> 12) ^ (pc0 >> 18);
+  return &symbol_cache_[pc0 % SYMBOL_CACHE_LINES];
+}
+
+void Symbolizer::AgeSymbols(SymbolCacheLine *line) {
+  for (uint32_t &age : line->age) {
+    ++age;
+  }
+}
+
+const char *Symbolizer::FindSymbolInCache(const void *const pc) {
+  if (pc == nullptr) return nullptr;
+
+  SymbolCacheLine *line = GetCacheLine(pc);
+  for (size_t i = 0; i < ABSL_ARRAYSIZE(line->pc); ++i) {
+    if (line->pc[i] == pc) {
+      AgeSymbols(line);
+      line->age[i] = 0;
+      return line->name[i];
+    }
+  }
+  return nullptr;
+}
+
+const char *Symbolizer::InsertSymbolInCache(const void *const pc,
+                                            const char *name) {
+  SAFE_ASSERT(pc != nullptr);
+
+  SymbolCacheLine *line = GetCacheLine(pc);
+  uint32_t max_age = 0;
+  size_t oldest_index = 0;
+  bool found_oldest_index = false;
+  for (size_t i = 0; i < ABSL_ARRAYSIZE(line->pc); ++i) {
+    if (line->pc[i] == nullptr) {
+      AgeSymbols(line);
+      line->pc[i] = pc;
+      line->name[i] = CopyString(name);
+      line->age[i] = 0;
+      return line->name[i];
+    }
+    if (line->age[i] >= max_age) {
+      max_age = line->age[i];
+      oldest_index = i;
+      found_oldest_index = true;
+    }
+  }
+
+  AgeSymbols(line);
+  ABSL_RAW_CHECK(found_oldest_index, "Corrupt cache");
+  base_internal::LowLevelAlloc::Free(line->name[oldest_index]);
+  line->pc[oldest_index] = pc;
+  line->name[oldest_index] = CopyString(name);
+  line->age[oldest_index] = 0;
+  return line->name[oldest_index];
+}
+
+static void MaybeOpenFdFromSelfExe(ObjFile *obj) {
+  if (memcmp(obj->start_addr, ELFMAG, SELFMAG) != 0) {
+    return;
+  }
+  int fd = open("/proc/self/exe", O_RDONLY);
+  if (fd == -1) {
+    return;
+  }
+  // Verify that contents of /proc/self/exe matches in-memory image of
+  // the binary. This can fail if the "deleted" binary is in fact not
+  // the main executable, or for binaries that have the first PT_LOAD
+  // segment smaller than 4K. We do it in four steps so that the
+  // buffer is smaller and we don't consume too much stack space.
+  const char *mem = reinterpret_cast<const char *>(obj->start_addr);
+  for (int i = 0; i < 4; ++i) {
+    char buf[1024];
+    ssize_t n = read(fd, buf, sizeof(buf));
+    if (n != sizeof(buf) || memcmp(buf, mem, sizeof(buf)) != 0) {
+      close(fd);
+      return;
+    }
+    mem += sizeof(buf);
+  }
+  obj->fd = fd;
+}
+
+static bool MaybeInitializeObjFile(ObjFile *obj) {
+  if (obj->fd < 0) {
+    obj->fd = open(obj->filename, O_RDONLY);
+
+    if (obj->fd < 0) {
+      // Getting /proc/self/exe here means that we were hinted.
+      if (strcmp(obj->filename, "/proc/self/exe") == 0) {
+        // /proc/self/exe may be inaccessible (due to setuid, etc.), so try
+        // accessing the binary via argv0.
+        if (argv0_value != nullptr) {
+          obj->fd = open(argv0_value, O_RDONLY);
+        }
+      } else {
+        MaybeOpenFdFromSelfExe(obj);
+      }
+    }
+
+    if (obj->fd < 0) {
+      ABSL_RAW_LOG(WARNING, "%s: open failed: errno=%d", obj->filename, errno);
+      return false;
+    }
+    obj->elf_type = FileGetElfType(obj->fd);
+    if (obj->elf_type < 0) {
+      ABSL_RAW_LOG(WARNING, "%s: wrong elf type: %d", obj->filename,
+                   obj->elf_type);
+      return false;
+    }
+
+    if (!ReadFromOffsetExact(obj->fd, &obj->elf_header, sizeof(obj->elf_header),
+                             0)) {
+      ABSL_RAW_LOG(WARNING, "%s: failed to read elf header", obj->filename);
+      return false;
+    }
+    const int phnum = obj->elf_header.e_phnum;
+    const int phentsize = obj->elf_header.e_phentsize;
+    auto phoff = static_cast<off_t>(obj->elf_header.e_phoff);
+    size_t num_executable_load_segments = 0;
+    for (int j = 0; j < phnum; j++) {
+      ElfW(Phdr) phdr;
+      if (!ReadFromOffsetExact(obj->fd, &phdr, sizeof(phdr), phoff)) {
+        ABSL_RAW_LOG(WARNING, "%s: failed to read program header %d",
+                     obj->filename, j);
+        return false;
+      }
+      phoff += phentsize;
+      constexpr int rx = PF_X | PF_R;
+      if (phdr.p_type != PT_LOAD || (phdr.p_flags & rx) != rx) {
+        // Not a LOAD segment, or not executable code.
+        continue;
+      }
+      if (num_executable_load_segments < obj->phdr.size()) {
+        memcpy(&obj->phdr[num_executable_load_segments++], &phdr, sizeof(phdr));
+      } else {
+        ABSL_RAW_LOG(
+            WARNING, "%s: too many executable LOAD segments: %zu >= %zu",
+            obj->filename, num_executable_load_segments, obj->phdr.size());
+        break;
+      }
+    }
+    if (num_executable_load_segments == 0) {
+      // This object has no "r-x" LOAD segments. That's unexpected.
+      ABSL_RAW_LOG(WARNING, "%s: no executable LOAD segments", obj->filename);
+      return false;
+    }
+  }
+  return true;
+}
+
+// The implementation of our symbolization routine.  If it
+// successfully finds the symbol containing "pc" and obtains the
+// symbol name, returns pointer to that symbol. Otherwise, returns nullptr.
+// If any symbol decorators have been installed via InstallSymbolDecorator(),
+// they are called here as well.
+// To keep stack consumption low, we would like this function to not
+// get inlined.
+const char *Symbolizer::GetUncachedSymbol(const void *pc) {
+  ObjFile *const obj = FindObjFile(pc, 1);
+  ptrdiff_t relocation = 0;
+  int fd = -1;
+  if (obj != nullptr) {
+    if (MaybeInitializeObjFile(obj)) {
+      const size_t start_addr = reinterpret_cast<size_t>(obj->start_addr);
+      if (obj->elf_type == ET_DYN && start_addr >= obj->offset) {
+        // This object was relocated.
+        //
+        // For obj->offset > 0, adjust the relocation since a mapping at offset
+        // X in the file will have a start address of [true relocation]+X.
+        relocation = static_cast<ptrdiff_t>(start_addr - obj->offset);
+
+        // Note: some binaries have multiple "rx" LOAD segments. We must
+        // find the right one.
+        ElfW(Phdr) *phdr = nullptr;
+        for (size_t j = 0; j < obj->phdr.size(); j++) {
+          ElfW(Phdr) &p = obj->phdr[j];
+          if (p.p_type != PT_LOAD) {
+            // We only expect PT_LOADs. This must be PT_NULL that we didn't
+            // write over (i.e. we exhausted all interesting PT_LOADs).
+            ABSL_RAW_CHECK(p.p_type == PT_NULL, "unexpected p_type");
+            break;
+          }
+          if (pc < reinterpret_cast<void *>(start_addr + p.p_memsz)) {
+            phdr = &p;
+            break;
+          }
+        }
+        if (phdr == nullptr) {
+          // That's unexpected. Hope for the best.
+          ABSL_RAW_LOG(
+              WARNING,
+              "%s: unable to find LOAD segment for pc: %p, start_addr: %zx",
+              obj->filename, pc, start_addr);
+        } else {
+          // Adjust relocation in case phdr.p_vaddr != 0.
+          // This happens for binaries linked with `lld --rosegment`, and for
+          // binaries linked with BFD `ld -z separate-code`.
+          relocation -= phdr->p_vaddr - phdr->p_offset;
+        }
+      }
+
+      fd = obj->fd;
+      if (GetSymbolFromObjectFile(*obj, pc, relocation, symbol_buf_,
+                                  sizeof(symbol_buf_), tmp_buf_,
+                                  sizeof(tmp_buf_)) == SYMBOL_FOUND) {
+        // Only try to demangle the symbol name if it fit into symbol_buf_.
+        DemangleInplace(symbol_buf_, sizeof(symbol_buf_), tmp_buf_,
+                        sizeof(tmp_buf_));
+      }
+    }
+  } else {
+#if ABSL_HAVE_VDSO_SUPPORT
+    VDSOSupport vdso;
+    if (vdso.IsPresent()) {
+      VDSOSupport::SymbolInfo symbol_info;
+      if (vdso.LookupSymbolByAddress(pc, &symbol_info)) {
+        // All VDSO symbols are known to be short.
+        size_t len = strlen(symbol_info.name);
+        ABSL_RAW_CHECK(len + 1 < sizeof(symbol_buf_),
+                       "VDSO symbol unexpectedly long");
+        memcpy(symbol_buf_, symbol_info.name, len + 1);
+      }
+    }
+#endif
+  }
+
+  if (g_decorators_mu.TryLock()) {
+    if (g_num_decorators > 0) {
+      SymbolDecoratorArgs decorator_args = {
+          pc,       relocation,       fd,     symbol_buf_, sizeof(symbol_buf_),
+          tmp_buf_, sizeof(tmp_buf_), nullptr};
+      for (int i = 0; i < g_num_decorators; ++i) {
+        decorator_args.arg = g_decorators[i].arg;
+        g_decorators[i].fn(&decorator_args);
+      }
+    }
+    g_decorators_mu.Unlock();
+  }
+  if (symbol_buf_[0] == '\0') {
+    return nullptr;
+  }
+  symbol_buf_[sizeof(symbol_buf_) - 1] = '\0';  // Paranoia.
+  return InsertSymbolInCache(pc, symbol_buf_);
+}
+
+const char *Symbolizer::GetSymbol(const void *pc) {
+  const char *entry = FindSymbolInCache(pc);
+  if (entry != nullptr) {
+    return entry;
+  }
+  symbol_buf_[0] = '\0';
+
+#ifdef __hppa__
+  {
+    // In some contexts (e.g., return addresses), PA-RISC uses the lowest two
+    // bits of the address to indicate the privilege level. Clear those bits
+    // before trying to symbolize.
+    const auto pc_bits = reinterpret_cast<uintptr_t>(pc);
+    const auto address = pc_bits & ~0x3;
+    entry = GetUncachedSymbol(reinterpret_cast<const void *>(address));
+    if (entry != nullptr) {
+      return entry;
+    }
+
+    // In some contexts, PA-RISC also uses bit 1 of the address to indicate that
+    // this is a cross-DSO function pointer. Such function pointers actually
+    // point to a procedure label, a struct whose first 32-bit (pointer) element
+    // actually points to the function text. With no symbol found for this
+    // address so far, try interpreting it as a cross-DSO function pointer and
+    // see how that goes.
+    if (pc_bits & 0x2) {
+      return GetUncachedSymbol(*reinterpret_cast<const void *const *>(address));
+    }
+
+    return nullptr;
+  }
+#else
+  return GetUncachedSymbol(pc);
+#endif
+}
+
+bool RemoveAllSymbolDecorators(void) {
+  if (!g_decorators_mu.TryLock()) {
+    // Someone else is using decorators. Get out.
+    return false;
+  }
+  g_num_decorators = 0;
+  g_decorators_mu.Unlock();
+  return true;
+}
+
+bool RemoveSymbolDecorator(int ticket) {
+  if (!g_decorators_mu.TryLock()) {
+    // Someone else is using decorators. Get out.
+    return false;
+  }
+  for (int i = 0; i < g_num_decorators; ++i) {
+    if (g_decorators[i].ticket == ticket) {
+      while (i < g_num_decorators - 1) {
+        g_decorators[i] = g_decorators[i + 1];
+        ++i;
+      }
+      g_num_decorators = i;
+      break;
+    }
+  }
+  g_decorators_mu.Unlock();
+  return true;  // Decorator is known to be removed.
+}
+
+int InstallSymbolDecorator(SymbolDecorator decorator, void *arg) {
+  static int ticket = 0;
+
+  if (!g_decorators_mu.TryLock()) {
+    // Someone else is using decorators. Get out.
+    return -2;
+  }
+  int ret = ticket;
+  if (g_num_decorators >= kMaxDecorators) {
+    ret = -1;
+  } else {
+    g_decorators[g_num_decorators] = {decorator, arg, ticket++};
+    ++g_num_decorators;
+  }
+  g_decorators_mu.Unlock();
+  return ret;
+}
+
+bool RegisterFileMappingHint(const void *start, const void *end, uint64_t offset,
+                             const char *filename) {
+  SAFE_ASSERT(start <= end);
+  SAFE_ASSERT(filename != nullptr);
+
+  InitSigSafeArena();
+
+  if (!g_file_mapping_mu.TryLock()) {
+    return false;
+  }
+
+  bool ret = true;
+  if (g_num_file_mapping_hints >= kMaxFileMappingHints) {
+    ret = false;
+  } else {
+    // TODO(ckennelly): Move this into a string copy routine.
+    size_t len = strlen(filename);
+    char *dst = static_cast<char *>(
+        base_internal::LowLevelAlloc::AllocWithArena(len + 1, SigSafeArena()));
+    ABSL_RAW_CHECK(dst != nullptr, "out of memory");
+    memcpy(dst, filename, len + 1);
+
+    auto &hint = g_file_mapping_hints[g_num_file_mapping_hints++];
+    hint.start = start;
+    hint.end = end;
+    hint.offset = offset;
+    hint.filename = dst;
+  }
+
+  g_file_mapping_mu.Unlock();
+  return ret;
+}
+
+bool GetFileMappingHint(const void **start, const void **end, uint64_t *offset,
+                        const char **filename) {
+  if (!g_file_mapping_mu.TryLock()) {
+    return false;
+  }
+  bool found = false;
+  for (int i = 0; i < g_num_file_mapping_hints; i++) {
+    if (g_file_mapping_hints[i].start <= *start &&
+        *end <= g_file_mapping_hints[i].end) {
+      // We assume that the start_address for the mapping is the base
+      // address of the ELF section, but when [start_address,end_address) is
+      // not strictly equal to [hint.start, hint.end), that assumption is
+      // invalid.
+      //
+      // This uses the hint's start address (even though hint.start is not
+      // necessarily equal to start_address) to ensure the correct
+      // relocation is computed later.
+      *start = g_file_mapping_hints[i].start;
+      *end = g_file_mapping_hints[i].end;
+      *offset = g_file_mapping_hints[i].offset;
+      *filename = g_file_mapping_hints[i].filename;
+      found = true;
+      break;
+    }
+  }
+  g_file_mapping_mu.Unlock();
+  return found;
+}
+
+}  // namespace debugging_internal
+
+bool Symbolize(const void *pc, char *out, int out_size) {
+  // Symbolization is very slow under tsan.
+  ABSL_ANNOTATE_IGNORE_READS_AND_WRITES_BEGIN();
+  SAFE_ASSERT(out_size >= 0);
+  debugging_internal::Symbolizer *s = debugging_internal::AllocateSymbolizer();
+  const char *name = s->GetSymbol(pc);
+  bool ok = false;
+  if (name != nullptr && out_size > 0) {
+    strncpy(out, name, static_cast<size_t>(out_size));
+    ok = true;
+    if (out[static_cast<size_t>(out_size) - 1] != '\0') {
+      // strncpy() does not '\0' terminate when it truncates.  Do so, with
+      // trailing ellipsis.
+      static constexpr char kEllipsis[] = "...";
+      size_t ellipsis_size =
+          std::min(strlen(kEllipsis), static_cast<size_t>(out_size) - 1);
+      memcpy(out + static_cast<size_t>(out_size) - ellipsis_size - 1, kEllipsis,
+             ellipsis_size);
+      out[static_cast<size_t>(out_size) - 1] = '\0';
+    }
+  }
+  debugging_internal::FreeSymbolizer(s);
+  ABSL_ANNOTATE_IGNORE_READS_AND_WRITES_END();
+  return ok;
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+extern "C" bool AbslInternalGetFileMappingHint(const void **start,
+                                               const void **end, uint64_t *offset,
+                                               const char **filename) {
+  return absl::debugging_internal::GetFileMappingHint(start, end, offset,
+                                                      filename);
+}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize_emscripten.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize_emscripten.inc
new file mode 100644
index 0000000000000000000000000000000000000000..c226c4566600d048a44f6fb38e3ea2cf25ae27e3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize_emscripten.inc
@@ -0,0 +1,72 @@
+// Copyright 2020 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <cxxabi.h>
+#include <emscripten.h>
+
+#include <algorithm>
+#include <cstring>
+
+#include "absl/base/internal/raw_logging.h"
+#include "absl/debugging/internal/demangle.h"
+#include "absl/strings/numbers.h"
+#include "absl/strings/str_cat.h"
+#include "absl/strings/string_view.h"
+
+extern "C" {
+const char* emscripten_pc_get_function(const void* pc);
+}
+
+// clang-format off
+EM_JS(bool, HaveOffsetConverter, (),
+      { return typeof wasmOffsetConverter !== 'undefined'; });
+// clang-format on
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+void InitializeSymbolizer(const char*) {
+  if (!HaveOffsetConverter()) {
+    ABSL_RAW_LOG(INFO,
+                 "Symbolization unavailable. Rebuild with -sWASM=1 "
+                 "and -sUSE_OFFSET_CONVERTER=1.");
+  }
+}
+
+bool Symbolize(const void* pc, char* out, int out_size) {
+  // Check if we have the offset converter necessary for pc_get_function.
+  // Without it, the program will abort().
+  if (!HaveOffsetConverter()) {
+    return false;
+  }
+  const char* func_name = emscripten_pc_get_function(pc);
+  if (func_name == nullptr) {
+    return false;
+  }
+
+  strncpy(out, func_name, out_size);
+
+  if (out[out_size - 1] != '\0') {
+    // strncpy() does not '\0' terminate when it truncates.
+    static constexpr char kEllipsis[] = "...";
+    int ellipsis_size = std::min<int>(sizeof(kEllipsis) - 1, out_size - 1);
+    memcpy(out + out_size - ellipsis_size - 1, kEllipsis, ellipsis_size);
+    out[out_size - 1] = '\0';
+  }
+
+  return true;
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize_unimplemented.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize_unimplemented.inc
new file mode 100644
index 0000000000000000000000000000000000000000..db24456b0afaf974ce063638f1cfb5fce42a93e2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize_unimplemented.inc
@@ -0,0 +1,40 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <cstdint>
+
+#include "absl/base/internal/raw_logging.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+namespace debugging_internal {
+
+int InstallSymbolDecorator(SymbolDecorator, void*) { return -1; }
+bool RemoveSymbolDecorator(int) { return false; }
+bool RemoveAllSymbolDecorators(void) { return false; }
+bool RegisterFileMappingHint(const void *, const void *, uint64_t, const char *) {
+  return false;
+}
+bool GetFileMappingHint(const void **, const void **, uint64_t *, const char **) {
+  return false;
+}
+
+}  // namespace debugging_internal
+
+void InitializeSymbolizer(const char*) {}
+bool Symbolize(const void *, char *, int) { return false; }
+
+ABSL_NAMESPACE_END
+}  // namespace absl
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize_win32.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize_win32.inc
new file mode 100644
index 0000000000000000000000000000000000000000..53a099a1814933cfc25e29be36a94acd3818b77f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/debugging/symbolize_win32.inc
@@ -0,0 +1,82 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// See "Retrieving Symbol Information by Address":
+// https://msdn.microsoft.com/en-us/library/windows/desktop/ms680578(v=vs.85).aspx
+
+#include <windows.h>
+
+// MSVC header dbghelp.h has a warning for an ignored typedef.
+#pragma warning(push)
+#pragma warning(disable:4091)
+#include <dbghelp.h>
+#pragma warning(pop)
+
+#pragma comment(lib, "dbghelp.lib")
+
+#include <algorithm>
+#include <cstring>
+
+#include "absl/base/internal/raw_logging.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+static HANDLE process = NULL;
+
+void InitializeSymbolizer(const char*) {
+  if (process != nullptr) {
+    return;
+  }
+  process = GetCurrentProcess();
+
+  // Symbols are not loaded until a reference is made requiring the
+  // symbols be loaded. This is the fastest, most efficient way to use
+  // the symbol handler.
+  SymSetOptions(SYMOPT_DEFERRED_LOADS | SYMOPT_UNDNAME);
+  if (!SymInitialize(process, nullptr, true)) {
+    // GetLastError() returns a Win32 DWORD, but we assign to
+    // unsigned long long to simplify the ABSL_RAW_LOG case below.  The uniform
+    // initialization guarantees this is not a narrowing conversion.
+    const unsigned long long error{GetLastError()};  // NOLINT(runtime/int)
+    ABSL_RAW_LOG(FATAL, "SymInitialize() failed: %llu", error);
+  }
+}
+
+bool Symbolize(const void* pc, char* out, int out_size) {
+  if (out_size <= 0) {
+    return false;
+  }
+  alignas(SYMBOL_INFO) char buf[sizeof(SYMBOL_INFO) + MAX_SYM_NAME];
+  SYMBOL_INFO* symbol = reinterpret_cast<SYMBOL_INFO*>(buf);
+  symbol->SizeOfStruct = sizeof(SYMBOL_INFO);
+  symbol->MaxNameLen = MAX_SYM_NAME;
+  if (!SymFromAddr(process, reinterpret_cast<DWORD64>(pc), nullptr, symbol)) {
+    return false;
+  }
+  const size_t out_size_t = static_cast<size_t>(out_size);
+  strncpy(out, symbol->Name, out_size_t);
+  if (out[out_size_t - 1] != '\0') {
+    // strncpy() does not '\0' terminate when it truncates.
+    static constexpr char kEllipsis[] = "...";
+    size_t ellipsis_size =
+        std::min(sizeof(kEllipsis) - 1, out_size_t - 1);
+    memcpy(out + out_size_t - ellipsis_size - 1, kEllipsis, ellipsis_size);
+    out[out_size_t - 1] = '\0';
+  }
+  return true;
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/commandlineflag.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/commandlineflag.h
new file mode 100644
index 0000000000000000000000000000000000000000..f2fa08977fd46347bb91cf30da822a5fd7fa80e3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/commandlineflag.h
@@ -0,0 +1,200 @@
+//
+// Copyright 2020 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: commandlineflag.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines the `CommandLineFlag`, which acts as a type-erased
+// handle for accessing metadata about the Abseil Flag in question.
+//
+// Because an actual Abseil flag is of an unspecified type, you should not
+// manipulate or interact directly with objects of that type. Instead, use the
+// CommandLineFlag type as an intermediary.
+#ifndef ABSL_FLAGS_COMMANDLINEFLAG_H_
+#define ABSL_FLAGS_COMMANDLINEFLAG_H_
+
+#include <memory>
+#include <string>
+
+#include "absl/base/config.h"
+#include "absl/base/internal/fast_type_id.h"
+#include "absl/flags/internal/commandlineflag.h"
+#include "absl/strings/string_view.h"
+#include "absl/types/optional.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace flags_internal {
+class PrivateHandleAccessor;
+}  // namespace flags_internal
+
+// CommandLineFlag
+//
+// This type acts as a type-erased handle for an instance of an Abseil Flag and
+// holds reflection information pertaining to that flag. Use CommandLineFlag to
+// access a flag's name, location, help string etc.
+//
+// To obtain an absl::CommandLineFlag, invoke `absl::FindCommandLineFlag()`
+// passing it the flag name string.
+//
+// Example:
+//
+//   // Obtain reflection handle for a flag named "flagname".
+//   const absl::CommandLineFlag* my_flag_data =
+//        absl::FindCommandLineFlag("flagname");
+//
+//   // Now you can get flag info from that reflection handle.
+//   std::string flag_location = my_flag_data->Filename();
+//   ...
+class CommandLineFlag {
+ public:
+  constexpr CommandLineFlag() = default;
+
+  // Not copyable/assignable.
+  CommandLineFlag(const CommandLineFlag&) = delete;
+  CommandLineFlag& operator=(const CommandLineFlag&) = delete;
+
+  // absl::CommandLineFlag::IsOfType()
+  //
+  // Return true iff flag has type T.
+  template <typename T>
+  inline bool IsOfType() const {
+    return TypeId() == base_internal::FastTypeId<T>();
+  }
+
+  // absl::CommandLineFlag::TryGet()
+  //
+  // Attempts to retrieve the flag value. Returns value on success,
+  // absl::nullopt otherwise.
+  template <typename T>
+  absl::optional<T> TryGet() const {
+    if (IsRetired() || !IsOfType<T>()) {
+      return absl::nullopt;
+    }
+
+    // Implementation notes:
+    //
+    // We are wrapping a union around the value of `T` to serve three purposes:
+    //
+    //  1. `U.value` has correct size and alignment for a value of type `T`
+    //  2. The `U.value` constructor is not invoked since U's constructor does
+    //     not do it explicitly.
+    //  3. The `U.value` destructor is invoked since U's destructor does it
+    //     explicitly. This makes `U` a kind of RAII wrapper around non default
+    //     constructible value of T, which is destructed when we leave the
+    //     scope. We do need to destroy U.value, which is constructed by
+    //     CommandLineFlag::Read even though we left it in a moved-from state
+    //     after std::move.
+    //
+    // All of this serves to avoid requiring `T` being default constructible.
+    union U {
+      T value;
+      U() {}
+      ~U() { value.~T(); }
+    };
+    U u;
+
+    Read(&u.value);
+    // allow retired flags to be "read", so we can report invalid access.
+    if (IsRetired()) {
+      return absl::nullopt;
+    }
+    return std::move(u.value);
+  }
+
+  // absl::CommandLineFlag::Name()
+  //
+  // Returns name of this flag.
+  virtual absl::string_view Name() const = 0;
+
+  // absl::CommandLineFlag::Filename()
+  //
+  // Returns name of the file where this flag is defined.
+  virtual std::string Filename() const = 0;
+
+  // absl::CommandLineFlag::Help()
+  //
+  // Returns help message associated with this flag.
+  virtual std::string Help() const = 0;
+
+  // absl::CommandLineFlag::IsRetired()
+  //
+  // Returns true iff this object corresponds to retired flag.
+  virtual bool IsRetired() const;
+
+  // absl::CommandLineFlag::DefaultValue()
+  //
+  // Returns the default value for this flag.
+  virtual std::string DefaultValue() const = 0;
+
+  // absl::CommandLineFlag::CurrentValue()
+  //
+  // Returns the current value for this flag.
+  virtual std::string CurrentValue() const = 0;
+
+  // absl::CommandLineFlag::ParseFrom()
+  //
+  // Sets the value of the flag based on specified string `value`. If the flag
+  // was successfully set to new value, it returns true. Otherwise, sets `error`
+  // to indicate the error, leaves the flag unchanged, and returns false.
+  bool ParseFrom(absl::string_view value, std::string* error);
+
+ protected:
+  ~CommandLineFlag() = default;
+
+ private:
+  friend class flags_internal::PrivateHandleAccessor;
+
+  // Sets the value of the flag based on specified string `value`. If the flag
+  // was successfully set to new value, it returns true. Otherwise, sets `error`
+  // to indicate the error, leaves the flag unchanged, and returns false. There
+  // are three ways to set the flag's value:
+  //  * Update the current flag value
+  //  * Update the flag's default value
+  //  * Update the current flag value if it was never set before
+  // The mode is selected based on `set_mode` parameter.
+  virtual bool ParseFrom(absl::string_view value,
+                         flags_internal::FlagSettingMode set_mode,
+                         flags_internal::ValueSource source,
+                         std::string& error) = 0;
+
+  // Returns id of the flag's value type.
+  virtual flags_internal::FlagFastTypeId TypeId() const = 0;
+
+  // Interface to save flag to some persistent state. Returns current flag state
+  // or nullptr if flag does not support saving and restoring a state.
+  virtual std::unique_ptr<flags_internal::FlagStateInterface> SaveState() = 0;
+
+  // Copy-construct a new value of the flag's type in a memory referenced by
+  // the dst based on the current flag's value.
+  virtual void Read(void* dst) const = 0;
+
+  // To be deleted. Used to return true if flag's current value originated from
+  // command line.
+  virtual bool IsSpecifiedOnCommandLine() const = 0;
+
+  // Validates supplied value usign validator or parseflag routine
+  virtual bool ValidateInputValue(absl::string_view value) const = 0;
+
+  // Checks that flags default value can be converted to string and back to the
+  // flag's value type.
+  virtual void CheckDefaultValueParsingRoundtrip() const = 0;
+};
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FLAGS_COMMANDLINEFLAG_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/config.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/config.h
new file mode 100644
index 0000000000000000000000000000000000000000..14c4235bb3cbc20057d2277502d1bb10fd5242bb
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/config.h
@@ -0,0 +1,68 @@
+//
+//  Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_FLAGS_CONFIG_H_
+#define ABSL_FLAGS_CONFIG_H_
+
+// Determine if we should strip string literals from the Flag objects.
+// By default we strip string literals on mobile platforms.
+#if !defined(ABSL_FLAGS_STRIP_NAMES)
+
+#if defined(__ANDROID__)
+#define ABSL_FLAGS_STRIP_NAMES 1
+
+#elif defined(__APPLE__)
+#include <TargetConditionals.h>
+#if defined(TARGET_OS_IPHONE) && TARGET_OS_IPHONE
+#define ABSL_FLAGS_STRIP_NAMES 1
+#elif defined(TARGET_OS_EMBEDDED) && TARGET_OS_EMBEDDED
+#define ABSL_FLAGS_STRIP_NAMES 1
+#endif  // TARGET_OS_*
+#endif
+
+#endif  // !defined(ABSL_FLAGS_STRIP_NAMES)
+
+#if !defined(ABSL_FLAGS_STRIP_NAMES)
+// If ABSL_FLAGS_STRIP_NAMES wasn't set on the command line or above,
+// the default is not to strip.
+#define ABSL_FLAGS_STRIP_NAMES 0
+#endif
+
+#if !defined(ABSL_FLAGS_STRIP_HELP)
+// By default, if we strip names, we also strip help.
+#define ABSL_FLAGS_STRIP_HELP ABSL_FLAGS_STRIP_NAMES
+#endif
+
+// These macros represent the "source of truth" for the list of supported
+// built-in types.
+#define ABSL_FLAGS_INTERNAL_BUILTIN_TYPES(A) \
+  A(bool, bool)                              \
+  A(short, short)                            \
+  A(unsigned short, unsigned_short)          \
+  A(int, int)                                \
+  A(unsigned int, unsigned_int)              \
+  A(long, long)                              \
+  A(unsigned long, unsigned_long)            \
+  A(long long, long_long)                    \
+  A(unsigned long long, unsigned_long_long)  \
+  A(double, double)                          \
+  A(float, float)
+
+#define ABSL_FLAGS_INTERNAL_SUPPORTED_TYPES(A) \
+  ABSL_FLAGS_INTERNAL_BUILTIN_TYPES(A)         \
+  A(std::string, std_string)                   \
+  A(std::vector<std::string>, std_vector_of_string)
+
+#endif  // ABSL_FLAGS_CONFIG_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/declare.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/declare.h
new file mode 100644
index 0000000000000000000000000000000000000000..d1437bb9f6635924786a6485ec960076b70e2558
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/declare.h
@@ -0,0 +1,73 @@
+//
+//  Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: declare.h
+// -----------------------------------------------------------------------------
+//
+// This file defines the ABSL_DECLARE_FLAG macro, allowing you to declare an
+// `absl::Flag` for use within a translation unit. You should place this
+// declaration within the header file associated with the .cc file that defines
+// and owns the `Flag`.
+
+#ifndef ABSL_FLAGS_DECLARE_H_
+#define ABSL_FLAGS_DECLARE_H_
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace flags_internal {
+
+// absl::Flag<T> represents a flag of type 'T' created by ABSL_FLAG.
+template <typename T>
+class Flag;
+
+}  // namespace flags_internal
+
+// Flag
+//
+// Forward declaration of the `absl::Flag` type for use in defining the macro.
+#if defined(_MSC_VER) && !defined(__clang__)
+template <typename T>
+class Flag;
+#else
+template <typename T>
+using Flag = flags_internal::Flag<T>;
+#endif
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+// ABSL_DECLARE_FLAG()
+//
+// This macro is a convenience for declaring use of an `absl::Flag` within a
+// translation unit. This macro should be used within a header file to
+// declare usage of the flag within any .cc file including that header file.
+//
+// The ABSL_DECLARE_FLAG(type, name) macro expands to:
+//
+//   extern absl::Flag<type> FLAGS_name;
+#define ABSL_DECLARE_FLAG(type, name) ABSL_DECLARE_FLAG_INTERNAL(type, name)
+
+// Internal implementation of ABSL_DECLARE_FLAG to allow macro expansion of its
+// arguments. Clients must use ABSL_DECLARE_FLAG instead.
+#define ABSL_DECLARE_FLAG_INTERNAL(type, name)               \
+  extern absl::Flag<type> FLAGS_##name;                      \
+  namespace absl /* block flags in namespaces */ {}          \
+  /* second redeclaration is to allow applying attributes */ \
+  extern absl::Flag<type> FLAGS_##name
+
+#endif  // ABSL_FLAGS_DECLARE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/flag.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/flag.h
new file mode 100644
index 0000000000000000000000000000000000000000..b7f94be7c59cc4e46b69bfe2f6018c1c6ac1efd9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/flag.h
@@ -0,0 +1,310 @@
+//
+//  Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: flag.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines the `absl::Flag<T>` type for holding command-line
+// flag data, and abstractions to create, get and set such flag data.
+//
+// It is important to note that this type is **unspecified** (an implementation
+// detail) and you do not construct or manipulate actual `absl::Flag<T>`
+// instances. Instead, you define and declare flags using the
+// `ABSL_FLAG()` and `ABSL_DECLARE_FLAG()` macros, and get and set flag values
+// using the `absl::GetFlag()` and `absl::SetFlag()` functions.
+
+#ifndef ABSL_FLAGS_FLAG_H_
+#define ABSL_FLAGS_FLAG_H_
+
+#include <string>
+#include <type_traits>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/base/optimization.h"
+#include "absl/flags/config.h"
+#include "absl/flags/internal/flag.h"
+#include "absl/flags/internal/registry.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// Flag
+//
+// An `absl::Flag` holds a command-line flag value, providing a runtime
+// parameter to a binary. Such flags should be defined in the global namespace
+// and (preferably) in the module containing the binary's `main()` function.
+//
+// You should not construct and cannot use the `absl::Flag` type directly;
+// instead, you should declare flags using the `ABSL_DECLARE_FLAG()` macro
+// within a header file, and define your flag using `ABSL_FLAG()` within your
+// header's associated `.cc` file. Such flags will be named `FLAGS_name`.
+//
+// Example:
+//
+//    .h file
+//
+//      // Declares usage of a flag named "FLAGS_count"
+//      ABSL_DECLARE_FLAG(int, count);
+//
+//    .cc file
+//
+//      // Defines a flag named "FLAGS_count" with a default `int` value of 0.
+//      ABSL_FLAG(int, count, 0, "Count of items to process");
+//
+// No public methods of `absl::Flag<T>` are part of the Abseil Flags API.
+//
+// For type support of Abseil Flags, see the marshalling.h header file, which
+// discusses supported standard types, optional flags, and additional Abseil
+// type support.
+#if !defined(_MSC_VER) || defined(__clang__)
+template <typename T>
+using Flag = flags_internal::Flag<T>;
+#else
+#include "absl/flags/internal/flag_msvc.inc"
+#endif
+
+// GetFlag()
+//
+// Returns the value (of type `T`) of an `absl::Flag<T>` instance, by value. Do
+// not construct an `absl::Flag<T>` directly and call `absl::GetFlag()`;
+// instead, refer to flag's constructed variable name (e.g. `FLAGS_name`).
+// Because this function returns by value and not by reference, it is
+// thread-safe, but note that the operation may be expensive; as a result, avoid
+// `absl::GetFlag()` within any tight loops.
+//
+// Example:
+//
+//   // FLAGS_count is a Flag of type `int`
+//   int my_count = absl::GetFlag(FLAGS_count);
+//
+//   // FLAGS_firstname is a Flag of type `std::string`
+//   std::string first_name = absl::GetFlag(FLAGS_firstname);
+template <typename T>
+ABSL_MUST_USE_RESULT T GetFlag(const absl::Flag<T>& flag) {
+  return flags_internal::FlagImplPeer::InvokeGet<T>(flag);
+}
+
+// SetFlag()
+//
+// Sets the value of an `absl::Flag` to the value `v`. Do not construct an
+// `absl::Flag<T>` directly and call `absl::SetFlag()`; instead, use the
+// flag's variable name (e.g. `FLAGS_name`). This function is
+// thread-safe, but is potentially expensive. Avoid setting flags in general,
+// but especially within performance-critical code.
+template <typename T>
+void SetFlag(absl::Flag<T>* flag, const T& v) {
+  flags_internal::FlagImplPeer::InvokeSet(*flag, v);
+}
+
+// Overload of `SetFlag()` to allow callers to pass in a value that is
+// convertible to `T`. E.g., use this overload to pass a "const char*" when `T`
+// is `std::string`.
+template <typename T, typename V>
+void SetFlag(absl::Flag<T>* flag, const V& v) {
+  T value(v);
+  flags_internal::FlagImplPeer::InvokeSet(*flag, value);
+}
+
+// GetFlagReflectionHandle()
+//
+// Returns the reflection handle corresponding to specified Abseil Flag
+// instance. Use this handle to access flag's reflection information, like name,
+// location, default value etc.
+//
+// Example:
+//
+//   std::string = absl::GetFlagReflectionHandle(FLAGS_count).DefaultValue();
+
+template <typename T>
+const CommandLineFlag& GetFlagReflectionHandle(const absl::Flag<T>& f) {
+  return flags_internal::FlagImplPeer::InvokeReflect(f);
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+
+// ABSL_FLAG()
+//
+// This macro defines an `absl::Flag<T>` instance of a specified type `T`:
+//
+//   ABSL_FLAG(T, name, default_value, help);
+//
+// where:
+//
+//   * `T` is a supported flag type (see the list of types in `marshalling.h`),
+//   * `name` designates the name of the flag (as a global variable
+//     `FLAGS_name`),
+//   * `default_value` is an expression holding the default value for this flag
+//     (which must be implicitly convertible to `T`),
+//   * `help` is the help text, which can also be an expression.
+//
+// This macro expands to a flag named 'FLAGS_name' of type 'T':
+//
+//   absl::Flag<T> FLAGS_name = ...;
+//
+// Note that all such instances are created as global variables.
+//
+// For `ABSL_FLAG()` values that you wish to expose to other translation units,
+// it is recommended to define those flags within the `.cc` file associated with
+// the header where the flag is declared.
+//
+// Note: do not construct objects of type `absl::Flag<T>` directly. Only use the
+// `ABSL_FLAG()` macro for such construction.
+#define ABSL_FLAG(Type, name, default_value, help) \
+  ABSL_FLAG_IMPL(Type, name, default_value, help)
+
+// ABSL_FLAG().OnUpdate()
+//
+// Defines a flag of type `T` with a callback attached:
+//
+//   ABSL_FLAG(T, name, default_value, help).OnUpdate(callback);
+//
+// `callback` should be convertible to `void (*)()`.
+//
+// After any setting of the flag value, the callback will be called at least
+// once. A rapid sequence of changes may be merged together into the same
+// callback. No concurrent calls to the callback will be made for the same
+// flag. Callbacks are allowed to read the current value of the flag but must
+// not mutate that flag.
+//
+// The update mechanism guarantees "eventual consistency"; if the callback
+// derives an auxiliary data structure from the flag value, it is guaranteed
+// that eventually the flag value and the derived data structure will be
+// consistent.
+//
+// Note: ABSL_FLAG.OnUpdate() does not have a public definition. Hence, this
+// comment serves as its API documentation.
+
+// -----------------------------------------------------------------------------
+// Implementation details below this section
+// -----------------------------------------------------------------------------
+
+// ABSL_FLAG_IMPL macro definition conditional on ABSL_FLAGS_STRIP_NAMES
+#if !defined(_MSC_VER) || defined(__clang__)
+#define ABSL_FLAG_IMPL_FLAG_PTR(flag) flag
+#define ABSL_FLAG_IMPL_HELP_ARG(name)                      \
+  absl::flags_internal::HelpArg<AbslFlagHelpGenFor##name>( \
+      FLAGS_help_storage_##name)
+#define ABSL_FLAG_IMPL_DEFAULT_ARG(Type, name) \
+  absl::flags_internal::DefaultArg<Type, AbslFlagDefaultGenFor##name>(0)
+#else
+#define ABSL_FLAG_IMPL_FLAG_PTR(flag) flag.GetImpl()
+#define ABSL_FLAG_IMPL_HELP_ARG(name) &AbslFlagHelpGenFor##name::NonConst
+#define ABSL_FLAG_IMPL_DEFAULT_ARG(Type, name) &AbslFlagDefaultGenFor##name::Gen
+#endif
+
+#if ABSL_FLAGS_STRIP_NAMES
+#define ABSL_FLAG_IMPL_FLAGNAME(txt) ""
+#define ABSL_FLAG_IMPL_FILENAME() ""
+#define ABSL_FLAG_IMPL_REGISTRAR(T, flag)                                      \
+  absl::flags_internal::FlagRegistrar<T, false>(ABSL_FLAG_IMPL_FLAG_PTR(flag), \
+                                                nullptr)
+#else
+#define ABSL_FLAG_IMPL_FLAGNAME(txt) txt
+#define ABSL_FLAG_IMPL_FILENAME() __FILE__
+#define ABSL_FLAG_IMPL_REGISTRAR(T, flag)                                     \
+  absl::flags_internal::FlagRegistrar<T, true>(ABSL_FLAG_IMPL_FLAG_PTR(flag), \
+                                               __FILE__)
+#endif
+
+// ABSL_FLAG_IMPL macro definition conditional on ABSL_FLAGS_STRIP_HELP
+
+#if ABSL_FLAGS_STRIP_HELP
+#define ABSL_FLAG_IMPL_FLAGHELP(txt) absl::flags_internal::kStrippedFlagHelp
+#else
+#define ABSL_FLAG_IMPL_FLAGHELP(txt) txt
+#endif
+
+// AbslFlagHelpGenFor##name is used to encapsulate both immediate (method Const)
+// and lazy (method NonConst) evaluation of help message expression. We choose
+// between the two via the call to HelpArg in absl::Flag instantiation below.
+// If help message expression is constexpr evaluable compiler will optimize
+// away this whole struct.
+// TODO(rogeeff): place these generated structs into local namespace and apply
+// ABSL_INTERNAL_UNIQUE_SHORT_NAME.
+// TODO(rogeeff): Apply __attribute__((nodebug)) to FLAGS_help_storage_##name
+#define ABSL_FLAG_IMPL_DECLARE_HELP_WRAPPER(name, txt)                       \
+  struct AbslFlagHelpGenFor##name {                                          \
+    /* The expression is run in the caller as part of the   */               \
+    /* default value argument. That keeps temporaries alive */               \
+    /* long enough for NonConst to work correctly.          */               \
+    static constexpr absl::string_view Value(                                \
+        absl::string_view absl_flag_help = ABSL_FLAG_IMPL_FLAGHELP(txt)) {   \
+      return absl_flag_help;                                                 \
+    }                                                                        \
+    static std::string NonConst() { return std::string(Value()); }           \
+  };                                                                         \
+  constexpr auto FLAGS_help_storage_##name ABSL_INTERNAL_UNIQUE_SMALL_NAME() \
+      ABSL_ATTRIBUTE_SECTION_VARIABLE(flags_help_cold) =                     \
+          absl::flags_internal::HelpStringAsArray<AbslFlagHelpGenFor##name>( \
+              0);
+
+#define ABSL_FLAG_IMPL_DECLARE_DEF_VAL_WRAPPER(name, Type, default_value)     \
+  struct AbslFlagDefaultGenFor##name {                                        \
+    Type value = absl::flags_internal::InitDefaultValue<Type>(default_value); \
+    static void Gen(void* absl_flag_default_loc) {                            \
+      new (absl_flag_default_loc) Type(AbslFlagDefaultGenFor##name{}.value);  \
+    }                                                                         \
+  };
+
+// ABSL_FLAG_IMPL
+//
+// Note: Name of registrar object is not arbitrary. It is used to "grab"
+// global name for FLAGS_no<flag_name> symbol, thus preventing the possibility
+// of defining two flags with names foo and nofoo.
+#define ABSL_FLAG_IMPL(Type, name, default_value, help)                       \
+  extern ::absl::Flag<Type> FLAGS_##name;                                     \
+  namespace absl /* block flags in namespaces */ {}                           \
+  ABSL_FLAG_IMPL_DECLARE_DEF_VAL_WRAPPER(name, Type, default_value)           \
+  ABSL_FLAG_IMPL_DECLARE_HELP_WRAPPER(name, help)                             \
+  ABSL_CONST_INIT absl::Flag<Type> FLAGS_##name{                              \
+      ABSL_FLAG_IMPL_FLAGNAME(#name), ABSL_FLAG_IMPL_FILENAME(),              \
+      ABSL_FLAG_IMPL_HELP_ARG(name), ABSL_FLAG_IMPL_DEFAULT_ARG(Type, name)}; \
+  extern absl::flags_internal::FlagRegistrarEmpty FLAGS_no##name;             \
+  absl::flags_internal::FlagRegistrarEmpty FLAGS_no##name =                   \
+      ABSL_FLAG_IMPL_REGISTRAR(Type, FLAGS_##name)
+
+// ABSL_RETIRED_FLAG
+//
+// Designates the flag (which is usually pre-existing) as "retired." A retired
+// flag is a flag that is now unused by the program, but may still be passed on
+// the command line, usually by production scripts. A retired flag is ignored
+// and code can't access it at runtime.
+//
+// This macro registers a retired flag with given name and type, with a name
+// identical to the name of the original flag you are retiring. The retired
+// flag's type can change over time, so that you can retire code to support a
+// custom flag type.
+//
+// This macro has the same signature as `ABSL_FLAG`. To retire a flag, simply
+// replace an `ABSL_FLAG` definition with `ABSL_RETIRED_FLAG`, leaving the
+// arguments unchanged (unless of course you actually want to retire the flag
+// type at this time as well).
+//
+// `default_value` is only used as a double check on the type. `explanation` is
+// unused.
+// TODO(rogeeff): replace RETIRED_FLAGS with FLAGS once forward declarations of
+// retired flags are cleaned up.
+#define ABSL_RETIRED_FLAG(type, name, default_value, explanation)      \
+  static absl::flags_internal::RetiredFlag<type> RETIRED_FLAGS_##name; \
+  ABSL_ATTRIBUTE_UNUSED static const auto RETIRED_FLAGS_REG_##name =   \
+      (RETIRED_FLAGS_##name.Retire(#name),                             \
+       ::absl::flags_internal::FlagRegistrarEmpty{})
+
+#endif  // ABSL_FLAGS_FLAG_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/commandlineflag.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/commandlineflag.h
new file mode 100644
index 0000000000000000000000000000000000000000..ebfe81ba1e3a016a3cca4d8160ed02cc65f69902
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/commandlineflag.h
@@ -0,0 +1,68 @@
+//
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_FLAGS_INTERNAL_COMMANDLINEFLAG_H_
+#define ABSL_FLAGS_INTERNAL_COMMANDLINEFLAG_H_
+
+#include "absl/base/config.h"
+#include "absl/base/internal/fast_type_id.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace flags_internal {
+
+// An alias for flag fast type id. This value identifies the flag value type
+// similarly to typeid(T), without relying on RTTI being available. In most
+// cases this id is enough to uniquely identify the flag's value type. In a few
+// cases we'll have to resort to using actual RTTI implementation if it is
+// available.
+using FlagFastTypeId = absl::base_internal::FastTypeIdType;
+
+// Options that control SetCommandLineOptionWithMode.
+enum FlagSettingMode {
+  // update the flag's value unconditionally (can call this multiple times).
+  SET_FLAGS_VALUE,
+  // update the flag's value, but *only if* it has not yet been updated
+  // with SET_FLAGS_VALUE, SET_FLAG_IF_DEFAULT, or "FLAGS_xxx = nondef".
+  SET_FLAG_IF_DEFAULT,
+  // set the flag's default value to this.  If the flag has not been updated
+  // yet (via SET_FLAGS_VALUE, SET_FLAG_IF_DEFAULT, or "FLAGS_xxx = nondef")
+  // change the flag's current value to the new default value as well.
+  SET_FLAGS_DEFAULT
+};
+
+// Options that control ParseFrom: Source of a value.
+enum ValueSource {
+  // Flag is being set by value specified on a command line.
+  kCommandLine,
+  // Flag is being set by value specified in the code.
+  kProgrammaticChange,
+};
+
+// Handle to FlagState objects. Specific flag state objects will restore state
+// of a flag produced this flag state from method CommandLineFlag::SaveState().
+class FlagStateInterface {
+ public:
+  virtual ~FlagStateInterface();
+
+  // Restores the flag originated this object to the saved state.
+  virtual void Restore() const = 0;
+};
+
+}  // namespace flags_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FLAGS_INTERNAL_COMMANDLINEFLAG_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/flag.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/flag.h
new file mode 100644
index 0000000000000000000000000000000000000000..6154638c411948459fa46864abacb893558871bd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/flag.h
@@ -0,0 +1,800 @@
+//
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_FLAGS_INTERNAL_FLAG_H_
+#define ABSL_FLAGS_INTERNAL_FLAG_H_
+
+#include <stddef.h>
+#include <stdint.h>
+
+#include <atomic>
+#include <cstring>
+#include <memory>
+#include <new>
+#include <string>
+#include <type_traits>
+#include <typeinfo>
+
+#include "absl/base/attributes.h"
+#include "absl/base/call_once.h"
+#include "absl/base/casts.h"
+#include "absl/base/config.h"
+#include "absl/base/optimization.h"
+#include "absl/base/thread_annotations.h"
+#include "absl/flags/commandlineflag.h"
+#include "absl/flags/config.h"
+#include "absl/flags/internal/commandlineflag.h"
+#include "absl/flags/internal/registry.h"
+#include "absl/flags/internal/sequence_lock.h"
+#include "absl/flags/marshalling.h"
+#include "absl/meta/type_traits.h"
+#include "absl/strings/string_view.h"
+#include "absl/synchronization/mutex.h"
+#include "absl/utility/utility.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+///////////////////////////////////////////////////////////////////////////////
+// Forward declaration of absl::Flag<T> public API.
+namespace flags_internal {
+template <typename T>
+class Flag;
+}  // namespace flags_internal
+
+#if defined(_MSC_VER) && !defined(__clang__)
+template <typename T>
+class Flag;
+#else
+template <typename T>
+using Flag = flags_internal::Flag<T>;
+#endif
+
+template <typename T>
+ABSL_MUST_USE_RESULT T GetFlag(const absl::Flag<T>& flag);
+
+template <typename T>
+void SetFlag(absl::Flag<T>* flag, const T& v);
+
+template <typename T, typename V>
+void SetFlag(absl::Flag<T>* flag, const V& v);
+
+template <typename U>
+const CommandLineFlag& GetFlagReflectionHandle(const absl::Flag<U>& f);
+
+///////////////////////////////////////////////////////////////////////////////
+// Flag value type operations, eg., parsing, copying, etc. are provided
+// by function specific to that type with a signature matching FlagOpFn.
+
+namespace flags_internal {
+
+enum class FlagOp {
+  kAlloc,
+  kDelete,
+  kCopy,
+  kCopyConstruct,
+  kSizeof,
+  kFastTypeId,
+  kRuntimeTypeId,
+  kParse,
+  kUnparse,
+  kValueOffset,
+};
+using FlagOpFn = void* (*)(FlagOp, const void*, void*, void*);
+
+// Forward declaration for Flag value specific operations.
+template <typename T>
+void* FlagOps(FlagOp op, const void* v1, void* v2, void* v3);
+
+// Allocate aligned memory for a flag value.
+inline void* Alloc(FlagOpFn op) {
+  return op(FlagOp::kAlloc, nullptr, nullptr, nullptr);
+}
+// Deletes memory interpreting obj as flag value type pointer.
+inline void Delete(FlagOpFn op, void* obj) {
+  op(FlagOp::kDelete, nullptr, obj, nullptr);
+}
+// Copies src to dst interpreting as flag value type pointers.
+inline void Copy(FlagOpFn op, const void* src, void* dst) {
+  op(FlagOp::kCopy, src, dst, nullptr);
+}
+// Construct a copy of flag value in a location pointed by dst
+// based on src - pointer to the flag's value.
+inline void CopyConstruct(FlagOpFn op, const void* src, void* dst) {
+  op(FlagOp::kCopyConstruct, src, dst, nullptr);
+}
+// Makes a copy of flag value pointed by obj.
+inline void* Clone(FlagOpFn op, const void* obj) {
+  void* res = flags_internal::Alloc(op);
+  flags_internal::CopyConstruct(op, obj, res);
+  return res;
+}
+// Returns true if parsing of input text is successfull.
+inline bool Parse(FlagOpFn op, absl::string_view text, void* dst,
+                  std::string* error) {
+  return op(FlagOp::kParse, &text, dst, error) != nullptr;
+}
+// Returns string representing supplied value.
+inline std::string Unparse(FlagOpFn op, const void* val) {
+  std::string result;
+  op(FlagOp::kUnparse, val, &result, nullptr);
+  return result;
+}
+// Returns size of flag value type.
+inline size_t Sizeof(FlagOpFn op) {
+  // This sequence of casts reverses the sequence from
+  // `flags_internal::FlagOps()`
+  return static_cast<size_t>(reinterpret_cast<intptr_t>(
+      op(FlagOp::kSizeof, nullptr, nullptr, nullptr)));
+}
+// Returns fast type id coresponding to the value type.
+inline FlagFastTypeId FastTypeId(FlagOpFn op) {
+  return reinterpret_cast<FlagFastTypeId>(
+      op(FlagOp::kFastTypeId, nullptr, nullptr, nullptr));
+}
+// Returns fast type id coresponding to the value type.
+inline const std::type_info* RuntimeTypeId(FlagOpFn op) {
+  return reinterpret_cast<const std::type_info*>(
+      op(FlagOp::kRuntimeTypeId, nullptr, nullptr, nullptr));
+}
+// Returns offset of the field value_ from the field impl_ inside of
+// absl::Flag<T> data. Given FlagImpl pointer p you can get the
+// location of the corresponding value as:
+//      reinterpret_cast<char*>(p) + ValueOffset().
+inline ptrdiff_t ValueOffset(FlagOpFn op) {
+  // This sequence of casts reverses the sequence from
+  // `flags_internal::FlagOps()`
+  return static_cast<ptrdiff_t>(reinterpret_cast<intptr_t>(
+      op(FlagOp::kValueOffset, nullptr, nullptr, nullptr)));
+}
+
+// Returns an address of RTTI's typeid(T).
+template <typename T>
+inline const std::type_info* GenRuntimeTypeId() {
+#ifdef ABSL_INTERNAL_HAS_RTTI
+  return &typeid(T);
+#else
+  return nullptr;
+#endif
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// Flag help auxiliary structs.
+
+// This is help argument for absl::Flag encapsulating the string literal pointer
+// or pointer to function generating it as well as enum descriminating two
+// cases.
+using HelpGenFunc = std::string (*)();
+
+template <size_t N>
+struct FixedCharArray {
+  char value[N];
+
+  template <size_t... I>
+  static constexpr FixedCharArray<N> FromLiteralString(
+      absl::string_view str, absl::index_sequence<I...>) {
+    return (void)str, FixedCharArray<N>({{str[I]..., '\0'}});
+  }
+};
+
+template <typename Gen, size_t N = Gen::Value().size()>
+constexpr FixedCharArray<N + 1> HelpStringAsArray(int) {
+  return FixedCharArray<N + 1>::FromLiteralString(
+      Gen::Value(), absl::make_index_sequence<N>{});
+}
+
+template <typename Gen>
+constexpr std::false_type HelpStringAsArray(char) {
+  return std::false_type{};
+}
+
+union FlagHelpMsg {
+  constexpr explicit FlagHelpMsg(const char* help_msg) : literal(help_msg) {}
+  constexpr explicit FlagHelpMsg(HelpGenFunc help_gen) : gen_func(help_gen) {}
+
+  const char* literal;
+  HelpGenFunc gen_func;
+};
+
+enum class FlagHelpKind : uint8_t { kLiteral = 0, kGenFunc = 1 };
+
+struct FlagHelpArg {
+  FlagHelpMsg source;
+  FlagHelpKind kind;
+};
+
+extern const char kStrippedFlagHelp[];
+
+// These two HelpArg overloads allows us to select at compile time one of two
+// way to pass Help argument to absl::Flag. We'll be passing
+// AbslFlagHelpGenFor##name as Gen and integer 0 as a single argument to prefer
+// first overload if possible. If help message is evaluatable on constexpr
+// context We'll be able to make FixedCharArray out of it and we'll choose first
+// overload. In this case the help message expression is immediately evaluated
+// and is used to construct the absl::Flag. No additionl code is generated by
+// ABSL_FLAG Otherwise SFINAE kicks in and first overload is dropped from the
+// consideration, in which case the second overload will be used. The second
+// overload does not attempt to evaluate the help message expression
+// immediately and instead delays the evaluation by returing the function
+// pointer (&T::NonConst) genering the help message when necessary. This is
+// evaluatable in constexpr context, but the cost is an extra function being
+// generated in the ABSL_FLAG code.
+template <typename Gen, size_t N>
+constexpr FlagHelpArg HelpArg(const FixedCharArray<N>& value) {
+  return {FlagHelpMsg(value.value), FlagHelpKind::kLiteral};
+}
+
+template <typename Gen>
+constexpr FlagHelpArg HelpArg(std::false_type) {
+  return {FlagHelpMsg(&Gen::NonConst), FlagHelpKind::kGenFunc};
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// Flag default value auxiliary structs.
+
+// Signature for the function generating the initial flag value (usually
+// based on default value supplied in flag's definition)
+using FlagDfltGenFunc = void (*)(void*);
+
+union FlagDefaultSrc {
+  constexpr explicit FlagDefaultSrc(FlagDfltGenFunc gen_func_arg)
+      : gen_func(gen_func_arg) {}
+
+#define ABSL_FLAGS_INTERNAL_DFLT_FOR_TYPE(T, name) \
+  T name##_value;                                  \
+  constexpr explicit FlagDefaultSrc(T value) : name##_value(value) {}  // NOLINT
+  ABSL_FLAGS_INTERNAL_BUILTIN_TYPES(ABSL_FLAGS_INTERNAL_DFLT_FOR_TYPE)
+#undef ABSL_FLAGS_INTERNAL_DFLT_FOR_TYPE
+
+  void* dynamic_value;
+  FlagDfltGenFunc gen_func;
+};
+
+enum class FlagDefaultKind : uint8_t {
+  kDynamicValue = 0,
+  kGenFunc = 1,
+  kOneWord = 2  // for default values UP to one word in size
+};
+
+struct FlagDefaultArg {
+  FlagDefaultSrc source;
+  FlagDefaultKind kind;
+};
+
+// This struct and corresponding overload to InitDefaultValue are used to
+// facilitate usage of {} as default value in ABSL_FLAG macro.
+// TODO(rogeeff): Fix handling types with explicit constructors.
+struct EmptyBraces {};
+
+template <typename T>
+constexpr T InitDefaultValue(T t) {
+  return t;
+}
+
+template <typename T>
+constexpr T InitDefaultValue(EmptyBraces) {
+  return T{};
+}
+
+template <typename ValueT, typename GenT,
+          typename std::enable_if<std::is_integral<ValueT>::value, int>::type =
+              ((void)GenT{}, 0)>
+constexpr FlagDefaultArg DefaultArg(int) {
+  return {FlagDefaultSrc(GenT{}.value), FlagDefaultKind::kOneWord};
+}
+
+template <typename ValueT, typename GenT>
+constexpr FlagDefaultArg DefaultArg(char) {
+  return {FlagDefaultSrc(&GenT::Gen), FlagDefaultKind::kGenFunc};
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// Flag current value auxiliary structs.
+
+constexpr int64_t UninitializedFlagValue() {
+  return static_cast<int64_t>(0xababababababababll);
+}
+
+template <typename T>
+using FlagUseValueAndInitBitStorage = std::integral_constant<
+    bool, absl::type_traits_internal::is_trivially_copyable<T>::value &&
+              std::is_default_constructible<T>::value && (sizeof(T) < 8)>;
+
+template <typename T>
+using FlagUseOneWordStorage = std::integral_constant<
+    bool, absl::type_traits_internal::is_trivially_copyable<T>::value &&
+              (sizeof(T) <= 8)>;
+
+template <class T>
+using FlagUseSequenceLockStorage = std::integral_constant<
+    bool, absl::type_traits_internal::is_trivially_copyable<T>::value &&
+              (sizeof(T) > 8)>;
+
+enum class FlagValueStorageKind : uint8_t {
+  kValueAndInitBit = 0,
+  kOneWordAtomic = 1,
+  kSequenceLocked = 2,
+  kAlignedBuffer = 3,
+};
+
+template <typename T>
+static constexpr FlagValueStorageKind StorageKind() {
+  return FlagUseValueAndInitBitStorage<T>::value
+             ? FlagValueStorageKind::kValueAndInitBit
+         : FlagUseOneWordStorage<T>::value
+             ? FlagValueStorageKind::kOneWordAtomic
+         : FlagUseSequenceLockStorage<T>::value
+             ? FlagValueStorageKind::kSequenceLocked
+             : FlagValueStorageKind::kAlignedBuffer;
+}
+
+struct FlagOneWordValue {
+  constexpr explicit FlagOneWordValue(int64_t v) : value(v) {}
+  std::atomic<int64_t> value;
+};
+
+template <typename T>
+struct alignas(8) FlagValueAndInitBit {
+  T value;
+  // Use an int instead of a bool to guarantee that a non-zero value has
+  // a bit set.
+  uint8_t init;
+};
+
+template <typename T,
+          FlagValueStorageKind Kind = flags_internal::StorageKind<T>()>
+struct FlagValue;
+
+template <typename T>
+struct FlagValue<T, FlagValueStorageKind::kValueAndInitBit> : FlagOneWordValue {
+  constexpr FlagValue() : FlagOneWordValue(0) {}
+  bool Get(const SequenceLock&, T& dst) const {
+    int64_t storage = value.load(std::memory_order_acquire);
+    if (ABSL_PREDICT_FALSE(storage == 0)) {
+      return false;
+    }
+    dst = absl::bit_cast<FlagValueAndInitBit<T>>(storage).value;
+    return true;
+  }
+};
+
+template <typename T>
+struct FlagValue<T, FlagValueStorageKind::kOneWordAtomic> : FlagOneWordValue {
+  constexpr FlagValue() : FlagOneWordValue(UninitializedFlagValue()) {}
+  bool Get(const SequenceLock&, T& dst) const {
+    int64_t one_word_val = value.load(std::memory_order_acquire);
+    if (ABSL_PREDICT_FALSE(one_word_val == UninitializedFlagValue())) {
+      return false;
+    }
+    std::memcpy(&dst, static_cast<const void*>(&one_word_val), sizeof(T));
+    return true;
+  }
+};
+
+template <typename T>
+struct FlagValue<T, FlagValueStorageKind::kSequenceLocked> {
+  bool Get(const SequenceLock& lock, T& dst) const {
+    return lock.TryRead(&dst, value_words, sizeof(T));
+  }
+
+  static constexpr int kNumWords =
+      flags_internal::AlignUp(sizeof(T), sizeof(uint64_t)) / sizeof(uint64_t);
+
+  alignas(T) alignas(
+      std::atomic<uint64_t>) std::atomic<uint64_t> value_words[kNumWords];
+};
+
+template <typename T>
+struct FlagValue<T, FlagValueStorageKind::kAlignedBuffer> {
+  bool Get(const SequenceLock&, T&) const { return false; }
+
+  alignas(T) char value[sizeof(T)];
+};
+
+///////////////////////////////////////////////////////////////////////////////
+// Flag callback auxiliary structs.
+
+// Signature for the mutation callback used by watched Flags
+// The callback is noexcept.
+// TODO(rogeeff): add noexcept after C++17 support is added.
+using FlagCallbackFunc = void (*)();
+
+struct FlagCallback {
+  FlagCallbackFunc func;
+  absl::Mutex guard;  // Guard for concurrent callback invocations.
+};
+
+///////////////////////////////////////////////////////////////////////////////
+// Flag implementation, which does not depend on flag value type.
+// The class encapsulates the Flag's data and access to it.
+
+struct DynValueDeleter {
+  explicit DynValueDeleter(FlagOpFn op_arg = nullptr);
+  void operator()(void* ptr) const;
+
+  FlagOpFn op;
+};
+
+class FlagState;
+
+class FlagImpl final : public CommandLineFlag {
+ public:
+  constexpr FlagImpl(const char* name, const char* filename, FlagOpFn op,
+                     FlagHelpArg help, FlagValueStorageKind value_kind,
+                     FlagDefaultArg default_arg)
+      : name_(name),
+        filename_(filename),
+        op_(op),
+        help_(help.source),
+        help_source_kind_(static_cast<uint8_t>(help.kind)),
+        value_storage_kind_(static_cast<uint8_t>(value_kind)),
+        def_kind_(static_cast<uint8_t>(default_arg.kind)),
+        modified_(false),
+        on_command_line_(false),
+        callback_(nullptr),
+        default_value_(default_arg.source),
+        data_guard_{} {}
+
+  // Constant access methods
+  int64_t ReadOneWord() const ABSL_LOCKS_EXCLUDED(*DataGuard());
+  bool ReadOneBool() const ABSL_LOCKS_EXCLUDED(*DataGuard());
+  void Read(void* dst) const override ABSL_LOCKS_EXCLUDED(*DataGuard());
+  void Read(bool* value) const ABSL_LOCKS_EXCLUDED(*DataGuard()) {
+    *value = ReadOneBool();
+  }
+  template <typename T,
+            absl::enable_if_t<flags_internal::StorageKind<T>() ==
+                                  FlagValueStorageKind::kOneWordAtomic,
+                              int> = 0>
+  void Read(T* value) const ABSL_LOCKS_EXCLUDED(*DataGuard()) {
+    int64_t v = ReadOneWord();
+    std::memcpy(value, static_cast<const void*>(&v), sizeof(T));
+  }
+  template <typename T,
+            typename std::enable_if<flags_internal::StorageKind<T>() ==
+                                        FlagValueStorageKind::kValueAndInitBit,
+                                    int>::type = 0>
+  void Read(T* value) const ABSL_LOCKS_EXCLUDED(*DataGuard()) {
+    *value = absl::bit_cast<FlagValueAndInitBit<T>>(ReadOneWord()).value;
+  }
+
+  // Mutating access methods
+  void Write(const void* src) ABSL_LOCKS_EXCLUDED(*DataGuard());
+
+  // Interfaces to operate on callbacks.
+  void SetCallback(const FlagCallbackFunc mutation_callback)
+      ABSL_LOCKS_EXCLUDED(*DataGuard());
+  void InvokeCallback() const ABSL_EXCLUSIVE_LOCKS_REQUIRED(*DataGuard());
+
+  // Used in read/write operations to validate source/target has correct type.
+  // For example if flag is declared as absl::Flag<int> FLAGS_foo, a call to
+  // absl::GetFlag(FLAGS_foo) validates that the type of FLAGS_foo is indeed
+  // int. To do that we pass the "assumed" type id (which is deduced from type
+  // int) as an argument `type_id`, which is in turn is validated against the
+  // type id stored in flag object by flag definition statement.
+  void AssertValidType(FlagFastTypeId type_id,
+                       const std::type_info* (*gen_rtti)()) const;
+
+ private:
+  template <typename T>
+  friend class Flag;
+  friend class FlagState;
+
+  // Ensures that `data_guard_` is initialized and returns it.
+  absl::Mutex* DataGuard() const
+      ABSL_LOCK_RETURNED(reinterpret_cast<absl::Mutex*>(data_guard_));
+  // Returns heap allocated value of type T initialized with default value.
+  std::unique_ptr<void, DynValueDeleter> MakeInitValue() const
+      ABSL_EXCLUSIVE_LOCKS_REQUIRED(*DataGuard());
+  // Flag initialization called via absl::call_once.
+  void Init();
+
+  // Offset value access methods. One per storage kind. These methods to not
+  // respect const correctness, so be very carefull using them.
+
+  // This is a shared helper routine which encapsulates most of the magic. Since
+  // it is only used inside the three routines below, which are defined in
+  // flag.cc, we can define it in that file as well.
+  template <typename StorageT>
+  StorageT* OffsetValue() const;
+  // This is an accessor for a value stored in an aligned buffer storage
+  // used for non-trivially-copyable data types.
+  // Returns a mutable pointer to the start of a buffer.
+  void* AlignedBufferValue() const;
+
+  // The same as above, but used for sequencelock-protected storage.
+  std::atomic<uint64_t>* AtomicBufferValue() const;
+
+  // This is an accessor for a value stored as one word atomic. Returns a
+  // mutable reference to an atomic value.
+  std::atomic<int64_t>& OneWordValue() const;
+
+  // Attempts to parse supplied `value` string. If parsing is successful,
+  // returns new value. Otherwise returns nullptr.
+  std::unique_ptr<void, DynValueDeleter> TryParse(absl::string_view value,
+                                                  std::string& err) const
+      ABSL_EXCLUSIVE_LOCKS_REQUIRED(*DataGuard());
+  // Stores the flag value based on the pointer to the source.
+  void StoreValue(const void* src) ABSL_EXCLUSIVE_LOCKS_REQUIRED(*DataGuard());
+
+  // Copy the flag data, protected by `seq_lock_` into `dst`.
+  //
+  // REQUIRES: ValueStorageKind() == kSequenceLocked.
+  void ReadSequenceLockedData(void* dst) const
+      ABSL_LOCKS_EXCLUDED(*DataGuard());
+
+  FlagHelpKind HelpSourceKind() const {
+    return static_cast<FlagHelpKind>(help_source_kind_);
+  }
+  FlagValueStorageKind ValueStorageKind() const {
+    return static_cast<FlagValueStorageKind>(value_storage_kind_);
+  }
+  FlagDefaultKind DefaultKind() const
+      ABSL_EXCLUSIVE_LOCKS_REQUIRED(*DataGuard()) {
+    return static_cast<FlagDefaultKind>(def_kind_);
+  }
+
+  // CommandLineFlag interface implementation
+  absl::string_view Name() const override;
+  std::string Filename() const override;
+  std::string Help() const override;
+  FlagFastTypeId TypeId() const override;
+  bool IsSpecifiedOnCommandLine() const override
+      ABSL_LOCKS_EXCLUDED(*DataGuard());
+  std::string DefaultValue() const override ABSL_LOCKS_EXCLUDED(*DataGuard());
+  std::string CurrentValue() const override ABSL_LOCKS_EXCLUDED(*DataGuard());
+  bool ValidateInputValue(absl::string_view value) const override
+      ABSL_LOCKS_EXCLUDED(*DataGuard());
+  void CheckDefaultValueParsingRoundtrip() const override
+      ABSL_LOCKS_EXCLUDED(*DataGuard());
+
+  int64_t ModificationCount() const ABSL_EXCLUSIVE_LOCKS_REQUIRED(*DataGuard());
+
+  // Interfaces to save and restore flags to/from persistent state.
+  // Returns current flag state or nullptr if flag does not support
+  // saving and restoring a state.
+  std::unique_ptr<FlagStateInterface> SaveState() override
+      ABSL_LOCKS_EXCLUDED(*DataGuard());
+
+  // Restores the flag state to the supplied state object. If there is
+  // nothing to restore returns false. Otherwise returns true.
+  bool RestoreState(const FlagState& flag_state)
+      ABSL_LOCKS_EXCLUDED(*DataGuard());
+
+  bool ParseFrom(absl::string_view value, FlagSettingMode set_mode,
+                 ValueSource source, std::string& error) override
+      ABSL_LOCKS_EXCLUDED(*DataGuard());
+
+  // Immutable flag's state.
+
+  // Flags name passed to ABSL_FLAG as second arg.
+  const char* const name_;
+  // The file name where ABSL_FLAG resides.
+  const char* const filename_;
+  // Type-specific operations "vtable".
+  const FlagOpFn op_;
+  // Help message literal or function to generate it.
+  const FlagHelpMsg help_;
+  // Indicates if help message was supplied as literal or generator func.
+  const uint8_t help_source_kind_ : 1;
+  // Kind of storage this flag is using for the flag's value.
+  const uint8_t value_storage_kind_ : 2;
+
+  uint8_t : 0;  // The bytes containing the const bitfields must not be
+                // shared with bytes containing the mutable bitfields.
+
+  // Mutable flag's state (guarded by `data_guard_`).
+
+  // def_kind_ is not guard by DataGuard() since it is accessed in Init without
+  // locks.
+  uint8_t def_kind_ : 2;
+  // Has this flag's value been modified?
+  bool modified_ : 1 ABSL_GUARDED_BY(*DataGuard());
+  // Has this flag been specified on command line.
+  bool on_command_line_ : 1 ABSL_GUARDED_BY(*DataGuard());
+
+  // Unique tag for absl::call_once call to initialize this flag.
+  absl::once_flag init_control_;
+
+  // Sequence lock / mutation counter.
+  flags_internal::SequenceLock seq_lock_;
+
+  // Optional flag's callback and absl::Mutex to guard the invocations.
+  FlagCallback* callback_ ABSL_GUARDED_BY(*DataGuard());
+  // Either a pointer to the function generating the default value based on the
+  // value specified in ABSL_FLAG or pointer to the dynamically set default
+  // value via SetCommandLineOptionWithMode. def_kind_ is used to distinguish
+  // these two cases.
+  FlagDefaultSrc default_value_;
+
+  // This is reserved space for an absl::Mutex to guard flag data. It will be
+  // initialized in FlagImpl::Init via placement new.
+  // We can't use "absl::Mutex data_guard_", since this class is not literal.
+  // We do not want to use "absl::Mutex* data_guard_", since this would require
+  // heap allocation during initialization, which is both slows program startup
+  // and can fail. Using reserved space + placement new allows us to avoid both
+  // problems.
+  alignas(absl::Mutex) mutable char data_guard_[sizeof(absl::Mutex)];
+};
+
+///////////////////////////////////////////////////////////////////////////////
+// The Flag object parameterized by the flag's value type. This class implements
+// flag reflection handle interface.
+
+template <typename T>
+class Flag {
+ public:
+  constexpr Flag(const char* name, const char* filename, FlagHelpArg help,
+                 const FlagDefaultArg default_arg)
+      : impl_(name, filename, &FlagOps<T>, help,
+              flags_internal::StorageKind<T>(), default_arg),
+        value_() {}
+
+  // CommandLineFlag interface
+  absl::string_view Name() const { return impl_.Name(); }
+  std::string Filename() const { return impl_.Filename(); }
+  std::string Help() const { return impl_.Help(); }
+  // Do not use. To be removed.
+  bool IsSpecifiedOnCommandLine() const {
+    return impl_.IsSpecifiedOnCommandLine();
+  }
+  std::string DefaultValue() const { return impl_.DefaultValue(); }
+  std::string CurrentValue() const { return impl_.CurrentValue(); }
+
+ private:
+  template <typename, bool>
+  friend class FlagRegistrar;
+  friend class FlagImplPeer;
+
+  T Get() const {
+    // See implementation notes in CommandLineFlag::Get().
+    union U {
+      T value;
+      U() {}
+      ~U() { value.~T(); }
+    };
+    U u;
+
+#if !defined(NDEBUG)
+    impl_.AssertValidType(base_internal::FastTypeId<T>(), &GenRuntimeTypeId<T>);
+#endif
+
+    if (ABSL_PREDICT_FALSE(!value_.Get(impl_.seq_lock_, u.value))) {
+      impl_.Read(&u.value);
+    }
+    return std::move(u.value);
+  }
+  void Set(const T& v) {
+    impl_.AssertValidType(base_internal::FastTypeId<T>(), &GenRuntimeTypeId<T>);
+    impl_.Write(&v);
+  }
+
+  // Access to the reflection.
+  const CommandLineFlag& Reflect() const { return impl_; }
+
+  // Flag's data
+  // The implementation depends on value_ field to be placed exactly after the
+  // impl_ field, so that impl_ can figure out the offset to the value and
+  // access it.
+  FlagImpl impl_;
+  FlagValue<T> value_;
+};
+
+///////////////////////////////////////////////////////////////////////////////
+// Trampoline for friend access
+
+class FlagImplPeer {
+ public:
+  template <typename T, typename FlagType>
+  static T InvokeGet(const FlagType& flag) {
+    return flag.Get();
+  }
+  template <typename FlagType, typename T>
+  static void InvokeSet(FlagType& flag, const T& v) {
+    flag.Set(v);
+  }
+  template <typename FlagType>
+  static const CommandLineFlag& InvokeReflect(const FlagType& f) {
+    return f.Reflect();
+  }
+};
+
+///////////////////////////////////////////////////////////////////////////////
+// Implementation of Flag value specific operations routine.
+template <typename T>
+void* FlagOps(FlagOp op, const void* v1, void* v2, void* v3) {
+  switch (op) {
+    case FlagOp::kAlloc: {
+      std::allocator<T> alloc;
+      return std::allocator_traits<std::allocator<T>>::allocate(alloc, 1);
+    }
+    case FlagOp::kDelete: {
+      T* p = static_cast<T*>(v2);
+      p->~T();
+      std::allocator<T> alloc;
+      std::allocator_traits<std::allocator<T>>::deallocate(alloc, p, 1);
+      return nullptr;
+    }
+    case FlagOp::kCopy:
+      *static_cast<T*>(v2) = *static_cast<const T*>(v1);
+      return nullptr;
+    case FlagOp::kCopyConstruct:
+      new (v2) T(*static_cast<const T*>(v1));
+      return nullptr;
+    case FlagOp::kSizeof:
+      return reinterpret_cast<void*>(static_cast<uintptr_t>(sizeof(T)));
+    case FlagOp::kFastTypeId:
+      return const_cast<void*>(base_internal::FastTypeId<T>());
+    case FlagOp::kRuntimeTypeId:
+      return const_cast<std::type_info*>(GenRuntimeTypeId<T>());
+    case FlagOp::kParse: {
+      // Initialize the temporary instance of type T based on current value in
+      // destination (which is going to be flag's default value).
+      T temp(*static_cast<T*>(v2));
+      if (!absl::ParseFlag<T>(*static_cast<const absl::string_view*>(v1), &temp,
+                              static_cast<std::string*>(v3))) {
+        return nullptr;
+      }
+      *static_cast<T*>(v2) = std::move(temp);
+      return v2;
+    }
+    case FlagOp::kUnparse:
+      *static_cast<std::string*>(v2) =
+          absl::UnparseFlag<T>(*static_cast<const T*>(v1));
+      return nullptr;
+    case FlagOp::kValueOffset: {
+      // Round sizeof(FlagImp) to a multiple of alignof(FlagValue<T>) to get the
+      // offset of the data.
+      size_t round_to = alignof(FlagValue<T>);
+      size_t offset =
+          (sizeof(FlagImpl) + round_to - 1) / round_to * round_to;
+      return reinterpret_cast<void*>(offset);
+    }
+  }
+  return nullptr;
+}
+
+///////////////////////////////////////////////////////////////////////////////
+// This class facilitates Flag object registration and tail expression-based
+// flag definition, for example:
+// ABSL_FLAG(int, foo, 42, "Foo help").OnUpdate(NotifyFooWatcher);
+struct FlagRegistrarEmpty {};
+template <typename T, bool do_register>
+class FlagRegistrar {
+ public:
+  explicit FlagRegistrar(Flag<T>& flag, const char* filename) : flag_(flag) {
+    if (do_register)
+      flags_internal::RegisterCommandLineFlag(flag_.impl_, filename);
+  }
+
+  FlagRegistrar OnUpdate(FlagCallbackFunc cb) && {
+    flag_.impl_.SetCallback(cb);
+    return *this;
+  }
+
+  // Make the registrar "die" gracefully as an empty struct on a line where
+  // registration happens. Registrar objects are intended to live only as
+  // temporary.
+  operator FlagRegistrarEmpty() const { return {}; }  // NOLINT
+
+ private:
+  Flag<T>& flag_;  // Flag being registered (not owned).
+};
+
+}  // namespace flags_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FLAGS_INTERNAL_FLAG_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/flag_msvc.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/flag_msvc.inc
new file mode 100644
index 0000000000000000000000000000000000000000..c31bd27fd8c63ade27216b80dca9e832a47d5957
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/flag_msvc.inc
@@ -0,0 +1,116 @@
+//
+//  Copyright 2021 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// Do not include this file directly.
+// Include absl/flags/flag.h instead.
+
+// MSVC debug builds do not implement initialization with constexpr constructors
+// correctly. To work around this we add a level of indirection, so that the
+// class `absl::Flag` contains an `internal::Flag*` (instead of being an alias
+// to that class) and dynamically allocates an instance when necessary. We also
+// forward all calls to internal::Flag methods via trampoline methods. In this
+// setup the `absl::Flag` class does not have constructor and virtual methods,
+// all the data members are public and thus MSVC is able to initialize it at
+// link time. To deal with multiple threads accessing the flag for the first
+// time concurrently we use an atomic boolean indicating if flag object is
+// initialized. We also employ the double-checked locking pattern where the
+// second level of protection is a global Mutex, so if two threads attempt to
+// construct the flag concurrently only one wins.
+//
+// This solution is based on a recomendation here:
+// https://developercommunity.visualstudio.com/content/problem/336946/class-with-constexpr-constructor-not-using-static.html?childToView=648454#comment-648454
+
+namespace flags_internal {
+absl::Mutex* GetGlobalConstructionGuard();
+}  // namespace flags_internal
+
+// Public methods of `absl::Flag<T>` are NOT part of the Abseil Flags API.
+// See https://abseil.io/docs/cpp/guides/flags
+template <typename T>
+class Flag {
+ public:
+  // No constructor and destructor to ensure this is an aggregate type.
+  // Visual Studio 2015 still requires the constructor for class to be
+  // constexpr initializable.
+#if _MSC_VER <= 1900
+  constexpr Flag(const char* name, const char* filename,
+                 const flags_internal::HelpGenFunc help_gen,
+                 const flags_internal::FlagDfltGenFunc default_value_gen)
+      : name_(name),
+        filename_(filename),
+        help_gen_(help_gen),
+        default_value_gen_(default_value_gen),
+        inited_(false),
+        impl_(nullptr) {}
+#endif
+
+  flags_internal::Flag<T>& GetImpl() const {
+    if (!inited_.load(std::memory_order_acquire)) {
+      absl::MutexLock l(flags_internal::GetGlobalConstructionGuard());
+
+      if (inited_.load(std::memory_order_acquire)) {
+        return *impl_;
+      }
+
+      impl_ = new flags_internal::Flag<T>(
+          name_, filename_,
+          {flags_internal::FlagHelpMsg(help_gen_),
+           flags_internal::FlagHelpKind::kGenFunc},
+          {flags_internal::FlagDefaultSrc(default_value_gen_),
+           flags_internal::FlagDefaultKind::kGenFunc});
+      inited_.store(true, std::memory_order_release);
+    }
+
+    return *impl_;
+  }
+
+  // Public methods of `absl::Flag<T>` are NOT part of the Abseil Flags API.
+  // See https://abseil.io/docs/cpp/guides/flags
+  bool IsRetired() const { return GetImpl().IsRetired(); }
+  absl::string_view Name() const { return GetImpl().Name(); }
+  std::string Help() const { return GetImpl().Help(); }
+  bool IsModified() const { return GetImpl().IsModified(); }
+  bool IsSpecifiedOnCommandLine() const {
+    return GetImpl().IsSpecifiedOnCommandLine();
+  }
+  std::string Filename() const { return GetImpl().Filename(); }
+  std::string DefaultValue() const { return GetImpl().DefaultValue(); }
+  std::string CurrentValue() const { return GetImpl().CurrentValue(); }
+  template <typename U>
+  inline bool IsOfType() const {
+    return GetImpl().template IsOfType<U>();
+  }
+  T Get() const {
+    return flags_internal::FlagImplPeer::InvokeGet<T>(GetImpl());
+  }
+  void Set(const T& v) {
+    flags_internal::FlagImplPeer::InvokeSet(GetImpl(), v);
+  }
+  void InvokeCallback() { GetImpl().InvokeCallback(); }
+
+  const CommandLineFlag& Reflect() const {
+    return flags_internal::FlagImplPeer::InvokeReflect(GetImpl());
+  }
+
+  // The data members are logically private, but they need to be public for
+  // this to be an aggregate type.
+  const char* name_;
+  const char* filename_;
+  const flags_internal::HelpGenFunc help_gen_;
+  const flags_internal::FlagDfltGenFunc default_value_gen_;
+
+  mutable std::atomic<bool> inited_;
+  mutable flags_internal::Flag<T>* impl_;
+};
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/path_util.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/path_util.h
new file mode 100644
index 0000000000000000000000000000000000000000..a6594d3347e187beab8a261a396794d73e4f8695
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/path_util.h
@@ -0,0 +1,62 @@
+//
+//  Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_FLAGS_INTERNAL_PATH_UTIL_H_
+#define ABSL_FLAGS_INTERNAL_PATH_UTIL_H_
+
+#include "absl/base/config.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace flags_internal {
+
+// A portable interface that returns the basename of the filename passed as an
+// argument. It is similar to basename(3)
+// <https://linux.die.net/man/3/basename>.
+// For example:
+//     flags_internal::Basename("a/b/prog/file.cc")
+// returns "file.cc"
+//     flags_internal::Basename("file.cc")
+// returns "file.cc"
+inline absl::string_view Basename(absl::string_view filename) {
+  auto last_slash_pos = filename.find_last_of("/\\");
+
+  return last_slash_pos == absl::string_view::npos
+             ? filename
+             : filename.substr(last_slash_pos + 1);
+}
+
+// A portable interface that returns the directory name of the filename
+// passed as an argument, including the trailing slash.
+// Returns the empty string if a slash is not found in the input file name.
+// For example:
+//      flags_internal::Package("a/b/prog/file.cc")
+// returns "a/b/prog/"
+//      flags_internal::Package("file.cc")
+// returns ""
+inline absl::string_view Package(absl::string_view filename) {
+  auto last_slash_pos = filename.find_last_of("/\\");
+
+  return last_slash_pos == absl::string_view::npos
+             ? absl::string_view()
+             : filename.substr(0, last_slash_pos + 1);
+}
+
+}  // namespace flags_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FLAGS_INTERNAL_PATH_UTIL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/private_handle_accessor.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/private_handle_accessor.h
new file mode 100644
index 0000000000000000000000000000000000000000..c64435cd61999bff3c3403d385f7dcb171b9c416
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/private_handle_accessor.h
@@ -0,0 +1,61 @@
+//
+// Copyright 2020 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_FLAGS_INTERNAL_PRIVATE_HANDLE_ACCESSOR_H_
+#define ABSL_FLAGS_INTERNAL_PRIVATE_HANDLE_ACCESSOR_H_
+
+#include <memory>
+#include <string>
+
+#include "absl/base/config.h"
+#include "absl/flags/commandlineflag.h"
+#include "absl/flags/internal/commandlineflag.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace flags_internal {
+
+// This class serves as a trampoline to access private methods of
+// CommandLineFlag. This class is intended for use exclusively internally inside
+// of the Abseil Flags implementation.
+class PrivateHandleAccessor {
+ public:
+  // Access to CommandLineFlag::TypeId.
+  static FlagFastTypeId TypeId(const CommandLineFlag& flag);
+
+  // Access to CommandLineFlag::SaveState.
+  static std::unique_ptr<FlagStateInterface> SaveState(CommandLineFlag& flag);
+
+  // Access to CommandLineFlag::IsSpecifiedOnCommandLine.
+  static bool IsSpecifiedOnCommandLine(const CommandLineFlag& flag);
+
+  // Access to CommandLineFlag::ValidateInputValue.
+  static bool ValidateInputValue(const CommandLineFlag& flag,
+                                 absl::string_view value);
+
+  // Access to CommandLineFlag::CheckDefaultValueParsingRoundtrip.
+  static void CheckDefaultValueParsingRoundtrip(const CommandLineFlag& flag);
+
+  static bool ParseFrom(CommandLineFlag& flag, absl::string_view value,
+                        flags_internal::FlagSettingMode set_mode,
+                        flags_internal::ValueSource source, std::string& error);
+};
+
+}  // namespace flags_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FLAGS_INTERNAL_PRIVATE_HANDLE_ACCESSOR_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/program_name.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/program_name.h
new file mode 100644
index 0000000000000000000000000000000000000000..b99b94fe18ab18ecff9d84032e6d11bc59f9e99a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/program_name.h
@@ -0,0 +1,50 @@
+//
+//  Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_FLAGS_INTERNAL_PROGRAM_NAME_H_
+#define ABSL_FLAGS_INTERNAL_PROGRAM_NAME_H_
+
+#include <string>
+
+#include "absl/base/config.h"
+#include "absl/strings/string_view.h"
+
+// --------------------------------------------------------------------
+// Program name
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace flags_internal {
+
+// Returns program invocation name or "UNKNOWN" if `SetProgramInvocationName()`
+// is never called. At the moment this is always set to argv[0] as part of
+// library initialization.
+std::string ProgramInvocationName();
+
+// Returns base name for program invocation name. For example, if
+//   ProgramInvocationName() == "a/b/mybinary"
+// then
+//   ShortProgramInvocationName() == "mybinary"
+std::string ShortProgramInvocationName();
+
+// Sets program invocation name to a new value. Should only be called once
+// during program initialization, before any threads are spawned.
+void SetProgramInvocationName(absl::string_view prog_name_str);
+
+}  // namespace flags_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FLAGS_INTERNAL_PROGRAM_NAME_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/registry.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/registry.h
new file mode 100644
index 0000000000000000000000000000000000000000..4b68c85f5c44a8c6047c5c9fbf00d55bc2a75a26
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/registry.h
@@ -0,0 +1,97 @@
+//
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_FLAGS_INTERNAL_REGISTRY_H_
+#define ABSL_FLAGS_INTERNAL_REGISTRY_H_
+
+#include <functional>
+
+#include "absl/base/config.h"
+#include "absl/flags/commandlineflag.h"
+#include "absl/flags/internal/commandlineflag.h"
+#include "absl/strings/string_view.h"
+
+// --------------------------------------------------------------------
+// Global flags registry API.
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace flags_internal {
+
+// Executes specified visitor for each non-retired flag in the registry. While
+// callback are executed, the registry is locked and can't be changed.
+void ForEachFlag(std::function<void(CommandLineFlag&)> visitor);
+
+//-----------------------------------------------------------------------------
+
+bool RegisterCommandLineFlag(CommandLineFlag&, const char* filename);
+
+void FinalizeRegistry();
+
+//-----------------------------------------------------------------------------
+// Retired registrations:
+//
+// Retired flag registrations are treated specially. A 'retired' flag is
+// provided only for compatibility with automated invocations that still
+// name it.  A 'retired' flag:
+//   - is not bound to a C++ FLAGS_ reference.
+//   - has a type and a value, but that value is intentionally inaccessible.
+//   - does not appear in --help messages.
+//   - is fully supported by _all_ flag parsing routines.
+//   - consumes args normally, and complains about type mismatches in its
+//     argument.
+//   - emits a complaint but does not die (e.g. LOG(ERROR)) if it is
+//     accessed by name through the flags API for parsing or otherwise.
+//
+// The registrations for a flag happen in an unspecified order as the
+// initializers for the namespace-scope objects of a program are run.
+// Any number of weak registrations for a flag can weakly define the flag.
+// One non-weak registration will upgrade the flag from weak to non-weak.
+// Further weak registrations of a non-weak flag are ignored.
+//
+// This mechanism is designed to support moving dead flags into a
+// 'graveyard' library.  An example migration:
+//
+//   0: Remove references to this FLAGS_flagname in the C++ codebase.
+//   1: Register as 'retired' in old_lib.
+//   2: Make old_lib depend on graveyard.
+//   3: Add a redundant 'retired' registration to graveyard.
+//   4: Remove the old_lib 'retired' registration.
+//   5: Eventually delete the graveyard registration entirely.
+//
+
+// Retire flag with name "name" and type indicated by ops.
+void Retire(const char* name, FlagFastTypeId type_id, char* buf);
+
+constexpr size_t kRetiredFlagObjSize = 3 * sizeof(void*);
+constexpr size_t kRetiredFlagObjAlignment = alignof(void*);
+
+// Registered a retired flag with name 'flag_name' and type 'T'.
+template <typename T>
+class RetiredFlag {
+ public:
+  void Retire(const char* flag_name) {
+    flags_internal::Retire(flag_name, base_internal::FastTypeId<T>(), buf_);
+  }
+
+ private:
+  alignas(kRetiredFlagObjAlignment) char buf_[kRetiredFlagObjSize];
+};
+
+}  // namespace flags_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FLAGS_INTERNAL_REGISTRY_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/sequence_lock.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/sequence_lock.h
new file mode 100644
index 0000000000000000000000000000000000000000..36318ab9d36497992fa0c050fe1613c06f7643c9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/internal/sequence_lock.h
@@ -0,0 +1,187 @@
+//
+// Copyright 2020 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_FLAGS_INTERNAL_SEQUENCE_LOCK_H_
+#define ABSL_FLAGS_INTERNAL_SEQUENCE_LOCK_H_
+
+#include <stddef.h>
+#include <stdint.h>
+
+#include <atomic>
+#include <cassert>
+#include <cstring>
+
+#include "absl/base/optimization.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace flags_internal {
+
+// Align 'x' up to the nearest 'align' bytes.
+inline constexpr size_t AlignUp(size_t x, size_t align) {
+  return align * ((x + align - 1) / align);
+}
+
+// A SequenceLock implements lock-free reads. A sequence counter is incremented
+// before and after each write, and readers access the counter before and after
+// accessing the protected data. If the counter is verified to not change during
+// the access, and the sequence counter value was even, then the reader knows
+// that the read was race-free and valid. Otherwise, the reader must fall back
+// to a Mutex-based code path.
+//
+// This particular SequenceLock starts in an "uninitialized" state in which
+// TryRead() returns false. It must be enabled by calling MarkInitialized().
+// This serves as a marker that the associated flag value has not yet been
+// initialized and a slow path needs to be taken.
+//
+// The memory reads and writes protected by this lock must use the provided
+// `TryRead()` and `Write()` functions. These functions behave similarly to
+// `memcpy()`, with one oddity: the protected data must be an array of
+// `std::atomic<uint64>`. This is to comply with the C++ standard, which
+// considers data races on non-atomic objects to be undefined behavior. See "Can
+// Seqlocks Get Along With Programming Language Memory Models?"[1] by Hans J.
+// Boehm for more details.
+//
+// [1] https://www.hpl.hp.com/techreports/2012/HPL-2012-68.pdf
+class SequenceLock {
+ public:
+  constexpr SequenceLock() : lock_(kUninitialized) {}
+
+  // Mark that this lock is ready for use.
+  void MarkInitialized() {
+    assert(lock_.load(std::memory_order_relaxed) == kUninitialized);
+    lock_.store(0, std::memory_order_release);
+  }
+
+  // Copy "size" bytes of data from "src" to "dst", protected as a read-side
+  // critical section of the sequence lock.
+  //
+  // Unlike traditional sequence lock implementations which loop until getting a
+  // clean read, this implementation returns false in the case of concurrent
+  // calls to `Write`. In such a case, the caller should fall back to a
+  // locking-based slow path.
+  //
+  // Returns false if the sequence lock was not yet marked as initialized.
+  //
+  // NOTE: If this returns false, "dst" may be overwritten with undefined
+  // (potentially uninitialized) data.
+  bool TryRead(void* dst, const std::atomic<uint64_t>* src, size_t size) const {
+    // Acquire barrier ensures that no loads done by f() are reordered
+    // above the first load of the sequence counter.
+    int64_t seq_before = lock_.load(std::memory_order_acquire);
+    if (ABSL_PREDICT_FALSE(seq_before & 1) == 1) return false;
+    RelaxedCopyFromAtomic(dst, src, size);
+    // Another acquire fence ensures that the load of 'lock_' below is
+    // strictly ordered after the RelaxedCopyToAtomic call above.
+    std::atomic_thread_fence(std::memory_order_acquire);
+    int64_t seq_after = lock_.load(std::memory_order_relaxed);
+    return ABSL_PREDICT_TRUE(seq_before == seq_after);
+  }
+
+  // Copy "size" bytes from "src" to "dst" as a write-side critical section
+  // of the sequence lock. Any concurrent readers will be forced to retry
+  // until they get a read that does not conflict with this write.
+  //
+  // This call must be externally synchronized against other calls to Write,
+  // but may proceed concurrently with reads.
+  void Write(std::atomic<uint64_t>* dst, const void* src, size_t size) {
+    // We can use relaxed instructions to increment the counter since we
+    // are extenally synchronized. The std::atomic_thread_fence below
+    // ensures that the counter updates don't get interleaved with the
+    // copy to the data.
+    int64_t orig_seq = lock_.load(std::memory_order_relaxed);
+    assert((orig_seq & 1) == 0);  // Must be initially unlocked.
+    lock_.store(orig_seq + 1, std::memory_order_relaxed);
+
+    // We put a release fence between update to lock_ and writes to shared data.
+    // Thus all stores to shared data are effectively release operations and
+    // update to lock_ above cannot be re-ordered past any of them. Note that
+    // this barrier is not for the fetch_add above.  A release barrier for the
+    // fetch_add would be before it, not after.
+    std::atomic_thread_fence(std::memory_order_release);
+    RelaxedCopyToAtomic(dst, src, size);
+    // "Release" semantics ensure that none of the writes done by
+    // RelaxedCopyToAtomic() can be reordered after the following modification.
+    lock_.store(orig_seq + 2, std::memory_order_release);
+  }
+
+  // Return the number of times that Write() has been called.
+  //
+  // REQUIRES: This must be externally synchronized against concurrent calls to
+  // `Write()` or `IncrementModificationCount()`.
+  // REQUIRES: `MarkInitialized()` must have been previously called.
+  int64_t ModificationCount() const {
+    int64_t val = lock_.load(std::memory_order_relaxed);
+    assert(val != kUninitialized && (val & 1) == 0);
+    return val / 2;
+  }
+
+  // REQUIRES: This must be externally synchronized against concurrent calls to
+  // `Write()` or `ModificationCount()`.
+  // REQUIRES: `MarkInitialized()` must have been previously called.
+  void IncrementModificationCount() {
+    int64_t val = lock_.load(std::memory_order_relaxed);
+    assert(val != kUninitialized);
+    lock_.store(val + 2, std::memory_order_relaxed);
+  }
+
+ private:
+  // Perform the equivalent of "memcpy(dst, src, size)", but using relaxed
+  // atomics.
+  static void RelaxedCopyFromAtomic(void* dst, const std::atomic<uint64_t>* src,
+                                    size_t size) {
+    char* dst_byte = static_cast<char*>(dst);
+    while (size >= sizeof(uint64_t)) {
+      uint64_t word = src->load(std::memory_order_relaxed);
+      std::memcpy(dst_byte, &word, sizeof(word));
+      dst_byte += sizeof(word);
+      src++;
+      size -= sizeof(word);
+    }
+    if (size > 0) {
+      uint64_t word = src->load(std::memory_order_relaxed);
+      std::memcpy(dst_byte, &word, size);
+    }
+  }
+
+  // Perform the equivalent of "memcpy(dst, src, size)", but using relaxed
+  // atomics.
+  static void RelaxedCopyToAtomic(std::atomic<uint64_t>* dst, const void* src,
+                                  size_t size) {
+    const char* src_byte = static_cast<const char*>(src);
+    while (size >= sizeof(uint64_t)) {
+      uint64_t word;
+      std::memcpy(&word, src_byte, sizeof(word));
+      dst->store(word, std::memory_order_relaxed);
+      src_byte += sizeof(word);
+      dst++;
+      size -= sizeof(word);
+    }
+    if (size > 0) {
+      uint64_t word = 0;
+      std::memcpy(&word, src_byte, size);
+      dst->store(word, std::memory_order_relaxed);
+    }
+  }
+
+  static constexpr int64_t kUninitialized = -1;
+  std::atomic<int64_t> lock_;
+};
+
+}  // namespace flags_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FLAGS_INTERNAL_SEQUENCE_LOCK_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/marshalling.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/marshalling.h
new file mode 100644
index 0000000000000000000000000000000000000000..325e75e516361bb6576e8ae58327c7ab3e7364d3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/marshalling.h
@@ -0,0 +1,356 @@
+//
+//  Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: marshalling.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines the API for extending Abseil flag support to
+// custom types, and defines the set of overloads for fundamental types.
+//
+// Out of the box, the Abseil flags library supports the following types:
+//
+// * `bool`
+// * `int16_t`
+// * `uint16_t`
+// * `int32_t`
+// * `uint32_t`
+// * `int64_t`
+// * `uint64_t`
+// * `float`
+// * `double`
+// * `std::string`
+// * `std::vector<std::string>`
+// * `std::optional<T>`
+// * `absl::LogSeverity` (provided natively for layering reasons)
+//
+// Note that support for integral types is implemented using overloads for
+// variable-width fundamental types (`short`, `int`, `long`, etc.). However,
+// you should prefer the fixed-width integral types (`int32_t`, `uint64_t`,
+// etc.) we've noted above within flag definitions.
+//
+// In addition, several Abseil libraries provide their own custom support for
+// Abseil flags. Documentation for these formats is provided in the type's
+// `AbslParseFlag()` definition.
+//
+// The Abseil time library provides the following support for civil time values:
+//
+// * `absl::CivilSecond`
+// * `absl::CivilMinute`
+// * `absl::CivilHour`
+// * `absl::CivilDay`
+// * `absl::CivilMonth`
+// * `absl::CivilYear`
+//
+// and also provides support for the following absolute time values:
+//
+// * `absl::Duration`
+// * `absl::Time`
+//
+// Additional support for Abseil types will be noted here as it is added.
+//
+// You can also provide your own custom flags by adding overloads for
+// `AbslParseFlag()` and `AbslUnparseFlag()` to your type definitions. (See
+// below.)
+//
+// -----------------------------------------------------------------------------
+// Optional Flags
+// -----------------------------------------------------------------------------
+//
+// The Abseil flags library supports flags of type `std::optional<T>` where
+// `T` is a type of one of the supported flags. We refer to this flag type as
+// an "optional flag." An optional flag is either "valueless", holding no value
+// of type `T` (indicating that the flag has not been set) or a value of type
+// `T`. The valueless state in C++ code is represented by a value of
+// `std::nullopt` for the optional flag.
+//
+// Using `std::nullopt` as an optional flag's default value allows you to check
+// whether such a flag was ever specified on the command line:
+//
+//   if (absl::GetFlag(FLAGS_foo).has_value()) {
+//     // flag was set on command line
+//   } else {
+//     // flag was not passed on command line
+//   }
+//
+// Using an optional flag in this manner avoids common workarounds for
+// indicating such an unset flag (such as using sentinel values to indicate this
+// state).
+//
+// An optional flag also allows a developer to pass a flag in an "unset"
+// valueless state on the command line, allowing the flag to later be set in
+// binary logic. An optional flag's valueless state is indicated by the special
+// notation of passing the value as an empty string through the syntax `--flag=`
+// or `--flag ""`.
+//
+//   $ binary_with_optional --flag_in_unset_state=
+//   $ binary_with_optional --flag_in_unset_state ""
+//
+// Note: as a result of the above syntax requirements, an optional flag cannot
+// be set to a `T` of any value which unparses to the empty string.
+//
+// -----------------------------------------------------------------------------
+// Adding Type Support for Abseil Flags
+// -----------------------------------------------------------------------------
+//
+// To add support for your user-defined type, add overloads of `AbslParseFlag()`
+// and `AbslUnparseFlag()` as free (non-member) functions to your type. If `T`
+// is a class type, these functions can be friend function definitions. These
+// overloads must be added to the same namespace where the type is defined, so
+// that they can be discovered by Argument-Dependent Lookup (ADL).
+//
+// Example:
+//
+//   namespace foo {
+//
+//   enum OutputMode { kPlainText, kHtml };
+//
+//   // AbslParseFlag converts from a string to OutputMode.
+//   // Must be in same namespace as OutputMode.
+//
+//   // Parses an OutputMode from the command line flag value `text`. Returns
+//   // `true` and sets `*mode` on success; returns `false` and sets `*error`
+//   // on failure.
+//   bool AbslParseFlag(absl::string_view text,
+//                      OutputMode* mode,
+//                      std::string* error) {
+//     if (text == "plaintext") {
+//       *mode = kPlainText;
+//       return true;
+//     }
+//     if (text == "html") {
+//       *mode = kHtml;
+//      return true;
+//     }
+//     *error = "unknown value for enumeration";
+//     return false;
+//  }
+//
+//  // AbslUnparseFlag converts from an OutputMode to a string.
+//  // Must be in same namespace as OutputMode.
+//
+//  // Returns a textual flag value corresponding to the OutputMode `mode`.
+//  std::string AbslUnparseFlag(OutputMode mode) {
+//    switch (mode) {
+//      case kPlainText: return "plaintext";
+//      case kHtml: return "html";
+//    }
+//    return absl::StrCat(mode);
+//  }
+//
+// Notice that neither `AbslParseFlag()` nor `AbslUnparseFlag()` are class
+// members, but free functions. `AbslParseFlag/AbslUnparseFlag()` overloads
+// for a type should only be declared in the same file and namespace as said
+// type. The proper `AbslParseFlag/AbslUnparseFlag()` implementations for a
+// given type will be discovered via Argument-Dependent Lookup (ADL).
+//
+// `AbslParseFlag()` may need, in turn, to parse simpler constituent types
+// using `absl::ParseFlag()`. For example, a custom struct `MyFlagType`
+// consisting of a `std::pair<int, std::string>` would add an `AbslParseFlag()`
+// overload for its `MyFlagType` like so:
+//
+// Example:
+//
+//   namespace my_flag_type {
+//
+//   struct MyFlagType {
+//     std::pair<int, std::string> my_flag_data;
+//   };
+//
+//   bool AbslParseFlag(absl::string_view text, MyFlagType* flag,
+//                      std::string* err);
+//
+//   std::string AbslUnparseFlag(const MyFlagType&);
+//
+//   // Within the implementation, `AbslParseFlag()` will, in turn invoke
+//   // `absl::ParseFlag()` on its constituent `int` and `std::string` types
+//   // (which have built-in Abseil flag support).
+//
+//   bool AbslParseFlag(absl::string_view text, MyFlagType* flag,
+//                      std::string* err) {
+//     std::pair<absl::string_view, absl::string_view> tokens =
+//         absl::StrSplit(text, ',');
+//     if (!absl::ParseFlag(tokens.first, &flag->my_flag_data.first, err))
+//         return false;
+//     if (!absl::ParseFlag(tokens.second, &flag->my_flag_data.second, err))
+//         return false;
+//     return true;
+//   }
+//
+//   // Similarly, for unparsing, we can simply invoke `absl::UnparseFlag()` on
+//   // the constituent types.
+//   std::string AbslUnparseFlag(const MyFlagType& flag) {
+//     return absl::StrCat(absl::UnparseFlag(flag.my_flag_data.first),
+//                         ",",
+//                         absl::UnparseFlag(flag.my_flag_data.second));
+//   }
+#ifndef ABSL_FLAGS_MARSHALLING_H_
+#define ABSL_FLAGS_MARSHALLING_H_
+
+#include "absl/base/config.h"
+
+#if defined(ABSL_HAVE_STD_OPTIONAL) && !defined(ABSL_USES_STD_OPTIONAL)
+#include <optional>
+#endif
+#include <string>
+#include <vector>
+
+#include "absl/strings/string_view.h"
+#include "absl/types/optional.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// Forward declaration to be used inside composable flag parse/unparse
+// implementations
+template <typename T>
+inline bool ParseFlag(absl::string_view input, T* dst, std::string* error);
+template <typename T>
+inline std::string UnparseFlag(const T& v);
+
+namespace flags_internal {
+
+// Overloads of `AbslParseFlag()` and `AbslUnparseFlag()` for fundamental types.
+bool AbslParseFlag(absl::string_view, bool*, std::string*);
+bool AbslParseFlag(absl::string_view, short*, std::string*);           // NOLINT
+bool AbslParseFlag(absl::string_view, unsigned short*, std::string*);  // NOLINT
+bool AbslParseFlag(absl::string_view, int*, std::string*);             // NOLINT
+bool AbslParseFlag(absl::string_view, unsigned int*, std::string*);    // NOLINT
+bool AbslParseFlag(absl::string_view, long*, std::string*);            // NOLINT
+bool AbslParseFlag(absl::string_view, unsigned long*, std::string*);   // NOLINT
+bool AbslParseFlag(absl::string_view, long long*, std::string*);       // NOLINT
+bool AbslParseFlag(absl::string_view, unsigned long long*,             // NOLINT
+                   std::string*);
+bool AbslParseFlag(absl::string_view, float*, std::string*);
+bool AbslParseFlag(absl::string_view, double*, std::string*);
+bool AbslParseFlag(absl::string_view, std::string*, std::string*);
+bool AbslParseFlag(absl::string_view, std::vector<std::string>*, std::string*);
+
+template <typename T>
+bool AbslParseFlag(absl::string_view text, absl::optional<T>* f,
+                   std::string* err) {
+  if (text.empty()) {
+    *f = absl::nullopt;
+    return true;
+  }
+  T value;
+  if (!absl::ParseFlag(text, &value, err)) return false;
+
+  *f = std::move(value);
+  return true;
+}
+
+#if defined(ABSL_HAVE_STD_OPTIONAL) && !defined(ABSL_USES_STD_OPTIONAL)
+template <typename T>
+bool AbslParseFlag(absl::string_view text, std::optional<T>* f,
+                   std::string* err) {
+  if (text.empty()) {
+    *f = std::nullopt;
+    return true;
+  }
+  T value;
+  if (!absl::ParseFlag(text, &value, err)) return false;
+
+  *f = std::move(value);
+  return true;
+}
+#endif
+
+template <typename T>
+bool InvokeParseFlag(absl::string_view input, T* dst, std::string* err) {
+  // Comment on next line provides a good compiler error message if T
+  // does not have AbslParseFlag(absl::string_view, T*, std::string*).
+  return AbslParseFlag(input, dst, err);  // Is T missing AbslParseFlag?
+}
+
+// Strings and std:: containers do not have the same overload resolution
+// considerations as fundamental types. Naming these 'AbslUnparseFlag' means we
+// can avoid the need for additional specializations of Unparse (below).
+std::string AbslUnparseFlag(absl::string_view v);
+std::string AbslUnparseFlag(const std::vector<std::string>&);
+
+template <typename T>
+std::string AbslUnparseFlag(const absl::optional<T>& f) {
+  return f.has_value() ? absl::UnparseFlag(*f) : "";
+}
+
+#if defined(ABSL_HAVE_STD_OPTIONAL) && !defined(ABSL_USES_STD_OPTIONAL)
+template <typename T>
+std::string AbslUnparseFlag(const std::optional<T>& f) {
+  return f.has_value() ? absl::UnparseFlag(*f) : "";
+}
+#endif
+
+template <typename T>
+std::string Unparse(const T& v) {
+  // Comment on next line provides a good compiler error message if T does not
+  // have UnparseFlag.
+  return AbslUnparseFlag(v);  // Is T missing AbslUnparseFlag?
+}
+
+// Overloads for builtin types.
+std::string Unparse(bool v);
+std::string Unparse(short v);               // NOLINT
+std::string Unparse(unsigned short v);      // NOLINT
+std::string Unparse(int v);                 // NOLINT
+std::string Unparse(unsigned int v);        // NOLINT
+std::string Unparse(long v);                // NOLINT
+std::string Unparse(unsigned long v);       // NOLINT
+std::string Unparse(long long v);           // NOLINT
+std::string Unparse(unsigned long long v);  // NOLINT
+std::string Unparse(float v);
+std::string Unparse(double v);
+
+}  // namespace flags_internal
+
+// ParseFlag()
+//
+// Parses a string value into a flag value of type `T`. Do not add overloads of
+// this function for your type directly; instead, add an `AbslParseFlag()`
+// free function as documented above.
+//
+// Some implementations of `AbslParseFlag()` for types which consist of other,
+// constituent types which already have Abseil flag support, may need to call
+// `absl::ParseFlag()` on those consituent string values. (See above.)
+template <typename T>
+inline bool ParseFlag(absl::string_view input, T* dst, std::string* error) {
+  return flags_internal::InvokeParseFlag(input, dst, error);
+}
+
+// UnparseFlag()
+//
+// Unparses a flag value of type `T` into a string value. Do not add overloads
+// of this function for your type directly; instead, add an `AbslUnparseFlag()`
+// free function as documented above.
+//
+// Some implementations of `AbslUnparseFlag()` for types which consist of other,
+// constituent types which already have Abseil flag support, may want to call
+// `absl::UnparseFlag()` on those constituent types. (See above.)
+template <typename T>
+inline std::string UnparseFlag(const T& v) {
+  return flags_internal::Unparse(v);
+}
+
+// Overloads for `absl::LogSeverity` can't (easily) appear alongside that type's
+// definition because it is layered below flags.  See proper documentation in
+// base/log_severity.h.
+enum class LogSeverity : int;
+bool AbslParseFlag(absl::string_view, absl::LogSeverity*, std::string*);
+std::string AbslUnparseFlag(absl::LogSeverity);
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FLAGS_MARSHALLING_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/reflection.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/reflection.h
new file mode 100644
index 0000000000000000000000000000000000000000..e6baf5de4b0c569f8cb96f8a7265e6bffb67fce6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/reflection.h
@@ -0,0 +1,90 @@
+//
+// Copyright 2020 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: reflection.h
+// -----------------------------------------------------------------------------
+//
+// This file defines the routines to access and operate on an Abseil Flag's
+// reflection handle.
+
+#ifndef ABSL_FLAGS_REFLECTION_H_
+#define ABSL_FLAGS_REFLECTION_H_
+
+#include <string>
+
+#include "absl/base/config.h"
+#include "absl/container/flat_hash_map.h"
+#include "absl/flags/commandlineflag.h"
+#include "absl/flags/internal/commandlineflag.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace flags_internal {
+class FlagSaverImpl;
+}  // namespace flags_internal
+
+// FindCommandLineFlag()
+//
+// Returns the reflection handle of an Abseil flag of the specified name, or
+// `nullptr` if not found. This function will emit a warning if the name of a
+// 'retired' flag is specified.
+absl::CommandLineFlag* FindCommandLineFlag(absl::string_view name);
+
+// Returns current state of the Flags registry in a form of mapping from flag
+// name to a flag reflection handle.
+absl::flat_hash_map<absl::string_view, absl::CommandLineFlag*> GetAllFlags();
+
+//------------------------------------------------------------------------------
+// FlagSaver
+//------------------------------------------------------------------------------
+//
+// A FlagSaver object stores the state of flags in the scope where the FlagSaver
+// is defined, allowing modification of those flags within that scope and
+// automatic restoration of the flags to their previous state upon leaving the
+// scope.
+//
+// A FlagSaver can be used within tests to temporarily change the test
+// environment and restore the test case to its previous state.
+//
+// Example:
+//
+//   void MyFunc() {
+//    absl::FlagSaver fs;
+//    ...
+//    absl::SetFlag(&FLAGS_myFlag, otherValue);
+//    ...
+//  } // scope of FlagSaver left, flags return to previous state
+//
+// This class is thread-safe.
+
+class FlagSaver {
+ public:
+  FlagSaver();
+  ~FlagSaver();
+
+  FlagSaver(const FlagSaver&) = delete;
+  void operator=(const FlagSaver&) = delete;
+
+ private:
+  flags_internal::FlagSaverImpl* impl_;
+};
+
+//-----------------------------------------------------------------------------
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FLAGS_REFLECTION_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/usage_config.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/usage_config.h
new file mode 100644
index 0000000000000000000000000000000000000000..ded70300f0970856a9b69f0186bce3ce5d1c3d4e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/flags/usage_config.h
@@ -0,0 +1,135 @@
+//
+//  Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: usage_config.h
+// -----------------------------------------------------------------------------
+//
+// This file defines the main usage reporting configuration interfaces and
+// documents Abseil's supported built-in usage flags. If these flags are found
+// when parsing a command-line, Abseil will exit the program and display
+// appropriate help messages.
+#ifndef ABSL_FLAGS_USAGE_CONFIG_H_
+#define ABSL_FLAGS_USAGE_CONFIG_H_
+
+#include <functional>
+#include <string>
+
+#include "absl/base/config.h"
+#include "absl/strings/string_view.h"
+
+// -----------------------------------------------------------------------------
+// Built-in Usage Flags
+// -----------------------------------------------------------------------------
+//
+// Abseil supports the following built-in usage flags. When passed, these flags
+// exit the program and :
+//
+// * --help
+//     Shows help on important flags for this binary
+// * --helpfull
+//     Shows help on all flags
+// * --helpshort
+//     Shows help on only the main module for this program
+// * --helppackage
+//     Shows help on all modules in the main package
+// * --version
+//     Shows the version and build info for this binary and exits
+// * --only_check_args
+//     Exits after checking all flags
+// * --helpon
+//     Shows help on the modules named by this flag value
+// * --helpmatch
+//     Shows help on modules whose name contains the specified substring
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+namespace flags_internal {
+using FlagKindFilter = std::function<bool (absl::string_view)>;
+}  // namespace flags_internal
+
+// FlagsUsageConfig
+//
+// This structure contains the collection of callbacks for changing the behavior
+// of the usage reporting routines in Abseil Flags.
+struct FlagsUsageConfig {
+  // Returns true if flags defined in the given source code file should be
+  // reported with --helpshort flag. For example, if the file
+  // "path/to/my/code.cc" defines the flag "--my_flag", and
+  // contains_helpshort_flags("path/to/my/code.cc") returns true, invoking the
+  // program with --helpshort will include information about --my_flag in the
+  // program output.
+  flags_internal::FlagKindFilter contains_helpshort_flags;
+
+  // Returns true if flags defined in the filename should be reported with
+  // --help flag. For example, if the file
+  // "path/to/my/code.cc" defines the flag "--my_flag", and
+  // contains_help_flags("path/to/my/code.cc") returns true, invoking the
+  // program with --help will include information about --my_flag in the
+  // program output.
+  flags_internal::FlagKindFilter contains_help_flags;
+
+  // Returns true if flags defined in the filename should be reported with
+  // --helppackage flag. For example, if the file
+  // "path/to/my/code.cc" defines the flag "--my_flag", and
+  // contains_helppackage_flags("path/to/my/code.cc") returns true, invoking the
+  // program with --helppackage will include information about --my_flag in the
+  // program output.
+  flags_internal::FlagKindFilter contains_helppackage_flags;
+
+  // Generates string containing program version. This is the string reported
+  // when user specifies --version in a command line.
+  std::function<std::string()> version_string;
+
+  // Normalizes the filename specific to the build system/filesystem used. This
+  // routine is used when we report the information about the flag definition
+  // location. For instance, if your build resides at some location you do not
+  // want to expose in the usage output, you can trim it to show only relevant
+  // part.
+  // For example:
+  //   normalize_filename("/my_company/some_long_path/src/project/file.cc")
+  // might produce
+  //   "project/file.cc".
+  std::function<std::string(absl::string_view)> normalize_filename;
+};
+
+// SetFlagsUsageConfig()
+//
+// Sets the usage reporting configuration callbacks. If any of the callbacks are
+// not set in usage_config instance, then the default value of the callback is
+// used.
+void SetFlagsUsageConfig(FlagsUsageConfig usage_config);
+
+namespace flags_internal {
+
+FlagsUsageConfig GetUsageConfig();
+
+void ReportUsageError(absl::string_view msg, bool is_fatal);
+
+}  // namespace flags_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+extern "C" {
+
+// Additional report of fatal usage error message before we std::exit. Error is
+// fatal if is_fatal argument to ReportUsageError is true.
+void ABSL_INTERNAL_C_SYMBOL(AbslInternalReportFatalUsageError)(
+    absl::string_view);
+
+}  // extern "C"
+
+#endif  // ABSL_FLAGS_USAGE_CONFIG_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/any_invocable.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/any_invocable.h
new file mode 100644
index 0000000000000000000000000000000000000000..3e783c871d90eba7042a860c59199b7132568984
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/any_invocable.h
@@ -0,0 +1,316 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: any_invocable.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines an `absl::AnyInvocable` type that assumes ownership
+// and wraps an object of an invocable type. (Invocable types adhere to the
+// concept specified in https://en.cppreference.com/w/cpp/concepts/invocable.)
+//
+// In general, prefer `absl::AnyInvocable` when you need a type-erased
+// function parameter that needs to take ownership of the type.
+//
+// NOTE: `absl::AnyInvocable` is similar to the C++23 `std::move_only_function`
+// abstraction, but has a slightly different API and is not designed to be a
+// drop-in replacement or C++11-compatible backfill of that type.
+//
+// Credits to Matt Calabrese (https://github.com/mattcalabrese) for the original
+// implementation.
+
+#ifndef ABSL_FUNCTIONAL_ANY_INVOCABLE_H_
+#define ABSL_FUNCTIONAL_ANY_INVOCABLE_H_
+
+#include <cstddef>
+#include <initializer_list>
+#include <type_traits>
+#include <utility>
+
+#include "absl/base/config.h"
+#include "absl/functional/internal/any_invocable.h"
+#include "absl/meta/type_traits.h"
+#include "absl/utility/utility.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// absl::AnyInvocable
+//
+// `absl::AnyInvocable` is a functional wrapper type, like `std::function`, that
+// assumes ownership of an invocable object. Unlike `std::function`, an
+// `absl::AnyInvocable` is more type-safe and provides the following additional
+// benefits:
+//
+// * Properly adheres to const correctness of the underlying type
+// * Is move-only so avoids concurrency problems with copied invocables and
+//   unnecessary copies in general.
+// * Supports reference qualifiers allowing it to perform unique actions (noted
+//   below).
+//
+// `absl::AnyInvocable` is a template, and an `absl::AnyInvocable` instantiation
+// may wrap any invocable object with a compatible function signature, e.g.
+// having arguments and return types convertible to types matching the
+// `absl::AnyInvocable` signature, and also matching any stated reference
+// qualifiers, as long as that type is moveable. It therefore provides broad
+// type erasure for functional objects.
+//
+// An `absl::AnyInvocable` is typically used as a type-erased function parameter
+// for accepting various functional objects:
+//
+// // Define a function taking an AnyInvocable parameter.
+// void my_func(absl::AnyInvocable<int()> f) {
+//   ...
+// };
+//
+// // That function can accept any invocable type:
+//
+// // Accept a function reference. We don't need to move a reference.
+// int func1() { return 0; };
+// my_func(func1);
+//
+// // Accept a lambda. We use std::move here because otherwise my_func would
+// // copy the lambda.
+// auto lambda = []() { return 0; };
+// my_func(std::move(lambda));
+//
+// // Accept a function pointer. We don't need to move a function pointer.
+// func2 = &func1;
+// my_func(func2);
+//
+// // Accept an std::function by moving it. Note that the lambda is copyable
+// // (satisfying std::function requirements) and moveable (satisfying
+// // absl::AnyInvocable requirements).
+// std::function<int()> func6 = []() { return 0; };
+// my_func(std::move(func6));
+//
+// `AnyInvocable` also properly respects `const` qualifiers, reference
+// qualifiers, and the `noexcept` specification (only in C++ 17 and beyond) as
+// part of the user-specified function type (e.g.
+// `AnyInvocable<void()&& const noexcept>`). These qualifiers will be applied to
+// the `AnyInvocable` object's `operator()`, and the underlying invocable must
+// be compatible with those qualifiers.
+//
+// Comparison of const and non-const function types:
+//
+//   // Store a closure inside of `func` with the function type `int()`.
+//   // Note that we have made `func` itself `const`.
+//   const AnyInvocable<int()> func = [](){ return 0; };
+//
+//   func();  // Compile-error: the passed type `int()` isn't `const`.
+//
+//   // Store a closure inside of `const_func` with the function type
+//   // `int() const`.
+//   // Note that we have also made `const_func` itself `const`.
+//   const AnyInvocable<int() const> const_func = [](){ return 0; };
+//
+//   const_func();  // Fine: `int() const` is `const`.
+//
+// In the above example, the call `func()` would have compiled if
+// `std::function` were used even though the types are not const compatible.
+// This is a bug, and using `absl::AnyInvocable` properly detects that bug.
+//
+// In addition to affecting the signature of `operator()`, the `const` and
+// reference qualifiers of the function type also appropriately constrain which
+// kinds of invocable objects you are allowed to place into the `AnyInvocable`
+// instance. If you specify a function type that is const-qualified, then
+// anything that you attempt to put into the `AnyInvocable` must be callable on
+// a `const` instance of that type.
+//
+// Constraint example:
+//
+//   // Fine because the lambda is callable when `const`.
+//   AnyInvocable<int() const> func = [=](){ return 0; };
+//
+//   // This is a compile-error because the lambda isn't callable when `const`.
+//   AnyInvocable<int() const> error = [=]() mutable { return 0; };
+//
+// An `&&` qualifier can be used to express that an `absl::AnyInvocable`
+// instance should be invoked at most once:
+//
+//   // Invokes `continuation` with the logical result of an operation when
+//   // that operation completes (common in asynchronous code).
+//   void CallOnCompletion(AnyInvocable<void(int)&&> continuation) {
+//     int result_of_foo = foo();
+//
+//     // `std::move` is required because the `operator()` of `continuation` is
+//     // rvalue-reference qualified.
+//     std::move(continuation)(result_of_foo);
+//   }
+//
+// Attempting to call `absl::AnyInvocable` multiple times in such a case
+// results in undefined behavior.
+template <class Sig>
+class AnyInvocable : private internal_any_invocable::Impl<Sig> {
+ private:
+  static_assert(
+      std::is_function<Sig>::value,
+      "The template argument of AnyInvocable must be a function type.");
+
+  using Impl = internal_any_invocable::Impl<Sig>;
+
+ public:
+  // The return type of Sig
+  using result_type = typename Impl::result_type;
+
+  // Constructors
+
+  // Constructs the `AnyInvocable` in an empty state.
+  AnyInvocable() noexcept = default;
+  AnyInvocable(std::nullptr_t) noexcept {}  // NOLINT
+
+  // Constructs the `AnyInvocable` from an existing `AnyInvocable` by a move.
+  // Note that `f` is not guaranteed to be empty after move-construction,
+  // although it may be.
+  AnyInvocable(AnyInvocable&& /*f*/) noexcept = default;
+
+  // Constructs an `AnyInvocable` from an invocable object.
+  //
+  // Upon construction, `*this` is only empty if `f` is a function pointer or
+  // member pointer type and is null, or if `f` is an `AnyInvocable` that is
+  // empty.
+  template <class F, typename = absl::enable_if_t<
+                         internal_any_invocable::CanConvert<Sig, F>::value>>
+  AnyInvocable(F&& f)  // NOLINT
+      : Impl(internal_any_invocable::ConversionConstruct(),
+             std::forward<F>(f)) {}
+
+  // Constructs an `AnyInvocable` that holds an invocable object of type `T`,
+  // which is constructed in-place from the given arguments.
+  //
+  // Example:
+  //
+  //   AnyInvocable<int(int)> func(
+  //       absl::in_place_type<PossiblyImmovableType>, arg1, arg2);
+  //
+  template <class T, class... Args,
+            typename = absl::enable_if_t<
+                internal_any_invocable::CanEmplace<Sig, T, Args...>::value>>
+  explicit AnyInvocable(absl::in_place_type_t<T>, Args&&... args)
+      : Impl(absl::in_place_type<absl::decay_t<T>>,
+             std::forward<Args>(args)...) {
+    static_assert(std::is_same<T, absl::decay_t<T>>::value,
+                  "The explicit template argument of in_place_type is required "
+                  "to be an unqualified object type.");
+  }
+
+  // Overload of the above constructor to support list-initialization.
+  template <class T, class U, class... Args,
+            typename = absl::enable_if_t<internal_any_invocable::CanEmplace<
+                Sig, T, std::initializer_list<U>&, Args...>::value>>
+  explicit AnyInvocable(absl::in_place_type_t<T>,
+                        std::initializer_list<U> ilist, Args&&... args)
+      : Impl(absl::in_place_type<absl::decay_t<T>>, ilist,
+             std::forward<Args>(args)...) {
+    static_assert(std::is_same<T, absl::decay_t<T>>::value,
+                  "The explicit template argument of in_place_type is required "
+                  "to be an unqualified object type.");
+  }
+
+  // Assignment Operators
+
+  // Assigns an `AnyInvocable` through move-assignment.
+  // Note that `f` is not guaranteed to be empty after move-assignment
+  // although it may be.
+  AnyInvocable& operator=(AnyInvocable&& /*f*/) noexcept = default;
+
+  // Assigns an `AnyInvocable` from a nullptr, clearing the `AnyInvocable`. If
+  // not empty, destroys the target, putting `*this` into an empty state.
+  AnyInvocable& operator=(std::nullptr_t) noexcept {
+    this->Clear();
+    return *this;
+  }
+
+  // Assigns an `AnyInvocable` from an existing `AnyInvocable` instance.
+  //
+  // Upon assignment, `*this` is only empty if `f` is a function pointer or
+  // member pointer type and is null, or if `f` is an `AnyInvocable` that is
+  // empty.
+  template <class F, typename = absl::enable_if_t<
+                         internal_any_invocable::CanAssign<Sig, F>::value>>
+  AnyInvocable& operator=(F&& f) {
+    *this = AnyInvocable(std::forward<F>(f));
+    return *this;
+  }
+
+  // Assigns an `AnyInvocable` from a reference to an invocable object.
+  // Upon assignment, stores a reference to the invocable object in the
+  // `AnyInvocable` instance.
+  template <
+      class F,
+      typename = absl::enable_if_t<
+          internal_any_invocable::CanAssignReferenceWrapper<Sig, F>::value>>
+  AnyInvocable& operator=(std::reference_wrapper<F> f) noexcept {
+    *this = AnyInvocable(f);
+    return *this;
+  }
+
+  // Destructor
+
+  // If not empty, destroys the target.
+  ~AnyInvocable() = default;
+
+  // absl::AnyInvocable::swap()
+  //
+  // Exchanges the targets of `*this` and `other`.
+  void swap(AnyInvocable& other) noexcept { std::swap(*this, other); }
+
+  // abl::AnyInvocable::operator bool()
+  //
+  // Returns `true` if `*this` is not empty.
+  explicit operator bool() const noexcept { return this->HasValue(); }
+
+  // Invokes the target object of `*this`. `*this` must not be empty.
+  //
+  // Note: The signature of this function call operator is the same as the
+  //       template parameter `Sig`.
+  using Impl::operator();
+
+  // Equality operators
+
+  // Returns `true` if `*this` is empty.
+  friend bool operator==(const AnyInvocable& f, std::nullptr_t) noexcept {
+    return !f.HasValue();
+  }
+
+  // Returns `true` if `*this` is empty.
+  friend bool operator==(std::nullptr_t, const AnyInvocable& f) noexcept {
+    return !f.HasValue();
+  }
+
+  // Returns `false` if `*this` is empty.
+  friend bool operator!=(const AnyInvocable& f, std::nullptr_t) noexcept {
+    return f.HasValue();
+  }
+
+  // Returns `false` if `*this` is empty.
+  friend bool operator!=(std::nullptr_t, const AnyInvocable& f) noexcept {
+    return f.HasValue();
+  }
+
+  // swap()
+  //
+  // Exchanges the targets of `f1` and `f2`.
+  friend void swap(AnyInvocable& f1, AnyInvocable& f2) noexcept { f1.swap(f2); }
+
+ private:
+  // Friending other instantiations is necessary for conversions.
+  template <bool /*SigIsNoexcept*/, class /*ReturnType*/, class... /*P*/>
+  friend class internal_any_invocable::CoreImpl;
+};
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FUNCTIONAL_ANY_INVOCABLE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/bind_front.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/bind_front.h
new file mode 100644
index 0000000000000000000000000000000000000000..f9075bd1d503dfe9c5df48087030ffc2b434753a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/bind_front.h
@@ -0,0 +1,193 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: bind_front.h
+// -----------------------------------------------------------------------------
+//
+// `absl::bind_front()` returns a functor by binding a number of arguments to
+// the front of a provided (usually more generic) functor. Unlike `std::bind`,
+// it does not require the use of argument placeholders. The simpler syntax of
+// `absl::bind_front()` allows you to avoid known misuses with `std::bind()`.
+//
+// `absl::bind_front()` is meant as a drop-in replacement for C++20's upcoming
+// `std::bind_front()`, which similarly resolves these issues with
+// `std::bind()`. Both `bind_front()` alternatives, unlike `std::bind()`, allow
+// partial function application. (See
+// https://en.wikipedia.org/wiki/Partial_application).
+
+#ifndef ABSL_FUNCTIONAL_BIND_FRONT_H_
+#define ABSL_FUNCTIONAL_BIND_FRONT_H_
+
+#if defined(__cpp_lib_bind_front) && __cpp_lib_bind_front >= 201907L
+#include <functional>  // For std::bind_front.
+#endif  // defined(__cpp_lib_bind_front) && __cpp_lib_bind_front >= 201907L
+
+#include "absl/functional/internal/front_binder.h"
+#include "absl/utility/utility.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// bind_front()
+//
+// Binds the first N arguments of an invocable object and stores them by value.
+//
+// Like `std::bind()`, `absl::bind_front()` is implicitly convertible to
+// `std::function`.  In particular, it may be used as a simpler replacement for
+// `std::bind()` in most cases, as it does not require  placeholders to be
+// specified. More importantly, it provides more reliable correctness guarantees
+// than `std::bind()`; while `std::bind()` will silently ignore passing more
+// parameters than expected, for example, `absl::bind_front()` will report such
+// mis-uses as errors. In C++20, `absl::bind_front` is replaced by
+// `std::bind_front`.
+//
+// absl::bind_front(a...) can be seen as storing the results of
+// std::make_tuple(a...).
+//
+// Example: Binding a free function.
+//
+//   int Minus(int a, int b) { return a - b; }
+//
+//   assert(absl::bind_front(Minus)(3, 2) == 3 - 2);
+//   assert(absl::bind_front(Minus, 3)(2) == 3 - 2);
+//   assert(absl::bind_front(Minus, 3, 2)() == 3 - 2);
+//
+// Example: Binding a member function.
+//
+//   struct Math {
+//     int Double(int a) const { return 2 * a; }
+//   };
+//
+//   Math math;
+//
+//   assert(absl::bind_front(&Math::Double)(&math, 3) == 2 * 3);
+//   // Stores a pointer to math inside the functor.
+//   assert(absl::bind_front(&Math::Double, &math)(3) == 2 * 3);
+//   // Stores a copy of math inside the functor.
+//   assert(absl::bind_front(&Math::Double, math)(3) == 2 * 3);
+//   // Stores std::unique_ptr<Math> inside the functor.
+//   assert(absl::bind_front(&Math::Double,
+//                           std::unique_ptr<Math>(new Math))(3) == 2 * 3);
+//
+// Example: Using `absl::bind_front()`, instead of `std::bind()`, with
+//          `std::function`.
+//
+//   class FileReader {
+//    public:
+//     void ReadFileAsync(const std::string& filename, std::string* content,
+//                        const std::function<void()>& done) {
+//       // Calls Executor::Schedule(std::function<void()>).
+//       Executor::DefaultExecutor()->Schedule(
+//           absl::bind_front(&FileReader::BlockingRead, this,
+//                            filename, content, done));
+//     }
+//
+//    private:
+//     void BlockingRead(const std::string& filename, std::string* content,
+//                       const std::function<void()>& done) {
+//       CHECK_OK(file::GetContents(filename, content, {}));
+//       done();
+//     }
+//   };
+//
+// `absl::bind_front()` stores bound arguments explicitly using the type passed
+// rather than implicitly based on the type accepted by its functor.
+//
+// Example: Binding arguments explicitly.
+//
+//   void LogStringView(absl::string_view sv) {
+//     LOG(INFO) << sv;
+//   }
+//
+//   Executor* e = Executor::DefaultExecutor();
+//   std::string s = "hello";
+//   absl::string_view sv = s;
+//
+//   // absl::bind_front(LogStringView, arg) makes a copy of arg and stores it.
+//   e->Schedule(absl::bind_front(LogStringView, sv)); // ERROR: dangling
+//                                                     // string_view.
+//
+//   e->Schedule(absl::bind_front(LogStringView, s));  // OK: stores a copy of
+//                                                     // s.
+//
+// To store some of the arguments passed to `absl::bind_front()` by reference,
+//  use std::ref()` and `std::cref()`.
+//
+// Example: Storing some of the bound arguments by reference.
+//
+//   class Service {
+//    public:
+//     void Serve(const Request& req, std::function<void()>* done) {
+//       // The request protocol buffer won't be deleted until done is called.
+//       // It's safe to store a reference to it inside the functor.
+//       Executor::DefaultExecutor()->Schedule(
+//           absl::bind_front(&Service::BlockingServe, this, std::cref(req),
+//           done));
+//     }
+//
+//    private:
+//     void BlockingServe(const Request& req, std::function<void()>* done);
+//   };
+//
+// Example: Storing bound arguments by reference.
+//
+//   void Print(const std::string& a, const std::string& b) {
+//     std::cerr << a << b;
+//   }
+//
+//   std::string hi = "Hello, ";
+//   std::vector<std::string> names = {"Chuk", "Gek"};
+//   // Doesn't copy hi.
+//   for_each(names.begin(), names.end(),
+//            absl::bind_front(Print, std::ref(hi)));
+//
+//   // DO NOT DO THIS: the functor may outlive "hi", resulting in
+//   // dangling references.
+//   foo->DoInFuture(absl::bind_front(Print, std::ref(hi), "Guest"));  // BAD!
+//   auto f = absl::bind_front(Print, std::ref(hi), "Guest"); // BAD!
+//
+// Example: Storing reference-like types.
+//
+//   void Print(absl::string_view a, const std::string& b) {
+//     std::cerr << a << b;
+//   }
+//
+//   std::string hi = "Hello, ";
+//   // Copies "hi".
+//   absl::bind_front(Print, hi)("Chuk");
+//
+//   // Compile error: std::reference_wrapper<const string> is not implicitly
+//   // convertible to string_view.
+//   // absl::bind_front(Print, std::cref(hi))("Chuk");
+//
+//   // Doesn't copy "hi".
+//   absl::bind_front(Print, absl::string_view(hi))("Chuk");
+//
+#if defined(__cpp_lib_bind_front) && __cpp_lib_bind_front >= 201907L
+using std::bind_front;
+#else   // defined(__cpp_lib_bind_front) && __cpp_lib_bind_front >= 201907L
+template <class F, class... BoundArgs>
+constexpr functional_internal::bind_front_t<F, BoundArgs...> bind_front(
+    F&& func, BoundArgs&&... args) {
+  return functional_internal::bind_front_t<F, BoundArgs...>(
+      absl::in_place, absl::forward<F>(func),
+      absl::forward<BoundArgs>(args)...);
+}
+#endif  // defined(__cpp_lib_bind_front) && __cpp_lib_bind_front >= 201907L
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FUNCTIONAL_BIND_FRONT_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/function_ref.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/function_ref.h
new file mode 100644
index 0000000000000000000000000000000000000000..f9779607fb59236a247bf70469aa4cb4708429fa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/function_ref.h
@@ -0,0 +1,143 @@
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: function_ref.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines the `absl::FunctionRef` type for holding a
+// non-owning reference to an object of any invocable type. This function
+// reference is typically most useful as a type-erased argument type for
+// accepting function types that neither take ownership nor copy the type; using
+// the reference type in this case avoids a copy and an allocation. Best
+// practices of other non-owning reference-like objects (such as
+// `absl::string_view`) apply here.
+//
+//  An `absl::FunctionRef` is similar in usage to a `std::function` but has the
+//  following differences:
+//
+//  * It doesn't own the underlying object.
+//  * It doesn't have a null or empty state.
+//  * It never performs deep copies or allocations.
+//  * It's much faster and cheaper to construct.
+//  * It's trivially copyable and destructable.
+//
+// Generally, `absl::FunctionRef` should not be used as a return value, data
+// member, or to initialize a `std::function`. Such usages will often lead to
+// problematic lifetime issues. Once you convert something to an
+// `absl::FunctionRef` you cannot make a deep copy later.
+//
+// This class is suitable for use wherever a "const std::function<>&"
+// would be used without making a copy. ForEach functions and other versions of
+// the visitor pattern are a good example of when this class should be used.
+//
+// This class is trivial to copy and should be passed by value.
+#ifndef ABSL_FUNCTIONAL_FUNCTION_REF_H_
+#define ABSL_FUNCTIONAL_FUNCTION_REF_H_
+
+#include <cassert>
+#include <functional>
+#include <type_traits>
+
+#include "absl/base/attributes.h"
+#include "absl/functional/internal/function_ref.h"
+#include "absl/meta/type_traits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// FunctionRef
+//
+// Dummy class declaration to allow the partial specialization based on function
+// types below.
+template <typename T>
+class FunctionRef;
+
+// FunctionRef
+//
+// An `absl::FunctionRef` is a lightweight wrapper to any invokable object with
+// a compatible signature. Generally, an `absl::FunctionRef` should only be used
+// as an argument type and should be preferred as an argument over a const
+// reference to a `std::function`. `absl::FunctionRef` itself does not allocate,
+// although the wrapped invokable may.
+//
+// Example:
+//
+//   // The following function takes a function callback by const reference
+//   bool Visitor(const std::function<void(my_proto&,
+//                                         absl::string_view)>& callback);
+//
+//   // Assuming that the function is not stored or otherwise copied, it can be
+//   // replaced by an `absl::FunctionRef`:
+//   bool Visitor(absl::FunctionRef<void(my_proto&, absl::string_view)>
+//                  callback);
+//
+// Note: the assignment operator within an `absl::FunctionRef` is intentionally
+// deleted to prevent misuse; because the `absl::FunctionRef` does not own the
+// underlying type, assignment likely indicates misuse.
+template <typename R, typename... Args>
+class FunctionRef<R(Args...)> {
+ private:
+  // Used to disable constructors for objects that are not compatible with the
+  // signature of this FunctionRef.
+  template <typename F,
+            typename FR = absl::base_internal::invoke_result_t<F, Args&&...>>
+  using EnableIfCompatible =
+      typename std::enable_if<std::is_void<R>::value ||
+                              std::is_convertible<FR, R>::value>::type;
+
+ public:
+  // Constructs a FunctionRef from any invokable type.
+  template <typename F, typename = EnableIfCompatible<const F&>>
+  // NOLINTNEXTLINE(runtime/explicit)
+  FunctionRef(const F& f ABSL_ATTRIBUTE_LIFETIME_BOUND)
+      : invoker_(&absl::functional_internal::InvokeObject<F, R, Args...>) {
+    absl::functional_internal::AssertNonNull(f);
+    ptr_.obj = &f;
+  }
+
+  // Overload for function pointers. This eliminates a level of indirection that
+  // would happen if the above overload was used (it lets us store the pointer
+  // instead of a pointer to a pointer).
+  //
+  // This overload is also used for references to functions, since references to
+  // functions can decay to function pointers implicitly.
+  template <
+      typename F, typename = EnableIfCompatible<F*>,
+      absl::functional_internal::EnableIf<absl::is_function<F>::value> = 0>
+  FunctionRef(F* f)  // NOLINT(runtime/explicit)
+      : invoker_(&absl::functional_internal::InvokeFunction<F*, R, Args...>) {
+    assert(f != nullptr);
+    ptr_.fun = reinterpret_cast<decltype(ptr_.fun)>(f);
+  }
+
+  // To help prevent subtle lifetime bugs, FunctionRef is not assignable.
+  // Typically, it should only be used as an argument type.
+  FunctionRef& operator=(const FunctionRef& rhs) = delete;
+  FunctionRef(const FunctionRef& rhs) = default;
+
+  // Call the underlying object.
+  R operator()(Args... args) const {
+    return invoker_(ptr_, std::forward<Args>(args)...);
+  }
+
+ private:
+  absl::functional_internal::VoidPtr ptr_;
+  absl::functional_internal::Invoker<R, Args...> invoker_;
+};
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FUNCTIONAL_FUNCTION_REF_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/internal/any_invocable.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/internal/any_invocable.h
new file mode 100644
index 0000000000000000000000000000000000000000..6bfbda18a70fcf80ede0608386c393949055f21f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/internal/any_invocable.h
@@ -0,0 +1,878 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Implementation details for `absl::AnyInvocable`
+
+#ifndef ABSL_FUNCTIONAL_INTERNAL_ANY_INVOCABLE_H_
+#define ABSL_FUNCTIONAL_INTERNAL_ANY_INVOCABLE_H_
+
+////////////////////////////////////////////////////////////////////////////////
+//                                                                            //
+// This implementation of the proposed `any_invocable` uses an approach that  //
+// chooses between local storage and remote storage for the contained target  //
+// object based on the target object's size, alignment requirements, and      //
+// whether or not it has a nothrow move constructor. Additional optimizations //
+// are performed when the object is a trivially copyable type [basic.types].  //
+//                                                                            //
+// There are three datamembers per `AnyInvocable` instance                    //
+//                                                                            //
+// 1) A union containing either                                               //
+//        - A pointer to the target object referred to via a void*, or        //
+//        - the target object, emplaced into a raw char buffer                //
+//                                                                            //
+// 2) A function pointer to a "manager" function operation that takes a       //
+//    discriminator and logically branches to either perform a move operation //
+//    or destroy operation based on that discriminator.                       //
+//                                                                            //
+// 3) A function pointer to an "invoker" function operation that invokes the  //
+//    target object, directly returning the result.                           //
+//                                                                            //
+// When in the logically empty state, the manager function is an empty        //
+// function and the invoker function is one that would be undefined-behavior  //
+// to call.                                                                   //
+//                                                                            //
+// An additional optimization is performed when converting from one           //
+// AnyInvocable to another where only the noexcept specification and/or the   //
+// cv/ref qualifiers of the function type differ. In these cases, the         //
+// conversion works by "moving the guts", similar to if they were the same    //
+// exact type, as opposed to having to perform an additional layer of         //
+// wrapping through remote storage.                                           //
+//                                                                            //
+////////////////////////////////////////////////////////////////////////////////
+
+// IWYU pragma: private, include "absl/functional/any_invocable.h"
+
+#include <cassert>
+#include <cstddef>
+#include <cstring>
+#include <functional>
+#include <initializer_list>
+#include <memory>
+#include <new>
+#include <type_traits>
+#include <utility>
+
+#include "absl/base/config.h"
+#include "absl/base/internal/invoke.h"
+#include "absl/base/macros.h"
+#include "absl/base/optimization.h"
+#include "absl/meta/type_traits.h"
+#include "absl/utility/utility.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// Helper macro used to prevent spelling `noexcept` in language versions older
+// than C++17, where it is not part of the type system, in order to avoid
+// compilation failures and internal compiler errors.
+#if ABSL_INTERNAL_CPLUSPLUS_LANG >= 201703L
+#define ABSL_INTERNAL_NOEXCEPT_SPEC(noex) noexcept(noex)
+#else
+#define ABSL_INTERNAL_NOEXCEPT_SPEC(noex)
+#endif
+
+// Defined in functional/any_invocable.h
+template <class Sig>
+class AnyInvocable;
+
+namespace internal_any_invocable {
+
+// Constants relating to the small-object-storage for AnyInvocable
+enum StorageProperty : std::size_t {
+  kAlignment = alignof(std::max_align_t),  // The alignment of the storage
+  kStorageSize = sizeof(void*) * 2         // The size of the storage
+};
+
+////////////////////////////////////////////////////////////////////////////////
+//
+// A metafunction for checking if a type is an AnyInvocable instantiation.
+// This is used during conversion operations.
+template <class T>
+struct IsAnyInvocable : std::false_type {};
+
+template <class Sig>
+struct IsAnyInvocable<AnyInvocable<Sig>> : std::true_type {};
+//
+////////////////////////////////////////////////////////////////////////////////
+
+// A type trait that tells us whether or not a target function type should be
+// stored locally in the small object optimization storage
+template <class T>
+using IsStoredLocally = std::integral_constant<
+    bool, sizeof(T) <= kStorageSize && alignof(T) <= kAlignment &&
+              kAlignment % alignof(T) == 0 &&
+              std::is_nothrow_move_constructible<T>::value>;
+
+// An implementation of std::remove_cvref_t of C++20.
+template <class T>
+using RemoveCVRef =
+    typename std::remove_cv<typename std::remove_reference<T>::type>::type;
+
+////////////////////////////////////////////////////////////////////////////////
+//
+// An implementation of the C++ standard INVOKE<R> pseudo-macro, operation is
+// equivalent to std::invoke except that it forces an implicit conversion to the
+// specified return type. If "R" is void, the function is executed and the
+// return value is simply ignored.
+template <class ReturnType, class F, class... P,
+          typename = absl::enable_if_t<std::is_void<ReturnType>::value>>
+void InvokeR(F&& f, P&&... args) {
+  absl::base_internal::invoke(std::forward<F>(f), std::forward<P>(args)...);
+}
+
+template <class ReturnType, class F, class... P,
+          absl::enable_if_t<!std::is_void<ReturnType>::value, int> = 0>
+ReturnType InvokeR(F&& f, P&&... args) {
+  return absl::base_internal::invoke(std::forward<F>(f),
+                                     std::forward<P>(args)...);
+}
+
+//
+////////////////////////////////////////////////////////////////////////////////
+
+////////////////////////////////////////////////////////////////////////////////
+///
+// A metafunction that takes a "T" corresponding to a parameter type of the
+// user's specified function type, and yields the parameter type to use for the
+// type-erased invoker. In order to prevent observable moves, this must be
+// either a reference or, if the type is trivial, the original parameter type
+// itself. Since the parameter type may be incomplete at the point that this
+// metafunction is used, we can only do this optimization for scalar types
+// rather than for any trivial type.
+template <typename T>
+T ForwardImpl(std::true_type);
+
+template <typename T>
+T&& ForwardImpl(std::false_type);
+
+// NOTE: We deliberately use an intermediate struct instead of a direct alias,
+// as a workaround for b/206991861 on MSVC versions < 1924.
+template <class T>
+struct ForwardedParameter {
+  using type = decltype((
+      ForwardImpl<T>)(std::integral_constant<bool,
+                                             std::is_scalar<T>::value>()));
+};
+
+template <class T>
+using ForwardedParameterType = typename ForwardedParameter<T>::type;
+//
+////////////////////////////////////////////////////////////////////////////////
+
+// A discriminator when calling the "manager" function that describes operation
+// type-erased operation should be invoked.
+//
+// "relocate_from_to" specifies that the manager should perform a move.
+//
+// "dispose" specifies that the manager should perform a destroy.
+enum class FunctionToCall : bool { relocate_from_to, dispose };
+
+// The portion of `AnyInvocable` state that contains either a pointer to the
+// target object or the object itself in local storage
+union TypeErasedState {
+  struct {
+    // A pointer to the type-erased object when remotely stored
+    void* target;
+    // The size of the object for `RemoteManagerTrivial`
+    std::size_t size;
+  } remote;
+
+  // Local-storage for the type-erased object when small and trivial enough
+  alignas(kAlignment) char storage[kStorageSize];
+};
+
+// A typed accessor for the object in `TypeErasedState` storage
+template <class T>
+T& ObjectInLocalStorage(TypeErasedState* const state) {
+  // We launder here because the storage may be reused with the same type.
+#if ABSL_INTERNAL_CPLUSPLUS_LANG >= 201703L
+  return *std::launder(reinterpret_cast<T*>(&state->storage));
+#elif ABSL_HAVE_BUILTIN(__builtin_launder)
+  return *__builtin_launder(reinterpret_cast<T*>(&state->storage));
+#else
+
+  // When `std::launder` or equivalent are not available, we rely on undefined
+  // behavior, which works as intended on Abseil's officially supported
+  // platforms as of Q2 2022.
+#if !defined(__clang__) && defined(__GNUC__)
+#pragma GCC diagnostic ignored "-Wstrict-aliasing"
+#pragma GCC diagnostic push
+#endif
+  return *reinterpret_cast<T*>(&state->storage);
+#if !defined(__clang__) && defined(__GNUC__)
+#pragma GCC diagnostic pop
+#endif
+
+#endif
+}
+
+// The type for functions issuing lifetime-related operations: move and dispose
+// A pointer to such a function is contained in each `AnyInvocable` instance.
+// NOTE: When specifying `FunctionToCall::`dispose, the same state must be
+// passed as both "from" and "to".
+using ManagerType = void(FunctionToCall /*operation*/,
+                         TypeErasedState* /*from*/, TypeErasedState* /*to*/)
+    ABSL_INTERNAL_NOEXCEPT_SPEC(true);
+
+// The type for functions issuing the actual invocation of the object
+// A pointer to such a function is contained in each AnyInvocable instance.
+template <bool SigIsNoexcept, class ReturnType, class... P>
+using InvokerType = ReturnType(TypeErasedState*, ForwardedParameterType<P>...)
+    ABSL_INTERNAL_NOEXCEPT_SPEC(SigIsNoexcept);
+
+// The manager that is used when AnyInvocable is empty
+inline void EmptyManager(FunctionToCall /*operation*/,
+                         TypeErasedState* /*from*/,
+                         TypeErasedState* /*to*/) noexcept {}
+
+// The manager that is used when a target function is in local storage and is
+// a trivially copyable type.
+inline void LocalManagerTrivial(FunctionToCall /*operation*/,
+                                TypeErasedState* const from,
+                                TypeErasedState* const to) noexcept {
+  // This single statement without branching handles both possible operations.
+  //
+  // For FunctionToCall::dispose, "from" and "to" point to the same state, and
+  // so this assignment logically would do nothing.
+  //
+  // Note: Correctness here relies on http://wg21.link/p0593, which has only
+  // become standard in C++20, though implementations do not break it in
+  // practice for earlier versions of C++.
+  //
+  // The correct way to do this without that paper is to first placement-new a
+  // default-constructed T in "to->storage" prior to the memmove, but doing so
+  // requires a different function to be created for each T that is stored
+  // locally, which can cause unnecessary bloat and be less cache friendly.
+  *to = *from;
+
+  // Note: Because the type is trivially copyable, the destructor does not need
+  // to be called ("trivially copyable" requires a trivial destructor).
+}
+
+// The manager that is used when a target function is in local storage and is
+// not a trivially copyable type.
+template <class T>
+void LocalManagerNontrivial(FunctionToCall operation,
+                            TypeErasedState* const from,
+                            TypeErasedState* const to) noexcept {
+  static_assert(IsStoredLocally<T>::value,
+                "Local storage must only be used for supported types.");
+  static_assert(!std::is_trivially_copyable<T>::value,
+                "Locally stored types must be trivially copyable.");
+
+  T& from_object = (ObjectInLocalStorage<T>)(from);
+
+  switch (operation) {
+    case FunctionToCall::relocate_from_to:
+      // NOTE: Requires that the left-hand operand is already empty.
+      ::new (static_cast<void*>(&to->storage)) T(std::move(from_object));
+      ABSL_FALLTHROUGH_INTENDED;
+    case FunctionToCall::dispose:
+      from_object.~T();  // Must not throw. // NOLINT
+      return;
+  }
+  ABSL_UNREACHABLE();
+}
+
+// The invoker that is used when a target function is in local storage
+// Note: QualTRef here is the target function type along with cv and reference
+// qualifiers that must be used when calling the function.
+template <bool SigIsNoexcept, class ReturnType, class QualTRef, class... P>
+ReturnType LocalInvoker(
+    TypeErasedState* const state,
+    ForwardedParameterType<P>... args) noexcept(SigIsNoexcept) {
+  using RawT = RemoveCVRef<QualTRef>;
+  static_assert(
+      IsStoredLocally<RawT>::value,
+      "Target object must be in local storage in order to be invoked from it.");
+
+  auto& f = (ObjectInLocalStorage<RawT>)(state);
+  return (InvokeR<ReturnType>)(static_cast<QualTRef>(f),
+                               static_cast<ForwardedParameterType<P>>(args)...);
+}
+
+// The manager that is used when a target function is in remote storage and it
+// has a trivial destructor
+inline void RemoteManagerTrivial(FunctionToCall operation,
+                                 TypeErasedState* const from,
+                                 TypeErasedState* const to) noexcept {
+  switch (operation) {
+    case FunctionToCall::relocate_from_to:
+      // NOTE: Requires that the left-hand operand is already empty.
+      to->remote = from->remote;
+      return;
+    case FunctionToCall::dispose:
+#if defined(__cpp_sized_deallocation)
+      ::operator delete(from->remote.target, from->remote.size);
+#else   // __cpp_sized_deallocation
+      ::operator delete(from->remote.target);
+#endif  // __cpp_sized_deallocation
+      return;
+  }
+  ABSL_UNREACHABLE();
+}
+
+// The manager that is used when a target function is in remote storage and the
+// destructor of the type is not trivial
+template <class T>
+void RemoteManagerNontrivial(FunctionToCall operation,
+                             TypeErasedState* const from,
+                             TypeErasedState* const to) noexcept {
+  static_assert(!IsStoredLocally<T>::value,
+                "Remote storage must only be used for types that do not "
+                "qualify for local storage.");
+
+  switch (operation) {
+    case FunctionToCall::relocate_from_to:
+      // NOTE: Requires that the left-hand operand is already empty.
+      to->remote.target = from->remote.target;
+      return;
+    case FunctionToCall::dispose:
+      ::delete static_cast<T*>(from->remote.target);  // Must not throw.
+      return;
+  }
+  ABSL_UNREACHABLE();
+}
+
+// The invoker that is used when a target function is in remote storage
+template <bool SigIsNoexcept, class ReturnType, class QualTRef, class... P>
+ReturnType RemoteInvoker(
+    TypeErasedState* const state,
+    ForwardedParameterType<P>... args) noexcept(SigIsNoexcept) {
+  using RawT = RemoveCVRef<QualTRef>;
+  static_assert(!IsStoredLocally<RawT>::value,
+                "Target object must be in remote storage in order to be "
+                "invoked from it.");
+
+  auto& f = *static_cast<RawT*>(state->remote.target);
+  return (InvokeR<ReturnType>)(static_cast<QualTRef>(f),
+                               static_cast<ForwardedParameterType<P>>(args)...);
+}
+
+////////////////////////////////////////////////////////////////////////////////
+//
+// A metafunction that checks if a type T is an instantiation of
+// absl::in_place_type_t (needed for constructor constraints of AnyInvocable).
+template <class T>
+struct IsInPlaceType : std::false_type {};
+
+template <class T>
+struct IsInPlaceType<absl::in_place_type_t<T>> : std::true_type {};
+//
+////////////////////////////////////////////////////////////////////////////////
+
+// A constructor name-tag used with CoreImpl (below) to request the
+// conversion-constructor. QualDecayedTRef is the decayed-type of the object to
+// wrap, along with the cv and reference qualifiers that must be applied when
+// performing an invocation of the wrapped object.
+template <class QualDecayedTRef>
+struct TypedConversionConstruct {};
+
+// A helper base class for all core operations of AnyInvocable. Most notably,
+// this class creates the function call operator and constraint-checkers so that
+// the top-level class does not have to be a series of partial specializations.
+//
+// Note: This definition exists (as opposed to being a declaration) so that if
+// the user of the top-level template accidentally passes a template argument
+// that is not a function type, they will get a static_assert in AnyInvocable's
+// class body rather than an error stating that Impl is not defined.
+template <class Sig>
+class Impl {};  // Note: This is partially-specialized later.
+
+// A std::unique_ptr deleter that deletes memory allocated via ::operator new.
+#if defined(__cpp_sized_deallocation)
+class TrivialDeleter {
+ public:
+  explicit TrivialDeleter(std::size_t size) : size_(size) {}
+
+  void operator()(void* target) const {
+    ::operator delete(target, size_);
+  }
+
+ private:
+  std::size_t size_;
+};
+#else   // __cpp_sized_deallocation
+class TrivialDeleter {
+ public:
+  explicit TrivialDeleter(std::size_t) {}
+
+  void operator()(void* target) const { ::operator delete(target); }
+};
+#endif  // __cpp_sized_deallocation
+
+template <bool SigIsNoexcept, class ReturnType, class... P>
+class CoreImpl;
+
+constexpr bool IsCompatibleConversion(void*, void*) { return false; }
+template <bool NoExceptSrc, bool NoExceptDest, class... T>
+constexpr bool IsCompatibleConversion(CoreImpl<NoExceptSrc, T...>*,
+                                      CoreImpl<NoExceptDest, T...>*) {
+  return !NoExceptDest || NoExceptSrc;
+}
+
+// A helper base class for all core operations of AnyInvocable that do not
+// depend on the cv/ref qualifiers of the function type.
+template <bool SigIsNoexcept, class ReturnType, class... P>
+class CoreImpl {
+ public:
+  using result_type = ReturnType;
+
+  CoreImpl() noexcept : manager_(EmptyManager), invoker_(nullptr) {}
+
+  enum class TargetType : int {
+    kPointer = 0,
+    kCompatibleAnyInvocable = 1,
+    kIncompatibleAnyInvocable = 2,
+    kOther = 3,
+  };
+
+  // Note: QualDecayedTRef here includes the cv-ref qualifiers associated with
+  // the invocation of the Invocable. The unqualified type is the target object
+  // type to be stored.
+  template <class QualDecayedTRef, class F>
+  explicit CoreImpl(TypedConversionConstruct<QualDecayedTRef>, F&& f) {
+    using DecayedT = RemoveCVRef<QualDecayedTRef>;
+
+    constexpr TargetType kTargetType =
+        (std::is_pointer<DecayedT>::value ||
+         std::is_member_pointer<DecayedT>::value)
+            ? TargetType::kPointer
+        : IsCompatibleAnyInvocable<DecayedT>::value
+            ? TargetType::kCompatibleAnyInvocable
+        : IsAnyInvocable<DecayedT>::value
+            ? TargetType::kIncompatibleAnyInvocable
+            : TargetType::kOther;
+    // NOTE: We only use integers instead of enums as template parameters in
+    // order to work around a bug on C++14 under MSVC 2017.
+    // See b/236131881.
+    Initialize<static_cast<int>(kTargetType), QualDecayedTRef>(
+        std::forward<F>(f));
+  }
+
+  // Note: QualTRef here includes the cv-ref qualifiers associated with the
+  // invocation of the Invocable. The unqualified type is the target object
+  // type to be stored.
+  template <class QualTRef, class... Args>
+  explicit CoreImpl(absl::in_place_type_t<QualTRef>, Args&&... args) {
+    InitializeStorage<QualTRef>(std::forward<Args>(args)...);
+  }
+
+  CoreImpl(CoreImpl&& other) noexcept {
+    other.manager_(FunctionToCall::relocate_from_to, &other.state_, &state_);
+    manager_ = other.manager_;
+    invoker_ = other.invoker_;
+    other.manager_ = EmptyManager;
+    other.invoker_ = nullptr;
+  }
+
+  CoreImpl& operator=(CoreImpl&& other) noexcept {
+    // Put the left-hand operand in an empty state.
+    //
+    // Note: A full reset that leaves us with an object that has its invariants
+    // intact is necessary in order to handle self-move. This is required by
+    // types that are used with certain operations of the standard library, such
+    // as the default definition of std::swap when both operands target the same
+    // object.
+    Clear();
+
+    // Perform the actual move/destory operation on the target function.
+    other.manager_(FunctionToCall::relocate_from_to, &other.state_, &state_);
+    manager_ = other.manager_;
+    invoker_ = other.invoker_;
+    other.manager_ = EmptyManager;
+    other.invoker_ = nullptr;
+
+    return *this;
+  }
+
+  ~CoreImpl() { manager_(FunctionToCall::dispose, &state_, &state_); }
+
+  // Check whether or not the AnyInvocable is in the empty state.
+  bool HasValue() const { return invoker_ != nullptr; }
+
+  // Effects: Puts the object into its empty state.
+  void Clear() {
+    manager_(FunctionToCall::dispose, &state_, &state_);
+    manager_ = EmptyManager;
+    invoker_ = nullptr;
+  }
+
+  template <int target_type, class QualDecayedTRef, class F,
+            absl::enable_if_t<target_type == 0, int> = 0>
+  void Initialize(F&& f) {
+// This condition handles types that decay into pointers, which includes
+// function references. Since function references cannot be null, GCC warns
+// against comparing their decayed form with nullptr.
+// Since this is template-heavy code, we prefer to disable these warnings
+// locally instead of adding yet another overload of this function.
+#if !defined(__clang__) && defined(__GNUC__)
+#pragma GCC diagnostic ignored "-Wpragmas"
+#pragma GCC diagnostic ignored "-Waddress"
+#pragma GCC diagnostic ignored "-Wnonnull-compare"
+#pragma GCC diagnostic push
+#endif
+    if (static_cast<RemoveCVRef<QualDecayedTRef>>(f) == nullptr) {
+#if !defined(__clang__) && defined(__GNUC__)
+#pragma GCC diagnostic pop
+#endif
+      manager_ = EmptyManager;
+      invoker_ = nullptr;
+      return;
+    }
+    InitializeStorage<QualDecayedTRef>(std::forward<F>(f));
+  }
+
+  template <int target_type, class QualDecayedTRef, class F,
+            absl::enable_if_t<target_type == 1, int> = 0>
+  void Initialize(F&& f) {
+    // In this case we can "steal the guts" of the other AnyInvocable.
+    f.manager_(FunctionToCall::relocate_from_to, &f.state_, &state_);
+    manager_ = f.manager_;
+    invoker_ = f.invoker_;
+
+    f.manager_ = EmptyManager;
+    f.invoker_ = nullptr;
+  }
+
+  template <int target_type, class QualDecayedTRef, class F,
+            absl::enable_if_t<target_type == 2, int> = 0>
+  void Initialize(F&& f) {
+    if (f.HasValue()) {
+      InitializeStorage<QualDecayedTRef>(std::forward<F>(f));
+    } else {
+      manager_ = EmptyManager;
+      invoker_ = nullptr;
+    }
+  }
+
+  template <int target_type, class QualDecayedTRef, class F,
+            typename = absl::enable_if_t<target_type == 3>>
+  void Initialize(F&& f) {
+    InitializeStorage<QualDecayedTRef>(std::forward<F>(f));
+  }
+
+  // Use local (inline) storage for applicable target object types.
+  template <class QualTRef, class... Args,
+            typename = absl::enable_if_t<
+                IsStoredLocally<RemoveCVRef<QualTRef>>::value>>
+  void InitializeStorage(Args&&... args) {
+    using RawT = RemoveCVRef<QualTRef>;
+    ::new (static_cast<void*>(&state_.storage))
+        RawT(std::forward<Args>(args)...);
+
+    invoker_ = LocalInvoker<SigIsNoexcept, ReturnType, QualTRef, P...>;
+    // We can simplify our manager if we know the type is trivially copyable.
+    InitializeLocalManager<RawT>();
+  }
+
+  // Use remote storage for target objects that cannot be stored locally.
+  template <class QualTRef, class... Args,
+            absl::enable_if_t<!IsStoredLocally<RemoveCVRef<QualTRef>>::value,
+                              int> = 0>
+  void InitializeStorage(Args&&... args) {
+    InitializeRemoteManager<RemoveCVRef<QualTRef>>(std::forward<Args>(args)...);
+    // This is set after everything else in case an exception is thrown in an
+    // earlier step of the initialization.
+    invoker_ = RemoteInvoker<SigIsNoexcept, ReturnType, QualTRef, P...>;
+  }
+
+  template <class T,
+            typename = absl::enable_if_t<std::is_trivially_copyable<T>::value>>
+  void InitializeLocalManager() {
+    manager_ = LocalManagerTrivial;
+  }
+
+  template <class T,
+            absl::enable_if_t<!std::is_trivially_copyable<T>::value, int> = 0>
+  void InitializeLocalManager() {
+    manager_ = LocalManagerNontrivial<T>;
+  }
+
+  template <class T>
+  using HasTrivialRemoteStorage =
+      std::integral_constant<bool, std::is_trivially_destructible<T>::value &&
+                                       alignof(T) <=
+                                           ABSL_INTERNAL_DEFAULT_NEW_ALIGNMENT>;
+
+  template <class T, class... Args,
+            typename = absl::enable_if_t<HasTrivialRemoteStorage<T>::value>>
+  void InitializeRemoteManager(Args&&... args) {
+    // unique_ptr is used for exception-safety in case construction throws.
+    std::unique_ptr<void, TrivialDeleter> uninitialized_target(
+        ::operator new(sizeof(T)), TrivialDeleter(sizeof(T)));
+    ::new (uninitialized_target.get()) T(std::forward<Args>(args)...);
+    state_.remote.target = uninitialized_target.release();
+    state_.remote.size = sizeof(T);
+    manager_ = RemoteManagerTrivial;
+  }
+
+  template <class T, class... Args,
+            absl::enable_if_t<!HasTrivialRemoteStorage<T>::value, int> = 0>
+  void InitializeRemoteManager(Args&&... args) {
+    state_.remote.target = ::new T(std::forward<Args>(args)...);
+    manager_ = RemoteManagerNontrivial<T>;
+  }
+
+  //////////////////////////////////////////////////////////////////////////////
+  //
+  // Type trait to determine if the template argument is an AnyInvocable whose
+  // function type is compatible enough with ours such that we can
+  // "move the guts" out of it when moving, rather than having to place a new
+  // object into remote storage.
+
+  template <typename Other>
+  struct IsCompatibleAnyInvocable {
+    static constexpr bool value = false;
+  };
+
+  template <typename Sig>
+  struct IsCompatibleAnyInvocable<AnyInvocable<Sig>> {
+    static constexpr bool value =
+        (IsCompatibleConversion)(static_cast<
+                                     typename AnyInvocable<Sig>::CoreImpl*>(
+                                     nullptr),
+                                 static_cast<CoreImpl*>(nullptr));
+  };
+
+  //
+  //////////////////////////////////////////////////////////////////////////////
+
+  TypeErasedState state_;
+  ManagerType* manager_;
+  InvokerType<SigIsNoexcept, ReturnType, P...>* invoker_;
+};
+
+// A constructor name-tag used with Impl to request the
+// conversion-constructor
+struct ConversionConstruct {};
+
+////////////////////////////////////////////////////////////////////////////////
+//
+// A metafunction that is normally an identity metafunction except that when
+// given a std::reference_wrapper<T>, it yields T&. This is necessary because
+// currently std::reference_wrapper's operator() is not conditionally noexcept,
+// so when checking if such an Invocable is nothrow-invocable, we must pull out
+// the underlying type.
+template <class T>
+struct UnwrapStdReferenceWrapperImpl {
+  using type = T;
+};
+
+template <class T>
+struct UnwrapStdReferenceWrapperImpl<std::reference_wrapper<T>> {
+  using type = T&;
+};
+
+template <class T>
+using UnwrapStdReferenceWrapper =
+    typename UnwrapStdReferenceWrapperImpl<T>::type;
+//
+////////////////////////////////////////////////////////////////////////////////
+
+// An alias that always yields std::true_type (used with constraints) where
+// substitution failures happen when forming the template arguments.
+template <class... T>
+using TrueAlias =
+    std::integral_constant<bool, sizeof(absl::void_t<T...>*) != 0>;
+
+/*SFINAE constraints for the conversion-constructor.*/
+template <class Sig, class F,
+          class = absl::enable_if_t<
+              !std::is_same<RemoveCVRef<F>, AnyInvocable<Sig>>::value>>
+using CanConvert = TrueAlias<
+    absl::enable_if_t<!IsInPlaceType<RemoveCVRef<F>>::value>,
+    absl::enable_if_t<Impl<Sig>::template CallIsValid<F>::value>,
+    absl::enable_if_t<
+        Impl<Sig>::template CallIsNoexceptIfSigIsNoexcept<F>::value>,
+    absl::enable_if_t<std::is_constructible<absl::decay_t<F>, F>::value>>;
+
+/*SFINAE constraints for the std::in_place constructors.*/
+template <class Sig, class F, class... Args>
+using CanEmplace = TrueAlias<
+    absl::enable_if_t<Impl<Sig>::template CallIsValid<F>::value>,
+    absl::enable_if_t<
+        Impl<Sig>::template CallIsNoexceptIfSigIsNoexcept<F>::value>,
+    absl::enable_if_t<std::is_constructible<absl::decay_t<F>, Args...>::value>>;
+
+/*SFINAE constraints for the conversion-assign operator.*/
+template <class Sig, class F,
+          class = absl::enable_if_t<
+              !std::is_same<RemoveCVRef<F>, AnyInvocable<Sig>>::value>>
+using CanAssign = TrueAlias<
+    absl::enable_if_t<Impl<Sig>::template CallIsValid<F>::value>,
+    absl::enable_if_t<
+        Impl<Sig>::template CallIsNoexceptIfSigIsNoexcept<F>::value>,
+    absl::enable_if_t<std::is_constructible<absl::decay_t<F>, F>::value>>;
+
+/*SFINAE constraints for the reference-wrapper conversion-assign operator.*/
+template <class Sig, class F>
+using CanAssignReferenceWrapper = TrueAlias<
+    absl::enable_if_t<
+        Impl<Sig>::template CallIsValid<std::reference_wrapper<F>>::value>,
+    absl::enable_if_t<Impl<Sig>::template CallIsNoexceptIfSigIsNoexcept<
+        std::reference_wrapper<F>>::value>>;
+
+////////////////////////////////////////////////////////////////////////////////
+//
+// The constraint for checking whether or not a call meets the noexcept
+// callability requirements. This is a preprocessor macro because specifying it
+// this way as opposed to a disjunction/branch can improve the user-side error
+// messages and avoids an instantiation of std::is_nothrow_invocable_r in the
+// cases where the user did not specify a noexcept function type.
+//
+#define ABSL_INTERNAL_ANY_INVOCABLE_NOEXCEPT_CONSTRAINT(inv_quals, noex) \
+  ABSL_INTERNAL_ANY_INVOCABLE_NOEXCEPT_CONSTRAINT_##noex(inv_quals)
+
+// The disjunction below is because we can't rely on std::is_nothrow_invocable_r
+// to give the right result when ReturnType is non-moveable in toolchains that
+// don't treat non-moveable result types correctly. For example this was the
+// case in libc++ before commit c3a24882 (2022-05).
+#define ABSL_INTERNAL_ANY_INVOCABLE_NOEXCEPT_CONSTRAINT_true(inv_quals)      \
+  absl::enable_if_t<absl::disjunction<                                       \
+      std::is_nothrow_invocable_r<                                           \
+          ReturnType, UnwrapStdReferenceWrapper<absl::decay_t<F>> inv_quals, \
+          P...>,                                                             \
+      std::conjunction<                                                      \
+          std::is_nothrow_invocable<                                         \
+              UnwrapStdReferenceWrapper<absl::decay_t<F>> inv_quals, P...>,  \
+          std::is_same<                                                      \
+              ReturnType,                                                    \
+              absl::base_internal::invoke_result_t<                          \
+                  UnwrapStdReferenceWrapper<absl::decay_t<F>> inv_quals,     \
+                  P...>>>>::value>
+
+#define ABSL_INTERNAL_ANY_INVOCABLE_NOEXCEPT_CONSTRAINT_false(inv_quals)
+//
+////////////////////////////////////////////////////////////////////////////////
+
+// A macro to generate partial specializations of Impl with the different
+// combinations of supported cv/reference qualifiers and noexcept specifier.
+//
+// Here, `cv` are the cv-qualifiers if any, `ref` is the ref-qualifier if any,
+// inv_quals is the reference type to be used when invoking the target, and
+// noex is "true" if the function type is noexcept, or false if it is not.
+//
+// The CallIsValid condition is more complicated than simply using
+// absl::base_internal::is_invocable_r because we can't rely on it to give the
+// right result when ReturnType is non-moveable in toolchains that don't treat
+// non-moveable result types correctly. For example this was the case in libc++
+// before commit c3a24882 (2022-05).
+#define ABSL_INTERNAL_ANY_INVOCABLE_IMPL_(cv, ref, inv_quals, noex)            \
+  template <class ReturnType, class... P>                                      \
+  class Impl<ReturnType(P...) cv ref ABSL_INTERNAL_NOEXCEPT_SPEC(noex)>        \
+      : public CoreImpl<noex, ReturnType, P...> {                              \
+   public:                                                                     \
+    /*The base class, which contains the datamembers and core operations*/     \
+    using Core = CoreImpl<noex, ReturnType, P...>;                             \
+                                                                               \
+    /*SFINAE constraint to check if F is invocable with the proper signature*/ \
+    template <class F>                                                         \
+    using CallIsValid = TrueAlias<absl::enable_if_t<absl::disjunction<         \
+        absl::base_internal::is_invocable_r<ReturnType,                        \
+                                            absl::decay_t<F> inv_quals, P...>, \
+        std::is_same<ReturnType,                                               \
+                     absl::base_internal::invoke_result_t<                     \
+                         absl::decay_t<F> inv_quals, P...>>>::value>>;         \
+                                                                               \
+    /*SFINAE constraint to check if F is nothrow-invocable when necessary*/    \
+    template <class F>                                                         \
+    using CallIsNoexceptIfSigIsNoexcept =                                      \
+        TrueAlias<ABSL_INTERNAL_ANY_INVOCABLE_NOEXCEPT_CONSTRAINT(inv_quals,   \
+                                                                  noex)>;      \
+                                                                               \
+    /*Put the AnyInvocable into an empty state.*/                              \
+    Impl() = default;                                                          \
+                                                                               \
+    /*The implementation of a conversion-constructor from "f*/                 \
+    /*This forwards to Core, attaching inv_quals so that the base class*/      \
+    /*knows how to properly type-erase the invocation.*/                       \
+    template <class F>                                                         \
+    explicit Impl(ConversionConstruct, F&& f)                                  \
+        : Core(TypedConversionConstruct<                                       \
+                   typename std::decay<F>::type inv_quals>(),                  \
+               std::forward<F>(f)) {}                                          \
+                                                                               \
+    /*Forward along the in-place construction parameters.*/                    \
+    template <class T, class... Args>                                          \
+    explicit Impl(absl::in_place_type_t<T>, Args&&... args)                    \
+        : Core(absl::in_place_type<absl::decay_t<T> inv_quals>,                \
+               std::forward<Args>(args)...) {}                                 \
+                                                                               \
+    InvokerType<noex, ReturnType, P...>* ExtractInvoker() cv {                 \
+      using QualifiedTestType = int cv ref;                                    \
+      auto* invoker = this->invoker_;                                          \
+      if (!std::is_const<QualifiedTestType>::value &&                          \
+          std::is_rvalue_reference<QualifiedTestType>::value) {                \
+        ABSL_HARDENING_ASSERT([this]() {                                       \
+          /* We checked that this isn't const above, so const_cast is safe */  \
+          const_cast<Impl*>(this)->invoker_ =                                  \
+              [](TypeErasedState*,                                             \
+                 ForwardedParameterType<P>...) noexcept(noex) -> ReturnType {  \
+            ABSL_HARDENING_ASSERT(false && "AnyInvocable use-after-move");     \
+            std::terminate();                                                  \
+          };                                                                   \
+          return this->HasValue();                                             \
+        }());                                                                  \
+      }                                                                        \
+      return invoker;                                                          \
+    }                                                                          \
+                                                                               \
+    /*The actual invocation operation with the proper signature*/              \
+    ReturnType operator()(P... args) cv ref noexcept(noex) {                   \
+      assert(this->invoker_ != nullptr);                                       \
+      return this->ExtractInvoker()(                                           \
+          const_cast<TypeErasedState*>(&this->state_),                         \
+          static_cast<ForwardedParameterType<P>>(args)...);                    \
+    }                                                                          \
+  }
+
+// Define the `noexcept(true)` specialization only for C++17 and beyond, when
+// `noexcept` is part of the type system.
+#if ABSL_INTERNAL_CPLUSPLUS_LANG >= 201703L
+// A convenience macro that defines specializations for the noexcept(true) and
+// noexcept(false) forms, given the other properties.
+#define ABSL_INTERNAL_ANY_INVOCABLE_IMPL(cv, ref, inv_quals)    \
+  ABSL_INTERNAL_ANY_INVOCABLE_IMPL_(cv, ref, inv_quals, false); \
+  ABSL_INTERNAL_ANY_INVOCABLE_IMPL_(cv, ref, inv_quals, true)
+#else
+#define ABSL_INTERNAL_ANY_INVOCABLE_IMPL(cv, ref, inv_quals) \
+  ABSL_INTERNAL_ANY_INVOCABLE_IMPL_(cv, ref, inv_quals, false)
+#endif
+
+// Non-ref-qualified partial specializations
+ABSL_INTERNAL_ANY_INVOCABLE_IMPL(, , &);
+ABSL_INTERNAL_ANY_INVOCABLE_IMPL(const, , const&);
+
+// Lvalue-ref-qualified partial specializations
+ABSL_INTERNAL_ANY_INVOCABLE_IMPL(, &, &);
+ABSL_INTERNAL_ANY_INVOCABLE_IMPL(const, &, const&);
+
+// Rvalue-ref-qualified partial specializations
+ABSL_INTERNAL_ANY_INVOCABLE_IMPL(, &&, &&);
+ABSL_INTERNAL_ANY_INVOCABLE_IMPL(const, &&, const&&);
+
+// Undef the detail-only macros.
+#undef ABSL_INTERNAL_ANY_INVOCABLE_IMPL
+#undef ABSL_INTERNAL_ANY_INVOCABLE_IMPL_
+#undef ABSL_INTERNAL_ANY_INVOCABLE_NOEXCEPT_CONSTRAINT_false
+#undef ABSL_INTERNAL_ANY_INVOCABLE_NOEXCEPT_CONSTRAINT_true
+#undef ABSL_INTERNAL_ANY_INVOCABLE_NOEXCEPT_CONSTRAINT
+#undef ABSL_INTERNAL_NOEXCEPT_SPEC
+
+}  // namespace internal_any_invocable
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FUNCTIONAL_INTERNAL_ANY_INVOCABLE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/internal/front_binder.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/internal/front_binder.h
new file mode 100644
index 0000000000000000000000000000000000000000..45f52de73d25ef73c521a97c265804956051573d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/internal/front_binder.h
@@ -0,0 +1,95 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// Implementation details for `absl::bind_front()`.
+
+#ifndef ABSL_FUNCTIONAL_INTERNAL_FRONT_BINDER_H_
+#define ABSL_FUNCTIONAL_INTERNAL_FRONT_BINDER_H_
+
+#include <cstddef>
+#include <type_traits>
+#include <utility>
+
+#include "absl/base/internal/invoke.h"
+#include "absl/container/internal/compressed_tuple.h"
+#include "absl/meta/type_traits.h"
+#include "absl/utility/utility.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace functional_internal {
+
+// Invoke the method, expanding the tuple of bound arguments.
+template <class R, class Tuple, size_t... Idx, class... Args>
+R Apply(Tuple&& bound, absl::index_sequence<Idx...>, Args&&... free) {
+  return base_internal::invoke(
+      absl::forward<Tuple>(bound).template get<Idx>()...,
+      absl::forward<Args>(free)...);
+}
+
+template <class F, class... BoundArgs>
+class FrontBinder {
+  using BoundArgsT = absl::container_internal::CompressedTuple<F, BoundArgs...>;
+  using Idx = absl::make_index_sequence<sizeof...(BoundArgs) + 1>;
+
+  BoundArgsT bound_args_;
+
+ public:
+  template <class... Ts>
+  constexpr explicit FrontBinder(absl::in_place_t, Ts&&... ts)
+      : bound_args_(absl::forward<Ts>(ts)...) {}
+
+  template <class... FreeArgs, class R = base_internal::invoke_result_t<
+                                   F&, BoundArgs&..., FreeArgs&&...>>
+  R operator()(FreeArgs&&... free_args) & {
+    return functional_internal::Apply<R>(bound_args_, Idx(),
+                                         absl::forward<FreeArgs>(free_args)...);
+  }
+
+  template <class... FreeArgs,
+            class R = base_internal::invoke_result_t<
+                const F&, const BoundArgs&..., FreeArgs&&...>>
+  R operator()(FreeArgs&&... free_args) const& {
+    return functional_internal::Apply<R>(bound_args_, Idx(),
+                                         absl::forward<FreeArgs>(free_args)...);
+  }
+
+  template <class... FreeArgs, class R = base_internal::invoke_result_t<
+                                   F&&, BoundArgs&&..., FreeArgs&&...>>
+  R operator()(FreeArgs&&... free_args) && {
+    // This overload is called when *this is an rvalue. If some of the bound
+    // arguments are stored by value or rvalue reference, we move them.
+    return functional_internal::Apply<R>(absl::move(bound_args_), Idx(),
+                                         absl::forward<FreeArgs>(free_args)...);
+  }
+
+  template <class... FreeArgs,
+            class R = base_internal::invoke_result_t<
+                const F&&, const BoundArgs&&..., FreeArgs&&...>>
+  R operator()(FreeArgs&&... free_args) const&& {
+    // This overload is called when *this is an rvalue. If some of the bound
+    // arguments are stored by value or rvalue reference, we move them.
+    return functional_internal::Apply<R>(absl::move(bound_args_), Idx(),
+                                         absl::forward<FreeArgs>(free_args)...);
+  }
+};
+
+template <class F, class... BoundArgs>
+using bind_front_t = FrontBinder<decay_t<F>, absl::decay_t<BoundArgs>...>;
+
+}  // namespace functional_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FUNCTIONAL_INTERNAL_FRONT_BINDER_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/internal/function_ref.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/internal/function_ref.h
new file mode 100644
index 0000000000000000000000000000000000000000..b5bb8b430a814ecb6788ba3660f92d92dc934fc2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/functional/internal/function_ref.h
@@ -0,0 +1,106 @@
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_FUNCTIONAL_INTERNAL_FUNCTION_REF_H_
+#define ABSL_FUNCTIONAL_INTERNAL_FUNCTION_REF_H_
+
+#include <cassert>
+#include <functional>
+#include <type_traits>
+
+#include "absl/base/internal/invoke.h"
+#include "absl/meta/type_traits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace functional_internal {
+
+// Like a void* that can handle function pointers as well. The standard does not
+// allow function pointers to round-trip through void*, but void(*)() is fine.
+//
+// Note: It's important that this class remains trivial and is the same size as
+// a pointer, since this allows the compiler to perform tail-call optimizations
+// when the underlying function is a callable object with a matching signature.
+union VoidPtr {
+  const void* obj;
+  void (*fun)();
+};
+
+// Chooses the best type for passing T as an argument.
+// Attempt to be close to SystemV AMD64 ABI. Objects with trivial copy ctor are
+// passed by value.
+template <typename T>
+constexpr bool PassByValue() {
+  return !std::is_lvalue_reference<T>::value &&
+         absl::is_trivially_copy_constructible<T>::value &&
+         absl::is_trivially_copy_assignable<
+             typename std::remove_cv<T>::type>::value &&
+         std::is_trivially_destructible<T>::value &&
+         sizeof(T) <= 2 * sizeof(void*);
+}
+
+template <typename T>
+struct ForwardT : std::conditional<PassByValue<T>(), T, T&&> {};
+
+// An Invoker takes a pointer to the type-erased invokable object, followed by
+// the arguments that the invokable object expects.
+//
+// Note: The order of arguments here is an optimization, since member functions
+// have an implicit "this" pointer as their first argument, putting VoidPtr
+// first allows the compiler to perform tail-call optimization in many cases.
+template <typename R, typename... Args>
+using Invoker = R (*)(VoidPtr, typename ForwardT<Args>::type...);
+
+//
+// InvokeObject and InvokeFunction provide static "Invoke" functions that can be
+// used as Invokers for objects or functions respectively.
+//
+// static_cast<R> handles the case the return type is void.
+template <typename Obj, typename R, typename... Args>
+R InvokeObject(VoidPtr ptr, typename ForwardT<Args>::type... args) {
+  auto o = static_cast<const Obj*>(ptr.obj);
+  return static_cast<R>(
+      absl::base_internal::invoke(*o, std::forward<Args>(args)...));
+}
+
+template <typename Fun, typename R, typename... Args>
+R InvokeFunction(VoidPtr ptr, typename ForwardT<Args>::type... args) {
+  auto f = reinterpret_cast<Fun>(ptr.fun);
+  return static_cast<R>(
+      absl::base_internal::invoke(f, std::forward<Args>(args)...));
+}
+
+template <typename Sig>
+void AssertNonNull(const std::function<Sig>& f) {
+  assert(f != nullptr);
+  (void)f;
+}
+
+template <typename F>
+void AssertNonNull(const F&) {}
+
+template <typename F, typename C>
+void AssertNonNull(F C::*f) {
+  assert(f != nullptr);
+  (void)f;
+}
+
+template <bool C>
+using EnableIf = typename ::std::enable_if<C, int>::type;
+
+}  // namespace functional_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_FUNCTIONAL_INTERNAL_FUNCTION_REF_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/hash/hash.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/hash/hash.h
new file mode 100644
index 0000000000000000000000000000000000000000..74e2d7c0534c00f2f5b5b585caf74a7ff70515cd
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/hash/hash.h
@@ -0,0 +1,421 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: hash.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines the Abseil `hash` library and the Abseil hashing
+// framework. This framework consists of the following:
+//
+//   * The `absl::Hash` functor, which is used to invoke the hasher within the
+//     Abseil hashing framework. `absl::Hash<T>` supports most basic types and
+//     a number of Abseil types out of the box.
+//   * `AbslHashValue`, an extension point that allows you to extend types to
+//     support Abseil hashing without requiring you to define a hashing
+//     algorithm.
+//   * `HashState`, a type-erased class which implements the manipulation of the
+//     hash state (H) itself; contains member functions `combine()`,
+//     `combine_contiguous()`, and `combine_unordered()`; and which you can use
+//     to contribute to an existing hash state when hashing your types.
+//
+// Unlike `std::hash` or other hashing frameworks, the Abseil hashing framework
+// provides most of its utility by abstracting away the hash algorithm (and its
+// implementation) entirely. Instead, a type invokes the Abseil hashing
+// framework by simply combining its state with the state of known, hashable
+// types. Hashing of that combined state is separately done by `absl::Hash`.
+//
+// One should assume that a hash algorithm is chosen randomly at the start of
+// each process.  E.g., `absl::Hash<int>{}(9)` in one process and
+// `absl::Hash<int>{}(9)` in another process are likely to differ.
+//
+// `absl::Hash` may also produce different values from different dynamically
+// loaded libraries. For this reason, `absl::Hash` values must never cross
+// boundries in dynamically loaded libraries (including when used in types like
+// hash containers.)
+//
+// `absl::Hash` is intended to strongly mix input bits with a target of passing
+// an [Avalanche Test](https://en.wikipedia.org/wiki/Avalanche_effect).
+//
+// Example:
+//
+//   // Suppose we have a class `Circle` for which we want to add hashing:
+//   class Circle {
+//    public:
+//     ...
+//    private:
+//     std::pair<int, int> center_;
+//     int radius_;
+//   };
+//
+//   // To add hashing support to `Circle`, we simply need to add a free
+//   // (non-member) function `AbslHashValue()`, and return the combined hash
+//   // state of the existing hash state and the class state. You can add such a
+//   // free function using a friend declaration within the body of the class:
+//   class Circle {
+//    public:
+//     ...
+//     template <typename H>
+//     friend H AbslHashValue(H h, const Circle& c) {
+//       return H::combine(std::move(h), c.center_, c.radius_);
+//     }
+//     ...
+//   };
+//
+// For more information, see Adding Type Support to `absl::Hash` below.
+//
+#ifndef ABSL_HASH_HASH_H_
+#define ABSL_HASH_HASH_H_
+
+#include <tuple>
+#include <utility>
+
+#include "absl/functional/function_ref.h"
+#include "absl/hash/internal/hash.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// -----------------------------------------------------------------------------
+// `absl::Hash`
+// -----------------------------------------------------------------------------
+//
+// `absl::Hash<T>` is a convenient general-purpose hash functor for any type `T`
+// satisfying any of the following conditions (in order):
+//
+//  * T is an arithmetic or pointer type
+//  * T defines an overload for `AbslHashValue(H, const T&)` for an arbitrary
+//    hash state `H`.
+//  - T defines a specialization of `std::hash<T>`
+//
+// `absl::Hash` intrinsically supports the following types:
+//
+//   * All integral types (including bool)
+//   * All enum types
+//   * All floating-point types (although hashing them is discouraged)
+//   * All pointer types, including nullptr_t
+//   * std::pair<T1, T2>, if T1 and T2 are hashable
+//   * std::tuple<Ts...>, if all the Ts... are hashable
+//   * std::unique_ptr and std::shared_ptr
+//   * All string-like types including:
+//     * absl::Cord
+//     * std::string
+//     * std::string_view (as well as any instance of std::basic_string that
+//       uses char and std::char_traits)
+//  * All the standard sequence containers (provided the elements are hashable)
+//  * All the standard associative containers (provided the elements are
+//    hashable)
+//  * absl types such as the following:
+//    * absl::string_view
+//    * absl::uint128
+//    * absl::Time, absl::Duration, and absl::TimeZone
+//  * absl containers (provided the elements are hashable) such as the
+//    following:
+//    * absl::flat_hash_set, absl::node_hash_set, absl::btree_set
+//    * absl::flat_hash_map, absl::node_hash_map, absl::btree_map
+//    * absl::btree_multiset, absl::btree_multimap
+//    * absl::InlinedVector
+//    * absl::FixedArray
+//
+// When absl::Hash is used to hash an unordered container with a custom hash
+// functor, the elements are hashed using default absl::Hash semantics, not
+// the custom hash functor.  This is consistent with the behavior of
+// operator==() on unordered containers, which compares elements pairwise with
+// operator==() rather than the custom equality functor.  It is usually a
+// mistake to use either operator==() or absl::Hash on unordered collections
+// that use functors incompatible with operator==() equality.
+//
+// Note: the list above is not meant to be exhaustive. Additional type support
+// may be added, in which case the above list will be updated.
+//
+// -----------------------------------------------------------------------------
+// absl::Hash Invocation Evaluation
+// -----------------------------------------------------------------------------
+//
+// When invoked, `absl::Hash<T>` searches for supplied hash functions in the
+// following order:
+//
+//   * Natively supported types out of the box (see above)
+//   * Types for which an `AbslHashValue()` overload is provided (such as
+//     user-defined types). See "Adding Type Support to `absl::Hash`" below.
+//   * Types which define a `std::hash<T>` specialization
+//
+// The fallback to legacy hash functions exists mainly for backwards
+// compatibility. If you have a choice, prefer defining an `AbslHashValue`
+// overload instead of specializing any legacy hash functors.
+//
+// -----------------------------------------------------------------------------
+// The Hash State Concept, and using `HashState` for Type Erasure
+// -----------------------------------------------------------------------------
+//
+// The `absl::Hash` framework relies on the Concept of a "hash state." Such a
+// hash state is used in several places:
+//
+// * Within existing implementations of `absl::Hash<T>` to store the hashed
+//   state of an object. Note that it is up to the implementation how it stores
+//   such state. A hash table, for example, may mix the state to produce an
+//   integer value; a testing framework may simply hold a vector of that state.
+// * Within implementations of `AbslHashValue()` used to extend user-defined
+//   types. (See "Adding Type Support to absl::Hash" below.)
+// * Inside a `HashState`, providing type erasure for the concept of a hash
+//   state, which you can use to extend the `absl::Hash` framework for types
+//   that are otherwise difficult to extend using `AbslHashValue()`. (See the
+//   `HashState` class below.)
+//
+// The "hash state" concept contains three member functions for mixing hash
+// state:
+//
+// * `H::combine(state, values...)`
+//
+//   Combines an arbitrary number of values into a hash state, returning the
+//   updated state. Note that the existing hash state is move-only and must be
+//   passed by value.
+//
+//   Each of the value types T must be hashable by H.
+//
+//   NOTE:
+//
+//     state = H::combine(std::move(state), value1, value2, value3);
+//
+//   must be guaranteed to produce the same hash expansion as
+//
+//     state = H::combine(std::move(state), value1);
+//     state = H::combine(std::move(state), value2);
+//     state = H::combine(std::move(state), value3);
+//
+// * `H::combine_contiguous(state, data, size)`
+//
+//    Combines a contiguous array of `size` elements into a hash state,
+//    returning the updated state. Note that the existing hash state is
+//    move-only and must be passed by value.
+//
+//    NOTE:
+//
+//      state = H::combine_contiguous(std::move(state), data, size);
+//
+//    need NOT be guaranteed to produce the same hash expansion as a loop
+//    (it may perform internal optimizations). If you need this guarantee, use a
+//    loop instead.
+//
+// * `H::combine_unordered(state, begin, end)`
+//
+//    Combines a set of elements denoted by an iterator pair into a hash
+//    state, returning the updated state.  Note that the existing hash
+//    state is move-only and must be passed by value.
+//
+//    Unlike the other two methods, the hashing is order-independent.
+//    This can be used to hash unordered collections.
+//
+// -----------------------------------------------------------------------------
+// Adding Type Support to `absl::Hash`
+// -----------------------------------------------------------------------------
+//
+// To add support for your user-defined type, add a proper `AbslHashValue()`
+// overload as a free (non-member) function. The overload will take an
+// existing hash state and should combine that state with state from the type.
+//
+// Example:
+//
+//   template <typename H>
+//   H AbslHashValue(H state, const MyType& v) {
+//     return H::combine(std::move(state), v.field1, ..., v.fieldN);
+//   }
+//
+// where `(field1, ..., fieldN)` are the members you would use on your
+// `operator==` to define equality.
+//
+// Notice that `AbslHashValue` is not a class member, but an ordinary function.
+// An `AbslHashValue` overload for a type should only be declared in the same
+// file and namespace as said type. The proper `AbslHashValue` implementation
+// for a given type will be discovered via ADL.
+//
+// Note: unlike `std::hash', `absl::Hash` should never be specialized. It must
+// only be extended by adding `AbslHashValue()` overloads.
+//
+template <typename T>
+using Hash = absl::hash_internal::Hash<T>;
+
+// HashOf
+//
+// absl::HashOf() is a helper that generates a hash from the values of its
+// arguments.  It dispatches to absl::Hash directly, as follows:
+//  * HashOf(t) == absl::Hash<T>{}(t)
+//  * HashOf(a, b, c) == HashOf(std::make_tuple(a, b, c))
+//
+// HashOf(a1, a2, ...) == HashOf(b1, b2, ...) is guaranteed when
+//  * The argument lists have pairwise identical C++ types
+//  * a1 == b1 && a2 == b2 && ...
+//
+// The requirement that the arguments match in both type and value is critical.
+// It means that `a == b` does not necessarily imply `HashOf(a) == HashOf(b)` if
+// `a` and `b` have different types. For example, `HashOf(2) != HashOf(2.0)`.
+template <int&... ExplicitArgumentBarrier, typename... Types>
+size_t HashOf(const Types&... values) {
+  auto tuple = std::tie(values...);
+  return absl::Hash<decltype(tuple)>{}(tuple);
+}
+
+// HashState
+//
+// A type erased version of the hash state concept, for use in user-defined
+// `AbslHashValue` implementations that can't use templates (such as PImpl
+// classes, virtual functions, etc.). The type erasure adds overhead so it
+// should be avoided unless necessary.
+//
+// Note: This wrapper will only erase calls to
+//     combine_contiguous(H, const unsigned char*, size_t)
+//     RunCombineUnordered(H, CombinerF)
+//
+// All other calls will be handled internally and will not invoke overloads
+// provided by the wrapped class.
+//
+// Users of this class should still define a template `AbslHashValue` function,
+// but can use `absl::HashState::Create(&state)` to erase the type of the hash
+// state and dispatch to their private hashing logic.
+//
+// This state can be used like any other hash state. In particular, you can call
+// `HashState::combine()` and `HashState::combine_contiguous()` on it.
+//
+// Example:
+//
+//   class Interface {
+//    public:
+//     template <typename H>
+//     friend H AbslHashValue(H state, const Interface& value) {
+//       state = H::combine(std::move(state), std::type_index(typeid(*this)));
+//       value.HashValue(absl::HashState::Create(&state));
+//       return state;
+//     }
+//    private:
+//     virtual void HashValue(absl::HashState state) const = 0;
+//   };
+//
+//   class Impl : Interface {
+//    private:
+//     void HashValue(absl::HashState state) const override {
+//       absl::HashState::combine(std::move(state), v1_, v2_);
+//     }
+//     int v1_;
+//     std::string v2_;
+//   };
+class HashState : public hash_internal::HashStateBase<HashState> {
+ public:
+  // HashState::Create()
+  //
+  // Create a new `HashState` instance that wraps `state`. All calls to
+  // `combine()` and `combine_contiguous()` on the new instance will be
+  // redirected to the original `state` object. The `state` object must outlive
+  // the `HashState` instance.
+  template <typename T>
+  static HashState Create(T* state) {
+    HashState s;
+    s.Init(state);
+    return s;
+  }
+
+  HashState(const HashState&) = delete;
+  HashState& operator=(const HashState&) = delete;
+  HashState(HashState&&) = default;
+  HashState& operator=(HashState&&) = default;
+
+  // HashState::combine()
+  //
+  // Combines an arbitrary number of values into a hash state, returning the
+  // updated state.
+  using HashState::HashStateBase::combine;
+
+  // HashState::combine_contiguous()
+  //
+  // Combines a contiguous array of `size` elements into a hash state, returning
+  // the updated state.
+  static HashState combine_contiguous(HashState hash_state,
+                                      const unsigned char* first, size_t size) {
+    hash_state.combine_contiguous_(hash_state.state_, first, size);
+    return hash_state;
+  }
+  using HashState::HashStateBase::combine_contiguous;
+
+ private:
+  HashState() = default;
+
+  friend class HashState::HashStateBase;
+
+  template <typename T>
+  static void CombineContiguousImpl(void* p, const unsigned char* first,
+                                    size_t size) {
+    T& state = *static_cast<T*>(p);
+    state = T::combine_contiguous(std::move(state), first, size);
+  }
+
+  template <typename T>
+  void Init(T* state) {
+    state_ = state;
+    combine_contiguous_ = &CombineContiguousImpl<T>;
+    run_combine_unordered_ = &RunCombineUnorderedImpl<T>;
+  }
+
+  template <typename HS>
+  struct CombineUnorderedInvoker {
+    template <typename T, typename ConsumerT>
+    void operator()(T inner_state, ConsumerT inner_cb) {
+      f(HashState::Create(&inner_state),
+        [&](HashState& inner_erased) { inner_cb(inner_erased.Real<T>()); });
+    }
+
+    absl::FunctionRef<void(HS, absl::FunctionRef<void(HS&)>)> f;
+  };
+
+  template <typename T>
+  static HashState RunCombineUnorderedImpl(
+      HashState state,
+      absl::FunctionRef<void(HashState, absl::FunctionRef<void(HashState&)>)>
+          f) {
+    // Note that this implementation assumes that inner_state and outer_state
+    // are the same type.  This isn't true in the SpyHash case, but SpyHash
+    // types are move-convertible to each other, so this still works.
+    T& real_state = state.Real<T>();
+    real_state = T::RunCombineUnordered(
+        std::move(real_state), CombineUnorderedInvoker<HashState>{f});
+    return state;
+  }
+
+  template <typename CombinerT>
+  static HashState RunCombineUnordered(HashState state, CombinerT combiner) {
+    auto* run = state.run_combine_unordered_;
+    return run(std::move(state), std::ref(combiner));
+  }
+
+  // Do not erase an already erased state.
+  void Init(HashState* state) {
+    state_ = state->state_;
+    combine_contiguous_ = state->combine_contiguous_;
+    run_combine_unordered_ = state->run_combine_unordered_;
+  }
+
+  template <typename T>
+  T& Real() {
+    return *static_cast<T*>(state_);
+  }
+
+  void* state_;
+  void (*combine_contiguous_)(void*, const unsigned char*, size_t);
+  HashState (*run_combine_unordered_)(
+      HashState state,
+      absl::FunctionRef<void(HashState, absl::FunctionRef<void(HashState&)>)>);
+};
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_HASH_HASH_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/hash/internal/city.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/hash/internal/city.h
new file mode 100644
index 0000000000000000000000000000000000000000..393da0b95d9f8c13fa5649234f686845fbd7b1ab
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/hash/internal/city.h
@@ -0,0 +1,78 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// https://code.google.com/p/cityhash/
+//
+// This file provides a few functions for hashing strings.  All of them are
+// high-quality functions in the sense that they pass standard tests such
+// as Austin Appleby's SMHasher.  They are also fast.
+//
+// For 64-bit x86 code, on short strings, we don't know of anything faster than
+// CityHash64 that is of comparable quality.  We believe our nearest competitor
+// is Murmur3.  For 64-bit x86 code, CityHash64 is an excellent choice for hash
+// tables and most other hashing (excluding cryptography).
+//
+// For 32-bit x86 code, we don't know of anything faster than CityHash32 that
+// is of comparable quality.  We believe our nearest competitor is Murmur3A.
+// (On 64-bit CPUs, it is typically faster to use the other CityHash variants.)
+//
+// Functions in the CityHash family are not suitable for cryptography.
+//
+// Please see CityHash's README file for more details on our performance
+// measurements and so on.
+//
+// WARNING: This code has been only lightly tested on big-endian platforms!
+// It is known to work well on little-endian platforms that have a small penalty
+// for unaligned reads, such as current Intel and AMD moderate-to-high-end CPUs.
+// It should work on all 32-bit and 64-bit platforms that allow unaligned reads;
+// bug reports are welcome.
+//
+// By the way, for some hash functions, given strings a and b, the hash
+// of a+b is easily derived from the hashes of a and b.  This property
+// doesn't hold for any hash functions in this file.
+
+#ifndef ABSL_HASH_INTERNAL_CITY_H_
+#define ABSL_HASH_INTERNAL_CITY_H_
+
+#include <stdint.h>
+#include <stdlib.h>  // for size_t.
+
+#include <utility>
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace hash_internal {
+
+// Hash function for a byte array.
+uint64_t CityHash64(const char *s, size_t len);
+
+// Hash function for a byte array.  For convenience, a 64-bit seed is also
+// hashed into the result.
+uint64_t CityHash64WithSeed(const char *s, size_t len, uint64_t seed);
+
+// Hash function for a byte array.  For convenience, two seeds are also
+// hashed into the result.
+uint64_t CityHash64WithSeeds(const char *s, size_t len, uint64_t seed0,
+                             uint64_t seed1);
+
+// Hash function for a byte array.  Most useful in 32-bit binaries.
+uint32_t CityHash32(const char *s, size_t len);
+
+}  // namespace hash_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_HASH_INTERNAL_CITY_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/hash/internal/hash.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/hash/internal/hash.h
new file mode 100644
index 0000000000000000000000000000000000000000..ccf4cc1ad566d5c0b7cc943db440cac2db4880a7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/hash/internal/hash.h
@@ -0,0 +1,1305 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: hash.h
+// -----------------------------------------------------------------------------
+//
+#ifndef ABSL_HASH_INTERNAL_HASH_H_
+#define ABSL_HASH_INTERNAL_HASH_H_
+
+#include <algorithm>
+#include <array>
+#include <bitset>
+#include <cmath>
+#include <cstddef>
+#include <cstring>
+#include <deque>
+#include <forward_list>
+#include <functional>
+#include <iterator>
+#include <limits>
+#include <list>
+#include <map>
+#include <memory>
+#include <set>
+#include <string>
+#include <tuple>
+#include <type_traits>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+#include <vector>
+
+#include "absl/base/config.h"
+#include "absl/base/internal/unaligned_access.h"
+#include "absl/base/port.h"
+#include "absl/container/fixed_array.h"
+#include "absl/hash/internal/city.h"
+#include "absl/hash/internal/low_level_hash.h"
+#include "absl/meta/type_traits.h"
+#include "absl/numeric/bits.h"
+#include "absl/numeric/int128.h"
+#include "absl/strings/string_view.h"
+#include "absl/types/optional.h"
+#include "absl/types/variant.h"
+#include "absl/utility/utility.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+class HashState;
+
+namespace hash_internal {
+
+// Internal detail: Large buffers are hashed in smaller chunks.  This function
+// returns the size of these chunks.
+constexpr size_t PiecewiseChunkSize() { return 1024; }
+
+// PiecewiseCombiner
+//
+// PiecewiseCombiner is an internal-only helper class for hashing a piecewise
+// buffer of `char` or `unsigned char` as though it were contiguous.  This class
+// provides two methods:
+//
+//   H add_buffer(state, data, size)
+//   H finalize(state)
+//
+// `add_buffer` can be called zero or more times, followed by a single call to
+// `finalize`.  This will produce the same hash expansion as concatenating each
+// buffer piece into a single contiguous buffer, and passing this to
+// `H::combine_contiguous`.
+//
+//  Example usage:
+//    PiecewiseCombiner combiner;
+//    for (const auto& piece : pieces) {
+//      state = combiner.add_buffer(std::move(state), piece.data, piece.size);
+//    }
+//    return combiner.finalize(std::move(state));
+class PiecewiseCombiner {
+ public:
+  PiecewiseCombiner() : position_(0) {}
+  PiecewiseCombiner(const PiecewiseCombiner&) = delete;
+  PiecewiseCombiner& operator=(const PiecewiseCombiner&) = delete;
+
+  // PiecewiseCombiner::add_buffer()
+  //
+  // Appends the given range of bytes to the sequence to be hashed, which may
+  // modify the provided hash state.
+  template <typename H>
+  H add_buffer(H state, const unsigned char* data, size_t size);
+  template <typename H>
+  H add_buffer(H state, const char* data, size_t size) {
+    return add_buffer(std::move(state),
+                      reinterpret_cast<const unsigned char*>(data), size);
+  }
+
+  // PiecewiseCombiner::finalize()
+  //
+  // Finishes combining the hash sequence, which may may modify the provided
+  // hash state.
+  //
+  // Once finalize() is called, add_buffer() may no longer be called. The
+  // resulting hash state will be the same as if the pieces passed to
+  // add_buffer() were concatenated into a single flat buffer, and then provided
+  // to H::combine_contiguous().
+  template <typename H>
+  H finalize(H state);
+
+ private:
+  unsigned char buf_[PiecewiseChunkSize()];
+  size_t position_;
+};
+
+// is_hashable()
+//
+// Trait class which returns true if T is hashable by the absl::Hash framework.
+// Used for the AbslHashValue implementations for composite types below.
+template <typename T>
+struct is_hashable;
+
+// HashStateBase
+//
+// An internal implementation detail that contains common implementation details
+// for all of the "hash state objects" objects generated by Abseil.  This is not
+// a public API; users should not create classes that inherit from this.
+//
+// A hash state object is the template argument `H` passed to `AbslHashValue`.
+// It represents an intermediate state in the computation of an unspecified hash
+// algorithm. `HashStateBase` provides a CRTP style base class for hash state
+// implementations. Developers adding type support for `absl::Hash` should not
+// rely on any parts of the state object other than the following member
+// functions:
+//
+//   * HashStateBase::combine()
+//   * HashStateBase::combine_contiguous()
+//   * HashStateBase::combine_unordered()
+//
+// A derived hash state class of type `H` must provide a public member function
+// with a signature similar to the following:
+//
+//    `static H combine_contiguous(H state, const unsigned char*, size_t)`.
+//
+// It must also provide a private template method named RunCombineUnordered.
+//
+// A "consumer" is a 1-arg functor returning void.  Its argument is a reference
+// to an inner hash state object, and it may be called multiple times.  When
+// called, the functor consumes the entropy from the provided state object,
+// and resets that object to its empty state.
+//
+// A "combiner" is a stateless 2-arg functor returning void.  Its arguments are
+// an inner hash state object and an ElementStateConsumer functor.  A combiner
+// uses the provided inner hash state object to hash each element of the
+// container, passing the inner hash state object to the consumer after hashing
+// each element.
+//
+// Given these definitions, a derived hash state class of type H
+// must provide a private template method with a signature similar to the
+// following:
+//
+//    `template <typename CombinerT>`
+//    `static H RunCombineUnordered(H outer_state, CombinerT combiner)`
+//
+// This function is responsible for constructing the inner state object and
+// providing a consumer to the combiner.  It uses side effects of the consumer
+// and combiner to mix the state of each element in an order-independent manner,
+// and uses this to return an updated value of `outer_state`.
+//
+// This inside-out approach generates efficient object code in the normal case,
+// but allows us to use stack storage to implement the absl::HashState type
+// erasure mechanism (avoiding heap allocations while hashing).
+//
+// `HashStateBase` will provide a complete implementation for a hash state
+// object in terms of these two methods.
+//
+// Example:
+//
+//   // Use CRTP to define your derived class.
+//   struct MyHashState : HashStateBase<MyHashState> {
+//       static H combine_contiguous(H state, const unsigned char*, size_t);
+//       using MyHashState::HashStateBase::combine;
+//       using MyHashState::HashStateBase::combine_contiguous;
+//       using MyHashState::HashStateBase::combine_unordered;
+//     private:
+//       template <typename CombinerT>
+//       static H RunCombineUnordered(H state, CombinerT combiner);
+//   };
+template <typename H>
+class HashStateBase {
+ public:
+  // HashStateBase::combine()
+  //
+  // Combines an arbitrary number of values into a hash state, returning the
+  // updated state.
+  //
+  // Each of the value types `T` must be separately hashable by the Abseil
+  // hashing framework.
+  //
+  // NOTE:
+  //
+  //   state = H::combine(std::move(state), value1, value2, value3);
+  //
+  // is guaranteed to produce the same hash expansion as:
+  //
+  //   state = H::combine(std::move(state), value1);
+  //   state = H::combine(std::move(state), value2);
+  //   state = H::combine(std::move(state), value3);
+  template <typename T, typename... Ts>
+  static H combine(H state, const T& value, const Ts&... values);
+  static H combine(H state) { return state; }
+
+  // HashStateBase::combine_contiguous()
+  //
+  // Combines a contiguous array of `size` elements into a hash state, returning
+  // the updated state.
+  //
+  // NOTE:
+  //
+  //   state = H::combine_contiguous(std::move(state), data, size);
+  //
+  // is NOT guaranteed to produce the same hash expansion as a for-loop (it may
+  // perform internal optimizations).  If you need this guarantee, use the
+  // for-loop instead.
+  template <typename T>
+  static H combine_contiguous(H state, const T* data, size_t size);
+
+  template <typename I>
+  static H combine_unordered(H state, I begin, I end);
+
+  using AbslInternalPiecewiseCombiner = PiecewiseCombiner;
+
+  template <typename T>
+  using is_hashable = absl::hash_internal::is_hashable<T>;
+
+ private:
+  // Common implementation of the iteration step of a "combiner", as described
+  // above.
+  template <typename I>
+  struct CombineUnorderedCallback {
+    I begin;
+    I end;
+
+    template <typename InnerH, typename ElementStateConsumer>
+    void operator()(InnerH inner_state, ElementStateConsumer cb) {
+      for (; begin != end; ++begin) {
+        inner_state = H::combine(std::move(inner_state), *begin);
+        cb(inner_state);
+      }
+    }
+  };
+};
+
+// is_uniquely_represented
+//
+// `is_uniquely_represented<T>` is a trait class that indicates whether `T`
+// is uniquely represented.
+//
+// A type is "uniquely represented" if two equal values of that type are
+// guaranteed to have the same bytes in their underlying storage. In other
+// words, if `a == b`, then `memcmp(&a, &b, sizeof(T))` is guaranteed to be
+// zero. This property cannot be detected automatically, so this trait is false
+// by default, but can be specialized by types that wish to assert that they are
+// uniquely represented. This makes them eligible for certain optimizations.
+//
+// If you have any doubt whatsoever, do not specialize this template.
+// The default is completely safe, and merely disables some optimizations
+// that will not matter for most types. Specializing this template,
+// on the other hand, can be very hazardous.
+//
+// To be uniquely represented, a type must not have multiple ways of
+// representing the same value; for example, float and double are not
+// uniquely represented, because they have distinct representations for
+// +0 and -0. Furthermore, the type's byte representation must consist
+// solely of user-controlled data, with no padding bits and no compiler-
+// controlled data such as vptrs or sanitizer metadata. This is usually
+// very difficult to guarantee, because in most cases the compiler can
+// insert data and padding bits at its own discretion.
+//
+// If you specialize this template for a type `T`, you must do so in the file
+// that defines that type (or in this file). If you define that specialization
+// anywhere else, `is_uniquely_represented<T>` could have different meanings
+// in different places.
+//
+// The Enable parameter is meaningless; it is provided as a convenience,
+// to support certain SFINAE techniques when defining specializations.
+template <typename T, typename Enable = void>
+struct is_uniquely_represented : std::false_type {};
+
+// is_uniquely_represented<unsigned char>
+//
+// unsigned char is a synonym for "byte", so it is guaranteed to be
+// uniquely represented.
+template <>
+struct is_uniquely_represented<unsigned char> : std::true_type {};
+
+// is_uniquely_represented for non-standard integral types
+//
+// Integral types other than bool should be uniquely represented on any
+// platform that this will plausibly be ported to.
+template <typename Integral>
+struct is_uniquely_represented<
+    Integral, typename std::enable_if<std::is_integral<Integral>::value>::type>
+    : std::true_type {};
+
+// is_uniquely_represented<bool>
+//
+//
+template <>
+struct is_uniquely_represented<bool> : std::false_type {};
+
+// hash_bytes()
+//
+// Convenience function that combines `hash_state` with the byte representation
+// of `value`.
+template <typename H, typename T>
+H hash_bytes(H hash_state, const T& value) {
+  const unsigned char* start = reinterpret_cast<const unsigned char*>(&value);
+  return H::combine_contiguous(std::move(hash_state), start, sizeof(value));
+}
+
+// -----------------------------------------------------------------------------
+// AbslHashValue for Basic Types
+// -----------------------------------------------------------------------------
+
+// Note: Default `AbslHashValue` implementations live in `hash_internal`. This
+// allows us to block lexical scope lookup when doing an unqualified call to
+// `AbslHashValue` below. User-defined implementations of `AbslHashValue` can
+// only be found via ADL.
+
+// AbslHashValue() for hashing bool values
+//
+// We use SFINAE to ensure that this overload only accepts bool, not types that
+// are convertible to bool.
+template <typename H, typename B>
+typename std::enable_if<std::is_same<B, bool>::value, H>::type AbslHashValue(
+    H hash_state, B value) {
+  return H::combine(std::move(hash_state),
+                    static_cast<unsigned char>(value ? 1 : 0));
+}
+
+// AbslHashValue() for hashing enum values
+template <typename H, typename Enum>
+typename std::enable_if<std::is_enum<Enum>::value, H>::type AbslHashValue(
+    H hash_state, Enum e) {
+  // In practice, we could almost certainly just invoke hash_bytes directly,
+  // but it's possible that a sanitizer might one day want to
+  // store data in the unused bits of an enum. To avoid that risk, we
+  // convert to the underlying type before hashing. Hopefully this will get
+  // optimized away; if not, we can reopen discussion with c-toolchain-team.
+  return H::combine(std::move(hash_state),
+                    static_cast<typename std::underlying_type<Enum>::type>(e));
+}
+// AbslHashValue() for hashing floating-point values
+template <typename H, typename Float>
+typename std::enable_if<std::is_same<Float, float>::value ||
+                            std::is_same<Float, double>::value,
+                        H>::type
+AbslHashValue(H hash_state, Float value) {
+  return hash_internal::hash_bytes(std::move(hash_state),
+                                   value == 0 ? 0 : value);
+}
+
+// Long double has the property that it might have extra unused bytes in it.
+// For example, in x86 sizeof(long double)==16 but it only really uses 80-bits
+// of it. This means we can't use hash_bytes on a long double and have to
+// convert it to something else first.
+template <typename H, typename LongDouble>
+typename std::enable_if<std::is_same<LongDouble, long double>::value, H>::type
+AbslHashValue(H hash_state, LongDouble value) {
+  const int category = std::fpclassify(value);
+  switch (category) {
+    case FP_INFINITE:
+      // Add the sign bit to differentiate between +Inf and -Inf
+      hash_state = H::combine(std::move(hash_state), std::signbit(value));
+      break;
+
+    case FP_NAN:
+    case FP_ZERO:
+    default:
+      // Category is enough for these.
+      break;
+
+    case FP_NORMAL:
+    case FP_SUBNORMAL:
+      // We can't convert `value` directly to double because this would have
+      // undefined behavior if the value is out of range.
+      // std::frexp gives us a value in the range (-1, -.5] or [.5, 1) that is
+      // guaranteed to be in range for `double`. The truncation is
+      // implementation defined, but that works as long as it is deterministic.
+      int exp;
+      auto mantissa = static_cast<double>(std::frexp(value, &exp));
+      hash_state = H::combine(std::move(hash_state), mantissa, exp);
+  }
+
+  return H::combine(std::move(hash_state), category);
+}
+
+// AbslHashValue() for hashing pointers
+template <typename H, typename T>
+H AbslHashValue(H hash_state, T* ptr) {
+  auto v = reinterpret_cast<uintptr_t>(ptr);
+  // Due to alignment, pointers tend to have low bits as zero, and the next few
+  // bits follow a pattern since they are also multiples of some base value.
+  // Mixing the pointer twice helps prevent stuck low bits for certain alignment
+  // values.
+  return H::combine(std::move(hash_state), v, v);
+}
+
+// AbslHashValue() for hashing nullptr_t
+template <typename H>
+H AbslHashValue(H hash_state, std::nullptr_t) {
+  return H::combine(std::move(hash_state), static_cast<void*>(nullptr));
+}
+
+// AbslHashValue() for hashing pointers-to-member
+template <typename H, typename T, typename C>
+H AbslHashValue(H hash_state, T C::* ptr) {
+  auto salient_ptm_size = [](std::size_t n) -> std::size_t {
+#if defined(_MSC_VER)
+    // Pointers-to-member-function on MSVC consist of one pointer plus 0, 1, 2,
+    // or 3 ints. In 64-bit mode, they are 8-byte aligned and thus can contain
+    // padding (namely when they have 1 or 3 ints). The value below is a lower
+    // bound on the number of salient, non-padding bytes that we use for
+    // hashing.
+    if (alignof(T C::*) == alignof(int)) {
+      // No padding when all subobjects have the same size as the total
+      // alignment. This happens in 32-bit mode.
+      return n;
+    } else {
+      // Padding for 1 int (size 16) or 3 ints (size 24).
+      // With 2 ints, the size is 16 with no padding, which we pessimize.
+      return n == 24 ? 20 : n == 16 ? 12 : n;
+    }
+#else
+    // On other platforms, we assume that pointers-to-members do not have
+    // padding.
+#ifdef __cpp_lib_has_unique_object_representations
+    static_assert(std::has_unique_object_representations<T C::*>::value);
+#endif  // __cpp_lib_has_unique_object_representations
+    return n;
+#endif
+  };
+  return H::combine_contiguous(std::move(hash_state),
+                               reinterpret_cast<unsigned char*>(&ptr),
+                               salient_ptm_size(sizeof ptr));
+}
+
+// -----------------------------------------------------------------------------
+// AbslHashValue for Composite Types
+// -----------------------------------------------------------------------------
+
+// AbslHashValue() for hashing pairs
+template <typename H, typename T1, typename T2>
+typename std::enable_if<is_hashable<T1>::value && is_hashable<T2>::value,
+                        H>::type
+AbslHashValue(H hash_state, const std::pair<T1, T2>& p) {
+  return H::combine(std::move(hash_state), p.first, p.second);
+}
+
+// hash_tuple()
+//
+// Helper function for hashing a tuple. The third argument should
+// be an index_sequence running from 0 to tuple_size<Tuple> - 1.
+template <typename H, typename Tuple, size_t... Is>
+H hash_tuple(H hash_state, const Tuple& t, absl::index_sequence<Is...>) {
+  return H::combine(std::move(hash_state), std::get<Is>(t)...);
+}
+
+// AbslHashValue for hashing tuples
+template <typename H, typename... Ts>
+#if defined(_MSC_VER)
+// This SFINAE gets MSVC confused under some conditions. Let's just disable it
+// for now.
+H
+#else   // _MSC_VER
+typename std::enable_if<absl::conjunction<is_hashable<Ts>...>::value, H>::type
+#endif  // _MSC_VER
+AbslHashValue(H hash_state, const std::tuple<Ts...>& t) {
+  return hash_internal::hash_tuple(std::move(hash_state), t,
+                                   absl::make_index_sequence<sizeof...(Ts)>());
+}
+
+// -----------------------------------------------------------------------------
+// AbslHashValue for Pointers
+// -----------------------------------------------------------------------------
+
+// AbslHashValue for hashing unique_ptr
+template <typename H, typename T, typename D>
+H AbslHashValue(H hash_state, const std::unique_ptr<T, D>& ptr) {
+  return H::combine(std::move(hash_state), ptr.get());
+}
+
+// AbslHashValue for hashing shared_ptr
+template <typename H, typename T>
+H AbslHashValue(H hash_state, const std::shared_ptr<T>& ptr) {
+  return H::combine(std::move(hash_state), ptr.get());
+}
+
+// -----------------------------------------------------------------------------
+// AbslHashValue for String-Like Types
+// -----------------------------------------------------------------------------
+
+// AbslHashValue for hashing strings
+//
+// All the string-like types supported here provide the same hash expansion for
+// the same character sequence. These types are:
+//
+//  - `absl::Cord`
+//  - `std::string` (and std::basic_string<char, std::char_traits<char>, A> for
+//      any allocator A)
+//  - `absl::string_view` and `std::string_view`
+//
+// For simplicity, we currently support only `char` strings. This support may
+// be broadened, if necessary, but with some caution - this overload would
+// misbehave in cases where the traits' `eq()` member isn't equivalent to `==`
+// on the underlying character type.
+template <typename H>
+H AbslHashValue(H hash_state, absl::string_view str) {
+  return H::combine(
+      H::combine_contiguous(std::move(hash_state), str.data(), str.size()),
+      str.size());
+}
+
+// Support std::wstring, std::u16string and std::u32string.
+template <typename Char, typename Alloc, typename H,
+          typename = absl::enable_if_t<std::is_same<Char, wchar_t>::value ||
+                                       std::is_same<Char, char16_t>::value ||
+                                       std::is_same<Char, char32_t>::value>>
+H AbslHashValue(
+    H hash_state,
+    const std::basic_string<Char, std::char_traits<Char>, Alloc>& str) {
+  return H::combine(
+      H::combine_contiguous(std::move(hash_state), str.data(), str.size()),
+      str.size());
+}
+
+// -----------------------------------------------------------------------------
+// AbslHashValue for Sequence Containers
+// -----------------------------------------------------------------------------
+
+// AbslHashValue for hashing std::array
+template <typename H, typename T, size_t N>
+typename std::enable_if<is_hashable<T>::value, H>::type AbslHashValue(
+    H hash_state, const std::array<T, N>& array) {
+  return H::combine_contiguous(std::move(hash_state), array.data(),
+                               array.size());
+}
+
+// AbslHashValue for hashing std::deque
+template <typename H, typename T, typename Allocator>
+typename std::enable_if<is_hashable<T>::value, H>::type AbslHashValue(
+    H hash_state, const std::deque<T, Allocator>& deque) {
+  // TODO(gromer): investigate a more efficient implementation taking
+  // advantage of the chunk structure.
+  for (const auto& t : deque) {
+    hash_state = H::combine(std::move(hash_state), t);
+  }
+  return H::combine(std::move(hash_state), deque.size());
+}
+
+// AbslHashValue for hashing std::forward_list
+template <typename H, typename T, typename Allocator>
+typename std::enable_if<is_hashable<T>::value, H>::type AbslHashValue(
+    H hash_state, const std::forward_list<T, Allocator>& list) {
+  size_t size = 0;
+  for (const T& t : list) {
+    hash_state = H::combine(std::move(hash_state), t);
+    ++size;
+  }
+  return H::combine(std::move(hash_state), size);
+}
+
+// AbslHashValue for hashing std::list
+template <typename H, typename T, typename Allocator>
+typename std::enable_if<is_hashable<T>::value, H>::type AbslHashValue(
+    H hash_state, const std::list<T, Allocator>& list) {
+  for (const auto& t : list) {
+    hash_state = H::combine(std::move(hash_state), t);
+  }
+  return H::combine(std::move(hash_state), list.size());
+}
+
+// AbslHashValue for hashing std::vector
+//
+// Do not use this for vector<bool> on platforms that have a working
+// implementation of std::hash. It does not have a .data(), and a fallback for
+// std::hash<> is most likely faster.
+template <typename H, typename T, typename Allocator>
+typename std::enable_if<is_hashable<T>::value && !std::is_same<T, bool>::value,
+                        H>::type
+AbslHashValue(H hash_state, const std::vector<T, Allocator>& vector) {
+  return H::combine(H::combine_contiguous(std::move(hash_state), vector.data(),
+                                          vector.size()),
+                    vector.size());
+}
+
+// AbslHashValue special cases for hashing std::vector<bool>
+
+#if defined(ABSL_IS_BIG_ENDIAN) && \
+    (defined(__GLIBCXX__) || defined(__GLIBCPP__))
+
+// std::hash in libstdc++ does not work correctly with vector<bool> on Big
+// Endian platforms therefore we need to implement a custom AbslHashValue for
+// it. More details on the bug:
+// https://gcc.gnu.org/bugzilla/show_bug.cgi?id=102531
+template <typename H, typename T, typename Allocator>
+typename std::enable_if<is_hashable<T>::value && std::is_same<T, bool>::value,
+                        H>::type
+AbslHashValue(H hash_state, const std::vector<T, Allocator>& vector) {
+  typename H::AbslInternalPiecewiseCombiner combiner;
+  for (const auto& i : vector) {
+    unsigned char c = static_cast<unsigned char>(i);
+    hash_state = combiner.add_buffer(std::move(hash_state), &c, sizeof(c));
+  }
+  return H::combine(combiner.finalize(std::move(hash_state)), vector.size());
+}
+#else
+// When not working around the libstdc++ bug above, we still have to contend
+// with the fact that std::hash<vector<bool>> is often poor quality, hashing
+// directly on the internal words and on no other state.  On these platforms,
+// vector<bool>{1, 1} and vector<bool>{1, 1, 0} hash to the same value.
+//
+// Mixing in the size (as we do in our other vector<> implementations) on top
+// of the library-provided hash implementation avoids this QOI issue.
+template <typename H, typename T, typename Allocator>
+typename std::enable_if<is_hashable<T>::value && std::is_same<T, bool>::value,
+                        H>::type
+AbslHashValue(H hash_state, const std::vector<T, Allocator>& vector) {
+  return H::combine(std::move(hash_state),
+                    std::hash<std::vector<T, Allocator>>{}(vector),
+                    vector.size());
+}
+#endif
+
+// -----------------------------------------------------------------------------
+// AbslHashValue for Ordered Associative Containers
+// -----------------------------------------------------------------------------
+
+// AbslHashValue for hashing std::map
+template <typename H, typename Key, typename T, typename Compare,
+          typename Allocator>
+typename std::enable_if<is_hashable<Key>::value && is_hashable<T>::value,
+                        H>::type
+AbslHashValue(H hash_state, const std::map<Key, T, Compare, Allocator>& map) {
+  for (const auto& t : map) {
+    hash_state = H::combine(std::move(hash_state), t);
+  }
+  return H::combine(std::move(hash_state), map.size());
+}
+
+// AbslHashValue for hashing std::multimap
+template <typename H, typename Key, typename T, typename Compare,
+          typename Allocator>
+typename std::enable_if<is_hashable<Key>::value && is_hashable<T>::value,
+                        H>::type
+AbslHashValue(H hash_state,
+              const std::multimap<Key, T, Compare, Allocator>& map) {
+  for (const auto& t : map) {
+    hash_state = H::combine(std::move(hash_state), t);
+  }
+  return H::combine(std::move(hash_state), map.size());
+}
+
+// AbslHashValue for hashing std::set
+template <typename H, typename Key, typename Compare, typename Allocator>
+typename std::enable_if<is_hashable<Key>::value, H>::type AbslHashValue(
+    H hash_state, const std::set<Key, Compare, Allocator>& set) {
+  for (const auto& t : set) {
+    hash_state = H::combine(std::move(hash_state), t);
+  }
+  return H::combine(std::move(hash_state), set.size());
+}
+
+// AbslHashValue for hashing std::multiset
+template <typename H, typename Key, typename Compare, typename Allocator>
+typename std::enable_if<is_hashable<Key>::value, H>::type AbslHashValue(
+    H hash_state, const std::multiset<Key, Compare, Allocator>& set) {
+  for (const auto& t : set) {
+    hash_state = H::combine(std::move(hash_state), t);
+  }
+  return H::combine(std::move(hash_state), set.size());
+}
+
+// -----------------------------------------------------------------------------
+// AbslHashValue for Unordered Associative Containers
+// -----------------------------------------------------------------------------
+
+// AbslHashValue for hashing std::unordered_set
+template <typename H, typename Key, typename Hash, typename KeyEqual,
+          typename Alloc>
+typename std::enable_if<is_hashable<Key>::value, H>::type AbslHashValue(
+    H hash_state, const std::unordered_set<Key, Hash, KeyEqual, Alloc>& s) {
+  return H::combine(
+      H::combine_unordered(std::move(hash_state), s.begin(), s.end()),
+      s.size());
+}
+
+// AbslHashValue for hashing std::unordered_multiset
+template <typename H, typename Key, typename Hash, typename KeyEqual,
+          typename Alloc>
+typename std::enable_if<is_hashable<Key>::value, H>::type AbslHashValue(
+    H hash_state,
+    const std::unordered_multiset<Key, Hash, KeyEqual, Alloc>& s) {
+  return H::combine(
+      H::combine_unordered(std::move(hash_state), s.begin(), s.end()),
+      s.size());
+}
+
+// AbslHashValue for hashing std::unordered_set
+template <typename H, typename Key, typename T, typename Hash,
+          typename KeyEqual, typename Alloc>
+typename std::enable_if<is_hashable<Key>::value && is_hashable<T>::value,
+                        H>::type
+AbslHashValue(H hash_state,
+              const std::unordered_map<Key, T, Hash, KeyEqual, Alloc>& s) {
+  return H::combine(
+      H::combine_unordered(std::move(hash_state), s.begin(), s.end()),
+      s.size());
+}
+
+// AbslHashValue for hashing std::unordered_multiset
+template <typename H, typename Key, typename T, typename Hash,
+          typename KeyEqual, typename Alloc>
+typename std::enable_if<is_hashable<Key>::value && is_hashable<T>::value,
+                        H>::type
+AbslHashValue(H hash_state,
+              const std::unordered_multimap<Key, T, Hash, KeyEqual, Alloc>& s) {
+  return H::combine(
+      H::combine_unordered(std::move(hash_state), s.begin(), s.end()),
+      s.size());
+}
+
+// -----------------------------------------------------------------------------
+// AbslHashValue for Wrapper Types
+// -----------------------------------------------------------------------------
+
+// AbslHashValue for hashing std::reference_wrapper
+template <typename H, typename T>
+typename std::enable_if<is_hashable<T>::value, H>::type AbslHashValue(
+    H hash_state, std::reference_wrapper<T> opt) {
+  return H::combine(std::move(hash_state), opt.get());
+}
+
+// AbslHashValue for hashing absl::optional
+template <typename H, typename T>
+typename std::enable_if<is_hashable<T>::value, H>::type AbslHashValue(
+    H hash_state, const absl::optional<T>& opt) {
+  if (opt) hash_state = H::combine(std::move(hash_state), *opt);
+  return H::combine(std::move(hash_state), opt.has_value());
+}
+
+// VariantVisitor
+template <typename H>
+struct VariantVisitor {
+  H&& hash_state;
+  template <typename T>
+  H operator()(const T& t) const {
+    return H::combine(std::move(hash_state), t);
+  }
+};
+
+// AbslHashValue for hashing absl::variant
+template <typename H, typename... T>
+typename std::enable_if<conjunction<is_hashable<T>...>::value, H>::type
+AbslHashValue(H hash_state, const absl::variant<T...>& v) {
+  if (!v.valueless_by_exception()) {
+    hash_state = absl::visit(VariantVisitor<H>{std::move(hash_state)}, v);
+  }
+  return H::combine(std::move(hash_state), v.index());
+}
+
+// -----------------------------------------------------------------------------
+// AbslHashValue for Other Types
+// -----------------------------------------------------------------------------
+
+// AbslHashValue for hashing std::bitset is not defined on Little Endian
+// platforms, for the same reason as for vector<bool> (see std::vector above):
+// It does not expose the raw bytes, and a fallback to std::hash<> is most
+// likely faster.
+
+#if defined(ABSL_IS_BIG_ENDIAN) && \
+    (defined(__GLIBCXX__) || defined(__GLIBCPP__))
+// AbslHashValue for hashing std::bitset
+//
+// std::hash in libstdc++ does not work correctly with std::bitset on Big Endian
+// platforms therefore we need to implement a custom AbslHashValue for it. More
+// details on the bug: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=102531
+template <typename H, size_t N>
+H AbslHashValue(H hash_state, const std::bitset<N>& set) {
+  typename H::AbslInternalPiecewiseCombiner combiner;
+  for (int i = 0; i < N; i++) {
+    unsigned char c = static_cast<unsigned char>(set[i]);
+    hash_state = combiner.add_buffer(std::move(hash_state), &c, sizeof(c));
+  }
+  return H::combine(combiner.finalize(std::move(hash_state)), N);
+}
+#endif
+
+// -----------------------------------------------------------------------------
+
+// hash_range_or_bytes()
+//
+// Mixes all values in the range [data, data+size) into the hash state.
+// This overload accepts only uniquely-represented types, and hashes them by
+// hashing the entire range of bytes.
+template <typename H, typename T>
+typename std::enable_if<is_uniquely_represented<T>::value, H>::type
+hash_range_or_bytes(H hash_state, const T* data, size_t size) {
+  const auto* bytes = reinterpret_cast<const unsigned char*>(data);
+  return H::combine_contiguous(std::move(hash_state), bytes, sizeof(T) * size);
+}
+
+// hash_range_or_bytes()
+template <typename H, typename T>
+typename std::enable_if<!is_uniquely_represented<T>::value, H>::type
+hash_range_or_bytes(H hash_state, const T* data, size_t size) {
+  for (const auto end = data + size; data < end; ++data) {
+    hash_state = H::combine(std::move(hash_state), *data);
+  }
+  return hash_state;
+}
+
+#if defined(ABSL_INTERNAL_LEGACY_HASH_NAMESPACE) && \
+    ABSL_META_INTERNAL_STD_HASH_SFINAE_FRIENDLY_
+#define ABSL_HASH_INTERNAL_SUPPORT_LEGACY_HASH_ 1
+#else
+#define ABSL_HASH_INTERNAL_SUPPORT_LEGACY_HASH_ 0
+#endif
+
+// HashSelect
+//
+// Type trait to select the appropriate hash implementation to use.
+// HashSelect::type<T> will give the proper hash implementation, to be invoked
+// as:
+//   HashSelect::type<T>::Invoke(state, value)
+// Also, HashSelect::type<T>::value is a boolean equal to `true` if there is a
+// valid `Invoke` function. Types that are not hashable will have a ::value of
+// `false`.
+struct HashSelect {
+ private:
+  struct State : HashStateBase<State> {
+    static State combine_contiguous(State hash_state, const unsigned char*,
+                                    size_t);
+    using State::HashStateBase::combine_contiguous;
+  };
+
+  struct UniquelyRepresentedProbe {
+    template <typename H, typename T>
+    static auto Invoke(H state, const T& value)
+        -> absl::enable_if_t<is_uniquely_represented<T>::value, H> {
+      return hash_internal::hash_bytes(std::move(state), value);
+    }
+  };
+
+  struct HashValueProbe {
+    template <typename H, typename T>
+    static auto Invoke(H state, const T& value) -> absl::enable_if_t<
+        std::is_same<H,
+                     decltype(AbslHashValue(std::move(state), value))>::value,
+        H> {
+      return AbslHashValue(std::move(state), value);
+    }
+  };
+
+  struct LegacyHashProbe {
+#if ABSL_HASH_INTERNAL_SUPPORT_LEGACY_HASH_
+    template <typename H, typename T>
+    static auto Invoke(H state, const T& value) -> absl::enable_if_t<
+        std::is_convertible<
+            decltype(ABSL_INTERNAL_LEGACY_HASH_NAMESPACE::hash<T>()(value)),
+            size_t>::value,
+        H> {
+      return hash_internal::hash_bytes(
+          std::move(state),
+          ABSL_INTERNAL_LEGACY_HASH_NAMESPACE::hash<T>{}(value));
+    }
+#endif  // ABSL_HASH_INTERNAL_SUPPORT_LEGACY_HASH_
+  };
+
+  struct StdHashProbe {
+    template <typename H, typename T>
+    static auto Invoke(H state, const T& value)
+        -> absl::enable_if_t<type_traits_internal::IsHashable<T>::value, H> {
+      return hash_internal::hash_bytes(std::move(state), std::hash<T>{}(value));
+    }
+  };
+
+  template <typename Hash, typename T>
+  struct Probe : Hash {
+   private:
+    template <typename H, typename = decltype(H::Invoke(
+                              std::declval<State>(), std::declval<const T&>()))>
+    static std::true_type Test(int);
+    template <typename U>
+    static std::false_type Test(char);
+
+   public:
+    static constexpr bool value = decltype(Test<Hash>(0))::value;
+  };
+
+ public:
+  // Probe each implementation in order.
+  // disjunction provides short circuiting wrt instantiation.
+  template <typename T>
+  using Apply = absl::disjunction<         //
+      Probe<UniquelyRepresentedProbe, T>,  //
+      Probe<HashValueProbe, T>,            //
+      Probe<LegacyHashProbe, T>,           //
+      Probe<StdHashProbe, T>,              //
+      std::false_type>;
+};
+
+template <typename T>
+struct is_hashable
+    : std::integral_constant<bool, HashSelect::template Apply<T>::value> {};
+
+// MixingHashState
+class ABSL_DLL MixingHashState : public HashStateBase<MixingHashState> {
+  // absl::uint128 is not an alias or a thin wrapper around the intrinsic.
+  // We use the intrinsic when available to improve performance.
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+  using uint128 = __uint128_t;
+#else   // ABSL_HAVE_INTRINSIC_INT128
+  using uint128 = absl::uint128;
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+
+  static constexpr uint64_t kMul =
+      sizeof(size_t) == 4 ? uint64_t{0xcc9e2d51}
+                          : uint64_t{0x9ddfea08eb382d69};
+
+  template <typename T>
+  using IntegralFastPath =
+      conjunction<std::is_integral<T>, is_uniquely_represented<T>>;
+
+ public:
+  // Move only
+  MixingHashState(MixingHashState&&) = default;
+  MixingHashState& operator=(MixingHashState&&) = default;
+
+  // MixingHashState::combine_contiguous()
+  //
+  // Fundamental base case for hash recursion: mixes the given range of bytes
+  // into the hash state.
+  static MixingHashState combine_contiguous(MixingHashState hash_state,
+                                            const unsigned char* first,
+                                            size_t size) {
+    return MixingHashState(
+        CombineContiguousImpl(hash_state.state_, first, size,
+                              std::integral_constant<int, sizeof(size_t)>{}));
+  }
+  using MixingHashState::HashStateBase::combine_contiguous;
+
+  // MixingHashState::hash()
+  //
+  // For performance reasons in non-opt mode, we specialize this for
+  // integral types.
+  // Otherwise we would be instantiating and calling dozens of functions for
+  // something that is just one multiplication and a couple xor's.
+  // The result should be the same as running the whole algorithm, but faster.
+  template <typename T, absl::enable_if_t<IntegralFastPath<T>::value, int> = 0>
+  static size_t hash(T value) {
+    return static_cast<size_t>(Mix(Seed(), static_cast<uint64_t>(value)));
+  }
+
+  // Overload of MixingHashState::hash()
+  template <typename T, absl::enable_if_t<!IntegralFastPath<T>::value, int> = 0>
+  static size_t hash(const T& value) {
+    return static_cast<size_t>(combine(MixingHashState{}, value).state_);
+  }
+
+ private:
+  // Invoked only once for a given argument; that plus the fact that this is
+  // move-only ensures that there is only one non-moved-from object.
+  MixingHashState() : state_(Seed()) {}
+
+  friend class MixingHashState::HashStateBase;
+
+  template <typename CombinerT>
+  static MixingHashState RunCombineUnordered(MixingHashState state,
+                                             CombinerT combiner) {
+    uint64_t unordered_state = 0;
+    combiner(MixingHashState{}, [&](MixingHashState& inner_state) {
+      // Add the hash state of the element to the running total, but mix the
+      // carry bit back into the low bit.  This in intended to avoid losing
+      // entropy to overflow, especially when unordered_multisets contain
+      // multiple copies of the same value.
+      auto element_state = inner_state.state_;
+      unordered_state += element_state;
+      if (unordered_state < element_state) {
+        ++unordered_state;
+      }
+      inner_state = MixingHashState{};
+    });
+    return MixingHashState::combine(std::move(state), unordered_state);
+  }
+
+  // Allow the HashState type-erasure implementation to invoke
+  // RunCombinedUnordered() directly.
+  friend class absl::HashState;
+
+  // Workaround for MSVC bug.
+  // We make the type copyable to fix the calling convention, even though we
+  // never actually copy it. Keep it private to not affect the public API of the
+  // type.
+  MixingHashState(const MixingHashState&) = default;
+
+  explicit MixingHashState(uint64_t state) : state_(state) {}
+
+  // Implementation of the base case for combine_contiguous where we actually
+  // mix the bytes into the state.
+  // Dispatch to different implementations of the combine_contiguous depending
+  // on the value of `sizeof(size_t)`.
+  static uint64_t CombineContiguousImpl(uint64_t state,
+                                        const unsigned char* first, size_t len,
+                                        std::integral_constant<int, 4>
+                                        /* sizeof_size_t */);
+  static uint64_t CombineContiguousImpl(uint64_t state,
+                                        const unsigned char* first, size_t len,
+                                        std::integral_constant<int, 8>
+                                        /* sizeof_size_t */);
+
+  // Slow dispatch path for calls to CombineContiguousImpl with a size argument
+  // larger than PiecewiseChunkSize().  Has the same effect as calling
+  // CombineContiguousImpl() repeatedly with the chunk stride size.
+  static uint64_t CombineLargeContiguousImpl32(uint64_t state,
+                                               const unsigned char* first,
+                                               size_t len);
+  static uint64_t CombineLargeContiguousImpl64(uint64_t state,
+                                               const unsigned char* first,
+                                               size_t len);
+
+  // Reads 9 to 16 bytes from p.
+  // The least significant 8 bytes are in .first, the rest (zero padded) bytes
+  // are in .second.
+  static std::pair<uint64_t, uint64_t> Read9To16(const unsigned char* p,
+                                                 size_t len) {
+    uint64_t low_mem = absl::base_internal::UnalignedLoad64(p);
+    uint64_t high_mem = absl::base_internal::UnalignedLoad64(p + len - 8);
+#ifdef ABSL_IS_LITTLE_ENDIAN
+    uint64_t most_significant = high_mem;
+    uint64_t least_significant = low_mem;
+#else
+    uint64_t most_significant = low_mem;
+    uint64_t least_significant = high_mem;
+#endif
+    return {least_significant, most_significant};
+  }
+
+  // Reads 4 to 8 bytes from p. Zero pads to fill uint64_t.
+  static uint64_t Read4To8(const unsigned char* p, size_t len) {
+    uint32_t low_mem = absl::base_internal::UnalignedLoad32(p);
+    uint32_t high_mem = absl::base_internal::UnalignedLoad32(p + len - 4);
+#ifdef ABSL_IS_LITTLE_ENDIAN
+    uint32_t most_significant = high_mem;
+    uint32_t least_significant = low_mem;
+#else
+    uint32_t most_significant = low_mem;
+    uint32_t least_significant = high_mem;
+#endif
+    return (static_cast<uint64_t>(most_significant) << (len - 4) * 8) |
+           least_significant;
+  }
+
+  // Reads 1 to 3 bytes from p. Zero pads to fill uint32_t.
+  static uint32_t Read1To3(const unsigned char* p, size_t len) {
+    unsigned char mem0 = p[0];
+    unsigned char mem1 = p[len / 2];
+    unsigned char mem2 = p[len - 1];
+#ifdef ABSL_IS_LITTLE_ENDIAN
+    unsigned char significant2 = mem2;
+    unsigned char significant1 = mem1;
+    unsigned char significant0 = mem0;
+#else
+    unsigned char significant2 = mem0;
+    unsigned char significant1 = mem1;
+    unsigned char significant0 = mem2;
+#endif
+    return static_cast<uint32_t>(significant0 |                     //
+                                 (significant1 << (len / 2 * 8)) |  //
+                                 (significant2 << ((len - 1) * 8)));
+  }
+
+  ABSL_ATTRIBUTE_ALWAYS_INLINE static uint64_t Mix(uint64_t state, uint64_t v) {
+    // Though the 128-bit product on AArch64 needs two instructions, it is
+    // still a good balance between speed and hash quality.
+    using MultType =
+        absl::conditional_t<sizeof(size_t) == 4, uint64_t, uint128>;
+    // We do the addition in 64-bit space to make sure the 128-bit
+    // multiplication is fast. If we were to do it as MultType the compiler has
+    // to assume that the high word is non-zero and needs to perform 2
+    // multiplications instead of one.
+    MultType m = state + v;
+    m *= kMul;
+    return static_cast<uint64_t>(m ^ (m >> (sizeof(m) * 8 / 2)));
+  }
+
+  // An extern to avoid bloat on a direct call to LowLevelHash() with fixed
+  // values for both the seed and salt parameters.
+  static uint64_t LowLevelHashImpl(const unsigned char* data, size_t len);
+
+  ABSL_ATTRIBUTE_ALWAYS_INLINE static uint64_t Hash64(const unsigned char* data,
+                                                      size_t len) {
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+    return LowLevelHashImpl(data, len);
+#else
+    return hash_internal::CityHash64(reinterpret_cast<const char*>(data), len);
+#endif
+  }
+
+  // Seed()
+  //
+  // A non-deterministic seed.
+  //
+  // The current purpose of this seed is to generate non-deterministic results
+  // and prevent having users depend on the particular hash values.
+  // It is not meant as a security feature right now, but it leaves the door
+  // open to upgrade it to a true per-process random seed. A true random seed
+  // costs more and we don't need to pay for that right now.
+  //
+  // On platforms with ASLR, we take advantage of it to make a per-process
+  // random value.
+  // See https://en.wikipedia.org/wiki/Address_space_layout_randomization
+  //
+  // On other platforms this is still going to be non-deterministic but most
+  // probably per-build and not per-process.
+  ABSL_ATTRIBUTE_ALWAYS_INLINE static uint64_t Seed() {
+#if (!defined(__clang__) || __clang_major__ > 11) && \
+    !defined(__apple_build_version__)
+    return static_cast<uint64_t>(reinterpret_cast<uintptr_t>(&kSeed));
+#else
+    // Workaround the absence of
+    // https://github.com/llvm/llvm-project/commit/bc15bf66dcca76cc06fe71fca35b74dc4d521021.
+    return static_cast<uint64_t>(reinterpret_cast<uintptr_t>(kSeed));
+#endif
+  }
+  static const void* const kSeed;
+
+  uint64_t state_;
+};
+
+// MixingHashState::CombineContiguousImpl()
+inline uint64_t MixingHashState::CombineContiguousImpl(
+    uint64_t state, const unsigned char* first, size_t len,
+    std::integral_constant<int, 4> /* sizeof_size_t */) {
+  // For large values we use CityHash, for small ones we just use a
+  // multiplicative hash.
+  uint64_t v;
+  if (len > 8) {
+    if (ABSL_PREDICT_FALSE(len > PiecewiseChunkSize())) {
+      return CombineLargeContiguousImpl32(state, first, len);
+    }
+    v = hash_internal::CityHash32(reinterpret_cast<const char*>(first), len);
+  } else if (len >= 4) {
+    v = Read4To8(first, len);
+  } else if (len > 0) {
+    v = Read1To3(first, len);
+  } else {
+    // Empty ranges have no effect.
+    return state;
+  }
+  return Mix(state, v);
+}
+
+// Overload of MixingHashState::CombineContiguousImpl()
+inline uint64_t MixingHashState::CombineContiguousImpl(
+    uint64_t state, const unsigned char* first, size_t len,
+    std::integral_constant<int, 8> /* sizeof_size_t */) {
+  // For large values we use LowLevelHash or CityHash depending on the platform,
+  // for small ones we just use a multiplicative hash.
+  uint64_t v;
+  if (len > 16) {
+    if (ABSL_PREDICT_FALSE(len > PiecewiseChunkSize())) {
+      return CombineLargeContiguousImpl64(state, first, len);
+    }
+    v = Hash64(first, len);
+  } else if (len > 8) {
+    // This hash function was constructed by the ML-driven algorithm discovery
+    // using reinforcement learning. We fed the agent lots of inputs from
+    // microbenchmarks, SMHasher, low hamming distance from generated inputs and
+    // picked up the one that was good on micro and macrobenchmarks.
+    auto p = Read9To16(first, len);
+    uint64_t lo = p.first;
+    uint64_t hi = p.second;
+    // Rotation by 53 was found to be most often useful when discovering these
+    // hashing algorithms with ML techniques.
+    lo = absl::rotr(lo, 53);
+    state += kMul;
+    lo += state;
+    state ^= hi;
+    uint128 m = state;
+    m *= lo;
+    return static_cast<uint64_t>(m ^ (m >> 64));
+  } else if (len >= 4) {
+    v = Read4To8(first, len);
+  } else if (len > 0) {
+    v = Read1To3(first, len);
+  } else {
+    // Empty ranges have no effect.
+    return state;
+  }
+  return Mix(state, v);
+}
+
+struct AggregateBarrier {};
+
+// HashImpl
+
+// Add a private base class to make sure this type is not an aggregate.
+// Aggregates can be aggregate initialized even if the default constructor is
+// deleted.
+struct PoisonedHash : private AggregateBarrier {
+  PoisonedHash() = delete;
+  PoisonedHash(const PoisonedHash&) = delete;
+  PoisonedHash& operator=(const PoisonedHash&) = delete;
+};
+
+template <typename T>
+struct HashImpl {
+  size_t operator()(const T& value) const {
+    return MixingHashState::hash(value);
+  }
+};
+
+template <typename T>
+struct Hash
+    : absl::conditional_t<is_hashable<T>::value, HashImpl<T>, PoisonedHash> {};
+
+template <typename H>
+template <typename T, typename... Ts>
+H HashStateBase<H>::combine(H state, const T& value, const Ts&... values) {
+  return H::combine(hash_internal::HashSelect::template Apply<T>::Invoke(
+                        std::move(state), value),
+                    values...);
+}
+
+// HashStateBase::combine_contiguous()
+template <typename H>
+template <typename T>
+H HashStateBase<H>::combine_contiguous(H state, const T* data, size_t size) {
+  return hash_internal::hash_range_or_bytes(std::move(state), data, size);
+}
+
+// HashStateBase::combine_unordered()
+template <typename H>
+template <typename I>
+H HashStateBase<H>::combine_unordered(H state, I begin, I end) {
+  return H::RunCombineUnordered(std::move(state),
+                                CombineUnorderedCallback<I>{begin, end});
+}
+
+// HashStateBase::PiecewiseCombiner::add_buffer()
+template <typename H>
+H PiecewiseCombiner::add_buffer(H state, const unsigned char* data,
+                                size_t size) {
+  if (position_ + size < PiecewiseChunkSize()) {
+    // This partial chunk does not fill our existing buffer
+    memcpy(buf_ + position_, data, size);
+    position_ += size;
+    return state;
+  }
+
+  // If the buffer is partially filled we need to complete the buffer
+  // and hash it.
+  if (position_ != 0) {
+    const size_t bytes_needed = PiecewiseChunkSize() - position_;
+    memcpy(buf_ + position_, data, bytes_needed);
+    state = H::combine_contiguous(std::move(state), buf_, PiecewiseChunkSize());
+    data += bytes_needed;
+    size -= bytes_needed;
+  }
+
+  // Hash whatever chunks we can without copying
+  while (size >= PiecewiseChunkSize()) {
+    state = H::combine_contiguous(std::move(state), data, PiecewiseChunkSize());
+    data += PiecewiseChunkSize();
+    size -= PiecewiseChunkSize();
+  }
+  // Fill the buffer with the remainder
+  memcpy(buf_, data, size);
+  position_ = size;
+  return state;
+}
+
+// HashStateBase::PiecewiseCombiner::finalize()
+template <typename H>
+H PiecewiseCombiner::finalize(H state) {
+  // Hash the remainder left in the buffer, which may be empty
+  return H::combine_contiguous(std::move(state), buf_, position_);
+}
+
+}  // namespace hash_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_HASH_INTERNAL_HASH_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/hash/internal/low_level_hash.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/hash/internal/low_level_hash.h
new file mode 100644
index 0000000000000000000000000000000000000000..439968aa9808d7aaacb82de872b76c4fc8b5b261
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/hash/internal/low_level_hash.h
@@ -0,0 +1,50 @@
+// Copyright 2020 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// This file provides the Google-internal implementation of LowLevelHash.
+//
+// LowLevelHash is a fast hash function for hash tables, the fastest we've
+// currently (late 2020) found that passes the SMHasher tests. The algorithm
+// relies on intrinsic 128-bit multiplication for speed. This is not meant to be
+// secure - just fast.
+//
+// It is closely based on a version of wyhash, but does not maintain or
+// guarantee future compatibility with it.
+
+#ifndef ABSL_HASH_INTERNAL_LOW_LEVEL_HASH_H_
+#define ABSL_HASH_INTERNAL_LOW_LEVEL_HASH_H_
+
+#include <stdint.h>
+#include <stdlib.h>
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace hash_internal {
+
+// Hash function for a byte array. A 64-bit seed and a set of five 64-bit
+// integers are hashed into the result.
+//
+// To allow all hashable types (including string_view and Span) to depend on
+// this algorithm, we keep the API low-level, with as few dependencies as
+// possible.
+uint64_t LowLevelHash(const void* data, size_t len, uint64_t seed,
+                      const uint64_t salt[5]);
+
+}  // namespace hash_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_HASH_INTERNAL_LOW_LEVEL_HASH_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/check.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/check.h
new file mode 100644
index 0000000000000000000000000000000000000000..33145a57e9de19c2354645a61df438c8128f33a1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/check.h
@@ -0,0 +1,183 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/check.h
+// -----------------------------------------------------------------------------
+//
+// This header declares a family of `CHECK` macros.
+//
+// `CHECK` macros terminate the program with a fatal error if the specified
+// condition is not true.
+//
+// Except for those whose names begin with `DCHECK`, these macros are not
+// controlled by `NDEBUG` (cf. `assert`), so the check will be executed
+// regardless of compilation mode. `CHECK` and friends are thus useful for
+// confirming invariants in situations where continuing to run would be worse
+// than terminating, e.g., due to risk of data corruption or security
+// compromise.  It is also more robust and portable to deliberately terminate
+// at a particular place with a useful message and backtrace than to assume some
+// ultimately unspecified and unreliable crashing behavior (such as a
+// "segmentation fault").
+
+#ifndef ABSL_LOG_CHECK_H_
+#define ABSL_LOG_CHECK_H_
+
+#include "absl/log/internal/check_impl.h"
+#include "absl/log/internal/check_op.h"     // IWYU pragma: export
+#include "absl/log/internal/conditions.h"   // IWYU pragma: export
+#include "absl/log/internal/log_message.h"  // IWYU pragma: export
+#include "absl/log/internal/strip.h"        // IWYU pragma: export
+
+// CHECK()
+//
+// `CHECK` terminates the program with a fatal error if `condition` is not true.
+//
+// The message may include additional information such as stack traces, when
+// available.
+//
+// Example:
+//
+//   CHECK(!cheese.empty()) << "Out of Cheese";
+//
+// Might produce a message like:
+//
+//   Check failed: !cheese.empty() Out of Cheese
+#define CHECK(condition) ABSL_CHECK_IMPL((condition), #condition)
+
+// QCHECK()
+//
+// `QCHECK` behaves like `CHECK` but does not print a full stack trace and does
+// not run registered error handlers (as `QFATAL`).  It is useful when the
+// problem is definitely unrelated to program flow, e.g. when validating user
+// input.
+#define QCHECK(condition) ABSL_QCHECK_IMPL((condition), #condition)
+
+// PCHECK()
+//
+// `PCHECK` behaves like `CHECK` but appends a description of the current state
+// of `errno` to the failure message.
+//
+// Example:
+//
+//   int fd = open("/var/empty/missing", O_RDONLY);
+//   PCHECK(fd != -1) << "posix is difficult";
+//
+// Might produce a message like:
+//
+//   Check failed: fd != -1 posix is difficult: No such file or directory [2]
+#define PCHECK(condition) ABSL_PCHECK_IMPL((condition), #condition)
+
+// DCHECK()
+//
+// `DCHECK` behaves like `CHECK` in debug mode and does nothing otherwise (as
+// `DLOG`).  Unlike with `CHECK` (but as with `assert`), it is not safe to rely
+// on evaluation of `condition`: when `NDEBUG` is enabled, DCHECK does not
+// evaluate the condition.
+#define DCHECK(condition) ABSL_DCHECK_IMPL((condition), #condition)
+
+// `CHECK_EQ` and friends are syntactic sugar for `CHECK(x == y)` that
+// automatically output the expression being tested and the evaluated values on
+// either side.
+//
+// Example:
+//
+//   int x = 3, y = 5;
+//   CHECK_EQ(2 * x, y) << "oops!";
+//
+// Might produce a message like:
+//
+//   Check failed: 2 * x == y (6 vs. 5) oops!
+//
+// The values must implement the appropriate comparison operator as well as
+// `operator<<(std::ostream&, ...)`.  Care is taken to ensure that each
+// argument is evaluated exactly once, and that anything which is legal to pass
+// as a function argument is legal here.  In particular, the arguments may be
+// temporary expressions which will end up being destroyed at the end of the
+// statement,
+//
+// Example:
+//
+//   CHECK_EQ(std::string("abc")[1], 'b');
+//
+// WARNING: Passing `NULL` as an argument to `CHECK_EQ` and similar macros does
+// not compile.  Use `nullptr` instead.
+#define CHECK_EQ(val1, val2) ABSL_CHECK_EQ_IMPL((val1), #val1, (val2), #val2)
+#define CHECK_NE(val1, val2) ABSL_CHECK_NE_IMPL((val1), #val1, (val2), #val2)
+#define CHECK_LE(val1, val2) ABSL_CHECK_LE_IMPL((val1), #val1, (val2), #val2)
+#define CHECK_LT(val1, val2) ABSL_CHECK_LT_IMPL((val1), #val1, (val2), #val2)
+#define CHECK_GE(val1, val2) ABSL_CHECK_GE_IMPL((val1), #val1, (val2), #val2)
+#define CHECK_GT(val1, val2) ABSL_CHECK_GT_IMPL((val1), #val1, (val2), #val2)
+#define QCHECK_EQ(val1, val2) ABSL_QCHECK_EQ_IMPL((val1), #val1, (val2), #val2)
+#define QCHECK_NE(val1, val2) ABSL_QCHECK_NE_IMPL((val1), #val1, (val2), #val2)
+#define QCHECK_LE(val1, val2) ABSL_QCHECK_LE_IMPL((val1), #val1, (val2), #val2)
+#define QCHECK_LT(val1, val2) ABSL_QCHECK_LT_IMPL((val1), #val1, (val2), #val2)
+#define QCHECK_GE(val1, val2) ABSL_QCHECK_GE_IMPL((val1), #val1, (val2), #val2)
+#define QCHECK_GT(val1, val2) ABSL_QCHECK_GT_IMPL((val1), #val1, (val2), #val2)
+#define DCHECK_EQ(val1, val2) ABSL_DCHECK_EQ_IMPL((val1), #val1, (val2), #val2)
+#define DCHECK_NE(val1, val2) ABSL_DCHECK_NE_IMPL((val1), #val1, (val2), #val2)
+#define DCHECK_LE(val1, val2) ABSL_DCHECK_LE_IMPL((val1), #val1, (val2), #val2)
+#define DCHECK_LT(val1, val2) ABSL_DCHECK_LT_IMPL((val1), #val1, (val2), #val2)
+#define DCHECK_GE(val1, val2) ABSL_DCHECK_GE_IMPL((val1), #val1, (val2), #val2)
+#define DCHECK_GT(val1, val2) ABSL_DCHECK_GT_IMPL((val1), #val1, (val2), #val2)
+
+// `CHECK_OK` and friends validate that the provided `absl::Status` or
+// `absl::StatusOr<T>` is OK.  If it isn't, they print a failure message that
+// includes the actual status and terminate the program.
+//
+// As with all `DCHECK` variants, `DCHECK_OK` has no effect (not even
+// evaluating its argument) if `NDEBUG` is enabled.
+//
+// Example:
+//
+//   CHECK_OK(FunctionReturnsStatus(x, y, z)) << "oops!";
+//
+// Might produce a message like:
+//
+//   Check failed: FunctionReturnsStatus(x, y, z) is OK (ABORTED: timeout) oops!
+#define CHECK_OK(status) ABSL_CHECK_OK_IMPL((status), #status)
+#define QCHECK_OK(status) ABSL_QCHECK_OK_IMPL((status), #status)
+#define DCHECK_OK(status) ABSL_DCHECK_OK_IMPL((status), #status)
+
+// `CHECK_STREQ` and friends provide `CHECK_EQ` functionality for C strings,
+// i.e., nul-terminated char arrays.  The `CASE` versions are case-insensitive.
+//
+// Example:
+//
+//   CHECK_STREQ(argv[0], "./skynet");
+//
+// Note that both arguments may be temporary strings which are destroyed by the
+// compiler at the end of the current full expression.
+//
+// Example:
+//
+//   CHECK_STREQ(Foo().c_str(), Bar().c_str());
+#define CHECK_STREQ(s1, s2) ABSL_CHECK_STREQ_IMPL((s1), #s1, (s2), #s2)
+#define CHECK_STRNE(s1, s2) ABSL_CHECK_STRNE_IMPL((s1), #s1, (s2), #s2)
+#define CHECK_STRCASEEQ(s1, s2) ABSL_CHECK_STRCASEEQ_IMPL((s1), #s1, (s2), #s2)
+#define CHECK_STRCASENE(s1, s2) ABSL_CHECK_STRCASENE_IMPL((s1), #s1, (s2), #s2)
+#define QCHECK_STREQ(s1, s2) ABSL_QCHECK_STREQ_IMPL((s1), #s1, (s2), #s2)
+#define QCHECK_STRNE(s1, s2) ABSL_QCHECK_STRNE_IMPL((s1), #s1, (s2), #s2)
+#define QCHECK_STRCASEEQ(s1, s2) \
+  ABSL_QCHECK_STRCASEEQ_IMPL((s1), #s1, (s2), #s2)
+#define QCHECK_STRCASENE(s1, s2) \
+  ABSL_QCHECK_STRCASENE_IMPL((s1), #s1, (s2), #s2)
+#define DCHECK_STREQ(s1, s2) ABSL_DCHECK_STREQ_IMPL((s1), #s1, (s2), #s2)
+#define DCHECK_STRNE(s1, s2) ABSL_DCHECK_STRNE_IMPL((s1), #s1, (s2), #s2)
+#define DCHECK_STRCASEEQ(s1, s2) \
+  ABSL_DCHECK_STRCASEEQ_IMPL((s1), #s1, (s2), #s2)
+#define DCHECK_STRCASENE(s1, s2) \
+  ABSL_DCHECK_STRCASENE_IMPL((s1), #s1, (s2), #s2)
+
+#endif  // ABSL_LOG_CHECK_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/globals.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/globals.h
new file mode 100644
index 0000000000000000000000000000000000000000..32b87db0586b504c1d48a188b1fb5e067a7169ac
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/globals.h
@@ -0,0 +1,165 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/globals.h
+// -----------------------------------------------------------------------------
+//
+// This header declares global logging library configuration knobs.
+
+#ifndef ABSL_LOG_GLOBALS_H_
+#define ABSL_LOG_GLOBALS_H_
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/base/log_severity.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+//------------------------------------------------------------------------------
+//  Minimum Log Level
+//------------------------------------------------------------------------------
+//
+// Messages logged at or above this severity are directed to all registered log
+// sinks or skipped otherwise. This parameter can also be modified using
+// command line flag --minloglevel.
+// See absl/base/log_severity.h for descriptions of severity levels.
+
+// MinLogLevel()
+//
+// Returns the value of the Minimum Log Level parameter.
+// This function is async-signal-safe.
+ABSL_MUST_USE_RESULT absl::LogSeverityAtLeast MinLogLevel();
+
+// SetMinLogLevel()
+//
+// Updates the value of Minimum Log Level parameter.
+// This function is async-signal-safe.
+void SetMinLogLevel(absl::LogSeverityAtLeast severity);
+
+namespace log_internal {
+
+// ScopedMinLogLevel
+//
+// RAII type used to temporarily update the Min Log Level parameter.
+class ScopedMinLogLevel final {
+ public:
+  explicit ScopedMinLogLevel(absl::LogSeverityAtLeast severity);
+  ScopedMinLogLevel(const ScopedMinLogLevel&) = delete;
+  ScopedMinLogLevel& operator=(const ScopedMinLogLevel&) = delete;
+  ~ScopedMinLogLevel();
+
+ private:
+  absl::LogSeverityAtLeast saved_severity_;
+};
+
+}  // namespace log_internal
+
+//------------------------------------------------------------------------------
+// Stderr Threshold
+//------------------------------------------------------------------------------
+//
+// Messages logged at or above this level are directed to stderr in
+// addition to other registered log sinks. This parameter can also be modified
+// using command line flag --stderrthreshold.
+// See absl/base/log_severity.h for descriptions of severity levels.
+
+// StderrThreshold()
+//
+// Returns the value of the Stderr Threshold parameter.
+// This function is async-signal-safe.
+ABSL_MUST_USE_RESULT absl::LogSeverityAtLeast StderrThreshold();
+
+// SetStderrThreshold()
+//
+// Updates the Stderr Threshold parameter.
+// This function is async-signal-safe.
+void SetStderrThreshold(absl::LogSeverityAtLeast severity);
+inline void SetStderrThreshold(absl::LogSeverity severity) {
+  absl::SetStderrThreshold(static_cast<absl::LogSeverityAtLeast>(severity));
+}
+
+// ScopedStderrThreshold
+//
+// RAII type used to temporarily update the Stderr Threshold parameter.
+class ScopedStderrThreshold final {
+ public:
+  explicit ScopedStderrThreshold(absl::LogSeverityAtLeast severity);
+  ScopedStderrThreshold(const ScopedStderrThreshold&) = delete;
+  ScopedStderrThreshold& operator=(const ScopedStderrThreshold&) = delete;
+  ~ScopedStderrThreshold();
+
+ private:
+  absl::LogSeverityAtLeast saved_severity_;
+};
+
+//------------------------------------------------------------------------------
+// Log Backtrace At
+//------------------------------------------------------------------------------
+//
+// Users can request backtrace to be logged at specific locations, specified
+// by file and line number.
+
+// ShouldLogBacktraceAt()
+//
+// Returns true if we should log a backtrace at the specified location.
+namespace log_internal {
+ABSL_MUST_USE_RESULT bool ShouldLogBacktraceAt(absl::string_view file,
+                                               int line);
+}  // namespace log_internal
+
+// SetLogBacktraceLocation()
+//
+// Sets the location the backtrace should be logged at.
+void SetLogBacktraceLocation(absl::string_view file, int line);
+
+//------------------------------------------------------------------------------
+// Prepend Log Prefix
+//------------------------------------------------------------------------------
+//
+// This option tells the logging library that every logged message
+// should include the prefix (severity, date, time, PID, etc.)
+
+// ShouldPrependLogPrefix()
+//
+// Returns the value of the Prepend Log Prefix option.
+// This function is async-signal-safe.
+ABSL_MUST_USE_RESULT bool ShouldPrependLogPrefix();
+
+// EnableLogPrefix()
+//
+// Updates the value of the Prepend Log Prefix option.
+// This function is async-signal-safe.
+void EnableLogPrefix(bool on_off);
+
+namespace log_internal {
+
+using LoggingGlobalsListener = void (*)();
+void SetLoggingGlobalsListener(LoggingGlobalsListener l);
+
+// Internal implementation for the setter routines. These are used
+// to break circular dependencies between flags and globals. Each "Raw"
+// routine corresponds to the non-"Raw" counterpart and used to set the
+// configuration parameter directly without calling back to the listener.
+void RawSetMinLogLevel(absl::LogSeverityAtLeast severity);
+void RawSetStderrThreshold(absl::LogSeverityAtLeast severity);
+void RawEnableLogPrefix(bool on_off);
+
+}  // namespace log_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_LOG_GLOBALS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/append_truncated.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/append_truncated.h
new file mode 100644
index 0000000000000000000000000000000000000000..f0e7912c2ac51109ca7f8ea849b136f97ae9c1d5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/append_truncated.h
@@ -0,0 +1,47 @@
+// Copyright 2022 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_LOG_INTERNAL_APPEND_TRUNCATED_H_
+#define ABSL_LOG_INTERNAL_APPEND_TRUNCATED_H_
+
+#include <cstddef>
+#include <cstring>
+
+#include "absl/base/config.h"
+#include "absl/strings/string_view.h"
+#include "absl/types/span.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace log_internal {
+// Copies into `dst` as many bytes of `src` as will fit, then truncates the
+// copied bytes from the front of `dst` and returns the number of bytes written.
+inline size_t AppendTruncated(absl::string_view src, absl::Span<char> &dst) {
+  if (src.size() > dst.size()) src = src.substr(0, dst.size());
+  memcpy(dst.data(), src.data(), src.size());
+  dst.remove_prefix(src.size());
+  return src.size();
+}
+// Likewise, but `n` copies of `c`.
+inline size_t AppendTruncated(char c, size_t n, absl::Span<char> &dst) {
+  if (n > dst.size()) n = dst.size();
+  memset(dst.data(), c, n);
+  dst.remove_prefix(n);
+  return n;
+}
+}  // namespace log_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_LOG_INTERNAL_APPEND_TRUNCATED_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/check_impl.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/check_impl.h
new file mode 100644
index 0000000000000000000000000000000000000000..c9c28e3ea6a8a5d18783df0c78128628e6843f36
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/check_impl.h
@@ -0,0 +1,150 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_LOG_INTERNAL_CHECK_IMPL_H_
+#define ABSL_LOG_INTERNAL_CHECK_IMPL_H_
+
+#include "absl/base/optimization.h"
+#include "absl/log/internal/check_op.h"
+#include "absl/log/internal/conditions.h"
+#include "absl/log/internal/log_message.h"
+#include "absl/log/internal/strip.h"
+
+// CHECK
+#define ABSL_CHECK_IMPL(condition, condition_text)                    \
+  ABSL_LOG_INTERNAL_CONDITION_FATAL(STATELESS,                        \
+                                    ABSL_PREDICT_FALSE(!(condition))) \
+  ABSL_LOG_INTERNAL_CHECK(condition_text).InternalStream()
+
+#define ABSL_QCHECK_IMPL(condition, condition_text)                    \
+  ABSL_LOG_INTERNAL_CONDITION_QFATAL(STATELESS,                        \
+                                     ABSL_PREDICT_FALSE(!(condition))) \
+  ABSL_LOG_INTERNAL_QCHECK(condition_text).InternalStream()
+
+#define ABSL_PCHECK_IMPL(condition, condition_text) \
+  ABSL_CHECK_IMPL(condition, condition_text).WithPerror()
+
+#ifndef NDEBUG
+#define ABSL_DCHECK_IMPL(condition, condition_text) \
+  ABSL_CHECK_IMPL(condition, condition_text)
+#else
+#define ABSL_DCHECK_IMPL(condition, condition_text) \
+  ABSL_CHECK_IMPL(true || (condition), "true")
+#endif
+
+// CHECK_EQ
+#define ABSL_CHECK_EQ_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_CHECK_OP(Check_EQ, ==, val1, val1_text, val2, val2_text)
+#define ABSL_CHECK_NE_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_CHECK_OP(Check_NE, !=, val1, val1_text, val2, val2_text)
+#define ABSL_CHECK_LE_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_CHECK_OP(Check_LE, <=, val1, val1_text, val2, val2_text)
+#define ABSL_CHECK_LT_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_CHECK_OP(Check_LT, <, val1, val1_text, val2, val2_text)
+#define ABSL_CHECK_GE_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_CHECK_OP(Check_GE, >=, val1, val1_text, val2, val2_text)
+#define ABSL_CHECK_GT_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_CHECK_OP(Check_GT, >, val1, val1_text, val2, val2_text)
+#define ABSL_QCHECK_EQ_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_QCHECK_OP(Check_EQ, ==, val1, val1_text, val2, val2_text)
+#define ABSL_QCHECK_NE_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_QCHECK_OP(Check_NE, !=, val1, val1_text, val2, val2_text)
+#define ABSL_QCHECK_LE_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_QCHECK_OP(Check_LE, <=, val1, val1_text, val2, val2_text)
+#define ABSL_QCHECK_LT_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_QCHECK_OP(Check_LT, <, val1, val1_text, val2, val2_text)
+#define ABSL_QCHECK_GE_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_QCHECK_OP(Check_GE, >=, val1, val1_text, val2, val2_text)
+#define ABSL_QCHECK_GT_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_QCHECK_OP(Check_GT, >, val1, val1_text, val2, val2_text)
+#ifndef NDEBUG
+#define ABSL_DCHECK_EQ_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_CHECK_EQ_IMPL(val1, val1_text, val2, val2_text)
+#define ABSL_DCHECK_NE_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_CHECK_NE_IMPL(val1, val1_text, val2, val2_text)
+#define ABSL_DCHECK_LE_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_CHECK_LE_IMPL(val1, val1_text, val2, val2_text)
+#define ABSL_DCHECK_LT_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_CHECK_LT_IMPL(val1, val1_text, val2, val2_text)
+#define ABSL_DCHECK_GE_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_CHECK_GE_IMPL(val1, val1_text, val2, val2_text)
+#define ABSL_DCHECK_GT_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_CHECK_GT_IMPL(val1, val1_text, val2, val2_text)
+#else  // ndef NDEBUG
+#define ABSL_DCHECK_EQ_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_DCHECK_NOP(val1, val2)
+#define ABSL_DCHECK_NE_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_DCHECK_NOP(val1, val2)
+#define ABSL_DCHECK_LE_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_DCHECK_NOP(val1, val2)
+#define ABSL_DCHECK_LT_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_DCHECK_NOP(val1, val2)
+#define ABSL_DCHECK_GE_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_DCHECK_NOP(val1, val2)
+#define ABSL_DCHECK_GT_IMPL(val1, val1_text, val2, val2_text) \
+  ABSL_LOG_INTERNAL_DCHECK_NOP(val1, val2)
+#endif  // def NDEBUG
+
+// CHECK_OK
+#define ABSL_CHECK_OK_IMPL(status, status_text) \
+  ABSL_LOG_INTERNAL_CHECK_OK(status, status_text)
+#define ABSL_QCHECK_OK_IMPL(status, status_text) \
+  ABSL_LOG_INTERNAL_QCHECK_OK(status, status_text)
+#ifndef NDEBUG
+#define ABSL_DCHECK_OK_IMPL(status, status_text) \
+  ABSL_LOG_INTERNAL_CHECK_OK(status, status_text)
+#else
+#define ABSL_DCHECK_OK_IMPL(status, status_text) \
+  ABSL_LOG_INTERNAL_DCHECK_NOP(status, nullptr)
+#endif
+
+// CHECK_STREQ
+#define ABSL_CHECK_STREQ_IMPL(s1, s1_text, s2, s2_text) \
+  ABSL_LOG_INTERNAL_CHECK_STROP(strcmp, ==, true, s1, s1_text, s2, s2_text)
+#define ABSL_CHECK_STRNE_IMPL(s1, s1_text, s2, s2_text) \
+  ABSL_LOG_INTERNAL_CHECK_STROP(strcmp, !=, false, s1, s1_text, s2, s2_text)
+#define ABSL_CHECK_STRCASEEQ_IMPL(s1, s1_text, s2, s2_text) \
+  ABSL_LOG_INTERNAL_CHECK_STROP(strcasecmp, ==, true, s1, s1_text, s2, s2_text)
+#define ABSL_CHECK_STRCASENE_IMPL(s1, s1_text, s2, s2_text) \
+  ABSL_LOG_INTERNAL_CHECK_STROP(strcasecmp, !=, false, s1, s1_text, s2, s2_text)
+#define ABSL_QCHECK_STREQ_IMPL(s1, s1_text, s2, s2_text) \
+  ABSL_LOG_INTERNAL_QCHECK_STROP(strcmp, ==, true, s1, s1_text, s2, s2_text)
+#define ABSL_QCHECK_STRNE_IMPL(s1, s1_text, s2, s2_text) \
+  ABSL_LOG_INTERNAL_QCHECK_STROP(strcmp, !=, false, s1, s1_text, s2, s2_text)
+#define ABSL_QCHECK_STRCASEEQ_IMPL(s1, s1_text, s2, s2_text) \
+  ABSL_LOG_INTERNAL_QCHECK_STROP(strcasecmp, ==, true, s1, s1_text, s2, s2_text)
+#define ABSL_QCHECK_STRCASENE_IMPL(s1, s1_text, s2, s2_text)             \
+  ABSL_LOG_INTERNAL_QCHECK_STROP(strcasecmp, !=, false, s1, s1_text, s2, \
+                                 s2_text)
+#ifndef NDEBUG
+#define ABSL_DCHECK_STREQ_IMPL(s1, s1_text, s2, s2_text) \
+  ABSL_CHECK_STREQ_IMPL(s1, s1_text, s2, s2_text)
+#define ABSL_DCHECK_STRCASEEQ_IMPL(s1, s1_text, s2, s2_text) \
+  ABSL_CHECK_STRCASEEQ_IMPL(s1, s1_text, s2, s2_text)
+#define ABSL_DCHECK_STRNE_IMPL(s1, s1_text, s2, s2_text) \
+  ABSL_CHECK_STRNE_IMPL(s1, s1_text, s2, s2_text)
+#define ABSL_DCHECK_STRCASENE_IMPL(s1, s1_text, s2, s2_text) \
+  ABSL_CHECK_STRCASENE_IMPL(s1, s1_text, s2, s2_text)
+#else  // ndef NDEBUG
+#define ABSL_DCHECK_STREQ_IMPL(s1, s1_text, s2, s2_text) \
+  ABSL_LOG_INTERNAL_DCHECK_NOP(s1, s2)
+#define ABSL_DCHECK_STRCASEEQ_IMPL(s1, s1_text, s2, s2_text) \
+  ABSL_LOG_INTERNAL_DCHECK_NOP(s1, s2)
+#define ABSL_DCHECK_STRNE_IMPL(s1, s1_text, s2, s2_text) \
+  ABSL_LOG_INTERNAL_DCHECK_NOP(s1, s2)
+#define ABSL_DCHECK_STRCASENE_IMPL(s1, s1_text, s2, s2_text) \
+  ABSL_LOG_INTERNAL_DCHECK_NOP(s1, s2)
+#endif  // def NDEBUG
+
+#endif  // ABSL_LOG_INTERNAL_CHECK_IMPL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/check_op.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/check_op.h
new file mode 100644
index 0000000000000000000000000000000000000000..4907b89bcfb434f9cfad9d7d59fabf4e5b3c9ed3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/check_op.h
@@ -0,0 +1,392 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/internal/check_op.h
+// -----------------------------------------------------------------------------
+//
+// This file declares helpers routines and macros used to implement `CHECK`
+// macros.
+
+#ifndef ABSL_LOG_INTERNAL_CHECK_OP_H_
+#define ABSL_LOG_INTERNAL_CHECK_OP_H_
+
+#include <stdint.h>
+
+#include <ostream>
+#include <sstream>
+#include <string>
+#include <utility>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/base/optimization.h"
+#include "absl/log/internal/nullguard.h"
+#include "absl/log/internal/nullstream.h"
+#include "absl/log/internal/strip.h"
+
+// `ABSL_LOG_INTERNAL_STRIP_STRING_LITERAL` wraps string literals that
+// should be stripped when `ABSL_MIN_LOG_LEVEL` exceeds `kFatal`.
+#ifdef ABSL_MIN_LOG_LEVEL
+#define ABSL_LOG_INTERNAL_STRIP_STRING_LITERAL(literal)         \
+  (::absl::LogSeverity::kFatal >=                               \
+           static_cast<::absl::LogSeverity>(ABSL_MIN_LOG_LEVEL) \
+       ? (literal)                                              \
+       : "")
+#else
+#define ABSL_LOG_INTERNAL_STRIP_STRING_LITERAL(literal) (literal)
+#endif
+
+#ifdef NDEBUG
+// `NDEBUG` is defined, so `DCHECK_EQ(x, y)` and so on do nothing.  However, we
+// still want the compiler to parse `x` and `y`, because we don't want to lose
+// potentially useful errors and warnings.
+#define ABSL_LOG_INTERNAL_DCHECK_NOP(x, y)   \
+  while (false && ((void)(x), (void)(y), 0)) \
+  ::absl::log_internal::NullStream().InternalStream()
+#endif
+
+#define ABSL_LOG_INTERNAL_CHECK_OP(name, op, val1, val1_text, val2, val2_text) \
+  while (                                                                      \
+      ::std::string* absl_log_internal_check_op_result ABSL_ATTRIBUTE_UNUSED = \
+          ::absl::log_internal::name##Impl(                                    \
+              ::absl::log_internal::GetReferenceableValue(val1),               \
+              ::absl::log_internal::GetReferenceableValue(val2),               \
+              ABSL_LOG_INTERNAL_STRIP_STRING_LITERAL(val1_text                 \
+                                                     " " #op " " val2_text)))  \
+  ABSL_LOG_INTERNAL_CHECK(*absl_log_internal_check_op_result).InternalStream()
+#define ABSL_LOG_INTERNAL_QCHECK_OP(name, op, val1, val1_text, val2, \
+                                    val2_text)                       \
+  while (::std::string* absl_log_internal_qcheck_op_result =         \
+             ::absl::log_internal::name##Impl(                       \
+                 ::absl::log_internal::GetReferenceableValue(val1),  \
+                 ::absl::log_internal::GetReferenceableValue(val2),  \
+                 ABSL_LOG_INTERNAL_STRIP_STRING_LITERAL(             \
+                     val1_text " " #op " " val2_text)))              \
+  ABSL_LOG_INTERNAL_QCHECK(*absl_log_internal_qcheck_op_result).InternalStream()
+#define ABSL_LOG_INTERNAL_CHECK_STROP(func, op, expected, s1, s1_text, s2,     \
+                                      s2_text)                                 \
+  while (::std::string* absl_log_internal_check_strop_result =                 \
+             ::absl::log_internal::Check##func##expected##Impl(                \
+                 (s1), (s2),                                                   \
+                 ABSL_LOG_INTERNAL_STRIP_STRING_LITERAL(s1_text " " #op        \
+                                                                " " s2_text))) \
+  ABSL_LOG_INTERNAL_CHECK(*absl_log_internal_check_strop_result)               \
+      .InternalStream()
+#define ABSL_LOG_INTERNAL_QCHECK_STROP(func, op, expected, s1, s1_text, s2,    \
+                                       s2_text)                                \
+  while (::std::string* absl_log_internal_qcheck_strop_result =                \
+             ::absl::log_internal::Check##func##expected##Impl(                \
+                 (s1), (s2),                                                   \
+                 ABSL_LOG_INTERNAL_STRIP_STRING_LITERAL(s1_text " " #op        \
+                                                                " " s2_text))) \
+  ABSL_LOG_INTERNAL_QCHECK(*absl_log_internal_qcheck_strop_result)             \
+      .InternalStream()
+// This one is tricky:
+// * We must evaluate `val` exactly once, yet we need to do two things with it:
+//   evaluate `.ok()` and (sometimes) `.ToString()`.
+// * `val` might be an `absl::Status` or some `absl::StatusOr<T>`.
+// * `val` might be e.g. `ATemporary().GetStatus()`, which may return a
+//   reference to a member of `ATemporary` that is only valid until the end of
+//   the full expression.
+// * We don't want this file to depend on `absl::Status` `#include`s or linkage,
+//   nor do we want to move the definition to status and introduce a dependency
+//   in the other direction.  We can be assured that callers must already have a
+//   `Status` and the necessary `#include`s and linkage.
+// * Callsites should be small and fast (at least when `val.ok()`): one branch,
+//   minimal stack footprint.
+//   * In particular, the string concat stuff should be out-of-line and emitted
+//     in only one TU to save linker input size
+// * We want the `val.ok()` check inline so static analyzers and optimizers can
+//   see it.
+// * As usual, no braces so we can stream into the expansion with `operator<<`.
+// * Also as usual, it must expand to a single (partial) statement with no
+//   ambiguous-else problems.
+#define ABSL_LOG_INTERNAL_CHECK_OK(val, val_text)                        \
+  for (::std::pair<const ::absl::Status*, ::std::string*>                \
+           absl_log_internal_check_ok_goo;                               \
+       absl_log_internal_check_ok_goo.first =                            \
+           ::absl::log_internal::AsStatus(val),                          \
+       absl_log_internal_check_ok_goo.second =                           \
+           ABSL_PREDICT_TRUE(absl_log_internal_check_ok_goo.first->ok()) \
+               ? nullptr                                                 \
+               : ::absl::status_internal::MakeCheckFailString(           \
+                     absl_log_internal_check_ok_goo.first,               \
+                     ABSL_LOG_INTERNAL_STRIP_STRING_LITERAL(val_text     \
+                                                            " is OK")),  \
+       !ABSL_PREDICT_TRUE(absl_log_internal_check_ok_goo.first->ok());)  \
+  ABSL_LOG_INTERNAL_CHECK(*absl_log_internal_check_ok_goo.second)        \
+      .InternalStream()
+#define ABSL_LOG_INTERNAL_QCHECK_OK(val, val_text)                       \
+  for (::std::pair<const ::absl::Status*, ::std::string*>                \
+           absl_log_internal_check_ok_goo;                               \
+       absl_log_internal_check_ok_goo.first =                            \
+           ::absl::log_internal::AsStatus(val),                          \
+       absl_log_internal_check_ok_goo.second =                           \
+           ABSL_PREDICT_TRUE(absl_log_internal_check_ok_goo.first->ok()) \
+               ? nullptr                                                 \
+               : ::absl::status_internal::MakeCheckFailString(           \
+                     absl_log_internal_check_ok_goo.first,               \
+                     ABSL_LOG_INTERNAL_STRIP_STRING_LITERAL(val_text     \
+                                                            " is OK")),  \
+       !ABSL_PREDICT_TRUE(absl_log_internal_check_ok_goo.first->ok());)  \
+  ABSL_LOG_INTERNAL_QCHECK(*absl_log_internal_check_ok_goo.second)       \
+      .InternalStream()
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+class Status;
+template <typename T>
+class StatusOr;
+
+namespace status_internal {
+std::string* MakeCheckFailString(const absl::Status* status,
+                                 const char* prefix);
+}  // namespace status_internal
+
+namespace log_internal {
+
+// Convert a Status or a StatusOr to its underlying status value.
+//
+// (This implementation does not require a dep on absl::Status to work.)
+inline const absl::Status* AsStatus(const absl::Status& s) { return &s; }
+template <typename T>
+const absl::Status* AsStatus(const absl::StatusOr<T>& s) {
+  return &s.status();
+}
+
+// A helper class for formatting `expr (V1 vs. V2)` in a `CHECK_XX` statement.
+// See `MakeCheckOpString` for sample usage.
+class CheckOpMessageBuilder final {
+ public:
+  // Inserts `exprtext` and ` (` to the stream.
+  explicit CheckOpMessageBuilder(const char* exprtext);
+  ~CheckOpMessageBuilder() = default;
+  // For inserting the first variable.
+  std::ostream& ForVar1() { return stream_; }
+  // For inserting the second variable (adds an intermediate ` vs. `).
+  std::ostream& ForVar2();
+  // Get the result (inserts the closing `)`).
+  std::string* NewString();
+
+ private:
+  std::ostringstream stream_;
+};
+
+// This formats a value for a failing `CHECK_XX` statement.  Ordinarily, it uses
+// the definition for `operator<<`, with a few special cases below.
+template <typename T>
+inline void MakeCheckOpValueString(std::ostream& os, const T& v) {
+  os << log_internal::NullGuard<T>::Guard(v);
+}
+
+// Overloads for char types provide readable values for unprintable characters.
+void MakeCheckOpValueString(std::ostream& os, char v);
+void MakeCheckOpValueString(std::ostream& os, signed char v);
+void MakeCheckOpValueString(std::ostream& os, unsigned char v);
+void MakeCheckOpValueString(std::ostream& os, const void* p);
+
+namespace detect_specialization {
+
+// MakeCheckOpString is being specialized for every T and U pair that is being
+// passed to the CHECK_op macros. However, there is a lot of redundancy in these
+// specializations that creates unnecessary library and binary bloat.
+// The number of instantiations tends to be O(n^2) because we have two
+// independent inputs. This technique works by reducing `n`.
+//
+// Most user-defined types being passed to CHECK_op end up being printed as a
+// builtin type. For example, enums tend to be implicitly converted to its
+// underlying type when calling operator<<, and pointers are printed with the
+// `const void*` overload.
+// To reduce the number of instantiations we coerce these values before calling
+// MakeCheckOpString instead of inside it.
+//
+// To detect if this coercion is needed, we duplicate all the relevant
+// operator<< overloads as specified in the standard, just in a different
+// namespace. If the call to `stream << value` becomes ambiguous, it means that
+// one of these overloads is the one selected by overload resolution. We then
+// do overload resolution again just with our overload set to see which one gets
+// selected. That tells us which type to coerce to.
+// If the augmented call was not ambiguous, it means that none of these were
+// selected and we can't coerce the input.
+//
+// As a secondary step to reduce code duplication, we promote integral types to
+// their 64-bit variant. This does not change the printed value, but reduces the
+// number of instantiations even further. Promoting an integer is very cheap at
+// the call site.
+int64_t operator<<(std::ostream&, short value);           // NOLINT
+int64_t operator<<(std::ostream&, unsigned short value);  // NOLINT
+int64_t operator<<(std::ostream&, int value);
+int64_t operator<<(std::ostream&, unsigned int value);
+int64_t operator<<(std::ostream&, long value);                 // NOLINT
+uint64_t operator<<(std::ostream&, unsigned long value);       // NOLINT
+int64_t operator<<(std::ostream&, long long value);            // NOLINT
+uint64_t operator<<(std::ostream&, unsigned long long value);  // NOLINT
+float operator<<(std::ostream&, float value);
+double operator<<(std::ostream&, double value);
+long double operator<<(std::ostream&, long double value);
+bool operator<<(std::ostream&, bool value);
+const void* operator<<(std::ostream&, const void* value);
+const void* operator<<(std::ostream&, std::nullptr_t);
+
+// These `char` overloads are specified like this in the standard, so we have to
+// write them exactly the same to ensure the call is ambiguous.
+// If we wrote it in a different way (eg taking std::ostream instead of the
+// template) then one call might have a higher rank than the other and it would
+// not be ambiguous.
+template <typename Traits>
+char operator<<(std::basic_ostream<char, Traits>&, char);
+template <typename Traits>
+signed char operator<<(std::basic_ostream<char, Traits>&, signed char);
+template <typename Traits>
+unsigned char operator<<(std::basic_ostream<char, Traits>&, unsigned char);
+template <typename Traits>
+const char* operator<<(std::basic_ostream<char, Traits>&, const char*);
+template <typename Traits>
+const signed char* operator<<(std::basic_ostream<char, Traits>&,
+                              const signed char*);
+template <typename Traits>
+const unsigned char* operator<<(std::basic_ostream<char, Traits>&,
+                                const unsigned char*);
+
+// This overload triggers when the call is not ambiguous.
+// It means that T is being printed with some overload not on this list.
+// We keep the value as `const T&`.
+template <typename T, typename = decltype(std::declval<std::ostream&>()
+                                          << std::declval<const T&>())>
+const T& Detect(int);
+
+// This overload triggers when the call is ambiguous.
+// It means that T is either one from this list or printed as one from this
+// list. Eg an enum that decays to `int` for printing.
+// We ask the overload set to give us the type we want to convert it to.
+template <typename T>
+decltype(detect_specialization::operator<<(std::declval<std::ostream&>(),
+                                           std::declval<const T&>()))
+Detect(char);
+
+}  // namespace detect_specialization
+
+template <typename T>
+using CheckOpStreamType = decltype(detect_specialization::Detect<T>(0));
+
+// Build the error message string.  Specify no inlining for code size.
+template <typename T1, typename T2>
+ABSL_ATTRIBUTE_RETURNS_NONNULL std::string* MakeCheckOpString(
+    T1 v1, T2 v2, const char* exprtext) ABSL_ATTRIBUTE_NOINLINE;
+
+template <typename T1, typename T2>
+std::string* MakeCheckOpString(T1 v1, T2 v2, const char* exprtext) {
+  CheckOpMessageBuilder comb(exprtext);
+  MakeCheckOpValueString(comb.ForVar1(), v1);
+  MakeCheckOpValueString(comb.ForVar2(), v2);
+  return comb.NewString();
+}
+
+// Add a few commonly used instantiations as extern to reduce size of objects
+// files.
+#define ABSL_LOG_INTERNAL_DEFINE_MAKE_CHECK_OP_STRING_EXTERN(x) \
+  extern template std::string* MakeCheckOpString(x, x, const char*)
+ABSL_LOG_INTERNAL_DEFINE_MAKE_CHECK_OP_STRING_EXTERN(bool);
+ABSL_LOG_INTERNAL_DEFINE_MAKE_CHECK_OP_STRING_EXTERN(int64_t);
+ABSL_LOG_INTERNAL_DEFINE_MAKE_CHECK_OP_STRING_EXTERN(uint64_t);
+ABSL_LOG_INTERNAL_DEFINE_MAKE_CHECK_OP_STRING_EXTERN(float);
+ABSL_LOG_INTERNAL_DEFINE_MAKE_CHECK_OP_STRING_EXTERN(double);
+ABSL_LOG_INTERNAL_DEFINE_MAKE_CHECK_OP_STRING_EXTERN(char);
+ABSL_LOG_INTERNAL_DEFINE_MAKE_CHECK_OP_STRING_EXTERN(unsigned char);
+ABSL_LOG_INTERNAL_DEFINE_MAKE_CHECK_OP_STRING_EXTERN(const std::string&);
+ABSL_LOG_INTERNAL_DEFINE_MAKE_CHECK_OP_STRING_EXTERN(const absl::string_view&);
+ABSL_LOG_INTERNAL_DEFINE_MAKE_CHECK_OP_STRING_EXTERN(const char*);
+ABSL_LOG_INTERNAL_DEFINE_MAKE_CHECK_OP_STRING_EXTERN(const signed char*);
+ABSL_LOG_INTERNAL_DEFINE_MAKE_CHECK_OP_STRING_EXTERN(const unsigned char*);
+ABSL_LOG_INTERNAL_DEFINE_MAKE_CHECK_OP_STRING_EXTERN(const void*);
+#undef ABSL_LOG_INTERNAL_DEFINE_MAKE_CHECK_OP_STRING_EXTERN
+
+// Helper functions for `ABSL_LOG_INTERNAL_CHECK_OP` macro family.  The
+// `(int, int)` override works around the issue that the compiler will not
+// instantiate the template version of the function on values of unnamed enum
+// type.
+#define ABSL_LOG_INTERNAL_CHECK_OP_IMPL(name, op)                        \
+  template <typename T1, typename T2>                                    \
+  inline constexpr ::std::string* name##Impl(const T1& v1, const T2& v2, \
+                                             const char* exprtext) {     \
+    using U1 = CheckOpStreamType<T1>;                                    \
+    using U2 = CheckOpStreamType<T2>;                                    \
+    return ABSL_PREDICT_TRUE(v1 op v2)                                   \
+               ? nullptr                                                 \
+               : MakeCheckOpString<U1, U2>(v1, v2, exprtext);            \
+  }                                                                      \
+  inline constexpr ::std::string* name##Impl(int v1, int v2,             \
+                                             const char* exprtext) {     \
+    return name##Impl<int, int>(v1, v2, exprtext);                       \
+  }
+
+ABSL_LOG_INTERNAL_CHECK_OP_IMPL(Check_EQ, ==)
+ABSL_LOG_INTERNAL_CHECK_OP_IMPL(Check_NE, !=)
+ABSL_LOG_INTERNAL_CHECK_OP_IMPL(Check_LE, <=)
+ABSL_LOG_INTERNAL_CHECK_OP_IMPL(Check_LT, <)
+ABSL_LOG_INTERNAL_CHECK_OP_IMPL(Check_GE, >=)
+ABSL_LOG_INTERNAL_CHECK_OP_IMPL(Check_GT, >)
+#undef ABSL_LOG_INTERNAL_CHECK_OP_IMPL
+
+std::string* CheckstrcmptrueImpl(const char* s1, const char* s2,
+                                 const char* exprtext);
+std::string* CheckstrcmpfalseImpl(const char* s1, const char* s2,
+                                  const char* exprtext);
+std::string* CheckstrcasecmptrueImpl(const char* s1, const char* s2,
+                                     const char* exprtext);
+std::string* CheckstrcasecmpfalseImpl(const char* s1, const char* s2,
+                                      const char* exprtext);
+
+// `CHECK_EQ` and friends want to pass their arguments by reference, however
+// this winds up exposing lots of cases where people have defined and
+// initialized static const data members but never declared them (i.e. in a .cc
+// file), meaning they are not referenceable.  This function avoids that problem
+// for integers (the most common cases) by overloading for every primitive
+// integer type, even the ones we discourage, and returning them by value.
+template <typename T>
+inline constexpr const T& GetReferenceableValue(const T& t) {
+  return t;
+}
+inline constexpr char GetReferenceableValue(char t) { return t; }
+inline constexpr unsigned char GetReferenceableValue(unsigned char t) {
+  return t;
+}
+inline constexpr signed char GetReferenceableValue(signed char t) { return t; }
+inline constexpr short GetReferenceableValue(short t) { return t; }  // NOLINT
+inline constexpr unsigned short GetReferenceableValue(               // NOLINT
+    unsigned short t) {                                              // NOLINT
+  return t;
+}
+inline constexpr int GetReferenceableValue(int t) { return t; }
+inline unsigned int GetReferenceableValue(unsigned int t) { return t; }
+inline constexpr long GetReferenceableValue(long t) { return t; }  // NOLINT
+inline constexpr unsigned long GetReferenceableValue(              // NOLINT
+    unsigned long t) {                                             // NOLINT
+  return t;
+}
+inline constexpr long long GetReferenceableValue(long long t) {  // NOLINT
+  return t;
+}
+inline constexpr unsigned long long GetReferenceableValue(  // NOLINT
+    unsigned long long t) {                                 // NOLINT
+  return t;
+}
+
+}  // namespace log_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_LOG_INTERNAL_CHECK_OP_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/conditions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/conditions.h
new file mode 100644
index 0000000000000000000000000000000000000000..b89f1dfd7bca15a011f3e5390bebf4370986f352
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/conditions.h
@@ -0,0 +1,222 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/internal/conditions.h
+// -----------------------------------------------------------------------------
+//
+// This file contains implementation of conditional log statements, like LOG_IF
+// including all the ABSL_LOG_INTERNAL_..._CONDITION_... macros and
+// various condition classes like LogEveryNState.
+
+#ifndef ABSL_LOG_INTERNAL_CONDITIONS_H_
+#define ABSL_LOG_INTERNAL_CONDITIONS_H_
+
+#ifdef _WIN32
+#include <cstdlib>
+#else
+#include <unistd.h>
+#endif
+#include <stdlib.h>
+
+#include <atomic>
+#include <cstdint>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/log/internal/voidify.h"
+
+// `ABSL_LOG_INTERNAL_CONDITION` prefixes another macro that expands to a
+// temporary `LogMessage` instantiation followed by zero or more streamed
+// expressions.  This definition is tricky to read correctly.  It evaluates to
+// either
+//
+//   (void)0;
+//
+// or
+//
+//   ::absl::log_internal::Voidify() &&
+//       ::absl::log_internal::LogMessage(...) << "the user's message";
+//
+// If the condition is evaluable at compile time, as is often the case, it
+// compiles away to just one side or the other.
+//
+// Although this is not used anywhere a statement (e.g. `if`) could not go,
+// the ternary expression does a better job avoiding spurious diagnostics
+// (dangling else, missing switch case) and preserving noreturn semantics (e.g.
+// on `LOG(FATAL)`) without requiring braces.
+#define ABSL_LOG_INTERNAL_STATELESS_CONDITION(condition) \
+  switch (0)                                             \
+  case 0:                                                \
+    !(condition) ? (void)0 : ::absl::log_internal::Voidify()&&
+
+// `ABSL_LOG_INTERNAL_STATEFUL_CONDITION` applies a condition like
+// `ABSL_LOG_INTERNAL_CONDITION` but adds to that a series of variable
+// declarations, including a local static object which stores the state needed
+// to implement the stateful macros like `LOG_EVERY_N`.
+//
+// `for`-loops are used to declare scoped variables without braces (to permit
+// streaming into the macro's expansion) and without the dangling-`else`
+// problems/diagnostics that come with `if`.
+//
+// Two more variables are declared in separate `for`-loops:
+//
+// * `COUNTER` implements a streamable token whose value when streamed is the
+//   number of times execution has passed through the macro.
+// * A boolean flag is used to prevent any of the `for`-loops from ever actually
+//   looping.
+#define ABSL_LOG_INTERNAL_STATEFUL_CONDITION(condition)             \
+  for (bool absl_log_internal_stateful_condition_do_log(condition); \
+       absl_log_internal_stateful_condition_do_log;                 \
+       absl_log_internal_stateful_condition_do_log = false)         \
+  ABSL_LOG_INTERNAL_STATEFUL_CONDITION_IMPL
+#define ABSL_LOG_INTERNAL_STATEFUL_CONDITION_IMPL(kind, ...)              \
+  for (static ::absl::log_internal::Log##kind##State                      \
+           absl_log_internal_stateful_condition_state;                    \
+       absl_log_internal_stateful_condition_do_log &&                     \
+       absl_log_internal_stateful_condition_state.ShouldLog(__VA_ARGS__); \
+       absl_log_internal_stateful_condition_do_log = false)               \
+    for (const uint32_t COUNTER ABSL_ATTRIBUTE_UNUSED =                   \
+             absl_log_internal_stateful_condition_state.counter();        \
+         absl_log_internal_stateful_condition_do_log;                     \
+         absl_log_internal_stateful_condition_do_log = false)
+
+// `ABSL_LOG_INTERNAL_CONDITION_*` serve to combine any conditions from the
+// macro (e.g. `LOG_IF` or `VLOG`) with inherent conditions (e.g.
+// `ABSL_MIN_LOG_LEVEL`) into a single boolean expression.  We could chain
+// ternary operators instead, however some versions of Clang sometimes issue
+// spurious diagnostics after such expressions due to a control flow analysis
+// bug.
+#ifdef ABSL_MIN_LOG_LEVEL
+#define ABSL_LOG_INTERNAL_CONDITION_INFO(type, condition) \
+  ABSL_LOG_INTERNAL_##type##_CONDITION(                   \
+      (condition) && ::absl::LogSeverity::kInfo >=        \
+                         static_cast<::absl::LogSeverity>(ABSL_MIN_LOG_LEVEL))
+#define ABSL_LOG_INTERNAL_CONDITION_WARNING(type, condition) \
+  ABSL_LOG_INTERNAL_##type##_CONDITION(                      \
+      (condition) && ::absl::LogSeverity::kWarning >=        \
+                         static_cast<::absl::LogSeverity>(ABSL_MIN_LOG_LEVEL))
+#define ABSL_LOG_INTERNAL_CONDITION_ERROR(type, condition) \
+  ABSL_LOG_INTERNAL_##type##_CONDITION(                    \
+      (condition) && ::absl::LogSeverity::kError >=        \
+                         static_cast<::absl::LogSeverity>(ABSL_MIN_LOG_LEVEL))
+// NOTE: Use ternary operators instead of short-circuiting to mitigate
+// https://bugs.llvm.org/show_bug.cgi?id=51928.
+#define ABSL_LOG_INTERNAL_CONDITION_FATAL(type, condition)                 \
+  ABSL_LOG_INTERNAL_##type##_CONDITION(                                    \
+      ((condition)                                                         \
+           ? (::absl::LogSeverity::kFatal >=                               \
+                      static_cast<::absl::LogSeverity>(ABSL_MIN_LOG_LEVEL) \
+                  ? true                                                   \
+                  : (::absl::log_internal::AbortQuietly(), false))         \
+           : false))
+// NOTE: Use ternary operators instead of short-circuiting to mitigate
+// https://bugs.llvm.org/show_bug.cgi?id=51928.
+#define ABSL_LOG_INTERNAL_CONDITION_QFATAL(type, condition)                \
+  ABSL_LOG_INTERNAL_##type##_CONDITION(                                    \
+      ((condition)                                                         \
+           ? (::absl::LogSeverity::kFatal >=                               \
+                      static_cast<::absl::LogSeverity>(ABSL_MIN_LOG_LEVEL) \
+                  ? true                                                   \
+                  : (::absl::log_internal::ExitQuietly(), false))          \
+           : false))
+
+#define ABSL_LOG_INTERNAL_CONDITION_LEVEL(severity)                    \
+  for (int log_internal_severity_loop = 1; log_internal_severity_loop; \
+       log_internal_severity_loop = 0)                                 \
+    for (const absl::LogSeverity log_internal_severity =               \
+             ::absl::NormalizeLogSeverity(severity);                   \
+         log_internal_severity_loop; log_internal_severity_loop = 0)   \
+  ABSL_LOG_INTERNAL_CONDITION_LEVEL_IMPL
+#define ABSL_LOG_INTERNAL_CONDITION_LEVEL_IMPL(type, condition)    \
+  ABSL_LOG_INTERNAL_##type##_CONDITION(                            \
+      (condition) &&                                               \
+      (log_internal_severity >=                                    \
+           static_cast<::absl::LogSeverity>(ABSL_MIN_LOG_LEVEL) || \
+       (log_internal_severity == ::absl::LogSeverity::kFatal &&    \
+        (::absl::log_internal::AbortQuietly(), false))))
+#else  // ndef ABSL_MIN_LOG_LEVEL
+#define ABSL_LOG_INTERNAL_CONDITION_INFO(type, condition) \
+  ABSL_LOG_INTERNAL_##type##_CONDITION(condition)
+#define ABSL_LOG_INTERNAL_CONDITION_WARNING(type, condition) \
+  ABSL_LOG_INTERNAL_##type##_CONDITION(condition)
+#define ABSL_LOG_INTERNAL_CONDITION_ERROR(type, condition) \
+  ABSL_LOG_INTERNAL_##type##_CONDITION(condition)
+#define ABSL_LOG_INTERNAL_CONDITION_FATAL(type, condition) \
+  ABSL_LOG_INTERNAL_##type##_CONDITION(condition)
+#define ABSL_LOG_INTERNAL_CONDITION_QFATAL(type, condition) \
+  ABSL_LOG_INTERNAL_##type##_CONDITION(condition)
+#define ABSL_LOG_INTERNAL_CONDITION_LEVEL(severity)                    \
+  for (int log_internal_severity_loop = 1; log_internal_severity_loop; \
+       log_internal_severity_loop = 0)                                 \
+    for (const absl::LogSeverity log_internal_severity =               \
+             ::absl::NormalizeLogSeverity(severity);                   \
+         log_internal_severity_loop; log_internal_severity_loop = 0)   \
+  ABSL_LOG_INTERNAL_CONDITION_LEVEL_IMPL
+#define ABSL_LOG_INTERNAL_CONDITION_LEVEL_IMPL(type, condition) \
+  ABSL_LOG_INTERNAL_##type##_CONDITION(condition)
+#endif  // ndef ABSL_MIN_LOG_LEVEL
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace log_internal {
+
+// Stateful condition class name should be "Log" + name + "State".
+class LogEveryNState final {
+ public:
+  bool ShouldLog(int n);
+  uint32_t counter() { return counter_.load(std::memory_order_relaxed); }
+
+ private:
+  std::atomic<uint32_t> counter_{0};
+};
+
+class LogFirstNState final {
+ public:
+  bool ShouldLog(int n);
+  uint32_t counter() { return counter_.load(std::memory_order_relaxed); }
+
+ private:
+  std::atomic<uint32_t> counter_{0};
+};
+
+class LogEveryPow2State final {
+ public:
+  bool ShouldLog();
+  uint32_t counter() { return counter_.load(std::memory_order_relaxed); }
+
+ private:
+  std::atomic<uint32_t> counter_{0};
+};
+
+class LogEveryNSecState final {
+ public:
+  bool ShouldLog(double seconds);
+  uint32_t counter() { return counter_.load(std::memory_order_relaxed); }
+
+ private:
+  std::atomic<uint32_t> counter_{0};
+  // Cycle count according to CycleClock that we should next log at.
+  std::atomic<int64_t> next_log_time_cycles_{0};
+};
+
+// Helper routines to abort the application quietly
+
+ABSL_ATTRIBUTE_NORETURN inline void AbortQuietly() { abort(); }
+ABSL_ATTRIBUTE_NORETURN inline void ExitQuietly() { _exit(1); }
+}  // namespace log_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_LOG_INTERNAL_CONDITIONS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/config.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/config.h
new file mode 100644
index 0000000000000000000000000000000000000000..379e9ab974c392fcb33f440b8ff5a9d682cd7f21
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/config.h
@@ -0,0 +1,45 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/internal/config.h
+// -----------------------------------------------------------------------------
+//
+
+#ifndef ABSL_LOG_INTERNAL_CONFIG_H_
+#define ABSL_LOG_INTERNAL_CONFIG_H_
+
+#include "absl/base/config.h"
+
+#ifdef _WIN32
+#include <cstdint>
+#else
+#include <sys/types.h>
+#endif
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace log_internal {
+
+#ifdef _WIN32
+using Tid = uint32_t;
+#else
+using Tid = pid_t;
+#endif
+
+}  // namespace log_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_LOG_INTERNAL_CONFIG_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/globals.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/globals.h
new file mode 100644
index 0000000000000000000000000000000000000000..27bc0d09847dc77ffbf5ae1ca509ffd67bb0baf0
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/globals.h
@@ -0,0 +1,101 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/internal/globals.h
+// -----------------------------------------------------------------------------
+//
+// This header file contains various global objects and static helper routines
+// use in logging implementation.
+
+#ifndef ABSL_LOG_INTERNAL_GLOBALS_H_
+#define ABSL_LOG_INTERNAL_GLOBALS_H_
+
+#include "absl/base/config.h"
+#include "absl/base/log_severity.h"
+#include "absl/strings/string_view.h"
+#include "absl/time/time.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace log_internal {
+
+// IsInitialized returns true if the logging library is initialized.
+// This function is async-signal-safe
+bool IsInitialized();
+
+// SetLoggingInitialized is called once after logging initialization is done.
+void SetInitialized();
+
+// Unconditionally write a `message` to stderr. If `severity` exceeds kInfo
+// we also flush the stderr stream.
+void WriteToStderr(absl::string_view message, absl::LogSeverity severity);
+
+// Set the TimeZone used for human-friendly times (for example, the log message
+// prefix) printed by the logging library. This may only be called once.
+void SetTimeZone(absl::TimeZone tz);
+
+// Returns the TimeZone used for human-friendly times (for example, the log
+// message prefix) printed by the logging library Returns nullptr prior to
+// initialization.
+const absl::TimeZone* TimeZone();
+
+// Returns true if stack traces emitted by the logging library should be
+// symbolized. This function is async-signal-safe.
+bool ShouldSymbolizeLogStackTrace();
+
+// Enables or disables symbolization of stack traces emitted by the
+// logging library. This function is async-signal-safe.
+void EnableSymbolizeLogStackTrace(bool on_off);
+
+// Returns the maximum number of frames that appear in stack traces
+// emitted by the logging library. This function is async-signal-safe.
+int MaxFramesInLogStackTrace();
+
+// Sets the maximum number of frames that appear in stack traces emitted by
+// the logging library. This function is async-signal-safe.
+void SetMaxFramesInLogStackTrace(int max_num_frames);
+
+// Determines whether we exit the program for a LOG(DFATAL) message in
+// debug mode.  It does this by skipping the call to Fail/FailQuietly.
+// This is intended for testing only.
+//
+// This can have some effects on LOG(FATAL) as well. Failure messages
+// are always allocated (rather than sharing a buffer), the crash
+// reason is not recorded, the "gwq" status message is not updated,
+// and the stack trace is not recorded.  The LOG(FATAL) *will* still
+// exit the program. Since this function is used only in testing,
+// these differences are acceptable.
+//
+// Additionally, LOG(LEVEL(FATAL)) is indistinguishable from LOG(DFATAL) and
+// will not terminate the program if SetExitOnDFatal(false) has been called.
+bool ExitOnDFatal();
+
+// SetExitOnDFatal() sets the ExitOnDFatal() status
+void SetExitOnDFatal(bool on_off);
+
+// Determines if the logging library should suppress logging of stacktraces in
+// the `SIGABRT` handler, typically because we just logged a stacktrace as part
+// of `LOG(FATAL)` and are about to send ourselves a `SIGABRT` to end the
+// program.
+bool SuppressSigabortTrace();
+
+// Sets the SuppressSigabortTrace() status and returns the previous state.
+bool SetSuppressSigabortTrace(bool on_off);
+
+}  // namespace log_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_LOG_INTERNAL_GLOBALS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/log_format.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/log_format.h
new file mode 100644
index 0000000000000000000000000000000000000000..95a45edf6102fce8da4fe5cf838cbf349b22dbc9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/log_format.h
@@ -0,0 +1,78 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/internal/log_format.h
+// -----------------------------------------------------------------------------
+//
+// This file declares routines implementing formatting of log message and log
+// prefix.
+
+#ifndef ABSL_LOG_INTERNAL_LOG_FORMAT_H_
+#define ABSL_LOG_INTERNAL_LOG_FORMAT_H_
+
+#include <stddef.h>
+
+#include <string>
+
+#include "absl/base/config.h"
+#include "absl/base/log_severity.h"
+#include "absl/log/internal/config.h"
+#include "absl/strings/string_view.h"
+#include "absl/time/civil_time.h"
+#include "absl/time/time.h"
+#include "absl/types/span.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace log_internal {
+
+enum class PrefixFormat {
+  kNotRaw,
+  kRaw,
+};
+
+// Formats log message based on provided data.
+std::string FormatLogMessage(absl::LogSeverity severity,
+                             absl::CivilSecond civil_second,
+                             absl::Duration subsecond, log_internal::Tid tid,
+                             absl::string_view basename, int line,
+                             PrefixFormat format, absl::string_view message);
+
+// Formats various entry metadata into a text string meant for use as a
+// prefix on a log message string.  Writes into `buf`, advances `buf` to point
+// at the remainder of the buffer (i.e. past any written bytes), and returns the
+// number of bytes written.
+//
+// In addition to calling `buf->remove_prefix()` (or the equivalent), this
+// function may also do `buf->remove_suffix(buf->size())` in cases where no more
+// bytes (i.e. no message data) should be written into the buffer.  For example,
+// if the prefix ought to be:
+//   I0926 09:00:00.000000 1234567 foo.cc:123]
+// `buf` is too small, the function might fill the whole buffer:
+//   I0926 09:00:00.000000 1234
+// (note the apparrently incorrect thread ID), or it might write less:
+//   I0926 09:00:00.000000
+// In this case, it might also empty `buf` prior to returning to prevent
+// message data from being written into the space where a reader would expect to
+// see a thread ID.
+size_t FormatLogPrefix(absl::LogSeverity severity, absl::Time timestamp,
+                       log_internal::Tid tid, absl::string_view basename,
+                       int line, PrefixFormat format, absl::Span<char>& buf);
+
+}  // namespace log_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_LOG_INTERNAL_LOG_FORMAT_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/log_impl.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/log_impl.h
new file mode 100644
index 0000000000000000000000000000000000000000..82b5ed84d6877e6d539c331554e38d7d39ae334a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/log_impl.h
@@ -0,0 +1,212 @@
+// Copyright 2022 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_LOG_INTERNAL_LOG_IMPL_H_
+#define ABSL_LOG_INTERNAL_LOG_IMPL_H_
+
+#include "absl/log/internal/conditions.h"
+#include "absl/log/internal/log_message.h"
+#include "absl/log/internal/strip.h"
+
+// ABSL_LOG()
+#define ABSL_LOG_IMPL(severity)                          \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATELESS, true) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+// ABSL_PLOG()
+#define ABSL_PLOG_IMPL(severity)                           \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATELESS, true)   \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream() \
+          .WithPerror()
+
+// ABSL_DLOG()
+#ifndef NDEBUG
+#define ABSL_DLOG_IMPL(severity)                         \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATELESS, true) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+#else
+#define ABSL_DLOG_IMPL(severity)                          \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATELESS, false) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+#endif
+
+#define ABSL_LOG_IF_IMPL(severity, condition)                 \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATELESS, condition) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+#define ABSL_PLOG_IF_IMPL(severity, condition)                \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATELESS, condition) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()    \
+          .WithPerror()
+
+#ifndef NDEBUG
+#define ABSL_DLOG_IF_IMPL(severity, condition)                \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATELESS, condition) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+#else
+#define ABSL_DLOG_IF_IMPL(severity, condition)                           \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATELESS, false && (condition)) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+#endif
+
+// ABSL_LOG_EVERY_N
+#define ABSL_LOG_EVERY_N_IMPL(severity, n)                         \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, true)(EveryN, n) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+// ABSL_LOG_FIRST_N
+#define ABSL_LOG_FIRST_N_IMPL(severity, n)                         \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, true)(FirstN, n) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+// ABSL_LOG_EVERY_POW_2
+#define ABSL_LOG_EVERY_POW_2_IMPL(severity)                        \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, true)(EveryPow2) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+// ABSL_LOG_EVERY_N_SEC
+#define ABSL_LOG_EVERY_N_SEC_IMPL(severity, n_seconds)                        \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, true)(EveryNSec, n_seconds) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_PLOG_EVERY_N_IMPL(severity, n)                        \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, true)(EveryN, n) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()         \
+          .WithPerror()
+
+#define ABSL_PLOG_FIRST_N_IMPL(severity, n)                        \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, true)(FirstN, n) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()         \
+          .WithPerror()
+
+#define ABSL_PLOG_EVERY_POW_2_IMPL(severity)                       \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, true)(EveryPow2) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()         \
+          .WithPerror()
+
+#define ABSL_PLOG_EVERY_N_SEC_IMPL(severity, n_seconds)                       \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, true)(EveryNSec, n_seconds) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()                    \
+          .WithPerror()
+
+#ifndef NDEBUG
+#define ABSL_DLOG_EVERY_N_IMPL(severity, n)        \
+  ABSL_LOG_INTERNAL_CONDITION_INFO(STATEFUL, true) \
+  (EveryN, n) ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_DLOG_FIRST_N_IMPL(severity, n)        \
+  ABSL_LOG_INTERNAL_CONDITION_INFO(STATEFUL, true) \
+  (FirstN, n) ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_DLOG_EVERY_POW_2_IMPL(severity)       \
+  ABSL_LOG_INTERNAL_CONDITION_INFO(STATEFUL, true) \
+  (EveryPow2) ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_DLOG_EVERY_N_SEC_IMPL(severity, n_seconds) \
+  ABSL_LOG_INTERNAL_CONDITION_INFO(STATEFUL, true)      \
+  (EveryNSec, n_seconds) ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#else  // def NDEBUG
+#define ABSL_DLOG_EVERY_N_IMPL(severity, n)         \
+  ABSL_LOG_INTERNAL_CONDITION_INFO(STATEFUL, false) \
+  (EveryN, n) ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_DLOG_FIRST_N_IMPL(severity, n)         \
+  ABSL_LOG_INTERNAL_CONDITION_INFO(STATEFUL, false) \
+  (FirstN, n) ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_DLOG_EVERY_POW_2_IMPL(severity)        \
+  ABSL_LOG_INTERNAL_CONDITION_INFO(STATEFUL, false) \
+  (EveryPow2) ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_DLOG_EVERY_N_SEC_IMPL(severity, n_seconds) \
+  ABSL_LOG_INTERNAL_CONDITION_INFO(STATEFUL, false)     \
+  (EveryNSec, n_seconds) ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+#endif  // def NDEBUG
+
+#define ABSL_LOG_IF_EVERY_N_IMPL(severity, condition, n)                \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, condition)(EveryN, n) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_LOG_IF_FIRST_N_IMPL(severity, condition, n)                \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, condition)(FirstN, n) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_LOG_IF_EVERY_POW_2_IMPL(severity, condition)               \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, condition)(EveryPow2) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_LOG_IF_EVERY_N_SEC_IMPL(severity, condition, n_seconds)    \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, condition)(EveryNSec, \
+                                                             n_seconds) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_PLOG_IF_EVERY_N_IMPL(severity, condition, n)               \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, condition)(EveryN, n) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()              \
+          .WithPerror()
+
+#define ABSL_PLOG_IF_FIRST_N_IMPL(severity, condition, n)               \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, condition)(FirstN, n) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()              \
+          .WithPerror()
+
+#define ABSL_PLOG_IF_EVERY_POW_2_IMPL(severity, condition)              \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, condition)(EveryPow2) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()              \
+          .WithPerror()
+
+#define ABSL_PLOG_IF_EVERY_N_SEC_IMPL(severity, condition, n_seconds)   \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, condition)(EveryNSec, \
+                                                             n_seconds) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()              \
+          .WithPerror()
+
+#ifndef NDEBUG
+#define ABSL_DLOG_IF_EVERY_N_IMPL(severity, condition, n)               \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, condition)(EveryN, n) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_DLOG_IF_FIRST_N_IMPL(severity, condition, n)               \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, condition)(FirstN, n) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_DLOG_IF_EVERY_POW_2_IMPL(severity, condition)              \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, condition)(EveryPow2) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_DLOG_IF_EVERY_N_SEC_IMPL(severity, condition, n_seconds)   \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, condition)(EveryNSec, \
+                                                             n_seconds) \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#else  // def NDEBUG
+#define ABSL_DLOG_IF_EVERY_N_IMPL(severity, condition, n)                \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, false && (condition))( \
+      EveryN, n) ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_DLOG_IF_FIRST_N_IMPL(severity, condition, n)                \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, false && (condition))( \
+      FirstN, n) ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_DLOG_IF_EVERY_POW_2_IMPL(severity, condition)               \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, false && (condition))( \
+      EveryPow2) ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+
+#define ABSL_DLOG_IF_EVERY_N_SEC_IMPL(severity, condition, n_seconds)    \
+  ABSL_LOG_INTERNAL_CONDITION##severity(STATEFUL, false && (condition))( \
+      EveryNSec, n_seconds)                                              \
+      ABSL_LOGGING_INTERNAL_LOG##severity.InternalStream()
+#endif  // def NDEBUG
+
+#endif  // ABSL_LOG_INTERNAL_LOG_IMPL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/log_message.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/log_message.h
new file mode 100644
index 0000000000000000000000000000000000000000..3744276bfb4fe3aca0849bbaadf1fa96290408b5
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/log_message.h
@@ -0,0 +1,355 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/internal/log_message.h
+// -----------------------------------------------------------------------------
+//
+// This file declares `class absl::log_internal::LogMessage`. This class more or
+// less represents a particular log message. LOG/CHECK macros create a
+// temporary instance of `LogMessage` and then stream values to it.  At the end
+// of the LOG/CHECK statement, LogMessage instance goes out of scope and
+// `~LogMessage` directs the message to the registered log sinks.
+// Heap-allocation of `LogMessage` is unsupported.  Construction outside of a
+// `LOG` macro is unsupported.
+
+#ifndef ABSL_LOG_INTERNAL_LOG_MESSAGE_H_
+#define ABSL_LOG_INTERNAL_LOG_MESSAGE_H_
+
+#include <ios>
+#include <memory>
+#include <ostream>
+#include <streambuf>
+#include <string>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/base/internal/errno_saver.h"
+#include "absl/base/log_severity.h"
+#include "absl/log/internal/nullguard.h"
+#include "absl/log/log_entry.h"
+#include "absl/log/log_sink.h"
+#include "absl/strings/internal/has_absl_stringify.h"
+#include "absl/strings/string_view.h"
+#include "absl/time/time.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace log_internal {
+constexpr int kLogMessageBufferSize = 15000;
+
+class LogMessage {
+ public:
+  // Used for `LOG`.
+  LogMessage(const char* file, int line,
+             absl::LogSeverity severity) ABSL_ATTRIBUTE_COLD;
+  LogMessage(const LogMessage&) = delete;
+  LogMessage& operator=(const LogMessage&) = delete;
+  ~LogMessage() ABSL_ATTRIBUTE_COLD;
+
+  // Overrides the location inferred from the callsite.  The string pointed to
+  // by `file` must be valid until the end of the statement.
+  LogMessage& AtLocation(absl::string_view file, int line);
+  // Omits the prefix from this line.  The prefix includes metadata about the
+  // logged data such as source code location and timestamp.
+  LogMessage& NoPrefix();
+  // Sets the verbosity field of the logged message as if it was logged by
+  // `VLOG(verbose_level)`.  Unlike `VLOG`, this method does not affect
+  // evaluation of the statement when the specified `verbose_level` has been
+  // disabled.  The only effect is on `absl::LogSink` implementations which
+  // make use of the `absl::LogSink::verbosity()` value.  The value
+  // `absl::LogEntry::kNoVerbosityLevel` can be specified to mark the message
+  // not verbose.
+  LogMessage& WithVerbosity(int verbose_level);
+  // Uses the specified timestamp instead of one collected in the constructor.
+  LogMessage& WithTimestamp(absl::Time timestamp);
+  // Uses the specified thread ID instead of one collected in the constructor.
+  LogMessage& WithThreadID(absl::LogEntry::tid_t tid);
+  // Copies all metadata (but no data) from the specified `absl::LogEntry`.
+  LogMessage& WithMetadataFrom(const absl::LogEntry& entry);
+  // Appends to the logged message a colon, a space, a textual description of
+  // the current value of `errno` (as by strerror(3)), and the numerical value
+  // of `errno`.
+  LogMessage& WithPerror();
+  // Sends this message to `*sink` in addition to whatever other sinks it would
+  // otherwise have been sent to.  `sink` must not be null.
+  LogMessage& ToSinkAlso(absl::LogSink* sink);
+  // Sends this message to `*sink` and no others.  `sink` must not be null.
+  LogMessage& ToSinkOnly(absl::LogSink* sink);
+
+  // Don't call this method from outside this library.
+  LogMessage& InternalStream() { return *this; }
+
+  // By-value overloads for small, common types let us overlook common failures
+  // to define globals and static data members (i.e. in a .cc file).
+  // clang-format off
+  // The CUDA toolchain cannot handle these <<<'s:
+  LogMessage& operator<<(char v) { return operator<< <char>(v); }
+  LogMessage& operator<<(signed char v) { return operator<< <signed char>(v); }
+  LogMessage& operator<<(unsigned char v) {
+    return operator<< <unsigned char>(v);
+  }
+  LogMessage& operator<<(signed short v) {  // NOLINT
+    return operator<< <signed short>(v);  // NOLINT
+  }
+  LogMessage& operator<<(signed int v) { return operator<< <signed int>(v); }
+  LogMessage& operator<<(signed long v) {  // NOLINT
+    return operator<< <signed long>(v);  // NOLINT
+  }
+  LogMessage& operator<<(signed long long v) {  // NOLINT
+    return operator<< <signed long long>(v);  // NOLINT
+  }
+  LogMessage& operator<<(unsigned short v) {  // NOLINT
+    return operator<< <unsigned short>(v);  // NOLINT
+  }
+  LogMessage& operator<<(unsigned int v) {
+    return operator<< <unsigned int>(v);
+  }
+  LogMessage& operator<<(unsigned long v) {  // NOLINT
+    return operator<< <unsigned long>(v);  // NOLINT
+  }
+  LogMessage& operator<<(unsigned long long v) {  // NOLINT
+    return operator<< <unsigned long long>(v);  // NOLINT
+  }
+  LogMessage& operator<<(void* v) { return operator<< <void*>(v); }
+  LogMessage& operator<<(const void* v) { return operator<< <const void*>(v); }
+  LogMessage& operator<<(float v) { return operator<< <float>(v); }
+  LogMessage& operator<<(double v) { return operator<< <double>(v); }
+  LogMessage& operator<<(bool v) { return operator<< <bool>(v); }
+  // clang-format on
+
+  // These overloads are more efficient since no `ostream` is involved.
+  LogMessage& operator<<(const std::string& v);
+  LogMessage& operator<<(absl::string_view v);
+
+  // Handle stream manipulators e.g. std::endl.
+  LogMessage& operator<<(std::ostream& (*m)(std::ostream& os));
+  LogMessage& operator<<(std::ios_base& (*m)(std::ios_base& os));
+
+  // Literal strings.  This allows us to record C string literals as literals in
+  // the logging.proto.Value.
+  //
+  // Allow this overload to be inlined to prevent generating instantiations of
+  // this template for every value of `SIZE` encountered in each source code
+  // file. That significantly increases linker input sizes. Inlining is cheap
+  // because the argument to this overload is almost always a string literal so
+  // the call to `strlen` can be replaced at compile time. The overload for
+  // `char[]` below should not be inlined. The compiler typically does not have
+  // the string at compile time and cannot replace the call to `strlen` so
+  // inlining it increases the binary size. See the discussion on
+  // cl/107527369.
+  template <int SIZE>
+  LogMessage& operator<<(const char (&buf)[SIZE]);
+
+  // This prevents non-const `char[]` arrays from looking like literals.
+  template <int SIZE>
+  LogMessage& operator<<(char (&buf)[SIZE]) ABSL_ATTRIBUTE_NOINLINE;
+
+  // Types that support `AbslStringify()` are serialized that way.
+  template <typename T,
+            typename std::enable_if<
+                strings_internal::HasAbslStringify<T>::value, int>::type = 0>
+  LogMessage& operator<<(const T& v) ABSL_ATTRIBUTE_NOINLINE;
+
+  // Types that don't support `AbslStringify()` but do support streaming into a
+  // `std::ostream&` are serialized that way.
+  template <typename T,
+            typename std::enable_if<
+                !strings_internal::HasAbslStringify<T>::value, int>::type = 0>
+  LogMessage& operator<<(const T& v) ABSL_ATTRIBUTE_NOINLINE;
+
+  // Note: We explicitly do not support `operator<<` for non-const references
+  // because it breaks logging of non-integer bitfield types (i.e., enums).
+
+ protected:
+  // Call `abort()` or similar to perform `LOG(FATAL)` crash.  It is assumed
+  // that the caller has already generated and written the trace as appropriate.
+  ABSL_ATTRIBUTE_NORETURN static void FailWithoutStackTrace();
+
+  // Similar to `FailWithoutStackTrace()`, but without `abort()`.  Terminates
+  // the process with an error exit code.
+  ABSL_ATTRIBUTE_NORETURN static void FailQuietly();
+
+  // Dispatches the completed `absl::LogEntry` to applicable `absl::LogSink`s.
+  // This might as well be inlined into `~LogMessage` except that
+  // `~LogMessageFatal` needs to call it early.
+  void Flush();
+
+  // After this is called, failures are done as quiet as possible for this log
+  // message.
+  void SetFailQuietly();
+
+ private:
+  struct LogMessageData;  // Opaque type containing message state
+  friend class AsLiteralImpl;
+  friend class StringifySink;
+
+  // This streambuf writes directly into the structured logging buffer so that
+  // arbitrary types can be encoded as string data (using
+  // `operator<<(std::ostream &, ...)` without any extra allocation or copying.
+  // Space is reserved before the data to store the length field, which is
+  // filled in by `~OstreamView`.
+  class OstreamView final : public std::streambuf {
+   public:
+    explicit OstreamView(LogMessageData& message_data);
+    ~OstreamView() override;
+    OstreamView(const OstreamView&) = delete;
+    OstreamView& operator=(const OstreamView&) = delete;
+    std::ostream& stream();
+
+   private:
+    LogMessageData& data_;
+    absl::Span<char> encoded_remaining_copy_;
+    absl::Span<char> message_start_;
+    absl::Span<char> string_start_;
+  };
+
+  enum class StringType {
+    kLiteral,
+    kNotLiteral,
+  };
+  void CopyToEncodedBuffer(absl::string_view str,
+                           StringType str_type) ABSL_ATTRIBUTE_NOINLINE;
+  void CopyToEncodedBuffer(char ch, size_t num,
+                           StringType str_type) ABSL_ATTRIBUTE_NOINLINE;
+
+  // Returns `true` if the message is fatal or enabled debug-fatal.
+  bool IsFatal() const;
+
+  // Records some tombstone-type data in anticipation of `Die`.
+  void PrepareToDie();
+  void Die();
+
+  void SendToLog();
+
+  // Checks `FLAGS_log_backtrace_at` and appends a backtrace if appropriate.
+  void LogBacktraceIfNeeded();
+
+  // This should be the first data member so that its initializer captures errno
+  // before any other initializers alter it (e.g. with calls to new) and so that
+  // no other destructors run afterward an alter it (e.g. with calls to delete).
+  absl::base_internal::ErrnoSaver errno_saver_;
+
+  // We keep the data in a separate struct so that each instance of `LogMessage`
+  // uses less stack space.
+  std::unique_ptr<LogMessageData> data_;
+};
+
+// Helper class so that `AbslStringify()` can modify the LogMessage.
+class StringifySink final {
+ public:
+  explicit StringifySink(LogMessage& message) : message_(message) {}
+
+  void Append(size_t count, char ch) {
+    message_.CopyToEncodedBuffer(ch, count,
+                                 LogMessage::StringType::kNotLiteral);
+  }
+
+  void Append(absl::string_view v) {
+    message_.CopyToEncodedBuffer(v, LogMessage::StringType::kNotLiteral);
+  }
+
+  // For types that implement `AbslStringify` using `absl::Format()`.
+  friend void AbslFormatFlush(StringifySink* sink, absl::string_view v) {
+    sink->Append(v);
+  }
+
+ private:
+  LogMessage& message_;
+};
+
+// Note: the following is declared `ABSL_ATTRIBUTE_NOINLINE`
+template <typename T,
+          typename std::enable_if<strings_internal::HasAbslStringify<T>::value,
+                                  int>::type>
+LogMessage& LogMessage::operator<<(const T& v) {
+  StringifySink sink(*this);
+  // Replace with public API.
+  AbslStringify(sink, v);
+  return *this;
+}
+
+// Note: the following is declared `ABSL_ATTRIBUTE_NOINLINE`
+template <typename T,
+          typename std::enable_if<!strings_internal::HasAbslStringify<T>::value,
+                                  int>::type>
+LogMessage& LogMessage::operator<<(const T& v) {
+  OstreamView view(*data_);
+  view.stream() << log_internal::NullGuard<T>().Guard(v);
+  return *this;
+}
+
+template <int SIZE>
+LogMessage& LogMessage::operator<<(const char (&buf)[SIZE]) {
+  CopyToEncodedBuffer(buf, StringType::kLiteral);
+  return *this;
+}
+
+// Note: the following is declared `ABSL_ATTRIBUTE_NOINLINE`
+template <int SIZE>
+LogMessage& LogMessage::operator<<(char (&buf)[SIZE]) {
+  CopyToEncodedBuffer(buf, StringType::kNotLiteral);
+  return *this;
+}
+// We instantiate these specializations in the library's TU to save space in
+// other TUs.  Since the template is marked `ABSL_ATTRIBUTE_NOINLINE` we will be
+// emitting a function call either way.
+extern template LogMessage& LogMessage::operator<<(const char& v);
+extern template LogMessage& LogMessage::operator<<(const signed char& v);
+extern template LogMessage& LogMessage::operator<<(const unsigned char& v);
+extern template LogMessage& LogMessage::operator<<(const short& v);  // NOLINT
+extern template LogMessage& LogMessage::operator<<(
+    const unsigned short& v);  // NOLINT
+extern template LogMessage& LogMessage::operator<<(const int& v);
+extern template LogMessage& LogMessage::operator<<(
+    const unsigned int& v);                                         // NOLINT
+extern template LogMessage& LogMessage::operator<<(const long& v);  // NOLINT
+extern template LogMessage& LogMessage::operator<<(
+    const unsigned long& v);  // NOLINT
+extern template LogMessage& LogMessage::operator<<(
+    const long long& v);  // NOLINT
+extern template LogMessage& LogMessage::operator<<(
+    const unsigned long long& v);  // NOLINT
+extern template LogMessage& LogMessage::operator<<(void* const& v);
+extern template LogMessage& LogMessage::operator<<(const void* const& v);
+extern template LogMessage& LogMessage::operator<<(const float& v);
+extern template LogMessage& LogMessage::operator<<(const double& v);
+extern template LogMessage& LogMessage::operator<<(const bool& v);
+
+// `LogMessageFatal` ensures the process will exit in failure after logging this
+// message.
+class LogMessageFatal final : public LogMessage {
+ public:
+  LogMessageFatal(const char* file, int line) ABSL_ATTRIBUTE_COLD;
+  LogMessageFatal(const char* file, int line,
+                  absl::string_view failure_msg) ABSL_ATTRIBUTE_COLD;
+  ABSL_ATTRIBUTE_NORETURN ~LogMessageFatal();
+};
+
+class LogMessageQuietlyFatal final : public LogMessage {
+ public:
+  LogMessageQuietlyFatal(const char* file, int line) ABSL_ATTRIBUTE_COLD;
+  LogMessageQuietlyFatal(const char* file, int line,
+                         absl::string_view failure_msg) ABSL_ATTRIBUTE_COLD;
+  ABSL_ATTRIBUTE_NORETURN ~LogMessageQuietlyFatal();
+};
+
+}  // namespace log_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+extern "C" ABSL_ATTRIBUTE_WEAK void ABSL_INTERNAL_C_SYMBOL(
+    AbslInternalOnFatalLogMessage)(const absl::LogEntry&);
+
+#endif  // ABSL_LOG_INTERNAL_LOG_MESSAGE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/log_sink_set.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/log_sink_set.h
new file mode 100644
index 0000000000000000000000000000000000000000..88ab073bbd0969947021c070579fcc94ab54dee6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/log_sink_set.h
@@ -0,0 +1,54 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/internal/log_sink_set.h
+// -----------------------------------------------------------------------------
+
+#ifndef ABSL_LOG_INTERNAL_LOG_SINK_SET_H_
+#define ABSL_LOG_INTERNAL_LOG_SINK_SET_H_
+
+#include "absl/base/config.h"
+#include "absl/log/log_entry.h"
+#include "absl/log/log_sink.h"
+#include "absl/types/span.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace log_internal {
+
+// Returns true if a globally-registered `LogSink`'s `Send()` is currently
+// being invoked on this thread.
+bool ThreadIsLoggingToLogSink();
+
+// This function may log to two sets of sinks:
+//
+// * If `extra_sinks_only` is true, it will dispatch only to `extra_sinks`.
+//   `LogMessage::ToSinkAlso` and `LogMessage::ToSinkOnly` are used to attach
+//    extra sinks to the entry.
+// * Otherwise it will also log to the global sinks set. This set is managed
+//   by `absl::AddLogSink` and `absl::RemoveLogSink`.
+void LogToSinks(const absl::LogEntry& entry,
+                absl::Span<absl::LogSink*> extra_sinks, bool extra_sinks_only);
+
+// Implementation for operations with log sink set.
+void AddLogSink(absl::LogSink* sink);
+void RemoveLogSink(absl::LogSink* sink);
+void FlushLogSinks();
+
+}  // namespace log_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_LOG_INTERNAL_LOG_SINK_SET_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/nullguard.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/nullguard.h
new file mode 100644
index 0000000000000000000000000000000000000000..926f61bbdc799ab02cba5214dda6a8f6558b6c7a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/nullguard.h
@@ -0,0 +1,88 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/internal/nullguard.h
+// -----------------------------------------------------------------------------
+//
+// NullGuard exists such that NullGuard<T>::Guard(v) returns v, unless passed a
+// nullptr_t, or a null char* or const char*, in which case it returns "(null)".
+// This allows streaming NullGuard<T>::Guard(v) to an output stream without
+// hitting undefined behavior for null values.
+
+#ifndef ABSL_LOG_INTERNAL_NULLGUARD_H_
+#define ABSL_LOG_INTERNAL_NULLGUARD_H_
+
+#include <array>
+#include <cstddef>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace log_internal {
+
+ABSL_DLL ABSL_CONST_INIT extern const std::array<char, 7> kCharNull;
+ABSL_DLL ABSL_CONST_INIT extern const std::array<signed char, 7>
+    kSignedCharNull;
+ABSL_DLL ABSL_CONST_INIT extern const std::array<unsigned char, 7>
+    kUnsignedCharNull;
+
+template <typename T>
+struct NullGuard final {
+  static const T& Guard(const T& v) { return v; }
+};
+template <>
+struct NullGuard<char*> final {
+  static const char* Guard(const char* v) { return v ? v : kCharNull.data(); }
+};
+template <>
+struct NullGuard<const char*> final {
+  static const char* Guard(const char* v) { return v ? v : kCharNull.data(); }
+};
+template <>
+struct NullGuard<signed char*> final {
+  static const signed char* Guard(const signed char* v) {
+    return v ? v : kSignedCharNull.data();
+  }
+};
+template <>
+struct NullGuard<const signed char*> final {
+  static const signed char* Guard(const signed char* v) {
+    return v ? v : kSignedCharNull.data();
+  }
+};
+template <>
+struct NullGuard<unsigned char*> final {
+  static const unsigned char* Guard(const unsigned char* v) {
+    return v ? v : kUnsignedCharNull.data();
+  }
+};
+template <>
+struct NullGuard<const unsigned char*> final {
+  static const unsigned char* Guard(const unsigned char* v) {
+    return v ? v : kUnsignedCharNull.data();
+  }
+};
+template <>
+struct NullGuard<std::nullptr_t> final {
+  static const char* Guard(const std::nullptr_t&) { return kCharNull.data(); }
+};
+
+}  // namespace log_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_LOG_INTERNAL_NULLGUARD_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/nullstream.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/nullstream.h
new file mode 100644
index 0000000000000000000000000000000000000000..8ed63d5295f655bf2c2e59859820209b324271d6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/nullstream.h
@@ -0,0 +1,134 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/internal/nullstream.h
+// -----------------------------------------------------------------------------
+//
+// Classes `NullStream`, `NullStreamMaybeFatal ` and `NullStreamFatal`
+// implement a subset of the `LogMessage` API and are used instead when logging
+// of messages has been disabled.
+
+#ifndef ABSL_LOG_INTERNAL_NULLSTREAM_H_
+#define ABSL_LOG_INTERNAL_NULLSTREAM_H_
+
+#ifdef _WIN32
+#include <cstdlib>
+#else
+#include <unistd.h>
+#endif
+#include <ios>
+#include <ostream>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/base/log_severity.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace log_internal {
+
+// A `NullStream` implements the API of `LogMessage` (a few methods and
+// `operator<<`) but does nothing.  All methods are defined inline so the
+// compiler can eliminate the whole instance and discard anything that's
+// streamed in.
+class NullStream {
+ public:
+  NullStream& AtLocation(absl::string_view, int) { return *this; }
+  template <typename SourceLocationType>
+  NullStream& AtLocation(SourceLocationType) {
+    return *this;
+  }
+  NullStream& NoPrefix() { return *this; }
+  NullStream& WithVerbosity(int) { return *this; }
+  template <typename TimeType>
+  NullStream& WithTimestamp(TimeType) {
+    return *this;
+  }
+  template <typename Tid>
+  NullStream& WithThreadID(Tid) {
+    return *this;
+  }
+  template <typename LogEntryType>
+  NullStream& WithMetadataFrom(const LogEntryType&) {
+    return *this;
+  }
+  NullStream& WithPerror() { return *this; }
+  template <typename LogSinkType>
+  NullStream& ToSinkAlso(LogSinkType*) {
+    return *this;
+  }
+  template <typename LogSinkType>
+  NullStream& ToSinkOnly(LogSinkType*) {
+    return *this;
+  }
+  template <typename LogSinkType>
+  NullStream& OutputToSink(LogSinkType*, bool) {
+    return *this;
+  }
+  NullStream& InternalStream() { return *this; }
+};
+template <typename T>
+inline NullStream& operator<<(NullStream& str, const T&) {
+  return str;
+}
+inline NullStream& operator<<(NullStream& str,
+                              std::ostream& (*)(std::ostream& os)) {
+  return str;
+}
+inline NullStream& operator<<(NullStream& str,
+                              std::ios_base& (*)(std::ios_base& os)) {
+  return str;
+}
+
+// `NullStreamMaybeFatal` implements the process termination semantics of
+// `LogMessage`, which is used for `DFATAL` severity and expression-defined
+// severity e.g. `LOG(LEVEL(HowBadIsIt()))`.  Like `LogMessage`, it terminates
+// the process when destroyed if the passed-in severity equals `FATAL`.
+class NullStreamMaybeFatal final : public NullStream {
+ public:
+  explicit NullStreamMaybeFatal(absl::LogSeverity severity)
+      : fatal_(severity == absl::LogSeverity::kFatal) {}
+  ~NullStreamMaybeFatal() {
+    if (fatal_) _exit(1);
+  }
+
+ private:
+  bool fatal_;
+};
+
+// `NullStreamFatal` implements the process termination semantics of
+// `LogMessageFatal`, which means it always terminates the process.  `DFATAL`
+// and expression-defined severity use `NullStreamMaybeFatal` above.
+class NullStreamFatal final : public NullStream {
+ public:
+  NullStreamFatal() {}
+  // ABSL_ATTRIBUTE_NORETURN doesn't seem to work on destructors with msvc, so
+  // disable msvc's warning about the d'tor never returning.
+#if defined(_MSC_VER) && !defined(__clang__)
+#pragma warning(push)
+#pragma warning(disable : 4722)
+#endif
+  ABSL_ATTRIBUTE_NORETURN ~NullStreamFatal() { _exit(1); }
+#if defined(_MSC_VER) && !defined(__clang__)
+#pragma warning(pop)
+#endif
+};
+
+}  // namespace log_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_LOG_INTERNAL_GLOBALS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/proto.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/proto.h
new file mode 100644
index 0000000000000000000000000000000000000000..c8d14acc4715124cc3629ed15b3360e9e1e5dcfa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/proto.h
@@ -0,0 +1,288 @@
+// Copyright 2020 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// -----------------------------------------------------------------------------
+// File: internal/proto.h
+// -----------------------------------------------------------------------------
+//
+// Declares functions for serializing and deserializing data to and from memory
+// buffers in protocol buffer wire format.  This library takes no steps to
+// ensure that the encoded data matches with any message specification.
+
+#ifndef ABSL_LOG_INTERNAL_PROTO_H_
+#define ABSL_LOG_INTERNAL_PROTO_H_
+
+#include <cstddef>
+#include <cstdint>
+#include <limits>
+
+#include "absl/base/attributes.h"
+#include "absl/base/casts.h"
+#include "absl/base/config.h"
+#include "absl/strings/string_view.h"
+#include "absl/types/span.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace log_internal {
+
+// absl::Span<char> represents a view into the available space in a mutable
+// buffer during encoding.  Encoding functions shrink the span as they go so
+// that the same view can be passed to a series of Encode functions.  If the
+// data do not fit, nothing is encoded, the view is set to size zero (so that
+// all subsequent encode calls fail), and false is returned.  Otherwise true is
+// returned.
+
+// In particular, attempting to encode a series of data into an insufficient
+// buffer has consistent and efficient behavior without any caller-side error
+// checking.  Individual values will be encoded in their entirety or not at all
+// (unless one of the `Truncate` functions is used).  Once a value is omitted
+// because it does not fit, no subsequent values will be encoded to preserve
+// ordering; the decoded sequence will be a prefix of the original sequence.
+
+// There are two ways to encode a message-typed field:
+//
+// * Construct its contents in a separate buffer and use `EncodeBytes` to copy
+//   it into the primary buffer with type, tag, and length.
+// * Use `EncodeMessageStart` to write type and tag fields and reserve space for
+//   the length field, then encode the contents directly into the buffer, then
+//   use `EncodeMessageLength` to write the actual length into the reserved
+//   bytes.  This works fine if the actual length takes fewer bytes to encode
+//   than were reserved, although you don't get your extra bytes back.
+//   This approach will always produce a valid encoding, but your protocol may
+//   require that the whole message field by omitted if the buffer is too small
+//   to contain all desired subfields.  In this case, operate on a copy of the
+//   buffer view and assign back only if everything fit, i.e. if the last
+//   `Encode` call returned true.
+
+// Encodes the specified integer as a varint field and returns true if it fits.
+// Used for int32_t, int64_t, uint32_t, uint64_t, bool, and enum field types.
+// Consumes up to kMaxVarintSize * 2 bytes (20).
+bool EncodeVarint(uint64_t tag, uint64_t value, absl::Span<char> *buf);
+inline bool EncodeVarint(uint64_t tag, int64_t value, absl::Span<char> *buf) {
+  return EncodeVarint(tag, static_cast<uint64_t>(value), buf);
+}
+inline bool EncodeVarint(uint64_t tag, uint32_t value, absl::Span<char> *buf) {
+  return EncodeVarint(tag, static_cast<uint64_t>(value), buf);
+}
+inline bool EncodeVarint(uint64_t tag, int32_t value, absl::Span<char> *buf) {
+  return EncodeVarint(tag, static_cast<uint64_t>(value), buf);
+}
+
+// Encodes the specified integer as a varint field using ZigZag encoding and
+// returns true if it fits.
+// Used for sint32 and sint64 field types.
+// Consumes up to kMaxVarintSize * 2 bytes (20).
+inline bool EncodeVarintZigZag(uint64_t tag, int64_t value,
+                               absl::Span<char> *buf) {
+  if (value < 0)
+    return EncodeVarint(tag, 2 * static_cast<uint64_t>(-(value + 1)) + 1, buf);
+  return EncodeVarint(tag, 2 * static_cast<uint64_t>(value), buf);
+}
+
+// Encodes the specified integer as a 64-bit field and returns true if it fits.
+// Used for fixed64 and sfixed64 field types.
+// Consumes up to kMaxVarintSize + 8 bytes (18).
+bool Encode64Bit(uint64_t tag, uint64_t value, absl::Span<char> *buf);
+inline bool Encode64Bit(uint64_t tag, int64_t value, absl::Span<char> *buf) {
+  return Encode64Bit(tag, static_cast<uint64_t>(value), buf);
+}
+inline bool Encode64Bit(uint64_t tag, uint32_t value, absl::Span<char> *buf) {
+  return Encode64Bit(tag, static_cast<uint64_t>(value), buf);
+}
+inline bool Encode64Bit(uint64_t tag, int32_t value, absl::Span<char> *buf) {
+  return Encode64Bit(tag, static_cast<uint64_t>(value), buf);
+}
+
+// Encodes the specified double as a 64-bit field and returns true if it fits.
+// Used for double field type.
+// Consumes up to kMaxVarintSize + 8 bytes (18).
+inline bool EncodeDouble(uint64_t tag, double value, absl::Span<char> *buf) {
+  return Encode64Bit(tag, absl::bit_cast<uint64_t>(value), buf);
+}
+
+// Encodes the specified integer as a 32-bit field and returns true if it fits.
+// Used for fixed32 and sfixed32 field types.
+// Consumes up to kMaxVarintSize + 4 bytes (14).
+bool Encode32Bit(uint64_t tag, uint32_t value, absl::Span<char> *buf);
+inline bool Encode32Bit(uint64_t tag, int32_t value, absl::Span<char> *buf) {
+  return Encode32Bit(tag, static_cast<uint32_t>(value), buf);
+}
+
+// Encodes the specified float as a 32-bit field and returns true if it fits.
+// Used for float field type.
+// Consumes up to kMaxVarintSize + 4 bytes (14).
+inline bool EncodeFloat(uint64_t tag, float value, absl::Span<char> *buf) {
+  return Encode32Bit(tag, absl::bit_cast<uint32_t>(value), buf);
+}
+
+// Encodes the specified bytes as a length-delimited field and returns true if
+// they fit.
+// Used for string, bytes, message, and packed-repeated field type.
+// Consumes up to kMaxVarintSize * 2 + value.size() bytes (20 + value.size()).
+bool EncodeBytes(uint64_t tag, absl::Span<const char> value,
+                 absl::Span<char> *buf);
+
+// Encodes as many of the specified bytes as will fit as a length-delimited
+// field and returns true as long as the field header (`tag_type` and `length`)
+// fits.
+// Used for string, bytes, message, and packed-repeated field type.
+// Consumes up to kMaxVarintSize * 2 + value.size() bytes (20 + value.size()).
+bool EncodeBytesTruncate(uint64_t tag, absl::Span<const char> value,
+                         absl::Span<char> *buf);
+
+// Encodes the specified string as a length-delimited field and returns true if
+// it fits.
+// Used for string, bytes, message, and packed-repeated field type.
+// Consumes up to kMaxVarintSize * 2 + value.size() bytes (20 + value.size()).
+inline bool EncodeString(uint64_t tag, absl::string_view value,
+                         absl::Span<char> *buf) {
+  return EncodeBytes(tag, value, buf);
+}
+
+// Encodes as much of the specified string as will fit as a length-delimited
+// field and returns true as long as the field header (`tag_type` and `length`)
+// fits.
+// Used for string, bytes, message, and packed-repeated field type.
+// Consumes up to kMaxVarintSize * 2 + value.size() bytes (20 + value.size()).
+inline bool EncodeStringTruncate(uint64_t tag, absl::string_view value,
+                                 absl::Span<char> *buf) {
+  return EncodeBytesTruncate(tag, value, buf);
+}
+
+// Encodes the header for a length-delimited field containing up to `max_size`
+// bytes or the number remaining in the buffer, whichever is less.  If the
+// header fits, a non-nullptr `Span` is returned; this must be passed to
+// `EncodeMessageLength` after all contents are encoded to finalize the length
+// field.  If the header does not fit, a nullptr `Span` is returned which is
+// safe to pass to `EncodeMessageLength` but need not be.
+// Used for string, bytes, message, and packed-repeated field type.
+// Consumes up to kMaxVarintSize * 2 bytes (20).
+ABSL_MUST_USE_RESULT absl::Span<char> EncodeMessageStart(uint64_t tag,
+                                                         uint64_t max_size,
+                                                         absl::Span<char> *buf);
+
+// Finalizes the length field in `msg` so that it encompasses all data encoded
+// since the call to `EncodeMessageStart` which returned `msg`.  Does nothing if
+// `msg` is a `nullptr` `Span`.
+void EncodeMessageLength(absl::Span<char> msg, const absl::Span<char> *buf);
+
+enum class WireType : uint64_t {
+  kVarint = 0,
+  k64Bit = 1,
+  kLengthDelimited = 2,
+  k32Bit = 5,
+};
+
+constexpr size_t VarintSize(uint64_t value) {
+  return value < 128 ? 1 : 1 + VarintSize(value >> 7);
+}
+constexpr size_t MinVarintSize() {
+  return VarintSize((std::numeric_limits<uint64_t>::min)());
+}
+constexpr size_t MaxVarintSize() {
+  return VarintSize((std::numeric_limits<uint64_t>::max)());
+}
+
+constexpr uint64_t MaxVarintForSize(size_t size) {
+  return size >= 10 ? (std::numeric_limits<uint64_t>::max)()
+                    : (static_cast<uint64_t>(1) << size * 7) - 1;
+}
+
+// `BufferSizeFor` returns a number of bytes guaranteed to be sufficient to
+// store encoded fields of the specified WireTypes regardless of tag numbers and
+// data values.  This only makes sense for `WireType::kLengthDelimited` if you
+// add in the length of the contents yourself, e.g. for string and bytes fields
+// by adding the lengths of any encoded strings to the return value or for
+// submessage fields by enumerating the fields you may encode into their
+// contents.
+constexpr size_t BufferSizeFor() { return 0; }
+template <typename... T>
+constexpr size_t BufferSizeFor(WireType type, T... tail) {
+  // tag_type + data + ...
+  return MaxVarintSize() +
+         (type == WireType::kVarint ? MaxVarintSize() :              //
+              type == WireType::k64Bit ? 8 :                         //
+                  type == WireType::k32Bit ? 4 : MaxVarintSize()) +  //
+         BufferSizeFor(tail...);
+}
+
+// absl::Span<const char> represents a view into the un-processed space in a
+// buffer during decoding.  Decoding functions shrink the span as they go so
+// that the same view can be decoded iteratively until all data are processed.
+// In general, if the buffer is exhausted but additional bytes are expected by
+// the decoder, it will return values as if the additional bytes were zeros.
+// Length-delimited fields are an exception - if the encoded length field
+// indicates more data bytes than are available in the buffer, the `bytes_value`
+// and `string_value` accessors will return truncated views.
+
+class ProtoField final {
+ public:
+  // Consumes bytes from `data` and returns true if there were any bytes to
+  // decode.
+  bool DecodeFrom(absl::Span<const char> *data);
+  uint64_t tag() const { return tag_; }
+  WireType type() const { return type_; }
+
+  // These value accessors will return nonsense if the data were not encoded in
+  // the corresponding wiretype from the corresponding C++ (or other language)
+  // type.
+
+  double double_value() const { return absl::bit_cast<double>(value_); }
+  float float_value() const {
+    return absl::bit_cast<float>(static_cast<uint32_t>(value_));
+  }
+  int32_t int32_value() const { return static_cast<int32_t>(value_); }
+  int64_t int64_value() const { return static_cast<int64_t>(value_); }
+  int32_t sint32_value() const {
+    if (value_ % 2) return static_cast<int32_t>(0 - ((value_ - 1) / 2) - 1);
+    return static_cast<int32_t>(value_ / 2);
+  }
+  int64_t sint64_value() const {
+    if (value_ % 2) return 0 - ((value_ - 1) / 2) - 1;
+    return value_ / 2;
+  }
+  uint32_t uint32_value() const { return static_cast<uint32_t>(value_); }
+  uint64_t uint64_value() const { return value_; }
+  bool bool_value() const { return value_ != 0; }
+  // To decode an enum, call int32_value() and cast to the appropriate type.
+  // Note that the official C++ proto compiler treats enum fields with values
+  // that do not correspond to a defined enumerator as unknown fields.
+
+  // To decode fields within a submessage field, call
+  // `DecodeNextField(field.BytesValue())`.
+  absl::Span<const char> bytes_value() const { return data_; }
+  absl::string_view string_value() const {
+    const auto data = bytes_value();
+    return absl::string_view(data.data(), data.size());
+  }
+  // Returns the encoded length of a length-delimited field.  This equals
+  // `bytes_value().size()` except when the latter has been truncated due to
+  // buffer underrun.
+  uint64_t encoded_length() const { return value_; }
+
+ private:
+  uint64_t tag_;
+  WireType type_;
+  // For `kTypeVarint`, `kType64Bit`, and `kType32Bit`, holds the decoded value.
+  // For `kTypeLengthDelimited`, holds the decoded length.
+  uint64_t value_;
+  absl::Span<const char> data_;
+};
+
+}  // namespace log_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_LOG_INTERNAL_PROTO_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/strip.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/strip.h
new file mode 100644
index 0000000000000000000000000000000000000000..848c38672d9ae167f872e0b2a07c66adbe9d6f95
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/strip.h
@@ -0,0 +1,71 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/internal/strip.h
+// -----------------------------------------------------------------------------
+//
+
+#ifndef ABSL_LOG_INTERNAL_STRIP_H_
+#define ABSL_LOG_INTERNAL_STRIP_H_
+
+#include "absl/base/log_severity.h"
+#include "absl/log/internal/log_message.h"
+#include "absl/log/internal/nullstream.h"
+
+// `ABSL_LOGGING_INTERNAL_LOG_*` evaluates to a temporary `LogMessage` object or
+// to a related object with a compatible API but different behavior.  This set
+// of defines comes in three flavors: vanilla, plus two variants that strip some
+// logging in subtly different ways for subtly different reasons (see below).
+#if defined(STRIP_LOG) && STRIP_LOG
+#define ABSL_LOGGING_INTERNAL_LOG_INFO ::absl::log_internal::NullStream()
+#define ABSL_LOGGING_INTERNAL_LOG_WARNING ::absl::log_internal::NullStream()
+#define ABSL_LOGGING_INTERNAL_LOG_ERROR ::absl::log_internal::NullStream()
+#define ABSL_LOGGING_INTERNAL_LOG_FATAL ::absl::log_internal::NullStreamFatal()
+#define ABSL_LOGGING_INTERNAL_LOG_QFATAL ::absl::log_internal::NullStreamFatal()
+#define ABSL_LOGGING_INTERNAL_LOG_DFATAL \
+  ::absl::log_internal::NullStreamMaybeFatal(::absl::kLogDebugFatal)
+#define ABSL_LOGGING_INTERNAL_LOG_LEVEL(severity) \
+  ::absl::log_internal::NullStreamMaybeFatal(log_internal_severity)
+#define ABSL_LOG_INTERNAL_CHECK(failure_message) ABSL_LOGGING_INTERNAL_LOG_FATAL
+#define ABSL_LOG_INTERNAL_QCHECK(failure_message) \
+  ABSL_LOGGING_INTERNAL_LOG_QFATAL
+#else  // !defined(STRIP_LOG) || !STRIP_LOG
+#define ABSL_LOGGING_INTERNAL_LOG_INFO                 \
+  ::absl::log_internal::LogMessage(__FILE__, __LINE__, \
+                                   ::absl::LogSeverity::kInfo)
+#define ABSL_LOGGING_INTERNAL_LOG_WARNING              \
+  ::absl::log_internal::LogMessage(__FILE__, __LINE__, \
+                                   ::absl::LogSeverity::kWarning)
+#define ABSL_LOGGING_INTERNAL_LOG_ERROR                \
+  ::absl::log_internal::LogMessage(__FILE__, __LINE__, \
+                                   ::absl::LogSeverity::kError)
+#define ABSL_LOGGING_INTERNAL_LOG_FATAL \
+  ::absl::log_internal::LogMessageFatal(__FILE__, __LINE__)
+#define ABSL_LOGGING_INTERNAL_LOG_QFATAL \
+  ::absl::log_internal::LogMessageQuietlyFatal(__FILE__, __LINE__)
+#define ABSL_LOGGING_INTERNAL_LOG_DFATAL \
+  ::absl::log_internal::LogMessage(__FILE__, __LINE__, ::absl::kLogDebugFatal)
+#define ABSL_LOGGING_INTERNAL_LOG_LEVEL(severity) \
+  ::absl::log_internal::LogMessage(__FILE__, __LINE__, log_internal_severity)
+// These special cases dispatch to special-case constructors that allow us to
+// avoid an extra function call and shrink non-LTO binaries by a percent or so.
+#define ABSL_LOG_INTERNAL_CHECK(failure_message) \
+  ::absl::log_internal::LogMessageFatal(__FILE__, __LINE__, failure_message)
+#define ABSL_LOG_INTERNAL_QCHECK(failure_message)                  \
+  ::absl::log_internal::LogMessageQuietlyFatal(__FILE__, __LINE__, \
+                                               failure_message)
+#endif  // !defined(STRIP_LOG) || !STRIP_LOG
+
+#endif  // ABSL_LOG_INTERNAL_STRIP_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/voidify.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/voidify.h
new file mode 100644
index 0000000000000000000000000000000000000000..8f62da20a0483708f374fff00e49d6b9e9e212d1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/internal/voidify.h
@@ -0,0 +1,44 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/internal/voidify.h
+// -----------------------------------------------------------------------------
+//
+// This class is used to explicitly ignore values in the conditional logging
+// macros. This avoids compiler warnings like "value computed is not used" and
+// "statement has no effect".
+
+#ifndef ABSL_LOG_INTERNAL_VOIDIFY_H_
+#define ABSL_LOG_INTERNAL_VOIDIFY_H_
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace log_internal {
+
+class Voidify final {
+ public:
+  // This has to be an operator with a precedence lower than << but higher than
+  // ?:
+  template <typename T>
+  void operator&&(const T&) const&& {}
+};
+
+}  // namespace log_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_LOG_INTERNAL_VOIDIFY_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/log.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/log.h
new file mode 100644
index 0000000000000000000000000000000000000000..e060a0b69b7034812d2480de81d7500c56b44390
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/log.h
@@ -0,0 +1,308 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/log.h
+// -----------------------------------------------------------------------------
+//
+// This header declares a family of LOG macros.
+//
+// Basic invocation looks like this:
+//
+//   LOG(INFO) << "Found " << num_cookies << " cookies";
+//
+// Most `LOG` macros take a severity level argument.  The severity levels are
+// `INFO`, `WARNING`, `ERROR`, and `FATAL`.  They are defined
+// in absl/base/log_severity.h.
+// * The `FATAL` severity level terminates the program with a stack trace after
+//   logging its message.  Error handlers registered with `RunOnFailure`
+//   (process_state.h) are run, but exit handlers registered with `atexit(3)`
+//   are not.
+// * The `QFATAL` pseudo-severity level is equivalent to `FATAL` but triggers
+//   quieter termination messages, e.g. without a full stack trace, and skips
+//   running registered error handlers.
+// Some preprocessor shenanigans are used to ensure that e.g. `LOG(INFO)` has
+// the same meaning even if a local symbol or preprocessor macro named `INFO` is
+// defined.  To specify a severity level using an expression instead of a
+// literal, use `LEVEL(expr)`.
+// Example:
+//
+//   LOG(LEVEL(stale ? absl::LogSeverity::kWarning : absl::LogSeverity::kInfo))
+//       << "Cookies are " << days << " days old";
+
+// `LOG` macros evaluate to an unterminated statement.  The value at the end of
+// the statement supports some chainable methods:
+//
+//   * .AtLocation(absl::string_view file, int line)
+//     .AtLocation(absl::SourceLocation loc)
+//     Overrides the location inferred from the callsite.  The string pointed to
+//     by `file` must be valid until the end of the statement.
+//   * .NoPrefix()
+//     Omits the prefix from this line.  The prefix includes metadata about the
+//     logged data such as source code location and timestamp.
+//   * .WithTimestamp(absl::Time timestamp)
+//     Uses the specified timestamp instead of one collected at the time of
+//     execution.
+//   * .WithThreadID(absl::LogEntry::tid_t tid)
+//     Uses the specified thread ID instead of one collected at the time of
+//     execution.
+//   * .WithMetadataFrom(const absl::LogEntry &entry)
+//     Copies all metadata (but no data) from the specified `absl::LogEntry`.
+//     This can be used to change the severity of a message, but it has some
+//     limitations:
+//     * `ABSL_MIN_LOG_LEVEL` is evaluated against the severity passed into
+//       `LOG` (or the implicit `FATAL` level of `CHECK`).
+//     * `LOG(FATAL)` and `CHECK` terminate the process unconditionally, even if
+//       the severity is changed later.
+//     `.WithMetadataFrom(entry)` should almost always be used in combination
+//     with `LOG(LEVEL(entry.log_severity()))`.
+//   * .WithPerror()
+//     Appends to the logged message a colon, a space, a textual description of
+//     the current value of `errno` (as by `strerror(3)`), and the numerical
+//     value of `errno`.
+//   * .ToSinkAlso(absl::LogSink* sink)
+//     Sends this message to `*sink` in addition to whatever other sinks it
+//     would otherwise have been sent to.  `sink` must not be null.
+//   * .ToSinkOnly(absl::LogSink* sink)
+//     Sends this message to `*sink` and no others.  `sink` must not be null.
+//
+// No interfaces in this header are async-signal-safe; their use in signal
+// handlers is unsupported and may deadlock your program or eat your lunch.
+//
+// Many logging statements are inherently conditional.  For example,
+// `LOG_IF(INFO, !foo)` does nothing if `foo` is true.  Even seemingly
+// unconditional statements like `LOG(INFO)` might be disabled at
+// compile-time to minimize binary size or for security reasons.
+//
+// * Except for the condition in a `CHECK` or `QCHECK` statement, programs must
+//   not rely on evaluation of expressions anywhere in logging statements for
+//   correctness.  For example, this is ok:
+//
+//     CHECK((fp = fopen("config.ini", "r")) != nullptr);
+//
+//   But this is probably not ok:
+//
+//     LOG(INFO) << "Server status: " << StartServerAndReturnStatusString();
+//
+//   The example below is bad too; the `i++` in the `LOG_IF` condition might
+//   not be evaluated, resulting in an infinite loop:
+//
+//     for (int i = 0; i < 1000000;)
+//       LOG_IF(INFO, i++ % 1000 == 0) << "Still working...";
+//
+// * Except where otherwise noted, conditions which cause a statement not to log
+//   also cause expressions not to be evaluated.  Programs may rely on this for
+//   performance reasons, e.g. by streaming the result of an expensive function
+//   call into a `DLOG` or `LOG_EVERY_N` statement.
+// * Care has been taken to ensure that expressions are parsed by the compiler
+//   even if they are never evaluated.  This means that syntax errors will be
+//   caught and variables will be considered used for the purposes of
+//   unused-variable diagnostics.  For example, this statement won't compile
+//   even if `INFO`-level logging has been compiled out:
+//
+//     int number_of_cakes = 40;
+//     LOG(INFO) << "Number of cakes: " << number_of_cake;  // Note the typo!
+//
+//   Similarly, this won't produce unused-variable compiler diagnostics even
+//   if `INFO`-level logging is compiled out:
+//
+//     {
+//       char fox_line1[] = "Hatee-hatee-hatee-ho!";
+//       LOG_IF(ERROR, false) << "The fox says " << fox_line1;
+//       char fox_line2[] = "A-oo-oo-oo-ooo!";
+//       LOG(INFO) << "The fox also says " << fox_line2;
+//     }
+//
+//   This error-checking is not perfect; for example, symbols that have been
+//   declared but not defined may not produce link errors if used in logging
+//   statements that compile away.
+//
+// Expressions streamed into these macros are formatted using `operator<<` just
+// as they would be if streamed into a `std::ostream`, however it should be
+// noted that their actual type is unspecified.
+//
+// To implement a custom formatting operator for a type you own, there are two
+// options: `AbslStringify()` or `std::ostream& operator<<(std::ostream&, ...)`.
+// It is recommended that users make their types loggable through
+// `AbslStringify()` as it is a universal stringification extension that also
+// enables `absl::StrFormat` and `absl::StrCat` support. If both
+// `AbslStringify()` and `std::ostream& operator<<(std::ostream&, ...)` are
+// defined, `AbslStringify()` will be used.
+//
+// To use the `AbslStringify()` API, define a friend function template in your
+// type's namespace with the following signature:
+//
+//   template <typename Sink>
+//   void AbslStringify(Sink& sink, const UserDefinedType& value);
+//
+// `Sink` has the same interface as `absl::FormatSink`, but without
+// `PutPaddedString()`.
+//
+// Example:
+//
+//   struct Point {
+//     template <typename Sink>
+//     friend void AbslStringify(Sink& sink, const Point& p) {
+//       absl::Format(&sink, "(%v, %v)", p.x, p.y);
+//     }
+//
+//     int x;
+//     int y;
+//   };
+//
+// To use `std::ostream& operator<<(std::ostream&, ...)`, define
+// `std::ostream& operator<<(std::ostream&, ...)` in your type's namespace (for
+// ADL) just as you would to stream it to `std::cout`.
+//
+// Currently `AbslStringify()` ignores output manipulators but this is not
+// guaranteed behavior and may be subject to change in the future. If you would
+// like guaranteed behavior regarding output manipulators, please use
+// `std::ostream& operator<<(std::ostream&, ...)` to make custom types loggable
+// instead.
+//
+// Those macros that support streaming honor output manipulators and `fmtflag`
+// changes that output data (e.g. `std::ends`) or control formatting of data
+// (e.g. `std::hex` and `std::fixed`), however flushing such a stream is
+// ignored.  The message produced by a log statement is sent to registered
+// `absl::LogSink` instances at the end of the statement; those sinks are
+// responsible for their own flushing (e.g. to disk) semantics.
+//
+// Flag settings are not carried over from one `LOG` statement to the next; this
+// is a bit different than e.g. `std::cout`:
+//
+//   LOG(INFO) << std::hex << 0xdeadbeef;  // logs "0xdeadbeef"
+//   LOG(INFO) << 0xdeadbeef;              // logs "3735928559"
+
+#ifndef ABSL_LOG_LOG_H_
+#define ABSL_LOG_LOG_H_
+
+#include "absl/log/internal/log_impl.h"
+
+// LOG()
+//
+// `LOG` takes a single argument which is a severity level.  Data streamed in
+// comprise the logged message.
+// Example:
+//
+//   LOG(INFO) << "Found " << num_cookies << " cookies";
+#define LOG(severity) ABSL_LOG_IMPL(_##severity)
+
+// PLOG()
+//
+// `PLOG` behaves like `LOG` except that a description of the current state of
+// `errno` is appended to the streamed message.
+#define PLOG(severity) ABSL_PLOG_IMPL(_##severity)
+
+// DLOG()
+//
+// `DLOG` behaves like `LOG` in debug mode (i.e. `#ifndef NDEBUG`).  Otherwise
+// it compiles away and does nothing.  Note that `DLOG(FATAL)` does not
+// terminate the program if `NDEBUG` is defined.
+#define DLOG(severity) ABSL_DLOG_IMPL(_##severity)
+
+// `LOG_IF` and friends add a second argument which specifies a condition.  If
+// the condition is false, nothing is logged.
+// Example:
+//
+//   LOG_IF(INFO, num_cookies > 10) << "Got lots of cookies";
+#define LOG_IF(severity, condition) ABSL_LOG_IF_IMPL(_##severity, condition)
+#define PLOG_IF(severity, condition) ABSL_PLOG_IF_IMPL(_##severity, condition)
+#define DLOG_IF(severity, condition) ABSL_DLOG_IF_IMPL(_##severity, condition)
+
+// LOG_EVERY_N
+//
+// An instance of `LOG_EVERY_N` increments a hidden zero-initialized counter
+// every time execution passes through it and logs the specified message when
+// the counter's value is a multiple of `n`, doing nothing otherwise.  Each
+// instance has its own counter.  The counter's value can be logged by streaming
+// the symbol `COUNTER`.  `LOG_EVERY_N` is thread-safe.
+// Example:
+//
+//   LOG_EVERY_N(WARNING, 1000) << "Got a packet with a bad CRC (" << COUNTER
+//                              << " total)";
+#define LOG_EVERY_N(severity, n) ABSL_LOG_EVERY_N_IMPL(_##severity, n)
+
+// LOG_FIRST_N
+//
+// `LOG_FIRST_N` behaves like `LOG_EVERY_N` except that the specified message is
+// logged when the counter's value is less than `n`.  `LOG_FIRST_N` is
+// thread-safe.
+#define LOG_FIRST_N(severity, n) ABSL_LOG_FIRST_N_IMPL(_##severity, n)
+
+// LOG_EVERY_POW_2
+//
+// `LOG_EVERY_POW_2` behaves like `LOG_EVERY_N` except that the specified
+// message is logged when the counter's value is a power of 2.
+// `LOG_EVERY_POW_2` is thread-safe.
+#define LOG_EVERY_POW_2(severity) ABSL_LOG_EVERY_POW_2_IMPL(_##severity)
+
+// LOG_EVERY_N_SEC
+//
+// An instance of `LOG_EVERY_N_SEC` uses a hidden state variable to log the
+// specified message at most once every `n_seconds`.  A hidden counter of
+// executions (whether a message is logged or not) is also maintained and can be
+// logged by streaming the symbol `COUNTER`.  `LOG_EVERY_N_SEC` is thread-safe.
+// Example:
+//
+//   LOG_EVERY_N_SEC(INFO, 2.5) << "Got " << COUNTER << " cookies so far";
+#define LOG_EVERY_N_SEC(severity, n_seconds) \
+  ABSL_LOG_EVERY_N_SEC_IMPL(_##severity, n_seconds)
+
+#define PLOG_EVERY_N(severity, n) ABSL_PLOG_EVERY_N_IMPL(_##severity, n)
+#define PLOG_FIRST_N(severity, n) ABSL_PLOG_FIRST_N_IMPL(_##severity, n)
+#define PLOG_EVERY_POW_2(severity) ABSL_PLOG_EVERY_POW_2_IMPL(_##severity)
+#define PLOG_EVERY_N_SEC(severity, n_seconds) \
+  ABSL_PLOG_EVERY_N_SEC_IMPL(_##severity, n_seconds)
+
+#define DLOG_EVERY_N(severity, n) ABSL_DLOG_EVERY_N_IMPL(_##severity, n)
+#define DLOG_FIRST_N(severity, n) ABSL_DLOG_FIRST_N_IMPL(_##severity, n)
+#define DLOG_EVERY_POW_2(severity) ABSL_DLOG_EVERY_POW_2_IMPL(_##severity)
+#define DLOG_EVERY_N_SEC(severity, n_seconds) \
+  ABSL_DLOG_EVERY_N_SEC_IMPL(_##severity, n_seconds)
+
+// `LOG_IF_EVERY_N` and friends behave as the corresponding `LOG_EVERY_N`
+// but neither increment a counter nor log a message if condition is false (as
+// `LOG_IF`).
+// Example:
+//
+//   LOG_IF_EVERY_N(INFO, (size > 1024), 10) << "Got the " << COUNTER
+//                                           << "th big cookie";
+#define LOG_IF_EVERY_N(severity, condition, n) \
+  ABSL_LOG_IF_EVERY_N_IMPL(_##severity, condition, n)
+#define LOG_IF_FIRST_N(severity, condition, n) \
+  ABSL_LOG_IF_FIRST_N_IMPL(_##severity, condition, n)
+#define LOG_IF_EVERY_POW_2(severity, condition) \
+  ABSL_LOG_IF_EVERY_POW_2_IMPL(_##severity, condition)
+#define LOG_IF_EVERY_N_SEC(severity, condition, n_seconds) \
+  ABSL_LOG_IF_EVERY_N_SEC_IMPL(_##severity, condition, n_seconds)
+
+#define PLOG_IF_EVERY_N(severity, condition, n) \
+  ABSL_PLOG_IF_EVERY_N_IMPL(_##severity, condition, n)
+#define PLOG_IF_FIRST_N(severity, condition, n) \
+  ABSL_PLOG_IF_FIRST_N_IMPL(_##severity, condition, n)
+#define PLOG_IF_EVERY_POW_2(severity, condition) \
+  ABSL_PLOG_IF_EVERY_POW_2_IMPL(_##severity, condition)
+#define PLOG_IF_EVERY_N_SEC(severity, condition, n_seconds) \
+  ABSL_PLOG_IF_EVERY_N_SEC_IMPL(_##severity, condition, n_seconds)
+
+#define DLOG_IF_EVERY_N(severity, condition, n) \
+  ABSL_DLOG_IF_EVERY_N_IMPL(_##severity, condition, n)
+#define DLOG_IF_FIRST_N(severity, condition, n) \
+  ABSL_DLOG_IF_FIRST_N_IMPL(_##severity, condition, n)
+#define DLOG_IF_EVERY_POW_2(severity, condition) \
+  ABSL_DLOG_IF_EVERY_POW_2_IMPL(_##severity, condition)
+#define DLOG_IF_EVERY_N_SEC(severity, condition, n_seconds) \
+  ABSL_DLOG_IF_EVERY_N_SEC_IMPL(_##severity, condition, n_seconds)
+
+#endif  // ABSL_LOG_LOG_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/log_entry.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/log_entry.h
new file mode 100644
index 0000000000000000000000000000000000000000..9e4ae8eb7627f8eb186d4fd1fa292f0d5a924c40
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/log_entry.h
@@ -0,0 +1,220 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/log_entry.h
+// -----------------------------------------------------------------------------
+//
+// This header declares `class absl::LogEntry`, which represents a log record as
+// passed to `LogSink::Send`. Data returned by pointer or by reference or by
+// `absl::string_view` must be copied if they are needed after the lifetime of
+// the `absl::LogEntry`.
+
+#ifndef ABSL_LOG_LOG_ENTRY_H_
+#define ABSL_LOG_LOG_ENTRY_H_
+
+#include <cstddef>
+#include <string>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/base/log_severity.h"
+#include "absl/log/internal/config.h"
+#include "absl/strings/string_view.h"
+#include "absl/time/time.h"
+#include "absl/types/span.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+namespace log_internal {
+// Test only friend.
+class LogEntryTestPeer;
+class LogMessage;
+}  // namespace log_internal
+
+// LogEntry
+//
+// Represents a single entry in a log, i.e., one `LOG` statement or failed
+// `CHECK`.
+//
+// `LogEntry` is thread-compatible.
+class LogEntry final {
+ public:
+  using tid_t = log_internal::Tid;
+
+  // For non-verbose log entries, `verbosity()` returns `kNoVerbosityLevel`.
+  static constexpr int kNoVerbosityLevel = -1;
+  static constexpr int kNoVerboseLevel = -1;  // TO BE removed
+
+  // Pass `LogEntry` by reference, and do not store it as its state does not
+  // outlive the call to `LogSink::Send()`.
+  LogEntry(const LogEntry&) = delete;
+  LogEntry& operator=(const LogEntry&) = delete;
+
+  // Source file and line where the log message occurred.  Taken from `__FILE__`
+  // and `__LINE__` unless overridden by `LOG(...).AtLocation(...)`.
+  //
+  // Take special care not to use the values returned by `source_filename()` and
+  // `source_basename()` after the lifetime of the entry.  This is always
+  // incorrect, but it will often work in practice because they usually point
+  // into a statically allocated character array obtained from `__FILE__`.
+  // Statements like `LOG(INFO).AtLocation(std::string(...), ...)` will expose
+  // the bug.  If you need the data later, you must copy them.
+  absl::string_view source_filename() const ABSL_ATTRIBUTE_LIFETIME_BOUND {
+    return full_filename_;
+  }
+  absl::string_view source_basename() const ABSL_ATTRIBUTE_LIFETIME_BOUND {
+    return base_filename_;
+  }
+  int source_line() const { return line_; }
+
+  // LogEntry::prefix()
+  //
+  // True unless the metadata prefix was suppressed once by
+  // `LOG(...).NoPrefix()` or globally by `absl::EnableLogPrefix(false)`.
+  // Implies `text_message_with_prefix() == text_message()`.
+  bool prefix() const { return prefix_; }
+
+  // LogEntry::log_severity()
+  //
+  // Returns this entry's severity.  For `LOG`, taken from the first argument;
+  // for `CHECK`, always `absl::LogSeverity::kFatal`.
+  absl::LogSeverity log_severity() const { return severity_; }
+
+  // LogEntry::verbosity()
+  //
+  // Returns this entry's verbosity, or `kNoVerbosityLevel` for a non-verbose
+  // entry.  Verbosity control is not available outside of Google yet.
+  int verbosity() const { return verbose_level_; }
+
+  // LogEntry::timestamp()
+  //
+  // Returns the time at which this entry was written.  Captured during
+  // evaluation of `LOG`, but can be overridden by
+  // `LOG(...).WithTimestamp(...)`.
+  //
+  // Take care not to rely on timestamps increasing monotonically, or even to
+  // rely on timestamps having any particular relationship with reality (since
+  // they can be overridden).
+  absl::Time timestamp() const { return timestamp_; }
+
+  // LogEntry::tid()
+  //
+  // Returns the ID of the thread that wrote this entry.  Captured during
+  // evaluation of `LOG`, but can be overridden by `LOG(...).WithThreadID(...)`.
+  //
+  // Take care not to *rely* on reported thread IDs as they can be overridden as
+  // specified above.
+  tid_t tid() const { return tid_; }
+
+  // Text-formatted version of the log message.  An underlying buffer holds
+  // these contiguous data:
+  //
+  // * A prefix formed by formatting metadata (timestamp, filename, line number,
+  //   etc.)
+  //   The prefix may be empty - see `LogEntry::prefix()` - and may rarely be
+  //   truncated if the metadata are very long.
+  // * The streamed data
+  //   The data may be empty if nothing was streamed, or may be truncated to fit
+  //   the buffer.
+  // * A newline
+  // * A nul terminator
+  //
+  // The newline and nul terminator will be present even if the prefix and/or
+  // data are truncated.
+  //
+  // These methods give access to the most commonly useful substrings of the
+  // buffer's contents.  Other combinations can be obtained with substring
+  // arithmetic.
+  //
+  // The buffer does not outlive the entry; if you need the data later, you must
+  // copy them.
+  absl::string_view text_message_with_prefix_and_newline() const
+      ABSL_ATTRIBUTE_LIFETIME_BOUND {
+    return absl::string_view(
+        text_message_with_prefix_and_newline_and_nul_.data(),
+        text_message_with_prefix_and_newline_and_nul_.size() - 1);
+  }
+  absl::string_view text_message_with_prefix() const
+      ABSL_ATTRIBUTE_LIFETIME_BOUND {
+    return absl::string_view(
+        text_message_with_prefix_and_newline_and_nul_.data(),
+        text_message_with_prefix_and_newline_and_nul_.size() - 2);
+  }
+  absl::string_view text_message_with_newline() const
+      ABSL_ATTRIBUTE_LIFETIME_BOUND {
+    return absl::string_view(
+        text_message_with_prefix_and_newline_and_nul_.data() + prefix_len_,
+        text_message_with_prefix_and_newline_and_nul_.size() - prefix_len_ - 1);
+  }
+  absl::string_view text_message() const ABSL_ATTRIBUTE_LIFETIME_BOUND {
+    return absl::string_view(
+        text_message_with_prefix_and_newline_and_nul_.data() + prefix_len_,
+        text_message_with_prefix_and_newline_and_nul_.size() - prefix_len_ - 2);
+  }
+  const char* text_message_with_prefix_and_newline_c_str() const
+      ABSL_ATTRIBUTE_LIFETIME_BOUND {
+    return text_message_with_prefix_and_newline_and_nul_.data();
+  }
+
+  // Returns a serialized protobuf holding the operands streamed into this
+  // log message.  The message definition is not yet published.
+  //
+  // The buffer does not outlive the entry; if you need the data later, you must
+  // copy them.
+  absl::string_view encoded_message() const ABSL_ATTRIBUTE_LIFETIME_BOUND {
+    return encoding_;
+  }
+
+  // LogEntry::stacktrace()
+  //
+  // Optional stacktrace, e.g. for `FATAL` logs and failed `CHECK`s.
+  //
+  // Fatal entries are dispatched to each sink twice: first with all data and
+  // metadata but no stacktrace, and then with the stacktrace.  This is done
+  // because stacktrace collection is sometimes slow and fallible, and it's
+  // critical to log enough information to diagnose the failure even if the
+  // stacktrace collection hangs.
+  //
+  // The buffer does not outlive the entry; if you need the data later, you must
+  // copy them.
+  absl::string_view stacktrace() const ABSL_ATTRIBUTE_LIFETIME_BOUND {
+    return stacktrace_;
+  }
+
+ private:
+  LogEntry() = default;
+
+  absl::string_view full_filename_;
+  absl::string_view base_filename_;
+  int line_;
+  bool prefix_;
+  absl::LogSeverity severity_;
+  int verbose_level_;  // >=0 for `VLOG`, etc.; otherwise `kNoVerbosityLevel`.
+  absl::Time timestamp_;
+  tid_t tid_;
+  absl::Span<const char> text_message_with_prefix_and_newline_and_nul_;
+  size_t prefix_len_;
+  absl::string_view encoding_;
+  std::string stacktrace_;
+
+  friend class log_internal::LogEntryTestPeer;
+  friend class log_internal::LogMessage;
+};
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_LOG_LOG_ENTRY_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/log_sink.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/log_sink.h
new file mode 100644
index 0000000000000000000000000000000000000000..9bfa6f86243ab3a5771de7730423972fbbdca137
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/log_sink.h
@@ -0,0 +1,64 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/log_sink.h
+// -----------------------------------------------------------------------------
+//
+// This header declares the interface class `absl::LogSink`.
+
+#ifndef ABSL_LOG_LOG_SINK_H_
+#define ABSL_LOG_LOG_SINK_H_
+
+#include "absl/base/config.h"
+#include "absl/log/log_entry.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// absl::LogSink
+//
+// `absl::LogSink` is an interface which can be extended to intercept and
+// process particular messages (with `LOG.ToSinkOnly()` or
+// `LOG.ToSinkAlso()`) or all messages (if registered with
+// `absl::AddLogSink`).  Implementations must be thread-safe, and should take
+// care not to take any locks that might be held by the `LOG` caller.
+class LogSink {
+ public:
+  virtual ~LogSink() = default;
+
+  // LogSink::Send()
+  //
+  // `Send` is called synchronously during the log statement.
+  //
+  // It is safe to use `LOG` within an implementation of `Send`.  `ToSinkOnly`
+  // and `ToSinkAlso` are safe in general but can be used to create an infinite
+  // loop if you try.
+  virtual void Send(const absl::LogEntry& entry) = 0;
+
+  // LogSink::Flush()
+  //
+  // Sinks that buffer messages should override this method to flush the buffer
+  // and return.
+  virtual void Flush() {}
+
+ private:
+  // https://lld.llvm.org/missingkeyfunction.html#missing-key-function
+  virtual void KeyFunction() const final;  // NOLINT(readability/inheritance)
+};
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_LOG_LOG_SINK_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/log_sink_registry.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/log_sink_registry.h
new file mode 100644
index 0000000000000000000000000000000000000000..bf76cceeae7976b73f79c9160bdffd07fbf58af9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/log/log_sink_registry.h
@@ -0,0 +1,61 @@
+// Copyright 2022 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: log/log_sink_registry.h
+// -----------------------------------------------------------------------------
+//
+// This header declares APIs to operate on global set of registered log sinks.
+
+#ifndef ABSL_LOG_LOG_SINK_REGISTRY_H_
+#define ABSL_LOG_LOG_SINK_REGISTRY_H_
+
+#include "absl/base/config.h"
+#include "absl/log/internal/log_sink_set.h"
+#include "absl/log/log_sink.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// AddLogSink(), RemoveLogSink()
+//
+// Adds or removes a `absl::LogSink` as a consumer of logging data.
+//
+// These functions are thread-safe.
+//
+// It is an error to attempt to add a sink that's already registered or to
+// attempt to remove one that isn't.
+//
+// To avoid unbounded recursion, dispatch to registered `absl::LogSink`s is
+// disabled per-thread while running the `Send()` method of registered
+// `absl::LogSink`s.  Affected messages are dispatched to a special internal
+// sink instead which writes them to `stderr`.
+//
+// Do not call these inside `absl::LogSink::Send`.
+inline void AddLogSink(absl::LogSink* sink) { log_internal::AddLogSink(sink); }
+inline void RemoveLogSink(absl::LogSink* sink) {
+  log_internal::RemoveLogSink(sink);
+}
+
+// FlushLogSinks()
+//
+// Calls `absl::LogSink::Flush` on all registered sinks.
+//
+// Do not call this inside `absl::LogSink::Send`.
+inline void FlushLogSinks() { log_internal::FlushLogSinks(); }
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_LOG_LOG_SINK_REGISTRY_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/memory/memory.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/memory/memory.h
new file mode 100644
index 0000000000000000000000000000000000000000..3508135c3338bbb2c2d8b4aed5904b8b4b931d1b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/memory/memory.h
@@ -0,0 +1,278 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: memory.h
+// -----------------------------------------------------------------------------
+//
+// This header file contains utility functions for managing the creation and
+// conversion of smart pointers. This file is an extension to the C++
+// standard <memory> library header file.
+
+#ifndef ABSL_MEMORY_MEMORY_H_
+#define ABSL_MEMORY_MEMORY_H_
+
+#include <cstddef>
+#include <limits>
+#include <memory>
+#include <new>
+#include <type_traits>
+#include <utility>
+
+#include "absl/base/macros.h"
+#include "absl/meta/type_traits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// -----------------------------------------------------------------------------
+// Function Template: WrapUnique()
+// -----------------------------------------------------------------------------
+//
+// Adopts ownership from a raw pointer and transfers it to the returned
+// `std::unique_ptr`, whose type is deduced. Because of this deduction, *do not*
+// specify the template type `T` when calling `WrapUnique`.
+//
+// Example:
+//   X* NewX(int, int);
+//   auto x = WrapUnique(NewX(1, 2));  // 'x' is std::unique_ptr<X>.
+//
+// Do not call WrapUnique with an explicit type, as in
+// `WrapUnique<X>(NewX(1, 2))`.  The purpose of WrapUnique is to automatically
+// deduce the pointer type. If you wish to make the type explicit, just use
+// `std::unique_ptr` directly.
+//
+//   auto x = std::unique_ptr<X>(NewX(1, 2));
+//                  - or -
+//   std::unique_ptr<X> x(NewX(1, 2));
+//
+// While `absl::WrapUnique` is useful for capturing the output of a raw
+// pointer factory, prefer 'absl::make_unique<T>(args...)' over
+// 'absl::WrapUnique(new T(args...))'.
+//
+//   auto x = WrapUnique(new X(1, 2));  // works, but nonideal.
+//   auto x = make_unique<X>(1, 2);     // safer, standard, avoids raw 'new'.
+//
+// Note that `absl::WrapUnique(p)` is valid only if `delete p` is a valid
+// expression. In particular, `absl::WrapUnique()` cannot wrap pointers to
+// arrays, functions or void, and it must not be used to capture pointers
+// obtained from array-new expressions (even though that would compile!).
+template <typename T>
+std::unique_ptr<T> WrapUnique(T* ptr) {
+  static_assert(!std::is_array<T>::value, "array types are unsupported");
+  static_assert(std::is_object<T>::value, "non-object types are unsupported");
+  return std::unique_ptr<T>(ptr);
+}
+
+// -----------------------------------------------------------------------------
+// Function Template: make_unique<T>()
+// -----------------------------------------------------------------------------
+//
+// Creates a `std::unique_ptr<>`, while avoiding issues creating temporaries
+// during the construction process. `absl::make_unique<>` also avoids redundant
+// type declarations, by avoiding the need to explicitly use the `new` operator.
+//
+// https://en.cppreference.com/w/cpp/memory/unique_ptr/make_unique
+//
+// For more background on why `std::unique_ptr<T>(new T(a,b))` is problematic,
+// see Herb Sutter's explanation on
+// (Exception-Safe Function Calls)[https://herbsutter.com/gotw/_102/].
+// (In general, reviewers should treat `new T(a,b)` with scrutiny.)
+//
+// Historical note: Abseil once provided a C++11 compatible implementation of
+// the C++14's `std::make_unique`. Now that C++11 support has been sunsetted,
+// `absl::make_unique` simply uses the STL-provided implementation. New code
+// should use `std::make_unique`.
+using std::make_unique;
+
+// -----------------------------------------------------------------------------
+// Function Template: RawPtr()
+// -----------------------------------------------------------------------------
+//
+// Extracts the raw pointer from a pointer-like value `ptr`. `absl::RawPtr` is
+// useful within templates that need to handle a complement of raw pointers,
+// `std::nullptr_t`, and smart pointers.
+template <typename T>
+auto RawPtr(T&& ptr) -> decltype(std::addressof(*ptr)) {
+  // ptr is a forwarding reference to support Ts with non-const operators.
+  return (ptr != nullptr) ? std::addressof(*ptr) : nullptr;
+}
+inline std::nullptr_t RawPtr(std::nullptr_t) { return nullptr; }
+
+// -----------------------------------------------------------------------------
+// Function Template: ShareUniquePtr()
+// -----------------------------------------------------------------------------
+//
+// Adopts a `std::unique_ptr` rvalue and returns a `std::shared_ptr` of deduced
+// type. Ownership (if any) of the held value is transferred to the returned
+// shared pointer.
+//
+// Example:
+//
+//     auto up = absl::make_unique<int>(10);
+//     auto sp = absl::ShareUniquePtr(std::move(up));  // shared_ptr<int>
+//     CHECK_EQ(*sp, 10);
+//     CHECK(up == nullptr);
+//
+// Note that this conversion is correct even when T is an array type, and more
+// generally it works for *any* deleter of the `unique_ptr` (single-object
+// deleter, array deleter, or any custom deleter), since the deleter is adopted
+// by the shared pointer as well. The deleter is copied (unless it is a
+// reference).
+//
+// Implements the resolution of [LWG 2415](http://wg21.link/lwg2415), by which a
+// null shared pointer does not attempt to call the deleter.
+template <typename T, typename D>
+std::shared_ptr<T> ShareUniquePtr(std::unique_ptr<T, D>&& ptr) {
+  return ptr ? std::shared_ptr<T>(std::move(ptr)) : std::shared_ptr<T>();
+}
+
+// -----------------------------------------------------------------------------
+// Function Template: WeakenPtr()
+// -----------------------------------------------------------------------------
+//
+// Creates a weak pointer associated with a given shared pointer. The returned
+// value is a `std::weak_ptr` of deduced type.
+//
+// Example:
+//
+//    auto sp = std::make_shared<int>(10);
+//    auto wp = absl::WeakenPtr(sp);
+//    CHECK_EQ(sp.get(), wp.lock().get());
+//    sp.reset();
+//    CHECK(wp.lock() == nullptr);
+//
+template <typename T>
+std::weak_ptr<T> WeakenPtr(const std::shared_ptr<T>& ptr) {
+  return std::weak_ptr<T>(ptr);
+}
+
+// -----------------------------------------------------------------------------
+// Class Template: pointer_traits
+// -----------------------------------------------------------------------------
+//
+// Historical note: Abseil once provided an implementation of
+// `std::pointer_traits` for platforms that had not yet provided it. Those
+// platforms are no longer supported. New code should simply use
+// `std::pointer_traits`.
+using std::pointer_traits;
+
+// -----------------------------------------------------------------------------
+// Class Template: allocator_traits
+// -----------------------------------------------------------------------------
+//
+// Historical note: Abseil once provided an implementation of
+// `std::allocator_traits` for platforms that had not yet provided it. Those
+// platforms are no longer supported. New code should simply use
+// `std::allocator_traits`.
+using std::allocator_traits;
+
+namespace memory_internal {
+
+// ExtractOr<E, O, D>::type evaluates to E<O> if possible. Otherwise, D.
+template <template <typename> class Extract, typename Obj, typename Default,
+          typename>
+struct ExtractOr {
+  using type = Default;
+};
+
+template <template <typename> class Extract, typename Obj, typename Default>
+struct ExtractOr<Extract, Obj, Default, void_t<Extract<Obj>>> {
+  using type = Extract<Obj>;
+};
+
+template <template <typename> class Extract, typename Obj, typename Default>
+using ExtractOrT = typename ExtractOr<Extract, Obj, Default, void>::type;
+
+// This template alias transforms Alloc::is_nothrow into a metafunction with
+// Alloc as a parameter so it can be used with ExtractOrT<>.
+template <typename Alloc>
+using GetIsNothrow = typename Alloc::is_nothrow;
+
+}  // namespace memory_internal
+
+// ABSL_ALLOCATOR_NOTHROW is a build time configuration macro for user to
+// specify whether the default allocation function can throw or never throws.
+// If the allocation function never throws, user should define it to a non-zero
+// value (e.g. via `-DABSL_ALLOCATOR_NOTHROW`).
+// If the allocation function can throw, user should leave it undefined or
+// define it to zero.
+//
+// allocator_is_nothrow<Alloc> is a traits class that derives from
+// Alloc::is_nothrow if present, otherwise std::false_type. It's specialized
+// for Alloc = std::allocator<T> for any type T according to the state of
+// ABSL_ALLOCATOR_NOTHROW.
+//
+// default_allocator_is_nothrow is a class that derives from std::true_type
+// when the default allocator (global operator new) never throws, and
+// std::false_type when it can throw. It is a convenience shorthand for writing
+// allocator_is_nothrow<std::allocator<T>> (T can be any type).
+// NOTE: allocator_is_nothrow<std::allocator<T>> is guaranteed to derive from
+// the same type for all T, because users should specialize neither
+// allocator_is_nothrow nor std::allocator.
+template <typename Alloc>
+struct allocator_is_nothrow
+    : memory_internal::ExtractOrT<memory_internal::GetIsNothrow, Alloc,
+                                  std::false_type> {};
+
+#if defined(ABSL_ALLOCATOR_NOTHROW) && ABSL_ALLOCATOR_NOTHROW
+template <typename T>
+struct allocator_is_nothrow<std::allocator<T>> : std::true_type {};
+struct default_allocator_is_nothrow : std::true_type {};
+#else
+struct default_allocator_is_nothrow : std::false_type {};
+#endif
+
+namespace memory_internal {
+template <typename Allocator, typename Iterator, typename... Args>
+void ConstructRange(Allocator& alloc, Iterator first, Iterator last,
+                    const Args&... args) {
+  for (Iterator cur = first; cur != last; ++cur) {
+    ABSL_INTERNAL_TRY {
+      std::allocator_traits<Allocator>::construct(alloc, std::addressof(*cur),
+                                                  args...);
+    }
+    ABSL_INTERNAL_CATCH_ANY {
+      while (cur != first) {
+        --cur;
+        std::allocator_traits<Allocator>::destroy(alloc, std::addressof(*cur));
+      }
+      ABSL_INTERNAL_RETHROW;
+    }
+  }
+}
+
+template <typename Allocator, typename Iterator, typename InputIterator>
+void CopyRange(Allocator& alloc, Iterator destination, InputIterator first,
+               InputIterator last) {
+  for (Iterator cur = destination; first != last;
+       static_cast<void>(++cur), static_cast<void>(++first)) {
+    ABSL_INTERNAL_TRY {
+      std::allocator_traits<Allocator>::construct(alloc, std::addressof(*cur),
+                                                  *first);
+    }
+    ABSL_INTERNAL_CATCH_ANY {
+      while (cur != destination) {
+        --cur;
+        std::allocator_traits<Allocator>::destroy(alloc, std::addressof(*cur));
+      }
+      ABSL_INTERNAL_RETHROW;
+    }
+  }
+}
+}  // namespace memory_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_MEMORY_MEMORY_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/meta/type_traits.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/meta/type_traits.h
new file mode 100644
index 0000000000000000000000000000000000000000..b1656c39c8c091ee5ebbb15df8910d4b40113150
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/meta/type_traits.h
@@ -0,0 +1,889 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// type_traits.h
+// -----------------------------------------------------------------------------
+//
+// This file contains C++11-compatible versions of standard <type_traits> API
+// functions for determining the characteristics of types. Such traits can
+// support type inference, classification, and transformation, as well as
+// make it easier to write templates based on generic type behavior.
+//
+// See https://en.cppreference.com/w/cpp/header/type_traits
+//
+// WARNING: use of many of the constructs in this header will count as "complex
+// template metaprogramming", so before proceeding, please carefully consider
+// https://google.github.io/styleguide/cppguide.html#Template_metaprogramming
+//
+// WARNING: using template metaprogramming to detect or depend on API
+// features is brittle and not guaranteed. Neither the standard library nor
+// Abseil provides any guarantee that APIs are stable in the face of template
+// metaprogramming. Use with caution.
+#ifndef ABSL_META_TYPE_TRAITS_H_
+#define ABSL_META_TYPE_TRAITS_H_
+
+#include <cstddef>
+#include <functional>
+#include <type_traits>
+
+#include "absl/base/config.h"
+
+// MSVC constructibility traits do not detect destructor properties and so our
+// implementations should not use them as a source-of-truth.
+#if defined(_MSC_VER) && !defined(__clang__) && !defined(__GNUC__)
+#define ABSL_META_INTERNAL_STD_CONSTRUCTION_TRAITS_DONT_CHECK_DESTRUCTION 1
+#endif
+
+// Defines the default alignment. `__STDCPP_DEFAULT_NEW_ALIGNMENT__` is a C++17
+// feature.
+#if defined(__STDCPP_DEFAULT_NEW_ALIGNMENT__)
+#define ABSL_INTERNAL_DEFAULT_NEW_ALIGNMENT __STDCPP_DEFAULT_NEW_ALIGNMENT__
+#else  // defined(__STDCPP_DEFAULT_NEW_ALIGNMENT__)
+#define ABSL_INTERNAL_DEFAULT_NEW_ALIGNMENT alignof(std::max_align_t)
+#endif  // defined(__STDCPP_DEFAULT_NEW_ALIGNMENT__)
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// Defined and documented later on in this file.
+template <typename T>
+struct is_trivially_destructible;
+
+// Defined and documented later on in this file.
+template <typename T>
+struct is_trivially_move_assignable;
+
+namespace type_traits_internal {
+
+// Silence MSVC warnings about the destructor being defined as deleted.
+#if defined(_MSC_VER) && !defined(__GNUC__)
+#pragma warning(push)
+#pragma warning(disable : 4624)
+#endif  // defined(_MSC_VER) && !defined(__GNUC__)
+
+template <class T>
+union SingleMemberUnion {
+  T t;
+};
+
+// Restore the state of the destructor warning that was silenced above.
+#if defined(_MSC_VER) && !defined(__GNUC__)
+#pragma warning(pop)
+#endif  // defined(_MSC_VER) && !defined(__GNUC__)
+
+template <class T>
+struct IsTriviallyMoveConstructibleObject
+    : std::integral_constant<
+          bool, std::is_move_constructible<
+                    type_traits_internal::SingleMemberUnion<T>>::value &&
+                    absl::is_trivially_destructible<T>::value> {};
+
+template <class T>
+struct IsTriviallyCopyConstructibleObject
+    : std::integral_constant<
+          bool, std::is_copy_constructible<
+                    type_traits_internal::SingleMemberUnion<T>>::value &&
+                    absl::is_trivially_destructible<T>::value> {};
+
+template <class T>
+struct IsTriviallyMoveAssignableReference : std::false_type {};
+
+template <class T>
+struct IsTriviallyMoveAssignableReference<T&>
+    : absl::is_trivially_move_assignable<T>::type {};
+
+template <class T>
+struct IsTriviallyMoveAssignableReference<T&&>
+    : absl::is_trivially_move_assignable<T>::type {};
+
+template <typename... Ts>
+struct VoidTImpl {
+  using type = void;
+};
+
+////////////////////////////////
+// Library Fundamentals V2 TS //
+////////////////////////////////
+
+// NOTE: The `is_detected` family of templates here differ from the library
+// fundamentals specification in that for library fundamentals, `Op<Args...>` is
+// evaluated as soon as the type `is_detected<Op, Args...>` undergoes
+// substitution, regardless of whether or not the `::value` is accessed. That
+// is inconsistent with all other standard traits and prevents lazy evaluation
+// in larger contexts (such as if the `is_detected` check is a trailing argument
+// of a `conjunction`. This implementation opts to instead be lazy in the same
+// way that the standard traits are (this "defect" of the detection idiom
+// specifications has been reported).
+
+template <class Enabler, template <class...> class Op, class... Args>
+struct is_detected_impl {
+  using type = std::false_type;
+};
+
+template <template <class...> class Op, class... Args>
+struct is_detected_impl<typename VoidTImpl<Op<Args...>>::type, Op, Args...> {
+  using type = std::true_type;
+};
+
+template <template <class...> class Op, class... Args>
+struct is_detected : is_detected_impl<void, Op, Args...>::type {};
+
+template <class Enabler, class To, template <class...> class Op, class... Args>
+struct is_detected_convertible_impl {
+  using type = std::false_type;
+};
+
+template <class To, template <class...> class Op, class... Args>
+struct is_detected_convertible_impl<
+    typename std::enable_if<std::is_convertible<Op<Args...>, To>::value>::type,
+    To, Op, Args...> {
+  using type = std::true_type;
+};
+
+template <class To, template <class...> class Op, class... Args>
+struct is_detected_convertible
+    : is_detected_convertible_impl<void, To, Op, Args...>::type {};
+
+template <typename T>
+using IsCopyAssignableImpl =
+    decltype(std::declval<T&>() = std::declval<const T&>());
+
+template <typename T>
+using IsMoveAssignableImpl = decltype(std::declval<T&>() = std::declval<T&&>());
+
+}  // namespace type_traits_internal
+
+// MSVC 19.20 has a regression that causes our workarounds to fail, but their
+// std forms now appear to be compliant.
+#if defined(_MSC_VER) && !defined(__clang__) && (_MSC_VER >= 1920)
+
+template <typename T>
+using is_copy_assignable = std::is_copy_assignable<T>;
+
+template <typename T>
+using is_move_assignable = std::is_move_assignable<T>;
+
+#else
+
+template <typename T>
+struct is_copy_assignable : type_traits_internal::is_detected<
+                                type_traits_internal::IsCopyAssignableImpl, T> {
+};
+
+template <typename T>
+struct is_move_assignable : type_traits_internal::is_detected<
+                                type_traits_internal::IsMoveAssignableImpl, T> {
+};
+
+#endif
+
+// void_t()
+//
+// Ignores the type of any its arguments and returns `void`. In general, this
+// metafunction allows you to create a general case that maps to `void` while
+// allowing specializations that map to specific types.
+//
+// This metafunction is designed to be a drop-in replacement for the C++17
+// `std::void_t` metafunction.
+//
+// NOTE: `absl::void_t` does not use the standard-specified implementation so
+// that it can remain compatible with gcc < 5.1. This can introduce slightly
+// different behavior, such as when ordering partial specializations.
+template <typename... Ts>
+using void_t = typename type_traits_internal::VoidTImpl<Ts...>::type;
+
+// conjunction
+//
+// Performs a compile-time logical AND operation on the passed types (which
+// must have  `::value` members convertible to `bool`. Short-circuits if it
+// encounters any `false` members (and does not compare the `::value` members
+// of any remaining arguments).
+//
+// This metafunction is designed to be a drop-in replacement for the C++17
+// `std::conjunction` metafunction.
+template <typename... Ts>
+struct conjunction : std::true_type {};
+
+template <typename T, typename... Ts>
+struct conjunction<T, Ts...>
+    : std::conditional<T::value, conjunction<Ts...>, T>::type {};
+
+template <typename T>
+struct conjunction<T> : T {};
+
+// disjunction
+//
+// Performs a compile-time logical OR operation on the passed types (which
+// must have  `::value` members convertible to `bool`. Short-circuits if it
+// encounters any `true` members (and does not compare the `::value` members
+// of any remaining arguments).
+//
+// This metafunction is designed to be a drop-in replacement for the C++17
+// `std::disjunction` metafunction.
+template <typename... Ts>
+struct disjunction : std::false_type {};
+
+template <typename T, typename... Ts>
+struct disjunction<T, Ts...> :
+      std::conditional<T::value, T, disjunction<Ts...>>::type {};
+
+template <typename T>
+struct disjunction<T> : T {};
+
+// negation
+//
+// Performs a compile-time logical NOT operation on the passed type (which
+// must have  `::value` members convertible to `bool`.
+//
+// This metafunction is designed to be a drop-in replacement for the C++17
+// `std::negation` metafunction.
+template <typename T>
+struct negation : std::integral_constant<bool, !T::value> {};
+
+// is_function()
+//
+// Determines whether the passed type `T` is a function type.
+//
+// This metafunction is designed to be a drop-in replacement for the C++11
+// `std::is_function()` metafunction for platforms that have incomplete C++11
+// support (such as libstdc++ 4.x).
+//
+// This metafunction works because appending `const` to a type does nothing to
+// function types and reference types (and forms a const-qualified type
+// otherwise).
+template <typename T>
+struct is_function
+    : std::integral_constant<
+          bool, !(std::is_reference<T>::value ||
+                  std::is_const<typename std::add_const<T>::type>::value)> {};
+
+// is_trivially_destructible()
+//
+// Determines whether the passed type `T` is trivially destructible.
+//
+// This metafunction is designed to be a drop-in replacement for the C++11
+// `std::is_trivially_destructible()` metafunction for platforms that have
+// incomplete C++11 support (such as libstdc++ 4.x). On any platforms that do
+// fully support C++11, we check whether this yields the same result as the std
+// implementation.
+//
+// NOTE: the extensions (__has_trivial_xxx) are implemented in gcc (version >=
+// 4.3) and clang. Since we are supporting libstdc++ > 4.7, they should always
+// be present. These  extensions are documented at
+// https://gcc.gnu.org/onlinedocs/gcc/Type-Traits.html#Type-Traits.
+template <typename T>
+struct is_trivially_destructible
+#ifdef ABSL_HAVE_STD_IS_TRIVIALLY_DESTRUCTIBLE
+    : std::is_trivially_destructible<T> {
+#else
+    : std::integral_constant<bool, __has_trivial_destructor(T) &&
+                                   std::is_destructible<T>::value> {
+#endif
+#ifdef ABSL_HAVE_STD_IS_TRIVIALLY_DESTRUCTIBLE
+ private:
+  static constexpr bool compliant = std::is_trivially_destructible<T>::value ==
+                                    is_trivially_destructible::value;
+  static_assert(compliant || std::is_trivially_destructible<T>::value,
+                "Not compliant with std::is_trivially_destructible; "
+                "Standard: false, Implementation: true");
+  static_assert(compliant || !std::is_trivially_destructible<T>::value,
+                "Not compliant with std::is_trivially_destructible; "
+                "Standard: true, Implementation: false");
+#endif  // ABSL_HAVE_STD_IS_TRIVIALLY_DESTRUCTIBLE
+};
+
+// is_trivially_default_constructible()
+//
+// Determines whether the passed type `T` is trivially default constructible.
+//
+// This metafunction is designed to be a drop-in replacement for the C++11
+// `std::is_trivially_default_constructible()` metafunction for platforms that
+// have incomplete C++11 support (such as libstdc++ 4.x). On any platforms that
+// do fully support C++11, we check whether this yields the same result as the
+// std implementation.
+//
+// NOTE: according to the C++ standard, Section: 20.15.4.3 [meta.unary.prop]
+// "The predicate condition for a template specialization is_constructible<T,
+// Args...> shall be satisfied if and only if the following variable
+// definition would be well-formed for some invented variable t:
+//
+// T t(declval<Args>()...);
+//
+// is_trivially_constructible<T, Args...> additionally requires that the
+// variable definition does not call any operation that is not trivial.
+// For the purposes of this check, the call to std::declval is considered
+// trivial."
+//
+// Notes from https://en.cppreference.com/w/cpp/types/is_constructible:
+// In many implementations, is_nothrow_constructible also checks if the
+// destructor throws because it is effectively noexcept(T(arg)). Same
+// applies to is_trivially_constructible, which, in these implementations, also
+// requires that the destructor is trivial.
+// GCC bug 51452: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=51452
+// LWG issue 2116: http://cplusplus.github.io/LWG/lwg-active.html#2116.
+//
+// "T obj();" need to be well-formed and not call any nontrivial operation.
+// Nontrivially destructible types will cause the expression to be nontrivial.
+template <typename T>
+struct is_trivially_default_constructible
+#if defined(ABSL_HAVE_STD_IS_TRIVIALLY_CONSTRUCTIBLE)
+    : std::is_trivially_default_constructible<T> {
+#else
+    : std::integral_constant<bool, __has_trivial_constructor(T) &&
+                                   std::is_default_constructible<T>::value &&
+                                   is_trivially_destructible<T>::value> {
+#endif
+#if defined(ABSL_HAVE_STD_IS_TRIVIALLY_CONSTRUCTIBLE) && \
+    !defined(                                            \
+        ABSL_META_INTERNAL_STD_CONSTRUCTION_TRAITS_DONT_CHECK_DESTRUCTION)
+ private:
+  static constexpr bool compliant =
+      std::is_trivially_default_constructible<T>::value ==
+      is_trivially_default_constructible::value;
+  static_assert(compliant || std::is_trivially_default_constructible<T>::value,
+                "Not compliant with std::is_trivially_default_constructible; "
+                "Standard: false, Implementation: true");
+  static_assert(compliant || !std::is_trivially_default_constructible<T>::value,
+                "Not compliant with std::is_trivially_default_constructible; "
+                "Standard: true, Implementation: false");
+#endif  // ABSL_HAVE_STD_IS_TRIVIALLY_CONSTRUCTIBLE
+};
+
+// is_trivially_move_constructible()
+//
+// Determines whether the passed type `T` is trivially move constructible.
+//
+// This metafunction is designed to be a drop-in replacement for the C++11
+// `std::is_trivially_move_constructible()` metafunction for platforms that have
+// incomplete C++11 support (such as libstdc++ 4.x). On any platforms that do
+// fully support C++11, we check whether this yields the same result as the std
+// implementation.
+//
+// NOTE: `T obj(declval<T>());` needs to be well-formed and not call any
+// nontrivial operation.  Nontrivially destructible types will cause the
+// expression to be nontrivial.
+template <typename T>
+struct is_trivially_move_constructible
+#if defined(ABSL_HAVE_STD_IS_TRIVIALLY_CONSTRUCTIBLE)
+    : std::is_trivially_move_constructible<T> {
+#else
+    : std::conditional<
+          std::is_object<T>::value && !std::is_array<T>::value,
+          type_traits_internal::IsTriviallyMoveConstructibleObject<T>,
+          std::is_reference<T>>::type::type {
+#endif
+#if defined(ABSL_HAVE_STD_IS_TRIVIALLY_CONSTRUCTIBLE) && \
+    !defined(                                            \
+        ABSL_META_INTERNAL_STD_CONSTRUCTION_TRAITS_DONT_CHECK_DESTRUCTION)
+ private:
+  static constexpr bool compliant =
+      std::is_trivially_move_constructible<T>::value ==
+      is_trivially_move_constructible::value;
+  static_assert(compliant || std::is_trivially_move_constructible<T>::value,
+                "Not compliant with std::is_trivially_move_constructible; "
+                "Standard: false, Implementation: true");
+  static_assert(compliant || !std::is_trivially_move_constructible<T>::value,
+                "Not compliant with std::is_trivially_move_constructible; "
+                "Standard: true, Implementation: false");
+#endif  // ABSL_HAVE_STD_IS_TRIVIALLY_CONSTRUCTIBLE
+};
+
+// is_trivially_copy_constructible()
+//
+// Determines whether the passed type `T` is trivially copy constructible.
+//
+// This metafunction is designed to be a drop-in replacement for the C++11
+// `std::is_trivially_copy_constructible()` metafunction for platforms that have
+// incomplete C++11 support (such as libstdc++ 4.x). On any platforms that do
+// fully support C++11, we check whether this yields the same result as the std
+// implementation.
+//
+// NOTE: `T obj(declval<const T&>());` needs to be well-formed and not call any
+// nontrivial operation.  Nontrivially destructible types will cause the
+// expression to be nontrivial.
+template <typename T>
+struct is_trivially_copy_constructible
+    : std::conditional<
+          std::is_object<T>::value && !std::is_array<T>::value,
+          type_traits_internal::IsTriviallyCopyConstructibleObject<T>,
+          std::is_lvalue_reference<T>>::type::type {
+#if defined(ABSL_HAVE_STD_IS_TRIVIALLY_CONSTRUCTIBLE) && \
+    !defined(                                            \
+        ABSL_META_INTERNAL_STD_CONSTRUCTION_TRAITS_DONT_CHECK_DESTRUCTION)
+ private:
+  static constexpr bool compliant =
+      std::is_trivially_copy_constructible<T>::value ==
+      is_trivially_copy_constructible::value;
+  static_assert(compliant || std::is_trivially_copy_constructible<T>::value,
+                "Not compliant with std::is_trivially_copy_constructible; "
+                "Standard: false, Implementation: true");
+  static_assert(compliant || !std::is_trivially_copy_constructible<T>::value,
+                "Not compliant with std::is_trivially_copy_constructible; "
+                "Standard: true, Implementation: false");
+#endif  // ABSL_HAVE_STD_IS_TRIVIALLY_CONSTRUCTIBLE
+};
+
+// is_trivially_move_assignable()
+//
+// Determines whether the passed type `T` is trivially move assignable.
+//
+// This metafunction is designed to be a drop-in replacement for the C++11
+// `std::is_trivially_move_assignable()` metafunction for platforms that have
+// incomplete C++11 support (such as libstdc++ 4.x). On any platforms that do
+// fully support C++11, we check whether this yields the same result as the std
+// implementation.
+//
+// NOTE: `is_assignable<T, U>::value` is `true` if the expression
+// `declval<T>() = declval<U>()` is well-formed when treated as an unevaluated
+// operand. `is_trivially_assignable<T, U>` requires the assignment to call no
+// operation that is not trivial. `is_trivially_copy_assignable<T>` is simply
+// `is_trivially_assignable<T&, T>`.
+template <typename T>
+struct is_trivially_move_assignable
+    : std::conditional<
+          std::is_object<T>::value && !std::is_array<T>::value &&
+              std::is_move_assignable<T>::value,
+          std::is_move_assignable<type_traits_internal::SingleMemberUnion<T>>,
+          type_traits_internal::IsTriviallyMoveAssignableReference<T>>::type::
+          type {
+#ifdef ABSL_HAVE_STD_IS_TRIVIALLY_ASSIGNABLE
+ private:
+  static constexpr bool compliant =
+      std::is_trivially_move_assignable<T>::value ==
+      is_trivially_move_assignable::value;
+  static_assert(compliant || std::is_trivially_move_assignable<T>::value,
+                "Not compliant with std::is_trivially_move_assignable; "
+                "Standard: false, Implementation: true");
+  static_assert(compliant || !std::is_trivially_move_assignable<T>::value,
+                "Not compliant with std::is_trivially_move_assignable; "
+                "Standard: true, Implementation: false");
+#endif  // ABSL_HAVE_STD_IS_TRIVIALLY_ASSIGNABLE
+};
+
+// is_trivially_copy_assignable()
+//
+// Determines whether the passed type `T` is trivially copy assignable.
+//
+// This metafunction is designed to be a drop-in replacement for the C++11
+// `std::is_trivially_copy_assignable()` metafunction for platforms that have
+// incomplete C++11 support (such as libstdc++ 4.x). On any platforms that do
+// fully support C++11, we check whether this yields the same result as the std
+// implementation.
+//
+// NOTE: `is_assignable<T, U>::value` is `true` if the expression
+// `declval<T>() = declval<U>()` is well-formed when treated as an unevaluated
+// operand. `is_trivially_assignable<T, U>` requires the assignment to call no
+// operation that is not trivial. `is_trivially_copy_assignable<T>` is simply
+// `is_trivially_assignable<T&, const T&>`.
+template <typename T>
+struct is_trivially_copy_assignable
+#ifdef ABSL_HAVE_STD_IS_TRIVIALLY_ASSIGNABLE
+    : std::is_trivially_copy_assignable<T> {
+#else
+    : std::integral_constant<
+          bool, __has_trivial_assign(typename std::remove_reference<T>::type) &&
+                    absl::is_copy_assignable<T>::value> {
+#endif
+#ifdef ABSL_HAVE_STD_IS_TRIVIALLY_ASSIGNABLE
+ private:
+  static constexpr bool compliant =
+      std::is_trivially_copy_assignable<T>::value ==
+      is_trivially_copy_assignable::value;
+  static_assert(compliant || std::is_trivially_copy_assignable<T>::value,
+                "Not compliant with std::is_trivially_copy_assignable; "
+                "Standard: false, Implementation: true");
+  static_assert(compliant || !std::is_trivially_copy_assignable<T>::value,
+                "Not compliant with std::is_trivially_copy_assignable; "
+                "Standard: true, Implementation: false");
+#endif  // ABSL_HAVE_STD_IS_TRIVIALLY_ASSIGNABLE
+};
+
+#if defined(__cpp_lib_remove_cvref) && __cpp_lib_remove_cvref >= 201711L
+template <typename T>
+using remove_cvref = std::remove_cvref<T>;
+
+template <typename T>
+using remove_cvref_t = typename std::remove_cvref<T>::type;
+#else
+// remove_cvref()
+//
+// C++11 compatible implementation of std::remove_cvref which was added in
+// C++20.
+template <typename T>
+struct remove_cvref {
+  using type =
+      typename std::remove_cv<typename std::remove_reference<T>::type>::type;
+};
+
+template <typename T>
+using remove_cvref_t = typename remove_cvref<T>::type;
+#endif
+
+namespace type_traits_internal {
+// is_trivially_copyable()
+//
+// Determines whether the passed type `T` is trivially copyable.
+//
+// This metafunction is designed to be a drop-in replacement for the C++11
+// `std::is_trivially_copyable()` metafunction for platforms that have
+// incomplete C++11 support (such as libstdc++ 4.x). We use the C++17 definition
+// of TriviallyCopyable.
+//
+// NOTE: `is_trivially_copyable<T>::value` is `true` if all of T's copy/move
+// constructors/assignment operators are trivial or deleted, T has at least
+// one non-deleted copy/move constructor/assignment operator, and T is trivially
+// destructible. Arrays of trivially copyable types are trivially copyable.
+//
+// We expose this metafunction only for internal use within absl.
+
+#if defined(ABSL_HAVE_STD_IS_TRIVIALLY_COPYABLE)
+template <typename T>
+struct is_trivially_copyable : std::is_trivially_copyable<T> {};
+#else
+template <typename T>
+class is_trivially_copyable_impl {
+  using ExtentsRemoved = typename std::remove_all_extents<T>::type;
+  static constexpr bool kIsCopyOrMoveConstructible =
+      std::is_copy_constructible<ExtentsRemoved>::value ||
+      std::is_move_constructible<ExtentsRemoved>::value;
+  static constexpr bool kIsCopyOrMoveAssignable =
+      absl::is_copy_assignable<ExtentsRemoved>::value ||
+      absl::is_move_assignable<ExtentsRemoved>::value;
+
+ public:
+  static constexpr bool kValue =
+      (__has_trivial_copy(ExtentsRemoved) || !kIsCopyOrMoveConstructible) &&
+      (__has_trivial_assign(ExtentsRemoved) || !kIsCopyOrMoveAssignable) &&
+      (kIsCopyOrMoveConstructible || kIsCopyOrMoveAssignable) &&
+      is_trivially_destructible<ExtentsRemoved>::value &&
+      // We need to check for this explicitly because otherwise we'll say
+      // references are trivial copyable when compiled by MSVC.
+      !std::is_reference<ExtentsRemoved>::value;
+};
+
+template <typename T>
+struct is_trivially_copyable
+    : std::integral_constant<
+          bool, type_traits_internal::is_trivially_copyable_impl<T>::kValue> {};
+#endif
+}  // namespace type_traits_internal
+
+// -----------------------------------------------------------------------------
+// C++14 "_t" trait aliases
+// -----------------------------------------------------------------------------
+
+template <typename T>
+using remove_cv_t = typename std::remove_cv<T>::type;
+
+template <typename T>
+using remove_const_t = typename std::remove_const<T>::type;
+
+template <typename T>
+using remove_volatile_t = typename std::remove_volatile<T>::type;
+
+template <typename T>
+using add_cv_t = typename std::add_cv<T>::type;
+
+template <typename T>
+using add_const_t = typename std::add_const<T>::type;
+
+template <typename T>
+using add_volatile_t = typename std::add_volatile<T>::type;
+
+template <typename T>
+using remove_reference_t = typename std::remove_reference<T>::type;
+
+template <typename T>
+using add_lvalue_reference_t = typename std::add_lvalue_reference<T>::type;
+
+template <typename T>
+using add_rvalue_reference_t = typename std::add_rvalue_reference<T>::type;
+
+template <typename T>
+using remove_pointer_t = typename std::remove_pointer<T>::type;
+
+template <typename T>
+using add_pointer_t = typename std::add_pointer<T>::type;
+
+template <typename T>
+using make_signed_t = typename std::make_signed<T>::type;
+
+template <typename T>
+using make_unsigned_t = typename std::make_unsigned<T>::type;
+
+template <typename T>
+using remove_extent_t = typename std::remove_extent<T>::type;
+
+template <typename T>
+using remove_all_extents_t = typename std::remove_all_extents<T>::type;
+
+namespace type_traits_internal {
+// This trick to retrieve a default alignment is necessary for our
+// implementation of aligned_storage_t to be consistent with any
+// implementation of std::aligned_storage.
+template <size_t Len, typename T = std::aligned_storage<Len>>
+struct default_alignment_of_aligned_storage;
+
+template <size_t Len, size_t Align>
+struct default_alignment_of_aligned_storage<
+    Len, std::aligned_storage<Len, Align>> {
+  static constexpr size_t value = Align;
+};
+}  // namespace type_traits_internal
+
+// TODO(b/260219225): std::aligned_storage(_t) is deprecated in C++23.
+template <size_t Len, size_t Align = type_traits_internal::
+                          default_alignment_of_aligned_storage<Len>::value>
+using aligned_storage_t = typename std::aligned_storage<Len, Align>::type;
+
+template <typename T>
+using decay_t = typename std::decay<T>::type;
+
+template <bool B, typename T = void>
+using enable_if_t = typename std::enable_if<B, T>::type;
+
+template <bool B, typename T, typename F>
+using conditional_t = typename std::conditional<B, T, F>::type;
+
+template <typename... T>
+using common_type_t = typename std::common_type<T...>::type;
+
+template <typename T>
+using underlying_type_t = typename std::underlying_type<T>::type;
+
+
+namespace type_traits_internal {
+
+#if (defined(__cpp_lib_is_invocable) && __cpp_lib_is_invocable >= 201703L) || \
+    (defined(_MSVC_LANG) && _MSVC_LANG >= 201703L)
+// std::result_of is deprecated (C++17) or removed (C++20)
+template<typename> struct result_of;
+template<typename F, typename... Args>
+struct result_of<F(Args...)> : std::invoke_result<F, Args...> {};
+#else
+template<typename F> using result_of = std::result_of<F>;
+#endif
+
+}  // namespace type_traits_internal
+
+template<typename F>
+using result_of_t = typename type_traits_internal::result_of<F>::type;
+
+namespace type_traits_internal {
+// In MSVC we can't probe std::hash or stdext::hash because it triggers a
+// static_assert instead of failing substitution. Libc++ prior to 4.0
+// also used a static_assert.
+//
+#if defined(_MSC_VER) || (defined(_LIBCPP_VERSION) && \
+                          _LIBCPP_VERSION < 4000 && _LIBCPP_STD_VER > 11)
+#define ABSL_META_INTERNAL_STD_HASH_SFINAE_FRIENDLY_ 0
+#else
+#define ABSL_META_INTERNAL_STD_HASH_SFINAE_FRIENDLY_ 1
+#endif
+
+#if !ABSL_META_INTERNAL_STD_HASH_SFINAE_FRIENDLY_
+template <typename Key, typename = size_t>
+struct IsHashable : std::true_type {};
+#else   // ABSL_META_INTERNAL_STD_HASH_SFINAE_FRIENDLY_
+template <typename Key, typename = void>
+struct IsHashable : std::false_type {};
+
+template <typename Key>
+struct IsHashable<
+    Key,
+    absl::enable_if_t<std::is_convertible<
+        decltype(std::declval<std::hash<Key>&>()(std::declval<Key const&>())),
+        std::size_t>::value>> : std::true_type {};
+#endif  // !ABSL_META_INTERNAL_STD_HASH_SFINAE_FRIENDLY_
+
+struct AssertHashEnabledHelper {
+ private:
+  static void Sink(...) {}
+  struct NAT {};
+
+  template <class Key>
+  static auto GetReturnType(int)
+      -> decltype(std::declval<std::hash<Key>>()(std::declval<Key const&>()));
+  template <class Key>
+  static NAT GetReturnType(...);
+
+  template <class Key>
+  static std::nullptr_t DoIt() {
+    static_assert(IsHashable<Key>::value,
+                  "std::hash<Key> does not provide a call operator");
+    static_assert(
+        std::is_default_constructible<std::hash<Key>>::value,
+        "std::hash<Key> must be default constructible when it is enabled");
+    static_assert(
+        std::is_copy_constructible<std::hash<Key>>::value,
+        "std::hash<Key> must be copy constructible when it is enabled");
+    static_assert(absl::is_copy_assignable<std::hash<Key>>::value,
+                  "std::hash<Key> must be copy assignable when it is enabled");
+    // is_destructible is unchecked as it's implied by each of the
+    // is_constructible checks.
+    using ReturnType = decltype(GetReturnType<Key>(0));
+    static_assert(std::is_same<ReturnType, NAT>::value ||
+                      std::is_same<ReturnType, size_t>::value,
+                  "std::hash<Key> must return size_t");
+    return nullptr;
+  }
+
+  template <class... Ts>
+  friend void AssertHashEnabled();
+};
+
+template <class... Ts>
+inline void AssertHashEnabled() {
+  using Helper = AssertHashEnabledHelper;
+  Helper::Sink(Helper::DoIt<Ts>()...);
+}
+
+}  // namespace type_traits_internal
+
+// An internal namespace that is required to implement the C++17 swap traits.
+// It is not further nested in type_traits_internal to avoid long symbol names.
+namespace swap_internal {
+
+// Necessary for the traits.
+using std::swap;
+
+// This declaration prevents global `swap` and `absl::swap` overloads from being
+// considered unless ADL picks them up.
+void swap();
+
+template <class T>
+using IsSwappableImpl = decltype(swap(std::declval<T&>(), std::declval<T&>()));
+
+// NOTE: This dance with the default template parameter is for MSVC.
+template <class T,
+          class IsNoexcept = std::integral_constant<
+              bool, noexcept(swap(std::declval<T&>(), std::declval<T&>()))>>
+using IsNothrowSwappableImpl = typename std::enable_if<IsNoexcept::value>::type;
+
+// IsSwappable
+//
+// Determines whether the standard swap idiom is a valid expression for
+// arguments of type `T`.
+template <class T>
+struct IsSwappable
+    : absl::type_traits_internal::is_detected<IsSwappableImpl, T> {};
+
+// IsNothrowSwappable
+//
+// Determines whether the standard swap idiom is a valid expression for
+// arguments of type `T` and is noexcept.
+template <class T>
+struct IsNothrowSwappable
+    : absl::type_traits_internal::is_detected<IsNothrowSwappableImpl, T> {};
+
+// Swap()
+//
+// Performs the swap idiom from a namespace where valid candidates may only be
+// found in `std` or via ADL.
+template <class T, absl::enable_if_t<IsSwappable<T>::value, int> = 0>
+void Swap(T& lhs, T& rhs) noexcept(IsNothrowSwappable<T>::value) {
+  swap(lhs, rhs);
+}
+
+// StdSwapIsUnconstrained
+//
+// Some standard library implementations are broken in that they do not
+// constrain `std::swap`. This will effectively tell us if we are dealing with
+// one of those implementations.
+using StdSwapIsUnconstrained = IsSwappable<void()>;
+
+}  // namespace swap_internal
+
+namespace type_traits_internal {
+
+// Make the swap-related traits/function accessible from this namespace.
+using swap_internal::IsNothrowSwappable;
+using swap_internal::IsSwappable;
+using swap_internal::Swap;
+using swap_internal::StdSwapIsUnconstrained;
+
+}  // namespace type_traits_internal
+
+// absl::is_trivially_relocatable<T>
+// Detects whether a type is "trivially relocatable" -- meaning it can be
+// relocated without invoking the constructor/destructor, using a form of move
+// elision.
+//
+// Example:
+//
+// if constexpr (absl::is_trivially_relocatable<T>::value) {
+//   memcpy(new_location, old_location, sizeof(T));
+// } else {
+//   new(new_location) T(std::move(*old_location));
+//   old_location->~T();
+// }
+//
+// Upstream documentation:
+//
+// https://clang.llvm.org/docs/LanguageExtensions.html#:~:text=__is_trivially_relocatable
+//
+#if ABSL_HAVE_BUILTIN(__is_trivially_relocatable)
+template <class T>
+struct is_trivially_relocatable
+    : std::integral_constant<bool, __is_trivially_relocatable(T)> {};
+#else
+template <class T>
+struct is_trivially_relocatable : std::integral_constant<bool, false> {};
+#endif
+
+// absl::is_constant_evaluated()
+//
+// Detects whether the function call occurs within a constant-evaluated context.
+// Returns true if the evaluation of the call occurs within the evaluation of an
+// expression or conversion that is manifestly constant-evaluated; otherwise
+// returns false.
+//
+// This function is implemented in terms of `std::is_constant_evaluated` for
+// c++20 and up. For older c++ versions, the function is implemented in terms
+// of `__builtin_is_constant_evaluated` if available, otherwise the function
+// will fail to compile.
+//
+// Applications can inspect `ABSL_HAVE_CONSTANT_EVALUATED` at compile time
+// to check if this function is supported.
+//
+// Example:
+//
+// constexpr MyClass::MyClass(int param) {
+// #ifdef ABSL_HAVE_CONSTANT_EVALUATED
+//   if (!absl::is_constant_evaluated()) {
+//     ABSL_LOG(INFO) << "MyClass(" << param << ")";
+//   }
+// #endif  // ABSL_HAVE_CONSTANT_EVALUATED
+// }
+//
+// Upstream documentation:
+//
+// http://en.cppreference.com/w/cpp/types/is_constant_evaluated
+// http://gcc.gnu.org/onlinedocs/gcc/Other-Builtins.html#:~:text=__builtin_is_constant_evaluated
+//
+#if defined(ABSL_HAVE_CONSTANT_EVALUATED)
+constexpr bool is_constant_evaluated() noexcept {
+#ifdef __cpp_lib_is_constant_evaluated
+  return std::is_constant_evaluated();
+#elif ABSL_HAVE_BUILTIN(__builtin_is_constant_evaluated)
+  return __builtin_is_constant_evaluated();
+#endif
+}
+#endif  // ABSL_HAVE_CONSTANT_EVALUATED
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_META_TYPE_TRAITS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/bits.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/bits.h
new file mode 100644
index 0000000000000000000000000000000000000000..df81b9a929e4e3be80e5c82164fb9453509bad3d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/bits.h
@@ -0,0 +1,177 @@
+// Copyright 2020 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: bits.h
+// -----------------------------------------------------------------------------
+//
+// This file contains implementations of C++20's bitwise math functions, as
+// defined by:
+//
+// P0553R4:
+//  http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2019/p0553r4.html
+// P0556R3:
+//  http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2018/p0556r3.html
+// P1355R2:
+//  http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2019/p1355r2.html
+// P1956R1:
+//  http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2020/p1956r1.pdf
+//
+// When using a standard library that implements these functions, we use the
+// standard library's implementation.
+
+#ifndef ABSL_NUMERIC_BITS_H_
+#define ABSL_NUMERIC_BITS_H_
+
+#include <cstdint>
+#include <limits>
+#include <type_traits>
+
+#if (defined(__cpp_lib_int_pow2) && __cpp_lib_int_pow2 >= 202002L) || \
+    (defined(__cpp_lib_bitops) && __cpp_lib_bitops >= 201907L)
+#include <bit>
+#endif
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/numeric/internal/bits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+#if !(defined(__cpp_lib_bitops) && __cpp_lib_bitops >= 201907L)
+// rotating
+template <class T>
+ABSL_MUST_USE_RESULT constexpr
+    typename std::enable_if<std::is_unsigned<T>::value, T>::type
+    rotl(T x, int s) noexcept {
+  return numeric_internal::RotateLeft(x, s);
+}
+
+template <class T>
+ABSL_MUST_USE_RESULT constexpr
+    typename std::enable_if<std::is_unsigned<T>::value, T>::type
+    rotr(T x, int s) noexcept {
+  return numeric_internal::RotateRight(x, s);
+}
+
+// Counting functions
+//
+// While these functions are typically constexpr, on some platforms, they may
+// not be marked as constexpr due to constraints of the compiler/available
+// intrinsics.
+template <class T>
+ABSL_INTERNAL_CONSTEXPR_CLZ inline
+    typename std::enable_if<std::is_unsigned<T>::value, int>::type
+    countl_zero(T x) noexcept {
+  return numeric_internal::CountLeadingZeroes(x);
+}
+
+template <class T>
+ABSL_INTERNAL_CONSTEXPR_CLZ inline
+    typename std::enable_if<std::is_unsigned<T>::value, int>::type
+    countl_one(T x) noexcept {
+  // Avoid integer promotion to a wider type
+  return countl_zero(static_cast<T>(~x));
+}
+
+template <class T>
+ABSL_INTERNAL_CONSTEXPR_CTZ inline
+    typename std::enable_if<std::is_unsigned<T>::value, int>::type
+    countr_zero(T x) noexcept {
+  return numeric_internal::CountTrailingZeroes(x);
+}
+
+template <class T>
+ABSL_INTERNAL_CONSTEXPR_CTZ inline
+    typename std::enable_if<std::is_unsigned<T>::value, int>::type
+    countr_one(T x) noexcept {
+  // Avoid integer promotion to a wider type
+  return countr_zero(static_cast<T>(~x));
+}
+
+template <class T>
+ABSL_INTERNAL_CONSTEXPR_POPCOUNT inline
+    typename std::enable_if<std::is_unsigned<T>::value, int>::type
+    popcount(T x) noexcept {
+  return numeric_internal::Popcount(x);
+}
+#else  // defined(__cpp_lib_bitops) && __cpp_lib_bitops >= 201907L
+
+using std::countl_one;
+using std::countl_zero;
+using std::countr_one;
+using std::countr_zero;
+using std::popcount;
+using std::rotl;
+using std::rotr;
+
+#endif
+
+#if !(defined(__cpp_lib_int_pow2) && __cpp_lib_int_pow2 >= 202002L)
+// Returns: true if x is an integral power of two; false otherwise.
+template <class T>
+constexpr inline typename std::enable_if<std::is_unsigned<T>::value, bool>::type
+has_single_bit(T x) noexcept {
+  return x != 0 && (x & (x - 1)) == 0;
+}
+
+// Returns: If x == 0, 0; otherwise one plus the base-2 logarithm of x, with any
+// fractional part discarded.
+template <class T>
+ABSL_INTERNAL_CONSTEXPR_CLZ inline
+    typename std::enable_if<std::is_unsigned<T>::value, int>::type
+    bit_width(T x) noexcept {
+  return std::numeric_limits<T>::digits - countl_zero(x);
+}
+
+// Returns: If x == 0, 0; otherwise the maximal value y such that
+// has_single_bit(y) is true and y <= x.
+template <class T>
+ABSL_INTERNAL_CONSTEXPR_CLZ inline
+    typename std::enable_if<std::is_unsigned<T>::value, T>::type
+    bit_floor(T x) noexcept {
+  return x == 0 ? 0 : T{1} << (bit_width(x) - 1);
+}
+
+// Returns: N, where N is the smallest power of 2 greater than or equal to x.
+//
+// Preconditions: N is representable as a value of type T.
+template <class T>
+ABSL_INTERNAL_CONSTEXPR_CLZ inline
+    typename std::enable_if<std::is_unsigned<T>::value, T>::type
+    bit_ceil(T x) {
+  // If T is narrower than unsigned, T{1} << bit_width will be promoted.  We
+  // want to force it to wraparound so that bit_ceil of an invalid value are not
+  // core constant expressions.
+  //
+  // BitCeilNonPowerOf2 triggers an overflow in constexpr contexts if we would
+  // undergo promotion to unsigned but not fit the result into T without
+  // truncation.
+  return has_single_bit(x) ? T{1} << (bit_width(x) - 1)
+                           : numeric_internal::BitCeilNonPowerOf2(x);
+}
+#else  // defined(__cpp_lib_int_pow2) && __cpp_lib_int_pow2 >= 202002L
+
+using std::bit_ceil;
+using std::bit_floor;
+using std::bit_width;
+using std::has_single_bit;
+
+#endif
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_NUMERIC_BITS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/int128.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/int128.h
new file mode 100644
index 0000000000000000000000000000000000000000..7a899eec84c3a98be0d37bba5dcfe04f5d10ea43
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/int128.h
@@ -0,0 +1,1165 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: int128.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines 128-bit integer types, `uint128` and `int128`.
+//
+// TODO(absl-team): This module is inconsistent as many inline `uint128` methods
+// are defined in this file, while many inline `int128` methods are defined in
+// the `int128_*_intrinsic.inc` files.
+
+#ifndef ABSL_NUMERIC_INT128_H_
+#define ABSL_NUMERIC_INT128_H_
+
+#include <cassert>
+#include <cmath>
+#include <cstdint>
+#include <cstring>
+#include <iosfwd>
+#include <limits>
+#include <utility>
+
+#include "absl/base/config.h"
+#include "absl/base/macros.h"
+#include "absl/base/port.h"
+
+#if defined(_MSC_VER)
+// In very old versions of MSVC and when the /Zc:wchar_t flag is off, wchar_t is
+// a typedef for unsigned short.  Otherwise wchar_t is mapped to the __wchar_t
+// builtin type.  We need to make sure not to define operator wchar_t()
+// alongside operator unsigned short() in these instances.
+#define ABSL_INTERNAL_WCHAR_T __wchar_t
+#if defined(_M_X64) && !defined(_M_ARM64EC)
+#include <intrin.h>
+#pragma intrinsic(_umul128)
+#endif  // defined(_M_X64)
+#else   // defined(_MSC_VER)
+#define ABSL_INTERNAL_WCHAR_T wchar_t
+#endif  // defined(_MSC_VER)
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+class int128;
+
+// uint128
+//
+// An unsigned 128-bit integer type. The API is meant to mimic an intrinsic type
+// as closely as is practical, including exhibiting undefined behavior in
+// analogous cases (e.g. division by zero). This type is intended to be a
+// drop-in replacement once C++ supports an intrinsic `uint128_t` type; when
+// that occurs, existing well-behaved uses of `uint128` will continue to work
+// using that new type.
+//
+// Note: code written with this type will continue to compile once `uint128_t`
+// is introduced, provided the replacement helper functions
+// `Uint128(Low|High)64()` and `MakeUint128()` are made.
+//
+// A `uint128` supports the following:
+//
+//   * Implicit construction from integral types
+//   * Explicit conversion to integral types
+//
+// Additionally, if your compiler supports `__int128`, `uint128` is
+// interoperable with that type. (Abseil checks for this compatibility through
+// the `ABSL_HAVE_INTRINSIC_INT128` macro.)
+//
+// However, a `uint128` differs from intrinsic integral types in the following
+// ways:
+//
+//   * Errors on implicit conversions that do not preserve value (such as
+//     loss of precision when converting to float values).
+//   * Requires explicit construction from and conversion to floating point
+//     types.
+//   * Conversion to integral types requires an explicit static_cast() to
+//     mimic use of the `-Wnarrowing` compiler flag.
+//   * The alignment requirement of `uint128` may differ from that of an
+//     intrinsic 128-bit integer type depending on platform and build
+//     configuration.
+//
+// Example:
+//
+//     float y = absl::Uint128Max();  // Error. uint128 cannot be implicitly
+//                                    // converted to float.
+//
+//     absl::uint128 v;
+//     uint64_t i = v;                         // Error
+//     uint64_t i = static_cast<uint64_t>(v);  // OK
+//
+class
+#if defined(ABSL_HAVE_INTRINSIC_INT128)
+    alignas(unsigned __int128)
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+        uint128 {
+ public:
+  uint128() = default;
+
+  // Constructors from arithmetic types
+  constexpr uint128(int v);                 // NOLINT(runtime/explicit)
+  constexpr uint128(unsigned int v);        // NOLINT(runtime/explicit)
+  constexpr uint128(long v);                // NOLINT(runtime/int)
+  constexpr uint128(unsigned long v);       // NOLINT(runtime/int)
+  constexpr uint128(long long v);           // NOLINT(runtime/int)
+  constexpr uint128(unsigned long long v);  // NOLINT(runtime/int)
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+  constexpr uint128(__int128 v);           // NOLINT(runtime/explicit)
+  constexpr uint128(unsigned __int128 v);  // NOLINT(runtime/explicit)
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+  constexpr uint128(int128 v);  // NOLINT(runtime/explicit)
+  explicit uint128(float v);
+  explicit uint128(double v);
+  explicit uint128(long double v);
+
+  // Assignment operators from arithmetic types
+  uint128& operator=(int v);
+  uint128& operator=(unsigned int v);
+  uint128& operator=(long v);                // NOLINT(runtime/int)
+  uint128& operator=(unsigned long v);       // NOLINT(runtime/int)
+  uint128& operator=(long long v);           // NOLINT(runtime/int)
+  uint128& operator=(unsigned long long v);  // NOLINT(runtime/int)
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+  uint128& operator=(__int128 v);
+  uint128& operator=(unsigned __int128 v);
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+  uint128& operator=(int128 v);
+
+  // Conversion operators to other arithmetic types
+  constexpr explicit operator bool() const;
+  constexpr explicit operator char() const;
+  constexpr explicit operator signed char() const;
+  constexpr explicit operator unsigned char() const;
+  constexpr explicit operator char16_t() const;
+  constexpr explicit operator char32_t() const;
+  constexpr explicit operator ABSL_INTERNAL_WCHAR_T() const;
+  constexpr explicit operator short() const;  // NOLINT(runtime/int)
+  // NOLINTNEXTLINE(runtime/int)
+  constexpr explicit operator unsigned short() const;
+  constexpr explicit operator int() const;
+  constexpr explicit operator unsigned int() const;
+  constexpr explicit operator long() const;  // NOLINT(runtime/int)
+  // NOLINTNEXTLINE(runtime/int)
+  constexpr explicit operator unsigned long() const;
+  // NOLINTNEXTLINE(runtime/int)
+  constexpr explicit operator long long() const;
+  // NOLINTNEXTLINE(runtime/int)
+  constexpr explicit operator unsigned long long() const;
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+  constexpr explicit operator __int128() const;
+  constexpr explicit operator unsigned __int128() const;
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+  explicit operator float() const;
+  explicit operator double() const;
+  explicit operator long double() const;
+
+  // Trivial copy constructor, assignment operator and destructor.
+
+  // Arithmetic operators.
+  uint128& operator+=(uint128 other);
+  uint128& operator-=(uint128 other);
+  uint128& operator*=(uint128 other);
+  // Long division/modulo for uint128.
+  uint128& operator/=(uint128 other);
+  uint128& operator%=(uint128 other);
+  uint128 operator++(int);
+  uint128 operator--(int);
+  uint128& operator<<=(int);
+  uint128& operator>>=(int);
+  uint128& operator&=(uint128 other);
+  uint128& operator|=(uint128 other);
+  uint128& operator^=(uint128 other);
+  uint128& operator++();
+  uint128& operator--();
+
+  // Uint128Low64()
+  //
+  // Returns the lower 64-bit value of a `uint128` value.
+  friend constexpr uint64_t Uint128Low64(uint128 v);
+
+  // Uint128High64()
+  //
+  // Returns the higher 64-bit value of a `uint128` value.
+  friend constexpr uint64_t Uint128High64(uint128 v);
+
+  // MakeUInt128()
+  //
+  // Constructs a `uint128` numeric value from two 64-bit unsigned integers.
+  // Note that this factory function is the only way to construct a `uint128`
+  // from integer values greater than 2^64.
+  //
+  // Example:
+  //
+  //   absl::uint128 big = absl::MakeUint128(1, 0);
+  friend constexpr uint128 MakeUint128(uint64_t high, uint64_t low);
+
+  // Uint128Max()
+  //
+  // Returns the highest value for a 128-bit unsigned integer.
+  friend constexpr uint128 Uint128Max();
+
+  // Support for absl::Hash.
+  template <typename H>
+  friend H AbslHashValue(H h, uint128 v) {
+    return H::combine(std::move(h), Uint128High64(v), Uint128Low64(v));
+  }
+
+ private:
+  constexpr uint128(uint64_t high, uint64_t low);
+
+  // TODO(strel) Update implementation to use __int128 once all users of
+  // uint128 are fixed to not depend on alignof(uint128) == 8. Also add
+  // alignas(16) to class definition to keep alignment consistent across
+  // platforms.
+#if defined(ABSL_IS_LITTLE_ENDIAN)
+  uint64_t lo_;
+  uint64_t hi_;
+#elif defined(ABSL_IS_BIG_ENDIAN)
+  uint64_t hi_;
+  uint64_t lo_;
+#else  // byte order
+#error "Unsupported byte order: must be little-endian or big-endian."
+#endif  // byte order
+};
+
+// Prefer to use the constexpr `Uint128Max()`.
+//
+// TODO(absl-team) deprecate kuint128max once migration tool is released.
+ABSL_DLL extern const uint128 kuint128max;
+
+// allow uint128 to be logged
+std::ostream& operator<<(std::ostream& os, uint128 v);
+
+// TODO(strel) add operator>>(std::istream&, uint128)
+
+constexpr uint128 Uint128Max() {
+  return uint128((std::numeric_limits<uint64_t>::max)(),
+                 (std::numeric_limits<uint64_t>::max)());
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+// Specialized numeric_limits for uint128.
+namespace std {
+template <>
+class numeric_limits<absl::uint128> {
+ public:
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_signed = false;
+  static constexpr bool is_integer = true;
+  static constexpr bool is_exact = true;
+  static constexpr bool has_infinity = false;
+  static constexpr bool has_quiet_NaN = false;
+  static constexpr bool has_signaling_NaN = false;
+  static constexpr float_denorm_style has_denorm = denorm_absent;
+  static constexpr bool has_denorm_loss = false;
+  static constexpr float_round_style round_style = round_toward_zero;
+  static constexpr bool is_iec559 = false;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = true;
+  static constexpr int digits = 128;
+  static constexpr int digits10 = 38;
+  static constexpr int max_digits10 = 0;
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = 0;
+  static constexpr int min_exponent10 = 0;
+  static constexpr int max_exponent = 0;
+  static constexpr int max_exponent10 = 0;
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+  static constexpr bool traps = numeric_limits<unsigned __int128>::traps;
+#else   // ABSL_HAVE_INTRINSIC_INT128
+  static constexpr bool traps = numeric_limits<uint64_t>::traps;
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+  static constexpr bool tinyness_before = false;
+
+  static constexpr absl::uint128 (min)() { return 0; }
+  static constexpr absl::uint128 lowest() { return 0; }
+  static constexpr absl::uint128 (max)() { return absl::Uint128Max(); }
+  static constexpr absl::uint128 epsilon() { return 0; }
+  static constexpr absl::uint128 round_error() { return 0; }
+  static constexpr absl::uint128 infinity() { return 0; }
+  static constexpr absl::uint128 quiet_NaN() { return 0; }
+  static constexpr absl::uint128 signaling_NaN() { return 0; }
+  static constexpr absl::uint128 denorm_min() { return 0; }
+};
+}  // namespace std
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// int128
+//
+// A signed 128-bit integer type. The API is meant to mimic an intrinsic
+// integral type as closely as is practical, including exhibiting undefined
+// behavior in analogous cases (e.g. division by zero).
+//
+// An `int128` supports the following:
+//
+//   * Implicit construction from integral types
+//   * Explicit conversion to integral types
+//
+// However, an `int128` differs from intrinsic integral types in the following
+// ways:
+//
+//   * It is not implicitly convertible to other integral types.
+//   * Requires explicit construction from and conversion to floating point
+//     types.
+
+// Additionally, if your compiler supports `__int128`, `int128` is
+// interoperable with that type. (Abseil checks for this compatibility through
+// the `ABSL_HAVE_INTRINSIC_INT128` macro.)
+//
+// The design goal for `int128` is that it will be compatible with a future
+// `int128_t`, if that type becomes a part of the standard.
+//
+// Example:
+//
+//     float y = absl::int128(17);  // Error. int128 cannot be implicitly
+//                                  // converted to float.
+//
+//     absl::int128 v;
+//     int64_t i = v;                        // Error
+//     int64_t i = static_cast<int64_t>(v);  // OK
+//
+class int128 {
+ public:
+  int128() = default;
+
+  // Constructors from arithmetic types
+  constexpr int128(int v);                 // NOLINT(runtime/explicit)
+  constexpr int128(unsigned int v);        // NOLINT(runtime/explicit)
+  constexpr int128(long v);                // NOLINT(runtime/int)
+  constexpr int128(unsigned long v);       // NOLINT(runtime/int)
+  constexpr int128(long long v);           // NOLINT(runtime/int)
+  constexpr int128(unsigned long long v);  // NOLINT(runtime/int)
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+  constexpr int128(__int128 v);  // NOLINT(runtime/explicit)
+  constexpr explicit int128(unsigned __int128 v);
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+  constexpr explicit int128(uint128 v);
+  explicit int128(float v);
+  explicit int128(double v);
+  explicit int128(long double v);
+
+  // Assignment operators from arithmetic types
+  int128& operator=(int v);
+  int128& operator=(unsigned int v);
+  int128& operator=(long v);                // NOLINT(runtime/int)
+  int128& operator=(unsigned long v);       // NOLINT(runtime/int)
+  int128& operator=(long long v);           // NOLINT(runtime/int)
+  int128& operator=(unsigned long long v);  // NOLINT(runtime/int)
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+  int128& operator=(__int128 v);
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+
+  // Conversion operators to other arithmetic types
+  constexpr explicit operator bool() const;
+  constexpr explicit operator char() const;
+  constexpr explicit operator signed char() const;
+  constexpr explicit operator unsigned char() const;
+  constexpr explicit operator char16_t() const;
+  constexpr explicit operator char32_t() const;
+  constexpr explicit operator ABSL_INTERNAL_WCHAR_T() const;
+  constexpr explicit operator short() const;  // NOLINT(runtime/int)
+  // NOLINTNEXTLINE(runtime/int)
+  constexpr explicit operator unsigned short() const;
+  constexpr explicit operator int() const;
+  constexpr explicit operator unsigned int() const;
+  constexpr explicit operator long() const;  // NOLINT(runtime/int)
+  // NOLINTNEXTLINE(runtime/int)
+  constexpr explicit operator unsigned long() const;
+  // NOLINTNEXTLINE(runtime/int)
+  constexpr explicit operator long long() const;
+  // NOLINTNEXTLINE(runtime/int)
+  constexpr explicit operator unsigned long long() const;
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+  constexpr explicit operator __int128() const;
+  constexpr explicit operator unsigned __int128() const;
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+  explicit operator float() const;
+  explicit operator double() const;
+  explicit operator long double() const;
+
+  // Trivial copy constructor, assignment operator and destructor.
+
+  // Arithmetic operators
+  int128& operator+=(int128 other);
+  int128& operator-=(int128 other);
+  int128& operator*=(int128 other);
+  int128& operator/=(int128 other);
+  int128& operator%=(int128 other);
+  int128 operator++(int);  // postfix increment: i++
+  int128 operator--(int);  // postfix decrement: i--
+  int128& operator++();    // prefix increment:  ++i
+  int128& operator--();    // prefix decrement:  --i
+  int128& operator&=(int128 other);
+  int128& operator|=(int128 other);
+  int128& operator^=(int128 other);
+  int128& operator<<=(int amount);
+  int128& operator>>=(int amount);
+
+  // Int128Low64()
+  //
+  // Returns the lower 64-bit value of a `int128` value.
+  friend constexpr uint64_t Int128Low64(int128 v);
+
+  // Int128High64()
+  //
+  // Returns the higher 64-bit value of a `int128` value.
+  friend constexpr int64_t Int128High64(int128 v);
+
+  // MakeInt128()
+  //
+  // Constructs a `int128` numeric value from two 64-bit integers. Note that
+  // signedness is conveyed in the upper `high` value.
+  //
+  //   (absl::int128(1) << 64) * high + low
+  //
+  // Note that this factory function is the only way to construct a `int128`
+  // from integer values greater than 2^64 or less than -2^64.
+  //
+  // Example:
+  //
+  //   absl::int128 big = absl::MakeInt128(1, 0);
+  //   absl::int128 big_n = absl::MakeInt128(-1, 0);
+  friend constexpr int128 MakeInt128(int64_t high, uint64_t low);
+
+  // Int128Max()
+  //
+  // Returns the maximum value for a 128-bit signed integer.
+  friend constexpr int128 Int128Max();
+
+  // Int128Min()
+  //
+  // Returns the minimum value for a 128-bit signed integer.
+  friend constexpr int128 Int128Min();
+
+  // Support for absl::Hash.
+  template <typename H>
+  friend H AbslHashValue(H h, int128 v) {
+    return H::combine(std::move(h), Int128High64(v), Int128Low64(v));
+  }
+
+ private:
+  constexpr int128(int64_t high, uint64_t low);
+
+#if defined(ABSL_HAVE_INTRINSIC_INT128)
+  __int128 v_;
+#else  // ABSL_HAVE_INTRINSIC_INT128
+#if defined(ABSL_IS_LITTLE_ENDIAN)
+  uint64_t lo_;
+  int64_t hi_;
+#elif defined(ABSL_IS_BIG_ENDIAN)
+  int64_t hi_;
+  uint64_t lo_;
+#else  // byte order
+#error "Unsupported byte order: must be little-endian or big-endian."
+#endif  // byte order
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+};
+
+std::ostream& operator<<(std::ostream& os, int128 v);
+
+// TODO(absl-team) add operator>>(std::istream&, int128)
+
+constexpr int128 Int128Max() {
+  return int128((std::numeric_limits<int64_t>::max)(),
+                (std::numeric_limits<uint64_t>::max)());
+}
+
+constexpr int128 Int128Min() {
+  return int128((std::numeric_limits<int64_t>::min)(), 0);
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+// Specialized numeric_limits for int128.
+namespace std {
+template <>
+class numeric_limits<absl::int128> {
+ public:
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_signed = true;
+  static constexpr bool is_integer = true;
+  static constexpr bool is_exact = true;
+  static constexpr bool has_infinity = false;
+  static constexpr bool has_quiet_NaN = false;
+  static constexpr bool has_signaling_NaN = false;
+  static constexpr float_denorm_style has_denorm = denorm_absent;
+  static constexpr bool has_denorm_loss = false;
+  static constexpr float_round_style round_style = round_toward_zero;
+  static constexpr bool is_iec559 = false;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = false;
+  static constexpr int digits = 127;
+  static constexpr int digits10 = 38;
+  static constexpr int max_digits10 = 0;
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = 0;
+  static constexpr int min_exponent10 = 0;
+  static constexpr int max_exponent = 0;
+  static constexpr int max_exponent10 = 0;
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+  static constexpr bool traps = numeric_limits<__int128>::traps;
+#else   // ABSL_HAVE_INTRINSIC_INT128
+  static constexpr bool traps = numeric_limits<uint64_t>::traps;
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+  static constexpr bool tinyness_before = false;
+
+  static constexpr absl::int128 (min)() { return absl::Int128Min(); }
+  static constexpr absl::int128 lowest() { return absl::Int128Min(); }
+  static constexpr absl::int128 (max)() { return absl::Int128Max(); }
+  static constexpr absl::int128 epsilon() { return 0; }
+  static constexpr absl::int128 round_error() { return 0; }
+  static constexpr absl::int128 infinity() { return 0; }
+  static constexpr absl::int128 quiet_NaN() { return 0; }
+  static constexpr absl::int128 signaling_NaN() { return 0; }
+  static constexpr absl::int128 denorm_min() { return 0; }
+};
+}  // namespace std
+
+// --------------------------------------------------------------------------
+//                      Implementation details follow
+// --------------------------------------------------------------------------
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+constexpr uint128 MakeUint128(uint64_t high, uint64_t low) {
+  return uint128(high, low);
+}
+
+// Assignment from integer types.
+
+inline uint128& uint128::operator=(int v) { return *this = uint128(v); }
+
+inline uint128& uint128::operator=(unsigned int v) {
+  return *this = uint128(v);
+}
+
+inline uint128& uint128::operator=(long v) {  // NOLINT(runtime/int)
+  return *this = uint128(v);
+}
+
+// NOLINTNEXTLINE(runtime/int)
+inline uint128& uint128::operator=(unsigned long v) {
+  return *this = uint128(v);
+}
+
+// NOLINTNEXTLINE(runtime/int)
+inline uint128& uint128::operator=(long long v) {
+  return *this = uint128(v);
+}
+
+// NOLINTNEXTLINE(runtime/int)
+inline uint128& uint128::operator=(unsigned long long v) {
+  return *this = uint128(v);
+}
+
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+inline uint128& uint128::operator=(__int128 v) {
+  return *this = uint128(v);
+}
+
+inline uint128& uint128::operator=(unsigned __int128 v) {
+  return *this = uint128(v);
+}
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+
+inline uint128& uint128::operator=(int128 v) {
+  return *this = uint128(v);
+}
+
+// Arithmetic operators.
+
+constexpr uint128 operator<<(uint128 lhs, int amount);
+constexpr uint128 operator>>(uint128 lhs, int amount);
+constexpr uint128 operator+(uint128 lhs, uint128 rhs);
+constexpr uint128 operator-(uint128 lhs, uint128 rhs);
+uint128 operator*(uint128 lhs, uint128 rhs);
+uint128 operator/(uint128 lhs, uint128 rhs);
+uint128 operator%(uint128 lhs, uint128 rhs);
+
+inline uint128& uint128::operator<<=(int amount) {
+  *this = *this << amount;
+  return *this;
+}
+
+inline uint128& uint128::operator>>=(int amount) {
+  *this = *this >> amount;
+  return *this;
+}
+
+inline uint128& uint128::operator+=(uint128 other) {
+  *this = *this + other;
+  return *this;
+}
+
+inline uint128& uint128::operator-=(uint128 other) {
+  *this = *this - other;
+  return *this;
+}
+
+inline uint128& uint128::operator*=(uint128 other) {
+  *this = *this * other;
+  return *this;
+}
+
+inline uint128& uint128::operator/=(uint128 other) {
+  *this = *this / other;
+  return *this;
+}
+
+inline uint128& uint128::operator%=(uint128 other) {
+  *this = *this % other;
+  return *this;
+}
+
+constexpr uint64_t Uint128Low64(uint128 v) { return v.lo_; }
+
+constexpr uint64_t Uint128High64(uint128 v) { return v.hi_; }
+
+// Constructors from integer types.
+
+#if defined(ABSL_IS_LITTLE_ENDIAN)
+
+constexpr uint128::uint128(uint64_t high, uint64_t low)
+    : lo_{low}, hi_{high} {}
+
+constexpr uint128::uint128(int v)
+    : lo_{static_cast<uint64_t>(v)},
+      hi_{v < 0 ? (std::numeric_limits<uint64_t>::max)() : 0} {}
+constexpr uint128::uint128(long v)  // NOLINT(runtime/int)
+    : lo_{static_cast<uint64_t>(v)},
+      hi_{v < 0 ? (std::numeric_limits<uint64_t>::max)() : 0} {}
+constexpr uint128::uint128(long long v)  // NOLINT(runtime/int)
+    : lo_{static_cast<uint64_t>(v)},
+      hi_{v < 0 ? (std::numeric_limits<uint64_t>::max)() : 0} {}
+
+constexpr uint128::uint128(unsigned int v) : lo_{v}, hi_{0} {}
+// NOLINTNEXTLINE(runtime/int)
+constexpr uint128::uint128(unsigned long v) : lo_{v}, hi_{0} {}
+// NOLINTNEXTLINE(runtime/int)
+constexpr uint128::uint128(unsigned long long v) : lo_{v}, hi_{0} {}
+
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+constexpr uint128::uint128(__int128 v)
+    : lo_{static_cast<uint64_t>(v & ~uint64_t{0})},
+      hi_{static_cast<uint64_t>(static_cast<unsigned __int128>(v) >> 64)} {}
+constexpr uint128::uint128(unsigned __int128 v)
+    : lo_{static_cast<uint64_t>(v & ~uint64_t{0})},
+      hi_{static_cast<uint64_t>(v >> 64)} {}
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+
+constexpr uint128::uint128(int128 v)
+    : lo_{Int128Low64(v)}, hi_{static_cast<uint64_t>(Int128High64(v))} {}
+
+#elif defined(ABSL_IS_BIG_ENDIAN)
+
+constexpr uint128::uint128(uint64_t high, uint64_t low)
+    : hi_{high}, lo_{low} {}
+
+constexpr uint128::uint128(int v)
+    : hi_{v < 0 ? (std::numeric_limits<uint64_t>::max)() : 0},
+      lo_{static_cast<uint64_t>(v)} {}
+constexpr uint128::uint128(long v)  // NOLINT(runtime/int)
+    : hi_{v < 0 ? (std::numeric_limits<uint64_t>::max)() : 0},
+      lo_{static_cast<uint64_t>(v)} {}
+constexpr uint128::uint128(long long v)  // NOLINT(runtime/int)
+    : hi_{v < 0 ? (std::numeric_limits<uint64_t>::max)() : 0},
+      lo_{static_cast<uint64_t>(v)} {}
+
+constexpr uint128::uint128(unsigned int v) : hi_{0}, lo_{v} {}
+// NOLINTNEXTLINE(runtime/int)
+constexpr uint128::uint128(unsigned long v) : hi_{0}, lo_{v} {}
+// NOLINTNEXTLINE(runtime/int)
+constexpr uint128::uint128(unsigned long long v) : hi_{0}, lo_{v} {}
+
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+constexpr uint128::uint128(__int128 v)
+    : hi_{static_cast<uint64_t>(static_cast<unsigned __int128>(v) >> 64)},
+      lo_{static_cast<uint64_t>(v & ~uint64_t{0})} {}
+constexpr uint128::uint128(unsigned __int128 v)
+    : hi_{static_cast<uint64_t>(v >> 64)},
+      lo_{static_cast<uint64_t>(v & ~uint64_t{0})} {}
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+
+constexpr uint128::uint128(int128 v)
+    : hi_{static_cast<uint64_t>(Int128High64(v))}, lo_{Int128Low64(v)} {}
+
+#else  // byte order
+#error "Unsupported byte order: must be little-endian or big-endian."
+#endif  // byte order
+
+// Conversion operators to integer types.
+
+constexpr uint128::operator bool() const { return lo_ || hi_; }
+
+constexpr uint128::operator char() const { return static_cast<char>(lo_); }
+
+constexpr uint128::operator signed char() const {
+  return static_cast<signed char>(lo_);
+}
+
+constexpr uint128::operator unsigned char() const {
+  return static_cast<unsigned char>(lo_);
+}
+
+constexpr uint128::operator char16_t() const {
+  return static_cast<char16_t>(lo_);
+}
+
+constexpr uint128::operator char32_t() const {
+  return static_cast<char32_t>(lo_);
+}
+
+constexpr uint128::operator ABSL_INTERNAL_WCHAR_T() const {
+  return static_cast<ABSL_INTERNAL_WCHAR_T>(lo_);
+}
+
+// NOLINTNEXTLINE(runtime/int)
+constexpr uint128::operator short() const { return static_cast<short>(lo_); }
+
+constexpr uint128::operator unsigned short() const {  // NOLINT(runtime/int)
+  return static_cast<unsigned short>(lo_);            // NOLINT(runtime/int)
+}
+
+constexpr uint128::operator int() const { return static_cast<int>(lo_); }
+
+constexpr uint128::operator unsigned int() const {
+  return static_cast<unsigned int>(lo_);
+}
+
+// NOLINTNEXTLINE(runtime/int)
+constexpr uint128::operator long() const { return static_cast<long>(lo_); }
+
+constexpr uint128::operator unsigned long() const {  // NOLINT(runtime/int)
+  return static_cast<unsigned long>(lo_);            // NOLINT(runtime/int)
+}
+
+constexpr uint128::operator long long() const {  // NOLINT(runtime/int)
+  return static_cast<long long>(lo_);            // NOLINT(runtime/int)
+}
+
+constexpr uint128::operator unsigned long long() const {  // NOLINT(runtime/int)
+  return static_cast<unsigned long long>(lo_);            // NOLINT(runtime/int)
+}
+
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+constexpr uint128::operator __int128() const {
+  return (static_cast<__int128>(hi_) << 64) + lo_;
+}
+
+constexpr uint128::operator unsigned __int128() const {
+  return (static_cast<unsigned __int128>(hi_) << 64) + lo_;
+}
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+
+// Conversion operators to floating point types.
+
+inline uint128::operator float() const {
+  return static_cast<float>(lo_) + std::ldexp(static_cast<float>(hi_), 64);
+}
+
+inline uint128::operator double() const {
+  return static_cast<double>(lo_) + std::ldexp(static_cast<double>(hi_), 64);
+}
+
+inline uint128::operator long double() const {
+  return static_cast<long double>(lo_) +
+         std::ldexp(static_cast<long double>(hi_), 64);
+}
+
+// Comparison operators.
+
+constexpr bool operator==(uint128 lhs, uint128 rhs) {
+#if defined(ABSL_HAVE_INTRINSIC_INT128)
+  return static_cast<unsigned __int128>(lhs) ==
+         static_cast<unsigned __int128>(rhs);
+#else
+  return (Uint128Low64(lhs) == Uint128Low64(rhs) &&
+          Uint128High64(lhs) == Uint128High64(rhs));
+#endif
+}
+
+constexpr bool operator!=(uint128 lhs, uint128 rhs) { return !(lhs == rhs); }
+
+constexpr bool operator<(uint128 lhs, uint128 rhs) {
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+  return static_cast<unsigned __int128>(lhs) <
+         static_cast<unsigned __int128>(rhs);
+#else
+  return (Uint128High64(lhs) == Uint128High64(rhs))
+             ? (Uint128Low64(lhs) < Uint128Low64(rhs))
+             : (Uint128High64(lhs) < Uint128High64(rhs));
+#endif
+}
+
+constexpr bool operator>(uint128 lhs, uint128 rhs) { return rhs < lhs; }
+
+constexpr bool operator<=(uint128 lhs, uint128 rhs) { return !(rhs < lhs); }
+
+constexpr bool operator>=(uint128 lhs, uint128 rhs) { return !(lhs < rhs); }
+
+// Unary operators.
+
+constexpr inline uint128 operator+(uint128 val) {
+  return val;
+}
+
+constexpr inline int128 operator+(int128 val) {
+  return val;
+}
+
+constexpr uint128 operator-(uint128 val) {
+#if defined(ABSL_HAVE_INTRINSIC_INT128)
+  return -static_cast<unsigned __int128>(val);
+#else
+  return MakeUint128(
+      ~Uint128High64(val) + static_cast<unsigned long>(Uint128Low64(val) == 0),
+      ~Uint128Low64(val) + 1);
+#endif
+}
+
+constexpr inline bool operator!(uint128 val) {
+#if defined(ABSL_HAVE_INTRINSIC_INT128)
+  return !static_cast<unsigned __int128>(val);
+#else
+  return !Uint128High64(val) && !Uint128Low64(val);
+#endif
+}
+
+// Logical operators.
+
+constexpr inline uint128 operator~(uint128 val) {
+#if defined(ABSL_HAVE_INTRINSIC_INT128)
+  return ~static_cast<unsigned __int128>(val);
+#else
+  return MakeUint128(~Uint128High64(val), ~Uint128Low64(val));
+#endif
+}
+
+constexpr inline uint128 operator|(uint128 lhs, uint128 rhs) {
+#if defined(ABSL_HAVE_INTRINSIC_INT128)
+  return static_cast<unsigned __int128>(lhs) |
+         static_cast<unsigned __int128>(rhs);
+#else
+  return MakeUint128(Uint128High64(lhs) | Uint128High64(rhs),
+                     Uint128Low64(lhs) | Uint128Low64(rhs));
+#endif
+}
+
+constexpr inline uint128 operator&(uint128 lhs, uint128 rhs) {
+#if defined(ABSL_HAVE_INTRINSIC_INT128)
+  return static_cast<unsigned __int128>(lhs) &
+         static_cast<unsigned __int128>(rhs);
+#else
+  return MakeUint128(Uint128High64(lhs) & Uint128High64(rhs),
+                     Uint128Low64(lhs) & Uint128Low64(rhs));
+#endif
+}
+
+constexpr inline uint128 operator^(uint128 lhs, uint128 rhs) {
+#if defined(ABSL_HAVE_INTRINSIC_INT128)
+  return static_cast<unsigned __int128>(lhs) ^
+         static_cast<unsigned __int128>(rhs);
+#else
+  return MakeUint128(Uint128High64(lhs) ^ Uint128High64(rhs),
+                     Uint128Low64(lhs) ^ Uint128Low64(rhs));
+#endif
+}
+
+inline uint128& uint128::operator|=(uint128 other) {
+  *this = *this | other;
+  return *this;
+}
+
+inline uint128& uint128::operator&=(uint128 other) {
+  *this = *this & other;
+  return *this;
+}
+
+inline uint128& uint128::operator^=(uint128 other) {
+  *this = *this ^ other;
+  return *this;
+}
+
+// Arithmetic operators.
+
+constexpr uint128 operator<<(uint128 lhs, int amount) {
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+  return static_cast<unsigned __int128>(lhs) << amount;
+#else
+  // uint64_t shifts of >= 64 are undefined, so we will need some
+  // special-casing.
+  return amount >= 64 ? MakeUint128(Uint128Low64(lhs) << (amount - 64), 0)
+         : amount == 0 ? lhs
+                       : MakeUint128((Uint128High64(lhs) << amount) |
+                                         (Uint128Low64(lhs) >> (64 - amount)),
+                                     Uint128Low64(lhs) << amount);
+#endif
+}
+
+constexpr uint128 operator>>(uint128 lhs, int amount) {
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+  return static_cast<unsigned __int128>(lhs) >> amount;
+#else
+  // uint64_t shifts of >= 64 are undefined, so we will need some
+  // special-casing.
+  return amount >= 64 ? MakeUint128(0, Uint128High64(lhs) >> (amount - 64))
+         : amount == 0 ? lhs
+                       : MakeUint128(Uint128High64(lhs) >> amount,
+                                     (Uint128Low64(lhs) >> amount) |
+                                         (Uint128High64(lhs) << (64 - amount)));
+#endif
+}
+
+#if !defined(ABSL_HAVE_INTRINSIC_INT128)
+namespace int128_internal {
+constexpr uint128 AddResult(uint128 result, uint128 lhs) {
+  // check for carry
+  return (Uint128Low64(result) < Uint128Low64(lhs))
+             ? MakeUint128(Uint128High64(result) + 1, Uint128Low64(result))
+             : result;
+}
+}  // namespace int128_internal
+#endif
+
+constexpr uint128 operator+(uint128 lhs, uint128 rhs) {
+#if defined(ABSL_HAVE_INTRINSIC_INT128)
+  return static_cast<unsigned __int128>(lhs) +
+         static_cast<unsigned __int128>(rhs);
+#else
+  return int128_internal::AddResult(
+      MakeUint128(Uint128High64(lhs) + Uint128High64(rhs),
+                  Uint128Low64(lhs) + Uint128Low64(rhs)),
+      lhs);
+#endif
+}
+
+#if !defined(ABSL_HAVE_INTRINSIC_INT128)
+namespace int128_internal {
+constexpr uint128 SubstructResult(uint128 result, uint128 lhs, uint128 rhs) {
+  // check for carry
+  return (Uint128Low64(lhs) < Uint128Low64(rhs))
+             ? MakeUint128(Uint128High64(result) - 1, Uint128Low64(result))
+             : result;
+}
+}  // namespace int128_internal
+#endif
+
+constexpr uint128 operator-(uint128 lhs, uint128 rhs) {
+#if defined(ABSL_HAVE_INTRINSIC_INT128)
+  return static_cast<unsigned __int128>(lhs) -
+         static_cast<unsigned __int128>(rhs);
+#else
+  return int128_internal::SubstructResult(
+      MakeUint128(Uint128High64(lhs) - Uint128High64(rhs),
+                  Uint128Low64(lhs) - Uint128Low64(rhs)),
+      lhs, rhs);
+#endif
+}
+
+inline uint128 operator*(uint128 lhs, uint128 rhs) {
+#if defined(ABSL_HAVE_INTRINSIC_INT128)
+  // TODO(strel) Remove once alignment issues are resolved and unsigned __int128
+  // can be used for uint128 storage.
+  return static_cast<unsigned __int128>(lhs) *
+         static_cast<unsigned __int128>(rhs);
+#elif defined(_MSC_VER) && defined(_M_X64) && !defined(_M_ARM64EC)
+  uint64_t carry;
+  uint64_t low = _umul128(Uint128Low64(lhs), Uint128Low64(rhs), &carry);
+  return MakeUint128(Uint128Low64(lhs) * Uint128High64(rhs) +
+                         Uint128High64(lhs) * Uint128Low64(rhs) + carry,
+                     low);
+#else   // ABSL_HAVE_INTRINSIC128
+  uint64_t a32 = Uint128Low64(lhs) >> 32;
+  uint64_t a00 = Uint128Low64(lhs) & 0xffffffff;
+  uint64_t b32 = Uint128Low64(rhs) >> 32;
+  uint64_t b00 = Uint128Low64(rhs) & 0xffffffff;
+  uint128 result =
+      MakeUint128(Uint128High64(lhs) * Uint128Low64(rhs) +
+                      Uint128Low64(lhs) * Uint128High64(rhs) + a32 * b32,
+                  a00 * b00);
+  result += uint128(a32 * b00) << 32;
+  result += uint128(a00 * b32) << 32;
+  return result;
+#endif  // ABSL_HAVE_INTRINSIC128
+}
+
+#if defined(ABSL_HAVE_INTRINSIC_INT128)
+inline uint128 operator/(uint128 lhs, uint128 rhs) {
+  return static_cast<unsigned __int128>(lhs) /
+         static_cast<unsigned __int128>(rhs);
+}
+
+inline uint128 operator%(uint128 lhs, uint128 rhs) {
+  return static_cast<unsigned __int128>(lhs) %
+         static_cast<unsigned __int128>(rhs);
+}
+#endif
+
+// Increment/decrement operators.
+
+inline uint128 uint128::operator++(int) {
+  uint128 tmp(*this);
+  *this += 1;
+  return tmp;
+}
+
+inline uint128 uint128::operator--(int) {
+  uint128 tmp(*this);
+  *this -= 1;
+  return tmp;
+}
+
+inline uint128& uint128::operator++() {
+  *this += 1;
+  return *this;
+}
+
+inline uint128& uint128::operator--() {
+  *this -= 1;
+  return *this;
+}
+
+constexpr int128 MakeInt128(int64_t high, uint64_t low) {
+  return int128(high, low);
+}
+
+// Assignment from integer types.
+inline int128& int128::operator=(int v) {
+  return *this = int128(v);
+}
+
+inline int128& int128::operator=(unsigned int v) {
+  return *this = int128(v);
+}
+
+inline int128& int128::operator=(long v) {  // NOLINT(runtime/int)
+  return *this = int128(v);
+}
+
+// NOLINTNEXTLINE(runtime/int)
+inline int128& int128::operator=(unsigned long v) {
+  return *this = int128(v);
+}
+
+// NOLINTNEXTLINE(runtime/int)
+inline int128& int128::operator=(long long v) {
+  return *this = int128(v);
+}
+
+// NOLINTNEXTLINE(runtime/int)
+inline int128& int128::operator=(unsigned long long v) {
+  return *this = int128(v);
+}
+
+// Arithmetic operators.
+constexpr int128 operator-(int128 v);
+constexpr int128 operator+(int128 lhs, int128 rhs);
+constexpr int128 operator-(int128 lhs, int128 rhs);
+int128 operator*(int128 lhs, int128 rhs);
+int128 operator/(int128 lhs, int128 rhs);
+int128 operator%(int128 lhs, int128 rhs);
+constexpr int128 operator|(int128 lhs, int128 rhs);
+constexpr int128 operator&(int128 lhs, int128 rhs);
+constexpr int128 operator^(int128 lhs, int128 rhs);
+constexpr int128 operator<<(int128 lhs, int amount);
+constexpr int128 operator>>(int128 lhs, int amount);
+
+inline int128& int128::operator+=(int128 other) {
+  *this = *this + other;
+  return *this;
+}
+
+inline int128& int128::operator-=(int128 other) {
+  *this = *this - other;
+  return *this;
+}
+
+inline int128& int128::operator*=(int128 other) {
+  *this = *this * other;
+  return *this;
+}
+
+inline int128& int128::operator/=(int128 other) {
+  *this = *this / other;
+  return *this;
+}
+
+inline int128& int128::operator%=(int128 other) {
+  *this = *this % other;
+  return *this;
+}
+
+inline int128& int128::operator|=(int128 other) {
+  *this = *this | other;
+  return *this;
+}
+
+inline int128& int128::operator&=(int128 other) {
+  *this = *this & other;
+  return *this;
+}
+
+inline int128& int128::operator^=(int128 other) {
+  *this = *this ^ other;
+  return *this;
+}
+
+inline int128& int128::operator<<=(int amount) {
+  *this = *this << amount;
+  return *this;
+}
+
+inline int128& int128::operator>>=(int amount) {
+  *this = *this >> amount;
+  return *this;
+}
+
+// Forward declaration for comparison operators.
+constexpr bool operator!=(int128 lhs, int128 rhs);
+
+namespace int128_internal {
+
+// Casts from unsigned to signed while preserving the underlying binary
+// representation.
+constexpr int64_t BitCastToSigned(uint64_t v) {
+  // Casting an unsigned integer to a signed integer of the same
+  // width is implementation defined behavior if the source value would not fit
+  // in the destination type. We step around it with a roundtrip bitwise not
+  // operation to make sure this function remains constexpr. Clang, GCC, and
+  // MSVC optimize this to a no-op on x86-64.
+  return v & (uint64_t{1} << 63) ? ~static_cast<int64_t>(~v)
+                                 : static_cast<int64_t>(v);
+}
+
+}  // namespace int128_internal
+
+#if defined(ABSL_HAVE_INTRINSIC_INT128)
+#include "absl/numeric/int128_have_intrinsic.inc"  // IWYU pragma: export
+#else  // ABSL_HAVE_INTRINSIC_INT128
+#include "absl/numeric/int128_no_intrinsic.inc"  // IWYU pragma: export
+#endif  // ABSL_HAVE_INTRINSIC_INT128
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#undef ABSL_INTERNAL_WCHAR_T
+
+#endif  // ABSL_NUMERIC_INT128_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/int128_have_intrinsic.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/int128_have_intrinsic.inc
new file mode 100644
index 0000000000000000000000000000000000000000..3945fa2983709797017eafa2646748b3178e50f1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/int128_have_intrinsic.inc
@@ -0,0 +1,296 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// This file contains :int128 implementation details that depend on internal
+// representation when ABSL_HAVE_INTRINSIC_INT128 is defined. This file is
+// included by int128.h and relies on ABSL_INTERNAL_WCHAR_T being defined.
+
+namespace int128_internal {
+
+// Casts from unsigned to signed while preserving the underlying binary
+// representation.
+constexpr __int128 BitCastToSigned(unsigned __int128 v) {
+  // Casting an unsigned integer to a signed integer of the same
+  // width is implementation defined behavior if the source value would not fit
+  // in the destination type. We step around it with a roundtrip bitwise not
+  // operation to make sure this function remains constexpr. Clang and GCC
+  // optimize this to a no-op on x86-64.
+  return v & (static_cast<unsigned __int128>(1) << 127)
+             ? ~static_cast<__int128>(~v)
+             : static_cast<__int128>(v);
+}
+
+}  // namespace int128_internal
+
+inline int128& int128::operator=(__int128 v) {
+  v_ = v;
+  return *this;
+}
+
+constexpr uint64_t Int128Low64(int128 v) {
+  return static_cast<uint64_t>(v.v_ & ~uint64_t{0});
+}
+
+constexpr int64_t Int128High64(int128 v) {
+  // Initially cast to unsigned to prevent a right shift on a negative value.
+  return int128_internal::BitCastToSigned(
+      static_cast<uint64_t>(static_cast<unsigned __int128>(v.v_) >> 64));
+}
+
+constexpr int128::int128(int64_t high, uint64_t low)
+    // Initially cast to unsigned to prevent a left shift that overflows.
+    : v_(int128_internal::BitCastToSigned(static_cast<unsigned __int128>(high)
+                                           << 64) |
+         low) {}
+
+
+constexpr int128::int128(int v) : v_{v} {}
+
+constexpr int128::int128(long v) : v_{v} {}       // NOLINT(runtime/int)
+
+constexpr int128::int128(long long v) : v_{v} {}  // NOLINT(runtime/int)
+
+constexpr int128::int128(__int128 v) : v_{v} {}
+
+constexpr int128::int128(unsigned int v) : v_{v} {}
+
+constexpr int128::int128(unsigned long v) : v_{v} {}  // NOLINT(runtime/int)
+
+// NOLINTNEXTLINE(runtime/int)
+constexpr int128::int128(unsigned long long v) : v_{v} {}
+
+constexpr int128::int128(unsigned __int128 v) : v_{static_cast<__int128>(v)} {}
+
+inline int128::int128(float v) {
+  v_ = static_cast<__int128>(v);
+}
+
+inline int128::int128(double v) {
+  v_ = static_cast<__int128>(v);
+}
+
+inline int128::int128(long double v) {
+  v_ = static_cast<__int128>(v);
+}
+
+constexpr int128::int128(uint128 v) : v_{static_cast<__int128>(v)} {}
+
+constexpr int128::operator bool() const { return static_cast<bool>(v_); }
+
+constexpr int128::operator char() const { return static_cast<char>(v_); }
+
+constexpr int128::operator signed char() const {
+  return static_cast<signed char>(v_);
+}
+
+constexpr int128::operator unsigned char() const {
+  return static_cast<unsigned char>(v_);
+}
+
+constexpr int128::operator char16_t() const {
+  return static_cast<char16_t>(v_);
+}
+
+constexpr int128::operator char32_t() const {
+  return static_cast<char32_t>(v_);
+}
+
+constexpr int128::operator ABSL_INTERNAL_WCHAR_T() const {
+  return static_cast<ABSL_INTERNAL_WCHAR_T>(v_);
+}
+
+constexpr int128::operator short() const {  // NOLINT(runtime/int)
+  return static_cast<short>(v_);            // NOLINT(runtime/int)
+}
+
+constexpr int128::operator unsigned short() const {  // NOLINT(runtime/int)
+  return static_cast<unsigned short>(v_);            // NOLINT(runtime/int)
+}
+
+constexpr int128::operator int() const {
+  return static_cast<int>(v_);
+}
+
+constexpr int128::operator unsigned int() const {
+  return static_cast<unsigned int>(v_);
+}
+
+constexpr int128::operator long() const {  // NOLINT(runtime/int)
+  return static_cast<long>(v_);            // NOLINT(runtime/int)
+}
+
+constexpr int128::operator unsigned long() const {  // NOLINT(runtime/int)
+  return static_cast<unsigned long>(v_);            // NOLINT(runtime/int)
+}
+
+constexpr int128::operator long long() const {  // NOLINT(runtime/int)
+  return static_cast<long long>(v_);            // NOLINT(runtime/int)
+}
+
+constexpr int128::operator unsigned long long() const {  // NOLINT(runtime/int)
+  return static_cast<unsigned long long>(v_);            // NOLINT(runtime/int)
+}
+
+constexpr int128::operator __int128() const { return v_; }
+
+constexpr int128::operator unsigned __int128() const {
+  return static_cast<unsigned __int128>(v_);
+}
+
+// Clang on PowerPC sometimes produces incorrect __int128 to floating point
+// conversions. In that case, we do the conversion with a similar implementation
+// to the conversion operators in int128_no_intrinsic.inc.
+#if defined(__clang__) && !defined(__ppc64__)
+inline int128::operator float() const { return static_cast<float>(v_); }
+
+inline int128::operator double() const { return static_cast<double>(v_); }
+
+inline int128::operator long double() const {
+  return static_cast<long double>(v_);
+}
+
+#else  // Clang on PowerPC
+// Forward declaration for conversion operators to floating point types.
+constexpr int128 operator-(int128 v);
+constexpr bool operator!=(int128 lhs, int128 rhs);
+
+inline int128::operator float() const {
+  // We must convert the absolute value and then negate as needed, because
+  // floating point types are typically sign-magnitude. Otherwise, the
+  // difference between the high and low 64 bits when interpreted as two's
+  // complement overwhelms the precision of the mantissa.
+  //
+  // Also check to make sure we don't negate Int128Min()
+  return v_ < 0 && *this != Int128Min()
+             ? -static_cast<float>(-*this)
+             : static_cast<float>(Int128Low64(*this)) +
+                   std::ldexp(static_cast<float>(Int128High64(*this)), 64);
+}
+
+inline int128::operator double() const {
+  // See comment in int128::operator float() above.
+  return v_ < 0 && *this != Int128Min()
+             ? -static_cast<double>(-*this)
+             : static_cast<double>(Int128Low64(*this)) +
+                   std::ldexp(static_cast<double>(Int128High64(*this)), 64);
+}
+
+inline int128::operator long double() const {
+  // See comment in int128::operator float() above.
+  return v_ < 0 && *this != Int128Min()
+             ? -static_cast<long double>(-*this)
+             : static_cast<long double>(Int128Low64(*this)) +
+                   std::ldexp(static_cast<long double>(Int128High64(*this)),
+                              64);
+}
+#endif  // Clang on PowerPC
+
+// Comparison operators.
+
+constexpr bool operator==(int128 lhs, int128 rhs) {
+  return static_cast<__int128>(lhs) == static_cast<__int128>(rhs);
+}
+
+constexpr bool operator!=(int128 lhs, int128 rhs) {
+  return static_cast<__int128>(lhs) != static_cast<__int128>(rhs);
+}
+
+constexpr bool operator<(int128 lhs, int128 rhs) {
+  return static_cast<__int128>(lhs) < static_cast<__int128>(rhs);
+}
+
+constexpr bool operator>(int128 lhs, int128 rhs) {
+  return static_cast<__int128>(lhs) > static_cast<__int128>(rhs);
+}
+
+constexpr bool operator<=(int128 lhs, int128 rhs) {
+  return static_cast<__int128>(lhs) <= static_cast<__int128>(rhs);
+}
+
+constexpr bool operator>=(int128 lhs, int128 rhs) {
+  return static_cast<__int128>(lhs) >= static_cast<__int128>(rhs);
+}
+
+// Unary operators.
+
+constexpr int128 operator-(int128 v) { return -static_cast<__int128>(v); }
+
+constexpr bool operator!(int128 v) { return !static_cast<__int128>(v); }
+
+constexpr int128 operator~(int128 val) { return ~static_cast<__int128>(val); }
+
+// Arithmetic operators.
+
+constexpr int128 operator+(int128 lhs, int128 rhs) {
+  return static_cast<__int128>(lhs) + static_cast<__int128>(rhs);
+}
+
+constexpr int128 operator-(int128 lhs, int128 rhs) {
+  return static_cast<__int128>(lhs) - static_cast<__int128>(rhs);
+}
+
+inline int128 operator*(int128 lhs, int128 rhs) {
+  return static_cast<__int128>(lhs) * static_cast<__int128>(rhs);
+}
+
+inline int128 operator/(int128 lhs, int128 rhs) {
+  return static_cast<__int128>(lhs) / static_cast<__int128>(rhs);
+}
+
+inline int128 operator%(int128 lhs, int128 rhs) {
+  return static_cast<__int128>(lhs) % static_cast<__int128>(rhs);
+}
+
+inline int128 int128::operator++(int) {
+  int128 tmp(*this);
+  ++v_;
+  return tmp;
+}
+
+inline int128 int128::operator--(int) {
+  int128 tmp(*this);
+  --v_;
+  return tmp;
+}
+
+inline int128& int128::operator++() {
+  ++v_;
+  return *this;
+}
+
+inline int128& int128::operator--() {
+  --v_;
+  return *this;
+}
+
+constexpr int128 operator|(int128 lhs, int128 rhs) {
+  return static_cast<__int128>(lhs) | static_cast<__int128>(rhs);
+}
+
+constexpr int128 operator&(int128 lhs, int128 rhs) {
+  return static_cast<__int128>(lhs) & static_cast<__int128>(rhs);
+}
+
+constexpr int128 operator^(int128 lhs, int128 rhs) {
+  return static_cast<__int128>(lhs) ^ static_cast<__int128>(rhs);
+}
+
+constexpr int128 operator<<(int128 lhs, int amount) {
+  return static_cast<__int128>(lhs) << amount;
+}
+
+constexpr int128 operator>>(int128 lhs, int amount) {
+  return static_cast<__int128>(lhs) >> amount;
+}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/int128_no_intrinsic.inc b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/int128_no_intrinsic.inc
new file mode 100644
index 0000000000000000000000000000000000000000..8834804cec4d2230dc6e70ed997a47c89f2d3868
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/int128_no_intrinsic.inc
@@ -0,0 +1,311 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// This file contains :int128 implementation details that depend on internal
+// representation when ABSL_HAVE_INTRINSIC_INT128 is *not* defined. This file
+// is included by int128.h and relies on ABSL_INTERNAL_WCHAR_T being defined.
+
+constexpr uint64_t Int128Low64(int128 v) { return v.lo_; }
+
+constexpr int64_t Int128High64(int128 v) { return v.hi_; }
+
+#if defined(ABSL_IS_LITTLE_ENDIAN)
+
+constexpr int128::int128(int64_t high, uint64_t low) :
+    lo_(low), hi_(high) {}
+
+constexpr int128::int128(int v)
+    : lo_{static_cast<uint64_t>(v)}, hi_{v < 0 ? ~int64_t{0} : 0} {}
+constexpr int128::int128(long v)  // NOLINT(runtime/int)
+    : lo_{static_cast<uint64_t>(v)}, hi_{v < 0 ? ~int64_t{0} : 0} {}
+constexpr int128::int128(long long v)  // NOLINT(runtime/int)
+    : lo_{static_cast<uint64_t>(v)}, hi_{v < 0 ? ~int64_t{0} : 0} {}
+
+constexpr int128::int128(unsigned int v) : lo_{v}, hi_{0} {}
+// NOLINTNEXTLINE(runtime/int)
+constexpr int128::int128(unsigned long v) : lo_{v}, hi_{0} {}
+// NOLINTNEXTLINE(runtime/int)
+constexpr int128::int128(unsigned long long v) : lo_{v}, hi_{0} {}
+
+constexpr int128::int128(uint128 v)
+    : lo_{Uint128Low64(v)}, hi_{static_cast<int64_t>(Uint128High64(v))} {}
+
+#elif defined(ABSL_IS_BIG_ENDIAN)
+
+constexpr int128::int128(int64_t high, uint64_t low) :
+    hi_{high}, lo_{low} {}
+
+constexpr int128::int128(int v)
+    : hi_{v < 0 ? ~int64_t{0} : 0}, lo_{static_cast<uint64_t>(v)} {}
+constexpr int128::int128(long v)  // NOLINT(runtime/int)
+    : hi_{v < 0 ? ~int64_t{0} : 0}, lo_{static_cast<uint64_t>(v)} {}
+constexpr int128::int128(long long v)  // NOLINT(runtime/int)
+    : hi_{v < 0 ? ~int64_t{0} : 0}, lo_{static_cast<uint64_t>(v)} {}
+
+constexpr int128::int128(unsigned int v) : hi_{0}, lo_{v} {}
+// NOLINTNEXTLINE(runtime/int)
+constexpr int128::int128(unsigned long v) : hi_{0}, lo_{v} {}
+// NOLINTNEXTLINE(runtime/int)
+constexpr int128::int128(unsigned long long v) : hi_{0}, lo_{v} {}
+
+constexpr int128::int128(uint128 v)
+    : hi_{static_cast<int64_t>(Uint128High64(v))}, lo_{Uint128Low64(v)} {}
+
+#else  // byte order
+#error "Unsupported byte order: must be little-endian or big-endian."
+#endif  // byte order
+
+constexpr int128::operator bool() const { return lo_ || hi_; }
+
+constexpr int128::operator char() const {
+  // NOLINTNEXTLINE(runtime/int)
+  return static_cast<char>(static_cast<long long>(*this));
+}
+
+constexpr int128::operator signed char() const {
+  // NOLINTNEXTLINE(runtime/int)
+  return static_cast<signed char>(static_cast<long long>(*this));
+}
+
+constexpr int128::operator unsigned char() const {
+  return static_cast<unsigned char>(lo_);
+}
+
+constexpr int128::operator char16_t() const {
+  return static_cast<char16_t>(lo_);
+}
+
+constexpr int128::operator char32_t() const {
+  return static_cast<char32_t>(lo_);
+}
+
+constexpr int128::operator ABSL_INTERNAL_WCHAR_T() const {
+  // NOLINTNEXTLINE(runtime/int)
+  return static_cast<ABSL_INTERNAL_WCHAR_T>(static_cast<long long>(*this));
+}
+
+constexpr int128::operator short() const {  // NOLINT(runtime/int)
+  // NOLINTNEXTLINE(runtime/int)
+  return static_cast<short>(static_cast<long long>(*this));
+}
+
+constexpr int128::operator unsigned short() const {  // NOLINT(runtime/int)
+  return static_cast<unsigned short>(lo_);           // NOLINT(runtime/int)
+}
+
+constexpr int128::operator int() const {
+  // NOLINTNEXTLINE(runtime/int)
+  return static_cast<int>(static_cast<long long>(*this));
+}
+
+constexpr int128::operator unsigned int() const {
+  return static_cast<unsigned int>(lo_);
+}
+
+constexpr int128::operator long() const {  // NOLINT(runtime/int)
+  // NOLINTNEXTLINE(runtime/int)
+  return static_cast<long>(static_cast<long long>(*this));
+}
+
+constexpr int128::operator unsigned long() const {  // NOLINT(runtime/int)
+  return static_cast<unsigned long>(lo_);           // NOLINT(runtime/int)
+}
+
+constexpr int128::operator long long() const {  // NOLINT(runtime/int)
+  // We don't bother checking the value of hi_. If *this < 0, lo_'s high bit
+  // must be set in order for the value to fit into a long long. Conversely, if
+  // lo_'s high bit is set, *this must be < 0 for the value to fit.
+  return int128_internal::BitCastToSigned(lo_);
+}
+
+constexpr int128::operator unsigned long long() const {  // NOLINT(runtime/int)
+  return static_cast<unsigned long long>(lo_);           // NOLINT(runtime/int)
+}
+
+inline int128::operator float() const {
+  // We must convert the absolute value and then negate as needed, because
+  // floating point types are typically sign-magnitude. Otherwise, the
+  // difference between the high and low 64 bits when interpreted as two's
+  // complement overwhelms the precision of the mantissa.
+  //
+  // Also check to make sure we don't negate Int128Min()
+  return hi_ < 0 && *this != Int128Min()
+             ? -static_cast<float>(-*this)
+             : static_cast<float>(lo_) +
+                   std::ldexp(static_cast<float>(hi_), 64);
+}
+
+inline int128::operator double() const {
+  // See comment in int128::operator float() above.
+  return hi_ < 0 && *this != Int128Min()
+             ? -static_cast<double>(-*this)
+             : static_cast<double>(lo_) +
+                   std::ldexp(static_cast<double>(hi_), 64);
+}
+
+inline int128::operator long double() const {
+  // See comment in int128::operator float() above.
+  return hi_ < 0 && *this != Int128Min()
+             ? -static_cast<long double>(-*this)
+             : static_cast<long double>(lo_) +
+                   std::ldexp(static_cast<long double>(hi_), 64);
+}
+
+// Comparison operators.
+
+constexpr bool operator==(int128 lhs, int128 rhs) {
+  return (Int128Low64(lhs) == Int128Low64(rhs) &&
+          Int128High64(lhs) == Int128High64(rhs));
+}
+
+constexpr bool operator!=(int128 lhs, int128 rhs) { return !(lhs == rhs); }
+
+constexpr bool operator<(int128 lhs, int128 rhs) {
+  return (Int128High64(lhs) == Int128High64(rhs))
+             ? (Int128Low64(lhs) < Int128Low64(rhs))
+             : (Int128High64(lhs) < Int128High64(rhs));
+}
+
+constexpr bool operator>(int128 lhs, int128 rhs) {
+  return (Int128High64(lhs) == Int128High64(rhs))
+             ? (Int128Low64(lhs) > Int128Low64(rhs))
+             : (Int128High64(lhs) > Int128High64(rhs));
+}
+
+constexpr bool operator<=(int128 lhs, int128 rhs) { return !(lhs > rhs); }
+
+constexpr bool operator>=(int128 lhs, int128 rhs) { return !(lhs < rhs); }
+
+// Unary operators.
+
+constexpr int128 operator-(int128 v) {
+  return MakeInt128(~Int128High64(v) + (Int128Low64(v) == 0),
+                    ~Int128Low64(v) + 1);
+}
+
+constexpr bool operator!(int128 v) {
+  return !Int128Low64(v) && !Int128High64(v);
+}
+
+constexpr int128 operator~(int128 val) {
+  return MakeInt128(~Int128High64(val), ~Int128Low64(val));
+}
+
+// Arithmetic operators.
+
+namespace int128_internal {
+constexpr int128 SignedAddResult(int128 result, int128 lhs) {
+  // check for carry
+  return (Int128Low64(result) < Int128Low64(lhs))
+             ? MakeInt128(Int128High64(result) + 1, Int128Low64(result))
+             : result;
+}
+}  // namespace int128_internal
+constexpr int128 operator+(int128 lhs, int128 rhs) {
+  return int128_internal::SignedAddResult(
+      MakeInt128(Int128High64(lhs) + Int128High64(rhs),
+                 Int128Low64(lhs) + Int128Low64(rhs)),
+      lhs);
+}
+
+namespace int128_internal {
+constexpr int128 SignedSubstructResult(int128 result, int128 lhs, int128 rhs) {
+  // check for carry
+  return (Int128Low64(lhs) < Int128Low64(rhs))
+             ? MakeInt128(Int128High64(result) - 1, Int128Low64(result))
+             : result;
+}
+}  // namespace int128_internal
+constexpr int128 operator-(int128 lhs, int128 rhs) {
+  return int128_internal::SignedSubstructResult(
+      MakeInt128(Int128High64(lhs) - Int128High64(rhs),
+                 Int128Low64(lhs) - Int128Low64(rhs)),
+      lhs, rhs);
+}
+
+inline int128 operator*(int128 lhs, int128 rhs) {
+  return MakeInt128(
+      int128_internal::BitCastToSigned(Uint128High64(uint128(lhs) * rhs)),
+      Uint128Low64(uint128(lhs) * rhs));
+}
+
+inline int128 int128::operator++(int) {
+  int128 tmp(*this);
+  *this += 1;
+  return tmp;
+}
+
+inline int128 int128::operator--(int) {
+  int128 tmp(*this);
+  *this -= 1;
+  return tmp;
+}
+
+inline int128& int128::operator++() {
+  *this += 1;
+  return *this;
+}
+
+inline int128& int128::operator--() {
+  *this -= 1;
+  return *this;
+}
+
+constexpr int128 operator|(int128 lhs, int128 rhs) {
+  return MakeInt128(Int128High64(lhs) | Int128High64(rhs),
+                    Int128Low64(lhs) | Int128Low64(rhs));
+}
+
+constexpr int128 operator&(int128 lhs, int128 rhs) {
+  return MakeInt128(Int128High64(lhs) & Int128High64(rhs),
+                    Int128Low64(lhs) & Int128Low64(rhs));
+}
+
+constexpr int128 operator^(int128 lhs, int128 rhs) {
+  return MakeInt128(Int128High64(lhs) ^ Int128High64(rhs),
+                    Int128Low64(lhs) ^ Int128Low64(rhs));
+}
+
+constexpr int128 operator<<(int128 lhs, int amount) {
+  // int64_t shifts of >= 64 are undefined, so we need some special-casing.
+  return amount >= 64
+             ? MakeInt128(
+                   static_cast<int64_t>(Int128Low64(lhs) << (amount - 64)), 0)
+         : amount == 0
+             ? lhs
+             : MakeInt128(
+                   (Int128High64(lhs) << amount) |
+                       static_cast<int64_t>(Int128Low64(lhs) >> (64 - amount)),
+                   Int128Low64(lhs) << amount);
+}
+
+constexpr int128 operator>>(int128 lhs, int amount) {
+  // int64_t shifts of >= 64 are undefined, so we need some special-casing.
+  // The (Int128High64(lhs) >> 32) >> 32 "trick" causes the the most significant
+  // int64 to be inititialized with all zeros or all ones correctly. It takes
+  // into account whether the number is negative or positive, and whether the
+  // current architecture does arithmetic or logical right shifts for negative
+  // numbers.
+  return amount >= 64
+             ? MakeInt128(
+                   (Int128High64(lhs) >> 32) >> 32,
+                   static_cast<uint64_t>(Int128High64(lhs) >> (amount - 64)))
+         : amount == 0
+             ? lhs
+             : MakeInt128(Int128High64(lhs) >> amount,
+                          (Int128Low64(lhs) >> amount) |
+                              (static_cast<uint64_t>(Int128High64(lhs))
+                               << (64 - amount)));
+}
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/internal/bits.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/internal/bits.h
new file mode 100644
index 0000000000000000000000000000000000000000..bfef06bce1ff0419064d84c4f52502caf1999b13
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/internal/bits.h
@@ -0,0 +1,358 @@
+// Copyright 2020 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_NUMERIC_INTERNAL_BITS_H_
+#define ABSL_NUMERIC_INTERNAL_BITS_H_
+
+#include <cstdint>
+#include <limits>
+#include <type_traits>
+
+// Clang on Windows has __builtin_clzll; otherwise we need to use the
+// windows intrinsic functions.
+#if defined(_MSC_VER) && !defined(__clang__)
+#include <intrin.h>
+#endif
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+
+#if defined(__GNUC__) && !defined(__clang__)
+// GCC
+#define ABSL_NUMERIC_INTERNAL_HAVE_BUILTIN_OR_GCC(x) 1
+#else
+#define ABSL_NUMERIC_INTERNAL_HAVE_BUILTIN_OR_GCC(x) ABSL_HAVE_BUILTIN(x)
+#endif
+
+#if ABSL_NUMERIC_INTERNAL_HAVE_BUILTIN_OR_GCC(__builtin_popcountl) && \
+    ABSL_NUMERIC_INTERNAL_HAVE_BUILTIN_OR_GCC(__builtin_popcountll)
+#define ABSL_INTERNAL_CONSTEXPR_POPCOUNT constexpr
+#define ABSL_INTERNAL_HAS_CONSTEXPR_POPCOUNT 1
+#else
+#define ABSL_INTERNAL_CONSTEXPR_POPCOUNT
+#define ABSL_INTERNAL_HAS_CONSTEXPR_POPCOUNT 0
+#endif
+
+#if ABSL_NUMERIC_INTERNAL_HAVE_BUILTIN_OR_GCC(__builtin_clz) && \
+    ABSL_NUMERIC_INTERNAL_HAVE_BUILTIN_OR_GCC(__builtin_clzll)
+#define ABSL_INTERNAL_CONSTEXPR_CLZ constexpr
+#define ABSL_INTERNAL_HAS_CONSTEXPR_CLZ 1
+#else
+#define ABSL_INTERNAL_CONSTEXPR_CLZ
+#define ABSL_INTERNAL_HAS_CONSTEXPR_CLZ 0
+#endif
+
+#if ABSL_NUMERIC_INTERNAL_HAVE_BUILTIN_OR_GCC(__builtin_ctz) && \
+    ABSL_NUMERIC_INTERNAL_HAVE_BUILTIN_OR_GCC(__builtin_ctzll)
+#define ABSL_INTERNAL_CONSTEXPR_CTZ constexpr
+#define ABSL_INTERNAL_HAS_CONSTEXPR_CTZ 1
+#else
+#define ABSL_INTERNAL_CONSTEXPR_CTZ
+#define ABSL_INTERNAL_HAS_CONSTEXPR_CTZ 0
+#endif
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace numeric_internal {
+
+constexpr bool IsPowerOf2(unsigned int x) noexcept {
+  return x != 0 && (x & (x - 1)) == 0;
+}
+
+template <class T>
+ABSL_MUST_USE_RESULT ABSL_ATTRIBUTE_ALWAYS_INLINE constexpr T RotateRight(
+    T x, int s) noexcept {
+  static_assert(std::is_unsigned<T>::value, "T must be unsigned");
+  static_assert(IsPowerOf2(std::numeric_limits<T>::digits),
+                "T must have a power-of-2 size");
+
+  return static_cast<T>(x >> (s & (std::numeric_limits<T>::digits - 1))) |
+         static_cast<T>(x << ((-s) & (std::numeric_limits<T>::digits - 1)));
+}
+
+template <class T>
+ABSL_MUST_USE_RESULT ABSL_ATTRIBUTE_ALWAYS_INLINE constexpr T RotateLeft(
+    T x, int s) noexcept {
+  static_assert(std::is_unsigned<T>::value, "T must be unsigned");
+  static_assert(IsPowerOf2(std::numeric_limits<T>::digits),
+                "T must have a power-of-2 size");
+
+  return static_cast<T>(x << (s & (std::numeric_limits<T>::digits - 1))) |
+         static_cast<T>(x >> ((-s) & (std::numeric_limits<T>::digits - 1)));
+}
+
+ABSL_ATTRIBUTE_ALWAYS_INLINE ABSL_INTERNAL_CONSTEXPR_POPCOUNT inline int
+Popcount32(uint32_t x) noexcept {
+#if ABSL_NUMERIC_INTERNAL_HAVE_BUILTIN_OR_GCC(__builtin_popcount)
+  static_assert(sizeof(unsigned int) == sizeof(x),
+                "__builtin_popcount does not take 32-bit arg");
+  return __builtin_popcount(x);
+#else
+  x -= ((x >> 1) & 0x55555555);
+  x = ((x >> 2) & 0x33333333) + (x & 0x33333333);
+  return static_cast<int>((((x + (x >> 4)) & 0xF0F0F0F) * 0x1010101) >> 24);
+#endif
+}
+
+ABSL_ATTRIBUTE_ALWAYS_INLINE ABSL_INTERNAL_CONSTEXPR_POPCOUNT inline int
+Popcount64(uint64_t x) noexcept {
+#if ABSL_NUMERIC_INTERNAL_HAVE_BUILTIN_OR_GCC(__builtin_popcountll)
+  static_assert(sizeof(unsigned long long) == sizeof(x),  // NOLINT(runtime/int)
+                "__builtin_popcount does not take 64-bit arg");
+  return __builtin_popcountll(x);
+#else
+  x -= (x >> 1) & 0x5555555555555555ULL;
+  x = ((x >> 2) & 0x3333333333333333ULL) + (x & 0x3333333333333333ULL);
+  return static_cast<int>(
+      (((x + (x >> 4)) & 0xF0F0F0F0F0F0F0FULL) * 0x101010101010101ULL) >> 56);
+#endif
+}
+
+template <class T>
+ABSL_ATTRIBUTE_ALWAYS_INLINE ABSL_INTERNAL_CONSTEXPR_POPCOUNT inline int
+Popcount(T x) noexcept {
+  static_assert(std::is_unsigned<T>::value, "T must be unsigned");
+  static_assert(IsPowerOf2(std::numeric_limits<T>::digits),
+                "T must have a power-of-2 size");
+  static_assert(sizeof(x) <= sizeof(uint64_t), "T is too large");
+  return sizeof(x) <= sizeof(uint32_t) ? Popcount32(x) : Popcount64(x);
+}
+
+ABSL_ATTRIBUTE_ALWAYS_INLINE ABSL_INTERNAL_CONSTEXPR_CLZ inline int
+CountLeadingZeroes32(uint32_t x) {
+#if ABSL_NUMERIC_INTERNAL_HAVE_BUILTIN_OR_GCC(__builtin_clz)
+  // Use __builtin_clz, which uses the following instructions:
+  //  x86: bsr, lzcnt
+  //  ARM64: clz
+  //  PPC: cntlzd
+
+  static_assert(sizeof(unsigned int) == sizeof(x),
+                "__builtin_clz does not take 32-bit arg");
+  // Handle 0 as a special case because __builtin_clz(0) is undefined.
+  return x == 0 ? 32 : __builtin_clz(x);
+#elif defined(_MSC_VER) && !defined(__clang__)
+  unsigned long result = 0;  // NOLINT(runtime/int)
+  if (_BitScanReverse(&result, x)) {
+    return 31 - result;
+  }
+  return 32;
+#else
+  int zeroes = 28;
+  if (x >> 16) {
+    zeroes -= 16;
+    x >>= 16;
+  }
+  if (x >> 8) {
+    zeroes -= 8;
+    x >>= 8;
+  }
+  if (x >> 4) {
+    zeroes -= 4;
+    x >>= 4;
+  }
+  return "\4\3\2\2\1\1\1\1\0\0\0\0\0\0\0"[x] + zeroes;
+#endif
+}
+
+ABSL_ATTRIBUTE_ALWAYS_INLINE ABSL_INTERNAL_CONSTEXPR_CLZ inline int
+CountLeadingZeroes16(uint16_t x) {
+#if ABSL_HAVE_BUILTIN(__builtin_clzs)
+  static_assert(sizeof(unsigned short) == sizeof(x),  // NOLINT(runtime/int)
+                "__builtin_clzs does not take 16-bit arg");
+  return x == 0 ? 16 : __builtin_clzs(x);
+#else
+  return CountLeadingZeroes32(x) - 16;
+#endif
+}
+
+ABSL_ATTRIBUTE_ALWAYS_INLINE ABSL_INTERNAL_CONSTEXPR_CLZ inline int
+CountLeadingZeroes64(uint64_t x) {
+#if ABSL_NUMERIC_INTERNAL_HAVE_BUILTIN_OR_GCC(__builtin_clzll)
+  // Use __builtin_clzll, which uses the following instructions:
+  //  x86: bsr, lzcnt
+  //  ARM64: clz
+  //  PPC: cntlzd
+  static_assert(sizeof(unsigned long long) == sizeof(x),  // NOLINT(runtime/int)
+                "__builtin_clzll does not take 64-bit arg");
+
+  // Handle 0 as a special case because __builtin_clzll(0) is undefined.
+  return x == 0 ? 64 : __builtin_clzll(x);
+#elif defined(_MSC_VER) && !defined(__clang__) && \
+    (defined(_M_X64) || defined(_M_ARM64))
+  // MSVC does not have __buitin_clzll. Use _BitScanReverse64.
+  unsigned long result = 0;  // NOLINT(runtime/int)
+  if (_BitScanReverse64(&result, x)) {
+    return 63 - result;
+  }
+  return 64;
+#elif defined(_MSC_VER) && !defined(__clang__)
+  // MSVC does not have __buitin_clzll. Compose two calls to _BitScanReverse
+  unsigned long result = 0;  // NOLINT(runtime/int)
+  if ((x >> 32) &&
+      _BitScanReverse(&result, static_cast<unsigned long>(x >> 32))) {
+    return 31 - result;
+  }
+  if (_BitScanReverse(&result, static_cast<unsigned long>(x))) {
+    return 63 - result;
+  }
+  return 64;
+#else
+  int zeroes = 60;
+  if (x >> 32) {
+    zeroes -= 32;
+    x >>= 32;
+  }
+  if (x >> 16) {
+    zeroes -= 16;
+    x >>= 16;
+  }
+  if (x >> 8) {
+    zeroes -= 8;
+    x >>= 8;
+  }
+  if (x >> 4) {
+    zeroes -= 4;
+    x >>= 4;
+  }
+  return "\4\3\2\2\1\1\1\1\0\0\0\0\0\0\0"[x] + zeroes;
+#endif
+}
+
+template <typename T>
+ABSL_ATTRIBUTE_ALWAYS_INLINE ABSL_INTERNAL_CONSTEXPR_CLZ inline int
+CountLeadingZeroes(T x) {
+  static_assert(std::is_unsigned<T>::value, "T must be unsigned");
+  static_assert(IsPowerOf2(std::numeric_limits<T>::digits),
+                "T must have a power-of-2 size");
+  static_assert(sizeof(T) <= sizeof(uint64_t), "T too large");
+  return sizeof(T) <= sizeof(uint16_t)
+             ? CountLeadingZeroes16(static_cast<uint16_t>(x)) -
+                   (std::numeric_limits<uint16_t>::digits -
+                    std::numeric_limits<T>::digits)
+             : (sizeof(T) <= sizeof(uint32_t)
+                    ? CountLeadingZeroes32(static_cast<uint32_t>(x)) -
+                          (std::numeric_limits<uint32_t>::digits -
+                           std::numeric_limits<T>::digits)
+                    : CountLeadingZeroes64(x));
+}
+
+ABSL_ATTRIBUTE_ALWAYS_INLINE ABSL_INTERNAL_CONSTEXPR_CTZ inline int
+CountTrailingZeroesNonzero32(uint32_t x) {
+#if ABSL_NUMERIC_INTERNAL_HAVE_BUILTIN_OR_GCC(__builtin_ctz)
+  static_assert(sizeof(unsigned int) == sizeof(x),
+                "__builtin_ctz does not take 32-bit arg");
+  return __builtin_ctz(x);
+#elif defined(_MSC_VER) && !defined(__clang__)
+  unsigned long result = 0;  // NOLINT(runtime/int)
+  _BitScanForward(&result, x);
+  return result;
+#else
+  int c = 31;
+  x &= ~x + 1;
+  if (x & 0x0000FFFF) c -= 16;
+  if (x & 0x00FF00FF) c -= 8;
+  if (x & 0x0F0F0F0F) c -= 4;
+  if (x & 0x33333333) c -= 2;
+  if (x & 0x55555555) c -= 1;
+  return c;
+#endif
+}
+
+ABSL_ATTRIBUTE_ALWAYS_INLINE ABSL_INTERNAL_CONSTEXPR_CTZ inline int
+CountTrailingZeroesNonzero64(uint64_t x) {
+#if ABSL_NUMERIC_INTERNAL_HAVE_BUILTIN_OR_GCC(__builtin_ctzll)
+  static_assert(sizeof(unsigned long long) == sizeof(x),  // NOLINT(runtime/int)
+                "__builtin_ctzll does not take 64-bit arg");
+  return __builtin_ctzll(x);
+#elif defined(_MSC_VER) && !defined(__clang__) && \
+    (defined(_M_X64) || defined(_M_ARM64))
+  unsigned long result = 0;  // NOLINT(runtime/int)
+  _BitScanForward64(&result, x);
+  return result;
+#elif defined(_MSC_VER) && !defined(__clang__)
+  unsigned long result = 0;  // NOLINT(runtime/int)
+  if (static_cast<uint32_t>(x) == 0) {
+    _BitScanForward(&result, static_cast<unsigned long>(x >> 32));
+    return result + 32;
+  }
+  _BitScanForward(&result, static_cast<unsigned long>(x));
+  return result;
+#else
+  int c = 63;
+  x &= ~x + 1;
+  if (x & 0x00000000FFFFFFFF) c -= 32;
+  if (x & 0x0000FFFF0000FFFF) c -= 16;
+  if (x & 0x00FF00FF00FF00FF) c -= 8;
+  if (x & 0x0F0F0F0F0F0F0F0F) c -= 4;
+  if (x & 0x3333333333333333) c -= 2;
+  if (x & 0x5555555555555555) c -= 1;
+  return c;
+#endif
+}
+
+ABSL_ATTRIBUTE_ALWAYS_INLINE ABSL_INTERNAL_CONSTEXPR_CTZ inline int
+CountTrailingZeroesNonzero16(uint16_t x) {
+#if ABSL_HAVE_BUILTIN(__builtin_ctzs)
+  static_assert(sizeof(unsigned short) == sizeof(x),  // NOLINT(runtime/int)
+                "__builtin_ctzs does not take 16-bit arg");
+  return __builtin_ctzs(x);
+#else
+  return CountTrailingZeroesNonzero32(x);
+#endif
+}
+
+template <class T>
+ABSL_ATTRIBUTE_ALWAYS_INLINE ABSL_INTERNAL_CONSTEXPR_CTZ inline int
+CountTrailingZeroes(T x) noexcept {
+  static_assert(std::is_unsigned<T>::value, "T must be unsigned");
+  static_assert(IsPowerOf2(std::numeric_limits<T>::digits),
+                "T must have a power-of-2 size");
+  static_assert(sizeof(T) <= sizeof(uint64_t), "T too large");
+  return x == 0 ? std::numeric_limits<T>::digits
+                : (sizeof(T) <= sizeof(uint16_t)
+                       ? CountTrailingZeroesNonzero16(static_cast<uint16_t>(x))
+                       : (sizeof(T) <= sizeof(uint32_t)
+                              ? CountTrailingZeroesNonzero32(
+                                    static_cast<uint32_t>(x))
+                              : CountTrailingZeroesNonzero64(x)));
+}
+
+// If T is narrower than unsigned, T{1} << bit_width will be promoted.  We
+// want to force it to wraparound so that bit_ceil of an invalid value are not
+// core constant expressions.
+template <class T>
+ABSL_ATTRIBUTE_ALWAYS_INLINE ABSL_INTERNAL_CONSTEXPR_CLZ inline
+    typename std::enable_if<std::is_unsigned<T>::value, T>::type
+    BitCeilPromotionHelper(T x, T promotion) {
+  return (T{1} << (x + promotion)) >> promotion;
+}
+
+template <class T>
+ABSL_ATTRIBUTE_ALWAYS_INLINE ABSL_INTERNAL_CONSTEXPR_CLZ inline
+    typename std::enable_if<std::is_unsigned<T>::value, T>::type
+    BitCeilNonPowerOf2(T x) {
+  // If T is narrower than unsigned, it undergoes promotion to unsigned when we
+  // shift.  We calculate the number of bits added by the wider type.
+  return BitCeilPromotionHelper(
+      static_cast<T>(std::numeric_limits<T>::digits - CountLeadingZeroes(x)),
+      T{sizeof(T) >= sizeof(unsigned) ? 0
+                                      : std::numeric_limits<unsigned>::digits -
+                                            std::numeric_limits<T>::digits});
+}
+
+}  // namespace numeric_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_NUMERIC_INTERNAL_BITS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/internal/representation.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/internal/representation.h
new file mode 100644
index 0000000000000000000000000000000000000000..82d332fdde31a97d887b791c1a1c8d7195e26261
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/numeric/internal/representation.h
@@ -0,0 +1,55 @@
+// Copyright 2021 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_NUMERIC_INTERNAL_REPRESENTATION_H_
+#define ABSL_NUMERIC_INTERNAL_REPRESENTATION_H_
+
+#include <limits>
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace numeric_internal {
+
+// Returns true iff long double is represented as a pair of doubles added
+// together.
+inline constexpr bool IsDoubleDouble() {
+  // A double-double value always has exactly twice the precision of a double
+  // value--one double carries the high digits and one double carries the low
+  // digits. This property is not shared with any other common floating-point
+  // representation, so this test won't trigger false positives. For reference,
+  // this table gives the number of bits of precision of each common
+  // floating-point representation:
+  //
+  //                type     precision
+  //         IEEE single          24 b
+  //         IEEE double          53
+  //     x86 long double          64
+  //       double-double         106
+  //      IEEE quadruple         113
+  //
+  // Note in particular that a quadruple-precision float has greater precision
+  // than a double-double float despite taking up the same amount of memory; the
+  // quad has more of its bits allocated to the mantissa than the double-double
+  // has.
+  return std::numeric_limits<long double>::digits ==
+         2 * std::numeric_limits<double>::digits;
+}
+
+}  // namespace numeric_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_NUMERIC_INTERNAL_REPRESENTATION_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/profiling/internal/exponential_biased.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/profiling/internal/exponential_biased.h
new file mode 100644
index 0000000000000000000000000000000000000000..d31f7782e83dca4461073c443b9b04582a5921df
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/profiling/internal/exponential_biased.h
@@ -0,0 +1,130 @@
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_PROFILING_INTERNAL_EXPONENTIAL_BIASED_H_
+#define ABSL_PROFILING_INTERNAL_EXPONENTIAL_BIASED_H_
+
+#include <stdint.h>
+
+#include "absl/base/config.h"
+#include "absl/base/macros.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace profiling_internal {
+
+// ExponentialBiased provides a small and fast random number generator for a
+// rounded exponential distribution. This generator manages very little state,
+// and imposes no synchronization overhead. This makes it useful in specialized
+// scenarios requiring minimum overhead, such as stride based periodic sampling.
+//
+// ExponentialBiased provides two closely related functions, GetSkipCount() and
+// GetStride(), both returning a rounded integer defining a number of events
+// required before some event with a given mean probability occurs.
+//
+// The distribution is useful to generate a random wait time or some periodic
+// event with a given mean probability. For example, if an action is supposed to
+// happen on average once every 'N' events, then we can get a random 'stride'
+// counting down how long before the event to happen. For example, if we'd want
+// to sample one in every 1000 'Frobber' calls, our code could look like this:
+//
+//   Frobber::Frobber() {
+//     stride_ = exponential_biased_.GetStride(1000);
+//   }
+//
+//   void Frobber::Frob(int arg) {
+//     if (--stride == 0) {
+//       SampleFrob(arg);
+//       stride_ = exponential_biased_.GetStride(1000);
+//     }
+//     ...
+//   }
+//
+// The rounding of the return value creates a bias, especially for smaller means
+// where the distribution of the fraction is not evenly distributed. We correct
+// this bias by tracking the fraction we rounded up or down on each iteration,
+// effectively tracking the distance between the cumulative value, and the
+// rounded cumulative value. For example, given a mean of 2:
+//
+//   raw = 1.63076, cumulative = 1.63076, rounded = 2, bias = -0.36923
+//   raw = 0.14624, cumulative = 1.77701, rounded = 2, bias =  0.14624
+//   raw = 4.93194, cumulative = 6.70895, rounded = 7, bias = -0.06805
+//   raw = 0.24206, cumulative = 6.95101, rounded = 7, bias =  0.24206
+//   etc...
+//
+// Adjusting with rounding bias is relatively trivial:
+//
+//    double value = bias_ + exponential_distribution(mean)();
+//    double rounded_value = std::rint(value);
+//    bias_ = value - rounded_value;
+//    return rounded_value;
+//
+// This class is thread-compatible.
+class ExponentialBiased {
+ public:
+  // The number of bits set by NextRandom.
+  static constexpr int kPrngNumBits = 48;
+
+  // `GetSkipCount()` returns the number of events to skip before some chosen
+  // event happens. For example, randomly tossing a coin, we will on average
+  // throw heads once before we get tails. We can simulate random coin tosses
+  // using GetSkipCount() as:
+  //
+  //   ExponentialBiased eb;
+  //   for (...) {
+  //     int number_of_heads_before_tail = eb.GetSkipCount(1);
+  //     for (int flips = 0; flips < number_of_heads_before_tail; ++flips) {
+  //       printf("head...");
+  //     }
+  //     printf("tail\n");
+  //   }
+  //
+  int64_t GetSkipCount(int64_t mean);
+
+  // GetStride() returns the number of events required for a specific event to
+  // happen. See the class comments for a usage example. `GetStride()` is
+  // equivalent to `GetSkipCount(mean - 1) + 1`. When to use `GetStride()` or
+  // `GetSkipCount()` depends mostly on what best fits the use case.
+  int64_t GetStride(int64_t mean);
+
+  // Computes a random number in the range [0, 1<<(kPrngNumBits+1) - 1]
+  //
+  // This is public to enable testing.
+  static uint64_t NextRandom(uint64_t rnd);
+
+ private:
+  void Initialize();
+
+  uint64_t rng_{0};
+  double bias_{0};
+  bool initialized_{false};
+};
+
+// Returns the next prng value.
+// pRNG is: aX+b mod c with a = 0x5DEECE66D, b =  0xB, c = 1<<48
+// This is the lrand64 generator.
+inline uint64_t ExponentialBiased::NextRandom(uint64_t rnd) {
+  const uint64_t prng_mult = uint64_t{0x5DEECE66D};
+  const uint64_t prng_add = 0xB;
+  const uint64_t prng_mod_power = 48;
+  const uint64_t prng_mod_mask =
+      ~((~static_cast<uint64_t>(0)) << prng_mod_power);
+  return (prng_mult * rnd + prng_add) & prng_mod_mask;
+}
+
+}  // namespace profiling_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_PROFILING_INTERNAL_EXPONENTIAL_BIASED_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/profiling/internal/sample_recorder.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/profiling/internal/sample_recorder.h
new file mode 100644
index 0000000000000000000000000000000000000000..371f6c473ffc0fb9050a48a4dc40d2db44abdc68
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/profiling/internal/sample_recorder.h
@@ -0,0 +1,253 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: sample_recorder.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines a lock-free linked list for recording samples
+// collected from a random/stochastic process.
+//
+// This utility is internal-only. Use at your own risk.
+
+#ifndef ABSL_PROFILING_INTERNAL_SAMPLE_RECORDER_H_
+#define ABSL_PROFILING_INTERNAL_SAMPLE_RECORDER_H_
+
+#include <atomic>
+#include <cstddef>
+#include <functional>
+
+#include "absl/base/config.h"
+#include "absl/base/thread_annotations.h"
+#include "absl/synchronization/mutex.h"
+#include "absl/time/time.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace profiling_internal {
+
+// Sample<T> that has members required for linking samples in the linked list of
+// samples maintained by the SampleRecorder.  Type T defines the sampled data.
+template <typename T>
+struct Sample {
+  // Guards the ability to restore the sample to a pristine state.  This
+  // prevents races with sampling and resurrecting an object.
+  absl::Mutex init_mu;
+  T* next = nullptr;
+  T* dead ABSL_GUARDED_BY(init_mu) = nullptr;
+  int64_t weight;  // How many sampling events were required to sample this one.
+};
+
+// Holds samples and their associated stack traces with a soft limit of
+// `SetHashtablezMaxSamples()`.
+//
+// Thread safe.
+template <typename T>
+class SampleRecorder {
+ public:
+  SampleRecorder();
+  ~SampleRecorder();
+
+  // Registers for sampling.  Returns an opaque registration info.
+  template <typename... Targs>
+  T* Register(Targs&&... args);
+
+  // Unregisters the sample.
+  void Unregister(T* sample);
+
+  // The dispose callback will be called on all samples the moment they are
+  // being unregistered. Only affects samples that are unregistered after the
+  // callback has been set.
+  // Returns the previous callback.
+  using DisposeCallback = void (*)(const T&);
+  DisposeCallback SetDisposeCallback(DisposeCallback f);
+
+  // Iterates over all the registered `StackInfo`s.  Returning the number of
+  // samples that have been dropped.
+  int64_t Iterate(const std::function<void(const T& stack)>& f);
+
+  size_t GetMaxSamples() const;
+  void SetMaxSamples(size_t max);
+
+ private:
+  void PushNew(T* sample);
+  void PushDead(T* sample);
+  template <typename... Targs>
+  T* PopDead(Targs... args);
+
+  std::atomic<size_t> dropped_samples_;
+  std::atomic<size_t> size_estimate_;
+  std::atomic<size_t> max_samples_{1 << 20};
+
+  // Intrusive lock free linked lists for tracking samples.
+  //
+  // `all_` records all samples (they are never removed from this list) and is
+  // terminated with a `nullptr`.
+  //
+  // `graveyard_.dead` is a circular linked list.  When it is empty,
+  // `graveyard_.dead == &graveyard`.  The list is circular so that
+  // every item on it (even the last) has a non-null dead pointer.  This allows
+  // `Iterate` to determine if a given sample is live or dead using only
+  // information on the sample itself.
+  //
+  // For example, nodes [A, B, C, D, E] with [A, C, E] alive and [B, D] dead
+  // looks like this (G is the Graveyard):
+  //
+  //           +---+    +---+    +---+    +---+    +---+
+  //    all -->| A |--->| B |--->| C |--->| D |--->| E |
+  //           |   |    |   |    |   |    |   |    |   |
+  //   +---+   |   | +->|   |-+  |   | +->|   |-+  |   |
+  //   | G |   +---+ |  +---+ |  +---+ |  +---+ |  +---+
+  //   |   |         |        |        |        |
+  //   |   | --------+        +--------+        |
+  //   +---+                                    |
+  //     ^                                      |
+  //     +--------------------------------------+
+  //
+  std::atomic<T*> all_;
+  T graveyard_;
+
+  std::atomic<DisposeCallback> dispose_;
+};
+
+template <typename T>
+typename SampleRecorder<T>::DisposeCallback
+SampleRecorder<T>::SetDisposeCallback(DisposeCallback f) {
+  return dispose_.exchange(f, std::memory_order_relaxed);
+}
+
+template <typename T>
+SampleRecorder<T>::SampleRecorder()
+    : dropped_samples_(0), size_estimate_(0), all_(nullptr), dispose_(nullptr) {
+  absl::MutexLock l(&graveyard_.init_mu);
+  graveyard_.dead = &graveyard_;
+}
+
+template <typename T>
+SampleRecorder<T>::~SampleRecorder() {
+  T* s = all_.load(std::memory_order_acquire);
+  while (s != nullptr) {
+    T* next = s->next;
+    delete s;
+    s = next;
+  }
+}
+
+template <typename T>
+void SampleRecorder<T>::PushNew(T* sample) {
+  sample->next = all_.load(std::memory_order_relaxed);
+  while (!all_.compare_exchange_weak(sample->next, sample,
+                                     std::memory_order_release,
+                                     std::memory_order_relaxed)) {
+  }
+}
+
+template <typename T>
+void SampleRecorder<T>::PushDead(T* sample) {
+  if (auto* dispose = dispose_.load(std::memory_order_relaxed)) {
+    dispose(*sample);
+  }
+
+  absl::MutexLock graveyard_lock(&graveyard_.init_mu);
+  absl::MutexLock sample_lock(&sample->init_mu);
+  sample->dead = graveyard_.dead;
+  graveyard_.dead = sample;
+}
+
+template <typename T>
+template <typename... Targs>
+T* SampleRecorder<T>::PopDead(Targs... args) {
+  absl::MutexLock graveyard_lock(&graveyard_.init_mu);
+
+  // The list is circular, so eventually it collapses down to
+  //   graveyard_.dead == &graveyard_
+  // when it is empty.
+  T* sample = graveyard_.dead;
+  if (sample == &graveyard_) return nullptr;
+
+  absl::MutexLock sample_lock(&sample->init_mu);
+  graveyard_.dead = sample->dead;
+  sample->dead = nullptr;
+  sample->PrepareForSampling(std::forward<Targs>(args)...);
+  return sample;
+}
+
+template <typename T>
+template <typename... Targs>
+T* SampleRecorder<T>::Register(Targs&&... args) {
+  size_t size = size_estimate_.fetch_add(1, std::memory_order_relaxed);
+  if (size > max_samples_.load(std::memory_order_relaxed)) {
+    size_estimate_.fetch_sub(1, std::memory_order_relaxed);
+    dropped_samples_.fetch_add(1, std::memory_order_relaxed);
+    return nullptr;
+  }
+
+  T* sample = PopDead(args...);
+  if (sample == nullptr) {
+    // Resurrection failed.  Hire a new warlock.
+    sample = new T();
+    {
+      absl::MutexLock sample_lock(&sample->init_mu);
+      // If flag initialization happens to occur (perhaps in another thread)
+      // while in this block, it will lock `graveyard_` which is usually always
+      // locked before any sample. This will appear as a lock inversion.
+      // However, this code is run exactly once per sample, and this sample
+      // cannot be accessed until after it is returned from this method.  This
+      // means that this lock state can never be recreated, so we can safely
+      // inform the deadlock detector to ignore it.
+      sample->init_mu.ForgetDeadlockInfo();
+      sample->PrepareForSampling(std::forward<Targs>(args)...);
+    }
+    PushNew(sample);
+  }
+
+  return sample;
+}
+
+template <typename T>
+void SampleRecorder<T>::Unregister(T* sample) {
+  PushDead(sample);
+  size_estimate_.fetch_sub(1, std::memory_order_relaxed);
+}
+
+template <typename T>
+int64_t SampleRecorder<T>::Iterate(
+    const std::function<void(const T& stack)>& f) {
+  T* s = all_.load(std::memory_order_acquire);
+  while (s != nullptr) {
+    absl::MutexLock l(&s->init_mu);
+    if (s->dead == nullptr) {
+      f(*s);
+    }
+    s = s->next;
+  }
+
+  return dropped_samples_.load(std::memory_order_relaxed);
+}
+
+template <typename T>
+void SampleRecorder<T>::SetMaxSamples(size_t max) {
+  max_samples_.store(max, std::memory_order_release);
+}
+
+template <typename T>
+size_t SampleRecorder<T>::GetMaxSamples() const {
+  return max_samples_.load(std::memory_order_acquire);
+}
+
+}  // namespace profiling_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_PROFILING_INTERNAL_SAMPLE_RECORDER_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/bernoulli_distribution.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/bernoulli_distribution.h
new file mode 100644
index 0000000000000000000000000000000000000000..d81b6ae6b1c87db43aeafd6f82f2a03610fd1d24
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/bernoulli_distribution.h
@@ -0,0 +1,200 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_BERNOULLI_DISTRIBUTION_H_
+#define ABSL_RANDOM_BERNOULLI_DISTRIBUTION_H_
+
+#include <cstdint>
+#include <istream>
+#include <limits>
+
+#include "absl/base/optimization.h"
+#include "absl/random/internal/fast_uniform_bits.h"
+#include "absl/random/internal/iostream_state_saver.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// absl::bernoulli_distribution is a drop in replacement for
+// std::bernoulli_distribution. It guarantees that (given a perfect
+// UniformRandomBitGenerator) the acceptance probability is *exactly* equal to
+// the given double.
+//
+// The implementation assumes that double is IEEE754
+class bernoulli_distribution {
+ public:
+  using result_type = bool;
+
+  class param_type {
+   public:
+    using distribution_type = bernoulli_distribution;
+
+    explicit param_type(double p = 0.5) : prob_(p) {
+      assert(p >= 0.0 && p <= 1.0);
+    }
+
+    double p() const { return prob_; }
+
+    friend bool operator==(const param_type& p1, const param_type& p2) {
+      return p1.p() == p2.p();
+    }
+    friend bool operator!=(const param_type& p1, const param_type& p2) {
+      return p1.p() != p2.p();
+    }
+
+   private:
+    double prob_;
+  };
+
+  bernoulli_distribution() : bernoulli_distribution(0.5) {}
+
+  explicit bernoulli_distribution(double p) : param_(p) {}
+
+  explicit bernoulli_distribution(param_type p) : param_(p) {}
+
+  // no-op
+  void reset() {}
+
+  template <typename URBG>
+  bool operator()(URBG& g) {  // NOLINT(runtime/references)
+    return Generate(param_.p(), g);
+  }
+
+  template <typename URBG>
+  bool operator()(URBG& g,  // NOLINT(runtime/references)
+                  const param_type& param) {
+    return Generate(param.p(), g);
+  }
+
+  param_type param() const { return param_; }
+  void param(const param_type& param) { param_ = param; }
+
+  double p() const { return param_.p(); }
+
+  result_type(min)() const { return false; }
+  result_type(max)() const { return true; }
+
+  friend bool operator==(const bernoulli_distribution& d1,
+                         const bernoulli_distribution& d2) {
+    return d1.param_ == d2.param_;
+  }
+
+  friend bool operator!=(const bernoulli_distribution& d1,
+                         const bernoulli_distribution& d2) {
+    return d1.param_ != d2.param_;
+  }
+
+ private:
+  static constexpr uint64_t kP32 = static_cast<uint64_t>(1) << 32;
+
+  template <typename URBG>
+  static bool Generate(double p, URBG& g);  // NOLINT(runtime/references)
+
+  param_type param_;
+};
+
+template <typename CharT, typename Traits>
+std::basic_ostream<CharT, Traits>& operator<<(
+    std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
+    const bernoulli_distribution& x) {
+  auto saver = random_internal::make_ostream_state_saver(os);
+  os.precision(random_internal::stream_precision_helper<double>::kPrecision);
+  os << x.p();
+  return os;
+}
+
+template <typename CharT, typename Traits>
+std::basic_istream<CharT, Traits>& operator>>(
+    std::basic_istream<CharT, Traits>& is,  // NOLINT(runtime/references)
+    bernoulli_distribution& x) {            // NOLINT(runtime/references)
+  auto saver = random_internal::make_istream_state_saver(is);
+  auto p = random_internal::read_floating_point<double>(is);
+  if (!is.fail()) {
+    x.param(bernoulli_distribution::param_type(p));
+  }
+  return is;
+}
+
+template <typename URBG>
+bool bernoulli_distribution::Generate(double p,
+                                      URBG& g) {  // NOLINT(runtime/references)
+  random_internal::FastUniformBits<uint32_t> fast_u32;
+
+  while (true) {
+    // There are two aspects of the definition of `c` below that are worth
+    // commenting on.  First, because `p` is in the range [0, 1], `c` is in the
+    // range [0, 2^32] which does not fit in a uint32_t and therefore requires
+    // 64 bits.
+    //
+    // Second, `c` is constructed by first casting explicitly to a signed
+    // integer and then casting explicitly to an unsigned integer of the same
+    // size.  This is done because the hardware conversion instructions produce
+    // signed integers from double; if taken as a uint64_t the conversion would
+    // be wrong for doubles greater than 2^63 (not relevant in this use-case).
+    // If converted directly to an unsigned integer, the compiler would end up
+    // emitting code to handle such large values that are not relevant due to
+    // the known bounds on `c`.  To avoid these extra instructions this
+    // implementation converts first to the signed type and then convert to
+    // unsigned (which is a no-op).
+    const uint64_t c = static_cast<uint64_t>(static_cast<int64_t>(p * kP32));
+    const uint32_t v = fast_u32(g);
+    // FAST PATH: this path fails with probability 1/2^32.  Note that simply
+    // returning v <= c would approximate P very well (up to an absolute error
+    // of 1/2^32); the slow path (taken in that range of possible error, in the
+    // case of equality) eliminates the remaining error.
+    if (ABSL_PREDICT_TRUE(v != c)) return v < c;
+
+    // It is guaranteed that `q` is strictly less than 1, because if `q` were
+    // greater than or equal to 1, the same would be true for `p`. Certainly `p`
+    // cannot be greater than 1, and if `p == 1`, then the fast path would
+    // necessary have been taken already.
+    const double q = static_cast<double>(c) / kP32;
+
+    // The probability of acceptance on the fast path is `q` and so the
+    // probability of acceptance here should be `p - q`.
+    //
+    // Note that `q` is obtained from `p` via some shifts and conversions, the
+    // upshot of which is that `q` is simply `p` with some of the
+    // least-significant bits of its mantissa set to zero. This means that the
+    // difference `p - q` will not have any rounding errors. To see why, pretend
+    // that double has 10 bits of resolution and q is obtained from `p` in such
+    // a way that the 4 least-significant bits of its mantissa are set to zero.
+    // For example:
+    //   p   = 1.1100111011 * 2^-1
+    //   q   = 1.1100110000 * 2^-1
+    // p - q = 1.011        * 2^-8
+    // The difference `p - q` has exactly the nonzero mantissa bits that were
+    // "lost" in `q` producing a number which is certainly representable in a
+    // double.
+    const double left = p - q;
+
+    // By construction, the probability of being on this slow path is 1/2^32, so
+    // P(accept in slow path) = P(accept| in slow path) * P(slow path),
+    // which means the probability of acceptance here is `1 / (left * kP32)`:
+    const double here = left * kP32;
+
+    // The simplest way to compute the result of this trial is to repeat the
+    // whole algorithm with the new probability. This terminates because even
+    // given  arbitrarily unfriendly "random" bits, each iteration either
+    // multiplies a tiny probability by 2^32 (if c == 0) or strips off some
+    // number of nonzero mantissa bits. That process is bounded.
+    if (here == 0) return false;
+    p = here;
+  }
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_BERNOULLI_DISTRIBUTION_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/beta_distribution.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/beta_distribution.h
new file mode 100644
index 0000000000000000000000000000000000000000..c154066fb8132941685a7a0e3261584296332472
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/beta_distribution.h
@@ -0,0 +1,427 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_BETA_DISTRIBUTION_H_
+#define ABSL_RANDOM_BETA_DISTRIBUTION_H_
+
+#include <cassert>
+#include <cmath>
+#include <istream>
+#include <limits>
+#include <ostream>
+#include <type_traits>
+
+#include "absl/meta/type_traits.h"
+#include "absl/random/internal/fast_uniform_bits.h"
+#include "absl/random/internal/fastmath.h"
+#include "absl/random/internal/generate_real.h"
+#include "absl/random/internal/iostream_state_saver.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// absl::beta_distribution:
+// Generate a floating-point variate conforming to a Beta distribution:
+//   pdf(x) \propto x^(alpha-1) * (1-x)^(beta-1),
+// where the params alpha and beta are both strictly positive real values.
+//
+// The support is the open interval (0, 1), but the return value might be equal
+// to 0 or 1, due to numerical errors when alpha and beta are very different.
+//
+// Usage note: One usage is that alpha and beta are counts of number of
+// successes and failures. When the total number of trials are large, consider
+// approximating a beta distribution with a Gaussian distribution with the same
+// mean and variance. One could use the skewness, which depends only on the
+// smaller of alpha and beta when the number of trials are sufficiently large,
+// to quantify how far a beta distribution is from the normal distribution.
+template <typename RealType = double>
+class beta_distribution {
+ public:
+  using result_type = RealType;
+
+  class param_type {
+   public:
+    using distribution_type = beta_distribution;
+
+    explicit param_type(result_type alpha, result_type beta)
+        : alpha_(alpha), beta_(beta) {
+      assert(alpha >= 0);
+      assert(beta >= 0);
+      assert(alpha <= (std::numeric_limits<result_type>::max)());
+      assert(beta <= (std::numeric_limits<result_type>::max)());
+      if (alpha == 0 || beta == 0) {
+        method_ = DEGENERATE_SMALL;
+        x_ = (alpha >= beta) ? 1 : 0;
+        return;
+      }
+      // a_ = min(beta, alpha), b_ = max(beta, alpha).
+      if (beta < alpha) {
+        inverted_ = true;
+        a_ = beta;
+        b_ = alpha;
+      } else {
+        inverted_ = false;
+        a_ = alpha;
+        b_ = beta;
+      }
+      if (a_ <= 1 && b_ >= ThresholdForLargeA()) {
+        method_ = DEGENERATE_SMALL;
+        x_ = inverted_ ? result_type(1) : result_type(0);
+        return;
+      }
+      // For threshold values, see also:
+      // Evaluation of Beta Generation Algorithms, Ying-Chao Hung, et. al.
+      // February, 2009.
+      if ((b_ < 1.0 && a_ + b_ <= 1.2) || a_ <= ThresholdForSmallA()) {
+        // Choose Joehnk over Cheng when it's faster or when Cheng encounters
+        // numerical issues.
+        method_ = JOEHNK;
+        a_ = result_type(1) / alpha_;
+        b_ = result_type(1) / beta_;
+        if (std::isinf(a_) || std::isinf(b_)) {
+          method_ = DEGENERATE_SMALL;
+          x_ = inverted_ ? result_type(1) : result_type(0);
+        }
+        return;
+      }
+      if (a_ >= ThresholdForLargeA()) {
+        method_ = DEGENERATE_LARGE;
+        // Note: on PPC for long double, evaluating
+        // `std::numeric_limits::max() / ThresholdForLargeA` results in NaN.
+        result_type r = a_ / b_;
+        x_ = (inverted_ ? result_type(1) : r) / (1 + r);
+        return;
+      }
+      x_ = a_ + b_;
+      log_x_ = std::log(x_);
+      if (a_ <= 1) {
+        method_ = CHENG_BA;
+        y_ = result_type(1) / a_;
+        gamma_ = a_ + a_;
+        return;
+      }
+      method_ = CHENG_BB;
+      result_type r = (a_ - 1) / (b_ - 1);
+      y_ = std::sqrt((1 + r) / (b_ * r * 2 - r + 1));
+      gamma_ = a_ + result_type(1) / y_;
+    }
+
+    result_type alpha() const { return alpha_; }
+    result_type beta() const { return beta_; }
+
+    friend bool operator==(const param_type& a, const param_type& b) {
+      return a.alpha_ == b.alpha_ && a.beta_ == b.beta_;
+    }
+
+    friend bool operator!=(const param_type& a, const param_type& b) {
+      return !(a == b);
+    }
+
+   private:
+    friend class beta_distribution;
+
+#ifdef _MSC_VER
+    // MSVC does not have constexpr implementations for std::log and std::exp
+    // so they are computed at runtime.
+#define ABSL_RANDOM_INTERNAL_LOG_EXP_CONSTEXPR
+#else
+#define ABSL_RANDOM_INTERNAL_LOG_EXP_CONSTEXPR constexpr
+#endif
+
+    // The threshold for whether std::exp(1/a) is finite.
+    // Note that this value is quite large, and a smaller a_ is NOT abnormal.
+    static ABSL_RANDOM_INTERNAL_LOG_EXP_CONSTEXPR result_type
+    ThresholdForSmallA() {
+      return result_type(1) /
+             std::log((std::numeric_limits<result_type>::max)());
+    }
+
+    // The threshold for whether a * std::log(a) is finite.
+    static ABSL_RANDOM_INTERNAL_LOG_EXP_CONSTEXPR result_type
+    ThresholdForLargeA() {
+      return std::exp(
+          std::log((std::numeric_limits<result_type>::max)()) -
+          std::log(std::log((std::numeric_limits<result_type>::max)())) -
+          ThresholdPadding());
+    }
+
+#undef ABSL_RANDOM_INTERNAL_LOG_EXP_CONSTEXPR
+
+    // Pad the threshold for large A for long double on PPC. This is done via a
+    // template specialization below.
+    static constexpr result_type ThresholdPadding() { return 0; }
+
+    enum Method {
+      JOEHNK,    // Uses algorithm Joehnk
+      CHENG_BA,  // Uses algorithm BA in Cheng
+      CHENG_BB,  // Uses algorithm BB in Cheng
+
+      // Note: See also:
+      //   Hung et al. Evaluation of beta generation algorithms. Communications
+      //   in Statistics-Simulation and Computation 38.4 (2009): 750-770.
+      // especially:
+      //   Zechner, Heinz, and Ernst Stadlober. Generating beta variates via
+      //   patchwork rejection. Computing 50.1 (1993): 1-18.
+
+      DEGENERATE_SMALL,  // a_ is abnormally small.
+      DEGENERATE_LARGE,  // a_ is abnormally large.
+    };
+
+    result_type alpha_;
+    result_type beta_;
+
+    result_type a_;  // the smaller of {alpha, beta}, or 1.0/alpha_ in JOEHNK
+    result_type b_;  // the larger of {alpha, beta}, or 1.0/beta_ in JOEHNK
+    result_type x_;  // alpha + beta, or the result in degenerate cases
+    result_type log_x_;  // log(x_)
+    result_type y_;      // "beta" in Cheng
+    result_type gamma_;  // "gamma" in Cheng
+
+    Method method_;
+
+    // Placing this last for optimal alignment.
+    // Whether alpha_ != a_, i.e. true iff alpha_ > beta_.
+    bool inverted_;
+
+    static_assert(std::is_floating_point<RealType>::value,
+                  "Class-template absl::beta_distribution<> must be "
+                  "parameterized using a floating-point type.");
+  };
+
+  beta_distribution() : beta_distribution(1) {}
+
+  explicit beta_distribution(result_type alpha, result_type beta = 1)
+      : param_(alpha, beta) {}
+
+  explicit beta_distribution(const param_type& p) : param_(p) {}
+
+  void reset() {}
+
+  // Generating functions
+  template <typename URBG>
+  result_type operator()(URBG& g) {  // NOLINT(runtime/references)
+    return (*this)(g, param_);
+  }
+
+  template <typename URBG>
+  result_type operator()(URBG& g,  // NOLINT(runtime/references)
+                         const param_type& p);
+
+  param_type param() const { return param_; }
+  void param(const param_type& p) { param_ = p; }
+
+  result_type(min)() const { return 0; }
+  result_type(max)() const { return 1; }
+
+  result_type alpha() const { return param_.alpha(); }
+  result_type beta() const { return param_.beta(); }
+
+  friend bool operator==(const beta_distribution& a,
+                         const beta_distribution& b) {
+    return a.param_ == b.param_;
+  }
+  friend bool operator!=(const beta_distribution& a,
+                         const beta_distribution& b) {
+    return a.param_ != b.param_;
+  }
+
+ private:
+  template <typename URBG>
+  result_type AlgorithmJoehnk(URBG& g,  // NOLINT(runtime/references)
+                              const param_type& p);
+
+  template <typename URBG>
+  result_type AlgorithmCheng(URBG& g,  // NOLINT(runtime/references)
+                             const param_type& p);
+
+  template <typename URBG>
+  result_type DegenerateCase(URBG& g,  // NOLINT(runtime/references)
+                             const param_type& p) {
+    if (p.method_ == param_type::DEGENERATE_SMALL && p.alpha_ == p.beta_) {
+      // Returns 0 or 1 with equal probability.
+      random_internal::FastUniformBits<uint8_t> fast_u8;
+      return static_cast<result_type>((fast_u8(g) & 0x10) !=
+                                      0);  // pick any single bit.
+    }
+    return p.x_;
+  }
+
+  param_type param_;
+  random_internal::FastUniformBits<uint64_t> fast_u64_;
+};
+
+#if defined(__powerpc64__) || defined(__PPC64__) || defined(__powerpc__) || \
+    defined(__ppc__) || defined(__PPC__)
+// PPC needs a more stringent boundary for long double.
+template <>
+constexpr long double
+beta_distribution<long double>::param_type::ThresholdPadding() {
+  return 10;
+}
+#endif
+
+template <typename RealType>
+template <typename URBG>
+typename beta_distribution<RealType>::result_type
+beta_distribution<RealType>::AlgorithmJoehnk(
+    URBG& g,  // NOLINT(runtime/references)
+    const param_type& p) {
+  using random_internal::GeneratePositiveTag;
+  using random_internal::GenerateRealFromBits;
+  using real_type =
+      absl::conditional_t<std::is_same<RealType, float>::value, float, double>;
+
+  // Based on Joehnk, M. D. Erzeugung von betaverteilten und gammaverteilten
+  // Zufallszahlen. Metrika 8.1 (1964): 5-15.
+  // This method is described in Knuth, Vol 2 (Third Edition), pp 134.
+
+  result_type u, v, x, y, z;
+  for (;;) {
+    u = GenerateRealFromBits<real_type, GeneratePositiveTag, false>(
+        fast_u64_(g));
+    v = GenerateRealFromBits<real_type, GeneratePositiveTag, false>(
+        fast_u64_(g));
+
+    // Direct method. std::pow is slow for float, so rely on the optimizer to
+    // remove the std::pow() path for that case.
+    if (!std::is_same<float, result_type>::value) {
+      x = std::pow(u, p.a_);
+      y = std::pow(v, p.b_);
+      z = x + y;
+      if (z > 1) {
+        // Reject if and only if `x + y > 1.0`
+        continue;
+      }
+      if (z > 0) {
+        // When both alpha and beta are small, x and y are both close to 0, so
+        // divide by (x+y) directly may result in nan.
+        return x / z;
+      }
+    }
+
+    // Log transform.
+    // x = log( pow(u, p.a_) ), y = log( pow(v, p.b_) )
+    // since u, v <= 1.0,  x, y < 0.
+    x = std::log(u) * p.a_;
+    y = std::log(v) * p.b_;
+    if (!std::isfinite(x) || !std::isfinite(y)) {
+      continue;
+    }
+    // z = log( pow(u, a) + pow(v, b) )
+    z = x > y ? (x + std::log(1 + std::exp(y - x)))
+              : (y + std::log(1 + std::exp(x - y)));
+    // Reject iff log(x+y) > 0.
+    if (z > 0) {
+      continue;
+    }
+    return std::exp(x - z);
+  }
+}
+
+template <typename RealType>
+template <typename URBG>
+typename beta_distribution<RealType>::result_type
+beta_distribution<RealType>::AlgorithmCheng(
+    URBG& g,  // NOLINT(runtime/references)
+    const param_type& p) {
+  using random_internal::GeneratePositiveTag;
+  using random_internal::GenerateRealFromBits;
+  using real_type =
+      absl::conditional_t<std::is_same<RealType, float>::value, float, double>;
+
+  // Based on Cheng, Russell CH. Generating beta variates with nonintegral
+  // shape parameters. Communications of the ACM 21.4 (1978): 317-322.
+  // (https://dl.acm.org/citation.cfm?id=359482).
+  static constexpr result_type kLogFour =
+      result_type(1.3862943611198906188344642429163531361);  // log(4)
+  static constexpr result_type kS =
+      result_type(2.6094379124341003746007593332261876);  // 1+log(5)
+
+  const bool use_algorithm_ba = (p.method_ == param_type::CHENG_BA);
+  result_type u1, u2, v, w, z, r, s, t, bw_inv, lhs;
+  for (;;) {
+    u1 = GenerateRealFromBits<real_type, GeneratePositiveTag, false>(
+        fast_u64_(g));
+    u2 = GenerateRealFromBits<real_type, GeneratePositiveTag, false>(
+        fast_u64_(g));
+    v = p.y_ * std::log(u1 / (1 - u1));
+    w = p.a_ * std::exp(v);
+    bw_inv = result_type(1) / (p.b_ + w);
+    r = p.gamma_ * v - kLogFour;
+    s = p.a_ + r - w;
+    z = u1 * u1 * u2;
+    if (!use_algorithm_ba && s + kS >= 5 * z) {
+      break;
+    }
+    t = std::log(z);
+    if (!use_algorithm_ba && s >= t) {
+      break;
+    }
+    lhs = p.x_ * (p.log_x_ + std::log(bw_inv)) + r;
+    if (lhs >= t) {
+      break;
+    }
+  }
+  return p.inverted_ ? (1 - w * bw_inv) : w * bw_inv;
+}
+
+template <typename RealType>
+template <typename URBG>
+typename beta_distribution<RealType>::result_type
+beta_distribution<RealType>::operator()(URBG& g,  // NOLINT(runtime/references)
+                                        const param_type& p) {
+  switch (p.method_) {
+    case param_type::JOEHNK:
+      return AlgorithmJoehnk(g, p);
+    case param_type::CHENG_BA:
+      ABSL_FALLTHROUGH_INTENDED;
+    case param_type::CHENG_BB:
+      return AlgorithmCheng(g, p);
+    default:
+      return DegenerateCase(g, p);
+  }
+}
+
+template <typename CharT, typename Traits, typename RealType>
+std::basic_ostream<CharT, Traits>& operator<<(
+    std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
+    const beta_distribution<RealType>& x) {
+  auto saver = random_internal::make_ostream_state_saver(os);
+  os.precision(random_internal::stream_precision_helper<RealType>::kPrecision);
+  os << x.alpha() << os.fill() << x.beta();
+  return os;
+}
+
+template <typename CharT, typename Traits, typename RealType>
+std::basic_istream<CharT, Traits>& operator>>(
+    std::basic_istream<CharT, Traits>& is,  // NOLINT(runtime/references)
+    beta_distribution<RealType>& x) {       // NOLINT(runtime/references)
+  using result_type = typename beta_distribution<RealType>::result_type;
+  using param_type = typename beta_distribution<RealType>::param_type;
+  result_type alpha, beta;
+
+  auto saver = random_internal::make_istream_state_saver(is);
+  alpha = random_internal::read_floating_point<result_type>(is);
+  if (is.fail()) return is;
+  beta = random_internal::read_floating_point<result_type>(is);
+  if (!is.fail()) {
+    x.param(param_type(alpha, beta));
+  }
+  return is;
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_BETA_DISTRIBUTION_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/discrete_distribution.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/discrete_distribution.h
new file mode 100644
index 0000000000000000000000000000000000000000..171aa11a1eb439abc62a8b47a8e98c2113c21de4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/discrete_distribution.h
@@ -0,0 +1,247 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_DISCRETE_DISTRIBUTION_H_
+#define ABSL_RANDOM_DISCRETE_DISTRIBUTION_H_
+
+#include <cassert>
+#include <cmath>
+#include <istream>
+#include <limits>
+#include <numeric>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+#include "absl/random/bernoulli_distribution.h"
+#include "absl/random/internal/iostream_state_saver.h"
+#include "absl/random/uniform_int_distribution.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// absl::discrete_distribution
+//
+// A discrete distribution produces random integers i, where 0 <= i < n
+// distributed according to the discrete probability function:
+//
+//     P(i|p0,...,pn−1)=pi
+//
+// This class is an implementation of discrete_distribution (see
+// [rand.dist.samp.discrete]).
+//
+// The algorithm used is Walker's Aliasing algorithm, described in Knuth, Vol 2.
+// absl::discrete_distribution takes O(N) time to precompute the probabilities
+// (where N is the number of possible outcomes in the distribution) at
+// construction, and then takes O(1) time for each variate generation.  Many
+// other implementations also take O(N) time to construct an ordered sequence of
+// partial sums, plus O(log N) time per variate to binary search.
+//
+template <typename IntType = int>
+class discrete_distribution {
+ public:
+  using result_type = IntType;
+
+  class param_type {
+   public:
+    using distribution_type = discrete_distribution;
+
+    param_type() { init(); }
+
+    template <typename InputIterator>
+    explicit param_type(InputIterator begin, InputIterator end)
+        : p_(begin, end) {
+      init();
+    }
+
+    explicit param_type(std::initializer_list<double> weights) : p_(weights) {
+      init();
+    }
+
+    template <class UnaryOperation>
+    explicit param_type(size_t nw, double xmin, double xmax,
+                        UnaryOperation fw) {
+      if (nw > 0) {
+        p_.reserve(nw);
+        double delta = (xmax - xmin) / static_cast<double>(nw);
+        assert(delta > 0);
+        double t = delta * 0.5;
+        for (size_t i = 0; i < nw; ++i) {
+          p_.push_back(fw(xmin + i * delta + t));
+        }
+      }
+      init();
+    }
+
+    const std::vector<double>& probabilities() const { return p_; }
+    size_t n() const { return p_.size() - 1; }
+
+    friend bool operator==(const param_type& a, const param_type& b) {
+      return a.probabilities() == b.probabilities();
+    }
+
+    friend bool operator!=(const param_type& a, const param_type& b) {
+      return !(a == b);
+    }
+
+   private:
+    friend class discrete_distribution;
+
+    void init();
+
+    std::vector<double> p_;                     // normalized probabilities
+    std::vector<std::pair<double, size_t>> q_;  // (acceptance, alternate) pairs
+
+    static_assert(std::is_integral<result_type>::value,
+                  "Class-template absl::discrete_distribution<> must be "
+                  "parameterized using an integral type.");
+  };
+
+  discrete_distribution() : param_() {}
+
+  explicit discrete_distribution(const param_type& p) : param_(p) {}
+
+  template <typename InputIterator>
+  explicit discrete_distribution(InputIterator begin, InputIterator end)
+      : param_(begin, end) {}
+
+  explicit discrete_distribution(std::initializer_list<double> weights)
+      : param_(weights) {}
+
+  template <class UnaryOperation>
+  explicit discrete_distribution(size_t nw, double xmin, double xmax,
+                                 UnaryOperation fw)
+      : param_(nw, xmin, xmax, std::move(fw)) {}
+
+  void reset() {}
+
+  // generating functions
+  template <typename URBG>
+  result_type operator()(URBG& g) {  // NOLINT(runtime/references)
+    return (*this)(g, param_);
+  }
+
+  template <typename URBG>
+  result_type operator()(URBG& g,  // NOLINT(runtime/references)
+                         const param_type& p);
+
+  const param_type& param() const { return param_; }
+  void param(const param_type& p) { param_ = p; }
+
+  result_type(min)() const { return 0; }
+  result_type(max)() const {
+    return static_cast<result_type>(param_.n());
+  }  // inclusive
+
+  // NOTE [rand.dist.sample.discrete] returns a std::vector<double> not a
+  // const std::vector<double>&.
+  const std::vector<double>& probabilities() const {
+    return param_.probabilities();
+  }
+
+  friend bool operator==(const discrete_distribution& a,
+                         const discrete_distribution& b) {
+    return a.param_ == b.param_;
+  }
+  friend bool operator!=(const discrete_distribution& a,
+                         const discrete_distribution& b) {
+    return a.param_ != b.param_;
+  }
+
+ private:
+  param_type param_;
+};
+
+// --------------------------------------------------------------------------
+// Implementation details only below
+// --------------------------------------------------------------------------
+
+namespace random_internal {
+
+// Using the vector `*probabilities`, whose values are the weights or
+// probabilities of an element being selected, constructs the proportional
+// probabilities used by the discrete distribution.  `*probabilities` will be
+// scaled, if necessary, so that its entries sum to a value sufficiently close
+// to 1.0.
+std::vector<std::pair<double, size_t>> InitDiscreteDistribution(
+    std::vector<double>* probabilities);
+
+}  // namespace random_internal
+
+template <typename IntType>
+void discrete_distribution<IntType>::param_type::init() {
+  if (p_.empty()) {
+    p_.push_back(1.0);
+    q_.emplace_back(1.0, 0);
+  } else {
+    assert(n() <= (std::numeric_limits<IntType>::max)());
+    q_ = random_internal::InitDiscreteDistribution(&p_);
+  }
+}
+
+template <typename IntType>
+template <typename URBG>
+typename discrete_distribution<IntType>::result_type
+discrete_distribution<IntType>::operator()(
+    URBG& g,  // NOLINT(runtime/references)
+    const param_type& p) {
+  const auto idx = absl::uniform_int_distribution<result_type>(0, p.n())(g);
+  const auto& q = p.q_[idx];
+  const bool selected = absl::bernoulli_distribution(q.first)(g);
+  return selected ? idx : static_cast<result_type>(q.second);
+}
+
+template <typename CharT, typename Traits, typename IntType>
+std::basic_ostream<CharT, Traits>& operator<<(
+    std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
+    const discrete_distribution<IntType>& x) {
+  auto saver = random_internal::make_ostream_state_saver(os);
+  const auto& probabilities = x.param().probabilities();
+  os << probabilities.size();
+
+  os.precision(random_internal::stream_precision_helper<double>::kPrecision);
+  for (const auto& p : probabilities) {
+    os << os.fill() << p;
+  }
+  return os;
+}
+
+template <typename CharT, typename Traits, typename IntType>
+std::basic_istream<CharT, Traits>& operator>>(
+    std::basic_istream<CharT, Traits>& is,  // NOLINT(runtime/references)
+    discrete_distribution<IntType>& x) {    // NOLINT(runtime/references)
+  using param_type = typename discrete_distribution<IntType>::param_type;
+  auto saver = random_internal::make_istream_state_saver(is);
+
+  size_t n;
+  std::vector<double> p;
+
+  is >> n;
+  if (is.fail()) return is;
+  if (n > 0) {
+    p.reserve(n);
+    for (IntType i = 0; i < n && !is.fail(); ++i) {
+      auto tmp = random_internal::read_floating_point<double>(is);
+      if (is.fail()) return is;
+      p.push_back(tmp);
+    }
+  }
+  x.param(param_type(p.begin(), p.end()));
+  return is;
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_DISCRETE_DISTRIBUTION_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/distributions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/distributions.h
new file mode 100644
index 0000000000000000000000000000000000000000..37fc3aa7fd2ffa4cc7755a9f3628941baeb86269
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/distributions.h
@@ -0,0 +1,452 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: distributions.h
+// -----------------------------------------------------------------------------
+//
+// This header defines functions representing distributions, which you use in
+// combination with an Abseil random bit generator to produce random values
+// according to the rules of that distribution.
+//
+// The Abseil random library defines the following distributions within this
+// file:
+//
+//   * `absl::Uniform` for uniform (constant) distributions having constant
+//     probability
+//   * `absl::Bernoulli` for discrete distributions having exactly two outcomes
+//   * `absl::Beta` for continuous distributions parameterized through two
+//     free parameters
+//   * `absl::Exponential` for discrete distributions of events occurring
+//     continuously and independently at a constant average rate
+//   * `absl::Gaussian` (also known as "normal distributions") for continuous
+//     distributions using an associated quadratic function
+//   * `absl::LogUniform` for continuous uniform distributions where the log
+//     to the given base of all values is uniform
+//   * `absl::Poisson` for discrete probability distributions that express the
+//     probability of a given number of events occurring within a fixed interval
+//   * `absl::Zipf` for discrete probability distributions commonly used for
+//     modelling of rare events
+//
+// Prefer use of these distribution function classes over manual construction of
+// your own distribution classes, as it allows library maintainers greater
+// flexibility to change the underlying implementation in the future.
+
+#ifndef ABSL_RANDOM_DISTRIBUTIONS_H_
+#define ABSL_RANDOM_DISTRIBUTIONS_H_
+
+#include <algorithm>
+#include <cmath>
+#include <limits>
+#include <random>
+#include <type_traits>
+
+#include "absl/base/internal/inline_variable.h"
+#include "absl/random/bernoulli_distribution.h"
+#include "absl/random/beta_distribution.h"
+#include "absl/random/exponential_distribution.h"
+#include "absl/random/gaussian_distribution.h"
+#include "absl/random/internal/distribution_caller.h"  // IWYU pragma: export
+#include "absl/random/internal/uniform_helper.h"  // IWYU pragma: export
+#include "absl/random/log_uniform_int_distribution.h"
+#include "absl/random/poisson_distribution.h"
+#include "absl/random/uniform_int_distribution.h"
+#include "absl/random/uniform_real_distribution.h"
+#include "absl/random/zipf_distribution.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalClosedClosedTag, IntervalClosedClosed,
+                               {});
+ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalClosedClosedTag, IntervalClosed, {});
+ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalClosedOpenTag, IntervalClosedOpen, {});
+ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalOpenOpenTag, IntervalOpenOpen, {});
+ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalOpenOpenTag, IntervalOpen, {});
+ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalOpenClosedTag, IntervalOpenClosed, {});
+
+// -----------------------------------------------------------------------------
+// absl::Uniform<T>(tag, bitgen, lo, hi)
+// -----------------------------------------------------------------------------
+//
+// `absl::Uniform()` produces random values of type `T` uniformly distributed in
+// a defined interval {lo, hi}. The interval `tag` defines the type of interval
+// which should be one of the following possible values:
+//
+//   * `absl::IntervalOpenOpen`
+//   * `absl::IntervalOpenClosed`
+//   * `absl::IntervalClosedOpen`
+//   * `absl::IntervalClosedClosed`
+//
+// where "open" refers to an exclusive value (excluded) from the output, while
+// "closed" refers to an inclusive value (included) from the output.
+//
+// In the absence of an explicit return type `T`, `absl::Uniform()` will deduce
+// the return type based on the provided endpoint arguments {A lo, B hi}.
+// Given these endpoints, one of {A, B} will be chosen as the return type, if
+// a type can be implicitly converted into the other in a lossless way. The
+// lack of any such implicit conversion between {A, B} will produce a
+// compile-time error
+//
+// See https://en.wikipedia.org/wiki/Uniform_distribution_(continuous)
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//
+//   // Produce a random float value between 0.0 and 1.0, inclusive
+//   auto x = absl::Uniform(absl::IntervalClosedClosed, bitgen, 0.0f, 1.0f);
+//
+//   // The most common interval of `absl::IntervalClosedOpen` is available by
+//   // default:
+//
+//   auto x = absl::Uniform(bitgen, 0.0f, 1.0f);
+//
+//   // Return-types are typically inferred from the arguments, however callers
+//   // can optionally provide an explicit return-type to the template.
+//
+//   auto x = absl::Uniform<float>(bitgen, 0, 1);
+//
+template <typename R = void, typename TagType, typename URBG>
+typename absl::enable_if_t<!std::is_same<R, void>::value, R>  //
+Uniform(TagType tag,
+        URBG&& urbg,  // NOLINT(runtime/references)
+        R lo, R hi) {
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = random_internal::UniformDistributionWrapper<R>;
+
+  auto a = random_internal::uniform_lower_bound(tag, lo, hi);
+  auto b = random_internal::uniform_upper_bound(tag, lo, hi);
+  if (!random_internal::is_uniform_range_valid(a, b)) return lo;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t>(&urbg, tag, lo, hi);
+}
+
+// absl::Uniform<T>(bitgen, lo, hi)
+//
+// Overload of `Uniform()` using the default closed-open interval of [lo, hi),
+// and returning values of type `T`
+template <typename R = void, typename URBG>
+typename absl::enable_if_t<!std::is_same<R, void>::value, R>  //
+Uniform(URBG&& urbg,  // NOLINT(runtime/references)
+        R lo, R hi) {
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = random_internal::UniformDistributionWrapper<R>;
+  constexpr auto tag = absl::IntervalClosedOpen;
+
+  auto a = random_internal::uniform_lower_bound(tag, lo, hi);
+  auto b = random_internal::uniform_upper_bound(tag, lo, hi);
+  if (!random_internal::is_uniform_range_valid(a, b)) return lo;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t>(&urbg, lo, hi);
+}
+
+// absl::Uniform(tag, bitgen, lo, hi)
+//
+// Overload of `Uniform()` using different (but compatible) lo, hi types. Note
+// that a compile-error will result if the return type cannot be deduced
+// correctly from the passed types.
+template <typename R = void, typename TagType, typename URBG, typename A,
+          typename B>
+typename absl::enable_if_t<std::is_same<R, void>::value,
+                           random_internal::uniform_inferred_return_t<A, B>>
+Uniform(TagType tag,
+        URBG&& urbg,  // NOLINT(runtime/references)
+        A lo, B hi) {
+  using gen_t = absl::decay_t<URBG>;
+  using return_t = typename random_internal::uniform_inferred_return_t<A, B>;
+  using distribution_t = random_internal::UniformDistributionWrapper<return_t>;
+
+  auto a = random_internal::uniform_lower_bound<return_t>(tag, lo, hi);
+  auto b = random_internal::uniform_upper_bound<return_t>(tag, lo, hi);
+  if (!random_internal::is_uniform_range_valid(a, b)) return lo;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t>(&urbg, tag, static_cast<return_t>(lo),
+                                static_cast<return_t>(hi));
+}
+
+// absl::Uniform(bitgen, lo, hi)
+//
+// Overload of `Uniform()` using different (but compatible) lo, hi types and the
+// default closed-open interval of [lo, hi). Note that a compile-error will
+// result if the return type cannot be deduced correctly from the passed types.
+template <typename R = void, typename URBG, typename A, typename B>
+typename absl::enable_if_t<std::is_same<R, void>::value,
+                           random_internal::uniform_inferred_return_t<A, B>>
+Uniform(URBG&& urbg,  // NOLINT(runtime/references)
+        A lo, B hi) {
+  using gen_t = absl::decay_t<URBG>;
+  using return_t = typename random_internal::uniform_inferred_return_t<A, B>;
+  using distribution_t = random_internal::UniformDistributionWrapper<return_t>;
+
+  constexpr auto tag = absl::IntervalClosedOpen;
+  auto a = random_internal::uniform_lower_bound<return_t>(tag, lo, hi);
+  auto b = random_internal::uniform_upper_bound<return_t>(tag, lo, hi);
+  if (!random_internal::is_uniform_range_valid(a, b)) return lo;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t>(&urbg, static_cast<return_t>(lo),
+                                static_cast<return_t>(hi));
+}
+
+// absl::Uniform<unsigned T>(bitgen)
+//
+// Overload of Uniform() using the minimum and maximum values of a given type
+// `T` (which must be unsigned), returning a value of type `unsigned T`
+template <typename R, typename URBG>
+typename absl::enable_if_t<!std::is_signed<R>::value, R>  //
+Uniform(URBG&& urbg) {  // NOLINT(runtime/references)
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = random_internal::UniformDistributionWrapper<R>;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t>(&urbg);
+}
+
+// -----------------------------------------------------------------------------
+// absl::Bernoulli(bitgen, p)
+// -----------------------------------------------------------------------------
+//
+// `absl::Bernoulli` produces a random boolean value, with probability `p`
+// (where 0.0 <= p <= 1.0) equaling `true`.
+//
+// Prefer `absl::Bernoulli` to produce boolean values over other alternatives
+// such as comparing an `absl::Uniform()` value to a specific output.
+//
+// See https://en.wikipedia.org/wiki/Bernoulli_distribution
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//   ...
+//   if (absl::Bernoulli(bitgen, 1.0/3721.0)) {
+//     std::cout << "Asteroid field navigation successful.";
+//   }
+//
+template <typename URBG>
+bool Bernoulli(URBG&& urbg,  // NOLINT(runtime/references)
+               double p) {
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = absl::bernoulli_distribution;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t>(&urbg, p);
+}
+
+// -----------------------------------------------------------------------------
+// absl::Beta<T>(bitgen, alpha, beta)
+// -----------------------------------------------------------------------------
+//
+// `absl::Beta` produces a floating point number distributed in the closed
+// interval [0,1] and parameterized by two values `alpha` and `beta` as per a
+// Beta distribution. `T` must be a floating point type, but may be inferred
+// from the types of `alpha` and `beta`.
+//
+// See https://en.wikipedia.org/wiki/Beta_distribution.
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//   ...
+//   double sample = absl::Beta(bitgen, 3.0, 2.0);
+//
+template <typename RealType, typename URBG>
+RealType Beta(URBG&& urbg,  // NOLINT(runtime/references)
+              RealType alpha, RealType beta) {
+  static_assert(
+      std::is_floating_point<RealType>::value,
+      "Template-argument 'RealType' must be a floating-point type, in "
+      "absl::Beta<RealType, URBG>(...)");
+
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = typename absl::beta_distribution<RealType>;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t>(&urbg, alpha, beta);
+}
+
+// -----------------------------------------------------------------------------
+// absl::Exponential<T>(bitgen, lambda = 1)
+// -----------------------------------------------------------------------------
+//
+// `absl::Exponential` produces a floating point number representing the
+// distance (time) between two consecutive events in a point process of events
+// occurring continuously and independently at a constant average rate. `T` must
+// be a floating point type, but may be inferred from the type of `lambda`.
+//
+// See https://en.wikipedia.org/wiki/Exponential_distribution.
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//   ...
+//   double call_length = absl::Exponential(bitgen, 7.0);
+//
+template <typename RealType, typename URBG>
+RealType Exponential(URBG&& urbg,  // NOLINT(runtime/references)
+                     RealType lambda = 1) {
+  static_assert(
+      std::is_floating_point<RealType>::value,
+      "Template-argument 'RealType' must be a floating-point type, in "
+      "absl::Exponential<RealType, URBG>(...)");
+
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = typename absl::exponential_distribution<RealType>;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t>(&urbg, lambda);
+}
+
+// -----------------------------------------------------------------------------
+// absl::Gaussian<T>(bitgen, mean = 0, stddev = 1)
+// -----------------------------------------------------------------------------
+//
+// `absl::Gaussian` produces a floating point number selected from the Gaussian
+// (ie. "Normal") distribution. `T` must be a floating point type, but may be
+// inferred from the types of `mean` and `stddev`.
+//
+// See https://en.wikipedia.org/wiki/Normal_distribution
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//   ...
+//   double giraffe_height = absl::Gaussian(bitgen, 16.3, 3.3);
+//
+template <typename RealType, typename URBG>
+RealType Gaussian(URBG&& urbg,  // NOLINT(runtime/references)
+                  RealType mean = 0, RealType stddev = 1) {
+  static_assert(
+      std::is_floating_point<RealType>::value,
+      "Template-argument 'RealType' must be a floating-point type, in "
+      "absl::Gaussian<RealType, URBG>(...)");
+
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = typename absl::gaussian_distribution<RealType>;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t>(&urbg, mean, stddev);
+}
+
+// -----------------------------------------------------------------------------
+// absl::LogUniform<T>(bitgen, lo, hi, base = 2)
+// -----------------------------------------------------------------------------
+//
+// `absl::LogUniform` produces random values distributed where the log to a
+// given base of all values is uniform in a closed interval [lo, hi]. `T` must
+// be an integral type, but may be inferred from the types of `lo` and `hi`.
+//
+// I.e., `LogUniform(0, n, b)` is uniformly distributed across buckets
+// [0], [1, b-1], [b, b^2-1] .. [b^(k-1), (b^k)-1] .. [b^floor(log(n, b)), n]
+// and is uniformly distributed within each bucket.
+//
+// The resulting probability density is inversely related to bucket size, though
+// values in the final bucket may be more likely than previous values. (In the
+// extreme case where n = b^i the final value will be tied with zero as the most
+// probable result.
+//
+// If `lo` is nonzero then this distribution is shifted to the desired interval,
+// so LogUniform(lo, hi, b) is equivalent to LogUniform(0, hi-lo, b)+lo.
+//
+// See http://ecolego.facilia.se/ecolego/show/Log-Uniform%20Distribution
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//   ...
+//   int v = absl::LogUniform(bitgen, 0, 1000);
+//
+template <typename IntType, typename URBG>
+IntType LogUniform(URBG&& urbg,  // NOLINT(runtime/references)
+                   IntType lo, IntType hi, IntType base = 2) {
+  static_assert(random_internal::IsIntegral<IntType>::value,
+                "Template-argument 'IntType' must be an integral type, in "
+                "absl::LogUniform<IntType, URBG>(...)");
+
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = typename absl::log_uniform_int_distribution<IntType>;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t>(&urbg, lo, hi, base);
+}
+
+// -----------------------------------------------------------------------------
+// absl::Poisson<T>(bitgen, mean = 1)
+// -----------------------------------------------------------------------------
+//
+// `absl::Poisson` produces discrete probabilities for a given number of events
+// occurring within a fixed interval within the closed interval [0, max]. `T`
+// must be an integral type.
+//
+// See https://en.wikipedia.org/wiki/Poisson_distribution
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//   ...
+//   int requests_per_minute = absl::Poisson<int>(bitgen, 3.2);
+//
+template <typename IntType, typename URBG>
+IntType Poisson(URBG&& urbg,  // NOLINT(runtime/references)
+                double mean = 1.0) {
+  static_assert(random_internal::IsIntegral<IntType>::value,
+                "Template-argument 'IntType' must be an integral type, in "
+                "absl::Poisson<IntType, URBG>(...)");
+
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = typename absl::poisson_distribution<IntType>;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t>(&urbg, mean);
+}
+
+// -----------------------------------------------------------------------------
+// absl::Zipf<T>(bitgen, hi = max, q = 2, v = 1)
+// -----------------------------------------------------------------------------
+//
+// `absl::Zipf` produces discrete probabilities commonly used for modelling of
+// rare events over the closed interval [0, hi]. The parameters `v` and `q`
+// determine the skew of the distribution. `T`  must be an integral type, but
+// may be inferred from the type of `hi`.
+//
+// See http://mathworld.wolfram.com/ZipfDistribution.html
+//
+// Example:
+//
+//   absl::BitGen bitgen;
+//   ...
+//   int term_rank = absl::Zipf<int>(bitgen);
+//
+template <typename IntType, typename URBG>
+IntType Zipf(URBG&& urbg,  // NOLINT(runtime/references)
+             IntType hi = (std::numeric_limits<IntType>::max)(), double q = 2.0,
+             double v = 1.0) {
+  static_assert(random_internal::IsIntegral<IntType>::value,
+                "Template-argument 'IntType' must be an integral type, in "
+                "absl::Zipf<IntType, URBG>(...)");
+
+  using gen_t = absl::decay_t<URBG>;
+  using distribution_t = typename absl::zipf_distribution<IntType>;
+
+  return random_internal::DistributionCaller<gen_t>::template Call<
+      distribution_t>(&urbg, hi, q, v);
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_DISTRIBUTIONS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/exponential_distribution.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/exponential_distribution.h
new file mode 100644
index 0000000000000000000000000000000000000000..b5caf8a1e1f3674bfcea0b7d29533d7d5e8f49f7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/exponential_distribution.h
@@ -0,0 +1,165 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_EXPONENTIAL_DISTRIBUTION_H_
+#define ABSL_RANDOM_EXPONENTIAL_DISTRIBUTION_H_
+
+#include <cassert>
+#include <cmath>
+#include <istream>
+#include <limits>
+#include <type_traits>
+
+#include "absl/meta/type_traits.h"
+#include "absl/random/internal/fast_uniform_bits.h"
+#include "absl/random/internal/generate_real.h"
+#include "absl/random/internal/iostream_state_saver.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// absl::exponential_distribution:
+// Generates a number conforming to an exponential distribution and is
+// equivalent to the standard [rand.dist.pois.exp] distribution.
+template <typename RealType = double>
+class exponential_distribution {
+ public:
+  using result_type = RealType;
+
+  class param_type {
+   public:
+    using distribution_type = exponential_distribution;
+
+    explicit param_type(result_type lambda = 1) : lambda_(lambda) {
+      assert(lambda > 0);
+      neg_inv_lambda_ = -result_type(1) / lambda_;
+    }
+
+    result_type lambda() const { return lambda_; }
+
+    friend bool operator==(const param_type& a, const param_type& b) {
+      return a.lambda_ == b.lambda_;
+    }
+
+    friend bool operator!=(const param_type& a, const param_type& b) {
+      return !(a == b);
+    }
+
+   private:
+    friend class exponential_distribution;
+
+    result_type lambda_;
+    result_type neg_inv_lambda_;
+
+    static_assert(
+        std::is_floating_point<RealType>::value,
+        "Class-template absl::exponential_distribution<> must be parameterized "
+        "using a floating-point type.");
+  };
+
+  exponential_distribution() : exponential_distribution(1) {}
+
+  explicit exponential_distribution(result_type lambda) : param_(lambda) {}
+
+  explicit exponential_distribution(const param_type& p) : param_(p) {}
+
+  void reset() {}
+
+  // Generating functions
+  template <typename URBG>
+  result_type operator()(URBG& g) {  // NOLINT(runtime/references)
+    return (*this)(g, param_);
+  }
+
+  template <typename URBG>
+  result_type operator()(URBG& g,  // NOLINT(runtime/references)
+                         const param_type& p);
+
+  param_type param() const { return param_; }
+  void param(const param_type& p) { param_ = p; }
+
+  result_type(min)() const { return 0; }
+  result_type(max)() const {
+    return std::numeric_limits<result_type>::infinity();
+  }
+
+  result_type lambda() const { return param_.lambda(); }
+
+  friend bool operator==(const exponential_distribution& a,
+                         const exponential_distribution& b) {
+    return a.param_ == b.param_;
+  }
+  friend bool operator!=(const exponential_distribution& a,
+                         const exponential_distribution& b) {
+    return a.param_ != b.param_;
+  }
+
+ private:
+  param_type param_;
+  random_internal::FastUniformBits<uint64_t> fast_u64_;
+};
+
+// --------------------------------------------------------------------------
+// Implementation details follow
+// --------------------------------------------------------------------------
+
+template <typename RealType>
+template <typename URBG>
+typename exponential_distribution<RealType>::result_type
+exponential_distribution<RealType>::operator()(
+    URBG& g,  // NOLINT(runtime/references)
+    const param_type& p) {
+  using random_internal::GenerateNegativeTag;
+  using random_internal::GenerateRealFromBits;
+  using real_type =
+      absl::conditional_t<std::is_same<RealType, float>::value, float, double>;
+
+  const result_type u = GenerateRealFromBits<real_type, GenerateNegativeTag,
+                                             false>(fast_u64_(g));  // U(-1, 0)
+
+  // log1p(-x) is mathematically equivalent to log(1 - x) but has more
+  // accuracy for x near zero.
+  return p.neg_inv_lambda_ * std::log1p(u);
+}
+
+template <typename CharT, typename Traits, typename RealType>
+std::basic_ostream<CharT, Traits>& operator<<(
+    std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
+    const exponential_distribution<RealType>& x) {
+  auto saver = random_internal::make_ostream_state_saver(os);
+  os.precision(random_internal::stream_precision_helper<RealType>::kPrecision);
+  os << x.lambda();
+  return os;
+}
+
+template <typename CharT, typename Traits, typename RealType>
+std::basic_istream<CharT, Traits>& operator>>(
+    std::basic_istream<CharT, Traits>& is,    // NOLINT(runtime/references)
+    exponential_distribution<RealType>& x) {  // NOLINT(runtime/references)
+  using result_type = typename exponential_distribution<RealType>::result_type;
+  using param_type = typename exponential_distribution<RealType>::param_type;
+  result_type lambda;
+
+  auto saver = random_internal::make_istream_state_saver(is);
+  lambda = random_internal::read_floating_point<result_type>(is);
+  if (!is.fail()) {
+    x.param(param_type(lambda));
+  }
+  return is;
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_EXPONENTIAL_DISTRIBUTION_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/gaussian_distribution.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/gaussian_distribution.h
new file mode 100644
index 0000000000000000000000000000000000000000..4b07a5c0af9d5e09a20658e7a64207ef93822d88
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/gaussian_distribution.h
@@ -0,0 +1,275 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_GAUSSIAN_DISTRIBUTION_H_
+#define ABSL_RANDOM_GAUSSIAN_DISTRIBUTION_H_
+
+// absl::gaussian_distribution implements the Ziggurat algorithm
+// for generating random gaussian numbers.
+//
+// Implementation based on "The Ziggurat Method for Generating Random Variables"
+// by George Marsaglia and Wai Wan Tsang: http://www.jstatsoft.org/v05/i08/
+//
+
+#include <cmath>
+#include <cstdint>
+#include <istream>
+#include <limits>
+#include <type_traits>
+
+#include "absl/base/config.h"
+#include "absl/random/internal/fast_uniform_bits.h"
+#include "absl/random/internal/generate_real.h"
+#include "absl/random/internal/iostream_state_saver.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// absl::gaussian_distribution_base implements the underlying ziggurat algorithm
+// using the ziggurat tables generated by the gaussian_distribution_gentables
+// binary.
+//
+// The specific algorithm has some of the improvements suggested by the
+// 2005 paper, "An Improved Ziggurat Method to Generate Normal Random Samples",
+// Jurgen A Doornik.  (https://www.doornik.com/research/ziggurat.pdf)
+class ABSL_DLL gaussian_distribution_base {
+ public:
+  template <typename URBG>
+  inline double zignor(URBG& g);  // NOLINT(runtime/references)
+
+ private:
+  friend class TableGenerator;
+
+  template <typename URBG>
+  inline double zignor_fallback(URBG& g,  // NOLINT(runtime/references)
+                                bool neg);
+
+  // Constants used for the gaussian distribution.
+  static constexpr double kR = 3.442619855899;  // Start of the tail.
+  static constexpr double kRInv = 0.29047645161474317;  // ~= (1.0 / kR) .
+  static constexpr double kV = 9.91256303526217e-3;
+  static constexpr uint64_t kMask = 0x07f;
+
+  // The ziggurat tables store the pdf(f) and inverse-pdf(x) for equal-area
+  // points on one-half of the normal distribution, where the pdf function,
+  // pdf = e ^ (-1/2 *x^2), assumes that the mean = 0 & stddev = 1.
+  //
+  // These tables are just over 2kb in size; larger tables might improve the
+  // distributions, but also lead to more cache pollution.
+  //
+  // x = {3.71308, 3.44261, 3.22308, ..., 0}
+  // f = {0.00101, 0.00266, 0.00554, ..., 1}
+  struct Tables {
+    double x[kMask + 2];
+    double f[kMask + 2];
+  };
+  static const Tables zg_;
+  random_internal::FastUniformBits<uint64_t> fast_u64_;
+};
+
+}  // namespace random_internal
+
+// absl::gaussian_distribution:
+// Generates a number conforming to a Gaussian distribution.
+template <typename RealType = double>
+class gaussian_distribution : random_internal::gaussian_distribution_base {
+ public:
+  using result_type = RealType;
+
+  class param_type {
+   public:
+    using distribution_type = gaussian_distribution;
+
+    explicit param_type(result_type mean = 0, result_type stddev = 1)
+        : mean_(mean), stddev_(stddev) {}
+
+    // Returns the mean distribution parameter.  The mean specifies the location
+    // of the peak.  The default value is 0.0.
+    result_type mean() const { return mean_; }
+
+    // Returns the deviation distribution parameter.  The default value is 1.0.
+    result_type stddev() const { return stddev_; }
+
+    friend bool operator==(const param_type& a, const param_type& b) {
+      return a.mean_ == b.mean_ && a.stddev_ == b.stddev_;
+    }
+
+    friend bool operator!=(const param_type& a, const param_type& b) {
+      return !(a == b);
+    }
+
+   private:
+    result_type mean_;
+    result_type stddev_;
+
+    static_assert(
+        std::is_floating_point<RealType>::value,
+        "Class-template absl::gaussian_distribution<> must be parameterized "
+        "using a floating-point type.");
+  };
+
+  gaussian_distribution() : gaussian_distribution(0) {}
+
+  explicit gaussian_distribution(result_type mean, result_type stddev = 1)
+      : param_(mean, stddev) {}
+
+  explicit gaussian_distribution(const param_type& p) : param_(p) {}
+
+  void reset() {}
+
+  // Generating functions
+  template <typename URBG>
+  result_type operator()(URBG& g) {  // NOLINT(runtime/references)
+    return (*this)(g, param_);
+  }
+
+  template <typename URBG>
+  result_type operator()(URBG& g,  // NOLINT(runtime/references)
+                         const param_type& p);
+
+  param_type param() const { return param_; }
+  void param(const param_type& p) { param_ = p; }
+
+  result_type(min)() const {
+    return -std::numeric_limits<result_type>::infinity();
+  }
+  result_type(max)() const {
+    return std::numeric_limits<result_type>::infinity();
+  }
+
+  result_type mean() const { return param_.mean(); }
+  result_type stddev() const { return param_.stddev(); }
+
+  friend bool operator==(const gaussian_distribution& a,
+                         const gaussian_distribution& b) {
+    return a.param_ == b.param_;
+  }
+  friend bool operator!=(const gaussian_distribution& a,
+                         const gaussian_distribution& b) {
+    return a.param_ != b.param_;
+  }
+
+ private:
+  param_type param_;
+};
+
+// --------------------------------------------------------------------------
+// Implementation details only below
+// --------------------------------------------------------------------------
+
+template <typename RealType>
+template <typename URBG>
+typename gaussian_distribution<RealType>::result_type
+gaussian_distribution<RealType>::operator()(
+    URBG& g,  // NOLINT(runtime/references)
+    const param_type& p) {
+  return p.mean() + p.stddev() * static_cast<result_type>(zignor(g));
+}
+
+template <typename CharT, typename Traits, typename RealType>
+std::basic_ostream<CharT, Traits>& operator<<(
+    std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
+    const gaussian_distribution<RealType>& x) {
+  auto saver = random_internal::make_ostream_state_saver(os);
+  os.precision(random_internal::stream_precision_helper<RealType>::kPrecision);
+  os << x.mean() << os.fill() << x.stddev();
+  return os;
+}
+
+template <typename CharT, typename Traits, typename RealType>
+std::basic_istream<CharT, Traits>& operator>>(
+    std::basic_istream<CharT, Traits>& is,  // NOLINT(runtime/references)
+    gaussian_distribution<RealType>& x) {   // NOLINT(runtime/references)
+  using result_type = typename gaussian_distribution<RealType>::result_type;
+  using param_type = typename gaussian_distribution<RealType>::param_type;
+
+  auto saver = random_internal::make_istream_state_saver(is);
+  auto mean = random_internal::read_floating_point<result_type>(is);
+  if (is.fail()) return is;
+  auto stddev = random_internal::read_floating_point<result_type>(is);
+  if (!is.fail()) {
+    x.param(param_type(mean, stddev));
+  }
+  return is;
+}
+
+namespace random_internal {
+
+template <typename URBG>
+inline double gaussian_distribution_base::zignor_fallback(URBG& g, bool neg) {
+  using random_internal::GeneratePositiveTag;
+  using random_internal::GenerateRealFromBits;
+
+  // This fallback path happens approximately 0.05% of the time.
+  double x, y;
+  do {
+    // kRInv = 1/r, U(0, 1)
+    x = kRInv *
+        std::log(GenerateRealFromBits<double, GeneratePositiveTag, false>(
+            fast_u64_(g)));
+    y = -std::log(
+        GenerateRealFromBits<double, GeneratePositiveTag, false>(fast_u64_(g)));
+  } while ((y + y) < (x * x));
+  return neg ? (x - kR) : (kR - x);
+}
+
+template <typename URBG>
+inline double gaussian_distribution_base::zignor(
+    URBG& g) {  // NOLINT(runtime/references)
+  using random_internal::GeneratePositiveTag;
+  using random_internal::GenerateRealFromBits;
+  using random_internal::GenerateSignedTag;
+
+  while (true) {
+    // We use a single uint64_t to generate both a double and a strip.
+    // These bits are unused when the generated double is > 1/2^5.
+    // This may introduce some bias from the duplicated low bits of small
+    // values (those smaller than 1/2^5, which all end up on the left tail).
+    uint64_t bits = fast_u64_(g);
+    int i = static_cast<int>(bits & kMask);  // pick a random strip
+    double j = GenerateRealFromBits<double, GenerateSignedTag, false>(
+        bits);  // U(-1, 1)
+    const double x = j * zg_.x[i];
+
+    // Retangular box. Handles >97% of all cases.
+    // For any given box, this handles between 75% and 99% of values.
+    // Equivalent to U(01) < (x[i+1] / x[i]), and when i == 0, ~93.5%
+    if (std::abs(x) < zg_.x[i + 1]) {
+      return x;
+    }
+
+    // i == 0: Base box. Sample using a ratio of uniforms.
+    if (i == 0) {
+      // This path happens about 0.05% of the time.
+      return zignor_fallback(g, j < 0);
+    }
+
+    // i > 0: Wedge samples using precomputed values.
+    double v = GenerateRealFromBits<double, GeneratePositiveTag, false>(
+        fast_u64_(g));  // U(0, 1)
+    if ((zg_.f[i + 1] + v * (zg_.f[i] - zg_.f[i + 1])) <
+        std::exp(-0.5 * x * x)) {
+      return x;
+    }
+
+    // The wedge was missed; reject the value and try again.
+  }
+}
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_GAUSSIAN_DISTRIBUTION_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/distribution_caller.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/distribution_caller.h
new file mode 100644
index 0000000000000000000000000000000000000000..0f162a4e29f3129f934cfb0aa299447ed956c65e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/distribution_caller.h
@@ -0,0 +1,95 @@
+//
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+#ifndef ABSL_RANDOM_INTERNAL_DISTRIBUTION_CALLER_H_
+#define ABSL_RANDOM_INTERNAL_DISTRIBUTION_CALLER_H_
+
+#include <utility>
+#include <type_traits>
+
+#include "absl/base/config.h"
+#include "absl/base/internal/fast_type_id.h"
+#include "absl/utility/utility.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// DistributionCaller provides an opportunity to overload the general
+// mechanism for calling a distribution, allowing for mock-RNG classes
+// to intercept such calls.
+template <typename URBG>
+struct DistributionCaller {
+  static_assert(!std::is_pointer<URBG>::value,
+                "You must pass a reference, not a pointer.");
+  // SFINAE to detect whether the URBG type includes a member matching
+  // bool InvokeMock(base_internal::FastTypeIdType, void*, void*).
+  //
+  // These live inside BitGenRef so that they have friend access
+  // to MockingBitGen. (see similar methods in DistributionCaller).
+  template <template <class...> class Trait, class AlwaysVoid, class... Args>
+  struct detector : std::false_type {};
+  template <template <class...> class Trait, class... Args>
+  struct detector<Trait, absl::void_t<Trait<Args...>>, Args...>
+      : std::true_type {};
+
+  template <class T>
+  using invoke_mock_t = decltype(std::declval<T*>()->InvokeMock(
+      std::declval<::absl::base_internal::FastTypeIdType>(),
+      std::declval<void*>(), std::declval<void*>()));
+
+  using HasInvokeMock = typename detector<invoke_mock_t, void, URBG>::type;
+
+  // Default implementation of distribution caller.
+  template <typename DistrT, typename... Args>
+  static typename DistrT::result_type Impl(std::false_type, URBG* urbg,
+                                           Args&&... args) {
+    DistrT dist(std::forward<Args>(args)...);
+    return dist(*urbg);
+  }
+
+  // Mock implementation of distribution caller.
+  // The underlying KeyT must match the KeyT constructed by MockOverloadSet.
+  template <typename DistrT, typename... Args>
+  static typename DistrT::result_type Impl(std::true_type, URBG* urbg,
+                                           Args&&... args) {
+    using ResultT = typename DistrT::result_type;
+    using ArgTupleT = std::tuple<absl::decay_t<Args>...>;
+    using KeyT = ResultT(DistrT, ArgTupleT);
+
+    ArgTupleT arg_tuple(std::forward<Args>(args)...);
+    ResultT result;
+    if (!urbg->InvokeMock(::absl::base_internal::FastTypeId<KeyT>(), &arg_tuple,
+                          &result)) {
+      auto dist = absl::make_from_tuple<DistrT>(arg_tuple);
+      result = dist(*urbg);
+    }
+    return result;
+  }
+
+  // Default implementation of distribution caller.
+  template <typename DistrT, typename... Args>
+  static typename DistrT::result_type Call(URBG* urbg, Args&&... args) {
+    return Impl<DistrT, Args...>(HasInvokeMock{}, urbg,
+                                 std::forward<Args>(args)...);
+  }
+};
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_DISTRIBUTION_CALLER_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/fast_uniform_bits.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/fast_uniform_bits.h
new file mode 100644
index 0000000000000000000000000000000000000000..8d8ed04515e597e3587dc27d902d9d523e60c704
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/fast_uniform_bits.h
@@ -0,0 +1,270 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_FAST_UNIFORM_BITS_H_
+#define ABSL_RANDOM_INTERNAL_FAST_UNIFORM_BITS_H_
+
+#include <cstddef>
+#include <cstdint>
+#include <limits>
+#include <type_traits>
+
+#include "absl/base/config.h"
+#include "absl/meta/type_traits.h"
+#include "absl/random/internal/traits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+// Returns true if the input value is zero or a power of two. Useful for
+// determining if the range of output values in a URBG
+template <typename UIntType>
+constexpr bool IsPowerOfTwoOrZero(UIntType n) {
+  return (n == 0) || ((n & (n - 1)) == 0);
+}
+
+// Computes the length of the range of values producible by the URBG, or returns
+// zero if that would encompass the entire range of representable values in
+// URBG::result_type.
+template <typename URBG>
+constexpr typename URBG::result_type RangeSize() {
+  using result_type = typename URBG::result_type;
+  static_assert((URBG::max)() != (URBG::min)(), "URBG range cannot be 0.");
+  return ((URBG::max)() == (std::numeric_limits<result_type>::max)() &&
+          (URBG::min)() == std::numeric_limits<result_type>::lowest())
+             ? result_type{0}
+             : ((URBG::max)() - (URBG::min)() + result_type{1});
+}
+
+// Computes the floor of the log. (i.e., std::floor(std::log2(N));
+template <typename UIntType>
+constexpr UIntType IntegerLog2(UIntType n) {
+  return (n <= 1) ? 0 : 1 + IntegerLog2(n >> 1);
+}
+
+// Returns the number of bits of randomness returned through
+// `PowerOfTwoVariate(urbg)`.
+template <typename URBG>
+constexpr size_t NumBits() {
+  return RangeSize<URBG>() == 0
+             ? std::numeric_limits<typename URBG::result_type>::digits
+             : IntegerLog2(RangeSize<URBG>());
+}
+
+// Given a shift value `n`, constructs a mask with exactly the low `n` bits set.
+// If `n == 0`, all bits are set.
+template <typename UIntType>
+constexpr UIntType MaskFromShift(size_t n) {
+  return ((n % std::numeric_limits<UIntType>::digits) == 0)
+             ? ~UIntType{0}
+             : (UIntType{1} << n) - UIntType{1};
+}
+
+// Tags used to dispatch FastUniformBits::generate to the simple or more complex
+// entropy extraction algorithm.
+struct SimplifiedLoopTag {};
+struct RejectionLoopTag {};
+
+// FastUniformBits implements a fast path to acquire uniform independent bits
+// from a type which conforms to the [rand.req.urbg] concept.
+// Parameterized by:
+//  `UIntType`: the result (output) type
+//
+// The std::independent_bits_engine [rand.adapt.ibits] adaptor can be
+// instantiated from an existing generator through a copy or a move. It does
+// not, however, facilitate the production of pseudorandom bits from an un-owned
+// generator that will outlive the std::independent_bits_engine instance.
+template <typename UIntType = uint64_t>
+class FastUniformBits {
+ public:
+  using result_type = UIntType;
+
+  static constexpr result_type(min)() { return 0; }
+  static constexpr result_type(max)() {
+    return (std::numeric_limits<result_type>::max)();
+  }
+
+  template <typename URBG>
+  result_type operator()(URBG& g);  // NOLINT(runtime/references)
+
+ private:
+  static_assert(IsUnsigned<UIntType>::value,
+                "Class-template FastUniformBits<> must be parameterized using "
+                "an unsigned type.");
+
+  // Generate() generates a random value, dispatched on whether
+  // the underlying URBG must use rejection sampling to generate a value,
+  // or whether a simplified loop will suffice.
+  template <typename URBG>
+  result_type Generate(URBG& g,  // NOLINT(runtime/references)
+                       SimplifiedLoopTag);
+
+  template <typename URBG>
+  result_type Generate(URBG& g,  // NOLINT(runtime/references)
+                       RejectionLoopTag);
+};
+
+template <typename UIntType>
+template <typename URBG>
+typename FastUniformBits<UIntType>::result_type
+FastUniformBits<UIntType>::operator()(URBG& g) {  // NOLINT(runtime/references)
+  // kRangeMask is the mask used when sampling variates from the URBG when the
+  // width of the URBG range is not a power of 2.
+  // Y = (2 ^ kRange) - 1
+  static_assert((URBG::max)() > (URBG::min)(),
+                "URBG::max and URBG::min may not be equal.");
+
+  using tag = absl::conditional_t<IsPowerOfTwoOrZero(RangeSize<URBG>()),
+                                  SimplifiedLoopTag, RejectionLoopTag>;
+  return Generate(g, tag{});
+}
+
+template <typename UIntType>
+template <typename URBG>
+typename FastUniformBits<UIntType>::result_type
+FastUniformBits<UIntType>::Generate(URBG& g,  // NOLINT(runtime/references)
+                                    SimplifiedLoopTag) {
+  // The simplified version of FastUniformBits works only on URBGs that have
+  // a range that is a power of 2. In this case we simply loop and shift without
+  // attempting to balance the bits across calls.
+  static_assert(IsPowerOfTwoOrZero(RangeSize<URBG>()),
+                "incorrect Generate tag for URBG instance");
+
+  static constexpr size_t kResultBits =
+      std::numeric_limits<result_type>::digits;
+  static constexpr size_t kUrbgBits = NumBits<URBG>();
+  static constexpr size_t kIters =
+      (kResultBits / kUrbgBits) + (kResultBits % kUrbgBits != 0);
+  static constexpr size_t kShift = (kIters == 1) ? 0 : kUrbgBits;
+  static constexpr auto kMin = (URBG::min)();
+
+  result_type r = static_cast<result_type>(g() - kMin);
+  for (size_t n = 1; n < kIters; ++n) {
+    r = static_cast<result_type>(r << kShift) +
+        static_cast<result_type>(g() - kMin);
+  }
+  return r;
+}
+
+template <typename UIntType>
+template <typename URBG>
+typename FastUniformBits<UIntType>::result_type
+FastUniformBits<UIntType>::Generate(URBG& g,  // NOLINT(runtime/references)
+                                    RejectionLoopTag) {
+  static_assert(!IsPowerOfTwoOrZero(RangeSize<URBG>()),
+                "incorrect Generate tag for URBG instance");
+  using urbg_result_type = typename URBG::result_type;
+
+  // See [rand.adapt.ibits] for more details on the constants calculated below.
+  //
+  // It is preferable to use roughly the same number of bits from each generator
+  // call, however this is only possible when the number of bits provided by the
+  // URBG is a divisor of the number of bits in `result_type`. In all other
+  // cases, the number of bits used cannot always be the same, but it can be
+  // guaranteed to be off by at most 1. Thus we run two loops, one with a
+  // smaller bit-width size (`kSmallWidth`) and one with a larger width size
+  // (satisfying `kLargeWidth == kSmallWidth + 1`). The loops are run
+  // `kSmallIters` and `kLargeIters` times respectively such
+  // that
+  //
+  //    `kResultBits == kSmallIters * kSmallBits
+  //                    + kLargeIters * kLargeBits`
+  //
+  // where `kResultBits` is the total number of bits in `result_type`.
+  //
+  static constexpr size_t kResultBits =
+      std::numeric_limits<result_type>::digits;                      // w
+  static constexpr urbg_result_type kUrbgRange = RangeSize<URBG>();  // R
+  static constexpr size_t kUrbgBits = NumBits<URBG>();               // m
+
+  // compute the initial estimate of the bits used.
+  // [rand.adapt.ibits] 2 (c)
+  static constexpr size_t kA =  // ceil(w/m)
+      (kResultBits / kUrbgBits) + ((kResultBits % kUrbgBits) != 0);  // n'
+
+  static constexpr size_t kABits = kResultBits / kA;  // w0'
+  static constexpr urbg_result_type kARejection =
+      ((kUrbgRange >> kABits) << kABits);  // y0'
+
+  // refine the selection to reduce the rejection frequency.
+  static constexpr size_t kTotalIters =
+      ((kUrbgRange - kARejection) <= (kARejection / kA)) ? kA : (kA + 1);  // n
+
+  // [rand.adapt.ibits] 2 (b)
+  static constexpr size_t kSmallIters =
+      kTotalIters - (kResultBits % kTotalIters);                   // n0
+  static constexpr size_t kSmallBits = kResultBits / kTotalIters;  // w0
+  static constexpr urbg_result_type kSmallRejection =
+      ((kUrbgRange >> kSmallBits) << kSmallBits);  // y0
+
+  static constexpr size_t kLargeBits = kSmallBits + 1;  // w0+1
+  static constexpr urbg_result_type kLargeRejection =
+      ((kUrbgRange >> kLargeBits) << kLargeBits);  // y1
+
+  //
+  // Because `kLargeBits == kSmallBits + 1`, it follows that
+  //
+  //     `kResultBits == kSmallIters * kSmallBits + kLargeIters`
+  //
+  // and therefore
+  //
+  //     `kLargeIters == kTotalWidth % kSmallWidth`
+  //
+  // Intuitively, each iteration with the large width accounts for one unit
+  // of the remainder when `kTotalWidth` is divided by `kSmallWidth`. As
+  // mentioned above, if the URBG width is a divisor of `kTotalWidth`, then
+  // there would be no need for any large iterations (i.e., one loop would
+  // suffice), and indeed, in this case, `kLargeIters` would be zero.
+  static_assert(kResultBits == kSmallIters * kSmallBits +
+                                   (kTotalIters - kSmallIters) * kLargeBits,
+                "Error in looping constant calculations.");
+
+  // The small shift is essentially small bits, but due to the potential
+  // of generating a smaller result_type from a larger urbg type, the actual
+  // shift might be 0.
+  static constexpr size_t kSmallShift = kSmallBits % kResultBits;
+  static constexpr auto kSmallMask =
+      MaskFromShift<urbg_result_type>(kSmallShift);
+  static constexpr size_t kLargeShift = kLargeBits % kResultBits;
+  static constexpr auto kLargeMask =
+      MaskFromShift<urbg_result_type>(kLargeShift);
+
+  static constexpr auto kMin = (URBG::min)();
+
+  result_type s = 0;
+  for (size_t n = 0; n < kSmallIters; ++n) {
+    urbg_result_type v;
+    do {
+      v = g() - kMin;
+    } while (v >= kSmallRejection);
+
+    s = (s << kSmallShift) + static_cast<result_type>(v & kSmallMask);
+  }
+
+  for (size_t n = kSmallIters; n < kTotalIters; ++n) {
+    urbg_result_type v;
+    do {
+      v = g() - kMin;
+    } while (v >= kLargeRejection);
+
+    s = (s << kLargeShift) + static_cast<result_type>(v & kLargeMask);
+  }
+  return s;
+}
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_FAST_UNIFORM_BITS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/fastmath.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/fastmath.h
new file mode 100644
index 0000000000000000000000000000000000000000..963b7690f1a9fb3551933935723aff1c682a1803
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/fastmath.h
@@ -0,0 +1,57 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_FASTMATH_H_
+#define ABSL_RANDOM_INTERNAL_FASTMATH_H_
+
+// This file contains fast math functions (bitwise ops as well as some others)
+// which are implementation details of various absl random number distributions.
+
+#include <cassert>
+#include <cmath>
+#include <cstdint>
+
+#include "absl/numeric/bits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// Compute log2(n) using integer operations.
+// While std::log2 is more accurate than std::log(n) / std::log(2), for
+// very large numbers--those close to std::numeric_limits<uint64_t>::max() - 2,
+// for instance--std::log2 rounds up rather than down, which introduces
+// definite skew in the results.
+inline int IntLog2Floor(uint64_t n) {
+  return (n <= 1) ? 0 : (63 - countl_zero(n));
+}
+inline int IntLog2Ceil(uint64_t n) {
+  return (n <= 1) ? 0 : (64 - countl_zero(n - 1));
+}
+
+inline double StirlingLogFactorial(double n) {
+  assert(n >= 1);
+  // Using Stirling's approximation.
+  constexpr double kLog2PI = 1.83787706640934548356;
+  const double logn = std::log(n);
+  const double ninv = 1.0 / static_cast<double>(n);
+  return n * logn - n + 0.5 * (kLog2PI + logn) + (1.0 / 12.0) * ninv -
+         (1.0 / 360.0) * ninv * ninv * ninv;
+}
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_FASTMATH_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/generate_real.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/generate_real.h
new file mode 100644
index 0000000000000000000000000000000000000000..b569450cf73310a95c5ae1d169fdd00c9b2e3c6a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/generate_real.h
@@ -0,0 +1,144 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_GENERATE_REAL_H_
+#define ABSL_RANDOM_INTERNAL_GENERATE_REAL_H_
+
+// This file contains some implementation details which are used by one or more
+// of the absl random number distributions.
+
+#include <cstdint>
+#include <cstring>
+#include <limits>
+#include <type_traits>
+
+#include "absl/meta/type_traits.h"
+#include "absl/numeric/bits.h"
+#include "absl/random/internal/fastmath.h"
+#include "absl/random/internal/traits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// Tristate tag types controlling the output of GenerateRealFromBits.
+struct GeneratePositiveTag {};
+struct GenerateNegativeTag {};
+struct GenerateSignedTag {};
+
+// GenerateRealFromBits generates a single real value from a single 64-bit
+// `bits` with template fields controlling the output.
+//
+// The `SignedTag` parameter controls whether positive, negative,
+// or either signed/unsigned may be returned.
+//   When SignedTag == GeneratePositiveTag, range is U(0, 1)
+//   When SignedTag == GenerateNegativeTag, range is U(-1, 0)
+//   When SignedTag == GenerateSignedTag, range is U(-1, 1)
+//
+// When the `IncludeZero` parameter is true, the function may return 0 for some
+// inputs, otherwise it never returns 0.
+//
+// When a value in U(0,1) is required, use:
+//   GenerateRealFromBits<double, PositiveValueT, true>;
+//
+// When a value in U(-1,1) is required, use:
+//   GenerateRealFromBits<double, SignedValueT, false>;
+//
+//   This generates more distinct values than the mathematical equivalent
+//   `U(0, 1) * 2.0 - 1.0`.
+//
+// Scaling the result by powers of 2 (and avoiding a multiply) is also possible:
+//   GenerateRealFromBits<double>(..., -1);  => U(0, 0.5)
+//   GenerateRealFromBits<double>(..., 1);   => U(0, 2)
+//
+template <typename RealType,  // Real type, either float or double.
+          typename SignedTag = GeneratePositiveTag,  // Whether a positive,
+                                                     // negative, or signed
+                                                     // value is generated.
+          bool IncludeZero = true>
+inline RealType GenerateRealFromBits(uint64_t bits, int exp_bias = 0) {
+  using real_type = RealType;
+  using uint_type = absl::conditional_t<std::is_same<real_type, float>::value,
+                                        uint32_t, uint64_t>;
+
+  static_assert(
+      (std::is_same<double, real_type>::value ||
+       std::is_same<float, real_type>::value),
+      "GenerateRealFromBits must be parameterized by either float or double.");
+
+  static_assert(sizeof(uint_type) == sizeof(real_type),
+                "Mismatched unsinged and real types.");
+
+  static_assert((std::numeric_limits<real_type>::is_iec559 &&
+                 std::numeric_limits<real_type>::radix == 2),
+                "RealType representation is not IEEE 754 binary.");
+
+  static_assert((std::is_same<SignedTag, GeneratePositiveTag>::value ||
+                 std::is_same<SignedTag, GenerateNegativeTag>::value ||
+                 std::is_same<SignedTag, GenerateSignedTag>::value),
+                "");
+
+  static constexpr int kExp = std::numeric_limits<real_type>::digits - 1;
+  static constexpr uint_type kMask = (static_cast<uint_type>(1) << kExp) - 1u;
+  static constexpr int kUintBits = sizeof(uint_type) * 8;
+
+  int exp = exp_bias + int{std::numeric_limits<real_type>::max_exponent - 2};
+
+  // Determine the sign bit.
+  // Depending on the SignedTag, this may use the left-most bit
+  // or it may be a constant value.
+  uint_type sign = std::is_same<SignedTag, GenerateNegativeTag>::value
+                       ? (static_cast<uint_type>(1) << (kUintBits - 1))
+                       : 0;
+  if (std::is_same<SignedTag, GenerateSignedTag>::value) {
+    if (std::is_same<uint_type, uint64_t>::value) {
+      sign = bits & uint64_t{0x8000000000000000};
+    }
+    if (std::is_same<uint_type, uint32_t>::value) {
+      const uint64_t tmp = bits & uint64_t{0x8000000000000000};
+      sign = static_cast<uint32_t>(tmp >> 32);
+    }
+    // adjust the bits and the exponent to account for removing
+    // the leading bit.
+    bits = bits & uint64_t{0x7FFFFFFFFFFFFFFF};
+    exp++;
+  }
+  if (IncludeZero) {
+    if (bits == 0u) return 0;
+  }
+
+  // Number of leading zeros is mapped to the exponent: 2^-clz
+  // bits is 0..01xxxxxx. After shifting, we're left with 1xxx...0..0
+  int clz = countl_zero(bits);
+  bits <<= (IncludeZero ? clz : (clz & 63));  // remove 0-bits.
+  exp -= clz;                                 // set the exponent.
+  bits >>= (63 - kExp);
+
+  // Construct the 32-bit or 64-bit IEEE 754 floating-point value from
+  // the individual fields: sign, exp, mantissa(bits).
+  uint_type val = sign | (static_cast<uint_type>(exp) << kExp) |
+                  (static_cast<uint_type>(bits) & kMask);
+
+  // bit_cast to the output-type
+  real_type result;
+  memcpy(static_cast<void*>(&result), static_cast<const void*>(&val),
+         sizeof(result));
+  return result;
+}
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_GENERATE_REAL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/iostream_state_saver.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/iostream_state_saver.h
new file mode 100644
index 0000000000000000000000000000000000000000..e6e242ee1e669afff1532941918fbfc5642a7302
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/iostream_state_saver.h
@@ -0,0 +1,245 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_IOSTREAM_STATE_SAVER_H_
+#define ABSL_RANDOM_INTERNAL_IOSTREAM_STATE_SAVER_H_
+
+#include <cmath>
+#include <iostream>
+#include <limits>
+#include <type_traits>
+
+#include "absl/meta/type_traits.h"
+#include "absl/numeric/int128.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// The null_state_saver does nothing.
+template <typename T>
+class null_state_saver {
+ public:
+  using stream_type = T;
+  using flags_type = std::ios_base::fmtflags;
+
+  null_state_saver(T&, flags_type) {}
+  ~null_state_saver() {}
+};
+
+// ostream_state_saver is a RAII object to save and restore the common
+// basic_ostream flags used when implementing `operator <<()` on any of
+// the absl random distributions.
+template <typename OStream>
+class ostream_state_saver {
+ public:
+  using ostream_type = OStream;
+  using flags_type = std::ios_base::fmtflags;
+  using fill_type = typename ostream_type::char_type;
+  using precision_type = std::streamsize;
+
+  ostream_state_saver(ostream_type& os,  // NOLINT(runtime/references)
+                      flags_type flags, fill_type fill)
+      : os_(os),
+        flags_(os.flags(flags)),
+        fill_(os.fill(fill)),
+        precision_(os.precision()) {
+    // Save state in initialized variables.
+  }
+
+  ~ostream_state_saver() {
+    // Restore saved state.
+    os_.precision(precision_);
+    os_.fill(fill_);
+    os_.flags(flags_);
+  }
+
+ private:
+  ostream_type& os_;
+  const flags_type flags_;
+  const fill_type fill_;
+  const precision_type precision_;
+};
+
+#if defined(__NDK_MAJOR__) && __NDK_MAJOR__ < 16
+#define ABSL_RANDOM_INTERNAL_IOSTREAM_HEXFLOAT 1
+#else
+#define ABSL_RANDOM_INTERNAL_IOSTREAM_HEXFLOAT 0
+#endif
+
+template <typename CharT, typename Traits>
+ostream_state_saver<std::basic_ostream<CharT, Traits>> make_ostream_state_saver(
+    std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
+    std::ios_base::fmtflags flags = std::ios_base::dec | std::ios_base::left |
+#if ABSL_RANDOM_INTERNAL_IOSTREAM_HEXFLOAT
+                                    std::ios_base::fixed |
+#endif
+                                    std::ios_base::scientific) {
+  using result_type = ostream_state_saver<std::basic_ostream<CharT, Traits>>;
+  return result_type(os, flags, os.widen(' '));
+}
+
+template <typename T>
+typename absl::enable_if_t<!std::is_base_of<std::ios_base, T>::value,
+                           null_state_saver<T>>
+make_ostream_state_saver(T& is,  // NOLINT(runtime/references)
+                         std::ios_base::fmtflags flags = std::ios_base::dec) {
+  std::cerr << "null_state_saver";
+  using result_type = null_state_saver<T>;
+  return result_type(is, flags);
+}
+
+// stream_precision_helper<type>::kPrecision returns the base 10 precision
+// required to stream and reconstruct a real type exact binary value through
+// a binary->decimal->binary transition.
+template <typename T>
+struct stream_precision_helper {
+  // max_digits10 may be 0 on MSVC; if so, use digits10 + 3.
+  static constexpr int kPrecision =
+      (std::numeric_limits<T>::max_digits10 > std::numeric_limits<T>::digits10)
+          ? std::numeric_limits<T>::max_digits10
+          : (std::numeric_limits<T>::digits10 + 3);
+};
+
+template <>
+struct stream_precision_helper<float> {
+  static constexpr int kPrecision = 9;
+};
+template <>
+struct stream_precision_helper<double> {
+  static constexpr int kPrecision = 17;
+};
+template <>
+struct stream_precision_helper<long double> {
+  static constexpr int kPrecision = 36;  // assuming fp128
+};
+
+// istream_state_saver is a RAII object to save and restore the common
+// std::basic_istream<> flags used when implementing `operator >>()` on any of
+// the absl random distributions.
+template <typename IStream>
+class istream_state_saver {
+ public:
+  using istream_type = IStream;
+  using flags_type = std::ios_base::fmtflags;
+
+  istream_state_saver(istream_type& is,  // NOLINT(runtime/references)
+                      flags_type flags)
+      : is_(is), flags_(is.flags(flags)) {}
+
+  ~istream_state_saver() { is_.flags(flags_); }
+
+ private:
+  istream_type& is_;
+  flags_type flags_;
+};
+
+template <typename CharT, typename Traits>
+istream_state_saver<std::basic_istream<CharT, Traits>> make_istream_state_saver(
+    std::basic_istream<CharT, Traits>& is,  // NOLINT(runtime/references)
+    std::ios_base::fmtflags flags = std::ios_base::dec |
+                                    std::ios_base::scientific |
+                                    std::ios_base::skipws) {
+  using result_type = istream_state_saver<std::basic_istream<CharT, Traits>>;
+  return result_type(is, flags);
+}
+
+template <typename T>
+typename absl::enable_if_t<!std::is_base_of<std::ios_base, T>::value,
+                           null_state_saver<T>>
+make_istream_state_saver(T& is,  // NOLINT(runtime/references)
+                         std::ios_base::fmtflags flags = std::ios_base::dec) {
+  using result_type = null_state_saver<T>;
+  return result_type(is, flags);
+}
+
+// stream_format_type<T> is a helper struct to convert types which
+// basic_iostream cannot output as decimal numbers into types which
+// basic_iostream can output as decimal numbers. Specifically:
+// * signed/unsigned char-width types are converted to int.
+// * TODO(lar): __int128 => uint128, except there is no operator << yet.
+//
+template <typename T>
+struct stream_format_type
+    : public std::conditional<(sizeof(T) == sizeof(char)), int, T> {};
+
+// stream_u128_helper allows us to write out either absl::uint128 or
+// __uint128_t types in the same way, which enables their use as internal
+// state of PRNG engines.
+template <typename T>
+struct stream_u128_helper;
+
+template <>
+struct stream_u128_helper<absl::uint128> {
+  template <typename IStream>
+  inline absl::uint128 read(IStream& in) {
+    uint64_t h = 0;
+    uint64_t l = 0;
+    in >> h >> l;
+    return absl::MakeUint128(h, l);
+  }
+
+  template <typename OStream>
+  inline void write(absl::uint128 val, OStream& out) {
+    uint64_t h = absl::Uint128High64(val);
+    uint64_t l = absl::Uint128Low64(val);
+    out << h << out.fill() << l;
+  }
+};
+
+#ifdef ABSL_HAVE_INTRINSIC_INT128
+template <>
+struct stream_u128_helper<__uint128_t> {
+  template <typename IStream>
+  inline __uint128_t read(IStream& in) {
+    uint64_t h = 0;
+    uint64_t l = 0;
+    in >> h >> l;
+    return (static_cast<__uint128_t>(h) << 64) | l;
+  }
+
+  template <typename OStream>
+  inline void write(__uint128_t val, OStream& out) {
+    uint64_t h = static_cast<uint64_t>(val >> 64u);
+    uint64_t l = static_cast<uint64_t>(val);
+    out << h << out.fill() << l;
+  }
+};
+#endif
+
+template <typename FloatType, typename IStream>
+inline FloatType read_floating_point(IStream& is) {
+  static_assert(std::is_floating_point<FloatType>::value, "");
+  FloatType dest;
+  is >> dest;
+  // Parsing a double value may report a subnormal value as an error
+  // despite being able to represent it.
+  // See https://stackoverflow.com/q/52410931/3286653
+  // It may also report an underflow when parsing DOUBLE_MIN as an
+  // ERANGE error, as the parsed value may be smaller than DOUBLE_MIN
+  // and rounded up.
+  // See: https://stackoverflow.com/q/42005462
+  if (is.fail() &&
+      (std::fabs(dest) == (std::numeric_limits<FloatType>::min)() ||
+       std::fpclassify(dest) == FP_SUBNORMAL)) {
+    is.clear(is.rdstate() & (~std::ios_base::failbit));
+  }
+  return dest;
+}
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_IOSTREAM_STATE_SAVER_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/nonsecure_base.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/nonsecure_base.h
new file mode 100644
index 0000000000000000000000000000000000000000..c3b80335aed8536ad41831aebfd1e21128c12335
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/nonsecure_base.h
@@ -0,0 +1,161 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_NONSECURE_BASE_H_
+#define ABSL_RANDOM_INTERNAL_NONSECURE_BASE_H_
+
+#include <algorithm>
+#include <cstdint>
+#include <iterator>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+#include "absl/base/macros.h"
+#include "absl/container/inlined_vector.h"
+#include "absl/meta/type_traits.h"
+#include "absl/random/internal/pool_urbg.h"
+#include "absl/random/internal/salted_seed_seq.h"
+#include "absl/random/internal/seed_material.h"
+#include "absl/types/span.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// RandenPoolSeedSeq is a custom seed sequence type where generate() fills the
+// provided buffer via the RandenPool entropy source.
+class RandenPoolSeedSeq {
+ private:
+  struct ContiguousTag {};
+  struct BufferTag {};
+
+  // Generate random unsigned values directly into the buffer.
+  template <typename Contiguous>
+  void generate_impl(ContiguousTag, Contiguous begin, Contiguous end) {
+    const size_t n = static_cast<size_t>(std::distance(begin, end));
+    auto* a = &(*begin);
+    RandenPool<uint8_t>::Fill(
+        absl::MakeSpan(reinterpret_cast<uint8_t*>(a), sizeof(*a) * n));
+  }
+
+  // Construct a buffer of size n and fill it with values, then copy
+  // those values into the seed iterators.
+  template <typename RandomAccessIterator>
+  void generate_impl(BufferTag, RandomAccessIterator begin,
+                     RandomAccessIterator end) {
+    const size_t n = std::distance(begin, end);
+    absl::InlinedVector<uint32_t, 8> data(n, 0);
+    RandenPool<uint32_t>::Fill(absl::MakeSpan(data.begin(), data.end()));
+    std::copy(std::begin(data), std::end(data), begin);
+  }
+
+ public:
+  using result_type = uint32_t;
+
+  size_t size() { return 0; }
+
+  template <typename OutIterator>
+  void param(OutIterator) const {}
+
+  template <typename RandomAccessIterator>
+  void generate(RandomAccessIterator begin, RandomAccessIterator end) {
+    // RandomAccessIterator must be assignable from uint32_t
+    if (begin != end) {
+      using U = typename std::iterator_traits<RandomAccessIterator>::value_type;
+      // ContiguousTag indicates the common case of a known contiguous buffer,
+      // which allows directly filling the buffer. In C++20,
+      // std::contiguous_iterator_tag provides a mechanism for testing this
+      // capability, however until Abseil's support requirements allow us to
+      // assume C++20, limit checks to a few common cases.
+      using TagType = absl::conditional_t<
+          (std::is_pointer<RandomAccessIterator>::value ||
+           std::is_same<RandomAccessIterator,
+                        typename std::vector<U>::iterator>::value),
+          ContiguousTag, BufferTag>;
+
+      generate_impl(TagType{}, begin, end);
+    }
+  }
+};
+
+// Each instance of NonsecureURBGBase<URBG> will be seeded by variates produced
+// by a thread-unique URBG-instance.
+template <typename URBG, typename Seeder = RandenPoolSeedSeq>
+class NonsecureURBGBase {
+ public:
+  using result_type = typename URBG::result_type;
+
+  // Default constructor
+  NonsecureURBGBase() : urbg_(ConstructURBG()) {}
+
+  // Copy disallowed, move allowed.
+  NonsecureURBGBase(const NonsecureURBGBase&) = delete;
+  NonsecureURBGBase& operator=(const NonsecureURBGBase&) = delete;
+  NonsecureURBGBase(NonsecureURBGBase&&) = default;
+  NonsecureURBGBase& operator=(NonsecureURBGBase&&) = default;
+
+  // Constructor using a seed
+  template <class SSeq, typename = typename absl::enable_if_t<
+                            !std::is_same<SSeq, NonsecureURBGBase>::value>>
+  explicit NonsecureURBGBase(SSeq&& seq)
+      : urbg_(ConstructURBG(std::forward<SSeq>(seq))) {}
+
+  // Note: on MSVC, min() or max() can be interpreted as MIN() or MAX(), so we
+  // enclose min() or max() in parens as (min)() and (max)().
+  // Additionally, clang-format requires no space before this construction.
+
+  // NonsecureURBGBase::min()
+  static constexpr result_type(min)() { return (URBG::min)(); }
+
+  // NonsecureURBGBase::max()
+  static constexpr result_type(max)() { return (URBG::max)(); }
+
+  // NonsecureURBGBase::operator()()
+  result_type operator()() { return urbg_(); }
+
+  // NonsecureURBGBase::discard()
+  void discard(unsigned long long values) {  // NOLINT(runtime/int)
+    urbg_.discard(values);
+  }
+
+  bool operator==(const NonsecureURBGBase& other) const {
+    return urbg_ == other.urbg_;
+  }
+
+  bool operator!=(const NonsecureURBGBase& other) const {
+    return !(urbg_ == other.urbg_);
+  }
+
+ private:
+  static URBG ConstructURBG() {
+    Seeder seeder;
+    return URBG(seeder);
+  }
+
+  template <typename SSeq>
+  static URBG ConstructURBG(SSeq&& seq) {  // NOLINT(runtime/references)
+    auto salted_seq =
+        random_internal::MakeSaltedSeedSeq(std::forward<SSeq>(seq));
+    return URBG(salted_seq);
+  }
+
+  URBG urbg_;
+};
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_NONSECURE_BASE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/pcg_engine.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/pcg_engine.h
new file mode 100644
index 0000000000000000000000000000000000000000..e1f4ef3317146298058352df64b484096c892e9c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/pcg_engine.h
@@ -0,0 +1,287 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_PCG_ENGINE_H_
+#define ABSL_RANDOM_INTERNAL_PCG_ENGINE_H_
+
+#include <type_traits>
+
+#include "absl/base/config.h"
+#include "absl/meta/type_traits.h"
+#include "absl/numeric/bits.h"
+#include "absl/numeric/int128.h"
+#include "absl/random/internal/fastmath.h"
+#include "absl/random/internal/iostream_state_saver.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// pcg_engine is a simplified implementation of Melissa O'Neil's PCG engine in
+// C++.  PCG combines a linear congruential generator (LCG) with output state
+// mixing functions to generate each random variate.  pcg_engine supports only a
+// single sequence (oneseq), and does not support streams.
+//
+// pcg_engine is parameterized by two types:
+//   Params, which provides the multiplier and increment values;
+//   Mix, which mixes the state into the result.
+//
+template <typename Params, typename Mix>
+class pcg_engine {
+  static_assert(std::is_same<typename Params::state_type,
+                             typename Mix::state_type>::value,
+                "Class-template absl::pcg_engine must be parameterized by "
+                "Params and Mix with identical state_type");
+
+  static_assert(std::is_unsigned<typename Mix::result_type>::value,
+                "Class-template absl::pcg_engine must be parameterized by "
+                "an unsigned Mix::result_type");
+
+  using params_type = Params;
+  using mix_type = Mix;
+  using state_type = typename Mix::state_type;
+
+ public:
+  // C++11 URBG interface:
+  using result_type = typename Mix::result_type;
+
+  static constexpr result_type(min)() {
+    return (std::numeric_limits<result_type>::min)();
+  }
+
+  static constexpr result_type(max)() {
+    return (std::numeric_limits<result_type>::max)();
+  }
+
+  explicit pcg_engine(uint64_t seed_value = 0) { seed(seed_value); }
+
+  template <class SeedSequence,
+            typename = typename absl::enable_if_t<
+                !std::is_same<SeedSequence, pcg_engine>::value>>
+  explicit pcg_engine(SeedSequence&& seq) {
+    seed(seq);
+  }
+
+  pcg_engine(const pcg_engine&) = default;
+  pcg_engine& operator=(const pcg_engine&) = default;
+  pcg_engine(pcg_engine&&) = default;
+  pcg_engine& operator=(pcg_engine&&) = default;
+
+  result_type operator()() {
+    // Advance the LCG state, always using the new value to generate the output.
+    state_ = lcg(state_);
+    return Mix{}(state_);
+  }
+
+  void seed(uint64_t seed_value = 0) {
+    state_type tmp = seed_value;
+    state_ = lcg(tmp + Params::increment());
+  }
+
+  template <class SeedSequence>
+  typename absl::enable_if_t<
+      !std::is_convertible<SeedSequence, uint64_t>::value, void>
+  seed(SeedSequence&& seq) {
+    reseed(seq);
+  }
+
+  void discard(uint64_t count) { state_ = advance(state_, count); }
+
+  bool operator==(const pcg_engine& other) const {
+    return state_ == other.state_;
+  }
+
+  bool operator!=(const pcg_engine& other) const { return !(*this == other); }
+
+  template <class CharT, class Traits>
+  friend typename absl::enable_if_t<(sizeof(state_type) == 16),
+                                    std::basic_ostream<CharT, Traits>&>
+  operator<<(
+      std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
+      const pcg_engine& engine) {
+    auto saver = random_internal::make_ostream_state_saver(os);
+    random_internal::stream_u128_helper<state_type> helper;
+    helper.write(pcg_engine::params_type::multiplier(), os);
+    os << os.fill();
+    helper.write(pcg_engine::params_type::increment(), os);
+    os << os.fill();
+    helper.write(engine.state_, os);
+    return os;
+  }
+
+  template <class CharT, class Traits>
+  friend typename absl::enable_if_t<(sizeof(state_type) <= 8),
+                                    std::basic_ostream<CharT, Traits>&>
+  operator<<(
+      std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
+      const pcg_engine& engine) {
+    auto saver = random_internal::make_ostream_state_saver(os);
+    os << pcg_engine::params_type::multiplier() << os.fill();
+    os << pcg_engine::params_type::increment() << os.fill();
+    os << engine.state_;
+    return os;
+  }
+
+  template <class CharT, class Traits>
+  friend typename absl::enable_if_t<(sizeof(state_type) == 16),
+                                    std::basic_istream<CharT, Traits>&>
+  operator>>(
+      std::basic_istream<CharT, Traits>& is,  // NOLINT(runtime/references)
+      pcg_engine& engine) {                   // NOLINT(runtime/references)
+    random_internal::stream_u128_helper<state_type> helper;
+    auto mult = helper.read(is);
+    auto inc = helper.read(is);
+    auto tmp = helper.read(is);
+    if (mult != pcg_engine::params_type::multiplier() ||
+        inc != pcg_engine::params_type::increment()) {
+      // signal failure by setting the failbit.
+      is.setstate(is.rdstate() | std::ios_base::failbit);
+    }
+    if (!is.fail()) {
+      engine.state_ = tmp;
+    }
+    return is;
+  }
+
+  template <class CharT, class Traits>
+  friend typename absl::enable_if_t<(sizeof(state_type) <= 8),
+                                    std::basic_istream<CharT, Traits>&>
+  operator>>(
+      std::basic_istream<CharT, Traits>& is,  // NOLINT(runtime/references)
+      pcg_engine& engine) {                   // NOLINT(runtime/references)
+    state_type mult{}, inc{}, tmp{};
+    is >> mult >> inc >> tmp;
+    if (mult != pcg_engine::params_type::multiplier() ||
+        inc != pcg_engine::params_type::increment()) {
+      // signal failure by setting the failbit.
+      is.setstate(is.rdstate() | std::ios_base::failbit);
+    }
+    if (!is.fail()) {
+      engine.state_ = tmp;
+    }
+    return is;
+  }
+
+ private:
+  state_type state_;
+
+  // Returns the linear-congruential generator next state.
+  static inline constexpr state_type lcg(state_type s) {
+    return s * Params::multiplier() + Params::increment();
+  }
+
+  // Returns the linear-congruential arbitrary seek state.
+  inline state_type advance(state_type s, uint64_t n) const {
+    state_type mult = Params::multiplier();
+    state_type inc = Params::increment();
+    state_type m = 1;
+    state_type i = 0;
+    while (n > 0) {
+      if (n & 1) {
+        m *= mult;
+        i = i * mult + inc;
+      }
+      inc = (mult + 1) * inc;
+      mult *= mult;
+      n >>= 1;
+    }
+    return m * s + i;
+  }
+
+  template <class SeedSequence>
+  void reseed(SeedSequence& seq) {
+    using sequence_result_type = typename SeedSequence::result_type;
+    constexpr size_t kBufferSize =
+        sizeof(state_type) / sizeof(sequence_result_type);
+    sequence_result_type buffer[kBufferSize];
+    seq.generate(std::begin(buffer), std::end(buffer));
+    // Convert the seed output to a single state value.
+    state_type tmp = buffer[0];
+    for (size_t i = 1; i < kBufferSize; i++) {
+      tmp <<= (sizeof(sequence_result_type) * 8);
+      tmp |= buffer[i];
+    }
+    state_ = lcg(tmp + params_type::increment());
+  }
+};
+
+// Parameterized implementation of the PCG 128-bit oneseq state.
+// This provides state_type, multiplier, and increment for pcg_engine.
+template <uint64_t kMultA, uint64_t kMultB, uint64_t kIncA, uint64_t kIncB>
+class pcg128_params {
+ public:
+  using state_type = absl::uint128;
+  static inline constexpr state_type multiplier() {
+    return absl::MakeUint128(kMultA, kMultB);
+  }
+  static inline constexpr state_type increment() {
+    return absl::MakeUint128(kIncA, kIncB);
+  }
+};
+
+// Implementation of the PCG xsl_rr_128_64 128-bit mixing function, which
+// accepts an input of state_type and mixes it into an output of result_type.
+struct pcg_xsl_rr_128_64 {
+  using state_type = absl::uint128;
+  using result_type = uint64_t;
+
+  inline uint64_t operator()(state_type state) {
+    // This is equivalent to the xsl_rr_128_64 mixing function.
+    uint64_t rotate = static_cast<uint64_t>(state >> 122u);
+    state ^= state >> 64;
+    uint64_t s = static_cast<uint64_t>(state);
+    return rotr(s, static_cast<int>(rotate));
+  }
+};
+
+// Parameterized implementation of the PCG 64-bit oneseq state.
+// This provides state_type, multiplier, and increment for pcg_engine.
+template <uint64_t kMult, uint64_t kInc>
+class pcg64_params {
+ public:
+  using state_type = uint64_t;
+  static inline constexpr state_type multiplier() { return kMult; }
+  static inline constexpr state_type increment() { return kInc; }
+};
+
+// Implementation of the PCG xsh_rr_64_32 64-bit mixing function, which accepts
+// an input of state_type and mixes it into an output of result_type.
+struct pcg_xsh_rr_64_32 {
+  using state_type = uint64_t;
+  using result_type = uint32_t;
+  inline uint32_t operator()(uint64_t state) {
+    return rotr(static_cast<uint32_t>(((state >> 18) ^ state) >> 27),
+                state >> 59);
+  }
+};
+
+// Stable pcg_engine implementations:
+// This is a 64-bit generator using 128-bits of state.
+// The output sequence is equivalent to Melissa O'Neil's pcg64_oneseq.
+using pcg64_2018_engine = pcg_engine<
+    random_internal::pcg128_params<0x2360ed051fc65da4ull, 0x4385df649fccf645ull,
+                                   0x5851f42d4c957f2d, 0x14057b7ef767814f>,
+    random_internal::pcg_xsl_rr_128_64>;
+
+// This is a 32-bit generator using 64-bits of state.
+// This is equivalent to Melissa O'Neil's pcg32_oneseq.
+using pcg32_2018_engine = pcg_engine<
+    random_internal::pcg64_params<0x5851f42d4c957f2dull, 0x14057b7ef767814full>,
+    random_internal::pcg_xsh_rr_64_32>;
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_PCG_ENGINE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/platform.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/platform.h
new file mode 100644
index 0000000000000000000000000000000000000000..bbdb4e6208169b72631085b71f4589fac6a88287
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/platform.h
@@ -0,0 +1,171 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_PLATFORM_H_
+#define ABSL_RANDOM_INTERNAL_PLATFORM_H_
+
+// HERMETIC NOTE: The randen_hwaes target must not introduce duplicate
+// symbols from arbitrary system and other headers, since it may be built
+// with different flags from other targets, using different levels of
+// optimization, potentially introducing ODR violations.
+
+// -----------------------------------------------------------------------------
+// Platform Feature Checks
+// -----------------------------------------------------------------------------
+
+// Currently supported operating systems and associated preprocessor
+// symbols:
+//
+//   Linux and Linux-derived           __linux__
+//   Android                           __ANDROID__ (implies __linux__)
+//   Linux (non-Android)               __linux__ && !__ANDROID__
+//   Darwin (macOS and iOS)            __APPLE__
+//   Akaros (http://akaros.org)        __ros__
+//   Windows                           _WIN32
+//   NaCL                              __native_client__
+//   AsmJS                             __asmjs__
+//   WebAssembly                       __wasm__
+//   Fuchsia                           __Fuchsia__
+//
+// Note that since Android defines both __ANDROID__ and __linux__, one
+// may probe for either Linux or Android by simply testing for __linux__.
+//
+// NOTE: For __APPLE__ platforms, we use #include <TargetConditionals.h>
+// to distinguish os variants.
+//
+// http://nadeausoftware.com/articles/2012/01/c_c_tip_how_use_compiler_predefined_macros_detect_operating_system
+
+#if defined(__APPLE__)
+#include <TargetConditionals.h>
+#endif
+
+// -----------------------------------------------------------------------------
+// Architecture Checks
+// -----------------------------------------------------------------------------
+
+// These preprocessor directives are trying to determine CPU architecture,
+// including necessary headers to support hardware AES.
+//
+// ABSL_ARCH_{X86/PPC/ARM} macros determine the platform.
+#if defined(__x86_64__) || defined(__x86_64) || defined(_M_AMD64) || \
+    defined(_M_X64)
+#define ABSL_ARCH_X86_64
+#elif defined(__i386) || defined(_M_IX86)
+#define ABSL_ARCH_X86_32
+#elif defined(__aarch64__) || defined(__arm64__) || defined(_M_ARM64)
+#define ABSL_ARCH_AARCH64
+#elif defined(__arm__) || defined(__ARMEL__) || defined(_M_ARM)
+#define ABSL_ARCH_ARM
+#elif defined(__powerpc64__) || defined(__PPC64__) || defined(__powerpc__) || \
+    defined(__ppc__) || defined(__PPC__)
+#define ABSL_ARCH_PPC
+#else
+// Unsupported architecture.
+//  * https://sourceforge.net/p/predef/wiki/Architectures/
+//  * https://msdn.microsoft.com/en-us/library/b0084kay.aspx
+//  * for gcc, clang: "echo | gcc -E -dM -"
+#endif
+
+// -----------------------------------------------------------------------------
+// Attribute Checks
+// -----------------------------------------------------------------------------
+
+// ABSL_RANDOM_INTERNAL_RESTRICT annotates whether pointers may be considered
+// to be unaliased.
+#if defined(__clang__) || defined(__GNUC__)
+#define ABSL_RANDOM_INTERNAL_RESTRICT __restrict__
+#elif defined(_MSC_VER)
+#define ABSL_RANDOM_INTERNAL_RESTRICT __restrict
+#else
+#define ABSL_RANDOM_INTERNAL_RESTRICT
+#endif
+
+// ABSL_HAVE_ACCELERATED_AES indicates whether the currently active compiler
+// flags (e.g. -maes) allow using hardware accelerated AES instructions, which
+// implies us assuming that the target platform supports them.
+#define ABSL_HAVE_ACCELERATED_AES 0
+
+#if defined(ABSL_ARCH_X86_64)
+
+#if defined(__AES__) || defined(__AVX__)
+#undef ABSL_HAVE_ACCELERATED_AES
+#define ABSL_HAVE_ACCELERATED_AES 1
+#endif
+
+#elif defined(ABSL_ARCH_PPC)
+
+// Rely on VSX and CRYPTO extensions for vcipher on PowerPC.
+#if (defined(__VEC__) || defined(__ALTIVEC__)) && defined(__VSX__) && \
+    defined(__CRYPTO__)
+#undef ABSL_HAVE_ACCELERATED_AES
+#define ABSL_HAVE_ACCELERATED_AES 1
+#endif
+
+#elif defined(ABSL_ARCH_ARM) || defined(ABSL_ARCH_AARCH64)
+
+// http://infocenter.arm.com/help/topic/com.arm.doc.ihi0053c/IHI0053C_acle_2_0.pdf
+// Rely on NEON+CRYPTO extensions for ARM.
+#if defined(__ARM_NEON) && defined(__ARM_FEATURE_CRYPTO)
+#undef ABSL_HAVE_ACCELERATED_AES
+#define ABSL_HAVE_ACCELERATED_AES 1
+#endif
+
+#endif
+
+// NaCl does not allow AES.
+#if defined(__native_client__)
+#undef ABSL_HAVE_ACCELERATED_AES
+#define ABSL_HAVE_ACCELERATED_AES 0
+#endif
+
+// ABSL_RANDOM_INTERNAL_AES_DISPATCH indicates whether the currently active
+// platform has, or should use run-time dispatch for selecting the
+// acclerated Randen implementation.
+#define ABSL_RANDOM_INTERNAL_AES_DISPATCH 0
+
+#if defined(ABSL_ARCH_X86_64)
+// Dispatch is available on x86_64
+#undef ABSL_RANDOM_INTERNAL_AES_DISPATCH
+#define ABSL_RANDOM_INTERNAL_AES_DISPATCH 1
+#elif defined(__linux__) && defined(ABSL_ARCH_PPC)
+// Or when running linux PPC
+#undef ABSL_RANDOM_INTERNAL_AES_DISPATCH
+#define ABSL_RANDOM_INTERNAL_AES_DISPATCH 1
+#elif defined(__linux__) && defined(ABSL_ARCH_AARCH64)
+// Or when running linux AArch64
+#undef ABSL_RANDOM_INTERNAL_AES_DISPATCH
+#define ABSL_RANDOM_INTERNAL_AES_DISPATCH 1
+#elif defined(__linux__) && defined(ABSL_ARCH_ARM) && (__ARM_ARCH >= 8)
+// Or when running linux ARM v8 or higher.
+// (This captures a lot of Android configurations.)
+#undef ABSL_RANDOM_INTERNAL_AES_DISPATCH
+#define ABSL_RANDOM_INTERNAL_AES_DISPATCH 1
+#endif
+
+// NaCl does not allow dispatch.
+#if defined(__native_client__)
+#undef ABSL_RANDOM_INTERNAL_AES_DISPATCH
+#define ABSL_RANDOM_INTERNAL_AES_DISPATCH 0
+#endif
+
+// iOS does not support dispatch, even on x86, since applications
+// should be bundled as fat binaries, with a different build tailored for
+// each specific supported platform/architecture.
+#if (defined(TARGET_OS_IPHONE) && TARGET_OS_IPHONE) || \
+    (defined(TARGET_OS_IPHONE_SIMULATOR) && TARGET_OS_IPHONE_SIMULATOR)
+#undef ABSL_RANDOM_INTERNAL_AES_DISPATCH
+#define ABSL_RANDOM_INTERNAL_AES_DISPATCH 0
+#endif
+
+#endif  // ABSL_RANDOM_INTERNAL_PLATFORM_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/pool_urbg.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/pool_urbg.h
new file mode 100644
index 0000000000000000000000000000000000000000..05721929f54e6a5fb1adfa467de3c37d0bf49f23
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/pool_urbg.h
@@ -0,0 +1,131 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_POOL_URBG_H_
+#define ABSL_RANDOM_INTERNAL_POOL_URBG_H_
+
+#include <cinttypes>
+#include <limits>
+
+#include "absl/random/internal/traits.h"
+#include "absl/types/span.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// RandenPool is a thread-safe random number generator [random.req.urbg] that
+// uses an underlying pool of Randen generators to generate values.  Each thread
+// has affinity to one instance of the underlying pool generators.  Concurrent
+// access is guarded by a spin-lock.
+template <typename T>
+class RandenPool {
+ public:
+  using result_type = T;
+  static_assert(std::is_unsigned<result_type>::value,
+                "RandenPool template argument must be a built-in unsigned "
+                "integer type");
+
+  static constexpr result_type(min)() {
+    return (std::numeric_limits<result_type>::min)();
+  }
+
+  static constexpr result_type(max)() {
+    return (std::numeric_limits<result_type>::max)();
+  }
+
+  RandenPool() {}
+
+  // Returns a single value.
+  inline result_type operator()() { return Generate(); }
+
+  // Fill data with random values.
+  static void Fill(absl::Span<result_type> data);
+
+ protected:
+  // Generate returns a single value.
+  static result_type Generate();
+};
+
+extern template class RandenPool<uint8_t>;
+extern template class RandenPool<uint16_t>;
+extern template class RandenPool<uint32_t>;
+extern template class RandenPool<uint64_t>;
+
+// PoolURBG uses an underlying pool of random generators to implement a
+// thread-compatible [random.req.urbg] interface with an internal cache of
+// values.
+template <typename T, size_t kBufferSize>
+class PoolURBG {
+  // Inheritance to access the protected static members of RandenPool.
+  using unsigned_type = typename make_unsigned_bits<T>::type;
+  using PoolType = RandenPool<unsigned_type>;
+  using SpanType = absl::Span<unsigned_type>;
+
+  static constexpr size_t kInitialBuffer = kBufferSize + 1;
+  static constexpr size_t kHalfBuffer = kBufferSize / 2;
+
+ public:
+  using result_type = T;
+
+  static_assert(std::is_unsigned<result_type>::value,
+                "PoolURBG must be parameterized by an unsigned integer type");
+
+  static_assert(kBufferSize > 1,
+                "PoolURBG must be parameterized by a buffer-size > 1");
+
+  static_assert(kBufferSize <= 256,
+                "PoolURBG must be parameterized by a buffer-size <= 256");
+
+  static constexpr result_type(min)() {
+    return (std::numeric_limits<result_type>::min)();
+  }
+
+  static constexpr result_type(max)() {
+    return (std::numeric_limits<result_type>::max)();
+  }
+
+  PoolURBG() : next_(kInitialBuffer) {}
+
+  // copy-constructor does not copy cache.
+  PoolURBG(const PoolURBG&) : next_(kInitialBuffer) {}
+  const PoolURBG& operator=(const PoolURBG&) {
+    next_ = kInitialBuffer;
+    return *this;
+  }
+
+  // move-constructor does move cache.
+  PoolURBG(PoolURBG&&) = default;
+  PoolURBG& operator=(PoolURBG&&) = default;
+
+  inline result_type operator()() {
+    if (next_ >= kBufferSize) {
+      next_ = (kBufferSize > 2 && next_ > kBufferSize) ? kHalfBuffer : 0;
+      PoolType::Fill(SpanType(reinterpret_cast<unsigned_type*>(state_ + next_),
+                              kBufferSize - next_));
+    }
+    return state_[next_++];
+  }
+
+ private:
+  // Buffer size.
+  size_t next_;  // index within state_
+  result_type state_[kBufferSize];
+};
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_POOL_URBG_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen.h
new file mode 100644
index 0000000000000000000000000000000000000000..9ff4a7a5547806c8660783ff9b76d9b783d31ed3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen.h
@@ -0,0 +1,96 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_RANDEN_H_
+#define ABSL_RANDOM_INTERNAL_RANDEN_H_
+
+#include <cstddef>
+
+#include "absl/random/internal/platform.h"
+#include "absl/random/internal/randen_hwaes.h"
+#include "absl/random/internal/randen_slow.h"
+#include "absl/random/internal/randen_traits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// RANDen = RANDom generator or beetroots in Swiss High German.
+// 'Strong' (well-distributed, unpredictable, backtracking-resistant) random
+// generator, faster in some benchmarks than std::mt19937_64 and pcg64_c32.
+//
+// Randen implements the basic state manipulation methods.
+class Randen {
+ public:
+  static constexpr size_t kStateBytes = RandenTraits::kStateBytes;
+  static constexpr size_t kCapacityBytes = RandenTraits::kCapacityBytes;
+  static constexpr size_t kSeedBytes = RandenTraits::kSeedBytes;
+
+  ~Randen() = default;
+
+  Randen();
+
+  // Generate updates the randen sponge. The outer portion of the sponge
+  // (kCapacityBytes .. kStateBytes) may be consumed as PRNG state.
+  // REQUIRES: state points to kStateBytes of state.
+  inline void Generate(void* state) const {
+#if ABSL_RANDOM_INTERNAL_AES_DISPATCH
+    // HW AES Dispatch.
+    if (has_crypto_) {
+      RandenHwAes::Generate(keys_, state);
+    } else {
+      RandenSlow::Generate(keys_, state);
+    }
+#elif ABSL_HAVE_ACCELERATED_AES
+    // HW AES is enabled.
+    RandenHwAes::Generate(keys_, state);
+#else
+    // HW AES is disabled.
+    RandenSlow::Generate(keys_, state);
+#endif
+  }
+
+  // Absorb incorporates additional seed material into the randen sponge.  After
+  // absorb returns, Generate must be called before the state may be consumed.
+  // REQUIRES: seed points to kSeedBytes of seed.
+  // REQUIRES: state points to kStateBytes of state.
+  inline void Absorb(const void* seed, void* state) const {
+#if ABSL_RANDOM_INTERNAL_AES_DISPATCH
+    // HW AES Dispatch.
+    if (has_crypto_) {
+      RandenHwAes::Absorb(seed, state);
+    } else {
+      RandenSlow::Absorb(seed, state);
+    }
+#elif ABSL_HAVE_ACCELERATED_AES
+    // HW AES is enabled.
+    RandenHwAes::Absorb(seed, state);
+#else
+    // HW AES is disabled.
+    RandenSlow::Absorb(seed, state);
+#endif
+  }
+
+ private:
+  const void* keys_;
+#if ABSL_RANDOM_INTERNAL_AES_DISPATCH
+  bool has_crypto_;
+#endif
+};
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_RANDEN_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen_detect.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen_detect.h
new file mode 100644
index 0000000000000000000000000000000000000000..f283f4322647d102383d3903185fcc8df05f962a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen_detect.h
@@ -0,0 +1,33 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_RANDEN_DETECT_H_
+#define ABSL_RANDOM_INTERNAL_RANDEN_DETECT_H_
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// Returns whether the current CPU supports RandenHwAes implementation.
+// This typically involves supporting cryptographic extensions on whichever
+// platform is currently running.
+bool CPUSupportsRandenHwAes();
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_RANDEN_DETECT_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen_engine.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen_engine.h
new file mode 100644
index 0000000000000000000000000000000000000000..b47086649e2b15ec2aaa8f4ad999d310272cbaff
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen_engine.h
@@ -0,0 +1,264 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_RANDEN_ENGINE_H_
+#define ABSL_RANDOM_INTERNAL_RANDEN_ENGINE_H_
+
+#include <algorithm>
+#include <cinttypes>
+#include <cstdlib>
+#include <iostream>
+#include <iterator>
+#include <limits>
+#include <type_traits>
+
+#include "absl/base/internal/endian.h"
+#include "absl/meta/type_traits.h"
+#include "absl/random/internal/iostream_state_saver.h"
+#include "absl/random/internal/randen.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// Deterministic pseudorandom byte generator with backtracking resistance
+// (leaking the state does not compromise prior outputs). Based on Reverie
+// (see "A Robust and Sponge-Like PRNG with Improved Efficiency") instantiated
+// with an improved Simpira-like permutation.
+// Returns values of type "T" (must be a built-in unsigned integer type).
+//
+// RANDen = RANDom generator or beetroots in Swiss High German.
+// 'Strong' (well-distributed, unpredictable, backtracking-resistant) random
+// generator, faster in some benchmarks than std::mt19937_64 and pcg64_c32.
+template <typename T>
+class alignas(8) randen_engine {
+ public:
+  // C++11 URBG interface:
+  using result_type = T;
+  static_assert(std::is_unsigned<result_type>::value,
+                "randen_engine template argument must be a built-in unsigned "
+                "integer type");
+
+  static constexpr result_type(min)() {
+    return (std::numeric_limits<result_type>::min)();
+  }
+
+  static constexpr result_type(max)() {
+    return (std::numeric_limits<result_type>::max)();
+  }
+
+  randen_engine() : randen_engine(0) {}
+  explicit randen_engine(result_type seed_value) { seed(seed_value); }
+
+  template <class SeedSequence,
+            typename = typename absl::enable_if_t<
+                !std::is_same<SeedSequence, randen_engine>::value>>
+  explicit randen_engine(SeedSequence&& seq) {
+    seed(seq);
+  }
+
+  // alignment requirements dictate custom copy and move constructors.
+  randen_engine(const randen_engine& other)
+      : next_(other.next_), impl_(other.impl_) {
+    std::memcpy(state(), other.state(), kStateSizeT * sizeof(result_type));
+  }
+  randen_engine& operator=(const randen_engine& other) {
+    next_ = other.next_;
+    impl_ = other.impl_;
+    std::memcpy(state(), other.state(), kStateSizeT * sizeof(result_type));
+    return *this;
+  }
+
+  // Returns random bits from the buffer in units of result_type.
+  result_type operator()() {
+    // Refill the buffer if needed (unlikely).
+    auto* begin = state();
+    if (next_ >= kStateSizeT) {
+      next_ = kCapacityT;
+      impl_.Generate(begin);
+    }
+    return little_endian::ToHost(begin[next_++]);
+  }
+
+  template <class SeedSequence>
+  typename absl::enable_if_t<
+      !std::is_convertible<SeedSequence, result_type>::value>
+  seed(SeedSequence&& seq) {
+    // Zeroes the state.
+    seed();
+    reseed(seq);
+  }
+
+  void seed(result_type seed_value = 0) {
+    next_ = kStateSizeT;
+    // Zeroes the inner state and fills the outer state with seed_value to
+    // mimic the behaviour of reseed
+    auto* begin = state();
+    std::fill(begin, begin + kCapacityT, 0);
+    std::fill(begin + kCapacityT, begin + kStateSizeT, seed_value);
+  }
+
+  // Inserts entropy into (part of) the state. Calling this periodically with
+  // sufficient entropy ensures prediction resistance (attackers cannot predict
+  // future outputs even if state is compromised).
+  template <class SeedSequence>
+  void reseed(SeedSequence& seq) {
+    using sequence_result_type = typename SeedSequence::result_type;
+    static_assert(sizeof(sequence_result_type) == 4,
+                  "SeedSequence::result_type must be 32-bit");
+    constexpr size_t kBufferSize =
+        Randen::kSeedBytes / sizeof(sequence_result_type);
+    alignas(16) sequence_result_type buffer[kBufferSize];
+
+    // Randen::Absorb XORs the seed into state, which is then mixed by a call
+    // to Randen::Generate. Seeding with only the provided entropy is preferred
+    // to using an arbitrary generate() call, so use [rand.req.seed_seq]
+    // size as a proxy for the number of entropy units that can be generated
+    // without relying on seed sequence mixing...
+    const size_t entropy_size = seq.size();
+    if (entropy_size < kBufferSize) {
+      // ... and only request that many values, or 256-bits, when unspecified.
+      const size_t requested_entropy = (entropy_size == 0) ? 8u : entropy_size;
+      std::fill(buffer + requested_entropy, buffer + kBufferSize, 0);
+      seq.generate(buffer, buffer + requested_entropy);
+#ifdef ABSL_IS_BIG_ENDIAN
+      // Randen expects the seed buffer to be in Little Endian; reverse it on
+      // Big Endian platforms.
+      for (sequence_result_type& e : buffer) {
+        e = absl::little_endian::FromHost(e);
+      }
+#endif
+      // The Randen paper suggests preferentially initializing even-numbered
+      // 128-bit vectors of the randen state (there are 16 such vectors).
+      // The seed data is merged into the state offset by 128-bits, which
+      // implies prefering seed bytes [16..31, ..., 208..223]. Since the
+      // buffer is 32-bit values, we swap the corresponding buffer positions in
+      // 128-bit chunks.
+      size_t dst = kBufferSize;
+      while (dst > 7) {
+        // leave the odd bucket as-is.
+        dst -= 4;
+        size_t src = dst >> 1;
+        // swap 128-bits into the even bucket
+        std::swap(buffer[--dst], buffer[--src]);
+        std::swap(buffer[--dst], buffer[--src]);
+        std::swap(buffer[--dst], buffer[--src]);
+        std::swap(buffer[--dst], buffer[--src]);
+      }
+    } else {
+      seq.generate(buffer, buffer + kBufferSize);
+    }
+    impl_.Absorb(buffer, state());
+
+    // Generate will be called when operator() is called
+    next_ = kStateSizeT;
+  }
+
+  void discard(uint64_t count) {
+    uint64_t step = std::min<uint64_t>(kStateSizeT - next_, count);
+    count -= step;
+
+    constexpr uint64_t kRateT = kStateSizeT - kCapacityT;
+    auto* begin = state();
+    while (count > 0) {
+      next_ = kCapacityT;
+      impl_.Generate(*reinterpret_cast<result_type(*)[kStateSizeT]>(begin));
+      step = std::min<uint64_t>(kRateT, count);
+      count -= step;
+    }
+    next_ += step;
+  }
+
+  bool operator==(const randen_engine& other) const {
+    const auto* begin = state();
+    return next_ == other.next_ &&
+           std::equal(begin, begin + kStateSizeT, other.state());
+  }
+
+  bool operator!=(const randen_engine& other) const {
+    return !(*this == other);
+  }
+
+  template <class CharT, class Traits>
+  friend std::basic_ostream<CharT, Traits>& operator<<(
+      std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
+      const randen_engine<T>& engine) {       // NOLINT(runtime/references)
+    using numeric_type =
+        typename random_internal::stream_format_type<result_type>::type;
+    auto saver = random_internal::make_ostream_state_saver(os);
+    auto* it = engine.state();
+    for (auto* end = it + kStateSizeT; it < end; ++it) {
+      // In the case that `elem` is `uint8_t`, it must be cast to something
+      // larger so that it prints as an integer rather than a character. For
+      // simplicity, apply the cast all circumstances.
+      os << static_cast<numeric_type>(little_endian::FromHost(*it))
+         << os.fill();
+    }
+    os << engine.next_;
+    return os;
+  }
+
+  template <class CharT, class Traits>
+  friend std::basic_istream<CharT, Traits>& operator>>(
+      std::basic_istream<CharT, Traits>& is,  // NOLINT(runtime/references)
+      randen_engine<T>& engine) {             // NOLINT(runtime/references)
+    using numeric_type =
+        typename random_internal::stream_format_type<result_type>::type;
+    result_type state[kStateSizeT];
+    size_t next;
+    for (auto& elem : state) {
+      // It is not possible to read uint8_t from wide streams, so it is
+      // necessary to read a wider type and then cast it to uint8_t.
+      numeric_type value;
+      is >> value;
+      elem = little_endian::ToHost(static_cast<result_type>(value));
+    }
+    is >> next;
+    if (is.fail()) {
+      return is;
+    }
+    std::memcpy(engine.state(), state, sizeof(state));
+    engine.next_ = next;
+    return is;
+  }
+
+ private:
+  static constexpr size_t kStateSizeT =
+      Randen::kStateBytes / sizeof(result_type);
+  static constexpr size_t kCapacityT =
+      Randen::kCapacityBytes / sizeof(result_type);
+
+  // Returns the state array pointer, which is aligned to 16 bytes.
+  // The first kCapacityT are the `inner' sponge; the remainder are available.
+  result_type* state() {
+    return reinterpret_cast<result_type*>(
+        (reinterpret_cast<uintptr_t>(&raw_state_) & 0xf) ? (raw_state_ + 8)
+                                                         : raw_state_);
+  }
+  const result_type* state() const {
+    return const_cast<randen_engine*>(this)->state();
+  }
+
+  // raw state array, manually aligned in state(). This overallocates
+  // by 8 bytes since C++ does not guarantee extended heap alignment.
+  alignas(8) char raw_state_[Randen::kStateBytes + 8];
+  size_t next_;  // index within state()
+  Randen impl_;
+};
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_RANDEN_ENGINE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen_hwaes.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen_hwaes.h
new file mode 100644
index 0000000000000000000000000000000000000000..71a7f69f25b4c883f1cd071c0897aee78f089bb7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen_hwaes.h
@@ -0,0 +1,50 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_RANDEN_HWAES_H_
+#define ABSL_RANDOM_INTERNAL_RANDEN_HWAES_H_
+
+#include "absl/base/config.h"
+
+// HERMETIC NOTE: The randen_hwaes target must not introduce duplicate
+// symbols from arbitrary system and other headers, since it may be built
+// with different flags from other targets, using different levels of
+// optimization, potentially introducing ODR violations.
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// RANDen = RANDom generator or beetroots in Swiss High German.
+// 'Strong' (well-distributed, unpredictable, backtracking-resistant) random
+// generator, faster in some benchmarks than std::mt19937_64 and pcg64_c32.
+//
+// RandenHwAes implements the basic state manipulation methods.
+class RandenHwAes {
+ public:
+  static void Generate(const void* keys, void* state_void);
+  static void Absorb(const void* seed_void, void* state_void);
+  static const void* GetKeys();
+};
+
+// HasRandenHwAesImplementation returns true when there is an accelerated
+// implementation, and false otherwise.  If there is no implementation,
+// then attempting to use it will abort the program.
+bool HasRandenHwAesImplementation();
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_RANDEN_HWAES_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen_slow.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen_slow.h
new file mode 100644
index 0000000000000000000000000000000000000000..532c3a8991e2a46e7ac06e39bfa63896cd097b1d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen_slow.h
@@ -0,0 +1,40 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_RANDEN_SLOW_H_
+#define ABSL_RANDOM_INTERNAL_RANDEN_SLOW_H_
+
+#include <cstddef>
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// RANDen = RANDom generator or beetroots in Swiss High German.
+// RandenSlow implements the basic state manipulation methods for
+// architectures lacking AES hardware acceleration intrinsics.
+class RandenSlow {
+ public:
+  static void Generate(const void* keys, void* state_void);
+  static void Absorb(const void* seed_void, void* state_void);
+  static const void* GetKeys();
+};
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_RANDEN_SLOW_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen_traits.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen_traits.h
new file mode 100644
index 0000000000000000000000000000000000000000..120022c9fb893e52554959d506cdca05de6a2171
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/randen_traits.h
@@ -0,0 +1,88 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_RANDEN_TRAITS_H_
+#define ABSL_RANDOM_INTERNAL_RANDEN_TRAITS_H_
+
+// HERMETIC NOTE: The randen_hwaes target must not introduce duplicate
+// symbols from arbitrary system and other headers, since it may be built
+// with different flags from other targets, using different levels of
+// optimization, potentially introducing ODR violations.
+
+#include <cstddef>
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// RANDen = RANDom generator or beetroots in Swiss High German.
+// 'Strong' (well-distributed, unpredictable, backtracking-resistant) random
+// generator, faster in some benchmarks than std::mt19937_64 and pcg64_c32.
+//
+// High-level summary:
+// 1) Reverie (see "A Robust and Sponge-Like PRNG with Improved Efficiency") is
+//    a sponge-like random generator that requires a cryptographic permutation.
+//    It improves upon "Provably Robust Sponge-Based PRNGs and KDFs" by
+//    achieving backtracking resistance with only one Permute() per buffer.
+//
+// 2) "Simpira v2: A Family of Efficient Permutations Using the AES Round
+//    Function" constructs up to 1024-bit permutations using an improved
+//    Generalized Feistel network with 2-round AES-128 functions. This Feistel
+//    block shuffle achieves diffusion faster and is less vulnerable to
+//    sliced-biclique attacks than the Type-2 cyclic shuffle.
+//
+// 3) "Improving the Generalized Feistel" and "New criterion for diffusion
+//    property" extends the same kind of improved Feistel block shuffle to 16
+//    branches, which enables a 2048-bit permutation.
+//
+// Combine these three ideas and also change Simpira's subround keys from
+// structured/low-entropy counters to digits of Pi (or other random source).
+
+// RandenTraits contains the basic algorithm traits, such as the size of the
+// state, seed, sponge, etc.
+struct RandenTraits {
+  // Size of the entire sponge / state for the randen PRNG.
+  static constexpr size_t kStateBytes = 256;  // 2048-bit
+
+  // Size of the 'inner' (inaccessible) part of the sponge. Larger values would
+  // require more frequent calls to RandenGenerate.
+  static constexpr size_t kCapacityBytes = 16;  // 128-bit
+
+  // Size of the default seed consumed by the sponge.
+  static constexpr size_t kSeedBytes = kStateBytes - kCapacityBytes;
+
+  // Assuming 128-bit blocks, the number of blocks in the state.
+  // Largest size for which security proofs are known.
+  static constexpr size_t kFeistelBlocks = 16;
+
+  // Ensures SPRP security and two full subblock diffusions.
+  // Must be > 4 * log2(kFeistelBlocks).
+  static constexpr size_t kFeistelRounds = 16 + 1;
+
+  // Size of the key. A 128-bit key block is used for every-other
+  // feistel block (Type-2 generalized Feistel network) in each round.
+  static constexpr size_t kKeyBytes = 16 * kFeistelRounds * kFeistelBlocks / 2;
+};
+
+// Randen key arrays. In randen_round_keys.cc
+extern const unsigned char kRandenRoundKeys[RandenTraits::kKeyBytes];
+extern const unsigned char kRandenRoundKeysBE[RandenTraits::kKeyBytes];
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_RANDEN_TRAITS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/salted_seed_seq.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/salted_seed_seq.h
new file mode 100644
index 0000000000000000000000000000000000000000..06291865e3dcf370923fd2d16df6bccf6e762697
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/salted_seed_seq.h
@@ -0,0 +1,165 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_SALTED_SEED_SEQ_H_
+#define ABSL_RANDOM_INTERNAL_SALTED_SEED_SEQ_H_
+
+#include <cstdint>
+#include <cstdlib>
+#include <initializer_list>
+#include <iterator>
+#include <memory>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+#include "absl/container/inlined_vector.h"
+#include "absl/meta/type_traits.h"
+#include "absl/random/internal/seed_material.h"
+#include "absl/types/optional.h"
+#include "absl/types/span.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// This class conforms to the C++ Standard "Seed Sequence" concept
+// [rand.req.seedseq].
+//
+// A `SaltedSeedSeq` is meant to wrap an existing seed sequence and modify
+// generated sequence by mixing with extra entropy. This entropy may be
+// build-dependent or process-dependent. The implementation may change to be
+// have either or both kinds of entropy. If salt is not available sequence is
+// not modified.
+template <typename SSeq>
+class SaltedSeedSeq {
+ public:
+  using inner_sequence_type = SSeq;
+  using result_type = typename SSeq::result_type;
+
+  SaltedSeedSeq() : seq_(absl::make_unique<SSeq>()) {}
+
+  template <typename Iterator>
+  SaltedSeedSeq(Iterator begin, Iterator end)
+      : seq_(absl::make_unique<SSeq>(begin, end)) {}
+
+  template <typename T>
+  SaltedSeedSeq(std::initializer_list<T> il)
+      : SaltedSeedSeq(il.begin(), il.end()) {}
+
+  SaltedSeedSeq(const SaltedSeedSeq&) = delete;
+  SaltedSeedSeq& operator=(const SaltedSeedSeq&) = delete;
+
+  SaltedSeedSeq(SaltedSeedSeq&&) = default;
+  SaltedSeedSeq& operator=(SaltedSeedSeq&&) = default;
+
+  template <typename RandomAccessIterator>
+  void generate(RandomAccessIterator begin, RandomAccessIterator end) {
+    using U = typename std::iterator_traits<RandomAccessIterator>::value_type;
+
+    // The common case is that generate is called with ContiguousIterators
+    // to uint arrays. Such contiguous memory regions may be optimized,
+    // which we detect here.
+    using TagType = absl::conditional_t<
+        (std::is_same<U, uint32_t>::value &&
+         (std::is_pointer<RandomAccessIterator>::value ||
+          std::is_same<RandomAccessIterator,
+                       typename std::vector<U>::iterator>::value)),
+        ContiguousAndUint32Tag, DefaultTag>;
+    if (begin != end) {
+      generate_impl(TagType{}, begin, end, std::distance(begin, end));
+    }
+  }
+
+  template <typename OutIterator>
+  void param(OutIterator out) const {
+    seq_->param(out);
+  }
+
+  size_t size() const { return seq_->size(); }
+
+ private:
+  struct ContiguousAndUint32Tag {};
+  struct DefaultTag {};
+
+  // Generate which requires the iterators are contiguous pointers to uint32_t.
+  // Fills the initial seed buffer the underlying SSeq::generate() call,
+  // then mixes in the salt material.
+  template <typename Contiguous>
+  void generate_impl(ContiguousAndUint32Tag, Contiguous begin, Contiguous end,
+                     size_t n) {
+    seq_->generate(begin, end);
+    const uint32_t salt = absl::random_internal::GetSaltMaterial().value_or(0);
+    auto span = absl::Span<uint32_t>(&*begin, n);
+    MixIntoSeedMaterial(absl::MakeConstSpan(&salt, 1), span);
+  }
+
+  // The uncommon case for generate is that it is called with iterators over
+  // some other buffer type which is assignable from a 32-bit value. In this
+  // case we allocate a temporary 32-bit buffer and then copy-assign back
+  // to the initial inputs.
+  template <typename RandomAccessIterator>
+  void generate_impl(DefaultTag, RandomAccessIterator begin,
+                     RandomAccessIterator, size_t n) {
+    // Allocates a seed buffer of `n` elements, generates the seed, then
+    // copies the result into the `out` iterator.
+    absl::InlinedVector<uint32_t, 8> data(n, 0);
+    generate_impl(ContiguousAndUint32Tag{}, data.begin(), data.end(), n);
+    std::copy(data.begin(), data.end(), begin);
+  }
+
+  // Because [rand.req.seedseq] is not required to be copy-constructible,
+  // copy-assignable nor movable, we wrap it with unique pointer to be able
+  // to move SaltedSeedSeq.
+  std::unique_ptr<SSeq> seq_;
+};
+
+// is_salted_seed_seq indicates whether the type is a SaltedSeedSeq.
+template <typename T, typename = void>
+struct is_salted_seed_seq : public std::false_type {};
+
+template <typename T>
+struct is_salted_seed_seq<
+    T, typename std::enable_if<std::is_same<
+           T, SaltedSeedSeq<typename T::inner_sequence_type>>::value>::type>
+    : public std::true_type {};
+
+// MakeSaltedSeedSeq returns a salted variant of the seed sequence.
+// When provided with an existing SaltedSeedSeq, returns the input parameter,
+// otherwise constructs a new SaltedSeedSeq which embodies the original
+// non-salted seed parameters.
+template <
+    typename SSeq,  //
+    typename EnableIf = absl::enable_if_t<is_salted_seed_seq<SSeq>::value>>
+SSeq MakeSaltedSeedSeq(SSeq&& seq) {
+  return SSeq(std::forward<SSeq>(seq));
+}
+
+template <
+    typename SSeq,  //
+    typename EnableIf = absl::enable_if_t<!is_salted_seed_seq<SSeq>::value>>
+SaltedSeedSeq<typename std::decay<SSeq>::type> MakeSaltedSeedSeq(SSeq&& seq) {
+  using sseq_type = typename std::decay<SSeq>::type;
+  using result_type = typename sseq_type::result_type;
+
+  absl::InlinedVector<result_type, 8> data;
+  seq.param(std::back_inserter(data));
+  return SaltedSeedSeq<sseq_type>(data.begin(), data.end());
+}
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_SALTED_SEED_SEQ_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/seed_material.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/seed_material.h
new file mode 100644
index 0000000000000000000000000000000000000000..4be10e92568532a9ac3927d13e5780665f8fe1a7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/seed_material.h
@@ -0,0 +1,104 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_SEED_MATERIAL_H_
+#define ABSL_RANDOM_INTERNAL_SEED_MATERIAL_H_
+
+#include <cassert>
+#include <cstdint>
+#include <cstdlib>
+#include <string>
+#include <vector>
+
+#include "absl/base/attributes.h"
+#include "absl/random/internal/fast_uniform_bits.h"
+#include "absl/types/optional.h"
+#include "absl/types/span.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// Returns the number of 32-bit blocks needed to contain the given number of
+// bits.
+constexpr size_t SeedBitsToBlocks(size_t seed_size) {
+  return (seed_size + 31) / 32;
+}
+
+// Amount of entropy (measured in bits) used to instantiate a Seed Sequence,
+// with which to create a URBG.
+constexpr size_t kEntropyBitsNeeded = 256;
+
+// Amount of entropy (measured in 32-bit blocks) used to instantiate a Seed
+// Sequence, with which to create a URBG.
+constexpr size_t kEntropyBlocksNeeded =
+    random_internal::SeedBitsToBlocks(kEntropyBitsNeeded);
+
+static_assert(kEntropyBlocksNeeded > 0,
+              "Entropy used to seed URBGs must be nonzero.");
+
+// Attempts to fill a span of uint32_t-values using an OS-provided source of
+// true entropy (eg. /dev/urandom) into an array of uint32_t blocks of data. The
+// resulting array may be used to initialize an instance of a class conforming
+// to the C++ Standard "Seed Sequence" concept [rand.req.seedseq].
+//
+// If values.data() == nullptr, the behavior is undefined.
+ABSL_MUST_USE_RESULT
+bool ReadSeedMaterialFromOSEntropy(absl::Span<uint32_t> values);
+
+// Attempts to fill a span of uint32_t-values using variates generated by an
+// existing instance of a class conforming to the C++ Standard "Uniform Random
+// Bit Generator" concept [rand.req.urng]. The resulting data may be used to
+// initialize an instance of a class conforming to the C++ Standard
+// "Seed Sequence" concept [rand.req.seedseq].
+//
+// If urbg == nullptr or values.data() == nullptr, the behavior is undefined.
+template <typename URBG>
+ABSL_MUST_USE_RESULT bool ReadSeedMaterialFromURBG(
+    URBG* urbg, absl::Span<uint32_t> values) {
+  random_internal::FastUniformBits<uint32_t> distr;
+
+  assert(urbg != nullptr && values.data() != nullptr);
+  if (urbg == nullptr || values.data() == nullptr) {
+    return false;
+  }
+
+  for (uint32_t& seed_value : values) {
+    seed_value = distr(*urbg);
+  }
+  return true;
+}
+
+// Mixes given sequence of values with into given sequence of seed material.
+// Time complexity of this function is O(sequence.size() *
+// seed_material.size()).
+//
+// Algorithm is based on code available at
+// https://gist.github.com/imneme/540829265469e673d045
+// by Melissa O'Neill.
+void MixIntoSeedMaterial(absl::Span<const uint32_t> sequence,
+                         absl::Span<uint32_t> seed_material);
+
+// Returns salt value.
+//
+// Salt is obtained only once and stored in static variable.
+//
+// May return empty value if optaining the salt was not possible.
+absl::optional<uint32_t> GetSaltMaterial();
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_SEED_MATERIAL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/traits.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/traits.h
new file mode 100644
index 0000000000000000000000000000000000000000..f874a0f78b0a61c0c7a0aa44825d569e8363fdb9
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/traits.h
@@ -0,0 +1,149 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_TRAITS_H_
+#define ABSL_RANDOM_INTERNAL_TRAITS_H_
+
+#include <cstdint>
+#include <limits>
+#include <type_traits>
+
+#include "absl/base/config.h"
+#include "absl/numeric/bits.h"
+#include "absl/numeric/int128.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// random_internal::is_widening_convertible<A, B>
+//
+// Returns whether a type A is widening-convertible to a type B.
+//
+// A is widening-convertible to B means:
+//   A a = <any number>;
+//   B b = a;
+//   A c = b;
+//   EXPECT_EQ(a, c);
+template <typename A, typename B>
+class is_widening_convertible {
+  // As long as there are enough bits in the exact part of a number:
+  // - unsigned can fit in float, signed, unsigned
+  // - signed can fit in float, signed
+  // - float can fit in float
+  // So we define rank to be:
+  // - rank(float) -> 2
+  // - rank(signed) -> 1
+  // - rank(unsigned) -> 0
+  template <class T>
+  static constexpr int rank() {
+    return !std::numeric_limits<T>::is_integer +
+           std::numeric_limits<T>::is_signed;
+  }
+
+ public:
+  // If an arithmetic-type B can represent at least as many digits as a type A,
+  // and B belongs to a rank no lower than A, then A can be safely represented
+  // by B through a widening-conversion.
+  static constexpr bool value =
+      std::numeric_limits<A>::digits <= std::numeric_limits<B>::digits &&
+      rank<A>() <= rank<B>();
+};
+
+template <typename T>
+struct IsIntegral : std::is_integral<T> {};
+template <>
+struct IsIntegral<absl::int128> : std::true_type {};
+template <>
+struct IsIntegral<absl::uint128> : std::true_type {};
+
+template <typename T>
+struct MakeUnsigned : std::make_unsigned<T> {};
+template <>
+struct MakeUnsigned<absl::int128> {
+  using type = absl::uint128;
+};
+template <>
+struct MakeUnsigned<absl::uint128> {
+  using type = absl::uint128;
+};
+
+template <typename T>
+struct IsUnsigned : std::is_unsigned<T> {};
+template <>
+struct IsUnsigned<absl::int128> : std::false_type {};
+template <>
+struct IsUnsigned<absl::uint128> : std::true_type {};
+
+// unsigned_bits<N>::type returns the unsigned int type with the indicated
+// number of bits.
+template <size_t N>
+struct unsigned_bits;
+
+template <>
+struct unsigned_bits<8> {
+  using type = uint8_t;
+};
+template <>
+struct unsigned_bits<16> {
+  using type = uint16_t;
+};
+template <>
+struct unsigned_bits<32> {
+  using type = uint32_t;
+};
+template <>
+struct unsigned_bits<64> {
+  using type = uint64_t;
+};
+
+template <>
+struct unsigned_bits<128> {
+  using type = absl::uint128;
+};
+
+// 256-bit wrapper for wide multiplications.
+struct U256 {
+  uint128 hi;
+  uint128 lo;
+};
+template <>
+struct unsigned_bits<256> {
+  using type = U256;
+};
+
+template <typename IntType>
+struct make_unsigned_bits {
+  using type = typename unsigned_bits<
+      std::numeric_limits<typename MakeUnsigned<IntType>::type>::digits>::type;
+};
+
+template <typename T>
+int BitWidth(T v) {
+  // Workaround for bit_width not supporting int128.
+  // Don't hardcode `64` to make sure this code does not trigger compiler
+  // warnings in smaller types.
+  constexpr int half_bits = sizeof(T) * 8 / 2;
+  if (sizeof(T) == 16 && (v >> half_bits) != 0) {
+    return bit_width(static_cast<uint64_t>(v >> half_bits)) + half_bits;
+  } else {
+    return bit_width(static_cast<uint64_t>(v));
+  }
+}
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_TRAITS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/uniform_helper.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/uniform_helper.h
new file mode 100644
index 0000000000000000000000000000000000000000..e68b82ee5c47fa80ccabc89a4c5d6c0f5d27a5a7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/uniform_helper.h
@@ -0,0 +1,244 @@
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+#ifndef ABSL_RANDOM_INTERNAL_UNIFORM_HELPER_H_
+#define ABSL_RANDOM_INTERNAL_UNIFORM_HELPER_H_
+
+#include <cmath>
+#include <limits>
+#include <type_traits>
+
+#include "absl/base/config.h"
+#include "absl/meta/type_traits.h"
+#include "absl/random/internal/traits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+template <typename IntType>
+class uniform_int_distribution;
+
+template <typename RealType>
+class uniform_real_distribution;
+
+// Interval tag types which specify whether the interval is open or closed
+// on either boundary.
+
+namespace random_internal {
+template <typename T>
+struct TagTypeCompare {};
+
+template <typename T>
+constexpr bool operator==(TagTypeCompare<T>, TagTypeCompare<T>) {
+  // Tags are mono-states. They always compare equal.
+  return true;
+}
+template <typename T>
+constexpr bool operator!=(TagTypeCompare<T>, TagTypeCompare<T>) {
+  return false;
+}
+
+}  // namespace random_internal
+
+struct IntervalClosedClosedTag
+    : public random_internal::TagTypeCompare<IntervalClosedClosedTag> {};
+struct IntervalClosedOpenTag
+    : public random_internal::TagTypeCompare<IntervalClosedOpenTag> {};
+struct IntervalOpenClosedTag
+    : public random_internal::TagTypeCompare<IntervalOpenClosedTag> {};
+struct IntervalOpenOpenTag
+    : public random_internal::TagTypeCompare<IntervalOpenOpenTag> {};
+
+namespace random_internal {
+
+// In the absence of an explicitly provided return-type, the template
+// "uniform_inferred_return_t<A, B>" is used to derive a suitable type, based on
+// the data-types of the endpoint-arguments {A lo, B hi}.
+//
+// Given endpoints {A lo, B hi}, one of {A, B} will be chosen as the
+// return-type, if one type can be implicitly converted into the other, in a
+// lossless way. The template "is_widening_convertible" implements the
+// compile-time logic for deciding if such a conversion is possible.
+//
+// If no such conversion between {A, B} exists, then the overload for
+// absl::Uniform() will be discarded, and the call will be ill-formed.
+// Return-type for absl::Uniform() when the return-type is inferred.
+template <typename A, typename B>
+using uniform_inferred_return_t =
+    absl::enable_if_t<absl::disjunction<is_widening_convertible<A, B>,
+                                        is_widening_convertible<B, A>>::value,
+                      typename std::conditional<
+                          is_widening_convertible<A, B>::value, B, A>::type>;
+
+// The functions
+//    uniform_lower_bound(tag, a, b)
+// and
+//    uniform_upper_bound(tag, a, b)
+// are used as implementation-details for absl::Uniform().
+//
+// Conceptually,
+//    [a, b] == [uniform_lower_bound(IntervalClosedClosed, a, b),
+//               uniform_upper_bound(IntervalClosedClosed, a, b)]
+//    (a, b) == [uniform_lower_bound(IntervalOpenOpen, a, b),
+//               uniform_upper_bound(IntervalOpenOpen, a, b)]
+//    [a, b) == [uniform_lower_bound(IntervalClosedOpen, a, b),
+//               uniform_upper_bound(IntervalClosedOpen, a, b)]
+//    (a, b] == [uniform_lower_bound(IntervalOpenClosed, a, b),
+//               uniform_upper_bound(IntervalOpenClosed, a, b)]
+//
+template <typename IntType, typename Tag>
+typename absl::enable_if_t<
+    absl::conjunction<
+        IsIntegral<IntType>,
+        absl::disjunction<std::is_same<Tag, IntervalOpenClosedTag>,
+                          std::is_same<Tag, IntervalOpenOpenTag>>>::value,
+    IntType>
+uniform_lower_bound(Tag, IntType a, IntType) {
+  return a < (std::numeric_limits<IntType>::max)() ? (a + 1) : a;
+}
+
+template <typename FloatType, typename Tag>
+typename absl::enable_if_t<
+    absl::conjunction<
+        std::is_floating_point<FloatType>,
+        absl::disjunction<std::is_same<Tag, IntervalOpenClosedTag>,
+                          std::is_same<Tag, IntervalOpenOpenTag>>>::value,
+    FloatType>
+uniform_lower_bound(Tag, FloatType a, FloatType b) {
+  return std::nextafter(a, b);
+}
+
+template <typename NumType, typename Tag>
+typename absl::enable_if_t<
+    absl::disjunction<std::is_same<Tag, IntervalClosedClosedTag>,
+                      std::is_same<Tag, IntervalClosedOpenTag>>::value,
+    NumType>
+uniform_lower_bound(Tag, NumType a, NumType) {
+  return a;
+}
+
+template <typename IntType, typename Tag>
+typename absl::enable_if_t<
+    absl::conjunction<
+        IsIntegral<IntType>,
+        absl::disjunction<std::is_same<Tag, IntervalClosedOpenTag>,
+                          std::is_same<Tag, IntervalOpenOpenTag>>>::value,
+    IntType>
+uniform_upper_bound(Tag, IntType, IntType b) {
+  return b > (std::numeric_limits<IntType>::min)() ? (b - 1) : b;
+}
+
+template <typename FloatType, typename Tag>
+typename absl::enable_if_t<
+    absl::conjunction<
+        std::is_floating_point<FloatType>,
+        absl::disjunction<std::is_same<Tag, IntervalClosedOpenTag>,
+                          std::is_same<Tag, IntervalOpenOpenTag>>>::value,
+    FloatType>
+uniform_upper_bound(Tag, FloatType, FloatType b) {
+  return b;
+}
+
+template <typename IntType, typename Tag>
+typename absl::enable_if_t<
+    absl::conjunction<
+        IsIntegral<IntType>,
+        absl::disjunction<std::is_same<Tag, IntervalClosedClosedTag>,
+                          std::is_same<Tag, IntervalOpenClosedTag>>>::value,
+    IntType>
+uniform_upper_bound(Tag, IntType, IntType b) {
+  return b;
+}
+
+template <typename FloatType, typename Tag>
+typename absl::enable_if_t<
+    absl::conjunction<
+        std::is_floating_point<FloatType>,
+        absl::disjunction<std::is_same<Tag, IntervalClosedClosedTag>,
+                          std::is_same<Tag, IntervalOpenClosedTag>>>::value,
+    FloatType>
+uniform_upper_bound(Tag, FloatType, FloatType b) {
+  return std::nextafter(b, (std::numeric_limits<FloatType>::max)());
+}
+
+// Returns whether the bounds are valid for the underlying distribution.
+// Inputs must have already been resolved via uniform_*_bound calls.
+//
+// The c++ standard constraints in [rand.dist.uni.int] are listed as:
+//    requires: lo <= hi.
+//
+// In the uniform_int_distrubtion, {lo, hi} are closed, closed. Thus:
+// [0, 0] is legal.
+// [0, 0) is not legal, but [0, 1) is, which translates to [0, 0].
+// (0, 1) is not legal, but (0, 2) is, which translates to [1, 1].
+// (0, 0] is not legal, but (0, 1] is, which translates to [1, 1].
+//
+// The c++ standard constraints in [rand.dist.uni.real] are listed as:
+//    requires: lo <= hi.
+//    requires: (hi - lo) <= numeric_limits<T>::max()
+//
+// In the uniform_real_distribution, {lo, hi} are closed, open, Thus:
+// [0, 0] is legal, which is [0, 0+epsilon).
+// [0, 0) is legal.
+// (0, 0) is not legal, but (0-epsilon, 0+epsilon) is.
+// (0, 0] is not legal, but (0, 0+epsilon] is.
+//
+template <typename FloatType>
+absl::enable_if_t<std::is_floating_point<FloatType>::value, bool>
+is_uniform_range_valid(FloatType a, FloatType b) {
+  return a <= b && std::isfinite(b - a);
+}
+
+template <typename IntType>
+absl::enable_if_t<IsIntegral<IntType>::value, bool>
+is_uniform_range_valid(IntType a, IntType b) {
+  return a <= b;
+}
+
+// UniformDistribution selects either absl::uniform_int_distribution
+// or absl::uniform_real_distribution depending on the NumType parameter.
+template <typename NumType>
+using UniformDistribution =
+    typename std::conditional<IsIntegral<NumType>::value,
+                              absl::uniform_int_distribution<NumType>,
+                              absl::uniform_real_distribution<NumType>>::type;
+
+// UniformDistributionWrapper is used as the underlying distribution type
+// by the absl::Uniform template function. It selects the proper Abseil
+// uniform distribution and provides constructor overloads that match the
+// expected parameter order as well as adjusting distribtuion bounds based
+// on the tag.
+template <typename NumType>
+struct UniformDistributionWrapper : public UniformDistribution<NumType> {
+  template <typename TagType>
+  explicit UniformDistributionWrapper(TagType, NumType lo, NumType hi)
+      : UniformDistribution<NumType>(
+            uniform_lower_bound<NumType>(TagType{}, lo, hi),
+            uniform_upper_bound<NumType>(TagType{}, lo, hi)) {}
+
+  explicit UniformDistributionWrapper(NumType lo, NumType hi)
+      : UniformDistribution<NumType>(
+            uniform_lower_bound<NumType>(IntervalClosedOpenTag(), lo, hi),
+            uniform_upper_bound<NumType>(IntervalClosedOpenTag(), lo, hi)) {}
+
+  explicit UniformDistributionWrapper()
+      : UniformDistribution<NumType>(std::numeric_limits<NumType>::lowest(),
+                                     (std::numeric_limits<NumType>::max)()) {}
+};
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_UNIFORM_HELPER_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/wide_multiply.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/wide_multiply.h
new file mode 100644
index 0000000000000000000000000000000000000000..891e3630b7972706881c91d911f0611a28200959
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/internal/wide_multiply.h
@@ -0,0 +1,96 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_INTERNAL_WIDE_MULTIPLY_H_
+#define ABSL_RANDOM_INTERNAL_WIDE_MULTIPLY_H_
+
+#include <cstdint>
+#include <limits>
+#include <type_traits>
+
+#if (defined(_WIN32) || defined(_WIN64)) && defined(_M_IA64)
+#include <intrin.h>  // NOLINT(build/include_order)
+#pragma intrinsic(_umul128)
+#define ABSL_INTERNAL_USE_UMUL128 1
+#endif
+
+#include "absl/base/config.h"
+#include "absl/numeric/bits.h"
+#include "absl/numeric/int128.h"
+#include "absl/random/internal/traits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace random_internal {
+
+// wide_multiply<T> multiplies two N-bit values to a 2N-bit result.
+template <typename UIntType>
+struct wide_multiply {
+  static constexpr size_t kN = std::numeric_limits<UIntType>::digits;
+  using input_type = UIntType;
+  using result_type = typename random_internal::unsigned_bits<kN * 2>::type;
+
+  static result_type multiply(input_type a, input_type b) {
+    return static_cast<result_type>(a) * b;
+  }
+
+  static input_type hi(result_type r) {
+    return static_cast<input_type>(r >> kN);
+  }
+  static input_type lo(result_type r) { return static_cast<input_type>(r); }
+
+  static_assert(std::is_unsigned<UIntType>::value,
+                "Class-template wide_multiply<> argument must be unsigned.");
+};
+
+// MultiplyU128ToU256 multiplies two 128-bit values to a 256-bit value.
+inline U256 MultiplyU128ToU256(uint128 a, uint128 b) {
+  const uint128 a00 = static_cast<uint64_t>(a);
+  const uint128 a64 = a >> 64;
+  const uint128 b00 = static_cast<uint64_t>(b);
+  const uint128 b64 = b >> 64;
+
+  const uint128 c00 = a00 * b00;
+  const uint128 c64a = a00 * b64;
+  const uint128 c64b = a64 * b00;
+  const uint128 c128 = a64 * b64;
+
+  const uint64_t carry =
+      static_cast<uint64_t>(((c00 >> 64) + static_cast<uint64_t>(c64a) +
+                             static_cast<uint64_t>(c64b)) >>
+                            64);
+
+  return {c128 + (c64a >> 64) + (c64b >> 64) + carry,
+          c00 + (c64a << 64) + (c64b << 64)};
+}
+
+
+template <>
+struct wide_multiply<uint128> {
+  using input_type = uint128;
+  using result_type = U256;
+
+  static result_type multiply(input_type a, input_type b) {
+    return MultiplyU128ToU256(a, b);
+  }
+
+  static input_type hi(result_type r) { return r.hi; }
+  static input_type lo(result_type r) { return r.lo; }
+};
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_INTERNAL_WIDE_MULTIPLY_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/log_uniform_int_distribution.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/log_uniform_int_distribution.h
new file mode 100644
index 0000000000000000000000000000000000000000..4afff8f604ea61c90498083be8485bcb8df2e77f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/log_uniform_int_distribution.h
@@ -0,0 +1,256 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_LOG_UNIFORM_INT_DISTRIBUTION_H_
+#define ABSL_RANDOM_LOG_UNIFORM_INT_DISTRIBUTION_H_
+
+#include <algorithm>
+#include <cassert>
+#include <cmath>
+#include <istream>
+#include <limits>
+#include <ostream>
+#include <type_traits>
+
+#include "absl/numeric/bits.h"
+#include "absl/random/internal/fastmath.h"
+#include "absl/random/internal/generate_real.h"
+#include "absl/random/internal/iostream_state_saver.h"
+#include "absl/random/internal/traits.h"
+#include "absl/random/uniform_int_distribution.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// log_uniform_int_distribution:
+//
+// Returns a random variate R in range [min, max] such that
+// floor(log(R-min, base)) is uniformly distributed.
+// We ensure uniformity by discretization using the
+// boundary sets [0, 1, base, base * base, ... min(base*n, max)]
+//
+template <typename IntType = int>
+class log_uniform_int_distribution {
+ private:
+  using unsigned_type =
+      typename random_internal::make_unsigned_bits<IntType>::type;
+
+ public:
+  using result_type = IntType;
+
+  class param_type {
+   public:
+    using distribution_type = log_uniform_int_distribution;
+
+    explicit param_type(
+        result_type min = 0,
+        result_type max = (std::numeric_limits<result_type>::max)(),
+        result_type base = 2)
+        : min_(min),
+          max_(max),
+          base_(base),
+          range_(static_cast<unsigned_type>(max_) -
+                 static_cast<unsigned_type>(min_)),
+          log_range_(0) {
+      assert(max_ >= min_);
+      assert(base_ > 1);
+
+      if (base_ == 2) {
+        // Determine where the first set bit is on range(), giving a log2(range)
+        // value which can be used to construct bounds.
+        log_range_ = (std::min)(random_internal::BitWidth(range()),
+                                std::numeric_limits<unsigned_type>::digits);
+      } else {
+        // NOTE: Computing the logN(x) introduces error from 2 sources:
+        // 1. Conversion of int to double loses precision for values >=
+        // 2^53, which may cause some log() computations to operate on
+        // different values.
+        // 2. The error introduced by the division will cause the result
+        // to differ from the expected value.
+        //
+        // Thus a result which should equal K may equal K +/- epsilon,
+        // which can eliminate some values depending on where the bounds fall.
+        const double inv_log_base = 1.0 / std::log(static_cast<double>(base_));
+        const double log_range = std::log(static_cast<double>(range()) + 0.5);
+        log_range_ = static_cast<int>(std::ceil(inv_log_base * log_range));
+      }
+    }
+
+    result_type(min)() const { return min_; }
+    result_type(max)() const { return max_; }
+    result_type base() const { return base_; }
+
+    friend bool operator==(const param_type& a, const param_type& b) {
+      return a.min_ == b.min_ && a.max_ == b.max_ && a.base_ == b.base_;
+    }
+
+    friend bool operator!=(const param_type& a, const param_type& b) {
+      return !(a == b);
+    }
+
+   private:
+    friend class log_uniform_int_distribution;
+
+    int log_range() const { return log_range_; }
+    unsigned_type range() const { return range_; }
+
+    result_type min_;
+    result_type max_;
+    result_type base_;
+    unsigned_type range_;  // max - min
+    int log_range_;        // ceil(logN(range_))
+
+    static_assert(random_internal::IsIntegral<IntType>::value,
+                  "Class-template absl::log_uniform_int_distribution<> must be "
+                  "parameterized using an integral type.");
+  };
+
+  log_uniform_int_distribution() : log_uniform_int_distribution(0) {}
+
+  explicit log_uniform_int_distribution(
+      result_type min,
+      result_type max = (std::numeric_limits<result_type>::max)(),
+      result_type base = 2)
+      : param_(min, max, base) {}
+
+  explicit log_uniform_int_distribution(const param_type& p) : param_(p) {}
+
+  void reset() {}
+
+  // generating functions
+  template <typename URBG>
+  result_type operator()(URBG& g) {  // NOLINT(runtime/references)
+    return (*this)(g, param_);
+  }
+
+  template <typename URBG>
+  result_type operator()(URBG& g,  // NOLINT(runtime/references)
+                         const param_type& p) {
+    return static_cast<result_type>((p.min)() + Generate(g, p));
+  }
+
+  result_type(min)() const { return (param_.min)(); }
+  result_type(max)() const { return (param_.max)(); }
+  result_type base() const { return param_.base(); }
+
+  param_type param() const { return param_; }
+  void param(const param_type& p) { param_ = p; }
+
+  friend bool operator==(const log_uniform_int_distribution& a,
+                         const log_uniform_int_distribution& b) {
+    return a.param_ == b.param_;
+  }
+  friend bool operator!=(const log_uniform_int_distribution& a,
+                         const log_uniform_int_distribution& b) {
+    return a.param_ != b.param_;
+  }
+
+ private:
+  // Returns a log-uniform variate in the range [0, p.range()]. The caller
+  // should add min() to shift the result to the correct range.
+  template <typename URNG>
+  unsigned_type Generate(URNG& g,  // NOLINT(runtime/references)
+                         const param_type& p);
+
+  param_type param_;
+};
+
+template <typename IntType>
+template <typename URBG>
+typename log_uniform_int_distribution<IntType>::unsigned_type
+log_uniform_int_distribution<IntType>::Generate(
+    URBG& g,  // NOLINT(runtime/references)
+    const param_type& p) {
+  // sample e over [0, log_range]. Map the results of e to this:
+  // 0 => 0
+  // 1 => [1, b-1]
+  // 2 => [b, (b^2)-1]
+  // n => [b^(n-1)..(b^n)-1]
+  const int e = absl::uniform_int_distribution<int>(0, p.log_range())(g);
+  if (e == 0) {
+    return 0;
+  }
+  const int d = e - 1;
+
+  unsigned_type base_e, top_e;
+  if (p.base() == 2) {
+    base_e = static_cast<unsigned_type>(1) << d;
+
+    top_e = (e >= std::numeric_limits<unsigned_type>::digits)
+                ? (std::numeric_limits<unsigned_type>::max)()
+                : (static_cast<unsigned_type>(1) << e) - 1;
+  } else {
+    const double r = std::pow(static_cast<double>(p.base()), d);
+    const double s = (r * static_cast<double>(p.base())) - 1.0;
+
+    base_e =
+        (r > static_cast<double>((std::numeric_limits<unsigned_type>::max)()))
+            ? (std::numeric_limits<unsigned_type>::max)()
+            : static_cast<unsigned_type>(r);
+
+    top_e =
+        (s > static_cast<double>((std::numeric_limits<unsigned_type>::max)()))
+            ? (std::numeric_limits<unsigned_type>::max)()
+            : static_cast<unsigned_type>(s);
+  }
+
+  const unsigned_type lo = (base_e >= p.range()) ? p.range() : base_e;
+  const unsigned_type hi = (top_e >= p.range()) ? p.range() : top_e;
+
+  // choose uniformly over [lo, hi]
+  return absl::uniform_int_distribution<result_type>(
+      static_cast<result_type>(lo), static_cast<result_type>(hi))(g);
+}
+
+template <typename CharT, typename Traits, typename IntType>
+std::basic_ostream<CharT, Traits>& operator<<(
+    std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
+    const log_uniform_int_distribution<IntType>& x) {
+  using stream_type =
+      typename random_internal::stream_format_type<IntType>::type;
+  auto saver = random_internal::make_ostream_state_saver(os);
+  os << static_cast<stream_type>((x.min)()) << os.fill()
+     << static_cast<stream_type>((x.max)()) << os.fill()
+     << static_cast<stream_type>(x.base());
+  return os;
+}
+
+template <typename CharT, typename Traits, typename IntType>
+std::basic_istream<CharT, Traits>& operator>>(
+    std::basic_istream<CharT, Traits>& is,       // NOLINT(runtime/references)
+    log_uniform_int_distribution<IntType>& x) {  // NOLINT(runtime/references)
+  using param_type = typename log_uniform_int_distribution<IntType>::param_type;
+  using result_type =
+      typename log_uniform_int_distribution<IntType>::result_type;
+  using stream_type =
+      typename random_internal::stream_format_type<IntType>::type;
+
+  stream_type min;
+  stream_type max;
+  stream_type base;
+
+  auto saver = random_internal::make_istream_state_saver(is);
+  is >> min >> max >> base;
+  if (!is.fail()) {
+    x.param(param_type(static_cast<result_type>(min),
+                       static_cast<result_type>(max),
+                       static_cast<result_type>(base)));
+  }
+  return is;
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_LOG_UNIFORM_INT_DISTRIBUTION_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/poisson_distribution.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/poisson_distribution.h
new file mode 100644
index 0000000000000000000000000000000000000000..f4573082e10804fb7deeb4c21fcf40706d3167f6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/poisson_distribution.h
@@ -0,0 +1,261 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_POISSON_DISTRIBUTION_H_
+#define ABSL_RANDOM_POISSON_DISTRIBUTION_H_
+
+#include <cassert>
+#include <cmath>
+#include <istream>
+#include <limits>
+#include <ostream>
+#include <type_traits>
+
+#include "absl/random/internal/fast_uniform_bits.h"
+#include "absl/random/internal/fastmath.h"
+#include "absl/random/internal/generate_real.h"
+#include "absl/random/internal/iostream_state_saver.h"
+#include "absl/random/internal/traits.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// absl::poisson_distribution:
+// Generates discrete variates conforming to a Poisson distribution.
+//   p(n) = (mean^n / n!) exp(-mean)
+//
+// Depending on the parameter, the distribution selects one of the following
+// algorithms:
+// * The standard algorithm, attributed to Knuth, extended using a split method
+// for larger values
+// * The "Ratio of Uniforms as a convenient method for sampling from classical
+// discrete distributions", Stadlober, 1989.
+// http://www.sciencedirect.com/science/article/pii/0377042790903495
+//
+// NOTE: param_type.mean() is a double, which permits values larger than
+// poisson_distribution<IntType>::max(), however this should be avoided and
+// the distribution results are limited to the max() value.
+//
+// The goals of this implementation are to provide good performance while still
+// beig thread-safe: This limits the implementation to not using lgamma provided
+// by <math.h>.
+//
+template <typename IntType = int>
+class poisson_distribution {
+ public:
+  using result_type = IntType;
+
+  class param_type {
+   public:
+    using distribution_type = poisson_distribution;
+    explicit param_type(double mean = 1.0);
+
+    double mean() const { return mean_; }
+
+    friend bool operator==(const param_type& a, const param_type& b) {
+      return a.mean_ == b.mean_;
+    }
+
+    friend bool operator!=(const param_type& a, const param_type& b) {
+      return !(a == b);
+    }
+
+   private:
+    friend class poisson_distribution;
+
+    double mean_;
+    double emu_;  // e ^ -mean_
+    double lmu_;  // ln(mean_)
+    double s_;
+    double log_k_;
+    int split_;
+
+    static_assert(random_internal::IsIntegral<IntType>::value,
+                  "Class-template absl::poisson_distribution<> must be "
+                  "parameterized using an integral type.");
+  };
+
+  poisson_distribution() : poisson_distribution(1.0) {}
+
+  explicit poisson_distribution(double mean) : param_(mean) {}
+
+  explicit poisson_distribution(const param_type& p) : param_(p) {}
+
+  void reset() {}
+
+  // generating functions
+  template <typename URBG>
+  result_type operator()(URBG& g) {  // NOLINT(runtime/references)
+    return (*this)(g, param_);
+  }
+
+  template <typename URBG>
+  result_type operator()(URBG& g,  // NOLINT(runtime/references)
+                         const param_type& p);
+
+  param_type param() const { return param_; }
+  void param(const param_type& p) { param_ = p; }
+
+  result_type(min)() const { return 0; }
+  result_type(max)() const { return (std::numeric_limits<result_type>::max)(); }
+
+  double mean() const { return param_.mean(); }
+
+  friend bool operator==(const poisson_distribution& a,
+                         const poisson_distribution& b) {
+    return a.param_ == b.param_;
+  }
+  friend bool operator!=(const poisson_distribution& a,
+                         const poisson_distribution& b) {
+    return a.param_ != b.param_;
+  }
+
+ private:
+  param_type param_;
+  random_internal::FastUniformBits<uint64_t> fast_u64_;
+};
+
+// -----------------------------------------------------------------------------
+// Implementation details follow
+// -----------------------------------------------------------------------------
+
+template <typename IntType>
+poisson_distribution<IntType>::param_type::param_type(double mean)
+    : mean_(mean), split_(0) {
+  assert(mean >= 0);
+  assert(mean <=
+         static_cast<double>((std::numeric_limits<result_type>::max)()));
+  // As a defensive measure, avoid large values of the mean.  The rejection
+  // algorithm used does not support very large values well.  It my be worth
+  // changing algorithms to better deal with these cases.
+  assert(mean <= 1e10);
+  if (mean_ < 10) {
+    // For small lambda, use the knuth method.
+    split_ = 1;
+    emu_ = std::exp(-mean_);
+  } else if (mean_ <= 50) {
+    // Use split-knuth method.
+    split_ = 1 + static_cast<int>(mean_ / 10.0);
+    emu_ = std::exp(-mean_ / static_cast<double>(split_));
+  } else {
+    // Use ratio of uniforms method.
+    constexpr double k2E = 0.7357588823428846;
+    constexpr double kSA = 0.4494580810294493;
+
+    lmu_ = std::log(mean_);
+    double a = mean_ + 0.5;
+    s_ = kSA + std::sqrt(k2E * a);
+    const double mode = std::ceil(mean_) - 1;
+    log_k_ = lmu_ * mode - absl::random_internal::StirlingLogFactorial(mode);
+  }
+}
+
+template <typename IntType>
+template <typename URBG>
+typename poisson_distribution<IntType>::result_type
+poisson_distribution<IntType>::operator()(
+    URBG& g,  // NOLINT(runtime/references)
+    const param_type& p) {
+  using random_internal::GeneratePositiveTag;
+  using random_internal::GenerateRealFromBits;
+  using random_internal::GenerateSignedTag;
+
+  if (p.split_ != 0) {
+    // Use Knuth's algorithm with range splitting to avoid floating-point
+    // errors. Knuth's algorithm is: Ui is a sequence of uniform variates on
+    // (0,1); return the number of variates required for product(Ui) <
+    // exp(-lambda).
+    //
+    // The expected number of variates required for Knuth's method can be
+    // computed as follows:
+    // The expected value of U is 0.5, so solving for 0.5^n < exp(-lambda) gives
+    // the expected number of uniform variates
+    // required for a given lambda, which is:
+    //  lambda = [2, 5,  9, 10, 11, 12, 13, 14, 15, 16, 17]
+    //  n      = [3, 8, 13, 15, 16, 18, 19, 21, 22, 24, 25]
+    //
+    result_type n = 0;
+    for (int split = p.split_; split > 0; --split) {
+      double r = 1.0;
+      do {
+        r *= GenerateRealFromBits<double, GeneratePositiveTag, true>(
+            fast_u64_(g));  // U(-1, 0)
+        ++n;
+      } while (r > p.emu_);
+      --n;
+    }
+    return n;
+  }
+
+  // Use ratio of uniforms method.
+  //
+  // Let u ~ Uniform(0, 1), v ~ Uniform(-1, 1),
+  //     a = lambda + 1/2,
+  //     s = 1.5 - sqrt(3/e) + sqrt(2(lambda + 1/2)/e),
+  //     x = s * v/u + a.
+  // P(floor(x) = k | u^2 < f(floor(x))/k), where
+  // f(m) = lambda^m exp(-lambda)/ m!, for 0 <= m, and f(m) = 0 otherwise,
+  // and k = max(f).
+  const double a = p.mean_ + 0.5;
+  for (;;) {
+    const double u = GenerateRealFromBits<double, GeneratePositiveTag, false>(
+        fast_u64_(g));  // U(0, 1)
+    const double v = GenerateRealFromBits<double, GenerateSignedTag, false>(
+        fast_u64_(g));  // U(-1, 1)
+
+    const double x = std::floor(p.s_ * v / u + a);
+    if (x < 0) continue;  // f(negative) = 0
+    const double rhs = x * p.lmu_;
+    // clang-format off
+    double s = (x <= 1.0) ? 0.0
+             : (x == 2.0) ? 0.693147180559945
+             : absl::random_internal::StirlingLogFactorial(x);
+    // clang-format on
+    const double lhs = 2.0 * std::log(u) + p.log_k_ + s;
+    if (lhs < rhs) {
+      return x > static_cast<double>((max)())
+                 ? (max)()
+                 : static_cast<result_type>(x);  // f(x)/k >= u^2
+    }
+  }
+}
+
+template <typename CharT, typename Traits, typename IntType>
+std::basic_ostream<CharT, Traits>& operator<<(
+    std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
+    const poisson_distribution<IntType>& x) {
+  auto saver = random_internal::make_ostream_state_saver(os);
+  os.precision(random_internal::stream_precision_helper<double>::kPrecision);
+  os << x.mean();
+  return os;
+}
+
+template <typename CharT, typename Traits, typename IntType>
+std::basic_istream<CharT, Traits>& operator>>(
+    std::basic_istream<CharT, Traits>& is,  // NOLINT(runtime/references)
+    poisson_distribution<IntType>& x) {     // NOLINT(runtime/references)
+  using param_type = typename poisson_distribution<IntType>::param_type;
+
+  auto saver = random_internal::make_istream_state_saver(is);
+  double mean = random_internal::read_floating_point<double>(is);
+  if (!is.fail()) {
+    x.param(param_type(mean));
+  }
+  return is;
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_POISSON_DISTRIBUTION_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/random.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/random.h
new file mode 100644
index 0000000000000000000000000000000000000000..767208671c1481de4d2277e1c40fde57fc3df53d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/random.h
@@ -0,0 +1,189 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: random.h
+// -----------------------------------------------------------------------------
+//
+// This header defines the recommended Uniform Random Bit Generator (URBG)
+// types for use within the Abseil Random library. These types are not
+// suitable for security-related use-cases, but should suffice for most other
+// uses of generating random values.
+//
+// The Abseil random library provides the following URBG types:
+//
+//   * BitGen, a good general-purpose bit generator, optimized for generating
+//     random (but not cryptographically secure) values
+//   * InsecureBitGen, a slightly faster, though less random, bit generator, for
+//     cases where the existing BitGen is a drag on performance.
+
+#ifndef ABSL_RANDOM_RANDOM_H_
+#define ABSL_RANDOM_RANDOM_H_
+
+#include <random>
+
+#include "absl/random/distributions.h"  // IWYU pragma: export
+#include "absl/random/internal/nonsecure_base.h"  // IWYU pragma: export
+#include "absl/random/internal/pcg_engine.h"  // IWYU pragma: export
+#include "absl/random/internal/pool_urbg.h"
+#include "absl/random/internal/randen_engine.h"
+#include "absl/random/seed_sequences.h"  // IWYU pragma: export
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// -----------------------------------------------------------------------------
+// absl::BitGen
+// -----------------------------------------------------------------------------
+//
+// `absl::BitGen` is a general-purpose random bit generator for generating
+// random values for use within the Abseil random library. Typically, you use a
+// bit generator in combination with a distribution to provide random values.
+//
+// Example:
+//
+//   // Create an absl::BitGen. There is no need to seed this bit generator.
+//   absl::BitGen gen;
+//
+//   // Generate an integer value in the closed interval [1,6]
+//   int die_roll = absl::uniform_int_distribution<int>(1, 6)(gen);
+//
+// `absl::BitGen` is seeded by default with non-deterministic data to produce
+// different sequences of random values across different instances, including
+// different binary invocations. This behavior is different than the standard
+// library bit generators, which use golden values as their seeds. Default
+// construction intentionally provides no stability guarantees, to avoid
+// accidental dependence on such a property.
+//
+// `absl::BitGen` may be constructed with an optional seed sequence type,
+// conforming to [rand.req.seed_seq], which will be mixed with additional
+// non-deterministic data as detailed below.
+//
+// Example:
+//
+//  // Create an absl::BitGen using an std::seed_seq seed sequence
+//  std::seed_seq seq{1,2,3};
+//  absl::BitGen gen_with_seed(seq);
+//
+//  // Generate an integer value in the closed interval [1,6]
+//  int die_roll2 = absl::uniform_int_distribution<int>(1, 6)(gen_with_seed);
+//
+// Constructing two `absl::BitGen`s with the same seed sequence in the same
+// process will produce the same sequence of variates, but need not do so across
+// multiple processes even if they're executing the same binary.
+//
+// `absl::BitGen` meets the requirements of the Uniform Random Bit Generator
+// (URBG) concept as per the C++17 standard [rand.req.urng] though differs
+// slightly with [rand.req.eng]. Like its standard library equivalents (e.g.
+// `std::mersenne_twister_engine`) `absl::BitGen` is not cryptographically
+// secure.
+//
+// This type has been optimized to perform better than Mersenne Twister
+// (https://en.wikipedia.org/wiki/Mersenne_Twister) and many other complex URBG
+// types on modern x86, ARM, and PPC architectures.
+//
+// This type is thread-compatible, but not thread-safe.
+
+// ---------------------------------------------------------------------------
+// absl::BitGen member functions
+// ---------------------------------------------------------------------------
+
+// absl::BitGen::operator()()
+//
+// Calls the BitGen, returning a generated value.
+
+// absl::BitGen::min()
+//
+// Returns the smallest possible value from this bit generator.
+
+// absl::BitGen::max()
+//
+// Returns the largest possible value from this bit generator.
+
+// absl::BitGen::discard(num)
+//
+// Advances the internal state of this bit generator by `num` times, and
+// discards the intermediate results.
+// ---------------------------------------------------------------------------
+
+using BitGen = random_internal::NonsecureURBGBase<
+    random_internal::randen_engine<uint64_t>>;
+
+// -----------------------------------------------------------------------------
+// absl::InsecureBitGen
+// -----------------------------------------------------------------------------
+//
+// `absl::InsecureBitGen` is an efficient random bit generator for generating
+// random values, recommended only for performance-sensitive use cases where
+// `absl::BitGen` is not satisfactory when compute-bounded by bit generation
+// costs.
+//
+// Example:
+//
+//   // Create an absl::InsecureBitGen
+//   absl::InsecureBitGen gen;
+//   for (size_t i = 0; i < 1000000; i++) {
+//
+//     // Generate a bunch of random values from some complex distribution
+//     auto my_rnd = some_distribution(gen, 1, 1000);
+//   }
+//
+// Like `absl::BitGen`, `absl::InsecureBitGen` is seeded by default with
+// non-deterministic data to produce different sequences of random values across
+// different instances, including different binary invocations. (This behavior
+// is different than the standard library bit generators, which use golden
+// values as their seeds.)
+//
+// `absl::InsecureBitGen` may be constructed with an optional seed sequence
+// type, conforming to [rand.req.seed_seq], which will be mixed with additional
+// non-deterministic data, as detailed in the `absl::BitGen` comment.
+//
+// `absl::InsecureBitGen` meets the requirements of the Uniform Random Bit
+// Generator (URBG) concept as per the C++17 standard [rand.req.urng] though
+// its implementation differs slightly with [rand.req.eng]. Like its standard
+// library equivalents (e.g. `std::mersenne_twister_engine`)
+// `absl::InsecureBitGen` is not cryptographically secure.
+//
+// Prefer `absl::BitGen` over `absl::InsecureBitGen` as the general type is
+// often fast enough for the vast majority of applications.
+
+using InsecureBitGen =
+    random_internal::NonsecureURBGBase<random_internal::pcg64_2018_engine>;
+
+// ---------------------------------------------------------------------------
+// absl::InsecureBitGen member functions
+// ---------------------------------------------------------------------------
+
+// absl::InsecureBitGen::operator()()
+//
+// Calls the InsecureBitGen, returning a generated value.
+
+// absl::InsecureBitGen::min()
+//
+// Returns the smallest possible value from this bit generator.
+
+// absl::InsecureBitGen::max()
+//
+// Returns the largest possible value from this bit generator.
+
+// absl::InsecureBitGen::discard(num)
+//
+// Advances the internal state of this bit generator by `num` times, and
+// discards the intermediate results.
+// ---------------------------------------------------------------------------
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_RANDOM_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/seed_gen_exception.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/seed_gen_exception.h
new file mode 100644
index 0000000000000000000000000000000000000000..5353900564372008d26542d10a9ba336d1b055ca
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/seed_gen_exception.h
@@ -0,0 +1,55 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: seed_gen_exception.h
+// -----------------------------------------------------------------------------
+//
+// This header defines an exception class which may be thrown if unpredictable
+// events prevent the derivation of suitable seed-material for constructing a
+// bit generator conforming to [rand.req.urng] (eg. entropy cannot be read from
+// /dev/urandom on a Unix-based system).
+//
+// Note: if exceptions are disabled, `std::terminate()` is called instead.
+
+#ifndef ABSL_RANDOM_SEED_GEN_EXCEPTION_H_
+#define ABSL_RANDOM_SEED_GEN_EXCEPTION_H_
+
+#include <exception>
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+//------------------------------------------------------------------------------
+// SeedGenException
+//------------------------------------------------------------------------------
+class SeedGenException : public std::exception {
+ public:
+  SeedGenException() = default;
+  ~SeedGenException() override;
+  const char* what() const noexcept override;
+};
+
+namespace random_internal {
+
+// throw delegator
+[[noreturn]] void ThrowSeedGenException();
+
+}  // namespace random_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_SEED_GEN_EXCEPTION_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/seed_sequences.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/seed_sequences.h
new file mode 100644
index 0000000000000000000000000000000000000000..c3af4b00a477ce9588a4206342cc68e7a5e38e53
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/seed_sequences.h
@@ -0,0 +1,111 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: seed_sequences.h
+// -----------------------------------------------------------------------------
+//
+// This header contains utilities for creating and working with seed sequences
+// conforming to [rand.req.seedseq]. In general, direct construction of seed
+// sequences is discouraged, but use-cases for construction of identical bit
+// generators (using the same seed sequence) may be helpful (e.g. replaying a
+// simulation whose state is derived from variates of a bit generator).
+
+#ifndef ABSL_RANDOM_SEED_SEQUENCES_H_
+#define ABSL_RANDOM_SEED_SEQUENCES_H_
+
+#include <iterator>
+#include <random>
+
+#include "absl/base/config.h"
+#include "absl/random/internal/salted_seed_seq.h"
+#include "absl/random/internal/seed_material.h"
+#include "absl/random/seed_gen_exception.h"
+#include "absl/types/span.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// -----------------------------------------------------------------------------
+// absl::SeedSeq
+// -----------------------------------------------------------------------------
+//
+// `absl::SeedSeq` constructs a seed sequence according to [rand.req.seedseq]
+// for use within bit generators. `absl::SeedSeq`, unlike `std::seed_seq`
+// additionally salts the generated seeds with extra implementation-defined
+// entropy. For that reason, you can use `absl::SeedSeq` in combination with
+// standard library bit generators (e.g. `std::mt19937`) to introduce
+// non-determinism in your seeds.
+//
+// Example:
+//
+//   absl::SeedSeq my_seed_seq({a, b, c});
+//   std::mt19937 my_bitgen(my_seed_seq);
+//
+using SeedSeq = random_internal::SaltedSeedSeq<std::seed_seq>;
+
+// -----------------------------------------------------------------------------
+// absl::CreateSeedSeqFrom(bitgen*)
+// -----------------------------------------------------------------------------
+//
+// Constructs a seed sequence conforming to [rand.req.seedseq] using variates
+// produced by a provided bit generator.
+//
+// You should generally avoid direct construction of seed sequences, but
+// use-cases for reuse of a seed sequence to construct identical bit generators
+// may be helpful (eg. replaying a simulation whose state is derived from bit
+// generator values).
+//
+// If bitgen == nullptr, then behavior is undefined.
+//
+// Example:
+//
+//   absl::BitGen my_bitgen;
+//   auto seed_seq = absl::CreateSeedSeqFrom(&my_bitgen);
+//   absl::BitGen new_engine(seed_seq); // derived from my_bitgen, but not
+//                                      // correlated.
+//
+template <typename URBG>
+SeedSeq CreateSeedSeqFrom(URBG* urbg) {
+  SeedSeq::result_type
+      seed_material[random_internal::kEntropyBlocksNeeded];
+
+  if (!random_internal::ReadSeedMaterialFromURBG(
+          urbg, absl::MakeSpan(seed_material))) {
+    random_internal::ThrowSeedGenException();
+  }
+  return SeedSeq(std::begin(seed_material), std::end(seed_material));
+}
+
+// -----------------------------------------------------------------------------
+// absl::MakeSeedSeq()
+// -----------------------------------------------------------------------------
+//
+// Constructs an `absl::SeedSeq` salting the generated values using
+// implementation-defined entropy. The returned sequence can be used to create
+// equivalent bit generators correlated using this sequence.
+//
+// Example:
+//
+//   auto my_seed_seq = absl::MakeSeedSeq();
+//   std::mt19937 rng1(my_seed_seq);
+//   std::mt19937 rng2(my_seed_seq);
+//   EXPECT_EQ(rng1(), rng2());
+//
+SeedSeq MakeSeedSeq();
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_SEED_SEQUENCES_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/uniform_int_distribution.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/uniform_int_distribution.h
new file mode 100644
index 0000000000000000000000000000000000000000..fae80252e39b5e9456ba929aba0c4cd82329395b
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/uniform_int_distribution.h
@@ -0,0 +1,275 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: uniform_int_distribution.h
+// -----------------------------------------------------------------------------
+//
+// This header defines a class for representing a uniform integer distribution
+// over the closed (inclusive) interval [a,b]. You use this distribution in
+// combination with an Abseil random bit generator to produce random values
+// according to the rules of the distribution.
+//
+// `absl::uniform_int_distribution` is a drop-in replacement for the C++11
+// `std::uniform_int_distribution` [rand.dist.uni.int] but is considerably
+// faster than the libstdc++ implementation.
+
+#ifndef ABSL_RANDOM_UNIFORM_INT_DISTRIBUTION_H_
+#define ABSL_RANDOM_UNIFORM_INT_DISTRIBUTION_H_
+
+#include <cassert>
+#include <istream>
+#include <limits>
+#include <type_traits>
+
+#include "absl/base/optimization.h"
+#include "absl/random/internal/fast_uniform_bits.h"
+#include "absl/random/internal/iostream_state_saver.h"
+#include "absl/random/internal/traits.h"
+#include "absl/random/internal/wide_multiply.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// absl::uniform_int_distribution<T>
+//
+// This distribution produces random integer values uniformly distributed in the
+// closed (inclusive) interval [a, b].
+//
+// Example:
+//
+//   absl::BitGen gen;
+//
+//   // Use the distribution to produce a value between 1 and 6, inclusive.
+//   int die_roll = absl::uniform_int_distribution<int>(1, 6)(gen);
+//
+template <typename IntType = int>
+class uniform_int_distribution {
+ private:
+  using unsigned_type =
+      typename random_internal::make_unsigned_bits<IntType>::type;
+
+ public:
+  using result_type = IntType;
+
+  class param_type {
+   public:
+    using distribution_type = uniform_int_distribution;
+
+    explicit param_type(
+        result_type lo = 0,
+        result_type hi = (std::numeric_limits<result_type>::max)())
+        : lo_(lo),
+          range_(static_cast<unsigned_type>(hi) -
+                 static_cast<unsigned_type>(lo)) {
+      // [rand.dist.uni.int] precondition 2
+      assert(lo <= hi);
+    }
+
+    result_type a() const { return lo_; }
+    result_type b() const {
+      return static_cast<result_type>(static_cast<unsigned_type>(lo_) + range_);
+    }
+
+    friend bool operator==(const param_type& a, const param_type& b) {
+      return a.lo_ == b.lo_ && a.range_ == b.range_;
+    }
+
+    friend bool operator!=(const param_type& a, const param_type& b) {
+      return !(a == b);
+    }
+
+   private:
+    friend class uniform_int_distribution;
+    unsigned_type range() const { return range_; }
+
+    result_type lo_;
+    unsigned_type range_;
+
+    static_assert(random_internal::IsIntegral<result_type>::value,
+                  "Class-template absl::uniform_int_distribution<> must be "
+                  "parameterized using an integral type.");
+  };  // param_type
+
+  uniform_int_distribution() : uniform_int_distribution(0) {}
+
+  explicit uniform_int_distribution(
+      result_type lo,
+      result_type hi = (std::numeric_limits<result_type>::max)())
+      : param_(lo, hi) {}
+
+  explicit uniform_int_distribution(const param_type& param) : param_(param) {}
+
+  // uniform_int_distribution<T>::reset()
+  //
+  // Resets the uniform int distribution. Note that this function has no effect
+  // because the distribution already produces independent values.
+  void reset() {}
+
+  template <typename URBG>
+  result_type operator()(URBG& gen) {  // NOLINT(runtime/references)
+    return (*this)(gen, param());
+  }
+
+  template <typename URBG>
+  result_type operator()(
+      URBG& gen, const param_type& param) {  // NOLINT(runtime/references)
+    return static_cast<result_type>(param.a() + Generate(gen, param.range()));
+  }
+
+  result_type a() const { return param_.a(); }
+  result_type b() const { return param_.b(); }
+
+  param_type param() const { return param_; }
+  void param(const param_type& params) { param_ = params; }
+
+  result_type(min)() const { return a(); }
+  result_type(max)() const { return b(); }
+
+  friend bool operator==(const uniform_int_distribution& a,
+                         const uniform_int_distribution& b) {
+    return a.param_ == b.param_;
+  }
+  friend bool operator!=(const uniform_int_distribution& a,
+                         const uniform_int_distribution& b) {
+    return !(a == b);
+  }
+
+ private:
+  // Generates a value in the *closed* interval [0, R]
+  template <typename URBG>
+  unsigned_type Generate(URBG& g,  // NOLINT(runtime/references)
+                         unsigned_type R);
+  param_type param_;
+};
+
+// -----------------------------------------------------------------------------
+// Implementation details follow
+// -----------------------------------------------------------------------------
+template <typename CharT, typename Traits, typename IntType>
+std::basic_ostream<CharT, Traits>& operator<<(
+    std::basic_ostream<CharT, Traits>& os,
+    const uniform_int_distribution<IntType>& x) {
+  using stream_type =
+      typename random_internal::stream_format_type<IntType>::type;
+  auto saver = random_internal::make_ostream_state_saver(os);
+  os << static_cast<stream_type>(x.a()) << os.fill()
+     << static_cast<stream_type>(x.b());
+  return os;
+}
+
+template <typename CharT, typename Traits, typename IntType>
+std::basic_istream<CharT, Traits>& operator>>(
+    std::basic_istream<CharT, Traits>& is,
+    uniform_int_distribution<IntType>& x) {
+  using param_type = typename uniform_int_distribution<IntType>::param_type;
+  using result_type = typename uniform_int_distribution<IntType>::result_type;
+  using stream_type =
+      typename random_internal::stream_format_type<IntType>::type;
+
+  stream_type a;
+  stream_type b;
+
+  auto saver = random_internal::make_istream_state_saver(is);
+  is >> a >> b;
+  if (!is.fail()) {
+    x.param(
+        param_type(static_cast<result_type>(a), static_cast<result_type>(b)));
+  }
+  return is;
+}
+
+template <typename IntType>
+template <typename URBG>
+typename random_internal::make_unsigned_bits<IntType>::type
+uniform_int_distribution<IntType>::Generate(
+    URBG& g,  // NOLINT(runtime/references)
+    typename random_internal::make_unsigned_bits<IntType>::type R) {
+  random_internal::FastUniformBits<unsigned_type> fast_bits;
+  unsigned_type bits = fast_bits(g);
+  const unsigned_type Lim = R + 1;
+  if ((R & Lim) == 0) {
+    // If the interval's length is a power of two range, just take the low bits.
+    return bits & R;
+  }
+
+  // Generates a uniform variate on [0, Lim) using fixed-point multiplication.
+  // The above fast-path guarantees that Lim is representable in unsigned_type.
+  //
+  // Algorithm adapted from
+  // http://lemire.me/blog/2016/06/30/fast-random-shuffling/, with added
+  // explanation.
+  //
+  // The algorithm creates a uniform variate `bits` in the interval [0, 2^N),
+  // and treats it as the fractional part of a fixed-point real value in [0, 1),
+  // multiplied by 2^N.  For example, 0.25 would be represented as 2^(N - 2),
+  // because 2^N * 0.25 == 2^(N - 2).
+  //
+  // Next, `bits` and `Lim` are multiplied with a wide-multiply to bring the
+  // value into the range [0, Lim).  The integral part (the high word of the
+  // multiplication result) is then very nearly the desired result.  However,
+  // this is not quite accurate; viewing the multiplication result as one
+  // double-width integer, the resulting values for the sample are mapped as
+  // follows:
+  //
+  // If the result lies in this interval:       Return this value:
+  //        [0, 2^N)                                    0
+  //        [2^N, 2 * 2^N)                              1
+  //        ...                                         ...
+  //        [K * 2^N, (K + 1) * 2^N)                    K
+  //        ...                                         ...
+  //        [(Lim - 1) * 2^N, Lim * 2^N)                Lim - 1
+  //
+  // While all of these intervals have the same size, the result of `bits * Lim`
+  // must be a multiple of `Lim`, and not all of these intervals contain the
+  // same number of multiples of `Lim`.  In particular, some contain
+  // `F = floor(2^N / Lim)` and some contain `F + 1 = ceil(2^N / Lim)`.  This
+  // difference produces a small nonuniformity, which is corrected by applying
+  // rejection sampling to one of the values in the "larger intervals" (i.e.,
+  // the intervals containing `F + 1` multiples of `Lim`.
+  //
+  // An interval contains `F + 1` multiples of `Lim` if and only if its smallest
+  // value modulo 2^N is less than `2^N % Lim`.  The unique value satisfying
+  // this property is used as the one for rejection.  That is, a value of
+  // `bits * Lim` is rejected if `(bit * Lim) % 2^N < (2^N % Lim)`.
+
+  using helper = random_internal::wide_multiply<unsigned_type>;
+  auto product = helper::multiply(bits, Lim);
+
+  // Two optimizations here:
+  // * Rejection occurs with some probability less than 1/2, and for reasonable
+  //   ranges considerably less (in particular, less than 1/(F+1)), so
+  //   ABSL_PREDICT_FALSE is apt.
+  // * `Lim` is an overestimate of `threshold`, and doesn't require a divide.
+  if (ABSL_PREDICT_FALSE(helper::lo(product) < Lim)) {
+    // This quantity is exactly equal to `2^N % Lim`, but does not require high
+    // precision calculations: `2^N % Lim` is congruent to `(2^N - Lim) % Lim`.
+    // Ideally this could be expressed simply as `-X` rather than `2^N - X`, but
+    // for types smaller than int, this calculation is incorrect due to integer
+    // promotion rules.
+    const unsigned_type threshold =
+        ((std::numeric_limits<unsigned_type>::max)() - Lim + 1) % Lim;
+    while (helper::lo(product) < threshold) {
+      bits = fast_bits(g);
+      product = helper::multiply(bits, Lim);
+    }
+  }
+
+  return helper::hi(product);
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_UNIFORM_INT_DISTRIBUTION_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/uniform_real_distribution.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/uniform_real_distribution.h
new file mode 100644
index 0000000000000000000000000000000000000000..196833415e86c33f335dc778d1f020762d31311e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/uniform_real_distribution.h
@@ -0,0 +1,202 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: uniform_real_distribution.h
+// -----------------------------------------------------------------------------
+//
+// This header defines a class for representing a uniform floating-point
+// distribution over a half-open interval [a,b). You use this distribution in
+// combination with an Abseil random bit generator to produce random values
+// according to the rules of the distribution.
+//
+// `absl::uniform_real_distribution` is a drop-in replacement for the C++11
+// `std::uniform_real_distribution` [rand.dist.uni.real] but is considerably
+// faster than the libstdc++ implementation.
+//
+// Note: the standard-library version may occasionally return `1.0` when
+// default-initialized. See https://bugs.llvm.org//show_bug.cgi?id=18767
+// `absl::uniform_real_distribution` does not exhibit this behavior.
+
+#ifndef ABSL_RANDOM_UNIFORM_REAL_DISTRIBUTION_H_
+#define ABSL_RANDOM_UNIFORM_REAL_DISTRIBUTION_H_
+
+#include <cassert>
+#include <cmath>
+#include <cstdint>
+#include <istream>
+#include <limits>
+#include <type_traits>
+
+#include "absl/meta/type_traits.h"
+#include "absl/random/internal/fast_uniform_bits.h"
+#include "absl/random/internal/generate_real.h"
+#include "absl/random/internal/iostream_state_saver.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// absl::uniform_real_distribution<T>
+//
+// This distribution produces random floating-point values uniformly distributed
+// over the half-open interval [a, b).
+//
+// Example:
+//
+//   absl::BitGen gen;
+//
+//   // Use the distribution to produce a value between 0.0 (inclusive)
+//   // and 1.0 (exclusive).
+//   double value = absl::uniform_real_distribution<double>(0, 1)(gen);
+//
+template <typename RealType = double>
+class uniform_real_distribution {
+ public:
+  using result_type = RealType;
+
+  class param_type {
+   public:
+    using distribution_type = uniform_real_distribution;
+
+    explicit param_type(result_type lo = 0, result_type hi = 1)
+        : lo_(lo), hi_(hi), range_(hi - lo) {
+      // [rand.dist.uni.real] preconditions 2 & 3
+      assert(lo <= hi);
+
+      // NOTE: For integral types, we can promote the range to an unsigned type,
+      // which gives full width of the range. However for real (fp) types, this
+      // is not possible, so value generation cannot use the full range of the
+      // real type.
+      assert(range_ <= (std::numeric_limits<result_type>::max)());
+    }
+
+    result_type a() const { return lo_; }
+    result_type b() const { return hi_; }
+
+    friend bool operator==(const param_type& a, const param_type& b) {
+      return a.lo_ == b.lo_ && a.hi_ == b.hi_;
+    }
+
+    friend bool operator!=(const param_type& a, const param_type& b) {
+      return !(a == b);
+    }
+
+   private:
+    friend class uniform_real_distribution;
+    result_type lo_, hi_, range_;
+
+    static_assert(std::is_floating_point<RealType>::value,
+                  "Class-template absl::uniform_real_distribution<> must be "
+                  "parameterized using a floating-point type.");
+  };
+
+  uniform_real_distribution() : uniform_real_distribution(0) {}
+
+  explicit uniform_real_distribution(result_type lo, result_type hi = 1)
+      : param_(lo, hi) {}
+
+  explicit uniform_real_distribution(const param_type& param) : param_(param) {}
+
+  // uniform_real_distribution<T>::reset()
+  //
+  // Resets the uniform real distribution. Note that this function has no effect
+  // because the distribution already produces independent values.
+  void reset() {}
+
+  template <typename URBG>
+  result_type operator()(URBG& gen) {  // NOLINT(runtime/references)
+    return operator()(gen, param_);
+  }
+
+  template <typename URBG>
+  result_type operator()(URBG& gen,  // NOLINT(runtime/references)
+                         const param_type& p);
+
+  result_type a() const { return param_.a(); }
+  result_type b() const { return param_.b(); }
+
+  param_type param() const { return param_; }
+  void param(const param_type& params) { param_ = params; }
+
+  result_type(min)() const { return a(); }
+  result_type(max)() const { return b(); }
+
+  friend bool operator==(const uniform_real_distribution& a,
+                         const uniform_real_distribution& b) {
+    return a.param_ == b.param_;
+  }
+  friend bool operator!=(const uniform_real_distribution& a,
+                         const uniform_real_distribution& b) {
+    return a.param_ != b.param_;
+  }
+
+ private:
+  param_type param_;
+  random_internal::FastUniformBits<uint64_t> fast_u64_;
+};
+
+// -----------------------------------------------------------------------------
+// Implementation details follow
+// -----------------------------------------------------------------------------
+template <typename RealType>
+template <typename URBG>
+typename uniform_real_distribution<RealType>::result_type
+uniform_real_distribution<RealType>::operator()(
+    URBG& gen, const param_type& p) {  // NOLINT(runtime/references)
+  using random_internal::GeneratePositiveTag;
+  using random_internal::GenerateRealFromBits;
+  using real_type =
+      absl::conditional_t<std::is_same<RealType, float>::value, float, double>;
+
+  while (true) {
+    const result_type sample =
+        GenerateRealFromBits<real_type, GeneratePositiveTag, true>(
+            fast_u64_(gen));
+    const result_type res = p.a() + (sample * p.range_);
+    if (res < p.b() || p.range_ <= 0 || !std::isfinite(p.range_)) {
+      return res;
+    }
+    // else sample rejected, try again.
+  }
+}
+
+template <typename CharT, typename Traits, typename RealType>
+std::basic_ostream<CharT, Traits>& operator<<(
+    std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
+    const uniform_real_distribution<RealType>& x) {
+  auto saver = random_internal::make_ostream_state_saver(os);
+  os.precision(random_internal::stream_precision_helper<RealType>::kPrecision);
+  os << x.a() << os.fill() << x.b();
+  return os;
+}
+
+template <typename CharT, typename Traits, typename RealType>
+std::basic_istream<CharT, Traits>& operator>>(
+    std::basic_istream<CharT, Traits>& is,     // NOLINT(runtime/references)
+    uniform_real_distribution<RealType>& x) {  // NOLINT(runtime/references)
+  using param_type = typename uniform_real_distribution<RealType>::param_type;
+  using result_type = typename uniform_real_distribution<RealType>::result_type;
+  auto saver = random_internal::make_istream_state_saver(is);
+  auto a = random_internal::read_floating_point<result_type>(is);
+  if (is.fail()) return is;
+  auto b = random_internal::read_floating_point<result_type>(is);
+  if (!is.fail()) {
+    x.param(param_type(a, b));
+  }
+  return is;
+}
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_UNIFORM_REAL_DISTRIBUTION_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/zipf_distribution.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/zipf_distribution.h
new file mode 100644
index 0000000000000000000000000000000000000000..03497b1b26a2934e4c2bd6c90dc063a87f6ecb99
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/random/zipf_distribution.h
@@ -0,0 +1,272 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_RANDOM_ZIPF_DISTRIBUTION_H_
+#define ABSL_RANDOM_ZIPF_DISTRIBUTION_H_
+
+#include <cassert>
+#include <cmath>
+#include <istream>
+#include <limits>
+#include <ostream>
+#include <type_traits>
+
+#include "absl/random/internal/iostream_state_saver.h"
+#include "absl/random/internal/traits.h"
+#include "absl/random/uniform_real_distribution.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// absl::zipf_distribution produces random integer-values in the range [0, k],
+// distributed according to the unnormalized discrete probability function:
+//
+//  P(x) = (v + x) ^ -q
+//
+// The parameter `v` must be greater than 0 and the parameter `q` must be
+// greater than 1. If either of these parameters take invalid values then the
+// behavior is undefined.
+//
+// IntType is the result_type generated by the generator. It must be of integral
+// type; a static_assert ensures this is the case.
+//
+// The implementation is based on W.Hormann, G.Derflinger:
+//
+// "Rejection-Inversion to Generate Variates from Monotone Discrete
+// Distributions"
+//
+// http://eeyore.wu-wien.ac.at/papers/96-04-04.wh-der.ps.gz
+//
+template <typename IntType = int>
+class zipf_distribution {
+ public:
+  using result_type = IntType;
+
+  class param_type {
+   public:
+    using distribution_type = zipf_distribution;
+
+    // Preconditions: k > 0, v > 0, q > 1
+    // The precondidtions are validated when NDEBUG is not defined via
+    // a pair of assert() directives.
+    // If NDEBUG is defined and either or both of these parameters take invalid
+    // values, the behavior of the class is undefined.
+    explicit param_type(result_type k = (std::numeric_limits<IntType>::max)(),
+                        double q = 2.0, double v = 1.0);
+
+    result_type k() const { return k_; }
+    double q() const { return q_; }
+    double v() const { return v_; }
+
+    friend bool operator==(const param_type& a, const param_type& b) {
+      return a.k_ == b.k_ && a.q_ == b.q_ && a.v_ == b.v_;
+    }
+    friend bool operator!=(const param_type& a, const param_type& b) {
+      return !(a == b);
+    }
+
+   private:
+    friend class zipf_distribution;
+    inline double h(double x) const;
+    inline double hinv(double x) const;
+    inline double compute_s() const;
+    inline double pow_negative_q(double x) const;
+
+    // Parameters here are exactly the same as the parameters of Algorithm ZRI
+    // in the paper.
+    IntType k_;
+    double q_;
+    double v_;
+
+    double one_minus_q_;  // 1-q
+    double s_;
+    double one_minus_q_inv_;  // 1 / 1-q
+    double hxm_;              // h(k + 0.5)
+    double hx0_minus_hxm_;    // h(x0) - h(k + 0.5)
+
+    static_assert(random_internal::IsIntegral<IntType>::value,
+                  "Class-template absl::zipf_distribution<> must be "
+                  "parameterized using an integral type.");
+  };
+
+  zipf_distribution()
+      : zipf_distribution((std::numeric_limits<IntType>::max)()) {}
+
+  explicit zipf_distribution(result_type k, double q = 2.0, double v = 1.0)
+      : param_(k, q, v) {}
+
+  explicit zipf_distribution(const param_type& p) : param_(p) {}
+
+  void reset() {}
+
+  template <typename URBG>
+  result_type operator()(URBG& g) {  // NOLINT(runtime/references)
+    return (*this)(g, param_);
+  }
+
+  template <typename URBG>
+  result_type operator()(URBG& g,  // NOLINT(runtime/references)
+                         const param_type& p);
+
+  result_type k() const { return param_.k(); }
+  double q() const { return param_.q(); }
+  double v() const { return param_.v(); }
+
+  param_type param() const { return param_; }
+  void param(const param_type& p) { param_ = p; }
+
+  result_type(min)() const { return 0; }
+  result_type(max)() const { return k(); }
+
+  friend bool operator==(const zipf_distribution& a,
+                         const zipf_distribution& b) {
+    return a.param_ == b.param_;
+  }
+  friend bool operator!=(const zipf_distribution& a,
+                         const zipf_distribution& b) {
+    return a.param_ != b.param_;
+  }
+
+ private:
+  param_type param_;
+};
+
+// --------------------------------------------------------------------------
+// Implementation details follow
+// --------------------------------------------------------------------------
+
+template <typename IntType>
+zipf_distribution<IntType>::param_type::param_type(
+    typename zipf_distribution<IntType>::result_type k, double q, double v)
+    : k_(k), q_(q), v_(v), one_minus_q_(1 - q) {
+  assert(q > 1);
+  assert(v > 0);
+  assert(k > 0);
+  one_minus_q_inv_ = 1 / one_minus_q_;
+
+  // Setup for the ZRI algorithm (pg 17 of the paper).
+  // Compute: h(i max) => h(k + 0.5)
+  constexpr double kMax = 18446744073709549568.0;
+  double kd = static_cast<double>(k);
+  // TODO(absl-team): Determine if this check is needed, and if so, add a test
+  // that fails for k > kMax
+  if (kd > kMax) {
+    // Ensure that our maximum value is capped to a value which will
+    // round-trip back through double.
+    kd = kMax;
+  }
+  hxm_ = h(kd + 0.5);
+
+  // Compute: h(0)
+  const bool use_precomputed = (v == 1.0 && q == 2.0);
+  const double h0x5 = use_precomputed ? (-1.0 / 1.5)  // exp(-log(1.5))
+                                      : h(0.5);
+  const double elogv_q = (v_ == 1.0) ? 1 : pow_negative_q(v_);
+
+  // h(0) = h(0.5) - exp(log(v) * -q)
+  hx0_minus_hxm_ = (h0x5 - elogv_q) - hxm_;
+
+  // And s
+  s_ = use_precomputed ? 0.46153846153846123 : compute_s();
+}
+
+template <typename IntType>
+double zipf_distribution<IntType>::param_type::h(double x) const {
+  // std::exp(one_minus_q_ * std::log(v_ + x)) * one_minus_q_inv_;
+  x += v_;
+  return (one_minus_q_ == -1.0)
+             ? (-1.0 / x)  // -exp(-log(x))
+             : (std::exp(std::log(x) * one_minus_q_) * one_minus_q_inv_);
+}
+
+template <typename IntType>
+double zipf_distribution<IntType>::param_type::hinv(double x) const {
+  // std::exp(one_minus_q_inv_ * std::log(one_minus_q_ * x)) - v_;
+  return -v_ + ((one_minus_q_ == -1.0)
+                    ? (-1.0 / x)  // exp(-log(-x))
+                    : std::exp(one_minus_q_inv_ * std::log(one_minus_q_ * x)));
+}
+
+template <typename IntType>
+double zipf_distribution<IntType>::param_type::compute_s() const {
+  // 1 - hinv(h(1.5) - std::exp(std::log(v_ + 1) * -q_));
+  return 1.0 - hinv(h(1.5) - pow_negative_q(v_ + 1.0));
+}
+
+template <typename IntType>
+double zipf_distribution<IntType>::param_type::pow_negative_q(double x) const {
+  // std::exp(std::log(x) * -q_);
+  return q_ == 2.0 ? (1.0 / (x * x)) : std::exp(std::log(x) * -q_);
+}
+
+template <typename IntType>
+template <typename URBG>
+typename zipf_distribution<IntType>::result_type
+zipf_distribution<IntType>::operator()(
+    URBG& g, const param_type& p) {  // NOLINT(runtime/references)
+  absl::uniform_real_distribution<double> uniform_double;
+  double k;
+  for (;;) {
+    const double v = uniform_double(g);
+    const double u = p.hxm_ + v * p.hx0_minus_hxm_;
+    const double x = p.hinv(u);
+    k = rint(x);              // std::floor(x + 0.5);
+    if (k > static_cast<double>(p.k())) continue;  // reject k > max_k
+    if (k - x <= p.s_) break;
+    const double h = p.h(k + 0.5);
+    const double r = p.pow_negative_q(p.v_ + k);
+    if (u >= h - r) break;
+  }
+  IntType ki = static_cast<IntType>(k);
+  assert(ki <= p.k_);
+  return ki;
+}
+
+template <typename CharT, typename Traits, typename IntType>
+std::basic_ostream<CharT, Traits>& operator<<(
+    std::basic_ostream<CharT, Traits>& os,  // NOLINT(runtime/references)
+    const zipf_distribution<IntType>& x) {
+  using stream_type =
+      typename random_internal::stream_format_type<IntType>::type;
+  auto saver = random_internal::make_ostream_state_saver(os);
+  os.precision(random_internal::stream_precision_helper<double>::kPrecision);
+  os << static_cast<stream_type>(x.k()) << os.fill() << x.q() << os.fill()
+     << x.v();
+  return os;
+}
+
+template <typename CharT, typename Traits, typename IntType>
+std::basic_istream<CharT, Traits>& operator>>(
+    std::basic_istream<CharT, Traits>& is,  // NOLINT(runtime/references)
+    zipf_distribution<IntType>& x) {        // NOLINT(runtime/references)
+  using result_type = typename zipf_distribution<IntType>::result_type;
+  using param_type = typename zipf_distribution<IntType>::param_type;
+  using stream_type =
+      typename random_internal::stream_format_type<IntType>::type;
+  stream_type k;
+  double q;
+  double v;
+
+  auto saver = random_internal::make_istream_state_saver(is);
+  is >> k >> q >> v;
+  if (!is.fail()) {
+    x.param(param_type(static_cast<result_type>(k), q, v));
+  }
+  return is;
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_RANDOM_ZIPF_DISTRIBUTION_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/status/internal/status_internal.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/status/internal/status_internal.h
new file mode 100644
index 0000000000000000000000000000000000000000..873eb5c245d4f121f22da19af88882f6ae51f1c7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/status/internal/status_internal.h
@@ -0,0 +1,87 @@
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#ifndef ABSL_STATUS_INTERNAL_STATUS_INTERNAL_H_
+#define ABSL_STATUS_INTERNAL_STATUS_INTERNAL_H_
+
+#include <memory>
+#include <string>
+#include <utility>
+
+#include "absl/base/attributes.h"
+#include "absl/container/inlined_vector.h"
+#include "absl/strings/cord.h"
+
+#ifndef SWIG
+// Disabled for SWIG as it doesn't parse attributes correctly.
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+// Returned Status objects may not be ignored. Codesearch doesn't handle ifdefs
+// as part of a class definitions (b/6995610), so we use a forward declaration.
+//
+// TODO(b/176172494): ABSL_MUST_USE_RESULT should expand to the more strict
+// [[nodiscard]]. For now, just use [[nodiscard]] directly when it is available.
+#if ABSL_HAVE_CPP_ATTRIBUTE(nodiscard)
+class [[nodiscard]] Status;
+#else
+class ABSL_MUST_USE_RESULT Status;
+#endif
+ABSL_NAMESPACE_END
+}  // namespace absl
+#endif  // !SWIG
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+enum class StatusCode : int;
+
+namespace status_internal {
+
+// Container for status payloads.
+struct Payload {
+  std::string type_url;
+  absl::Cord payload;
+};
+
+using Payloads = absl::InlinedVector<Payload, 1>;
+
+// Reference-counted representation of Status data.
+struct StatusRep {
+  StatusRep(absl::StatusCode code_arg, absl::string_view message_arg,
+            std::unique_ptr<status_internal::Payloads> payloads_arg)
+      : ref(int32_t{1}),
+        code(code_arg),
+        message(message_arg),
+        payloads(std::move(payloads_arg)) {}
+
+  std::atomic<int32_t> ref;
+  absl::StatusCode code;
+  std::string message;
+  std::unique_ptr<status_internal::Payloads> payloads;
+};
+
+absl::StatusCode MapToLocalCode(int value);
+
+// Returns a pointer to a newly-allocated string with the given `prefix`,
+// suitable for output as an error message in assertion/`CHECK()` failures.
+//
+// This is an internal implementation detail for Abseil logging.
+std::string* MakeCheckFailString(const absl::Status* status,
+                                 const char* prefix);
+
+}  // namespace status_internal
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STATUS_INTERNAL_STATUS_INTERNAL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/status/internal/statusor_internal.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/status/internal/statusor_internal.h
new file mode 100644
index 0000000000000000000000000000000000000000..eaac2c0b14c63ca53a26fc81d1362ec93c40a536
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/status/internal/statusor_internal.h
@@ -0,0 +1,396 @@
+// Copyright 2020 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#ifndef ABSL_STATUS_INTERNAL_STATUSOR_INTERNAL_H_
+#define ABSL_STATUS_INTERNAL_STATUSOR_INTERNAL_H_
+
+#include <type_traits>
+#include <utility>
+
+#include "absl/base/attributes.h"
+#include "absl/meta/type_traits.h"
+#include "absl/status/status.h"
+#include "absl/utility/utility.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+template <typename T>
+class ABSL_MUST_USE_RESULT StatusOr;
+
+namespace internal_statusor {
+
+// Detects whether `U` has conversion operator to `StatusOr<T>`, i.e. `operator
+// StatusOr<T>()`.
+template <typename T, typename U, typename = void>
+struct HasConversionOperatorToStatusOr : std::false_type {};
+
+template <typename T, typename U>
+void test(char (*)[sizeof(std::declval<U>().operator absl::StatusOr<T>())]);
+
+template <typename T, typename U>
+struct HasConversionOperatorToStatusOr<T, U, decltype(test<T, U>(0))>
+    : std::true_type {};
+
+// Detects whether `T` is constructible or convertible from `StatusOr<U>`.
+template <typename T, typename U>
+using IsConstructibleOrConvertibleFromStatusOr =
+    absl::disjunction<std::is_constructible<T, StatusOr<U>&>,
+                      std::is_constructible<T, const StatusOr<U>&>,
+                      std::is_constructible<T, StatusOr<U>&&>,
+                      std::is_constructible<T, const StatusOr<U>&&>,
+                      std::is_convertible<StatusOr<U>&, T>,
+                      std::is_convertible<const StatusOr<U>&, T>,
+                      std::is_convertible<StatusOr<U>&&, T>,
+                      std::is_convertible<const StatusOr<U>&&, T>>;
+
+// Detects whether `T` is constructible or convertible or assignable from
+// `StatusOr<U>`.
+template <typename T, typename U>
+using IsConstructibleOrConvertibleOrAssignableFromStatusOr =
+    absl::disjunction<IsConstructibleOrConvertibleFromStatusOr<T, U>,
+                      std::is_assignable<T&, StatusOr<U>&>,
+                      std::is_assignable<T&, const StatusOr<U>&>,
+                      std::is_assignable<T&, StatusOr<U>&&>,
+                      std::is_assignable<T&, const StatusOr<U>&&>>;
+
+// Detects whether direct initializing `StatusOr<T>` from `U` is ambiguous, i.e.
+// when `U` is `StatusOr<V>` and `T` is constructible or convertible from `V`.
+template <typename T, typename U>
+struct IsDirectInitializationAmbiguous
+    : public absl::conditional_t<
+          std::is_same<absl::remove_cv_t<absl::remove_reference_t<U>>,
+                       U>::value,
+          std::false_type,
+          IsDirectInitializationAmbiguous<
+              T, absl::remove_cv_t<absl::remove_reference_t<U>>>> {};
+
+template <typename T, typename V>
+struct IsDirectInitializationAmbiguous<T, absl::StatusOr<V>>
+    : public IsConstructibleOrConvertibleFromStatusOr<T, V> {};
+
+// Checks against the constraints of the direction initialization, i.e. when
+// `StatusOr<T>::StatusOr(U&&)` should participate in overload resolution.
+template <typename T, typename U>
+using IsDirectInitializationValid = absl::disjunction<
+    // Short circuits if T is basically U.
+    std::is_same<T, absl::remove_cv_t<absl::remove_reference_t<U>>>,
+    absl::negation<absl::disjunction<
+        std::is_same<absl::StatusOr<T>,
+                     absl::remove_cv_t<absl::remove_reference_t<U>>>,
+        std::is_same<absl::Status,
+                     absl::remove_cv_t<absl::remove_reference_t<U>>>,
+        std::is_same<absl::in_place_t,
+                     absl::remove_cv_t<absl::remove_reference_t<U>>>,
+        IsDirectInitializationAmbiguous<T, U>>>>;
+
+// This trait detects whether `StatusOr<T>::operator=(U&&)` is ambiguous, which
+// is equivalent to whether all the following conditions are met:
+// 1. `U` is `StatusOr<V>`.
+// 2. `T` is constructible and assignable from `V`.
+// 3. `T` is constructible and assignable from `U` (i.e. `StatusOr<V>`).
+// For example, the following code is considered ambiguous:
+// (`T` is `bool`, `U` is `StatusOr<bool>`, `V` is `bool`)
+//   StatusOr<bool> s1 = true;  // s1.ok() && s1.ValueOrDie() == true
+//   StatusOr<bool> s2 = false;  // s2.ok() && s2.ValueOrDie() == false
+//   s1 = s2;  // ambiguous, `s1 = s2.ValueOrDie()` or `s1 = bool(s2)`?
+template <typename T, typename U>
+struct IsForwardingAssignmentAmbiguous
+    : public absl::conditional_t<
+          std::is_same<absl::remove_cv_t<absl::remove_reference_t<U>>,
+                       U>::value,
+          std::false_type,
+          IsForwardingAssignmentAmbiguous<
+              T, absl::remove_cv_t<absl::remove_reference_t<U>>>> {};
+
+template <typename T, typename U>
+struct IsForwardingAssignmentAmbiguous<T, absl::StatusOr<U>>
+    : public IsConstructibleOrConvertibleOrAssignableFromStatusOr<T, U> {};
+
+// Checks against the constraints of the forwarding assignment, i.e. whether
+// `StatusOr<T>::operator(U&&)` should participate in overload resolution.
+template <typename T, typename U>
+using IsForwardingAssignmentValid = absl::disjunction<
+    // Short circuits if T is basically U.
+    std::is_same<T, absl::remove_cv_t<absl::remove_reference_t<U>>>,
+    absl::negation<absl::disjunction<
+        std::is_same<absl::StatusOr<T>,
+                     absl::remove_cv_t<absl::remove_reference_t<U>>>,
+        std::is_same<absl::Status,
+                     absl::remove_cv_t<absl::remove_reference_t<U>>>,
+        std::is_same<absl::in_place_t,
+                     absl::remove_cv_t<absl::remove_reference_t<U>>>,
+        IsForwardingAssignmentAmbiguous<T, U>>>>;
+
+class Helper {
+ public:
+  // Move type-agnostic error handling to the .cc.
+  static void HandleInvalidStatusCtorArg(Status*);
+  ABSL_ATTRIBUTE_NORETURN static void Crash(const absl::Status& status);
+};
+
+// Construct an instance of T in `p` through placement new, passing Args... to
+// the constructor.
+// This abstraction is here mostly for the gcc performance fix.
+template <typename T, typename... Args>
+ABSL_ATTRIBUTE_NONNULL(1) void PlacementNew(void* p, Args&&... args) {
+  new (p) T(std::forward<Args>(args)...);
+}
+
+// Helper base class to hold the data and all operations.
+// We move all this to a base class to allow mixing with the appropriate
+// TraitsBase specialization.
+template <typename T>
+class StatusOrData {
+  template <typename U>
+  friend class StatusOrData;
+
+ public:
+  StatusOrData() = delete;
+
+  StatusOrData(const StatusOrData& other) {
+    if (other.ok()) {
+      MakeValue(other.data_);
+      MakeStatus();
+    } else {
+      MakeStatus(other.status_);
+    }
+  }
+
+  StatusOrData(StatusOrData&& other) noexcept {
+    if (other.ok()) {
+      MakeValue(std::move(other.data_));
+      MakeStatus();
+    } else {
+      MakeStatus(std::move(other.status_));
+    }
+  }
+
+  template <typename U>
+  explicit StatusOrData(const StatusOrData<U>& other) {
+    if (other.ok()) {
+      MakeValue(other.data_);
+      MakeStatus();
+    } else {
+      MakeStatus(other.status_);
+    }
+  }
+
+  template <typename U>
+  explicit StatusOrData(StatusOrData<U>&& other) {
+    if (other.ok()) {
+      MakeValue(std::move(other.data_));
+      MakeStatus();
+    } else {
+      MakeStatus(std::move(other.status_));
+    }
+  }
+
+  template <typename... Args>
+  explicit StatusOrData(absl::in_place_t, Args&&... args)
+      : data_(std::forward<Args>(args)...) {
+    MakeStatus();
+  }
+
+  explicit StatusOrData(const T& value) : data_(value) {
+    MakeStatus();
+  }
+  explicit StatusOrData(T&& value) : data_(std::move(value)) {
+    MakeStatus();
+  }
+
+  template <typename U,
+            absl::enable_if_t<std::is_constructible<absl::Status, U&&>::value,
+                              int> = 0>
+  explicit StatusOrData(U&& v) : status_(std::forward<U>(v)) {
+    EnsureNotOk();
+  }
+
+  StatusOrData& operator=(const StatusOrData& other) {
+    if (this == &other) return *this;
+    if (other.ok())
+      Assign(other.data_);
+    else
+      AssignStatus(other.status_);
+    return *this;
+  }
+
+  StatusOrData& operator=(StatusOrData&& other) {
+    if (this == &other) return *this;
+    if (other.ok())
+      Assign(std::move(other.data_));
+    else
+      AssignStatus(std::move(other.status_));
+    return *this;
+  }
+
+  ~StatusOrData() {
+    if (ok()) {
+      status_.~Status();
+      data_.~T();
+    } else {
+      status_.~Status();
+    }
+  }
+
+  template <typename U>
+  void Assign(U&& value) {
+    if (ok()) {
+      data_ = std::forward<U>(value);
+    } else {
+      MakeValue(std::forward<U>(value));
+      status_ = OkStatus();
+    }
+  }
+
+  template <typename U>
+  void AssignStatus(U&& v) {
+    Clear();
+    status_ = static_cast<absl::Status>(std::forward<U>(v));
+    EnsureNotOk();
+  }
+
+  bool ok() const { return status_.ok(); }
+
+ protected:
+  // status_ will always be active after the constructor.
+  // We make it a union to be able to initialize exactly how we need without
+  // waste.
+  // Eg. in the copy constructor we use the default constructor of Status in
+  // the ok() path to avoid an extra Ref call.
+  union {
+    Status status_;
+  };
+
+  // data_ is active iff status_.ok()==true
+  struct Dummy {};
+  union {
+    // When T is const, we need some non-const object we can cast to void* for
+    // the placement new. dummy_ is that object.
+    Dummy dummy_;
+    T data_;
+  };
+
+  void Clear() {
+    if (ok()) data_.~T();
+  }
+
+  void EnsureOk() const {
+    if (ABSL_PREDICT_FALSE(!ok())) Helper::Crash(status_);
+  }
+
+  void EnsureNotOk() {
+    if (ABSL_PREDICT_FALSE(ok())) Helper::HandleInvalidStatusCtorArg(&status_);
+  }
+
+  // Construct the value (ie. data_) through placement new with the passed
+  // argument.
+  template <typename... Arg>
+  void MakeValue(Arg&&... arg) {
+    internal_statusor::PlacementNew<T>(&dummy_, std::forward<Arg>(arg)...);
+  }
+
+  // Construct the status (ie. status_) through placement new with the passed
+  // argument.
+  template <typename... Args>
+  void MakeStatus(Args&&... args) {
+    internal_statusor::PlacementNew<Status>(&status_,
+                                            std::forward<Args>(args)...);
+  }
+};
+
+// Helper base classes to allow implicitly deleted constructors and assignment
+// operators in `StatusOr`. For example, `CopyCtorBase` will explicitly delete
+// the copy constructor when T is not copy constructible and `StatusOr` will
+// inherit that behavior implicitly.
+template <typename T, bool = std::is_copy_constructible<T>::value>
+struct CopyCtorBase {
+  CopyCtorBase() = default;
+  CopyCtorBase(const CopyCtorBase&) = default;
+  CopyCtorBase(CopyCtorBase&&) = default;
+  CopyCtorBase& operator=(const CopyCtorBase&) = default;
+  CopyCtorBase& operator=(CopyCtorBase&&) = default;
+};
+
+template <typename T>
+struct CopyCtorBase<T, false> {
+  CopyCtorBase() = default;
+  CopyCtorBase(const CopyCtorBase&) = delete;
+  CopyCtorBase(CopyCtorBase&&) = default;
+  CopyCtorBase& operator=(const CopyCtorBase&) = default;
+  CopyCtorBase& operator=(CopyCtorBase&&) = default;
+};
+
+template <typename T, bool = std::is_move_constructible<T>::value>
+struct MoveCtorBase {
+  MoveCtorBase() = default;
+  MoveCtorBase(const MoveCtorBase&) = default;
+  MoveCtorBase(MoveCtorBase&&) = default;
+  MoveCtorBase& operator=(const MoveCtorBase&) = default;
+  MoveCtorBase& operator=(MoveCtorBase&&) = default;
+};
+
+template <typename T>
+struct MoveCtorBase<T, false> {
+  MoveCtorBase() = default;
+  MoveCtorBase(const MoveCtorBase&) = default;
+  MoveCtorBase(MoveCtorBase&&) = delete;
+  MoveCtorBase& operator=(const MoveCtorBase&) = default;
+  MoveCtorBase& operator=(MoveCtorBase&&) = default;
+};
+
+template <typename T, bool = std::is_copy_constructible<T>::value&&
+                          std::is_copy_assignable<T>::value>
+struct CopyAssignBase {
+  CopyAssignBase() = default;
+  CopyAssignBase(const CopyAssignBase&) = default;
+  CopyAssignBase(CopyAssignBase&&) = default;
+  CopyAssignBase& operator=(const CopyAssignBase&) = default;
+  CopyAssignBase& operator=(CopyAssignBase&&) = default;
+};
+
+template <typename T>
+struct CopyAssignBase<T, false> {
+  CopyAssignBase() = default;
+  CopyAssignBase(const CopyAssignBase&) = default;
+  CopyAssignBase(CopyAssignBase&&) = default;
+  CopyAssignBase& operator=(const CopyAssignBase&) = delete;
+  CopyAssignBase& operator=(CopyAssignBase&&) = default;
+};
+
+template <typename T, bool = std::is_move_constructible<T>::value&&
+                          std::is_move_assignable<T>::value>
+struct MoveAssignBase {
+  MoveAssignBase() = default;
+  MoveAssignBase(const MoveAssignBase&) = default;
+  MoveAssignBase(MoveAssignBase&&) = default;
+  MoveAssignBase& operator=(const MoveAssignBase&) = default;
+  MoveAssignBase& operator=(MoveAssignBase&&) = default;
+};
+
+template <typename T>
+struct MoveAssignBase<T, false> {
+  MoveAssignBase() = default;
+  MoveAssignBase(const MoveAssignBase&) = default;
+  MoveAssignBase(MoveAssignBase&&) = default;
+  MoveAssignBase& operator=(const MoveAssignBase&) = default;
+  MoveAssignBase& operator=(MoveAssignBase&&) = delete;
+};
+
+ABSL_ATTRIBUTE_NORETURN void ThrowBadStatusOrAccess(absl::Status status);
+
+}  // namespace internal_statusor
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STATUS_INTERNAL_STATUSOR_INTERNAL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/status/status.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/status/status.h
new file mode 100644
index 0000000000000000000000000000000000000000..4e8292fc0e35135056e30bebb08b86be7d734689
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/status/status.h
@@ -0,0 +1,892 @@
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: status.h
+// -----------------------------------------------------------------------------
+//
+// This header file defines the Abseil `status` library, consisting of:
+//
+//   * An `absl::Status` class for holding error handling information
+//   * A set of canonical `absl::StatusCode` error codes, and associated
+//     utilities for generating and propagating status codes.
+//   * A set of helper functions for creating status codes and checking their
+//     values
+//
+// Within Google, `absl::Status` is the primary mechanism for communicating
+// errors in C++, and is used to represent error state in both in-process
+// library calls as well as RPC calls. Some of these errors may be recoverable,
+// but others may not. Most functions that can produce a recoverable error
+// should be designed to return an `absl::Status` (or `absl::StatusOr`).
+//
+// Example:
+//
+// absl::Status myFunction(absl::string_view fname, ...) {
+//   ...
+//   // encounter error
+//   if (error condition) {
+//     return absl::InvalidArgumentError("bad mode");
+//   }
+//   // else, return OK
+//   return absl::OkStatus();
+// }
+//
+// An `absl::Status` is designed to either return "OK" or one of a number of
+// different error codes, corresponding to typical error conditions.
+// In almost all cases, when using `absl::Status` you should use the canonical
+// error codes (of type `absl::StatusCode`) enumerated in this header file.
+// These canonical codes are understood across the codebase and will be
+// accepted across all API and RPC boundaries.
+#ifndef ABSL_STATUS_STATUS_H_
+#define ABSL_STATUS_STATUS_H_
+
+#include <ostream>
+#include <string>
+#include <utility>
+
+#include "absl/functional/function_ref.h"
+#include "absl/status/internal/status_internal.h"
+#include "absl/strings/cord.h"
+#include "absl/strings/string_view.h"
+#include "absl/types/optional.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// absl::StatusCode
+//
+// An `absl::StatusCode` is an enumerated type indicating either no error ("OK")
+// or an error condition. In most cases, an `absl::Status` indicates a
+// recoverable error, and the purpose of signalling an error is to indicate what
+// action to take in response to that error. These error codes map to the proto
+// RPC error codes indicated in https://cloud.google.com/apis/design/errors.
+//
+// The errors listed below are the canonical errors associated with
+// `absl::Status` and are used throughout the codebase. As a result, these
+// error codes are somewhat generic.
+//
+// In general, try to return the most specific error that applies if more than
+// one error may pertain. For example, prefer `kOutOfRange` over
+// `kFailedPrecondition` if both codes apply. Similarly prefer `kNotFound` or
+// `kAlreadyExists` over `kFailedPrecondition`.
+//
+// Because these errors may cross RPC boundaries, these codes are tied to the
+// `google.rpc.Code` definitions within
+// https://github.com/googleapis/googleapis/blob/master/google/rpc/code.proto
+// The string value of these RPC codes is denoted within each enum below.
+//
+// If your error handling code requires more context, you can attach payloads
+// to your status. See `absl::Status::SetPayload()` and
+// `absl::Status::GetPayload()` below.
+enum class StatusCode : int {
+  // StatusCode::kOk
+  //
+  // kOK (gRPC code "OK") does not indicate an error; this value is returned on
+  // success. It is typical to check for this value before proceeding on any
+  // given call across an API or RPC boundary. To check this value, use the
+  // `absl::Status::ok()` member function rather than inspecting the raw code.
+  kOk = 0,
+
+  // StatusCode::kCancelled
+  //
+  // kCancelled (gRPC code "CANCELLED") indicates the operation was cancelled,
+  // typically by the caller.
+  kCancelled = 1,
+
+  // StatusCode::kUnknown
+  //
+  // kUnknown (gRPC code "UNKNOWN") indicates an unknown error occurred. In
+  // general, more specific errors should be raised, if possible. Errors raised
+  // by APIs that do not return enough error information may be converted to
+  // this error.
+  kUnknown = 2,
+
+  // StatusCode::kInvalidArgument
+  //
+  // kInvalidArgument (gRPC code "INVALID_ARGUMENT") indicates the caller
+  // specified an invalid argument, such as a malformed filename. Note that use
+  // of such errors should be narrowly limited to indicate the invalid nature of
+  // the arguments themselves. Errors with validly formed arguments that may
+  // cause errors with the state of the receiving system should be denoted with
+  // `kFailedPrecondition` instead.
+  kInvalidArgument = 3,
+
+  // StatusCode::kDeadlineExceeded
+  //
+  // kDeadlineExceeded (gRPC code "DEADLINE_EXCEEDED") indicates a deadline
+  // expired before the operation could complete. For operations that may change
+  // state within a system, this error may be returned even if the operation has
+  // completed successfully. For example, a successful response from a server
+  // could have been delayed long enough for the deadline to expire.
+  kDeadlineExceeded = 4,
+
+  // StatusCode::kNotFound
+  //
+  // kNotFound (gRPC code "NOT_FOUND") indicates some requested entity (such as
+  // a file or directory) was not found.
+  //
+  // `kNotFound` is useful if a request should be denied for an entire class of
+  // users, such as during a gradual feature rollout or undocumented allow list.
+  // If a request should be denied for specific sets of users, such as through
+  // user-based access control, use `kPermissionDenied` instead.
+  kNotFound = 5,
+
+  // StatusCode::kAlreadyExists
+  //
+  // kAlreadyExists (gRPC code "ALREADY_EXISTS") indicates that the entity a
+  // caller attempted to create (such as a file or directory) is already
+  // present.
+  kAlreadyExists = 6,
+
+  // StatusCode::kPermissionDenied
+  //
+  // kPermissionDenied (gRPC code "PERMISSION_DENIED") indicates that the caller
+  // does not have permission to execute the specified operation. Note that this
+  // error is different than an error due to an *un*authenticated user. This
+  // error code does not imply the request is valid or the requested entity
+  // exists or satisfies any other pre-conditions.
+  //
+  // `kPermissionDenied` must not be used for rejections caused by exhausting
+  // some resource. Instead, use `kResourceExhausted` for those errors.
+  // `kPermissionDenied` must not be used if the caller cannot be identified.
+  // Instead, use `kUnauthenticated` for those errors.
+  kPermissionDenied = 7,
+
+  // StatusCode::kResourceExhausted
+  //
+  // kResourceExhausted (gRPC code "RESOURCE_EXHAUSTED") indicates some resource
+  // has been exhausted, perhaps a per-user quota, or perhaps the entire file
+  // system is out of space.
+  kResourceExhausted = 8,
+
+  // StatusCode::kFailedPrecondition
+  //
+  // kFailedPrecondition (gRPC code "FAILED_PRECONDITION") indicates that the
+  // operation was rejected because the system is not in a state required for
+  // the operation's execution. For example, a directory to be deleted may be
+  // non-empty, an "rmdir" operation is applied to a non-directory, etc.
+  //
+  // Some guidelines that may help a service implementer in deciding between
+  // `kFailedPrecondition`, `kAborted`, and `kUnavailable`:
+  //
+  //  (a) Use `kUnavailable` if the client can retry just the failing call.
+  //  (b) Use `kAborted` if the client should retry at a higher transaction
+  //      level (such as when a client-specified test-and-set fails, indicating
+  //      the client should restart a read-modify-write sequence).
+  //  (c) Use `kFailedPrecondition` if the client should not retry until
+  //      the system state has been explicitly fixed. For example, if a "rmdir"
+  //      fails because the directory is non-empty, `kFailedPrecondition`
+  //      should be returned since the client should not retry unless
+  //      the files are deleted from the directory.
+  kFailedPrecondition = 9,
+
+  // StatusCode::kAborted
+  //
+  // kAborted (gRPC code "ABORTED") indicates the operation was aborted,
+  // typically due to a concurrency issue such as a sequencer check failure or a
+  // failed transaction.
+  //
+  // See the guidelines above for deciding between `kFailedPrecondition`,
+  // `kAborted`, and `kUnavailable`.
+  kAborted = 10,
+
+  // StatusCode::kOutOfRange
+  //
+  // kOutOfRange (gRPC code "OUT_OF_RANGE") indicates the operation was
+  // attempted past the valid range, such as seeking or reading past an
+  // end-of-file.
+  //
+  // Unlike `kInvalidArgument`, this error indicates a problem that may
+  // be fixed if the system state changes. For example, a 32-bit file
+  // system will generate `kInvalidArgument` if asked to read at an
+  // offset that is not in the range [0,2^32-1], but it will generate
+  // `kOutOfRange` if asked to read from an offset past the current
+  // file size.
+  //
+  // There is a fair bit of overlap between `kFailedPrecondition` and
+  // `kOutOfRange`.  We recommend using `kOutOfRange` (the more specific
+  // error) when it applies so that callers who are iterating through
+  // a space can easily look for an `kOutOfRange` error to detect when
+  // they are done.
+  kOutOfRange = 11,
+
+  // StatusCode::kUnimplemented
+  //
+  // kUnimplemented (gRPC code "UNIMPLEMENTED") indicates the operation is not
+  // implemented or supported in this service. In this case, the operation
+  // should not be re-attempted.
+  kUnimplemented = 12,
+
+  // StatusCode::kInternal
+  //
+  // kInternal (gRPC code "INTERNAL") indicates an internal error has occurred
+  // and some invariants expected by the underlying system have not been
+  // satisfied. This error code is reserved for serious errors.
+  kInternal = 13,
+
+  // StatusCode::kUnavailable
+  //
+  // kUnavailable (gRPC code "UNAVAILABLE") indicates the service is currently
+  // unavailable and that this is most likely a transient condition. An error
+  // such as this can be corrected by retrying with a backoff scheme. Note that
+  // it is not always safe to retry non-idempotent operations.
+  //
+  // See the guidelines above for deciding between `kFailedPrecondition`,
+  // `kAborted`, and `kUnavailable`.
+  kUnavailable = 14,
+
+  // StatusCode::kDataLoss
+  //
+  // kDataLoss (gRPC code "DATA_LOSS") indicates that unrecoverable data loss or
+  // corruption has occurred. As this error is serious, proper alerting should
+  // be attached to errors such as this.
+  kDataLoss = 15,
+
+  // StatusCode::kUnauthenticated
+  //
+  // kUnauthenticated (gRPC code "UNAUTHENTICATED") indicates that the request
+  // does not have valid authentication credentials for the operation. Correct
+  // the authentication and try again.
+  kUnauthenticated = 16,
+
+  // StatusCode::DoNotUseReservedForFutureExpansionUseDefaultInSwitchInstead_
+  //
+  // NOTE: this error code entry should not be used and you should not rely on
+  // its value, which may change.
+  //
+  // The purpose of this enumerated value is to force people who handle status
+  // codes with `switch()` statements to *not* simply enumerate all possible
+  // values, but instead provide a "default:" case. Providing such a default
+  // case ensures that code will compile when new codes are added.
+  kDoNotUseReservedForFutureExpansionUseDefaultInSwitchInstead_ = 20
+};
+
+// StatusCodeToString()
+//
+// Returns the name for the status code, or "" if it is an unknown value.
+std::string StatusCodeToString(StatusCode code);
+
+// operator<<
+//
+// Streams StatusCodeToString(code) to `os`.
+std::ostream& operator<<(std::ostream& os, StatusCode code);
+
+// absl::StatusToStringMode
+//
+// An `absl::StatusToStringMode` is an enumerated type indicating how
+// `absl::Status::ToString()` should construct the output string for a non-ok
+// status.
+enum class StatusToStringMode : int {
+  // ToString will not contain any extra data (such as payloads). It will only
+  // contain the error code and message, if any.
+  kWithNoExtraData = 0,
+  // ToString will contain the payloads.
+  kWithPayload = 1 << 0,
+  // ToString will include all the extra data this Status has.
+  kWithEverything = ~kWithNoExtraData,
+  // Default mode used by ToString. Its exact value might change in the future.
+  kDefault = kWithPayload,
+};
+
+// absl::StatusToStringMode is specified as a bitmask type, which means the
+// following operations must be provided:
+inline constexpr StatusToStringMode operator&(StatusToStringMode lhs,
+                                              StatusToStringMode rhs) {
+  return static_cast<StatusToStringMode>(static_cast<int>(lhs) &
+                                         static_cast<int>(rhs));
+}
+inline constexpr StatusToStringMode operator|(StatusToStringMode lhs,
+                                              StatusToStringMode rhs) {
+  return static_cast<StatusToStringMode>(static_cast<int>(lhs) |
+                                         static_cast<int>(rhs));
+}
+inline constexpr StatusToStringMode operator^(StatusToStringMode lhs,
+                                              StatusToStringMode rhs) {
+  return static_cast<StatusToStringMode>(static_cast<int>(lhs) ^
+                                         static_cast<int>(rhs));
+}
+inline constexpr StatusToStringMode operator~(StatusToStringMode arg) {
+  return static_cast<StatusToStringMode>(~static_cast<int>(arg));
+}
+inline StatusToStringMode& operator&=(StatusToStringMode& lhs,
+                                      StatusToStringMode rhs) {
+  lhs = lhs & rhs;
+  return lhs;
+}
+inline StatusToStringMode& operator|=(StatusToStringMode& lhs,
+                                      StatusToStringMode rhs) {
+  lhs = lhs | rhs;
+  return lhs;
+}
+inline StatusToStringMode& operator^=(StatusToStringMode& lhs,
+                                      StatusToStringMode rhs) {
+  lhs = lhs ^ rhs;
+  return lhs;
+}
+
+// absl::Status
+//
+// The `absl::Status` class is generally used to gracefully handle errors
+// across API boundaries (and in particular across RPC boundaries). Some of
+// these errors may be recoverable, but others may not. Most
+// functions which can produce a recoverable error should be designed to return
+// either an `absl::Status` (or the similar `absl::StatusOr<T>`, which holds
+// either an object of type `T` or an error).
+//
+// API developers should construct their functions to return `absl::OkStatus()`
+// upon success, or an `absl::StatusCode` upon another type of error (e.g
+// an `absl::StatusCode::kInvalidArgument` error). The API provides convenience
+// functions to construct each status code.
+//
+// Example:
+//
+// absl::Status myFunction(absl::string_view fname, ...) {
+//   ...
+//   // encounter error
+//   if (error condition) {
+//     // Construct an absl::StatusCode::kInvalidArgument error
+//     return absl::InvalidArgumentError("bad mode");
+//   }
+//   // else, return OK
+//   return absl::OkStatus();
+// }
+//
+// Users handling status error codes should prefer checking for an OK status
+// using the `ok()` member function. Handling multiple error codes may justify
+// use of switch statement, but only check for error codes you know how to
+// handle; do not try to exhaustively match against all canonical error codes.
+// Errors that cannot be handled should be logged and/or propagated for higher
+// levels to deal with. If you do use a switch statement, make sure that you
+// also provide a `default:` switch case, so that code does not break as other
+// canonical codes are added to the API.
+//
+// Example:
+//
+//   absl::Status result = DoSomething();
+//   if (!result.ok()) {
+//     LOG(ERROR) << result;
+//   }
+//
+//   // Provide a default if switching on multiple error codes
+//   switch (result.code()) {
+//     // The user hasn't authenticated. Ask them to reauth
+//     case absl::StatusCode::kUnauthenticated:
+//       DoReAuth();
+//       break;
+//     // The user does not have permission. Log an error.
+//     case absl::StatusCode::kPermissionDenied:
+//       LOG(ERROR) << result;
+//       break;
+//     // Propagate the error otherwise.
+//     default:
+//       return true;
+//   }
+//
+// An `absl::Status` can optionally include a payload with more information
+// about the error. Typically, this payload serves one of several purposes:
+//
+//   * It may provide more fine-grained semantic information about the error to
+//     facilitate actionable remedies.
+//   * It may provide human-readable contexual information that is more
+//     appropriate to display to an end user.
+//
+// Example:
+//
+//   absl::Status result = DoSomething();
+//   // Inform user to retry after 30 seconds
+//   // See more error details in googleapis/google/rpc/error_details.proto
+//   if (absl::IsResourceExhausted(result)) {
+//     google::rpc::RetryInfo info;
+//     info.retry_delay().seconds() = 30;
+//     // Payloads require a unique key (a URL to ensure no collisions with
+//     // other payloads), and an `absl::Cord` to hold the encoded data.
+//     absl::string_view url = "type.googleapis.com/google.rpc.RetryInfo";
+//     result.SetPayload(url, info.SerializeAsCord());
+//     return result;
+//   }
+//
+// For documentation see https://abseil.io/docs/cpp/guides/status.
+//
+// Returned Status objects may not be ignored. status_internal.h has a forward
+// declaration of the form
+// class ABSL_MUST_USE_RESULT Status;
+class Status final {
+ public:
+  // Constructors
+
+  // This default constructor creates an OK status with no message or payload.
+  // Avoid this constructor and prefer explicit construction of an OK status
+  // with `absl::OkStatus()`.
+  Status();
+
+  // Creates a status in the canonical error space with the specified
+  // `absl::StatusCode` and error message.  If `code == absl::StatusCode::kOk`,  // NOLINT
+  // `msg` is ignored and an object identical to an OK status is constructed.
+  //
+  // The `msg` string must be in UTF-8. The implementation may complain (e.g.,  // NOLINT
+  // by printing a warning) if it is not.
+  Status(absl::StatusCode code, absl::string_view msg);
+
+  Status(const Status&);
+  Status& operator=(const Status& x);
+
+  // Move operators
+
+  // The moved-from state is valid but unspecified.
+  Status(Status&&) noexcept;
+  Status& operator=(Status&&);
+
+  ~Status();
+
+  // Status::Update()
+  //
+  // Updates the existing status with `new_status` provided that `this->ok()`.
+  // If the existing status already contains a non-OK error, this update has no
+  // effect and preserves the current data. Note that this behavior may change
+  // in the future to augment a current non-ok status with additional
+  // information about `new_status`.
+  //
+  // `Update()` provides a convenient way of keeping track of the first error
+  // encountered.
+  //
+  // Example:
+  //   // Instead of "if (overall_status.ok()) overall_status = new_status"
+  //   overall_status.Update(new_status);
+  //
+  void Update(const Status& new_status);
+  void Update(Status&& new_status);
+
+  // Status::ok()
+  //
+  // Returns `true` if `this->code()` == `absl::StatusCode::kOk`,
+  // indicating the absence of an error.
+  // Prefer checking for an OK status using this member function.
+  ABSL_MUST_USE_RESULT bool ok() const;
+
+  // Status::code()
+  //
+  // Returns the canonical error code of type `absl::StatusCode` of this status.
+  absl::StatusCode code() const;
+
+  // Status::raw_code()
+  //
+  // Returns a raw (canonical) error code corresponding to the enum value of
+  // `google.rpc.Code` definitions within
+  // https://github.com/googleapis/googleapis/blob/master/google/rpc/code.proto.
+  // These values could be out of the range of canonical `absl::StatusCode`
+  // enum values.
+  //
+  // NOTE: This function should only be called when converting to an associated
+  // wire format. Use `Status::code()` for error handling.
+  int raw_code() const;
+
+  // Status::message()
+  //
+  // Returns the error message associated with this error code, if available.
+  // Note that this message rarely describes the error code.  It is not unusual
+  // for the error message to be the empty string. As a result, prefer
+  // `operator<<` or `Status::ToString()` for debug logging.
+  absl::string_view message() const;
+
+  friend bool operator==(const Status&, const Status&);
+  friend bool operator!=(const Status&, const Status&);
+
+  // Status::ToString()
+  //
+  // Returns a string based on the `mode`. By default, it returns combination of
+  // the error code name, the message and any associated payload messages. This
+  // string is designed simply to be human readable and its exact format should
+  // not be load bearing. Do not depend on the exact format of the result of
+  // `ToString()` which is subject to change.
+  //
+  // The printed code name and the message are generally substrings of the
+  // result, and the payloads to be printed use the status payload printer
+  // mechanism (which is internal).
+  std::string ToString(
+      StatusToStringMode mode = StatusToStringMode::kDefault) const;
+
+  // Status::IgnoreError()
+  //
+  // Ignores any errors. This method does nothing except potentially suppress
+  // complaints from any tools that are checking that errors are not dropped on
+  // the floor.
+  void IgnoreError() const;
+
+  // swap()
+  //
+  // Swap the contents of one status with another.
+  friend void swap(Status& a, Status& b);
+
+  //----------------------------------------------------------------------------
+  // Payload Management APIs
+  //----------------------------------------------------------------------------
+
+  // A payload may be attached to a status to provide additional context to an
+  // error that may not be satisfied by an existing `absl::StatusCode`.
+  // Typically, this payload serves one of several purposes:
+  //
+  //   * It may provide more fine-grained semantic information about the error
+  //     to facilitate actionable remedies.
+  //   * It may provide human-readable contexual information that is more
+  //     appropriate to display to an end user.
+  //
+  // A payload consists of a [key,value] pair, where the key is a string
+  // referring to a unique "type URL" and the value is an object of type
+  // `absl::Cord` to hold the contextual data.
+  //
+  // The "type URL" should be unique and follow the format of a URL
+  // (https://en.wikipedia.org/wiki/URL) and, ideally, provide some
+  // documentation or schema on how to interpret its associated data. For
+  // example, the default type URL for a protobuf message type is
+  // "type.googleapis.com/packagename.messagename". Other custom wire formats
+  // should define the format of type URL in a similar practice so as to
+  // minimize the chance of conflict between type URLs.
+  // Users should ensure that the type URL can be mapped to a concrete
+  // C++ type if they want to deserialize the payload and read it effectively.
+  //
+  // To attach a payload to a status object, call `Status::SetPayload()`,
+  // passing it the type URL and an `absl::Cord` of associated data. Similarly,
+  // to extract the payload from a status, call `Status::GetPayload()`. You
+  // may attach multiple payloads (with differing type URLs) to any given
+  // status object, provided that the status is currently exhibiting an error
+  // code (i.e. is not OK).
+
+  // Status::GetPayload()
+  //
+  // Gets the payload of a status given its unique `type_url` key, if present.
+  absl::optional<absl::Cord> GetPayload(absl::string_view type_url) const;
+
+  // Status::SetPayload()
+  //
+  // Sets the payload for a non-ok status using a `type_url` key, overwriting
+  // any existing payload for that `type_url`.
+  //
+  // NOTE: This function does nothing if the Status is ok.
+  void SetPayload(absl::string_view type_url, absl::Cord payload);
+
+  // Status::ErasePayload()
+  //
+  // Erases the payload corresponding to the `type_url` key.  Returns `true` if
+  // the payload was present.
+  bool ErasePayload(absl::string_view type_url);
+
+  // Status::ForEachPayload()
+  //
+  // Iterates over the stored payloads and calls the
+  // `visitor(type_key, payload)` callable for each one.
+  //
+  // NOTE: The order of calls to `visitor()` is not specified and may change at
+  // any time.
+  //
+  // NOTE: Any mutation on the same 'absl::Status' object during visitation is
+  // forbidden and could result in undefined behavior.
+  void ForEachPayload(
+      absl::FunctionRef<void(absl::string_view, const absl::Cord&)> visitor)
+      const;
+
+ private:
+  friend Status CancelledError();
+
+  // Creates a status in the canonical error space with the specified
+  // code, and an empty error message.
+  explicit Status(absl::StatusCode code);
+
+  static void UnrefNonInlined(uintptr_t rep);
+  static void Ref(uintptr_t rep);
+  static void Unref(uintptr_t rep);
+
+  // REQUIRES: !ok()
+  // Ensures rep_ is not shared with any other Status.
+  void PrepareToModify();
+
+  const status_internal::Payloads* GetPayloads() const;
+  status_internal::Payloads* GetPayloads();
+
+  static bool EqualsSlow(const absl::Status& a, const absl::Status& b);
+
+  // MSVC 14.0 limitation requires the const.
+  static constexpr const char kMovedFromString[] =
+      "Status accessed after move.";
+
+  static const std::string* EmptyString();
+  static const std::string* MovedFromString();
+
+  // Returns whether rep contains an inlined representation.
+  // See rep_ for details.
+  static bool IsInlined(uintptr_t rep);
+
+  // Indicates whether this Status was the rhs of a move operation. See rep_
+  // for details.
+  static bool IsMovedFrom(uintptr_t rep);
+  static uintptr_t MovedFromRep();
+
+  // Convert between error::Code and the inlined uintptr_t representation used
+  // by rep_. See rep_ for details.
+  static uintptr_t CodeToInlinedRep(absl::StatusCode code);
+  static absl::StatusCode InlinedRepToCode(uintptr_t rep);
+
+  // Converts between StatusRep* and the external uintptr_t representation used
+  // by rep_. See rep_ for details.
+  static uintptr_t PointerToRep(status_internal::StatusRep* r);
+  static status_internal::StatusRep* RepToPointer(uintptr_t r);
+
+  std::string ToStringSlow(StatusToStringMode mode) const;
+
+  // Status supports two different representations.
+  //  - When the low bit is off it is an inlined representation.
+  //    It uses the canonical error space, no message or payload.
+  //    The error code is (rep_ >> 2).
+  //    The (rep_ & 2) bit is the "moved from" indicator, used in IsMovedFrom().
+  //  - When the low bit is on it is an external representation.
+  //    In this case all the data comes from a heap allocated Rep object.
+  //    (rep_ - 1) is a status_internal::StatusRep* pointer to that structure.
+  uintptr_t rep_;
+};
+
+// OkStatus()
+//
+// Returns an OK status, equivalent to a default constructed instance. Prefer
+// usage of `absl::OkStatus()` when constructing such an OK status.
+Status OkStatus();
+
+// operator<<()
+//
+// Prints a human-readable representation of `x` to `os`.
+std::ostream& operator<<(std::ostream& os, const Status& x);
+
+// IsAborted()
+// IsAlreadyExists()
+// IsCancelled()
+// IsDataLoss()
+// IsDeadlineExceeded()
+// IsFailedPrecondition()
+// IsInternal()
+// IsInvalidArgument()
+// IsNotFound()
+// IsOutOfRange()
+// IsPermissionDenied()
+// IsResourceExhausted()
+// IsUnauthenticated()
+// IsUnavailable()
+// IsUnimplemented()
+// IsUnknown()
+//
+// These convenience functions return `true` if a given status matches the
+// `absl::StatusCode` error code of its associated function.
+ABSL_MUST_USE_RESULT bool IsAborted(const Status& status);
+ABSL_MUST_USE_RESULT bool IsAlreadyExists(const Status& status);
+ABSL_MUST_USE_RESULT bool IsCancelled(const Status& status);
+ABSL_MUST_USE_RESULT bool IsDataLoss(const Status& status);
+ABSL_MUST_USE_RESULT bool IsDeadlineExceeded(const Status& status);
+ABSL_MUST_USE_RESULT bool IsFailedPrecondition(const Status& status);
+ABSL_MUST_USE_RESULT bool IsInternal(const Status& status);
+ABSL_MUST_USE_RESULT bool IsInvalidArgument(const Status& status);
+ABSL_MUST_USE_RESULT bool IsNotFound(const Status& status);
+ABSL_MUST_USE_RESULT bool IsOutOfRange(const Status& status);
+ABSL_MUST_USE_RESULT bool IsPermissionDenied(const Status& status);
+ABSL_MUST_USE_RESULT bool IsResourceExhausted(const Status& status);
+ABSL_MUST_USE_RESULT bool IsUnauthenticated(const Status& status);
+ABSL_MUST_USE_RESULT bool IsUnavailable(const Status& status);
+ABSL_MUST_USE_RESULT bool IsUnimplemented(const Status& status);
+ABSL_MUST_USE_RESULT bool IsUnknown(const Status& status);
+
+// AbortedError()
+// AlreadyExistsError()
+// CancelledError()
+// DataLossError()
+// DeadlineExceededError()
+// FailedPreconditionError()
+// InternalError()
+// InvalidArgumentError()
+// NotFoundError()
+// OutOfRangeError()
+// PermissionDeniedError()
+// ResourceExhaustedError()
+// UnauthenticatedError()
+// UnavailableError()
+// UnimplementedError()
+// UnknownError()
+//
+// These convenience functions create an `absl::Status` object with an error
+// code as indicated by the associated function name, using the error message
+// passed in `message`.
+Status AbortedError(absl::string_view message);
+Status AlreadyExistsError(absl::string_view message);
+Status CancelledError(absl::string_view message);
+Status DataLossError(absl::string_view message);
+Status DeadlineExceededError(absl::string_view message);
+Status FailedPreconditionError(absl::string_view message);
+Status InternalError(absl::string_view message);
+Status InvalidArgumentError(absl::string_view message);
+Status NotFoundError(absl::string_view message);
+Status OutOfRangeError(absl::string_view message);
+Status PermissionDeniedError(absl::string_view message);
+Status ResourceExhaustedError(absl::string_view message);
+Status UnauthenticatedError(absl::string_view message);
+Status UnavailableError(absl::string_view message);
+Status UnimplementedError(absl::string_view message);
+Status UnknownError(absl::string_view message);
+
+// ErrnoToStatusCode()
+//
+// Returns the StatusCode for `error_number`, which should be an `errno` value.
+// See https://en.cppreference.com/w/cpp/error/errno_macros and similar
+// references.
+absl::StatusCode ErrnoToStatusCode(int error_number);
+
+// ErrnoToStatus()
+//
+// Convenience function that creates a `absl::Status` using an `error_number`,
+// which should be an `errno` value.
+Status ErrnoToStatus(int error_number, absl::string_view message);
+
+//------------------------------------------------------------------------------
+// Implementation details follow
+//------------------------------------------------------------------------------
+
+inline Status::Status() : rep_(CodeToInlinedRep(absl::StatusCode::kOk)) {}
+
+inline Status::Status(absl::StatusCode code) : rep_(CodeToInlinedRep(code)) {}
+
+inline Status::Status(const Status& x) : rep_(x.rep_) { Ref(rep_); }
+
+inline Status& Status::operator=(const Status& x) {
+  uintptr_t old_rep = rep_;
+  if (x.rep_ != old_rep) {
+    Ref(x.rep_);
+    rep_ = x.rep_;
+    Unref(old_rep);
+  }
+  return *this;
+}
+
+inline Status::Status(Status&& x) noexcept : rep_(x.rep_) {
+  x.rep_ = MovedFromRep();
+}
+
+inline Status& Status::operator=(Status&& x) {
+  uintptr_t old_rep = rep_;
+  if (x.rep_ != old_rep) {
+    rep_ = x.rep_;
+    x.rep_ = MovedFromRep();
+    Unref(old_rep);
+  }
+  return *this;
+}
+
+inline void Status::Update(const Status& new_status) {
+  if (ok()) {
+    *this = new_status;
+  }
+}
+
+inline void Status::Update(Status&& new_status) {
+  if (ok()) {
+    *this = std::move(new_status);
+  }
+}
+
+inline Status::~Status() { Unref(rep_); }
+
+inline bool Status::ok() const {
+  return rep_ == CodeToInlinedRep(absl::StatusCode::kOk);
+}
+
+inline absl::string_view Status::message() const {
+  return !IsInlined(rep_)
+             ? RepToPointer(rep_)->message
+             : (IsMovedFrom(rep_) ? absl::string_view(kMovedFromString)
+                                  : absl::string_view());
+}
+
+inline bool operator==(const Status& lhs, const Status& rhs) {
+  return lhs.rep_ == rhs.rep_ || Status::EqualsSlow(lhs, rhs);
+}
+
+inline bool operator!=(const Status& lhs, const Status& rhs) {
+  return !(lhs == rhs);
+}
+
+inline std::string Status::ToString(StatusToStringMode mode) const {
+  return ok() ? "OK" : ToStringSlow(mode);
+}
+
+inline void Status::IgnoreError() const {
+  // no-op
+}
+
+inline void swap(absl::Status& a, absl::Status& b) {
+  using std::swap;
+  swap(a.rep_, b.rep_);
+}
+
+inline const status_internal::Payloads* Status::GetPayloads() const {
+  return IsInlined(rep_) ? nullptr : RepToPointer(rep_)->payloads.get();
+}
+
+inline status_internal::Payloads* Status::GetPayloads() {
+  return IsInlined(rep_) ? nullptr : RepToPointer(rep_)->payloads.get();
+}
+
+inline bool Status::IsInlined(uintptr_t rep) { return (rep & 1) == 0; }
+
+inline bool Status::IsMovedFrom(uintptr_t rep) {
+  return IsInlined(rep) && (rep & 2) != 0;
+}
+
+inline uintptr_t Status::MovedFromRep() {
+  return CodeToInlinedRep(absl::StatusCode::kInternal) | 2;
+}
+
+inline uintptr_t Status::CodeToInlinedRep(absl::StatusCode code) {
+  return static_cast<uintptr_t>(code) << 2;
+}
+
+inline absl::StatusCode Status::InlinedRepToCode(uintptr_t rep) {
+  assert(IsInlined(rep));
+  return static_cast<absl::StatusCode>(rep >> 2);
+}
+
+inline status_internal::StatusRep* Status::RepToPointer(uintptr_t rep) {
+  assert(!IsInlined(rep));
+  return reinterpret_cast<status_internal::StatusRep*>(rep - 1);
+}
+
+inline uintptr_t Status::PointerToRep(status_internal::StatusRep* rep) {
+  return reinterpret_cast<uintptr_t>(rep) + 1;
+}
+
+inline void Status::Ref(uintptr_t rep) {
+  if (!IsInlined(rep)) {
+    RepToPointer(rep)->ref.fetch_add(1, std::memory_order_relaxed);
+  }
+}
+
+inline void Status::Unref(uintptr_t rep) {
+  if (!IsInlined(rep)) {
+    UnrefNonInlined(rep);
+  }
+}
+
+inline Status OkStatus() { return Status(); }
+
+// Creates a `Status` object with the `absl::StatusCode::kCancelled` error code
+// and an empty message. It is provided only for efficiency, given that
+// message-less kCancelled errors are common in the infrastructure.
+inline Status CancelledError() { return Status(absl::StatusCode::kCancelled); }
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STATUS_STATUS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/status/status_payload_printer.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/status/status_payload_printer.h
new file mode 100644
index 0000000000000000000000000000000000000000..5e0937f67da8b232e7e471ea363b5c83f5a86aea
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/status/status_payload_printer.h
@@ -0,0 +1,51 @@
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+#ifndef ABSL_STATUS_STATUS_PAYLOAD_PRINTER_H_
+#define ABSL_STATUS_STATUS_PAYLOAD_PRINTER_H_
+
+#include <string>
+
+#include "absl/strings/cord.h"
+#include "absl/strings/string_view.h"
+#include "absl/types/optional.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace status_internal {
+
+// By default, `Status::ToString` and `operator<<(Status)` print a payload by
+// dumping the type URL and the raw bytes. To help debugging, we provide an
+// extension point, which is a global printer function that can be set by users
+// to specify how to print payloads. The function takes the type URL and the
+// payload as input, and should return a valid human-readable string on success
+// or `absl::nullopt` on failure (in which case it falls back to the default
+// approach of printing the raw bytes).
+// NOTE: This is an internal API and the design is subject to change in the
+// future in a non-backward-compatible way. Since it's only meant for debugging
+// purpose, you should not rely on it in any critical logic.
+using StatusPayloadPrinter = absl::optional<std::string> (*)(absl::string_view,
+                                                             const absl::Cord&);
+
+// Sets the global payload printer. Only one printer should be set per process.
+// If multiple printers are set, it's undefined which one will be used.
+void SetStatusPayloadPrinter(StatusPayloadPrinter);
+
+// Returns the global payload printer if previously set, otherwise `nullptr`.
+StatusPayloadPrinter GetStatusPayloadPrinter();
+
+}  // namespace status_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STATUS_STATUS_PAYLOAD_PRINTER_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/status/statusor.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/status/statusor.h
new file mode 100644
index 0000000000000000000000000000000000000000..a76e7201530d895f20a37531b354c963d5b520fa
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/status/statusor.h
@@ -0,0 +1,776 @@
+// Copyright 2020 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: statusor.h
+// -----------------------------------------------------------------------------
+//
+// An `absl::StatusOr<T>` represents a union of an `absl::Status` object
+// and an object of type `T`. The `absl::StatusOr<T>` will either contain an
+// object of type `T` (indicating a successful operation), or an error (of type
+// `absl::Status`) explaining why such a value is not present.
+//
+// In general, check the success of an operation returning an
+// `absl::StatusOr<T>` like you would an `absl::Status` by using the `ok()`
+// member function.
+//
+// Example:
+//
+//   StatusOr<Foo> result = Calculation();
+//   if (result.ok()) {
+//     result->DoSomethingCool();
+//   } else {
+//     LOG(ERROR) << result.status();
+//   }
+#ifndef ABSL_STATUS_STATUSOR_H_
+#define ABSL_STATUS_STATUSOR_H_
+
+#include <exception>
+#include <initializer_list>
+#include <new>
+#include <string>
+#include <type_traits>
+#include <utility>
+
+#include "absl/base/attributes.h"
+#include "absl/base/call_once.h"
+#include "absl/meta/type_traits.h"
+#include "absl/status/internal/statusor_internal.h"
+#include "absl/status/status.h"
+#include "absl/types/variant.h"
+#include "absl/utility/utility.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// BadStatusOrAccess
+//
+// This class defines the type of object to throw (if exceptions are enabled),
+// when accessing the value of an `absl::StatusOr<T>` object that does not
+// contain a value. This behavior is analogous to that of
+// `std::bad_optional_access` in the case of accessing an invalid
+// `std::optional` value.
+//
+// Example:
+//
+// try {
+//   absl::StatusOr<int> v = FetchInt();
+//   DoWork(v.value());  // Accessing value() when not "OK" may throw
+// } catch (absl::BadStatusOrAccess& ex) {
+//   LOG(ERROR) << ex.status();
+// }
+class BadStatusOrAccess : public std::exception {
+ public:
+  explicit BadStatusOrAccess(absl::Status status);
+  ~BadStatusOrAccess() override = default;
+
+  BadStatusOrAccess(const BadStatusOrAccess& other);
+  BadStatusOrAccess& operator=(const BadStatusOrAccess& other);
+  BadStatusOrAccess(BadStatusOrAccess&& other);
+  BadStatusOrAccess& operator=(BadStatusOrAccess&& other);
+
+  // BadStatusOrAccess::what()
+  //
+  // Returns the associated explanatory string of the `absl::StatusOr<T>`
+  // object's error code. This function contains information about the failing
+  // status, but its exact formatting may change and should not be depended on.
+  //
+  // The pointer of this string is guaranteed to be valid until any non-const
+  // function is invoked on the exception object.
+  const char* what() const noexcept override;
+
+  // BadStatusOrAccess::status()
+  //
+  // Returns the associated `absl::Status` of the `absl::StatusOr<T>` object's
+  // error.
+  const absl::Status& status() const;
+
+ private:
+  void InitWhat() const;
+
+  absl::Status status_;
+  mutable absl::once_flag init_what_;
+  mutable std::string what_;
+};
+
+// Returned StatusOr objects may not be ignored.
+template <typename T>
+#if ABSL_HAVE_CPP_ATTRIBUTE(nodiscard)
+// TODO(b/176172494): ABSL_MUST_USE_RESULT should expand to the more strict
+// [[nodiscard]]. For now, just use [[nodiscard]] directly when it is available.
+class [[nodiscard]] StatusOr;
+#else
+class ABSL_MUST_USE_RESULT StatusOr;
+#endif  // ABSL_HAVE_CPP_ATTRIBUTE(nodiscard)
+
+// absl::StatusOr<T>
+//
+// The `absl::StatusOr<T>` class template is a union of an `absl::Status` object
+// and an object of type `T`. The `absl::StatusOr<T>` models an object that is
+// either a usable object, or an error (of type `absl::Status`) explaining why
+// such an object is not present. An `absl::StatusOr<T>` is typically the return
+// value of a function which may fail.
+//
+// An `absl::StatusOr<T>` can never hold an "OK" status (an
+// `absl::StatusCode::kOk` value); instead, the presence of an object of type
+// `T` indicates success. Instead of checking for a `kOk` value, use the
+// `absl::StatusOr<T>::ok()` member function. (It is for this reason, and code
+// readability, that using the `ok()` function is preferred for `absl::Status`
+// as well.)
+//
+// Example:
+//
+//   StatusOr<Foo> result = DoBigCalculationThatCouldFail();
+//   if (result.ok()) {
+//     result->DoSomethingCool();
+//   } else {
+//     LOG(ERROR) << result.status();
+//   }
+//
+// Accessing the object held by an `absl::StatusOr<T>` should be performed via
+// `operator*` or `operator->`, after a call to `ok()` confirms that the
+// `absl::StatusOr<T>` holds an object of type `T`:
+//
+// Example:
+//
+//   absl::StatusOr<int> i = GetCount();
+//   if (i.ok()) {
+//     updated_total += *i
+//   }
+//
+// NOTE: using `absl::StatusOr<T>::value()` when no valid value is present will
+// throw an exception if exceptions are enabled or terminate the process when
+// exceptions are not enabled.
+//
+// Example:
+//
+//   StatusOr<Foo> result = DoBigCalculationThatCouldFail();
+//   const Foo& foo = result.value();    // Crash/exception if no value present
+//   foo.DoSomethingCool();
+//
+// A `absl::StatusOr<T*>` can be constructed from a null pointer like any other
+// pointer value, and the result will be that `ok()` returns `true` and
+// `value()` returns `nullptr`. Checking the value of pointer in an
+// `absl::StatusOr<T*>` generally requires a bit more care, to ensure both that
+// a value is present and that value is not null:
+//
+//  StatusOr<std::unique_ptr<Foo>> result = FooFactory::MakeNewFoo(arg);
+//  if (!result.ok()) {
+//    LOG(ERROR) << result.status();
+//  } else if (*result == nullptr) {
+//    LOG(ERROR) << "Unexpected null pointer";
+//  } else {
+//    (*result)->DoSomethingCool();
+//  }
+//
+// Example factory implementation returning StatusOr<T>:
+//
+//  StatusOr<Foo> FooFactory::MakeFoo(int arg) {
+//    if (arg <= 0) {
+//      return absl::Status(absl::StatusCode::kInvalidArgument,
+//                          "Arg must be positive");
+//    }
+//    return Foo(arg);
+//  }
+template <typename T>
+class StatusOr : private internal_statusor::StatusOrData<T>,
+                 private internal_statusor::CopyCtorBase<T>,
+                 private internal_statusor::MoveCtorBase<T>,
+                 private internal_statusor::CopyAssignBase<T>,
+                 private internal_statusor::MoveAssignBase<T> {
+  template <typename U>
+  friend class StatusOr;
+
+  typedef internal_statusor::StatusOrData<T> Base;
+
+ public:
+  // StatusOr<T>::value_type
+  //
+  // This instance data provides a generic `value_type` member for use within
+  // generic programming. This usage is analogous to that of
+  // `optional::value_type` in the case of `std::optional`.
+  typedef T value_type;
+
+  // Constructors
+
+  // Constructs a new `absl::StatusOr` with an `absl::StatusCode::kUnknown`
+  // status. This constructor is marked 'explicit' to prevent usages in return
+  // values such as 'return {};', under the misconception that
+  // `absl::StatusOr<std::vector<int>>` will be initialized with an empty
+  // vector, instead of an `absl::StatusCode::kUnknown` error code.
+  explicit StatusOr();
+
+  // `StatusOr<T>` is copy constructible if `T` is copy constructible.
+  StatusOr(const StatusOr&) = default;
+  // `StatusOr<T>` is copy assignable if `T` is copy constructible and copy
+  // assignable.
+  StatusOr& operator=(const StatusOr&) = default;
+
+  // `StatusOr<T>` is move constructible if `T` is move constructible.
+  StatusOr(StatusOr&&) = default;
+  // `StatusOr<T>` is moveAssignable if `T` is move constructible and move
+  // assignable.
+  StatusOr& operator=(StatusOr&&) = default;
+
+  // Converting Constructors
+
+  // Constructs a new `absl::StatusOr<T>` from an `absl::StatusOr<U>`, when `T`
+  // is constructible from `U`. To avoid ambiguity, these constructors are
+  // disabled if `T` is also constructible from `StatusOr<U>.`. This constructor
+  // is explicit if and only if the corresponding construction of `T` from `U`
+  // is explicit. (This constructor inherits its explicitness from the
+  // underlying constructor.)
+  template <
+      typename U,
+      absl::enable_if_t<
+          absl::conjunction<
+              absl::negation<std::is_same<T, U>>,
+              std::is_constructible<T, const U&>,
+              std::is_convertible<const U&, T>,
+              absl::negation<
+                  internal_statusor::IsConstructibleOrConvertibleFromStatusOr<
+                      T, U>>>::value,
+          int> = 0>
+  StatusOr(const StatusOr<U>& other)  // NOLINT
+      : Base(static_cast<const typename StatusOr<U>::Base&>(other)) {}
+  template <
+      typename U,
+      absl::enable_if_t<
+          absl::conjunction<
+              absl::negation<std::is_same<T, U>>,
+              std::is_constructible<T, const U&>,
+              absl::negation<std::is_convertible<const U&, T>>,
+              absl::negation<
+                  internal_statusor::IsConstructibleOrConvertibleFromStatusOr<
+                      T, U>>>::value,
+          int> = 0>
+  explicit StatusOr(const StatusOr<U>& other)
+      : Base(static_cast<const typename StatusOr<U>::Base&>(other)) {}
+
+  template <
+      typename U,
+      absl::enable_if_t<
+          absl::conjunction<
+              absl::negation<std::is_same<T, U>>, std::is_constructible<T, U&&>,
+              std::is_convertible<U&&, T>,
+              absl::negation<
+                  internal_statusor::IsConstructibleOrConvertibleFromStatusOr<
+                      T, U>>>::value,
+          int> = 0>
+  StatusOr(StatusOr<U>&& other)  // NOLINT
+      : Base(static_cast<typename StatusOr<U>::Base&&>(other)) {}
+  template <
+      typename U,
+      absl::enable_if_t<
+          absl::conjunction<
+              absl::negation<std::is_same<T, U>>, std::is_constructible<T, U&&>,
+              absl::negation<std::is_convertible<U&&, T>>,
+              absl::negation<
+                  internal_statusor::IsConstructibleOrConvertibleFromStatusOr<
+                      T, U>>>::value,
+          int> = 0>
+  explicit StatusOr(StatusOr<U>&& other)
+      : Base(static_cast<typename StatusOr<U>::Base&&>(other)) {}
+
+  // Converting Assignment Operators
+
+  // Creates an `absl::StatusOr<T>` through assignment from an
+  // `absl::StatusOr<U>` when:
+  //
+  //   * Both `absl::StatusOr<T>` and `absl::StatusOr<U>` are OK by assigning
+  //     `U` to `T` directly.
+  //   * `absl::StatusOr<T>` is OK and `absl::StatusOr<U>` contains an error
+  //      code by destroying `absl::StatusOr<T>`'s value and assigning from
+  //      `absl::StatusOr<U>'
+  //   * `absl::StatusOr<T>` contains an error code and `absl::StatusOr<U>` is
+  //      OK by directly initializing `T` from `U`.
+  //   * Both `absl::StatusOr<T>` and `absl::StatusOr<U>` contain an error
+  //     code by assigning the `Status` in `absl::StatusOr<U>` to
+  //     `absl::StatusOr<T>`
+  //
+  // These overloads only apply if `absl::StatusOr<T>` is constructible and
+  // assignable from `absl::StatusOr<U>` and `StatusOr<T>` cannot be directly
+  // assigned from `StatusOr<U>`.
+  template <
+      typename U,
+      absl::enable_if_t<
+          absl::conjunction<
+              absl::negation<std::is_same<T, U>>,
+              std::is_constructible<T, const U&>,
+              std::is_assignable<T, const U&>,
+              absl::negation<
+                  internal_statusor::
+                      IsConstructibleOrConvertibleOrAssignableFromStatusOr<
+                          T, U>>>::value,
+          int> = 0>
+  StatusOr& operator=(const StatusOr<U>& other) {
+    this->Assign(other);
+    return *this;
+  }
+  template <
+      typename U,
+      absl::enable_if_t<
+          absl::conjunction<
+              absl::negation<std::is_same<T, U>>, std::is_constructible<T, U&&>,
+              std::is_assignable<T, U&&>,
+              absl::negation<
+                  internal_statusor::
+                      IsConstructibleOrConvertibleOrAssignableFromStatusOr<
+                          T, U>>>::value,
+          int> = 0>
+  StatusOr& operator=(StatusOr<U>&& other) {
+    this->Assign(std::move(other));
+    return *this;
+  }
+
+  // Constructs a new `absl::StatusOr<T>` with a non-ok status. After calling
+  // this constructor, `this->ok()` will be `false` and calls to `value()` will
+  // crash, or produce an exception if exceptions are enabled.
+  //
+  // The constructor also takes any type `U` that is convertible to
+  // `absl::Status`. This constructor is explicit if an only if `U` is not of
+  // type `absl::Status` and the conversion from `U` to `Status` is explicit.
+  //
+  // REQUIRES: !Status(std::forward<U>(v)).ok(). This requirement is DCHECKed.
+  // In optimized builds, passing absl::OkStatus() here will have the effect
+  // of passing absl::StatusCode::kInternal as a fallback.
+  template <
+      typename U = absl::Status,
+      absl::enable_if_t<
+          absl::conjunction<
+              std::is_convertible<U&&, absl::Status>,
+              std::is_constructible<absl::Status, U&&>,
+              absl::negation<std::is_same<absl::decay_t<U>, absl::StatusOr<T>>>,
+              absl::negation<std::is_same<absl::decay_t<U>, T>>,
+              absl::negation<std::is_same<absl::decay_t<U>, absl::in_place_t>>,
+              absl::negation<internal_statusor::HasConversionOperatorToStatusOr<
+                  T, U&&>>>::value,
+          int> = 0>
+  StatusOr(U&& v) : Base(std::forward<U>(v)) {}
+
+  template <
+      typename U = absl::Status,
+      absl::enable_if_t<
+          absl::conjunction<
+              absl::negation<std::is_convertible<U&&, absl::Status>>,
+              std::is_constructible<absl::Status, U&&>,
+              absl::negation<std::is_same<absl::decay_t<U>, absl::StatusOr<T>>>,
+              absl::negation<std::is_same<absl::decay_t<U>, T>>,
+              absl::negation<std::is_same<absl::decay_t<U>, absl::in_place_t>>,
+              absl::negation<internal_statusor::HasConversionOperatorToStatusOr<
+                  T, U&&>>>::value,
+          int> = 0>
+  explicit StatusOr(U&& v) : Base(std::forward<U>(v)) {}
+
+  template <
+      typename U = absl::Status,
+      absl::enable_if_t<
+          absl::conjunction<
+              std::is_convertible<U&&, absl::Status>,
+              std::is_constructible<absl::Status, U&&>,
+              absl::negation<std::is_same<absl::decay_t<U>, absl::StatusOr<T>>>,
+              absl::negation<std::is_same<absl::decay_t<U>, T>>,
+              absl::negation<std::is_same<absl::decay_t<U>, absl::in_place_t>>,
+              absl::negation<internal_statusor::HasConversionOperatorToStatusOr<
+                  T, U&&>>>::value,
+          int> = 0>
+  StatusOr& operator=(U&& v) {
+    this->AssignStatus(std::forward<U>(v));
+    return *this;
+  }
+
+  // Perfect-forwarding value assignment operator.
+
+  // If `*this` contains a `T` value before the call, the contained value is
+  // assigned from `std::forward<U>(v)`; Otherwise, it is directly-initialized
+  // from `std::forward<U>(v)`.
+  // This function does not participate in overload unless:
+  // 1. `std::is_constructible_v<T, U>` is true,
+  // 2. `std::is_assignable_v<T&, U>` is true.
+  // 3. `std::is_same_v<StatusOr<T>, std::remove_cvref_t<U>>` is false.
+  // 4. Assigning `U` to `T` is not ambiguous:
+  //  If `U` is `StatusOr<V>` and `T` is constructible and assignable from
+  //  both `StatusOr<V>` and `V`, the assignment is considered bug-prone and
+  //  ambiguous thus will fail to compile. For example:
+  //    StatusOr<bool> s1 = true;  // s1.ok() && *s1 == true
+  //    StatusOr<bool> s2 = false;  // s2.ok() && *s2 == false
+  //    s1 = s2;  // ambiguous, `s1 = *s2` or `s1 = bool(s2)`?
+  template <
+      typename U = T,
+      typename = typename std::enable_if<absl::conjunction<
+          std::is_constructible<T, U&&>, std::is_assignable<T&, U&&>,
+          absl::disjunction<
+              std::is_same<absl::remove_cv_t<absl::remove_reference_t<U>>, T>,
+              absl::conjunction<
+                  absl::negation<std::is_convertible<U&&, absl::Status>>,
+                  absl::negation<internal_statusor::
+                                     HasConversionOperatorToStatusOr<T, U&&>>>>,
+          internal_statusor::IsForwardingAssignmentValid<T, U&&>>::value>::type>
+  StatusOr& operator=(U&& v) {
+    this->Assign(std::forward<U>(v));
+    return *this;
+  }
+
+  // Constructs the inner value `T` in-place using the provided args, using the
+  // `T(args...)` constructor.
+  template <typename... Args>
+  explicit StatusOr(absl::in_place_t, Args&&... args);
+  template <typename U, typename... Args>
+  explicit StatusOr(absl::in_place_t, std::initializer_list<U> ilist,
+                    Args&&... args);
+
+  // Constructs the inner value `T` in-place using the provided args, using the
+  // `T(U)` (direct-initialization) constructor. This constructor is only valid
+  // if `T` can be constructed from a `U`. Can accept move or copy constructors.
+  //
+  // This constructor is explicit if `U` is not convertible to `T`. To avoid
+  // ambiguity, this constructor is disabled if `U` is a `StatusOr<J>`, where
+  // `J` is convertible to `T`.
+  template <
+      typename U = T,
+      absl::enable_if_t<
+          absl::conjunction<
+              internal_statusor::IsDirectInitializationValid<T, U&&>,
+              std::is_constructible<T, U&&>, std::is_convertible<U&&, T>,
+              absl::disjunction<
+                  std::is_same<absl::remove_cv_t<absl::remove_reference_t<U>>,
+                               T>,
+                  absl::conjunction<
+                      absl::negation<std::is_convertible<U&&, absl::Status>>,
+                      absl::negation<
+                          internal_statusor::HasConversionOperatorToStatusOr<
+                              T, U&&>>>>>::value,
+          int> = 0>
+  StatusOr(U&& u)  // NOLINT
+      : StatusOr(absl::in_place, std::forward<U>(u)) {}
+
+  template <
+      typename U = T,
+      absl::enable_if_t<
+          absl::conjunction<
+              internal_statusor::IsDirectInitializationValid<T, U&&>,
+              absl::disjunction<
+                  std::is_same<absl::remove_cv_t<absl::remove_reference_t<U>>,
+                               T>,
+                  absl::conjunction<
+                      absl::negation<std::is_constructible<absl::Status, U&&>>,
+                      absl::negation<
+                          internal_statusor::HasConversionOperatorToStatusOr<
+                              T, U&&>>>>,
+              std::is_constructible<T, U&&>,
+              absl::negation<std::is_convertible<U&&, T>>>::value,
+          int> = 0>
+  explicit StatusOr(U&& u)  // NOLINT
+      : StatusOr(absl::in_place, std::forward<U>(u)) {}
+
+  // StatusOr<T>::ok()
+  //
+  // Returns whether or not this `absl::StatusOr<T>` holds a `T` value. This
+  // member function is analogous to `absl::Status::ok()` and should be used
+  // similarly to check the status of return values.
+  //
+  // Example:
+  //
+  // StatusOr<Foo> result = DoBigCalculationThatCouldFail();
+  // if (result.ok()) {
+  //    // Handle result
+  // else {
+  //    // Handle error
+  // }
+  ABSL_MUST_USE_RESULT bool ok() const { return this->status_.ok(); }
+
+  // StatusOr<T>::status()
+  //
+  // Returns a reference to the current `absl::Status` contained within the
+  // `absl::StatusOr<T>`. If `absl::StatusOr<T>` contains a `T`, then this
+  // function returns `absl::OkStatus()`.
+  const Status& status() const&;
+  Status status() &&;
+
+  // StatusOr<T>::value()
+  //
+  // Returns a reference to the held value if `this->ok()`. Otherwise, throws
+  // `absl::BadStatusOrAccess` if exceptions are enabled, or is guaranteed to
+  // terminate the process if exceptions are disabled.
+  //
+  // If you have already checked the status using `this->ok()`, you probably
+  // want to use `operator*()` or `operator->()` to access the value instead of
+  // `value`.
+  //
+  // Note: for value types that are cheap to copy, prefer simple code:
+  //
+  //   T value = statusor.value();
+  //
+  // Otherwise, if the value type is expensive to copy, but can be left
+  // in the StatusOr, simply assign to a reference:
+  //
+  //   T& value = statusor.value();  // or `const T&`
+  //
+  // Otherwise, if the value type supports an efficient move, it can be
+  // used as follows:
+  //
+  //   T value = std::move(statusor).value();
+  //
+  // The `std::move` on statusor instead of on the whole expression enables
+  // warnings about possible uses of the statusor object after the move.
+  const T& value() const& ABSL_ATTRIBUTE_LIFETIME_BOUND;
+  T& value() & ABSL_ATTRIBUTE_LIFETIME_BOUND;
+  const T&& value() const&& ABSL_ATTRIBUTE_LIFETIME_BOUND;
+  T&& value() && ABSL_ATTRIBUTE_LIFETIME_BOUND;
+
+  // StatusOr<T>:: operator*()
+  //
+  // Returns a reference to the current value.
+  //
+  // REQUIRES: `this->ok() == true`, otherwise the behavior is undefined.
+  //
+  // Use `this->ok()` to verify that there is a current value within the
+  // `absl::StatusOr<T>`. Alternatively, see the `value()` member function for a
+  // similar API that guarantees crashing or throwing an exception if there is
+  // no current value.
+  const T& operator*() const& ABSL_ATTRIBUTE_LIFETIME_BOUND;
+  T& operator*() & ABSL_ATTRIBUTE_LIFETIME_BOUND;
+  const T&& operator*() const&& ABSL_ATTRIBUTE_LIFETIME_BOUND;
+  T&& operator*() && ABSL_ATTRIBUTE_LIFETIME_BOUND;
+
+  // StatusOr<T>::operator->()
+  //
+  // Returns a pointer to the current value.
+  //
+  // REQUIRES: `this->ok() == true`, otherwise the behavior is undefined.
+  //
+  // Use `this->ok()` to verify that there is a current value.
+  const T* operator->() const ABSL_ATTRIBUTE_LIFETIME_BOUND;
+  T* operator->() ABSL_ATTRIBUTE_LIFETIME_BOUND;
+
+  // StatusOr<T>::value_or()
+  //
+  // Returns the current value if `this->ok() == true`. Otherwise constructs a
+  // value using the provided `default_value`.
+  //
+  // Unlike `value`, this function returns by value, copying the current value
+  // if necessary. If the value type supports an efficient move, it can be used
+  // as follows:
+  //
+  //   T value = std::move(statusor).value_or(def);
+  //
+  // Unlike with `value`, calling `std::move()` on the result of `value_or` will
+  // still trigger a copy.
+  template <typename U>
+  T value_or(U&& default_value) const&;
+  template <typename U>
+  T value_or(U&& default_value) &&;
+
+  // StatusOr<T>::IgnoreError()
+  //
+  // Ignores any errors. This method does nothing except potentially suppress
+  // complaints from any tools that are checking that errors are not dropped on
+  // the floor.
+  void IgnoreError() const;
+
+  // StatusOr<T>::emplace()
+  //
+  // Reconstructs the inner value T in-place using the provided args, using the
+  // T(args...) constructor. Returns reference to the reconstructed `T`.
+  template <typename... Args>
+  T& emplace(Args&&... args) {
+    if (ok()) {
+      this->Clear();
+      this->MakeValue(std::forward<Args>(args)...);
+    } else {
+      this->MakeValue(std::forward<Args>(args)...);
+      this->status_ = absl::OkStatus();
+    }
+    return this->data_;
+  }
+
+  template <
+      typename U, typename... Args,
+      absl::enable_if_t<
+          std::is_constructible<T, std::initializer_list<U>&, Args&&...>::value,
+          int> = 0>
+  T& emplace(std::initializer_list<U> ilist, Args&&... args) {
+    if (ok()) {
+      this->Clear();
+      this->MakeValue(ilist, std::forward<Args>(args)...);
+    } else {
+      this->MakeValue(ilist, std::forward<Args>(args)...);
+      this->status_ = absl::OkStatus();
+    }
+    return this->data_;
+  }
+
+ private:
+  using internal_statusor::StatusOrData<T>::Assign;
+  template <typename U>
+  void Assign(const absl::StatusOr<U>& other);
+  template <typename U>
+  void Assign(absl::StatusOr<U>&& other);
+};
+
+// operator==()
+//
+// This operator checks the equality of two `absl::StatusOr<T>` objects.
+template <typename T>
+bool operator==(const StatusOr<T>& lhs, const StatusOr<T>& rhs) {
+  if (lhs.ok() && rhs.ok()) return *lhs == *rhs;
+  return lhs.status() == rhs.status();
+}
+
+// operator!=()
+//
+// This operator checks the inequality of two `absl::StatusOr<T>` objects.
+template <typename T>
+bool operator!=(const StatusOr<T>& lhs, const StatusOr<T>& rhs) {
+  return !(lhs == rhs);
+}
+
+//------------------------------------------------------------------------------
+// Implementation details for StatusOr<T>
+//------------------------------------------------------------------------------
+
+// TODO(sbenza): avoid the string here completely.
+template <typename T>
+StatusOr<T>::StatusOr() : Base(Status(absl::StatusCode::kUnknown, "")) {}
+
+template <typename T>
+template <typename U>
+inline void StatusOr<T>::Assign(const StatusOr<U>& other) {
+  if (other.ok()) {
+    this->Assign(*other);
+  } else {
+    this->AssignStatus(other.status());
+  }
+}
+
+template <typename T>
+template <typename U>
+inline void StatusOr<T>::Assign(StatusOr<U>&& other) {
+  if (other.ok()) {
+    this->Assign(*std::move(other));
+  } else {
+    this->AssignStatus(std::move(other).status());
+  }
+}
+template <typename T>
+template <typename... Args>
+StatusOr<T>::StatusOr(absl::in_place_t, Args&&... args)
+    : Base(absl::in_place, std::forward<Args>(args)...) {}
+
+template <typename T>
+template <typename U, typename... Args>
+StatusOr<T>::StatusOr(absl::in_place_t, std::initializer_list<U> ilist,
+                      Args&&... args)
+    : Base(absl::in_place, ilist, std::forward<Args>(args)...) {}
+
+template <typename T>
+const Status& StatusOr<T>::status() const& {
+  return this->status_;
+}
+template <typename T>
+Status StatusOr<T>::status() && {
+  return ok() ? OkStatus() : std::move(this->status_);
+}
+
+template <typename T>
+const T& StatusOr<T>::value() const& {
+  if (!this->ok()) internal_statusor::ThrowBadStatusOrAccess(this->status_);
+  return this->data_;
+}
+
+template <typename T>
+T& StatusOr<T>::value() & {
+  if (!this->ok()) internal_statusor::ThrowBadStatusOrAccess(this->status_);
+  return this->data_;
+}
+
+template <typename T>
+const T&& StatusOr<T>::value() const&& {
+  if (!this->ok()) {
+    internal_statusor::ThrowBadStatusOrAccess(std::move(this->status_));
+  }
+  return std::move(this->data_);
+}
+
+template <typename T>
+T&& StatusOr<T>::value() && {
+  if (!this->ok()) {
+    internal_statusor::ThrowBadStatusOrAccess(std::move(this->status_));
+  }
+  return std::move(this->data_);
+}
+
+template <typename T>
+const T& StatusOr<T>::operator*() const& {
+  this->EnsureOk();
+  return this->data_;
+}
+
+template <typename T>
+T& StatusOr<T>::operator*() & {
+  this->EnsureOk();
+  return this->data_;
+}
+
+template <typename T>
+const T&& StatusOr<T>::operator*() const&& {
+  this->EnsureOk();
+  return std::move(this->data_);
+}
+
+template <typename T>
+T&& StatusOr<T>::operator*() && {
+  this->EnsureOk();
+  return std::move(this->data_);
+}
+
+template <typename T>
+const T* StatusOr<T>::operator->() const {
+  this->EnsureOk();
+  return &this->data_;
+}
+
+template <typename T>
+T* StatusOr<T>::operator->() {
+  this->EnsureOk();
+  return &this->data_;
+}
+
+template <typename T>
+template <typename U>
+T StatusOr<T>::value_or(U&& default_value) const& {
+  if (ok()) {
+    return this->data_;
+  }
+  return std::forward<U>(default_value);
+}
+
+template <typename T>
+template <typename U>
+T StatusOr<T>::value_or(U&& default_value) && {
+  if (ok()) {
+    return std::move(this->data_);
+  }
+  return std::forward<U>(default_value);
+}
+
+template <typename T>
+void StatusOr<T>::IgnoreError() const {
+  // no-op
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STATUS_STATUSOR_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/ascii.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/ascii.h
new file mode 100644
index 0000000000000000000000000000000000000000..42eadaea6c8d760b62dc6117e7ee1ff204905f9e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/ascii.h
@@ -0,0 +1,242 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: ascii.h
+// -----------------------------------------------------------------------------
+//
+// This package contains functions operating on characters and strings
+// restricted to standard ASCII. These include character classification
+// functions analogous to those found in the ANSI C Standard Library <ctype.h>
+// header file.
+//
+// C++ implementations provide <ctype.h> functionality based on their
+// C environment locale. In general, reliance on such a locale is not ideal, as
+// the locale standard is problematic (and may not return invariant information
+// for the same character set, for example). These `ascii_*()` functions are
+// hard-wired for standard ASCII, much faster, and guaranteed to behave
+// consistently.  They will never be overloaded, nor will their function
+// signature change.
+//
+// `ascii_isalnum()`, `ascii_isalpha()`, `ascii_isascii()`, `ascii_isblank()`,
+// `ascii_iscntrl()`, `ascii_isdigit()`, `ascii_isgraph()`, `ascii_islower()`,
+// `ascii_isprint()`, `ascii_ispunct()`, `ascii_isspace()`, `ascii_isupper()`,
+// `ascii_isxdigit()`
+//   Analogous to the <ctype.h> functions with similar names, these
+//   functions take an unsigned char and return a bool, based on whether the
+//   character matches the condition specified.
+//
+//   If the input character has a numerical value greater than 127, these
+//   functions return `false`.
+//
+// `ascii_tolower()`, `ascii_toupper()`
+//   Analogous to the <ctype.h> functions with similar names, these functions
+//   take an unsigned char and return a char.
+//
+//   If the input character is not an ASCII {lower,upper}-case letter (including
+//   numerical values greater than 127) then the functions return the same value
+//   as the input character.
+
+#ifndef ABSL_STRINGS_ASCII_H_
+#define ABSL_STRINGS_ASCII_H_
+
+#include <algorithm>
+#include <string>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace ascii_internal {
+
+// Declaration for an array of bitfields holding character information.
+ABSL_DLL extern const unsigned char kPropertyBits[256];
+
+// Declaration for the array of characters to upper-case characters.
+ABSL_DLL extern const char kToUpper[256];
+
+// Declaration for the array of characters to lower-case characters.
+ABSL_DLL extern const char kToLower[256];
+
+}  // namespace ascii_internal
+
+// ascii_isalpha()
+//
+// Determines whether the given character is an alphabetic character.
+inline bool ascii_isalpha(unsigned char c) {
+  return (ascii_internal::kPropertyBits[c] & 0x01) != 0;
+}
+
+// ascii_isalnum()
+//
+// Determines whether the given character is an alphanumeric character.
+inline bool ascii_isalnum(unsigned char c) {
+  return (ascii_internal::kPropertyBits[c] & 0x04) != 0;
+}
+
+// ascii_isspace()
+//
+// Determines whether the given character is a whitespace character (space,
+// tab, vertical tab, formfeed, linefeed, or carriage return).
+inline bool ascii_isspace(unsigned char c) {
+  return (ascii_internal::kPropertyBits[c] & 0x08) != 0;
+}
+
+// ascii_ispunct()
+//
+// Determines whether the given character is a punctuation character.
+inline bool ascii_ispunct(unsigned char c) {
+  return (ascii_internal::kPropertyBits[c] & 0x10) != 0;
+}
+
+// ascii_isblank()
+//
+// Determines whether the given character is a blank character (tab or space).
+inline bool ascii_isblank(unsigned char c) {
+  return (ascii_internal::kPropertyBits[c] & 0x20) != 0;
+}
+
+// ascii_iscntrl()
+//
+// Determines whether the given character is a control character.
+inline bool ascii_iscntrl(unsigned char c) {
+  return (ascii_internal::kPropertyBits[c] & 0x40) != 0;
+}
+
+// ascii_isxdigit()
+//
+// Determines whether the given character can be represented as a hexadecimal
+// digit character (i.e. {0-9} or {A-F}).
+inline bool ascii_isxdigit(unsigned char c) {
+  return (ascii_internal::kPropertyBits[c] & 0x80) != 0;
+}
+
+// ascii_isdigit()
+//
+// Determines whether the given character can be represented as a decimal
+// digit character (i.e. {0-9}).
+inline bool ascii_isdigit(unsigned char c) { return c >= '0' && c <= '9'; }
+
+// ascii_isprint()
+//
+// Determines whether the given character is printable, including spaces.
+inline bool ascii_isprint(unsigned char c) { return c >= 32 && c < 127; }
+
+// ascii_isgraph()
+//
+// Determines whether the given character has a graphical representation.
+inline bool ascii_isgraph(unsigned char c) { return c > 32 && c < 127; }
+
+// ascii_isupper()
+//
+// Determines whether the given character is uppercase.
+inline bool ascii_isupper(unsigned char c) { return c >= 'A' && c <= 'Z'; }
+
+// ascii_islower()
+//
+// Determines whether the given character is lowercase.
+inline bool ascii_islower(unsigned char c) { return c >= 'a' && c <= 'z'; }
+
+// ascii_isascii()
+//
+// Determines whether the given character is ASCII.
+inline bool ascii_isascii(unsigned char c) { return c < 128; }
+
+// ascii_tolower()
+//
+// Returns an ASCII character, converting to lowercase if uppercase is
+// passed. Note that character values > 127 are simply returned.
+inline char ascii_tolower(unsigned char c) {
+  return ascii_internal::kToLower[c];
+}
+
+// Converts the characters in `s` to lowercase, changing the contents of `s`.
+void AsciiStrToLower(std::string* s);
+
+// Creates a lowercase string from a given absl::string_view.
+ABSL_MUST_USE_RESULT inline std::string AsciiStrToLower(absl::string_view s) {
+  std::string result(s);
+  absl::AsciiStrToLower(&result);
+  return result;
+}
+
+// ascii_toupper()
+//
+// Returns the ASCII character, converting to upper-case if lower-case is
+// passed. Note that characters values > 127 are simply returned.
+inline char ascii_toupper(unsigned char c) {
+  return ascii_internal::kToUpper[c];
+}
+
+// Converts the characters in `s` to uppercase, changing the contents of `s`.
+void AsciiStrToUpper(std::string* s);
+
+// Creates an uppercase string from a given absl::string_view.
+ABSL_MUST_USE_RESULT inline std::string AsciiStrToUpper(absl::string_view s) {
+  std::string result(s);
+  absl::AsciiStrToUpper(&result);
+  return result;
+}
+
+// Returns absl::string_view with whitespace stripped from the beginning of the
+// given string_view.
+ABSL_MUST_USE_RESULT inline absl::string_view StripLeadingAsciiWhitespace(
+    absl::string_view str) {
+  auto it = std::find_if_not(str.begin(), str.end(), absl::ascii_isspace);
+  return str.substr(static_cast<size_t>(it - str.begin()));
+}
+
+// Strips in place whitespace from the beginning of the given string.
+inline void StripLeadingAsciiWhitespace(std::string* str) {
+  auto it = std::find_if_not(str->begin(), str->end(), absl::ascii_isspace);
+  str->erase(str->begin(), it);
+}
+
+// Returns absl::string_view with whitespace stripped from the end of the given
+// string_view.
+ABSL_MUST_USE_RESULT inline absl::string_view StripTrailingAsciiWhitespace(
+    absl::string_view str) {
+  auto it = std::find_if_not(str.rbegin(), str.rend(), absl::ascii_isspace);
+  return str.substr(0, static_cast<size_t>(str.rend() - it));
+}
+
+// Strips in place whitespace from the end of the given string
+inline void StripTrailingAsciiWhitespace(std::string* str) {
+  auto it = std::find_if_not(str->rbegin(), str->rend(), absl::ascii_isspace);
+  str->erase(static_cast<size_t>(str->rend() - it));
+}
+
+// Returns absl::string_view with whitespace stripped from both ends of the
+// given string_view.
+ABSL_MUST_USE_RESULT inline absl::string_view StripAsciiWhitespace(
+    absl::string_view str) {
+  return StripTrailingAsciiWhitespace(StripLeadingAsciiWhitespace(str));
+}
+
+// Strips in place whitespace from both ends of the given string
+inline void StripAsciiWhitespace(std::string* str) {
+  StripTrailingAsciiWhitespace(str);
+  StripLeadingAsciiWhitespace(str);
+}
+
+// Removes leading, trailing, and consecutive internal whitespace.
+void RemoveExtraAsciiWhitespace(std::string*);
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_ASCII_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/charconv.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/charconv.h
new file mode 100644
index 0000000000000000000000000000000000000000..7c50981245dee62ac28d2607b3879fba220668ff
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/charconv.h
@@ -0,0 +1,120 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_CHARCONV_H_
+#define ABSL_STRINGS_CHARCONV_H_
+
+#include <system_error>  // NOLINT(build/c++11)
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// Workalike compatibilty version of std::chars_format from C++17.
+//
+// This is an bitfield enumerator which can be passed to absl::from_chars to
+// configure the string-to-float conversion.
+enum class chars_format {
+  scientific = 1,
+  fixed = 2,
+  hex = 4,
+  general = fixed | scientific,
+};
+
+// The return result of a string-to-number conversion.
+//
+// `ec` will be set to `invalid_argument` if a well-formed number was not found
+// at the start of the input range, `result_out_of_range` if a well-formed
+// number was found, but it was out of the representable range of the requested
+// type, or to std::errc() otherwise.
+//
+// If a well-formed number was found, `ptr` is set to one past the sequence of
+// characters that were successfully parsed.  If none was found, `ptr` is set
+// to the `first` argument to from_chars.
+struct from_chars_result {
+  const char* ptr;
+  std::errc ec;
+};
+
+// Workalike compatibilty version of std::from_chars from C++17.  Currently
+// this only supports the `double` and `float` types.
+//
+// This interface incorporates the proposed resolutions for library issues
+// DR 3080 and DR 3081.  If these are adopted with different wording,
+// Abseil's behavior will change to match the standard.  (The behavior most
+// likely to change is for DR 3081, which says what `value` will be set to in
+// the case of overflow and underflow.  Code that wants to avoid possible
+// breaking changes in this area should not depend on `value` when the returned
+// from_chars_result indicates a range error.)
+//
+// Searches the range [first, last) for the longest matching pattern beginning
+// at `first` that represents a floating point number.  If one is found, store
+// the result in `value`.
+//
+// The matching pattern format is almost the same as that of strtod(), except
+// that (1) C locale is not respected, (2) an initial '+' character in the
+// input range will never be matched, and (3) leading whitespaces are not
+// ignored.
+//
+// If `fmt` is set, it must be one of the enumerator values of the chars_format.
+// (This is despite the fact that chars_format is a bitmask type.)  If set to
+// `scientific`, a matching number must contain an exponent.  If set to `fixed`,
+// then an exponent will never match.  (For example, the string "1e5" will be
+// parsed as "1".)  If set to `hex`, then a hexadecimal float is parsed in the
+// format that strtod() accepts, except that a "0x" prefix is NOT matched.
+// (In particular, in `hex` mode, the input "0xff" results in the largest
+// matching pattern "0".)
+absl::from_chars_result from_chars(const char* first, const char* last,
+                                   double& value,  // NOLINT
+                                   chars_format fmt = chars_format::general);
+
+absl::from_chars_result from_chars(const char* first, const char* last,
+                                   float& value,  // NOLINT
+                                   chars_format fmt = chars_format::general);
+
+// std::chars_format is specified as a bitmask type, which means the following
+// operations must be provided:
+inline constexpr chars_format operator&(chars_format lhs, chars_format rhs) {
+  return static_cast<chars_format>(static_cast<int>(lhs) &
+                                   static_cast<int>(rhs));
+}
+inline constexpr chars_format operator|(chars_format lhs, chars_format rhs) {
+  return static_cast<chars_format>(static_cast<int>(lhs) |
+                                   static_cast<int>(rhs));
+}
+inline constexpr chars_format operator^(chars_format lhs, chars_format rhs) {
+  return static_cast<chars_format>(static_cast<int>(lhs) ^
+                                   static_cast<int>(rhs));
+}
+inline constexpr chars_format operator~(chars_format arg) {
+  return static_cast<chars_format>(~static_cast<int>(arg));
+}
+inline chars_format& operator&=(chars_format& lhs, chars_format rhs) {
+  lhs = lhs & rhs;
+  return lhs;
+}
+inline chars_format& operator|=(chars_format& lhs, chars_format rhs) {
+  lhs = lhs | rhs;
+  return lhs;
+}
+inline chars_format& operator^=(chars_format& lhs, chars_format rhs) {
+  lhs = lhs ^ rhs;
+  return lhs;
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_CHARCONV_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/cord.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/cord.h
new file mode 100644
index 0000000000000000000000000000000000000000..c4a0d5aaae04aa2e0dc86ad7c7b20595e4b0a24e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/cord.h
@@ -0,0 +1,1633 @@
+// Copyright 2020 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: cord.h
+// -----------------------------------------------------------------------------
+//
+// This file defines the `absl::Cord` data structure and operations on that data
+// structure. A Cord is a string-like sequence of characters optimized for
+// specific use cases. Unlike a `std::string`, which stores an array of
+// contiguous characters, Cord data is stored in a structure consisting of
+// separate, reference-counted "chunks."
+//
+// Because a Cord consists of these chunks, data can be added to or removed from
+// a Cord during its lifetime. Chunks may also be shared between Cords. Unlike a
+// `std::string`, a Cord can therefore accommodate data that changes over its
+// lifetime, though it's not quite "mutable"; it can change only in the
+// attachment, detachment, or rearrangement of chunks of its constituent data.
+//
+// A Cord provides some benefit over `std::string` under the following (albeit
+// narrow) circumstances:
+//
+//   * Cord data is designed to grow and shrink over a Cord's lifetime. Cord
+//     provides efficient insertions and deletions at the start and end of the
+//     character sequences, avoiding copies in those cases. Static data should
+//     generally be stored as strings.
+//   * External memory consisting of string-like data can be directly added to
+//     a Cord without requiring copies or allocations.
+//   * Cord data may be shared and copied cheaply. Cord provides a copy-on-write
+//     implementation and cheap sub-Cord operations. Copying a Cord is an O(1)
+//     operation.
+//
+// As a consequence to the above, Cord data is generally large. Small data
+// should generally use strings, as construction of a Cord requires some
+// overhead. Small Cords (<= 15 bytes) are represented inline, but most small
+// Cords are expected to grow over their lifetimes.
+//
+// Note that because a Cord is made up of separate chunked data, random access
+// to character data within a Cord is slower than within a `std::string`.
+//
+// Thread Safety
+//
+// Cord has the same thread-safety properties as many other types like
+// std::string, std::vector<>, int, etc -- it is thread-compatible. In
+// particular, if threads do not call non-const methods, then it is safe to call
+// const methods without synchronization. Copying a Cord produces a new instance
+// that can be used concurrently with the original in arbitrary ways.
+
+#ifndef ABSL_STRINGS_CORD_H_
+#define ABSL_STRINGS_CORD_H_
+
+#include <algorithm>
+#include <cstddef>
+#include <cstdint>
+#include <cstring>
+#include <iosfwd>
+#include <iterator>
+#include <string>
+#include <type_traits>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/base/internal/endian.h"
+#include "absl/base/internal/per_thread_tls.h"
+#include "absl/base/macros.h"
+#include "absl/base/port.h"
+#include "absl/container/inlined_vector.h"
+#include "absl/crc/internal/crc_cord_state.h"
+#include "absl/functional/function_ref.h"
+#include "absl/meta/type_traits.h"
+#include "absl/strings/cord_analysis.h"
+#include "absl/strings/cord_buffer.h"
+#include "absl/strings/internal/cord_data_edge.h"
+#include "absl/strings/internal/cord_internal.h"
+#include "absl/strings/internal/cord_rep_btree.h"
+#include "absl/strings/internal/cord_rep_btree_reader.h"
+#include "absl/strings/internal/cord_rep_crc.h"
+#include "absl/strings/internal/cord_rep_ring.h"
+#include "absl/strings/internal/cordz_functions.h"
+#include "absl/strings/internal/cordz_info.h"
+#include "absl/strings/internal/cordz_statistics.h"
+#include "absl/strings/internal/cordz_update_scope.h"
+#include "absl/strings/internal/cordz_update_tracker.h"
+#include "absl/strings/internal/resize_uninitialized.h"
+#include "absl/strings/internal/string_constant.h"
+#include "absl/strings/string_view.h"
+#include "absl/types/optional.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+class Cord;
+class CordTestPeer;
+template <typename Releaser>
+Cord MakeCordFromExternal(absl::string_view, Releaser&&);
+void CopyCordToString(const Cord& src, std::string* dst);
+
+// Cord memory accounting modes
+enum class CordMemoryAccounting {
+  // Counts the *approximate* number of bytes held in full or in part by this
+  // Cord (which may not remain the same between invocations). Cords that share
+  // memory could each be "charged" independently for the same shared memory.
+  kTotal,
+
+  // Counts the *approximate* number of bytes held in full or in part by this
+  // Cord weighted by the sharing ratio of that data. For example, if some data
+  // edge is shared by 4 different Cords, then each cord is attributed 1/4th of
+  // the total memory usage as a 'fair share' of the total memory usage.
+  kFairShare,
+};
+
+// Cord
+//
+// A Cord is a sequence of characters, designed to be more efficient than a
+// `std::string` in certain circumstances: namely, large string data that needs
+// to change over its lifetime or shared, especially when such data is shared
+// across API boundaries.
+//
+// A Cord stores its character data in a structure that allows efficient prepend
+// and append operations. This makes a Cord useful for large string data sent
+// over in a wire format that may need to be prepended or appended at some point
+// during the data exchange (e.g. HTTP, protocol buffers). For example, a
+// Cord is useful for storing an HTTP request, and prepending an HTTP header to
+// such a request.
+//
+// Cords should not be used for storing general string data, however. They
+// require overhead to construct and are slower than strings for random access.
+//
+// The Cord API provides the following common API operations:
+//
+// * Create or assign Cords out of existing string data, memory, or other Cords
+// * Append and prepend data to an existing Cord
+// * Create new Sub-Cords from existing Cord data
+// * Swap Cord data and compare Cord equality
+// * Write out Cord data by constructing a `std::string`
+//
+// Additionally, the API provides iterator utilities to iterate through Cord
+// data via chunks or character bytes.
+//
+class Cord {
+ private:
+  template <typename T>
+  using EnableIfString =
+      absl::enable_if_t<std::is_same<T, std::string>::value, int>;
+
+ public:
+  // Cord::Cord() Constructors.
+
+  // Creates an empty Cord.
+  constexpr Cord() noexcept;
+
+  // Creates a Cord from an existing Cord. Cord is copyable and efficiently
+  // movable. The moved-from state is valid but unspecified.
+  Cord(const Cord& src);
+  Cord(Cord&& src) noexcept;
+  Cord& operator=(const Cord& x);
+  Cord& operator=(Cord&& x) noexcept;
+
+  // Creates a Cord from a `src` string. This constructor is marked explicit to
+  // prevent implicit Cord constructions from arguments convertible to an
+  // `absl::string_view`.
+  explicit Cord(absl::string_view src);
+  Cord& operator=(absl::string_view src);
+
+  // Creates a Cord from a `std::string&&` rvalue. These constructors are
+  // templated to avoid ambiguities for types that are convertible to both
+  // `absl::string_view` and `std::string`, such as `const char*`.
+  template <typename T, EnableIfString<T> = 0>
+  explicit Cord(T&& src);
+  template <typename T, EnableIfString<T> = 0>
+  Cord& operator=(T&& src);
+
+  // Cord::~Cord()
+  //
+  // Destructs the Cord.
+  ~Cord() {
+    if (contents_.is_tree()) DestroyCordSlow();
+  }
+
+  // MakeCordFromExternal()
+  //
+  // Creates a Cord that takes ownership of external string memory. The
+  // contents of `data` are not copied to the Cord; instead, the external
+  // memory is added to the Cord and reference-counted. This data may not be
+  // changed for the life of the Cord, though it may be prepended or appended
+  // to.
+  //
+  // `MakeCordFromExternal()` takes a callable "releaser" that is invoked when
+  // the reference count for `data` reaches zero. As noted above, this data must
+  // remain live until the releaser is invoked. The callable releaser also must:
+  //
+  //   * be move constructible
+  //   * support `void operator()(absl::string_view) const` or `void operator()`
+  //
+  // Example:
+  //
+  // Cord MakeCord(BlockPool* pool) {
+  //   Block* block = pool->NewBlock();
+  //   FillBlock(block);
+  //   return absl::MakeCordFromExternal(
+  //       block->ToStringView(),
+  //       [pool, block](absl::string_view v) {
+  //         pool->FreeBlock(block, v);
+  //       });
+  // }
+  //
+  // WARNING: Because a Cord can be reference-counted, it's likely a bug if your
+  // releaser doesn't do anything. For example, consider the following:
+  //
+  // void Foo(const char* buffer, int len) {
+  //   auto c = absl::MakeCordFromExternal(absl::string_view(buffer, len),
+  //                                       [](absl::string_view) {});
+  //
+  //   // BUG: If Bar() copies its cord for any reason, including keeping a
+  //   // substring of it, the lifetime of buffer might be extended beyond
+  //   // when Foo() returns.
+  //   Bar(c);
+  // }
+  template <typename Releaser>
+  friend Cord MakeCordFromExternal(absl::string_view data, Releaser&& releaser);
+
+  // Cord::Clear()
+  //
+  // Releases the Cord data. Any nodes that share data with other Cords, if
+  // applicable, will have their reference counts reduced by 1.
+  ABSL_ATTRIBUTE_REINITIALIZES void Clear();
+
+  // Cord::Append()
+  //
+  // Appends data to the Cord, which may come from another Cord or other string
+  // data.
+  void Append(const Cord& src);
+  void Append(Cord&& src);
+  void Append(absl::string_view src);
+  template <typename T, EnableIfString<T> = 0>
+  void Append(T&& src);
+
+  // Appends `buffer` to this cord, unless `buffer` has a zero length in which
+  // case this method has no effect on this cord instance.
+  // This method is guaranteed to consume `buffer`.
+  void Append(CordBuffer buffer);
+
+  // Returns a CordBuffer, re-using potential existing capacity in this cord.
+  //
+  // Cord instances may have additional unused capacity in the last (or first)
+  // nodes of the underlying tree to facilitate amortized growth. This method
+  // allows applications to explicitly use this spare capacity if available,
+  // or create a new CordBuffer instance otherwise.
+  // If this cord has a final non-shared node with at least `min_capacity`
+  // available, then this method will return that buffer including its data
+  // contents. I.e.; the returned buffer will have a non-zero length, and
+  // a capacity of at least `buffer.length + min_capacity`. Otherwise, this
+  // method will return `CordBuffer::CreateWithDefaultLimit(capacity)`.
+  //
+  // Below an example of using GetAppendBuffer. Notice that in this example we
+  // use `GetAppendBuffer()` only on the first iteration. As we know nothing
+  // about any initial extra capacity in `cord`, we may be able to use the extra
+  // capacity. But as we add new buffers with fully utilized contents after that
+  // we avoid calling `GetAppendBuffer()` on subsequent iterations: while this
+  // works fine, it results in an unnecessary inspection of cord contents:
+  //
+  //   void AppendRandomDataToCord(absl::Cord &cord, size_t n) {
+  //     bool first = true;
+  //     while (n > 0) {
+  //       CordBuffer buffer = first ? cord.GetAppendBuffer(n)
+  //                                 : CordBuffer::CreateWithDefaultLimit(n);
+  //       absl::Span<char> data = buffer.available_up_to(n);
+  //       FillRandomValues(data.data(), data.size());
+  //       buffer.IncreaseLengthBy(data.size());
+  //       cord.Append(std::move(buffer));
+  //       n -= data.size();
+  //       first = false;
+  //     }
+  //   }
+  CordBuffer GetAppendBuffer(size_t capacity, size_t min_capacity = 16);
+
+  // Returns a CordBuffer, re-using potential existing capacity in this cord.
+  //
+  // This function is identical to `GetAppendBuffer`, except that in the case
+  // where a new `CordBuffer` is allocated, it is allocated using the provided
+  // custom limit instead of the default limit. `GetAppendBuffer` will default
+  // to `CordBuffer::CreateWithDefaultLimit(capacity)` whereas this method
+  // will default to `CordBuffer::CreateWithCustomLimit(block_size, capacity)`.
+  // This method is equivalent to `GetAppendBuffer` if `block_size` is zero.
+  // See the documentation for `CreateWithCustomLimit` for more details on the
+  // restrictions and legal values for `block_size`.
+  CordBuffer GetCustomAppendBuffer(size_t block_size, size_t capacity,
+                                   size_t min_capacity = 16);
+
+  // Cord::Prepend()
+  //
+  // Prepends data to the Cord, which may come from another Cord or other string
+  // data.
+  void Prepend(const Cord& src);
+  void Prepend(absl::string_view src);
+  template <typename T, EnableIfString<T> = 0>
+  void Prepend(T&& src);
+
+  // Prepends `buffer` to this cord, unless `buffer` has a zero length in which
+  // case this method has no effect on this cord instance.
+  // This method is guaranteed to consume `buffer`.
+  void Prepend(CordBuffer buffer);
+
+  // Cord::RemovePrefix()
+  //
+  // Removes the first `n` bytes of a Cord.
+  void RemovePrefix(size_t n);
+  void RemoveSuffix(size_t n);
+
+  // Cord::Subcord()
+  //
+  // Returns a new Cord representing the subrange [pos, pos + new_size) of
+  // *this. If pos >= size(), the result is empty(). If
+  // (pos + new_size) >= size(), the result is the subrange [pos, size()).
+  Cord Subcord(size_t pos, size_t new_size) const;
+
+  // Cord::swap()
+  //
+  // Swaps the contents of the Cord with `other`.
+  void swap(Cord& other) noexcept;
+
+  // swap()
+  //
+  // Swaps the contents of two Cords.
+  friend void swap(Cord& x, Cord& y) noexcept { x.swap(y); }
+
+  // Cord::size()
+  //
+  // Returns the size of the Cord.
+  size_t size() const;
+
+  // Cord::empty()
+  //
+  // Determines whether the given Cord is empty, returning `true` is so.
+  bool empty() const;
+
+  // Cord::EstimatedMemoryUsage()
+  //
+  // Returns the *approximate* number of bytes held by this cord.
+  // See CordMemoryAccounting for more information on the accounting method.
+  size_t EstimatedMemoryUsage(CordMemoryAccounting accounting_method =
+                                  CordMemoryAccounting::kTotal) const;
+
+  // Cord::Compare()
+  //
+  // Compares 'this' Cord with rhs. This function and its relatives treat Cords
+  // as sequences of unsigned bytes. The comparison is a straightforward
+  // lexicographic comparison. `Cord::Compare()` returns values as follows:
+  //
+  //   -1  'this' Cord is smaller
+  //    0  two Cords are equal
+  //    1  'this' Cord is larger
+  int Compare(absl::string_view rhs) const;
+  int Compare(const Cord& rhs) const;
+
+  // Cord::StartsWith()
+  //
+  // Determines whether the Cord starts with the passed string data `rhs`.
+  bool StartsWith(const Cord& rhs) const;
+  bool StartsWith(absl::string_view rhs) const;
+
+  // Cord::EndsWith()
+  //
+  // Determines whether the Cord ends with the passed string data `rhs`.
+  bool EndsWith(absl::string_view rhs) const;
+  bool EndsWith(const Cord& rhs) const;
+
+  // Cord::operator std::string()
+  //
+  // Converts a Cord into a `std::string()`. This operator is marked explicit to
+  // prevent unintended Cord usage in functions that take a string.
+  explicit operator std::string() const;
+
+  // CopyCordToString()
+  //
+  // Copies the contents of a `src` Cord into a `*dst` string.
+  //
+  // This function optimizes the case of reusing the destination string since it
+  // can reuse previously allocated capacity. However, this function does not
+  // guarantee that pointers previously returned by `dst->data()` remain valid
+  // even if `*dst` had enough capacity to hold `src`. If `*dst` is a new
+  // object, prefer to simply use the conversion operator to `std::string`.
+  friend void CopyCordToString(const Cord& src, std::string* dst);
+
+  class CharIterator;
+
+  //----------------------------------------------------------------------------
+  // Cord::ChunkIterator
+  //----------------------------------------------------------------------------
+  //
+  // A `Cord::ChunkIterator` allows iteration over the constituent chunks of its
+  // Cord. Such iteration allows you to perform non-const operations on the data
+  // of a Cord without modifying it.
+  //
+  // Generally, you do not instantiate a `Cord::ChunkIterator` directly;
+  // instead, you create one implicitly through use of the `Cord::Chunks()`
+  // member function.
+  //
+  // The `Cord::ChunkIterator` has the following properties:
+  //
+  //   * The iterator is invalidated after any non-const operation on the
+  //     Cord object over which it iterates.
+  //   * The `string_view` returned by dereferencing a valid, non-`end()`
+  //     iterator is guaranteed to be non-empty.
+  //   * Two `ChunkIterator` objects can be compared equal if and only if they
+  //     remain valid and iterate over the same Cord.
+  //   * The iterator in this case is a proxy iterator; the `string_view`
+  //     returned by the iterator does not live inside the Cord, and its
+  //     lifetime is limited to the lifetime of the iterator itself. To help
+  //     prevent lifetime issues, `ChunkIterator::reference` is not a true
+  //     reference type and is equivalent to `value_type`.
+  //   * The iterator keeps state that can grow for Cords that contain many
+  //     nodes and are imbalanced due to sharing. Prefer to pass this type by
+  //     const reference instead of by value.
+  class ChunkIterator {
+   public:
+    using iterator_category = std::input_iterator_tag;
+    using value_type = absl::string_view;
+    using difference_type = ptrdiff_t;
+    using pointer = const value_type*;
+    using reference = value_type;
+
+    ChunkIterator() = default;
+
+    ChunkIterator& operator++();
+    ChunkIterator operator++(int);
+    bool operator==(const ChunkIterator& other) const;
+    bool operator!=(const ChunkIterator& other) const;
+    reference operator*() const;
+    pointer operator->() const;
+
+    friend class Cord;
+    friend class CharIterator;
+
+   private:
+    using CordRep = absl::cord_internal::CordRep;
+    using CordRepBtree = absl::cord_internal::CordRepBtree;
+    using CordRepBtreeReader = absl::cord_internal::CordRepBtreeReader;
+
+    // Constructs a `begin()` iterator from `tree`. `tree` must not be null.
+    explicit ChunkIterator(cord_internal::CordRep* tree);
+
+    // Constructs a `begin()` iterator from `cord`.
+    explicit ChunkIterator(const Cord* cord);
+
+    // Initializes this instance from a tree. Invoked by constructors.
+    void InitTree(cord_internal::CordRep* tree);
+
+    // Removes `n` bytes from `current_chunk_`. Expects `n` to be smaller than
+    // `current_chunk_.size()`.
+    void RemoveChunkPrefix(size_t n);
+    Cord AdvanceAndReadBytes(size_t n);
+    void AdvanceBytes(size_t n);
+
+    // Btree specific operator++
+    ChunkIterator& AdvanceBtree();
+    void AdvanceBytesBtree(size_t n);
+
+    // A view into bytes of the current `CordRep`. It may only be a view to a
+    // suffix of bytes if this is being used by `CharIterator`.
+    absl::string_view current_chunk_;
+    // The current leaf, or `nullptr` if the iterator points to short data.
+    // If the current chunk is a substring node, current_leaf_ points to the
+    // underlying flat or external node.
+    absl::cord_internal::CordRep* current_leaf_ = nullptr;
+    // The number of bytes left in the `Cord` over which we are iterating.
+    size_t bytes_remaining_ = 0;
+
+    // Cord reader for cord btrees. Empty if not traversing a btree.
+    CordRepBtreeReader btree_reader_;
+  };
+
+  // Cord::chunk_begin()
+  //
+  // Returns an iterator to the first chunk of the `Cord`.
+  //
+  // Generally, prefer using `Cord::Chunks()` within a range-based for loop for
+  // iterating over the chunks of a Cord. This method may be useful for getting
+  // a `ChunkIterator` where range-based for-loops are not useful.
+  //
+  // Example:
+  //
+  //   absl::Cord::ChunkIterator FindAsChunk(const absl::Cord& c,
+  //                                         absl::string_view s) {
+  //     return std::find(c.chunk_begin(), c.chunk_end(), s);
+  //   }
+  ChunkIterator chunk_begin() const;
+
+  // Cord::chunk_end()
+  //
+  // Returns an iterator one increment past the last chunk of the `Cord`.
+  //
+  // Generally, prefer using `Cord::Chunks()` within a range-based for loop for
+  // iterating over the chunks of a Cord. This method may be useful for getting
+  // a `ChunkIterator` where range-based for-loops may not be available.
+  ChunkIterator chunk_end() const;
+
+  //----------------------------------------------------------------------------
+  // Cord::ChunkRange
+  //----------------------------------------------------------------------------
+  //
+  // `ChunkRange` is a helper class for iterating over the chunks of the `Cord`,
+  // producing an iterator which can be used within a range-based for loop.
+  // Construction of a `ChunkRange` will return an iterator pointing to the
+  // first chunk of the Cord. Generally, do not construct a `ChunkRange`
+  // directly; instead, prefer to use the `Cord::Chunks()` method.
+  //
+  // Implementation note: `ChunkRange` is simply a convenience wrapper over
+  // `Cord::chunk_begin()` and `Cord::chunk_end()`.
+  class ChunkRange {
+   public:
+    // Fulfill minimum c++ container requirements [container.requirements]
+    // These (partial) container type definitions allow ChunkRange to be used
+    // in various utilities expecting a subset of [container.requirements].
+    // For example, the below enables using `::testing::ElementsAre(...)`
+    using value_type = absl::string_view;
+    using reference = value_type&;
+    using const_reference = const value_type&;
+    using iterator = ChunkIterator;
+    using const_iterator = ChunkIterator;
+
+    explicit ChunkRange(const Cord* cord) : cord_(cord) {}
+
+    ChunkIterator begin() const;
+    ChunkIterator end() const;
+
+   private:
+    const Cord* cord_;
+  };
+
+  // Cord::Chunks()
+  //
+  // Returns a `Cord::ChunkRange` for iterating over the chunks of a `Cord` with
+  // a range-based for-loop. For most iteration tasks on a Cord, use
+  // `Cord::Chunks()` to retrieve this iterator.
+  //
+  // Example:
+  //
+  //   void ProcessChunks(const Cord& cord) {
+  //     for (absl::string_view chunk : cord.Chunks()) { ... }
+  //   }
+  //
+  // Note that the ordinary caveats of temporary lifetime extension apply:
+  //
+  //   void Process() {
+  //     for (absl::string_view chunk : CordFactory().Chunks()) {
+  //       // The temporary Cord returned by CordFactory has been destroyed!
+  //     }
+  //   }
+  ChunkRange Chunks() const;
+
+  //----------------------------------------------------------------------------
+  // Cord::CharIterator
+  //----------------------------------------------------------------------------
+  //
+  // A `Cord::CharIterator` allows iteration over the constituent characters of
+  // a `Cord`.
+  //
+  // Generally, you do not instantiate a `Cord::CharIterator` directly; instead,
+  // you create one implicitly through use of the `Cord::Chars()` member
+  // function.
+  //
+  // A `Cord::CharIterator` has the following properties:
+  //
+  //   * The iterator is invalidated after any non-const operation on the
+  //     Cord object over which it iterates.
+  //   * Two `CharIterator` objects can be compared equal if and only if they
+  //     remain valid and iterate over the same Cord.
+  //   * The iterator keeps state that can grow for Cords that contain many
+  //     nodes and are imbalanced due to sharing. Prefer to pass this type by
+  //     const reference instead of by value.
+  //   * This type cannot act as a forward iterator because a `Cord` can reuse
+  //     sections of memory. This fact violates the requirement for forward
+  //     iterators to compare equal if dereferencing them returns the same
+  //     object.
+  class CharIterator {
+   public:
+    using iterator_category = std::input_iterator_tag;
+    using value_type = char;
+    using difference_type = ptrdiff_t;
+    using pointer = const char*;
+    using reference = const char&;
+
+    CharIterator() = default;
+
+    CharIterator& operator++();
+    CharIterator operator++(int);
+    bool operator==(const CharIterator& other) const;
+    bool operator!=(const CharIterator& other) const;
+    reference operator*() const;
+    pointer operator->() const;
+
+    friend Cord;
+
+   private:
+    explicit CharIterator(const Cord* cord) : chunk_iterator_(cord) {}
+
+    ChunkIterator chunk_iterator_;
+  };
+
+  // Cord::AdvanceAndRead()
+  //
+  // Advances the `Cord::CharIterator` by `n_bytes` and returns the bytes
+  // advanced as a separate `Cord`. `n_bytes` must be less than or equal to the
+  // number of bytes within the Cord; otherwise, behavior is undefined. It is
+  // valid to pass `char_end()` and `0`.
+  static Cord AdvanceAndRead(CharIterator* it, size_t n_bytes);
+
+  // Cord::Advance()
+  //
+  // Advances the `Cord::CharIterator` by `n_bytes`. `n_bytes` must be less than
+  // or equal to the number of bytes remaining within the Cord; otherwise,
+  // behavior is undefined. It is valid to pass `char_end()` and `0`.
+  static void Advance(CharIterator* it, size_t n_bytes);
+
+  // Cord::ChunkRemaining()
+  //
+  // Returns the longest contiguous view starting at the iterator's position.
+  //
+  // `it` must be dereferenceable.
+  static absl::string_view ChunkRemaining(const CharIterator& it);
+
+  // Cord::char_begin()
+  //
+  // Returns an iterator to the first character of the `Cord`.
+  //
+  // Generally, prefer using `Cord::Chars()` within a range-based for loop for
+  // iterating over the chunks of a Cord. This method may be useful for getting
+  // a `CharIterator` where range-based for-loops may not be available.
+  CharIterator char_begin() const;
+
+  // Cord::char_end()
+  //
+  // Returns an iterator to one past the last character of the `Cord`.
+  //
+  // Generally, prefer using `Cord::Chars()` within a range-based for loop for
+  // iterating over the chunks of a Cord. This method may be useful for getting
+  // a `CharIterator` where range-based for-loops are not useful.
+  CharIterator char_end() const;
+
+  // Cord::CharRange
+  //
+  // `CharRange` is a helper class for iterating over the characters of a
+  // producing an iterator which can be used within a range-based for loop.
+  // Construction of a `CharRange` will return an iterator pointing to the first
+  // character of the Cord. Generally, do not construct a `CharRange` directly;
+  // instead, prefer to use the `Cord::Chars()` method shown below.
+  //
+  // Implementation note: `CharRange` is simply a convenience wrapper over
+  // `Cord::char_begin()` and `Cord::char_end()`.
+  class CharRange {
+   public:
+    // Fulfill minimum c++ container requirements [container.requirements]
+    // Theses (partial) container type definitions allow CharRange to be used
+    // in various utilities expecting a subset of [container.requirements].
+    // For example, the below enables using `::testing::ElementsAre(...)`
+    using value_type = char;
+    using reference = value_type&;
+    using const_reference = const value_type&;
+    using iterator = CharIterator;
+    using const_iterator = CharIterator;
+
+    explicit CharRange(const Cord* cord) : cord_(cord) {}
+
+    CharIterator begin() const;
+    CharIterator end() const;
+
+   private:
+    const Cord* cord_;
+  };
+
+  // Cord::Chars()
+  //
+  // Returns a `Cord::CharRange` for iterating over the characters of a `Cord`
+  // with a range-based for-loop. For most character-based iteration tasks on a
+  // Cord, use `Cord::Chars()` to retrieve this iterator.
+  //
+  // Example:
+  //
+  //   void ProcessCord(const Cord& cord) {
+  //     for (char c : cord.Chars()) { ... }
+  //   }
+  //
+  // Note that the ordinary caveats of temporary lifetime extension apply:
+  //
+  //   void Process() {
+  //     for (char c : CordFactory().Chars()) {
+  //       // The temporary Cord returned by CordFactory has been destroyed!
+  //     }
+  //   }
+  CharRange Chars() const;
+
+  // Cord::operator[]
+  //
+  // Gets the "i"th character of the Cord and returns it, provided that
+  // 0 <= i < Cord.size().
+  //
+  // NOTE: This routine is reasonably efficient. It is roughly
+  // logarithmic based on the number of chunks that make up the cord. Still,
+  // if you need to iterate over the contents of a cord, you should
+  // use a CharIterator/ChunkIterator rather than call operator[] or Get()
+  // repeatedly in a loop.
+  char operator[](size_t i) const;
+
+  // Cord::TryFlat()
+  //
+  // If this cord's representation is a single flat array, returns a
+  // string_view referencing that array.  Otherwise returns nullopt.
+  absl::optional<absl::string_view> TryFlat() const;
+
+  // Cord::Flatten()
+  //
+  // Flattens the cord into a single array and returns a view of the data.
+  //
+  // If the cord was already flat, the contents are not modified.
+  absl::string_view Flatten();
+
+  // Supports absl::Cord as a sink object for absl::Format().
+  friend void AbslFormatFlush(absl::Cord* cord, absl::string_view part) {
+    cord->Append(part);
+  }
+
+  // Cord::SetExpectedChecksum()
+  //
+  // Stores a checksum value with this non-empty cord instance, for later
+  // retrieval.
+  //
+  // The expected checksum is a number stored out-of-band, alongside the data.
+  // It is preserved across copies and assignments, but any mutations to a cord
+  // will cause it to lose its expected checksum.
+  //
+  // The expected checksum is not part of a Cord's value, and does not affect
+  // operations such as equality or hashing.
+  //
+  // This field is intended to store a CRC32C checksum for later validation, to
+  // help support end-to-end checksum workflows.  However, the Cord API itself
+  // does no CRC validation, and assigns no meaning to this number.
+  //
+  // This call has no effect if this cord is empty.
+  void SetExpectedChecksum(uint32_t crc);
+
+  // Returns this cord's expected checksum, if it has one.  Otherwise, returns
+  // nullopt.
+  absl::optional<uint32_t> ExpectedChecksum() const;
+
+  template <typename H>
+  friend H AbslHashValue(H hash_state, const absl::Cord& c) {
+    absl::optional<absl::string_view> maybe_flat = c.TryFlat();
+    if (maybe_flat.has_value()) {
+      return H::combine(std::move(hash_state), *maybe_flat);
+    }
+    return c.HashFragmented(std::move(hash_state));
+  }
+
+  // Create a Cord with the contents of StringConstant<T>::value.
+  // No allocations will be done and no data will be copied.
+  // This is an INTERNAL API and subject to change or removal. This API can only
+  // be used by spelling absl::strings_internal::MakeStringConstant, which is
+  // also an internal API.
+  template <typename T>
+  // NOLINTNEXTLINE(google-explicit-constructor)
+  constexpr Cord(strings_internal::StringConstant<T>);
+
+ private:
+  using CordRep = absl::cord_internal::CordRep;
+  using CordRepFlat = absl::cord_internal::CordRepFlat;
+  using CordzInfo = cord_internal::CordzInfo;
+  using CordzUpdateScope = cord_internal::CordzUpdateScope;
+  using CordzUpdateTracker = cord_internal::CordzUpdateTracker;
+  using InlineData = cord_internal::InlineData;
+  using MethodIdentifier = CordzUpdateTracker::MethodIdentifier;
+
+  // Creates a cord instance with `method` representing the originating
+  // public API call causing the cord to be created.
+  explicit Cord(absl::string_view src, MethodIdentifier method);
+
+  friend class CordTestPeer;
+  friend bool operator==(const Cord& lhs, const Cord& rhs);
+  friend bool operator==(const Cord& lhs, absl::string_view rhs);
+
+  friend const CordzInfo* GetCordzInfoForTesting(const Cord& cord);
+
+  // Calls the provided function once for each cord chunk, in order.  Unlike
+  // Chunks(), this API will not allocate memory.
+  void ForEachChunk(absl::FunctionRef<void(absl::string_view)>) const;
+
+  // Allocates new contiguous storage for the contents of the cord. This is
+  // called by Flatten() when the cord was not already flat.
+  absl::string_view FlattenSlowPath();
+
+  // Actual cord contents are hidden inside the following simple
+  // class so that we can isolate the bulk of cord.cc from changes
+  // to the representation.
+  //
+  // InlineRep holds either a tree pointer, or an array of kMaxInline bytes.
+  class InlineRep {
+   public:
+    static constexpr unsigned char kMaxInline = cord_internal::kMaxInline;
+    static_assert(kMaxInline >= sizeof(absl::cord_internal::CordRep*), "");
+
+    constexpr InlineRep() : data_() {}
+    explicit InlineRep(InlineData::DefaultInitType init) : data_(init) {}
+    InlineRep(const InlineRep& src);
+    InlineRep(InlineRep&& src);
+    InlineRep& operator=(const InlineRep& src);
+    InlineRep& operator=(InlineRep&& src) noexcept;
+
+    explicit constexpr InlineRep(absl::string_view sv, CordRep* rep);
+
+    void Swap(InlineRep* rhs);
+    bool empty() const;
+    size_t size() const;
+    const char* data() const;  // Returns nullptr if holding pointer
+    void set_data(const char* data, size_t n);  // Discards pointer, if any
+    char* set_data(size_t n);                   // Write data to the result
+    // Returns nullptr if holding bytes
+    absl::cord_internal::CordRep* tree() const;
+    absl::cord_internal::CordRep* as_tree() const;
+    const char* as_chars() const;
+    // Returns non-null iff was holding a pointer
+    absl::cord_internal::CordRep* clear();
+    // Converts to pointer if necessary.
+    void reduce_size(size_t n);    // REQUIRES: holding data
+    void remove_prefix(size_t n);  // REQUIRES: holding data
+    void AppendArray(absl::string_view src, MethodIdentifier method);
+    absl::string_view FindFlatStartPiece() const;
+
+    // Creates a CordRepFlat instance from the current inlined data with `extra'
+    // bytes of desired additional capacity.
+    CordRepFlat* MakeFlatWithExtraCapacity(size_t extra);
+
+    // Sets the tree value for this instance. `rep` must not be null.
+    // Requires the current instance to hold a tree, and a lock to be held on
+    // any CordzInfo referenced by this instance. The latter is enforced through
+    // the CordzUpdateScope argument. If the current instance is sampled, then
+    // the CordzInfo instance is updated to reference the new `rep` value.
+    void SetTree(CordRep* rep, const CordzUpdateScope& scope);
+
+    // Identical to SetTree(), except that `rep` is allowed to be null, in
+    // which case the current instance is reset to an empty value.
+    void SetTreeOrEmpty(CordRep* rep, const CordzUpdateScope& scope);
+
+    // Sets the tree value for this instance, and randomly samples this cord.
+    // This function disregards existing contents in `data_`, and should be
+    // called when a Cord is 'promoted' from an 'uninitialized' or 'inlined'
+    // value to a non-inlined (tree / ring) value.
+    void EmplaceTree(CordRep* rep, MethodIdentifier method);
+
+    // Identical to EmplaceTree, except that it copies the parent stack from
+    // the provided `parent` data if the parent is sampled.
+    void EmplaceTree(CordRep* rep, const InlineData& parent,
+                     MethodIdentifier method);
+
+    // Commits the change of a newly created, or updated `rep` root value into
+    // this cord. `old_rep` indicates the old (inlined or tree) value of the
+    // cord, and determines if the commit invokes SetTree() or EmplaceTree().
+    void CommitTree(const CordRep* old_rep, CordRep* rep,
+                    const CordzUpdateScope& scope, MethodIdentifier method);
+
+    void AppendTreeToInlined(CordRep* tree, MethodIdentifier method);
+    void AppendTreeToTree(CordRep* tree, MethodIdentifier method);
+    void AppendTree(CordRep* tree, MethodIdentifier method);
+    void PrependTreeToInlined(CordRep* tree, MethodIdentifier method);
+    void PrependTreeToTree(CordRep* tree, MethodIdentifier method);
+    void PrependTree(CordRep* tree, MethodIdentifier method);
+
+    bool IsSame(const InlineRep& other) const { return data_ == other.data_; }
+
+    void CopyTo(std::string* dst) const {
+      // memcpy is much faster when operating on a known size. On most supported
+      // platforms, the small string optimization is large enough that resizing
+      // to 15 bytes does not cause a memory allocation.
+      absl::strings_internal::STLStringResizeUninitialized(dst, kMaxInline);
+      data_.copy_max_inline_to(&(*dst)[0]);
+      // erase is faster than resize because the logic for memory allocation is
+      // not needed.
+      dst->erase(inline_size());
+    }
+
+    // Copies the inline contents into `dst`. Assumes the cord is not empty.
+    void CopyToArray(char* dst) const;
+
+    bool is_tree() const { return data_.is_tree(); }
+
+    // Returns true if the Cord is being profiled by cordz.
+    bool is_profiled() const { return data_.is_tree() && data_.is_profiled(); }
+
+    // Returns the available inlined capacity, or 0 if is_tree() == true.
+    size_t remaining_inline_capacity() const {
+      return data_.is_tree() ? 0 : kMaxInline - data_.inline_size();
+    }
+
+    // Returns the profiled CordzInfo, or nullptr if not sampled.
+    absl::cord_internal::CordzInfo* cordz_info() const {
+      return data_.cordz_info();
+    }
+
+    // Sets the profiled CordzInfo. `cordz_info` must not be null.
+    void set_cordz_info(cord_internal::CordzInfo* cordz_info) {
+      assert(cordz_info != nullptr);
+      data_.set_cordz_info(cordz_info);
+    }
+
+    // Resets the current cordz_info to null / empty.
+    void clear_cordz_info() { data_.clear_cordz_info(); }
+
+   private:
+    friend class Cord;
+
+    void AssignSlow(const InlineRep& src);
+    // Unrefs the tree and stops profiling.
+    void UnrefTree();
+
+    void ResetToEmpty() { data_ = {}; }
+
+    void set_inline_size(size_t size) { data_.set_inline_size(size); }
+    size_t inline_size() const { return data_.inline_size(); }
+
+    // Empty cords that carry a checksum have a CordRepCrc node with a null
+    // child node. The code can avoid lots of special cases where it would
+    // otherwise transition from tree to inline storage if we just remove the
+    // CordRepCrc node before mutations. Must never be called inside a
+    // CordzUpdateScope since it untracks the cordz info.
+    void MaybeRemoveEmptyCrcNode();
+
+    cord_internal::InlineData data_;
+  };
+  InlineRep contents_;
+
+  // Helper for GetFlat() and TryFlat().
+  static bool GetFlatAux(absl::cord_internal::CordRep* rep,
+                         absl::string_view* fragment);
+
+  // Helper for ForEachChunk().
+  static void ForEachChunkAux(
+      absl::cord_internal::CordRep* rep,
+      absl::FunctionRef<void(absl::string_view)> callback);
+
+  // The destructor for non-empty Cords.
+  void DestroyCordSlow();
+
+  // Out-of-line implementation of slower parts of logic.
+  void CopyToArraySlowPath(char* dst) const;
+  int CompareSlowPath(absl::string_view rhs, size_t compared_size,
+                      size_t size_to_compare) const;
+  int CompareSlowPath(const Cord& rhs, size_t compared_size,
+                      size_t size_to_compare) const;
+  bool EqualsImpl(absl::string_view rhs, size_t size_to_compare) const;
+  bool EqualsImpl(const Cord& rhs, size_t size_to_compare) const;
+  int CompareImpl(const Cord& rhs) const;
+
+  template <typename ResultType, typename RHS>
+  friend ResultType GenericCompare(const Cord& lhs, const RHS& rhs,
+                                   size_t size_to_compare);
+  static absl::string_view GetFirstChunk(const Cord& c);
+  static absl::string_view GetFirstChunk(absl::string_view sv);
+
+  // Returns a new reference to contents_.tree(), or steals an existing
+  // reference if called on an rvalue.
+  absl::cord_internal::CordRep* TakeRep() const&;
+  absl::cord_internal::CordRep* TakeRep() &&;
+
+  // Helper for Append().
+  template <typename C>
+  void AppendImpl(C&& src);
+
+  // Appends / Prepends `src` to this instance, using precise sizing.
+  // This method does explicitly not attempt to use any spare capacity
+  // in any pending last added private owned flat.
+  // Requires `src` to be <= kMaxFlatLength.
+  void AppendPrecise(absl::string_view src, MethodIdentifier method);
+  void PrependPrecise(absl::string_view src, MethodIdentifier method);
+
+  CordBuffer GetAppendBufferSlowPath(size_t block_size, size_t capacity,
+                                     size_t min_capacity);
+
+  // Prepends the provided data to this instance. `method` contains the public
+  // API method for this action which is tracked for Cordz sampling purposes.
+  void PrependArray(absl::string_view src, MethodIdentifier method);
+
+  // Assigns the value in 'src' to this instance, 'stealing' its contents.
+  // Requires src.length() > kMaxBytesToCopy.
+  Cord& AssignLargeString(std::string&& src);
+
+  // Helper for AbslHashValue().
+  template <typename H>
+  H HashFragmented(H hash_state) const {
+    typename H::AbslInternalPiecewiseCombiner combiner;
+    ForEachChunk([&combiner, &hash_state](absl::string_view chunk) {
+      hash_state = combiner.add_buffer(std::move(hash_state), chunk.data(),
+                                       chunk.size());
+    });
+    return H::combine(combiner.finalize(std::move(hash_state)), size());
+  }
+
+  friend class CrcCord;
+  void SetCrcCordState(crc_internal::CrcCordState state);
+  const crc_internal::CrcCordState* MaybeGetCrcCordState() const;
+};
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// allow a Cord to be logged
+extern std::ostream& operator<<(std::ostream& out, const Cord& cord);
+
+// ------------------------------------------------------------------
+// Internal details follow.  Clients should ignore.
+
+namespace cord_internal {
+
+// Does non-template-specific `CordRepExternal` initialization.
+// Requires `data` to be non-empty.
+void InitializeCordRepExternal(absl::string_view data, CordRepExternal* rep);
+
+// Creates a new `CordRep` that owns `data` and `releaser` and returns a pointer
+// to it. Requires `data` to be non-empty.
+template <typename Releaser>
+// NOLINTNEXTLINE - suppress clang-tidy raw pointer return.
+CordRep* NewExternalRep(absl::string_view data, Releaser&& releaser) {
+  assert(!data.empty());
+  using ReleaserType = absl::decay_t<Releaser>;
+  CordRepExternal* rep = new CordRepExternalImpl<ReleaserType>(
+      std::forward<Releaser>(releaser), 0);
+  InitializeCordRepExternal(data, rep);
+  return rep;
+}
+
+// Overload for function reference types that dispatches using a function
+// pointer because there are no `alignof()` or `sizeof()` a function reference.
+// NOLINTNEXTLINE - suppress clang-tidy raw pointer return.
+inline CordRep* NewExternalRep(absl::string_view data,
+                               void (&releaser)(absl::string_view)) {
+  return NewExternalRep(data, &releaser);
+}
+
+}  // namespace cord_internal
+
+template <typename Releaser>
+Cord MakeCordFromExternal(absl::string_view data, Releaser&& releaser) {
+  Cord cord;
+  if (ABSL_PREDICT_TRUE(!data.empty())) {
+    cord.contents_.EmplaceTree(::absl::cord_internal::NewExternalRep(
+                                   data, std::forward<Releaser>(releaser)),
+                               Cord::MethodIdentifier::kMakeCordFromExternal);
+  } else {
+    using ReleaserType = absl::decay_t<Releaser>;
+    cord_internal::InvokeReleaser(
+        cord_internal::Rank0{}, ReleaserType(std::forward<Releaser>(releaser)),
+        data);
+  }
+  return cord;
+}
+
+constexpr Cord::InlineRep::InlineRep(absl::string_view sv, CordRep* rep)
+    : data_(sv, rep) {}
+
+inline Cord::InlineRep::InlineRep(const Cord::InlineRep& src)
+    : data_(InlineData::kDefaultInit) {
+  if (CordRep* tree = src.tree()) {
+    EmplaceTree(CordRep::Ref(tree), src.data_,
+                CordzUpdateTracker::kConstructorCord);
+  } else {
+    data_ = src.data_;
+  }
+}
+
+inline Cord::InlineRep::InlineRep(Cord::InlineRep&& src) : data_(src.data_) {
+  src.ResetToEmpty();
+}
+
+inline Cord::InlineRep& Cord::InlineRep::operator=(const Cord::InlineRep& src) {
+  if (this == &src) {
+    return *this;
+  }
+  if (!is_tree() && !src.is_tree()) {
+    data_ = src.data_;
+    return *this;
+  }
+  AssignSlow(src);
+  return *this;
+}
+
+inline Cord::InlineRep& Cord::InlineRep::operator=(
+    Cord::InlineRep&& src) noexcept {
+  if (is_tree()) {
+    UnrefTree();
+  }
+  data_ = src.data_;
+  src.ResetToEmpty();
+  return *this;
+}
+
+inline void Cord::InlineRep::Swap(Cord::InlineRep* rhs) {
+  if (rhs == this) {
+    return;
+  }
+  std::swap(data_, rhs->data_);
+}
+
+inline const char* Cord::InlineRep::data() const {
+  return is_tree() ? nullptr : data_.as_chars();
+}
+
+inline const char* Cord::InlineRep::as_chars() const {
+  assert(!data_.is_tree());
+  return data_.as_chars();
+}
+
+inline absl::cord_internal::CordRep* Cord::InlineRep::as_tree() const {
+  assert(data_.is_tree());
+  return data_.as_tree();
+}
+
+inline absl::cord_internal::CordRep* Cord::InlineRep::tree() const {
+  if (is_tree()) {
+    return as_tree();
+  } else {
+    return nullptr;
+  }
+}
+
+inline bool Cord::InlineRep::empty() const { return data_.is_empty(); }
+
+inline size_t Cord::InlineRep::size() const {
+  return is_tree() ? as_tree()->length : inline_size();
+}
+
+inline cord_internal::CordRepFlat* Cord::InlineRep::MakeFlatWithExtraCapacity(
+    size_t extra) {
+  static_assert(cord_internal::kMinFlatLength >= sizeof(data_), "");
+  size_t len = data_.inline_size();
+  auto* result = CordRepFlat::New(len + extra);
+  result->length = len;
+  data_.copy_max_inline_to(result->Data());
+  return result;
+}
+
+inline void Cord::InlineRep::EmplaceTree(CordRep* rep,
+                                         MethodIdentifier method) {
+  assert(rep);
+  data_.make_tree(rep);
+  CordzInfo::MaybeTrackCord(data_, method);
+}
+
+inline void Cord::InlineRep::EmplaceTree(CordRep* rep, const InlineData& parent,
+                                         MethodIdentifier method) {
+  data_.make_tree(rep);
+  CordzInfo::MaybeTrackCord(data_, parent, method);
+}
+
+inline void Cord::InlineRep::SetTree(CordRep* rep,
+                                     const CordzUpdateScope& scope) {
+  assert(rep);
+  assert(data_.is_tree());
+  data_.set_tree(rep);
+  scope.SetCordRep(rep);
+}
+
+inline void Cord::InlineRep::SetTreeOrEmpty(CordRep* rep,
+                                            const CordzUpdateScope& scope) {
+  assert(data_.is_tree());
+  if (rep) {
+    data_.set_tree(rep);
+  } else {
+    data_ = {};
+  }
+  scope.SetCordRep(rep);
+}
+
+inline void Cord::InlineRep::CommitTree(const CordRep* old_rep, CordRep* rep,
+                                        const CordzUpdateScope& scope,
+                                        MethodIdentifier method) {
+  if (old_rep) {
+    SetTree(rep, scope);
+  } else {
+    EmplaceTree(rep, method);
+  }
+}
+
+inline absl::cord_internal::CordRep* Cord::InlineRep::clear() {
+  if (is_tree()) {
+    CordzInfo::MaybeUntrackCord(cordz_info());
+  }
+  absl::cord_internal::CordRep* result = tree();
+  ResetToEmpty();
+  return result;
+}
+
+inline void Cord::InlineRep::CopyToArray(char* dst) const {
+  assert(!is_tree());
+  size_t n = inline_size();
+  assert(n != 0);
+  cord_internal::SmallMemmove(dst, data_.as_chars(), n);
+}
+
+inline void Cord::InlineRep::MaybeRemoveEmptyCrcNode() {
+  CordRep* rep = tree();
+  if (rep == nullptr || ABSL_PREDICT_TRUE(rep->length > 0)) {
+    return;
+  }
+  assert(rep->IsCrc());
+  assert(rep->crc()->child == nullptr);
+  CordzInfo::MaybeUntrackCord(cordz_info());
+  CordRep::Unref(rep);
+  ResetToEmpty();
+}
+
+constexpr inline Cord::Cord() noexcept {}
+
+inline Cord::Cord(absl::string_view src)
+    : Cord(src, CordzUpdateTracker::kConstructorString) {}
+
+template <typename T>
+constexpr Cord::Cord(strings_internal::StringConstant<T>)
+    : contents_(strings_internal::StringConstant<T>::value,
+                strings_internal::StringConstant<T>::value.size() <=
+                        cord_internal::kMaxInline
+                    ? nullptr
+                    : &cord_internal::ConstInitExternalStorage<
+                          strings_internal::StringConstant<T>>::value) {}
+
+inline Cord& Cord::operator=(const Cord& x) {
+  contents_ = x.contents_;
+  return *this;
+}
+
+template <typename T, Cord::EnableIfString<T>>
+Cord& Cord::operator=(T&& src) {
+  if (src.size() <= cord_internal::kMaxBytesToCopy) {
+    return operator=(absl::string_view(src));
+  } else {
+    return AssignLargeString(std::forward<T>(src));
+  }
+}
+
+inline Cord::Cord(const Cord& src) : contents_(src.contents_) {}
+
+inline Cord::Cord(Cord&& src) noexcept : contents_(std::move(src.contents_)) {}
+
+inline void Cord::swap(Cord& other) noexcept {
+  contents_.Swap(&other.contents_);
+}
+
+inline Cord& Cord::operator=(Cord&& x) noexcept {
+  contents_ = std::move(x.contents_);
+  return *this;
+}
+
+extern template Cord::Cord(std::string&& src);
+
+inline size_t Cord::size() const {
+  // Length is 1st field in str.rep_
+  return contents_.size();
+}
+
+inline bool Cord::empty() const { return size() == 0; }
+
+inline size_t Cord::EstimatedMemoryUsage(
+    CordMemoryAccounting accounting_method) const {
+  size_t result = sizeof(Cord);
+  if (const absl::cord_internal::CordRep* rep = contents_.tree()) {
+    if (accounting_method == CordMemoryAccounting::kFairShare) {
+      result += cord_internal::GetEstimatedFairShareMemoryUsage(rep);
+    } else {
+      result += cord_internal::GetEstimatedMemoryUsage(rep);
+    }
+  }
+  return result;
+}
+
+inline absl::optional<absl::string_view> Cord::TryFlat() const {
+  absl::cord_internal::CordRep* rep = contents_.tree();
+  if (rep == nullptr) {
+    return absl::string_view(contents_.data(), contents_.size());
+  }
+  absl::string_view fragment;
+  if (GetFlatAux(rep, &fragment)) {
+    return fragment;
+  }
+  return absl::nullopt;
+}
+
+inline absl::string_view Cord::Flatten() {
+  absl::cord_internal::CordRep* rep = contents_.tree();
+  if (rep == nullptr) {
+    return absl::string_view(contents_.data(), contents_.size());
+  } else {
+    absl::string_view already_flat_contents;
+    if (GetFlatAux(rep, &already_flat_contents)) {
+      return already_flat_contents;
+    }
+  }
+  return FlattenSlowPath();
+}
+
+inline void Cord::Append(absl::string_view src) {
+  contents_.AppendArray(src, CordzUpdateTracker::kAppendString);
+}
+
+inline void Cord::Prepend(absl::string_view src) {
+  PrependArray(src, CordzUpdateTracker::kPrependString);
+}
+
+inline void Cord::Append(CordBuffer buffer) {
+  if (ABSL_PREDICT_FALSE(buffer.length() == 0)) return;
+  absl::string_view short_value;
+  if (CordRep* rep = buffer.ConsumeValue(short_value)) {
+    contents_.AppendTree(rep, CordzUpdateTracker::kAppendCordBuffer);
+  } else {
+    AppendPrecise(short_value, CordzUpdateTracker::kAppendCordBuffer);
+  }
+}
+
+inline void Cord::Prepend(CordBuffer buffer) {
+  if (ABSL_PREDICT_FALSE(buffer.length() == 0)) return;
+  absl::string_view short_value;
+  if (CordRep* rep = buffer.ConsumeValue(short_value)) {
+    contents_.PrependTree(rep, CordzUpdateTracker::kPrependCordBuffer);
+  } else {
+    PrependPrecise(short_value, CordzUpdateTracker::kPrependCordBuffer);
+  }
+}
+
+inline CordBuffer Cord::GetAppendBuffer(size_t capacity, size_t min_capacity) {
+  if (empty()) return CordBuffer::CreateWithDefaultLimit(capacity);
+  return GetAppendBufferSlowPath(0, capacity, min_capacity);
+}
+
+inline CordBuffer Cord::GetCustomAppendBuffer(size_t block_size,
+                                              size_t capacity,
+                                              size_t min_capacity) {
+  if (empty()) {
+    return block_size ? CordBuffer::CreateWithCustomLimit(block_size, capacity)
+                      : CordBuffer::CreateWithDefaultLimit(capacity);
+  }
+  return GetAppendBufferSlowPath(block_size, capacity, min_capacity);
+}
+
+extern template void Cord::Append(std::string&& src);
+extern template void Cord::Prepend(std::string&& src);
+
+inline int Cord::Compare(const Cord& rhs) const {
+  if (!contents_.is_tree() && !rhs.contents_.is_tree()) {
+    return contents_.data_.Compare(rhs.contents_.data_);
+  }
+
+  return CompareImpl(rhs);
+}
+
+// Does 'this' cord start/end with rhs
+inline bool Cord::StartsWith(const Cord& rhs) const {
+  if (contents_.IsSame(rhs.contents_)) return true;
+  size_t rhs_size = rhs.size();
+  if (size() < rhs_size) return false;
+  return EqualsImpl(rhs, rhs_size);
+}
+
+inline bool Cord::StartsWith(absl::string_view rhs) const {
+  size_t rhs_size = rhs.size();
+  if (size() < rhs_size) return false;
+  return EqualsImpl(rhs, rhs_size);
+}
+
+inline void Cord::ChunkIterator::InitTree(cord_internal::CordRep* tree) {
+  tree = cord_internal::SkipCrcNode(tree);
+  if (tree->tag == cord_internal::BTREE) {
+    current_chunk_ = btree_reader_.Init(tree->btree());
+  } else {
+    current_leaf_ = tree;
+    current_chunk_ = cord_internal::EdgeData(tree);
+  }
+}
+
+inline Cord::ChunkIterator::ChunkIterator(cord_internal::CordRep* tree) {
+  bytes_remaining_ = tree->length;
+  InitTree(tree);
+}
+
+inline Cord::ChunkIterator::ChunkIterator(const Cord* cord) {
+  if (CordRep* tree = cord->contents_.tree()) {
+    bytes_remaining_ = tree->length;
+    if (ABSL_PREDICT_TRUE(bytes_remaining_ != 0)) {
+      InitTree(tree);
+    } else {
+      current_chunk_ = {};
+    }
+  } else {
+    bytes_remaining_ = cord->contents_.inline_size();
+    current_chunk_ = {cord->contents_.data(), bytes_remaining_};
+  }
+}
+
+inline Cord::ChunkIterator& Cord::ChunkIterator::AdvanceBtree() {
+  current_chunk_ = btree_reader_.Next();
+  return *this;
+}
+
+inline void Cord::ChunkIterator::AdvanceBytesBtree(size_t n) {
+  assert(n >= current_chunk_.size());
+  bytes_remaining_ -= n;
+  if (bytes_remaining_) {
+    if (n == current_chunk_.size()) {
+      current_chunk_ = btree_reader_.Next();
+    } else {
+      size_t offset = btree_reader_.length() - bytes_remaining_;
+      current_chunk_ = btree_reader_.Seek(offset);
+    }
+  } else {
+    current_chunk_ = {};
+  }
+}
+
+inline Cord::ChunkIterator& Cord::ChunkIterator::operator++() {
+  ABSL_HARDENING_ASSERT(bytes_remaining_ > 0 &&
+                        "Attempted to iterate past `end()`");
+  assert(bytes_remaining_ >= current_chunk_.size());
+  bytes_remaining_ -= current_chunk_.size();
+  if (bytes_remaining_ > 0) {
+    if (btree_reader_) {
+      return AdvanceBtree();
+    } else {
+      assert(!current_chunk_.empty());  // Called on invalid iterator.
+    }
+    current_chunk_ = {};
+  }
+  return *this;
+}
+
+inline Cord::ChunkIterator Cord::ChunkIterator::operator++(int) {
+  ChunkIterator tmp(*this);
+  operator++();
+  return tmp;
+}
+
+inline bool Cord::ChunkIterator::operator==(const ChunkIterator& other) const {
+  return bytes_remaining_ == other.bytes_remaining_;
+}
+
+inline bool Cord::ChunkIterator::operator!=(const ChunkIterator& other) const {
+  return !(*this == other);
+}
+
+inline Cord::ChunkIterator::reference Cord::ChunkIterator::operator*() const {
+  ABSL_HARDENING_ASSERT(bytes_remaining_ != 0);
+  return current_chunk_;
+}
+
+inline Cord::ChunkIterator::pointer Cord::ChunkIterator::operator->() const {
+  ABSL_HARDENING_ASSERT(bytes_remaining_ != 0);
+  return &current_chunk_;
+}
+
+inline void Cord::ChunkIterator::RemoveChunkPrefix(size_t n) {
+  assert(n < current_chunk_.size());
+  current_chunk_.remove_prefix(n);
+  bytes_remaining_ -= n;
+}
+
+inline void Cord::ChunkIterator::AdvanceBytes(size_t n) {
+  assert(bytes_remaining_ >= n);
+  if (ABSL_PREDICT_TRUE(n < current_chunk_.size())) {
+    RemoveChunkPrefix(n);
+  } else if (n != 0) {
+    if (btree_reader_) {
+      AdvanceBytesBtree(n);
+    } else {
+      bytes_remaining_ = 0;
+    }
+  }
+}
+
+inline Cord::ChunkIterator Cord::chunk_begin() const {
+  return ChunkIterator(this);
+}
+
+inline Cord::ChunkIterator Cord::chunk_end() const { return ChunkIterator(); }
+
+inline Cord::ChunkIterator Cord::ChunkRange::begin() const {
+  return cord_->chunk_begin();
+}
+
+inline Cord::ChunkIterator Cord::ChunkRange::end() const {
+  return cord_->chunk_end();
+}
+
+inline Cord::ChunkRange Cord::Chunks() const { return ChunkRange(this); }
+
+inline Cord::CharIterator& Cord::CharIterator::operator++() {
+  if (ABSL_PREDICT_TRUE(chunk_iterator_->size() > 1)) {
+    chunk_iterator_.RemoveChunkPrefix(1);
+  } else {
+    ++chunk_iterator_;
+  }
+  return *this;
+}
+
+inline Cord::CharIterator Cord::CharIterator::operator++(int) {
+  CharIterator tmp(*this);
+  operator++();
+  return tmp;
+}
+
+inline bool Cord::CharIterator::operator==(const CharIterator& other) const {
+  return chunk_iterator_ == other.chunk_iterator_;
+}
+
+inline bool Cord::CharIterator::operator!=(const CharIterator& other) const {
+  return !(*this == other);
+}
+
+inline Cord::CharIterator::reference Cord::CharIterator::operator*() const {
+  return *chunk_iterator_->data();
+}
+
+inline Cord::CharIterator::pointer Cord::CharIterator::operator->() const {
+  return chunk_iterator_->data();
+}
+
+inline Cord Cord::AdvanceAndRead(CharIterator* it, size_t n_bytes) {
+  assert(it != nullptr);
+  return it->chunk_iterator_.AdvanceAndReadBytes(n_bytes);
+}
+
+inline void Cord::Advance(CharIterator* it, size_t n_bytes) {
+  assert(it != nullptr);
+  it->chunk_iterator_.AdvanceBytes(n_bytes);
+}
+
+inline absl::string_view Cord::ChunkRemaining(const CharIterator& it) {
+  return *it.chunk_iterator_;
+}
+
+inline Cord::CharIterator Cord::char_begin() const {
+  return CharIterator(this);
+}
+
+inline Cord::CharIterator Cord::char_end() const { return CharIterator(); }
+
+inline Cord::CharIterator Cord::CharRange::begin() const {
+  return cord_->char_begin();
+}
+
+inline Cord::CharIterator Cord::CharRange::end() const {
+  return cord_->char_end();
+}
+
+inline Cord::CharRange Cord::Chars() const { return CharRange(this); }
+
+inline void Cord::ForEachChunk(
+    absl::FunctionRef<void(absl::string_view)> callback) const {
+  absl::cord_internal::CordRep* rep = contents_.tree();
+  if (rep == nullptr) {
+    callback(absl::string_view(contents_.data(), contents_.size()));
+  } else {
+    ForEachChunkAux(rep, callback);
+  }
+}
+
+// Nonmember Cord-to-Cord relational operators.
+inline bool operator==(const Cord& lhs, const Cord& rhs) {
+  if (lhs.contents_.IsSame(rhs.contents_)) return true;
+  size_t rhs_size = rhs.size();
+  if (lhs.size() != rhs_size) return false;
+  return lhs.EqualsImpl(rhs, rhs_size);
+}
+
+inline bool operator!=(const Cord& x, const Cord& y) { return !(x == y); }
+inline bool operator<(const Cord& x, const Cord& y) { return x.Compare(y) < 0; }
+inline bool operator>(const Cord& x, const Cord& y) { return x.Compare(y) > 0; }
+inline bool operator<=(const Cord& x, const Cord& y) {
+  return x.Compare(y) <= 0;
+}
+inline bool operator>=(const Cord& x, const Cord& y) {
+  return x.Compare(y) >= 0;
+}
+
+// Nonmember Cord-to-absl::string_view relational operators.
+//
+// Due to implicit conversions, these also enable comparisons of Cord with
+// with std::string, ::string, and const char*.
+inline bool operator==(const Cord& lhs, absl::string_view rhs) {
+  size_t lhs_size = lhs.size();
+  size_t rhs_size = rhs.size();
+  if (lhs_size != rhs_size) return false;
+  return lhs.EqualsImpl(rhs, rhs_size);
+}
+
+inline bool operator==(absl::string_view x, const Cord& y) { return y == x; }
+inline bool operator!=(const Cord& x, absl::string_view y) { return !(x == y); }
+inline bool operator!=(absl::string_view x, const Cord& y) { return !(x == y); }
+inline bool operator<(const Cord& x, absl::string_view y) {
+  return x.Compare(y) < 0;
+}
+inline bool operator<(absl::string_view x, const Cord& y) {
+  return y.Compare(x) > 0;
+}
+inline bool operator>(const Cord& x, absl::string_view y) { return y < x; }
+inline bool operator>(absl::string_view x, const Cord& y) { return y < x; }
+inline bool operator<=(const Cord& x, absl::string_view y) { return !(y < x); }
+inline bool operator<=(absl::string_view x, const Cord& y) { return !(y < x); }
+inline bool operator>=(const Cord& x, absl::string_view y) { return !(x < y); }
+inline bool operator>=(absl::string_view x, const Cord& y) { return !(x < y); }
+
+// Some internals exposed to test code.
+namespace strings_internal {
+class CordTestAccess {
+ public:
+  static size_t FlatOverhead();
+  static size_t MaxFlatLength();
+  static size_t SizeofCordRepExternal();
+  static size_t SizeofCordRepSubstring();
+  static size_t FlatTagToLength(uint8_t tag);
+  static uint8_t LengthToTag(size_t s);
+};
+}  // namespace strings_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_CORD_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/cord_analysis.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/cord_analysis.h
new file mode 100644
index 0000000000000000000000000000000000000000..7041ad1aa519dfaa46e8e5244b04b5550a4bd45f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/cord_analysis.h
@@ -0,0 +1,44 @@
+// Copyright 2021 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_CORD_ANALYSIS_H_
+#define ABSL_STRINGS_CORD_ANALYSIS_H_
+
+#include <cstddef>
+#include <cstdint>
+
+#include "absl/base/config.h"
+#include "absl/strings/internal/cord_internal.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// Returns the *approximate* number of bytes held in full or in part by this
+// Cord (which may not remain the same between invocations). Cords that share
+// memory could each be "charged" independently for the same shared memory.
+size_t GetEstimatedMemoryUsage(const CordRep* rep);
+
+// Returns the *approximate* number of bytes held in full or in part by this
+// CordRep weighted by the sharing ratio of that data. For example, if some data
+// edge is shared by 4 different Cords, then each cord is attribute 1/4th of
+// the total memory usage as a 'fair share' of the total memory usage.
+size_t GetEstimatedFairShareMemoryUsage(const CordRep* rep);
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+
+#endif  // ABSL_STRINGS_CORD_ANALYSIS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/cord_buffer.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/cord_buffer.h
new file mode 100644
index 0000000000000000000000000000000000000000..15494b31e063b6390a1262ab53cbb3b2685c9087
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/cord_buffer.h
@@ -0,0 +1,575 @@
+// Copyright 2021 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: cord_buffer.h
+// -----------------------------------------------------------------------------
+//
+// This file defines an `absl::CordBuffer` data structure to hold data for
+// eventual inclusion within an existing `Cord` data structure. Cord buffers are
+// useful for building large Cords that may require custom allocation of its
+// associated memory.
+//
+#ifndef ABSL_STRINGS_CORD_BUFFER_H_
+#define ABSL_STRINGS_CORD_BUFFER_H_
+
+#include <algorithm>
+#include <cassert>
+#include <cstddef>
+#include <cstdint>
+#include <memory>
+#include <utility>
+
+#include "absl/base/config.h"
+#include "absl/base/macros.h"
+#include "absl/numeric/bits.h"
+#include "absl/strings/internal/cord_internal.h"
+#include "absl/strings/internal/cord_rep_flat.h"
+#include "absl/types/span.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+class Cord;
+class CordBufferTestPeer;
+
+// CordBuffer
+//
+// CordBuffer manages memory buffers for purposes such as zero-copy APIs as well
+// as applications building cords with large data requiring granular control
+// over the allocation and size of cord data. For example, a function creating
+// a cord of random data could use a CordBuffer as follows:
+//
+//   absl::Cord CreateRandomCord(size_t length) {
+//     absl::Cord cord;
+//     while (length > 0) {
+//       CordBuffer buffer = CordBuffer::CreateWithDefaultLimit(length);
+//       absl::Span<char> data = buffer.available_up_to(length);
+//       FillRandomValues(data.data(), data.size());
+//       buffer.IncreaseLengthBy(data.size());
+//       cord.Append(std::move(buffer));
+//       length -= data.size();
+//     }
+//     return cord;
+//   }
+//
+// CordBuffer instances are by default limited to a capacity of `kDefaultLimit`
+// bytes. `kDefaultLimit` is currently just under 4KiB, but this default may
+// change in the future and/or for specific architectures. The default limit is
+// aimed to provide a good trade-off between performance and memory overhead.
+// Smaller buffers typically incur more compute cost while larger buffers are
+// more CPU efficient but create significant memory overhead because of such
+// allocations being less granular. Using larger buffers may also increase the
+// risk of memory fragmentation.
+//
+// Applications create a buffer using one of the `CreateWithDefaultLimit()` or
+// `CreateWithCustomLimit()` methods. The returned instance will have a non-zero
+// capacity and a zero length. Applications use the `data()` method to set the
+// contents of the managed memory, and once done filling the buffer, use the
+// `IncreaseLengthBy()` or 'SetLength()' method to specify the length of the
+// initialized data before adding the buffer to a Cord.
+//
+// The `CreateWithCustomLimit()` method is intended for applications needing
+// larger buffers than the default memory limit, allowing the allocation of up
+// to a capacity of `kCustomLimit` bytes minus some minimum internal overhead.
+// The usage of `CreateWithCustomLimit()` should be limited to only those use
+// cases where the distribution of the input is relatively well known, and/or
+// where the trade-off between the efficiency gains outweigh the risk of memory
+// fragmentation. See the documentation for `CreateWithCustomLimit()` for more
+// information on using larger custom limits.
+//
+// The capacity of a `CordBuffer` returned by one of the `Create` methods may
+// be larger than the requested capacity due to rounding, alignment and
+// granularity of the memory allocator. Applications should use the `capacity`
+// method to obtain the effective capacity of the returned instance as
+// demonstrated in the provided example above.
+//
+// CordBuffer is a move-only class. All references into the managed memory are
+// invalidated when an instance is moved into either another CordBuffer instance
+// or a Cord. Writing to a location obtained by a previous call to `data()`
+// after an instance was moved will lead to undefined behavior.
+//
+// A `moved from` CordBuffer instance will have a valid, but empty state.
+// CordBuffer is thread compatible.
+class CordBuffer {
+ public:
+  // kDefaultLimit
+  //
+  // Default capacity limits of allocated CordBuffers.
+  // See the class comments for more information on allocation limits.
+  static constexpr size_t kDefaultLimit = cord_internal::kMaxFlatLength;
+
+  // kCustomLimit
+  //
+  // Maximum size for CreateWithCustomLimit() allocated buffers.
+  // Note that the effective capacity may be slightly less
+  // because of internal overhead of internal cord buffers.
+  static constexpr size_t kCustomLimit = 64U << 10;
+
+  // Constructors, Destructors and Assignment Operators
+
+  // Creates an empty CordBuffer.
+  CordBuffer() = default;
+
+  // Destroys this CordBuffer instance and, if not empty, releases any memory
+  // managed by this instance, invalidating previously returned references.
+  ~CordBuffer();
+
+  // CordBuffer is move-only
+  CordBuffer(CordBuffer&& rhs) noexcept;
+  CordBuffer& operator=(CordBuffer&&) noexcept;
+  CordBuffer(const CordBuffer&) = delete;
+  CordBuffer& operator=(const CordBuffer&) = delete;
+
+  // CordBuffer::MaximumPayload()
+  //
+  // Returns the guaranteed maximum payload for a CordBuffer returned by the
+  // `CreateWithDefaultLimit()` method. While small, each internal buffer inside
+  // a Cord incurs an overhead to manage the length, type and reference count
+  // for the buffer managed inside the cord tree. Applications can use this
+  // method to get approximate number of buffers required for a given byte
+  // size, etc.
+  //
+  // For example:
+  //   const size_t payload = absl::CordBuffer::MaximumPayload();
+  //   const size_t buffer_count = (total_size + payload - 1) / payload;
+  //   buffers.reserve(buffer_count);
+  static constexpr size_t MaximumPayload();
+
+  // Overload to the above `MaximumPayload()` except that it returns the
+  // maximum payload for a CordBuffer returned by the `CreateWithCustomLimit()`
+  // method given the provided `block_size`.
+  static constexpr size_t MaximumPayload(size_t block_size);
+
+  // CordBuffer::CreateWithDefaultLimit()
+  //
+  // Creates a CordBuffer instance of the desired `capacity`, capped at the
+  // default limit `kDefaultLimit`. The returned buffer has a guaranteed
+  // capacity of at least `min(kDefaultLimit, capacity)`. See the class comments
+  // for more information on buffer capacities and intended usage.
+  static CordBuffer CreateWithDefaultLimit(size_t capacity);
+
+
+  // CordBuffer::CreateWithCustomLimit()
+  //
+  // Creates a CordBuffer instance of the desired `capacity` rounded to an
+  // appropriate power of 2 size less than, or equal to `block_size`.
+  // Requires `block_size` to be a power of 2.
+  //
+  // If `capacity` is less than or equal to `kDefaultLimit`, then this method
+  // behaves identical to `CreateWithDefaultLimit`, which means that the caller
+  // is guaranteed to get a buffer of at least the requested capacity.
+  //
+  // If `capacity` is greater than or equal to `block_size`, then this method
+  // returns a buffer with an `allocated size` of `block_size` bytes. Otherwise,
+  // this methods returns a buffer with a suitable smaller power of 2 block size
+  // to satisfy the request. The actual size depends on a number of factors, and
+  // is typically (but not necessarily) the highest or second highest power of 2
+  // value less than or equal to `capacity`.
+  //
+  // The 'allocated size' includes a small amount of overhead required for
+  // internal state, which is currently 13 bytes on 64-bit platforms. For
+  // example: a buffer created with `block_size` and `capacity' set to 8KiB
+  // will have an allocated size of 8KiB, and an effective internal `capacity`
+  // of 8KiB - 13 = 8179 bytes.
+  //
+  // To demonstrate this in practice, let's assume we want to read data from
+  // somewhat larger files using approximately 64KiB buffers:
+  //
+  //   absl::Cord ReadFromFile(int fd, size_t n) {
+  //     absl::Cord cord;
+  //     while (n > 0) {
+  //       CordBuffer buffer = CordBuffer::CreateWithCustomLimit(64 << 10, n);
+  //       absl::Span<char> data = buffer.available_up_to(n);
+  //       ReadFileDataOrDie(fd, data.data(), data.size());
+  //       buffer.IncreaseLengthBy(data.size());
+  //       cord.Append(std::move(buffer));
+  //       n -= data.size();
+  //     }
+  //     return cord;
+  //   }
+  //
+  // If we'd use this function to read a file of 659KiB, we may get the
+  // following pattern of allocated cord buffer sizes:
+  //
+  //   CreateWithCustomLimit(64KiB, 674816) --> ~64KiB (65523)
+  //   CreateWithCustomLimit(64KiB, 674816) --> ~64KiB (65523)
+  //   ...
+  //   CreateWithCustomLimit(64KiB,  19586) --> ~16KiB (16371)
+  //   CreateWithCustomLimit(64KiB,   3215) -->   3215 (at least 3215)
+  //
+  // The reason the method returns a 16K buffer instead of a roughly 19K buffer
+  // is to reduce memory overhead and fragmentation risks. Using carefully
+  // chosen power of 2 values reduces the entropy of allocated memory sizes.
+  //
+  // Additionally, let's assume we'd use the above function on files that are
+  // generally smaller than 64K. If we'd use 'precise' sized buffers for such
+  // files, than we'd get a very wide distribution of allocated memory sizes
+  // rounded to 4K page sizes, and we'd end up with a lot of unused capacity.
+  //
+  // In general, application should only use custom sizes if the data they are
+  // consuming or storing is expected to be many times the chosen block size,
+  // and be based on objective data and performance metrics. For example, a
+  // compress function may work faster and consume less CPU when using larger
+  // buffers. Such an application should pick a size offering a reasonable
+  // trade-off between expected data size, compute savings with larger buffers,
+  // and the cost or fragmentation effect of larger buffers.
+  // Applications must pick a reasonable spot on that curve, and make sure their
+  // data meets their expectations in size distributions such as "mostly large".
+  static CordBuffer CreateWithCustomLimit(size_t block_size, size_t capacity);
+
+  // CordBuffer::available()
+  //
+  // Returns the span delineating the available capacity in this buffer
+  // which is defined as `{ data() + length(), capacity() - length() }`.
+  absl::Span<char> available();
+
+  // CordBuffer::available_up_to()
+  //
+  // Returns the span delineating the available capacity in this buffer limited
+  // to `size` bytes. This is equivalent to `available().subspan(0, size)`.
+  absl::Span<char> available_up_to(size_t size);
+
+  // CordBuffer::data()
+  //
+  // Returns a non-null reference to the data managed by this instance.
+  // Applications are allowed to write up to `capacity` bytes of instance data.
+  // CordBuffer data is uninitialized by default. Reading data from an instance
+  // that has not yet been initialized will lead to undefined behavior.
+  char* data();
+  const char* data() const;
+
+  // CordBuffer::length()
+  //
+  // Returns the length of this instance. The default length of a CordBuffer is
+  // 0, indicating an 'empty' CordBuffer. Applications must specify the length
+  // of the data in a CordBuffer before adding it to a Cord.
+  size_t length() const;
+
+  // CordBuffer::capacity()
+  //
+  // Returns the capacity of this instance. All instances have a non-zero
+  // capacity: default and `moved from` instances have a small internal buffer.
+  size_t capacity() const;
+
+  // CordBuffer::IncreaseLengthBy()
+  //
+  // Increases the length of this buffer by the specified 'n' bytes.
+  // Applications must make sure all data in this buffer up to the new length
+  // has been initialized before adding a CordBuffer to a Cord: failure to do so
+  // will lead to undefined behavior.  Requires `length() + n <= capacity()`.
+  // Typically, applications will use 'available_up_to()` to get a span of the
+  // desired capacity, and use `span.size()` to increase the length as in:
+  //   absl::Span<char> span = buffer.available_up_to(desired);
+  //   buffer.IncreaseLengthBy(span.size());
+  //   memcpy(span.data(), src, span.size());
+  //   etc...
+  void IncreaseLengthBy(size_t n);
+
+  // CordBuffer::SetLength()
+  //
+  // Sets the data length of this instance. Applications must make sure all data
+  // of the specified length has been initialized before adding a CordBuffer to
+  // a Cord: failure to do so will lead to undefined behavior.
+  // Setting the length to a small value or zero does not release any memory
+  // held by this CordBuffer instance. Requires `length <= capacity()`.
+  // Applications should preferably use the `IncreaseLengthBy()` method above
+  // in combination with the 'available()` or `available_up_to()` methods.
+  void SetLength(size_t length);
+
+ private:
+  // Make sure we don't accidentally over promise.
+  static_assert(kCustomLimit <= cord_internal::kMaxLargeFlatSize, "");
+
+  // Assume the cost of an 'uprounded' allocation to CeilPow2(size) versus
+  // the cost of allocating at least 1 extra flat <= 4KB:
+  // - Flat overhead = 13 bytes
+  // - Btree amortized cost / node =~ 13 bytes
+  // - 64 byte granularity of tcmalloc at 4K =~ 32 byte average
+  // CPU cost and efficiency requires we should at least 'save' something by
+  // splitting, as a poor man's measure, we say the slop needs to be
+  // at least double the cost offset to make it worth splitting: ~128 bytes.
+  static constexpr size_t kMaxPageSlop = 128;
+
+  // Overhead for allocation a flat.
+  static constexpr size_t kOverhead = cord_internal::kFlatOverhead;
+
+  using CordRepFlat = cord_internal::CordRepFlat;
+
+  // `Rep` is the internal data representation of a CordBuffer. The internal
+  // representation has an internal small size optimization similar to
+  // std::string (SSO).
+  struct Rep {
+    // Inline SSO size of a CordBuffer
+    static constexpr size_t kInlineCapacity = sizeof(intptr_t) * 2 - 1;
+
+    // Creates a default instance with kInlineCapacity.
+    Rep() : short_rep{} {}
+
+    // Creates an instance managing an allocated non zero CordRep.
+    explicit Rep(cord_internal::CordRepFlat* rep) : long_rep{rep} {
+      assert(rep != nullptr);
+    }
+
+    // Returns true if this instance manages the SSO internal buffer.
+    bool is_short() const {
+      constexpr size_t offset = offsetof(Short, raw_size);
+      return (reinterpret_cast<const char*>(this)[offset] & 1) != 0;
+    }
+
+    // Returns the available area of the internal SSO data
+    absl::Span<char> short_available() {
+      const size_t length = short_length();
+      return absl::Span<char>(short_rep.data + length,
+                              kInlineCapacity - length);
+    }
+
+    // Returns the available area of the internal SSO data
+    absl::Span<char> long_available() {
+      assert(!is_short());
+      const size_t length = long_rep.rep->length;
+      return absl::Span<char>(long_rep.rep->Data() + length,
+                              long_rep.rep->Capacity() - length);
+    }
+
+    // Returns the length of the internal SSO data.
+    size_t short_length() const {
+      assert(is_short());
+      return static_cast<size_t>(short_rep.raw_size >> 1);
+    }
+
+    // Sets the length of the internal SSO data.
+    // Disregards any previously set CordRep instance.
+    void set_short_length(size_t length) {
+      short_rep.raw_size = static_cast<char>((length << 1) + 1);
+    }
+
+    // Adds `n` to the current short length.
+    void add_short_length(size_t n) {
+      assert(is_short());
+      short_rep.raw_size += static_cast<char>(n << 1);
+    }
+
+    // Returns reference to the internal SSO data buffer.
+    char* data() {
+      assert(is_short());
+      return short_rep.data;
+    }
+    const char* data() const {
+      assert(is_short());
+      return short_rep.data;
+    }
+
+    // Returns a pointer the external CordRep managed by this instance.
+    cord_internal::CordRepFlat* rep() const {
+      assert(!is_short());
+      return long_rep.rep;
+    }
+
+    // The internal representation takes advantage of the fact that allocated
+    // memory is always on an even address, and uses the least significant bit
+    // of the first or last byte (depending on endianness) as the inline size
+    // indicator overlapping with the least significant byte of the CordRep*.
+#if defined(ABSL_IS_BIG_ENDIAN)
+    struct Long {
+      explicit Long(cord_internal::CordRepFlat* rep_arg) : rep(rep_arg) {}
+      void* padding;
+      cord_internal::CordRepFlat* rep;
+    };
+    struct Short {
+      char data[sizeof(Long) - 1];
+      char raw_size = 1;
+    };
+#else
+    struct Long {
+      explicit Long(cord_internal::CordRepFlat* rep_arg) : rep(rep_arg) {}
+      cord_internal::CordRepFlat* rep;
+      void* padding;
+    };
+    struct Short {
+      char raw_size = 1;
+      char data[sizeof(Long) - 1];
+    };
+#endif
+
+    union {
+      Long long_rep;
+      Short short_rep;
+    };
+  };
+
+  // Power2 functions
+  static bool IsPow2(size_t size) { return absl::has_single_bit(size); }
+  static size_t Log2Floor(size_t size) {
+    return static_cast<size_t>(absl::bit_width(size) - 1);
+  }
+  static size_t Log2Ceil(size_t size) {
+    return static_cast<size_t>(absl::bit_width(size - 1));
+  }
+
+  // Implementation of `CreateWithCustomLimit()`.
+  // This implementation allows for future memory allocation hints to
+  // be passed down into the CordRepFlat allocation function.
+  template <typename... AllocationHints>
+  static CordBuffer CreateWithCustomLimitImpl(size_t block_size,
+                                              size_t capacity,
+                                              AllocationHints... hints);
+
+  // Consumes the value contained in this instance and resets the instance.
+  // This method returns a non-null Cordrep* if the current instances manages a
+  // CordRep*, and resets the instance to an empty SSO instance. If the current
+  // instance is an SSO instance, then this method returns nullptr and sets
+  // `short_value` to the inlined data value. In either case, the current
+  // instance length is reset to zero.
+  // This method is intended to be used by Cord internal functions only.
+  cord_internal::CordRep* ConsumeValue(absl::string_view& short_value) {
+    cord_internal::CordRep* rep = nullptr;
+    if (rep_.is_short()) {
+      short_value = absl::string_view(rep_.data(), rep_.short_length());
+    } else {
+      rep = rep_.rep();
+    }
+    rep_.set_short_length(0);
+    return rep;
+  }
+
+  // Internal constructor.
+  explicit CordBuffer(cord_internal::CordRepFlat* rep) : rep_(rep) {
+    assert(rep != nullptr);
+  }
+
+  Rep rep_;
+
+  friend class Cord;
+  friend class CordBufferTestPeer;
+};
+
+inline constexpr size_t CordBuffer::MaximumPayload() {
+  return cord_internal::kMaxFlatLength;
+}
+
+inline constexpr size_t CordBuffer::MaximumPayload(size_t block_size) {
+  // TODO(absl-team): Use std::min when C++11 support is dropped.
+  return (kCustomLimit < block_size ? kCustomLimit : block_size) -
+         cord_internal::kFlatOverhead;
+}
+
+inline CordBuffer CordBuffer::CreateWithDefaultLimit(size_t capacity) {
+  if (capacity > Rep::kInlineCapacity) {
+    auto* rep = cord_internal::CordRepFlat::New(capacity);
+    rep->length = 0;
+    return CordBuffer(rep);
+  }
+  return CordBuffer();
+}
+
+template <typename... AllocationHints>
+inline CordBuffer CordBuffer::CreateWithCustomLimitImpl(
+    size_t block_size, size_t capacity, AllocationHints... hints) {
+  assert(IsPow2(block_size));
+  capacity = (std::min)(capacity, kCustomLimit);
+  block_size = (std::min)(block_size, kCustomLimit);
+  if (capacity + kOverhead >= block_size) {
+    capacity = block_size;
+  } else if (capacity <= kDefaultLimit) {
+    capacity = capacity + kOverhead;
+  } else if (!IsPow2(capacity)) {
+    // Check if rounded up to next power 2 is a good enough fit
+    // with limited waste making it an acceptable direct fit.
+    const size_t rounded_up = size_t{1} << Log2Ceil(capacity);
+    const size_t slop = rounded_up - capacity;
+    if (slop >= kOverhead && slop <= kMaxPageSlop + kOverhead) {
+      capacity = rounded_up;
+    } else {
+      // Round down to highest power of 2 <= capacity.
+      // Consider a more aggressive step down if that may reduce the
+      // risk of fragmentation where 'people are holding it wrong'.
+      const size_t rounded_down = size_t{1} << Log2Floor(capacity);
+      capacity = rounded_down;
+    }
+  }
+  const size_t length = capacity - kOverhead;
+  auto* rep = CordRepFlat::New(CordRepFlat::Large(), length, hints...);
+  rep->length = 0;
+  return CordBuffer(rep);
+}
+
+inline CordBuffer CordBuffer::CreateWithCustomLimit(size_t block_size,
+                                                    size_t capacity) {
+  return CreateWithCustomLimitImpl(block_size, capacity);
+}
+
+inline CordBuffer::~CordBuffer() {
+  if (!rep_.is_short()) {
+    cord_internal::CordRepFlat::Delete(rep_.rep());
+  }
+}
+
+inline CordBuffer::CordBuffer(CordBuffer&& rhs) noexcept : rep_(rhs.rep_) {
+  rhs.rep_.set_short_length(0);
+}
+
+inline CordBuffer& CordBuffer::operator=(CordBuffer&& rhs) noexcept {
+  if (!rep_.is_short()) cord_internal::CordRepFlat::Delete(rep_.rep());
+  rep_ = rhs.rep_;
+  rhs.rep_.set_short_length(0);
+  return *this;
+}
+
+inline absl::Span<char> CordBuffer::available() {
+  return rep_.is_short() ? rep_.short_available() : rep_.long_available();
+}
+
+inline absl::Span<char> CordBuffer::available_up_to(size_t size) {
+  return available().subspan(0, size);
+}
+
+inline char* CordBuffer::data() {
+  return rep_.is_short() ? rep_.data() : rep_.rep()->Data();
+}
+
+inline const char* CordBuffer::data() const {
+  return rep_.is_short() ? rep_.data() : rep_.rep()->Data();
+}
+
+inline size_t CordBuffer::capacity() const {
+  return rep_.is_short() ? Rep::kInlineCapacity : rep_.rep()->Capacity();
+}
+
+inline size_t CordBuffer::length() const {
+  return rep_.is_short() ? rep_.short_length() : rep_.rep()->length;
+}
+
+inline void CordBuffer::SetLength(size_t length) {
+  ABSL_HARDENING_ASSERT(length <= capacity());
+  if (rep_.is_short()) {
+    rep_.set_short_length(length);
+  } else {
+    rep_.rep()->length = length;
+  }
+}
+
+inline void CordBuffer::IncreaseLengthBy(size_t n) {
+  ABSL_HARDENING_ASSERT(n <= capacity() && length() + n <= capacity());
+  if (rep_.is_short()) {
+    rep_.add_short_length(n);
+  } else {
+    rep_.rep()->length += n;
+  }
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_CORD_BUFFER_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/escaping.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/escaping.h
new file mode 100644
index 0000000000000000000000000000000000000000..7c082fef3de058eafc540f63d6ce4d64456ebfc8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/escaping.h
@@ -0,0 +1,169 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: escaping.h
+// -----------------------------------------------------------------------------
+//
+// This header file contains string utilities involved in escaping and
+// unescaping strings in various ways.
+
+#ifndef ABSL_STRINGS_ESCAPING_H_
+#define ABSL_STRINGS_ESCAPING_H_
+
+#include <cstddef>
+#include <string>
+#include <vector>
+
+#include "absl/base/macros.h"
+#include "absl/strings/ascii.h"
+#include "absl/strings/str_join.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// CUnescape()
+//
+// Unescapes a `source` string and copies it into `dest`, rewriting C-style
+// escape sequences (https://en.cppreference.com/w/cpp/language/escape) into
+// their proper code point equivalents, returning `true` if successful.
+//
+// The following unescape sequences can be handled:
+//
+//   * ASCII escape sequences ('\n','\r','\\', etc.) to their ASCII equivalents
+//   * Octal escape sequences ('\nnn') to byte nnn. The unescaped value must
+//     resolve to a single byte or an error will occur. E.g. values greater than
+//     0xff will produce an error.
+//   * Hexadecimal escape sequences ('\xnn') to byte nn. While an arbitrary
+//     number of following digits are allowed, the unescaped value must resolve
+//     to a single byte or an error will occur. E.g. '\x0045' is equivalent to
+//     '\x45', but '\x1234' will produce an error.
+//   * Unicode escape sequences ('\unnnn' for exactly four hex digits or
+//     '\Unnnnnnnn' for exactly eight hex digits, which will be encoded in
+//     UTF-8. (E.g., `\u2019` unescapes to the three bytes 0xE2, 0x80, and
+//     0x99).
+//
+// If any errors are encountered, this function returns `false`, leaving the
+// `dest` output parameter in an unspecified state, and stores the first
+// encountered error in `error`. To disable error reporting, set `error` to
+// `nullptr` or use the overload with no error reporting below.
+//
+// Example:
+//
+//   std::string s = "foo\\rbar\\nbaz\\t";
+//   std::string unescaped_s;
+//   if (!absl::CUnescape(s, &unescaped_s) {
+//     ...
+//   }
+//   EXPECT_EQ(unescaped_s, "foo\rbar\nbaz\t");
+bool CUnescape(absl::string_view source, std::string* dest, std::string* error);
+
+// Overload of `CUnescape()` with no error reporting.
+inline bool CUnescape(absl::string_view source, std::string* dest) {
+  return CUnescape(source, dest, nullptr);
+}
+
+// CEscape()
+//
+// Escapes a 'src' string using C-style escapes sequences
+// (https://en.cppreference.com/w/cpp/language/escape), escaping other
+// non-printable/non-whitespace bytes as octal sequences (e.g. "\377").
+//
+// Example:
+//
+//   std::string s = "foo\rbar\tbaz\010\011\012\013\014\x0d\n";
+//   std::string escaped_s = absl::CEscape(s);
+//   EXPECT_EQ(escaped_s, "foo\\rbar\\tbaz\\010\\t\\n\\013\\014\\r\\n");
+std::string CEscape(absl::string_view src);
+
+// CHexEscape()
+//
+// Escapes a 'src' string using C-style escape sequences, escaping
+// other non-printable/non-whitespace bytes as hexadecimal sequences (e.g.
+// "\xFF").
+//
+// Example:
+//
+//   std::string s = "foo\rbar\tbaz\010\011\012\013\014\x0d\n";
+//   std::string escaped_s = absl::CHexEscape(s);
+//   EXPECT_EQ(escaped_s, "foo\\rbar\\tbaz\\x08\\t\\n\\x0b\\x0c\\r\\n");
+std::string CHexEscape(absl::string_view src);
+
+// Utf8SafeCEscape()
+//
+// Escapes a 'src' string using C-style escape sequences, escaping bytes as
+// octal sequences, and passing through UTF-8 characters without conversion.
+// I.e., when encountering any bytes with their high bit set, this function
+// will not escape those values, whether or not they are valid UTF-8.
+std::string Utf8SafeCEscape(absl::string_view src);
+
+// Utf8SafeCHexEscape()
+//
+// Escapes a 'src' string using C-style escape sequences, escaping bytes as
+// hexadecimal sequences, and passing through UTF-8 characters without
+// conversion.
+std::string Utf8SafeCHexEscape(absl::string_view src);
+
+// Base64Escape()
+//
+// Encodes a `src` string into a base64-encoded 'dest' string with padding
+// characters. This function conforms with RFC 4648 section 4 (base64) and RFC
+// 2045. See also CalculateBase64EscapedLen().
+void Base64Escape(absl::string_view src, std::string* dest);
+std::string Base64Escape(absl::string_view src);
+
+// WebSafeBase64Escape()
+//
+// Encodes a `src` string into a base64 string, like Base64Escape() does, but
+// outputs '-' instead of '+' and '_' instead of '/', and does not pad 'dest'.
+// This function conforms with RFC 4648 section 5 (base64url).
+void WebSafeBase64Escape(absl::string_view src, std::string* dest);
+std::string WebSafeBase64Escape(absl::string_view src);
+
+// Base64Unescape()
+//
+// Converts a `src` string encoded in Base64 (RFC 4648 section 4) to its binary
+// equivalent, writing it to a `dest` buffer, returning `true` on success. If
+// `src` contains invalid characters, `dest` is cleared and returns `false`.
+// If padding is included (note that `Base64Escape()` does produce it), it must
+// be correct. In the padding, '=' and '.' are treated identically.
+bool Base64Unescape(absl::string_view src, std::string* dest);
+
+// WebSafeBase64Unescape()
+//
+// Converts a `src` string encoded in "web safe" Base64 (RFC 4648 section 5) to
+// its binary equivalent, writing it to a `dest` buffer. If `src` contains
+// invalid characters, `dest` is cleared and returns `false`. If padding is
+// included (note that `WebSafeBase64Escape()` does not produce it), it must be
+// correct. In the padding, '=' and '.' are treated identically.
+bool WebSafeBase64Unescape(absl::string_view src, std::string* dest);
+
+// HexStringToBytes()
+//
+// Converts an ASCII hex string into bytes, returning binary data of length
+// `from.size()/2`.
+std::string HexStringToBytes(absl::string_view from);
+
+// BytesToHexString()
+//
+// Converts binary data into an ASCII text string, returning a string of size
+// `2*from.size()`.
+std::string BytesToHexString(absl::string_view from);
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_ESCAPING_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/char_map.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/char_map.h
new file mode 100644
index 0000000000000000000000000000000000000000..70a9034336dc2b5a3856ad22401113cc5da9cd29
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/char_map.h
@@ -0,0 +1,158 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// Character Map Class
+//
+// A fast, bit-vector map for 8-bit unsigned characters.
+// This class is useful for non-character purposes as well.
+
+#ifndef ABSL_STRINGS_INTERNAL_CHAR_MAP_H_
+#define ABSL_STRINGS_INTERNAL_CHAR_MAP_H_
+
+#include <cstddef>
+#include <cstdint>
+#include <cstring>
+
+#include "absl/base/macros.h"
+#include "absl/base/port.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace strings_internal {
+
+class Charmap {
+ public:
+  constexpr Charmap() : m_() {}
+
+  // Initializes with a given char*.  Note that NUL is not treated as
+  // a terminator, but rather a char to be flicked.
+  Charmap(const char* str, int len) : m_() {
+    while (len--) SetChar(*str++);
+  }
+
+  // Initializes with a given char*.  NUL is treated as a terminator
+  // and will not be in the charmap.
+  explicit Charmap(const char* str) : m_() {
+    while (*str) SetChar(*str++);
+  }
+
+  constexpr bool contains(unsigned char c) const {
+    return (m_[c / 64] >> (c % 64)) & 0x1;
+  }
+
+  // Returns true if and only if a character exists in both maps.
+  bool IntersectsWith(const Charmap& c) const {
+    for (size_t i = 0; i < ABSL_ARRAYSIZE(m_); ++i) {
+      if ((m_[i] & c.m_[i]) != 0) return true;
+    }
+    return false;
+  }
+
+  bool IsZero() const {
+    for (uint64_t c : m_) {
+      if (c != 0) return false;
+    }
+    return true;
+  }
+
+  // Containing only a single specified char.
+  static constexpr Charmap Char(char x) {
+    return Charmap(CharMaskForWord(x, 0), CharMaskForWord(x, 1),
+                   CharMaskForWord(x, 2), CharMaskForWord(x, 3));
+  }
+
+  // Containing all the chars in the C-string 's'.
+  static constexpr Charmap FromString(const char* s) {
+    Charmap ret;
+    while (*s) ret = ret | Char(*s++);
+    return ret;
+  }
+
+  // Containing all the chars in the closed interval [lo,hi].
+  static constexpr Charmap Range(char lo, char hi) {
+    return Charmap(RangeForWord(lo, hi, 0), RangeForWord(lo, hi, 1),
+                   RangeForWord(lo, hi, 2), RangeForWord(lo, hi, 3));
+  }
+
+  friend constexpr Charmap operator&(const Charmap& a, const Charmap& b) {
+    return Charmap(a.m_[0] & b.m_[0], a.m_[1] & b.m_[1], a.m_[2] & b.m_[2],
+                   a.m_[3] & b.m_[3]);
+  }
+
+  friend constexpr Charmap operator|(const Charmap& a, const Charmap& b) {
+    return Charmap(a.m_[0] | b.m_[0], a.m_[1] | b.m_[1], a.m_[2] | b.m_[2],
+                   a.m_[3] | b.m_[3]);
+  }
+
+  friend constexpr Charmap operator~(const Charmap& a) {
+    return Charmap(~a.m_[0], ~a.m_[1], ~a.m_[2], ~a.m_[3]);
+  }
+
+ private:
+  constexpr Charmap(uint64_t b0, uint64_t b1, uint64_t b2, uint64_t b3)
+      : m_{b0, b1, b2, b3} {}
+
+  static constexpr uint64_t RangeForWord(char lo, char hi, uint64_t word) {
+    return OpenRangeFromZeroForWord(static_cast<unsigned char>(hi) + 1, word) &
+           ~OpenRangeFromZeroForWord(static_cast<unsigned char>(lo), word);
+  }
+
+  // All the chars in the specified word of the range [0, upper).
+  static constexpr uint64_t OpenRangeFromZeroForWord(uint64_t upper,
+                                                     uint64_t word) {
+    return (upper <= 64 * word)
+               ? 0
+               : (upper >= 64 * (word + 1))
+                     ? ~static_cast<uint64_t>(0)
+                     : (~static_cast<uint64_t>(0) >> (64 - upper % 64));
+  }
+
+  static constexpr uint64_t CharMaskForWord(char x, uint64_t word) {
+    const auto unsigned_x = static_cast<unsigned char>(x);
+    return (unsigned_x / 64 == word)
+               ? (static_cast<uint64_t>(1) << (unsigned_x % 64))
+               : 0;
+  }
+
+  void SetChar(char c) {
+    const auto unsigned_c = static_cast<unsigned char>(c);
+    m_[unsigned_c / 64] |= static_cast<uint64_t>(1) << (unsigned_c % 64);
+  }
+
+  uint64_t m_[4];
+};
+
+// Mirror the char-classifying predicates in <cctype>
+constexpr Charmap UpperCharmap() { return Charmap::Range('A', 'Z'); }
+constexpr Charmap LowerCharmap() { return Charmap::Range('a', 'z'); }
+constexpr Charmap DigitCharmap() { return Charmap::Range('0', '9'); }
+constexpr Charmap AlphaCharmap() { return LowerCharmap() | UpperCharmap(); }
+constexpr Charmap AlnumCharmap() { return DigitCharmap() | AlphaCharmap(); }
+constexpr Charmap XDigitCharmap() {
+  return DigitCharmap() | Charmap::Range('A', 'F') | Charmap::Range('a', 'f');
+}
+constexpr Charmap PrintCharmap() { return Charmap::Range(0x20, 0x7e); }
+constexpr Charmap SpaceCharmap() { return Charmap::FromString("\t\n\v\f\r "); }
+constexpr Charmap CntrlCharmap() {
+  return Charmap::Range(0, 0x7f) & ~PrintCharmap();
+}
+constexpr Charmap BlankCharmap() { return Charmap::FromString("\t "); }
+constexpr Charmap GraphCharmap() { return PrintCharmap() & ~SpaceCharmap(); }
+constexpr Charmap PunctCharmap() { return GraphCharmap() & ~AlnumCharmap(); }
+
+}  // namespace strings_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CHAR_MAP_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/charconv_bigint.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/charconv_bigint.h
new file mode 100644
index 0000000000000000000000000000000000000000..8f702976a80d1e689cb911a3da66274f78513fba
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/charconv_bigint.h
@@ -0,0 +1,423 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CHARCONV_BIGINT_H_
+#define ABSL_STRINGS_INTERNAL_CHARCONV_BIGINT_H_
+
+#include <algorithm>
+#include <cstdint>
+#include <iostream>
+#include <string>
+
+#include "absl/base/config.h"
+#include "absl/strings/ascii.h"
+#include "absl/strings/internal/charconv_parse.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace strings_internal {
+
+// The largest power that 5 that can be raised to, and still fit in a uint32_t.
+constexpr int kMaxSmallPowerOfFive = 13;
+// The largest power that 10 that can be raised to, and still fit in a uint32_t.
+constexpr int kMaxSmallPowerOfTen = 9;
+
+ABSL_DLL extern const uint32_t
+    kFiveToNth[kMaxSmallPowerOfFive + 1];
+ABSL_DLL extern const uint32_t kTenToNth[kMaxSmallPowerOfTen + 1];
+
+// Large, fixed-width unsigned integer.
+//
+// Exact rounding for decimal-to-binary floating point conversion requires very
+// large integer math, but a design goal of absl::from_chars is to avoid
+// allocating memory.  The integer precision needed for decimal-to-binary
+// conversions is large but bounded, so a huge fixed-width integer class
+// suffices.
+//
+// This is an intentionally limited big integer class.  Only needed operations
+// are implemented.  All storage lives in an array data member, and all
+// arithmetic is done in-place, to avoid requiring separate storage for operand
+// and result.
+//
+// This is an internal class.  Some methods live in the .cc file, and are
+// instantiated only for the values of max_words we need.
+template <int max_words>
+class BigUnsigned {
+ public:
+  static_assert(max_words == 4 || max_words == 84,
+                "unsupported max_words value");
+
+  BigUnsigned() : size_(0), words_{} {}
+  explicit constexpr BigUnsigned(uint64_t v)
+      : size_((v >> 32) ? 2 : v ? 1 : 0),
+        words_{static_cast<uint32_t>(v & 0xffffffffu),
+               static_cast<uint32_t>(v >> 32)} {}
+
+  // Constructs a BigUnsigned from the given string_view containing a decimal
+  // value.  If the input string is not a decimal integer, constructs a 0
+  // instead.
+  explicit BigUnsigned(absl::string_view sv) : size_(0), words_{} {
+    // Check for valid input, returning a 0 otherwise.  This is reasonable
+    // behavior only because this constructor is for unit tests.
+    if (std::find_if_not(sv.begin(), sv.end(), ascii_isdigit) != sv.end() ||
+        sv.empty()) {
+      return;
+    }
+    int exponent_adjust =
+        ReadDigits(sv.data(), sv.data() + sv.size(), Digits10() + 1);
+    if (exponent_adjust > 0) {
+      MultiplyByTenToTheNth(exponent_adjust);
+    }
+  }
+
+  // Loads the mantissa value of a previously-parsed float.
+  //
+  // Returns the associated decimal exponent.  The value of the parsed float is
+  // exactly *this * 10**exponent.
+  int ReadFloatMantissa(const ParsedFloat& fp, int significant_digits);
+
+  // Returns the number of decimal digits of precision this type provides.  All
+  // numbers with this many decimal digits or fewer are representable by this
+  // type.
+  //
+  // Analagous to std::numeric_limits<BigUnsigned>::digits10.
+  static constexpr int Digits10() {
+    // 9975007/1035508 is very slightly less than log10(2**32).
+    return static_cast<uint64_t>(max_words) * 9975007 / 1035508;
+  }
+
+  // Shifts left by the given number of bits.
+  void ShiftLeft(int count) {
+    if (count > 0) {
+      const int word_shift = count / 32;
+      if (word_shift >= max_words) {
+        SetToZero();
+        return;
+      }
+      size_ = (std::min)(size_ + word_shift, max_words);
+      count %= 32;
+      if (count == 0) {
+        std::copy_backward(words_, words_ + size_ - word_shift, words_ + size_);
+      } else {
+        for (int i = (std::min)(size_, max_words - 1); i > word_shift; --i) {
+          words_[i] = (words_[i - word_shift] << count) |
+                      (words_[i - word_shift - 1] >> (32 - count));
+        }
+        words_[word_shift] = words_[0] << count;
+        // Grow size_ if necessary.
+        if (size_ < max_words && words_[size_]) {
+          ++size_;
+        }
+      }
+      std::fill(words_, words_ + word_shift, 0u);
+    }
+  }
+
+
+  // Multiplies by v in-place.
+  void MultiplyBy(uint32_t v) {
+    if (size_ == 0 || v == 1) {
+      return;
+    }
+    if (v == 0) {
+      SetToZero();
+      return;
+    }
+    const uint64_t factor = v;
+    uint64_t window = 0;
+    for (int i = 0; i < size_; ++i) {
+      window += factor * words_[i];
+      words_[i] = window & 0xffffffff;
+      window >>= 32;
+    }
+    // If carry bits remain and there's space for them, grow size_.
+    if (window && size_ < max_words) {
+      words_[size_] = window & 0xffffffff;
+      ++size_;
+    }
+  }
+
+  void MultiplyBy(uint64_t v) {
+    uint32_t words[2];
+    words[0] = static_cast<uint32_t>(v);
+    words[1] = static_cast<uint32_t>(v >> 32);
+    if (words[1] == 0) {
+      MultiplyBy(words[0]);
+    } else {
+      MultiplyBy(2, words);
+    }
+  }
+
+  // Multiplies in place by 5 to the power of n.  n must be non-negative.
+  void MultiplyByFiveToTheNth(int n) {
+    while (n >= kMaxSmallPowerOfFive) {
+      MultiplyBy(kFiveToNth[kMaxSmallPowerOfFive]);
+      n -= kMaxSmallPowerOfFive;
+    }
+    if (n > 0) {
+      MultiplyBy(kFiveToNth[n]);
+    }
+  }
+
+  // Multiplies in place by 10 to the power of n.  n must be non-negative.
+  void MultiplyByTenToTheNth(int n) {
+    if (n > kMaxSmallPowerOfTen) {
+      // For large n, raise to a power of 5, then shift left by the same amount.
+      // (10**n == 5**n * 2**n.)  This requires fewer multiplications overall.
+      MultiplyByFiveToTheNth(n);
+      ShiftLeft(n);
+    } else if (n > 0) {
+      // We can do this more quickly for very small N by using a single
+      // multiplication.
+      MultiplyBy(kTenToNth[n]);
+    }
+  }
+
+  // Returns the value of 5**n, for non-negative n.  This implementation uses
+  // a lookup table, and is faster then seeding a BigUnsigned with 1 and calling
+  // MultiplyByFiveToTheNth().
+  static BigUnsigned FiveToTheNth(int n);
+
+  // Multiplies by another BigUnsigned, in-place.
+  template <int M>
+  void MultiplyBy(const BigUnsigned<M>& other) {
+    MultiplyBy(other.size(), other.words());
+  }
+
+  void SetToZero() {
+    std::fill(words_, words_ + size_, 0u);
+    size_ = 0;
+  }
+
+  // Returns the value of the nth word of this BigUnsigned.  This is
+  // range-checked, and returns 0 on out-of-bounds accesses.
+  uint32_t GetWord(int index) const {
+    if (index < 0 || index >= size_) {
+      return 0;
+    }
+    return words_[index];
+  }
+
+  // Returns this integer as a decimal string.  This is not used in the decimal-
+  // to-binary conversion; it is intended to aid in testing.
+  std::string ToString() const;
+
+  int size() const { return size_; }
+  const uint32_t* words() const { return words_; }
+
+ private:
+  // Reads the number between [begin, end), possibly containing a decimal point,
+  // into this BigUnsigned.
+  //
+  // Callers are required to ensure [begin, end) contains a valid number, with
+  // one or more decimal digits and at most one decimal point.  This routine
+  // will behave unpredictably if these preconditions are not met.
+  //
+  // Only the first `significant_digits` digits are read.  Digits beyond this
+  // limit are "sticky": If the final significant digit is 0 or 5, and if any
+  // dropped digit is nonzero, then that final significant digit is adjusted up
+  // to 1 or 6.  This adjustment allows for precise rounding.
+  //
+  // Returns `exponent_adjustment`, a power-of-ten exponent adjustment to
+  // account for the decimal point and for dropped significant digits.  After
+  // this function returns,
+  //   actual_value_of_parsed_string ~= *this * 10**exponent_adjustment.
+  int ReadDigits(const char* begin, const char* end, int significant_digits);
+
+  // Performs a step of big integer multiplication.  This computes the full
+  // (64-bit-wide) values that should be added at the given index (step), and
+  // adds to that location in-place.
+  //
+  // Because our math all occurs in place, we must multiply starting from the
+  // highest word working downward.  (This is a bit more expensive due to the
+  // extra carries involved.)
+  //
+  // This must be called in steps, for each word to be calculated, starting from
+  // the high end and working down to 0.  The first value of `step` should be
+  //   `std::min(original_size + other.size_ - 2, max_words - 1)`.
+  // The reason for this expression is that multiplying the i'th word from one
+  // multiplicand and the j'th word of another multiplicand creates a
+  // two-word-wide value to be stored at the (i+j)'th element.  The highest
+  // word indices we will access are `original_size - 1` from this object, and
+  // `other.size_ - 1` from our operand.  Therefore,
+  // `original_size + other.size_ - 2` is the first step we should calculate,
+  // but limited on an upper bound by max_words.
+
+  // Working from high-to-low ensures that we do not overwrite the portions of
+  // the initial value of *this which are still needed for later steps.
+  //
+  // Once called with step == 0, *this contains the result of the
+  // multiplication.
+  //
+  // `original_size` is the size_ of *this before the first call to
+  // MultiplyStep().  `other_words` and `other_size` are the contents of our
+  // operand.  `step` is the step to perform, as described above.
+  void MultiplyStep(int original_size, const uint32_t* other_words,
+                    int other_size, int step);
+
+  void MultiplyBy(int other_size, const uint32_t* other_words) {
+    const int original_size = size_;
+    const int first_step =
+        (std::min)(original_size + other_size - 2, max_words - 1);
+    for (int step = first_step; step >= 0; --step) {
+      MultiplyStep(original_size, other_words, other_size, step);
+    }
+  }
+
+  // Adds a 32-bit value to the index'th word, with carry.
+  void AddWithCarry(int index, uint32_t value) {
+    if (value) {
+      while (index < max_words && value > 0) {
+        words_[index] += value;
+        // carry if we overflowed in this word:
+        if (value > words_[index]) {
+          value = 1;
+          ++index;
+        } else {
+          value = 0;
+        }
+      }
+      size_ = (std::min)(max_words, (std::max)(index + 1, size_));
+    }
+  }
+
+  void AddWithCarry(int index, uint64_t value) {
+    if (value && index < max_words) {
+      uint32_t high = value >> 32;
+      uint32_t low = value & 0xffffffff;
+      words_[index] += low;
+      if (words_[index] < low) {
+        ++high;
+        if (high == 0) {
+          // Carry from the low word caused our high word to overflow.
+          // Short circuit here to do the right thing.
+          AddWithCarry(index + 2, static_cast<uint32_t>(1));
+          return;
+        }
+      }
+      if (high > 0) {
+        AddWithCarry(index + 1, high);
+      } else {
+        // Normally 32-bit AddWithCarry() sets size_, but since we don't call
+        // it when `high` is 0, do it ourselves here.
+        size_ = (std::min)(max_words, (std::max)(index + 1, size_));
+      }
+    }
+  }
+
+  // Divide this in place by a constant divisor.  Returns the remainder of the
+  // division.
+  template <uint32_t divisor>
+  uint32_t DivMod() {
+    uint64_t accumulator = 0;
+    for (int i = size_ - 1; i >= 0; --i) {
+      accumulator <<= 32;
+      accumulator += words_[i];
+      // accumulator / divisor will never overflow an int32_t in this loop
+      words_[i] = static_cast<uint32_t>(accumulator / divisor);
+      accumulator = accumulator % divisor;
+    }
+    while (size_ > 0 && words_[size_ - 1] == 0) {
+      --size_;
+    }
+    return static_cast<uint32_t>(accumulator);
+  }
+
+  // The number of elements in words_ that may carry significant values.
+  // All elements beyond this point are 0.
+  //
+  // When size_ is 0, this BigUnsigned stores the value 0.
+  // When size_ is nonzero, is *not* guaranteed that words_[size_ - 1] is
+  // nonzero.  This can occur due to overflow truncation.
+  // In particular, x.size_ != y.size_ does *not* imply x != y.
+  int size_;
+  uint32_t words_[max_words];
+};
+
+// Compares two big integer instances.
+//
+// Returns -1 if lhs < rhs, 0 if lhs == rhs, and 1 if lhs > rhs.
+template <int N, int M>
+int Compare(const BigUnsigned<N>& lhs, const BigUnsigned<M>& rhs) {
+  int limit = (std::max)(lhs.size(), rhs.size());
+  for (int i = limit - 1; i >= 0; --i) {
+    const uint32_t lhs_word = lhs.GetWord(i);
+    const uint32_t rhs_word = rhs.GetWord(i);
+    if (lhs_word < rhs_word) {
+      return -1;
+    } else if (lhs_word > rhs_word) {
+      return 1;
+    }
+  }
+  return 0;
+}
+
+template <int N, int M>
+bool operator==(const BigUnsigned<N>& lhs, const BigUnsigned<M>& rhs) {
+  int limit = (std::max)(lhs.size(), rhs.size());
+  for (int i = 0; i < limit; ++i) {
+    if (lhs.GetWord(i) != rhs.GetWord(i)) {
+      return false;
+    }
+  }
+  return true;
+}
+
+template <int N, int M>
+bool operator!=(const BigUnsigned<N>& lhs, const BigUnsigned<M>& rhs) {
+  return !(lhs == rhs);
+}
+
+template <int N, int M>
+bool operator<(const BigUnsigned<N>& lhs, const BigUnsigned<M>& rhs) {
+  return Compare(lhs, rhs) == -1;
+}
+
+template <int N, int M>
+bool operator>(const BigUnsigned<N>& lhs, const BigUnsigned<M>& rhs) {
+  return rhs < lhs;
+}
+template <int N, int M>
+bool operator<=(const BigUnsigned<N>& lhs, const BigUnsigned<M>& rhs) {
+  return !(rhs < lhs);
+}
+template <int N, int M>
+bool operator>=(const BigUnsigned<N>& lhs, const BigUnsigned<M>& rhs) {
+  return !(lhs < rhs);
+}
+
+// Output operator for BigUnsigned, for testing purposes only.
+template <int N>
+std::ostream& operator<<(std::ostream& os, const BigUnsigned<N>& num) {
+  return os << num.ToString();
+}
+
+// Explicit instantiation declarations for the sizes of BigUnsigned that we
+// are using.
+//
+// For now, the choices of 4 and 84 are arbitrary; 4 is a small value that is
+// still bigger than an int128, and 84 is a large value we will want to use
+// in the from_chars implementation.
+//
+// Comments justifying the use of 84 belong in the from_chars implementation,
+// and will be added in a follow-up CL.
+extern template class BigUnsigned<4>;
+extern template class BigUnsigned<84>;
+
+}  // namespace strings_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CHARCONV_BIGINT_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/charconv_parse.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/charconv_parse.h
new file mode 100644
index 0000000000000000000000000000000000000000..505998b5394a8aafb7d50986b208a63cfa6e4db8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/charconv_parse.h
@@ -0,0 +1,99 @@
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CHARCONV_PARSE_H_
+#define ABSL_STRINGS_INTERNAL_CHARCONV_PARSE_H_
+
+#include <cstdint>
+
+#include "absl/base/config.h"
+#include "absl/strings/charconv.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace strings_internal {
+
+// Enum indicating whether a parsed float is a number or special value.
+enum class FloatType { kNumber, kInfinity, kNan };
+
+// The decomposed parts of a parsed `float` or `double`.
+struct ParsedFloat {
+  // Representation of the parsed mantissa, with the decimal point adjusted to
+  // make it an integer.
+  //
+  // During decimal scanning, this contains 19 significant digits worth of
+  // mantissa value.  If digits beyond this point are found, they
+  // are truncated, and if any of these dropped digits are nonzero, then
+  // `mantissa` is inexact, and the full mantissa is stored in [subrange_begin,
+  // subrange_end).
+  //
+  // During hexadecimal scanning, this contains 15 significant hex digits worth
+  // of mantissa value.  Digits beyond this point are sticky -- they are
+  // truncated, but if any dropped digits are nonzero, the low bit of mantissa
+  // will be set.  (This allows for precise rounding, and avoids the need
+  // to store the full mantissa in [subrange_begin, subrange_end).)
+  uint64_t mantissa = 0;
+
+  // Floating point expontent.  This reflects any decimal point adjustments and
+  // any truncated digits from the mantissa.  The absolute value of the parsed
+  // number is represented by mantissa * (base ** exponent), where base==10 for
+  // decimal floats, and base==2 for hexadecimal floats.
+  int exponent = 0;
+
+  // The literal exponent value scanned from the input, or 0 if none was
+  // present.  This does not reflect any adjustments applied to mantissa.
+  int literal_exponent = 0;
+
+  // The type of number scanned.
+  FloatType type = FloatType::kNumber;
+
+  // When non-null, [subrange_begin, subrange_end) marks a range of characters
+  // that require further processing.  The meaning is dependent on float type.
+  // If type == kNumber and this is set, this is a "wide input": the input
+  // mantissa contained more than 19 digits.  The range contains the full
+  // mantissa.  It plus `literal_exponent` need to be examined to find the best
+  // floating point match.
+  // If type == kNan and this is set, the range marks the contents of a
+  // matched parenthesized character region after the NaN.
+  const char* subrange_begin = nullptr;
+  const char* subrange_end = nullptr;
+
+  // One-past-the-end of the successfully parsed region, or nullptr if no
+  // matching pattern was found.
+  const char* end = nullptr;
+};
+
+// Read the floating point number in the provided range, and populate
+// ParsedFloat accordingly.
+//
+// format_flags is a bitmask value specifying what patterns this API will match.
+// `scientific` and `fixed`  are honored per std::from_chars rules
+// ([utility.from.chars], C++17): if exactly one of these bits is set, then an
+// exponent is required, or dislallowed, respectively.
+//
+// Template parameter `base` must be either 10 or 16.  For base 16, a "0x" is
+// *not* consumed.  The `hex` bit from format_flags is ignored by ParseFloat.
+template <int base>
+ParsedFloat ParseFloat(const char* begin, const char* end,
+                       absl::chars_format format_flags);
+
+extern template ParsedFloat ParseFloat<10>(const char* begin, const char* end,
+                                           absl::chars_format format_flags);
+extern template ParsedFloat ParseFloat<16>(const char* begin, const char* end,
+                                           absl::chars_format format_flags);
+
+}  // namespace strings_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+#endif  // ABSL_STRINGS_INTERNAL_CHARCONV_PARSE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_data_edge.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_data_edge.h
new file mode 100644
index 0000000000000000000000000000000000000000..e18b33e1b0fb6ff13f7087f42876e48f6a67e69d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_data_edge.h
@@ -0,0 +1,63 @@
+// Copyright 2022 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORD_DATA_EDGE_H_
+#define ABSL_STRINGS_INTERNAL_CORD_DATA_EDGE_H_
+
+#include <cassert>
+#include <cstddef>
+
+#include "absl/base/config.h"
+#include "absl/strings/internal/cord_internal.h"
+#include "absl/strings/internal/cord_rep_flat.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// Returns true if the provided rep is a FLAT, EXTERNAL or a SUBSTRING node
+// holding a FLAT or EXTERNAL child rep. Requires `rep != nullptr`.
+inline bool IsDataEdge(const CordRep* edge) {
+  assert(edge != nullptr);
+
+  // The fast path is that `edge` is an EXTERNAL or FLAT node, making the below
+  // if a single, well predicted branch. We then repeat the FLAT or EXTERNAL
+  // check in the slow path of the SUBSTRING check to optimize for the hot path.
+  if (edge->tag == EXTERNAL || edge->tag >= FLAT) return true;
+  if (edge->tag == SUBSTRING) edge = edge->substring()->child;
+  return edge->tag == EXTERNAL || edge->tag >= FLAT;
+}
+
+// Returns the `absl::string_view` data reference for the provided data edge.
+// Requires 'IsDataEdge(edge) == true`.
+inline absl::string_view EdgeData(const CordRep* edge) {
+  assert(IsDataEdge(edge));
+
+  size_t offset = 0;
+  const size_t length = edge->length;
+  if (edge->IsSubstring()) {
+    offset = edge->substring()->start;
+    edge = edge->substring()->child;
+  }
+  return edge->tag >= FLAT
+             ? absl::string_view{edge->flat()->Data() + offset, length}
+             : absl::string_view{edge->external()->base + offset, length};
+}
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CORD_DATA_EDGE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_internal.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_internal.h
new file mode 100644
index 0000000000000000000000000000000000000000..e6f0d544b6f9cd92fe55cf9c71842e9c36b5679c
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_internal.h
@@ -0,0 +1,915 @@
+// Copyright 2021 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORD_INTERNAL_H_
+#define ABSL_STRINGS_INTERNAL_CORD_INTERNAL_H_
+
+#include <atomic>
+#include <cassert>
+#include <cstddef>
+#include <cstdint>
+#include <type_traits>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/base/internal/endian.h"
+#include "absl/base/internal/invoke.h"
+#include "absl/base/optimization.h"
+#include "absl/container/internal/compressed_tuple.h"
+#include "absl/container/internal/container_memory.h"
+#include "absl/meta/type_traits.h"
+#include "absl/strings/string_view.h"
+
+// We can only add poisoning if we can detect consteval executions.
+#if defined(ABSL_HAVE_CONSTANT_EVALUATED) && \
+    (defined(ABSL_HAVE_ADDRESS_SANITIZER) || \
+     defined(ABSL_HAVE_MEMORY_SANITIZER))
+#define ABSL_INTERNAL_CORD_HAVE_SANITIZER 1
+#endif
+
+#define ABSL_CORD_INTERNAL_NO_SANITIZE \
+  ABSL_ATTRIBUTE_NO_SANITIZE_ADDRESS ABSL_ATTRIBUTE_NO_SANITIZE_MEMORY
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// The overhead of a vtable is too much for Cord, so we roll our own subclasses
+// using only a single byte to differentiate classes from each other - the "tag"
+// byte.  Define the subclasses first so we can provide downcasting helper
+// functions in the base class.
+struct CordRep;
+struct CordRepConcat;
+struct CordRepExternal;
+struct CordRepFlat;
+struct CordRepSubstring;
+struct CordRepCrc;
+class CordRepRing;
+class CordRepBtree;
+
+class CordzInfo;
+
+// Default feature enable states for cord ring buffers
+enum CordFeatureDefaults {
+  kCordEnableRingBufferDefault = false,
+  kCordShallowSubcordsDefault = false
+};
+
+extern std::atomic<bool> cord_ring_buffer_enabled;
+extern std::atomic<bool> shallow_subcords_enabled;
+
+// `cord_btree_exhaustive_validation` can be set to force exhaustive validation
+// in debug assertions, and code that calls `IsValid()` explicitly. By default,
+// assertions should be relatively cheap and AssertValid() can easily lead to
+// O(n^2) complexity as recursive / full tree validation is O(n).
+extern std::atomic<bool> cord_btree_exhaustive_validation;
+
+inline void enable_cord_ring_buffer(bool enable) {
+  cord_ring_buffer_enabled.store(enable, std::memory_order_relaxed);
+}
+
+inline void enable_shallow_subcords(bool enable) {
+  shallow_subcords_enabled.store(enable, std::memory_order_relaxed);
+}
+
+enum Constants {
+  // The inlined size to use with absl::InlinedVector.
+  //
+  // Note: The InlinedVectors in this file (and in cord.h) do not need to use
+  // the same value for their inlined size. The fact that they do is historical.
+  // It may be desirable for each to use a different inlined size optimized for
+  // that InlinedVector's usage.
+  //
+  // TODO(jgm): Benchmark to see if there's a more optimal value than 47 for
+  // the inlined vector size (47 exists for backward compatibility).
+  kInlinedVectorSize = 47,
+
+  // Prefer copying blocks of at most this size, otherwise reference count.
+  kMaxBytesToCopy = 511
+};
+
+// Emits a fatal error "Unexpected node type: xyz" and aborts the program.
+ABSL_ATTRIBUTE_NORETURN void LogFatalNodeType(CordRep* rep);
+
+// Fast implementation of memmove for up to 15 bytes. This implementation is
+// safe for overlapping regions. If nullify_tail is true, the destination is
+// padded with '\0' up to 15 bytes.
+template <bool nullify_tail = false>
+inline void SmallMemmove(char* dst, const char* src, size_t n) {
+  if (n >= 8) {
+    assert(n <= 15);
+    uint64_t buf1;
+    uint64_t buf2;
+    memcpy(&buf1, src, 8);
+    memcpy(&buf2, src + n - 8, 8);
+    if (nullify_tail) {
+      memset(dst + 7, 0, 8);
+    }
+    memcpy(dst, &buf1, 8);
+    memcpy(dst + n - 8, &buf2, 8);
+  } else if (n >= 4) {
+    uint32_t buf1;
+    uint32_t buf2;
+    memcpy(&buf1, src, 4);
+    memcpy(&buf2, src + n - 4, 4);
+    if (nullify_tail) {
+      memset(dst + 4, 0, 4);
+      memset(dst + 7, 0, 8);
+    }
+    memcpy(dst, &buf1, 4);
+    memcpy(dst + n - 4, &buf2, 4);
+  } else {
+    if (n != 0) {
+      dst[0] = src[0];
+      dst[n / 2] = src[n / 2];
+      dst[n - 1] = src[n - 1];
+    }
+    if (nullify_tail) {
+      memset(dst + 7, 0, 8);
+      memset(dst + n, 0, 8);
+    }
+  }
+}
+
+// Compact class for tracking the reference count and state flags for CordRep
+// instances.  Data is stored in an atomic int32_t for compactness and speed.
+class RefcountAndFlags {
+ public:
+  constexpr RefcountAndFlags() : count_{kRefIncrement} {}
+  struct Immortal {};
+  explicit constexpr RefcountAndFlags(Immortal) : count_(kImmortalFlag) {}
+
+  // Increments the reference count. Imposes no memory ordering.
+  inline void Increment() {
+    count_.fetch_add(kRefIncrement, std::memory_order_relaxed);
+  }
+
+  // Asserts that the current refcount is greater than 0. If the refcount is
+  // greater than 1, decrements the reference count.
+  //
+  // Returns false if there are no references outstanding; true otherwise.
+  // Inserts barriers to ensure that state written before this method returns
+  // false will be visible to a thread that just observed this method returning
+  // false.  Always returns false when the immortal bit is set.
+  inline bool Decrement() {
+    int32_t refcount = count_.load(std::memory_order_acquire) & kRefcountMask;
+    assert(refcount > 0 || refcount & kImmortalFlag);
+    return refcount != kRefIncrement &&
+           (count_.fetch_sub(kRefIncrement, std::memory_order_acq_rel) &
+            kRefcountMask) != kRefIncrement;
+  }
+
+  // Same as Decrement but expect that refcount is greater than 1.
+  inline bool DecrementExpectHighRefcount() {
+    int32_t refcount =
+        count_.fetch_sub(kRefIncrement, std::memory_order_acq_rel) &
+        kRefcountMask;
+    assert(refcount > 0 || refcount & kImmortalFlag);
+    return refcount != kRefIncrement;
+  }
+
+  // Returns the current reference count using acquire semantics.
+  inline size_t Get() const {
+    return static_cast<size_t>(count_.load(std::memory_order_acquire) >>
+                               kNumFlags);
+  }
+
+  // Returns whether the atomic integer is 1.
+  // If the reference count is used in the conventional way, a
+  // reference count of 1 implies that the current thread owns the
+  // reference and no other thread shares it.
+  // This call performs the test for a reference count of one, and
+  // performs the memory barrier needed for the owning thread
+  // to act on the object, knowing that it has exclusive access to the
+  // object.  Always returns false when the immortal bit is set.
+  inline bool IsOne() {
+    return (count_.load(std::memory_order_acquire) & kRefcountMask) ==
+           kRefIncrement;
+  }
+
+  bool IsImmortal() const {
+    return (count_.load(std::memory_order_relaxed) & kImmortalFlag) != 0;
+  }
+
+ private:
+  // We reserve the bottom bits for flags.
+  // kImmortalBit indicates that this entity should never be collected; it is
+  // used for the StringConstant constructor to avoid collecting immutable
+  // constant cords.
+  // kReservedFlag is reserved for future use.
+  enum Flags {
+    kNumFlags = 2,
+
+    kImmortalFlag = 0x1,
+    kReservedFlag = 0x2,
+    kRefIncrement = (1 << kNumFlags),
+
+    // Bitmask to use when checking refcount by equality.  This masks out
+    // all flags except kImmortalFlag, which is part of the refcount for
+    // purposes of equality.  (A refcount of 0 or 1 does not count as 0 or 1
+    // if the immortal bit is set.)
+    kRefcountMask = ~kReservedFlag,
+  };
+
+  std::atomic<int32_t> count_;
+};
+
+// Various representations that we allow
+enum CordRepKind {
+  UNUSED_0 = 0,
+  SUBSTRING = 1,
+  CRC = 2,
+  BTREE = 3,
+  RING = 4,
+  EXTERNAL = 5,
+
+  // We have different tags for different sized flat arrays,
+  // starting with FLAT, and limited to MAX_FLAT_TAG. The below values map to an
+  // allocated range of 32 bytes to 256 KB. The current granularity is:
+  // - 8 byte granularity for flat sizes in [32 - 512]
+  // - 64 byte granularity for flat sizes in (512 - 8KiB]
+  // - 4KiB byte granularity for flat sizes in (8KiB, 256 KiB]
+  // If a new tag is needed in the future, then 'FLAT' and 'MAX_FLAT_TAG' should
+  // be adjusted as well as the Tag <---> Size mapping logic so that FLAT still
+  // represents the minimum flat allocation size. (32 bytes as of now).
+  FLAT = 6,
+  MAX_FLAT_TAG = 248
+};
+
+// There are various locations where we want to check if some rep is a 'plain'
+// data edge, i.e. an external or flat rep. By having FLAT == EXTERNAL + 1, we
+// can perform this check in a single branch as 'tag >= EXTERNAL'
+// Likewise, we have some locations where we check for 'ring or external/flat',
+// so likewise align RING to EXTERNAL.
+// Note that we can leave this optimization to the compiler. The compiler will
+// DTRT when it sees a condition like `tag == EXTERNAL || tag >= FLAT`.
+static_assert(RING == BTREE + 1, "BTREE and RING not consecutive");
+static_assert(EXTERNAL == RING + 1, "BTREE and EXTERNAL not consecutive");
+static_assert(FLAT == EXTERNAL + 1, "EXTERNAL and FLAT not consecutive");
+
+struct CordRep {
+  // Result from an `extract edge` operation. Contains the (possibly changed)
+  // tree node as well as the extracted edge, or {tree, nullptr} if no edge
+  // could be extracted.
+  // On success, the returned `tree` value is null if `extracted` was the only
+  // data edge inside the tree, a data edge if there were only two data edges in
+  // the tree, or the (possibly new / smaller) remaining tree with the extracted
+  // data edge removed.
+  struct ExtractResult {
+    CordRep* tree;
+    CordRep* extracted;
+  };
+
+  CordRep() = default;
+  constexpr CordRep(RefcountAndFlags::Immortal immortal, size_t l)
+      : length(l), refcount(immortal), tag(EXTERNAL), storage{} {}
+
+  // The following three fields have to be less than 32 bytes since
+  // that is the smallest supported flat node size. Some code optimizations rely
+  // on the specific layout of these fields. Notably: the non-trivial field
+  // `refcount` being preceded by `length`, and being tailed by POD data
+  // members only.
+  // # LINT.IfChange
+  size_t length;
+  RefcountAndFlags refcount;
+  // If tag < FLAT, it represents CordRepKind and indicates the type of node.
+  // Otherwise, the node type is CordRepFlat and the tag is the encoded size.
+  uint8_t tag;
+
+  // `storage` provides two main purposes:
+  // - the starting point for FlatCordRep.Data() [flexible-array-member]
+  // - 3 bytes of additional storage for use by derived classes.
+  // The latter is used by CordrepConcat and CordRepBtree. CordRepConcat stores
+  // a 'depth' value in storage[0], and the (future) CordRepBtree class stores
+  // `height`, `begin` and `end` in the 3 entries. Otherwise we would need to
+  // allocate room for these in the derived class, as not all compilers reuse
+  // padding space from the base class (clang and gcc do, MSVC does not, etc)
+  uint8_t storage[3];
+  // # LINT.ThenChange(cord_rep_btree.h:copy_raw)
+
+  // Returns true if this instance's tag matches the requested type.
+  constexpr bool IsRing() const { return tag == RING; }
+  constexpr bool IsSubstring() const { return tag == SUBSTRING; }
+  constexpr bool IsCrc() const { return tag == CRC; }
+  constexpr bool IsExternal() const { return tag == EXTERNAL; }
+  constexpr bool IsFlat() const { return tag >= FLAT; }
+  constexpr bool IsBtree() const { return tag == BTREE; }
+
+  inline CordRepRing* ring();
+  inline const CordRepRing* ring() const;
+  inline CordRepSubstring* substring();
+  inline const CordRepSubstring* substring() const;
+  inline CordRepCrc* crc();
+  inline const CordRepCrc* crc() const;
+  inline CordRepExternal* external();
+  inline const CordRepExternal* external() const;
+  inline CordRepFlat* flat();
+  inline const CordRepFlat* flat() const;
+  inline CordRepBtree* btree();
+  inline const CordRepBtree* btree() const;
+
+  // --------------------------------------------------------------------
+  // Memory management
+
+  // Destroys the provided `rep`.
+  static void Destroy(CordRep* rep);
+
+  // Increments the reference count of `rep`.
+  // Requires `rep` to be a non-null pointer value.
+  static inline CordRep* Ref(CordRep* rep);
+
+  // Decrements the reference count of `rep`. Destroys rep if count reaches
+  // zero. Requires `rep` to be a non-null pointer value.
+  static inline void Unref(CordRep* rep);
+};
+
+struct CordRepSubstring : public CordRep {
+  size_t start;  // Starting offset of substring in child
+  CordRep* child;
+
+  // Creates a substring on `child`, adopting a reference on `child`.
+  // Requires `child` to be either a flat or external node, and `pos` and `n` to
+  // form a non-empty partial sub range of `'child`, i.e.:
+  // `n > 0 && n < length && n + pos <= length`
+  static inline CordRepSubstring* Create(CordRep* child, size_t pos, size_t n);
+
+  // Creates a substring of `rep`. Does not adopt a reference on `rep`.
+  // Requires `IsDataEdge(rep) && n > 0 && pos + n <= rep->length`.
+  // If `n == rep->length` then this method returns `CordRep::Ref(rep)`
+  // If `rep` is a substring of a flat or external node, then this method will
+  // return a new substring of that flat or external node with `pos` adjusted
+  // with the original `start` position.
+  static inline CordRep* Substring(CordRep* rep, size_t pos, size_t n);
+};
+
+// Type for function pointer that will invoke the releaser function and also
+// delete the `CordRepExternalImpl` corresponding to the passed in
+// `CordRepExternal`.
+using ExternalReleaserInvoker = void (*)(CordRepExternal*);
+
+// External CordReps are allocated together with a type erased releaser. The
+// releaser is stored in the memory directly following the CordRepExternal.
+struct CordRepExternal : public CordRep {
+  CordRepExternal() = default;
+  explicit constexpr CordRepExternal(absl::string_view str)
+      : CordRep(RefcountAndFlags::Immortal{}, str.size()),
+        base(str.data()),
+        releaser_invoker(nullptr) {}
+
+  const char* base;
+  // Pointer to function that knows how to call and destroy the releaser.
+  ExternalReleaserInvoker releaser_invoker;
+
+  // Deletes (releases) the external rep.
+  // Requires rep != nullptr and rep->IsExternal()
+  static void Delete(CordRep* rep);
+};
+
+struct Rank1 {};
+struct Rank0 : Rank1 {};
+
+template <typename Releaser, typename = ::absl::base_internal::invoke_result_t<
+                                 Releaser, absl::string_view>>
+void InvokeReleaser(Rank0, Releaser&& releaser, absl::string_view data) {
+  ::absl::base_internal::invoke(std::forward<Releaser>(releaser), data);
+}
+
+template <typename Releaser,
+          typename = ::absl::base_internal::invoke_result_t<Releaser>>
+void InvokeReleaser(Rank1, Releaser&& releaser, absl::string_view) {
+  ::absl::base_internal::invoke(std::forward<Releaser>(releaser));
+}
+
+// We use CompressedTuple so that we can benefit from EBCO.
+template <typename Releaser>
+struct CordRepExternalImpl
+    : public CordRepExternal,
+      public ::absl::container_internal::CompressedTuple<Releaser> {
+  // The extra int arg is so that we can avoid interfering with copy/move
+  // constructors while still benefitting from perfect forwarding.
+  template <typename T>
+  CordRepExternalImpl(T&& releaser, int)
+      : CordRepExternalImpl::CompressedTuple(std::forward<T>(releaser)) {
+    this->releaser_invoker = &Release;
+  }
+
+  ~CordRepExternalImpl() {
+    InvokeReleaser(Rank0{}, std::move(this->template get<0>()),
+                   absl::string_view(base, length));
+  }
+
+  static void Release(CordRepExternal* rep) {
+    delete static_cast<CordRepExternalImpl*>(rep);
+  }
+};
+
+inline CordRepSubstring* CordRepSubstring::Create(CordRep* child, size_t pos,
+                                                  size_t n) {
+  assert(child != nullptr);
+  assert(n > 0);
+  assert(n < child->length);
+  assert(pos < child->length);
+  assert(n <= child->length - pos);
+
+  // TODO(b/217376272): Harden internal logic.
+  // Move to strategical places inside the Cord logic and make this an assert.
+  if (ABSL_PREDICT_FALSE(!(child->IsExternal() || child->IsFlat()))) {
+    LogFatalNodeType(child);
+  }
+
+  CordRepSubstring* rep = new CordRepSubstring();
+  rep->length = n;
+  rep->tag = SUBSTRING;
+  rep->start = pos;
+  rep->child = child;
+  return rep;
+}
+
+inline CordRep* CordRepSubstring::Substring(CordRep* rep, size_t pos,
+                                            size_t n) {
+  assert(rep != nullptr);
+  assert(n != 0);
+  assert(pos < rep->length);
+  assert(n <= rep->length - pos);
+  if (n == rep->length) return CordRep::Ref(rep);
+  if (rep->IsSubstring()) {
+    pos += rep->substring()->start;
+    rep = rep->substring()->child;
+  }
+  CordRepSubstring* substr = new CordRepSubstring();
+  substr->length = n;
+  substr->tag = SUBSTRING;
+  substr->start = pos;
+  substr->child = CordRep::Ref(rep);
+  return substr;
+}
+
+inline void CordRepExternal::Delete(CordRep* rep) {
+  assert(rep != nullptr && rep->IsExternal());
+  auto* rep_external = static_cast<CordRepExternal*>(rep);
+  assert(rep_external->releaser_invoker != nullptr);
+  rep_external->releaser_invoker(rep_external);
+}
+
+template <typename Str>
+struct ConstInitExternalStorage {
+  ABSL_CONST_INIT static CordRepExternal value;
+};
+
+template <typename Str>
+ABSL_CONST_INIT CordRepExternal
+    ConstInitExternalStorage<Str>::value(Str::value);
+
+enum {
+  kMaxInline = 15,
+};
+
+constexpr char GetOrNull(absl::string_view data, size_t pos) {
+  return pos < data.size() ? data[pos] : '\0';
+}
+
+// We store cordz_info as 64 bit pointer value in little endian format. This
+// guarantees that the least significant byte of cordz_info matches the first
+// byte of the inline data representation in `data`, which holds the inlined
+// size or the 'is_tree' bit.
+using cordz_info_t = int64_t;
+
+// Assert that the `cordz_info` pointer value perfectly overlaps the last half
+// of `data` and can hold a pointer value.
+static_assert(sizeof(cordz_info_t) * 2 == kMaxInline + 1, "");
+static_assert(sizeof(cordz_info_t) >= sizeof(intptr_t), "");
+
+// LittleEndianByte() creates a little endian representation of 'value', i.e.:
+// a little endian value where the first byte in the host's representation
+// holds 'value`, with all other bytes being 0.
+static constexpr cordz_info_t LittleEndianByte(unsigned char value) {
+#if defined(ABSL_IS_BIG_ENDIAN)
+  return static_cast<cordz_info_t>(value) << ((sizeof(cordz_info_t) - 1) * 8);
+#else
+  return value;
+#endif
+}
+
+class InlineData {
+ public:
+  // DefaultInitType forces the use of the default initialization constructor.
+  enum DefaultInitType { kDefaultInit };
+
+  // kNullCordzInfo holds the little endian representation of intptr_t(1)
+  // This is the 'null' / initial value of 'cordz_info'. The null value
+  // is specifically big endian 1 as with 64-bit pointers, the last
+  // byte of cordz_info overlaps with the last byte holding the tag.
+  static constexpr cordz_info_t kNullCordzInfo = LittleEndianByte(1);
+
+  // kTagOffset contains the offset of the control byte / tag. This constant is
+  // intended mostly for debugging purposes: do not remove this constant as it
+  // is actively inspected and used by gdb pretty printing code.
+  static constexpr size_t kTagOffset = 0;
+
+  // Implement `~InlineData()` conditionally: we only need this destructor to
+  // unpoison poisoned instances under *SAN, and it will only compile correctly
+  // if the current compiler supports `absl::is_constant_evaluated()`.
+#ifdef ABSL_INTERNAL_CORD_HAVE_SANITIZER
+  ~InlineData() noexcept { unpoison(); }
+#endif
+
+  constexpr InlineData() noexcept { poison_this(); }
+
+  explicit InlineData(DefaultInitType) noexcept : rep_(kDefaultInit) {
+    poison_this();
+  }
+
+  explicit InlineData(CordRep* rep) noexcept : rep_(rep) {
+    ABSL_ASSERT(rep != nullptr);
+  }
+
+  // Explicit constexpr constructor to create a constexpr InlineData
+  // value. Creates an inlined SSO value if `rep` is null, otherwise
+  // creates a tree instance value.
+  constexpr InlineData(absl::string_view sv, CordRep* rep) noexcept
+      : rep_(rep ? Rep(rep) : Rep(sv)) {
+    poison();
+  }
+
+  constexpr InlineData(const InlineData& rhs) noexcept;
+  InlineData& operator=(const InlineData& rhs) noexcept;
+
+  friend bool operator==(const InlineData& lhs, const InlineData& rhs) {
+#ifdef ABSL_INTERNAL_CORD_HAVE_SANITIZER
+    const Rep l = lhs.rep_.SanitizerSafeCopy();
+    const Rep r = rhs.rep_.SanitizerSafeCopy();
+    return memcmp(&l, &r, sizeof(l)) == 0;
+#else
+    return memcmp(&lhs, &rhs, sizeof(lhs)) == 0;
+#endif
+  }
+  friend bool operator!=(const InlineData& lhs, const InlineData& rhs) {
+    return !operator==(lhs, rhs);
+  }
+
+  // Poisons the unused inlined SSO data if the current instance
+  // is inlined, else un-poisons the entire instance.
+  constexpr void poison();
+
+  // Un-poisons this instance.
+  constexpr void unpoison();
+
+  // Poisons the current instance. This is used on default initialization.
+  constexpr void poison_this();
+
+  // Returns true if the current instance is empty.
+  // The 'empty value' is an inlined data value of zero length.
+  bool is_empty() const { return rep_.tag() == 0; }
+
+  // Returns true if the current instance holds a tree value.
+  bool is_tree() const { return (rep_.tag() & 1) != 0; }
+
+  // Returns true if the current instance holds a cordz_info value.
+  // Requires the current instance to hold a tree value.
+  bool is_profiled() const {
+    assert(is_tree());
+    return rep_.cordz_info() != kNullCordzInfo;
+  }
+
+  // Returns true if either of the provided instances hold a cordz_info value.
+  // This method is more efficient than the equivalent `data1.is_profiled() ||
+  // data2.is_profiled()`. Requires both arguments to hold a tree.
+  static bool is_either_profiled(const InlineData& data1,
+                                 const InlineData& data2) {
+    assert(data1.is_tree() && data2.is_tree());
+    return (data1.rep_.cordz_info() | data2.rep_.cordz_info()) !=
+           kNullCordzInfo;
+  }
+
+  // Returns the cordz_info sampling instance for this instance, or nullptr
+  // if the current instance is not sampled and does not have CordzInfo data.
+  // Requires the current instance to hold a tree value.
+  CordzInfo* cordz_info() const {
+    assert(is_tree());
+    intptr_t info = static_cast<intptr_t>(absl::little_endian::ToHost64(
+        static_cast<uint64_t>(rep_.cordz_info())));
+    assert(info & 1);
+    return reinterpret_cast<CordzInfo*>(info - 1);
+  }
+
+  // Sets the current cordz_info sampling instance for this instance, or nullptr
+  // if the current instance is not sampled and does not have CordzInfo data.
+  // Requires the current instance to hold a tree value.
+  void set_cordz_info(CordzInfo* cordz_info) {
+    assert(is_tree());
+    uintptr_t info = reinterpret_cast<uintptr_t>(cordz_info) | 1;
+    rep_.set_cordz_info(
+        static_cast<cordz_info_t>(absl::little_endian::FromHost64(info)));
+  }
+
+  // Resets the current cordz_info to null / empty.
+  void clear_cordz_info() {
+    assert(is_tree());
+    rep_.set_cordz_info(kNullCordzInfo);
+  }
+
+  // Returns a read only pointer to the character data inside this instance.
+  // Requires the current instance to hold inline data.
+  const char* as_chars() const {
+    assert(!is_tree());
+    return rep_.as_chars();
+  }
+
+  // Returns a mutable pointer to the character data inside this instance.
+  // Should be used for 'write only' operations setting an inlined value.
+  // Applications can set the value of inlined data either before or after
+  // setting the inlined size, i.e., both of the below are valid:
+  //
+  //   // Set inlined data and inline size
+  //   memcpy(data_.as_chars(), data, size);
+  //   data_.set_inline_size(size);
+  //
+  //   // Set inlined size and inline data
+  //   data_.set_inline_size(size);
+  //   memcpy(data_.as_chars(), data, size);
+  //
+  // It's an error to read from the returned pointer without a preceding write
+  // if the current instance does not hold inline data, i.e.: is_tree() == true.
+  char* as_chars() { return rep_.as_chars(); }
+
+  // Returns the tree value of this value.
+  // Requires the current instance to hold a tree value.
+  CordRep* as_tree() const {
+    assert(is_tree());
+    return rep_.tree();
+  }
+
+  void set_inline_data(const char* data, size_t n) {
+    ABSL_ASSERT(n <= kMaxInline);
+    unpoison();
+    rep_.set_tag(static_cast<int8_t>(n << 1));
+    SmallMemmove<true>(rep_.as_chars(), data, n);
+    poison();
+  }
+
+  void copy_max_inline_to(char* dst) const {
+    assert(!is_tree());
+    memcpy(dst, rep_.SanitizerSafeCopy().as_chars(), kMaxInline);
+  }
+
+  // Initialize this instance to holding the tree value `rep`,
+  // initializing the cordz_info to null, i.e.: 'not profiled'.
+  void make_tree(CordRep* rep) {
+    unpoison();
+    rep_.make_tree(rep);
+  }
+
+  // Set the tree value of this instance to 'rep`.
+  // Requires the current instance to already hold a tree value.
+  // Does not affect the value of cordz_info.
+  void set_tree(CordRep* rep) {
+    assert(is_tree());
+    rep_.set_tree(rep);
+  }
+
+  // Returns the size of the inlined character data inside this instance.
+  // Requires the current instance to hold inline data.
+  size_t inline_size() const { return rep_.inline_size(); }
+
+  // Sets the size of the inlined character data inside this instance.
+  // Requires `size` to be <= kMaxInline.
+  // See the documentation on 'as_chars()' for more information and examples.
+  void set_inline_size(size_t size) {
+    unpoison();
+    rep_.set_inline_size(size);
+    poison();
+  }
+
+  // Compares 'this' inlined data  with rhs. The comparison is a straightforward
+  // lexicographic comparison. `Compare()` returns values as follows:
+  //
+  //   -1  'this' InlineData instance is smaller
+  //    0  the InlineData instances are equal
+  //    1  'this' InlineData instance larger
+  int Compare(const InlineData& rhs) const {
+    return Compare(rep_.SanitizerSafeCopy(), rhs.rep_.SanitizerSafeCopy());
+  }
+
+ private:
+  struct Rep {
+    // See cordz_info_t for forced alignment and size of `cordz_info` details.
+    struct AsTree {
+      explicit constexpr AsTree(absl::cord_internal::CordRep* tree)
+          : rep(tree) {}
+      cordz_info_t cordz_info = kNullCordzInfo;
+      absl::cord_internal::CordRep* rep;
+    };
+
+    explicit Rep(DefaultInitType) {}
+    constexpr Rep() : data{0} {}
+    constexpr Rep(const Rep&) = default;
+    constexpr Rep& operator=(const Rep&) = default;
+
+    explicit constexpr Rep(CordRep* rep) : as_tree(rep) {}
+
+    explicit constexpr Rep(absl::string_view chars)
+        : data{static_cast<char>((chars.size() << 1)),
+               GetOrNull(chars, 0),
+               GetOrNull(chars, 1),
+               GetOrNull(chars, 2),
+               GetOrNull(chars, 3),
+               GetOrNull(chars, 4),
+               GetOrNull(chars, 5),
+               GetOrNull(chars, 6),
+               GetOrNull(chars, 7),
+               GetOrNull(chars, 8),
+               GetOrNull(chars, 9),
+               GetOrNull(chars, 10),
+               GetOrNull(chars, 11),
+               GetOrNull(chars, 12),
+               GetOrNull(chars, 13),
+               GetOrNull(chars, 14)} {}
+
+    // Disable sanitizer as we must always be able to read `tag`.
+    ABSL_CORD_INTERNAL_NO_SANITIZE
+    int8_t tag() const { return reinterpret_cast<const int8_t*>(this)[0]; }
+    void set_tag(int8_t rhs) { reinterpret_cast<int8_t*>(this)[0] = rhs; }
+
+    char* as_chars() { return data + 1; }
+    const char* as_chars() const { return data + 1; }
+
+    bool is_tree() const { return (tag() & 1) != 0; }
+
+    size_t inline_size() const {
+      ABSL_ASSERT(!is_tree());
+      return static_cast<size_t>(tag()) >> 1;
+    }
+
+    void set_inline_size(size_t size) {
+      ABSL_ASSERT(size <= kMaxInline);
+      set_tag(static_cast<int8_t>(size << 1));
+    }
+
+    CordRep* tree() const { return as_tree.rep; }
+    void set_tree(CordRep* rhs) { as_tree.rep = rhs; }
+
+    cordz_info_t cordz_info() const { return as_tree.cordz_info; }
+    void set_cordz_info(cordz_info_t rhs) { as_tree.cordz_info = rhs; }
+
+    void make_tree(CordRep* tree) {
+      as_tree.rep = tree;
+      as_tree.cordz_info = kNullCordzInfo;
+    }
+
+#ifdef ABSL_INTERNAL_CORD_HAVE_SANITIZER
+    constexpr Rep SanitizerSafeCopy() const {
+      if (!absl::is_constant_evaluated()) {
+        Rep res;
+        if (is_tree()) {
+          res = *this;
+        } else {
+          res.set_tag(tag());
+          memcpy(res.as_chars(), as_chars(), inline_size());
+        }
+        return res;
+      } else {
+        return *this;
+      }
+    }
+#else
+    constexpr const Rep& SanitizerSafeCopy() const { return *this; }
+#endif
+
+    // If the data has length <= kMaxInline, we store it in `data`, and
+    // store the size in the first char of `data` shifted left + 1.
+    // Else we store it in a tree and store a pointer to that tree in
+    // `as_tree.rep` with a tagged pointer to make `tag() & 1` non zero.
+    union {
+      char data[kMaxInline + 1];
+      AsTree as_tree;
+    };
+  };
+
+  // Private implementation of `Compare()`
+  static inline int Compare(const Rep& lhs, const Rep& rhs) {
+    uint64_t x, y;
+    memcpy(&x, lhs.as_chars(), sizeof(x));
+    memcpy(&y, rhs.as_chars(), sizeof(y));
+    if (x == y) {
+      memcpy(&x, lhs.as_chars() + 7, sizeof(x));
+      memcpy(&y, rhs.as_chars() + 7, sizeof(y));
+      if (x == y) {
+        if (lhs.inline_size() == rhs.inline_size()) return 0;
+        return lhs.inline_size() < rhs.inline_size() ? -1 : 1;
+      }
+    }
+    x = absl::big_endian::FromHost64(x);
+    y = absl::big_endian::FromHost64(y);
+    return x < y ? -1 : 1;
+  }
+
+  Rep rep_;
+};
+
+static_assert(sizeof(InlineData) == kMaxInline + 1, "");
+
+#ifdef ABSL_INTERNAL_CORD_HAVE_SANITIZER
+
+constexpr InlineData::InlineData(const InlineData& rhs) noexcept
+    : rep_(rhs.rep_.SanitizerSafeCopy()) {
+  poison();
+}
+
+inline InlineData& InlineData::operator=(const InlineData& rhs) noexcept {
+  unpoison();
+  rep_ = rhs.rep_.SanitizerSafeCopy();
+  poison();
+  return *this;
+}
+
+constexpr void InlineData::poison_this() {
+  if (!absl::is_constant_evaluated()) {
+    container_internal::SanitizerPoisonObject(this);
+  }
+}
+
+constexpr void InlineData::unpoison() {
+  if (!absl::is_constant_evaluated()) {
+    container_internal::SanitizerUnpoisonObject(this);
+  }
+}
+
+constexpr void InlineData::poison() {
+  if (!absl::is_constant_evaluated()) {
+    if (is_tree()) {
+      container_internal::SanitizerUnpoisonObject(this);
+    } else if (const size_t size = inline_size()) {
+      if (size < kMaxInline) {
+        const char* end = rep_.as_chars() + size;
+        container_internal::SanitizerPoisonMemoryRegion(end, kMaxInline - size);
+      }
+    } else {
+      container_internal::SanitizerPoisonObject(this);
+    }
+  }
+}
+
+#else  // ABSL_INTERNAL_CORD_HAVE_SANITIZER
+
+constexpr InlineData::InlineData(const InlineData&) noexcept = default;
+inline InlineData& InlineData::operator=(const InlineData&) noexcept = default;
+
+constexpr void InlineData::poison_this() {}
+constexpr void InlineData::unpoison() {}
+constexpr void InlineData::poison() {}
+
+#endif  // ABSL_INTERNAL_CORD_HAVE_SANITIZER
+
+inline CordRepSubstring* CordRep::substring() {
+  assert(IsSubstring());
+  return static_cast<CordRepSubstring*>(this);
+}
+
+inline const CordRepSubstring* CordRep::substring() const {
+  assert(IsSubstring());
+  return static_cast<const CordRepSubstring*>(this);
+}
+
+inline CordRepExternal* CordRep::external() {
+  assert(IsExternal());
+  return static_cast<CordRepExternal*>(this);
+}
+
+inline const CordRepExternal* CordRep::external() const {
+  assert(IsExternal());
+  return static_cast<const CordRepExternal*>(this);
+}
+
+inline CordRep* CordRep::Ref(CordRep* rep) {
+  // ABSL_ASSUME is a workaround for
+  // https://gcc.gnu.org/bugzilla/show_bug.cgi?id=105585
+  ABSL_ASSUME(rep != nullptr);
+  rep->refcount.Increment();
+  return rep;
+}
+
+inline void CordRep::Unref(CordRep* rep) {
+  assert(rep != nullptr);
+  // Expect refcount to be 0. Avoiding the cost of an atomic decrement should
+  // typically outweigh the cost of an extra branch checking for ref == 1.
+  if (ABSL_PREDICT_FALSE(!rep->refcount.DecrementExpectHighRefcount())) {
+    Destroy(rep);
+  }
+}
+
+}  // namespace cord_internal
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+#endif  // ABSL_STRINGS_INTERNAL_CORD_INTERNAL_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_btree.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_btree.h
new file mode 100644
index 0000000000000000000000000000000000000000..4209e51256b699df36fab6a168ea7a14b7498f19
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_btree.h
@@ -0,0 +1,936 @@
+// Copyright 2021 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORD_REP_BTREE_H_
+#define ABSL_STRINGS_INTERNAL_CORD_REP_BTREE_H_
+
+#include <cassert>
+#include <cstdint>
+#include <iosfwd>
+
+#include "absl/base/config.h"
+#include "absl/base/internal/raw_logging.h"
+#include "absl/base/optimization.h"
+#include "absl/strings/internal/cord_data_edge.h"
+#include "absl/strings/internal/cord_internal.h"
+#include "absl/strings/internal/cord_rep_flat.h"
+#include "absl/strings/string_view.h"
+#include "absl/types/span.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+class CordRepBtreeNavigator;
+
+// CordRepBtree is as the name implies a btree implementation of a Cordrep tree.
+// Data is stored at the leaf level only, non leaf nodes contain down pointers
+// only. Allowed types of data edges are FLAT, EXTERNAL and SUBSTRINGs of FLAT
+// or EXTERNAL nodes. The implementation allows for data to be added to either
+// end of the tree only, it does not provide any 'insert' logic. This has the
+// benefit that we can expect good fill ratios: all nodes except the outer
+// 'legs' will have 100% fill ratios for trees built using Append/Prepend
+// methods. Merged trees will typically have a fill ratio well above 50% as in a
+// similar fashion, one side of the merged tree will typically have a 100% fill
+// ratio, and the 'open' end will average 50%. All operations are O(log(n)) or
+// better, and the tree never needs balancing.
+//
+// All methods accepting a CordRep* or CordRepBtree* adopt a reference on that
+// input unless explicitly stated otherwise. All functions returning a CordRep*
+// or CordRepBtree* instance transfer a reference back to the caller.
+// Simplified, callers both 'donate' and 'consume' a reference count on each
+// call, simplifying the API. An example of building a tree:
+//
+//   CordRepBtree* tree = CordRepBtree::Create(MakeFlat("Hello"));
+//   tree = CordRepBtree::Append(tree, MakeFlat("world"));
+//
+// In the above example, all inputs are consumed, making each call affecting
+// `tree` reference count neutral. The returned `tree` value can be different
+// from the input if the input is shared with other threads, or if the tree
+// grows in height, but callers typically never have to concern themselves with
+// that and trust that all methods DTRT at all times.
+class CordRepBtree : public CordRep {
+ public:
+  // EdgeType identifies `front` and `back` enum values.
+  // Various implementations in CordRepBtree such as `Add` and `Edge` are
+  // generic and templated on operating on either of the boundary edges.
+  // For more information on the possible edges contained in a CordRepBtree
+  // instance see the documentation for `edges_`.
+  enum class EdgeType { kFront, kBack };
+
+  // Convenience constants into `EdgeType`
+  static constexpr EdgeType kFront = EdgeType::kFront;
+  static constexpr EdgeType kBack = EdgeType::kBack;
+
+  // Maximum number of edges: based on experiments and performance data, we can
+  // pick suitable values resulting in optimum cacheline aligned values. The
+  // preferred values are based on 64-bit systems where we aim to align this
+  // class onto 64 bytes, i.e.:  6 = 64 bytes, 14 = 128 bytes, etc.
+  // TODO(b/192061034): experiment with alternative sizes.
+  static constexpr size_t kMaxCapacity = 6;
+
+  // Reasonable maximum height of the btree. We can expect a fill ratio of at
+  // least 50%: trees are always expanded at the front or back. Concatenating
+  // trees will then typically fold at the top most node, where the lower nodes
+  // are at least at capacity on one side of joined inputs. At a lower fill
+  // rate of 4 edges per node, we have capacity for ~16 million leaf nodes.
+  // We will fail / abort if an application ever exceeds this height, which
+  // should be extremely rare (near impossible) and be an indication of an
+  // application error: we do not assume it reasonable for any application to
+  // operate correctly with such monster trees.
+  // Another compelling reason for the number `12` is that any contextual stack
+  // required for navigation or insertion requires 12 words and 12 bytes, which
+  // fits inside 2 cache lines with some room to spare, and is reasonable as a
+  // local stack variable compared to Cord's current near 400 bytes stack use.
+  // The maximum `height` value of a node is then `kMaxDepth - 1` as node height
+  // values start with a value of 0 for leaf nodes.
+  static constexpr size_t kMaxDepth = 12;
+  // See comments on height() for why this is an int and not a size_t.
+  static constexpr int kMaxHeight = static_cast<int>(kMaxDepth - 1);
+
+  // `Action` defines the action for unwinding changes done at the btree's leaf
+  // level that need to be propagated up to the parent node(s). Each operation
+  // on a node has an effect / action defined as follows:
+  // - kSelf
+  //   The operation (add / update, etc) was performed directly on the node as
+  //   the node is private to the current thread (i.e.: not shared directly or
+  //   indirectly through a refcount > 1). Changes can be propagated directly to
+  //   all parent nodes as all parent nodes are also then private to the current
+  //   thread.
+  // - kCopied
+  //   The operation (add / update, etc) was performed on a copy of the original
+  //   node, as the node is (potentially) directly or indirectly shared with
+  //   other threads. Changes need to be propagated into the parent nodes where
+  //   the old down pointer must be unreffed and replaced with this new copy.
+  //   Such changes to parent nodes may themselves require a copy if the parent
+  //   node is also shared. A kCopied action can propagate all the way to the
+  //   top node where we then must unref the `tree` input provided by the
+  //   caller, and return the new copy.
+  // - kPopped
+  //   The operation (typically add) could not be satisfied due to insufficient
+  //   capacity in the targeted node, and a new 'leg' was created that needs to
+  //   be added into the parent node. For example, adding a FLAT inside a leaf
+  //   node that is at capacity will create a new leaf node containing that
+  //   FLAT, that needs to be 'popped' up the btree. Such 'pop' actions can
+  //   cascade up the tree if parent nodes are also at capacity. A 'Popped'
+  //   action propagating all the way to the top of the tree will result in
+  //   the tree becoming one level higher than the current tree through a final
+  //   `CordRepBtree::New(tree, popped)` call, resulting in a new top node
+  //   referencing the old tree and the new (fully popped upwards) 'leg'.
+  enum Action { kSelf, kCopied, kPopped };
+
+  // Result of an operation on a node. See the `Action` enum for details.
+  struct OpResult {
+    CordRepBtree* tree;
+    Action action;
+  };
+
+  // Return value of the CopyPrefix and CopySuffix methods which can
+  // return a node or data edge at any height inside the tree.
+  // A height of 0 defines the lowest (leaf) node, a height of -1 identifies
+  // `edge` as being a plain data node: EXTERNAL / FLAT or SUBSTRING thereof.
+  struct CopyResult {
+    CordRep* edge;
+    int height;
+  };
+
+  // Logical position inside a node:
+  // - index: index of the edge.
+  // - n: size or offset value depending on context.
+  struct Position {
+    size_t index;
+    size_t n;
+  };
+
+  // Creates a btree from the given input. Adopts a ref of `rep`.
+  // If the input `rep` is itself a btree, i.e., `IsBtree()`, then this
+  // function immediately returns `rep->btree()`. If the input is a valid data
+  // edge (see IsDataEdge()), then a new leaf node is returned containing `rep`
+  // as the sole data edge. Else, the input is assumed to be a (legacy) concat
+  // tree, and the input is consumed and transformed into a btree().
+  static CordRepBtree* Create(CordRep* rep);
+
+  // Destroys the provided tree. Should only be called by cord internal API's,
+  // typically after a ref_count.Decrement() on the last reference count.
+  static void Destroy(CordRepBtree* tree);
+
+  // Destruction
+  static void Delete(CordRepBtree* tree) { delete tree; }
+
+  // Use CordRep::Unref() as we overload for absl::Span<CordRep* const>.
+  using CordRep::Unref;
+
+  // Unrefs all edges in `edges` which are assumed to be 'likely one'.
+  static void Unref(absl::Span<CordRep* const> edges);
+
+  // Appends / Prepends an existing CordRep instance to this tree.
+  // The below methods accept three types of input:
+  // 1) `rep` is a data node (See `IsDataNode` for valid data edges).
+  // `rep` is appended or prepended to this tree 'as is'.
+  // 2) `rep` is a BTREE.
+  // `rep` is merged into `tree` respecting the Append/Prepend order.
+  // 3) `rep` is some other (legacy) type.
+  // `rep` is converted in place and added to `tree`
+  // Requires `tree` and `rep` to be not null.
+  static CordRepBtree* Append(CordRepBtree* tree, CordRep* rep);
+  static CordRepBtree* Prepend(CordRepBtree* tree, CordRep* rep);
+
+  // Append/Prepend the data in `data` to this tree.
+  // The `extra` parameter defines how much extra capacity should be allocated
+  // for any additional FLAT being allocated. This is an optimization hint from
+  // the caller. For example, a caller may need to add 2 string_views of data
+  // "abc" and "defghi" which are not consecutive. The caller can in this case
+  // invoke `AddData(tree, "abc", 6)`, and any newly added flat is allocated
+  // where possible with at least 6 bytes of extra capacity beyond `length`.
+  // This helps avoiding data getting fragmented over multiple flats.
+  // There is no limit on the size of `data`. If `data` can not be stored inside
+  // a single flat, then the function will iteratively add flats until all data
+  // has been consumed and appended or prepended to the tree.
+  static CordRepBtree* Append(CordRepBtree* tree, string_view data,
+                              size_t extra = 0);
+  static CordRepBtree* Prepend(CordRepBtree* tree, string_view data,
+                               size_t extra = 0);
+
+  // Returns a new tree, containing `n` bytes of data from this instance
+  // starting at offset `offset`. Where possible, the returned tree shares
+  // (re-uses) data edges and nodes with this instance to minimize the
+  // combined memory footprint of both trees.
+  // Requires `offset + n <= length`. Returns `nullptr` if `n` is zero.
+  CordRep* SubTree(size_t offset, size_t n);
+
+  // Removes `n` trailing bytes from `tree`, and returns the resulting tree
+  // or data edge. Returns `tree` if n is zero, and nullptr if n == length.
+  // This function is logically identical to:
+  //   result = tree->SubTree(0, tree->length - n);
+  //   Unref(tree);
+  //   return result;
+  // However, the actual implementation will as much as possible perform 'in
+  // place' modifications on the tree on all nodes and edges that are mutable.
+  // For example, in a fully privately owned tree with the last edge being a
+  // flat of length 12, RemoveSuffix(1) will simply set the length of that data
+  // edge to 11, and reduce the length of all nodes on the edge path by 1.
+  static CordRep* RemoveSuffix(CordRepBtree* tree, size_t n);
+
+  // Returns the character at the given offset.
+  char GetCharacter(size_t offset) const;
+
+  // Returns true if this node holds a single data edge, and if so, sets
+  // `fragment` to reference the contained data. `fragment` is an optional
+  // output parameter and allowed to be null.
+  bool IsFlat(absl::string_view* fragment) const;
+
+  // Returns true if the data of `n` bytes starting at offset `offset`
+  // is contained in a single data edge, and if so, sets fragment to reference
+  // the contained data. `fragment` is an optional output parameter and allowed
+  // to be null.
+  bool IsFlat(size_t offset, size_t n, absl::string_view* fragment) const;
+
+  // Returns a span (mutable range of bytes) of up to `size` bytes into the
+  // last FLAT data edge inside this tree under the following conditions:
+  // - none of the nodes down into the FLAT node are shared.
+  // - the last data edge in this tree is a non-shared FLAT.
+  // - the referenced FLAT has additional capacity available.
+  // If all these conditions are met, a non-empty span is returned, and the
+  // length of the flat node and involved tree nodes have been increased by
+  // `span.length()`. The caller is responsible for immediately assigning values
+  // to all uninitialized data reference by the returned span.
+  // Requires `this->refcount.IsOne()`: this function forces the caller to do
+  // this fast path check on the top level node, as this is the most commonly
+  // shared node of a cord tree.
+  Span<char> GetAppendBuffer(size_t size);
+
+  // Extracts the right-most data edge from this tree iff:
+  // - the tree and all internal edges to the right-most node are not shared.
+  // - the right-most node is a FLAT node and not shared.
+  // - the right-most node has at least the desired extra capacity.
+  //
+  // Returns {tree, nullptr} if any of the above conditions are not met.
+  // This method effectively removes data from the tree. The intent of this
+  // method is to allow applications appending small string data to use
+  // pre-existing capacity, and add the modified rep back to the tree.
+  //
+  // Simplified such code would look similar to this:
+  //   void MyTreeBuilder::Append(string_view data) {
+  //     ExtractResult result = CordRepBtree::ExtractAppendBuffer(tree_, 1);
+  //     if (CordRep* rep = result.extracted) {
+  //       size_t available = rep->Capacity() - rep->length;
+  //       size_t n = std::min(data.size(), n);
+  //       memcpy(rep->Data(), data.data(), n);
+  //       rep->length += n;
+  //       data.remove_prefix(n);
+  //       if (!result.tree->IsBtree()) {
+  //         tree_ = CordRepBtree::Create(result.tree);
+  //       }
+  //       tree_ = CordRepBtree::Append(tree_, rep);
+  //     }
+  //     ...
+  //     // Remaining edge in `result.tree`.
+  //   }
+  static ExtractResult ExtractAppendBuffer(CordRepBtree* tree,
+                                           size_t extra_capacity = 1);
+
+  // Returns the `height` of the tree. The height of a tree is limited to
+  // kMaxHeight. `height` is implemented as an `int` as in some places we
+  // use negative (-1) values for 'data edges'.
+  int height() const { return static_cast<int>(storage[0]); }
+
+  // Properties: begin, back, end, front/back boundary indexes.
+  size_t begin() const { return static_cast<size_t>(storage[1]); }
+  size_t back() const { return static_cast<size_t>(storage[2]) - 1; }
+  size_t end() const { return static_cast<size_t>(storage[2]); }
+  size_t index(EdgeType edge) const {
+    return edge == kFront ? begin() : back();
+  }
+
+  // Properties: size and capacity.
+  // `capacity` contains the current capacity of this instance, where
+  // `kMaxCapacity` contains the maximum capacity of a btree node.
+  // For now, `capacity` and `kMaxCapacity` return the same value, but this may
+  // change in the future if we see benefit in dynamically sizing 'small' nodes
+  // to 'large' nodes for large data trees.
+  size_t size() const { return end() - begin(); }
+  size_t capacity() const { return kMaxCapacity; }
+
+  // Edge access
+  inline CordRep* Edge(size_t index) const;
+  inline CordRep* Edge(EdgeType edge_type) const;
+  inline absl::Span<CordRep* const> Edges() const;
+  inline absl::Span<CordRep* const> Edges(size_t begin, size_t end) const;
+
+  // Returns reference to the data edge at `index`.
+  // Requires this instance to be a leaf node, and `index` to be valid index.
+  inline absl::string_view Data(size_t index) const;
+
+  // Diagnostics: returns true if `tree` is valid and internally consistent.
+  // If `shallow` is false, then the provided top level node and all child nodes
+  // below it are recursively checked. If `shallow` is true, only the provided
+  // node in `tree` and the cumulative length, type and height of the direct
+  // child nodes of `tree` are checked. The value of `shallow` is ignored if the
+  // internal `cord_btree_exhaustive_validation` diagnostics variable is true,
+  // in which case the performed validations works as if `shallow` were false.
+  // This function is intended for debugging and testing purposes only.
+  static bool IsValid(const CordRepBtree* tree, bool shallow = false);
+
+  // Diagnostics: asserts that the provided tree is valid.
+  // `AssertValid()` performs a shallow validation by default. `shallow` can be
+  // set to false in which case an exhaustive validation is performed. This
+  // function is implemented in terms of calling `IsValid()` and asserting the
+  // return value to be true. See `IsValid()` for more information.
+  // This function is intended for debugging and testing purposes only.
+  static CordRepBtree* AssertValid(CordRepBtree* tree, bool shallow = true);
+  static const CordRepBtree* AssertValid(const CordRepBtree* tree,
+                                         bool shallow = true);
+
+  // Diagnostics: dump the contents of this tree to `stream`.
+  // This function is intended for debugging and testing purposes only.
+  static void Dump(const CordRep* rep, std::ostream& stream);
+  static void Dump(const CordRep* rep, absl::string_view label,
+                   std::ostream& stream);
+  static void Dump(const CordRep* rep, absl::string_view label,
+                   bool include_contents, std::ostream& stream);
+
+  // Adds the edge `edge` to this node if possible. `owned` indicates if the
+  // current node is potentially shared or not with other threads. Returns:
+  // - {kSelf, <this>}
+  //   The edge was directly added to this node.
+  // - {kCopied, <node>}
+  //   The edge was added to a copy of this node.
+  // - {kPopped, New(edge, height())}
+  //   A new leg with the edge was created as this node has no extra capacity.
+  template <EdgeType edge_type>
+  inline OpResult AddEdge(bool owned, CordRep* edge, size_t delta);
+
+  // Replaces the front or back edge with the provided new edge. Returns:
+  // - {kSelf, <this>}
+  //   The edge was directly set in this node. The old edge is unreffed.
+  // - {kCopied, <node>}
+  //   A copy of this node was created with the new edge value.
+  // In both cases, the function adopts a reference on `edge`.
+  template <EdgeType edge_type>
+  OpResult SetEdge(bool owned, CordRep* edge, size_t delta);
+
+  // Creates a new empty node at the specified height.
+  static CordRepBtree* New(int height = 0);
+
+  // Creates a new node containing `rep`, with the height being computed
+  // automatically based on the type of `rep`.
+  static CordRepBtree* New(CordRep* rep);
+
+  // Creates a new node containing both `front` and `back` at height
+  // `front.height() + 1`. Requires `back.height() == front.height()`.
+  static CordRepBtree* New(CordRepBtree* front, CordRepBtree* back);
+
+  // Creates a fully balanced tree from the provided tree by rebuilding a new
+  // tree from all data edges in the input. This function is automatically
+  // invoked internally when the tree exceeds the maximum height.
+  static CordRepBtree* Rebuild(CordRepBtree* tree);
+
+ private:
+  CordRepBtree() = default;
+  ~CordRepBtree() = default;
+
+  // Initializes the main properties `tag`, `begin`, `end`, `height`.
+  inline void InitInstance(int height, size_t begin = 0, size_t end = 0);
+
+  // Direct property access begin / end
+  void set_begin(size_t begin) { storage[1] = static_cast<uint8_t>(begin); }
+  void set_end(size_t end) { storage[2] = static_cast<uint8_t>(end); }
+
+  // Decreases the value of `begin` by `n`, and returns the new value. Notice
+  // how this returns the new value unlike atomic::fetch_add which returns the
+  // old value. This is because this is used to prepend edges at 'begin - 1'.
+  size_t sub_fetch_begin(size_t n) {
+    storage[1] -= static_cast<uint8_t>(n);
+    return storage[1];
+  }
+
+  // Increases the value of `end` by `n`, and returns the previous value. This
+  // function is typically used to append edges at 'end'.
+  size_t fetch_add_end(size_t n) {
+    const uint8_t current = storage[2];
+    storage[2] = static_cast<uint8_t>(current + n);
+    return current;
+  }
+
+  // Returns the index of the last edge starting on, or before `offset`, with
+  // `n` containing the relative offset of `offset` inside that edge.
+  // Requires `offset` < length.
+  Position IndexOf(size_t offset) const;
+
+  // Returns the index of the last edge starting before `offset`, with `n`
+  // containing the relative offset of `offset` inside that edge.
+  // This function is useful to find the edges for some span of bytes ending at
+  // `offset` (i.e., `n` bytes). For example:
+  //
+  //   Position pos = IndexBefore(n)
+  //   edges = Edges(begin(), pos.index)     // All full edges (may be empty)
+  //   last = Sub(Edge(pos.index), 0, pos.n) // Last partial edge (may be empty)
+  //
+  // Requires 0 < `offset` <= length.
+  Position IndexBefore(size_t offset) const;
+
+  // Returns the index of the edge ending at (or on) length `length`, and the
+  // number of bytes inside that edge up to `length`. For example, if we have a
+  // Node with 2 edges, one of 10 and one of 20 long, then IndexOfLength(27)
+  // will return {1, 17}, and IndexOfLength(10) will return {0, 10}.
+  Position IndexOfLength(size_t n) const;
+
+  // Identical to the above function except starting from the position `front`.
+  // This function is equivalent to `IndexBefore(front.n + offset)`, with
+  // the difference that this function is optimized to start at `front.index`.
+  Position IndexBefore(Position front, size_t offset) const;
+
+  // Returns the index of the edge directly beyond the edge containing offset
+  // `offset`, with `n` containing the distance of that edge from `offset`.
+  // This function is useful for iteratively finding suffix nodes and remaining
+  // partial bytes in left-most suffix nodes as for example in CopySuffix.
+  // Requires `offset` < length.
+  Position IndexBeyond(size_t offset) const;
+
+  // Creates a new leaf node containing as much data as possible from `data`.
+  // The data is added either forwards or reversed depending on `edge_type`.
+  // Callers must check the length of the returned node to determine if all data
+  // was copied or not.
+  // See the `Append/Prepend` function for the meaning and purpose of `extra`.
+  template <EdgeType edge_type>
+  static CordRepBtree* NewLeaf(absl::string_view data, size_t extra);
+
+  // Creates a raw copy of this Btree node with the specified length, copying
+  // all properties, but without adding any references to existing edges.
+  CordRepBtree* CopyRaw(size_t new_length) const;
+
+  // Creates a full copy of this Btree node, adding a reference on all edges.
+  CordRepBtree* Copy() const;
+
+  // Creates a partial copy of this Btree node, copying all edges up to `end`,
+  // adding a reference on each copied edge, and sets the length of the newly
+  // created copy to `new_length`.
+  CordRepBtree* CopyBeginTo(size_t end, size_t new_length) const;
+
+  // Returns a tree containing the edges [tree->begin(), end) and length
+  // of `new_length`. This method consumes a reference on the provided
+  // tree, and logically performs the following operation:
+  //   result = tree->CopyBeginTo(end, new_length);
+  //   CordRep::Unref(tree);
+  //   return result;
+  static CordRepBtree* ConsumeBeginTo(CordRepBtree* tree, size_t end,
+                                      size_t new_length);
+
+  // Creates a partial copy of this Btree node, copying all edges starting at
+  // `begin`, adding a reference on each copied edge, and sets the length of
+  // the newly created copy to `new_length`.
+  CordRepBtree* CopyToEndFrom(size_t begin, size_t new_length) const;
+
+  // Extracts and returns the front edge from the provided tree.
+  // This method consumes a reference on the provided tree, and logically
+  // performs the following operation:
+  //   edge = CordRep::Ref(tree->Edge(kFront));
+  //   CordRep::Unref(tree);
+  //   return edge;
+  static CordRep* ExtractFront(CordRepBtree* tree);
+
+  // Returns a tree containing the result of appending `right` to `left`.
+  static CordRepBtree* MergeTrees(CordRepBtree* left, CordRepBtree* right);
+
+  // Fallback functions for `Create()`, `Append()` and `Prepend()` which
+  // deal with legacy / non conforming input, i.e.: CONCAT trees.
+  static CordRepBtree* CreateSlow(CordRep* rep);
+  static CordRepBtree* AppendSlow(CordRepBtree*, CordRep* rep);
+  static CordRepBtree* PrependSlow(CordRepBtree*, CordRep* rep);
+
+  // Recursively rebuilds `tree` into `stack`. If 'consume` is set to true, the
+  // function will consume a reference on `tree`. `stack` is a null terminated
+  // array containing the new tree's state, with the current leaf node at
+  // stack[0], and parent nodes above that, or null for 'top of tree'.
+  static void Rebuild(CordRepBtree** stack, CordRepBtree* tree, bool consume);
+
+  // Aligns existing edges to start at index 0, to allow for a new edge to be
+  // added to the back of the current edges.
+  inline void AlignBegin();
+
+  // Aligns existing edges to end at `capacity`, to allow for a new edge to be
+  // added in front of the current edges.
+  inline void AlignEnd();
+
+  // Adds the provided edge to this node.
+  // Requires this node to have capacity for the edge. Realigns / moves
+  // existing edges as needed to prepend or append the new edge.
+  template <EdgeType edge_type>
+  inline void Add(CordRep* rep);
+
+  // Adds the provided edges to this node.
+  // Requires this node to have capacity for the edges. Realigns / moves
+  // existing edges as needed to prepend or append the new edges.
+  template <EdgeType edge_type>
+  inline void Add(absl::Span<CordRep* const>);
+
+  // Adds data from `data` to this node until either all data has been consumed,
+  // or there is no more capacity for additional flat nodes inside this node.
+  // Requires the current node to be a leaf node, data to be non empty, and the
+  // current node to have capacity for at least one more data edge.
+  // Returns any remaining data from `data` that was not added, which is
+  // depending on the edge type (front / back) either the remaining prefix of
+  // suffix of the input.
+  // See the `Append/Prepend` function for the meaning and purpose of `extra`.
+  template <EdgeType edge_type>
+  absl::string_view AddData(absl::string_view data, size_t extra);
+
+  // Replace the front or back edge with the provided value.
+  // Adopts a reference on `edge` and unrefs the old edge.
+  template <EdgeType edge_type>
+  inline void SetEdge(CordRep* edge);
+
+  // Returns a partial copy of the current tree containing the first `n` bytes
+  // of data. `CopyResult` contains both the resulting edge and its height. The
+  // resulting tree may be less high than the current tree, or even be a single
+  // matching data edge if `allow_folding` is set to true.
+  // For example, if `n == 1`, then the result will be the single data edge, and
+  // height will be set to -1 (one below the owning leaf node). If n == 0, this
+  // function returns null. Requires `n <= length`
+  CopyResult CopyPrefix(size_t n, bool allow_folding = true);
+
+  // Returns a partial copy of the current tree containing all data starting
+  // after `offset`. `CopyResult` contains both the resulting edge and its
+  // height. The resulting tree may be less high than the current tree, or even
+  // be a single matching data edge. For example, if `n == length - 1`, then the
+  // result will be a single data edge, and height will be set to -1 (one below
+  // the owning leaf node).
+  // Requires `offset < length`
+  CopyResult CopySuffix(size_t offset);
+
+  // Returns a OpResult value of {this, kSelf} or {Copy(), kCopied}
+  // depending on the value of `owned`.
+  inline OpResult ToOpResult(bool owned);
+
+  // Adds `rep` to the specified tree, returning the modified tree.
+  template <EdgeType edge_type>
+  static CordRepBtree* AddCordRep(CordRepBtree* tree, CordRep* rep);
+
+  // Adds `data` to the specified tree, returning the modified tree.
+  // See the `Append/Prepend` function for the meaning and purpose of `extra`.
+  template <EdgeType edge_type>
+  static CordRepBtree* AddData(CordRepBtree* tree, absl::string_view data,
+                               size_t extra = 0);
+
+  // Merges `src` into `dst` with `src` being added either before (kFront) or
+  // after (kBack) `dst`. Requires the height of `dst` to be greater than or
+  // equal to the height of `src`.
+  template <EdgeType edge_type>
+  static CordRepBtree* Merge(CordRepBtree* dst, CordRepBtree* src);
+
+  // Fallback version of GetAppendBuffer for large trees: GetAppendBuffer()
+  // implements an inlined version for trees of limited height (3 levels),
+  // GetAppendBufferSlow implements the logic for large trees.
+  Span<char> GetAppendBufferSlow(size_t size);
+
+  // `edges_` contains all edges starting from this instance.
+  // These are explicitly `child` edges only, a cord btree (or any cord tree in
+  // that respect) does not store `parent` pointers anywhere: multiple trees /
+  // parents can reference the same shared child edge. The type of these edges
+  // depends on the height of the node. `Leaf nodes` (height == 0) contain `data
+  // edges` (external or flat nodes, or sub-strings thereof). All other nodes
+  // (height > 0) contain pointers to BTREE nodes with a height of `height - 1`.
+  CordRep* edges_[kMaxCapacity];
+
+  friend class CordRepBtreeTestPeer;
+  friend class CordRepBtreeNavigator;
+};
+
+inline CordRepBtree* CordRep::btree() {
+  assert(IsBtree());
+  return static_cast<CordRepBtree*>(this);
+}
+
+inline const CordRepBtree* CordRep::btree() const {
+  assert(IsBtree());
+  return static_cast<const CordRepBtree*>(this);
+}
+
+inline void CordRepBtree::InitInstance(int height, size_t begin, size_t end) {
+  tag = BTREE;
+  storage[0] = static_cast<uint8_t>(height);
+  storage[1] = static_cast<uint8_t>(begin);
+  storage[2] = static_cast<uint8_t>(end);
+}
+
+inline CordRep* CordRepBtree::Edge(size_t index) const {
+  assert(index >= begin());
+  assert(index < end());
+  return edges_[index];
+}
+
+inline CordRep* CordRepBtree::Edge(EdgeType edge_type) const {
+  return edges_[edge_type == kFront ? begin() : back()];
+}
+
+inline absl::Span<CordRep* const> CordRepBtree::Edges() const {
+  return {edges_ + begin(), size()};
+}
+
+inline absl::Span<CordRep* const> CordRepBtree::Edges(size_t begin,
+                                                      size_t end) const {
+  assert(begin <= end);
+  assert(begin >= this->begin());
+  assert(end <= this->end());
+  return {edges_ + begin, static_cast<size_t>(end - begin)};
+}
+
+inline absl::string_view CordRepBtree::Data(size_t index) const {
+  assert(height() == 0);
+  return EdgeData(Edge(index));
+}
+
+inline CordRepBtree* CordRepBtree::New(int height) {
+  CordRepBtree* tree = new CordRepBtree;
+  tree->length = 0;
+  tree->InitInstance(height);
+  return tree;
+}
+
+inline CordRepBtree* CordRepBtree::New(CordRep* rep) {
+  CordRepBtree* tree = new CordRepBtree;
+  int height = rep->IsBtree() ? rep->btree()->height() + 1 : 0;
+  tree->length = rep->length;
+  tree->InitInstance(height, /*begin=*/0, /*end=*/1);
+  tree->edges_[0] = rep;
+  return tree;
+}
+
+inline CordRepBtree* CordRepBtree::New(CordRepBtree* front,
+                                       CordRepBtree* back) {
+  assert(front->height() == back->height());
+  CordRepBtree* tree = new CordRepBtree;
+  tree->length = front->length + back->length;
+  tree->InitInstance(front->height() + 1, /*begin=*/0, /*end=*/2);
+  tree->edges_[0] = front;
+  tree->edges_[1] = back;
+  return tree;
+}
+
+inline void CordRepBtree::Unref(absl::Span<CordRep* const> edges) {
+  for (CordRep* edge : edges) {
+    if (ABSL_PREDICT_FALSE(!edge->refcount.Decrement())) {
+      CordRep::Destroy(edge);
+    }
+  }
+}
+
+inline CordRepBtree* CordRepBtree::CopyRaw(size_t new_length) const {
+  CordRepBtree* tree = new CordRepBtree;
+
+  // `length` and `refcount` are the first members of `CordRepBtree`.
+  // We initialize `length` using the given length, have `refcount` be set to
+  // ref = 1 through its default constructor, and copy all data beyond
+  // 'refcount' which starts with `tag` using a single memcpy: all contents
+  // except `refcount` is trivially copyable, and the compiler does not
+  // efficiently coalesce member-wise copy of these members.
+  // See https://gcc.godbolt.org/z/qY8zsca6z
+  // # LINT.IfChange(copy_raw)
+  tree->length = new_length;
+  uint8_t* dst = &tree->tag;
+  const uint8_t* src = &tag;
+  const ptrdiff_t offset = src - reinterpret_cast<const uint8_t*>(this);
+  memcpy(dst, src, sizeof(CordRepBtree) - static_cast<size_t>(offset));
+  return tree;
+  // # LINT.ThenChange()
+}
+
+inline CordRepBtree* CordRepBtree::Copy() const {
+  CordRepBtree* tree = CopyRaw(length);
+  for (CordRep* rep : Edges()) CordRep::Ref(rep);
+  return tree;
+}
+
+inline CordRepBtree* CordRepBtree::CopyToEndFrom(size_t begin,
+                                                 size_t new_length) const {
+  assert(begin >= this->begin());
+  assert(begin <= this->end());
+  CordRepBtree* tree = CopyRaw(new_length);
+  tree->set_begin(begin);
+  for (CordRep* edge : tree->Edges()) CordRep::Ref(edge);
+  return tree;
+}
+
+inline CordRepBtree* CordRepBtree::CopyBeginTo(size_t end,
+                                               size_t new_length) const {
+  assert(end <= capacity());
+  assert(end >= this->begin());
+  CordRepBtree* tree = CopyRaw(new_length);
+  tree->set_end(end);
+  for (CordRep* edge : tree->Edges()) CordRep::Ref(edge);
+  return tree;
+}
+
+inline void CordRepBtree::AlignBegin() {
+  // The below code itself does not need to be fast as typically we have
+  // mono-directional append/prepend calls, and `begin` / `end` are typically
+  // adjusted no more than once. But we want to avoid potential register clobber
+  // effects, making the compiler emit register save/store/spills, and minimize
+  // the size of code.
+  const size_t delta = begin();
+  if (ABSL_PREDICT_FALSE(delta != 0)) {
+    const size_t new_end = end() - delta;
+    set_begin(0);
+    set_end(new_end);
+    // TODO(mvels): we can write this using 2 loads / 2 stores depending on
+    // total size for the kMaxCapacity = 6 case. I.e., we can branch (switch) on
+    // size, and then do overlapping load/store of up to 4 pointers (inlined as
+    // XMM, YMM or ZMM load/store) and up to 2 pointers (XMM / YMM), which is a)
+    // compact and b) not clobbering any registers.
+    ABSL_ASSUME(new_end <= kMaxCapacity);
+#ifdef __clang__
+#pragma unroll 1
+#endif
+    for (size_t i = 0; i < new_end; ++i) {
+      edges_[i] = edges_[i + delta];
+    }
+  }
+}
+
+inline void CordRepBtree::AlignEnd() {
+  // See comments in `AlignBegin` for motivation on the hand-rolled for loops.
+  const size_t delta = capacity() - end();
+  if (delta != 0) {
+    const size_t new_begin = begin() + delta;
+    const size_t new_end = end() + delta;
+    set_begin(new_begin);
+    set_end(new_end);
+    ABSL_ASSUME(new_end <= kMaxCapacity);
+#ifdef __clang__
+#pragma unroll 1
+#endif
+    for (size_t i = new_end - 1; i >= new_begin; --i) {
+      edges_[i] = edges_[i - delta];
+    }
+  }
+}
+
+template <>
+inline void CordRepBtree::Add<CordRepBtree::kBack>(CordRep* rep) {
+  AlignBegin();
+  edges_[fetch_add_end(1)] = rep;
+}
+
+template <>
+inline void CordRepBtree::Add<CordRepBtree::kBack>(
+    absl::Span<CordRep* const> edges) {
+  AlignBegin();
+  size_t new_end = end();
+  for (CordRep* edge : edges) edges_[new_end++] = edge;
+  set_end(new_end);
+}
+
+template <>
+inline void CordRepBtree::Add<CordRepBtree::kFront>(CordRep* rep) {
+  AlignEnd();
+  edges_[sub_fetch_begin(1)] = rep;
+}
+
+template <>
+inline void CordRepBtree::Add<CordRepBtree::kFront>(
+    absl::Span<CordRep* const> edges) {
+  AlignEnd();
+  size_t new_begin = begin() - edges.size();
+  set_begin(new_begin);
+  for (CordRep* edge : edges) edges_[new_begin++] = edge;
+}
+
+template <CordRepBtree::EdgeType edge_type>
+inline void CordRepBtree::SetEdge(CordRep* edge) {
+  const int idx = edge_type == kFront ? begin() : back();
+  CordRep::Unref(edges_[idx]);
+  edges_[idx] = edge;
+}
+
+inline CordRepBtree::OpResult CordRepBtree::ToOpResult(bool owned) {
+  return owned ? OpResult{this, kSelf} : OpResult{Copy(), kCopied};
+}
+
+inline CordRepBtree::Position CordRepBtree::IndexOf(size_t offset) const {
+  assert(offset < length);
+  size_t index = begin();
+  while (offset >= edges_[index]->length) offset -= edges_[index++]->length;
+  return {index, offset};
+}
+
+inline CordRepBtree::Position CordRepBtree::IndexBefore(size_t offset) const {
+  assert(offset > 0);
+  assert(offset <= length);
+  size_t index = begin();
+  while (offset > edges_[index]->length) offset -= edges_[index++]->length;
+  return {index, offset};
+}
+
+inline CordRepBtree::Position CordRepBtree::IndexBefore(Position front,
+                                                        size_t offset) const {
+  size_t index = front.index;
+  offset = offset + front.n;
+  while (offset > edges_[index]->length) offset -= edges_[index++]->length;
+  return {index, offset};
+}
+
+inline CordRepBtree::Position CordRepBtree::IndexOfLength(size_t n) const {
+  assert(n <= length);
+  size_t index = back();
+  size_t strip = length - n;
+  while (strip >= edges_[index]->length) strip -= edges_[index--]->length;
+  return {index, edges_[index]->length - strip};
+}
+
+inline CordRepBtree::Position CordRepBtree::IndexBeyond(
+    const size_t offset) const {
+  // We need to find the edge which `starting offset` is beyond (>=)`offset`.
+  // For this we can't use the `offset -= length` logic of IndexOf. Instead, we
+  // track the offset of the `current edge` in `off`, which we increase as we
+  // iterate over the edges until we find the matching edge.
+  size_t off = 0;
+  size_t index = begin();
+  while (offset > off) off += edges_[index++]->length;
+  return {index, off - offset};
+}
+
+inline CordRepBtree* CordRepBtree::Create(CordRep* rep) {
+  if (IsDataEdge(rep)) return New(rep);
+  return CreateSlow(rep);
+}
+
+inline Span<char> CordRepBtree::GetAppendBuffer(size_t size) {
+  assert(refcount.IsOne());
+  CordRepBtree* tree = this;
+  const int height = this->height();
+  CordRepBtree* n1 = tree;
+  CordRepBtree* n2 = tree;
+  CordRepBtree* n3 = tree;
+  switch (height) {
+    case 3:
+      tree = tree->Edge(kBack)->btree();
+      if (!tree->refcount.IsOne()) return {};
+      n2 = tree;
+      ABSL_FALLTHROUGH_INTENDED;
+    case 2:
+      tree = tree->Edge(kBack)->btree();
+      if (!tree->refcount.IsOne()) return {};
+      n1 = tree;
+      ABSL_FALLTHROUGH_INTENDED;
+    case 1:
+      tree = tree->Edge(kBack)->btree();
+      if (!tree->refcount.IsOne()) return {};
+      ABSL_FALLTHROUGH_INTENDED;
+    case 0:
+      CordRep* edge = tree->Edge(kBack);
+      if (!edge->refcount.IsOne()) return {};
+      if (edge->tag < FLAT) return {};
+      size_t avail = edge->flat()->Capacity() - edge->length;
+      if (avail == 0) return {};
+      size_t delta = (std::min)(size, avail);
+      Span<char> span = {edge->flat()->Data() + edge->length, delta};
+      edge->length += delta;
+      switch (height) {
+        case 3:
+          n3->length += delta;
+          ABSL_FALLTHROUGH_INTENDED;
+        case 2:
+          n2->length += delta;
+          ABSL_FALLTHROUGH_INTENDED;
+        case 1:
+          n1->length += delta;
+          ABSL_FALLTHROUGH_INTENDED;
+        case 0:
+          tree->length += delta;
+          return span;
+      }
+      break;
+  }
+  return GetAppendBufferSlow(size);
+}
+
+extern template CordRepBtree* CordRepBtree::AddCordRep<CordRepBtree::kBack>(
+    CordRepBtree* tree, CordRep* rep);
+
+extern template CordRepBtree* CordRepBtree::AddCordRep<CordRepBtree::kFront>(
+    CordRepBtree* tree, CordRep* rep);
+
+inline CordRepBtree* CordRepBtree::Append(CordRepBtree* tree, CordRep* rep) {
+  if (ABSL_PREDICT_TRUE(IsDataEdge(rep))) {
+    return CordRepBtree::AddCordRep<kBack>(tree, rep);
+  }
+  return AppendSlow(tree, rep);
+}
+
+inline CordRepBtree* CordRepBtree::Prepend(CordRepBtree* tree, CordRep* rep) {
+  if (ABSL_PREDICT_TRUE(IsDataEdge(rep))) {
+    return CordRepBtree::AddCordRep<kFront>(tree, rep);
+  }
+  return PrependSlow(tree, rep);
+}
+
+#ifdef NDEBUG
+
+inline CordRepBtree* CordRepBtree::AssertValid(CordRepBtree* tree,
+                                               bool /* shallow */) {
+  return tree;
+}
+
+inline const CordRepBtree* CordRepBtree::AssertValid(const CordRepBtree* tree,
+                                                     bool /* shallow */) {
+  return tree;
+}
+
+#endif
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CORD_REP_BTREE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_btree_navigator.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_btree_navigator.h
new file mode 100644
index 0000000000000000000000000000000000000000..3d581c877e23c1b892eb63ab7f1b0ca752e968da
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_btree_navigator.h
@@ -0,0 +1,267 @@
+// Copyright 2021 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORD_REP_BTREE_NAVIGATOR_H_
+#define ABSL_STRINGS_INTERNAL_CORD_REP_BTREE_NAVIGATOR_H_
+
+#include <cassert>
+#include <iostream>
+
+#include "absl/strings/internal/cord_internal.h"
+#include "absl/strings/internal/cord_rep_btree.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// CordRepBtreeNavigator is a bi-directional navigator allowing callers to
+// navigate all the (leaf) data edges in a CordRepBtree instance.
+//
+// A CordRepBtreeNavigator instance is by default empty. Callers initialize a
+// navigator instance by calling one of `InitFirst()`, `InitLast()` or
+// `InitOffset()`, which establishes a current position. Callers can then
+// navigate using the `Next`, `Previous`, `Skip` and `Seek` methods.
+//
+// The navigator instance does not take or adopt a reference on the provided
+// `tree` on any of the initialization calls. Callers are responsible for
+// guaranteeing the lifecycle of the provided tree. A navigator instance can
+// be reset to the empty state by calling `Reset`.
+//
+// A navigator only keeps positional state on the 'current data edge', it does
+// explicitly not keep any 'offset' state. The class does accept and return
+// offsets in the `Read()`, `Skip()` and 'Seek()` methods as these would
+// otherwise put a big burden on callers. Callers are expected to maintain
+// (returned) offset info if they require such granular state.
+class CordRepBtreeNavigator {
+ public:
+  // The logical position as returned by the Seek() and Skip() functions.
+  // Returns the current leaf edge for the desired seek or skip position and
+  // the offset of that position inside that edge.
+  struct Position {
+    CordRep* edge;
+    size_t offset;
+  };
+
+  // The read result as returned by the Read() function.
+  // `tree` contains the resulting tree which is identical to the result
+  // of calling CordRepBtree::SubTree(...) on the tree being navigated.
+  // `n` contains the number of bytes used from the last navigated to
+  // edge of the tree.
+  struct ReadResult {
+    CordRep* tree;
+    size_t n;
+  };
+
+  // Returns true if this instance is not empty.
+  explicit operator bool() const;
+
+  // Returns the tree for this instance or nullptr if empty.
+  CordRepBtree* btree() const;
+
+  // Returns the data edge of the current position.
+  // Requires this instance to not be empty.
+  CordRep* Current() const;
+
+  // Resets this navigator to `tree`, returning the first data edge in the tree.
+  CordRep* InitFirst(CordRepBtree* tree);
+
+  // Resets this navigator to `tree`, returning the last data edge in the tree.
+  CordRep* InitLast(CordRepBtree* tree);
+
+  // Resets this navigator to `tree` returning the data edge at position
+  // `offset` and the relative offset of `offset` into that data edge.
+  // Returns `Position.edge = nullptr` if the provided offset is greater
+  // than or equal to the length of the tree, in which case the state of
+  // the navigator instance remains unchanged.
+  Position InitOffset(CordRepBtree* tree, size_t offset);
+
+  // Navigates to the next data edge.
+  // Returns the next data edge or nullptr if there is no next data edge, in
+  // which case the current position remains unchanged.
+  CordRep* Next();
+
+  // Navigates to the previous data edge.
+  // Returns the previous data edge or nullptr if there is no previous data
+  // edge, in which case the current position remains unchanged.
+  CordRep* Previous();
+
+  // Navigates to the data edge at position `offset`. Returns the navigated to
+  // data edge in `Position.edge` and the relative offset of `offset` into that
+  // data edge in `Position.offset`. Returns `Position.edge = nullptr` if the
+  // provide offset is greater than or equal to the tree's length.
+  Position Seek(size_t offset);
+
+  // Reads `n` bytes of data starting at offset `edge_offset` of the current
+  // data edge, and returns the result in `ReadResult.tree`. `ReadResult.n`
+  // contains the 'bytes used` from the last / current data edge in the tree.
+  // This allows users that mix regular navigation (using string views) and
+  // 'read into cord' navigation to keep track of the current state, and which
+  // bytes have been consumed from a navigator.
+  // This function returns `ReadResult.tree = nullptr` if the requested length
+  // exceeds the length of the tree starting at the current data edge.
+  ReadResult Read(size_t edge_offset, size_t n);
+
+  // Skips `n` bytes forward from the current data edge, returning the navigated
+  // to data edge in `Position.edge` and `Position.offset` containing the offset
+  // inside that data edge. Note that the state of the navigator is left
+  // unchanged if `n` is smaller than the length of the current data edge.
+  Position Skip(size_t n);
+
+  // Resets this instance to the default / empty state.
+  void Reset();
+
+ private:
+  // Slow path for Next() if Next() reached the end of a leaf node. Backtracks
+  // up the stack until it finds a node that has a 'next' position available,
+  // and then does a 'front dive' towards the next leaf node.
+  CordRep* NextUp();
+
+  // Slow path for Previous() if Previous() reached the beginning of a leaf
+  // node. Backtracks up the stack until it finds a node that has a 'previous'
+  // position available, and then does a 'back dive' towards the previous leaf
+  // node.
+  CordRep* PreviousUp();
+
+  // Generic implementation of InitFirst() and InitLast().
+  template <CordRepBtree::EdgeType edge_type>
+  CordRep* Init(CordRepBtree* tree);
+
+  // `height_` contains the height of the current tree, or -1 if empty.
+  int height_ = -1;
+
+  // `index_` and `node_` contain the navigation state as the 'path' to the
+  // current data edge which is at `node_[0]->Edge(index_[0])`. The contents
+  // of these are undefined until the instance is initialized (`height_ >= 0`).
+  uint8_t index_[CordRepBtree::kMaxDepth];
+  CordRepBtree* node_[CordRepBtree::kMaxDepth];
+};
+
+// Returns true if this instance is not empty.
+inline CordRepBtreeNavigator::operator bool() const { return height_ >= 0; }
+
+inline CordRepBtree* CordRepBtreeNavigator::btree() const {
+  return height_ >= 0 ? node_[height_] : nullptr;
+}
+
+inline CordRep* CordRepBtreeNavigator::Current() const {
+  assert(height_ >= 0);
+  return node_[0]->Edge(index_[0]);
+}
+
+inline void CordRepBtreeNavigator::Reset() { height_ = -1; }
+
+inline CordRep* CordRepBtreeNavigator::InitFirst(CordRepBtree* tree) {
+  return Init<CordRepBtree::kFront>(tree);
+}
+
+inline CordRep* CordRepBtreeNavigator::InitLast(CordRepBtree* tree) {
+  return Init<CordRepBtree::kBack>(tree);
+}
+
+template <CordRepBtree::EdgeType edge_type>
+inline CordRep* CordRepBtreeNavigator::Init(CordRepBtree* tree) {
+  assert(tree != nullptr);
+  assert(tree->size() > 0);
+  assert(tree->height() <= CordRepBtree::kMaxHeight);
+  int height = height_ = tree->height();
+  size_t index = tree->index(edge_type);
+  node_[height] = tree;
+  index_[height] = static_cast<uint8_t>(index);
+  while (--height >= 0) {
+    tree = tree->Edge(index)->btree();
+    node_[height] = tree;
+    index = tree->index(edge_type);
+    index_[height] = static_cast<uint8_t>(index);
+  }
+  return node_[0]->Edge(index);
+}
+
+inline CordRepBtreeNavigator::Position CordRepBtreeNavigator::Seek(
+    size_t offset) {
+  assert(btree() != nullptr);
+  int height = height_;
+  CordRepBtree* edge = node_[height];
+  if (ABSL_PREDICT_FALSE(offset >= edge->length)) return {nullptr, 0};
+  CordRepBtree::Position index = edge->IndexOf(offset);
+  index_[height] = static_cast<uint8_t>(index.index);
+  while (--height >= 0) {
+    edge = edge->Edge(index.index)->btree();
+    node_[height] = edge;
+    index = edge->IndexOf(index.n);
+    index_[height] = static_cast<uint8_t>(index.index);
+  }
+  return {edge->Edge(index.index), index.n};
+}
+
+inline CordRepBtreeNavigator::Position CordRepBtreeNavigator::InitOffset(
+    CordRepBtree* tree, size_t offset) {
+  assert(tree != nullptr);
+  assert(tree->height() <= CordRepBtree::kMaxHeight);
+  if (ABSL_PREDICT_FALSE(offset >= tree->length)) return {nullptr, 0};
+  height_ = tree->height();
+  node_[height_] = tree;
+  return Seek(offset);
+}
+
+inline CordRep* CordRepBtreeNavigator::Next() {
+  CordRepBtree* edge = node_[0];
+  return index_[0] == edge->back() ? NextUp() : edge->Edge(++index_[0]);
+}
+
+inline CordRep* CordRepBtreeNavigator::Previous() {
+  CordRepBtree* edge = node_[0];
+  return index_[0] == edge->begin() ? PreviousUp() : edge->Edge(--index_[0]);
+}
+
+inline CordRep* CordRepBtreeNavigator::NextUp() {
+  assert(index_[0] == node_[0]->back());
+  CordRepBtree* edge;
+  size_t index;
+  int height = 0;
+  do {
+    if (++height > height_) return nullptr;
+    edge = node_[height];
+    index = index_[height] + 1;
+  } while (index == edge->end());
+  index_[height] = static_cast<uint8_t>(index);
+  do {
+    node_[--height] = edge = edge->Edge(index)->btree();
+    index_[height] = static_cast<uint8_t>(index = edge->begin());
+  } while (height > 0);
+  return edge->Edge(index);
+}
+
+inline CordRep* CordRepBtreeNavigator::PreviousUp() {
+  assert(index_[0] == node_[0]->begin());
+  CordRepBtree* edge;
+  size_t index;
+  int height = 0;
+  do {
+    if (++height > height_) return nullptr;
+    edge = node_[height];
+    index = index_[height];
+  } while (index == edge->begin());
+  index_[height] = static_cast<uint8_t>(--index);
+  do {
+    node_[--height] = edge = edge->Edge(index)->btree();
+    index_[height] = static_cast<uint8_t>(index = edge->back());
+  } while (height > 0);
+  return edge->Edge(index);
+}
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CORD_REP_BTREE_NAVIGATOR_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_btree_reader.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_btree_reader.h
new file mode 100644
index 0000000000000000000000000000000000000000..8db8f8dd171845faa4a4b7d4ecad969acb232777
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_btree_reader.h
@@ -0,0 +1,212 @@
+// Copyright 2021 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORD_REP_BTREE_READER_H_
+#define ABSL_STRINGS_INTERNAL_CORD_REP_BTREE_READER_H_
+
+#include <cassert>
+
+#include "absl/base/config.h"
+#include "absl/strings/internal/cord_data_edge.h"
+#include "absl/strings/internal/cord_internal.h"
+#include "absl/strings/internal/cord_rep_btree.h"
+#include "absl/strings/internal/cord_rep_btree_navigator.h"
+#include "absl/strings/internal/cord_rep_flat.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// CordRepBtreeReader implements logic to iterate over cord btrees.
+// References to the underlying data are returned as absl::string_view values.
+// The most typical use case is a forward only iteration over tree data.
+// The class also provides `Skip()`, `Seek()` and `Read()` methods similar to
+// CordRepBtreeNavigator that allow more advanced navigation.
+//
+// Example: iterate over all data inside a cord btree:
+//
+//   CordRepBtreeReader reader;
+//   for (string_view sv = reader.Init(tree); !sv.Empty(); sv = sv.Next()) {
+//     DoSomethingWithDataIn(sv);
+//   }
+//
+// All navigation methods always return the next 'chunk' of data. The class
+// assumes that all data is directly 'consumed' by the caller. For example:
+// invoking `Skip()` will skip the desired number of bytes, and directly
+// read and return the next chunk of data directly after the skipped bytes.
+//
+// Example: iterate over all data inside a btree skipping the first 100 bytes:
+//
+//   CordRepBtreeReader reader;
+//   absl::string_view sv = reader.Init(tree);
+//   if (sv.length() > 100) {
+//     sv.RemovePrefix(100);
+//   } else {
+//     sv = reader.Skip(100 - sv.length());
+//   }
+//   while (!sv.empty()) {
+//     DoSomethingWithDataIn(sv);
+//     absl::string_view sv = reader.Next();
+//   }
+//
+// It is important to notice that `remaining` is based on the end position of
+// the last data edge returned to the caller, not the cumulative data returned
+// to the caller which can be less in cases of skipping or seeking over data.
+//
+// For example, consider a cord btree with five data edges: "abc", "def", "ghi",
+// "jkl" and "mno":
+//
+//   absl::string_view sv;
+//   CordRepBtreeReader reader;
+//
+//   sv = reader.Init(tree); // sv = "abc", remaining = 12
+//   sv = reader.Skip(4);    // sv = "hi",  remaining = 6
+//   sv = reader.Skip(2);    // sv = "l",   remaining = 3
+//   sv = reader.Next();     // sv = "mno", remaining = 0
+//   sv = reader.Seek(1);    // sv = "bc", remaining = 12
+//
+class CordRepBtreeReader {
+ public:
+  using ReadResult = CordRepBtreeNavigator::ReadResult;
+  using Position = CordRepBtreeNavigator::Position;
+
+  // Returns true if this instance is not empty.
+  explicit operator bool() const { return navigator_.btree() != nullptr; }
+
+  // Returns the tree referenced by this instance or nullptr if empty.
+  CordRepBtree* btree() const { return navigator_.btree(); }
+
+  // Returns the current data edge inside the referenced btree.
+  // Requires that the current instance is not empty.
+  CordRep* node() const { return navigator_.Current(); }
+
+  // Returns the length of the referenced tree.
+  // Requires that the current instance is not empty.
+  size_t length() const;
+
+  // Returns the number of remaining bytes available for iteration, which is the
+  // number of bytes directly following the end of the last chunk returned.
+  // This value will be zero if we iterated over the last edge in the bound
+  // tree, in which case any call to Next() or Skip() will return an empty
+  // string_view reflecting the EOF state.
+  // Note that a call to `Seek()` resets `remaining` to a value based on the
+  // end position of the chunk returned by that call.
+  size_t remaining() const { return remaining_; }
+
+  // Resets this instance to an empty value.
+  void Reset() { navigator_.Reset(); }
+
+  // Initializes this instance with `tree`. `tree` must not be null.
+  // Returns a reference to the first data edge of the provided tree.
+  absl::string_view Init(CordRepBtree* tree);
+
+  // Navigates to and returns the next data edge of the referenced tree.
+  // Returns an empty string_view if an attempt is made to read beyond the end
+  // of the tree, i.e.: if `remaining()` is zero indicating an EOF condition.
+  // Requires that the current instance is not empty.
+  absl::string_view Next();
+
+  // Skips the provided amount of bytes and returns a reference to the data
+  // directly following the skipped bytes.
+  absl::string_view Skip(size_t skip);
+
+  // Reads `n` bytes into `tree`.
+  // If `chunk_size` is zero, starts reading at the next data edge. If
+  // `chunk_size` is non zero, the read starts at the last `chunk_size` bytes of
+  // the last returned data edge. Effectively, this means that the read starts
+  // at offset `consumed() - chunk_size`.
+  // Requires that `chunk_size` is less than or equal to the length of the
+  // last returned data edge. The purpose of `chunk_size` is to simplify code
+  // partially consuming a returned chunk and wanting to include the remaining
+  // bytes in the Read call. For example, the below code will read 1000 bytes of
+  // data into a cord tree if the first chunk starts with "big:":
+  //
+  //   CordRepBtreeReader reader;
+  //   absl::string_view sv = reader.Init(tree);
+  //   if (absl::StartsWith(sv, "big:")) {
+  //     CordRepBtree tree;
+  //     sv = reader.Read(1000, sv.size() - 4 /* "big:" */, &tree);
+  //   }
+  //
+  // This method will return an empty string view if all remaining data was
+  // read. If `n` exceeded the amount of remaining data this function will
+  // return an empty string view and `tree` will be set to nullptr.
+  // In both cases, `consumed` will be set to `length`.
+  absl::string_view Read(size_t n, size_t chunk_size, CordRep*& tree);
+
+  // Navigates to the chunk at offset `offset`.
+  // Returns a reference into the navigated to chunk, adjusted for the relative
+  // position of `offset` into that chunk. For example, calling `Seek(13)` on a
+  // cord tree containing 2 chunks of 10 and 20 bytes respectively will return
+  // a string view into the second chunk starting at offset 3 with a size of 17.
+  // Returns an empty string view if `offset` is equal to or greater than the
+  // length of the referenced tree.
+  absl::string_view Seek(size_t offset);
+
+ private:
+  size_t remaining_ = 0;
+  CordRepBtreeNavigator navigator_;
+};
+
+inline size_t CordRepBtreeReader::length() const {
+  assert(btree() != nullptr);
+  return btree()->length;
+}
+
+inline absl::string_view CordRepBtreeReader::Init(CordRepBtree* tree) {
+  assert(tree != nullptr);
+  const CordRep* edge = navigator_.InitFirst(tree);
+  remaining_ = tree->length - edge->length;
+  return EdgeData(edge);
+}
+
+inline absl::string_view CordRepBtreeReader::Next() {
+  if (remaining_ == 0) return {};
+  const CordRep* edge = navigator_.Next();
+  assert(edge != nullptr);
+  remaining_ -= edge->length;
+  return EdgeData(edge);
+}
+
+inline absl::string_view CordRepBtreeReader::Skip(size_t skip) {
+  // As we are always positioned on the last 'consumed' edge, we
+  // need to skip the current edge as well as `skip`.
+  const size_t edge_length = navigator_.Current()->length;
+  CordRepBtreeNavigator::Position pos = navigator_.Skip(skip + edge_length);
+  if (ABSL_PREDICT_FALSE(pos.edge == nullptr)) {
+    remaining_ = 0;
+    return {};
+  }
+  // The combined length of all edges skipped before `pos.edge` is `skip -
+  // pos.offset`, all of which are 'consumed', as well as the current edge.
+  remaining_ -= skip - pos.offset + pos.edge->length;
+  return EdgeData(pos.edge).substr(pos.offset);
+}
+
+inline absl::string_view CordRepBtreeReader::Seek(size_t offset) {
+  const CordRepBtreeNavigator::Position pos = navigator_.Seek(offset);
+  if (ABSL_PREDICT_FALSE(pos.edge == nullptr)) {
+    remaining_ = 0;
+    return {};
+  }
+  absl::string_view chunk = EdgeData(pos.edge).substr(pos.offset);
+  remaining_ = length() - offset - chunk.length();
+  return chunk;
+}
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CORD_REP_BTREE_READER_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_consume.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_consume.h
new file mode 100644
index 0000000000000000000000000000000000000000..d46fca2b2173847b8be5b5dd24ca6d2d5b024ad3
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_consume.h
@@ -0,0 +1,50 @@
+// Copyright 2021 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORD_REP_CONSUME_H_
+#define ABSL_STRINGS_INTERNAL_CORD_REP_CONSUME_H_
+
+#include <functional>
+
+#include "absl/functional/function_ref.h"
+#include "absl/strings/internal/cord_internal.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// Functor for the Consume() and ReverseConsume() functions:
+//   void ConsumeFunc(CordRep* rep, size_t offset, size_t length);
+// See the Consume() and ReverseConsume() function comments for documentation.
+using ConsumeFn = FunctionRef<void(CordRep*, size_t, size_t)>;
+
+// Consume() and ReverseConsume() consume CONCAT based trees and invoke the
+// provided functor with the contained nodes in the proper forward or reverse
+// order, which is used to convert CONCAT trees into other tree or cord data.
+// All CONCAT and SUBSTRING nodes are processed internally. The 'offset`
+// parameter of the functor is non-zero for any nodes below SUBSTRING nodes.
+// It's up to the caller to form these back into SUBSTRING nodes or otherwise
+// store offset / prefix information. These functions are intended to be used
+// only for migration / transitional code where due to factors such as ODR
+// violations, we can not 100% guarantee that all code respects 'new format'
+// settings and flags, so we need to be able to parse old data on the fly until
+// all old code is deprecated / no longer the default format.
+void Consume(CordRep* rep, ConsumeFn consume_fn);
+void ReverseConsume(CordRep* rep, ConsumeFn consume_fn);
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CORD_REP_CONSUME_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_crc.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_crc.h
new file mode 100644
index 0000000000000000000000000000000000000000..379d7a604e976708c490e7efba3fc44f4e03d53f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_crc.h
@@ -0,0 +1,103 @@
+// Copyright 2021 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORD_REP_CRC_H_
+#define ABSL_STRINGS_INTERNAL_CORD_REP_CRC_H_
+
+#include <cassert>
+#include <cstdint>
+
+#include "absl/base/config.h"
+#include "absl/base/optimization.h"
+#include "absl/crc/internal/crc_cord_state.h"
+#include "absl/strings/internal/cord_internal.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// CordRepCrc is a CordRep node intended only to appear at the top level of a
+// cord tree.  It associates an "expected CRC" with the contained data, to allow
+// for easy passage of checksum data in Cord data flows.
+//
+// From Cord's perspective, the crc value has no semantics; any validation of
+// the contained checksum is the user's responsibility.
+struct CordRepCrc : public CordRep {
+  CordRep* child;
+  absl::crc_internal::CrcCordState crc_cord_state;
+
+  // Consumes `child` and returns a CordRepCrc prefixed tree containing `child`.
+  // If the specified `child` is itself a CordRepCrc node, then this method
+  // either replaces the existing node, or directly updates the crc state in it
+  // depending on the node being shared or not, i.e.: refcount.IsOne().
+  // `child` must only be null if the Cord is empty. Never returns null.
+  static CordRepCrc* New(CordRep* child, crc_internal::CrcCordState state);
+
+  // Destroys (deletes) the provided node. `node` must not be null.
+  static void Destroy(CordRepCrc* node);
+};
+
+// Consumes `rep` and returns a CordRep* with any outer CordRepCrc wrapper
+// removed.  This is usually a no-op (returning `rep`), but this will remove and
+// unref an outer CordRepCrc node.
+inline CordRep* RemoveCrcNode(CordRep* rep) {
+  assert(rep != nullptr);
+  if (ABSL_PREDICT_FALSE(rep->IsCrc())) {
+    CordRep* child = rep->crc()->child;
+    if (rep->refcount.IsOne()) {
+      delete rep->crc();
+    } else {
+      CordRep::Ref(child);
+      CordRep::Unref(rep);
+    }
+    return child;
+  }
+  return rep;
+}
+
+// Returns `rep` if it is not a CordRepCrc node, or its child if it is.
+// Does not consume or create a reference on `rep` or the returned value.
+inline CordRep* SkipCrcNode(CordRep* rep) {
+  assert(rep != nullptr);
+  if (ABSL_PREDICT_FALSE(rep->IsCrc())) {
+    return rep->crc()->child;
+  } else {
+    return rep;
+  }
+}
+
+inline const CordRep* SkipCrcNode(const CordRep* rep) {
+  assert(rep != nullptr);
+  if (ABSL_PREDICT_FALSE(rep->IsCrc())) {
+    return rep->crc()->child;
+  } else {
+    return rep;
+  }
+}
+
+inline CordRepCrc* CordRep::crc() {
+  assert(IsCrc());
+  return static_cast<CordRepCrc*>(this);
+}
+
+inline const CordRepCrc* CordRep::crc() const {
+  assert(IsCrc());
+  return static_cast<const CordRepCrc*>(this);
+}
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CORD_REP_CRC_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_flat.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_flat.h
new file mode 100644
index 0000000000000000000000000000000000000000..e3e27fcd7c46214577e30e09b513f46675787f83
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_flat.h
@@ -0,0 +1,187 @@
+// Copyright 2020 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORD_REP_FLAT_H_
+#define ABSL_STRINGS_INTERNAL_CORD_REP_FLAT_H_
+
+#include <cassert>
+#include <cstddef>
+#include <cstdint>
+#include <memory>
+
+#include "absl/base/config.h"
+#include "absl/base/macros.h"
+#include "absl/strings/internal/cord_internal.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// Note: all constants below are never ODR used and internal to cord, we define
+// these as static constexpr to avoid 'in struct' definition and usage clutter.
+
+// Largest and smallest flat node lengths we are willing to allocate
+// Flat allocation size is stored in tag, which currently can encode sizes up
+// to 4K, encoded as multiple of either 8 or 32 bytes.
+// If we allow for larger sizes, we need to change this to 8/64, 16/128, etc.
+// kMinFlatSize is bounded by tag needing to be at least FLAT * 8 bytes, and
+// ideally a 'nice' size aligning with allocation and cacheline sizes like 32.
+// kMaxFlatSize is bounded by the size resulting in a computed tag no greater
+// than MAX_FLAT_TAG. MAX_FLAT_TAG provides for additional 'high' tag values.
+static constexpr size_t kFlatOverhead = offsetof(CordRep, storage);
+static constexpr size_t kMinFlatSize = 32;
+static constexpr size_t kMaxFlatSize = 4096;
+static constexpr size_t kMaxFlatLength = kMaxFlatSize - kFlatOverhead;
+static constexpr size_t kMinFlatLength = kMinFlatSize - kFlatOverhead;
+static constexpr size_t kMaxLargeFlatSize = 256 * 1024;
+static constexpr size_t kMaxLargeFlatLength = kMaxLargeFlatSize - kFlatOverhead;
+
+// kTagBase should make the Size <--> Tag computation resilient
+// against changes to the value of FLAT when we add a new tag..
+static constexpr uint8_t kTagBase = FLAT - 4;
+
+// Converts the provided rounded size to the corresponding tag
+constexpr uint8_t AllocatedSizeToTagUnchecked(size_t size) {
+  return static_cast<uint8_t>(size <= 512 ? kTagBase + size / 8
+                              : size <= 8192
+                                  ? kTagBase + 512 / 8 + size / 64 - 512 / 64
+                                  : kTagBase + 512 / 8 + ((8192 - 512) / 64) +
+                                        size / 4096 - 8192 / 4096);
+}
+
+// Converts the provided tag to the corresponding allocated size
+constexpr size_t TagToAllocatedSize(uint8_t tag) {
+  return (tag <= kTagBase + 512 / 8) ? tag * 8 - kTagBase * 8
+         : (tag <= kTagBase + (512 / 8) + ((8192 - 512) / 64))
+             ? 512 + tag * 64 - kTagBase * 64 - 512 / 8 * 64
+             : 8192 + tag * 4096 - kTagBase * 4096 -
+                   ((512 / 8) + ((8192 - 512) / 64)) * 4096;
+}
+
+static_assert(AllocatedSizeToTagUnchecked(kMinFlatSize) == FLAT, "");
+static_assert(AllocatedSizeToTagUnchecked(kMaxLargeFlatSize) == MAX_FLAT_TAG,
+              "");
+
+// RoundUp logically performs `((n + m - 1) / m) * m` to round up to the nearest
+// multiple of `m`, optimized for the invariant that `m` is a power of 2.
+constexpr size_t RoundUp(size_t n, size_t m) {
+  return (n + m - 1) & (0 - m);
+}
+
+// Returns the size to the nearest equal or larger value that can be
+// expressed exactly as a tag value.
+inline size_t RoundUpForTag(size_t size) {
+  return RoundUp(size, (size <= 512) ? 8 : (size <= 8192 ? 64 : 4096));
+}
+
+// Converts the allocated size to a tag, rounding down if the size
+// does not exactly match a 'tag expressible' size value. The result is
+// undefined if the size exceeds the maximum size that can be encoded in
+// a tag, i.e., if size is larger than TagToAllocatedSize(<max tag>).
+inline uint8_t AllocatedSizeToTag(size_t size) {
+  const uint8_t tag = AllocatedSizeToTagUnchecked(size);
+  assert(tag <= MAX_FLAT_TAG);
+  return tag;
+}
+
+// Converts the provided tag to the corresponding available data length
+constexpr size_t TagToLength(uint8_t tag) {
+  return TagToAllocatedSize(tag) - kFlatOverhead;
+}
+
+// Enforce that kMaxFlatSize maps to a well-known exact tag value.
+static_assert(TagToAllocatedSize(MAX_FLAT_TAG) == kMaxLargeFlatSize,
+              "Bad tag logic");
+
+struct CordRepFlat : public CordRep {
+  // Tag for explicit 'large flat' allocation
+  struct Large {};
+
+  // Creates a new flat node.
+  template <size_t max_flat_size, typename... Args>
+  static CordRepFlat* NewImpl(size_t len, Args... args ABSL_ATTRIBUTE_UNUSED) {
+    if (len <= kMinFlatLength) {
+      len = kMinFlatLength;
+    } else if (len > max_flat_size - kFlatOverhead) {
+      len = max_flat_size - kFlatOverhead;
+    }
+
+    // Round size up so it matches a size we can exactly express in a tag.
+    const size_t size = RoundUpForTag(len + kFlatOverhead);
+    void* const raw_rep = ::operator new(size);
+    CordRepFlat* rep = new (raw_rep) CordRepFlat();
+    rep->tag = AllocatedSizeToTag(size);
+    return rep;
+  }
+
+  static CordRepFlat* New(size_t len) { return NewImpl<kMaxFlatSize>(len); }
+
+  static CordRepFlat* New(Large, size_t len) {
+    return NewImpl<kMaxLargeFlatSize>(len);
+  }
+
+  // Deletes a CordRepFlat instance created previously through a call to New().
+  // Flat CordReps are allocated and constructed with raw ::operator new and
+  // placement new, and must be destructed and deallocated accordingly.
+  static void Delete(CordRep*rep) {
+    assert(rep->tag >= FLAT && rep->tag <= MAX_FLAT_TAG);
+
+#if defined(__cpp_sized_deallocation)
+    size_t size = TagToAllocatedSize(rep->tag);
+    rep->~CordRep();
+    ::operator delete(rep, size);
+#else
+    rep->~CordRep();
+    ::operator delete(rep);
+#endif
+  }
+
+  // Create a CordRepFlat containing `data`, with an optional additional
+  // extra capacity of up to `extra` bytes. Requires that `data.size()`
+  // is less than kMaxFlatLength.
+  static CordRepFlat* Create(absl::string_view data, size_t extra = 0) {
+    assert(data.size() <= kMaxFlatLength);
+    CordRepFlat* flat = New(data.size() + (std::min)(extra, kMaxFlatLength));
+    memcpy(flat->Data(), data.data(), data.size());
+    flat->length = data.size();
+    return flat;
+  }
+
+  // Returns a pointer to the data inside this flat rep.
+  char* Data() { return reinterpret_cast<char*>(storage); }
+  const char* Data() const { return reinterpret_cast<const char*>(storage); }
+
+  // Returns the maximum capacity (payload size) of this instance.
+  size_t Capacity() const { return TagToLength(tag); }
+
+  // Returns the allocated size (payload + overhead) of this instance.
+  size_t AllocatedSize() const { return TagToAllocatedSize(tag); }
+};
+
+// Now that CordRepFlat is defined, we can define CordRep's helper casts:
+inline CordRepFlat* CordRep::flat() {
+  assert(tag >= FLAT && tag <= MAX_FLAT_TAG);
+  return reinterpret_cast<CordRepFlat*>(this);
+}
+
+inline const CordRepFlat* CordRep::flat() const {
+  assert(tag >= FLAT && tag <= MAX_FLAT_TAG);
+  return reinterpret_cast<const CordRepFlat*>(this);
+}
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CORD_REP_FLAT_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_ring.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_ring.h
new file mode 100644
index 0000000000000000000000000000000000000000..2000e21ea0a3007f0120c898b3d89dd7f48aa850
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_ring.h
@@ -0,0 +1,607 @@
+// Copyright 2020 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORD_REP_RING_H_
+#define ABSL_STRINGS_INTERNAL_CORD_REP_RING_H_
+
+#include <cassert>
+#include <cstddef>
+#include <cstdint>
+#include <iosfwd>
+#include <limits>
+#include <memory>
+
+#include "absl/container/internal/layout.h"
+#include "absl/strings/internal/cord_internal.h"
+#include "absl/strings/internal/cord_rep_flat.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// All operations modifying a ring buffer are implemented as static methods
+// requiring a CordRepRing instance with a reference adopted by the method.
+//
+// The methods return the modified ring buffer, which may be equal to the input
+// if the input was not shared, and having large enough capacity to accommodate
+// any newly added node(s). Otherwise, a copy of the input rep with the new
+// node(s) added is returned.
+//
+// Any modification on non shared ring buffers with enough capacity will then
+// require minimum atomic operations. Caller should where possible provide
+// reasonable `extra` hints for both anticipated extra `flat` byte space, as
+// well as anticipated extra nodes required for complex operations.
+//
+// Example of code creating a ring buffer, adding some data to it,
+// and discarding the buffer when done:
+//
+//   void FunWithRings() {
+//     // Create ring with 3 flats
+//     CordRep* flat = CreateFlat("Hello");
+//     CordRepRing* ring = CordRepRing::Create(flat, 2);
+//     ring = CordRepRing::Append(ring, CreateFlat(" "));
+//     ring = CordRepRing::Append(ring, CreateFlat("world"));
+//     DoSomethingWithRing(ring);
+//     CordRep::Unref(ring);
+//   }
+//
+// Example of code Copying an existing ring buffer and modifying it:
+//
+//   void MoreFunWithRings(CordRepRing* src) {
+//     CordRepRing* ring = CordRep::Ref(src)->ring();
+//     ring = CordRepRing::Append(ring, CreateFlat("Hello"));
+//     ring = CordRepRing::Append(ring, CreateFlat(" "));
+//     ring = CordRepRing::Append(ring, CreateFlat("world"));
+//     DoSomethingWithRing(ring);
+//     CordRep::Unref(ring);
+//   }
+//
+class CordRepRing : public CordRep {
+ public:
+  // `pos_type` represents a 'logical position'. A CordRepRing instance has a
+  // `begin_pos` (default 0), and each node inside the buffer will have an
+  // `end_pos` which is the `end_pos` of the previous node (or `begin_pos`) plus
+  // this node's length. The purpose is to allow for a binary search on this
+  // position, while allowing O(1) prepend and append operations.
+  using pos_type = size_t;
+
+  // `index_type` is the type for the `head`, `tail` and `capacity` indexes.
+  // Ring buffers are limited to having no more than four billion entries.
+  using index_type = uint32_t;
+
+  // `offset_type` is the type for the data offset inside a child rep's data.
+  using offset_type = uint32_t;
+
+  // Position holds the node index and relative offset into the node for
+  // some physical offset in the contained data as returned by the Find()
+  // and FindTail() methods.
+  struct Position {
+    index_type index;
+    size_t offset;
+  };
+
+  // The maximum # of child nodes that can be hosted inside a CordRepRing.
+  static constexpr size_t kMaxCapacity = (std::numeric_limits<uint32_t>::max)();
+
+  // CordRepring can not be default constructed, moved, copied or assigned.
+  CordRepRing() = delete;
+  CordRepRing(const CordRepRing&) = delete;
+  CordRepRing& operator=(const CordRepRing&) = delete;
+
+  // Returns true if this instance is valid, false if some or all of the
+  // invariants are broken. Intended for debug purposes only.
+  // `output` receives an explanation of the broken invariants.
+  bool IsValid(std::ostream& output) const;
+
+  // Returns the size in bytes for a CordRepRing with `capacity' entries.
+  static constexpr size_t AllocSize(size_t capacity);
+
+  // Returns the distance in bytes from `pos` to `end_pos`.
+  static constexpr size_t Distance(pos_type pos, pos_type end_pos);
+
+  // Creates a new ring buffer from the provided `rep`. Adopts a reference
+  // on `rep`. The returned ring buffer has a capacity of at least `extra + 1`
+  static CordRepRing* Create(CordRep* child, size_t extra = 0);
+
+  // `head`, `tail` and `capacity` indexes defining the ring buffer boundaries.
+  index_type head() const { return head_; }
+  index_type tail() const { return tail_; }
+  index_type capacity() const { return capacity_; }
+
+  // Returns the number of entries in this instance.
+  index_type entries() const { return entries(head_, tail_); }
+
+  // Returns the logical begin position of this instance.
+  pos_type begin_pos() const { return begin_pos_; }
+
+  // Returns the number of entries for a given head-tail range.
+  // Requires `head` and `tail` values to be less than `capacity()`.
+  index_type entries(index_type head, index_type tail) const {
+    assert(head < capacity_ && tail < capacity_);
+    return tail - head + ((tail > head) ? 0 : capacity_);
+  }
+
+  // Returns the logical end position of entry `index`.
+  pos_type const& entry_end_pos(index_type index) const {
+    assert(IsValidIndex(index));
+    return Layout::Partial().Pointer<0>(data_)[index];
+  }
+
+  // Returns the child pointer of entry `index`.
+  CordRep* const& entry_child(index_type index) const {
+    assert(IsValidIndex(index));
+    return Layout::Partial(capacity()).Pointer<1>(data_)[index];
+  }
+
+  // Returns the data offset of entry `index`
+  offset_type const& entry_data_offset(index_type index) const {
+    assert(IsValidIndex(index));
+    return Layout::Partial(capacity(), capacity()).Pointer<2>(data_)[index];
+  }
+
+  // Appends the provided child node to the `rep` instance.
+  // Adopts a reference from `rep` and `child` which may not be null.
+  // If the provided child is a FLAT or EXTERNAL node, or a SUBSTRING node
+  // containing a FLAT or EXTERNAL node, then flat or external the node is added
+  // 'as is', with an offset added for the SUBSTRING case.
+  // If the provided child is a RING or CONCAT tree, or a SUBSTRING of a RING or
+  // CONCAT tree, then all child nodes not excluded by any start offset or
+  // length values are added recursively.
+  static CordRepRing* Append(CordRepRing* rep, CordRep* child);
+
+  // Appends the provided string data to the `rep` instance.
+  // This function will attempt to utilize any remaining capacity in the last
+  // node of the input if that node is not shared (directly or indirectly), and
+  // of type FLAT. Remaining data will be added as one or more FLAT nodes.
+  // Any last node added to the ring buffer will be allocated with up to
+  // `extra` bytes of capacity for (anticipated) subsequent append actions.
+  static CordRepRing* Append(CordRepRing* rep, string_view data,
+                             size_t extra = 0);
+
+  // Prepends the provided child node to the `rep` instance.
+  // Adopts a reference from `rep` and `child` which may not be null.
+  // If the provided child is a FLAT or EXTERNAL node, or a SUBSTRING node
+  // containing a FLAT or EXTERNAL node, then flat or external the node is
+  // prepended 'as is', with an optional offset added for the SUBSTRING case.
+  // If the provided child is a RING or CONCAT tree, or a SUBSTRING of a RING
+  // or CONCAT tree, then all child nodes not excluded by any start offset or
+  // length values are added recursively.
+  static CordRepRing* Prepend(CordRepRing* rep, CordRep* child);
+
+  // Prepends the provided string data to the `rep` instance.
+  // This function will attempt to utilize any remaining capacity in the first
+  // node of the input if that node is not shared (directly or indirectly), and
+  // of type FLAT. Remaining data will be added as one or more FLAT nodes.
+  // Any first node prepnded to the ring buffer will be allocated with up to
+  // `extra` bytes of capacity for (anticipated) subsequent prepend actions.
+  static CordRepRing* Prepend(CordRepRing* rep, string_view data,
+                              size_t extra = 0);
+
+  // Returns a span referencing potentially unused capacity in the last node.
+  // The returned span may be empty if no such capacity is available, or if the
+  // current instance is shared. Else, a span of size `n <= size` is returned.
+  // If non empty, the ring buffer is adjusted to the new length, with the newly
+  // added capacity left uninitialized. Callers should assign a value to the
+  // entire span before any other operations on this instance.
+  Span<char> GetAppendBuffer(size_t size);
+
+  // Returns a span referencing potentially unused capacity in the first node.
+  // This function is identical to GetAppendBuffer except that it returns a span
+  // referencing up to `size` capacity directly before the existing data.
+  Span<char> GetPrependBuffer(size_t size);
+
+  // Returns a cord ring buffer containing `len` bytes of data starting at
+  // `offset`. If the input is not shared, this function will remove all head
+  // and tail child nodes outside of the requested range, and adjust the new
+  // head and tail nodes as required. If the input is shared, this function
+  // returns a new instance sharing some or all of the nodes from the input.
+  static CordRepRing* SubRing(CordRepRing* r, size_t offset, size_t len,
+                              size_t extra = 0);
+
+  // Returns a cord ring buffer with the first `len` bytes removed.
+  // If the input is not shared, this function will remove all head child nodes
+  // fully inside the first `length` bytes, and adjust the new head as required.
+  // If the input is shared, this function returns a new instance sharing some
+  // or all of the nodes from the input.
+  static CordRepRing* RemoveSuffix(CordRepRing* r, size_t len,
+                                   size_t extra = 0);
+
+  // Returns a cord ring buffer with the last `len` bytes removed.
+  // If the input is not shared, this function will remove all head child nodes
+  // fully inside the first `length` bytes, and adjust the new head as required.
+  // If the input is shared, this function returns a new instance sharing some
+  // or all of the nodes from the input.
+  static CordRepRing* RemovePrefix(CordRepRing* r, size_t len,
+                                   size_t extra = 0);
+
+  // Returns the character at `offset`. Requires that `offset < length`.
+  char GetCharacter(size_t offset) const;
+
+  // Returns true if this instance manages a single contiguous buffer, in which
+  // case the (optional) output parameter `fragment` is set. Otherwise, the
+  // function returns false, and `fragment` is left unchanged.
+  bool IsFlat(absl::string_view* fragment) const;
+
+  // Returns true if the data starting at `offset` with length `len` is
+  // managed by this instance inside a single contiguous buffer, in which case
+  // the (optional) output parameter `fragment` is set to the contiguous memory
+  // starting at offset `offset` with length `length`. Otherwise, the function
+  // returns false, and `fragment` is left unchanged.
+  bool IsFlat(size_t offset, size_t len, absl::string_view* fragment) const;
+
+  // Testing only: set capacity to requested capacity.
+  void SetCapacityForTesting(size_t capacity);
+
+  // Returns the CordRep data pointer for the provided CordRep.
+  // Requires that the provided `rep` is either a FLAT or EXTERNAL CordRep.
+  static const char* GetLeafData(const CordRep* rep);
+
+  // Returns the CordRep data pointer for the provided CordRep.
+  // Requires that `rep` is either a FLAT, EXTERNAL, or SUBSTRING CordRep.
+  static const char* GetRepData(const CordRep* rep);
+
+  // Advances the provided position, wrapping around capacity as needed.
+  // Requires `index` < capacity()
+  inline index_type advance(index_type index) const;
+
+  // Advances the provided position by 'n`, wrapping around capacity as needed.
+  // Requires `index` < capacity() and `n` <= capacity.
+  inline index_type advance(index_type index, index_type n) const;
+
+  // Retreats the provided position, wrapping around 0 as needed.
+  // Requires `index` < capacity()
+  inline index_type retreat(index_type index) const;
+
+  // Retreats the provided position by 'n', wrapping around 0 as needed.
+  // Requires `index` < capacity()
+  inline index_type retreat(index_type index, index_type n) const;
+
+  // Returns the logical begin position of entry `index`
+  pos_type const& entry_begin_pos(index_type index) const {
+    return (index == head_) ? begin_pos_ : entry_end_pos(retreat(index));
+  }
+
+  // Returns the physical start offset of entry `index`
+  size_t entry_start_offset(index_type index) const {
+    return Distance(begin_pos_, entry_begin_pos(index));
+  }
+
+  // Returns the physical end offset of entry `index`
+  size_t entry_end_offset(index_type index) const {
+    return Distance(begin_pos_, entry_end_pos(index));
+  }
+
+  // Returns the data length for entry `index`
+  size_t entry_length(index_type index) const {
+    return Distance(entry_begin_pos(index), entry_end_pos(index));
+  }
+
+  // Returns the data for entry `index`
+  absl::string_view entry_data(index_type index) const;
+
+  // Returns the position for `offset` as {index, prefix}. `index` holds the
+  // index of the entry at the specified offset and `prefix` holds the relative
+  // offset inside that entry.
+  // Requires `offset` < length.
+  //
+  // For example we can implement GetCharacter(offset) as:
+  //   char GetCharacter(size_t offset) {
+  //     Position pos = this->Find(offset);
+  //     return this->entry_data(pos.pos)[pos.offset];
+  //   }
+  inline Position Find(size_t offset) const;
+
+  // Find starting at `head`
+  inline Position Find(index_type head, size_t offset) const;
+
+  // Returns the tail position for `offset` as {tail index, suffix}.
+  // `tail index` holds holds the index of the entry holding the offset directly
+  // before 'offset` advanced by one. 'suffix` holds the relative offset from
+  // that relative offset in the entry to the end of the entry.
+  // For example, FindTail(length) will return {tail(), 0}, FindTail(length - 5)
+  // will return {retreat(tail), 5)} provided the preceding entry contains at
+  // least 5 bytes of data.
+  // Requires offset >= 1 && offset <= length.
+  //
+  // This function is very useful in functions that need to clip the end of some
+  // ring buffer such as 'RemovePrefix'.
+  // For example, we could implement RemovePrefix for non shared instances as:
+  //   void RemoveSuffix(size_t n) {
+  //     Position pos = FindTail(length - n);
+  //     UnrefEntries(pos.pos, this->tail_);
+  //     this->tail_ = pos.pos;
+  //     entry(retreat(pos.pos)).end_pos -= pos.offset;
+  //   }
+  inline Position FindTail(size_t offset) const;
+
+  // Find tail starting at `head`
+  inline Position FindTail(index_type head, size_t offset) const;
+
+  // Invokes f(index_type index) for each entry inside the range [head, tail>
+  template <typename F>
+  void ForEach(index_type head, index_type tail, F&& f) const {
+    index_type n1 = (tail > head) ? tail : capacity_;
+    for (index_type i = head; i < n1; ++i) f(i);
+    if (tail <= head) {
+      for (index_type i = 0; i < tail; ++i) f(i);
+    }
+  }
+
+  // Invokes f(index_type index) for each entry inside this instance.
+  template <typename F>
+  void ForEach(F&& f) const {
+    ForEach(head_, tail_, std::forward<F>(f));
+  }
+
+  // Dump this instance's data tp stream `s` in human readable format, excluding
+  // the actual data content itself. Intended for debug purposes only.
+  friend std::ostream& operator<<(std::ostream& s, const CordRepRing& rep);
+
+ private:
+  enum class AddMode { kAppend, kPrepend };
+
+  using Layout = container_internal::Layout<pos_type, CordRep*, offset_type>;
+
+  class Filler;
+  class Transaction;
+  class CreateTransaction;
+
+  static constexpr size_t kLayoutAlignment = Layout::Partial().Alignment();
+
+  // Creates a new CordRepRing.
+  explicit CordRepRing(index_type capacity) : capacity_(capacity) {}
+
+  // Returns true if `index` is a valid index into this instance.
+  bool IsValidIndex(index_type index) const;
+
+  // Debug use only: validates the provided CordRepRing invariants.
+  // Verification of all CordRepRing methods can be enabled by defining
+  // EXTRA_CORD_RING_VALIDATION, i.e.: `--copts=-DEXTRA_CORD_RING_VALIDATION`
+  // Verification is VERY expensive, so only do it for debugging purposes.
+  static CordRepRing* Validate(CordRepRing* rep, const char* file = nullptr,
+                               int line = 0);
+
+  // Allocates a CordRepRing large enough to hold `capacity + extra' entries.
+  // The returned capacity may be larger if the allocated memory allows for it.
+  // The maximum capacity of a CordRepRing is capped at kMaxCapacity.
+  // Throws `std::length_error` if `capacity + extra' exceeds kMaxCapacity.
+  static CordRepRing* New(size_t capacity, size_t extra);
+
+  // Deallocates (but does not destroy) the provided ring buffer.
+  static void Delete(CordRepRing* rep);
+
+  // Destroys the provided ring buffer, decrementing the reference count of all
+  // contained child CordReps. The provided 1\`rep` should have a ref count of
+  // one (pre decrement destroy call observing `refcount.IsOne()`) or zero
+  // (post decrement destroy call observing `!refcount.Decrement()`).
+  static void Destroy(CordRepRing* rep);
+
+  // Returns a mutable reference to the logical end position array.
+  pos_type* entry_end_pos() {
+    return Layout::Partial().Pointer<0>(data_);
+  }
+
+  // Returns a mutable reference to the child pointer array.
+  CordRep** entry_child() {
+    return Layout::Partial(capacity()).Pointer<1>(data_);
+  }
+
+  // Returns a mutable reference to the data offset array.
+  offset_type* entry_data_offset() {
+    return Layout::Partial(capacity(), capacity()).Pointer<2>(data_);
+  }
+
+  // Find implementations for the non fast path 0 / length cases.
+  Position FindSlow(index_type head, size_t offset) const;
+  Position FindTailSlow(index_type head, size_t offset) const;
+
+  // Finds the index of the first node that is inside a reasonable distance
+  // of the node at `offset` from which we can continue with a linear search.
+  template <bool wrap>
+  index_type FindBinary(index_type head, index_type tail, size_t offset) const;
+
+  // Fills the current (initialized) instance from the provided source, copying
+  // entries [head, tail). Adds a reference to copied entries if `ref` is true.
+  template <bool ref>
+  void Fill(const CordRepRing* src, index_type head, index_type tail);
+
+  // Create a copy of 'rep', copying all entries [head, tail), allocating room
+  // for `extra` entries. Adds a reference on all copied entries.
+  static CordRepRing* Copy(CordRepRing* rep, index_type head, index_type tail,
+                           size_t extra = 0);
+
+  // Returns a Mutable CordRepRing reference from `rep` with room for at least
+  // `extra` additional nodes. Adopts a reference count from `rep`.
+  // This function will return `rep` if, and only if:
+  // - rep.entries + extra <= rep.capacity
+  // - rep.refcount == 1
+  // Otherwise, this function will create a new copy of `rep` with additional
+  // capacity to satisfy `extra` extra nodes, and unref the old `rep` instance.
+  //
+  // If a new CordRepRing can not be allocated, or the new capacity would exceed
+  // the maxmimum capacity, then the input is consumed only, and an exception is
+  // thrown.
+  static CordRepRing* Mutable(CordRepRing* rep, size_t extra);
+
+  // Slow path for Append(CordRepRing* rep, CordRep* child). This function is
+  // exercised if the provided `child` in Append() is not a leaf node, i.e., a
+  // ring buffer or old (concat) cord tree.
+  static CordRepRing* AppendSlow(CordRepRing* rep, CordRep* child);
+
+  // Appends the provided leaf node. Requires `child` to be FLAT or EXTERNAL.
+  static CordRepRing* AppendLeaf(CordRepRing* rep, CordRep* child,
+                                 size_t offset, size_t length);
+
+  // Prepends the provided leaf node. Requires `child` to be FLAT or EXTERNAL.
+  static CordRepRing* PrependLeaf(CordRepRing* rep, CordRep* child,
+                                  size_t offset, size_t length);
+
+  // Slow path for Prepend(CordRepRing* rep, CordRep* child). This function is
+  // exercised if the provided `child` in Prepend() is not a leaf node, i.e., a
+  // ring buffer or old (concat) cord tree.
+  static CordRepRing* PrependSlow(CordRepRing* rep, CordRep* child);
+
+  // Slow path for Create(CordRep* child, size_t extra). This function is
+  // exercised if the provided `child` in Prepend() is not a leaf node, i.e., a
+  // ring buffer or old (concat) cord tree.
+  static CordRepRing* CreateSlow(CordRep* child, size_t extra);
+
+  // Creates a new ring buffer from the provided `child` leaf node. Requires
+  // `child` to be FLAT or EXTERNAL. on `rep`.
+  // The returned ring buffer has a capacity of at least `1 + extra`
+  static CordRepRing* CreateFromLeaf(CordRep* child, size_t offset,
+                                     size_t length, size_t extra);
+
+  // Appends or prepends (depending on AddMode) the ring buffer in `ring' to
+  // `rep` starting at `offset` with length `len`.
+  template <AddMode mode>
+  static CordRepRing* AddRing(CordRepRing* rep, CordRepRing* ring,
+                              size_t offset, size_t len);
+
+  // Increases the data offset for entry `index` by `n`.
+  void AddDataOffset(index_type index, size_t n);
+
+  // Descreases the length for entry `index` by `n`.
+  void SubLength(index_type index, size_t n);
+
+  index_type head_;
+  index_type tail_;
+  index_type capacity_;
+  pos_type begin_pos_;
+
+  alignas(kLayoutAlignment) char data_[kLayoutAlignment];
+
+  friend struct CordRep;
+};
+
+constexpr size_t CordRepRing::AllocSize(size_t capacity) {
+  return sizeof(CordRepRing) - sizeof(data_) +
+         Layout(capacity, capacity, capacity).AllocSize();
+}
+
+inline constexpr size_t CordRepRing::Distance(pos_type pos, pos_type end_pos) {
+  return (end_pos - pos);
+}
+
+inline const char* CordRepRing::GetLeafData(const CordRep* rep) {
+  return rep->tag != EXTERNAL ? rep->flat()->Data() : rep->external()->base;
+}
+
+inline const char* CordRepRing::GetRepData(const CordRep* rep) {
+  if (rep->tag >= FLAT) return rep->flat()->Data();
+  if (rep->tag == EXTERNAL) return rep->external()->base;
+  return GetLeafData(rep->substring()->child) + rep->substring()->start;
+}
+
+inline CordRepRing::index_type CordRepRing::advance(index_type index) const {
+  assert(index < capacity_);
+  return ++index == capacity_ ? 0 : index;
+}
+
+inline CordRepRing::index_type CordRepRing::advance(index_type index,
+                                                    index_type n) const {
+  assert(index < capacity_ && n <= capacity_);
+  return (index += n) >= capacity_ ? index - capacity_ : index;
+}
+
+inline CordRepRing::index_type CordRepRing::retreat(index_type index) const {
+  assert(index < capacity_);
+  return (index > 0 ? index : capacity_) - 1;
+}
+
+inline CordRepRing::index_type CordRepRing::retreat(index_type index,
+                                                    index_type n) const {
+  assert(index < capacity_ && n <= capacity_);
+  return index >= n ? index - n : capacity_ - n + index;
+}
+
+inline absl::string_view CordRepRing::entry_data(index_type index) const {
+  size_t data_offset = entry_data_offset(index);
+  return {GetRepData(entry_child(index)) + data_offset, entry_length(index)};
+}
+
+inline bool CordRepRing::IsValidIndex(index_type index) const {
+  if (index >= capacity_) return false;
+  return (tail_ > head_) ? (index >= head_ && index < tail_)
+                         : (index >= head_ || index < tail_);
+}
+
+#ifndef EXTRA_CORD_RING_VALIDATION
+inline CordRepRing* CordRepRing::Validate(CordRepRing* rep,
+                                          const char* /*file*/, int /*line*/) {
+  return rep;
+}
+#endif
+
+inline CordRepRing::Position CordRepRing::Find(size_t offset) const {
+  assert(offset < length);
+  return (offset == 0) ? Position{head_, 0} : FindSlow(head_, offset);
+}
+
+inline CordRepRing::Position CordRepRing::Find(index_type head,
+                                               size_t offset) const {
+  assert(offset < length);
+  assert(IsValidIndex(head) && offset >= entry_start_offset(head));
+  return (offset == 0) ? Position{head_, 0} : FindSlow(head, offset);
+}
+
+inline CordRepRing::Position CordRepRing::FindTail(size_t offset) const {
+  assert(offset > 0 && offset <= length);
+  return (offset == length) ? Position{tail_, 0} : FindTailSlow(head_, offset);
+}
+
+inline CordRepRing::Position CordRepRing::FindTail(index_type head,
+                                                   size_t offset) const {
+  assert(offset > 0 && offset <= length);
+  assert(IsValidIndex(head) && offset >= entry_start_offset(head) + 1);
+  return (offset == length) ? Position{tail_, 0} : FindTailSlow(head, offset);
+}
+
+// Now that CordRepRing is defined, we can define CordRep's helper casts:
+inline CordRepRing* CordRep::ring() {
+  assert(IsRing());
+  return static_cast<CordRepRing*>(this);
+}
+
+inline const CordRepRing* CordRep::ring() const {
+  assert(IsRing());
+  return static_cast<const CordRepRing*>(this);
+}
+
+inline bool CordRepRing::IsFlat(absl::string_view* fragment) const {
+  if (entries() == 1) {
+    if (fragment) *fragment = entry_data(head());
+    return true;
+  }
+  return false;
+}
+
+inline bool CordRepRing::IsFlat(size_t offset, size_t len,
+                                absl::string_view* fragment) const {
+  const Position pos = Find(offset);
+  const absl::string_view data = entry_data(pos.index);
+  if (data.length() >= len && data.length() - len >= pos.offset) {
+    if (fragment) *fragment = data.substr(pos.offset, len);
+    return true;
+  }
+  return false;
+}
+
+std::ostream& operator<<(std::ostream& s, const CordRepRing& rep);
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CORD_REP_RING_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_ring_reader.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_ring_reader.h
new file mode 100644
index 0000000000000000000000000000000000000000..7ceeaa000ee725637bf4c4acc30e97944c3b568d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cord_rep_ring_reader.h
@@ -0,0 +1,118 @@
+// Copyright 2021 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORD_REP_RING_READER_H_
+#define ABSL_STRINGS_INTERNAL_CORD_REP_RING_READER_H_
+
+#include <cassert>
+#include <cstddef>
+#include <cstdint>
+
+#include "absl/strings/internal/cord_internal.h"
+#include "absl/strings/internal/cord_rep_ring.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// CordRepRingReader provides basic navigation over CordRepRing data.
+class CordRepRingReader {
+ public:
+  // Returns true if this instance is not empty.
+  explicit operator bool() const { return ring_ != nullptr; }
+
+  // Returns the ring buffer reference for this instance, or nullptr if empty.
+  CordRepRing* ring() const { return ring_; }
+
+  // Returns the current node index inside the ring buffer for this instance.
+  // The returned value is undefined if this instance is empty.
+  CordRepRing::index_type index() const { return index_; }
+
+  // Returns the current node inside the ring buffer for this instance.
+  // The returned value is undefined if this instance is empty.
+  CordRep* node() const { return ring_->entry_child(index_); }
+
+  // Returns the length of the referenced ring buffer.
+  // Requires the current instance to be non empty.
+  size_t length() const {
+    assert(ring_);
+    return ring_->length;
+  }
+
+  // Returns the end offset of the last navigated-to chunk, which represents the
+  // total bytes 'consumed' relative to the start of the ring. The returned
+  // value is never zero. For example, initializing a reader with a ring buffer
+  // with a first chunk of 19 bytes will return consumed() = 19.
+  // Requires the current instance to be non empty.
+  size_t consumed() const {
+    assert(ring_);
+    return ring_->entry_end_offset(index_);
+  }
+
+  // Returns the number of bytes remaining beyond the last navigated-to chunk.
+  // Requires the current instance to be non empty.
+  size_t remaining() const {
+    assert(ring_);
+    return length() - consumed();
+  }
+
+  // Resets this instance to an empty value
+  void Reset() { ring_ = nullptr; }
+
+  // Resets this instance to the start of `ring`. `ring` must not be null.
+  // Returns a reference into the first chunk of the provided ring.
+  absl::string_view Reset(CordRepRing* ring) {
+    assert(ring);
+    ring_ = ring;
+    index_ = ring_->head();
+    return ring_->entry_data(index_);
+  }
+
+  // Navigates to the next chunk inside the reference ring buffer.
+  // Returns a reference into the navigated-to chunk.
+  // Requires remaining() to be non zero.
+  absl::string_view Next() {
+    assert(remaining());
+    index_ = ring_->advance(index_);
+    return ring_->entry_data(index_);
+  }
+
+  // Navigates to the chunk at offset `offset`.
+  // Returns a reference into the navigated-to chunk, adjusted for the relative
+  // position of `offset` into that chunk. For example, calling Seek(13) on a
+  // ring buffer containing 2 chunks of 10 and 20 bytes respectively will return
+  // a string view into the second chunk starting at offset 3 with a size of 17.
+  // Requires `offset` to be less than `length()`
+  absl::string_view Seek(size_t offset) {
+    assert(offset < length());
+    size_t current = ring_->entry_end_offset(index_);
+    CordRepRing::index_type hint = (offset >= current) ? index_ : ring_->head();
+    const CordRepRing::Position head = ring_->Find(hint, offset);
+    index_ = head.index;
+    auto data = ring_->entry_data(head.index);
+    data.remove_prefix(head.offset);
+    return data;
+  }
+
+ private:
+  CordRepRing* ring_ = nullptr;
+  CordRepRing::index_type index_;
+};
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CORD_REP_RING_READER_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_functions.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..ed108bf1b4430bbf324135ceb488990f409515c8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_functions.h
@@ -0,0 +1,77 @@
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORDZ_FUNCTIONS_H_
+#define ABSL_STRINGS_INTERNAL_CORDZ_FUNCTIONS_H_
+
+#include <stdint.h>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/base/optimization.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// Returns the current sample rate. This represents the average interval
+// between samples.
+int32_t get_cordz_mean_interval();
+
+// Sets the sample rate with the average interval between samples.
+void set_cordz_mean_interval(int32_t mean_interval);
+
+// Cordz is only enabled on Linux with thread_local support.
+#if defined(ABSL_INTERNAL_CORDZ_ENABLED)
+#error ABSL_INTERNAL_CORDZ_ENABLED cannot be set directly
+#elif defined(__linux__) && defined(ABSL_HAVE_THREAD_LOCAL)
+#define ABSL_INTERNAL_CORDZ_ENABLED 1
+#endif
+
+#ifdef ABSL_INTERNAL_CORDZ_ENABLED
+
+// cordz_next_sample is the number of events until the next sample event. If
+// the value is 1 or less, the code will check on the next event if cordz is
+// enabled, and if so, will sample the Cord. cordz is only enabled when we can
+// use thread locals.
+ABSL_CONST_INIT extern thread_local int64_t cordz_next_sample;
+
+// Determines if the next sample should be profiled. If it is, the value pointed
+// at by next_sample will be set with the interval until the next sample.
+bool cordz_should_profile_slow();
+
+// Returns true if the next cord should be sampled.
+inline bool cordz_should_profile() {
+  if (ABSL_PREDICT_TRUE(cordz_next_sample > 1)) {
+    cordz_next_sample--;
+    return false;
+  }
+  return cordz_should_profile_slow();
+}
+
+// Sets the interval until the next sample (for testing only)
+void cordz_set_next_sample_for_testing(int64_t next_sample);
+
+#else  // ABSL_INTERNAL_CORDZ_ENABLED
+
+inline bool cordz_should_profile() { return false; }
+inline void cordz_set_next_sample_for_testing(int64_t) {}
+
+#endif  // ABSL_INTERNAL_CORDZ_ENABLED
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CORDZ_FUNCTIONS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_handle.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_handle.h
new file mode 100644
index 0000000000000000000000000000000000000000..3c800b433fbfb6270ec0114929c1d81c5f1b26f4
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_handle.h
@@ -0,0 +1,131 @@
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORDZ_HANDLE_H_
+#define ABSL_STRINGS_INTERNAL_CORDZ_HANDLE_H_
+
+#include <atomic>
+#include <vector>
+
+#include "absl/base/config.h"
+#include "absl/base/internal/raw_logging.h"
+#include "absl/base/internal/spinlock.h"
+#include "absl/synchronization/mutex.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// This base class allows multiple types of object (CordzInfo and
+// CordzSampleToken) to exist simultaneously on the delete queue (pointed to by
+// global_dq_tail and traversed using dq_prev_ and dq_next_). The
+// delete queue guarantees that once a profiler creates a CordzSampleToken and
+// has gained visibility into a CordzInfo object, that CordzInfo object will not
+// be deleted prematurely. This allows the profiler to inspect all CordzInfo
+// objects that are alive without needing to hold a global lock.
+class CordzHandle {
+ public:
+  CordzHandle() : CordzHandle(false) {}
+
+  bool is_snapshot() const { return is_snapshot_; }
+
+  // Returns true if this instance is safe to be deleted because it is either a
+  // snapshot, which is always safe to delete, or not included in the global
+  // delete queue and thus not included in any snapshot.
+  // Callers are responsible for making sure this instance can not be newly
+  // discovered by other threads. For example, CordzInfo instances first de-list
+  // themselves from the global CordzInfo list before determining if they are
+  // safe to be deleted directly.
+  // If SafeToDelete returns false, callers MUST use the Delete() method to
+  // safely queue CordzHandle instances for deletion.
+  bool SafeToDelete() const;
+
+  // Deletes the provided instance, or puts it on the delete queue to be deleted
+  // once there are no more sample tokens (snapshot) instances potentially
+  // referencing the instance. `handle` should not be null.
+  static void Delete(CordzHandle* handle);
+
+  // Returns the current entries in the delete queue in LIFO order.
+  static std::vector<const CordzHandle*> DiagnosticsGetDeleteQueue();
+
+  // Returns true if the provided handle is nullptr or guarded by this handle.
+  // Since the CordzSnapshot token is itself a CordzHandle, this method will
+  // allow tests to check if that token is keeping an arbitrary CordzHandle
+  // alive.
+  bool DiagnosticsHandleIsSafeToInspect(const CordzHandle* handle) const;
+
+  // Returns the current entries in the delete queue, in LIFO order, that are
+  // protected by this. CordzHandle objects are only placed on the delete queue
+  // after CordzHandle::Delete is called with them as an argument. Only
+  // CordzHandle objects that are not also CordzSnapshot objects will be
+  // included in the return vector. For each of the handles in the return
+  // vector, the earliest that their memory can be freed is when this
+  // CordzSnapshot object is deleted.
+  std::vector<const CordzHandle*> DiagnosticsGetSafeToInspectDeletedHandles();
+
+ protected:
+  explicit CordzHandle(bool is_snapshot);
+  virtual ~CordzHandle();
+
+ private:
+  // Global queue data. CordzHandle stores a pointer to the global queue
+  // instance to harden against ODR violations.
+  struct Queue {
+    constexpr explicit Queue(absl::ConstInitType)
+        : mutex(absl::kConstInit,
+                absl::base_internal::SCHEDULE_COOPERATIVE_AND_KERNEL) {}
+
+    absl::base_internal::SpinLock mutex;
+    std::atomic<CordzHandle*> dq_tail ABSL_GUARDED_BY(mutex){nullptr};
+
+    // Returns true if this delete queue is empty. This method does not acquire
+    // the lock, but does a 'load acquire' observation on the delete queue tail.
+    // It is used inside Delete() to check for the presence of a delete queue
+    // without holding the lock. The assumption is that the caller is in the
+    // state of 'being deleted', and can not be newly discovered by a concurrent
+    // 'being constructed' snapshot instance. Practically, this means that any
+    // such discovery (`find`, 'first' or 'next', etc) must have proper 'happens
+    // before / after' semantics and atomic fences.
+    bool IsEmpty() const ABSL_NO_THREAD_SAFETY_ANALYSIS {
+      return dq_tail.load(std::memory_order_acquire) == nullptr;
+    }
+  };
+
+  void ODRCheck() const {
+#ifndef NDEBUG
+    ABSL_RAW_CHECK(queue_ == &global_queue_, "ODR violation in Cord");
+#endif
+  }
+
+  ABSL_CONST_INIT static Queue global_queue_;
+  Queue* const queue_ = &global_queue_;
+  const bool is_snapshot_;
+
+  // dq_prev_ and dq_next_ require the global queue mutex to be held.
+  // Unfortunately we can't use thread annotations such that the thread safety
+  // analysis understands that queue_ and global_queue_ are one and the same.
+  CordzHandle* dq_prev_  = nullptr;
+  CordzHandle* dq_next_ = nullptr;
+};
+
+class CordzSnapshot : public CordzHandle {
+ public:
+  CordzSnapshot() : CordzHandle(true) {}
+};
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CORDZ_HANDLE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_info.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_info.h
new file mode 100644
index 0000000000000000000000000000000000000000..17eaa91c77de5dedf4ee5a00ee9dcbea0d052f3e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_info.h
@@ -0,0 +1,298 @@
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORDZ_INFO_H_
+#define ABSL_STRINGS_INTERNAL_CORDZ_INFO_H_
+
+#include <atomic>
+#include <cstdint>
+#include <functional>
+
+#include "absl/base/config.h"
+#include "absl/base/internal/raw_logging.h"
+#include "absl/base/internal/spinlock.h"
+#include "absl/base/thread_annotations.h"
+#include "absl/strings/internal/cord_internal.h"
+#include "absl/strings/internal/cordz_functions.h"
+#include "absl/strings/internal/cordz_handle.h"
+#include "absl/strings/internal/cordz_statistics.h"
+#include "absl/strings/internal/cordz_update_tracker.h"
+#include "absl/synchronization/mutex.h"
+#include "absl/types/span.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// CordzInfo tracks a profiled Cord. Each of these objects can be in two places.
+// If a Cord is alive, the CordzInfo will be in the global_cordz_infos map, and
+// can also be retrieved via the linked list starting with
+// global_cordz_infos_head and continued via the cordz_info_next() method. When
+// a Cord has reached the end of its lifespan, the CordzInfo object will be
+// migrated out of the global_cordz_infos list and the global_cordz_infos_map,
+// and will either be deleted or appended to the global_delete_queue. If it is
+// placed on the global_delete_queue, the CordzInfo object will be cleaned in
+// the destructor of a CordzSampleToken object.
+class ABSL_LOCKABLE CordzInfo : public CordzHandle {
+ public:
+  using MethodIdentifier = CordzUpdateTracker::MethodIdentifier;
+
+  // TrackCord creates a CordzInfo instance which tracks important metrics of
+  // a sampled cord, and stores the created CordzInfo instance into `cord'. All
+  // CordzInfo instances are placed in a global list which is used to discover
+  // and snapshot all actively tracked cords. Callers are responsible for
+  // calling UntrackCord() before the tracked Cord instance is deleted, or to
+  // stop tracking the sampled Cord. Callers are also responsible for guarding
+  // changes to the 'tree' value of a Cord (InlineData.tree) through the Lock()
+  // and Unlock() calls. Any change resulting in a new tree value for the cord
+  // requires a call to SetCordRep() before the old tree has been unreffed
+  // and/or deleted. `method` identifies the Cord public API method initiating
+  // the cord to be sampled.
+  // Requires `cord` to hold a tree, and `cord.cordz_info()` to be null.
+  static void TrackCord(InlineData& cord, MethodIdentifier method);
+
+  // Identical to TrackCord(), except that this function fills the
+  // `parent_stack` and `parent_method` properties of the returned CordzInfo
+  // instance from the provided `src` instance if `src` is sampled.
+  // This function should be used for sampling 'copy constructed' and 'copy
+  // assigned' cords. This function allows 'cord` to be already sampled, in
+  // which case the CordzInfo will be newly created from `src`.
+  static void TrackCord(InlineData& cord, const InlineData& src,
+                        MethodIdentifier method);
+
+  // Maybe sample the cord identified by 'cord' for method 'method'.
+  // Uses `cordz_should_profile` to randomly pick cords to be sampled, and if
+  // so, invokes `TrackCord` to start sampling `cord`.
+  static void MaybeTrackCord(InlineData& cord, MethodIdentifier method);
+
+  // Maybe sample the cord identified by 'cord' for method 'method'.
+  // `src` identifies a 'parent' cord which is assigned to `cord`, typically the
+  // input cord for a copy constructor, or an assign method such as `operator=`
+  // `cord` will be sampled if (and only if) `src` is sampled.
+  // If `cord` is currently being sampled and `src` is not being sampled, then
+  // this function will stop sampling the cord and reset the cord's cordz_info.
+  //
+  // Previously this function defined that `cord` will be sampled if either
+  // `src` is sampled, or if `cord` is randomly picked for sampling. However,
+  // this can cause issues, as there may be paths where some cord is assigned an
+  // indirect copy of it's own value. As such a 'string of copies' would then
+  // remain sampled (`src.is_profiled`), then assigning such a cord back to
+  // 'itself' creates a cycle where the cord will converge to 'always sampled`.
+  //
+  // For example:
+  //
+  //   Cord x;
+  //   for (...) {
+  //     // Copy ctor --> y.is_profiled := x.is_profiled | random(...)
+  //     Cord y = x;
+  //     ...
+  //     // Assign x = y --> x.is_profiled = y.is_profiled | random(...)
+  //     //              ==> x.is_profiled |= random(...)
+  //     //              ==> x converges to 'always profiled'
+  //     x = y;
+  //   }
+  static void MaybeTrackCord(InlineData& cord, const InlineData& src,
+                             MethodIdentifier method);
+
+  // Stops tracking changes for a sampled cord, and deletes the provided info.
+  // This function must be called before the sampled cord instance is deleted,
+  // and before the root cordrep of the sampled cord is unreffed.
+  // This function may extend the lifetime of the cordrep in cases where the
+  // CordInfo instance is being held by a concurrent collection thread.
+  void Untrack();
+
+  // Invokes UntrackCord() on `info` if `info` is not null.
+  static void MaybeUntrackCord(CordzInfo* info);
+
+  CordzInfo() = delete;
+  CordzInfo(const CordzInfo&) = delete;
+  CordzInfo& operator=(const CordzInfo&) = delete;
+
+  // Retrieves the oldest existing CordzInfo.
+  static CordzInfo* Head(const CordzSnapshot& snapshot)
+      ABSL_NO_THREAD_SAFETY_ANALYSIS;
+
+  // Retrieves the next oldest existing CordzInfo older than 'this' instance.
+  CordzInfo* Next(const CordzSnapshot& snapshot) const
+      ABSL_NO_THREAD_SAFETY_ANALYSIS;
+
+  // Locks this instance for the update identified by `method`.
+  // Increases the count for `method` in `update_tracker`.
+  void Lock(MethodIdentifier method) ABSL_EXCLUSIVE_LOCK_FUNCTION(mutex_);
+
+  // Unlocks this instance. If the contained `rep` has been set to null
+  // indicating the Cord has been cleared or is otherwise no longer sampled,
+  // then this method will delete this CordzInfo instance.
+  void Unlock() ABSL_UNLOCK_FUNCTION(mutex_);
+
+  // Asserts that this CordzInfo instance is locked.
+  void AssertHeld() ABSL_ASSERT_EXCLUSIVE_LOCK(mutex_);
+
+  // Updates the `rep` property of this instance. This methods is invoked by
+  // Cord logic each time the root node of a sampled Cord changes, and before
+  // the old root reference count is deleted. This guarantees that collection
+  // code can always safely take a reference on the tracked cord.
+  // Requires a lock to be held through the `Lock()` method.
+  // TODO(b/117940323): annotate with ABSL_EXCLUSIVE_LOCKS_REQUIRED once all
+  // Cord code is in a state where this can be proven true by the compiler.
+  void SetCordRep(CordRep* rep);
+
+  // Returns the current `rep` property of this instance with a reference
+  // added, or null if this instance represents a cord that has since been
+  // deleted or untracked.
+  CordRep* RefCordRep() const ABSL_LOCKS_EXCLUDED(mutex_);
+
+  // Returns the current value of `rep_` for testing purposes only.
+  CordRep* GetCordRepForTesting() const ABSL_NO_THREAD_SAFETY_ANALYSIS {
+    return rep_;
+  }
+
+  // Sets the current value of `rep_` for testing purposes only.
+  void SetCordRepForTesting(CordRep* rep) ABSL_NO_THREAD_SAFETY_ANALYSIS {
+    rep_ = rep;
+  }
+
+  // Returns the stack trace for where the cord was first sampled. Cords are
+  // potentially sampled when they promote from an inlined cord to a tree or
+  // ring representation, which is not necessarily the location where the cord
+  // was first created. Some cords are created as inlined cords, and only as
+  // data is added do they become a non-inlined cord. However, typically the
+  // location represents reasonably well where the cord is 'created'.
+  absl::Span<void* const> GetStack() const;
+
+  // Returns the stack trace for a sampled cord's 'parent stack trace'. This
+  // value may be set if the cord is sampled (promoted) after being created
+  // from, or being assigned the value of an existing (sampled) cord.
+  absl::Span<void* const> GetParentStack() const;
+
+  // Retrieves the CordzStatistics associated with this Cord. The statistics
+  // are only updated when a Cord goes through a mutation, such as an Append
+  // or RemovePrefix.
+  CordzStatistics GetCordzStatistics() const;
+
+ private:
+  using SpinLock = absl::base_internal::SpinLock;
+  using SpinLockHolder = ::absl::base_internal::SpinLockHolder;
+
+  // Global cordz info list. CordzInfo stores a pointer to the global list
+  // instance to harden against ODR violations.
+  struct List {
+    constexpr explicit List(absl::ConstInitType)
+        : mutex(absl::kConstInit,
+                absl::base_internal::SCHEDULE_COOPERATIVE_AND_KERNEL) {}
+
+    SpinLock mutex;
+    std::atomic<CordzInfo*> head ABSL_GUARDED_BY(mutex){nullptr};
+  };
+
+  static constexpr size_t kMaxStackDepth = 64;
+
+  explicit CordzInfo(CordRep* rep, const CordzInfo* src,
+                     MethodIdentifier method);
+  ~CordzInfo() override;
+
+  // Sets `rep_` without holding a lock.
+  void UnsafeSetCordRep(CordRep* rep) ABSL_NO_THREAD_SAFETY_ANALYSIS;
+
+  void Track();
+
+  // Returns the parent method from `src`, which is either `parent_method_` or
+  // `method_` depending on `parent_method_` being kUnknown.
+  // Returns kUnknown if `src` is null.
+  static MethodIdentifier GetParentMethod(const CordzInfo* src);
+
+  // Fills the provided stack from `src`, copying either `parent_stack_` or
+  // `stack_` depending on `parent_stack_` being empty, returning the size of
+  // the parent stack.
+  // Returns 0 if `src` is null.
+  static size_t FillParentStack(const CordzInfo* src, void** stack);
+
+  void ODRCheck() const {
+#ifndef NDEBUG
+    ABSL_RAW_CHECK(list_ == &global_list_, "ODR violation in Cord");
+#endif
+  }
+
+  // Non-inlined implementation of `MaybeTrackCord`, which is executed if
+  // either `src` is sampled or `cord` is sampled, and either untracks or
+  // tracks `cord` as documented per `MaybeTrackCord`.
+  static void MaybeTrackCordImpl(InlineData& cord, const InlineData& src,
+                                 MethodIdentifier method);
+
+  ABSL_CONST_INIT static List global_list_;
+  List* const list_ = &global_list_;
+
+  // ci_prev_ and ci_next_ require the global list mutex to be held.
+  // Unfortunately we can't use thread annotations such that the thread safety
+  // analysis understands that list_ and global_list_ are one and the same.
+  std::atomic<CordzInfo*> ci_prev_{nullptr};
+  std::atomic<CordzInfo*> ci_next_{nullptr};
+
+  mutable absl::Mutex mutex_;
+  CordRep* rep_ ABSL_GUARDED_BY(mutex_);
+
+  void* stack_[kMaxStackDepth];
+  void* parent_stack_[kMaxStackDepth];
+  const size_t stack_depth_;
+  const size_t parent_stack_depth_;
+  const MethodIdentifier method_;
+  const MethodIdentifier parent_method_;
+  CordzUpdateTracker update_tracker_;
+  const absl::Time create_time_;
+};
+
+inline ABSL_ATTRIBUTE_ALWAYS_INLINE void CordzInfo::MaybeTrackCord(
+    InlineData& cord, MethodIdentifier method) {
+  if (ABSL_PREDICT_FALSE(cordz_should_profile())) {
+    TrackCord(cord, method);
+  }
+}
+
+inline ABSL_ATTRIBUTE_ALWAYS_INLINE void CordzInfo::MaybeTrackCord(
+    InlineData& cord, const InlineData& src, MethodIdentifier method) {
+  if (ABSL_PREDICT_FALSE(InlineData::is_either_profiled(cord, src))) {
+    MaybeTrackCordImpl(cord, src, method);
+  }
+}
+
+inline ABSL_ATTRIBUTE_ALWAYS_INLINE void CordzInfo::MaybeUntrackCord(
+    CordzInfo* info) {
+  if (ABSL_PREDICT_FALSE(info)) {
+    info->Untrack();
+  }
+}
+
+inline void CordzInfo::AssertHeld() ABSL_ASSERT_EXCLUSIVE_LOCK(mutex_) {
+#ifndef NDEBUG
+  mutex_.AssertHeld();
+#endif
+}
+
+inline void CordzInfo::SetCordRep(CordRep* rep) {
+  AssertHeld();
+  rep_ = rep;
+}
+
+inline void CordzInfo::UnsafeSetCordRep(CordRep* rep) { rep_ = rep; }
+
+inline CordRep* CordzInfo::RefCordRep() const ABSL_LOCKS_EXCLUDED(mutex_) {
+  MutexLock lock(&mutex_);
+  return rep_ ? CordRep::Ref(rep_) : nullptr;
+}
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CORDZ_INFO_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_statistics.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_statistics.h
new file mode 100644
index 0000000000000000000000000000000000000000..9f558df49415dd4ecc43f8931fef8dd93cb5963f
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_statistics.h
@@ -0,0 +1,88 @@
+// Copyright 2019 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORDZ_STATISTICS_H_
+#define ABSL_STRINGS_INTERNAL_CORDZ_STATISTICS_H_
+
+#include <cstdint>
+
+#include "absl/base/config.h"
+#include "absl/strings/internal/cordz_update_tracker.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// CordzStatistics captures some meta information about a Cord's shape.
+struct CordzStatistics {
+  using MethodIdentifier = CordzUpdateTracker::MethodIdentifier;
+
+  // Node counts information
+  struct NodeCounts {
+    size_t flat = 0;       // #flats
+    size_t flat_64 = 0;    // #flats up to 64 bytes
+    size_t flat_128 = 0;   // #flats up to 128 bytes
+    size_t flat_256 = 0;   // #flats up to 256 bytes
+    size_t flat_512 = 0;   // #flats up to 512 bytes
+    size_t flat_1k = 0;    // #flats up to 1K bytes
+    size_t external = 0;   // #external reps
+    size_t substring = 0;  // #substring reps
+    size_t concat = 0;     // #concat reps
+    size_t ring = 0;       // #ring buffer reps
+    size_t btree = 0;      // #btree reps
+    size_t crc = 0;        // #crc reps
+  };
+
+  // The size of the cord in bytes. This matches the result of Cord::size().
+  size_t size = 0;
+
+  // The estimated memory used by the sampled cord. This value matches the
+  // value as reported by Cord::EstimatedMemoryUsage().
+  // A value of 0 implies the property has not been recorded.
+  size_t estimated_memory_usage = 0;
+
+  // The effective memory used by the sampled cord, inversely weighted by the
+  // effective indegree of each allocated node. This is a representation of the
+  // fair share of memory usage that should be attributed to the sampled cord.
+  // This value is more useful for cases where one or more nodes are referenced
+  // by multiple Cord instances, and for cases where a Cord includes the same
+  // node multiple times (either directly or indirectly).
+  // A value of 0 implies the property has not been recorded.
+  size_t estimated_fair_share_memory_usage = 0;
+
+  // The total number of nodes referenced by this cord.
+  // For ring buffer Cords, this includes the 'ring buffer' node.
+  // For btree Cords, this includes all 'CordRepBtree' tree nodes as well as all
+  // the substring, flat and external nodes referenced by the tree.
+  // A value of 0 implies the property has not been recorded.
+  size_t node_count = 0;
+
+  // Detailed node counts per type
+  NodeCounts node_counts;
+
+  // The cord method responsible for sampling the cord.
+  MethodIdentifier method = MethodIdentifier::kUnknown;
+
+  // The cord method responsible for sampling the parent cord if applicable.
+  MethodIdentifier parent_method = MethodIdentifier::kUnknown;
+
+  // Update tracker tracking invocation count per cord method.
+  CordzUpdateTracker update_tracker;
+};
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CORDZ_STATISTICS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_update_scope.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_update_scope.h
new file mode 100644
index 0000000000000000000000000000000000000000..57ba75de93c850feabf21aa357d9373fa03225f7
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_update_scope.h
@@ -0,0 +1,71 @@
+// Copyright 2021 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORDZ_UPDATE_SCOPE_H_
+#define ABSL_STRINGS_INTERNAL_CORDZ_UPDATE_SCOPE_H_
+
+#include "absl/base/config.h"
+#include "absl/base/optimization.h"
+#include "absl/base/thread_annotations.h"
+#include "absl/strings/internal/cord_internal.h"
+#include "absl/strings/internal/cordz_info.h"
+#include "absl/strings/internal/cordz_update_tracker.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// CordzUpdateScope scopes an update to the provided CordzInfo.
+// The class invokes `info->Lock(method)` and `info->Unlock()` to guard
+// cordrep updates. This class does nothing if `info` is null.
+// See also the 'Lock`, `Unlock` and `SetCordRep` methods in `CordzInfo`.
+class ABSL_SCOPED_LOCKABLE CordzUpdateScope {
+ public:
+  CordzUpdateScope(CordzInfo* info, CordzUpdateTracker::MethodIdentifier method)
+      ABSL_EXCLUSIVE_LOCK_FUNCTION(info)
+      : info_(info) {
+    if (ABSL_PREDICT_FALSE(info_)) {
+      info->Lock(method);
+    }
+  }
+
+  // CordzUpdateScope can not be copied or assigned to.
+  CordzUpdateScope(CordzUpdateScope&& rhs) = delete;
+  CordzUpdateScope(const CordzUpdateScope&) = delete;
+  CordzUpdateScope& operator=(CordzUpdateScope&& rhs) = delete;
+  CordzUpdateScope& operator=(const CordzUpdateScope&) = delete;
+
+  ~CordzUpdateScope() ABSL_UNLOCK_FUNCTION() {
+    if (ABSL_PREDICT_FALSE(info_)) {
+      info_->Unlock();
+    }
+  }
+
+  void SetCordRep(CordRep* rep) const {
+    if (ABSL_PREDICT_FALSE(info_)) {
+      info_->SetCordRep(rep);
+    }
+  }
+
+  CordzInfo* info() const { return info_; }
+
+ private:
+  CordzInfo* info_;
+};
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CORDZ_UPDATE_SCOPE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_update_tracker.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_update_tracker.h
new file mode 100644
index 0000000000000000000000000000000000000000..c5170662bf4eac2b8a0c0a87521ed6c8f14ae4fc
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/cordz_update_tracker.h
@@ -0,0 +1,123 @@
+// Copyright 2021 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_CORDZ_UPDATE_TRACKER_H_
+#define ABSL_STRINGS_INTERNAL_CORDZ_UPDATE_TRACKER_H_
+
+#include <atomic>
+#include <cstdint>
+
+#include "absl/base/config.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace cord_internal {
+
+// CordzUpdateTracker tracks counters for Cord update methods.
+//
+// The purpose of CordzUpdateTracker is to track the number of calls to methods
+// updating Cord data for sampled cords. The class internally uses 'lossy'
+// atomic operations: Cord is thread-compatible, so there is no need to
+// synchronize updates. However, Cordz collection threads may call 'Value()' at
+// any point, so the class needs to provide thread safe access.
+//
+// This class is thread-safe. But as per above comments, all non-const methods
+// should be used single-threaded only: updates are thread-safe but lossy.
+class CordzUpdateTracker {
+ public:
+  // Tracked update methods.
+  enum MethodIdentifier {
+    kUnknown,
+    kAppendCord,
+    kAppendCordBuffer,
+    kAppendExternalMemory,
+    kAppendString,
+    kAssignCord,
+    kAssignString,
+    kClear,
+    kConstructorCord,
+    kConstructorString,
+    kCordReader,
+    kFlatten,
+    kGetAppendBuffer,
+    kGetAppendRegion,
+    kMakeCordFromExternal,
+    kMoveAppendCord,
+    kMoveAssignCord,
+    kMovePrependCord,
+    kPrependCord,
+    kPrependCordBuffer,
+    kPrependString,
+    kRemovePrefix,
+    kRemoveSuffix,
+    kSetExpectedChecksum,
+    kSubCord,
+
+    // kNumMethods defines the number of entries: must be the last entry.
+    kNumMethods,
+  };
+
+  // Constructs a new instance. All counters are zero-initialized.
+  constexpr CordzUpdateTracker() noexcept : values_{} {}
+
+  // Copy constructs a new instance.
+  CordzUpdateTracker(const CordzUpdateTracker& rhs) noexcept { *this = rhs; }
+
+  // Assigns the provided value to this instance.
+  CordzUpdateTracker& operator=(const CordzUpdateTracker& rhs) noexcept {
+    for (int i = 0; i < kNumMethods; ++i) {
+      values_[i].store(rhs.values_[i].load(std::memory_order_relaxed),
+                       std::memory_order_relaxed);
+    }
+    return *this;
+  }
+
+  // Returns the value for the specified method.
+  int64_t Value(MethodIdentifier method) const {
+    return values_[method].load(std::memory_order_relaxed);
+  }
+
+  // Increases the value for the specified method by `n`
+  void LossyAdd(MethodIdentifier method, int64_t n = 1) {
+    auto& value = values_[method];
+    value.store(value.load(std::memory_order_relaxed) + n,
+                std::memory_order_relaxed);
+  }
+
+  // Adds all the values from `src` to this instance
+  void LossyAdd(const CordzUpdateTracker& src) {
+    for (int i = 0; i < kNumMethods; ++i) {
+      MethodIdentifier method = static_cast<MethodIdentifier>(i);
+      if (int64_t value = src.Value(method)) {
+        LossyAdd(method, value);
+      }
+    }
+  }
+
+ private:
+  // Until C++20 std::atomic is not constexpr default-constructible, so we need
+  // a wrapper for this class to be constexpr constructible.
+  class Counter : public std::atomic<int64_t> {
+   public:
+    constexpr Counter() noexcept : std::atomic<int64_t>(0) {}
+  };
+
+  Counter values_[kNumMethods];
+};
+
+}  // namespace cord_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_CORDZ_UPDATE_TRACKER_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/damerau_levenshtein_distance.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/damerau_levenshtein_distance.h
new file mode 100644
index 0000000000000000000000000000000000000000..7a4bd64462682d42dda2914b6a765fbbe7da271a
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/damerau_levenshtein_distance.h
@@ -0,0 +1,34 @@
+// Copyright 2022 The Abseil Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_DAMERAU_LEVENSHTEIN_DISTANCE_H_
+#define ABSL_STRINGS_INTERNAL_DAMERAU_LEVENSHTEIN_DISTANCE_H_
+
+#include <cstdint>
+
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace strings_internal {
+// Calculate DamerauLevenshtein distance between two strings.
+// When the distance is larger than cutoff, the code just returns cutoff + 1.
+uint8_t CappedDamerauLevenshteinDistance(absl::string_view s1,
+                                         absl::string_view s2, uint8_t cutoff);
+
+}  // namespace strings_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_DAMERAU_LEVENSHTEIN_DISTANCE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/escaping.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/escaping.h
new file mode 100644
index 0000000000000000000000000000000000000000..b04033ffc9a07c9fc1fec62d0d81cf57731b2bf1
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/internal/escaping.h
@@ -0,0 +1,56 @@
+// Copyright 2020 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef ABSL_STRINGS_INTERNAL_ESCAPING_H_
+#define ABSL_STRINGS_INTERNAL_ESCAPING_H_
+
+#include <cassert>
+
+#include "absl/strings/internal/resize_uninitialized.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace strings_internal {
+
+ABSL_CONST_INIT extern const char kBase64Chars[];
+
+// Calculates the length of a Base64 encoding (RFC 4648) of a string of length
+// `input_len`, with or without padding per `do_padding`. Note that 'web-safe'
+// encoding (section 5 of the RFC) does not change this length.
+size_t CalculateBase64EscapedLenInternal(size_t input_len, bool do_padding);
+
+// Base64-encodes `src` using the alphabet provided in `base64` (which
+// determines whether to do web-safe encoding or not) and writes the result to
+// `dest`. If `do_padding` is true, `dest` is padded with '=' chars until its
+// length is a multiple of 3. Returns the length of `dest`.
+size_t Base64EscapeInternal(const unsigned char* src, size_t szsrc, char* dest,
+                            size_t szdest, const char* base64, bool do_padding);
+template <typename String>
+void Base64EscapeInternal(const unsigned char* src, size_t szsrc, String* dest,
+                          bool do_padding, const char* base64_chars) {
+  const size_t calc_escaped_size =
+      CalculateBase64EscapedLenInternal(szsrc, do_padding);
+  STLStringResizeUninitialized(dest, calc_escaped_size);
+
+  const size_t escaped_len = Base64EscapeInternal(
+      src, szsrc, &(*dest)[0], dest->size(), base64_chars, do_padding);
+  assert(calc_escaped_size == escaped_len);
+  dest->erase(escaped_len);
+}
+
+}  // namespace strings_internal
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_INTERNAL_ESCAPING_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/match.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/match.h
new file mode 100644
index 0000000000000000000000000000000000000000..038cbb3fa8b5e0d8a76c7856fa99f2e599654245
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/match.h
@@ -0,0 +1,100 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: match.h
+// -----------------------------------------------------------------------------
+//
+// This file contains simple utilities for performing string matching checks.
+// All of these function parameters are specified as `absl::string_view`,
+// meaning that these functions can accept `std::string`, `absl::string_view` or
+// NUL-terminated C-style strings.
+//
+// Examples:
+//   std::string s = "foo";
+//   absl::string_view sv = "f";
+//   assert(absl::StrContains(s, sv));
+//
+// Note: The order of parameters in these functions is designed to mimic the
+// order an equivalent member function would exhibit;
+// e.g. `s.Contains(x)` ==> `absl::StrContains(s, x).
+#ifndef ABSL_STRINGS_MATCH_H_
+#define ABSL_STRINGS_MATCH_H_
+
+#include <cstring>
+
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// StrContains()
+//
+// Returns whether a given string `haystack` contains the substring `needle`.
+inline bool StrContains(absl::string_view haystack,
+                        absl::string_view needle) noexcept {
+  return haystack.find(needle, 0) != haystack.npos;
+}
+
+inline bool StrContains(absl::string_view haystack, char needle) noexcept {
+  return haystack.find(needle) != haystack.npos;
+}
+
+// StartsWith()
+//
+// Returns whether a given string `text` begins with `prefix`.
+inline bool StartsWith(absl::string_view text,
+                       absl::string_view prefix) noexcept {
+  return prefix.empty() ||
+         (text.size() >= prefix.size() &&
+          memcmp(text.data(), prefix.data(), prefix.size()) == 0);
+}
+
+// EndsWith()
+//
+// Returns whether a given string `text` ends with `suffix`.
+inline bool EndsWith(absl::string_view text,
+                     absl::string_view suffix) noexcept {
+  return suffix.empty() ||
+         (text.size() >= suffix.size() &&
+          memcmp(text.data() + (text.size() - suffix.size()), suffix.data(),
+                 suffix.size()) == 0);
+}
+
+// EqualsIgnoreCase()
+//
+// Returns whether given ASCII strings `piece1` and `piece2` are equal, ignoring
+// case in the comparison.
+bool EqualsIgnoreCase(absl::string_view piece1,
+                      absl::string_view piece2) noexcept;
+
+// StartsWithIgnoreCase()
+//
+// Returns whether a given ASCII string `text` starts with `prefix`,
+// ignoring case in the comparison.
+bool StartsWithIgnoreCase(absl::string_view text,
+                          absl::string_view prefix) noexcept;
+
+// EndsWithIgnoreCase()
+//
+// Returns whether a given ASCII string `text` ends with `suffix`, ignoring
+// case in the comparison.
+bool EndsWithIgnoreCase(absl::string_view text,
+                        absl::string_view suffix) noexcept;
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_MATCH_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/numbers.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/numbers.h
new file mode 100644
index 0000000000000000000000000000000000000000..86c84ed39b7ace921bd99bb048fb237509fada2e
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/numbers.h
@@ -0,0 +1,303 @@
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: numbers.h
+// -----------------------------------------------------------------------------
+//
+// This package contains functions for converting strings to numbers. For
+// converting numbers to strings, use `StrCat()` or `StrAppend()` in str_cat.h,
+// which automatically detect and convert most number values appropriately.
+
+#ifndef ABSL_STRINGS_NUMBERS_H_
+#define ABSL_STRINGS_NUMBERS_H_
+
+#ifdef __SSSE3__
+#include <tmmintrin.h>
+#endif
+
+#ifdef _MSC_VER
+#include <intrin.h>
+#endif
+
+#include <cstddef>
+#include <cstdlib>
+#include <cstring>
+#include <ctime>
+#include <limits>
+#include <string>
+#include <type_traits>
+
+#include "absl/base/config.h"
+#include "absl/base/internal/endian.h"
+#include "absl/base/macros.h"
+#include "absl/base/port.h"
+#include "absl/numeric/bits.h"
+#include "absl/numeric/int128.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// SimpleAtoi()
+//
+// Converts the given string (optionally followed or preceded by ASCII
+// whitespace) into an integer value, returning `true` if successful. The string
+// must reflect a base-10 integer whose value falls within the range of the
+// integer type (optionally preceded by a `+` or `-`). If any errors are
+// encountered, this function returns `false`, leaving `out` in an unspecified
+// state.
+template <typename int_type>
+ABSL_MUST_USE_RESULT bool SimpleAtoi(absl::string_view str, int_type* out);
+
+// SimpleAtof()
+//
+// Converts the given string (optionally followed or preceded by ASCII
+// whitespace) into a float, which may be rounded on overflow or underflow,
+// returning `true` if successful.
+// See https://en.cppreference.com/w/c/string/byte/strtof for details about the
+// allowed formats for `str`, except SimpleAtof() is locale-independent and will
+// always use the "C" locale. If any errors are encountered, this function
+// returns `false`, leaving `out` in an unspecified state.
+ABSL_MUST_USE_RESULT bool SimpleAtof(absl::string_view str, float* out);
+
+// SimpleAtod()
+//
+// Converts the given string (optionally followed or preceded by ASCII
+// whitespace) into a double, which may be rounded on overflow or underflow,
+// returning `true` if successful.
+// See https://en.cppreference.com/w/c/string/byte/strtof for details about the
+// allowed formats for `str`, except SimpleAtod is locale-independent and will
+// always use the "C" locale. If any errors are encountered, this function
+// returns `false`, leaving `out` in an unspecified state.
+ABSL_MUST_USE_RESULT bool SimpleAtod(absl::string_view str, double* out);
+
+// SimpleAtob()
+//
+// Converts the given string into a boolean, returning `true` if successful.
+// The following case-insensitive strings are interpreted as boolean `true`:
+// "true", "t", "yes", "y", "1". The following case-insensitive strings
+// are interpreted as boolean `false`: "false", "f", "no", "n", "0". If any
+// errors are encountered, this function returns `false`, leaving `out` in an
+// unspecified state.
+ABSL_MUST_USE_RESULT bool SimpleAtob(absl::string_view str, bool* out);
+
+// SimpleHexAtoi()
+//
+// Converts a hexadecimal string (optionally followed or preceded by ASCII
+// whitespace) to an integer, returning `true` if successful. Only valid base-16
+// hexadecimal integers whose value falls within the range of the integer type
+// (optionally preceded by a `+` or `-`) can be converted. A valid hexadecimal
+// value may include both upper and lowercase character symbols, and may
+// optionally include a leading "0x" (or "0X") number prefix, which is ignored
+// by this function. If any errors are encountered, this function returns
+// `false`, leaving `out` in an unspecified state.
+template <typename int_type>
+ABSL_MUST_USE_RESULT bool SimpleHexAtoi(absl::string_view str, int_type* out);
+
+// Overloads of SimpleHexAtoi() for 128 bit integers.
+ABSL_MUST_USE_RESULT inline bool SimpleHexAtoi(absl::string_view str,
+                                               absl::int128* out);
+ABSL_MUST_USE_RESULT inline bool SimpleHexAtoi(absl::string_view str,
+                                               absl::uint128* out);
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+// End of public API.  Implementation details follow.
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace numbers_internal {
+
+// Digit conversion.
+ABSL_DLL extern const char kHexChar[17];  // 0123456789abcdef
+ABSL_DLL extern const char
+    kHexTable[513];  // 000102030405060708090a0b0c0d0e0f1011...
+ABSL_DLL extern const char
+    two_ASCII_digits[100][2];  // 00, 01, 02, 03...
+
+// Writes a two-character representation of 'i' to 'buf'. 'i' must be in the
+// range 0 <= i < 100, and buf must have space for two characters. Example:
+//   char buf[2];
+//   PutTwoDigits(42, buf);
+//   // buf[0] == '4'
+//   // buf[1] == '2'
+inline void PutTwoDigits(size_t i, char* buf) {
+  assert(i < 100);
+  memcpy(buf, two_ASCII_digits[i], 2);
+}
+
+// safe_strto?() functions for implementing SimpleAtoi()
+
+bool safe_strto32_base(absl::string_view text, int32_t* value, int base);
+bool safe_strto64_base(absl::string_view text, int64_t* value, int base);
+bool safe_strto128_base(absl::string_view text, absl::int128* value,
+                         int base);
+bool safe_strtou32_base(absl::string_view text, uint32_t* value, int base);
+bool safe_strtou64_base(absl::string_view text, uint64_t* value, int base);
+bool safe_strtou128_base(absl::string_view text, absl::uint128* value,
+                         int base);
+
+static const int kFastToBufferSize = 32;
+static const int kSixDigitsToBufferSize = 16;
+
+// Helper function for fast formatting of floating-point values.
+// The result is the same as printf's "%g", a.k.a. "%.6g"; that is, six
+// significant digits are returned, trailing zeros are removed, and numbers
+// outside the range 0.0001-999999 are output using scientific notation
+// (1.23456e+06). This routine is heavily optimized.
+// Required buffer size is `kSixDigitsToBufferSize`.
+size_t SixDigitsToBuffer(double d, char* buffer);
+
+// These functions are intended for speed. All functions take an output buffer
+// as an argument and return a pointer to the last byte they wrote, which is the
+// terminating '\0'. At most `kFastToBufferSize` bytes are written.
+char* FastIntToBuffer(int32_t, char*);
+char* FastIntToBuffer(uint32_t, char*);
+char* FastIntToBuffer(int64_t, char*);
+char* FastIntToBuffer(uint64_t, char*);
+
+// For enums and integer types that are not an exact match for the types above,
+// use templates to call the appropriate one of the four overloads above.
+template <typename int_type>
+char* FastIntToBuffer(int_type i, char* buffer) {
+  static_assert(sizeof(i) <= 64 / 8,
+                "FastIntToBuffer works only with 64-bit-or-less integers.");
+  // TODO(jorg): This signed-ness check is used because it works correctly
+  // with enums, and it also serves to check that int_type is not a pointer.
+  // If one day something like std::is_signed<enum E> works, switch to it.
+  // These conditions are constexpr bools to suppress MSVC warning C4127.
+  constexpr bool kIsSigned = static_cast<int_type>(1) - 2 < 0;
+  constexpr bool kUse64Bit = sizeof(i) > 32 / 8;
+  if (kIsSigned) {
+    if (kUse64Bit) {
+      return FastIntToBuffer(static_cast<int64_t>(i), buffer);
+    } else {
+      return FastIntToBuffer(static_cast<int32_t>(i), buffer);
+    }
+  } else {
+    if (kUse64Bit) {
+      return FastIntToBuffer(static_cast<uint64_t>(i), buffer);
+    } else {
+      return FastIntToBuffer(static_cast<uint32_t>(i), buffer);
+    }
+  }
+}
+
+// Implementation of SimpleAtoi, generalized to support arbitrary base (used
+// with base different from 10 elsewhere in Abseil implementation).
+template <typename int_type>
+ABSL_MUST_USE_RESULT bool safe_strtoi_base(absl::string_view s, int_type* out,
+                                           int base) {
+  static_assert(sizeof(*out) == 4 || sizeof(*out) == 8,
+                "SimpleAtoi works only with 32-bit or 64-bit integers.");
+  static_assert(!std::is_floating_point<int_type>::value,
+                "Use SimpleAtof or SimpleAtod instead.");
+  bool parsed;
+  // TODO(jorg): This signed-ness check is used because it works correctly
+  // with enums, and it also serves to check that int_type is not a pointer.
+  // If one day something like std::is_signed<enum E> works, switch to it.
+  // These conditions are constexpr bools to suppress MSVC warning C4127.
+  constexpr bool kIsSigned = static_cast<int_type>(1) - 2 < 0;
+  constexpr bool kUse64Bit = sizeof(*out) == 64 / 8;
+  if (kIsSigned) {
+    if (kUse64Bit) {
+      int64_t val;
+      parsed = numbers_internal::safe_strto64_base(s, &val, base);
+      *out = static_cast<int_type>(val);
+    } else {
+      int32_t val;
+      parsed = numbers_internal::safe_strto32_base(s, &val, base);
+      *out = static_cast<int_type>(val);
+    }
+  } else {
+    if (kUse64Bit) {
+      uint64_t val;
+      parsed = numbers_internal::safe_strtou64_base(s, &val, base);
+      *out = static_cast<int_type>(val);
+    } else {
+      uint32_t val;
+      parsed = numbers_internal::safe_strtou32_base(s, &val, base);
+      *out = static_cast<int_type>(val);
+    }
+  }
+  return parsed;
+}
+
+// FastHexToBufferZeroPad16()
+//
+// Outputs `val` into `out` as if by `snprintf(out, 17, "%016x", val)` but
+// without the terminating null character. Thus `out` must be of length >= 16.
+// Returns the number of non-pad digits of the output (it can never be zero
+// since 0 has one digit).
+inline size_t FastHexToBufferZeroPad16(uint64_t val, char* out) {
+#ifdef ABSL_INTERNAL_HAVE_SSSE3
+  uint64_t be = absl::big_endian::FromHost64(val);
+  const auto kNibbleMask = _mm_set1_epi8(0xf);
+  const auto kHexDigits = _mm_setr_epi8('0', '1', '2', '3', '4', '5', '6', '7',
+                                        '8', '9', 'a', 'b', 'c', 'd', 'e', 'f');
+  auto v = _mm_loadl_epi64(reinterpret_cast<__m128i*>(&be));  // load lo dword
+  auto v4 = _mm_srli_epi64(v, 4);                            // shift 4 right
+  auto il = _mm_unpacklo_epi8(v4, v);                        // interleave bytes
+  auto m = _mm_and_si128(il, kNibbleMask);                   // mask out nibbles
+  auto hexchars = _mm_shuffle_epi8(kHexDigits, m);           // hex chars
+  _mm_storeu_si128(reinterpret_cast<__m128i*>(out), hexchars);
+#else
+  for (int i = 0; i < 8; ++i) {
+    auto byte = (val >> (56 - 8 * i)) & 0xFF;
+    auto* hex = &absl::numbers_internal::kHexTable[byte * 2];
+    std::memcpy(out + 2 * i, hex, 2);
+  }
+#endif
+  // | 0x1 so that even 0 has 1 digit.
+  return 16 - static_cast<size_t>(countl_zero(val | 0x1) / 4);
+}
+
+}  // namespace numbers_internal
+
+template <typename int_type>
+ABSL_MUST_USE_RESULT bool SimpleAtoi(absl::string_view str, int_type* out) {
+  return numbers_internal::safe_strtoi_base(str, out, 10);
+}
+
+ABSL_MUST_USE_RESULT inline bool SimpleAtoi(absl::string_view str,
+                                            absl::int128* out) {
+  return numbers_internal::safe_strto128_base(str, out, 10);
+}
+
+ABSL_MUST_USE_RESULT inline bool SimpleAtoi(absl::string_view str,
+                                            absl::uint128* out) {
+  return numbers_internal::safe_strtou128_base(str, out, 10);
+}
+
+template <typename int_type>
+ABSL_MUST_USE_RESULT bool SimpleHexAtoi(absl::string_view str, int_type* out) {
+  return numbers_internal::safe_strtoi_base(str, out, 16);
+}
+
+ABSL_MUST_USE_RESULT inline bool SimpleHexAtoi(absl::string_view str,
+                                               absl::int128* out) {
+  return numbers_internal::safe_strto128_base(str, out, 16);
+}
+
+ABSL_MUST_USE_RESULT inline bool SimpleHexAtoi(absl::string_view str,
+                                               absl::uint128* out) {
+  return numbers_internal::safe_strtou128_base(str, out, 16);
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_NUMBERS_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/str_cat.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/str_cat.h
new file mode 100644
index 0000000000000000000000000000000000000000..730b4d8c603041ef4c80aa7e8465923891475ffe
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/str_cat.h
@@ -0,0 +1,462 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: str_cat.h
+// -----------------------------------------------------------------------------
+//
+// This package contains functions for efficiently concatenating and appending
+// strings: `StrCat()` and `StrAppend()`. Most of the work within these routines
+// is actually handled through use of a special AlphaNum type, which was
+// designed to be used as a parameter type that efficiently manages conversion
+// to strings and avoids copies in the above operations.
+//
+// Any routine accepting either a string or a number may accept `AlphaNum`.
+// The basic idea is that by accepting a `const AlphaNum &` as an argument
+// to your function, your callers will automagically convert bools, integers,
+// and floating point values to strings for you.
+//
+// NOTE: Use of `AlphaNum` outside of the //absl/strings package is unsupported
+// except for the specific case of function parameters of type `AlphaNum` or
+// `const AlphaNum &`. In particular, instantiating `AlphaNum` directly as a
+// stack variable is not supported.
+//
+// Conversion from 8-bit values is not accepted because, if it were, then an
+// attempt to pass ':' instead of ":" might result in a 58 ending up in your
+// result.
+//
+// Bools convert to "0" or "1". Pointers to types other than `char *` are not
+// valid inputs. No output is generated for null `char *` pointers.
+//
+// Floating point numbers are formatted with six-digit precision, which is
+// the default for "std::cout <<" or printf "%g" (the same as "%.6g").
+//
+// You can convert to hexadecimal output rather than decimal output using the
+// `Hex` type contained here. To do so, pass `Hex(my_int)` as a parameter to
+// `StrCat()` or `StrAppend()`. You may specify a minimum hex field width using
+// a `PadSpec` enum.
+//
+// User-defined types can be formatted with the `AbslStringify()` customization
+// point. The API relies on detecting an overload in the user-defined type's
+// namespace of a free (non-member) `AbslStringify()` function as a definition
+// (typically declared as a friend and implemented in-line.
+// with the following signature:
+//
+// class MyClass { ... };
+//
+// template <typename Sink>
+// void AbslStringify(Sink& sink, const MyClass& value);
+//
+// An `AbslStringify()` overload for a type should only be declared in the same
+// file and namespace as said type.
+//
+// Note that `AbslStringify()` also supports use with `absl::StrFormat()` and
+// `absl::Substitute()`.
+//
+// Example:
+//
+// struct Point {
+//   // To add formatting support to `Point`, we simply need to add a free
+//   // (non-member) function `AbslStringify()`. This method specifies how
+//   // Point should be printed when absl::StrCat() is called on it. You can add
+//   // such a free function using a friend declaration within the body of the
+//   // class. The sink parameter is a templated type to avoid requiring
+//   // dependencies.
+//   template <typename Sink> friend void AbslStringify(Sink&
+//   sink, const Point& p) {
+//     absl::Format(&sink, "(%v, %v)", p.x, p.y);
+//   }
+//
+//   int x;
+//   int y;
+// };
+// -----------------------------------------------------------------------------
+
+#ifndef ABSL_STRINGS_STR_CAT_H_
+#define ABSL_STRINGS_STR_CAT_H_
+
+#include <array>
+#include <cstdint>
+#include <string>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+#include "absl/base/port.h"
+#include "absl/strings/internal/has_absl_stringify.h"
+#include "absl/strings/internal/stringify_sink.h"
+#include "absl/strings/numbers.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+namespace strings_internal {
+// AlphaNumBuffer allows a way to pass a string to StrCat without having to do
+// memory allocation.  It is simply a pair of a fixed-size character array, and
+// a size.  Please don't use outside of absl, yet.
+template <size_t max_size>
+struct AlphaNumBuffer {
+  std::array<char, max_size> data;
+  size_t size;
+};
+
+}  // namespace strings_internal
+
+// Enum that specifies the number of significant digits to return in a `Hex` or
+// `Dec` conversion and fill character to use. A `kZeroPad2` value, for example,
+// would produce hexadecimal strings such as "0a","0f" and a 'kSpacePad5' value
+// would produce hexadecimal strings such as "    a","    f".
+enum PadSpec : uint8_t {
+  kNoPad = 1,
+  kZeroPad2,
+  kZeroPad3,
+  kZeroPad4,
+  kZeroPad5,
+  kZeroPad6,
+  kZeroPad7,
+  kZeroPad8,
+  kZeroPad9,
+  kZeroPad10,
+  kZeroPad11,
+  kZeroPad12,
+  kZeroPad13,
+  kZeroPad14,
+  kZeroPad15,
+  kZeroPad16,
+  kZeroPad17,
+  kZeroPad18,
+  kZeroPad19,
+  kZeroPad20,
+
+  kSpacePad2 = kZeroPad2 + 64,
+  kSpacePad3,
+  kSpacePad4,
+  kSpacePad5,
+  kSpacePad6,
+  kSpacePad7,
+  kSpacePad8,
+  kSpacePad9,
+  kSpacePad10,
+  kSpacePad11,
+  kSpacePad12,
+  kSpacePad13,
+  kSpacePad14,
+  kSpacePad15,
+  kSpacePad16,
+  kSpacePad17,
+  kSpacePad18,
+  kSpacePad19,
+  kSpacePad20,
+};
+
+// -----------------------------------------------------------------------------
+// Hex
+// -----------------------------------------------------------------------------
+//
+// `Hex` stores a set of hexadecimal string conversion parameters for use
+// within `AlphaNum` string conversions.
+struct Hex {
+  uint64_t value;
+  uint8_t width;
+  char fill;
+
+  template <typename Int>
+  explicit Hex(
+      Int v, PadSpec spec = absl::kNoPad,
+      typename std::enable_if<sizeof(Int) == 1 &&
+                              !std::is_pointer<Int>::value>::type* = nullptr)
+      : Hex(spec, static_cast<uint8_t>(v)) {}
+  template <typename Int>
+  explicit Hex(
+      Int v, PadSpec spec = absl::kNoPad,
+      typename std::enable_if<sizeof(Int) == 2 &&
+                              !std::is_pointer<Int>::value>::type* = nullptr)
+      : Hex(spec, static_cast<uint16_t>(v)) {}
+  template <typename Int>
+  explicit Hex(
+      Int v, PadSpec spec = absl::kNoPad,
+      typename std::enable_if<sizeof(Int) == 4 &&
+                              !std::is_pointer<Int>::value>::type* = nullptr)
+      : Hex(spec, static_cast<uint32_t>(v)) {}
+  template <typename Int>
+  explicit Hex(
+      Int v, PadSpec spec = absl::kNoPad,
+      typename std::enable_if<sizeof(Int) == 8 &&
+                              !std::is_pointer<Int>::value>::type* = nullptr)
+      : Hex(spec, static_cast<uint64_t>(v)) {}
+  template <typename Pointee>
+  explicit Hex(Pointee* v, PadSpec spec = absl::kNoPad)
+      : Hex(spec, reinterpret_cast<uintptr_t>(v)) {}
+
+ private:
+  Hex(PadSpec spec, uint64_t v)
+      : value(v),
+        width(spec == absl::kNoPad
+                  ? 1
+                  : spec >= absl::kSpacePad2 ? spec - absl::kSpacePad2 + 2
+                                             : spec - absl::kZeroPad2 + 2),
+        fill(spec >= absl::kSpacePad2 ? ' ' : '0') {}
+};
+
+// -----------------------------------------------------------------------------
+// Dec
+// -----------------------------------------------------------------------------
+//
+// `Dec` stores a set of decimal string conversion parameters for use
+// within `AlphaNum` string conversions.  Dec is slower than the default
+// integer conversion, so use it only if you need padding.
+struct Dec {
+  uint64_t value;
+  uint8_t width;
+  char fill;
+  bool neg;
+
+  template <typename Int>
+  explicit Dec(Int v, PadSpec spec = absl::kNoPad,
+               typename std::enable_if<(sizeof(Int) <= 8)>::type* = nullptr)
+      : value(v >= 0 ? static_cast<uint64_t>(v)
+                     : uint64_t{0} - static_cast<uint64_t>(v)),
+        width(spec == absl::kNoPad
+                  ? 1
+                  : spec >= absl::kSpacePad2 ? spec - absl::kSpacePad2 + 2
+                                             : spec - absl::kZeroPad2 + 2),
+        fill(spec >= absl::kSpacePad2 ? ' ' : '0'),
+        neg(v < 0) {}
+};
+
+// -----------------------------------------------------------------------------
+// AlphaNum
+// -----------------------------------------------------------------------------
+//
+// The `AlphaNum` class acts as the main parameter type for `StrCat()` and
+// `StrAppend()`, providing efficient conversion of numeric, boolean, decimal,
+// and hexadecimal values (through the `Dec` and `Hex` types) into strings.
+// `AlphaNum` should only be used as a function parameter. Do not instantiate
+//  `AlphaNum` directly as a stack variable.
+
+class AlphaNum {
+ public:
+  // No bool ctor -- bools convert to an integral type.
+  // A bool ctor would also convert incoming pointers (bletch).
+
+  AlphaNum(int x)  // NOLINT(runtime/explicit)
+      : piece_(digits_, static_cast<size_t>(
+                            numbers_internal::FastIntToBuffer(x, digits_) -
+                            &digits_[0])) {}
+  AlphaNum(unsigned int x)  // NOLINT(runtime/explicit)
+      : piece_(digits_, static_cast<size_t>(
+                            numbers_internal::FastIntToBuffer(x, digits_) -
+                            &digits_[0])) {}
+  AlphaNum(long x)  // NOLINT(*)
+      : piece_(digits_, static_cast<size_t>(
+                            numbers_internal::FastIntToBuffer(x, digits_) -
+                            &digits_[0])) {}
+  AlphaNum(unsigned long x)  // NOLINT(*)
+      : piece_(digits_, static_cast<size_t>(
+                            numbers_internal::FastIntToBuffer(x, digits_) -
+                            &digits_[0])) {}
+  AlphaNum(long long x)  // NOLINT(*)
+      : piece_(digits_, static_cast<size_t>(
+                            numbers_internal::FastIntToBuffer(x, digits_) -
+                            &digits_[0])) {}
+  AlphaNum(unsigned long long x)  // NOLINT(*)
+      : piece_(digits_, static_cast<size_t>(
+                            numbers_internal::FastIntToBuffer(x, digits_) -
+                            &digits_[0])) {}
+
+  AlphaNum(float f)  // NOLINT(runtime/explicit)
+      : piece_(digits_, numbers_internal::SixDigitsToBuffer(f, digits_)) {}
+  AlphaNum(double f)  // NOLINT(runtime/explicit)
+      : piece_(digits_, numbers_internal::SixDigitsToBuffer(f, digits_)) {}
+
+  AlphaNum(Hex hex);  // NOLINT(runtime/explicit)
+  AlphaNum(Dec dec);  // NOLINT(runtime/explicit)
+
+  template <size_t size>
+  AlphaNum(  // NOLINT(runtime/explicit)
+      const strings_internal::AlphaNumBuffer<size>& buf)
+      : piece_(&buf.data[0], buf.size) {}
+
+  AlphaNum(const char* c_str)                     // NOLINT(runtime/explicit)
+      : piece_(NullSafeStringView(c_str)) {}      // NOLINT(runtime/explicit)
+  AlphaNum(absl::string_view pc) : piece_(pc) {}  // NOLINT(runtime/explicit)
+
+  template <typename T, typename = typename std::enable_if<
+                            strings_internal::HasAbslStringify<T>::value>::type>
+  AlphaNum(                                         // NOLINT(runtime/explicit)
+      const T& v,                                   // NOLINT(runtime/explicit)
+      strings_internal::StringifySink&& sink = {})  // NOLINT(runtime/explicit)
+      : piece_(strings_internal::ExtractStringification(sink, v)) {}
+
+  template <typename Allocator>
+  AlphaNum(  // NOLINT(runtime/explicit)
+      const std::basic_string<char, std::char_traits<char>, Allocator>& str)
+      : piece_(str) {}
+
+  // Use string literals ":" instead of character literals ':'.
+  AlphaNum(char c) = delete;  // NOLINT(runtime/explicit)
+
+  AlphaNum(const AlphaNum&) = delete;
+  AlphaNum& operator=(const AlphaNum&) = delete;
+
+  absl::string_view::size_type size() const { return piece_.size(); }
+  const char* data() const { return piece_.data(); }
+  absl::string_view Piece() const { return piece_; }
+
+  // Normal enums are already handled by the integer formatters.
+  // This overload matches only scoped enums.
+  template <typename T,
+            typename = typename std::enable_if<
+                std::is_enum<T>{} && !std::is_convertible<T, int>{} &&
+                !strings_internal::HasAbslStringify<T>::value>::type>
+  AlphaNum(T e)  // NOLINT(runtime/explicit)
+      : AlphaNum(static_cast<typename std::underlying_type<T>::type>(e)) {}
+
+  // vector<bool>::reference and const_reference require special help to
+  // convert to `AlphaNum` because it requires two user defined conversions.
+  template <
+      typename T,
+      typename std::enable_if<
+          std::is_class<T>::value &&
+          (std::is_same<T, std::vector<bool>::reference>::value ||
+           std::is_same<T, std::vector<bool>::const_reference>::value)>::type* =
+          nullptr>
+  AlphaNum(T e) : AlphaNum(static_cast<bool>(e)) {}  // NOLINT(runtime/explicit)
+
+ private:
+  absl::string_view piece_;
+  char digits_[numbers_internal::kFastToBufferSize];
+};
+
+// -----------------------------------------------------------------------------
+// StrCat()
+// -----------------------------------------------------------------------------
+//
+// Merges given strings or numbers, using no delimiter(s), returning the merged
+// result as a string.
+//
+// `StrCat()` is designed to be the fastest possible way to construct a string
+// out of a mix of raw C strings, string_views, strings, bool values,
+// and numeric values.
+//
+// Don't use `StrCat()` for user-visible strings. The localization process
+// works poorly on strings built up out of fragments.
+//
+// For clarity and performance, don't use `StrCat()` when appending to a
+// string. Use `StrAppend()` instead. In particular, avoid using any of these
+// (anti-)patterns:
+//
+//   str.append(StrCat(...))
+//   str += StrCat(...)
+//   str = StrCat(str, ...)
+//
+// The last case is the worst, with a potential to change a loop
+// from a linear time operation with O(1) dynamic allocations into a
+// quadratic time operation with O(n) dynamic allocations.
+//
+// See `StrAppend()` below for more information.
+
+namespace strings_internal {
+
+// Do not call directly - this is not part of the public API.
+std::string CatPieces(std::initializer_list<absl::string_view> pieces);
+void AppendPieces(std::string* dest,
+                  std::initializer_list<absl::string_view> pieces);
+
+}  // namespace strings_internal
+
+ABSL_MUST_USE_RESULT inline std::string StrCat() { return std::string(); }
+
+ABSL_MUST_USE_RESULT inline std::string StrCat(const AlphaNum& a) {
+  return std::string(a.data(), a.size());
+}
+
+ABSL_MUST_USE_RESULT std::string StrCat(const AlphaNum& a, const AlphaNum& b);
+ABSL_MUST_USE_RESULT std::string StrCat(const AlphaNum& a, const AlphaNum& b,
+                                        const AlphaNum& c);
+ABSL_MUST_USE_RESULT std::string StrCat(const AlphaNum& a, const AlphaNum& b,
+                                        const AlphaNum& c, const AlphaNum& d);
+
+// Support 5 or more arguments
+template <typename... AV>
+ABSL_MUST_USE_RESULT inline std::string StrCat(
+    const AlphaNum& a, const AlphaNum& b, const AlphaNum& c, const AlphaNum& d,
+    const AlphaNum& e, const AV&... args) {
+  return strings_internal::CatPieces(
+      {a.Piece(), b.Piece(), c.Piece(), d.Piece(), e.Piece(),
+       static_cast<const AlphaNum&>(args).Piece()...});
+}
+
+// -----------------------------------------------------------------------------
+// StrAppend()
+// -----------------------------------------------------------------------------
+//
+// Appends a string or set of strings to an existing string, in a similar
+// fashion to `StrCat()`.
+//
+// WARNING: `StrAppend(&str, a, b, c, ...)` requires that none of the
+// a, b, c, parameters be a reference into str. For speed, `StrAppend()` does
+// not try to check each of its input arguments to be sure that they are not
+// a subset of the string being appended to. That is, while this will work:
+//
+//   std::string s = "foo";
+//   s += s;
+//
+// This output is undefined:
+//
+//   std::string s = "foo";
+//   StrAppend(&s, s);
+//
+// This output is undefined as well, since `absl::string_view` does not own its
+// data:
+//
+//   std::string s = "foobar";
+//   absl::string_view p = s;
+//   StrAppend(&s, p);
+
+inline void StrAppend(std::string*) {}
+void StrAppend(std::string* dest, const AlphaNum& a);
+void StrAppend(std::string* dest, const AlphaNum& a, const AlphaNum& b);
+void StrAppend(std::string* dest, const AlphaNum& a, const AlphaNum& b,
+               const AlphaNum& c);
+void StrAppend(std::string* dest, const AlphaNum& a, const AlphaNum& b,
+               const AlphaNum& c, const AlphaNum& d);
+
+// Support 5 or more arguments
+template <typename... AV>
+inline void StrAppend(std::string* dest, const AlphaNum& a, const AlphaNum& b,
+                      const AlphaNum& c, const AlphaNum& d, const AlphaNum& e,
+                      const AV&... args) {
+  strings_internal::AppendPieces(
+      dest, {a.Piece(), b.Piece(), c.Piece(), d.Piece(), e.Piece(),
+             static_cast<const AlphaNum&>(args).Piece()...});
+}
+
+// Helper function for the future StrCat default floating-point format, %.6g
+// This is fast.
+inline strings_internal::AlphaNumBuffer<
+    numbers_internal::kSixDigitsToBufferSize>
+SixDigits(double d) {
+  strings_internal::AlphaNumBuffer<numbers_internal::kSixDigitsToBufferSize>
+      result;
+  result.size = numbers_internal::SixDigitsToBuffer(d, &result.data[0]);
+  return result;
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_STR_CAT_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/str_format.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/str_format.h
new file mode 100644
index 0000000000000000000000000000000000000000..3536b70e9d0b5cd0c1885b356e93a2e5994234be
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/str_format.h
@@ -0,0 +1,874 @@
+//
+// Copyright 2018 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: str_format.h
+// -----------------------------------------------------------------------------
+//
+// The `str_format` library is a typesafe replacement for the family of
+// `printf()` string formatting routines within the `<cstdio>` standard library
+// header. Like the `printf` family, `str_format` uses a "format string" to
+// perform argument substitutions based on types. See the `FormatSpec` section
+// below for format string documentation.
+//
+// Example:
+//
+//   std::string s = absl::StrFormat(
+//                      "%s %s You have $%d!", "Hello", name, dollars);
+//
+// The library consists of the following basic utilities:
+//
+//   * `absl::StrFormat()`, a type-safe replacement for `std::sprintf()`, to
+//     write a format string to a `string` value.
+//   * `absl::StrAppendFormat()` to append a format string to a `string`
+//   * `absl::StreamFormat()` to more efficiently write a format string to a
+//     stream, such as`std::cout`.
+//   * `absl::PrintF()`, `absl::FPrintF()` and `absl::SNPrintF()` as
+//     replacements for `std::printf()`, `std::fprintf()` and `std::snprintf()`.
+//
+//     Note: a version of `std::sprintf()` is not supported as it is
+//     generally unsafe due to buffer overflows.
+//
+// Additionally, you can provide a format string (and its associated arguments)
+// using one of the following abstractions:
+//
+//   * A `FormatSpec` class template fully encapsulates a format string and its
+//     type arguments and is usually provided to `str_format` functions as a
+//     variadic argument of type `FormatSpec<Arg...>`. The `FormatSpec<Args...>`
+//     template is evaluated at compile-time, providing type safety.
+//   * A `ParsedFormat` instance, which encapsulates a specific, pre-compiled
+//     format string for a specific set of type(s), and which can be passed
+//     between API boundaries. (The `FormatSpec` type should not be used
+//     directly except as an argument type for wrapper functions.)
+//
+// The `str_format` library provides the ability to output its format strings to
+// arbitrary sink types:
+//
+//   * A generic `Format()` function to write outputs to arbitrary sink types,
+//     which must implement a `FormatRawSink` interface.
+//
+//   * A `FormatUntyped()` function that is similar to `Format()` except it is
+//     loosely typed. `FormatUntyped()` is not a template and does not perform
+//     any compile-time checking of the format string; instead, it returns a
+//     boolean from a runtime check.
+//
+// In addition, the `str_format` library provides extension points for
+// augmenting formatting to new types.  See "StrFormat Extensions" below.
+
+#ifndef ABSL_STRINGS_STR_FORMAT_H_
+#define ABSL_STRINGS_STR_FORMAT_H_
+
+#include <cstdio>
+#include <string>
+
+#include "absl/strings/internal/str_format/arg.h"  // IWYU pragma: export
+#include "absl/strings/internal/str_format/bind.h"  // IWYU pragma: export
+#include "absl/strings/internal/str_format/checker.h"  // IWYU pragma: export
+#include "absl/strings/internal/str_format/extension.h"  // IWYU pragma: export
+#include "absl/strings/internal/str_format/parser.h"  // IWYU pragma: export
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// UntypedFormatSpec
+//
+// A type-erased class that can be used directly within untyped API entry
+// points. An `UntypedFormatSpec` is specifically used as an argument to
+// `FormatUntyped()`.
+//
+// Example:
+//
+//   absl::UntypedFormatSpec format("%d");
+//   std::string out;
+//   CHECK(absl::FormatUntyped(&out, format, {absl::FormatArg(1)}));
+class UntypedFormatSpec {
+ public:
+  UntypedFormatSpec() = delete;
+  UntypedFormatSpec(const UntypedFormatSpec&) = delete;
+  UntypedFormatSpec& operator=(const UntypedFormatSpec&) = delete;
+
+  explicit UntypedFormatSpec(string_view s) : spec_(s) {}
+
+ protected:
+  explicit UntypedFormatSpec(const str_format_internal::ParsedFormatBase* pc)
+      : spec_(pc) {}
+
+ private:
+  friend str_format_internal::UntypedFormatSpecImpl;
+  str_format_internal::UntypedFormatSpecImpl spec_;
+};
+
+// FormatStreamed()
+//
+// Takes a streamable argument and returns an object that can print it
+// with '%s'. Allows printing of types that have an `operator<<` but no
+// intrinsic type support within `StrFormat()` itself.
+//
+// Example:
+//
+//   absl::StrFormat("%s", absl::FormatStreamed(obj));
+template <typename T>
+str_format_internal::StreamedWrapper<T> FormatStreamed(const T& v) {
+  return str_format_internal::StreamedWrapper<T>(v);
+}
+
+// FormatCountCapture
+//
+// This class provides a way to safely wrap `StrFormat()` captures of `%n`
+// conversions, which denote the number of characters written by a formatting
+// operation to this point, into an integer value.
+//
+// This wrapper is designed to allow safe usage of `%n` within `StrFormat(); in
+// the `printf()` family of functions, `%n` is not safe to use, as the `int *`
+// buffer can be used to capture arbitrary data.
+//
+// Example:
+//
+//   int n = 0;
+//   std::string s = absl::StrFormat("%s%d%n", "hello", 123,
+//                       absl::FormatCountCapture(&n));
+//   EXPECT_EQ(8, n);
+class FormatCountCapture {
+ public:
+  explicit FormatCountCapture(int* p) : p_(p) {}
+
+ private:
+  // FormatCountCaptureHelper is used to define FormatConvertImpl() for this
+  // class.
+  friend struct str_format_internal::FormatCountCaptureHelper;
+  // Unused() is here because of the false positive from -Wunused-private-field
+  // p_ is used in the templated function of the friend FormatCountCaptureHelper
+  // class.
+  int* Unused() { return p_; }
+  int* p_;
+};
+
+// FormatSpec
+//
+// The `FormatSpec` type defines the makeup of a format string within the
+// `str_format` library. It is a variadic class template that is evaluated at
+// compile-time, according to the format string and arguments that are passed to
+// it.
+//
+// You should not need to manipulate this type directly. You should only name it
+// if you are writing wrapper functions which accept format arguments that will
+// be provided unmodified to functions in this library. Such a wrapper function
+// might be a class method that provides format arguments and/or internally uses
+// the result of formatting.
+//
+// For a `FormatSpec` to be valid at compile-time, it must be provided as
+// either:
+//
+// * A `constexpr` literal or `absl::string_view`, which is how it most often
+//   used.
+// * A `ParsedFormat` instantiation, which ensures the format string is
+//   valid before use. (See below.)
+//
+// Example:
+//
+//   // Provided as a string literal.
+//   absl::StrFormat("Welcome to %s, Number %d!", "The Village", 6);
+//
+//   // Provided as a constexpr absl::string_view.
+//   constexpr absl::string_view formatString = "Welcome to %s, Number %d!";
+//   absl::StrFormat(formatString, "The Village", 6);
+//
+//   // Provided as a pre-compiled ParsedFormat object.
+//   // Note that this example is useful only for illustration purposes.
+//   absl::ParsedFormat<'s', 'd'> formatString("Welcome to %s, Number %d!");
+//   absl::StrFormat(formatString, "TheVillage", 6);
+//
+// A format string generally follows the POSIX syntax as used within the POSIX
+// `printf` specification. (Exceptions are noted below.)
+//
+// (See http://pubs.opengroup.org/onlinepubs/9699919799/functions/fprintf.html)
+//
+// In specific, the `FormatSpec` supports the following type specifiers:
+//   * `c` for characters
+//   * `s` for strings
+//   * `d` or `i` for integers
+//   * `o` for unsigned integer conversions into octal
+//   * `x` or `X` for unsigned integer conversions into hex
+//   * `u` for unsigned integers
+//   * `f` or `F` for floating point values into decimal notation
+//   * `e` or `E` for floating point values into exponential notation
+//   * `a` or `A` for floating point values into hex exponential notation
+//   * `g` or `G` for floating point values into decimal or exponential
+//     notation based on their precision
+//   * `p` for pointer address values
+//   * `n` for the special case of writing out the number of characters
+//     written to this point. The resulting value must be captured within an
+//     `absl::FormatCountCapture` type.
+//   * `v` for values using the default format for a deduced type. These deduced
+//     types include many of the primitive types denoted here as well as
+//     user-defined types containing the proper extensions. (See below for more
+//     information.)
+//
+// Implementation-defined behavior:
+//   * A null pointer provided to "%s" or "%p" is output as "(nil)".
+//   * A non-null pointer provided to "%p" is output in hex as if by %#x or
+//     %#lx.
+//
+// NOTE: `o`, `x\X` and `u` will convert signed values to their unsigned
+// counterpart before formatting.
+//
+// Examples:
+//     "%c", 'a'                -> "a"
+//     "%c", 32                 -> " "
+//     "%s", "C"                -> "C"
+//     "%s", std::string("C++") -> "C++"
+//     "%d", -10                -> "-10"
+//     "%o", 10                 -> "12"
+//     "%x", 16                 -> "10"
+//     "%f", 123456789          -> "123456789.000000"
+//     "%e", .01                -> "1.00000e-2"
+//     "%a", -3.0               -> "-0x1.8p+1"
+//     "%g", .01                -> "1e-2"
+//     "%p", (void*)&value      -> "0x7ffdeb6ad2a4"
+//
+//     int n = 0;
+//     std::string s = absl::StrFormat(
+//         "%s%d%n", "hello", 123, absl::FormatCountCapture(&n));
+//     EXPECT_EQ(8, n);
+//
+// NOTE: the `v` specifier (for "value") is a type specifier not present in the
+// POSIX specification. %v will format values according to their deduced type.
+// `v` uses `d` for signed integer values, `u` for unsigned integer values, `g`
+// for floating point values, and formats boolean values as "true"/"false"
+// (instead of 1 or 0 for booleans formatted using d). `const char*` is not
+// supported; please use `std:string` and `string_view`. `char` is also not
+// supported due to ambiguity of the type. This specifier does not support
+// modifiers.
+//
+// The `FormatSpec` intrinsically supports all of these fundamental C++ types:
+//
+// *   Characters: `char`, `signed char`, `unsigned char`
+// *   Integers: `int`, `short`, `unsigned short`, `unsigned`, `long`,
+//         `unsigned long`, `long long`, `unsigned long long`
+// *   Floating-point: `float`, `double`, `long double`
+//
+// However, in the `str_format` library, a format conversion specifies a broader
+// C++ conceptual category instead of an exact type. For example, `%s` binds to
+// any string-like argument, so `std::string`, `absl::string_view`, and
+// `const char*` are all accepted. Likewise, `%d` accepts any integer-like
+// argument, etc.
+
+template <typename... Args>
+using FormatSpec = str_format_internal::FormatSpecTemplate<
+    str_format_internal::ArgumentToConv<Args>()...>;
+
+// ParsedFormat
+//
+// A `ParsedFormat` is a class template representing a preparsed `FormatSpec`,
+// with template arguments specifying the conversion characters used within the
+// format string. Such characters must be valid format type specifiers, and
+// these type specifiers are checked at compile-time.
+//
+// Instances of `ParsedFormat` can be created, copied, and reused to speed up
+// formatting loops. A `ParsedFormat` may either be constructed statically, or
+// dynamically through its `New()` factory function, which only constructs a
+// runtime object if the format is valid at that time.
+//
+// Example:
+//
+//   // Verified at compile time.
+//   absl::ParsedFormat<'s', 'd'> formatString("Welcome to %s, Number %d!");
+//   absl::StrFormat(formatString, "TheVillage", 6);
+//
+//   // Verified at runtime.
+//   auto format_runtime = absl::ParsedFormat<'d'>::New(format_string);
+//   if (format_runtime) {
+//     value = absl::StrFormat(*format_runtime, i);
+//   } else {
+//     ... error case ...
+//   }
+
+#if defined(__cpp_nontype_template_parameter_auto)
+// If C++17 is available, an 'extended' format is also allowed that can specify
+// multiple conversion characters per format argument, using a combination of
+// `absl::FormatConversionCharSet` enum values (logically a set union)
+//  via the `|` operator. (Single character-based arguments are still accepted,
+// but cannot be combined). Some common conversions also have predefined enum
+// values, such as `absl::FormatConversionCharSet::kIntegral`.
+//
+// Example:
+//   // Extended format supports multiple conversion characters per argument,
+//   // specified via a combination of `FormatConversionCharSet` enums.
+//   using MyFormat = absl::ParsedFormat<absl::FormatConversionCharSet::d |
+//                                       absl::FormatConversionCharSet::x>;
+//   MyFormat GetFormat(bool use_hex) {
+//     if (use_hex) return MyFormat("foo %x bar");
+//     return MyFormat("foo %d bar");
+//   }
+//   // `format` can be used with any value that supports 'd' and 'x',
+//   // like `int`.
+//   auto format = GetFormat(use_hex);
+//   value = StringF(format, i);
+template <auto... Conv>
+using ParsedFormat = absl::str_format_internal::ExtendedParsedFormat<
+    absl::str_format_internal::ToFormatConversionCharSet(Conv)...>;
+#else
+template <char... Conv>
+using ParsedFormat = str_format_internal::ExtendedParsedFormat<
+    absl::str_format_internal::ToFormatConversionCharSet(Conv)...>;
+#endif  // defined(__cpp_nontype_template_parameter_auto)
+
+// StrFormat()
+//
+// Returns a `string` given a `printf()`-style format string and zero or more
+// additional arguments. Use it as you would `sprintf()`. `StrFormat()` is the
+// primary formatting function within the `str_format` library, and should be
+// used in most cases where you need type-safe conversion of types into
+// formatted strings.
+//
+// The format string generally consists of ordinary character data along with
+// one or more format conversion specifiers (denoted by the `%` character).
+// Ordinary character data is returned unchanged into the result string, while
+// each conversion specification performs a type substitution from
+// `StrFormat()`'s other arguments. See the comments for `FormatSpec` for full
+// information on the makeup of this format string.
+//
+// Example:
+//
+//   std::string s = absl::StrFormat(
+//       "Welcome to %s, Number %d!", "The Village", 6);
+//   EXPECT_EQ("Welcome to The Village, Number 6!", s);
+//
+// Returns an empty string in case of error.
+template <typename... Args>
+ABSL_MUST_USE_RESULT std::string StrFormat(const FormatSpec<Args...>& format,
+                                           const Args&... args) {
+  return str_format_internal::FormatPack(
+      str_format_internal::UntypedFormatSpecImpl::Extract(format),
+      {str_format_internal::FormatArgImpl(args)...});
+}
+
+// StrAppendFormat()
+//
+// Appends to a `dst` string given a format string, and zero or more additional
+// arguments, returning `*dst` as a convenience for chaining purposes. Appends
+// nothing in case of error (but possibly alters its capacity).
+//
+// Example:
+//
+//   std::string orig("For example PI is approximately ");
+//   std::cout << StrAppendFormat(&orig, "%12.6f", 3.14);
+template <typename... Args>
+std::string& StrAppendFormat(std::string* dst,
+                             const FormatSpec<Args...>& format,
+                             const Args&... args) {
+  return str_format_internal::AppendPack(
+      dst, str_format_internal::UntypedFormatSpecImpl::Extract(format),
+      {str_format_internal::FormatArgImpl(args)...});
+}
+
+// StreamFormat()
+//
+// Writes to an output stream given a format string and zero or more arguments,
+// generally in a manner that is more efficient than streaming the result of
+// `absl:: StrFormat()`. The returned object must be streamed before the full
+// expression ends.
+//
+// Example:
+//
+//   std::cout << StreamFormat("%12.6f", 3.14);
+template <typename... Args>
+ABSL_MUST_USE_RESULT str_format_internal::Streamable StreamFormat(
+    const FormatSpec<Args...>& format, const Args&... args) {
+  return str_format_internal::Streamable(
+      str_format_internal::UntypedFormatSpecImpl::Extract(format),
+      {str_format_internal::FormatArgImpl(args)...});
+}
+
+// PrintF()
+//
+// Writes to stdout given a format string and zero or more arguments. This
+// function is functionally equivalent to `std::printf()` (and type-safe);
+// prefer `absl::PrintF()` over `std::printf()`.
+//
+// Example:
+//
+//   std::string_view s = "Ulaanbaatar";
+//   absl::PrintF("The capital of Mongolia is %s", s);
+//
+//   Outputs: "The capital of Mongolia is Ulaanbaatar"
+//
+template <typename... Args>
+int PrintF(const FormatSpec<Args...>& format, const Args&... args) {
+  return str_format_internal::FprintF(
+      stdout, str_format_internal::UntypedFormatSpecImpl::Extract(format),
+      {str_format_internal::FormatArgImpl(args)...});
+}
+
+// FPrintF()
+//
+// Writes to a file given a format string and zero or more arguments. This
+// function is functionally equivalent to `std::fprintf()` (and type-safe);
+// prefer `absl::FPrintF()` over `std::fprintf()`.
+//
+// Example:
+//
+//   std::string_view s = "Ulaanbaatar";
+//   absl::FPrintF(stdout, "The capital of Mongolia is %s", s);
+//
+//   Outputs: "The capital of Mongolia is Ulaanbaatar"
+//
+template <typename... Args>
+int FPrintF(std::FILE* output, const FormatSpec<Args...>& format,
+            const Args&... args) {
+  return str_format_internal::FprintF(
+      output, str_format_internal::UntypedFormatSpecImpl::Extract(format),
+      {str_format_internal::FormatArgImpl(args)...});
+}
+
+// SNPrintF()
+//
+// Writes to a sized buffer given a format string and zero or more arguments.
+// This function is functionally equivalent to `std::snprintf()` (and
+// type-safe); prefer `absl::SNPrintF()` over `std::snprintf()`.
+//
+// In particular, a successful call to `absl::SNPrintF()` writes at most `size`
+// bytes of the formatted output to `output`, including a NUL-terminator, and
+// returns the number of bytes that would have been written if truncation did
+// not occur. In the event of an error, a negative value is returned and `errno`
+// is set.
+//
+// Example:
+//
+//   std::string_view s = "Ulaanbaatar";
+//   char output[128];
+//   absl::SNPrintF(output, sizeof(output),
+//                  "The capital of Mongolia is %s", s);
+//
+//   Post-condition: output == "The capital of Mongolia is Ulaanbaatar"
+//
+template <typename... Args>
+int SNPrintF(char* output, std::size_t size, const FormatSpec<Args...>& format,
+             const Args&... args) {
+  return str_format_internal::SnprintF(
+      output, size, str_format_internal::UntypedFormatSpecImpl::Extract(format),
+      {str_format_internal::FormatArgImpl(args)...});
+}
+
+// -----------------------------------------------------------------------------
+// Custom Output Formatting Functions
+// -----------------------------------------------------------------------------
+
+// FormatRawSink
+//
+// FormatRawSink is a type erased wrapper around arbitrary sink objects
+// specifically used as an argument to `Format()`.
+//
+// All the object has to do define an overload of `AbslFormatFlush()` for the
+// sink, usually by adding a ADL-based free function in the same namespace as
+// the sink:
+//
+//   void AbslFormatFlush(MySink* dest, absl::string_view part);
+//
+// where `dest` is the pointer passed to `absl::Format()`. The function should
+// append `part` to `dest`.
+//
+// FormatRawSink does not own the passed sink object. The passed object must
+// outlive the FormatRawSink.
+class FormatRawSink {
+ public:
+  // Implicitly convert from any type that provides the hook function as
+  // described above.
+  template <typename T,
+            typename = typename std::enable_if<std::is_constructible<
+                str_format_internal::FormatRawSinkImpl, T*>::value>::type>
+  FormatRawSink(T* raw)  // NOLINT
+      : sink_(raw) {}
+
+ private:
+  friend str_format_internal::FormatRawSinkImpl;
+  str_format_internal::FormatRawSinkImpl sink_;
+};
+
+// Format()
+//
+// Writes a formatted string to an arbitrary sink object (implementing the
+// `absl::FormatRawSink` interface), using a format string and zero or more
+// additional arguments.
+//
+// By default, `std::string`, `std::ostream`, and `absl::Cord` are supported as
+// destination objects. If a `std::string` is used the formatted string is
+// appended to it.
+//
+// `absl::Format()` is a generic version of `absl::StrAppendFormat()`, for
+// custom sinks. The format string, like format strings for `StrFormat()`, is
+// checked at compile-time.
+//
+// On failure, this function returns `false` and the state of the sink is
+// unspecified.
+template <typename... Args>
+bool Format(FormatRawSink raw_sink, const FormatSpec<Args...>& format,
+            const Args&... args) {
+  return str_format_internal::FormatUntyped(
+      str_format_internal::FormatRawSinkImpl::Extract(raw_sink),
+      str_format_internal::UntypedFormatSpecImpl::Extract(format),
+      {str_format_internal::FormatArgImpl(args)...});
+}
+
+// FormatArg
+//
+// A type-erased handle to a format argument specifically used as an argument to
+// `FormatUntyped()`. You may construct `FormatArg` by passing
+// reference-to-const of any printable type. `FormatArg` is both copyable and
+// assignable. The source data must outlive the `FormatArg` instance. See
+// example below.
+//
+using FormatArg = str_format_internal::FormatArgImpl;
+
+// FormatUntyped()
+//
+// Writes a formatted string to an arbitrary sink object (implementing the
+// `absl::FormatRawSink` interface), using an `UntypedFormatSpec` and zero or
+// more additional arguments.
+//
+// This function acts as the most generic formatting function in the
+// `str_format` library. The caller provides a raw sink, an unchecked format
+// string, and (usually) a runtime specified list of arguments; no compile-time
+// checking of formatting is performed within this function. As a result, a
+// caller should check the return value to verify that no error occurred.
+// On failure, this function returns `false` and the state of the sink is
+// unspecified.
+//
+// The arguments are provided in an `absl::Span<const absl::FormatArg>`.
+// Each `absl::FormatArg` object binds to a single argument and keeps a
+// reference to it. The values used to create the `FormatArg` objects must
+// outlive this function call.
+//
+// Example:
+//
+//   std::optional<std::string> FormatDynamic(
+//       const std::string& in_format,
+//       const vector<std::string>& in_args) {
+//     std::string out;
+//     std::vector<absl::FormatArg> args;
+//     for (const auto& v : in_args) {
+//       // It is important that 'v' is a reference to the objects in in_args.
+//       // The values we pass to FormatArg must outlive the call to
+//       // FormatUntyped.
+//       args.emplace_back(v);
+//     }
+//     absl::UntypedFormatSpec format(in_format);
+//     if (!absl::FormatUntyped(&out, format, args)) {
+//       return std::nullopt;
+//     }
+//     return std::move(out);
+//   }
+//
+ABSL_MUST_USE_RESULT inline bool FormatUntyped(
+    FormatRawSink raw_sink, const UntypedFormatSpec& format,
+    absl::Span<const FormatArg> args) {
+  return str_format_internal::FormatUntyped(
+      str_format_internal::FormatRawSinkImpl::Extract(raw_sink),
+      str_format_internal::UntypedFormatSpecImpl::Extract(format), args);
+}
+
+//------------------------------------------------------------------------------
+// StrFormat Extensions
+//------------------------------------------------------------------------------
+//
+// AbslStringify()
+//
+// A simpler customization API for formatting user-defined types using
+// absl::StrFormat(). The API relies on detecting an overload in the
+// user-defined type's namespace of a free (non-member) `AbslStringify()`
+// function as a friend definition with the following signature:
+//
+// template <typename Sink>
+// void AbslStringify(Sink& sink, const X& value);
+//
+// An `AbslStringify()` overload for a type should only be declared in the same
+// file and namespace as said type.
+//
+// Note that unlike with AbslFormatConvert(), AbslStringify() does not allow
+// customization of allowed conversion characters. AbslStringify() uses `%v` as
+// the underlying conversion specififer. Additionally, AbslStringify() supports
+// use with absl::StrCat while AbslFormatConvert() does not.
+//
+// Example:
+//
+// struct Point {
+//   // To add formatting support to `Point`, we simply need to add a free
+//   // (non-member) function `AbslStringify()`. This method prints in the
+//   // request format using the underlying `%v` specifier. You can add such a
+//   // free function using a friend declaration within the body of the class.
+//   // The sink parameter is a templated type to avoid requiring dependencies.
+//   template <typename Sink>
+//   friend void AbslStringify(Sink& sink, const Point& p) {
+//     absl::Format(&sink, "(%v, %v)", p.x, p.y);
+//   }
+//
+//   int x;
+//   int y;
+// };
+//
+// AbslFormatConvert()
+//
+// The StrFormat library provides a customization API for formatting
+// user-defined types using absl::StrFormat(). The API relies on detecting an
+// overload in the user-defined type's namespace of a free (non-member)
+// `AbslFormatConvert()` function, usually as a friend definition with the
+// following signature:
+//
+// absl::FormatConvertResult<...> AbslFormatConvert(
+//     const X& value,
+//     const absl::FormatConversionSpec& spec,
+//     absl::FormatSink *sink);
+//
+// An `AbslFormatConvert()` overload for a type should only be declared in the
+// same file and namespace as said type.
+//
+// The abstractions within this definition include:
+//
+// * An `absl::FormatConversionSpec` to specify the fields to pull from a
+//   user-defined type's format string
+// * An `absl::FormatSink` to hold the converted string data during the
+//   conversion process.
+// * An `absl::FormatConvertResult` to hold the status of the returned
+//   formatting operation
+//
+// The return type encodes all the conversion characters that your
+// AbslFormatConvert() routine accepts.  The return value should be {true}.
+// A return value of {false} will result in `StrFormat()` returning
+// an empty string.  This result will be propagated to the result of
+// `FormatUntyped`.
+//
+// Example:
+//
+// struct Point {
+//   // To add formatting support to `Point`, we simply need to add a free
+//   // (non-member) function `AbslFormatConvert()`.  This method interprets
+//   // `spec` to print in the request format. The allowed conversion characters
+//   // can be restricted via the type of the result, in this example
+//   // string and integral formatting are allowed (but not, for instance
+//   // floating point characters like "%f").  You can add such a free function
+//   // using a friend declaration within the body of the class:
+//   friend absl::FormatConvertResult<absl::FormatConversionCharSet::kString |
+//                                    absl::FormatConversionCharSet::kIntegral>
+//   AbslFormatConvert(const Point& p, const absl::FormatConversionSpec& spec,
+//                     absl::FormatSink* s) {
+//     if (spec.conversion_char() == absl::FormatConversionChar::s) {
+//       absl::Format(s, "x=%vy=%v", p.x, p.y);
+//     } else {
+//       absl::Format(s, "%v,%v", p.x, p.y);
+//     }
+//     return {true};
+//   }
+//
+//   int x;
+//   int y;
+// };
+
+// clang-format off
+
+// FormatConversionChar
+//
+// Specifies the formatting character provided in the format string
+// passed to `StrFormat()`.
+enum class FormatConversionChar : uint8_t {
+  c, s,                    // text
+  d, i, o, u, x, X,        // int
+  f, F, e, E, g, G, a, A,  // float
+  n, p, v                  // misc
+};
+// clang-format on
+
+// FormatConversionSpec
+//
+// Specifies modifications to the conversion of the format string, through use
+// of one or more format flags in the source format string.
+class FormatConversionSpec {
+ public:
+  // FormatConversionSpec::is_basic()
+  //
+  // Indicates that width and precision are not specified, and no additional
+  // flags are set for this conversion character in the format string.
+  bool is_basic() const { return impl_.is_basic(); }
+
+  // FormatConversionSpec::has_left_flag()
+  //
+  // Indicates whether the result should be left justified for this conversion
+  // character in the format string. This flag is set through use of a '-'
+  // character in the format string. E.g. "%-s"
+  bool has_left_flag() const { return impl_.has_left_flag(); }
+
+  // FormatConversionSpec::has_show_pos_flag()
+  //
+  // Indicates whether a sign column is prepended to the result for this
+  // conversion character in the format string, even if the result is positive.
+  // This flag is set through use of a '+' character in the format string.
+  // E.g. "%+d"
+  bool has_show_pos_flag() const { return impl_.has_show_pos_flag(); }
+
+  // FormatConversionSpec::has_sign_col_flag()
+  //
+  // Indicates whether a mandatory sign column is added to the result for this
+  // conversion character. This flag is set through use of a space character
+  // (' ') in the format string. E.g. "% i"
+  bool has_sign_col_flag() const { return impl_.has_sign_col_flag(); }
+
+  // FormatConversionSpec::has_alt_flag()
+  //
+  // Indicates whether an "alternate" format is applied to the result for this
+  // conversion character. Alternative forms depend on the type of conversion
+  // character, and unallowed alternatives are undefined. This flag is set
+  // through use of a '#' character in the format string. E.g. "%#h"
+  bool has_alt_flag() const { return impl_.has_alt_flag(); }
+
+  // FormatConversionSpec::has_zero_flag()
+  //
+  // Indicates whether zeroes should be prepended to the result for this
+  // conversion character instead of spaces. This flag is set through use of the
+  // '0' character in the format string. E.g. "%0f"
+  bool has_zero_flag() const { return impl_.has_zero_flag(); }
+
+  // FormatConversionSpec::conversion_char()
+  //
+  // Returns the underlying conversion character.
+  FormatConversionChar conversion_char() const {
+    return impl_.conversion_char();
+  }
+
+  // FormatConversionSpec::width()
+  //
+  // Returns the specified width (indicated through use of a non-zero integer
+  // value or '*' character) of the conversion character. If width is
+  // unspecified, it returns a negative value.
+  int width() const { return impl_.width(); }
+
+  // FormatConversionSpec::precision()
+  //
+  // Returns the specified precision (through use of the '.' character followed
+  // by a non-zero integer value or '*' character) of the conversion character.
+  // If precision is unspecified, it returns a negative value.
+  int precision() const { return impl_.precision(); }
+
+ private:
+  explicit FormatConversionSpec(
+      str_format_internal::FormatConversionSpecImpl impl)
+      : impl_(impl) {}
+
+  friend str_format_internal::FormatConversionSpecImpl;
+
+  absl::str_format_internal::FormatConversionSpecImpl impl_;
+};
+
+// Type safe OR operator for FormatConversionCharSet to allow accepting multiple
+// conversion chars in custom format converters.
+constexpr FormatConversionCharSet operator|(FormatConversionCharSet a,
+                                            FormatConversionCharSet b) {
+  return static_cast<FormatConversionCharSet>(static_cast<uint64_t>(a) |
+                                              static_cast<uint64_t>(b));
+}
+
+// FormatConversionCharSet
+//
+// Specifies the _accepted_ conversion types as a template parameter to
+// FormatConvertResult for custom implementations of `AbslFormatConvert`.
+// Note the helper predefined alias definitions (kIntegral, etc.) below.
+enum class FormatConversionCharSet : uint64_t {
+  // text
+  c = str_format_internal::FormatConversionCharToConvInt('c'),
+  s = str_format_internal::FormatConversionCharToConvInt('s'),
+  // integer
+  d = str_format_internal::FormatConversionCharToConvInt('d'),
+  i = str_format_internal::FormatConversionCharToConvInt('i'),
+  o = str_format_internal::FormatConversionCharToConvInt('o'),
+  u = str_format_internal::FormatConversionCharToConvInt('u'),
+  x = str_format_internal::FormatConversionCharToConvInt('x'),
+  X = str_format_internal::FormatConversionCharToConvInt('X'),
+  // Float
+  f = str_format_internal::FormatConversionCharToConvInt('f'),
+  F = str_format_internal::FormatConversionCharToConvInt('F'),
+  e = str_format_internal::FormatConversionCharToConvInt('e'),
+  E = str_format_internal::FormatConversionCharToConvInt('E'),
+  g = str_format_internal::FormatConversionCharToConvInt('g'),
+  G = str_format_internal::FormatConversionCharToConvInt('G'),
+  a = str_format_internal::FormatConversionCharToConvInt('a'),
+  A = str_format_internal::FormatConversionCharToConvInt('A'),
+  // misc
+  n = str_format_internal::FormatConversionCharToConvInt('n'),
+  p = str_format_internal::FormatConversionCharToConvInt('p'),
+  v = str_format_internal::FormatConversionCharToConvInt('v'),
+
+  // Used for width/precision '*' specification.
+  kStar = static_cast<uint64_t>(
+      absl::str_format_internal::FormatConversionCharSetInternal::kStar),
+  // Some predefined values:
+  kIntegral = d | i | u | o | x | X,
+  kFloating = a | e | f | g | A | E | F | G,
+  kNumeric = kIntegral | kFloating,
+  kString = s,
+  kPointer = p,
+};
+
+// FormatSink
+//
+// A format sink is a generic abstraction to which conversions may write their
+// formatted string data. `absl::FormatConvert()` uses this sink to write its
+// formatted string.
+//
+class FormatSink {
+ public:
+  // FormatSink::Append()
+  //
+  // Appends `count` copies of `ch` to the format sink.
+  void Append(size_t count, char ch) { sink_->Append(count, ch); }
+
+  // Overload of FormatSink::Append() for appending the characters of a string
+  // view to a format sink.
+  void Append(string_view v) { sink_->Append(v); }
+
+  // FormatSink::PutPaddedString()
+  //
+  // Appends `precision` number of bytes of `v` to the format sink. If this is
+  // less than `width`, spaces will be appended first (if `left` is false), or
+  // after (if `left` is true) to ensure the total amount appended is
+  // at least `width`.
+  bool PutPaddedString(string_view v, int width, int precision, bool left) {
+    return sink_->PutPaddedString(v, width, precision, left);
+  }
+
+  // Support `absl::Format(&sink, format, args...)`.
+  friend void AbslFormatFlush(FormatSink* sink, absl::string_view v) {
+    sink->Append(v);
+  }
+
+ private:
+  friend str_format_internal::FormatSinkImpl;
+  explicit FormatSink(str_format_internal::FormatSinkImpl* s) : sink_(s) {}
+  str_format_internal::FormatSinkImpl* sink_;
+};
+
+// FormatConvertResult
+//
+// Indicates whether a call to AbslFormatConvert() was successful.
+// This return type informs the StrFormat extension framework (through
+// ADL but using the return type) of what conversion characters are supported.
+// It is strongly discouraged to return {false}, as this will result in an
+// empty string in StrFormat.
+template <FormatConversionCharSet C>
+struct FormatConvertResult {
+  bool value;
+};
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_STR_FORMAT_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/str_join.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/str_join.h
new file mode 100644
index 0000000000000000000000000000000000000000..ee5ae7efdf6d50b9f9fc1d2bde2c3642a37452d6
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/str_join.h
@@ -0,0 +1,287 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: str_join.h
+// -----------------------------------------------------------------------------
+//
+// This header file contains functions for joining a range of elements and
+// returning the result as a std::string. StrJoin operations are specified by
+// passing a range, a separator string to use between the elements joined, and
+// an optional Formatter responsible for converting each argument in the range
+// to a string. If omitted, a default `AlphaNumFormatter()` is called on the
+// elements to be joined, using the same formatting that `absl::StrCat()` uses.
+// This package defines a number of default formatters, and you can define your
+// own implementations.
+//
+// Ranges are specified by passing a container with `std::begin()` and
+// `std::end()` iterators, container-specific `begin()` and `end()` iterators, a
+// brace-initialized `std::initializer_list`, or a `std::tuple` of heterogeneous
+// objects. The separator string is specified as an `absl::string_view`.
+//
+// Because the default formatter uses the `absl::AlphaNum` class,
+// `absl::StrJoin()`, like `absl::StrCat()`, will work out-of-the-box on
+// collections of strings, ints, floats, doubles, etc.
+//
+// Example:
+//
+//   std::vector<std::string> v = {"foo", "bar", "baz"};
+//   std::string s = absl::StrJoin(v, "-");
+//   EXPECT_EQ("foo-bar-baz", s);
+//
+// See comments on the `absl::StrJoin()` function for more examples.
+
+#ifndef ABSL_STRINGS_STR_JOIN_H_
+#define ABSL_STRINGS_STR_JOIN_H_
+
+#include <cstdio>
+#include <cstring>
+#include <initializer_list>
+#include <iterator>
+#include <string>
+#include <tuple>
+#include <type_traits>
+#include <utility>
+
+#include "absl/base/macros.h"
+#include "absl/strings/internal/str_join_internal.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// -----------------------------------------------------------------------------
+// Concept: Formatter
+// -----------------------------------------------------------------------------
+//
+// A Formatter is a function object that is responsible for formatting its
+// argument as a string and appending it to a given output std::string.
+// Formatters may be implemented as function objects, lambdas, or normal
+// functions. You may provide your own Formatter to enable `absl::StrJoin()` to
+// work with arbitrary types.
+//
+// The following is an example of a custom Formatter that uses
+// `absl::FormatDuration` to join a list of `absl::Duration`s.
+//
+//   std::vector<absl::Duration> v = {absl::Seconds(1), absl::Milliseconds(10)};
+//   std::string s =
+//       absl::StrJoin(v, ", ", [](std::string* out, absl::Duration dur) {
+//         absl::StrAppend(out, absl::FormatDuration(dur));
+//       });
+//   EXPECT_EQ("1s, 10ms", s);
+//
+// The following standard formatters are provided within this file:
+//
+// - `AlphaNumFormatter()` (the default)
+// - `StreamFormatter()`
+// - `PairFormatter()`
+// - `DereferenceFormatter()`
+
+// AlphaNumFormatter()
+//
+// Default formatter used if none is specified. Uses `absl::AlphaNum` to convert
+// numeric arguments to strings.
+inline strings_internal::AlphaNumFormatterImpl AlphaNumFormatter() {
+  return strings_internal::AlphaNumFormatterImpl();
+}
+
+// StreamFormatter()
+//
+// Formats its argument using the << operator.
+inline strings_internal::StreamFormatterImpl StreamFormatter() {
+  return strings_internal::StreamFormatterImpl();
+}
+
+// Function Template: PairFormatter(Formatter, absl::string_view, Formatter)
+//
+// Formats a `std::pair` by putting a given separator between the pair's
+// `.first` and `.second` members. This formatter allows you to specify
+// custom Formatters for both the first and second member of each pair.
+template <typename FirstFormatter, typename SecondFormatter>
+inline strings_internal::PairFormatterImpl<FirstFormatter, SecondFormatter>
+PairFormatter(FirstFormatter f1, absl::string_view sep, SecondFormatter f2) {
+  return strings_internal::PairFormatterImpl<FirstFormatter, SecondFormatter>(
+      std::move(f1), sep, std::move(f2));
+}
+
+// Function overload of PairFormatter() for using a default
+// `AlphaNumFormatter()` for each Formatter in the pair.
+inline strings_internal::PairFormatterImpl<
+    strings_internal::AlphaNumFormatterImpl,
+    strings_internal::AlphaNumFormatterImpl>
+PairFormatter(absl::string_view sep) {
+  return PairFormatter(AlphaNumFormatter(), sep, AlphaNumFormatter());
+}
+
+// Function Template: DereferenceFormatter(Formatter)
+//
+// Formats its argument by dereferencing it and then applying the given
+// formatter. This formatter is useful for formatting a container of
+// pointer-to-T. This pattern often shows up when joining repeated fields in
+// protocol buffers.
+template <typename Formatter>
+strings_internal::DereferenceFormatterImpl<Formatter> DereferenceFormatter(
+    Formatter&& f) {
+  return strings_internal::DereferenceFormatterImpl<Formatter>(
+      std::forward<Formatter>(f));
+}
+
+// Function overload of `DereferenceFormatter()` for using a default
+// `AlphaNumFormatter()`.
+inline strings_internal::DereferenceFormatterImpl<
+    strings_internal::AlphaNumFormatterImpl>
+DereferenceFormatter() {
+  return strings_internal::DereferenceFormatterImpl<
+      strings_internal::AlphaNumFormatterImpl>(AlphaNumFormatter());
+}
+
+// -----------------------------------------------------------------------------
+// StrJoin()
+// -----------------------------------------------------------------------------
+//
+// Joins a range of elements and returns the result as a std::string.
+// `absl::StrJoin()` takes a range, a separator string to use between the
+// elements joined, and an optional Formatter responsible for converting each
+// argument in the range to a string.
+//
+// If omitted, the default `AlphaNumFormatter()` is called on the elements to be
+// joined.
+//
+// Example 1:
+//   // Joins a collection of strings. This pattern also works with a collection
+//   // of `absl::string_view` or even `const char*`.
+//   std::vector<std::string> v = {"foo", "bar", "baz"};
+//   std::string s = absl::StrJoin(v, "-");
+//   EXPECT_EQ("foo-bar-baz", s);
+//
+// Example 2:
+//   // Joins the values in the given `std::initializer_list<>` specified using
+//   // brace initialization. This pattern also works with an initializer_list
+//   // of ints or `absl::string_view` -- any `AlphaNum`-compatible type.
+//   std::string s = absl::StrJoin({"foo", "bar", "baz"}, "-");
+//   EXPECT_EQ("foo-bar-baz", s);
+//
+// Example 3:
+//   // Joins a collection of ints. This pattern also works with floats,
+//   // doubles, int64s -- any `StrCat()`-compatible type.
+//   std::vector<int> v = {1, 2, 3, -4};
+//   std::string s = absl::StrJoin(v, "-");
+//   EXPECT_EQ("1-2-3--4", s);
+//
+// Example 4:
+//   // Joins a collection of pointer-to-int. By default, pointers are
+//   // dereferenced and the pointee is formatted using the default format for
+//   // that type; such dereferencing occurs for all levels of indirection, so
+//   // this pattern works just as well for `std::vector<int**>` as for
+//   // `std::vector<int*>`.
+//   int x = 1, y = 2, z = 3;
+//   std::vector<int*> v = {&x, &y, &z};
+//   std::string s = absl::StrJoin(v, "-");
+//   EXPECT_EQ("1-2-3", s);
+//
+// Example 5:
+//   // Dereferencing of `std::unique_ptr<>` is also supported:
+//   std::vector<std::unique_ptr<int>> v
+//   v.emplace_back(new int(1));
+//   v.emplace_back(new int(2));
+//   v.emplace_back(new int(3));
+//   std::string s = absl::StrJoin(v, "-");
+//   EXPECT_EQ("1-2-3", s);
+//
+// Example 6:
+//   // Joins a `std::map`, with each key-value pair separated by an equals
+//   // sign. This pattern would also work with, say, a
+//   // `std::vector<std::pair<>>`.
+//   std::map<std::string, int> m = {
+//       std::make_pair("a", 1),
+//       std::make_pair("b", 2),
+//       std::make_pair("c", 3)};
+//   std::string s = absl::StrJoin(m, ",", absl::PairFormatter("="));
+//   EXPECT_EQ("a=1,b=2,c=3", s);
+//
+// Example 7:
+//   // These examples show how `absl::StrJoin()` handles a few common edge
+//   // cases:
+//   std::vector<std::string> v_empty;
+//   EXPECT_EQ("", absl::StrJoin(v_empty, "-"));
+//
+//   std::vector<std::string> v_one_item = {"foo"};
+//   EXPECT_EQ("foo", absl::StrJoin(v_one_item, "-"));
+//
+//   std::vector<std::string> v_empty_string = {""};
+//   EXPECT_EQ("", absl::StrJoin(v_empty_string, "-"));
+//
+//   std::vector<std::string> v_one_item_empty_string = {"a", ""};
+//   EXPECT_EQ("a-", absl::StrJoin(v_one_item_empty_string, "-"));
+//
+//   std::vector<std::string> v_two_empty_string = {"", ""};
+//   EXPECT_EQ("-", absl::StrJoin(v_two_empty_string, "-"));
+//
+// Example 8:
+//   // Joins a `std::tuple<T...>` of heterogeneous types, converting each to
+//   // a std::string using the `absl::AlphaNum` class.
+//   std::string s = absl::StrJoin(std::make_tuple(123, "abc", 0.456), "-");
+//   EXPECT_EQ("123-abc-0.456", s);
+
+template <typename Iterator, typename Formatter>
+std::string StrJoin(Iterator start, Iterator end, absl::string_view sep,
+                    Formatter&& fmt) {
+  return strings_internal::JoinAlgorithm(start, end, sep, fmt);
+}
+
+template <typename Range, typename Formatter>
+std::string StrJoin(const Range& range, absl::string_view separator,
+                    Formatter&& fmt) {
+  return strings_internal::JoinRange(range, separator, fmt);
+}
+
+template <typename T, typename Formatter>
+std::string StrJoin(std::initializer_list<T> il, absl::string_view separator,
+                    Formatter&& fmt) {
+  return strings_internal::JoinRange(il, separator, fmt);
+}
+
+template <typename... T, typename Formatter>
+std::string StrJoin(const std::tuple<T...>& value, absl::string_view separator,
+                    Formatter&& fmt) {
+  return strings_internal::JoinAlgorithm(value, separator, fmt);
+}
+
+template <typename Iterator>
+std::string StrJoin(Iterator start, Iterator end, absl::string_view separator) {
+  return strings_internal::JoinRange(start, end, separator);
+}
+
+template <typename Range>
+std::string StrJoin(const Range& range, absl::string_view separator) {
+  return strings_internal::JoinRange(range, separator);
+}
+
+template <typename T>
+std::string StrJoin(std::initializer_list<T> il,
+                    absl::string_view separator) {
+  return strings_internal::JoinRange(il, separator);
+}
+
+template <typename... T>
+std::string StrJoin(const std::tuple<T...>& value,
+                    absl::string_view separator) {
+  return strings_internal::JoinAlgorithm(value, separator, AlphaNumFormatter());
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_STR_JOIN_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/str_replace.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/str_replace.h
new file mode 100644
index 0000000000000000000000000000000000000000..273c7077353ebf3a61c62311a18391e0f96c15e8
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/str_replace.h
@@ -0,0 +1,219 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: str_replace.h
+// -----------------------------------------------------------------------------
+//
+// This file defines `absl::StrReplaceAll()`, a general-purpose string
+// replacement function designed for large, arbitrary text substitutions,
+// especially on strings which you are receiving from some other system for
+// further processing (e.g. processing regular expressions, escaping HTML
+// entities, etc.). `StrReplaceAll` is designed to be efficient even when only
+// one substitution is being performed, or when substitution is rare.
+//
+// If the string being modified is known at compile-time, and the substitutions
+// vary, `absl::Substitute()` may be a better choice.
+//
+// Example:
+//
+// std::string html_escaped = absl::StrReplaceAll(user_input, {
+//                                                {"&", "&amp;"},
+//                                                {"<", "&lt;"},
+//                                                {">", "&gt;"},
+//                                                {"\"", "&quot;"},
+//                                                {"'", "&#39;"}});
+#ifndef ABSL_STRINGS_STR_REPLACE_H_
+#define ABSL_STRINGS_STR_REPLACE_H_
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "absl/base/attributes.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// StrReplaceAll()
+//
+// Replaces character sequences within a given string with replacements provided
+// within an initializer list of key/value pairs. Candidate replacements are
+// considered in order as they occur within the string, with earlier matches
+// taking precedence, and longer matches taking precedence for candidates
+// starting at the same position in the string. Once a substitution is made, the
+// replaced text is not considered for any further substitutions.
+//
+// Example:
+//
+//   std::string s = absl::StrReplaceAll(
+//       "$who bought $count #Noun. Thanks $who!",
+//       {{"$count", absl::StrCat(5)},
+//        {"$who", "Bob"},
+//        {"#Noun", "Apples"}});
+//   EXPECT_EQ("Bob bought 5 Apples. Thanks Bob!", s);
+ABSL_MUST_USE_RESULT std::string StrReplaceAll(
+    absl::string_view s,
+    std::initializer_list<std::pair<absl::string_view, absl::string_view>>
+        replacements);
+
+// Overload of `StrReplaceAll()` to accept a container of key/value replacement
+// pairs (typically either an associative map or a `std::vector` of `std::pair`
+// elements). A vector of pairs is generally more efficient.
+//
+// Examples:
+//
+//   std::map<const absl::string_view, const absl::string_view> replacements;
+//   replacements["$who"] = "Bob";
+//   replacements["$count"] = "5";
+//   replacements["#Noun"] = "Apples";
+//   std::string s = absl::StrReplaceAll(
+//       "$who bought $count #Noun. Thanks $who!",
+//       replacements);
+//   EXPECT_EQ("Bob bought 5 Apples. Thanks Bob!", s);
+//
+//   // A std::vector of std::pair elements can be more efficient.
+//   std::vector<std::pair<const absl::string_view, std::string>> replacements;
+//   replacements.push_back({"&", "&amp;"});
+//   replacements.push_back({"<", "&lt;"});
+//   replacements.push_back({">", "&gt;"});
+//   std::string s = absl::StrReplaceAll("if (ptr < &foo)",
+//                                  replacements);
+//   EXPECT_EQ("if (ptr &lt; &amp;foo)", s);
+template <typename StrToStrMapping>
+std::string StrReplaceAll(absl::string_view s,
+                          const StrToStrMapping& replacements);
+
+// Overload of `StrReplaceAll()` to replace character sequences within a given
+// output string *in place* with replacements provided within an initializer
+// list of key/value pairs, returning the number of substitutions that occurred.
+//
+// Example:
+//
+//   std::string s = std::string("$who bought $count #Noun. Thanks $who!");
+//   int count;
+//   count = absl::StrReplaceAll({{"$count", absl::StrCat(5)},
+//                               {"$who", "Bob"},
+//                               {"#Noun", "Apples"}}, &s);
+//  EXPECT_EQ(count, 4);
+//  EXPECT_EQ("Bob bought 5 Apples. Thanks Bob!", s);
+int StrReplaceAll(
+    std::initializer_list<std::pair<absl::string_view, absl::string_view>>
+        replacements,
+    std::string* target);
+
+// Overload of `StrReplaceAll()` to replace patterns within a given output
+// string *in place* with replacements provided within a container of key/value
+// pairs.
+//
+// Example:
+//
+//   std::string s = std::string("if (ptr < &foo)");
+//   int count = absl::StrReplaceAll({{"&", "&amp;"},
+//                                    {"<", "&lt;"},
+//                                    {">", "&gt;"}}, &s);
+//  EXPECT_EQ(count, 2);
+//  EXPECT_EQ("if (ptr &lt; &amp;foo)", s);
+template <typename StrToStrMapping>
+int StrReplaceAll(const StrToStrMapping& replacements, std::string* target);
+
+// Implementation details only, past this point.
+namespace strings_internal {
+
+struct ViableSubstitution {
+  absl::string_view old;
+  absl::string_view replacement;
+  size_t offset;
+
+  ViableSubstitution(absl::string_view old_str,
+                     absl::string_view replacement_str, size_t offset_val)
+      : old(old_str), replacement(replacement_str), offset(offset_val) {}
+
+  // One substitution occurs "before" another (takes priority) if either
+  // it has the lowest offset, or it has the same offset but a larger size.
+  bool OccursBefore(const ViableSubstitution& y) const {
+    if (offset != y.offset) return offset < y.offset;
+    return old.size() > y.old.size();
+  }
+};
+
+// Build a vector of ViableSubstitutions based on the given list of
+// replacements. subs can be implemented as a priority_queue. However, it turns
+// out that most callers have small enough a list of substitutions that the
+// overhead of such a queue isn't worth it.
+template <typename StrToStrMapping>
+std::vector<ViableSubstitution> FindSubstitutions(
+    absl::string_view s, const StrToStrMapping& replacements) {
+  std::vector<ViableSubstitution> subs;
+  subs.reserve(replacements.size());
+
+  for (const auto& rep : replacements) {
+    using std::get;
+    absl::string_view old(get<0>(rep));
+
+    size_t pos = s.find(old);
+    if (pos == s.npos) continue;
+
+    // Ignore attempts to replace "". This condition is almost never true,
+    // but above condition is frequently true. That's why we test for this
+    // now and not before.
+    if (old.empty()) continue;
+
+    subs.emplace_back(old, get<1>(rep), pos);
+
+    // Insertion sort to ensure the last ViableSubstitution comes before
+    // all the others.
+    size_t index = subs.size();
+    while (--index && subs[index - 1].OccursBefore(subs[index])) {
+      std::swap(subs[index], subs[index - 1]);
+    }
+  }
+  return subs;
+}
+
+int ApplySubstitutions(absl::string_view s,
+                       std::vector<ViableSubstitution>* subs_ptr,
+                       std::string* result_ptr);
+
+}  // namespace strings_internal
+
+template <typename StrToStrMapping>
+std::string StrReplaceAll(absl::string_view s,
+                          const StrToStrMapping& replacements) {
+  auto subs = strings_internal::FindSubstitutions(s, replacements);
+  std::string result;
+  result.reserve(s.size());
+  strings_internal::ApplySubstitutions(s, &subs, &result);
+  return result;
+}
+
+template <typename StrToStrMapping>
+int StrReplaceAll(const StrToStrMapping& replacements, std::string* target) {
+  auto subs = strings_internal::FindSubstitutions(*target, replacements);
+  if (subs.empty()) return 0;
+
+  std::string result;
+  result.reserve(target->size());
+  int substitutions =
+      strings_internal::ApplySubstitutions(*target, &subs, &result);
+  target->swap(result);
+  return substitutions;
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_STR_REPLACE_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/str_split.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/str_split.h
new file mode 100644
index 0000000000000000000000000000000000000000..7bbb68a3435ccccfc05b789b5cb9f8bab6143c98
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/str_split.h
@@ -0,0 +1,547 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: str_split.h
+// -----------------------------------------------------------------------------
+//
+// This file contains functions for splitting strings. It defines the main
+// `StrSplit()` function, several delimiters for determining the boundaries on
+// which to split the string, and predicates for filtering delimited results.
+// `StrSplit()` adapts the returned collection to the type specified by the
+// caller.
+//
+// Example:
+//
+//   // Splits the given string on commas. Returns the results in a
+//   // vector of strings.
+//   std::vector<std::string> v = absl::StrSplit("a,b,c", ',');
+//   // Can also use ","
+//   // v[0] == "a", v[1] == "b", v[2] == "c"
+//
+// See StrSplit() below for more information.
+#ifndef ABSL_STRINGS_STR_SPLIT_H_
+#define ABSL_STRINGS_STR_SPLIT_H_
+
+#include <algorithm>
+#include <cstddef>
+#include <map>
+#include <set>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "absl/base/internal/raw_logging.h"
+#include "absl/base/macros.h"
+#include "absl/strings/internal/str_split_internal.h"
+#include "absl/strings/string_view.h"
+#include "absl/strings/strip.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+//------------------------------------------------------------------------------
+// Delimiters
+//------------------------------------------------------------------------------
+//
+// `StrSplit()` uses delimiters to define the boundaries between elements in the
+// provided input. Several `Delimiter` types are defined below. If a string
+// (`const char*`, `std::string`, or `absl::string_view`) is passed in place of
+// an explicit `Delimiter` object, `StrSplit()` treats it the same way as if it
+// were passed a `ByString` delimiter.
+//
+// A `Delimiter` is an object with a `Find()` function that knows how to find
+// the first occurrence of itself in a given `absl::string_view`.
+//
+// The following `Delimiter` types are available for use within `StrSplit()`:
+//
+//   - `ByString` (default for string arguments)
+//   - `ByChar` (default for a char argument)
+//   - `ByAnyChar`
+//   - `ByLength`
+//   - `MaxSplits`
+//
+// A Delimiter's `Find()` member function will be passed an input `text` that is
+// to be split and a position (`pos`) to begin searching for the next delimiter
+// in `text`. The returned absl::string_view should refer to the next occurrence
+// (after `pos`) of the represented delimiter; this returned absl::string_view
+// represents the next location where the input `text` should be broken.
+//
+// The returned absl::string_view may be zero-length if the Delimiter does not
+// represent a part of the string (e.g., a fixed-length delimiter). If no
+// delimiter is found in the input `text`, a zero-length absl::string_view
+// referring to `text.end()` should be returned (e.g.,
+// `text.substr(text.size())`). It is important that the returned
+// absl::string_view always be within the bounds of the input `text` given as an
+// argument--it must not refer to a string that is physically located outside of
+// the given string.
+//
+// The following example is a simple Delimiter object that is created with a
+// single char and will look for that char in the text passed to the `Find()`
+// function:
+//
+//   struct SimpleDelimiter {
+//     const char c_;
+//     explicit SimpleDelimiter(char c) : c_(c) {}
+//     absl::string_view Find(absl::string_view text, size_t pos) {
+//       auto found = text.find(c_, pos);
+//       if (found == absl::string_view::npos)
+//         return text.substr(text.size());
+//
+//       return text.substr(found, 1);
+//     }
+//   };
+
+// ByString
+//
+// A sub-string delimiter. If `StrSplit()` is passed a string in place of a
+// `Delimiter` object, the string will be implicitly converted into a
+// `ByString` delimiter.
+//
+// Example:
+//
+//   // Because a string literal is converted to an `absl::ByString`,
+//   // the following two splits are equivalent.
+//
+//   std::vector<std::string> v1 = absl::StrSplit("a, b, c", ", ");
+//
+//   using absl::ByString;
+//   std::vector<std::string> v2 = absl::StrSplit("a, b, c",
+//                                                ByString(", "));
+//   // v[0] == "a", v[1] == "b", v[2] == "c"
+class ByString {
+ public:
+  explicit ByString(absl::string_view sp);
+  absl::string_view Find(absl::string_view text, size_t pos) const;
+
+ private:
+  const std::string delimiter_;
+};
+
+// ByChar
+//
+// A single character delimiter. `ByChar` is functionally equivalent to a
+// 1-char string within a `ByString` delimiter, but slightly more efficient.
+//
+// Example:
+//
+//   // Because a char literal is converted to a absl::ByChar,
+//   // the following two splits are equivalent.
+//   std::vector<std::string> v1 = absl::StrSplit("a,b,c", ',');
+//   using absl::ByChar;
+//   std::vector<std::string> v2 = absl::StrSplit("a,b,c", ByChar(','));
+//   // v[0] == "a", v[1] == "b", v[2] == "c"
+//
+// `ByChar` is also the default delimiter if a single character is given
+// as the delimiter to `StrSplit()`. For example, the following calls are
+// equivalent:
+//
+//   std::vector<std::string> v = absl::StrSplit("a-b", '-');
+//
+//   using absl::ByChar;
+//   std::vector<std::string> v = absl::StrSplit("a-b", ByChar('-'));
+//
+class ByChar {
+ public:
+  explicit ByChar(char c) : c_(c) {}
+  absl::string_view Find(absl::string_view text, size_t pos) const;
+
+ private:
+  char c_;
+};
+
+// ByAnyChar
+//
+// A delimiter that will match any of the given byte-sized characters within
+// its provided string.
+//
+// Note: this delimiter works with single-byte string data, but does not work
+// with variable-width encodings, such as UTF-8.
+//
+// Example:
+//
+//   using absl::ByAnyChar;
+//   std::vector<std::string> v = absl::StrSplit("a,b=c", ByAnyChar(",="));
+//   // v[0] == "a", v[1] == "b", v[2] == "c"
+//
+// If `ByAnyChar` is given the empty string, it behaves exactly like
+// `ByString` and matches each individual character in the input string.
+//
+class ByAnyChar {
+ public:
+  explicit ByAnyChar(absl::string_view sp);
+  absl::string_view Find(absl::string_view text, size_t pos) const;
+
+ private:
+  const std::string delimiters_;
+};
+
+// ByLength
+//
+// A delimiter for splitting into equal-length strings. The length argument to
+// the constructor must be greater than 0.
+//
+// Note: this delimiter works with single-byte string data, but does not work
+// with variable-width encodings, such as UTF-8.
+//
+// Example:
+//
+//   using absl::ByLength;
+//   std::vector<std::string> v = absl::StrSplit("123456789", ByLength(3));
+
+//   // v[0] == "123", v[1] == "456", v[2] == "789"
+//
+// Note that the string does not have to be a multiple of the fixed split
+// length. In such a case, the last substring will be shorter.
+//
+//   using absl::ByLength;
+//   std::vector<std::string> v = absl::StrSplit("12345", ByLength(2));
+//
+//   // v[0] == "12", v[1] == "34", v[2] == "5"
+class ByLength {
+ public:
+  explicit ByLength(ptrdiff_t length);
+  absl::string_view Find(absl::string_view text, size_t pos) const;
+
+ private:
+  const ptrdiff_t length_;
+};
+
+namespace strings_internal {
+
+// A traits-like metafunction for selecting the default Delimiter object type
+// for a particular Delimiter type. The base case simply exposes type Delimiter
+// itself as the delimiter's Type. However, there are specializations for
+// string-like objects that map them to the ByString delimiter object.
+// This allows functions like absl::StrSplit() and absl::MaxSplits() to accept
+// string-like objects (e.g., ',') as delimiter arguments but they will be
+// treated as if a ByString delimiter was given.
+template <typename Delimiter>
+struct SelectDelimiter {
+  using type = Delimiter;
+};
+
+template <>
+struct SelectDelimiter<char> {
+  using type = ByChar;
+};
+template <>
+struct SelectDelimiter<char*> {
+  using type = ByString;
+};
+template <>
+struct SelectDelimiter<const char*> {
+  using type = ByString;
+};
+template <>
+struct SelectDelimiter<absl::string_view> {
+  using type = ByString;
+};
+template <>
+struct SelectDelimiter<std::string> {
+  using type = ByString;
+};
+
+// Wraps another delimiter and sets a max number of matches for that delimiter.
+template <typename Delimiter>
+class MaxSplitsImpl {
+ public:
+  MaxSplitsImpl(Delimiter delimiter, int limit)
+      : delimiter_(delimiter), limit_(limit), count_(0) {}
+  absl::string_view Find(absl::string_view text, size_t pos) {
+    if (count_++ == limit_) {
+      return absl::string_view(text.data() + text.size(),
+                               0);  // No more matches.
+    }
+    return delimiter_.Find(text, pos);
+  }
+
+ private:
+  Delimiter delimiter_;
+  const int limit_;
+  int count_;
+};
+
+}  // namespace strings_internal
+
+// MaxSplits()
+//
+// A delimiter that limits the number of matches which can occur to the passed
+// `limit`. The last element in the returned collection will contain all
+// remaining unsplit pieces, which may contain instances of the delimiter.
+// The collection will contain at most `limit` + 1 elements.
+// Example:
+//
+//   using absl::MaxSplits;
+//   std::vector<std::string> v = absl::StrSplit("a,b,c", MaxSplits(',', 1));
+//
+//   // v[0] == "a", v[1] == "b,c"
+template <typename Delimiter>
+inline strings_internal::MaxSplitsImpl<
+    typename strings_internal::SelectDelimiter<Delimiter>::type>
+MaxSplits(Delimiter delimiter, int limit) {
+  typedef
+      typename strings_internal::SelectDelimiter<Delimiter>::type DelimiterType;
+  return strings_internal::MaxSplitsImpl<DelimiterType>(
+      DelimiterType(delimiter), limit);
+}
+
+//------------------------------------------------------------------------------
+// Predicates
+//------------------------------------------------------------------------------
+//
+// Predicates filter the results of a `StrSplit()` by determining whether or not
+// a resultant element is included in the result set. A predicate may be passed
+// as an optional third argument to the `StrSplit()` function.
+//
+// Predicates are unary functions (or functors) that take a single
+// `absl::string_view` argument and return a bool indicating whether the
+// argument should be included (`true`) or excluded (`false`).
+//
+// Predicates are useful when filtering out empty substrings. By default, empty
+// substrings may be returned by `StrSplit()`, which is similar to the way split
+// functions work in other programming languages.
+
+// AllowEmpty()
+//
+// Always returns `true`, indicating that all strings--including empty
+// strings--should be included in the split output. This predicate is not
+// strictly needed because this is the default behavior of `StrSplit()`;
+// however, it might be useful at some call sites to make the intent explicit.
+//
+// Example:
+//
+//  std::vector<std::string> v = absl::StrSplit(" a , ,,b,", ',', AllowEmpty());
+//
+//  // v[0] == " a ", v[1] == " ", v[2] == "", v[3] = "b", v[4] == ""
+struct AllowEmpty {
+  bool operator()(absl::string_view) const { return true; }
+};
+
+// SkipEmpty()
+//
+// Returns `false` if the given `absl::string_view` is empty, indicating that
+// `StrSplit()` should omit the empty string.
+//
+// Example:
+//
+//   std::vector<std::string> v = absl::StrSplit(",a,,b,", ',', SkipEmpty());
+//
+//   // v[0] == "a", v[1] == "b"
+//
+// Note: `SkipEmpty()` does not consider a string containing only whitespace
+// to be empty. To skip such whitespace as well, use the `SkipWhitespace()`
+// predicate.
+struct SkipEmpty {
+  bool operator()(absl::string_view sp) const { return !sp.empty(); }
+};
+
+// SkipWhitespace()
+//
+// Returns `false` if the given `absl::string_view` is empty *or* contains only
+// whitespace, indicating that `StrSplit()` should omit the string.
+//
+// Example:
+//
+//   std::vector<std::string> v = absl::StrSplit(" a , ,,b,",
+//                                               ',', SkipWhitespace());
+//   // v[0] == " a ", v[1] == "b"
+//
+//   // SkipEmpty() would return whitespace elements
+//   std::vector<std::string> v = absl::StrSplit(" a , ,,b,", ',', SkipEmpty());
+//   // v[0] == " a ", v[1] == " ", v[2] == "b"
+struct SkipWhitespace {
+  bool operator()(absl::string_view sp) const {
+    sp = absl::StripAsciiWhitespace(sp);
+    return !sp.empty();
+  }
+};
+
+template <typename T>
+using EnableSplitIfString =
+    typename std::enable_if<std::is_same<T, std::string>::value ||
+                            std::is_same<T, const std::string>::value,
+                            int>::type;
+
+//------------------------------------------------------------------------------
+//                                  StrSplit()
+//------------------------------------------------------------------------------
+
+// StrSplit()
+//
+// Splits a given string based on the provided `Delimiter` object, returning the
+// elements within the type specified by the caller. Optionally, you may pass a
+// `Predicate` to `StrSplit()` indicating whether to include or exclude the
+// resulting element within the final result set. (See the overviews for
+// Delimiters and Predicates above.)
+//
+// Example:
+//
+//   std::vector<std::string> v = absl::StrSplit("a,b,c,d", ',');
+//   // v[0] == "a", v[1] == "b", v[2] == "c", v[3] == "d"
+//
+// You can also provide an explicit `Delimiter` object:
+//
+// Example:
+//
+//   using absl::ByAnyChar;
+//   std::vector<std::string> v = absl::StrSplit("a,b=c", ByAnyChar(",="));
+//   // v[0] == "a", v[1] == "b", v[2] == "c"
+//
+// See above for more information on delimiters.
+//
+// By default, empty strings are included in the result set. You can optionally
+// include a third `Predicate` argument to apply a test for whether the
+// resultant element should be included in the result set:
+//
+// Example:
+//
+//   std::vector<std::string> v = absl::StrSplit(" a , ,,b,",
+//                                               ',', SkipWhitespace());
+//   // v[0] == " a ", v[1] == "b"
+//
+// See above for more information on predicates.
+//
+//------------------------------------------------------------------------------
+// StrSplit() Return Types
+//------------------------------------------------------------------------------
+//
+// The `StrSplit()` function adapts the returned collection to the collection
+// specified by the caller (e.g. `std::vector` above). The returned collections
+// may contain `std::string`, `absl::string_view` (in which case the original
+// string being split must ensure that it outlives the collection), or any
+// object that can be explicitly created from an `absl::string_view`. This
+// behavior works for:
+//
+// 1) All standard STL containers including `std::vector`, `std::list`,
+//    `std::deque`, `std::set`,`std::multiset`, 'std::map`, and `std::multimap`
+// 2) `std::pair` (which is not actually a container). See below.
+//
+// Example:
+//
+//   // The results are returned as `absl::string_view` objects. Note that we
+//   // have to ensure that the input string outlives any results.
+//   std::vector<absl::string_view> v = absl::StrSplit("a,b,c", ',');
+//
+//   // Stores results in a std::set<std::string>, which also performs
+//   // de-duplication and orders the elements in ascending order.
+//   std::set<std::string> a = absl::StrSplit("b,a,c,a,b", ',');
+//   // v[0] == "a", v[1] == "b", v[2] = "c"
+//
+//   // `StrSplit()` can be used within a range-based for loop, in which case
+//   // each element will be of type `absl::string_view`.
+//   std::vector<std::string> v;
+//   for (const auto sv : absl::StrSplit("a,b,c", ',')) {
+//     if (sv != "b") v.emplace_back(sv);
+//   }
+//   // v[0] == "a", v[1] == "c"
+//
+//   // Stores results in a map. The map implementation assumes that the input
+//   // is provided as a series of key/value pairs. For example, the 0th element
+//   // resulting from the split will be stored as a key to the 1st element. If
+//   // an odd number of elements are resolved, the last element is paired with
+//   // a default-constructed value (e.g., empty string).
+//   std::map<std::string, std::string> m = absl::StrSplit("a,b,c", ',');
+//   // m["a"] == "b", m["c"] == ""     // last component value equals ""
+//
+// Splitting to `std::pair` is an interesting case because it can hold only two
+// elements and is not a collection type. When splitting to a `std::pair` the
+// first two split strings become the `std::pair` `.first` and `.second`
+// members, respectively. The remaining split substrings are discarded. If there
+// are less than two split substrings, the empty string is used for the
+// corresponding `std::pair` member.
+//
+// Example:
+//
+//   // Stores first two split strings as the members in a std::pair.
+//   std::pair<std::string, std::string> p = absl::StrSplit("a,b,c", ',');
+//   // p.first == "a", p.second == "b"       // "c" is omitted.
+//
+// The `StrSplit()` function can be used multiple times to perform more
+// complicated splitting logic, such as intelligently parsing key-value pairs.
+//
+// Example:
+//
+//   // The input string "a=b=c,d=e,f=,g" becomes
+//   // { "a" => "b=c", "d" => "e", "f" => "", "g" => "" }
+//   std::map<std::string, std::string> m;
+//   for (absl::string_view sp : absl::StrSplit("a=b=c,d=e,f=,g", ',')) {
+//     m.insert(absl::StrSplit(sp, absl::MaxSplits('=', 1)));
+//   }
+//   EXPECT_EQ("b=c", m.find("a")->second);
+//   EXPECT_EQ("e", m.find("d")->second);
+//   EXPECT_EQ("", m.find("f")->second);
+//   EXPECT_EQ("", m.find("g")->second);
+//
+// WARNING: Due to a legacy bug that is maintained for backward compatibility,
+// splitting the following empty string_views produces different results:
+//
+//   absl::StrSplit(absl::string_view(""), '-');  // {""}
+//   absl::StrSplit(absl::string_view(), '-');    // {}, but should be {""}
+//
+// Try not to depend on this distinction because the bug may one day be fixed.
+template <typename Delimiter>
+strings_internal::Splitter<
+    typename strings_internal::SelectDelimiter<Delimiter>::type, AllowEmpty,
+    absl::string_view>
+StrSplit(strings_internal::ConvertibleToStringView text, Delimiter d) {
+  using DelimiterType =
+      typename strings_internal::SelectDelimiter<Delimiter>::type;
+  return strings_internal::Splitter<DelimiterType, AllowEmpty,
+                                    absl::string_view>(
+      text.value(), DelimiterType(d), AllowEmpty());
+}
+
+template <typename Delimiter, typename StringType,
+          EnableSplitIfString<StringType> = 0>
+strings_internal::Splitter<
+    typename strings_internal::SelectDelimiter<Delimiter>::type, AllowEmpty,
+    std::string>
+StrSplit(StringType&& text, Delimiter d) {
+  using DelimiterType =
+      typename strings_internal::SelectDelimiter<Delimiter>::type;
+  return strings_internal::Splitter<DelimiterType, AllowEmpty, std::string>(
+      std::move(text), DelimiterType(d), AllowEmpty());
+}
+
+template <typename Delimiter, typename Predicate>
+strings_internal::Splitter<
+    typename strings_internal::SelectDelimiter<Delimiter>::type, Predicate,
+    absl::string_view>
+StrSplit(strings_internal::ConvertibleToStringView text, Delimiter d,
+         Predicate p) {
+  using DelimiterType =
+      typename strings_internal::SelectDelimiter<Delimiter>::type;
+  return strings_internal::Splitter<DelimiterType, Predicate,
+                                    absl::string_view>(
+      text.value(), DelimiterType(d), std::move(p));
+}
+
+template <typename Delimiter, typename Predicate, typename StringType,
+          EnableSplitIfString<StringType> = 0>
+strings_internal::Splitter<
+    typename strings_internal::SelectDelimiter<Delimiter>::type, Predicate,
+    std::string>
+StrSplit(StringType&& text, Delimiter d, Predicate p) {
+  using DelimiterType =
+      typename strings_internal::SelectDelimiter<Delimiter>::type;
+  return strings_internal::Splitter<DelimiterType, Predicate, std::string>(
+      std::move(text), DelimiterType(d), std::move(p));
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_STR_SPLIT_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/string_view.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/string_view.h
new file mode 100644
index 0000000000000000000000000000000000000000..eae11b2ab676f9ab87c0c7fcca7afa7183257da2
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/string_view.h
@@ -0,0 +1,704 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: string_view.h
+// -----------------------------------------------------------------------------
+//
+// This file contains the definition of the `absl::string_view` class. A
+// `string_view` points to a contiguous span of characters, often part or all of
+// another `std::string`, double-quoted string literal, character array, or even
+// another `string_view`.
+//
+// This `absl::string_view` abstraction is designed to be a drop-in
+// replacement for the C++17 `std::string_view` abstraction.
+#ifndef ABSL_STRINGS_STRING_VIEW_H_
+#define ABSL_STRINGS_STRING_VIEW_H_
+
+#include <algorithm>
+#include <cassert>
+#include <cstddef>
+#include <cstring>
+#include <iosfwd>
+#include <iterator>
+#include <limits>
+#include <string>
+
+#include "absl/base/attributes.h"
+#include "absl/base/config.h"
+#include "absl/base/internal/throw_delegate.h"
+#include "absl/base/macros.h"
+#include "absl/base/optimization.h"
+#include "absl/base/port.h"
+
+#ifdef ABSL_USES_STD_STRING_VIEW
+
+#include <string_view>  // IWYU pragma: export
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+using string_view = std::string_view;
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#else  // ABSL_USES_STD_STRING_VIEW
+
+#if ABSL_HAVE_BUILTIN(__builtin_memcmp) ||        \
+    (defined(__GNUC__) && !defined(__clang__)) || \
+    (defined(_MSC_VER) && _MSC_VER >= 1928)
+#define ABSL_INTERNAL_STRING_VIEW_MEMCMP __builtin_memcmp
+#else  // ABSL_HAVE_BUILTIN(__builtin_memcmp)
+#define ABSL_INTERNAL_STRING_VIEW_MEMCMP memcmp
+#endif  // ABSL_HAVE_BUILTIN(__builtin_memcmp)
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// absl::string_view
+//
+// A `string_view` provides a lightweight view into the string data provided by
+// a `std::string`, double-quoted string literal, character array, or even
+// another `string_view`. A `string_view` does *not* own the string to which it
+// points, and that data cannot be modified through the view.
+//
+// You can use `string_view` as a function or method parameter anywhere a
+// parameter can receive a double-quoted string literal, `const char*`,
+// `std::string`, or another `absl::string_view` argument with no need to copy
+// the string data. Systematic use of `string_view` within function arguments
+// reduces data copies and `strlen()` calls.
+//
+// Because of its small size, prefer passing `string_view` by value:
+//
+//   void MyFunction(absl::string_view arg);
+//
+// If circumstances require, you may also pass one by const reference:
+//
+//   void MyFunction(const absl::string_view& arg);  // not preferred
+//
+// Passing by value generates slightly smaller code for many architectures.
+//
+// In either case, the source data of the `string_view` must outlive the
+// `string_view` itself.
+//
+// A `string_view` is also suitable for local variables if you know that the
+// lifetime of the underlying object is longer than the lifetime of your
+// `string_view` variable. However, beware of binding a `string_view` to a
+// temporary value:
+//
+//   // BAD use of string_view: lifetime problem
+//   absl::string_view sv = obj.ReturnAString();
+//
+//   // GOOD use of string_view: str outlives sv
+//   std::string str = obj.ReturnAString();
+//   absl::string_view sv = str;
+//
+// Due to lifetime issues, a `string_view` is sometimes a poor choice for a
+// return value and usually a poor choice for a data member. If you do use a
+// `string_view` this way, it is your responsibility to ensure that the object
+// pointed to by the `string_view` outlives the `string_view`.
+//
+// A `string_view` may represent a whole string or just part of a string. For
+// example, when splitting a string, `std::vector<absl::string_view>` is a
+// natural data type for the output.
+//
+// For another example, a Cord is a non-contiguous, potentially very
+// long string-like object.  The Cord class has an interface that iteratively
+// provides string_view objects that point to the successive pieces of a Cord
+// object.
+//
+// When constructed from a source which is NUL-terminated, the `string_view`
+// itself will not include the NUL-terminator unless a specific size (including
+// the NUL) is passed to the constructor. As a result, common idioms that work
+// on NUL-terminated strings do not work on `string_view` objects. If you write
+// code that scans a `string_view`, you must check its length rather than test
+// for nul, for example. Note, however, that nuls may still be embedded within
+// a `string_view` explicitly.
+//
+// You may create a null `string_view` in two ways:
+//
+//   absl::string_view sv;
+//   absl::string_view sv(nullptr, 0);
+//
+// For the above, `sv.data() == nullptr`, `sv.length() == 0`, and
+// `sv.empty() == true`. Also, if you create a `string_view` with a non-null
+// pointer then `sv.data() != nullptr`. Thus, you can use `string_view()` to
+// signal an undefined value that is different from other `string_view` values
+// in a similar fashion to how `const char* p1 = nullptr;` is different from
+// `const char* p2 = "";`. However, in practice, it is not recommended to rely
+// on this behavior.
+//
+// Be careful not to confuse a null `string_view` with an empty one. A null
+// `string_view` is an empty `string_view`, but some empty `string_view`s are
+// not null. Prefer checking for emptiness over checking for null.
+//
+// There are many ways to create an empty string_view:
+//
+//   const char* nullcp = nullptr;
+//   // string_view.size() will return 0 in all cases.
+//   absl::string_view();
+//   absl::string_view(nullcp, 0);
+//   absl::string_view("");
+//   absl::string_view("", 0);
+//   absl::string_view("abcdef", 0);
+//   absl::string_view("abcdef" + 6, 0);
+//
+// All empty `string_view` objects whether null or not, are equal:
+//
+//   absl::string_view() == absl::string_view("", 0)
+//   absl::string_view(nullptr, 0) == absl::string_view("abcdef"+6, 0)
+class string_view {
+ public:
+  using traits_type = std::char_traits<char>;
+  using value_type = char;
+  using pointer = char*;
+  using const_pointer = const char*;
+  using reference = char&;
+  using const_reference = const char&;
+  using const_iterator = const char*;
+  using iterator = const_iterator;
+  using const_reverse_iterator = std::reverse_iterator<const_iterator>;
+  using reverse_iterator = const_reverse_iterator;
+  using size_type = size_t;
+  using difference_type = std::ptrdiff_t;
+
+  static constexpr size_type npos = static_cast<size_type>(-1);
+
+  // Null `string_view` constructor
+  constexpr string_view() noexcept : ptr_(nullptr), length_(0) {}
+
+  // Implicit constructors
+
+  template <typename Allocator>
+  string_view(  // NOLINT(runtime/explicit)
+      const std::basic_string<char, std::char_traits<char>, Allocator>& str
+          ABSL_ATTRIBUTE_LIFETIME_BOUND) noexcept
+      // This is implemented in terms of `string_view(p, n)` so `str.size()`
+      // doesn't need to be reevaluated after `ptr_` is set.
+      // The length check is also skipped since it is unnecessary and causes
+      // code bloat.
+      : string_view(str.data(), str.size(), SkipCheckLengthTag{}) {}
+
+  // Implicit constructor of a `string_view` from NUL-terminated `str`. When
+  // accepting possibly null strings, use `absl::NullSafeStringView(str)`
+  // instead (see below).
+  // The length check is skipped since it is unnecessary and causes code bloat.
+  constexpr string_view(const char* str)  // NOLINT(runtime/explicit)
+      : ptr_(str), length_(str ? StrlenInternal(str) : 0) {}
+
+  // Implicit constructor of a `string_view` from a `const char*` and length.
+  constexpr string_view(const char* data, size_type len)
+      : ptr_(data), length_(CheckLengthInternal(len)) {}
+
+  // NOTE: Harmlessly omitted to work around gdb bug.
+  //   constexpr string_view(const string_view&) noexcept = default;
+  //   string_view& operator=(const string_view&) noexcept = default;
+
+  // Iterators
+
+  // string_view::begin()
+  //
+  // Returns an iterator pointing to the first character at the beginning of the
+  // `string_view`, or `end()` if the `string_view` is empty.
+  constexpr const_iterator begin() const noexcept { return ptr_; }
+
+  // string_view::end()
+  //
+  // Returns an iterator pointing just beyond the last character at the end of
+  // the `string_view`. This iterator acts as a placeholder; attempting to
+  // access it results in undefined behavior.
+  constexpr const_iterator end() const noexcept { return ptr_ + length_; }
+
+  // string_view::cbegin()
+  //
+  // Returns a const iterator pointing to the first character at the beginning
+  // of the `string_view`, or `end()` if the `string_view` is empty.
+  constexpr const_iterator cbegin() const noexcept { return begin(); }
+
+  // string_view::cend()
+  //
+  // Returns a const iterator pointing just beyond the last character at the end
+  // of the `string_view`. This pointer acts as a placeholder; attempting to
+  // access its element results in undefined behavior.
+  constexpr const_iterator cend() const noexcept { return end(); }
+
+  // string_view::rbegin()
+  //
+  // Returns a reverse iterator pointing to the last character at the end of the
+  // `string_view`, or `rend()` if the `string_view` is empty.
+  const_reverse_iterator rbegin() const noexcept {
+    return const_reverse_iterator(end());
+  }
+
+  // string_view::rend()
+  //
+  // Returns a reverse iterator pointing just before the first character at the
+  // beginning of the `string_view`. This pointer acts as a placeholder;
+  // attempting to access its element results in undefined behavior.
+  const_reverse_iterator rend() const noexcept {
+    return const_reverse_iterator(begin());
+  }
+
+  // string_view::crbegin()
+  //
+  // Returns a const reverse iterator pointing to the last character at the end
+  // of the `string_view`, or `crend()` if the `string_view` is empty.
+  const_reverse_iterator crbegin() const noexcept { return rbegin(); }
+
+  // string_view::crend()
+  //
+  // Returns a const reverse iterator pointing just before the first character
+  // at the beginning of the `string_view`. This pointer acts as a placeholder;
+  // attempting to access its element results in undefined behavior.
+  const_reverse_iterator crend() const noexcept { return rend(); }
+
+  // Capacity Utilities
+
+  // string_view::size()
+  //
+  // Returns the number of characters in the `string_view`.
+  constexpr size_type size() const noexcept { return length_; }
+
+  // string_view::length()
+  //
+  // Returns the number of characters in the `string_view`. Alias for `size()`.
+  constexpr size_type length() const noexcept { return size(); }
+
+  // string_view::max_size()
+  //
+  // Returns the maximum number of characters the `string_view` can hold.
+  constexpr size_type max_size() const noexcept { return kMaxSize; }
+
+  // string_view::empty()
+  //
+  // Checks if the `string_view` is empty (refers to no characters).
+  constexpr bool empty() const noexcept { return length_ == 0; }
+
+  // string_view::operator[]
+  //
+  // Returns the ith element of the `string_view` using the array operator.
+  // Note that this operator does not perform any bounds checking.
+  constexpr const_reference operator[](size_type i) const {
+    return ABSL_HARDENING_ASSERT(i < size()), ptr_[i];
+  }
+
+  // string_view::at()
+  //
+  // Returns the ith element of the `string_view`. Bounds checking is performed,
+  // and an exception of type `std::out_of_range` will be thrown on invalid
+  // access.
+  constexpr const_reference at(size_type i) const {
+    return ABSL_PREDICT_TRUE(i < size())
+               ? ptr_[i]
+               : ((void)base_internal::ThrowStdOutOfRange(
+                      "absl::string_view::at"),
+                  ptr_[i]);
+  }
+
+  // string_view::front()
+  //
+  // Returns the first element of a `string_view`.
+  constexpr const_reference front() const {
+    return ABSL_HARDENING_ASSERT(!empty()), ptr_[0];
+  }
+
+  // string_view::back()
+  //
+  // Returns the last element of a `string_view`.
+  constexpr const_reference back() const {
+    return ABSL_HARDENING_ASSERT(!empty()), ptr_[size() - 1];
+  }
+
+  // string_view::data()
+  //
+  // Returns a pointer to the underlying character array (which is of course
+  // stored elsewhere). Note that `string_view::data()` may contain embedded nul
+  // characters, but the returned buffer may or may not be NUL-terminated;
+  // therefore, do not pass `data()` to a routine that expects a NUL-terminated
+  // string.
+  constexpr const_pointer data() const noexcept { return ptr_; }
+
+  // Modifiers
+
+  // string_view::remove_prefix()
+  //
+  // Removes the first `n` characters from the `string_view`. Note that the
+  // underlying string is not changed, only the view.
+  constexpr void remove_prefix(size_type n) {
+    ABSL_HARDENING_ASSERT(n <= length_);
+    ptr_ += n;
+    length_ -= n;
+  }
+
+  // string_view::remove_suffix()
+  //
+  // Removes the last `n` characters from the `string_view`. Note that the
+  // underlying string is not changed, only the view.
+  constexpr void remove_suffix(size_type n) {
+    ABSL_HARDENING_ASSERT(n <= length_);
+    length_ -= n;
+  }
+
+  // string_view::swap()
+  //
+  // Swaps this `string_view` with another `string_view`.
+  constexpr void swap(string_view& s) noexcept {
+    auto t = *this;
+    *this = s;
+    s = t;
+  }
+
+  // Explicit conversion operators
+
+  // Converts to `std::basic_string`.
+  template <typename A>
+  explicit operator std::basic_string<char, traits_type, A>() const {
+    if (!data()) return {};
+    return std::basic_string<char, traits_type, A>(data(), size());
+  }
+
+  // string_view::copy()
+  //
+  // Copies the contents of the `string_view` at offset `pos` and length `n`
+  // into `buf`.
+  size_type copy(char* buf, size_type n, size_type pos = 0) const {
+    if (ABSL_PREDICT_FALSE(pos > length_)) {
+      base_internal::ThrowStdOutOfRange("absl::string_view::copy");
+    }
+    size_type rlen = (std::min)(length_ - pos, n);
+    if (rlen > 0) {
+      const char* start = ptr_ + pos;
+      traits_type::copy(buf, start, rlen);
+    }
+    return rlen;
+  }
+
+  // string_view::substr()
+  //
+  // Returns a "substring" of the `string_view` (at offset `pos` and length
+  // `n`) as another string_view. This function throws `std::out_of_bounds` if
+  // `pos > size`.
+  // Use absl::ClippedSubstr if you need a truncating substr operation.
+  constexpr string_view substr(size_type pos = 0, size_type n = npos) const {
+    return ABSL_PREDICT_FALSE(pos > length_)
+               ? (base_internal::ThrowStdOutOfRange(
+                      "absl::string_view::substr"),
+                  string_view())
+               : string_view(ptr_ + pos, Min(n, length_ - pos));
+  }
+
+  // string_view::compare()
+  //
+  // Performs a lexicographical comparison between this `string_view` and
+  // another `string_view` `x`, returning a negative value if `*this` is less
+  // than `x`, 0 if `*this` is equal to `x`, and a positive value if `*this`
+  // is greater than `x`.
+  constexpr int compare(string_view x) const noexcept {
+    return CompareImpl(length_, x.length_,
+                       Min(length_, x.length_) == 0
+                           ? 0
+                           : ABSL_INTERNAL_STRING_VIEW_MEMCMP(
+                                 ptr_, x.ptr_, Min(length_, x.length_)));
+  }
+
+  // Overload of `string_view::compare()` for comparing a substring of the
+  // 'string_view` and another `absl::string_view`.
+  constexpr int compare(size_type pos1, size_type count1, string_view v) const {
+    return substr(pos1, count1).compare(v);
+  }
+
+  // Overload of `string_view::compare()` for comparing a substring of the
+  // `string_view` and a substring of another `absl::string_view`.
+  constexpr int compare(size_type pos1, size_type count1, string_view v,
+                        size_type pos2, size_type count2) const {
+    return substr(pos1, count1).compare(v.substr(pos2, count2));
+  }
+
+  // Overload of `string_view::compare()` for comparing a `string_view` and a
+  // a different C-style string `s`.
+  constexpr int compare(const char* s) const { return compare(string_view(s)); }
+
+  // Overload of `string_view::compare()` for comparing a substring of the
+  // `string_view` and a different string C-style string `s`.
+  constexpr int compare(size_type pos1, size_type count1, const char* s) const {
+    return substr(pos1, count1).compare(string_view(s));
+  }
+
+  // Overload of `string_view::compare()` for comparing a substring of the
+  // `string_view` and a substring of a different C-style string `s`.
+  constexpr int compare(size_type pos1, size_type count1, const char* s,
+                        size_type count2) const {
+    return substr(pos1, count1).compare(string_view(s, count2));
+  }
+
+  // Find Utilities
+
+  // string_view::find()
+  //
+  // Finds the first occurrence of the substring `s` within the `string_view`,
+  // returning the position of the first character's match, or `npos` if no
+  // match was found.
+  size_type find(string_view s, size_type pos = 0) const noexcept;
+
+  // Overload of `string_view::find()` for finding the given character `c`
+  // within the `string_view`.
+  size_type find(char c, size_type pos = 0) const noexcept;
+
+  // Overload of `string_view::find()` for finding a substring of a different
+  // C-style string `s` within the `string_view`.
+  size_type find(const char* s, size_type pos, size_type count) const {
+    return find(string_view(s, count), pos);
+  }
+
+  // Overload of `string_view::find()` for finding a different C-style string
+  // `s` within the `string_view`.
+  size_type find(const char* s, size_type pos = 0) const {
+    return find(string_view(s), pos);
+  }
+
+  // string_view::rfind()
+  //
+  // Finds the last occurrence of a substring `s` within the `string_view`,
+  // returning the position of the first character's match, or `npos` if no
+  // match was found.
+  size_type rfind(string_view s, size_type pos = npos) const noexcept;
+
+  // Overload of `string_view::rfind()` for finding the last given character `c`
+  // within the `string_view`.
+  size_type rfind(char c, size_type pos = npos) const noexcept;
+
+  // Overload of `string_view::rfind()` for finding a substring of a different
+  // C-style string `s` within the `string_view`.
+  size_type rfind(const char* s, size_type pos, size_type count) const {
+    return rfind(string_view(s, count), pos);
+  }
+
+  // Overload of `string_view::rfind()` for finding a different C-style string
+  // `s` within the `string_view`.
+  size_type rfind(const char* s, size_type pos = npos) const {
+    return rfind(string_view(s), pos);
+  }
+
+  // string_view::find_first_of()
+  //
+  // Finds the first occurrence of any of the characters in `s` within the
+  // `string_view`, returning the start position of the match, or `npos` if no
+  // match was found.
+  size_type find_first_of(string_view s, size_type pos = 0) const noexcept;
+
+  // Overload of `string_view::find_first_of()` for finding a character `c`
+  // within the `string_view`.
+  size_type find_first_of(char c, size_type pos = 0) const noexcept {
+    return find(c, pos);
+  }
+
+  // Overload of `string_view::find_first_of()` for finding a substring of a
+  // different C-style string `s` within the `string_view`.
+  size_type find_first_of(const char* s, size_type pos,
+                                    size_type count) const {
+    return find_first_of(string_view(s, count), pos);
+  }
+
+  // Overload of `string_view::find_first_of()` for finding a different C-style
+  // string `s` within the `string_view`.
+  size_type find_first_of(const char* s, size_type pos = 0) const {
+    return find_first_of(string_view(s), pos);
+  }
+
+  // string_view::find_last_of()
+  //
+  // Finds the last occurrence of any of the characters in `s` within the
+  // `string_view`, returning the start position of the match, or `npos` if no
+  // match was found.
+  size_type find_last_of(string_view s, size_type pos = npos) const noexcept;
+
+  // Overload of `string_view::find_last_of()` for finding a character `c`
+  // within the `string_view`.
+  size_type find_last_of(char c, size_type pos = npos) const noexcept {
+    return rfind(c, pos);
+  }
+
+  // Overload of `string_view::find_last_of()` for finding a substring of a
+  // different C-style string `s` within the `string_view`.
+  size_type find_last_of(const char* s, size_type pos, size_type count) const {
+    return find_last_of(string_view(s, count), pos);
+  }
+
+  // Overload of `string_view::find_last_of()` for finding a different C-style
+  // string `s` within the `string_view`.
+  size_type find_last_of(const char* s, size_type pos = npos) const {
+    return find_last_of(string_view(s), pos);
+  }
+
+  // string_view::find_first_not_of()
+  //
+  // Finds the first occurrence of any of the characters not in `s` within the
+  // `string_view`, returning the start position of the first non-match, or
+  // `npos` if no non-match was found.
+  size_type find_first_not_of(string_view s, size_type pos = 0) const noexcept;
+
+  // Overload of `string_view::find_first_not_of()` for finding a character
+  // that is not `c` within the `string_view`.
+  size_type find_first_not_of(char c, size_type pos = 0) const noexcept;
+
+  // Overload of `string_view::find_first_not_of()` for finding a substring of a
+  // different C-style string `s` within the `string_view`.
+  size_type find_first_not_of(const char* s, size_type pos,
+                              size_type count) const {
+    return find_first_not_of(string_view(s, count), pos);
+  }
+
+  // Overload of `string_view::find_first_not_of()` for finding a different
+  // C-style string `s` within the `string_view`.
+  size_type find_first_not_of(const char* s, size_type pos = 0) const {
+    return find_first_not_of(string_view(s), pos);
+  }
+
+  // string_view::find_last_not_of()
+  //
+  // Finds the last occurrence of any of the characters not in `s` within the
+  // `string_view`, returning the start position of the last non-match, or
+  // `npos` if no non-match was found.
+  size_type find_last_not_of(string_view s,
+                             size_type pos = npos) const noexcept;
+
+  // Overload of `string_view::find_last_not_of()` for finding a character
+  // that is not `c` within the `string_view`.
+  size_type find_last_not_of(char c, size_type pos = npos) const noexcept;
+
+  // Overload of `string_view::find_last_not_of()` for finding a substring of a
+  // different C-style string `s` within the `string_view`.
+  size_type find_last_not_of(const char* s, size_type pos,
+                             size_type count) const {
+    return find_last_not_of(string_view(s, count), pos);
+  }
+
+  // Overload of `string_view::find_last_not_of()` for finding a different
+  // C-style string `s` within the `string_view`.
+  size_type find_last_not_of(const char* s, size_type pos = npos) const {
+    return find_last_not_of(string_view(s), pos);
+  }
+
+ private:
+  // The constructor from std::string delegates to this constructor.
+  // See the comment on that constructor for the rationale.
+  struct SkipCheckLengthTag {};
+  string_view(const char* data, size_type len, SkipCheckLengthTag) noexcept
+      : ptr_(data), length_(len) {}
+
+  static constexpr size_type kMaxSize =
+      (std::numeric_limits<difference_type>::max)();
+
+  static constexpr size_type CheckLengthInternal(size_type len) {
+    return ABSL_HARDENING_ASSERT(len <= kMaxSize), len;
+  }
+
+  static constexpr size_type StrlenInternal(const char* str) {
+#if defined(_MSC_VER) && _MSC_VER >= 1910 && !defined(__clang__)
+    // MSVC 2017+ can evaluate this at compile-time.
+    const char* begin = str;
+    while (*str != '\0') ++str;
+    return str - begin;
+#elif ABSL_HAVE_BUILTIN(__builtin_strlen) || \
+    (defined(__GNUC__) && !defined(__clang__))
+    // GCC has __builtin_strlen according to
+    // https://gcc.gnu.org/onlinedocs/gcc-4.7.0/gcc/Other-Builtins.html, but
+    // ABSL_HAVE_BUILTIN doesn't detect that, so we use the extra checks above.
+    // __builtin_strlen is constexpr.
+    return __builtin_strlen(str);
+#else
+    return str ? strlen(str) : 0;
+#endif
+  }
+
+  static constexpr size_t Min(size_type length_a, size_type length_b) {
+    return length_a < length_b ? length_a : length_b;
+  }
+
+  static constexpr int CompareImpl(size_type length_a, size_type length_b,
+                                   int compare_result) {
+    return compare_result == 0 ? static_cast<int>(length_a > length_b) -
+                                     static_cast<int>(length_a < length_b)
+                               : (compare_result < 0 ? -1 : 1);
+  }
+
+  const char* ptr_;
+  size_type length_;
+};
+
+// This large function is defined inline so that in a fairly common case where
+// one of the arguments is a literal, the compiler can elide a lot of the
+// following comparisons.
+constexpr bool operator==(string_view x, string_view y) noexcept {
+  return x.size() == y.size() &&
+         (x.empty() ||
+          ABSL_INTERNAL_STRING_VIEW_MEMCMP(x.data(), y.data(), x.size()) == 0);
+}
+
+constexpr bool operator!=(string_view x, string_view y) noexcept {
+  return !(x == y);
+}
+
+constexpr bool operator<(string_view x, string_view y) noexcept {
+  return x.compare(y) < 0;
+}
+
+constexpr bool operator>(string_view x, string_view y) noexcept {
+  return y < x;
+}
+
+constexpr bool operator<=(string_view x, string_view y) noexcept {
+  return !(y < x);
+}
+
+constexpr bool operator>=(string_view x, string_view y) noexcept {
+  return !(x < y);
+}
+
+// IO Insertion Operator
+std::ostream& operator<<(std::ostream& o, string_view piece);
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#undef ABSL_INTERNAL_STRING_VIEW_MEMCMP
+
+#endif  // ABSL_USES_STD_STRING_VIEW
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// ClippedSubstr()
+//
+// Like `s.substr(pos, n)`, but clips `pos` to an upper bound of `s.size()`.
+// Provided because std::string_view::substr throws if `pos > size()`
+inline string_view ClippedSubstr(string_view s, size_t pos,
+                                 size_t n = string_view::npos) {
+  pos = (std::min)(pos, static_cast<size_t>(s.size()));
+  return s.substr(pos, n);
+}
+
+// NullSafeStringView()
+//
+// Creates an `absl::string_view` from a pointer `p` even if it's null-valued.
+// This function should be used where an `absl::string_view` can be created from
+// a possibly-null pointer.
+constexpr string_view NullSafeStringView(const char* p) {
+  return p ? string_view(p) : string_view();
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_STRING_VIEW_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/strip.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/strip.h
new file mode 100644
index 0000000000000000000000000000000000000000..341e66fc928128d39e03e8ccdf3e60db87edc781
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/strip.h
@@ -0,0 +1,93 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: strip.h
+// -----------------------------------------------------------------------------
+//
+// This file contains various functions for stripping substrings from a string.
+#ifndef ABSL_STRINGS_STRIP_H_
+#define ABSL_STRINGS_STRIP_H_
+
+#include <cstddef>
+#include <string>
+
+#include "absl/base/macros.h"
+#include "absl/strings/ascii.h"
+#include "absl/strings/match.h"
+#include "absl/strings/string_view.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+
+// ConsumePrefix()
+//
+// Strips the `expected` prefix, if found, from the start of `str`.
+// If the operation succeeded, `true` is returned.  If not, `false`
+// is returned and `str` is not modified.
+//
+// Example:
+//
+//   absl::string_view input("abc");
+//   EXPECT_TRUE(absl::ConsumePrefix(&input, "a"));
+//   EXPECT_EQ(input, "bc");
+inline bool ConsumePrefix(absl::string_view* str, absl::string_view expected) {
+  if (!absl::StartsWith(*str, expected)) return false;
+  str->remove_prefix(expected.size());
+  return true;
+}
+// ConsumeSuffix()
+//
+// Strips the `expected` suffix, if found, from the end of `str`.
+// If the operation succeeded, `true` is returned.  If not, `false`
+// is returned and `str` is not modified.
+//
+// Example:
+//
+//   absl::string_view input("abcdef");
+//   EXPECT_TRUE(absl::ConsumeSuffix(&input, "def"));
+//   EXPECT_EQ(input, "abc");
+inline bool ConsumeSuffix(absl::string_view* str, absl::string_view expected) {
+  if (!absl::EndsWith(*str, expected)) return false;
+  str->remove_suffix(expected.size());
+  return true;
+}
+
+// StripPrefix()
+//
+// Returns a view into the input string `str` with the given `prefix` removed,
+// but leaving the original string intact. If the prefix does not match at the
+// start of the string, returns the original string instead.
+ABSL_MUST_USE_RESULT inline absl::string_view StripPrefix(
+    absl::string_view str, absl::string_view prefix) {
+  if (absl::StartsWith(str, prefix)) str.remove_prefix(prefix.size());
+  return str;
+}
+
+// StripSuffix()
+//
+// Returns a view into the input string `str` with the given `suffix` removed,
+// but leaving the original string intact. If the suffix does not match at the
+// end of the string, returns the original string instead.
+ABSL_MUST_USE_RESULT inline absl::string_view StripSuffix(
+    absl::string_view str, absl::string_view suffix) {
+  if (absl::EndsWith(str, suffix)) str.remove_suffix(suffix.size());
+  return str;
+}
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_STRIP_H_
diff --git a/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/substitute.h b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/substitute.h
new file mode 100644
index 0000000000000000000000000000000000000000..d6a5a690d7f4d70419cdba3e29d492a01663be2d
--- /dev/null
+++ b/miniconda3/envs/active_proaction/lib/python3.10/site-packages/tensorflow/include/absl/strings/substitute.h
@@ -0,0 +1,755 @@
+//
+// Copyright 2017 The Abseil Authors.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//      https://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+// -----------------------------------------------------------------------------
+// File: substitute.h
+// -----------------------------------------------------------------------------
+//
+// This package contains functions for efficiently performing string
+// substitutions using a format string with positional notation:
+// `Substitute()` and `SubstituteAndAppend()`.
+//
+// Unlike printf-style format specifiers, `Substitute()` functions do not need
+// to specify the type of the substitution arguments. Supported arguments
+// following the format string, such as strings, string_views, ints,
+// floats, and bools, are automatically converted to strings during the
+// substitution process. (See below for a full list of supported types.)
+//
+// `Substitute()` does not allow you to specify *how* to format a value, beyond
+// the default conversion to string. For example, you cannot format an integer
+// in hex.
+//
+// The format string uses positional identifiers indicated by a dollar sign ($)
+// and single digit positional ids to indicate which substitution arguments to
+// use at that location within the format string.
+//
+// A '$$' sequence in the format string causes a literal '$' character to be
+// output.
+//
+// Example 1:
+//   std::string s = Substitute("$1 purchased $0 $2 for $$10. Thanks $1!",
+//                              5, "Bob", "Apples");
+//   EXPECT_EQ("Bob purchased 5 Apples for $10. Thanks Bob!", s);
+//
+// Example 2:
+//   std::string s = "Hi. ";
+//   SubstituteAndAppend(&s, "My name is $0 and I am $1 years old.", "Bob", 5);
+//   EXPECT_EQ("Hi. My name is Bob and I am 5 years old.", s);
+//
+// Supported types:
+//   * absl::string_view, std::string, const char* (null is equivalent to "")
+//   * int32_t, int64_t, uint32_t, uint64_t
+//   * float, double
+//   * bool (Printed as "true" or "false")
+//   * pointer types other than char* (Printed as "0x<lower case hex string>",
+//     except that null is printed as "NULL")
+//   * user-defined types via the `AbslStringify()` customization point. See the
+//     documentation for `absl::StrCat` for an explanation on how to use this.
+//
+// If an invalid format string is provided, Substitute returns an empty string
+// and SubstituteAndAppend does not change the provided output string.
+// A format string is invalid if it:
+//   * ends in an unescaped $ character,
+//     e.g. "Hello $", or
+//   * calls for a position argument which is not provided,
+//     e.g. Substitute("Hello $2", "world"), or
+//   * specifies a non-digit, non-$ character after an unescaped $ character,
+//     e.g. "Hello $f".
+// In debug mode, i.e. #ifndef NDEBUG, such errors terminate the program.
+
+#ifndef ABSL_STRINGS_SUBSTITUTE_H_
+#define ABSL_STRINGS_SUBSTITUTE_H_
+
+#include <cstring>
+#include <string>
+#include <type_traits>
+#include <vector>
+
+#include "absl/base/macros.h"
+#include "absl/base/port.h"
+#include "absl/strings/ascii.h"
+#include "absl/strings/escaping.h"
+#include "absl/strings/internal/stringify_sink.h"
+#include "absl/strings/numbers.h"
+#include "absl/strings/str_cat.h"
+#include "absl/strings/str_split.h"
+#include "absl/strings/string_view.h"
+#include "absl/strings/strip.h"
+
+namespace absl {
+ABSL_NAMESPACE_BEGIN
+namespace substitute_internal {
+
+// Arg
+//
+// This class provides an argument type for `absl::Substitute()` and
+// `absl::SubstituteAndAppend()`. `Arg` handles implicit conversion of various
+// types to a string. (`Arg` is very similar to the `AlphaNum` class in
+// `StrCat()`.)
+//
+// This class has implicit constructors.
+class Arg {
+ public:
+  // Overloads for string-y things
+  //
+  // Explicitly overload `const char*` so the compiler doesn't cast to `bool`.
+  Arg(const char* value)  // NOLINT(google-explicit-constructor)
+      : piece_(absl::NullSafeStringView(value)) {}
+  template <typename Allocator>
+  Arg(  // NOLINT
+      const std::basic_string<char, std::char_traits<char>, Allocator>&
+          value) noexcept
+      : piece_(value) {}
+  Arg(absl::string_view value)  // NOLINT(google-explicit-constructor)
+      : piece_(value) {}
+
+  // Overloads for primitives
+  //
+  // No overloads are available for signed and unsigned char because if people
+  // are explicitly declaring their chars as signed or unsigned then they are
+  // probably using them as 8-bit integers and would probably prefer an integer
+  // representation. However, we can't really know, so we make the caller decide
+  // what to do.
+  Arg(char value)  // NOLINT(google-explicit-constructor)
+      : piece_(scratch_, 1) {
+    scratch_[0] = value;
+  }
+  Arg(short value)  // NOLINT(*)
+      : piece_(scratch_,
+               static_cast<size_t>(
+                   numbers_internal::FastIntToBuffer(value, scratch_) -
+                   scratch_)) {}
+  Arg(unsigned short value)  // NOLINT(*)
+      : piece_(scratch_,
+               static_cast<size_t>(
+                   numbers_internal::FastIntToBuffer(value, scratch_) -
+                   scratch_)) {}
+  Arg(int value)  // NOLINT(google-explicit-constructor)
+      : piece_(scratch_,
+               static_cast<size_t>(
+                   numbers_internal::FastIntToBuffer(value, scratch_) -
+                   scratch_)) {}
+  Arg(unsigned int value)  // NOLINT(google-explicit-constructor)
+      : piece_(scratch_,
+               static_cast<size_t>(
+                   numbers_internal::FastIntToBuffer(value, scratch_) -
+                   scratch_)) {}
+  Arg(long value)  // NOLINT(*)
+      : piece_(scratch_,
+               static_cast<size_t>(
+                   numbers_internal::FastIntToBuffer(value, scratch_) -
+                   scratch_)) {}
+  Arg(unsigned long value)  // NOLINT(*)
+      : piece_(scratch_,
+               static_cast<size_t>(
+                   numbers_internal::FastIntToBuffer(value, scratch_) -
+                   scratch_)) {}
+  Arg(long long value)  // NOLINT(*)
+      : piece_(scratch_,
+               static_cast<size_t>(
+                   numbers_internal::FastIntToBuffer(value, scratch_) -
+                   scratch_)) {}
+  Arg(unsigned long long value)  // NOLINT(*)
+      : piece_(scratch_,
+               static_cast<size_t>(
+                   numbers_internal::FastIntToBuffer(value, scratch_) -
+                   scratch_)) {}
+  Arg(float value)  // NOLINT(google-explicit-constructor)
+      : piece_(scratch_, numbers_internal::SixDigitsToBuffer(value, scratch_)) {
+  }
+  Arg(double value)  // NOLINT(google-explicit-constructor)
+      : piece_(scratch_, numbers_internal::SixDigitsToBuffer(value, scratch_)) {
+  }
+  Arg(bool value)  // NOLINT(google-explicit-constructor)
+      : piece_(value ? "true" : "false") {}
+
+  template <typename T, typename = typename std::enable_if<
+                            strings_internal::HasAbslStringify<T>::value>::type>
+  Arg(  // NOLINT(google-explicit-constructor)
+      const T& v, strings_internal::StringifySink&& sink = {})
+      : piece_(strings_internal::ExtractStringification(sink, v)) {}
+
+  Arg(Hex hex);  // NOLINT(google-explicit-constructor)
+  Arg(Dec dec);  // NOLINT(google-explicit-constructor)
+
+  // vector<bool>::reference and const_reference require special help to convert
+  // to `Arg` because it requires two user defined conversions.
+  template <typename T,
+            absl::enable_if_t<
+                std::is_class<T>::value &&
+                (std::is_same<T, std::vector<bool>::reference>::value ||
+                 std::is_same<T, std::vector<bool>::const_reference>::value)>* =
+                nullptr>
+  Arg(T value)  // NOLINT(google-explicit-constructor)
+      : Arg(static_cast<bool>(value)) {}
+
+  // `void*` values, with the exception of `char*`, are printed as
+  // "0x<hex value>". However, in the case of `nullptr`, "NULL" is printed.
+  Arg(const void* value);  // NOLINT(google-explicit-constructor)
+
+  // Normal enums are already handled by the integer formatters.
+  // This overload matches only scoped enums.
+  template <typename T,
+            typename = typename std::enable_if<
+                std::is_enum<T>{} && !std::is_convertible<T, int>{} &&
+                !strings_internal::HasAbslStringify<T>::value>::type>
+  Arg(T value)  // NOLINT(google-explicit-constructor)
+      : Arg(static_cast<typename std::underlying_type<T>::type>(value)) {}
+
+  Arg(const Arg&) = delete;
+  Arg& operator=(const Arg&) = delete;
+
+  absl::string_view piece() const { return piece_; }
+
+ private:
+  absl::string_view piece_;
+  char scratch_[numbers_internal::kFastToBufferSize];
+};
+
+// Internal helper function. Don't call this from outside this implementation.
+// This interface may change without notice.
+void SubstituteAndAppendArray(std::string* output, absl::string_view format,
+                              const absl::string_view* args_array,
+                              size_t num_args);
+
+#if defined(ABSL_BAD_CALL_IF)
+constexpr int CalculateOneBit(const char* format) {
+  // Returns:
+  // * 2^N for '$N' when N is in [0-9]
+  // * 0 for correct '$' escaping: '$$'.
+  // * -1 otherwise.
+  return (*format < '0' || *format > '9') ? (*format == '$' ? 0 : -1)
+                                          : (1 << (*format - '0'));
+}
+
+constexpr const char* SkipNumber(const char* format) {
+  return !*format ? format : (format + 1);
+}
+
+constexpr int PlaceholderBitmask(const char* format) {
+  return !*format
+             ? 0
+             : *format != '$' ? PlaceholderBitmask(format + 1)
+                              : (CalculateOneBit(format + 1) |
+                                 PlaceholderBitmask(SkipNumber(format + 1)));
+}
+#endif  // ABSL_BAD_CALL_IF
+
+}  // namespace substitute_internal
+
+//
+// PUBLIC API
+//
+
+// SubstituteAndAppend()
+//
+// Substitutes variables into a given format string and appends to a given
+// output string. See file comments above for usage.
+//
+// The declarations of `SubstituteAndAppend()` below consist of overloads
+// for passing 0 to 10 arguments, respectively.
+//
+// NOTE: A zero-argument `SubstituteAndAppend()` may be used within variadic
+// templates to allow a variable number of arguments.
+//
+// Example:
+//  template <typename... Args>
+//  void VarMsg(std::string* boilerplate, absl::string_view format,
+//      const Args&... args) {
+//    absl::SubstituteAndAppend(boilerplate, format, args...);
+//  }
+//
+inline void SubstituteAndAppend(std::string* output, absl::string_view format) {
+  substitute_internal::SubstituteAndAppendArray(output, format, nullptr, 0);
+}
+
+inline void SubstituteAndAppend(std::string* output, absl::string_view format,
+                                const substitute_internal::Arg& a0) {
+  const absl::string_view args[] = {a0.piece()};
+  substitute_internal::SubstituteAndAppendArray(output, format, args,
+                                                ABSL_ARRAYSIZE(args));
+}
+
+inline void SubstituteAndAppend(std::string* output, absl::string_view format,
+                                const substitute_internal::Arg& a0,
+                                const substitute_internal::Arg& a1) {
+  const absl::string_view args[] = {a0.piece(), a1.piece()};
+  substitute_internal::SubstituteAndAppendArray(output, format, args,
+                                                ABSL_ARRAYSIZE(args));
+}
+
+inline void SubstituteAndAppend(std::string* output, absl::string_view format,
+                                const substitute_internal::Arg& a0,
+                                const substitute_internal::Arg& a1,
+                                const substitute_internal::Arg& a2) {
+  const absl::string_view args[] = {a0.piece(), a1.piece(), a2.piece()};
+  substitute_internal::SubstituteAndAppendArray(output, format, args,
+                                                ABSL_ARRAYSIZE(args));
+}
+
+inline void SubstituteAndAppend(std::string* output, absl::string_view format,
+                                const substitute_internal::Arg& a0,
+                                const substitute_internal::Arg& a1,
+                                const substitute_internal::Arg& a2,
+                                const substitute_internal::Arg& a3) {
+  const absl::string_view args[] = {a0.piece(), a1.piece(), a2.piece(),
+                                    a3.piece()};
+  substitute_internal::SubstituteAndAppendArray(output, format, args,
+                                                ABSL_ARRAYSIZE(args));
+}
+
+inline void SubstituteAndAppend(std::string* output, absl::string_view format,
+                                const substitute_internal::Arg& a0,
+                                const substitute_internal::Arg& a1,
+                                const substitute_internal::Arg& a2,
+                                const substitute_internal::Arg& a3,
+                                const substitute_internal::Arg& a4) {
+  const absl::string_view args[] = {a0.piece(), a1.piece(), a2.piece(),
+                                    a3.piece(), a4.piece()};
+  substitute_internal::SubstituteAndAppendArray(output, format, args,
+                                                ABSL_ARRAYSIZE(args));
+}
+
+inline void SubstituteAndAppend(std::string* output, absl::string_view format,
+                                const substitute_internal::Arg& a0,
+                                const substitute_internal::Arg& a1,
+                                const substitute_internal::Arg& a2,
+                                const substitute_internal::Arg& a3,
+                                const substitute_internal::Arg& a4,
+                                const substitute_internal::Arg& a5) {
+  const absl::string_view args[] = {a0.piece(), a1.piece(), a2.piece(),
+                                    a3.piece(), a4.piece(), a5.piece()};
+  substitute_internal::SubstituteAndAppendArray(output, format, args,
+                                                ABSL_ARRAYSIZE(args));
+}
+
+inline void SubstituteAndAppend(std::string* output, absl::string_view format,
+                                const substitute_internal::Arg& a0,
+                                const substitute_internal::Arg& a1,
+                                const substitute_internal::Arg& a2,
+                                const substitute_internal::Arg& a3,
+                                const substitute_internal::Arg& a4,
+                                const substitute_internal::Arg& a5,
+                                const substitute_internal::Arg& a6) {
+  const absl::string_view args[] = {a0.piece(), a1.piece(), a2.piece(),
+                                    a3.piece(), a4.piece(), a5.piece(),
+                                    a6.piece()};
+  substitute_internal::SubstituteAndAppendArray(output, format, args,
+                                                ABSL_ARRAYSIZE(args));
+}
+
+inline void SubstituteAndAppend(
+    std::string* output, absl::string_view format,
+    const substitute_internal::Arg& a0, const substitute_internal::Arg& a1,
+    const substitute_internal::Arg& a2, const substitute_internal::Arg& a3,
+    const substitute_internal::Arg& a4, const substitute_internal::Arg& a5,
+    const substitute_internal::Arg& a6, const substitute_internal::Arg& a7) {
+  const absl::string_view args[] = {a0.piece(), a1.piece(), a2.piece(),
+                                    a3.piece(), a4.piece(), a5.piece(),
+                                    a6.piece(), a7.piece()};
+  substitute_internal::SubstituteAndAppendArray(output, format, args,
+                                                ABSL_ARRAYSIZE(args));
+}
+
+inline void SubstituteAndAppend(
+    std::string* output, absl::string_view format,
+    const substitute_internal::Arg& a0, const substitute_internal::Arg& a1,
+    const substitute_internal::Arg& a2, const substitute_internal::Arg& a3,
+    const substitute_internal::Arg& a4, const substitute_internal::Arg& a5,
+    const substitute_internal::Arg& a6, const substitute_internal::Arg& a7,
+    const substitute_internal::Arg& a8) {
+  const absl::string_view args[] = {a0.piece(), a1.piece(), a2.piece(),
+                                    a3.piece(), a4.piece(), a5.piece(),
+                                    a6.piece(), a7.piece(), a8.piece()};
+  substitute_internal::SubstituteAndAppendArray(output, format, args,
+                                                ABSL_ARRAYSIZE(args));
+}
+
+inline void SubstituteAndAppend(
+    std::string* output, absl::string_view format,
+    const substitute_internal::Arg& a0, const substitute_internal::Arg& a1,
+    const substitute_internal::Arg& a2, const substitute_internal::Arg& a3,
+    const substitute_internal::Arg& a4, const substitute_internal::Arg& a5,
+    const substitute_internal::Arg& a6, const substitute_internal::Arg& a7,
+    const substitute_internal::Arg& a8, const substitute_internal::Arg& a9) {
+  const absl::string_view args[] = {
+      a0.piece(), a1.piece(), a2.piece(), a3.piece(), a4.piece(),
+      a5.piece(), a6.piece(), a7.piece(), a8.piece(), a9.piece()};
+  substitute_internal::SubstituteAndAppendArray(output, format, args,
+                                                ABSL_ARRAYSIZE(args));
+}
+
+#if defined(ABSL_BAD_CALL_IF)
+// This body of functions catches cases where the number of placeholders
+// doesn't match the number of data arguments.
+void SubstituteAndAppend(std::string* output, const char* format)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 0,
+        "There were no substitution arguments "
+        "but this format string either has a $[0-9] in it or contains "
+        "an unescaped $ character (use $$ instead)");
+
+void SubstituteAndAppend(std::string* output, const char* format,
+                         const substitute_internal::Arg& a0)
+    ABSL_BAD_CALL_IF(substitute_internal::PlaceholderBitmask(format) != 1,
+                     "There was 1 substitution argument given, but "
+                     "this format string is missing its $0, contains "
+                     "one of $1-$9, or contains an unescaped $ character (use "
+                     "$$ instead)");
+
+void SubstituteAndAppend(std::string* output, const char* format,
+                         const substitute_internal::Arg& a0,
+                         const substitute_internal::Arg& a1)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 3,
+        "There were 2 substitution arguments given, but this format string is "
+        "missing its $0/$1, contains one of $2-$9, or contains an "
+        "unescaped $ character (use $$ instead)");
+
+void SubstituteAndAppend(std::string* output, const char* format,
+                         const substitute_internal::Arg& a0,
+                         const substitute_internal::Arg& a1,
+                         const substitute_internal::Arg& a2)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 7,
+        "There were 3 substitution arguments given, but "
+        "this format string is missing its $0/$1/$2, contains one of "
+        "$3-$9, or contains an unescaped $ character (use $$ instead)");
+
+void SubstituteAndAppend(std::string* output, const char* format,
+                         const substitute_internal::Arg& a0,
+                         const substitute_internal::Arg& a1,
+                         const substitute_internal::Arg& a2,
+                         const substitute_internal::Arg& a3)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 15,
+        "There were 4 substitution arguments given, but "
+        "this format string is missing its $0-$3, contains one of "
+        "$4-$9, or contains an unescaped $ character (use $$ instead)");
+
+void SubstituteAndAppend(std::string* output, const char* format,
+                         const substitute_internal::Arg& a0,
+                         const substitute_internal::Arg& a1,
+                         const substitute_internal::Arg& a2,
+                         const substitute_internal::Arg& a3,
+                         const substitute_internal::Arg& a4)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 31,
+        "There were 5 substitution arguments given, but "
+        "this format string is missing its $0-$4, contains one of "
+        "$5-$9, or contains an unescaped $ character (use $$ instead)");
+
+void SubstituteAndAppend(std::string* output, const char* format,
+                         const substitute_internal::Arg& a0,
+                         const substitute_internal::Arg& a1,
+                         const substitute_internal::Arg& a2,
+                         const substitute_internal::Arg& a3,
+                         const substitute_internal::Arg& a4,
+                         const substitute_internal::Arg& a5)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 63,
+        "There were 6 substitution arguments given, but "
+        "this format string is missing its $0-$5, contains one of "
+        "$6-$9, or contains an unescaped $ character (use $$ instead)");
+
+void SubstituteAndAppend(
+    std::string* output, const char* format, const substitute_internal::Arg& a0,
+    const substitute_internal::Arg& a1, const substitute_internal::Arg& a2,
+    const substitute_internal::Arg& a3, const substitute_internal::Arg& a4,
+    const substitute_internal::Arg& a5, const substitute_internal::Arg& a6)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 127,
+        "There were 7 substitution arguments given, but "
+        "this format string is missing its $0-$6, contains one of "
+        "$7-$9, or contains an unescaped $ character (use $$ instead)");
+
+void SubstituteAndAppend(
+    std::string* output, const char* format, const substitute_internal::Arg& a0,
+    const substitute_internal::Arg& a1, const substitute_internal::Arg& a2,
+    const substitute_internal::Arg& a3, const substitute_internal::Arg& a4,
+    const substitute_internal::Arg& a5, const substitute_internal::Arg& a6,
+    const substitute_internal::Arg& a7)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 255,
+        "There were 8 substitution arguments given, but "
+        "this format string is missing its $0-$7, contains one of "
+        "$8-$9, or contains an unescaped $ character (use $$ instead)");
+
+void SubstituteAndAppend(
+    std::string* output, const char* format, const substitute_internal::Arg& a0,
+    const substitute_internal::Arg& a1, const substitute_internal::Arg& a2,
+    const substitute_internal::Arg& a3, const substitute_internal::Arg& a4,
+    const substitute_internal::Arg& a5, const substitute_internal::Arg& a6,
+    const substitute_internal::Arg& a7, const substitute_internal::Arg& a8)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 511,
+        "There were 9 substitution arguments given, but "
+        "this format string is missing its $0-$8, contains a $9, or "
+        "contains an unescaped $ character (use $$ instead)");
+
+void SubstituteAndAppend(
+    std::string* output, const char* format, const substitute_internal::Arg& a0,
+    const substitute_internal::Arg& a1, const substitute_internal::Arg& a2,
+    const substitute_internal::Arg& a3, const substitute_internal::Arg& a4,
+    const substitute_internal::Arg& a5, const substitute_internal::Arg& a6,
+    const substitute_internal::Arg& a7, const substitute_internal::Arg& a8,
+    const substitute_internal::Arg& a9)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 1023,
+        "There were 10 substitution arguments given, but this "
+        "format string either doesn't contain all of $0 through $9 or "
+        "contains an unescaped $ character (use $$ instead)");
+#endif  // ABSL_BAD_CALL_IF
+
+// Substitute()
+//
+// Substitutes variables into a given format string. See file comments above
+// for usage.
+//
+// The declarations of `Substitute()` below consist of overloads for passing 0
+// to 10 arguments, respectively.
+//
+// NOTE: A zero-argument `Substitute()` may be used within variadic templates to
+// allow a variable number of arguments.
+//
+// Example:
+//  template <typename... Args>
+//  void VarMsg(absl::string_view format, const Args&... args) {
+//    std::string s = absl::Substitute(format, args...);
+
+ABSL_MUST_USE_RESULT inline std::string Substitute(absl::string_view format) {
+  std::string result;
+  SubstituteAndAppend(&result, format);
+  return result;
+}
+
+ABSL_MUST_USE_RESULT inline std::string Substitute(
+    absl::string_view format, const substitute_internal::Arg& a0) {
+  std::string result;
+  SubstituteAndAppend(&result, format, a0);
+  return result;
+}
+
+ABSL_MUST_USE_RESULT inline std::string Substitute(
+    absl::string_view format, const substitute_internal::Arg& a0,
+    const substitute_internal::Arg& a1) {
+  std::string result;
+  SubstituteAndAppend(&result, format, a0, a1);
+  return result;
+}
+
+ABSL_MUST_USE_RESULT inline std::string Substitute(
+    absl::string_view format, const substitute_internal::Arg& a0,
+    const substitute_internal::Arg& a1, const substitute_internal::Arg& a2) {
+  std::string result;
+  SubstituteAndAppend(&result, format, a0, a1, a2);
+  return result;
+}
+
+ABSL_MUST_USE_RESULT inline std::string Substitute(
+    absl::string_view format, const substitute_internal::Arg& a0,
+    const substitute_internal::Arg& a1, const substitute_internal::Arg& a2,
+    const substitute_internal::Arg& a3) {
+  std::string result;
+  SubstituteAndAppend(&result, format, a0, a1, a2, a3);
+  return result;
+}
+
+ABSL_MUST_USE_RESULT inline std::string Substitute(
+    absl::string_view format, const substitute_internal::Arg& a0,
+    const substitute_internal::Arg& a1, const substitute_internal::Arg& a2,
+    const substitute_internal::Arg& a3, const substitute_internal::Arg& a4) {
+  std::string result;
+  SubstituteAndAppend(&result, format, a0, a1, a2, a3, a4);
+  return result;
+}
+
+ABSL_MUST_USE_RESULT inline std::string Substitute(
+    absl::string_view format, const substitute_internal::Arg& a0,
+    const substitute_internal::Arg& a1, const substitute_internal::Arg& a2,
+    const substitute_internal::Arg& a3, const substitute_internal::Arg& a4,
+    const substitute_internal::Arg& a5) {
+  std::string result;
+  SubstituteAndAppend(&result, format, a0, a1, a2, a3, a4, a5);
+  return result;
+}
+
+ABSL_MUST_USE_RESULT inline std::string Substitute(
+    absl::string_view format, const substitute_internal::Arg& a0,
+    const substitute_internal::Arg& a1, const substitute_internal::Arg& a2,
+    const substitute_internal::Arg& a3, const substitute_internal::Arg& a4,
+    const substitute_internal::Arg& a5, const substitute_internal::Arg& a6) {
+  std::string result;
+  SubstituteAndAppend(&result, format, a0, a1, a2, a3, a4, a5, a6);
+  return result;
+}
+
+ABSL_MUST_USE_RESULT inline std::string Substitute(
+    absl::string_view format, const substitute_internal::Arg& a0,
+    const substitute_internal::Arg& a1, const substitute_internal::Arg& a2,
+    const substitute_internal::Arg& a3, const substitute_internal::Arg& a4,
+    const substitute_internal::Arg& a5, const substitute_internal::Arg& a6,
+    const substitute_internal::Arg& a7) {
+  std::string result;
+  SubstituteAndAppend(&result, format, a0, a1, a2, a3, a4, a5, a6, a7);
+  return result;
+}
+
+ABSL_MUST_USE_RESULT inline std::string Substitute(
+    absl::string_view format, const substitute_internal::Arg& a0,
+    const substitute_internal::Arg& a1, const substitute_internal::Arg& a2,
+    const substitute_internal::Arg& a3, const substitute_internal::Arg& a4,
+    const substitute_internal::Arg& a5, const substitute_internal::Arg& a6,
+    const substitute_internal::Arg& a7, const substitute_internal::Arg& a8) {
+  std::string result;
+  SubstituteAndAppend(&result, format, a0, a1, a2, a3, a4, a5, a6, a7, a8);
+  return result;
+}
+
+ABSL_MUST_USE_RESULT inline std::string Substitute(
+    absl::string_view format, const substitute_internal::Arg& a0,
+    const substitute_internal::Arg& a1, const substitute_internal::Arg& a2,
+    const substitute_internal::Arg& a3, const substitute_internal::Arg& a4,
+    const substitute_internal::Arg& a5, const substitute_internal::Arg& a6,
+    const substitute_internal::Arg& a7, const substitute_internal::Arg& a8,
+    const substitute_internal::Arg& a9) {
+  std::string result;
+  SubstituteAndAppend(&result, format, a0, a1, a2, a3, a4, a5, a6, a7, a8, a9);
+  return result;
+}
+
+#if defined(ABSL_BAD_CALL_IF)
+// This body of functions catches cases where the number of placeholders
+// doesn't match the number of data arguments.
+std::string Substitute(const char* format)
+    ABSL_BAD_CALL_IF(substitute_internal::PlaceholderBitmask(format) != 0,
+                     "There were no substitution arguments "
+                     "but this format string either has a $[0-9] in it or "
+                     "contains an unescaped $ character (use $$ instead)");
+
+std::string Substitute(const char* format, const substitute_internal::Arg& a0)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 1,
+        "There was 1 substitution argument given, but "
+        "this format string is missing its $0, contains one of $1-$9, "
+        "or contains an unescaped $ character (use $$ instead)");
+
+std::string Substitute(const char* format, const substitute_internal::Arg& a0,
+                       const substitute_internal::Arg& a1)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 3,
+        "There were 2 substitution arguments given, but "
+        "this format string is missing its $0/$1, contains one of "
+        "$2-$9, or contains an unescaped $ character (use $$ instead)");
+
+std::string Substitute(const char* format, const substitute_internal::Arg& a0,
+                       const substitute_internal::Arg& a1,
+                       const substitute_internal::Arg& a2)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 7,
+        "There were 3 substitution arguments given, but "
+        "this format string is missing its $0/$1/$2, contains one of "
+        "$3-$9, or contains an unescaped $ character (use $$ instead)");
+
+std::string Substitute(const char* format, const substitute_internal::Arg& a0,
+                       const substitute_internal::Arg& a1,
+                       const substitute_internal::Arg& a2,
+                       const substitute_internal::Arg& a3)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 15,
+        "There were 4 substitution arguments given, but "
+        "this format string is missing its $0-$3, contains one of "
+        "$4-$9, or contains an unescaped $ character (use $$ instead)");
+
+std::string Substitute(const char* format, const substitute_internal::Arg& a0,
+                       const substitute_internal::Arg& a1,
+                       const substitute_internal::Arg& a2,
+                       const substitute_internal::Arg& a3,
+                       const substitute_internal::Arg& a4)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 31,
+        "There were 5 substitution arguments given, but "
+        "this format string is missing its $0-$4, contains one of "
+        "$5-$9, or contains an unescaped $ character (use $$ instead)");
+
+std::string Substitute(const char* format, const substitute_internal::Arg& a0,
+                       const substitute_internal::Arg& a1,
+                       const substitute_internal::Arg& a2,
+                       const substitute_internal::Arg& a3,
+                       const substitute_internal::Arg& a4,
+                       const substitute_internal::Arg& a5)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 63,
+        "There were 6 substitution arguments given, but "
+        "this format string is missing its $0-$5, contains one of "
+        "$6-$9, or contains an unescaped $ character (use $$ instead)");
+
+std::string Substitute(const char* format, const substitute_internal::Arg& a0,
+                       const substitute_internal::Arg& a1,
+                       const substitute_internal::Arg& a2,
+                       const substitute_internal::Arg& a3,
+                       const substitute_internal::Arg& a4,
+                       const substitute_internal::Arg& a5,
+                       const substitute_internal::Arg& a6)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 127,
+        "There were 7 substitution arguments given, but "
+        "this format string is missing its $0-$6, contains one of "
+        "$7-$9, or contains an unescaped $ character (use $$ instead)");
+
+std::string Substitute(const char* format, const substitute_internal::Arg& a0,
+                       const substitute_internal::Arg& a1,
+                       const substitute_internal::Arg& a2,
+                       const substitute_internal::Arg& a3,
+                       const substitute_internal::Arg& a4,
+                       const substitute_internal::Arg& a5,
+                       const substitute_internal::Arg& a6,
+                       const substitute_internal::Arg& a7)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 255,
+        "There were 8 substitution arguments given, but "
+        "this format string is missing its $0-$7, contains one of "
+        "$8-$9, or contains an unescaped $ character (use $$ instead)");
+
+std::string Substitute(
+    const char* format, const substitute_internal::Arg& a0,
+    const substitute_internal::Arg& a1, const substitute_internal::Arg& a2,
+    const substitute_internal::Arg& a3, const substitute_internal::Arg& a4,
+    const substitute_internal::Arg& a5, const substitute_internal::Arg& a6,
+    const substitute_internal::Arg& a7, const substitute_internal::Arg& a8)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 511,
+        "There were 9 substitution arguments given, but "
+        "this format string is missing its $0-$8, contains a $9, or "
+        "contains an unescaped $ character (use $$ instead)");
+
+std::string Substitute(
+    const char* format, const substitute_internal::Arg& a0,
+    const substitute_internal::Arg& a1, const substitute_internal::Arg& a2,
+    const substitute_internal::Arg& a3, const substitute_internal::Arg& a4,
+    const substitute_internal::Arg& a5, const substitute_internal::Arg& a6,
+    const substitute_internal::Arg& a7, const substitute_internal::Arg& a8,
+    const substitute_internal::Arg& a9)
+    ABSL_BAD_CALL_IF(
+        substitute_internal::PlaceholderBitmask(format) != 1023,
+        "There were 10 substitution arguments given, but this "
+        "format string either doesn't contain all of $0 through $9 or "
+        "contains an unescaped $ character (use $$ instead)");
+#endif  // ABSL_BAD_CALL_IF
+
+ABSL_NAMESPACE_END
+}  // namespace absl
+
+#endif  // ABSL_STRINGS_SUBSTITUTE_H_